pax_global_header00006660000000000000000000000064152565065160014525gustar00rootroot0000000000000052 comment=199fc53c8a1b87fe4f11d7a8f69f0d1408d9ef71 geopandas-1.2.0/000077500000000000000000000000001525650651600134665ustar00rootroot00000000000000geopandas-1.2.0/.coveragerc000066400000000000000000000005071525650651600156110ustar00rootroot00000000000000[report] exclude_lines = pragma: no cover if TYPE_CHECKING: def __repr__ raise AssertionError raise NotImplementedError if __name__ == .__main__.: omit = geopandas/tests/* geopandas/io/tests/* geopandas/tools/tests/* geopandas/_version.py geopandas/datasets/naturalearth_creation.py geopandas-1.2.0/.git-blame-ignore-revs000066400000000000000000000004711525650651600175700ustar00rootroot00000000000000# black-ification of code 479b6e706293ff1a484e4e121682f3a105a53aa9 7bc3166cfecdd120b544701cff5e37b4482cbbb5 f4b749d148f0eb13e7e92fbbf7e2eae79de2543a 373ac8bf731db1002e09859587c475a5f56c4e8b 50c59d395bc539eb411f9a5c6cdf04d37510060a 5dc982edbfc2633cd2c7a22ebd314d1b9b810a9a 44dba4fde90e8010ea6c71c6cf51b34413440177 geopandas-1.2.0/.gitattributes000066400000000000000000000001711525650651600163600ustar00rootroot00000000000000geopandas/_version.py export-subst # GitHub syntax highlighting pixi.lock linguist-language=YAML linguist-generated=true geopandas-1.2.0/.github/000077500000000000000000000000001525650651600150265ustar00rootroot00000000000000geopandas-1.2.0/.github/ISSUE_TEMPLATE/000077500000000000000000000000001525650651600172115ustar00rootroot00000000000000geopandas-1.2.0/.github/ISSUE_TEMPLATE/bug_report.md000066400000000000000000000016451525650651600217110ustar00rootroot00000000000000--- name: Bug Report about: Create a bug report to help us improve geopandas title: "BUG:" labels: "bug, needs triage" --- - [ ] I have checked that this issue has not already been reported. - [ ] I have confirmed this bug exists on the latest version of geopandas. - [ ] (optional) I have confirmed this bug exists on the main branch of geopandas. --- **Note**: Please read [this guide](https://matthewrocklin.com/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug. #### Code Sample, a copy-pastable example ```python # Your code here ``` #### Problem description [this should explain **why** the current behaviour is a problem and why the expected output is a better solution] #### Expected Output #### Output of ``geopandas.show_versions()``
[paste the output of ``geopandas.show_versions()`` here leaving a blank line after the details tag]
geopandas-1.2.0/.github/ISSUE_TEMPLATE/config.yml000066400000000000000000000003241525650651600212000ustar00rootroot00000000000000blank_issues_enabled: true contact_links: - name: GeoPandas Discussions url: https://github.com/geopandas/geopandas/discussions about: Ask a general question about GeoPandas (e.g., usage, installation) geopandas-1.2.0/.github/ISSUE_TEMPLATE/feature_request.md000066400000000000000000000016351525650651600227430ustar00rootroot00000000000000--- name: Feature Request about: Suggest an idea for geopandas title: "ENH:" labels: "enhancement" --- #### Is your feature request related to a problem? [this should provide a description of what the problem is, e.g. "I wish I could use geopandas to do [...]"] #### Describe the solution you'd like [this should provide a description of the feature request, e.g. "`GeoDataFrame.foo` should get a new parameter `bar` that [...]", try to write a docstring for the desired feature] #### API breaking implications [this should provide a description of how this feature will affect the API] #### Describe alternatives you've considered [this should provide a description of any alternative solutions or features you've considered] #### Additional context [add any other context, code examples, or references to existing implementations about the feature request here] ```python # Your code here, if applicable ``` geopandas-1.2.0/.github/dependabot.yml000066400000000000000000000003321525650651600176540ustar00rootroot00000000000000version: 2 updates: # Maintain dependencies for GitHub Actions - package-ecosystem: "github-actions" directory: "/" schedule: # Check for updates to GitHub Actions every week interval: "weekly" geopandas-1.2.0/.github/workflows/000077500000000000000000000000001525650651600170635ustar00rootroot00000000000000geopandas-1.2.0/.github/workflows/release.yml000066400000000000000000000021511525650651600212250ustar00rootroot00000000000000name: Publish geopandas to PyPI / GitHub on: push: tags: - "v*" jobs: build-n-publish: name: Build and publish geopandas to PyPI runs-on: ubuntu-latest environment: name: pypi url: https://pypi.org/p/geopandas permissions: contents: write # this permission is required for the Github release action id-token: write # IMPORTANT: this permission is mandatory for trusted publishing steps: - name: Checkout source uses: actions/checkout@v7 - name: Set up Python uses: actions/setup-python@v7 with: python-version: "3.x" - name: Build source and wheel distributions run: | python -m pip install --upgrade build twine python -m build twine check --strict dist/* - name: Publish distribution to PyPI uses: pypa/gh-action-pypi-publish@release/v1 - name: Create GitHub Release uses: softprops/action-gh-release@v3 with: tag_name: ${{ github.ref_name }} draft: true prerelease: false files: dist/*.tar.gz geopandas-1.2.0/.github/workflows/tests.yaml000066400000000000000000000067261525650651600211240ustar00rootroot00000000000000name: Tests on: push: branches: [main, 0.**] pull_request: branches: [main, 0.**] schedule: - cron: "0 0 * * *" concurrency: group: ${{ github.workflow }}-${{ github.ref }} cancel-in-progress: true jobs: Test: name: ${{ matrix.os }}, ${{ matrix.env }} runs-on: ${{ matrix.os }} defaults: run: shell: bash -l {0} # skip scheduled runs on forks if: (github.event_name == 'schedule' && github.repository == 'geopandas/geopandas') || (github.event_name != 'schedule') strategy: fail-fast: false matrix: os: [ubuntu-latest] postgis: [false] dev: [false] doctest: [false] env: - ci/envs/311-latest.yaml - ci/envs/311-minimal.yaml # - ci/envs/311-pd23.yaml -> in postgis env below # - ci/envs/312-latest.yaml -> in postgis env below - ci/envs/313-latest-no-pyogrio.yaml - ci/envs/313-latest-no-pyproj.yaml # - ci/envs/313-latest.yaml -> in doctest env below - ci/envs/313-no-optional-deps.yaml - ci/envs/314-latest.yaml # - ci/envs/313-dev.yaml -> in dev env below include: # PostGIS envs - env: ci/envs/311-pd23.yaml postgis: true os: ubuntu-latest - env: ci/envs/312-latest.yaml postgis: true os: ubuntu-latest # doctest env - env: ci/envs/313-latest.yaml doctest: true os: ubuntu-latest # MacOS & Windows envs - env: ci/envs/313-latest-no-pyogrio.yaml os: macos-latest - env: ci/envs/313-latest.yaml os: macos-latest - env: ci/envs/313-latest-no-pyogrio.yaml os: windows-latest - env: ci/envs/313-latest.yaml os: windows-latest # Dev env - env: ci/envs/313-dev.yaml os: ubuntu-latest dev: true steps: - uses: actions/checkout@v7 - name: Install Conda environment with Micromamba uses: mamba-org/setup-micromamba@v3 with: environment-file: ${{ matrix.env }} - name: Install package # install without deps to avoid installing default but optional pyproj/pyogrio run: pip install -e . --no-deps - name: Check and Log Environment run: | python -V python -c "import geopandas; geopandas.show_versions();" micromamba info micromamba list - name: Test env: PYTHONWARNINGS: ${{ matrix.dev && 'error' || 'default' }} run: | pytest -v -r a -n auto --color=yes --cov=geopandas --cov-append --cov-report term-missing --cov-report xml geopandas/ - name: Test with PostGIS if: ${{ matrix.postgis }} env: PGUSER: postgres PGPASSWORD: postgres PGHOST: "127.0.0.1" PGPORT: 5432 run: | conda install postgis -c conda-forge sh ci/scripts/setup_postgres.sh set -o pipefail pytest -v -r a --color=yes --cov=geopandas --cov-append --cov-report term-missing --cov-report xml geopandas/io/tests/test_sql.py | tee /dev/stderr | if grep SKIPPED >/dev/null;then echo "TESTS SKIPPED, FAILING" && exit 1;fi - name: Test docstrings if: ${{ matrix.doctest }} run: | pytest -v --color=yes --doctest-only geopandas --ignore=geopandas/datasets - uses: codecov/codecov-action@v7 geopandas-1.2.0/.gitignore000066400000000000000000000017221525650651600154600ustar00rootroot00000000000000# Autogenerated geopandas/_version.py # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] *$py.class # C extensions *.so # Distribution / packaging .Python env/ venv*/ build/ dist/ eggs/ .eggs/ lib/ lib64/ parts/ sdist/ var/ wheels/ *.egg-info/ .installed.cfg *.egg # Unit test / coverage reports htmlcov/ .tox/ .coverage .coverage.* .cache nosetests.xml coverage.xml *.cover .hypothesis/ result_images # Sphinx documentation doc/_build/ .buildinfo # Jupyter Notebook .ipynb_checkpoints # pytest .pytest_cache .mypy_cache # projects .spyderproject .spyproject .idea .ropeproject .vscode *.py~ .DS_Store examples/nybb_*.zip doc/source/savefig doc/source/reference doc/source/docs/reference/api/*.rst geopandas.egg-info geopandas/version.py geopandas/datasets/ne_110m_admin_0_countries.zip .asv doc/source/getting_started/my_file.geojson .ruff_cache .env doc/test_fiona.gpkg doc/test_pyogrio.gpkg # pixi environments .pixi *.egg-info pixi.toml pixi.lock geopandas-1.2.0/.pre-commit-config.yaml000066400000000000000000000005161525650651600177510ustar00rootroot00000000000000ci: autofix_prs: false autoupdate_schedule: quarterly files: 'geopandas\/' repos: - repo: https://github.com/astral-sh/ruff-pre-commit rev: "v0.15.20" hooks: - id: ruff-format - id: ruff-check name: sort imports with ruff args: [--select, I, --fix] - id: ruff-checkgeopandas-1.2.0/CHANGELOG.md000066400000000000000000002162031525650651600153030ustar00rootroot00000000000000# Changelog ## Version 1.2.0 (September 28, 2026) New features and improvements: - Static plotting in GeoPandas has been re-implemented. The new tooling shall be mostly backwards compatible while bringing a set of enhancements: - Categorical plotting now allows dict-based mapping of categories to colors and other styles. - Legends now reflect the geometry type and complete set of styles, not just a color. - Plotting of continuous variables with a classification scheme can now produce a colorbar instead of a categorical legend when using `colorbar=True` within `legend_kwds`. - `vmin` and `vmax` keywords are supported when `scheme` is defined, not ignored. - When using categorical colormap with categorical plots, colors are now assigned in order to optimize the contrast of the resulting map. - Colorbar size is automatically adjusted to be approximately the size of the plot. - Colorbar now indicates extensions if the `vmin` and/or `vmax` clip the values. - Legends are created automatically by matplotlib. This also means that you can create a single legend from a layered map by calling `ax.legend()` directly. - GeoPandas now labels axes using the information on units from CRS. This can be controlled via the `add_labels` keyword. - Static plots can include a basemap defined by the `tiles` keyword, exposing `contextily.add_basemap` from within the `plot` method. Requires `contextily` package to be installed. - `scheme` keyword can take a value `"greedy"` resulting in a greedy (topological) coloring where no two adjacent polygons share a color. Requires `mapclassify` and `libpysal` packages. The related refactoring of the plotting infrastructure has changed the internal representation of the figure generated by GeoPandas. This can be considered a breaking change if you rely the composition of collections, as that has changed. A number of plotting-related bugs has been fixed along as a side effect. All changes are reflected in an updated user guide. (#3708, #3728, #3748, #3752, #3753, #3763, #3767, #3689). - GeoParquet improvements: - The upcoming GeoParquet 2.0 specification is now supported for both writing and reading (#3632). - `read_parquet` now support direct reading from HTTP/HTTPS protocols (#3699). - `GeoDataFrame.to_parquet` and `read_parquet` will now write and read ``attrs`` respectively (#3597). - Added `geopandas.make_grid` and `GeoSeries.make_grid` to generate a square or hexagonal grids covering the input geometry (#2062). - Added `geopandas.read_file_info` to expose information about file layers, including layer metadata (this is an alias for `pyogrio.read_info`) (#3759). - Add ``grid_size`` parameter to ``union``, ``difference``, ``symmetric_difference`` and ``intersection`` (#3593). - Document how to access the feature ID (`fid`) as the index when reading files with `read_file` and the pyogrio engine, using `fid_as_index=True` (#3829). Deprecations and compatibility notes: - The `resolution` keyword to `buffer` has been deprecated to align with the convention in shapely, `quad_segs` should be used instead (#3600). - Expired deprecations: option `use_pygeos` which had no functionality, `seed` keyword in sample_points (replaced by `rng`) (#3613). Bug fixes: - Fix `GeoSeries.sample_points` looping without end on zero-area polygons, such as a collapsed ring; it now returns an empty `MultiPoint`, as it already did for empty and missing geometries (#3851). - Fix ``read_parquet`` ignoring the ``partitioning`` keyword when auto-detecting the columns to read, so that ``partitioning=None`` no longer raises (#3459). - Fix ``GeoDataFrame.explode()`` raising a cryptic error when passing multiple (non-geometry) columns; it now falls back to the pandas multi-column explode (#2753). - Fix `GeoDataFrame.from_features` raising a `ValueError` for an empty list of features when a `crs` is provided; it now returns an empty `GeoDataFrame` with a `geometry` column (#3777). - Fix the index level of the parts created by `explode(index_parts=True)` and `get_coordinates(index_parts=True)` being `int32` instead of `int64` on 32-bit platforms (#3865). - Various compatibility fixes for pandas 3.1 (#3802, #3831). Community: - GeoPandas now uses the NumFOCUS Code of Conduct. - GeoPandas has a new software paper which can be used as canonical citation, see https://geopandas.org/en/latest/about/citing.html Notes on dependencies: - GeoPandas 1.2 now requires Python 3.11 or greater and pandas 2.2, numpy 2.0, pyproj 3.7, and shapely 2.1, are now the minimum required version for these dependencies. Furthermore, the minimum tested version for optional dependencies has been updated to fiona 1.8.21, scipy 1.9, matplotlib 3.9, mapclassify 2.7 folium 0.15, pyarrow 15.0 and SQLAlchemy 2.0. Older versions of these libraries may continue to work, but are no longer considered supported (#3371, #3581). ## Version 1.1.4 (June 26, 2026) Bug fixes: - Further hardening of ``to_postgis`` against SQL injection (#3800). - Ensure that points generated by ``sample_points`` are not sorted along x-axis (#3773). - Fix ``GeoDataFrame.explore()`` ignoring custom ``legend_kwds={"labels": ...}`` for categorical and boolean columns (#3496). - More graceful handling of ``keep_geom_type`` in the ``overlay()`` function with empty input (#3745). ## Version 1.1.3 (March 10, 2026) This release addresses a handful of small compatibility issues with pandas 3.0 and backports some bugfixes. Bug fixes: - Improved compatibility with pandas 3.0 Copy-on-Write feature, making use of deferred copies where possible (#3298, #3711). - Fix `GeoSeries.sample_points` not accepting list-like `size` when generating points using `pointpaterns` (#3710). - Fix `from_wkt/wkb` to correctly handle missing values with pandas 3 (where the new `str` dtype is used) (#3714). - Fix `to_postgis` to correctly handle missing values with pandas 3 (where the new `str` dtype is used) (#3722). - Using `loc` to assign column values to a new row index now correctly preserves the column CRS and geometry dtype on pandas 3.1, due to an upstream bug fix (#3741, Pandas #62523) - Random states in `pointpats` methods of `sample_points`` can now be fixed with `rng` (#3737). ## Version 1.1.2 (December 22, 2025) Bug fixes: - Fix an issue that caused an error in `GeoDataFrame.from_features` when there is no `properties` field (#3599). - Fix `read_file` and `to_file` errors (#3682) - Fix `read_parquet` with `to_pandas_kwargs` for complex (list/struct) arrow types (#3640) - `value_counts` on GeoSeries now preserves CRS in index (#3669) - Fix f-string placeholders appearing in error messages when `pyogrio` cannot be imported (#3682). - Fix `read_parquet` with `to_pandas_kwargs` for complex (list/struct) arrow types (#3640). - `.to_json` now provides a clearer error message when called on a GeoDataFrame without an active geometry column (#3648). - Calling `del gdf["geometry"]` now will downcast to a `pd.DataFrame` if there are no geometry columns left in the dataframe (#3648). - Fix SQL injection in `to_postgis` via geometry column name (#3681). ## Version 1.1.1 (June 27, 2025) Bug fixes: - Fix regression in the GeoDataFrame constructor when np.nan is given as an only geometry (#3591). - Fix regression in `overlay` with `how="identity"` when input dataframes have column names that are equal (#3596). ## Version 1.1.0 (June 1, 2025) Notes on dependencies: - GeoPandas 1.1 now requires Python 3.10 or greater and pandas 2.0, numpy 1.24, pyproj 3.5, are now the minimum required version for these dependencies. Furthermore, the minimum tested version for optional dependencies has been updated to fiona 1.8.21, scipy 1.9, matplotlib 3.7, mapclassify 2.5, folium 0.12 and SQLAlchemy 2.0. Older versions of these libraries may continue to work, but are no longer considered supported (#3371). New features and improvements: - Added options to return the result of `SpatialIndex.query` in a form of a dense or a sparse boolean array. This adds optional dependency on `scipy` for the sparse output. Note that this also changes the previously undocumented behaviour of the `output_format` keyword (#1674). - Add ``grid_size`` parameter to ``union_all`` and ``dissolve`` (#3445). - `GeoDataFrame.plot` now supports `pd.Index` as an input for the `column` keyword (#3463). - Added `disjoint_subset` union algorithm for `union_all` and `dissolve` (#3534). - Added `constrained_delaunay_triangles` method to GeoSeries/GeoDataFrame (#3552). - Added `to_pandas_kwargs` argument to `from_arrow`, `read_parquet` and `read_feather` to allow better control of conversion of non-geometric Arrow data to DataFrames (#3466). - Added `is_valid_coverage` and `invalid_coverage_edges` to GeoSeries/GeoDataFrame to allow validation of polygonal coverage (#3545). - Added `maximum_inscribed_circle` method from shapely to GeoSeries/GeoDataFrame (#3544). - Added `minimum_clearance_line` method from shapely to GeoSeries/GeoDataFrame (#3543). - Added `orient_polygons` method from shapely to GeoSeries/GeoDataFrame (#3559). - Added ``method`` and ``keep_collapsed`` argument to ``make_valid`` (#3548). - Added `simplify_coverage` method for topological simplification of polygonal coverages to GeoSeries/GeoDataFrame (#3541). - Added initial support of M coordinates (`m` and `has_m` properties, `include_m` in `get_coordinates`) (#3561). - Added `geom_equals_identical` method exposing `equals_identical` from shapely to GeoSeries/GeoDataFrame (#3560). - GeoPandas now attempts to use a range request when reading from an URL even if the header does not directly indicate its support (#3572). - Added `geopandas.accessors` module. Import this module to register a `pandas.Series.geo` accessor, which exposes GeoSeries methods via pandas's extension mechanism (#3272). - Improve performance of `overlay` with `how=identity` (#3504). - A warning message is raised in `read_file` when a GeoDataFrame or GeoSeries mask and/or the source dataset is missing a defined CRS (#3464). - GeoDataFrame no longer hard-codes the class internally, allowing easier subclassing (#3505). Bug fixes: - Fix an issue that showed numpy dtypes in bbox in `to_geo_dict` and `__geo_interface__`. (#3436). - Fix an issue in `sample_points` that could occasionally result in non-uniform distribution (#3470). - Fix unspecified layer warning being emitted while reading multilayer datasets, even when layer is specified when using the mask or bbox keywords (#3378). - Properly support named aggregations over a geometry column in `GroupBy.agg` (#3368). - Support GeoDataFrame constructor receiving arguments to `geometry` which are not (Geo)Series, but instead should be interpreted as column names, like Enums (#3384). - Fix regression where constructing a GeoSeries from a pd.Series with GeometryDtype values failed when `crs` was provided (#3383). - Fix regression where `overlay` with `keep_geom_type` returns wrong results if the input contains invalid geometries (#3395). - Fix the dtype of the GeometryArray backing data being incorrect for zero length GeoDataFrames causing errors in `overlay` (#3424). - Fix regression where constructing a GeoSeries from a pd.Series with GeometryDtype values failed when `crs` was provided (#3383). - Fix plotting of polygons with holes by normalizing the coordinate order prior to plotting (#3483). - Fix an issue in plotting when polygon patches were not closed (#3576). - Fix ambiguous error when GeoDataFrame is initialised with a column called "crs" (#3502). - Avoid change of the plot aspect when plotting missing values (#3438). Deprecations and compatibility notes: - The `GeoSeries.select` method wrapping the pandas `Series.select` method has been removed. The upstream method no longer exists in all supported version of pandas (#3394). - The deprecated `geom_almost_equals` method has been removed. Use `geom_equals_exact` instead (#3522). ## Version 1.0.1 (July 2, 2024) Bug fixes: - Support a named datetime or object dtype index in `explore()` (#3360, #3364). - Fix a regression preventing a Series as an argument for geometric methods (#3363). ## Version 1.0.0 (June 24, 2024) Notes on dependencies: - GeoPandas 1.0 drops support for shapely<2 and PyGEOS. The only geometry engine that is currently supported is shapely >= 2. As a consequence, spatial indexing based on the rtree package has also been removed (#3035). - The I/O engine now defaults to Pyogrio which is now installed with GeoPandas instead of Fiona (#3223). New methods: - Added `count_geometries` method from shapely to GeoSeries/GeoDataFrame (#3154). - Added `count_interior_rings` method from shapely to GeoSeries/GeoDataFrame (#3154). - Added `relate_pattern` method from shapely to GeoSeries/GeoDataFrame (#3211). - Added `intersection_all` method from shapely to GeoSeries/GeoDataFrame (#3228). - Added `line_merge` method from shapely to GeoSeries/GeoDataFrame (#3214). - Added `set_precision` and `get_precision` methods from shapely to GeoSeries/GeoDataFrame (#3175). - Added `count_coordinates` method from shapely to GeoSeries/GeoDataFrame (#3026). - Added `minimum_clearance` method from shapely to GeoSeries/GeoDataFrame (#2989). - Added `shared_paths` method from shapely to GeoSeries/GeoDataFrame (#3215). - Added `is_ccw` method from shapely to GeoSeries/GeoDataFrame (#3027). - Added `is_closed` attribute from shapely to GeoSeries/GeoDataFrame (#3092). - Added `force_2d` and `force_3d` methods from shapely to GeoSeries/GeoDataFrame (#3090). - Added `voronoi_polygons` method from shapely to GeoSeries/GeoDataFrame (#3177). - Added `contains_properly` method from shapely to GeoSeries/GeoDataFrame (#3105). - Added `build_area` method exposing `build_area` shapely to GeoSeries/GeoDataFrame (#3202). - Added `snap` method from shapely to GeoSeries/GeoDataFrame (#3086). - Added `transform` method from shapely to GeoSeries/GeoDataFrame (#3075). - Added `get_geometry` method from shapely to GeoSeries/GeoDataFrame (#3287). - Added `dwithin` method to check for a "distance within" predicate on GeoSeries/GeoDataFrame (#3153). - Added `to_geo_dict` method to generate GeoJSON-like dictionary from a GeoDataFrame (#3132). - Added `polygonize` method exposing both `polygonize` and `polygonize_full` from shapely to GeoSeries/GeoDataFrame (#2963). - Added `is_valid_reason` method from shapely to GeoSeries/GeoDataFrame (#3176). - Added `to_arrow` method and `from_arrow` class method to GeoSeries/GeoDataFrame to export and import to/from Arrow data with GeoArrow extension types (#3219, #3301). New features and improvements: - Added ``predicate="dwithin"`` option and ``distance`` argument to the ``sindex.query()`` method and ``sjoin`` (#2882). - GeoSeries and GeoDataFrame `__repr__` now trims trailing zeros for a more readable output (#3087). - Add `on_invalid` parameter to `from_wkt` and `from_wkb` (#3110). - `make_valid` option in `overlay` now uses the `make_valid` method instead of `buffer(0)` (#3113). - Passing `"geometry"` as `dtype` to `pd.read_csv` will now return a GeoSeries for the specified columns (#3101). - Added support to ``read_file`` for the ``mask`` keyword for the pyogrio engine (#3062). - Added support to ``read_file`` for the ``columns`` keyword for the fiona engine (#3133). - Added support to ``to_parquet`` and ``read_parquet`` for writing and reading files using the GeoArrow-based native geometry encoding of GeoParquet 1.1 (#3253, #3275). - Add `sort` keyword to `clip` method for GeoSeries and GeoDataFrame to allow optional preservation of the original order of observations (#3233). - Added `show_bbox`, `drop_id` and `to_wgs84` arguments to allow further customization of `GeoSeries.to_json` (#3226). - `explore` now supports `GeoDataFrame`s with additional columns containing datetimes, uuids and other non JSON serializable objects (#3261). - The `GeoSeries.fillna` method now supports the `limit` keyword (#3290). - Added ``on_attribute`` option argument to the ``sjoin()`` method, allowing to restrict joins to the observations with matching attributes (#3231). - Added support for `bbox` covering encoding in geoparquet. Can filter reading of parquet files based on a bounding box, and write out a bounding box column to parquet files (#3282). - `align` keyword in binary methods now defaults to `None`, treated as True. Explicit True will silence the warning about mismatched indices (#3212). - `GeoSeries.set_crs` can now be used to remove CRS information by passing `crs=None, allow_override=True` (#3316). - Added ``autolim`` keyword argument to ``GeoSeries.plot()`` and ``GeoDataFrame.plot()`` (#2817). - Added `metadata` parameter to `GeoDataFrame.to_file` (#2850). - Updated documentation to clarify that passing a named (Geo)Series as the `geometry` argument to the GeoDataFrame constructor will not use the name but will always produce a GeoDataFrame with an active geometry column named "geometry" (#3337). - `read_postgis` will query the spatial_ref_sys table to determine the CRS authority instead of its current behaviour of assuming EPSG. In the event the spiatal_ref_sys table is not present, or the SRID is not present, `read_postgis` will fallback on assuming EPSG CRS authority (#3329). - Added ``GeoDataFrame.active_geometry_name`` property returning the active geometry column's name or None if no active geometry column is set (#2943). Backwards incompatible API changes: - The `sjoin` method will now preserve the name of the index of the right GeoDataFrame, if it has one, instead of always using `"index_right"` as the name for the resulting column in the return value (#846, #2144). - GeoPandas now raises a ValueError when an unaligned Series is passed as a method argument to avoid confusion of whether the automatic alignment happens or not (#3271). - The deprecated default value of GeoDataFrame/ GeoSeries `explode(.., index_parts=True)` is now set to false for consistency with pandas (#3174). - The behaviour of `set_geometry` has been changed when passed a (Geo)Series `ser` with a name. The new active geometry column name in this case will be `ser.name`, if not None, rather than the previous active geometry column name. This means that if the new and old names are different, then both columns will be preserved in the GeoDataFrame. To replicate the previous behaviour, you can instead call `gdf.set_geometry(ser.rename(gdf.active_geometry_name))` (#3237). Note that this behaviour change does not affect the `GeoDataFrame` constructor, passing a named GeoSeries `ser` to `GeoDataFrame(df, geometry=ser)` will always produce a GeoDataFrame with a geometry column named "geometry" to preserve backwards compatibility. If you would like to instead propagate the name of `ser` when constructing a GeoDataFrame, you can instead call `df.set_geometry(ser)` or `GeoDataFrame(df, geometry=ser).rename_geometry(ser.name)` (#3337). - `delaunay_triangles` now considers all geometries together when creating the Delaunay triangulation instead of performing the operation element-wise. If you want to generate Delaunay triangles for each geometry separately, use ``shapely.delaunay_triangles`` instead. (#3273) - Reading a data source that does not have a geometry field using ``read_file`` now returns a Pandas DataFrame instead of a GeoDataFrame with an empty ``geometry`` column. Enforced deprecations: - The deprecation of `geopandas.datasets` has been enforced and the module has been removed. New sample datasets are now available in the [geodatasets](https://geodatasets.readthedocs.io/en/latest/) package (#3084). - Many longstanding deprecated functions, methods and properties have been removed (#3174), (#3190) - Removed deprecated functions `geopandas.io.read_file`, `geopandas.io.to_file` and `geopandas.io.sql.read_postgis`. `geopandas.read_file`, `geopandas.read_postgis` and the GeoDataFrame/GeoSeries `to_file(..)` method should be used instead. - Removed deprecated `GeometryArray.data` property, `np.asarray(..)` or the `to_numpy()` method should be used instead. - Removed deprecated `sindex.query_bulk` method, using `sindex.query` instead. - Removed deprecated `sjoin` parameter `op`, `predicate` should be supplied instead. - Removed deprecated GeoSeries/ GeoDataFrame methods `__xor__`, `__or__`, `__and__` and `__sub__`. Instead use methods `symmetric_difference`, `union`, `intersection` and `difference` respectively. - Removed deprecated plotting functions `plot_polygon_collection`, `plot_linestring_collection` and `plot_point_collection`, use the GeoSeries/GeoDataFrame `.plot` method directly instead. - Removed deprecated GeoSeries/GeoDataFrame `.plot` parameters `axes` and `colormap`, instead use `ax` and `cmap` respectively. - Removed compatibility for specifying the `version` keyword in `to_parquet` and `to_feather`. This keyword will now be passed through to pyarrow and use `schema_version` to specify the GeoParquet specification version (#3334). New deprecations: - `unary_union` attribute is now deprecated and replaced by the `union_all()` method (#3007) allowing opting for a faster union algorithm for coverages (#3151). - The ``include_fields`` and ``ignore_fields`` keywords in ``read_file()`` are deprecated for the default pyogrio engine. Currently those are translated to the ``columns`` keyword for backwards compatibility, but you should directly use the ``columns`` keyword instead to select which columns to read (#3133). - The `drop` keyword in `set_geometry` has been deprecated, and in future the `drop=True` behaviour will be removed (#3237). To prepare for this change, you should remove any explicit `drop=False` calls in your code (the default behaviour already is the same as `drop=False`). To replicate the previous `drop=True` behaviour you should replace `gdf.set_geometry(new_geo_col, drop=True)` with ```python geo_col_name = gdf.active_geometry_name gdf.set_geometry(new_geo_col).drop(columns=geo_col_name).rename_geometry(geo_col_name) ``` - The `geopandas.use_pygeos` option has been deprecated and will be removed in GeoPandas 1.1 (#3283) - Manual overriding of an existing CRS of a GeoSeries or GeoDataFrame by setting the `crs` property has been deprecated and will be disabled in future. Use the `set_crs()` method instead (#3085). Bug fixes: - Fix `GeoDataFrame.merge()` incorrectly returning a `DataFrame` instead of a `GeoDataFrame` when the `suffixes` argument is applied to the active geometry column (#2933). - Fix bug in `GeoDataFrame` constructor where if `geometry` is given a named `GeoSeries` the name was not used as the active geometry column name (#3237). - Fix bug in `GeoSeries` constructor when passing a Series and specifying a `crs` to not change the original input data (#2492). - Fix regression preventing reading from file paths containing hashes in `read_file` with the fiona engine (#3280). An analogous fix for pyogrio is included in pyogrio 0.8.1. - Fix `to_parquet` to write correct metadata in case of 3D geometries (#2824). - Fixes for compatibility with psycopg (#3167). - Fix to allow appending dataframes with no CRS to PostGIS tables with no CRS (#3328) - Fix plotting of all-empty GeoSeries using `explore` (#3316). ## Version 0.14.4 (April 26, 2024) - Several fixes for compatibility with the upcoming pandas 3.0, numpy 2.0 and fiona 1.10 releases. ## Version 0.14.3 (Jan 31, 2024) - Several fixes for compatibility with the latest pandas 2.2 release. - Fix bug in `pandas.concat` CRS consistency checking where CRS differing by WKT whitespace only were treated as incompatible (#3023). ## Version 0.14.2 (Jan 4, 2024) - Fix regression in `overlay` where using `buffer(0)` instead of `make_valid` internally produced invalid results (#3074). - Fix `explore()` method when the active geometry contains missing and empty geometries (#3094). ## Version 0.14.1 (Nov 11, 2023) - The Parquet and Feather IO functions now support the latest 1.0.0 version of the GeoParquet specification (geoparquet.org) (#2663). - Fix `read_parquet` and `read_feather` for [CVE-2023-47248](https://www.cve.org/CVERecord?id=CVE-2023-47248>) (#3070). ## Version 0.14 (Sep 15, 2023) GeoPandas will use Shapely 2.0 by default instead of PyGEOS when both Shapely >= 2.0 and PyGEOS are installed. PyGEOS will continue to be used by default when PyGEOS is installed alongside Shapely < 2.0. Support for PyGEOS and Shapely < 2.0 will be removed in GeoPandas 1.0. (#2999) API changes: - ``seed`` keyword in ``sample_points`` is deprecated. Use ``rng`` instead. (#2913). New methods: - Added ``concave_hull`` method from shapely to GeoSeries/GeoDataFrame (#2903). - Added ``delaunay_triangles`` method from shapely to GeoSeries/GeoDataFrame (#2907). - Added ``extract_unique_points`` method from shapely to GeoSeries/GeoDataFrame (#2915). - Added ``frechet_distance()`` method from shapely to GeoSeries/GeoDataFrame (#2929). - Added ``hausdorff_distance`` method from shapely to GeoSeries/GeoDataFrame (#2909). - Added ``minimum_rotated_rectangle`` method from shapely to GeoSeries/GeoDataFrame (#2541). - Added ``offset_curve`` method from shapely to GeoSeries/GeoDataFrame (#2902). - Added ``remove_repeated_points`` method from shapely to GeoSeries/GeoDataFrame (#2940). - Added ``reverse`` method from shapely to GeoSeries/GeoDataFrame (#2988). - Added ``segmentize`` method from shapely to GeoSeries/GeoDataFrame (#2910). - Added ``shortest_line`` method from shapely to GeoSeries/GeoDataFrame (#2960). New features and improvements: - Added ``exclusive`` parameter to ``sjoin_nearest`` method for Shapely >= 2.0 (#2877) - The ``to_file()`` method will now automatically detect the FlatGeoBuf driver for files with the `.fgb` extension (#2958) Bug fixes: - Fix ambiguous error when GeoDataFrame is initialized with a column called ``"crs"`` (#2944) - Fix a color assignment in ``explore`` when using ``UserDefined`` bins (#2923) - Fix bug in `apply` with `axis=1` where the given user defined function returns nested data in the geometry column (#2959) - Properly infer schema for ``np.int32`` and ``pd.Int32Dtype`` columns (#2950) - ``assert_geodataframe_equal`` now handles GeoDataFrames with no active geometry (#2498) Notes on (optional) dependencies: - GeoPandas 0.14 drops support for Python 3.8 and pandas 1.3 and below (the minimum supported pandas version is now 1.4). Further, the minimum required versions for the listed dependencies have now changed to shapely 1.8.0, fiona 1.8.21, pyproj 3.3.0 and matplotlib 3.5.0 (#3001) Deprecations and compatibility notes: - `geom_almost_equals()` methods have been deprecated and `geom_equals_exact()` should be used instead (#2604). ## Version 0.13.2 (Jun 6, 2023) Bug fix: - Fix a regression in reading from local file URIs (``file://..``) using ``geopandas.read_file`` (#2948). ## Version 0.13.1 (Jun 5, 2023) Bug fix: - Fix a regression in reading from URLs using ``geopandas.read_file`` (#2908). This restores the behaviour to download all data up-front before passing it to the underlying engine (fiona or pyogrio), except if the server supports partial requests (to support reading a subset of a large file). ## Version 0.13 (May 6, 2023) New methods: - Added ``sample_points`` method to sample random points from Polygon or LineString geometries (#2860). - New ``hilbert_distance()`` method that calculates the distance along a Hilbert curve for each geometry in a GeoSeries/GeoDataFrame (#2297). - Support for sorting geometries (for example, using ``sort_values()``) based on the distance along the Hilbert curve (#2070). - Added ``get_coordinates()`` method from shapely to GeoSeries/GeoDataFrame (#2624). - Added ``minimum_bounding_circle()`` method from shapely to GeoSeries/GeoDataFrame (#2621). - Added `minimum_bounding_radius()` as GeoSeries method (#2827). Other new features and improvements: - The Parquet and Feather IO functions now support the latest 1.0.0-beta.1 version of the GeoParquet specification () (#2663). - Added support to fill missing values in `GeoSeries.fillna` via another `GeoSeries` (#2535). - Support specifying ``min_zoom`` and ``max_zoom`` inside the ``map_kwds`` argument for ``.explore()`` (#2599). - Added support for append (``mode="a"`` or ``append=True``) in ``to_file()`` using ``engine="pyogrio"`` (#2788). - Added a ``to_wgs84`` keyword to ``to_json`` allowing automatic re-projecting to follow the 2016 GeoJSON specification (#416). - ``to_json`` output now includes a ``"crs"`` field if the CRS is not the default WGS84 (#1774). - Improve error messages when accessing the `geometry` attribute of GeoDataFrame without an active geometry column related to the default name `"geometry"` being provided in the constructor (#2577) Deprecations and compatibility notes: - Added warning that ``unary_union`` will return ``'GEOMETRYCOLLECTION EMPTY'`` instead of None for all-None GeoSeries. (#2618) - The ``query_bulk()`` method of the spatial index `.sindex` property is deprecated in favor of ``query()`` (#2823). Bug fixes: - Ensure that GeoDataFrame created from DataFrame is a copy, not a view (#2667) - Fix mismatch between geometries and colors in ``plot()`` if an empty or missing geometry is present (#2224) - Escape special characters to avoid TemplateSyntaxError in ``explore()`` (#2657) - Fix `to_parquet`/`to_feather` to not write an invalid bbox (with NaNs) in the metadata in case of an empty GeoDataFrame (#2653) - Fix `to_parquet`/`to_feather` to use correct WKB flavor for 3D geometries (#2654) - Fix `read_file` to avoid reading all file bytes prior to calling Fiona or Pyogrio if provided a URL as input (#2796) - Fix `copy()` downcasting GeoDataFrames without an active geometry column to a DataFrame (#2775) - Fix geometry column name propagation when GeoDataFrame columns are a multiindex (#2088) - Fix `iterfeatures()` method of GeoDataFrame to correctly handle non-scalar values when `na='drop'` is specified (#2811) - Fix issue with passing custom legend labels to `plot` (#2886) Notes on (optional) dependencies: - GeoPandas 0.13 drops support pandas 1.0.5 (the minimum supported pandas version is now 1.1). Further, the minimum required versions for the listed dependencies have now changed to shapely 1.7.1, fiona 1.8.19, pyproj 3.0.1 and matplotlib 3.3.4 (#2655) ## Version 0.12.2 (December 10, 2022) Bug fixes: - Correctly handle geometries with Z dimension in ``to_crs()`` when using PyGEOS or Shapely >= 2.0 (previously the z coordinates were lost) (#1345). - Assign Crimea to Ukraine in the ``naturalearth_lowres`` built-in dataset (#2670) ## Version 0.12.1 (October 29, 2022) Small bug-fix release removing the shapely<2 pin in the installation requirements. ## Version 0.12 (October 24, 2022) The highlight of this release is the support for Shapely 2.0. This makes it possible to test Shapely 2.0 (currently 2.0b1) alongside GeoPandas. Note that if you also have PyGEOS installed, you need to set an environment variable (`USE_PYGEOS=0`) before importing geopandas to actually test Shapely 2.0 features instead of PyGEOS. See for more details. New features and improvements: - Added ``normalize()`` method from shapely to GeoSeries/GeoDataFrame (#2537). - Added ``make_valid()`` method from shapely to GeoSeries/GeoDataFrame (#2539). - Added ``where`` filter to ``read_file`` (#2552). - Updated the distributed natural earth datasets (*naturalearth_lowres* and *naturalearth_cities*) to version 5.1 (#2555). Deprecations and compatibility notes: - Accessing the `crs` of a `GeoDataFrame` without active geometry column was deprecated and this now raises an AttributeError (#2578). - Resolved colormap-related warning in ``.explore()`` for recent Matplotlib versions (#2596). Bug fixes: - Fix cryptic error message in ``geopandas.clip()`` when clipping with an empty geometry (#2589). - Accessing `gdf.geometry` where the active geometry column is missing, and a column named `"geometry"` is present will now raise an `AttributeError`, rather than returning `gdf["geometry"]` (#2575). - Combining GeoSeries/GeoDataFrames with ``pandas.concat`` will no longer silently override CRS information if not all inputs have the same CRS (#2056). ## Version 0.11.1 (July 24, 2022) Small bug-fix release: - Fix regression (RecursionError) in reshape methods such as ``unstack()`` and ``pivot()`` involving MultiIndex, or GeoDataFrame construction with MultiIndex (#2486). - Fix regression in ``GeoDataFrame.explode()`` with non-default geometry column name. - Fix regression in ``apply()`` causing row-wise all nan float columns to be casted to GeometryDtype (#2482). - Fix a crash in datetime column reading where the file contains mixed timezone offsets (#2479). These will be read as UTC localized values. - Fix a crash in datetime column reading where the file contains datetimes outside the range supported by [ns] precision (#2505). - Fix regression in passing the Parquet or Feather format ``version`` in ``to_parquet`` and ``to_feather``. As a result, the ``version`` parameter for the ``to_parquet`` and ``to_feather`` methods has been replaced with ``schema_version``. ``version`` will be passed directly to underlying feather or parquet writer. ``version`` will only be used to set ``schema_version`` if ``version`` is one of 0.1.0 or 0.4.0 (#2496). Version 0.11 (June 20, 2022) ---------------------------- Highlights of this release: - The ``geopandas.read_file()`` and `GeoDataFrame.to_file()` methods to read and write GIS file formats can now optionally use the [pyogrio](https://github.com/geopandas/pyogrio/) package under the hood through the ``engine="pyogrio"`` keyword. The pyogrio package implements vectorized IO for GDAL/OGR vector data sources, and is faster compared to the ``fiona``-based engine (#2225). - GeoParquet support updated to implement [v0.4.0](https://github.com/opengeospatial/geoparquet/releases/tag/v0.4.0) of the OpenGeospatial/GeoParquet specification (#2441). Backwards compatibility with v0.1.0 of the metadata spec (implemented in the previous releases of GeoPandas) is guaranteed, and reading and writing Parquet and Feather files will no longer produce a ``UserWarning`` (#2327). New features and improvements: - Improved handling of GeoDataFrame when the active geometry column is lost from the GeoDataFrame. Previously, square bracket indexing ``gdf[[...]]`` returned a GeoDataFrame when the active geometry column was retained and a DataFrame was returned otherwise. Other pandas indexing methods (``loc``, ``iloc``, etc) did not follow the same rules. The new behaviour for all indexing/reshaping operations is now as follows (#2329, #2060): - If operations produce a ``DataFrame`` containing the active geometry column, a GeoDataFrame is returned - If operations produce a ``DataFrame`` containing ``GeometryDtype`` columns, but not the active geometry column, a ``GeoDataFrame`` is returned, where the active geometry column is set to ``None`` (set the new geometry column with ``set_geometry()``) - If operations produce a ``DataFrame`` containing no ``GeometryDtype`` columns, a ``DataFrame`` is returned (this can be upcast again by calling ``set_geometry()`` or the ``GeoDataFrame`` constructor) - If operations produce a ``Series`` of ``GeometryDtype``, a ``GeoSeries`` is returned, otherwise ``Series`` is returned. - Error messages for having an invalid geometry column have been improved, indicating the name of the last valid active geometry column set and whether other geometry columns can be promoted to the active geometry column (#2329). - Datetime fields are now read and written correctly for GIS formats which support them (e.g. GPKG, GeoJSON) with fiona 1.8.14 or higher. Previously, datetimes were read as strings (#2202). - ``folium.Map`` keyword arguments can now be specified as the ``map_kwds`` argument to ``GeoDataFrame.explore()`` method (#2315). - Add a new parameter ``style_function`` to ``GeoDataFrame.explore()`` to enable plot styling based on GeoJSON properties (#2377). - It is now possible to write an empty ``GeoDataFrame`` to a file for supported formats (#2240). Attempting to do so will now emit a ``UserWarning`` instead of a ``ValueError``. - Fast rectangle clipping has been exposed as ``GeoSeries/GeoDataFrame.clip_by_rect()`` (#1928). - The ``mask`` parameter of ``GeoSeries/GeoDataFrame.clip()`` now accepts a rectangular mask as a list-like to perform fast rectangle clipping using the new ``GeoSeries/GeoDataFrame.clip_by_rect()`` (#2414). - Bundled demo dataset ``naturalearth_lowres`` has been updated to version 5.0.1 of the source, with field ``ISO_A3`` manually corrected for some cases (#2418). Deprecations and compatibility notes: - The active development branch of geopandas on GitHub has been renamed from master to main (#2277). - Deprecated methods ``GeometryArray.equals_exact()`` and ``GeometryArray.almost_equals()`` have been removed. They should be replaced with ``GeometryArray.geom_equals_exact()`` and ``GeometryArray.geom_almost_equals()`` respectively (#2267). - Deprecated CRS functions ``explicit_crs_from_epsg()``, ``epsg_from_crs()`` and ``get_epsg_file_contents()`` were removed (#2340). - Warning about the behaviour change to ``GeoSeries.isna()`` with empty geometries present has been removed (#2349). - Specifying a CRS in the ``GeoDataFrame/GeoSeries`` constructor which contradicted the underlying ``GeometryArray`` now raises a ``ValueError`` (#2100). - Specifying a CRS in the ``GeoDataFrame`` constructor when no geometry column is provided and calling ``GeoDataFrame. set_crs`` on a ``GeoDataFrame`` without an active geometry column now raise a ``ValueError`` (#2100) - Passing non-geometry data to the``GeoSeries`` constructor is now fully deprecated and will raise a ``TypeError`` (#2314). Previously, a ``pandas.Series`` was returned for non-geometry data. - Deprecated ``GeoSeries/GeoDataFrame`` set operations ``__xor__()``, ``__or__()``, ``__and__()`` and ``__sub__()``, ``geopandas.io.file.read_file``/``to_file`` and ``geopandas.io.sql.read_postgis`` now emit ``FutureWarning`` instead of ``DeprecationWarning`` and will be completely removed in a future release. - Accessing the ``crs`` of a ``GeoDataFrame`` without active geometry column is deprecated and will be removed in GeoPandas 0.12 (#2373). Bug fixes: - ``GeoSeries.to_frame`` now creates a ``GeoDataFrame`` with the geometry column name set correctly (#2296) - Fix pickle files created with pygeos installed can not being readable when pygeos is not installed (#2237). - Fixed ``UnboundLocalError`` in ``GeoDataFrame.plot()`` using ``legend=True`` and ``missing_kwds`` (#2281). - Fix ``explode()`` incorrectly relating index to columns, including where the input index is not unique (#2292) - Fix ``GeoSeries.[xyz]`` raising an ``IndexError`` when the underlying GeoSeries contains empty points (#2335). Rows corresponding to empty points now contain ``np.nan``. - Fix ``GeoDataFrame.iloc`` raising a ``TypeError`` when indexing a ``GeoDataFrame`` with only a single column of ``GeometryDtype`` (#1970). - Fix ``GeoDataFrame.iterfeatures()`` not returning features with the same field order as ``GeoDataFrame.columns`` (#2396). - Fix ``GeoDataFrame.from_features()`` to support reading GeoJSON with null properties (#2243). - Fix ``GeoDataFrame.to_parquet()`` not intercepting ``engine`` keyword argument, breaking consistency with pandas (#2227) - Fix ``GeoDataFrame.explore()`` producing an error when ``column`` is of boolean dtype (#2403). - Fix an issue where ``GeoDataFrame.to_postgis()`` output the wrong SRID for ESRI authority CRS (#2414). - Fix ``GeoDataFrame.from_dict/from_features`` classmethods using ``GeoDataFrame`` rather than ``cls`` as the constructor. - Fix ``GeoDataFrame.plot()`` producing incorrect colors with mixed geometry types when ``colors`` keyword is provided. (#2420) Notes on (optional) dependencies: - GeoPandas 0.11 drops support for Python 3.7 and pandas 0.25 (the minimum supported pandas version is now 1.0.5). Further, the minimum required versions for the listed dependencies have now changed to shapely 1.7, fiona 1.8.13.post1, pyproj 2.6.1.post1, matplotlib 3.2, mapclassify 2.4.0 (#2358, #2391) Version 0.10.2 (October 16, 2021) --------------------------------- Small bug-fix release: - Fix regression in ``overlay()`` in case no geometries are intersecting (but have overlapping total bounds) (#2172). - Fix regression in ``overlay()`` with ``keep_geom_type=True`` in case the overlay of two geometries in a GeometryCollection with other geometry types (#2177). - Fix ``overlay()`` to honor the ``keep_geom_type`` keyword for the ``op="difference"`` case (#2164). - Fix regression in ``plot()`` with a mapclassify ``scheme`` in case the formatted legend labels have duplicates (#2166). - Fix a bug in the ``explore()`` method ignoring the ``vmin`` and ``vmax`` keywords in case they are set to 0 (#2175). - Fix ``unary_union`` to correctly handle a GeoSeries with missing values (#2181). - Avoid internal deprecation warning in ``clip()`` (#2179). Version 0.10.1 (October 8, 2021) -------------------------------- Small bug-fix release: - Fix regression in ``overlay()`` with non-overlapping geometries and a non-default ``how`` (i.e. not "intersection") (#2157). Version 0.10.0 (October 3, 2021) -------------------------------- Highlights of this release: - A new ``sjoin_nearest()`` method to join based on proximity, with the ability to set a maximum search radius (#1865). In addition, the ``sindex`` attribute gained a new method for a "nearest" spatial index query (#1865, #2053). - A new ``explore()`` method on GeoDataFrame and GeoSeries with native support for interactive visualization based on folium / leaflet.js (#1953) - The ``geopandas.sjoin()``/``overlay()``/``clip()`` functions are now also available as methods on the GeoDataFrame (#2141, #1984, #2150). New features and improvements: - Add support for pandas' ``value_counts()`` method for geometry dtype (#2047). - The ``explode()`` method has a new ``ignore_index`` keyword (consistent with pandas' explode method) to reset the index in the result, and a new ``index_parts`` keywords to control whether a cumulative count indexing the parts of the exploded multi-geometries should be added (#1871). - ``points_from_xy()`` is now available as a GeoSeries method ``from_xy`` (#1936). - The ``to_file()`` method will now attempt to detect the driver (if not specified) based on the extension of the provided filename, instead of defaulting to ESRI Shapefile (#1609). - Support for the ``storage_options`` keyword in ``read_parquet()`` for specifying filesystem-specific options (e.g. for S3) based on fsspec (#2107). - The read/write functions now support ``~`` (user home directory) expansion (#1876). - Support the ``convert_dtypes()`` method from pandas to preserve the GeoDataFrame class (#2115). - Support WKB values in the hex format in ``GeoSeries.from_wkb()`` (#2106). - Update the ``estimate_utm_crs()`` method to handle crossing the antimeridian with pyproj 3.1+ (#2049). - Improved heuristic to decide how many decimals to show in the repr based on whether the CRS is projected or geographic (#1895). - Switched the default for ``geocode()`` from GeoCode.Farm to the Photon geocoding API () (#2007). Deprecations and compatibility notes: - The ``op=`` keyword of ``sjoin()`` to indicate which spatial predicate to use for joining is being deprecated and renamed in favor of a new ``predicate=`` keyword (#1626). - The ``cascaded_union`` attribute is deprecated, use ``unary_union`` instead (#2074). - Constructing a GeoDataFrame with a duplicated "geometry" column is now disallowed. This can also raise an error in the ``pd.concat(.., axis=1)`` function if this results in duplicated active geometry columns (#2046). - The ``explode()`` method currently returns a GeoSeries/GeoDataFrame with a MultiIndex, with an additional level with indices of the parts of the exploded multi-geometries. For consistency with pandas, this will change in the future and the new ``index_parts`` keyword is added to control this. Bug fixes: - Fix in the ``clip()`` function to correctly clip MultiPoints instead of leaving them intact when partly outside of the clip bounds (#2148). - Fix ``GeoSeries.isna()`` to correctly return a boolean Series in case of an empty GeoSeries (#2073). - Fix the GeoDataFrame constructor to preserve the geometry name when the argument is already a GeoDataFrame object (i.e. ``GeoDataFrame(gdf)``) (#2138). - Fix loss of the values' CRS when setting those values as a column (``GeoDataFrame.__setitem__``) (#1963) - Fix in ``GeoDataFrame.apply()`` to preserve the active geometry column name (#1955). - Fix in ``sjoin()`` to not ignore the suffixes in case of a right-join (``how="right``) (#2065). - Fix ``GeoDataFrame.explode()`` with a MultiIndex (#1945). - Fix the handling of missing values in ``to/from_wkb`` and ``to_from_wkt`` (#1891). - Fix ``to_file()`` and ``to_json()`` when DataFrame has duplicate columns to raise an error (#1900). - Fix bug in the colors shown with user-defined classification scheme (#2019). - Fix handling of the ``path_effects`` keyword in ``plot()`` (#2127). - Fix ``GeoDataFrame.explode()`` to preserve ``attrs`` (#1935) Notes on (optional) dependencies: - GeoPandas 0.10.0 dropped support for Python 3.6 and pandas 0.24. Further, the minimum required versions are numpy 1.18, shapely 1.6, fiona 1.8, matplotlib 3.1 and pyproj 2.2. - Plotting with a classification schema now requires mapclassify version >= 2.4 (#1737). - Compatibility fixes for the latest numpy in combination with Shapely 1.7 (#2072) - Compatibility fixes for the upcoming Shapely 1.8 (#2087). - Compatibility fixes for the latest PyGEOS (#1872, #2014) and matplotlib (colorbar issue, #2066). Version 0.9.0 (February 28, 2021) --------------------------------- Many documentation improvements and a restyled and restructured website with a new logo (#1564, #1579, #1617, #1668, #1731, #1750, #1757, #1759). New features and improvements: - The ``geopandas.read_file`` function now accepts more general file-like objects (e.g. ``fsspec`` open file objects). It will now also automatically recognize zipped files (#1535). - The ``GeoDataFrame.plot()`` method now provides access to the pandas plotting functionality for the non-geometry columns, either using the ``kind`` keyword or the accessor method (e.g. ``gdf.plot(kind="bar")`` or ``gdf.plot.bar()``) (#1465). - New ``from_wkt()``, ``from_wkb()``, ``to_wkt()``, ``to_wkb()`` methods for GeoSeries to construct a GeoSeries from geometries in WKT or WKB representation, or to convert a GeoSeries to a pandas Seriew with WKT or WKB values (#1710). - New ``GeoSeries.z`` attribute to access the z-coordinates of Point geometries (similar to the existing ``.x`` and ``.y`` attributes) (#1773). - The ``to_crs()`` method now handles missing values (#1618). - Support for pandas' new ``.attrs`` functionality (#1658). - The ``dissolve()`` method now allows dissolving by no column (``by=None``) to create a union of all geometries (single-row GeoDataFrame) (#1568). - New ``estimate_utm_crs()`` method on GeoSeries/GeoDataFrame to determine the UTM CRS based on the bounds (#1646). - ``GeoDataFrame.from_dict()`` now accepts ``geometry`` and ``crs`` keywords (#1619). - ``GeoDataFrame.to_postgis()`` and ``geopandas.read_postgis()`` now supports both sqlalchemy engine and connection objects (#1638). - The ``GeoDataFrame.explode()`` method now allows exploding based on a non-geometry column, using the pandas implementation (#1720). - Performance improvement in ``GeoDataFrame/GeoSeries.explode()`` when using the PyGEOS backend (#1693). - The binary operation and predicate methods (eg ``intersection()``, ``intersects()``) have a new ``align`` keyword which allows optionally not aligning on the index before performing the operation with ``align=False`` (#1668). - The ``GeoDataFrame.dissolve()`` method now supports all relevant keywords of ``groupby()``, i.e. the ``level``, ``sort``, ``observed`` and ``dropna`` keywords (#1845). - The ``geopandas.overlay()`` function now accepts ``make_valid=False`` to skip the step to ensure the input geometries are valid using ``buffer(0)`` (#1802). - The ``GeoDataFrame.to_json()`` method gained a ``drop_id`` keyword to optionally not write the GeoDataFrame's index as the "id" field in the resulting JSON (#1637). - A new ``aspect`` keyword in the plotting methods to optionally allow retaining the original aspect (#1512) - A new ``interval`` keyword in the ``legend_kwds`` group of the ``plot()`` method to control the appearance of the legend labels when using a classification scheme (#1605). - The spatial index of a GeoSeries (accessed with the ``sindex`` attribute) is now stored on the underlying array. This ensures that the spatial index is preserved in more operations where possible, and that multiple geometry columns of a GeoDataFrame can each have a spatial index (#1444). - Addition of a ``has_sindex`` attribute on the GeoSeries/GeoDataFrame to check if a spatial index has already been initialized (#1627). - The ``geopandas.testing.assert_geoseries_equal()`` and ``assert_geodataframe_equal()`` testing utilities now have a ``normalize`` keyword (False by default) to normalize geometries before comparing for equality (#1826). Those functions now also give a more informative error message when failing (#1808). Deprecations and compatibility notes: - The ``is_ring`` attribute currently returns True for Polygons. In the future, this will be False (#1631). In addition, start to check it for LineStrings and LinearRings (instead of always returning False). - The deprecated ``objects`` keyword in the ``intersection()`` method of the ``GeoDataFrame/GeoSeries.sindex`` spatial index object has been removed (#1444). Bug fixes: - Fix regression in the ``plot()`` method raising an error with empty geometries (#1702, #1828). - Fix ``geopandas.overlay()`` to preserve geometries of the correct type which are nested within a GeometryCollection as a result of the overlay operation (#1582). In addition, a warning will now be raised if geometries of different type are dropped from the result (#1554). - Fix the repr of an empty GeoSeries to not show spurious warnings (#1673). - Fix the ``.crs`` for empty GeoDataFrames (#1560). - Fix ``geopandas.clip`` to preserve the correct geometry column name (#1566). - Fix bug in ``plot()`` method when using ``legend_kwds`` with multiple subplots (#1583) - Fix spurious warning with ``missing_kwds`` keyword of the ``plot()`` method when there are no areas with missing data (#1600). - Fix the ``plot()`` method to correctly align values passed to the ``column`` keyword as a pandas Series (#1670). - Fix bug in plotting MultiPoints when passing values to determine the color (#1694) - The ``rename_geometry()`` method now raises a more informative error message when a duplicate column name is used (#1602). - Fix ``explode()`` method to preserve the CRS (#1655) - Fix the ``GeoSeries.apply()`` method to again accept the ``convert_dtype`` keyword to be consistent with pandas (#1636). - Fix ``GeoDataFrame.apply()`` to preserve the CRS when possible (#1848). - Fix bug in containment test as ``geom in geoseries`` (#1753). - The ``shift()`` method of a GeoSeries/GeoDataFrame now preserves the CRS (#1744). - The PostGIS IO functionality now quotes table names to ensure it works with case-sensitive names (#1825). - Fix the ``GeoSeries`` constructor without passing data but only an index (#1798). Notes on (optional) dependencies: - GeoPandas 0.9.0 dropped support for Python 3.5. Further, the minimum required versions are pandas 0.24, numpy 1.15 and shapely 1.6 and fiona 1.8. - The ``descartes`` package is no longer required for plotting polygons. This functionality is now included by default in GeoPandas itself, when matplotlib is available (#1677). - Fiona is now only imported when used in ``read_file``/``to_file``. This means you can now force geopandas to install without fiona installed (although it is still a default requirement) (#1775). - Compatibility with the upcoming Shapely 1.8 (#1659, #1662, #1819). Version 0.8.2 (January 25, 2021) -------------------------------- Small bug-fix release for compatibility with PyGEOS 0.9. Version 0.8.1 (July 15, 2020) ----------------------------- Small bug-fix release: - Fix a regression in the ``plot()`` method when visualizing with a JenksCaspallSampled or FisherJenksSampled scheme (#1486). - Fix spurious warning in ``GeoDataFrame.to_postgis`` (#1497). - Fix the un-pickling with ``pd.read_pickle`` of files written with older GeoPandas versions (#1511). Version 0.8.0 (June 24, 2020) ----------------------------- **Experimental**: optional use of PyGEOS to speed up spatial operations (#1155). PyGEOS is a faster alternative for Shapely (being contributed back to a future version of Shapely), and is used in element-wise spatial operations and for spatial index in e.g. ``sjoin`` (#1343, #1401, #1421, #1427, #1428). See the [installation docs](https://geopandas.readthedocs.io/en/latest/install.html#using-the-optional-pygeos-dependency) for more info and how to enable it. New features and improvements: - IO enhancements: - New ``GeoDataFrame.to_postgis()`` method to write to PostGIS database (#1248). - New Apache Parquet and Feather file format support (#1180, #1435) - Allow appending to files with ``GeoDataFrame.to_file`` (#1229). - Add support for the ``ignore_geometry`` keyword in ``read_file`` to only read the attribute data. If set to True, a pandas DataFrame without geometry is returned (#1383). - ``geopandas.read_file`` now supports reading from file-like objects (#1329). - ``GeoDataFrame.to_file`` now supports specifying the CRS to write to the file (#802). By default it still uses the CRS of the GeoDataFrame. - New ``chunksize`` keyword in ``geopandas.read_postgis`` to read a query in chunks (#1123). - Improvements related to geometry columns and CRS: - Any column of the GeoDataFrame that has a "geometry" dtype is now returned as a GeoSeries. This means that when having multiple geometry columns, not only the "active" geometry column is returned as a GeoSeries, but also accessing another geometry column (``gdf["other_geom_column"]``) gives a GeoSeries (#1336). - Multiple geometry columns in a GeoDataFrame can now each have a different CRS. The global ``gdf.crs`` attribute continues to returns the CRS of the "active" geometry column. The CRS of other geometry columns can be accessed from the column itself (eg ``gdf["other_geom_column"].crs``) (#1339). - New ``set_crs()`` method on GeoDataFrame/GeoSeries to set the CRS of naive geometries (#747). - Improvements related to plotting: - The y-axis is now scaled depending on the center of the plot when using a geographic CRS, instead of using an equal aspect ratio (#1290). - When passing a column of categorical dtype to the ``column=`` keyword of the GeoDataFrame ``plot()``, we now honor all categories and its order (#1483). In addition, a new ``categories`` keyword allows to specify all categories and their order otherwise (#1173). - For choropleths using a classification scheme (using ``scheme=``), the ``legend_kwds`` accept two new keywords to control the formatting of the legend: ``fmt`` with a format string for the bin edges (#1253), and ``labels`` to pass fully custom class labels (#1302). - New ``covers()`` and ``covered_by()`` methods on GeoSeries/GeoDataFrame for the equivalent spatial predicates (#1460, #1462). - GeoPandas now warns when using distance-based methods with data in a geographic projection (#1378). Deprecations: - When constructing a GeoSeries or GeoDataFrame from data that already has a CRS, a deprecation warning is raised when both CRS don't match, and in the future an error will be raised in such a case. You can use the new ``set_crs`` method to override an existing CRS. See [the docs](https://geopandas.readthedocs.io/en/latest/projections.html#projection-for-multiple-geometry-columns). - The helper functions in the ``geopandas.plotting`` module are deprecated for public usage (#656). - The ``geopandas.io`` functions are deprecated, use the top-level ``read_file`` and ``to_file`` instead (#1407). - The set operators (``&``, ``|``, ``^``, ``-``) are deprecated, use the ``intersection()``, ``union()``, ``symmetric_difference()``, ``difference()`` methods instead (#1255). - The ``sindex`` for empty dataframe will in the future return an empty spatial index instead of ``None`` (#1438). - The ``objects`` keyword in the ``intersection`` method of the spatial index returned by the ``sindex`` attribute is deprecated and will be removed in the future (#1440). Bug fixes: - Fix the ``total_bounds()`` method to ignore missing and empty geometries (#1312). - Fix ``geopandas.clip`` when masking with non-overlapping area resulting in an empty GeoDataFrame (#1309, #1365). - Fix error in ``geopandas.sjoin`` when joining on an empty geometry column (#1318). - CRS related fixes: ``pandas.concat`` preserves CRS when concatenating GeoSeries objects (#1340), preserve the CRS in ``geopandas.clip`` (#1362) and in ``GeoDataFrame.astype`` (#1366). - Fix bug in ``GeoDataFrame.explode()`` when 'level_1' is one of the column names (#1445). - Better error message when rtree is not installed (#1425). - Fix bug in ``GeoSeries.equals()`` (#1451). - Fix plotting of multi-part geometries with additional style keywords (#1385). And we now have a [Code of Conduct](https://github.com/geopandas/geopandas/blob/main/CODE_OF_CONDUCT.md)! GeoPandas 0.8.0 is the last release to support Python 3.5. The next release will require Python 3.6, pandas 0.24, numpy 1.15 and shapely 1.6 or higher. Version 0.7.0 (February 16, 2020) --------------------------------- Support for Python 2.7 has been dropped. GeoPandas now works with Python >= 3.5. The important API change of this release is that GeoPandas now requires PROJ > 6 and pyproj > 2.2, and that the ``.crs`` attribute of a GeoSeries and GeoDataFrame no longer stores the CRS information as a proj4 string or dict, but as a ``pyproj.CRS`` object (#1101). This gives a better user interface and integrates improvements from pyproj and PROJ 6, but might also require some changes in your code. Check the [migration guide](https://geopandas.readthedocs.io/en/latest/projections.html#upgrading-to-geopandas-0-7-with-pyproj-2-2-and-proj-6) in the documentation. Other API changes; - The ``GeoDataFrame.to_file`` method will now also write the GeoDataFrame index to the file, if the index is named and/or non-integer. You can use the ``index=True/False`` keyword to overwrite this default inference (#1059). New features and improvements: - A new ``geopandas.clip`` function to clip a GeoDataFrame to the spatial extent of another shape (#1128). - The ``geopandas.overlay`` function now works for all geometry types, including points and linestrings in addition to polygons (#1110). - The ``plot()`` method gained support for missing values (in the column that determines the colors). By default it doesn't plot the corresponding geometries, but using the new ``missing_kwds`` argument you can specify how to style those geometries (#1156). - The ``plot()`` method now also supports plotting GeometryCollection and LinearRing objects (#1225). - Added support for filtering with a geometry or reading a subset of the rows in ``geopandas.read_file`` (#1160). - Added support for the new nullable integer data type of pandas in ``GeoDataFrame.to_file`` (#1220). Bug fixes: - ``GeoSeries.reset_index()`` now correctly results in a GeoDataFrame instead of DataFrame (#1252). - Fixed the ``geopandas.sjoin`` function to handle MultiIndex correctly (#1159). - Fixed the ``geopandas.sjoin`` function to preserve the index name of the left GeoDataFrame (#1150). Version 0.6.3 (February 6, 2020) --------------------------------- Small bug-fix release: - Compatibility with Shapely 1.7 and pandas 1.0 (#1244). - Fix ``GeoDataFrame.fillna`` to accept non-geometry values again when there are no missing values in the geometry column. This should make it easier to fill the numerical columns of the GeoDataFrame (#1279). Version 0.6.2 (November 18, 2019) --------------------------------- Small bug-fix release fixing a few regressions: - Fix a regression in passing an array of RRB(A) tuples to the ``.plot()`` method (#1178, #1211). - Fix the ``bounds`` and ``total_bounds`` attributes for empty GeoSeries, which also fixes the repr of an empty or all-NA GeoSeries (#1184, #1195). - Fix filtering of a GeoDataFrame to preserve the index type when ending up with an empty result (#1190). Version 0.6.1 (October 12, 2019) -------------------------------- Small bug-fix release fixing a few regressions: - Fix ``astype`` when converting to string with Multi geometries (#1145) or when converting a dataframe without geometries (#1144). - Fix ``GeoSeries.fillna`` to accept ``np.nan`` again (#1149). Version 0.6.0 (September 27, 2019) ---------------------------------- Important note! This will be the last release to support Python 2.7 (#1031) API changes: - A refactor of the internals based on the pandas ExtensionArray interface (#1000). The main user visible changes are: - The ``.dtype`` of a GeoSeries is now a ``'geometry'`` dtype (and no longer a numpy ``object`` dtype). - The ``.values`` of a GeoSeries now returns a custom ``GeometryArray``, and no longer a numpy array. To get back a numpy array of Shapely scalars, you can convert explicitly using ``np.asarray(..)``. - The ``GeoSeries`` constructor now raises a warning when passed non-geometry data. Currently the constructor falls back to return a pandas ``Series``, but in the future this will raise an error (#1085). - The missing value handling has been changed to now separate the concepts of missing geometries and empty geometries (#601, 1062). In practice this means that (see [the docs](https://geopandas.readthedocs.io/en/v0.6.0/missing_empty.html) for more details): - ``GeoSeries.isna`` now considers only missing values, and if you want to check for empty geometries, you can use ``GeoSeries.is_empty`` (``GeoDataFrame.isna`` already only looked at missing values). - ``GeoSeries.dropna`` now actually drops missing values (before it didn't drop either missing or empty geometries) - ``GeoSeries.fillna`` only fills missing values (behaviour unchanged). - ``GeoSeries.align`` uses missing values instead of empty geometries by default to fill non-matching index entries. New features and improvements: - Addition of a ``GeoSeries.affine_transform`` method, equivalent of Shapely's function (#1008). - Addition of a ``GeoDataFrame.rename_geometry`` method to easily rename the active geometry column (#1053). - Addition of ``geopandas.show_versions()`` function, which can be used to give an overview of the installed libraries in bug reports (#899). - The ``legend_kwds`` keyword of the ``plot()`` method can now also be used to specify keywords for the color bar (#1102). - Performance improvement in the ``sjoin()`` operation by reusing existing spatial index of the input dataframes, if available (#789). - Updated documentation to work with latest version of geoplot and contextily (#1044, #1088). - A new ``geopandas.options`` configuration, with currently a single option to control the display precision of the coordinates (``options.display_precision``). The default is now to show less coordinates (3 for projected and 5 for geographic coordinates), but the default can be overridden with the option. Bug fixes: - Also try to use ``pysal`` instead of ``mapclassify`` if available (#1082). - The ``GeoDataFrame.astype()`` method now correctly returns a ``GeoDataFrame`` if the geometry column is preserved (#1009). - The ``to_crs`` method now uses ``always_xy=True`` to ensure correct lon/lat order handling for pyproj>=2.2.0 (#1122). - Fixed passing list-like colors in the ``plot()`` method in case of "multi" geometries (#1119). - Fixed the coloring of shapes and colorbar when passing a custom ``norm`` in the ``plot()`` method (#1091, #1089). - Fixed ``GeoDataFrame.to_file`` to preserve VFS file paths (e.g. when a "s3://" path is specified) (#1124). - Fixed failing case in ``geopandas.sjoin`` with empty geometries (#1138). In addition, the minimum required versions of some dependencies have been increased: GeoPandas now requires pandas >=0.23.4 and matplotlib >=2.0.1 (#1002). Version 0.5.1 (July 11, 2019) ----------------------------- - Compatibility with latest mapclassify version 2.1.0 (#1025). Version 0.5.0 (April 25, 2019) ------------------------------ Improvements: - Significant performance improvement (around 10x) for ``GeoDataFrame.iterfeatures``, which also improves ``GeoDataFrame.to_file`` (#864). - File IO enhancements based on Fiona 1.8: - Support for writing bool dtype (#855) and datetime dtype, if the file format supports it (#728). - Support for writing dataframes with multiple geometry types, if the file format allows it (e.g. GeoJSON for all types, or ESRI Shapefile for Polygon+MultiPolygon) (#827, #867, #870). - Compatibility with pyproj >= 2 (#962). - A new ``geopandas.points_from_xy()`` helper function to convert x and y coordinates to Point objects (#896). - The ``buffer`` and ``interpolate`` methods now accept an array-like to specify a variable distance for each geometry (#781). - Addition of a ``relate`` method, corresponding to the shapely method that returns the DE-9IM matrix (#853). - Plotting improvements: - Performance improvement in plotting by only flattening the geometries if there are actually 'Multi' geometries (#785). - Choropleths: access to all ``mapclassify`` classification schemes and addition of the ``classification_kwds`` keyword in the ``plot`` method to specify options for the scheme (#876). - Ability to specify a matplotlib axes object on which to plot the color bar with the ``cax`` keyword, in order to have more control over the color bar placement (#894). - Changed the default provider in ``geopandas.tools.geocode`` from Google (now requires an API key) to Geocode.Farm (#907, #975). Bug fixes: - Remove the edge in the legend marker (#807). - Fix the ``align`` method to preserve the CRS (#829). - Fix ``geopandas.testing.assert_geodataframe_equal`` to correctly compare left and right dataframes (#810). - Fix in choropleth mapping when the values contain missing values (#877). - Better error message in ``sjoin`` if the input is not a GeoDataFrame (#842). - Fix in ``read_postgis`` to handle nullable (missing) geometries (#856). - Correctly passing through the ``parse_dates`` keyword in ``read_postgis`` to the underlying pandas method (#860). - Fixed the shape of Antarctica in the included demo dataset 'naturalearth_lowres' (by updating to the latest version) (#804). Version 0.4.1 (March 5, 2019) ----------------------------- Small bug-fix release for compatibility with the latest Fiona and PySAL releases: - Compatibility with Fiona 1.8: fix deprecation warning (#854). - Compatibility with PySAL 2.0: switched to ``mapclassify`` instead of ``PySAL`` as dependency for choropleth mapping with the ``scheme`` keyword (#872). - Fix for new ``overlay`` implementation in case the intersection is empty (#800). Version 0.4.0 (July 15, 2018) ----------------------------- Improvements: - Improved ``overlay`` function (better performance, several incorrect behaviours fixed) (#429) - Pass keywords to control legend behavior (``legend_kwds``) to ``plot`` (#434) - Add basic support for reading remote datasets in ``read_file`` (#531) - Pass kwargs for ``buffer`` operation on GeoSeries (#535) - Expose all geopy services as options in geocoding (#550) - Faster write speeds to GeoPackage (#605) - Permit ``read_file`` filtering with a bounding box from a GeoDataFrame (#613) - Set CRS on GeoDataFrame returned by ``read_postgis`` (#627) - Permit setting markersize for Point GeoSeries plots with column values (#633) - Started an example gallery (#463, #690, #717) - Support for plotting MultiPoints (#683) - Testing functionality (e.g. ``assert_geodataframe_equal``) is now publicly exposed (#707) - Add ``explode`` method to GeoDataFrame (similar to the GeoSeries method) (#671) - Set equal aspect on active axis on multi-axis figures (#718) - Pass array of values to column argument in ``plot`` (#770) Bug fixes: - Ensure that colorbars are plotted on the correct axis (#523) - Handle plotting empty GeoDataFrame (#571) - Save z-dimension when writing files (#652) - Handle reading empty shapefiles (#653) - Correct dtype for empty result of spatial operations (#685) - Fix empty ``sjoin`` handling for pandas>=0.23 (#762) Version 0.3.0 (August 29, 2017) ------------------------------- Improvements: - Improve plotting performance using ``matplotlib.collections`` (#267) - Improve default plotting appearance. The defaults now follow the new matplotlib defaults (#318, #502, #510) - Provide access to x/y coordinates as attributes for Point GeoSeries (#383) - Make the NYBB dataset available through ``geopandas.datasets`` (#384) - Enable ``sjoin`` on non-integer-index GeoDataFrames (#422) - Add ``cx`` indexer to GeoDataFrame (#482) - ``GeoDataFrame.from_features`` now also accepts a Feature Collection (#225, #507) - Use index label instead of integer id in output of ``iterfeatures`` and ``to_json`` (#421) - Return empty data frame rather than raising an error when performing a spatial join with non overlapping geodataframes (#335) Bug fixes: - Compatibility with shapely 1.6.0 (#512) - Fix ``fiona.filter`` results when bbox is not None (#372) - Fix ``dissolve`` to retain CRS (#389) - Fix ``cx`` behavior when using index of 0 (#478) - Fix display of lower bin in legend label of choropleth plots using a PySAL scheme (#450) Version 0.2.0 ------------- Improvements: - Complete overhaul of the documentation - Addition of ``overlay`` to perform spatial overlays with polygons (#142) - Addition of ``sjoin`` to perform spatial joins (#115, #145, #188) - Addition of ``__geo_interface__`` that returns a python data structure to represent the ``GeoSeries`` as a GeoJSON-like ``FeatureCollection`` (#116) and ``iterfeatures`` method (#178) - Addition of the ``explode`` (#146) and ``dissolve`` (#310, #311) methods. - Addition of the ``sindex`` attribute, a Spatial Index using the optional dependency ``rtree`` (``libspatialindex``) that can be used to speed up certain operations such as overlays (#140, #141). - Addition of the ``GeoSeries.cx`` coordinate indexer to slice a GeoSeries based on a bounding box of the coordinates (#55). - Improvements to plotting: ability to specify edge colors (#173), support for the ``vmin``, ``vmax``, ``figsize``, ``linewidth`` keywords (#207), legends for chloropleth plots (#210), color points by specifying a colormap (#186) or a single color (#238). - Larger flexibility of ``to_crs``, accepting both dicts and proj strings (#289) - Addition of embedded example data, accessible through ``geopandas.datasets.get_path``. API changes: - In the ``plot`` method, the ``axes`` keyword is renamed to ``ax`` for consistency with pandas, and the ``colormap`` keyword is renamed to ``cmap`` for consistency with matplotlib (#208, #228, #240). Bug fixes: - Properly handle rows with missing geometries (#139, #193). - Fix ``GeoSeries.to_json`` (#263). - Correctly serialize metadata when pickling (#199, #206). - Fix ``merge`` and ``concat`` to return correct GeoDataFrame (#247, #320, #322). geopandas-1.2.0/CITATION.cff000066400000000000000000000025041525650651600153610ustar00rootroot00000000000000cff-version: "1.2.0" message: "If you use this software, please cite the article below." title: GeoPandas authors: - family-names: Fleischmann given-names: Martin orcid: "https://orcid.org/0000-0003-3319-3366" - family-names: "Van den Bossche" given-names: Joris orcid: "https://orcid.org/0000-0003-3284-2977" - family-names: Jordahl given-names: Kelsey - family-names: Richards given-names: "Matthew John" - family-names: McBride given-names: James - family-names: Wasserman given-names: Jacob - family-names: Ward given-names: Brendan - family-names: Wolf given-names: "Levi John" preferred-citation: type: article title: "GeoPandas: Fundamental Data Structures for Vector Spatial Data in Python" authors: - family-names: Fleischmann given-names: Martin - family-names: "Van Den Bossche" given-names: Joris - family-names: Jordahl given-names: Kelsey - family-names: Richards given-names: "Matthew John" - family-names: McBride given-names: James - family-names: Wasserman given-names: Jacob - family-names: Ward given-names: Brendan - family-names: Wolf given-names: "Levi John" year: 2026 month: 12 journal: "Computers, Environment and Urban Systems" volume: 130 start: 102495 issn: "0198-9715" doi: 10.1016/j.compenvurbsys.2026.102495 languages: - en geopandas-1.2.0/CITATION.md000066400000000000000000000021241525650651600152210ustar00rootroot00000000000000To cite GeoPandas please use the following [software paper](https://doi.org/10.1016/j.compenvurbsys.2026.102495) published in the Computers, Environment and Urban Systems. > Fleischmann, M., Van den Bossche, J., Jordahl, K., Richards, M. J., McBride, J., > Wasserman, J., Ward, B., & Wolf, L. J. (2026). GeoPandas: Fundamental data structures > for vector spatial data in Python. _Computers, Environment and Urban Systems_, 130, > 102495. https://doi.org/10.1016/j.compenvurbsys.2026.102495 BibTeX: ```bibtex @article{fleischmann2026GeoPandas, title = {{{GeoPandas}}: {{Fundamental}} Data Structures for Vector Spatial Data in {{Python}}}, shorttitle = {{{GeoPandas}}}, author = {Fleischmann, Martin and Van den Bossche, Joris and Jordahl, Kelsey and Richards, Matthew John and McBride, James and Wasserman, Jacob and Ward, Brendan and Wolf, Levi John}, year = 2026, month = dec, journal = {Computers, Environment and Urban Systems}, volume = {130}, pages = {102495}, issn = {01989715}, doi = {10.1016/j.compenvurbsys.2026.102495}, urldate = {2026-08-01}, langid = {english}, } ```geopandas-1.2.0/CODE_OF_CONDUCT.md000066400000000000000000000032441525650651600162700ustar00rootroot00000000000000# NumFOCUS Code of Conduct You can find the whole document here [https://numfocus.org/code-of-conduct](https://numfocus.org/code-of-conduct). ## The short version NumFOCUS is dedicated to providing a harassment-free community for everyone, regardless of gender, sexual orientation, gender identity and expression, disability, physical appearance, body size, race, or religion. We do not tolerate harassment of community members in any form. Be kind to others. Do not insult or put down others. Behave professionally. Remember that harassment and sexist, racist, or exclusionary jokes are not appropriate for NumFOCUS. All communication should be appropriate for a professional audience including people of many different backgrounds. Sexual language and imagery is not appropriate. Thank you for helping make this a welcoming, friendly community for all. ## Long version You can find the long version of the Code of Conduct on the NumFOCUS website [https://numfocus.org/code-of-conduct](https://numfocus.org/code-of-conduct) ## How to report If you feel that the Code of Conduct has been violated, feel free to submit a report, by using the form: [NumFOCUS Code of Conduct Reporting Form](https://forms.monday.com/forms/f130e8cddb99568fa86cf077b8912a60?r=use1)   ## Who will receive your report Your report will be received and handled by NumFOCUS Code of Conduct Working Group; trained, and experienced contributors with diverse backgrounds. The group is making decisions independently from the project, PyData, NumFOCUS or any other organization. You can learn more about the current group members, as well as the reporting procedure **[HERE](https://numfocus.org/code-of-conduct)** geopandas-1.2.0/CONTRIBUTING.md000066400000000000000000000374641525650651600157350ustar00rootroot00000000000000# Contributing to GeoPandas (Contribution guidelines largely copied from [pandas](http://pandas.pydata.org/pandas-docs/stable/development/contributing.html)) ## Overview Contributions to GeoPandas are very welcome. They are likely to be accepted more quickly if they follow these guidelines. At this stage of GeoPandas development, the priorities are to define a simple, usable, and stable API and to have clean, maintainable, readable code. Performance matters, but not at the expense of those goals. In general, GeoPandas follows the conventions of the pandas project where applicable. In particular, when submitting a pull request: - All existing tests should pass. Please make sure that the test suite passes, both locally and on [GitHub Actions](https://github.com/geopandas/geopandas/actions). Status on GHA will be visible on a pull request. GHA are automatically enabled on your own fork as well. To trigger a check, make a PR to your own fork. - New functionality should include tests. Please write reasonable tests for your code and make sure that they pass on your pull request. - Classes, methods, functions, etc. should have docstrings. The first line of a docstring should be a standalone summary. Parameters and return values should be documented explicitly. - Follow PEP 8 when possible. We use [ruff](https://docs.astral.sh/ruff/) to ensure a consistent code format throughout the project. For more details see the section _Style Guide_ below. - Imports should be grouped with standard library imports first, 3rd-party libraries next, and GeoPandas imports third. Within each grouping, imports should be alphabetized. Always use absolute imports when possible, and explicit relative imports for local imports when necessary in tests. - GeoPandas supports Python 3.10+ only. - Unless your PR implements minor changes or internal work only, make sure it contains a note describing the changes in the `CHANGELOG.md` file. ### Seven Steps for Contributing There are seven basic steps to contributing to GeoPandas: 1. Fork the GeoPandas git repository 2. Create a development environment 3. Install GeoPandas dependencies 4. Make a `development` build of GeoPandas 5. Make changes to code and add tests 6. Update the documentation 7. Submit a Pull Request Each of these 7 steps is detailed below. ## 1) Forking the GeoPandas repository using Git To the new user, working with Git is one of the more daunting aspects of contributing to GeoPandas. It can very quickly become overwhelming, but sticking to the guidelines below will help keep the process straightforward and mostly trouble free. As always, if you are having difficulties please feel free to ask for help. The code is hosted on [GitHub](https://github.com/geopandas/geopandas). To contribute you will need to sign up for a [free GitHub account](https://github.com/signup/free). We use [Git](http://git-scm.com/) for version control to allow many people to work together on the project. Some great resources for learning Git: - Software Carpentry\'s [Git Tutorial](http://swcarpentry.github.io/git-novice/) - [Atlassian](https://www.atlassian.com/git/tutorials/what-is-version-control) - the [GitHub help pages](http://help.github.com/). - Matthew Brett\'s [Pydagogue](https://matthew-brett.github.io/pydagogue/). ### Getting started with Git [GitHub has instructions](https://github.com/git-guides/install-git) for installing git, setting up your SSH key, and configuring git. All these steps need to be completed before you can work seamlessly between your local repository and GitHub. ### Forking You will need your own fork to work on the code. Go to the [GeoPandas project page](https://github.com/geopandas/geopandas) and hit the `Fork` button. You will want to clone your fork to your machine: git clone git@github.com:your-user-name/geopandas.git geopandas-yourname cd geopandas-yourname git remote add upstream git://github.com/geopandas/geopandas.git This creates the directory `geopandas-yourname` and connects your repository to the upstream (main project) GeoPandas repository. The testing suite will run automatically on GitHub Actions once your pull request is submitted. The test suite will also automatically run on your branch so you can check it prior to submitting the pull request. ### Creating a branch You want your main branch to reflect only production-ready code, so create a feature branch for making your changes. For example: git branch shiny-new-feature git checkout shiny-new-feature The above can be simplified to: git checkout -b shiny-new-feature This changes your working directory to the shiny-new-feature branch. Keep any changes in this branch specific to one bug or feature so it is clear what the branch brings to GeoPandas. You can have many shiny-new-features and switch in between them using the git checkout command. To update this branch, you need to retrieve the changes from the main branch: git fetch upstream git rebase upstream/main This will replay your commits on top of the latest GeoPandas git main. If this leads to merge conflicts, you must resolve these before submitting your pull request. If you have uncommitted changes, you will need to `stash` them prior to updating. This will effectively store your changes and they can be reapplied after updating. ## 2) Creating a development environment A development environment is a virtual space where you can keep an independent installation of GeoPandas. This makes it easy to keep both a stable version of python in one place you use for work, and a development version (which you may break while playing with code) in another. An easy way to create a GeoPandas development environment is as follows: - Install either [Anaconda or miniconda](https://www.anaconda.com/docs/getting-started/concepts/anaconda-or-miniconda) - Make sure that you have cloned the repository - `cd` to the `geopandas` source directory ### Using the provided environment GeoPandas provides an environment which includes the required dependencies for development. The environment file is located in the top level of the repo and is named `environment-dev.yml`. You can create this environment by navigating to the the `geopandas` source directory and running: conda env create -f environment-dev.yml This will create a new conda environment named `geopandas-dev`. ### Creating the environment manually Alternatively, it is possible to create a development environment manually. To do this, tell conda to create a new environment named `geopandas-dev`, or any other name you would like for this environment, by running: conda create -n geopandas-dev python This will create the new environment, and not touch any of your existing environments, nor any existing python installation. ### Working with the environment To work in this environment, you need to `activate` it. The instructions below should work for both Windows, Mac and Linux: conda activate geopandas-dev Once your environment is activated, you will see a confirmation message to indicate you are in the new development environment. To view your environments: conda info -e To return to you home root environment: conda deactivate See the full conda docs [here](http://conda.pydata.org/docs). At this point you can easily do a *development* install, as detailed in the next sections. ## 3) Installing Dependencies To run GeoPandas in a development environment, you must first install the dependencies of GeoPandas. If you used the provided environment in section 2, skip this step and continue to section 4. If you created the environment manually, we suggest installing dependencies using the following commands (executed after your development environment has been activated): conda install -c conda-forge pandas pyogrio shapely pyproj pytest This should install all necessary dependencies. ## 4) Making a development build Once dependencies are in place, make an in-place build by navigating to the git clone of the GeoPandas repository and running: python -m pip install -e . ## 5) Making changes and writing tests GeoPandas is serious about testing and strongly encourages contributors to embrace [test-driven development (TDD)](http://en.wikipedia.org/wiki/Test-driven_development). This development process "relies on the repetition of a very short development cycle: first the developer writes an (initially failing) automated test case that defines a desired improvement or new function, then produces the minimum amount of code to pass that test." So, before actually writing any code, you should write your tests. Often the test can be taken from the original GitHub issue. However, it is always worth considering additional use cases and writing corresponding tests. Adding tests is one of the most common requests after code is pushed to GeoPandas. Therefore, it is worth getting in the habit of writing tests ahead of time so this is never an issue. GeoPandas uses the [pytest testing system](http://doc.pytest.org/en/latest/) and the convenient extensions in [numpy.testing](http://docs.scipy.org/doc/numpy/reference/routines.testing.html). ### Writing tests All tests should go into each module's `tests` sub-directory. This folder contains many current examples of tests, and we suggest looking to these for inspiration. The `geopandas.testing` module has some special `assert` functions that make it easier to make statements about whether GeoSeries or GeoDataFrame objects are equivalent. The easiest way to verify that your code is correct is to explicitly construct the result you expect, then compare the actual result to the expected correct result, using eg the function `assert_geoseries_equal`. ### Running the test suite The tests can then be run directly inside your Git clone (without having to install GeoPandas) by typing: pytest ## 6) Updating the Documentation GeoPandas documentation resides in the `doc` folder. Changes to the docs are made by modifying the appropriate file in the `source` folder within `doc`. GeoPandas docs use mixture of reStructuredText syntax for `rst` files, [which is explained here](http://www.sphinx-doc.org/en/stable/rest.html#rst-primer) and MyST syntax for `md` files [explained here](https://myst-parser.readthedocs.io/en/latest/index.html). The docstrings follow the [Numpy Docstring standard](https://numpydoc.readthedocs.io/en/latest/format.html#docstring-standard). Some pages and examples are Jupyter notebooks converted to docs using [nbsphinx](https://nbsphinx.readthedocs.io/). Jupyter notebooks should be stored without the output. We highly encourage you to follow the [Google developer documentation style guide](https://developers.google.com/style/highlights) when updating or creating new documentation. Once you have made your changes, you may try if they render correctly by building the docs using sphinx. To do so, you can navigate to the `doc` folder: cd doc and type: make html The resulting html pages will be located in `doc/build/html`. In case of any errors, you can try to use `make html` within a new environment based on environment.yml specification in the `doc` folder. You may need to register Jupyter kernel as `geopandas_docs`. Using conda: cd doc conda env create -f environment.yml conda activate geopandas_docs python -m ipykernel install --user --name geopandas_docs make html For minor updates, you can skip the `make html` part as reStructuredText and MyST syntax are usually quite straightforward. ## 7) Submitting a Pull Request Once you've made changes and pushed them to your forked repository, you then submit a pull request to have them integrated into the GeoPandas code base. You can find a pull request (or PR) tutorial in the [GitHub's Help Docs](https://help.github.com/articles/using-pull-requests/). ## Style Guide & Linting GeoPandas follows the [PEP8](http://www.python.org/dev/peps/pep-0008/) standard and uses [ruff](https://docs.astral.sh/ruff/) to ensure a consistent code format throughout the project. Continuous Integration (GitHub Actions) will run those tools and report any stylistic errors in your code. Therefore, it is helpful before submitting code to run the check yourself: ruff format geopandas ruff check geopandas git diff upstream/main -u -- "*.py" | ruff check . to auto-format your code. Additionally, many editors have plugins that will apply `ruff` as you edit files. Optionally (but recommended), you can setup [pre-commit hooks](https://pre-commit.com/) to automatically run `ruff` when you make a git commit. If you did not use the provided development environment in `environment-dev.yml`, you must first install `pre-commit`: $ python -m pip install pre-commit From the root of the geopandas repository, you should then install the `pre-commit` included in GeoPandas: $ pre-commit install Then `ruff` will be run automatically each time you commit changes. You can skip these checks with `git commit --no-verify`. ## Commit message conventions Commit your changes to your local repository with an explanatory message. GeoPandas uses the pandas convention for commit message prefixes and layout. Here are some common prefixes along with general guidelines for when to use them: - ENH: Enhancement, new functionality - BUG: Bug fix - DOC: Additions/updates to documentation - TST: Additions/updates to tests - BLD: Updates to the build process/scripts - PERF: Performance improvement - TYP: Type annotations - CLN: Code cleanup The following defines how a commit message should be structured. Please refer to the relevant GitHub issues in your commit message using GH1234 or #1234. Either style is fine, but the former is generally preferred: - a subject line with < 80 characters. - One blank line. - Optionally, a commit message body. Now you can commit your changes in your local repository: git commit -m ## AI Assisted Contributions We recognise that AI coding assistants are now a regular part of many developers\' workflows. However, AI-generated PRs can sometimes lead to undesirable additional maintainer burden. PRs that appear to be fully generated by AI with little to no engagement from the author may be closed without further review. Remember that maintainers volunteer to support GeoPandas, and low quality AI generated Pull Requests are not an effective use of that precious time - a contribution should be worth more to the project than the time it takes to review it. Ultimately, the person submitting an issue or PR is responsible for its content, regardless of whether AI tools were used in its creation and they must ensure the contribution meets the GeoPandas guidelines. We are not opposed to the use of AI tools in writing PRs, but require the following: - Only submit a PR if you are able to debug and own the changes yourself - review all generated code to understand every detail - Match the style and conventions used in the rest of the codebase, including PR titles and descriptions - Be upfront about AI usage and summarise what was AI-generated - If there are parts you don\'t fully understand, leave comments on your own PR explaining what steps you took to verify correctness - Watch for AI\'s tendency to generate overly verbose comments, unnecessary test cases, and incorrect fixes - Break down large PRs into smaller ones to make review easier - AI agents should never tag or ping maintainers - AI generated PRs will not be accepted unless they resolve an existing issue where there is consensus on the solution to implement. If no issue exists, open a new issue and engage to gather consensus. When interacting with developers (forums, discussions, issues, pull requests, etc.) do not use AI to speak for you, except for translation or grammar editing. If the developers want to chat with a chatbot, they can do so themselves. Human-to-human communication is essential for an open source community to thrive. This policy is adapted in part from the Apache Arrow AI-generated code Contribution guidelines. geopandas-1.2.0/LICENSE.txt000066400000000000000000000027321525650651600153150ustar00rootroot00000000000000Copyright (c) 2013-2022, GeoPandas developers. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of GeoPandas nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. geopandas-1.2.0/README.md000066400000000000000000000201421525650651600147440ustar00rootroot00000000000000[![pypi](https://img.shields.io/pypi/v/geopandas.svg)](https://pypi.python.org/pypi/geopandas/) [![Actions Status](https://github.com/geopandas/geopandas/workflows/Tests/badge.svg)](https://github.com/geopandas/geopandas/actions?query=workflow%3ATests) [![Coverage Status](https://codecov.io/gh/geopandas/geopandas/branch/main/graph/badge.svg)](https://codecov.io/gh/geopandas/geopandas) [![Join the chat at https://gitter.im/geopandas/geopandas](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/geopandas/geopandas?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) [![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/geopandas/geopandas/main) [![DOI](https://zenodo.org/badge/11002815.svg)](https://zenodo.org/badge/latestdoi/11002815) [![Powered by NumFOCUS](https://img.shields.io/badge/powered%20by-NumFOCUS-orange.svg?style=flat&colorA=E1523D&colorB=007D8A)](https://numfocus.org) GeoPandas --------- Python tools for geographic data Introduction ------------ GeoPandas is a project to add support for geographic data to [pandas](http://pandas.pydata.org) objects. It currently implements `GeoSeries` and `GeoDataFrame` types which are subclasses of `pandas.Series` and `pandas.DataFrame` respectively. GeoPandas objects can act on [shapely](http://shapely.readthedocs.io/en/latest/) geometry objects and perform geometric operations. GeoPandas geometry operations are cartesian. The coordinate reference system (crs) can be stored as an attribute on an object, and is automatically set when loading from a file. Objects may be transformed to new coordinate systems with the `to_crs()` method. There is currently no enforcement of like coordinates for operations, but that may change in the future. Documentation is available at [geopandas.org](http://geopandas.org) (current release) and [Read the Docs](http://geopandas.readthedocs.io/en/latest/) (release and development versions). [//]: # (numfocus-fiscal-sponsor-attribution) The GeoPandas project uses an [open governance model](https://github.com/geopandas/governance/blob/main/Governance.md) and is fiscally sponsored by [NumFOCUS](https://numfocus.org/). Consider making a [tax-deductible donation](https://numfocus.org/donate-for-geopandas) to help the project pay for developer time, professional services, travel, workshops, and a variety of other needs.

Install -------- See the [installation docs](https://geopandas.readthedocs.io/en/latest/install.html) for all details. GeoPandas depends on the following packages: - ``pandas`` - ``shapely`` - ``pyogrio`` - ``pyproj`` - ``packaging`` Further, ``matplotlib`` is an optional dependency, required for plotting. Those packages depend on several low-level libraries for geospatial analysis, which can be a challenge to install. Therefore, we recommend to install GeoPandas using the [conda package manager](https://conda.io/en/latest/). See the [installation docs](https://geopandas.readthedocs.io/en/latest/install.html) for more details. Get in touch ------------ - Ask usage questions ("How do I?") on [StackOverflow](https://stackoverflow.com/questions/tagged/geopandas) or [GIS StackExchange](https://gis.stackexchange.com/questions/tagged/geopandas). - Get involved in [discussions on GitHub](https://github.com/geopandas/geopandas/discussions) - Report bugs, suggest features or view the source code [on GitHub](https://github.com/geopandas/geopandas). - For a quick question about a bug report or feature request, or Pull Request, head over to the [gitter channel](https://gitter.im/geopandas/geopandas). - For less well defined questions or ideas, or to announce other projects of interest to GeoPandas users, ... use the [mailing list](https://groups.google.com/forum/#!forum/geopandas). Examples -------- >>> import geopandas >>> from shapely.geometry import Polygon >>> p1 = Polygon([(0, 0), (1, 0), (1, 1)]) >>> p2 = Polygon([(0, 0), (1, 0), (1, 1), (0, 1)]) >>> p3 = Polygon([(2, 0), (3, 0), (3, 1), (2, 1)]) >>> g = geopandas.GeoSeries([p1, p2, p3]) >>> g 0 POLYGON ((0 0, 1 0, 1 1, 0 0)) 1 POLYGON ((0 0, 1 0, 1 1, 0 1, 0 0)) 2 POLYGON ((2 0, 3 0, 3 1, 2 1, 2 0)) dtype: geometry ![Example 1](doc/source/gallery/test.png) Some geographic operations return normal pandas objects. The `area` property of a `GeoSeries` will return a `pandas.Series` containing the area of each item in the `GeoSeries`: >>> print(g.area) 0 0.5 1 1.0 2 1.0 dtype: float64 Other operations return GeoPandas objects: >>> g.buffer(0.5) 0 POLYGON ((-0.3535533905932737 0.35355339059327... 1 POLYGON ((-0.5 0, -0.5 1, -0.4975923633360985 ... 2 POLYGON ((1.5 0, 1.5 1, 1.502407636663901 1.04... dtype: geometry ![Example 2](doc/source/gallery/test_buffer.png) GeoPandas objects also know how to plot themselves. GeoPandas uses [matplotlib](http://matplotlib.org) for plotting. To generate a plot of a `GeoSeries`, use: >>> g.plot() GeoPandas also implements alternate constructors that can read any data format recognized by [pyogrio](http://pyogrio.readthedocs.io/en/latest/). To read a zip file containing an ESRI shapefile with the [boroughs boundaries of New York City](https://data.cityofnewyork.us/City-Government/Borough-Boundaries/tqmj-j8zm) (the example can be fetched using the [`geodatasets`](https://geodatasets.readthedocs.io/en/latest/) package): >>> import geodatasets >>> nybb_path = geodatasets.get_path('nybb') >>> boros = geopandas.read_file(nybb_path) >>> boros = boros.set_index('BoroCode').sort_index() >>> boros BoroName Shape_Leng Shape_Area \ BoroCode 1 Manhattan 359299.096471 6.364715e+08 2 Bronx 464392.991824 1.186925e+09 3 Brooklyn 741080.523166 1.937479e+09 4 Queens 896344.047763 3.045213e+09 5 Staten Island 330470.010332 1.623820e+09 geometry BoroCode 1 MULTIPOLYGON (((981219.0557861328 188655.31579... 2 MULTIPOLYGON (((1012821.805786133 229228.26458... 3 MULTIPOLYGON (((1021176.479003906 151374.79699... 4 MULTIPOLYGON (((1029606.076599121 156073.81420... 5 MULTIPOLYGON (((970217.0223999023 145643.33221... ![New York City boroughs](doc/source/gallery/nyc.png) >>> boros['geometry'].convex_hull BoroCode 1 POLYGON ((977855.4451904297 188082.3223876953,... 2 POLYGON ((1017949.977600098 225426.8845825195,... 3 POLYGON ((988872.8212280273 146772.0317993164,... 4 POLYGON ((1000721.531799316 136681.776184082, ... 5 POLYGON ((915517.6877458114 120121.8812543372,... dtype: geometry ![Convex hulls of New York City boroughs](doc/source/gallery/nyc_hull.png) How to cite ----------- To cite GeoPandas please use the following [software paper](https://doi.org/10.1016/j.compenvurbsys.2026.102495) published in the Computers, Environment and Urban Systems. > Fleischmann, M., Van den Bossche, J., Jordahl, K., Richards, M. J., McBride, J., Wasserman, J., Ward, B., & Wolf, L. J. (2026). GeoPandas: Fundamental data structures for vector spatial data in Python. _Computers, Environment and Urban Systems_, 130, 102495. https://doi.org/10.1016/j.compenvurbsys.2026.102495 BibTeX: ```bibtex @article{fleischmann2026GeoPandas, title = {{{GeoPandas}}: {{Fundamental}} Data Structures for Vector Spatial Data in {{Python}}}, shorttitle = {{{GeoPandas}}}, author = {Fleischmann, Martin and Van den Bossche, Joris and Jordahl, Kelsey and Richards, Matthew John and McBride, James and Wasserman, Jacob and Ward, Brendan and Wolf, Levi John}, year = 2026, month = dec, journal = {Computers, Environment and Urban Systems}, volume = {130}, pages = {102495}, issn = {01989715}, doi = {10.1016/j.compenvurbsys.2026.102495}, urldate = {2026-08-01}, langid = {english}, } ```geopandas-1.2.0/asv.conf.json000066400000000000000000000140271525650651600161020ustar00rootroot00000000000000{ // The version of the config file format. Do not change, unless // you know what you are doing. "version": 1, // The name of the project being benchmarked "project": "geopandas", // The project's homepage "project_url": "http://geopandas.org/", // The URL or local path of the source code repository for the // project being benchmarked "repo": ".", // List of branches to benchmark. If not provided, defaults to "master" // (for git) or "default" (for mercurial). // "branches": ["master"], // for git // "branches": ["default"], // for mercurial // The DVCS being used. If not set, it will be automatically // determined from "repo" by looking at the protocol in the URL // (if remote), or by looking for special directories, such as // ".git" (if local). // "dvcs": "git", // The tool to use to create environments. May be "conda", // "virtualenv" or other value depending on the plugins in use. // If missing or the empty string, the tool will be automatically // determined by looking for tools on the PATH environment // variable. "environment_type": "conda", "conda_channels": ["conda-forge", "defaults"], // timeout in seconds for installing any dependencies in environment // defaults to 10 min //"install_timeout": 600, // the base URL to show a commit for the project. "show_commit_url": "http://github.com/geopandas/geopandas/commit/", // The Pythons you'd like to test against. If not provided, defaults // to the current version of Python used to run `asv`. // "pythons": ["2.7", "3.3"], // The matrix of dependencies to test. Each key is the name of a // package (in PyPI) and the values are version numbers. An empty // list or empty string indicates to just test against the default // (latest) version. null indicates that the package is to not be // installed. If the package to be tested is only available from // PyPi, and the 'environment_type' is conda, then you can preface // the package name by 'pip+', and the package will be installed via // pip (with all the conda available packages installed first, // followed by the pip installed packages). // "matrix": { "pandas": [], "shapely": [], "pyogrio": [], "pyproj": [], "matplotlib": [], }, // "numpy": ["1.6", "1.7"], // "six": ["", null], // test with and without six installed // "pip+emcee": [""], // emcee is only available for install with pip. // }, // Combinations of libraries/python versions can be excluded/included // from the set to test. Each entry is a dictionary containing additional // key-value pairs to include/exclude. // // An exclude entry excludes entries where all values match. The // values are regexps that should match the whole string. // // An include entry adds an environment. Only the packages listed // are installed. The 'python' key is required. The exclude rules // do not apply to includes. // // In addition to package names, the following keys are available: // // - python // Python version, as in the *pythons* variable above. // - environment_type // Environment type, as above. // - sys_platform // Platform, as in sys.platform. Possible values for the common // cases: 'linux2', 'win32', 'cygwin', 'darwin'. // // "exclude": [ // {"python": "3.2", "sys_platform": "win32"}, // skip py3.2 on windows // {"environment_type": "conda", "six": null}, // don't run without six on conda // ], // // "include": [ // // additional env for python2.7 // {"python": "2.7", "numpy": "1.8"}, // // additional env if run on windows+conda // {"platform": "win32", "environment_type": "conda", "python": "2.7", "libpython": ""}, // ], // The directory (relative to the current directory) that benchmarks are // stored in. If not provided, defaults to "benchmarks" // "benchmark_dir": "benchmarks", // The directory (relative to the current directory) to cache the Python // environments in. If not provided, defaults to "env" "env_dir": ".asv/env", // The directory (relative to the current directory) that raw benchmark // results are stored in. If not provided, defaults to "results". "results_dir": ".asv/results", // The directory (relative to the current directory) that the html tree // should be written to. If not provided, defaults to "html". "html_dir": ".asv/html", // The number of characters to retain in the commit hashes. // "hash_length": 8, // `asv` will cache wheels of the recent builds in each // environment, making them faster to install next time. This is // number of builds to keep, per environment. // "wheel_cache_size": 0 // The commits after which the regression search in `asv publish` // should start looking for regressions. Dictionary whose keys are // regexps matching to benchmark names, and values corresponding to // the commit (exclusive) after which to start looking for // regressions. The default is to start from the first commit // with results. If the commit is `null`, regression detection is // skipped for the matching benchmark. // // "regressions_first_commits": { // "some_benchmark": "352cdf", // Consider regressions only after this commit // "another_benchmark": null, // Skip regression detection altogether // } // The thresholds for relative change in results, after which `asv // publish` starts reporting regressions. Dictionary of the same // form as in ``regressions_first_commits``, with values // indicating the thresholds. If multiple entries match, the // maximum is taken. If no entry matches, the default is 5%. // // "regressions_thresholds": { // "some_benchmark": 0.01, // Threshold of 1% // "another_benchmark": 0.5, // Threshold of 50% // } } geopandas-1.2.0/benchmarks/000077500000000000000000000000001525650651600156035ustar00rootroot00000000000000geopandas-1.2.0/benchmarks/__init__.py000066400000000000000000000000011525650651600177030ustar00rootroot00000000000000 geopandas-1.2.0/benchmarks/clip.py000066400000000000000000000013351525650651600171060ustar00rootroot00000000000000from geopandas import read_file, clip from shapely.geometry import box try: from geopandas.tests.util import _NATURALEARTH_LOWRES from geopandas.tests.util import _NATURALEARTH_CITIES except: # geopandas <=0.14 compat from geopandas.datasets import get_path _NATURALEARTH_LOWRES = get_path("naturalearth_lowres") _NATURALEARTH_CITIES = get_path("naturalearth_cities") class Bench: def setup(self, *args): world = read_file(_NATURALEARTH_LOWRES) capitals = read_file(_NATURALEARTH_CITIES) self.bounds = [box(*geom.bounds) for geom in world.geometry] self.points = capitals def time_clip(self): for bound in self.bounds: clip(self.points, bound) geopandas-1.2.0/benchmarks/geom_methods.py000066400000000000000000000101741525650651600206320ustar00rootroot00000000000000import random import numpy as np from geopandas import GeoSeries from shapely.geometry import Point, Polygon, MultiPolygon def with_attributes(**attrs): def decorator(func): for key, value in attrs.items(): setattr(func, key, value) return func return decorator class Bench: def setup(self, *args): self.points = GeoSeries([Point(i, i) for i in range(100000)]) triangles = GeoSeries( [ Polygon([(random.random(), random.random()) for _ in range(3)]) for _ in range(1000) ] ) triangles2 = triangles.copy().iloc[np.random.choice(1000, 1000)] triangles3 = GeoSeries( [ Polygon([(random.random(), random.random()) for _ in range(3)]) for _ in range(10000) ] ) triangles4 = GeoSeries( [ MultiPolygon( [Polygon([(random.random(), random.random()) for _ in range(3)])] ) for _ in range(10000) ] ) triangle = Polygon([(random.random(), random.random()) for _ in range(3)]) self.triangles, self.triangles2 = triangles, triangles2 self.triangles_big = triangles3 self.multi_triangles = triangles4 self.triangle = triangle @with_attributes( param_names=["op"], params=[ ( "contains", "crosses", "disjoint", "intersects", "overlaps", "touches", "within", "geom_equals", "geom_equals_exact", ) ], ) def time_binary_predicate(self, op): getattr(self.triangles, op)(self.triangle) @with_attributes( param_names=["op"], params=[ ( "contains", "crosses", "disjoint", "intersects", "overlaps", "touches", "within", "geom_equals", ) ], ) # 'geom_equals_exact')]) def time_binary_predicate_vector(self, op): getattr(self.triangles, op)(self.triangles2) @with_attributes(param_names=["op"], params=[("distance")]) def time_binary_float(self, op): getattr(self.triangles, op)(self.triangle) @with_attributes(param_names=["op"], params=[("distance")]) def time_binary_float_vector(self, op): getattr(self.triangles, op)(self.triangles2) @with_attributes( param_names=["op"], params=[("difference", "symmetric_difference", "union", "intersection")], ) def time_binary_geo(self, op): getattr(self.triangles, op)(self.triangle) @with_attributes( param_names=["op"], params=[("difference", "symmetric_difference", "union", "intersection")], ) def time_binary_geo_vector(self, op): getattr(self.triangles, op)(self.triangles2) @with_attributes( param_names=["op"], params=[("is_valid", "is_empty", "is_simple", "is_ring")] ) def time_unary_predicate(self, op): getattr(self.triangles, op) @with_attributes(param_names=["op"], params=[("area", "length")]) def time_unary_float(self, op): getattr(self.triangles_big, op) @with_attributes( param_names=["op"], params=[ ("boundary", "centroid", "convex_hull", "envelope", "exterior", "interiors") ], ) def time_unary_geo(self, op): getattr(self.triangles, op) def time_unary_geo_representative_point(self, *args): getattr(self.triangles, "representative_point")() def time_geom_type(self, *args): self.triangles_big.geom_type def time_bounds(self, *args): self.triangles.bounds def time_union_all(self, *args): self.triangles.union_all() def time_buffer(self, *args): self.points.buffer(2) def time_explode(self, *args): self.multi_triangles.explode() # TODO # project, interpolate, affine_transform, translate, rotate, scale, skew # cx indexer geopandas-1.2.0/benchmarks/io.py000066400000000000000000000061671525650651600165760ustar00rootroot00000000000000import os import shutil import tempfile import warnings import numpy as np from shapely.geometry import Point from geopandas import GeoDataFrame, GeoSeries, read_file, read_parquet, read_feather # TEMP: hide warning from to_parquet warnings.filterwarnings("ignore", message=".*initial implementation of Parquet.*") format_dict = { "ESRI Shapefile": ( ".shp", lambda gdf, filename: gdf.to_file(filename, driver="ESRI Shapefile"), lambda filename: read_file(filename, driver="ESRI Shapefile"), ), "GeoJSON": ( ".json", lambda gdf, filename: gdf.to_file(filename, driver="GeoJSON"), lambda filename: read_file(filename, driver="GeoJSON"), ), "GPKG": ( ".gpkg", lambda gdf, filename: gdf.to_file(filename, driver="GeoJSON"), lambda filename: read_file(filename, driver="GeoJSON"), ), "Parquet": ( ".parquet", lambda gdf, filename: gdf.to_parquet(filename), lambda filename: read_parquet(filename), ), "Feather": ( ".feather", lambda gdf, filename: gdf.to_feather(filename), lambda filename: read_feather(filename), ), } class Bench: params = ["ESRI Shapefile", "GeoJSON", "GPKG", "Parquet", "Feather"] param_names = ["file_format"] def setup(self, file_format): self.ext, self.writer, self.reader = format_dict[file_format] num_points = 20000 xs = np.random.rand(num_points) ys = np.random.rand(num_points) self.points = GeoSeries([Point(x, y) for (x, y) in zip(xs, ys)]) self.df = GeoDataFrame( { "geometry": self.points, "x": xs, "y": ys, "s": np.zeros(num_points, dtype="object"), } ) self.tmpdir = tempfile.mkdtemp() self.filename = os.path.join(self.tmpdir, "frame" + self.ext) self.writer(self.df, self.filename) def teardown(self, file_format): shutil.rmtree(self.tmpdir) class BenchFrame(Bench): params = ["ESRI Shapefile", "GeoJSON", "GPKG", "Parquet", "Feather"] param_names = ["file_format"] def time_write(self, file_format): with tempfile.TemporaryDirectory() as tmpdir: out_filename = os.path.join(tmpdir, "frame" + self.ext) self.writer(self.df, out_filename) def time_read(self, file_format): self.reader(self.filename) class BenchSeries(Bench): params = ["ESRI Shapefile", "GeoJSON", "GPKG"] param_names = ["file_format"] def setup(self, file_format): super().setup(file_format) self.filename_series = os.path.join(self.tmpdir, "series" + self.ext) self.writer(self.points, self.filename_series) def time_write_series(self, file_format): with tempfile.TemporaryDirectory() as tmpdir: out_filename = os.path.join(tmpdir, "series" + self.ext) self.writer(self.points, out_filename) def time_read_series(self, file_format): GeoSeries.from_file(self.filename_series) def time_read_series_from_frame(self, file_format): GeoSeries.from_file(self.filename) geopandas-1.2.0/benchmarks/overlay.py000066400000000000000000000042741525650651600176450ustar00rootroot00000000000000from geopandas import GeoDataFrame, GeoSeries, read_file, overlay import numpy as np from shapely.geometry import Point, Polygon from geopandas.tests.util import _NATURALEARTH_LOWRES from geopandas.tests.util import _NATURALEARTH_CITIES class Countries: param_names = ["how"] params = [ ("intersection", "union", "identity", "symmetric_difference", "difference") ] def setup(self, *args): world = read_file(_NATURALEARTH_LOWRES) capitals = read_file(_NATURALEARTH_CITIES) countries = world[["geometry", "name"]] countries = countries.to_crs("+init=epsg:3395")[countries.name != "Antarctica"] capitals = capitals.to_crs("+init=epsg:3395") capitals["geometry"] = capitals.buffer(500000) self.countries = countries self.capitals = capitals def time_overlay(self, how): overlay(self.countries, self.capitals, how=how) class Small: param_names = ["how"] params = [ ("intersection", "union", "identity", "symmetric_difference", "difference") ] def setup(self, *args): polys1 = GeoSeries( [ Polygon([(0, 0), (2, 0), (2, 2), (0, 2)]), Polygon([(2, 2), (4, 2), (4, 4), (2, 4)]), ] ) polys2 = GeoSeries( [ Polygon([(1, 1), (3, 1), (3, 3), (1, 3)]), Polygon([(3, 3), (5, 3), (5, 5), (3, 5)]), ] ) df1 = GeoDataFrame({"geometry": polys1, "df1": [1, 2]}) df2 = GeoDataFrame({"geometry": polys2, "df2": [1, 2]}) self.df1, self.df2 = df1, df2 def time_overlay(self, how): overlay(self.df1, self.df2, how=how) class ManyPoints: param_names = ["how"] params = [ ("intersection", "union", "identity", "symmetric_difference", "difference") ] def setup(self, *args): points = GeoDataFrame(geometry=[Point(i, i) for i in range(1000)]) base = np.array([[0, 0], [0, 100], [100, 100], [100, 0]]) polys = GeoDataFrame(geometry=[Polygon(base + i * 100) for i in range(10)]) self.df1, self.df2 = points, polys def time_overlay(self, how): overlay(self.df1, self.df2, how=how) geopandas-1.2.0/benchmarks/plotting.py000066400000000000000000000044711525650651600200230ustar00rootroot00000000000000import random from geopandas import GeoDataFrame, GeoSeries from shapely.geometry import Point, LineString, Polygon, MultiPolygon import numpy as np class Bench: param_names = ["geom_type"] params = [("Point", "LineString", "Polygon", "MultiPolygon", "mixed")] def setup(self, geom_type): if geom_type == "Point": geoms = GeoSeries([Point(i, i) for i in range(1000)]) elif geom_type == "LineString": geoms = GeoSeries( [ LineString([(random.random(), random.random()) for _ in range(5)]) for _ in range(100) ] ) elif geom_type == "Polygon": geoms = GeoSeries( [ Polygon([(random.random(), random.random()) for _ in range(3)]) for _ in range(100) ] ) elif geom_type == "MultiPolygon": geoms = GeoSeries( [ MultiPolygon( [ Polygon( [(random.random(), random.random()) for _ in range(3)] ) for _ in range(3) ] ) for _ in range(20) ] ) elif geom_type == "mixed": g1 = GeoSeries([Point(i, i) for i in range(100)]) g2 = GeoSeries( [ LineString([(random.random(), random.random()) for _ in range(5)]) for _ in range(100) ] ) g3 = GeoSeries( [ Polygon([(random.random(), random.random()) for _ in range(3)]) for _ in range(100) ] ) geoms = g1 geoms.iloc[np.random.randint(0, 100, 50)] = g2.iloc[:50] geoms.iloc[np.random.randint(0, 100, 33)] = g3.iloc[:33] print(geoms.geom_type.value_counts()) df = GeoDataFrame({"geometry": geoms, "values": np.random.randn(len(geoms))}) self.geoms = geoms self.df = df def time_plot_series(self, *args): self.geoms.plot() def time_plot_values(self, *args): self.df.plot(column="values") geopandas-1.2.0/benchmarks/sindex.py000066400000000000000000000065711525650651600174600ustar00rootroot00000000000000from shapely.geometry import Point from geopandas.tests.util import _NATURALEARTH_LOWRES from geopandas.tests.util import _NATURALEARTH_CITIES from geopandas import read_file, GeoSeries # Derive list of valid query predicates based on underlying index backend; # we have to create a non-empty instance of the index to get these index = GeoSeries([Point(0, 0)]).sindex predicates = sorted(p for p in index.valid_query_predicates if p is not None) geom_types = ("mixed", "points", "polygons") def generate_test_df(): world = read_file(_NATURALEARTH_LOWRES) capitals = read_file(_NATURALEARTH_CITIES) countries = world.to_crs("epsg:3395")[["geometry"]] capitals = capitals.to_crs("epsg:3395")[["geometry"]] mixed = capitals.append(countries) # get a mix of geometries points = capitals polygons = countries # filter out invalid geometries data = { "mixed": mixed[mixed.is_valid], "points": points[points.is_valid], "polygons": polygons[polygons.is_valid], } # ensure index is pre-generated for data_type in data.keys(): data[data_type].sindex.query(data[data_type].geometry.values[0]) return data class BenchIntersection: param_names = ["input_geom_type", "tree_geom_type"] params = [ geom_types, geom_types, ] def setup(self, *args): self.data = generate_test_df() # cache bounds so that bound creation is not counted in benchmarks self.bounds = { data_type: [g.bounds for g in self.data[data_type].geometry] for data_type in self.data.keys() } def time_intersects(self, input_geom_type, tree_geom_type): tree = self.data[tree_geom_type].sindex for bounds in self.bounds[input_geom_type]: tree.intersection(bounds) class BenchIndexCreation: param_names = ["tree_geom_type"] params = [ geom_types, ] def setup(self, *args): self.data = generate_test_df() def time_index_creation(self, tree_geom_type): """Time creation of spatial index. Note: requires running a single query to ensure that lazy-building indexes are actually built. """ # Note: the GeoDataFram._sindex_generated attribute will # be removed by GH#1444 but is kept here (in the benchmarks # so that we can compare pre GH#1444 to post GH#1444 if needed self.data[tree_geom_type]._sindex_generated = None self.data[tree_geom_type].geometry.values._sindex = None tree = self.data[tree_geom_type].sindex # also do a single query to ensure the index is actually # generated and used tree.query(self.data[tree_geom_type].geometry.values[0]) class BenchQuery: param_names = ["predicate", "input_geom_type", "tree_geom_type"] params = [ predicates, geom_types, geom_types, ] def setup(self, *args): self.data = generate_test_df() def time_query_bulk(self, predicate, input_geom_type, tree_geom_type): self.data[tree_geom_type].sindex.query( self.data[input_geom_type].geometry.values, predicate=predicate, ) def time_query(self, predicate, input_geom_type, tree_geom_type): tree = self.data[tree_geom_type].sindex for geom in self.data[input_geom_type].geometry.values: tree.query(geom, predicate=predicate) geopandas-1.2.0/benchmarks/sjoin.py000066400000000000000000000017021525650651600172770ustar00rootroot00000000000000import random from geopandas import GeoDataFrame, GeoSeries, sjoin from shapely.geometry import Point, LineString, Polygon import numpy as np class Bench: param_names = ["op"] params = [("intersects", "contains", "within")] def setup(self, *args): triangles = GeoSeries( [ Polygon([(random.random(), random.random()) for _ in range(3)]) for _ in range(1000) ] ) points = GeoSeries( [ Point(x, y) for x, y in zip(np.random.random(10000), np.random.random(10000)) ] ) df1 = GeoDataFrame( {"val1": np.random.randn(len(triangles)), "geometry": triangles} ) df2 = GeoDataFrame({"val1": np.random.randn(len(points)), "geometry": points}) self.df1, self.df2 = df1, df2 def time_sjoin(self, predicate): sjoin(self.df1, self.df2, predicate=predicate) geopandas-1.2.0/benchmarks/transform.py000066400000000000000000000014311525650651600201670ustar00rootroot00000000000000from geopandas import GeoDataFrame, GeoSeries, read_file import numpy as np import pandas as pd from shapely.geometry import Point from geopandas.tests.util import _NYBB class CRS: def setup(self): nybb = read_file(_NYBB) self.long_nybb = GeoDataFrame(pd.concat(10 * [nybb]), crs=nybb.crs) num_points = 20000 longitudes = np.random.rand(num_points) - 120 latitudes = np.random.rand(num_points) + 38 self.point_df = GeoSeries( [Point(x, y) for (x, y) in zip(longitudes, latitudes)] ) self.point_df.crs = {"init": "epsg:4326"} def time_transform_wgs84(self): self.long_nybb.to_crs({"init": "epsg:4326"}) def time_transform_many_points(self): self.point_df.to_crs({"init": "epsg:32610"}) geopandas-1.2.0/ci/000077500000000000000000000000001525650651600140615ustar00rootroot00000000000000geopandas-1.2.0/ci/envs/000077500000000000000000000000001525650651600150345ustar00rootroot00000000000000geopandas-1.2.0/ci/envs/311-latest.yaml000066400000000000000000000012431525650651600175160ustar00rootroot00000000000000name: test channels: - conda-forge dependencies: - python=3.11 # required - pandas - shapely - pyogrio - pyproj - packaging # testing - pytest - pytest-cov - pytest-xdist - fsspec - requests - aiohttp - pytz # optional - matplotlib-base - mapclassify - libpysal - folium - xyzservices - contextily - scipy - geopy - pointpats - geodatasets # installed in tests.yaml, because not available on windows # - postgis - sqlalchemy<2.1 # test oldest support sqlalchemy version - psycopg2 - libspatialite - geoalchemy2 - pyarrow - geoarrow-pyarrow - fiona - pyogrio # doctest testing - pytest-doctestplus geopandas-1.2.0/ci/envs/311-minimal.yaml000066400000000000000000000006261525650651600176540ustar00rootroot00000000000000name: test channels: - conda-forge dependencies: - python=3.11 # required - numpy=2.0 - pandas=2.2 - shapely=2.1.0 - pyogrio=0.8 - pyproj=3.7.0 - packaging # testing - pytest - pytest-cov - pytest-xdist - fsspec - requests - aiohttp - pytz # optional - matplotlib=3.9.1 - mapclassify=2.7.0 - geopy - sqlalchemy - libspatialite - pyarrow=15.0 - geodatasets geopandas-1.2.0/ci/envs/311-pd23.yaml000066400000000000000000000011761525650651600167770ustar00rootroot00000000000000name: test channels: - conda-forge dependencies: - python=3.11 # required - pandas=2.3 - numpy=2.3 # released 3 days after pandas 2.3 - shapely - pyogrio - pyproj - packaging # testing - pytest - pytest-cov - pytest-xdist - fsspec - requests - aiohttp - pytz # optional - matplotlib - mapclassify - folium - xyzservices - scipy - geopy - geodatasets # installed in tests.yaml, because not available on windows # - postgis - sqlalchemy<2.1 # test oldest support sqlalchemy version - psycopg2 - libspatialite - geoalchemy2 - pyarrow # doctest testing - pytest-doctestplus geopandas-1.2.0/ci/envs/312-latest.yaml000066400000000000000000000011611525650651600175160ustar00rootroot00000000000000name: test channels: - conda-forge dependencies: - python=3.12 # required - pandas - shapely - pyogrio - pyproj - packaging # testing - pytest - pytest-cov - pytest-xdist - fsspec - requests - aiohttp - pytz # optional - fiona - matplotlib - mapclassify - libpysal - folium - xyzservices - contextily - scipy - geopy - pointpats - geodatasets # installed in tests.yaml, because not available on windows # - postgis - sqlalchemy - psycopg - psycopg2 - libspatialite - geoalchemy2 - pyarrow - geoarrow-pyarrow # doctest testing - pytest-doctestplus geopandas-1.2.0/ci/envs/313-dev.yaml000066400000000000000000000020561525650651600170050ustar00rootroot00000000000000name: test channels: - conda-forge dependencies: - python=3.13 - cython # required - pyproj - gdal - geos - packaging # testing - pytest - pytest-cov - pytest-xdist - fsspec - requests - aiohttp # optional #- geopy - sqlalchemy<2.1 # test oldest support sqlalchemy version - libspatialite - rasterio - pip - pip: - geopy - mapclassify>=2.6.0 # dev versions of packages - --pre --index-url https://pypi.anaconda.org/scientific-python-nightly-wheels/simple --extra-index-url https://pypi.fury.io/arrow-nightlies/ --extra-index-url https://pypi.org/simple - numpy>=2.0.0 - fiona - pandas - matplotlib - pyarrow - shapely - git+https://github.com/python-visualization/folium.git@main - git+https://github.com/python-visualization/branca.git@main - git+https://github.com/geopandas/xyzservices.git@main - git+https://github.com/geopandas/contextily.git@main - git+https://github.com/geopandas/geodatasets.git@main - git+https://github.com/geopandas/pyogrio.git@main geopandas-1.2.0/ci/envs/313-latest-no-pyogrio.yaml000066400000000000000000000007441525650651600216250ustar00rootroot00000000000000name: test channels: - conda-forge dependencies: - python=3.13 # required - pandas - shapely # - pyogrio # build with only fiona - libgdal - pyproj - packaging # testing - pytest - pytest-cov - pytest-xdist - pytz # - fsspec # to have one non-minimal build without fsspec # optional - matplotlib>=3.6 - mapclassify - folium - xyzservices - scipy - geopy - sqlalchemy - libspatialite - pyarrow - fiona - pointpats - geodatasetsgeopandas-1.2.0/ci/envs/313-latest-no-pyproj.yaml000066400000000000000000000007161525650651600214570ustar00rootroot00000000000000name: test channels: - conda-forge dependencies: - python=3.13 # required - pandas - shapely # - pyogrio # - pyproj - packaging # testing - pytest - pytest-cov - pytest-xdist - fsspec - requests - aiohttp - pytz # optional - fiona - matplotlib - mapclassify - folium - xyzservices - scipy - geopy # - pointpats - geodatasets # - sqlalchemy>=2 # - psycopg2 # - libspatialite # - geoalchemy2 - pyarrow geopandas-1.2.0/ci/envs/313-latest.yaml000066400000000000000000000012131525650651600175150ustar00rootroot00000000000000name: test channels: - conda-forge dependencies: - python=3.13 # required - pandas - shapely - pyogrio - pyproj - packaging # testing - pytest - pytest-cov - pytest-xdist - fsspec - requests - aiohttp - pytz # optional - fiona - matplotlib - mapclassify - libpysal - folium - xyzservices - contextily - scipy - geopy - pointpats - geodatasets # installed in tests.yaml, because not available on windows # - postgis - sqlalchemy - psycopg - psycopg2 - libspatialite - geoalchemy2 - pyarrow - geoarrow-pyarrow - libgdal-arrow-parquet # doctest testing - pytest-doctestplus geopandas-1.2.0/ci/envs/313-no-optional-deps.yaml000066400000000000000000000003121525650651600214100ustar00rootroot00000000000000name: test channels: - conda-forge dependencies: - python=3.13 # required - pandas - shapely - pyogrio>=0.7.2 - pyproj - packaging # testing - pytest - pytest-cov - pytest-xdist geopandas-1.2.0/ci/envs/314-latest.yaml000066400000000000000000000012101525650651600175130ustar00rootroot00000000000000name: test channels: - conda-forge dependencies: - python=3.14 # required - pandas - shapely - pyogrio - pyproj - packaging # testing - pytest - pytest-cov - pytest-xdist - fsspec - requests - aiohttp - pytz # optional # - fiona # no 3.14 build yet - matplotlib - mapclassify - libpysal - folium - xyzservices - contextily - scipy - geopy - pointpats - geodatasets # installed in tests.yaml, because not available on windows # - postgis - sqlalchemy - psycopg - psycopg2 - libspatialite - geoalchemy2 - pyarrow - geoarrow-pyarrow # doctest testing - pytest-doctestplus geopandas-1.2.0/ci/scripts/000077500000000000000000000000001525650651600155505ustar00rootroot00000000000000geopandas-1.2.0/ci/scripts/setup_postgres.sh000066400000000000000000000013621525650651600211740ustar00rootroot00000000000000#!/bin/sh set -e if [ -z "${PGUSER}" ] || [ -z "${PGPORT}" ]; then echo "Environment variables PGUSER and PGPORT must be set" exit 1 fi PGDATA=$(mktemp -d /tmp/postgres.XXXXXX) echo "Setting up PostgreSQL in ${PGDATA} on port ${PGPORT}" pg_ctl -D ${PGDATA} initdb pg_ctl -D ${PGDATA} start SOCKETPATH="/tmp/.s.PGSQL.${PGPORT}" echo -n 'waiting for postgres' while [ ! -e ${SOCKETPATH} ]; do sleep 1 echo -n '.' done echo echo "Done setting up PostgreSQL. When finished, stop and cleanup using:" echo echo " pg_ctl -D ${PGDATA} stop" echo " rm -rf ${PGDATA}" echo createuser -U ${USER} -s ${PGUSER} createdb --owner=${PGUSER} test_geopandas psql -d test_geopandas -q -c "CREATE EXTENSION postgis" echo "PostGIS server ready." geopandas-1.2.0/codecov.yml000066400000000000000000000002431525650651600156320ustar00rootroot00000000000000coverage: status: project: default: target: 95% # the required coverage value threshold: 0.2% # the leniency in hitting the target geopandas-1.2.0/doc/000077500000000000000000000000001525650651600142335ustar00rootroot00000000000000geopandas-1.2.0/doc/Makefile000066400000000000000000000130731525650651600156770ustar00rootroot00000000000000# Makefile for Sphinx documentation # # You can set these variables from the command line. SPHINXOPTS = SPHINXBUILD = sphinx-build PAPER = BUILDDIR = build # Internal variables. PAPEROPT_a4 = -D latex_paper_size=a4 PAPEROPT_letter = -D latex_paper_size=letter ALLSPHINXOPTS = -d $(BUILDDIR)/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) source # the i18n builder cannot share the environment and doctrees with the others I18NSPHINXOPTS = $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) source .PHONY: help clean html dirhtml singlehtml pickle json htmlhelp qthelp devhelp epub latex latexpdf text man changes linkcheck doctest gettext help: @echo "Please use \`make ' where is one of" @echo " html to make standalone HTML files" @echo " dirhtml to make HTML files named index.html in directories" @echo " singlehtml to make a single large HTML file" @echo " pickle to make pickle files" @echo " json to make JSON files" @echo " htmlhelp to make HTML files and an HTML help project" @echo " qthelp to make HTML files and a qthelp project" @echo " devhelp to make HTML files and a Devhelp project" @echo " epub to make an epub" @echo " latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter" @echo " latexpdf to make LaTeX files and run them through pdflatex" @echo " text to make text files" @echo " man to make manual pages" @echo " texinfo to make Texinfo files" @echo " info to make Texinfo files and run them through makeinfo" @echo " gettext to make PO message catalogs" @echo " changes to make an overview of all changed/added/deprecated items" @echo " linkcheck to check all external links for integrity" @echo " doctest to run all doctests embedded in the documentation (if enabled)" clean: -rm -rf $(BUILDDIR)/* -rm -rf source/reference/* html: $(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html @echo @echo "Build finished. The HTML pages are in $(BUILDDIR)/html." dirhtml: $(SPHINXBUILD) -b dirhtml $(ALLSPHINXOPTS) $(BUILDDIR)/dirhtml @echo @echo "Build finished. The HTML pages are in $(BUILDDIR)/dirhtml." singlehtml: $(SPHINXBUILD) -b singlehtml $(ALLSPHINXOPTS) $(BUILDDIR)/singlehtml @echo @echo "Build finished. The HTML page is in $(BUILDDIR)/singlehtml." pickle: $(SPHINXBUILD) -b pickle $(ALLSPHINXOPTS) $(BUILDDIR)/pickle @echo @echo "Build finished; now you can process the pickle files." json: $(SPHINXBUILD) -b json $(ALLSPHINXOPTS) $(BUILDDIR)/json @echo @echo "Build finished; now you can process the JSON files." htmlhelp: $(SPHINXBUILD) -b htmlhelp $(ALLSPHINXOPTS) $(BUILDDIR)/htmlhelp @echo @echo "Build finished; now you can run HTML Help Workshop with the" \ ".hhp project file in $(BUILDDIR)/htmlhelp." qthelp: $(SPHINXBUILD) -b qthelp $(ALLSPHINXOPTS) $(BUILDDIR)/qthelp @echo @echo "Build finished; now you can run "qcollectiongenerator" with the" \ ".qhcp project file in $(BUILDDIR)/qthelp, like this:" @echo "# qcollectiongenerator $(BUILDDIR)/qthelp/GeoPandas.qhcp" @echo "To view the help file:" @echo "# assistant -collectionFile $(BUILDDIR)/qthelp/GeoPandas.qhc" devhelp: $(SPHINXBUILD) -b devhelp $(ALLSPHINXOPTS) $(BUILDDIR)/devhelp @echo @echo "Build finished." @echo "To view the help file:" @echo "# mkdir -p $$HOME/.local/share/devhelp/GeoPandas" @echo "# ln -s $(BUILDDIR)/devhelp $$HOME/.local/share/devhelp/GeoPandas" @echo "# devhelp" epub: $(SPHINXBUILD) -b epub $(ALLSPHINXOPTS) $(BUILDDIR)/epub @echo @echo "Build finished. The epub file is in $(BUILDDIR)/epub." latex: $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex @echo @echo "Build finished; the LaTeX files are in $(BUILDDIR)/latex." @echo "Run \`make' in that directory to run these through (pdf)latex" \ "(use \`make latexpdf' here to do that automatically)." latexpdf: $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex @echo "Running LaTeX files through pdflatex..." $(MAKE) -C $(BUILDDIR)/latex all-pdf @echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex." text: $(SPHINXBUILD) -b text $(ALLSPHINXOPTS) $(BUILDDIR)/text @echo @echo "Build finished. The text files are in $(BUILDDIR)/text." man: $(SPHINXBUILD) -b man $(ALLSPHINXOPTS) $(BUILDDIR)/man @echo @echo "Build finished. The manual pages are in $(BUILDDIR)/man." texinfo: $(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo @echo @echo "Build finished. The Texinfo files are in $(BUILDDIR)/texinfo." @echo "Run \`make' in that directory to run these through makeinfo" \ "(use \`make info' here to do that automatically)." info: $(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo @echo "Running Texinfo files through makeinfo..." make -C $(BUILDDIR)/texinfo info @echo "makeinfo finished; the Info files are in $(BUILDDIR)/texinfo." gettext: $(SPHINXBUILD) -b gettext $(I18NSPHINXOPTS) $(BUILDDIR)/locale @echo @echo "Build finished. The message catalogs are in $(BUILDDIR)/locale." changes: $(SPHINXBUILD) -b changes $(ALLSPHINXOPTS) $(BUILDDIR)/changes @echo @echo "The overview file is in $(BUILDDIR)/changes." linkcheck: $(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck @echo @echo "Link check complete; look for any errors in the above output " \ "or in $(BUILDDIR)/linkcheck/output.txt." doctest: $(SPHINXBUILD) -b doctest $(ALLSPHINXOPTS) $(BUILDDIR)/doctest @echo "Testing of doctests in the sources finished, look at the " \ "results in $(BUILDDIR)/doctest/output.txt." dash: html rm -rf geopandas.docset doc2dash --name=geopandas _build/html geopandas-1.2.0/doc/environment.yml000066400000000000000000000014201525650651600173170ustar00rootroot00000000000000name: geopandas_docs channels: - conda-forge dependencies: - python=3.13 - pandas - shapely - geopy - matplotlib - mapclassify - sphinx - pydata-sphinx-theme - numpydoc - ipython - pillow - mock - pyepsg - sphinx-gallery - jinja2 - doc2dash - matplotlib-scalebar # specify additional dependencies to reduce solving for conda - geos - nbsphinx - jupyter_client - ipykernel - myst-parser - folium - xyzservices - packaging - pip - pip: - sphinx-toggleprompt - geodatasets # proj/pyproj deps from pip to avoid Proj env warning from conda+readthedocs - pyproj - cartopy - fiona - pyogrio - rasterio - contextily - pointpats - libpysal - matplotlib_map_utils geopandas-1.2.0/doc/make.bat000066400000000000000000000117701525650651600156460ustar00rootroot00000000000000@ECHO OFF REM Command file for Sphinx documentation if "%SPHINXBUILD%" == "" ( set SPHINXBUILD=sphinx-build ) set BUILDDIR=build set ALLSPHINXOPTS=-d %BUILDDIR%/doctrees %SPHINXOPTS% source set I18NSPHINXOPTS=%SPHINXOPTS% source if NOT "%PAPER%" == "" ( set ALLSPHINXOPTS=-D latex_paper_size=%PAPER% %ALLSPHINXOPTS% set I18NSPHINXOPTS=-D latex_paper_size=%PAPER% %I18NSPHINXOPTS% ) if "%1" == "" goto help if "%1" == "help" ( :help echo.Please use `make ^` where ^ is one of echo. html to make standalone HTML files echo. dirhtml to make HTML files named index.html in directories echo. singlehtml to make a single large HTML file echo. pickle to make pickle files echo. json to make JSON files echo. htmlhelp to make HTML files and an HTML help project echo. qthelp to make HTML files and a qthelp project echo. devhelp to make HTML files and a Devhelp project echo. epub to make an epub echo. latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter echo. text to make text files echo. man to make manual pages echo. texinfo to make Texinfo files echo. gettext to make PO message catalogs echo. changes to make an overview over all changed/added/deprecated items echo. linkcheck to check all external links for integrity echo. doctest to run all doctests embedded in the documentation if enabled goto end ) if "%1" == "clean" ( for /d %%i in (%BUILDDIR%\*) do rmdir /q /s %%i del /q /s %BUILDDIR%\* goto end ) if "%1" == "html" ( %SPHINXBUILD% -b html %ALLSPHINXOPTS% %BUILDDIR%/html if errorlevel 1 exit /b 1 echo. echo.Build finished. The HTML pages are in %BUILDDIR%/html. goto end ) if "%1" == "dirhtml" ( %SPHINXBUILD% -b dirhtml %ALLSPHINXOPTS% %BUILDDIR%/dirhtml if errorlevel 1 exit /b 1 echo. echo.Build finished. The HTML pages are in %BUILDDIR%/dirhtml. goto end ) if "%1" == "singlehtml" ( %SPHINXBUILD% -b singlehtml %ALLSPHINXOPTS% %BUILDDIR%/singlehtml if errorlevel 1 exit /b 1 echo. echo.Build finished. The HTML pages are in %BUILDDIR%/singlehtml. goto end ) if "%1" == "pickle" ( %SPHINXBUILD% -b pickle %ALLSPHINXOPTS% %BUILDDIR%/pickle if errorlevel 1 exit /b 1 echo. echo.Build finished; now you can process the pickle files. goto end ) if "%1" == "json" ( %SPHINXBUILD% -b json %ALLSPHINXOPTS% %BUILDDIR%/json if errorlevel 1 exit /b 1 echo. echo.Build finished; now you can process the JSON files. goto end ) if "%1" == "htmlhelp" ( %SPHINXBUILD% -b htmlhelp %ALLSPHINXOPTS% %BUILDDIR%/htmlhelp if errorlevel 1 exit /b 1 echo. echo.Build finished; now you can run HTML Help Workshop with the ^ .hhp project file in %BUILDDIR%/htmlhelp. goto end ) if "%1" == "qthelp" ( %SPHINXBUILD% -b qthelp %ALLSPHINXOPTS% %BUILDDIR%/qthelp if errorlevel 1 exit /b 1 echo. echo.Build finished; now you can run "qcollectiongenerator" with the ^ .qhcp project file in %BUILDDIR%/qthelp, like this: echo.^> qcollectiongenerator %BUILDDIR%\qthelp\GeoPandas.qhcp echo.To view the help file: echo.^> assistant -collectionFile %BUILDDIR%\qthelp\GeoPandas.ghc goto end ) if "%1" == "devhelp" ( %SPHINXBUILD% -b devhelp %ALLSPHINXOPTS% %BUILDDIR%/devhelp if errorlevel 1 exit /b 1 echo. echo.Build finished. goto end ) if "%1" == "epub" ( %SPHINXBUILD% -b epub %ALLSPHINXOPTS% %BUILDDIR%/epub if errorlevel 1 exit /b 1 echo. echo.Build finished. The epub file is in %BUILDDIR%/epub. goto end ) if "%1" == "latex" ( %SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex if errorlevel 1 exit /b 1 echo. echo.Build finished; the LaTeX files are in %BUILDDIR%/latex. goto end ) if "%1" == "text" ( %SPHINXBUILD% -b text %ALLSPHINXOPTS% %BUILDDIR%/text if errorlevel 1 exit /b 1 echo. echo.Build finished. The text files are in %BUILDDIR%/text. goto end ) if "%1" == "man" ( %SPHINXBUILD% -b man %ALLSPHINXOPTS% %BUILDDIR%/man if errorlevel 1 exit /b 1 echo. echo.Build finished. The manual pages are in %BUILDDIR%/man. goto end ) if "%1" == "texinfo" ( %SPHINXBUILD% -b texinfo %ALLSPHINXOPTS% %BUILDDIR%/texinfo if errorlevel 1 exit /b 1 echo. echo.Build finished. The Texinfo files are in %BUILDDIR%/texinfo. goto end ) if "%1" == "gettext" ( %SPHINXBUILD% -b gettext %I18NSPHINXOPTS% %BUILDDIR%/locale if errorlevel 1 exit /b 1 echo. echo.Build finished. The message catalogs are in %BUILDDIR%/locale. goto end ) if "%1" == "changes" ( %SPHINXBUILD% -b changes %ALLSPHINXOPTS% %BUILDDIR%/changes if errorlevel 1 exit /b 1 echo. echo.The overview file is in %BUILDDIR%/changes. goto end ) if "%1" == "linkcheck" ( %SPHINXBUILD% -b linkcheck %ALLSPHINXOPTS% %BUILDDIR%/linkcheck if errorlevel 1 exit /b 1 echo. echo.Link check complete; look for any errors in the above output ^ or in %BUILDDIR%/linkcheck/output.txt. goto end ) if "%1" == "doctest" ( %SPHINXBUILD% -b doctest %ALLSPHINXOPTS% %BUILDDIR%/doctest if errorlevel 1 exit /b 1 echo. echo.Testing of doctests in the sources finished, look at the ^ results in %BUILDDIR%/doctest/output.txt. goto end ) :end geopandas-1.2.0/doc/nyc_boros.py000066400000000000000000000045151525650651600166070ustar00rootroot00000000000000""" Visualizing NYC Boroughs ------------------------ Visualize the Boroughs of New York City with Geopandas. This example generates many images that are used in the documentation. See the `Geometric Manipulations ` example for more details. First we'll import a dataset containing each borough in New York City. We'll use the ``datasets`` module to handle this quickly. """ import numpy as np import matplotlib.pyplot as plt from shapely.geometry import Point from geopandas import GeoSeries, GeoDataFrame import geopandas as gpd import geodatasets np.random.seed(1) DPI = 100 path_nybb = geodatasets.get_path("nybb") boros = GeoDataFrame.from_file(path_nybb) boros = boros.set_index("BoroCode") boros ############################################################################## # Next, we'll plot the raw data ax = boros.plot() plt.xticks(rotation=90) plt.savefig("nyc.png", dpi=DPI, bbox_inches="tight") ############################################################################## # We can easily retrieve the convex hull of each shape. This corresponds to # the outer edge of the shapes. boros.geometry.convex_hull.plot() plt.xticks(rotation=90) # Grab the limits which we'll use later xmin, xmax = plt.gca().get_xlim() ymin, ymax = plt.gca().get_ylim() plt.savefig("nyc_hull.png", dpi=DPI, bbox_inches="tight") ############################################################################## # We'll generate some random dots scattered throughout our data, and will # use them to perform some set operations with our boroughs. We can use # GeoPandas to perform unions, intersections, etc. N = 2000 # number of random points R = 2000 # radius of buffer in feet # xmin, xmax, ymin, ymax = 900000, 1080000, 120000, 280000 xc = (xmax - xmin) * np.random.random(N) + xmin yc = (ymax - ymin) * np.random.random(N) + ymin pts = GeoSeries([Point(x, y) for x, y in zip(xc, yc)]) mp = pts.buffer(R).union_all() boros_with_holes = boros.geometry - mp boros_with_holes.plot() plt.xticks(rotation=90) plt.savefig("boros_with_holes.png", dpi=DPI, bbox_inches="tight") ############################################################################## # Finally, we'll show the holes that were taken out of our boroughs. holes = boros.geometry & mp holes.plot() plt.xticks(rotation=90) plt.savefig("holes.png", dpi=DPI, bbox_inches="tight") plt.show() geopandas-1.2.0/doc/source/000077500000000000000000000000001525650651600155335ustar00rootroot00000000000000geopandas-1.2.0/doc/source/_static/000077500000000000000000000000001525650651600171615ustar00rootroot00000000000000geopandas-1.2.0/doc/source/_static/SponsoredProject.svg000066400000000000000000000172561525650651600232200ustar00rootroot00000000000000SponsoredProjectgeopandas-1.2.0/doc/source/_static/binary_geo-difference.svg000066400000000000000000000017511525650651600241140ustar00rootroot00000000000000geopandas-1.2.0/doc/source/_static/binary_geo-intersection.svg000066400000000000000000000020601525650651600245220ustar00rootroot00000000000000geopandas-1.2.0/doc/source/_static/binary_geo-symm_diff.svg000066400000000000000000000023341525650651600237750ustar00rootroot00000000000000geopandas-1.2.0/doc/source/_static/binary_geo-union.svg000066400000000000000000000021341525650651600231460ustar00rootroot00000000000000geopandas-1.2.0/doc/source/_static/binary_op-01.svg000066400000000000000000000321211525650651600221010ustar00rootroot00000000000000geopandas-1.2.0/doc/source/_static/binary_op-02.svg000066400000000000000000000340361525650651600221110ustar00rootroot00000000000000geopandas-1.2.0/doc/source/_static/binary_op-03.svg000066400000000000000000000206501525650651600221070ustar00rootroot00000000000000geopandas-1.2.0/doc/source/_static/boros_with_holes.png000066400000000000000000004421401525650651600232450ustar00rootroot00000000000000PNG  IHDRxFsBIT|d pHYsaa?i IDATxyX9-TRJY2c؍J&La,cd>c!ɾ36QKR:=?:9vau]]?srMYT(!BlC; B!$xB!ٌ$xB!ٌ$xB!ٌ$xB!ٌ$xB!ٌ$xB!ٌ$xB!ٌ$xB!ٌ^ x☙_3}tnܸ*vӦMtGGGʕ+9vXZZjaaaAժU1cFqqqqу%Kbcc+aaaiƞ>}lll(Y$={$>>>XTkf|B!5 r%zƍiѢ+WTR:uJ7aZlӧO6m{رcԭ[={9k,|||R AAA4mڔ3p@$ׯφ 4hK.EPn]BCCub?NݺuXldԫWFۿ ;AAATT ooo̙'B9RpTn߾)͛7^+V_EGG+FFFJ۶m^zXYY)>>>:FRt TT*rAׯ_+%JP\]]u;;;+%KT8T*%((HOѣu{{{+J|||B!gP$0(P+꼦:qs&Gim۶'OЩS'؎;2~x~W ʕ+qtt^zڸ9ry666İw^F6~.\+WҪU+~W4 ;vLUؾ};ÇٳGGGLLL2؄B!Y||}4RSǏh4:B!6;q1vXs/22ӬY3~(VgϞՉ"../B{hѢDDDKdd$* GGGSJΔؿYX1^x͛7S:B!6z'x~~~;QF1rT *-[tym۶agg7nnnXYYb +V`dd3LJ(:ڵkquuųٙ@!!!ܺu:hmCC4۷]ߏE!ⳣ<F ...6^:ys,]֭[ӢE ={ڵke֬Y9s2j(뇅΄G~tTjժnݚ &`cc {.:9r$5o{0bUF-qՋW_sN5k6+u^^^ (yM7{k,^zZVT*U/ZKLLTvܩSqttTʖ-Oʑ#GҬ7 @YbnnTREO3...N޽Rxq%o޼fSggg%o޼Jŕ={qӕʕ++JZիWg9)@m !B|7TIé\2aaaq'11]_2f̘O!BGVSvm޽;.\˗ d7oή]x$y"[O!GիWquudɒx7oҨQO۹7j(mrzO!$xB!>RT)011y:̙ӧO߿?fffYC8u?m O9xB!> ;;;:u]hh(}aԨQINNΒ=zׯ_k_nmڴݝƍ%)H's 6mϧnݺ9rwwwѣW^}CBB4lؐ RLw}Ç)S GβRBٽ{7?fTVjժv͛Yx1 .B 4jԈ!C`mmw3f gΜL2*Uhmۖ1c0n8vɓ'QTYB|D2'DEEèQljtFόpwwgҥl۶ ooobbb6m666|ozY&M}{{{/^+OׯO&?CFB|mrqY̍KJJJ~|ݻ7{&00kײ~n݊Zb(P U۷oV믿ΰ;w&117oIHHoߦ`B|L2'lڴ9sjvyu k֬Iw.w}ǖ-[ػw/Æ #O<̞=GGGj֬˵+RP!nܸ7|i? *ɓ>}:SNرc|B|l ! 1B}pp?Ȱa055ߟΝ;n9rвeKVXMڵkt +++<<< W\NprrPB|J*EQO݉p*WLXX*UBƹs(WժUc˖-ei3f`/_ŋAQBBB)H'"K賡qݻ7̌… fZvȐ!*TH5kfεkX|9㫯Ԕ-[ʈ#8z(gδbŊQhQ$xB!1ɓ)]4nÇU4nܘ7n9ږĭ[y&o޼=z>}СCtŅ }&NH\2tҘs8njj;}H޻w/:$B'qF˗/Kr O8K.q=ƎKpp0*T:jذ!mڴ 333سg W\O>DFFҭ[4Os" B:uGѽ{w,YB߾})^8 4 ((۷[6))VZahhH.]t 6DԩCTTTGLG>>2`&Õ([,ZbժU9rɓ'7o^eٲetЁ;wRpazUصkWB|(yE+"CѣG599֭[y%TT֭[j*N8v;;w 22___ͩULpp0qqqrNNN8991tP&Lȑ#eԩ|W̛7ŋ`&˟zRd/ !ŋ111E2,Y߿ORR^"E$66ŋb iذg͙3Wr9=Jb|$$$o>H'"]{RrerȡWs1h =zDŊqwwԔ$KBoߞ>c-..>}oOhР]vɓ9GQP!j׮&&&;m4XjvۋǏo>j5^)ߏŋ̌ӧsƍ4,YB "O<ԪUǏ[jj‚U2cƌ4닋G,Y\]] K3Ӹ`ccCɒ%ٳgYR Ԯ]իWq!Fpp0Ϟ=Yfzߺu޽{R8x aaa̝;)S0}t\r9s(W9r$ zr鉍`ܹ??? *D\\͛7ٻw/ժU˴ޜ9sҠA=ի߿?LP J.\ȥKٳvʕ+)UN҉m۶-}N:]_WWW^z7k,|||R AAA4mڔ3p@$ׯφ 4hK.EPn]BCCub?NݺuXldԫW/3d RJx{{3g>9!XlzϿ;v,p13V)\;޽{1}w.ݻwPti@ܘ={6Eٳ4j^f իWg̙9rAqt75ezAulHݻwS]}bdd4o\{m͚5JRaoXv-k׮չƦh`ϟ-BV/;w```*IΝ;:iƥӋէN+++ tb2sLnݪ%ɝ";ٲe 'NPB$''gcO۷IHH 11ӧ&&&L:5ݍccc7n5j`ҤI(š5k2۶mܹ޽{cnn fffԯ_ӷo_ǏsUu }nlmmiݺ5nnn :T{wJS̜9}<___l‚ (QvOk."///lmm_|Rާ#MݙUVXܹsۢysnk{)K.EQĉ)[,<?NTT͛7gXB}f166TR899LMM)WʕKDrr2 6|LBDDGa,Y-[Rvm+FZ>u' ޘ1c8q"&M[n)CY2Vi֬+VիWʕ 4 O>M5}>|)sRbS^<|pǏhBݻw{{{O x wޥG,[ ###mҹ3q^Qh!}Ӻuk،ywwwXz5g֭ܺuLLL066OOOZj#ϟƆ2edڎ̙39p;v`ҥԪU_9::ҭ[7waʕ/vL7S7vXƍرc:tν|)hŊٳ:裢9shѢDDD+22J [HT*gϞM/H_x͛7u/DFF|3StnnD޽{*U/2tP~gj5֭FQ)΢=044~}B$gΜ5~aaa3ѯ_?촱TZ֭[3alllX`w% @#GQF|tޝFAjtqW^?;wY> B17wxd/*[B{=17o[X'O040 Ia"w0`!!!;h Rm[/333n޼w/ș3'ɬX@?~cee_^IˤIיBտg}65jHuo̙jϟOɒ%y/^L'ޞ9rdgz)jZQTjNlttҦMPBR~t7 Pj֬+UTQӌSw/^\ɛ7NRy*ŋWzӧOW*W+jRV^  !/^(JŔ9:_*jZ_Ν;+Snݺ?~\ׯN|׮]FfӧOWT*7赑rZ_=zP?9?ȑCqssS*WJժUŋ+ΝSÕiӦ)7oRO>(Ǐ6:wrIvZ])TR|T?$03JQd9é\2aaaz %C2uT&Utu ˶J'<ԩS+WoՋoCCC=ʂ ߩX"{ǏNznjdŔ+WH&NO?^/R|yH&M 000KdÆ tEr̙YwΛSL/9K%]V2R!N> 9+i>`GirSƔ-Z kJŁL8pfffzI)R=']taѢE^}uٳ'ÕcŔ.]sѩS$prrҞnnnNFf۶mYNpp0kfɒ%Ջ;k׮Tq)?:t(kצ^zTPM6ei?OB=޽K.uV̟O)Ӆ\PkL3zIY@ڶmҥKٱcnh4mh"݉EQ(S ݺuKTݛh~gOiҤ :tٳgBr(PW^eƌqիok Fdd$Zb̘1 <8VN0zqUlmmuN144$))W[WF#n&qFFF-[Çs5uWſ&k޼93gPtiZjw}GRRl޼ŋ3x`F .]sZԩSׯQQQx{{ckkK޼yԩSx{{/p5J*N[]rr2>>>͛7ͽWӒhi4rŲe(QÆ #_|4h777իGtt{N:$$$Ν;ǝ;w믿֮_Nt6F>p{E|Z2'B+Gl߾'''7+hGpwTdf]ҥK 8|q!*TS&..ooo֬Y1zҫ-JEi۶-Oŋh4-J5{cooWVٳgԩSx֢E?عs'ޙ}e o_-/_e\]ݺqFw-[ÇsyXbe˗/ѣGy 'NAiI'B!tؑ#.RBi>}Foޖ… :nj ǏO3255eÆ zj6mJ"EZL2ڒ;wnts}JJJC⵵eȐ!,^Eex={PlT4hMiܸ1v/="O<_N}8z(M6.]:2SHHϞ=ٳgDEEI#',Br]HVzŮ]Nmۆ 8P{ÇΝsss Ν;Ƥ]vE ҥ #Fȑ#۷D~GrAn8vnnn:SDEEQF ߿Mj5 ,nݺ̝;*T*"Epa)QDWinݺвeK{ѣGy>{Vu*9s0|=*ݿ !HeۑYfSǚ5kgʕx4^ۂ)R$c֬YÅ X`]vM3AڵSG\\˗/@8;;_N8PT3&ͣ*GL6g2vXΞ=s/ 6•ӱ۬^sG\prr"66Pʖ-u:z(ƍcܸq-Z"E˗ӧ~J94etTBQQQL0x{6e6nܨsbŊ$&&sLh4<\{yz6mDҥ3Vzu5kFHH tޝ˗Stiv#3f ԭ[wZd˝;w(^8((2sLnݺEN4iZb˖-SdILLLؽ{7 46֭˰av'w̜9W^̴nݚh.^eu ! ţE mh-[RzuvI޽ׯ%#GiӦ&'OѣG6@tt4ӫ_^^^lܸVJ2e;w.ϟ?HgQ_Ϟ=cĉ+V0V() ۷/+VnݺlذMdV2=̙34j(ݶ&Nȭ[Xv-s̡pœ:u={ҦMlْe;88dI=Ӑ077 /_*:utRؾ}Nr7o ӧOGVkϲZ*=۷o端Jx2# Bϼ=bT/Wx*}]se˖mۖ`J(ٳv111lݺWWW(P@Y\\}KI:ׂ}RtT6SN1ڳH"\tI{w>}WWW-[Am?IBЅsWa``^h*\xa 0 &kKbŰoeԬY_~%նٱo>T|y*Ẃܹ3_֫(ܺu0R%>XbzՕUrMBBε;vhܶm799N:Qtiz(>ٳgo>>}nnnN144ҒGJ>{|.aaaܿ5kr1֬YCÆ }z=z111a޼yqTǟϟ$xB!vM||{9r[T*Uq1119ӨQ#ڶmKRa˖-Ү];&L@ݺuӬK.۷vڱi&ߟG1a<<;wЭ[7bcc[.͛7ё۷oe144ٳgs=<==VZ9f"X"o޼вeK>}: `Μ9YR))繈LSre¨TҧB|vN8 lذ!Eo޼ˋXqqqIsm4h۷ HhwҥK-I"۵k{aȑlٲ%{.u֥e˖X[[pB,,,2,hs 4Ы OOOuV%O<$''smw^޽Ǐuw1sLXXXP|yG6m8p Ntlܸ$mY___T*UÇ3VZHy!AAA:t[rJVXMtSFH˔)oeS3 @]͛y{}.' BdcG$'lhժ yĂ |ǎ-Z@lll2-FIoz,--8x`q5k_cק ҫX"nnnTP5kү_?vb<-[bdd5N:^###Ǘ]tDquuk`ҥxx;"yYLƒ8wc)W6}gcǎ !D6s=ʔ)C>}(_ܑMzgc7\|ܹ5 6VL0'ҫT*ܸqzQ8::gNXf z%..5k ņ 8? !D6h6m[3ozO]Ӹx6mę3g租~bѢEܸqcvǭ7oА~IvSeZhA駟Dĉ4lcc4^z[ZիsN񖖖ܿ{ҫW/hѢ;wԻ]>|H.]wvvzmv%h|x9'$$$-MښݻgմoߞKbbbȝ;;mBys^ըQ;vJ,5k7n̙3(P333ZhŠoϲmذ!7n,6www|}}ٿv5oFv5jvvvWΝK3Ky-h2ݰZ|$Bl@8=x K/9r6j(ԩmڴK.rJ055e֬YYrN;3ϟѣGEQ(S yɓ݀I& 6/^pYvޝf}J|QrK.1p@ǡCRm77ndҥ-[}YO?DDDt)Ӷ---)[,{_0`)Uvnպt颳Wc[oߞ;w0b*]"#xB$]FFT(+W޽{Yx1͚5ѣGԫW/yYy///6lA݃tv{1fLLL8~x',a^^zlذǏիR SLKK WS;FxxNrPz7#Ν;nzcǎRUM6@ܼy[b <… ɴj^+wmmm)W111ٳ'OrT*Kr~6nHPP^^^CΝ;G&M202P@IS 2DgC=_W;I'ԩS162[+&5+h˂;w(*F˵$''SrLWf&,,AHӦMiڴ)666ܸqʕ+SV-#F7ݺu#000E>$wzXvڽ~˗ÇӢE r̙n}ݻw'))~F~U#lĉu711MKaڴiC2exo?K=lw!ΝK:u{nϐi}v"""XUdnnϹ~Gɷ'|>D'? wL&W}fV'.^Wj>|J|SҦM\\\5X?$)5sH(qMCvvvvŜ9s[nXx1vvv\z *皇/b``Ǐ=q%rF]@TTR)zʕ ;rhedѼysꨞ;w3gIǎi۶-r6mZ܏@LL .}zATT۷o',,Ν;Õ+WXf ǎ6oLZZ۷o/G dΜ9hkks9=ʊ+7n\s(Uo߾%66VĂ, |r9ǏǺ҉z\COODXg+W'N`ccSiӑ# &MvڬZ>OҥKӣG&MSW^֖ڵkwٳg3{l ~I&O̼yxe]yuuu*=+E nÆ <|iz19= \磭UZ{3gpܹ|ߧ}oߞnݺEZZ*UqhjjH$Ԯ];OZ\qqq:`37nܠsoԫWF)GnZZ("Aedd1s&+Y %u)d+=Vdd$wãD;#7n\Q5jP|}6mڔj*UpssAqMCCMJ߿+b nݪ@Ne A(I␅ l|)+]v]rNwީ;wP@2"<<;w+L&cڴi̝;ҥK3|9y$3f ==wwwϘq6}Vrf(Y%VAP2˖QB9U>lNn!HTysbթY&cƌaÆJdN::991k,Ν;177Ғ[nλwUP#rE17Ω7^~MXX)))+WK|ܬY3V\ɖ-[6mRs|9Wٰa6ls_~xyyѽ{w~7455d2ԩfff@N`+W9D"aٲe4i---nܸR(W\jժ*=7a*]ԯ LժUեe˖,Y~rVZ!H 4uV7oN2eǧvɸQzuLMMԩaaa cǎRzuƌCjjjmiذ!eʔEl۶= Aχ16mѣG<g-%)R)Zr2d:ub̙,Xqٳ샫VUTLJO.SV-LMMJ*Ç_>Wfҥx<===y۹s\MMM&VSNyСztŵӧOwV\g͚AMLL䩩~<,, ZϞ=Z7/ڽfժ/+cǎK.-_|Y*r\GGG޺uŋrmmmyV...rCCC9 D?38p@!:::ܟ*^~-իiFZ'N.]Z Wo(xCUBիWk#FLn СC3lذ<ׇJVV~~~k[lGGG5---˱c5 _|ɉ'ׯ_[ʊ-[(СCÇn |FAzF&# msm%&Ͷm ]j߾=֭#22R;wHNNV(Jٲeqtt̜9'OrQ z%R믿f޽өS''22c]]]ʗ/O@@=z`…oƌРAr9qb)E||'PBk``Z߾}OcW/xkK[C([l\p߿?ȑ# 4{жm[S~};FFF*jMTU׮]޿IIIdff*]-Wvv6&55333VZEJ3mNaaa4i҄(^x׎;رcGkn/L䮾͜9Sqٳgx"7>{ T/H055ٳgyTZ&ܶ-2}!J-ҥK9x`"s @=|#ڵkTZWBYeggGTT>,VG֭ILLD.믿܇T*%66VZ>}rvvV˼xBQvɒ%ܿ۷o"7χ(V7sL8ժUS\5j,*U# {ۼSX(5 466&##\NՁϵk022ʳu&W߾}`ԨQJEY 4mڔ}zjVXAիL<ի3}t~w/UǏoob޽{,^'N~F@ZZqqqCoR9駟X`?_?~E[YYYYXXX]ׯP|maaQ~޻ܶ-2}ő —"55+WPB9*[-A$_UγR)jjjall\s111S~0dN>MTi^EҥKܹ777ƏϾ}ח}EҥiӦ 'NTu6DGG+*q(+>>777޽{TRt)&LիW4h9+Pwٳ3ggΝ;deeѤI?֯_!@Np7o#22d*WVJnݺo.ᨩamm {QSSgnۿiccCbb"QQQy | С/_!ݿe*lmmb˖-H$ ltu\./w}GϞ=ٱcָw^>xbm7oAAA$%%1gE8APRhcǎt1_&M`eeUob ZjZ[[OOO&L@2eС.]b޼yL0s^-t{{{z155eg֭G˗̘1FhW|yƎܹs恁lٲ |*VHjj*VX9m$;I[_ e[45YR7uҽ{wYx1cXZZϋ/xeʔaΜ9tAqtt$))#H^:ь1FqFtTwF&ѰaݳgVPFw{ ̝;-Zķ~ ҬY3fΜɌ3dee)^ ɕ8mкukΞ=[55|U".xۺu+k֬TV~1q|RRRpwwԩSѨQ#͛WྎP<<۶AL[]=},XaƍѣG~C.Ϡ vݻy7ndڥӥK7o۷/[n1bڴiѣGUܺuu뢧G144iӦ;p=zޕrVCBB -$ +322"5= ^뱴իVR@8{,$&&8)))cffV! .Ǐ 0p@\\\1bf 6 H`` k֬˗|wEw+WDOO3g(*,%$$(V԰eڴiT^t_N>}T d7oXbϟg:t";ѱ iӆ/p֭"Zr011aJm_j2 wA9r$=ׯ_Ғ:uꠦ...;v ___8}4 F3k,J."\a233s*8@.] %!![n1eؽ{w}ԨQ˗/Ӳe|#tӧOgƍѫW/lll>;U455B+-D58x _O~?~{{{w˖-cÆ ǡC>+ZA܊9А`LMMSm۶-M6%))'2k,޽ˤIx % .d…-_i߾=nnnx{{СC9x ...E_Ɏ;v!!!2~x4559pãʕCKK+*`IW^ҥ [lޞ#GtR&L֪U ebO ųgϊZTo:}t&00xٲeK$ ٱpBޘ5kpyK TFȑ#2*RҢEvׯ/رcILLť|ŕȑ#45o g ##Ν;+4lؐ *)˨[r!mbzY`A:vȝ;wXn{HH$Ο? ;wdΜ9ܻw FQ"+Xʚ2e ͛7g׮]dee)ǓÇ'(({{]τ#&aaaz* _߸qvފv6lPĉRT^B~[>skDMM~yzzzI} Ç\WWW-]>r@%7nXț5k& ʧM&Օr===<##d7 IDATDȐ;::ʣK?ῡ$ 'egg|2L 1ףiӦ̜9o*ܹsnݺ1i$רB3>~e2 АKl3- 2ӢER{XYYq 2d%>tޝ;w2p@ʖ-Kdd$15rϞ=9|0Ǐ'11Xn޼Y"khhE' '6|=cڰٝV?>FFF`P ;H-Y9٣& M4ѣGLLLk׮ѬNu̙i)@*$$$|&uuu"""HJJɓ'| ׯҥKx?N:sNƍL&CWWAQbE7<<7*$Z>X"ѱ %͛7TT  3|NT\)/^7K԰K&uxl4m 9~II|͉q}7=#C~`ԨѬX_ʐdx{{ӲeK7n @rr2ǎյd1w\<==:t(cƌ ))IQV޽;Ȉ;vR F$:A;TJ$-- 6… X׳''NPSuO\۶mH$xxxмysv1hoo_+aT\.'::TZ`A<=x @:ww3OI.sf9q:...h={Ν:afG|K7 \ۤ>LӦMիW=z:аax%y;;;nݺUd~ctttH$$&&m6xyxx# yzzzTT)S?APVI"SA( r;[[^Ds|Ohjh.]"w1jV&a$?QWWŅ]v2x`IML4mђ?湟F!s^x-S: !!!n###rV)^P! A/жm۸s.S@SCLN]A(2231ꝇzcim-2066a :--7h <==z8m`@O6\;Ccٲey]c*G Fܜ7o(}}}EpYwr7nT,!D'/dx̜1};'x)Njj7WWW̸{.[n%11ʔ)Ҹ `tiy;5KЕ[{vR^\\ݻw ::_K1?PȬZ 77O<#\ /Zt)QO}߮L]eRۮG%--Ty9K.5kңGOPPy8vں] 7ƾk֬Q\)͘1c8zRc@ Tcq5oI&}{河iݦq1sLN)c|' `ŸjeqǏŋtAqHmm}S‚Wn%ouPSSJ*53;\U*=ɓ'4BSөS`Μ94*\ÇUzF.cblǸbܖS~}bcc?L* KΝoз I&}ll۶$:\.? 5Ɯ9s011),X@JٳRquuEGGc*Ν;иqcvRZuuu\B۶m~Zj͐}6֕ACC#F| gO_˩W2 ɩ$&0k֬O2-Z`cckw3Q?|߿zfΜbm*ȑ#XY[Џv1oHII!++ 6`V֌vڱ`ڴiQSSc>|+V(}0AAA 4?/#{ Jԩ'?MRR-'N䩓Ȳeso6.X{ͨQj}lwWK& ;}v@N@DD,]={{n sЩ! #%!E#3`BBBDbO#4iYYLSBYR҈dss6 r+?J)}6l@r>ٜ 'ҥK?ƇJIIeY_\)szւlnZ Xv-Zs=<{bڙ7a1lh4^amlKS녎)8ukVcneΏQJ={|\dO#ߟMQE"dɒbʫW\KIIa„ O}ߟgxCg0b!C5dddĉYj'L& |omfm镓RJP1FCx)Zbr7*D"u m˰H$"H$)]4VJW7>yʅ χA>ǣ!>Uq M쪳~:\t_2 7nL hР]v-[ٳ gbѣ֭SuLcYg͛7{*a238{Ru;j5IXXXɡUaddĉ'hҤ *^>b?33Bd2$ ˗/@G?]UJ < GrEN:k&7oLJk䄯og###1b?#K,aTf޼y4Y.-ǎΝӰ͚5ի%%%EQzuʔ)ɓ'Wsy{{#JU٢6lX*{{{wεIOr%ݻwƎ˔)STO*GVF7QwI/MjUJ >m_Z4=8z(WVyEAH&N^R5=2l31x`f̘A)U=ƍ!rM u*W0gtuh@ܻD\'/@AAAԮ]yк_ʗ/ÇqlyEq͚5L:cccӧݺuSJ?{n"B#WyžuT9qY~=JRzʕ+Sf "B}MMMHIIIE 6h٭E~NG.ѣU Gp._[Δ*)^t#3degcK6Mڞ2zIIK@W'k%ƕwl:!!!*wy%55;rZaΡ#ؽ{7Ԯ]ZjѱcG\\\PiO)G\L L ?_d nnnl߾.-ؾ};GΩ}5_`ɵk*թO[NVZnݚ={PZ/,KD]96_UxV"PR9IJJ*UZ#օpTՓILNSNJҥ O0/^$;;;OܿPSScˏtх^zҦWkʼnӬ,BX>q={$66V۷oӰaCR\N6m2eG;T]JF 6ͳX-#1>Vt ԴɩAlgϾ7MKK#<vBl`lu u8t - |K,aɒ%޽777Z9jehx>)IJӡM?;!xD|И6sSWlYb'ihh =$Kfv}۫um'ZjŠAظq# ,Pi_RRiiiEˍb6jDL5n;셬X"[k٭;dԩ\tyyzzRpwuסN:*?gbaBzUyp!} id GԻwoBCC.MG/<ړ[y,ZeIȄ'y407!;YW%a"൞>~AS#ÇٳgRG֜g̔- @`RRYreˠwA-8b\Aif sa޾YMl׮:ub۶m\z2l0lmmyyŲŚu =ټy3jbDEE+prrbXV>xb^Z4=ZǽOs/)2 Kq&-Z`ƌmTJLL ˗J" D'‡]1OHÇmʔ)xyyainʫw,[#G*5k=z43fMMM={ɓ';;={gϞb̙3ݛd ѦT4+%E&W_qmŵ/cƎA&g65СZjhV=VeÛE lFCKvl OO$ qB_IJZ:|pBӧM5y1FۛvXZZCtt4۷oWp?>:t4&84XU.r988p9ׯӇ-ݻ}6C<sU^K\\{4h 4qI:Ux-2ko\sH$|eW6%޷2%qr):tтbddQ&`ADFF޼u022ؘWo;Sw'+5իW3uT-[޽{y֭իgVR/>WA>@+]v1zhbbbbbbpsss#&QmΝ=iqOHڱ}bkTҨQ#ILL,t5&L@OOɓ'/p-=WD,;'[PP+Vdȑw^EeqB"J:9]jժܻwO>|H5 .-3)gD\lJ?trupvvgϞlڼI B1%&&|mM ֩kP&&PB֮YC5ع}]邚ZHQFDDzo׮]+*WM[xmݯKPP?FWWQtiLMMiݦ5\|*6U} ~C\Ӄ6mur֪먳qFLϞ=uL0??Bd,\ÆRL͕W+i*W̪UpvvV{}I&1|E/_ou93L_A^ǿC*.O>t>M:5!y,ܣqL6}YZ_'Qٮ2YYl6A߯Ӣe d̫פ$$3ϊ69YPSS#66K.KիƔՓ`+b玝y~f͚ٟx_R"}LlYH@*YyF7oz]g<oPRMKK^z<~qycd2]|&M(޸qJGJ"+x ȅ u[tm ttaɤoY2[/pi݄%۷;;;*WDŊ1(Sk++,,,(_<֯DZmnծ#X CӲرr9`ń3h@F-\xnݺQڠ4P.:miӧYb}ϪWΎ;{Ǒ́TiMƵhxMظq#!C02b~TCʺX"®XpE$MKIóO s~w222HOO'$$G=܁T<(23iҤ 'OÇiԸ<`\۽ IDAT :Htt4k֬)<?WSS+VlAu" B1 4q}vMѲ-N_b%`IΎ|Ӿ%Lz.^#e,R jڦ),8oߞCZ !>> Rv%j{B/̍HOMǩO[J7zs?=nˋ2e(߼yL,'''qd(@ٿ",, GGGŦv}23ϱ'HOȁ#w\U6^Vf?Mtuٰs^ujFqF 9Xr%Ǐgo^8 ˪L0/ٙ5jPn]455w׮]CS[Er;RT K.rN,XB(|\JxAAAsի ݻwW,d2|||8z(w%==:BN |FR8AUV[vxoύIHNE+W'Gl,˽?mM vhE\$Ey4,к7(0;H8p@rWw,H$8kWsR[~d57lقMUtuty111HRӕ˻I&nPD-'Ջu-+t,X3xM>nU%%;;$%Φ8RS]+ǯK'URAu9vGرc|2wlWZ6_}̋pd(F&a`b@^vYus՚lWe-X;wISdΞ=KǎU_(R{zӧOӧjl޼p9IJJԔoGGGʕ+Çٺu+y弿.1ґU:yȊ"{fMFFF2RiR{O7;G>~??}u5h???bqss㯿bʔ)6mk~{ͥm۶|'''tuuټy3oʕ+XXXСC숎fԬY;wH|_lܸ bbbٴinnnLRtgY .ϟ?SPG9ԯW*U#00;$&fEMc ##C޺ӦMc-W.dɺHLjCѩS'~& ?}lieG 2 GN:CIKM 11Q"ZQQQ}x7]k @Z\/_Zj5NIlܸSҦM]Omtt4UJ~VZ:u PH||<*IVt0oo֬uwNHHϞ=~:;wȑ#  F̟?ŋ>|8ބ!%sdu5j{HɓĬUSfj\lC$FEK(5y YYY1.ӆs= 6sLoHEEU+ӰM#*U-15Ν;WF;ډ42\RS8}4l"""0mgJ ܸqCq b^ۇ,=>|])%{pU.\^z?u "o;===~7BBBD ;@؁HW'!!QFI؎9\2}QF s /^+c˗/3x`fffo>CWgȑqܹR 2~-z-w+++.]$܁s'>#t#YŨnysbxΝ;ٹsDV@-O<%&& ot_$&&Ț5kHi |$^?|^sgcnMc ޽-[`ך߳BSPTRdͥΟ͛7cT9:z bM>KtVիWJuҐ;UjOËaٷw_޿!e^JZ| j"!t>{9u) OOϟ:ǯwY$$$sMq7zk#881l+WN .JӦM'44BBB/ҶI&E(88MMM 1A-ӧKT`ccSu2dDz$q[bgxUviێ_$+;}}}*Us|{\ ӧoՕAqOݿu7̙31b;vPqM*U7ٹp78;;3tB'N$66=zm]m;ƕ+WuL @ /S'Ѻ#?b"//Ϯ]3f ܎ʔJU .J"28'o>PHC]2NmQ>㏑%IN(ENE_KMJS'T˩k[be+V ue} 8'N{B6e[ǐ!Ce˖- OOBlA%^PI9̝;333###/$)a[ <]֭[)*#G/22H;mmm%/˶heE7o111Ĉ >\b(Ç`ܹ,zSbڶ-wqbw1j(n߾ҥK^׀8}4GAIQ]v1dȐ2_Ŋp7caz.Gw4n͛7KP-lmܸqA.tqԨQUUU.?tӞFXR-~C...%:x͛7R ~[9^zJ:[nc䐣r^y߲>SNB[W1ap˓+CJK/-PWx2?| [KU]&m`;g0:Ջ2ؒXNbʕ̝۾߮eI'vɎ; a *آ|xyynݺbEJjj␷4||;LY +'\kULN_7$;uGzzxAZ,X@nnt˫LPvmv9jjj\rE*UUUN<Ɍ3x- .,Ņt°g㾬 PX5t+:B!Cɉ/J5n5?+S),71a&228ϟχWXߑblj D'aÆR͛]^Q0k,*UM7Q[lGdϲ}h(k0o<<<<زe ݻv̵ͧkwȬoQ{n[Z*Uc$SPTaJ*JHŋb ?~|6XXXOիWd266Vb+z|\D l3>>< 2QWzzze}٨s+j> J:(*(u2Q@HKN#k R.Gq4oޜkזiz9rD$q'6ԤTWWb=ս{wݻW[nim`Fr1lقmgMMM{Mq+vn՞((ciYd0zhjժǏ UV$$%0e ɉׁqrrƆ'r _RJU6Lb石sUפbaaǏ6_RދdJ%KtR,Yœ9sIHHҒ0|||hԨQ!*OJ %--Zjծ]gϞɉmլY99Bc~=f:uHIIP.$leȐk"_*Jܾ][ [[[' p=99L0-[RYG---~~U!spp)\:iq_3%$k"IҵkWtuuyt1,4qO?~<})vLccc %Ayu?ɓ& GAUwzf:-[y2d6mJ۶mINKf榿hR$1}0c ϟ_.YZw UUv-*~."(XbccoܸÇ?ͣf͚ϠeÜ 2g044,Q:R;xNNN,YGGbCXZZFFFEmmB[{EQQQ"dᄆJgeeI=Ĺxt֍Ç-%,,LBr(((9Jի4D a\RqzlXӧO_⭫s&ϟ?'##LwγgxeLDD:|_p3AZJ:Oo2_v7o^Dδ4QFyyyfT,a˜m#[Sq&@Br8L~^~9^ixOǸ1nWcDw8}4gΝ!); qY{q5MMl{HJqvv.atzԩS ~LTHT=bP'*VX182e SNƆFѶm[\\\(WCfKp9Hrt M5q߉TY8::ҽ{wwݻw%ΛA׮]y1nnndeeIU\YSQQёӧEnݸ{.NNNL>]Я_?ZnY|9:::lݺ(/ baa'NӧO̟?ccc+V'O'ݶioooߏ[2do͛"_#,:гgO|}} }(ի166eT,Ϙ4ivvvlٲqrss9}4T$cϞ=YsQe ylY٨T+#J-kgg {N⼦&TVU-myyyxpas?QB yشi9\8rE4"o1ko&UQȑ#Yt)|P)-sboݻwK\Ξ=ˊ+x&M<ЌO1PE=X6t9ZZxyy$}H={999.\ $ɑǧO@NNScĈSNB l۶={P^=V^]U<ׯ_ޞUV17n(Tjbb/ .dԨQhkk3p@֬YS[+zzzxxxJӦM9pwUɐ!EQb}MPSQAM=faվ4mWut]s-x9u_!{6)@YUYB$GC[RD٭gS~m ;_Jeʅ9ϧO߿R۶J|L 2d9r$YYYj!Q/-e K'Q=*+@СCyY*\VЭ\[[[>U6 )5k.ؿD@\\hWȑ#4cJQ[SaP߀qR苻P($ {{{ׯ/%{=Zq^w?;Ύ v-aڲ:u3wN?~DII*U :-.O(TTT3f !BI|ܴW[Y~}c3"++58 L1JGIy5LFFFQHnn.-[veEQ}[1m""hfԌӧOy 2O 2yD}3F͛K4k,ΝC^rr2/F^ ^ ޅEu6"""nR^ 9pWΙgb-%Kգ W*Ϙ%p͍;WEaE+UkTegVMD!Q!$%=@@jR*BΝ;K5affFrر#mڴa 4k׮511GGGpuu͛7\55(ȝ,͛7O Vw1&/'~^ IDAT]X>}TX@VVYYT/\ʢO,C:g͚5e!C )PRR"\ԤG CsƍGjJ FG 0u*K cˣqVQ'0ldٳſ2O 2 44x]!3[S[!!R# %:___=U6\x"k֬aܹŚ4:u 9 [Kؾ'A5Z8xeNNvyy"\߲p PVSf1ڧ%zZUy1۷om۶hhŹs?W0aBc''R7DժWkkkѩբC  |2Ǐ 9ߒŋզYUTh֮/_1<2dȐkkk Xwȋ?.sƍ+^.>ɓ'өS'n޼)ռL Fhis+OŋIHHoP34ieъcǏa_vZ8͋;/?~(չz*/u}k֢ Z'<&k7K괨î9n2zؐѬQ-[sTT۷9wFFݻw'88!mJ-V)h_}א9x2dȐ!sÇ\|Eu&@]PRT``˫8^x!OjժR DOOO;qD"""h׼{\[nߟ`՘SЮ͠A~cannNZz#(Syy 3III<իGb(WyD@@4iۤD;5M5sU7R^}$m&OǏ37DU].F~Dz $q?̞=RUUUTʳlzW̔ QSfkk U;wٳgܾ} PVM_NGk /j7"sdȐɓ'HѣG}梦DEr<~XԩStUeE,M]bQzz:}A]]srQG:{@M9uGFIUQFܝɗϧ]vCDD̙3:uƯaC2?zu")) ʗ/OfXr%+rլ… *̾, :݇w/u~v}dرR4vʫW0mk#~MR\IqIRS˗ic҆[Ү)݆uCK[ = /JС/ɓcmmŨ}3r%+hccGZnݚ-[ϫݻttΝ+ aܸqѮ];VX2sv̢+[ivZK<2d2qDƌ5*U*.6m]9@޽3pV~ׯJ/7Θ1c2eJVf7n@]]o@nݰ2J56}}Yhϟ?g,qb}Yy֙̚sQt邾>Ƭ^JYrd1=~3޽{'[ZZZ}m'c%hhݪ `ll,`ڴiÛ7o8rwiM'ݻ=z.͕2hhhڳX%44TtFJΝ2d*j*L[7Ub>Gdedaܥ5݆uCTc oA|>m۶eҤIޮ%ɓ'aHqԫGEkk< ̝;999λ{VHN@m#Qt6бcGrss /f=ʪTQ3^R%{zztEe"B!gnF^M={gmF(bG[Uqtt,ۍUUUD vq7 47n̍7g2_/k;rq 104ATЮ… {m9x2de9s&JJJv:NF^^/prr"%-Quknγp%E-[\2vvv=ҥK:u*k׮%$$~v}Pۺw{aR)\q z߻Pvu\Nouزe {6mNdp$^;rE,k.q Fgϱ.g8ԩÐCxɸecp#U]ILH !Cw&oM;Mu5Wދ5;@(+)m;JVWD~dСC:t(\pe˖`6l(c(?i"wo/c߾}lw^0i,s #BU]zztލf-znAsdQhjiJեcĈLk:>jكyu5ۗ~lnܸOҢE 222x";wfRUIK˟ݻyyƖOGOl?`!II{9x2d%9{c F Seر FI5@ @EELF-yV\ɔ)SPU())1ydU `<2dpm7F5]:1ݝ:5ҥKuJ5_BB)Vך(*/,Vpp0yyy~ӧsڷoρW^^={˖-AQWWWiPjZj|jO<'377ԩShjjJuRe˖Kk,X͛̎3\V_8K֭[֕V fRRVUf\nå8z(風"ڵc٨ex3 EɔN +; rJ$p׏H+[XXub// |JD,O $ŵZfgKm* >!CHTC3aqJAAA*M6! 4i.>v1پ}{]qӸqc|||8wϟ/oooÙ?~(W&$$s-ZXtBQܹӿqNY 4uaܥ5Hִg[YE#ܜի;wfΜ9x ) GծFHhH!IHLL$<,yssso.2: :C,--%Z)ȥ|p!_15+hN^^^2d<2d9WETUT9|Kw#xxx@N@bR5ŃWAىJ"445kи!j_?/Uб:С4+WNuEe Fbm4{PVTرcesﾽ:ٳgiܸ1+W&66/_@sF-QrhŋE:e˖Ş={6+|2o,^5k֔8ŋWTVW[ICDD7nޠEKVMx9LxX8+W.B__ )?cUzrdffϫ,'C_/֩b*JJ<0mpoէm: t7,G-\k֬!99%bhDzV_xzzQ7QdstcرI=իWsfp9~'.3?$$"u-uSF>~(v$'jR֭CEE%iVfV˗8[eeeѧOSi<5ݽ2%? GGGINJfݺu|Na%%v) 9x2d%v2.Wem-Fyp\`BV"6A-m#yUqa~[2 Ā %+#~Oϗ':#2n84*h@BLw/*{f~b!aiT/MMM5kFh`hsSNRJX[[KB˹shc҆k׊EΝ;4oޜGa;gU E\ -%},x***L>'źnded~ahUEGG%@ɐ!㗥y/p+- ILN%5K>H5OLL J#7:5Œ'Uٰa-[DGG:1yyDF+k׮]1 ƳgϨ^:a#,]FbT"GHH$F%v>uHMNCCCCN:::dvX4Tim Zjq.&MjsΡ)QLtŠ/t_f͚jJ&44W^:i044ÃD:![ɐ!$!A;ɓ'T'orԛAHU?V,IXwq=x K,)u7nPZa]ωI$ݻ$9904i"۷wwwҾ>| :9 ȨL4mڔhРgvN:򻅮ئF,Y;;;ڷoֹ1on #fy ˓*b'T{}7o^ ᒸtHOKt_G6mUVӧ#GᵛӠa^}De>;KT,ۢ!C'Ofҥ(((9e;z/F.r#0#ٙKbbڞkϙjJ lڴٸq#S5 bQ4hڴԽc~ѪU+6oٌaCVtb+ԮE&28"''Ǿ}k˳~|鲄s5|S[MM%UG||<Ϟ=V[ٙF~Ҳta 6;@BQiӦ >cވpvņ8s SMi͍ۢ1q7*UNEdȐK@~~>-[ӜϥiհѳgO<==)Wu!!!ѣ_OzZ΀'4mH_ C-Ln޼IΝ˜CekkZo?P(dxyyΪ63B [7888`hhS7H~~>7ofEVF.]ҥKvӇS^EAT(r 7OʠAJwee  KRVrssd$Y2|09eʽĉ((׆i4,{㟋037biiQTTD',P($%!eUe Eq̙L? ɐ!㗤I&xxx0sLx1uvxyyrj֬ɽw?РwԩSÇ%~zLvv6+>\(ILUU]eڿ?{!%%E|ݻwԭ[gggqQhh(˖-#Qĵk׸vK.N:ڵ}3ys0b033#44ӧŋXmeggӴiS޾}S(.]EdPdsqq̵^䧉lt#)<&N1h~]P(O&a(g=zׯӭk7e់Ģ)ԨQCNBٿ?00P=5/p"Fvr :N}ҒҸRmې9x2dȐ(?#GLߝT/ؘ+0l:>DH)XzutUcg:P)¼y^:rrrԬYGGGn^^^{qtܙΝ;h"23[5 4TY)&1#7fќ;wGI߾}.'V]vSeL&fǎy OO:ʗ%觧JQd2qww'88Çӷo_ ;Pȉ'9s&5j`۶mjˈ*D,aT7><@ٳ&@ SNxyyo>""8s"7'͊Z0fbEWeIc|i( qk맹9aCzj,X@M~̗=f{7j]y-[Çh!]Sn2d7C]Ml䰷ťcxEY^hSn؇?&&&%Ε4 IDATZ:3/^s^aA}L¤>ӒҘg.]ʌۗSL> 6Ñf 3 +q ֫KdD$&MƮ/KA,CCCn޸YLZjV>>7'''Vݻw1jnD`` ;w/yƎƍ|nmڴqȊ,dȐ!;zٹD[SSS9}$L]e+WZm樂z9^ Mu#-)˗.ӱcG [eee9z(&3-擞A`S5A=R#9Eq _BGGvBܹÑGpq>_Eyn7<5VNpsscM4k׌֖pwwȈɓ'yqssD%b_n/`ǟ=%'; &F-q[nU;~ؼX6݌ѭޥXﶞu}KQܜ\\\\hԨJ9rL^kI/ϣɦMJLڵ2exK!CƍHMȑ#9r/%#W:Y?Lq>csbŊ8p ׯ_gDEWG&&? .ү_?<==QRR2dk֬9ۼЪEW.d-.]X e˼Ш2SL̘1333B!/_f5ܹ}&7!+#Yfŋ%}Xh...=Kńr-msRDꢜɻ'Uk\4UT ˝u5uїYm۶Uvss豣}DFC{8 $]Ex4kG>/kT S2d@thA86l œ"ۢ!C2" >;(@En{D5P ;{WAD@EBr~2R{}F9Ϲ|4@"=" kK76wܻw=z ң!.ASSٳg: @7{)R8,[4E~qy6lqdfWE"]2iggNj/HJLŋꓕ ~~tuuu455ӧ<|%RRxHIJePRR_~L<}bhd;MmBXd8.U%ղT'1񨨨PV2sjj*,Zbbѩ[FYyz9*ӧOKL}W|땧Et@MPUW }'&7G'ZRE7oN  gtMtH gG#G22j(?~ sua|IukECC:uBnn.133)RJWHYr*L!;w,HZQQ-[`gg7hׯ-JJJܹ͛ 8::Ҷm[7oYvb)o#* }M`RҨV՜F&~yL`۶me^;--iSbXQ&VFG{zz" mbǭ`f̘cdi&Ĺy̴ K$#&ũͧ(8~xGݻw]~@zJzk4И^{PV-vϟ`ƌ4j܈La&,BgNܺu WWax S11j;4.r!&&l޼SNsu+܇YVquuu&MTѣGKWfϙ-=ZY;xr CSMaVVC#R SM:qe &E۷/6חFb`b@eQR*=A `jjqMc*Utԉ#G &_Yȑ#"99P~X`˗//P"^3eq)_555cc >HLxy?~sJ J:$~ΐvB( L0 hq!I&-Z/49<666̟?l[[[x???5HP>=BC[oHIHR5 ]tkwiJζO(8@Sg459፵7ѣ騩IC,j*W*OmQ6Ԗ3\RB8D"Νc>yJS&zL$Ee%&5/ȝwyQr*R݈N:Ѻw2KLʧT4ԕFjԭ oߞ.];N:l{{{4iB˖-eʯ_E!놊rJ$((FC6lގ9rCI0H$Szd&sjU H뎣ܿMaAvƍΝ; \16 $ǹ;BB^~z6m|pT+GvN000S'D d\I—CY:̭eYijהĊ16-ֵi׶p 1ԲŜmPU/mZFZ& HrB>mndeb%2|p֯__qoȫhȑ2tP"""qSZZZ(ҹxŋJsgg]EzLϛv!OKv2Š+Hi$/"&1RZ_qpvӈxQj:uy/ٳ<W[Ǡ͓p 5e׮]?L\9 tܙuu 4 "" |kt VbŊX[[JݺuXA*t+se$Oȑ#<7pBvITd˖-U+~XXv#רW+V`Ϟ=,^Ê[#r;[R,#&'i yyyIĢj%5+rع7Ғ1c"stޞӧ~z~?ry[þ&[Ɠ'Oc?רz4%cUԅ :l(M*Wsfz&>ND^t~:%Sڵk:C(;Ht*zd S3sL-ź5***DDF6=ѰMa1iZs%>&'OIKNE[Wz-WDϞ=8qwpFTTSM^CʩA +v̕:}hD"3:\X,ٽg<̴LW*Oö 06+r XL$TʡUHĽKqpp(1`Μ9ڵ+} 9xݻwMe=z4m>cFŅ˗/<9rȑ%KCuhZYz7;xVE]}GR1,8;/gϞl8#tRڵk'> . Ć1g۬BTg=,ZH UfϞ 6N0}*j*&S*ϟ/թQ\v @@z5փ+8<}m"S$M ФStjRz>\qw (((PN,!'6ڌ0T(ֳĄĐ-HhWТN: 5 zL*ӽуkʖvz={,5OuĈ2@)<O9rd3XWEњu*86oğMGHrݪTԤrEuČ*j2@A9|zU@ΎWxbỷ5yo^5X?~Ƭc5[n7'iDkľӳT.77?(וT:ysԵF$\);b$MfaxxxNs\ dn^+(Fr.b5G3 4ݷ]v#FPfM455iٲ%k׮&""޽{cddDjprr*Oh޼9:::ڲnݺ"ǥ믿bii<|ȱt===,--4iEqТE <(!G\^yiRN8//24_e{ RҲ "%%L+W_kn۾D0WWWu99^b^ZL6%G0hjה$ *sunEF1vRLJ.]`nn 4(LNNݺu#99s%kvxwя'˦M?~<3gӼ y[n%4ZhD"^[&e(GpBB^`osM ޽3f`.]ӧO120bM?R\!LjԨZl+H?929x۶m#44I&q)z;}Uq[<==Yt)ޞ67l#hܸ1ǎk׮̙3YfK۶m9y$gf݈D"Zn wHˋYfqqڴiShƌ̝;ݻs16lٸq9r~N8AVVf[ksvU賯$]_;e&؛.QRfhj{ݻx wkZENv2(kԨ5`4l=Z\UwaX6iݢ|͓LrJ299TЫh}36۩$72uTw޸{ޗocT͈ݚQɨ'T^N:QjUtBK_rrL֭c̙,>Fl'7\Ӈ? m 222x+unEvvw7#:nڴ)B`b1 0бD6mHHHٳg;vAqMZt9V&:::K"## tqq͍xx=FFF,Yŋ[Z5j׮-}jYj'** SSS鸛7oҮ];;F~ ݯ#߉H$b޽tb1,Y7$ѦYfjժB_Z)))8v}VOEGҿ<9yՏ^)_I&1p@¢P7zu66:p%q>(ʤ"cҺYea&O?}hժa1a,3y=~bܸqݻc"ڵ$^xAڒ ||#ifllL-Tqqqܾs$,vl\]E1ḼuVcq#Cf"G.ܻxܜ\IDVRPTTA}{֬^ôiӾ{ővć>n5?+[ܿ={@ 铧XXX8gܹ{A㛫hxEnԩSݻ󝵿533CSS@n_ddd6򈎎FOO(kccCDėH 8w6"s f酞|ߗ!G}Is444A*czu$֮]˙3eI(ovF … ɇ@Juʒ߼yba===|EgUy 6bb9"vnPCrJԪVf PcqsWqww9(+)Q)R7Ъ:W#aH8>,$1i$TUٻoMeeeUEනIHY=z[Ġ3 8Hز|rl  8Ew2`X|hfVEY9;;[j{޽{qrr*0zxyy1j(޽{ǀ[VfꚒjb=A׮]ɦ NÜسwI)Iԫ_uoST(?=|pBΞ=֭[K !tIݎ9?cǎe$f"jkGv|JI%u<*唨QըؖdePSC}DUEٳg,󬪪͛7{ՙ1c_Y #$$ZM )s`ee˗/9plٲhԭW~Ƹ1ꀟ8q\ L*sUƌSȹ-[~D"/_;y$X7Dwoѵ̚=N:}ٽ{7=u<Ȱaq량2W]\\X|9+V`UHD#҈]*B?~[ͯ-m}Yl&&&" #GοSSS:vWmԭY9wb|ľYBOrСB8880zh##VPa#KTшD"2%vKi-SZjUTaȐ!0sLT"S׌Uh"^2!,8m vx-c:n]Hk:Drr2-*vNNK,~>ĽGAAww23]2wxb*Ua;ɩr9e6WС7 kH5**tСYrB}V:t HdMZiM@@gvCG)Xooϙsgpuuso߾%((tCH⽡C9([mc~| 8m);K?9V&"(;y6Pe=구˚5kؼys{6mtݕo1bT@.GruڵF322ppp " IHHȑ#r;!B\otgj988ȹH'N sss{ö~zvҥKywܸq~SbX8J}L7XHEΝY`Zu(((кO+TTˑ^#rJF&>]taŅ3׬Y3̘3g,]ёdL:߿ϲe˘>}:k֬D4k֌hc֭rߧClْ'{/9~~~@fΜ'-iӦ\tM6aLR I#ˈ#8}V \ɫ(jW7Y=Y^^S8uݏ37II #3 UUUΞ=KN6PSSoƍ3g65 ծ&^gҳ$:j_d;YqViQ߿ɓ'F^^<}}}R 򢪅1.H:&ܻwOTWWG-D. ,`ر\N<ŋ CEMaNGEڊadfZ2?VlÇiժ*"RRRPRVԱA%^=z4+WDOOx>}ʐ!C -Z2d`4)ulO@X׶fMm۶߿}3=\p"W!(( | k۶-o.GAA@H;22ٳgs=TTThժ֭bŊ8pm۶3,,,2dH⌌ fϞիWILLI&,[ț dѢEKNXfM>|0ԩĉKl,wwq*T3ib>&FYQ]-lDDPC{ɕibD4*R8l0LMM8pTI$fHj޶)/::cbb1{%1Q>I*rIl˃Y``FZ&s#'3GXh۷3eм{sTU94;vL$({2zhZiŸenzBS§b7HŊyej t҅W2rH$IqЩS'H_ >sm 6D;nnnR -[0qǗFTTmڶ!6&Zֵ+R"))5jйCh՞ ʼ>tQǏ;۷kߎ(\.ʔLj={;{.]gCɑO>=s)s Gƒ%D55`a\xJr$OKKZjakkK֭k֬RJTRͩY&7XoQQQA(DX?^$""v*$jչ`UnV-~? : Y1z% osT\h.\@ȋ̬p SߧlԤTlll7n'O.u}lVy-=cŘ,ZK8rʼ%(`ʔ)hiPͼ:ʸYRk}LJJgffRByU)XGj-d?~7G-]- -XZZbakm˴|z HxΝ;3tPy=gϞࡃdgg3 4D:Ɠr_7~baaQjg'GοcJ /_nݻS~}B!~~~\y,?_^ža;yݺucǎԨQ$͊<aW^%66 9{,'O@Q!SXX,04b/\g8yLm19o<ɽKHO$//@mqdB_us;acc#-t+ǏS~}F80Q {Cu͛ǪU011)T`8::6PpaXNn/sЭKNvXZ1rpY9p@6ʉ'㻎280~)e#KF.q sb1[m=lll6m+W;wi&n޼ɴiX~79rN޽9x4-hu}]+4o5y5jS=_#̠QÆ.]ʄ G | KKK׸uRhTظuKN@ [m+))rmhV7Y7mm((3d D >SQfԵ4cO.PuEUU/flХ 0={2sLҥKZiGIqq2|½ X7m=z`ժU>%FNv3o\4u4|INv;vi:paBVZK\X9Tnь\4p> -(r@ `IX6s/^ϟ'x'Gsĉ"#offfܻw+++{+ /sŋyܼylxb7oӼ{NzMEYG3pIMVdTߏ@F!) ]mu쭍Q)sC괜'o20}SVG]b_'sDTAAkJCGnYK/q̙skc\8tJxF {&S[ϟ?Dm0cκ+ko7H鯢(++S^ 4u$!9H3  #5uMu,֤NR_"C*9ْ." ks\uDzl&b]Ҷ["F$H1Mv4AN<9r߿D'BKK wwBΜ~AMϟiڴ)e94rMEZIsr5Ǘ=$|2}-fϞabC:&2V$c7/MƳbmʺM6ٳgdefQF|rA|@Iըߺ?)10H:t?D}˰ woNേlݺUڊ8ͥU())Qr2="''#/o'OիW6ߛcddDŊ~O"I_T_9PWSgaj 555>|Qlo`Hq.7"3֣YW{zD:FQYLȆ< sX,.H*[=%44TZmZdSt $e<sI 7gK^~zdfeɔ)S4iFFFdeeG֭e cufR+מaۅǠgX ;G ___5kVs 6؀NE!KIKץ;pB=Wiڴ)bE1k\rvbIz۷_~v^J׮]hdWA>}u4hNb hWq&t*Y;nݺņ pqqnݺϞ=c„ 03s"j yҸqc.]TH)C$1uB**Ղ eD^>PU/*Y>j/C1c+W,atM8B!fHLJ$;+#G0h ޿ٷo; ,(v͛7Сͺ<(6Ureڵ̞=W^Qfغ `mٲEi*sDsKC/]!jԨ!请4Wi&khCTd$:jߋbppa?Wp%;vMd|ǧDt[سguE-$**la6 gҪ_ٹdj*\8tcccׯOժUYv-&&+VPRRRh޽9\9m[N:]իWكdCr IDAT-s2{L_/ˇA\ݻugԨQN>-H`Ν%:w=z`׮]$'PDb‚È ad Yv-^^^4k֌ʕ+;޽ϟyMQ> dĈ'&LP6mЭ[7._FdfsdEɑ#_[ZZYjtb֬Y={---RSS^RPgEG6'(Y9TД|d?/gϞ2ͱA6:e% +45Q^p Ғ}>.ťD$Ҽ{3jy ?6p~.ߟծ]3|pq"666-Pۦ6;buԶ5i\r@_իcooaӒ%Kعs'Oߦߔ\.)8u-u5KvV*j*hV+Ѽys<<<2 ,gϞ%CJ:rEV9xrƍ]Ѫeo^D"[l!""TihgkbƮ>G[{ J5CD2DV1ݝ;wJULT}HA7edsϳyEu6**̝;T{K,AUUkh3!0f 4j-m-S'''jԬCWH`Ϟ=E:whkkvZ>'wQ+7,8>ooQR%^W>9w&**(vލUM+>}ڌ|{#Vi|~T?gYUKY-I)rO9ogԩIt4_(?@R~Mɗ\wx]zAy-Ϟ=ݻő#GJ}h߰Jqhu${-/^Lr=z3w tilF fvƼĴtQ`aal0s?俏@5dPŨ ) )T5JƍK߯_?445x|1QϣX9vbE./:[( ~M` zѣ133̌ѣGC.\Hu@՚(**r= G5oG#G?iR2 ˖-3H$<֟a,\77Bd9N~%;uD!OĎYey\LffLN!j gggjﳘU.O7{ 'QĘV`p:Zj[\Z=qëp.8P'\~.]^}c9k-9#>jcЂ;+Wƣƈ@ɂ@ =-~%$>})͍Μ8C1]7֧Zm3yzjTU B>,]3''ɓ(*)RfU6s#xrÔ)S8q"1o#QQQ2|]ٿD"E,?u)=HP4hu$޽ߟ드e07eɈ6$0f̘2\reedƼ9>#]uGKĄ`PՀؘXtiԮ])S~zBCC Yƭ.2wiLn;ljle8NKESJ%"##e֯_EEEe:o޲bŊ,_\zt*K 6@\9$ޟ PгgO1xxx(o߲~,XYAܜ|9r_efϞĉ9h6::v[;<#o>0v/2z1̜9]2j(N82]CInYzLވ#GЧwoʴg@MbСDFEcߓĢEx {49 jc0CȂ<{vEm233,Mf̘Ϋ0VkppwQfmRJLPT.lQkMT^F\BP1P)Qa~2\v9=33<}ì>>>||ӧOiPG,ǎ+rxzzF`` Ey=222hV@EED |axzzuVTU04J*UOVVwωt0nciio1\v wPK.e|~g۶m`+*߆4"EׯgǎKr޼y#~L&F>Q7$'{"++築4ddfvMM~͸pk׎9s(ѽ,--ȑ#Xl)CnzJVl֌Yǥٳg3f̠VZ\?=ϥٳ'<̣֨Q#>>ӏc7xLɎr.;C;A;tbǹ'XNOZȃs.}a .4Z* Ӝ2fI޿M] y!##CƍF҃nݺś7oxΟ#55aID& ,&xRH[y%55QF3w\vrޞd=Zή]9r$e6u8|0{.>e.Ђ}3f 7nܐ(ʗ/IJ7m{HVZUhr:Kד(!#O(0t-ѣ%vay :RPR` !&f#+Wp͜,Cf1`Z<@ 媷3i@MطoVVV 2$9Ϟ=̙31c# %eSPR`a옻VVp$zׯqX3g;"#%Өdƽ{f>}+NJ᫐ɖʕ+t9Tڷo/^IScaST7111Rr#"EʬYx5!,^EEELLLJ5ʕ+ٻw/ǎAARA&w:::Tb v3p+nݺa*LRڴiCt\ש[מ Vd@ }F=z>:ʕ+$&%b׮.#I#&E˔BWiqM/4hio3Úg~=#F}Wlݖ6-8___WNϞ=Yd K,gϞT^gϞ1qV=Ņ O.qr-^y晇/!;$91_7PRRbXuwn|+D)RVDI h{\x'''LkCǥ+PR*GJZ\N#-ǎa֠g.սr~JʚGbR $0`N"}8)ɴk׎;wWDӓARaSVEOO2洴4N8aCqP([^!#`T׈.i~S)DzKtn'quu@YYddd1ԔƢZu~tܑntν;#z1p~^~D6r?(s#M]MZto_\Ν;z*Y2K8&$$kU )RV<==qwwgi>0>~X2x0u4ٷx wX7ZfSRRpqqaIhޜ6֬YDPHx'7*Xlnj\N~੫o(*h Qzޱ}ܹs| F߾}ѣG=YYY*h(s}P +F>k,"##e,B!^+}YAzeh>}xpω0uT;4)=*UfƖi:w<ȏQhikQX[[xbԩtFUrrrIJBx~v)%blli^zIʫ*qh^/zXlnr͏HȢ2ͪrVY2\N6m ]wFFooooΉ'-#1.*=#Gr͏Q4T1bj"88ÇcvR9/[S9<:W}}Ajz&U+h`k]FEa8h3 {^ \U59؇vMj8&4"fk֮einʊ+ey<ŵ#G*W.x:""9s  PHI}=D)ë{jժ{ :vE#HD}"3#%e% Mb3;MI^BDGr|$LJ3gΰr/.?mFu t ."ߐ&x@IϪUptt[n\tIoPȜ9s_ŏ\]2Xw>ct"l*kբCrČWrڋ6ݻSUW7~D|K ,2*TXaь]Σ.##CSc=βAJWBl+~ed_IȠQJgݻ4i"Y" s%f6ͪfDcc$AhijP999޿Ȱ"fٰddݻb?p ;wڟ>zKfuMJUxIˋphݺ5ZZZqu"##1hΔ_'!'_1^l72cƌ"7֭+dڵlڴ ՗9OYImT^%ǝo@9r_r )Rĸq _ճ'Z)((ΥGoFYNQ8.\oҲDtmȑ#yޛI}, F%u um4c=1I/Gg/" B!~9_(}aڵԪU un߾+u$N@܊eP&{9uG?ݧ1SgW**$['zzzܼyN:qfYu@z-aÈɜIȠMTTgϞw܉dff&fb֭8_!i)ihW¢&Ms;Jző5fڴiE_(2`TUU%.6THMJ͛7J|JKŊ;f,6o"K,G/H<)R5!!5j%B@Ѿq5XNXG"%5)ݺ\#F`?/4+j5qYYqeu눈Mi$9>FFŞ?~C#CNo?Ô_'9 v*}n߾ yEpvvFII7"bq5L ΁*p[{ MMMm&@ @: kHVf +a٭ IDAT9 J L4[ [[[6nȋ/2n"m"Eʿ#G*;Ƶk\q jT*;>$%:VX oaX4kƻOe6Q});w.Tz899I@ӦM!}؈ˊiQ2r2|ԤT6o\9+Te K(^AZu7kג@\\<_˺u눉e2.ȱ5hڤ)A$AUEڍƁ&a;5ץv޽0ˡYڵ!"49EyбH<)RgϞ&quPܜD;{U*eTx}䄚&+-'o΢Etށ ҳe0k찧(oѢE VZ$J4^x%***̞=gs|+ZZZܼy55BfMbb"QQQ=n22٫g֚ <}Xc555W7OQRVWRJ^ )R<{rA5Nj7MAc kS;U._k, #PEe6gjj*޽cu]qEjdEGGLKNN@@*gRRJH`` ӧO/'O`kƍ̤M"zhڡ)߿/ּH&M^:IPn=""IOMGQlXH<)Rk0^ ey xY#ژ=|H3z5Y.ײ<_IKKghyy"]ǂ}7a PŲ=r:'C!2*>`ll3Z_6mcGXv-5ԤvZUm@bBb/_0q8&U  .V"[|y sss&xRHzM=?^w@KK wwwJ׿ͰCqh2ZaɩeMJj*^*YjЦoO؋CW< Id#ډ7S/۠j刈(/\ohEƗ f۷166bwruz똾wZ!(ԍ7XX"+'%߾}Ҭ-{~ s>$m۶H<)Rkiߥ|ބ3tذbLbb"ݻw̲mYi֕%ۛ"'+`ݺuԩSFUI~_~|~^cƧp5z4gb|]>DVϟ3btuݽXc{dTS3f jjj())Qre-[NUֹ;w%Wp}b:zNjWҦi=z9?´E=4t4TToyi3g #;, i'E-V\9Q=wIʣW"Q;z}q)ʱ|t2X]OJ>Nem5z2#\|Tsm1åC({Yc͛'H m޽=va_2p3(ߢaVXYG'OΜ2sL7oNVv)q ޏXt4&Kq=h i9-à# '66vڕ*[bT=?-KŪeΜ9-߾}m"$ϸc^:T`ƍ;GF0K 5DHuHȈ5k {]eZcRRR&NȫWػw/rqR))ްG" YƵ+ph+O>,\(UVoddď\͇h\\\ 1h j֬ɼsY{OFpu`j[7DI^:wÇThff&˖-cdffru 򤥦!##C:LX=%oY-EϣӦY F-e.ԬYckkKŊ BIIHg`qsͩ_?ohhhP۸6A/ Mer,=-~znJڵiܸ1pOѪLnӶУGMHH`\v}{={{ԪHʤHgrMll@SaCSkՁvƐGGGVZY6L|罩WAh޼9ܿWDfrs? tqsHݟ9t#GwxeZG M "2X|9/fFl$cHi#Ȁ< 68|Gy*^/yTtthłU*_qYq/HOZ.hѵ'~=Iː\deeӧOfddТE |||WǴ)O}$+Ohժ!YQP($2,kQv< ;w8yWDF@ @6?/Jy-No?͙044dʔ)L2yyyBCCqrr칳$$$mDW*Vȁ n?_4"K{zmZ&~5m}KP(AZ,O*;,=`;;\s[[/^yrjk1׋|Hmok˰aPVV.^cu^Hְm0Ν;Zwnݸsa(q,9IaHKN;ⓐWT@:ШMb$|S6Eݻ7Ƶk`eKR(Ι+ qtt̵СCgܿ/ɄJVPfMd,9Dzj:xp1ʪhܶ1CA]S=B@c򷭧8NNN,Y ':*eL[g:^y"99Lvߑ&x1O6۷֭[\] ҧ F5 q>ۮdd .dժD] -jv[lي6! i޼9m:sv|KbԨQ8pnݚ7 ;9]X͡04n͛7ϟӠA~؉Ek} gʩCIY ] V,f^hihr LL <߾}^zkQP~|ɛxuuuLMMQQQ!44oߢCۼ.3`>|u~uc gUD}B֭۷/5j << .pU1glV/kq.CXf ʕ۷̟?dddHjr*~$"ߐ6YH"?ŋ_*V ޴zW_ď;~~cNS6S,]qXt^5jDv6~Ztp q>Lv]h@ `ǎ$$7*JiÝMhP:uHfAǯ|KSb uu5|J&Rr2$Cp@0žoi8a[n܁HĉDGܝUY|dwBmڄ|/YYkMw̒X%:u`5$~K… ܹsSҽ{wƍ;=BIkǭ'6:DCPߴ>߽Y3WV-N:o\2׮]$weB)۷o, =^P-WrY0u EO>zW222\Z=.so>$9)Mry ˊ/>Pj,lCӦM\;Y@pկ_,ֹg_E? 9LBaŔHI\dde?#Z%%59^0d"_ݻӬY3]~Đvy7jݐF N?v9ĠuDFplllmҤ ׯ_凘C<>>o1T?AȏUtcTE7sH":G!3#~ 5+"^YYrD +- ľ>}Gzj?%A=-] I"?ݻȧqƼzKP (O.unTTgΜʬ* gȧ/E@ w!\ =22ܸqC"#dzcǎR}x5$f$>yd 7}8}ߧjժ#76x9c::J)>O) \\\ޗ>u ;1BT^N6YWχO [u_O> s -%^Ǐ9v>+))۷cbb˗эzFJ cjf oo_hP(dҤSġg\ٷ'Kz"33yvڅO* bذa؏o{W<*jdgɡwޓq!;vYfqY޼yC5j윻ޯeaEXz, iצބzgl%){qcpuuh}wܡA|Hi _ӂu~ʘ1դkaaɪ0jH~DP@;-ms&xRH׳h"F.**(**G4hhkm C<OΝw_e˖wt%0ʪCEMM 5Uՙ|8YYYybׯ{|5Usv¡V403ʕ+ZwiYj,{.oI-MכF;#&MWСC%~nRF/119sЩS'tuu,]4سgboogܿ?#GвeKʗ/96m7.))'bll.ݺuçot]]]4iR7niӦ/_VZqQ ^ )R1JN^Тy3عs'/^p @9NvKka> euF"BQ |J #Er$: QF4lؐZjo>,YKW `ʕ,ZckkKLL ...xyyq%:wn)̌32e ݺuÇXiӦ믿233$44+VPbEoq͛y!:tuL8ϟ?pBUƃrIϘ1[h"7oΥKضmL2%k :"ݻqqq333,,,066f߾}ܿ?,˗/gܹՊ3FDddj#F1>͐ȅ0]e p4nmڵkYl)zZ*G]%m{pr 8 av kVbvtiVt-ԃGt?SSST<1ؐoԌ;ZmڴI#)) UUUDkdܸq}Twzd}M6tڕʕ+ą  -@]dz?# yp!.> YB4+jШmc:<ɩm>ţ[k׮Œ̌d<< \agWm\)$&& :+zzz FEIh^Λ7oѣD,}V aIW.]H6mQqqq<|74hKfE?:[٢fZ=& 5Ԯ]5Ξ=Cv;Q]>5lZb <Ǹӷ]c7os*ܹsɓ'3z;/ݥ%%%yhѢBtʣc IDAT6ݿ aQOڵkrʕ+Szus=MZL:)ѤgeY2:mYsp xzzF2sL.\Ȁx%%%vY,\ɓ'}ӇǮSr% x{"B"04%i8q'Nz,6 ~'xD__;>>}lD @WWO>DN\ussstuus~)8---deesg6o,="Ddd$mMn 122"$$@u#:bgr_,3)C:=kWFe w`m r'B_v^go3BIIOCCC]MS=̢whc݆7nJ|W^=̥˗iѢEq SNL2Ǐ_W0[w# 2''\mڴI|wGGG\]]NG)I^jr*Oo=_~EZ9::r!nIfz&7(9rw3rrryʺ B PR%bbHKIb­AsD[7СCЧOسg&&&XZZ)?ܡTR~RHl2^yC6uҡ~Ǐ'++lؽ{7YƇ'OSYYY۷T,@kU* !,~_Hyw`]| \t;t" |DoNTB3‚}PR*Ǚ3gU% 'QR'/qss͍/x!Y!,:p=?~HNN_i9rD$fg`iie ;v pa !qlYx1={D<ϟ?ϕ܁$رcݻ!_zOYKWdg0tP⭭#I IJ CE{ѓ'OXhKIIAAQ%rP P(dXXXpqرcz*YYYXBOVVVې_|A__?Wl@q9oA7rJ"埋XKR35N|=::-Z |+.hܸq&L  *oYx1.]nݺkL8hh`ڭOWR<< $619s۲eKԩǕ(V^ͫׯQTTeV9~Goڟ)S6mfu<yYB"cIȔ&)VyP$r]œ5jǷ|z p۾GW_clHLH_~)4>##@X~=nnnXBW_=:ص… ̞=ؿUdqHH1w&cǎe 0>}lj'{ŋ>}:˗K.s.)Rx2 s.F.]D{9;~!|G"ڵbRo *jxo>#D ܜׯ_iuáCؼi~~h*QH@qrnW딗fddH4Gqu uQ'ٳ3!+'KVf_uUTv:t3x"A:w4gΝ0< ԭ[?& MLL };1#׆?:,m'! *(b ݽv*vwv` Jw12R9@}gy﹟.#c??WHP)###nRTV,nݺիWz*уݻ {qك 6?#߸TΝ˭[YfYnV.]ʣGѡSNjDmݺ'O@ 2eJHHcʔ)9s&߱ ЬN%޽{8qb=|+++έȀ6_R3deeIt?~}To޼'''tX|t! BƵc7t!'/f `?d Pl֬Y3 ‚vvNKLIGV:wſ.rlPP5kDYQʺǡÖ-[2c \\\ܮ> (qؿ=o2fҘ>6䖫ĝ⿝Hmڴ)eeei&V^M%UG:RKz۾};M4)p7GKf<† GJtg߉>}pqxyyѷ__X}v%pxA DNZ45=#C>GT0[5-حx>իWʕ+EN _v/^]<:_FIrM:uwj $,7tdر9omY2l2]rI R8333qgoIHHk)++Qz S1Ey9~ߌ:YZy ^w߾BݩX"5k֔hƵkmbɁuuuƌǰaÊ Ǿxzz?z 9B98qaÆI|~9}t(,,, c?# .M'^Zhذ!tYsn) )zNSJY...4n8777*T +ܒ*mmmN>]Y_ i/# Hui׶-w%M  KϛpB2d#ȚŲeؼy3&<1:gdtކ٬\2OͰ;111xhh( U%y׭B+ӪLc~iYX~'ٓ] |||Xn77_SS-8k#0f++ ]!YSB>cƌ1tTɖQ7 :tXYYqƂ4iGŎ=^׽ +F`ʕXuP^2Zm^ԭ%ѥKz聾>޽9<ر#7o!/RF[I"E`oK:?^G~p!Q(*(PNi"ӧOsyZY R%bbbxEl\<]A?JYIcґM6as'uEUUА?|@^^.ҪUٳg6lTX5pOe^\.?^`GAMYX|}}%`n֬o&.ECAA@2yw1ob^lp0m4VXm6& x;n\rE|LK*Ri'Ecy 666?{)֭[\t͛77Oa`K6N=k^]~ -vɸq/www&M?>(ˡ@ں׍n_X 4Քi}`GY@Ld_vcG!2}sQTclБ3wyE/awts+V u%F,򜒞"UT)r TT CCC]j}Xttt~2V_AZY$ }TUU%SVk\wwH@QY5+i EZ嵘i BpcѭK@杖H<)RbggǼyv-\__CJl힛ޯ9 r&2tE+ϽҒ/^0a<Ȟ?3W"ʒ֍ӺQu;sip"&M*ܕ+WraǏYjo|߰nW؈@OB!XżyV ӧOG޽\885y>3228p oHIII7;:* ;7oNFE{YG'//EN%R엕E(lϘjwS\|9$>uٵೋEM kfjya8;;coo]k/{ε,dz쉜~Ȅiت˗/G__:HQ `dMr]t)vvv{Ӂ+xLq"?D%ŋqnnn]:&&4oCCCZ*222bҊ+yf">}b]^<)֭!W'OFY dСҤILMM;w]7Ŕ)S044$==߾hMiN̥y_!IXXXH4_ny\*4U | hbc0Zzosi}|}(]QbEݻc6^*`ʲqb{޼}K߾} ޽;ӧO?)-t9|ȱBǏǫW017ˬpQӧOܾ}mv܉x}FFF4i~L\3#9!n:th7nLnx|['ob@o1cfffP D[ZQ)߇4"?@֭ٙx֭[GΝi߾=K,Aj. erM׏;F˖-QUQa Ǽo^:,`DfIOOgP&X8#%[-knݺEΝbϞ= 8x"1 }~ϲb_D)Yvh޽srPV,}>iٜ>}HOnkkkƍ5@#㋜sdF,fիW1];;;޿Oll,ׯ_o>J:`kkK'|}}K8 cƌhkkK|||ٳ'2221j(qqGĻHU{ >;;@}xs zYL<ݻ7<ر |b=x`#)3~6ODu....TU`` :::L6Mxb%gƎԘ-^“ ӡˋOrr22#Fݝ կϬ)) ;qFSCf͚ammM*U81wl typ \›7o M60}tl>?Ɗ<7h~wwwVBS2 s-S177ۻwoԩÊ#wZv{LMMWx][[[B!gnB-lVgȈӥK_sfT;EL:˗S|y^|I ڢĝ$ZW 4(KvQM^^+VoS<==9p<†QF&z```@IՕMboUgV:VYMJw_^dWETKG`\J" E  //OϞ=űQ IDATGS;wk.<= e˖ܽ{yr>}B__9&MiwlYʆ2d^<累/Q\~vKkoߞgϟ̌_kQ̟?_,pvZ\,,,ؽgÇG' 0WWBlnݺEQT,c1(<\q}dt4UjP5cbͶζ^hkko 6lHzz:wukL%’50e 4T͌u;3{g3Q'*>%  _-Ƶ h\DK]l׺.g݀(xX":IO 5-M|NRR^ʔ)L2DkbhhH߾}Yf #FhikB/upttdƌ%N:u7oޗa;YY$%1ʺ((q &LP켹Bn1v(^N:uJN)! %w; '''7k7o bΜ9|ٳgl%%%ݻ˗/K-rrr޽sҧOu놚=bl>s9(*(PVMT˩& xVܻwL̍K-* VYzҰnPz)+bQ2*KBrrrCAASN1\vhhϡ-3gΔ*={6Vœf {ZPGGG޼yI!@J)^%@EEHH ȊRJAOO555h׮+WF(ŋbҥ{7hHʕۨ5X`+dd`dhH*Uw/v:&L@FL *hlzj4hPd[ k KmӇ$QݧOhӦMXZ^~͜9s|D7.`y/MZf6̜93_Pٴq# Xܗmn_~Q)=_ۇGw&~d=mڴh>ܹ 4dtwwGQQQ RڵۃO砪א]X,{|x,*j׺,KՐ`w΃ٻnL*J &!#u4"kƷtܙw<}MM)ŋ4lؐAgRo8sb)jլÇiժU600R]:?v #Cѣ*TٳϟS\9^~]ϟieՊаPdeeƂ1~ ._dرcSSu.NM%0* YE}jKYVv[ɓ@t;v,rrdO $9r$W.' s\ Spkv튳sҒyqh2Ru_h߿l6mwo9;ZLAgr%|.˗/gժU,iſOb˜qdTE%%"""HMMø.'?k%A211R*Ԯ-B|۝;qFVV[nfii{ۙ%qƸ8q&:wp{F]]{6l~~~_^/ 2-JJj% ?s9]떒i,LJ &pj(E¡_g%:PQmCU+W5={ңAtPlpWʊeu5lfΜ)9 4( BnggWؐҦQ!9-w0Vl`58|oBc:AϞ=~&MV*wNJ%V lٲ+WRAOrZѲeKg0+ 7޽Çicĉ^;?2qDE_RSSKpꌏzĉ fɢE?;ualbL x}alvĚs^:7w)Vչnݺ-%? _oE>+yKnD11bʪtX׫''0&Ӿ(2CdD*HЩoӠR%ի3fv!++ E:ĨQhѢEcB!CAF& '+[yI0#oTSV ..N~56lKF&("%9̍+1SC4TfbS9z9zUr1:uĶ ^,V=N>$r"3Tjjj~{yR74dddƤ 90j׮-rQD.'88~i\Tk* ss2uٻw/gΜЀ 2Dڷkρ4cghjiHݺuٶm.]"445MUXWOdH3xR~IԈ$---&\Qxxx"6.999|}} '##dߏ&;ƶ;w0Pe]sg呗 _'F-j*H95E:p|BBB݃SblFl$2L> 2HNJ*/$&$$Ȩ"ϟ=+ͿP(DMYUc᰸fj 5df~屯/4g?DZl6ĕ+NL $Ɩ+Z]ER?UUU^<{I`Z Zɨzxzz ;Vy-: iOdp$rT3u+tDSKSpppH ICC,.ޏ^ѰaCN͸iѢx{{Ӷm[4h ~4uU%fmGʑ͛t!-[uVΝKvvĵT^dƵKTTz)'9s Vʕ+,s.ze^h-q2V }wΜ9źMsEdddhӦ ˖-m۶W5xR~ IQPP`رʊ;q4@,,,0Q+++6l*W^h܎I`=~Lukwn`,t ukw/OBCʜ :ѣiؼ9w?avl/^;h5ʔWWog23v\bE}nv+Qp' uEn1n8||}%>Ȉ˗/3|xת+F%K&NHÆ iѢ3gΤ-[W=PwŰa 8qS| Ģ>,>xؤ4] @ԩ:g.\O <<}{ru0p >=lqusR@. 5jp?_, ₥i|ݚ%ea1ΛW~?aܸqzLFJ~z76cYO&xϡy8{'09ϟHԠPVݻw+.^m۶߿Dkձ 66ql߶)he]o˧OXvmPPP`S ^u,>>>Jd\-߿G3vMeety4m5EE^sK}t vnnt֍^1n8444Ą;v兵5W^eܸq̙33rqfѣZYdQ@VASw]:::R|y&M>|8/0kqF<<}L\7}1b8rU4W ^ddd1c&bx'RoV2c6fh41Yݰ17$,4exIQRg4AU !ZqziOLu~Lzfح(ddd-!D75ȱT+^`r@3XnnnwcUJlݺKKbVۓ;X؈J N8@Ddfm e5e&NHbbD뒕E StзL2s2g;Q  )>O/9FFF%:'==;nnU!@VV*,QQWV jT|e 6޽{⦏8tIIIL8I";e4R32|9YR (+++˾}Xj/\`ӦMTT#F0p@ZnMHH ,Ғٳg׺ XmF\R:rrr @ 7`٪jjjߵܭo Դ&qܺu뻮%)`v\Y? 78ۛ_?_j*+sQh\K ۦed+ug6Q2YCi^6S߽iQ2ڷoO9r{I5*Ubǎ45"ESoQzkeYݑuǐsxRL:x>&iju%ܲ؁QQL:ر$gܕuuuڵ+W^ɓ0RXDD"I=ؾ]G>Ykiqy>+WfgJB`` *J T]@ QTT:$KV7>ǧ|ApppHNN摷7M >O>@]]DrQl۶ѣG2 D1Yw!}Ʈ\Br :ihmm-7?9sN:ˢ4W$K=]=⾾OOGXX#Fʊ IشiS\ٳ`P@,FvV83e-R~-,Yp ׭e9AԩKF8x G 8 _~e2d/A;\^,ѹ11Xn/^Ыo_]Fp 1ce͛ E4&~gϞ%'''TVDFF^]qne " AYQȺ&Iqs3f r@M#bk CCCLM)/)S6"9}G?cޞ,:x֭Tf,]vy֓Oqyf!2>+WͥKeyR O <`Ԩ%^ C W^AN9 xվҜ9}4NNNZde$%eȐ!DD3vmwsqߨ Ip45,_j&Uqqq)q4"?"00^}pӓ^U"iR?XduŔ氇deg~ygΜsc޽4oՊ8F u*x;]<-WeD-?GQ&Uun;wuer¢1)F(vuHѲe 5ַNcѬnez`j o6e˖RBR0vEZz&{.J4\pnݺЫW/7o^aZ>iPT?Yָc#||?DsxOmuKZFF;Afz&&&ZuĬYpqq쁧'ӧOߟ@Xd ӧMF\wɛoaR~OFFF8::i&BаA?K]&)423]8xDb%WWWK6nb''n:?[55us mݻbǯXŌ] c׮]ի~Ĉ pCW;2d##'+#] A гgO6oݻ6mիUcvh IDAT i_}sz+".C˗ӧOqtt֭[?u_N*U$)L2.]qvlܸM aq?*@IQSNjkP.222ԪU YYY<̙3QTVfh~3ԐNE.+gϞ-պ b֭l۶]3wQi TQ eWg͚ի믿555*TP1R~<OpӃB$H"'XPGIfrcխ+:QԩSҥK|I+b ԤGϞ}wWij!Ԯ]O:8p^&TelW36`9%֨dС,[ YrЍkø45Q7{z,{Vq̔(BBB)D bb|,ٳhijjaaaAjz&Kđ+ \(]a[}Ϟ=c rmpӯ_?zt.+˗/c``ӧE0d0'&6L<i:6M˗KX?~L9 U4Tɥ4pټsAB|Nzdg ϕqtIFG5鵉88q_Gl؁{Ǡ(щ4*KHKKewY˩1oY9ԬU!C5Ѣ-[ΝիWٸqD|aa/FwfĈ`(6G7C4Ц]{\* Ʒ+kV$$eT컙|qjؠAee͚5rt13}ɩ71g(++###CDpi)ic$掊qTZ M NJXyz9" .OKzJm۶555BDd$ Ka(++Kv!YnMxw%JB!C ![ KN<"[(DGMU2ƆU޸eeePV&((_]kƍ9}4fUޞÇӨQrGDb~AIU*GQ/"#3s3;w.z\vzŌ.h0) ii3(SN%::P物S"//ϤI?aBB;vdE,_Yݘϕ&jj&NX{Fvv6}!;+a޳())ѽ{wKJ֭[n ~WΥ9t222ޘLZL;D`DseժU #w%11f-I=z`4;lDn-mV&Ƹ憩)999)r9s&FMMM,KTIIItԉd,l,Ԑڣ7).kǮcZ"i'cbbBPP< *+(аR%V )a}O|BB"(ܶm[¨\Yr=&66+++Fբjjj};wp}vfn=@lKS233%b w9LwD%呷7٤gqɛB-Z-y^L09ņu&fZ@ ?QK\x1oBB}^|~Z|qɉ:5 Ù3g߿EK9BYP~}>}J-ؿ L!A_,Y.GMѣ%F=x9Lm_\n +#;vhBH%wk t֭ mG?33IBOMMa"Ӣطl@FfB^efRQ_֪T¨Q8tпZDի:]~N:|`uWe;fk?عdع`llf*v/[/޼%:wfذa%b+-[ҿ_? #9-}6mD~(_<?f߾}8w_HMһwR][II;ԩ3O3W '+ʕ+G.zjugL +dq?!99:RFu45IxDZɘwRdddYݣG-mpssZժՋf͚NNIMIa$dC.]TFp^zA-̹2dhw?---:cddmĊK.gϞq'%Çܿw>wQaQ|x+O&Oٷo_$b )Xܵf͚hkkӾ}{N>͍7Mɢ낓BS _*oAYY;뀨ҷ _30"e*v 6؉k `bbv t #H Gy{ssߜ.6U>H$"#{a577W6pіWΓڷU~eV{iw5uk^ [bǿvZˈ8| IcCMvcL2ǎH;[F1o*{E,3k#\IK`޼y%v>F^^^ܽ{7[pWT̤yɪ+-ٽ{7Z[J)򏓐 JKU~s=DzS|x޽i&&N#% ٲe VBSST∋'.>gQQP 15ӧ)Xn/fʕxzz~Xߧ/lٲÇciiɰaԩS}(Hdd$666~jY2ɜ}tM3ѼF7PX7Af7,sOP(:[0ҜM QEŋ`+%H[[BCC9gMÇ9q86&l8ar\Z;LET-c7[KY~~~2YUUU̙S`aènhOʗSAN( 44DA|GemU>~PR;ǎsd=Mk{dN<~OK̚=&Sɠ6Nh< b|Ey&._B 6&99y1tji҃7]uBNNA% +id B#Shٗ/_x! żĤ"% 'Ndʔ)OMMGe2ƿp&qp)$%166Ϗ[2hРp)Rqԩ@pw4]DeUTI ٳgeڿ;t͛3iHlll ˥Kݯ'N,RGVpebbGx:]AoB՚%XY+iѢJJp5tttCQQ+WP~}DB!xyyѰcŴU+N8R *irYV\Y^|\zg9rkkbDyIU(mgrcա[,N:CӇݸh>خ>M:uСZH@@g z7u}=o};v/9{{{qqqE.rx^-ڮ];t+WLnEB5:߿ 60tP֟?83%ݺ#'\Lը@BȶN%((uU9gP+ܮ>e3| L<K;K} 98]hҤ1'N,Trԩ<}/_e9bRgVX ̷Ġr,X{{{?I5-tFM$%- *9PSV`6X\ɃVj\ɨ*b֠>>>XMH!TUVKߦ4W?" :w`˼g޽9_I2e8x ׯ$N -Z(Rnl۶G%Yƍc%2oU==>PBn\^ MMMΞ=Knr߿2ϓX!$**'QLyjI+B!Wf lllhܸ/^mۢL믿lܸuJc]msrQ4k֬Pxyy_Nqx<6  xGe^;v,BjUZAVVp0gSi` мNί,n97 oCfj۷meԨQ25-qaY #!!???pqq}hwF91o\f´DtB]]vʪ+ٽ{77oޔ®${.NNNstݫw|1cKKGIJJߌ΅AkQUəmV[ fh#ii߃7@[s `o6}&#-?tPDhjjү_?bbpf@ +k`؀&bcCmU˗EWW%%%ZnMv~qH߾}nk60es7),*(SD[?H$Z%c=t9WOOsʼYba. k҆ꆆt CCCcȑ2ɪ>}"2E|`3>4iDڙ\iӶ-~wc`>ŋEy06ǖ7Xz5G.$&&MJJbر2GZ Y8- %26_VWFWGRɣןx4Qճ;+'b_".7QU*LqEfX('7 |GjGTEBN[3\Ǵkґ혹MҠ-;%E{wO6:(h^ƍGTT$ I528s%W]$%%2"yZԩ"NIQĞ9l>k1mc~bbph+]؞t{'R%Kpԩ)4ݜdBŁر#˖IU=~wƏ/x9xF.q`M>($%mVYlcƌASC RFl?ZEOO PF PUP`lV\r?#>dI(_ߚg/>}DN/q̘1c033ĤǏg޼yʕqvvƮk#N[S[]$7F5*aۥ#:7'dNN~ȑMdT߿gq$$sN:Uy\rJhYuxLK7o`Bw}:}W "vϻr)}> ﳍQRըE$p/ TnLM?QEژH"}jl'fZCbrqGބE DbժU BeD")b-Vx'Yvׯ7d3>>233 e' ϾQF">|'UJ#sٳ@GGaV=NNNoMMM444hժw1j uuu5kƆ r/!! &PV-tttڵ+u]tAGGZj1q_} }Yf:h޾ ӓ.] v0CTLnݘ5k3f6.==sss;̙3ٽ{7m6;whVgf̘#Gh׮]'[{{{fϞM=8|07P{) x* kis^z&GGGbbc\FeyyYhk#gС\}Mb1+|~U+z!!55x|E$1nܸ%%L~=tGď w_3q|%0zСCIIINc֮]ZC,:ubʕ4C bGFىV5TiӠ˗-3"3f w||}H+ Ww+ 7aߥadmkR yyIZuE{?קL*"?~̃Ej]oIJI IҥKsODWϞLwp/$/߲&ьrqEg\\'##33|8wÚعBJ@̴ir|a TWիǙ'HNKCQ$B$/1c};Ԕ>}Pzu>~<=1Ѧ$}Ӯki16L6mҴiS=ܹsDGGsΝiiiL<'''ҥN>Qײx5SLa׮]2 ֭["ߟ[Δ)Sسg*򸏟@QvdQreqرb;L6ϟ}Rp]؏1ϢwYyźu cN⳨]6kfʔ)>|rΒ+ڰcƏ'))3f`ۥ&u+F 7*կZ,M.S0yKh)}E~e~Ia^ru3yү1O#9~9 U7%eAY1bWٳgdS !5.f͚IuDaaaDNN._U7.іvvvUt:ӧE\g唕ϟ W߾o^w ֪K="" iʏ8q;v*3}vJkذ!-[,sw)))9=*<|^VjЫW4ne%%Ogi.owa۶mܝ>}-cJrJJF۶&GG.?":.5kb c2xzp7 dp~ʊDlmmY*?[ 3ޞlai?hBE \\3Yf|SMoUt)Y&4i[lAZ32PRR^zܼwiب̬֮D<޿tn޼ {nܸALL…ѝpiPըDdLkosRaTVd&%%qI  '^zue222{ְa2dHc333=g6::ylr?L2RWno"6lX1Gn %P2)xLL ǎc޼y 2;wgg~^.))Ba)%Lh٢k8XHĒy~Ϲ7k6#yE͚5y=kkk/_ΓOyVr$˗5߿?ff@_Ƴ\x֭[cff~5},,,ԩUtu%e2FEgpӦ<3FF$$&ɓ\Ǜ~zB!sO,yDYE%M Mصk頪"SsH$E˖fqտM,:b1fO7g0t :YX4$P׷/0O x>'ձ/)Gd:-:[DD$ҡ}{Fqm{.IIIzD޽K6mJ?bj׮Ÿq㰰@ ە/''y6ל6mp-"e\XmdŅ/^+**Jٵkö$vwƁr̔QPP*$p3^G %Ebl2_VkXJ)OWWWT~NBz۷oeddd䚆tiee7MFFFZ.\ǏK[DOSVիSB=Jr}N~D(reBBBʊuҦM"g~ѣGX[[ӽ[7ʫ09߿ϫ1PTbmc1"yy\R?.iiƒ\{ CޞiӦqS52/fի,- FELl,,\bErFԬͼslYפIS~uIivդ=YoMMG}HM&n<էdZ*ixb,ͺ X4~?~}xu@Rl߾-[J8EEE -+\|7orD`( 4חr\@+k̝eYX4nܘ3gr޽\9~ɉN;1:-I9Y]]/ YLYʤCXCx˗GM-g d}ɍx8mpp0 ~xT^sH000@ 3ks< @"R~-e˖i.YG_h?1{Μ_R,d,,hޜǏӧ >3f{w<'M&&0U+i^nn|"ۅ$O?vM6%K`gg!WlMGb ˖e[^5 ,Ew(+({b3"PK]d<|BE}IIJoqB߫WRY\L[Ӌ rk d_Q';t!*ѪU+*T >?}H^%Pz>&-=CZz b㓥理s#&NҠ$8jؔhkio$x!\~vڱaƏ_ y _pvv.py :<0hhhf6)Z}! BBB?]#lȔTd%g233ͱ H:wU)(O `kkˇΩSSJJbݻz߾}Dl5666suk)))ҵkWi-^ ܹ3nnn'%CCC1b#//6,=m333ssI1ϝvnJJj*&Ldא!$3IRR"yz&&&0qDZlP(ڵkR1 6h"\2eдY3"6)iG`j;8z:-dşlrHˬeD":Ԩs'|3i ㌃%G,ZPv/>F(}zvs'$$ԁ\d#ݻիW-[Ldȑ#VJ\!?A-WQQ4|`̟}[,]{̙ćRְ!zXfui_BtΟ?OBBqqq5H»u높҂qQJlǏ9<~rttdڴiL2Ν;òe˘6mk׮Ԕ`/_۶m//l70:v숙Ǐ'<Ш"fi~w3Fڔu!)&!$gѣTAj0ݻ#Gv\'fX.œ^k=TOͮē'O8۠ ӯ1:*oxs]D,/Ɉ燑o޼wbhh(Ug8z(+}dffr vŋQTU$=5%%bc%޵!/s])%KPR%ƌß'_[6ŷoY2d)&&&ܻ{DFI K@Z) ~˜1cw!>>r=x ۷o'005k2x`sKLLd̙xyyoƲer=q???.\Ƚ{‚k.U١Cxgyv x454Ը1 g+i$={̑o>pydjf~Sf:IhTrrr(+HFFڬ݇FURa C/r3@͛7羟e!>$̌N:IS'OKX[[[֭[c \,3sL6n 5RF$*:ӵk0֯_ܹs,kdiiɴiҥK?{ݻw`8 ~!!!;w *DGGKjj*rr'y(Ec{b5͊xXGGG=[flW^J0&Ӣn9˃'O`ll\xuJ_O͉xI_u8;Z 3fdԩJHOOuV<|TTUiߡ$&&sTQe -X)%W^$:!mUU:֪E##I}?ƸrbIIn䙙ݿv޶mۘ0a+zbY<̲ YE۶mrJ~%W\%M 8|` 5p"13vv0s^g|Y|r,X@帳e$rBv"%N<ƍG[[|'HMM%$$wwwAbӄKŽ5zxxGv"~իi۷ocْj(2F bMGA͚5y5#I=gqY#^kР-ILMd5GfvMBtm۴Vw:udF2t4n7;%o^)u{ߺ^fРA8p@Oݻ4ZImvߏ^^>}yromP(ctU;}d t;;;222((}8n>Ceڷoŋ3Ɔݻwgf\GʪH~qf@z/tItҕ.]R|y^xK=\Z34۹^?595ƭ?K|rwo$1ӦMe˖TTڳ,W.UYpB:nF~HX♲a ZHs=˗/WR3x{{Fo٨]$֭EٲN]I qf&zBN(D^^T:,^\~#T,[D=?}gO*5kbT@ X,}<|Hpt4TTS@S qf&Z6iH|jIj <͛728~hP2>Ȭ%ͪ9.,&PW\ycȑ9k׮ѧw/:tqL^ U\G,4$IT,^^9|%nԫW/oߎqqqݻeq<~q=^T)˗/WO}~9 *p+/_"44$ٚR{Q$ RޅݺG+WA:uBy}s,[  bPUPm'y|K"mF)/5xҽ[7Ξ;NjN]Nce!]&Yй 3ڷ'C,MLQUֿLݨÆ !<ƍg%On~R]]k62A{<:ԩ[yyy޼~Md>k\V4*r)T}ڼ^%u+U޼=B ?pWʴ6rHKe+LN͎׷$W^cǎޘ݊~$ 0PǏ_~؉;x_,3ANcjjw͛2e ZբoF PO\[bi1icnc… K:pd1ظN:1cSCN-Xɋw(g{xÇu"H*s-̚5g{B#x@t4Vrt\In]ƍǜ9s~ 7l'ҥ ׯ{SS;wk΁7'H U##>gݚ/_FAQ9$A|9kL@Æ _` .:wFFw 1edd0Y~}‚::ĸ g](#2ѳUM߇rrrT%3y1{qb׮]>߱߈.ct{&<"qSD4Yq5GEQĠs'yT0Pax8;;l2 ^5]YͲ2wXf'v07NqY}DI=_Fz***:VPZ3}tBCX`Aip/4WJ3xTC xA͚ܿ? UC%53\^c; e+,'Ndc1+/ytQQQqҫZ]'qKz={-p[C422 tӸ6 W^TTϟ憧'qn\œq VVV[ůLJ%Kpښsy0$WB>Mn? K|2JeuxcWwKl¾~ZwQr\X[[$}VZ,^eeeSNz*˖.MMM޽ˈ#x bĒDy U6M``{I>cyk IKK_2wDUE'Imֻ5؍.qZo%5j ..[޷G9rjφIiT1.]**h ͔xVVVx;]z(:tE\d /fqnطo똭73iIeq?ϟk׮e^;<ç%7yp^ȪK 8O886(*OpkRȢ)*Ϥ!{ #3nrk"PS\9Ot5k0{l&nƖ]Ӌ /ѴISU#66{q<vْCPQ67o%Jjhװ$S(5={bŊ2f +k2HkF uD[.W+0nX2HM8:5BX̙3g={_fUK nE9UE{A߸v/^vVKZŋRRYJ,J)hْxVy]BZ5zC-1g(?sgϲ22mG4ekyZi2l0hڴic~D[[H(W'UUU"0]%{Y=e˖%EhislhRRBTBz4R999e%%QM 9~"5k޷n9S}!ݺve}٬j>؄Uˎ3kڣ +k \?gZIp׻U-Ip_YSLq&:YXgܹ3SLA.Ln'_}Bf͸}vP(W^tڕ.]: һu-w֓ٻwLU233iԨ?F^$ϐكf#;Wim?ڛC$/^>|գsNܼe V6\”vg=S z qFtvL~ff&;د_ٷoL^`` ZԮXS~1ߤce|-]SQPOn2)խ}yFEuuaa]ATbŮ^nl`c^wEPcvA|?DڠxXY޻>N`f9{{[$4.\ȱ|:.]pȑ|6Vуβa+hUdwʢ_~>2;wۦVZyf֬^EXT,['^ XsJw$ 1҄?~5cԳ`۬?qkJK$4Ԕ 8 yy(rStʖǧ#EEEE||s]<<<طw/eձða a̙yGEELHRR}޽{ki[j/kpp|qԋ-Çq^B#%%WPw4ꛙqrOD5زy3>>>2_>!_#7jğ1zgVV?85kF%KKh۶-&8;;DyMMN:%S]PQT }Pgƌ%{Qڵkk.?x{0a]TQq5LLdG,؇ET3O®^=n߹/ISS V;vi۵kW:89C3jkǝ۷ȔΝ;$2 QatڅW^;̌s3-fjn ~ʟg&ЮfE&&\|eeeEr<ę[|3lAoջLiuaǎ"69yͪ0gӮ];|$[a׮]iժ۶mê^8ȉuWܕhzJVd633(B'1tPRSSqo*=f`>(DѨ-?% ô=΀?$&&Ɠwh6v+,,lْ7Ѹ?P.o}w*?Kdd$'N̔>}pi_cg3ud\&|UYBt"6珁 tcZ#-%={,JPdm`ذaرx( vZ[sbm?G,Sreb#"\:wCCI@[Ez+2fu|?cĉҾ9v=jfM޴Yaa8;;yfTTJg"''0$>>sq84gn!Ξa/*j [[[-60r~ׯϹsxE>V?ܹS`&::===kdNSwsv0˗/gԨQƥбcG.^Y=[,4?(WdÆ :uߟ;vƄkBe}\ ֧$%%`dOViܸ1/bhdU83:)SEjUy)64А6.IMJ~_kGjR*/%" ۚS]DZb |r}B&)nd t)_FBߌ/^`ڴiɳg8E\|<}XѣLF{<{SS?dڴimlm}d$?GN%3;۷83SS^zUGիL5ߓ\RfeX"TZ{{{455y9/]"==QM-a3kOOOQ Kμ -3 &dɒ/^ 믿Ҿ}{Μ9S}pp0xj>w^"wGD#99gϲmVbbb?e#Ǯ}EJxBF ɓm?K+\򎄄N{… \cǎyX'zCt׏_}"1999 1ԥNhj_>ӠA1*Z-K.ȋDhRӨzU\d2e%^;ʸ+ɰaXs8qsο]=Yl@b Μ>C {ZݓϩZ*'$pp>2)Fss iC cDžƲ͚5VZ߸5iߝ?Mrr2 ? 4/tuI%:: %Eyݿȑ#cveC<GZfUV};/#c{֭[g8''ŋ3m4 o,##-urʗQT#szc#tu֥MJJ*U8tn2PSSqMpp0ATT^Ӝ9}зa*+J9}tK(Ntj*jyV;2w%nUԩSe˒k֬)vX,FMM m=-ƭGE+D"gY1~,])GE+{]Œ}R22y-l-ZDDD99};WWW6m,F޳GYj1Ƞ9qii(@jj*gϦR%KfΜɉX=z*1ұ>>> /Dۧv&::GGG&OLćn%۷[[[.]D"mߎ)mXWH K}lPӔMb XhW6T^Ν;~zބڵkt45TW7nPzR oJ ۷#"J*mv0u=WqR! PR#Ze˖B7oPW/8>}:JJJL6- ,tEFHHf"6. %##B߿iSRؘnjx~ed7'"2RӴrJUUå7{"..5*urg f@ޒIf&aiT073#ݺu ssn= bT1|"ɱxb/^ ;e5rv-VwL^^C2c ^aa5PUަybZӦȜ9sJ5GaÆRQ7opm*W&̅ ָZd\2מ IDAT0k֬RS<<<ݻ7?3lٲE<|=cg >8FhKpD[:jՊ{ZRz:7BBHLOP 5)T(MG[[LͤwlJXQ4butylڴ){L9rA"å0e.yCTtL<`̞=߿DžU߼y5:l K#:G16@ݺիWܾ} XLՊh*"LTYqʄk[)΍9| aQh)ӴFEu˖-cܸS?NΝqiґFNȢLOz~4spޞq}7 E=!p ʰ]ל#=36m>aZg̘1[N(1B ͳH&9µWdmP xRBq#2?zDRRX?eϞ=ӎDnӆqc$bSR8)o fXZXP+++RSSs6_TWC}Qp0QcN=zĬYX`AիWz_4ק|ZO1gҸne5U1d)YlڴAIf Օ/\ˍS9z _D, VqBK]y y[e+H·(+⵨// 81bL}NҤkŬ[)SLZf8ŝD&Q-}| >888䳭P.\M6"ݛUۇzx:7ROLkݻK|׹aZ'Hެ=r[fy.}6vvv .uĨQN і1b֭CUQ,T__=IILJkxL@`$}'}yx{{(eee鉋7=?~TzDNN2eQ&۵"pK׮aϞ=8W̛73gRre={M*QFyf2228p`VҵqeE:ƣ~Zl@ՉJlom/.&$WV$ Rl(ʗϻ#۷bŊ_) oGѬY3t133+2w&&&gg[AL>2wCjװCfxw&NDz D"o-b̙|DSLf͚ܭ]kkϭ[JͯW;;;bBB:[CpپRn]qttcǎyy{{3b99|8vfx޹'Cn:\®],--yq],--DrfϞoBN>s>Dܜ8 /<^K`ܡCHVNHb2PQTgy9s^ ƞI>3}LY͢}hִ)6orWFEE1vX݋}eN.p3V"ͭa$c'aLKʹۥ)Uo',,}燮.1hii) |E?$g8;S3O 9!'-#5k`bl O>pAf899q ZjG lVZQQՋiӦI3o>>߿?O>^VV^[##T52$=+QSʊ^{ӱcGtuuy:u*!7;w=Q,aAE;x'=zsޞ'OzjFÇKG\s6-#0޼&=}~}s7ee%ʕC_@NNvڡ@ƍIIInݺt nKxWnݼ# <7 ^zX7f޼y~qC; )*(0Y3F~+ `2dȐB;bc9t4j===޽{+Wslqu![B=ž]ضmҥKqww/Y]\\8q޼E>eҤIO5j#G ZZRjk7044TnQXBTB*/by@TBj7nLjѣ666IPMM{A,3o<,XS\q>_VUoӓ 6x]@߀ 墟J!hE?XbEvjjj\_~L?OZf&14dTϞZzz:Ԍ GdPJJlo@EYٳgk䄜***k#I^(ʄ^cS g\ʋx2044|y}B;\1J̝7t Tr(ܻwWWW=zuj s8 [TPe]oW, < b^z֭%-+ }MMjTEY55[0ʡZd߿?cnj1MH¢\9UFK`MMY&*QؘIG)3n-VOGRrew{*T@K4ǏDll,%e&GڶiÜs%$$Я_?8z(<~  }L:UVyChѽy7Օ$R<_Ņk׮!c`MLPWWZX'>DEYkkk.<՗/U~mێ-Z󙒙YdOD,pCN~H5{w4 ʖܹs2>|E@ #;{Jj e޽U!//O*UgϞ=D"l޼@8,YYYl߾}&x{{3{lކvZ<ZeeϏ{G̻jԨ (!_Cqb1-B,CKK MMM޼yÃo={bҥy`ii?ΠI2ҥEQWT2sÆ H$~'qW{/6[t//"[z4T uO17%#P4lܸ pI:uꄷ7[ٙ}I:4ߘڝ-ee8G7׺'< &H;9ȋDT!Z"!3'UU x.3mZ~ ڵkVre.^Dǎˋլgj*okh7IOO)'͛ӼysΞ=K]y~c׶m9z>{4m4Mpp0-7GN,n%ЮZ5=ʙ3gptt,~?L||o *9;B*U 333^E]]Vbj۶ɰC?PhkO1fV\Ɍ3 Օ8fU+pvw僗defЮ};֬^E nJ# x_ɓYx1zQ^_DB߾EQ^1cBy>[pavEkpvvѣ#us᧟~"ߐ{l\fj5J1qL=v'*Q~}tuus7oDCYuA8.<}J-1X}E6viҤ W^xE+QQQԨQ,X潴XdVMZ+$%%[~% |5B/Z>>>X+ǟ;S|yTPQeKNeO>0Ѕ ԩzu::ܿwDUAdpp0eԊMMe}DVNrrrH$E"o^dß 2`?x@5]D"aؾywDBp/())ѱcGǍǦMHIIASSdԵK7G.SC$s3sE +3}yrfY}uuu7oիחYL\@࿊pD+M!g7vgfݿW"ͩVVhlٲ/ZÛ7o8CB_Sw_u|[_ѴPи8g<ƥo_޽Kvv6III<{u;O߾}QSUqLřGIƌ3Fgw^/Y^^^о_{,jeD_FЫ8G\dffͮ?wK~KKK,YD"!99'Objj*w;x---w?(3;vPؘ}a$Cu"6ƍ')))t9YNNڸٱ%ƍM. Hׯ[,F,{f33STFu?E^^==="KMeե888'jժy'lٲTۯ}3d+(Vp<;;@ƒQͺ\:7\tWòexCˉ1407ݝaʔ),]ToX&((eݺc[@"}*رc-b1W^e\p!؄:uꄗ㚷j!T;nѻwx,_pSggkW@@V?~ѨiТgnw3f'''=JУ !(A&2)իWiղ%92tLc2ߓCcaaaaD|S,ĐQ2ރ6ժ?"1{rs dee!͕QS./CB}],ބ~U_PP666hȤ֭ y=mW$.5CCCÿx>/%$$KKKuK6ǯmF߾w~TJ#h7n#XbO##*()*ɵ/Zfӵ<'-22eR6 %9#;]]*땧Mժɴ&--3ϣ8zQRvc:U˗9;n%[,FG]ssiK-c|ɑHPVTUL,ZeEE֬Ɏ7x2ͩofFzV֯#.5@>oaÐW}0^N~ׯk֬aĈ*?Ɣ)SXb[ XZH#Yg%>=TR22x˅O10@QAT8{3X"A[U4TUUe~ϑH$L2///D",YLvNN:XZ*ě/K,!&&w([,nBQQQfݣrʨ}V9ŠA꒓CZrjj$$reҒӸ}Wcl"x Ȅ3蔊OCCChkվb1#<=w6MLp0;;;233|2<~7dj۶訫["06]͛caaARRׯ_gu)0 ҰǹQ#TUUy G\Fn޼5ٜ9se˖q]D򸸺2bԩC\\dɒ%\|cc!( 8pZj!-;7oh߾=N5..Zj;uM4AAA[nsѢGsaF}*m9޽XS!P%G+?b͛6lbqAPPdgm--XL;v5/1tP֮]gƉoooԔۡ#5#GX =z`۶m>|D##;FzYFڵk\+Atr2QkfҤI4mڔnݺ}Mhب!AAAiPR-9=Я_?om[< ȜgffF\\111lڴH~Wv) ?%--9sh׮ 0qD ?:t۷os7o.QsԩSRsĄcǎIw*WLV8|0={ ,,UV1c f͚?| IDAT%9s:t.H3f [>8Z[ckhCuׯWϵf9[,O%S0p 2TB`Ç135ex]` 3;oҬY3eRJ|Hļy󤹍fff‹/8` ({"2Bm*תL~80;vd۶m+HXҥK1c8x!9!{{{/^L&MG(/:%moСmۖΝ;xĉ1hР<Hvv6w^۾};fff@YYgggΞ=KTT;w#(3vX ԩ>|];wd_ޑ v~ WD@@#F¢9>|HhX [xbt54hlnίcY@)?#î[i駟Xx1)yv,#JN&!&XHTxFCvv6OcǎE}L_GͺUHI$%1olreh֖{qtt;_Wˋ /\̈́s*T@NNNېQQQTPA *k.O[,>cccc_C"0|pnݺ%SNƆ6mڰvZ?ܜiǏQil Wє .O?ss::vA6|8dPK_~Ey%wJ$zoLBf&'N,2h̤cǎtܙ} _;4?2I&]Xf k?IJb {e?N pMN@_Bx=";;{{{ttt?7nlٲ>}@~޽<>BBBHIIɳdii hJ*ׯF[H$׏S~}֯__ ¶V-=À;E Z T`BNN͛7۷\rlذ+Wry^cW>O`իg7_ҡBC?> h[) 55[o%Cŋ={ÇӠAjժň#}6njh2S[NSV\µׄ(BJ50`~~~y~|?v???7n @Na۶my|l۶ EEE\]]OHH/^^`޽899Isi߾=#33SjǛ7o0` _\9:uT/wǐ!Cؽ{7溺2D"ڶmkט0ag ۷]}n݊f///f̘4͛7߁O144KԫWN"*)0Y.ƹsӾl[+'Q:RE_f$22Rcbbxi. #" 6Y3m4f̘AMz*.]*oU@@ArΜ9CJJ I>|=zС VP!ϛ ƍC[[ϼy7nRnݺaggG^?cڵ PfΜIܹ֭3#F 22ӧS~͎;X|yvl^X|9[l_,鿖nɒ%\~\ս{ӲJfܹcJ9994?-ZB}˳k.N5*UBG]֗/_HŊ x"W^eHü/bbYLtM6EEEo^gOڮZ1112С7n~+;dΜ9J9^z1x`8pzw|0foέ[ݻ7Nb,Y$QPP CEtQ %ё-[D"< fX&>y LV*::O9h07'Mk׬KKK9IU>a"Z}}ʴލ7pCG D5x% ' P*|NW^y={u꠯Ő q?|K2qD}ƛX6_n9[X϶ ~]8<7BBhX@EH$bq׮mќ-ׯL*ҺuId9.xNlJ FZ8TGSrۈO9yK4˗vQZAYYm۶QZ5f::gg7d@ӦMC"0qD4>=++ӭfMuڇiϭ[zɓYpa[fMBbc >b\,9:OW]n5k4w9k^I.Gk.bgg'S~G?VVVԪUcZHġCgbɓR?X9WWW.\yE6}.!?_,,,X`A[ɇw}me޽lOFVV}JjՐcfggsxX[[j?~xi@DΝ;~ x?őPgFdYH6mŋ033cciiIn8q'O$*5n7³ի̭>ĉ:m{sqc9E " mlʕ+(vDEGSVUH<".XYYǓɜ3D"6Δ,ޝRZ5֭K>}ܹ3eʔaʕEvttdEcΙ3H$i̒" 45ٺu+DDFU0ڻ(w6eM@Mp5IwV\*af枘id .2s0'6aܟ:g޹w/.~ǯl(,ģGssؠN- oIII~fffXhzg1<ӵkW4t~@Q:tt(7vFҞ=xTQQxx8xy!'HkϿ=6/^DXX<8gϞ8Cbo%Cyq[9\QPҿ/(effO>_ AĉW[13<3@ ۶c6}'++,:dΚY Vw%%0714ٳgcǎSNUzFL$!>>ncnbb>22 oiz8~BBB#Gжm[4lT٭[7#prtUci  P֭1q"~ϼ c@~^Fjt|[[[TY[TTݻO?ţ;w0)Ojypp0ϜW嗳gPG~8C{ȡC[[|;;a:x0jԨ1 σgaog+.uBX[& բD"_T,M'O %9~h寿۶Eg5jL`_~?ܬ~=0{2'LR7 c6ijA (?4fu^Oc:<3`%%#v-R^ *vv?p 6~Ə_ &M|wPpƻo㑚xy}hh(Ν;.aڵQTZu~5|\S/PP,Ǧcǔx.!** ˶cU/ Go\ p9TTT@"o,,ަMX|V5 b1^ BJJұR)&MիWch6ռ= aldcSNUoԨQw͛3awо]'pcbbhe^+ggܺ}[c5abbw{WsfܺY{ ߟ`1V'xg̙IKCtt4Zl H{{{?.]B |/ ݺ]c?^}UDFFbx+V ?? 9oGQEN_H(DEE!euu1|pdJꫯd62#D"DFF1̙LN_kV~ڱC-}2c8V'O}HOOGvN(++C//ԗJqOPTV/_!2"3& {, v/Lƍ$ViiiyՍ3}xyam4p$V\qhm373Cc+ktr+Q ))hѢE;33xoV%SSSDLw;oj_"g<ܚJ;8x"*RVY"zݻ{0tq*oذa WY7߄&LHDRc;sYc7^eӻTeŊ(++Ö-[pexxxGc&)ZV-[[[㲲ZkW>͛r= ?P98D7ol2p_ a}dɧ[YlƏ“~}cg 'FYړ'Ob:u*߿>W`1xgv؁lƢu2\4@{77Ĭ['?;WvӧOW [{L&CԮ_0x:<j*`Xx%c1xgp>6)) 3Ӻ =Em,2 ۷MOOٳgk|^+&UX޽q6#ƍ1Ƙ.;v,{kll,FrRc!hdmE?h"ڸbMbܸqHJJɓ'___t-HPh!zEG%q ,\w}1Ƙ,  W1>څ.]8tvƏK}жm܉ocb=cge2~?|*IR|5 'OW砑3lۆcڶ-pHR11d(Im̵rvƯơixP\ 7[[ \cǎ/_7ob[Ô{XZ1p<'cǎŪUp4l՜c1t 5kЧeKč n:4W}[<.-ET GGZ#..ӧOG77?y^+`ifO{BW\5ڳ0cxg֭[iK۪U?yGB @c ia;*,'VSLL`jl7nԨѣGc1}!i ]qIoh׮۩T {OQX&`ܹ;vCyEq̙Jr ;tk82uZz$"Y N\K`ҤIUgggҥK 0c@ >LjH>cǐSPظ8|믿3ի|fƹ۷Z)_F裏z*=<d2B˗ѴiScDk@aeikyy(.)_U>J92 IDAT2FtTz/~z\v_-W܃px{k.\X`1S<bnnݺb1͛W>Ǐ>}m߾k׬E-__G/ı,L>999u69  00+KKqHc6777Ͷmm6l޼ZDÇ ZY!woL<D^s =Æ C{;qnڵ+ϟ777)S ;''88Re2cx036Ʋ@ EDD}:>\eo666q|?>?lc ^ɢtq%pw QTV?la,&ȽyhlkK;wyNxC)} 06~ p%eTcL'cO"4tzz\}SSl h.ܹ2tk}C`XvJgZ89afUFY~] ƍ)HV\D,9۷(1>g6Dai¶2_/7L$]#nၙ)NI0i~ `ڴikp-3SLR̆7Ϋ8/  XaA3}wru>3H#(@ /DC8x -ZwyZL>yoUǏO>A޽Hٳg+d2,YhԨ;`۶m(**RyXtDzeTah֬Я_?=q 4k &L@IIڥKCF׮]W_N~:e2rsmRb=9sFi?H uˌ1'O`Ȑ!Aj1c b`kk1cƠgϞxB_Pt ߿?>cL:UAAAؾ};MC*"00GUMKKC`` `Æ :u*mۆ=z(m:yd|'8p Ю];95謈 n@ЊQ.!~Vi=c zyyy$͞=[aT*%DT_ sNbP?֭[m$%''˷RӦM_~ Ӈ5kFeeem"A(!!A͛$i̙ 9D)Gzz:t_"6%=:uJvZP{8~7Cʫ =EK,_+`oo}?deeɷڵ 5jBmxx8Ν͛7cڴiM6=zיb;w.$ p]۷3gTX+((شi|M͛!J_;@… Ϲx"v܉m۶!$$԰!.<|z669kd2N<_aa6}yoWRgn/:&巿WLhݺ|[ff6`033S@DΆ@D*k}}}q?Sdff J Ǐ-zŋLvҥpaܸq cLgI$,...+r^}μRR{yya )y!Ē%KǏ+gii#Gbʔ)Ryyuј3g߿022z0qDNr^ݔN:ɓ'4h/HHKK)r^ykKS$9)//d2d2ˣd %A(..N4y92d+':thvW]<tB'NnĉԥK:H8jW7Z^gggJHH.!!#⼪q^ݤɼXeddO>o.㼪q^dhyMMMQVVVm]YYLMM#⼪q^ݤɼr^ݵl2t M6EѺukxl3gpaxxx`ٲeZq^yk.>b1 @ԤI"+++inSm8/强`jѢ $SVh…6Նr^[=& ؼy3233ƈ#ШQ#-w^Cgiy}1sttD~~~tYW5Ϋ -yU㼺Iy_tԩC}Jku*㼺.Yh̙~vv6͚5hɒ%ZP8"Ϋ4Sĉ Dƍ1bKƍHIIAἜɓ'cʕJ=yyyxb1"""tRmriii3gۇA@>}gW_Ryyu oy&wUxZL38/'1ƞc1Ƙʪ!HpZL38/'W*⧟~¦MxA/u!C@$ҏ8/3x8z(ΝD3f@ǎԡzqp^gHyKJJУG?~`% 222pu %%fffZp^ykx*#00...>|8~[l[viy9/]&Lm۰uVIII[oz +WՋ*㼺Kyk5۷/ݛJKK+)--۷ovW]V༪q^ݤɼq[͎? &ԴSSS7gvW]V༪q^ݤɼI T*((WPP@)))F NՃr^Ϋ?~L$yzzߧL$PQQ}irޚ^%/_N$ @bb}nSm8/强ɓ'O}f͚YZZRͩ@nSm8/iRPRR]vɗ:ΣL|)L^C׼1<1c^ɢ  4r'L***gtvvvnG㊋!͵JX_}r3~'?zNB븫xxx`ܹј"::gϞ DŽ ,|qq1899+?3Ѹqc|puu݁|)Yo… UVdnn.wܜ|}}iTXX6զvI˖-vqguATm6G(l'oooD$חۧ/11FASL "":|0X,&WWWr3e266&AҒӓ,,,H211SjMY|9ؐ ԸqcڸqRMZZD"-t^'Nt?)))$[NM 2F-}urvv&A‚ׯO kSu5mT>&" T^=lmmƍ:6TH$MFFFH4{l={6EDDP`` iݗcر/ kQvvB]ZZ .'""&Ngر$ :TMuܙ͛'ciԨQ4j(####:|;Uؘ,,,֖rssݝ\]]i̘1D"H/G%Ay'PNH>|}%D"~a@B[n~7vIT\\L;v [[[;v;U˗=-X֯_OM6QF;%&&ғ'Oh׮]J+Wձy»KtʕJk^J{ag@_i͚5ԠAjԨ;wN^O_u 4ݻw_wЁH*ʷ=yڵkGAAAhQMF:u7o҃(44\]])88X>(//ݻSdd}yrGdd$Ga۲eؘ DD?W&&&Bw)|7$EFFʷ:sssJIIvrr(O?5 3f_ܹAPF=/U5'www7a#FmYYYԺukrppӧO|av֍\\\hΝJ  _0ƆׯOqqqJu6l [[ۺlM#|}}iڵ$(ԭ^|}}=R޽{ʪ:Ҭ͛Sll={)׏ܹsԱcGrss]v)Jn)iӦ }vvvTGuٚFў={ܹC PRRBݮ]x2u(++M(++4+11 PdgϞ8uꔖSTDFForAЙfmiii׮~R͛7aaaQiģG`cc#&ߌommǏio`h+wܾ}J틝;wСC4hJKKНl&NaÆ߆D"v[ӹsg,_\ףG]Vnݺuh۶m]v;v!:99Y.55]v23( @a+((2dHvΕڿ>k׎lmm)::Z/,  [)99ϟOt1yѣGQF4l0-u>cƌ7|SzdffFÇW{7i„ uݞ[tRR^ܓ˗/Sn֖6lؠ瓻;hтbbb۵hтONӧO͛H$۷kݗb 3gʕ+ˋ&L@δyfz!ő• W^%pZlRll,-[֖6lH׮]v/gϞZԾ}{&&&Gt1$">|8 @]tcǒ-D"rqq!5jD7ov/~"Aˋ:t@&&&ta^zQtt4H$R4 i厏>H&UZo>F&Qtt4YZZ0ѷ頋/R޽+/E;޽Kl ""_]F۷kulW^Œ%Kb,,,{aʔ)hҤ:T%K`ƌqU"$$ǏL&5/77~!PQQ?k˖-={6._2e ܴԝz}ظq#aÆaӦM>}:ܹ=z4k[0;._}aذappp1s:dgg#""ΝC||<:vѣHOOǵkPQQ`011v{jSVV'N׹~۷xj=7+!p=/̆ B$ҟe2Q^*sݻ؏?s!,, nG#06lWVE&!##nnn 1ƘcC[y7bo5H$͛7!&MeCvv6Ӽ5oaa!bbbi&dffʟ@433C&MѣGZ˝Geye0y ϗ./ɓ'1gJO?ŤI`jjՋ>CʛN:!++ ]v4D BZZίy9/!u> /Ν;G$cǎԻwo211!ccc0233={V$༜.C[y 7T1*IDAT*㼺Kyy AI& $?|O!!!D1nXL ,Vjy9/]U&M՟GA㈈|%"##~p>a { box-shadow: 0px 4px 14px -7px #999999; /* background-color: white; */ border: 1px solid #bbbbbb; display: inline-block; cursor: pointer; color: #139C5A; padding: 1em 1em; text-align: center; text-decoration: none; font-size: 140%; margin: 2%; width: 40%; } .button>p>a:hover { border-color: rgb(19, 156, 90); color: rgb(231, 4, 136); } .button>p>a:active { position: relative; top: 1px; }geopandas-1.2.0/doc/source/_static/dataframe.svg000066400000000000000000000614561525650651600216420ustar00rootroot00000000000000 geopandas-1.2.0/doc/source/_static/holes.png000066400000000000000000004572021525650651600210130ustar00rootroot00000000000000PNG  IHDRxFsBIT|d pHYsaa?i IDATxy\W,:0e]C`0md_[,a Fc1S ]V(:90y?y~}}]QjB!DxB!o$xB!E$xB!E$xB!E$xB!E$xB!E$xB!E$xB!E$xB!ELCIj066ٙEq}4Ǟ={677W{zzjΜ9Su :,,Ls,==]]zuuwqqQרQC9Qo۶MOzzzYfi]߿uҥiii/0B!szI˗/瘍 u-WZq&&&R\9ͱ_Ǐ3p@0g6o_ @`` lRghhs!!!KKK a֬Yhb[jEJ gϞl޼Rɀƍݻ=zy-Ŋ+cB!xeiiiо}{J.Zm*ǏJFIY`z")) 7nPvm6mmm166Joܸ'VZjbbb͛|c~VTT)O@s ׯ ?!BeӦM}/5zꅵ5m۶eڴiX[[&ݻRTLll,_|X>}Ks΅XB!;xbfΜ+y 7n333K֮];ݻw'99[&Ȉ3g2vXJ, ̝;cjTqsswww͛%+W޽{lذA3fO?k׮ 68MFÆ ޽&ʊ#G2g5jľ}ذaZeV>t *yޢMh->}c)ײeKBPOPh233S1BmkkvppPOH !x#Jpp0'O`ĉ)S>}2IB,--100@WW=z0vXHNNf֭lܸ]wS$xB!Hbb"J:vR AAA 2DsN:ԩS~ApH'⵩T*߿OŊsssի JŽ{8<ϟgȐ!XZZswBM !xmgϞ%--ZjiW(ԩSGҮ];y۷onݺw(B)zB!~w󜳲 ""GGGlmmIMM4jԈRJѴiS>}ʶmطo'OfٴiӆѣGӶm[tttg(O!k[v-FFF7s׿UF۶m144|a&&& 4AqlBXXVVVӣG1(rdV!kQ*ܸq#l. :uy̟?ν{ y[]~%B !x-(Juێ'$$2e0~A(U:::lٲ_oJO!+"55gggͱ-[rJj֬Iǎk׎v1rH6oL@@:tȑ#T8++qƱbŊ<̘8q"&M,(O!+ !;;[+R Ǐ*Jñcpuuuo߾dffb / T8{, 4B +Wٸq#SNĉ/"C}۱5BPаaC[>ETrU.]Dvv6~W^y>|8͚5c<|I$xB! ---WRN׺>"11]]]zygccS˖-Kzz:cO]vPzu)^82lذ3f 7̙Ν;;zh&O\!Z!sNJ%ܿyq}o^k(JeʔڱѣGh.Z)S`hhHVU K.T*>|xW\3336mڤuD 4???x"˖-ǧvxW$BQh`ܿ_S.::5kx}LLV"׶m[ 888xӧ\2ׯСClٲ:uxbLMM_r̙3$4iAAApq;5U !(ܚq+WfDEE͛ \xVZi+WM/7l[|2d|ǬZꍓ;ƍBɒ%)QNNN,_3fpYtBxx8[ޞBm !(Lj5GzɡC8z fuCV3d߿ׯ'  ㏅*&v튻;G1fʖ-K֭ٽ{!:wЙ3g_>ԫW]wG! )))|'2x`<==՜q3fʕ+,_Çi_~O>(J>S177'&&{r9tuuYzk/xiiilRU… ٳP)_Νc„ 4hЀÇSbwm2D+YZZsNINN֭[,Yqq͗&wUVe$&&?ӧ166QF44C~<ԩ6l`rAy:ķ~lذAkEo$xB!ޚݻw^]Ɲ;w8s ~~~XZZtN>͎;"11ƍ.dLLLعsKbaaZA`aaxzzRtiuݻ%o$xB! Jqammĉٻw/gϞlٲ4jҥKܹs}R/}a ٳY|9&&&#n޼VZǏ144qٓ?ﷷoNB!޺Tٹs'o&;;JB  LJtߋn۶mܺuׯsytttXl&KOO租~ÇbllLǎmKGGCȎ;8~8-Zdff2fVZ= '''P*DDDpN:ő#Giݺ5>_\$xB!ު'OҵkWQ]vXb(J<<<(W&ڵkL>k׮1lذw,\+++zܹs׏8ʗ/#Æ +puܸq߿___ GPP|<ҬY35kZ&** ;ƾ}066E77o)"CIj066ٙEq|Zn9fff4k֌5j`iiI7ӸbiiI51biii.^ PdIl2-\~mےϦM4iGfܸqZFٲe {vگ|?xɚv޽͛#%%9sЩS'VX  4`޽ӭ[7:wLbb"^Cڵ?>d4lؐ#GЫW/ҥ Gy@JVZ˗1b;w{booϩSbØ1ch޼9[netЁgϞiI ضm;vdĉL0A+.;;VZ?_vZJ%-Z ""B+6<<-Zn:&L5nܸqxyyѹsgmFz߿?˖-+''"ڵk =wHPI lܸP։'V.[ fΜ7o~Us?fرٳ>}~WjGWW͛pB:DVx)+[&55&B-~wܡrtQ3t֭ۗPZlҰsά_^s|֬Y,X[niO?fFFkצjժZ']]]y&ϟK M6Owbkk˴iӘ={zOOO۷}.,ŢY&9ߞ={3gŊرcoܞJRILL ///|}}裏z°ahӦ _|A@N=333^VοE~slll[07M6}ir9?~0lbkkզ!8p gSHH}њtڪU+*UD``͛Q* GػwK/"ݻwV 56ر7nKV3gbbbFPo>h޼96lxؘ7ҥK=zĴiPTagglj'եO>o~gd>~(z̓r -Z`r6mݺu\{ <Ylmm166Joܸ_ZjVҒ7oVuppZmRR.ܽ=zVřu,]Th9%J ++ _z___oΨQׯV\ .vzޱc4-YSSSIKK???]Sutt^y<ߎwB___pqqyk+UIIIܹsBŋ?E+s={6g :Ts|;;; Yk%>>>$%%{*My{{7ͤIΕ)Sor{ҥKӹs~,BAKIIa,_ \]]2d˅><<^z婃ZO>3etttPTU*'Od͚5!Ko IDAT_iӦĔ)S;JŖ-[7o7nAVX :;j*]tAEPs/^̙3quu5Oƍ0~xoߎ_}666,_$"FFF̜9cRdI\\\`ܹ;Vज़3o<,--Yr%cÆ Z1c~)]veذa1m46lH5qVVV99s9-dž ' ! 88-[.J}}}-Zի_ҥKQ*L8allLzzz>}Jɒ%QTO||V)u}glܸQKPfz*3gƍ=bŊq 4igoŊo5疗E[_hZTX'O2a֬YCJJ uyVŽ=333VZŏ?HeܸqZq_pB5۳|3f0p@qww/ŋcccÖ-[Xx1jbƍZoBu" ===";U(i8s cʔ)ٴi{gϞ]ёݻwsM~/Bll,iӆÇ3x`ԩCff&O"G%99___J*x41b8991oQ_??:uoԩSyI#rB1w6 ;vݝ+… 133c̙̚5 ZMzzk [)S#G닿?cƌ7n۶mڵ֭[B.]B ԫW`pww'((GҼyB<<<}}}pvvD4k֌C2gf̘Axx8III*U ''' )"> W!>`7oɓLЃy0]o۩]Nst)Sذaʕ}\v(N<ʕ+  44+WOŊקXb,YvJCٸq#~#11dꫯXp! 6 <ӁiǓOJ`[lԘ.ooojԨA޽5IIIܻ.1NMϠLI3/N hٲ%vĉtQSST^'OPV-ڴi!7o$""'''F͒%K ŋìYΛ0qDMm1_Jb}vMb2233155%"".]|r6nHj022"--9s`ll̕+Wr gΜA:vH۶mٻw//^\sёǏc``@tt4rI#;;I _RC}MJ k&53N=DZziiiX[[k)ǚسgҥKJ*˵jANٳgtR+Nj0aƍGXX(Jpqq{%K,oΝ;5{8qw5LLL4ۅYZZΝ;Yf /_ѣG$''J޽qttԩS={gjڵkҡC>Ç_?Pti4h'G,) L(8p ?#cG|GVZ'LЃJ3-vw\x0 =?0yd|}} -?tp$&&Ҷm[Μ9CժUqvvʊD9wVVVcnnN*U^:? ť!111ƩT*?ѣFU޽{̞=3g0mڴDK.Ν7 > E)&}Y?-T9^^^,Z4BCC)Q222P*oeeŽ{ |H&O'ҥK2dcƌa<} ЩS'4iB&**ӧOO`` m۶} ٹs''N`\rE|ՉW^ԫWݻwSB_'Sn]޽RV)777233ɓ'tޝ˓All,z/oC(:dB|`z%k\s `py.]$=MEOO&Oy6zh֬Yu3E/T*yڵ{iիG/]6͛7gdff2gN8AӦM`Ŋ|\|> .ЧOݻܸq+~zN>ӧٲe !!!L0/ҤI =?O>‚ ʳ(w|@qqqcHKKɓ'/\%|9>}Zgm !f<|?gfޱf|=j{4mF.\E }6k֬O>Ӗu[۷ok:t(.]bҥ|gݛf͚Qxq.\?Lxx8}K%Jbbb=-- Gҿ<Vpp0vvv{^׮]_z5SgO>븹}v7o.Gjժ :333lll믿4,XҥKkA>{ҴiSΝKzz:zӯ܄Ν;8;;nEBč7駟h) fI:tqP(8֬߆;/^#$/7o۷ݻw@BB@7{K,a֭Gi߾=&L 22;r+^8*U-H*UY&?#Κ7bQT\r0.]gx^Uvɓ'4mڔǏ #F{bŊ g޽l)fݰaC>}:@!dwǎ-[:u[@YH'aÆZgUm}BIDDD$>yʆa, gͻt˗gԨQ/DZRJݻ77o899i7`޽+WmY>}pE=Z/^@&L_TFE2eQ[KKK:uD׮]/#++UV:u͛73fΜ v.^{yڳcǎɊ+8u]ժUxgϞ}ko7Ż#CB8v;}\sES"-=FriogVdI=zIJe4IΞ=ӧ>>4k,OL\\Æ #!!'O;9CϭZΝ;]RŅO>PsԬYj-?>|XSpML0 !DtR ╯agr1~|r\pZ*۷i\2eҥ |g[nO oN@@[m۶lْ޽{s)FҥKx":tlܹåK%;;;ObrKf KY!D;D;a:z83ܽŊT hO 5-\BJƆ:Oϟ篿SNaddT` ? " !Dgbbӧݫ~ f4GOW$3.p}ؘ'O~[n jP7m zi\Z4n{Q`urjԨu4eD ۷2e ժU7͍.]m6=JJTfffnx2D+E\ff&\r*wBO{nʗ+]! @WO'O0i$*Vo:::8;;se222 B=I pݚ5k`РAq/pB033ÃRR%ʔ)=U]r^G>}XjSLO>Jr'I~:5j`޼yƳS+J϶03.FDDK,aܼDŽ%눌]؄eXؐJoΝ;wزeK]voAAAҭLLL=z4ӧO'((y{n&NȂ b$%%1{BUB"jԨQ$'AP3½#G壏>bƍTRYS>$y'˗TRBԯ_???_ذaÇgРAuʊ2ejvaddxddd0ydMLvv6G!==]ڸ8FIHHÇoj͔(Q333ԩwE!oJOOg?ӲA-|\~ʮC3ĘEFFȑ#_,]+W?%o-l~ ]NPΦM xt֍]jb"""4CԨQ%K͛7eڴi̝;6''']ٳٽ{7 6|ǽy'EÇIKOKk) ~Ltt4YYYMb:t( ^w%([,{~* ƏիWIJJ"**wի_)zVxq={}rpp_~aZ6mJƍ|2 2tP._^$w.]|=|mO?%::N$B"֭[M Jfԏ>-[OϞ=YhΝDDD0sL /}WX888hJFbb"Æ CCC+Fɒ%i׮e˖%66VkWqㆦ}]ϣP(X|9۷GWWݻwɊ+-tI/_sqƬ[Zy'>I}8i@{nݺq)ԩO?ĠAܹ3#GtЁ/-?TZ~2e3x`6lHDD{ETtۚ5k#Fॱ'OFOOŋx>|1666xzzr.\.&e2O!VZP(=y9x };? %11ڴi?8{,]tTR,_5jhg&44ݻwSJ<<>>4m]]]:vȊ+Xh^^^︗& BA4i҄=GdPXzq2v쯋@N(˗/q=)_ԩSӱcG?/+++bccsWӧ%K[n9/[l!!!1cnSZ5ۗT*%!!|whHHH@*OR000(r.; A%JeO===T"fҥgY\9BSXd we֬Ydffɓмyszo/_ɉ*UeZ-[___ձƆACjjjc5j <O_.""Buɒ%9€HIIYf4jԈbŊ& EԼUdѣݝH%_۷jԨC kٲe$%%dɒ7x1b<==G[[???MƊ+zE]]=ױϭ,{O_.:^W^o߾,Ym۶Q|wpp0eʔQO7777n̙3qqqEvN[[ ݕ{)Tдe~w _xwww퉎;Oz1 {ɓ'Opww~D'/pB\ۣWKp5Y:߿?ʕ+\pdjԨ jjj>&MĉYhVVViӦH-DFF6\~sss*U?d"|=z|JdddpE$ u)ru TJٳg+Vёu뒑˗9r={d„ P|y޽KVVJJJh"R)ŊTv\SS:c+ihh ¿D"AWWŋH$ zK>QQQaÆ,]VZ)ĉe&O,6n{{{Ν*Tr\x>|P`99dOkkk˝oذ!۷o',,L{-q[A-[W.k)]4RW^}uuuQVV,YEѳgO"##iӦ s-% ׈*K,v8;;ӻwo)0 aÆaeegߐ=+4QD-ZA~T(_$5ԉzǏB Eٳg={x*Uuݧ׮]^z8::bggPqqqhiiRTrr2ӧO… ,]uuunܸAVVfff+?///>\k׮1b4559vX!uFll,'67nd`hhY+u FN:СCZ*:::hтUVѣ| Dl닥%ŋQFNT^}}}:u˗lNǎקz;<ۺҰaC/N $A~ V3WcѸ<~斖^~BCCiܸ1:t耞vvvr}խ[sssds ӿڴi͛7<_&TV3g2ydvݻ>}:*U_~pRn]&OBroX*r1LMMEp' 𼽽scǎ?gϞlݺ33|q[N{)E : {n:wԩSqvvk5cʔ)lڴLZlŋچвeK 쌳3{UV.&MĴiڵ+w~ 2A(7o{nf̘A*nh@7X=՞Br8q͛7'22i&VXAÆ ٶmuɓ'kylBA\۷o`Ĉ888o~<<99cccvʖ-[dϟҥKy~vb>}ZSjj*昚tؑGquB`` mڴa׮]LLL={6 ,]?tP=JtttI  `Zh!~/N73k캉sWoe7;wٳPx\mBBB6m+V͛(++#Jեf͚xyyqqqуvQ\9/[jg^DPP3f ==]]]N>Ee׮]\e wݻ[ݦhJ illEӴ؟I|||5Ç'##???ٱ[bbb"7,΀8~͋/8y$rFX"[n#33ÇׯeA~RC`Ԃ9# z$%%|:z(4iӦM5kZ K.vS~}.]Ć #66۷o[իpႴlْ˗SfM5j5ɼ|R{t%%%^xkט={6VVVtԉ@ؽ{羂 |u&%>>7nЧO>>>J%g\ :::rAÇsSYYY<~}}}=zDVVVmk׮̓UPN:-m%J;6` Pu k۶m<|лm|ըd 3cι;?<ϴ;vLJiӦq ;%"̸x"iiiQ\9٠nYTVVVylKZZ;w&%%`bbbǏٶm.\ ==###QƗ?jǎرCXBBiRrF̙#;Ô)SdAYY9(ȵ-]tr*gɒ%QVVkwww:$Gw |+Ra\4=[7)ٛ8ꊫ+ӵO$Ϟ=+˪eXM6%66/KQ 3wޑVQ}EHHA~ U4`\1"k\ Yޜ9s8x nnnTVMv|̘1XXX0r~O aA%*:.-:t*:{gBBϧ &Q\GK]%J 55TJzdHJ*+ dφd*Fܹsٳg;wdtޝ۷oNzXv-׮]S=zIZZYe2a„~Έh߾=6667Or9[`K,aҥ=Zv|޽8q˗ ٛ"rvRd\\FFFL.m?kHo޼!33S ?-g{qD?ZƎg133˵mÆ $&&2{DZZɅx/_DUUDBӦM)S {)|2y700`̙3gΜ 8%%Wr1/_ ΝEġCB8ѴiS8~8*** 49(m۶4hЀuqunܸiܸ1VVV~G +R/pB~O.w"##&MPdINѣGR @ߚ?~̇\X*U~zgqJJJPR%Mƍr}{9 wh]5%-%^NxG.K*iAtuĄ4/_D"aԩDFF5iiiW͟?N8AqwwԩSB޽9ydϗ%J=wcǎQn] khժv[n9s~[lIVVo߾&NRΝ;GÆ 9wԩS>|Ç4lؐ" @E/0o޼|3:ubL6-nԈlmmSܻ}?!۷og̘1ݻw-[V:OF%Z*+Wsz[__'N6L6-/x#D"%OLL,Ν;Gڵ׸$^RGGGGG<==Rh+ͣcǎt1W&MPb\Rsn`޼yL8ŋӡC.^ȢE8q\!^zѨQ#ŋˋXm&wsҶm[w/^`OSАq믿=ʶms ?!>>k'%5 SSS6Ѿ}{<ȁAKK y!Iv]PRRbܸq=۷cooϟɸq̙3+W---޼yׯ)Y$zA=xW^EIIIiӦ 2y,H@@RT ̜9+X رcSRRx@ CGG'ҫW/m_n]?Ϡ] Pıc? ۛ~jժrJ&M$NYY@LŠ+x ?AAA5i҄3g0w\(Y$%on}v\]]Sr B);󀞮YݻGhh(5WhQx19xXԨB@x_>`ooD"ӓSIxx8TZm2z ԩS@zɤI044dԨQt%>222А;x_~ PF ֮]K͚5m}v6l ޽{;weee:wunbȑ/_7[34h7of֭3d.\H d5!? UJ |O۶mcС ښɃ{f'BmmTWeϝaeeDZl^w޼K9hhh`mm-(H۶mi׮?7==<==fΜ9yyLJ___-Z`ooOŊyGe4k ??? IMMIT%33S`[n4i҄K.ѩS':wƍ;w.iii,\ m"A̸]lǚ[ԢeިkhE <~|G?oF:uhѢgϞ=DEE%]tAUUH5kƃ5jÇѣG̝;cǒԩSiڴ)ԪU mmm7opǏZgϞqmlmmiժeʔJ*8p@3355e„ 2j( ڵk+Ν;e_Ϛ5 j11E+b/`T#{|dW?~}XnW&%%;vPbEFgԼݻWWWfϞMÆ 7o)))rmvAff& ,@K+ H~qҥD*qݽ{7϶>|TYd\pتU+$ H$XzB*o#x ?~0׾7Y@,ȭ4iiii̘1.\HNptt$44===k׮ͳ5m۶UTTXbK.Zjpqnݺѣ)_}pss(JUU???TTTHII!>>###8|9-[Fjj*\t͛7sgҥKQUU@VL2>Ozz:b׮]@v.zjERR~~~۷W^\]]qppˌ;իӺukhg}V ֬YC%X72/ysG ?P.dȐ!+Vׯ_s!wSk,--ٹs'f͢y/HʊիWFٻw/=zPEQikkӲeK>|@Fغu+hiixbhǚ4i eȐ!ܼycccYƍ7lݻwoE޽Yp!W\=ە+WHMM%++ Tʧ)h{ER\2&M_ ˖-JV… _D'+Q$&6<ٵR Uߔ4Vl}{nuƘ1cdOMFYvȃ'Ix ?Ð9n' ?ݏeŠ y昘vQ%&۟T*eɦݴ5AЁ BPWWeWQ˱g̘!}.]|V077RJQFɾ3f nȏ2#G͛d)E5f޽{G@@O<^z,]+W}vlmm9s 5jCCCbbDAݻ77f֬Y;v@Z/SSS\]]-٥!RJlذ͛Szy8zhոq8~,/l +VVVyۈ# Ãٳg{,_>n:]FӦMIOOŋׯg\x4XhWA ӡC44eҸu]vwyV?>]LCC+V|g{.g]Vyxxw=z(t/d爻q\$hԨo&11}):2*UKˍ7N6&/&,, }}}RRRXp! .…  D'/6c $3y䳌+;[L8t uɓ'5k k׮1{lY*!(([[[/^LPP[Ξ=ӧOsTsEptt$<<9sN.::/v\T* c***=ӧO/9;;seuǏyʼ{}}. ^|͛y! `„ <{jժ}~}HNNfʕ8;;0h N8A\\\JRڴiÙ3ghڴ)uf(++3fϥK;v,ƘR\9- DxߙARRRطoaaa$''O`` .]"33SNUUtJeLtou5UatKp_OZj 6$]&uwwgҤI@vE߿+H_>lٲҥKcccCټy3c۶m> |xCAdeel2/[wPVBjZŴ5߾աTq]G?hp8):DW[5`ӁTm6|FԨQGaoo/;>eBCC )LHH`ڴi-[& YpgaaٳiҤ -[̳bŊo޼_֭[}vYM D'ʢ޽&7f5H$MLBS]5տG*`Ӵ)qJZ~]vt1϶AAAt҅tkSJ[{%J}݋/PWWǂ}6zp&LP`_?}h"/^LNm)ZAK2k,l;d>oy QGʇ=5PWQb 7U[ڎ4+J*QiѢ~tҲs,Yw}\_9sO$D% A/[yUbɸŽ- &!^(o@il_2Ruyݻwgܸq$&&ʮKLLd8::wzzz1erw;wDIIk׮```_Sٴi,Ѥv̛7O(O;۷oo߽áoSZ}*T(W#G:g30-o(wf n\̂PSŋoÉaӦM[.bddԩSٹsT*e: ~~O>eɴiӆ-[0rH̦LrFH'~f"APVԼF፿rDGG˾7nܠN:$K^mqv2Ν;Yx1Ϟ=c֭{Ϡ &MDHH U `񄇇sy455c4o\{vwkϟ?ݣrJZd+SXU5ULTTAAA_AFx Yrr2**H22oٲ֭g*+'!QRbС\|'O9 ~In=Qpp0NBII޽{ιs8z(ŊTZGGG|||w˕+W8z(v^z۷/FFFk3glZnI2rmtl=zGiAZD'U\Դ4|=çϩT2 >\v̙3*K' 6-_FL9x קL2$44o>:w @hh(eʔAGGTۉGUU'N`ggGVV>>>SbE#WKܽ{ӹsg\\\8p=9$ ׯ'59 GCl͈é\OQF,ZA>%[rj+sB?r;vJڵ .ɲB嶊hmP2ݺuW^joߦRJnWWWv ٳ`|}}ܹ3‚yɩ[*>۷VȏT*˗y&vf֜Z̝;7PT"ALLLh׮ _'JYOZE;yq˼9y1;3x9V`c_2{;JJJDDD0qD޻GHHYYY hEFSE*z}4^z1 W;eee̘7o˖-#::f͚q޽/jj*l>iЦ> Z7ݺuKQlBaƦTg9-=S"ѱ ?`֭ԭ[_1Ӯ/Z$w>y 6:ZD?Ź+x ZJ*\x @ꕘ>,+)iil?cˎ:t.]pI>} @qpb/ӗPyK7AD )Ǒ#G(VZZZ^Ӻukʔ)رcUC ֠Al¾3u,7CbC>lJ딕q\sǩ\26P"ABNΗSjDx'djd,\\\ff5P`ܱR#~Y  {SNtܙcr{gD=jUS˒ 333^ʝ;w^ծ]ݻwj*6mă8uT7L^IȖ]q%СB}ihh*ʦQBY +r"= EL C*W̭[ĴzM<#Z'**D^xARRWȂ;Ȯ6-f7([*,ڦMݻ7&Lرc ԊNC)QLMu5GaS\7YDZvZ"#кu9k׎xXe˖eŊ8::r~"] .\Hՙ._͠b4qȦ"=z4Ll7 =% {Uu) |}u֬]|I$8̳y5##mmmʔ)k$H*rAխY>K}6KBB옞aaalܸfu͘67-rv2.¾W{>d_9DT*v-7fʔ) B͘1pLLL8x`Ԟ=</fȐ!>--";<M:6X m9$>Q%%%9uM5X?k O+e߾}>15"| ۷4P{ --- ˝z}5f'Nwp=\\Zj'133] 4`DEE\:x` '111888RRRBOO'O۷ }<==زhgo;d(***X[[ק ŅDl?{11/S"Avѣ1.[PU: m֭ZqUɸөS'4hNNNܻwt֮] fffԩ]={r+Rx1ׯ_ĉܽw#Q\Gkz?]6V[ĺ5wr̟?'cܸqȞZ޸qc6GF[[#G믿uVnݺkӧ4jԈC!JtϞ=ZjQfMYOcRsssnܸ*ze0Q˸8.BV(^xOA BbرjWmGdƎJY"""hР/_.ӭ9?RuHVMy3](]ր;w HصkyOi.^4b^B*BY"!#3%0/Izf&OCۛ~ 2M67_֖I&ѬYSթ N*o Ay-%Jdru3jӷYYYxLZC0N8AvHLL4EC$2RJ+]pGGG޽Krr2:::ʓ'OpwwD 6 SSS߿ݺuﯷ7 a؜!r_>}ɒHJH"""UdʕL6 VZ42M {ϰyok. /Jrr2C<<ߩٔ4.\V eqrr~ٳ J }#׵jjt֑]nbZܹs/)KG,@EE֭[sA+coo}spih۶-pttD.^CIIInZS5k$==D½z:jjjnݚ5k`jjoECf͘?>n;WL&MbŊǯR):t1;52ܼpSNѦMo,·#iO_QMSgBh9r?+W`bX)[JQ>ƨQX~ܵϟy4K!Jy:2?3D8޽{Yj]-q\>FZ1bwZr>)[l?M\p,e&22#G`nnԍ72j(IIII(_<51kJm"\u۷k*-- }}}޽{'OiӦ_SF!v |5j R҈y =hӧO  %pwA͚f,[,׵<~ӧO3SZԶhiӸ>;˖-Ąӧ9y$VQ wFUgk7&cl9P IDAT]ƹ>&,^#G#{ߟ;{{{H?Slͤ2m귲;v  ݻDZl  Zbco{ppiVH$&N_"%%/__BOO/kfʕ9s'Ol2*V#=חcnY nz}[RwW֞YM`t[wٳ888`cc#7ѪWqAk SLbbbXd 'O~M"""(VƦwB_-xbBCC?..ÇSE]6gIOOg̘1 4TE/(Nx u()) cWnH ֔)O/WoHxHTVVF__H5P󓔔Ć zO>K^geeŻ7𙷉̌L$ }<-{ck֬e˖x{{/7*եysu@C>ehh̙3?{ٿMv\Ŗߏw&MR &hjk2g윟VVV}-A>ʱdn^'}u;nÄ022fݴiӖ˷UIx(aÆ{U 9 `YQǜ@QsŒ #`@AGE0!fA4,&T3"9їhg-rNiN8š!SɖiMmM44 Fsٵ|7'q():~8jjl ل]xq~Ϋ7@@RRhMMMTTT۷>>Q~!wJ#GίرcLjqBY|ZTQVb M. <{J8w˖-Ņ+3ݪ/v=-,… `1Ur[ĵ,[իWK̔ȅfڴiy011֖QFFZPj4 k%%y}>}С_Ziiit؁'ܼ&^z A}JPVWftښJƹx=Z̟?///R"[w+++zxqŎmvnMDDup322pssDF(bo@qrrZj^wN損;=G&ϑqABLVbɒ%LۛW^1c ?~~~^:...L~gvs1,XݻˣK.ܾ}[֭[t"*a9r];w. .d>|͛3f6o\WP9H_>[n!GwM4aʔ)ʊ&Mu^?K[ϜfԨQT\I&ITFDDUVu;w/us#// 233QoҥܺuK浩w^ݫ7UuRN- a\٪-UTJ%e"O?QQHuM,Nܹ3/_Kw)~~~LXâ<3nbС3̮p?c`8l߾]⸺:+Vɓ'$%&›7oرcG;cKȓOLۘT[ד.kjȲeˈ,r$I gӦMTRE}Ϟ=6luŅ'N$zZj,]g2h @԰j*ƌƍŶ`|ǎ޽{\|]m7nX`ժUԨQSNs ׯO9~8VVV|-[c&%%j*&OD#GN9s&[n},AsIi>Ν;)ɡ}<~2g@tD۠BKwl{C޽ tj#p TXJ*k]]]155eݴmV?~LPPNѣG"K.xxx#F'o1ie"u]=CYI2HkRW6r|I=G&MpppёX֬Yòe˨TIzwNl\,w.a:ԢI&XN UTV Mk[bʋ/pvvFAA J'4ʩ3ed^ɓ'+ٹ+z4k֌wމRTQ $lǏOnn.vb@UU#Fp9bccQ .0rHjsssjժ؁c揋%&rgr9mF-^6C¹Q 17qԃ@͚֭5k׮,]ϟ?3sL=zr)v@gfuK.Wlv…iݚD4P<= 7RVm X.cС? Nǯjޕ'NHGhh( 6iӦ8::!nú2e$&,G͸z*-Z۷op_"Gо]R?0x_Gvv6aÆqFFWpqq۷غJ8w?[YֳWLeUe~2bjjʻw2˒zz(**y&g->}СC  X~sڵkS\9 g022RjOFOTT*U;Q4ۆ [1-y~$Qi̙3G5d?(ȑ#?NyMvv#p~8k׮eԩrJөRIrP tkn7eTWc!45]oXA)WN ooo Bj HOOLJ5kPjU[nQ}hݺ5&&&hkk3d޿_y֭[Qe[q|}}߿?qqqe#F)~.+e988p)oߎTx >>>2_՘aȑSZ޼yCnn.fukd_6wƼU#ϟT(:Rr%|W!3;RNhڴ׮]`ڴiEX/]8;;fxqu}F,;4|>K,_XϨ`ю맮7*ת\֌oM|L<`DK999 H910ldHssQ5.vzux.EL˖-|IaXΰ;)!7-sBG &// dpZ Yj׮-CʢO>\ru0v ӛ>s .OGG 1~Eg044M6߿v-Y4n܈giҤIRފ+pqq[[[6у/_pB[ժU#//Oj@Gg߿K+8עle3>>2Wɑ#MA7RPSUn|Li;UPWU^z4kc##FwPV} ͱɉ*u+3v6\BG` HJL歛t-]-Q׬6c.ѹ]]fV\|+W|m\\aƔ󭋴SPP|hW<{+ҒR\96m$EaccCHbO - Yw)XX}ikk exz3v1}N:(((c\ȶ `%ϧOEee #Gߛ룦WXֱDȏ~=4EU-ݲO>JJJf 8::J*={A,ɕ! ϫTƒ/+W5]"<<3gۈQnFXʾ9qD!G/<<+Wo|_խZ}\=~UD|TTSt(zqORǭ\2:ao'N޽{R޽ˠA+=~R9$'%,;wѢVZ%jhiPAMcnn13g$nX֭}AAAOOOv{AQQ˙%Ҕ(QQPT`#0f޼yh*#3xNy5.]7ot6l@SQzr}?/H.IJoPQW`>@Vdn~~>[- b+F"hi ka6l܀Ѭ:t[+|x%-qssm۶ q9)兓}O>۶mKFFzÇlܸ, ;}}}qdLMM '''̙Cݻ7of̙3ʕ---iժÆ c͚5TT۷B$уA1m4bbbXl[xZZ*3gY4צMΞ=޽{ٸq9rÃ֭[3sv/%B(p/ɢ Σȑ#:vȅ QZ``NJrWqCx^Ǹ ۊ ?~,vJJ 2N~j>/_5k++B)GϞ=~+h8@ -%MjoRNz%3UUUn݊.E:?qE'Nl;Appnϟe+.G[O0c oÇIOMz%e%1g-]FJҫ~u}E @ @NPRVa#n=ƶEY?{ СC3ydV#(Ȫb;>ILyW~d]rDڵk-ѣ=ztvl۶M1[l)s޼yrl9rHTTDf0k,fA9 uiAJڏϞkfH06dǎRmF*̴t|V֙[9 ݻ !CjYt$v8_~0 IDAT4h!C.0`8}΃;;|؏X[[cnnΨQ(_^Tz1,-^w8|괩<@|N:\ !;3ѩC劕166&;;5ݤU+#0Ֆ͸y;w\v5kNR\Ls}#??===1mcBڅ•kUq2\əg;"F2*ziSeZ,,,i~B!_"39r֭[ *p5$ɲ9p.]ǏI-dѓ: ±f:GDzg 7A oy$q&"F(2ydmۆ"999jՊgϟ1d%&Xnn.ܿASѥ'$&DfZ{QUUeԨQlٲE}P(А$])WXU Ur2IKIGMMMܗuܹs띜Xz5ʪ47o;yCCC^J׮]>w ,rNTkؐYf_<@Yy|16QzM?^lJ.y [C7<}#cq4:)) fb" }%+kWkWn 2ϳ23qQuȑ#^zI|s yY 0 Ǐg߾}$''0g2k uWFAAAV^)eV~F(2J$+m[*==yQNUO./%,, ooo: M6xflb./grk[Ǭo0_cƗ~tʣs}ާ,HNC-HII+**KZbNWm-}o'B,ٵa_GGG0}}}}ƸqpvvI켳:?]te˖WE'|Kg/ ꇻ/qtj)~WWbmg͜ER\'4=6gii⢒Sڂ|U=+]U-s-s4\S~̘1 W!wȑ#_$ӗ@ʩ#W*NSy[nz?=b@Z_0wCeZ݇aܹbڟ;w.3aL5 h٣%Rttt~:Ȍ:s ̾}$[bZ[%}o'-@EU@q cmLxUhK.KY5ʙ}k !uuh57pۣ㉍???̇ve((SA_g ׀44X5z5\)t u$-) 9gTPnCMR+t n~EmmINH!UCCC333N< ^,vM|xXk{ʟQRR"/lEH /f>RS^rȑwbլYFugfXSgfT=ǡC+t={+%oU65Czk?ʑ75j^^^?}ZvoYl?XN;r=^Z(jYlU+V`ݺu4lgLhVYmKԋ(vʕ+ <7oްl2Ν;E=9@ `첱 u{ɺu u87?mq~իKխ[[n1x`v9#TSEE}%>& PTTnE-ͭW7T5l֌ /-U B(rڏ맮1q,q'r92D3_cx3f+FEHaCC衐@94h*Ue44---9vX wȑ#8/v捒/2Y"^Fɴ4n`wR3ҽ}wK\8x׏^(>ÄEvV6~.{<{, ;+l?Ɩ-[W;xrȑ>MӦMINNfXKZI*( ɕzmNN̝*Y|2z_*T… R *uP^^Iqɀh;4^Efy}`Pn?3rssiвA0aDFMz]EEEqݻՓ BgN1Si,k>}eqp߾}m۶$&%0ax 35is9Zj/YN@^nG6z 229vP(mɮE5E )(xDTZ6r7ѣ+W'99rCHHO9y>̄Lakk+S[&Lݳw<^$%zm۶$ qqq*9T2cEFE 5511AQQ7Oo-󷑚9sez-ZDB|-s' ====,--{.oË>gSH$""/}x7]jj8-cȌ[)**2d22S~}@hҿeü.\ l0p4k2ΊFJ*QhuըLi;kK;;3)|W*;C ԤXYrO9x177|9M͛(foYe3Τ_\v ŋ~#gN>\|WzLԗo,ٴZ5k^UVo3RRX3ΕFYI 7''$W9>%6Zi$c%zJB q$EgOà˗/gݺu%ڟ8q-פ^g=Dёm۶d^ܓ^wôo[[[sbR8 >2y9/[7޽{ǩS$~Vc܈w0x"8Pkbo~#~W^qx6w_K\gA^j:,ݞ:w,S[u9r/jnުj%ҵkҀh2rHQզx=.I;|#崰眼r[-@fv6 _ABEƅ P`(go鍧}Hz3ƞ={hҤIcu '}ZrSOgr[^JЮӘ60Çsl1%y}vV6KvP|y:\qt-KQRY}/^\|Xڙi# PȓOY>b%¨QK/1kX5ڙq缨e[~c֭RĄϟ3h ]$7f8̉3>g1m`۷9rc|݋a2kjkJH??S)x؍[k1;ss5OFYI@*(ķDtٹsed.]ćѡ{4JKNN %{P׬L}c( <~UEi!!!JY^hק *r2Y9xbטϒ%KX~=4ԄEۗFg %>>^j~zz:݋QJvC.߿?Æ +2ZF]( 8$&<($,oáC3fLΝP(dpՔq r-4ڌ5G 6|B9hnwoή]x *2e zغN)5Uo)Y9YL:+**reƌwu6s~ǩ@ W;xrȑ/ʾ@Q"c~$%'/qu-[gϞ=2,pLMMZ*gTV J5VI:w̅^455x%xzz@OpPؐ=J0BYpuuƍ 2Kuš*R+pԥa7ێ_ XXasƌS>?"ap*U(N.;SN' *v/T>}:'OaÆ())gXvf)Zvo\2?(*NիW̱CCԨTSwDɑ#Gοhذ!I'%hMZf:Z2mee 4osss~*MMMEAAA픟]ve͚5ʼnlmm6lIXL`,ݽĥammĉ`:vꈮ.Uԣ{>}Zݻ5ogLZd?Ӿo;fn\\\0lTqcQ:.y^ Vm#{zzRi=\iYޅCAAlmmҥ G6cz=y[\23f(qޓ'OAJl uMuq /OOO5k!>\H[49eLy+#M:5v͔_&.ܮO[rrr$$J!֭?͛;v-ߊ\X9rŔ)SX~=abmwH~>Ԩ\QQUQ&==]'Oܩy蔆"3&>}ʸqz*ԯ_ׯ_q@l] Pl6l|||_)MDݿǀD[<l*WL޽1c߿'%%CvZTUiի%mzyrLZp7v hѭUjWՕsѾ}{̙T 8y|UBgnQ~} SPP 88p| B]Ŭ)zUzų[|1j޾Hg, *TÇ>DMjl+GC鸋UV(RvԮ"Q_.Pz%@T,d9r ###,--9q8-Lѿs+v7=%)_NSSIMϠFc 4إ5&v455znBPjׯ1na\dOP@T)$|K f8VvC8yǡ FEUʵ+珹|2/昵1e9hhit㛏iXf%whh;K/ BCCY~=,]ŋ( hԡxa?J$7|K$TWWҥK2D| rs@ɑ#Ɋ+',, mmm<ӬR(R5./pmQRpl^Bϗ(ضmQQѸۍkf̙`mmͫW02upww'<nS<զn׮X@$zh%6#e m]- '彗C el߾EF1A v`sdedYC3?P&v2)rȑSFRRR6֦:#**WWWYX`VZEDDC [ɑ3KgHׂH!'7 2p@ pw@9(**2}ں8::nݼUR9wᷞ ɓ'cƎ@d&d_IaÆaddīWJր~zuţPQUJںgZijۢ"eUe.+sͺZ EEEF@I@zՙfcj,[vΨ6D}cǎܼymM<9r iUZŋ~8#u˗/ԩ*[. PeVv6E/_m9GȑDEUֽZ233 &??;jNڵ ???*Vȗ/_̟?SSShΣGd?$$U5UwmZ5sBz꬟GW45#\|0DfzWlذdl]\B+**kPRW9 yS?+%p{ҏ޶ۮmi?=sp2QV'r~ya:iit|F3quAMյEq@G֞t^9Xt)ڵ#m-pы/-xA۶m9yLǣ^N]] Z:0ah/X VkTO{r v=r*ٻwDŋѯawˆ˧H٥YJC>_Qq"##=i9rqTV͛7S^=#͛уSbEW==FIFJǚσo1iPX7---v4<==Y}t5:ev;o:|PHd;Ve^Gd;yU que;5k*45puuE.z0tF 53T\ TTtqEtI$%]^[깣GRۤFMdxxbqssǏԀbbWpqNI$8l9`g_ݏTOBO7lkF5j?qL_~CCbu W;xrìYx 2uTqDmɒ%tܙoѩ1v#0cX?LjV#׏Zjp)#-?s *c4p-R{(OTЯP{WsiOQ2$$MF "{l#"DR![ R):Z\y>=:~^nVsUBGb1;w\8x݊/c"1f*p/55XL9ϲXJe+{ЮZKHa\y'PLJ^zJ3fC(_Q֯HT HD\\ʕ2m6R<9rrhh|_oik8v.*Ҥ)s*WӦ|y>'cJl?kw=Dv3gΔɮi̙ҿ?fׂLj9a F0f0,<6'-)1o[۷ 9>Y]gW4~ >ܹPx~=gᦗ?#F`߾};I z>G.b1k ]o.VHVF/#}2GQ4hiIfF&EEENNNDEѼkszU(D"L0 .X,LJ=zмys?ދ8nvbQgۚo>6k/0)) *~nR߼>/_,4hЀCȜns <]Z:ZF 1r~$t {ԟX>& M}zssx4]rbӿbbpaJ>=3i$$EH"|||ЫKZÀtW*u$Nf?Uww<9r9\vN:⹽E"6gSQQQفCzة{"#)OHhW,Oׯ `H(%\}@E%EDB۶m˗+TZ"ݏTTtTUԄmYN}_c^ߜ 5FFF2=׻w7nDUUUD"\15 PYY8e~?1~LzׯoZ:5._N"66V* ϓ;|;p Xx $''ӨQ#b?2]Gv)px;/s=5kF˖-y7++Ì.HKdxxx0ЭZŇ6;3$%*U%~'Np{\`w޳yfͥFyL.BrȑSFPWWEúeꯨ ف} homۼ 3\vXycǎASkk23}jW.Ou`UarflЮ ۺyZ\\C!;'a]Cx`H$GFM)[Y9|5W"9& 4)CO>2w/_fࠁ;Eիٳgy=S&OA_[׏^8/J@ߤU';'dΟ?Ovf66/L&X3څgAdmnKWٳg;rԷ_l%e%F- xdȑEgL8xb6ŠfUTTI珟)_dϞ=<{9BttP'%%cǢ'kmVFpSVXAJeoGɑ#C,GVV)_Koq$xH+f@dwScRdd$(**"QF}nJXK}()ٱ)JəE~}ٳgQUWwq&7o">>VbfUi[3qE1|pgyD L-MYwf-sx޾}KN{.&l׾N9ϟQVVg/\~ss*T@R|_BN9s`ut^vnyU#],ߝNQVQưay}GIiZocccBCBիtvR62c50015;;;*UD߾}|T^ <ڵkuii ===ϟ/אxr9r$;;'c8:ˈ(((%S/3pnӅАDZ `)xxiBy-5T >}#w^uPϟ?iرc2bLGroػw/ƍCYYkǮjT иMc7ϲ+ Ⴧ@ ÇxxxSA52e[}ԤECNJ ukN+VoԬY3_@X,F$*PSTcŋ{/HOKc/_=zlB+z#aaaj 1b~_?V=[=8DvPVV&-x^-Z^V9rCF m-KoQ(-zԨQG/':t@jފw!it!.n\*Ut%.D-άȴ^\?V??? *ʴ#e=ѡu<{kPQSal2DzePUU?y56_Ut wCfFDFF_dpиqcjժ#W.]B$vO?wiɾ%ѫ^ྎ$!51HOIGUUX,fD"XZ hnoʓ(  z hA[rary|MFM4XBWVl֬Y@г|sY{=xV^E~)vutlⓨUL #33޽{HzeWN'22^ Vҥ~ssn˗,]uY⾸"=`V'޿ĉegƍ|)[Ƿ1lF* 8888.?O6%ѡCl^=zUlBI;w_0mUW->C&++Qhayɓ'V"9r~9D"={JtJ}xb>eNyɷ4Q܇8]a sg444>m:as,ɟY>tz- s*۷жkƢW=5q_}@ _'jj Pbֽ[YNՂ?7B ,\k_Aɑ#$O+4(((P\|-m,ܭr;q I<֣ב12vX:wLPPtWcR/пKE==^Hv$|~>~Ic}yvҢZ 6uUe38؎f[Q}ٵkW ̌AdgY0'GrsO[sNmF@@o޼A[[[`+ ?#G/úuPRRF 2nݺ1{ln? cɎ#29Dbq{MMM)Ø5o'C$—/EI\zHDLl,r$9ue^7H%cTT [[[= :w̫h@v={6߿gډ0/{r=H4̞ChV_Mjl՛{ߛvfvl5#?l沵;055[7㊮+bsvz'ϭ'IM(Ƿ JnKNҶm[BݻYr%Ǐ'!!OOϟFMNv  `lded1zh=eee0aC i|O9 OHFV6hjjР}x *>9u17Z,#5%}JUǴi8riiyyw_zYlԔN:tԉ'nQѹҾ1|8m%K:w$&2\Nl$m49Y9DGqdz-[7RJEYp!t: j㧏5vڑ^sjj*&M~EB__իrJD"'NЈM6.6NY{ܷ IDATҩ .\@$?oF'G_4h9FG ގTX5TQ+WdP kbTX1_iѪT#nnn4jYl)8:FSe^Gy>Ƶz6m>+)/ω'hӻZ\s|'oC2rHuN9BF q%_~{%S[ne()Iý{7ng3~ ضeKkN6meʦɴfX,3x`ooAWHHtsg5KQѩC|gx3c%K())ѢE qtt%*b˂<#G/Nj/ VQUXr#Jѻ@ R H$!9KKK+k6#v0[cllT-,q׌^^l޼3gJEGGw^޽{*VVV 6 mu%O9w7jbҥ|'''޿<9,DGz iiӦ|}}}@^8(|1e"93+VtVaÆܿ7ope\2T.̜9k64,p_AAıٳg&Mu#+'%a$r_|ؽ`/dggs޽{GrGrFɑ#,^?| *.D(EW%> OnnO'ݛ2ax6y1%m=w}dL:X:9%^BC[Q3{lMի ݑiݺ5 <{EEK0"_E#^>| ׮]CMMZjɇH5C{-\V"c<' xl"""PWWPDCCC>|dfgQf]fL Tb_΍7HJJ޽{Ӱa,}tt4GD"~X`okWZl!Ӻ7&<Yf4kVtAF" ^G颫+,&F̘1̯jaF()+f0ڍLY>~˥KXdIc͚k\őʅ?/R~}Ég˖-hhh;P/tQ;P`IJPSJ'#5;hڴT,b BvY6 ǹCӴ6gϝ~ۻp:7ضի;ݻ"6ݚ8(;`ln st~y>`XS*+#|Z@@^rlۭ9"Hf9ߑ)~:#FvhiiѺuk6m }-}5j0lذ"5iٲ%:::4mڴ2t~wק[nk׮cffɓ9͛FGGVZq:XrCSS&ySӰ*\U)1q.ɓ~bHYrPSU"/kpǴl3 ;cSۘuBo֭رc+.vެ,-kWسg'57ҧCB!C0q#;dRY;v3G9B6LķolڴT}ĉ9vc-\2AvtՍn3Xn-嫔Q6PVD"ϟg…51cNZ5MI}XwtܙӪWK\oletѕ>z3o֝vA y,SeܮQ888H\dM-QSW {ˑݻw+&O7}СCԫWH۽y[[[bbbpuueŊܽ{[[۶mcĈX[[Iݙ3gfN(Ҿ}{={6G$Ѷm[޽mPPT̚5'OҮ]V'3f`ܹOOO>|8۷9r7nAժHY[ܹsmiܸ1^33;O_. C(үo_ArhE?⾰/S4ePo޼nݺ0g222022gϞ콷Tzs99l!V%)9VZ2auvvK/ukD"/qw2}R hܸV,N޽HVZjOٳ$%%5jBGJ:tip/s%> @\\W\aȐ!}T^=GE$1jԨ'$$p9Y>9rH$b]KΙTWcrWb6)p 5=+9WWWlmmٱc>k*z]Cl<28 :u*TD^zaո1͚5ch{N:>MHLr ~> 6I"""ׯʸae @TEEE0k\Y;f2~8BCB.Gb#>ڵkԠn2329riv3>ˍi~o=Kgjθ8u?}z ` <~r?`P$";G& ,˗a(][Xa^Y95 55Ո###6<{ .64| =g8s !o^4u-u4դGzzz#˴VQm9 ŬΧjIy8ʭM6e~V;Ʊc]kZ[Y(sxb{dadffL剎FQQ@6oq niӦ%oꢨoغuV!,,,bƌ$''NƍҒ?x JJ.Zq S3PVQM֤|Iag_bX"W^YfkpǠGL,Ikw04_=zL7RRsNZp'|MM&';̌,ttt>|8ϟ=gJ*L\3a]xd(xX0Եԩ\29i9GY@PfMXq|.q߉eKo#\W+ EԮ]777oS z1W6  a%ݿPD`rС̝;ѠE(ZiM/,YŽ;<}=[аe.UkTٳgeW˖-c͚544R(LϨIw DnA~Y:V^ۢqޒeDD"Qr'/-*2ipG$IcL6 __|Zt܉-[r>G%3o^gԨQԬY\Ws \ZqӇO̝;/^[YreW.,@8_2X7~=qqlݺ5NСCYnoaZs#O>PvLfM  Z/WBUV|r U0WPP`ذa?~\k[nn:i=z==t~"X̽Kqw9B|rޘ5ChעE rk֬VZ(D>eaܻ|+P,P+K0 K2V[O 51+V0hРΙ3֭[3w\}nw&d4/.C 0j(.}uI!ݿ䟂L͛qvvk׮tڵKY̙39u]taΜ9dddb j֬رcpvvf`ooݻwYjӧOJɺiӦ 8իWϮ]ɴK,cǎݛI&ǢEh֬}300`ʔ)\.\Çٶm[>9ro_Ӱ 1_02bm,jHv\\1g5ѽ{Μ>_}~Ylfff())1a Է)?׸KzW,jtt]ËHKLVOy AAARUUUU2d3fVy- k#{1l0i9mvs ֜z걨bȇUUUEYI561+t=Xq|9͛7!@ӇO욷S~m27uIIH!e${#1./7oi;ˆlXO͛7' O޳<Sy$sMlll044iӦxyy-o)qdefU^ gmd7Ե$"ݚZm+-{(H:L\3Oql޼}www޽{ǘѭ\t^Ӣ=˗/dedqdr #cU# gƍG{'ՐB9{ 0///7nח))_^;lܸQ '''N.ENlwS2QWS#-+0i$Jok:ućԲe+KWM4s5իW2d 6tZ ܖ[ %::===^JΝ;SwRɤ|Ia*j3)]vf6]cg5Ջl3iX|{Iv*3f45!b* yGzJ:]˭3QRPB__O1lJEq̲Ce!**6m!--iٳ IMH IDATG9D(#Gӕ\x1W~Xg;y S$x:xrȑ󿀗ժUcٔҠMC̪-!{Ξ9Å طo_'NdӦLr+ӪK;HH͠.8};0U=-,Ԯ]a/YlW LMMٸq#3fݩS'޾}ڵkqqq ?~Շ;v)SЩS'ڵo?Q16xle UT!%;M-M883~X垑֓QTTm۶bԚ @Qkݱ 7$;;̩J<{Yfq(O)))Ѫu+ܶQ~+VIIa]hԶ!JJJ=xD"*TPu<9rcɓÇtia+K"]v3rH*U$TID"ݻBsΡHǎQVVFII _Ӵi݊&`ȶLe$+!i6ғyZ|hTة ԨQoycvZ֮]KZZHs \rYSZ%fV Ut JFssyކJrZ*(*)d(ZJ(nѯ_?6űWEMaf͚qEtuuy1Xi^>wRAggg3177gر """(_<[.l߾UWw|9s&-*պ~e9r+}fl9/u9rfaq}MSRR XYY#D G۶miذ!wg@]bۥc]Xl;+.KHLLDOO͛V)F[[mR(j/@_| IIIREVZ1iҤB|P(~tY^YJ~79rcEF8 ҆~z@,ӥK]5jIҮ];ie`z8卞^;u^A3FoߞӮ]HpW y[EdQ?]OMM)b,Y___YjjDi\ƬͧW9 +pёt^|>O#5j ɫVȶވsԭ[Wz-i*Uˆ'x!ɱ<|gggTgڵر޿Kn͙f}CIYrȖd93,T+rrrETL·7INN,Hhպt܁S}fF&׎\Nsk L }YYz2餦2u<;u( l н,2)2ЩS';gPςCsԏ"11}%%% Lr (zZ]V/'W5.2!+rTW5BMEQIE&{wѯ_?b>0k eVnaVŌ,?DIN8p@;-[ƍO̫gˀ-DwzN`:::lܸ*qR!!Y=~-̙3-zs'^O2a6mDZ`:7.rZꥶN\, Q΢[3,*}sC|z?iWcy%6o1cԴLTU^_y hڜ9,,,ܹ3vXL 0`%R@N${@=e, *,8q"ҥK\_q\"ƍ,"! -#Hy!r r,\$MC $$9۶ygD}vOlq=*ŝ9~Q dfdsKiѢ>ׯ(ի]]EZ~،ZO_ ,ȭ$es+Wf$3ܥh9QRآԭl㱴yBn]Ƚذ~=קI4W  M+v ;ϻ0p>WW\\\011a޼y^3z4saRfH۟TSơuMD4>Bakk#~'HNNʕ+}J4Na0l޼9׽S2}tLa:6iTnl`ʼneT1fL:5ϸz")>I3U~!C">B?!'')*j*s?+.zFzĂ8z{oJ_uVt9xm:zĦ~fʼn2թ`͚5̟?ha" ;i xM/onMڗW%%: D_pm*թDE)H|L<:9p=z(8˔F!x@ $**m۶Q~}ڴi{RSS122"))ImɪCw8HvvŻFҠZdǽ6Y89  />m} ޷^O>eٲeܿwPZյE$ǎcܹMd{tfի}r#""06|/ʎe%| Zj@9rVgü9)Tn&?232uٱMBzjM&XT y1_7aB*^ЇןQ)٬ g۱I-3,w`)?ƭ?a8) kkk> 1œp)2wux>>> 6 # >'Yyj9tW0j&Sr,@D,HǔBTB";6ދ$$$䒓S>}Թ+XE5 fZ2| $NqI 8P*+w^އcn\n[Xggoر#k׮ё7IKG|q#e˖铧\p///BBCW~ :Tٶm[Ν;G_ lZJ_ IIt) t킜H3 1]fbbbŽ;طoAAAQGӧ:ōc7}_=|E 2oВ˸DEqA[{(a;x;SjU$1gD_ }7)}J/X3+oy?7 îVnΒ2|Iv_ŋT[xҤIFXR[Z:z>A7oߕxSVM>} (*`;f B>Ȓc0SEg^.(7s j*r.9~$S'Uf-`};JJ߂{~n޼If򌝖ŋ9yАP233QWW~cΜ9j/9s&[n%))6ӝH$ԪUKx ###Ο?OZETMQRcג[332s._b*1gcc×/ _8ƝL6w;OFޠ500v,[PͶhշ4s>|գ<qWWW&NX2oE$ӧO'44i{Ҷ6m̆Uns}ہǼnl^j? #O((*0b)J󸱮&12<}8n@Yu5^M*_S4s W ܿx?Y>|ɓ'ޡNvŜsc=u.]פNRR7oBLL 'Odɒ%,[/)3fHS5jԠ{8z>e?6I&AhF7>|dѠ%iߗ6 6H;A")99gش!++۷oM͉Kei,=A384H`;{C5i]i!x$_wUsWWSeBԔ9D"gg3r_H$jٕ`_<̘5k6#de&Qu+̻wwbΎֹׯ6f!&Ѱ/^]c%:3C`ddĮ]#EJthcI믟#GD"QݿPRRk׮̙3Yfaoop={^:fnaT7FHEFyV;neN8Yl]6l@XPm:v5G71\ǮƱT޿ cÆ ?۴leu ½qgϞ%((HzoԨQ1c Th'_\pAl/ԩSQSScƆdfe座W.ҥKyKIJۦuG֫@zzz,X#od׽eNhi˥!`ү "9]W;3yCsqãq&q̋/0\^΍rΠ{"9_j׮-='Hfƍ+^IPWW޽{ tȽ q4jY_zꍟ_]Ə3gt؉o_^бCG>yʄ r9trrrt^$DZΝ;SJʗ/ԩS9{,:6,vaV2e_x+P(B/k֬a̘1 W_GQ!669111lٲ&1c];uwFwbWױyv%A"D&Yf ^Ǐ?S4>' hj޿{ IDATjѲx 7QZy5TILTfݤ6)nNiԹ1ڠͳO9zH:V`` dee!_@%< r|,^OqxlߍD"aʕYR̜9ɓ' |+LOMG$Iz+++;rc۷hhi_m5zQ#ԵswYf {'p2bŔ)SÇ ?%ҥ ^:c"0#FRV*jpڷo/mӪU\gl#9RablL9XX;. 1tҏvNH.*.)ǁ*=g>-`R:ɓ'2eJggghԡ!믮e4֔=1jlzV̙3G0QDFFҲUKYz5׼qEgH+ZA4ь[PP}I$1iݟh Zhg%9dR " ˗/V-ܕ*ZQAݺ}|aEEEѢys_ĶPc8 ix-釱nkT\2M "6gϨP.Esj֬&{'MҀLdpZm#h)iゆC yRRnWm3zyhO\x kkk:n ?BCS]4iܹs*ӧOXZZbYے9fhq'3lĥ!tM" HPP(y-,, a?-]FvFC+G$*,akUɩÃ~>C$ux? wB oX{AdJr}Άv$s &Mbݺu^D"СC߿7oŘa``ի12 4I$sq @ߏ8FjUD"$|IJJbYda[ {.?6'wSF ׯԩS7IOLҾ w{wx3=NNN7xSUQd;ξ+pݰбnJ!2.KJQVRBOO+Whm" lCpp0f5J@"2IYhG)W 5Ώ{栬n{ ; u?KpINHfXױf}ԬY777&O: )2e4ˠ @̤bŊ 0uuuŠҦA8ϕ+W_!066&<N!/$0;ҰaC6,eݺuINHchcuh۶-a(N_dOs @0 m ,BчًbȊdd7o^)Z*陼|]*=|>4hP? ۷oErI{<덫 5jTO>|)c녴,7!CwYx1/dz\?zԜ{|L<17beۣKf9g9_z c ] x47olٲ|oÇ%ԓ] v{̅oPSSc,Krr2FFFtޝWqNqz)iiD"{n3Spf:4eqfi]7/_bldĬYuVmҥ@NV.gȕ]#+3 Ow9~ QS̙3%ի!\|3g2rH,YB@@ݧf͚lܰT [NJBɃSO}Bc# ٳgejN X?y#\Jw$,uuun -}̌"2k+d#ȤҸ=z֖&9gT/#c];>{nnipiϟ4x2w<=zHvh¬MJiƑ/r]UP3QR[H9;b qPQQ!--\\veb[Nqb Yb}! iѴiS݋iiӆ6mƽ{̌FƄ ZR]ju8 j:Gtx4L4(oQdUSWW}γ}d]sRRx7o004sҀ`Ջ0uӔ|SRS9t1)0'sm˩㹠7ͬzW'OH-ϗcSЊb^o_ё5k<ٳquu%l,[m͗/<!9!͛sq^ݻwINNܜ޽{ceeUg-0&LKIOA~ݻ73g'y:nm[|d"=&YrT!E`޿>5kSl˞={嫗Ҁ̔>WqvvFXvaE>DOn䤍quu~!ɓ'ԭ[WuINe Mt-$ UlBW?- Cx莢bL:oׯbŊc$>EѲ][1}4Ԋ䘹 .Ftʕ0.ōuCN3sIhkk/((˗ϨRFMƎK.]p uFZZ6mliSCm>|{<|ܼqHĵk׈G__yN]6k.O޳i>Y?yɴ.vShRRSS5ks̷ uڵk[)M||<(*=w\i؋gInOL7oܹsط';#07cƌ{ÇX,^2К#((3~xZnȏL4y{vm^V}Z2mT.$,ڵkGdZxs}ƎQ%#Vsaߥ̆LphSGCW*fhWw )S!Crvڿ:U$qZhOSac,˗I;6UqagϞ`[%`G+ɪUXUGJ.(_a& )))\rP޼nTCku///^.w|rNo@@4-/oBL<lڴ7ob o 5-c:IL0Adĉ(*1}e=`^՜Wױ+夾!T |-OH޽{2 BBޡԾ~zkޡDڵKaeJ $;;׎W.2rv̙CeVjC:T,UDϼ$fҳPJff&!!!`bb"sGaܺu lԾb \wp5WVV"pݷ*XشٳJ#ǏÐk}MILfB Fk$m6Сdǎdd|yrqqoVhD$nE((}ŵj91M"99j&ѨQ#~oe&#r8A5ct)xcG"?N8-eʔ 333ݻ7!!!?5vttj11@bq$)6DKTx4|@f}hؾ>iiiODMA&W2/!x4kgc##F͹'||3'`ldľ}8tS3h?c?YF.2qE}*Iv6^344ں6U֢Q 6;vjȚxFDD8[SIH$ 2=zL^`;`e[gNSf 9ϖ-[A&ЦM.]g#{Ds47n\qpp@,snW:Oo=%62V:@YNti_҅wD~iV*wppp/\#F`\Nˮz:!$V8.b\oҸB9x))HΦݍ1lٺ5Wq"~fP"0~9˔a̘1\§;X\fq֌\NgZjk`mӰLqsdՙhh1h nݺETTcرjѪ_K*ԭ={k׎}$iӦ+l@,--#) [[[8qC ly;144V"(PLDΎafiTdfdZ<;B' KW5YqδmhSH$C<>̝3 4eЂHȢʹ}8,9fht\A+es%i$$$0|7jDK VBn8}'\y˼ 22Zɽp/YBٲB3w\.>xC[8ȲmJߟ-[Р]}F-|:Mq>5M5/_0|0_Yxq ;S6Nf-7:ӓrQxyyDxt ?.}›З1DpA444X:d)W]c۟Y=100Ԅ'2}TTT#55^~]\-PVVI٭4Ԉ@1@y IDATdO=zzzD5jԪU ''s{Ύebkkڵkm¸qR zzztرs ~~~t===T:?flll([,M4Z~*V#Fo/gM =RQQKYF999~,: (#Y3';gUVZ073cɒ%|xMEx_L׮]iܸ ؏,^QF?2'nw S7]|zNbڴiL<91 "!γ$d%AKC rʕ+Wgs?eʖ}`ƻMy)(*0lPx̦xOϞ=1b7n\rr%/ۧ}p_A59sf Ԕ{waiaN]m2xR&Ċ?V( iѢ̛go=бCC⇵5 6@CCkkkԤ[n{zzۀw2?q%c^בnLL ۷oN:у;v[UtR͛G޽qqq!..֮]ٳgi׮qttdĉ̟?___Cs'YYYlْP,Y͛7)9vח6mЬY3#sϏ;w,tttdƍ̛7 rYL\\'N,$diړBjQwl?OBII͠jf[Ne~ݻ1|8Ut,%<â4hP'O{i֭oߞ rIE"ԩî}Ki߾}2d;y!5-~ꙊXr)nnn9s~7:e^}=l^$PAuj4@lwwwz*Y:d9lRim G xGչ~L>QB?߿7oߐN5:+&L@YY̙áÇؿr:/6F q=t uh;-fUHK'~ /]rܹHzyy1tPL*3y-9֓3i$b#?ڒ&JdgXp!urH$\jXZZҧOХK<<5YXX0g.\(?dΝ;GXXXoB@̙3Yr%ǧbPp9W 999 Ք=tҍcǎ:CBBY 4i*sѣg\?rrrԩS'WJ6!![CBBڴlْ#Gvr6>?\A"͕VdzȉE]*Ѵ{SL*&E{?u%˼N<˗/c̙x$&:FԔÇ3o޼d% v1#Vfj\*HۄK]g(((N[ =(Z?Q.| ĕ+Wh֬iiir,s;W_@: hjj2ߜ, xjhh ӧc6,i 9N-,,2 … ҴçOte˖{n#6lX?̙3gdx*_SK,2l#wjI's:8[n5nݺڵjх :46:^dգ6-Zy湂;WWW1cwo]#M.blT]yhPR"^Wdd={$[Ɉko•dg\$Y!D4E[=q8t['^>{:y>{7iiݎn 9-|Kq,^X,Ԭk˴S76/nyg>$\:!B-[:[l!>.7o0gTTT022BFF7oH<@۶mIMKe9,>{^;%s.^M )2P-DMS#G  ٺu+ X2VSQsҥKȐ.4qD }!997X i&UؘrV (# GWW0Ba+:FFF L<M͂+ -EdggvZF&YħߡCbff6uT"%=O"*SꜿpO0#;;*UУG:tP,fժU̙=5*kfW KﴜNJq1 ի9͛7QBT1K׮̜96ɓqssE*lrHcN Ù#F.G.3+ٿ_f%.˖-MT۶o/2x5.+V`Ӥ:7GuA KgehW.%3&?/{k.޼yàA?17o͛,^c$~ Ǐ}{\i}b:8...ܸqE%EF,Vl<#zΞe{?ؼy3ZzZk^zgxZ`lll8{,)))߿Cm>|Ç :|{קO a޼yH %JDGGmժUq^o֬~4uuu EGG\WWW)n޼Y9]*()*ry4Tx dРA,_ &MX5qSN-$^,>}:[l&;; j(+sb:۶mvZۿMC̟O\^;RJ\|˗@m#]~']y ggo`;///6nH_pdABu44&phMGI48|%' >.hQװȸ>mѮIuϱk.bcc.bf,|dJt)Uuu\nNnZ~g]x #ޡzIŏ̈';7Ers :hiqA>{m?$KOO4iZbr`77nyfrs0cJeKصfϲ yyyhnrrrhTP۷ddd`hfHL֭[KZ E-|o޽L4 ;;;zIrr2GʊnC)RG8y$?^97fkEfV.tdplFⷔD _FVNK1m ()cP݀ǯcNƌSV{qUiխD5ttunRMcT>'K.Zc痓-Ҿ%M&n'x9kUO 0g,,,8t9ϓű _wuj>~oqD~~~}H!<<a\IDdˬ~OewjD40HVN+Qw9^F%`bcJbٲe=w[r8 xs,jt0A|ل_= '/_@9eyy,4k=QhWb$!Q(BK4˫pfE?N-Syő+A\ gԩ}{Qzۊmk5 Me#qpm9 $02vkkWB/'w ]5oAuW_hђCOcKf!r4ϳ6^.mӸqc5kFBL"u,Dɓ'%=y򄘘uVy999:v숯/yy;O-U'==? qa'O ##1 Rǖ)Rɓ'455ÇDDD J"ªh\9AYIQURw4; ^d_G'qAǀ 5mIBNN6в!KZlNy.E|Bi4Yv۩[~|{JhTmb{D3f@s 3Uz`16Е^ԮSPTTu0̶QxMUUUojes_ZH$*~S"B*W@f0eTɞ\-O IqIUE5nԘgdI&[RIMt&tpA5j4u4}wMV zyyx>77ooo*U$;ҥ څكB!Ceggsa:u$ūX"9rk׈,o߿<]vA)?/K/`kaq/:T۵ 2@hl:-&D~ Fz` AɬY|SRٷtіRH([< GIA KOӔWU~5R>8n<ǤM1oؐ7nHxx8994$Z|{a翔d E ^fgPTTFJF(QEKm۶qBDQ/`ddĬYLКC%|/EŻ >### ptt@RsΑ.{)Ǐ;w#GdΝ888`ggGJJ &..777X,\ɓ'-,_ɓ'Tٳ'͚5gggtuuٺu+o߾-`ڵkG7n̛7 >3qD-[%gϞe߾}Y"EHR?+R,MYܟ;wʵkHLLXL0@YYw2@f7ϳoq=OX1T,Kdeeݥ )ٍfx~SAAšq\ rDqmdQFW}{*T7'm5]&ƅ敓Ce>,B~~>222NGG-[r&GwCKʜ9shݺ5={E1 ŧf0*.+VOc=l"'#Ýh +cbߧ1yyN):կP]M5LI򅳜ΕHfΜɡC ԩC/{{v{NŞγ7}/{:(~OXz5NcO1[/rEqLG˚ipw";5*k#cz.*S rԚ?:Y3^}MOMg̭2G| o~ \)((k__:ذgbjBMaR:TDϞ=cڵ4nL&M"2BLZz*ڵK.>{rXZ5v튍MM6QN.WL>sB9 )QHw/++wΖFW+xR{ \t ooowQQQ )]P&/77{7mjEӦMxS-Y2W>Wy ?d>V9VP***XYYaee 4}"33V-[00nVf`E-QȽoYq&nc^]G;I4.M DpnUl-J - >9=\wJbʋqJNN@I&Ntupu-p!<xN,9+yDSV{`h2>d]QW\C.- tхK^+;2K?-^M&;'FO<&5=- !XXXpN3f`ڵ+EfE pW~[ojLe;tw3K[| &LÃSիW)X^[5б&<9D[uyĻrWzĞ{ѩÑu_^{HߩeZC m1*5hφN͛3Mi<(~SE{ ^=z>~G 555͙9s&=zHfs&EtVx IDATcrrr6mt֍#GpE߿_ӦMk<igsb>>&z</0#hbيӧOsſm@]]/puvIRⲔHH%[&w.x_29'O,$Wݻ<<<5z,FÃo%#+{8mPSU-4##%y薼ŷ鏻 Yy6rR27Rӳua)џizj֭[ǃhj5-,762Tj6 :_DjUEVVΝ;soDѯRF4^E5_K}0ˣMǎ-m-Ҥe˖O===qUBCCq_fQ%ć3qD|O}<)R9y$ׯGMQ/^GFFkkk+v@~ʝ- 7XիJq^>ؾ};Ff+uִnZCclҍǗ =ٵkK,)5^SS1czm۶شkǚ#4G Lt"XhQ/QWW''/z>'/WA@p4kǵ/6^YI׉#֜fy_" vٍ" +ذa> _w`g pIvҥKIOOjՊ̬L{s j[¦YsNxȫW#ٶiI勓ݲAnESG5s999Ƕm8sFIVVuxL6-8;;Sߣ_G'E1={DR%ʫpwLbmjʲ]ie]֩êU~/&55?9h13utYxxg!<,Ȕk-'_ dk^ʥ-l<@6ZQX[[#ԶquzZunSZ WJyG"xGNn {F"6߅˰a͙]>hhR^=,Yˆ#ѽCQQӧOiٲ%[lazl~ݸS]]Nӯ֮'7&O!#3ٳg#7ihZ-,-~۷oM @l'dee9Iݳg&'8zCf6d}Ӧklmm\ //8(摕S򪒬,`Q?bJN_|,\2rrrEvzf|`݄ tRmFǎ9qdXp![nӓ={UQQx7{{{9pYY8|0i$ذa~~~ޗގ߁YD~$2i$*V(H4&44}.222$&&O.]]6/^ݸs222hkkSvm055cǎ4nܘx@dԢE @EY uuvJppA֭[{  ݿӥKu-ׯʊ oܹss -P(Mt4jH4^'KQg@d+U&$Z2ɒ 133H#]oI V9nlÊ,VETv [t}62pѪF40tpe%WUG J+T@ݹ~:Dih0f\xzz2gqqqMbbbMv!u*+þݻI"P65хJkU*#:6󄇇8A3g>! ۄ}ORj^a7v R1M0SgV,J:v̾b2͡UغkGM;y2f #fͨȫッLLBvv6C % ߿0 71M۷o… nKy.$#=//Rcccc}:,\J=c&xRu:tʕ+++ѷj"K.;VtFFf&%p]r ޸ڵk) [hP($::///N<ׯ 8;;d\]]V>gϠlߟݺo͞CMƎ["&&VVBiBn.۶m!.\߹ʕ+Ȃ^׆6?iؠ/_DFF] IhIv+HdeB /vS( ƍG}c,Q[բVU/^_z2WQ3$?_c XOOOdU"lB5:ȅ 2Oi] G(68mAY ޴i0~BCYq&J<^&P^^sg1n8k5=$"""-++"ݻI|HHZqp>z-+Vļy/h1.n:nj.W^ CW1-ޙBAQiaF+W$} ?ӧ'77 jj$~4OY yVEd DWW>}0rH֭[G^8y$͹%B!ԪUX*\z###WcDdd$f5jм^c[CkSV&"26h@|l,mԦM!!L8v,X{{Zk֬a̙ti^$Zp@ xu,Y? TЦȿ͛7QN'"A(+H4>@~~Nyvښ7oE:uB^9=]ԭO  aÇܺutLMMԩ**ӹ}6[FQQƍ)ѮBn>dKGcn^*U y LwƿOì<  /_x'AAA,\3>gPPRˈ ny#nnn7Owtn2`^ߜ;w{ \:ۖ:xt1QUUG!Mʀ4gK.T)__G';6c%=mjKq&> !- Ņb?OzoͺsX>BVVSZY[ٳ_>Ǐg|ɓ'ݝs]b@ѣGٷo/\䈑QtNJf&6'2) C2iҤB?*aVw,1էYz 6oѴiZw`^i9x1sΜ9sCCC7o/&[|bkNdiyu=r{Շd}?׎6%Χ(U%Z5+V͛y1YYTT/bka¹UK=@ @օFy2ϳ,ѷo"'+F9r)7|pvŦIÂTMOXL*+\\1bD[? ۗGECi`݀-3...̞Y9 KKKLK̔'g۷oӼysYYYܽ{$u=&;if^ $%9E,l,d ߌ4o''h!5;k Fbz:Kϝ%>- m{ϸq`͚5mFFFDFFz~Ȑ!bgجiS޼Όc`>oٳgnٜuzxLjf& WXo$ܺuVZaߦD+K&ܩ``B@@+WdΜ9X2`\; ]mޢ:ujsO|.˥mCc6@Np~!+ % .dٲe9v~aBwk<E4<}z0fmLV5dY5eE3(Blѥ(/ MLmB*F5?Rp~;w2|RرcDFF5jԠ]v꠬0; 3vYyBvM>}9~OvbeDEFQi-~MDD*ǎO>llƢKהDfz&-2qD\]] ___ ů9 &0Llٔz$3oF3<~(NjIOMxK/Ͻ?f𾽌1Pf̘֭>Gˤzu de93nD/mr2AKjLFՋ9sФIBtt4=z@EE7nӂK=bO>!!QܹsBWS '; .pMrJ*2n6xlkk%$wޥiӦ_}ΟLϟ;~'#=L gggΝˆ 6m*1ethʄ֎Xfۗ,7(з%EҁHMHǘ|BOMHHm@qeJ@d$~udN\ !֭wm7fϞ%Z߇Ǐ.^$gfJ?_PsP5j?<޽{ܸq#%TSV 7ݤI ή'Ii\{TK Xw+i0t^m1f( s^(TTdiUV!gV"ֱ ^3:sD$wv_Z`'O)eggӿ_?խxxǜ<uj_5^zQ\9Ur Lާu THKwM7HIIy]ºu눌<5:lܸw2sLj4Jߔ܁833>jۅѼcsZ_;Vra m9#.|"b8.3IK@IiIJJMdd5j45J6+/+?H:::ORRB}L_KF?{/:B+V$!!r׳#A[] "/+ĮGrssٿ?1qNh_h{K5ظq#oQ:>`E)P7L}ҩchjْG2Ц>o=yt28=mӺm.rIuj\Z;&PY[Vȑ#9s [:qwwgDGGSW\Ν;̘18kjjo>qGjll,3g$//im5nQ& Yw !Ԃ#pz7:``L)/jըͭ9I: WWWPCWՖ_k`͛#'''Bf*YK /{ $Rrg LD'r;[)|FFnݤ/uJZQ;sL… PWSURج2U+jr5'%]MwXzvk꺚rVbNc={6gnuttlڙݳS-//.M}4Ջl摞 6ΖM6V-Z0w_\qh?/oqeJ=@ ݝU qrrbժUTEӨQC?.6VZ*ZnMzz:=ꉼJUԿ9,Lٻg7111jJZIK"j$.N7ШFe/X@= 7o&&&]RR{즛}_EW}s.&F-[>9ߧuÇ4lԈ[0w/HY m6prrBS1Kx} z#לFQQ#K`4i{!͏+ ,XΝ;ehn䍓PTzm/,ۋ̴L9r$6m`DJ$.eeeOtOOCJJ jߟM0ҲtJݻ ~N/{{Q__ PAU돦jUQi#F<{7RȌ8}',E ̳ϋLlB~}em93DߡVZ!flX1U*O5cc<==ֽ;[JTC*!ܹ Ϟ=̬0Ҕc6;uѮ8::@ ꖬjj|iX#Gz~~>'OǺ!7(V%!9!4l^H ߟ[\ IDAT *DǎyJdڵhL K.#/7@3cѢEt {;{֬YÛ7oؾ};tyyynzIɭHVVVe)<OOêU 㷚5ۡƀ111#GШQ#6 m6Ȱ z@@ חŋ3}t݉(֭[ini**WL={WER>Om(;xBWNNGGǏSRy5v*rn.]ɉ&5*zI*' "J=*/OG:4hނ[.|~?'qzDǎ߶z3΢ؘɛΣ{Xĉb`@-h۶1KVVCpk4iҤ؏~ܴiӆ|B!1 &v$Mׯg:t}1}]ZZZk׎[g~]jdak֬j )i'ƍ8p qrhKY^cǎeD+W8~;VRi.a2^눤>ٗ>|Ui׮K,u^:666ܿ 0~xކW#c4lHmu |/\y8::.]uuu*hk ٤ffk2ax*iqu2 \=ѩob_rsQQTdu?dey?=sδlْ5kҩS':$ӧyt҅cǎ7h׉ԫXI [Ş 1gܯ 4޾߿ 155eϞ=l߾5*q I`5a'AVFJʣ$OlR:iUʞ{i֬::”# p3QQR`|8hKi`RO ?dpvre D E222ժQB9,kQSQP Fpp0?vԈФ"*+і ::hj$W'rsrs>YYDEERҴ~8SNeժUѲeKZQ|vf6i?22ҥ k׮GMoH,|RQ)Srzah-X@,!(Ƌ]P:44kb[-p1cC7gi߁65 ȗWVfϠ*᠏>>>0x\]]QR|u8*U@(jǏw7F\yɼP'#;'N ''G~ؾ}˗/7WB!UV%91)4 v _ŭ[Yf4kLciiI\\W.C -sʚ< gdЪU+iݺi)??-%\e:t@ÇYz7G jD *mgPJ)[QQk-VU1põQlDP\BQ~ t}ssΜsҽWoߞ̌tZZZ{Eqh]%1K^X}l"U|rΝ;իW9z(1@5Жck=cXz/Ӭc.>sB*Vt]Yif Jՙ:m7oޤaNǏjK׹sg U%vH]Ю];UX~ *iVl/+D- ǰ,rZBXpj\t[nUw?1^9߿*JU8߉KI!22%Kh…D'&2Ϣ۰0&C,ԕTQ^=}b݅ TV (7<ʠ"-)Sa1>o+~><*E_kRj^|,^իWB|8LUx,ꇾ,ט;bk['ufDܹsŋ:t@||<11ܹׯCټi#[ٳ(ȋG7nBΎ4jԐ|IJ#K]HKKޞ7o`XՌIRe5n۲3XYYѣGr;++KR'*^c-s޿?#3^9?~D)9K),]ccƏ.Ν;ӿp֭K`{q4eFܸqgϖh߷oban'Ԩ͛_ʎU2{llmmqN">!D?yǏa˳>>>=zt@%O(FVV%%e>'<4O4nܸ1gϞf*(RnDYR2aaaxxxpaƎ 6 tAIQ\ە71 !VVVszѮ ֭-נ'?=JfΜfҺXUuC#>)]P%#SȆ $ϟIPcCSk:m}id<+%))3gr99{,/sWGQQNJƍ;.srx* s"##yS a#!p<3g޽o߾ /Fƍ'6֫cg {)>YYY8t@Ϝ7CPʡicEؚºu066fͨIX~$Æ cذae|2335;W@$>}Nu|¡ٵ jqonlNeU&nNJa߾}0_EXcA u4%5m6JճA|/ږ>LVػw/ZX4eK3cմmۖ}z~ r~-}deپ}opԩe =*,*111!K`gg^Ƣsg;w.ׯw V]Ţ nMF`Ļr+ 9YYWgœpԫׯq:.oCz8t b3g$!!KCMMTkeCXYZb3 W7Q ˷,:t FI͚53f B cG2m4֬YÓ'OΦN: :TR/# *C YK_8pIƌMQ{phfBFF)+'N$2&o~\]]Yz5մ*Ĺ -k$/0iQFU5^?~/eY9K1\zŀIS=kkPօ (/G* +#`cCG ϳgy9"1p@"Yb$+7~xɾMZKgiVFEE1cJ[Ǔ'O DD욽pqح[u6663:~86EyVXMFZGϻ+( s455/CcLǾNv%SWx^ ؘ;¾JdvQUF/u8uXr%?x9]??Bšnݺ\x/ v8u)/WhՠlŞVGSMV }P(B^NxݻϪҼޞzu051rm) KSkfrB2QQQA[x#hӶ TZ%'}{E9'$w3BNN+Vr*qpLVP:* s<=mLLѣG~nP9I<]1%DSEWԕq03㮟_kҊJ-S2Jyo^jifnlΩ%oRUfb۲RWWG3f,ׂ"h?{_#ʄpd~o_۫iUbn_a F?[b/\|Cm UF͚5mO:EtL,GCV>&iN;q0wM?!_YOΝhٲ%r(ɒ #GՕ={ _h]iui eJԪU 55>tlI߳QVCFF]b D"U*":8-&vZ!^ #xV`IA|cVA(#cǎ@ZZk֬AQQ*UPjU455ѩM}ura)SmH$$~Nk?ɢnݺs¹s\"M6e̛;7 _~<,)OĘۇd]<_$_P>''yN(ƍQUPUt4>.qYfΜ)y0ҸIn>x@PX&,[4Nř qӧO+\AA޽zg^*W[lCCk#JրRL4^zI$8p 6$":yvo/uՅ:ɖu~ܺu ? ttd C*jDٴ0k]*,1m46n,va}\\J 嚽 EF;>U6 .)dΜ9_}1c cQ_1Cl*I朢Yqמ) uu8:1}B%m'w2b&uXv-K-c䨑ԩS[uʋu[P,,,!eId{22fƍmhذˆW\8ddeۄb?]]] *11dtuu%7k׮c>RWjs&uttD$h"6mHjZ~S#So2Ys|Kht4kv1ci]qTƹ`ݺufsfϞMݙiҧT("[6_?)rб#sΥJK{DժcWNjJ l@6hk?嶯/8~ KjU:sE9vڙqA)C!+#`鈶ES.xkLy ϟˋp2339u1Qq)I@C]`Ĉ;TZ4ҳPR,`xVq[aS& =g^"j*ƍW.ˋ,$ $++TdeD"֟ÊcIIˠif,\>ÇK7oXp*`t6m, kͿnMN$/_‚10f\}eKfw#ɨ5lz.'O.\]^z\!Cwn}}}mۆtڑQQQVZe tuuٳw/bb۲v9///ckq1FXFF}~*s2wJz$1!l!,Ln"+zii|b"۶nV͚'v IDAT~ח+3q|YsNJA D%&0du6p99m8;;ķXZ3}n\ί\`gFF֍/Cزe Q9egr%()o7;;x sDؘ7rMx"sM$((^zr3ydh߾=kbّ[1l՟zԌx^{ʟq4ʕ+cmmy5_&1)˗*<{ %,Lt g݈H__28~Jb߾}İaèV*@'O>ёd2EK=^6-7aZ߄$Sqtt~yb§mqyKʕY`Auw>|[nEDD.\`ѢE[α?~&%%//"l^~ 4w޼YogA՛NiiZu xUUT䟹P~J~|C./?E 9[II{sktnOÆeȐ!>}: s>].x@۹^T攩c 111tIX[[###ӷ5жHAPǎz¢o() bW^l -|FdzY湖,YBfMz65І,&dԪYk׮oăFmIMqG/q3(s7lj",aȪ?133c׮]R`jjMȐ~)FpCYp%<<=Yf v?ٸq#ˋcKk%88X.gDHْzǏ{.]tA$,m*(3cѢEԩS'cD"͛)ϟ(7UVfiԬ *o$-- p񫔑re!9jTUreZ׬ɕ˗DBJ (|#+Q\{7.1-m1,Ej\u 6ofQgϞhjh`^l`.k[ϟ;&&&X۶q*jud <MpqqsssBahULHDm +YӔX3CXG=zԩSxx}ٟ16.aXàDutvZ.])QthYE='1|8FCSZ֯C?q? L<[[[\ƏfAjRCWL}&95xzzkgΜWϞѣGI .``i}ԫY࿥"WOGDD 4`ƍgOG`K߾t`Nr?f5  9XYiIJ$* ,֭s))bbc%oiҨ4Ջ4nԘ/_."-[ (F-yjےmӦM+E}A CASM q}Qy16664i҄ڵk_ZA+*Xr%/^s#=Ja]Z?h0rj>%$c;a}os<ޞ}Ne^M ,4h7%RSkh:3g<|EEEBaf!''P$bm(ѭ[7JuM3f̠reM&mPyer#~YYY\Y]=Mӱ{YvޏXKiQ^GGň:}Yrekݺ5>M|z6]p;הGWC1Ӏ 6:4t0)[nQDCq=!##௿ڮIj~x jYQRRM6e6e3YBii$SF >~ۦOk߶_^Nǻ׶S[KYוcޅO>lwoTTIIVZ}vK͚5Y7v'c]0axdddPVS!59#Gxٹs'Ǐ,_Uw^ osG{;~|x@{ҷf6_ڈgU4p"6l`E.رc! ѣnnnl{?d޼}ˌv6fu1sr254+3ʪX#L+ /4hoݢA;**06殟Ga)xB $^ur:4dőT Ǝݻw9xr]L\wFʓ g,X޽{իW]>Ç6m )) gggN>H(˓+W"-!XQRV&!p\r3%qkʓ'O̙3Bڵ;vlF$wۨ.QH$=Y~|rmUH@JB [<~g/Ϝ9#=c[g"L̿ڰmǝ~,[e˖y|ۄB!g۶m<999u_ҥK]|dWЬY3iڴ}:xkfs!. 'cPQWȈ{1Ҋ}R !̬,teΜ9^[|H&M3#*\,RǬ^D"="22/_pu8"M2bpTs^lD"_:w?ΠA_KM233Ģu t(*+,>ĻRiU_]Vv#0s;w.߾Qc̖/cXl?JAqe kkXpavڬY!!ddd" %WiS::8 bldƍK,L ۊޅx˨. b>b3mzKG>̨Qؘ&MMMS={Fk;;V絧/,~O#ꊲr^) JΆ r |qƢ +o84"A8y)Ck ˗]Ws&ꪅ/IIggd4TªUJ@n]G&&tn^m B"> FѤIcRS [LŊ IN_OɛO><*&00Pb״mcfԎ13$(( q®]8s ={DCK׷JNUVRϳo9z(Æ cʖ_\U+9.a֭Ŗ0 XYY?JR|2k֬aΜ9lٲ,,,?۷/&&ፇ ;vHtzFm6j9ǎ;Yb:(xTMńK.e˖w V"cȑRG9~83g7occc9qrrbDGGCTT,^X###>, ?P(̓1bGٳׯ8 ʑq$w nplڴ'ODڵ",,yqLJO9.**E":AE(Pw].?gҤIAUQ)h,ʺty|1s"#q#CCΝsuuulI066Xp5_7n`3{*7nSCqзo3 4 @ܕw,^s~"5KCg6lZZZH:u숢#4ӗ|H{vu);.ҴY3F8'K\- JƷ /1|@zJ:^zdggsΦ$; uСChhi``)VZi[z|$.'dˆeyOaЮׯ^ٸ#y:pmU9pӦMCEEX]A(.d^|I6mXz5&""z*e}ǚ1jjjyFj@۷o͛7dgg;~~ZjNuuu3ngԩyT۝pr*]a{_+gؑXӽ~}L|EG#)7@<~|IM^x8kՒ:nfV4nܘ=zpA9BLl,'MB>}bl0꥔/CBx=i_ج,~SZgϝ+5:vXTTTTdddTl{[lX((ŋsa=+LSC:ff|W/^܂6k5 j f͢5JC˖-?سgOe IDATx~~~x9N-̴َu˖bx<~PTdW %=kOiӦ<i%!..oK P.im=vɸq۷o#زe \z.;Ǐ_WBBBXjտwƆ;v"`IMM,)}6${7@\vͰ0K.qV,3˭IIs,Ĥx+W höX-[wbb۷_2/AD[5˕ .\ysqzzz,X OM~|\P3GI~ %%%֮]ڵk #,, ]]]о=sXyAӸ1 t-pNUE|; Ԕ;11ѲN̠ngeaT ZڈE(+)FRzFѓÇxEF2|p֬YÐ!Cr ?; zd3L:3gΔ DSSeWzyNZ;l>:VHuѺU^^z *D~8y$/Ø%ܘw8x@<23!D~thjOD?]VdÆ XXX/U GDD+nޤ"@^NK6_ϒ%Ko~rB0OL׏CiJA5ٳ'F;$53w'ֻsb;U +'Kt @YCN^U/қwQ;v7ܾU6{ )׻~RPj$z쉛))) %O/::Z2WuEW߂~?|?P([d)o>h()qi$*c݆ Dj"YZxW9 8/iܘ <mmn )GRZu-uAAZZ\6m]greJ}aggUS 2`2rH͹{.?&88o|?H@@O[3NAYI*j$?~_ԩͼWqqռ EѬv5^"99Y,Kӧy@ ؘ}£GpssH7/PW RSS$))`B!W^)nDq?qeed2ƒ.=:ԎUj#$YqYwvvfѢE\/.⒥ :qWܺ/^HUVV];|2W\AUUDD0`@^OI%3#ωTR|}ơS]we)OWWH,ẒG̪߾}Krr2m5k֔ǏY=oZjHXXT^u˷ǯ޽{<{ݻkp bc2rr8Ȟ={ شl,ڭיo]&صFCY/87F%]r=o.jԨQ}Q 4_'OSR ]jѢ-Zږ?Ñ#Gسg*]V(O7dE!##'vZa=gtY-$Be[7n4>bR2kh!'LzE,ڕhhdHb>4?|NBA^ܖә:u*o߾X"˖-cRLWfTFX鐑%ɛOMDAN*kc[ L}V2dffbg j0q&hhh6ӧO/Dw{TR777 yyygggqdlzz:tURGΝ9y$͓_NxxA?>nnnR7"777ѣGyi*(r6Ϥds,ʪ6m¦E 9|7t* iA`x8ZgX~8Tib`@Ç3g~?uXccƍϬYJ)((ޞ3gΐI@@@GgҒ pyEreYYY4$^A^J]pdS/knPVVfxyyѧOƏ;v˗/RJJJ,ZSAΝ{.+V`ԩTVO>XYY1`V\w9rD.\Hի...DEEJ% n4iښsnyT ~)ݏ",6nbiiYiӦlٺɓ'|:fwp`@&()(Ys (*(S޻QRwh0* :uiMڷAGMX?}ܹs8yWK[z\:ҥ ]L+Abb"С)O>:[6os2͛u(wӓs8zwaQeo0(&(*-kǢbk`'*b" ꢀA*Hw P~yǹsϝ~9ԪUS2&2x0 1풳r]Vk%Ajת-[䩓ܿ@ '5+)JKZvz]?t%r{VːN-,,$8rrr"999%o޼a̙ܹsLիDzeˤV:t;vݙ2e $^ղߊԜlm>`W]K- x 8:b$MT{g`]GgdիǣG077_A^^f13 `oty*N:q/AQ˕?` _3fE+F HtY9u=a+RʊIlҹS)UE$s B"$m%?ߧ#Q~{߶r ]Z}klxnj=}aooϵk$۟QDG]2gH}) boooHLLDWWΝ;Ӷm[dĈxMBRH#='E )Y<<}7E-yY`M6-fRwr% @=J9Gֽnnn%ճACY5CkW^&/?t{}035Hx-uF ܟMKt.N. /ФiS̙!C@gMYIB ?_}0[O827UK/QʪHMMEB} (xocаAB>c J S,^K*UT31c7ndlLk! `R)L8-[EPpΜ9^ZGDDŊO h֬=z(Ճubb"2aѡ7q?c֭_;"gːVb֭/#;෱܏-ǎ{{İLJ=w,0}tAYNX~=`ݺuo^G3** :_i6IDtQ-c络PbEl h 70(*Ʀ \Ƶpv̙3hWfDd9r4ugn=cͱ[4°UT^+Wࢣ1ի+8i>*((|rk„ dfdpePO}rwHms]0223տ> 90 (((;uў-jX~I۶mu{E4*3bYR2rJNPTP`ȑeX,]]]Qʹs$###U6TdVDbnN۷uuuFQl_‚-J3Wok#F`]GGG ٽ{aɒ%<xΦ[~F)+=3;2PvQ:d,))),wv.PeX|?'2)#tپno'l|L77WoR 8'7/p6nŪbGFE$1Ɩ2mjaPrrJ(XXX#:ů7ѮΙiTM=.N\=Z"Y(7pDžu41 .$ 07u9|hhh`lTqq IOO%MjbnRۘ:z<sMΝ)'8<:V&/'0~>%"7O/ag``$eb7a7gn=#&1O_Im-<+sDuA,ӱcG߿&f5L :D"/U|7 R#9ݻϙSW^o[clmmGAXxp>/`6LA2ƍcԩ_k̜9W{?=rbIPBI7![m6srXX.y{Y"B߇@@@^A$heą}҇}*UH_~'hiiѡCjX,СC=zWDT12z`aak|L۶m8!3۵wݾEH2ObcTRPZ Wӻ7\ϲvxo`\i֞gB%3d;v XӧOIJJB,Zjqa 9B*~B-j{V<#%#0h56mȤI ҡtb*4w0_L){7'}[܊8.:k1oosS}MW3϶۹s'RgIuF9{, ]%hӦ ӧO/!1AAA8::]taĉ\z3rHb#cfϞ͑#G^:gϝ%35'ȕ+WñD"֮]![yˣ{EB]bbeRl.Miܩ1~֭[g…R%sRSSqvvD|zƵC+<+yݬYowQ2d9xeA6mڄ]v%ϏЅ 4{̼yD9s{Cjݻ7ԬYRI;zSeE\qCٱs|W^ѣGBBBР. y"HB^ #7Bڴn͡Ç16"Ͼ> b?f޼yѷo_.LHˏ|z18G?oE,co߆{wzp2f ìC8n<{EԂU b '`ZZ'vdZ19X|"%&h"ܹCLl .]>;cWd`Jj˺_K]뺤椲šR|ޥ{y3fЬY3.]ʓ'O7gƩX6 cĄŠD B2۲%/_oEVd!?Kppp@Bv̛h:w&)9_շ Kv%IuݧxJu]Rhi-ut1*Tc^<=j::5JEٙxL&M>WȲ=<av6ݻu+&pQ֬YC:u4 SI[^^SfMIpPreZjk@Qɟy{􈇯_3T333z1껞÷Hrr ܹSZV\Ǒދ\Y"~_3jɮhvwqG EEy뛗8ѫ8vהҦ1VMPSUrr[0eoЦ=>}ʄ 8ǷcBRr >#0<}3>|>ȄU?v  Jt-[7O?a``Y%;=g?\+XZ޽ɬ),®C#6l@~Mq#Ϭw]wu6 LayϞ=#**B?Ypmx2V|}}ڵ+< {PUU%!#?""}ۢĥK>ۮ@xd)tUVS(3eҪUccdzGG^|)qg,uu9<|g 4y2Rٹs'c"?pkL3f(4%y 8A_Q$d}+}$z=uBZ}4/A<W!99xȒyO^x^bShԨ&&>}UUU4h@ZX|9jj^ta~|aի ]y@@+x/]^>{'//իWGCCUUU`֬Yde} P.cJA~o? QӮ&K]oj>"P IDATU\s=z=XȖhˀl[ CRg W#44SuuOčm׬FJUJ}66tkq ڋqƍo.Oȑ# :6E{%|֭[-fժUܽGJJ ԬYǍcž9r ߸ 53k*ru0?pu"ٹ(*(_P֦LՈ *T3LE5jFnXRiObba=hidzfNpQ"4TI%;W& k;ߛc-xW )vĉD:vǿ:gɉK..o{5b:-Z84o{QI`hnHNV.$ =}=.@/^T6̼=s뗯zMZbœ<_DOAJ^}S}!RRҥ /^Gd9x1!QVV VFI r=*UW3~xZW#geV{yj*Ν+ͦMprrm쏴Y\瑆wH}pv=_rTİd.]JR''2Rx=1yd]@E g\zxT+TԔv1mڴ2_? ۴A@ [mٟ^k1g7&Of֭/_ 1$-+ĥdҴI.\XZ5K UhQq L޽ImjӳZ4Wœ7oK~`]QA;W]W`$&&2{lDTSbkS+9ٻu&Mbٲehjj&FGa-ixyyѡCG5a@v+̟?+Wbю 3~:bEEEOkx +w`4~xvE+V^<%!v:rriժ7n@YUaBqQh PPT@II5j`{{{ڷo)))%KDEEUu+vԩSe֟@oɓ% Ǵrb:ɉf,]ueeթ99bR߼'qo?>&M*Q B!Ъ /Y[=51CDEvf=/{*-(kkx-kו0 N>zDVvssIIIagg<...pr"3+ V K(;$*y~:mڴ)9vZIf?aÇB&8#ښz&Xjf^u6m ~ آuQ"/Cﮯ/<4deeLJJ .>Ռ*Wq0qDS6W۷yYm#|7@A T~N~ 88:b``oٽեx{7nвe[9sPE[]?SwoIyɫ0PXXHϞ=pBUV[r?ݻ4oޜf]2eb㓹2A7xdU +yB찰ʊ:u`cc#>f:d_,vTPJےewiZ1tܹy#aR30:a s)aGE=SSgנ4j)WXX ~r;Ezn.c7g}>_<{rÃKJ jĽp^D{_23)ZTs^dB6mbƌ54dɘcٵkd)u^?1|;QqiħfsFTxr ػo`nnN8"ٹcC QRRxxx0tbˈZ$QG!c>XUŽ1lvq` P m¡-%pXʥKܹsU1D_]ۿFoM{ j9غu+/_Çr%z7nxAЍzFlD,B֭KΝIMMeʔ)ؔ^#QGo {Fn.5;cߡC1(ʻybgggN81 ygܹ%Fb޽{zz2|0ѡ"8bҰZ̬,T ;Sw?6^GvR17>x]-mK#%;˝ڳ''Ol[|K\ϱ!'PE{?OC,3tP;Vb?ҰFEwk^@ E|=wkĝy=d3蓓-jT,ZNOtbARzR1"H"PR3?;; uu5E]S-gokε #{- caY =]=||}\ PUL CcŞ>}>}puW!pܼr]UUUrss3%).QUUUj֪IV2e _H߆L&Eƿ aR5r;EPȂ JSPPVZԩSC/~H-OP۷#G߹sg#"pvvFҒt^`U>;v sV^Mdrx$qrrԔr20ib]ň=z~1py"}:/ ݻ2JJJ0qDɦ,Xy.13V6*JZiS%@ZF S =HSM#53{}q \#>%jn(rʗGSSSjpEzͯNj[F&A^l؁ :saBra,* 0ı{EKCJg$Hu_ajfͭԱ;<`ƍUYB)N2~xHCCi߳o_n1m4_M6Ij 0rH;:oo Yԥ'.O)lB||<'88iNr 6W,x G!99L<~ ϧO矱~[h+WDUQ֖lА-[e |IvӮfѲv&;vKl8y3}M&;gQ"ަrM6FΊ7Yq&g`l?iҧUMzzҢE PTRF5=zZ>G*UxĬ^$qmZn!G, Xv&ƍ ysb' TmEx9JJJ4lGGb942< ~~EOGˋx|^ 9+ѣGZɾm[8@tJ2&Zeigd&,Ȟ={c` qq&O.Þ;w}/DLQ@Lb:+F*S ooo֮]K`P (+EAA b"1**((( //)={dرD2-OBnn.O)4c…/!F\X͗lWI-[ gl|FOdӦM$''s*++3j(.\@nAs~up;~۷oo>N:El%X&5e]]΅  T;wfJ)b"˴KТ%'ƌ:~*F\:fדSX(sSL8cJ8ǬXB*< ;wdΝbʵ$tR^VlW屏QRTdRV%ihplh<ѣ}% OOOb=TuyαsNnݼ7RRA(*>C+ WF$sР5նΟsDZeZl֭[jt(, 4yiL͓ןRŰ cE$q~n=kOWP bsd`:YJ1Lߓh뇃*TI&QOQZ5_|F?z(*ȓ_.] W"رcg[Ij;c݊BK-N^1GMCIĘ㮮_|+-_ "## ݝ={k׿i?~Rٴ4\7]$ ''6::: e( *2 琑\8HUdQ!!!!AWsmmnTfJ;s'кՌ"˖a/Z} $%qk/hAۂyyF||<Æ[dg砥Q"ζQQQ!33K˪9+[.їH$ìERӤ2LZ1+ < P: {455TT?p-[FPWWիhI؈Zx>t/K\'##uuRk[>OP!>% @Sptt!!!={1cưl2ETPdHJJzZi5lذ 8#0tΟUܽtxX5FbԨQ_!Ϸ7d3x28plv]#//5n@Q$bY kI++\J֏ߠ!9L{'[2!8eg[$ee2yEɉHS)+BzNyEl0_@OO^^B\]]9}4X:#sY7oN'ާ s@[ . YbEˤob*i+''2+3udj4(˜s.>>>ڢQQ>Oe Udʥ{JVz7S(!kx2L#3~Yڣ(/̶iaVA#4%.=/Eǭ֖aSK߀| ?$$&gOO6h@߾}|~ H Ysr̬qssä.-Z`ll b<ȋZ2| ưa>y|f_={6ʊEbtuQVV¡yO杽G^^.Yׇ0VZkUܾG%u%x?@EyyQQ"//|VF[X,tTEлϟ$o߾ёwH'|EEE3vXz͖eY'AFF~~EFFFGff&FFFӇ&MHk1SN@U]ӧuʥK󿨧O?|GMTt4.]*wInx&ͬ PWU",6GqQ-^̼y>??̓'O8{,_F]]-[R^=iZtU*XmKu@ xyy!FTd򦋸x<$//\kyx/_b_f󏡠ʕ+rJ1F};_SPȤIطo *|lXqʹX[#//Skj=~AO\7{QΥ%֭c„ }>![ d3mTn޼I?QQTDEQ\֮]K9x ֘MV^j) j:|W#hmYs!OyBJJ DEE;z&&T!'?7ox=<0+,ĉqwwgK}z^x8+/_/P,tKNNE&'2ڌM`ϧϺuX⌈@AF4D⢊"K*/_͍-7s&'|2g#*HbnkkS'ߚΝ; jW>b\3gU&go~4ȵkEк|ˋ? "<ݛ3vTJi9^^~Lnv.mۖӬY_ dm:uÇ#Ҩ]jFG]D50-'=~~lKf͸A޽ٰan©m4b1c߹W^7JJdegCO?tRn\NpHf,f̘IСCڷoϖkc[*%g)?+errĔ+ۯWWW5j;qqxJz%jkpmp<IJeKݻwLLLPVV"y鄹#ޣS\bGGGC heI m-K{NCV.ÇܷKu/(C~<\a𭹹9rrrWqvvjժR54roKxIIۻcDzdɒ޽DE7ĉף:חܽG p|QLlf%JǛ7o?>^OmU"@努iJr*v#!''}}}:uD~իWۗ l~fɨW<$-GЍTҪ3?S~e^<)MM==NF5аtضŋs!7kF2h0J!3sg133cEK'ױ[w:ժ@ =7ݷoޤ/dT﷼2EsԔQW#!3 硬[\zjժ}g. IDAT9quu%$$@  loӋ>"I|%&&&s[[[N8%ƲŃ?DOK}V|9 ^O2]$<-[Ǐ3v<~kBa*ə'p% )='&)?^B]s=4ԔK@r_Wy033+V=:y_'v6*m@ `ĉYFG!+2y|rayi|;wЧwob߾!PSR{:[yILJMr~D6_x'$YhkVSC '/x6ɭZZ ahhiYs?~<'\]磬@! ,(@UEo>v¬~ˆCz'_q]5kƪ}U֝OY>9s_iec=n-ٗm]rTPi!߃CH2 DdaT +Vdҥ߿O&''vq44~s+O=3<]teƍXXX|QF'_GTd$ƥ^ ML9>P$MKV^\xܼ<\ncE ]= D&'c4mp<މקCD(s" $1yOi'kx!ǔ;֪عiӦ2Eyn͚6%44zpjۖUxůWٷo򂢢_3!t p߽Rήhܸ1YׇsCnMZFtkZsOVYBz{͚5TTe˖Qs6UF)<H cƎeǎoϞe[ҬM52e (++#''Wb0&&55 Q]UUU8{,q>{Wpz;!hjj2a,XEw2Nd3xe@6uDDD`aaNAYߺ%Z .g5XL:uHIIaȉ8ЩVmtԈȠc͚cо܈ 9%̺q;uի CzRuϸӧ-KvȫƬY!M 99[UI=1g{a[Q1! 0[֯_[HNIFC]1}2qD"233/o}[{n޼echXҦ=b81a13uTfΜ\%Ϸ7d^x_޽{3f 7f`}]v0)۷iٲ%7~^ 1 413CG]ش4EFM>}8vX ŋL:kGn1 `̑#\~VR>ؕJaa!O>ȑ#?NXx8jq}*}7Ck֬aRIKKc۶mINN]XU ~h>vZZ< U$b1n9agL]lRBSS'''}?J*Uz{sy ]t)u5?`̙?`ׂsw94,ڃ:w&""iӦɂ;:d9x22N<"@Ō>r*VHv$ 4˗Zɉ*UH/0 ƥ*ԨϞgggciiI^JU9= 4`ʔ)gdcU. ӧO.SE~ӧO/<άXɛ/1gEniii,X5k֔8.!bU[[$!Cpȑ֭#11ǎ/;'7;o!߆lO_BXXlҤ\WQtپ /(:ˠ Rù͢E8qr)TH`Æ XZZvZ}ffdZECEBΜ9?ŋ:t(: 6RlUϋjciٲeڿGJ)D\5j6m*9|xcwӏ3gbk[%ױYѣGSXXHbb$PС\22/ si٢%W^%((Ȃ;zd3x2\@ڵKz TD2SSy6tQGI~?H~~̞=tKYk_ D"֭ pѣܾu ;w000b?dÆ \yJ OLÇk߾qFӓk2nܸ0ߏ` GWW7n̔)S[QC5r r'!㟌Ȣ Ȋ,O֭ ~%KJ<?ׯK^ ZX AGGe߿Ϣ.]ٮdb{p n߾M&M$v2//A+/CBz_ɡf&&rbhYT-&<1;w"$8[neԩ3ݸ33pϞaggG&M3fL2WWWMFJj ">:oi/p1ڴi TY_,+~=#bb5wr޽LY)CKTR} ~Ȱ(,L͊q+Ç dW-Z![ؽ{7)5yn4=3rHoNB(]tnwd4)S.$^~}:v p|9c>W.g͚5Zru/6S<m={(knN_2+WT)ۧX~=oi?\ YHٳg2L&C&Çd4@4pqs Ubڎiӭ[7>jHԉOrSN%22:tj7|pssK͛7pbٔT,jAxDIo޼ pn ZX??~6l@pHÇkүcXս;۵Ȑ1]=:vӧ:~ٱs'UJ-Oٳt :tUVt5pZl}-]6/oQbE$IJsE9ޕϟk % +144dF&#䭶x@}K(.y!&U1@hjٵk>>>d2.YB…93|z+ZÆQ{\zG(ӝM>A=ɓ/ tU[NHHa֬Y*R(YؓP~ˀ25 T*V'Ohb> `A+ec͛HEMӐ4 _~ݻxd2$#s]"yآme׮]tQq(P3f0cƌtO=zKˎ;xM]%}'8::baaA>}֭8JܹsӇAZ[&aZ$ DJm۶?׹Z5F}i BzEFF2sL=W/W%J>JuQ&ođG9('N 222IPNy{]*b5 AЖxORׯ_gĆ?:3ǏӱcGW΋/:~3O?]L`̙kzEro/W@$n݂J7nJE-  dw,BKK yjtup_JX2ϏJ*1m4^˜" S־ e2mdݧukʜ(]Ֆ>h#40РTǣT*ٿķ,AЖ'd_:iS~QJŲgI n޺YǙ>}:gϜ}%ӜP'؏C4wn3^~5k(Y2|sľS6-.WZ oؐ R)ۮ]m4EШ(l7neʔxUVqۛ;Qԗh^ǎsDDTwB±*bq_~;w.l~IIkYQo˽ D'dիWcl`@jՁf Vm/iGPpIzmqBȣ0|Jvy "&Eٻq#-ZH%K͛у'N W*85Ú+ZbɣǓ@رc 6`Q@o'z5Aaaqs˖-c31bš (S싎[tЁvRfy9%M0kHX۷lٲ 2A}x!8}T*666䋋̈ݽe흮eYd 7o$** sss֭.W^%,, RlYZhAJҵѣG===WN7o;w 8 ,Hڵ.pꂋ jBv'hMTP$IlڴK.tggu#5bٹlٲ)Sh= *xboԩS9z(*I&Cڵ5kV,J.YVZqM %""?__:D %44?}{KT*!, } 3j2a>=|+V$]+Ƭٳʋĩ7ƈQh"}XAP?8V[w7n\@޼yt6mڵ+Y zj틭-J"<<KKK6mĎ;(]4^^^ 6;vO?PĄ~ujÍ QȀNUҷN*[Xɞwt]v%((g(BdٲeDEDWj ?5҈ C e>А7~qA G k~M`X[nMR~z}\~*s)% TkkԨQZڷOrkLuv}-b5̞3juÇh"~T%]W?o6zI81tUeaŹIO_jZ[NɒD}|Vp֬YtR+Y5a5?JYG7PP!N<͉m'gl+W̤hӦM[iyxx8cƌYf!˙M4 #^1ŋq.^~h?@/((+WGv퀴Zx1)Ο?WWWUV믌5*I]||<ܹѣGzjJ% 4ŋIj/\@ үYQF}v6lRLR;rHƌ3TV=zpBm,(ddv8OD*UniJj1aOԷs3ݻ $1cs)ɡèS$'w料iȣǒΩ?f_8Ţ@t_}w@VvN*Ue֬Y+XNU \:^x|ɜs9tnnn_<!...xzz0_қ7o8rݺuS7w(Q׫mڴ RI^ݻw߿?͌\.g!\KjzF/wwF{߆MJqFlݺy4~B;By2ӹ ߧYӦT*uXzz%>/EV_8σׯS.dA_`˗/… I~H7ocƌι m4Iݧ%8yWsEs'qYŪUzzMoVT)v+++KRy$,B/I$IkIrLcccJ(@nĈVKœMYLL Oٳgаa$9'Mĉ'c> djs3z39{ӝ0pU8~۳ 0bĈd5|N4\/)nbBR܍gX{:uR$RV-1Wio\]]믿8[ IDATogOG<ٳg4kڔXG.QΟr,Zhپ};/^S׮< gS}W~pʖ)C=? dرÇ ޿}R2d`` oaaA"ٻT1Q*Ijs{`oWXS:VJEp,]y:r1˗Cbnnӑ$5jaz{ȝ;S=$I شpY#"TʛwBil2KuBH˗/T*mJbf5ׯeL&ǺXt7}ZYZ2vL9{DGGSBy6l@*҇iۧ0s_7n~ 27Zr%+WdݴiӆR9uzZHx6""%K*U۷o';;wdXYY`mmL&֭[j?oƆ0>}9;w$9Negg7&FFl՛m7M pix=~̣G7n7n`d2-ʨѣ>}:meaɚ!RIk%FۗZI?u;FB__?͉zUx5(<H5۹ziCҥS9!Ŋ_ǿ*/^{JXa4{n{ygM)C Ǐy5]Ft퀤//SƝo~ي!9@^ٳ'^^^I~;wdSNW^lr3gaON>zzg} .:&YsP8? Zb3ojKժUqep# 'Yl՘1i~-&L-S<MZ/]Bɓi0\֮fDGDm|XlSN%,,/^УGLJ-&^D9dT*gk}qAt];t}\ۛ;v (Q"[,,,_L0#F?~x"ӦMcĈ.\X]۾}{ܹ3ӧǑKK6lؐ<ǏI&mۖAOjԨhѢ 2DVb͚59x 6l`IYɉ?KX~ڴj@gbԨQ"Xf ͛7gĉ:vL}@&Qxq aS ,SXPܥfڴiIMLԸqc :oP >[mS||ϟ]1|vEzk6Qmkk tu|2֯N:rwW_|9%Jˋ uvJt^+؈x=y$qiiH3+0߿hl>- |HbXt)Ͻ m[?ӹsgL&ctܙ.]KaÆ~z\B.]8pgfΜ9I 'OC̚5>} 9u5kLR[V-ݻ7gϦsxyy%G͍Yfo>tqwwOqY$uS{| cIw҅9pϟ?vPS'c;ZZALLL QGs- iRJ*i~Fyc9A,o߾E&qHć7oΟ~lF%K&:ttt([ G<`$k*\BATTTJ???1}H h`cÆqssCP$;}>|8˗/-Pz #42H 8αY1hݦ K,I6_#G\M96\$,$XYt))Nо}{^ʏ] KԵ=s%r9 .LJ |Wږ-ZrJ=u֔+WSNkmƒ%Kسgٹsɸ?^LٽO= @_*Uhԭ[cǎѱcG?'1+VC B. bgƍLx;{hZ]~ cǎq1  'oބ֯_޽{Б)[klvE +lIٽbŊ%y7%tYswaaϽAY[666={6BA3fرc[ED4x\.ӓNN߿5/CJkgE?ձJU2bWx2k׮9{ 9BLj2c Ȉݻw۷oɗ/=?Z>ӗ^-Z`ȑ_̄4ƮT*GE{, d޽{ӻwo= A"q6322,[}?'Oh||Ry+DBl)ijsŊl^y%N͛ăr w\q?DvXz5gm׏,^8ùSStiV|Z;AױAoE\˅֮]O\΀0`VbذaԘ3"E(YȔ8<淦M(6-W?c6f30w[xL KΜN[L Ktkj( $Iԓ'߿!Cܹs3uHɅ *ן=y9k}NxmVc  | ^.F޽g߾}qY'|RE?ը)DƤ]>7+MZLFC[[N Aqssc׮]>GJnܸAMɧ˿X`U_?  _J4xL'޵NŇ"ExbݿOлwo:hhm++`h`'O$I8| gόI>r5XZ#wsqA81lʔIPW% DL6M܍7Ѧ&$$kufS"%6> a]_|~AA,˅:t^ao&Jqzi  8_>P?X&E%ۿh}}/jZ[iu. W.\U}jvڅjJԘ3^^+WÇsʕ/>  d&R'ND<{ŋ3}tΝ;$%mڴi&l9y>u`ʛKpD +tЁQvqח5?ֽF L8''N>M6mY&G> "&&/_y KIJx=/CCl_八bbرNA ^.%ѣq6Spar9W.njR7n=v =##Z-[J p`?nǏ3xVOƲgh߾=?NsDszH||ڤ)5c;8!:7RR0 n<{a24 L.gҡMۿzukoA&\JT}/RDc.{1#GZj<~#F//&:Ȏ۷.W;v>ƻw8r0TB I4iS()//FAoo/FmSRmۘ| 4`__Κid1ۮ_GOOcǏcnn)A&\ٳR3ӣ:QZ 66ܹsVZp?/?|HRwLJW.#I̔wV\=#wǩW]e BV ^.n:ttheW1ĚSN%ٮ 2,&akV*V QQlт pf/椫ocïM"pqq200sԫW%K2 [ԣG052(Z<<?6g/޿,_4?CRq71IV#Ddl +~AWW3gdAkWr'{[4%76iܸ12]7ojUŽk}jjܹ֘s=.AADK%>SÚtC6m/^tԗ,biIISS~k,ZBAݒ%e( hrC!ƴ<`7:vH۹,_hKTkOɍ@c[OxDD% 9hr)GGGöѝH K}mtSX1.]sU\  ~,~ٹcps^JYpZ8C @fPAr E.|rtttXb< >*O <=3|.QN(_~ LuROrhl ӈ+x\.gҥ={Mp747sׇJdѢEܼu L9_ 8y$Aޱw^6nӧQ(S'[z /BC8p`OAr qOv۷﫝/8;; S(J3J{ OmvIRF zAlK4xbccYp!wgd2ܹ3CPyA_O!Zݝ>>UÙ  ;k0`vڱv$k:u %=z4aOq]\ΠA(mk˝;w}n\Nٲe91$Y?ač7)P@) 9U((abBٽ{7'L`M( (Ϯ~i\Lqѻzp"!C߿? jmCҬpOOO޽{ys BN'\***Z5kCԭ˟N-08݈Jb;ٳM1G; ˍuZʖ4Z0.]pto߾lXɇ`S@ժUEs' )hsr}wuک;HmڶRe2 ۦs۵%13clӦܹsOOtE&qӓ*U0vV?]>EV~z! hr0Jѣ[z7oزy3*Wcժ)~P޼6 \b[zzxxx{FFF :[3d߻ڵ(U*  vmKF8{۷slٲ-[M~[7v߾ǵk)+(<>77O n ~߳puxlƍذq/5k`-dzr*蘂 ۈ,rk4lLMe˖qUn倧wjAoT*u}L J*tޝG $#"kߞkbddE/X .^dʕtڕҥKA!7 ^~޸kn|mp%uƪ}Xw% &,U*!/V7S^=>77nѣGr*T@Ϟ=Jv 0|p֮]KϞ=QH&L  $ ^(P@cMpdU o޼ٳ7o|rҒ6m?&B ̘1#qSR%FA!DXXX!::ŚRf޽ &0eTҥK3e qqqdI3goeʱAAЎx">=hcԩS?~UVs) 7 $vjL|\5 ާ/Ջ? 888ίT*9z(m3؂  hO4x9H)fa{R,ܙH,XidT\e˖a``iA!}Dd2:tčgxjmE(fbӧ5|2FLSܼydh  d0GFGGǎYk'o[ԬQ5k榞WY2-[|!|U"o&l"0oЀjmhtTO>MR| zzX[[s%Je""l"o& Ѻ g̘14k 333r9'ONRR;w.M4hѢ*Tݻ}v"""4ݝu?~7oƺ~gʔ)-[Ljç^J-033L2 <(nnnۓ?~իKm9w4Z_ky`j֬6|pWκu Ύ5j A!vPP+W$..v |}*22qaii_͛111_~4nܘowΟ?WWWUV믌5*I]||<ܹѣGzjJ% 4ŋIj/\@ uMGiذaP#Gd̘18;;Ajѣ .ԲeK/^w}ƚ1E.3l0{#^+.A!2 ((HdɓlW*R```3fH2Lڽ{z[dddbb"&0a+|Rm֭L&N<-.]Zjٲe;99IeʔbbbN8!d2C璎4q$ѣdjj*EEE%q5 ]4qDI&I1aLIDAT]{J̕ϙ+=2U*`h(ȑ#z$6>[zț9ț嶼ԭ[7CߟcHF_xB@>cffƋ/֫WOc]~g4]E433K2ۋ/4֙P(?Qٸq#VVV4AADtt4~~~4o</88mے'O֬Y>qR՜SSSw)cAAHI:uY6Epp0M4իW=z4٭P J%>Eڠ u?oJ388RVA!;ɒ/$$MѣG)_|nݺd?,YRTRܾ};1ܹL& kkkd2Yc&~zLxi: I  Bv ^hh(M6ߟGRreu[Ąu%پn:tuu uuuߟSNİeZl~'''nJllˋz֭[7ttt4gg ~AA-ŤI&i[|!nݺŝ;wػw/fffd2ݻ54i҄ׯcnnտbcc)X :::ƍ6mÇ}-[OOOV\IyF޽{Z KKKumRXhϟؘӧO3h 3g9|ٳ$h,Xʕ+1cAAVdR:4VҔdd2~~~T*J,dߧze www-[ݻw)]4ݺucȑ>ѣ9vԨQiӦQZdW^e\r5kƜ9s000HV͛y1+VdРAE A@,;9+$7] ^nK/Y$I&S D^7'My}}};w.'ٗ/_>z/7ayO*hRpBL»wP(lMMM?~.\E^W-CK2LѣtI)((HRTJ'OJ nD^W;ڵk'988H޽K&88XrppڷoG5DD++OuJCMnСRݺuˆȫț=嶼E<<<Ҭ .FD^D)+fDٝ;whժUu-[TϛSn˫OLLLu111e-W37{ʼ֖/Yw%lmmˆȫț=嶼ueԩX˔)SȲțț}ee,]6֭cƌA$zlOn:NʬY(3+׼y]6K~TT SSS a[nq7o7yE^WKe+1Bёd2?~TRRɒ%%cccI&Iȑ#03+׻w酪3gJʕ %L&d2PP4k,)88[3ӈ"ț61MJ*^xMM2 ݺuhѢxKysܖS*Tys67cD' ÈgArܴr $Y7o%񒇇ԪU+\r?~|R֭;vHJ[3ӈ"ț}-X@255U?~Q&Ifff҂ 03+i R5$L&YYYI[z%KjݺTD I&I5k֔p+Wn[CyE^OY233N8bɓ'%333i_qdYCMN;r[ܶrȫț=ee^iPtiii֭XB #Z"f"oKi:tH266 #Z"f"oyJSH4  #Z"f"o涕;D^D)Kfɜ$''')&&&Śu]v'&'f_-ܹs%B!M8QO_4iP(s~f.7)7{ʼb< ^J (VݺuӸt֭[y^^^Wy#Gh"J%Ƙ"IAAAÐ!CpssC"+M4x)pSLȑ#|%d8991~xjժFDyܖr"țdE^͛7&,k"oN $  B#*KC`` IXYYaffG5D^7'-yJ%vb[<2LQϞ=i׮ryΘ8AyE޴+x)x"SNS3<-[dܸqԬY0s'f)oTT 6ʕ+(Q;;;uȝ;wFxyya``GeD^WՎh4v 4*V e^|ɩSV7yE^7>Rn] *"##d~|5rBCCS79,,7nP~|… }62 cGFFr9 .LŊ;{ӓbŊ1`,--wLcHf͒*T 1449sHz&66Vڽ{4o<k|||^z}}]L&N:iΟ?/;v,ɶ[J666L!΃C iX]Y:DXnV,ti*W" qNZ;gRwE?Zo~|sv|>}~ ( C K,Aqq1Bvv6jQQQS\\!T*두0!W^y1B{]FinnB@::v?>ꫩ Bl߾].\BiӘ2e  Chh(HNNF:oSJs,@NN jgΜgoVIIICff&L&PVVɄL!996q  &>qBx(L&^zWQQxg}nx뭷{BI&aX|9"##Cy0s>|~~~@XXj5jb @PȢ!RRR###B9\. ^钒={W]] Jh.}IΝwW[[K9]tLCCCnHZ/>>I~P:;x fϧEL\#--mVKO1%T*pk6W;Ξ=KJr'xjjjo%H׮]@:>}:9rV^Mϓjt,={6K?yѶmF۾};; :r䈴aiΜ9pLAegAzzŋHOO… ݘ5L2W;fΜ Z YEndee!<<\vGo߾hll[ZҘ"77C)gŊXxn:!//aŋj*ws:_[cƌ<%~!!!AפS{{;Ν Z;w~t:L6 vӦMCII JJJB?qڻﾋbXjL>W\Auu5&OpfNoZƲe˰i&TUU 6mBAAj5wmXtJT*̛7;`@^^x衇PTTZ BFF!bccw/zfB`` :J{ ʂBp85|i厗_~qqqcihh~ 7vP* &r;^:u ,eHMMFs=pMll,Ξ=;6nHUUU488^XXSqq1%&&z(lܸ֮]KgΜI&:ҥKMG%愮G+W͛7Iv㏩+JZlS\\9oMv2LKw漣:wҖ-[( iWVhooͥ1>|*--uc:!D>2 t---tqꢛ7oFdɡ@Os!:vMu^eE ~AzDxCVzJ`L"?^P^]ܡ6a,b@ BjiH[-@RC1|V^nhPq/'7AP1K2awn k!PO` P0Bpb= Tݐ,̽0[q ۮ<̵ );_:9 dSUȪ?j B7wPE|O?4>+O$M>D@y_.'L+wQ-nVFxM $'hpA.aͤ2|~B-ǔWǕ^Du {F2 .$6ݥrnqT*IyʱSHBOa+c~dpJuy*< zHU_&=2!4o- Zw&\\lgWHyDKȃsCoB0ҩK)'ŒeL8zH0̻4{@ΛH.@юH &FUv('S#roEH2bHxpDC8X@$Bp=6nYO  hV6~b ,XR529t E W񇏌>LH|g;(4DHaAP@0sxDB!ݠ)!?uqqwKx*HirYW z.>~ȴ_kx!xoPW_63ᕀZʽvzw}yS7VN|䝜H{~1~Dgy&|am^l)> n<]3Q[JyO=؆S lx->xG@؈3*Nj|k&.Cu"rQ@5c 2cOOWS5T}D|Y!%5Rf3)r&6; G㇄Q 4<mlAh, d]TA&~_oyq۶[dž'}ͯp}R0rPlzz']}+_:ӗ}9\gIL0 pi>l{@$\& ʡ`kZ @WK`a#q?Zŭ@U,`+m~ 6FxhDc/j8& r{MK~I@+hm(F?sr(@֢*g@7jj-I1.Nej @gYVpqJ-~Ζ` Nvn(?vccXf  W+-C=#?榮~U,os[懰q2rG<ŗr`v] fR͗; ڡ;^4vVh<,.=BfhNF(F(^0p H 8]][~)6=I ,?Pb؀YM*8ui@ ŭ|sc0QnD3g=D^}Q>H3 [ڽ3QF[bE6Vm|Dg|]:7.m ;~'2iE\*_6MixA4M6KP_d@,ޯ&6pSHCb#](q< )23Md+Yǎ/amUa}g l;}zaBNsRfa.Bo7 ӫ_C7D䡏=uUkSCDW"`* tМG&]glh-u(T\D2Z+.3Xʘ+@ bPda;s1 F )L+zOqxFvW.'{ 0*‰bY0)+|x  pT"@o2yؾ0A"rY].z fbؖ'yq-S#,XXeŞIENDB`geopandas-1.2.0/doc/source/_static/logo/geopandas_icon.png000066400000000000000000000715061525650651600236110ustar00rootroot00000000000000PNG  IHDRu pHYs.#.#x?v IDATxU$ٕ6-,YP:.^a@4mhb,` > @ߊC+$3~Ŕ57>WSJS::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::r EP"PwQUUG$/*$tc0aG 7# 97;?ofh9= #ꮩChXmujr3:Pz!tcZ(L,s8Hގchҋ#P02\4>vO$P:#nP)!|p@ȞHN5a鋀4x$ȉVmmtG #$ ]ܭx*\ozFڲ DK,*t/2 yӾ}7rhj M&l u/26D:0Bb;7CU$=H kI ]!;![wD!䭣v!t t1>Ƥxݐ6f:@6FK !lOPk>Jx !x&t̅N3!#!$ t"37!;!BGH\5 fwB؎ :@½#'Hؗ(6dB ݌!{L=yc&tHmt3 uH]#Dbn3 `qFy nBGXƦ1h,1}c #t1h<76 0 c#, 硣4|7ȯ :L9s?#@0q:P[h `ABGؓ 9Hv hȆf t-Yl؏PwQUUF[l$^AYj>(4@ل横ka##@ᄎ a9 {j~P|2TwM]UEUU?;_`eO!xt:dxl tPwin|_WC/` A E:Y,SH6ůakq7&q0x1:Y #q'' yZ.F>wEZhEehҋAd#|UEx\5Mj6<$~A:Ndh#`HS[:I3N 0Rz0n dK$85dE;;9zBz$tdu0{ 9~(+$Ǹ5#Ԑ)XݐF!)ƭld$!B5T I0n dA$8qd2BZB'ql8n}c eBG zu׌?:)sNFGxwj1 D秡 "#p Xs8<m8 o۱ǯ3SUU]xFD)=u⹛4^—.HXxɹqk %BG :uלWU/'.3 i`qO!xQz BG azS==w3]㟇 ehKb't:'nmM1 =4%tVWwiBh{cx.ٸXU5UU)!gbȏ&G ZBG`5u\&#ho>@DG,.|5n{{ ˗@r6 `y"t.u#y;l&3N>y @,Е%py A㕠] סw7zM,񎙟UKht9&7Y0B؊i=m:0~>uL'N^e (#0k;c'Cx4 ۯ/t?«‚` BG`2u\[`E~Ry9  ojGxϕKxc{$xFD*t?^x fLd:;~sFw#$$p5 l C%pT i <^:N e509#na,_*GG! 7Cߔ^`BG]7j>F Pwi^S tVw͵7Km(엡/}::1jDA/]@GyhsJ)U̍]{/K{#݄7]!,86^{C ½&A(}l}.BGU!pu{ #.J`gG !xMD ҙKKS:d`BG=ER2t:m`6G2'x&t~'p$SF'|$ccx=BG7G2$l`uG2j7dD@tdJ| 'p$#F'|$CGUBG(LH|ܡPkꪪ%qUU] m H?:BaB8v;{:k>'N   G:d(|$UG7BG(=Ώ @ 9 }w Gk"]s:澪S\BGȜD}atE:($E:B6# aC? M$F:B[OIcƁƒkfH $t ISlrh>a!x!3GbI L , BGȈD6[X69#B86H"P#d@H{tPp>*9Nq]jx1K6~\?L2B5SQv`C5@lf; u$p5Jxu#vxHD=7dΝa+:)R>[8F;;~p jq#l'#@ybѳ%; BGJGg(T{!t(ǏN-dPlǺk HA;y6[H*=li|s7#/xH*#q#3y< o&O{ű^u~KEdƫ^N7ZGUDdͽ+lA yhhA(j\ yԨ5~G%t$g:#{:A6l̚ Im|HM :cռHX#ɩ梪'dž1p ǺkN :k<#kcBHjlfOG #ƬɆБdC_) cdAHU8+ )c$OH*UȜ1k't$zƪp_U(1k&t$jƪ0C˜5:;cՌ@Y,#2VM pJf̚$ Rh4*_U(ص'5BGbeG9$EHt9꣓)OC{~Z #Q cՂ&>mU|e!t$6ƪehҋt't$a׏NhUU #!vBGbMlOaSPz!p3w$x IDAT)S"a ס( cach{Pv2.a9+# m?;m"dk}t?Ok VƫxD̄qiȉS(J/~ ט@t*S`?>W%#k BI#ߟTH3L",ihYrT 0Б]s1|~`-!&BGǸkLjg0k%BGvmyL;FJ/-!BGڼ?xq,X2DAȒ\j[1p|( T(Y1ܖ^Y*ꄎb}8`5ʰ*#K<8ҋ"KeXБYvO\qq̹1Ӈ8#sT<C-TM5x סOʰ#)EБ)yZR_,ߙБI]s.%Cۻ| ]'uל9?$tdoa =^w Б)\h߅{pv3,%Qyhҋ0:/ϖ=@v|c.BGvVR" m?^ ]vdBG˱ m"dR td'Dw?p oБ]Y(q"B :naerhҋPOa&!td~(LFȻr,8V}^z tۑZX-CPБ8V}|5v;Z*ބl%t9^VU`ʑ)ٖ.`tv:΄|.":mEwvd/BG1o^LxA#{:&]E0V k43#ߣ1oƪݎL7]s1kOZ>7#o5c|nGv"tUd7v݄|_(Ǫ=`[y7#1{ƪx'IБxzK/ۑw:u6VgX5քtacu(L#[:;]Y)%[:IcLA#[:]Yuhҋdt;]BGrӓ&6v;)*o:Lc.J/#&#UAc~.S[Tob_$O ]!C mPznGI舧y"SvJX4Bc~,`)9Rm^:Mc~17EcBIuȐv6v;֪&cSVrl:(ܵ:dI;;+&cS<5TUux&t,Lx@^.N:"<}  u!oPhб i.ǼI5NcYܭ_(DG#BR !#OUU]^#GS6c9rh"-9DᄎO>^<m]S;r B^'ٞ9r 3*5wCߔ^`m;Oȃ_0,@BǗoK/IKJ蘱4:d/jR1,0BǼ yhҋ@:f*]$\Gfi4 }(2$tLThcuH.Gpc嘾]P+Ȉ1]B]^:&UU^}"@)\%G&i>]G3"tL´r?dcbŪ!qN9R<ӣ1mCߖ^G& KU7\(NiǃҋǡK/ >cZt9MgLKCžVV4]e@?pi~(qT&tLTvYzX'N蘀k:. ",Qf3qB4AK.GwꞬk! :AKq-+3mBDŽ #g:yV21 &tt=[K/t;ˈu„3ZҋB:FhuV,4BK1QBǸJ Be:QBǸNuEh s :&H)I{^%tL1^~e3@dBs2i2b cV38.cb2Z/DdZ:R<5Th|d:1B8!J7 rEuZzR"tL5uUU'!QGȅf eҤI+!BJAҠ1Mu|(:G蘦; di$] B|, iy%ɽ:Fd= m/tHqi:"@c,H2钟$@?4ir@b4$K~c$될ǡ^Di"II5uE1/ @BɣKn aRM3I(:F l]:. 2CEHf4t14yaH\h&s9F1cy:dQ&I1c>^ m?^Lh*INLj WVwT>M^2J>;=DH>|z:4U"%t\Nx!Lh.yrɑDJ踢|XlaGsWd+-6V?^BtK12BuAHBNa5G!&Buӣ,>Uv:AZV[z4yED踰{X_:}nƈu:.@z<(&]"Brc:뮩K/҄Ѻ/eɑ,L8B.T-&R&t1BGvd:4 : `L(D„/ĽILfaBy' ^ ؇{ӡIlABy&N}\Mb :(ǿT$ <,I^Ǥ$tX5Zu0 :2 '5OQLASK:6 :N7o4ÄB: S3.D8=KdҠI m?TUI,Oϙi,㴄ދS93u׸qBi ɹ+%)OOLjh1t|R$ to4x|L'RwM]Uat`>oN6 OC?^f1 yUq'#tLX޵ .׼qcB)dohGׯ%A83P 4hq:B4|LgfB Gqlu/ʄ]3:N7j{ !tL_*L=r93:NC2-,AK,q9% >K3 B 4~viD6#4sKdiD6#qhoB_OX=I踣kt9A#kU蝔^9w> m/t`M>R$t::cqĄ?f6(qw:;~lyr 81t:O ;1CqL@ |>沉 wgzcQ b* 'tubqbBI^SxiLL> xm`ubBI}m3텎:ih{ ‹Cq3)m4~g]vѲnÒ~W4C۟UUuit܌{;[Qw͸DfG'ߙq3ic`[4¤A3fo5B4tq3B` KN7EM0 ^ L?l)s:n΋3~klN7?#KsW\78[zXܑ'7řk`I יqs^i%N ֙7řXҾjGqh{:f"tܐg2t:W|%C#* IDATӸa`1ʼ Ekt;? ϙq^iG% 0'4H>KpW4H8 d t`VJB '0mGi pWz&tkd ͛S:T$h[qz.#MI]c.s\qz^i/LFcK:t:.D8=/D]M)~ :NOo LD m/YqbCۏcVT)p*&,I8yl`SȤC^ <,I{ؐ:.H8/txs`WC^ <]WUծe g`LR4 #=O{X,L8dMu>„bN7 VRẃ9laB^GV%a :$2Twq5`%Bth ۂu:UE9]WU՞ch{#f:5r3sRU'_J/&C(b2[`z^^]+i1^B[I7 t< m?^m:._0#4xU5uUU a["tw:l-VbzKNN5WiXIf-:ϋ;-<xNt<mlK3~4_2/BG*LN2[$t\y:å.Ǵ>"2Ӣ}1--:.Ë<-\a*PU1fÓ+^Ҡ mߕ/jL?l#ODqϳk@4w-:.V蘔c6aTNxr/9T#)r-:.Ĩnrv+|q I~(&t\MIlt;P~_:.ˋ>-d.L^_" t\}Z뮩K/@t9G$o.e39FB<:EP0Z+BykbiDI%B] g @{r:.lh۪'t(Lzܓ>LjCo2рyKDAbfPJCo60Z-:nA_71=*;""tܒ퇪 oGcWۥ7=|2Uw͞$W"$t.I|zXKI+:ncXlM O.\aGdۧ8=ބ2Fkr*:n7#$MrH O"&#y:1\]G[6x@EHiE5UU:r1R7%ҧ$M  O{&[ :?4yHL5UU;$N1.B47+qꬒ8$BLj m?|/fp7J1BǓc e1;+IF!t1M;$钛$B:$ʛ@٫9%CEH1NR47/NtK"tEH7/RK1!B(o^dN1^471ZL&F/cXDI#tWF_wAE̙I8Z}_zR#t?]"QwM]UձH &tLqxS`-I|$ABLjNڎ a:]F%t4?]FvCV'J?cvqgviI\$QBN77-f, :A&4hu„i,m.,X^mg4a%L蘀z( ;. ۯjGѓhuڄk5X$qBtN{D:&bh[#IۭF#m)mBǴvLަ1BǴvXwQEW<m/Ȁ1!ak]uHt9fBi>]Wz:G蘾 _01BĄMKC>]S^xAc#L#B-N!q7wa.:Kcvt;oȌ1]ZwRz :fF蘨oǭN!qu(8Ӧ1}gr̐1muLnGJ8|#CBDŽNKCt;P*]:?vT˔1q:dP0Iy혾kJ/E1c#̓(ţy:f "ߔ^ vWC;_B|΃nGJ`j3sBL maLt;q̭SΛ1/r,1/av<.dB3bLVBc:uל^ɦBX(w; d "t̓nv, Aa3.h,ҋ@Rt9J?T<,1sCߎ^|"0Ieݎׂ  .l}(9"Q @|o-бW[wͩ Baꒂ  .n1*/guԥˑ;%(CENC<`+)K]t,I:d懺kJ/8ӖNX#qwT:fhqm}ua5G S t,qltб@CۏCun5-7yNX.U~N뮩K/[!g kθ%yC~#t,TX_/GKeX=бl$L`)awб`]sZzX*#tf 3{ȷ _^ Y*|䛄TZe sy:# 9]UBG4. t9&#_ m[UՍjdҋdt9&#ӸT61;&o:u;fҋ.nxБt;""tt;fmǛȻ T~"y&tOt;fϘ5J|ڭ"9+:UҋrdeBG^)F.J/"`eBGIckt9#oJ͘5 #Xc|.G&t=<ț1kwE&tM`t9#FY0zT%؄БwXc·TMYʼnjeϘ5@.ҋ愎[RŲg\БU8f@yt92#+X9*9T4< ouCrd2BGVzC];ujLJȺu;C5ǥ swCk:aRBG=)8fWz2vpБT0{;Ƭu3ejBG6۱ u8k,l$pETwAEȥ.G"td ~(U5uEȄ.Gf#tdcȍJaYqh{G\LD% ǪΝs:2tR5q~Gd m?8>$tdJi Ǭu92;# OI*ǾHe,BȤ7_=j1> 7C_9. tdi}\xjrp#@.uN,E\<=){:K:2Ru~G8寰#s: OS(¢&v>]xYҋ%?mlw*Bƥ28]WU՞È',M"Щ7/bX`)'>{F/t;f%[*S}O3.RnMbSS/Ǻk=|" BGܨz~X`V[%&BGa_QeLE1DE&c4..<ي,)ָX"L#tdNTX G߯БNX?]`33БmTlX,p}DGVKn,u5{` LjБ~Ikh}e5J.õe%#8uE7Z۴>G;#QF뮹(p+h Fc5|&ňБظ:GߛIБKpv*;"p6#1: Oo(׸Z𛻡ϕTN W8cxQwM]|%|O&1BG.t:@~+ى1kBx@Ʊ3OjD˘5|Fb$t$jƬyc5.MJjR%t$ƬyC5%xX5:0f-O~<8 ߇!IBG08V{H1Lc Iɱ1k@A& BGa5/TUu-x2c,Imּ<) U #)͚v¨u*@7VM6$ǘ5_:@|%+BGd̚<Ǫo9: &KBGe̚o<*q_"GBGzya E"w6C"GBGr09I^(x"v3"W9YR7Y]3Y0ya ׈1|pj't$ 㖯k~#1c𻓰elm_7N0j N.UW\m@NHnNB:$x`VVUU{gOB8 :0eo<0p/*+US # m#8?nX(Б\u#87aCHBG6&p}?DBGy y8R"#Y|lcwʼBgH#o9)qhgoǫkj-!pPnhSETBG~GCUUׂG^#pƗŢdBG0uUU?:mް/x[4b )gw\mx't4'w|1~&V;ݍ]BG 3{5x1x@B_φ/8P<#<:883BG4y'#@ag[#+p# tdw䝞G y'8W)Vg+w_z88f= BG6JSL̄˿ Y{oh>^VS5kDqg8· j > Ww͹5܅7o o뮩 =!8)NA{`Eaa|h<><";2plq Mτ8.Մmv@”u kXH A/ m?x.h't<TxH|/pdMC۟+8ǿD$<`Ucc1#|˰_D" MȚƆ8j ណka܆h5v տ86p+:+}˰G#†+ЏC_)"No;X l k |La=BGxCb6ڂ6Tw؀!,9R+6xR 0W cprlq lF.^' fWw͹1L84: X X00g c~PS& :ŠG6nԍ Y0YrM a=6Z3_-X]5!p0)܅`"BGXCPFk&2.D9o0CazBGXSX,s~Lc5yp'eb"6ULpL=ƏjĄNBC 01#lFk&GLMW :lf_y4n 372afBGLmfOe҄OT :D6Z3q6!8޳wę؝ #LhhLl%[5 몪v6$tg ~=@8' (4 : …߫-3ǭ9R\ Ʃ?8TfrDa&!xT_f0[=lrHqjW :ŒF4#sƭmc\`aBGiؐsHqjrA-:f##3Hqjr'p:Bx6\[00N}m܅`)HqA3o`r8_%t= H~UUZqPr%#.P#*#P GG2"|G5BGg㍊iUoqh1xT2c5~8%t$|<##zT:_ǿz kꮩ47:4CNUUcUU!p!t^>`|>p@BJC)GG 1u9t@w!pucbⰪ[:FLB[q>@),J96%tV>$P{_XLEl 2~ ?}5hH_X[`W`a>^z ϝ05#{)n0zm5 mᤀ9*GG㳭קDrd5f~ݏr|X_40u衪J%7C o)0piUU; ria #0+_9WsG#L'<<Aum?m>Rlx)m;|h9$t6WwAj<]?0XU5 _]ՔBa=a|8ozt#-BG`vʗXdBO}P:x1# XD0*+y|6~V1>BGx[86> k #G`k,9a 6B|="qY̙qj4BG ku]' B蘖,Yx]n4N Gd:6c\@qOlO((F5ㇾ3&t\γ@ ճQ1t7^.P2#P0"w-`n?¸P෱^Swbe+4%BGHxُ2 sC{/0",#P,Kf,+׺ʁBXBGhaDo|"Z?S6?:3|<]FxCnt/7|YpY#t:PwxG Ga8n}>Ʃ ^BGWz(cJ/oP$ݍ:G"n``"BG5zȊF ֤ f tP5'UUzHCEM63:8G#9] N:L:,Amg`BG]s`knݍ`9BG]3>Q?h`QaB'tX@5㢙z2Rl`AʢHf Qsb!tؒkBףk}ݍDDeuלѢ{[RDH#f BGxQjD"Fl(5@b #קUU}rF@Q곡Ͻ"t\5{a rF#@"956:ht SwiqyaCH A{22x>Cȇ a{FL 2{'Ɠ@$1 N oBG Y6Df\s6( Sϖ͜ -$PBG̅e3XcXvwnI @> ԖLPPG`"7UUxNP>w.^ `%7ZxIuל]^!lMBG@|w:UG 62#>@MF6&t`%GȖXKU&l`rBG6RwQ|>@Z>WUu.l`BG&!|d\{G\LNa#:0kBqcԃs`)BGfUwM:vT a# t`!|<ݏ ] 6 X\5'UUX: 5D @lMx}GX0DIօ3!t#nrb&t }WdR?8! wPdk 4B}FR$t Frߩ4 iBGc5yjr"t Y$t?Z<@j,G[ȍ,X<@"-Է \ JXZ>.CG 5+2> o:3ƯmgۧO ϶_Ha#F4pqO A#LLk@$B& H t 2N_ `YBG`+[&t-O=dBx-h:]s,u+{x|#D򤪪}gMh^ NBG̳{ a|~6:m $@{6} (ͳo~O8 SB:>R[zY;0%r ΪΟ!dO/`F6GSf`.>yY3w3f`!.Ȼr9 LɱL2d`DGXҜF>4+qRDF+%BWQ#NH%2>a}v`aF/~>Et>lsZܙE'\MCDݐ0m. HFH!~N IFt!rh6 diGe"re5#lm =H[Oz.2:i2%Y+"k3)œRb$7&$"5(":#ZE6C;=㱅>E.FI"(߸wqlġ1ogW*5N`9?ġijGф"QyM׏twwڏz#]D^0 #lP1croǘBt~@D@D@D@D@D@D@D@D@D@D@D@D@D@D@D@D@D@D@D@D@D@D@D@D@D@D@D@D@D@D@D@D@D@D@D@0 i[IENDB`geopandas-1.2.0/doc/source/_static/logo/geopandas_icon.svg000066400000000000000000000017171525650651600236210ustar00rootroot00000000000000geopandas-1.2.0/doc/source/_static/logo/geopandas_icon_green.png000066400000000000000000001044261525650651600247670ustar00rootroot00000000000000PNG  IHDRu pHYs.#.#x?v IDATx_'ǝ 'HZ!h1v⋑ 9vfZlr-̅(ZJX}3Z08j5 tRVW3RM+wq͟s}>)ҭprW_k##D`AZՈ7uiWYPFKxT@6oT% V;gkQ%9p]0 BG>*<[6tNiv+&o[Wj,t%%+4ÕOXJ(@ŖvbT_ݺۺmF޾atpqBivvsH w*-;W,  #?9ת%5TC˦ b{c|fj#6Ӛ %Xt1ڥJ+!d̆l!t2Ԟ ӹ*!6#=qo;'Sڱ#d(KG5c;dj4geE:@kKGfrreF+t*/\wBG%ڧc)i)گ]zU4@T5 K#[OSׇ.o %:<=voՏL@^T5ՏH@bVcșG !tqbhvN| @ =[OlvhfҡKZՏ/|MP1ql4ߝPp#Nz @T:S,ޝ=m 9^ @oh'z @W]B}![`lg^tU?h 5,_l1mƴW91qlp_+p4e#5&tz;/]s߾p": #Չ0}ټF聘c_@ XuNzj@E@ҙCOK 6#KG_J  6^BG6mg5d*6^G#&th:4ɞZ7ms@W]Y N;xM߷Uٸ`zҭ4~P.#@b#ĮWu@6MoιraKil>Z{wtz~ẋ( 76R8BG\Ēh\,E-ז4bIH 4e3'tmԯoF:9aѼG:Qm%1@&>zϼGf:ԉ@{h#@ƨl?}E;{m&}yy:/R<ӭe3Z$tZՍ[6z|EmJ=kim`.ݚoU=jȆz:ser #@7Vɡ׵Rk,:VjΈ9' P?BGNݾ/|e[:SG-"tڎVu ztH;APV*_߾ϙk3iGu[BG* <ךhQ @oE˧ąnw+aQ @=Y4SBG(, #REuݛ9; \YѪzr4@/]5nèi{w +rǺ/;{m&|hxpvjU4@'_Hs\֯{TZQɞ/{p;#ij,#L V(&m憐[||Hn4xcAJ|*}ᄪG:yǿ&F[?ܻiPPrGkJI&wKf>%tòqbV:'8_ݡBxKfZ@3Lzղ%~u ɢnT/^v[Z s &txNmlޚPF>Xi)|q@@KCi|zd@%2L|^pm:e1^ٺAƏo} M$r϶(K[ 'tvj7!#ԓh(#PO1ԈviӃ!d3܇itvkI@ăQS ^25w^ ў iv(hh#Psj~-?;o4"~FdTBz]玦nȝۻ^uQӿ;>4 nsjHΞn[dLV夁3:6mvd@BgĜs?ȅ3@[<:a~#TK@ Ջ9'2;#y1M`@P#SJt֡th5 tL:iA#(2>'x) sG;A@Ln~#,@Il1If@baLoܽiӅEpUxlvڌ&H@ >G|@jETT8ZmQ9|"Eで)̌N #P=/5 ڧ]zߜFE0Ƃ&@5,kOtʜFeگ,j@1h'>liPhyzUo`1#|1=GYQA_NjX>#E1%GG: wjՏp`f[6[&t.O0wPPK"x?wԂćɡ/$5֮~۾gH)(n:gC5*NjL||hg5%BG`q&v^߾iQh>tVU y۳y[:ֽnb}:He 5@:ݛ6h[GjBGvHSjD ,jE>/C)"x?w5"BG"ØHiƦlTEF3:4b9LT6:X;3:"L|^pe+%t>!pb9̱_XKg(A|n>쮁:)?rNFQh9 U>tG`Pɡ ItCc/uΛƉS?lX#LH =*woi#LBG(Q C?7|(Qԁ&qg,JFHHܹw7 |+]9n:BI4i#BG(6cmN,Jc@fZj&[F ]GE8:}$M͝w܇iQa#"| Ʀx#46ӪlĮWd#|*M\8 ߷ynNAV^o?q" 8z-}K_qda՟?0u:sm& |}c+ oh&nGzJ#4JG #Սz Zjuk|+: o/ ދx_|CHϜ0?8@qwҁ~z FQM=Ӗ x 05w22^JB/D1Ft !oBGv85uxxGzM3zDHăa#(f=K5^ʁ2{GNu#T55֚:BěWutٍJHD#t.8mgͤ=?cՍzW##t}G&;J <Px/5&tKQK;]AlJ?Ntk6?}ӭ__wtEl7t@H]0<\+&>z/_ٍ@W֡qe1@n:fP,jKf#TddP滎4F;Pҙ3i_9TjN#t z͑Qxqk: PV";H1|8C^ ~ Nޜ IDAT$B*QDg6 ] tEX{Ç˧[7<^tDw9To؀:"B`I;ĜG$g5t 6UaX VƜGE%tæjoo9Tem"31 V*OF EwNe#+E&6ZCg!bGl88xhMzgΤoȊFkezBGx Yx~˧"@*9{m1"Qtb TKcS5+cq; J޲`(>#t,Wu$,1c +'QTC aa @łsc E(@5X×OYP1ffkVb,agQGP#1&lrY,+'txǰ @|`fkbX9#E8尡 6[Q2Ug(Q^_WϢEN|p@&Z/z*ټ͕$}t֜JG99,'u 'ZabX:#E9[6|-Zr y1fk"UbX#`g"p> Zc><'t(1et๣Gxs<^YjY(^ݾρ" )F{#,V|y4P1>'vbm#,Бb1,ND3h,EO&t1qm.'1Zmeh1#O&tqdځcPcŊQ<БF3Ǒ8 xd)%\:h81g_Vx2A|Gx4#e#c玶 “DpBGGCbA#GsV9BGGCYwvp^#c#OrڌErҷGZm[JYdex0xI BG5כ!>ҭ4~NM; BG$"pA}3`5:h_&pr4XNHņ/m,"i)sڪInͧ bi)\zE4"pxg6:$tgb0n ȥ\Nx@ OBGz"`\4qؽi[:Η]lnͧå@c?U/󿺮 s^MSs-$+BG.Db .eqxnbqڌ skDWI6WU1wL[uܻ۪pv`iL{6osjdCHWxCC⭜McT9:5ϵR#L[6L6tU[o)Sl6K `bvr.:}Fzˠt܎ucG~f@btY0::qڪdS5@Leu%tFYS#6UtȚU[̠t145wce#͠tLqYԻ8'>LN~ ]؇:ǔ)T =1,FAoZcq @oD2ԍБYS&czcK߆4:ӧVT2Y[|7mt ԆБJścrڌ150k:RxoV(ǕigqQg{6wF=7*cjdruA-DI<,8fb/zNH%I)ɹS@-mZ $tdFkI 1qtX:{:"䠲bܹwG uoڱ~U3BGV$5J1n#@6&v#߷1},Dq<>;]1d#W#I9qȓůБe7%v:BX:K:,ǔ9Yg1eBGldPk.s'/r/BJȒr,Ý{wqhgZ "tdIƟ}53YMBG-?>[×O3WgJ?Fye޴cK+,ZXcl]5-ib׫.:(1!?l84[,ݳrX:OȢP/HoΕ~ n:DQshfSnqdk<9ꑟS1C#&tbCFeBl۠ڑ:X|4߻ҏ8̿vjOGy$UweF?ҏHQ8:˧#<*>`cv#O#U:M[5ڑ  UͦGj·J*fV Db$"tSbi.m,+[vBGZT96jKQU:/:9jM#U:ʱܻ۪rPJ INV vdS\gͤ4cلSL_)XU!"t,*:tT_^1kVN/9BEX0Ut|pcY.cT96~TD#%t,Ԙ0Sՙҏ vd9ڳy[\_14N,y[[5Sr dc3;n߻[19VDXrܽi[8gͤӥ{!F,б0)d ,б }U96P,xscT;Bǂx#<McʰHBBD+[~ cy 4/DX}AX@/d1Xj dy "vp<б/5V~ ic虱f;xBmZ$C@)4ʡ˧{wK?jll,:64yҏ/mBdž1ĕfx UdalG tl[#N~ dbl>Wŧ C[cx+0:}M5V:0! 8{m&:S11mOkWn mb#fp] 1MBcr g:1IB8`fBcHG@Ƣ3:2)бK[6~ 4pc s tlUa#M` BmLv~ ̤\B̍ +C#M3lB̙ /R@DgfthR&c"p\cTܻFyZT;_F8tT}n@i.1SQ{Ӷҏ!{Q8yҏP\BLY h:Վe:frʔI! dA#P 95:_48tȔ:ք$?_Gz_N}V2ź5:_ޠ1"R BZfZo !- t ~қ3R/-:ր@}n:1+:֓бCs̋y<#Ec= ;<Ǽ(gs˥@'HeQ>?s52{i‰ҏ:njM\#1/}ҏ/\Onͧu1^1ԊбvhܟNrȞN#jb-tǷ㳥BhLb 7ecV,chǕyxP cW6*uեC+c@Z<\(:ڱ^31/m$KQBNJ,H K?ՙt]ǔoҏ624gl`BNtK#K:1^=hB>pD& 9Ћ5{Il=4 t1XByC<2ms/@ {L:|@|Laө%tQcۮ\,,7s/}\kc"LбG!h51 ۇƈA:t8"@|u2:@n:/^dƖTGWvmZ&Y c#t+Hik~_6Q#t4ص@}#r/4:4ܺD斾VDLTcgt4FcV.GJ:\J86B.=s'piL9"X ťwU="råo?3:B.c i t:@t}e9S.X@kEsne$hK `: ql M5N 0~@٬J6SU:vaxul 3^]=cWA@٬J ?Gdc2iбCĺj|@UIKP)cH#2Tz44訔бCF>UqL& +%tO%J@UJó*%taIpDh|INj ;4:1vW@uV׊۫T >;:@tV]KP*&v8$u{ :nap?_ NջLF>:E.M4Zcxݚ]vdKZl7u%͇Vnr34Bhg[+dș1ۄ  >BG_T=Ir Z+kNFtD,BǐvmeK%cH^#WOxn Sdg``LОKP#/LD~s/P1Տ!z|xOȒБd5 n>'#j hb@@F=7cnkO, 7ض+ʟ7Q5"$t$Y+kГѡBGL9%[dI dⶰ̽:nMor/@@_ J`}My#6f^)%_^Tވ5ʽ:Y׿%tφ vBML :hoW#@r I}3%:o<8H :}%tJ踉ѡ vBMш :}%t(Kv@_ :}%t$IsK7=8H :}%tJБ$5Fv{p:ƶ]@@_ :hbǘ#@F|JyɎ+&Es@섎_^ :}%tJБ$M^%:'Hφ=5W^RJ4Ӵ:}%tDy~q+BG46ۃБ$5HBC :h߈ƙ.̽:}%tJ踉Ď1Б$mNM3IPQM3k>{ J!t [Uc$1qsK7wmT9J%})%KBG5/БdM}z%:>{l@Ȓ1L Iֳ.=XXn*_I$kac:f+^"1zQ&WDIH ^.WGovf%(1Vf:%t|e;c[#b5:gC%A^Ion5We:>Ɯeِ-#@ v)k^Iڳ;t:1^]1b's/:gܣ![BGְUe:}#cOg\B-̺`!#gUr't$y= 6ցob%(q +&Oc1v]_˽:na~Yɽ^"3ӑ -}{ J%t܂;{ S Y:na|?С;J{TB6<cfn፜@ۄ`Zк@,t:FoNDz ?Ec."rӱtB6Ϙ;xk>tlB64ދg̝_bаgбlB6x!/tb0!h0+бMG/vmt) 'tlc2AiW4UCئyA/vmldg9e:+/5 "B6$~Μ@4UC&AjKƲj{ mƫS`BBxQkebNi(бogA1^? e:v` 3z.X0Ca:v 3 #Thu{ *#t793b @6M׹2B-Lg͙_Ta4tбC8ŽT]ΫбC^klr4~W^J ;|; 9"KP)c@07LTcš$бC/|bWj$бCa/|*rش]騖бCZq`U1mw:%tr397PbаZ'!j д (Ϟizf%б _M:v^4<?%sD& F+'t79me3e d:van8&@IбzB.`ImĄ]:\бK.XA;e1 .WбK.Xٝ{ (N4\=B.o-4\=B.;Ũ&t-}{ B%F@MrL:"4L}&gni:@RI50{KgωžSod&z:@Z{kq`=Â_thcV׊۫:ot068Byv@`=Jn‚@\NF:hnxcWȤ{ JأC;Sa#rD& бGջŃ @sLK0PB>^Gz% бf%@sL#2't/t@sL#2'tӡnO+kUWS`#ĒGdO'FNGd t쓯&^G:ec:бO15B%C9&+ӨQx"]>4\L3%$.a{zc:-AGag{lrϸ?$;zf15net awiBJRбt;11fhtKa7<{ʠ1-qڇ in0"goOͽ $NcZhб+kv?aȮbb ;)t&t$i9%" a1^]ݎi,e WZ1:+ {R|KwMq:դQQ .a4NBǒ4WWVWghбD^i7lVGKXYI96k5Fvo%t,NL}&dkj?13_^h K:.,7k#mz=> +RzXhud-:V䒶ߤȃ~鹽zhͽ :VDozߩ?3c]q==F18)'fhu gC/0b PoMҤ1 B Yr7ɽ/{j4B }*ON`.P/FVR!tМc2I2b P?ǟ6ٖ"73!t6b P?i'73HбbڀFC=0s7L[i:VLp\4S~i&ȭ+ڀC;0iq >4iFhNOhH[8}hSL̥빗 9B&K]fν:v4ѡֱP319B~Hf4-,7ːN6|t99I:c2i m$' GBIGxj :Hh G;>@zLZ112$I8@l);u繗 9HL4wLjuj8n*+InbK8@=l);u@#@*|Zch1YBOHBcdwqhϸ Gx&t0^̽ ;u𨇔(i:X¤AiIд #Sh8(8cbEV҄..ee:] #څXA89 6:Fˆu.t-,7V׀1چ @|?$ꣿ|{ jA # e&jE[,Ӡ ts/@4L+V7W^Z:FĈuALkM&!t봝M:YLև12Jכ T@ hhu#c:]Fv0@&vnKP+BNۛO Zf4bՊ1BFuhxki1 c@#M+jE!#izǴBGN|{^eHVXZٝ{mFG)ݎ$tG^:pν %$tE# >4\L{5^Ւ1bvr/.BDŽ3}ș|~|k62$O#9 k8 tĻ$/t;N{̹<^{wr/q@&?B}#^21x6|t/B:@.>gcwKs/Ct;&nngcX׃nG< L=v.jʒ1a!t; ^j[dJ0e!t;<@ 9 /cSvVɛ1qFaаnGj/?@3&tL2x,ȗбt;փnGbf2:r/AmvݐUNXᛃpndz/ړ YW,/z:քEcdwe Q8N/,7s/Cm~K@nOd:9k#t;NϽ $܍-B $c̈́o7 #&Q mÑ[ t!,K8:4{б7 aȮ{Dq[>cM^N8Z4Fv^"G:T!|@=lnY@L5>O(J}o{52n|V_̽?1~(0xXY_Sz~BcoVξV% "# k,|q>/^DK׋]G:֜o't; ^,k.|P+Fv'<20@ bnG繗vN8Z4Fv^䜋lA蘁;}CÎ0Wo]Q|K  @d C蘉^ξkOk٧~NjO AaR.GAHWB38g~0bE tk~XDK{~G 0 t'b#@:gv] Бs+b&wHCL 0(BGz6}CË3Ƞ Y8TȌoͶ&aEXLԒ`}M#Q:+~e'wMeʹ[0hBG&, jKoyab tωGs/ab t/֒G.&vy&gP~!t羚|珜pX`@N~'<mve%w ?.R0w\#>)(!BBGJqͧ_nQ g]2*3J095neJ<bc҄1k3ܫTb%t4sK7ɔ2 ]o]Qi$tTa.GБR-2?&&ttrS3˜un+/|DMH%׉G^q<D8*seTvtXwx*IT&vtT&O̱3 |>JHT&~cZo]:wr\|$AHB xh'Oٗ~t(h='F2T.h !ZIБʅVsdEkì@+D@NyEk-IU #5pZpuŊ2$td`BkcY /piz +H@۫<ULF=azڇJB Th?y傇;ٗ~{.4C*#ƬC8y{czfq:2't$ Ÿ Gr/x <#Qv/Uܑ@ݸlZ:p˘5wq,~N-1k6\|bbzph::cl>4\;#xp,/zԆБfCEѡa5jX5u$t$JƬppG-cԑБ(@]D˘5dz/ZM0VM 1k[;X5u&t$jƬ1#P}hZ:=cH;77.{ԚБ$Bx|u>۞D't$ a~a玼-xջ't$sK7 # cDHR7BWyh]>&ȇБf3G f'|L x“&5c1ܫ?+ 0@G\l1'AV$엟{8G {oadx5 0G0j @BgO>XaEsr$t$Yջ=@I@ϿVlVk) ȑБK=DI@YFXĦ޹Z :W.vd7#[ #'?@ȚБ!x#8yJ0șБZW2î ،^ iǷf/Ԋ +#̀v_Vd:R7agF؝<)#:ߵVBGj&3qKHN_^#vg\qكlEH.-^/ ? t}X,,7=\Kf&v^ȖǓW.(Б:ߑ- ߅qؙbа8?%t~[yca#-G:q,n<БZxJY- 8҉wngD`BGjow-?2v}=#m J/`sBGj/;6w!x  c 횺=#YXM[…B#@N8*p#} <Бl,~Q\q-c@=:K.-^/\ m:p~G <Pjh{]@{d',ߑvE!x;f5VL8ґ?GБ4W,]!xؙւqm_(әi{cBG4t8?zt$,<)̙:Љoo]Q3Бl`Dǰ2g.O8Bwd-y8,C<&UBnt_n ZXGeT> #@N8ښT8ҍp #t${apX ^0@\d/Е>l'th-pRб(&Q.Nk2ǜz&tnǫw|E¸/s`#[+pQЕ@^$t% :k` ܯknlYB\²`e։G] 1_BGeՙ~51BGxpX4t-̿{fJ &Ͻ(6qOKוoØ30YB5r8#t8yBMGf'LcU?5[c$BGxﮗ9,Cc#@/߳K DBGB^Asar$L@?(P#!\1;uga (y{Q13 7.:B›Rj @%c __3LE8 ᰌs1L٥DE8>:B\yGN7:CT :B\6ofM -|?3uƸ5P+'-f^odK-,7[?:B7vhϸqk6Ʃw<.U֥jd77)qk uƩ)[+p"!t>ZMNHY6ƭ#HqjpO+/5DB}›%[bjE5ƩƩ)۩+sKCLPf,aW8x RauK02aq5$M/AN8ڍv$bbXkuj :B›_x2(fiJ*Z'\PkJ-av%]|$f1}1DLcxS{;ɑ*|mCĄP(x ?2:zO:BE›*m # u׊!t 70j$7u9rהdPS-/z#T,I7K#U~V1u 9GH@ (fGrGH$iS9]"o] ABG/ZoP5]Ni:€7Q#]qY:B Gbbڇ $Nc6eqrg@ 5 to›, JzؙKoCÞ8B!2MVȠ8p85g@P?BGU|?_yG#YXn )#86B1z&Uf~PCBGXl.bfL1ssgbB !rGbrhx1'G?#$ Mb 8B,x$&fucdg#$") Q.58B^#1~G<'~@:Bb,\;+$t ƕGNȘ%tD I!oBGHln{ ؋@'x$aG}5'p #Cx3Ƿf=Q}@ BGW.IF8ZH>#Ԍ|G<;D :B IMy[wn :BM IQ86plfrgcj]#BG1# f>=vFY#|x#\#1+t ##d엟|Qcc/_#'&p!tL\uEHX0@aJCku?ګОѡo>:l.` #Î:xuE/_lbrxr6-aBq0ǰ;Н>,߸z@4@90 ?~_>/fdcc|  O^#PЭ5/T\[2ut|c/ ]r0(M KϾ)ЙaF{7W,޽Vy{ϦdCUЁYXH"^:ZکIa")4M3jAI>zB IbئBƻ&Z~ +yߐ|@W(=>L i6,3A#ea PBG'h X&$PF莃3NL"{ŅwyL@'9@m_$@o[}I]t\ #s[&75jfdhu_;j4@] 1a?[H13#W<@Dڗ1 rKC?/\+:oL]9qj]AT3M%m[3 O^I@ JU/?R/Q0"&:2}d:su&_<@fh6 #!:?}7he1kGLx}h[wE@Jv=KW^wIP/.N>2h$#Pi1&*'k{zSnh,#Py1f٢)jdhuazDh:@ Z7_JS[JlhbܸSlvi?@Z6Wf[(*.U1BhHԎvk _;f1BiJRnm5U5<0(`|A#vj̈́@v3W~HGڭ蛘^F[],z`v:m%tj̕wvk-jtKY(\%ک3̈́@#DʞvRXZx;EhVQ<Է8t2ۭ17 0Z\I6\,کE Ɖ̀{w:iozgN "eXV*hn81{NmY m˙M%MOK&7?/\+*_fm9_s]3 Ab()B1F@Eu|VtpBGUKf:8{L]UV-[KeX=2bKG,kU@Dݤuh bhhѳd2bYLvm_*ޡ'ᥓ3^ Rlz\7QNIg;hXXh 81{!m]#pgGT7da#@RFtƭi4=?t4- ?x;T7t`#@3n*3ŬGQ}*GzS3*GR[* V8> yZ:p dzF#RUS~**:TM{*idhu~uڶfQTsEu̺P\s iҡsxNn8JG ׻;+@:|}xC!tX4㺡U#RTjna?}ע.E1%p]f @^Tj^ْ`o>KӗN:Bj h9st`lW|u]>&}nܺj@#@hx2Jac6WkJ PkګI5b+uZK#@EDuT=lv%@+:&}l+5@iiz-@kp F:TՁ ;ӡM{ӊAw4Rm<|dVjf:T`sW!tx`lW:ae3@%M}%1$t%P )bַfw %1!thX6mz4y#a#%#@St t[lƣO9k:4M@nFCG`~u3tFjG6vx"FӁ]G,̈́X&#G/.N>6BG"|16R-*`O.:pGh&a#]&t FzDٷ~"ڴ7Z&>@pXs|^:<>DڶfI@Ea˧\ t EՙtɢHqIDAT@^賈v7ow#'f/×Jg8r@@w Nv6^@w|h[wTLv7OX:D @ 蝨z<8+q,F^,ys>06@s+#stC@E _-5PcBG%Znؕvw6Z#Z_<Ϟw4g\#UO^{q"ܰS5-QajF#3|q"m[3jB @Ci>1>lV@|:UY@ r]il,;ՏTIT5~rbN1f(~_j? hxplgF#^0&tيiwg җY}C_G/j KR.ƹy't}bLT?F亡Ux($BG-:[l.>SR  -~tLSO ^?s6: .Z au菧BGxRH1ڦ#lNK!>A#K PF#$r̙3F#tKkGk-iwgsՙOmj:@/  =-iڗJ)zf:se&}9{hq:@?DZ 3.:@s _{aKڶf}f>#4$ڰ׾TTAF5*Hm:2ih#T;i|ͨ*HQb +3EȨIur{hQ 9rx6+?f:@],hZ*Yi:^l.C2dBGh0!$M2 tBKxJBGh!7 ܧ\/? :  [c;v֌:h犭2k x,!#T UQ"h 3#j $͆Y_-DT1= tgdhH軳w/Ftf1}"t7k͆|`E#P!BG75;Bh %V8}xX23#\qcLWee+;iHZ)DbT/FȨ=1#PMHjqbFh40BG^"ynUѦwZ͌jʪŨT[7߮ Ŏg\5P'<2,Șd%6Ϫ+bƭ c֢hT:P.2^*ɰmͨ/sox{geopandas-1.2.0/doc/source/_static/logo/geopandas_logo.png000066400000000000000000003207321525650651600236170ustar00rootroot00000000000000PNG  IHDRBZ pHYs.#.#x?v IDATx1 0aGb@8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8ܶq~ػ;1VK@9t`wt u@U0 bFdD_|x&p,@2±$# p,@2±$# p,@2±$# p,@2±$# p,@2±$# p,@2±$# p,@2±$# p,@2±$# p,@2±$# p,@2±$# p,@2±$# p,@2±$# p,@2±$# p,@2±$# p,@2±$# p,@2±$# p,@2±$# p,@2±$# p,@2±$# p,@2±$# p,@2±$# p,@2±$# p,@2±$# p,@2±$# p,@24JS]?9ϵG8[E0z}w/g."v{Zx±@JC}_!޾Qv.ߏ% (8 Ȥ= ?sXn?4a v v8.uuq$^4/°},v~nަΑX;q/\";ˊpVc`JS=aFaswdϮn`jc ۿ 3#OLp,T@J V Asml7XTeŽߥ-(vQLvp,$7ĮF؅CDQLSdJSB"aLVp,TWK{1ٮn )MUFP{:nM7Rj9 n D9"vuq Bo 쇐죐,τc 눸i}t\BN($qu' ?PDĵP,9"]n p~c`4jt`v!h' ZB~,|}HGLp,S9.gldG!٭A8Wj9DbXG3u>tup,g4joae#mDypJSO IDATb/, *_lvtK8*Mb?ZhF}v' >{xsxC"# @zbip,iZ-,0"G& bC,GdƜF8]D<p,'Q }tu#w}Hۭ~N87U9ɥ|Ed;Gp,GW*"nwė$ QZFuD#bҾٍs' o)Mu3c?$Gu#X^4UD xC_#s" *q눸t5Nl}<ۭN8*M!{Z$]8W±|4UbXIlDuu5pNcT!U>"s  0sJD\G:".~&owCDvkN`cf42"nhЗ! `\`jcf4U]G0")TeD>""ⶫۭ_ 3X3Vj"ⓝOq탳 Tوs* X3QDD]U}SDuu LLi`;8]ޮn8X(Mu눸}D[HnFVs@ۭI  Pi]dcHD0&K@8*XJS-#F0&i>±hF';Y~p,;~p, l " p@@xX7$ ,S±o@0x!Y/ Qi |C8F])>٭ X T7}-"n> s`XWNlwxT!wdos|8±42"c$vC@ pc~`F'7z# p4U.HKn! 0R&"c ODvuO8UDG= no& Zi0[gen $ Ri/{#Sj=Dc/! A8TK3]^ihmEMWNy g4mD#;ۭ{;#M5<yRM[ dAdtqȂUX|e+r&=uET՚co N8X5I.X?m Vo  W$Irj] p,PkΓLXW9emj4tMIrM=rGI=rdRw±9K276#u~烺1}'tK,` Rfz}"CF`oqX)]sh7B&Զpp,&cl&ɱ%+ >ZG?ܗ-ocW+]3Irz aؚY\^²{\'m_^tMI2Mj q؇kvfUd%IDe{Lr.2^d݈8ag#xsh=5"'~mevp,J\&b)kֶ(,_隣1({6e>6=$B2X/)InZ }/a>6M隓g!_{e>cI±*]s6FcE`.{l5Gc@d?tPy_k_:3F85Cxe`'IJO$&{";iOj?8/B8kJ$V8bgc$v_cLYL4c<9±9tP3ˍc>a{\v±˻LUnH쬶`Jל<CV>'kWg`[ +]S$9>65;b Vtѳ,lX`+ *]s6Fc`#ݎ?Bda|mdlXCk.QbgNYH|U6˷$]±GJה$$q> )]s,${궰w6=1h챛|5[`?g!C\~l:Xk&I7boL俟Gc@v~4 umd±J VY,)]Sƀyչ~j?M$ ;j S7}57VkNLƘ!ac.^m 7vI隣1 ;wm?1!  [tE^cDv2^v1׶`c` ^>}V隓$C|<Ɂw,1mg2VE8H隒d=![\UfN|4 |.׶ U-Qd`g/qzgkO (]sdSgdZ۾mPEskOL2 +]3~q'y$WX3:5-!,Z+]3Mɍ7XjX5GI.ƈ7xYX4X@kJ!}?C(vֶ3 zS'1' t-SC0`9A|طg}&ୄc`J\&$3=kJ$óΡ8x,o% kTfGC,6Umkƀ쩓gx줶Z±kJ45Gc@V8}2cx-XX1;Krl{m?ul~5d'I.=0c'o*]sd*MrY~11Fd ,K !y`svGJ KY" +P,ɍh,;l^ֶpdc SCîkO# K6~3;h>cX1) o Ʋ`m_ee&9L%)]3ξؗ" Ȳyc` JL|-;B0! ˎc`Dc!l,YvԶs\" %ˎ`kȲc3X" Q$%96)[`,H@" ;Ʋ#^m` Ȳ#ceJLƀ x줶'Xx#Xmw .e|m?uH# oP(ɍh,[h^ֶ9tHrl!X=$ T$Lt-8c[k]3± lr<5GcO&aˈXx!X$WwC\x,!˖xaNkl/ `7 _e ̓\ `qJ%fe]׶8?!䢶c9Cff XX6cImCꔮ`;F8e͓\ֶr$X5GINXƲ\ԶW,4ɡs>׶: X6l5ggH6x,`ƲIj_:l5GI@Sacp,{M4 -EmQ|'9t66x,`oƲa`v*]S\& [J84FDc?A`_P0: ! c;c4v&O2m{J\ qx_V2±X6^%90,+t]~bp  (]s(+e(]sdx,`P IDAT`'YeLjX5CY!_czk܉Ʋ"?'m_ E $FgE>׶`}cjkJ+0Or^~fl`g$?8+" F±l-XV[Imjt`Jל'b _pVlh,+cmsX`SԶIrQX!N<+]sbhl5C4˱DI& l5I8Kė){m8h,tL4tgaOX$5ӓk&I~q1ddR6$%mf``kEX$ l5I~r@躶' V(]sdXk_MCeIֶ4.r Jה$wW,<ɤq]2>CO|tXsmqG86fI]Or^*]s'fIUθlh,KpL4ugGf fkN ===Tf~rh䢶Ԩ>)]S$9ux~RjX`O6Q隉h, 8Dc3D=k۟%,qQO8SfZ2,з$' IO2F`Kɦl5'IfI\:9Jr3?mXʠ! (]Shx0O2mcM?'@{X X6F!{",3&+]s'3 8yx?X6BiO|՘/S,MIN<nk&,US~6>ܛ8L23$VkΒ Ymj<*]3I3׶1 `Vd*]ló̝791D,U$_\pg $9tD`JלFEc6Wm$'c^8jM8)]s$̓|_{`նIΒ|s+c_{zz W$%9.0Dcj `7wm~=X+X^^4`{ն$ꄼ#<# •Q,+I>%tdf4`Jל$Ū·1[jO|dq+B)IZm?1Tbyyk `h*+×J%30-]sd0XtE>1;D@:HrS w±[隳$?YSc5əx,p`g"<:Zkit 8ꋼd,mM,l%Z<|EӢ__|}!"""fvDc*5|p6DA!|TNEcy'zuMEAD|aS{j/_8>OJ^Eq>{5?>(pl Dg,6?8OJDci20 ±uDͫ9"jjx D*("ݻz}4kڮ!*͆l=X 0qG@SGp,@h̭w5y#D*c` L\*("ݹz?6Tr!"kڍ!px VUDݸj# 0Q䃈8qߪ}vQw ]:±ӵv۪}Gx+[0=UD:mhl״]CLQ*yO ~,=}DĎ#D8`bR}f꣱]^>w୒j`CX]9&-~p DY0  0!䣈wj5TTrǮiWLm  0}4tUGx!,J8`R{qUiڇx!,J8`6X-Xw2<\*yX=}W!-TrVc״"FgwxX uDܰ:]>X mxs±#J>ceD> mxs±%\S>5mWoxxx T2"vݮ:ˮik5XI%'*}vSd?0>cƧܭ*(x_a.oB8`DR{qfUGa.oB8`\UiG'ނp,H"b߽S״ge6XXUU~vU[h?|0kTrU5.#bQ[l8krKwQ״]#ly*~눘S5kGW! R{qFku#DcE8`2"02'G^p,J%EľTkڮFhhk^kO]Ӟ>Hxi±[(TS״G9^p,I%UD>즒 $XTkڮ&b5< ElTe~̝&csz% lUDd>wM+&0=TrrW%lTND]EĢ&j6<لc-&dx8 lTAD}v]X92\±APf"*qJql9cY* "a]vPc! dc״Pa1O" Rɋ]FIJ*rx੄cޗ-jRB815Y#Tn]Tr'ҍ1" >1-j;d-j{y ±o p,K״gn S Cx}4vfIU#(>% Ri2Mi;>;$5i#$+wxE4cX+O4O%.XGcg6U״]#,+# JRi2=]+4O%/|p,/3N 0/eeI[c^45y#b9ۄc^|vr.#b]8Xoc^ʮ욶}^~*M±/,]'S״/c^\EĢxUׄc^xtr]^>nibp,Zsr.uWa*y@TADtr+X|xSǩ±/`rrp@8 99?uM{QE*9& LC؎rG"bi~p, LϪkڮxW @݄c!c'S״GSP1X9 'eUl  <|: [m 'JmJNw{K;?w5 l%XJ%?oX>`-܊u|ˎ㤒jB{x&c״(i;+|p#{JƝ݊܈~_kx>Ƕ+`F-"bCw" ؙ:4eòïgqJI`ZRi^Gb*݇TB8}h]vY9)ۭPluRn_J}Dvx{4 maIVHJ%_Gbbyu|L]ɞ Lp,݄cAD)=u]n!{9/lKHoJ%#b:L<"w]Ӟ;:cJ];`i>2-v5 7±o_ ]7}+${z#$9@+c:|7"q셇x$ } LªTb0<5sH=^l״Tb>[!0{˞I8J^]vM}&o)>RF(@,ٿ/oDdOu\:,!?*i^ Ai^p,-2 9@8X*h=C蘂>rҿRWCȞ 7Cy·,cM8v.>UiO^~LD͆ r5,FLuDk O% psުbz/(7>R";J%_!3% w e״G*䝮i/'nbw xyD7"g6" p6uP#ӥO8DNN $|5?Jf7GSɗ7~Qfc \7ӑ)Fjac{>J GXxw9".633=|J>L?/vM>DD>j%"blkr+0`״Nh+|9'{i`~pT0z~*yԦƦ"ߢ0_J%_zL%'xyD{,+9Fc״7xHb0j&7䍇M4D,l0z[`nc]MZɎ#wY?Xر5y#-TQ"XB=7x6Y9Xj7@־ȭjrTA*\0#n|ﱬSɞ0±@햵0r=}X&ajw䕀,p,Pufd}0Z}2|:c]\dMf"C8C#&OT`; 5qh0&}A9! {J?ُI%}mp,P#X><@zTH5d$'z|}p,PxvM><`kwRg!Ti/x} $j%2nG8ClT:"~}^aD}M iW.Rɾޙp,P!S״޽qk=p#  \E@8)Bs-(-F DT/HSdo^Cwo+z̅4y`B' lq~姦ooT #8YH^\*{k/Qӷ4`ء/M^ 8x!y`?5:3ź" !/֛W{؟oc2ޓ 'BwMZ+x< T&zO^y/+,vۓoc`,^~j&<][ F@JB׵`ϼ;14¯!c2k!Is+j'8r݌0^}.A(qӷGKa2vBۛvm%έ텲v >b]^L\9Tj6B8TiqpBxMZC- D0J>ݰ9aB7:lGӷ'MyJ (]xwq~F`5[ aJ:n"Y)hxQZp]9x8?cO6Kp,PhaykƤX`#by XA* ț…xBx,lXQ%4 3c7)<27^8L~m֚$lXcuU{2}\M6o_?XIx,lXFHn9aB58rIaBxz|X5}Ptk/@ P,?Oc++j Lc7M|3FEzmS{xh6~rH/>3/ 82 K̙MM!w*+ /#8ocʁQ.U(r ૚= !,CǪ  ݩ.j/@ nrHMxTjllǦoNp,0wgFH׵0WHMߚ;wM6Mُ| O'8oOBF8!ڋPq M!w*}r%p`v~'hvwM 0YZ 8rG6}{:vÓ`saM *NlֽJٷ) .}ǹUspU쩰 6p3ki&-'려߅'8(^j,gy$4ּ8BX]L1\Ohnr$PZUӷ՗:6}{1vUc985Ź[-JBcٶIHBHˍݰLu\99o vn1[~$L4}geӷs&8A&eOT"6}{B848lHP@>ƲE'a߱&QY2:_ql/C [X P>%6^^LBc p>O7b&C'!/uݬ6}WSz}ӷW`EkG @Gp,@>FJbvq7 9iY.;\Ej,ͭSYL0[c -˃t:o\ż @d BGonli o&AGkA[n2E =v9 L1, J',7r}E(aӷ'؟oυLb+0W^Md|E7_i6W.S0_ +P7(c潰<ؓo5!6v Bc3vC (.ߒNB!|Vbk"0_5@R#Wc7,_W)0,pB8V(?q]8rB8T\90R`c˲|!,_v&p-h !kfs| wc7ĀH¿C gc7\!D9ޥC`v%\R@9h"XW+:k~ != %&!1@!@.Cqg)e`%{c"h  #M^f"zBc7ݰT6%~n !S Cx܉wۣڋ+ڋQ(Uq@ӷ!jM> ٶnR !;}Hs'qg1k/Wc J`IYnj/0MFʼno [1lYӷ'!+u^ A׵HslZtq! P4orEc\ڋJub"8f!EEmjҵ/;00v]؆o4zKv;" )jc7wi\I=7 h:5pŸ,TzOU؞ؼ~UHc7`r/@nX̓d0/c4}{^P؂oc[C&~^nt!|6@nُ>Y:N`%TRA@ǐ%j/0v] G TKxlάo0cb\{&ye1'9!C}c7Ɛڋ<@mf ^Ap,Po4eQ{&e9EbVᇱnCRlYjE`PR 7 Q+ixoBn0Ti1MX P9n"P>@i~솻ڋץ[%*ƛ !¿n85?=藺@5|5|^'8(͉+MIToPӷ!cq c솫 &O+sw*<6c(X4oX1)ixoCc7pڋ2v2!8X`Np,PXO9]c7x,Jgj !\,{U{!9; _S. pk/e D0I9>0^g*}2pn|!j~P?`Ǐw*(X$Iʱ20v8vYq=ʘ4x{c?:E d/5",j/⽲cwm>x?Cc 9&6@@nk?8l:' r5Wk,Fak/.;)J IDAT !<0@mosP@ ǖCὲk&mBWjRpQ{$c7܄B?8$c7܅~4h{!8J`^nX^^.5H>(a=1pPACck/hq솳w](Gp,P!$e &,T*B)HB-%4f [ `솸ڹCdP@֚m=T>lre@-$4fd솻3(ĕڋ g &8ȝr`_ü PocsP(0Sc7@1nauN"8ȝ 1v`^meA% K5(XnW6@UuN"8ȝye\{بrۦ"jlB5fF8BcBc7\B7@nn !l`vӚ~_&8ݱ*@6{f9Uj"ph"4*6v2=~92tcPv $`cnY@umBF(? nn?T_ 7cw:' IeU"jtB80ň?c7Aڇ)㯆 Ș yeX^"o+m^.M6)82 3vMӷq-{ﻫZGkF>RM ~~?CK_T S5;c: ̆2,k/[}1pk""~%׉;U,={s j mӷdx1>`qbxpzJ 8ș2h`ox5 )8 s yӷw! ۣ4WCajp0 ]}eCwir}CnXlgN[<=͕}]|&qfhΡ|Kܧ3ݣ3N{n.$8RjSly1`nX6}`~qa"Up_*Y|Ӎpmn\nmD>?=]^xyǵOVݣ$)bsL?Lst_ }rScٙq2qVPoW47"`uu=x> 䚸Oo"??Ouúoks/9Uj\ |X W/n7[G{ d<0{H6/1vMĚ3}TkHb솱 yvg^~0 Xɼ#vth Gfsbo {r] C8׿®_ tZ9 djwg{q?،J6e0_'8ȕ2`Bp4{.vp8vÕ/7͕*R䒟7$ijYیX5]6-=Nby+ OE lyحӚ~Y^/́Vg~40i}ó/2Y[[K/W߿b+?gzIam1 ]\=9@&ߧ#ZB #[o@8R l2Ժ T"m0ٻ5N&6WL W͋i3rڈ| uxw6 upk${mooLS Lʙ sCƭVrcË)@&A٫9]t{yvWHk'ksY_ڞd!Zåg5nZ{p`:12E6y,D:vBhObSչ=yicrM /uVA!.&kv{#k O)&Ț`[cwF"؝NkZb$g3y!)|^d f`muN~ϱ |LœVٮy09@duĞcnḰX Wc m7R# (5 _]H 'g6&o*L}&Ť[=TۣI@[c_յC^^6j:؞ԜbP;A`30ɁdSUӷ{Ca&r:b{**7u}qަuDkdCp,#A#eR.x,@ `΍j~-51 J)zʵAϤ+ <48sjf׀,Q؂U=pȡ c.̃eop9 Y(4DV!GzYʞG]*cgsUãoWA;%@J>^vjiaqNGp,0F2k֤&Ƌt2Oo\t Ŧ7:5 [`s΅1Is2.}8~ogE'q^j%.\yI,$8ȑg]Z-‰q9H|l4?M߮R7%15?zF KT؆X>~&+Y$aCsfgq84TT}| M0 d&R\Zl;cOTۓt]SS `gYI/P2^`6 ennWA)Yh~ HŞ|i"i^!MhЮql!M^0衁<4Cӷ3q,Z5Yo5paب,-|Ɂ8T doM څ`A30F2[ ø1L#6?^͏ 4C3;@nX6}kwCo4hW)w_:> װ_D Bo:O B&kOU&Z9gJ="@nNH,K#aSP$c#gcnZ 2bǸ+}@ԴsU3gw%0j~aZ%Y?R,cIjۓ㽑YMGn'!fx6@n ^EcϚM!,=MYPym^=P|*N,m'spӷgq^/RxYӷz ̕XauPYp`]!5q !I}c~},wc )mҌ *6L qP,k|ǜd ~`PPrdlL>UC,MMߞ`A|5\(HzWs$Xܧgk/*dȀdI샍ee ׵:oz/قU]ӷ L\g?\Sc )1Mz>V8'>6gt5ƷƯ Y3Ű9 v?9k6O> fki"+%Gc95}pI,L%;7zY ifǾ4&o=SYfj|`;Bavl_MZ vX/{-O!RIBo.j$D ^h[1 ھL(_FlD`d Լ}Bm 69}Ja\gD :w v6a}min`^ I(F:8N4?&ʿ: 9H{rɁX t%59ZM c`S P*BWșIyoٳKӷ63'T("d%9sb;P{-t昮tp?a+-iOM|oSe؍tmg&V[o~P9@NlXm`>+IXo\v0GqSrӷwi#A&#RʅAd%$W@.RZ ; v*0Ag9]Z6} K4Dv&!P;pNt"%XWf\)E _LK\Kp,k/{%<.ǵ(ҹaC Kj8)fu~#ث^SYlq_^@ⳝVh5n!@ \؃ BPɚ{#Gަ.d#8ȉ-j/{%8̝˱dleڬ %X\-JjRzFY XjwtP̯؁^`o {YCl}O)H@y:}9Laי uaM  P<iE@#mA(/c7\^!nĜ4V A 9K;}])_6 C d{bD<(&|JŰS.>˅~JFޤC*\ :"CbvG ^ /\wV TN,@E9qiOKˠ (/mHAh8aVauOD0'Jso)#gt%Suw;ҳ ]!ox?ɡG| E a~#@cxݰT"^(Ni (ZlLl0'b3G۳(: /sD4u"FgᫌͳߕqS>.Ls#woҁmF 39|AG.C[p}@TP @ C4EH VV.k/J۫Fqj|t;7!4F]K=wJ>ohew]Hת.VDz5x(L\6ȇ(e %K)!4qW]̋XjRd dOsP r?v"50Rd]IaTt l~ji-'=f!G_`FӞs IBëX 6Op,9palP!pU{(8 :Kmh lߪRIqOEbF 7P(;ᱼdZsC*:) I,{Y3+A?.raK @F p{^lF$)$SH &6z)p@ŇtXm\[U3:H"mm)ziN ELA!=R՘ =z7e~^EVra9woOk=שh M^ƐL!)'5Z&~\gab՝y^`۫'o@6[ k(ѻO략8Ǟipe*q~ca~ؕ:}<79O{T$~j 8| ɯX(YQ3#8ȅI=^ro8ٙU )01\]q"&M}BƵwGnXV>Ӥ1͎7N}nM^DO) {VJ_rkfKh,l T^"ɝȞ gTq!ܥf_5&t]zLјMo@v71C=M.] kF_=oa[ov>XY(%r`!0yȉd)m{ct{fxGiًp|~ U4/vc^;HE,,?ަvIx&/s?anu^lX S 'ȞFV WB0vuE O^%6"\u)N8'^?p}Oh';~?d~؄Ix[eܯ=!OSߑ}< 'cu6Kp, r H$chBg BdI6lp~> ATP1d_"mE7gOoq~r?(dAx"x{'IwhQ`Vج۱F5@ln̟Xrb*6f:52ٸ'mӴ [!_BcacQ51xng"Uf%8ȁŠi#GnƝx]⮀\ ch*Q+h D-!W ҽ4W0нy#5Q*|d"'s" ȯ/9$}6-ɈEaYU t˺UN~PE ᱗.=N5l>=Ch,nX =ku Kyx176i X1|Kq"Ȃ9eHɹoKo`qUU$8 `wuiY }W0޷W ^]cϊX`dx[X ,3WAQ|\t8E+@Zx,0b,]Bca:sM _gM`%6B 4Q[0$˪N]nVLp,$ RUIF % 8E dۛ?g؍ m_Dpl{/BxKJxCvm߿v0|&4fslkג´kaaXV+C+RX6c |X9q>%|Ium}HN/ ߗ$h#4YggY œu X ,q>y"/e: XL5 M !\¥ʋ*lGA|VG::)Ɔy]𾅝4\ ٽ`5{y`^}9Qs rcBplXhևlHɀX` c^@_xv%쑾 e񾅝:L.?1hL,ً/$,pH<5 k {tqxipr1@~d@h, y:86wrp@ 沪W.%J#Pz;n$wmPgN8V3X`i& i.`uhN.`nuל lĜ6^P>\j \(PB^tU5!'r$.>nxufHgu(˘b- '8XڋȏM#@<` cY;U,X8tOu\h@:,ZA0ؐb"sd.?XE:bؓg9 8n}sdhh{u; ׬rC$LM#ǦA M뱪=X+FJZb~x~ G1 mf@nXфҥLK5}A!)u\$L!@Zl55@: ;r&8  djyCۛk`pQv8濋{{$pKnBѾU%~Xcham_ȼ$ Yu ,Xڥv,FB$01 Cn{~Mw?oCd#T, Ma ><9 }y}vHi5WC_yrA)rUUՕK 2~_iABKC!iχ:Tߡ;0/ ؐ@‚W(i']s$+ 60k) .$a#T?y,7 Xޫk·7ǰUL!BBRg=1I6M5anF &sYw͵94ĵJo;a_Z0{{|-`c @l\K (L.4Lc,mSbu,d:BxsP0>k.ʞP>mVs`&&8YŦHXߑb>K uh^<PT,vOBdOb>Z)^]s> =y #'cPXuޜ3|?| $(k= (J5󸢱@'ȓH[O'qHC-Dck< i/r^Ll5:u(Xh}1'֧P#zQwU 4kgV,qO101>FCf7վT\q ?ʖM5qF,!+wv\ k^Fu{Xk<ּ>]{Ǩ bdKܿ P2@ـdꮹ)ygޅѸ)h .7d6 ~q>V<Ck?"ӳ?z݄)‹RSw͑EduV-Gx¼ܐ='scPK~qja/ 3wN6{g`Fc%i=d">OfY%oD`Zu\TUZ3/"l2gsCܣzYp4b5~=b\s9L sȬFx`5P tbLn1>6P~ pXڀ 8<V.ufD ;1Nw9ioyqɧagɺ #8/Q`@s'LI]s WbE Vd;fm߫`c? (^xq|֟ztϹ>NvO 6m ssu瑾7R2^~z>lH +w޺|s'k6?׬}^` ޓ ; PmO/@a0mZ~q60b ŀC_G7@Ŷ; Œ n} Cocj1bL!(ˡե"L/u1!}z|'ޗOg1e!S!‡Ϳ)> Cd/'\?~xnÌ%|8 o_XhhPrm*'#8YQؙZ Jkg6_LYQ[wm,r&0[|z3> =|,Tw͝B(`mb )/~Ko?χͰ?ÅaòJ g7n+~8<8wBZ99p<|>Ij:!P/'8εšCvޢ:Aid}`& ,I|0.-nAGFmz-+uׄpcI{̝o"y\Đ>y c)Rb\Tw͕~{Qbmk.a/ysO@Fn],X\X8a_L [-c)o6]]sd9,(_RO'l&{x R؋񄵁sG+7np4gP N~lrtYN.`Tq"yB7»X4dVFwyycmB|Nu}psǸ2r~!o!{nVq.3 i2)c\gYo3=U]߯Gڿ@!B/6wC_.!Ǘ5̙Hp, EN:MY9vc3,XqӮz1<*/X0<!0\³8uלiߩBAץ7 XN·YpR1r#DRǍ*#xCqoGɊt汭]+ksu)nŵŵ6[̞PrxH}q2 `JGs0klǏ4c0ʭQk;̓gFHꮹ+%X8x.&87"~} zc"\Ys%x??4n#b9:}9#(XxXmkfv8\ L>)/8(^_%8vBc3ƒ|`&{ `@¦74/0aCBQ 61N F̿Ǭꮹ>[tb]s%Ft'75A bY̍Sץo|*\?qqä ]pٷANgvEpnʻg-U\1?Q5W:}|w}"]C.'ßϏIP_N>n8(aτn/燶 mR;{߬D ǵIk.$kjEPXwMH6{i7~]s>KRlNG,?Rkndƻdq9)m] __l]7k "ma~2>' |4s؟r}x Ga^.mxɲ'FX ݝؕBp5AdLUs,!GFMT0|9kz}vh4 m8]8]$/ 9\Y]sֻJhB@!n8BwCj CH`UU}]WIv._s\h˜-B_/ <(ui# c־ cc7o*\?Fw]P0|&ɳP ^ ЊXyVz;Ipݤ IF@Տ &S?B{g0 aN &Z窪 $@ֻ$Or ϧM/^C*}a c?Ld#$9iJ5hyTpxc#Lp,s 76 %A14)vBc3N~^5}Rls9?C_ǀ P $Z^2ns~Geœ[aNO1x;F]v\;wvM^ >>(Rqߕ'|1|܈CIC`͵grcYQe BBd.ߕ{OD_RBaߧg8z) {L wN(=, q8j30ʼB[Nh>!lγz9q78(/+oAlJp*ojYNp,ke)`g1Ŧi˥3-KB(0>d,\p*w?va>K}lпXzC`hN`ƾPN:ƾ-]slk T(/8/aM:JC\W áGnͰi;P6",0!!R >L] 3q붓mX*SXNb@6@ ׮F2yGs`3Nl:9,(]'4.}a~NO ^?LBI'99tA>k#@=Np,l6`'uׄo-\z;Ƚ;a !g )oP`E{UwEUU? xwH_ vrPD/G&û$INezc k9 cޗaGY1@ xPCXkl͊GPK}JLp,ca?1$r]85(<6=g7Kq`L 4.|CA֖ɕYV\{`>@p,5/0#]FoJ FKogB2gqy6cߗ JhlbWp$-7 m7c!+Jo`ciMؗ,)@ OVy6cщ,֬HO,`(.lh`+uׄ͌ZmT8bE߇)a|Sy.[P9}~Wz[,$f_́E]o~r JSl8 71y'|c#89*IOq!gHk朦p=$+זO-!nO7{ c\`Zc Z-⒋ uJ R|]o~3ż$&]s'8!Y!'>Y pLzޅs,ck|p`i8h̽)H| eżOӕLN\  @,N-[&lx?jz/pJoeTwMΧY|}MO23]s;uQZ8Hsw 1X gaZ2b@{}5$ ] M˓CL-Io [r0e)n`[¼hgva *23᱋x]A5a;iYp$IhlyhE\WPܡq[FYY IDATX!Q+aLYz[*;J˓2ik,Dp,BA1?zG)X(]w&0P[wmUU? LH}F SRDx,8$p>!uY#H1XI:&=K lJ`(Ck˕T@~g(Bc p.PdvuVwMx WZ3 9͏Chlx2#Chl}@cKo(H :ZRtX!9( |R¾x?d aBppUUu%8]T=)Bc &<H\<|Wz;,:d>Pلrȕ4-uרuF!85Py5#Dܸg Bp,&EA5܀ (x;c8W,>+&Oꮩ뮹էW6.XXhho|͗!twqh?a~½J8ya}f Bp,SS0lBqF,2|Լ[9nC|c- i< w]S!0Uȩ |]15|I|:̄f,4u_z;,|+nxU0.`Y]pl5g gűv6OPSwe5UU޹`e e㐇'Dh:wXF{p xx0{E9w֢E/?j!Ȗ}GN`t U(]awc&G2 0!8R;C96b_UUTUձ[`u]s\C{7 mAXLƮD p/v!l+^>YJn^nhka-0%QH$8v;k 4R͔G[]sVwu56b_u?MF_rd8Y>ץ7>$ w.BۈkƦ$';C]ϙqa䐧W%8I]sRUձc~3~˝ %xL- г.{B9^]sWwTUkUU?#~p\14lehՀ ]:.i^c,؅}k"SB<U8Cܨ]KP\xQ^,/|.gCۇsA;ϛOXx(Q=E{?]. PwE|1?N+~=[pO7Œ]&_%QJh }3k$3m9֢Y&ъPL{@] qXX E QP" 63>up+g}֯:5XKcY\SAZbPƳ}' -2:?B~!HBW8Ԫxg+G2X?pǢ]_69IY] ` F oW}9~=Ţ]M*]UUCH`hPwNAUKCƃu^%QJ$ЉQ mC8Chl53B4+20 }YgwOMˌ^Uo^P`kc`q*(3lamH࣬ݕg. ?BEp9cUUיyD:1ЇFp92 Z{kw>I c4 歖!фqf5ozU!+7algoh1Ƥ`q+N?lzXw_4Ţ {%V2"ZI3?VUV;yhWocy NLb%u q[`c`q=BcE;ɶ<N࣬"]MO ?s;Xg19 ՁC7ǚ|r?,n}P'FzPRO>aTq.'sGs\w`l6K1'XY+&~yy-c~OК)Gx(uԥ|߇S>JI8Į}sOÎLpcZx, F$5Zg+g}O 6& fq*PĤb_V^RJ8w1wYΔ|,&hY>"L~ȓX0&w/Ĩ {\ oCUZ]SWUuG)I(P'%m/lvQZygps>&;~/"81)0W`a7 7N2y)y#zҧg.B8x2椪[0k וuZfdL˜[aG*I`B >j](G5'UURX T&.@jOW]d3k}zf&0N[ΦXc=mSwfAc.,zck-Čw XwZ |ΉVF8rzFkYnci)#I g!p ]!?T^%J$ }}FC,cekvֽY*}2;b-X4=TUu뵉?0 s|)> p8;bv@:0-RlXk:tMG qY*ޛ) \HNT<T|p!g\;OXa,rKr1D~g##$8v>k57=Np, E4 a |L~,@Z.WGn&x87>eg c<ü`Ld^7%}Y#/sz]UǺk@0&fKP]2i)" C(g-@:^ cClYT Iu'C43I <'C}2w/KrSq~7뮹(&85s`9uUU^HMqh{>D(t P?w1& =IYa/գ9)\ &`j> X\;շa*ƺ _Xweܻ Bp,eplAq !Y]s|RB8ɍi XӊS#$ m1P4$֙\Nc> HQY{8W)>a*ƺ W`%'Z Lͦ'sI\7z7ĄpX`U,r~ ~X5Nǽc ;ן3cI mGyGP'p2MAUUoW57ux r&.}5s0:y f#tޭ(.Ρs9*]rG)C#\(ҕ7Xa_ qk݀Y`L'xt >at1X}z^WUk5뮹t(=X`IOZ oqs8:qYluHtnXoi8ֽKEP{P|K Ӂ8bVrHc2o5ǭ>9t?UUkn뮹>CX XrL@9/tvCyNGS(>~1D~mA "Uzx'RZjZ1`u10zg߻2KHPc`c^UUKUU-D'8/1r"8`q|kBLFpnr(^8NtNH&^?D9(p"$^ʞuNcHc>ǻ87)>a c#D2'8/1 @@l() 楎j(6N*N`z[wM&Q~h{&̘6_ؕy'YCۇbO'>!Y1+Ę\ Pwe5@0!pp2ctC5 wx Jo=9'dm_؅1<1*-|z'$ohkd̅պk6BdJoX_\`ANL[]zT Q0>ӹY,Cۇw.$c5Z{hledBȅ'9[25ΫcL)K?kd%|IVA1'jUU׺kkꮱ  8XM[i3 @_ G@lg4ca-x4ϰv?dA^N`jzʮOgx0UYN$^X g c%`Duׄ$6(} 6Qr~fWZ-Yڿ ®[Ml8hآ'HC57u\}p0- L ȦNI(ܛ=VehpO?ǻWywLﾐ{ L:l<Ɇ5p&}Bn޻b)S5*qUU]3]s[w͕`c%ٸ6vfʤ``|^k$ykR3 59ϓNZO1dH?]j-ڤȑ{)^UU_knKؙ6U`QP& W nq/c@ `/o 1 E} c>iaL'G[}2=Kre Z-~&H}`^´\%Y_wMaQ*N k8, 1gcنu髑#}}2=ZS>p ʡ*TU?ꮹ#- $8/IRPEe0.(cbգ; da*j:Xا!.k!8X(DqyNah{(i](qhs]pTc߃k8cZXc ^dZ' HZDf@ȚXFQ)s v PrzK1i(ti(v6"?90ȟ|r%yc4)c 0o5-7xhLEb]KD#,TH07c1@!@E\I)J8u C۟"@L}Z!!cbC{=y!>a-[1 ) TU]sSw}{dCp,4 iS @lsXa*g4Qa~(CUUU֬?ZCCb159s hR9X&ט0{]Uկu|B=  hv$c3沄]A aHILCXPvh/=1krchA@dO1ƴ1Xi=ű> Sd"%뮹֟ UcK #H,sep>͘=JcNd@Iއbr_{bZ֜Yu%1ei&g&5mUUZTUCUU3f"!}&HE7dh{E2 P`TӰ/Ҹ秡 ౪?7Ǿ5L(C?`OwcZd _}2-VÚ3duUU]sWw͹@ 0A zP&@R/%)J㞟/fOUUhh{a9Cg`up@3^0-ZY s9|ɴOXTWyiUUUwǺk!(LO?))}B %{^Q_0³8^\!qOzV|W0ڸ`YC<8(!_$,;i]= ~Ǯ10 cC2{~kR@B`߆^9A߆U_ ķjb%武ڸMB, h!$L#}6K ZyeRb.]9 }UUɁ`cUX1kd@.VY mlڬYf'8H Ig~ IDAT$Ϡe X1Y Xeo[ݗoFr?G?~ZW} |Q5cKS/FѼOWs]5g.0S DBlz E63E'8`ݬK08oC۟3ix} |1ƴX#|xF'' VUk I[ )})eyt)*} @Nl_ XPds ^7z3kXkd}XK`nXW5u.4c f9X0BXĶfLd-!{2A8HǪT bO+k來ݱVO,s6kCkFP2 K(R %'FlRв]sVX?c2 g ~v{2!,R)V(1}ṽO~_%u ciY7`uR;04ػ99;مP~_5!dS@JjW#y6),Z֙^dJ9\kJe>dn<y chF}0PrUUr (`,}cHx A) E)=? 9@BR c~)ZXVmUUUU? ECߺ60;0 m! ᱷ]jt4t7NcU4}gk:)Gkb4Pnp.2B?6^R$<BcLM:v$ioj_Vc[4}f"~έLccA7 n<>_ḾXz Rf4}zꤡ>=[;\7mZqE`dw;İoC,ڄǞp P@JnMHQ PH&T],`cBf!4 c77}{k/-ل X %H^S*黩}] ]AӷM ٺOWj"۷!+"ThOx,@]~ q> ouǕ~ <)xks+%\O {BX! n/NZE5}ƊgA!vku~иGj)`o X8jѽy ޮd 37/K۶oѾ{:TB5,[`=@zx&8H<8` ?<ȑIiPt'Y mũDO 幞Po]OOl5pc܅ $gkEK,ABas&;ovVfYJYkԼK*%I_3j"n>'j솩B|k$8HՃI&& \]ڹ$վi)%؉ P  (Xޞ5@'LS ! !(# YȓX Uv&}M*`6M߾QX g9@\Nu| ` 噇|d^P'zP΀ D[[Hr=a|!֒\,R!&6L 8RR 36lvK(7?7vy4SxΘ^ӷP)́\OҨYj~ !A̡۷fC P+=ӊrgvNc 0u-Tƽ0dV ^ TM<3Ro8{=Epv Sd=qlq%8HB<.i] &~0yx.z]UJoc7h)F4r<3G#gVu$Jp,F/p]|M@nr6=aiA0<}[o*Wh?ʙ|$}Kp:RΦ^:)xד+]b c7L7'X*HX % ]g6@[EiR:=3y]T1< p?o5#[ jtaדtmS"$x^:]AӷoJpTE|Rޣ.}I%`٭$x^HX 'b2,\Yl(juntBF4xVǞkTKp|;y:-Y w8C-Ov*.¿WTnn[\;#8ȉC|9靈aPn}̺AT k]w_R95`{b/k{ox ߧE~V|tEash7e @B9qHi솱L,lRTMe6vuӷ7 M4K_Ɖ@N *]iޙXr{;/5krqlW]3$;ve4}{&cĸECGq¥zż5 a/\xqd3Mz~̋FGM) nKa|\NbgMBxv@jN53`jIIӷo g\O܊Ԩ2vTn O?vmS"D4@eg>'Ȍy`W\S#D$fv9'M߾ÒXvlإCuCEhc7L;; @ N}5|O,j;Hp. O#Y{D IDATco,l5!|^8Bp>0 ~9JH`6Jr gv帞xvzh'#X 7 1 =/eHQgڦ0`%Lwu^'M߾)R AT,8XGr*Bo.İj2דL˰6g@5sc7LOv}y&8ȊB{<7E8gkC#0Qjep4+kr6%\ifz,VcPNrtkײLP h]`I!sigumS#Kc~U2vgeyoN$a !|‹1593cP$@d[!@oߚ6O_h/M>:XwX;=M7!4+ -Y k,Ir뱒3,@Mϲ8ÎT?$% ȁd^45V%+ca솩I$2 [ӨC+4ۦH\wU`51rĢp+q,"2%,[3,Y)PXowj# dg{7Y1ߧ`;}{Lx,2n>æo hמg;RC Nl̬nb="=p0óyX5tW{;( ;W-0E JXI>5-6X8ƌk<0? W-ۦH^4=+/!VX ŝ4}{TgfgAz5e-8b)pY25Hy٘^־L60)gə"ʼ<Ăcog5J&<(N`>fƒ4@o=DM1rԼ/Y Gpkdux`iU_4B_sj̰#~4@p,+r@֦C[ {Ƕ0+a`oVL iJXr9wKA;AS~WY]i`Wc笄wMkæoOnsK&}0(CLUv3W0aFGxAq[v<ݹllc־3<7=C<S{U&q!o%\Y9+a6!j ;; Y5kqnpawc -q ?h< 0NW6O2yކ@ߡUVvnfø`Vg8g%y'$snW~v`R?7vthȝX g 0;S}c7\n,Z6H8ʢ.m*P3 |˸Zi`Iឞj,p`U\لUl^d-c7N6`q14v":x/ !c ;w͎958+c0}{C? $&V 4ߣ&yg^;' c7j<%ԤpԫC,?'8esKJp,8J]^LTX;/4e+`b8i˘yA | Ѱ./*pN^ҽ: a@;=:$d=֞Wg$Z*r'8ȝh8{x;뗕{찜 !/M߾)q9n0N" ` +smSbӡc8z UVpjH\=foQ7c7|m QqÃYŞ{-^J'*xLjw^atoMJ&8ȝؼ5}+$BFQB^R ":fi*#q(g빘=Ԯiï1 Hc7\ Żx s9w~HIxz5}{Ve:p*x Hɺԩl&杕x!'l !cŦGu: œu2]쌐e&4 ϭ)y9E.a~2g,uIZ4'Ԍ0}1s3Nux/' J $/cy0? v  mA^39pqqq=3So4}{[Y允ˋ 4S1Xr`Uc7܇>ۅU]S4}{6Y, XBp=Yס0rb`A c˞aun=+w1Ay?ZlM5}+49,J:5u1ܳFr7}k@to1c7 G7@?~P@&?s@KYw|SӷӿwV@-ty Pρ 0a;s^fQ*K"E !|ygç*؋ {~SyojZأ{P#t}'Mzgˊrz|M\r`qĥaЫFWųV}eA)@Bp}Mugf솫`c7LV;{^F:Rx/vw=jg_u@*Ks~6ٵs G1] h"),w)aph,Ӫ887o07v J 2?g/@%4Z ~kʯ< !OmcbS̞'x~5ע؊>/pBc9KÉ,¿`d$ 3J*mZMoBj>'Jqm's\T_~8w![mϦYn=cVӷ}}m] Ou%jqc7}f # {DxlŚ7Nj_ {jX:n%0YPxfym()Z ~צow!FHXnfe/6uPXoc:5♶rl.< S'˵dej  Bh6kQBHXI\:> X J:NIh,^{cX$BMql әֹ(˜o69wnMV%<,4}{->y JdYBoυQ"f@ת  #-c+#4R ᱕KNq>:0X4?>SJrm7#8lc0nyQCn!|Tڄ*%9SӷSOa.j _))6B{{@p$}}E%5Ic+!4᱕KW?5};7d-=}na 1vthGL`af`;.f23ph)t}-\'~ܹ<;fXD&睩/e/eMP ^,6+~l.Emݽ;;E`fe(x_fpf {l}9 Hۡ`l v Tgk咶//?qHD=O6QFBվdoC~/@WVyIp,P+< BS=c(Yf'0 LM 6*9«]O|ϟ>M Jӵ=ꦠ&),`eۄ] J_ e` *eQ6=oj_ųE!d/,¹ {wW||$Jp,P8 d~]k_p)ܩ:dVXXRgijVsMm^X9)!695H`l"b#{4Q1lo~kx %0T'%O!6 "%Mߞ4vùGYD~1(-  fˉYsHXTn@E$@<&zg_4J}B?&EGSq AG@) U^Ʒ,u8v.D Jueg o Q1{*|GڏM\@&Ʀx+dk)g;w\8Jbe -IKN=[$74#SԴ/*)? (yy/clJ8{LS/x~ WH vM_wye$Lp,PLcwh Oq:F< HX ?9TX@o}ԴBa]!(LmBO/>[wYlHvwc7]8c7\&Z2}|;Qv!,co>m.^OS[@<%ѦZ=! DW_L)@%s#'yK)Tmux% gJxc  #c7LO"1n~ * Zl6}2kDZ.^{ע0Ծ l~*¥/c(A5O^djM;{ȟ؄C_>`eq(9֐6vy 왷]M/c00+񏆖LC65OSz c,6 D&Ї\$M~k>`Sf=Eۛ ]:;ح4$]8[16\ʻ?j=!2}c3ҕ|!s_N]/@E!¡LEcwf6@6gy8l}ӷ6m~m,|K:|'yjdV>Qp˩py*wϻo/vֹHQ b˝zZx M)M9HqO2^OzG-,\Ǿ4w vF]@ś bC Hڙɖ ˌb}M@pf+V@,gno6נ]"(B#>SLOYlnbse 5[g~4xx>j";}Ϲ{ycxZ/x^a:33H };!Z`1O074GvrY|px=ug j. IDATlO/rJo:Q,Pܓ/M˂CSccl&oH+6lnǐ_M5hdn[[kV 55}Mٙ~jm~o?FugtgA\$֑:pܻlx5?7_ @Z4j볉DZRA#KjChAl2 [_?&_a+61 WM~ZA?5sRkf7BbY 1hSU~oNW1>^oz˗gkJLAc( zg( }؋a攇)D66[ hTc(=.L߬#LX-/6vy|(c/uclbhtV P@m4 fL @NmC<[TDx,5۷uި~oMQe&TP\`ic7J3jiJ) svh>.:V&_~[!nv^Hp,P:|X}pPTb+0عXY?;.OxV.|8Z?4@nj{;Y s>@,a䐥Gy,|[濿3o <0/ɀ荷1[wu3vudcWuS략V[᱗!wfZ؝[E~А/vld.> 4vY|7``1_/R"tC aۑΗrGso@/ϱaʍp*<Rm0nfS >Ŧabs3Hߙ@' c7 ,L/YT #=loR| 3vÕƬyX籼bxVp.$`b`L䳵d}6nl؇k@ዀ^F~ls ic7l#'l{k587q % JXJu@ }kGg!|)6`%dw6`#^kW;sD Mӿ3{ƀ Hv$-.A x1 scZ])RЧ#HP -- un !)ӿS@ ܌pBd )MPny T)Nz:lMOteX@VA f0vù>ed trf@$S%PL*$. cz? LhJ<{Oz7V  cg : a@6 dwt_#dL6֔Ch*@QNAp,PMT#'M6 Kh,O2p$ &FM'3 <&4V8qΙ=k AB@lD p}jZ0Xt\ ̍|-[|4L0_YU_ ;:y[7G~QqA2z`Ԅ  w@> +З~F)Oy#<x'~s7:9 *\عn87abhj$a/ND Y@鄧i5TK!#<xXؿiXw`N@ں9';wuv J[7]Q=?ں5 ܛnã"{bh~099 =c{#w&ъ='m\d-1˯v⼭E)r3c@V0rO]UCmj 'cGx,#4$B,V=Xp,0v.nN1]F?<ՑJ  Eq{['{7qaZ48P@.lO6r 8 C`{ށca{ۺ9S_JRت]M%8odFp,@Jj0zָ(jf9 ='tG.6a[s֍s)@q֜G&Cx,ljVp ub0j_YKp,PX谐8nĭ5'l\@5u ,`{ P`Ɲ7syoS1 ؝=y7Pj>9H)-0b7q; _nJYp`>=c1ār KYP͊Bc`<Ƽ44v 1`c^\P>ӑXx^yR #v0@w!A{_'>` JCtAd\؂qXAv0.^ #j ,W{mh,l2@p,.bNK/Ż0غnnۺB6qF Y;qKX{ƀ =zxńƲ caN әZ/|rPz(I\[7҇R~͇')Mx5G(yش8\r.ᣰ,?NlBc29xM*?ၧdhIJ)-0B_[Cp,@*߬P4m\šM@. nņك8E6+[ơ c y OrXaj@zy߁m246 #7< ?yR #}S8rS5^tͶ&ݏџں9 m2Mwag]20pb\`C.d4w&.eca7  :ph,e rUAE(w4n声= 3)0\^ 1O cR(ā\ s`'k!/!:WFMrn(>oq\J/M\xg(XrK[f\`ں.KJ駮ٺ+Y8xy:hyj#X 0\ݻ.MB;Qv P6l)21s {V?Jt~n-%JE,p (X5cv>UAE0Xq4ucrMmںqp< Rr {̱0Hz` K^[sg'(Ѝv؜ 8ҏJ",!%;; X<`k7S3K(A5O)ۺd_oT?(̅-3 |6I幋N=vo r͡vجbLInBe!lU,KՆ <|擃ҋ@\ɮ0 蚫yL[7Wy07~)'˸D3^RU2];Ke tSL 4lG%3!\>xo X{˲E1dn!ց]~'zx`/pAp,ÄS5^un1k"P׼9Ϯ^#e~BN|ş=2Cs,l*{jxL;Ś>](^/OtI^5,ԐܤvBE=[_ x@>8Y [@8h>fs# lQ7|h <Ŧij85Ev~j.Koh kCȝGhr9غWRX'8qg꓅O|rPz2.,us7"и l2<1ޡ٦.ظh>4-B-"xn~;Rn-5 H/>c]s {G .C )͹op"7ͅd;'\wь`Fjv%Wϱ;Wrhf%w̱*1^(#uA( Po^+c5tCLr~g{;=Yؘx  h򁚇O|rPzȊu)OtȺiDpK]D0<]OA.!.'JA\麃dAq0pZX_F 2.4>ؚ?v(+?wtu!Q=Ox2mY 35ʆ Pn"Y)ޕw fOv+cOcup~kd] yh<"§_f2W ??eV dE?$86)LK5^1DL,5~l s!uaU`ÕޝK/?7cxl9`W'{uFү29X38p;^㑄z1~}=Jcb~5t*W(ѝhƟUI~Q"e\؅j@&KyT'ؿ|k)@wr&,m3Rx$ Ba5̬e><5[߭O럫=3l21r) {zZ'Y8H^ k$낢SJov_Bbb)S{ ]gy.ӘcOޫgk"?Nw7qY~ыj>=V~ ; uszϓ ,yOxXz"1O02L5plXnYxM - 7j>تҕXxUl:]G [*F@`,{.nκ\cؒXK]7uwbX.pE1_{4z o vBp,]D͢#vh"!L`~O~^:@X?xj>񓕮9.`: ʇ9b GOE®x>:8;0}bAwzSVϮ7*y7ꛠl±wñ5> @V\T17'=/(zLB ƥ_|H3T'kRǬu3- XMp,lg.e[7nzF9Ξ %֞W.S73 -p, xڪeއȚٝ쭻"Dv}FMp,+A[󳛢$X\H'm,򠁌-[FHu UЅ My߾6 %Z0pcq >@qz6b~B $kO~ՅWǁMsG֪~!,Q tQEʜ?Hc7"@ɷ@p:u ض8xٵ96|kѐ;6u0p,rq΋ĺU Ek_.UqIQ\W-s͢"b7Hc7K[7ҋppX~@M Za/ϯ׹phXu0=GGªM8@ͭu39\{N=O 0xsc8pa|l@ilH|zfk[7A˾ -̥`C(7w2lź*lo" vJ~ F#ê| l]a(n8gLk6ރ]j{=Ajd{׽=R%Z@Af$cr_~ ; }M^8Kc6O.RJ(kv~Y$X\ CYSdɷFV@,b;sM`$XKX?$6p_sR[2s*\zq9Sbo:Lz{qVϏUJ//|j} x?`flmS \ T ]CTH6]H/L?V 3Pa,1cAR?( ]e]yO`}IqDexn}~SAPRt?QSQ+'Ě )Vjmpᾃ{7kIVKjH\,EGϨCQ-lL[Ϊ*[r.4ئ^&z `&&5sPX}.%iC$ ]"鿯~q>EȔX (TuJfM^bI(T z!O\otkۺ 0j>Y[h؎,Z' ذׄ3=kcP+ ymtKҢq3% /yzW'nJ9ucEJiYUAE`#\<Kp,v ӛYEծۺRF>cEJiYOK/AcOLΪ*Ȳ+"8`ںYta0ꜥx!ߓ0|Z'GgYE3 Tl 9\0X |b|xe\0(;0Ko|rXz!O"8`mQzx2.!;0K5ֻj> C*Sj>J/uskm 0`>M[zFb,`[ _BsgOf`fmݴ !4Ʃ(UQEn&#a47ƪ(|R^`ǩ*ʛ.<" y[7W6#4ƫ(" ]Jif@] 0.Vɱnn  cF$iΌYq|rXzl "8`d"fi܊&c GSvIp,8K)^=nJ]jO[7|rRzoOu#@ةj>>(nvXBk_{Uz!:pixͭzwbtQ'`)3Ap,E ey᱕qتӶnZ%Ap,Exͩ1,һ`;ۺP[`_\\"}YEؒ$8 ]͝,o|r\z6K[7 X D͙,֢OK/,ۺ)&oc26KY7)j>J/LynS)ҋJmAp,@F"ܘ]5,J/ ݹ9ON=ںi  eW5LK/3\us`P[7.OK/ۢI)]UIUz!~K[7 j 1E e[735Hp,@"]J"<`0P ߩ1.j>Y^5_ۺR`d.Bp}YE˔o$`a8j>^i[7BC&8#0οH%]us:h*xRC tb`,e!9MJJx,Pi[7@p,@A"gj̉E5Tⲭ XDHΥq'.t%<(T Pi@{Q|Mۺi20&c a9ScOx_' 2u֍ 3 P͹4Oc ݹ8+eF=S #ӶnZ XExαz~擩l@c%8pm\^oݥ|&8,T zc6m5  EP 輭 #X~UJj =T`$)S@p,h ׹Q\ FbMk`TEXFx,0_ۺ2P@.n kCvͩr nf|rRz8c"`=h*7Lߍ(Bw5C- ^mFx, ֵu3ko)mݜ)#<ҹ ql]5tY[y]JXq\ GNSJKUcؚj>9Lۺi ȕX!<Ǫq|H)Key׶n.șX~o)/*1IEyMJi@@$mt<תjBcyi[7b sLSJw*bBcym|S,cxnn#<"<g]usXٻ>L9+&1xU;#0`}lQj"xPogZhlY棁]*_+c>I~! rG} kc>I-w| ;DcYn0`PSz|  VKiR4;㕱]# a\$9w  L$SU} "X<ɅXZ;Mrb(`W XILsC&Xc7cxeg֒Pzd!=؇`.0NSKrGocv2;@;؇X`%0N!ПMثj1*mcB}O\۔;z5EcSiuh,t܇ @8;IrkWp=0XFc?8)Cƅp,+Շ&̪]VJ碱<} cX>I~,J<`VI9#tHL8'ч,GrSSDꭕy7j1)'J<<>k±<>$^O12XRZ%-ɞcx}Եy$7#yN#a>Xq[{IJ'xRZ'y<> =XMƾCL_J3<);1=pчqn@Ya\$ykuCiUl VLm#|C A8gׇq~^XGxSZ*#'91 "0Β|>jVz$$3/p;Ec0v#܏p,/$gc#FiVI~2)+pڇqaHax+{I~+qhOƔ}xX^TE+?VTZ=JrvE3C +:h_գ$7I a\`Po IDAT5cX+}{$.ÊIrUZ=0(?I>%3+cƹAVG8Ӈqd2aEiĨ*ɋxfT`-aLuX$VSZ=Jrb}}`ma'p!V}iZ 2{$Fx±>$+*MiȰ6+'%ɞCbIN0v< X6v)Vl}*6Ճ";a\0)Jk~*^VqmPZ=I2='V4 )ubVO lAiuzxB} VKŁx);7`Dz%){± KzUZ=r'@4`Kx,kUOjq(h,Dz%) ?2±ⱬ$JWc@4`; 7Dz^%:/8R±co,Jgh;E4` wDz!_dgDcp, X6~իl-X  w ˆyeih,;eNyi8 !±plwInJg6X'X6^Iՙ#l){܇qdE8@< CiF@`0Xx6d} ^V`8Xx)ۇq&{7Y" p,x,[@@`=\'9@8VD<-u@Q=xcvc`l(dŗhl77xiodGXH8@ƹx,[K@z+sчQ4?'$6fN[iJ@Ѧȟ|p,<>IcBdgXG8Xƅx,[뀬ݼxj+G82{l) z# ]S4vn"p,<>=ɱx,[nYi88?&OXJ8W 妀$S@z}^ܕp,<)ۇq&ˎK.?J;:ɑh,% /d7I>VJ'){܇Ʊ/XxA}ϒuv̫$VoJle4;.! /<%u v~Lyi }gWIcQ{I$GizlS4x,XX}I\ ;U_J7jb62;w0VA8Hƞ8ɯŽOS.Kǻ>֦hh,$ kfaS''s*{V?'9G0vFi lba$ݬ0^&9NrT'S$]ZB'&xIދ_z+ S ø($JrdMo9=3܄c`CaV;M7SZۤzdzCsc`Me*ɡ[½M1wӳΗSF,Vv^xeV^p,l>=Qiu ^qOx)~zJˈ쥈,JPX,2=/J8DYi*7G;L|ø0-JGX+`u~M2[~8]2&7dVij\;ՇjG`-ϖ}'c3.%`a\VXqKX}F๔VHX,<)܇XB}{<7WZ-HX잳^Fc`8+^%~JHr"xV.#Xr}E) JS$xwx?a<7=L8v@Ei eWng3K,ENߏ}u' ;cu$Fi,#_bp8@8vLUy=+~=4)&ԇzRo2!$}NՇf;OP,l$> ` اVijz`וVW#XHINw`# S@v^Z]$'9zU$vu 62;wB6p,O})wUFՃe K$CָN2:)@8>}mV=J@X쾃VHr|? [A8>"<ɡXwՃ";nعmcoø('9OJϧ?bvuF±_اWi2\U vz|@-߯VVQ) {h1V% ;)߸>`zj(p,p'}o_i,{pW߈~{ ]  KƳUy}PZ=g ?NC- .28c_[`;V?K{x,y=+kW W_}=뾫e|ǯ㯾E>5E±V\&9&naٯ,?ӟx?/~o7g}/]& Li,{@a` +UZ=Jrd߲m>vV"ٰucX7 $JII- 7؇0g±*e<XN2ø0|p,,J'ˀv}Ow};±)e<v$>WNO8xvՓe@v;$g}s/Z.>'arbXEVO=*?'9؝O8xqղǾv wS kz<ɾl09,d!H#HD~W&F+uMˈ>}DroL ' ^eDDękmkJ8X`2R׬":"N\WคcII]YD|wYWk@cIJ]sq/ږ`썉.±YEUD,\>sg@}cK]"2"~smUn)^±lYFMD:0CCL?@cI]s^ f`׹C851N| &xoL} ƮI8eDDę={,[Gqؓ<"#]FUnkGixF_}Q١`:c^&EeyNTG nG8UD|Ps .XWJ],o61"V0±odlmclX_$ |m ^G8R׬J@vaK !{>I8$ E8 ! 9Rפ,ωD c8Y^E8d`c78Y X^@v E8kx앀,6"n"Z0x/X |XJTI04±K]sWPgsn'>p,H*"CD\   02kqq~L p,HIqYYx?J0vc[Єc& ujE=]q71 LH=sWx36}6pl±fYw~>"sߚ p,IqX56"PUnI DU Ȟ93q>55qqmXT]01ۈ)؍c# =/ٯ`XlDώ p,I]Uy`$C(v_;0±<+uE ~zbC46}v$`Jc58׎Lp,""8!;DcsgS'g IDAT I* Iq3cso ̉p,4".#""N s涿5&0W±|5jUaŖ`l60w±|5)"Vba^%6RלED, 0{m;1^" 9ߋȞX`V f9X"uE ȊLCD\p,G&d ؛8'"% c,@b>p,X,0:k.B'.;X`JDDd n](bIO8H]sZe^qmk6`R,K@<"2< ].±BꚋՁF]K8I]sZCH'],6Cp,&.$&mw59I8YEdbsۯ `c'RלEdlTࡄbr:4 9/Y!YP#v/-0}±B'x^(vcd$$ ]$bOTB{1{bBgeopandas-1.2.0/doc/source/_static/logo/geopandas_logo_green.png000066400000000000000000003646351525650651600250110ustar00rootroot00000000000000PNG  IHDRBZ pHYs.#.#x?v IDATx1@I@b𒰑bB|@^3sqjb c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c ?;q]B%N F 0פ9j[/H+J59r$Zҕ I_~<$_)"ܞX$F8 1±Hp,@bc# X$F8 1±Hp,@bc# X$F8 1±Hp,@bc# X$F8 1±Hp,@bc# X$F8 1±Hp,@bc# X$F8 1±Hp,@bc# X$F8 1±Hp,@bc# X$F8 1±Hp,@bc# X$F8 1±Hp,@bc# X$F8 1±Hp,@bc# X$F8 1±Hp,@bc# X$F8 1±Hp,@bc# X|lW*Dkv/;hϗj'@ cvl9sSo>աRO})6n~? McNk'ډv9q\$o0Z^/zm;4<ÿp,_s(Ftf$ Eb;?}bxҹ7*+۵02^=,A8 :Aاa?e~9hlFR\e]DdR:Trha8{..{ ՟edNT }8l03qa/Ǯ=tڰv-يY\VT}4~w Y-ɞK- [tnZݍzs'gϝ,=p,Е^<o5N<ܫ X }Qҙ*Vb VkYDᓟcmkOn9@1?~v߲PBp6Y@᳐lh8-@cq,φhapڭX۫ǽ,@Zc1YąrLϋB~]zNp,p:aN$,Pɕm6w콭YTvyfN% |b% N+Y0v wUq+۵g1٧p,nJpEb;PlV->9Eexo±b'*G .A4$W3oM8-XฬlVMḦ́c $)9k IDATXޮړǎe0p, gt̎M@.nEdރp,Ra$f1Wa[PړoK8^evl:(OqHl6wc~NXvQ?" .-WcY4v w UG^E86Ly"W:Tspڭh8?pH84YWcx Zi) }O81tUha$h p,r}z%6]ee7~ZB@_7 1Sꕨ @W;hxb4;' @o)Fbz9fǦc .=v~Xqw fY0ҹE ݘ_77b2:X`.c1WaKЗڭXߏnEKA8S*ĵWc<Z̯/B}I؀tDU/ǥs+t61~'nn,~TЅ>~W\> ^x5F vwpneh4w]XT*'1臍{qcn,o՜' @Z:kƗcSc]?-͍%t b% ^:Wn\pK@$ ꕨ,݊GoLitt.^° ͍__Fs$pc89||]_M @~ظ# ; }z9ry.! F8S*'ӂVkY@vyfJ8±N0ūؔ@#  N8' I@p,O0Y8±;XUdc! L87õ& M8; }z9ryeqa^\pK@L8ߚkhaec ~vˁՄcxJ08IV?Z˄cdc/)S ^p+{/±r| IlfٛK Ra$]_MHv- .oՌ@?||]s^\@:ٯw4; ٱ/=Zn~e<p,@/,V°un-#Oh܇dΣXE5aPle-\M P"zdJ}CF}eˆCMfejXہJj ;Y,V%B~&Mi^`$/h±ͨ+Ɵ>{/4n,3/^uTp,@3iO1}Mj@vNёhV±bdj4vS*@K8;w)F8*e41۱-\)ǫ@3hTm]1P fv!n,HTL϶4XF4@پ?6k1zLVnFvAhL>61[{1vhX_[w[W?}8lu)tse!F bj~d4±unt}9gGk1zLVnFqAO[{c<;6}>[q>Ʈ3% aCC9@n늱݇3V͕)YP. ԑ)v:;w)F8*e3PcA_g&Ƴñ=rW\XԞJDZbx~wX'oQZ XCmԁwߌz  S`f͕)Y`=]|kgdEcԶ1P5eCCYۺbñgK͕r&ūNZ( Pc#DZbS*mj4Y#ST);kE8V:316hp˕rNGx)X 03y .EcĦT:w4SigcVQ_g&j>Y`fWL뇑;/5% JFwKmR&)7}/&Tک XיoO=gJ2a2;ĥ~6#@ ڔJF{*%m]1Wc@ fv~L1X±_1ǎnMt{1T0ܗpwۺbñgKB썁1R8S<`6?/鏩o~_4ms=b>d0Ƀfbtgc0J@˸|kjP o[©Stsچׇ>2[ƊlfK~74$R?O,Uu/L}?$YOvdgKZ5ɱ+gbyp7. ;SϙXs ~]z1?שa}m]~ x:J+?E8:31vV] aKu\cN೑فOT>ّ?ֶr#ӑ/^8_D8C=}(66I]zZ,(l$.׮|gaY~W~\}hOc<;]-%q-Dqv5TmLZuf%[՟Ee5X)q>O8hm[{Xq?n,Div\O"-ۉ/哈lɎL?=[zcW±@y Fz+Z NzIPsIL1jPѮmxhqk1R8%T@-=gƞ-^BfK1}1X~/GDB{VߔJJ9.2@8h-LgA|kw⧑XhTm]e;3՘l Mn7c9gh]±@}(wqh23H,-1پ(:4$>T);/@_כxhvmhp7WaKFbg~'yOvfb`I,W1t<# 4\?ƳcS*Ѐ.ߚ](㯥_BM"Ovd{?51v Xy>04$DbSN3}ػjHv[fCI'~v"*egh~±@iOc♣gKB[qKI$lL):j@VHG9t:@Kw46. ux߯ B11v4/Xyl>PGfKvjL:g'!od_١LHT,U.|cמJxv83\͕j X4L loسu$CNs4X%}Gc[fu2Y(vhI}`wko2ދa-W1zLLyk'cŦTa \Y|j\~HtuE.+n}"3lܥ)2=@sS޾`\5o՘5wfT:>Wc"vfK˿Kp,X3Gcϖ^Zcj~6.^|p_:3q'[6&X><.؄cƑD/ƦTՠ\xX,@-tuE.+>,ؑ阘+1 a''] V͕j$6NϚXSIDv`ko|#]Ƈ8;w)F L xc7{ObobNѬ@]hO#Y,E.J,Uh±@Jr=[z] X,HDda-Wʑ<9q3ƶ.( Mb&" Ht' ԟ$7=Riׁ/)h*m]슃?;:ѩc7X<O=*%M.;1W08ؓmm˷fcg'bR6@}C{*cuK389NWE߀י#ۿ̮ؔJpגg©S4@_^ݮ͕j((62q']\)Ѕ15?k." L_MKpvR]Z%al|iqz G cb`*! \?ƳEc ,X-UfGIG.s n+gLPc1}s+yVPO6>=[zM dZN?X`gϘY,əw"_*ۺȎՐTsI.g% T:&9{Z"bR|ZNL)`$؃g=g$)Q֏p,6ۺ\YW|j,Uʭ>@HW<̮ؔJ; -/ǎNWYXs±@uf"?-ܥxcbL϶u=\ֶٱhyG cb35Xxh,-kR7Ŀr  l#ۿ]NGKK#S>ZZaҒfKqr杘+x48cFV|zʏcRX_j}(+-ܥxcb52@iO#c;Ķ.LI+wQ8Xuxgai˕rL(vx1ݳi)IgމJٍ)YYcTG8rvF`= ҬfK˿ wo<;MA0z" K3/±YPdi6L8Sj4v0R44X̷< IDAT,$yM;Ew{±KKJ4w! ,c4X^? @C|)± ЬzՀ춶nLCk±]5WdidI<6yf+#G_g&/ƦT*4$s15?pѝhHG cp,1X͕j0Vp V֞JȎSypcō<}h+x_1ŢKՇWE.t K4F31>N,UnD|7PkƑ阘+8g^6&XٹKqk/^? /UӋc=YGie!p,(X[qp:oJ.-ǧI@& {cn,ı+gpoo~_y0k*nh}#{@-Wqy'*eUO=[zJ];R8sGZō<XdZ ]x-ū$ʞ8Kzs!u*BL/hUEXhԳ+ q‰xW\ 51~|Ć dg&ũ;@V KZ/l{Jd튞;@ +nh}#h^ԫsbʏcRv#[{c<;6;uHtLh%7>lNI4z4XcxDie6ēxxCNCeŢ( MJ4z\)kj46~ij~6νw%ܜ.Wn-F8h,fx-.UT)\!Kzs%uB<h!±lDc'7WHt{T_y0Qc! D4zr/^ ~"__{'Jc  ͢=o*˺Y,Ł & Gy,UՀrY֕x,04$Ͻ$˺Zc7cc1}G 1 q֬NfG_g!'ݮȺIq#OGi#@ C>u|An/h,-tr&r㢱Ђ& dnpq~8T6>@e]5#O ULgW#5M;Ei<;,˺H\/qXSwcx($Z|1ۺh ±Ѐh`fӱ\KpT)Ѕ|c'9' &>߳XSI- %!%8H"Il>ZK@2h,kxjn%$:病Yc@31ԓ5ގ]9(6<pO|OkdM%ر6:а6>l-ƞv%[q_oA{QZY[7>dTD_g&2>V 4p,Թ\?~7Lwߌ#ӱT)XUӋ8ޕxrs&ۺ˚p,Գ$p4h5cnp&/Dcmvjjx-~ݘ54n*r&^xZw4:3vp,ԙgƎng]9CNTCm _Z_%jOmmJ>cEX @Ǟ-NB,b?7;I>?c8TM՞J[±P'v{:5svR6v\5vseݑϽ$ -XC=޾)J9^x-F bR62[bx$;G{i`2;&fK@y{_; 5י̣Ex@=)>~@x PnEi嶁6~|ʂCR썡q" 뤻+/ƦT XU˕rZ^9cXiL)VkJM\߸@uОJ3GEcYu3xh:Kr ]8̘ ±h쎎nӳ]\(6,oB\YphVU˰56=[zΪYHtNRlX%L)[YgU%>SiJ8|^jn,DnxL $wt|VՎ~< @2q2{Ҭ8Die@KR<o|`|/"c/^5)j3㢱wߌ 'bR6+@D59 1X21ԓ5(.cSRiSsߖ+8x~ΘSZ+qviX5'c`kA5' 5T^eU,"7y<ūKrNű+gLĪw4:3֔p,oǎns&"% pooLJ\ܷ@ƳX+±P##={}{p"Dx녘Y,|(3G X\?^}ȴܗJ9^x-Ʈ3$=*܎\,^3!mϖXyeSľƇ=~5"}z{ذ!ӋECT|zSj4vS*mU[1ֱ#DƮ#ӱ\)ەr2{MhoJT VUUCE.}H& 5`,%fIZf ^u13=Rɛn!{E8v+]jmр{8?{F8v`%. MhgK+5F=&o|Vn>Tя G~aZع#KٶGOcxxǍ/o~8ّDŽOݴ}suȶ=x㿞Vpf^'`.]% `Ë1P1!vmvL8uZOb?CGE^F8vh~\Ƕ->ڢh,@x9&o~6: ֕˷-±Ӄ04a:mv%f^5@l5=96| vp,lӑå=g6eQs, q\SmgOxW± ݹ|,T.DW.o6IXhr\[dl/+[ X؆f4vPdlكkhgF@bӱmK\l=>S±Eա8U:f.h,@w=fjWm(҇~#0x9go%b˱ި ?__?k'eKވ}/<^6]7Yw.K\[vmh,@X"&o~9m=z v [,}x1ry3%hlh,lUFJ`c fGbl"dvE4 xx|͹ْc$7&Xkci%hJXXoclb̏3e± ݹ|,}p4lh,@cٮ C1Y:n7`Kc>]ixF=N* oVne,TGk,ୄc/̎LX_,Q3;ylPkcEXHIo/&DcxXjPh ׺sX9x#XDZ|uckK^\փkU3x%>^C8"bvd*ʦZ%wL튱x%GL^?_X9\5k}]oԍkOcthVŝFm eݹ|,}pѯ^K4wRFGO56XPg5Dc lEQ& YӃ8U:$ nm̏~d'XT,9Dc x[ q-±$iËѕ;>! ^-*[ّ>TtxC48hGN?p,i/ӮwbzHp,crX4=9r.0X1?Q zj郋GP4 83x±yh,Κ̍L$J8crޡyh, . Mx٭ A±dBBt 6c郋GQiա8U:ȼѨgEc| ( X2X荹)f4v'q٪Q|et !±d+w`ZDcNP9>dIա+;.-@4GY\pKHp,sp)F+@&*k=dp,P]2S7V0R^ SfGbPQiiFcVj2X2N88rGO;(-_~* dX_9C & @f,T.8&-Vn2mnd*^G fGbP1iEcECו2J8W,ЄCI+` cx \!׌N8͏n@ѪC1Wv=|ڊƮ7O$B@бޘrm41eX iJbt< ScX \7yl5br>} ! @G,%nvE4kUG?2dp,;yKݫRK}<Z,,T.DW.p r;J}Wj~fx±tr*s}rTkZ4t8X:Fw. bSf#KW@B{%jQoEcԧȤfn~㶑ٻWghW=Сc͘ӎf4v'$)2;MlÇQ\: N0?zΝ6{j 672%J8WC%jOci ;ҕB ֺsrD][>;9U:dN# @[(ryGJЃk1{W+*ZC85_3'(AzL8ԧ4&tXB$j'l(z B84;2ILJ {b! @)zcfhatmv,R`όctch;+w|rTkwwڞp,meJ%16o/>(Du V*$1zLv9ԧ7GOGkphc±ّ(8Hbf^VS`-Ƅch Bo M8Fb܎Z3rLڔp,mand*ryHȃkQ->=Цc8p83x!Ѩ䍏S卅Z IDAT zbvd! pG?rLv9gh 3CQ,:X`%!!_~w/>@cndAp`s= w=Ι1_vh#± 6F mkndqp ޘ=z r7ж1=Xq h±sOݫqj3 ڀp,nJ R# z:|ұ G?2z"m>jm1:D L8}5=XCE'b˱ި>@Gcnd og2qj3t3'`TO@ 嘿w=:9$ cfh hcScp@cEW.oLv9gȄ" +z C'`᭸x92cӃ  =g@̍LEw.τcS8U:fhԣZ[L}L(Du>`O͍L8]VS f&;w`G±r q o>@uQ>c3 ͸F={90{t D8=1=XBq3Zz3$cndʱ`'dE3$(zp,:4fأͧ1{W$`Wu2 cQO}$<e=& \ިVy Ih =XvMw.3CͰGOҽrSp,:|2ryfXz3s#Sf=$ cfh˜Vy |mes`WTOFW.ǒz4.ݻ )u134a cQO}X_9f=  ;\ސVy F qahˆhi\w=xrLvp,dndʀV-z 9.`NJ83x€Vy l@'+]$ ͍L/6tz3 >OK8)z eTv%g` =1_6Xvd2̍L vp,֝Є2hQjm1ءr@8m\ptXoSw072e>±lKw.3CFˠϿZRw4W -ѕ-f^M}vȔ) eݹ| M,4?[M}vX_9w  UOFW.oy4&es#S&w  M+f^F=ec}8rdV!Xdz]2:iZ!XdndPѨG߮>{(zM ;  [MVbc[$c^Әw=gODkj&Xh#eL*mF 1w/>hfh"sy6Z+(cv% g]|TOA8:;q2fOcmI3pV  + qfq2Xxg zbZ<L8W2L\w=g|lp,ѝda2ci BO@8\0r'M̍L9lp,qvb r;<^N}X_9^%Ӄ(%#6tz3fFB8 4HX| 3';wxX^/A2bQI}T5F±03$ؒ%wz 7XScȈGOci BOLk 23+ȈϿZ;S0|eW &Kǣ+7DF\w= gw.x2 s#SoVnǝ˩@93xgJw.G^(bz}jQΟ[o_q7L8 q(44UbǴ>v ȡRt7s8lX_y[F~zɋXG:p,@fN>Af\[ vOѼ܏[1o?>TLb\ұzcOOcuɋlHX/sl41{W.@G(`%VjlI8?F9TjESٺobc}[ l?#XMWq[ި>{±wuqp@X_Reͨڟ²͘l{vp,@f&?6Xxg5cdBo+8ۑ|@ǯ6Ռ6*{' 6O?~;H`%ryπ_> Ȅ1_#Jq"MPw6xme ${πF=&ȈұE ?u D9Μfwod?!;S ;cT,[b:dFW.㱴RsTp߄bÇNKɞ*8z=x֊~܊ʮm>14X͍L9{<|>33|R8:בؿ-eGn. My,Xt1Y:pI4 jŽ.g mXmb] =qfD[Ӄ8^(tmhSSxF,ʬkj~!H6s*sW`u(aϖCE/YF*rHG["Ynk˦ps {E P?7̉{ $B:XXOvaU}F3:3n=$;ZڤglPAp,@iWŅڳ#lvAokG8Qv uMx@ Ym]׆pvXF.r5xc`'lmddq!\2P`?7/x kn'f YpD.LM825c!cRFBdW z qFn_ ^ HXxpko8955ءuwAf=X}]B22u%WFXBp,@U7žl(-BokG.z) lw9?}4,Wn7/7Xx@zo:8c`'.0~P2@8X -vK5tn22u%+ 2ґbљpn경XvkMٛ^ vMP8106$Ġ pg=1@Xڲ) 8NvrF2;_( esfx̄ ̺0X c3J@!nG> &#“ţapnYe evpaE(1d׀M\ ,@~,lRh,4n2@=h<τVX5X1 zo:zX)z1@C ЀdX6h6LM J'* ai`l 7I, >75( Ep,@,2 \kmm7d`ȱO ^P 7yP ~ps@ hc`'s4YwH{9h O85vMLG}vlU.'8NIۨXx8R cHU 7 =X-}gǧB[fE Yc氯ː&B87ue<{[; ܋a106@ H刟{Ge>L[=^$Jp,@IWr#<^\0LV[P<F?S>6΄Oe'6:hnk}h & E/. w75?aϖ}]aϳ mʉ cD',S\[?W:rCp,@Z6gIF Fg3 ,>$c_iWva)6,ј\ .Cí &#QF7šH֖M3Y5˳gKG>׉$ȇD8^ٛ Hz[;^΁0C׆6ï˃gf 1Oy#+ kkz,.yR\kg]Ss,9VE VgwUPaNt5E^w&nW:Bp,@K _ŅP02/8UoY"T ': m/Q#SW^\}$Ja?uMj /}4?0ԔXDoj$0;eȥ[+aѬIP , @Rl89|ѡ>ԔXD BIE/@L]6@oe:΄ mJ; @- H 4=Y\c3c\H`iQ >FuM͊V$h}Ss'%IBcB(Wn֎hk&?9w(+&<Z m-Q#SW^$ܾlS1`|C׆6eڇ IDAT ` H4]7f I Z75Nt΄uMJ Z .ÖNj^L]6` ڲ)to, ,9;vQe`Ma,uM͆-1OBreHy\zt<Cc˃ts nWcVX >IQr#<^\(zq_L𣢗BR m@M  R!KwE/@.VŀE/@C}gº梗}4knAp,@B­- Yb?sE/@ʕټv5:9F .tln%Ap,@Bzj;Sa&g' 9G+Ac? H㗠}]E/@ăc <е-kxCp,@"y\7f5yԣYCROKPq>75(Mp,@"K Ub^w*7 fblBh<^w&sߞ2T,XbzKPFg hzt Gh,)ڳ# )w[+E/@C8#-Eh,Eps 3*_[:NbʕE/@!_>eȝ,= |!?=ZyS䒰7!m< VCfo3j}/=ڳ)M׆ps@#L^Ip,@0xhMhqBұ9 #4wȯx.;]^ޛ`+ܝYHl#AհׅVe@_e'~X6|BKZïk-q}%sb=;lM SBcSS\/z %w zXjI }T ssl5XviI}a' foϫ=1Dvdk.Z)C81EKKRIxKP(q(86-}/뛚ǧƲ͆ɹ;ᇇ, UHUv_9'1PvRpg&"WUTŵm-,q/ܾ&/ Sq';&M8ڲ'¡Zt?eUb'Ѝ;"&alN 8P 7&:?[GzaYcr(nfhR\/z )wôOCȃߞΪr]PlTNZ6ڑxKV#a#DfiXeI̸(8,r\<ԣ0wat !'b|.?0 A'o†CgqmP{5;gE/ȡVSdqt\%D|yP+rr#\\ςG/.(bgֲ9 ɮkj.zި2tpkg 6]963^R^ OLR(z mrnZ]B6M͂(vq΄06sy `'k <xnv`|؟;  #8 gbpI 0!:煂c{c)S|jly[ Ϯ[:‘?, bh|vdd)`ș\B:,.(8/uME/E2Z;꠭es(;5apaExA|O׹W`iY7`' >+89KHGT 1P{_ {jo}Ss)Aԣ02u9 vH,@viɮO^!׉΁,:m:zW %ih#&!;ʐ mY!sxv}4~s:͌ eE!_1{?}qJx 5K]E/@ ȑX|Zt &Iy0+Rq21sC_[+ ê李kCGMsvNlE/@ %izo:@ q~(*-KE/@M zЎM; [a묹!ĐgAk'q34[ڿyҧ|R.zK4͡M4G&禋^Hܝ%" k,t?:޼fƋ^ $}%{v#C}(nnûuME/InqHo6ÑrFK4 ivt˙>M<1{h75s X(9+7)ޭG0񩰵E:5/z _ ;@~tmh3u`8OE*<` 5N-tGV>|Bh;uѬ Ju;ޟ5mx䄠+׋^^|1-ۊ^5qs jS܂yZ`,ɉgiŅl˙ oZK=E/@!  A%isp_(86)}pvQ-/n_co^(z)Ht,QB;1:ٵ%($9!$-eA@B z4P*z V0)E-͡kchp@ ͧ mY7Ep,@N"86*7^ ǒuMV[[6)iܝ_, ?B6-  "OG^ J21w% .V+OBz5Xá>,ߜOa١kCWE"<6 (9 $-ʍ7SaxQlpWMo^^ (W!,(9 z^B JqwA_2q珤UC5SAuӓŅp0X,zX1^RECbs)aXB JQ\/z xڲ)vbC; m ؠbD7s پ*4(aѬ΁C}qX:b@ is3LKo붢`E7ЎנN_;˟ezX~W_Lp /.dt,.,!8erNK;(W+SBb!?j (_pern:~s:a&:C/80ԙ`t\7; ;Ha΃kC5/n_ɂ/c2c3?VA!/g& n+ʚԙ`tL ݙG{>wYy|0t|K"6"y!l?4е w:mV(ԙ`tL)z Xrr%b]Ssh]?V dlS]` ϲ`fA*@#sNaLl??@QHIHܴzX O,֖M -{9Z~qҮE"8RFK hBMl6:5 'ƿCMߵ#\g47/fkjhlcH I:&d:xsUT¹㊒ )W.Un: )FV^o6Uh`c[pl,.[+E/!+۳(,1`"T;U14vlfe$=^\s{l.dt;U]E!8N#ɹiULf?O';T#QBc1o> Sf(\=F롷u[~h PH1!U$` Y-$40FH't%8crNK*0Lv*"ABcq ߼_,ܝ`\k0jh?2@a6+{"&禋^Ve:G!ABcyE7å ڨʚ۳Ep,@ P:=>48P `ǵ0ABc8/.9vި5z$1  6JK*E/P`ûb:wJ wfGQhLcjl}SsXԬ jҠ$p{ڲ'Bc=ϴ;Rc=hHcjLH:&"tX(6=;Oȗ3+E/Ņ0X, uKLrÐԘIp,@nSܝqIm@%P<'-|"bh/^`‘ox0hpP _ +@mZ6x&禋^@ojSڜ@%P,뛚0t^ʕJеM‹Z_[Z;~Q=[~gu/M,ه4,D/5 oG tkj>XԘ4E/k 7֖ME/P0'fɿ`^+?{ Ykok5 {CnWϾ-B}_ b,lꬭW}rNXЦ d:@%P뛚c$t? *^'N58Ikk>ؔ9 Bg0Z?K5\yKHlCd+?֙ ޳Lډ<hlql;iL4Ꞣlꅟv{g(%{t?_8#j$8CY`$k|3q h|'6TN#;17e9wJsY9Vvxë^>߅Òȓsw@b ]_Ce FA0$P&>#=g\jBˇ7jg&i(g'%x]F Sfw=[g/?s*f OױVvD@ ګ 09D׆uޤ65k-54p{_s9 n HIys%`CreV ^;/cR>XSahTGm ];sWO5Lx@Y݄+ׅCTz%l=61)Wn< Qj`PZ`f!gkX:>9ö0XڥQ;zy)s㺓 ~gfKHK\z"+ au9|ozAX8l_)hz{̺X|ŀ?{V<iƑё+r0` +@-Uk\?/?=gW/F Hp,@<݀GޙP qi3y}`=hLw497vees;a:9;@B„0VOԲ2:3.prz R ; ]\7-F4l @'C vv̥ٟg<*.;+?߼߽b0νPr$΁?&*?@?G(?Ⱦyu[*\A7znl`$˓Ņl4D@H&G)Ppk3 &zr(?a1!RW=RxcjD)8^1dQp (Nv59vb~!b<5mPTic}k/!}>s׮qusET11{/?tt6{;c ?x.oS-c r^wzk]9+ލ"@ {t6y=X {f"ޥ]lɻt8`[֩G(yH" (%V ?~!D1}Y#::@S#M_~,lpk.ѵ١˧#Ab~(SBq("u Q&eٰ_i9iLgaU_eGhbssv7}#h *4gr^.L/~ %D McJHW?@ibj5}^p h M]Z/e"Fk&O#' @oH+"/m!vQ-xƿGnP2!W5?@6֛&/n@Wo564yPO@l39nf. g?<"|cJW Ҙo"Hؙî q0yI#2WwĊ*Ea顑=ӊZʽo۰=۰hD f.tiGU{ĞwsGZZ<țfk+I`,E޺{;9tTvue PUc`ն%!V}lv3Q];km֊־ YxnAX ( zc\16bG :X;Y HA gm{5?g{5Cqd-gޝHކ=܍֭h8(7]:v9 (C/I)6b>zT@4׮W7Aay_> ,Ax&v{+w nd8;%2G#'5nVɁ\!w ) 9/% 6@G/ .0ajj#.]>j?tǍ7>KSh5ih7usEų6 s;_>}Zgu}cY ~<9, 3/)wxĿ&?9 P>ZѦ3;@h۔W%6pu{~wpkȒrsV ؔCal9SKCo14:bƚӵSkV4g:Xv7/tc6;t/X1"}o|>@MEDdhJC_ )]ٓM-&r zŇsSSXO n뾷%/{~Sc5M٣tc}1 ƲXL:k\}",Nx{魹B{W,Hb>vphwyR39M#nqo:1uo6_r>E P0]gAP 4~c:]E}h8)((p1>KL,O_? KbGޚ\{Eݼ{@ N&AG.ԁ,h"C꽣k@*]XsIb^AZg=2^cNhLG*"髷<j&/3ä"Pc-5m&@LG@uKA P' s;mj.$G0[TpB X!Eܦ(wt٤Hs b>*=@1e57ψ@o|Rk޳ah%0^ׯ}f  xCȮ緺\ uN6yTۇ0F04GRtEgg{G'uItyT-HϺEw6㨃gL~6fN I $oGg/wM#8@l. J71?؀rX(-.>z i^Bɑ݀ YX>Ň@"'8P''ShqkxKQ-;Eڭa$d޲ Eh?Kyu+s`/ 6 Pho!*g~|jbɥJHO^:ap; nB(RHY׭gb!G{bv`!!"(ShS~?C=jjǻIXxGsߣz b_ Ne*Οo8~U{»>`C#{\Xw2}ۇ* *!.v ZϞ[U* U4 hQqbǻP(fpfiѬT7vhG)L@G=흐 %OGo87 Olk sɹ4ׯ9(y3kSV;Smj&`F_zåzW KEPgVIQߠibOqkbԅX˞+Mcwi@oȞnX m;&]1ݼ{$8@ @ .0:$g׷}lrrQɼ֟`P%= jD1,DFߠzqו I*U5R@ehM߸@P,hˌo{? eXxuO eVCN}d I_:8;kTX(8} ͊|h{Џ{OA<ĎwKmӿڐJ \w k{An((4 tFh-2YKߴ 0?EtklwmjAwp!ؒM~+d aC]hSNhmH7Z#%. $*GAbX:]!JEhb-(\36MO9}F )5$6/DX{ mz4Ga7!gׯR(8t'W#qڲnk ͻw> $(M@&rCcB1bC^Y8s {& $&N8 >Qqz.1@^Ԥ47Ԓo`Bcaio % {' 4Fx,hX;@A_1@ (HJ]S>@&$Ǯixv!'.Ό:AA_1@ech?+-)LHÉmBcZ^ݯH^| *޳Xk Zjx=4-j-ZBh,crWqE(LS?@_1@ecaX߽c Hɖu/ g8iBt~=cNplfo'86B-@q&vCxjY_"v)# 7cO\}T`bPrHhgs5]1f`XZI' R4c{ XubN̜oP1(GMh,p?Kzvf3BI@suCv+<jn9aUy 3JZ bQd@p0<܀ 剿w841Y(bxe!wswU 0Gp,ԖN@W1@٧̅ܦj N׏Xq,\y*7F:'B[!ޏn8X/uk}Bc\>FF{PM ڷGކ׭M_bIڲC3>T$ M6PFNB X(3T/5qhqߏ!@9^j ߹1՘\ׯ4{\jGgSȺA{k@} mD!Ui{^4`:~Ð5ut=BawtCro#B?7s}4{I[MrBcp ~kz~(BEs'ف\zb!Pu3awd;:\NQ xћ i \wpqŻW4$w (O-$8 !cDA@G/\3y>n {^ȶ[/L61ϭ\-JmE{,cFO z+z*6kZ3H_p90J }61ەDD_ry АsU9X?1rU+cr`Z*֠#djչD8tݚ np zob }v}o7gAԑٳHW錆-q6wt3rnE^u/trmֿjMַ) 9tcy пj*;TBu@ qnc[wdS3>]X̱kgz!6lw`dO)V}=ȞvPxmxײE "P+:y@$AMj./…"`=Bl?Kodی^Tg߆mF?Q1}J3yvU9}gs& ]=&N _ @X?e;1 9}ɉ"TD8' t*9!qPyvN% ŋ߸{,[$/7[V=",-}ļ-0\/ [z"xx 3g[֭`Nqq.p5oSy %xX|v N]1͵$;?L6LQ-jӹ=x07we_eՋ5fAr%ub|+A*mLhq"7f1_~3įjQ$'fhN>AobsGD{ k8l[\j>`R-f&Bm5kB|'8\7ܺ{~A [sJ_0$(6urBvڞ)Z/Aq  @(2۸ZplH(?1,cg{/@XY61"ۂLnݓNVߌ`^! F^M\h9^lGhgS ~߽h(sɑhqOٹwP8lnWwUk{k B6x쫹 \#i6UkM+7Ov3POux׮Y7ȺW/t3Bv l5e-AhYk'%8 1@BU P(uNvlmڹß_~Sm"`KxpjrdOR+Eѯ?ˎ];K8TZ}[?s{T hQ\X;:$T)"\f7fִP<<9~H߁Jw<1 xVݿn51?<"3FBF].8GYnYBz=59۲΁ &[ T_( M vDB2هIAa(WƿkRn`=t{!Q\op{o~"|=\BMolq]+PgJhNFsdߋ" $G/#Ç]-@DX lV̙" m _yF! ?9qE]?HOnseZ(B Yϔx_}Yr5;vgH#րs~q{?[X=:/u @yh63Zk+&)2)ׁsG@CD@jTNESo^;u^ #݀ϏdFɲ~ iV|MgDֱku

QlJ(Ztx'g.t ,V/^1Mx:Q6a{6*DȞ}_C @MW0O* OnKx?ttmC wfoliS~{.ڳkfM[֭6$,qMkbh99s!;t5Kf~ov֡9zZ(~2Zt_~[C1Hw" /y+ MșݼSۇkꢨˁTL}O<Nѵwh'+IVsXZo@^.jU,];VgC=^ڞFf_8@Z.p_zquLjT( ͊ $46(g! =Ycۯp#Q_~ r@hlzß[(IzncIܷEuN\G@k'F5vp_[@@yb#{pUM^>]xg}kDN-ލBc97Xw7לu11L XX{?~z$b9翊oL_:P q90R_kh(Jd4M͜sDQ>{0=.&1MT͂c6lGb;D ĚG4Ήhra,x7:Ns9ѵnP$멷n֭כk$mm,˧jkQy6ƛ~C chM^V( "8 GN`+oSmD} +7{1˰s`DaA|h]#Q,;F>1W)=vBiOal HP{uDL\g$IXދP!k.Չ(hc#xۺ?XDVj uJ[E*'>~͹WW'{5_FϭQ֭5^|淮L^:UL  #8 16ōeV]-,G(Z,FgL͜oWksQDMJgrdв[KGoS}ۄIwѩ? QHə `ɺ6RQrG;<؅@u,(m4\M-׸_M&Fub*-F 5MVlb -%@vc@c a U⍙6}ilvA!~-AKF;k(< q?'"`E~{Z 1CdV,8QxwYAXѩ?yOP/)85k&/}ᤱ/~kF ?i;gz֭1?&xuڳ\]cg{,Xyl7^.ݘQߩm%ٷa{ ?u1&G6kʽ`|7Tx{.ՉZ{=uHTp:-œ"D.ۧ-5x,"3k4 Jќ"}-l9ə m Zoœپm.51HX\Mht\%~vržfGsK,XZ6PG/vJ3k`Y&vŞο} ~q<涀ڰ_Be/=rM tlQ4>:' TMhlz".bNiyy9E ;:&" ,H]Z^EМ"mB ;?m3a{Lz!ujv+׺~dHq6}# @k\a 'N쏂cXFdr6~H6+Խ%50ROOf5RuA~!Q>T#Dža>g蔮x0wv" t>aBm$)9.,i!S?#66!*?GԖ^b|1_,+`ݝ4I4MX~ MK[lφOdn̶}(hX}'85iIUk\EOXX8cN,PP{CšϿuwv lF|:KDcʵm@I>35GA< tSS>Fim?j/cJ!I IDATƄ'!E4NsBcj{aKcm}{ˇGR#Op,@Lܼ#ن.[N\Ȧf7k8nT)BFTuq?ՙܺ_(AoM9m[\N^l]cß"sܑV~ [({{ƿ؆oœپm.lBb-# 4#ݵIVq|'8 G ˪WX(qʍ7}EBtj¥xsPhcx8PNx7{zœF;aG}VsNi^c@ѩ?es +4ytڲW! nOsU ǽ.MX:hhm 4kB/&@>@W\ u&H~]>X%]:!X]#5KWS.ţD @- m OѸlKoEhQ⽾]uϩ<P5[lLJm#<&ٷU*ƶXRg>ԅXXW;7V)0W'O^:դ#L͜WH^]!`R ,B?DM_|PO4dDpkxC]n~`>bfia  jC4|)5_n:rEX+ ;1n%zxɑ߿IDc7+ Bc|h,S΅IX> Kckz[s ĄqQ<_coźU4+bokg> }kP:UctXR`:␟ Ph8u6.΁칕kj+ w,IM|+`m``s7o9{{`q:5VwNՋL͜ QßTqTgfAk`$O^ +`75KfG mO\0&$cx .l\ Mp,\5(N8ןEnxPrn3,dzϿjJ g~`Ko5 j 0_|ژXϨzujf!Bc/ccO0xj"I5ԪfGWx[m6nbiYh8BE CF;V dʭ8dZ9W9.,W"l|SϺP^~|PBL|q.@"J͏ n˦_?}f/!Х Zد ^1(ϿQ6g3tkွ*~/RIqFWJ$8տJ?j߆M];.7!aM ʳ$wi*oyB64[ߊ'9Co{phwau+Ps亰>RqQ0iy`"4yԠ)~V(TG{;?XW{T#uZ$ ȡ✗fP١K+gw)IM!Ql%EY9F;@3Dae/(vʎmsZNAkG>lg=ҍY.-Bc' o*WۛISə BX8p#);=O*or#r 5%BGD͖:ܞhϿ 7fZ_05s>!')^xYN9&/v=U\^d:uAf[֭6tiG0W]tؙd1y߿oDhԟ hCE=mR\{O>A%"O7ӯv=^ST#?ETq.qˏ0?ex{`KwOp,$&Nxe!I|V!&yl_iWb^p"dl6"PPZN\hs *Ǿ BBo4=Xjk58 |w&4ZB8<ݺ{'7PM6)Q5 1Ǩs!5_EՈXsxZgcJMYɭ{ѩ"Lp,}.ݘ5$f 5)ԨF7坞< 2zV\^΅-mDÑ6'FG+x8nIm'U8sk I~b4b E{Cp7 +,+ xcx,B5xKv(txQKⷷs`D~ n@)B}yY'6I|蝞\ kߙMch>['jw&yW(i|:{!RQV</ؚЧi(b]B1}_~9YyhgS]>j6eEasE`>v6;vkhVkc˺.cTQ7ه}s Kk J/@D3cmCp,,G Snݽ#RPxu b$Im K7f] 3j77lR.;` b?~/eP]k0W6)(ﲉ/?6Tb׀s=e?Ep΁Dau61NR` M:PU Z(VB o߆M +ٜh܉(͚ދ;Lv9!`4<%hBKs 4yTbԺUbM܀9ΗADɑ.@p,@ҷ)c tVqIrGQH8[շ|[&TDa_Sx_3m~qG;܁"M͜,X\aƀP F1ʧ9EӠ*8SXK8vL۾6%yepV-_ccau]#.GΦ_i!_U!kgyXrA*L^>]k5ٜ=>I{M:'g.L\0%iSs kSP *(s=峖@!PJZz} 7c1"D+ֳ\^둚Wl "gkM^>RK7fcδSxW#}9sYMYʵ̕l>er\k Po;5X>dϭ\crt7KxXQ7{&EjJx^dS07@|XulZQW!dՁG4HS;SP<9sSs\(SE(W7fqAY@m,KVڰoիUW,S* ?.Ps$+N})ޛLj)sI \`ӥƢ}OR.KTox>4.+iA5!TA o[UۣHؔclk ѿrm5'Gׯ6@J~U' u; >;A%9ݼ{WF{" ė OzO)g:֯%h*Qhʳʢ)UÇ"h_8{bǻڝw D?{u~i}D x݉#H  a;w#Q{ 0IwGGE$ݝ`jOM"Lj#̟X20ĊAKWZMw?z<3ioZbȔ)!~y9v\bFzC _. +Xl;YH݇ )!SMl̤yKdw% Bc\zו=Of7 uCfGbSNzWن5,`5r2޼4Eyq?g̤b {xǾp e4i246$!¨*Q?ghTtƱ*؅k:I%O:86Q}R{l~RC7hrw";SȎ}֠T?NO=]ކixZh$῿j7v0$ k;é=d}*Ɔwu|GbӓCP>vdIraf]8c9bՆĞ,>sDb{&+sxjjN[ e$8 3?Ph_)@/黨5ET*[)86_U46 D߸noM +%dհ(e @^;rW~ScZ![7j<:DVe"8Z@ yzLԔu@ N&BpXзbTU@mVhɝB" j2JM4fp2 a4+5e;⽔) ~/H)h:eL'V0z P_ LN^PE@UpHaChwG1bz#<<͉X? +6+͛^R-BY-A5F) !Ǥo`F~py?GOctVӵ@F9"(^ @Ħ ]ObjV#弐dvi6dCc^bqkQJdjIkWɭ3F8`̍5e)No6.L06r8ٱot?y4_5#U,ڽـ@A\h!(jh. nU{$xf28[ߵ+mj^݇kMT=&VkpzUk,ލ޸H<ݏdIa 7w og⽖ft֣E# @ *@[`v8p(~;{G0)46jlpnj? c oV\رlݙkEΔ-VCEzQ6~$p +)6Eٻe[8O/0>z"{0P+6j=}l Bn@q4)ʅ4=THK5)Kgp& uZFt,>sDչ P:˂)![VszZ9e! )hzJ(H= Ip,@PmͅGf(ԗ._vL[p @ +0Jj~,=]5M>QMSBS:G'~^)bMO9e% GB>+:#5i|Vj$9ДGZxu:p_#jTXN,H\ nPO LKT:SkRMcpy, a03QS>JT=&"׆f䩱:#LŠkGX~(+ H?Wlcw83~ RG v$e2p PnԈSuS RSpw`̵';#=;Uפ@5caX IDATƠ&~YViY#PfܓBv;O֡߅fx @ P/KoW)L)֣&%{B#exf2*{.;>Xd^' ϬU9lzT)3AĠ}(3_R9u뒱%]{yw[" h3 zC@Ɔ)s?Gq @56jTtGs/!/wpdl_|Vof&P{fܥcM=ΠYQZR)kHDxRs#_CdGO#; P;.eRNP/BQ5Htgo#EL)أb«4u| WpPv֤P{399e'>IO9e /q~Dݲ-sh)D[ FԜX\,XS`^C}یh" 95K[W7'}c|PvS82|Ek3@1} r@RoÙ"; ! (ϛlXF\:ȸww3@4%iܙky(Ԁ"46WV%~.#Wӵ>K *=e|qO:댔nkNAX+=^1N ivDxJC9s% 1D[Z~Fk P#cP7i=հSz\4Uv)(t.ػEp+'Ø"߿3Է=Q*|C]ۛ&?ҚWǣ&1X>4u bgl)Rs8Ʈὁ0%Lpd> "*Np,@F4{M[Q'1|H^ֈԅ"tEuwOC#pP -PS1^t UL*1sƗ,mN9~F!*Ip,@mLlW't\z*ԃ.QM`j^ KKHr2 "zg}ß~Nߺ@%ȱ 2_ f "&  3>{xdߊ}BBf&!g Ow  P!c4*/~e耎YTAJr, _Q= k|udY8~\o):ΙU)*u&BUhKjG&cJakpf{F&PK3hpBmP=^1@np7#zcH0z&+͛aݫ}nVgY9J̵'@QmR%̼P=4sJ1'(]w5\x뗾JLp,@箱as݇ t7-s!@տA@Vf1=gzi.<2 +jg Iմ P&4Z{89'T E8vRg('p``XhJ/ n$P=4+AIanfsV$Ԭ}.M(5J?|MҚ_ʏGeIP5ݓZ (>sqnΌY @- G cx.ev+zzLcQp:WS@+RM"TٻWcOߺd~ɒMF@ j,h#U)/EQ96ڽY,@Heu`5~е޸eNI;gcPu;{(MNBucOxf2p9^xZAP-96cM"/9-!TYcM%( ]u24z5wNY d.Fv~P߶*>P3ß~T+T=pwJL73aA'ɋ8N;>(v9MețXjgzK5ԷΏPyn(@?ϯMO++G(udOuy3}d?R{کT-gf #U4=kեfP%_N ȝ͘OrwC :I1qgT^cf \~녝 @?W$;t@}<3v]E8Gּ*H 21RjjE'ɋ ڷJFG'xa|{& 4y-FӲQ@΄Ճ5!uu;p*hlPC,={R`쑉B YI.kZOFdZO[Q=PnnC$8˟B> .wNlH @utbvR"PE{]i($~. ^ ~N`,7LU'P'T5}BEiKѦfqM8cL7kl\! #-s.Ew+7(JTc#Ixz}yҼ~رW33UFy֢?[wx k `]OМN8}YTƮԞCa'P6s P ԖMbr2@eg:ho{m|ivSMsMM)Kgo ?>p೏x鐭ݛkE{x5)Mޯ|Cc=Mh#gÝQv6'=4(XDZOjΡ#9Yܤ @ZO  M_PO.^dx0xp%MJhZ(2f کS-Ƴ0; oob]fv-cO3ƃu5uc@6wy3\nf&"2+59^9PA-lοOKg18y^p2@cƆu aB2bs;@.L;(` M'2kA#k]4/E:,vy(\N :uF2ޯWJBS))Ěcυ3#_jc0o>XDoC٘KJ0@;9\L蠡-ۜT@> 7/~K9i+x `5ܑoy> 903KlZ.?޽Nݺ(@ 6V CJ .up0vj۟zSgݛ2rgf-/66FZOc5D a=]HXJmZ7  ~[zCb/=א]"(Vfjf6KVb Ιppp c"nf36Է%<:.ϑ7ga)3e&pw)k9"l,}ߛsM+`L s8琡vbhr;p :cRn,i֧콰qxZ>;y!^Z=]܃ǡ(LGPlX8w6)\:gO# ~+$8 ! kl\! ֧Ų9YB{ȅ)|a[O_aů k9!Wf&ec])~n)H }Ȁ)+5%кFк&,Kñ0Ǡ %ƀ㱑4nN9vma82q,XKf @t>-{u C1`mC b @фק1faG}@z+ kcxl< $K9 m S/p n7J 9oc)@lPxO7ԫtp`$l\;,ma@44_5P/F^ s0TK_ c{XQjQp,Rl9L b245{/tHup6gtT&@Μ|C3OYxo`؈Q;14Yx, X,u=uCN>PS֥ȑڲMp,dҝ\=Xx`bڑ2dN ~7aIu:/ 8 !w6Ümj(l rץC _o=9ڛaպ>>|Xz~f@ABc&xc/'L5Å~6v7rJ͏?!<$3CW>tPUM 8c ;KF`f E/ -)[7PF\M376ڿ/3\U_|FOqvmtjAp,@b__!t,w|NOuެ=*@+{`oNUq֥0csJ k Ţ}NY;{zB-3oҳ3 J'ٻW+uGt 1g~g=Z>@&g]#@7iL}iy#ַ~.}dxQ_ 87ЙV!G!vex鰃Y59L =]PL[7*|`sP>S'ٻWcFp`3Ü9ݰ AkᑿW/ ni/> Ǯ ß~̵6p8cjMp,@n5 stG`y4T%8ecE-@@I7oa\ Pb_\̄j 'ibik`MCX3@5/> G&ΆO? jAuڝ$8îM?P rk}yF>Ğ C[(2^C 55E3 o_9,<3v6=?3@(ȓg:saP$rh=u@ awPRs(EMUO+(]&JjlVCO*u[0xptdh&kݲC@CˡCȸ,xmCPJ_8 MBR_`̵ b"=Weæ 4 3d"8 Oe"XHl+͛ÎWozx@MM #R_%b8x1k `ܳ+5$@*ص>jCp,@A u ֟zj?(fœ#zȤv6*Dw0i=$ "'mo1rz֛T(0TFNdxȎ}gK`Ntn5>@ ѝ?Ҵ5@[),ڥg|]ZO}ǧ$*<xk `1xJ ۂuWNk0J8jAp,@Ap `Y_(g~YҼi:lgo# m@kQ {QUiO@I]n0u88f݇t4HC:@{)nvo6 t ZWnV,!82q6y8߇ "s tr %Sh\plW> 3FVPDSOg/AxZmarܙkDnCDf*-!kTM:VZ.[~4oKJ,T72P`@J֝+*Pf9y8;uRQ`u0@{oРzR ^d~+cPsT0d`93ؚ̋h U3, by#죰/ۑp``D%8@ͅGv@B֝в S](i{,=ÔPfo9O//2/#Ɔ~:H2ϭ3RΠJ4e;H)6W{5 ^ axf2@]p'^ӭ,ZOvZ& 9Y#ڻe[R,9댔R!2'ICloij#gC~4okk}8&$RhX.ug9(`,ݱObh-<2U Ҙ' Mԍ"4p:F3۵f L*gEo>(y8:[!dCPUc аXxl*3[1@y/~eǒ{-E,;"yY16 yȓ*f}u쑉qgg^UBb@(X멅L m.pgU!*̄@ 9;uR4]P_ѿA8ذL'3/c$aT=?`1mFUmK^C40*|A^ƛ7±ySXs```"8`!/As9>rs8c_ht wQ KYp; >GҲOBUAV3㕐uU=1$]~X݇VnN\&cwv0ݫ11@(\Ӑ hKMw P!GJ)̈́K's/WPD]vi|imZ:`m uFZ4 )Uw 2޼]?>9vl IDAT\,my5Dp,@b 4Էx콺P1g&Li& 9ixA} ԁo4U}ZO|SvHe@CZCsv6BO*?"5 k~ d=ofYo<2,.T,8*ah#E32P5}|sCxdIjXXԓ(vݯݛsB>rڢXuFRMVXV #2$;:~2 ǯ W7Icf*Cp,@ؔ,>%Ut~f¼fbm.dnzNV P_؉sw+{,ǵSB8(;kR`43J:2L嚚g5-0vj8Gqad? }^*Ap,@Lڔ,`F֙  @֜1+ 4jRuRr?f=]+|T`43JϞ*efJ(Q@M%9#և{}$ܤ ԑ{2s۷.d$=8;0jYg7܀vo6u,CSVu> s?ß~_?=ojOp,@!)a dNݺd3LƐW-dҰ4N'6刅Cq4١ Vi=VʾUzΡ)D#{5 ?gg^  R=@ IKÅ< AdaO2D ܓI#;Ba^{BX ǡl|k15{%6*z&yixDٷay#~. ^ ppyVȐ{@ S…<º<CuqUsx}s sp`FM5_]Z+~ X)|ͪ?"` 1p k*ܙkP6vy^ >(y8:R^k7Pnc:dz!WY XW`(.\g}62#T+Q3d4̤oViz;{KP_|,<6i8G?j9NO#$ƽVTzD82q64<BdK];tHܐ(!"Xxi%^R!/c#@FZ &/32 joTRIY@]&I?j.1Д=VhBdK*~8JLp,@LWrדfÅdNNݺd3/{@Vf\ԇg񷕪{hƖ[Vi1f m5mެRgqJ2 t!?G'~4oY3e&8&]. ]X!q}^Z c?iII+_ܓIGU!IsbcFUL*@X Hs&ZhW ДA[{6,\>(pO m%&8ta/a°CҰOѩ[{Fu,L FIm`PK4EUFHGSVKb$w1i+ C 'ep5ǬG9 xj-< o] ??%&8-cX!HJC#PG2 (ڽ9@yg&#a(POOV.6Qw8uxLJL %g1R6keߪGwcRbyo'îL n XE C+6v7f%PWn]2zo`8p SL$ .7oDf%mNh՚g C!]uF1MSgx,ĽU2SG& ̀wPfc:Lay m.ȔG,u=O{)d IZކ5vtM`"͛|.1,ܙk{d@gݞkB o]0@Wu̅'ѰãS.O}k}9FT@gL XO&ǽaP/_4lTD,=Ź >U b1ihZFw/:9 7< (Fl0v&+1)QZ}ȾUzZXrRG%ul{wZ"tR*Ep,@-uOMC čc ƆoT k㰮gK iņbwB7o|uѝZB yXB q)pafOKYtRu (@l 8ݡ9MOshrGTA|\:#:琵rA7O?6\}XxPGy,5S۲F:'A8I{1Ģ(6;s-X3E9;uyTJBuF1bPl1IabshI Nx8/!8x9rx[0ehCg&DΌpN,T<-QMǤq?Y}u"'&g߂hPANg>h)wz stǾLL P5LsgO4 NJ/PVc2 <\`9Ru@dl| n:DaZ_ ub4oDv64T| 5-m"8Bȅ"5Fqnٶ:-}Xv8d ".[C9tU^&^ >(|<3iD2/ݎXE&7BE <~ܕIWx+~ ƃK=Fb,6@2)98Kw݄ĽSg@Rt2d# PU.BJԭK3Nz> q7 |B K~ƛ7zB /5 9Nk^)7dHPqn@>)0{@Ħ+G& M`rԃX . co{݇x S"|[ܯ>sQX,8P݇pڻeF,P3qqge:s_ej!qK:g&.muHa:I=]֔@zSJ3O:)MIHA=@O$g@] m? 屿w݇tSO )Aޱo2=pѬ' =.Yצ`qY#&:^ rdL5(G :4<*N vN>Lx,Ap,@>\+7>|x<4n©[NΌ-@&w6Xf.L>e P; @8 CP4@Ѭ3%lN8cR$tyN{qΎ]\odbzi*Jm|r^ ̝s( n0FVz K^bӊ+ “a?=]j [Z> @9;(s"Y~NLs*k[05{8(Aow~,>35%8 6$Ev˨Rzh?eL5>O(M(ݲjr)O(b@'$Pcҹ3Z }*NQba@1UCQ4(\$P⩉)E{q9u뒑)&4b',|S,E#>bٕM#9:?3aS Ÿ,>3 ŀN{Dxz?c7R{~VGC0߭Aw5HGkg41(HmUU5& u)-~)a{݇mJ(~nX};z}k.|>视9XXrw[*͛ὁaH\/"#g+|"b\@BqcMU طT 0g@\n-Rxn0Z=3;;Qx|JSBsxFOM ,mn] 0*5#8 #SBPdX_{<߿/? }(Db_yOWcߍwZ.'&7 J)OɥPnHiy#_I%tJ\c/S8h? >nNAzM{l3:5& SKJ%n(d/6%e`*c7}(: pb]LE͎M[HN\сIN< V!َ¤wGmFޓsΙH  $ۉE0L4Il6ionwQKKߟ$'dZZw|^Iw;{+漢ćh5sq{%= )-qF,*2,a5=>E0L æ7cZ&(ǎ-KsCX! a(#珻C{2VB/!ܳcs/0 ]7Fπ erN)YAcq`K=W3l }i6Fę@h%yFC UUmYPw? R)(9c+GT=ܣNU! /ff_72&/mVm^#쓻vj!@]8 Ŭ 1AH=CX}"8e2KnKA k[4dg$M;v {؅Ix 9uDxA!,p.ʞаǨ$,f maq}Lπ['_4$h ܘ1>}܅VȠw?/0_[ʞ~@r {-d2bYvLV3qo$Ozo۴y3pka`rߩSeŒב=OV.=>W\3LcZh^Iq K5a:{:咫YU7E2}AH( D2`z닽3Z%kKm`f>=4N 7]/[k䦝}sr~<6Wլac#灞   -ezYvL ?$A- l(w;E ! 5\r(gjT'wu1ߥkc2 hمnMaD+~ nb]"~;݄v1:i>Kx/lr{l9}D Aٳa[}1sD/ö`Ic@$%A-ȞכO_cPPJ珻Oca6΢\kj ! B)?k9"ً9]gc]rkCJ3sl XQΔ2J`,zv΢e6v{^ 4&P&-fY4&j臼=r+z<_=nr;t9n fE%tEbϾZgX>wi;qb'W5:" i科ږ(\r7ڳa[=J[%oem\}ⱺkrN h-vnOanxvk*™_:pDu̳x)M!f̥kVo^<骍X ߬7<[G/ /SOз6wFOϯɠ @!fNW_j@3@ɦ큵BBBR-x{h4Vf{X'}:vL}7XgRce)ܔg轴a[ 5?ŌU{0PvH{W%4(sjZ"-?C0DXzLfC+L1gaGb˚7}hSfw\]ڽ]\cZ,&%Tƒ- 9,Mz5׹#9j,Sw߼x᱔& i$HC[l4a9FF'ARBCxleo,{d;]!JCX`bdYVx~A+r7 rBMNۭ8\CFiO+!/mV? W IDATG\1K)kWFn/VSw`BAi;z ʸ@K;*ᱴ t!J"<mMO <R|^Yͳܯܴer62> Kp,@]8fc}_`Q`1z:jFVy4FMxU{{ h'/(^<[h hh [u}**p@L;ci!4l^ |'}Ff\6yص_#Jp,@ 6)O^tS=FY ]ZfBa[]f?{d?PZ#-ixJ;D'd(>>Yg^, i˽͠{@W9mOni窏}޶Hc?nK~ӟo^ìfw\(m[s/7XK|6M{ݓnla(2 1|ۯ͙~[]C64&|? OܹL_8 )(>>id>9@!:}j7 Ak;M>\=8q@ٷjU;Mxl Tw׃SgΆQټazdx P=6& _6(gcjaOStT AhOI; mSOK2i3SgX*c7yPϯ%Iӱ3Y4$cc7ޘpn&؄9-[~WO 'J:M;-S} RV24;&sqWZFc CXPgtnahEP`qiJNgd UcRXLq36ig=$=rN}/۷xYs,@AؽIq @p,@!iϽhtލO2]_{] S'0WGܧJwQ iy&P:,ݒ`Vm70{Y@싔a/X۷T/sXrT_gg;%,g~)1tS+pir;H`l{f} ݱcjv[ N2}\Yg5kB!Uyzi6w` Aexh =lV0:<_̕hNo}: =jfJh!]eKd߰8_U'8 '-@[<@nԀSYxZk+ܠJy xx&E,W9-|\xR]cLկv.R,14%ŏ҆mW(ڹN-&9zuf-Z$/ʓg^ӼO;LV~HS(Ga8% \{ʱusȻ 8|qΰ҂,zt[umZ+˫aOkr]!8 );[VlB (Sivt/@^s?Oijq Mo}Q }Hp^Ci\_=Ԅ?n&Wڣ R+SY?n9~/Rg8Dޓ p%$' Pp{=689M3hnnysyr&3Nu=aߊ>hx~RK}Q?fsk tX_-+nEzFab2C;Bgu7ZA¶'^ #߹ y귂謼L:"ϡ&@Vy>p 34OiϺ澼Y{˞| (Sޓ _/Lٗ|PAL%W9C0bxw7W0zٷJH}+n[/lyG::Rkec 3sq-+P6%E q( )Oa%hIF{v:xF:-C:+;h7Δ} V΀R1xyH-N]G `Lٷ2>R#뼬Sgz{Z{L_t?e9B]0a<;& (ÎݩB͚ PjM|VxIGvʔLfD11}6ezeme I~nJ=;S#ߵ4ֺ2HM;f|'5B. }HC3jG/?^u|+߳ڛ{9}r%*?o^=A>}1`̀wZANÓelKd^-Ooh& F䤶@]Aٖf(k|Ԗ,f-gn;; :pZ!Qyz C߄QkJ@J?di> {O^Po !%g![<)n#L<ݳp O {9!)- =^?uH/kńR*{o)W6knh عԟ/!J 'ywky{,0X9̾Sue; IMOwsmu(ZlsT/g_'^; TV tX싯STPQjP pg%(*M# MK##OA+ｓzU<&}wGsNF,ማ<+Rf/RZ~GL[4dI kH2aη&0v0"yz-KQ-g3 90v〝?T ď qheim]}=Cp%G)1RG|N5ɇ;[}vn.}ZoY҆mO"۬𑾄!8P?p wӂcZ,mX˰󔟥J)|KA4|i]/ka?'V c׫g_qwPB&a)]l<A.d%NׄP(kp棹IŰ?j_$8@}u2 I GӐ}E^UP5 xݢЫ|fw 'N@q=|\>)kϪZ :tc1 #K7~j{fiCz>;އo|g➅uk3 K3ijyxwqxgU|."3wNT[֬7ZlAJ{&{V͠jɞUCgϪMow&A\g\z_O?doNgdC\. P4D-uO h4SG=c5:ie'ow|?Z, mwAœ/ }Ϯ,6+E׿HpOZ)y6;3>9{Vj4-$nw/F=ք<-}wK0Gس72fً^Et.P <rX3 m{>_P~Ta?Z` ~P<#{\ng0K_~V-,\oC͌hbI2׬:<?4Dytw6C&4*[XlNzC@m-:ڣK-  TSHG~n /OhJNx&mKor?뻒|<䝟4ygTSU?^mYpgQ[&OL< Js/&%4[rC= n_|pCdB߿Whn:{zfqg|ͯwPŐjF8, ߳5j>WdXꀲ0|tϟ?K3,2$CzHX. @njvOf-tp[5, ]**xƔn\n^7݄(<_)ricJ?|Ɣ<;ͳ4Vݕ@~=b-ĊY R#/. 5\KUA5Aͬ͂dS 'm85aF^]%8pGN;(u[ȾM;ݎB.%A h50r74gjߺBg`M!%!ڹiȟƣ&(O w?^7 |8W]!` 2)!=o9cУO:tH3(,}|_ڝ/]j6?ڲjbhہ PمUUi,Q^~3!=`r9\G[X )}~`R1[Ϲ=hϾ .}i?WMwۨy{n?{|vk} p[YY~{+.t5C ^Zdտ@zP욇тb#/tX%S w6-{ݓnC+=#<>HcJ} #Ī¹{,򳜦ΦiVk6;e͞}! \]ܟ;e!!=Fu'>B3@:?Y } H/qc:iKU gkEmvL/@/ ]`&O!eiVn{njOK{@_zj(+0eh aP Lߑ1J(Ѭ~h׬ ߿޿:&pvz?w2|폮TP2$|~1t骪^t+ wjԡ_.~"Qp7y_u`K:(+_ P3b;bmB8<c:{ڲfJ(Ȟ/e0r(G 8MYn}1w;@0@RBʴk>fb]Rœ} tԹ1 0d9P>.}ڮt.wP]/=6J|@r‰җ]|GJݒYNp,@GRsoc_0QYtXfԃNtR ^Mw{ŕ.٫(C־rݚr@5!=F־7僂ck= 8#fmߦ}cw.|f.KU{^wI$ Jx`_TMz5I>s92jgY 4,a䆔Be`U5Bck2Yc:"Ъ2׺Js=nt\?5iJ dc`}-^&keI=vJ!Nr߈[ټa:f;j !GNۻq[/,۬}n!)  @%Lx,%9RW7wV;pDœnHws)g,ez:եk@o4+)/\_]e -|I{ɜ  >Nem4e+ Pevk]%rFwc_B#͆LN=rD/tF="8cfθ۷ig/>Smejp7cchb4 Vŵ=kSC wA: X IDAT肺+%YB&O6PԪۦðO~#?~|ϽtĞ= 1GN{~ݖ> f.ι@,K@vZu  NI0X ,X({oUls0&>=QVUO~r;\AGɇլ-tL&RmƧ~ H+3yP(. 0;B5+ ۼf}/-Cwc:*S -|EiJr0{ I  @*;m<FZNCc>. u :@x, ݜ.>zh} P<3vTJmKYrd@'4_ Ryw~vPp&4kXgpw&4Bx,=?m.TkWeRqPNhP H/ծ_/w#u`޳XJsP*(S3(AXtX{ X2]h,,Xw?Ph,y.&$)nMp,@۴]wfk M}uq;@W-AkK0zڧux@t(RӛjvRyntCAҐ:>w {YK JIg8)|W20[֬w19]?@p,@dSpϯR]I $)nF1)γ-mRC!r3(CVwȟ~ wPݐ/  U[2 އޢXwoqh&?p{^c e{V}B k3  oξ_zDh,mv% T N/uIqW|A˥2`@9~{؜]zDeM5u%4`D6ݽ̖u[V25^)JIxW4%y"dҵOݿZ™y-ܻ\>Yٚ8yC}}0X s@f~; N~vGYS0}-kvh!+#8Gflt¾M;~ 4 )&}ɀV͋'({2tZ"4z5 w=[߲ g DÓ}|3W^H p':_J0.)γ(Y[+c%?fw.{YOznwWB+/h^.a Yg-0ϷS*z~.g{!䍫_Zm%;E0z> +-U~(.d kz-EȤxuߩCġ,;5p.3F#ݗ{p +Y4!k&|ȰNatIޙsiHV*vLJ䳜r=CE>jaR+ 4}vQG;ej_ODK ZK hW.4{~KMg, ^>.Xw̾R7Cɲא@ׄe%˻rޙgx4C &t ,6JͿY XI$wNp,@(m֮Z@ǤҵOV:#k@| BE&sпϘ˞/ O{ ' )NCgx%2'`0|:9 ;k D}o\|~emcNSԦlwqN- ijdwտ>̺'0X.x; C?}v13-%fV*Es)FM\9Bj#kK,;SU?Ah w.i:nd?߀JsO$<ܕM29%i` .` t>+B23 Og,|_ ͚Y 0l٣<{ (/C*fHm^Lijw چ8d/ #@'x>Qa c珻oξ_ b%XY fġJ5S|S \'0A:[l Z@>R(X,O՚!j|Js?=n){ ~ΞAaR78|>kKd3Zռ_pm+?O@5YP @ {jƧU?_4|N5kqPrf9IpG3pyOHw|hn6'0Q񌥯.r‰Ź֗9 M=`= m͐WNIJ1N tOjOb8o2C :J]S5={o|0BcznqiHp`@iw&n ݽnB2(X*gٳa[].¸4M; sm=]oVm^zq0PGA]'4s> ?ԩm:w`)3Pe֚n20U9H`x sYg1y 8ѻ,0ȡ,Y.8*\8q=@v  !tI/о1P{&+W. SCMOօ?7pgPYa] #'J~Ov̾R]ڻq[]쀮H ecXuDRtL(ADfٟa'𭦱Q!%KKj+;67>c'}=Xc-cmJukl LTjgر3{FɁ p.9L?QM=B}1WɀSoлĨegpj!vL<^u=E;xDy<Ei.O(˽=@ rXI1>&,v\unp:ؒ7{z3Gs(XKn\?nn&5Bڵn>`%C1ޅ?`p^@|.?9. N~󻔟G~:Qҥ @- &wC6h9%xKaC;.3w{L" sʥjb.4*Heb棹:\` Y~ڑi24]֬ͳ `)g܊X`91Yn9. Wjڞ׵_J1Gp, 4dRl $M`|D]л{ݓa@Rvم3^`406izIPl*<&;@di c>\! dQ pc/}Y[ ͋'t:)` F͠実f NTm>X @-/9P &m @`RSodDI/ =yz}hg9玉@3tM[  ۱3㞹@/ o :֜u=1  Kq]Y9u}PM~~.׸}ُzGC ]^f~.TCgpG,=Z裬O (K=/2Datڻn\q J=Jvc5!܎qn d]H䦝[5K@ ҠbON?W3ipIхӊa@~<*pۛhc岮A>ȞuPao%] 9=/wE\O3p`9aT3:?woYwCRȟ~ELO*?_{f/oBch! U/8iFyX/#(Ɣ;;&(6̀7?M8%MGuY5tڽnkЪ1Gj^R %QfO`< UK'3:ܔ0yzxXMp, \O>v̾Jcz&, 5J}[ey槱sOXUhiB7VK@ c{$]$DvtPO> ,K\_=ǫ_>Ea!8KA -?঄صXMp, \n.4lY^Me Oܭ&Te˚=i e_5@! &+sگ 1 q:yV'Pj0}tQ'ߝf}ӆ/w-'< d(@/ l}z~WCj+} MkX߹Y o|Ш-Ed_V".Q&*`- A5z3Osu@h肜]oL-QGzZx7@ fpFZ85C/OnqrC uiS^3x>q @ӛ\_?32?gMX,?GvL6RwZ8Ke讫_U.*AF u`f`Ҡ滫Z =|9оNZi] 5_-]oJ5TdO;kwM A;oÛR#(R#͋[ku>  ƢyN7<\ g cмk`v]n֗vL o1ig5s.Jտ7}G܂ g0w.}"xLod0(M׮Z];,[ w~'1T>w@lk{z8.X>jdq:rhP Y7Vq,1/<4$VZ?Vda=pxhp FKT IPkX$?O,mhM@s">Ί^J|vK2E9U~=ϭrN}teEYP<}ߋ%50Z࣒G]j"Ƀ4=\+} j(ļTyMcɃ蕛N*7mg)Uy @=$@ڽ}ɧGeZ ?+y,KO50a}ndqڨ{L!$վ/"[ VKgS$ ˓_Ix_>ڵ3:$ %-5W㧵XCN?#Xhh^5lz'AMk{5|f8Pe hPD_SoGWd_M T\2'p,2S|;њН 7̧i*ifcP˥[5Zjki1=}fcOb\2\辘8i@phRIE{Kkg\U@p$'S. }~擸sFgFU" lK12k&5|j|ٹ!<}&*Ob\2`g>08m."Xl\Y6| 7>jo H8c/?rɈM*7ٝi@ p,R3ixr57VMU& 嗟Nd& 7>18mXm\YC6D_gwg-O_ƪYc)O[FM5" @UlKb1M+rn}x\|j+bAXo;3\#.LjH8Y6hg>@M5% @UMk6ގJ 1P{±TxL>5\gHX}ƄcɵB,o3ҚR cdPGmr$\Rec}C  @Moӗmr@Ŵ ;R䗟8l8!±qS -i鍭vP3M5&ڋב_~ PS "{jghZ#ӱ].9`8A±Z3r ܹp#:>@y:& Np,5[x;咡\[Kk3i&*Xjn\YC@oGW}j9P8±)@ŴF$XNv cD>s+[Z>@CZUD83V m-1F "XNT0nt\.H ic}S$ X/W//?;N' Np,'.ׇFO+534.2XNZ3|JW>@Cx:+ p,"(v%ĥ"wFg{¸C:! .ble)sin=}p,ftq:^v)2yv}CuD8S-;ikiɯn}32뻯 XNj<}Rs1?N#p,.ׇ!e_i.&b\rPgc8u뻯bD3;3\+8C±ԅ)FF>3-ip,ua\„Hގp*b~cPP/& o=Ξs&)rrmG~e6S'l'LbiD呩 PWPJʌV17V>pS/-3-iNtlK:@=Y,ʬ3I+̧i斷#p,ugdq:v%2m-1hoiM5[xd`h±ԝr)r &z;b>@LƪP& M 7>@콨aAPr 'r_|>@U,NvdTh±ԭbJ|Vuv}XZʬ1POkBm-1yv} cƍ F8].Ena!g>{iXWfcihDh0±Խx\|R+p$;R,Np, !0!n\Nzp, a}U,N;Lg8/Wc`РchXZw`)6?}ivʥ Pqbm-1sn} YWF& @CY,o?;ۋxȯ̚p, '$^vp)_N -p, g\!.=̰!X,p, i~c5^ 7>#$chXGS.9}M d>@d :p, +d  1_~v@jƪfqC$&܎nCbu,N;xh2±4la5{2l9HHXRy\|pXx,^쾎i)! @c\rN<hdu\H XV:`x,ЈWfc~c@f:_ cdqa@΋0sx,.p,M/$دU<{KE)$ @*ov`?X-o3@Z$}1z;9cKE$7;R bpI0߁*ؾnu!0뻯p,Yi?$.NυZ@r±N~ev/o$ db,sּN¸qX)$1xx,$ ]@p,iDxx, njƪ=±VI<.Th, ; Ւ(OwaX>h\_ ±^I"=.Gsن|PRWFp,yHx,G!ALZV?HO0Dc9#V; IgykW%;ؓ1%A %{$ o$laJ<[Zch,<+>H8ޒ{|x,&;3p/z;G}¸wH">I>&ǎ Dc9_vd7ࣄcO_|fT)-ˡY͢Hb> p 7{.^4]XqyWf p,|Z!Lā$ؙj0&Vƶ}¸C-"d {8ގxlיODhX#Pr Y4ph±pI'A%fM`H4CZɵ#Jb?+ZZc]X CYZ#G& [؋A%ع`Of 5&܎=S./b\2pd±pH3E02"{D׺8la2p,±pHR ?#ʯڜ8k{uf'v7|N^쾎lat@1ֺ 9ގX6@]c[#)b].X8$ 4˃HV[Kk\mpJ[Z#8,0KEU% Ǵ*}`F$t%ؙ{q9٘\+:Xո0aJl,s+a>]&Gε P#±P%٘Z3'Gvr ܍V#@Řv7>?9 Ԍp,TQ JApTΞ{mCȞ?].9rio\2#P3±Pe3Ez;bs6gcȤ[06jJ8l\?r,m-kw#{!) ܋=MɱY3C5' 5Y„i9gbg^_gw,}cvtYcZʬ!! 52VqA!f' pH=~E,oG0nJ@ &q9ގX6&31e*ޘcyk[[Kk}}?{2x֘>7{.)b].8Q±PcI\h`TXV3{Q4;1z;Bd cihP  ؋>AUtga"fL X8!K-LcQԗ# IDATjoi|f8FK@UME~e֨4V;TQ[Kkt]aV u:cf^𩪧/W#[7*pcWfcjmTUobfnH1snvt9pjyk=y`T )USUΞ#mXOW6|l\ڋnKNp,$Fn~3#{auӽP:@xC2/PclK{$N6?WnG{Km0}1ߏގ.JM&bih\n)Z}&u)每D_g6??].D?Z@]SYlc&>?I}}?h8]g> ,C M͉uI8LYY<jf! t_ˁQ3I46[70PcNLb|eqP3Ξo/?;gdn%w&zO -oGnᑉ% u$0SksIkvw/Po:cf^ l$;0}lKp,ԙla<\u,Tc>]g>54P7FˁPS;R @4{±Pֺ(=BxCr@:ČfZZc(&܎V}KE 4XSI<6[rQs׺K_~v4?ƪp,Աb 5每D_ghx+@Ccs Obp49R@1V?:.NTobHuvDcq=ms2O2g($PI :]0bVp<]aVt1tA{%9n0q4)pHgpB;f??W2Ӵπ{Y±PCc {:bOb &~$ x쪍R1~Y4+±P؅3kGr46{Rl>r|@.Dzt7.hhN$x_-88PwccITGcȦm% ;XTTK|EG.l9X@K?Gp,XIS"#gbٛ l(E4h±Pc6{:bObL4'ड़_\ A8ԓxFhb1RN. h±P*c*MdeF.{!zp,ԹՂx,UȾy=͉x*XQ @ ?'?ΌX @5%19k@"K;+9v!3ёj@#K-8ODs"i3" Xh@ԂD2Gb3c3  o±Р؞/?oZ{%2g#}m]S$ lTlx,5Ⱦ؅|H p,48Xjͱ_bL4'ab{h,SԤGcg1|,@ *Gc/%X2,K*Gc^w@EbTt;ʑ}]ͱ ^.o"±K_-R~9_ѹ)"±K+c8VTȎed\;^.RS-?dDQ*F>±+[/+{I]+ddG{DG@*Gc/j`[y9-8(u)G㻓`Mh( k+凢H8RC+Ga쏙>\BdӇ Խr46XjsSG194#*clFL.smyalh雟Ɔp& =#gbg1TPDcvp,mr~I<ԔHƩ_"aof_a±_-DڊCӰ/?Y ?͉7Pq17.KMCkOH(?1F? PK+5.vz9:זg"Tg|sB̾?hN$ k+1Z0 .vP~"߹=19'?`M; ȦG[SS~X|<X/cL4%S1p#&FG5LZ\L}±(1 ;vA<?Hʫop7rÕX!'g8RyN:f~=_~GCS쯼o>cceSJ,v3#RcTz*# R11A% <]{%VblF%V͉d%{D97/W>" rr(?G1|Awccq6JDQcDZ!x}Cp,P5Fb_bL4%P$_b blF̯{}m]qȦynpmy T/XL.c~cD_NuW^7Gp7(?ugo:Nwf">K:Ry Ue;t^P98hUȖ#jX,lR1n^K Pc^*Fc<3:/Y>pZX[/y+r{"@UHF6}(N, [z^幺 X/NZ!&(Ix-XHXL%۞j1loѹ)A±@M}}_N9)Gbۺbam1]Q±@X| Nuj@s"c,ȦES"i6f k+1ՂS0Xw#gU#ZľǑ}]&tp\~n 5i|q:f.E؅hJ$,>m]_S-]ps2A8YØGqd_!`uZ#>moƱ!]Q*1` uD8ibdsbL ?jL&͉dG_[WZJds+X. ߹3bX/?`Kr:Ue={;8n^ՂC^k|{GєH n?\?a.Kzذrǟ͉?-owVזgb_+?n TkLD@(d~](l>ʿS~syY?s;?~/{@)\_\x±g>pܝx)./z<[;ME>] Q*;W#$ x|L,N;kam%/ŸG^k͟|xϯ+&(IWWF*FWqux1z𓅵Ǣl X^*q槱Q*:!@Xlb̯lך?1^^s"8> O%nfTOcTtM18}_`[N 5 Ʊ!S7P~B0tu:b ܼo~ԙh,wfלHxf0.@8[;5X/c8}(]F~U|h,;J8` w+i! k+ctnlך?vF_[Wg=UjTiXv-X֜HP;1|Tb֕X|dvp,nٛ`t.(c՘\Λj  ۆODS"i ]pmy&5KEZTTkcC!7P~"f,99F8dӇ+T]_ܔPn4#WCb.Э+ij5>Ot#6J_rJ@)4KXX/Z! P 8D ?j/tR ߹'Ԓ=Gu 7l`rމU±h3T~fbq:FbTtjp,@jN$c>p܆@ûp)n]G~ p,@H[gȾ.[ s5rԋ F{Ŧ@(clq:F禌 @=7C')-P-zh`p,@=jN$+G ԍbdn*f,z' P:R18@ͺ8\\h±+F{?=jFc1:7e4p,@#'=bwM,NT 4D2߉sFS"i\/܋;Wceahd± Nu{åK s" IDAT8,+o>`,p,?kdr`ە#sS1wl5X~I@Nb-Nܔ;o:3l{ŅW$;p#KEgdW5Wbcx><#sS~'?MO% L8' <`,lX^,cxuИc`ud1`= ȞD{ŅN`{ tfPc` 3dl18 7bT,XvV9 {LPc*;ںP%o> `w;ߊS]$;w~±TTk?x"CєHZK`%# @uiN$c8,lk31x#W K85ЙOD{J 6JŘ\tl>rJ~±TJ@Ⱦ.k 8nT륢@vtZ+SVgp)#WLPc=͉d u;3ўj L-ވՂ@m tf8˒4|/܈Rћp,go:'CєHZwR||L. G8ҜH@g&uPW6JTl>2./XW_[WNK25mam%nDp7KEc@#Pf,U^x#;3ўj75L||X^k|{(Õ!Po>1RHИn А:R1FmOx6J˷b1XȦǻqoZX[+܍R'cD2;֝pdy$ˆ?^M+9 v;SM dG6a‘k R^ C2ɔHW{ѕe7ɦp.œxIt X"~?c㵺pj?wţZ±Gm+YI6xr<<'% '5QW.nN6 O'1$?96?G, 176mĿ^ģZ<pZ±pZVwT܎Z<;ώbÁgeQUZ*^ " pmX\xpٚՏ"M`bwEw:, LD`4H8>'Yb$c࢈  $$ YFd1^ 9z~]ʈF:Ʀ y:e,v-v#2槊* {XM8۱kbFp,L,6ZUDg= ŮtU±0ʖ*"[dƦrXXlyv@8."g[q鉶3۲Wwcyc2cq#6ZT,ش=#ko8ep,\ͱXl^P㵺=}Վ^;wb}cYU# WTBܙm nߍf-RL8Xlbq#6ZBgr;V{XmΚP,c%$ o1 LHTn6c׎P,>T۸QEekuS+Q$ W}$! ^sl *qsip߮+Xضu-X|!2"{k"ƦMFWX鵫Xlyu|-_7`>p,Y9֏ⲥ[ELo7'f=b^oGweQk_p>,(âw>ͱh~9]Wa/;sc6skiXuy^% u+>!Ph|C->~{3,7=>3! 0̞48!zO'xwR ?'z.ka4b$# p,@2±$# p,@2±$# p,@2±$# p,@2±$# p,@2±$# p,@2±$# p,@2±$# p,@2±$# p,@2±$# p,@2±$# p,@2±$# p,@2±$# p,@2±$# p,@2±$# p,@2±$# p,@2±$# p,@2±$# p,@2±$# p,@2±$# p,@2±$# p,@2±$# p,@2±$# p,@2±$# p,@2±$# p,@2±$# p,@2±$# p,@2±$# p,@2±$# p,@2±$# p,@2±$# p,@2±$# p,@2±$# p,@2±$# p,@2±$# p,vaS[cq,@8 F #Ĉcbı1Xq,@8 F #Ĉcbı1Xq,@8 F #Ĉcbı1Xq,@8 F #Ĉcbı1Xq,@8 F #Ĉcbı1Xq,@8 F #"VIDATcbı1Xq,@8 F #Ĉcbı1Xq,@8 F #Ĉcbı1Xq,@8 F #Ĉcbı1Xq,@8 F #Ĉcbı1Xq,@8 F #Ĉcbı1Xq,@8 F #Ĉcbı1Xq,@8 F #Ĉcbı1Xq,@8 F #Ĉcbı1Xq,@8 F #Ĉcbı1Xq,@8 F #Ĉcbı1Xq,@8 F #Ĉcbı1Xq,@ɶl}EIENDB`geopandas-1.2.0/doc/source/_static/logo/geopandas_logo_green.svg000066400000000000000000000072101525650651600250030ustar00rootroot00000000000000geopandas-1.2.0/doc/source/_static/logo/geopandas_logo_web.svg000066400000000000000000000077161525650651600244730ustar00rootroot00000000000000 geopandas-1.2.0/doc/source/_static/nyc.png000066400000000000000000002505501525650651600204670ustar00rootroot00000000000000PNG  IHDRxFsBIT|d pHYsaa?i IDATxyXU0l* *"j* X i[`Z&).(nY\\*Yg/: 8fs3>9Qh4 B!0(!BO!O!O!O!O!O!O!O!W?ǰ-[|rbbbr޽?GGG~m&O/g7n XbEq)))9:u`ccCN3̙3`ccC:u5jyꊅlڴ)?_B!k-_ իr Fb׮]t֍ 6Pn]"##q ,G$%%tRƌ/'isʕJHH'Ofĉ:qYYYiӆ;w2i$֭[JӓSNĞ}ĪU066IhРN$ѹ~m4 F3Ņ[MsssWM Ϲ`NB!eӦM߿P:˹6}t>}`ooO6m\p;;;m\\\J *jƆ8XwssӞWkccu+ RjԨdu3?~<_|Eq㕑-zK6ޒ4]K0`6(B%xӧO'44oڵkkoذqѭ[7wNRR۶mElڴ)D4{BQ!vO~Pn]7nq*TPjzK:ޒMd+m ; ufϞ… YhÆ W|4mڔ-[гgOQT̟?_kooJ")))W^'y|6PT:B!o%xsaܹ̙3S|vܹC.]t+J|||'++ gggΝ;KJJ NNNc5kr @SB͜اtvvѣG\v-W+Bw7|̙̙31cFϫU@hhL¨\2O}X~N122ҙPOll,ǎKOO'88N:i֭[FDDpu>1??? ښΝ;kB!xk^@@3g\7o%CeݺuݛnݺLpp0weʕxSSSfΜ㱰ۛSN1|ƏSR{ѻwo,X | n"((Hg3f̠]v2b6mM6[n8;;;Fͼyh֬{%((+WY)WCxzK6ޒMd+mS,uB6.##Cw^ͨQ4Ϳ/?gAAA-Zh5B!7IB!JIB!JIB!JIB!JIB!JIB!JIB!JIB!JIB!JIB!JIB!JIBǏRB$xB!^Z133M6ڵ FSCTO!Kٻw/NԔsѣGWμy䮞DI'~wRSSy_F ƎKXX+WŅ5jDYd =*QZk׮](I':[300ݝUVqǏ:u*t҅Çy3go>M6iB* !(uajj}(_;;;>ժUcܾ};soܸ666ltؑvQN҈ƍEqIB$xB! TT={PСCL:@ժUiժ7o&++ ӧSZ5bbbС ۮV/f,Y_~HG}O!D={0tЫW/رc 0 Į]5&LPz- 9|pQ _O!DB{(TZ5>3ǒ%K]6۶mҥKL6mҤImƃ=d!ʰ ͲgT*[.m۶m۶Ç,T{&LL0 =VO!D޽eҼyJ+WTԬY(=J!hBǩ]6=W.##+ī& B|ƍ6Q(n$ B|ۺu+^^^}K.;v*^JV^͕+W5jv[nlذuǸqhժl޼N:+W+!!!{L<'eeeѦMvɤIXn* OOON:{I<== dĉl߾֭[Z~>aLBΝ q 4/2ߜBXXX׈RUV,Yp-[F&M m۶k@h4/ }6UT9vMxؽ{7ߟ#G<Tܹ3ׯ5k-ڵkmF~tLOOA8;;g>>>pyGŶmիqqq8::2m4fϞ=ߟ{rƍ|gr@FtΙ3-Zozaaa$&&2d&++͛7kmذGGG6MLLׯ޽{sAt&iӆիa͛7R |VfŠiӦxxx;pҥ\1Փyn<}ڋv鄆b j׮ g/));w駟ҿNW( <[nqi,BCCWeʔJD d222coLOO'88N:i֭[FDDpu>1??? ښΝ;kB!99///nîra 6k׮H:uHNNѣGabbJ͐/,, B}طog BKDj?&Nw}ǏywȵvرXZZzj{j׮ҥK0aNRѣL4%Kݞ+Y7oNDD3f`Ȑ!XYYѻwo-[kX@@lٲ~m6nܨsP!JG[Z:d200@V!|RsXFw=9eD5j xxx`֬YCVV:qE;v‚իSlY8J۷_EN"d!(L²eؼp".5u~9:9Uep?N]y232`dΝKN^ex'$''ӧO̙ .ѣX[[kwW_嚎tm'e9>sy2D>#(tSNǴnݚ$f͚3IY$BRl$%'ᨼ200`_ѻCKV؏iYܹCPPs #Fի셋Gg…T\m۶z䚣CԫWO?;;;uvvӧk5vJ%?7Ԅ۲e ftLLL([,UVJL^J`` Cy{Ѿ}{bbb>|3K.Ⱦ}?~X!Ct9rDOW$^5'X/5hCX8i餦booWj-[777"##ߵ޽GGG瘕ŬY8x`]iݺ3mР'N 11ӧOӶm[]xBRwԫWO/mw5kԾ766\[n1jԨ<O?r=mi3---dzo0 !D) URq>vvṲ]v^g``Uhٲ%... >e˖Qvmڴi뜻wСCYv-ָϯZ1RB*WLZZK_xu$BRjΜ9Z6޶);P\95k wiZ|9]:e˖G~4 ǏS*_Ps##""HNN&>>^6{H'JСTaT FfV±$** OOOyW Ϩ^=cwN3 w˖-˵?222_ EY>|E;w'''ʗ/wwwTגKBRfܿIcWҰaCt^''H||<_nkѼATgd5jzgiChh(k&((sss.]SV8q"'O&%%}ᇤsڵkQQQ;y$ԨQ'NUׁ<BRdɤ2mhB?\UVƍ|w4lPϣ,=֬Yd5۰aCowiժJRusHHH`ƍhт瓖F>}rP޼y-[R5!wf۶mxd_'ײ]qojՊ7nзo_I^RP(<|-ZȆ ppp`ԨQ >\5h =Jjմۍ%%%ѴiS̘>}:=/^̎;FJՊWIX"J=z0i$N>'|$xBQ©jFM׹@V}]lY=zŋiڴYcH֯_1NNN$%%=nڃX"nnnܹsG{|DDD… ^*CCC۷/ժU㧟~zO!Jsy>ց2֟>>ԯ_@{>Ͻ{HOO׾ " ~AxBQM> ݪ.Y j5L4FXzjիWu?D###h֬W\!##ooo:u@ffuxw$ܹs|L< ڵkyoɓ'Z 3vXَ "w/9s oыOx7o(/UVaT6ݼyWҵkW6n?Swy;w @͚5_[d99UikkkLmݽg0fFdԩTVFZl֫W/ /N3f̠L2̟?^|m!ϙ3gG!u}>9O<(J-[r*$xBQL2;;;eQ{wū֖=_Y۷4inznlJ4iϟѣ<|*U:ۛ+WRPT\|7o>Gڕ۷o/ʼnb' B*K0}hol$feM ?P̌uVQ䏯/=:YMlܸmA䰰04 4ОT*qwwg۶mcϼ8y-?V!Jem6wjopXʗ̌}s)&&&,ZիW0А˼y011СC*RbE:RR|f?c@w.xsH'%!NN5x8m#̸}6},b+Gr5>cz\1G/9`zT{U233IMMҒukl" B JԚgOqdfeQvm)l HNN~;wf1"/_־Is$/^̳{ُeq͛K]޽O-{*T@ٲe{μy_ٕ+W{wd+Iǎ166ѣG;w?Mz{Jlv>^~s$nœF͵;&վ}\^f]j5gΜѾ?|mԯ_ui(> !D Ҷm[lKv9:u%tT*ƍsq s.^XqoNYjzf `(J*]fO!JrIZ7̤u888%%%/pY.]Y_Sn]uVٳG'.66wzNJVV/5Q<$BdӦMThIJ}ή_122"33oo"QbE~͵ǬӉ`СӇ?Ǐwg6'iٙsΞB(@j5+bhh(BhGGGcgg3e֭[Ǿ}Xv-˗/l;gL֯_@֭6vO!J+V`ld|q"IRHNNI&XZZ?=]ɓ'>Yp[^=/_W_}=>o<2e|! Bٳoצ|2eʐJn݊p%ZܹsdddMN>#G{nN1c }} |||W$B`ǎ<|^}ΝD]+R\9Z,fɿo>Cf̘6+V̞/駟Ԕ]h"VXܹsH?~<]eQ ZJGY$B`ǎ+cJF}Ά#jܹ&&&E8’!1?k֖-[|2ժUٳzii+V )) Z͡Ch4T\5j{x3I'o8FÉ?N-|>RYCK:zD{*T@LLL͉KII 88777ܹ#KI wEݻOEK׾_xdsyw2eʐă3`"O$B7\S][@փ'033###JCW}QÃ={RbET*ь;cjj !x-Ϙ-|gfe񟿮Qٙ7oJ 11XXXХKj._/d9۠AjԨʕ+ڏx}!|ֶV"=](ׯGDFFSO !*33˗SсTj* d'ʕ{6kԨAJ;! B-[ƽ{ջSγҾ %ѣG `jJom;99q B+WWWWBBBx66>7Y155s2h m;hXh&MsNΜ9Ñ#Gsر# 4`޼yٳGܹsZ*PV-صkz ..ӧ3k,m̝;>H@ !^;'Ne˖-Sc#gF_/ɓ'پ};cǎ{OCOXX 2Dbc6lQ綸 cܻwx<6hӦ իWgÆ c7oFR1x\?x~9߆BzjAtxӇQݚnEyj5T䃏OvTΝ;MƓ'OhҤ Dޗ( Ebb".\gϞ:ǿ;Μ9˗ynΣ wttL'iٷ5 F3Ņ[MsssWMs !{Bl tO[H!-((f}܏Zt{feqr.Uk׮qFy3)**͛s5ܹd'kLppα9/ ^ݷӧk1i$-[qqq(\I qqq:y|=:Ǐ봙WJR|4n1B/jY3g`kMoyʖpIO>,YzIԡC<ӧ6l~-˗/ٳgiҤK[2)ӧO'44+VPvmÇӨQ#Zi4vُB6n7x޶(WxK%]׮]׭[׶c裏HKK+*gf…,ZaÆiر矓 ==dGRy ޽{kOܻ؜?+6?m&$$Rtbz~~ ܬju_t84066m۶߿_5&Nƍ9}4gΜsgԩzKJ̙ܹs3gNEͱvZ,--[Ԭ=1UcccIIIyTPfMΟ?k,.\@PdoP(z6y׮]ȣ !kBڬli^")ŕԭ[Rs|RPX3gfΜɌ3r}ѣG֭مX~NׯȈk˱cǴ SNڹxx{{uVw"""~:|yomm-&ժf6˚ىųܞ^ȧcffp! ,_,9s&>>>*0ܼysWkUj{{{}LMM9s&ǏoooN:?~<+Wv777z͂ o֭[}s̘1vˈ#gڴi4mTGf޼y4k֌{ʕ+uʬ!DqJLLoyzk\ZJt[۷O:--y9s4*-_ ^XX } ًKD~;KKKV^OڵYt)&LЉS*=zI&dhڴ)ǎ˵yDDD0c {fٲe[l! ~7=B6p@T*sGUeevK-[f?S(;SҫW/ƍϢr|Y%eRE.((>ܖOeݽ9-[-[`_m[[[ⰴ$$$WUtZs\sDɠ|'!(8ƌs*_-ɞ%K=KT[yÇԫW'O0f̘Wݿcǎ1vX-Z/M6%44U`'TBB!N߾}IOKc1E2?ej5oޯ˗/?ұcG*o+2aBCC->h߾=-}vZOFt@O!^;6lĉ>Nׇ)+gjlڵ;vi޼9=z(ioҥKIJxyCaggǘ1c9~^zEBF4 g֚/~g׾|#f͚i޿~!Cx_ipuue̙R={6e˖nRRR9 ;qYYY̝;###ƍ'BF[bcc?r@Y+2)*,ѻwot֭[ ݻh4vAÆ 6_d֭]Kdd$~-ժUĉȍ7y&...wzz:;w&44T3RI=[.ԭ[˳pB>3 lڵy&1bD!J>' Jżya_ΞMNh4m4|i jժ| VZ߻wݻs^ӧdee1xN@@6+S ~~~@?$$9s0p@FIjj*UViի 6L;\rO!^˗/֭[4BQ}Oz}]l")JKݻ_?+W`7oҽ{wj5IKKHLPRgɾ}x"={6ϸOOO ?d͚55mrm_xmtO!^,_:x5-Uw$i_ZnE\rxzzr ׯφ ̤jժ8;;SV-#Dʕ+CIKK#2223g;tRvؑguuFͭ[011%ɓ')SF+޽;v.@ڵ0a˖-cL4Ǐ믿j"/V!^_|wet_I ɏIÇ_f߿?XXXw1k,O˖-ׯ}}L<96r:t(vvvԯ_+Vh?o۶-cϥ3rH=zIMM%==֭[sMʔ)ﻳǏgϞХKSLחJ|83fxyyMll,.\zx3I',]uм+뷓{*V0hd IDAT~uֽ!ݻ333-[&MzOz|k׮ѰaC-ZoƖ-[#""z `ggG\\! !D1=z4i+А]s5kd͚5\rݻw/4mڔ йsg\]]quue,^8W066nݺ\33333cZxyyCxP&>>ByrUP_~o>:vȮ]oi֬v7|S$o(QB={&MEƍ{8B7ܹshҤ <`ԫ/{h?m9qw1P*Yfɻz*8u/aXYYxzAǎr}M Ԗ}6Çzjj5^^^DDD`jjJÆ !>>hjԨQoFǎwNNNbff/vvv+/}rO!ȑ#156bb_PtTYXZZr9MFFF8v~~~,XcǎQre4h@rr2Ǐ͛4jh޽{=P-YZi$&&[o_ѪU+ڵk;;w&wnnnMGEΝ%>>RJW%7jԈwHwqQ]A:bذ+X#XbQXkDcƮ1vbWbP X`Uݻs瞻?3gpvvӓGQB|C:x *sN;ƀέ0W]afԒa;cݼz{޽7#GTTaÆ@h\.'99\\\8~8˖-U7.\Ç8p{GЌz*!J166&!!B QtiF&vvv_x7Tg͍fΜi׮-8xD#ZA\ {$4yd 4pKHLL$0`eʔ>>}J׮]y+^^^ՋݻwiiilْCQ^=lmmپ};<|TʠAѣy...r]?xD+ Zz5/_槎-TAKsq G۷=zpС\ \rE͝;իW̙3s<_CC[pq-ZDtt4$&&B`` ˖-#)) nܸWrΝ;%CxD+d2>X6ťM#UЪGϯ&55mti̓DZ<ΨQ8uTsիW/1cb؂Fx{{Sn]7nm "-- 2eW\ԩSYP^-i_@>}bDf 2D +1'{Ý*Y/e.t<~tbpWt3?lJ+{͌ Y3mܾ};AbbMbb"{g:t(JzjFž}hٲ֭C"p)b~@OOc\|333ԩCd9>nnn,[Yf[Hhhiˢå˗*m?CO!|yAATX+:,^d־{1nVlI.ʤhkjr Zjٳټy3nnnDGGl2ǼrqqҒѣGn:qLR[;;;|}}w=͛7W<YfЯ_?S$ -{u ɤtR [EL6Mr$ l߾~ϺӧO|}}yg%|}D ʤ<<|8'NÇϳpB\'ݻgJz;w.0E 2UKܹ9UTp9^ ?,gD]͋MT[fԨQXZZ޽˨Qظq☮.VSNC[L 7,_*!Hٳ'i9בI$r޽vJX9rIH$tvGTʜ9s(TG| 2K8s;w;w =XΑ#G?@U݊lu KKKt IM XsuCfch4G0o\귫hנSvsΥxt 6bƍl^c 5Bի( BIKOcj$gsԯ_cǎajd{)W(c˖-QN:Ŗ-[شiNNN@悅… g_ݻqwwG.tRORڵ+ɟ~qvvv8991{l/d2 g_ӐMDE4؀ V\?555-ZDʫ5WW yvUݸqc#od2ۧx߸S# (R]v!=ɽ Bq6le*$(?1&( Pre9z(msݖQ!}!JС;wF*pdȐ!lذ#GrJؼy35j 22򣮟7۲ihi(gGɊ%7V\ aU4@骥127?ҟL&ӧ=rF?*ڹ;F>))I]慛[iܿwGYB BD]>U2]2p@.\={8iDFqnر TJʕ0a3fԯ_7o~SRR"r-}3;5QF}Ԯ̙3gHIIQ:~00rGw$U&Ƽy3P^uܠZ:Z̘1#OcHAɓ'ٳgbGaLx,0.\Ⱦ}+)zLyb}z.FGW |v<&&&<|iڴ) .$))Uү_??Jaq{ϑ$&$̕+WLtɟJ10|X JU͔k1ҤKcoAoHAȑ#ӥCQd I_Ν;D֭VUgMb'c"1Zp!)S͛7Fܻ*WիW~G݋5ׯaÆ,[-ZJ#r}γϸx$@QyϞ=kC9\:YU.;wtR\\\۷/wܻ{OѾx4 K?q!ł%A;w =mmUq<|¢Eh߾=CW&ؔ/IIM_h+q#z5tܙW^Ѯ];8@r0`ێm_CVnݺ899ѠAl-ZpAz쉧'}XXX0k,/_΂ ?駟r}~DD~~~TȐ_s/u@@C QꍮNhƞ{R lذBsd2(bUrͳ+FFγTSSX1{ܹsM"`llݻwd!::Z10 eÃc& 6|rvl{>M4f3{MՕXFn̙3~ѸqcJ,IZT'O#ڴi W\!!!]F>G.>{z5NPM2ܔ+:~yffTQnү1]O y`և:vg/3h ^Fc;kZL%jlܸ555[w/13ifȑ# 񷅀---ٽ{7C aϞ=z*<}}Z*kצf͚:tHzΝ;[x1͛5K0Ej Tʋ$7b%ƍѣ ơCf9g 0ϳsrrXXXORRҖf*Tmv ԩSs\r,^qK|b^01񤦤"Ha`괪{^hjkdwңG믏#55:{n&Lm۩W^D+AL̝;*eJPbUk9rŋADFEj*E1lCO_ƏceohسgjQ;IΓ;--M5ϽˉGʕjVbԩ;rq].FՉr>kuMuBGgVƍj?~;wr5._8>|0.͇qv9gzr…x"7oT*U 6uDFD닭-zzz4j111l߾ 5q/1` "<|8ϟ?ˋyaSC?9#s#a{jjg*9u 6ɼs3C}Ƹecٳf/asSzuƌĉs=Zͤ~o ި_ҒB }ӧOcUՊK(IRHJ%XUm6jԨxbO?pebKϹ>?Ѷ=tܾ}}}}>| Qɓ-W9sPV%< X̌[/qFhjk2|{HKIqwti(W,~~~pܹ\Ÿ`hР"?pF5g5iٲ͛fۖ_eKׯϲe"dD'ڴiCdd$;B_W[dѾQ*Ѕ *кuknܸAֵ(g֭[iڴ)r59S&3~X >\+sYOTƏ8ޠAr9tjƅπ{ueҤId'J޽;/ôɧwF.TRݻdǑ?ƺnUflF׏^zEc2OܹGҷC3\7Uu8٪kSuTiWYfA 6?''?Nǎ5els硝ׯ7>&3ٔVɓ'SJe=s555Bw4wi΃~o}܈)pք5kPպ*bK? O> NőP%32oɓ'̜9Ӵ]:(vGzz:gΜPlAvO >'1OhԨ䟒"w4؀b2g;v,l[}JRcEcooϞ={TLߏ+˗G__ ۷md24oޜbŊajjJϞ=ٰaIIIrJ044VZ̙ORRC bŊӶm[/ M6SbEذY&4hЀUV?>SN<{ JeP:תeddۺ.l[n%))>{+USSK =|UMeK ִiptLBc_fS5{fӠc6l@R ̱}FF}\9D"I7Gnݼe1ʻͩS_s^zL &NNN \%x\~CҩS'BCC\2O2}||(^8ӦMc͚5п5k 7@PPԬYѣRj#6mbԨQ,[ 7neǏӸqr˗ņ hҤI#G/о}{֯_}~oP!""|||]6Z6&kޯŜ[~̙dڵD T2SV]^/ I IDAT}[[񹑹g_CGOA0q/h5ʶ./j˲ >ݝx.uc_ӨX"k.ǁ!$r\F=hѢJ> r%vlߪW2%̙3Yʦ\z'''ܹu)g[d;(S vlٲ__";ŋSF cZ55,H}m6?R[777YzXhh(VVV@KK gggvELL'Oسg...ёRJ8zj222psS3͍gϞ}~' ,:tPz{"y[2&(%JҾ}{t题&ӣG܈p=˺UV\q BYr{vޕju777.]@t*bDݎe˖+}^Jn߾Ma?:<>דRF 6m/p>yωk׮mwalmVxr }}}d0***K;r9wܜ۷o#˳mkmmB'** od4;#FH阳3=OoOBB*TƒMP=g4U}:̶M\\͛7ѣGݻ7ˣPKKK222Rmlll|sj>Pj+B5qDWS~x?-VAqE p@0&L@2Epܽ^N23HJH҉˜} }}}Sj#Jٵkl'~A1KFcV̔qX\"""H$J.D"盶Y\9^|ݻw P0+meaD~Ak(6~AÏ-qFbΝ$&'臆LZ3VZ:|7Bz۸ҿ@f4sss"##GޢE +ƥWHz\5U =f_+6-{$&&[[[~5;}VպU4Kرc_:gL0__lӢE ܹ޽{VZڵkIG!!!hhhгgO1WWWܹ?K*%%kҶm[\< Znͺu;tSl₺z733}JA۷/˱U+|u+]dжm[nݺŅҭ +wƆݻw&Kcʆ >6] ]ÃӧNSpɺFx4dŴU,}fb߳}Q< Jʫ/~-L LŚ9s&uUv"@&L@6mhӦMuիWjՊsDJJR… +Fƴ0a#FА֭[s 1bEQ׹sgjժEݙ:u*,\fKر#ɓ'x{{Svm:uzXbxxx0eԩΝ;YbAAAJeVA(x_θqYk NIeLP(yl[q./^@pp0(Z`pܗكiӦ AAAl?rHVٵrjjjdoW|s1UfS; eKF"U 3b>&S˫X"[lf͚_Zߺ\:vtt;H$dddpʔ)D"ɶ]߾},Xr%\t *ȑ#f׾}vg5<<___N> -[d. d͚5ܼy4z_б  Yj|ujZɃr ,gߩ l'N~3C6ѣGm߆Ei Mb ־esoW\AWWʕ+|r:wn߾;R͊XUvZTY)"v<[gn߯=^?ٵ¼'#5ukѮ׻/Fa$.3T\?; ,`С*&HH|4<{@ / BA#V _Ur<%T";v$,,Mq^ݻi\$w00<[n &ၦ&~&q߇es=wf׊]d|EsKsnǪ >\QzA&0g5; Iغuk^k&~jAukk{h:v*sVmnݺ̙3ឞK8T̍o,XSu}|W*wϦvZX =_BZ*&׳sƺtJYPSS#hhF!>|nq׏[7Ç$LMM nsg1gU'f *;xL&co+kv:Yv-2b3o<5jDϞ=fOU,ߤ3ƍ%O|=g 3///455IOOgUr%;%J]vLL2G̬Y}6}f32.ÇUF ֲeO tU {ҿСCIJJbǎ@h;v F2\A#OG H۶mS=Zр\.g+IYf 9Wj*KDD"RU]3nY'w|R=2-9jw}GOĄ` @Q3$b=p_|cٳg##3Oν%2"N|R=*\0wUq$_'B x")IEHhڴ)~K>|YQST䫜ܝDKGZ~r?DW_{R)bOgҤIJ*SIc~pp0)d$H"=-Hz =OXCFDGG:/N Pݖܺ{ PP۬߰ cY#xLT]Y13jE(d׵3VUfɓ'T PL2 222crnc׮M6ugRҐ33y˶k.ѡ_{>aU=S[?%nFQGtCD'Bˮ]hѢ7辶<›4M&ɉuֱ3d' :fָ4AGSl{D*SOsr)>֭ʘ%UROfr A(]{VA(5k5*k H+h׮]ˌ3ֵ+Ν76xtԦwje0{17$6bf4!R4onDa'w郧,^5oIwG  HBC#[~RRRvժUgk,Rlȣ#>e`.ϱu-C"@JJJW6VZ]z?4f^ ?WLBpuţs.C nGE'B#055uJ > V4UVD9;w.ru^nzZ$%qoSo߆/:<#_?^mO\!2" hjj:<#'3o_u_^ƹ 4OfQX%99SQUʫa!\7eVܸq+fؘȇ1}2Ō9dM^AtK"@&H"T@106 $>6ճbhh"@iܤ ϟc?wmUgP=R/pU(aƽtp=?M@.yą#' M/rMAo/[DEDro<'rUuB$x ӧOgϞ=XZZbll,H䵔ץ)KKK#>>5hh[[HJa۱;xosM\Π5!/sl1N 'b$ RLYѣGlmmUi ƅ ޽{瑑dd/qoSeh zZlaKx-M_*Wık/z-ARr9¯g"^"e2FFFԭSjժEݺu122Ru*#]/YU,9q%g/^Y<RJ6HɒXXXbJVL[ yqsЪU+r9ӧOgԨQmڴp8@&Mmmmm2eҰ%J`˖-hjjP|y5kƟInLJ'*LHHϏiߨ _T.\HQS#t59];,# iڴ̝-[h7r$.fVr/t%_7JU*'@iߚ\eWxԨQCV&rJ:vT9\r4hЀPűm۶sݕtss#==իW+beeHpvvf׮]dnك"pttTRJ_z5egؾ}{nAoʢ܊l7Fvw<$x6r`kι5 m3Wo[#=.A`Wׯ_85K'?v dN^NNNv7-[xJsrS.s͹}6r<۶D> (UT>lkĈY?qvv g.deH2X r)F+d^DEo?bҰ GA&,!"|s2o<:nkP\I`d2j\)`U_ڦ5~z C.!~#!1s7S~}˧:Ń1f{Lz>>IiU)C{ e%)))oƌ̜93f?V`AsNݻG/tppHחX+:Vxq?S"`oo@ѢEH$iDGG痢Vú@~y*16L݇NjYg|}?.p,ǽ#֠ q lῧ B)Yjل/Kw7{lf̘ԩS2eJʎٳ'VŋGuUVyf>6oތ.ݻwWݻ7;wNu,>>]vѢE սx4k֌ݻwj˗/֭:::ܜ֭["B @>#k8B#)bG˖-^v^ǦFz|fqg$pb/ڂ]U/ 4I{,YԩSi޼9͛7OQ`z;?M2n8UbŊ1`U{LԩS=z4&&&4k֌˗/3{lFmprrٙ9s`aa5k`֭jqL2FѶm[JPP&Mjժo^Ɔ#F0k,UƱcغu+˗/W+"Bv%fcYwBRSʝ)yɁ3?+DƼ8d2wAKw٘ei|i13GOC|B1&62ѲolP,͐xqڍ 'Ǐsq$jŋs%9r$zzzԩSKJYv-6mdɒxxx0fvڜ={www.\HXXUVܹs)VV^///LB~033ٙE}2i-T;wdɒ%TPm۶ }Bc鯻"I.u W?z뷡$&~' Q1DĒq=zPH\\\Tr9-KU|ڱ!?O˴ rn: |"RٳXb8p ]m{IϞ=lg``91Ec2vt!w}6zRLeDnǙkؘ2eТ]'֭۷oYh&攭lŊD8::bii˗/ߘ;w.ӦMC,Tkj kC׹"K}#b \+ٿѡ B6:u̗%3tӗ.Xϛ05kFVhܸ1)J/ >H_JZt6!ƭ6f;ˡuE'|SRq^FK i9r---fۭkwq>YXPKhӺ5 mj3O4z4z|Q֭[z~<6׎U3>5e)·#3x 2zƌ35IeRlj]$ |f͚JJ*W~Dzw ~ccc ,v7o=:e:#k2]JۙgoM)WԒNK O dT&%K/ XSPؚdԮgΜ˼|29rիWMtt4-Ѣzɯ[i]|ȧt4{C+l6LJ\HK,! U7gɥˆ Lbb":::i֞@?fހ$'"Q١ g"Aܫ+t yŋՊܹ{K"E ""BFBt*9# ݶq F5ÂȲ 7qzicZ eDgXB W&xQQQ羚M|j 'dzrJ 03re;20 &ٽ={vnm1%5!o0k evıa!!Ȥڜߕ/A$' K;+ DG5IT"F=zePXQf͚euu놟=Sߩ"UȯSgBU'O}vԎ 駧M#X r[UfhCQs"OHT)i ׾j"Ax1>>L:===J.U}Ν;нE=TE5#}F(~zGP0s `zU,T-m @2#8 HJLbsUno/BIsL'^YXg9撔{g'i,.Cc 9a~ ܅ԉ>{K{h8d ?>DD?Ь2Y bjPuoFHaF!]_?͘n{`eǰ|Q y흹`!161D^"!{9ǏK:s@T; LxIg֬YT\xqYFd\BM4>2.u(n7>o8\ ({ĎzA/0>]-BӻϤR-uhI\!דdH$B"eֺunݺq}n߾ZD1`6lP&Y7g+2Q3[:k? ,c-nWe˹}w.ܣϔ^lY:[iG#G Yv{YH"K' *T^zek-[dǎ ǫ;]f[+ ;Wg^%Q?4kTc {}XR$@DH-(ZZ$%%i:\A\!Oؼy3NCc9rD<22xbR[)d޼!Mo@ ..CEpk@ONQaQ;OAt!hذ!3ydע)^8c 4(k]ae!cTՔ5F"4⛌cjGP{ţkYrb7G>3B y /&c*YP?ܮ'|;%,=΃#8 5Fkt%}銝,I| 46mɉ=; pvi/q3DG^jռ Das#fcAd2!. gݺuw 2e2w67dxB6X >A܏ͷ6j:+$% _&fAȳN7 ?6mh:\C$x i˒}&sIf]P(t(B=֭[k8C$x i^4{IO@4&իd^CȸPEp$HAȳ UTk4Nw?K](k]8QɤNS4^JuU\i y﷪ZUv~~AoSu>ވCƮAK n-Mі>=oyC\\!&"F$w$6{.PBh0nˆ?e G$GlT,FFF#W yZbŋԩ]PuzuI+Ѹ"/"\~b/Ӽ9e-o]맮k8"!=bb1g0r yZ%T=zĹ>?#Ysw> UNJ\2/rﱴt!O+SǏU/_LGfi3iZr0O0JA e:DEi: !œ/_>MO !!@׮ݻMPP˗W=oXnhk˼1I I$%%i:! k:\E|b ˗Sՙ3gbhh1=.]P|9׫ǹsUw[J %{p$BZ 3" ^LL ǏI&XXXŌ3> 4{{{*Tq?v۶mOҥK?.66aÆQT),,,hѢ7nlׯӼys,,,(UÇW+%K舉 jbi;Vݻ5WbŊYp gggWΡC\49W}䒆#b`gO5F/$$דHH$)͙3;#Gnݺjm/_N޽qttd޽lْq.))~ٰarurK.Qn]6n܈^zrcƌanݚ{?ҫW/VX ԩSYv-իWgرl޼rXn]:PPaN\o7 si3eo|h8"!-a$ċ!INNNIXXX0}tNZ%P(x{@hѢtܙ;v ul޼Yvڴi̛7???سg]v̙3ԫWx*VGU߼ys|||{.RgҰaCCΝU13i$O:w;vW^!͛7R 7nMY>>3ӯߞQ2mr3ۉT&rSst{(\M7T III<v̟>'D/'22Rӡd72uޗrB+++Ԏ%ΞP IDATӯ_?}%))Ilٲ{{{UrG׮]9qʪAAA_tMԯ_"Eeձ;v ۷oCCC9rDThHRll9s E_WĀ?!^z.[IY1e~F-`V9sͳs 6cT1,z#YߕC/}fc`TOJ,7bzżypqq!""_TLx+ooo>>>jmB $''닅>>>$''mf_xA|(RH>kј tڕ]z Ð!CqyHpp机C24u*h4^HGG}6o3O5fVfiٛl_@@㡁aqmv-ڍcVSTzXضm}t8Yj׮]ڵKXDDW 4nܘI&`kk˽{QG[[;E`aaZZj}݇םT瘛 ~8sV-[&. B.Duy$BBB7̎4%$iⶉ>O#Rd|=QJy!c W|ްd2chY/Ÿč"Dؽx/s &>\YmٲQFѾ}{:t@DD{f͚l߾sd퇂 B3g't[=]mvRIuuѨ!!$% } dtr툉DϕWT]r=? | b-Ң^~=zV\,P(_Z*;wSN 0'N ˙={-rӐڪ IyÇ{>ڦϰ0rZ[Ar;wzj8$K,ք }t㻈**p8씩}2gѭL6sWc_P=v_"ЕRBqܥN[>p$C&x#006mڨ֦y>}ZU1P~~חXŋݻO"`oo@ѢEH$)>/E;@ WlC#ͱ Tl˝R)}MKW-zs"C?nyV2< )-`]@$ssLMS+COLL+++շVZaffV"`ҽ{wձ޽{˹sTٵk-ZP݋giiIfؽ{7 xyyKݺuCGG㛛ӺuLAΝ˛ǻ/hM P5tz2=,lp?L8}L^}[Jdz\AYh>zqbccVcyc˖-ɟ?? `Æ 8;;Ӿ}{"##ٵko߾Um ɘ:u*GĄf͚qefϞѣJt'''3gYnݪ)ShԨm۶eС1i$V* `ccÈ#5kժUرclݺ˗Y!y>3"BԔ-by>ބGDp d:6% S)^8.=T+Z>c `dbHV?o247l0߿?}쌋~UVq ,ĉYj5kܹs1B?WWWlµkpqqѣxxxh"vڜ={;pBΝZjjmWgggR| ,Y 鉋 7od۶m92#?ArptBٲeٽgk[&<5vߎ2}WO%Ϻ)q֗ٗTwXSD24uu6Ŭ ݻw%N+ | ,`„ )/j4:,&(o36O<ի{:{j'tu5*~e{:Lۛut4{lC#CծSBD'B0wΜb5홪_wq:}MJHbZo=o\lY2}#} \,BArr2QQ+_TuޓO3$!>7rl LX:#8c32-/&(0K̯^O<'wI|4v,s B? I SCFwT.k=iW?ov?.3 c l>`H}Z\Qw.,(3CjcnyR۲LPz|1S\L>s-ʕ*~رc14LbYHITXhh( ,ԩSXZZұcG033Ғ… k:/:uGGG<4ݥ& 7ptBF/^:ƣ-nKi_8[ԏrQ֩ EJC7o=GSSxq6o;'PyC_o#FRZ5jԨ}mW3xZt)v h"3p@6mvvvԫWM_&L`ʕӮ][ƭ=Ib\Ñ Uqq_tY6)=^bcqNPi&2)SNJW&{͡}s_sVZ%FFF\xC#C:u_;Nr Ŝ\4* i.?WY|9ݺuSN%dH;ĪU(Ycǎ9uʸq<2SboӖjq(W,6lt蟵crS$^|: IȤ?õCU,{xl_]埱Τ{XQ㏳>$<,n㺒,_10ܔ3c Oغe+RaÆÃ駟Xz5S&Oa۳q&ãwn}aAat[Up̈́8ԫW?_?ϟA6FK EGGHBB+[*V666… پu+o(namS-Up---תZT Q[0p@$ 666ԫW,uþ%KK:u~fC G&dMU$눖{u=xձ23Nsw $5N5-E, eԩ}]___5СCUǷoN`ٵ?!CcD?T_yw3z(SsUɓ'9(tq .^Hdd$3g$$$LMMi۶-,_&Mh:,ѴiSՇjՊ۷nCrr2QQH$ZYK@$z.'jmF=2FYv-C˗3A[Ƌ 9)T.%iߧfu9~hkѭay E-?Pة#yTg Q^{G^L؛^%*,X޾~~W^eŊ:}7P"Σ:S\ѴO"B">~U>-&h ?'Ndw9^V"׏ǏxmѢE888Ю]; :w''TdĉQlYڶmKOf4N:\pA|D:8XX6ZZhiI#>{=M Q9S&>:Μ9ïJYd FP-VBrN]y@J CҬ OOOֹb`*_C"y5\N9y$v *-[2ePxqJ(7{ϡo̝;ĤDU۸Y}1dƟ; fjw\c([fǎ 1[ ߝÇӧO DёUx;Οh׮#F`۶m8qTܮ>}'e"##ӿoƌH$gHLlڔ'Ӧi,hRcBɯBuĽǖ mQuR$1{vbzzbldD"El?bxKqu*qiڪX ^O#m =ܖI&֗v޽{QԴD'OŅ"͜^(+:+? Ptv1gZ^zѼysXd 'OV%sV *. fzvJ.si:vH![[LMMAK%0`SL!66 믿D"A"0}}zύ5.L2k׮1VUR˲yD"aZ\q$.!r FR<7{XYYE7Q~,^`bcq}fϞbARNP|y8;w)QglbD=Wn2Ğ'w)|~ϛqz{#V ˗i׶-69%*>gr ŧOS=٧/<~Ujո| f(/^ƵWPfoOR<}ϔ=|`FŪU>o*5Cd0̾#mm}WA1|,!g [&}cRGVa eoĐ%GKJf֭dgj>l[w9=J.͛7ٸq#fbɈev,M媅-cfiFXPoUׯ_dhyH޽{4kCmm}J&"JՎ%''㿻1xѿ=zUi+W<=y)zzz$''caaAڢP( 3eU?m+T-Au-W.OO>Ј"@yk=+չsg͓;ڵk*3Mr0[-foW.45ƪ6;iڴ)yߤӅҼys=[ɗ/}u@ @ݙSzc/w]ԕ͵SN___]bEEٳgԬQE\ AYܳlroڦvl۶ {{/դLVvv2Op!?ڲcGL (d0'*6}f<}=ߺuKZϘ|BJNN߹D9Cnnb#ѵzɟBK10-?O`[fhD,Y… ذaCۇѨq#Xvjn)X AAASsN͌߹-z5$g'z>=fVfV" !Oۼy3J"! nrv₊ .|6Y;zajlً}+(YВ5G""|И={Ç cV\YUc)k}$ 'H9st%UwHux9 ѣ4lؐr徼r9K,ή0&Lqt‹/xٳguAAAtԉE^fMJ*X>Ԕ[r \?6 Oʙ[>=Zr}>wD'H>o߾$''S'*ڦ}R&KDx,"|6roȶ˟٭[ӪB6@/Jsշ|>k۶-={B묳VHו3HR8:2Sr]c  4ԩSK +Vή0nnnX(Z܋PUݻwS=QFШKCJT.q4h(eyw}͏@N:~ׯSn==[qܿ 퓳տ8k. {̒%K;Oȑ|}}Q.*Uʖ1׸1ݟR.7½aC$@@TTeE P{>xTZf۷Qƌje exBV΂z}UTi*Yr .L(B3g6^}AVpCe ?>]LLL(Y$Oo=c٬7֯_/fff?{~fUR'O{n ܾ󰲳~ootuuILLL2,_C+ pU˗/0`2Ν;W^Qn|||)YtĥAd4_jW͛ ~̐y$'bmo7N L*.]жm[.\Ț5kXᶊ%'a`ldDh`(W΅+W[n^GlrF311Q#1e"r8:t,X{I]bJ+VFK}9u(#)M6k֬Y~{ =|TWh#5Gfƍ\>~0n/56nɓ33e6l@PPR(zҴ;"bqri;Lb\"kfڵjm?rܸqTʦ+5ߓsEFFRP!._L5UCw(\$rG~ИDU`rvlC]HD,QZuLL/_Lpǝʕ+G"E8g)f)/_t_l2ywcIIdOfYo{Dܾ}3gPdI?$''poH$xB@F ^ɒ,j^! P(xAbŲeWG4H.{gm?y:̕+Wiol߾аpFj+O`y)?Ǐ3b RF 4hB܍7&mx}Zf}x/^D"0f@Y)))d֮]ˋ>5U2mbzx6wTsƙ3g}N%Z!GR gI>#:>P!{իW#a@ Qs2'yJAyOJwomO yM޽-[\f Vi˖ǰ|+PR%իs© *Ufñ[mOOOn߹M{3TۏիW <3h ֭P(033#2RBĄϟO IDAT;x` K-e8sY{gdhG$%eVӧOR0B q111ܿm--`&q] 7-P(8q85"7G8pbuۗcX[Go4< 7ԩ;w̒c_|ɵkװ.`?0b H&P_#}= (w\27… 9ͥ&VfF'-9B@" J8::v*% ҲeK<};wpQݥK^g :CzjiZ:K/oNMxM;.-[d/^8}45"22PVZ .YL:uxR]=j^^^4i҄J*끫4$ɓYz5qqqԯ_Juсׯ_ӣGjժŋ/ "&" U111ݛ޽{D֭3bڵ.Qk{kr9{eRLq~Tmz9q/_LOи@L5CILJ۷l߾d~,۷}gsxwPc2duҿ#CgN+_&ݛw^q___yEutq]:H EQQl{^[hWTK,1QXTPϓdܙs7sgڵ)S >fnM9uPgϰaøw<+VÃ6NZØ[Hcvxw]tA ;(H$*nSB SSt$4G5igfbmnsRR2j%pTɩ49@siӦQڈGG#qwiz WbJjqtAwY]^tkR΍*QF_yn[mc4)q>| _FJttUϯ@G< %)'''5jīWPPT .*TK$yܿeeԅ(vV%64'-mmdڵՋjժ***d\(OxǏD$1M**4smz 5'%r qqqӮOKEѥKD(vLLL{.>U!Ԙ(ZD";u 4mڔ/zAqeeғ (@|x 5԰*SuUe޽{.]p1陬Y&Obcce󺺺!‚bx"(\)CР=z777ś陼 ߢcZ1K}Һǎy!mkRjjܼyRSSQT!YzfŁ+6ƍ˫@]hh(7o|`&*#&<%%%Y ___ »w:K6Tw @ժUx1 DeK @ժUc{̘1tHD"VӚDDDɓ]@"8xN͚5~$-1${ԩ2vzxFxM7VPmۆX,fK۳ل$NΝ_>n"#3C61m_U{~:Cw|,-J;xaRڄׯөS'7n0`J*>2u+UČ3H$ hyrby7jVZ5V}Z_:ӧO |}Ň ;`"(y%PXmuKK/?9NGa2O?ժ abR+#=z2zX՗;'0N0իG*> ET6m+W^зo_lm+H$r 2-z~*j*٘q<==>}: ˭[ׯ U3f &#-[oSj5 5z 3ܝh6mgmٙ3gҶm[RS羏 &2(޽}ǡC:(Vݻ/=kBG~ 8;S‚U*(nnnBNňun?}CBRU͹v5d}G<N*U0vX<==ƍy)o"9vCEOO'~Č3HJM'"6 лW/DɴmFjj*AAAN;{kD"ٳg֝ݻ @Vf1/c07oh =+fhl_#O;7ݻw 4ŋ5j'ȓvONPPiԱQۥ_k]Hk~=5jԠB Cb|=GO࿏ +CMYY>% ٯ?Ÿs>/CUPl6_zDh~FgkϒH:6}s1S]m$# 1h 4ź} 555,--)Gxl" 9%WQ Z p֥ѱB֭151Ņŋ/Xt)#M$׮]#((H6WRBgwr?w|j΍P/󳷷\uʶ ƫǯ?6{lϟU0¢9 7A[]W&11 w|s ŏ@Gg֤6G޹uޏ?É>\0+`HAIi2*JԷ5\Z=ԌljjjjLy_|ՇۃҭJӢt۵[XYe˖J CiT-w ܨ8.)iPyd5}[d ]vɮܸq166fÌM'5 6쫶ggg3c lm(WZZZl# $˯[\o({amcMbb".]BWW7(>O&,,q> X3i- ļ9kW| H?5j`bbH$B$ʈ#Ml=v ۺlٲEȷĔ)ShѢ(((| ߼y3M6EOO]]]rJ~;wԩʕ+/99#Gbcc...ƍnlll5jg0VXAڵѡaÆڵ+@aI鏔~fvAGC5W;DׯwEU[,!gqO*b\JSm}svf>Qڕ_޾}KHsΞ=K֭__߀$R䝷L-;luUtWb۴i#;VF M7;vD,ӵkWׯĉdժUd~0ȪػH E¨SK,!,& j6"% ؐƲI~X,fߊ? C=z4>Wre줱PBƎK*8}c>ܗ(Wujz-2q/Ể۠.=nlaLIn$%%hѢ/^o/&&͛7)+9QŞ={2n85j޽{ٽ{7..._~Ԯ]ЦM&O+u;++ '''4i[n%;;ƍ˄ sr K<==8q"I&w'L)Sh׮f͚ۗիWcNa$8x?%lp˗/quuF3At/ga*u^j()IWL rgl9/{egg2%Ԕ ֭[ٓCٿW^D%Syz^ 33w ͜TPͣŻ/)+LY|9wAEU;p)NJ+VŋtU.Lcvv6ᣇ m+N/ Y}fnM ʊ F}sq@ȭ^Z./99΄v ǏС\r+++sٓ'][y֝{cҺxʞG>ZM޾|lܴ^wU< .Nkm{o>Ο?O&Md]\\2o<틇-[D"h"&MTۛ7nȍٲeKV9yCy([,ǎP͚5t YYf믿LHH`޼y 2D.Ptdgg# ?+.+ӫv|8K%7jĠe X7Emrb _?ϢtI޿~鞴BBPPKQQKKKB_E׮]ee̘5k\CbbbB}Ds:ǥ{pik[aB̌rnO IDAT$*vUy&'O}eu+\pA˾}hԨӧO}]AQ{M1z-ʎ+(3跁$ptؘח/&Hܥ3Ac u[E"˶逴Ǐ6K.{B1燖[fZݙ9s7!uK*sΥAr]^?~8(w|dee{nٱ;v`aa!7*=zD/ٳgٳ\h'''ٱݻε$>`bcc9q(,,-y)̌pt$+;[[[RzuΛǍp,e|?80s[o>1UÒ4.+p6޾KaE®JelllٖiӦ.u"szrrhӦ Fc7>מR^=6g\h\b*4h۷cxAzJ:dd|QR%$ ǎlUئ{''^߶m)ClA7o4oޜ/r@rY:HRgQ$vt9P@@"~}ۻ TuC:[n͵'Px#prr2?r,Z+I:u8zT^ $D*^ZjUhjj5 DVH$O*UxMjA T*]эC۶m2~x@";[7 -/r;i{qy;.}7=IHJd}f{M:foIYv##Hx5<~mWTTIѣBx>E_8p!ƍc޼y1ӏ'csxx HhJ|r9[M&-)M$堫͛ٺu+l9; Mfؿr? :***^dxΞ=c~6mbnV.WDtPPP(r͒X2/i߾}Vݛ˧?~w-s!4449r$fffZΝ;w^"%K.b`` W.,,y <9''.^(7f^PTTbͽ?_If߾}x\cjm@];Xq&lB=yuףǪUed4\((ػ@,˙ei9Yw ~IkjMX4)aѬYF]] 8s.g?CYI5j|92G) CpT*%gɓ'_kiիСW'k.^H||,3kў.M 'l?xMl"[ ^g͚5H$Mjj*;wAȪ@tQz7T!+++AWWWΜ9é>q->&I }(H2u &#wE`hhꪲ$¤vtՙѿ6mգG\nݢVgBurb,n]o=022b֬Y2K|d8OӬY3gٹsT42Cj_?I-5މD"jU&Mm_eEL)oK CjӤ&څaO͟Yy*щ >۷Em+&tCWH$ q?ϵgiQuxeee~w=|H ׯG"ׯ:u,...ׯ_3vX/^DKC_GYI$pc/[o&~),ÇE*KvEE\ۢ5͚d ׭dfIW{Usvv6$~ZAٲei߾= [.jуӦg^,ۋJwNUUD2x?ؑu_i,ݗN#:*]v|HгݾNVv6 traN}x*ӈ[k R &6D #bfGJ}ȶ !QI2sZpn8q#F`aamǪUr5 &~8]]ݘ>}?u"%%5u5>H#XNxppl\uuu^͛r9J( 2ڵs0 ])utt[Ⱥ0 tm)TH^䬢/_@ /HNN\O VVVrH9ܻwH -( cǎ̟˗/ݓ x~<@Ѣ-[hҤ Ϟen۶m@achD@$I+**bgg'i[غu+5e%L qaΈujeQy6̬l6m9y z;.}ВH$*5D">Kz~) \n-s&+CVh2 j]ddesy᱉()W˄f5e Q)+տ۷oSfM~W4iBd]ff& FٲT^ttt_~A[jorxTvUΝ;QQ2%)I)۳d*׳eȂwpB̙#믦o_sϞ=_T߿Lʛ9wi,H$PGwqDGN<˞={)IHĀwtرc~(ܶm[2@,CЯ_?^x!޽{qqqѪU+'tBCC߿XϞ=QRRs~}}}ڵk@hܸ1q>g)xݢBUU{{{l™3g:tn={mdw6xoz#0ƺ?/բ< 1ۈOF"Pu%]XtgS3]XhH3 sct+ mݹ.9{}ZGlsĖW_W\~???@#Gr--j[QFS+s7[6odܹrTTT Ź8s )))To\]vc_Q+&˸6oL~P~P(QB C@Wܽt}Λ4i<p~ղPTTDLMp( ɲ#<СC4\]]_~ҹsg)k֬!>>^.AMM9s0~xttthժW^e?ҥKvܙ:u… 100`%gٳqvv=EFF2sL֭+ (S Gf4ԩSxyyQh_jӯ_?s9$+?߇O|I﫰i޼9͛7eYSw3:QB;3| a1H$}*7о5a1ڢ̝@v>-eb;TT*++KŇtp)-amk1g_g*%CɎĖvuKA,ܹs166Wɰsx%%:7VGGG:ȍݴ&'#Gl4BGGi?|WDЍ ~X;17nDBL*OVF+ B$qA\]]qssÙq5ٲe &MBGG .;;vpu8y$˖-crK.,]A"7Փ[~}.\Yl\p!WŊ+Xt)Ǐ͍[nsNƌSE޽;*}M("b5-a(J*ŬYx2#G?:x#^FMōq)-XT\[NޢT)=xCgR:Bn%%ܚV)xG]mu*s̙/qris&3+yFdžz L:ENR{y%uԑ+y)%#R^x1eM˲j*}JvV6Cru|StZKK+bhh]tY,XnI]YTaQRV")!RdIVX! svj>>qVSѵOIHJmEݺu2e '1)IGEYY+p,Z-LflE{.YGK.>,M8w͚}HU&Ԣ4a/ZEhdzz0rhيW^dfgg 9y$:tH#ZPbBޱ^&899N>X,& MMM4k֌ .a4)}r!sfvMZqFj{u-:s\|Jj̐f O/̺))Xrl1?H| U-"Z_V((aQRReSFʤ|O&.._,CO];126漟ֹiҹ3KCOK="%5~22ɶ)ؖ?vӜl? j]e墓03ҡz_۸q#oSM6kۖcǎi~qHD&M7n\.Os:uŋD"Znùp.Z{Z>ヒ]tɝ's򊬏AILʛPR$iE%9!dRR.11XXU['LGx^Jͭ76^Bs%b@\+ ] S9+x? 'NΝ;LprXL+d֭?s/T(_}9k7AWSm^f*fdOjX2c6EjZeMM//AMKZWT#ew3>e˖\v c cy̺,{:<l*ΫQQQKr`llׯ'L7-|qHúubƶL!,3#eg+9q=LLݹ}6%<$Ulm(U^z%gUުŎ]tAGCNM~XL; ArA? Ёcä;Io`ñ݋۴BEYY}"w)6fZ4`>Aes}n<"5=ZaP,GxVV3,]mKOMgۼ4S&zť?/aF^Z>ؑw1*kD+-Sˆ@"}'=OQT)VXȑ#p!VXp...deeٳzmw21ݻ/˅yob @DxT*O^QE$ꧬșee]Ts7$ ^E'~}ĥ~@pww~ׯ_g„ |K`6BhSiE֭[==ʜ;m=B)-defq=m<ܢqbIڵkGXX&L!1x˛7o06uuwݢXw8~.}puue߾}mw{nzt g@4W:݌} oWeQZ2Ų*Fǿ%4viYYYrT?e͌1BMۢE b&LLJ` L ] Ǽ}m5)))W-cүN%6Bpء!C FCY232 ׄ>~EBLz(2^523´ i~F\t[ɓ1x?,be޻֨QII=trJoߞZ5kpm:;V*f|o}}25j˜1c箘2J[O=hco]\f*ZVcǙ@s7٭K^Y~'66 tQy'H Q$&z4nͶZIeYpy@JxX6̙cп?g}}yD"b͚59s@jԨѣGu~bb"ݻw'00:%9K[VN,s5^a@"Fʗ/sS(FŒ%KnjZܦ|'Q/؏X,&***϶z5EGӚ2/Q7)%63r1%[{-ZDvXg wSB]c ’njXp~Eo%j*?~޽{G>}1sQQQ/n_',1a1\={82PSWچn1z*(ի@XBVl~7 IDAT*bSS9z(:t(n +Vw\ǟϳ{Y~ܱ'(Sڈ^z3Νcǎ˗iܸ1;}2KՐ32{l/^,;XєB4iLPP0-5'{:~ BbҒҸpo_ϋ/e$KaUΊa憭mQ\  9^_)_e~ 6Hk+WJr22t&ꖧ7;Y?I򃗪зe5?btЁQF$$PBkEEE,,,xmcoMZexuۛǯb./wyy H8xŴN]Nőoqgo1+kʾ -ȑ#EP=|Xjp?4WkBl>;NsUâD顯OjX"ZEГ(UX}.7u - c۶maddD h߾==zɓ|AjiEFV6?)oԱHMixvEn(.{%wLIII/:::$$$ޗ">CK1111©S9uk0}\$Hc2O>R]vvvXZXpID~.5=bƌr˕+Gj՘ӟ5GΕq=b޼y_G,Mqpp@CCoKll,[l@P:Ҏky_Sb1vU1i j 8x_ExD-Zi^Nyyyq?))),[ŋv 5SYb***())RdIԠ4l…m6LtOLE$a꺒 OEG3o!ΌVFJix!=3 e%%Z"4IKK###QUUeƌ̙3Gn|KKK@dG_rիWp*!ϟҤ|===p7Οe4o?ghj斪>|u-[|չKHHo߾8qE%EljYF|L<'N`"U5Ub65t|+yw0tPƌ+G@{<+W$Y(.\`/[ã¨.]Gq￿Sa닛+qq8V5c۔X葞ũ'BZFa1js#8׮Ѫuk>}#ʗ'S,{H J}%Mu u<#3s7C8(*("*ʊrڑ {{{YIPnܸA (S ZZZ`_FF|"DEU~->ʕ#<<%%|>}~S&tm-p$ ڥ,CJR*wi׮!PdNjj*~RX[˚/0q9~7m2aa LJ#44Ν;WBC}hZ諪+飺,wܿ///TUV:yI|4&g*()M[EYpձQd˩@QUU6ٳgNUkر9Zh!?3Ο=%׵{]\WHժJ}cԩ6Rpv={ 5jPqšw޸up04>O9qT67N(5xD|8z 333Oѻ7mCUFc;BX ^YZ6zC=8Ua%]Lφ^rRR -Zl/Lk׮ŤI b" _v׋8 ݀@ ȑ#PcG*JOOyQbPH>ϞӋR 23kC0 qlmm.IaΟIqw.`;C_(/=/ 1Iխ9,,q􄎶6h|y8YYY/W׿nuh?=X,ƕcWqx<} ̝;'Nb'&D#Js9gLA+3?]֮AH3ǎus19s}b]86 M2#1ix^EF.$,d|M-PNՃӫW`hh}} b1Ν #JX|50DJJJ+x=33q!4mڔ8ԫWG@@0o!ѩS',_Ho# W&tgX ;Puǎ@̘1J?HoVxyZCKK YJƟH$BضS:{ O=AqQ1S+g5(&c.EM;r8v\\\$׌)ۭ[sEѦM^ 'Ojk޽ hݳ##k>A?q]ɶؼy'UE QgΠ%l>m"_wy=w߿.M )9ټB>_xx8/_.yܩS' )) +V,u/ƬY`hb>cK-/2?7#%ۜѱcG,]T.A*(˗/0&#[&w*Uu],,,4!%%%@ :;v@EÊ041DFN:w 044DRR,--QTTݻwCtt4Fƭ[٨&, _QA]"3%Se˖Xv-ի'$jB=~bll.E#1e3NFG/_SSSzC PsBbbbfpP1h T},M_GBoFNN JFo٧gR\T]v0ɶJ*m۶Xz5,,,h91# ̏$0)#,Vرc.<,\xs@K굪c0glӡJBu`jaP]t~ϛ935 6W:51rHL83n# 42 ЦMDDD]QqqqСbccQŦ TB`ig'د: zD"Cq_Sڇ#jB=~P"\}8 JJVR Cѷo_`hQ 1/p8.011AгgOуQ%ڸq#зqK1^cP 2`ii ]Qk֬q`hjYgSKN lRLp\vm3fPz=2jˆb<}vѦo5e/*Bukh҄kabǏG`` cPEE8BN#;=%%%VbرFӦMV !~c!447n@\\uwIcՅ™3gЮM,Md;<,Yt$%%!)) ^n]<ZZr?H|T4iq%x\2 C An.XB5x\~gD޼Ɂ BCCo>ԬY;v,ۇlMM1y ?+W`oO޽{Qɨ"LR>rj"Eň /!1&QTk.}5S#qF >bHxx5`׮]Rn݊X[cc5Ϳ/^(*)v%^##;;͚7ѧ-N z07+{ʑEj0g0,--qׯ_ǔ)S+!srr0etBskchݺ5ƌ#sLHHZhcccJ-{\5 jՂP(DΝ%sldd$:uPZjaϗ9vٲepqq1Zl۷0&y&L]--x7q#xY~=[@uܾ}MUǭSqq8US/yyyG(t~.|`#1tro cNm7gv;N:[g˗qat3gΤ*TaÆٳ'6l Y`,WFll,rJ?cƌYp5L<IIIRkO>ܹsaaaիWM68sE׮]CѺuklڴ ϟ?nj3WBKחx̙hڴ)?"##̆T@[[~~~ƥ li)5{➽*;>}mw0kg.=/mWӴ4ɶuaѢks3C6|r4jHC4n쐑HKKÆ >8>>>ӧOGHHzY|cXr%1,X&MMsΡm۶ 4@@@?.ٯ?UÇKArrrBvp ̘1~~~}feeÆ Ӹ|c;v,6mH¢"t[Ts*%UID"ׯ1|p_#eڹs']n,*,‘Gq:49Y9pwwǒ%KвeK!c3l0tر )= 2DjQRRPɶ[N.7%|OF~ָ-n* Hz4;vͣGJZ:>Bqqq`'OH @7n8T\Yj7|*022qP6QW¿ #F4mڔ #dii<@[>qrko:l`!j ,;wΝ;eav7xɘ4i,Y *(P(DrrXY B󮮮ט{E}gjj ---ߊ+и1?K(JLL 8xԬw9 G/ ]3$=I]mۏE8¶!;#7?:w !'ѭ[xz7x#F@F0t!~XE] IDATsY$%%E85j4.p02baÆ n pdQ#;34lڵ+5v6xéSpR J*A[[D,u/%%֒HMM-~o{;>=Vzz:D"Xuȑ#?RJ9YYj"E"JL۷QSN4b ɆUTPk쮳ʁбcGEI#6x())y=Ppp0q!t;wMϠ\888H9::ݻy=p{{{p;w+5uPF dggӧRݻw_00`bݺuT"t<j<#q79^gB]~L;rds͓?!UPPRD'6'͚bႅRτ!ǧ1xxxgϞ_Q^=@n`jj-[H}lٲ:::߿d۠Aзo_\pA2;wDΝ%YXX v´i$wFDD !!Ajn~aزedonnnj~СC {33`mlyLÇXv,_Dznd{}++Ϻu|mBz:F *UvB`HKK2'j7V湸'Ȇ ,YB!*'O"77۷oK.-uW[hݺ䱞f͚q^^^~:Νqơjժz+~̛7Bk֬gϰm69s&ڷo=z`ȑx%~wIgee___q l۶ +WfE]\6m77Зrܓ'uضu+~ܼ k~?6qz]x*"?8\xQrt5wj qlql4jХKƎ5OO>}BܤqPzuŪUJ=kA͚5ѯ_??Ը7` U*V?\ₓ'Ok 0a„rUeHKO#.Em]}R{{VOOG#E֒HHOGm[aV nݺ*~/W^psv,ޅl|5kZjwyPDsIIt3߬ŋ2̟?+VhiAK @QK!D0ǏGNa!3Or7%w)Ǎllŋ+>B!< _|X mP<''Rcbcc%yGӧ3JBOä.=yČ Qm"w`jj h׮]qޱ:׮V+!Biso?ƍ2~ '`nIptģǏ!ѵK4jNNN())A^^2kUDrf&z7rxBi>} %%Gg0'%_͈KME:uо}{L6 p qe$>~ m--c۶Eu菅hP>-CF!DP#GĔɓq3KQ1Xb1ߏ^zz )^֯WJB4]b1rpyz;t/_F@@@<<<`P޸Q5.Çohڴ2J&PZZZFF~>ߥ|#"0(d ѻwo7N *p#<|R޳g}\] B!JA kժK.6|G(f;-Zŋ(Xox3M׵kPe!(]D"N<|j\E pY~5Bz6}gŠGDZ UB!tOc]|YYYՕR:ORSQ} \DEE{kMO{wabn7nN:*B)< eTF|x׮Ĥ$8;;~VZBܹcb`ggj !)Z ʕ!40^1ư04ܹTD˗cر}͜~GyJ!5xh(]>}+T^z􀽽=O.YzBQEiWpmܘRx!11r&?1rH"B< JHGMq-.;w͛QJ"B< 4|p>|7/_΢>2Ea|F!(E5b1ߥ(H$°sLx bE8y;B!j< UXXr0.G^sD$&&".>۷:Evލ ̞=`vj['())A-1ZYaHHLĝwѣG5wB4iiiqPfM<~D\?T,c ?nٌ0}t̛7O򜭭-8˗/e˖`7WB!|h% 5ӣG^KVS#x ^NNL;B(B `Μ9Rcb1.] fff߿?݋\ A-`ll WWW,_\\5 jՂP(DΝVmxWdd$:uPZjaϗ9vٲepqq1Zl۷K?~kԨl<}8Rc !BTLt7|#s\׮]ajj-[Hm߲e tttN4pd[aa!v܉Ν;Kųvڅ"؈$$$G_~֖֭g~!B5{|IܹsÑ#G  q߿{{{}uXXX ))I򧨨&&&mmm?̯}v̝;+z%yڵk#,, 011Abb"&NcÆ uttDPP^ ###\x#G3,Y"Xx1c(((U |oB!p48NrqX,s)uEHH֮]5kD~0~RäIp sEƍK̙3qMcǎXd̉-[;vѣG_>FI7RBTVJJdkkkuEy):gOj4MAA;8/u&5:WhRX,]!!!ɑzĄ `ooSEy>E GZZ@KK $ٚc̙3f o5奼W5ƢYf(..FQ~}o)))w> ]]]\vM(R^KyˉRBBBql,55b&Yjj*;<4h8m߾r奼Wuٓ2Ǥ3WWW֫W/%V4ʫ<Zhƌqcƌa-ZPBEEyeIZZZ={|tܞ={XժUPbQ^(jRd^Mtݻ.]|t\Ν%2+UMWWWWXX]]]%TXW6ʫ<pqPbQ^(jҴ-Z@@@bcc jJ)-.EUZ_~2e c}-[ -J˗YfY&Zn eܹK.˗//Gy)/-8eƍǴqؘ9::2fdd8c:::l|)7R^Օ.\ԩq8ի-Z.Sn(/奼GӤ|@rr2BCC+5V5Я_?XYY\|Q^ʫN4-Ncffs%Ay<B!BԀ&P^dQRRútԩÌ1[.ڵ+۷oD|)7R^յj*fnn.vG[[[r "qL(UV]P^Ky?<򘛛8ٱ]]2[[[qswwg|(/奼KV5x25 Bvܹ2ǜ? B6zh%V4ʫ4-AyeIyf͚lݺu~z䤄FyU522baaawIfddFyU"J2,,,FyUմ;(lW5)4vˋyyy2JƩ:[U]wҥLKKRdzٳg3---tR*/+6EydD6mPZ5O!vBbb""""s_R^ʫƏ D"1";;Ųe.U.(/奼G ^]>}8fΜMT|QOr奼Dy/_xt @dd$J}޽xX"Ky)jӴ;(Q^է9/# ^zAKKRR^ʫ4mKyՉRbMIDATG!fESe8q"QRRB0Ɛ\`̘1Xd ߥ奼W5iRri5IJe˘5k+|\\cZZZlٲe/P(4ʫ4-AyKKyŅ}tsqqQ|A FyeI򚛛ÇtܡC*R,+UMK+Yf͚}t\ӦMFyUմ;(lW5)2/5x28;;ĉggg%TXW6ʫ4-ە;>4ە;ڷoFyUB~aEum6q4hpȐ<"""8m۶JR^ʫrrr+8۳=zϬGޞqXnn.~1Ky)oPW+V0SSSq8ikk3mmmc333rJ˔Ky)*..fvb:ubjbАծ]u҅ٳ]P^Ky?I|=zTf5jk׮B,eխ[7ɫiꚗBE !B,)33Pre+!TRR'NE033q)))}ioeffrUʓ;;;?~͛7y5q]pWWW=Zmʕ+Z*ׯÇ#,, ժU/O۱O% h"V^=/vG__9;;%KL˔"v!|rvYcS&LݻcҥKӓikk3s3a8c3qP8q"e͊+ 8UVmٲԘk׮1@Cuɢd`DZK={CJ'$$0KKKq300`*Ub1'''c3VfMIѾ}{c]veDZ+2㘩)KLL}S'CJJ QbmڴalΜ9lΜ9חiӆiii1'''w_,''K8c~-w5qOUʏ/3f?ÇgDZ^zI͛sJJI&f͚$ lllX% @QQkݺ5:u*~9M[cԩKj˙޽;+**bU 5 d7ozjq:udgUVe{qsppPfi f̘!y|!q6|Rer`ggWmlmm_׏Jmc 4`Bݾ}1>ZbСC`I}:&y\R%}R6mLMMYB8;;uIGEE1XppԸ5k0ggge'wFFFRe9y$322RBEUvmRj'.ܹ3+,,TϫhΪWΎ=*+uoa6l<633c{)5nX);q/qԩSR=JKCaaa.:Gaa*R0tUjΟ?k7O߅ 0uT 0{L qԸ .e˖sjcnnnR3/##={*2Ű,~ZZkܸ1355ejqq}rʒ;뙾>;}Hϟ?1oooq}aGVvyri+w4hO:Z\֪U+fjj6mڤ73[[[VN,ޮN:lڴilڴivL ][j311a~~~,((988ѣG3KKK^~Ͷo,,,L5x2<~ Bfjj̖/_BBBXHH[|9aJ*ɓ'|ڵk TI&;.Mpp0366fm۶e7nPLfDZ-ZÇ3SSS&5f1+++w_8\\\X إK1С d۷g@Ԉ*Ӥ;ƍǪWbqcN>-@݈b %7ՃXǎ˼.s˗/$[c}R7XYYϟҾi2<~K.EHH3000a8::T,]3f@bb"ē'n޼ X /99#FSPRR?kΝ3gbbbb0aWSu5o4e厘>}}P(,s˗qY):剏/k.]]~QQQx JJJ`mm '''t*T<),,Ddd$,,,PF o ӧakk|ܬ}X,ƫW>0T@}._CŊ?+??/_S}!::>>>͕RZ]s!bCե#uC !M[y7yo9KIIӧOmmmԨQjsZ}j7%%HNN֘733غu+bcc%w >>>:t(yT>ʫɫi2ܺu 8vXkLLLo_.O?ʫ4)ojj*5k8lח\{pppkT~eKy)o9nu͌@ `cǎB LGGM:0===֮]ɥ*(/奼KVޡQ^եȼн{w(uKׯY.]'cml…|)7R^եi+P^(jRd^Tn޼ ___TZUSNExx8PV-xzzѣ(/eG#FJ*ZА5=~2R^qt <ԩS_!//uɓѳgOɘw( AEk1ɋx=L&`$l Hn.&IV7Nz'^ׂn[}!zs;, !|O;ћyb.)OczT<v4q NS֧d#Zm6SiիoTltmn64QYz[z8t:|>De}LoJP%ŻG GX$cHƂ  @2<dL%iIENDB`geopandas-1.2.0/doc/source/_static/nyc_hull.png000066400000000000000000001575211525650651600215170ustar00rootroot00000000000000PNG  IHDRxFsBIT|d pHYsaa?i IDATxy\ " #(" jmZ^֩̾&(EK縶h)Zj.ʾ sb^ۭ7y_y͘eV!BaB!nIB!00B! ~y7l@nݰߟ?q999xyyT*߿? =y$(J8q"yyykkkwƍ+u!Bj x}.\`ĉر!C7'NЌ[d Æ #337x~@߯5+ Ϗ8BBB>}:SNW\\L^ؾ};ӦMcڵT*9~cǎu:u*[n%((J5v̘1G;v,+W'BQ+!%%ܱ7nMMMՃkժCk͛7զQFimmmaaaZcΝ655z͛7ԇWnZݿRh#}5ly |^V>1c4:"""gմnZs<**z!C0tP233ٲe ]veƍRZ?722NuOeMtRWmפIzYA>ky:Yt)˖-W^/))w! M6}RXxf3*r󧥥5ݻ+{?>lleLOOGRiB!k,X… Y`3gzٳܾ}{NB 88P\\ @V_&%%X˖-9sLZΞ=@򩑑Q9yVZŵkʍ !B5x/f̝;9s{xEEEڵ LLJW -ׯ~zLMM.( #))#GhK5ӯ_?6oLaafÇ~:ƍ5j&&&"BQTHΝKpp06ܹ3666_bڵ1!Cp}bccs+VЌ777gܹcmmM~8~8/&<<\kKCψ#Xd JիWBttVs̡O> 4^{TfϞM@@C ьsrrbҤI,ZN:gYb6+]hhKx6M>ao*Y^PPXmddT?ccc͸B={'NT}||oﾫphu׮]VVVj???udddrrrԯSݴiSupp:!!±'NPOݴiSzĉݸx;ݺuSoܸo5B!)] #SNcǎ$$$Ի;yBx"oT.Z!BQIB!00B! tLaa!$%%LJJ ܻw?ǏuN ATҥK{=⑒'~|||j2-ZEύ7~:7o֭[ܹs4֮]֭[Yp!z\BT>A_ӲeK.]*˶V'JJJJ8v.屳`lڴ-Z0{l8pKBӺukϟJwi'J|6m;PTTD="O!D޽;; *̌ɓ'uV|||xwiٲ%_}K>`hѢl+*>3.\ĉٱcC !** oooN85vԨQ[ٓXbbb߿?ZVXAXX~~~ә:uָbzۙ6mk׮ERǵ;vLu1uTnJPP*Jkɓ1c$..:0vXV\YoN! Ν;),,gw)oXZZ2|prrbԩ4GFjZOnݺֱ7oAHH;w 66ѣGsA:_^^... 8kϛ7e˖q5mٲP9 h׮ZoќLbb"gΜ CѻwolHNNݝٳg3|aaaٳ7n`nn^SNѱcGdO!:t(8x rEO]sNVZŃ2dWFT,QpBi,X@׮]6ڵ ^z%Ǐͱ(ݵlР۷4RSSo5j& 7774bbbPT?߻wݻwmBQ<!CϠAe˖,[LmꞘٳgyk5.^H`` ˖-#**7oٳl_sGLzz:*JkB'ɄOrr2֍-xLM066###166722б} ,]TB^-ӴiS>"##ܹ3:|<Uw߂ Xp! ,`̙Z5k֌ƍe]VZ믿jDNNc-[̙3:{,FFFТE Y6sjՊ,]Vn5V!ꋳgҡC{.#O.tƷ'OnE[OcdKT逷xb,Xܹs3gN׍?~<)))OӶm[6l"`2zhͱ08rXAA_s-=cj>|ׯk](ۗ\Lx5T~2,^ښH{=߿OPP/2.MHh###;w.`sL2[2tP^}U\\\OԺCܜs5,^p4c?#F`ɒ%(JV^MJJ Zu̙3>}0h ^{5RSS={6 2D3ɉI&h":uĞ={fŊZ۬!D}0uTa7r7jeeوw_.#j~,Y_|ѣG_* V- xvȈ{w^׌4Opuufԩ|dggOrrw¾g_Һukr* :ĴixHOO' #Gs>|9sK/akkˈ#]  6m"22___6lؠ=B ))|Ц%NؘN|rrrhRUGҒe˖Ȝ9s;v,qqq.KPޡC*=37o_|_|YXX駟VjN??r!aL”)S*5V! [oj53 4,ajRrkcЯ_?~n΄ ]I!_~ݻw3?v6ƺsMwv>}:^^^oQ{70oiɸO 6ܼ|,-e.R(DFFP(:t(EEE.ITVVV*Sc]Ƥ#[<$v¾}4{ފAB#,]n΄t+zMHyUvd}"@B# .$--cTzMH$fJ7NJJo!J$ !D='+Wѻ%|ʽ-RseД=͛zDT+V)/o[tEJVv.Pzǥ0,7o'w $ !yb渡>GW[-J3,n"++-AB۷o~zo-*}HɒgN< @``+U!O! ĬY(.*bFXw-Rrs111Qb[Μ9C h߾KU O! ۷G96ҹ" ;' hQ\t wwJm#j xBaʺwUs5"JE`Q7IB:&;;5" 77_g ̝;wOߥ*'u\Mw-RrrL>}AgW@V'seѨQ#%{i;knnrHBZ.55MwEX4_湹XJ)))a֬Y.E,-u2xʞ;۩S'<==] xBQ)&O=A g b̕:i޽RU*8p0<==G,_ćVٳ'Ƽٰaݺu> R$$$T8ɓT*bĉU8622???޽;7noB!Gսn^^~lRGmٲGGGy}"tR>… L8;v0dĉj*^ QWXAXX~~~ә:uָbzۙ6mk׮ERǵ;v@֭[ԩSٺu+AAAT'Ofƌ 88:tرcYree!xeDX%ڼ< xuѕ+W? Jm~Gۗ#FҥKٹs$f͚ņ 2dHXp!cǎeŊ<쳨j-[ƴi4}vN< Ҍm׮-o̻pB7oN||!""yi> .d„ rBe/頃V155-\u_]ڷo_2 ۷/U|;]ॗ^:>~x]4h@hh(#-- (~˨Q4W^9Jb޽{޽o !xutEVcm=㑛ˑ#G۷ZT5{+{_sNE Q*=zTkΊڢP(޿"}:t1BQ]{`jbBaa͗h/Inۛnݺ黔zѩSرcҺukW_}jWtFuuB{`bb%ڬҀW?EO?q y,ro,]e˖+ho۶ߗk-p}5j ΨT*233ܽ{\eޕ-ac:;zeQTZcqz\;(ux Α*u,X… Y`3gz~Ν;ckk?Ɔ={вeK~W9Cse˖9s\-gϞwwwZhQ9yVZŵkʍ !wPz.^Vni\ѻ{. 3KZnɓ) :ĴixHOO' #Gs>|9sK/akkˈ# 6m"22___6lؠ=B%--Tҽ/'7 F:H7AEQѣ<쳚-̄a'z@ST%%5yyyD[m޼|riQ(0m4ƌ,YA ݻ? x;K>#f7|{GCѩcs򰴴|U~yyy\r˗/INNذaCv7\]]quuaÆ8pSPP%]taРAbkkO)j3 xBՕݟyx ~vkٹ [syXYYRRTܸqK.ȵkHNN&%% j5 4vvt톃`D4n嫗q_sNJ%ڵgϞmۖvɓC?'ݻwTz>>L8WW*}6QIBGwid;r5"Vm 19Kr ^|E]"iii\t+Wp5͒jZZfIUP`kgO'7'\ZĶIKGNpi.q݈72f̘G˗-nuk6kJ3f8gw\Q<|J̏q.7nR*McxƟg13ȝwtg\`ߏ۷Vx0` 믿f|G0rH|}}IJJ,pmJO6*]RkfG6-ܻsV#-\hLKu5~ ';sqccc[9?,n5cccMB@_?VNYرcټy37nF;\WHB/ݻ;wM60k,>C %UNT*.Lzj:T+̸_IDQAjגKHl^Uܳ9˾Z8Eӓ5k0tP}&*AB@}}c#c/_oF۹h͗.!]hV^wwl1ΆUٳu/!#C X^II W_ء~wRSQ4uӳҿ{uB`P: `մO6lÇgݺu ZNB@}]y g=z QQQ,YO hX?ߥ:ďcǪDfO{,rN~K_"~6 [7繐tN A+ߖD/9Q~4^>xBԗ}^[) ҺgΜ;7Oݣ%%%GvA;(<7j ]tIK}c~g~:EX4VKpg,[}?N3?##5cDzzj{}ݻ߮\_cll1V{>cccz N]9;v~k#] hGv^x?M#= g.ӑN@颤tߙ֊2ooyŁظq#={wiOWBݻwqsuw3+:S/GeW_?|O1'zAwv%붑[Pw@f9t5<^Ã(/©~Bvos--hC@v+S==&sVZOߥ{޽ˆhs6AWEsyLhܐocY437÷]}4rX3 ڸѵg5[[Yzx~ڵ+SLaҥkG {OhVr Ԋ#?]fk137[M7zA<\~f7ͶhӦK$Z !D5{M'T.;'3(lY\G;k],X2st^^<ĤDwYĄşbdT¼)xaXWWUv=#=usRיִIv~6{tg͚5.IB*0]ʝ -=N^,;Vaudd0rp 8tW^aܸqT*}ewBT!trr?#ҳF0cC{bjSTU$:Į ' #%%Ee$ xBQIн[iYF0m\*վ<#t :Q( ^^/^^/~?;ڵСC.HBJHOOݻR~gZ֬1y?O]vBQ by`gwIR鉥%=z`$&&jƔ|r郓M6elݺ ݰaݺu> R$$$T8ɓT*bĉU8622???޽;7n!fϞ]wۊ1#t~mOWcerve|%3f`ذa,Pgq&NȎ;2dQQQx{{s rssŅKi&lmm0a{Hk+V_.!!!L>Sj+..W^l߾iӦvZT*?~\kc `ݺuL:[TBɓ'3c H\\:t`ر\jߠ१Uw'Ξx/ +ϡ.Dx>+U#FDD͢`v~vO#))IeyFjZOnݺj7oÃvIII i{w5k;wdР5Y~fyXl׮]Ӽߖ-[ PPP@vhժ|`9s ffKdݙ={6לƞ={q徇SNѱcGС?}mBkfv xjӗGb*|s.l\\sB?Ŝ/031cƍ P?Q_q)] IDAT ۷, w&y9w׮]dddK/i?~>>ٳooo}s5ʖhkZۤDDDχ~H֭+ΠA`ݺuUr:O%.=Bs;g_ݻ{ٸ;:?Fq1ƌ11V'?{aLYNZzlٲE%Uxgҥ,[W^y1Ӈ[nR3*dZZZc޽[ḲVftT*}UY1FHE=skZjh|ӬȭmRK-s"h9&E9yӣu9:|> !*zhZ^}IcnG4 JeNiвӷNŶ-cȑhZc'*&Mɓ4iqci߾=ofԮ]Ξ=0RSS{ۛs'|8sLxxxq@BIW޺͞мVE[9 ?Bw991}T_'88-[t,VoԩL4 &%!!_~v튽}#RV\JW9`sРAq233ٰa;w{RJtؑ>! <8cll\֭[q!D9UVf~k Szv/rH.^˭,tB<9ޣ~9Nz\xQX:X,̙Ä ԩ:u*ppƍIOOCosʘZf„ 9:vHHHSNeȑTϳ"zߟ>}0m4Xhcr?vѣG Fdd$cǎ% ={suueĈL2@ի ẘջ_ɩ'iџg붆`faF_*tBE9eH2L'D044dx}P.\@z8sұ'BwwG?hC~#xqXO K+>jӻ5} )4mڔ7*IQR !zMҷWPcZ-w¹m.tB k2mT*ѯ_?&Lt$H'𒒒Xz}k8Ҭq\&&6ￌ|Łi[ԗ)Sгg|V›9s&iJ<\XG; thP东 7oWvmZ ({&j>^:weǎ4jԈKXeJ p4^@ZWx{z*?P:ROQ-YQXVv6EjgO3r-!`?g(%111J(R !*x6oL矣qȆ e@@j~d'φѸSNnNePbmgE\\1JxB eddd0og< lۏox5[B026Z!ģ:ْtR!| OjWQ:Y~;i cQn݉;49B)DYs#3#$8)Ƃ ^D""t0Tȱk̔WF-tB/;g;NRBTZVf ì062oƏw}._OWbacQF_.xB6mDxx8V:9-C/ӵM9v,,KeN+GH'fΜ =[?zLl=fj]:5|3ns#,u !<)oƙ3gݮ)8^D+ԭ6kٵ T&*fj/8B4qD,ܽQpoqNNDDU6('̬̈T:FOQ]~CѹYC&u`ȟWcZ-vġ=/tBVDGG++@!Jӄ 024`Ֆ xU_%%~;Ĥ4mWE%702zm\!DA+I'(bbbؾm-`kt'v?<Ѱra~9~L*H(KKƧ1JxBrkԩdee1~%{Ę Y̚1we'MX-sޙKjδX%Bqڔc^͚VtcƚkVO_KR|k$DIu99)I'(IHH/W[F3G Zu!>%Q26䕎 ~?mB 5?ێdudge+S.@uzqH'(wZ- ,z5W}k*穥gA 7KҰ5>v4kP94)-2-~mٳj/w;u|!t^[t'Y~=w#"Mν?/](L x|_[s(>|ͷf͒@RcxvSʩ/7"Q1JxBrg֬YTvKFJGy*Qq|0g9% NNfɿhT;W 7vmGmkZϴ!IIH)B VKh|}CII'(W~Ο?+/4׫deeތEdff2Y\2s` W9Ԭ҂}+<3GΖF|!w"00P(B>&M9;R:S|J}QLR6 *Q544ϱjJ ,5|lFS?hӦIJxBrŋtkZ{#eXZ놃,t 6|}1=8A>)H~!IP*>OQnL8o(\$=[ ,gpb4m\qwyjS#f `g=DzÁg'w[F |85kɉΝ;sԩBǞC6mDVh4Ǝ5>nݺqFxX`A=!"ݻǞ={hXk˂dFZy0|Ď Ve`/6^w <*lݖ=871]?"B?%IqI$%۳Ə%_x郗ӧOg۶m|Wh4~Glmm VZ,[1cN8G/-0}ipN<#:v@lQ26d[~saL?{zET<7X4o>b*UРA۴Z5kУGLJf͚jժvI||pMo1C}{)0u##lU%fvܧֲ ;ů=73eTbB^ؘ b&>>/EZZ+0ח˗{hh(@XZZ{/449sss ɉ[n[غur|sZ[[^`Ng mȑX~ѯ_G~͚5db/0T{-ohe:{G |Ҕ$-|;9m7Q׏ysʛot4!] J*a6lؐ>4{6n8mOGǂQ;99ABBDDD`ddTHz8\m֬Y~?rH9 gooQf޼y=Ai!Dh4,]:VP:Nt/VoiugR;tӶ ?LWMk5Ia5iڠ>μiѳ9C?Dw:/2/6V:F>->}|$ƍǎ;;w.5jx J!t߲eˈ⍞핎Rx>-.6jflϰ|<*0Q~l ٥teۜvtkY#ێqOsұDF|tBYyoĉL>3f/u777BDFVعh ]ΛآuwF7V;̙CN tBeeof&X^ JfĊ߱Tsh`-`cNBx&*#ӈ٣IgٽbұDvUrssiڴQx&MbL4?8לٳx{{@aaa!ޜ;w9 "9}||HNN&<<8 X!nعs'ׯ_N-R WrN$36ٺqW00ps,z,9i9%LrcvfݬL2c kVԩS4i&L`'24dVn޼o^ڵ+\2+WDRꫯ6h 8|pklذΝ;=WR%:vOV?Ϫ:t۷o~166.t֭oL6 {+zͿWnǞcgޮ-9.5#ͮع,IN!G_Ԭ҂{+<3G 1.Di  JG)d1g&L@NԩS7~|͛Сcƌ;ˋ7x#oZf„ 9:vHHHSNeȑT\9ol^O>L6 '''-ZĽ{XzuǏ]vуaÆرc gϞy\]]1bSL 00={zjujwN:P?ްRs ~/fƝTo4u/rUD 89E144dx49۳i۷ ƽV[F p}|k=zSyROʝ;wb``޽{ٻwouرc=w}SSSZhܹsD7 IDAT}vvv,^+VPF KFo Gf֬YÇ <(ٸqc::t(Ӈٳgؙ6gT3gY&B7L03S{+""h*<,?MZQkqN DE׮,6Mo |}1=9žreF-GV()YYF++vb{mԩSfJ}6tmސ('L2؄$ vE_7baМy뜻AeFxzdבL,9?S:(.3ٲe zR:cD!d:mĉ0kKs%<2Y}%ps~p$.^;~R][Tc6xbJf=4crꩫbwR !tVRR7M^8,y;%BBYr2|X>`ⰳ@edhfbH,;b%cr0׭ϑ$Bg͘1tE( 9WW[}g0cpeW RQZt?gahhȨuY6&,f13סa{"Bxc]'B'egg˨W݃<*';:jEY=p>Ͼ?Iu7|5U]*J>F1-*g^ƾ4QJz.';ԯ):iѢEF'=#XʐC2.# |\8RQ\VӏYX(׀{|Sm9o."'; )):)88JlXW(y-Xɽf|<\l}X>{qhcwʞK<< UMY:nsߛGFZ{Q'кuk-):g֭2K+YO\`D4t*t̯W3zq:[lIL8@UK6jÀ>:x1puc =z!;{;\]0SI'9ӧO֚^mtaY5+1uB~/>Xs7'+-|Gے=ւTG, hH= d3yoFـ!-=TW:FOSBBB8u/k-RRoq4e櫅w8~#Clo_#{ZNvfhҔIJ׎uر>9c%tV%:"z*NB? 35+ 77O-'&1bVsfg; ByJvȼ `k m'Cqm> GwU:a!+3@9):#44ұjSr>ƨNuS'Cyw1lשl_:~qv }ѿ75lz"~5Yxr @6OAFI'_|ޝŸp>qV #V/|7GR1x߼Xyv[zt %tXY[t2'B'hZn5pU4Kbr*[SXw 5-Jտ{Ŗ&ILؐo1d'%3D-.0DwV:"Bm۶K(#771_-%159cfbo1lQM}P<*M1jeaZeH\9GZ6raǼ4aǦL8$c iZ"DtEH' | Vt jhK|Ե5\oaVۂxؕi6;K*@7agf|o0/$y;  WR !ǯJFu=sY]Aٹ[~EmCv+颴9XO,hk2Z] C+K/ЪU+e(D U]Z-^BzFsag_IW8.e(-֒*GvDGp92v,ZhpH'Pڵk9r$oūͦ)]Q1zQ_.A0 T>ƥ2ha4Z95]K5Ǔx؏VYOXZػbQD~&&4l))dܹܻw)S`X;˻5z:lMT|B_ėlD|*9DfHsl(Oʈv.\:~I>`\{5wE]RxB`iiɸq8s,aaa|۰x>^9}?w"~? OׯVeo7; Y~*kW)dUvx-6^ ң]Q:(CԩSGOsUƴiӸ|2W^eȑdbhXu{NDd4/Ulӽw,`M}O\xҮ<ݬeѝI ").ĸ }{B5jW_qMΞ=˛oE\Z_B7?c`|I,L߉Kҙ QcefL\txt$>*SO+E/Ь2;u!nW| | !!!,]ݻw3i|r+~"_ф݋_Q܋܋M„WBFKuX4 6jyЏV 'ѵE5Z{߾AgcllL&M()zq4nVÇYl? 722B`oeFÚ.Tu歮 gJ-222C\'^ u#/J8UTD(O iݺ5[FղtR ƈ#WիWsrĢ702ߧS&(C ^Mo 7H8EE`JPxBrА~~;tԩZeuqцF+C9[Р=!{1,5)8B߿?YI :ւiW4w!)>wS:(%WN^%77M*EqR !ʭǏ}vO&5S",HL!Wxbm]pS~Rru i޼Q'Z`jhtSΒm.ҏIҵYUn~}Rv) ,--8)Fa]_ K򳛮veѣ"Խ:FFlQD)>5kg%\OHi-q.+E[pvv^(:A 5kDΝ9uTcOS:Jpve%w;#b8{Qy>+k+S]İl2ٳ'M6^zK}]=J˖- fРA4jԈ7ҥKƌÇ~o\NN[f˖-=˗hhٲ%!!!;v-[wi&ZjF7vԨQ|Gt֍7kƂ !ty000`D ;+ TF&HWRzB{"wV:1.@o-tʕ+dƍyuOOOV^MHOOgɼkСCrss1cG-[pI~&:t^zL2ݻw]kTZ;v`bb@i۶-[nwDDDpBƍ7gbb"'O7DVB(DղukVyghh Iҁh®_/}oIiZDJG9O ^nnn_\rFVVVRrvI||;TEVҏ QFî}y; *( ^q1^{3fзo_?>&&&Pۚ=<<֭[ח\prr֭[:nݺܼy3ߜָw9r$^ׯ+{c7`ifʠ V9ZeyYSˎ;`h(uݥ- 6aÆ|%$$<%^777ڷoرcpssNȨ@DDDD͚5+tï}cc9ocdd5oҒ]2mڴ;a{=Xx1+VF|嗌5*8###~FͬY# Çڸqc::t(ӇٳgxsΜ9TR3g|}}YfMC!9v™;Qm-m]pS~)twp掑QtAl%-ۖO:%Ǥ~c¬b'fbu&צOZJ)>Ϫ]7g..J1pZhöm۔RJސ}B={h{-,L131".QіAݫcddv*EGR\qIr{Bד5R:,MKnVmGN?Av9K. PhxB8Y5Qgo)hK>%she166I&JGYR !Ftt4ǎGrr=`&!EVJSqu6T:+TRENxE+PFvv6 ƒ@||<$$$HRRIIILJJ iiix{{SV-V믿&''_ T:Np1\X1ʽ-x.q +% IOO'..8󊲤$INN&))dRSSIII!55TRHH'#=L233y|*SccLU*LQT1S26&.>+Αק900M7lXstV:Np1'!U +m:v'~5B8^jR* rcH'D1hZ%>>>ouaA({:,=-42I #3Ǟoh`PSTdb5掎Xbij+SSl̰6Sc6VfzVW"#y<;FPP;wfymp]ԡˠkl-HL~ʄ9i20QmA%]9y\6mt&(veZj*餧IFFY+3+-cy٣甎RDޣFJ R '0i$ת7*G\|Ԯi&rssGp6#>YY^m=Pw^T(99ދ~JG R 5rH ԩơcddt$/cdhRlqil,ps,)R%\t 1 Jǩ0nINNAAAJG R pBY>k 3taǎR˗vւiWVFaJlQV|uk98䷦9Νc̘1UwNWpΜ9SskZ¨Ts'v$搫*BtGe[*C~s =z`fdqQm˗/}JvhsILe_G/9yQ*S]zC~{ 2p@Yԧ/.6Jagn_ժ޵h^Ͻ.O\+wZU6eߚRiZ#WOZxBg,]͛7FtSG8^nЀ;vD=qf*sw,y˛JYaeȐAn\:1bS_MVfryqI't˗5r$G'пa#LU*.]Z^pʶr1ܜtH?ZE }:kyɯb\9q6m(D' zDl2D~KjػgO>l~ujUvtlg% \-hPӞ!h4[7χbemQ&۽{7W^e\ǎTS:x }%2_tt41qUw)3##l-LKnJɇ~U)ܽqo/oc)̙9C+E<>~ 131aٲe%2 kW);;KiW.8کiOJG)Z-QwU:^O(*""#Go=iC*Ao_ܦt k ^q[KcRbhhHfU paS W/j| ~ ޽ޟ344 3SJ YdmF+SԠ]+ܹrAVZ)DH'hذ~=;gX|w+~~WY9ҵN]lXb3͓E **[ aNJIN{pZ-I1bHMJ_(zG~ E̜9##mJ(?{_>pswdD#FUv͢t|֮REk7m)JR_ZJkEh1S 9k "ɓ\K\Whry24]MYU:TE~Ӯ,UHʯG\y.JN>\Qߦ<%.--];wҾV--ӮԋnZ9,,IL} Q/W xEФIC)s%n$0}{CudR X8iW+xrHݺzUbhht(exĭ^ZNδ&=kرcIqoAU6PELxJIN{JHKHիtexDsu5٠fIKKcƍ: y20xԏ6Az*%5-&f&J7cIM۳$(Q .̌[T:QZW 6m*~L"yoA[J?Zgcd";XnZH&)D^&)WZ?NrrsF䕻~r1#>I bgcl,^Q~:FFFl) C uU:2K kV:2M QFJRKNNftZ3g|UVѡCUV?~<ϸ 6ЪU+lmmbΗѣ]6jݻs|ѭ[7j5k>xbOL___<==uօn$r (ʵŋܺu빏6ljydei!:`mi! R)a'h3jfժUdeeѧOT*UcL6mؼy36m{-d֭уI&1a„\㲳i߾=;v`ĉYFC۶m 5m &m6ڵkFøq,\щ,g1D?LMT:rGђ\Pm'w~^Hem6o̖-[8tP?ݻƬY4h~~~tNܹs8q"...ر\sv҅ 2{lٓ3Y\2vÃ;?o˙6m3fș3!!Yf1|p؋?>&&j-ʻu~/ի^^|cjV̗UB\âVK899aoot(eZtO|o̙lJ\\C7ٹԔ~o>?ZoߞU?伶i&4 yݻx]J e^>\:o)i|PG?l̈K+x%[99*~rZj)FW,RRRvcܹԩS+++عsg!!!4l0XYYk4hNGXXt|֯_?眏紱jժy|<ד;^zUvLlŤ3}CwbŊBߢE :uĪ=gK.Vі| Spʼlb/+Jټysbر/_}vzVѸRe ]cѲE~p&#KKJaaaPzjԨ JjժRx?Y&III+  8 blTO=W.x{eߩG)#g8Ĥ>c(,nT~5>- ͻxP: PJۛwrĉ׳ٵk/ zױgݺuXn 0 ٝ 6oLssvvk׮lٲϭ[2dHk(;::ҳgrK ,@2NW|R&FFy|^sښ_^DGh]YH0Mg,(bZ|J\XKJJ III\x۷УG?~<۶mo߾PR%/_N|||fff|g;[[[vJ`` _|cǎbŊ9c틗_~%jow~\1N>N:ѻwoFEdd$~)M6٠^3f gYfo_??\۬h4lټfTupP:Q R.+Ɔc1sLNf]YlxE-+[YGѲ{YPR¯"3#f͚)^xo94U*۶mc۶mT*BCCR UTɓL0իW/}vvvXkRV-.\ȸqr344L8 KӦM9rHլ͛7ߟӧ3tPywXhQNTT___4h r]=~zcbX\OVsZ<6uTV\{hG[4- 4=ڽNSWA*7?e˖8p |8 ޽{{?@c-_ QҡRچG{WMΘ1ca9w&FXI?"jhqN^{]IoZ/"i(={~M^P,IMKCվ\FFL_FG[tZ-HMrw8ݹA5CoObQ$͛cڴU:Q[X鈍}} ?$h_E]`m&hȌot8zMnRI'^XJJ E--GrܿH{7ittihc.ʊ5o)\vqv\{#xa˗/'9%EΊy\=ި(.&6!~=}hkAx/dƿٲ/VYÔGege6hڵS6=";ъfjj;;ӼZ5Cefٜm۶}/> JւxiWVh+]fkxvl¨>JZ-g|2iT^^zI\/\q!o( Gn ҥgf3i"y؏6 ],b)oro]a],+bZG7n)#[%\=uN^ڵk5/BrO bmf-E zA7]w"pB *+NoH&)4S90&1. ڴiy7166V:D$((8[/* #"YEo/f||W.ەJW@ŜS~5f FEݸp? GcjfJxׯfffJo( 8u\<'s|ʕ+3h`֬^xåQUt :6y,`Ɗ3T06~%[r.<1b011Yf՟b),()DhZ6n@ʕt801!!!X^`[6oeX],(\+~pS¹3m ^!w9gѤIMСCQ:rK -3~U+C*b#c9"fpaLFAŊPx]~'N0ukYi& ̌bĄNʸqt:5,sFvV&%J?\ g0䳍Q!ZSܺzVK:u2e #F]SH'ۼy0T]{CeW##}K0m?:/WFB$c2m4]V⿥$v)FC5;v,>>>xxx(( )sdZ&($ssRӊwsΣ_~,qo7/&6܎W:Rd??~oqtQ- !dggSj|||~J( A <\V\IBb"[>E܂b.}]͝˜ӫ fc G[ UBʲW{l/=*+23׿8OnAVf..x{{3b<==Q )sYbiWҡ2҂lbF+__^ԉY3cp~9/Gncekc^n˟\=} j5iݺ!"$(@.^gݺ)(mxzb/:vȫmڰ| &;kTl5Z ϳ;t2iѽ9fRMt4;r*i)i> 6:S)D͟? SSFOy8Z>ܖ*~~~x6iY5WOiqi89wF:yK1D$z-^ja(V˹9KHIJƆ2tPw.E]9 (8K:u9Z=,:K|5gϞlػRٶDΫgG,bSρw9ŕҳ144+³cc5ҡV˥8ݟHR|VkێӷorRZx1k/}gŋS[YUr /Y޽{>~;+ғve1i?~.<'fvu^Ү7.`m?ziݺ5 wDگWRgj[\=PaWRW{n!4=]mSF {I;\ϽTl쬨ި [7Y9)2Bփ@ld,&&lђ{an^ ]x;w[J"􄕙FƖy.\ȶ?Ѳ}^2I&jiޤc( 6f%mjzp~ w$sQNlR I$fGf` VX;S8:TىU+\YI&<9aN?kR]?777#ex4 [6o;UG+SSE KȽ8C=ƂkwS򼞐I$Dp'-Ө43M$19]0 TuCE\q[9$'sh?' J8Z&Mȑ#^!2N uU:DGO_V/]Eɰ13#Y-cÆ |G46cj^Vq\K8#Ƚd$F|RmcCbu;^vNv8:se'*VuBʏL:ʟs q3rH5jtBOI'uΞ;Ď嶋(Q: Hn2x0~__c>4]2+8S2v'܋Vdw̃4bHJIG `k#NU777VJ5Y&[la֬Yef}$eefמ\;šL@FA͕QR|͝;Sccƴmt(BOU07#-tl={1="^j^t| Wor/{zxLTbqI餤^jjb]TSVT\9WVJg~hS{TQ) YٜǸz*888[oӦM,Jx"~:֪ܾ’̬,1+%O׮]2x0KfTz E, څ5qssJ*TZ5kRV"qs?Z bj9s ;r%RӨPwaÆѹsg)by,]TƵot(B9X>p}ݕ _ٳgt:tn>ǿڇϼwNGbb"۷ogm<<>>ԯ__(QR ͛JҹBRZf&_;'#;>}xbܔ\su}آ,n^x_/NVf..xfĈxzz*KOOgY'kkeFaYKM},]z) {1׭p4E#;+}'8WO_##='''ۏÇӺukCTHLJ⣶E1?:ŗΝ8 ?/^L۶mKv2FCЁy˧=}ðaС<^"ĿHWέ\ꎎUKPDeQ:W7T:,ʕ+_jZ=.^"%)u醷7ݻwN;~8._nݕEgo]8JEgg/_ΨQl)fffyiWjt;$'aieI7P0( 999ɓ'ӹsgj5̜9t:ڴi~ac6l@Vˋŋ;.%%ѣGSvmj5ݻw [njj׮|@ZZ[鉭-[fƍOI̟? SS|1g:,Jl 3f ->)kkkbKwwUVw%c~Ĝ8{-`Æ IJ{n|M)xͪUʢO>T_ı~~~ <OOOnJ=4i&L5.;;۳c&NȚ5kh4mۖ\c?τ ضmڵC;n8&OLϞ=ٺu+7fРA,[_2+..[ݺX*(bRRy3^ Hnݾ.ٕ!$~XƴfsbIoĪUޓkBRoѺ9V^aaaL: 6ƬY4h~~~tNܹs8q"...ر :ӷK.4lؐٳggϞyg͚EʕٵkW7:v?)""˗3m4f̘3gBBfbᘙS8;+%-3/c-O,Y"S-DF(wn?GTDx5ߴ~ 2+++CBoN{Çӹsgz+qqq :4dggiӦ~s5%755_~۷("##?߿O}۷jժ9mڴ Fwİ{gXViZ֭]KC77mFaC|}4[syn*]@Zr,"#8Ndr)rwWw-ZDdd$1cHq'D+EW&((+WcbkkK׮] /`رTX1gl߾}w/DVr]֯_<ӧOSNݛQFɧ~JӦMsʘ1c={6͚5~c| RҡbhXϲ#ħұcG.]J]/w\]]QT*𲳲9;qUSqptf!J*FMxx8p¶mضm*МOGaggNJ+Xv-jb…7.8CCC>ĉY`4mڔ#GYڼys>}:CޞwyEyחJ*?K ذaCܹs;wɝ^VǠ w٤ KrKKKb}_rv҉K&QBzON{eJWe˧O.sۤg}6[ss˗{7W##Y EF?nT̤o'ݟ/5;vdK:!TFlt,JjRӷnɮ ǥbE[F!?E *S9͟ ǂIK‚m2x`+#R D7MKHt4^ښ3gW~H<._ A_bffFVxuB)ʁ'NHO+o/3zh̙#b9::͊+4hr%_'^9@G)%#/cdj4[,Y$׊s!xSx!D!^9VVJ" 'Y@|j*:ubҥ1@!IW\vJJ!VeS)wd%BgF WWaߥ[\{I-vm{B!ēHncYA[3] ǵbEV\/[!x.R@^~p\N7k'^ƆYf1eD!DHN> @'zħ.Ą1c0gdAB  'kkCIZz|}}e!EB <=w5*Ut⑬luܹ3K.VZJ&BHnL )NղI 4b-ZP:4!zH <=LtL u5R:rmŋL߳PÃ]W^J%BIN8Ne7YASaLٵ[puqcذaBb'A++hKVeW9۷`cٳOd!%F <=xҡtIɓ6mly"IǮ^-V<`x ^~ef2p@ O!D9%  -7K9]veڴi4mTЄB)u^~YPFZf&='O}1SNEV+BC <=u)t:d f &9=/y6BRI <=uIemazC9^{SҺukCB!J <=cmn,xYY|_ 4ԮB9R0< q߃9rv3e:vpdB!DIRRRNJR*eegD k"aa|TREB&YA[IVӽ|b۹ĥPzuNZȘ1c033S:[nJ&B )Ppp0VffMFa|\w+KK ȴiӨQ !J <=TWF''{ [Μ!&9U_2n8͕O!(R0;:*F:{6 իdihޜ&ѫW/ O!(QR陔DEQ^}C)vZ-O\‚MF:uO!Pxz&((NGJnJRlRSYz0O 1Jnn̘1ccctxB!3Vz߽e2dĉ[rV!)*)Jj|<pLMML>B/)􌾬MLOg?Nq7>L2I&agūBQ\3ᡡ4*+h]cG9ri4~ǀ6BQ@R鑴4DGS^=Cy.|}4q'.k++zĉT:bƍCB y >-MHrKdmq6q= i|GB!xzRRu& fdPZ5>S>cB!OjbSR!:9o9cbk|ǴiFBD <=䄣._K켙YYl={AA#[aÆ3y"B)􊁁o߭\ɝbfsv,afdV3ۛSnb;B!Nn3'OF,/=YYx}`O[8E7ZLw7oV:%7WIi3hޜϣh^xgΰ9((ߢn޽',[[[Znƍ %)& QII޺Pgfeu^gfE=SuU/:!Bstt4V"++>}RrIMMeڴiTT9s?boy)ґIDAToر#YYY1x`<==ٺu+=z`ҤIL0!׸lڷoώ;8q"k֬Aжm[s=~8m۶g„ l۶v幢5n8&OLϞ=ٺu+7fРA,[_RW^ >-Xzpy\]JιbEKTtuB!DQEի󌱰۹8ҥ nnnL:~޽{Κ5A3V1w\&N ;v ((CѮ] 6dٳ'|f͢rڵ <<<ر#?3o6,_iӦ1cƌ95kÇ/VX+Wo/NNt˧?mdb";ϟg9 %xwo B.]x7ڵ! VAݛSگJ\\C5ۛٳgi&&N?{Nq`jjJ~={6QQQj"##?1cFNqо}{V?Smڴ Fwoذݻw[oz/==˗/?KT*̙3~ːֵͪV%04?Cnr=P֖VmгgOZhsܹss̙FHMo~+O>3xYA6)4l0結<+VVV<4hN#,, ZMhh(:.߱͛洱jժyrǯ{OO/O|/>!|oҤIqU*IMo~+oѪUB[^XXSL}t! PBcj5ƶn:q9&j:||L~144uǺtƍqww{BQ, K.X Xz}-/([OȀ$!B'iٲ _l!cccԩcynh?EEEkltttߟ4 sƢhrB!(K^֭[߿^z)Ϙ5kp\Bs^Q.\3Gpp0* wwwUJ3f͚$%%gkB!DYR^||<aaa߿_~9q:[.֭8׭Ƒ#Gr^`t=Y}:~b1%W|˦ZhAVVzA9f׮]r2CQ|%҉<6lؠSTA>iZVEGG>:///]LL꼼t}-Ȋ䛗[vgRUV?>C]VJ %O-[...[>s֭[u+V,?ɷl*|m,88=zs܉'(?ɷl*ojՊٳg1!!!̚5W_}#+o^oUk/ڲL<Nw6k[ٳg`,:+]/EԪU6mаaCmϟ?ѣGqwwg G$_W-B2.Ǝ322ҩT*Fիlllt*Jgll7naW|ˮݼytuՙT*NRu-X@tEF|%gmR"""M6k5kҿ\]]hI>)oadHM-)B!<'zSպ>#+o^oU-o;$IeSq+^>jժ[r3}w:xI|˦򖯍n߾}w^M DT$IeSq+,39;;[/7oT-o;$IeS[تSuU׵kW]FFddd+$߼$߲W_ u3fЅy?,,L u_}-77ɷl+|e|Ѷm[*WLmen߾? Gb$_W-ƍh4_{U, rx[NVh_F-sZhy/b!f¨5MP ƭmѫW/&}:Nr l>|RRRN:ՎS[t!&}:Nr ]V8H}do}#""/jTTTŋ}6L&k}.Z+wNSN0:&//LBm8p_KW3(&&ڴiT+wMVܽ{uFG@'ڷoO{!WWW?}oWjTVV͛>&$$htTIp_}VWK<c1kcL#eǸ/ \B֭488^uU8/{kO+wq'W_>E['RFFUVV2t]rttӜ9s* }:ܗrߧT"ۉyPTTd~QQy>ĸ9nVྖzٗ'xVDDD ==q鈈?̸uW쭯7mVLxyy uW+YXQXXH5С uW쭯}$g_YN;vq\vv6 uW;+w.L%^_+jի!@RRPZZjzHNNWV04/uoB_~5jF~!((BDFFݻJ}fܗrߧj,XB!M^^^ϔ)}z=|6l@^мys-Z@>}i&TVV*S2ܗrߚcR޽{eZhR߾}YdZW^j_'1ci dʈ~ 'ar;vPNK8+//'!(f*++IޡϞVxc۷رcԵkW7oR`` m߾:vtܺu222ĉ$SJJf////nݚ:m6Φ&MЛoI`O%kFii)>S2moݺBx{{cȑ9r${*T탃K.! 3f |}}4V8phѢ>DGGCD ѵrB@ә{iaB燍7 dffغu+ Ǝ`Ri,Xޘ5k/_f͚aȑ@vv6>|,c…lYo 44gΜvٳgÇ0<6m|'8uKԯ_7FAAiy]}vvDD"###Ӿ]v֭%{!::/^͛7ř&ڵ+Nm厴4l舄TTT ~~~_mZhHKK3S;暶}}}1eq&MBpp-">>SN5mgffBa=/U&z>ի  6 fЦMsҥ i^ii)fDvvinݺXvŸ+V`02,ñdѣG!e-^ᶎ'z~?^z6H$-Z`͚5w؁:uwxfWBӦMe㕖.)yW_i&qk׮- ;v0m_zBܹl\VV/U&|lP~.]qΝ;rss5ӴiӨl;1&L@M6U(>cZr%Rjj* :VZE'OWѣiѢEpZiCC%j۷vM6Lg;ŔJaR:8@GsQee%QXXۗ'Б#GaÆJD=s׮]C4|Z?'x5ׯ04h@:v._rruu}b{ѵk(00vloJNNݻf h''ORӦMͮc1 ci}7[GbFx"z ͜*..k4.]d7}oYY-[VZEDggg d=z4yxx(Tuqq`ko_[ ^5~>}:}לyzzҤIwޡ:u(PZoWoII EGGSQQuܙZnm6h4ɓ'i߾}L_cr_D+ N(NNNpttDZZݻ?N.ܗ}-q_/OHHH@HH-ɷnB>}۸z=f͚TLp_}Vq_uvnÇ)55|}}M)--vE7oޤ͛S||3}Z;wѣGԯ_?jӦ%%%믿w}Gz)88Xφr_j{:u pttDLL  !/^lXJr_ٙ3g0n8ԭ[bY'www8{1%}/WӧOSZZ:t˩UVSMc ɉL* };}Hݗ'x1fݻGFQ3 ׄj61l߾a3W۸oxcvNp_mOk2ƘM65+((Aq_븯:ٗc1Bz葌i}}Gξ|1TˋFMF_^k8}/}:|1T^3gΐǹ(uW1Ƙmۖ?^㸺uR@@ ɋZ}Iξ| c؝;wbW۸txc11|1cLcxc113gPbEt‡~/Vl߾gϞ1a„۪L9~#vXuq+VF)X7Ãeޟ%Jސ9=֠ðm&<8Fn-۷ol""*`^|ɸqXl~!={41QZgϞiܸ1ƍAcZl߾[npĉK?@/yx`նKD>φ;?i#\5ȕ+.\I;_Aם > ¡aСf{Hb /^~zd®]ر#2e֖dɒ࠲E8ꫯhҤ >>>,YҪ2}6L Nl4~-K.M˄ Xb_J\9r3f*J6yNq$ރ!z'|UV̙S Xwׯ_g…ԩS[bcc+۶mݝO?iӦaooOLȜ93;wvlݺ///*,\Cڵkɓ'1 3dN8;}V1;ofݴ.Q;|h"W3;>P4oM|\r\X'N0qD~RdnkyioooiqY}8p< m¨ Y-]:[C|4jMɜ9V(<`׮];v 777n޼IrH")SLuٳgbkkӧOq͛TPN:[n͚5cرK;vʘ1c8V1YL2CR]4~Ռ ;'C& ۗ&MhŸz*ƍcƌ ;/;c鮝-Zg:u3"ܽ{}quWNҦGɓ'qCѢEI,ʪ˗… i۶wÃ={2b"&֭[,_ISǑZgԽ[allaРA*b֎?Ό3ܵ2^lا b_ppiJkZn뜋 `00o<*WLٲeMFGG@ԩVf9ʪZp! >Ν;IJqF:vH9r$wuX|R c2[D( SѣGS\9RL/føCZIYFU`ժU*b6&MDxx8-ZԬ9sy&M4IeULҥKӧo߾ -lذ֭[3x`z1$ט(8/˗'uZĤ:t-(vvVðރI- l۶t)1f̘A֬Yiܸ1f;,&&ϟ3eTB*UTV_&M0az@]Y&cƌ_~ DÙ5kkޡ^OWzwP$+}N:Z!$-[L1goaGTIVZ?^-[ٽ{7m~F*|VB]K"S {&tYavxW#ނغu[*P;vk׮>}ZeUYd ;v$K, $ lْk.!i߾}4hЀ}5LyF@xs5kP$^ԬYm_y$i`VcamҤ wU(N.^HСxcl޼9 ʪ={6Ga$L2ɓݻwSN]A7]p6lÇ;wP~}ʷD*-}iF[|2'M4)(͚5k׮:uJeUΥKصk]tQ&ӓ{q!k۶m4oE"*"5 {?Ka{Y`ݦD>+ދl߾]v۷o]v+x(-b˖-ܻwOeUܲeȗ/eʔQ&"wxxx0m4 ,c5U:=Մ lْP"dZUIVXAL4 *oݴi]tVo% 8;rIUo< ={* ӱcGvMPP8ET͇+nMVZS p$U9ȉ'˗/ew0Ȅ ޽{aUYO>lٲ d̘Ν;70m4r jcf{Qعs7C-[|%8eZ_3}?p{cǎ #ϟ]v1o޼êj^|ɠA?~0QÆ ޽{( ̙3]f(Ė*- &'""BYd )s]`eea |`}J.^01w\ZjSA)ݻ77n$66VeUСCԩٳgW&l,\[n) +ĉ*Pcǎ) y#;`J3.?ٳo^)%|`gWj*|}}5oޜXVZ0Xpp0?.H&ctaÆQe ( SRze;cj+yѣGY?Pa$ݻ3c ^z*͟?*U?~a\\\/5jT&!oR+Ts8vv GU>|ȬYh<ȠٺMLPw4ӧOOg3f /Q$3gNn޼*FAAAرΝ;+ 3ѬY32d~0/š h2DY6o߾ Bќ9s_Y* STloh"!ҦMuƙ3gTVhɓ+ 3;wnZjɓuG+4b1`c,LA+#FP?_ΏSl]0d L8|)$dcc} jBBBQf[n0}taE<;,LC*p &p}"zjp?ؤQa$2eʰm6Uk7sL>Sʔ)0̌36m JL:R^ˢ,LIњM`P Yb6z* S.+ΝVl׮]ԨQCA۷UVUxx8f L+W9sjf`+qJf8f:݌hkf`C@@v.A,AfRVdϞa"rIΝmBQU3srE)RYӦM2VdC] 0f(sakMҥKz2{J* „dɒ77x;jfON˖-ɐ!1q.]bTk,LU4P',^(.h.csJœ4J0oF֭ɞ=01_~%JRY1U111SM4ٔ)lC:u°bL6&Ѭf&+Ν;  ֮]0dd9s'-Z`kkҥK{. ͥ.5 Mիp°RUkQlY`!\]]ׯ@ӧONW b..^RY3LƱbN>0eʔyBUV͈?ZRŋ AĄ$XrkS, ,?ҥO>dɒEX___/RZjŽ8gSq0'ktS^~!*agWY0;w${ L_!CN"&/66V!{y)_IɁ841?WvZ|i,"fKeL>>X" TmZ~ayYLc]eUKX}ʴie1DLСмysaJ7 &0P]HDΜ9,qI1CQQQxBe՚8͢E(VŊS,mfe bnR C:u0CGNU11yH"*֤qƤ 3watb ë}akk0mr 7o*3*6eРA _v][UVX߰kr06l EԮ][aX8g) srz <*B֭[RĊ+ KSrea>;wTfڵkɓGeڔ,Yy@YO2w\0@Pq*6VyCa +.֭[ ڵN`U?^aҥK窱1GGGwz]Y4h0Dp ܽ,Ր;C%jԨ0\j`xQaQ~°Yu3}ta&V\IŊUVU 9RG^czɓqt=b֍n{&M!4'˗ Äރ= l۷悰"u{3y@Ǐserȡjݻ7S* 5aԩγWk”RUf͚ C(P&J(.JRV}QFa0꺦իCe4mڔW>uWMPPPZ6`*8 uAti18RGWMS8ʉ^z) +rL>>̟?_h_5G~4&oP(], CJ10 wwwvޭ@Q2Ιs躪2-_0n8bccH&UT~*ٙ;wrp\Y-U tܙKG)`z[xK7&гgOUfg^àlGn͞={J"x 3Z;ҧ,<==Ca)mӦM+V9rI2%W]<֭W&_| DH MFT56xŠAL7ooo"D=<==9|I@a0 u#Xϟo1nܸU+%KZoK*rG_>ƨ[sLAt+]' G D[k1ժUSaM `{* e5 ѨQ#޽@XHHZ"{ 6ʪpqq98<)uQzł } XLk988:#bc`/2>}(6ɠ@h%nnnݻWe:t]Hzg>@͹wBI ?ʕ+`Ztұm6L.1ĉtEcT彥v![. O(d0hS{|||+;ʵyg-J<лL+$MG&MxBgO>[nDGG'RY2XUTXڵ+V" DmT`ưƗ0 ]ϡC DM2h;6GU(s 鲈͛+ygNwc".MF8f;Dhw{#%}p-8\J ^P~}߯@B]!-_XDݺu0dD:ưPԩwUV[l:%_?~nݺtR>C" >+!p5b+ogz;_;$*; *Eft =yr͚5S w D9f ~?fذa3͛'dJ*?ҦM˲E?2{ó.]sу˗JH1a}e&bc`|:[GT%AP.͛7ȑ# _DEXdG˖-$s ڏBCCѣ|I]*`JrÎ!ufa0K.,Y͛73ojRaa`oSHs=W __ƍm6_UV0ah bDl޺2x"~~~8::r Nʍ7(TVIt)3H tD>%}Rhtɔd4\{SNM fwbXԯ__HIǩY& P~}Ο?O:uq]]ȑ#ٲ89cj8-xhiРdҥlٲ%f9'NR)50Aע^7 ___"&1U:L+$k3k<==sGmcZjEʕiڴiN*o-_| 4'8>#®C4i҄ <==qvvB*- yKB|?ܻlYhxjT]p&z "&/Ghp)G.~~~\rf0iƞ={شikJ!I.u:KaӬY3N|8\zN:iɳMGELj}!u<[0C fho._&_*x3ooQpu/M=8}E=gal rzri-ZDIqd%j>#((hիWܽ{;2atbRi57ecc+˗5;2Z7E/_d߾}.]:|||puu%cƌZbv254z 3>N7jV?q^&}P\9beϧ­B1{ ݝ=z~!wOphI\RaaTXQ;Ĥuk-Xex5nC-~͛7oߓ!CV=FUyu4T^UqF Ɖi9ٌךK*>$22 ƏϹs7nMV Q^OdݡH0my*^=imw*V/Bv")qUd7ߟiӚM{n"tpI ʄ}tPwac~6M\DM%T)I޽- p%5kF%Lq;K,zj&yOay($aǣG3fLk׮/_>|}} ؝`|Uڥ&_~&@S]CbŴŢ71~Y)sqv=z G&7MZ&׋*UhoBþPm,K-tMϞ=~~%ׯȑ#ooooL1JÃW^}o͚5ݾ=!""e˗Qy1Ζ_ŋ^bN:dʔ))W9Cp'26O1\sp}W/>6\rag)A9O86Lg|Wxyy kխ)\o Р7zv?bVg)SƬKHH7oȑOOO9sC[o'ONL{@mԩS2:wڵkӧ...|TJg֬Y?~|my!M4ZbRwMaZJɣpx5^D\590>q*pvYfܸqqoߞŋ6mZ>{,A_~p%\<ƣMj,͎1(QB;ĢCï;aOԪ |wΝ/۾&44~]vQ|y6mjv뿋$Z-Zhד&M}2FӬY3n޼ٳg:th%K$O< R 8f VX68kp#'iAltpɼɽTƣc8Ѹ|vϳdBR5~iPB"^} ZGGN2f͚Yoqggg֭$Vh-Ǚl#?Q#Gӷo_a+VZEPP B fʪB DI4ƍrk.Lk!-y>}Qt߳IfT;Iƀw/ö_v`=ёƣS'ۆ=TjՎ9r9w.~ f~Օ{b(jlxlƯp|믩S.w. |Rt`J)3.]$mʔ)DFF;-Z0ZDD,_Ə?[5[caHEzN?6˿O8.~ }Ϝ9U7}xT_/mo#Y3inypw5=BeIΤa~>6EчeR>w_uΟlN?OTZ5>'NΝwra'ONZ eʔKblQYRM_!2 ^F?xn{ ]D5 oy>!nfYdaߺq1cFgI$vO}r+xq%' ,֭[｡,"*RX{yp/ s;'qS,x ҥKӢE +Fraaa?~k׮q9ݹs ٙ[E΋W^g b~Q$2g ;7o&:::uҾ}{+SZ5֮]F?ŋ-o}"VTVΝ7p`Y9s@ }v0`ѣG%o۶m*U%JPD mN9XYկ_?lmm9<ٙu2zh?>L2ԩS}LƟ(ǡ`k h&*j(j`4h}4L&4B F#51!Vb4Đ×%pza? {w{$gfnVQv۶m(ظq#{?X,|hiiC0j(f,Xϗ|?_3޼={\L2IIIZq֭~1cܹ3<4PeժU^j/5EE󃎁Zlp8Æ ʕ+t:y)ܹs A۷ogxx8߿O|YXXdɓu֑$SSSY]]Mt:5kpweXX333 vttgϞNSإKh2xqdii)M&/_vo;%ƄR]]ͬ,fм.]vd^^WϭerrfABCCi&СC &oakk+ ~iwu_~%U}{Dþޓq@EQƽba$@`TFѼh~1tĖ5-**x1M&sss5;͚5>_~Mw0$$O>3gSdPP7y$y}*¦&edeeYV^ڐ1={6CBBIP[[p (͐=zތ!c?K^pyyy:u`ݻwKAAAPϱlF͆p$>|-[;wv^ ڗzKCC.\ն֭ȑ#cǎngϞY⇞jͨ/MƍRѣG1sLY * oٌX=zVիW! # ; l÷~ӧOBUA27  IENDB`geopandas-1.2.0/doc/source/_static/test.png000066400000000000000000000200071525650651600206450ustar00rootroot00000000000000PNG  IHDRasBIT|d pHYsaa?iIDATxpU m;6EJQuPZ+B%Y3Fۉ:Δ0Je:OS@~T#]ukuN*EgwZdG>Hs?|Ƅܓ{﹯̝o9;_˹$I\U' la @&[2A Ld R^{-n7o^TWWGUUUZ_nܸ1z})r2o/ɓtlq]w{eǺϚ u˟5?k{./vu[!tK.)SD[[[}ɓ'{YFeϚ u˟5?k6p}0G}}}@ [z8~xO~W]uU1",Y7o;vlDD\q1gΜ2eJrhmmx Fk֬P&$l.]o!hiirq7n\tvvFggg[Ƌ/oVŋcʕ|d(^=dgƺϚ u˟5+-$C~wޘ1cFٳǍ%(^+{l7Ld - la @&[2A Ld - la @&[2A Ld - la @&[2A Ld - la @&[2A Ld - la @&[2A Ld - la @&[2A Ld - la @&[2A Ld - la @&[2A k7|s̛7/*VZ?K.'Fuuu̟??ٓ(WmGGG]6zXhQDDr~{ɸ+VOS\ve{4 @kkkرc?!֭[shkk_1{숈hhhiӦm;vHc!6IߴiSvEmD9ѣG d|S*4 gc׮]ŞC'Β Ȭ7wuWL>ɮS̍+lȲuE˯3?S!N^[lYL<ȳ!˞{o{3fJ6lkjj{kkjjzJ6cy1~|AFmy-?C@M<9f͚UiCd? /gy}"EmmO ۖg1Ş Wav/ޛo1|/2p*jg_s=2.[z8~xO~W]uU1",Y7o;vlDD| _kbQ]]?㥗^͛7=m`}ՁcŞz.]4:o %ZZZ"Łbܸqvذa裏Ɗ+;N3gƮ]0 J{Ê=%*@a{3aÆذaC#G~4DP,%{-P~D-$lMP -0(R!la [ oR$lZJMPʄ-/R'l3a IP.-pZr"l^Zʍz#a t#j(W"j(gQ @ -T8Q @V[`,PD-Y#lZHBd "j2a Bu*[8Q @aJ"l!D-FBZ*T*a "jd2BP-da eOۄ-1Q PD-t'l ZIB;a eD [(&l Z83a %N@[(aOBa %H@-Q #lZ8a %B[(OBZ( a E$jp-P Oa CH@z- Q 0D-OBD- a )0t-DQ COBZ(a $jxRۓ'O-uuu1z;wnܹmܸ1z}iO&j W'= ,m۶ŢExƍcҤI=ܴ "jR 'x"ow}w\qWƑ#Gbʕ ۩SӜ& Ґ6m#Gŋ755ų>mmmgF$iMM@H?~|1x]]]DD8p .>`舚?~{ycԨQ1NC@ߚ+K5l>cƌ1^]]9[oYfEUUU;v숧~ϸ뮻ܾ@D-YoO,ݻ7f̘Kc ]?MMMq饗׿CE@iJۚ8zhSc555y]K.)S\Hҕj؎?>8qD}ED\0rA %jMMMǽ5$Ilڴ)N=X߿? =U蓨җ=pXlY;v,&L[n}z<1o޼c:;;{֭[_z+jkkcűrG>!"D-vذazXziٰaClذwܑԠOKBP~-$l]D-/a &l!D-dZaKE–%j [-I@[*lTQ %lMRD-d%D-TaKZ–PY-$j[2G@ed%l Q Mؒ =Q D[ʜN-Q ,Zང-eGQ lZ/– j3Fm.""8_|iϝ6mZ񺺺H$<ՅtzW#"ǴFMcNJ<3/bO qo"τ{=#=|p3 =|p~3Ӟ|5jThll+SO=|DDyS"rbπ;{mE ;+K-l#7u]1}&nؾ}{۷/&OÇ # ԧ= 2o.(>y6dݨQb̙ŞFŽ{ƌ3R~ULMMM9rckjjN8m|߾}q /ڷo_r-\(mSSSqv%I6mStSSSXzuTWWx饗biM2+ZZZf_>~yu$IذaG+Vw3gΌ]vyQ%.2wUv{%(^K[JLd - la @&[2A Ld - la @&[2A Ld - la @&[2A Ld - la @&[2A Ld - la @&[2A Ld - la @&[2A Ld - la @&[2A Ld - la @&[S(K-l`[XY"l/YYnfcgJKa{ɸ[.Fs΍;w܍7FUUU_~9iPy񫯾:~իcĉq=Ă b۶mhѢ~]cƍ1iҤnc{nOя&ɁN{Ζ-[(ǖ-[=Çc̘1=ƫޗ??n֘5kVTUUcǎx駣9 e˖#FpN8_;$g_1gΜ~]𩧞iӦE]]]3&yn_6X|y^}K/ŋ{{ 3&Muƍ8rH=zKbʔ)֖d[΋n! ?>~_lj'}{_}"" .z|.ٔ4455믿{oX$iӦ:uj̘1#k>c/TȀ^6'O/<:Ocƌqe#<fŸbh v?eu/#֮]]tQ,Z(֭[+y fN(^|XlY|wq7=XU^̚Ri,"bܸqquEMMMW;_iϭԽ65; {;vޝrdÆ gϞL2={4˿KG}kĉɄ 005^GGGGUVJkU~{饗zYg,\s+u f*uΛo$&MJkrz+Bg}o b0kiӦٳgws9?p[eM!SR>+u@M?____+u fN}v:g}v|>3z5;{ö=ƏS""/ ⬳ΊڸkڴǴizE$Xb?eq/RZoǎ}^{GJgIɓ'w]|[ߊyN﵁)ke}C=ǿƼyⓟdo`׵\ ^JSq=o>5"n8cڴio;~hllJkYAP >hx駓$IS&sqC%\.;ǯ$%=蟩6%KߵkWւεTz?ee/ѣy+Z{]T~7rG?ޖϚ /%k׮M-ZٟYs*} dz3н"`֦&:::tn9+~^JS4T~ַ~{|ߎ9Zk֛Jgcǎcƌ3+_J\verXdITUޕf^+5 g1o߾rQ[[[S4U^KS%UV??ƪUuN﵁Y_*abŊ_WϚ%ｖs%'Lr\qƮdI]]݀_*rɵ^[i`f۶mI.K~_vz 6b+~^G^{ q+Bn-r??u^%﵁Yo*eδiӒ:+y7N{L%gz3нVa$IwygcǎkM:p Ó^xks\r'۷oO~ӟ&_ѣG'{nU֦u9yd2spvHݻ Yҙg?KZZZ;r5\$---믿$֛[~I.Kϟ޽;o~qY$Io1YbEr&&#FH&OmrRӟm]$I?tM?ϓ+"ޤ{kt&]/ϧ^iVmݏw f*u%~6N>dرɼyݎ15+^%w^)Ld - la @&[2A @9]⌞IENDB`geopandas-1.2.0/doc/source/_static/test_buffer.png000066400000000000000000000475471525650651600222200ustar00rootroot00000000000000PNG  IHDRasBIT|d pHYsaa?i IDATxyxLWwfJ!5(j-R[mAKk*R(JikZKbK#~iQj*,!Bdf?VmYΙ|7fs NS@DDDD,DDDD$ ,DDDD$ ,DDDD$ ,DDDD$ ,DDDD$ ,DDDD$ ,DDDD$ ۴4L8۷J%McT*__RRɉȔXH=@rr2~'xzz{Xb EZj=dɒIDDDD&Nbꊔ{bŊnݺw4""""αt=̇wlK.HNNF дiS̚5 헜{666JDDDDxtz=Qr/@ƍT*ec׮]8ssv޽mݺu0`^iOw0x`4o&MBHH3WRݠYFAll,bbbpU$$$ 1޽,BՊIRJ666ptt ׯ6m<9O`1yu-u-''Z^YXX@VxpvvFJꊚ5k# qIr]7b"oԩǏ?3kkk}SRP:-Wqu՝Q,Ԗמsh^A2S ʋ!K wh ǑH|V{yj-`i?k*;Å;B^0W^ŢEh"ՖT슮Ȟ>}J:e˖EjリU ժUZ~9w؁uJ]v+ڵkaR+z{dˈG\\BCC JիqfYtϣ_~̑#Gd =--CNZP$/~2n|ZdU)V\ѦFY2X^?9:q?Ng ڶkŋbŊIum7<.GFЪU+)SFpJ"+]45kf͚=ݦhpI)Sd'OA8qI=hiӦ!(h5RSfբF;W21(,qIYz/3[Ty -+sviӦ N'\ׂ mzYBj8x n݊cǎ!33ޘ:u*:w,:^IXluLAVfZԯCZGeHf?œuX'й#&TWXj&9-7AdhuZ E`aark?#33^^^9r$<==EG# Y7oFJJ j׮I&Fm>J|2}6h5t𮆙AjeEG#OA@ 4z-I>QөhBdf-]i wuI{zkۡhдiS5 5jHrsso߆+&L#GBzj5"擱۴4|'X ѼU˛]32?l? %0 &| xSg w,#:rm:ҿ_Ob²LU}Qʖw#_f Xp:4ܹoX&aŊϑ֭[O?ERD"2{ :|1$ 8{,6ć~'{%~[6NRKzw1Z#ځJԾH +Crr27nf͚!''Gt4uY4VxqO={6K-h߾=n݊ ""j_|F#:Xl%CA/\xߍhsF-Hf2r08{KީM<vj6@)[ :tv>}hFZ\x| BCCQ~}ш?j5ƍ7vژ9s&Uݻw&)[ =z=!vǘЧ?H囨?,v =pn\3 vL_9]TM]KUetL:nnnϼ\=znnn m???^׈\Ŋl2|7DΝ"77Wt4IgfB͑}!tjo,n#:MU`qp%vnH nq%,鈈ѱ5k7oL :@֭uVt +V'^*:ޱIJJ ڶiɓ'I4 mE20-=hS>jƕ9Q83>jR9hӦ .:A׵z!,, 5 IVcڴi6mP~}]Vt,bՃ(v~[x$qհi7J.aX(?+Dǒ\TToaX|9DΝ~z8;;cРA,E4k,mJM" ”A-EG"Zw4JM"5DG2M]Kl,YK2kmj|r|ᇢ#UX6l@ݱaԭ[cm!iZ5 'OF \HZKwXW<@m±1oV-Z`ӦMcտko¸T*L<g͛7ѨQ#={Vt"a-F^zaҥخ~E"ht`F|8F5. `ˬMku}:zy扎u /=&2mڴˑf͚!22RtBc-LjksDVv}_c0+eD/aX( &`ܸq#Iff&Zjp 8ӧOu̸#((vvvԩ6n(:RUGFpa|7=Nt$ԴL#%g>5EGhZ 6 ,@^DG*'׵?| Ǝ+: RbE]ʕCh"ё 6n޼^^|1aMDG"}/o\3ҿ.v^Fi{\Uؼy3Zh!:Rܼy^^^tKDD9::"88kرc(:RCjj*Zh;>/':{hJܼy{9ё5J[!~bKnݺ/hѢܹycǎ#ŏ?&M`ƌ&,kǧ]K@h`Oo%HOJmRJv煷\KDdce-h1zhё^u׮]_ƍDDFχ'&N 6/,jѩcG9suF&HomXy o ,YٳgBZM4 ͛7J?WWW 2Xl_#"0} 8$S,hƱ1M`T`ҤI 9~~~#Lfcmme˖dɒٳ'N:%:+ؾĸq1=!w4H,S=">l0`8 :SOk}!CD!"h׮bccEGz)Xx1,Xw[c@IV8"=@Cm$:Zv駟CD&\rXd кukB, xת)=E!bk;cfjhPؕT\j׮3g BDFq _tb,ӭlJlٗo!IԚͫK1ܹs YYY֭0o<^׈H6m~aDyp2x '#賮p.wԚ}4B1+%°yf?h $$$`ԩ(]KQ}Ǩ]6>SDGG ۿX0kCMt!ZdR!bX#(<0+V@XXziroE#"T*wAVwAVVHO'c³3~At!Z\Sڸ"//`mcbb0vXԬYG2e0uT)/Z=wJŶB2􌥖 uMx1|pIj޽;J%͛J%xDdZl={"44k׮-fϞ 11#_ud7aeO?$`gFLL ,8DDpuuń b 0.wgIStZ/ s;ׄNC˖-%Ν;.]H2* wƏ/:y#F ;; +?*: K-*r + <<\|]F``MD"^^^իq9Y̶8p۷mÐp$:K-ή@`z=m6tUT빉^?Z| 4Y[VÆt ;+:K-Gx۽VŰaqDD!Cѣ ,ٳqe|7-l-E!`V ZJ/͞=/_رcammDD3p@bĉ$3booWE!`P YΝtL|[nEDTXJHNNƗ_~)&Q3gDG!`Qe8ڨpĉ"lΜ9x>c=#"*8///ԯ_+WF2*Xt ,+C2RKE5m5t:ݻP?-;̝;cU]x1"p4Gya%}vJDDD ''?%  ooo~F1fSlZ-̟N8dXjI>iV ZiZ̟?UTDDXlA5bvZ$\6LK-۔nR)efkڵk}x]#"oL2Xh4ҥKame]B&fRrHOOGttk_t)УG#"*8R ?~o6̘E Fݿ.QC& eFѣGr?F_~olmm P}]hZ,Y ɾnݺSR0(dXjɐ|;BGr["%%]v5P2"©\2T7d<ە+WD1jY[t21,$B%m۶t+WXbhӦNvpE?^d]l333u:xWJ%_􌥖D;`/yff&8&M@R2Q Xxcɺ!#3#zB&DQ%Up LɈ uy7Q"b򥋣eʢ`%c{ihZZ깟 ^^^Np-=zTqd[lZ-N>&+B&gx<_N^=hLK۶mP(}vIǑm=uRS] `%cR=,U ;w폯khԨdDD~tm۶A5%: 9Z2FC=@˖-&#"*ڵk̙3j!b2%\^t2b,dV/ XfmQQQpvvFҥE""*ƍѣG.%bjqix*/: 1Z2f7ykgP6&"ԢE ( I#Ȳ^xkXjwodDDdɓ'%C~{~(dXjT>==FЩS'8qYYʲkԒ( IdWEHNNO?\t@/yyy͛+WBѠe˖8r+[RKr\[[" )J]]]wVXc7oތǏ#""Z^z1cvcmXl K-əDDzekk+ٹ N+puu}Zj Þ={pݗk,lL21,$wJHvvvh'Ƣ^zmNC||Kf5,d*=7NE(7oYfmwrrzsoo{t̶~_%!Xj\Yiڻw/̶GI6ۢ<(Ѣ Bc2&,dXlm111dWիnkk%KHLK-α%RKDDDbKF Ŗ K-- Z"""* [2 ,DDDTT,$K-- RKDDDbK°>ؒ,DDDo,dp,DDD$[2(Z""" - K-IŖ bKc%"""C`%IؒdXjȐXlI,DDDdh,w,DDD$-K-bKzRKDDD"ؒ^h,Td,DDDd XlHXjXR1aBa%"""cbKRKDDDƈŖ -K-3[Z"""2v,Z,DDDd XlXjTK)ab%"""SbKa%"""SbK`%"""SbKO)c%,DDDdXlḏ\ؚ1Z"""[3RKDDDrbkXjHXl K-a%"""9c5,DDD$w,f̱`1Z"""2',2RKDDDVXj K-+[a%"""sb+,DDDdXle=bkXjbkXjbkXjbkbXj^ք`%"""z5[RKDDDz,F(XlK-Q)Z"""! pttD6mwR-K-QYH=@޽o>̜9nnnXv-t邍7{=>((jzf[ɒ%+K-QHZl=m۶aժUx;vDbb"Lb[n]xyyIhSakk>}<}믿pמCIϨwlcccQzu< ]tArr2*TMb֬Ypuu}1G_C!l׺.(/a,DDD$G{}٪GI6͛([s۝eʕ+/7RĖ-[]v̙3,=\:wK-ɕ/|}}???IƓѡC <͛7ǤIMK-H:ǶB {s۟lPB[oN:kXjKb[zu\zYYYlTZPU(E&K-IZl >ݦuA:[Q Hα;＃ѣG#%%5kq!lܸ~CŚ5kJ*ڵk֭[N:P(֭[ٳgK[2,DDDDґᱰ0bʕqsNo>ZZ5k=<<~z\~puuE>}0e/_^zRKDDD$-ɋJ̙31s̗zj^m͓:IO9RKDDDd(,b%"""2[Xj VXj`#Z""""qXlH,[=`%"""ŶXjmBb%"""2.,RKDDDd|Xl 8K-Tz Ja Yj ãlx _XK-ӠgAP)Xmk^wl_Aաozpz1ޱ}n?QAt""""zޱ}i+b/0W^_; bJZQ>ǥ;h]ީ':dW(f>P*#Q>x~8;Di;+qxoď{§+ZqXlL{Ьr1>,FDDDd̾j:2 JyDDDD.rǮ$~u\Ft""""*$c{/5 AѮީ72.v!#' lsX1Mʣj){q̲j:ԘMt""""|xl閃8wR%:ݱÌuЦ./:ݱ]~wR=QHm^ 7C*Ѡ8DDDDGfUl> |֪(DDDDgfSlZ  +ءu2Mun> WQHfSlgoѵWB """#('bؕ{-( qHfQlW:{+%yV$"beteHd_ldDDD$g/{N%|k:BDDDDu/ͽTJ>4FDDD$g."Naf7CcDDDDr'buUKZaEGQHb-ZG.F*%DG!""""mhU(DDDDd-{\R$: l3 YDG!""""e連1ѴrqQ@dYlg>_[k̅,ϯQFt""""2YԴ,TtPZt""""2Y۴\T.ƈ̉,mzf. H63WD """"eZN#ɶ.5l̉,@eG[1ȀdYlm,P*cɲZXj̍6//#ڴi{t5 nnnprrBNpĉ|Z""""#iݻ7.\CbÆ ([,tWl޼Xr%4 Zl#Gv\ %-GŶm۰j*{;"11SLA_z͛qqDDDUV___ԫW3f]^96-mpp0lmmѧOg<?c]]]ZB~gܽ{c8ǖHVlcccQzup3ɊmQ(4DD)&L/|}}???IƓJYB΅}B <699P QVjfxq&!77WsKVlWW"++UjժٳmB+<0LS$+FFFBCCntFݺuѠAW۲N:=k2Z["#UFFdl7y=)))Y&֯_CaƍO:t(֬YXTT УGx{{w̙3䄥K֭[Xfkh l*==]sKXXXrJܸq^^^عs'ڷotV V * 7|QFk["$ ̙31s̗zj^XdIհL$=.Z-""yHKKܲ\?3H.OEH?Xl H 'W#:Q]N~]ZZ4^׈ݾ}[s˲W_C|RFXqΝ;[W@DTdYy\xQp"{KEL"߫ND/[CٲeT*qUq[UDQ·iIz5k"== ͽ{{+>ɲZY[~&~$"Ӷ+& ФIܽ{MLLTŋ#3k~i;ruݻwGժU$8Q\|Y˲VR:'nH7Hjo=B<==*29DDR;w+&eYl}]!"*G9(֭ͅ &eYl[jB)ʙTi7uARpQɈ ')) wAÆ %CqQDD8ȑ#n{7$H*Oqjժdcȶ6h5޵%"[( 6@D&?#jԨ!-Gp8^l"B'̳H.\OOOIǐm֭ %pe"2-_ēyǎp̯t[(Y$"O(fG>~uG}4hhh\LǞ={<^[J.={V=Nt"|}XXX/???ؽ{Ndd$ׯWWWǒ}RsIRDhth۶mebΝJFDT8111~:kd_l-,,PFMdj?]t"j-[J%vs!55ш %,, j#F0x/W_Fo=/8 ыef 888GCHH\nn.ФI83R1b۫W/"22D!"zgǏC eJBCC|g,-/ -: 34l ++5}ԩSq}IZ-BBBPvmt`M1cj5OȈȨС踷~jBHHDFDFeǎs :[4h:|K(DDO .\Xc?s$&&_~ QYׯA5btRT*>~Kt""]DF=z¢8... QEFF"!!%W [5ͪZXXk׮aV%qu(J]P+J7qqqs:"[b ׿UJD%p- HѢuPՅ>Ϙ1cPL,^smHH\x}T*7bVѯd0xYqLefaT*l۶HRT1cbcca=%$"*\|wpqqĉd0b ֭ ˷#O L<E>߰a+VmdD$Ē%Kp,^B2eUWCӪ㢣9r-dɒ6mλrJdeeaz;'Q~ܺu 7nDvХKa9̶իWGlJ6֞.:NvڥzzzٳjED3c (JXBh- odDd_yhܸ17n/\ꫯ Ddd$;cEh.+VD~3̥f<}`lQxq̜9Xn$c=sRJ>}8]l`Cd8D$c<-^fx7|r\|YqN$,_\cf qHFG#.% |[Ζ|<"2?QFcǎ`xxx`xz) 'Ǐf%ggg#11_~A$"s ,)ۿ͘1KG[E!"HAuGDF cocȑ_uVKD򖓓 &@Vc۶mB02,rqX[[cűD|Wt"&K [øq$Yu.\cܹ3D$?_}_+V_XlEV#22ciη%"<.%gf͚7o * ۷o%Qܹ ޽{hܸ1ƍlsGN.׷%`, k++:uJhXׯ_ǘ1cDTGYၥKB,/0osFҥwޘ3gN<q`ĉpvvFDDQ,",/sN4h taqD?GCP ""nnn#=c3g8Dd"nܸ>BҥEGz)W8~8*WNa!"u%9 WVE#=gܹׯ°rJqȥ`Ĉh4_*:+ؾƕ+WP{.Ǩp.FD/YoA>}DGzu֡m۶XlE!"#Ç#%%Xl_ ׮'DG""#( sGV1>Sё^ITb׮]x70gرCt$"221b6mڈ/,`oo.ˎ»NDDFxEpBёu3,:d 4.]ŋbͧ+"11~Z,w]䕻h0c#Hqh-. 4III EG*(QQbEyj y\,|&:>ߊT(ؿ?z 6 ZVt,"ܹs2dgCtc- \~C,ΎBzPFdN=pB-[0fёDTbƍ3f ۇѣG#'5"srA98tQ,toy47@t$"2?”_caaaÇ@EGқf‰'0h ܾ}[t$"2 6O?3;u늎Th,Ew^ 6 Z4['#:I$Q̎DX]իx7EһI&!((nB߾}믿DDIOOرc1o<4lNBJD*"Z|9v KK|54Xxyc;uU rѴiSEt, 8'N@3f 77Wt,"ңhGE@@<ѱV:ujժ8r"q XD݃MAS`ܹ8t,,,Dǒ\Z ???l۶ ǵkD""=Xr% طofΜ RPxQC0v_@d$t%J˗1~xѱ JVc͚5Xn۷ED#Gbҥh֬.\ѱV/^(XYYadi9.>L.Cd̂]CW2lmmi&߿_t,ӧOcڴir 9s ;;[t4"zӧOO>'N`ʔ)cIV"j8{,ʔ)P~|ш?$ַQ5GZz:z):QQT ĕ+WM6[nسghD)))0l0888/СCqEш^nn.VZݻ#** C A||< ":Aĉ֭[A}CQшNrZ6:n7 [30BN᭥bruVܿ666 ĭ[pBt:L6 ڵÔ)Sp`A+~ng,-- ֭ѵkWݻ8{,ߏ&M,! pttD6m ((J%%%Ij56oތ,|'p,Yeaxx C%&3|_BY:wl>wY 11ݺu,T*|LjEhh(5jH :]tw}7Cnn.vލw1Q]mgdSWkي€ڒ4^@RL]%MVb1ESPqA5 HDl)033%71g/g(ٳظq#^J0' oj÷~X!>>^kD-.( vvvANN#UU8чd;`O$/Īr8O!36.Şm\7̔7'*LD$&&(..Fyy9JKKP!$$&5nݺCR3g˱vZ= bSNʕ+K.ō7uV'x,5 ={֭ӧދ^l8tSeP;#99.k8v6:C~ k֬AVV,vf_ׯGgg'+߿}JJJ䄹s"$$/Fpp0Ot$ܺu ǏǩSp%tuu0m4wAll,d2~t4_TT)S ))iD{jj*V^ J_|qѨ m_xxaa!pM֋_~ǺN`WgΞ禍?8X܆Mΐu=@8ZnG_؏ !!QQQ ,1qZ HHH@__N8z466ptt \BwwQct'f8CjߏV?pMe˖!""~~~꬟;wb0ֆi7B)88X]Rt/(( !yxx͙3} ||(..6ubvtt]U.زe BCC!?P(4DGG 'O6}0c1@KK ~mA4g555ΝCPPJ%Q]]=6( ޽7n4huuu CRRU-c1,Owlo>.8{ln\[^y@Rܗ1c6&/3oQQQpuuL&CVV5S&ռ=z񁋋 °k._Y3fR˖-Ü9s0e̟?6mߡ:R3sf͢[L4 ۷ouWPPmwm{ W_Ekk+>3/Dxx8:w1L͓fIbǾ}~^Y3nä{?>.^Bdffɓ:SjL0 ޽ٳgcŊ•+WPQQ?999ذaSJ5klYfqPCC !`D{DDx3gXhǔ|$cǎחbbb,0ډr CB|fIMyhkoo'{{{WY3fRn趽yyy8t?#88gJmL2m9K$լ[lyg}k9 ܹUUUHNNGY3ff͚3ɱcHqy"" %Kh\c8jkkIA˗/7Ɠ):u*i?~TQQaֱNΓd=+*լ=кi#żeff;깜 6RY[[T*ͥxrvvZgg͘icl,*!x^26^^^153wl%K$լYm6ر;wĻ;5kr&!˱pBYغu+ i[Y3f 3m{˔̝;.\holl ,'kϒ%I5k$eeeO>AVV>CH=kL)L7x7oFSSOz֞Ot18k7<$BuEDDR4'N !k”ڔjjjmKZly,Џԥ'QO?%!}r֌6R=ݿH9kS3m͚UOlHFNN>|RRR^իW^{5ڱc8p@>}:EFFґ#G4[r%d2jmmUmڴhڴi4ydzޣѧ6BDt=JOO'9s&-]Tt[3[h ˝2lğYdlݤuzd8kR3/&WWWrpp \NQQQtq1f̚ ╗c1Ƙulc1[c1fxbc1lOlc1ƘM-c1 {% endif %} geopandas-1.2.0/doc/source/_templates/redirect.html000066400000000000000000000004661525650651600223650ustar00rootroot00000000000000{% set redirect = redirects[pagename.split("/")[-1]] %} This page has moved

This page has moved here.

geopandas-1.2.0/doc/source/about.md000066400000000000000000000053761525650651600172020ustar00rootroot00000000000000# About GeoPandas ```{toctree} :maxdepth: 2 :caption: About :hidden: Team Roadmap Citing Logo ``` GeoPandas is an open source project to add support for geographic data to pandas objects. It currently implements `GeoSeries` and `GeoDataFrame` types which are subclasses of `pandas.Series` and `pandas.DataFrame` respectively. GeoPandas objects can act on `shapely` geometry objects and perform geometric operations. GeoPandas is a community-led project written, used and supported by a wide range of people from all around of world of a large variety of backgrounds. Want to get involved in the community? See our [community guidelines](community). GeoPandas will always be 100% open source software, free for all to use and released under the liberal terms of the BSD-3-Clause license. ```{container} button {doc}`Team ` {doc}`Roadmap ` {doc}`Citing ` {doc}`Logo ` ``` GeoPandas is a fiscally sponsored project of NumFOCUS, a nonprofit dedicated to supporting the open-source scientific computing community. If you like GeoPandas and want to support our mission, please consider making a [donation](https://numfocus.org/donate-for-geopandas) to support our efforts. NumFOCUS is a 501(c)(3) non-profit charity in the United States; as such, donations to NumFOCUS are tax-deductible as allowed by law. As with any donation, you should consult with your personal tax adviser or the IRS about your particular tax situation. ```{image} _static/SponsoredProject.svg :alt: numfocus :width: 400px :align: center :target: ``` ## Project history Kelsey Jordahl founded GeoPandas project in 2013 during the Scipy Conference and released a version 0.1.0 in July 2014. In 2016, Joris Van den Bossche took the lead and became the maintainer of the project. Since the beginning, GeoPandas is a BSD-licensed open-source project supported by a [community of contributors](https://github.com/geopandas/geopandas/graphs/contributors) from around the world and is now maintained by a [team](about/team) of core developers. In 2020 GeoPandas became [NumFOCUS Affiliated Project](https://numfocus.org/sponsored-projects/affiliated-projects) and received two [Small Development Grants](https://numfocus.org/programs/sustainability) to support its development. In 2023, GeoPandas became [NumFOCUS Sponsored Project](https://numfocus.org/project/geopandas). ## Timeline - **2013**: Beginning of the development - **2014**: GeoPandas 0.1.0 released - **2020**: GeoPandas became [NumFOCUS Affiliated Project](https://numfocus.org/sponsored-projects/affiliated-projects) - **2023**: GeoPandas became [NumFOCUS Sponsored Project](https://numfocus.org/project/geopandas) geopandas-1.2.0/doc/source/about/000077500000000000000000000000001525650651600166455ustar00rootroot00000000000000geopandas-1.2.0/doc/source/about/citing.md000066400000000000000000000063421525650651600204510ustar00rootroot00000000000000# Citing ## Canonical citation To cite GeoPandas please use the following [software paper](https://doi.org/10.1016/j.compenvurbsys.2026.102495) published in the Computers, Environment and Urban Systems. > Fleischmann, M., Van den Bossche, J., Jordahl, K., Richards, M. J., McBride, J., > Wasserman, J., Ward, B., & Wolf, L. J. (2026). GeoPandas: Fundamental data structures > for vector spatial data in Python. _Computers, Environment and Urban Systems_, 130, > 102495. https://doi.org/10.1016/j.compenvurbsys.2026.102495 BibTeX: ```bibtex @article{fleischmann2026GeoPandas, title = {{{GeoPandas}}: {{Fundamental}} Data Structures for Vector Spatial Data in {{Python}}}, shorttitle = {{{GeoPandas}}}, author = {Fleischmann, Martin and Van den Bossche, Joris and Jordahl, Kelsey and Richards, Matthew John and McBride, James and Wasserman, Jacob and Ward, Brendan and Wolf, Levi John}, year = 2026, month = dec, journal = {Computers, Environment and Urban Systems}, volume = {130}, pages = {102495}, issn = {01989715}, doi = {10.1016/j.compenvurbsys.2026.102495}, urldate = {2026-08-01}, langid = {english}, } ``` ## Citing specific version If you'd like to refer to a specific version, you can use [Zenodo DOI](https://doi.org/10.5281/zenodo.2585848) for each release alongside the canonical citation above. Below is the example of resulting BiBTeX record for GeoPandas 1.1.4 and a reference using APA 6th ed. > Joris Van den Bossche, Kelsey Jordahl, Martin Fleischmann, Matt Richards, James > McBride, Jacob Wasserman, Adrian Garcia Badaracco, Alan D. Snow, Pieter Roggemans, > Brendan Ward, Jeff Tratner, Jeffrey Gerard, Mike Taves, Matthew Perry, carsonfarmer, > Geir Arne Hjelle, Nicholas YS Tan, Ray Bell, rraymondgh, … Sergio Rey. (2026). > geopandas/geopandas: Version 1.1.4 (Version v1.1.4) [Computer software]. Zenodo. > https://doi.org/10.5281/zenodo.20938197 ``` @software{joris_van_den_bossche_2026_20938197, author = {Joris Van den Bossche and Kelsey Jordahl and Martin Fleischmann and Matt Richards and James McBride and Jacob Wasserman and Adrian Garcia Badaracco and Alan D. Snow and Pieter Roggemans and Brendan Ward and Jeff Tratner and Jeffrey Gerard and Mike Taves and Matthew Perry and carsonfarmer and Geir Arne Hjelle and Nicholas YS Tan and Ray Bell and rraymondgh and Micah D. Cochran and Giacomo Caria and Ewout ter Hoeven and Christine P. Chai and Lucas Culbertson and Matt Bartos and Nick Eubank and sangarshanan and John Flavin and Sergio Rey}, title = {geopandas/geopandas: Version 1.1.4}, month = jun, year = 2026, publisher = {Zenodo}, version = {v1.1.4}, doi = {10.5281/zenodo.20938197}, url = {https://doi.org/10.5281/zenodo.20938197}, } ``` geopandas-1.2.0/doc/source/about/logo.md000066400000000000000000000035451525650651600201360ustar00rootroot00000000000000# GeoPandas logo GeoPandas project uses a logo derived from [`pandas` logo](https://pandas.pydata.org/about/citing.html), enclosing it in a globe illustrating the geographic nature of our data. ## Versions We have four versions of our logo: ### Primary logo Use the primary logo in the majority of cases. Use the inverted logo or icon only when necessary. ```{image} ../_static/logo/geopandas_logo.png :alt: geopandas-logo :align: center ``` ### Inverted colors If you want to place the GeoPandas logo on a dark background, use the inverted version. ```{image} ../_static/logo/geopandas_logo_green.png :alt: geopandas-logo-green :align: center ``` ### Icon Although it is possible to use icon independently, we would prefer using the complete variant above. ```{image} ../_static/logo/geopandas_icon.png :alt: geopandas-icon :width: 25% :align: center ``` ### Inverted icon ```{image} ../_static/logo/geopandas_icon_green.png :alt: geopandas-icon-green :width: 25% :align: center ``` ## Download You can download all version in SVG and PNG from [GitHub repository](https://github.com/geopandas/geopandas/tree/main/doc/source/_static/logo). ## Colors Pink and yellow accent colors are shared with `pandas`. ### Green ```{raw} html ``` **HEX:** #139C5A **RGB:** (19, 156, 90) ### Yellow ```{raw} html ``` **HEX:** #FFCA00 **RGB:** (255, 202, 0) ### Pink ```{raw} html ``` **HEX:** #E70488 **RGB:** (231, 4, 136) geopandas-1.2.0/doc/source/about/roadmap.md000066400000000000000000000045121525650651600206140ustar00rootroot00000000000000# Roadmap This page provides an overview of the strategic goals for development of GeoPandas. Some of the tasks may happen sooner given the appropriate funding, other later with no specified date, and some may not happen at all if the implementation proves to be against the will of the community or face technical issues preventing their inclusion in the code base. The current roadmap reflects longer-term vision covering enhancements that should happen in upcoming releases. ## S2 geometry engine GeoPandas uses `shapely` as its geometry engine, which serves as a Python API for `GEOS`. It means that all geometry operations in GeoPandas are planar, using (possibly) projected coordinate reference systems. Some applications focusing on the global context may find planar operations limiting as they come with troubles around anti-meridian and poles. One solution is an implementation of a spherical geometry engine, namely `S2`, that should eliminate these limitations and offer an alternative to `GEOS`. The GeoPandas community is currently working together with the R-spatial community that has already exposed `S2` in an R counterpart of GeoPandas `sf` on Python bindings for `S2`, which GeoPandas should use as a secondary geometry engine. ## Prepared geometries GeoPandas uses spatial indexing for the operations that may benefit from it. Developers can achieve further performance gains using prepared geometries. Preparation creates a spatial index of individual line segments of geometries, greatly enhancing the speed of spatial predicates like `intersects` or `contains`. Given that the preparation has become less computationally expensive in `shapely` 2.0, GeoPandas should expose the preparation to the user but, more importantly, use smart automatic geometry preparation under the hood. ## Static plotting improvements GeoPandas currently covers a broad range of geospatial tasks, from data exploration to advanced analysis. However, one moment may tempt the user to use different software - plotting. GeoPandas can create static maps based on ``matplotlib``, but they are a bit basic at the moment. It isn't straightforward to generate a complex map in a production-quality which can go straight to an academic journal or an infographic. We want to change this and remove barriers which we currently have and make it simple to create beautiful maps. geopandas-1.2.0/doc/source/about/team.md000066400000000000000000000013411525650651600201140ustar00rootroot00000000000000# Team ## Contributors GeoPandas is developed by more than [100 volunteer contributors](https://github.com/geopandas/geopandas/graphs/contributors). ## Core developers - Joris Van den Bossche - **lead maintainer** | [@jorisvandenbossche](https://github.com/jorisvandenbossche) - Martin Fleischmann | [@martinfleis](https://github.com/martinfleis) - James McBride | [@jdmcbr](https://github.com/jdmcbr) - Brendan Ward | [@brendan-ward](https://github.com/brendan-ward) - Levi Wolf | [@ljwolf](https://github.com/ljwolf) - Matt Richards | [@m-richards](https://github.com/m-richards) ## Founder - Kelsey Jordahl | [@kjordahl](https://github.com/kjordahl) ## Alumni developers - Jacob Wasserman | [@jwass](https://github.com/jwass) geopandas-1.2.0/doc/source/community.rst000066400000000000000000000023631525650651600203150ustar00rootroot00000000000000Community --------- GeoPandas is a community-led project written, used and supported by a wide range of people from all around of world of a large variety of backgrounds. Everyone is welcome, each small contribution, no matter if it is a fix of a typo in the documentation, bug report, an idea, or a question, is valuable. As a member of our community, you should adhere to the principles presented in the :doc:`Code of Conduct `. If you'd like to contribute, please read the :doc:`Contributing guide `. It will help you to understand the way GeoPandas development works and us to review your contribution. GeoPandas is a part of the broader Python :doc:`ecosystem `. It depends on a range of great tools, and various packages are built on top of GeoPandas addressing specific needs in geospatial data processing, analysis and visualization. .. container:: button :doc:`Contributing ` :doc:`Code of Conduct ` :doc:`Ecosystem ` .. toctree:: :maxdepth: 2 :caption: Community :hidden: Contributing Code of Conduct Ecosystem geopandas-1.2.0/doc/source/community/000077500000000000000000000000001525650651600175575ustar00rootroot00000000000000geopandas-1.2.0/doc/source/community/code_of_conduct.md000066400000000000000000000032441525650651600232210ustar00rootroot00000000000000# NumFOCUS Code of Conduct You can find the whole document here [https://numfocus.org/code-of-conduct](https://numfocus.org/code-of-conduct). ## The short version NumFOCUS is dedicated to providing a harassment-free community for everyone, regardless of gender, sexual orientation, gender identity and expression, disability, physical appearance, body size, race, or religion. We do not tolerate harassment of community members in any form. Be kind to others. Do not insult or put down others. Behave professionally. Remember that harassment and sexist, racist, or exclusionary jokes are not appropriate for NumFOCUS. All communication should be appropriate for a professional audience including people of many different backgrounds. Sexual language and imagery is not appropriate. Thank you for helping make this a welcoming, friendly community for all. ## Long version You can find the long version of the Code of Conduct on the NumFOCUS website [https://numfocus.org/code-of-conduct](https://numfocus.org/code-of-conduct) ## How to report If you feel that the Code of Conduct has been violated, feel free to submit a report, by using the form: [NumFOCUS Code of Conduct Reporting Form](https://forms.monday.com/forms/f130e8cddb99568fa86cf077b8912a60?r=use1)   ## Who will receive your report Your report will be received and handled by NumFOCUS Code of Conduct Working Group; trained, and experienced contributors with diverse backgrounds. The group is making decisions independently from the project, PyData, NumFOCUS or any other organization. You can learn more about the current group members, as well as the reporting procedure **[HERE](https://numfocus.org/code-of-conduct)** geopandas-1.2.0/doc/source/community/contributing.md000066400000000000000000000000531525650651600226060ustar00rootroot00000000000000``` {include} ../../../CONTRIBUTING.md ``` geopandas-1.2.0/doc/source/community/ecosystem.md000066400000000000000000000412771525650651600221270ustar00rootroot00000000000000# Ecosystem ## GeoPandas dependencies GeoPandas brings together the full capability of `pandas` and the open-source geospatial tools `Shapely`, which brings manipulation and analysis of geometric objects backed by [`GEOS`](https://trac.osgeo.org/geos) library, `pyogrio`, allowing us to read and write geographic data files using [`GDAL`](https://gdal.org), and `pyproj`, a library for cartographic projections and coordinate transformations, which is a Python interface to [`PROJ`](https://proj.org). Furthermore, GeoPandas has several optional dependencies as `mapclassify`, or `geopy`. ### Required dependencies #### [pandas](https://github.com/pandas-dev/pandas) `pandas` is a Python package that provides fast, flexible, and expressive data structures designed to make working with structured (tabular, multidimensional, potentially heterogeneous) and time series data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis / manipulation tool available in any language. It is already well on its way toward this goal. #### [Shapely](https://github.com/Toblerity/Shapely) `Shapely` is a BSD-licensed Python package for manipulation and analysis of planar geometric objects. It is based on the widely deployed `GEOS` (the engine of PostGIS) and `JTS` (from which `GEOS` is ported) libraries. `Shapely` is not concerned with data formats or coordinate systems, but can be readily integrated with packages that are. #### [pyogrio](https://github.com/geopandas/pyogrio) Pyogrio provides a GeoPandas-oriented API to OGR vector data sources, such as ESRI Shapefile, GeoPackage, and GeoJSON. Vector data sources have geometries, such as points, lines, or polygons, and associated records with potentially many columns worth of data. #### [pyproj](https://github.com/pyproj4/pyproj) `pyproj` is a Python interface to `PROJ` (cartographic projections and coordinate transformations library). GeoPandas uses a `pyproj.crs.CRS` object to keep track of the projection of each `GeoSeries` and its `Transformer` object to manage re-projections. ### Optional dependencies #### [mapclassify](https://github.com/pysal/mapclassify) `mapclassify` provides functionality for Choropleth map classification. Currently, fifteen different classification schemes are available, including a highly-optimized implementation of Fisher-Jenks optimal classification. Each scheme inherits a common structure that ensures computations are scalable and supports applications in streaming contexts. #### [geopy](https://github.com/geopy/geopy) `geopy` is a Python client for several popular geocoding web services. `geopy` makes it easy for Python developers to locate the coordinates of addresses, cities, countries, and landmarks across the globe using third-party geocoders and other data sources. #### [matplotlib](https://github.com/matplotlib/matplotlib) `Matplotlib` is a comprehensive library for creating static, animated, and interactive visualizations in Python. Matplotlib produces publication-quality figures in a variety of hardcopy formats and interactive environments across platforms. Matplotlib can be used in Python scripts, the Python and IPython shell, web application servers, and various graphical user interface toolkits. #### [Fiona](https://github.com/Toblerity/Fiona) `Fiona` is `GDAL’s` neat and nimble vector API for Python programmers. Fiona is designed to be simple and dependable. It focuses on reading and writing data in standard Python IO style and relies upon familiar Python types and protocols such as files, dictionaries, mappings, and iterators instead of classes specific to `OGR`. Fiona can read and write real-world data using multi-layered GIS formats and zipped virtual file systems and integrates readily with other Python GIS packages such as `pyproj`, `Rtree`, and `Shapely`. ## GeoPandas ecosystem Various packages are built on top of GeoPandas addressing specific geospatial data processing needs, analysis, and visualization. Below is an incomplete list (in no particular order) of tools which form the GeoPandas-related Python ecosystem. ### Spatial analysis and Machine Learning #### [PySAL](https://github.com/pysal/pysal) `PySAL`, the Python spatial analysis library, is an open source cross-platform library for geospatial data science with an emphasis on geospatial vector data written in Python. `PySAL` is a family of packages, some of which are listed below. ##### [libpysal](https://github.com/pysal/libpysal) `libpysal` provides foundational algorithms and data structures that support the rest of the library. This currently includes the following modules: input/output (`io`), which provides readers and writers for common geospatial file formats; weights (`weights`), which provides the main class to store spatial weights matrices, as well as several utilities to manipulate and operate on them; computational geometry (`cg`), with several algorithms, such as Voronoi tessellations or alpha shapes that efficiently process geometric shapes; and an additional module with example data sets (`examples`). ##### [esda](https://github.com/pysal/esda) `esda` implements methods for the analysis of both global (map-wide) and local (focal) spatial autocorrelation, for both continuous and binary data. In addition, the package increasingly offers cutting-edge statistics about boundary strength and measures of aggregation error in statistical analyses. ##### [segregation](https://github.com/pysal/segregation) `segregation` package calculates over 40 different segregation indices and provides a suite of additional features for measurement, visualization, and hypothesis testing that together represent the state of the art in quantitative segregation analysis. ##### [mgwr](https://github.com/pysal/mgwr) `mgwr` provides scalable algorithms for estimation, inference, and prediction using single- and multi-scale geographically weighted regression models in a variety of generalized linear model frameworks, as well as model diagnostics tools. ##### [tobler](https://github.com/pysal/tobler) `tobler` provides functionality for areal interpolation and dasymetric mapping. `tobler` includes functionality for interpolating data using area-weighted approaches, regression model-based approaches that leverage remotely-sensed raster data as auxiliary information, and hybrid approaches. #### [movingpandas](https://github.com/anitagraser/movingpandas) `MovingPandas` is a package for dealing with movement data. `MovingPandas` implements a `Trajectory` class and corresponding methods based on GeoPandas. A trajectory has a time-ordered series of point geometries. These points and associated attributes are stored in a `GeoDataFrame`. `MovingPandas` implements spatial and temporal data access and analysis functions as well as plotting functions. #### [momepy](https://github.com/martinfleis/momepy) `momepy` is a library for quantitative analysis of urban form - urban morphometrics. It is built on top of `GeoPandas`, `PySAL` and `networkX`. `momepy` aims to provide a wide range of tools for a systematic and exhaustive analysis of urban form. It can work with a wide range of elements, while focused on building footprints and street networks. #### [geosnap](https://github.com/spatialucr/geosnap) `geosnap` makes it easier to explore, model, analyze, and visualize the social and spatial dynamics of neighborhoods. `geosnap` provides a suite of tools for creating socio-spatial datasets, harmonizing those datasets into consistent set of time-static boundaries, modeling bespoke neighborhoods and prototypical neighborhood types, and modeling neighborhood change using classic and spatial statistical methods. It also provides a set of static and interactive visualization tools to help you display and understand the critical information at each step of the process. #### [mesa-geo](https://github.com/Corvince/mesa-geo) `mesa-geo` implements a GeoSpace that can host GIS-based GeoAgents, which are like normal Agents, except they have a shape attribute that is a `Shapely` object. You can use `Shapely` directly to create arbitrary shapes, but in most cases you will want to import your shapes from a file. Mesa-geo allows you to create GeoAgents from any vector data file (e.g. shapefiles), valid GeoJSON objects or a GeoPandas `GeoDataFrame`. #### [Pyspatialml](https://github.com/stevenpawley/Pyspatialml) `Pyspatialml` is a Python module for applying `scikit-learn` machine learning models to 'stacks' of raster datasets. Pyspatialml includes functions and classes for working with multiple raster datasets and performing a typical machine learning workflow consisting of extracting training data and applying the predict or `predict_proba` methods of `scikit-learn` estimators to a stack of raster datasets. Pyspatialml is built upon the `rasterio` Python module for all of the heavy lifting, and is also designed for working with vector data using the `geopandas` module. #### [PyGMI](https://github.com/Patrick-Cole/pygmi) `PyGMI` stands for Python Geoscience Modelling and Interpretation. It is a modelling and interpretation suite aimed at magnetic, gravity and other datasets. ### Visualization #### [hvPlot](https://hvplot.holoviz.org/user_guide/Geographic_Data.html#Geopandas) `hvPlot` provides interactive Bokeh-based plotting for GeoPandas dataframes and series using the same API as the Matplotlib `.plot()` support that comes with GeoPandas. hvPlot makes it simple to pan and zoom into your plots, use widgets to explore multidimensional data, and render even the largest datasets in web browsers using [Datashader](https://datashader.org). #### [contextily](https://github.com/geopandas/contextily) `contextily` is a small Python 3 (3.6 and above) package to retrieve tile maps from the internet. It can add those tiles as basemap to `matplotlib` figures or write tile maps to disk into geospatial raster files. Bounding boxes can be passed in both WGS84 (EPSG:4326) and Spheric Mercator (EPSG:3857). #### [cartopy](https://github.com/SciTools/cartopy) `Cartopy` is a Python package designed to make drawing maps for data analysis and visualisation easy. It features: object oriented projection definitions; point, line, polygon and image transformations between projections; integration to expose advanced mapping in `Matplotlib` with a simple and intuitive interface; powerful vector data handling by integrating shapefile reading with `Shapely` capabilities. #### [bokeh](https://github.com/bokeh/bokeh) `Bokeh` is an interactive visualization library for modern web browsers. It provides elegant, concise construction of versatile graphics, and affords high-performance interactivity over large or streaming datasets. `Bokeh` can help anyone who would like to quickly and easily make interactive plots, dashboards, and data applications. #### [folium](https://github.com/python-visualization/folium) `folium` builds on the data wrangling strengths of the Python ecosystem and the mapping strengths of the `Leaflet.js` library. Manipulate your data in Python, then visualize it in a `Leaflet` map via `folium`. #### [kepler.gl](https://github.com/keplergl/kepler.gl) `Kepler.gl` is a data-agnostic, high-performance web-based application for visual exploration of large-scale geolocation data sets. Built on top of Mapbox GL and `deck.gl`, `kepler.gl` can render millions of points representing thousands of trips and perform spatial aggregations on the fly. #### [geoplot](https://github.com/ResidentMario/geoplot) `geoplot` is a high-level Python geospatial plotting library. It's an extension to `cartopy` and `matplotlib` which makes mapping easy: like `seaborn` for geospatial. It comes with the high-level plotting API, native projection support and compatibility with `matplotlib`. #### [GeoViews](https://github.com/holoviz/geoviews) `GeoViews` is a Python library that makes it easy to explore and visualize any data that includes geographic locations, with native support for GeoPandas dataframes and series objects. It has particularly powerful support for multidimensional meteorological and oceanographic datasets, such as those used in weather, climate, and remote sensing research, but is useful for almost anything that you would want to plot on a map! #### [EarthPy](https://github.com/earthlab/earthpy) `EarthPy` is a python package that makes it easier to plot and work with spatial raster and vector data using open source tools. `Earthpy` depends upon `geopandas` which has a focus on vector data and `rasterio` with facilitates input and output of raster data files. It also requires `matplotlib` for plotting operations. `EarthPy’s` goal is to make working with spatial data easier for scientists. #### [splot](https://github.com/pysal/splot) `splot` provides statistical visualizations for spatial analysis. It methods for visualizing global and local spatial autocorrelation (through Moran scatterplots and cluster maps), temporal analysis of cluster dynamics (through heatmaps and rose diagrams), and multivariate choropleth mapping (through value-by-alpha maps). A high level API supports the creation of publication-ready visualizations #### [legendgram](https://github.com/pysal/legendgram) `legendgram` is a small package that provides "legendgrams" legends that visualize the distribution of observations by color in a given map. These distributional visualizations for map classification schemes assist in analytical cartography and spatial data visualization. #### [buckaroo](https://github.com/paddymul/buckaroo) `buckaroo` is a modern data table for Jupyter that expedites the most common exploratory data analysis tasks. It provides scrollable tables, histograms, and summary stats. Buckaroo supports many DataFrame libraries including `geopandas`. It can display `GeoDataFrame`s as tables, it also supports rendering the Geometry as an SVG in the table. ### Geometry manipulation #### [TopoJSON](https://github.com/mattijn/topojson) `topojson` is a library for creating a TopoJSON encoding of nearly any geographical object in Python. With topojson it is possible to reduce the size of your geographical data, typically by orders of magnitude. It is able to do so through eliminating redundancy through computation of a topology, fixed-precision integer encoding of coordinates, and simplification and quantization of arcs. #### [geocube](https://github.com/corteva/geocube) Tool to convert geopandas vector data into rasterized `xarray` data. ### Data retrieval #### [OSMnx](https://github.com/gboeing/osmnx) `OSMnx` is a Python package that lets you download spatial data from OpenStreetMap and model, project, visualize, and analyze real-world street networks. You can download and model walkable, drivable, or bikeable urban networks with a single line of Python code and then easily analyze and visualize them. You can just as easily download and work with other infrastructure types, amenities/points of interest, building footprints, elevation data, street bearings/orientations, and speed/travel time. #### [pyrosm](https://github.com/HTenkanen/pyrosm) `Pyrosm` is a Python library for reading OpenStreetMap data from Protocolbuffer Binary Format -files (`*.osm.pbf`) into Geopandas `GeoDataFrames`. `Pyrosm` makes it easy to extract various datasets from OpenStreetMap pbf-dumps including e.g. road networks, buildings, Points of Interest (POI), landuse and natural elements. Also fully customized queries are supported which makes it possible to parse the data from OSM with more specific filters. #### [geobr](https://github.com/ipeaGIT/geobr) `geobr` is a computational package to download official spatial data sets of Brazil. The package includes a wide range of geospatial data in geopackage format (like shapefiles but better), available at various geographic scales and for various years with harmonized attributes, projection and topology. #### [cenpy](https://github.com/cenpy-devs/cenpy) An interface to explore and query the US Census API and return Pandas `Dataframes`. This package is intended for exploratory data analysis and draws inspiration from sqlalchemy-like interfaces and `acs.R`. With separate APIs for application developers and folks who only want to get their data quickly & painlessly, `cenpy` should meet the needs of most who aim to get US Census Data into Python. #### [pygadm](https://github.com/12rambau/pygadm) `pygadm` is a Python package that lets you request spatial data from [GADM](https://gadm.org/) without manually downloading any file. This package aims at simplifying the requests of the data using few parameters such as the name and the subdivision levels. Outputs are served as `GeoDataFrame` in `epsg:4326`. ```{admonition} Expand this page Do know a package which should be here? [Let us know](https://github.com/geopandas/geopandas/issues) or [add it by yourself](contributing)! ``` geopandas-1.2.0/doc/source/conf.py000066400000000000000000000376071525650651600170470ustar00rootroot00000000000000# -*- coding: utf-8 -*- # # GeoPandas documentation build configuration file, created by # sphinx-quickstart on Tue Oct 15 08:08:14 2013. # # This file is execfile()d with the current directory set to its containing dir. # # Note that not all possible configuration values are present in this # autogenerated file. # # All configuration values have a default; values that are commented out # serve to show the default. import sys, os import warnings import geodatasets geodatasets.fetch( [ "geoda.chile_labor", "ny.bb", "geoda.malaria", "geoda.chicago_health", "geoda.chicago_commpop", "geoda.groceries", "geoda.natregimes", "geoda.nepal", ] ) sys.path.insert(0, os.path.abspath("../..")) import geopandas # noqa # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. # sys.path.insert(0, os.path.abspath('.')) # -- General configuration ----------------------------------------------------- # If your documentation needs a minimal Sphinx version, state it here. # needs_sphinx = '1.0' # Add any Sphinx extension module names here, as strings. They can be extensions # coming with Sphinx (named 'sphinx.ext.*') or your custom ones. extensions = [ "IPython.sphinxext.ipython_console_highlighting", "IPython.sphinxext.ipython_directive", "sphinx_gallery.load_style", "sphinx.ext.autosummary", "sphinx.ext.intersphinx", "sphinx.ext.autodoc", "sphinx.ext.linkcode", "myst_parser", "nbsphinx", "numpydoc", "sphinx_toggleprompt", "matplotlib.sphinxext.plot_directive", ] # continue doc build and only print warnings/errors in examples ipython_warning_is_error = False ipython_exec_lines = [ # ensure that dataframes are not truncated in the IPython code blocks "import pandas as _pd", '_pd.set_option("display.max_columns", 20)', '_pd.set_option("display.width", 100)', ] # Fix issue with warnings from numpydoc (see discussion in PR #534) numpydoc_show_class_members = False autodoc_typehints = "none" def setup(app): app.add_css_file("custom.css") # may also be a URL # Add any paths that contain templates here, relative to this directory. templates_path = ["_templates"] autosummary_generate = True nbsphinx_execute = "always" nbsphinx_allow_errors = True nbsphinx_kernel_name = "python3" # suppress matplotlib warning in examples warnings.filterwarnings( "ignore", category=UserWarning, message="Matplotlib is currently using agg, which is a" " non-GUI backend, so cannot show the figure.", ) # The suffix of source filenames. source_suffix = [".rst", ".md"] # The encoding of source files. # source_encoding = 'utf-8-sig' # The master toctree document. master_doc = "index" # General information about the project. project = "GeoPandas" copyright = "2013–, GeoPandas developers" # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. import geopandas release = release = geopandas.__version__ version = release if "+" in version: version, remainder = release.split("+") if not remainder.startswith("0"): version = version + ".dev+" + remainder.split(".")[0] # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. # language = None # There are two options for replacing |today|: either, you set today to some # non-false value, then it is used: # today = '' # Else, today_fmt is used as the format for a strftime call. # today_fmt = '%B %d, %Y' # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. exclude_patterns = [] # The reST default role (used for this markup: `text`) to use for all documents. # default_role = None # If true, '()' will be appended to :func: etc. cross-reference text. # add_function_parentheses = True # If true, the current module name will be prepended to all description # unit titles (such as .. function::). # add_module_names = True # If true, sectionauthor and moduleauthor directives will be shown in the # output. They are ignored by default. # show_authors = False # The name of the Pygments (syntax highlighting) style to use. pygments_style = "sphinx" # A list of ignored prefixes for module index sorting. # modindex_common_prefix = [] # -- Options for HTML output --------------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. html_theme = "pydata_sphinx_theme" # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. html_theme_options = { "icon_links": [ { "name": "GitHub", "url": "https://github.com/geopandas/geopandas", "icon": "fab fa-github-square fa-xl", }, ] } # Add any paths that contain custom themes here, relative to this directory. # html_theme_path = [] # The name for this set of Sphinx documents. If None, it defaults to # " v documentation". # html_title = None # A shorter title for the navigation bar. Default is the same as html_title. # html_short_title = None # The name of an image file (relative to this directory) to place at the top # of the sidebar. html_logo = "_static/logo/geopandas_logo_web.svg" # The name of an image file (within the static path) to use as favicon of the # docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 # pixels large. html_favicon = "_static/logo/favicon.png" # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ["_static"] # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, # using the given strftime format. # html_last_updated_fmt = '%b %d, %Y' # If true, SmartyPants will be used to convert quotes and dashes to # typographically correct entities. # html_use_smartypants = True # Custom sidebar templates, maps document names to template names. # html_sidebars = {} # Additional templates that should be rendered to pages, maps page names to # template names. # html_additional_pages = {} # Add redirect for previously existing pages, each item is like `(from_old, to_new)` moved_pages = [ # user guide ("aggregation_with_dissolve", "docs/user_guide/aggregation_with_dissolve"), ("data_structures", "docs/user_guide/data_structures"), ("geocoding", "docs/user_guide/geocoding"), ("geometric_manipulations", "docs/user_guide/geometric_manipulations"), ("indexing", "docs/user_guide/indexing"), ("io", "docs/user_guide/io"), ("mapping", "docs/user_guide/mapping"), ("mergingdata", "docs/user_guide/mergingdata"), ("missing_empty", "docs/user_guide/missing_empty"), ("projections", "docs/user_guide/projections"), ("set_operations", "docs/user_guide/set_operations"), # other ("install", "getting_started/install"), ("reference", "docs/reference"), ("changelog", "docs/changelog"), ("code_of_conduct", "community/code_of_conduct"), ("contributing", "community/contributing"), ] html_additional_pages = {page[0]: "redirect.html" for page in moved_pages} html_context = {"redirects": {old: new for old, new in moved_pages}} # Define the canonical URL if you are using a custom domain on Read the Docs html_baseurl = os.environ.get("READTHEDOCS_CANONICAL_URL", "") # Tell Jinja2 templates the build is running on Read the Docs if os.environ.get("READTHEDOCS", "") == "True": html_context["READTHEDOCS"] = True # If false, no module index is generated. # html_domain_indices = True # If false, no index is generated. # html_use_index = True # If true, the index is split into individual pages for each letter. # html_split_index = False # If true, links to the reST sources are added to the pages. # html_show_sourcelink = True # If true, "Created using Sphinx" is shown in the HTML footer. Default is True. # html_show_sphinx = True # If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. # html_show_copyright = True # If true, an OpenSearch description file will be output, and all pages will # contain a tag referring to it. The value of this option must be the # base URL from which the finished HTML is served. # html_use_opensearch = '' # This is the file name suffix for HTML files (e.g. ".xhtml"). # html_file_suffix = None # Output file base name for HTML help builder. htmlhelp_basename = "GeoPandasdoc" # -- Options for LaTeX output -------------------------------------------------- latex_elements = { # The paper size ('letterpaper' or 'a4paper'). #'papersize': 'letterpaper', # The font size ('10pt', '11pt' or '12pt'). #'pointsize': '10pt', # Additional stuff for the LaTeX preamble. #'preamble': '', } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, author, documentclass [howto/manual]). latex_documents = [ ("index", "GeoPandas.tex", "GeoPandas Documentation", "Kelsey Jordahl", "manual") ] # The name of an image file (relative to this directory) to place at the top of # the title page. # latex_logo = None # For "manual" documents, if this is true, then toplevel headings are parts, # not chapters. # latex_use_parts = False # If true, show page references after internal links. # latex_show_pagerefs = False # If true, show URL addresses after external links. # latex_show_urls = False # Documents to append as an appendix to all manuals. # latex_appendices = [] # If false, no module index is generated. # latex_domain_indices = True # -- Options for manual page output -------------------------------------------- # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [("index", "geopandas", "GeoPandas Documentation", ["Kelsey Jordahl"], 1)] # If true, show URL addresses after external links. # man_show_urls = False # -- Options for Texinfo output ------------------------------------------------ # Grouping the document tree into Texinfo files. List of tuples # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ ( "index", "GeoPandas", "GeoPandas Documentation", "Kelsey Jordahl", "GeoPandas", "One line description of project.", "Miscellaneous", ) ] # Documents to append as an appendix to all manuals. # texinfo_appendices = [] # If false, no module index is generated. # texinfo_domain_indices = True # How to display URL addresses: 'footnote', 'no', or 'inline'. # texinfo_show_urls = 'footnote' nbsphinx_prolog = r""" {% set docname = env.doc2path(env.docname, base=None) %} .. only:: html .. role:: raw-html(raw) :format: html .. note:: | This page was generated from `{{ docname }}`__. | Interactive online version: :raw-html:`Binder badge` __ https://github.com/geopandas/geopandas/blob/main/doc/source/{{ docname }} """ # --Options for sphinx extensions ----------------------------------------------- # connect docs in other projects intersphinx_mapping = { "cartopy": ( "https://cartopy.readthedocs.io/stable/", "https://cartopy.readthedocs.io/stable/objects.inv", ), "contextily": ( "https://contextily.readthedocs.io/en/stable/", "https://contextily.readthedocs.io/en/stable/objects.inv", ), "fiona": ( "https://fiona.readthedocs.io/en/stable/", "https://fiona.readthedocs.io/en/stable/objects.inv", ), "folium": ( "https://python-visualization.github.io/folium/latest", "https://python-visualization.github.io/folium/latest/objects.inv", ), "geoplot": ( "https://residentmario.github.io/geoplot/index.html", "https://residentmario.github.io/geoplot/objects.inv", ), "geopy": ( "https://geopy.readthedocs.io/en/stable/", "https://geopy.readthedocs.io/en/stable/objects.inv", ), "libpysal": ( "https://pysal.org/libpysal/stable/", "https://pysal.org/libpysal/stable/objects.inv", ), "mapclassify": ( "https://pysal.org/mapclassify/", "https://pysal.org/mapclassify/objects.inv", ), "matplotlib": ( "https://matplotlib.org/stable/", "https://matplotlib.org/stable/objects.inv", ), "pandas": ( "https://pandas.pydata.org/pandas-docs/stable/", "https://pandas.pydata.org/pandas-docs/stable/objects.inv", ), "pyarrow": ("https://arrow.apache.org/docs/", None), "pyepsg": ( "https://pyepsg.readthedocs.io/en/stable/", "https://pyepsg.readthedocs.io/en/stable/objects.inv", ), "pyproj": ( "https://pyproj4.github.io/pyproj/stable/", "https://pyproj4.github.io/pyproj/stable/objects.inv", ), "python": ( "https://docs.python.org/3", "https://docs.python.org/3/objects.inv", ), "rasterio": ( "https://rasterio.readthedocs.io/en/stable/", "https://rasterio.readthedocs.io/en/stable/objects.inv", ), "shapely": ( "https://shapely.readthedocs.io/en/stable/", "https://shapely.readthedocs.io/en/stable/objects.inv", ), "branca": ( "https://python-visualization.github.io/branca/", "https://python-visualization.github.io/branca/objects.inv", ), "xyzservices": ( "https://xyzservices.readthedocs.io/en/stable/", "https://xyzservices.readthedocs.io/en/stable/objects.inv", ), "pyogrio": ( "https://pyogrio.readthedocs.io/en/stable/", "https://pyogrio.readthedocs.io/en/stable/objects.inv", ), } # based on pandas implementation with added support of properties def linkcode_resolve(domain, info): """ Determine the URL corresponding to Python object """ import inspect if domain != "py": return None modname = info["module"] fullname = info["fullname"] submod = sys.modules.get(modname) if submod is None: return None obj = submod for part in fullname.split("."): try: with warnings.catch_warnings(): # Accessing deprecated objects will generate noisy warnings warnings.simplefilter("ignore", FutureWarning) obj = getattr(obj, part) except AttributeError: return None try: fn = inspect.getsourcefile(inspect.unwrap(obj)) except TypeError: try: # property fn = inspect.getsourcefile(inspect.unwrap(obj.fget)) except AttributeError: fn = None if not fn: return None try: source, lineno = inspect.getsourcelines(obj) except TypeError: try: # property source, lineno = inspect.getsourcelines(obj.fget) except AttributeError: lineno = None except OSError: lineno = None if lineno: linespec = f"#L{lineno}-L{lineno + len(source) - 1}" else: linespec = "" fn = os.path.relpath(fn, start=os.path.dirname(geopandas.__file__)) if "+" in geopandas.__version__: return ( f"https://github.com/geopandas/geopandas/blob/main/geopandas/{fn}{linespec}" ) else: return ( f"https://github.com/geopandas/geopandas/blob/" f"v{geopandas.__version__}/geopandas/{fn}{linespec}" ) geopandas-1.2.0/doc/source/docs.rst000066400000000000000000000014261525650651600172200ustar00rootroot00000000000000Documentation ------------- The documentation of GeoPandas consists of four parts - :doc:`User Guide ` with explanation of the basic functionality, :doc:`Advanced Guide ` covering topics which assume knowledge of basics, :doc:`Examples `, and :doc:`API reference ` detailing every class, method, function and attribute used implemented by GeoPandas. .. container:: button :doc:`User guide ` :doc:`Advanced guide ` :doc:`Examples ` :doc:`API reference ` .. toctree:: :maxdepth: 2 :caption: Documentation User guide Advanced guide API reference Changelog geopandas-1.2.0/doc/source/docs/000077500000000000000000000000001525650651600164635ustar00rootroot00000000000000geopandas-1.2.0/doc/source/docs/advanced_guide.rst000066400000000000000000000013251525650651600221400ustar00rootroot00000000000000Advanced guide ============== The advanced guide covers advanced usage of GeoPandas. Each page focuses on a single topic and outlines how it is implemented in GeoPandas, with reproducible examples. If you don't know anything about GeoPandas, start with the :doc:`Introduction to GeoPandas <../getting_started/introduction>`. Basic topics can be found in the :doc:`User guide ` and further specification in the :doc:`API reference `. .. note:: This section is currently work in progress. See the available pages below. .. toctree:: :maxdepth: 2 user_guide/spatial_indexing user_guide/missing_empty user_guide/reproject_fiona user_guide/pygeos_to_shapely user_guide/fiona_to_pyogrio geopandas-1.2.0/doc/source/docs/changelog.md000066400000000000000000000000501525650651600207270ustar00rootroot00000000000000``` {include} ../../../CHANGELOG.md ``` geopandas-1.2.0/doc/source/docs/reference.rst000066400000000000000000000011671525650651600211600ustar00rootroot00000000000000.. _reference: API reference ============= The API reference provides an overview of all public objects, functions and methods implemented in GeoPandas. All classes and function exposed in ``geopandas.*`` namespace plus those listed in the reference are public. .. warning:: The ``geopandas.array`` and ``geopandas.base`` modules are private. Stable functionality in such modules is not guaranteed. .. toctree:: :maxdepth: 2 GeoSeries GeoDataFrame Input/output Tools Spatial index Testing geopandas-1.2.0/doc/source/docs/reference/000077500000000000000000000000001525650651600204215ustar00rootroot00000000000000geopandas-1.2.0/doc/source/docs/reference/geodataframe.rst000066400000000000000000000041721525650651600235760ustar00rootroot00000000000000============ GeoDataFrame ============ .. currentmodule:: geopandas A ``GeoDataFrame`` is a tabular data structure that contains at least one ``GeoSeries`` column storing geometry. Constructor ----------- .. autosummary:: :toctree: api/ GeoDataFrame Serialization / IO / conversion ------------------------------- .. autosummary:: :toctree: api/ GeoDataFrame.from_file GeoDataFrame.from_features GeoDataFrame.from_postgis GeoDataFrame.from_arrow GeoDataFrame.to_file GeoDataFrame.to_json GeoDataFrame.to_geo_dict GeoDataFrame.to_parquet GeoDataFrame.to_arrow GeoDataFrame.to_feather GeoDataFrame.to_postgis GeoDataFrame.to_wkb GeoDataFrame.to_wkt Projection handling ------------------- .. autosummary:: :toctree: api/ GeoDataFrame.crs GeoDataFrame.set_crs GeoDataFrame.to_crs GeoDataFrame.estimate_utm_crs Active geometry handling ------------------------ .. autosummary:: :toctree: api/ GeoDataFrame.rename_geometry GeoDataFrame.set_geometry GeoDataFrame.active_geometry_name Aggregating and exploding ------------------------- .. autosummary:: :toctree: api/ GeoDataFrame.dissolve GeoDataFrame.explode Spatial joins ------------- .. autosummary:: :toctree: api/ GeoDataFrame.sjoin GeoDataFrame.sjoin_nearest Overlay operations ------------------ .. autosummary:: :toctree: api/ GeoDataFrame.clip GeoDataFrame.overlay Plotting -------- .. autosummary:: :toctree: api/ GeoDataFrame.explore .. autosummary:: :toctree: api/ :template: accessor_callable.rst GeoDataFrame.plot Spatial index ------------- .. autosummary:: :toctree: api/ GeoDataFrame.sindex GeoDataFrame.has_sindex Indexing -------- .. autosummary:: :toctree: api/ GeoDataFrame.cx Interface --------- .. autosummary:: :toctree: api/ GeoDataFrame.__geo_interface__ GeoDataFrame.iterfeatures All pandas ``DataFrame`` methods are also available, although they may not operate in a meaningful way on the ``geometry`` column. All methods listed in `GeoSeries `__ work directly on an active geometry column of GeoDataFrame. geopandas-1.2.0/doc/source/docs/reference/geoseries.rst000066400000000000000000000113211525650651600231360ustar00rootroot00000000000000========= GeoSeries ========= .. currentmodule:: geopandas Constructor ----------- .. autosummary:: :toctree: api/ GeoSeries General methods and attributes ------------------------------ .. autosummary:: :toctree: api/ GeoSeries.area GeoSeries.boundary GeoSeries.bounds GeoSeries.total_bounds GeoSeries.length GeoSeries.geom_type GeoSeries.offset_curve GeoSeries.distance GeoSeries.hausdorff_distance GeoSeries.frechet_distance GeoSeries.representative_point GeoSeries.exterior GeoSeries.interiors GeoSeries.minimum_bounding_radius GeoSeries.minimum_clearance GeoSeries.x GeoSeries.y GeoSeries.z GeoSeries.m GeoSeries.get_coordinates GeoSeries.count_coordinates GeoSeries.count_geometries GeoSeries.count_interior_rings GeoSeries.set_precision GeoSeries.get_precision GeoSeries.get_geometry Unary predicates ---------------- .. autosummary:: :toctree: api/ GeoSeries.is_closed GeoSeries.is_empty GeoSeries.is_ring GeoSeries.is_simple GeoSeries.is_valid GeoSeries.is_valid_reason GeoSeries.is_valid_coverage GeoSeries.invalid_coverage_edges GeoSeries.has_m GeoSeries.has_z GeoSeries.is_ccw Binary predicates ----------------- .. autosummary:: :toctree: api/ GeoSeries.contains GeoSeries.contains_properly GeoSeries.crosses GeoSeries.disjoint GeoSeries.dwithin GeoSeries.geom_equals GeoSeries.geom_equals_exact GeoSeries.geom_equals_identical GeoSeries.intersects GeoSeries.overlaps GeoSeries.touches GeoSeries.within GeoSeries.covers GeoSeries.covered_by GeoSeries.relate GeoSeries.relate_pattern Set-theoretic methods --------------------- .. autosummary:: :toctree: api/ GeoSeries.clip_by_rect GeoSeries.difference GeoSeries.intersection GeoSeries.symmetric_difference GeoSeries.union Constructive methods and attributes ----------------------------------- .. autosummary:: :toctree: api/ GeoSeries.boundary GeoSeries.buffer GeoSeries.centroid GeoSeries.concave_hull GeoSeries.convex_hull GeoSeries.envelope GeoSeries.extract_unique_points GeoSeries.force_2d GeoSeries.force_3d GeoSeries.make_grid GeoSeries.make_valid GeoSeries.minimum_bounding_circle GeoSeries.maximum_inscribed_circle GeoSeries.minimum_clearance GeoSeries.minimum_clearance_line GeoSeries.minimum_rotated_rectangle GeoSeries.normalize GeoSeries.orient_polygons GeoSeries.remove_repeated_points GeoSeries.reverse GeoSeries.sample_points GeoSeries.segmentize GeoSeries.shortest_line GeoSeries.simplify GeoSeries.simplify_coverage GeoSeries.snap GeoSeries.transform Affine transformations ---------------------- .. autosummary:: :toctree: api/ GeoSeries.affine_transform GeoSeries.rotate GeoSeries.scale GeoSeries.skew GeoSeries.translate Linestring operations --------------------- .. autosummary:: :toctree: api/ GeoSeries.interpolate GeoSeries.line_merge GeoSeries.project GeoSeries.shared_paths Aggregating and exploding ------------------------- .. autosummary:: :toctree: api/ GeoSeries.build_area GeoSeries.constrained_delaunay_triangles GeoSeries.delaunay_triangles GeoSeries.explode GeoSeries.intersection_all GeoSeries.polygonize GeoSeries.union_all GeoSeries.voronoi_polygons Serialization / IO / conversion ------------------------------- .. autosummary:: :toctree: api/ GeoSeries.from_arrow GeoSeries.from_file GeoSeries.from_wkb GeoSeries.from_wkt GeoSeries.from_xy GeoSeries.to_arrow GeoSeries.to_file GeoSeries.to_json GeoSeries.to_wkb GeoSeries.to_wkt Projection handling ------------------- .. autosummary:: :toctree: api/ GeoSeries.crs GeoSeries.set_crs GeoSeries.to_crs GeoSeries.estimate_utm_crs Missing values -------------- .. autosummary:: :toctree: api/ GeoSeries.fillna GeoSeries.isna GeoSeries.notna Overlay operations ------------------ .. autosummary:: :toctree: api/ GeoSeries.clip Plotting -------- .. autosummary:: :toctree: api/ GeoSeries.plot GeoSeries.explore Spatial index ------------- .. autosummary:: :toctree: api/ GeoSeries.sindex GeoSeries.has_sindex Indexing -------- .. autosummary:: :toctree: api/ GeoSeries.cx Interface --------- .. autosummary:: :toctree: api/ GeoSeries.__geo_interface__ Methods of pandas ``Series`` objects are also available, although not all are applicable to geometric objects and some may return a ``Series`` rather than a ``GeoSeries`` result when appropriate. The methods ``isna()`` and ``fillna()`` have been implemented specifically for ``GeoSeries`` and are expected to work correctly. geopandas-1.2.0/doc/source/docs/reference/io.rst000066400000000000000000000007621525650651600215670ustar00rootroot00000000000000============ Input/output ============ .. currentmodule:: geopandas GIS vector files ---------------- .. autosummary:: :toctree: api/ read_file_info list_layers read_file GeoDataFrame.to_file PostGIS ------- .. autosummary:: :toctree: api/ read_postgis GeoDataFrame.to_postgis Feather ------- .. autosummary:: :toctree: api/ read_feather GeoDataFrame.to_feather Parquet ------- .. autosummary:: :toctree: api/ read_parquet GeoDataFrame.to_parquet geopandas-1.2.0/doc/source/docs/reference/sindex.rst000066400000000000000000000014511525650651600224460ustar00rootroot00000000000000============= Spatial index ============= .. currentmodule:: geopandas GeoPandas will use the STRtree implementation provided by the Shapely (``shapely.STRtree``) ``GeoSeries.sindex`` creates a spatial index, which can use the methods and properties documented below. See the User Guide page :doc:`../user_guide/spatial_indexing` for more. Constructor ----------- .. autosummary:: :toctree: api/ GeoSeries.sindex Spatial index object -------------------- The spatial index object returned from :attr:`GeoSeries.sindex` has the following methods: .. currentmodule:: geopandas.sindex.SpatialIndex .. autosummary:: :toctree: api/ intersection is_empty nearest query size valid_query_predicates The spatial index offers the full capability of :class:`shapely.STRtree`.geopandas-1.2.0/doc/source/docs/reference/testing.rst000066400000000000000000000004301525650651600226250ustar00rootroot00000000000000======= Testing ======= .. currentmodule:: geopandas GeoPandas includes specific functions to test its objects. .. autosummary:: :toctree: api/ .. testing.geom_equals .. testing.geom_almost_equals testing.assert_geoseries_equal testing.assert_geodataframe_equal geopandas-1.2.0/doc/source/docs/reference/tools.rst000066400000000000000000000003161525650651600223130ustar00rootroot00000000000000===== Tools ===== .. currentmodule:: geopandas .. autosummary:: :toctree: api/ make_grid sjoin sjoin_nearest overlay clip geocode reverse_geocode tools.collect points_from_xygeopandas-1.2.0/doc/source/docs/user_guide.rst000066400000000000000000000021401525650651600213450ustar00rootroot00000000000000User guide ========== The user guide covers different parts of basic usage of GeoPandas. Each page focuses on a single topic and outlines how it is implemented in GeoPandas, with reproducible examples. If you don't know anything about GeoPandas, start with the :doc:`Introduction to GeoPandas <../getting_started/introduction>`. Advanced topics can be found in the :doc:`Advanced Guide ` and further specification in the :doc:`API Reference `. .. toctree:: :maxdepth: 2 Data structures Reading and writing files Indexing and selecting data Making maps and plots Interactive mapping Projections Geometric manipulations Set operations with overlay Aggregation with dissolve Merging data Geocoding Sampling points How to... geopandas-1.2.0/doc/source/docs/user_guide/000077500000000000000000000000001525650651600206165ustar00rootroot00000000000000geopandas-1.2.0/doc/source/docs/user_guide/aggregation_with_dissolve.rst000066400000000000000000000074551525650651600266150ustar00rootroot00000000000000.. ipython:: python :suppress: import geopandas import matplotlib orig = matplotlib.rcParams['figure.figsize'] matplotlib.rcParams['figure.figsize'] = [orig[0] * 1.5, orig[1] * 1.5] Aggregation with dissolve ============================= Spatial data are often more granular than needed. For example, you might have data on sub-national units, but you're actually interested in studying patterns at the level of countries. In a non-spatial setting, when you need summary statistics of the data, you can aggregate data using the :meth:`~pandas.DataFrame.groupby` function. But for spatial data, you sometimes also need to aggregate geometric features. In the GeoPandas library, you can aggregate geometric features using the :meth:`~geopandas.GeoDataFrame.dissolve` function. :meth:`~geopandas.GeoDataFrame.dissolve` can be thought of as doing three things: (a) it dissolves all the geometries within a given group together into a single geometric feature (using the :meth:`~geopandas.GeoSeries.union_all` method), and (b) it aggregates all the rows of data in a group using :ref:`groupby.aggregate `, and (c) it combines those two results. :meth:`~geopandas.GeoDataFrame.dissolve` Example ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Take example of administrative areas in Nepal. You have districts, which are smaller, and zones, which are larger. A group of districts always compose a single zone. Suppose you are interested in Nepalese zone, but you only have Nepalese district-level data like the `geoda.nepal` dataset included in `geodatasets`. You can easily convert this to a zone-level dataset. First, let's look at the most simple case where you just want zone shapes and names. .. ipython:: python import geodatasets nepal = geopandas.read_file(geodatasets.get_path('geoda.nepal')) nepal = nepal.rename(columns={"name_2": "zone"}) # rename to remember the column nepal[["zone", "geometry"]].head() By default, :meth:`~geopandas.GeoDataFrame.dissolve` will pass ``'first'`` to :ref:`groupby.aggregate `. .. ipython:: python nepal_zone = nepal[['zone', 'geometry']] zones = nepal_zone.dissolve(by='zone') @savefig zones1.png zones.plot(); zones.head() If you are interested in aggregate populations, however, you can pass different functions to the :meth:`~geopandas.GeoDataFrame.dissolve` method to aggregate populations using the ``aggfunc =`` argument: .. ipython:: python nepal_pop = nepal[['zone', 'geometry', 'population']] zones = nepal_pop.dissolve(by='zone', aggfunc='sum') @savefig zones2.png zones.plot(column = 'population', scheme='quantiles', cmap='YlOrRd'); zones.head() .. ipython:: python :suppress: matplotlib.rcParams['figure.figsize'] = orig .. toctree:: :maxdepth: 2 Dissolve arguments ~~~~~~~~~~~~~~~~~~ The ``aggfunc =`` argument defaults to 'first' which means that the first row of attributes values found in the dissolve routine will be assigned to the resultant dissolved geodataframe. However it also accepts other summary statistic options as allowed by :meth:`pandas.groupby ` including: * 'first' * 'last' * 'min' * 'max' * 'sum' * 'mean' * 'median' * function * string function name * list of functions and/or function names, e.g. [np.sum, 'mean'] * dict of axis labels -> functions, function names or list of such. For example, to get the number of countries on each continent, as well as the populations of the largest and smallest country of each, you can aggregate the ``'name'`` column using ``'count'``, and the ``'pop_est'`` column using ``'min'`` and ``'max'``: .. ipython:: python zones = nepal.dissolve( by="zone", aggfunc={ "district": "count", "population": ["min", "max"], }, ) zones.head() geopandas-1.2.0/doc/source/docs/user_guide/categorical.rst000066400000000000000000000107271525650651600236340ustar00rootroot00000000000000.. currentmodule:: geopandas .. ipython:: python :suppress: import geopandas import matplotlib.pyplot as plt import matplotlib plt.close('all') matplotlib.rcParams['savefig.bbox'] = 'tight' Categorical maps ================ Categorical maps visualize distinct categories linked to individual observations. As such, they usually use different colormaps and users have different expectations about their behavior. GeoPandas can create categorical maps natively by passing the column containing those values to the ``column`` argument of the :meth:`~GeoDataFrame.plot` method. The method infers whether the value is categorical based on its dtype. Alternatively, you can force a numeric column to be treated as categorical by passing ``categorical=True``. .. ipython:: python import geodatasets guerry = geopandas.read_file( geodatasets.get_path("geoda.guerry") ) Default behavior ---------------- By default, when GeoPandas detects categorical data, it switches the default colormap to ``"tab10"`` (or ``"tab20"`` for more than 10 unique categories) for better distinction between the classes. .. ipython:: python @savefig guerry.png guerry.plot("Region"); You can request a legend using ``legend=True``, which shows one legend item for each unique category, with a label reflecting the style mapped to the class. Additional keywords to adapt the legend can be passed in a ``legend_kwds`` dictionary. .. ipython:: python @savefig guerry_legend.png guerry.plot("Region", legend=True, legend_kwds={"loc": "lower left"}); Alternatively, you can use Matplotlib's tooling to generate the legend from the figure using :func:`matplotlib.pyplot.legend`. .. ipython:: python guerry.plot("Region") @savefig guerry_legend_plt.png plt.legend(loc="lower left"); Colormap treatment ------------------ While GeoPandas uses a categorical colormap by default, any colormap understood by Matplotlib can be used. GeoPandas attempts to detect whether the colormap is categorical or continuous and adapts the mapping of categories to colors accordingly. With categorical colormaps (those with less than 32 unique colors), it iterates over colors sequentially. For continuous colormaps, it retrieves colors proportionally from the entire extent of the colormap for optimal contrast. .. ipython:: python fig, axs = plt.subplots(1, 2) cmaps = [ "Set2", # categorical "Reds", # continuous ] @savefig guerry_color_mapping.png for cmap, ax in zip(cmaps, axs): guerry.plot("Region", cmap=cmap, ax=ax, legend=True) ax.set_title(cmap) ax.set_axis_off() Mapping styles to categories ---------------------------- However, a common use case is mapping custom colors to individual categories. This can be done using a dictionary with unique categories as keys and colors as values, where colors can be specified in any way understood by matplotlib. .. ipython:: python colors = { 'C': '#f8da20', 'E': '#924b4e', 'N': '#99cfba', 'S': '#332737', 'W': '#aaa08c', } @savefig guerry_custom_cmap.png guerry.plot("Region", cmap=colors, legend=True, legend_kwds={"loc": "lower left"}); Other styles can be mapped in an analogous way. .. ipython:: python hatches = { 'C': '//', 'E': r'\\', 'N': '||', 'S': 'O', 'W': 'o', } edgecolors = { 'C': 'black', 'E': 'black', 'N': 'white', 'S': 'white', 'W': 'white', } @savefig guerry_custom_mapping.png guerry.plot( "Region", cmap=colors, hatch=hatches, edgecolor=edgecolors, linewidth=0, legend=True, legend_kwds={"loc": "lower left"}, ); Another option is to pass an array of the same length as the GeoDataFrame to be mapped to individual geometries. Maps with multiple layers ------------------------- When creating a map with multiple layers, legend entries for all data plotted on the same Axes are merged. You can also see that the legend handles reflect the geometry types and their specific symbology for easier identification. .. ipython:: python ax = guerry.plot("Region", cmap=colors) ax = guerry.set_geometry(guerry.centroid).iloc[:10].plot( "Dprtmnt", ax=ax, cmap="tab10", edgecolor="k", markersize="Wealth", marker="X", ) @savefig guerry_layers.png ax.legend(bbox_to_anchor=(1.45, 1), frameon=False); geopandas-1.2.0/doc/source/docs/user_guide/choropleth.rst000066400000000000000000000137561525650651600235330ustar00rootroot00000000000000.. currentmodule:: geopandas .. ipython:: python :suppress: import geopandas import matplotlib.pyplot as plt import matplotlib plt.close('all') matplotlib.rcParams['savefig.bbox'] = 'tight' Choropleth maps =============== Choropleth maps represent a continuous or ordinal variable using a color assigned to each geometry. GeoPandas can create choropleth maps natively by passing the column containing those values to the ``column`` argument of the :meth:`~GeoDataFrame.plot` method. Loading some example data: .. ipython:: python import geodatasets south = geopandas.read_file( geodatasets.get_path("geoda.south") ) Default behavior ---------------- The dataset contains counties in southern US states, with sociodemographic data associated with each county. To plot the percentage of female-headed households in 1990, pass its column name, ``"FH90"``, to ``column``: .. ipython:: python @savefig south.png south.plot(column='FH90'); If you would like to show a legend, pass ``legend=True``. By default, this will create a colorbar. .. ipython:: python @savefig south_legend.png south.plot(column='FH90', legend=True); The default color mapping stretches the colors from the minimum to the maximum of the observed values. These bounds can be customized with the ``vmin`` and ``vmax`` keywords, which specify the minimum and maximum values respectively. In that case, the colorbar indicates that some values go beyond the extremes. .. ipython:: python @savefig south_vmax.png south.plot(column='FH90', legend=True, vmax=30); Classification schemes ---------------------- The default mapping of values to colors is continuous and linear which may not provide an optimal cartographic visualization of the data. The mapping can be adjusted using the ``scheme`` keyword, which specifies a classification scheme. You can, for example, map colors to quantiles, where each color bin contains the same number of geometries. Note that using ``scheme`` requires the optional ``mapclassify`` dependency. .. ipython:: python @savefig south_quantiles.png south.plot(column='FH90', legend=True, scheme='quantiles'); This changes the mapping of colors but, by default, also changes the style of the legend. This default exists for legacy reasons and can be useful on its own. However, a more suitable option is often to keep the colorbar, which you can request with ``legend_kwds``. .. ipython:: python @savefig south_scheme_cbar.png south.plot( column='FH90', legend=True, scheme='quantiles', legend_kwds={"colorbar": True}, ); There are two options for spacing colors in the colorbar. The default is proportional spacing, where each bin gets a portion of the colorbar proportional to the range of values it represents. When bin edges are densely packed around the same values, this can yield a suboptimal result. In that case, it may be more useful to use uniform spacing, where each bin gets an equal portion of the colorbar and each color has enough space. .. ipython:: python @savefig south_scheme_cbar_uniform.png south.plot( column='FH90', legend=True, scheme='quantiles', legend_kwds={"colorbar": True, "spacing": "uniform"}, ); Most schemes support the ``k`` argument, which indicates the number of bins to use. Larger values are recommended when using a colorbar legend. .. ipython:: python @savefig south_scheme_k.png south.plot( column='FH90', legend=True, scheme='quantiles', k=10, legend_kwds={"colorbar": True}, ); Classification schemes are provided by the `mapclassify `__ package, and any of its supported schemes can be used here. The current options include: * ``"BoxPlot"`` * ``"EqualInterval"`` * ``"FisherJenks"`` * ``"FisherJenksSampled"`` * ``"HeadTailBreaks"`` * ``"JenksCaspall"`` * ``"JenksCaspallForced"`` * ``"JenksCaspallSampled"`` * ``"MaxP"`` * ``"MaximumBreaks"`` * ``"NaturalBreaks"`` * ``"Percentiles"`` * ``"PrettyBreaks"`` * ``"Quantiles"`` * ``"StdMean"`` * ``"UserDefined"`` Different classification schemes can emphasize different patterns in the same data. .. ipython:: python fig, axs = plt.subplots(2, 2) schemes = ["EqualInterval", "Quantiles", "NaturalBreaks", "FisherJenks"] for scheme, ax in zip(schemes, axs.flat): south.plot( column="FH90", scheme=scheme, legend=True, legend_kwds={"colorbar": True}, ax=ax) ax.set_title(scheme) ax.set_axis_off() @savefig schemes.png plt.tight_layout() GeoPandas also supports ``scheme="greedy"``, which uses :func:`mapclassify.greedy` to derive a topological coloring where neighboring geometries do not share the same color. This scheme cannot be used together with ``column`` because it does not classify attribute values. .. ipython:: python @savefig greedy.png south.plot(scheme="greedy", cmap="Set3"); Arguments for the classification scheme can be passed as a dictionary to ``classification_kwds``. See the `mapclassify documentation `__ for details on the available schemes and their parameters. .. ipython:: python @savefig class_kwds.png south.plot( column="FH90", legend=True, scheme="UserDefined", classification_kwds={ "bins": [10, 20, 30], "lowest": 0 }, legend_kwds={"colorbar": True}, ); Colormaps --------- The colormap used in the choropleth map can be any string recognized by Matplotlib or any :class:`matplotlib.colors.Colormap` object. .. ipython:: python fig, axs = plt.subplots(2, 2, sharex=True, sharey=True) cmaps = ["viridis", "cividis", "plasma", "managua"] for cmap, ax in zip(cmaps, axs.flat): south.plot(column='FH90', cmap=cmap, ax=ax, add_labels=False) ax.set_title(cmap) @savefig cmaps.png plt.tight_layout() .. ipython:: python :suppress: plt.close('all') geopandas-1.2.0/doc/source/docs/user_guide/data_structures.rst000066400000000000000000000173631525650651600245760ustar00rootroot00000000000000.. currentmodule:: geopandas .. ipython:: python :suppress: import geopandas import matplotlib orig = matplotlib.rcParams['figure.figsize'] matplotlib.rcParams['figure.figsize'] = [orig[0] * 1.5, orig[1] * 1.5] Data structures ========================================= GeoPandas implements two main data structures, a :class:`GeoSeries` and a :class:`GeoDataFrame`. These are subclasses of :class:`pandas.Series` and :class:`pandas.DataFrame`, respectively. GeoSeries --------- A :class:`GeoSeries` is essentially a vector where each entry in the vector is a set of shapes corresponding to one observation. An entry may consist of only one shape (like a single polygon) or multiple shapes that are meant to be thought of as one observation (like the many polygons that make up the State of Hawaii or a country like Indonesia). GeoPandas has three basic classes of geometric objects (which are actually `Shapely `__ objects): * Points / Multi-Points * Lines / Multi-Lines * Polygons / Multi-Polygons Note that all entries in a :class:`GeoSeries` do not need to be of the same geometric type, although certain export operations will fail if this is not the case. Overview of attributes and methods ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The :class:`GeoSeries` class implements nearly all of the attributes and methods of Shapely objects. When applied to a :class:`GeoSeries`, they will apply elementwise to all geometries in the series. Binary operations can be applied between two :class:`GeoSeries`, in which case the operation is carried out elementwise. The two series will be aligned by matching indices. Binary operations can also be applied to a single geometry, in which case the operation is carried out for each element of the series with that geometry. In either case, a :class:`~pandas.Series` or a :class:`GeoSeries` will be returned, as appropriate. A short summary of a few attributes and methods for GeoSeries is presented here, and a full list can be found in the :doc:`GeoSeries API reference <../reference/geoseries>`. There is also a family of methods for creating new shapes by expanding existing shapes or applying set-theoretic operations like "union" described in :doc:`Geometric manipulations `. Attributes ^^^^^^^^^^^^^^^ * :attr:`~GeoSeries.area`: shape area (units of projection -- see :doc:`projections `) * :attr:`~GeoSeries.bounds`: tuple of max and min coordinates on each axis for each shape * :attr:`~GeoSeries.total_bounds`: tuple of max and min coordinates on each axis for entire GeoSeries * :attr:`~GeoSeries.geom_type`: type of geometry. * :attr:`~GeoSeries.is_valid`: tests if coordinates make a shape that is reasonable geometric shape according to the `Simple Feature Access `_ standard. Basic methods ^^^^^^^^^^^^^^ * :meth:`~GeoSeries.distance`: returns :class:`~pandas.Series` with minimum distance from each entry to ``other`` * :attr:`~GeoSeries.centroid`: returns :class:`GeoSeries` of centroids * :meth:`~GeoSeries.representative_point`: returns :class:`GeoSeries` of points that are guaranteed to be within each geometry. It does **NOT** return centroids. * :meth:`~GeoSeries.to_crs`: change coordinate reference system. See :doc:`projections ` * :meth:`~GeoSeries.plot`: plot :class:`GeoSeries`. See :doc:`mapping `. Relationship tests ^^^^^^^^^^^^^^^^^^^ * :meth:`~GeoSeries.geom_equals_exact`: is shape the same as ``other`` (up to a specified decimal place tolerance) * :meth:`~GeoSeries.contains`: is shape contained within ``other`` * :meth:`~GeoSeries.intersects`: does shape intersect ``other`` GeoDataFrame ------------ A :class:`GeoDataFrame` is a tabular data structure that contains a :class:`GeoSeries`. The most important property of a :class:`GeoDataFrame` is that it always has one :class:`GeoSeries` column that holds a special status - the "active geometry column". When a spatial method is applied to a :class:`GeoDataFrame` (or a spatial attribute like ``area`` is called), these operations will always act on the active geometry column. The active geometry column -- no matter the name of the corresponding :class:`GeoSeries` -- can be accessed through the :attr:`~GeoDataFrame.geometry` attribute (``gdf.geometry``), and the name of the ``geometry`` column can be found by typing ``gdf.geometry.name`` or ``gdf.active_geometry_name``. A :class:`GeoDataFrame` may also contain other columns with geometrical (shapely) objects, but only one column can be the active geometry at a time. To change which column is the active geometry column, use the :meth:`GeoDataFrame.set_geometry` method. An example using the ``geoda.malaria`` dataset from ``geodatasets`` containing the counties of Colombia: .. ipython:: python import geodatasets colombia = geopandas.read_file(geodatasets.get_path('geoda.malaria')) colombia.head() # Plot countries @savefig colombia_borders.png colombia.plot(markersize=.5); Currently, the column named "geometry" with county borders is the active geometry column: .. ipython:: python colombia.geometry.name You can also rename this column to "borders": .. ipython:: python colombia = colombia.rename_geometry('borders') colombia.geometry.name Now, you create centroids and make it the geometry: .. ipython:: python :okwarning: colombia['centroid_column'] = colombia.centroid colombia = colombia.set_geometry('centroid_column') @savefig colombia_centroids.png colombia.plot(); **Note:** A :class:`GeoDataFrame` keeps track of the active column by name, so if you rename the active geometry column, you must also reset the geometry:: gdf = gdf.rename(columns={'old_name': 'new_name'}).set_geometry('new_name') **Note 2:** Somewhat confusingly, by default when you use the :func:`~geopandas.read_file` command, the column containing spatial objects from the file is named "geometry" by default, and will be set as the active geometry column. However, despite using the same term for the name of the column and the name of the special attribute that keeps track of the active column, they are distinct. You can easily shift the active geometry column to a different :class:`GeoSeries` with the :meth:`~GeoDataFrame.set_geometry` command. Further, ``gdf.geometry`` will always return the active geometry column, *not* the column named ``geometry``. If you wish to call a column named "geometry", and a different column is the active geometry column, use ``gdf['geometry']``, not ``gdf.geometry``. Attributes and methods ~~~~~~~~~~~~~~~~~~~~~~ Any of the attributes calls or methods described for a :class:`GeoSeries` will work on a :class:`GeoDataFrame` -- they are just applied to the active geometry column :class:`GeoSeries`. However, :class:`GeoDataFrames ` also have a number few extra methods for: * :doc:`Reading and writing files ` * :ref:`Spatial joins ` * :doc:`Spatial aggregations ` * :doc:`Geocoding ` .. ipython:: python :suppress: matplotlib.rcParams['figure.figsize'] = orig Display options --------------- GeoPandas has an ``options`` attribute with global configuration attributes: .. ipython:: python import geopandas geopandas.options The ``geopandas.options.display_precision`` option can control the number of decimals to show in the display of coordinates in the geometry column. In the ``colombia`` example of above, the default is to show 5 decimals for geographic coordinates: .. ipython:: python colombia['centroid_column'].head() If you want to change this, for example to see more decimals, you can do: .. ipython:: python geopandas.options.display_precision = 9 colombia['centroid_column'].head() geopandas-1.2.0/doc/source/docs/user_guide/fiona_to_pyogrio.rst000066400000000000000000000047071525650651600247260ustar00rootroot00000000000000.. currentmodule:: geopandas .. ipython:: python :suppress: import geopandas Migration from the Fiona to the Pyogrio read/write engine ========================================================= Since version 0.11, GeoPandas started supporting two engines to read and write files: `Fiona `__ and `Pyogrio `__. It became possible to choose the engine using the ``engine=`` parameter in :func:`geopandas.read_file` and :func:`geopandas.GeoDataFrame.to_file`. It became also possible to change the default engine globally with: .. code-block:: python geopandas.options.io_engine = "pyogrio" For Geopandas versions <1.0, GeoPandas defaulted to use Fiona. Starting from GeoPandas version 1.0, the global default has changed from Fiona to Pyogrio. The main reason for this change is performance. Pyogrio is optimized for the use case relevant for GeoPandas: reading and writing in bulk. Because of this, in many cases speedups >5-20x can be observed. This guide outlines the (known) functional differences between both, so you can account for them when switching to Pyogrio. Write an attribute table to a file ---------------------------------- Using the Fiona engine, it was possible to write an attribute table (a table without geometry column) to a file using the ``schema`` parameter to specify that the "geometry" column of a GeoDataFrame should be ignored. With Pyogrio you can write an attribute table by using :func:`pyogrio.write_dataframe` and passing a pandas DataFrame to it: .. code-block:: python >>> import pyogrio >>> df = pd.DataFrame({"data_column": [1, 2, 3]}) >>> pyogrio.write_dataframe(df, "test_attribute_table.gpkg") No support for ``schema`` parameter to write files -------------------------------------------------- Pyogrio does not support specifying the `schema` parameter to write files. This means it is not possible to specify the types of attributes being written explicitly. Writing EMPTY geometries ------------------------ Pyogrio writes EMPTY and None geometries as such to e.g. GPKG files, Fiona writes both as None. .. ipython:: python :okwarning: import shapely gdf = geopandas.GeoDataFrame(geometry=[shapely.Polygon(), None], crs=31370) gdf.to_file("test_fiona.gpkg", engine="fiona") gdf.to_file("test_pyogrio.gpkg", engine="pyogrio") geopandas.read_file("test_fiona.gpkg").head() geopandas.read_file("test_pyogrio.gpkg").head() geopandas-1.2.0/doc/source/docs/user_guide/geocoding.rst000066400000000000000000000036751525650651600233210ustar00rootroot00000000000000.. currentmodule:: geopandas .. ipython:: python :suppress: import geopandas Geocoding ========== GeoPandas supports geocoding (i.e., converting place names to location on Earth) through `geopy`_, an optional dependency of GeoPandas. The following example shows how to get the locations of boroughs in New York City, and plots those locations along with the detailed borough boundary file included within GeoPandas. .. _geopy: http://geopy.readthedocs.io/ .. ipython:: python import geodatasets boros = geopandas.read_file(geodatasets.get_path("nybb")) boros.BoroName boro_locations = geopandas.geocode(boros.BoroName) boro_locations import matplotlib.pyplot as plt fig, ax = plt.subplots() boros.to_crs("EPSG:4326").plot(ax=ax, color="white", edgecolor="black"); @savefig boro_centers_over_bounds.png boro_locations.plot(ax=ax, color="red"); By default, the :func:`~geopandas.geocode` function uses the `Photon geocoding API `__. But a different geocoding service can be specified with the ``provider`` keyword. The argument to ``provider`` can either be a string referencing geocoding services, such as ``'google'``, ``'bing'``, ``'yahoo'``, and ``'openmapquest'``, or an instance of a :mod:`Geocoder ` from :mod:`geopy`. See ``geopy.geocoders.SERVICE_TO_GEOCODER`` for the full list. For many providers, parameters such as API keys need to be passed as ``**kwargs`` in the :func:`~geopandas.geocode` call. For example, to use the OpenStreetMap Nominatim geocoder, you need to specify a user agent: .. code-block:: python geopandas.geocode(boros.BoroName, provider='nominatim', user_agent="my-application") .. attention:: Please consult the Terms of Service for the chosen provider. The example above uses ``'photon'`` (the default), which expects fair usage - extensive usage will be throttled. (`Photon's Terms of Use `_). geopandas-1.2.0/doc/source/docs/user_guide/geometric_manipulations.rst000066400000000000000000000230301525650651600262670ustar00rootroot00000000000000.. _geometric_manipulations: Geometric manipulations ======================== GeoPandas makes available all the tools for geometric manipulations in the `Shapely library `_. Note that documentation for all set-theoretic tools for creating new shapes using the relationship between two different spatial datasets -- like creating intersections, or differences -- can be found at :doc:`Set operations with overlay `. Constructive methods ~~~~~~~~~~~~~~~~~~~~ .. method:: GeoSeries.buffer(distance, resolution=16) Returns a :class:`~geopandas.GeoSeries` of geometries representing all points within a given `distance` of each geometric object. .. attribute:: GeoSeries.boundary Returns a :class:`~geopandas.GeoSeries` of lower dimensional objects representing each geometry's set-theoretic `boundary`. .. attribute:: GeoSeries.centroid Returns a :class:`~geopandas.GeoSeries` of points for each geometric centroid. .. attribute:: GeoSeries.concave_hull Returns a :class:`~geopandas.GeoSeries` of geometries representing the smallest concave `Polygon` containing all the points in each object unless the number of points in the object is less than three. For two points, the concave hull collapses to a `LineString`; for 1, a `Point`. .. attribute:: GeoSeries.convex_hull Returns a :class:`~geopandas.GeoSeries` of geometries representing the smallest convex `Polygon` containing all the points in each object unless the number of points in the object is less than three. For two points, the convex hull collapses to a `LineString`; for 1, a `Point`. .. method:: GeoSeries.constrained_delaunay_triangles Returns a :class:`~geopandas.GeoSeries` with the constrained Delaunay triangulation of polygons. A constrained Delaunay triangulation requires the edges of the input polygon(s) to be in the set of resulting triangle edges. An unconstrained delaunay triangulation only triangulates based on the vertices, hence triangle edges could cross polygon boundaries. .. method:: GeoSeries.delaunay_triangles(tolerance, preserve_topology=True) Returns a :class:`~geopandas.GeoSeries` consisting of polygons (default) or linestrings (`only_edges=True`) representing the computed Delaunay triangulation around the vertices of an input geometry. .. attribute:: GeoSeries.envelope Returns a :class:`~geopandas.GeoSeries` of geometries representing the point or smallest rectangular polygon (with sides parallel to the coordinate axes) that contains each object. .. method:: GeoSeries.extract_unique_points Returns a :class:`~geopandas.GeoSeries` of geometries containing all distinct vertices of each input geometry as a multipoint. .. method:: GeoSeries.offset_curve(distance, quad_segs=8, join_style="round", mitre_limit=5.0) Returns a :class:`~geopandas.GeoSeries` containing a `Linestring` or `MultiLineString` geometry at a distance from the object on its right or its left side. .. method:: GeoSeries.remove_repeated_points Returns a :class:`~geopandas.GeoSeries` containing a copy of the input geometry with repeated points removed. .. method:: GeoSeries.simplify(tolerance, preserve_topology=True) Returns a :class:`~geopandas.GeoSeries` containing a simplified representation of each object. .. method:: GeoSeries.segmentize(max_segment_length) Returns a :class:`~geopandas.GeoSeries` with additional vertices added to line segments based on max_segment_length. .. method:: GeoSeries.union_all() Return a geometry containing the union of all geometries in the :class:`~geopandas.GeoSeries`. Affine transformations ~~~~~~~~~~~~~~~~~~~~~~~~ .. method:: GeoSeries.affine_transform(self, matrix) Transform the geometries of the :class:`~geopandas.GeoSeries` using an affine transformation matrix .. method:: GeoSeries.rotate(self, angle, origin='center', use_radians=False) Rotate the coordinates of the :class:`~geopandas.GeoSeries`. .. method:: GeoSeries.scale(self, xfact=1.0, yfact=1.0, zfact=1.0, origin='center') Scale the geometries of the :class:`~geopandas.GeoSeries` along each (x, y, z) dimension. .. method:: GeoSeries.skew(self, angle, origin='center', use_radians=False) Shear/Skew the geometries of the :class:`~geopandas.GeoSeries` by angles along x and y dimensions. .. method:: GeoSeries.translate(self, xoff=0.0, yoff=0.0, zoff=0.0) Shift the coordinates of the :class:`~geopandas.GeoSeries`. Examples of geometric manipulations ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. sourcecode:: python >>> import geopandas >>> from geopandas import GeoSeries >>> from shapely.geometry import Polygon >>> p1 = Polygon([(0, 0), (1, 0), (1, 1)]) >>> p2 = Polygon([(0, 0), (1, 0), (1, 1), (0, 1)]) >>> p3 = Polygon([(2, 0), (3, 0), (3, 1), (2, 1)]) >>> g = GeoSeries([p1, p2, p3]) >>> g 0 POLYGON ((0 0, 1 0, 1 1, 0 0)) 1 POLYGON ((0 0, 1 0, 1 1, 0 1, 0 0)) 2 POLYGON ((2 0, 3 0, 3 1, 2 1, 2 0)) dtype: geometry .. image:: ../../_static/test.png Some geographic operations return normal pandas objects. The :attr:`~geopandas.GeoSeries.area` property of a :class:`~geopandas.GeoSeries` will return a :class:`pandas.Series` containing the area of each item in the :class:`~geopandas.GeoSeries`: .. sourcecode:: python >>> print(g.area) 0 0.5 1 1.0 2 1.0 dtype: float64 Other operations return GeoPandas objects: .. sourcecode:: python >>> g.buffer(0.5) 0 POLYGON ((-0.3535533905932737 0.35355339059327... 1 POLYGON ((-0.5 0, -0.5 1, -0.4975923633360985 ... 2 POLYGON ((1.5 0, 1.5 1, 1.502407636663901 1.04... dtype: geometry .. image:: ../../_static/test_buffer.png GeoPandas objects also know how to plot themselves. GeoPandas uses `matplotlib`_ for plotting. To generate a plot of a :class:`~geopandas.GeoSeries`, use: .. sourcecode:: python >>> g.plot() GeoPandas also implements alternate constructors that can read any data format recognized by `Pyogrio`_. To read a zip file containing an ESRI shapefile with the `borough boundaries of New York City`_ (provided by the ``geodatasets`` package): .. sourcecode:: python >>> import geodatasets >>> nybb_path = geodatasets.get_path('nybb') >>> boros = geopandas.read_file(nybb_path) >>> boros = boros.set_index('BoroCode').sort_index() >>> boros BoroName Shape_Leng Shape_Area \ BoroCode 1 Manhattan 359299.096471 6.364715e+08 2 Bronx 464392.991824 1.186925e+09 3 Brooklyn 741080.523166 1.937479e+09 4 Queens 896344.047763 3.045213e+09 5 Staten Island 330470.010332 1.623820e+09 geometry BoroCode 1 MULTIPOLYGON (((981219.0557861328 188655.31579... 2 MULTIPOLYGON (((1012821.805786133 229228.26458... 3 MULTIPOLYGON (((1021176.479003906 151374.79699... 4 MULTIPOLYGON (((1029606.076599121 156073.81420... 5 MULTIPOLYGON (((970217.0223999023 145643.33221... .. image:: ../../_static/nyc.png .. sourcecode:: python >>> boros['geometry'].convex_hull BoroCode 1 POLYGON ((977855.4451904297 188082.3223876953,... 2 POLYGON ((1017949.977600098 225426.8845825195,... 3 POLYGON ((988872.8212280273 146772.0317993164,... 4 POLYGON ((1000721.531799316 136681.776184082, ... 5 POLYGON ((915517.6877458114 120121.8812543372,... dtype: geometry .. image:: ../../_static/nyc_hull.png To demonstrate a more complex operation, generate a :class:`~geopandas.GeoSeries` containing 2000 random points: .. sourcecode:: python >>> import numpy as np >>> from shapely.geometry import Point >>> xmin, xmax, ymin, ymax = 900000, 1080000, 120000, 280000 >>> xc = (xmax - xmin) * np.random.random(2000) + xmin >>> yc = (ymax - ymin) * np.random.random(2000) + ymin >>> pts = GeoSeries([Point(x, y) for x, y in zip(xc, yc)]) Now draw a circle with fixed radius around each point: .. sourcecode:: python >>> circles = pts.buffer(2000) You can collapse these circles into a single :class:`MultiPolygon` geometry with .. sourcecode:: python >>> mp = circles.union_all() To extract the part of this geometry contained in each borough, you can just use: .. sourcecode:: python >>> holes = boros['geometry'].intersection(mp) .. image:: ../../_static/holes.png and to get the area outside of the holes: .. sourcecode:: python >>> boros_with_holes = boros['geometry'].difference(mp) .. image:: ../../_static/boros_with_holes.png Note that this can be simplified a bit, since ``geometry`` is available as an attribute on a :class:`~geopandas.GeoDataFrame`, and the :meth:`~geopandas.GeoSeries.intersection` and :meth:`~geopandas.GeoSeries.difference` methods are implemented with the "&" and "-" operators, respectively. For example, the latter could have been expressed simply as ``boros.geometry - mp``. It's easy to do things like calculate the fractional area in each borough that are in the holes: .. sourcecode:: python >>> holes.area / boros.geometry.area BoroCode 1 0.579939 2 0.586833 3 0.608174 4 0.582172 5 0.558075 dtype: float64 .. _matplotlib: http://matplotlib.org .. _pyogrio: http://pyogrio.readthedocs.io/en/latest/ .. _geopy: https://github.com/geopy/geopy .. _geo_interface: https://gist.github.com/sgillies/2217756 .. _borough boundaries of New York City: https://data.cityofnewyork.us/City-Government/Borough-Boundaries/tqmj-j8zm .. toctree:: :maxdepth: 2 geopandas-1.2.0/doc/source/docs/user_guide/how_to.rst000066400000000000000000000025621525650651600226540ustar00rootroot00000000000000.. _how_to: How to... ========= Drop duplicate geometry in all situations ----------------------------------------- Using the standard Pandas :meth:`~pandas.DataFrame.drop_duplicates` function on a geometry column can lead to some duplicate geometries not being dropped, in certain circumstances. When used on a geometry column, the Pandas function compares the WKB of each geometry object. This is sensitive to the orders of various components of the geometry - for example, a line with coordinates in the order left-to-right should be equal to a line with the same coordinates in the order right-to-left, but the WKB representations will be different. The same applies for the order of rings of polygons and parts in multipart geometries. To deal with this problem, use the :meth:`~geopandas.GeoSeries.normalize` method first to order the coordinates in a canonincal form, and then use the standard :meth:`~pandas.DataFrame.drop_duplicates` method:: gdf["geometry"] = gdf.normalize() gdf.drop_duplicates() The effect of the :meth:`~geopandas.GeoSeries.normalize` method can be seen in the following example:: >>> geopandas.GeoSeries([ ... shapely.LineString([(0, 0), (1, 0), (2, 0)]), ... shapely.LineString([(2, 0), (1, 0), (0, 0)]), ... ]).normalize().to_wkt() 0 LINESTRING (0 0, 1 0, 2 0) 1 LINESTRING (0 0, 1 0, 2 0) dtype: object geopandas-1.2.0/doc/source/docs/user_guide/indexing.rst000066400000000000000000000026411525650651600231600ustar00rootroot00000000000000.. currentmodule:: geopandas .. ipython:: python :suppress: import geopandas Indexing and selecting data =========================== GeoPandas inherits the standard pandas_ methods for indexing/selecting data. This includes label based indexing with :attr:`~pandas.DataFrame.loc` and integer position based indexing with :attr:`~pandas.DataFrame.iloc`, which apply to both :class:`GeoSeries` and :class:`GeoDataFrame` objects. For more information on indexing/selecting, see the pandas_ documentation. .. _pandas: http://pandas.pydata.org/pandas-docs/stable/indexing.html In addition to the standard pandas_ methods, GeoPandas also provides coordinate based indexing with the :attr:`~GeoDataFrame.cx` indexer, which slices using a bounding box. Geometries in the :class:`GeoSeries` or :class:`GeoDataFrame` that intersect the bounding box will be returned. Using the ``geoda.chile_labor`` dataset, you can use this functionality to quickly select parts of Chile whose boundaries extend south of the -50 degrees latitude. You can first check the original GeoDataFrame. .. ipython:: python import geodatasets chile = geopandas.read_file(geodatasets.get_path('geoda.chile_labor')) @savefig chile.png chile.plot(figsize=(8, 8)); And then select only the southern part of the country. .. ipython:: python southern_chile = chile.cx[:, :-50] @savefig chile_southern.png southern_chile.plot(figsize=(8, 8)); geopandas-1.2.0/doc/source/docs/user_guide/interactive_mapping.ipynb000066400000000000000000000154521525650651600257200ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "id": "c554e753", "metadata": {}, "source": [ "# Interactive mapping\n", "\n", "Alongside static plots, `geopandas` can create interactive maps based on the [folium](https://python-visualization.github.io/folium/) library.\n", "\n", "Creating maps for interactive exploration mirrors the API of static [plots](../reference/api/geopandas.GeoDataFrame.plot.html) in an [explore()](../reference/api/geopandas.GeoDataFrame.explore.html) method of a GeoSeries or GeoDataFrame.\n", "\n", "Loading some example data:" ] }, { "cell_type": "code", "execution_count": null, "id": "caf2fbd5", "metadata": {}, "outputs": [], "source": [ "import geopandas\n", "import geodatasets\n", "\n", "nybb = geopandas.read_file(geodatasets.get_path(\"nybb\"))\n", "chicago = geopandas.read_file(geodatasets.get_path(\"geoda.chicago_commpop\"))\n", "groceries = geopandas.read_file(geodatasets.get_path(\"geoda.groceries\")).explode(ignore_index=True)" ] }, { "cell_type": "markdown", "id": "56bf1bcf", "metadata": {}, "source": [ "The simplest option is to use `GeoDataFrame.explore()`:" ] }, { "cell_type": "code", "execution_count": null, "id": "6b484ecc", "metadata": {}, "outputs": [], "source": [ "nybb.explore()" ] }, { "cell_type": "markdown", "id": "7a797389", "metadata": {}, "source": [ "Interactive plotting offers largely the same customisation as static one plus some features on top of that. Check the code below which plots a customised choropleth map. You can use `\"BoroName\"` column with NY boroughs names as an input of the choropleth, show (only) its name in the tooltip on hover but show all values on click. You can also pass custom background tiles (either a name supported by folium, a name recognized by `xyzservices.providers.query_name()`, XYZ URL or `xyzservices.TileProvider` object), specify colormap (all supported by `matplotlib`) and specify black outline." ] }, { "cell_type": "markdown", "id": "798bf532", "metadata": {}, "source": [ "
\n", "Note\n", "\n", "Note that the GeoDataFrame needs to have a CRS set if you want to use background tiles.\n", "
" ] }, { "cell_type": "code", "execution_count": null, "id": "94b4ff24", "metadata": {}, "outputs": [], "source": [ "nybb.explore(\n", " column=\"BoroName\", # make choropleth based on \"BoroName\" column\n", " tooltip=\"BoroName\", # show \"BoroName\" value in tooltip (on hover)\n", " popup=True, # show all values in popup (on click)\n", " tiles=\"OpenStreetMap HOT\", # use humanitarian OpenStreetMap tiles\n", " cmap=\"Set1\", # use \"Set1\" matplotlib colormap\n", " style_kwds=dict(color=\"black\"), # use black outline\n", ")" ] }, { "cell_type": "markdown", "id": "5a10291e", "metadata": {}, "source": [ "The `explore()` method returns a `folium.Map` object, which can also be passed directly (as you do with `ax` in `plot()`). You can then use folium functionality directly on the resulting map. In the example below, you can plot two GeoDataFrames on the same map and add layer control using folium. You can also add additional tiles allowing you to change the background directly in the map." ] }, { "cell_type": "code", "execution_count": null, "id": "cba9970b", "metadata": {}, "outputs": [], "source": [ "import folium\n", "\n", "m = chicago.explore(\n", " column=\"POP2010\", # make choropleth based on \"POP2010\" column\n", " scheme=\"naturalbreaks\", # use mapclassify's natural breaks scheme\n", " legend=True, # show legend\n", " k=10, # use 10 bins\n", " tooltip=False, # hide tooltip\n", " popup=[\"POP2010\", \"POP2000\"], # show popup (on-click)\n", " legend_kwds=dict(colorbar=False), # do not use colorbar\n", " name=\"chicago\", # name of the layer in the map\n", ")\n", "\n", "groceries.explore(\n", " m=m, # pass the map object\n", " color=\"red\", # use red color on all points\n", " marker_kwds=dict(radius=5, fill=True), # make marker radius 10px with fill\n", " tooltip=\"Address\", # show \"name\" column in the tooltip\n", " tooltip_kwds=dict(labels=False), # do not show column label in the tooltip\n", " name=\"groceries\", # name of the layer in the map\n", ")\n", "\n", "folium.TileLayer(\"OpenStreetMap HOT\", show=False).add_to(\n", " m\n", ") # use folium to add alternative tiles\n", "folium.LayerControl().add_to(m) # use folium to add layer control\n", "\n", "m # show map" ] }, { "cell_type": "code", "execution_count": null, "id": "4d09db10", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "default", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.14.6" } }, "nbformat": 4, "nbformat_minor": 5 } geopandas-1.2.0/doc/source/docs/user_guide/io.rst000066400000000000000000000273571525650651600217750ustar00rootroot00000000000000.. _io: Reading and writing files ========================= Reading spatial data --------------------- GeoPandas can read almost any vector-based spatial data format including ESRI shapefile, GeoJSON files and more using the :func:`geopandas.read_file` command:: geopandas.read_file(...) which returns a GeoDataFrame object. This is possible because GeoPandas makes use of the massive open-source program called `GDAL/OGR `_ designed to facilitate spatial data transformations, through the Python packages `Pyogrio `_ or `Fiona `_, which both provide bindings to GDAL. Any arguments passed to :func:`geopandas.read_file` after the file name will be passed directly to :func:`pyogrio.read_dataframe` or :func:`fiona.open`, which does the actual data importation. In general, :func:`geopandas.read_file` is pretty smart and should do what you want without extra arguments, but for more help, type:: import pyogrio; help(pyogrio.read_dataframe) import fiona; help(fiona.open) .. note:: For faster data reading, pass ``use_arrow=True`` when using the default pyogrio engine. This can be 2-4 times faster than the default reading behavior and works with all drivers. See `pyogrio.read_dataframe `_ for full details. Note that this requires the ``pyarrow`` dependency to exist in your environment. Among other things, one can explicitly set the driver (shapefile, GeoJSON) with the ``driver`` keyword, or pick a single layer from a multi-layered file with the ``layer`` keyword:: countries_gdf = geopandas.read_file("package.gpkg", layer='countries') If you have a file with multiple layers, you can list them using :func:`geopandas.list_layers`. To read properties of each layer (including layer metadata), you can use :func:`geopandas.read_file_info`. Note that these functions requires Pyogrio. GeoPandas can also load resources directly from a web URL, for example for GeoJSON files from `geojson.xyz `_:: url = "http://d2ad6b4ur7yvpq.cloudfront.net/naturalearth-3.3.0/ne_110m_land.geojson" df = geopandas.read_file(url) You can also load ZIP files that contain your data:: zipfile = "zip:///Users/name/Downloads/cb_2017_us_state_500k.zip" states = geopandas.read_file(zipfile) If the dataset is in a folder in the ZIP file, you have to append its name:: zipfile = "zip:///Users/name/Downloads/gadm36_AFG_shp.zip!data" If there are multiple datasets in a folder in the ZIP file, you also have to specify the filename:: zipfile = "zip:///Users/name/Downloads/gadm36_AFG_shp.zip!data/gadm36_AFG_1.shp" It is also possible to read any file-like objects with a :func:`~os.read` method, such as a file handler (e.g. via built-in :func:`open` function) or :class:`~io.StringIO`:: filename = "test.geojson" file = open(filename) df = geopandas.read_file(file) File-like objects from `fsspec `_ can also be used to read data, allowing for any combination of storage backends and caching supported by that project:: path = "simplecache::http://download.geofabrik.de/antarctica-latest-free.shp.zip" with fsspec.open(path) as file: df = geopandas.read_file(file) You can also read path objects:: import pathlib path_object = pathlib.Path(filename) df = geopandas.read_file(path_object) Using Arrow for faster reading ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ For faster data reading, pass ``use_arrow=True`` when using the default pyogrio engine. This can be 2-4 times faster than the default reading behavior and works with all drivers. See `pyogrio.read_dataframe `_ for full details. It is also possible to enable this by default by setting the environment variable ``PYOGRIO_USE_ARROW=1`` (which will also enable writing data using arrow). Note that this requires the ``pyarrow`` dependency to exist in your environment. Reading subsets of the data ~~~~~~~~~~~~~~~~~~~~~~~~~~~ Since geopandas is powered by GDAL, you can take advantage of pre-filtering when loading in larger datasets. This can be done geospatially with a geometry or bounding box. You can also filter rows loaded with a slice. Read more at :func:`geopandas.read_file`. Geometry filter ^^^^^^^^^^^^^^^ The geometry filter only loads data that intersects with the geometry. .. code-block:: python import geodatasets gdf_mask = geopandas.read_file( geodatasets.get_path("geoda.nyc") ) gdf = geopandas.read_file( geodatasets.get_path("geoda.nyc education"), mask=gdf_mask[gdf_mask.name=="Coney Island"], ) Bounding box filter ^^^^^^^^^^^^^^^^^^^ The bounding box filter only loads data that intersects with the bounding box. .. code-block:: python bbox = ( 1031051.7879884212, 224272.49231459625, 1047224.3104931959, 244317.30894023244 ) gdf = geopandas.read_file( geodatasets.get_path("nybb"), bbox=bbox, ) Row filter ^^^^^^^^^^ Filter the rows loaded in from the file using an integer (for the first n rows) or a slice object. .. code-block:: python gdf = geopandas.read_file( geodatasets.get_path("geoda.nyc"), rows=10, ) gdf = geopandas.read_file( geodatasets.get_path("geoda.nyc"), rows=slice(10, 20), ) Field/column filters ^^^^^^^^^^^^^^^^^^^^ Load in a subset of fields from the file using the ``columns`` keyword (this requires pyogrio or Fiona 1.9+): .. code-block:: python gdf = geopandas.read_file( geodatasets.get_path("geoda.nyc"), columns=["name", "rent2008", "kids2000"], ) Skip loading geometry from the file: .. note:: Returns :obj:`pandas.DataFrame` .. code-block:: python pdf = geopandas.read_file( geodatasets.get_path("geoda.nyc"), ignore_geometry=True, ) Accessing the feature ID (fid) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Some formats (e.g. GeoPackage) store a feature ID (``fid``) per feature that is not included in the GeoDataFrame's columns by default. Pass ``fid_as_index=True`` to use it as the GeoDataFrame's index instead of the default integer index: .. note:: Requires the pyogrio engine. .. code-block:: python gdf = geopandas.read_file( geodatasets.get_path("spdata.columbus"), fid_as_index=True, ) SQL WHERE filter ^^^^^^^^^^^^^^^^ .. versionadded:: 0.12 Load in a subset of data with a `SQL WHERE clause `__. .. note:: Requires Fiona 1.9+ or the pyogrio engine. .. code-block:: python gdf = geopandas.read_file( geodatasets.get_path("geoda.nyc"), where="subborough='Coney Island'", ) Supported drivers / file formats ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ When using pyogrio, all drivers supported by the GDAL installation are enabled, and you can check those with:: import pyogrio; pyogrio.list_drivers() where the values indicate whether reading, writing or both are supported for a given driver. Fiona only exposes a default subset of drivers. To display those, type:: import fiona; fiona.supported_drivers There is a `list of available drivers `_ which are unexposed by default but may be supported (depending on the GDAL-build). You can activate these at runtime by updating the `supported_drivers` dictionary like:: fiona.supported_drivers["NAS"] = "raw" Writing spatial data --------------------- GeoDataFrames can be exported to many different standard formats using the :meth:`geopandas.GeoDataFrame.to_file` method. For a full list of supported formats, type ``import pyogrio; pyogrio.list_drivers()``. In addition, GeoDataFrames can be uploaded to `PostGIS `__ database (starting with GeoPandas 0.8) by using the :meth:`geopandas.GeoDataFrame.to_postgis` method. .. note:: For faster data writing, pass ``use_arrow=True`` when using the default pyogrio engine. This can be 2-4 times faster than the default writing behavior and works with all drivers. See `pyogrio.write_dataframe `_ for full details. Note that this requires the ``pyarrow`` dependency to exist in your environment. .. note:: GeoDataFrame can contain more field types than supported by most of the file formats. For example tuples or lists can be easily stored in the GeoDataFrame, but saving them to e.g. GeoPackage or Shapefile will raise a ValueError. Before saving to a file, they need to be converted to a format supported by a selected driver. .. note:: One GeoDataFrame can contain multiple geometry (GeoSeries) columns, but most standard GIS file formats, e.g. GeoPackage or ESRI Shapefile, support only a single geometry column. To store multiple geometry columns, non-active GeoSeries need to be converted to an alternative representation like well-known text (WKT) or well-known binary (WKB) before saving to file. Alternatively, they can be saved as an Apache (Geo)Parquet or Feather file, both of which support multiple geometry columns natively. **Writing to Shapefile**:: countries_gdf.to_file("countries.shp") **Writing to Shapefile with via Arrow**:: countries_gdf.to_file("countries.shp", use_arrow=True) **Writing to GeoJSON**:: countries_gdf.to_file("countries.geojson", driver='GeoJSON') **Writing to GeoPackage**:: countries_gdf.to_file("package.gpkg", layer='countries', driver="GPKG") cities_gdf.to_file("package.gpkg", layer='cities', driver="GPKG") **Writing with multiple geometry columns**:: countries_gdf["country_center"] = countries_gdf["geometry"].centroid # Line below fails because GeoJSON can't contain multiple geometry columns # countries_gdf.to_file("countries.geojson", driver='GeoJSON') countries_gdf["country_center"] = countries_gdf["country_center"].to_wkt() countries_gdf.to_file("countries.geojson", driver='GeoJSON') For multi-layer formats such as GeoPackage, it is possible to write additional geometry columns to separate layers instead of saving them as WKT or WKB within a single layer. Spatial databases ----------------- GeoPandas can also get data from a PostGIS database using the :func:`geopandas.read_postgis` command. Writing to PostGIS:: from sqlalchemy import create_engine db_connection_url = "postgresql://myusername:mypassword@myhost:5432/mydatabase"; engine = create_engine(db_connection_url) countries_gdf.to_postgis("countries_table", con=engine) Apache Parquet and Feather file formats --------------------------------------- .. versionadded:: 0.8.0 GeoPandas supports writing and reading the Apache Parquet (`GeoParquet `__) and Feather file formats. `Apache Parquet `__ is an efficient, columnar storage format (originating from the Hadoop ecosystem). It is a widely used binary file format for tabular data. The Feather file format is the on-disk representation of the `Apache Arrow `__ memory format, an open standard for in-memory columnar data. The :func:`geopandas.read_parquet`, :func:`geopandas.read_feather`, :meth:`geopandas.GeoDataFrame.to_parquet` and :meth:`geopandas.GeoDataFrame.to_feather` methods enable fast roundtrip from GeoPandas to those binary file formats, preserving the spatial information. .. note:: The GeoParquet specification is developed at: https://github.com/opengeospatial/geoparquet. By default, the latest version is used when writing files, but older versions can be specified using the ``schema_version`` keyword. GeoPandas supports reading files encoded using any GeoParquet version. geopandas-1.2.0/doc/source/docs/user_guide/mapping.rst000066400000000000000000000304771525650651600230160ustar00rootroot00000000000000.. currentmodule:: geopandas .. ipython:: python :suppress: import geopandas import matplotlib.pyplot as plt import matplotlib plt.close('all') matplotlib.rcParams['savefig.bbox'] = 'tight' Mapping and plotting tools ========================== GeoPandas provides a high-level interface to the matplotlib_ library for making maps. Mapping shapes is as easy as using the :meth:`~GeoDataFrame.plot()` method on a :class:`GeoSeries` or :class:`GeoDataFrame`. .. _matplotlib: https://matplotlib.org/stable/ On this page, you can read a quickstart guide covering the most important aspects of plotting. You can further look into dedicated guides to :doc:`choropleth maps ` and :doc:`categorical maps `. Loading some example data: .. ipython:: python import geodatasets chicago = geopandas.read_file( geodatasets.get_path("geoda.chicago_commpop") ) groceries = geopandas.read_file( geodatasets.get_path("geoda.groceries") ) You can now plot those GeoDataFrames: .. ipython:: python # Examine the chicago GeoDataFrame chicago.head() # Basic plot, single color @savefig chicago_singlecolor.png chicago.plot(); Note that in general, any options one can pass to `pyplot `_ in matplotlib_ (or `style options that work for lines `_) can be passed to the :meth:`~GeoDataFrame.plot` method. Choropleth maps --------------- GeoPandas makes it easy to create Choropleth maps (maps where the color of each shape is based on the value of an associated variable). Simply use the plot command with the ``column`` argument set to the column whose values you want used to assign colors. .. ipython:: python :okwarning: # Plot by population @savefig chicago_population.png chicago.plot(column="POP2010"); Creating a legend ~~~~~~~~~~~~~~~~~ When plotting a map, one can enable a legend using the ``legend`` argument: .. ipython:: python # Plot population estimates with an accurate legend @savefig chicago_choro.png chicago.plot(column='POP2010', legend=True); The following example plots the color bar below the map and adds its label using ``legend_kwds``: .. ipython:: python # Plot population estimates with an accurate legend @savefig chicago_horizontal.png chicago.plot( column="POP2010", legend=True, legend_kwds={"label": "Population in 2010", "orientation": "horizontal"}, ); However, the default appearance of the legend and plot axes may not be desirable. One can define the plot axes (with ``ax``) and the legend axes (with ``cax``) and then pass those in to the :meth:`~GeoDataFrame.plot` call. The following example uses ``mpl_toolkits`` to horizontally align the plot axes and the legend axes and change the width: .. ipython:: python # Plot population estimates with an accurate legend import matplotlib.pyplot as plt from mpl_toolkits.axes_grid1 import make_axes_locatable fig, ax = plt.subplots(1, 1) divider = make_axes_locatable(ax) cax = divider.append_axes("bottom", size="5%", pad=0.5) @savefig chicago_cax.png chicago.plot( column="POP2010", ax=ax, legend=True, cax=cax, legend_kwds={"label": "Population in 2010", "orientation": "horizontal"}, ); Choosing colors ~~~~~~~~~~~~~~~ You can also modify the colors used by :meth:`~GeoDataFrame.plot` with the ``cmap`` option. For a full list of colormaps, see `Choosing Colormaps in Matplotlib `_. .. ipython:: python @savefig chicago_red.png chicago.plot(column='POP2010', cmap='OrRd'); To make the color transparent for when you just want to show the boundary, you have two options. One option is to do ``chicago.plot(facecolor="none", edgecolor="black")``. However, this can cause a lot of confusion because ``"none"`` and ``None`` are different in the context of using ``facecolor`` and they do opposite things. ``None`` does the "default behavior" based on matplotlib, and if you use it for ``facecolor``, it actually adds a color. The second option is to use ``chicago.boundary.plot()``. This option is more explicit and clear.: .. ipython:: python @savefig chicago_transparent.png chicago.boundary.plot(); The way color maps are scaled can also be manipulated with the ``scheme`` option (if you have ``mapclassify`` installed, which can be accomplished via ``conda install -c conda-forge mapclassify``). The ``scheme`` option can be set to any scheme provided by mapclassify (e.g. ``'box_plot'``, ``'equal_interval'``, ``'fisher_jenks'``, ``'fisher_jenks_sampled'``, ``'headtail_breaks'``, ``'jenks_caspall'``, ``'jenks_caspall_forced',`` ``'jenks_caspall_sampled'``, ``'max_p_classifier'``, ``'maximum_breaks'``, ``'natural_breaks'``, ``'quantiles'``, ``'percentiles'``, ``'std_mean'`` or ``'user_defined'``). Arguments can be passed in ``classification_kwds`` dict. See the `mapclassify documentation `_ for further details about these map classification schemes. .. ipython:: python @savefig chicago_quantiles.png chicago.plot(column='POP2010', cmap='OrRd', scheme='quantiles'); Missing data ~~~~~~~~~~~~ In some cases one may want to plot data which contains missing values - for some features one simply does not know the value. GeoPandas by defaults ignores such features. .. ipython:: python import numpy as np chicago.loc[np.random.choice(chicago.index, 30), 'POP2010'] = np.nan @savefig missing_vals.png chicago.plot(column='POP2010'); However, passing ``missing_kwds`` one can specify the style and label of features containing None or NaN. .. ipython:: python @savefig missing_vals_grey.png chicago.plot(column='POP2010', missing_kwds={'color': 'lightgrey'}); @savefig missing_vals_hatch.png chicago.plot( column="POP2010", legend=True, scheme="quantiles", figsize=(15, 10), missing_kwds={ "color": "lightgrey", "edgecolor": "red", "hatch": "///", "label": "Missing values", }, ); Background tiles ---------------- Background tiles add contextual web (or local) map imagery below the plotted geometries. Pass ``tiles=True`` to use the default provider (``OpenStreetMap.HOT``). Note that this requires `contextily `__ package to be installed. .. ipython:: python @savefig tiles.png groceries.plot(tiles=True); Alternatively, pass the name of any provider available through `xyzservices `__ or anything else supported by ``contextily``. .. ipython:: python @savefig tiles_osm.png groceries.plot(tiles="OpenStreetMap DE"); Tile providers typically require attribution. GeoPandas adds provider attribution by default when it is available, but you can customize it with ``attr`` or add your own when none is available. For example, if you want to show it only once, rather than in each subplot. .. ipython:: python tiles = [ "OpenStreetMap CAT", "OpenStreetMap DE", ] fig, axs = plt.subplots(1, 2) for ax, t in zip(axs, tiles): groceries.plot(tiles=t, attr="", ax=ax) ax.set_title(t) ax.set_axis_off() @savefig attr.png fig.suptitle("(C) OpenStreetMap contributors, Tiles courtesy of Breton OpenStreetMap Team", y=0.05); By default, GeoPandas forces ``contextily`` to warp the images to match the CRS of the data, which can occasionally result in blurred maps. To avoid that, re-project the data to Web Mercator (EPSG:3857) first. .. ipython:: python @savefig tiles_3857.png groceries.to_crs(epsg=3857).plot(tiles=True); Any other customisation of background tiles shall be done directly with ``contextily`` as illustrated in the :doc:`dedicated example <../../gallery/plotting_basemap_background>`. Other map customizations ------------------------ GeoPandas uses an information stored in the coordinate reference system about units each axis represents and includes them as labels, as shown above. This can be turned off with the ``add_labels`` keyword. .. ipython:: python @savefig labels_off.png ax = chicago.plot(add_labels=False); However, maps usually do not have to have coordinate indication either. You can turn the entire asex off using ``set_axis_off()`` or ``axis("off")`` axis methods. .. ipython:: python ax = chicago.plot() @savefig set_axis_off.png ax.set_axis_off(); Maps with layers ----------------- There are two strategies for making a map with multiple layers -- one more succinct, and one that is a little more flexible. Before combining maps, however, remember to always ensure they share a common CRS (so they will align). .. ipython:: python # Look at groceries # Note use of standard `pyplot` line style options @savefig capitals.png groceries.plot(marker='*', color='green', markersize=5); # Check crs groceries = groceries.to_crs(chicago.crs) # Now you can overlay over the outlines Method 1: let GeoPandas create the matplotlib figure ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The simple way is to let the first call to ``plot`` define the ``Axes`` which can be consumed by subsequent ``plot`` calls via the ``ax`` keyword: .. ipython:: python base = chicago.plot(color='white', edgecolor='black') @savefig groceries_over_chicago_1.png groceries.plot(ax=base, marker='o', color='red', markersize=5); Method 2: Create the matplotlib figure manually ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The other way is to define the ``Axes`` object before and pass it to both. .. ipython:: python fig, ax = plt.subplots() chicago.plot(ax=ax, color='white', edgecolor='black') @savefig groceries_over_chicago_2.png groceries.plot(ax=ax, marker='o', color='red', markersize=5) Legend for layers ~~~~~~~~~~~~~~~~~ You can define the ``label`` of each layer and use it to generate the legend for each layer using matplotlib. .. ipython:: python base = chicago.plot(color='white', edgecolor='black', label="Community") groceries.plot(ax=base, marker='o', color='red', markersize=5, label="Grocery") @savefig groceries_over_chicago_3.png plt.legend(); Control the order of multiple layers in a plot ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ When plotting multiple layers, use ``zorder`` to take control of the order of layers being plotted. The lower the ``zorder`` is, the lower the layer is on the map and vice versa. Without specified ``zorder``, cities (Points) gets plotted below world (Polygons), following the default order based on geometry types. .. ipython:: python ax = groceries.plot(color='k') @savefig zorder_default.png chicago.plot(ax=ax); You can set the ``zorder`` for cities higher than for world to move it of top. .. ipython:: python ax = groceries.plot(color='k', zorder=2) @savefig zorder_set.png chicago.plot(ax=ax, zorder=1); Pandas plots ------------ Plotting methods also allow for different plot styles from pandas along with the default ``geo`` plot. These methods can be accessed using the ``kind`` keyword argument in :meth:`~GeoDataFrame.plot`, and include: * ``geo`` for mapping * ``line`` for line plots * ``bar`` or ``barh`` for bar plots * ``hist`` for histogram * ``box`` for boxplot * ``kde`` or ``density`` for density plots * ``area`` for area plots * ``scatter`` for scatter plots * ``hexbin`` for hexagonal bin plots * ``pie`` for pie plots .. ipython:: python @savefig pandas_line_plot.png chicago.plot(kind="scatter", x="POP2010", y="POP2000") You can also create these other plots using the ``GeoDataFrame.plot.`` accessor methods instead of providing the ``kind`` keyword argument. For example, ``hist``, can be used to plot histograms of population for two different years from the Chicago dataset. .. ipython:: python @savefig pandas_hist_plot.png chicago[["POP2000", "POP2010", "geometry"]].plot.hist(alpha=.4) For more information, see `Chart visualization `_ in the pandas documentation. .. ipython:: python :suppress: import matplotlib.pyplot as plt plt.close('all') Other resources ----------------- .. toctree:: :maxdepth: 2 choropleth categoricalgeopandas-1.2.0/doc/source/docs/user_guide/mergingdata.rst000066400000000000000000000137121525650651600236360ustar00rootroot00000000000000.. currentmodule:: geopandas .. ipython:: python :suppress: import geopandas import pandas as pd Merging data ========================================= There are two ways to combine datasets in GeoPandas -- attribute joins and spatial joins. In an attribute join, a :class:`GeoSeries` or :class:`GeoDataFrame` is combined with a regular :class:`pandas.Series` or :class:`pandas.DataFrame` based on a common variable. This is analogous to normal merging or joining in *pandas*. In a spatial join, observations from two :class:`GeoSeries` or :class:`GeoDataFrame` are combined based on their spatial relationship to one another. In the following examples, these datasets are used: .. ipython:: python import geodatasets chicago = geopandas.read_file(geodatasets.get_path("geoda.chicago_commpop")) groceries = geopandas.read_file(geodatasets.get_path("geoda.groceries")) # For attribute join chicago_shapes = chicago[['geometry', 'NID']] chicago_names = chicago[['community', 'NID']] # For spatial join chicago = chicago[['geometry', 'community']].to_crs(groceries.crs) Appending --------- Appending :class:`GeoDataFrame` and :class:`GeoSeries` uses pandas :func:`~pandas.concat` function. Keep in mind, that appended geometry columns needs to have the same CRS. .. ipython:: python # Appending GeoSeries joined = pd.concat([chicago.geometry, groceries.geometry]) # Appending GeoDataFrames douglas = chicago[chicago.community == 'DOUGLAS'] oakland = chicago[chicago.community == 'OAKLAND'] douglas_oakland = pd.concat([douglas, oakland]) Attribute joins ---------------- Attribute joins are accomplished using the :meth:`~pandas.DataFrame.merge` method. In general, it is recommended to use the ``merge()`` method called from the spatial dataset. With that said, the stand-alone :func:`pandas.merge` function will work if the :class:`GeoDataFrame` is in the ``left`` argument; if a :class:`~pandas.DataFrame` is in the ``left`` argument and a :class:`GeoDataFrame` is in the ``right`` position, the result will no longer be a :class:`GeoDataFrame`. For example, consider the following merge that adds full names to a :class:`GeoDataFrame` that initially has only area ID for each geometry by merging it with a :class:`~pandas.DataFrame`. .. ipython:: python # `chicago_shapes` is GeoDataFrame with community shapes and area IDs chicago_shapes.head() # `chicago_names` is DataFrame with community names and area ID chicago_names.head() # Merge with `merge` method on shared variable (area ID): chicago_shapes = chicago_shapes.merge(chicago_names, on='NID') chicago_shapes.head() .. _mergingdata.spatial-joins: Spatial joins ---------------- In a spatial join, two geometry objects are merged based on their spatial relationship to one another. .. ipython:: python # One GeoDataFrame of communities, one of grocery stores. # Want to merge to get each grocery's community. chicago.head() groceries.head() # Execute spatial join groceries_with_community = groceries.sjoin(chicago, how="inner", predicate='intersects') groceries_with_community.head() GeoPandas provides two spatial-join functions: - :meth:`GeoDataFrame.sjoin`: joins based on binary predicates (intersects, contains, etc.) - :meth:`GeoDataFrame.sjoin_nearest`: joins based on proximity, with the ability to set a maximum search radius. .. note:: For historical reasons, both methods are also available as top-level functions :func:`sjoin` and :func:`sjoin_nearest`. It is recommended to use methods as the functions may be deprecated in the future. Binary predicate joins ~~~~~~~~~~~~~~~~~~~~~~ Binary predicate joins are available via :meth:`GeoDataFrame.sjoin`. :meth:`GeoDataFrame.sjoin` has two core arguments: ``how`` and ``predicate``. **predicate** The ``predicate`` argument specifies how GeoPandas decides whether or not to join the attributes of one object to another, based on their geometric relationship. The values for ``predicate`` correspond to the names of geometric binary predicates and depend on the spatial index implementation. The default spatial index in GeoPandas currently supports the following values for ``predicate`` which are defined in the `Shapely documentation `__: * `intersects` * `contains` * `within` * `touches` * `crosses` * `overlaps` **how** The `how` argument specifies the type of join that will occur and which geometry is retained in the resultant :class:`GeoDataFrame`. It accepts the following options: * ``left``: use the index from the first (or `left_df`) :class:`GeoDataFrame` that you provide to :meth:`GeoDataFrame.sjoin`; retain only the `left_df` geometry column * ``right``: use index from second (or `right_df`); retain only the `right_df` geometry column * ``inner``: use intersection of index values from both :class:`GeoDataFrame`; retain only the `left_df` geometry column Note more complicated spatial relationships can be studied by combining geometric operations with spatial join. To find all polygons within a given distance of a point, for example, one can first use the :meth:`~geopandas.GeoSeries.buffer` method to expand each point into a circle of appropriate radius, then intersect those buffered circles with the polygons in question. Nearest joins ~~~~~~~~~~~~~ Proximity-based joins can be done via :meth:`GeoDataFrame.sjoin_nearest`. :meth:`GeoDataFrame.sjoin_nearest` shares the ``how`` argument with :meth:`GeoDataFrame.sjoin`, and includes two additional arguments: ``max_distance`` and ``distance_col``. **max_distance** The ``max_distance`` argument specifies a maximum search radius for matching geometries. This can have a considerable performance impact in some cases. If you can, it is highly recommended that you use this parameter. **distance_col** If set, the resultant GeoDataFrame will include a column with this name containing the computed distances between an input geometry and the nearest geometry. geopandas-1.2.0/doc/source/docs/user_guide/missing_empty.rst000066400000000000000000000125131525650651600242410ustar00rootroot00000000000000.. currentmodule:: geopandas .. ipython:: python :suppress: import geopandas .. _missing-empty: Missing and empty geometries ============================ GeoPandas supports, just like in pandas, the concept of missing values (NA or null values). But for geometry values, there is an additional concept of empty geometries: - **Empty geometries** are actual geometry objects but that have no coordinates (and thus also no area, for example). They can for example originate from taking the intersection of two polygons that have no overlap. The scalar object (when accessing a single element of a GeoSeries) is still a Shapely geometry object. - **Missing geometries** are unknown values in a GeoSeries. They will typically be propagated in operations (for example in calculations of the area or of the intersection), or ignored in reductions such as :meth:`~GeoSeries.union_all`. The scalar object (when accessing a single element of a GeoSeries) is the Python ``None`` object. .. warning:: Starting from GeoPandas v0.6.0, those two concepts are more consistently separated. See :ref:`below ` for more details on what changed compared to earlier versions. Consider the following example GeoSeries with one polygon, one missing value and one empty polygon: .. ipython:: python from shapely.geometry import Polygon s = geopandas.GeoSeries([Polygon([(0, 0), (1, 1), (0, 1)]), None, Polygon([])]) s In spatial operations, missing geometries will typically propagate (be missing in the result as well), while empty geometries are treated as a geometry and the result will depend on the operation: .. ipython:: python s.area s.union(Polygon([(0, 0), (0, 1), (1, 1), (1, 0)])) s.intersection(Polygon([(0, 0), (0, 1), (1, 1), (1, 0)])) The :meth:`GeoSeries.isna` method will only check for missing values and not for empty geometries: .. ipython:: python :okwarning: s.isna() On the other hand, if you want to know which values are empty geometries, you can use the :attr:`GeoSeries.is_empty` attribute: .. ipython:: python s.is_empty To get only the actual geometry objects that are neither missing nor empty, you can use a combination of both: .. ipython:: python :okwarning: s.is_empty | s.isna() s[~(s.is_empty | s.isna())] .. _missing-empty.changes-0.6.0: Changes since GeoPandas v0.6.0 ------------------------------ In GeoPandas v0.6.0, the missing data handling was refactored and made more consistent across the library. Historically, missing ("NA") values in a GeoSeries could be represented by empty geometric objects, in addition to standard representations such as ``None`` and ``np.nan``. At least, this was the case in :meth:`GeoSeries.isna` or when a GeoSeries got aligned in geospatial operations. But, other methods like :meth:`~GeoSeries.dropna` and :meth:`~GeoSeries.fillna` did not follow this approach and did not consider empty geometries as missing. In GeoPandas v0.6.0, the most important change is :meth:`GeoSeries.isna` no longer treating empty as missing: * Using the small example from above, the old behaviour treated both the empty as missing geometry as "missing": .. code-block:: python >>> s 0 POLYGON ((0 0, 1 1, 0 1, 0 0)) 1 None 2 GEOMETRYCOLLECTION EMPTY dtype: object >>> s.isna() 0 False 1 True 2 True dtype: bool * Starting from GeoPandas v0.6.0, it will now only see actual missing values as missing: .. ipython:: python :okwarning: s.isna() For now, when ``isna()`` is called on a GeoSeries with empty geometries, a warning is raised to alert the user of the changed behaviour with an indication how to solve this. Additionally, the behaviour of :meth:`GeoSeries.align` changed to use missing values instead of empty geometries to fill non-matching indexes. Consider the following small toy example: .. ipython:: python from shapely.geometry import Point s1 = geopandas.GeoSeries([Point(0, 0), Point(1, 1)], index=[0, 1]) s2 = geopandas.GeoSeries([Point(1, 1), Point(2, 2)], index=[1, 2]) s1 s2 * Previously, the ``align`` method would use empty geometries to fill values: .. code-block:: python >>> s1_aligned, s2_aligned = s1.align(s2) >>> s1_aligned 0 POINT (0 0) 1 POINT (1 1) 2 GEOMETRYCOLLECTION EMPTY dtype: object >>> s2_aligned 0 GEOMETRYCOLLECTION EMPTY 1 POINT (1 1) 2 POINT (2 2) dtype: object This method is used under the hood when performing spatial operations on mis-aligned GeoSeries objects: .. code-block:: python >>> s1.intersection(s2) 0 GEOMETRYCOLLECTION EMPTY 1 POINT (1 1) 2 GEOMETRYCOLLECTION EMPTY dtype: object * Starting from GeoPandas v0.6.0, :meth:`GeoSeries.align` will use missing values to fill in the non-aligned indices, to be consistent with the behaviour in pandas: .. ipython:: python s1_aligned, s2_aligned = s1.align(s2) s1_aligned s2_aligned This has the consequence that spatial operations will also use missing values instead of empty geometries, which can have a different behaviour depending on the spatial operation: .. ipython:: python :okwarning: s1.intersection(s2) geopandas-1.2.0/doc/source/docs/user_guide/projections.rst000066400000000000000000000414031525650651600237110ustar00rootroot00000000000000.. currentmodule:: geopandas .. ipython:: python :suppress: import geopandas Projections =========== Coordinate reference systems ----------------------------- The coordinate reference system (CRS) is important because the geometric shapes in a GeoSeries or GeoDataFrame object are simply a collection of coordinates in an arbitrary space. A CRS tells Python how those coordinates relate to places on the Earth. For reference codes of the most commonly used projections, see `spatialreference.org `_. The same CRS can often be referred to in many ways. For example, one of the most commonly used CRS is the WGS84 latitude-longitude projection. This can be referred to using the authority code ``"EPSG:4326"``. GeoPandas can accept anything accepted by :meth:`pyproj.CRS.from_user_input() `: - CRS WKT string - An authority string (i.e. "epsg:4326") - An EPSG integer code (i.e. 4326) - A :class:`pyproj.CRS ` - An object with a to_wkt method. - PROJ string - Dictionary of PROJ parameters - PROJ keyword arguments for parameters - JSON string with PROJ parameters For reference, a few very common projections and their EPSG codes: * WGS84 Latitude/Longitude: EPSG:4326 * UTM Zones (North): EPSG:32633 * UTM Zones (South): EPSG:32733 What is the best format to store the CRS information? ----------------------------------------------------- Generally, WKT or SRID's are preferred over PROJ strings as they can contain more information about a given CRS. Conversions between WKT and PROJ strings will in most cases cause a loss of information, potentially leading to erroneous transformations. If possible WKT2 should be used. For more details, see `What is the best format for describing coordinate reference systems `_. Setting a projection ---------------------- There are two relevant operations for projections: setting a projection and re-projecting. Setting a projection may be necessary when for some reason GeoPandas has coordinate data (x-y values), but no information about how those coordinates refer to locations in the real world. Setting a projection is how one tells GeoPandas how to interpret coordinates. If no CRS is set, GeoPandas geometry operations will still work, but coordinate transformations will not be possible and exported files may not be interpreted correctly by other software. Be aware that **most of the time** you don't have to set a projection. Data loaded from a reputable source (using the :func:`geopandas.read_file()` command) *should* always include projection information. You can see an objects current CRS through the :attr:`GeoSeries.crs` attribute. From time to time, however, you may get data that does not include a projection. In this situation, you have to set the CRS so GeoPandas knows how to interpret the coordinates. For example, if you convert a spreadsheet of latitudes and longitudes into a GeoSeries by hand, you would set the projection by passing the WGS84 latitude-longitude CRS to the :meth:`GeoSeries.set_crs` method (or by setting the :attr:`GeoSeries.crs` attribute): .. sourcecode:: python my_geoseries = my_geoseries.set_crs("EPSG:4326") my_geoseries = my_geoseries.set_crs(epsg=4326) Re-projecting ---------------- Re-projecting is the process of changing the representation of locations from one coordinate system to another. All projections of locations on the Earth into a two-dimensional plane have distortions. See `Which projection is best `_ for more information. The projection that is best for your application may be different from the projection associated with the data you import. In these cases, data can be re-projected using the :meth:`GeoDataFrame.to_crs` command: .. ipython:: python import geodatasets # load example data usa = geopandas.read_file(geodatasets.get_path('geoda.natregimes')) # Check original projection # (it's Plate Carrée! x-y are long and lat) usa.crs # Visualize ax = usa.plot() @savefig usa_starting.png ax.set_title("WGS84 (lat/lon)"); # Reproject to Albers contiguous USA usa = usa.to_crs("ESRI:102003") ax = usa.plot() @savefig usa_reproj.png ax.set_title("NAD 1983 Albers contiguous USA"); Projection for multiple geometry columns ---------------------------------------- GeoPandas 0.8 implements support for different projections assigned to different geometry columns of the same GeoDataFrame. The projection is now stored together with geometries per column (directly on the GeometryArray level). Note that if GeometryArray has an assigned projection, it cannot be overridden by an another inconsistent projection during the creation of a GeoSeries or GeoDataFrame: .. code-block:: python >>> array.crs Name: WGS 84 Axis Info [ellipsoidal]: - Lat[north]: Geodetic latitude (degree) - Lon[east]: Geodetic longitude (degree) ... >>> GeoSeries(array, crs=4326) # crs=4326 is okay, as it matches the existing CRS >>> GeoSeries(array, crs=3395) # crs=3395 is forbidden as array already has CRS ValueError: CRS mismatch between CRS of the passed geometries and 'crs'. Use 'GeoSeries.set_crs(crs, allow_override=True)' to overwrite CRS or 'GeoSeries.to_crs(crs)' to reproject geometries. GeoSeries(array, crs=3395).crs If you want to overwrite the projection, you can then assign it to the GeoSeries manually or re-project geometries to the target projection using either ``GeoSeries.set_crs(epsg=3395, allow_override=True)`` or ``GeoSeries.to_crs(epsg=3395)``. All GeometryArray-based operations preserve projection; however, if you loop over a column containing geometry, this information might be lost. Upgrading to GeoPandas 0.7 with pyproj > 2.2 and PROJ > 6 --------------------------------------------------------- Starting with GeoPandas 0.7, the `.crs` attribute of a GeoSeries or GeoDataFrame stores the CRS information as a :class:`pyproj.CRS `, and no longer as a proj4 string or dict. Before, you might have seen this: .. code-block:: python >>> gdf.crs {'init': 'epsg:4326'} while now you will see something like this: .. code-block:: python >>> gdf.crs Name: WGS 84 Axis Info [ellipsoidal]: - Lat[north]: Geodetic latitude (degree) - Lon[east]: Geodetic longitude (degree) ... >>> type(gdf.crs) pyproj.crs.CRS This gives a better user interface and integrates improvements from pyproj and PROJ 6, but might also require some changes in your code. See `this blogpost `__ for some more information. The subsections below cover different possible migration issues. See the `pyproj documentation `__ for more on the :class:`pyproj.CRS ` object. Importing data from files ^^^^^^^^^^^^^^^^^^^^^^^^^ When reading geospatial files with :func:`geopandas.read_file`, things should mostly work out of the box. For example, reading the example countries dataset yields a proper CRS: .. ipython:: python df = geopandas.read_file(geodatasets.get_path('naturalearth.land')) df.crs However, in certain cases (with older CRS formats), the resulting CRS object might not be fully as expected. See the :ref:`section below ` for possible reasons and how to solve it. Manually specifying the CRS ^^^^^^^^^^^^^^^^^^^^^^^^^^^ When specifying the CRS manually in your code (e.g., because your data has not yet a CRS, or when converting to another CRS), this might require a change in your code. **"init" proj4 strings/dicts** Currently, a lot of people (and also the GeoPandas docs showed that before) specify the EPSG code using the "init" proj4 string: .. code-block:: python ## OLD GeoDataFrame(..., crs={'init': 'epsg:4326'}) # or gdf.crs = {'init': 'epsg:4326'} # or gdf.to_crs({'init': 'epsg:4326'}) The above will now raise a deprecation warning from pyproj, and instead of the "init" proj4 string, you should use only the EPSG code itself as follows: .. code-block:: python ## NEW GeoDataFrame(..., crs="EPSG:4326") # or gdf.crs = "EPSG:4326" # or gdf.to_crs("EPSG:4326") **proj4 strings/dicts** Although a full proj4 string is not deprecated (as opposed to the "init" string above), it is still recommended to change it with an EPSG code if possible. For example, *if* you know the EPSG code for the projection you are using, instead of: .. code-block:: python gdf.crs = "+proj=laea +lat_0=45 +lon_0=-100 +x_0=0 +y_0=0 +a=6370997 +b=6370997 +units=m +no_defs" this is recommended: .. code-block:: python gdf.crs = "EPSG:2163" One possible way to find out the EPSG code is using pyproj for this: .. code-block:: python >>> import pyproj >>> crs = pyproj.CRS("+proj=laea +lat_0=45 +lon_0=-100 +x_0=0 +y_0=0 +a=6370997 +b=6370997 +units=m +no_defs") >>> crs.to_epsg() 2163 (you might need to set the ``min_confidence`` keyword of ``to_epsg`` to a lower value if the match is not perfect) Further, on websites such as `Spatial Reference `__ and `epsg.org `__ the descriptions of many CRS can be found including their EPSG codes and proj4 string definitions. **Other formats** Next to the EPSG code mentioned above, there are also other ways to specify the CRS: an actual :class:`pyproj.CRS ` object, a WKT string, a PROJ JSON string, etc. Anything that is accepted by :meth:`pyproj.CRS.from_user_input() ` can by specified to the ``crs`` keyword/attribute in GeoPandas. Also compatible CRS objects, such as from the :mod:`rasterio` package, can be passed directly to GeoPandas. The axis order of a CRS ^^^^^^^^^^^^^^^^^^^^^^^ Starting with PROJ 6 / pyproj 2, the axis order of the official EPSG definition is honoured. For example, when using geographic coordinates (degrees of longitude and latitude) in the standard EPSG:4326, the CRS will look like: .. code-block:: python >>> pyproj.CRS(3EPSG:4326") ... Axis Info [ellipsoidal]: - Lat[north]: Geodetic latitude (degree) - Lon[east]: Geodetic longitude (degree) ... This mentions the order as (lat, lon), as that is the official order of coordinates in EPSG:4326. In GeoPandas, however, the coordinates are always stored as (x, y), and thus as (lon, lat) order, regardless of the CRS (i.e. the "traditional" order used in GIS). When reprojecting, GeoPandas and pyproj will under the hood take care of this difference in axis order, so the user doesn't need to care about this. .. _unrecognized-crs-reasons: Why is it not properly recognizing my CRS? ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ There are many file sources and CRS definitions out there "in the wild" that might have a CRS description that does not fully conform to the new standards of PROJ > 6 (proj4 strings, older WKT formats, ...). In such cases, you will get a :class:`pyproj.CRS ` object that might not be fully what you expected (e.g. not equal to the expected EPSG code). Below is a list of a few possible cases. I get a "Bound CRS"? ~~~~~~~~~~~~~~~~~~~~ Some CRS definitions include a *"towgs84" clause*, which can give problems in recognizing the actual CRS. For example, both the proj4 and WKT representations for EPSG:31370 (the local projection used in Belgium) as can be found at `EPSG:31370 `__ include this clause. When taking one of those definitions from that site, and creating a CRS object: .. code-block:: python >>> import pyproj >>> crs = pyproj.CRS("+proj=lcc +lat_1=51.16666723333333 +lat_2=49.8333339 +lat_0=90 +lon_0=4.367486666666666 +x_0=150000.013 +y_0=5400088.438 +ellps=intl +towgs84=106.869,-52.2978,103.724,-0.33657,0.456955,-1.84218,1 +units=m +no_defs") >>> crs Name: unknown Axis Info [cartesian]: - E[east]: Easting (metre) - N[north]: Northing (metre) Area of Use: - undefined Coordinate Operation: - name: Transformation from unknown to WGS84 - method: Position Vector transformation (geog2D domain) Datum: Unknown based on International 1909 (Hayford) ellipsoid - Ellipsoid: International 1909 (Hayford) - Prime Meridian: Greenwich Source CRS: unknown You notice that the above is not a "Projected CRS" as expected, but a "Bound CRS". This is because it is "bound" to a conversion to WGS84, and will always use this when reprojecting instead of letting PROJ determine the best conversion. To get the actual underlying projected CRS, you can use the ``.source_crs`` attribute: .. code-block:: python >>> crs.source_crs Name: unknown ... Now you have a "Projected CRS", and now it will also recognize the correct EPSG number: .. code-block:: python >>> crs.to_epsg() >>> crs.source_crs.to_epsg() 31370 I have a different axis order? ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ As mentioned above, pyproj now honours the axis order of the EPSG definition. However, proj4 strings or older WKT versions don't specify this correctly, which can be a reason that the CRS object is not equal to the expected EPSG code. Consider the following example of a Canadian projected CRS EPSG:2953. When constructing the CRS object from the WKT string as provided on `EPSG:2953 `__: .. code-block:: python >>> crs = pyproj.CRS("""PROJCS["NAD83(CSRS) / New Brunswick Stereographic", ... GEOGCS["NAD83(CSRS)", ... DATUM["NAD83_Canadian_Spatial_Reference_System", ... SPHEROID["GRS 1980",6378137,298.257222101, ... AUTHORITY["EPSG","7019"]], ... AUTHORITY["EPSG","6140"]], ... PRIMEM["Greenwich",0, ... AUTHORITY["EPSG","8901"]], ... UNIT["degree",0.0174532925199433, ... AUTHORITY["EPSG","9122"]], ... AUTHORITY["EPSG","4617"]], ... PROJECTION["Oblique_Stereographic"], ... PARAMETER["latitude_of_origin",46.5], ... PARAMETER["central_meridian",-66.5], ... PARAMETER["scale_factor",0.999912], ... PARAMETER["false_easting",2500000], ... PARAMETER["false_northing",7500000], ... UNIT["metre",1, ... AUTHORITY["EPSG","9001"]], ... AUTHORITY["EPSG","2953"]]""") >>> crs Name: NAD83(CSRS) / New Brunswick Stereographic Axis Info [cartesian]: - E[east]: Easting (metre) - N[north]: Northing (metre) ... Although this is the WKT string as found online for "EPSG:2953", this CRS object does not evaluate equal to this EPSG code: .. code-block:: python >>> crs == "EPSG:2953" False If you construct the CRS object from the EPSG code (truncated output): .. code-block:: python >>> pyproj.CRS("EPSG:2953") Name: NAD83(CSRS) / New Brunswick Stereographic Axis Info [cartesian]: - N[north]: Northing (metre) - E[east]: Easting (metre) ... You can see that the CRS object constructed from the WKT string has a "Easting, Northing" (i.e. x, y) axis order, while the CRS object constructed from the EPSG code has a (Northing, Easting) axis order. Only having this difference in axis order is no problem when using the CRS in GeoPandas, since GeoPandas always uses a (x, y) order to store the data regardless of the CRS definition. But, you might still want to verify it is equivalent to the expected EPSG code. By lowering the `min_confidence`, the axis order will be ignored: .. code-block:: python >>> crs.to_epsg() >>> crs.to_epsg(min_confidence=20) 2953 The ``.crs`` attribute is no longer a dict or string ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ If you relied on the ``.crs`` object being a dict or a string, such code can be broken given it is now a :class:`pyproj.CRS ` object. But this object actually provides a more robust interface to get information about the CRS. For example, if you used the following code to get the EPSG code: .. code-block:: python gdf.crs['init'] This will no longer work. To get the EPSG code from a ``crs`` object, you can use the :meth:`~pyproj.crs.CRS.to_epsg` method. Or to check if a CRS was a certain UTM zone: .. code-block:: python '+proj=utm ' in gdf.crs could be replaced with the more robust check (requires pyproj 2.6+): .. code-block:: python gdf.crs.utm_zone is not None And there are many other methods available on the :class:`pyproj.CRS ` class to get information about the CRS. geopandas-1.2.0/doc/source/docs/user_guide/pygeos_to_shapely.rst000066400000000000000000000065211525650651600251110ustar00rootroot00000000000000Migration from PyGEOS geometry backend to Shapely 2.0 ===================================================== Since the 0.8 version, GeoPandas includes an experimental support of PyGEOS as an alternative geometry backend to Shapely. Recently, PyGEOS codebase was merged into the Shapely project and released as part of Shapely 2.0. GeoPandas will therefore deprecate support of the PyGEOS backend and will go forward with Shapely 2.0 as the only geometry engine exposing GEOS functionality. Given that historically the PyGEOS engine was automatically used if the package is installed (this behaviour will changed in GeoPandas 0.14 where Shapely 2.0 is used by default if installed), some downstream code may depend on PyGEOS geometries being available as underlying data of a ``GeometryArray``. This guide outlines the migration from the PyGEOS-based code to the Shapely-based code. Migration period ---------------- The migration is planned for three releases spanning approximately one year, starting with 0.13 released in the second quarter of 2023. GeoPandas 0.13 ^^^^^^^^^^^^^^ - PyGEOS is still used as a default backend over Shapely (1.8 or 2.0) if installed, with a ``FutureWarning`` warning about upcoming changes. GeoPandas 0.14 ^^^^^^^^^^^^^^ - The default backend is Shapely 2.0 and the PyGEOS is used only if Shapely 1.8 is installed instead of 2.0 or newer. The PyGEOS backend is still supported, but a user needs to opt in using the environment variable ``USE_PYGEOS`` as explained in the `installation instructions <../../getting_started/install.rst>`__. GeoPandas 1.0 ^^^^^^^^^^^^^ - GeoPandas will remove support of both PyGEOS and Shapely<2. How to prepare your code for transition --------------------------------------- If you don't use PyGEOS explicitly, there nothing to be done as GeoPandas internals will take care of the transition. If you use PyGEOS directly and access an array of PyGEOS geometries using ``GeoSeries.values.data``, you will need to make some changes to avoid code breakage. The recommended way is using Shapely vectorized operations on the ``GeometryArray`` instead of accessing the NumPy array of geometries and using PyGEOS/Shapely operations on the array. This is a common pattern used with GeoPandas 0.12 (or earlier), that should now be avoided in new code: .. code-block:: python >>> import pygeos >>> geometries = gdf.geometry.values.data >>> mrr = pygeos.minimum_rotated_rectangle(geometries) The recommended way of refactoring this code would look like this (with Geopandas 0.12 or later): .. code-block:: python >>> import shapely # shapely 2.0 >>> mrr = shapely.minimum_rotated_rectangle(gdf.geometry.array) This code will work no matter which geometry backend GeoPandas actually uses, because on the ``GeometryArray`` level, it always returns Shapely geometry. Although keep in mind, that it may involve additional overhead cost of converting PyGEOS geometry to Shapely geometry. Note that while in most cases, a simple replacement of ``pygeos`` with ``shapely`` together with a change of ``gdf.geometry.values.data`` to ``gdf.geometry.values`` or analogous ``gdf.geometry.array`` should work, there are some differences between the API of PyGEOS and that of Shapely. Please consult the `Migrating from PyGEOS `__ document for details. geopandas-1.2.0/doc/source/docs/user_guide/reproject_fiona.rst000066400000000000000000000053531525650651600245270ustar00rootroot00000000000000Re-projecting using GDAL with Rasterio and Fiona ================================================ The simplest method of re-projecting is :meth:`GeoDataFrame.to_crs`. It uses pyproj as the engine and transforms the points within the geometries. These examples demonstrate how to use Fiona or rasterio as the engine to re-project your data. Fiona and rasterio are powered by GDAL and with algorithms that consider the geometry instead of just the points the geometry contains. This is particularly useful for antimeridian cutting. However, this also means the transformation is not as fast. Fiona example ------------- .. code-block:: python from functools import partial import fiona import geopandas from fiona.transform import transform_geom from packaging import version from pyproj import CRS from pyproj.enums import WktVersion from shapely.geometry import mapping, shape # set up Fiona transformer def crs_to_fiona(proj_crs): proj_crs = CRS.from_user_input(proj_crs) if version.parse(fiona.__gdal_version__) < version.parse("3.0.0"): fio_crs = proj_crs.to_wkt(WktVersion.WKT1_GDAL) else: # GDAL 3+ can use WKT2 fio_crs = proj_crs.to_wkt() return fio_crs def base_transformer(geom, src_crs, dst_crs): return shape( transform_geom( src_crs=crs_to_fiona(src_crs), dst_crs=crs_to_fiona(dst_crs), geom=mapping(geom), antimeridian_cutting=True, ) ) # load natural earth land data world = geopandas.read_file("https://naciscdn.org/naturalearth/110m/physical/ne_110m_land.zip") destination_crs = "EPSG:3395" forward_transformer = partial(base_transformer, src_crs=world.crs, dst_crs=destination_crs) # Reproject to Mercator (after dropping Antartica) world = world.drop(7) with fiona.Env(OGR_ENABLE_PARTIAL_REPROJECTION="YES"): mercator_world = world.set_geometry(world.geometry.apply(forward_transformer), crs=destination_crs) Rasterio example ---------------- This example requires rasterio 1.2+ and GDAL 3+. .. code-block:: python import geopandas import rasterio.warp from shapely.geometry import shape # load example data world = geopandas.read_file("https://naciscdn.org/naturalearth/110m/physical/ne_110m_land.zip") # Reproject to Mercator (after dropping Antartica) world = world.drop(7) destination_crs = "EPSG:3395" geometry = rasterio.warp.transform_geom( src_crs=world.crs, dst_crs=destination_crs, geom=world.geometry.values, ) mercator_world = world.set_geometry( [shape(geom) for geom in geometry], crs=destination_crs, ) geopandas-1.2.0/doc/source/docs/user_guide/sampling.ipynb000066400000000000000000000161221525650651600234750ustar00rootroot00000000000000{ "cells": [ { "attachments": {}, "cell_type": "markdown", "id": "f02b5a40-29b6-4d46-abb5-f84d5ee4da56", "metadata": {}, "source": [ "# Sampling Points" ] }, { "attachments": {}, "cell_type": "markdown", "id": "f2fa64d9-7781-4357-8381-6ff64eff7379", "metadata": {}, "source": [ "Learn how to sample random points using GeoPandas. \n", "\n", "The example below shows you how to sample random locations from shapes in GeoPandas GeoDataFrames." ] }, { "cell_type": "markdown", "id": "ae0cc935-8940-4cfb-9a62-d3174fc77687", "metadata": {}, "source": [ "## Import Packages\n", "\n", "To begin with, we need to import packages we'll use: " ] }, { "cell_type": "code", "execution_count": null, "id": "1b3b0e6e-221c-4b8e-baf8-92bf07e806ea", "metadata": {}, "outputs": [], "source": [ "import geopandas\n", "import geodatasets" ] }, { "attachments": {}, "cell_type": "markdown", "id": "9e5ee97a-6647-4686-a3a0-f3dfb7228cd1", "metadata": {}, "source": [ "For this example, we will use the New York Borough example data (`nybb`) provided by geodatasets. " ] }, { "cell_type": "code", "execution_count": null, "id": "d013124f-4af5-4380-bf45-aa5fd4887c63", "metadata": {}, "outputs": [], "source": [ "nybb = geopandas.read_file(geodatasets.get_path(\"nybb\"))\n", "# simplify geometry to save space when rendering many interactive maps\n", "nybb.geometry = nybb.simplify(200) " ] }, { "attachments": {}, "cell_type": "markdown", "id": "d4703589-f6b5-46a3-9540-ec9a52716747", "metadata": {}, "source": [ "To see what this looks like, visualize the data:" ] }, { "cell_type": "code", "execution_count": null, "id": "ec65cc25-2baa-431f-adea-9275c231ac47", "metadata": {}, "outputs": [], "source": [ "nybb.explore()" ] }, { "attachments": {}, "cell_type": "markdown", "id": "99883485-2e67-4da8-a3d8-d4724ef8b2a1", "metadata": {}, "source": [ "## Sampling random points\n", "\n", "To sample points from within a GeoDataFrame, use the `sample_points()` method.\n", "To specify the sample sizes, provide an explicit number of points to sample. For example, we can sample 200 points randomly from each feature: " ] }, { "cell_type": "code", "execution_count": null, "id": "76d8658d-7ee8-4ef0-84b4-b8883c921687", "metadata": {}, "outputs": [], "source": [ "n200_sampled_points = nybb.sample_points(200)\n", "m = nybb.explore()\n", "n200_sampled_points.explore(m=m, color='red')" ] }, { "cell_type": "markdown", "id": "14b8628f-3d5e-4fbb-b26e-28cc042ff755", "metadata": {}, "source": [ "This functionality also works for line geometries. For example, let's look only at the boundary of Manhattan Island:" ] }, { "cell_type": "code", "execution_count": null, "id": "c47472fc-7ca5-4f69-b86f-93c25a9f2b03", "metadata": {}, "outputs": [], "source": [ "manhattan_parts = nybb.iloc[[3]].explode(ignore_index=True)\n", "manhattan_island = manhattan_parts.iloc[[30]]\n", "manhattan_island.boundary.explore()" ] }, { "cell_type": "markdown", "id": "54280acd-e449-4528-ac7c-c1b2dbbcdc2f", "metadata": {}, "source": [ "Sampling randomly from along this boundary can use the same `sample_points()` method:" ] }, { "cell_type": "code", "execution_count": null, "id": "358822ef-a0c6-40cd-9e35-9a688c56f361", "metadata": {}, "outputs": [], "source": [ "manhattan_border_points = manhattan_island.boundary.sample_points(200)\n", "m = manhattan_island.explore()\n", "manhattan_border_points.explore(m=m, color='red')" ] }, { "cell_type": "markdown", "id": "fa0f5a05-e7cc-44cf-b6ac-40afd9753f23", "metadata": {}, "source": [ "Keep in mind that sampled points are returned as a single multi-part geometry, and that the distances over the line segments are calculated *along* the line. " ] }, { "cell_type": "code", "execution_count": null, "id": "b0ccad29-d29f-4da1-9151-692bfd20d533", "metadata": {}, "outputs": [], "source": [ "manhattan_border_points" ] }, { "cell_type": "markdown", "id": "125f19d9-b3f0-4ab9-b44a-41be1a8cc388", "metadata": {}, "source": [ "If you want to separate out the individual sampled points, use the `.explode()` method on the dataframe:" ] }, { "cell_type": "code", "execution_count": null, "id": "265f2194-a94f-4a3d-9ae8-f7da55893e90", "metadata": {}, "outputs": [], "source": [ "manhattan_border_points.explode(ignore_index=True).head()" ] }, { "attachments": {}, "cell_type": "markdown", "id": "21a1a9d5", "metadata": {}, "source": [ "## Variable number of points\n", "\n", "You can also sample different number of points from different geometries if you pass an array specifying the size of the sample per geometry." ] }, { "cell_type": "code", "execution_count": null, "id": "b76e4cd8", "metadata": {}, "outputs": [], "source": [ "variable_size = nybb.sample_points([10, 50, 100, 200, 500])\n", "m = nybb.explore()\n", "variable_size.explore(m=m, color='red')" ] }, { "attachments": {}, "cell_type": "markdown", "id": "7f041b05-7c12-4a2c-a360-35bc5c79f1f4", "metadata": {}, "source": [ "## Sampling from more complicated point pattern processes\n", "\n", "Finally, the `sample_points()` method can use different sampling processes than those described above, so long as they are implemented in the `pointpats` package for spatial point pattern analysis. For example, a \"cluster-poisson\" process is a spatially-random cluster process where the \"seeds\" of clusters are chosen randomly, and then points around these clusters are distributed according again randomly. \n", "\n", "To see what this looks like, consider the following, where ten points will be distributed around five seeds within each of the boroughs in New York City:" ] }, { "cell_type": "code", "execution_count": null, "id": "7868221c-ad0a-44e6-9f2f-41c4d6ac0fcf", "metadata": {}, "outputs": [], "source": [ "sample_t = nybb.sample_points(method='cluster_poisson', size=50, n_seeds=5, cluster_radius=7500)" ] }, { "cell_type": "code", "execution_count": null, "id": "abd12d6e-533d-4808-86d7-9df4c097c077", "metadata": {}, "outputs": [], "source": [ "m = nybb.explore()\n", "sample_t.explore(m=m, color='red')" ] } ], "metadata": { "kernelspec": { "display_name": "geopandas_dev", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.0" }, "vscode": { "interpreter": { "hash": "b1fe2ae8565152c84d3dbd08488d3746f754c9bdf2de9b61cf939da5306d3793" } } }, "nbformat": 4, "nbformat_minor": 5 } geopandas-1.2.0/doc/source/docs/user_guide/set_operations.rst000066400000000000000000000174451525650651600244210ustar00rootroot00000000000000.. currentmodule:: geopandas .. ipython:: python :suppress: import geopandas import matplotlib.pyplot as plt plt.close('all') Set operations with overlay ============================ When working with multiple spatial datasets -- especially multiple *polygon* or *line* datasets -- users often wish to create new shapes based on places where those datasets overlap (or don't overlap). These manipulations are often referred using the language of sets -- intersections, unions, and differences. These types of operations are made available in the GeoPandas library through the :meth:`~geopandas.GeoDataFrame.overlay` method. The basic idea is demonstrated by the graphic below but keep in mind that overlays operate at the DataFrame level, not on individual geometries, and the properties from both are retained. In effect, for every shape in the left :class:`~geopandas.GeoDataFrame`, this operation is executed against every other shape in the right :class:`~geopandas.GeoDataFrame`: .. image:: ../../_static/overlay_operations.png **Source: QGIS documentation** .. note:: Note to users familiar with the *shapely* library: :meth:`~geopandas.GeoDataFrame.overlay` can be thought of as offering versions of the standard *shapely* set operations that deal with the complexities of applying set operations to two *GeoSeries*. The standard *shapely* set operations are also available as :class:`~geopandas.GeoSeries` methods. The different overlay operations -------------------------------- First, create some example data: .. ipython:: python from shapely.geometry import Polygon polys1 = geopandas.GeoSeries([Polygon([(0,0), (2,0), (2,2), (0,2)]), Polygon([(2,2), (4,2), (4,4), (2,4)])]) polys2 = geopandas.GeoSeries([Polygon([(1,1), (3,1), (3,3), (1,3)]), Polygon([(3,3), (5,3), (5,5), (3,5)])]) df1 = geopandas.GeoDataFrame({'geometry': polys1, 'df1':[1,2]}) df2 = geopandas.GeoDataFrame({'geometry': polys2, 'df2':[1,2]}) These two GeoDataFrames have some overlapping areas: .. ipython:: python ax = df1.plot(color='red'); @savefig overlay_example.png width=5in df2.plot(ax=ax, color='green', alpha=0.5); The above example illustrates the different overlay modes. The :meth:`~geopandas.GeoDataFrame.overlay` method will determine the set of all individual geometries from overlaying the two input GeoDataFrames. This result covers the area covered by the two input GeoDataFrames, and also preserves all unique regions defined by the combined boundaries of the two GeoDataFrames. .. note:: For historical reasons, the overlay method is also available as a top-level function :func:`overlay`. It is recommended to use the method as the function may be deprecated in the future. When using ``how='union'``, all those possible geometries are returned: .. ipython:: python res_union = df1.overlay(df2, how='union') res_union ax = res_union.plot(alpha=0.5, cmap='tab10') df1.plot(ax=ax, facecolor='none', edgecolor='k'); @savefig overlay_example_union.png width=5in df2.plot(ax=ax, facecolor='none', edgecolor='k'); The other ``how`` operations will return different subsets of those geometries. With ``how='intersection'``, it returns only those geometries that are contained by both GeoDataFrames: .. ipython:: python res_intersection = df1.overlay(df2, how='intersection') res_intersection ax = res_intersection.plot(cmap='tab10') df1.plot(ax=ax, facecolor='none', edgecolor='k'); @savefig overlay_example_intersection.png width=5in df2.plot(ax=ax, facecolor='none', edgecolor='k'); ``how='symmetric_difference'`` is the opposite of ``'intersection'`` and returns the geometries that are only part of one of the GeoDataFrames but not of both: .. ipython:: python res_symdiff = df1.overlay(df2, how='symmetric_difference') res_symdiff ax = res_symdiff.plot(cmap='tab10') df1.plot(ax=ax, facecolor='none', edgecolor='k'); @savefig overlay_example_symdiff.png width=5in df2.plot(ax=ax, facecolor='none', edgecolor='k'); To obtain the geometries that are part of ``df1`` but are not contained in ``df2``, you can use ``how='difference'``: .. ipython:: python res_difference = df1.overlay(df2, how='difference') res_difference ax = res_difference.plot(cmap='tab10') df1.plot(ax=ax, facecolor='none', edgecolor='k'); @savefig overlay_example_difference.png width=5in df2.plot(ax=ax, facecolor='none', edgecolor='k'); Finally, with ``how='identity'``, the result consists of the surface of ``df1``, but with the geometries obtained from overlaying ``df1`` with ``df2``: .. ipython:: python res_identity = df1.overlay(df2, how='identity') res_identity ax = res_identity.plot(cmap='tab10') df1.plot(ax=ax, facecolor='none', edgecolor='k'); @savefig overlay_example_identity.png width=5in df2.plot(ax=ax, facecolor='none', edgecolor='k'); Overlay groceries example ------------------------- First, load the Chicago community areas and groceries example datasets and select : .. ipython:: python import geodatasets chicago = geopandas.read_file(geodatasets.get_path("geoda.chicago_commpop")) groceries = geopandas.read_file(geodatasets.get_path("geoda.groceries")) # Project to crs that uses meters as distance measure chicago = chicago.to_crs("ESRI:102003") groceries = groceries.to_crs("ESRI:102003") To illustrate the :meth:`~geopandas.GeoDataFrame.overlay` method, consider the following case in which one wishes to identify the "served" portion of each area -- defined as areas within 1km of a grocery store -- using a ``GeoDataFrame`` of community areas and a ``GeoDataFrame`` of groceries. .. ipython:: python # Look at Chicago: @savefig chicago_basic.png width=5in chicago.plot(); # Now buffer groceries to find area within 1km. # Check CRS -- USA Contiguous Albers Equal Area, units of meters. groceries.crs # make 1km buffer groceries['geometry']= groceries.buffer(1000) @savefig groceries_buffers.png width=5in groceries.plot(); To select only the portion of community areas within 1km of a grocery, specify the ``how`` option to be "intersect", which creates a new set of polygons where these two layers overlap: .. ipython:: python chicago_cores = chicago.overlay(groceries, how='intersection') @savefig chicago_cores.png width=5in chicago_cores.plot(alpha=0.5, edgecolor='k', cmap='tab10'); Changing the ``how`` option allows for different types of overlay operations. For example, if you were interested in the portions of Chicago *far* from groceries (the peripheries), you would compute the difference of the two. .. ipython:: python chicago_peripheries = chicago.overlay(groceries, how='difference') @savefig chicago_peripheries.png width=5in chicago_peripheries.plot(alpha=0.5, edgecolor='k', cmap='tab10'); .. ipython:: python :suppress: import matplotlib.pyplot as plt plt.close('all') keep_geom_type keyword ---------------------- In default settings, :meth:`~geopandas.GeoDataFrame.overlay` returns only geometries of the same geometry type as GeoDataFrame (left one) has, where Polygon and MultiPolygon is considered as a same type (other types likewise). You can control this behavior using ``keep_geom_type`` option, which is set to True by default. Once set to False, ``overlay`` will return all geometry types resulting from selected set-operation. Different types can result for example from intersection of touching geometries, where two polygons intersects in a line or a point. More examples ------------- A larger set of examples of the use of :meth:`~geopandas.GeoDataFrame.overlay` can be found `here `_ .. toctree:: :maxdepth: 2 geopandas-1.2.0/doc/source/docs/user_guide/spatial_indexing.ipynb000066400000000000000000000323551525650651600252130ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Spatial indexing\n", "\n", "When you want to know a spatial relationship (known as a spatial\n", "predicate) between a set of geometries A and a geometry B (or a set of\n", "them), you can compare geometry B against any geometry in a\n", "set A. However, that is not the most performant approach\n", "in most cases. A spatial index is a more efficient method for pre-filtering comparisons of geometries before using more computationally expensive spatial predicates.\n", "GeoPandas exposes the\n", "Sort-Tile-Recursive R-tree from shapely on any GeoDataFrame and\n", "GeoSeries using the [GeoSeries.sindex](../reference/api/geopandas.GeoSeries.sindex.html) property. This page outlines its options\n", "and common usage patterns.\n", "\n", "Note that for many operations where a spatial index provides significant\n", "performance benefits, GeoPandas already uses it automatically (like [sjoin()](../reference/api/geopandas.GeoDataFrame.sjoin.html),\n", "[overlay()](../reference/api/geopandas.GeoDataFrame.overlay.html), or [clip()](../reference/api/geopandas.GeoDataFrame.clip.html)). However, more advanced use cases may require\n", "a direct interaction with the index." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import geopandas\n", "import matplotlib.pyplot as plt\n", "import shapely\n", "\n", "from geodatasets import get_path" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Load data on New York City subboroughs to illustrate the spatial\n", "indexing." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "nyc = geopandas.read_file(get_path(\"geoda nyc\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## R-tree principle\n", "\n", "In principle, any R-tree index builds a hierarchical collection of\n", "bounding boxes (envelopes) representing first individual geometries and then their\n", "most efficient combinations (from a spatial query perspective). When\n", "creating one, you can imagine that your geometries are represented by\n", "their envelopes, as illustrated below." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fig, axs = plt.subplots(1, 2, sharey=True, figsize=(8, 4))\n", "\n", "nyc.plot(ax=axs[0], edgecolor=\"black\", linewidth=1)\n", "nyc.envelope.boundary.plot(ax=axs[1], color='black');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The left side of the figure shows the original geometries, while the\n", "right side their bounding boxes, extracted using the [envelope](../reference/api/geopandas.GeoSeries.envelope.html)\n", "property. Typically, the index works on top of those.\n", "\n", "Let’s generate two points now, both intersecting at least one bounding\n", "box but only one intersecting the actual geometry." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "point_inside = shapely.Point(950000, 155000)\n", "point_outside = shapely.Point(1050000, 150000)\n", "points = geopandas.GeoSeries([point_inside, point_outside], crs=nyc.crs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can verify that visually." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fig, axs = plt.subplots(1, 2, sharey=True, figsize=(8, 4))\n", "\n", "nyc.plot(ax=axs[0], edgecolor=\"black\", linewidth=1)\n", "nyc.envelope.boundary.plot(ax=axs[1], color='black')\n", "points.plot(ax=axs[0], color=\"limegreen\")\n", "points.plot(ax=axs[1], color=\"limegreen\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Querying the index\n", "\n", "### Scalar query\n", "\n", "\n", "You can now use the [sindex](../reference/api/geopandas.GeoSeries.sindex.html) property to query the index. The\n", "[query()](../reference/api/geopandas.sindex.SpatialIndex.query.html) method, by default, returns positions of all geometries\n", "whose bounding boxes intersect the bounding box of the input geometry.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "bbox_query_inside = nyc.sindex.query(point_inside)\n", "bbox_query_outside = nyc.sindex.query(point_outside)\n", "bbox_query_inside, bbox_query_outside" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Both the point we know is inside a geometry and the one that is outside\n", "a geometry return one hit as each intersects one bounding box in the\n", "tree." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fig, axs = plt.subplots(1, 2, sharey=True, figsize=(8, 4))\n", "\n", "nyc.plot(ax=axs[0], edgecolor=\"black\", linewidth=1)\n", "nyc.envelope.boundary.plot(ax=axs[1], color='black')\n", "points.plot(ax=axs[0], color=\"limegreen\", zorder=3, edgecolor=\"black\", linewidth=.5)\n", "points.plot(ax=axs[1], color=\"limegreen\", zorder=3, edgecolor=\"black\", linewidth=.5)\n", "nyc.iloc[bbox_query_inside].plot(ax=axs[0], color='orange')\n", "nyc.iloc[bbox_query_outside].plot(ax=axs[0], color='orange')\n", "nyc.envelope.iloc[bbox_query_inside].plot(ax=axs[1], color='orange')\n", "nyc.envelope.iloc[bbox_query_outside].plot(ax=axs[1], color='orange');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The image above provides a clear illustration of what happens. While you\n", "can see on the left image that only one intersects an orange geometry\n", "marked as a *hit*, the hits are quite clear when looking at the bounding\n", "box.\n", "\n", "Thankfully, the spatial index allows for further filtering based on the\n", "actual geometry. In this case, the tree is first queried as above but\n", "afterwards, each of the possible hits is checked using a spatial predicate." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pred_inside = nyc.sindex.query(point_inside, predicate=\"intersects\")\n", "pred_outside = nyc.sindex.query(point_outside, predicate=\"intersects\")\n", "pred_inside, pred_outside" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When you specify ``predicate=\"intersects\"``, the result is indeed\n", "different and the output of the query using the point that lies outside\n", "of any geometry is empty." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fig, axs = plt.subplots(1, 2, sharey=True, figsize=(8, 4))\n", "\n", "nyc.plot(ax=axs[0], edgecolor=\"black\", linewidth=1)\n", "nyc.envelope.boundary.plot(ax=axs[1], color='black')\n", "points.plot(ax=axs[0], color=\"limegreen\", zorder=3, edgecolor=\"black\", linewidth=.5)\n", "points.plot(ax=axs[1], color=\"limegreen\", zorder=3, edgecolor=\"black\", linewidth=.5)\n", "nyc.iloc[pred_inside].plot(ax=axs[0], color='orange')\n", "nyc.envelope.iloc[pred_inside].plot(ax=axs[1], color='orange');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can use any of the predicates available in [valid_query_predicates](../reference/api/geopandas.sindex.SpatialIndex.valid_query_predicates.html):" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "nyc.sindex.valid_query_predicates" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Array query\n", "\n", "Checking a single geometry against the tree is nice but not that\n", "efficient if you are interested in many-to-many relationships. The\n", "[query()](../reference/api/geopandas.sindex.SpatialIndex.query.html) method allows passing any 1-D array of geometries to be\n", "checked against the tree. If you do so, the output structure is slightly\n", "different:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "bbox_array_query = nyc.sindex.query(points)\n", "bbox_array_query" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By default, the method returns a 2-D array of indices where the query\n", "found a hit where the subarrays correspond to the indices of the input\n", "geometries and indices of the tree geometries associated with each. In\n", "the example above, the 0-th geometry in the ``points`` GeoSeries\n", "intersects the bounding box of the geometry at the position 1 from the\n", "``nyc`` GeoDataFrame, while the geometry 1 in the ``points`` matches\n", "geometry 16 in the ``nyc``. You may notice that these are the same\n", "indices as you’ve seen above.\n", "\n", "The other option is to return a boolean array with shape\n", "``(len(tree), n)`` with boolean values marking whether the bounding box\n", "of a geometry in the tree intersects a bounding box of a given geometry.\n", "This can be either a dense numpy array, or a sparse scipy array. Keep in\n", "mind that the output will be, in most cases, mostly filled with\n", "``False`` and the array can become really large, so it is recommended to\n", "use the sparse format, if possible.\n", "\n", "You can specify each using the ``output_format`` keyword:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "bbox_array_query_dense = nyc.sindex.query(points, output_format=\"dense\")\n", "bbox_array_query_dense" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The dense array above has rows aligned with the rows of ``nyc`` and\n", "columns aligned with the rows of ``points`` and indicates all pairs\n", "where a *hit* was found.\n", "\n", "The same array can be represented as a `scipy.sparse.coo_array`:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "bbox_array_query_sparse = nyc.sindex.query(points, output_format=\"sparse\")\n", "bbox_array_query_sparse" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For example, to find the number of neighboring geometries for each subborough, you can\n", "use the spatial index to compare all geometries against each other. Since you are using\n", "``nyc`` on both sides of the query here, the resulting array is square-shaped with\n", "diagonal filled with ``True``." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "neighbors = nyc.sindex.query(nyc.geometry, predicate=\"intersects\", output_format=\"dense\")\n", "neighbors" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Getting the sum along one axis can then give you the answer. Note that\n", "since a geometry always intersects itself, you need to subtract one." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "n_neighbors = neighbors.sum(axis=1) - 1\n", "n_neighbors" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The result is a numpy array you can directly plot on a map." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "nyc.plot(n_neighbors, legend=True);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Nearest geometry query\n", "\n", "While checking the spatial predicate using the spatial index is indeed extremely useful, GeoPandas\n", "also allows you to use the spatial index to find\n", "the nearest geometry. The API is similar as above:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "nearest_indices = nyc.sindex.nearest(points)\n", "nearest_indices" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can see that the nearest query returns the indices representation.\n", "If you are interested in how “near” the geometries actually are, the method\n", "can also return distances. In this case, the return format is a tuple of\n", "arrays." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "nearest_indices, distance = nyc.sindex.nearest(points, return_distance=True)\n", "distance" ] } ], "metadata": { "kernelspec": { "display_name": "default", "language": "python", "name": "python3" }, "language_info": { "name": "python", "version": "3.12.8" } }, "nbformat": 4, "nbformat_minor": 2 } geopandas-1.2.0/doc/source/gallery/000077500000000000000000000000001525650651600171725ustar00rootroot00000000000000geopandas-1.2.0/doc/source/gallery/cartopy_convert.ipynb000066400000000000000000000136231525650651600234630ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "# Plotting with CartoPy and GeoPandas\n", "\n", "Converting between GeoPandas and CartoPy for visualizing data.\n", "\n", "[CartoPy](https://cartopy.readthedocs.io/) is a Python library\n", "that specializes in creating geospatial\n", "visualizations. It has a slightly different way of representing\n", "Coordinate Reference Systems (CRS) as well as constructing plots.\n", "This example steps through a round-trip transfer of data\n", "between GeoPandas and CartoPy.\n", "\n", "First we'll load in the data using GeoPandas.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import geopandas\n", "from cartopy import crs as ccrs\n", "from geodatasets import get_path\n", "\n", "path = get_path(\"naturalearth.land\")\n", "df = geopandas.read_file(path)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First we'll visualize the map using GeoPandas\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plotting with CartoPy\n", "=====================\n", "\n", "Cartopy also handles Shapely objects well, but it uses a different system for\n", "CRS. To plot this data with CartoPy, we'll first need to project it into a\n", "new CRS. We'll use a CRS defined within CartoPy and use the GeoPandas\n", "``to_crs`` method to make the transformation.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Define the CartoPy CRS object.\n", "crs = ccrs.AzimuthalEquidistant()\n", "\n", "# This can be converted into a `proj4` string/dict compatible with GeoPandas\n", "crs_proj4 = crs.proj4_init\n", "df_ae = df.to_crs(crs_proj4)\n", "\n", "# Here's what the plot looks like in GeoPandas\n", "df_ae.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that our data is in a CRS based off of CartoPy, we can easily\n", "plot it.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fig, ax = plt.subplots(subplot_kw={\"projection\": crs})\n", "ax.add_geometries(df_ae[\"geometry\"], crs=crs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that we could have easily done this with an EPSG code like so:\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "crs_epsg = ccrs.epsg(\"3857\")\n", "df_epsg = df.to_crs(epsg=\"3857\")\n", "\n", "# Generate a figure with two axes, one for CartoPy, one for GeoPandas\n", "fig, axs = plt.subplots(1, 2, subplot_kw={\"projection\": crs_epsg}, figsize=(10, 5))\n", "# Make the CartoPy plot\n", "axs[0].add_geometries(\n", " df_epsg[\"geometry\"], crs=crs_epsg, facecolor=\"white\", edgecolor=\"black\"\n", ")\n", "# Make the GeoPandas plot\n", "df_epsg.plot(ax=axs[1], color=\"white\", edgecolor=\"black\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "CartoPy to GeoPandas\n", "====================\n", "\n", "Next we'll perform a CRS projection in CartoPy, and then convert it\n", "back into a GeoPandas object.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "crs_new = ccrs.AlbersEqualArea()\n", "new_geometries = [\n", " crs_new.project_geometry(ii, src_crs=crs) for ii in df_ae[\"geometry\"].values\n", "]\n", "\n", "fig, ax = plt.subplots(subplot_kw={\"projection\": crs_new})\n", "ax.add_geometries(new_geometries, crs=crs_new)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we've created new Shapely objects with the CartoPy CRS,\n", "we can use this to create a GeoDataFrame.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df_aea = geopandas.GeoDataFrame(\n", " df.drop(columns=\"geometry\"), geometry=new_geometries, crs=crs_new.proj4_init\n", ")\n", "df_aea.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can even combine these into the same figure. Here we'll plot the\n", "shapes of the countries with CartoPy. We'll then calculate the centroid\n", "of each with GeoPandas and plot it on top.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "nbsphinx-thumbnail" ] }, "outputs": [], "source": [ "# Generate a CartoPy figure and add the countries to it\n", "fig, ax = plt.subplots(subplot_kw={\"projection\": crs_new})\n", "ax.add_geometries(new_geometries, crs=crs_new)\n", "\n", "# Calculate centroids and plot\n", "df_aea_centroids = df_aea.geometry.centroid\n", "# Need to provide \"zorder\" to ensure the points are plotted above the polygons\n", "df_aea_centroids.plot(ax=ax, markersize=5, color=\"r\", zorder=10)\n", "\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.0" } }, "nbformat": 4, "nbformat_minor": 4 } geopandas-1.2.0/doc/source/gallery/choro_legends.ipynb000066400000000000000000022350051525650651600230570ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Choro legends" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import geopandas\n", "from geopandas import read_file" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'2.4.2'" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import mapclassify\n", "\n", "mapclassify.__version__" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'4.4.0'" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import libpysal\n", "\n", "libpysal.__version__" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Name Description Installed\n", "0 10740 Albuquerque, New Mexico, Census 2000 Tract Data. 10740 i... True\n", "1 AirBnB Airbnb rentals, socioeconomics, and crime in Chicago False\n", "2 Atlanta Atlanta, GA region homicide counts and rates False\n", "3 Baltimore Baltimore house sales prices and hedonics False\n", "4 Bostonhsg Boston housing and neighborhood data False\n", "5 Buenosaires Electoral Data for 1999 Argentinean Elections False\n", "6 Charleston1 2000 Census Tract Data for Charleston, SC MSA and counties False\n", "7 Charleston2 1998 and 2001 Zip Code Business Patterns (Census Bureau... False\n", "8 Chicago Health Chicago Health + Socio-Economics False\n", "9 Chicago commpop Chicago Community Area Population Percent Change for 20... False\n", "10 Chicago parcels Tax parcel polygons of Cook county False\n", "11 Chile Labor Labor Markets in Chile (1982-2002) False\n", "12 Chile Migration Internal Migration in Chile (1977-2002) False\n", "13 Cincinnati 2008 Cincinnati Crime + Socio-Demographics False\n", "14 Cleveland 2015 sales prices of homes in Cleveland, OH. False\n", "15 Columbus Columbus neighborhood crime False\n", "16 Elections 2012 and 2016 Presidential Elections False\n", "17 Grid100 Grid with simulated variables False\n", "18 Groceries 2015 Chicago supermarkets False\n", "19 Guerry Moral statistics of France (Guerry, 1833) False\n", "20 Health Indicators Chicago Health Indicators (2005-11) False\n", "21 Health+ 2000 Health, Income + Diversity False\n", "22 Hickory1 2000 Census Tract Data for Hickory, NC MSA and counties False\n", "23 Hickory2 1998 and 2001 Zip Code Business Patterns (Census Bureau... False\n", "24 Home Sales 2014-15 Home Sales in King County, WA False\n", "25 Houston Houston, TX region homicide counts and rates False\n", "26 Juvenile Cardiff juvenile delinquent residences False\n", "27 Lansing1 2000 Census Tract Data for Lansing, MI MSA and counties False\n", "28 Lansing2 1998 and 2001 Zip Code Business Patterns (Census Bureau... False\n", "29 Laozone Ozone measures at monitoring stations in Los Angeles basin False\n", "30 LasRosas Corn yield, fertilizer and field data for precision agr... False\n", "31 Line Line Shapefile True\n", "32 Liquor Stores 2015 Chicago Liquor Stores False\n", "33 Malaria Malaria incidence and population (1973, 95, 93 censuses... False\n", "34 Milwaukee1 2000 Census Tract Data for Milwaukee, WI MSA False\n", "35 Milwaukee2 1998 and 2001 Zip Code Business Patterns (Census Bureau... False\n", "36 NCOVR US county homicides 1960-1990 True\n", "37 NDVI Normalized Difference Vegetation Index grid False\n", "38 NYC Demographic and housing data for New York City subborou... False\n", "39 NYC Earnings Block-level Earnings in NYC (2002-14) False\n", "40 NYC Education NYC Education (2000) False\n", "41 NYC Neighborhoods Demographics for New York City neighborhoods False\n", "42 NYC Socio-Demographics NYC Education + Socio-Demographics False\n", "43 Natregimes NCOVR with regimes (book/PySAL) False\n", "44 Nepal Health, poverty and education indicators for Nepal dist... False\n", "45 Ohiolung Ohio lung cancer data, 1968, 1978, 1988 False\n", "46 Orlando1 2000 Census Tract Data for Orlando, FL MSA and counties False\n", "47 Orlando2 1998 and 2001 Zip Code Business Patterns (Census Bureau... False\n", "48 Oz9799 Monthly ozone data, 1997-99 False\n", "49 Phoenix ACS Phoenix American Community Survey Data (2010, 5-year av... False\n", "50 Pittsburgh Pittsburgh homicide locations False\n", "51 Point Point Shapefile True\n", "52 Police Police expenditures Mississippi counties False\n", "53 Polygon Polygon Shapefile True\n", "54 Polygon_Holes Example to test treatment of holes True\n", "55 Rio Grande do Sul Cities of the Brazilian State of Rio Grande do Sul True\n", "56 SIDS North Carolina county SIDS death counts False\n", "57 SIDS2 North Carolina county SIDS death counts and rates False\n", "58 Sacramento1 2000 Census Tract Data for Sacramento MSA True\n", "59 Sacramento2 1998 and 2001 Zip Code Business Patterns (Census Bureau... True\n", "60 SanFran Crime July-Dec 2012 crime incidents in San Francisco (points ... False\n", "61 Savannah1 2000 Census Tract Data for Savannah, GA MSA and counties False\n", "62 Savannah2 1998 and 2001 Zip Code Business Patterns (Census Bureau... False\n", "63 Scotlip Male lip cancer in Scotland, 1975-80 False\n", "64 Seattle1 2000 Census Tract Data for Seattle, WA MSA and counties False\n", "65 Seattle2 1998 and 2001 Zip Code Business Patterns (Census Bureau... False\n", "66 Snow John Snow & the 19th Century Cholera Epidemic False\n", "67 South US Southern county homicides 1960-1990 True\n", "68 Spirals Synthetic spiral points False\n", "69 StLouis St Louis region county homicide counts and rates False\n", "70 Tampa1 2000 Census Tract Data for Tampa, FL MSA and counties False\n", "71 US SDOH 2014 US Social Determinants of Health Data False\n", "72 arcgis arcgis testing files True\n", "73 baltim Baltimore house sales prices and hedonics, 1978. True\n", "74 berlin Prenzlauer Berg neighborhood AirBnB data from Berlin True\n", "75 book Synthetic data to illustrate spatial weights True\n", "76 burkitt Burkitt's lymphoma in the Western Nile district of Uganda True\n", "77 calemp Employment density for California counties True\n", "78 chicago Chicago neighborhoods True\n", "79 clearwater mgwr testing dataset False\n", "80 columbus Columbus neighborhood crime data 1980 True\n", "81 desmith Small dataset to illustrate Moran's I statistic True\n", "82 geodanet Datasets from GeoDaNet for network analysis True\n", "83 georgia Various socio-economic variables for counties within the... True\n", "84 juvenile Residences of juvenile offenders in Cardiff, UK True\n", "85 mexico Decennial per capita incomes of Mexican states 1940-2000 True\n", "86 networks Datasets used for network testing True\n", "87 newHaven Network testing dataset False\n", "88 nyc_bikes New York City Bike Trips False\n", "89 sids2 North Carolina county SIDS death counts and rates True\n", "90 snow_maps Public water pumps and Cholera deaths in London 1854 (Jo... True\n", "91 stl Homicides and selected socio-economic characteristics fo... True\n", "92 street_net_pts Street network points True\n", "93 taz Traffic Analysis Zones in So. California False\n", "94 tokyo Tokyo Mortality data True\n", "95 us_income Per-capita income for the lower 48 US states 1929-2009 True\n", "96 virginia Virginia counties shapefile True\n", "97 wmat Datasets used for spatial weights testing True\n" ] } ], "source": [ "libpysal.examples.available()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "_ = libpysal.examples.load_example(\"South\")\n", "pth = libpysal.examples.get_path(\"south.shp\")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "df = read_file(pth)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Default legend formatting" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "tags": [ "nbsphinx-thumbnail" ] }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "ax = df.plot(\n", " column=\"HR60\",\n", " scheme=\"QUANTILES\",\n", " k=4,\n", " cmap=\"BuPu\",\n", " legend=True,\n", " legend_kwds={\"loc\": \"center left\", \"bbox_to_anchor\": (1, 0.5)},\n", ")" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[' 0.00, 3.21', ' 3.21, 6.25', ' 6.25, 9.96', ' 9.96, 92.94']" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "labels = [t.get_text() for t in ax.get_legend().get_texts()]\n", "labels" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Quantiles \n", "\n", " Interval Count\n", "----------------------\n", "[ 0.00, 3.21] | 353\n", "( 3.21, 6.25] | 353\n", "( 6.25, 9.96] | 353\n", "( 9.96, 92.94] | 353" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "q4 = mapclassify.Quantiles(df.HR60, k=4)\n", "q4" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "labels == q4.get_legend_classes()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that in this case, the first interval is closed on the minimum value in the dataset. The other intervals have an open lower bound. This can be now displayed in the legend using `legend_kwds={'interval': True}`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Overriding numerical format" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax = df.plot(\n", " column=\"HR60\",\n", " scheme=\"QUANTILES\",\n", " k=4,\n", " cmap=\"BuPu\",\n", " legend=True,\n", " legend_kwds={\"loc\": \"center left\", \"bbox_to_anchor\": (1, 0.5)},\n", ")" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax = df.plot(\n", " column=\"HR60\",\n", " scheme=\"QUANTILES\",\n", " k=4,\n", " cmap=\"BuPu\",\n", " legend=True,\n", " legend_kwds={\"loc\": \"center left\", \"bbox_to_anchor\": (1, 0.5), \"fmt\": \"{:.4f}\"},\n", ")" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax = df.plot(\n", " column=\"HR60\",\n", " scheme=\"QUANTILES\",\n", " k=4,\n", " cmap=\"BuPu\",\n", " legend=True,\n", " legend_kwds={\"loc\": \"center left\", \"bbox_to_anchor\": (1, 0.5), \"fmt\": \"{:.0f}\"},\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The new legends_kwds arg `fmt` takes a string to set the numerical formatting." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## When first class lower bound < y.min()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax = df.plot(\n", " column=\"HR60\",\n", " scheme=\"BoxPlot\",\n", " cmap=\"BuPu\",\n", " legend=True,\n", " legend_kwds={\"loc\": \"center left\", \"bbox_to_anchor\": (1, 0.5), \"fmt\": \"{:.0f}\"},\n", ")" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "BoxPlot \n", "\n", " Interval Count\n", "----------------------\n", "( -inf, -6.90] | 0\n", "(-6.90, 3.21] | 353\n", "( 3.21, 6.25] | 353\n", "( 6.25, 9.96] | 353\n", "( 9.96, 20.07] | 311\n", "(20.07, 92.94] | 42" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bp = mapclassify.BoxPlot(df.HR60)\n", "bp" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['(-inf, -7]',\n", " '( -7, 3]',\n", " '( 3, 6]',\n", " '( 6, 10]',\n", " '( 10, 20]',\n", " '( 20, 93]']" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bp.get_legend_classes(fmt=\"{:.0f}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In some classifiers the user should be aware that the lower (upper) bound of the first (last) interval is not equal to the minimum (maximum) of the attribute values. This is useful to detect extreme values and highly skewed distributions." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Show interval bracket" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax = df.plot(\n", " column=\"HR60\",\n", " scheme=\"BoxPlot\",\n", " cmap=\"BuPu\",\n", " legend=True,\n", " legend_kwds={\"loc\": \"center left\", \"bbox_to_anchor\": (1, 0.5), \"interval\": True},\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Categorical Data" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax = df.plot(\n", " column=\"STATE_NAME\",\n", " categorical=True,\n", " legend=True,\n", " legend_kwds={\"loc\": \"center left\", \"bbox_to_anchor\": (1, 0.5), \"fmt\": \"{:.0f}\"},\n", ") # fmt is ignored for categorical data" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.1" }, "nbsphinx": { "execute": "never" } }, "nbformat": 4, "nbformat_minor": 4 } geopandas-1.2.0/doc/source/gallery/choropleths.ipynb000066400000000000000000012035471525650651600226030ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Choropleth classification schemes from PySAL for use with GeoPandas\n", "\n", "\n", "PySAL is a [Spatial Analysis Library](https://pysal.org), which packages fast spatial algorithms used in various fields. These include Exploratory spatial data analysis, spatial inequality analysis, spatial analysis on networks, spatial dynamics, and many more.\n", "\n", "It is used under the hood in geopandas when plotting measures with a set of colors. There are many ways to classify data into different bins, depending on a number of classification schemes.\n", "\n", "\n", "\n", "For example, if we have 20 countries whose average annual temperature varies between 5C and 25C, we can classify them in 4 bins by:\n", "\n", "* Quantiles\n", " - Separates the rows into equal parts, 5 countries per bin.\n", "* Equal Intervals\n", " - Separates the measure's interval into equal parts, 5C per bin.\n", "* Natural Breaks (Fischer Jenks)\n", " - This algorithm tries to split the rows into naturally occurring clusters. The numbers per bin will depend on how the observations are located on the interval." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2017-12-15T21:29:37.736444Z", "start_time": "2017-12-15T21:29:37.716444Z" } }, "outputs": [], "source": [ "import geopandas as gpd\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2017-12-15T21:29:39.866422Z", "start_time": "2017-12-15T21:29:39.846422Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Observations, Attributes: (49, 21)\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
AREAPERIMETERCOLUMBUS_COLUMBUS_IPOLYIDNEIGHOVALINCCRIMEOPEN...DISCBDXYNSANSBEWCPTHOUSNEIGNOgeometry
00.3094412.440629251580.46700319.53115.7259802.850747...5.0338.79999944.0700001.01.01.00.01000.01005.0POLYGON ((8.62413 14.23698, 8.55970 14.74245, ...
10.2593292.236939312144.56700121.23218.8017545.296720...4.2735.61999942.3800011.01.00.00.01000.01001.0POLYGON ((8.25279 14.23694, 8.28276 14.22994, ...
20.1924682.187547463626.35000015.95630.6267814.534649...3.8939.82000041.1800001.01.01.00.01000.01006.0POLYGON ((8.65331 14.00809, 8.81814 14.00205, ...
30.0838411.427635524233.2000014.47732.3877600.394427...3.7036.50000040.5200001.01.00.00.01000.01002.0POLYGON ((8.45950 13.82035, 8.47341 13.83227, ...
40.4888882.997133675723.22500011.25250.7315100.405664...2.8340.00999838.0000001.01.01.00.01000.01007.0POLYGON ((8.68527 13.63952, 8.67758 13.72221, ...
\n", "

5 rows × 21 columns

\n", "
" ], "text/plain": [ " AREA PERIMETER COLUMBUS_ COLUMBUS_I POLYID NEIG HOVAL \\\n", "0 0.309441 2.440629 2 5 1 5 80.467003 \n", "1 0.259329 2.236939 3 1 2 1 44.567001 \n", "2 0.192468 2.187547 4 6 3 6 26.350000 \n", "3 0.083841 1.427635 5 2 4 2 33.200001 \n", "4 0.488888 2.997133 6 7 5 7 23.225000 \n", "\n", " INC CRIME OPEN ... DISCBD X Y NSA NSB \\\n", "0 19.531 15.725980 2.850747 ... 5.03 38.799999 44.070000 1.0 1.0 \n", "1 21.232 18.801754 5.296720 ... 4.27 35.619999 42.380001 1.0 1.0 \n", "2 15.956 30.626781 4.534649 ... 3.89 39.820000 41.180000 1.0 1.0 \n", "3 4.477 32.387760 0.394427 ... 3.70 36.500000 40.520000 1.0 1.0 \n", "4 11.252 50.731510 0.405664 ... 2.83 40.009998 38.000000 1.0 1.0 \n", "\n", " EW CP THOUS NEIGNO geometry \n", "0 1.0 0.0 1000.0 1005.0 POLYGON ((8.62413 14.23698, 8.55970 14.74245, ... \n", "1 0.0 0.0 1000.0 1001.0 POLYGON ((8.25279 14.23694, 8.28276 14.22994, ... \n", "2 1.0 0.0 1000.0 1006.0 POLYGON ((8.65331 14.00809, 8.81814 14.00205, ... \n", "3 0.0 0.0 1000.0 1002.0 POLYGON ((8.45950 13.82035, 8.47341 13.83227, ... \n", "4 1.0 0.0 1000.0 1007.0 POLYGON ((8.68527 13.63952, 8.67758 13.72221, ... \n", "\n", "[5 rows x 21 columns]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# We use a PySAL example shapefile\n", "import libpysal as ps\n", "\n", "pth = ps.examples.get_path(\"columbus.shp\")\n", "tracts = gpd.GeoDataFrame.from_file(pth)\n", "print(\"Observations, Attributes:\", tracts.shape)\n", "tracts.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plotting the CRIME variable\n", "In this example, we are taking a look at neighbourhood-level statistics for the city of Columbus, OH. We'd like to have an idea of how the crime rate variable is distributed around the city.\n", "\n", "From the [shapefile's metadata](https://github.com/pysal/pysal_examples/tree/master/pysal_examples/columbus):\n", ">**CRIME**: residential burglaries and vehicle thefts per 1000 households" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAEiCAYAAAAF7Y7qAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAuF0lEQVR4nO3deZwcVbn/8c8XCBAIBDAQ2YOoKD8iCCOiLE5wQxFwAdGLCIjmugFqUPCqLO5ehetyryK4sEoEVJRFBYEBUbYEAmFVhMhOZGdiWALP749zOqk0vVTPTM/UZL7v12te01Vddc5TVafrqb0UEZiZmZWx3EgHYGZmo4eThpmZleakYWZmpTlpmJlZaU4aZmZWmpOGmZmV1vWkIek4SV8aorI2ktQvafnc3Sfpw0NRdi7v95L2G6ryOqj3q5IekvTAEJe7o6TbSg7bK+meFt+fKOmrQxddy1j2l3T5MNUVkl7a4TjDFl9dvaWX52gjaR9JF4xQ3fMkvakL5U7J7WuFoS57JA0qaeSZvVDSk5Iek/RXSR+VtLjciPhoRHylZFktF1xE3BUREyLiucHEnes7StKpdeW/LSJOGmzZHcaxITAD2DwiXjyUZUfEnyNis6Es00ZOlZfnYBNpRJwWEW8ZRP0vl3Rm3vh6XNINkj5T28Aca/LymCvp35IekPQjSWsUvn/B+i/3b7sRNRR7GrtFxGrAxsA3gcOAnw5BuUtZ1rJ1wcbAwxExf6QDqYJleDkPykjOl6ovE0mbAlcBdwNTI2IisBfQA6w2krGNBEkzgG8BnwUmAtuR1jMXSlpx0BVExID/gHnAm+r6bQs8D2yRu08Evpo/TwLOBR4DHgH+TEpcp+RxFgL9wOeAKUAABwJ3AZcV+q2Qy+sDvgFcDTwO/BZYK3/XC9zTKF5gF+AZ4Nlc3/WF8j6cPy8HfBH4JzAfOBmYmL+rxbFfju0h4Ast5tPEPP6/cnlfzOW/KU/z8zmOExuM2wvcQ9obmQ/cDxxQ+H4l4Ds5jgeB44DxjeYBsDVwHfAkcCbwy8KyaVfPibnsC/P4lwIbF75/PXBNXg7XAK9v1k6Ao4BT6+ZlcTnvD1yep+tR4E7gbYXx1wN+R2pDtwMfqZsf3wXuy3/fBVYqfP/ZPG33AR/Kdb80f/d24OY8ffcChzZZnvsDfwF+kKf3VuCN+bu9gNl1w88Azm5S1lrAz3M8j9aGKyyPw4AHSL+R+uU5L0/PDcAC0sbaZOD3eRr+BKxZGH474K+k39/1QG+b3/ZhueyngRWAw4F/5LJvBt6Vh30l8BTwHKkdP9aubTaZp5cXugP4KPD3PF/+D1CTcU8FzmuzrtoduClPex/wykbtk8L6qslvqPQ8Z0nbnp6X7/3AjLrfVKu6DiO1wyeB28htrM10rp6XwXvr+k8g/a4/VP8brBtu8e+haR3tgmgT4OKZXdf/LuBj9TOGtII/DhiX/3asNYT6sgoz/GRgVWA8jZPGvcAWeZhfsWRltNQCaNA4XjDTWDppfIi0QnpJnuG/Bk6pi+2EHNeWpB/WK5vMp5NJCW21PO7fgAObxVk3bi+wCPhynmdvB/7Nkob5XdIKdK1c/jnAN+rLBlYkJaxDcjnvJiXOr5as50RS492JtDL4HvlHnut+FNiXtHJ5f+5+UZNlu3jeN1nO+5MS+keA5YGPkX50tbZyKfBDYGVgK1Iyrq20vwxcCawDrE1aSX4lf7cLaeVVay+/YOmkcT+wY/68JrB1ixXcIuDTeV7tTUoea+V58whLr5SuA97TpKzzSMl7zVzWG+qWx7dymeNpvAK7krTSWp+0UrgWeHUe52LgyDzs+sDDebkuB7w5d6/d4rc9B9iQJRshe5ES9nJ5mhcA6xbmyeV1ZXyXJm2zyTytTxrnAmsAG+VlvEuTcR+gsIHT4PuX51jfnOfx50i/7RUbrBdOpH3SKDvPp+TpOJ3U3qbm6WhbF7AZac9pvUJZm5ZYJ++S280KDb47CTi92fqvMN9bJo1unQi/j9RQ6j0LrEvaQn020jHaaFPWURGxICIWNvn+lIi4MSIWAF8C3jtExzH3AY6NiDsioh/4PPC+ul31oyNiYURcT9py27K+kBzL3sDnI+LJiJgHHENawZb1LPDlPM/OJ21JbCZJpBXrpyPikYh4Evg68L4GZWxHWqF/P5fza9IeWtt6Ct+fFxGXRcTTwBeA1+VzMrsCf4+IUyJiUUScTtr63q2Daaxfzv+MiBMinb86idRuJuf6dgAOi4inImIO8BOWzM998jTMj4h/AUcXvnsv8PNCezmqwfRvLmn1iHg0Iq5tEe984Lt5Xv2StCW4a543vwQ+ACDp/5F+8OfWFyBpXeBtwEdzfc9GxKWFQZ4nrYCebtH+fxARD0bEvaQ996si4rocx29IKzNyPOdHxPkR8XxEXAjMIiWRZr4fEXfX6o6IMyPivjz+L0l7Ads2GrHDttnMNyPisYi4C7iEtIHQyItICb+ZvUlt98KIeJa09zOetHc8EGXnec3RuW3PJe1Vvr9EHc+RktDmksZFxLyI+EeJ8SYBD0XEogbf3Z+/r3lvPhe9+K9E+V1LGuuTtrbqfZuU4S+QdIekw0uUdXcH3/+TtCUxqcmwnVgvl1csewXSFkZN8Wqnf5P2SOpNYslWfrGs9TuI5eG6RlCra21gFWB2YaH/Ifevtx5wb12Srp+3zep5wfA5kT6Sy62fV9D5NNbHsnjeRsS/88cJua7aSqhRXY2W23qF7+rbS9F7SCvRf0q6VNLrWsRbPy+L9ZwE/Edece4LnJFXKPU2zNPyaJM6/hURT7WIAdKeU83CBt215bcxsFfdCmIHUjJuZqllIumDkuYUxt+C5r+1TtpmM2V+X5D2mFpNx1JtIiKeJ01bJ+2zqOw8r6lvc+vRRkTcDnyKtGEzX9JMSW3HIx0qn9TkPNS6+fuaMyJijeJfifKHPmlIeg1pYbzgSoq8pT0jIl5C2gr9jKQ31r5uUmS7PZENC583Im0tPkTaHV2lENfyLN1g25V7H+mHVix7EUs3kDIeyjHVl3Vvh+U0K3sh8P8KC35iRDT6cd0PrJ9XZDUbNhiulcXDS5pA2pusnTvYuG7Y4jQutSyARleJtVseNfcBa0kqnuAs1tVoud2XP9/PC9vLkgAiromIPUiHts4GzmgRR/28XFxPRFxJOvS3I/AfpPMRjdydp2WNJt+XnSdl3E3aKy+uJFaNiG+2GGdx/ZI2Jh2O/STpsOMawI2A6ofNOmmbg/UnUsJvZqk2kZfbhjT+DZZpq52qb3O19tiyroj4RUTsQIo9SIcq27mCdKj83cWeklYl7dVe1FHkDQxZ0pC0uqR3ADNJx8rmNhjmHZJemhfaE6RdsNrlsw+Szh906gOSNpe0Cul49ln5kMbfgJUl7SppHOnk80qF8R4EphQvD65zOvBpSZvkFeTXgV822e1rKsdyBvA1SavlH99nSCfvBiVvMZ0A/I+kdQAkrS/prQ0Gv4I0rz8paQVJe9Dk0EILb5e0Q74C4yuk3fK7gfOBl0v6j1z23sDmLDkkM4d0aG+cpB5gzw7rXSzX91fgG5JWlvQq0kn00/IgpwNflLS2pEnAESyZ12cA+xfay5G1ciWtqHSvwMR8CKPWPptZBzg4T9NepJPB5xe+Pxn4X2BRRDS8FDUi7iedQP2hpDVzWTt1Mj86cCqwm6S3Slo+z7teSRuUHH9V0orrXwCSDiDtadQ8CGxQuzqnw7Y5WEcCr5f0bUkvznW9VNKpOSGfAewq6Y15XTCDtGL9a4Oy5pDa+Vq5rE8NQXxfkrRKPlR5AOnwZcu6JG0maWdJK5EuMlhIbo95uTXcoIiIx0mHZH8gaZfcpqaQLny5h+YbMKUNRdI4R9KTpC2ZLwDHkmZMIy8jbRX0k1ZiP4yIvvzdN0g/9sckHdpB/aeQTig9QDoxejAsnnkfJx3vvpeU1Ys3r52Z/z8sqdGx65/lsi8jXb3zFHBQB3EVHZTrv4O0B/aLXP5QOIx0yO9KSU+Q5u8LruWPiGdIWx8Hkq4g+QBppd7osEkzvyD9QB8BtiGdPyAiHgbeQfoxPkw60fiOiKjtCn8J2JR0cvzoXM5gvJ90nuA+0jHkI/MxeoCvko7V3wDMJZ2k/GqO8/ekk7MXk+bZxXXl7gvMy/Pxo+TzEk1cRWrPDwFfA/bM86HmFNJKtd2PdF/SnuitpPMkn2oz/IDkZLsH8F+kFf/dpKuASq0DIuJm0rm4K0gJYirpCrKai0lXJz0gqbbcS7XNwcrH+l9HahM3SXqcdFHMLODJiLiNtCx/QFpeu5FuFXimQXGnkM5PzgMuYMkKfjAuJc2Hi4DvRETtJsZWda1EuoXhIdK6bR3SsoO053JFs8oi4r/zsN8hbfzULkd+Y5PDpB2pXY1iY5Ckq4DjIuLnIx3LskbSeFIS2Doi/j7S8diyQ9JPgDMj4o8jUX+lb9qxoSXpDaSrfB4i7SW8inRy0obex4BrnDBsqEXEkD06aSCcNMaWzUjHdyeQbtLaMx9XtyEkaR7pBPE7RzYSs6Hnw1NmZlaaH41uZmalOWmYmVlplTqnMWnSpJgyZUrH4y1YsIBVV1116APqktEWL4y+mB1vd422eGH0xVw23tmzZz8UEZ3caT840eYBWMP5t80228RAXHLJJQMab6SMtngjRl/Mjre7Rlu8EaMv5rLxArNiGNfTPjxlZmalOWmYmVlpThpmZlaak4aZmZXmpGFmZqV1NWlIWkPSWZJulXRLm5famJlZxXX7Po3vAX+IiD3zc/ZXaTeCmZlVV9eShqTVgZ1IL4yvvc+h0fPrzcxslOjaAwslbQUcD9wMbAnMBg6JiAV1w00HpgNMnjx5m5kzZ3ZcV39/PxMmdOMtkt1RhXjn3vt4R8NPHg8PLkyfp64/sQsRDa0qzONOON7uG20xl4132rRpsyOiZxhCArqbNHqAK4HtI+IqSd8DnoiILzUbp6enJ2bNmtVxXX19ffT29g441uFWhXinHH5eR8PPmLqIY+amHdN539y1GyENqSrM40443u4bbTGXjVfSsCaNbp4Ivwe4JyKuyt1nAVt3sT4zM+uyriWNiHgAuFtS7Z3AbyQdqjIzs1Gq21dPHQSclq+cugM4oMv1mZlZF3U1aUTEHGDYjrWZmVl3+Y5wMzMrzUnDzMxKc9IwM7PSnDTMzKw0Jw0zMyvNScPMzEpz0jAzs9KcNMzMrDQnDTMzK81Jw8zMSnPSMDOz0pw0zMysNCcNMzMrzUnDzMxKc9IwM7PSnDTMzKw0Jw0zMyvNScPMzEpz0jAzs9KcNMzMrDQnDTMzK81Jw8zMSnPSMDOz0pw0zMysNCcNMzMrbYVOBpa0HDAhIp4oOfw84EngOWBRRPR0HKGZmVVG2z0NSb+QtLqkVYGbgdskfbaDOqZFxFZOGGZmo1+Zw1Ob5z2LdwLnAxsB+3YzKDMzq6YySWOcpHGkpPHbiHgWiJLlB3CBpNmSpg8wRjMzqwhFtF7/SzoYOAy4HtiVtKdxakTs2LZwab2IuE/SOsCFwEERcVndMNOB6QCTJ0/eZubMmR1PRH9/PxMmTOh4vJEyVPHOvffxIYimnMnj4cGF6fPU9ScOW70DVaU2UWY5FedvUVXndZXmb1mjLeay8U6bNm32cB7+b5s0Go4krRARizoc5yigPyK+02yYnp6emDVrVsfx9PX10dvb2/F4I2Wo4p1y+HmDD6akGVMXcczcdN3EvG/uOmz1DlSV2kSZ5VScv0VVnddVmr9ljbaYy8YraViTRtOrpyR9ps24x7b6Mp84Xy4insyf3wJ8ufMQzcysKlpdcrta/r8Z8Brgd7l7N+CyhmMsbTLwG0m1en4REX8YYJxmZlYBTZNGRBwNIOkCYOuIeDJ3HwWc2a7giLgD2HJowjQzsyooc/XURsAzhe5ngCldicbMzCqtzB3hpwBXS/pN7n4ncFLXIjIzs8pqmzQi4muSfg/sSLrv4oCIuK7rkZmZWeWUffbUc8DzpKTxfPfCMTOzKivz7KlDgNOAScA6wKmSDup2YGZmVj1l9jQOBF4bEQsAJH0LuAL4QTcDMzOz6ilz9ZRIh6dqnsv9zMxsjCmzp/Fz4Kp89ZSAPYCfdjUqMzOrpDJXTx0rqQ/YIffy1VNmZmNUJ1dPBb56ysxsTPPVU2ZmVpqvnjIzs9J89ZSZmZXW6dVTkJ495aunzMzGoLJXT10KbE/aw/DVU2ZmY1TZq6fmAPfXhpe0UUTc1a2gzMysmtomjXyl1JHAgyw5nxHAq7obmpmZVU2ZPY1DgM0i4uFuB2NmZtVW5uqpu4HHux2ImZlVX9M9DUmfyR/vAPoknQc8Xfs+Io7tcmxmZlYxrQ5PrZb//xO4C1gx/5mZ2RjVNGlExNGSlgdOiogPDGNMZmZWUS3PaUTEc8DakryHYWZmpa6emgf8RdLvgAW1nj6nYWY29pRJGvflv+VYcp7DzMzGoDKPETl6OAIxM7PqK3NH+CWkO8CXEhE7l6kgn0yfBdwbEe/oOEIzM6uMMoenDi18Xhl4D7CogzoOAW4BVu9gHDMzq6Ayh6dm1/X6S37qbVuSNgB2Bb4GfKbN4GZmVnGKeMGRp6UHkNYqdC4HbAN8PyI2a1u4dBbwDdIJ9EMbHZ6SNB2YDjB58uRtZs6cWT76rL+/nwkTJnQ83kgZqnjn3jt8T3eZPB4eXJg+T11/4rDVW6/sNBfjrRmpuMvE3CheGNl53Uq7NjyYttmtaV5W1xPTpk2bHRE9wxASUO7w1GzSOQ2RDkvdSXoFbEuS3gHMj4jZknqbDRcRxwPHA/T09ERvb9NBm+rr62Mg442UoYp3/8PPG3wwJc2Yuohj5qbmMm+f3mGrt17ZaS7GWzNScZeJuVG8MLLzupV2bXgwbbNb0zxW1xNDrczhqU0GWPb2wO6S3k46F7K6pFN9d7mZ2ejV9im3ksZJOljSWfnvk5LGtRsvIj4fERtExBTgfcDFThhmZqNbmcNTPwLGAT/M3fvmfh/uVlBmZlZNZZLGayJiy0L3xZKu76SSiOgD+joZx8zMqqfMS5iek7RprUPSS0ivfTUzszGmzJ7GZ4FLJN1BuoJqY+CArkZlZmaVVObqqYskvQzYjJQ0bo2Ip9uMZmZmy6Ayz55aGfg4sAPpfo0/SzouIp7qdnBmZlYtZQ5PnQw8Cfwgd78fOAXYq1tBmZlZNZVJGpvVXT11SadXT5mZ2bKhzNVT10nartYh6bXAX7oXkpmZVVXTPQ1Jc0nnMMYBH5R0V+7eGLh5eMIzM7MqaXV4yi9MMjOzpTRNGhHxz9rn/Pa9ya2GNzOzZV+ZS24PAo4EHgSez70DeFUX4zIzswoqs+dwCOkKqoe7HYyZmVVbmaun7gaG7xVxZmZWWa2unqq90/sOoE/SecDix4dExLFdjs3MzCqm1eGp1fL/u/LfivnPzMzGqFZXTx09nIGYmVn1lbl66hzS1VJFjwOzgB/7wYVmZmNHmRPhdwD9wAn57wnS5bcvz91mZjZGlLnk9tURsVOh+xxJl0XETpJu6lZgZmZWPWX2NNaWtFGtI3+elDuf6UpUZmZWSWX2NGYAl0v6B+nNfZsAH5e0KnBSN4MzM7NqKfO61/Pz615fwZLXvdZOfn+3i7GZmVnFtLq5b+eIuFjSu+u+eokkIuLXXY7NzMwqptWexhuAi4HdGnwXgJOGmdkY0+rmviPz/wOGLxwzM6uytldPSZos6aeSfp+7N5d0YInxVpZ0taTrJd0kyXeYm5mNcmUuuT0R+COwXu7+G/CpEuM9DewcEVsCWwG7FN81bmZmo0+ZpDEpIs4gv4ApIhYBz7UbKZL+3Dku/9U/jsTMzEaRMkljgaQXkVf4eW+h1Ps1JC0vaQ4wH7gwIq4aaKBmZjbyFNF641/S1sAPgC2AG4G1gT0j4obSlUhrAL8BDoqIG+u+mw5MB5g8efI2M2fO7CR+APr7+5kwYULH442UoYp37r3D926syePhwYXp89T1Jw64nOGKuRjvUOj2NA91vDWDibuVdm14ONtmvWbTvKyuJ6ZNmzY7InqGISSgRNIAkLQCsBnp5r7bIuLZjiuSjgQWRMR3mg3T09MTs2bN6rRo+vr66O3t7Xi8kTJU8U45/LzBB1PSjKmLOGZuuthu3jd3HXA5wxVzMd6h0O1pHup4awYTdyvt2vBwts16zaZ5WV1PSBrWpFG2lW4LTMnDb51v7ju51QiS1gaejYjHJI0H3gR8azDBmpnZyCrzPo1TgE2BOSw5AR5Ay6QBrAucJGl50rmTMyLi3IGHamZmI63MnkYPsHmUOY5VkM95vHpAUZmZWSWVuXrqRuDF3Q7EzMyqr8yexiTgZklXk27YAyAidu9aVGZmVkllksZR3Q7CzMxGhzLv07h0OAIxM7PqK3NOw8zMDHDSMDOzDjRNGpIuyv99Q56ZmQGtz2msK+kNwO6SZpIeIbJYRFzb1cjMzKxyWiWNI4DDgQ2AY+u+C2DnbgVlZmbV1Op1r2cBZ0n6UkR8ZRhjMjOziipzye1XJO0O7JR79fkZUmZmY1OZd4R/AzgEuDn/HZL7mZnZGFPmjvBdga0i4nkASScB1wGf72ZgZmZWPWXv01ij8Lk7rwIzM7PKK7On8Q3gOkmXkC673QnvZZiZjUllToSfLqkPeA0paRwWEQ90OzAzM6ueUq97jYj7gd91ORYzM6s4P3vKzMxKc9IwM7PSWiYNSctJunG4gjEzs2prmTTyvRnXS9pomOIxM7MKK3MifF3gpvyO8AW1nn5HuJnZ2FMmaRzd9SjMzGxUKPWOcEkbAy+LiD9JWgVYvvuhmZlZ1ZR5YOFHgLOAH+de6wNndzEmMzOrqDKX3H4C2B54AiAi/g6s082gzMysmsokjacj4plah6QVSG/ua0nShpIukXSLpJskHTKYQM3MbOSVSRqXSvovYLykNwNnAueUGG8RMCMiXglsB3xC0uYDD9XMzEZamaRxOPAvYC7wn8D5wBfbjRQR90fEtfnzk8AtpPMhZmY2Simi7ZEmJK0IvIJ0WOq24uGqUpVIU4DLgC0i4om676YD0wEmT568zcyZMzspGoD+/n7ufPy5jsermbr+8L4ipL+/nwkTJgy6nLn3Pj4E0ZQzeTw8uHDYqhs0x5sMpm23al+jbf5CuZiHe13QStn1xLRp02ZHRM8whASUSBqSdgWOA/5BejT6JsB/RsTvS1UgTQAuBb4WEb9uNWxPT0/MmjWrTLFL6evrY/8/LGg/YBPzvrnrgMcdiL6+Pnp7ewddzpTDzxt8MCXNmLqIY+aWeihyJTjeZDBtu1X7Gm3zF8rFPNzrglbKrickDWvSKLPUjwGmRcTtAJI2Bc4D2iYNSeOAXwGntUsYZmZWfWXOacyvJYzsDmB+u5EkCfgpcEtEHDvA+MzMrEKa7mlIenf+eJOk84EzSOc09gKuKVH29sC+wFxJc3K//4qI8wcerpmZjaRWh6d2K3x+EHhD/vwvYM12BUfE5aRzIGZmtoxomjQi4oDhDMTMzKqv7YlwSZsABwFTisP70ehmZmNPmaunziad0D4HeL6r0ZiZWaWVSRpPRcT3ux6JmZlVXpmk8T1JRwIXAE/XetYeEWJmZmNHmaQxlXTp7M4sOTwVudvMzMaQMknjXcBLOn3elJmZLXvK3BF+PbBGl+MwM7NRoMyexmTgVknXsPQ5DV9ya2Y2xpRJGkd2PQozMxsV2iaNiLh0OAIxM7PqK3NH+JMseSf4isA4YEFErN7NwMzMrHrK7GmsVuyW9E5g224FZGZm1VXm6qmlRMTZ+B4NM7MxqczhqXcXOpcDelhyuMrMzMaQMldPFd+rsQiYB+zRlWjMzKzSypzT8Hs1zMwMaP261yNajBcR8ZUuxGNmZhXWak9jQYN+qwIHAi8CnDTMzMaYVq97Pab2WdJqwCHAAcBM4Jhm45mZ2bKr5TkNSWsBnwH2AU4Cto6IR4cjMDMzq55W5zS+DbwbOB6YGhH9wxaVmZlVUqub+2YA6wFfBO6T9ET+e1LSE8MTnpmZVUmrcxod3y1uZmbLNicGMzMrrWtJQ9LPJM2XdGO36jAzs+HVzT2NE4Fduli+mZkNs64ljYi4DHikW+Wbmdnw8zkNMzMrTRHde8q5pCnAuRGxRYthpgPTASZPnrzNzJkzO66nv7+fOx9/bqBhMnX9iQMedyD6+/uZMGHCoMuZe+/jQxBNOZPHw4MLh626QXO83TXa4oVqx9xoHVR2PTFt2rTZEdHTjbgaKfNo9K6KiONJNxDS09MTvb29HZfR19fHMZc3elRWOfP26bzOwejr62Mg01lv/8PPG3wwJc2Yuohj5o54cynN8XbXaIsXqh1zo3XQUK0nhpoPT5mZWWndvOT2dOAKYDNJ90g6sFt1mZnZ8OjavlpEvL9bZZuZ2cjw4SkzMyvNScPMzEpz0jAzs9KcNMzMrDQnDTMzK81Jw8zMSnPSMDOz0pw0zMysNCcNMzMrzUnDzMxKc9IwM7PSnDTMzKw0Jw0zMyvNScPMzEpz0jAzs9KcNMzMrDQnDTMzK81Jw8zMSnPSMDOz0pw0zMysNCcNMzMrzUnDzMxKc9IwM7PSnDTMzKw0Jw0zMyvNScPMzErratKQtIuk2yTdLunwbtZlZmbd17WkIWl54P+AtwGbA++XtHm36jMzs+7r5p7GtsDtEXFHRDwDzAT26GJ9ZmbWZYqI7hQs7QnsEhEfzt37Aq+NiE/WDTcdmJ47NwNuG0B1k4CHBhHucBtt8cLoi9nxdtdoixdGX8xl4904ItbudjA1K3SxbDXo94IMFRHHA8cPqiJpVkT0DKaM4TTa4oXRF7Pj7a7RFi+MvpirGm83D0/dA2xY6N4AuK+L9ZmZWZd1M2lcA7xM0iaSVgTeB/yui/WZmVmXde3wVEQskvRJ4I/A8sDPIuKmLlU3qMNbI2C0xQujL2bH212jLV4YfTFXMt6unQg3M7Nlj+8INzOz0pw0zMystFGdNEbDY0ok/UzSfEk3FvqtJelCSX/P/9ccyRiLJG0o6RJJt0i6SdIhuX8lY5a0sqSrJV2f4z06969kvDWSlpd0naRzc3fV450naa6kOZJm5X6VjVnSGpLOknRrbsuvq3i8m+V5W/t7QtKnqhjzqE0ao+gxJScCu9T1Oxy4KCJeBlyUu6tiETAjIl4JbAd8Is/Xqsb8NLBzRGwJbAXsImk7qhtvzSHALYXuqscLMC0itircO1DlmL8H/CEiXgFsSZrXlY03Im7L83YrYBvg38BvqGLMETEq/4DXAX8sdH8e+PxIx9Uk1inAjYXu24B18+d1gdtGOsYWsf8WePNoiBlYBbgWeG2V4yXds3QRsDNw7mhoE8A8YFJdv0rGDKwO3Em+0Kfq8TaI/y3AX6oa86jd0wDWB+4udN+T+40GkyPifoD8f50RjqchSVOAVwNXUeGY86GeOcB84MKIqHS8wHeBzwHPF/pVOV5IT3O4QNLs/OgfqG7MLwH+Bfw8HwL8iaRVqW689d4HnJ4/Vy7m0Zw0Sj2mxAZG0gTgV8CnIuKJkY6nlYh4LtJu/QbAtpK2GOGQmpL0DmB+RMwe6Vg6tH1EbE06HPwJSTuNdEAtrABsDfwoIl4NLKAKh3VKyDdC7w6cOdKxNDOak8ZofkzJg5LWBcj/549wPEuRNI6UME6LiF/n3pWOGSAiHgP6SOeQqhrv9sDukuaRnvy8s6RTqW68AETEffn/fNKx9m2pbsz3APfkPU6As0hJpKrxFr0NuDYiHszdlYt5NCeN0fyYkt8B++XP+5HOG1SCJAE/BW6JiGMLX1UyZklrS1ojfx4PvAm4lYrGGxGfj4gNImIKqc1eHBEfoKLxAkhaVdJqtc+kY+43UtGYI+IB4G5Jm+VebwRupqLx1nk/Sw5NQRVjHumTKoM8YfR24G/AP4AvjHQ8TWI8HbgfeJa0BXQg8CLSidC/5/9rjXSchXh3IB3muwGYk//eXtWYgVcB1+V4bwSOyP0rGW9d7L0sORFe2XhJ5wiuz3831X5rFY95K2BWbhdnA2tWOd4c8yrAw8DEQr/KxezHiJiZWWmj+fCUmZkNMycNMzMrzUnDzMxKc9IwM7PSnDTMzKw0Jw0bUyS9WNJMSf+QdLOk8yW9XNLC/HTRmyWdnG9wRFJv4Um0+0sKSW8slPeu3G/P3N2n9OTl2tNKzxqZKTXrjq697tWsavKNi78BToqI9+V+WwGTgX9ExFb56ckXAu8FTmtQzFzSDVgX5e73ke5fKNonImYN/RSYjTzvadhYMg14NiKOq/WIiDkUHnwZEc8BV9P84Zd/Jj3falx+PtdLSTdAmo0J3tOwsWQLoOWDAiWtTHq0+iFNBgngT8BbgYmkxzxsUjfMaZIW5s8XRsRnBxyxWcV4T8Ms2TQ/Xv1h4K6IuKHFsDNJh6WKj7Au2ifyC3WcMGxZ46RhY8lNpLeiNfKPSI9XfymwnaTdmxUSEVeT9lomRcTfhjxKswpz0rCx5GJgJUkfqfWQ9Bpg41p3pBfdHE56E2Qrnwf+qxtBmlWZk4aNGZGezvku4M35ktubgKN44XtYzgZWkbRji7J+HxGXNPn6tMIlt38agtDNKsNPuTUzs9K8p2FmZqU5aZiZWWmjPmlIei4fO75R0jm1V392WEaPpO83+W6epEkDjO2dkjYvdH9Z0pvajHNi7ZEUdf37JPUMJI4ScTass804H5X0wW7E00EMUyTdOATlLH5USIPvflJchg2+31/S/3ZQ11aS3l7oPkrSoR3Gu5ekWyRdkmN/fSfjDyVJO0m6VtKi+jYkaT9Jf89/+xX6byLpqtz/l/l1zSj5vqTbJd0gaesmdfZ3d6oa63Rd0KptjNQ0DIVRnzSAhfl6+C2AR4BPdFpARMyKiIOHPjTeCSxe4UTEEREx7CdG849xyJa1pBUi4riIOHmoyqyqiPhwRNw8hEVuRXp97mAcCHw8IqaRXhk7bEkjP2al6C5gf+AXdcOtBRxJulFyW+BISWvmr78F/E9EvAx4lDQ9AG8DXpb/pgM/6sIk2CAtC0mj6Ary4x8kbSrpD5JmS/qzpFfk/nvlvZLrJV2W+xUfSvciSRdIuk7SjwHVCpf0AUlX5z2bH9d+QJL6JX0tl3mlpMl562934Nt5+E2LW/SSjpB0TY7leEmivQ9I+mseZ9tczlJbqvm7KfnvFkk/BK4FNpT0JUm3SrpQ0umNtnCbxZX3dL4u6VLgkGK9nczruromSLoob6nOlbRH7l+L/QRJN+XlMT5/t00u7wqabCDkrdfi1vyJkt4jaXlJ387Td4Ok/yyMNkHSWXn+nFY33T358y451uslXVRXLZLWlvSrXP41krav+35F4MvA3rlN7J2/2jzXc4ekgwvDv6C9STqC9B734ySdCXwU+HQeZscS87xX0mWSfqP0cMbjlDcoJL1F0hV5Gs9UekxKbQv7CEmXA3sVy4uIeflGyOfrqnor6W74RyLiUdLzvHbJ83VnoPYgx5NIG1cAewAnR3IlsIakdeunIce01O8t99s4t6cb8v+Ncv+l9qSVt/IlrZvnRe1IxY6t5kN2UKG91tr5WpLOzvVeKelVDeLdJJd5jaSvFPo3jKHSRvol5UPwMvb+/H954Exgl9x9EfCy/Pm1wMX581xg/fx5jfy/Fzg3f/4+cET+vCvpsRGTgFcC5wDj8nc/BD6YPwewW/7838AX8+cTgT0LsS7upvCCeOCUwvhLjVMYpg84IX/eCbgxfz4KOLQw3I3AlPz3PLBd7t9DekbSeGA10ovqD+0grj7gh4XvjiqMX3pe103TCsDq+fMk4HZSkp4CLAK2yt+dAXwgf74BeEP+/O3afKgr912khxICrEh6ttR40tZrbdmsBMwiPQKkF3gc2IC0IXUFsENhunuAtXM5mxTnE2kr+3/z518UxtsIuKVBbIuHL8zHv+Z4JpHuSB9H6/bWB/Q0Wf7t5nkv8BTwEtJv5kJgz1z3ZcCqebjDWPI7mAd8rs3v8ESWbuuH1uZ17v5S7jcJuL3Qf0OWtOVza/Ov0K56GtTV7Pd2DrBf/vwh4OwmsdXWGTOALxTWH6uVmA8H5c8fB36SP/8AODJ/3hmY06Bt/K6w/D7RKoZO14HD/bcsPHtqvNLjH6aQnit0Yd4yeD1wppZswK+U//8FOFHSGcCvG5S3E/BugIg4T9Kjuf8bSXcTX5PLHA/Mz989Q2rw5BjeXCLuaZI+B6wCrEW6W/mcNuOcnuO6TNLqan/+5p+RttggbZ3+NiIWAkhqVleruH5ZP/Ag57WAr0vaiZTg1ic9cRbgzkgPE4Q0T6dImkhaEV6a+59COqRR7/fA9yWtBOwCXBYRCyW9BXhVYatzIulQyDPA1RFxT56mOaT2dHmhzO1yOXcCRMQjDep9E2mvoda9uqTVIuLJBsMWnRcRTwNPS5qf50Gr9tZKu3kOaVrvAJB0OqltPEU6lPqXXN+KpORZ84Jl30ajPedo0b/VOPWa/d5eR/7tktrGf7eJ8RrgZ0qPwT87IuZIegOt50Ntns4u1LUD8B6AiLhY6WjFxLq6tq8Nk2P7VrMY2sQ84paFpLEw0iOtJ5Ia0idIWxaPRXosxFIi4qOSXkvai5ij9GjsFwzWoJ9IW6+N7hR+NvKmAvAcbear0kPxfkjairpb0lHAyq3GaRJXkLbIi4cZi+UsKFbbrvAScS1oMNpydDCvI+LhwiD7kLbgt4mIZyXNK9T3dGG450grTdF42dTX+5SkPtIhkr1Z8nwokbYU/1g33b0N6qtfhmXqXg54XS0xd6BR3a3aW1Ml5jk0bkciHU56f5OiGy37Vu4h7dXUbEDaQ3qIdNhphYhYlPvfVxhnw7px6m+8hPK/t9owi38j+fDYirB442sn0rw6RdK3SedYWs2H2rIq1ls22b2gX6MYouLnCpeZcxoR8ThwMGkXeCFwp6S9YPGJ4C3z500j4qqIOILUgDesK+oy0soMSW8DaifvLgL2lLRO/m4tSRvT2pOkXd56tRXjQ3lLveyVS3vnuncAHs/TPA/YOvffmhc+cbXmcmA3SSvnOncdirgi4gkGPq8nAvNzwphG4XEeTep6DHg8Tz/k5dTETOAAYEegliT+CHxMS16w9HJJq7abxuwK4A2SNsnjrtVgmAuAT9Y6mmyQNGsT9cq2t6XKKzHPIT3afROlcxl7k9rGlcD2kl6ay1lF0stLxNnMH4G3SFpT6QT4W4A/5pX9JSxpW/sBv82ffwd8MLeh7Uht/P4O6vwr6SGSkNpGbU9xHkueObYH6fAfeX7Oj4gTgJ+SfkcDmQ/FdUYv8FD+XRT9pS42WsRQactM0gCIiOtIL8R5H2nBHCjpetIhlj3yYN9WOol1I2lh179A52hgJ0nXkhr6Xbnsm4EvAhdIuoF0LLjhSbqCmcBnlU6qb1qI8zHgBNLx57NJu6hlPCrpr8BxLLni5FfAWvmQyseAhg/Qi4hrSD/K60m72LNIx/GLwww0roHO69OAHkmzchm3lqjrAOD/lE6Et9qiv4B0qPFPEfFM7vcT4Gbg2hzTjym5tx0R/yKdE/l1ns5Gh2sOztNzg6SbSSep611COoRVPBHeqL6y7e0c4F25vB1pP88hJcBvks5/3Qn8Jk/f/sDpub4rgVc0i69G0msk3UM6Qf5jpUez1A7ffYXUhq4Bvlw4pHcY8BlJtwMvIq0sAc4H7iCd2zqBdN6gEwcDB+T492XJ4+1PICX8q0nn3Gp7Tb2kvbHrSIeOvjfA+XAUebmT5ut+DYY5BPiEpGtIG0s1L4ih7MSOFD9GZAyRNCEi+iWtQlqhTI+Ia0c6Lhs+eUv40Ih4xwiHYqPUsnBOw8o7XulGtZVJx8udMMysI97TMDOz0papcxpmZtZdThpmZlaak4aZmZXmpGFmZqU5aZiZWWlOGmZmVtr/BwQNjQmf6ze9AAAAAElFTkSuQmCC", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Let's take a look at how the CRIME variable is distributed with a histogram\n", "tracts[\"CRIME\"].hist(bins=20)\n", "plt.xlabel(\"CRIME\\nResidential burglaries and vehicle thefts per 1000 households\")\n", "plt.ylabel(\"Number of neighbourhoods\")\n", "plt.title(\"Distribution of neighbourhoods by crime rate in Columbus, OH\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's see what it looks like without a classification scheme:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2017-12-15T21:29:54.097280Z", "start_time": "2017-12-15T21:29:53.766283Z" }, "tags": [ "nbsphinx-thumbnail" ] }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWYAAADtCAYAAACMJt+5AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAABusElEQVR4nO2dd3gU1frHP2e2ZDfZ9EYSQhIglNClW6gqiCIqFsR61Yt67b397A2vXnu5145drIAiTUVURAGlF+kQkpBet++c3x+7ICVlN9lNNjif55lnd2Znzjmb7H73zHveIqSUaGhoaGiED0pbD0BDQ0ND41A0YdbQ0NAIMzRh1tDQ0AgzNGHW0NDQCDM0YdbQ0NAIM/RtPQANDQ2NtqSrENLq57mFMF9KOT6kA0ITZg0Njb85VmCan+c+CEmhHMt+NGHW0ND4WyMAXVsP4jA0YdbQ0PjbE26LbZowa2ho/K0RaMKsoaGhEXaIth7AYWjCrKGh8bdHmzFraGhohBnajFlDQ0MjjBCEnxCG23g0NDQ0Wh1txqyhoaERRmheGX6SlJQks7Oz23oYGhoaYc7KlStLpZTJLW1HE2Y/yM7OZsWKFW09DA0NjTBHCLErKO0Eo5EgEpbCrKGhodFaaCHZGhoaGmGIZsrQ0NDQCCO0xT8NDQ2NMESzMWtoaGiEGdqMWUNDQyOM0EwZGhrNYMaMGVRWVmIymTCbzZhMpkO2g49FRUWRlpaGEOF2c6oRrmheGRoazeC2W2/mlBP6EmE0Yne6sDlc2B0u7E4XdrvT+9zhwu5wsqewmG+//Y7jjjuurYet0Y7QZswaGgGSndWJK88bw7D+uU2eO+bSx3E6na0wKo2jiXC7vwq3HwoNjSPIzs5m594Sv85VVYlOF243phrhzH4bsz9ba6HNmDXCnuzOXdmZv8evcz0eVRNmjYAJtxmzJswaYU/nzl1YuXi1X+dKKbnlphuItkThcrtwu9243R5MpggslmgsFgtRlmiio2OwRMcQHR3N4MGDGTNmTIjfhUY4E26mA02YNcKe7OxsPiso8+vc/z5wKXuKytDrFAx6PXq9gk5RcDjd1Frt1NTZqLXaqbNWUltTxNq1Rcz64lOWLlse4nehEa4E2ytDCBEHvA70BiRwGbAZ+BjIBnYC50opKxpqQxNmjbAnJyeHnXuK/Tq3d7dMenfL9Lvtleu2c+XDHzZ3aBpHCUGeMT8HzJNSni2EMAKRwN3At1LK6UKIO4E7gTtaaTwaGsEnKyuLPYXFeDxq0Ns2m4zY7fagt6vRfgjm4p8QIgYYAbwBIKV0SikrgUnADN9pM4AzGmunyb6EEG8KIYqFEOvqee1WIYQUQiQ1cO1OIcRaIcQqIYSWYFmjWZhMJhLi4ygsafDOr/ltRxiw2TRh/rsj/NyAJCHEioO2aYc11RkoAd4SQvwhhHhdCBEFpEopCwF8jymNjccfU8bbwIvAO4e8ESEygZOA3U1cP1pKWepHPxoaDZKTncWO/BI6dkgMarvmCCN2hyOobWq0P4Tip1+GKkullIMaOUMPHANcJ6X8VQjxHF6zRUA0OWOWUi4Byut56RngdrzGbQ2NkJKTk8OOfP/szIGgzZg1hACdTvFr84N8IF9K+atv/1O8Qr1PCJHm7U+kAY1+mJtlYxZCnA7slVI25cMkgQVCiJX1TPkPb3Pa/tuDkhL/ggk0/j5k5XRhl59BJoFgNmkzZg0QQvi1NYWUsgjYI4To7js0FtgAzAYu8R27BJjVWDsBe2UIISKBe4CT/Tj9OCllgRAiBVgohNjkm4EfgZTyVeBVgEGDBmmzcI1D6Ny5Cz9+82vTJwZIhNGA0+lCVVUURVsL/3si/Ddl+Md1wPs+j4ztwD/wToJnCiEux2v+PaexBprjLtcFyAFW+35BOgK/CyGG+H4tDiClLPA9FgshvgCGAPUKs4ZGY2RnZ/Pu3uAvVQghiIgw4nA4MJvNQW9fo30QzGyEUspVQH126LH+thHwFEFKuVZKmSKlzJZSZuO1qRxzuCgLIaKEENH7n+OdYR/h2aGh4Q85OTnsDIGNGcAUYcRms4WkbY12gPAu/vmztRb+uMt9CPwCdBdC5Pum4g2dmy6EmOvbTQV+EkKsBn4DvpZSzgvGoDX+fmRmZlJYXIbL5Q5622ZThObL/DdGEDwbc7Bo0pQhpTy/idezD3peAEzwPd8O9Gvh+DQ0ADAYDHRISWZPURmdM1OD2rY2Y/6bIwSKfx4XrUZ4jUZDoxFycvxP/xkIWvSfhqIIv7bWQsuVodFuyMnpzI784AuzNmP+e7PflBFOaMKs0W7IyunCzvw1QW/XHKHNmP/WiAAi/1oJzZSh0W7o3LkzOwvrC0JtGd7oP23G/Hem3S3+aWgcjpSSffv2sXPnTnbt2sWOHTvYtXM7u3ZsZ+euXYDgg49m0r9//6D2m52dzc784PkySymxO1zodIo2Y/5b07qucP6gCbNGwPzwww+MHj2aXrlZ9OicRlZqDHnpCZzSqydZacexbmsBJ584hhtvvpXBgwcjpWT16tVsWLuKuIQkunXvcWAGotfrMRgMB6Lu3G43VVVVVFZWUlVZQWVlOXarFYfDQWVlJZu27mrx+F//5DtuePQdHA4nRqOBSLOZuLi4Frer0T4RgrDzytCEWSNgRo4cyWX/uITdm//g3YcvIsJoOOT1Prkdye2Uwrtff89jsz9EEQo9c5IZlJNKZfVuVn3vjTNSpYrbI3G5PUhfEL5OEcRaTMRGGUmzmOmeaSbSFEeEUY9B34mzfviJyupa4mIszR7/7oJSbr31Nh544AGtPqAGoC3+aRwFCCH436uvc945k7n0/nf58PHLjjhnUK9sBvXKDnrfHZJiWbZqK+NH9G92G06Xh0SLRRNljQO0piucP4TX/F2j3aDX63n7nff4YlHr1j/ISk9ixfrtLWrD6fZgNBqDNCKNdo+fC3/a4p9Gu0BKSUQrC1y3rFQ2bt3bojYcTrcmzBoHEGjuchpHEU6n8wj7cqjpnpXCzhYGmThdHiIiIoI0Io12jwBFJ/zaWgttxqzRbJxOJ0ZD636EsjOSKKuqbVEbTpdmytA4FG3xT+Oowel0YjS2sjCnJ1JdY21RGw6XW5sxaxxCuzNltLBK9nghxGYhxFYhRMAFCTXCm7aYMedkJFFd2zJhdro0G7PGXwgEivBvay38sTG/DYw//GBTVbKFEDrgJeAUIA84XwiR1+yRaoQdTqcTg751hTk1MQaXR6WopLLZbWimDI1DCMNE+f7kY14ihMiu56X9VbIbKio4BNjqy8uMEOIjYBLewoQaRwGxsbEUFJdTUFxJekpcq/QphCA5PpqptzxPt5x0LGYTMRYT0VFmoi1m4mIiibVEEh8TRXyMhfjYKOJiIg+p5+d0ujRThsYhHBU25oOrZDfyhjKAPQft5wNDG2lzGjANoFOnTs0ZlkYrk5mZybXXXseVj37I7GevarUPt8PuYMe2fJKEh912F1anC5vDTZ3Thd3pxuZ043C5cbg8OD0qbo+KXqegVxT0OoGUUF4e/GRIGu2ToyIkO4Aq2fV9Sxusfq1VyW6f3Hvf/QwdPJv3vl7GRacNb5U+UxJiuP3kPlx8Qk+/zldVid3lxuoT7bNeXEiHDh1CPEqN9kP4JTFqzs/EwVWyd/JXlezDP+n5QOZB+x2BguYMUiN8MRqNPPTIY7w1+7dW6zPaYmZftf8LgIoiiIwwkBRtJjMxmkhzBBs3bmTz5s2Ulpbi8XhCOFqN9kC7j/yTUq4FUvbv+8R5kJTy8HyMy4FcIUQOsBeYAkxt/lA1wpWTTjqJK/95Oas27aF/j8ymL2ghyfEWCiqb75lxcs903nruCf7zyP2UVtdSXWcj1mIhMT6OxIR4EhMTSUxKJiElhcSkFJKSkkhMTCQpKYnk5GTy8vIOsVlrtHMEiDD7fzYpzL4q2aOAJCFEPnC/lPKNBs5NB16XUk6QUrqFENcC8wEd8KaUcn3whq4RLphMJm674y6ueOhFHrnmNJLjo4m1mOmSmRySWUZ6Sjz5e4qaff09Ewdwz8QBB/Y9qkpFnYOyWvtfW00pZfvyKdvuYJfVTZnVRVmtnVXbC5n52eeMGzcuGG9FI0wIpinDN1mtATyAW0o5SAiRAHwMZAM7gXOllBUNtRGyKtm+/bnA3Kb60Gj/XH/99cTExPDIq6/gcDgoKS2jrq6Oq88dwQNXnRbUGWZ2eiJr12wNWns6RSEp2kxStLnJc8995Tuqq6uD1rdGOCC8K4DBZfRhVoQ7gW+llNN9MR13Anc0dHF4zd812i06nY7LL7+cpb+uYOWqtezOL+CP1WtZvLaEa6fPDEofqqry7pxfmDHrZ7bta3CyEVLMRp1W7eQoQwhQ9Dq/thYwCZjhez4DOKOxk7WQbI2Q0alTJ+Z8/Q2dMjvy9C2TMUU0nfDI6XRz/u2v8vuGnZRU1OL0eJPoRxh06ITAYjJw7qDOvPFT2wizSa9o9QGPQgIwuSUJIQ7Odfuqz6PsYCSwQAghgf/5Xk+VUhYCSCkLhRApNIImzBohJT4+nrwe3fhlzTZGD+7R6LlWm4P+Zz9IvEHw7OTBDMpOIj4yAkUIKm0Oah1uOidF4/KovPjdOlRVbfVFOLNBmzEfdQgB/tuYS6WUg5o45zgpZYFPfBcKITYFOiRNmDVCztiTx/Pdb2sZPbgHu/aW8vBrX1NTZ6Omzk6dzYnd7sLhdFFYUsmAzES+vPpEIgyH3jamGiJJ9T036nUY9Tr2lNWSlRzTqu8lQie0GfNRSDC9MnxrbUgpi4UQX+CNgt4nhEjzzZbTgOLG2tCEWSPknHDCCJ566GsA3vnqF76Yv5yzB3ehk8VAdKIJS4SBaJOB9LgoTsrLwOBHFFas2cjW4spWF2azQRPmo5FgeQ8JIaIARUpZ43t+MvAQMBu4BJjue2wolQWgCbNGKzB06FCWr9tGeWUtlTU2dDqF/15wXIvaTLCY2FFSE6QR+o/JoKfaWtfq/WqEDiEEQh+0GXMq8IVP6PXAB1LKeUKI5cBMIcTleBO/ndNYI5owa4SchIQEIo0G0sfeSk5yDJf7GUrdGDohePTzX3lnyQaMBh0mg44Igx6zQYfJqCfSaCDSoMMcoScqwkhkhJ7oCANRJoN3hm42Em0yEhtpJNpkIMZs9MtebTboKba1LO2oRvghRHCE2Ze0rV89x8uAsf62owmzRqvQo0cPrulj4axjcoLSntvloQfQy+bEaZXYJDikxK5CjZQ4VIldVXGo0repOKXEqaq4VIlTlbikxK1K3FLiwes7qlcEa5+4kC6pcfX2azLqsFn/Eubt27fzxx9/4HQ66908Hg/p6elkZmZSXFxMWloao0aNCsrfQCNIiPBLlK8Js0bIyc/PZ+269Rx7xulBazMuKoJeBh1Xp8cHpT0pJS4Jo9fsZntxdcPCbNDjqP3Lxnzd1VdSW7yLtMQY36KkcsgmgA0/WMkvrcHlchER14FRS34Oypg1gocmzBrtFlVVsVqtREVFBbRY8siDD3D5cd3oEBvZ5Llut8qeir9q+imKIC3WjPGwhPwdYs3sK6ryf/BNIITAKMCsU6i01u8OJ6Xko+U7GX+hNxxbVVWWLvuV9a9eTYeE6Cb7mLd8C4/P2cj69eupq6s7YrPb7Qgh0Ol0KIpy4PHg55GRkUyYMAF9KxcoOLoRECRTRrDQ/rsafvPYo4/ywIMPIqUkOjqa6GgLU8+fyhP//neD1xQUFPDJzI/ZeP8ZR7y2ZV8Vry7ZyKKNBXx9/TjS46KY8MI8fvyzEL3P3iuROD0qekVBpwiMOgVFCDyqJNXQokisevEKs7Pe1z75dQtFDoXrrr8BgI0bN5IQE+mXKAMkx0ayZ9cOzp44jiiTEYs5gkiT1+4dFaHHZNAhJahS4lHlX4/qX/tb95by+v9e4aNPPiMysukfOg0/0EwZGu0Ft9vNpNNPR6fTkZCQQHx8PMt+/ZXpD9/LjdddRU1NLUt+WsqV192GwWCge48ejBo1iszMQ7PL2e12LCYjiRYT4J1lPvz1H/x38UaqbU4Gd0xk7d5yRj35Nb0z4li6dR8fnT2MU3L/yiLrcHuwuT043CrVDheqhN8LK7jn2+DnxDLrFKrqEeYqq4NbZ/7GzC/nYDB4Ixh//vlnju3lf1GHgd0y2D7jhhaNz+ly88/nvmbMyBOY/fU3pKQ0GkCm4QeCoyBRvsbfA0VR+H7xYp576lEMBgPl5RVEjTyWU8adiF6vJz4+jlNPOZlXnpOsXruOzz/9mCeemM6HH36E2WwmKysLvV6PXq+ntKKawY98CUKyu7SWKKOe58f1Y0JuBww6hbX7qlhRUMHvRVUcn5XMsZmJh4wlQq8jwpenINUn8AC2EORRNisK7/28keO6pZEeF8X46V8SGaGn0upkxNhxHHfcX25+S39czPDurZtw32jQ8/Ytp/Pgez+Q16MbN9x4EzfdfAsWi6VVx3FUIQS0t7SfGn9PFEXhkosvZueuPTz64D31nqPX6zlz0qmcOelUpJScd9EVnD/lPGrr6tizJx9VVRFC0DUxmqk90nBLycCR8QzvmIjuoFvHPqmx9EmN5R8BjC/VEoHNFXxhHhlr5oOiSh74fBnnDetGZVUd53WIY6FHpXe//oec+/PPP3PTnRODPoamEELwwEWjuHBMH+5/bza5L73A8y+8zDnnntvqYzlaCDdThpCy8SpOQog3gdOAYillb9+xh/FmS1LxhhZeuj8M8bBrd3JYXlJ/BjVo0CC5YsWKpk/UCCk7duxg0KBB7Nmyqln2TCklg4aO4pR4lXtOaDxPRnPajp0+ix/7diLBENz5xScl1Ty8uxRVwoXpCdyZHsf88lqetir0692L6qoqyioq2LZnN5Vf3tPmSfNn/rCWV77fyQ8//9qm42gLhBAr/dWVhugXb5Hzxx7helwvaZ8tbXF//uDPJ+ptYPxhx56UUvaVUvYHvgLua+T60VLK/q3xZjSCS05ODoMGDmTWnG+adf0nn81i06bNXNQ3+MV1hRDEmYxstbuC3vaJ8VFMz0nhp/5Z3JkeB8DouCj+ZfJw4va1XFyZT3ppIYN7dGxzUQbIzUiksjJ4Hip/O4RAKIpfW2vRZE9SyiVA+WHHDs4UHkUjRVY12jdTpkzhy68CF+YdO3ZxxT//xSunDqCTH25yzSE5ysQOW/0eFC0hXq9jQoKFuIPy7xoVwaSkaE5OsHBsbCR7VBg/KDfofTeHuCgzVVry/mYjAKFT/Npai2bfAwohHgUuBqqA0Q2cVl9e0obamwZMA28eX43woFNWFiUlZQFdU1xcwnEjTuTCvp04p1fHEI0MOkSb2WNtm4RCRR6VkX2z26Tvw9lXWYvVpqUibTZh6C7X7J8AKeU9UspM4H3g2gZOO05KeQxwCnCNEGJEI+29KqUcJKUclJyc3NxhaQSZ6Ohoamprmz7xIMaedBpDk6OYPqZXiEblpWOMib1Od0j7qI+ddie1DhcDuqa1et+Hs3VvGec8+inPvfBSWw+lHdMOTRl+8AEwub4XDs5LCuzPS6rRjoiJiaG62v8sbus3bGLr9p28dEo/v9J3toQMi4mSEHhmNMVnJTUM6paBoWWlhlrM7F82csKtb3P/Q49x/vmNlubUaAIhhF9ba9EsU4YQIldKucW3ezpwRIb+RvKSarQjMjMzyd9bgMPhICIiosnzP/j4M+xuD91enE+0UU+s2Ui8OYIEs5GkSCNJJgPxJgMWo54oo44og56c+Cj6d4gLeGwdLBHUtkEo7TKbi3MGt719efonv/LiK69yzjmNZpDUaApBIBVMWoUmhVkI8SEwCm+tq3zgfmCCEKI7Xne5XcBVvnPTgdellBNoIC9pKN6ERuiIioqib98+zFvwLZMmTmjy/EcfvId77riJXbvzyd+7l/z8QgqKiti3r5iS0jLWlJZRU12F3WrD6XDgdDrYU1jMzhtPIc5kDGhsqVEm2iIBZ4GqMiIM7MsGvY60tLY3pxwNtKaZwh+aFGYpZX33SG80cG4BMMH3vN68pBrtj9tvu50nnnzCL2EGiIyMpGePbvTs0c2v8zMzc/l5dxmndgtMZDpYIrB51ICuaSmFDpc3lLxbRqv2Wx9GvQ6nM/heKX83hBBaSLZG+2PY8OFs274jZO337NOH73cVBCzMKVEmbK7WXfz7rLSGfl3SiTC2zVenoKyaOruLH9fuZEdBqSbMwaIV7cf+EF4/ExphSUxMDDU1gXlmBMLkM09n/rZ9AV+XEhWB1eVBVVtv1vxTnYNxg7q0Wn8Hk19SRf+r/8uEBz7l/eXFXHPzHQwbNqxNxnK0IRTh19ZaaDNmjSYxm83Y7XaklCFZmb7ognO5/qbbKbc5STD7b2eOMurRK4JCp5uMAO3TzWWvhFH9mleF5b9zfuXXTfnERpmIs5hIiDZj1Ouos7uwOd3YHC7s+x9dbhwuNw6nx/vocrO1sJJrr7+RBx7U1tCDivBtYYQmzBpNoigKRqMRh8OByWRq+oIAiYyMJDUxnh93lTKpR3pA1yZERrDN7moVYS5xuimptvLpkvV8+fNGpJRIvHk79udRlkik6tvH+4jvvM+XrGdUXkfKSquptjmptTtxeVQiDHpvBj2Db9PrMBv0RBgUYk16TJYIrE4Xv2zM58677g7a+1m5ciU7d+48kAVQCEFGRgbdunXDbDYHrZ92QZiZMjRh1vALi8XC/z3wOE8+/kBIZs29+vbju127AhbmFIuJHXYXDUYuBZGPi6uRwJ69pT6/1r/K3ns9rv46JvjrNUWAQHDR8T155sLj0TXDA+CL5VvZoyT69cNYUVHBjBkzGDp0KLm5uSQmJlJUVER5eTlOp5PS0lI2bNjAjTfeyKDOaXSIteBWVVQJ+RV1bC0qxWyKYO68BRx77LEBj7U9Ema6rAmzhn/MmzePq666knc/mMnFF5wX9PbPnjyJR+6uP71oY6RFm8mvrAv6eOrjF6uD208bxGPnDm+V/g7mxy37GDG23jiuQ3A4HJx52gT0FXt55wUP2/dVouh0CKmSGmfBqNcRHxlB5zgTc68cw8k9jlxwlVLy2ML1zPzog7+FMAshELrwUmZNmDX8YtCgQTzxxL+55l9Xc8GUs9Hpghv1dsGUyfzr2psoqXOQHNV0IMt+Okab2FlcGdSxNES+CqN6to2b3JItxbx8X0MpabyoqsqlF04l0V7CR1ecgKIIPKpKcY2DDjEmv+90hBBkxUfyx7ZtwRh6+yDMpsyaV4aG34wZM4a4+Hg+nPlZ0Ns2mUx0SErgp92lAV2XYTFR1gpeGZVuN2VWO8O7tm7FEoCKOjtbC8sYOHBgo+fdfcft7Fr9KzPOG4Ti8yDQKQppseaAzU8T8jKYu3ARTeVrP2pQ/Nz8QAihE0L8IYT4yrefIIRYKITY4ntssrS7JswafiOE4PnnX+CWO+9nz569QW+/z4ABfLszMGFOtURQ1wof4y9La8ntEE90AF4jweLnPwsZOnAARmPDfb/y8kt88cEMvrh4GOYg+Fg73Cpx0ZZWzQ/RlgQ5V8YNwMaD9u8EvpVS5gLf+vYbRRNmjYAYMmQIN95wIxf84yrc7uAGd0w550wWBujPnBplwtYKk7rvq22M65sV+o7qYcmfRYw48eQGX583bx4P3Xs3c/5xLEmW4HjNqD5Pk78F3pVa/7ammhKiI3Aq8PpBhycBM3zPZwBnNNWOJswaAXP7HXcQn5DEuInnsmDR97zx9nvEpubgcrWsmsi5Z59BcZ2Notojcwu7VZVf88tYsK2Izzbk89YfO3nh1y3M3VJItTP4VUwOZ7eEUT0D8xgJFku2ljBqVP325Y0bN3Lx1Cl8dMFQuiRFB63POevzGTnihKC1F+4InfBrw5szaMVB27TDmnoWuB1vHqH9pEopCwF8j02WNtcW/zQCRqfT8fkXX/DIww/z72dfITYmlurqGqqqqklKSmy6gQZ44JEniBAK3Z/7hmiDHlVK3wZuqeKUECcEBiEwAgbhnVmEOl9GnVultM7OcbmtL8w1Nicbd+9jyJAjM+aWlZUxcfzJPDE+j+M7N/ldD4hah5uOOW1zh9Am+G+xKW2oTJ4QYn9t1JVCiFEtGY4mzBrNQqfTcf8DDxzYz87Ooqa21m9hdrvdlJdXUFZeQVlZOZ/P+oq3/vc6g+Kj2FBWw4ORRvQCDIBeCK6usnKTlPSRkoPvsd3AJYBTVTGGKEPYnLIaspJjiQvAWyRYLN1SyMB+fY7wX7bZbEyaMJ6zuidw8ZDOQe93S4Wd/id3D3q7YUnwci0fB5wuhJgAmIAYIcR7wD4hRJqUslAIkYa3gHWj+JP2syVVsscDzwE6vOlAp/v7DjXaF9HR0X7l05g7byGTzpqKW0oMQmDQKZh0Cmadjmf65yAl/F+1jZ6GQ93xrKqkvhRHeiAC2GV3kRsZGuFcVGXl5KFtk395yeZCRow96ZBjUkouOPdssqji0fGDQ9LvL7vKuervlIcjCLospbwLuAvAN2O+VUp5oRDiSbzzh+m+x1lNteXPjPlt4EXgnYOOPSmlvNc3gOvxVsm+6uCLhBA64CXgJCAfWC6EmC2l3OBHnxrtDJPJhN3uaPB1VVXZsWMXz7zwX07JSOLlAdko9cxSdtc5qHIfWpXEISUeIKGBtqOFYJvdGTJh3iHhhh5t47/84/ZSHrz+UPvy448/zh+//syG20854BYXbHaWVNK9+99kxkzIa/5NB2YKIS4HdgNNVjbwJx/zEiFE9mHH/KmSPQTY6svLjBDiI7yzbE2Yj0JEE1OOHj0GsHtvIWmRETzeu1O9ogzQMdKIW1UpcKuk672miUpVEiEESgNuAnGKwm57aNJ/2lWVEquD47u3vn3Z6nCxenshw4d7Iw2tVis3Xn8tr73xFhcO7oIxhKWtUmItFBQU/H3EOci6LKVcDCz2PS8DxgZyfSirZGcAew7azweGNtKeViX7KGPjpj+pramlvLKSnXsLWDNuADGGxj9yihB0jo5kqcvF2XrvDLhSeoW5If+tBCDfERrPjLlltaTFR5EU3fpJfZZtKyKtQyrbt2/H4XBw6YVT6Zts5IqReRSU+l+HsTl07xDHtm3b/hbCLP5mVbLre6cNekZqVbLbPwdHib3y6lv0OeZYTj5xAlPPuYAzOqU0Kcr76RsXxRrXX54WlarE2MgXJ0FVKQpRwvz5FXWc2LttvBMSLSZyE02cN3Ecp407kZtHZPHOP0eTmxbPvhpbSPsWgMfT+oVu2wxF+Le1EsHwyvgA+BpvLcCDyQcyD9rvCByxQKhxdJDXK48LLrqC5OQkjKYI1q3fhCrhhKQYTIqCGZWH1u/GoleI1Omw6BWiDTosOh0xBj0xBh0xej2xRoU+MWZW7vtrzlAnJZVuDzfpdURIuMXj4eCf7ngp2eMOTTTEdgRX5rWNfblfp2TmXH9kYElOcgwl9fh6B5Nd5bVkZf193OXCLcAxZFWygeVArhAiB9gLTAGmNmuUGmHP1Vf/ixkz3mF0yT48eH+FnWYjjso6qoB90ruIZ5cSh++5Q0qcUuLyPbql1/0NwKIIvP4WMMKoJy02kkopeaLWziY4RJjjgJoQ+DI7VZV9dXZOaAP7cmN06xBPeV1ohTkvJYYff/yRvn37hrSfsMDPqL7WJGRVsqWUbiHEtcB8vO5yb0op14fmbWi0NUOHDmVQXh65u7czrIW5GipVyeTKOtyqil5R0AtxwH0uVqfDqR5qtogFrO7gC/PCijqSos10iIsKetstoWtqLFanG4+qNiu3sz8c3ymG9WtWhaTtcCTMdDl0VbJ9+3OBuc0enUa74vLrruOTu+5gGC0TyThFEKUI1rpVBhgPFR69gMOX+WIBuxp8e+i8ilrG9skOerstxWTUY9ApVFidQcuNcTiKENjtoZ2VhxVhtvinRf5pBI3zzz+f22+6iUqTjrgWftC7GAz85nQz4LDZt0EI9teFfkcIVut1OFWJR/UwdNUu3+qyNwHPfqvzgUcpD91nv6OHr0SU7+CB50juiA+v2TLAy4vWEGsyEhvCTHdju3XgmXe/DVn7YUUYemVowqwRNGJjYzltwgQWzfuas02GFrXVQ4GN9ZgnDIgDM+bfdTrOijTSP8LA1cVVfNgvi0i9gkD4yjn5yj3xVwIxxedxvf91IQTKYa8J32snLN3MiABLXYUaVVV59MvlPD6xPwZd8M0YqqryR34FczfuZefeQq649GI2b1jP7fc+wMSJE4PeX9gQZrYMTZg1gso/r72Wq75dxGTpaVH+gY4CvnSrXFdZh0QghXcWu8utskfAKkWh0uNmXFQ0GXodkYpArwi6RgXn1r7Y4cLmURnds2NQ2gsW0+esJEInuGBg8yp170dVVVbtrWDh5iJ+21XK9vI6SmodVFjtGHUK3ZJjuXxwF/qWrcPlLuenJUuOamEWYZZnUxNmjaAycuRIag0GplXUYhL7TQJ/CfQBM8KBQ+KA6cAlJYoi0AtBjUfFjWRIlPmAs73O15JOgA6vHTrdN2uM1enYbnWQF6RAkN8q60iJjkQJ0eJac1BVlefmr+LlyYPR+zlbVlWVdYVVzF6fz+q9FWwtraO01k651Y5BUeiWHMOAtHjGHJNMz+Ro8lJijyjt9d4qPd/t3B6KtxQ+aDNmjaMZRVE4ftQoZn76GdPiIg+YETji8a8g7v2mg/eqbfSIj2ZcxyQAHvl9G5OjTCTomxahBL2OPTZnk+f5y/yyWgbnpgatvWBw58dLSY6K4My+mUe8tl+AF2wu5LddpWytclJidVNRXeu9cxGCKb3SuXxAInkpMfRMjiGliYXDaruL537ZypxtZWT1aihTyVHAwR/SMEETZo2gc8ddd/PrwoVcEWsMyJwxx+pkatc0zu/qteu+s6WQn+0OJlqangUn6hQK7MEJy65xe1i4r5Ll158SlPaCgdPt5o3F63n3wuFs2FfJgk2F/LqrjK2VDkqsbspratHpdHTr2oVjBozgn/360CuvO7169qCquprBx53If08fEFCfa/dV8c7mMl5/+x2OP/74EL2ztkcgECGw17cETZjDkN9//527br4RnaKg0+kwRETw2FNP06NHj7Yeml8MGDCAMoeTGtVATABl4d1SYj6o+na/pBhWFpUz0dL0tSmKYF+Q8mV8va+KtFgLeRnNT/ofDJxuN/PX7OHrVdv5etUuXOg4791fEYqgW9cu9O93HJf160OvvB706tmd1NSUen8Iq6qr62m9aRIjjUQY9Jx00klNn9ze0UwZGk1RXFzM3j838uCwLnhUyUeb/+STmTO597772npofiGEQK9TAvZmdkuJ6SAhH5Bg4cPCcr+uTRKwzdlyX2aHR+WlXaVcNr5fi9sKBLdbZdGG3Xz1+06W7yimoKKWshobiRYTOYnR7Kuq45Ybr+Wm666iQ4fUgO5EpJTNulNPjoqgpLyiGVe2M/a76YQRmjCHIbm5uVS7PIzv7LVxSiRvfLsQ2okwA3RM7UCBrZy4AG4RXRLMB6Wy7BVvwV9ZiNcp1LRQmKWU3LelCEOkkf+bFJoE9Afz/PxVfPjLnxRU1FFSYyXeHMGg7GTO7pPBMZmJHJOZQJzZyKTXFnPs0EH8+7EHmtWPlLJZM0LvZeElWKFBhJ1bhibMYUhWVhbFVbXY3R5Meh3DMxKZtmgJbrcbvb59/Mt69+3DD98twFVPqs4IIegRcaSfs0dKTAcJeV68hQqnC1VVm/SOSFBEi8Oyn9tVyoLSav6YfkHIvTF+31nMPTOXcvfJfRmU5RXhxHpc/T79YydLd5SwY+v3ze5LyuatbdncHgx6PXPnzsVms2Gz2bBarUgpiY+PJzExkYSEBBITE0lNTSUiovVLbwWNMPsBah/f8r8Zer2erPQObK+sIy8phkSzkYxYC2vWrOGYY45p6+H5RVJGR16stjHLfeQstsLm5MmkGEYc5pblkRLTQTPmZLORCEVho8tDr4jGhTJep2D3M02l3a2ysc7G+ho7W+sc7LI62GVzss/lJj0hmnOe/waDzlv2KsKgJ8Kgw2TQYTboMRm9j6lxkdxxWr01Of1i2hvfc8Vx3blrXJ8GzymttXPlR8t46qkniIuLa3ZfzWVXhZXi8kqef/wezBEGIk0GzBF6BIKKWjvlVXWUV1kpLCln4KBBzJ23sNXHGBQ0U4aGv+TmdmVbRS15STEADE+LZcmSJe1GmOPi47jz+B7cN7LnEa+NnvEjW2qsRwizW0LkYVU5uidEs9Rmp1c9M+yDiVcOFeY5RZUsKq2m0uWh2q1S6fJQ43Jj9XhwSIgEEhWFFCHooKrUCEGnuEguz+uI06Pi8G12j8Tu9mC3ubDXOqh2q9g9Ki/vKGJcnyz6ZwWeO3zpnwX8WVjO3GkjGz3vX5/8RveePbnisosC7uNgZAMFBpqixuEiIyWOuc9Oa/S8H1b+yf0zljarj/BAgC501WCagybMYUpuz15sXfVXroIRHWJ47YP3uOGGG9qF3S8vrxcfz/6w3tcyYswUVBxZuNXDoV4ZAMckRrNmW9NFXuN0AofqFaC3d5cyfUshx+l0xKsqmVKSBCTiTReagO+Dr/5l+tisU7ggL5Mr+zcdUVdpd9HrzRKOe3AmekXx3QWLA3fDAq9jdkP/JYfbww2je5HSSDDMrvJa5q7bw57t85scT1PExcZgczjJe+UHxnS0cNvxPcjyIweI28/sdYqioKqhyYfdaoTZd0oT5jClW888fvt53oH9M7ql8czq33jv3Xe56OKL23Bk/tG3b1/uL6m//FHHGBPL68kG55FgPiyYpG+ChQU7m57NRAmBCryzxyvKdwD9AqjAIVSJx4+ZpZSS8+asoHNyDHOuHA1CICWoUiKlRJXexVq1CXN3TmLjPoCRvmovSUktd9lLS+vArs1/8NU3C3jj7fc48YPf2HJNfdXgDsWg1+H242/orfoV/LSrrYYAwijCE/zLx/wmcBpQLKXs7Tv2JDARcALbgH9IKSvruXYnUAN4ALeUsvlGub8Zubm5fFj9V9VpvaLw3IjuTLn5JvJ69WLgwIFtOLqmycjIoKiyfmHuFGPmdbfKGaU1jNEJro/3ipQKRB0mzHnxFir9EgeBScBjfxZyIxCos5siVb+E2a1Klu0to+7pqRhDuBCbGBWB0+3BarUSGRnZ4vZSUpK57JIL6Na1C+eef4lf10QZdLj8WFBVhEBt6pco3AmzGbM/PxNvA+MPO7YQ6C2l7Av8CdzVyPWjpZT9NVEOjNzcXLaVHRoYMCgtnoeGZDHxxDGcc8bpbN68uY1G1zSxsbHYnC7s9Sz+/fOYHBZcdAJjOqewyufipkpv6k3jYTOXbrFR1Djd1Dbxxd/udOORcCXe8uyBIqRXdJvCIyWKECEVZQBFEUSbjGzavKXpkwNqV2m48OZhRBr0uOv5/9XbZru2ZAjvjNmfrZVosicp5RKg/LBjC6SU+8tILMNbSUgjiGRmZlJeZ6XWeWi1jql5may6+Hj6VGznuMEDOWHIIJ5//nkKCsKrnGJRURFxUeZDvCz2Y9ApHJMWT058FDrfN9qN98N4uJtahE4hLcrEL43kwXCqKteU1HCKotD4clrDKIDLD2FWm+cSHDC1Dhduj4rHD2EMBCEE/ipztFGHy09TRrueMR/I9erH1lRTQpiEEL8JIVYLIdYLIR70HU8QQiwUQmzxPcY31k4wfgIuA75p4DUJLBBCrBRCNLq0K4SYJoRYIYRYUVJSEoRhtW8URaFLp0y2VdQd8VqUQc8tg7vw52WjuD5Dx7I3n6dXt1x653bhysv/wTvvvMOWLVtwu0NTOdof1q9fT4/UxhPfWIx67D4hdsuGP4x9k2L4rZE8GDeU1pIoJVNbIA46/J8xt8ZN74s/bCItrQODBwfXC0cI4SsLcCR2l5t5W4q4c95qTnrrByZ+/Jtf77WZ8SthhM8rw5+taRzAGCllP6A/MF4IMQy4E/hWSpkLfOvbb5AW3Y8JIe7BO9l5v4FTjpNSFgghUoCFQohNvhn4EUgpXwVeBRg0aFC7vjEKFt26dWNLRQn9UmPrfT1Cr2NClw5M6NIB1+ierC2pZtmOFcxZ8SP/V1BOYUU1D9x3L/fcd3gB89CzevVq8hIaz142KD2eJ6Rks8NNuao26Ep6TIKFz4vqjwF8p6qOzXYnz+IV1+birzDrhEARgtHPLeD7G46sYB0Mqm1Onli0jg/enxH0thVFQaqSX/eUsWjrPn7NL2dXnYsyq4OKGiuJsRZ652YwfOQxTOuazuBeTVfKdjhd7Ty4hKD9skivb+J+NyKDb5PAJLy1UwFmAIuBOxpqp9nCLIS4BO+i4FjZgKOkrwYgUspiIcQXeM1/9QqzxpF079OXLT/O9utcg07hmA5xHNMhjn/5jq3aV8k/3ni9QWGWUvL6668TGxtLTk4OOTk5JCYmBsUdb/GCeZyTVv8Pyn76d4jD4fFwRWEF0REGOsfU76nQK97C6/WMaa/LzRtVNm4BGr0v9AOvKaPpGXekQccvF45g4IzFuN0qej9SkgbK80s206FDGqdNCL7wFxQWUllTw8QPl9GzcxoDj+nB5NwM+nTNIK9zByyRgRcacLjc7VuYCai0VJIQYsVB+6/6JpV/tSWEDlgJdAVeklL+KoRIlVIWAkgpC32T1QZpljALIcbjVfuRUkprA+dEAYqUssb3/GTgoeb093elR8885s77rNnX90uJpa6mhrlz5/L7ypXs3PonRfuKmfX1XHQ6Hdu3b+f2m25gRE4au6tt7Cyrwq2qZGWke4U6txs5XXPJzs4mMzOTjh07kpKS0mS4sqqq/Lj0F164fESj55n0Ol45bSBXzlnJI0O6Mblzh3rP6xVvodJ5qCnjsxobT1TUkQYElsyyfnR4zSn+0DXeglGnUFRjo2OQawJW2Zw8tWgdM2e+G9R291NdU0OnDgls/uKBoLXpcLZ/YQ4gV0ZpU44MUkoP0F8IEQd8IYToHehw/HGX+xDvFDxJCJEP3I/XCyMCr3kCYJmU8iohRDrwupRyApDqG9T+fj6QUs6rpwuNBjjhhBO45bp9FFu7khIZ+AdfCMG4nGTOOess/tG3E31jzbyxaA1utxudTseGDRtItZh5bFgO2bFegam0u9hdbWVnVTW7Nyzhz9++5Vubm701NvZW1lBls5OenERGWhqZWVl0zO5MZlbWAeHu2LEjbrcbk15Hqh8VnCf3zEABps35nTm7S3hzRK8jhL9DZARCCLY63dSpKk/XOtjrUZnUPZ31W4vA0/KFJz3egAp/Mel17KuxB12Yn128iYyMdE45+cSgtrsfXQgi3LzCHJpq3a2CECEJyZZSVgohFuP1atsnhEjzzZbTgOLGrm1SmKWU59dz+I0Gzi0AJviebydwd1KNg+jcuTMXXXoJD/08nxdH5zWrjbsG5XBdv050S/CaCW78du2BREjDhw9n/DnnM+q9d+mTHMut/TMZ2SmJOFMsfVPqN0PY3R4Kau1eoa7Zxd7fNrN+iYcFNjcFNTbyK2sor7OSmxjj9xjP7JlBvw6xnP/Zcrp/+jPZFjOj0+IZ1zGJOKMBl6oSa9RzVVkNbuCyYzpzy7CuvLt6N5u27mvW3+Vw/PXK2E+UQU9RtRVvPGFwqLQ6efq7dXzxWf0Rk8FAEUqzQ7Qb4mgwZQQrJFsIkQy4fKJsBk4EngBmA5cA032PsxprR4v8C3Puf+gR+vf6jLfW7uYffToFfH36QWG/UkpiI03s2LGDrl27kpSUxLMvvMj0J5/i7bff5sYH7+WPC5Iabc+k19E5LorOcQ3PFP8sr2H4Oz/gVlX0fvp+do63sPSyUawoKGfxrlLmbt3Hm3/uxeWbDXdNjGZq70wu7pdFrK8Ct1P1tGjB72B0BCbMFqOeklpH0ycGwDOLN9KpUyYnjR2N3W5n9lfzmLfwO+645Tq6d8sNSh86nQ412MLc3mfMEEy3kjRghs/OrAAzpZRfCSF+AWYKIS4HdgPnNNaIJsxhTlxcHIt++JFRxw0nyqDj3B4ZzW5LCMFtg7sycdzJfDp7Dj179uS9d9/ljVdeYuwpE9hVVkmx1dEss8nBdEuIJtKoZ11xNf07xPl9nU4RDO2YyNCOidxxXPcmz3d5ZFCFORBTRpzJSFG1LSh9Syn5aVsxTy1aS8f0DDqkZFFWW0eiQY+QEpvNxofvvBaUvkIRDKJKiVKPv3q7wU8fZX+QUq6hnmUPKWUZMNbfdjRhbgd07dqV+d8t5sSRJ6ATMLl788X5hoE5JJr2MOq44XRITSXSUce/eqezZPb7mPQ6tlXUtliYAdItZn7eXRqQMAeKR6rs8nh4S1FIVlVOABr3A2kYr43Zf8VKMDdfmCusDj7+fSffrM9nw95K9lVbEUC/yAiG2KroExdBr7RoYnQKMyvq+HzlH83qpz50Ol3QTRlHBe0tV4ZGeNCrVy/mLvyWyRNPY/6eCqYf140Es7FZbV3YK5OBHeLYXWXl5JyeCCE4u0cGz47qiRKkmcPUvAyeXraFfw7sjDFEhS6vH9oVq8vD9vJaPtxeQpKUDGtmW3oCM2UkmY2U1NqbPE9VVb7fUsSsNXv4ZXsJu0trqHK6yIwwMjgqgmmRBvolJpNh0NXrptjLZOClvcGL6lQUEXRTxlFBmEXIaMLcjhgwYABrNm3m7ttvY+gHH/CfEd05PTetWW31TIymZ2L0IceCJcoANwzqykt/7OTV33dw7eAuQWv3YJIiTTx1snd9ucP0WaR6mi84OsARgCkj2WxgTdWR6UjXFlTwye87WbJ1HzuKayits2NWBH2iTIwy6enbIYY8kwGznzO03AgDNU4npaVlQck0p9PptRnz4WiJ8jVaisVi4fmXX+HcqRdw+UUX8Nn2Ep46oTvJQTA/BJv/jOrF5d/8jpRw3ZDQiDN4Z6V1HpXAU9b/RSCmjHK7k8JaG+uLKjnrte/ZW1FHflkttU43HinpGWlioEnPuXFmeqfFkNQC+6tREXQyGfnsizlc+c9Lm93OfhRFaMJ8BFqifI0gcfzxx7Nqwybu/797GPbG6zw0vAvn52UGddbbUibmpjErchhnf/kb2yvreGRUHlHG0HzkUswRPOdwcaeqNmtBsL6Q7Eq7k+92l/JzfhlrS6opqLZTbndg86ik6BQy9TqUrcUMQvKHzcmHOUn0iDAEvZBBf7ORBd9+HxRhFopo55ngQkQYfW9AE+Z2jdls5t//eZpzz5/KNf+8gtc3ruDfx3VlcFpLA5SDx/CMRH66YARnfPEbH63dzVMn9WVqn8ygipeiKKy+9mSynvqKvUCgToU2oADYVFLF+JlL2Vtto9zmwOpRSdIpdDbo6S5gvE4hx2KioyLQHzb+TxxuKt0qwtSy97XF7uLhfdWMjDLSzWQgN8JAH6OOj/5Y3aJ296MPweJfu5+BC6Et/mkEn0GDBvHLyt959513uOD2WzmpYwIvjO4RNrPnnLgoVv9jNB+s38Mdi9bx+h87eWlCf3omRQdNoC1GPVEGHTXOhjPqWYHVwAZgJ1Ch01GjqlilRAWiHU66VNRyoiLIiYogU6dg8HN86Xod62xOhvsR7djo+9AJVlkdlMcl4K6xUrG3EpeqEmVqeqHRHxRFCfrin9XmJDIyLqhttjph8l3ZjybMRwmKonDJpZdy1uTJpKWm8MixXYg3Nc9rI1RM7ZXJWd3TuGzuH5zw1mJMOoWhmUmMykpkcHoCFXYnG0qq2VJhZUxWEuf06hjQj4vZoKfW6aYWWAOsB3YBlT4BtklJghBkKQq9PB6yPB46AkbgRuB/0ZFkNDMpUbJOId/V8tzJaQY916bE8EldLfn5W9Dr9axbt4HS8vKmL/YDr7tcUJo6QI3VTkxCXHAbbW00YdYIJdHR0Qi86SnDEZNezwenD/YmOsov4/PNBXywNp+nf9lKhF5HamQE6ZYIbl24li83F/HOGQMxNOBuV1RrZ9G2ffySX8764ipK6+w8hzcPbYIQZCsKfT0eOnk8ZOINyTJICYclf/8S6KjXN1uUAcyALUiKd3lCFN/uLufs8y7hy8/ep3fv5oXj14c3wCS4ylxjdZKU7X8IftjRHmv+abQ/PKqKLszcfw5HURRGdkpmZKf6fSnKbU6GvPsDtyxYw5CMeNbuq+bPsmr2VNkprbVT5XDhkpJ4RSFVCFI8HtLxZoaZDuTUI8ANUaYodPC74FL9mIGyIFWK1gnBf9JimTxvAV/M+pozJ50alHYBdErwbcw1Nic50dFNnxi2BC/yL1howhwmSCkpLCxk27ZtB7ay0mKsdbVcfOnljBkzJoDWBNct3kynSB3pURFkWMykR5tIt5hIjowIG9tzYySYjcyZPIyhb3/PjN93kKPTkSAlWapKfyAJb5SfcpDvsR34DHgTeDiAvmqFwNLCHzITElsQqyt1NOq5PTWWyy6dxthdG4mJCc6MNBQ25hqrk+h2Lcy0P2FuYZXs8cBzeL2RXpdSTg/e0Ns/27dv59X//ZdlS39izbr16BSF3Ow0OndMonN6PD3jLWyqLOLlF58LSJi/W7yYTZs2sWfPHjbt3M63u3eRv3kve4uKqK61khYfQ3pMJJd3T2VKz+aHd4eanonRDEiNJ6K4kpP8mP2agERFwRNgiak6oGMLv5cRgDPIgndWrJlFtQ7GT5jM0p8WBqXNUOTKqK5zBO2Ho81ob8KMt0r2i8A7Bx1bCNwlpXQLIZ7Am5/5kDIpvuxKLwEnAfnAciHEbCnlhmAMvL2iqirz5s3jpRee5bfflnPJpOHcffGx9Mk9h9SkIzM9FBRX0O/sh/F4PH7n0h02bBjDhtUfnGy32ykoKOCLL77gkzdeCmthBrhzeC4XffkbY/GvQKVNCKxAJRDnZx91QGwLv5geKYNu1xdC8EiHGE5ftZoXX3mda6++osVt6nQ6mqPM5VW1rN6ylw3bCvlzdzG7CssoKquhqs7OvrJqptQeGQXZvmhnwiylXCKEyD7s2IKDdpcBZ9dz6RBgqy8vM0KIj/DWvfpbCnN5eTlvvvkmr7z0AvEWI1efewIzHz4LcxOeE+kp8aQmxrJq1SoGDhzY4nGYTCY6d+7MqaeeyitPhv8NzNisFNxANf4JbW+Phx8VhctVlQydjuf9mGlbpSS6haLqAMo8Hj4pr8ODxIO3orYHUJGoEtxSHjgmkbglOH3HXVLikuCSEjfe527pPSdawC233sVZk04lPb15Ifj7OTxXhtPpZsOOQtZuLWDzzn1s31vC3pIqKqpt1Fjt1Nkc1NkcuN0eEmKjSEuOo2NqAtnpyRx/TC7pKfH899OfDuT4breEly4HxcZ8GfBxPcczgD0H7ecDQxtqxFdFexpAp06B5x0OV9atW8fT/3mSL774glNH9OO9Ry9iSJ+cgPx3xwzpzqJFi4IizPvp3Lkz+eVVOD1qyJIMBYPnVmwlXlGI89M8kQvkqio24EmPBysQ2cQ1dimJbaGNeacq2eH08JlHogiBgkAR3vwj3s37XKeIAwVdFQFGRcGgKBh0gihFwagIjDoFgyJ8r3nP+XJbEfc//ASvvfJsi8Yp8N61pY+7kzqbA6vdiSXSRGpCDB1T4+mUnsjYoemkJ8eRkeLd0pPjSIyLavAzu3z9LgoKgpdoqdU52gJMmqiSXd9/scF7qKOtSvaePXu45647mD9/HjdcMIaNsx4iJYCqHgczdmh3Xpn1DXfc0WBR3YAxGo10TE1mZ5X1QHWTcOStNbsYFKDNGLxeEhYh2CAljRZow7toGNfCGbNUJdf178wjx/ZoUTsNkRsXxb1ffQ0tFGa9Xk+N1cG8V24kPTmOtKRYDIaW5YnISI5hz+5dLWqjzWmHNuZ68aNKdj6QedB+R7yRr0c1tbW1TJ/+GK+8/DJXnTOCP796mOgoc9MXNsLIQd25+O43sdvtmEzBqxSR27UrWytqw1aYd1TWUVBjY0ozr09UFP70eJoUZkcQZszRQlDuaDjqsKVMyEnlmu/Xsmr1Wvr369PsdvQGA5bICIb17Ry0sXVMTeCNN+aQlJxCp06dsFqtZGZmMmDAADp0qL/AbvhxFAizP1WygeVArhAiB9gLTAGmNmuU7YT58+dzxWWXMmJgF1Z+fA+d0oJTDy4uJpJeuZn88ssvjB49OihtAnTp0ZOtG38MWnvB5uZv15Kr02H20x/5cJKlxJ95nBOIb6EwxyjerHOhwqzXcVqXNO5/aDqzPqvvBtU/lBAkMZo4si8Op5t1m39gzU81RJoMvPrnXmweHavWrMNgMAS3w1AQXrocuirZPo+Na4H5eN3l3pRSrg/R+2hT6urquO3WW5gz63Nef+AiTjq2V9D7GDM4l0WLFgZFmO12O489/DAfffA+b5wUcGX1VuHxpZtYsrOYf7agjURVZZOiQCOmEDfexbiEFvQDEC8EBQ5XC1tpnHO6dOCmX39rURuhiPyLMkdwyenDDzkmpWT8tS/z4osvcNNNNwe1v5DQ3kwZza2S7dufC8xt9ujaAcuWLePiC6cyJC+DVZ/cS3xscMvZ72fssJ7c88o8Hn30Mb/Ot9vtlJSUUFJSQnFxMZmZmfTq1QspJYP796ODp46fzxtOx5iWmVlCwYy1u3jylz+5iJbVoE7A683RGFbAgFewWkKColDpx4xZVVWcqkqtS8XmdmN1e4jU68iMbmqJErrGRVFTW9eicep1+hbGOPqHEILnbp3MiCse5vzzp4a3SUPQ/oRZo36cTicPPnA/b7z+Ks/feR5nn9yUJbMlfbnRKYLfV6/l/fffx+VyUVVVRWVlJZUV5VRVllNaXOwV4ZISSsoqsDscJMfHkpIQw4Zte7jmmmt5+plnEEJw0aWX8saz/8GgC68PI8DsLQXcuGA1Z+NdlGgJCYC1iYXDOvA7g1xjxAjYU2Oly9vf4pESVZXeR9/mUX2uclJ6c5koAr0i0CsKTo+KxagnKyaS87qmcWWfrHp/KDpGm6h1OnE6nRiNzUtQFYoZc0P0yOnAP04fxu233sw7733QKn02DwHiKPLK+Luybt06LrrgfDISjPw+8//oUE9gSCCUVdSwZsteflu7nW2797GrsJx9pdVUVluprrNRa7UTHWmiW2YyH/3vSeIsZmIjjcRFGUiPMtEj2URybgeS47qSEh9FcqyFWIsJIQSf/bCWO99czIMPPXSgv9vvvAu73c7EV1/m6zMGtkr1E4fbw7KCCnolRZPUQH/vrd/NdfNWcTrQLQh9xuH1L27MZS5YwhwnoM7lYdGFI7yubjoFo05Bryh/ub/pvK5xh+cxcasqfxRV8v2uUl5evZMnVm7jxMwkHhzenQzLX3c0ETodAoHVam22MOv0rVuM9f+uOIVBFzzBzJkzOffcc1ut34DRZsztF4/Hw9NP/4d/T3+cx244g8vOPL5B305VVSkqrWL91gI27Shk255idhWUsa+smspaO3VWB1af877H4yE+1oLb48GgCC4aN5CcYd3JSUsgJy2erNR4TBGBL6AUldVw7XNzmP31vCNyGdx7/wPYbTZOf/9tvp40sNmFXRtje2UdC3cUs6iwhp92FmGxWJjSOZGHj+9+xLnP/LaFh3/cyJl4Z7qr8Joh9L7N4HsUeMXW6XssVhSsqooRb/rOCLyh2fufG4A5QD+8Qh3vO76fYAlzpKIQbdTTNyXwH2m9ojA4PYHB6QncNiyXxbtKeW7FNvq//wMpUSZOzEjk4eN6EGM0oAhwOptvyw7F4l9jREeZ+OCxSzn1X1cxaNAgOncOnjdIUAkvXdaE2V++++47rr3mav7cspWRg7rx8Te/8fpnP2KzO7E6XDicbpxOFw6nC6fTjd3pwqDXER9roUNyHBmpiWRldGDYwF6kpSSQlhxPWko86SnxxMdavOG3L37Cgu9+5d9XByeb2LaCMrI6dWLo0CPjeoQQPDr9CRwOB2d8/jFzJh1DbDPE/3C2V9bx0urdLNpTjtUjGTduPJdeOYn3TjyR7du3c8HEU3j4+EOvOe/L3/h6WxEAsw0G0pKT6T9gAEP79MFeV0ddbS11tbVYa2tRVZX4+Hhifdujjz3Gtf1y8AA1TjfVLg81Tjc2t4cKl4csl4slbpV5Lu8xh0dFEQKDIjAoCgKJ261yU42NQXqFkyMMJDfD3hyF8LtmYGMIIRidnczo7GSqHS4W7Sjmld93kPv2d/RMjMGtSpyu5guzTtHRSDhBSBiYl8Xdl53MlHMn89PSX5s92w8pQZoxCyEy8aav6ACowKtSyueEEAl4A/Gy8dZpOFdKWdFQO5ow+0FhYSFnnnE6Bp3CaWMGkxQfTVJ8NAmxFmJjooiLiSIu2vcYE0lcdBSx0ZEYjYEJXYzFjK2RChyBkpkSy97CwgZfF0Lw1DPPcp3DzplzZjPr9GOIbmFNvkU7i1mri+ez+Z/Qt2/fQ+4o4uPjqVNhc1kNcSYDc7YW8eWuSjZUOLjkkkuYNm0affv2xWLx3696zqefcHbXZPon+zdTlVJic6vUutzUutw8vnwLs7cV0ctiZondyVuVViJ1OpJ1gu7AaKOegXqlycXBKAVczQiEaYyYCANn9cjgrB4Z7K6ycvOitZjKdDjsjma3GYpE+f5w3fmj+W7FVu668w7+8/QzrT+AJgnalNkN3CKl/F0IEQ2sFEIsBC4FvpVSThdC3AncyWH5hQ5GE+YmqK2t5bQJ44mzRJDVMZXPX749ZH1ZoszYg1AFYz/piTFIj5u5c+cyYcKEes8RQvD8S69w5eUOzv5qId+cObBFaUG7xEVhtKr069ev3r4mnj6JMz//hCqHm1PGj+P6Jy5i/PjxmM3N8w7pmZfH5oodfguzEIJIg45Ig44UIkiOjGCIxcxNiVFAFC4p+dPhZrXdxQqnh0fq7NhUSaJBT4ZU6atXMErJHhWKVEm1QU+dENhUFX0Qc2AvLyhnZ6UVq8tNnctDnctDj0QLi3YUMeaUSSQnJSF8od3g8yrZvy8EQgiEUPDuChRFQQiBx+PB7fYw+oqncHtUVI/ErUoyU+N479HLMIWo6o0Qgjfum8qgC55g1OgxTJw4MST9NIsghmRLKQuBQt/zGiHERrzpKSbhdTsGmAEsRhPm5uF2uznvnMn0z4mj38k9efur5SHtL8YSiSOIwqzX65j5wPlMunAqewuKGowaVBSFhx+fTm7nj3F6VEz65ofodo23sPXHhguH3nXvfZw66QxOPPHEoEQx5vUfwOY5zc+LpeC939yPQQh6mQz0Mhl80VDRFLs9rLG7WOXw8FFVHTGREQzpmMQJsWY6xZjpGGMmI9pMdlzwXCWnfrWKYwYPISk5mShLNFHRMcRFR9PL8Tm7d2zjhqmjkXi9P6SUSAmqVL2PB455PUKkz0tE9T0O7zkRo0GPQa/DoNeh1+uY/d3vZI6/i8Q4C4oQCEX4hN/7+TDoFQw6HUaDjgijHo/Hg6pK6uwubHYXdqd3czjd5HVO4/vXbzniPSXGWXj/0UuZfPmlrFi5iszMzHreeRvh/29qkhBixUH7r/rSSRzZpDf52wDgVyDVJ9pIKQuFECmNdaIJcwMUFxdz8YVTEfYyXn5oGp8sWkmdLTgFMQ9HVVV2F5SyfXcRTldww3qP7Z2N0+di1ZgQfvD++0zqntEiUQboGG2mtLISq9VKZOSRvhCdOnUKapKqXr1789ZHzbe5CiGQTdhcU/Q6TrToONECG51uTu2XzV3HhyYnxn5UCf97awbp6emHHO/Zsyfvvvofbrz0lKD2d9Olp7Dg57VUVNXhUVWvu5/v0e3x4HC4sDtc2BxOrDYnT77xFTdeeCKZqfHERJuJi44kzueLPfHaF6iutRJjOfL/f2z/Ltw0dRRTzp3M4iU/h09UoP93iaVSyiZ9Y4UQFrx1G26UUlYHWnRYE+Z6+Pbbb7n4wqlcctpgHrhyGnq9joSYSGwhCrm9+bG3ee2jhSTFRzOib3bQ21dV2WQu57df+x/T+6a2uC+dIshKjGPr1q307du3xe01RV5eHpvLa5p9vSIOnTE3hQfQt0I2PiGo161t4MCB3LR+ewj6E4w73r//V3FZFc/M+IanbjmnXq+knl3S+c87i3jwX6fXe/2tF5/ED7//j3vuvot/P/lUi8YdPIJnhhJCGPCK8vtSys99h/cJIdJ8s+U0vFXQGkQT5oNwu93cf9+9vP3ma7z1wEWcOKzngdcS4yw4HKER5gijnhF9s/jmqZYEIDeMx+M5YvGqsLCQpUuXsvTnn1iy+Dt27tnNCac2fxa4r87OzE0FfLC1BBu6oCZbaoyuXbuSX1mN3e1p1mxfEYG5j6lSBtWW3BACUa8w5+TkYDSaWPrHnxw7IBje3oHjdHkDnhqaBf5z8vHc/O+ZvPLJD+h1OvR6BaNBz8nDe/LUTecQaTYy48GLGHjBdEaOGs2ppwavpmGzCZ5XhsAbGb1RSvn0QS/NBi7BW5LyEmBWY+1owuxjz549TJ1yDmZhY8X7d5J6WIrOxNgo7C3wH22MtOR4vq8OjZkEvGLyxx9/sGLFCpb9vISlS3+hpraGYb07M7xHGo9fPIwp92/lm+37OLWL/6GzNpeHr7cV8cHWUn7bW8qk00/n+funMWLEiBaHOPuLwWAgp2MGW6vq6N2MtKp6ReAOQJlVCa0RMNnQjFkIwa2338H012cw+6W2Eeb0lHhcbg9Olxuj4UgJmTZ5BCcNy/Ml2XditTvZV1bNCx9+R9qJtzKodxciIiJASq66chp78ve2wbs4CBHUYqzHARcBa4UQq3zH7sYryDOFEJcDu4FzGmtEE2Zg1qxZTLviMm66YDS3XnxivaKSEBuFPURJalIS46ixhy4BTpwlkmmXTGF4XiZje2Zw78SpdMtMPmTGM+XEAby88s8mhVlKyS97y/ngz33M2lLAwGOO4ZL/e4TPzzyTqKjQ5Alpip49e7K5oqBZwtw1NoqPAlhw9SAxtEZhAVH/jBngsssu55GHH2LN5t307d76RSUURcFkNFBeVVdv1KsQgs4dj6x+fv4pQ7j7+c/ZVGpk2pVXEh8fT0ZGmJQ2C5IwSyl/omG7yFh/2/lbC7PD4eC2W29h9hef8vlTVzC8X5cGz421mHG7PVitdiIjg3ubnpoUS10z7Ndut5uZ369h7rJNvH772Zgi6nd1KvziniYrppw3ph8fLvy9wdd3VNbxwca9fLSlGHNMLBdfMY1HLrooLL5Yef0HsGn+1mZdOygljpIAFlxVCfpWCN/dX2mkPkwmEzfedDP3v/g5nz9/XUDVcIJFhNFARbU14HQE8dGRpEckN+i+2WaEWUh2eGXuaEW2bNnC8KGD2LPxV1Z+cEejogw+/1ezkR35jdrsm0VqUhxWP4V5z74Kbn1pDn0ufYb40x7krtcX8OO63Vz7bMMmK3++uMN7dQIhmOeLwAOotLt4a80uTv7id8Z8toLafqP55JsFrNuyjTvuvDMsRBmgV+8+bLE2z5slJ8aMS0pmVlr5oc7OOruTQpcbZwOi6EFiaAUzjWhkxgxw7bXXUVDu5KGXvgz5WOpFgMsduGvn2ScP4uOPPsLpDF3u6max35zR1NZK+JOP+U28lUqKpZS9fcfOAR4AegJDpJQrGrh2J1CDdzHb7Y+bSWvw6aefcvWV/+SBKydw1Tkj/J5xxEVHsiO/mF7dgnv72CEpDlsD0VyqqjJn6QZen/Mbq7bvo7SihqH9unLl+Scy/oR+dM1K5aeVmzn1yqd4+pqJxFiaN5tXFIXzxvTnhT+2IITgw60lLNxexNjRo7jjmYc45ZRTwse16TDy8vLYVNG8Ks37AzPecbpRXIJapxuby43DrfoSD+kw6Lzh2wZFwSrg4WVbeHH1bnQC9AJ0QmAQXh9oo04hQqdgMihEKDpMBgWzXodJr2DS64g06L0BLnodkUY9kXodUUYdFoP+wGO0UQ8+/+OGiIyM5Ku58xg+bAiZaQlcNnlkc/98AZNfVEad1U737MC9eHIykpCqSk1NDYmJwSkkERTCbMbsjynjbeBFvPHf+1kHnAX8z4/rR0spSwMfWvBxu93cdecdfPrx+8x94V8MzMsK6Pr42Ch2F5QEfVwJcRacLjfVtXZiLCaKK2p56fOfmb10EzsKy4gwGpg0dhBXXjyBMUPziDQfmp3t+IHdGdSnM9Oe+oyPHrig2eMYP7Qb7y9Yyb932Lj4ypv53/nnk5DQ0hTyoadbt27sLKvE5VEDtv9W+xZ0t9wy4RA3OFWV1LncVDvc1Dhc1Pgez3x/KZed1J9e2Sk4nB5cbg8Ol9u3ebC73Ngc3uc2p5tqp4sSp/e43erA6bJ6z3W6cfoW0JxuDy63isvtweVRcXs8SAllZWWNJv1JTU3lm3kLGDnieNJT4hh/wpHRlqFg4dJ1ZKcnExFgyoH9mCKM2Gy2II+qJQjCLYuRP4nyl/giWA4+thH8u0UOF/bt28eUc8/GqNbw27u3kxgXeJ27pLhoCoobzDvSbMorazFFGDnznrfZVlTJvrJq+vXI4oIzTmDCiP707JLe5N/6ydvOZ9RFj1BaWUtSM94bwMeL13P1tTcw/YknmnV9W2EymeiYmsr2aivd4wN77yv3VZEYFXGEb7KiCKIjDERHGPCWdvUSZTJy4dh+9OuSFoyhN8jpD35Cfn4+gwcPbvS87t2789nnX3LGpNNY8Ppt9OsR2GSjOZiMBlTZvLwgG7YVYHM4cDian+8j6PwNE+VLYIEQQgL/ayh0EUAIMQ2YBgQ1Mgzgl19+4dyzz+LS0wZz37QL0DVzVT0lPprCAIVZVVU2b9/LirXbWPfnbv7cWcDeonIqa+qoqbVRU2fD5XKTlBBDRmYa/7r0NE4c3otYPypaHMwxedmMHtaLf0z/hDnT/xHQtQDf/76VhSu2su2TbwO+NhzI69mDTRVlAQvzqpIqsgK4RkqJrhVszL0z41mzZg1nnnlmk+ced9xxvPDiK0y65gZ+/uD/yEgN7V2OJdKEvZnJth55Yz7XXncjXbo0vqbTuggQLYt4DTahFubjpJQFvrjwhUKITVLKJfWd6BPtVwEGDRoUlPxXUkpefvllHrz//3jt3qlMHNmyW73kBAs7CqsO7LvdbkoraigqqaS0opqKqjoqq2vR6XVs3LqXVRt38/OKdegUQXpqAtkZyXTtlMoJA7qSmZZIVnoSndISSUmMCYrf753/PI0zrgk8c9f3v29lysMf8/Ennx+Rt7m9kJLZide/+YMqh4u8BAu9E6Mx6b0f7x1VdZzy9UoQ+yuHKCh4bcPVDic9kv1/z1K2TuRf7+xkZq1a6ff5U6ZMYceO7Uy8+lm+e/sO4mJC57oYbTHjbKYwe1RJnz7Nr/IdMv5OM2ZfDUCklMVCiC+AIUC9whxsrFYrV027gtUrfuGnN2+ha6dGc4YcQnllLT+v2sbKTbvZsHUvu4oqKa+uo6KqDofTRWSvKd4sXR4Vo0GPOcKA2RSBwaBjT2EZo0edwIknjefGSZfz3emn417/TquYffrkZlJZY8XpdGP0I33nxp37eOS9H/juj+18/MnnjBkzJuRjDDbLli3jiUcf5scff6RfViIvby2kqLKWaqsTs1FPVIQBl9vD6L7Z3HTaQGxON3affdflUVm6aS/zVvof4qxK2ew7rkDok5PKo599E9A1d955F0VFhYz751PMf+3WkIlzdGRE0HO6tDl/l9JSQogoQPGlvosCTgYeauKyoLBt2zbOOuN0+mTH8fNbtxAZYHWO9JNuJzkhhs6ZKXTPSWdI/+7kZKaQ0zGZ5PhooswRmCIMmCIMh8x0n37rGxat2sc38xcihMDlcqHT6VrNFm+JMhEXHcnP63Yy+piuDZ63s7CcB2Z8z/zlW7jpllt57fPrAsqBHA4sWbKEe++6nV3bt3HzhH688+JlRB2UstLtUSmqrGNveQ2FFXWM7tWJ2KgjS1plJ8fy8c+b/O5XSom+FUwZ3TsmsXN3Pna73e/wdiEEzz77PDfeeD3j/vkU8169NSTFgQ16PWpbJHUOKe1sxiyE+BBvHtEkIUQ+cD9QDrwAJANfCyFWSSnHCSHSgdellBOAVOALnyjpgQ+klPNC8zb+Ys6cOVx+2SXcd8V4rj53ZMCi6HS6cbs97Fn8fMB9P/3OfBYsWnygT28eXMHPv//Jcce0Tvhszy4ZLP5jW73C/OeeEl74fBkffbeaa6+7ni0fLSAmJvBouXDg0Qfu5YQ0hQXXXYyhnhwZep1Cx8RoOiY2bqbo2iGOKqsDVVX9Mid5Z8yh/xIbDXq6dExl48aNDBgwwO/r9ovzrbfczPEXPMKsl26ga1YYV6gOC1rXR9kf/PHKOL+Bl76o59wCYILv+Xa8pdZaBY/HwwP338fbb77G50/+k2P7N39xwWwy8uOKzZww6MjadI1RWV1LTk7OgX29Xs8777zLOddfQ8EPzzV7PIHQr0cWKzfvOrDvdnuYs3Qjr8xZwdrtRVx2+RVsfOVTUlL8N+2EI7k98oiv21SvKAdCQrQZnU5hS1kt3ZOb/pGSklZZ/APonZ3CunXrAhJm8Irzf55+hq65uZxw4f+x9MN7yenYvv/fIUUQdot/4WVYaSZlZWWceso4flzwJb+9e3uLRNlo1HPNlNFcdk+DDiT1oqoqdvuROY/PO+886qx2Kqvrmj2mQOidm8H2ogpm/bSea56dTeepT/HM1xv5x/V3szu/gMenP9HuRRkgr3cfNhU1P93nwWQlx/DjTv9c7VUpW2XxD6B3ZhxrVq9q9vVXX/0vJpx6GvOWNFy4QMNHe4v8C3dWrVrFmZMmEmsW3HLxScz9aS3H9utC9+zm375dc+4onv/guwZfX752O8tWbcFoNGA06Ikw6imrqKFb185H5D12uVzExcWyq6A0pCvl+8nrksG+8hpeXridcadOYsEj/yMvLy/k/bY2PXv25MP/VgalrR4dk/hjbwXqQBW7W8Xqi/yzu1VvAVe3B5vLW8hVVVV0rZD2E6B3Tir/+/GPFrVx7HEn8MPX73P11CANqoWUVtTiakEx2dDQDgNMwp2FCxeQ1SmTqKgo5qwsY9uOXazbWsh/bjm72W0mxllwudxIKeu1UT/zzkIqnCY6dcrE5azDYfc6zN9626ElvNxuN+efdw4De2bSq2vHZo8nEHp0TkNFYcF3P7SrAKBA6dmzJxt3Bydvic3u5I3VO3h9xXYU37qAThHoFMW76bwudjqdgsPlweMJbtHVhuiT3YF1/22ZX/nIkSN55MF7G/wstwbVtVaenLGAd2f/wp59FVxwRThF/fn4u3hltBa33XY7t932V4HUJ554grJNi1vUptGgIAGXy1Ov25nV4eLqa27hjDPOaLSd0tJS5s6bz8pPH2oVFyuAKHMEdocDl8sVnmXig0RqaipuVVJSZSU5NrBgnMPR6xSunjiU568+pVHxcrrcmCc+zPUvfU1FrZWSKhsIgc3pDbE+EJrtdHPN6UN44p/jm+zb5nDhdHtdL12+x/2b0+2hpKyciooK4uPjm/XecnNzcbpVtu3e12qLgNW1Vr75aT3f/baJH1f+yc6CMnplJXPvOccy44dNZGWFPjoxYMJsEtPuhflgpJQs/3UZAzKan5bT6XQzcOpjHJOXjV5fv5hWVtcRG9t0usMOHTrw0EMPMfDs+3C53MRGR/Lrxw/QOTN0Nt7Vm3aT16PbUS3Kqqry0ksvIpA4m5Hh7HBioyKoqnM0OaNUFMHlpwwCCYv+2Ead3cUzN55BfHQkcRaT79HM9Pe+48/8sib73VFYQY/LnyPSbEKv02EwGHwVP3To9Xr0Oj09u3fDZrM1W5iFEFx11dWced0LPHX7eZw4vHdQJwlut5sFv2zgy+9WsWFbAbsKyiipqCEtMZr+nTtw7fj+nDGsO+k+75h3lvzpTZIfdmjCHDJmz57NokULee3rRxs9b8bspbw9eymqyoGCk/srCBeUVJLTMYVvXr2tQfepopJKvzOt7Z/Rf/TRR9x+y/WkJITWPW3l+h0MGjwkpH20NY8/9iifv/MqPz10HhlNuMP5Q3yUiZ0VTd9e63U6Xr1hIgD5JZX07pbJ1Wced8R5ybFRFBeXN9leWY2Vvnk9WLl6XeCDDoD7H3iQzl26cu8zT3Hl/W9z3ilDGNgrm349OpGWHIdep0OnUzDodYd85qWU1NTZWL5mO8vXbefPnUWUV9ZQUFyJw+Gk04m3UVpVS1yUiWE9MxnfuyODzxrCcT0zsTQQO+BW1fDLUigEQgkvr4yjSpjHjRvH8OHDmfboB0w781gMeh19u3U8UL13Px988xtVdQ7OOmmwz4aooNd5H80mIxdPOr7BpPMAd087lannn8evv60gLc2/ZDYxMdFkdsomfeT1DMjrzFO3ncvgPg1nDmsuKzbs4dhxU4LebriwYsUKnnv6Pyx/bAqZScH5kUu0mFidH1gOFINewdHAbD3KbOCn9XvoeulzeNcJBYrirePnXdwXCMDp9qA3tU5gz0UXXcRFF13E6tWr+fLLL5n5wwrufm4WJaVleFQVt9uNoij06JJJpCmCPYWllJRXAqC63fTvkkbnDvFkRpvp1zuDi47NpUfHRPrlpJIS5/+ittujoteHo+xoM+aQYTKZ+GLWHK6/7hoe/2A5NpuNfUUFzHrmSnp1+asM/KnH9+G1L5fyf1ef0ax+Lp50PN/9uonPP/+ca665xq9rJkw4lQkTTqWoqIjc3K4hc7lasW4H198TFmmvQ8LjDz/A/ZMHB02UARKjzVRbA6u5aNDrcDZQkurqs47jmO6ZvrswFY8q8XhUJBzYV1XJlj0lfPNH69a769evH/361R9eYLVa2bBhA1arlaysLJKTk1m3bh3X/mMKy6YHx62jsRzTbYfQFv9Cjclk4tXX3jiw/847Mxh75U28/+gljB3qrXo95ZTB3PbMp9jsTsym5tlih/bN5o/f660P0Cgvv/wSk8YMZEBedrP6bYxtu/dRXF4dnkligoTq8ZAawAzNH1JiI6mxBpaG0qjX4XTXny8iJT6aicf3arKNlZv2MH91YUD9hpLIyEgGDTr0R91isVBrC161EYvJSG1t84oahJbwmjGH189ECLj44kt46JHH+M/7Pxw4lpIQQ1ZGMrc/9VGz201NjKW0ODB3rcLCQl568QUevq7pVI7N4e0vfuKCCy4MPxteEImIiMARQPFUf3CrapvM5ExGA/YGKteECxaLhZoA7yYaIzrSSHV1ddDaCxphFmBy1AszwAknnMCO/EMrj3zz4rXM+HIJi3/b2Kw2k+NjKCkNTJjvv+9eJo0ZQKf04JfUsTuczJj1M5ddfkXQ2w4njBERQfHEOJgPftzIsb0Cc+HyqGqLkxmZjPqwF+Z335lB76zgeRHFR0VQWhoWBY0ORSj+ba3E30KYTSYTFdW1rN6858DMqEtmCpPHDuCpN+cG3J6Ukve/+gUpA/sFTUlJZv7SjXQ++TauuPdNPvz6F/aVVjV9oR88+cY3DB4y9Kg2YwBUVVYSGxlcd6t5q3awYnM+0z9awuc/baCs2trkNS632uI8HRCuNlcva9as4dmnn+KVK8cGrc3huSl8v3B+0NoLDkIT5rYgOzubS/9xOZPvmEHX0x/g5v98yuIVmxkxsBvb9+wLuL3Siho+/PoXZs35OqDrHnn0cfL3FjJ/0WIGjDyTT37YSc/T7mLA5Pu59d8f8s2S1dTWBX7buH1PMc+/v5Bnn38x4GvbG1u3bWsyY1ygfHjjaQztksqsnzcy7dnZ3P/u901e41HVBv3c/cXmdBEZaW76xDbA6XRy8QVTmH7RCDolN+2z7y8TBnZl3oIFeDzBvetpEYKgCbMQ4k0hRLEQYt1BxxKEEAuFEFt8j006pYe6SvZ44DlAhzcd6PQm31kI0Ol0/PvJp3ji30+ydu1avvzyC2578TPWb9xEpMnIG58tZtTgnnTOTPErbNVo0KMoCsnJyQGPRQhBjx496NGjB9dddx1ut5sVK1awcOECnnx/Pufd/BIDe3dl7NBujB3ei8G9O6NvZGbm8ahccd9b3HHnXeEZURVESkpK2PDnNq58w4UQHOJ/fsFx3bhjUvO8Uc4e3oOzh/cA4NrXFvDl0o2kxkWhUxSEAEUInG4P1VYH1VYHNVYHv23eS7fO6U203DhWuxOzn7mWW5uHHryfjha4dGzfoLabmRxDx6QYZs+e7VfZrNYjaPbjtzmyePWdwLdSyulCiDt9+3fUc+0BQlYlWwihA14CTgLygeVCiNlSyg1+9BkShBD07duXvn37ct9997N161beffcdvl+3iftf+jc6ASMH92Dk4NxGhdpo0OMMUiIWvV7PsGHDGDZsGPfeex91dXX8+OOPLFwwn2se/5Sdu3YzYkgeJw7pztjhvejR+dDCrE+//Q3SEMMtt9walPGEM0lJSaxevRqXy+XLda2gKArLli1j5v+e5o5JLe8jMTqSylo7i9bsPiD6UkoMeh0WcwRRZiPR0VGce9JAzh7dsqy2NocLszn8ZszLly/ntf++wu9PXxKS/BpPXzqSKVf8g/KyMi6/IhzWRIK3sFdf8WpgEt6c9gAzgMW0VJhbUCV7CLDVl5cZIcRHvgG2mTAfTteuXXnwQW9RFSklW7Zs4fvvv+f7779tVKiNBj0OhzMkiWGioqIYP34848d78ywUFxfz3XffsXDBPP5z9fO43U7GDuvF2GE9SE2M5T8zFrB8xe9HZLU7Gtn/w3o4P/30E50Sg+NCl5eZSHpSLD+87J9/ekuw2l1ERrYsz0ewsdvtXHLh+Tx7+RjSEkJT/3FUnywWPzKFU+69C3OkmalTLwhJP4Hht1kqSQhxsIXg1caKTPtIlVIWAkgpC301UBsllH7MGcCeg/bzgaENnRzKKtn+IISgW7dudOvWjSuvvBIpJVu3buX7779n8fff8sDLTyKkysghPRk5KBfwJucPdRRTSkoKU6ZMYcqUKUgp2bZtGwsXLmTOwnn8suxLnnv+xaPehNEUUkoMQagqUl5j47EvfiMuunVmseEYBfefp54kLzWS804IbarYHh2TmH33mZx03TVkZ+dw7LHHhrS/JvHfw6ZUShnyCK5Qfirq+6Y0uAQdiirZLUEIQW5uLrm5uUybNu2AKC5evJjF3y1i4ID+bTKmrl270rVrV66++upW7z9c6du3L28+X9L0iU1wymOfk9EhgQ8evCgIo2oanSLCaxEM+HPTBoZ0aZ1CCn2yU3jz2vGcO/lMNmze0oZlzgQh9oPYJ4RI882W04Am/WxDOZp8IPOg/Y5AQQj7Cyn7RfGKK67gvQ8+YvnK38NutvN3Zfjw4ewuqWZncctcD/PLa7jz4rGtNmPW63S4G4gebCtuvOV2nvlqJXX24EX7NcaEQV0Z168Td9/ZqMk19IQ2wGQ2cInv+SXArKYuCKUwLwdyhRA5QggjMAXvADU0goper+fiSy7m0S+XN7uN/LJqyqqt9OvaMk+LQNDrlbCr5jFgwABGjBzFQx/93Gp9PnHxCD6d+RHLlzf//9dyhJ9bE614i1f/AnQXQuQLIS4HpgMnCSG24HWGaNI7rUlhrq8jIcSZvorZw/FWyZ7vOzddCDEXQErpBq4F5gMbgZlSyvVNvjMNjWZw3wMPMW9NPj+s39P0yYdx9wdL6Hnjm1xy6lBiLa3nJREfbaaisrLV+vOX51/6L7NWFfDsrNYRyoRoMw+ffxw3X39t2wTcCII2Y5ZSni+lTJNSGqSUHaWUb0gpy6SUY6WUub7HJnPCNinMDXT0he95hJQyVUo5zndugZRywkHXzpVSdpNSdpFSNp4kWUOjBcTGxvLam29z4UvzyS8LrEjrJ8u28sLNk/nf7c0vR9YckuMslJY1nbe5tUlNTWXR9z/wzNzVzFq2uVX6vHRsX0oL9/Dzz603U/8Lf2fLWjFWDY2AmTBhAtfeeAvnPvsai++bjNHgnwuh3eUiJz2hRX273B5sDhc2hwur3XnguXffhc3512t23/GqWjslZeVtWo+vITp16sSnX8xi4oRx9OiYRPeOwc/vcjA6ncLEQZ1ZuGABxx9/fEj7qhcRXu6mmjBrHFXccedd/PLTj9z2/o/ccuox2F1uHC5vpWu7043D7fHuu9zYXd7nNoeLr37ewC9rd3rF0+HB5nRjc3qwOlzYHG6vsB4iuE6sNgc2hxOb3YGqSiLNJsymCCIjzZhNJsxmE5HmSMxms3eLjCQyMgqTOcb7vKOFZ589I+xEeT9Dhw7lkceeYPL0B1n8yBSSYkLrc31sj3T+t/THkPbRIGH2P9CEWeOoQlEUZrz/ISePGcUJD3+BKSKCCKORiIgIIiIiMJkifM9NmEwmIkxR9Ok3AFtsN6piYomMjKJD5F9iGunHc7PZjMFgCFuBbQnTrrySXbt2kHX5M3RIjCUtIYaUuChizAaiTXqiI/RYTHriLWbSEiykJ1hIT4gmIzE64L+HQa+gqq1TgfxQWtdM4Q+aMGscdcTHx7P8j9VtPYyjhkcfm849/3cfhYWFFBUVsW/fPqqrq6mpqaG2tpbq6ip2lxQz//d8Cgo2kl9QiNvtYmiPTqTHRyGRvrwmeMPcpe+5L9eJ9B0vLKsiMTO3bd5kmP2oasKsoaHRJJGRkXTp0oUuXbr4df7evXtZtmwZZWVlh+Q1qW87+PW8vNBGHDaMJswaGhpHORkZGUyePLmth+E/2oxZQ0NDI4wQWjFWDQ0NjTBEE2YNDQ2N8EIzZWhoaGiEG5owa2hoaIQRmh+zhoaGRtghtMU/DQ0NjTBDszE3zcqVK0uFELta2EwSUBqM8bQi2phbB23MrUNrjDlIddU0YW4SKWVyS9sQQqxojdpcwUQbc+ugjbl1aDdj1vyYNTQ0NMIRbcasoaGhEV5oNuZW49W2HkAz0MbcOmhjbh3ayZjDz5Qh2qTGloaGhkaYMOiYfnLFj9/4da6wZKxsDbv50Txj1tDQ0PAPzZShoaGhEW6ElzCHl2ElSAgh4oQQnwohNgkhNgohhrf1mBpDCNFdCLHqoK1aCHFjW4+rKYQQNwkh1gsh1gkhPhRCmNp6TE0hhLjBN9714fo3FkK8KYQoFkKsO+hYghBioRBii+8xvi3HeDgNjPkc399ZFUKEsducz8bsz9ZKHJXCDDwHzJNS9gD6ARvbeDyNIqXcLKXsL6XsDwwErMAXbTuqxhFCZADXA4OklL0BHTClbUfVOEKI3sA/gSF4PxenCSHaqJZRo7wNjD/s2J3At1LKXOBb33448TZHjnkdcBawpNVHEwgCTZhDjRAiBhgBvAEgpXRKKSvbdFCBMRbYJqVsaeRja6AHzEIIPRAJFLTxeJqiJ7BMSmmVUrqBH4Az23hMRyClXAKUH3Z4EjDD93wGcEZrjqkp6huzlHKjlHJzGw0pQISfW+twNNqYOwMlwFtCiH7ASuAGKWVd2w7Lb6YAH7b1IJpCSrlXCPEUsBuwAQuklAvaeFhNsQ54VAiRiHfME4AVbTskv0mVUhYCSCkLhRApbT2go4WVv6+eL8xJSX6e3iph8UejMOuBY4DrpJS/CiGew3vbd2/bDqtphBBG4HTgrrYeS1P4bJyTgBygEvhECHGhlPK9Nh1YI0gpNwohngAWArXAasDdtqPSaGuklIebYNqco86UAeQD+VLKX337n+IV6vbAKcDvUsp9bT0QPzgR2CGlLJFSuoDPgWPbeExNIqV8Q0p5jJRyBN5b7y1tPSY/2SeESAPwPRa38Xg0QshRJ8xSyiJgjxCiu+/QWGBDGw4pEM6nHZgxfOwGhgkhIoUQAu/fOawXWQH2mwCEEJ3wLky1l7/3bOAS3/NLgFltOBaNEHNURv4JIfoDrwNGYDvwDyllRZsOqgmEEJHAHqCzlLKqrcfjD0KIB4Hz8JoD/gCukFI62nZUjSOE+BFIBFzAzVLKb9t4SEcghPgQGIU3beY+4H7gS2Am0Anvj+I5UsrDFwjbjAbGXA68ACTjNXetklKOa6MhtiuOSmHW0NDQaM8cdaYMDQ0NjfaOJswaGhoaYYYmzBoaGhphhibMGhoaGmGGJswaGhoaYYYmzBoaGhphhibMGhoaGmHG/wPgGut2L8rX8wAAAABJRU5ErkJggg==", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "tracts.plot(column=\"CRIME\", cmap=\"OrRd\", edgecolor=\"k\", legend=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "All the 49 neighbourhoods are colored along a white-to-dark-red gradient, but the human eye can have a hard time comparing the color of shapes that are distant one to the other. In this case, it is especially hard to rank the peripheral districts colored in beige.\n", "\n", "Instead, we'll classify them in color bins." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Classification by quantiles\n", ">QUANTILES will create attractive maps that place an equal number of observations in each class: If you have 30 counties and 6 data classes, you’ll have 5 counties in each class. The problem with quantiles is that you can end up with classes that have very different numerical ranges (e.g., 1-4, 4-9, 9-250)." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2017-12-15T21:30:30.408917Z", "start_time": "2017-12-15T21:30:30.088920Z" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Splitting the data in three shows some spatial clustering around the center\n", "tracts.plot(\n", " column=\"CRIME\", scheme=\"quantiles\", k=3, cmap=\"OrRd\", edgecolor=\"k\", legend=True\n", ")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2017-12-15T21:28:00.376417Z", "start_time": "2017-12-15T21:27:57.039Z" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# We can also see where the top and bottom halves are located\n", "tracts.plot(\n", " column=\"CRIME\", scheme=\"quantiles\", k=2, cmap=\"OrRd\", edgecolor=\"k\", legend=True\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Classification by equal intervals\n", ">EQUAL INTERVAL divides the data into equal size classes (e.g., 0-10, 10-20, 20-30, etc.) and works best on data that is generally spread across the entire range. CAUTION: Avoid equal interval if your data are skewed to one end or if you have one or two really large outlier values." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2017-12-15T21:28:00.376417Z", "start_time": "2017-12-15T21:27:57.045Z" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVUAAAD4CAYAAABc+XWqAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAB+BUlEQVR4nO2dd3yTVRfHvzc76Z50QVtm2QjIEJElW1EUVFQEFHHvvX2duF63iAPn61acqExBkI3svSktbelu08znvn+k1AIdaZq0BZ/v55MmefI895407ckd5/yOkFKioqKiouIfNI1tgIqKisrphOpUVVRUVPyI6lRVVFRU/IjqVFVUVFT8iOpUVVRUVPyIrrENqIro6GiZkpLS2GaoqKioVMnatWuPSiljqnqtSTrVlJQU1qxZ09hmqKioqFSJEOJAda+p038VFRUVP6I6VRUVFRU/ojpVFRUVFT/SJNdUVVT+zTidTtLT07HZbI1tyr8ek8lEUlISer3e62tUp6qi0sRIT08nJCSElJQUhBCNbc6/Fiklubm5pKenk5qa6vV16vRfRaWJYbPZiIqKUh1qIyOEICoqqs4zBtWpqqg0QVSH2jTw5XNQp/8qpy2KorBixQqEEJhMJkwmE2azueKxyWTCaDSe+g5MKuB2AAqgAa0BhDpeaizU37zKacuhQ4cYMGAAd9x+K1MmT2LshRcwcOAAunbtQnJyMmFhYWi1WsxmMxEREcTHx9OqVUtWrVrV2KZ7j8sK9nxwlYKrzHNvz/cc9xNr166lc+fOtG7dmltvvZWqNJhzc3MZNGgQwcHB3Hzzzce99vnnn9O5c2e6dOnCiBEjOHr0aI39VddWcXEx3bp1q7hFR0dz++23n3T9qlWrKs7p2rUrs2fPrnjN4XAwbdo02rZtS1paGt9++20dfxu1o45UVU5b4uLi0Gg0LF3wMzpd1X/qiqJgt9spK7Nhs9m4aurNZGRkNLClPuKyehxpla+VH9dZ6t3NDTfcwDvvvEOfPn0YNWoUv/32GyNHjjzuHJPJxJNPPsnmzZvZvHnzP2a4XNx2221s3bqV6Oho7r33Xt544w0ef/zxavurrq2QkBDWr19f8bxHjx5cdNFFJ13fqVMn1qxZg06nIzMzk65du3L++eej0+l4+umniY2NZefOnSiKQl5enu+/mGpQR6oqpy1Go5HY2BjSD1fvJDUaDWazmcjICBIS4jEaDWi12ga00kekUr1DPYarzHNePcjMzKSoqIi+ffsihOCqq67i+++/P+m8oKAgzj77bEwm0/FmSomUktLSUqSUFBUVkZCQUGOf1bVVmV27dpGdnU3//v1Pes1isVR8idpstuOWd2bNmsUDDzwAeD776OjoGm3xBdWpqpzWpCSnsP/AQa/PVxTl1HCqbod/z6uGw4cPk5SUVPE8KSmJw4cPe329Xq9nxowZdO7cmYSEBLZu3co111xTL5vAs6Rw6aWXVrsevnLlSjp27Ejnzp15++230el0FBQUAPDII4/QvXt3xo8fT1ZWVr1tORHVqaqc1qSmprJvv/dO1e3+x6m63W7sdjslJSUUFxejKPUb9fkXb22pn81VrZ/WZWPP6XQyY8YM/v77bzIyMujSpQvPPvtsvWwC+OKLL5gwYUK1r/fu3ZstW7awevVqnn32WWw2Gy6Xi/T0dPr168e6devo27cvd999d71tORF1TVXltCY1NbVOI9XgoCBGjx6NoigIIdDpdBXZNFarFbPZTHBwMMHBQZ77oGBCQkIICwvjjTffJCamSjW4AODteKh+46akpCTS09Mrnqenp9c6fa/MsTXQVq1aAXDJJZcwffr0etm0YcMGXC4XPXr0qPXc9u3bExQUxObNm+nRowcWi4WxY8cCMH78eN5///162VIVqlNVOa1JSU1l0fzfvT7/68/ex+12o9Pp0GiOd0iKomC1WikpKaWktJSSklKKi0soKS3ljnsfYdeuXQ3nVLUGz06/N+fVg/j4eEJCQlixYgW9e/fm448/5pZbbvH6+sTERLZu3UpOTg4xMTHMmzeP9u3bAzB79mxWrVpV55Hr559/XuModd++fTRv3hydTseBAwfYsWNHRXba+eefzx9//MHgwYNZsGABHTp0qFPf3qA6VZXTmtTUVGbVYaSq1WqrXVPVaDTlo9Tgk157/r9vNGyuvtCAzlzzZpXO7Jd41RkzZjB58mTKysoYOXJkxc7/jz/+yJo1a3jiiScAjw5yUVERDoeD77//nrlz59KhQwcee+wxzjnnHPR6PcnJyXz44YcA7Nmzh9DQ0Cr7rK4tgK+++oo5c+Ycd35lW5YuXcr06dPR6/VoNBreeuutig2p5557jokTJ3L77bcTExPDBx98UO/fz4mIqtZMGpuePXtKVaRaxR8cOHCAs8/ux6FdGwLaz6gLL+OmW25n9OjR9W5r27ZtFaO5WqkurEpn9ks4VSC58sorefnllxtwycQ3qvo8hBBrpZQ9qzq/1q8xIcQsIUS2EGJzFa/dLYSQQogq4xKEEPuFEJuEEOuFEKqXVGlwEhMTyc7OwW63B7Qfk8nUOKpSOgsYI0AXVO5Ig8qfN22HCvDpp582eYfqC97MDT4ERpx4UAjRHBgK1Da3GiSl7FadV1dRCSQ6nY7ExAQOHkqv/eR6YDaZKCurJW40UAgN6EweR6ozqSmqjUytv30p5RKgqrSDl4F7gaa3fqCiUglPBMChgPZhMhlV/VMVwMd4CyHEGOCwlLK2hSoJzBVCrBVCTKulzWlCiDVCiDU5OTm+mKWiUiUpySns219tnTa/0KgjVZUmRZ13/4UQFuAhYJgXp/eTUmYIIWKBeUKI7eUj35OQUr4DvAOejaq62qWiUh11TQDwBXWkqnIMX0aqrYBUYIMQYj+QBKwTQsSdeKKUMqP8PhuYDfTy3VQVFd9IaYDpv9lspszqP2WouiBdduSRTciDKzz3rsBuyqnUTJ2dqpRyk5QyVkqZIqVMAdKB7lLKI5XPE0IECSFCjj3GM7I9KYJARSXQpKamsq8Osaq+0FgjVXloJXLVO8jd85AH//Lcr3oHeWhlvdq9+uqriY2NpVOnTscdv/TSSytk9VJSUujWrdtJ19psNnr16kXXrl3p2LEjjz322HGvv/7667Rr146OHTty77331mhHbW0BvPjiiwghqpUUTElJoXPnznTr1o2ePU/eL6/t+rpS6/RfCPE5MBCIFkKkA49JKavM7RJCJADvSSlHAc2A2eV5wjrgMynlb36xWkWlDnim/4FfUz2anxvQPk5EHlqJPLDs5BcUZ8Vx0by3T21PnjyZm2++mauuuuq4419++WXF47vuuouwsLCTrjUajSxcuJDg4GCcTidnn302I0eOpE+fPixatIgffviBjRs3YjQayc7OrtGOmtoCj2buvHnzaNGiRY3tLFq0qEpFKm+vrwve7P5PkFLGSyn1UsqkEx1q+Yj1aPnjjHKHipRyr5Sya/mto5Tyab9ZraJSB+Li4igsLMIawOl5Q8epSpcdeahmMW15aJXPSwHnnHMOkZGR1bctJV999VWV6aJCiIqsM6fTidPprBBhmTFjBvfffz9GoxGA2NjYGu2oqS2AO+64g+eff97n6g31vb4q1IA2ldMejUZDcnKLgK6rms0mymwNuPt/dCcozprPUZxwdFdAuv/zzz9p1qwZbdq0qfJ1t9tNt27diI2NZejQofTu7Rkx79y5kz///JPevXszYMAAVq9eXWtf1bX1448/kpiYSNeuXWu8XgjBsGHD6NGjB++8807FcW+vrytq7r/Kv4LUFI9aVYf27QLSvsloxFbWgGuqDi/EVACcXp5XR2oTNdFqtaxfv56CggLGjh3L5s2b6dSpEy6Xi/z8fFasWMHq1au55JJL2Lt3b40jxaraatmyJU8//TRz586t1dZly5aRkJBAdnY2Q4cOJS0tjZ49e3p9fV1RR6oq/wpSUlICGlZlNpsbNk7VEOTdeXovz6sDLpeL7777jksvvbTWc8PDwxk4cCC//ebZTklKSuKiiy5CCEGvXr3QaDRebxBVbmvPnj3s27ePrl27kpKSQnp6Ot27d+fIkSMnXXdMqjA2NpaxY8eyatWqOl1fV1SnqvKvINCbVQ2++x/dFjT6ms/R6CG66ul5fZg/fz5paWnHVQSoTE5OToXKfllZWcX5ABdeeCELFy4EPEsBDoeD6OhoDh8+zJAhQ7xuq3PnzmRnZ7N//372799PUlIS69atIy7u+MjO0tJSiouLKx7PnTuXTp06eX29L6hOVeVfQWrLluw/GLj8f7O5YTOqhM6IaF5z2Ldo3guhM/rU/oQJE+jbty87duwgKSnpODHnqlT3MzIyGDVqFOCpazVo0CC6dOnCmWeeydChQznvvPMAT6jW3r176dSpE5dddhkfffQRQggyMzOrLM5YU1vVUdmWrKwszj77bLp27UqvXr0YPXo0I0acJGXiV1TpP5VGw2q1cuDAAQ4cOEDXrl2Jj48PWF+rVq3ihuunsfavBYFpf/U6br7zQVZ5sfFSG3WR/pOHVnqiACpvWmn0HofqYzhVY/DGG2/QokULxowZ09imnERdpf/UjSqVgLN7925+++039u3dy8H9e9i/bx8HDqVTVFxKckIM8VGh7M7I44effvGqRIYv+DtV1Wq1smbdeqzWMsrKbOzYubthd//LEc17Q3w3zy6/s9SzhhrdxucRamNx8803N7YJfkN1qioBZ86cOdx22208MHEIY7vEkzJ8EMnNImgWGVxRsmT2kk2MGDaE4cOG0bHLGbjdbrZs/Jtlf/2FRmg488yemM1mwBMipdMb0OsNCI1n11hxuykpLqKwoICCggIKi4ooKirGZrdjdziw2x2U2eykH84gKdH7GkvV8ennX/OfZ16iU8eOmM1mzGYzk66aVO92fUHojBDXqfYTVRoE1amqBJxbbrmFrVs2sXztUh6eNBST8eQNlrHndKZb6wQW/b2bbZvmoddqGNo6mscunIxEsm5nOk6XpzKo263gdJfichVVXC+EIDjWSHhIc8KC2hAWbCbUYsRs1GM06DDotHSc+AJ/LV/FJeMurPd7slrLGHfxxbz62mv1bkvl9EJ1qioBRwjBm2+9zZUTLuXKp7/imyeuqPK81IQoUhOiqnytXYuaM2+8oVViNOs3bvKLU3U4HBgM9Suqp3J6ojpVlQZBq9XywcefEhQUBFTtVANNuxYxbNvunwwjh9NZkWqpolIZ1amqNBiKomA01BJbGUDaJEazZtlev7RltzswmEL80lZ9UUpLsK1YhJKfiyYiClOfQWiCTq74qtIwqHGqKg2Gw+HAoG+87/Hk+Ajy8qqqDFR3HA4HhiYwUi359iNyrruQohnTKfniXYpmTCfnugsp+fYjn9tsanJ71bX19ddf07FjRzQaDdWFYO7YsaNCqrBbt26EhobyyiuveH29L6gjVZUGw+NUG2+kmhofSVFRsV/acjgaf/pf8u1HlHz+zknHpa2s4njwxXWPSGiKcntVtdWpUye+++47rrvuumqva9euHevXrwc8wiyJiYmMHTvW6+t9QR2pqjQYjT1STY2PorjEiqIo9W7L7rA36kaVUlpC6exPajyndPYnKNa6C6qcKnJ77du3p1077wVyFixYQKtWrUhOTvbpem9RnapKg2G32xvVqYaHmNFqNezZu6/ebTkczkZ1qrYVi5C1JBtIWxm25Yt8ar8pye1V11ZdqSq9NhB4o/w/CzgPyJZSdjrhtbuBF4CYY0LVJ7w+AngV0OKpCDDdL1arnJJ4RqraRrUhMSac5StX06Z1q3q109jTf8XLKgNKgW/VCJqS3F5VbZ1zzjl1asPhcPDjjz/y7LPP1uk6X/BmpPohcJICgRCiOTAUqDL3TwihBd4ERgIdgAlCiA4+W6pyyuNwONDrGteptkyMZsPGrfVup7Gn/5qIquN5Tzov3LvzqqMpyO1V1VZd+fXXX+nevTvNmjWr87V1pdaRqpRyiRAipYqXXgbuBX6o5tJewG4p5V4AIcQXwAVA/f+iVU5JEhMTOZJbxIbdGXRtXf9UUV9okxTFh598xt8bNmKxmLFYLAQHBREcZCE4OIjQ0FBCg4MJCw8lLDSM8LBQwsPDiIwMJzIiApPJBDT+9N/UZxDFH7xa4xKAMJkx9R1U57ZzcnLQ6/WEh4dXyO3dd999FXJ5x0hJSWHNmjUnbSCVlpaiKAohISEVcnuPPvpojdcfPnyYq666igULFnjVVl2pTVTbn/i0wCWEGAMcllJuqGGxORGoXL8iHahWNkcIMQ2YBvi1CJdK0yE6OprnXniRKdOfZMWMGxtlffXgkXzy8gvom2yipMxOqa2Y0iInOVY7VpuDUpsDq81Bmd2JzeG52R0u7E4XTpcbIQQ6rRaNgKHDRzW4/cfQBAUTNHZilbv/xwgaOxGNpe4i1ZmZmUyaNAm3242iKFxyySVeye1NnTqVOXPmkJWVVbHD7nK5uPzyy2uV26tO+q+mtmbPns0tt9xCTk4Oo0ePplu3bvz+++/H2QIe8Zt58+Yxc+bM49qu7vr64pX0X/lI9WcpZSchhAVYBAyTUhYKIfYDPU9cUxVCjAeGSymnlj+fCPSSUt5SW3+q9N/pi5SSMaNH0j1e8NiUoQ3e/00vfUNGbgmzn5lc52ullDhdbqw2J+Me+4w7H32uVmfjC3WR/iv59iNKZ39y3IhVmMwEjZ3oUzhVY/Fvl/5rBaQCx0apScA6IUQvKWXlxZF0oHml50lAhg/9qZxGCCH476uv0/+s3jwyaUiFSlVDkRwXyZodh326VgiBQa/DoNdVKQrTGARfPAnLyHHYli9CKchFEx6Fqe8gn0aojcm/WvpPSrkJqFC3qG6kCqwG2gghUoHDwGXA5b6bqnK60KZNGxISEvj5r62MObthJetaJ0aTlVf/BID4iCAuvPBCwkNDiIoMJzoqiqioKKKiY4iMjiEqOoaYmBjPsRNu/l6L1ViCsAzx/4hZxTe8Can6HBgIRAsh0oHHpJTvV3NuAp7QqVFSSpcQ4mbgdzwhVbOklFv8Z7rKqcxDj/6HJx6+m3O6tiI8xNxg/aalxJJXWP8Ko+/eezFv3Xkh+cVWcgutHC0sJbewlNwiK3mFu8jdspE9JXZyi8rIK7J6XissIa+wGJPRSFREOCkpKcxftBittnEjIlT8ize7/zVumUkpUyo9zgBGVXo+B5hTD/tUTlPGjh3Lb3N+ps3lz9OjfTJut0LL+HB6pSUyflBXQoNMAem3TWI0ZXYnTpe73uFdep2W2IgQYiO8F1aRUlJUaiO3yErXyf/FarUSEtI0hFlU/IOaUaXSKGg0Gt59/wP+3rCJ2x+ezv1PvUyXIZfy8+Zi+t04gwNH/CN8ciJarQajXueXJQBfEEIQFmymZUIUloauwKrSIKiCKiqNSosWLSpC6IYOHcott9zC888/x/jHZrJqpm+bF5v3ZrJk/R6sNgdCaEiIDiUs2MTbs/9i+ZaDlDmcLNu4j0vPPcOfb6XOmIx6v1RgtRUWsvWbbyjOzCQkPp4O48ZhCgvzg4UqvqCOVFWaHHfeeRe703PIzvd+NKkoChfeP4uIEQ/Rd9qrvPPjSr75YzNfLdrAo+/9xtTpXxEdHsLsZyfTq0My+wM0Eq4LZqOh3iPVJU8/zUuJifw4dSqLHnmEH6dO5aXERJY8/XS92m1KcnsFBQWMGzeOtLQ02rdvz/LlywFYv349ffr0qbCxukyrl19+mY4dO9KpUycmTJhQ8TvfsGEDffv2pXPnzpx//vkUFRVVeX1dUUeqKk0OnU7HgP5ns3Dtbi7zYjSpKAp9pr1GsdXOkjdvpmNqsxpDteKiQknPKfSnyT5R35HqkqefZuHDD5903FlaWnH8nIce8rn9piK3d9tttzFixAi++eYbHA4HVqsVgHvvvZfHHnuMkSNHMmfOHO69917++OOP4649fPgwr732Glu3bsVsNnPJJZfwxRdfMHnyZKZOncqLL77IgAEDmDVrFi+88AJPPvmk13ZVh+pUVZokg4eOYOHCryuc6vP/W8jGPZmUWO2UlNkpszuxO93YHC7yikqJiwpl+cxbvYokiIsMITPXP6OS+lCfkaqtsJA/axEH+fPZZ+l1yy2YQkN96qMqvE1KOEZVcnt1oaioiCVLlvDhhx8CYDAYKkLShBAVo8vCwsIKjYATcblclJWVodfrsVqtFeft2LGjQphl6NChDB8+XHWqKqcv/fv35903/lvx/JmPF3BOt5Ykx0USFmQkxGIkyGQg2GIkLiqUAd1aYfYyID8+Kpht+2sW8WgITAadzyPVrd98g7O05tAwZ2kpW7/5hu5XX13n9o/J7QkhuO6665g2bZpPdtZXbm/v3r3ExMQwZcoUNmzYQI8ePXj11VcJCgrilVdeYfjw4dx9990oisJff/110vWJiYncfffdtGjRArPZzLBhwxg2bBjgGTX/+OOPXHDBBXz99dccOnTopOt9QV1TVWmSdO7cmf0ZORSWlKEoCmHBZq4d04fX7xjLU9NGcd+VQ7h5XH8mj+rFiN5pXjtUgOiwIIqs9gBa7x0mo97nkWpxZqZX55V4ed6JLFu2jHXr1vHrr7/y5ptvsmTJkjq3cUxub/z48T7ZAJ5R5rp167jhhhv4+++/CQoKYvp0j4LojBkzePnllzl06BAvv/wy11xzzUnX5+fn88MPP7Bv3z4yMjIoLS3l008/BWDWrFm8+eab9OjRg+LiYr8lZagjVZUmiV6vJ7l5Il0nv0ReUSl6rRaLn1JDw4LNZOUVM+vnlQSZDQSbPSPfEIuJ0CAjIWYjYUEmjAFORa3PSDUkPt6r84K9PO9EqpLbq6uGqT/k9pKSkkhKSqoQyR43blyFU/3oo4949dVXARg/fjxTp0496fr58+eTmppKTEwMABdddBF//fUXV155JWlpaRXarjt37uSXX37x2c7KqE5VpcnSpm1bItw5PDJ5GMlxEfUuv3GMowWl5OUX88Lbv2B3KzgUBadbwaFInIqCS5G4yoWGtEKg1YgT7jWex1qN57FWg06rRaf7575FfCTfP3fyyKkyZoPO65GqlLLiptFo6DBuHL/edluNSwD6oCA6jBvn/S+mnMaQ26tO+i8uLo7mzZuzY8cO2rVrx4IFC+jQwSPLnJCQwOLFixk4cCALFy6kTZs2J7XbokULVqxYgdVqxWw2s2DBgopohuzsbGJjY1EUhaeeeorrr7++zu+xKlSnqtJkcbvc9O6UTEp8pF/bbZkQSZTJwOJzqy/pIaXHsToUia3c8TrcEruiYK/8uNwZV35c7HTxzIpttdpx4kj166+/5v577uLtd9/DbrcjpVLJmVLxpaIRAoPRQNspU9jyxhvVtt//gQd82qRqDLm96qT/AF5//XWuuOIKHA4HLVu25IMPPgDg3Xff5bbbbsPlcmEymSpKrVS2pXfv3owbN47u3buj0+k444wzKtaHP//8c958803AM4KdMmVKnX9XVeGV9F9Do0r/qaxcuZJxF57Hjk/v9rsi1J7DR+lyxXPsu7CPX9s9hlNRSJ69AsfiF2oM7brokU85/8qbKtYCL790HN1jHAyf8gBprZIRwuNIhRBUHqO7FAWHw40Q8Mt/nmfnBx8cN2LVBwXR/4EH6hVO1dD826X/VFS8Ys6cOTgcDkJCQjyK+qGhJCUlERRUuyzdow/dz0MTB3rtUK02Bw+8/QsOl7vimEGnJSU+kuDyddP46FDMBh2KBIdbweZWMGn9v1er12gQAkqsDkKDq9YwWLpxL2t2ZPBxpU2cv5Yv55HpE5EaDboa7NJpNOhMntfbT5tKjxtvZP+vv1J65AjmmBiaDx+GLiiIXTt3lleOlXDMMZ94j0AIz/Po6BjCGikT618t/aei4g0ul4vzzjuP80ePoLi4hKLiYrKyc+h1Zi++/e67Gq/dsWMHGzdu4MeHHzjptV2Hcpj5w3IWrNrBlv1ZZPz4ONHhwbzy1WI++3klfWPDK851KJIlZfaKqXmJ04Vb8czMJLCloIQeUf6L4ayMQaMht6i0SqfqdLm58b8/8vKrrxNaPj0/fPgwpSWltG0ew4469BMTFoTd6abt2PPQagQaIdBoPDet0KHReFynBJCy/L78OZ5lBQC3orB/314SEpMqNnVUfEN1qir1xuVy0aFDezQaDRHhEURGRhIaGorZbOaHr/+pTb96zTqGjLqYyy69lLS0NLr36FHldM9Tc14cpyL1+8od3PT8V2QcLaJnTBjnRgaxUUq6TJhOXGw4uzNymZwSy8Odkr2y+Zx5G9hRVBYwp2rUaMgrspKacHLhvVe++pMWrdoxrtIm0rJly+jbpWX5VN+zjurNxlxsRLDfbA42G9l9OAOHw0FCQoLfNgZPZXxZHlWdqkq90Wq1ZGfn8OUn7xISHExevqcO1EXnDzvuvJ49zmDZwl/YsGkzO3bu4dprp2IwfEzbtm2Jjo6uGLUBFOUX0vXK5zGb9WQcKSC/pIxb05K4tm87LOXOdmrreLYWWNlcWMr2+Eguan58SmVNNLMY2F8aOIUoo1bDAzN/4dnrRtMjrTk9Jr2E3eHEoNexL7uAvzduPs5pLf1zMf3ae8KYjIpHmzUqLKhBHZvJoKNdixj2ZeaxJS+PhMREIiL8F3VxqiGlJDc3t6LYo7eoTlWl3gghmHrNNfz6+wJeebF6IQ8hBJ07daBzJ09ITHKLJG6/7VYcDifZOTkoioJGo0FKSUKQiYmRQZS63KSkJTKgWXiFMz1GhEFPv9gw+sXWfR0wwWwkPYAJACOSolm09SBPfjCX756dwqZ9GTzbrSVWt8JLR+RJxS3/WrqEl68dCEBi2VYOZ8PRo0FIGsehORxO1v+dBUIQFRXdqJVjGxOTyURSUlKdrql1918IMQs4D8iWUnYqP/YknnLTCpANTC4XqD7x2v1AMeAGXNXtlp2Iuvt/6pGRkUGnTp3YvXkVkZERdb7e7XZjs9lwuxUGDT2Pgc4i7unQvPYLfWT6loOszi3i23MCV87l/d2ZTN+RjlmrRadI1o3qAcDQZbsYceUkzGYzRXl55OZk883335M758kmU/sKPEI1T3wwl93WUD778pvGNqdJUdPuvzdbnx8CJ9aXfUFK2UVK2Q34GagpMniQlLKbtw5V5dQkISGBoeeey1fffu/T9VqtlqCgILZt38m2Ldu4MOnktUh/0sykp9AV2HDCQc3CuaJ5DG91b8XKEf+obd3ZMprin7/C+s0HhC/5hawl82jXIrZJOVTwCIn3TGtBYUFBY5tySuFNOZUl5SWqKx+rLPETxLHNRJV/NROvuorpzzzN9df6FkRdVFTE6NEXcUeHFrQJtfjZuuOJMRkodbtrP7EetAwx83iXlJOOj4yPYGT8P6P537MKGN4nLaC2+EpYsInCwsaXSTyV8DlITwjxtBDiEHAF1Y9UJTBXCLFWCFGjzI0QYpoQYo0QYk1OTo6vZqk0In369GHbjroEBB1Pv/7DOCPUxM1tfMtXrwuxRj1WZ2Cdqrfsd7gY2K1lY5tRJWFBJgr9JN78b8FnpyqlfEhK2Rz4H1Bd5G4/KWV3YCRwkxCiWkUGKeU7UsqeUsqeapzcqUlISAjFxSU+XXv2OcMoyTjMjJ6tG2S3OdZkoMzV+E413+Ekr6SMszqnNLYpJ6EoCs99voTWrU/OqVepHn+kk3wGXFzVC8c2r6SU2cBsoJcf+lNpohiNRgDs9rrtqn/+5bds3bCJXwd1JljfMOWaY0x6ylzu8oyjxuObAzm0ToohxBKY6rG+4nK5uenl70kv1vDF1982tjmnFD45VSFE5a+uMcD2Ks4JEkKEHHsMDAM2+9KfyqlDWFgY+fkFdbrmwYceZ1iCZ41RaSAtiiCdFo2ALJuzQfqrjrlHChjeu12j2nAixVYbfW98i8O2IH7+9feKL0sV76h1o0oI8TkwEIgWQqQDjwGjhBDt8IRUHQCuLz83AXhPSjkKaAbMLp/K6YDPpJS/BeJNqDQdOnbswMbNW4mL815D0+1y8W3GUWYfPIpT8eTjB+t1hBp0hBv0RBj1RBp0hOu0hOgEFq0Wi05D+7Agekf7nhEVbtCzs8hKvKXxnMZep5t7zmjdaP1XxcGsAopskjVzfvvXBv7XB292/6sSRHy/mnMzgFHlj/cC1WurqZyWDBo4iG9m/8Swcwd5fc3B/f/I5DkcDg4dOsyBQ+mkHz5MRmYWWVnZZOccJb2wkNKSUsqsVmylVnZs3MquC3qhr0EJqiZizAb2lNgY4NPV9afE5eJosZWzu6Q2kgVVY9BpAe/SZFVORs2oUvErN99yC23btuWxB+8hMbHuu/gGg4FWrVJp1ap2RxMd3Zz1+SWc6WP+fpzZyIEApqrWxncHj5IcH+lVscKGxKDX4nA07rLIqYxao0rFr0RFRZHWrh379h8IeF8t27RmaY7v4T7xZgOHG7FW1a+ZeQzr1bTiU0vL7Py5YR8Op+pUfUV1qip+JzQ0tEFiG0eMHMaCLN8D0+ONOrLtjec89jgUBndv1Wj9V+aO138mdsx/aHnpc7y7YDdTfKjAquJBnf6r+B2LxUJZWeCn1VOnTOS55/6L3a1g9EFsOtZkoNjdOMmANpdCdnFpo62nSimxO5wUW+388fdevv9rB2vWbcDlctGqVSt1PbUeqE5Vxe9YLBasPlYJrQstmicRZjGzLq+YvjF1V6qKNeqxuhonTvWH9Bzio8OIDvddD/Wpj+bx1Ifz0Os06HVa9Fodep0WITyi025F4lYUlPLHivufY+7y+FydVovT5WbWrFmkpKT46d39u1GdqorfMZvNFBUVN0hfrdq1ZWlOrm9O1aTH6nIFwKra+fZQDtlFNpIu/E+FcIas9ONYuK5EVnqRSsr9EqvNwWNXD+Pq0b0pKbNTUuag2GpDSs8OvtGgO+7eoNdh1Osw6LUY9Tq0Wg35xVZSxj/LFVdc4ff3OH/+fO6/505CgoPR6/VoNBo0Gg3JLVuR1r4jsbGxjBo1qtFKuAQK1amq+J127dpx70OPYjIZmTplYkD7GjlqON+9NcMnmcDoRkxVPeCCq0f3YvzgrojyOlGVi/x5HnvOPfa4qvPSkmMx6HU0I8QnO5Zu3EfvM7t7rZdaUlLCFZeNJzg4hB69+tCxY0eCgoLYsGEDxcXF2O128nKPsn/PbuYtXMSkET25eGBnXG7P6NjlVtifmcPOP7/jq10ZLP5jIW/PfNcn25sqqlNV8Tt33X03o887j379+jFu7BjCwwM3Epk65Uqeevp5ytxuzNq6pbjGGD2pqi5FQedjrKsvOBSFrGIrD0wcQlyAyrl4y5IN+zln0Llenetyubh03EVEa4vp2yKYbSt/Yvan7+BwuenSMo6IIAMGvZbmwSb69Yll5vUPEBtRvbNfve0gN7w+z19vpcmgOlWVgJCWlsbo0aN4c+b7PHTfnQHrJyE+noggC2tzSzi7jhUAjFoNRq2G/aU2WocEVmqwMr8dziM6PKjRHSrAn5sO8uL1tSdqSCm58YbrcBVm8s6zVx1XP8xX4qNCOZiegc1mq3PJkqaMGlKlEjAeeOBBXnvrXXJz8wLaT+v27Vma41toVaRRz+7iwG+qVeaH9KOc27Px8/2LrTa27j1Mr1616xw9+8zTrP5zAV/9Z4JfHCpAUmw4KfGRrFy50i/tNRVUp6oSMNq3b88Vl1/BtTfe6VNVSm85//yRzPcxXjXWbGRvAzvVbXYXg3s0fr7/sk376dGtS62jxE8//YSZb77GT9Mn+V1Nq6TMTnS09wUbTwVUp6oSUJ6dPp19Bw/xzvsfBayPqZOvZGdhCVYfNp2amY0cbMCsKkVROFJSxoAmIEr954Z9nDO45vXUP//8kztvu5Wfpk8iIdr/a+NajabR5Rf9jepUVQKK0Wjk88+/4JEnnuPTz78CPKIpTz/3Xw4fzvRLH9HRUUSFBLM6t+5hXAkmPRlWh1/s8IZ5RwoIDTLRvFndiyP6myWbDjJwYPXrqXv37mX8xWP5+KFL6NTS/9UY9mfmkZ1ffNrFx6obVSoBJy0tjQULFnDhhRdw532PUlJSSllZGR3bp/kkulIVzZKSeHLTfn44FIxdUXAqEkf5fWaZgyyrHTcevVa3lLil57ECJDag9N/3h44yuEfbBuuvOqw2Bxt2HqJv375Vvl5UVMT5o0fw0JUDGNYrMOu/h7ILSGvbhpAQ38LBmiqqU1VpEDp37sz27TvIz8/HYrFw3bRplJT6VnqlMlarlZTUDhSXWtEKgabEjhbQAjop0UjJDik5F0jC8wevL7/XAduAraLhJmybypw80ATWU1dsOUCXju2xWE6OenA6nVw2/mIGdIjjpov6BcyG/GIrwcG+Z5Q1VVSnqtJg6PV6YmNjgbrXs/r8y29ZuPhPSkpKKS4pwVpqpcxqJeNwBilGLe+c3Z2+v6/jQrdy3JqWBFYAZwBVbbFEAKXOhsmqUhSFI6VlDDij8UVUlmzYyzmDhpx0XErJddOmIkuO8PIDVwXUhjXb0+nRu+qR8qmMN8r/s4DzgGwpZafyY08CF+BR/s8GJh+rR3XCtSOAV/EMHN6TUk73o+0qpzB1dao33nAbPSKCiDUZiNcKgrUaLDoN5lgLIxOSaGbSoxWCLKDygoINz8ZBdXvWwdBgWVV/ZhdiMuhIjY9skP5qYsnGQ9z/1B0nHX/xxRfYvHopC16e6rfQqepYtuUw910WuBjmxsKbkeqHwBvAx5WOvSClfARACHErnhLV11e+SAihBd4EhgLpwGohxI9Syq1+sFvlFEen01WIelRHUVERX3/3E+s3bKTUbmdW724Yash8ahsWxK7couOcqhXPdL86ggF7A+0+f3voKAPOaNPoClB2h4s12/Zz1llnHXd83rx5PPLww2z86G6CzIFfZ7banURENP6Gnb/xppzKEiFEygnHKotlBlEh93AcvYDd5WVVEEJ8gWd0qzpVFa8YOGgkWfv30yEihCe7tazRoQJ0iwhmae7xOq6l4Cm3Uo3jtAAuwOpyYdEFdjVsvdXBnb0av9zzqm0Had+uDaGhnowul8vFo488zAfvvYPL5SbRB3EaX2geG8aePXvo3bt3g/TXUPj8VySEeBq4CigEqorLSAQOVXqeDlT72xNCTAOmAbRo0cJXs1ROYRRFISPzCHabHZvdzs5de/h9UGdaeVlupHOYmbkGPVQqBVIK6GsYGWoAA7C72EaXiMBumhwptTGgWxNYT12/l+TUVuzbtw9FUbjqigkECSvr3r+NthOeJaeghBYNEPLVJiGcPXv2BLyfhsbnbU8p5UNSyubA/4Cbqzilqr/katNqpJTvSCl7Sil7xsTE+GqWyinMxZdOIqVNFzp37U3v3gPoFB7stUMFaB9qwXrCMSsep1kTwRoNe0oCm1W1MqcQjUbQtnnj/223Sowm58B2BvTrTedOHbmgRxxznptMs8gQzEY9OQX1j8rwBgG43Y2jEhZI/DHf+Qz4BU/p6sqkA5X12JKAkzazVP6dJCcnM+v998jLy8dsNmE2mZk/fyFjk2IYGh9BsF5LsE7D9sJSwvQ6QgxaLOV6nNXRNtRCsdOFg38cqRXId7uZhWf99FzgxG2iYCHYVxLYSgVfH8yhf7emoah/2bnduOzcboBnt7+yTWajgZyC0gaxY192EaOGNv7I3d/45FSFEG2klLvKn44Btldx2mqgjRAiFTgMXAZc7pOVKqcdk6dM4YYbb2T/3+sJ1mhwAyFuN4vTj7LwcC4uKT03PCEmbjzTHE35TQtohEAjBFoh0Gk8N42UbOOf2uhdADOeZYD1eMKrRp1gSxhwKMBVVdeW2LmpV+MH/Z/IiU7eYjSQk98wI9WoEBO7d+1skL4aEm9Cqj4HBgLRQoh0PCPSUUKIdnj+3g9QvvMvhEjAEzo1SkrpEkLcDPyO539glpRyS2Dehsqphslk4tabbmL9zJkMOlF9vxrxFQXPppKz/N4lJc5yx+tye44vFoLDUlY41TCgZ/njo1otriqmm8GKQmZZYFNVM8vsnNME1lNrI8jccNP/8QM7c+e73/OfJ55skP4aCm92/ydUcfj9as7NoNJAQEo5B5jjs3UqpzXXXn89Az74gAEul1eL+8c2lWpaIz0sJfuqeU2LZ8R7IsFSctgRuLW9DfnFuBWFDinNAtaHv4gINpOV1zBOVUqJydhwKcINhZpRpdJodOrUiaTmzdm7Ywf+StyMAzZqtVDFiFQLVNajcgEFeJYGMkttfLk/CwVP9JUbiSzXCHBLT6Uot/zn2LHniqT82D/nuPEMtpXyY39kFdC9XfMa14ObCk63G2sDle1OS45ly/Ydp51ItepUVRqV6269lXfuvZfWpf7ZHGkGWKvZUdZJybFeMoF38SQGaAG308UbuzIRCDTCszOtqVQbSiNAU6lGlIZja7pU1JQ6do7mhHMKnW7SGlC0xVfSswtYtfUgr99xUYP0FxsRQkJ0BLt376Zjx47Y7fbTwrmqTlWlUbn88su59667sOIJxK8v4fwzAg0/4TVt+SgSYBeQqNFwjaKQDXys0fDn0G5+sOBkBszfwNDeja/0XxtTnvmC8/p1pGNqXMD6UBSFv3ceZu7qnazasp/9B9K5fOwFHMrMwqkoFBYXo61jrbGmhupUVRqV8PBwRo0Ywabvv68+M6QOCCAE+EgIjOXTbVl+K3O7UYA3tFqsikKH8muCCFyqqs2tsK/IyqWDuwWkfX+x82A2yzfvZ8NHd/ulPUVRWL87g3mrdrByywF2H8zhaF4x+VYbRq2G1qFBdA4P4rEuqbQNMdMmrQNDlmwjOzub+Hj/a7c2JKpTVWl0rrv5Zi7+5Re2Oj1reSfu/Uv+ySSpeE2IiuNuKVE0Gozl4UEOqdBMkXR2uz1T80rXawCN240GSCyPMjDj2cCyuRRMOv+ue67LKybMbCQ6vGlL3E155ksmDO1Bq8S6lTZRFIWNuzP56a8trN95+B/nWWpDrxG0CvM4zysjg2ibEkO7UAtRxqrVGBKCg0hPT1edqopKfRk4cCCFTictgKjyY+KE+xMfI2XF802A1GuZ0jYBgPX5JazJKuQMLyX9jkUV7C0po0N4kG9vohrmHimgXcvATaf9wdodh9iw+zBfPVm91J+iKGzee4S5q7azYutBdmcWkltYRl5BEW5FQa/XcVF8OJdHWGiXHE3bUAvR1TjPE1meU8jv2UUcKCwmLy+wRSIbAtWpqjQ6Wq2WaVdfzZ4PPuBsHwoEHtFq6RofwfVtEwFYebSIpTlFtVx1PBYh2F3sX6fqUiRf7M/i4ycm+a3NQHDt9K+57oKzSIwJQ1EUtu7P4veVO1i5ZT+7MgvJKSwjv7AInVZHu7at6da1K9Mu6ETHDu3o2D6NP5Ys48G7H+CFbqk+9X/zhoNcce11/DpunFeVXZs6qlM9xZBS8vHHH1NWVoZOp0Or1RIZGckFF1zQ2KbVizEXXcTd33wDRXVzhuDZmDJq/5m2tw+zUORwoeC9uEWwRsMBP2dVLcrKx2jQc16/DrWf3AgcyS3ila+WsGH3YexuyVfjp5NfUIzQaGjbphXdu3Zm6pjOFc4zNjamyjTb+lbKjTCbuPyKK+jWrVu92mkqqE71FMNutzN58mQmtG0OCBTg10PZbNy2neTk5MY2z2dCQ0NRfMyLd8NxsoCheh2hBh377U68rVkaAhwu829V1ff2ZjG8iTjUowUlzF6yiXmrdrD1QA5ZeUUUW204XQpJiQnccNPNdOyQRsf27WjWLLZOGgWy0lKML0SbDGRnZ9ejhaaF6lRPMUwmE0mxMdyWGkNysCemr8St8Oeff57STjU4OJgyH0c8bsCoOf7fun1EMHuO5HvtVIOl5IgfU1VnH8rh77wSvrzxfL+16S0FxVZmL9nM3FXb2bwvm6y8IopKy2iZEM1ZnVO5bXx/eqYlsWZ7Og++8ys7Nq6oslaVt3hGqr6PVi1aDaV+ilNuCqhO9RSkdctU9pYUVzjV3kE6liyYz5VXXtnIlvlOWloaWVYrbjzB+HXBzfHTf4Du4UH8eCTf6zaCFYUcu39qVa3PL+GedXt56/5LiI1suEqhRwtK6Dr5JXILSkiOj6JvpxRuvrgfPdol0bllPEbDP//uGUcLuefNH3n37Tfq5VD9gU1RMJu9l3hs6qhO9RSkbfsO7Fu3uEIZvHd0KJ8vXtyoNtUXs9lMTGQkX2ZnV5vb34Z/1KcqU9VItWOYha+MBrB7N/oMAg74If9/V5GVS//cyq0TBnDl8J61X+BHpk7/iq6tE/nu6UmYath5l1JyzfSv6H7GGVw6fmy9+/VM/31fAMiz2Zn59lt89cVn2Mo8BR2tVo8ybmRUFBFR0URFxxIVFUVsbCzjxo3DYKhNJbfxUJ3qKUibDh3ZsXxBxfMOYUEcPrKT3NxcoqKiariyaSOQ6MKC6FLF6C7DamN5gZWuVThJt5SYTsjCaR9qoVTx3kkGASVehmBl2xyszytha2Epe0psHCq1kWN3UehwYXO7QcC73y7j/dnL0Wo0aDUCrVaDTqv13Ou06HQa9Dotep2WZlEhfPfs1V7bWhVHcotYsHYXy2feWqNDBfhiwd+s2naIA7v8o3VU342qXXmFdLQepk87ExaTCbPRI5YtpSS/uIy8wmzyMvazd4eDp+evo1WrVk26BIvqVE9B2rZty2/2fzKAdBpBj2ZRLFu2jDFjxjSiZfUjOSmJ24McnB17co2ktbnFXLW8KtneYyPV46f/qcFmbC6FEjzi1LURBB6HWM5f2YW8vP0QxS6FUpdCsdNFqdOFza2g4BG2DtdoiADC3G464ZEZ/ASYPaATFq0GhyJxKAp2t/LPY0XicCv/PFYUnlu2lSO5RcRFhXr1e6qKKc98yYjeaXRqWXPgfHZ+MTe++C2vvfxCRY2q+iKp35pqsMnAbZecQ8+05rWeu3pHBkoDFWr0FdWpnoK0bduWvUXHFw7pF6Lnsw8/OKWdapfuPdiyan6VTjXWpMfurvqfSQGM2uOnnzqNoEWwmZ3FVrp70bcFKtpfdbSQy5dupYuUxOApXxGEx2mG4cnAElJWqYRl0mpoFWwixuTd9PRImYMXtx4i+aInPCpW5WIsx0RaBHDsR00TbLeisO6Du2rt73+/ryMhIYFJE6tS9PSNpMQEDhYUcfaSHfQP0XFj20SaB3kvjKJI0Gq8Wz4Qov4j40CjOtVTkJYtW3K4sBinongqhQJTWjZjwKKFLFq0iEGDqqrD2PTpduaZLFoyt8rXok16bOW5+yfGnrqkxFxFemnXyBB21cGpOqVkc34Jly7ZykCgT93Mh3Lb3F7+z5e53YxfupVzurfm26eneKQCpcSteO6llChSoiiyOt3uCsxGPeFe1PMKthjR+VmwZOA5Z7N/+9/8/OtcPvnsK4Yv28TWYV28vl4jwFXNF+ZJ52rEqT9SFULMAs4DsqWUncqPvQCcDziAPcAUKWVBFdfuB4rxzNBcUsqGXbk/TTEYDCTERHOw1F5RGM+i0/JEWjw3XDOF5Wv/PiXrqSclJZHtrPofxqzVYtBomCUEOpebkXhk/qB8pFqFVmnXMDOr9TrwYq3UiOeP9II/NtNXCPr4+I8rhMDt5UhqW6GVXIeLbS9NazCt1WaRwZSUFPu93bi4ZkydMpERQ4fQvlvdvo40QuBwerf+LWj6TtWbT/JDYMQJx+YBnaSUXYCdwAM1XD9IStlNdaj+pXWrluw7oQLoiIRIBpoFbVKSee7ZZyp2UE8V4uPjyaohAP/Tfu2Z1qE5LovxuKJobjhpowo8G3hlXk4r7Xjy/7sC/evxT3tM4MUbFAl6nbZBxaubRYRQVha4yrEajah1VH3SNUJg91anQSOa/PS/1k9TSrkEyDvh2Fwp5bHfwgo8lVJVGpC27Tuy54QKoEIIHm+fwHe9W7H0vbdok9yCJx5/nO3bq97gaWoUFRURpK9+8nRWTBjT2iTQzGw8LpZVkRKz9uQ/5bQwC0VOT7pqbXyk0ZCi0TBUUeqVHSSEwKV490/vPqGSaUMQYjFSZgtcPS7P+6mb09NqBA6XlyNVwWkxUq2Nq4Ffq3lNAnOFEGuFENNqakQIMU0IsUYIsSYnJ8cPZp3etOvUiX32qv+42oZaePeMZGZ1SeTglx8wuE8vOrdpzeOPPsoff/zRZLNXtmzZQltL7cpGJ75rBap0qtFGPSatlsO1tPcLYJWSi+rpUMHzD+XyeqQq8XIg7Tdm/riCNq0DV4BQiLqNVHcUlmJ3unF4O1IVmiY/Uq3XRpUQ4iE8ehb/q+aUflLKDCFELDBPCLG9fOR7ElLKd4B3AHr27Nm0f2tNgHbt2vGNreY/xK4RwXSNCOaJDomszi3m928+4t4P3mVLTh5pLVvSb+BA0jp1pnfv3vTo0aOBLK+ezX//TRtj7d/zHcMsrMn9R3hFwbPrXhVp4UHszimkumCd7cAG4Bop8UfBEyHqNv13KxKXy4VOF/g944yjhbz/0wr+Wlz1ZqA/8CxlnPz+j5Q5WJCZz8rcInaWOTjqUsi3emZaaSlxtEmK8ap9p8uNXu+dpGBj4fMnKYSYhGcDa4is5qujvLoqUspsIcRsoBdQpVNVqRtt27Zlb6F3VS81QtA7OpTe0Z64RJs7hY35Jaz581dWLJzDQ4dzycrNa/QslTUrlnN/ZO3Se70ig/n1kJYlTjcOPN/qVY1UAc6ICGZeTmGVrzmAH4RgpJT4q86pQHg9Um0ZYsIoJc3H/IfMOYEv0/z0xwvo1LED3bp2DlgfQghcboUH/t7DlmIb2Yokv8yOzeEiNSGKM9o258p2iXRMjaNzq3jio0LrtARiczgxNvEKrD45VSHECOA+YICUssrdECFEEKCRUhaXPx4GPOGzpSrH0aJFC3KtZVhdbiy6uoXImLQaekWH0qvcyW4psbN06VIGDx5c7TVPPf4YmRmZpLZtS2pqasUtPDzcL+uCBQUFbNu9m+4jzqj13EHNwnlAkewwGWgdama8yUAzU9Wjl05hFn6qIl1VwVNnPUEIuvlxOinwjD69Id5sZO7gLnT9ZbXf+q+O9OwCPpqzilVLF9R+cj0oLi7B5XRyND6G0YMS6dQyns4t40mNj0RbzRdfXbA7Xae+UxVCfA4MBKKFEOnAY3h2+414pvQAK6SU1wshEoD3pJSj8ES8zC5/XQd8JqX8LSDv4l+IVqulZVIiu4vL6BJRv1IdQ8KNfPX5Z2zevJml8+eyY+cuWrZqxexfPGmMUkpeeOEFbm0Zy84/f2OhU3Kw1M7BgiKERkNKYiIpqamktmlLaps2tGjRgqSkJJo3b05sbKxXu9vLly+nW7OoaqfxlYkxGXizV1tuXLWT4fGRXN26+iyijmEWSk/Y2MgEvtXrKHG6GOiHddTKeKb/3p8frNPiVCSKogQ0CuDJj+bTtXMnOnUKrBShlBKjQccP06cEpH274zRwqlLKqlIv3q/m3AxgVPnjvVStf6HiJ0ZfMJYPf/6a/9bTqZ7bLJzh773P0OQ4xsSG0Nno5N116ypez8jIwO5wclZMKN0igitGplJKCpwuDpbaOVR4gENLdrJhvsJvLkmG1UFGSSlFZXbio6NIjI+neYsWNG/ViubJKTRv3pykpCSSkpJo1qwZO3bsoK3Z+4nTiIRI3u/TjutX7eSXzHz+1zetyvpSrULMlLrcWPEsE/wq4IBGw/VtEvjfnky0fq5xX5fpP3gyv7RCkFdkDVgdq4NZ+fzv9zWsXRF40Z1Ah4fZHM4mX8Zazag6hXnoscdo99GHbMwvqddotXN4EN+d05E+0Z71rQ35JXxn/2c6HRsby6OPPcZNM97C4s5kQnwIV6U2Q6/REGHQE2HQ07Wa/m1uhSNlDjLK7GRmbCNjz3rWO+FXl0JmmZOM4lIKy2wEGQ3cnOrdZsUxBsVFsGToGVy/ejfdfl1L62ATg+PCuSq1GdHlaaI6IMqk5wO7k2IEgxIieb1dIp3Cg/jfnky/hL9Upi5xqscw6TQcySsOmFN94oN5nNGtC+3T2gak/cpoddqA7s7bT9c1VZWmQVhYGM++8CI33Xc3s3u3qnAkdUUIQd+Yf/Lt9RpBflFRxZRUr9fz4MMPc/+DD/LHH39w+403EJ6ey8UtaneCJq2GlGATKcHVjy7sboVrV+xge0ndlfebmQ182789K48WsexoEb8fKeC/2w6h02jKU0YlwTotY1vFM61VHC0q5aQrEr9O/cHTnrdxqsewaLVkHi2qVQzFF/Zn5vHF/HX8veqf/WGr1cripX8x/NzBfh9ZajSaOgf/1wXVqaoEnClXX83eXbuYMOtdvu7dinBD/T/S9qEWolF48L77eHr6dLRaLQ6Hg08++YTo6GgmXjOVVe+9zsV+sB88AtMXNI9i+tZ0n67XlH8p9I0J4+72zXEpnmUJKSUaIYg06KrcTFOkrLMgdm0I4X2c6jFCDDqO5Pk/dRTg8Vm/k5zcgjfffp+lf/7F/j37KCorQwE+eOd1vwqrAOi02nLVqsDgVpQGCT+rD03bOhWveOKZZygpKebKb7/kizNbEayvn6sQQjCrezI3fvEJI1atZOI1U3nioQdJEm5sUrI+K4+WEf6RjTvG6IRo7ly7h3yHkwhD/eIQdRrhVXlkRUIBYKVceapevVZut25OJdygJ72asK+6knG0kM/n/c3vK7exdVcmOUWl6IHf9x6guctFZyAB+E2rZc7vC/zuVAM9Uj0VUJ3qaYAQgv++9jrXlZRw8W+/8EqnRNqH1a/UcqzJwBdntuSFnQeZ8fB9PJMSyTnNwgHYXZzAziL/6gqYdBpizSbmHM7jilR/RY3WTPvwIBbkFfNLeXTADUBkPdsUgKuOTiXSqOdIpWQGb1EUhbmrdvDNHxtZuXEfBzPzKXO5iNVoSAbOVhSSgFAA1/GJIoluN3+vXlvnPmvD41T/3V5VdaqnCUIIZs76gPfefZfx99zN1clR3Nw67rgqo3VFpxE8kJZ40vHWIWZaeyEzV1duaxvPM5sPcFGLaMx+lqeriu8GdKx43OLbv6ot41IXPGuqdctNjzLqyCmoPZFj895Mvl64gT/W7WbXvixyS6wYhaCFRkOy281ZQByg9aL/BGBxZmad7PQGnU6rjlQb2wAV/yGE4Npp0xg5ahTTJk9i1LINvNQpsdqd+abGlS3jeG1XJtO3HOLxzskNJjZy2GpDwSNEXV88u/91uyZar2NbwfF6DBlHC/l64Qbmrd7B5p0ZZOcXo0hJglZLc0VhkJQkACHViGXXRjPA6nSRnZ1DbGzdoi5qoro01X8TqlM9DUlKSuKXefP55OOPufK2W7k8KYI72sR7FVjf2LzXqw0Tlm3jiN3J6z1a1Wuk7S3r8kow4smyqu/4WEjvN6qOlDlYeCSfZdkF7DmQTdtxT1FYXEZJmR2blMRpNbSQ0FNRSMSzNCF8cKBVoQWiNRq+/f5nbpjmv0B9nU73L3epqlM9bRFCcNWkSQwbPpwbrrmaYUtX8FRafMW6aFOlS0QwS87tysg/tjDuz2280bPVcWFQgWBQXDjBRj2fO91c4YcMqxPjVI/aHCw4ks/Ko8VsLbKSaXVQ6HDikpIIjYY4oLuiEGZzEAqs0mjQSck4L9XwfaU5MHfBIr86VXVNVXWqpz1xcXF89/MvfP/999x58020P1zAY23jSK4hbrSxiTIZWDqsK1f+tZ0B89YzITWOBzs0r3dUQ3UE63QsH3EGaT+sopjyjR0fsAJWt5tP9h7hwz1HyCh3no5jzlMImrndtAdigHBAU8X652FFYZevb6YavtNo0EpJCymJBWKBREVh49r1fu1Hp9MFdPZ/Kjhs1an+CxBCMHbsWEaOHMlLLzzPyOefZ3JKDPe0jW9wkWRvMWg0fHV2B3YXW7lm5S56/57DS91bMSKhvvvzVWPR6TBoNJQpSq1OtQRPDaGDQDZQqtVS5nZjA3C5OZJfShspaYvHeUZQtfOsjgigTKv1aa20OnKk5IiUFCclsigrm1KnEwOgyfavdrFGowmYT5VSYi2zY7FYAtSDf1Cd6r8Ik8nEQ488yuSrryGtTWuuTonxKp6zMWkdYmHxuV15Z1cGt63ZjUmroXdsOP0ig+kRFUJaqAWXlOwssrK9yEozk4F+MWHofFB/Nmo9TvUYJcBuPM4zRwhKNRqsbjdOIFwI4jQa2rjdxLrdxABbhWC9EFxTT2X6UMDu5xHZBCl5E3j08Qe46orLsFqt/D5vEUVFdQ/lqoljGVoyAFUNrDYHRoNeDf5XaXokJiai12rRNs1BapVMa5PA1a3imJeZx68Z+XywP4vpWw9hdblQpCcrKdJooMDuJNqk55v+Hbz+wjhUamNxVgFWl5sfAKHVVjjPiHLn2dbtJqbceUYAmip23ddSv/pWx9BT9wSC2gjFI358w/W3M2r4UKKjoxh7wWi/9lGZQDjVYqudkGB/xGgEFtWp/ktxKwraJjr1rw6dRsPIxGhGJkZXHDtstRFm0BFcPnpRFIWLl27jgsWbmTe4S4XWrM2lsCG/mHV5JfydX8LukjKyyxwUO1y4gcjyNUc9MKTceYZTtfOsCgUolJLa1WBrR8/JJWP8QWdgu5ScO2wM69ctC0APHjx1pCT+DtxQnapKQHG73VitVtxuN+Hh4XW/XlHIt7sI1mnRnGLOtTKJluM33DQaDd+e3Z6+8zbQ59e1uCVYXW7sUmIGIjQaYoWghdtNdzxrnqGAUBR2CMFsKdkiBBfVcaTowBOm5A8/osP/I9VjnOd288b2nTzz/Ms8eO8dAelDIAKyrlpktRES0vRjrlWn2oRxu93s3buXTZs2sWnTJvbs3MbePXvYs28/2UfzMJsMOJwutm3bTqtWdSvm1q93b85bsZ6iUiuxIUEkBFuIN+mJ00ri9FoSLAbiTAbizQbizUaf1igbC41Gwwd92jB0/kYuAuKBMMpjUGuYnreTkgHAFiGoa1qQHf/9M+nxlN0OBGbgYin5z+PPcOnFF9KqVar/OykfqfqbYqud0BD/ak4EAm+U/2fhWY7JllJ2Kj/2AnA+ni/oPcAUKWVBFdeOAF7F8/f8npRyuv9MP33ZtGkTb7z+Kl988QURIUF0bp1Ip+QoBrSIYkqfXrRKHElCdCgajYbJz37N3LlzueGGG+rUx+9/eASL7XY7GRkZpKen/3Pbv59N+/ZyOD2dfel76RsZxNvdkgPxVgNGh7BgkiwmSq22OuXz2wCdD+uidvAsp/hhhBnodIeWwBlCMPjc89i3Z5Pf5f8EgQl9Kiq1ERx8eoxUPwTeAD6udGwe8ICU0iWEeA5PeZX7Kl8khNACbwJDgXRgtRDiRynlVn8YfrrhdDqZPXs2b772Mrt27mTa+b3Y8vFdJESH1XjdkDNS+Wnur3V2qscwGo0V9aaqYtOmTYw7d5BPbTc2E1vGMnPLIXrV4R/cLgS5UrIK6I73o09/OlU3/td5PZEhisKMrGxuuu0eZrz+kl/bFqLu0/+CYivrd2ewdd8Rdh7K4cCRfDLziikssVNSZsda5rnv0L6dX20NBN6UU1kihEg54VjlGrcrgHFVXNoL2F1eVgUhxBfABYDqVCuRmZnJzLdn8M7Mt2mTGMUNF5zJ2KcuQu9lMb8hPdtw55uv4na70QZAhKR169YcyC/ELeUpt7HVMSyI4jo6ud5SYhCCVcA8KbkEaOPFdQ78N8J044mfX1R+r5Tf1/T42HOl/Ppj9+5K56ERIDRIjcfpaZF8NOsTJl15GX16n+kn6z0o5aN9h8PF9oPZbNyTwY6D2ezNyCM9u5D8kjKKrXZKyzw3p0shMtRCXFQozZtFkBIXwVmdUkiICSMhOozE6DD2ZubyzNd/+9XOQOCPZaCrgS+rOJ4IHKr0PB3oXV0jQohpwDTwVAo93Vm+fDmvvPQCc+fN49Ih3fj1uUl0blV35feE6DCaRYawfv16evTo4Xc7zWYzsRERpJfam3QWVlX8d3s6Z2g0Na6jnkgEMFhKBgPfarVsdbu9dqo6P33p2PDU03I1i0CDZwSsER4xbo0QaPA81gr+OY7nHJ0Q6DUCQ/m9XggMmn+O64QGXflxnUbww+Fcnnj6Beb8+JVfbAcwG/W0vvRZrHYHVpuDIJOB2IgQkmLDSY6LZFD3ViTGhJMQHUpijMdhRoVZal2G0Os0pB/O8JudgaJeTlUI8RCez/9/Vb1cxbFqhw1SyneAdwB69uzZ9HPRfGTbtm3cc+ftbNm0ntvHncXbX95PWHD9ZPQGd2/J/PnzA+JUAdq0asWeksJTyqk6FIWNucVcU482ot1u9njbH/UXYzlGEKAXgm/Pbu+nFqsnNdjE1GV/+bXNYqudL5+4irTkWOIiQzH6oRoFeAYQGVnZAa88W198tkwIMQnPBtYVsupV6XQ8mg3HSAKa/tdMgMjJyeHGG67jnH59GdzGzLZP7uKWcf3r7VABhnRvxYLf5/jByqpp17Eje0tsAWs/ELy14zDhGg2x9WgjkvJ0US9wAFo/bc5Y8ChdOf2QSFAbfaJDEW4333z3o9/aNOh19OmYTHJcpN8cKoDRoCM8JJgPP/yQLVu2cPDgQXbs2IHD4WhSmgA+OdXyXf37gDFSyuok4FcDbYQQqUIIA3AZ4L9P7hRBURTeeON1OqS1RZ+3k62f3MXtl5yDQe+/P7YB3VqxfNUabLbAOL62HTux1+aq/cQmxP/2Z9Oznk4pEo5LW60JB+WJAn5AgydltsgZqMCqSn0JwWUpzfjvf1/3W5uCwMXZPnnNUH7+31uMHT2Mfr17MHrYIMxmM5eOuygg/fmCNyFVnwMDgWghRDrwGJ7dfiMwrzwVbYWU8nohRAKe0KlR5ZEBNwO/45kZzZJSbgnQ+2iSpKenc/WkiRRlH2Lxa9NISw5MmZDwEDMdWyayfPlyBg3y7079kSNHmP/rHIIDP2jyG/ev20Oe1U7nerYTCdikRKH20YcNMPjRkeg1giKni6gG0Ga4pEU0H/+xyW/t+SkIokquHdOHa8f0Oe6Yze6k69WvMmfOHEaNGhWYjutArSNVKeUEKWW8lFIvpUySUr4vpWwtpWwupexWfru+/NwMKeWoStfOkVK2lVK2klI+Hcg30pSQUvLpp5/SvVsXzmllYcnr1wXMoR5jcLcU5s393W/tSSl58/XX6dSuLS3Td/JMhwS/tR1IXtt+iM/3ZXEVUN8VYDOe0cBRL84t02jwZ4EZvUZDYQOMVAHahpixu9xk+K28imjQ6bjJqOeVW0Zz2y03YrfXvcy5v1EzqvzM0aNHuWHaVLZtXMuvz0/mjLZJDdLvkJ6teeiT3+HZqvMrpJQUFBSQk5NDTk4O2dnZ5OTkkJWVRU72EY5mZ1FQUMjM92bRvHlzbDYbjz/6CNOaR3BrFXWqmiLfHMjmxS2HuAJPuRB/EK7RcEBRal2bLRPCr05VI2HRkXwOltgoUxTsbgVb+c3hVrApCg5FYncr2BUFuyJxuj3HHIqCUaNhWHwkF7eIwaSreewkhCDWYmTlqnV+EVkRAcqoqomRfdoz86c1vPjiCzz00MMN2veJqE7Vj/z0009cP20qEwZ35qOZN2MK8NQtO6+YFVsPsG5HOpv2ZLJu/TauumICpSXFFBYWUlhYSEFhIYVFxRQUFWM2GomNCiMmPJjo8CBiwy3EhJpJCbNwMHMvB3McxMR46hWZzWbm/7GYoQMH0DbUEjAdU3/x46Gj3LlmNxcB/sz9ihECb8ZvViC61rO8RyB5fushkoPNntAojabi3lB+b9R6Hhs1Gow6QYhBYNR4bsUuhTd2ZfDghr3EBZm4okUMN7ZNQFfNrnm8xcjW7Tv841QB2QhFVV6+aRS9rnuBSy65lDZtvAmECwyqU/UDxcXF3HH7rSz4fQ7/e2gc53SrWx5+VZRYbWzae4St+7PYdSiHTXsyyMwtpszhorDERrG1DIfTTVxkKCkJkbRtHstjU4YRE+EmLDiK8OBEwoLMhAebCQs2ER5srnYnds/hozz72WLmL1yMyfTPpLlr167MmTefkecOQa8RDImLqPf7qgtHyhz8kZVPuEFfo1P/aE8mj6zfxwVAmp9tiHa72efFeWVS4s8ESr3Q8OaZbbioRf2K8h21O/n1cC5v7crkzV0Z9I8O5c72zekQfrzak0Gj8d/UWYgGH6kCpCZE8fjkIVw2/mL+Wrkao9HY4DaA6lTrzZIlS5h81RUM6tKCv9+/jdAa6im5XC6O5JWwLzOP7Qey2HnoKAeP5JGRV0Kh1UlxmZPSMjvWsjIcDgdhoaE0i40hMSGBozYDBSU2nrx2BKnxkaTERxIXGeKXeL0p07/loUceo2vXrie91rNnT3767XfOHz6MtzSC/rHh9e6vOuxuhdW5xfxxtJg/8svIKLFyVp++bFi7muHxEVXqc76w5QBvbD/MODyiKRuAHJ0ORatF63KhcbvR4xEpEXh26R1C4DIYsGu1HHQ6PNN2RUHjVjDAcbc8IF8I/pYSCxBcfgvi+H8em5SE+PF3ocEzja8v0UY9E1vGcWVqM1bmFvPe3izO+2MTwXodfaNC+E/XVOLMBrRC4HL5J8LDk/vvl6bqzI0X9WPh+v3cc/edvPb6m41ig+pUfaSwsJApUyYze/b3nNm+OYVFxVz04AcU21yUOdzYHG7sThd2pwuHw4nd7sDucGA0GAgJCSYmJpqkxARapHah64DmxMc1IyE+jvi4ZsTHNSMmJvo4h/nGjPd4f+YMrhjm/wD/3ek5jB9/SbWv9+nTh+9+/oWLzhvNO90EfWNq1iOoK18fyOaXvDKWZ+aS1ro1Iy6eyLujRnPmmWei1Wpp3TyJzYWldA4/fix40eItLD9aCMB3ej3x0dF0O+MMRp99NsHBwVitVqxWK6UlJZQWFeF0OgmPiiIsPJywsDDWrVvHuo8+4pluqVhdCiUuNyVuhWKXgtXtxupS0LncWJwutroVSp1urC43Nrcbh+JJ29VrBDqNBpfTxVwh2C4l7fGkttbr606C3Y+jPSEEfaJD6RMdikNpxfKcIt7Zc4Q+v60lOcSCQSNw+6vQoBABC6mqvWvBe/deRI+przN4yFAuvPDCBrdBdao+oCgKw84dzLZtWxl4Tj+iIiOJiookLTqKiPAwwsPDCA87dh9KREQ44WFhhIaG+FwKIiQkGLsrMHFNLeKiOHToEImJ1W9I9e/fn8+//Y4JF4/lg+6p9Izy37jsmZ1ZPPjU0/zv8suJioo66fULLrqY3xf+QOfwYPIdTuZm5PPDUSt7FS3XXHMNkydPpmvXroSE1M2mv/76i43zf2OKD+nBipSUuRVKnG5KXG7Onvs3PaUkT6vl5/J6VSFaLeFuN63wCETXRbROKJ4NqUBg0GgY0CycAc3COWpz8OL2w3y1P4uCAv+UVvGEVDVeMH5EiIVPH76Ei6+9hu7duzd42rvqVH3g3nvuJv3AXoIsZhb9/kOD9BkaEoI9QCE2bZOi+OnHH+jTp0+N55177rl8/MVXXHXpJXxyZku6RfhnFTE1PISOHTtW6VABLhw3jomffMS6Uhdrs/MZMmAA1951FWPGjCEoyHcl+Pbt27PraIFPpT80QhCk0xKk09IMz1LBmUBIeZWAEiDd7eagEGwRgkWKglEIQoUgQVHoCKTgyfHOALKAHCAfcOh15CnKSaWu/UGx00WpS8HqcmN1e+5HxEewPKeQ3+ct4Pa7H0Sj0SCEBo1GoNFoKt0EWo0WoREIIdBqtWiEBo228usa3C6Ft7//C5NRj9PpmbE5XW6uGN6D7g0UDXNW51TuGHcWEy4Zxx9/LkOvb7habKpTrSNvvPE6P3/3JV88fiUX3D+rwfoNDQ3B4QxMVtNz142gzw1v0u/s/rUGT48cOZLrbr2NT776yG9OtaVZx86dOxk8eHCVr/fr149xk6+md9++jBo1ym+amhEREQQHWThc5iDJUv9NjcouMBjPplmalCAlbuCIlBySkoNaLd+63TjwqEiFG/TEW4y0CDbR02KkudlAosVI32j/CjIvyy5kwl/biQoLJchsJshixmIJIigohPDU1qzasJE9e/ejSImiKEgpkeWPPbcTjkvln+eKRJESqSi0S2vH/K15GAx69Ho9er0O0DDglrcZ1rM1KXER6LQeJ6zTatFqBDqdlmCzgSCTgWCzkRCLidAgI2ajnoLiMjJyCzmSV0JOfglHC0rIK7JSUGLj1vH9GTfo5L0AgLsnDOCPDR/x8EMP8tzzL/j1d1kTqlOtAx988AHPPPE4f75xPUFmAzaHs8H6DgkOxuEKzEg1PjqUa0f3ZOnSP73KSFn4y89cH+2/ve5UvWDntuoVIbVaLS+9/LLf+qtM+7Zt2VlUVG+nKkTNAe9aPLJtiUCf8tHsE8DOMb0J1vtfsrEqSl1uhvY/m18W/XHSa7m5ubRs2ZIfvvk0YGIl6/7ewLMvvMKOwjIUxY3bpeByO1AUBbfbjc1mx2azYbPbsdsd2Gw2SkuK0Wo0JMaEER5iISLETFRYEK2SYsg4WsjD7/5WrVPVaDR89MB4ek57nXMGDGT06MAVOqyM6lS9oKSkhJtvvJ6VS//gtxevJjUhCpfLjc3hwuVyBbRk7v+++Ibvvv+Z9Rs34wygE5dSotHU/s+9c+dOdu/ZzaBz65sE+g8tg018s3mz39qrCx27dmPX8t8Z7IdwsbpO1iVgasCStjWtdUZFRREREc7uPXtp26Z1QPrvfkZXvv7sgzpdYwiJJfPHxwmxnBxVk5VXTOq4p8grshIZaqny+piIYD59+FIunTKJNevWk5QU+OWHpquf1UTYsGEDPbt3hby9rJp5E51aejY1dDotRr2W9PTACm/999W3yNi1ifvGn8n6WbcFrB9FylpFrouKinji8ccZEx+J3k+jmVKXm70lNvbs9VZkz7906NqVXbaGFzY45tqqC8YPBLUJnfTs0ZPlK9c0mD21kZeXh6JAsLnqWUSzyBC6tklk6vQv+Xrheuat2sHa7Yc4kJl3XHhY/64tuWVsHyZcMs5vYWM1oTrVapBS8uabb3Du4AE8eGkfZt0/jqATPtzQIDN79x8IqB0J8XH0bN+cqef3ISmAMaKKcrxTlVKyc+dOPvroI6ZNvYYuHdNIiI/jl59/IL+srF59uaXkz+wCbtt4kB7zNrKuWSv+++aM+r4Fn+jQoQO7yuo/A/BkEXlPY+jT1LZEceXEibw+470mI6MXGhqKlBJXDVEQj109jD2Hc3lw5hymPPMFw++cSceJzxM9+jEuefhDjuR6Ihruu2IgRncxjz4S+BRWdfpfBfn5+UydMol9Ozax9I0baNO86qyWiBALBw4cqvI1f5GUmED6zsCPHtyKZN/OnTzzzDMs//MPVqxag8Wko0/HFM7qkMg1t4+ga+sEfly6hTtfnu1TH7uKrHx9OI9vMwqIjYvnquvv4PUrryQ2tj6qp/WjQ4cO7Mj1LQKgMho8O/neIgl8HaoTqa0g35gxY3jooQeZv3AxQ4cMbDC7qkOn02E06MgvthIbUXW43IjeaYzofXIe3YotB3jm4wW0uuRpQkODKS4pw+lysf+olaeefiagIteqUz2B5cuXM+HS8Yzp04ZP37yhRpHdqPAgDvtN2adqEhPj2biqOsla/9EyMZJFc1YR685g4lnNeWvaLSRWEeQ/sk8ak0rL2FNcRquQ2iVE8uxOfkg/ytfZpWTaXVwxcSK/XX0NnTv7b022PsTExKDX6cm2OWlmNvjcjkWvo9DhxNuk0qboVDUaDfffdz/PPP9Kk3CqACaDntzC6p1qdfTpmMyPz11N1ymv8t+33uOss87CYrHU64vTW1SnWo6iKDz33HReeekFZt49ljFnd6r1mpjwYDIyjgTUrrhmsRRafZ+eHskt4vVv/iQxJowbLzq72vOuHt2bq0dXW0KsgiCzkSE92/Ha9nRePbNq0QqHojA/M59vskv460geo0aM4Nnp1zFkyJCAbur5Slqb1uwsttbLqYYb9RTUYSOx0ab/tWRpXTZhAo88+ggL/1jC4IHnNJBl1WMwGsgv9n1QYTLq0el09YpnritN7y+8EcjKyuKqKyZgzT3Mqpk307yZdzvBsRHBZOfkBNS2ZrExlNZBdV9RFOat3sXMH/5i7e5ssnML6NKpA9u2r+CSwd2IDq9/KNQVw87g3lePT3qQUrI+v4SvMwr48XAeHTu056oH7+GL8eMJDfVvvKW/6di1KzvX/lEvXYN4k57MYo9alYnaNysab6RaszvX6/XMfHsml0+exOK5P9CubeOpPYFn9FyfpJfLB3finRlv+l28vSa8Uf6fhacWVbaUslP5sfHA40B7oJeUsspFPyHEfqAYT4yzS0rZ0z9m+4/Fixdz+WWXMGV4Nx59eCo6L0tDA8SGW9i5Ny+A1nlGqlabo8ZzCoqtvDV7Gd8v3c7uw7lotTrOP28Er988kiGD+hMSEsKYi69gyrNf8dNzV9fbptF9O3D1M1+wr6QMg0bDt+m5fHOkCMVo5qprprJ60iRSU1Pr3U9D0aFrN9YtW1CvNoJ0Gv4ENgqBW0oM5ZoAeo1AKwQ6IdBKEFKiURSky40CXL18O0atBpNGYNRqMGs0mLQeWT+LTotF63kepNNg0Wox6zQE63QE6bQE6zQE6XS16qUeo7bp/zGGjxjB0089zagLJ/DXojk0a9Y4a942m428/CI6t4zzuY2zOqfw2Z8L/WhV7XgzUv0QeAP4uNKxzcBFwEwvrh8kpfRGPL1BkVLy0ksv8uJzz/LhA+MZ1qtdnduIDguiuDCwa6rNYmOxlp1ce+qvTft487tlLN+WQWZOPh3S2jL+0isYPXIoXTp3PGnt6LmnH6PnWUM4lJXv9Ui8OoItRgZ2b8OFS7fj0mgZN24cH069lr59+zbImpW/6dixI1+W1S/UJt/p5s72SdzToQVOpVycpVwXoMTlpvjYY6ebYpebgyVlzNqbRXKv9pQ5nNgdLgodTrLsHhEem8OF3e7E5nBVpHo6ytM9nS43TreCy63gKk8k0Go05TdPquixlFGtEJ7H5Tv/YZHeqb5eM3UqBw8e5LyLr+CP379v0OnzMX6ft5CosKB6za4Meh22Bq4GUKtTlVIuEUKknHBsG3BK/gOBR//0mimT2LdtPctn3EhynG8CzJGhQVitpX627nhiY6OxlpVRYrXx3s8r+XrRJnYezsPpdDNy+Lk8+/T1DB86mMjImh1l+7S2XHD+SCY/8xULXr2uXjYpikL60WKuvuU2HnvsseM0WE9F2rdvz878wnq1ked006I8QF2v0RBh0BBhqD7ffHN+Cd9kFfLmXfUvWOdyuXG4ylXRHOXKaM5KKmnljw9lFfDIB4u8bvfx//yHAwcPMGHSdcz+8qNa45j9jdPpqnc1VpNBR2GRf4RivCXQa6oSmCuEkMBMKeU71Z0ohJgGTAMCqiqzfft2LrrwfM5q14zFr11XL3X+yFALVqv/duYVRWHX7j2sWbeezVu2s2PXbtLTMwgOCiJ2zH9omZLMxWPH8srI4fTs0a3Of+RP/+chOnbvx44DWbSrR82sbxdvwhQSxTPPPHPKfrFWJj4+HqciOWp3Eu3j30OR00XzIO9TXZ2KROunsB6dTotOp8Viqnmjze1WuPG/sykuLvZK0UsIwTvvvMvIkSO4895HePWlZ/xir7cEBVuwO3yfQSiKwoUPfszFF4/zo1W1E2in2k9KmSGEiMVTeXW7lHJJVSeWO9x3AHr27BmQ6ONvvvmGG667lqenDmPq+bXvdNdGVKgFm61uUwtFUdi3/wCffPY1y5avJDvnKAWFhRQXl1BSUoJOpyM+Lo7UlBa0aplK3149SUluwTln96332lZqSjJXTriEaS/OZvHr1/vUxrod6dz66o98+c3s08Khgsd5tG/Vil1FVqKr0Yp1KQp/ZhcSpNMQptcRqtdh0XnWOA0aDSUOF83roB/gUBS0mob9/Wm1GtqnJrJly5ZaFcmOYTAY+Pbb7+jX7yxefWMmt91cv1lOXQgNrp+IkMutsC8jh5df9V/5bW8IqFOVUmaU32cLIWYDvYAqnWogcblcPHD/fXz9+af88txkeqY190u7JoMOq83GDz/NobCoiJLiUgqLisnLz6+oDVVaUkqp1YrZbKaouIRNm7disVjIysri7ttvomVqMsktmtOieRItmicRGupP/fiTueTiC5g85zefrp2/ZicTn/qSt2a+x8CBA/1rWCOSnZ2NU8AdGw8Qr9cQbzKQGmSiTaiFDmEWWgebuHzFDtYXWjHoddgdLhwuF263xK0oCAF6nbZO5aSdEjQN7FQBOqXGsmnTJq+dKkB4eDhz5vzKWWedRXRUJFdMGB9AC/8hLDQkYCJCgSRgTlUIEQRopJTF5Y+H4RHmaVCysrK4bPzF6J0FrJp5U50XvW12Bx/OWcP63YfZlZ5DdqGDglIbRSVWyspsRISHcetdD2IyGTGbTJjNZkJDQwgLCyU0JISkhHg++/JbuvfowdPPTKdLly5s3bqV++69mxee/U+A3nX1dEhrS15h3daY/vh7N098tIjDeWV88PH/mkRtdX+wd+9eXnh+Ol98/gXjBnWhx7ARHM4pZH9mPquO5DH7UC7ZG/djc7jQaTVs/PgeWicdv9EjpcTtVoi/4HE25pfQx8uqCA5FQadt+CzxTinRbNzwd52vS05OZu7cuQwdei5AgzjWkJDAyV0GEm9Cqj4HBgLRQoh04DE8pXteB2KAX4QQ66WUw4UQCcB7UspReKoEzy6fIuqAz6SUvg2RfGTFihWMv3gsk4Z24bEpF6L14Y/4yQ/n8dYPqxgyaAB9h/ShVcsUWqYm0zIlhcTE+FqD2Rct/pNvf/iFr7/+pmIHVVGURps6x8fHISXsOpRTbfrtMdbuOMSD785jX1Yxj/7nSS6//PImGbxfVw4fPsxdt9/K/AXzufa8Xmz5+E7ioqqPpS2zO7E5nESEnKyEJIRHC7RVYjRr8oq9dqpORQY0VbI6OrWM55fZ6326tmPHjsybN5+hQ89FIrlyQvUlePyBaISRvD/wZvd/QjUvnZQAXj7dH1X+eC9QtdBhgJFS8tZbb/KfRx/h3Xsv4vx+HX1uq8hqZ+CAs/nuy498uv6Nt9/n0UcePS4kRa/Xc/BQOrt276FN6/pXXq0LQghatUxh3uodVTpVKSUrtx7k5a+X8deWQzzy2ONcc83UBlVODzR//fUXe7b+zZ4v7qtSUu5EzEY95lqm9u1T4tiyaa/XNrik0qAKVcfo3DKeTVu+8lnr4JhjHTFiOPsPHOKh++48bdbW/cVpp1JltVqZNPEKZr76AkvfvKFeDhWgeWw4f6/fhM12cqyoNxQVldD8hGiG3r17M2XyFK6aenO9bPOVrp07sXzL8epaVpuD935awZnT3uSq6d/TZ+QEdu3Zx/XX33BaOVSAtLQ0SmxOrxyqt3RIjuGA3fs0VYcifZo51Ze4qBCkdJOVleVzGx07dmTlylX8OGce1918lx+tOz04rZzqnj176NurJ+6je/jrrRtOWv/yhbsnDESHiyenv+TT9WU2G2bz8cIjGo2Gm2+5hW3bdzaKzFq3Lh3ZcSiP0jI7v/y1lZte/p6US57lly3FPPvKDHbu2cddd92FxVK18O+pTps2bdiXno3Tj5sgrZvHkF+Hj9LZSE5VCEHn1kls2rSpXu0kJCSwcOEi/vfFN5TVUwrydOO0cao//fQTfXufybXDOvDxQ5f4bRNAo9FwxZAu/LV8VY3nFRUVU1BQSFlZGe7yLBdFUdi9Zy/Nm58cbRAdHY2UkqNHc/1iZ11on9aWvUcKSBj7JP/9eSsteo5k7d8b+eHnXxk+fHijrPU1JCaTiaSEZuw57L9Ev9aJ0RTZa04nrozLj3GqdaVTSky9nSpAcHAwHTq0Z+26DX6wyv80lizsqb/rgGdDasyYMej1Oh5891duf+173G43nz1+JZcOOaPe7YcGmSgtrf4f0G63E9siDZPJhN1ux263o9Fo0Ov1tGyZSkpKyknXPP/cc8THNSM4uOHT/zq0b4fRaOTAgYN1Lut8utA+LY3tB7JJq0cSRGVaJUZRYnMwdMkWFAQKVNykBDflxfLwCII7FAVjPRJP6kPHlFhW+RABUBVn9zubP/9awdn9vA/Ragh2Hcph6nNfY6ohqy1QnBZOtVevXuzatQuLxUJQUBAWi4Vrr56E1eafmk5hwaYaM6eKi0sICgoiN/efUafL5cJut1e5W/7LL7/w6muvsvrPeSctDTQEKcktyMvLPy128n2lXYfObD+w3W/tHctmmjiuP2HBZnRaDXqd9rj7yrdVWw/y7k8rj2ujuNTGgax8Dmblk3G0iMzcIrLzSygts/PGnRfXmjHlLZ1bxjFr/mK/tDVw0CBmvPk6D9xzu1/aqw+HsvKZ/ukCfl+1k8zcIkb2aY/B4J/fWV04Lf6rNBoNrVsfX6zM6XSi1/nnF7ppT2aNtX1KS60EBR2//qjT6ap1WmeccQZOp4uvv/uB6OhIQoKDGTLoHL+VXq6NI0eyCAkJOeVz9utD8xYtWDt3uV/bNOh0XDqkm1eCNdkFJWTmFhI96hFKy2w4XJ4MK4vJQGiQibAgMxGhFiLDLCxYvYML+nfmgv61a/wWW218+vta7A5XueDKP8IrLrfE6VYoLLWxZfuuelc7AOjfvz8TJ05kx85dDSoT6HK5+OmvrXy9cAPbDmSTk+8pW31211Y8MnkoY87uSInVTr9bqs2MDxinhVM9kY0bNzJv/gLuGVF/mbsH3v6ZD3//m99++Krac45kZdepJEhCQgKffPwx33//PQVrNvLlV1+x4NfvGkwUeM269fTs0eNfGwrzzNNP8fJ/X+Sdu+svZlIZg15LkdW7tOWx/TvT+s1oQixGJj7xPwZ1b8NzN55X5WfSbsKzlJZ51+6KLQd4/qsVjL3oYnQGPTq9Dp2uXKhZ77mP0+l468Jr/fL5R0ZG8uorr3D2kPOYfOVlXDP5StLa+de5ulwuFq/fy3eLN7JqWzqZuUUUFFsJCzYzuEcbrhndi06t4uneNonQoH8GCvnFZeh16vTfL7z77jt0TI6hY2rtOowXP/wJ6/dmoSieNS8pJYqUSMVzb7M7+eP3H+jRvVu1bZSUllJQUEBhYSFhYd4Ff48YOZIRI0fyyccfs3DRQswms2d03QDhS2vWrefMM88MeD9Nkc8++4yP35vB2ndv9XshRb1OS7GXTtVk1HNme0+oXUSoBZ1OW62TMxr0lNaiqXsMq81Jty6deeXV17wz2g9Mufpqzu7fn/ffe49BI8aSlBhPnzN70K1rJ9LatsFoNKDT6dBqtURFRhAfH4cQApfLRW5uHjlHc1m99m+WLV/Jrt17KcjPx1ZWRlmZFaNeR8jQBwkJMtGrQzKXDu5Kj7TmtGsRQ0J0zf9rTpcbvb7hXdxp6VSfe+55LjhvFFc+9QUXn9MRvU5Lp5ZxtEo8OcRqZ3ougwcPZtzY88s/eI3nXqNFp9ORkty8ViGTwQP7M3LoYM4//zwWL15SpxHAmb16cfmEy7nh9vvYt28/UyZO4JUXn67ze64Lq9eu5/obbwloH02RgwcPcvutN/PLc5MCUpnWoNdRVFr3eGa9VltjeFd4sJl73/qZR979DSEEQgBC4LkTFccEArfbTXLLhlfrb9OmDdOfe44nn3qKpUuX8ve6dSxetoZ3P/gMp8uJ2+3G5XKRnZ2Dy+XCbDaRlZVNZGQk0dFR5GZn0Tk1lv4dmhMfnUBYsImwIBORoRbSkmOrzGarDZdbaZQY69PSqVosFn74eQ733Xs3X645iNPpZPmLs3n7rgsZe87xBed6tYsjLy+fkcPP9bk/IQSvvvQMzdt0Zd++fbRs2dLra9PS0njl1VcBuPzyCQGPBpBSeqb/PZtcEYaAM3Pm20wY3Jke7fwjqHMiBp3O65FqZfQ6bY0lQ759ahIZuUW43QqKlCjls6gTnyuKwuL1e1if1XjLOnq9nkGDBtVYviQ7O5uysjKSkpIq5CsH9T+L+8d1ZXAP/30hNFap7dPSqYLHsb7+xlsVz9esWcOFY85j16Gj3HvFPx/4ef06cMvrv9W/RLFGQ6+e3Vm7dm2dnOox1q9fz8KFC3l748raT64Hq1avIyIigoSEhID20zQRRIUGLqHBaPBtpGrQaWpUY4qJCCYmwrtNzFKbgw1Z6XW2oSGpav8hODiYEi/Xjb0lxGKkuKTEr216w+kd5V2Jnj17snL1Wp75dBEZR/9ReR/eKw1bWRkffvJ5vftoFhtDdna2T9fed++9PHL/XQGX/pv18WdMmTzlX7lJZTKZsAdQ9UijET5J1Wk0GhTFP/VVzUa9zynVjUlIaKhPo/yaCA0yUVSsOtWAkpiYSPu2rdlz+J94UovJwNt3Xcgtd97Pvv0Hari6dmJjojjqQ3XVuXPnsm/fXqZdc1W9+q+N0tJSvv7uR66aNCmg/TRVjEYjDldgikMrikLm0ULaJ9ddSNzpdqOvQ8HJmjAZTj2n6nA42Lx5C2FB/o3ZDjEbKS6x+u0Ly1v+VU4VICoq6qRvxHGDutIrLZHHn37B53allNhsdnJ8cKr/+9+n2Ox2HnjkSX6bu4DS0sDUvXrh5TcYeu65JCYmBqT9po7NZsPgZeXRuvLujyvILSwlOS6izmt5Lpf/tFX1Oi0Oh/fpsk2BJ5/4D0kRekaf1d6v7ep0Wlo1j2PDhoZNo/3XOdWkFsnc+tpP3PvWLyzduBe32/MtdvdlA/jt9/k+t/v5l9/y9eyfmDhxYp2vff/9WXz11deER8Xx7Etv0Cy5AwOHXcBT019ixco1uFz1n7Lu2buPN96exYsv+SYMczqQnZVJTHhgNgKlhIgQC50nvoBlyH20v+I5Fv+9x6tr3W4FvZ+cqs3hPKWSOtasWcPMGW/xzt1jA7IkNbpPO3766Ue/t1sT/zqnOvPd9/nmhzmYW/XllrcWkHTx00x9/lvW786ocLC+UFhUxIjhI+hdhzIVx9DpdPTp04eHH3mExYuXcOTIEe574CHyi2xcd+s9RCe148JLruKNGe+xfceuOo+EpJTcdPt93HvPPVWKu/xbWLpkMeEhFnYczGbb/iy27DvCpj2ZbNidUa8CcwDXjz2Lo3OepGjes+z8/AG0GsHSjd7pq7oVBZ2fpv9lducpoy5ms9m46soJvHzL6FpjTn1ldN92zPnph4C0XR3eKP/PAs4DsqWUncqPjQceB9oDvaSUa6q5dgTwKqDFUxFgup/s9hkhBN27d6d79+48+eRT7Nu3j++//55vvvyMwqIihoy8iIHnnMXA/v3odWZ3jEbvirkZDAa/TbuCg4MZOXIkI0eOBDwlYRYuXMj8efN4/uU3UBSFcwedw7mDz2HIwHOIj685yWHGOx+Qm1/IHXfe6Rf7TkWklGh1ep7+bBkazXI0Gk3F7Uh2Do9PGswNY8/yS1/Nm0WQGBPGS1/8wewlmz1xpMCxgZjD5cbhVHC63ThdbnILSujdMdkvfVttzkbRk/CFhx96kA6JoVzmB9Gj6ji7Syp7937O559/zoQJ1ent+xdvQqo+BN4APq50bDNwETCzuouEEFrgTWAokA6sFkL8KKXc6rO1ASA1NZU77riDO+64g8LCQpYuXcofixZx1wP/Ydv27ZzZ4wyPkz2nH73P7FGtk/WnUz2RZs2aMWHCBCZMmICUkt27dzN/3jxm/zSPW+96iIT4uAonO6D/WccpT23bvpPHnnqeZcuWnXZi03VBCMHav6teW3vkkUfIPOTfULbmseEczilk8sieSKiUsQdmk54gkwGLyUCQ2UCQyUCXVvF+6bfM7sR8CoxUly1bxv8++ZD1798e0EgUg17H3JemcuHdt7Fj+zYee/w/AY988aacyhIhRMoJx7YBtRnXC9hdXlYFIcQXwAVAk3KqlQkLC2P06NGMHj0agMLCQpYtW8YfixZx9wNPsHXbNnr17F7hZHv17F6xfmXQ67Hb/RsSUhVCCNq0aUObNm244cYbcbvdrFu3jnlz5/LfN97lsqum0a1LZ84d3J/BA/tzx72P8NSTT9K2bduA23aqsmn9Gi7t6R8JwGOkxEey89BRbh7X36/t1kaZ3YnJ3LSdamlpKZMmXs6bt1/gdfxtfejSOoHlM25i6J3vEREZyW233R7Q/gIZ/J8IHKr0PB3oXd3JQohpwDSAFieUH2kswsLCGDVqVEX10KKiooqR7D0PPsmWrVsrRrIul7tRdl21Wi1nnnkmZ555Jg8+9BBWq5WlS5cyf948brvnETp17MS06xquVvupiMlkxlWP9fQT2bY/i/d+XOm3KX1dcCtKRZZSU+W56c/Su20cF56Q3RhImkWG8OOzkzj7pidp1ao15513XsD6CqRTrWoYW+0Oi5TyHeAdgJ49ezaSZnfNhIaGnuRkj41k//hjCT169GhkCz2ZZMOGDWPYsGGNbcopQ6eu3Vm3eQFXDPPP5zfg5je5fFgPnr9htF/aqwtajaai8kRTZcumDZzbueEHTinxkXzz5JVcMGki6zduDlhoYSB3/9OBylvNSUBGAPtrcEJDQxk5ciTPPf88K1et4q0ZMxrbJBUfGDNmDN8v3eaXXPGjBSUUlpTx/A2jMTSCQpJWK1CauFO9+bY7eenLpX6tEeYtfTomc/2Y3tx+y00B6yOQTnU10EYIkSqEMACXAQ0bMKai4gWdO3fGHBTCnOXb6t3WT8u2khIf1SgOFY6NVAOXiusPBg0aRMs2abz7o39Fwr3lgSsHsX7tKn766aeAtF+rUxVCfA4sB9oJIdKFENcIIcYKIdKBvsAvQojfy89NEELMAZBSuoCbgd+BbcBXUsotAXkXKir1QAjBf199ndte/xmrl7qlVfHaV0u4f8bPDO3Vzo/W1Y0Qi5HioqJG699b/vvq6zzx8R/MX7Ozwfs2GfW8cfv53H7rTQHZXK7VqUopJ0gp46WUeillkpTyfSnl7PLHRillMynl8PJzM6SUoypdO0dK2VZK2UpKGViRUBWVejBixAj6nNWf21//yadlgOWb93P/zF946ZYLeO22CwJgoXdEhwVx9Kj/qsQGik6dOvHt7B+48qkvWb3tYIP3P/TMdqQlRTBjxlu1n1xH/nUZVSoq1THzvVms2HmUt7+v+7R02/4sWsZHceXwHo1a4jsmPJicU8Cpgqe+1cx3Z3HJY5+RnV/c4P3ffnFfvvrsU7+3e9rqqaqo1JWQkBC+/+kXzurTi55pSRXlTrwhI7eIyDDvdQXcboUyu5MyuxOr3VHx2PO80mObgzK7C5vdSZnDidXmpMzhoszhxmp3eR5XOr+otIzi0lNHUGXs2LGsXrWSy5/4gt9euNpv6brecFbnVDZu+ZiioiJCQ0P91q5oLHXsmujZs6dcs6bKzFcVlYDz7bffcvdtN/LWHRfgdCnYnS7sThc2hxO7w43j2GOnC7tTwe50M3/1Do4WljKgR5rHwTlclNkqO0uHx0Ha7JTZHTidLswmI2aTCYvZhLn8ZjFbMJvNnpvFgsViwWS2YLYEYQkKwmIJwmw2Y7FUOu+E582aNTullMjcbjejhg+lRbCdZ64dQWSopcH0fs+45nVmffZNncMhhRBrpZRVls9QR6oqKidw8cUXs2vndl766TeMRmP5zYTJbMZgDMZoMmEMNmEyWwg2mYgyGpnQZSg2m422bdtW6ehOdIJGo/FfKRReFVqtlq+/+54rJ1xKm8ufp8xmJy46gmaRoYQGmQg2Gwg2Gwgx6wk1G2gWEUR8dCgJ0WEkxYSREh/p8+9SkUq1peR9RR2pqqioNCnKyso4cuQIWVlZFBcXU1JSUnFfUFDAkczDZB5OJzMjg/0HD2K1ltGrYyrJzUKRkvJ6XVTU7/IcU1Ak/9TzKq+cPG/lVlatXkv79nXTclVHqioqKqcMZrOZ1NRUUlNTvTo/MzOTFStWkJGRgVarPU6BrKqbEKLi8dV3GP2ui6E6VRUVlVOa+Ph4xo4d29hmVKCGVKmoqKj4EdWpqqioqPgR1amqqKio+BHVqaqoqKj4EdWpqqioqPgR1amqqKio+BHVqaqoqKj4EdWpqqioqPiRJpmmKoTIAQ74qblo4NTQQqsZ9X00LdT30bRo6PeRLKWMqeqFJulU/YkQYk11ObqnEur7aFqo76Np0ZTehzr9V1FRUfEjqlNVUVFR8SP/Bqf6TmMb4CfU99G0UN9H06LJvI/Tfk1VRUVFpSH5N4xUVVRUVBoM1amqqKio+JHT2qkKIcKFEN8IIbYLIbYJIfo2tk11RQjRTgixvtKtSAhxe2Pb5QtCiDuEEFuEEJuFEJ8LIUyNbZMvCCFuK38PW06lz0IIMUsIkS2E2FzpWKQQYp4QYlf5fURj2ugN1byP8eWfhyKEaNTQqtPaqQKvAr9JKdOArsC2Rranzkgpd0gpu0kpuwE9ACswu3GtqjtCiETgVqCnlLIToAUua1yr6o4QohNwLdALz9/UeUKINo1rldd8CIw44dj9wAIpZRtgQfnzps6HnPw+NgMXAUsa3JoTOG2dqhAiFDgHeB9ASumQUhY0qlH1ZwiwR0rpr2yzhkYHmIUQOsACZDSyPb7QHlghpbRKKV3AYqDp1PKoASnlEiDvhMMXAB+VP/4IuLAhbfKFqt6HlHKblHJHI5l0HKetUwVaAjnAB0KIv4UQ7wkhghrbqHpyGfB5YxvhC1LKw8CLwEEgEyiUUs5tXKt8YjNwjhAiSghhAUYBzRvZpvrQTEqZCVB+H9vI9pzynM5OVQd0B2ZIKc8ASjk1pjZVIoQwAGOArxvbFl8oX6u7AEgFEoAgIcSVjWtV3ZFSbgOeA+YBvwEbAFejGqXSpDidnWo6kC6lXFn+/Bs8TvZUZSSwTkqZ1diG+Mi5wD4pZY6U0gl8B5zVyDb5hJTyfSlldynlOXimobsa26Z6kCWEiAcov89uZHtOeU5bpyqlPAIcEkK0Kz80BNjaiCbVlwmcolP/cg4CfYQQFiGEwPN5nHIbhwBCiNjy+xZ4NkdO5c/lR2BS+eNJwA+NaMtpwWmdUSWE6Aa8BxiAvcAUKWV+oxrlA+Vrd4eAllLKwsa2x1eEEP8BLsUzXf4bmCqltDeuVXVHCPEnEAU4gTullAsa2SSvEEJ8DgzEI5OXBTwGfA98BbTA88U3Xkp54mZWk6Ka95EHvA7EAAXAeinl8Eax73R2qioqKioNzWk7/VdRUVFpDFSnqqKiouJHVKeqoqKi4kdUp6qioqLiR1SnqqKiouJHVKeqoqKi4kdUp6qioqLiR/4PJ0y0KAVnokQAAAAASUVORK5CYII=", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "tracts.plot(\n", " column=\"CRIME\",\n", " scheme=\"equal_interval\",\n", " k=4,\n", " cmap=\"OrRd\",\n", " edgecolor=\"k\",\n", " legend=True,\n", ")" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2017-12-15T21:28:00.386417Z", "start_time": "2017-12-15T21:27:57.048Z" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# No legend here as we'd be out of space\n", "tracts.plot(column=\"CRIME\", scheme=\"equal_interval\", k=12, cmap=\"OrRd\", edgecolor=\"k\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Classificaton by natural breaks\n", ">NATURAL BREAKS is a kind of “optimal” classification scheme that finds class breaks that will minimize within-class variance and maximize between-class differences. One drawback of this approach is each dataset generates a unique classification solution, and if you need to make comparison across maps, such as in an atlas or a series (e.g., one map each for 1980, 1990, 2000) you might want to use a single scheme that can be applied across all of the maps." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2017-12-15T21:28:00.376417Z", "start_time": "2017-12-15T21:27:57.042Z" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVUAAAD4CAYAAABc+XWqAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAB8B0lEQVR4nO2ddXxT1/vH3yfSJnXqSltKgVKKD3e3sQ2YwBSZM/3O3Zh+5/DbYK5szIBtfDfc3d21FGipt2lj9/z+SOmQlqZN0hZ2369XXrm5Ofec56bNJ0ee8zxCSomKioqKinvQ1LUBKioqKpcTqqiqqKiouBFVVFVUVFTciCqqKioqKm5EFVUVFRUVN6KrawMqIjQ0VCYkJNS1GSoqKioVsmHDhtNSyrCK3quXopqQkMD69evr2gwVFRWVChFCHKnsPXX4r6KiouJGVFFVUVFRcSOqqKqoqKi4kXo5p6qi8m/GarWSnp5OaWlpXZvyr8dgMBAbG4ter3f6GlVUVVTqGenp6fj7+5OQkIAQoq7N+dcipSQ7O5v09HQSExOdvk4d/quo1DNKS0sJCQlRBbWOEUIQEhJS7RGDKqoqKvUQVVDrBzX5O6jDf5XLFkVRWL16NUIIDAYDBoMBo9FYfmwwGPD29r70BUwqYLcACqABrRcItb9UV6ifvMply7Fjx+jZsycPPXg/Y2+7lWuuvopevXrSqlVL4uPjCQwMRKvVYjQaadCgAVFRUSQlNWLt2rV1bbrz2ExgzgVbMdhKHM/mXMd5N7FhwwbS0tJo3Lgx999/PxXFYM7OzqZ37974+fkxceLEc96bPn06aWlptGzZkkGDBnH69OmLtjdv3jzatWtHWloa7dq1Y+HCheXvDRo0iFatWpGamspdd92F3W6/4HqLxcLYsWNJS0ujVatWLF68GIDCwkJat25d/ggNDeXBBx+s/gdSFVLKevdo166dVFFxldLSUunl5SWthSelLDld4cNenClNOcdk9vF98viBbbJv757yt99+q1O7d+7c6VxBa3Gl9yVLTjvedwNXXHGFXLlypVQURQ4aNEjOmTPngjJFRUVy2bJl8qOPPpL33nvvPyZarTIsLExmZWVJKaV89NFH5fPPP3/R9jZu3CiPHz8upZRy27ZtMjo6uvy9/Px8KaWUiqLIESNGyOnTp19w/eTJk+Vtt90mpZTy1KlTsm3bttJut19Qrm3btnLJkiVV3H3Ffw9gvaxEv9Seqspli7e3N+HhYaQfz6i0jEajwWg0EhzcgOjoKLy9vdBqtbVoZQ2RiqNnejFsJY5yLnDixAkKCgro3LkzQghuueUWZs6ceUE5X19funXrhsFgONfMMqEpLi5GSklBQQHR0dEXbbNNmzblZVJTUyktLcVsNgMQEBDguDWbDYvFUuHUzc6dO+nbty8A4eHhBAUFXbDtfd++fWRmZtK9e3fnPohqoIqqymVNQnwCh48cdbq8oiiXhqjaLe4tVwnHjx8nNja2/HVsbCzHjx93+nq9Xs9HH31EWloa0dHR7Ny5k/Hjxzt9/S+//EKbNm3w9vYuPzdw4EDCw8Px9/dn1KhRF1zTqlUrZs2ahc1m49ChQ2zYsIFjx46dU2b69Olcf/31HplPV0VV5bImMTGRQ4edF1W7/R9RtdvtmM1mioqKKCwsRFFc6/W5F2dtcc1mWcH8aXWEyGq18tFHH7Fp0yYyMjJo2bIlr732mlPX7tixg8cff5ypU6eec/7vv//mxIkTmM3mc+ZbzzBu3DhiY2Np3749Dz74IF26dEGnO3dN/ocffmD06NFO30d1UFf/VS5rEhMTq9VT9fP1ZejQoSiKghACnU5XvpvGZDJhNBrx8/PDz8/X8ezrh7+/P4GBgUyeMoWwsAqjwXkAZ/tDrvWbYmNjSU9PL3+dnp5e5fD9bDZv3gxAUlISANdddx2vv/56ldelp6dzzTXX8PXXX5dfezYGg4Hhw4cza9Ys+vfvf857Op2Od999t/x1ly5dSE5OLn+9ZcsWbDYb7dq1c/o+qoMqqiqXNQmJiSya/7fT5X/6/jPsdjs6nQ6N5lxBUhQFk8lEUVExRcXFFBUVU1hYRFFxMQ899iz79u2rPVHVejlW+p0p5wJRUVH4+/uzevVqOnbsyNdff819993n9PUxMTHs3LmTrKwswsLCmDdvHikpKQD89ttvrF279oKea15eHkOHDuW1116ja9eu5efPjBiioqKw2WzMmTOnwjlRk8mElBJfX1/mzZuHTqejefPm5e9Pnz7dY71UUEVV5TInMTGRz6vRU9VqtZXOqWo0mrJeqt8F7735zuTa3asvNKAzXnyxSmd0i7/qRx99xG233UZJSQmDBw9m8ODBAMyePZv169fz0ksvAY44yAUFBVgsFmbOnMncuXNp3rw5zz//PD169ECv1xMfH8+XX34JwIEDB8oXns5m8uTJ7N+/n5dffpmXX34ZgLlz5yKlZPjw4ZjNZux2O3369OGuu+66wJbMzEwGDhyIRqMhJiaGb7755pz6Z8yYwZw5c1z+XCpDVDRnUte0b99eqkGqVdzBkSNH6NatK8f2bfFoO0OuvoF773uQoUOHulzXrl27yntzVWIzVSysOiPofFy2xZPcdNNNvPvuu7U4ZVIzKvp7CCE2SCnbV1S+yp8xIcTnQohMIcT2Ct57RAghhRChlVx7WAixTQixWQihqqRKrRMTE0NmZla5S46nMBgMdRNVSucD3g1A51smpL5lr+u3oAJ8++239V5Qa4IzY4MvgUHnnxRCxAH9garGVr2llK0rU3UVFU+i0+mIiYnm6LH0qgu7gNFgoKSkCr9RTyE0oDM4hFRnULeo1jFVfvpSyqVATgVvvQs8BtS/+QMVlbNweAAcq7qgCxgM3mr8UxWghv4WQojhwHEpZVUTVRKYK4TYIIS4o4o67xBCrBdCrM/KyqqJWSoqFZIQn8Chw5XmaXMLddpTValXVHv1XwjhAzwNDHCieFcpZYYQIhyYJ4TYXdbzvQAp5TRgGjgWqqprl4pKZVR3A0BNUHuqKmeoSU81CUgEtgghDgOxwEYhROT5BaWUGWXPmcBvQIeam6qiUjMSamH4bzQaKTG5LzJUdZBmE8q2RSirf0PZtghprhs7VBxUW1SllNuklOFSygQpZQKQDrSVUp48u5wQwlcI4X/mGEfP9gIPAhUVT5OYmMihaviq1oS66qkqq2eiTL0XOfcT5IqfkHM/QZl6L8rqmTWu89ixY/Tu3ZuUlBRSU1N5//33y9979tlnadmyJa1bt2bAgAFkZFQcrOb999+nRYsWpKam8t5775Wfv/7668tD7yUkJNC6dWunbLLb7bRp04Zhw4a5zRaADz/8kKZNm5Kamspjjz3mlC1V4YxL1XRgFdBUCJEuhKg0GoIQIloIccarNgJYLoTYAqwF/pRS/uUOo1VUqoNj+H/5zakqq2ciV8wA63nuYlYzcsWMGgurTqfj7bffZteuXaxevZopU6awc+dOAB599FG2bt3K5s2bGTZsWLnj/9ls376dTz75hLVr17Jlyxb++OMP9u3bB8CPP/7I5s2b2bx5MyNHjmTEiBFO2fT+++9f4Cvqqi2LFi1i1qxZbN26lR07dvDII49U63OqDGdW/0dLKaOklHopZayU8rPz3k+QUp4uO86QUg4pOz4opWxV9kiVUk5yi8UqKtUkMjKS/PwCTB4cnte2n6o0m5BrZ128zNpZNZoKiIqKom3btgD4+/uTkpJSHpnq7B1QxcXFFQZX2bVrF506dcLHxwedTkfPnj357bffzrVNSmbMmOHUdtH09HT+/PNPJkyYcM55V2356KOPeOKJJ8ojYIWHh1dpizOoDm0qlz0ajYb4+IYenVc1Gg2UlNZeT1XuXXNhD/V8rGbkXteyGBw+fJhNmzbRsWPH8nNPP/00cXFxfPfddxX2Dlu0aMHSpUvJzs7GZDIxZ86cC0LvLVu2jIiIiHMCnVTGgw8+yJtvvnlBLAZXbdm7dy/Lli2jY8eO9OzZk3Xr1lVpizOooqryryAxoXrRqqqLwdub0pJanFMtznNvuQooKipi5MiRvPfee+f0CidNmsSxY8e48cYbmTx58gXXpaSk8Pjjj9O/f//y9Cfnh95zNqjJH3/8QXh4eKURpVyxxWazkZuby+rVq3nrrbe47rrrKgx1WF1UUVX5V5CQkOBRtyqj0Vi7c6q+Qe4tdx5Wq5WRI0dy4403VjrvOWbMGH755ZcK3xs/fjwbN25k6dKlBAcHn9Mjtdls/Prrr1x//fVV2rFixQpmz55NQkICN9xwAwsXLuSmm25yiy2xsbGMGDECIQQdOnRAo9FUmT/LGVRRVflX4OnFqtpe/RdNOoLe++KF9N6IJtX3YpRSMn78eFJSUnj44YfPee/MIg84IkM1a9aswjoyMzMBOHr0KL/++us5vdL58+fTrFmzczIKHD9+vDwFytm89tprpKenc/jwYX744Qf69OnDt99+6xZbrr766vIg13v37sVisRAaWmEYk2qhhv5T+VeQ2KgR69au8lj9RmPtrv4Lbx9Eh6scq/+VlelwFcK7+oFVVqxYwTfffENaWlq5y9Orr77KkCFDeOKJJ9izZ0/ZPHU8H3/8MQAZGRlMmDChPKTeyJEjyc7ORq/XM2XKFBo0aFBef0VR90+cOHHBFEFVuGrLuHHjGDduHC1atMDLy4uvvvrKLelV1NB/KnWGyWTiyJEjHDlyhFatWhEVFeWxttauXcvdd93BhpULPFP/uo1MfPgp1rphsaM6of+U1TMdXgBnL1rpvREdrkLT6WqXbaktJk+eTMOGDRk+fHhdm3IB1Q39p/ZUVTzO/v37+euvvzh86BBHjhzh8JHDHDlylIKCAuIbxhEVGcH+g4eZNWuWx1JcuHurqslkYv3GzZhMJZSUlLJn7/5aXf0/g6bT1cg2Axyr/MV54BuEaNKhRj3UumTixIl1bYLbUEVVxeP8b84c7n/gAZ567CFGDh9IQnxD4hvGEhERXu4m89usPxk0aBADBwygRYsW2O12tm/fzoqVK9BoNFzR/gqMRiPgcJHS6/Xo9fry4ZqiKBQWFpKfn09eXh75BfkUFBRQWmrGbHY8SkpKSD+eQWyM8zmWKuPb6T/x4qtv0yI1FaPRiNFo5NZbbnW53pogvH0Qab3qpG2VC1FFVcXjTLzvPnbu3MnK1et49sn/XJAbHuCaq4bSulULFi1Zzs5de9Dr9Qzs240Xn34IKSUbN23FarMBji2LVqsVq9VWfr0QAn9/P4ICAwgMDCAwIICAAH+MBgPe3t54eelJad2FlavWct2oq12+J5OphFEjR/L+Bx+4XJfK5YUqqioeRwjB5ClTuOmmG7nxtrv45YcvKyyXmBBPYkJ8he81bVK1k3hVJCUlsnnrNreIqsViwcvLtaR6Kpcnqqiq1AparZYvvvgSX1/fOrOhWXJjdu3eV3VBJ7BYreXbG1VUzkYVVZVaQ1GUOhWi5MaNWLdhk1vqMpsteBn83VKXq5Tm57Pz558pPHEC/6gomo8ahSEwsK7N+teiOv+r1Bp1PWROiG9Ibl6eW+qyWCx41YOe6tJJk3g7JobZEyaw6NlnmT1hAm/HxLB0kuvxi+pLuL28vDxGjRpFs2bNSElJYdUqh7/x5s2b6dSpE61bt6Z9+/asXVtxnIN3332X1NRUWrRowejRo8s3aWzZsoXOnTuTlpbGlVdeSUFBgTMfS5WooqpSazhEVV9n7ScmNCS/oNAtdVksdT/8XzppEgufeQZrcfE5563FxSx85hmXhbW+hNt74IEHGDRoELt372bLli3lNj322GM8//zzbN68mZdeeqlCgT5+/DgffPAB69evZ/v27djtdn744QcAJkyYwOuvv862bdu45ppreOutt6r9GVWEKqoqtUZd91QTE+IpLChEURSX6zJbzHV6L6X5+Sx77bWLlln22muU1rD3VV/C7RUUFLB06VLGj3eEcfby8iIoKAhwLICe6V3m5+cTHV2xq5zNZqOkpASbzYbJZCovt2fPHnr06AFA//79K40dUF1UUVWpNcxmM176uhOioKBAtDotBw4ecrkui8Vap6K68+efL+ihno+1uJidP/9co/rrS7i9gwcPEhYWxtixY2nTpg0TJkyguOy+33vvPR599FHi4uJ45JFHeK2CH5mYmBgeeeQRGjZsSFRUFIGBgQwYMKDcztmzZwPw008/XRCesKY4E/n/cyFEphDiglQoQohHhBBSCFFhFAIhxCAhxB4hxH4hxBPuMFjl0qWuh/8AMdHRrFrj+lbSuh7+F5444VS5IifLnU19Crdns9nYuHEjd999N5s2bcLX15fXX38dcPR63333XY4dO8a7775b3ps9m9zcXGbNmsWhQ4fIyMiguLi4PCDL559/zpQpU2jXrh2FhYVu+5F0pqf6JTDo/JNCiDigP1Dh3j8hhBaYAgwGmgOjhRDNa2ypyiWPxWJBr69bUU1qlMCWrTtdrqeuh//+TsZJ8KtBPIX6FG4vNjaW2NjY8iDZo0aNYuPGjQB89dVX5WEJr7322goXqubPn09iYiJhYWHo9XpGjBjBypUrAWjWrBlz585lw4YNjB49mqSkpGp8SpVTpUuVlHKpECKhgrfeBR4DKsvp0AHYL6U8CCCE+AG4CnD9P1rlkiQmJoaTpzLZsnU7rVq2qBMbkpMa8eU337NpyxZ8fHzw8fHBz9cXP18f/Px8CQgIIMDPj8CgAAIDAgkKDCAoKJDg4CCCGzQo3w1W18P/5qNG8b8HHrjoFIDe15fmo0ZVu+7XXnutfCi9ePFi/vvf/54Tbu+MQFYVbi88PLw83N6ZFfsz4fZ69ep1Tri948ePc8stt7BgwbkBbyIjI4mLi2PPnj00bdqUBQsW0Ly5o28WHR3NkiVL6NWrFwsXLqwwi0DDhg1ZvXo1JpMJo9HIggULaN++/Tk2KorCK6+8wl133VXtz6oiauSnKoQYDhyXUm65SKisGODsSYp0oGMlZRFC3AHcAY4PQuXyIzQ0lDffeINbb5/I2mVz60SUjh47Rk5uHp11eRQWZVOca8dksZFlsVFssWOyWDFZ7JRYbZRY7ZitNsw2BbPNjtWuIIRApxVoEPQfOKTW7T+DITCQ7k8+ycJnnqm0TPcnn8Rw1sKSO/BUuL2Lhf778MMPufHGG7FYLDRq1IgvvvgCgE8++YQHHngAm82GwWBg2rRpF9jSsWNHRo0aRdu2bdHpdLRp04Y77rgDcGQfmDJlCgAjRoxg7NixbvmMnAr9V9ZT/UNK2UII4QMsAgZIKfOFEIeB9meS/511zbXAQCnlhLLXNwMdpJT3VdWeGvrv8kVKyfArr6Rd6+a88Mzjtd7+Pfc/SvrqBfx2a9dqXyulxGpXMFntXPv9eh5+c/I5Ppzuojqh/5ZOmsSy1147p8eq9/Wl+5NP0uPpp91um6f4t4f+SwISgTO91FhgoxCig5Ty5Fnl0oG4s17HAhV7Cqv8axBC8M6779K9ezeee+rRCleXPUlCfBzr/q5ZiD4hBF46LV46LQav+rEZscfTT9PhvvvY+fPPFJ04gd+ZHVVu7qF6mn916D8p5Tag3Lmssp4qsA5IFkIkAseBG4AxNTdV5XIhOTmZ6Oho/pjzN8OHDa7VtpOSEjlV6Hqq6ihfPVdffRVB/n6EBAUSGhJCSEgIIWFhBIdFEBIWTlhYmOPceQ93T3sYAgJoO26cW+tUqTlViqoQYjrQCwgVQqQDz0spP6ukbDTwqZRyiJTSJoSYCPwNaIHPpZQ73Ge6yqXMM08/wwuvvESPbl0ICqq9feopTZuQU1xFamcnmDaiDVOuakWuyUK2yczpYjM5JgvZxQfJ3reL7K02DpgVsktsZJss5BSXkl1UQk5hMQZvL0KCAklISGT+kmVotdoL6pdSuiW1h4pr1CQzijOr/xfNIyulTDjrOAMYctbrOcCcalulctlz9TXX8Ndff5GUegXt27bGZrOR1CiBjle05doRVxEQ4JlgJcmNG1FitWG1K+i1rk096LUawv0NhPtfGB+2MqSUFJRayTZZaP3uXEwmE/7+596rwWAgOzubkJAQVVjrECkl2dnZFcb/vRhqjiqVOuXo0aNs374dvV7Pnt27mTdvHvv372POb9OJj4+ruoJqoigKfkFR7Hp8KLFBdReGECDy5d/Zuc+xY+hsrFYr6enptZqdVaViDAYDsbGxF/hXqzmqVOotDRs2LHeh69+/PxPvu4+33nyTkWPGsn7F/BrVuX37TpYuX0VxSQkaIYiOiiQwMID/m/oZq5avoMRqZ8Wh01zfpm5F1aDXV5iBVa/Xk5iYWAcWqbgDVVRV6h0PPfwwk159lczMLMLDw6q+AEcP9OpRY1iyaCk2u52ksEAMei1SSnJMZorNNgalRPPrrV157I8tHM4t8vBdVI3RS6f2Ri9DVFFVqXfodDp69ujBwsXLuOG6EVWWVxSFDl36UHjiKEvu7UtqRCAaTeVzkRH+Bo7n137m0/Mx6HUV9lRVLm1UUVWpl/Tt25cFZ4nqm29/wJZtOygqKqKouBhTkQlzaQmlJSXkZOcQ6atj5X39CTJW7a4UFWDgRD0RVbWnevmhiqpKvaRb9+5Mmza1/PUrr7xO90bhJAT7EuCtwz9ch6+XD/7eAUT4x9MzKRyj3rl/50g/b3adzPOQ5c5j0GvVnupliCqqKvWStLQ0Dh85Sn5+Af7+fgT6+3F7x0ZcmRrjct2hvt4UmF0PVO0qRr1W7alehqiiqlIv0ev1xMfFkdaqAzm5eei1Gny8LnSSrwlBRj0nC018vuYAvl46/Aw6/L30+Bt0BHjr8ffWE2jQ4e1kz7emGHQatad6GaKKqkq9JblRAg2yzDwzvjPxDXzd5gifVWTmdEEJz/yyHhtgl7L82Q7YgTP9WM2ZhwANAo0QaIVAoxFoNQKdRuN41gr0Gq3jWashIcSXWeN7XtQOo07jdE9VURSsVit2ux2j0ahuCqjHqKKqUm+x2210jA8lIdjPrfU2CvbDTwjuvkiuKolDWO2ADbBJsCOxnRFge9n5s8uUHZcCf2XmV2mHQXtuT/Wnn37iif88iMVixWK1YrHayp9tdjteeh0aIfDS60mMi8FssZB+KotNm7fQuHHjGn8eKu5FFVWVesmaNWvYvGE9P/xngNvrbh4ZSHEVOwkFjoAVWqC64U/swDwcvcuLReHKK7Gcs7f8tx+nc1ercK5rHY+XVoNeq8FLq8FLp0GnEQghkFKSW2LhUE4x3loNN/y4AZPJ9QAxKu5DFVUVjzFnzhwsFgv+/v6OiPoBAcTGxuLrW/VOpueeeIynezXBoHduHtVksfHUn1uw2O3l57y0WuKDffHz0uFv0BHpb8So16KU9TZteOYLoMUhykUWGwGGiiV5+aEs1p8o4Otrry0/t3LVKp65qR2xQT6V1i2EINjHm2AfR34sKeHbb7/Fz8+P4qJCigsLKC4qxFRYRHFxESaTCSklWq0GjUaLVqtFo9GUPzuOdWh1Wm67424GDbogc5JKNVFFVcUj2Gw2hg0bxpVDB1FYWERBYSGnMrPocEUHfvn114teu2fPHrZu2cysJy4MC7gvq4Bpq/az8EAW2zNyOf781YT6GXhv6R5+2HyEHo0jy8uabWZWHsmm1GbHbLNTUGrFXjbk1wg4KR1Bfj2BFsgutlQoqla7wr2ztvLuh1PKUz4fP36c4uIimoRVL5DMXR3iObT2T7Q6QYCXligvHb5eOnz9dPgE6/Dx8kEAinTMGSuKxC4VpLRjV6TjvCIpMFu5bcz1THrzbcafl5ZapXqooqriMjabjebNU9BoNDQIakBwcDABAQEYjUZm/fRNebl16zfSd8hIbrj+epo1a0bbdu0qjPTu5+cHgnOiSM3dk8G9v27kRL6JTolhXNk8mi3Hc2j9zt9E+hvYl1XA3V2TeX1oa6dsbvHWHDJP5XtMVHVAjslMYsiF88HvLdtHw2YtGHVW/qgVK1bQuVFktReg7uvqvrnU7o3CGPbM4xw9cphnn3+h0vQmKhdH/dRUXEar1ZKZmcWP33yCv58fObm55OTmMeLKc+dD27drw4qFf7Jl23b27D3A7bdPwMvra5o0aUJoaGh5rw2gsMhEm3f+wqAVnCgyk1ts5sm+qdzfvQk+ZVH37+/ehC0ZeWzJyGX7yQLGtIl32uaoAB9yT1W9mFRT9ELw1JwtvDqkFe3iQmjz1hxKrAreeg1H80vZvGPnOQK6fMliukS7d0GuujQJC2DZXT24acY3pE3/jpdee5NRo0apngbVRA39p+IWHvnPf7CZi3nvv5OcvubTL77hv+/9HxaLlcysrPKFHSklcf5ePNAtmUKzlcah/vRLjsTX2319gLE/rmXTugOMdFuN5zJHq2Wf3U635tH8elt3vB/7kaGABVju7U1+UdE5PcH2aam80zOaronOBZDxJFJK5u49ybPz9yD8gpn6xVe0a9eurs2qV1ws9F+VoiqE+BwYBmRKKVuUnXsZR7ppBcgEbisLUH3+tYeBQsq8Tioz4nxUUb30yMjIoEWLFuzfvpbg4AbVvt5ut1NaWordrtB7wDCGhgueH5jmAUsdPPu/rUxfsINbPdYCrAGW6rToBGC181DZ+S/8/bnuzjsxGo3knj7N6cxMZs6exemXRji9MFcbKIrkpXnbORCQzPc/X3we/N+Gq/FUvwQmA1+fde4tKeWzZZXfDzwHVJY0u3cF+atULjOio6Pp368fM36ZyV23Vz/Vr1arxdfXlzVrN7B71x6+HjjQA1b+Q1SAAbu3HsxWj7XRGMi3KyRLydlJ1zsXFbHq/ffRWa14A4eAJjEN6pWgAmg0gvZxwWw8klvXplxSVJlPQkq5FMg571zBWS99cfhKq/zLufmWW/h2+i81vr6goIBhw0fydL8WNAv3bDbQSH8jdp1nRSwEGCAliTi8Ac7QTEr6Wa30AjoDNo1gUNMoj9pSUwINevLzPTf3fDlS40kqIcQk4BYgH+hdSTEJzBVCSGCqlHLaReq7A7gDKI8Er3Jp0alTJ3bt2VPj67v1HEjH2CAe693MjVZVTIS/AWs9WU8oMnjRMym86oJ1QKBBT35BQdUFVcqpceYzKeXTUso44DugsqTdXaWUbYHBwL1CiB4XqW+alLK9lLL9+Tl7VC4N/P39KSysWUT97r0GYjp9km9Hd6qV1eZIfwOl9rqPVGUC8sxWusSH1rUpF6AokjeXHaBx4+S6NuWSwrV0kg6+h4oXUc8sXkkpM4HfgA5uaE+lnuLt7djlYzZXLwX09B9/Yee27ay+fwD+Bn3VF7iBCH8DJVYbdS2rW4HGoQG1dt/OYrMrTJy1mXRNED/8OrOuzbmkqJGoCiHO/ukaDuyuoIyvEML/zDEwANhek/ZULh0CAwPJzc2r1jVPP/tieZxURamdIbmftx6NENR1pqq9QjCoWWTVBWuRwlIrXT5awnHfWP74e175j6WKc1Q5pyqEmA70AkKFEOnA88AQIURTHC5VRyhb+RdCRAOfSimHABHAb2VDOR3wvZTyL0/chEr9ITW1OVu37yQyMsLpa2w2G99vOMwPmw5jsSsYdFr8DV4EGb1o4ONFsK83oT7eBBv1BHiXbcP00pEWHUQ3F/w6g328ySoswbNLYhen2Fj/5lOP5pkokDrW/zVXdfyvAVWKqpRydAWnP6ukbAYwpOz4INDKJetULjl69+rNz7/9zoB+la1dXsjRgzvLjy0WC8eOHefIsXTSjx8n48QpTp3KJDPrNPvy8ykuLqY0p4QSUwF7f99E7qRR52xnrQ7h/gayC0tIqtHVrmPBMZ/qyg+DJ/DSakCiCmoNUbepqriViffdR5MmTXj+qUeJiam+m5CXlxdJSYkkJVWd9z48Ip51x7LpklAzUYoO9CE3o+58MLcC8cF+TiUrrE28tBosVs/5717uuGOhSkWlnJCQEJo1bcqhw0c83lZiUhKL9mfW+PrYQB/q0gNzjxAMqGf+qcVmG8sPZaqi6gKqqKq4nYCAgFrxbRw4qD//232yxtfHBhow1eEQt8jHmz6N68d86sN/biPipd9JevN/fHLAythx4+rapEsWdfiv4nZ8fHwoKfF8ltAJY2/mzbfexWyz412D3VGR/gYUgxeUVM8FzB3YgJxSS50FUJFSYrbaKTTbWHwwk5l7TrN+yzZsNhtJSUnqfKoLqKKq4nZ8fHww1UKW0IZxsQT6+rDmSDY9arCCHuFvxKapG/HYjmNON9S35u5Kk+Zt55X529FrNOi1WvQ6TfminSMAtXQ8lwWmPvucvcx1TacVWO2Szz//nISEBDfcmYoqqipux2g0UlBQWCttJSUns2j/qRqJaqS/AUsdbVXdCmQWlRD70qyyM9IRQEOeG0jj/Chysvw8mCxWnh+QxtgOjSgy2yiy2Cg025BS4qXV4K3Tlj078lydf06r0ZBrspD4+hxuvPFGt9/j/PnzeeLhB/H380Wv15enb4lPakyz1DTCw8MZMmQIgYGBbm+7LlFFVcXtNG3alMeefg6DwZsJY2/2aFuDBg9g9lef1ShMYIS/gVKbveqCHqDEx5txbeMZ1aohAhACBKLs2eHOdKYPLcQ/r88v1yw8AC+dlojqZWEpZ/mhLDq2bYOXl3MeCEVFRdx47Uj8/P1o16krqamp+Pr6smXLFgoLCzGbzeSczuTwvr3MW7yUW9snMKKFL7ay3rFNkRzO3MTePav4MSOPJQvn8/EnFXpoXrKooqridv7zyCMMHTaMrl27Muqa4QQFea4nMmHsTUx69S1KrDaM+ur9Ozu2qtpRqN0VWxuQa7HyRJ/mRAYYa7HlC1l6OJse/a6tuiCOTRrXj7ia0IJjdPYLZNfsz/h1WhEWu0KrCD+CvLV4aSDWoKdruC8fPzGUcH9DpfWtO5rNPfNXuOtW6g2qqKp4hGbNmjF06BCmTP2Mpx9/2GPtREdF0SDAl1WHT9MnuXrbPb11Wgw6DTlWO7UZzmQPEOprqHNBBVh2NJ+3e1e9UUNKyT133o4tYz9Tb+lU4w0XZxMVYORoxglKS0sxGCoX30sN1aVKxWM8+eRTfPB/n5CdnVN1YRdo3KQpC2vorxria6C2I6hvB/o2qXv/1MJSK7syTtOhQ9Vxjl6bNIl1C/7ix9FXuEVQAWKDfEgI9mPNmjVuqa++oIqqisdISUnhxjE3cvs9D1+w4OJOhg0bzP92n6jRtREBRrLdbE9V5Pt407dx3W9NXXE4i3YtW1TZS/z2m2+Y+sE7zL6lo9ujaRWZbYSG1r+wh66giqqKR3nt9dc5dPQY0z77ymNtTLjtJnadzKPYbKv2tdGBPuS536RKUYBci61G3gruZtnhbHr0G3DxMsuW8fD99zL7lk5EB/q43QatRqAodR2A0b2ooqriUby9vZk+/QeefekNvp0+A3AETZn0xjscP16z3uX5hIaGEBLoz8ojWdW+Ni7QWKtbVfcBAQY9cUG+tdhqxSw9WkCv3n0qff/gwYNce81VfHVtO1pEBbm9/cM5RWQWllx2/rHqQpWKx2nWrBkLFizg6quv4uHHn6OoqJiSkhJSU5rVKOhKRYRHx/DEH5uZEXsUs82Oxa5gsTkex/NNZOSYsJ9xfJeOhyIlChAoqLUsa9uAPvVgPtVksbH12Ck6d+5c4fsFBQVcOWgAT/Vo7LH4BMfyTDRLboy/fw39weopqqiq1AppaWns3r2H3NxcfHx8uPOOOygqdj1EtMlkIiauGUUmExpAZhaikY5Eezop0UjJDinpB8Ti+IfXlz3rgF3Aeo0W7LXjr5rr402/erDff/WR07RMaYaPz4VDeqvVyg0jr6FnpBf3dm3sMRvySiz4+fl5rP66QhVVlVpDr9cTHu4QlOrms5r+4y8sXLKMoqJiCouKKC42YSou5nh6BgGlpYwDPgCustnPmdOSwGqgDVDRckwDqLVdVQqQZ7XXi/nUpYdO06PvhWnApZTcOWEc8uRB3rm5o0dtWJeeS7vOV3q0jbrAmcj/nwPDgEwpZYuycy8DV+H4P8kEbjuTj+q8awcB7+PoOHwqpXzdjbarXMJUV1Rvv30iMYqCH+AlJXpFwR9IA5oBfjgWCE4BZw9WS8vOV7a+7UftieohwKDXkBhcD+ZTjxXw5EN9Lzj/37feYNvyBSwY381trlOVseJ4EU88XGku0EsWZ3qqXwKTga/POveWlPJZACHE/cBzlKVUOYMQQgtMAfoD6cA6IcRsKeVOVP716HQ67FWs+hYUFPDTr7+zectWSq1WrsPx61wZ4RoN+xTlHFE14RjuV4Yf1Fqq6q1Az8aRdR4Bymyzs+HwSbp06XLO+Xnz5vHsM8+w5T+D8fX2/CDWZLHToEEDj7dT2ziTTmWpECLhvHNnB8v0peJp/g7A/rK0KgghfsDRu1VFVcUpuvUYyNG9+4nUahmk0aCtQoSjcfx6n00xoNdooJJrfXBsG7UAno6/n2304u4mzufu8hRrj2aTkpxEQIAjO5fNZuO5Z57ii2lTsdkVYgJrZ6dXw0ADBw4coGNHz04z1DY1/jkSQkwCbgHygYr2ucUAx856nQ5U+ukJIe4A7gBo2LBhTc1SuYRRFIWMEycxl5opNZvZs3c/d0hJiM05/9NIReHweQJaDOgv0jPU4BDTbM6dNvAE+XaFHo3qwXzqwSzik5pw6NAhFEXhltHX42s6zYb7+9D09T/IKjLTsIHne6qNg7w4cOCAx9upbWo8aSKlfFpKGQd8B0ysoEhF/8mVjrOklNOklO2llO3Dwup+t4lK7TPi+lto2LglKS2uoG27bkQJQUg1rg8His8bypuougfqK4THt6oexeHo3iSs7t2HGof6k7l7Ez07tiMttTnDIxT+vLUzEf5GjHodWcW1E7RbCIG9lrwuahN3/Bx9D/yJI3X12aQDcWe9jgUuWMxS+XcSHx/P5599Sk5OLkajAaPByNy5C0kDmuAQQm9F4RRgxLHQpOPivYBwoETKc4byJiDXbudzHPOn/YDg867z12jI8fCXezPQPSmizudTAa5v3ZDrWztGg1LKc2wyeunIKvJ81gaAwwVWhiTVVS5bz1EjURVCJEsp95W9HA7srqDYOiBZCJEIHAduAMbUyEqVy47bxo7l7nvu4fCmzfhpNNiBQLudE0KQjmOe0yYlNhwuJnYcwxzN2Q8hznnWCoHWbmcX/+RGb4lDlItxCNtqynKon0UAeHyrapbBi7H1YD71fM4XeR+9jtO11FMNMWjZv3dvrbRVmzjjUjUd6AWECiHScfRIhwghmuL4fz9C2cq/ECIah+vUECmlTQgxEfgbx6Lt51LKHZ65DZVLDYPBwP333svmqVPp7eScqYJDbK38I7pW/hFfK7BECI5LWS6qgUD7suPTWi22Cnqk/opCzdMHOkeBlPViPrUqfL20ZBbWTk91VIso/jPzF158+eVaaa+2cGb1f3QFpysM1V3mqzrkrNdzgDk1tk7lsub2u+6i5xdf0NNmc2py/8yi0sXmSI9LyaFK3tPi6PGej5+UlF7EQ8BVMgBFSppH1P+0IQ2MejJrqacqJRi8a56jq76i7qhSqTNatGhBbFwcB/fswV2bISOBrdqKt51qgbPlwoZj2F8MFCgKm3FMMUgcveKzjznvXEXlKitzAGgbF4KmjpIMVgerXcFkrZ3Fo2bhAezYs/qyC1KtiqpKnXLn/fcz7bHHaFxc7Jb6IgBTJYtOOik508oJ4BMcGwPO9GBXaDSOPFDnPeCfBbJz3juTN6qKcqV2OwHe1U+hXduk55lYezSbD65pX3VhNxDubyA6yI/9+/eTmpqK2Wy+LMRVFVWVOmXMmDE89p//YMLhiO8qQfzTAw067z2tlOXD/31AjEbDeEUhE/hSCO710PD/I43GY5Ge3MnYH9cwLDWW1EjPTVMoisKm47nM23uStUezOXzqNDeOuoajGSex2u3kFxSi1db/H6CLoYqqSp0SFBTEkEGD2DZzZuU7Q6qBAPyBr4TAW+PoN54ZhpfYHUn+Jmu1mBSF5mXX+OK5rao24LSicF2r+r2hZW9mAasPZ7H5P4PdUp+iKGzOKBPPI9kcyDFxuriUXJMZb52WphGBtIlpwH+vbENKRCApEWm0fn8BmZmZREXV/x+gi6GKqkqdc+fEiYz88092Wq3AhTtEJP8Mr8vfE6L8vB2J1GjwEg4RtUiFCLtCmt1e4fBcY3dEsoopE1IjjuG/Dfd/IdIBP72WUL/6Pawd++MaRrdLJCm0epsTFEVh64l8ft+RzpaMPA7kFHO62ExOcSleWi1NwgNoExtMvyZRNI8MJDUykFDfihenYoP9SU9PV0VVRcVVevXqRb7VSkMo30Elzns+/xgpy19vE6Dx8WJiz2YArE/PYeWuE7QxW51q/+ytqu72JN0rBKnR9TtoyMZj2WzNyGXGLV0rLaMoCttP5jN3zwnWHMlmf4GV06U2cvOLsCsKer2OMa1i6ZMcQfMIh3iGOflDsvRAJrN2neRQZh45OZ5NElkbqKKqUudotVruGDeOA198QbcaDMNParR0ah7Dw71SAFh+KIuFe09Vqw4fITgtpVtFVQE2ScmPfZtXWbYuuf3n9dzRuQkxgT4oisLOU/n8veekQzzzLWSV2MgtLEKn1dG0SRKt2/fijpYtSG3elNSUZixeuoJnnnyaj0fWbIHrlhkbGD12AnNeH+VUZtf6jiqqlxhSSr7++mtKSkrQ6XRotVqCg4O56qqr6to0lxg+YgSP/PwzFBRUXfg8bAIMun8WN9IiA8k3W1FwPriFnxDkunledT9g8NIxtHmMW+t1FycLSnhv2W62HM/BLDX89OZccguKERpBk+Qk2rbpzoRWaeXiGR4eVuE2W1cz5Qb7Gxlz4420bt3apXrqC6qoXmKYzWZuu+02xnZpBgjsUjJz6xG27thFfHx8XZtXYwICAlBquC/eLsFL9498Bhq9CDToOVxsppGTdfgL4fYEgGs1Goalxbq51ppxuqiU37alM2/vSXZlFXKqoITCUgtWRRIbE8XdD91HavNmpKY0JSIivFoxClwV1TA/I5mZmS7VUZ9QRfUSw2AwEBcZzpM9k2kU4sjvU2ixs2zZsktaVP38/Cip4ZfTDnjrzu2TpkUHc2DfCadF1U9Kt4rqNhw7qd4c1saNtTpHnsnCzO3p/L3nBDsyCzlVYKKgxEKj0AA6J4bxQLdI2sUFs/5YDk//tYM9W9dUmKvKWeRZ89s1wddLS7Gb/JTrA6qoXoIkJzVi3+nCclHtHuvP0kULuOmmm+rYsprTrFkzTplM2Ll4dP+KsHPu8B+gQ8Ngftx/wuksqX6K4rYQahnAH8C06zsQ7l97q/6ni0pp/c5fZBeVEh/sT+fEMCZ2aUzb2AakRQXhfdZnlJFv4rHfN/PJJ//nkqC6gxKrHaOxdgJj1waqqF6CNG6Wwv6srQwscyjvlhjGp38sqWOrXMNoNBIWHMyPmZmV7u1P5p/oU2dTUU+1dVQQ3xgNYHIuOIgvYK1ke2t1OA18AzzcJ4Wb2iW6VFd1mfDTWlpFN+CXW7th0Ff+0ySlZMJP62jbri3XX3uNy+26Ovw/XWxm6tSp/DRjBiUlJZSUlGAymQAIDg4mODiYkJAQgkNCCA8PZ9SoUXh5eTpPQ81RRfUSJLlZc/b/sab8dcuoINJPnCQ7O5uQkOqEda5faDRgiAqifcPQC947llvM6vQcWpkuDPZhR2I8T0TSooMoqoZA+uJIqeIMRThiWZ7C4YaVB5i1WkqlxK4T6BB8tvYQX6w/jFajcYQk1Ah0GoFWo0FXdnzmEennzS9jXUuAd7KghIX7TrHyvv4XFVSAHzcfYe2xXI4sWe5Sm2dwdfi/50Q2zTvo6HxFS3yMRoxGI0ajASklubl5ZOfkkpOby6F9u5g06RWSkpLqdQoWVVQvQZo0acKC3H/ERafV0LFRFCtWrGD48OF1aJlrJDSM49k2AfRJjrzgvdVHTnPVZ0srvM4uwfs8IWkc4keJzU4RjuDUVXH+rqrDwFLAIgR2jQYzYFEUzFKi4PAWCBSCYCFIsNsJstsJBL6xwKJ7+uLrpcNsUzDb7JjtChab/Z/XNsXxsDuOn/9rKycLSogMqPkQeNyPaxjYLJoWUUEXLZdZWMo9v6zngw/eKc9R5SoSiSuxt/18fHhw4p20b1f1/PPaDZtQPLSd2F2oonoJ0qRJE/Zlnbus0qdhANO//vKSFtUWrduw9cSGCkU10t+A2VZxz1PhwjlVnVZDYogfezMLaOtE2z6ArezLehSYjmOqIVBKDHY7vjhiswbi2IElpHTErjsPo15LkzB/IvydE8iMfBMv/b2NhJdnoinbVisECES5UImykxfTLbsi2fDwoCrb+27jYaJjYrj15ooietaM2JhoDmXmkvLufPrFB/JIrxTig535KXMgpXR6v78QwuXpBk+jiuolSKNGjTh2Og+rXSnPzX5Pl2TS3pvPokWL6N27ojyM9Z/W7a5g1VcVD0kj/A2UWO0V+p7apMRHf+G/cru4ELZXQ1StOKJXfS8EvYFONfjyChwC5wwlVhv9py6iR+MIfrqlK4p0CIxdSqQsCy2oSBQpq1xvM+q1BBmrnmf099ah09Y4NV2F9OrRjcO7N/HH/+byzfczaD95MVnPDXP6eiEENicDlWs0mku/pyqE+BwYBmRKKVuUnXsLuBLHNNQBYKyUMq+Caw8DhZRtrZZS1k5MscscLy8vYiLCOJRTRJMwxxDO11vHu0NbcM/t41m5bsMlmU89NjaWjMKKZzaNeh3eeg1fSIHWamMw/2wpVaDCecT2MUEs36YHS9XbVb1x/JN+LQRdhKBTDb+4GuHwHXaGrRl5ZBWZ2fbI4PJeqqeJ8DdQVFjo9nojIyOYMPZmBvXvS0rrztW6VqsRWJz4G4FDgOu7qDrzl/wSOH9cMQ9oIaVsCewFnrzI9b2llK1VQXUvyUlJ7Ms698sxPDWGgQ19adIokTdef618BfVSISoqipOFJZW+P3tcTx4anIYS5HNOUjQ7YNRd+K+cFhVEaQXnK8KMY/9/GyHo7sKXVgiBze6cqCoS9DpNrQkqQLifgZKSyj9jV9FoBFX3qy+8xmxxLtuARlP/h/9V/jWllEuBnPPOzZVSnumvr8aRKVWlFmncrDn7Tp8rqkII3h6axqLbu7L2p89o0iiBl158kd27K8rLWP8oKCjA11tf6fu9GkfwQI9mRAX6nuPLqkiJj9eFg660qKDy7apV8ZVGQ4JGQ39FcWklWyPA5uTw3y4lmlrOrurvrafE7KyfQ/URQjjtG3wGrahGT5XLo6daFeOA/1XyngTmCiE2CCHuuFglQog7hBDrhRDrs7Ky3GDW5U3T5qnsz63YBzMlIpAZYzrw6+i2ZC2aQd9unWjZLJkXnn+exYsX19vdKzt27CA11LfKcsp5PRUFLnCpAgjzM+DjpeN4FfX9CZikZISLggqOL73NyS+9orjmilQTpq45QHJjdyWvuZDqLiTtPJmP2WrFYnFO6DUaTb3vqbq0UCWEeBpHGMrvKinSVUqZIYQIB+YJIXaX9XwvQEo5DZgG0L59+/r9qdUDmjZtysycizu2t4sNpl1sMO8MbcnKI6eZvfwXnvzhC7YezSSlcRJdevSkWWoLOnbsSLt27WrJ8srZvmUTKSFV70BqFR3E8qOny19XJqoAqVFB7D+YSVwlde0GtgDjpcQdKehENXuqdimx2RR0Tk5TuEJGvonPV+9n5bL5HmvDMZVx4f1n5JuYszuDFQez2JldwqkSOzkFjh/3lGZNSG6c5FT9VpsNvb7y0Ux9oMaiKoS4FccCVl9ZyU9HWXZVpJSZQojfgA443P9UXKRJkybsO5XnVFmNRtAtMYxuiWEAlFrtbEjPYdXhVWzeupinn3icU6ez63yXyoY1qxnZueo0zt0SQpm56QhLzVYsOH7VjRWs/gN0iAtm9sGKg3VYgFlCMNiNIf8EON1TbRLmj1GnJX7SbI4/f7WbLKicSQt20SI1ldat0jzWhmNOWeG+X9ex+WQhJ0oUcopKKC01k5gYT9vWbRk9qhUtUlNIS00hKiqyWsFbSktL8a7nGVhrJKpCiEHA40BPKWWFqyFCCF9AI6UsLDseALxUY0tVzqFhw4acLiym2GzD17t6f0aDXkvXxDC6lons5pOFLF++nD59+lR6zSsvvsCJExkkNm5CYmJi+SMoKKhaX4rKyMvLY+fe/XS6oerYowOaRlIqJfsCjTSLCOTmACPRARX3cFtHBzHD58LtqgqOPOvRQtDajcPJ6ixUxQT6sO7BgcS+9Jvb2q+M9DwTX687wNqVizzaTmFhETablfSgJAb1bU1aanPSWqSQmBDvltxTZrPl0hdVIcR0oBcQKoRIB57HsdrvjWNID7BaSnmXECIa+FRKOQSHx8tvZe/rgO+llH955C7+hWi1WpIaxrInq4C2scEu1TUkKZgZP0xn+/btrFg0nz1799KoUWN+/f0PwOE7+dZbb/Fkr2SO7V7BsgILh3OLOZSZixAaEuNiSExsRHzjxiQmJdOwYUNiY2OJi4sjPDzcqdXtVatW0T4xssotlgAR/ka+GdOZm75fxZXNo7m3W9NKy7aMbkDxeT3HE8CvBi8KSy30csM86tkIcNqlChx+oxa7gqIoHvUCeGXBTlq1TKNFC88GzJZSYvA2MPuXymYEXcNsNl/6oiqlrGjrxWeVlM0AhpQdH6Ti+BcqbmLI8Kv4aOUcPnFRVIemRNHhvU8Z2jKB61Ij6ZTsy3vrNpS/n5GRgdlioWdSBFfEBZf3TKWU5JZYOJRTzOHsIg4fWsHeLYtYUGghPc9Eek4h+aYSosNDiY2OJjYujtiEJOLi44mLiyM2NpbY2FgiIiLYs2cPqaHOR0sa3iKWn27txphvVvDr9uP8Oa4Hhgo8AJqG+VNksWLCMU3wPyE4qtPwcPemfLJiL9oKYgm4QnXmVMGx80srBDkmi8fyWB3NLea79QfZsHaZR+o/G41GU22XqupQegmksVZ3VF3CPP3s8zRr/BUb03Nc6q22iWnAwrv70r2RI7L7hvQcvtn3j4dAeHg4zz3/PDdN/Rg/jZ1xbWK4s3Nj9FoNwT7eBPt4066S9kutdo7nm0jPL+F4fibHdh9mz3or8wstHM8vIT2ngLziEvwM3jzWI7ladg9sGsX2x4Yy5rtVxL0yiyZhAQxqGsmdnZPLQ+7pNIIwPwNfmswUIhiYEs23/VJpHdOAacv3usX95WwEjjnF6uDjpeNkYYnHRPXl+Ttp06YVKc2aeKT+s9HqtMhq/KhUl8uip6pSfwkMDOTVN97i5mcfZ9Ht3Wscu1MIQY+kfxaI9BpBbn5B+ZBUr9fz1NPP8MSTT7F48WIemngPwT5HGdM2ocq6DXotSaH+F83SabbZuf7r5ezIrP5On6gAIwvu7M3yQ1ksPpDJH7symDR/BzqNBo1wDMX9vPWM7pzMA92akBjyz550V6MrVYRjoap6ouLjpeNEQSktPJBE9HBOET9sPMSm9f9s/zWZTCxZvpKB/fq4fcrB0z3Vy2JOVaV+M3bcOA4e2M/gLz9n/vhuNPBxfQU/LSqIcIPgqSceZ9Jrr6PVarFYLHzzzTeEhoZy09jxLJ/1uVOi6gzeOi3XtY7nmf9trdH1Go3jR6FHUjjPDWiBza6QW2JBkRKtRkOIj1eFi2mKlNUOiF0V1Vn9P0OAQX/RnWSu8OLcHcQnxDPl489Yvmwlhw8coqCkBAX4YtqHbg2sAqDTaiuKM+M27HY7Ol39lq36bZ2KU7z0yiSKCgsZ+tWv/D22C/4G1/z4hBD8emMHbpzxI4PXreWmseN5+blnSPDTYLIqbDh8kuQI1+Zxz2dEi1gm/LiGHJOZYB/XeiI6rcap9MiKlOQBJsoiT7nUqoPqBFQ5Q7CPN8fz3SOqGfkmpm86wt+7T7DrRD5ZxaXogb8PHSXOZiMNiAb+0mqZ8/cCt4vqpeCc72lUUb0MEELwzvsfcGdxEX0//YvPRrYhrYq4mlURGWDk77FdeH7eTj559VkmD2xMvyaOkHx7MgvYdcq9afIMXjoiA334bVs64zs65wjuKmkxwSw4ms2fZcGs7wZc/amoiaiG+HpzstC5DAVnoygKc/ec5Jdtx1hz6DRHc4opsdsJ12iIB7opCrFAAMB5UaBi7HY2nbUY6S5UUVVF9bJBCMHUTz/n008+of/jj3Jvp0Qe79UUL13NB7g6rYZJg1pccL5peABNw90T4PhsnuydwlNztjCmbXylzvzuZOG9fcuPjY9MrzSNS3WxVnOhKszPQFZR1aK6/UQuP205xpL9mezLLCDbZMZbCBpqNMTb7XQBIgGtE9MP0cCSEyeqZacz6HRaVVTr2gAV9yGE4PY77mDwkCHcOe42Ov3fEj4Z0brSlfn6xu2dG/PGol08879t/PfK1m7ZVOAMx3KLUXBE/3eV6vqpAoT7erEh59xFuox8Ez9tOcb8vSfYnpFPZmEJipREa7XEKQq9pSQa8JeyRnm1IgCT1UZmZhbh4WHVvr4yKtum+m9CFdXLkNjYWP74ex7ffPM1wx58gHHtGvJs3xSnHOvrmhm3dGHwJ0vIKCjhqxs6utTTdpY1R7PxxrHLytXWhHR+9T8j38Rfu0+waN9J9pwqoOmk38kvsVBktlIqJZEaDQ2B9opCDI6pCeFiYsIzaIFQjYZfZv7B3XeMdUudADqdzqMLVZcCqqhepgghuOWWWxkwYCD33D6e9pMX8d7QFuXzovWVtrEh7Hh0CB0/mEe/qYv46oZO57hBeYJBTSPx9/VmeomVG13dYSXlBX6qmYWl/G93BssPZbE1I4/0nGLySizYpKSBRkOkgLZ2hUBLEQHAWo0GnZSM8nCIuzhg7oJFbhVVdU4VRH38ANq3by/Xr19f12ZcNkgpmTlzJv+5fyItQw28NTiVRh4WKlex2GwM+2wZqw5nMbZDYyYNTnPZq+FimCw2Qp76iYmULezUpA7gM72OplGB2CQcyy4mr8SMpVw8BRF2O+FAGBBExbE3lwH7cMTUdBe/ajRopaShlIQD4cB2YHtMNAf218yVrSJsNhte/pEoJaerLlwD/MMSOH78uNuSFtYUIcSGygLvqz3VfwFCCK655hoGDx7M2/99i05vvcndnZJ4oX9Krc1bVhcvnY65d/ZmT2YBo75eQfJrh5l2bQeGt/BMPHQfLx1eWg0ldqVKUS3CkUPoKJAJFGu1lNjtlAJYbRw7lkOylPTEIZ4NAE01ep0NgBKttkZzpZWRJSUnpaQwNoZFpzIptlrxAjSZ7o1d7HD+9wxSSkwmEz4+zm9nrgtUUf0XYTAYePqZZ7lt7DiaNUlmYpdGTvlz1iVNwwPY9shg3luym7E/rsH463q6NQqnV6MwOsWH0iIyEJsi2Xkqnx0n84kMMNA7KaJGye28dVpKzhq6FwH7cYhnlhAUazSY7HasQJAQRGo0JNvthNvthAE7hWCzEIx3cdgeAJjdPIIcLSVTgOdeeJJbbrwBk8nE3/MWUVBQ4NZ2zuzQklK6/QfbZDLh7e2tOv+r1D9iYmLQ67RoNfWzl1oRD/ZsxsSuTfhj13FmbU/no1X7efavbRSbrShSEmj0IsTXQI7JTISfgfl39Xb6B+NIThFz95ykxGZnFiC02nLxbFAmnk3sdsLKxLMBoKlg1X0DuJTf6gx6Lsxu4CoBOIIf333XgwwZ2J/Q0BCuuWqoW9s4G0+IamFhEf7+lW93ri+oovovxW5X0NbToX9l6HQark6L4+q0f+L4H8stpoFRj5/B4WWqKAp9Pl5MjykL2PDQwPLcVaUWG+vTc1hz5DTr0nPYc7KAE/klFJRasAPBGg0aRUEP9C0TzyAqFs+KUIB8KWnjhvvUl9XnbtKA3VLSb8BwNm9c4YEWHJzJeOruuAKFRUX4+9fvtQBQRfWSxW63YzKZsNvtBAUFVf96RSHbZMHfW4/mEuqxnk9cg3O9SzUaDQvv6kXTN+bQ+JXZ2O2SYosVs5QYBTTQaAhHEG230xLHnGcAIBSFPULwm5TsEIIR1ewpWnC4KblDRnS4v6d6hmF2O5N37+XVN9/lqcce8kgb1c1T5SwFBYVqT1XFNex2OwcPHmTbtm1s37aN/fv3c/DgQQ4cPEhmZiZGoxGLxcKuXbtISqre1s5unTvR9eNl5BcVEdkggJgGfsQGGIj21RHr701soA/RgUZiA32ICTTWaI6yrtBoNPx6a1favfMXI4AoIBDQSuAiu52ali0u7RCC6jpbmnHfl0mPI+22JzACI6XkxRde5fqRV5OUlOiRdjyR8bSwqIgA/7pd9XcGZyL/f45jOiZTStmi7NxbwJU4fqAPAGOllHkVXDsIeB/Hj/inUsrX3Wf65cu2bduYMnky03/4gQYNgmjZojktmjejd/cOjL/lWpISE4mOjkSj0XDrhHuZN3cuSXffXa02/lrgSKthNpvJyMggPT29/HHs6GFWHznM8R3pHDp6jJ6JoXx/Q4XeI/WWtOgGNAzypTivuFr7+UsBXQ0EwYwj1bI7PN89/fPVCGgjBH36DePQgW1uH6Z7sqfq53d5DP+/BCYDX591bh7wpJTSJoR4A0d6lcfPvkgIoQWmAP2BdGCdEGK2lHKnOwy/3LBarfz2229MmTKZffv2cef4W9i1aQXR0RcPstmvT09m/Tmfu6opqmfw9vYuzzdVEdu2beP6If1rVHddc0eXxrzz11Y6VCPAiVkIsqVkLdAW53uf7hRVO+6JmHUx+ioKH53K5N4HHuWjD992a92Oj6F6n0NeXh6bt25n58497Nl3gCNHj3Hi5CnyCwooLCrGVGyiqLiY5s1T3GqrJ3AmncpSIUTCeefmnvVyNTCqgks7APvL0qoghPgBuApQRfUsTpw4wbSpU5k6bSrJSY24985xXHPVUKfT8Pbt1Z0HH30Gu93ulsRq59O4cWMOZuZgVxS0Hsyh5AlaxwRRWM2IUR2lxEsI1gLzpOQ6wJl8BBbc18O049g9v6jsWSl7vtjxmddK2fVnns8cSwFoNKARIDRIIdAh+frLb7n1phvo1PEKN1kPIMqH/xaLhd179rJ12052793HwUNHSE/PIDcvj4LCIopNJoqLTVitVoIbBBEZGUHD2BgS4hvSpdMVxERHER0dSUx0FAcPHeGVN953o52ewR3TQOOAHys4HwMcO+t1OtCxskqEEHcAd4AjU+jlzqpVq3jv3XeZO28eN1x7NX/PnkFaDZKyRUdHEREexubNm2nXrp3b7TQajUSEBHMk11Tvd2Gdz8tzd9BGo4FqOt73kZI+wC9aLTvtdqdFVecmb4pSHPm0RONItEKgEQKNhvJjrabsnBBoNZx1LNBpBF5aDV5aDXqNwEtXdqwV6DUa9FoNOo1Ar3Uc/7j5KC9Neos5s2e4xXYAo8FAo5R2mEpKMZlM+Pr6EB4WRmxMNAnxcfTu1Z3YmCiioxxiGRMdRUhIcJXTEHq9nvTj6W6z01O4JKpCiKdx/P0rSp1Y0X9Ypd0GKeU0YBo4tqm6Yld9ZteuXTz6yCPs2LGDh+67k2kfvkFgoGuT7317dWfB/PkeEVWAJo2T2JtVcEmJqsVmY8PRbMa7MBwPtds54Gx7uB6M5Qy+gF4rWHBXbzfVWDmNQ/257ttVbq2zsKiIn77/nGZNkomMDHdb+pPoqEgyMk54PPOsq9TYMiHErTgWsG6UFU+gpOOI2XCGWCCjpu1d6mRlZXHvPffQo0d3+vbszJ6tq7j/3jtcFlSAfn16MH/+fDdYWTHJKc3Zl1X9/FF1yX8X7yFICMKrLlopwZRtF3UCC6B10+KMD45IV9WNy1oTuieGIRQ7P/862211ent50alDe+Lj49yaT8rb25ugoEC+/PJLduzYwdGjR9mzZw8Wi6VeBXGpkaiWreo/DgyXUpoqKbYOSBZCJAohvIAbAPf95S4RFEVhyuTJNG/eHL1GYffmVTx0/914ebkrJDL07N6VVatXU1pa/ejxztAkJZV9uZ6p21N8snI/7V106wkGSpysw0LZRgE3oAG8tVryS61uqe+ibWkEt3VI4p33PnRfpcIzLlUAk154ij9/n8k1V19F165dGDpkMEajkeuvu84j7dUEZ1yqpgO9gFAhRDrwPI7Vfm9gXtlWtNVSyruEENE4XKeGlHkGTAT+xjEy+lxKucND91EvSU9PZ9zYsRQU5LFs/u80a1q9FMzOEhQUSGrzZqxatYrevd07ZDx58iQL/v4fwRZPeU66n4m/rCOnwESai/UEA6VSolB176MU8HJjb8lLpyGvxEKor+czh97cLoGpk9030vGUSxXA7eNu4fZxt5xzrrS0lLT2PZgzZw5DhgzxSLvVocqeqpRytJQySkqpl1LGSik/k1I2llLGSSlblz3uKiubIaUccta1c6SUTaSUSVLKSZ68kfqElJLvvv2Wtm3b0rNbB5Yv+MNjgnqGvr26M3/ePLfVJ6VkyuQPSUtpSnPbCT68sqXb6vYkry/Yzuer9nMz4GqoGCOO3oAzQexKNBqMLrZ3NnqNQ1Rrg+YRAZRabWS4Lb2K50S1IgwGAx+8/SoPPHA/ZrO51tqtDHVHlZs5ffo0d999F7t27uTv2T/SpnXtiFG/Pj148rnXqOyXS0pJXl4eWVlZZGVlkZmZ6Xg+dar8XF5eHlOnTSMuLo7S0lJeeO45HuycwBN9U2vlHlzl2w2HeOl/2xiDI12IOwjSaDiiKFXOzZYI4VZR1QJ/7z7BoZwiSix2Smx2Six2Su12zFY7pVY7Zruk1GrDbFMw2+xY7LLsWcFbp2VYajQ3tUnA4HXxr7kQgsgAI2vWbnRLkBWB54b/lTF4YD+af/oVb//3vzz19NO12vb5qKLqRv74/XfuvOtOxlw3km8+eR+DwbNh9TIzs1i9dj0bN21ly7YdbNy0iVtuvpni4mLyC/LJz88nLy+P/PwC8vLyMBqNhIeFEhYWSlhoiOM4NISE2HCOHj7A0WNHCQtz5CsyGo3MX7SYAX160Twi0GNxTN3FT1uOcPv01YwA4t1Yb5gQONN/MwGhbmwXCc//vY1GIf4Odyit1uEqpdPgpXMce+s0eOu0eOk0eHvpCdBp8NZq8dYJCsw23ly0mwd+3UB0kC/jOyTySM8UdLqKB6cxQb7s3L3HPaLqweH/xXjvrVdo37U/1153HcnJnh0ZXgxVVN1AYWEhDz/0EPPnz2P6V1Pp0a2Ly3UWFRWxbfsudu7aw979B9i2fScnTp3CZCohv6CQwsJCLBYrkRHhJMQ3pGmTxrzwzGOEh4USGBBAUFBg2XNA+evKVmIPHDzEq2+9x/z5C875IWjVqhV//j2PIQP6oddqGJwS7fJ9VYeMfBNz95wk2MfroqL+8cp9PPTreq4CmrnZhlC7nUNOlCuREnc6nOkFfDOmM6PbJrhUT1ZRKTO3p/Pfxbv57+Ld9E6K4NkBqbSMbnBOOS+txn1DZw8uVF2MxIR4XnzmMW644XpWrlzlVs+D6qCKqossXbqU2267lT49u7Fl7RICAiqPomOz2Th5KpNDh4+wa/c+9u0/wOEjx8g4eZL8vAKKiospKi7GZDJhsVgIDAggIjycmJgoTmfnkJubz6QXniIxIZ6E+DgiIyPc4q9364SJPPP0M7Rq1eqC99q3b8/sOX8xfMggvr1OQ99kz+W4MtvsrDiUxdx9Wcw9kM2x3EK6du7M1kXruTI1psL4nC/8tZU35+9gFI6gKVuALJ0ORatFa7OhsdvR4whSInCs0luEwOblhVmr5ZjdigEQdgVhs+MF5zxygFwh2CQlPoBf2cOXc788pVLizvhJAjDbXBemMD8Dt3dqzISOSSw/lMWHK/bT9cN5+Bu86NkojLeHtyE60AedRoPNZnPdcEDU8pzq2dx713gWLl7OY48+yvsffFAnNqiiWkPy8/MZN3Ysv/72Gx3atyUvL5+rr7uZoqJiTCUllJaaMZvNmC0WLGYLZosZs9mCt5cX/v5+hIWFEhsTTcO4GFq1TCUqMoLoqEiiIiOIiowgLCz0HMGc/NGnfP7Vd9w4+lq338v+g4cYdW3l9Xbq1IlfZv3OyOHD+HH0FfRIcsX780K+3XCIn3dlsXRfBinJjRl45dV8/MoQrrjiCrRaLckJcWzOyKVNzLmhUfr83wKWHswE4Fe9nqjQUFq3acPQbt3w8/PDZDJhMpkoLiqiuKAAq9VKUEgIgUFBBAYGsnHjRjZ+9RUfXNOOYouNIrONgrJHscVGscWOwWIjwGxlj9lGkdlKscVGidWGxaacszPJYrYyVwh2S0kKjq2trvzcCcUxP+ouhBB0bxRO90bhWGx2lhzM5P1l+0h+7XcahQTgpdNgd5dfrAClmtuD3YUQgs8+fo82nfvQp08frrr66lq3QRXVGqAoCgMG9GfXrl306tGNkOAGhIQE06xpMg2CAgkKCiQo8MxzAA0aBBEUGEhAgH+NU0H4+/tR6qGVzYZxsRw7doyYmJhKy3Tv3p3vf/qF668dya83daRzgvtmEJ/8exdPvvAyX44ZQ0hIyAXvD79mBLN3LKZNTDA5JjO/7zjOjzsy2ZNvY/z48dx22220atWq2rE2V65cyY7lC7ina5Nq26woEpPVRqHZRqHZSvM3/qQ9khytlj/K8lX5a7UE2e0k4QgQXZ1tHkJRMHvI+d9Lp6V/kyj6N4kis7CUl+Zt5+t1B+mY557UKnU1p3qGBg2C+P6Lj7nmhtto07ZtrW97V0W1Bjz+2GOkHzuGr68vi/6eWSttBvj7YzZ7xsWmaXJj/vj9dzp16nTRcv369eOr739gxJjr+f3WzrSPu1AAa0LjiCBSU1MrFFSAq0eM4tZrv2XNiSJWHzxFv969uO2pBxg+fDi+vr4VXuMMKSkp7M44XaPUHxqNwM9bj5+3niiMeAm4QoJ/WZaAIiDdbueoEOwQgkWKgrcQBAhBtKKQCiTg2OOdAZwCsoBcwOqtJ1tRLkh17Q4KSq1lPW57WW/cxvDUGJYcyGTuvAU8+MhTaDQahNCg0Qg0Gs1ZD4FWo0VoBEIItFotGqFBoz37fQ12u52PP/kCg9GAxWLBbLZgtVq5afS1tG1z4RSTJ+jSuQMP33cXo0ffwOLFS5wOUOQOVFGtJlMmT+b332cz49vPuHLkmFprNyDAH4vFM6L65qTn6NBjIF27dmVwFc7TgwcP5o5772faop/dJqpNgn3Yu3cvffr0qfD9rl27MuLGW+nYqTO/DBnitpiaDRo0wN/Pl2N5Jho2qLk4n+HsvpkfjkWzZlKClNiBk1JyTEqOarX8YrdjwRFBqoGPNzFBPiSE+NEj2Jf4Br40DPJx+zTLov2nGPLpEkIbBOFrNOLrY8TX1xdfX18CohPZtWkzBw4eRpESRVGQUiLLjh2P885L5Z/XikSREqkoNE1uzNyFi/HSe6HX69HrHTLTvd8wBvbrQ0J8HDqdDq1Wi06nRavRotPr8PP1wcfHF38/X/z9/QkI8MNoMJCXn09GxklOZmaRmXma09nZZOfkkJ9fwAP33smoEcMrvN9HH57I4mUrePaZZ3j9jTfc+lleDFVUq8GXX3zBpFcnsWLhn/j6+HhsOF4R/n5+WKye2bYYFRXJHeNuZvny5VWKKsCiv+fwaJq7PEGhcZAXe3fvqvR9rVbL2++867b2zqZ50ybsPJXvsqgKBPIiyZm1OMK2xQCdynqzLwG5r4zCz1A7vagis40BvXrwx7yFF7yXnZ1No0aNmPXztx4LVrJx0xZee+t9du/dj6Io2O12bDZ7+XFpaSmlpWZKzY71iFKzmeLiYrRaLTHRUQQFBRIcFERISDCNGzUi48RJnn5hUqWiqtFo+PrTKbTt0pcePXowZKjnEh2ejSqqTlBUVMTEifeyZvVq5v7+E4kJ8dhsNkpLzdhsNo+mzP3uh5/5deYfbN66HauHRBUcmwM0mqqDh+zdu5f9+/cz6Fr3bQdsEhbAip3b3VZfdUhJa8Xu42sY1Mx1d7HqziJKwKBzfwzcyrhY8OiQkBAaNAhi/4GDNElu7JH227ZpxU/ff16ta7wCIjl1ZFeF8+WnTmUS37QNOTk5BAdXnN8hLCyU77/4mGtvGs/69euJjfW8v3X9jZ9VT9iyZQvt27dDKFbWr5hHi1RH5HGdToe3tzfp6Z4NvPXOBx+RceIkTz7yAFvXLfVYO4qiVBnkuqCggJdefIFr02LRuylnVZHZyt6sAvYfOOiW+qpL87SW7Mp2PVhMdSOpnpG2ypzxPYHg4hH527drz6o162vNnqrIyclBUWSl0z0REeG0btmC8Xc9yIyfZzJvwSLWb9jMkSPHznEP696tM/ffM4HRo29wm9vYxVBFtRKklPzflCn069eXZx5/kC+mfXjBokhAgD8HDx/xqB3RkRFc0a41E8bdTGyM55zvFUVBc5aoSinZu3cvX331FXfecQctW6YRHR3NnDlzyCmqLDCZc9gVhQX7TjL2543EvzaHFdYw3v5wiqu3UCOaN2/OztPFbqmrOj3V2neNdwSzvpio3nTzzXz40af1JoxeQEAAUsqLCuELzzzG/oOHePK5V7h1wkQGDBtJSpvOhEQnc+2YsZw8eQqAJx55AIOXjuefe87jdqvD/wrIzc1lwvjxHDp0gJWL5pDcuOJMpQ2CAjly5FiF77mL2Jho0o+7K9BF5djtCgf37uW1V19l5cqVrF6zBh8fI507tqdLxyuYcMu1tGrZgtl//MXDDz9aozZ2ZxbwzcajfLf5GGERkdw8/k7+O+tGwsPduyBTHZo3b86u4zXzADgbjRDYqiFGEs/noTqfqnJHDR8+nKeffor5C5fQv2+v2jOsEhyjQS9yc/MIDw+rsMygAX0ZNKDvBedXr1nPK2+8Q2KztgQEBlBYWITVauXQkXRefuUVjwa5VkX1PFatWsXo0Tdw1dBBfP/F5ItudQsNCeG42yL7VExMTBRbd3g+rVdSowQWLllOREgAt44Zycfvv05MzIVJBwcP7MvNeUXszSqgSVjVnpfZxWZ+3HyEr7ee5HhBKWNuupk574wjLc3VwHzuISwsDJ1Oz8nCUqICah4SxddbR36JhYq/+hdSN6J68Z6qRqPhicef4NU336sXogpg8PYmOye3UlGtjE4d2/PHr9+T1r4777z7Pl26dMHHx8elH05nUUW1DEVRePONN3j3vXf5ZMo7DB82uMprQkODycg46VG7IiPCyc+vuVP2yZOn+OD/phEbHc09d42vtNy4W29k3K03Vlmfr68v/fr05PUFO/n8hor9Wi02O3N2ZfDN1hMs3neCoYMH8cqUSfTt29eji3o1pXnTZHaeyndJVIN9DdUK1VcXw/+q5lQBbhg9mmefe5aFi5fSp1eP2jHsInh5e5Obm1fj6w0GAzqdziV/5uqizqkCp06dYvCgQfz5x2zWL5/vlKACRISHk5mV5VHbIsLDKC52fg5TURT+nreQa667hYbJrYhv2oZ5C5fw2NMvcvp0tltsumn0tSw6dm56FSkla49mc//sLTR8bQ4f7rVw5b1PcvT4Cb6b8QsDBw6sl4IKkJLWkl2nXNtNFBNk5ASOaFXOCGbdDf8vbp1er2fqx1MZc9td7Nm7r5YsqxytRoPZUnPXxRuvH8m0qVPdaFHVOBP5/3McuagypZQtys5dC7wApAAdpJQVLhkKIQ4DhTgy5dqklO3dY7b7WLJkCWPGjGHcLaN5/ulHq/XFDw8L9fg/XmREOCbTxUU1Ly+PKVM/57dZf7L/wCG0Wi1XDhvEB2+/Rt/e3fH392f4yBu57faJ/PHbdJdtGjq4P7fdPpEDpwvx0mn4buNRvt2SgU3nxc1jx7P2y1tJTEx0uZ3aonlaK3b97FryO3+DjoXAVo3ALmVZNlNHXACtEOiEQItj+6nGrqBYbCjAqC+XYdBpMOi1GHRajHotRr0Og16Lj16Lr5cOo5cWPy8dvmUPP28d/t56/Lx0+HvrqoyXegZnA50MHDSISa9MYsjVo1m5aA4REXUz511aWkp2dg5pqdXPMnyGrp078N2M39xoVdU489f4EpgMfH3Wue3ACMCZn4DeUkpngqfXKlJK3nn7bd7671t8/ekUBvSrfhqS0JBgCguLPGDdP0SEh2MqKbng/IqVa5gy9TNWrVlHxolTNG/WlGtHDGfo4P60TEu9YO7ojUnP075LX44eS6dhnGu+en5+fvTu2Y2eHy3CgoZRo0bx2dNT6Ny5c63MWbmb1NRUfvn4ws+4OmQXW3imfwteGJiG1a5QaLZSWGqjwGyl0GyloNTxutBspcBs5eDpIv5v5T4iAnwotdkptSnkllootdmx2BRKbWXBqG12rHalPPi09ayHTVGw2R0iqdU4UlRrhWOr6Jk01mfOazQapJQEhTgnkOMnTODo0aMMG3kji/+eWavD5zP8PW8hISHBhIbWfOeel5eXx3K3VUaVoiqlXCqESDjv3C7gkvwCgSP+6fjx4zh08ABrlvxNfHxc1RdVQEhIgwoFz52Eh4diMpVQVFTEp198w48/z2LvvgNYrVYGD+zHqy8+w8D+fQgObnDRelKaNeGqK4dw64R7WfT3LJdsUhSFY8czuO3u+3j++ec9Hozb06SkpLArw7WpkdMmK4nBDuHRazUE+3gT7FP5Iufm47l8u/EwU0a4nlbcZlew2JXyDABm25nXdsx2BUvZ+fR8E88ucjbpNrzw4oscOXqE0bfeyW8/flWlH7O7sVptLsdENRi8yc/Pd5NFzuHpSS4JzBVCSGCqlHJaZQWFEHcAdwAejSqze/duRoy4hq6drmDZ/N9dEoTgBu4VVUVR2Lf/AOs3bmb7jt3s2bef9PQM/Hx9CY1tQqPEBEZePYz33ppE+3atq/1PPunFp0ht25U9e/fRtEnNI6P/8tvvGIw+vPrqq5fsD+vZREVFYbErZBWVEuZXs/+H/FILCcHOxySw2hW0Gvd8djqtBp1Wg08VCXrtisI9v22isLDQqYheQgimTfuEwYMH8fBjz/L+26+6xV5n8fXzcSlwtqIoDB91M6NGjnKjVVXjaVHtKqXMEEKE48i8ultKWeG2oDLBnQbQvn17j3gf//Lzz9x199289tLTTBh7s8v1hQQHU1pavT+6oigcOnyEb77/iRWr1pCZdZq8/HwKC4soKipCp9MRFRlJYkJDkhol0rlDexLiG9KjW2eX57YSE+K5afR13H7Pwyyd/3uN6ti4aQsTH36CH3+ccVkIKjjEo3mTxuw6VVCpqNpsCgsPnMTPS0+Q0YtAgx5fLx0+Xhq8dDoKSy0kVCN+gNlmR1vLn59WoyElOpQdO3ZUGZHsDF5eXvzyy6907dqF9ydP5YGJd3rYyn8I8HMtiJDNZuPQ4SO8865n4kZUhkdFVUqZUfacKYT4DegAeG6vZSXYbDaeevJJZvw0g//NnE77dm3cUq/B4E2JqYRZv88hv6CAosJi8gsKycnNJS8/n/yCQoqLiik2mTAajRQUFrFt+058fHw4deoUjzx4L40S44lvGEfDuFgaxsVeNHOAO7hu5FXcdvvEGl07f+ESbhx7Fx/930f06tXLvYbVIZmZmVjsCuNmrCEu0EhsoA9JIX6kRATSMjqIpmH+DPlsCeuOnsZLp3XMb9oU7FJiVyQCx5A/zK+KruJZWO0KGjf1VKtDiwh/tm3b5rSoAgQFBTFnzv/o0qULoSHBHgmUXhGBAf5YLJ6Ld+EpPCaqQghfQCOlLCw7HoAjME+tcurUKW644Xq8dBrWL59X7Unv0tJSvvh6Olu2bmfv/gOcyswiLz+fgoJCSkpKaRAUyP3/eQqDwRujwYDRaCQgwJ/AwAAC/P2JjY7i+x9/oW27dkx69XVatmzJzp07efyxR3jrtRc9dNeV07xZE3Kq6fe3eOlyXnjlLY6fOMmXX3zpVCSrS4GDBw/y1uuv8sMPPzAyLZb2vVNIzyvhcJ6JxYdO892mI2QWllJqs6PTCLY8MoTGoef+6MkyYY1+cSYbjuXS3clwfWabgq4OevqpIQa2btpY7evi4+OZO3cu/fv3A6gVYfX39/dYZDZP4oxL1XSgFxAqhEgHnseRuudDIAz4UwixWUo5UAgRDXwqpRyCI0vwb2VDRB3wvZTyL8/cRsWsXr2aa68dxW033cALzzxWo4n2l159iykff07f3j3o1KE9SY0SaJQYT6OEBGJioqp0wVq0ZBm/zPqTn376uXwFVVGUOhs6R0VFIqVk3/4DlW6/PcOGjZt58rlJHDx8hOefe57RY8bUW1/T6nD8+HH+c/9E5s+fz4QOiWx/qD+RF3H8L7HaKLUqNKhg0lIIgU4rSAr1Z9WRLKdF1dFTrX038RZRQczZXH1RBYeXxLx58+nfvx8SyU2jr3Ozdeci6qAn7w6cWf0fXclbFzh/lQ33h5QdHwRqJ8z3hXbw0f/9Hy+8+AKfffQeVw4dVOO6CguL6NWzG7/++FWNrp/88Wc89+xz57ik6PV6jh5Ld0rY3I0QgqRGCcybv7jCtqWUrFm7gXc++IgVq9fy3LPPMW78+FqNnO5pVq5cyYFNq9n/2CD8nYhlatTrMFZRLCUikC0ZeU7bYFUkujoQjbSoILb9tLHGsQ7OCOugQQM5fOQYTz/+8GUzt+4uLrsdVSaTiVtvvYWPP/4/Vi6a45KgAsTFxrBpy9Ya+7oVFBQRd543Q8eOHRl721humVCzuU1XaZXWgpVr1p1zzmQy8ekX39CuS19uGn8Pnbp0Z9++/dx5112XlaACNGvWjCKr4pSgOktKuB8Hc5zf+Wax2922+l8dIv0NSLudU6dO1biO1NRU1qxZy+w587hz4n/caN3lwWUlqgcOHKBz504o1lJWL/mLxkmNXK7zkYcmotfqePn1t2t0fUlpKUbjuUNLjUbDxPvuY9fuvXUSZq11y1T27N1PcXExf/5vLvc88CgNm7Tm9/8t5LXX32Tv3n08/J//4OPjU+u21QbJyckcyszF6sYcUMmh/pw2Oe8J4k6XquoghKBFbCjbtm1zqZ7o6GgWLlzEdz/8TImHfbUvNS4bUf3j99/p3Lkzd4y9iW8+/8htc38ajYYbR49i5ap1Fy1XUFBIXl4+JSUl2MvSZSiKwv4DB4mLu3BzQWhoKFJKt+3Hrw4pzZpw4NBhIhNS+e8HU2mYkMyGDRuZNXs2AwcOrJO5vtrEYDAQGxHOgWz37YZLCvUn3+S8+4/FrqCto885LczXZVEFx8665s1T2LBxixuscj91FRf20l91wLEgdeXw4ej1ep587hUeeOQp7HY7P3z9Cddfe43L9QcGBFBsqjyQsdlsJrxhMwwGA+ay/DoajQa9Xk+jRokkJCRccM2bb7xBVGQEfn61v/2veUpTvL29OXLkaLXTOl8upDRryp7MApqFVydxdOUkhfhRZLbS7t2/USQoUmKXEkX559iRRM+RIM9ss+Ndi1H/zyY1zJe1mza4pa5uXbuxbOVqunV13kWrNti3/wDj75hYq+lqznBZiGqHDh3Yt28fPj4++Pr64uPjw+0TJrhtt1NgQACm4srrKiwswtfXl+zsf3qdNpsNs9lcYY/5zz//5P0P3mfdsnkXTA3UBgnxDcnJyb0sVvJrStO0luzatZCrWrgnZ5FPWVCT0a0bEmTUo9No0GtF2bMGXdkefF3Z8dqj2Xy65twUMoWlFo7kFnM0r4SMAhMn8kvIKjZTbLbx4Yj25W24SouoQD5fstktdfXq3ZuPpnzIk48+6Jb6XOHosXRef+t9/przP05kZjE4JQYvr9pfD7gsvlUajYbGjc9NVma1WstT47rK1u07UC4SMq242ISv77nzjzqdrlLRatOmDVarjZ9+nUVoaDD+fn707d3DbamXq+LkyVP4+/tf8nv2XSGuYQIbVrk35beXTsP1bRoSF1T16COrqJQT+SZCn/0Fk1XBYrOh1WrwMfoQEOBHYEAAwcENCG4Qy/xFSxje4qRTPwCFpVa+3XAYs92OzS4dQVcU6XhIh9dBQamVHXuPuJztAKB79+7cfPPNLm99ri42m43f//ybGb/MZOf2HWSePEluYRHdkiJ5tls8V6Z2pchipdu0lbVm0xkuC1E9n61btzJv/nwef+gul+t68tmX+eKb6fw1a0alZU6eyqxWSpDo6Gi++fprZs6cSd76rfw4YwYL/vdrrQUFXr9xM+3btfvXusK8+srLvPvft5h2jXt21p3BS6uloNS5xHJXt4gj6V5//A16bvluJX2uHcObr75Q4d8kucUVFFucq3f10dO8tSada0aORKfXo9Pp0em90Ol0GPR6xzZonY7/uz3ELX//4OBg3n/vPbr1HcZtN93A+NtuollT94qrzWZjybIV/PLbH6xZvYYTx9PJLSwmyOhFn+RIxjUNpkXPWNrGBhNwlkdHbonFbR2r6nBZiuqnn3xCakpTUps3q7LsNdffwuYt28vmuxTHs1SQZXNfpSWlLP57Fu3atq60jqLiYvLy8sjPzycwMNApGwcNHsygwYP55uuvWbhoIUaDsax37fnhyvqNm7niiis83k595Pvvv+Prjz5g/X19iA1yr3eDXquh0OzcDiCDXssVDR27+xr4OESvMpEzeHtTbHZOVEssdlqnteC9DyY7Z7QbGDtuHN26d+ezTz+l96BriI2JotMV7WjdqgXNmiTj7e24P61WS0hwA6KiIhFCYLPZyM7OIet0Nus2bGLFqjXs23+AvOwcSk0mSkpMeGHHNygaf4OeDvFhXJcYSruu7Wka5k904MX/fla7gr4OprguS1F9/Y03uGr4cG687S5GXXMler2OFqkpJDW6MHDyvn0H6NOrO6OuubLsD69xPGu06HQ6EuLjqgxk0qdXdwb378OVVw5jyZKl1eoBXNGhA2NGj+HuBx/n0KHDjL15NO/9d1K177k6rNuwmbvuuc+jbdRHjh49yoMT7+WPWzu5XVABvHRaCkqrv61Sp9FcdDtmYFAgj8/dwXOL9iPEPyE3hRCIM8/CEYTarijEJ9R+gPDk5GRef+MNXn7lFZYvX86mjRtZsmI9n3zxPVabFbvdjs1mIzMzC5vNhtFo4NSpTIKDHfFSs09mkBbmR7eGDYhOCSDQEEKAQU+wjzfNwgMq3M1WFTZF1omP9WUpqj4+PsyaPZsnHn+c6T//jtVqZdXq1Uyb/DbXXDX0nLIdrmhHTm4ugwf2q3F7Qgjef/tV4pJbcejQIRo1ct4/tlmzZrz3/vsAjBkz2uPeAFJKx/C/fb1LwuBxpn78EaPTomkXG+yR+r10Goqc7FGejV6ruWg0pt9++IqMEyex2+0oiuLwIFCUf15Lpfz84qUr2LRtlyu34RJ6vZ7evXvTu3flQd8zMzMpKSkhNja2fOt47y4debylL32SI91mi+pS5WZ8fHz44MMPy1+vX7+eq6++mr37DvD4I/eXn79yyEDuffAx11MUazR0aN+WDRs2VEtUz7B582YWLlzIx1vX1NgGZ1i7biMNGjQgOjrao+3US4Qg2Oi5f3nvGvZUvbSai0ZjCgsLJSws1Km6iouL2bxtd7VtqE0qWn/w8/Oj2M0Rqfy99RQWVe4K6Skuby/vs2jfvj1r1qxh0pvvkpHxT1rpgf17U1paypffuJ67KSI8jMzMzBpd+/hjj/HsE//xeOi/z7/+nrG3jf1XLlIZDEbMbtxFdT4aHE791b5OA3bF7hYbjEZjracPcQf+AQEU1qCXfzECDHoKVFH1LDExMaQ0a8aBg4fLz/n4+DBtyrvc9/CTHDp8xKX6w8NCOF2D7Kpz587l0KGD3DH+Fpfar4ri4mJ++nU2t9x6q0fbqa94e3tj8ZCmKorCiYKSGm0msNklXjr3zP0ZvL0vOVG1WCxs37HjnJV7d+DvraPQVIKi1G5C8H+VqAKEhIRQWHTu9sRRI4bToX0bXpj0Vo3rlVJSWmomqwai+t1331JqNvPksy/z19wFFBd75tf1rXcn079fP2JiYjxSf32ntLQULw/9x3+y5gDZxaXEN/Ct9lye1a6gc5Prj16vdylafl3w8osvEKu3MjTFvVNSOq2GpMgQtmyp3W20/zpRjYuLY+JDT/Lok8+zfMXq8n36jz48kb/+nl/jeqf/+As//fY7N99c/TQtn332OTNm/ERQSCSvvT2ZiPjm9BpwFa+8/jar16zHZnN9WHTg4CEmf/w5/327ZoFhLgcyT2QQ5utaIrnKkNLhGtXyv3PwfWIGzd/8kyUHnJsKskuJ3k091VJz6SW1qWP9+vVM/b/JTL26lUempIY0Cef32bPdXu/F+NeJ6sdTp/LzL79g9GvAvQ8/SXSjFoy/6wG2bNmO3YVhQn5BAYMGDqJjNdJUnEGn09GpUyeeefZZlixZysmTJ3n8yafJLSjlzvsfJTS2KVdfdwuTP/qU3Xv2VbsnJKXk3gcf57FHH60wuMu/heVLFtHA6MWezAJ2ncpnx8l8tp3IY0tGLmaba3Oad3VJJuulkeS/ei17nhiGFlh+yElRVSQ6N+1RLykpvWSii5WWlnLL6Ot5d2iLKn1Oa8rQpuHMmfmrR+quDGci/38ODAMypZQtys5dC7wApAAdpJTrK7l2EPA+oMWREeB1N9ldY4QQtG3blrZt2/LSyy9z6NAhZs2cyU8//0R+fgF9B4+gV48u9OrelQ5XtHU6Ra6Xl5fbhl1+fn4MHjyYwYMHA46UMAsXLmT+vHm8+e5kFEWhX+8e9OvTg769ehAVdXE3lI+mfUF2bj4PPfywW+y7FJFSotV7MWllOprVx9FoNOWPk1mneaF3Mnd1cc9OoLgGvsQEGnln2QFm7sos8yH9B6sUWBSJVXEM/bPzi+jspt6lyVRSJ/EkasIzTz1B8wDB9a3jPdZGt8QwDv6wjunTv2f06DEea+dsnJnI+RKYDHx91rntwAhgamUXCSG0wBSgP5AOrBNCzJZS7qyxtR4gMTGRBx96iAcfeoj8/HyWL1/O4kWL+M+TL7Jr926uaNfGIbI9utLxinaViqw7RfV8IiIiGD16NKNHj0ZKyf79+5k/bx6//T6P+//zNNFRkeUi27N7l3MiT+3avZfnX3mTFStWXHbBpquDEIINWyoOd/fss8+SsfqCRBYuERfky3FtIGNvH3vObj0pJT4+RkfgH6MRX18ffH18aJmW6pZ2S0ovDVFdsWIF3335BZse6OtRTxQvnZa/x3fjmgcmsmfXLp5/8SWPe744k05lqRAi4bxzu4CqjOsA7C9Lq4IQ4gfgKqBeierZBAYGMnToUIYOdWwQyM/PZ8WKFSxetIhHnnyJnbt20aF923KR7dC+bfn8lZde71KOcmcRQpCcnExycjJ333MPdrudjRs3Mm/uXN6Z/Ak33HIHrVum0a9Pd/r06s5Djz3LKy+/TJMmTTxu26XKtvVruT7Sue3FzpIQ7Ms+vQ/33XO7W+utipKSC4Oi1zeKi4u5dcwNTLmqVaUpwd1Jy+gGrLynJwM+m0aD4GAeePAhj7bnSef/GODYWa/TgY6VFRZC3AHcAdDwvPQjdUVgYCBDhgxhSFn20IKCgvKe7KNPvcyOnTvLe7I2m71OVl21Wi1XXHEFV1xxBU89/TQmk4nly5czf948Hnj0WVqktuCOO2svV/uliMFowKYUuq2+Xafy+XRjOp26d3dbnc5it9trlOCyNnnjtVfpGGl0W9hFZ4jwNzLrlo50f/lFkhonM2zYMI+15UlRragbW+kKi5RyGjANoH379nWzv6wKAgICLhDZMz3ZxYuX0q5duzq20OF3O2DAAAYMGFDXplwytGjbng0LfmRMW/fU1/uT5Yy58QbeevUF91RYDbRabblHS31lx5bN9IsLqvV2E4L9+OnGDlx9841s3r7TY66Fnlz9TwfOXmqOBTI82F6tExAQwODBg3njzTdZs3Yt//fRR3VtkkoNGD78KmbtPOGWveKni0rJKzLx1qsv4OVV/SAgrqLVat22O8tTTHz4Ed5ecdCtOcKcpVN8KHd2SOTBe+/2WBueFNV1QLIQIlEI4QXcANSuw5iKihOkpaVhDAhizi7Xf/P/2JlBQnxcnQgqlImqi+5hnqZ37940atacT1YfqJP2n+zdlM1rVvL77797pP4qRVUIMR1YBTQVQqQLIcYLIa4RQqQDnYE/hRB/l5WNFkLMAZBS2oCJwN/ALmCGlHKHR+5CRcUFhBC88+EUHvxzOyYng0FXxAfL9vD43F0M6Fd5hCZP4+/nR2Gh++aHPcU7H/4fLy/ex/y9J2u9bYNey4fD03jw3rs9srhcpahKKUdLKaOklHopZayU8jMp5W9lx95Syggp5cCyshlSyiFnXTtHStlESpkkpfRskFAVFRcYNGgQnbr34qE/ttZoGmDV4Sye/GsH773zOh++U3fu2KGhwZw+fbrO2neWFi1a8Mus37l5xgbWHa39jML9m0TRrIEXH/3fFLfX/a/bUaWiUhlTP/+S1aftTK3BsHTXqQIaJTTkptHX1WmK77DQULJOVz/+RF3QvXt3pn7+JddPX0dmYe0HgXmgcwIzvv266oLV5LKNp6qiUl38/f2Z+ef/6NKhPe1iGpSnO3GGEwUlhIQ4H/zabrdTUlJCSUkpJlMJJaWl/7wuKanwuKS0rGxJSdlxafl1Z+ooKCiksLCoagPqCddccw3r1qxizI8/8NfYLui0tfeD1CUhlK1fr6SgoICAAPekKgdVVFVUzqFx48Z89MlnjL7ndqZc1QqrXcFiUzDb7JTa7JjLjx3PZrvEbIcFe09wWtEz5tY7y4SuFFOJySGGJWXCd0YcS0qwWq0YjUaMRiM+PsZ/jo0+GH2MGA1n3vM5q5wPRv9ggsPPPX9+uYiIiLr+GKvFy5NeY8jatUyctZlJA1MJ9vGqlXi/Rr2OpPBg9u3b51Z3SFVUVVTOY+TIkezbvZN3/pqDt7cBb28j3gYDBqMRLz8D3gYj3gYjBh8f/A0GQr29GT3ASmlpKU2aNKlQ6M4XQW9v739loPCK0Gq1/DRzNjddN4omb/1NidlMZIMAwgN8CPDW4++tw89Lg79ei79eEOHnRXSAkagAI7GBPiQE+9b4s1SkrDSVfE0RdZXH5WK0b99erl9fYYwWFRWVy5ySkhJOnjzJqVOnKCwspKioqPw5Ly+Pk8fTOZF+lBMZGRw+egxTSQkdEiOID/BGAoo885AoOMIyKhIUJIpy5rxESsG8nUdZu2EjKSkp1bJRCLFBSllhoje1p6qiolKvMBqNJCYmkpjoXFbYEydOsHr1ajIyMtBqtedEIKvoIYQoPx7n7e32uBiqqKqoqFzSREVFcc0119S1GeWoLlUqKioqbkQVVRUVFRU3ooqqioqKihtRRVVFRUXFjaiiqqKiouJGVFFVUVFRcSOqqKqoqKi4EVVUVVRUVNxIvdymKoTIAo64qbpQoP4HmKwa9T7qF+p91C9q+z7ipZRhFb1RL0XVnQgh1le2R/dSQr2P+oV6H/WL+nQf6vBfRUVFxY2ooqqioqLiRv4Nojqtrg1wE+p91C/U+6hf1Jv7uOznVFVUVFRqk39DT1VFRUWl1lBFVUVFRcWNXNaiKoQIEkL8LITYLYTYJYToXNc2VRchRFMhxOazHgVCiAfr2q6aIIR4SAixQwixXQgxXQhhqGubaoIQ4oGye9hxKf0thBCfCyEyhRDbzzoXLISYJ4TYV/bcoC5tdIZK7uPasr+HIoSoU9eqy1pUgfeBv6SUzYBWwK46tqfaSCn3SClbSylbA+0AE/Bb3VpVfYQQMcD9QHspZQtAC9xQt1ZVHyFEC+B2oAOO/6lhQojkurXKab4EBp137glggZQyGVhQ9rq+8yUX3sd2YASwtNatOY/LVlSFEAFAD+AzACmlRUqZV6dGuU5f4ICU0l27zWobHWAUQugAHyCjju2pCSnAaimlSUppA5YA9SeXx0WQUi4Fcs47fRXwVdnxV8DVtWlTTajoPqSUu6SUe+rIpHO4bEUVaARkAV8IITYJIT4VQvjWtVEucgMwva6NqAlSyuPAf4GjwAkgX0o5t26tqhHbgR5CiBAhhA8wBIirY5tcIUJKeQKg7Dm8ju255LmcRVUHtAU+klK2AYq5NIY2FSKE8AKGAz/VtS01oWyu7iogEYgGfIUQN9WtVdVHSrkLeAOYB/wFbAFsdWqUSr3ichbVdCBdSrmm7PXPOET2UmUwsFFKeaquDakh/YBDUsosKaUV+BXoUsc21Qgp5WdSyrZSyh44hqH76tomFzglhIgCKHvOrGN7LnkuW1GVUp4Ejgkhmpad6gvsrEOTXGU0l+jQv4yjQCchhI8QQuD4e1xyC4cAQojwsueGOBZHLuW/y2zg1rLjW4FZdWjLZcFlvaNKCNEa+BTwAg4CY6WUuXVqVA0om7s7BjSSUubXtT01RQjxInA9juHyJmCClNJct1ZVHyHEMiAEsAIPSykX1LFJTiGEmA70whEm7xTwPDATmAE0xPHDd62U8vzFrHpFJfeRA3wIhAF5wGYp5cA6se9yFlUVFRWV2uayHf6rqKio1AWqqKqoqKi4EVVUVVRUVNyIKqoqKioqbkQVVRUVFRU3ooqqioqKihtRRVVFRUXFjfw/njQNAmhU8tgAAAAASUVORK5CYII=", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Compare this to the previous 3-bin figure with quantiles\n", "tracts.plot(\n", " column=\"CRIME\",\n", " scheme=\"natural_breaks\",\n", " k=3,\n", " cmap=\"OrRd\",\n", " edgecolor=\"k\",\n", " legend=True,\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Other classification schemes in PySAL\n", "\n", "Geopandas includes only the most used classifiers found in PySAL. In order to use the others, you will need to add them as additional columns to your GeoDataFrame.\n", "\n", ">The max-p algorithm determines the number of regions (p) endogenously based on a set of areas, a matrix of attributes on each area and a floor constraint. The floor constraint defines the minimum bound that a variable must reach for each region; for example, a constraint might be the minimum population each region must have. max-p further enforces a contiguity constraint on the areas within regions." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
AREAPERIMETERCOLUMBUS_COLUMBUS_IPOLYIDNEIGHOVALINCCRIMEOPEN...XYNSANSBEWCPTHOUSNEIGNOgeometryMax_P
00.3094412.440629251580.46700319.53115.7259802.850747...38.79999944.0700001.01.01.00.01000.01005.0POLYGON ((8.62413 14.23698, 8.55970 14.74245, ...0
10.2593292.236939312144.56700121.23218.8017545.296720...35.61999942.3800011.01.00.00.01000.01001.0POLYGON ((8.25279 14.23694, 8.28276 14.22994, ...0
20.1924682.187547463626.35000015.95630.6267814.534649...39.82000041.1800001.01.01.00.01000.01006.0POLYGON ((8.65331 14.00809, 8.81814 14.00205, ...2
30.0838411.427635524233.2000014.47732.3877600.394427...36.50000040.5200001.01.00.00.01000.01002.0POLYGON ((8.45950 13.82035, 8.47341 13.83227, ...2
40.4888882.997133675723.22500011.25250.7315100.405664...40.00999838.0000001.01.01.00.01000.01007.0POLYGON ((8.68527 13.63952, 8.67758 13.72221, ...3
\n", "

5 rows × 22 columns

\n", "
" ], "text/plain": [ " AREA PERIMETER COLUMBUS_ COLUMBUS_I POLYID NEIG HOVAL \\\n", "0 0.309441 2.440629 2 5 1 5 80.467003 \n", "1 0.259329 2.236939 3 1 2 1 44.567001 \n", "2 0.192468 2.187547 4 6 3 6 26.350000 \n", "3 0.083841 1.427635 5 2 4 2 33.200001 \n", "4 0.488888 2.997133 6 7 5 7 23.225000 \n", "\n", " INC CRIME OPEN ... X Y NSA NSB EW CP \\\n", "0 19.531 15.725980 2.850747 ... 38.799999 44.070000 1.0 1.0 1.0 0.0 \n", "1 21.232 18.801754 5.296720 ... 35.619999 42.380001 1.0 1.0 0.0 0.0 \n", "2 15.956 30.626781 4.534649 ... 39.820000 41.180000 1.0 1.0 1.0 0.0 \n", "3 4.477 32.387760 0.394427 ... 36.500000 40.520000 1.0 1.0 0.0 0.0 \n", "4 11.252 50.731510 0.405664 ... 40.009998 38.000000 1.0 1.0 1.0 0.0 \n", "\n", " THOUS NEIGNO geometry Max_P \n", "0 1000.0 1005.0 POLYGON ((8.62413 14.23698, 8.55970 14.74245, ... 0 \n", "1 1000.0 1001.0 POLYGON ((8.25279 14.23694, 8.28276 14.22994, ... 0 \n", "2 1000.0 1006.0 POLYGON ((8.65331 14.00809, 8.81814 14.00205, ... 2 \n", "3 1000.0 1002.0 POLYGON ((8.45950 13.82035, 8.47341 13.83227, ... 2 \n", "4 1000.0 1007.0 POLYGON ((8.68527 13.63952, 8.67758 13.72221, ... 3 \n", "\n", "[5 rows x 22 columns]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def max_p(values, k):\n", " \"\"\"\n", " Given a list of values and `k` bins,\n", " returns a list of their Maximum P bin number.\n", " \"\"\"\n", " from mapclassify import MaxP\n", "\n", " binning = MaxP(values, k=k)\n", " return binning.yb\n", "\n", "\n", "tracts[\"Max_P\"] = max_p(tracts[\"CRIME\"].values, k=5)\n", "tracts.head()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "tracts.plot(column=\"Max_P\", cmap=\"OrRd\", edgecolor=\"k\", categorical=True, legend=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.6" }, "nbsphinx": { "execute": "never" } }, "nbformat": 4, "nbformat_minor": 4 } geopandas-1.2.0/doc/source/gallery/create_geopandas_from_pandas.ipynb000066400000000000000000000134441525650651600261000ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "# Creating a GeoDataFrame from a DataFrame with coordinates\n", "\n", "This example shows how to create a ``GeoDataFrame`` when starting from\n", "a *regular* ``DataFrame`` that has coordinates either WKT\n", "([well-known text](https://en.wikipedia.org/wiki/Well-known_text))\n", "format, or in\n", "two columns.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import geopandas\n", "import matplotlib.pyplot as plt\n", "from geodatasets import get_path" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From longitudes and latitudes\n", "=============================\n", "\n", "First, let's consider a ``DataFrame`` containing cities and their respective\n", "longitudes and latitudes.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df = pd.DataFrame(\n", " {\n", " \"City\": [\"Buenos Aires\", \"Brasilia\", \"Santiago\", \"Bogota\", \"Caracas\"],\n", " \"Country\": [\"Argentina\", \"Brazil\", \"Chile\", \"Colombia\", \"Venezuela\"],\n", " \"Latitude\": [-34.58, -15.78, -33.45, 4.60, 10.48],\n", " \"Longitude\": [-58.66, -47.91, -70.66, -74.08, -66.86],\n", " }\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A ``GeoDataFrame`` needs a ``shapely`` object. We use geopandas\n", "``points_from_xy()`` to transform **Longitude** and **Latitude** into a list\n", "of ``shapely.Point`` objects and set it as a ``geometry`` while creating the\n", "``GeoDataFrame``. (note that ``points_from_xy()`` is an enhanced wrapper for\n", "``[Point(x, y) for x, y in zip(df.Longitude, df.Latitude)]``). The ``crs``\n", "value is also set to explicitly state the geometry data defines latitude/\n", "longitude world geodetic degree values. This is important for the correct \n", "interpretation of the data, such as when plotting with data in other formats." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "gdf = geopandas.GeoDataFrame(\n", " df, geometry=geopandas.points_from_xy(df.Longitude, df.Latitude), crs=\"EPSG:4326\"\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "``gdf`` looks like this :\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(gdf.head())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we plot the coordinates over a country-level map.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "nbsphinx-thumbnail" ] }, "outputs": [], "source": [ "world = geopandas.read_file(get_path(\"naturalearth.land\"))\n", "\n", "# We restrict to South America.\n", "ax = world.clip([-90, -55, -25, 15]).plot(color=\"white\", edgecolor=\"black\")\n", "\n", "# We can now plot our ``GeoDataFrame``.\n", "gdf.plot(ax=ax, color=\"red\")\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From WKT format\n", "===============\n", "Here, we consider a ``DataFrame`` having coordinates in WKT format.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df = pd.DataFrame(\n", " {\n", " \"City\": [\"Buenos Aires\", \"Brasilia\", \"Santiago\", \"Bogota\", \"Caracas\"],\n", " \"Country\": [\"Argentina\", \"Brazil\", \"Chile\", \"Colombia\", \"Venezuela\"],\n", " \"Coordinates\": [\n", " \"POINT(-58.66 -34.58)\",\n", " \"POINT(-47.91 -15.78)\",\n", " \"POINT(-70.66 -33.45)\",\n", " \"POINT(-74.08 4.60)\",\n", " \"POINT(-66.86 10.48)\",\n", " ],\n", " }\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We use ``shapely.wkt`` sub-module to parse wkt format:\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from shapely import wkt\n", "\n", "df[\"Coordinates\"] = geopandas.GeoSeries.from_wkt(df[\"Coordinates\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The ``GeoDataFrame`` is constructed as follows :\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "gdf = geopandas.GeoDataFrame(df, geometry=\"Coordinates\")\n", "\n", "print(gdf.head())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Again, we can plot our ``GeoDataFrame``.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ax = world.clip([-90, -55, -25, 15]).plot(color=\"white\", edgecolor=\"black\")\n", "\n", "gdf.plot(ax=ax, color=\"red\")\n", "\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.0" } }, "nbformat": 4, "nbformat_minor": 4 } geopandas-1.2.0/doc/source/gallery/geopandas_rasterio_sample.ipynb000066400000000000000000000100351525650651600254460ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "# Using GeoPandas with Rasterio to sample point data\n", "\n", "This example shows how to use GeoPandas with Rasterio. [Rasterio](https://rasterio.readthedocs.io/en/latest/index.html) is a package for reading and writing raster data.\n", "\n", "In this example a set of vector points is used to sample raster data at those points.\n", "\n", "The raster data used is Copernicus Sentinel data 2018 for Sentinel data.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import geopandas\n", "import rasterio\n", "import matplotlib.pyplot as plt\n", "from shapely.geometry import Point" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create example vector data\n", "=============================\n", "\n", "Generate a geodataframe from a set of points\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Create sampling points\n", "points = [\n", " Point(625466, 5621289),\n", " Point(626082, 5621627),\n", " Point(627116, 5621680),\n", " Point(625095, 5622358),\n", "]\n", "gdf = geopandas.GeoDataFrame([1, 2, 3, 4], geometry=points, crs=32630)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The ``GeoDataFrame`` looks like this:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "gdf.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Open the raster data\n", "=============================\n", "\n", "Use ``rasterio`` to open the raster data to be sampled" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "src = rasterio.open(\"s2a_l2a_fishbourne.tif\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's see the raster data with the point data overlaid.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "nbsphinx-thumbnail" ] }, "outputs": [], "source": [ "from rasterio.plot import show\n", "\n", "fig, ax = plt.subplots()\n", "\n", "# transform rasterio plot to real world coords\n", "extent = [src.bounds[0], src.bounds[2], src.bounds[1], src.bounds[3]]\n", "ax = rasterio.plot.show(src, extent=extent, ax=ax, cmap=\"pink\")\n", "\n", "gdf.plot(ax=ax)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sampling the data\n", "===============\n", "Rasterio requires a list of the coordinates in x,y format rather than as the points that are in the geomentry column.\n", "\n", "This can be achieved using the code below" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "coord_list = [(x, y) for x, y in zip(gdf[\"geometry\"].x, gdf[\"geometry\"].y)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Carry out the sampling of the data and store the results in a new column called `value`. Note that if the image has more than one band, a value is returned for each band." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "gdf[\"value\"] = [x for x in src.sample(coord_list)]\n", "gdf.head()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.2" } }, "nbformat": 4, "nbformat_minor": 4 } geopandas-1.2.0/doc/source/gallery/index.rst000066400000000000000000000004051525650651600210320ustar00rootroot00000000000000Examples Gallery ================ The following examples show off the functionality in GeoPandas. They highlight many of the things you can do with this package, and show off some best-practices. .. nbgallery:: :name: nbshpinx-gallery :glob: ./*geopandas-1.2.0/doc/source/gallery/inset_map.ipynb000066400000000000000000014067061525650651600222320ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Adding Inset Maps to a Matplotlib Plot\n", "[Inset maps](https://en.wikipedia.org/wiki/Map_layout#Inset_map) are elements commonly added to maps to show additional detail of a smaller region, show the location of the map within a larger region, or to show another region that is discontiguous from the main portion of the map.\n", "\n", "Two options exist for easily adding these elements to the default `matplotlib` plots generated by GeoPandas' `plot()` function: either the functions included in the base `matplotlib` library, or the `matplotlib-map-utils` library (see [here](https://github.com/moss-xyz/matplotlib-map-utils)). The use of each is described below." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using `matplotlib`\n", "Two distinct functions within `matplotlib` provide the functionality to add inset axes to already-existing axes. Confusingly, both are named `inset_axes()`, but one lives within `matplotlib`, and the other within `mpl_toolkits.axes_grid1`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Using `matplotlib.axes.Axes.inset_axes()`\n", "\n", "The first option is to use the [inset_axes](https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.inset_axes.html) method available in matplotlib." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import geopandas as gpd\n", "from geodatasets import get_path" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Creating the Parent Axis\n", "The \"parent axis\" is the one that will contain the inset axes (also called a \"child axis\"). This is the axis that is returned by the `plot()` function." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nybb = gpd.read_file(get_path(\"nybb\"))\n", "nybb = nybb.to_crs(epsg=32619) # Convert the dataset to a coordinate system which uses meters\n", "\n", "ax = nybb.plot()\n", "print(type(ax))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Adding an Inset Axis\n", "The inset axis (or \"child axis\", relative to the \"parent axis\" created by `.plot()`) can be added via the `inset_axes()` method on an `axes.Axes` object.\n", "\n", "This function's first argument, `bounds`, requires a 4-tuple of integers of floats, matching `(x0, y0, width, height)`, where `(x0, y0)` is the lower-left corner of the inset axes, and `width` and `height` are the width and height of the inset axes. By default, these values should be in `ax.transAxes` scaling, i.e. ranging from zero to one, representing a fraction of the parent axis, though users may pass their own desired transformation via the `transform` argument.\n", "\n", "`inset_axes()` shares common kwargs with the regular `Axes` class along with others; see [the documentation](https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.inset_axes.html) for details.\n", "\n", "The axis returned by `inset_axes()` can then be used to plot data, as normal." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = nybb.plot()\n", "\n", "# Now adding an inset axis\n", "iax = ax.inset_axes([0.05, 0.70, 0.25, 0.25], xticks=[], yticks=[])\n", "# Additional data may now be plotted on this axis\n", "# ex. new_york_state.plot(ax=iax)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Using `mpl_toolkits.axes_grid1.inset_locator.inset_axes`\n", "\n", "Another option is available in the `mpl_toolkits` part of matplotlib, via the function [inset_axes](https://matplotlib.org/stable/api/_as_gen/mpl_toolkits.axes_grid1.inset_locator.inset_axes.html).\n", "\n", "This separate `inset_axes()` function provided by `matplotlib` is intended to be used in a similar way to the `inset_axes()` method of the Axes class. However, it may also (optionally) be paired with `mark_inset()` ([documentation](https://matplotlib.org/stable/api/_as_gen/mpl_toolkits.axes_grid1.inset_locator.mark_inset.html)) to connect the inset axes to the parent axes, in the case where the inset axes are showing additional detail of a small area of the parent axes." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "from mpl_toolkits.axes_grid1 import inset_locator" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Creating a Parent and Inset Axis\n", "At a high level, creating a simple parent and inset axis occurs with more-or-less the same workflow as above.\n", "\n", "However, this `inset_axes()` function is very different from the `inset_axes()` method of the Axes class; most notably: \n", "\n", "- It is applied as a standalone function (via `mpl_toolkits.axes_grid1.inset_locator.inset_axes()`) rather than as a method of an Axes object (like `ax.inset_axes()`). As such, the parent axes must be passed as the first argument.\n", "\n", "- Instead of providing a 4-tuple of values specifying the bottom-left corner and width and height of the inset axes, it requires separate values for `width` and `height`, as well as a `loc` keyword similar to how `Legend` objects are placed. \n", "\n", " - If integers or floats are passed to width and height, they are interpreted as *inches*. If strings are passed, they are interpreted as percentages of the parent axes, or to the bounding box specified under `bbox_to_anchor`.\n", "\n", " - The `loc` keyword is similar to the `loc` keyword of `Legend` objects. It can be a string or an integer. See [the documentation](https://matplotlib.org/stable/api/legend_api.html#matplotlib.legend.Legend) for more details.\n", "\n", " - Advanced positioning is also possible via `bbox_to_anchor`, and `bbox_transform`, just like `Legend`s. See [the documentation](https://matplotlib.org/stable/api/_as_gen/mpl_toolkits.axes_grid1.inset_locator.inset_axes.html) for more details.\n", "\n", "- While kwargs are accepted and passed along when creating the inset axes, they must be provided in for the form of a dictionary via the `axes_kwargs` keyword." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = nybb.plot()\n", "\n", "# Now adding an inset axis\n", "iax = inset_locator.inset_axes(ax, width=1, height=1, loc=\"upper left\", borderpad=1, axes_kwargs={\"xticks\":[], \"yticks\":[]})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Marking an Indicator\n", "Unique to the `mpl_toolkits.axes_grid1` approach, the `mark_inset()` function can be used to connect the inset axes to the parent axes, in the case that the inset axes is displaying a zoomed-in section of the parent axes. See [this documentation](https://matplotlib.org/stable/api/_as_gen/mpl_toolkits.axes_grid1.inset_locator.mark_inset.html) for more details.\n", "\n", "**Important:** For this function to work, both the inset and parent axes **must** be using the same coordinate system." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = nybb.plot()\n", "\n", "# Now adding an inset axis\n", "iax = inset_locator.inset_axes(ax, width=1, height=1, loc=\"upper left\", borderpad=1, axes_kwargs={\"xticks\":[], \"yticks\":[]})\n", "# Zooming into the neighborhood of Coney Island\n", "nyc = gpd.read_file(get_path('geoda.nyc')).to_crs(epsg=32619)\n", "coney = nyc.query(\"name=='Coney Island'\")\n", "minx, miny, maxx, maxy = coney.buffer(1000).total_bounds\n", "nybb.plot(ax=iax)\n", "coney.plot(ax=iax, color=\"red\", alpha=0.8)\n", "iax.set_xlim(minx, maxx)\n", "iax.set_ylim(miny, maxy)\n", "\n", "\n", "# Connecting the inset axis to the main axis\n", "# Note: loc1 and loc2 will accept the integers 1, 2, 3, or 4, each corresponding to a different corner\n", "# 1 = upper right, 2 = upper left, 3 = lower left, 4 = lower right\n", "_ = inset_locator.mark_inset(ax, iax, loc1=1, loc2=3, linewidth=0.5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using `matplotlib-map-utils`\n", "\n", "Other option is the [matplotlib-map-utils]((https://github.com/moss-xyz/matplotlib-map-utils)) library.\n", "\n", "This library is intended to provide an easier-to-use interface for constructing inset maps (along with scale bars and north arrows) for matplotlib plots than either the `inset_axes()` method or function.\n", "\n", "Using this package, an inset map and its relevant indicators can be made with either **functions** or **classes**, which are reusable across plots; for the purposes of this tutorial, only the *functions* will be used, though the *classes* work in much the same way. Tutorials for customizing the map are available [in the package documentation](https://moss-xyz.github.io/matplotlib-map-utils/)." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "from matplotlib_map_utils.core.inset_map import inset_map, indicate_extent, indicate_detail\n", "from matplotlib_map_utils import set_size\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Set-Up" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you are working with a common plot size, `matplotlib-map-utils` has a function called `set_size()` that bulk-updates a variety of settings so that the relevant object looks \"better\" at that size.\n", "\n", "GeoPandas' `plot()` function will create figures of `6.4\"x4.8\"` with the data used in this tutorial, which corresponds to a size of `small`." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "set_size(\"small\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Creating an inset map\n", "\n", "The `inset_map()` function takes in the following arguments:\n", "\n", "* `ax`: the axis on which to plot the inset map\n", "\n", "* `location`: a string indicating the location of the inset map on the plot (see `loc` under `matplotlib.pyplot.legend` ([documentation](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.legend.html)) e.g., \"upper left\", \"lower right\", etc.)\n", "\n", "* `scale`: the desired height of the north arrow, in *inches*; can be a single value for a square map, or a two-tuple of (width, height)\n", "\n", "* `pad`: the amount of padding between the inset map and the edge of the plot, in *inches*" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Making a basic arrow using the minimum amount of arguments\n", "ax = nybb.plot()\n", "# This is using the default values for `scale` and `pad`\n", "# xticks and yticks are kwargs passed along to the axis constructor\n", "iax = inset_map(ax, location=\"upper left\", xticks=[], yticks=[])\n", "# iax can now be plotted on like normal" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Optional additional arguments can be passed, such as `coords` and `transform` for positioning the map, `to_plot` for plotting additional data, and any `kwarg` accepted as by an Axes object; see [the documentation](https://moss-xyz.github.io/matplotlib-map-utils/inset_maps/) for details." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Creating indicators\n", "\n", "`matplotlib-map-utils` makes two indicators available:\n", "\n", "- *Extent indicators* may be used when the inset map shows a *zoomed-out* version of the parent axes, providing additional location context; in this instance, the extent indicator will show the boundary of the extent of the parent axes on the inset map.\n", "\n", "- *Detail indicators* may be used in the opposite scenario, when the inset map shows a *zoomed-in* version of the parent axes; in this instance, the detail indicator will show the boundary of the extent of the inset map on the parent axes, and connect the two together, just as `mark_inset())` does, above.\n", "\n", "To work, both indicators require the CRSs of both the parent and inset axes; however, they do not require the CRSs be the same, and will handle conversion between the two. Details on both of these types of indicators can be found [in the documentation](https://moss-xyz.github.io/matplotlib-map-utils/inset_maps/#inset-map-indicators)." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Detail indicator example\n", "ax = nybb.plot()\n", "\n", "# Now adding an inset axis\n", "iax = inset_map(ax, location=\"upper left\", xticks=[], yticks=[])\n", "# Zooming into the neighborhood of Coney Island\n", "coney = nyc.query(\"name=='Coney Island'\")\n", "minx, miny, maxx, maxy = coney.buffer(1000).total_bounds\n", "nybb.plot(ax=iax)\n", "coney.plot(ax=iax, color=\"red\", alpha=0.8)\n", "iax.set_xlim(minx, maxx)\n", "iax.set_ylim(miny, maxy)\n", "\n", "# Connecting the inset axis to the main axis\n", "indicate_detail(pax=ax, iax=iax, pcrs=32619, icrs=32619)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Extent indicator example\n", "# Note that we reverse which data is plotted on which axis!\n", "# Parent axis will have the neighborhood\n", "ax = nyc.query(\"name=='Coney Island'\").plot()\n", "\n", "# Now adding an inset axis\n", "iax = inset_map(ax, location=\"upper right\", xticks=[], yticks=[])\n", "\n", "# Inset axis will have the whole city\n", "nybb.plot(ax=iax)\n", "\n", "# Connecting the inset axis to the main axis\n", "# The indicator is the red box that appears in the inset axis\n", "indicate_extent(pax=iax, bax=ax, pcrs=32619, bcrs=32619)" ] } ], "metadata": { "kernelspec": { "display_name": "default", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.14.2" } }, "nbformat": 4, "nbformat_minor": 2 } geopandas-1.2.0/doc/source/gallery/matplotlib_scalebar.ipynb000066400000000000000000000364441525650651600242530ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Adding Scale Bars and North Arrows to a Matplotlib Plot\n", "Scale bars and north arrows are common elements added to maps to indicate the scale and orientation of the map, respectively. \n", "\n", "Two packages exist for easily adding these elements to the default `matplotlib` plots generated by GeoPandas' `plot()` function: [matplotlib-scalebar](https://pypi.org/project/matplotlib-scalebar/) and [matplotlib-map-utils](https://github.com/moss-xyz/matplotlib-map-utils). The use of each is described below." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using `matplotlib-scalebar`" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from geodatasets import get_path\n", "from matplotlib_scalebar.scalebar import ScaleBar\n", "\n", "import geopandas as gpd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Creating a ScaleBar object\n", "The only required parameter for creating a ScaleBar object is `dx`. This is equal to a size of one pixel in real world. Value of this parameter depends on units of your CRS.\n", "\n", "#### Projected coordinate system (meters)\n", "The easiest way to add a scale bar is using a projected coordinate system with meters as units. Just set `dx = 1`:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "nybb = gpd.read_file(get_path(\"nybb\"))\n", "nybb = nybb.to_crs(32619) # Convert the dataset to a coordinate\n", "# system which uses meters\n", "\n", "ax = nybb.plot()\n", "ax.add_artist(ScaleBar(1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Geographic coordinate system (degrees)\n", "With a geographic coordinate system with degrees as units, `dx` should be equal to a distance in meters of two points with the same latitude (Y coordinate) which are one full degree of longitude (X) apart. You can calculate this distance by online calculator [(e.g. the Great Circle calculator)](http://edwilliams.org/gccalc.htm) or in geopandas.\\\n", "\\\n", "Firstly, we will create a GeoSeries with two points that have roughly the coordinates of NYC. They are located on the same latitude but one degree of longitude from each other. Their initial coordinates are specified in a geographic coordinate system (geographic WGS 84). They are then converted to a projected system for the calculation:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from shapely.geometry.point import Point\n", "\n", "points = gpd.GeoSeries(\n", " [Point(-73.5, 40.5), Point(-74.5, 40.5)], crs=4326\n", ") # Geographic WGS 84 - degrees\n", "points = points.to_crs(32619) # Projected WGS 84 - meters" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After the conversion, we can calculate the distance between the points. The result slightly differs from the Great Circle Calculator but the difference is insignificant (84,921 and 84,767 meters):" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "distance_meters = points[0].distance(points[1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we are able to use geographic coordinate system in our plot. We set value of `dx` parameter to a distance we just calculated:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "nybb = gpd.read_file(get_path(\"nybb\"))\n", "nybb = nybb.to_crs(4326) # Using geographic WGS 84\n", "\n", "ax = nybb.plot()\n", "ax.add_artist(ScaleBar(distance_meters))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Using other units \n", "The default unit for `dx` is m (meter). You can change this unit by the `units` and `dimension` parameters. There is a list of some possible `units` for various values of `dimension` below:\n", "\n", "| dimension | units |\n", "| ----- |:-----:|\n", "| si-length | km, m, cm, um|\n", "| imperial-length |in, ft, yd, mi|\n", "|si-length-reciprocal|1/m, 1/cm|\n", "|angle|deg|\n", "\n", "In the following example, we will leave the dataset in its initial CRS which uses feet as units. The plot shows scale of 2 leagues (approximately 11 kilometers):" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "nybb = gpd.read_file(get_path(\"nybb\"))\n", "\n", "ax = nybb.plot()\n", "ax.add_artist(ScaleBar(1, dimension=\"imperial-length\", units=\"ft\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Customization of the scale bar" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "nybb = gpd.read_file(get_path(\"nybb\")).to_crs(32619)\n", "ax = nybb.plot()\n", "\n", "# Position and layout\n", "scale1 = ScaleBar(\n", " dx=1,\n", " label=\"Scale 1\",\n", " location=\"upper left\", # in relation to the whole plot\n", " label_loc=\"left\",\n", " scale_loc=\"bottom\", # in relation to the line\n", ")\n", "\n", "# Color\n", "scale2 = ScaleBar(\n", " dx=1,\n", " label=\"Scale 2\",\n", " location=\"center\",\n", " color=\"#b32400\",\n", " box_color=\"yellow\",\n", " box_alpha=0.8, # Slightly transparent box\n", ")\n", "\n", "# Font and text formatting\n", "scale3 = ScaleBar(\n", " dx=1,\n", " label=\"Scale 3\",\n", " font_properties={\n", " \"family\": \"serif\",\n", " \"size\": \"large\",\n", " }, # For more information, see the cell below\n", " scale_formatter=lambda value, unit: f\"> {value} {unit} <\",\n", ")\n", "\n", "ax.add_artist(scale1)\n", "ax.add_artist(scale2)\n", "ax.add_artist(scale3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Note:* Font is specified by six properties: `family`, `style`, `variant`, `stretch`, `weight`, `size` (and `math_fontfamily`). See [more](https://matplotlib.org/stable/api/font_manager_api.html#matplotlib.font_manager.FontProperties).\\\n", "\\\n", "For more information about matplotlib-scalebar library, see the [PyPI](https://pypi.org/project/matplotlib-scalebar/) or [GitHub](https://github.com/ppinard/matplotlib-scalebar) page." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using `matplotlib-map-utils`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using this package, north arrows and scale bars can be made with either **functions** or **classes**; for the purposes of this tutorial, only the *functions* will be used, though the *classes* work in much the same way. Tutorials for customizing each object are available in [the documentation](https://moss-xyz.github.io/matplotlib-map-utils/)." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from geodatasets import get_path\n", "from matplotlib_map_utils.core.north_arrow import NorthArrow, north_arrow\n", "from matplotlib_map_utils.core.scale_bar import ScaleBar, scale_bar\n", "\n", "import geopandas as gpd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Set-Up" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "nybb = gpd.read_file(get_path(\"nybb\"))\n", "# By default, the data is projected in feet\n", "nybb.crs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you are working with a common plot size, both `NorthArrow` and `ScaleBar` have a function called `set_size()` that bulk-updates a variety of settings so that the relevant object looks \"better\" at that size.\n", "\n", "GeoPandas' `plot()` function will create figures of `6.4\"x4.8\"` with the data used in this tutorial, which corresponds to a size of `small`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "NorthArrow.set_size(\"small\")\n", "ScaleBar.set_size(\"small\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### North Arrows\n", "\n", "The `north_arrow()` function takes in the following arguments:\n", "\n", "* `ax`: the axis on which to plot the north arrow\n", "\n", "* `location`: a string indicating the location of the north arrow on the plot (see `loc` under [matplotlib.pyplot.legend](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.legend.html) e.g., \"upper left\", \"lower right\", etc.)\n", "\n", "* `scale`: the desired height of the north arrow, in *inches*\n", "\n", "* `rotation`: a dictionary containing either a value for `degrees` (if rotation will be set manually), or arguments for `crs`, `reference`, and `coords` (if rotation will be calculated based on the provided CRS)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Making a basic arrow using the minimum amount of arguments\n", "ax = nybb.plot()\n", "north_arrow(\n", " ax, location=\"upper left\", rotation={\"crs\": nybb.crs, \"reference\": \"center\"}\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Optional additional arguments can be passed to `base`, `fancy`, `shadow`, `label`, `pack`, and `aob` to change the styling of the arrow; see [the documentation](https://moss-xyz.github.io/matplotlib-map-utils/north_arrows/) for details." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Making a more customized arrow\n", "ax = nybb.plot()\n", "north_arrow(\n", " ax,\n", " location=\"upper left\",\n", " scale=0.4,\n", " rotation={\"crs\": nybb.crs, \"reference\": \"center\"},\n", " base={\"edgecolor\": \"blue\", \"linewidth\": 2},\n", " fancy=False,\n", " shadow=False, # this turns off the component\n", " label={\"position\": \"top\", \"text\": \"North\", \"fontsize\": 8},\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Scale Bars\n", "\n", "The `scale_bar()` function takes in the following arguments:\n", "\n", "* `ax`: the axis on which to plot the scale bar\n", "\n", "* `location`: a string indicating the location of the scale bar on the plot (see `loc` under [matplotlib.pyplot.legend](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.legend.html) e.g., \"upper left\", \"lower right\", etc.)\n", "\n", "* `style`: the appearance of the arrow: can be either `ticks` or `boxes` (default)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Making a basic scale bar using the minimum amount of arguments\n", "# Note that the data is auto-converted to miles\n", "ax = nybb.plot()\n", "scale_bar(ax, location=\"upper left\", style=\"boxes\", bar={\"projection\": nybb.crs})" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Making the same scale bar but in the other style (ticks)\n", "ax = nybb.plot()\n", "scale_bar(ax, location=\"upper left\", style=\"ticks\", bar={\"projection\": nybb.crs})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The scale bar can handle converting between common unit types, as shown in the table below. \n", "\n", "| Unit Type | Conversion Factor | Accepted Inputs |\n", "|----------------|-------------------|--------------------------------------------------------|\n", "| Meters | 1 | `m`,`meter`,`metre`,`meters`,`meters` |\n", "| Kilometers | 1000 | `km`,`kilometer`,`kilometre`,`kilometers`,`kilometers` |\n", "| Feet | 0.3048 | `ft`,`ftUS`,`foot`,`feet`,`US survey foot` |\n", "| Yards | 0.9144 | `yd`,`yard`,`yards` |\n", "| Miles | 1609.34 | `mi`,`mile`,`miles` |\n", "| Nautical Miles | 1852 | `nmi`,`nautical`,`nautical mile`,`nautical miles` |\n", "\n", "The scale bar can also handle *unprojected* data (degrees) - it will convert it to metres using *great circle distance*, and then convert it into the units selected by the user. This will happen automatically when `projection` is set to a CRS of `4326` or similar." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Making a scale bar in kilometers instead, by changing bar[\"units\"]\n", "# Note that the data did not have to be reprojected to do this\n", "ax = nybb.plot()\n", "scale_bar(\n", " ax, location=\"upper left\", style=\"boxes\", bar={\"projection\": nybb.crs, \"unit\": \"km\"}\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Optional additional arguments can be passed to `bar`, `labels`, `units`, `text`, and `aob` to change the styling of the bar; see [the documentation](https://moss-xyz.github.io/matplotlib-map-utils/scale_bars/) for details.\n", "\n", "*Note that control of the length of the bar, as well as the number of major and minor divisions, is handled within the `bar` style dictionary.*" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Making a more formatted scale bar (ticks)\n", "ax = nybb.plot()\n", "scale_bar(\n", " ax,\n", " location=\"upper left\",\n", " style=\"ticks\",\n", " bar={\n", " \"projection\": nybb.crs,\n", " \"max\": 12,\n", " \"major_div\": 2,\n", " \"minor_div\": 3,\n", " \"minor_type\": \"first\",\n", " \"tick_loc\": \"middle\",\n", " \"tickcolors\": \"blue\",\n", " \"basecolors\": \"blue\",\n", " \"tickwidth\": 1.5,\n", " },\n", " labels={\"loc\": \"above\", \"style\": \"major\"},\n", " units={\"loc\": \"bar\", \"fontsize\": 8},\n", " text={\"fontfamily\": \"monospace\"},\n", ")" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.3" } }, "nbformat": 4, "nbformat_minor": 2 } geopandas-1.2.0/doc/source/gallery/nyc.png000066400000000000000000011771131525650651600205040ustar00rootroot00000000000000PNG  IHDRk.sBIT|d pHYs.#.#x?v IDATxyxMN$1DĐЈ!1TK)9PjVZMǹQNiLiqJ"jB$1S5WMc1H$~](kg{>{}]M]y峖jZp(7'a-L@X &  k`Z0a-L@X &  k`Z0a-L@X &  k`Z0a-L@X &(bܺuKgϞՙ3gtY%''ZjV]3\vM񊏏י3gTT))((H+Wg?}~g%$$ʕ+*[TfMլY@{KѣGu ]pAׯ_*VURO:}.\۷oB TݺuU|흞}ٳJHHPrrz)A-x28EX{uEGG+**Jqqq~zWPA=Ə|?~yEzw5hР| nSSS_*22Rvuw6lX׷oִi4|>|85k>}hРAȷ.] (>>9nnnjݺ>}[oI:qℾ -\P/_{^x 0@;wxXVՌ)))2e?(###pwwW߾}5~xyGjZxrcj\OzuСZkj*}qF:w\ׄ)22RuyK,ѐ!C kU\z[VM6M{饗4c )SdZX{i˵__ݻwe˖JMM}ue˖iڵkյkWz+/O%i֬Yz뭷SQQQj۶m4a„<-Ul٢u~4o޼ ޽{mM6ںu^T);V#F]w֨Qlֺ)**J:tȶ$ݻwOjҙ3g u^Z5Y^z~0}}}uq-[6Gau5tCf͚ZzUf_~ы/'NժUӑ#Gr(CaÆޢE -YD*TY{nuI׮]3[l͛7縷`Gsǎm/t҆jULLz衔g}ܹssO67 ՗_~gjYTX1M0A[n S5xݽ{MjX4o<5*ӠV4iu֩aÆzFFLŊw}g7\&Hy&N҃~~~J5k؄'Os߷ jkԨ+W j+WTѢE [q =ڦ޼ysEFF҃C7o,맟~q7oΝ;վ}<_cʔ)9r۷ogyCmРA'NP:u ˗]{mkXd#Ǐ׳>kXcǎrٮO?mK- 6Yfٮ߾}ڴic秓'OqZli-[V;v4(K_oP>>_dIokܹujEݺuUzw\a ʥMޱc{ }XaZƍ]%e2>*99ǁrÇmj;vU=kYX,yfVѣ<:u#-5jھ}Zjڄ t̙9rn޼i?>u?M-/jpN-gddرc6oZ0*UreC ۷oW^:~xk]_~ްaC=8ݜ.fOKKɓ'Mjŋg'@Nڰ6))Icƌ'֭f͚%={v׮](by5f={Pfe^cǎj/mvSX1N{ܹRJb?ڬg6˔)#7B;*##Cׯ_7jԨ"E gY(_|aS?)oo\_UV:r䈶mۦqiٮ)V># 0@h`8;[Zժ$(Q"҃dɒy]T)GmGVwXrqy'ё#GbyRڵK /W@wjΜ9e˖INN֧~:x`ګĒG;sXjv7*=q℺v{ꡡ6mZyQիWOז-[dZ~fXjժQ׮]ɓU^=7.Oˌ}vȻ^ MX{ymV/_6+WkתXbfLL5k'O5k=w|nܸ]virww78q^z%%''g\ϟmέGg(j3vgPʕ+j׮Ξ=k?SZns}ϫG6ﾫ>nƍqr]mjy =8p {_LPjƍkT7N޼yS:p^tiũnݺymj۷Wrr}޽{ۄ{B KCBBЎ_J-ZPKJJRZZCqʰj߾g*66V=zԦK5j0.^L<mI6ͩj:ݰڑ_? 9aX{uAw6}||vZ5j{?~ܦ8ۣ;)VN8k׶Y&OW^mSkq%Vk+&&P+RH+~Tambb^xرP/Y3K{-zjw5ñbM6?ܹsrJYiժM-66VUdjŊSӦMsѯ#'Μ9~P TPPPX"mff͚#iڤ$u}Zf}|eLJmխ[WYQӤ$\\ vکxYkݺ|}} SN?ݻgk׮6޺un޼{葫kaݻwչsgmݺP/QV^-Zk?{a]ΩǏ֭[Z:u]g/TN{4,4"EԩS'ۄWTTTLX 2=MIIQnݴi&Cx⊊R˖-󽧽v֭ZdIcZ5t]QzRҥ M6i9} 6Լoh}m7hΝ9Z?hZrrV6Sɓ9Zl2mٲP b_WgfɊ7ڵkʕ+h=`jX={jݺuzŵjժl߽WjܸM}Ȑ!:|gibkQ>>>z Gvrk /qXAj)))ҥN:ړ'OK.6;vsSǎ k׮cǎ~zkwء>}(##PeXr^} u5w._\ӟ 5777}9 H&GEGGEՊ+Ժu%Jjׯ_WXXLLǫG5jgo^x0|pp599Y]tѾ}IHHP׮]mv>Qߌ7N%K4Ԯ^;fv`'H"6ڵ.^hw͞={ԥK-ZP׮]sbhҤI6۷_׍7l>PLL^{5YVgׯh 믿nSU*Ul°hҤfΜy_}YfYU۷o_~/ 6(55fMZo>-Z4dzjժl> TDD7oC)66VG9Ck֬Q6mr7k׮UΝիWWDD5j]v)66ѿ҃]۶mѻz5sL$7njÆ Zn]aaaڶm[wgի7UjǺ.`n$&& Pp?Õp?Õp?Õp?Õp?Õp?Õp?ZR$S FZZ͛7uǺv2ertm6͙3xdV=,XV4hE{6&ϟٳ5{L׻iƌy j%C/4dĉ:qD뽼dɒ<4h 9sFg\_T)EEE)'ٳ?]veH@^Z>> RppT"wwi9rD/^ԵkTtiFz $=zTǏŋu +WN ͷ4+?N>ݹsG˗W@@² SCFFݫs… JIISO=5lP3֯\"??PظZBX 8 W\-OyY f  k`Z0a-L@X &  k`Z0a-L@X &  k`Z0a-L@X &  k`Z0a-L@X &  k`Z0a-L@X & 1{ ?$&&jqGfd.gϞ$+zWUhQldz~JRjjbcc5`)SF:tЖ-[LEX B?d(<<\aaa NGX Bmst5`jܸqJLL,,P߿RRR ի+ 5/^Ԅ TB:uTA @c߿P 7|N-RǎUhQ듒4gլYSm۶զM05v IDATjZtUjW\IõeC-..NK65w\\Rk׬]}]_>3vPxonկ_&$ooo :Tk֬ѸqTZL{a 4H;vn߾a- ژ1c\]jѢE1cy٥K|С[edd@r6sΩz9Z߸qc5nXϟ׬Y~z%''ۜĨlٲj߾7opթS籿mSˏnnnܹ.\ {*SLknݺJM6ZjM6=,pm(lbSk֬YQƎkjʔ)zUJyyye&99Y[nU֭ɓ'+%%%_k @gñʖ-[ ԢE oӷ~8 6L+Vr5rH_֮] d>N(8`8Yz/^\ʕ+5uT5iDŊD-^XM6UHH&MD DX B㧟~ڔ97oӧkZ`:wc=1cƨ|޽9ñVXPW_UݺuRJiСJMM5{D,VjWPrL畔$eddS=ځS̸ZZ:۷o7԰aCAi5kZnEfzS4aUXQ?/^l!>}ZÇ2Ç;h")b@nرp\T).]ڤiV~}կ__wҥKj*=7--M7nƍnݺiȑ qԮ/55UJII>}8`"$bg- ={+Vh$9WxqWK,ѼyԾ}{/^<^3gvjР&Ow:pbu==6=K.pTIDX B`&ɛM8Qqqq=zBBBdXkZ~9R˗WݵaYVO222Ԯ];}ٞٳg;`*"@ .j-zyiիʔ)ZbڴijժiJHHpąߜ9suVCI'Me:b,"@aj[6iѣGkڵ?f͚#MUVUxxϟtN\8}cŢ-[ڜs魷rX e)\zUڕ+WgD8pmٲqҥgD֭[ZdV^se{~rԽ{w5J5kt۷Utiedddy^u!9h2S_CP+ɥCRJ75w\o^ŋk׮i̙ QӦMzjN<==UHl3fàҥKe˖e;k'j @A\#Fo߾&MxiiiҊ+teP[4hPAcǎs???%$$H"t4hK.IBBB4sLhQGZBX 8 W@Apss '{yyy4.]HY[xqmVÆ SvdX4sٳg4i9˗װaôm6>sssSϞ=5m4+W GvZBX 8 W@A(]n޼VZ4q"`Ze-^X{>>zw4n8yxxt '\-O᝵(t=Cg1ibX/Bz饗| 4uTU^]7dN8iiiɓճg| j%裏TJ͜9s<pVjU9s?o&NT+&&F˗/ׁjҤ}A>$,YR~jѢݵVU6mRTT-wysUViM%4n8=)po.ӧO+((P?~:udDߍ7pBܹsٞ_dIuY|AxT^=ǽs*222:*TX=op֭[4k(] ˗kΜ9С3=Ν;oTN5jH_ueN;7o>=<<4p@]VFR͚5>;==]O~~5EGGWŋ4]&Lui fddh߾}UBY,uYIII:s?ߟ~^^^zW7hzgmΩ\rYIKK?`5mԤi\["EԩS'uIW\т n:]x1uQQQvܩ;hbvؑgڵS oPPzes6[VYVyxxK.z饗 dxg-$\䇫W*00P… Uzuz۷O_vڥg{~HH!C{DZ$7N]v-ޛ7o֟\:u^@Z\-O1pZMP[\9ZkذN( 2DjYcƌQվ}{-[LVZc֭[A<_WL Z8Z&ML҃|ԼyT\,OIIQllzk:pCf}4P.[6=s.]T@gc'jx\Pz 5777Y&ې5w\XB/_ֽ{=駟V޽{;IKKSɒ% mV~i={&IX,?Z:xai4d@ajy a-$\ Onnnڶm<==M YIJJҥKzj:u*K(={?Tppp>өST~}6|֭[{^z)>>+ 8)EgӦM :%J_~ׯN: hͺ~󓒒4w\͟?_Zѣվ}\LNNs=}ezNrԥK\]UZdLN'--MG1Ԟ}YA^kرZv>3=s{nzz6nܨ:r2d:>G2޽{͹~ZLkLɹHڸqRRR ppssS˖-5ydSj2=ܹs1cTn]PRRRʕ+3i"[om111*[I)..NW֏?h֞bŊbŊ:qgQQQPBAoÇ?;p_}8ŋUti#xxxcǎ/CfdA$j%SNsiԩ&M@X qFqhh( 0`,YHo^Ŋ t{*SyIGop* ͛4 VV-M8Q6lȑ#O+5stC}5nݺQF4p4Z8oViii-"""LCϟ^sQ6mlVZE&M{{ysddN:eDkTVZe8IYծ][&MRLL  GO-Wlv޻wOÇ7i"HE~p\~}&Aa~1KӦMոqcٳa-&&F/^SO=edNѣJHH0?3 @G6k۶m&MNc…c//BQ T"OOOC-99٤iikԨa`hJMM5BCCM8 a-BZZo5mԤio *TPÆ M8 a-BLL޽koޤiIMME=z͍=ҥK e˖UppIs%ݿPׯIG"SX~8,,̤IDz^f&FX ӥ(!!PkҤIULjV2iH0]LL222 ֭[4 XVzjDX -Yp\\94 x͚53޽[&M۴iA cǎw***ʤiT҅  6mژ4 `ի\rڣ;!Νk8.ZZje4y6lh8޺uIG! ǡ4i<(p:d4k`իWۄQœUV2ϟo4k`O>Ħ /0 `>j~V\)jT 4Mlٲ&L8-ZԺuʕ+kرva-Lsjݻw7i9 X,6?VŊբE }ڿ DX S_^IIIZ.]Lp*TyݻwO۷oĉՠAիWOp K.5nݺ&M8I&e˖X<zUjUM4IovД ?Xp W^v4f͚:~-[>3q"ܿ_Vҷ~cǎ)zxx(,,LzҰaIp WSY |N8alҤiUHu]Zxze6--M{՘1cﯞ={_~q kp .4twwWvLp~AAA2d+Ĩo߾d-[LЊ+x-NzjqժUUdI ooo1B5jV*btũ{ u1O !CYVܹPkܸI^y-]TVҐ!C/_Aϕmۦ۷ojm۶5iuTPAŋ5o<5o\nnlZ~ >\˗נAtԩ,?|լYS*ZW_)%% _4W^v4cذa6mc[.P @ޝ9sFs͛u,uwwWÆ շo_ 8PŊ{Ytt:u${?NV֭5p@rww߸Z߀P6l0שS( UTѸqI&gsӵ{n >\~~~ڵ(A$%&&jժU֭4`ݻ ,\;k'j =W^U hT֭[ZhJJJҭ[rէN:__~_WS/$fM&N}֬YRJZΝ;gɓ'5n8^z7n>x 2$\m>P2e ;^~e6q*矵pBmݺUoڵkkΜ9Zڶm-[ݻw+%%%>*Tаa4bjy a-$\ ڰaڴic͞=[aaa&M +Z~-[64hJ+V6mԨu~ s<pG 2D3fxxu͍7u֭[Zdbbb/I ԢEew}EumPV@;ػ(EXb^&KLLјD=Zr4 *X,PAQ:̼`fss9{8kصk;hGOO 6D>} ~DDTZ?ZRB^\rvvƝ;wr۵kUVi""RǓ'O3lzzzJILLD`` N8hddd:F"={Ę1cТENDD:@)lBDDDDDD8T^2,76w\ SԨQ*%"k6kJ ]{q!"""""" ނƍѼys-UDDeIVVN< \|)))H$prrF wwJSج%*%tŅ>}СCUTAPP$"(;;N‘#GpU$%%)5;vĐ!C0`sDDT\tf-Q)k/.DDDDDDD+++oVU|2mۆϸ511ѣGJ*\%iS.tZ4h!"]+V?~<_Tb֬YG~pH+kJ ]$t vvv￵Xw!88N7:jԨ^zaرhժj'"ld IDAT*tR*o޼Ǐ#<~iiipppԩ33%;;׮]Cll,?W^ժUCժUQn]TVMycbbpm"..+W-իzi<_^w{3$&&vvvpvvFZ=kgϞ!99ժU=7nUk\|?Fll,Pzuۣy水,BDDDDDDX[[ &>|8K-VEDINNqQDFF˗J|}}Ѯ];6nJ]맔fmbb"$&&|jо}{,\RÇw^?R_~}t;wF>}`bbVLlذ۷oDž }q>|8Nurr2~7رn֭[cȑ?~< 4ŋXv-vڅ*|F*K.=z4Fp=[w6CCCxzz}h~]{q!""""""Zlr% PF -VEDq!;w߇2oƍCvJJ""*S֬MOOǪUpd2ç~ ^#uEEEaʔ)yTqmVq׮]èQpMԮ]AǎUΗɓ'gɓ'JiҤ oߎF9}0yBܹ3lق5k)\.oٳg#--MqaeeUڋ +\{ݻwk"" S}A[[['OF͋B""RD)ZkĠnݺ6lQ4ϊ+0gdgg5^" ,,Lfmpp0/HYR7oѣUM0a"00ݻwW; h"ZXX 447V;ѣm6֨QᰵU;BDDDDDD۳gPfM'Nk*""L8qAAAr RSS}b„ Ŷ$Z?5k% ,,,4lqq8{ڟfZd ϟիiӦh֬6lto߾W>k׮Cx螽=z:8z(D 88]tQ:GE޽#K$888nnn8sLJcݺu5"""PN|ݻwmڴW+WbJ۷/:$999!<\F-~'|"# @ׯf̘TxUqN(Sz/Ri#GJ'""""""e\7k֌M "FFF0`6n܈cǎ)|c^Bhhh VHDDe֚ҲeK\.GddRc7T*_ ~Q*͡ye̟@AL"X8;wNG>}J$?. ųg}j"ennQFa׮] ѣEH$h:""* |Yfؕ+W w9y j۷oq1AL"`*ӴiSׯFTT fjj {xxB ѣGZSN͛7X:uиqc+~)jB#:teYKDDDDDD]ݬЭ[7-UCDTrU.]isrrR:""*|6>>^Sby -.ϟGff ֬Y3ԨQC-yxxH*T5xr~UW@ZдiSAɓ'6WQo69""B4Qy`AZd8p@Pw&"7kO8!:̙3kD&MhܺuKRk.EeK$KRQ:''wQy."""""""]pA$%% b|@,DXX ֮];?^KQYPo޼my,HТE%''j*UIuRR֯_^z~033C͚5???lٲW^U۷obꬪ{{{/%Id{(rЯHWmڴIp]rek"n:zJUlVV~xyyac 22111Q*".( 5jJ*xh+u[nń D[ӧ||||vZvQSe|a [٩YYY?z邘 5Z""""""*DG3oRi^+@DX۷OèQD޺u  {rcǠAp ,Z{aժU:uj~DDe唔̝;W9rdc?!f?>LԨϞ={nJ /H4,-(RRrrrD}ő_&$$=QY}v bR5DD%#""ׯ=+VNNNF׮]EQrܹ/^@ǎsצM~""Қ2۬;w.?~,Y[[+lɓ7߈Qx :T;QL݆>*V(d2heiQ+|W[XX@"h-'""""""U۶m\YKwׯ_ر# "\qqq H$X`^~… q͢MDDNyXn(|r:^;v %%%/FN T ܸqAAAXhs,[ Ck6 x0LNNνHIIi _sssr[XX@* V{nJII EDDDDDDD!11X.]T Qx%DqCCCމشiVZ|򽟓SNQFMDT|ܛ+1Yk^pǏGȀf-[Ğ={Pvm3FFFpss<==デޗdPwࢎ-㋚4'""""""*bbbD8::NK ===Cdggc. ?N_?Uj°a/J2Ӭ})w/_ 5kDpp0*T\u.\(j3fŗ.]Z8E+^_zTNEkT;11Q܊^ÇxZ)S ==]S+.۷(vĠ~5khQ~Fnݺٳ000PV"HGf˺2 r\\zǏ իWDZc`oo|ڠA|g*5w\_o޼ɍ=~6}rz իWW)y$J5;o޼ANNN9 WRmW)Ϟ%"""""R ׬Y0L<666ZCbb">SC.C.C*fB۶m5?oaaak}}}TXmڴ/D[ZZ"==]!Xn`K?~ 6Ν ===|DDZN$55*ўR6!!]vݻwq?~*ϙ_3}*okkaaM b2 wQ) ÜA@`ddh~ZUuYKDDDDDDT9Raɓ'퍟~#jxrrr ˑǏcի""M9uRJ۷FmZ`XYYר͛7o}n""*=Ju611ݻwGTT ^reAj͛':ԚOѸUVm*ں1yX٫E oeeT\.GRRRWgfma"""""""]񱴴4cҰ=s  |&!!x~"*ߞ?b#1e޽033 T^8GիW];YkxxxxJƍ=wÆ KYɬfsc@j_V-)))~jMSʺ[ WT J%K4^ˢEz.33C Qw""u_XJرf۷o/B [DXYYF r6ktDl&''gϞ| niiGYfEEaR*{>}9\./RTa$~""""""J.֭[ѼysX[[z~m6tM][6F%sg0`@v""ӧ _reر#EBÇѣGqlݺ ./SN!44GZ؍AJJƾ""ҎR׬}-zꅋ/ +VDpp0ZhQ&&& @޿_nu(^V+CDš_"_.?''GQ^="""""""* lllo>d2 6 nBjиqcL:ALL |}}E׮]'O˗G92+&0uhoT111Eӧ4o+W.t.cccwڵ+LM8J4hv)ٳ*NDDKj־{VZi,W.]D1EMWe(j2Hr }vh֬lmm1m4999X`n߾rW^wصkmGkWG¤DeffO>ARR5}PǎðasT3F&"")5ڔΝpaiF,tlA_ua;Э[7Q\ՆEjܸ1֭[8'''Q}))) Q)HH=zĤq]tm9ܼyS_,t\!  +pje4H9ʂ;wʕ+J?k,Zk׮풖?\f鉗/_*ߦI},'O#{?ooodgg+])))\]]/z=̙3G#yצh׮ vYH$KDDūT4k?lA&СC8Э[7AAAwJ󄄄օnoNE+,f~}}}-ފ؈ T)Ul.u0b爌@k=Ui0a\2Lt@_+f}}}gœE+ #0k, ⾾Yϟ\lR߃о}{Ph >Zo֦c8u nbb@tСXa„ Xvv6k ?էOB2*UN:+޽{Xf fdd>Lcƌm׻sNc׮]X*UnVN8Q[z 44TsvvVE(ʕ+CAGYR㉈J:uBddRϛVɓQzuA,66 Bfff~\|Ytkլ  H:胵kŋ |}}/R4_~E':/гgkܸqsQj6338v nbbгWj޼y{n/^W^llltBVX d9r$.\P؄xzz&MRYXn]?^KOOG~ =ׯL۷ ///$&&86""#G}D"Q*#ШQ#A޽{߿g߿_} &JO?)KNNGjQ +Yn w?~FoM׋bOydXB/3f ѽ;wbذaJ@cAhLll, pAхn/_^zx ^^=:t1ݻhuۢպqZh!:]vػwp%xyy!!!A4&6څ RWY&!$$0bl۶MyF}8d2kD"A.e8G3}ƍ1dXZZ*sh26nܨpKbpvvFXXBBB+zU9dQ\* lj'p1~4i3g(8?BDDDDDDe1kiif- m@5k7n ''n+Mݷ6\SVb.]R{>"*?~7HNN<3l0ٳ'It*N|DVVcR)6n܈1cƨ< 6`j5ȍpAC| ~GZXXhҤG;v5ᰳS;BDDDDDDeONNj׮O oRsD\ a%\CaSYպh &HDO啔> Ϟ=ˍkgΜ<׸qcܼy3W^XhZ9o߾ ___SNڵkڵk#...O?9s樕{b…x5+Vi>""Mӵ~VϬ--R)b 4޽{j"""аaCժU Ǐ/RxѣQJ4i .Q ?oa׮]Ri\Np"5j`۶mXj7x{{իi7n4j`OozE+"J*/_|!~_.ߤpװ6\\\TODC\\Km=z4jd }dDںubٲe^qΝ LVN6>>>Bll,V\)X)LDDfD"_Mn=3f͛7W_۸H$066FӦM1c DEEj>h޼9"##zjlٲ:5j%KƍԩSs@np ,^_ $ ݱf\x7H~ܼyCڵ}NOO]vş'OVZڵk>}:Vsׯo۷OPF2Lt֡UE3Ey'*(gbb"TCCܸR3q ; 7ؤ>wYp-J*]/!!!Ahh]1i$A֭[ZhV͛7 """`ll HMMU8v۶mjիWbӦMS{>""*ֶA. ]Gx-ѰaC8::VVLL s$$$RJׯ_Ν;ϑ*U...kڵkAll,޾}UM4) 2 x ={tT^puu+BזQٲxb|74ޘKyX /\di*ZgOc{͇@Gjժ!00$#2 K,8SSSl߾ ?3@T5S޽C^ԚR ++ nBEsrrT|DDA).4kڴ)6mܵk.piqkР4hKRlٲDDDDDDD$33˗/,L0o7窖 Y+++$&*ڶ|O&eC{ZСCEDeǞ={mZ[[pssē/5iD={Zx-1l0b޽HMM RSSagg5k֠j7jN뽈Ϭ%"""""""*V^$A>]adh\ W*FR1e8~jnEύ?$"2 nnnt钨Q ?h cǪ?| e6|.XKfmjS><۶m233`Ŋ8p?~p#Gj355Uk\^Z\.G||>666X\\?FDDDDDDE妙1~ 3l Nccx<r_!۳+ .y=GX:{vBESr 7+ފT 6gƈ# qDG⧟~süSLLLuj7jիW2dUgiiW^)wAƍ?r&&&B*UвeKKN"Z? CgϞim.m0K#u2WZ6tBˆE?;vx2I===䈞mժV^]DTXBԨ\#G ,[LԨH$h֬={"6ʕ+cƍ={<~111[W Tal޼9:55ŋZRB> BDDDDDDɓ~kc#&Xmݏ'r.xf8p`IFD(..v킱1F333ݼycƌ*oyU^zҥKZ#Ǐ{1c-[&߼y 6hGӦMRsO>FsG)]Tf)H$gJ"*>s1uT^Z;w/Fbb"6m$WF :hhȐ!={61uTL>'OUx۷={61w\|w>۬Y3COOiiig>s?C|ڵ@||D"#z={ŋgpBA5f\wƺup_w:KD6kʑ+VbMj|!H227oիW%Q ٰa/M6>wi={]v 7l[Lx]HMM9\h\R;bݺu%sF#F͛7 BHH޽ڵkc^.bٸq(vq,U<&0o+=˲I&%U !!!>w\rEFbŊ~~~<3cƌxs[4O  aÆi%?.ᙵDDDDDDDD\FFfRO_(8::@5D 077W{|:ur=&&͚5,::#FȷQ+JQbEXXXxpDDDf-Q93Fexd+4Pqrr¡C0x`܈ꫯ۪K$̜95@\\L"ڦxGrr2޼yׯ_#!!ݻw=kkk H|{H՜R7/'eW,UPApRR/P5kĬY/<(榑`1uT$%% ⦦Xj\ooo޳B~Ds 4@!"!e7q""""""""Rٳg]Ɲ) ۶m[R/^,ݻZ°ar_}sJ[UtR<|P^:N> ''|d2QlԩJ6k M+M] buŃrѥK.dXp!N8\.oiiQ<::AAAX֭Q~"$pIAbŊ8~x5H$ܯSNhРAk*n7nD@@Zl'N%Z֭(֣u3-T?_a3c5a UDDfbb˗/ _~_ǏGzzzn|ɽ7n 99Y۴i KAٳ'5RSqڿ?&L`UDDf-Q1`Q*)DƉ111k%*:v]va߾}{_}wsss?-xuH-G\[XX[_}n߾`TRE#5]r J76kʉPQmWvF ~.CrK__^^^%Y2իWTml:t̍K$L2Ec5=Cu֤ n߾=__lݟDDDDDDDD+##Cama^\…[тX֭-999ܼ$K#RFOOׁ/^6mHJJ£G}jd*.N5r+6kʁ'b>X&Ww-ڴG5kh ۗPEDTYYYI4;((H_OO|/U0b XXXp5DD_mT[ڵK¨e<׬lKMŗ/_.CK,"*>]__g榱aaaUmfB@@@nSzhѢLMM*T7fΜRJ2Qw c! HT~{Xٳ'9"u1]DT3€0sL<{Lƍh(un4:6]pޯ ;;.]{fffZ`HaÆ ޝK"zO b666Č0{J={gϞJ*)l֩S:uXϟ#%%E2d6???q==gpH͜9S,@hi%YXX 99Y:t(K4"*CF cccQWyn޼)GV4C>XY<HoDDDDDDDD7oވVZbV >cp#5j ::Zstt^%QӥKaaaHNN'<==5ݻkh46eggb<+Hul騹sb^[@-*Ka0w͟˗/1ccc\ETFիWpuu-3UiӦaڴiŖڵk[.m4i֬Y\ҥK\Qf-JNNƞ={J榘6*RX5g.M2j*҈,YW /[^=-UR|xqED$44TШӧk{^Çgج%"""""""1׮]:U1{[-UB999ĉ"*fk׮"""j4^p" 4j_|E!"Z""""""""3n8dgg^K7l)ըSNS5DT\駟v/ݻL3>3۷odd޽{q-\ՃN<"VLDDP6~C'""""""""lٲ.]:5FFeD5+WpU-ԬY8K"b"**g9Ν;cŢ3`K֭[nݺئMn.[,{]vּDD]l鈷ob֬Y7TK'4(W^)|J/"22Rooo,\L =z:!!WVk~7oVk޼222ӧO9DD DDDDDDDD:bԩ]`ceTwmoET*żyJUVb={66lpLvv6)DGHHnܸLVjժ bVRBW\^|N:}EΝX9pl .`ΝXj֘SKnLi=22 }N:W[nݺ#"M9y$ܹ#ծ]O>?7obŊ044T8̙3XbE9vލ޽{c޼y3f z쉿Kc_R) &=y*ͣ;;166V͙3{n;vHsQج%"""""""SL>J$v X~wvDvN|}}o>ܹ}-HSd2֮]+ҥK033ˍ͜9W^vډٳg\0ǻwvZդ$,YDc[BQ\9$ h ޽Z@VVOQ|ƌOУGL8Q(lqgϞصU7T l 8*תUK&M*ѣGXvv6V\XAWƙ3g0g|Wnٲ mڴI U&M""?e""""M) IDAT""""Ro\K$~ݴTb)040 .ɰH fdd_UtbŊtb+󽟞sB&a޼y{UTѣƒ1j( >>^lll+^ 999;vl :u:L&Çk׮ٳgLMMRhsZ?3Ξ=gϞB Xp!n݊7o Bpp0>|xzzͭ3#FNj~QSSmDDDDDDTDFFe˖/;6ڹLwi r>)?IA@t.j4"*]c֭>geegϞXOKKC&Mp=k;v,&O9s3g!H}&## 4@LLL^xUSHVV[[[۷Cͽ91T'b͚5HDDѵ~ A&"""""""yxWdߐ%Ծ.j+ֶo[jjQjik+Į Y%'3Ogf23^ss$s9jѢ)oflfނ @V#/~WxkLP 5jPKTB8::bҤI8<8uaԨQKIIAxxyWWW8pݱYYY&?[]wwŋjm۶jvttDhh(*V0`6l؀ݻST\528g""bڰa PrEe$TT"%Ql'3}Ŕ)SwUd2#"EÆ 1~x߿e˖!''G\pp0N>mG\Q!3իwJsfQ_s >3gӧOѠA/V͇$b}6&L h7UZ㗣OŽ/]vDTr5JKNNֺW-[Mz(>D_۷oTmj'ӧ7ܰh"%"ϊ/JOO7{Ύ;tYs*J;wK.EVVϐ OkߨQ [^= 4HST3f 9"UjDDDDDDDDDDv,66_} I#lvٯVӧ#00iQ1QlYAzzzGӼkPׯ_ǘ1cо}{76m2zpt Νmٲ}O<ofaVR!** F礏fqV}>:t o߾zEs""*)X%"""""""cGvv 6PGMƫW.u={vJDThk-Ux #22F¥KDܹ3g1krqqlNN틄'w;V\)B׮]mv.=biU[Z5QFѼV*UcǎVΆ#___Q,>>ެ9o߾ӧ/Dʕ+2e N:%?Drr2QPPGѣ|2fΜuG SRRt DD% DDDDDDDDvÇ_BQFqTуl761^QPP.]ƍ}ѣqIAF˖-u>;{lC_lXamc]כ .5^ͣtU*Xlo޽[t-ܹ"y,!]0g6p9yyX>QDd;΢bǏMoǎ;z JĀAzurr޽{1j(A<&&w.lkۥڪU+lݺU3fu_(W]{6lȑ#yf$%%i{u""* l ݽ{Νuj'GGed6BjzFѧO 2""{Rp!ܺu yyyC~~~>;O !!5j[h?rLϟGF5MP`Μ9Q_-|Q~wAdd$իWfϞK.!"":u 99Y4e%-- Cѣ:/ 믿nVnDD%DDDDDDDDvf޼yoB!;lQb?׺uk5 /Y=QT:uWC$&&/,͝;eʔÇE1BS"88u/]ƍkz*T*rc;KbĉۇL\pJmڴAll,-Zw"??aaa:ujyoc;kWW^y}ɓ 4`Ȏ$&&b͂X5Kk'9믿 "G֭3P @آmr\[ŋС碣mWWW\zիW7hwyGom0h ԭ[W={Ϲ#44]zuX  sNFbYYY۷o㭷ނE#"* xg-4i{wQ69u}Kv'mX}&")((?hҳ0`k)S*TĮ\Rs#RSS Z3""ϟ;fP*E4ЦMִG7o\޺u+BCC/ܹ;&LB-,ىb۶mX**2*J ;1LZ-⣏>25"#OӧO1OOO4j͛7G۶mѩS't}1l0L6 gΜA:֪UKоtRtYNOOG˖-dɒ"֭[c7xVf㏋Z͛5ޫW/xvٲe15kD߾}QIc@BB*8S.a~6̪h={q fUV "*AIII߬Y3;v}5\|!!!:yW~zbС5;CaԩhҤ |||,b )>-[cƌēi&?CNNAI&QƝDDDDDDDD6={"11Q `潒...OLP^^*'O 6o<d4r̟?_t|oAAN<󹂂~.!ه] ̙nހ糴Q߳:$_>_ b- -]8w ^h aQ$ b_ʴ;=bŊx7hZJs5kO?$k~Q^yQYf6m?==of'O_!++˨uL}m/_Us#"*)X%"""""""#GB&aҤI]XNf;p7sÞ掰ewߕ2-"SW^ի'y-BٲeM6zCժU{:p<;:nݺ1?# d2ʕ+ ZƒZR5k>|u0iWL"""""""" eddRJذa~BZU*Iqdd bj.:vJ'O'N<???,^Xüy|VXK߷n~;v4ӧOrʸsEyN\jйsg@XX/::U!"*X%"""""""鉸8c{=CI22ӂ. *`Ȑ!REDv,##C͵IFB .Y<|i>輒`,X ? Μ9Fi۷ok'&&"''н{w:^b-@&;/P lًaEsW B!eZDT@PW^HLLt׋vWױE;;ts ݻwӧc„ =Y,___hذ߾}Vɇ$bȊJ%BCCyk+#QVK?9gϞdCDŅJ޽{ѭ[7IsVF-bϞ=Zǟ:uJvttt%4oܨT"9FE1f̘4pvQx*苉k׬QIb-M>]/W_ۖ~ac?Avn1ZL&(#"*F }/^ԹZ,XoooAqQ|ׂXժUw9sj?r_4i$xyyisss{ｇʕ+k_d <==c.///YFKMMO?hѢxDDTR!RիWk7;|'I3N"*=֭)Shsvv+ /z8P^v-f͚,VZ۴iS˖-ݻIkĈ-[RpA\.I&EDTܱXKDDDDDDDd%QQQ7 _G^>WPP h7n/iQ11h kNܹ*Yfvo۷cP*~,X߿o۶mE>_R%-[V JRDdd$Ο?oR~E6m c۷OfPPʔDDDDDDDDV-Mk '˂XJ& 0@ʴiڴ(6aH x A,&&YYYX|h|zpe^Ŋuݺuˠ9Zl)r( 888Ehڴ)ƌcRE0axFYe}"Z""""""""+fuv^ ;Q,))I._<:u$eZDT 85jĔJ%UǏKOj\رk/" 䵺vנ9&O "}](X-6QIb-d2QQ⏋t_ףcpuA\rƎ+DTr|dxzz:֬Y#y>D}5FF8 ݻ 0iN,Xqnnn&ͯ˹s퀀DGG b[DD%DDDDDDDDVRmJHܼ@z0~x)"bnڵآE<;RW!ֶm[k=[nfU5^B5-6J¡C1wwwdddbݻwؚDDDDDDDDDDVyB!ߒQTXncd2f̘sGldggL&0dIze/_0V^=Z#""̞C{_$0m4Q1TΝy#hr4o"IȊoA2Fc(AM6eDD%k.[lA!N:Y_ÇkmkN "<<;vj]YYYZ6]qqqJ/~Qia$"""""""*n޼)hW-gL T*joz?(#"*)E}fΜQF!((VGGGt;vHuX@C$PSɓGe۷o/r̞={^=z.ro 6Xa8 IDATl]"Z""""""""+z7Evg-yzL2XܻKDAptt4⺛ԩZHMM5y^JUd6!!ݻw7y m֬Y^{qSLA||E&"*w7"""""""b؍6FY1uEIJ-4 .]OW!͚5?n8<.r?3BCCKKKĉ->QIb-UXQS*6DUCFV1&M("*ڵkSNӨ紝TP\{h۶(~U8pk+V0z^C) DDD $$D8S6"҆Z""""""""+>}(gLt/;bŊhҤDQI{Ϩgʕ+gl7h'HKK3j7o߿?݋D5 9@cgJk[nXJ{aCEꫯ$ʆJbɒ%>|8ƌm۶!%% .`<&LrtԩSG7|*'W^Ǻu댚8tʔ)prrDD%a d^^^ӧO cwu {[wԨQCl$x1  Yy%,X}]Kf38u*U#Gc/_ޠy:dGF߾}3vڈȑ# 4m{F=&QIDDDDDDDD|<=#F]ƣT1cQIw^[~}cʕ+'OVTbÆ Q1Ț[\͛7בK.a͚5,Z""""""""  z0#"* ͛5v&''#;;ۨg.^͛&m:u 55111Z 9y֬YwU7"""""""&-- 7njT@WcW-iA[\vmHnn޴iY?ZժUC|655XzY9r%%%}_~>}AԬY܈H,YuDG Fوt q)Jǎ+eJDTԮ][9s&~Wwݻw~޽2L}1y}S-X@k|E>;p@0d.DDDDDDDDVyfA;8ldlľ}DU[R%hBʔݻxDDsuǶRZ߲eK/_M|iqj&""iXKDDDDDDDda>˗-P()n U˻jȚ5>~xl߾=WY]N8aֲdɒ"Ǽ ""k,lʕB!6Hhݯ(ξ 0#"*mtT_z%ݱcn߾ptŠ{ܰm6( ֶ]j=zdt lQI}vAn)il pEAQpfF!ujDT#!!A[f {= ƌ30c @TT␓YP{TYk+$$Dt"?;ڵk':Eڿu44DDDDDDDDgDEE b=C(Fzf y'bÆ LԻun݊8899aذahڴӲÇc…ĉQrewښVZU泆#GAx?/,Yڵkk}ԣx 2i/( d,ShL-Jԋǘ1cg\pOɓjԬ]vZwĖ&޸$L8Q(999FDDVb-ٳXЦpsqQFBoŝX*kUuJQ,n<9rFIGW!W(hժT̟?ߨ9MZ"""""""" 9t2227Q6b:$h;99ӧX޽y&ݵÑ+q6PTooh_|ُǏgϞR3g,rb!""b-l۶M.UmPZFrC˖-KQnUVItKP*6>888`Ϟ=>j5?.ŵkD)Jчo>,;RYɓ'F(An~ y'fn"eZD{~Hƍصk(.0l0YٯPk>>G*:Enn.zexooo+gDDDb-D[ )h;;; AAAh֬)^C 99YLoܸq؉'Z󈊊Bzz:T*hvލ+W/~iӦ! @0|O"xIY+ɴu-飳4J+kL4k,^*Zf6虄a X L5+q&QR%[ ֭[cƌ{v" z-\t 6Ύ`g?ĺ4o*6_)¬233 /iIIIy־Ҳ㴠p: ""!%"""""""2)ST* SmÌ89UVՂW);J>LNʒ ^16ȄX%"""""""2R^^.\ uh9)IfMtHZ?@=$( """cH6l&el Ao:ǎ ggg 3F۶mE+W """c* {EURmOOO)!"--[;w뭷0h 3/{("""b+W~[(r- ͝D$|̟?6l9y2"³s쌥Krfe[ƍrss+ ""I hܿ111Fpp0QZ5xxx:Ewnܸ8$$$+VDZPV-Mܹs>DJJ QR%ԩSAAAV_ʕ+w>|tTP_>ʗ/oյJ%.^!;; DFPlYODDDDDD?Ɠ'O _Bj*e:D "" Zr9,XuJm[Xr־ɓ'͛gDDD$=(֦`ػw/:+T6m`٨S$9fggQF >}ka͚5شiΞ=s\1dL0tX7oƵktkѢ1cт899u떄َLJ믿޽{qi.W(6lfϞ@+diӦ:znĈX~Y_ro^j3UVņ jcĈ5 `ӦMWo߾ƍ+PcÆ RYkjXӧOGvvVwу B DDDDDDdaÆaƍmc'Z>@vn1GY^yyyhժ־Ub۶mKu=ҹ)Fo]+iYfԩS&jg~wY&K:y$k4k̨B-;7ko:u2P 4iÇ~7xèB-;v 6m#Fĉ*;ШQ#ř>ٷǏc۶mX -L&1111eCDǎ׾}R[4kLkvq d2ʔ)unݺx燨(ܼy'NGwEzШQ#擕#GZS\W\/ D.]жm[_8x _.R;J*Сٿtl޼YkX*(!YAǏKQ B\iW_aڴiJ¼yG(+"""uŚ5kٳg-+ 8qBtݻwLuq,[]V-|gٲe ݻwk-@Ŋk.qO<ܹsZΜ9x !VZDE+W4jYf@Y&v%(ԾiӦصk\\\'N`R0c QUVشiPL&CnݰqFd2A͛q'"""""O?}zWmq|꿏611QLH׉pjHOO8"""'ebz*F ggzA&M0|Q~H~9@.cݺu"_ b2 7nyGs-\f޽իW b...صkU5j`׮]s~ԩSطo ԲeKG}dq[ly]v>_~Xp V1sL&"""""!..k׮jT@ulqzONN("uFnҥZeʔ/`h֭8C"""˳i&O5k buY~{Ǐ֭[=s۷oڡCנk֬I& bذaAϯ_^!CТE oժ,%&&6UVb'O6ؗ]vq >FzڴiZ apOddཀ={"++K0fҠge&ukD+ʗ//geeڵk6>lRg_rr2CDGG#22GƯ*aDDDWhڴVqFӰaCο z}6_.k׮FߵkW b}XӱcDVV 7j}m_ڊКv%:.m۶:1f}kKɓ}ӧx`-3YZUH"iuMkɓ˓8eVʄHžX([t{| 8vԨQԩi),$$+W6z.m;E]}o3gj (( 6bcc,Vk[B9yl3gxQ1gL:qFJ$rʕ۟)Q&DׇYYYسg 2O3g4jQ 4}ѣX&MLСCXvmaRJXxɹW_}դ=k__&\.J%n޼i\DDDDDD$-[^3h!+NRlQs]}v_ұcG׊YY_.֦rrr1777GИ>DDDDDDoŊ{i`ƈ~aTto l gB?~,Q&DyΝ;%ƾ*V}}QqRlZ/:ts߿}]aK.5+?}ݳkj۠d2K R)Z_a-w٤$#""""""S*ŻjBRu̟<BbvO&WL&LsrrBPP־sI}׵02?YL-Μ9111?ycƌĖ.]jrCQYYY:ǫT*Rs#}eʔ(.)""""""_#..N67L ؾUX ho"vi'$$?8qIqZM2d<${~y4q(zٴi/_./^F5yd/h}Q <+j5233^_<==MZL2㨵R{^Xpss3YmDDDDDD]AA.\(+MBV"h;88 ??ӡ7zh]Vk*qF@g߭[0rH`u޽w{>E,*YPff$سbW={h', >#ك7˕++WcQQ0$##b9a/w\,ƪVϪjADDDDDTR,^Xt:vN¯KP RCDxZj!**Jw5DEEVZ6̶<==XF͹j*,Xĝ@DT $*Vߥܹ޽{#77W[.VXa\)))xw ˗7;OSz ]'""""""icłXyܭbYHKK2"C WXn؇sL&Cpp(ќ|lܹ3?~,WRQM8QIڰ0 >"]Prr2L/99YImmSRRLZW>m}mʘ7ݻwgJO>DtG:v$n/PDdW_}˗/zocPPPPbwms͛77|BiFп|rOd$""hbbY'ڛb?!!]tALL Ç#00Ш~Wlٲ5kX$WCxxxjڢZLRm|dddTdLKKR,r }}RKmy,o|1(2fVR YYYRCDO.W^X~OTO>Axx 2_gKtܹ;ј3gΘY)53;֭[?95j5_rr2Ǝ+͟?+W6;WCY`T*Et2V(pvv(>yUKz,/ 5.%r^7 FQqػ&$lQqBTnQā GV⬶u먫m@q[օ"*I~'Os]>99w(9m;r.\l /Mƍ .;_ǔeee&5"""U"y:u7neʔC}s۶m+*jKBsq Z0Tq^ނPuD+J~Z/+W._cDDDDDDiiiXj hg[(#)SVpb-QQbE;)J9#jڴi}ͱo>h5ӧO Q+ '&&sθv nooC~7..Np ~U{g޽[4FŊB'Nb,֩SG=nnn-5)jԨ۷oqpp(T RRRSS"bSQ-mm%νrHF m QQQxqNsEzz:4h+9L&͜97nyDDDP,o߾E.]p%AԪXQR[_[UQi4kJ%bccQf"_* W\cccQz"Je|ކmu ޒ,==J2_ۈbŊ*ܻw3@fgmm-[`Ϟ=>ʖ-ݻ)O㯿~WOZ,BDDTԊݣIIIATT nkkp4i@NݺuE}h,Uw*DJe8p fbbZji=ܻ[/+[VWiR333ȀRϲf̘l 2e u>ce˖6="""Qڵ+Ξ=+`h֬YH$'jsssAx֯g׮]<ڶm6mڈbDFFVsVZZZKsx!^*ZjZN=ؼymLDDDDDDEC.cŊ?[<$&&!"RGSٳc6%s@DDdhŦXnݺӂ5ۇ-Zd;wB.cǎ ѱcGQ\ۂkA~puux_͚5EСCZ! byӾ}{-GttVz;D} .gϞHOO[j??|-HpBQOTP(p TfggLG _ד'O0"""gϞEݺuJ*8r V?s?ޅΜ9믿.mW\Gߴi-[KKK۷/\+j~""""""* ˗ǖ a^Y+H,ޕ+WԶfYâE z+־@"} 5ŋ|rxzzj^z?>_md;;w.5+V **J1`DGGcڴiZ~2 :tocǎJ*:z*K899gff={b׮]ضm[ZMDDDDD1coQ|ʰ0 a*ьg󑜜 ѿ; > CTT2"""RMY҃p <} 3j֬ڵkn;wS~pvv \z<@\\gggxxxh,Bŋ#~طoDDT,Z"""""""2*SNŭ[ >B-jbM @LL |jã32^ . ޽{#,, H$(S (}X%"""""""qE,[L-ey(#35ѼWׯbŊ8O_i-[[[̛7& wڅݻ#&&+̚5 +WF`` =zdtb-\?XT2m ᙙ?Z"qZ*bŊ033\. ZgzFq=zt>zK,N|XE`ĈXb*U{*U)̈Cb-hϞ=C@@ (m]@Yj-WҥKU2 Cl߾=lmm ;v@HH`刌1`Qtl۶ uօNDDTX!"""""""* z聗/_ xk&ʪQ*56U%"pA +VD|| naaԌ۷oѱcG(/$$իWH$^ nnnI&{r>|FNsNX[[ """ǕDDDDDDDTbM4 .\*/Q Qɓp-[&ZUd )o)))Сk7`{c͚5pwwWpwwj\"""u8HXb faneF@Y? WRa̘1piv?8 ֩S'xyyt"h{9Ν;U۴i|ӤIТE d2QGa͚5ΕkD6lթz ʨxQ\ZRի BTTߏ/ޘ:u*N<­EѣG{PPK.y^Nm[6mxuŊ+ˋOz͘hBmBĉqر|cǢ_~x왨ʓ},Q3g bVsjUrCc;W֒:$=zر#qQns ǭ[1^jo߾-܌MRP&M'OkQvԙZGDD '""""""姟~p o!Xc;W֒:VǏcСM"̮ؼyTR駟DתI͚5+>˳Ott4vޭ:,Q)*r89^ʨȣ˕Nҥ t_JJ :>} 88؝mP(3 ;w`ԨQ3g>} Bx5ܹ# 0΢cIIICָqcks9j f͚v&NBdJDD/6NDDDDDDD%E>ݙ+4HJI\ Y%ufΜrTZ :̪p?=z{ر#:t/"44=z@fヶmb˖-o߹RSLQN; nʔ)>+W۷U\\\? 33ϟ?ŋu3}8iiiXl \ޭ Qɐ)\{ BNƍw^̞=u)'Oo fx9n6msN+333di=׍5BZT}:"55Ν_~Eamm˗k3vX$'' bXr fooWVZ055%" """""""ʏ9sׂ>]24kT*(J;%*AR)|}}[n!88]^^^pss+Lɓ'تUOP`̙S\]]5WVV IDAT?ݫW/CBBץJ<˗/dDDDYv-5jƢB X">38::jnSSS888 ~/9vʔ)8r䈨ĉP\_3DDDDDDDT%%%aŊXeѻ}seTrh%ν:Z/777,Zaii{3331eִiB߿,YDSJ]*ڒ0[PRDTT{K.8p/_Ɖ'9s۷oF .K8,Ԛ?/DDDyZ"""""""*[$%% bpUm>^*y2rĉcժUؾ}~VVVHMM͹~ /_I&+Uq!66?ľ}0}to^mP 4i"T*U~MWI.#88gΜkt:ŋqŜ2eիWSѽ{wx{{k#?MMM1{B%""҄Q 6b5*U@6ʨdytzʄ-Nr5"6v%\ͱl2jDLG&GڵUᅇcƌ*lllpA$$$G6LOĉh۶(feesp5$$$ ##666TT6l˗/(eee˨Q^~ ???߂>Xj5j$駢!333ϟiDD;VO6DDDDDDDT,}7BT*a Q&9UmT*etnܸq˗G|||6rfvjq{o>իEy&۷zjDEE!==@cXYY077Gff&p ̜9SW)055Ef{ŋѭ[79r$OVVO۷ K.ŸqpmѸW^ƎƍIDDDꔌ߄ryкQ]Uu1PF%SFf&Rc $k߾=6l(IRtM}6l٢ e׮]ۃquQ|̘1S/]r3f`Ȑ!8yd oƳgϰqFYGFFr9JފG˖-x̛7OsppEƙ3gV3fJ*xqMDD>kؙ8qb <0͐^̨dz<}вeKx{{c;v,,X0kޣGbrN^rO4oѢORTعsIE֭[nݺ=|Nr5l޼YP,ꫯpIQѣG mlٲK=2+H/@l"""""֭ۗsm"bY(_FsaĶm92LTp:}49Bz*'ּysDFFAzL[[[TT G-)r*H$ذa<<< bܽ{ vϟ?Wz,X)Sk|\*UV:99aݐd;%%hկT*E=0sL4jH˭[鉴4.]T\Gػw fbbSe̘18˗/P$Q[=Zb\ "&&kڲLkJCb\*^pAibҥؼysεT*͛7VZ!##P㛚J*VΝ;'Z%jw>3j]D .@*E_pU5kٳg׷yO<GuVx{{yB0ƍܒ s9OOv<pwwǴiZdflZ"""""""2z&Lj ÀliYkUEw+tmС011ɹV(7o4i[npwwcVlfeeݻ8t/^XQ(mѸqc4lի5k}?@tt+'O,y>c̙38v foo|jYrpEԫWOs|g9]\\O?iܾ}O󽓈t$.DDDDDDDDE/66ԮwʨεF--DPLxzz̙39;wb͚5h߾=ڷosn޼۷o;wcdggk=A:iӦ9奓e---1d,]4'ݻwW^ZrJ2 ;vPy1qrrBTT# 9rK.=BBBXr%n݊yaĈJ>P%("""""""2Z?`UD"A ?WIrJ!C߾}5kֈd2wޘ2e ֮]"&&III8z(fΜΝ;=T'%%EB*(~YO[[[Al۶mjzw ?X@@ڵkWJ ۷!!!*7oތ+Wŋ*۝#>>}7nٷDDDq"""""""2ϟ ζFV5edU"~ZPL>0^^^pqq֭[h߾=p<ҥKѪU+eee!"" i XYYb&L;w =vneʔg}&ݾ}ojk+++,^4k^BϞ=pB|79JZ:^'45kDjj*ԩ{{nlڴ ~~~믱~z,Y*Tزe \]]1w\smAODD DDDDDDDdPgΜXjEti@;R(SF˽JpHLLgZ0`,-- l˗mvZٳʸBx:tN:%zEBϟ b9֘XZZƍ*4i~'C">>ЦM?W۷1cjժp""2N,A}UHR3?ȳTuu㺂TQQB6•īWt2~ʕ࠲-NMׯ())HVQ~t۷/_fff uaٱcGgƢE7 ::ZT$bСʟ9BCCѱcGG׮]{1j(xxx۷oB(WKDD%!"""""""Y~=\"y{5DU  $Z.NٳضmSH@@:##?ƷPֿRgV~BϑG}5ۘ~TwtQ6~:?~x|w9m*U‘#Ga6aÆҥK91,\ׯG͚5UwacС>|8֭[ׯwѼP""*QX%"""""""HKK贲0Ƿ#(#T  b˗/ǣG%66QQQ5kMuPZujS9r}/u9J*%9sFgyt+VÇ#,,L-rrr2F =<<bڴi*ĦMi&QѣGqB"""*X%""""""":u\ŀaokmW@묬,QQ!-- G˗/KMMEǎѧO3}9C/~ BCCu2vttʸN_ӧO{"VT ={{+U>1}t$''$Aǎq)+WNW(ػw/z 6l7-Hзo_ڵ }= IXXNs'"Z"""""""һ{aڵsY@.-Y=6Uϟ?رcyb!dddɓ'E6m͛_̙3ѣG4m{Ƹqpyf_wҥK b **wup~ӋdU8uTx6vׯ .zꢶ7o`h׮uQFӰ´iӰexzzܒ:?('"Z"""""""һѣG###C2@ㆢ\τQݻؾ}>S+J%+4mZԮy&V\0<}#={_|.\sƨB@LLQl,,jg Tcbbt2EUV!@*ޠ DDD9Wvv(v-!T.]}B*ϟŋ/1rHJ*Xz5֭[':\T\Æ 1qDt ,Pm-t>ؾ};>>-ZT$Q Q> ŋs0QlYeDDDDDD{ kݻwsb&2[TPNÝ+K=+U$uȑ5j>S+гgOw `ѢEغukεT*ŃDD\\*VmԨQZ*!JQ^=xLtU5XYY!))I ̂JJJBժU+С 77… m6A_\>D ׯܹs_.]0w\ӧOǁrSX%*&ͅHEaʔ)GneIJMCϯ~7I`KRJaݢm?t W\]p<{ r\o?~ۜsG^9WØ1czBBlقs͛7hݺ5m&ڎӧ ȑ#۷8#Pvm~;v_8pNsP)  R.H0eOe{LL K.QFN([=Zb\rKKK`M)< VsM>ڭ[7̚5K sѣG+իW9~~ʶ(DFFb„ ʭM6Xp!LMM tM8؞={н{w͑gγ_zz:ϟ7&&UVw3|صkڳPga{f̘}Çv̞=[YUܹsh֬YKDd,b-Q1alo.DDDDDD}WX| 6mX? @}փ1$%oŭ /TVM [l?X{7mڄ!CݻضmΝ;SNI&|_|Wds!!!ӧ^{ň@JJJG!Sff&ի;w͑#>>^uz6mpq}JDT[=Zb\׷o_s~m!z.x3 A3E|2FO2eиqcԮ]&MBʕ 9R͟:*0+z? Y+-[/^P wL&WzT*驶u8q3Rmƌ;w fmm j,4š5kDN0K.5H>Xr%=tԪU rO WWעH OOOij`j*Uϩx"<۷o#-- o޼Rc Rh"AϟB-}PjժZjN1%*&I"""""0t]վ9f|Qo-jiiT~Qd2RRR4ddd@"+WF-0tPh˗۷/EmIIIsoߢt9-66:t{Dmpqqѣظq# ˜:R˖-C-1b`ƍŋZEDDDSX%*&ͅ>lJÍ7rbfطr&Jk e]ظpεD"G0p@:u@ca֬Yz䯾Jp2",,,_X_~ ֫W/ܹSg9jժ!!!Aj|kkk:tZ׿2 gϞEӦM `|/ {Wj]sj] U*X~=5kV11a,]T)EzaϞ=y+ѵkWQwekگma ӧOku'|"_~,3kH0k,A2(`q3qNlǎtd2KU*ؼy3r9&N˔ e֭Xh fbbUVݻw#<<\mQJxyy:WM ]vaZ[B4nCqq [UuV\|KFݺuѹsg ;v 7olmmrJs#"""PXKDDDDDDD:5g b:@Q~umDPv=zEaزe ֭['Nh=-[Ç29wP hݺuƸtlt .\@ժU fyĤIжm[5jkN49۷o… 1|p >O>Ř1cc_ѱJ R_~ݻ o2dH~ ""ZbX'""""۷oQrey&'VV̄L&3`foSn4劜7< O*@#&D{Ig9k͛7ر(޴iS={6_ߧϞ=C 1XZZΝ;xjrVcشiryN|ҤI*ӹ-\SNjN;;;$&&m !!AgX͛7Ǯ]DySXԩSZbM Q U+ /^ ޭper\s)/1g'hհRDppp_FR߳gOO{)9::b͚5(_ ޽{kh4k 7nDvv6>}͛7޽{*sj*:t?ugϞ= C DDD?,N<} Z<홝[ }5<{LԷ`jbrh^=6o@G{=Uw[lY5E2eʠtXb@ojp!/_1\]]}ѽ{wXN:z*^|lϟm۶*Z"""""""ґɓ'#--M  @QA*V*OTm^6lK?xD1\={.P(4i(naah5hϞ=!?Ѷ,g6KRٳ/^cǰ|r;>>>[.<<<0a88892&CKHH@f,>}-[č7Qab>n*կQ5PFTP?[Sm,ѠVU\@_r%uSSӂ5R={V׬YZߣX-s &&&ZuڵCvT/\v[deeqdhŋhժCEQDDDDDDDTh_5233s%&9[V~*vm8O.%&&bС>A-=zBv 333| SLɓ'qLիWcZQsΘ8q"'6ݻw7Pv^^^0779sKs5·=11>>>Xd  >S&""*X%""""""BDXX ֬^-4rn0LM! ?2RuJh=vn#߹sÖ-[T*  FR6c ԭdL:Uıc1Te˖>z܊sQSN8p뇆  QtWhSɡP(ϋ-0ed4Pv%|w+k'藕@TP/KDDDDDDDDDDT(ӧO)J193’ɄEDD,1O|P?333?W^8uTr$66* -[,J$,^cǎ-ըQuNՕ;b۶m|2Ξ=]}Bl=L&UnMk b]1{K*;cX`AKDDTRXKDDDDDDDve֯mfegeD uY& }Dɓ'0aWjwQY1NՑH$;w.˗s}ԩS=zӧlU2ކ  H `kk˗4>Hc0EzGgGtS>}t3ɅΑ$b l„ UR0#*,ByO>\i\Oq[7eGFmwbĈ>|8ݻ;r{m/_Ϊkgφh L~ӦM+?8}4ծݻw/Zju… oooک2elp}Zjbccն_2Nϔ%8 FZx.B5kPreY ErPB > """*F$J>~GT,xʕQlYeDDDDDDّ#GЩS'AcX:gtᤤŧ4ٵt:UcYXnlw\T~D"A֭1uT˚(Jĉu̙3Q_0֫WpUDGGe˖hҤI/zj† 0pBMe˖a>ptt,ӧO3gDNkkk}>g$my olT*Ugϊwqq be?cXKTLۛ   535| e ֋oq f8L}IK]+7)sss <#GicժUxÆ q juMTjbb 6`С:Ίڛ7o`ggg?___I)))hРhE37C22ٿw۶m۷ȉȸ[= BBBDgvoBHмjZF&^*<˕o%ߢyZjedd 88z޽{5_~Eoڴ)N:Uڸ8ܹ ʕ;;;#F  <ŋ?bϞ=EúuTn=P>j] `fi!p7PUU(]45j ܼyg7QƕDń= BDDDDDKPFɉ0ǁU`k]ʀ.܊ySjhgt{u!tKY1bׯGGGoߎ 65©S`ee|'"**ػ@(ߙ-UR!$-J NPڴyuJ괋Jus}B Ѣٳa~3c1}! c5j(=BBBСCdgg;OSS7oFz၈ƸoWWr"B'O˗aff@EE۷oǤI$ۿ?F)<d̚5 puuūWJm+uZ>"űmG95j vvvz*||| ??_|AZZ/K.ر#kP(Dvvv-iii8x """H())cǎ2dttt"!IuP*B!BM6a錘K52"kya7w"0gg;|QVVP(D~~>kLMM n\m۶aƌ޽{W999hҤ >~ׯx<,,,зov'OfrWTTѣGTWFmɒ%X|yᵃN:kf͒J-[eo?hJ*y 7 Ra|p͛7hҤ X[n 03 ׍7FddLGdǏhݺ5Ď+**~~~hժU%gG!S)T%no.B!I 18|%UpLZעEov ٴ~!WlA|rߣuָ~:d{۶m2eJ-XǏ{___̝;311A@@]Snj:nݺs%eӧO#++ f}v튫W2b... ,C\\\sD5J6n-$~mTG&Ρ8\Wv!i#\ A‡2AM6Dp8?řǏG "<<<@AAAƙBHTzq B!B!Ŋ+ZZ*Voc6mydC#8|*_1SNwL WbԩSbѵkشiSZke˖-d333aaa1c %g 񲳳g899A[[ 3|>lll wh>UPB-| 7oެpjjj+qҟ8a52/ʤڪ6_ 8v? 1l4jztDEEU3sE"ѬYRj6󃃃D" 3$Rk !B!R*ظq##V6*,FF36m7^KmP&͊;V>ӧʕ+34~b_xŋ *f̘CCCիaoo^^^Xx؟,0w3iӦ̹(++c˖-=z4c?~DӦMl2L:hZ]xÇ/UӧvZ'*mƣ[={Ɋh",YoѾ9WHW1oKpt۱J#99Ǐo茡޷1!&w#Fcޮ]о}{4_B!_љTխ:!B!zEƍSkWS,:t۷o办6Eo!uEwwwl߾].}aڴiY=zŎ}< i1%֝tk!c"B^AQI&V87>$"m >ٯ_?PQQK* +++DGGK zmp4n.v=go~FLQQ5j5"OխBZBB!B^FC1b[MBGrʈH[Ɨ/W޿'O D{ς ,]'o EŪqVrll,:w,qG[pp9s~:::8w\z*Ν[5jBΝT]il߾SNP(D[ͮ]0~xzankv}6ħ{EŴ|L0ٙم &rIXkN1jT_|z +Ʈ,2}jРbcc%#We~xx#cǎɩ¹B4Uzʯ.B!B!IDD?F؈ E7sNF\EY -TVjpםq*4ѣGpqq57o]]]4i۶mCN$}!Ȩi$%%!88>|||_\~&>>>-гgOD"Ɯ-`ypnK]af{CFݱ_GF`Ƅ;}t[q:_kX.zMxx8vUr %~xQ 36LGc3pZpB!CZB!B!k錢 qL/ Xc+x"#޶ES(*(TJ~йu ԯ͈- ˛&p]<?~DTT&NZjI\n\|Ҳjذ!p-L2ZZZ^O>J>U٫Wн{wP0 waQPU' FL6zA]K]3y I!B!̙3EE$nj4D"=gC(0ӭ[7l޼Xx1cL6[F뼼JAΟ?ϸxhݺLЩS'FK悂ٳg8ra f.\ a^-Zy!//***ؿ?FŚ#iY{*)+a X9nua,//'N̙3;!.!B!B:v/aq8hjj˖-ٳg1k,Vk܂ݻm۶eU pŷ&5<W㗹PMס]׹ 0RRRЬY3FoE11ԩSe\W% IDATB-EǕ-[cĞ=+\B!eGZB!B!D"̟?SQV1gv o ֭[;i322˘SK&v\.fLLL*=ib\KY9x<qag熄 /_CvQ0S_wbe0lnXω%k+E@ݻw} o:::05e Na_MJ_uаRsr]2߫Y{kʕ!QB!B!,dwI&t,޸9C"""q^={Ƙ7uJ8QV /[w1 vލ#FaV ѯ_?xBNIW9@> CYEYz2Q*2 ߢE wu ָv+"F0uuuY??+6Zűv`^=BtPB!B! yyyXl# }|@;((2@>|~׌kM j#lfacǎ۷o"mk###9er1w\x{{7ϟeT>}ĸ2_kƘlk. 5RlݕyNGY{Wp)`V9DwS8ɓhԨ+mHW1gЎպٙq=!b-!B!BVZXFltPYFK7Ɠ1333bܸqbjii12pc\}\Ë79]i3QPPPmdʠ<7%%ENH~xLL p9e&Ewf犝.N 7ʕnPƵ@ uiӦI3l*X BV{4HOg[XX׬]2?CCKƖM[na֬YeOBH*=B!B222qFF&\SF22t#^1ؼy3x< 4`mÊ53îӡR &_Ȉ>-ZiӦHNN.c65l=z4ktM6'F-̪h)8_ 1D~ndA]hhk #50VU}q._ݻw#99_|8oҚIWNJY݃#RSS cĉdV{:,]!^~߆ ```ٳgBaD!B!b:7P 嗒iެB-ǃ/m&#8pDDsRGws71uuulذAϒVZ1߼y#LJVn]]VDTTƍ333DDDY]u=(n:;?'~P >|(q/_ЫW/#((OիWu[ں/'ș"|3|F{ٲe000%e_ z^* ,uOB!lT%B!B !!vb֯])#RI鰛_q%%%ڵKl KPsVVVpvv5ڷ/>-CNw6+>g蔽gUաCu||33}χ{A$I-OigDŽB!BBBMðt 8rȮ|֯_ڵmb3R2c9eݛoA)SvٲeqFsZڴ#+aܰ"i\. ӧcY<L\g.F- B~6tf-!B!B[?rĎ}394nhւ^qZ:x9O>Ett4  Bx<|#''k˅@ @nn.rss2UhѾg|s\ͳ!6*B!B!gf_?n!呝+@ 'VX ={Ppq\R*ĎP{=ӇQ,*##w;wn:4n:t(˝E3r޵k]&mU5TG~9RHY)R7! 2!B! ɓ'-i9eDcSH`[D0`f;k,v5J|&AfO?n*FGG޾B8_+ФI 6 [ntdp֭R(ubhժ#www9eU+W 00PlV}:5ş[lt>>>~.A!]a@cƌѣG"&M¤IZUPS;&jFMoҹ"%q"!TDԩSKHH`B!B!foo˗/^s^ 1+Ri=m|㿑^zj󛝝u"##++b4kQ0 71HȂ*_㓐+ \u=˝wq"?O3{쑸^$k֬aW;ľd)u011S{ѥKEkײηrrr0b|UEEEAGGGyyy>Ƞi#x 1.-#MG>>>)ۅzX{sBKXȊϝ;;qM5 V۾SeUVaѢE!Tz k "ۛ !B!Ю];;"HTɰc+q}Rl="Ruuu1}t,X\:z(?| <`}a`` gɚ# g̘?S 22Ʃ=z1#_Dj9.^͛7cٲeǻ, ɏָۺe?\kyadF>wBB~5խs B!B;vQmPYXlm۶3̙*Z6kTZH}GY'OҲBG.Pk6N6>n-L_W7bˍb:n#R… R%0`pAYYP;}TS唉xyyy##fTNٔLCKs`ll3gΈRVDK>XAInK]ԵJjuSĮ[@QYC1;6`СwI R!B!BOI$a…_Y s SF^Ċ~gΜ_oƎ;s{s,jG9cĬѲeb%&&O>;>z(8q(S.Z`7 "xz)B_ub޽M6ŋ⨨UVw^a,$$DjZj1$̔?"++;_Džas#Z0|U2djhOo?+ϟ?8_&Laݪqk&Ʊ}%eTGcۢ_ykױ;uK 0///]VBHu$B!B!޽`{Ԭ!HyKěϲlѢ ??...c#y3b8x`B!'Xu%e.ԊWeKY<-Á}|mN6&b4ҥK+o,<{ LOOS&%%%bo×u69d&^=YKWcao{:äIp|PUx r;xı &iӦ 2v޾L+J(b_ 5k`hhXڱcnjvm-=eh[u1t`װg^\ b׷i9ͪ2;]uAZsPנMF-CsxxY~-mhW|H{md#!TwT%B!B~1aF U|?qmggիWCCCpWmhh(6mĘ)*-o=FSN-v?ψ)0w\jxp xtY<@LL N8:sY )Eegg۷hҤ ذa4hoo ҐRPTRD b߱u6{kiCt 敜-.6][MNX={` V.|N~BAQ>di?wIt !Z; BswԩÈ%$$ZB!BHE@__TGS,3#euiܗofRx-ЪU+<}1o1pضRqk-HT˗/o7o1)Lm[\PnXcZ•+W*;wdlmm MͪUL&//C Ǐ cHNNn߾ [_7eeeyR͛7 6ƏXTk`|4P PPP 93t8n#c @{!SݓB!B,[u^APr4ZCC#*v0Bm@0 [n-j" bjat/j;lgM5q*4$$~:%pY̝;{@ (ݺu 011AѨQ#ԬYJJJ077\ uB- >_P BQY]|CVV`-ZZp[Sj?WGiV--,5;Eĵ PK!@B!BH5& ѽ{w\zUx::ѫS&E*,mDez >Xv-c*[ +;[l G y D8q"!oAZEEE 666h߾=TTTʕ$ӧY9ر&L@^^XkkժD舳g;R4jHj%:DO?ۙm۶pvvƸq wBHenYK!B!TSׯ/P ?Y(ٳ *Զ6m\l,vQrسgOZܹ3+ߪd{N m-,,pt޽rعs'Ə1c 22L(A__-[DѱcG_ H'O <<>}BVVB!TUU ]]]Mbϟ?G^^t"vN|||^OqK,ݐ.T%D .ٛgat˱={bƍ011CfRZBIB!B|]x=z(vðgpǏ )}=7ohczNJ5=+]nnn/v%K|rV|T8H-GY-Ev]i.,, … +B~\.0555:uꄸ8Ks_>XEtQ"HlbU UsgP;6gTIHu%`\+WF+VSFT)T%no.B!T(*JȆH˛0lZ cx ĉ0} WmpFb~cL<"rXs| $_iz| Me׮]liHKK˗/q\v HHHɳm۶קO:>ku+uE Jʒ!LS;{ 5su5u1+BSNB!B!o8Ϲ*!"-?%bmB-x{{3 pMuFӧsѧT `[=<<<ܹ0 ++ ?Frr2Xʊq\?^꯭7|!~mJ'bW|=MKǏpl$lmmӧ2Ӳ,B~# G:?"-T%B!BsKf+Z6ЫSKNgE>SSSq-gsBPiP5GY󲳳ѻwoYZ:2QV;7qbNJ<o:%ssscD8eh;#ݻ1|p磦*wp$^HTW z"H77713B>XK!B!T3Xp!#WRĜQ)IƗ/Xcxf͚Ul;Zb\Gپkqq}FEPTTdЦM={V}0Afy…w2:} BƖjɓ'qIFLX].nwtqm+vڬXBLĦ9b*4'tBItߑfΜ)ԩS @hA!B!BH5/;vAMrʈd?ObΝ;044,}͛>ψ]yva|.]!8hbvSCA=ЧOYf铼"8?"7SyCBBOB!B$"7FtttaL][+KLAޮXMM 'N@׮]|۷cʔ)Dpք>ڶh fj 6!$֗˗矊D" /^d)0bpƎ̪&]<|,CCCxyyʕ+/_@~V-G<cƌM6m/F~x7-u-=g#=&jԨ .]v|]p={,q)0tF") )HM:1Qf جUe7!UOn/CEA[[>DFX9O>ečpRH"խ !B!"=6mbj`C*VaA0 *ĉ///sD|JOɟqS'5Ա &u*BTTL,/FrZnF = caͅZ=z4PYgUg2ʓ"!ݦ}:(+˶٣GL6돲Ҳ!CwUb!%qѴUSL󝊚ukJ=wBvhף-PKMM#@oC]CzC-A|['G.n߾=ܑ: Ɔhf'qoQ öm0~ܹs˘q1ֽdRX>v%B*홦x,DDDԴ~!$$16{LXuBpL\/,Ubi۷/liFjBjk^{<+!?A ,Ƨwv8uTgbx<,X˗/{1![=e!B!jӓQݩP[&0;v(￸sVXC%KJ?nƄB!='.[ݱcP~zV`UP \B:ے'VPfￗzK```*ԶlVab ۷oB-8qG̙3ann/O"DT45B~:J|%,U .3g`ɒ%rʪtPbnժUի233+!;BȯB!BH5u1buu0N> R HJe(];7o`ԨQի'vNÇ#$$?F\\< & JJJu<,y 6 /cLX~,1'eeeAAA?>kIcdRVyy-_xP"4u41ol BEWΐ}/ J>ǘƇ 5)E켱cǖ YY߿?жm[ݓ_U:txDNN|>Ϙ&mǫ׌yMZ5_۞.>q8c2y;wPSSTTTvk׮… %Ω 6܈{3bqF:y_gPT)04p8pwwǖ-[0BVOb-!UDu{s!B!Tcǎ "[_ ')#RZɟݓ|2v*qVX!vG$a+vǠ6mwwwXff&<<<~3ƚZ6O((JީYUxE=YqNXֹ8'N'ˀN|A>&LD3^߾} CCò'ٳgx>}/_E0mkʈ]8x N!sZwm\W£ˏ;ccc|BۼyXtx ԽyX4`1b2wӮ^~ȫCCC$%%a5ׄ? džҥ N> UUU1+ !SB!BOnѢEkU2';)R:Z5PC۷o6Ϟ=P>\.֭Cppp¢?H,Զi{aja?E^65eD8j@AIw]StXi?={D͚5]bڴiؽ{7=zTB-DsSHjl fZ^QǠ.6؈/_ 9993f ^x+WV8gBȯB!BO@\kYR=t|!CEk] x<\t ;v'3ѻwo1zZ /@뮖k'gdgeƨQJ}7n0<./DCFjm>Y)OU U< 7UAJc;FΝ1e_^n߳Ĕi~:%O"bƖM$JxÆ 1o޼J'44!1JZMKx^5nk \HUV!<<"c! cb?fҦT"i(T+!J-}>v]l/ DP)H=L4M29$53}!B!"4߻gB$Mc;m@AkԨd8;;ˤsNѣG63wI,l5SZeS &^`޼y"g7nzMm`djuj^z}ʫ%V_ 'Ƭ|[666Ѳ%V~>)C]_yqI(|]8kaх0eo1ۋ}!PB!BOٳѣG 7nd1v2) ƍ;# ҥ ƍ/^@Gl:FFF̙ 4v*&­x< 0Mqqqx׊c<.gPTa"4txNNN§Od0C6 4lPJq6'. ?oV{eZ_ϟg<5)u51wo ytA p2!9XK!B!eee,XqVq}=@&L `S&+xܲeK;v k׮2{ `ٰ tuuQvm@__ۑ,<ܹKKK,\o޼a+(0x |?]U37reݾ}٢]y&C{<mXm|>NV^-JbitƏuwG9̊i@'py|>ciqF\b9b w, b?N3QB!B3qqqصk#feZ-e4#" V+pfgg-Btt47oٳgHMMŮ]0dT^z&Μ9WWWmĬ3Yh;)Z x+###l۶k3 %6RP r 4X홙8q"X97mmm^Ϟ=۷o_ӓWs2%!:4uǗKp᯿b5hi+|]G `?՘7Pcر#$RPB!B􄅅 mvѬؚugǞ={`kk[;v쀏UƚըPG]}]KwE,ʂt___ RwݛZey5k1cư~B͚5555ݻWhĸDqBtZ3^Z.yGQdS&RGԐ;;;`©NJ}OPm1?|uƹ4u&TUI!X ![ǣyشimLB!B6lx\L]"t52:t_|il^^ڴi/_2\m{ԾpZh=z_'N3fVhXT_'o{*Җ/H/mW#KuMBڇ}({2pJJJb#!]skׯ_/ϩ&I=R%L >ceggD^^B/T%B!B@@@}Ɗ F2ZXrHcÊC `w\4K/g,fիWڭ쭰hhk4/R4mmٳŶx<0bo+˅`: ?N:w-mW+)WL44aVόۺu+.ռNNNݻ7+k#ռꁙf6w@ ݻѢE DGG|!k !B! .::GaĪhNJ n/YiX>q+&ٳظq#+݋׾w[X2 |;v쀵5N:sv͢մr1y$YYZصkJcaaxZ>S`r?TQe?|>8sssرC6M᭐UXKHQtwf̈yˆJ3HssJLDfZ&|F .͛7xm};Тs ϟ?cpvvF|||O^Y[EB;!*ЯLjmݺ]ժU{rWjj"|cX'p-4k _~-B*YO@ $$$۷HHHannsssA]]]j?ʠN IDATaaa+rdzgϐdVVV1xT^055zǏ#>>̄!LLL`kkjդ{TAAVCA~߿ӦM+:ǟ?|ϗ&Xvr)݆a7 W^Ez0yd̝;ܟ[bm^DŽx< 5+G)Spq 5lؐ[j((oIb쁨e] @yqqqåK ! QMKKٳg-[Yw +)) OFXX"##Knnn;w.jԨQ܅b ŝ;wgkk}b̘1-\gffbÆ سg"5má(?~Dpp0ۇ7orѦM 8Xnx֯_ӧOB())SN2d<==%B!B:u*+6ch+!yde3b-l֬Y##1]"u8 9ܜ'%X7[pp/ ,nQ +_vbm3k|ܻxNKfDIY =`Ӭ9ͷobhڴi]W喛y6M[GDD 77JR!:uɓ'ғӱg^ 5@*9Kfja}<_͛7hܸ1Ξ= GGG̏"[@ (`ڵ í[žÀ0|֋|֭CXX>|(v^R ƌ3gBG쇓?~)Uv؁֭[9dd$ wމ<_~:t~~~%;vf͚e-aL:UwG"6?ח !B!'''***`6<1VQQϟ?GڵOHH@ݺugu`եڮ85)E:CMSM'AyfdkO~=vqqAdddiڴ)n߾͈laJn嬠G3i___CUUUR cum6oGϛm6#vtUj9? 0{J-.-s_aуn#$"=P>~3fƍ*?_m߾ؽ{㢣`RjǏXjj׮ .:p9899U~nꊝ;w)-[жm[ 8::W^bjhРvA/V>F!))L !B!([laŌ%s(x}LF*P c7Oϕ3CpNrtMVM$Kf,--@ GXq 8TD<=zK`Ո}4X~~>osss9rDsaM-h3 jN]]]ݛ!]9czTi0fggW^6lc gBHWp8B1l0^ DϞ=add9Lׇ3]vaƌ񁶶6kLff&ߗ*ǏѳgO?|MLL0x`l۶ Ǐ Lj#8c9,,,0zhl۶ #G077ݻw/vl۶ .&M֭[1`J?00PhqƘ={66oތ={ ]A;xxx!B!F`` +ީ fCP <Ǐ/v̽{-Z{3̏HO`ɈթSG7Ǐm'ʐ! Ä~>~ݻcǎbccY_㖞ͥƮ+WpU1c+2Y߬>̃!3g@[^U%m?}}}5 Æ +,߿cѢEXr%ő߿--"Ftez\fMחlwiii5kBBBXp5p֭o28}4oУGܺu˗/+rdXZZ"++ӧann t^1077ӧOz111gѢE :t1wޅRSSVZʕ+"~hvwwgŻwM6ϟG׮]Ygv+am>!B!d7nu`bbӧ3^O0k֬aPoDvo=#<⠮^7nUV.,ի~Att4*̞={ˈ#**JɫVɓ:ٳ%-JeB!Rb;۷oؚdW"oG1cƔx_tt4 m D|IѭJz>~1ĮWaߊ X튊6l/_5lee/_zʻ%x?Yvf6Ʒ_1UUUXJJU?X@@HZVΌӧOqڵR;wHO8jbΟ?B!Bl~~~Ŷ~oimV kB%V^]Xa@eg}܈!kӀN^E3}BTթjƍQfM,\ySzI4m,o߾ӓu䡤۳jšvlEZj0 ~ T27nx,$IQѠAFرcXs_b,!,,p8?997odйxgO!BEFFܗ gCdMG}SRRž={X[ZjӃ?|> SϛHQ-Cp%**PPޘy 3 &bԵJMհM76c4̙3 -U_>w4.+x~aЂE^_%aÆE=ZF4rij#9"BIÇ+t,\pp8ҥX4hZ~q/_ӧ7ojjjbTv!<<ŗ/_FFF#fff[[[ z +Bv UVBH7?k !B!%y5޿/rZ3" + G.#>yd֍/_?gcҥPTTd8}#Zs'WNM =k FhwCr{3> g Z5@#Fh֩ x~(B$I_eddp8pppzV]]u)q-VJLL?K¥p8喟#66T~@ :B!?K֭E֩꘲+B\fĸ\.BBB`lluxEי8q"FFFB|s1g tl<B ÁkW]Fy*sg9@pָޥDJȟ{7Fx2bSNŁJuGJ:eTUttB8e=ܹy !D>uٳe5jԀsbMX1v6̟8-rrr1UUU())I,?k !B!ٺu+F].eϤH8{>V`͛ݻ#''HOO/Z?UTT͛7QFqIt$22KcJeb7o.z*j!z+| 4b_oƌBjʘi2 L J5OYSPT j3<==1yd9%lL>߿s'&& =[κ-mRGGGp$Z,-.zz: X=i׾!B!xX~]~wm_PTP(Yi{:>D>|8̙=z4޽{hWSQkc;~VZ Ç ,O!1>}[ w߳st_b `(bɁbbbĺ֫WX1.A]vY 9ÂBIy_AAV\f͚!))IF#kOFL__R燬,FCiׯ_Y }|>,+箩 #wtB!?ϭ[Pn]`̘1ޗ9POUUUkDrr0i6c?:;w2u^ KVL[[s˗/qesy!6!>M37#5yСCacc[{1X8h1d#\.&OY}3F<##nnnxVyk6Xx(5ثܹ;;;m۶m=zb{/Hd \>]b„ t3 Tb;w0|pV|Rĉ dgϞ ggg!#ePX[·0RYKB!"{˗/gYfvm[6}8}.#CQQׯ_Ѫ1Zu-6=vgȥ`m+-[@YYYvAh#ZN$VUEd_x;Txܶm+vJYTRV¸Enf4o>} Mifff%_XUիWƏ?qlذAЯ_?VUhW\c}|YsWB!k׮]E>tH9%AC[b۶mݻwѱcGPFdfիW/ca;hihIH ;v,<==Eag>?""ژ}TԘ7Rܺu SL)q:k|\?y]H\{ `hZvmԯ_2!DTrS}=ڵkǺ f͚8w\HBtt4Yg6lG`Ɗ}YHOa )Wŭs|T!B!7oވ5?\&چlg Æ CΝ nFz2g,yQFƬYXmFfF}*ȕzMg +W3g,-3-CkBgbi'BAI߸q#޽{WQFA[[ޥ2n^\Xm@pp fFHJS}VE'doߞuױ.\~ڷo/_0w.  9ТW^FF JQ\[yKe_\jjjC!BObƍbqgN-4#R޶ǃqկJh7ֹ㠡=]=E,YWWWѷwMMMI,C1b߿”Ю];F,3-KSp1v"cܸq"N&Q DpuuEll,#n``/BjЦM$''3▖tJu]I {U<UTX~IK|i"k !B!)<IJJQvm̜9 Z5!aL_ =@h[DDf̘![dmb]"n.0}mq*~eBH^Z~=ŋEw+v-ϯaieBfIJBW-~nnnx #J-wBBڴi3⦦Dt}aEfIK+l~EEE+)CRo}S?!B!pwwXҌHys ⓘ;tvpĉ1:hߴ!|>~c.dƍ?~95 5tsƂAPZڧD\0ž޽{nC>oe*5Bzmc[=ڵkXo߄0}T8wmj{q̌RX,fffCx#p4lX]qeWWW.5j@dd$j֬Y66 +墮_ rrB!B*;wNq?Ja6}G8x g텄ƵX|j~C__~~~ڗ`r?ބ CAQ,OxRxRh&Kuɓ'CA*S_{TBJ֠,g{b/((`?V]ZJmE#c\w/++ }E@@|fH*`6++ ;vݻwq ;wR˝6m׌xZjI$OzX3gΔZOfJ*VGT pyFLAAVVVb_B!R9?~To'YX22W:.32'Ƴ7 jkݺ5bbb .\_{L[",ۋoˈxEYx7n;3m޼5_Ӡkv"6w\\rW\;2225s+0b0 =R|D쿈-[^3p>4aJ Šx$|RSM֮[̈[p!ի//_nb>pl+E`®_V5Xmׯ_#>}$B*L6;;ygΠYfR˝ WWW]8t,--%O]]m۶e-X>y񌘭meKKKlDDD?""߿3h߾=TUCM6b^~ z8\.[ڵkKwUkB!B*T 44T]K‹Ts6AnU<+WsȑГ}}P+wsn;uyٳpwwׯ_URV„uPVU!bJ¥Cؿ`t(A 0ܧ xz yΝ;+V<|||X];q׽u6 l|n8 É'|MB*YOԩSh֬޼yF;[}\YbёhեpppZ"]X7xzzڵkN>-uOJJ ڶmϟ3ժUåKPNVTwu e_AA2o``*#,,LV5SB!?}T1gDQx<sqq'СCw֩x"vލp7Zzh-=[ plԮ/o`ds?l ܊!hOONLJ7XvyIt. >|8#{{)y[?e>ބ<mKNNFڵw^V%2RPPߒ:g&&&iӦO !%bmNNt˗/3⪪ CV;<$%-- ۷ǿ2ϰŋQn]ѣ˗/رc"oFJ*4hHڮw޽}Hoݺ}g*\9r$+n: vQ\zEX ֶEYf ޼yÈo5ki@ Z|>G7e4!:V2ʻȂ-ܸq{*j7LdΑf͚2!ksssѭ[7\!SNxjYddd Oǝ;wN: FXXvm999///YfA]]]-[;# www;6** |xbp8E]~P> W^ۻij{;I&1b\.K.)7!B!)#uLHyf;:[r„ H1b+zBS}q+++>}1/_^zň׮]Ϟ=c-Ntt4_-Z022bwݑSx풜={VvݻwǦMX+|>"""ボ/~an]JJ 4k֬3Uߴ~hٙػbߍŇxW0+E^@ȟ##5)=P1Nn1~mg&RSp"$ڔpM888`fW)2+ٳLMM*ƍ#$$EaΜ9 K1|?|[Fvv6nnnh޼9bbbO)**̙3h۶=w<==QPPjp~~:k(BBBnI pss5]$%% ** b5k,YŠs\8:: &&&t.\ tկ_^;5+B!B)))—/_[GW+H)NӖ#'7WLYY>DݺuQPPKKKh.-pđ71xcٲeFFFƯ8OݳaRO *7'c\ ʕ+hݺf[ngAaZzQd{I-{'9zVď6OAV aV뗯X8h1bXmjjj={6NJ]IP)2+رC ʵ]\\pҥ"͛ 4hm&֘3gyyybr ?7o_ UTɓ'Ѿ}R={6/^\rJVCbϞ=kbb[nzup!B!O0|plٲTc5XKxF<|^eP_aԩ~~PfnNX4)𞰐}rժUkt W^e~SJ"?}qPRRѬWbxYn)"Bno!{wdeBXa[ppaykhaUJ< Ñ#BualpwwGTTի'8SSS\xLZ9bocgg;wP .ľ}#8ggg]F3uָy&?+k#*(""5TQ <#mlWғ`ϲ<;nc|?d߿8| fGH%bYp8SsQF>֭[ƍ;cɒ%ssm۶ƢE`ccSl&Mݻwakk+zBLL f̘Zjُ;wDdd$LMM%?ƸqPZ"))) 'NC.0B!B*(޽{e8 ψgoEFjժ8pF:h|_/*W޳Wp+F[zYf1U1n?x< !ia`6l؀ׯ_hFLx fϞ 堻w ͌?O>h`!-b\g$?Ե1|_o߾8q fFH$mIS|ֆ1,--QN9^|> ==zzz066 y1㑔,TV ư+* |>ǻwҒJʶlB!L__ )/y S 6Ghg^LAbr*M^o9WJܽ{ ƒzRz6ȯswn:Fcǎ8{lcrrr,1dggwx<x<6lPqyzOA;z+YɗsjyXjƎ+?YeP p!B!]]KpF,v1)СΝ;p Kfu^'/AAA000Qq ;s7\.`E0 .;~=p8|2Zn藗 ӧ~rtOf kWKtժJχ.{ RB2>g` d~ھ6b5M3}Lrx<p8իe43lzO!B!ǥUU ]\Jp6<댘.y۷ѥK8/g'15XZ{{{L4 jKZB1x`"\ ㈈lܸcǎU֭[:W~:PTE wª+UUUR ĄE_ ZV<C55j0k֬7d4;B'B!BDN:zƙ~TCyyn mpaǏԩZ]ͪ:>$#m***8tf͚dF/WU굍Ѽ3s'O`ǎ[.0zhl޼56<< ZjŊkj'r !:u0IB>O@̭yFG*zMN>>ҜÏ?`ee%MSVRaչB]WγOJJu\9߿F!..N3#DQB!BQNNN\C³!N dnj``;wBCCh7-aQg.8nM6ń 0vX|oj_x<BJ:0b_|)vׯ/iTV s2ϭ\A!ZW('. ׄ%oߢqƸzfG|b-!B!"'b„ br8 R\Z|q8@CC`GB[*>}Ndžgjjjؿ?޽0FeCK89I R j"OMM̙38Ο?gggdddmnsٳg c)%B!BJХKhjjW]UWȧG/^c=\.۶m0bX.!k6V:Ƹf*U~m8ydF|g3"rp8x7o,ylڴ C ajՔb*Rx_jR*VYѯ<#f`bG֜ն~899!55U3#D>PB!B)AZpXYYwrqmƏ*ł4EHQ^CIi$(ǜbjݴ~>VgJ!W|R!ެY6M3g4̇׋$niY^{5m߾]a. (@y(5 j޼yki5w۶m*WZjÇK֬Y^zIjرvw[VoXMEҐj*y=}􌋫 EOG&0wlR ,pSu@E<2L8pʖ-p~K"8joN׉1x rJyyyI-[Grub~JIMӄ_@zyyiΜ92ﱼgp9uNoW^yE<ԩ0gΝV5k(KjjѢϟo7_:X\RfYs܄ JUy҈ SRRԷo_3MZ[o~J_ՙÆXp IDATppVZI҅ 裏rV|I!Co|?0W:g+XL/~Q^(b~8p@M6U.]+ٳzꩧԠA이9Wnjo&dnsp=٬.t=_)#ԬCxKv;葳[;+s_VHX!nZok׮JKKsSu@bl6w@Qxx!07Umʒ/wڥ9.ƩT,NzeVXmJ>jժ=Ζzip/<}dPШKAl/}4ިKco>JfY?vsg}|V #AAxڹܞ={T~}͛7 ;k~( ץKXjҺY*թ$I&hZjG/8?kXg6y)6Mڵk7U_ӦMә3g#]T \%#3S&~C^zZdIv#ĉ;vChJhBG՛o)I0a8`[Dx =7}!vdL_ K~ \+KP4l@-{b7wP.]$DNnj{&}<1U2̭[NM4իU\97U?v_qqqה?nH/j+dl6^2#""j*f=JJJ2 >x1MDncQ}I ;t%) 8@_OYJףcjnf @RAU3sbTgzZe!~AٱӧkÆ φ9ooo͝;WE4d%''r|(6?\$WsK<nbhЫB 3 eefeĨu֚3g>7V ~[~~9L&xvj)sH,]eY,͞=[7Ύ>}Z/!Ky3ϨiӦŋk*:UTUZ;pu߈0:ƭӨ`g| JOKwwZ>vIW^z7Th/:+fDP*ÍHKOj5_}Uׯ Tp}%^qOm~HLKKO?mxYCQ@2#HpwZÖ 4~ 5ij4f 0g8Ь.]|7j劊5ĺwﮱcbg֚5k "5]m^]gξxԨQ:y!]v*[S>p8׿@eee8+WX2QkWպuk]x G[.eQP@/ɳzFC,,,L!#Gb>^^z2}]wذaٍ}iƌ}> po>b9 }csCjm_~E7։'PZ4kp %%Eƍm@~>>n땙󔞑iM>] 8C=mU|3240y{{O> 93>p#//}O?)͖Uy/o/YuI2:nI6mrOqЬcvpC5^U;5ڶm[x,YbU,:K^|o>g{)7|uusfN||}tCws''&sE jA2ϟ?6mhn p>\!::Z3gδc /↊p=R9lb駟b 6l!e1Gl6kjv|ppN*IJJJҨQ >~>6q> (@fLl+j>[zjy{{gc,fMza=?u\0̅h ͎5J#{?J9 zizOhРA6lZ Y (~wgϕzmeEDD觟~!>`?١6ث?6}}}dըQ#;wԭި:<ymX73V!"l6͚5KzbbbT74kEޢEbUPJ?ZhY,} 6~q׭[7-[+_:4o{պu5p@. nUoT]o-SUTۼy4h~ yCP%&&Ş}*xn'OJ6z쩎;bO[F1qƆ}ΝOb}VZe'0#6i} s>|y7U䌿yg}V{[oƍW^ 9wTMZ_L 1ooo͙3G/LCFU:4Dw5˕+ybSO=eY,z'c\ sG5~ -]l>>Z` ͛i@GUW>\Rz۷Uvsݵ;Ѭ_*EVhpU,[I1jԨwy.wlco/xi^22 cƌmf;V v}7B5nKxVVm榪{4kEJʹ^|||ޜ!/Rv˗/7#B5ϕfkN/!dM8"rl6kġ2ٓ@Y (R9ݻ8߽M|)R!SOYfv~;f;dCVZZlL&Svܹsӧ;{>UoX>SxpСCnGP,^8ǹ#\y+돽/ׯI&z*ѬIW6$b_ X;xϟow$jսޫ\YlY믊0OLg3ڲe"#wS?U"*6T `f-Hؼy_m۶5[,fs-nW͚kCiӦz饗rV^~\X;nY0֭oƪPF/^tS%="_~wS%%%i(& $2C_ ,px$ BBBm6t JNN6kT: 6/] y{{"2.Y Wy}qի3+)! /jժOtRgSSSջwowߵ{ͯr!Ɲ6MgϞuS5Z@кu\;) 5Yoݶ]v;wN/v8WtiYFwqpBr!vӧOZdzlݻwAUr|y eyyyiΜ9?k*33.^Bm߾]b/jܹssսb\c((ܙ3gT `Dx /Sc5ȫ7>]ClȐ!jڴi|||b EDD8|C9?_݆ܓNJPPf-͚5+݇OS%ȫhٺ͆Xr4mڴ\ܹU=ҴiTN_}~WQ5EjPرc) l6s | Ҹ&I}|||r}l6kΝ_kv#F7wX*ש0Mp`Ddi֢Y x.]jΊ_Ѭ- _16{衻ۿ???9yΔKڽyvoޣ_7N\<%J0T `*mÆ yrq%ȋQgk %K?v:}zS:̗{IXR1w֢Y hWS^pWgdbSLQHHHNNСCeٲcfY}q,ޖ\7L .zz 㼞ZGKMMS^x󚁸>'hѓXdd0՚2ebbbә3g ;zܮ&mh!z) .s3\r 5^+E pS5eY hS^j\\ 惯3a#J{9رC 4ׯܹs eJ~RHО{c:OE+dMz- @hZͦ8s܋f-yʫX+Anm۩7i)SFC7FFnZϟٜ!b#Fj& }}||r}jx՛(b4kv hyʫ]+AN224铯eff̘!ooo3g┐:wݻ *rŃP `D౬V.^?W:k{U׮]L&X.]TD ٗ >.V"?Ɵ#~@~Y X֭S^U\[r4yb}WCTRs|^Ae'OԆ ۝(}ǻ2x )/2+#{ԗ߯Ϛ5KŋS?rvZկ__*Uu,UX\ffֵ BKcv֢ Y X}~٪{;"fӘ]{W}x{ֲedbIOKwrg T p u._8}ܚ!N_q8::MѬxW_}5Ϲ6I;wY-l͖VPPSz駟8QS@U<(0Yf-cz+ݵ.gZO&MR*U5y\s~) xHq8&&MѬxtu]JLL35*saE8?N_k·((Թ;yPp0^(hs5iD%K%94<3-ZH^^=+>>^G.Mm8up|._?_1T ㌌ EGG X{V޽%I)))ڽ{vڥGy.zcsqcV/$bɓbw]N_1\C+bi؁joW<&LݻwAڰa]nrePaqiĉj|`N_+? qο*שN~g*ȑ#n;k)11Q-[TRRRkW.⊊݇O>>>z'NTTWۙZeffjdž9|yݗp, 8@cǎ@xEQ+IeB]X 0-[, s?/VBl]TdI}{Zs(C 㨨(7Uf-#={fJN3+UuƏ0^>c 2ժs~t8eP\RA1Z?<ΦM/9jVI`+cV>Co1_u㶠u:]Dj߻*(BKO9}t IDAT@Y 8ӦMSVVVK qa5q]fSbqi)iqza]îZp+n ٮF.s)-0w1197dK)\Lw.NieY^BUW3J͓^\g9>λÃORR866VYYYX,nEZyw5<嶿k+*vL&Sغua\,&-~]7=-]_HVzjWܐtmZYڢMk{UW}̱3ivn%՚kn T(*\6,+^:u䦊էOu]%KTJJÜsi惌L8222wiG%˔TVV>}6~.]udڴg^ٲW嫗SG:eזvwyD Y=[i_1_=7}||\U:G>vg/٬?X!!!n EZӁTM&w<q\t_&醏;^'ҌgiԮW[EV uh!E>}WU5d &7@Q=kOzrSE?h&=z4'O"Hhժaˑg&0w]X1q=zƌ-[mU5W*׾WVe\;=I}JNpE75`8Z@pyø*|4E<&I_| RGzz~]t޲~]P%KO׈wК?^Zgjskx ~@7///c(8Y (~gχ󖿳qXR[W{ǵo>CErZ45YVO駬54.g+;]P![׆^x7U8FP$Ɖ)9dF|瓫\~}駆XHq?}z?lt'OĪ<ߑ]Gw^C{*U*Y (wV*J<[VvͺY*+++;f ]UdKvdm75˪Y>gT[vyI& 4kEJeKwWfxm@[v麎pQiw鄪<۱=Ǵ{C[nX*rFP${$/J<}^5Ǖ;V\F5s#y{\&$לGNk( ͟@6mz뭷X3kܹ7ă"VR٫4l%k[V 62k%k^I﫽VdnפK|B@JIe$͟@YY`W- ~xkiJR*w9jU*gۻwSXzuZIZ>j/>9_s>?٨M6jҤ*f-#]SMxY,vX?_5-;lxw@s^@Q0ee^Uk6[o" h<´iʑ]\ nkT0]vקN:Wp=q2*[*ޭ{,1Q1ڶCuEAx*::Z'NЁsNm۶M;v4uSEGj C9}QΝ 1 )N]]Za<#-C[Vnj |eUk2UBf-cf|^իnIl*,:֩nرcN{bw}Lefffw1> Qs:,gbuVCoWӦMTph<^bbaXM kT!j*cXd:t5ko7.K9ۺjsEÂ_)3#3{l2o"ѬxͦUV/ƍ=` Yj>ފ 5f͚%5kԨrb:Ύ\KK)9!9+p .hO -[yn>4kgϞ;e˖ """k.Cnpbnh[am6=c.]sنqZFZǥkǺ6U`1sND+&*<'Z8+ewվn>4k^LL.]jOKKӹs甖fȯҊa=;9sƍ5۷oʕ+bFlݵ ]GkՂ3k -ZnsSEY (vޭ< ra5Je|v'jʔ).[ܹs .[+?M~4|ZJfLOQKW@>[8+eebUŠf-;|ps|նY}V١}!f/j۶m.Y3(،_5> 2ŬoU/9}b| $\H?l2Ě7oVZ"xnԱc z08{^Qѱ:x_ |#=p1E?z9#GjN_g}vy,p_/_G| Z-\5W=Pv7X 5w)=-4inZ@wI8<4DuVTݪTt>>Q[vȎ:}"#s)z֭kxIAN]]}{uӰY:|:.ܬ,ݺO{٠b*Q:TY9RkSY,K~p._ԯ7b͚5S6mTp8PEEEa%1Lz}D_͗ZZ|׺뮻bڰWCqƺ;T4k^tݝ{Ҵ燨ecOeW8Z@7~x=JMMjnw\!\m,Q/6-f:7u jje f{GiƒNu.:N?*t.%;n^sq@Y (x@6mѣGu EEE̙3? ;-Ңmbhqղ *u 2٬j{ҷЯ;Ohs:|')#]gu~BJjRSuxqX'gzbwbIM}b>R&)jՎ_U۴o>;yN6ugUNmVzRfd6T1PЬZ_}UsIIIJJJm22 cŢF9u+V*Gprz׭zgs;0Kt=BPV_vbB'td2),,L*URZ԰aCr-馛䊁f-кrl^wA%ȋǣ 㰰0:uŞ~Gy~w[k:u=Od6⭾;Cs|ܷ}mr\p'iܕں?} UŊUzu5h@͚5Sjf- PrfRNEjl֖+g,raH7:3* *ySsdspaFz:$%'$kՂU:ul~8bQ͚5uwcǎjѢJ*Z@5qD1By~\xIVܜ5+W5rЮo2T.,H ^_8i 47W:?y|}x̌Lm7232LXXZh.]W\EZ@5x`C֭޽{u!;vLOֹs甐`drS8o-[֩є)Sϗ%ňOGՠji) cV\&?F~,K>Vjj/;uڵiRSL``ڷoG}T۷@PG YfeJ\JKW|;u~[.]纍]rj-zْ2(پa|;bzOsWەp>޺[5`=ɇ*EN8aGq=z~f͚9u ??zx]J͐SY>^ٻ%SNeZc~ۥg~NM4V%-UY^UW_APO~әgo2T~}WCUHHH>T  XG5+F:t0 RxxoZ_m-Djٳg5e9аӚ?=뭈;9.7|IF뗬WJO$Xu#FFP!PЬx(øZH7U}GO*Tps:R7RD>O=~aNDh9/'=chn߾]K.bѳ>\22tI}\-\5WW>հeW+=-]Kg,ʹ+ڤ U.]cyT!PtqQ#9sF/^4jW)jpYøVZN{jڵkuB{>D)?˿n4;V'zkSyY.dZ'Џ?bŊ]IqI?֐(Kڥ4-[=)-,F:u%K(::Z9Z njM&ժTM *:0nҤm6u ݺ^f٬JEZ2R5wNE]TjI v\&DՌ{.^xx kNӖ-[,ߨ;:T_II7y6Yɩ9ըQCÇ}vøDPqx||S 1g԰l2͆;TfCB4fN]=ؾY{A8p@?!U/ժӗR.MZjiĉѣG>Vg8T 7U?1-ӛ3f04j&!휺FQ6FznJedI9>sLedd(++ːk2ICfڴKivCKqqcStlϱs*Udʿf-#:t0._*eʔSXxa|s*T>j^8[| yWybYYYqOڲrcX"HSdY234 >pZjz'5b'pt)øJ7U' jժ9[l 7pgh^ ԡnfŢ;N.lֽ 6Ԕ)SԮ'>8;vйs ʺ8cׯ_gZu9edd(99Y:t0xiݍnh {C{+6G Gph]ھ};ZY }Euukz)"Xްwk4DM,nIg/1Ę^bQc+{A "K IDATëpa {=k&љuV9x |<+!óIX&M4/66G oooQ=Wkb&h*>F1Fv,١˴@ZU`S`[PPlH,[eڴi1 }B;-[n|#F͛7Ɉ[ѝ4pW]Ƕ ]DDDDFSN'"RAY֣GLxyy *>}[nLj *UOOOkREdd$޽'O ..pqqAQzR_޽{ Ǐ 899RJUK}7n 22?FJJ *TWWWԩSΥv~~>^(|c +TVZaܹ%\[b8ydGxzz⣏>Kpkb֭._:u^_디\۶m۷׬Y3 <#Gq˗c툈P۷+V`ǎ^111An'W^%>i?Dzeˊl5r7݉|uwwD"x}~Pfma~u5gnfVpE8QۗnXKDDc:61rsr_Ƕn݊ 1+"*@Ogee_~pEG*bС;w.\]]DDD gϪ<WF~ZxtȐ!ucT7M6ZI 6 *[.n݊ڵkk]0vb oj׮6n777.((ʕ+1yddff<WFZx899 bqqqptt,V/_FӦMlH$WfDo`b܏yܹܿSr9Zj/*l+ThnR#1|q>T7QFDDDʜ"ZFcFD%mXL6 ϟרP <GjհesvׯVyi69rM6UP <*CشiV_;vTP !!!hܸ1?gƀ*/ۯW뻊45l0L0AB-޽ 4'OZyfJ**s 4kҥEj-͌YՑ}`apdgf(#"""zA[ߵn޼4=eCD0c$HP\9x{{>>>pttăp=={VtPvv6Fڵk/&&={Djj:uBCի̙*U`Ȑ!j7n@+jsuuEΝѺuk 00w5jо}{ رcEqD///qƸt;w ϟoذW~ڵooo>}ǎ'''Gt*VTٳg+,KR4l]t?Ǐ ѣΞ=HDi{Jz/fRy<iYX-4K.+-?FLѺ^/<_OEQ-~]0|p=fEDHxzz~vttĘ1c0bT\qX`,Ylr lmmUΣE ,,,_s΢ϟ?ǨQ_ &&&~:j֬٨Y&=z$;99hԨh̓'O$Ehh(U^?..ժUWK4ٳ'q///ܹsG;loݺ "//Ooٲ%vڅ *\|=z@B֭[* ܽ{wQk֬ ^PPG_~~0xb/۶mHW!.9o^+^e}VdHMMsrO?TdwrVzN^@@kukaimì q)߫W/߿_c_ڵks*-9ϟgϊޞ1cT^{Ϟ=BT*֭[j۶m ӧ6,_\T577jBjRRڗϛ7OTuttÇjCXjZO6MTVjI&}?{ZrL2EoѢn**/wv[lD"m۶ 7nPy}"""""*9RسgӖL PwϮjjY)+o[&hi}C?VҺD"aHO}k \LHz+øuFSSS7n/:|̘1C{)kff v}p%OIIE1D-[iӦJ:88СC(_ j*DEE~DD֬Y#! JVZ߳ |qAA^UXͱyfQt#}v]^^^(? VPPS6i.##{#ѡCK.r f͚r Ƹ8*Afz5^.+:cFvkUgc%D=dEDDD _`|)?l8z-vE9&Njժ b~cO:ozzz/T;6g @k.۷G~T_Z5|Xvv66nܨ 6v~Gh֬J[h?P޽{UzjQlĉE-m۶ܹgj9sT>FB*UG\,'"""""L27'ObϞ=8~8.\~!99Y0Ƴ{t8A]v*JaVTCI Iu%x Z<].ȦzȌ|y 6;z̈)h4i"x.((PiK;gO>jݳgOǿ:Y_QU\?77$֏Å 1L={d}"""""\˫K)dBŹryǽ{Vy%K;ZbL^x|7cGb={zfGDDDXX[|2ge"CU拵Ů]tLAA'wqa˗GVϟJǥbH$j_^=ΐ<|PPܹsGZ\ DFFqN:uꨵ_/EED%nZb-QzcWW~ S qvv]'ɓGVʯ!ͥeov}ϰs;7OT#Y"6Tʙ"U]W^S&DT2_Ŋ+>;88 PT;u1.]BNN V~}T\Y,n}Ed??? 677xwww}GnxY-|lrPPxi"""""*HHH,Ma_κ15+.zqnWmڴ)ܲ05i//OV6`;KFjfN1?x"""Nz+U>kO8!5nX۷ob]t᮪ݻbwyn֗H$odd$*V޽{jEDDDDDŻ{ȽK亅sB\K[` rPLl]0IOپےRTrunԚܹ(޿7Zv*?*ZNn>YJl]*H5>ނDћLkEGK$4jH鸻wbjWWWQL\vDr7Jjb~"""""LHHX&E{;AMkW>0k2EB3RjcHU+aJ$GRl'.{\L<tj O"9v옞!"etÐXʕtbZlUenn333A,<<\Wih\q;;~nn.G%YXXD{b-Q(|}9NHwDUY~Mt΍R?cjWqĂO; /Qpr)ڿ~WBDDD%Kf,Se'Azʆ)cԩ[M>~snn.P|R__W.HJXlD/ת/ ߡEDDDDD%#44T\Or?ڶm<Zu8/Rs"یeFh\`X(g%|y}~qR|8{#}Gë'귮Nvf/NjܪWƓO^?߼ySQQکS"**JsttTX-,--M+]jjN֗d222/E;KY$~ri6>5Y4W3z;5I7o"%=S裏Ԟ'::Z\ݵ<**7*l{{., \@(;3-. /'Oi"""Nպ^N%"(;kn݊+VK,UJ`WPPpfgSRR맧Ҳص_kkk.WWgJXj+==UODDDDT=yDTDOҧg. Ѻuk8}4bcunT<9s;N*G hUM@רQxpzk/ϻtܾ}uSFDb:c\688#G?c@JXZŵi~DDh UA wGrTC%eee梧lHK7 ۴i##ZvS:7ŏ;/P\ Evnoyr ^WB+r~n%""*Mn5`bfױ'NXKEMo2U C>}-{{{cʕZͭ1ڎ5kODDDDDu󅅤Zzʆr/2^$ b Pk\G byXu~И&ī~OT\(+LPq ꌌT 11c.]cFDH)ĠSNxL GsqZPPϟkDrQbR__n/^hnQ >WWDDKl>""""*$$Dlb,Sy[=eCE%}ѳֹGUA|1ϡ=k> :4O_GXHd&edek)""2˭zeA߫MJ:ДšsΈ+VǏUJ`'H쬬W*J$"+UWnn.4*2&''n/g/LR}.KKK=KDDDDۂg[tcN+y066Vk7 YK/mSBBcT{qX>UټU7lklb_Oû iѣGgWg=eBE3Zj$vd\Rd[ӚQ4l,/|I&[j ێl *G"""^%/ iƍ/a謥8; \.ǹ۷oٴimkjQ7W09*շn9"4ʏJ݄ )bڔtW^mmmk5(V>\UĈbEl}###j]DDDDD,bOv#;CEr 駟5Gnn.N:%uiRG <|Z}|l9]zg(O"""*n5ϷnS&DkSSSѵkW\|Y#GШQ#!՞Xq:E:tHx}DG b2 ի+LJ4Td[\K0Rd?϶ٳZsǘ ^/v,ى_UTT/<2""yyyzʆ 3bmZZu& nmmÇiӦ%\\\x?^'mV___ ψv^? @,H]M6X`` Z;33gU/ Ξ=$ׯZsQ8Pd):a6TW)"w4M[Y9agW?i'Z빉H}n5]+^999xqqAwwj'MT۬,[TXݻ75k6666m &1x`+nݺ cƌQX鉑#G b~*޽{1cTZUV޽ ݻŋJa… !(GlРA] >}{w_I& bFFFTZPd[6Mt I.cm((/fbbS=פIDz5^DoT~3;+ޣIMYo-aj,)VD4yB-fΜf͂ ݻ544TaS;-ZYBCCѧO<}T{FV>-[O>*-HxbQ… 2dEmrGŠAP>Cԯ__H5999:D'ң2{`ʭ];wF Ԛ̙3駟D>jj#q?l=Gϒcif'8Ul%67ΫnUsFFFH7$N:m6 2D&M`ݺu*/bccEmԩڵkHի$ 6l؀?X\]6m =]a~~~hѢnݺ@ܹsGBǎ^ȑ#իcŪV ???4j  %|9NUU[NPNVZ8{,;'O"((..;c Ev###4n~~~puuʼn'pq~֭sΩ(prrռDDDDDeŋѢE A,po߮VڶmׯnݺZe/077WkￏׯRJZODDDDDEr๒21\yy]D i5K0pXOFPErB-L2cƨSRENjEś;d 2IE} )I(rNDDDTY Io;k%ꛇN>-[`ӦM8}˵% <<<b„ m֚jР\kb˖-|~>>>4hƏ_b;:;v숛7obŊضm_ߴiS <F& mbٲeؾ};"##Jh۶->chkj„ ֭/_;wٳg k׮OѫW[+ijzmR3pܸ"cIRUw6/GjQ?Y%޾D42y* DDD:##=eBDo۝.66!!!x)={kkkC띜ĝ;wS$$$...VjԨQ߻wx)x{{ݽ׿q"##...[.Kum\+W ::?FVV*V4lsv: `cc_틡=;1+Iĕ;az/wFѓ8'&C/x cbqylZ*.\>@y m""tїT: aE>/q_9)Std DDDQ -[O</cFDy)zYk*T v*UPJ_fMԬYSo׫WFFFhҤ 4ipAS6+6!ƃ\؄DgfAӷHnNNNV///xyyj͓5k 99ǏB`un,P5ӮWxvQcʺSx Y%""҃ sf Z""""""2HgΜ<ˤUUOٔ,x=DhpvvFJOOOtj87YGaСؽ{KU{ujTKLs7Q 2y‚w1|lJ!"""dz̆^)rvMszD;Iib+^Ư 5j@Æ ѪU+t¯HJ8ְG ^cPF YtolIÇEcΝ066Fn_&eZ.ؤ1jQ<'7_ye2)-=g@N\Ж:|ߏC Tk#{6A(u=\i=wkt?ޑ˱lr8wcz-"""RNf,, qg-a` Njjh WѶqhXOY.'/_g.֭ƍe˖h֬LM5/HڊFΝ_2Ltqrrh|L|N$ >ŀowFx,<]uzۮ9bvoEpQ޿033+uHDXKrDZ""""""28FFF {=k wFd2}L:Cv/|]}ѣG*MҦӢbZKu} c;K Ѿc1stTWT ""zWɌYKdQafffB-XY{ 8wvj 66Vw%ʆ |qw8Xrsr1g\YWdYKdX%""""""3sL6 k8]_/݀TwwwܼyzL+iu]A% nx0:)l߳b/|4W DDD9LdX%""""""k.n8r¶QF!""*TqVѯ_?,YD9"bJ(2T2׍_`1-!Iv"""RZ"b-\ĈVfCFKLISƚ5k Htvr9M6...7nr±iiiXzVrGbј8`_Qed`b/q62#""zpg-ab JPP1Jػd2R,>1Y99Ή۶m&M:-eff֭[r DR2 FFFcÆ CDD>}UVA*@4ĉq}r/55BZV /klr=dFDDv16LMNDDY*5A*)#۵;hw>\t ΝC<~Ϟ=CRRb뛊k }9,־s-+ f;v~ jx(ðx"[!C""DXZ"b-Fq^8TSF/ebkq1Jñm6 43͛7aDFFŋ8sL;ԱcGܹsGye ĥwÎo>DW/,$ SQqM=eHDDTv fCDO?DDDDDDdP|||P|yA՗ſIՅSWPKdTŽ~D>}իcȑШQ#طo_jzׯcݺuw.]>1yF^~}ܭ¶|;KQ[^n2;̈6LdX%""""""Sn]ƿAt$ #u{LX5lP1C޽_kmm 8;;RJr #!!X~=VX\ƍѨQ#5 kɓm6 <&7CvE"'nDr@|WUq)}ѢVEf!//OǙ]f0H t">>NNNX\\̞=+}\M't*S׳gOH=4V.25Zq1M{cܸqJ}D; A Bo&"""k DDDDDDd|}}!mV !Iv۶m5k(퓙o\Ov}Kb.8b=WW{mmYNgؿ?v]^އ,sފ bm᪹k8U>\Qؖ/14<"kȠG'X"}ً$Ǟ8qFF}vƏV,}C| wۢ <]p֭b狉OAoo⿝yvI[%//=mŭ`ZNx!"b ޲eEQyA Zݶ :k)Q ,PP^z!33Si|WfUq{S0}t<+u?]{Q ݲo 3pڦLoۑ! dddE{)#"""gVX @)6D[,k߾=u&=9;,nߏ'5ۑ]7333l߾sQ8~8tEWr9zǏw,ֵ*FжmGQkƣg/>@VI{Eqz#ɌE""wYzJ(& "ݒwD||="3($ܬY38qpvvF\\붿 WRE>3X~W@㚕4ZC?]KQ>n 'z_xg jsq %Kf͚} wHx }ݫQ˃HL[&`Lz C΅`z:ΊȰ9Af,X%?kʰhcȑc׮]D"AVV3+=?37oM.#5=%n h?ReœmS}W^TglF077.5uiW=qeTYCۖ|Z5=2ȻceFFF013Ē b-Q r\a{͚5JT*QNOUq9991b|}}rJd ZcZonN64+bx_-Ok|ٯ(.H XxHzz2!WX%"""""*8C*#h|[Oj&&&ɓn:aÆ~Cpp0~' 8ptt]y7ӷO =+Wi;(+Xoͷ$UAfHX%"""""*cN<#;;ؾoFӦMޝeVȦbłXڵ1|mSCdd$v-effbǎ}I  @5YbH">V5J"-zG pl_|| Dx Db-Q޽{#33Sa^YfxYisӦMh _`ٲeÜ9spekӑ./i-rF/aUGOڡJےKz/R/&ᭇzȆȰL;k322 b-Q]"--Ma{E;]-VN Bȯo߾HSgϪT`511AFиqcڵ > G7|333sPmϲ7p6fX[|{r/-͌a"wscʇJ=_z8ښb9Y9li2"""2\YKdpX%"""""*rrr٧ :k)cDW^-UVA~~>f͚m߱yf$''˗Wj\pȝυ5^u>NN7訧Ȑ;EY;+a'޿-F ڬ,=eBDW""""""׼ys\~]T|"RJ fEEEEaϞ=*?th)fZҫ.|dԪlHqfDDDD𜝝Lk ܕ+WD/PA9ZlYiQ!}rrTH@D%w6IMՁ |,|7'""zwkʠ%6}% ʕ+uݻ)::Z{/L~A+L| X~^> ˿^̈HXE$"]a[Rnӳ%q-$TXϟ?ǣGgXYY*wajjR>-kr6DMk!qT4vҒt~IY%24,#F_~M]y/X|||2w^׮]f͚biYC+RyZCѕʉqj. }rDDD:& ?S)"zZ"""""2`Xd`[XƍeFoZ|y}VSQa3?FB|_<_哖cȌ)"zZ"""""2/… !Q5 @6mpYyGƍSnf"C!җ:^<` ZvQ~Un9/b_83"""01^ۑLkʐ)S`ܹmϞ=q )wSll,F!CԩS>|8ܹ///v| 0L3Fb؎0RKTL8AG.>9""Rfb&ƢDqAQI-=dTvDމĢdffԷlm,J9#"ԳdixL|,2YYd Fzz]Z; *`A^h4hlD1b-51&Db ("f/˝Z9y_KdJj["d-ZHMMEff&Ґ@XOOO+9'['u[ĂCж;H`d-b-x 2m۶]毿BJy&XB頋uQT {m =/<:4,کrĒ%Kߢ{lMјuHLVV񫘺jD7hk?~ռ &wAfZfجm٤K Z""""""2 V]utԨsNZF&ߎ1qHx/^A!nٲe=ڇU @I+]_cdWcVH{E28#XZZxڕ\ K U91^L$-^LDDDDDDp+AtI< `g& G/ǂ]{{\&&zSd2Y<ktz-rHLzwAp l8O;NZˀǵC Ub7# &"4iXKDDDDD$h|СC 8iӧOѧOddd[71heKc'C#ǫ5|!"qϦ2%lD}IUf\l[FW'bʀVF٫[W6'w IZ"""""" Zj/uV8q;wDѼys9sFJB.]A_ELn:U*7h~PT~ͬ_N!,&>Dd*qӀf'{Dˌ7f{Zz4E3Pl h 2Oe[`xHZ,IhwEmZBhh(߿oq5Ạ ~9jsipڷ}vZ`RRR Gk 4̋jQEdc\3{ [}ɻdlE|ȔrkspCDb-h4 D5@,X&ʰؼy fkc3Pk/_꽆Zƾ} Ǫ 5TC5ec_ 1a1#잎/=K8/_ /5,IZ""""""ܸq9ZٳQN8p2'O?d23~z}gGrugϞǎC||N{ݸPMDd*ּȜd-D틿'NaQpj(_q={`ѢEMoOwh0{fꜘL&Y̙37[Cћkp{l3÷}P-Vz"d'dJi7{A;yD?6ւ6A&DDDDDDj۶-VXŋwNNL񄆆bĈPT]_["=z9/-- -Z@BB{e2tmW<~[,Z(}79/~JYW}ѯ7D%mEBH""26{6eBDDDDDDDf}رcai573+tYP1OP*1e A֭w)Sp ݴy$"(*!ki|SY9mn%z:#lZظ()A&DDŏs2!"Z""""""bmm5k 00͚5˹cǎŹs$]_j}ALL S&~X3,#cӅwhܜ9sPT n:fM3Gsq).ٯށDZ\#Gz ;~AՠD6֖'ޗ/u;J@)fSID/%<_AVMDEZ""""""27obر=Рo<t G-Uۇ_zw.:{,=-fr'Q<|RĂ1TQ|RFfWU<mE*JlWyT*:wwveP9ߎl9-o;{xK/EÄ<5V,| 6HΉEHQaecl$''K QƓDDDDDDd[nkB.DzF^bݘwk .rL:-ZwQvm 0g;'׌ڠBi6U >\B..!mU(W< ]p`_AKQNIJ("*m1,IZ""""""2i(_2W1yl|nt!{={… HJJJ’%K3gt:|ԥ>bOm^JhR,~ݚήnC(sNvv6ƷР0zOt8TfMDDD +++'&گKKKC~pQcʔtDod jhk&S4=?+ai)Q#=5C[6yhţr*hc.pO:`H$1C]l<"];u:5(#"FNIDRZ""""""2m ɩiXֱaQӅ< wݻSNN}{ZNHK>Үv2Kc~=\ ŕYJ,([ӕ9ڼ8YaodZu;JL;ZkÓDDDDDDd]vER۰$¢gfW"%`x+\unj{ a܀P*ZeR|^a=g^Z >+a֨ =%kZs+tq Ct{'=c= Y\^']BDDńO k(,--pB3F?+ oh̀0` 0@4hB-\ZWODKcձoc+ߟu:uO^~2ᳮ޹Q6CVrd)IKc4: ڮ'"5DDDDDDd41[uFB.]f͚AP9^IDy H?xD'nMϲߪB-8X کIeBDT|(2?';88H XKDDDDDDFcdԩFǏcÆ 0xoB}bڴiHOOw7OxPvE.`DyZ뻤w)G< N\7GVux}ӹ9 /3$ʄ]urr("b۶måK0bxyyA.w}GY}Q^==GRc܏ VDFDqUJЮYڢZyL@ZJipsē395|5}"uy9 ڊoE "QH"*j,Qhܸ16l؀"99wޅֱ&>yfG~ sܡCDBMD:[Z:,ߗOowc$?sWVVz/uW2K)h;;;K XKDDDDDDE UTz̙3)i{HNNs=,jY{[mZP\IA,bUeK[e#:MӱiQloQX7|l #"z;iAWXKDDDDDD&3uTAWF֭ ޠA0g>OOONj/FUbÌޟ(mjY5׹$Y[[QѾ3K=ZT‚MOM 0eZDDo5[“$ʄ,N> M4ѭ[7iӦk\۷o/J%CaeDD{luNBi'ru0+egg K U<1QFDDo?[;A;11QL'kȤڴiŋP?`ҤIzri7^6 ΁H;;k6;H@&'mRf:1 y_wODDwkyH:,CPZ-9;CQsQHD^=<>k&w}3дf]R?1, 2!"*\d>C$kj|ݻE> om,缁]- IDATtIDr.۫\\-ߏzͪybh긳qVO("#I-"eBD|g-[ncǎx +bQgD=}"B.Æ=ѶAŢJ am0*ˏ Ɯ }M[INII("Z""""""2[7nD͵j-2|A+8Yoyht; n_R%""#srL vWl.eBD,Y3f FtQ A.Qвֵ&ioYp%ʄ\B42!"k֭Fyucгe5Ag G~xjX>sKDDE)Q&DDowA;==jZl7 7Z.rzB? Ēp=[s9ۻ+lH\N[`Tӏ] 37|kkk #"zs)FJʹL#""""""vZ?˳P/\o3ˇcȶ 6sCQ,=%Wbv%Ȉp=AVVVeCT7V""""""2+Go0fJ!"")U(L{_lDi!"zĄUT("bJpp݇ˡk&Ά5VKgCDvz,h׫WOLZ""""""2yxD8Ç(WA {Θ>!"7"KIXӦM%ʆX%""""""_D] vO!UMi""zݽ~F{wIX%""""""QD nZgL G[+A;~D® %KDٲe%ʆX%""""""-[jsF&Έ̉w)kSS%ʄ~%LЮ\Db-ѣGcѢEZ~',L* eCDQT /hBl`ȥKt~?>v !""s3m-QұdBDf 9LAleCDDDDDDDdFʔ)/%= NQ&̈IUDGY%"׹5j$Q6DXKDDDDDDfGmdBDDv+CI h"nZl_,٘2eJ̒d&ʆQi7{A;9DY®#%)E6lDѿX%""""""aaa@DEEW^Z4e⬈Ȝԭ!h+eBDf9pFvuuE.]$ʆb- *͛Zxxޢ(z%TL, m۶\2B""""""2;ڷh9A{`8܊?@&OW+9^dAleCD:""""""233uvsahCg[SIDD2؝x*Q&DD$QlٲdBDڰXKDDDDDDf͛6lT*Uupqc;1+""2<]t2!"2o mL!X%""""""4rHxJe݌mJ)/Ej(FDT$'$ ("Rr;u]O.k׮8~8t1wmPR[F7L|~Kt:%"ˆN b͋ +# C e`mg=ߵnݏǭ?&Z\ǠݴMP(UɀX9Z.vגS'";$*2E yJMϻDeBDd-gggO:5 ֬YJeкRPb|q(>یX}>HŽ=xX~QkmDkw:%"""|dɒeBDڰXKDDDDDDfgذahҤ~ TTu3ٸ؈gv(xnik""{MpjiY<1ǾBg6}DDH>h&Q&D A&""""""ԠA\|YkӧO2ewa7!hDqk+ 4+,E6N^YM/%"|4ae!R;ތ0ejDDf!e͓D'k,ըQCgӧO JzlF|/1j{ _.y-n5K E:~%"z>Y.Q&D DDDDDDd*W/)ՇrSLdm*woT:N\?A""2K YYeBD$ Z4ҥKK ib-ի{JeNYfZ-\#Gp:dd*Ekh?񛖡@[k1CR#}DDd\]V5 bb$ʆ% [-u YKDDDDDDfjDFFZj3g"99%>5 r* q345+#KLe6*܋M“ZsҴ ,,= ×"p}d^D] 0SNa$DDdbwmKK<$"c̒\.+߻w/X+˱xb,^X4/k A"h__Oz5I+=9""Sqs\l&'v5\ŒL]/diȜd""""""2[:"#믿jtp4( r~(Q)d#h'ћqcA[LH>>ADDDDDDfZ^gǎFg`shZjwGԓdzQ ""y@vfz"S!hJ “DDDDDDdʕ+ѣGzqepmllжm[tzR#0!8YCﯓVp=2!VRڦh?#"4DdZfUMJJ•+Wrz*>|(V۷1w\9riidTLѣGpP(n:l޼/_9v>>[`՘1c222ׯ_?(QD]BB<|8PtiL8juAHHEk]u>n 9쬅!"2ڕ,?~\lHION2E;~t1x4lPUZ>>>h߾=|}}QbE$%%!22:ǬY ͛˗/E}^^^x[nsh=m7?~]vq___t5¥Kp ܿ_3QfM߰aF%d2ԪU :t@5p8q?¥KPL3g,X [XXAر#|||pIckѮ];={VwqqY0i$ Esr9Mbaqr3)m 2LLQVTvI"ћmHz L&֭>k֬APPRSS5k2 6mujWJ:y$4h j,_\VZ%*aZ PlY۷OTHMNN… xu8wGZ^r>,* GFFb͚5?sLч,UT}jW탭 ~Y߿_j5>sQEؼyP GNi-[͛zODDDDDԩwiӦi}6X}<{ ݺu̙3Vŋ]po """DWȅdBDdZvv$%'O 666:TPοw7o.occp;?** UVKmmmYf?<ڵk;"##800PN%Kŋ: ů۽{7(xp5ד[lСC1___\xQK.;II""""pL<QQQ:8::\rEjP^=4m+W\eZ ?@y?D% иr'""dg~e=$dB ڪGK~Κ/5kLt'ћmHz3BmQr(ֳgO֨\=ӲڵKӶmU^@4i 75Æ S֫P ::h A,!!zEɓ'U^]UܦMAΝ;҆_}ԩSQBAرc htǃ0o7qvDDVq]AXD$=IRqㆠ-PNש[hw޽bm-JR1LfEO[ZZ[n&ٟgÇ o߾Z fڴiɓ~zܹ=z@*U('O_uT/Ѕ{Tw}wmqCx#CHz7Em$-ȻqD1mW].55'Od2zeuu߿{Ν;:t`:tǏ#===yOƋ/+vK[:}!wgk̟J€tR(JAF5kp;vh{~ޥ-S`coڡ('"z<+>`VlY 2!"]eZjF[ ^'L&Z;K.ZWONӧO9̙3X NTŋ SP|y[ֶrU7/^]/MDDDDDeصkWc.\;mU;m-""_ʥErˡ(`!ٌ߮sFH~&I1x|6"#NP%VZ%A&DK,6nX˯Ș[FFc/// s}(֥Kk^S?e2YbJBXXkQ<,FLV}&j}Eq[kK8XV|6#KضdvAHIJ(,p!.,uzÈap!\}5CFZԩ5ż$Ȅt)1~"`Ƨ~dAlܹ  r U漿L&3jᢸh4~DDDDDTV\ /IT IDAT// 4@tt4DEM_akmYZ m+FzyIDD׭EuQajH!3=_"Sx3"SUo3 K1 ded??ct1ާX6eqZ&kж(v 2!"]  /F>}rb)))߿?lق*U蜫T*l2[NoРڊy՗yrRH-?&&=?DDDDDILLD~?h4~:6l={Av`oA-qnj;S 7y`֘ڿ9*8 ""YJuN,2'G:U*?tjU2aVР0U:ǩj?J@\NvA&Ѱm}TU B~AD@Iϒpswm۶@'$Q,/7nJ%ʕ+Y&FݻbŊPq}~@TT`-Z?k߄Q%J~^2̨Ҽ9T*i2LQ^GDDDDDӭ[7\tIk_RR:v숩SӧpFE[!$*}>sv|CtvNusvVU""*Rζ}~䙄'ezc>d"le66}vB2@kf j8¯5jT@zUдcfWX DPNHKKYѿmFʕ+cرFAvv6֭[':9e˖J__|)`hii ggguPVE'K y@ 9cobm^Q}b67B Bxx8^*?p.M-z_=dWԉjB9ׯ5WVγb hҡяF߻NcHzdԵRqܾxN(Wk;¨ͺ $(֦cŊ9sYѿ;k_׺ukܹsgΜA6mcgg~駟.aaN₡FAZZ^{ NNNrBȫRs޿ 򂭭-+mhuVڢW^ ?z-WG7%:`ޢC߫~0BDDա0E>&=Uo\2C r 9FmtSd*ǝdF/j{a1["TmPe+֯_/Q6DBXtꫯ?5'##}͛``-^+WsT bŊptt,?DDDDDokעo߾EVVvލD 1j(XZ /JHNGi+6ꃩcH:qK+Kxy8#cFϱ~i  #_}@Dڵ]A;**J;?*V(uFUaaa[.FG4L&|}}.711˖-Cݺu1{BQk ;M_ؽa"""""2ٳ1n8(JA<>>ϞN˜lڴ ݻw=𘖩ĺkwGʑxgN.i"5?aD}wB%0JRk\wG(\;u *,% kem7=v^[pD;k; e˖żyJ* %%wņ ~z3 .Dhh(6m;U/^bϞ=C2e uh BשJm/ OJ*ouS|\s޿ n5TjFof5w^X[O2լʼn_?,Ժ}#Rhu.U/;Ÿk*T/;RwvIO l0]L.÷V%֪$Aؾt'RB&AȽl:-kzPfwܹ8ydVAj Etev̙BmϞ=tRשSSRSSsk֬s1 *JtJ7AVgϞhbirr24M.Z"""""zeѢEZ EϷCC[+}7)FӕS0+[w.k_&hoaZFИ|֮K?y٠UVhգW{8sO_簲KIXXfZ M;49ӧO#&&˗03-[bX~A/_^tVT⫯s^ҥE ]aߕdoAh%J}ءhs!Z^YLDDDDT4]&9ZarfP 7׏A}\&õuc0o;^hػsRڴ'fg el2eN2]C,kQI+u?mπur~;.>>BOLLjz뒒޾d2ϟ ֶO!BL.te /D /Մ<[XyxkfV,yh b`^W*, !TB6LGNt/'D% 0_G&,EJ\D8r;,Nܗ;?AݽJ!5ho5 qvFGG U&Vv'eII ޽{'v|F.]T{ @*gg02߿Bxz@ !B! RRD?.?G =C饣;ߥ:ί Mu 6%jkR'jBES,1Fx6Lڈ@sx%s2sW1h؜*hM:--  !K֊:4==edd\3 zxx@UUgϞ!!!A{;wNޞKo^-882ݻPh:<<|/amm-Ϟ=+?2-[Œy-B!BhgϞEV/d8GhdRi[HaѩpzBOK]s<~Dn7ŧIB&IM ?'SYPQU8Rs8pKEYv```5EC钵Jz}DOOfffbkiiK]ք˗/'vڵk2ڵk(,,hҥ 444$ر#޿/{߿y}:g޽{s?` B!ls JG{HR TJB-Q9 pi*/M'j'/,f-;XH',,eX{{jBZ!!!Z+::mόJTRQݝK,+++gϞv###jJŋ2_ԮfJB!"?ͭܔ<|Γz!+Hw{*D1mM./35ptwOy+R9ŗ2SRRI&I~Y @۷.3; bԨQR=zPcǎR!?.fhh(u?jۺu+bbbܽ{WaÆh׮]l2l޼m]tA!B!HfΝ;:rB,/yW(-PQKr9'OH=z%'L uƍh+**B>}{scbbЧO3n,X---߶m[x{{ edd>zÇ*˲j*0 ?l0޽C߾}=700mL&k֬ބB! 憜g㉣RTBj2լHBB5*,Zlu#k}{whj/sg'eoCSCȼ5Ñ@B|tZ8r45?eff[lXJ$66 YƎ=zHPr}AXX9III۷T===,\P~h"hkk [hb%Yz5۷/E E>}PTT$ަMW{3 B<#%qU 6LCI&RߟB!"!y$KGw#"RX-`@SNJJƽװrwSRV)Qqv6; !5,>|P BR$l⮤Dbb`ҥK017o]g̘1[nE˖-[[[ ** QQQy&ls2e,KUUh߾=Kssst[ƫW,B@@ԩ#'OD޽QQQvf͚ɓ'* |%|YqqqۅJ߿fffpqqA׮]ѰaCܻw׮]'OУG̛7Whqk׮077͛7qu~]]]/ !B!Ⱦ{꫞-1gh[9FC!&6j322BZZP;S}("*@l_;MmLWfQx\ &~Ĺ.m]e!fS >[DژDH5`Tsr+++m=XJ=/!-ܸq7yKпp82LٳGyWwĉ+T^UUϟG.]*| bժU;wյGGVh9>|33 {02<$-- u3B!'''zzz2GidFD!!3 n`2`0BTU}[gx<p\>UuU #߼%C"Rɢ((coT7dtO).|m#EKƶo/Kڵ ǏƈNm˧Ժo|߿?nܸ-ZT^?>bcc+,~5j$gdVRsk >TRu,}]O;vģGpixxx@GGG JJJ_>&OXBWYӧغu+7o.v3V^pxzzV_yyy!<<+W{xx!!!pqqW^a޼y;NII :uq-XZZ>>>xN cccTTTЧO;w2' !B!ѵkWt 3f̐&ۛGP>Bj"+)ʦX_`<,u1$HLM=Blsk6φ)b1 8\&eJ^A>}X">>::: ,--$LMMaoo ğW!/o߾Ett4CCCIn RI^x8$%%!77055d<|<} #`bbSSS4mzzgKm۶O!BHy222 1N-$Ǩ!6w_G^!}vf5%Bo9\xGP! j~ר/](I}v@G_Gl>s-EaRߏRslOo^;;;#<\9Մ-\DuA:u^*`ee%qiUsttcݿq.+]YL&͛7ÙB!R9۶m͛7RD'!skLzs[x/Nqub^Ƥbޞ=\7n}v(%D%Dd@5Y!-ūWJLdmԳhJrh(DQQԪ1*B~>5!B!Z? !HZxUSDs5FКa('bs.F&_ 6)\X+wSʱ*z$o}[W3kWVS4(YK!B!Da3u %"R>:Z"ￅ{WOwM*Ke/GEMQ?\lĵnåRCy͍o/TMd-!B!)((c.rBȢ="|RZjV>U ƴ"Masv3LlMg`!MH>7;}mAR`hRzuuJ1RGYl j%k !B!(Ę1cfL."8bH;GB~TV0WS};΅")#Gβ>\߷ K/7Y5h /Myk^W m% :pI5 !5~=\."!En !B!T[BOOZZZhѢW#"Lg30Ӭp !䧵~BW>!"ٽ<8Ml_^v¢4 'Y4iXl_FREihi6()s1!&gY,V5EBϋB!B9s& ~,4}a]O*,%wDq0}"!csȳ_!*YIoĥQȇ5P//5'Ǒ}\M$ 35Su !ՏS\ojD IDAT%k !B!ݽ{dWR|y`lmǧ8x!l3CyI!YcP;S%nhJ5!`U|0Lٙ폍 ϲȧϺ &C☔%TW_aˌ@;%k Qv 3ª+O899UCd(YK!B!DlRy[I_U !:h`HO}w]g*KY {9K56!* Q {z)|Čx|1CGh7ZUuۗ& ݰbn=9m]%?&ñ1X~)DWRR!B!BlS6#9VxjnV., (+n,dfրȾs{' ܯb^y.!j%%c۬郋{+v)v؁&M$QM?JB!B+773e="!"+& =D?Y Hv1TcC`| 1Ckd`-8[VM GwGhjI,&DBbG_aQ(), ^^^X`ڶm+ !@NB!B+???GdB]~wif n 򷯦aCS]E\B!5ghkw Fvo> %jY,֬Y#Guڶm UUUktEd-}p,b?(OEU&u$C+^ǼJf"ZI\+}2RS$!/5!lܾ %j1j(DFF˔%B!BG!;ǁZ;BbZQ96BO/S.F g()51+VX, !om7'_'mڴ **_^ b0Сݻ>>@)xQi=,>f \khh`"2L0L{xprrBTTx%QRR!R>޵+ZgVκ#Fvr\*(.<ߒ26ywI{ŜKHmNŇ9 pttDƍѢE ((ZBB!B~RX|9aÆa̘1h֬LMMl2Tw7o4h>}A ]vMCzV"B$ZW ="=_A}m7TE`.ļ=7,3rMLLWPPM6JUQNb>d-jh*|fmnn.N:]]JǕ-+IJދJOHmqfG |:Mõc.~Qd aaax"t=yYR%eTBEzz:PnjB! ŋ~z򠪪.]VVVr_To<`eee,^-BNN뇇H`>bMUdȄCĨINƶs[G+(/Ʈ; (ɰWVC6 B8q"vQbԨQBuecL1k$QQS4ՠu- e!1:Q`\ff&[ >O>&EBB{5'VB!qN#'3M5)Q1}M>[\ٯڴiףe˖ Gm˧ZB!B~^%V\)6QRRrQ\\/QFTpD-p\,^ޘ6mnݺ%YxP !dl;^x<ǜ# +.Tj/bsBb4ħ Vh۶Ts9"pݏvVk؁`H.b#'3i i6oC %j֭ }}}_JC"** [n@+򾡡q r ~Fp\Y~TB07"F"96]Pm1\ şMԺ͛(QKOB!r[lA-(߾ W y*l0Ɛ(P̝;͚5ׯ+8qz!{/_Ɓ$z>-!Ϭް1n'1RG􅾎: (֝| gϞݻwZ{CV~]]i&ȑ#K+WIh>ɄРADP uDŽʃd`Nysȗ Ůyg ŋ Aǎ!fd-!B!Paa!VZ b(,,ƌA9ЮUT1[/ mU]4pvvƠA' >cE>VNA1&n(Ǩ!vPCGCEAėєpҒ_R)5hw- yyyIu>ӧfwv$1CzBmmoV8f0g;v ~~~}L&P󡭭- ˮ+W@GGG`cHO:NB!3%Sl_$tĘUGbL;g쩔غu+^|) %ک5!B!5Nrr2/_G"''GU`ĵjkwK-6qq0BzWT%%XxgN#4^xWͿ[na߾}ӧO~@RׯgϞaĈ8uT.L!kS}l ]@I9cӲT{2TTרARpsޤ(Oo/xÌ3!pmhRvfr77!іCTTbbb | >}Bff&> ҥ f͚&?+[F֭Eooannɓ'C[[ 6lXi= `ѡ "âNb3wP[##7+WgΜAFބjQ&BbEGGcp3i&L^K色=,ry"bPQQӧѫW*7Q7b޼y;7oQEQBHa\\YRRR.gZGMJYv]zmhhtxEƍKc p;f^+++#//O`kM)TR1b0y%?0p?ۻc}8 ,zx1f 1L=`Xr!?ږO2ȄB!^|^z D2u톧sVY&k9sig/f1x`Ub͜9׮] %j !DJ}饩Rh``ä">⯃8\_ZSSSי`RRUrtwrxI5E#޽{W],?qt1\{^Y11PU/eX0p! r CAq0***?CZBH)JB!JLL57n p\Ųh1v&:8?aV @_AA郬*(FK;Q( *Bt_:uoaƖ" #[Y3x-666FJJJ%gW)%m\!xbX[[Ν;֖8#44F˖-h X~=\'TЪU똘̜9BcKJJ?Ñ|@G,r2r:J`0---Hxxի[[1p@^닠D!\#}1<_ @}JLjc3Зu|>ǧҶΝ;R_z5ϟ/Ц{Qtdg /+YyG~v> 9Ҹ*-BvDM#ɤDNn Q—>-=۟C+t{.Ɠfff"BdS)%m\!?4$$$gϞ۹sg9sFd6lJTUU1n8lٲ*MӧO gz`nn/^e͐?gX.k[h|?hhۛFBMUq3]w{/=+f0F@p8pvvFTT@*M+EzR:R?!-1 Iw/!-ArR^###V*&E֭[qȱFF8ejM܅^](/,067x!No?Δ^[ZZ"..Nu!j[> !v+))={sNx'uuuJ/ @-!!;_~8}4}I'Ub…XreuQ!L&W"}p3Ϝ)~ 0p@)ł.tttvZ 3 {P !V9B_X,\V{i]O͆?96x=ӑ,|\0H s$\. ٳgŎndý;uR afku|N?K-:A-BZBj˅RM4 _G׮]q5su놠 $p x<4onnnPQQq;^CE`2=,PРYp53: >]FPQACz͛+\/mC۶mmꪘ ::x"1& iJXԤI\t &&& ?x1 &[tHmVTPӶҕ?/\qW~{#-'GP)%m\!ω'0n89e?~ %%y}s*u?~,mSVV1WWWhM6$c8~Tbg l,| ϟ#Ξ/) \L ?NS.[er"## X,ظq#w^Z4TpX̲܄J7uX/,=x*֍quphi-&n+QvaeeWbݺu|G_[ qǧAGSMn$=+7 w7nv-hӦ bbb"DmmmhkkCGGӃ!йsgU\|>lقUV!##CXΘ:"O c۸+fSsƝ3wK݅BdW)%m\!GJJ Zj%3U~ppp*A_ e0x)KYGånݺ!00,"3tP8qq={=6oɇ8|_m55n ?."oBX|<}#vZI\ .hT֞+>I`fŨM'!/'e0RDY3Cm5Y%m+d^XnfϞ-0~bw옡u{;G߾k4mO>itiFKL&ttt```deeo߾Xv-~ *PPPKb֭(**; +CE^%t2}q]QFJ6[S};:-{ de^/##mڴATTTY,֭ 水aeec^~DQQQXjBBB.+4FP=53mM!JRwg%)Tbbpn,՚'m@حg]vիW++!?ږOBRXX֭[MMam /.3g:9 IIcaÆ"-.۴i_nݺaŊPGY_;v ͚5̙3 Î;0uZZZX~= "q^S ik+"L 7`ŕX'w‘#G0lذjvR8b0 t4}qW cM}C%"BF>\yn^!]PvĤI^̑oՌ<<{菙HxȄ |+_Ѫx!<==իvUUUhܸ1<<ڙ Ƶn5}.DvTMMJM͛7k֬zM#JɛE f"fB*}Džw{FDD 99^^^:Ⱦ(ҳa4`@.bcc իO@ŹTXZg؍pDDnIߩl R3gΠ͛7}T _wI[Z</"[ϰuVD~s(z [o2!Q)IB!x<z!s{[bt6l0҂q))) -76>ׯ566@쉓kS|`$(:V\.mۆ  11bNJJ*38:rOjlիMvY[NMEY [t %jEC3ZލEx{h %j !._<)ȇ.] -Zh/;/V]}M_4P-;;@_B*tmOPfy a)2'j4iÇW_)))aРA1bs/^z{1e8b^%|SNahذ!믿]vXzȷO80 4j;w/,,D֭`0@Kr?(Q[}ʗ\)|6lŋpaEE!fjbg@GlĈ}JQ0#Xv--Yʓw+|;a+mJJJXd xbnݚuEPPv%,Dǭ]2l0qyؿعW\.=ϛBGe !j۶}BȏaX`~%6qUhX,QRR"h#ǃb+bŌ3ʝrJ̟?bJ)))ׯY+c̙ذa&Mg0,}iiirmbu>|;Wlll+vuptuB(.S³_~8{xg7I]ge _~h޼@10x;5x<>8B}$D8/x16֘w.4u[N.nXW7`>PzʽCS(YKH Q~Bj۷cԩBJhk+q&hY޿7m*x{0ätl6 l &#!CEEuODwwwܻwjjj!88۷3L$%%^^^yИ={`ر$oaIoZ ܳ#J=hժUFT{YXX 11Qm3-XMBQAaR<:|>!6ۑ(HLL""%`Myp XUxpx>n>ZK.r %Xr%,r</SBSDmAC}uL}GC~܁ׯc$gS)ɆB :tӦMJuwkZ`i^NԎ:|Hb6HXFqpyd͝k6g.e'Frr2 mmm1.]Zcƌѷo__^DmMMxc~nx:g..N6ٳg\c:w 6:T_kh6 @ĠMeUE~&Lj;~6]u%..ZBJJJ먩!x֫'sa!nD2::8`eT: e;ftCCɏcÍXvRIDD6ZÇDZcmMjBy\L̴o-EU%"K/׾$r]Dp(ne'Pve):W̴f}?9f}> Ƌb`=Z 'ǻ FFڴ ~GclrXn֯_9_gĠ}Rޱ$Ξ=I&!''Gq%U%C \%gGM[8QsFẐnnv.V_Q 22x !%#kB!cǎej P <b '&L(Qӧ9BlDff&ڵkWkT|ܺu *xh^.z4k?@b PSYMjXګ7&O) Uת]N0}`vn;I;qp*VqO>*vNF`cc///*ԖP(DRRRQL.!T U;ba| mX3f`Ϟ=x>@dvTNNNׇw@PIRdԨQ~:Zn-xvF6<^^qN%gGcGdqB-m۶\s#DB!& Æ Cvv6#nSI]j(+cF.=L7)0I5lż1gժUx{;8,[E( nnnHMM-_~ŵk!C bQC'}}4BVp﷥+pylLܹL?[ƭK5{N-NmUU8Oo##?; E&R3KJoxrssgϞȰj CÆ Eׯ"?lCBHurqy4''23E>,;A_&#DGGmaZZZ8w)טvڂtrJ gUz]tq%~O Ua&DGT/)[diP=E?]nyBdA&DJڲ}Bt %X[]]_%yy eV2HBrwXs SL~كWSNذa( ѽ{wjPVf?|(++#""vvvbcc p\Å֨P aٳe>_EAK`ۚQ'yS0͛7%lٳg5Bx!T/>Ƣܣ9S ]wK3`0a+pvv… žm۶-?]I&044ġC~难"[lw~ڒԯ_?ɓh bk~.\ӼHOȶ[ )*G]<͓cP6nX!%DJړ N:kiReNB!DD <><f#7n̈ׯO}95kǏa``Pn{?5U w/i^FB̨^=<_.v0}[0˗%l|644dŢ ~7֜d4mڴ\ v.B- uOxXK?p#CX+W '#cǰv"ZTR6B<#z(Q4//j㼼<,XXhtuu<-=+Ӌ/`cc:ڵkCNN\\\0xr{fϞ-P |cmm-YQp5Eծ ez VVV"ܽt;f:b\ 8^'51UԿB-}cj̙3@ZB!D%%%[n#^GM WE}Lz5U\+؝;w`gg[n=aVWqU0ce_Oyꠧ}t]# ,&כo'ԕ*MYryf\p9;gYK 3B!b~hU1I?P&ʹYxݻ}6O 25k׮_ey lmmabb___[xyyҲLѲrJoܸ3:(򘵵5:%7n`ժUEwtX9d?/IKBV9SRR\$TmT%BdLzz:w0F2̞SnEIYӯU+ֽBw2v66p67n·5 Fqkb8!m`u߾hqd^>4UT`mhU7?PT{yzzVr6֖nFPTPVB9MD*%>'53לA%>UdX!ץ6ȄўH YN|{MD~sЮaC dVd| !իrʕ+Psw8 -Ȃ1Ǐ˗"~HLL>8@'OfnvE@!ZO#3VCCfTfC[ݹ𯉘 nF@X̧[ RhDDq%-x\Xj (E7mw܁N9l0xxx Ȟ84y² \]]1bĈb%ܨQ#DDDR,##ӦM{y=C=,;jCҲstr\666rmBdShTg IDATe-!"ܹ%K}ZiT-!%yygNC.'$pĜ~e-_?!H\????d˱cuai/d!Hz8zu[00`s #=K {ʹR|VNN[laĤPg3pwwYq9daq~ ޽| Xvp)Ք2331zhl۶~,~5uL {[ Y3g //صk4h rbv8-56z>+y'͜9!!!pwwGzDqtt!2B 8|c͛KӦlxp2 "x,7SSȗjWM@]I\C1+PPSҋŃ[JՇB$<FzXΝQFxhx @o| K}W}kԨ///|(+** 'NDq <.:6Cٸu ~_M9FBYQuM WX `9|_]`?UO@ 5k0iҤ">}{,;;{VUXt)>ׯCWWW伫b" IyBȝRCm۶6lUwޕU鄐b-!"D;FWgNld4 |ڴq;3o\FAGrB.': 5ث2[w:V1PQܦ tJy]BI 󜝝.s9,IBf΋@A@|||*8D|KAG0u%{OѣG۷o^^bbb0uT4mNB+\֦M,\1e Ukݫ Y> bV4jшuɓ'˗/E7$%{쉈YF5IIp kGCJB2$U[!uZď9?6//YR=ўH YN\Ϟ=C۶m1ݚ5bplVe4HtD `XR\Z>yj֨9*ȰǏV?_g${ۂqx|x3""}kAO |W\qD0ܹ3VZ;;x<PCJǚ5kp)dee&9 K^vqəxӦM_[ɓ'յ`C. yػd OƸqX+VٲuǎX|y9d_5`bW +ʣUVhF-'휽 OoA]֭[{OOO >Uu"TdBZBr!T.;;;3b3tΡ$ AZD&&ڨ6&m۶Ez`ffիWmFD{1LMM?`ps eD!Xy;ψZ ۶m۷aggb3|rѶm[lڴIJDIHJJڵkꊌ s8у,kWiQ>D%ۢ㈊O+#55iƍի#Jܚsy?}:ƍCCC5j@ڵK.yiӦѣ5TU۷o@ vOScԒQ)cKKKܺu1GMM s|> kv@WGGGۗ_TVO6BHwyVI߾O ÁAݺhV\.:aI;*cL;xsM!0_+,-- VЫW/ ɈDV ''7bo3{"[i?oDӧEZ#Bܤ9cfo ٻ[u5œ?|@pp0k o޼'m:cPPb$K(=Bx{Ww޽* [[R'ڳ)!k= !]v aZݧ/V#!:tۘY"d5]V0 KŶ#B~IJG1"+' pT qݺuý{Dk۴Nf"<*`ܽ{wܾ} .`ĈXS|nxd%!3-iIJNt[2R30|s5jΜ9Sa_SU' akkW2 ň%ĘD8څOo??Rݺ rBz>F!DjԪ ޽%!nbmxx ɸ{.#6j !K bVFƎ[  d[7S{bHW#gьB-7BÇ]vxiA;񅨚kzLKjm\~:B!k6… EgP}p8-;PC XqAa 4o:xzzbȐ!;<j !g-!R\pAdzpu\zcLRתEBȎ"''`xD'%D!J1N<)ʏAXu# +K &LPaƍU>{zbPnחE"' ÖIې&z)?) )5 {'PUV!<<-[>x`YfxӦMrJ{999ܹsB정BHriL87WK #B52[EGGK(j\ kK(B!U/0sR3rq;wVBB!Ν;LjYjV*^nݺU*:Q/s_W]̴ؕ+WЭ[r,alSQ 42ҫ̪O1t ^=|/(++7nKڴi'BJB&B!1an~ӀȊB~]zڌqJJ RSS%M;|XBB [.,Ԯ\}Pfؼ!+wB#ƈM<)#T.ז%ׯ\ c6 *u7)ܜ\~ +pb?NJ,"((:thPRuoB!Deeevvv" 6lC!Ңeׯ%lpvvf<ԣ(ÜAU{AB!l>uB۶mPfٙ1=ZT}]0[ khh`~ԩ 60b<|O=7aipiӦ2+\` 3q~;f[́I_扜r1p@}[, ! B!Roԩxc0|ZXB$_NWp FW^/յB!._Gŋh۶-ܠZWX777<{ Sr(~K}8!!BZH#5kPX @WWzzz*.\X.eaG}}CDĘDב500ӧaffV BhT%B?Μ9#򘲼<{[WrFR}שSI{""" m$ !%J`赵ŋEuZUڵ yyq9ݥ:2C~?wXf̘QF|||A<22#Gwekk(~-bv6esrr ]]]1gȮ,tС.1&&&x!XǬЯ_?a߾}x">| v<==1] 㬌WkbqqqժX+ 1uT9OAAaaa;nݺ@׮]x8lFA]`=ܾ,v?Ę" .-!DΫ/B!DHOOguk^=ѯUk(!2 Baĺt.,??W^e,M@o!Uߣpl8q "(/6mejO$Zv;4#֟E| P'//ggg(**J s%|;rOE<Pv݆6]6?bNU|~~To`NJl?ح[0n8Fn?qaѺ!6u t6<.;oߞgqZ5ж[b3bz"gϞs|DGGزe 0x`;  89Grr2XɁz!rO|" y&Z7x>~n] !"ѻwB!Ddzb棡! BQX+ff h/l!ҲrY1QR3s5~AoVFjUҗ<{r7n޽{ɓ'C^^ЪN(͍h&:CT0_())r`f9ZI=u7웊Xvv6@ X׵cႧ~K}p8KyK" AAAb˗/6l(TIJJJCCCmŐ!Cp-yƌ\>\bP5|aAE8/$$p\XYY֭[ Bݺu%)!!R鲲DҒ@6R9רԟ>|<׭^K(B);Zy^T1/>Ƣc mCW ;=svvٳ%QFTsEOh2O(AIAJPV<堪%y)CMYjJ PSQ4T UEPUB UE(`3" Fjj*#nҬNVKV`؅VB}B!޼yÈ/-'̬MB8337oD޽Ys7o\J B888`޼ySNy[fZbޛo2R2|xmNNTyExx8̐mmm<%ĉ066f{/8~3}@(r{UQQY(v?_BJ}Zl[֤I$$$@  ::Z"7o{~EVFP(D{w|J_\dvҤIݻPKT%BDF'5`GD!viii믿`hh;ĉR!4)oѮy} eC!nxub,Mدsssk(++y"W wk~߾}Ǐ%Mڴi+d=|0^~͈i+Щ~@cWXFlҤI022{NPA٪s+իW/Ԫ[P ebccE%IZ,^wޅ)#. Een Jvcl80}ap8ҥ mۆL8qȘB*=zB!HP֬!ӪNs=z)S`=z4VXAR_UR-Q|BHէ[G7C5j]4jP_sp  @ߣSJD2:tx"99HOO˗/+@CCDVVV ;;9997z IDATAnn.|>k>ubػwoA<##5in4,\kihh?{P(ĢE1ڍ :S0ҥKʘW^Aٹg9!prμܹ3ڷoV=-)("p`bҥKhӦM%dE!# !jɓ'":B0sSRRp!4ipssP(Y~~>~ej\BYLuzwNdd$ҠEE Ș \n& %+\l>y\oӡCJJr`kk &`СqN:Ǐcƌŋo>4k prr›7o' 99A\\j} ""0f((0[{VeΈ-]TJ^ʂܹÈUX^=4j\z@ڜlvcߎxnnn9d*|}}i%9>'l)vFZBH@ZB!D }kgzܺB*4UTJ}^`` ƍW@VUCdd$kEʆB*N-uS*xNN---|+0 c=zȇ`G.8b c[xY9cǎ,%K(b022#~zs8v<}BSSػw/ QPP.]Z?bPf[ߛO#,ec&x\zk [u7ڙ}=}WχWF\YY7oŋŶ&YG}!)|j?&T#u2?xSSSÊ+b bΝD~~~܎;B[z&c\&=(!"q)8w%~;U<\.8O IP\}1Ƥ2/3>_^NOt<ܿMJ… 0k,$$$@NNԩSC ['A1 hd?_=9r *0+_Qb1wUrb;vl먟^y?VvV:>СC_>-[UUU9[gWg|||xo޽~:\.աV=zCCCC&M*<'QFTF^b B]ÍCEeEVG-ZwkѢ`bR5~BHEb-!"}cij !IXdggCII ֹsg\xطoPV-ԈauWO^"FZFXu8{>>1wN~}jBفiРAؑ1vX"ǯؽjra;}26lFs |a>|8ZF}y`mQV7o.up'' F۷~uGέEOKƖ).{ 9Y9{_`ʺjۥ\rˋ1Y&V.' B!R)5jH(B|0B555Z 7oބ;6lX)J{ծ"L!Dry9^C&/>xA,;>"$]qC#q{{{DFFbx9bccZN:X022cddd`eիYyG" l U%mbԕ\|CӦ}da݌нM#<8G5kV9880|^gXzSVVE^wٲeb .zۅcЬ.@ѣ`}6}ķNLLē'O7BnܸQwfuƍ1yy,p/r~zJ:6TdgYY8 N9\E~c;v,j !'T%B@RRcܠHb>@&UKrr2c\%T_kwxmڥ-\ M؇* òo|ȑ#pttpլV5BPPȕE(=zO9-L%((we4Օw >[vaܤήCe9Fz-Z5ԮC;wx$^^`x~ln=V`gg͛7# @l~mڴ%#v|gu>>~c5ktuuE^g\7VWR%Ъ%xvv6uLzr:Go{Z؃X{ \]]+ #l >99}G,FFXmb>Dzq\ԬY**qWR.aŖ-[K$YCZB!D„B!RRR1=MSȄ"dD(u%akjZBߩ9N/I=Į,ʧ \冁V (/OpGFСC2eJ9r˗/G-`gg'OHl_M Cvk.|1yfqՈ**yU<Ggc0shk1W*+`( v@imD'l7رrP-Ƙ]vvv6ϟu֡[n;v,Dm6p8 VrfdfN:ضmد{޽PTTyGnNs+JM?0}T6ay^H\Ytq+_#b sEժ/]zZZ&OQFo_B! Z"9"^E0bjjjغu+㑔 {077ӧOE~>qK37|8vRQBpVd$T& 0FFFhP(FY6 "~-u\:7&pEc\ F不Pvz,:- kbduo{Fb>}z޻< .rޞ>Mj\W48~ ;v 0006Zl sssF~ЬY3L6 Buyg.ׯWJDݱvZF^Xլ<~&¿&/qs)^gϞŚ5kDB(:7#WGZvWTVDnm5hf}ݡ`=XJ42c+Wv;6eyf=z0y<]Qp88r끁x</Zy 8RRD'ε3cmmmLn sss=z􀙙cigx .q8"%̙30aؕVfP(ۖ iY˗)\̜9666"0|y8V^B 3#44`k}=zꅃR'N XqcaҽsBBt#6qD;m۶ &Mb2R2l_#n(ʈM6= !b-!"a;vd !i'&&&L߾[0T@&R5p\`eKĴ0-<?Slb ̛7U1a > -tL׎uE:X6ʂKIIÇ<#""9f͚ZR?$#V^=8p=z(/lb 8$#&//sRPB0={0bϗBRڊhyS dRuZ}L{BHjTvw Z7[sj֬)ڿneoz.cExpiEy6MSGvK(+2 dyyyy_׬)j (!./wD_XLܾJcoOGfs|7mllжVqwl/3`b;6-r:t1FuC`W[6<-m-l>Ql_q[XFF֭[W?<{ #F`ط#Ư'n"51۴iTTJYNNgϞeţ>DEr;1bɓ'CGG'ꄊBPP`!$SD-B%**(>$$Dlϟ?g䠬$/l!ܪ!BŸmZdiӆq,O Ęm[@[/2b\.Z*WP+W{do=l bR,~h9LJg@]yV5PS8\ܟuaZQmm?~< #j/SIWreP7-) ZZvB](ݽ{k׮ ?tn]hؼiy<8/?g)ޅEGGj/_`mm4Fܠ8_i/ 7sUmÆ ˴uy?\;s_{{c***ؽ{wO!k !){Ƹt"ʢ ˗ʤj(\nPGz>%bps|=ػﰦ_4 ! v+u˪k/E׮kWbEJｄ_A=9w3]tB 6M6ܹ3#پ}fhi:O4hիW/Kxx84i5kְr0Kc? mMzޭ +&3;ڀv_S^z49=t,YݳgOٱKCXuUPA  5!&G1bUt؊z]eeebhWS PUW/q8\x(qLqTUU1c m۶j*5M0gs_ȑ#K\(!*8pvx~9+梍>}@K&ʆBH% \IMvGB~/VY޽cm$R]-u FwشiԩÈEc3%Kcp֟bݺu2?m۶ "+bTZ={f7O, {[Ғ@CUx Zcߊm;W)_E1b>>>HOQv…fΜU|d2YߨT^V [%Cw{!)y汾-bNNZn-ZK p$qX9|>888}0;uW@]S]̝Ϊ=;_OOTbaǏ_`*w|=V<>!(YK!(Xaa!>̈CBu(>>WVl*4FۂvBH5mKPl322pUV2F''؇a[Ѿ}{YѩS'L8YYY]C!֒ g&~իJ-=xECP,)TϺ̻m파 ٥DӦM7oTu~Z{ix^90i#wō;"Wǘd-KQb?aXn$9!]nn.Ye550o\?C،3ʴEFaa!z`tH ===B!L !Zt)+VP3!ysX1?|ڴi4M]ЇBdjx'zF`mm'OBE"? ƹ4JU -¬YJCVNLkkgf7\I_6nܘ/7S孷j0w۷h7zj\I SZt>|̙3^@gggܾ}WQU-`n+wYiYz#fnnsj8qBd|Jii:2R2/k:55U"B%k !ۻw/+6fB!p0CGF,99˖-SЌ*7##殯̈́B~M6Y %۷ok2 1vEə"+z }1> Cv.;//rk.M"xXgG%#9#kݺG#27(,юE:::3gϟ]]]I͒PZXARSh/,n+k IDATqFFF>._Jl$x/ngcX Ưv}moS kINp08^~͈qY9 +#"'b &싗%VD!3nܸ!9BȯBfB!5ٙUY`(}ٹ_]T1= !ꏎ DsNbԩ6lzN^zIXtƮE]q[hJJJr}=r)F[U%Z񶑰.{n?` \ |kΝ+5khUJW^EV:TvZGƫWP~}p\5_ ڣG2 ҹȾ1Fˤbʔ) ?;-Ѷm[2ʕEnn.{@U j:4EMӪeg>>F%_ի^ ÇE3\e.HNd\R}lnn.<`&-nXY{wvvƁ`gg;w.vSPP;we˖)v]EU6LS&Rì3}7l ۷KnhbJV׬YS _ %k !FP\є%4lu7.=Ν;1aάrp2'>B!SQ"` ^yoa]t֮]sի_>KNZjC_.?*Z^1u޽HJJB.]`jjŋ3hmy O=_`;Yuttu2ϥQF;(>W 5 5VlʕXhS`ӯ^*>v:ә۵cD@nۯmY_`rغu+Ǝ[طog"225ԉڰaxy%#֦MtA},N:u.d-!`fLjE&')h6XJJJX27nĸqhwaWXr98/jaG0Knݺχ;&sիWZ5gVFǏgkJ~{P&?U{f\Nv_615 ~7^SQdenx󆽘;k5K.ҥK ,ի9027lllйsgx7oKN[յ*K>yfH4VcLwXqD޲eK 2.u)sB]'B 3BB碌#:NBCCk.ͨ`?;B|yPɬxTT,Y+WJuuuEhh(S%jy<1wg=?!% Z?**61`/SߡC0+}˜Ü9s-Q -[?ϔQ|h&x<f̘8j^> 35_&N(YGEˏD^~n>Nl>Ɉ`ӦMXxx8ϺQU*jשWQmX'Fz:x+a޽{A%|ԫW,#!hg-!`/^dRsr0B<˗!3/(6a ygc=sĈ"1}t,Y{r\"+Nn>4Fl̙gĎ;tf6k Oƒȁ6ՃNE%UXX5G1MMMlܸT)Izw')PBQ5kb0B<1u~ׯ_affUkf>~Md-!ɽɜ}1N:%U#G0ڦhV\n,obb:8R5dy!O~_ Zu3f΍+rssakk[G8+ E`ðׯ+`ᫌ>b/_f?Ӫz)g/ǘ0|V߫WbgR@*Q{qGSSS2ݧnSNsk !D/6!sttd%l˗ !TS۶oҤ """*v2+SL!bY]{6e u5aU]5Ф 4%Cŋ ܹsФ\Hc%ka[1C"0jyx/bM4Ν;___V{?KKKԮ]B^{í<r\9 # #1d}rssž{m6N%u8r_ׯe ǃX{]:؋ʢ6Ǒ]Xr\Eƛ7o^⽅ƕ+WkkkܸqC#_%B*yaьبGP$2K!*5 h8899Ν;r?gˊiiv!2hV~Ц5&rf%gy<>?|^s.G@~7n@P?%CZZ9K Whqy<{/@PX,J:{ƌ3DVP4CCCKᄢYԲ?7 k<^?xeE}Я_?ݻW⳶mۆuֱ}%K b׏a< `qÙ;ЧOVϟ3Z mQ[ΉZ8kfOVfbE ,H2K!ʳBeggb|VbB~ybJ6xRRT%P(eA % !ϐvzVE>AS@|RÇm mh-12sKN469CVQ1i UVV+9XڵR&j :Gr~kdnڣȮphaϸ:uxoxxȸ|윿 ѿ#+̰޽c]☥w:88`̙rH>V4<},"_RBĈagK!e])*mJJ ڶm+hfZ- !(Noܹs0yyfgr/+Vyd]=@[pkdd>}:"""p=xzzVݩiSvI┕+ܿ{Ӷo޼)zrM:NOs%Sw>¢qqE,腄̳WgE1b::ԑ@_L F]7&iUUUѩS'\p_|aff&KP7n,t+Zbt"_AjB*Á-\]]%>#$$"#KWtS'<~.tЁk܆}<ƧbgΜnlDرcFǎ%>5V\]C"##3IBPBZl eeQNV !TNvƸ>qutlxxx  t"U Bb)))iɁmmMUtoQG'k9l0hjj ǏǁfVV.Ҭ|Zw.Ex`ɒ%kеkW.O+r}*ZVZ BkvE.EN:޽|<|P5aÆ1b!6B3l J]߼yÊ _8m}mF[KKKگ_ca.]#F@E(@Psv˅s/_:_B<@!_eH{BH%VwNC 1}we,_f͚5c3#A7!GGhQew5F۵%%+F[[[ׯ_ǡChܹRߓoff|܆ Ν; Z*BÆ KSwYw)Bi~~~X [y3g΄:#ܝ]JX5mQ]lm>Z O233>Ӻ`TTTSEM3ẁu:drttgL!??JB!/y_,E!;cܚ<uDV~Zb xB%3;‹4+`D-"NYc&| 72.9O#.%u]WW6mB`` Zn]*ٳKS^ òxy%ɓ'Çh۶mQQⳂ^ׇktwVu]U[uSǮ<Т|G%@ 0ZiӦ1J%C͢Pjz.qqq,!"(YK!TQ + !w_ nN:FFbĠsθzjάXXX*+G!'تU0߿PSF:ƽHts_qI - U Xv )))gϞ ɓRǒ|8vSqm( v?annoѫW/1<;u/1s !wV. B~SiiiX\r)&BicAhyw֭[RPRRB51 !BxÈ} .]h7gu})=CHMj`aa###Nj>wQB5=M%ggs{ntԩs.ܸq(,,χ@ @tt4>|0|7oޔy pB4^,$Yfabt3ի3b-tkƌ3k|>咵œ>&]n* Uy+eh!fDDH>nvbzK'*1|ح7HOKK =z8::ƍ"Mfr4i֮] =ɓ'ر#222w^[Nz)1Y&sKr\2r;1QUU7nĀD^OKKCHHc'b̘1?p${3eԩ͛7 x_ E44`0(*^^*Amhjۦkdά|V߄(FB!'EղfC!?GI~*5jhMUL!j;4c_(ױ2Y ʯ<]iR:UO= PjgƝIWRhÇ#)W569[<Al}wXlbcc+mժUkL>={`nn]]]p\/_5lقcΝ0aU''',^>SCM)tyI죩!ӳˋ+}j{-//˖-xNs'DmVsuE kvڲbIII6^xx8 FbKJrYJHBBH%hg"g͆B~V84l.OKJ__dGVʟ߫pK!w vixBZt Ҳ1e{ep|%`̽ _}7ôzLrW"1-[9Zl3gӧOXh _gϞݻwk|GNN9Rtx<b尵E͚51f}lrr23 _H5Q>} ggwRjjK^e.X[,He%dKIIa~?F~/@!C·545@5w ˋ+Q.cݝCQ0TeEF+BBH%!*pBHJܟ6]䵬,>}Z䵟ú_$ǣ_Wu[S.sh`h8ܹsvF0Ӑ(処vCYr!,:B+ N?p8O +,,ħOWWWp\())!88sINNf5XdgOl +իf͚!!!q{^:V5wޕx .]q}1QQ6sȾQa̟jjj011)jGagZ<-Om^5fy>`ҤIro߾ 1ed\8%BȯBH%qVvZ M!?] $^m55ֵQFW>YX[[3ڟbRDw$߈P}eݻrGȈťdZϿl=cǓ~pzs<{ kf͂J+>ֶ0/ *ΦM`aa߳URNJ첸ҝ[Q,jYe~%սիE^r+V$Op~EVfbh1X @MC '-5ȓ F-Ɋ2 IDAToݺM4S2ǏqVGπ#?!n(YK!Tg5-- iTA3"2\^ 0⩩֭e~}OWL!pg:3Vs!riUVW^rG>w2&)~ {{{L0~~~HMMǛ7oШQ333IPeB4iRqwwj)CGS 㙋FѣGcݺuغukݿ?N*򚆚2 k1{"hjk3\WJ}̙31qDFLOO{eU)aÆسgk5kaW>|8N8!F5z Ըmc%< tqiJ>88fxVau2;3 'ߝ !R!v؁/_0b]ݠLTBHY Xq˗/鉓'O*pfS^=F;69@.% !gpphnOt~gNz lѺ0KMMKi 033 t޽{CGr$͛kטɸ[L7]Av[|>^^^x)rss񐗗|SNغu+[???Ƴj[eh,v!Ϛ5T;:$ZN^VGf6kzվMMLE@$$堆 >#XB4-- /_DÆ ˖-hܸ1N#v{׼LBHE:v3V|K$*s9H>?ښ5>z`+OYu}d޾} ;;;1w(^ݺuEN[[7FN0`ذ=޾}[Զ0AĿS߇&oaa!:t۷oKիWcΜ9^ghhL 5MbGTR%w .Hcnز^8_Ms|XD`,FL]]^BZ۷COO3f̀@SU{&`͘uliUUU| 'NiX30c^Я.IӴiS<}T1=z3g޽{.Y@]]!~| -]!Bl۶mrkMZBjհup\lY| ```mߣjee=zԉZW@@#Q \!8xc7%N^x^dۜYۑʾ̿_5k`߾}zF9sqM[S-ň#DƱǧOf͚3TEŇ/6c\a^xt"} x^YiYe`Ixzz:,,]c~'^Ӊ'!(V2bŊHNNfSӡ UuUͭj[uٽ{\rСƍ#F`…С,--QJ(Iq - m!BHԮډB?vMc8~gVvsšC>* ^RyPsf$e# d^J6]Ǝi]K=gavPrXvݺu=x<444PNtzzze,8ƍ9ȋ <==e˖ؓ(0a`.kUʼno#aY|[n1odk]p[d0cvԩm۶ab_CDDvڅ$L2gƳgϰ~RQVV9rrrǺ. LLlq=U>3+Sz"EW8QZ99Eg׮];ԭ[W>r-g s/_Ǐy&BBBJI,(++ .]b3%ϳBňU;UA3"_^*w9dfO j׮ F5iw-! U5L0I[c-6ϧ-,,퍿˗/ǐ!C`bbaÆL̟??v@ q3ɔסIM\ġ{f7l?hk0wRx<^~-t C #<65g%''cРAիҊK%hXz5|||ЪU+$&&iӦ8vn޼Ybr_[[{Fhh(>}Xܸq;v,.ȐH'zbb1|>}D9ױ>yeLb3~!I[qO-폰)xd2NNMQҐ7++ ^^^rwg4m~~~hKC[9yg"#>xهN%y*5z7v%{*[VlE'W~V\I hMme.*\TPSPSFm gϞeߺi'`k^NĘ|KͪpV 3ֲeK+:u!99Xz5f;ETX^-1,}jbm"2nii}BpttĽ{`l޼Fnpٳqdd$z[D `Bm1vCi7^۪U+^z2u8`jKC؀9s&BCCS<4,>| 6HǏXr%LLL`aauuutޝ2ZB1OΦd-!TV5mƊ9sF*UٳpssCv cJ#p1v'$"=[ՕxGFhT GK'Bc2ЩMNNֳO`(OAAZhk׮A__c֯_G1b\:9IJ?ۢI Kqp˹)ܨTUUb׌nX0 Ѻ5u4S<|\pww/eQjUFBMM(Yݳ0Q#)j&a,>P8f6d--8aooI&gddE"}-ݏvBYE1,ڃgO__n݂cm߾SVVƛo PY=ں;wΝ;TV 7n܀@ ʕ+vZL6M!`ʕx!$MHH( . ,(B~]BQ?i99bzB).5k"""b'#gM4ZX6.?(.BCzu:O^U+-U;:<)Eމ} M&jDG_?c"r )67~_D㑔TD-}G`AfjY x<^xׯV-((30s&۲ؿ?4hVZʕ+%&jEL`B(YK!(LRR.Y/Bd3<{ӗ2ĬYVCARY$fƠMŽ?ϑ/(ĺe'=+f/ O}SLiɠA%kxԯYLSYYXJ;%{A|ݺu\z,Νׯ^KQ+@ZRrEןW - FSNٳ;ARs.n?q-&`_)Yڒħ`aE%;{pttx=&"W,s\.N_ӅX~=7n+W =lIt,\fff1b^zU煅ɓ'ez!Ee%(((@PP$իWG͚5all,q#""h]6j׮-{Brr2`ff;;;XYYAAA@TTall sssԯ_իuI|>?ϟ?#::99901195j*YJ!e!RP!6/>"=9R,`yAMM s[@`]gϞe-ch{!K6N3g!|W(/00_|a+/_Ɖ'pH[NmmڴA׮])Әؽ{7|}}Yo*_><==_ɵ$Hzz:oߎ#G͛7b5oC ѣ\FXlݺ/>}$A۶m1l0 2Dnc~ٶm;8}TUUѹsg1ݺugov&_D!D#G۷uxkk֬铵p)#<<(v=NDK}G!evIwΠ2NMmk4ط1c-ű6n!-#4"~400(XE `͚5x)TȆa(s98XY`ȖʸLD%2w vرǏ(|dS|Œyvk4b$k+,,ѣG={qؿ?@gÙPu[/7>/e۷\ 0vXڵKl^ŀѤCKs 6O|b#b#9}ɒwaҥ{RWWfff>ժN;B~E޽ՀsPK!;s+_ӧpqqa,|sCC"|E9GYEڼ.x{|6x;%_E=+FwmR7AaVuboc N;AF={j入K2bVVV?~<̙Saz*:w\()!a'w;ݴsSlG#֞/js\jիW1d$%%AWW'N@N$Ac۶m8qsw.+~옳C= *8e!元">>b7 'O^nJ|vߍW^ٲJMMEժUonkٞh*$|B64ZgIKPmF]PET$F'bfkf͚\B) g̛7N*Q[6n܈ ʜURR龫WٹTZJvҟ Pܞ={о}R%jo+ɜpƍ2xb 0` IDATTZs4hׯ_iÇcҤIJv'4jԈU”B% 29*"Rz X0DѾ}{F !0&g}oZ ]>0 &vU?25uk+k#T/Bs1F:AVQ޽ˊEFFbܹsmllmAa!$\>2ijjښկNhYzά:WFR%jw؁ݻ#)) } KKK`С??~|*,]톶b݉qqqhժ/_ϟ?#77WDIJCbL4esZ]]])Qsapss 0h `۶mX|9Wj3_Cbu8+ [ևCs윿[gHˢƬw6cR'jevf0!Q +7,X+)) 4@Æ aootDFF ˗/ȶ>(( ;v+^z3i}I)(edP$LSx:!sfNQr2I4Ѩa޿?^kmu.YkWkO^3g6m~ƐD@@\]]iqsss?F`` 6 (>zh薋<^v\x7 |)?;vEXΞ= '"055Ţť|YL/7B4hCqLJA{3لLaP:u$U?>e˖aݴʼ< 8aaahҤ ޿`ɧN… 0}t߿f1}tlٲEפ\:aӆ04iIT1wɴloE@@&ML&''fM6b,"쿷 Ŀžӻbظqa+ؽ{7g^zsQbSLÇq~ftyY;O$2UMW puBέ5քe#}Ϋ۷=\C OKVEEڵ5ڹs'P;x`=zFFFb'$$ܹsib899!??oР^Ν9)))3f =zaԨQnIzz:ƌCcڲeK\zr :qqqrss䄗/_T6::... .\Cïu+.zE<h&i5 "裏vϠU^xnnn uYf"9 Çr|>Vۥ+*O2>_7:{l9Byf{{JSw~~~=SYͅ=֮]0Ưᝋw1rבyؽx6\%.5g磔'&a 4WI1҈2O /V^~PAgXPRRB0sLxxxɓ'Ó'OAypP4QQQXZ+ Z܃~ժUըǾ}hjjjcj/_YdggcƍR ?PQbvm Zl (Xr%{-pe]t˗J???>L|z) N:XbaY^>}Fg:jj"Z`8rIo5 ri8G#P\"zC@ j}HlfJX 3***6F5ǽgoE 7FPP7#.]J5A^u8to2J8L2#֬Y7odbpixB988H׳gOFVI\RD:\.EZ+ cpwZZ._,ո򒑑^za֭4QYUc+7 ܉q*/ågx<9r1sgʐ.Ǹu!55jгgO N:X<{ GDD "$$/^'hҒR kN1x>?`jl8t@d"8!„ *TZs@` U?~.CnHNNfXҝt@ii)wOVƸ\._W{|q̌b5bp9RbXу_EE_FӦM%OLLDV(b*n߾E?8UC 0_- /ЫW/J~ )Wŋ7nvM6vӧ1eJ!!!Re'ܹY7h \vMb_ql xH9<];v^+"Bn`K@%fqY;V֭[ 6Pb t5p|#7J &` ͝u5: !CB0_;4">:t7dZ^zf!1[^*ˉU+4nݢX,`8^D%cS(۷/|}}|rEΧDy/َŌu2{5H|kmzyøwm>Add\ d~6=V3k ߒcǎQZ7n7j… J3P.\HɓR?~8kĉR @y ( \tI-Z$P [Ӈ{%;4u뤶^t)LMM)7nP$ 4k!@ 謴B |>̙C+c1o<pZ ѵ*P)+C~v|>] )& /*s9ReB 0  Z|›*Ԍc~~>/BEE]t+W{B__CWmQ}y|V ;wannÇӄZmiR|\wAl7o0y\aK.eaٲe5 wT?USGSb&E ɓ.WJjZ/Ŧeր$%IAQA.3 R繬 4ńe|^LK3+(--űcھscsLX+X5W'9_喲2,YWD ~$jUM|lZFņ.LKks~cX56zz:> q\ :F' ٦AwN(߿V4hÇ˥n1s>dk·DpmhO^n%^hPVfhrsTZ)CJBϗ/_ׯׯGRRJKKGGGaaa(t;aSa1,'},DNA1%njj,YYYY~*J!|$ePbtU)))aPPP@7h s6M̀ݷo_eIO%I4 9AږZQ . ĔTvYeUekڪ)Ljr<4hbbb 2i$*sd" 5Q y<-a]9VY8CgN}4NJˡ@Oj СU`uV BݠN>DTT% =G^^(+dCϟ#!!AlX|Аعa# _{.rrB5k&>C-˗!ݫW/ԫW@ Ȃpf*ɬ% qa(Tܹs1ax{{6e x`^X<|,EujiT{R|믌qq 0\%&1ITBYY6T)%6:v=qa7іk̈́_֭[˽c ׊*shBXeSp¥᪲j*{36o "zfcoo ={cId>ӧת;@1b?CCH?~L;ֱcG4iD汘;޽+S;;;hoQQ4˚U &&&4wIU(^a䐐=Aa[ - P[p9ņ‡Dq9r$hԗFJ&ɮ%~PU|@_PYW+nnn6֭]z.4$:wBCCÇ~YtSn!z-<,Z6/_0-&!`GRai,P=zgΜ9VfXOGZLOO6ljOkΜ9ya$fAS/46lDڨQ#2ʅ3ak[YjfV3 `ƍpvvybac  Z,6 fs?zyU6^dvKBkQ`L)VȤ7(/^brO8k/~~%l6&V1$&O|ר"Bܭ8ljSN <<<(R8<2r@ |?8ڴkA(9F\r"3a\L0T "@HgeŜ'SLUVZ4HLͦUUU3t4T_ms4WZYm۶e,߾}E)+ nOJ\+:wxfԩSC`l[ZHMfrMڡc ]AV¬YDg7LL<&;^=%WVUF_]EWD -`ܘ31^-kS\K3<UVDӦMaoo'OZLZ06g{b|>_5@ .]5[j1҂fč=zښ{)$!D|˪4m-7bASMv{ j6iֽ?4;^]U ZaLLL%%%MxN%WW~KNPX---8\u@ҹ}3?'Q{K=DR(++Rfݺuz 5 5[3R2*_b?Yٻw/ZC[ eKagĜу1ih۶m +7~xZlVpcFWb-:z Ç+d :'>yf[RUDF0w\ 66_|quر3f@N0adɸ$ J S?I9?C|||͟DTSWWD%һwou~q1B&(iiiPSSԩS///hjjRbgoGcᛵ"PX>Zsfى{Dr'!ĉ4VYi9X}6Fuv_Gbҥ033/^,\.|TPCC}K j[_3T1uРAmbɯvefa\d#G,w`<eeeX7/Y.͛g;!jX8pl60a455allg*D͛q-J 4N&ZJJJ4 Y}Ffh0T@̟?VD ~XGU6m3(ѦM\xQ|+]0gX Kg1b|b~E~E̔{<P "B3wo`W^biݺ5:DSĴ1gk~)e,.MĈ/,< fq@ia6a7:<ᢅB-5V#zI:y2tn]t:F__&Lj6 wwwt QQQpwwWxjQn^,/ĽHK.!/Z\z̘-{Di}2mڴZujr1~s:gΝ;/Gxѣ||&hd*xvdgPw]J+ 4vFm+j 7lgΜA^^޽{+dUVjrXgUeOV*Nllߞn"?Z꘿}.8\AٳVE wXgZUUUѼys+;v,VX!!˅6 H gkSX [kёG+Z7?@ Tj%%j 558 ϭGCܛ8q"V\I ,#jgQ_cнMc.iT?̟ӣ3mb31,?|blߺhe"1gxg((1ƍ7͚666)))_Ehh(6lM$삑WT vvvEo'fqˑ-o)-[׬l1F#C6܂g^ՄE߰0ZWW]d itb8mG⎎ʊ.m2;g?E{7sdΝ;i; SCMjy,tSV5oPlZ8tP嵎ۇ(͛7Ehh(֯_(n߾]d l股@ ``(e! :::Lsh{_ G ڴiCGZ@^ЪV,򠡛7oθcԩXia(.)ѓ@ a-KMD~_DN(,.=xQ17e*, keN~>zylnၸzAGNdZ$޾}7oHn`<<+nڥKq[ IDATE߳4݋wv m$U++h~j&&pDgVZ1ƫ)cϣv;eee5%55(..[ZW'ƌ Ot;vsۊn&Rb<&L`<@ ej ak~StoooC amm!C`ܸqHLLꊐ`Xush0ش1-1¸j, 餤8 ݻwouǏx1 P{nk 1)999Xf _^+S'N@JJ IS_@ 䡱6.=yH̡@nfaL߶Mɒ*0,)yVWƩQ8B^G+c7\Æ  Udd/sy֭1uTo"]ݘ(++={UUUt =z􀮮.˗/G\\l6q1ǯ.۷RSٜpIL6iӦ!**Q,cXG˦]f ׯ { &9]\.g֓{]r !I{_d?q6E,---8;;ceC3gP&l8ſy%<|IÇK\< <~R^)J"Ƞm&Ϲ4Far ocYb)}||h6]-gbH}rp"//Cŋ/#Ze֊066ƽ{$i4O<e^SuNUҏ_j{~P={6)oPeeԍЮ6ۤqaٸz*zM}cΜ9ر#444۷cٲe}]]]q)P'ܣNLy=_Lj5`4z6 l6"+))U߷gϞx=S)Ӈ҆w :oߢ_~vL5ě$ p9[gϟl3fP fƿzAqa1^`` ,X*pc|p?S K@_L޺ƲK؜KJJ'K]qXh-m6NdeeI5'L}EF?}b"-}}kCbb"G hÇZZ@ A]Eg#0֦ǏkieAYY7oĨQh?dO懔ZZ!@h2/xD7mumqYF笒R/X \.vvvoCCC߼y-Z%KDg }vp/ 3=}1>xbHtkٲ9+022Çmg۷cܸqر#fΜx\z .̙(2Օp%o5_w _0ޛ6m\Ǖ+W$)-VVV066]}Xq|>uw2j1bbb`kk+Ccd{yq|H>h^C!&&k[&U*hQ]1&~$?gtX+>k֭,X+V%"}<@PbO`y<^NN픤8^M=Rɛ.߬.N% =33Kyp޽^^^"*C^~ƍ011z>|@=ŹsGXJՒ>چ%BBN<0XZZaÆp8ptt!Ν;'պ$1p@uo!OڀC}+VRTن[޷i:t耈nZ!k&-- ߿Ěl.թ_G|wbchYw_v-K}v4o>>>2h uf(1gϞ2oC~eիWر+!''SS}j@EEVl>++K.Qbivv6\ZuA%??M4СCqͦ0BYlXJjsaÆ䄻wbذa,.HNErz.E~FVafe}t0k.`հ#5Ga1Z9|rgT 4J$dΞij." igieDGGs̡)))Kl8;;׸E,t <@)Ǘ;b% ,=gI(--X,t ,ԩSr U*6k~{gCšC'Oׯ)0m4m}OT7a~#}C~v> r % 0EECA`!CK9hׯ555V>UEQK}MQQQFz*j*ppm]WbFxbb"F[ڦ}RnРc\)b-bmÆ i Lb4lɕ-n~===l4@ϟ?KvQL{EĖ-[OqfMJC u5@ 0h/RS+7k̘1.]@ #xwg. C= C&4onưjn%of(,7@#ua=_Vjd-}a!pq~ 2> j6b6E`IOl0eeeJI&ԩS B6m0tP̜9͛7 ׯŊ 7nqxPp#2#bBM5e4}XXX(lLt B2(O:y!7ݻwѼys|ӵU O> ^m=OF|g1 \Xj^LsرcǏi`޽ծګW/!%%El,ςy޼yسg\ueAII {ƍ7*c9wF!ט|>ge+tޝt745)f#\eR;3f LMMkgqVsm۶eWGիW"۷nݚ&+2ݺukX[+))yx %0V&&&PUU!xP#sM?@ 0ѢE 8qSND8} ^Ӝkoq@K(bmxx8|7ߜiqEbFPV$JxyZۯۢq} b,K |>1'~̆Zo<9Fڌm͜a]C$-*)ŒB2Z4$f3)))Axxx1ƍ7uuu1Q|DFFb۶m>бcG̝;mڴAf͠"vnzz:|}}fѷo_bҤIbVWSѹט zY%]@aoEk7\]]+Vk׮JJJhBm˖-CH}mҤIO>8y=Rb3f`ɒ%j? ʪRekR|UԖddb2 ѣG1c b۶mXxBGVVX,4iBBQZr [!C9_>h ĥUQQΝ Z\@-X[H}Udy CUv6Q "\@O2UZ@dhfѺuk<{ONNK(ƏRL>}%:!ެf2 N:`ｻyyyF޽koQ'''899!77K$$$E:Ϊ'??&؀o{c" r ~1 Np728N>y%x&OߤmrѤIZȈW^~-[ŋܺu nݪ677AVPVVJ[XʁMq4ĤX>ԓNP-U. [[[KE-T&Ξ=(Ǐ;BBB0p@>A5!!W4)nnn2}UZLX(j3gu`1`j_aU$ib@ *eeeSk\.-[i@ɓQZZٳgW }}pZ^@ dlzjjȮR+?X[6&N'V޾}Ǐӧx%;EOyX}6Nx жs9P=>2KKאU4l6>na^_eA[[6lf2##f͂]Đ=(a1iikj:P+o0ؘVc9u{5 444p= 6OFUVIVQA^7Ԥ{{wEa~!GUJKK6yڴir`f0779y ,+W uuuhhh@SSZZZсtuu }}}c̘1 O[XXC۴{iiiغu+ t҅"rӗ=1)'+ɯI=)))b] {I.+-Ãߨ2.9gc(CttOq@ HOk_~4ItqVЭ[7PjDDDub˔k>}ôib<2tЭ[7oܸ|2v!H1I<ݻ7N>M,lw.2@ Tnݢl^D~OblN2@ 0faw_m}yaXh֬5kS%''#$$2B-((;fO>-wؾ?g).)Ŋ7TUQQVYP %}vuv؁,(++ x}elm}b}=hjN~KpAk .Ƌ/Dwuuz첲29ro߆6mDd())f͂JKKѪU+ŅRg'j}E@djFn^bII ⨙],0`\l/d344)))3EMM GUX򐓓ӧb?~R 3mc*x^#\.W~'SDh{MMV+#5M<1zhZL\YqݩɘE*,Jx-%־}{עE ((=~YN'$$P{HkĈ >v*)((Hu9R1AԤ_EYYf3A:Z4Zɛ93ӴiS;[nŕ+W|}Ν1l04nܘ7_zpo jAZw[#&G# ,Tv1~+,wxMfzqBƺz*N8 6***`Xŵk`ݻ2 J lK3mGTY#]85++~(f޽Sacccǎaɒ%ǏvZƾz* .ѣhҤ /J@l?~ ^zŋB/{nJLU]k}_) ۷o,t#Ѳ]YYI +YYYڵD6dG& sHqؘ#3IPצ_B M̬0`(']84o\qi"ׯ/߈#h}}}1| G/|}}1to>?˅cvp}o1 УGRe_V u46DMzϓRHfѪU(@y/йsgJS1cD\4k ?l\pAи8уbëׯ_iSC0 |RUUŝ;w$Gп>h`L#w\t cǎϯYXX ::,KbSNaԩX#B;ڵ KRkGu3Y@Z\5nC JJJдiS}2kV@ ߖq")))@n.]Lyc=@9xv/b|)-J|)*AE'yw1yɭSJ| 2>!;2֡C<}FjՊ&>Qx}Vmꓫǹs.I-k^ɁX{ cϞ=d0n[HGZ맯~g|MKKtVǁPR&?yy5@ hlzJ=jrJ7ŋ8wT7nHj4hm۶I쫭+WRb|>'OFhh^dffbȐ!z..0%j IDAT.R `ffٳgSbEEEpttX7>>V^-P 233aooxz*!!!|H08::åba֭Ç1eJs|>7nIh&L;J=?@ Hŋi'T|&5@ j 5ee4 M ##͛l޼'Oz|aS0_X8P b k}}}jsFœ[|U!8ܰt fIf6 ^h#yhog? Z񜏂kyJJ?YJNjJɡńd_:l`W}ݻaff'N=۷i)+&AMCM.\>D-iMOlccC[ljӦ5|oܺu 7FСleUetLw?>}:͛8&K~&Ga~gΜ`w"_,lN ~~jf=RRRDXabF11gl=<Ԭd2Œ3suiӦӓSRQBWb|ZV9s('NӕٽgfG/oIBBFE;Р)L9wj>cccp9ho7̄n$b^DCMS-:Lkݻ7=zD}HCU]f$'Pɧ\^*@ zZᚴՅb!11QjK`>QFr9zhxxx,V#F`<$ 6#G`2puueJ 0p@_fHk%I޽{{)Si}EallGqr_c@ (OsΔ?S©CZZ@ ǜg(ŋZ@ppH$%ͳlY)/E%niKGcxQYzԟ޸J j_PFvdUR>a&_Dĥ&Mukө >5#f#""Z{*())-{ŊХKJ"…`do 9"8}G[7zc]իWGF\Sݩ aaaϺ7$a vgpxJ˗/PS=pa?m0{&޵mۖ1u߽!F&Gsf'EYCnٲ%A4_嵙HKPϦtJel6.]wwwOϟK3CBBжm[֭[jSlhҤL,--Z-o={zzz2ӧ={V^^^ؽ{rF³g&(:uDB}YK!7_Ee˖Jl6F­[{,]TM1 ۽xfuUenIe{XH,x2F6"">}v=4-&YfFzxo\_ijP 3Tp o޼auFĦP Zj3wvNŢQ`Pr_ ё&?tNDɘh1GAaVs&[s.@B~t-<G͍h} EZZvڅ޽{bё&Ԫi){ЄVZ,'N{KUd+[e&SS@ԬڢZɳE!::˖-)\X,CXh^|)STQXYY!<<{A.]Į]vؼy3Ч6< 0QQQظq, ݺu޽{o/8&ƍh\"q8ܹ̿`DFFb…hذvptt˗qf@P&$@ oC󑞞^ !T```;v 99PQ׷} =_'I %Vת2/5a6]ڵke9+]]]Ux2ra4z'oG˼3 u5ѹ%U(g*Hbbb˝8qHƶ_@PVͰk`ğ^B5jQZ⋷i=%_E jyEˆʭ)^AӦMѠArW^!11ׇ9ШQ#$22HKKѤIHK0559J,ʂP(D`` ; hٲ%vl";/^sѺ~&B)1V\OOwEf).66SLkĎojkݠ]'"oh>iL把Bz|٫ѣxStÑ0.s/*)cc.gϞɭڵkdV|ƌصK﯉'E֦91)EFso+WwrgU;l۶ \.h׮]<܋}(SSSCnnK.{m߾ncX}ѣGann>}@ `ǎr_G#99IIIHJJÇx<@CC.]*q5&j[gnƬC; %G>^lիR5p@FB3oՙc3rav\\\TݻwQRSSTUQUai{-]*㜒~?RRR¾}nxt1DE| uօ̊%>έva'Ofihc:ٰ꟟ #s`:usjcc+WaÆJÿrދx=X5~5S3XcDuyvw$TMT%BaÆ1ߦ">X%EzbͱtJΊ!\;v@MM1LJgtu#|yc67i= ng(NJ-\8x FRu!ߨ}z,NYsxlE$6mp+ƱH$;wrǏ=*|;wdMze^)iYqV(";;СTsQ0qc޾cĆZwڅ=zb?b ˗/`\֍aꪰ@ yH',;K/6+\K.E֭c4ku@SWr iȊܹ^zz:ň٘1 Y-^y$!r%{úYt7b2!( YK!T:::ppp@w+^ⷞ=!N!aaoV\SS?m5Ӷm[wތ}AZN c`iB{SD_ ~[}La=.V|xx%j)<}cƌɓ'cbSr'ά.-%@H2kHka҆ nyJJ gϞ"22uSSv6~ս)&W @LԪs 2BYvr1L>'Odzu!-- {),Jl+%n =YÇezyڸq#.d ^j۷oА4*{c'''Zcشiq={_X3bǻ pF_O_3PSaѾT:[~kC%K`رc;vX3ױh`%F<''Gq΃T90w\eCQ!BXmh=B\Yyy{7R ۷oVIYY'NڵkYWSs}ᩂ#ݣ9.=-f/$$C P(9뺻/סBĄ*σrp8Pe؊˽|4hЀ5nff&yzzbȑX\rfnaoOP^7Ecǹ' BBB^4iP+-m{)4}=c= &s~Er~f͚0w;cѨUNvWHZ澫JJJen\:///xyy8s ͛''(M+x<̞=p>5R2:{/>A_U߿G=X;J˩#fQtlfm?,@ H(Jo)g|K:9{E5L4I ۲ Ǐ6V -h3+w^+Ʊ%1M'={,,IqIƟGܖ8hս88Tg{]C]5ѯbp$FM[<Ou5!+l@ƍ!*b-!R aaaX]=*BHeXr<.xٳhڴ"ְaC`sNQ\(a޿WC!!UǤ-9+nܸ:ubk7 _ Hv{amaSr ->-pJpqFFVZ5k~׮]cTUUV?Y׶ IDATpl߾E/_{΅ǻ/(DV0dbr2| q!?0ٻwk+9/>|f3\e.Y{~5 5>7@胗q.-Y,\e.Fu-`e>^{DB- !Uk !j`k !D<Ŏ uV|{"E[[7oĠAWD2fFRuIbwޅ6) #J|JJJy,z.8\}l:y/pqFpΝXbTTTJ|ͩ*}+mۆo{gjVhk`e ; C4g `gx>c򖛛 ooo8qaaaPUUE׮]j*XYB3FlvҟsT@a]x>}@ma\ 1$ܳ3qߏe@-${} 9!&&bIYY~XT=s55dbl o}[46D>r2s&Gk![lO!T# 8<#+(#BDc+b [;۸FJJJpˈ "v {}!U}1#& uuޖ=U_hiM3zz ! O[6 8==W^˽֮]_^z2]l<3/cRp.5>|CDy+8v=B!Ǝ]vUj6==ӧO1f̘[n!)) qqq8|07n#G۷|}})p1~آB-5M4y`QV.G^v#~zKs߽xO%gz ?\k\8n-=-XY獳d2/);H} k }XK!TqcǎE^]~Nm!ϧ=t#>yd,[LAYErE^̢@(~(,_7뚤lغmCоhQcLKCNHJ`D@d}5@(BV.QL22˗/;00=BPP^~@(BKK 666GFЬY3OKyq)&&h֮)"&-k\GG>>>_^pM5e]U,$yU| I IHKJCzj:22<";#056xj* E00uc+jP==r|f*F `RBĘp,NNfX5 JȄJJB bxi-rrUQƏM6PVᢐիW1mڴ0! BZB!JOOgUM$\A!.zo1b8wZ,M6ElllQң7A)0;Bص,#6x;kbAW񷋚5n055ETT뺎Т!kF\_[Y|4=PLt! VP6о}{oߞ/((@ ⲊO?ZQ]#LkZtr(|'e;g())1VLK;x ZzZOP5 5i#7+IqɈ Se!>~>~}n?]- o>c%e)) ; @*&G? GH@žFRCT>_K<Ν;T%ሤz5&KJJ LLmall!a0qB*&>- F|۶m3g"UճgЮ];jx=GRBY]9}i UU*t9|jjjb[pqkx+WP(^r_G+wm g5[u)*0ڡo߾rJxmv|?Wj27o¢111ppp@f,t u)(;:4Er_bDv+G_kDzY8,_)z/|ރꬦS!R\U8 ʈBjBgj]]]PKjٲ%NʈE$|±k/!e^9"w b>Th{kclJJ`˃ `Hbr;aOp8dX UP6K,ԪTVZiR OOJ-_> &ǮX{v K#~(c"I&PP8*ޟHډ~^###߃RuPBYS !Dzn߆3bvvv8rr-#ƍY{ DHUbcZ#fgW@FfPe.󡮮}}f℔ ^zf nv=m\ +y S=3I퐕ӧOKo;l O?k׮044:G. ccc4lڵC~͛cvN<3f_~ԩZn '''v***ؾ};Ə/uDx)O9oZ'x2{\$vlVqEe"B>뀅ؿrKkXYY|-uƍ:BHDB!ULff&BBB15ee_ !D |o'$0⚚x"TU%x DCCӧOb)sk=`Q,/[cN?){YRQᢩ1^Ƥ{q̪Bܱ1wlB w#SVxnn.889zv-q8Y9Rb!YDپ.+:~:Ӗ-[ML1k 9ڕis%+b>}۷o#..AAA8rrp hiiNNN;v,"v{q#F`ńB!ؔ@ܽ{W9 >ܚښSo&pfoca_ D BoWoCbN%Á%1tP 6 jjjHKKCNN֭8?<<PSSéSmݺ5ib>!f=k "jZuBHdddK.fKQPVR3׿;z(n XE}pr&?4A񹬕T O1c2; ?fӜJdEnn. bj*8<F̶:"3q8ȿ \.*p ^zKHH`l̙3bNFC  ]*-K} yU /G|GSbϞ=ν*D8vn޼ Hx>GI&EZD%/8B~qmmm̘1+Ws!U)+!REoejbA ʊBj|>w[#GVrFxpuueD"ZNdj >9q"!,FeGH'IPwM]]F<==*6;[}|> Ŀ.+a0li bEV'ҋ8"{3K< ƯAtXk`}ףn{AMJ8{,0fxyyZPUQ!2(/~OI*-;=k&ñM'jpBaÆ T% k !*_bOcZVTBF(b~/{'v̙3r6oj9vY<A&:2)}{8~h7*q|ذa8s9*ʎ;h"|cx^w8No?_JW 6IP(D̫> zRSRL,M`[7}{FwX6~*˗/S' !W)T%i?\!%vz%v5TU!f 7oBf֎˖-HM 777 55UV1q8.+$C[$acKŊ 0O s]|^0QG=zj,YRnݺs_{===5 +%ʲ|rZUCle×#*4Z?Ν;W<+@ 1Ҥ[=?p_^A8`ZH;tjKøUJA* /@p@^D}{Ȧ:5Np :ιW1HKb۷'OINi*REԴ.ݻnݺsHK o! Bt؊wf&LɦL//2o$thf)njbm1wؘ:GO6S2b+'vq]%-37Hu Koo)q8H>+ u56Y̶iii_x۷e:N:Q[ʕ+077Gxx8fEc|_cӧO\|Y&9˛H$joYgvT""bL9qƼʕ0egd#kD<BHjHJHD$'@XBYp83փs9 B J '(VMУBBH%۽{B- wtl![~їUm޼9ۧHM$_STIj NFIXGbƠ0,ձAɧtsŰaÊbB#0ch"88͚5}ѹsgƖ>`Ȑ!Xr%-[&*Ŏ;dŋsޗXFP +W&a9 شi^{]vƔ%pШURk@|}̐X >x*A9\.m۶Ŷmۊ2UWWGnnn99y%"L {߅BK.vѣG j`ʱOo۶ WPV6:u*.\??RϝзzW Y" W | H]JE}S8ؚ+SՅ:w\ VuuڨlY舋=n  j]MU\X=]ZXu@ ۯ?⓿.,,Ą  UM6b->s0e䢏 #`#jyzzZDҵ84#.Q6kS?mPQU)W]̫Ї/%d vonnGGGt}S9H$B`` ;APGٳ'={BGGk!YKHQzB>|33Ϲ4k:RBde=Έ8}Z*0BdI$ϟ~頀ǂC,FXro BO@sidBO[y]ˆ)#WxbbbJ$֪A Z:c YՊ̙3qqF!EǠW _tןE3⾾>[}7^nXa-|xRR-|ؾ};B 5縖iIikJ$@[_ ='sst'o" %"}{vvv㡬  ڵCakk+viii8w"## }}}BGGhԨ7oN jZ=T5 !ZCZ`С0֦ !DVv޹3 VcRQ\3I´m_[k,:cH IDATQ`?CY 1r)\2dΞ= ˊ3f0ߟ3CޥfbFl8vaHIIa9 "or跆*((=ĎT/(}ώ;" @K@@;ܾ}sxjBHɨXK!ٞ={ƩPK!WcC+dsl28;;+(+R[Xl1}MXLXw>bSmA,[HO%f魊߼KC=mUU?:2"cz`k`˖-_1np8P*bmÆ e6EXZ%#\ʼg/@ kZuuuܾ}(p\DFF˕]᳼bbbJ-6ikYgʴP[Ӆ>| }x%RhѫW/7FFFp8رc~!ڄB]~3եB-!ȁ{xlةS',]TAԩSǸ&o8CW!2w' Hİ?6}]yJj1Mu,u"]+]MU\\놎ͭd n>a=z dƣCIY600ɼx#VL^KK{Jl_6Ȣ|||0h [Uʕ+߿?|w( 2_9O]5v-J)!^xq?) ☙066ȑ#1m4hӖLp+!"gvvv b/B(.\+v-ZΝ;bNJ(NfNfmd! gD7ueS'/ =YZ^Ĉ70_>5Y=3}|):~Lݼy3X]U!^3}{􆱺vGݺ5vӧOT$&&^VQQfΜ 777g ܸq&L@||V[bw(Ӄ,"?|‹/FAr|2²C׮]|rjJB)/z$Bf`+.B.%+5j233`K*݊+pq~HaF7[rkCf@W6;0kҲŎZ{vuJ[(b}euNVBl!]j>f cZWGM8]qXXX˃/#֧u ADQ,;;6m–-[=ou* BA__Jγ~̜91 ȬjJ'W#-srhժ Ú:zBH@ZB!DZlW2b;vRP6RszccYK. ȊftڴiT)ՈnM߭Wyjca;ڣhdq**Bu1V/q~@ q;ko<5<-c2s}7|`=f#C%޿jk_.lhjw^df2exr?ĝMJ/՚b8JJJ044{ptt)c,//SLc QFpgv/  xDAZrr2sϗEiӦrʖBPBFbutk"k+.7ĎA!ѣG1k֬ ͱ`eCd9y|8ՓX #]ٮ$ v]Xٳ' 9޽"40}X}.PU$,}bO_a`ŦaX2sث E$VO%?u.1;;p8O=ɓ'1iҤr8o.-*OS1ݢXb"u"|$&&N˗/!rѿ8pxF8F2vU+M}g×HNHAtp8Bѯ_?L4 jjt@!DXK!ٸq0qDFY\PB*L(b8(v'+93R`Ѹ~z0'&sQV9&BV%1 WAEJTim:y =!Nkee 25ֲeK#p QFѣUj6R;to޼)1Ѭ=of\گ4A "K^[0a<==ďYHN˂VQ w%bKG/n8ztlaEGLL c%sYmڴQRUbme& 0u䋀?[|-Ǐ8}5Gn?ve̫oGD"%e^DR\R泳~VBH V_ !*ԔQU`6R  صOŴ7n8@Zׯ_/w&ί g2Ίs+t=P[w 3p1̕8tP>38_2s]x"-SUu~n>SJ,,;?@ZXXwww\pA깮\8ѲKOX/E0ggg]edd۷o+<.[ '"(G!>2 !0uԁ#455+bضmNK!k !J8N˥b-!TYZEa͚5 ʊ U U1 ~ގސ%56W93b.{dp8Vɏ^%i{>DdG"=Y4ӧN:_~z!;;;4n=z􀫫+bc Ecǯ͛7/#ʘ>}:?jW-}}}ɓReee1E|_|aĺt"k>`ٸx"JvC=YO V[b溙Аa^B!rr)/>-^***pttDV`ccaÆNv?!T?T%B*ϟ99$Rn\ǩ1xxx`LvԤI2?tl=X_SY"Y6E>_PyBtux=GvLTall/EOӧ8rΝ++حXCVt_x!233лwoY͛7{}HH6nJgHsRcll(ؾn/܊ J*ZU˖-e6m6m !!8J6ͳ`jiZV{[ %ceH/& 9>FN:gϞpss3%RqDќ"s)))_ԾqJ8㸮^-^R\~nB *SII ˜1c!4j%S\DV!}'p'$_c_0bNNNTk׮bvp]Si=#=^}V8f틷0k?揸xB۶m񠫫ѣGGԤBʆBCIÌM!T?wu)(+Bij,޷1S%fEHg\{+4c] 3C&x/}Zsss3f̀~:w~k-ʃ.k>|eUVtddAGl^I6گA(y3((#*֙[MG&@PX\.puuŜ9s]=BXK!Yjj*Mqm*(B~\CB4iӧ˶'!!W^WյhX\$< CWbB$TLJ7lͬqP̜8NݍmР9p8::yR1bfշbB7䌢'-[V={BG/wNytwF]9k#>'Oȑ#| LRޥcﾣ6?((("bׂ 5zƘb7QcKذ"403~0ʺs#{xS/|D"A1p@hrM6Y &"Z""QNN Q4a Tܹs&Q>={ D"`lφEAj9ybYl;+"ߓ(/:Šu1bUL"7#۱6[b1M 0oRN͚5C lNaxo F\F4$X8&|;v,TL.c޽ضmYY~<#\ e=É'sX,;lllOOOt%guN'""zZ""vck##a܅Rҥ ֬Y#P"ѶO啟Su}'琒8j6pwC`%MT"4:/24oШ$ǥ &1 م0"_on.1}Xim{D&b!3+{ڿϋOϟT g74:35Gͪt=z)PͰsĠ=j^kϥP@"vrѻwo8q"vIR3af\6ZONǞjZB uTG2# _?G@);;;lٲPY#R .`Æ pH8tZ $IQd+pf\:75( }}}tK,AӦMDDD%AR>b"*.OFEEVDDT\slid t;!K `}_/Щ:7rnpu{DL$N+kYRcpD*@#$Рfek Nd2 &o|RդI:uJ]~ڵS-(LBXyP} .K.ի1cƌcDac tbk=gt޽PYʪ~ VBxx8lllH"99Y;Kpjp3KRmo\>tO"%A,d2XXXK.ׯr9$ z ++bNMDDAy맰YKTJ7"z+==F̢uƣJDDT5YYU&իhْ(*lmmʖټ&~Ma/Uddek\gnn䜥Uo|}Y~m$H咋%F3;資qrr³gϞ3;[Y7AD!ͪnذ!>,ď=BFjںҡcSӳrf<{lD";z t/^,UkVEeXXXc/+wT*=\\\ VZ͋:]孟eJʕ+5j۴ QpCF-L>Z*S_"Sc -Tc%s \[.6n܈۫ՇtF%m*UGv9nTV Gagg[[[#55޸~:bccѡCԫWƃ##yX BNŒ900˗/w}W,+*f7){k,p"ݫaÆhԨZ~l?N>Ĥ_NhG߾}g)Cqy֯_P!-- Ά3ڵk޽{VYZx׮]EjZ[I6T),!#-׎^x4lEMLLлwo̘1-[|ys""ęDDy{jժng0kcc<7 IDATec*""]kӏxĔ 37nݻx ^zXիVZhР:w,\iӦ?DjjGJ>,pFcsc4l]uAƵ 5JpvY<)?3gbʔ)rU)YKDDTԎ5lF-Q.?zѨ6*=zܼyK,בq+bUVE=0c dذa֮]k׮|2}>굋J.cݺuXf ; qnܸ_Fjjjlܢհ&읫 6fDUX\{7R G~| _{|D׮]1n8t޽|-DDD™DDy{>E3:vҾJDDTzyv\}I9s`Ŋ:NEAzz:FCTk^`mm4$'' 4@V7o֮]N:f͚~MmܹЭKQ*)?v?`k3Ax!<D-HPfMhݻwG~`ffV,V*Xj6mڄ5k/^+###\xQ333`ƍxI߿UVqF߷0f̘$zeKcD#`)JTBrj B-ZsNxzz~k\.ǂ yfs#ӗa7,F橛xށHKX bر4i#"B)oά%""*!{֨ٙoDDɱ'O01F-2#nO< =kCP /ogM<!PF >gφxRqJ%݃3wF'Ep,:12S},z#g$63MhkV=0D%!G |}};v@"FW3srys~P(ru\\\x{{ѣG.ڪU+l߾G.߬ԩS1uT|y~!J\&f7ԇ>r_#"(̒YӠQ+'PWCbԩVZ '$""*ج%""*gϞř3gj2 0k4;Yy4jR)&MT'9#8rۇ'Nh,S>+VU+D"E& 4: (ЬY3ٲQh;^#c04ࢭ5\zB$xe ]FV6pfʖ&y^_ƒ׸|( |/_,Ư o~Λkܸ1HJJ–-[~<~K%`ƌիWID-0ooo<{L֣yM=Վ+Uӧmll~ٳg066FѩS'ܿgFZ JTbɒ%X|y&U:&;;~3?G-,Io=}&021۸UsZ{>w} Ԥ555EΝ1gmV .LTJiD]Ϟ=qY&M~p>g1zvd|0I&z*vލ7obڵ@)O?gΜ ֯_cQ.;I&î}G"c=lΕONy .? DF\p!Fob՘1cFE"ϣХ iZ;7 hQh"Sp//\.D"DGGZ{J(x"Μ9Ǐ#..kFVlӃD"AʕK2ӦMs' x|B9txb,YD^/$$UV@ <8mX.5' uoJX<1041)L-M`ji sksY@c5HPʡ+8,B;ڵرcѿH$$"Sج%*%ۛ QER`aa3{H*{F՛I5add$P;pL?ݻ ?CTj=oij= j[jzNoH,canlsCaKTڎhI5,z9{9::cǎ2dzT}!,B&MǹC$ R?:>>g)8p7 A 5oX |'NT[#} 3݆έt!.2,Z022!Cd899& +oiC0D"RZVPtP*d12?أׯ_/ZͳQ aaa:JCyyGvuǖCO +  OqGJ˗/K]055EXXX?dϏ95k_zT Qk߅wq76z8Ho5j{պ*s='\9W^&K}6|SL_~ ҹ3QYßԈJ}ؘ "Zg.\aÆ5;33D$mYi*@cƌ`fVq%""*il3BtT&DTťչ9V={`ذa:LV4.|dYh"c};(M5eF@|r:^zc7|q S2tOS#=,ݡxRVFY.`iӦ޽{sjQ]ܲHT*a1ɰ01%q#qy(^ ŋ1p")͞={hE< |4 ˿q}QF6lN NIDDD;"""۷j֜BDӱ'O0j6mZh־;6k/T\vM6C]Y-M 13L  4vmj LqDŧz?g05%Fj'N(Ic\zUmM'۬MːoG&P[scaxQ133e˖7nƏ_F* ˗/Ƿ~ \,l-0Ipo.@:݉_sw>"uօL[[[$""wج%""*f~eihε\JCD$[_Q 2 =R33Վmlltغu E xH$bti.]F&.GRZ&Tl _EmGY5e#^Ւi1d:u*ϟS#p4i0nQD%FabС_aaaͺ5'Pt+^/XC666h޼9jժǣ~:JIDDDbݻwP5tt,w?+^6"*Jx⒎U,޼yv,ۥDϫZ7- duj`jP3},Z3f`ٲeHOOϩm9fm֦eq.=|O24_ 44iѣG[ڵ ӦMCbb9#3#]8m n&b]~ <:DѧO3zzz:LIDDDaT*L0jJDD$~~a}c:vcҤIѣ(pVJ˘Ç#++ T}Ĕ $d܄NuͥN/]fm1211A6mp…$?"=3;;uAjо}{?01f9rDyn4XX8n( (Nm;\ǹa܃bѣG [ Hbbr]]6l333Zǖf=z@Æ 翳xb6j4}t9YJl=(}"*^k{ |8pZEp ܚuVVVHLLDVVЦMlڴ  R Ҋŋ1zh=ߑ1s-@Rp8"㵎JС,X;6 JN|Rfed}L4DD7 !]sfES,ҥKhѢ۵kO?T(G-/ًs٬%ҢM}Gf/J%bʗ>+x### 8WT*!J!rլY$z٘9s&6lBqޡfUm&T ]{q-ہ``b1 +WQ鈈(7Q1Ѷb*")Jxlq{͜9?.w,--Ay푘WIpL=( Q֣z/)))ϕT{{}D֭~ᇜ㌬lddf@?S钔'eH, dpDVRŪH;/?ﻁ,9qľQ:]5gϞEƍJfbb믿@R˖-CFFy+;+L[1nMk dEau62ҥK1n86 JDDTLsZ5JCD iihF]OOG-wd2F%t YNHC􏬬l:{DF'k L lo/ Mۿ;s1yd%"Үm۶jګC=SQyR1x |HNӾdiֶm[[NTm߾fBBB9== ^cz Ey&Ғr'Ѷm[,XݺuaB"""6k>hpԩ|`aa4Dy4hnݚsγk8dܪiGZI<xL(JG%tJH{NHCV.{IH$1zhwAff&R)d2rׇ> ```CCC ** 7nݣvhӦ :G9lll`cc[[[TT ...ptt,(K.i=P~*Utd˳sghhcpssaB"""%6kADv11$!"*9{ïk~:h msEVVٳ'S" _YZb\Flږs* Q EPd2 $-?F$ 2>陹7J>tweìYн{w8q;wF~.\@vv6J%d2Gmaii]v_5d2Yq} DBRa͚57oRRR4Kda_ DR߳^}#k@8;s΅ D**o HWQR%ZTTlmmJDDw^ >\iHx4n,P""u/(}ׯC\V3gVXHNNx"Ls2&z06ԃ>>qNѷ+LHKP(ߠC@x<2 IDAT#&& )IJ+T3UV \]]&Mp+1ozj*l8sqC^9VZ9s&&MslrS)W8p@T_?D4DDMBn٬Ѩ1bDNd2Tt۷o'՛vl%S2hh "UmаFeoQ>|R[~2 1MJCD\*"R (t###XYYjժh۶-<<<дiSb:* ?-[ 2}z !ٴJэ{V$UV?>zÄDDDTZM""T*qUZ$4 r9qbRS͚5Î;JET8CErr2fΜ|ǫF'!4: 'na3hߠ>ׁ6ʂD:|? DhtR󿨄bȐ!ժUCJJ \]]ƍf͚劉*ϟcĈxTg?Mkٜ}[NKuo:LHDDD A&*%۴}ԩZUDDDCTb?pWǏs5*s/`ǎ{)ܘs#g039UҠT*{/pO"}]###fH$­[իWcҥPTpssÇa``BӧO;v,ьDByrJdeei7GI }̌Lx\@J޻cXht(o6kJBT?7o9670DҥYADeB8Z nB:uJFT<>} ???DEE!&&޸wP(^O&0o-zӏp;qIV(#zpssCz;w}Avv6D"V\3fرclVw܁'wm)?MBe:N22pt1siI`bbO?}x#""H孟f-Q)Q\* -իn=7NDDDGP`Vd8r#P2sܹs8<_ jWRj<U^G$ 22nݺ5QZ5899f͚]6jԨ==8p:v숞={[6"ҔYf?7L 1sVvMMJš+HwxzzbѢElOfzDDD)<<\Yj%!"xJ#ohԊD"OlRgbbb`Æ 8uBCC5`;(MqKTfV6A*)v]xS#=#9- >AxLyٳ}v@-{n}4i&M8#]t cǎuy1ITI8.\999aΜ94iR~KDDDZR= BTQr-ӱ]nҞ={ZM"ė_~)P*"T*ܸqƍ˗/e?X>NRj}/(*dd2$Y/ ]sXbؽ{7J%M,L0ꛑh׿.>*V׉pJ͚51o<3˩Oc]DDDGc_U,:yBQ+vZL8QTDH$B6mpyt_|R׬Mː`&ux<d %`XlZm׮]ذa;TZ2رc4i"""4O]` 01/;Bo~=rͥߩS+3-LDDDb#W-Ip( Q._VD",_ZTV >>>8}4;<|0xga/i&\G"pQ TdDƧ"!%}uaÆxQα>-Z޽ݻW^)u$*cbcc1n8?~31ǸЬkY<+# {LZwww,XC aZ""vRWDT< Ō4>\;w.̙#P*K__ @fSg+,]xt\=)2Oɀ T*/<('nML dR$AԨ.͝;{A```Nm„ Xj`_L&î]0yd$&&GH$6Élټy3KkEhӧ5- #ޓOpq%< ?ҨQ#̟?d> %*%DEӦMq}ZŠ:hV9r$v%P*JAafj&&ɚ$b!5# jWYNLT@SgA,! <:͘1Vw\VVХKz nnnaggLܹ2dl#*'1j(\zUy*֘DkYO /?w7Fr|r-?|у? S84R)Je"LTb-&M`"*[T֬MJijѹ+9Kxu누 QNTRvvvظqƘ5k  ФI4oSNzva:u 2 իW?RRRKk""S*Xb.]Tbt¨vYJMJGp~dg-m+V@˖-u* 6k@RaРANlQe~oRӧ^FT@+Wυ~0aիXO>9͛#Gbɒ%#Gպtkߨ%Tۋ}U\RE '6"8]scpuu1`#""Z"""jX7l@ ul ~:wNfhh'Ne%0%  Z/iK.E&MJET65k /^Tu ݺu ܹJQF 'OĂ r5kD۶mm6{L0)))8r222 q.\iӐI&̄urrB6m {Ȑ!8vF}مM r+;;k׮ҥKu w u^{b?8777̞=044M8"""D*J%t"*bw=,Xs/"&""*~RZ7JETvݹs-Z9DUפԩShڴ)WT|]v${z;Bm]"*ۇaÆA,CTb߾}-[ ++ SaddwFGG8qDMZc >U(ѡظxF^֭[Lj#tS8-[TDGFF R()))vjԨWWWU(iii9r$\ǘ 6O?ʕ+c9݃:\ɱ<]J Շs(70p@,^5kqB"""*孟™DDD7hԾU$DD;٨%"* P$&&&ի1*ooo7Fkbb"sݻzzzHOOQXUBQglob~~Dc-xeZL&C.]i&8886 Qج%""*JsΩժYZk\ "7GB\ZZZjBFF@__&M"Q>} 11P)bmjͪ Efz& %D"Qޮ&&ZEI+ϔcgj_ڷo?ja:"""a233Vcg'P"ܭr_GtJ ^:;;ݻƭ[p5(-QŰo>;Vkզ5j5tELX }r.toڧZ©FU5/=!.2NyX6m`…֭DDDsNڂ\:J, cDغu+-[D˖-uٶm>s$$$h34~9ly6_z {/-z&?]8_nZS+bt'!&'ĝwJ}C}$'$k]X&I[N&"""*$&D$!6QyմiSܿ?,0?~n:qjcTrRSSÇhݺ5VX===ceddJlJXs!kxv9ٰ@M``dLt\9tiHFnYT*u Oo;FX,Ff4Ǐڵk٨%""2bWHDDT@k֬AFFZm~Ϟ!"R7qD&'f͚ѣGO.D k׮믿KNN_|8QrI[N6|pxC,n½{p]ZڎV\D~^wu777?nnnǿoz ]vudYKDDTwV;ooF"*]ñmZjO? ir=_f~E~~~5___L<GEDD,,,{nK>}>}@P1y!to3f…X`Zn*DDDD2DDDHJJ˗/j#,ED$A";d2lmm1a„\'%%0 Qooo6l؀@BB͛h[e˖R7jT~yw& ܨ;;LFDDDTzYKDD۷oCTՆ5i*P"luBBj={D߾}JDڸq#f̘}7oDQR%`ӦM.00d'$$ݻwǸq㐖vYEYUFUVX,FΝwTTo^Q)"(AE%cb<{,'=j$Kb7^"(X, H3HBZ{ݛ0sFJt ʕ+`ajN,,,/"ˏ?ccc[YYY066 ֯_҈Jb„ r rygdd@.ŋƐ!C`Q?d2,YK.UhUE+|u6*Xkޒ"&5/^DC*I&N:x`nnJJ6k '^n !"zό#YfQGQ~V\e˖!##؛DDL.cѢEXt)222~NFF6[l`ffVe9r$ܹ~uw4֕%_I!Jqzi\<䏨Q*i׮.\>}ݺuÈ#rQKDDDe DDDĵk\tS ?n|w bjœ9stTZ"BE߾}qҥ|UpϾF{UK>sNXXv܉1cl\8B 0eٳ aPG 1*M˵x s=<<'Nĉ4"""RZ""|_^𡋁H-[""*d2^y\J0a>HR,c͂CDDD_ >>^ឡ!^yj6w%c0{6%3Gzzz5\|f͛7a``ٳgc…d,^7nDJJn0QpTЗ[*r\?U___ҭ[7MHDDDT*YKDDQR%UCDeQWW$'9wh۶"""ҌH7'NPz6{l*8(r}]y1?LXWqJǬ[mڴAFr͛7q ?~2W*Gjj*\Z013A)}yXgUBG}͛7CN4P!Q%+ɗ...vvvXll,lmmuT@>}p떮89~+"$2) ;/^P5#QIO;w@VVCcC Z\ٸqBqzYcaddx5H$Bö #acgsJo5U:vĈz*+VDxx8Ϡ%""bo%""ʇUR*!&&9F-|lQq5u0g ծZ|h٭Zvk6A !Up# Uk;cC̽* |+~C_0`v ddd`ɒ%ƬYب%"""RW&عs's-|diêHd2Ȳ"""_hh(Ο?gϞ˗333HRd2HR߿_>m1웡026*zX L%ԫʈH-={VirHHH@ff&`ذaldggcطo]<ja7CQ^y?<{` l[/݇$S0Ƽj8mCtfyRߦ²%<]ѶWhPXӕпBk<$1hѢVZs/DDDxQ/SyH |B|_.vyD1 ︻c:?YYYڵ+<ƮlClh, b1Db1b1 \>+`%< A\d\MLLgaʕ\Ys+olC* s]EO!:9Y5j!""ORR:t۷o+ܫ`omaߪ>MxzI Ȥ'QzLDDDDyc(VVV7H@9SSUDD*1- [swpرcuTnܸ޽{#**JxC}HH@*ضpâ okk8ԨQ={ʕ+P%Z""|\rEpmd`#nFDcH֭[SBDD:"H0uTlܸق{b1FO|}/u3df4gڴiXb&"""16ki&ܽ{WkZ֍WH\~˵dѷo_UDDDe]bb"ڷo{)321إcТKsTFOM|Njb899!-- 7nƏj(?l\aC:;w0jo E""" ,, 1BCC+)QMQT^\2 >>>hٲ#""""uYKDDDbb"?~,5j+""'onݺi"""ݹs;wF||½F0nXXX[蠲-]:Ń~HSmch׮.\ʈج%""R"\hmJHEG;v`ȑZE*cnꨲᵇ8/TҜ> gφR)V*ج%""RB tP Ĵ4eKZ}6DD5[l  Hq + Ly*j7ʞBs.<\9"/7|ʈHج%""R n5tP 铁۶"aZoh"d՛+`Q٥*r!Bp|4+WG}___xzzf͚4Z""\^~)SbYG>8*īTO?˖-êU?͚5ADDTdeeaԨQعs½*5`ގ9`ʆ{۪)))w} DDTeff"&&UVӧOq1r&&ա#"} bǏ燭DD1׮] p-}033H$BJJJwi +@ꏬ,<ip j{FLXLxO>}n:8::jZ""""* ج%"2ݻw>|8N:tPf`߈H}{n\[YYa…:˗/1a:uJpT*Żw(3i%;;Q!Q~, LMQvNv9cB022D"D",X ,(J.6kH'_ݻw#33#Gć~HNNLLL4qpaAL* b|:H ;vKKKUCDDH&aXlY΋Yy0__7*ْl111ȑ#|rHR]66DDDDeDDU߿?N< \_쌈qBFмysܾ}?F-W_B\\H$>wЫW/)r޽۹3rd׹bccѬY3DFF{Ɛ!CЪU... YNٳg]v9:uǾ/(rDT6m|ϟÛ\󬬬X;v Ç۷o h޹>Yx6l:٢F057A%OЍ nA--xf͚\;w|ذaڵk|DDDDJ)%Y۷os*6 Ϟ=S7|pl۶9yf޽7os'  &yذaك+q͛7ǐ!C0j(~ {Ehh1bڵ1dȐb ϟ?JK.9r$w^B/bɈF|||> !!!*]6o[lAZZZϮT FB\"*C9-W*',e˖aܹ Gy@-bp餃J>I~;?\DthJs `hhϤ=q0zhH$իt"""2O):m>zsn߾D"hԩSvZF[:t(=zl߾mڴ)RnFʈ#6R~}޽E7 *裏}vTZHr96l؀3gL> 7nD }ͅD]prrBbbb+{˛qժhꊯ}3sH?v!7l؀qi&""?ӧO?70ŠiOtPUɗW6,--ѪU+:t+b1ЪU+<~`cc;;;ðar~ .]ݻrڈ }Y;e[Psݬr ڶm5Y{ix{{C"zX,ƯÇ_~ѣ՚kll'NOpXxZsq%xzzصkZsqu8::}ͅK_>177G؛7173LM!QT4bdKdibwLj-4_Db">a32שS'>}ZKUYp![#fl{g!TIR|DZJ;b|9r$Fcc<Ǿy044DDDDD}|?ϳQ[TׯF:t֭[8{,cd2FUuٳgDpssCǎѤIܸqΝË/㲲gիW^oݺUiV$;v;q9DG %z-u7n6j ШQ#t UVqy֭._\[R鋫WbӦMY:8<*FR33q}|u yfժaa3bPfM-TCDDرcXhBQ;/L^; FwdyÕ{wޘ3gjժs+VX\%QV"W֚M4}sO+k'M~)Yfq`+k333QN|R/7n0'** }#88P$Ƣf͚HIIkժ___)y>S<\wssCPPPΰ}5jlAe˖8pܺu ݺuS8uW97]*?3lڴ 666\.Ǚ3gлwodvȐ!عsg+ooPٖz!**J^KWW԰CzݽPϕUl @h|<ؕ_־=zoHsۭo cݘ={6ѬY3:u :JdԨQqqqG}Q}ks"_9cǎŤI`ffꈈH]Oi† &MӠA-d\\\_Zj V֪U k׮Uh۬0c A /^DӦM-Z $$D2e V^r'*lMjkkT^=yϟ?G-A|ڵ4iw___Af͚~z^~۷Wh=z=zP)L&>|(ᇸpB[:tD{.4hR7*:w3gcuݱ/4/C"A@X( Xkl`nж :ԩZHyݾXңQyLl333 VJRt""#GĶm?'먢΅;AD`:d D8pz]`hB0O>EժU ڵk V… h޼y]#333'fkkz*Zn-UXJWvA_0uwwÇUkϞ=:t 憀V'^ӧO:uSN87?ͅʮpT^+v­3a7Gݽha=VSD޿˹vo67\;̙3GDDDꈎe['[]q] Ÿ9RJXx1F&-Q)o.;88ۨՕ3f4j`hٲe= 矶kNF- ܫX333}voݺUaAԨ^:p@A,..Viƍb'OVQ Uq۶m \|Y ,PySE;slӦMFH$ž#4ިMbaKԯv ]ÇQ#6jH-2 }Q >PhԺa…(ʈ+W*25z(6j󿏯{}UW٨-WLP3Z""""*1tڬ-OA3˖-+GQy{{ƫ,e~D'Nb"Hkcccq5A~V;w`̙ؼy3رc`\#'g4VM+5D"ozЦУ~}'"R轿/8Ps~'nwLDD*nP)/>sr*|wvx֬YҎ`DDDDDf{RRR{)D"lٲŚH$BϞ=  (|^x` bرcwfVɝ={6a???}V^:<== _ٿ/eM܎;;n˗/r~6k EѴiS\cƌ3VZ-["22R0o#/UJDTt+ϝÁ{18b1:wɲy`0h߿n)\}9+RKKKL4 Xv-\%jج}[َ9/?7n@VV ְaC8;;YVw;_!ֱcBoGmff@@@@vU-TV 4""" lV+_F9w7 K铔=uʼn'['$$`ƌ`e[hT"bqy0?'D"ٳgpwWDDD!UjGGanyX+T |嗈?kkk-WIDDDDT8lܹsزeKu*U?{K!ֵkWl^U̟~HV~XЬJxIETaÆزe 233U#q -"*}^''ݻ!J9s`Y&]kQِ tTv=,K0Dsss;X~=lll\%Q꺀૯«Wr O?T#5ro*e Z٩!!\i+˗/!XgU``BV]7ob+! kL"bI|)j?$QBl^.p\K1ys k""]{ >θ755ʼn'`aa<333CQe`` JuTfeKqhayYJǘaXd lmm\!Q+ڤ$5JQywo 90 ~իjcvN*XHRLؿ/g^:T:u*ьd7JǘbРAXt)\!fɓsݳgOO9s7 a~V.#55UeJCCC+WNŐ+fiIίT57?DKKK$$$Bǫ$Aw7n*""":>Z?vuJ80|BV򕴆0`V\ -WHDDDD9%YmصkWε ~gD044T!ڢ"w -R]WGV-C.`B:{,v-h@Mn9FDz$53k/^{nUDDDƍ Sߦ"-% {WפR).쿈@\dލf͚aͨ_#""""mu :W& 3f zju.>KB~,;;]t劈4S.m*¸-ssjժ:_Bbd2ADTPc;淳SOTddds=*WiuTL?rR?bYF)\'.ڱFc9SQ IDAT'@.5j`-VHDDDDTfmTT:44 6Ty}?*UW3ԩK. b6 ͫ[nsԩ#hj3jԨgϞ)wwwuիW*=?g4Z\pԬYSb1ԩ pssx~L&wS7eK|4H.c# V^JL$bb'Ѻuk,\m۶bDDDDDX5S>]v} 7 C Qiɒ%*Z [DwO4icTzk׮x]C[[[ݻ78{ҥKԩԩԕss!RL&=plqt&Osظk """"*kVG&N\MOOG=p]s|;wnϛ7OaI\\v `bWWW?P.] CCFtt9oF=!l*DX|Bڵk:t(d29sV{hذ4E&W^ gPXܽ;U0V Sب%"ZQtTefgʋ ۹KǦ+rplo޼9:___ƍ8v?Ө///LA\,a]߾ڴjH";;801YWDT2m|ϝEBZZ>A*3|5tfL&CV91H={`IDDk-[ĵkr[tm?|YgffcsEKz縚5kbݺuܹs}Yb{H$BhhF-f-7.]QÇof͚n*r_ǣG_%q㏱c\xȍM84iRF1zcǎlR1c gw""*bԩ_f͚X~=bbbtRXXXG''cjLen!&:' V}IDD޽nSlUZyْl 1YqFjժa͚5xf̘F-|dR DGG#>>666pttD͚5Qvm DTT pww/i~(_?ds""" T\hԨʗ/l#,, kFVVVNIժsdVED%ټ?:?Μ9˗/Wُ?F۶m'ө3f؄qoo8UV֌DDLLL?Oqvlwoct2޽;K$jժ"BDDTL`dliُ3 [tn 0gӇ/>@)ILLļy\UKD-9} OTy#Gйs"7jJ*߿B| 1Iv6zmތ))xƍk׮"BDDTn֚A?L<[}6jԩ'O۷/DDDDD%R'"*en݊o*ē3uP TT߫W/ٳXkӧҸL&\O:x6:::000(֚J8w2E)Ɔ .]hD"""""RDDL&ǿznތ/\ T\.Ç{ ۷W󿯆;w-,,J*{MDDDQ||J:w`7Xv """""c7n7nN??!"8&VP9RT#cΝ ٘dry=X_ 6HMDDDQPPPmmm{n:u ZDD%n޼'N\r{2*#`ΝxAA.k CM{VΜF7{Sz-QY *U`x%媈8""*\!6a>TBD]cVk">ͱcDDDTڬXvvvڵkcڵ: DDz`ذa^ v;<\[~#W^={p.LAlܹpvvZ]DDDMkɓ'uIDDDDDTLج%"غu &JWt*""]h wɐd [kBXX"##&M ###ìY4^>033u DDDDDTج%"m۶8WMZ!"]itD"Fd2ԩSիW,--CCCرC{QYOƈFLDJHv ;{ӧӿ4nXC|lSSSu@X*!"]Qy||FsV\9076?h,""""""""Z""= >䉎*!"]wXAŀk#-+KaooJ6kHϞ=` :-C"AUɷzj۷/1?뇠\\kkk,YD#5&l鑙3g\[p:I*)o߾s:u ӦMw̔v6=/ &ʪk"""""""""*mج%"#۷B|ŋ:Di|ݺuŚٳgڵkcZbFOxJ0o޼bbHlܸQ!WL*!ja$''vbm?ƌ|__$ ͙3VQifxmڴI!R*!"mdgbp0D+?uTxyyᣏ>*RT kk̘WIIu ^V-L4H56k͛1eS:k'SS׮];DEErj24,G)>ߵ YlkB,.DDDDDDDDDeDDzdڵJ N"U!"M{۶ب׭[% spXWCB'{}tv~"""""""""}f-J2^n1DqSBJf۴iVYf8fˠd~0322?Vn"""""""""}f-ׯ_GRz!"MJ q###-ZjngϞ|ө5ju&bb :uꨕHZ""=pB`ǎH$ 7mIyf$Q)9Ǐ{ QN\v C1qD${7 >%oee5k֨HraDiqqqbccakk4KMME>}p{4tPKC~0R22U>}3w^XZZ[nWJfgg/ĦMO\2y':??Eaܹ!""""""""RSHYXXȑ#;v,vڅ9oհ+$"UHR󃏿۽{~r'A'&8zx i4{  0l{@0n߾}MDDDDDDDDTXOᡅDDzn̘1߿BtP D*b#?{u53)N( $Ћ A*4Q뮽ڻ( (((,Ho{  $Bk~|IȄLy>y>^"'o٨ٳz)m۶iV 6jk44j8v4<׾}{5 |dرc5k,eeeŷ8{v뎦ͼX+>wNN] |iӦ8qڷo9cccR[ `WeX6MlW޷X,ݒe4k/_^= C|ƍ4kb֭znJ^Zjz뭷c#?n~Ӥ9SteM&(bQŢ K2AAZש#0EIC_~jӦKuf-O?TK.՞={bSRX?MݴIçniI &($$HsԹ^=uWH]i㱣d2CL29Y ~l6СCXj\iܚ5ڲejɚ6mZa^}pRzVNcg-$eggb~g6ͦNQ{nժUѣ6oޜwIe((0}|Txի|'D`v_ z~毪}niJ 99vu9ek!iӦ]׼Y ~",,L&zl6B<m>MGym7oڵkb_Z)U :X@@MNѬ?ҥKb69⥊syu>wû^qnSO.^ ]jjIJHgyFv%G:ur" gúq IDAT|V&O#GTXX!.i>}kݺ{G4k̚5!t~/T'Noӥ|ܹN_u@ݵ'Ο͛ =5~C|5kfI ?Y?RL5itÁD?NiNʕŋk.kTb}rELH?~A6=/f2W_VZ,+4k\ݬ XT o(eCrzt~j֬YaÆbΞUbZZ6;^_> 2uf-VZ<?sF-3xddvޭ_^_аKZy`/ٷO}ǍSjf!^NM8Z3Nh*|3gUVժU慪8vjժة >;+.N&MlCFZdBBBN_x̛7OE^M-_uzo劊pEF6pmq6nӿPUFEE)66VZ#/hyꩧ e護zo|  w^֭պGNuR2gv!M4Qll*WrFDRR9burJy"pAZI2e)@Zw}49YG%I;ӦMXBe˖H'z$=ҩ*|3go8xqٳGf' {СC]zK.Zf Z f:Ԯj_fNFΘ[>@w_|45ioĈ4hP;l2sUY ~bܸqZ*2;"YgZ;J7l1H5Ll2 8P;vtxbGܹs[3O̟?_ XJ^q맟hϙ3>׭[7hwݻKRSSuwk˖-jҤ>sM\Eoް*n\YY믮٨}wFrJo689uD׮^l8vT֬Qp@9N9}nС={Ο?h9ÕѬ?;6S  2 QZ5eZq׬Y3M8QPr}8wbȀY:=p﮻_lAR4f.\д͛TP&]tCbhԨQ;wjժf-'xBP!>w7p}z{|%]$IZz__mƔ/_^3g/썒 _.Pt-h…yS/z"uzn毚_WdquդIO B@TT݈`:yý/Yڵ~7/_K"d!!!өJR5@l6p+L&=Z|9Z%;kL&õfSjk}ӪC\Wre}G^L@zbV זp|Fmxx.\[zy=:vZ7 vt%^*y Nн}sNޫ|wjԩX$СC駟aÆ. Z/6\/UJʖR5swit9' ￯gy/]~aOnEҥK שjѲLڠ>_!AA^>ZTp4k={!p$Mty OdZ{:Ԯ>o4btiýH-ZHM4)jhURRRAuSobcd>\,I2L*UŞ1Y3qo>ie˖Zd*W\]M:U:tb'N;cG=XntNVO>ViyZIJ'5hD>wl6[yw+ݻ6nH3WW;wNժU3UjU/U_tYMbp)4k׾5kZggB^mV:$ q}v|S8PBB^G‰=>m}V]N0ĪU5k֨nݺ^ |Z(l6ޛ>}zcVmGYA/]n1BBB4|P 8Jx۷Hc׮bޥw-4fƏ6 łf-Pj֬\d_}[Ce54짟c=쥪q 2Puԑn/#GedM7W_d4p]0{KU`g-@vȍZIε^!@KhYCQF_e2Tv@ t=ڈ 2"eh˶mx pB*UKY >MZnzp9Gڵk{"?4k|ʔ)tooHDEij}ZX,7c6j%)11Y %?'|2F$ըXQ/S/ӃHLԛ{ɓZ(A.]C*33>g*/Ճ99NWXQ#GpUh@ qFϡQk64M[ȊT!nt/6G{Ϟ1:t3fhՊVXXCJx@iiiX`MݰEx0%iڵX 믿*""BReh@ `ZuACb6k+*ti/UOKtIOT%@mk T&(H+^׼224db&IcǎUDD ("PX,*;;;/֭~}>,'7WkժC9=3Nm<&.Zz|Tj߯￿H܃f-f24j%)fR5(6MkΝ=tHS{ϞQqcWPtl6;V6k׮ &T?h}>|Cg^۷kv.ef*37WjȻLjݫ7/W~͡QkX4yd{Ь/ڼy}Qxp N?6n½{iXn?lVj ];XCd2_V׮]Zhh\tIڵsQCs W {85Gf\qqqӧ?w?jՈߔ!UM&|Mpv͙3-##=T \q4)IoXӧkoY6lPF]tҨ5zշob p}h|Afչ)j5ڵkΝ;k馛0m޼Y;w.riԩ&Y aaXɃՠ0v&$ݻ ݻkƍUÆ =VKٲerJ1B۵k͛7XM3OOΝSj DUZKrrrTbE]|*x*fi3y1Ţ[E^򋒓լY3uUUTjMP|RyYtiIpQ+ۍZIV2ׁc233c9׺fM3Z&&åK 2e/RE_Z/R'NpS'}/Tki{QFq^h,Xp]*0P ?V5kz"3g:BCC5b/TU cn^=5q:?(⅊f-x@V ST 򓑝.&LPf裏;Re_1ݻwg}w}6-M bUsiiZvmjر^ h4h!v1#f?zT&MTrz-Ze2<\Ь/]+t L2Uٲe=\_ZlXJ I;OҐ'ۨ~Z˗`UZ'M{CJz|Ҳ :uwފW۶m/zBSYD+FΘ3XÆ ~zUTKU \ݬ5IiӼ] p\F-XVõddR5f_X@@fϞp_4k vC%#!0`ڷo壘f-x@͚5 vIN1~hd2C\rT4k#ի *bcbW{aڵч5]٬͘1#/x>]R`/VΤ^ի5+.NGGGGBe4kC^xC63'GꟷŪn\.]RFvR.k]ڝpZ96.ddhlNUPASNp8Y Ү];5kLw΋i6kvss+jU*UZ/efuk5}VKNVzv9M&}]]o\7A<{Z~`7lŪ_ժ:Vq'OHr\LUzvW=[;4TCڵӷݦ@8ǧMGe_tcǎz#\ Lv{ Ν;jժbZ*BqPxxRSSbkЪglbew4)I-^G./ڡ:EEkXԶm[ }4eʔ΄M޸QCoŋϾgܹZryola\ӧ l2eԼys+WNj޼FF|{2Fe[SlY 8P-[TFt:u>C9ra\֭wߩe˖Qڔm޼9k˖-:qs67JHHмy4w\-_\.]*H7P͚5Rß5n8ڸqc5o\ ғO>/믿֏?ݻw-ܢ!CGU``9sF_~MG:}l6{!C-$:tH_}~'={3AAA;vk~XG;(C{TQ=sF-ԦǕpbϚfkN-Z˗~9-[1cرcU=z#nPl IDATOjv׮]7ol٢͛7رcc2d-έ$''?ܹsm۶"'|R/BCC\˟h׮]SNM4I]v˗/СC6ӢE 馛;34l0zmݦI&VZוn믿ֿ/eddz܀7߸?9rQթSm9P4h@ʻ֠<+*XҥKzaLٹ5ͺ;om۶K.L2((y(zƍ _z/a=×KX%g_맘oK/_~PZwعsQ+IYYYUn]-YY`ڷoRVcW^ttߪG.5j%iǎj۶u_]KZIZbnfܹ:T#GtQ+IZjvw9x.x,=6uf]Q۩S'[NSƍ=T%7YL&TMiӦj֬ʗ/{j߾}ZbΟ?oAi֭t9g\\>8܋TϞ=եKرC-Ҟ={ l6=cUwrEiذaqɤz)&&Fm۶Ն xbwHfggkZf5kr &wqnRLL6m+Wj:}ṋ/ꮻ҆ Tzu2eCbu_Zjiɒ%ZdCCĉ뮻#?q}UƏӌGUx?\R/>מ3g}& @S۶m5l0zWl릛nR۶m?… ŖVZ9rx-WJII+ޙ(66֥wfeeo߾ɭV͛6m8IHHнޫurrrt=\r'&&{u8RaÆ7oի:t$_j8555U}՞={\vO8z뭷jƌ ϋ=CM6鮻RRRR޽'OoV\Y;5 cǪRJyZp̼{qqq>|&OR~@5k.]hժUyCΩk`6_VҥVfӽsڨ5Lѣ>;SX'7*l2oY<ƌM6)--M6mҘ1cT|bݡCO:r䈞1ch̙6nܨ^{ͥ_~?nhΜ9NTF ͞=H .hԨQ.4CjժSÆ 5|Ç5fK54hٳgWj׮fϞRJⱱ3gNsl6vwI?Q'ɤ;CSLd2W&L^U7nTwGiΎ^ݻU_/h4hX-X@sZȫJMMնm4~x=jӦooTT֬YuA޽O;Ν[9RSS5zhCd2iʔ)j߾}cT+443f ѣ;v!VT)͞=[QQQ_fϞ`C|ԨQf͛7\u;j ӗ_~9(k6mûnիٳg+((s>Cn_,Tnjӝ6}\mz3NN.dZv!aÆZx-ZrHLH ǒ5bN7tƍ:4ke˖m۶]s\ZZ,YbL&ץ7|;vȑ#;xc)SF111.叉QHH!hѢk.^b.^hEEEy.w }ٳg;4t颊+^w~p={_7Ď%'bFFս},C<44T~nʎZܬ^Ν;+LoÆ 6Zl5k\C>}gb+VPݺurJXLLQ(&&Fg΋effjv)j?;qℎ9wQ.\bׯWnnnsڳV7NGb)ϝӧ˖iɓ̜]PvnB\ZʱZh޼nʚMvVXaL&mcwիWp6]/8d*R~jj߾}.\cǎ2Ķc?M̜=?WuivNБ$%\ʵٔt]RfnFmŊoѨѬ-ݻw;-[5jtͱ{ueW$EFFjd2y,foۯ0͚gʀ+WHܑw_g9 fiI-)))my> .vQQQ.p3|  _z/a=×KX%ϟv E;k iz⯾uVy0t/Ē#Kr+Ѭ-kh \W{%!?Oҥ]BѬ;wW^綷lR3gtax"llٲe ѻz|~^W❵8pz .M4ѢET|y,[.^wmەիƫ&iSWѥKdxV5stϓRo翖 hϞ=Xhhf~@l6f xG6Vݕh7h@˖-S*U4;V|X, VVVC4,pv{fm@@4iR ,,% [8v옺wӧOQQQZlu-gc0tvqժU]ޢp6vv?#UV9+?p%WWuICvZ|"""kƍ;,Lo Tv*U!D NRtt?n׬YS˗/WZ;GӦMbEr~vcff'??''!!Aݻwב#G -_\uqKf͚9ϟ_͛VЗL&"ZZp! <ZIgΜQtt:dW^]˖-SݺuݖC 6Ķmۦ'N<ٳ &Iݺu+pL׮]b-RVVK322h"C,$$D:tp9՟0?8C,22RQQQ.rիW;|-(00Yh8pײeԠA+[zwac;vk޼5 4p}ŋ0zҥK8{X!vڵ˥wq}l\pu߿KsuܹsѣgiٲejԨQuTtuw ,fu]^_Z5uܹs]lW3Zo)))ٳvmWZUK.Uƍ-ޫJ*b+VЬY 5СC/ ` :Pz!zN 6ju4m4CJ*nV>?\.3gjժUX&MԥK"5ϧ~Gb={TZ 5Y{Ehǎx*UtRcݭ|z饗 1ͦ!ChƍMJJҝwX|' ,[}QC,33S{֑#G {aʖ-[;wV^ $K)))]~ "f2L?x`tMءCԧOkw֬Yz 1٬ d. uQ|||޵dj-|iiiqEZre-]T-Z(ܮRF MuctjݺuxŊuAU\Ϟ= *--oذ͛z99x.:t[[ ;wT6mkz뭚>}W0fի:$Nc0`Ǝfiׯ233 )SzW^JHH޽{cP5L?f$5JCZj /dGoV߶mvt{QNk.-ZH{qx.00PW=\w6ׯiF7nԢE%zj1~xGKR&M(66V/vN"##~zըQF7j۶bbbe˖iɒ%NwhBW.bO^o^szL&=z4#|MnwCj„ .?А. ٬롇ryƍaÆ( 88XsQϞ=W_ջ[*Tʕ+k7<Hc###n:EDD9?ϽW^Z~5kҸڵkkҥը{L-R͚5]עE mܸ;hڴi ui\nݴ}>zʔ)Ҹ{G۷oQ "zd2y_ZҖ-[矫]v~M7iڹsuVWѣvܩQFiӦС mڴI͛7wK_vK/:ubQtt{-_\kvK#G*..NO=}.((H{ٳ5c ~ 2;vL{ӧJ*FjР5jTۧ:T5jiӦnk$..NǎSBB5jE6Sfi˖-:qN:LU^]5jP֭UbΑ}i޽ڻwK.ҥKe}UZUM4QӦMհaC^w KX%g3|ͥK˔)Gg3| 3Z5sL͜9Sׯj-ґ[Կ?w3ʼn _z/a=×K233`͜9S+VЩS\^&I֭;CJ*KX%g\4kb_|Q .t&I={_lWc=×KX%g3g護Ҕ)Ste]ti 2Do:7  _z/L(.]?i+f1cƨlٲŚ _z/a=×KVz-}n!J.g}Vo,Kb=×KX%g%$$讻R\\GhBSDDG·KX%g3|IZZb^o]ʕ+Ѽmg3| UV:qDT\Y111TʕL&N< *999߹###}vǃa=×KX%g uԩ_nݺNs 0 _z/a=×K|IkL2}'MdONN.I&g/S\Æ s'?c=×KX%f-&sqn6}QOf!9s-' _z/a=×ZFip{ O?ʕ+ݞ _z/a=×hnҷo__/RygΜi 1ӧOoc=×KX%g֔bŞO?uA{^63|  _z6❵\pA΋͟?_wqG/\PwyguPPΜ9+z$?|  _z/a=×\|YaaaJOOϋM4I>GO 3I<$3I<$|0V~~~vO0$ L0$ Lj{իWVիؽ{fO>bI$aI'kO>hӦ]QQSKKK1uTTVViskL3I<$3I<$}E˖-2̜9ngΜrce˖ׯ,3I<$3I>> DPPЩS'0$ L0$ LܼyHOOGzz:.^hqu0$ L0$Ic3'klLUUܹXnN>]})___5 G({KT3$aI$aI\#>>{EEETU~1p@=GFm[1$ L0$Ic̳E6l؀_iii6`` }]dDwbI$aI$INNƫ~VÇjbI$aIƜgN@~~>yܹ.^^m۶K=j\gy&Igy&IJJJ0m4|.t:&LKӦqbI$aI晓DVٵ/6n܈^zٵ.<$3I<$3IrY 99ٮu{~ڵk]y&Igy&I[8YKdE ˗]'$$#G/zKQ W^^~Zh_:tǣFy&Igy&Ig{9zO?P}}}믿$aI$aZ"+5EAxx8bccAN두;vR~xx8m֠DL0$ L0$vZ;v4ytEEׯq uƌ5kkDwbI$aI*Yʕ+UEQ4/777u͚56Guss3_۬&53I<$3I<$6l0˕NS?C?t:6lYMjgy&Igyd- 6L޽u٣hjGEEټ.<$3I<$3Igvڵkm^7>>X%٘gy&Igymh׮jC~R߿䄼<$3I<$̳虈4RRR4nh}m۶vEE9b$ L0$ L0$/9n///c [}y&Igy&Igs%ּ\Du<$3I<$3Ib~}0ͳgy&Igy6Z"+ر}Q[R~~>RRRjgy&Igy&Iڷoi>|EEEv_TTj٭><$3I<$̳9NYAHH:udlqơ 1~xͲ;"$$I&$aI$aI>}M6vEEN2.--ԩSQYYi\֦Ms=6M21$ L0$ lDV2aM{׮] Cvvjfgg#,, v/Du<$3I<$3IGyDlڵ`Y](]VN=T?3I<$3I>qFÇc=z4<==S1$ L0$ L;w">>֭ӧ/EQQFa5.3I<$3I<$ ˗]'$$<~)ɱiw-믿Cx0$ L0$ NYIyy9#Ghb„ Ett4Tؼy3j*jի8gpQ0$ L0$ LDFFbe <<kSNaHHH;PYYy?<<۶mkP "y&Ygy&I;DdK,QEѼ]v٬ݻUooo/YMjgy&Igy&IV\i+777u͚56Guss3_۬&53I<$3Iy>\x} gy&Igy&IΝ;i۽ִϟ?o> 3I<$3I.gy&Igy6Z"+״m8~fY׮]VdaI$aI$ر}Q[R~~>RRRjgy&Igy6Z"+@=mUU'NؼvNNbbb4˂`$L0$ L0$IHH:udlqơ 1~xͲ;"$$I&$aI$aqJ&Migffbػwj۷aaa̬/Du<$3I<$3I2aM{׮]  î]j Q]1$ L0$ 󬥨:D\r8}fyӦM1qD":: SZZ͛7#!!}nܸy)))f& $aI$aIsΡgϞfWz EppUj=z Xh.^y G]weZ81$ L0$ Z"+ڶm}9r$덯f͚i-))`0rss?"11W^r 11>Yy&Igy&Igd͚5xᇫ|OQt <{yyAQ*QUEEE<_999aժU.gy&Igy 'k?dz> ֿl),,Dyy9[_0prr’%K0eʔu 3I<$3I<$gܹsk]vEA-'!(b<ҥK<[_ݢDbI$aIN@bb"ƍbk֬V^#Fإ5.3I<$3I<$˗/3<7oڥ3,Yɓ'ۥ5.3I<$3I<:GwHhdee瞃길`ڴi&$aI$aIz FLLk9]h"Ygy&Igy敵D6w ,_֭CZZUQF'DΝO"K0$ L0$ Lڵ ˖-ƍqee˖ԩSq}YeD`I$aIƚgNQVVt7nܨr}WWWi B=0dt='2<$3I<$3IQ^^۷k򜝝]볚u:5yDaI$aIƖgNTVV%%%xzz=#;$aI$aIrlgOOOxyy=%L0$ LH3'k@5F%"""""""""""""rN9'k [^'<<<#cI$aI晈$d-`HKKCzz:222PPPk׮AUUͺ >>> DPPΝ;;y&Igy&IgڵkػwYQRR94 ? L0$ L4<+*77_~%qȑOQcx'о}{+23I<$3I<$%%%G||<qƍ Çѣ1zhxzzZDcI$aIƚgNو`ytRڤ+y̚5 z&5daI$aIr,]CAAMjx{{c̙6m\\\lR`I$aIƞgNm0f\t.Zl5k **.qaI$aI$IMME\\RsHHH@Ϟ=R$aI$a9YKdu_}Lri޼9z=z=E``˗-އ -['|>&L0$ L0$ɖ-[0vX\rNt:xyyAQN7ok֬QMgy&IgyDVcDFF;vX9򂳳sWTTyyy~z4gy&Igy&IΜ9i>}`Zgy&Igy6Z"+0 v^އ={jEEEv<$3I<$3IrM{vC5/ڽ$L0$ L08YKd[ִwi>޽[{H$aI$aI///MváC4VZٽ$L0$ L08YKdڼyi}v<$3I<$3IҮ];M4[<0$ L0$ lDV: IDATi噝aKF^^fYPP,3I<$3I<$vvv6V?;;Ǐ,ڵ,3I<$3I}ׯGBBoߎJ=zCФIաƍy&Igy&Igʕ+ӧ5˛6m'"66puumPRl޼ p ~~~HIIAT7$aI$a8YKdE߿?˫|_Qbȑ^7{)`ŏ?_~...ػw/zmHL0$ L0$ɶmU2͚5Ӭ[RRReq*( kgy&Igy/%M6a())vhuի͞E3I<$3I<$9}YTTTX;;; xvrr’%K0eʔu 3I<$3I$:wl}Yy&Igy&Ik9YKdGطoRSStn=?hӦ =z`t'!bI$aI$BRRYoܸQfy2dvjcI$aIƔgN9Heee'8999gDu<$3I<$Z""""""""""""""9DDDDDDDDDDDDDD'kDDDDDDDDDDDDDDZ""""""""""""""pvtJ\t ())AII jٲ%S""JLL Ӎ휜nܸ ҥK@=^.[II ?k!իW5Aj3yddgg?{CT?.]r<5إKмystV9YKd'HJJۑtdggf}EQGPPC!`;s N< m;UUl2,Xr=EQ| ,@VSjN:dlhݺEeffb֬Yؽ{7*EA˖-ѳgO#&&8r,Xt\p! ă>pqqqPo1*//Gzz:Zl[]aa!ϟݻw#33M4A`` 6}(_"Yվ}駟/ U? SLO"K]v +~l}EݭZ7ӧ˗[uDWTT8G$;Y|pxgq:mݺ5.\GyNŦMøx񠑢([o'xm_^{ K.5 YEQO?FӦM14rss_">>!EApp0F'xNٳ`޽n8={lZ8${|?@۶m-̙3x_AAA0hР:mGTXl}ocСUn[YYEƥKj9s^?NHFF^u$$$ج(x衇{! fuo`ƍ8vfrl޽{G_QQ0do=y(UUd}WT{%%x|׼=2Yݼyo֚>}`ӦM,;vl:`ժU sjc00o<,]6gyf2ܲe˰qF:tHsw}GŰa\w7Mgmذqqq`.]eѶK.wcJaԩV}UoŔ)SpjQo2cɒ%u9tP\wGV\PNnc~z⫯i-j0x`HKK3|bرSLApp0[""ٶo'<mvZ n.gm>T^^ѣG7ް(F\\7x+P\\\mjm6t .D-?F׮]uVաƭcƌi?eVTT -- _51b;vA5y] QLǃǏxn]tR)UU/q 4ifeM*6l؀"ny'֛"00Ps>`0 ((fh!!!ҥ t-Z ''DFFF\nbccapV9gŒ%KR啱:]vE`` .^\|};wn@T SLvwj޼y2[,[ O>d}MT;⫶nŋ׫.ǃdMoƃ˖-ܒvΜ9nzSCymck{k'N[x0;;W^r${ @^&LvgƜ9sܼyl777t/^D^^^Z`^z:8YKdE;vl999C\\F ___oq ~@BB(1f̘}";w+W{GdM:F"35Ĺs4# Çkn„ XzfYӦM /W_E-vnܶm"--Dw*++I&ѣBBBЬY39rɚg̙3xpBf͚a…8q"\]]5´i̮@@vv6ZnmOIɎ;YU;vDllq<gg*ׯ@aa! q<~z>>ƫjVvӧOŋ5ESNUnWQQ˗cΜ9׼׽{w$''I&4DNzѣG'{yyW^S47ogϞ5':;;^c=Ν;t8_|F#''͚5ܹs(եKR j RϟZ-j^z%|)zzz{sϩǏW[n]z_{V T;$$ĸN'Ƣ|ǃdMrXl8>}oǎn#v$ogj`kt<8}tuUVӸ,==!͛f\`昃)EQ0w\k 7C yynnÿ"RJJ`(дiSnڴ)>S[r 9rNiO<=P۵k6lܹs1{l{YYY2dmfJ"[0`|M|G?Ӛm۶Duu:XX}: ADD~g&O>c֜%ӫ^oz}C:SĉzqVZa޼yuǬYI&ڵk'N@xx8msd7ݺuCffo>̘1_|γJEAxxxGT=zo7ZUUZ'Nhuɦ麊[nuCn݌"''T/w mm۶ BZZ[yd-Kyy9rssmwww|oߤI|իi:tQQQزeKWY^Lr۷/ХK{…ZEQ7hD51=رcnsQ!44A} 5N֪4ߣz1s=ok(swn=td-oLdv,k3yYd0rС~bcceNBxxID/`ƌ333ӧkN( =5_m-Pڠ>?ޠQe{.KM'T-ܔi,e:xN:`_3}t]Vsyo+Ĉl%55F,۱cBBBG9Dx;v{гgOM;##Aɓv]ƃlٲ^ƃ%8|ӳ!;;[ӮϙI 8IIIg vi#++&>#$%%i~UVVbٳ'nY*Zi׮yyyq)MYf 5^/_ִrb`j;>Ktw82=xZTTd> 'b5 8lܸlrȑ#@AAAMd֭[?׮]/Aٛ`:Ykx1 ܇kx dM6okzْ[Wt/k?Z"+뮻4ۗ5|tpؠcǎf?*Ϝ9CZt"k'ODtt4&O9_S:u2ƍ >/hڦDС]MEEEE`z#oyElvi>޽[ӶrI&`PPP7l0$&&uQ :*D8q"RSS?I|߾}ӧ͛WDcԩStRի hAӻvpB}`:dwGd-!߾w=T_:kL^u ;v쀯fy~~>"""xwww,Zɪ/@=qFuΚK駟q8M;))ʰw^2 7"Ky"`>qZӉݛ7o 5L|yf!11QxꫬL.//o> ͛7E:t(Ν;D뮻~z|嗚<7@~,,#4m<*f̘Q6111Msu*߯YfzR &^-a:tRZ"+ٳYvo޼YsN[o WZe]t;;wHKKJ"K#%%}?}fff"<<gΜZ-ڴm?>ss{!44AW!رc.Й>}:9Rw}7ycٳ\IIIx5ƌcvw>"KiF㌌ Xxbg͛7-rJ1owww?VMDVbݵk0n8,\f5?c3׮],mV3[#)) &DEE!%%'O,/((#<ѣGO>vEEF{gܹs[nXbEg?~Ǐĉ5s5Pcgz5˼yjS0i$ <<[CBBm6h?~C5J֞z)=zњLs<_ݶmKU<==՗^zI׬_QQƚF>|:oYFС&{Glƌ7gk׮fu {}j)j፠V'N@hh(]?^`0rssy6f͚_u/66V< ॗ^zs!22ծ(="[r ^|*or> 44x58J{a̙վ( Zj???m۶otت">>xE#QCL0Wj4|Wx'5Gpp0PPPGU>۶!YErr2_sEAhh(FY?X...ػw/zmHDtt4lblfw DFFܹsծdo_[jt:L< ,'ɱ}Y|U( z=ڷooz|5EKDRR\\\"ݖ~'劢I&pss˗r燓'OO>?y/ cǎr:o[u]ظq#SmL_ԩS#''&i]pQQQ> r!92222qqq͛1d\v !!!ήwn݊zoOtM6a())v'uLիW=>-Z3fہV}v233i6&GPU˖-+b6O?#FxJUU U>VFkn5I=~딪 *D,!*8dΙ,S؜N񲸸-s,]$02-v:b'PJi~ʥ==㑜9$Vy91y䃺O&z*f͚}Qn2o޼9sfU]l„ QYYyd"vO#I&EeeA-]fڴi9ׯeҥKsg_Wc͚5m1:ujl޼@Gx+ z(yZTyyy,[,;vl~>}3<G}t֟={A-] /e˖~>s?>^|EZE]]SS_֨Q"CGd2wWs=7g1mڴ={vl߾@G;#8"nxg jR&M QG~1k֬ׯ_,\ß>Ama-СCcŊ1o޼5jT5jTO_~ i K.9}sŋc9?{b:2̸q_|0ƍץ߿tMQ[[\sM>u+s~oWVVO?t\uU9חSO=_ڽGoz{SN9%֮]?ϣy[_~,91]O|pȑ/ FK./l6{챨-n^xaY&~1ӥիW\vevڸW^]UK7~_ƭޚsxGj͢[fpdȳ樨 ƢEyj2dHL>=f̘SNfΜ9q]w\|83-[ /ʜ[ZZ&sPbŊ;wnKvڃVYYYL:5f͚'t]vʕ+cժUjժxף_~Q^^~z\~};?я':vZ}illg}vorժUq7 ?, ke٨شiSl۶%%%QRR =zt3& ()7ȹnĈysccc̙3'kXhQ\{\9[ʕ+6>ؼysl޼9ZZZ$:())Ƙ1cbNkޚ5kbŊvXn] 80ƍz8p`1*++;|3Έ?z~;***r;S,{ڰaC̙3' phii/ns~ >4hP=:Ǝ#GB>t؟X|>|ѻwBf!n֜%d2Xpa |[n]|o|^+QyfX P k  }ГeزeK466^o߾ѯ_۷o5AϤDϤDϤDƳXF+VKFUUUTUUźubΝOe2:thq1iҤ8묳3ㆽ3)3)3)3)kii͛7Gcccl۶-m%%%}F^ |p`z&%z&%z&%)3\UWWC=O?tlذ5jT\s5qWDLJLJLJL^}Ago@(..#GѣcqƔ)SȷZQ8z&%z&%z&%=gZȓ{/nxGߧO;n$䃞IIIIQeee<3DCCC쳴45\'O}B{虔虔虔Ԟ k!,Y3gΌƼ5iҤ~y_IϤDϤDϤDϤfڵq7ǢEF&/8~ӟ꙼3)3)3)=B{v;O>sصkWn߯_XdIL0k虔虔虔gL{ofv;֭{)SdFQQQvy|$z&%z&%z&%za-t_Wm~ٯ֭[۽?98N9-[z?0{WYo쪇DgRgRgRgR{e Цܹs ]VCCCvّܹ#GYo֢g3)3)3)s.Z"guV/gOwߝ/xmjjʞ}9?ò555y(ggRgRgRr뭷yg> oknڴ){gY?q֤g3)3)3)s.Z٢_oݡ}577gLҺ^zeyw}7;hР?.7|sG 虤虔虔Ԕ4u%dwؑuwؑ9sfeeey_陔虔虔9WQ3RP]]lu{1|(.햖x'y!C,XС!BϤEϤDϤDϤ7ވ߭ۙL&yݻwݻw<ÑdZ{뭷by_4陔虔虔-Z6lٞ0aB7~_rӧu{ݺuQ[[۩c3)3)3)3)""&Nݶ~iiiL8u;[om====eX ]>;clEEE1nܸl6IIIIɻﾛ}t1o3)3)3)s[=؜{Srx=?c3)3)3)3)ٺuk'p 'l766v1====eX ]`̘19ۯZltIϞ~ ^z&%z&%z&%z&%^tiCEEEAHIII2.A+W*++s|1ko8∜?S@ϥgRgRgRgRrql/Yۏa5:III2.ЧO1bDvmmm}իȹ=/6_>gKϤDϤDϤDϤdرdZ7m?|ǦMZ:=\z&%z&%z&%zn˰Ȟyuj۷o.,>䓜bo=pZIIIIűSN͹nƌhѢx1cFu{nSA{陔虔虔-Z"wqGvaۻv . n6_7O>d7.֮]sc/6 2}{gRgRgRgRrlo߾=.Ҹ{}3gΌ۷\?{켭IϠgRgRgR\l6-A@*㎸;\_ZZַbԨQ1r>|xlڴ)ꢶ6ī~m˖-1xO|:BϤDϤDϤDϤ%N˖-kO<19|Xxq,^8ώ^xk@IIIsBڹsgL81{L&{GqWn_r%10쇞IIII[oǏ}c|+1lذ(--msd2~7uQ|4tIII?cX ]lʕ1mڴ;3fĂ u;/***Zx:>D虴虔虔虔kqEƍ#/ <8}٘8qAEϤDϤDϤDϻB444_O\/====樨 ƢEbÆ ߐ!Cb1cƌ:ujuё陔虔虔Ԟ k,F]]]TUUEMMMĦMb۶m#<2JJJ$ 1z3fLQnz&%z&%z&%=gZ84? 8Z0(Z0(Bqhjjj> x49z&%z&%z&%Zϙl6-a&LիWG6L&ͅ>$0===CgAf{';===CgZDd2Bt===CgZ0(Z0(Z(.|E&)l`k====2C ====i  7>n[oƍmѳ虔虔虔 k:aذaQ__ߺo~3|n[zn[陔虔虔9 t©jժ tIIIstif͚صkW:GϤDϤDϤDϹ k:a>?Y3)3)3)s.ZNŦrKϤDϤDϤDϹ k:q '\ד_lMϤDϤDϤDϹ k:iO3)3)3)g k:SOٮO>[2dH,====y7mڴظqcv&>({AϢgRgRgRdl8 2@a-@a-@a-@a-@a-@a-@a-@a-@a-@a-@a-@a-@a-@"ӼR9IENDB`geopandas-1.2.0/doc/source/gallery/nyc_hull.png000066400000000000000000006377651525650651600215450ustar00rootroot00000000000000PNG  IHDRk.sBIT|d pHYs.#.#x?v IDATxyXVu}#*Uf2''kfZil7G+[E\ w-M-]TpeAGxn9u5sޟ97v˫t PV"Za-X,@X   k`Za-X,@X   k`Za-X,@X   k`Za-X,\ÇÇܹs WxxBCCsgRJJRRR&M(44T Uퟚ={(##C'OTf(EEE]ޒw^FYm%2eK8q92dF[oZbs:N+jZ`֯_/Qk\Yݫ믿Ve95n8=m۶Ν;+g}>}TE4j(9r{5c uϞ=[>eF}BBB￯o:w\yմi+keamjjªZ 6ԩSuWzMԻwo] ~Ν[K,U\\\v]V$}'PZ` PkUio&MzjU>ӧWio6m~zU?M.ljҤbcc+ @޽{f;v̴S֭SΝ+<ɓ'յkWBBB4`>}ZҎ;b {W5nܸ ~}w7kݻo߮e˖iݦݵdׯR%iٲe6lJJJ uͦp%$$[n|r/kjݺuj߾}O6M?nԡC%$$(66VV]֏?VZUYAtA)$$D+VЊ+\>۱cGYF ք;v~aiӦ}Νӄ oPOl .ZfޤI7NO#NSK.wܡBȑ#_T?mvԩSua?ܠVki͚505%%EcǎPM6Vͦӧg)3ݻkŊҥp84y {zyy)99eP+IAAAJJJ29990aB{K> |iPŋ j_EBC>TFFF*))eP+k$OOOC}͚5JNNpoág}Tիf̘a j_klDm۶˞я?AU'OSO=e(//{}]6ƌ><:w'N\vo^^ յl={?pn j߾} kzzzjʕٳe߿怀:tB^nzm5kL6l(3(o|;vTWM8׆ *tĉ3j ŋ/,|ZIHC@͛7jÇTwZLO˺2{lOWV 'xP;> 6m{ｷBA L͝;B?CSsZWP۽{i+/ KX+ս{wñO?t}[n5l6WǎM+:yj ]_ѰZ`fjɓ'?j 4-R#:u2նlr}I[76sXPl+ջcǎ;o߮_~}ݻ F)!!Re-[LgϞ-w\C-44TqqqU}$]޽{_[E|XiU$t6;Nm߾/cL׾ԏ?hέkܦMJwt_UVj jذaz{yyBmذ+T$md#G.V_٠\56l0^p·+W4պuv}>YeCK;w`o׮]СC+ջ}>5Kl*D{pamnn6M]vx@k ꩧc?cUZp"ׯm6[w8ڷo^]Ne~@aŋp8 6mڨyZ^^.8pܽ?S]tѨQ.Uwt+վ=ieb:tȲiii*,,4ԼQm kPu6-((s=g9׸'ݽyfo^cǎ… {n={VZv>EGGg5\W^ZdInUʦMVlj K럝SW?_eʪph`UsÆZ@@`^Ξ=?\/V^]=ΝӛoW^yE;vT?+CWXr ZNuyC=66V~w^uQ=V^-Yfp^՛I&cǎz饗{Y޵?Ovu&=z 'N!!!Zd*}ͥKgϞ:t萡O>D{ٳgu8qBڸqƎ+777Þ &;Թs.O:U[]ޕu^Em^Չo֞zW]z֭u릱cꡇҖ-[JΝ`M4>>>-=v::uZjUC26{_XU sssURRrdXjuz͛RAAABCC%I)))eϨOQp?>~F} 3ꃬ,jWO6++K7|gSDDDϛÇkĉs*>>^gΜ)zU KJJ r2vssSÆ M>uTBKsrrt:Jݛ5k*] Kw@@C 3g'ϨOQp?˟կA>} ݻw͚5]ܹSZ@@>J]m۶h/EZUCW0\wiOV}oڴ)t:ή\Tkڜ%$$h톺/_*_~0 ͛WZ{)|tu*dΰ4&&wwww4tMo۶<== HZiРA u???-[L:uݻTj۰aCEFFjǎS~~~{.}$x+ѣ~mt:knwVH0=sM6꾾Zdvz=8`]Iv铔NS,ۛj-R jYsVNJKJJtRCA~V2d6l`7nX/V=fffVzYYYcVwKz5lP۲e9RIII}-wO>}Le˖}9-[P__m۶j tWڵkMAٳ+}-\{jMX[PPaÆEn^^ys*_oǎc???nݺ}||ԿSەj),,}O |J_|Ν;g 8Pׯ _~ \z1n5ksJ]׮Z֞={Vz֬Yc7jH .ԍ7X\v8\CC*TӝίhXhe hذa 4.\Ђ *S̈́(B~ uooo-X@{5khٕc=V_ꮻk޼y}AZÆ 5jԨ 衇L9s~ _~f͚e5o޼a#5z)___=ȑ#qrfeeiȐ!`qر 4fCPv~r:tHvƍOtM:tCݰaF)al}>x~oΛ7OO?foV7 Y^pAw}/^l{zzjׯU?c 5jP;}5yd7%%Ewqy?!CTh?Opܹs?ܓÇ@Ӌ/XKjܸC\aaa.2.o ?|p;v͛7nSaa~7jm[o?~egg.]ONӰN:up4u!Mu???mUF_ߦK/ UϞ=piڿV\"Ӟh[lQ>}TPP`Z VBBz꥝;wjٲeڽ{c$~}tBBbbbf-_\󂃃aUqov]ݺuSBBrJXSZvm*.Kff ]wuWt]J7%.~F} 3g'ϨOQԷj <aÆ={vZI3f믛vء;vI&Z`AZI0a>DCphƍ}%upp.\xA-=~6OwߩGUߨQ# JIIQǎtCjÆ j߾}mV}z*-[6mTj_||6nܨ^Q^{Mfͪp~Q߾}uV_QӧkԾںuZn}EjB]רQ#9N9NgyϨOQp?>~F} 3꫺|?[lTV~a͙3G_|}}˼fBBB?QkfkeuY?yu޽l6:t7xC;vP߾}E׎;4aƖ׻ﾫM6)..Zs=ڹsrSwss7߬?\Զmj?I۶mO<-Zyn6%%%iٕ,fmmwԩW!>|X UXXڶm+77?55Uwֱcǔ%)22Rڵ%i޽:p;l5o\AAA<۶mSjj222thBAAA/7LC?9tUV R.]wUzɓ'pU5-O!j @uoy5Za-X,@X   k`Za-X,@X   k`Za-X,@X   k`Za-X,@X   k`Za-X,@X   k`Za-XաPo: )""BڵS-0!@0x`ZZÆ ﯀lR[V۶m(kNM6p#@7o޼2ZI:?Ǐkǎ.TӦMhth^ Zy/_P(oo@UVnxxF],6N={ve5iDϟWaaa:{Ξ=Gy5kRdd<==u.M^~eqF IDAT$(--MVzz?'NԩSVnnΟ?_+??_iii.m67nf͚>뮻Nj׮Qs!@5gٳP#666Ve^#//4=zN8'Oyyy***N򔗗v5nX͛7W``J݈kNaaarssPlNiLj'OT@@EPuY[n-=nԨ-ZTߍ6<*нpBݮ&MyjѢEiEGG+$$DvF[“RSS}vCmJթS'COVjj9RfffԩSQ^^JJJm&álegg.qss_iۺukiӦ4m׮ZnM `ZIӧO(=nР~aKfj޼7onݺ縻-[4V`` .U@X :iņ㨨(Z4vhB-Z_áǏ+55UG5ϯ@X4}"+ 膅͛7seCW^MS}vT9%%%JOOWZZ9ǏO. tNgUTT'Nĉڹss<== nppڴipEFF*&&FM4Z9Waa6dYnnn QHHH)==]JOOױct eee:Waa;cǎ%|6mjJ QXX-j 6#@3w\q-Զm[}<<<29>Ç+==]:ydigV\ΝSzzӵuV4jHM6U``! WTTڵk'//j *s֬Yc8ܹE] 6Tdd"##<ٳ@رcnvvrssMO8_ȑ#.m6||| n6mԶm@7**J:@X :eΝ0o4]9JMMՑ#GatsrrWLNSgΜљ3g&___5mT-Z0Rxxݫm. @2sLñn&:t:yNnnJ=~N>^e=z@… 6PNNrrrt!*!!A'OVPPPuFAAonxM/uPVVJ'Nɓ:}ti[RRRm3oŋ3襗^[]uٳU\\l 2ĢiPvݻ>9+WTO'ua-jlٳPݻEZiӦW_UfL녅8qBCC_[0!kkP}*)))=4hZ6d%''{i=##Cs[0!+kP%''CBB\>Ԕ +u9k֬Q\\q;w'ua-j5á 6jݺuh($$DSN՛o@zQQLPM>݂ @mFX Zm͚51l4k 5rHyxxO8x@7pvi6"@_}}}o4@<<7oF-OOOzVVƌnݺi˖-LFX Z+11QM p"rciΜ9իs~'CFR^^^ ODX Zk…㰰0X4 Pu-Z;Ｃ{O[6_pABCC5e_Ra-jmڴPѣEգgϞ;wyyyyO>\;wƍ-$ZJ˖-S~~6x`ƌyO>lso߮^z{UvvS@X Z믿67mT111MT͛kĉzbZ/))ѬY'jd!ZJV2wѢI{o㏫QF\=ӊڵk-\-uRSSj7d&v5JWeݻշo_y̴`JPkP$&&t_~N [fZw83g"""ojd8Z:-2GFFۢiױcG}_ƍ /(&&F,TZ*EEE ={Z4 `ݮ{WIII\ Pjjj\"@PPP۷jzhָq㔘8|wO?\ܹsMСC-駟jj֬iP'NTXXfϞm"Z 3nZZh6ltwݴ[}Ձ,VXnK.M/R;wvyիաCORaaa OۼyN3>`JZXfڵ6,E7xΝ1c˴~)=cҥ6nhW_}e8nܸ:ud4y͙3G}f3m6Kw/^#eV\i8οj@M8QSLQ6mL%%%9s4i$^ @%'_Xĉڿ֧Op9=zМ9s?Qަ=S׺u,5k 뮻eĂ ǡhﯷzKSNUhhipoQxxz-^ @kPJJJqFCgϞM:u꤯JO>|||Lyyyz#@[|Μ9c 4Ȣi\ ݮ#G*))I rj>|?nNqg6+66֢iT&Mh„ 6m"""LCɊ+ v!@u uA3g7ߡ/viٲeL@AX C_~Mj[nEnnns:kСJOO`JGX 5sL9 op"W?WLLitjŊҸqT\\lX5jņHy{{[4 ӧkܸq3={V"##Z0! @)..O?dp M1BIII1b4h`ZOKKmݦ*55|:w6x``FiܸqJLLT\b jxBja-j̜9s  hV g}jڴiPp1W6wɢIru]wݴ~Qy۷8`\={ѣG [4 S>fΜY_XzO' @HLL4{xxO>M6 '|_]͛77?^BCC5sL &zF,]p-P%$$(99Yw'?OzҞ={,AX ܹsڶmv 7X4 CO=ku9ׯWǎ5vX\9Z\usQQQ6l0P>?Oh´^\\>HaaaO-#U7ohٲ}]M4S.\Ч~뮻N~P1fΜi8*8nI?,OOOS裏sڼyP>Z\U˗/7͢i7nnn;vΝ޽{fٺuz쩑#G*77ׂ)pWMnnvexM> Կ/{jӦiD դIp8,#Z\5_}.\Pzl4x` 'P]3g}Qy{{ֳSOcǎZ~INN6iFMZa^III]yΝ馛tw+++˂) Up8LOu͢i\裏jZw8={[ojd@#U~z>}P8pEuE_}x s)66VV`BWŬY >>>ڵEv=JJJRBBv߷onf1B'N`JWʕ+ qqq.IM4믿 7;͟?_zWURRbk:@Ծ} ޽{[4 i֬YzgkZwkN˖-`Bn֬Yr8v] p"0%''kذarss3s! effBݺuSnԥKEDDxljmڴIѡCd%%%k׮JNNvl)>>^ٳzڶmvڥ,C:ԧO6mRFFT\\dv 0@IIIvMM6lؠLeggB))):]D7xècbbԿ ןg]veeZ^Z}7߬_M UVQPP+V>իUPP5|p͟?_}i/_7xC˖-SaaaիF#GWXXX}q_9r.\N:hZ=;TQQ.b=Sk.)33S;w֯4o3k-Zڴi[J[oO<ڸq7ugM4b:fugkĈj*mݺ}ZrFuj*wy1֭Ǝo^=  IDATիg{DEEiڵׯ_t:Կ;V}իWo͛{שN:Is:uꤏ?X@9==]k֬i 3К5kս{֢E,ZG}GyDtzJJ.d-O611Q7pZuر***t}9b֥K}i,?t;Ǎ{N ^gkzO6:=rz}:?'tMvv.}F?&&F;wT|||֖~G*))1={ԇ~;v׮]~nݴu&=v˖-:餓 6l|M%'';]sgĈ;/b=ӊ5裏tE9Dܔ)S/7i RLLˏMo©u9rD ,oG|ϰaϪO>)}gQffϟ`P+I{fkxm޽͛5{l{G˗/?nP+I]vܹsi&~^{NAmhh}A$jҥNAÇu}5zmI{DX)S˗/w wޭ'|Iϙ3)ѣ.]2SN9EK.UHH_fFp8tw:E9ϻ}ǍW^yŋkӦM^A${MѾ}{͚5K=k׮ՠA4}&? ,Y{a}7:s}{LzыPqqq֐!CaÆc \RGn\Q\\]7E]twܩO?ݸ}ھ}:utݫ^zaiHH>S 2W3f911Qwnqk׮Ո#^||֭[WoPko&Ol<o߾ڼysxbM:uM֭kdW9X~L:88!!A+Wp"O?TuwA=MO[bژ4sL蕕i퍺7ZI3֭[g4uﾻHvmܹ裏٨GuQt'Z4 XgztW}N骫i͛7[0!X~ rKr)F][[Q>Fm4w\֐w}ש7q&Z~UU>W_ Yc߿5ʢiZ1czz7Ȭ_^'|zxBvРAN~}WN?#x/))lIIOOw?jϞ= ^sNmݺ腇kرMZر 5zVrz6M]}^.]4`&U]ҥKU#F(&&ڪW_}ը۵kt*5)))Z`y8^UU^xA;wO?m|XkLK/9>|7:z4h:v{g5xM#WWGr4$44)୬Ժu뚼~SwJRZZӍVZAs[u9/ @[b իWjĈzt5($$CnI'#O?ԩ7x^_u@@趹ǩ7~fuuwͲ~4k}Vh۶mMB6m2zChN>}y >\N4d]yNOEEENGOnb@-66VaaaB=S7nzꥈuIǏG-\P7nlzU+IvQ<۝Z~ͻﾫ#GqY4 x:G?zWԥK`JY=@KXUǎu~3Kֽ˺띎*.//O?+WMLgvO2ڵ˩_ ~M6)믿}ꭷj%mnXר7z@@[҆/,,TMM~;;㦎wg6M\s}]9[l5eY0%5lX;{leffDn]NTM>]wuk!!!޽]/??_^zi֖R^sà EEE?kigiKwu]IIIwkvEwwX7*773Ƣi7O?4kjjxbu]˖-`B@kɰvѢE׿^aŋO`m޼Yڱc~z͟?epkÆ ]n`ؒs`X[[[OX +٬eZzMUVV%KuHHN?tvI'7Ԍ3znn&NI&Ȃ @|ׯu]~ר#%a`O9ܹkְj*ׯ_f+ĉO>8zmmy}Z`&Ol2ل|jg]4qD9rWOX[l1zÆ h/z`%'';~A]ve:43aO?:K0:uʕ+{5ϟ?) '3g:5xjԚ_  7޽{UZZڬhmo񆪫7n9S;袋.R``˗/WϞ=/Z0Ls򏺧:89//Og}233~JJ>c&ݯ[ӦMkҽfϞzx>@ff*++ }#""СCJIIiڿ %Y*6+d,**RMMqh5OVTY`ߨ###pp߿w}]whhN;4Uǎ-i׮Ν?_O?d^XXZ//T&iNR^^ Xڃjھ}С>u޽/L>|xVzzc=ám۶iРA^aMMӃe;%}СfK>f)a-p諯2zO-ȴ~:uw}V-6p@zǝN7O>D{ ^c.wC{j4TUU1cc4ڶmtu9NwzuW+88J79 &4W\\\s9G6l0111ZjU 7V߾}]cbbuhÇ~6VgwZwqUffzl6[7n4 ۺ[ݺȞ[h) ߿hm3_(0 P_+@hSmmq~'vڥ믿^</l5n8u]]7qzꫯgϞM ׅ%%%7n[+WOnaaa֭vmwޭ:۳gS#]]|&:ַ5\{=njZ-_\rK֮G}d􂂂jjj4ek._tӴ+4! 0m#ǞRyyűݻuugi7ڵ-Z ^Peety睚7oӣȥ:sn:+VSOuZFr ]n+Բ$vij߾&tRЙg5gqSoժU:rH֮pڡN;u4ƾ}i&g]>x /t:yȐ!.jĈt:̡л-  5{UQQQϕl6jk߿땕?ӵyf &_=;<}WF?""B˗/駟.b^CϙmHݣ%3"".Ҧ? ]6x]=+++իիbsg+,,Fӫ{і-[߯ /M8'֬YՍ].j=v4h~&j^];;f("^Ν;5k,X4ݮ nShh[l?"-//\5kp}6l3fފ+k׮&gN!obbUݝذtyY~j}MZծfZ?4x`ԩ쩞zvI}wTDx_~.\hPW\qz-}4W/q:ָ>>k>luԩQmۦ1c(?? ҍWmwK{n&޹vڷkg,Y"aTOO"""^߱c oYUUUL4=z&M?臅iٲe}jK͙3GFﭷj+v}iƍFCvk4gp84eʔ$u:7pC®]3z0aqݻ{nM0}we?~;xƏ]nL_u׵Nmصk&Nxg5kѳlz6ޮHƍSqqя Փs+5<,8(HfE7M>|XMsvj_R^6Zeamuu.RX臄ӨQZ}Hs=N+B7t=򺂂M8QszGu1[[n)\Є adggkĉN;Pcbbt]w5j_VddsNAq׮]u76ix@AAAN8qrrr\^wi„ 4Æ ĉv@@z!W_}S5á>HoU[k_A5z}иqo>){jOc#eY~qzEرâi{Qtt{ѣu58}zjNxbM:թ40)N9=쳍~_{Ω!COTZZmۦ7믿v{4?38CeeeNv;VCՖ-[j*mݺ}Z|ƌӤ%iʕ \>H{;vN>d_^Vr:Wy_~S1}YGKҀ4vXGk֬ի]n׺u딚gO<Էl>Y}ztw9sX} 7kp"b͟?_vy;Ó[bYXpB]}խr#GO?m&MK6{͋/XO>|u66M>&_gyFӧOoV@޾}{:쳛]wƊ6{Sjͺn믿 'o\ܹC탃ݷ(gcK|Ht5TifCڵM>‰:ܤbtUWgV?yw#(,,IצwoѬVƏu֩_~M.--M|IZūVRǎt~tjɒ%kڳmĂmd_i&۷O999*))Qկ_?u޽١pceddh֭TCzjյ%i۶mڹsrrrTXXo߾n Hi&edd(;;[%%%JJJRjj`C߯,UVV*%%E:餓*۶}o0a-[v=m.!QRb'lIߪ# ly1‰{TVVꡇ2>HRbbnݪX[d,==]閬ݹsw޽{wޖof)g˗St j%iǾ, Rbo}=CQ^'+i'U4{k~y~mvC=k kژJ=F/.:R42 %ǹ}RVVaMW^1cѫW\kkژsuUSQDG3_K͝x]vg"›tjNIeF s-GFwmDkѣGu}pMd|'+igT-JJ̝aaaMfBBBC=[4x?Z?4|k.vMYF=j%uvuQ^UU&OJFX g`)G6~r:&:R6o}[NX޴skСF/##)̷qq*..6z\vA̜|NmlV)-'O{k~xw,a-ٿ^~e׽c1?dI ;UhQQQ1c+((pz9zN [t"''~V(+0H&wF]SS,ua-زe.]jNUC4U*,)3z);UkRR?Wdddx~r~b̙>Vl}-瞬\c+ul=P^aQQQMmCDD-a-qbҲ`u, Rb|\i:GC@ 7ꬬz ma-T[[{ W]5Hl- 3hh;"""W0ڰa?lZ|̜|Nm=Zk,a-s8;^hvmD?;Q'%Ļ從tJUUF/67Cg%111FMX k|ؓO>ݻwKWlT[WPd$דJJz~=tP=G=zT{ы MsUXRf:SroO*kw{/..Ψ ,a-kMvr=Yr8F{ZGۓJ̝ hh;w@EX 裏n[c, Rb|\=^ťڰ0&nX[RRb$k|М9sv(4<mYݘHlr&HNN6#Gآi;Wm\vv^x͞ 8խduB\[)%efXn$ж8?|߾}Lދvm4{S/tCF{J)a-X"..sa-k|_F/g8+0Y''} O(-7Ya$жl6糲y7M>d̙>VtǴIn_Z%f|/(+7w!KX &ZvZ}Foؠ>=kʕ0z:}O(+0H&X4 x'Z?A@;VYk^sSpPZeV^n><'::ڨ,a-xwsɉ\J*4AZOllQ[4 x'  q8;^Hvښ97by+  k ,a-{gcw񘡊n|uRB|*쬍hh{MމUUU/ы ͗ߪ=9Uk-GT1z111MmOآI;x|PFoڄ ijkVUWuz\uz9ur=zԢixR=#FC\pV'+WuOتk:DX bOmmo4}kܹsUXXh_2^ s'opPZuRRZj6M_))L &DX 8uN G iwc1ё|HEP/j׮BBBa-_xnMfoo<4fu|\R' hhÍ-#2;vЛoiwO<~v!NNLȺ e999Z/3sLUUUtI[?Ȩ%`am^^E!"֭ӪU{+g_U]NY ֋6z ma-[p8A6;sjdKR[+:22ҢI튏7CNh{kIJe˴n:w9s6 %yl}w#řG>l$}kwaڷ ȣs)Ǩc#e*kjFMX _i j۶mFѧ+!6ʣsdVQ''@СQ:ma-jjjtwP͸ϒo>G0913SyEQ祤uMM, a-׿꧟~2zSϒWPdd=/5ꆵo> &CX `2=F/16JlRU]’2 v_Q[P#F/66֢i튈Ppp̴h.7o 'SpPgٓ i=>TVUMMыg 3ꬬ,&BX `|=FSr.>k%6 %Y2;.)q5LUUEw!ȬYTVf;|딉٬ͮr:&:ҲYܡDX VQii;S-~ vޭ_IcNMh")3'ߨc};,ݐYp4u~QOh.9s=ztǴHRv!NNLh(.5P&mFݱcGk΢ixw}+VIY4 MeFn$жܹӨc$}ktu`w>VvddupP,=JͰ6""¢I:00PÆ h>K/?ћ0T%X4)Ǩc#e+:22ҢIڴiQvZ4 xGh޼yF/",DD&2eu|rKviԽ{hNCFTdw.?dɉ M>Fe$vw'[4 x'ZVVQQF/>:R]4֢ ,}+̝Y*,,4zÇhNl޼y:xѻ~8 h"SUu ˌ^=բiܧ`Ψm6Fa4kZQAAz)1)Az΢=Yr8Fk'EӸO3/ٴiQ*""¢i;o]Fofo6 %%[4{TUU*GX cݻElFwgzE09m$v۷Ϩ d$|n/6g9r -~9F;PK˜zqqqLmSqq ϫg ;;[ԫNӢꗝȨ,}JJ̰6((HMmcu@@6ƢIܫ1ȑMmϦM:11Q MލMfϞJͻj%);Q''7Y ٶmQˢI/">!EWy<**ʢI0tk@X ZeF/͞j4U^gg-a-xFeeްa,a-@ ڽYp8^Nvq v6neȑ >|qW$m2 %%[4{U󈉉hh[~NHHPrrE#hWj{zZIړk1Q|EG*1zMm˶mیGM]3i&#|`|>--Pmmcg-xFFFQ[3ZfWo"?dɉ M^EeeNX &L C h ˻j%)ȨOH`$U\bhh;^}UѣG[4 Zf]U*(6C4{EӸWqIQųx۽Fݯ_?uѢi7U*@3ڽYp8^NvqҲr hh;Kniw4歹YFxq:Ϭ hh;/^l111-|a-@͙3ǧwJҞ\K%umذ]r%j׮E<$77W]gq1M~euڈ&_TMMͱ:00Pg϶p"Mj%);Q''&X4W5a-*^ :T]vh"-/j%)ȨOH`$WVZe˖r뭷Z4 ZF]U*(.3ziTq (**ʢI[Fm5qDCX vo9׵ݢiܯ<ZǶm۴c7m4lDX|hU+I3:8(HI M~儵/QohMrWm>VduLT_킪8bMTk׮5zƍc.4|J\iSO=բiw4vJR~a$Y49,ך5kcnFFX PU[U]b3LZ4:bbgڢE:66V^{Eo#ܹsnWެr8Fk'EӸ_I۷WppEƍ%\Zh&Zrssdj%i{FQ3kKˍ:,,̢I?⋪ճ5g 'FX ?=YF)ETRf%իްaԹsgk??_< k-￯b7k,@X P#%¨#"",[oeԝ:uo4k~şwJR~a$Y4I(-7YKX uVܹ]uU~u>XjW/hVAyLp=բiZ;ku,\ШCCCu뭷Z3 Zծځ=:=-Ƚf0z];-uUamddE(--ڵk޹瞫h&AX |V쪕YFwϬ-FEEY4 ŋȑ#FoΜ9M@VduLT=knXZháe˖jMſh&U+IyFc$F5k(7aoѢi6-쪕#%Ne/^lqqq-a-h®ZI/ Y;B$,  *HjVʢ}Wkj+ n`MEQ6!ld6d0Ifrxs9sh su}h=NVC,!6Ƥl\Zac 2믿.ָҒ%KLZУԬ\4_IٸFiY]w&d_mm6md]|JHH0)#pOk@~sǮZI:v*˰r3kKʌÞ7)֬YRCLZcZI:cXý*+7kk{u]gR6*zRW$e}YeVc600ФL{;tN8au]$nb-zRW$e6Yʬu@@I@O IDATկ~eN2(ڤܶV H2q&Z:kJKKcCkUppI{X zfj`R6W[WRq^z t_ojjjEK.51#pok@RWEnU+f gR6S^QiXSlZn!6j(3ƤQ=JO몕c ko//>>kL_S8]gxY[Vn, tojjjEK,11#yL\UU{N\r"""twhժU,JgzF{G.wٲeju5vX-ZBt %Khȑ;$}gJJJҥK[-JΝ;jܸq:tS_zFgyB$l6m޼Y?O5uTwxK5j قڵk5sLzy=V| rb-fӺu ѣGk&e=iڜ-YD_~l6[Q__˗kȐ!Zr3/B?˞'袋.js3--MSNoѡhڴih}رciӦOnkss֭5j<ء.-\{i̘1h9%ɻ陵nYkM>Lyyy<`R6su1?fXD%&&Wxx;Gj:}j1bƌ|***twu뭷8]y啺KtmذA6\ct})!!ASLi6lsbѠA4}t;VvƍuIu555={Km^of1bODm۶M7n/))ь3k.EGGy'|B.]uUJHHЦMi&rFFf̘۷st!-uՎO:Ϥe Úuaۤl2p-X@{[RO??1^UUٳgk޽ uZ^/։'Ǐ׮]j͚5KxDD֯_ /lr-Ckkku7۷[n;fСZ~ 뮻$Iǎu]JKK5k,>|Mg:tH ,OM>\&yв˗'ŋMz.ӊJNN֡Ct׷M;V=]?vJ~V0C?튄UZZg}X,Zr.V >}/?55U 1???]V hkڵvgO?]pKK_۷o_WS'NԊ+ K.ux\;cwAvZ4u7!?f֞qZ}+/k<88ؤLk͛ /\&e=ګCx衇4dCjs%GQZZZɓ;^)S覛nr!ChѢEXuu^uZΝ &8tItbo]졇jٳg.3>۷{7կ~b~rkUیZw((ȤLkny>ƍgX744h޽z͛ żw]ԇ~h9sfKܿV֭3,Kퟗ;vb^^^:e,]f؍A_gmeeabR&е4}ttbѣb~mWVV{qmXꫯ* lnM6bEfjsFey7߮[UU;wyvJR~4j(C,##ok\:[m:6yΝvږ6u3k+ k`70C @rb-[bcǎm׳6nܨW_}q?έ%}]km׳i,bivz=zt ]k:fcj><ݑ;*))kիW/2HݼX[RRb7b /lJKK pM9r.֞ZIs]yil6}vqgpί|tնǽϬ--(ʕ+ -.]jbFk> 6?뗿233su]\18]G5w߹:;ܿV'N0mSN6qks5U;kZuڒrX޽M&ͦubcƌQRRI~mVU/ϛ7JNNz:_k;g>}8^X,N-QQopoނv?vu.KWx m26;++7vlb>hR6ŋ+== oۚb͟?{]kkoB6WjX -IOO sӤ?M{nWݽ{]'$ym[+Wl\[/rs<W SSSpXۑ.FxFW.[ 06448-uOsZ-f˗mcG[0Q3gOLL_6=P!?Iγ=;v;wW>t:ο[ݩXks~pVuulb]~劉1)#@Sݦ633SW\qrss }'o؞ *''q}UW;tJC̙3nΜ9ck釒>YZXXخ}[ۿՖ+V w]U6~2q&}qMgZi~~~&v5ݢX+Rxtt6mڤ6=o͚5Ճ+8%WGPmhhpjbCIsZXRRskY,5{ LhƲemάqZkeadR&е|ᇆA4}t{TTTnˏA.((ԩSڼyܦ9sF ,0Ğ{9w8WG9`X__oץZ|}}3>_݋{tն<SFgZ+{ ۷O=cR6tbmaa >|۷6nܨaÆO<ח]v]Zhl\ a|M=#u=U:nٲevܚ<^Fڦ(oցZhIZe k:p!޻womܸQIIIznvvaѣG;|s?#gj>cذa 1gK=Æ )Y{{{k:vs'???UUg@!6|p4ۍ5nɟgbW^y8jëɔZ2q&jlkooo=c&f8S555曵i&C< @yjw%Kb6Mݻ[@\s]aq bUUUZ,I'N 7`weԫW/ubkUXX;wԼy 萤gy'?Fah̙<?b~9Av3kKbm@@GTWW7eEEE˵Nz q???YFSL1)γpBjeen}7ޓ3guoO<񄂂 |]{-v?~Big}V^^^vϟ9sN>=_nUUU'O̙3b瞳رCwq^lOn߮ѣG;?1t:kt kղ6ˣ>jR6GyXj>#y ;n8IS?ԥ^jK4//OcǎU\\OI&СCڰa>looo^Z}mZjz7Ə;!Ch>}.B޽[6l+^fXsIJJK/d7/Tll4}t >\۷oƍm8ZM{K5\%Kg5{=};vO8mٲE6mjwȑzۼ?uZ79jhR&`G }t4fZJ7xݯIff^{5k-᡿oKꫯK/vzV^m7RQשS3ؽvAvh@u.o7=Zx :((H-2)@[VX,:;og3fݫ/7n\1B><.)O6MO?V?~^xٳGIIINnӡCd߿<==5uTԯ_?pB߿_-Rdddnڵkzn IAA|MCڎvZkEaMg-nB̙39 KCGˤ:} ԻwohȐ!:\ѣGuq>}ZEEE SLLV ml)22R1119rdTglڻw222*EGG+&&F\pBCC]o~~""" cClرjZХpVkxy5\oR&UYibR&6lؠ3gbU Ze4U8ZgvbmE`2֑;wIڃb-2]t;]t kZ:k 'mN j]XwbmCMUUՆZ=иѣ>jbFX j]۫Igm]I8OEel6!jR6yuVClԩ4'!@Q|Y%eev1z_vpzLku=ocm-\}[ kB@fzСI!kކ;Y۴X'OO_+==?I:b-0?NWm'iYkڲ&ڀ2γb :((H/LQki rJCZiY[cˬ"?Z̙3ڽ{!6k,(Uy|}6w(6 4)+VP]]]CK.51#@GQsx59 7鬥X l6%''bƍӰaL k)\>MݿZYiX ^rr E Y(N\WtպAVPsZʰҜ9sL,k@kכMcXEgmbm^L\ɓ:r!6o`2Z|>>>zGM,k@~MάZ2q*:$$ĤLum6C+PxxIb-4t՚bmUU 8Oeb-ѪU>c (NCWy|jc644ԤLu֬YcXwyKMlk@\>>v<VuZf0>Z)5Wsݷ.Fi!`=&ep 5Ysg4LNJ+b{6!pٳgRFWX \nٲet՚K龝况|}}MoŊχj<<tٹy&eqk+WZ`I: Z4tvm dX[+WPhR6SYm,2@w{b7|LY(yF7@Ʒ>IٴVjC,44ԤlVXƵ,YbbFB8]]&b[PeUU wtMjh@wU__O>0a bRFD8?.jU̾bú\]ʤlڧjӧ @ǭ_^ņ?lR6Ft]MS'(6!un9~ҤڮܰP``I@Ǭ^ڰ7lR6Ft]݇Z0W1fkhcMqe?km.uQC?);>jUۥ]4bOmeݏMʨmʚ|0ZS}}}#QBWm+{/0)#ǕW-\ ]jn]uU = Zn-u^BWm+Pd!VSSJ2r\X@w着[xIB]&iy Gk˗L1bm^L>25qDb-hj<0O>^ث/kEI[܂LgΝ2~b-hjC1c!VZVW2)sY ka-XlfX ڌZm3fmj>ŤZg4Hg-$''G{5fϞ-2b-h3j݋biT}}58988ؤLmV}QVOOO-]ĬfX ڤZ74nPM8k>6)QНւ tWÆ'Nl^f'jhU\7K&(:"̌*+ P29rDڹsdٚWaEEEz7 1jGp~5o|XMmznL?l2YbR6pVMMmۦ͛7oՙ3gy^}U;2tUkuLЇ}ol>EL01*UߤCZfSrro߮#G7L ZfjU뎞WϨ1w G)0̤rX޽M@OctA%''kϞ=JOOWCCCzzzj>}͛1cƜ&@@80)R\d~zXiU/LlM1TUUiڲeۧիƏ3goWXX 3tWk9U{#L-*='1m*+;(:"ܔJk[\';;[ׯח_~^?2p.u뭷k 3% ΉڞSo\1:/]qD3Fuj^M?c_kÆ ڳgKIIIuwh.(VUsM/Hk2kv}͹jpyxxtJNeu``` kƍt۟ݒM4Ifm(v@Pg{sT]}ᵲr^}}}ywƎJNN֎;:X,߿N9shԩX ZDW-<<<9wz]%>TvEG,kEaݫW/1ԉ'/ooo_~JHHiy}Wڸqk:|F3fh޼y8p 3dk@FYsSzN5/VS&]V`( S:ϙ3g'N(--MVnnjkkC}QDD"##8%$$hӧSTKJJ?\REEŹo?}ɓuM7iٌYt Ytբ9IQzOU ٺzmvݧ[fL^BLyZ SN);;[999m:6M*((Ç^Sxx"""X%$$_~߿ιǵ~zܹS'Ofs(7Ţ!Chڴi;w&Mxc@X OUfyxxSuIӛk]irR^O~sgkwEea=c;XӧUXXN˥JȰ{b($$DኊRLL|ݻW~ Ocǎu]y)66֙_mF)**믿nU9+ҎG?yJ2HuthϊJ㙵k9sFǏp`l6CkhhPqquyَ%\ٳgk̙uY(;tբ-"eЮVdV5'ѥ꾟ܢ^; i6((]Eeealqzzc},[aPT\bb?V}#a$5TRT9U\L^ UR\k;xWGQ}}4l0y{{tAUt>Rkzy**3m6}cv}s@7^3M]<==۴Geeaἁ^:~RSSllq^ EF*:>Fqb%os9- UhP =:e+'rsUp@y9y:ҒR;w5Vvv kݻhJLLȑ#ӱ)8b-09W_YS'>M_xmEUp6|Cv.ϯ2YK@ǎSjjҔ8ɹ㣾}HEE)_) (bEGYYHKWZ):lN}} TPPCٽp(!!A СCu+))I'Y:x-WDD!p2)>>P=_VaY;;]P/mpC}n[}Vmmfݻ[b/jVU'N[뒱ݷ"(&(Fglqgl:q}e;y!q1i{}2[yy*PanJϔZyubQHH"##kРA>|Få9@On:k@j3)>zq}sǿt,=co F nUlTD3OJb{vz@G+##C999***r^ ll bW/QY/JkpڅtD늲 ePfJrr3Ϩ8XeEȗX SttԿwyJLLԨQ0" j߼C/Zf_%/oVHpᵓZ3G :qN:88??5c}}WaኊRt|b(UNVvlJTFj/ń p%M.Uͦ3( eVnzٮܜB)* EDD(66V5|p1BCuܭBg-DW-:&i%ҪuZ UU^g;vk}u#I*mYE.gZb[UTT8}?ϳc#(E(n@Ŏl:!޶GGՙ3~4QɊ<=<<p]8kj̔ NQnz N8HeimSVV28q5OOOGQQQW5dȐƮZw$ZLqm[ۚ&WfϘ/s/`mٲ҅өS8833SOVQQK RXd£v$D[%/cc-j]v:q*+?+60$PC Ѹ+j=>xQ\Pӕ}rN+/3_gr URPrZ^,PLL4p@wy1b?|t ܭB" K/d&e$5+G[v8𾽕4 SdLqֻˎ-vT]m9?ߣS;}?F_6Jpb\ig-*xOdtj2TpJUZT֦s}GaaaQ5ydz뭊qlVOX t@AW-;՟H^7p@_꤬Е0ㆱ999[]qIQlXEGvŎ*-.W[vj߮}J>UU'H]8LMqW 3u6MHR,Q~V URP*ki///M4ItEdp6wPw{(**RBBctբ+lZ ':SRu#FТEttbv 6Myyy͎-wb?_E),*LQ1QRlX6gWT}e;<co[<,I#4IP+IιΤlwPskۧ{GcƌѢE4b]凱Ǐɓ'L>}ڵc#ȘHEG+bŸbGl6>[w𷇕+9غA99I0Ysmp/o/WxC @oZ1 ъ!=flw^}^eV у5v4czra*lZTi྾Zx|Iyxxsz @u&e8ư{uM7ի튶 ڻwN;V렔z IDAT= ֙)w[:yN8T+++˵cCpEF=G"c#zbGӎM;t8:W ׄkk#{r/MlZuuz)}GZj bRw$ZtWJ?:cwm&Iŋ{辶=<<4n8=tvDnnn;'O*==]cm6,;Oaa >;8Gcl:vnۥ*?'{=<?4^.؁1.OPEikzꩧ_ڄ?ܭB" k…z B5 Jyhn;xGU {w\u9"9,Ž*9rij=eHs6,5Lsk G8@e(Ld~{83_.\o*#N)li }{vT-)w1frA,33666UKNNƬYpJ)Jѭ[7L>]rOdSRR[cĮ `lGeprujP^^ q:1Uz!ܽ݇u#;=> ۶m}WFDSج%p!"""""éZe+=Y*Ti|G ik``nݺyyyHHH@RRRRR'O^qXj&Nʈ>ҷ~ DZB.DDDDD}8UKqNGEk׮U^7oĬYpСJ500@=0c Ը 99}6RRRt>[lko2{8slԀG֥T KqHKMWz*Z"][nl㍩ΤcGC͛Ѹq:}맰YK%BDDDDD%//2Sn܃Gb;vK/T{^~fѣG+mW^1cC.#-- O#{][lcg[G[88pvwԥ"&2Qg敛U=Ʀh>+X)Qr9ُ~GY_/#B)li }{v>}:V^-qtAN^9sfV_zfBhhhM>}`ƌpppM޽{ÃPR">2 >p9c6n|T3ٹ?N'ȦZ@NFFjRob#-9M&H0n8lذSج%p!"""""ZenŁS!C4ϕ+WNe˖Fvv6ݻtjl{9QGWG8`hc\.GdkxpU"m됮hC^yy9v|l Ey.NNN_H[?Z"-o"""""%]u?+C/DI?Gmj'ylry-Įcepru:^ųp5y7MLЬ'inúr*tVbl"dIRL<k׮1iDOaHKÅNO4Xr>v#Bã,Hp)իV}.\#)) P۱Mlvp9cؠ5YpD8bbR{;+cmo 4z+%;eeؼWs rq[nEn4P}j"""""""Rs 4T n=AGJ7jp%\|W^Ebb"RRR&gBUY5 s#ݝacu\.Ǎ3._Gfz{R4wAm}X7ȚX)΃:cuy#X}6zSoDZB߾ BDDDDDǩZe,[KW,J*a8uʔxT#c#tĬ+.*FԟQx%J54_3t΃;â+%>%%0g 9͛7Ν;﯁H[?DDDDDDDzSߺ6j7l؀3g"??V Ȧ84=:¥+.ܼtSa!t$QBNt@XܽueJuO'ot ]ZC*$5_ƦƘTts6 /;O~Mt3gĢE {%M""""",NՒ.{m*gCCC\r-[Tx}ff&&Lcǎ)C"al Ny'6_BJ161F~]1jHX6T6z*ΜG|L<3khdזn=u+%] cg?± }}}{JDD[?Z"-o"""""ҬӧcՂتYow6H90ywȑ#w^o߾.rss N6ws npkcSZ{-v؅ؤJ165F1h#q5#~II(~\^FhѮ: @ajnJKpl\ y5SSS̝;g-)E)li }{ptلOƕĔ?ƍpww\W^yTx.xiprQgOŅa]HU5&&1;^",,ٴ+wnŹw1i)imh]tAv-H"|ZuEzǎk.YOaHKÅ4S¢ᝥkW^y7o87|{^Fi{6ݍ[WoWz)zᓆ¼'Js>ϜǍ+70{L>86QcD齧e6=*YXX`ҥx4P }맰YK%BDDDDDZe>\Դ?#99:vEi9z8oޅ[\gbHR55^4f+I3goSLPDDuDZB߾ BDDDDDu///...(((xT-\a/B5nРܹL6 7oVQ;-jjp|wVz9 a/t%OJ ]͸?Wz 6E:+lX)UG J=h l۶ u\i+}맰YK%BDDDDDuoƌXj wՒ.{C裏0tPL8))⣄%R W>#c*֢Bg^KW534&iሉEJb JKJخ1;DFl=_LX ~l=f>aƌXlgMf-з jId<Ĝ7#<.QO'4XXqe#%>G"p3:wG_ h-ڶ)qc<iȼlf=D^n PXPjkиZk;065UDT?a 8pvB֭2" OaHKÅԋSk~?u7GcqsO"9 p T2e"U܈Tܿ}Oh$ =кKktm026Rٽ6/; .̙35PS 5]UZxg H1XG#T})342w7Ɗ p|V\)] `8v>l231Dѓޛ*Jd"t"JªV#1&Im6~J"tDDDDDDD|lܸQkF-i\ԕx_ XAP~]P\R&lF- >>/| ƦƢcǎEؾ}#"DDDDDDD:b޼ywՎjIsJJKmxsZ+xߩ5wg` uX)GR^õh=77&LÑDDf-l_ǶIEu"g޲e `x?_,l F}zXQ=%a0429Fu~mdlr=e;f|{0^zaڵ36kjI[ܸ.gP.NJ% K玅Bmܺ%.ͭ\ @"y MI&TCQ}f-T-i\.;1HJY[`ΌrO< >//%""&}2EcƢ[" @uDT߰YKDDDDDD8UK|' / GiYhofX>w,Z6 a7q~ gt/4lP) _\[bccѶm[?~\Q}f-T-iJEE6;qtѺu#3̞6&&F5\!13cZk&"".Fg H$Zvv6ŋk:" 5])ƩZҔ;t&$Qޣ^}'M:2_20?,T^+QmIRLd"ya(~\t sADDv 33TZ"""""""-ũZkqV:'F&xhγ{qH ֠qxEKDDJ]wk |ȸ!X />͛kB"G<H qڃXkFmG_W|5olZ8t"yXsS'|ypz֒={h2"Wlisa9s YWz`iYя%e8tZ!F7 Ra/fΜ \ Hd"""""""-YT-C|~3bo*\k7^ƍ,#(xT, k j]+Q]9mkPW4.˱b \pDF4X%:NiNR]8HaoMt*:ۢKTR/&/Lvj /^@eD/ج%"""""""%uT_Gaah_>=[W{~E% '""mM`E6h޽{޽;֯_ʈHd"""""""-hvXTTd4߅Bњ i~m T_PSg1gO'ֽVi C#C4xyam(+){V\\z X~='jaHKT6Uۿ*"}QXT?nCȹ כ6)`oU “}h >xF"""f߾f=WTT_~˗qa888hJ"%ۮF#""v^~^hL IDAT ___>} #"f-T-Z,\o/rENVX<{T۶/e؄Yk|?"""]`ieϷCD@,_\3NaH pT)7f/oPQ!\30`d`|hȜ*σƈ!5_1zh̞=r;Q}f--XS~;[1K3c|:=-U+!HLytјc0sa\///ѿk:"6liH~~>6l qj"a>~= fafOgN lhd|W-Stb8{:N:֭[#66VaHC,X T-ķ[D:]Uߨ~2N:N\DDDK-FAEkwA.]yf TFDP(T 55EEE􄧧'<<<`iiقr͚5{Tm۸~:Ґ&M ͛7G՞?>>wrrr`gggggx{{McbbpmܻwpppL&/՚.]Bjj*PTTGGGd2J~P4U)T-U_9-z9_S۱?R𳑉&|4^-tf|;b7;QVZtǘ2e """fH#Y#G $$ǎCNNN;88{X`k;-- BHHN:GUyL&C`` ϟZ~VII ~'lݺQQQ^wQi:???mۆWVz]Ν1qD022RYǎ;p-HR*&N5k`Νxk1x`?СCUS5T /7ARtUKdzV6b3QU^O_|;}%o [ $"MTTTT<2+..ʕ+pj ,XLlZ !!!~LLL;O?uo1114iM6gϞ)L<wQzO6muVnݺكS>Qo{ƦMZGH}GƺuT[[[ܟG~~>\\\ZO#TU. w\/7'Sy.\Y =#iOE# #""G9XW'Xrppѹsg TF;O>ٳgkԨ>_~l٢+W/q*o߾>~Æ ׯ_Z͛qUQ OF۶mqʕZ>>AV`kk7o">>aaaHOO355ٳgܧOF߾}涱nذ!_x>}}666tR{'&&=zPxL&ÀУG"44׮]]gddGO>^x傸D"'H;v ɢ{4lgϞEVƍxEqD֭[#00>>>?q12 pttvyaѢEڵkǏ6۶mZIo!"""""8UK=8,䇑:<\.v"+/,Z`057Uy>""شW&N7UDB)ZӬo_wao.^+ߓl&M$|gX~hcǎ փɓ'hٲ%RRRq;;;:t۷IKKØ1c.[YY!!!M4Q:FFPPP 7oh͛71d$&& ➞vZ~qqqh׮nݺaϞ=ppp9<^xdee =ztn栠 Q|ѸqcAFbĉ믿V+BDDDDD͜9+Vľ?_>&¢b3?5id_ #C;z ~MYĨwF`ԻT>u69ăBmڴÇ쬁ʈS4v য়~Bjj*,XPḛh"Xv-Jԩv܉d̜9F-X[[cڵطoh-** sU:7|F|ŋW+_|!jȑ# ߍÇIIIXvm'F6jC[aaa8xҹr9>cQk׮غuQ =;h lٲDm6(|lذA9QKնfGQ Q[VV˂X& 1a*EDDTxchѾh-66m۶ʼn'4P5kp_I`ÇgϞExx8Ǝ z 3f!!!J#??K.$ lق> vZ*֭[1SSSãʽ͚5Cppl sVٳ8t ֤Igua駟BA;vu`ї5j(|WXEEfϞTn"""""ZR,>!yʬѵssX  1e$""Rgzusl """Rl{1sa\///ǒ%KB UGDڼ<ѱ۷W{W&k---ѢE~(VZdJ_K$:/q Q\U+**'""""ET15*_?9¿^;%KYGTxr\DDDT5^Xo:֎;___CRDtY{rA666j"+hEͼgU}ϛdR$%%i,JJ 1sss,?DDDDD?MvT-)/7 _dc Y9p| Է)zdK/A[nSNعs*#fmaa!fϞ-O8QݻK8wִYimmZH$*mV?77~ȯʿʪHpT- 6~d'ݱ[ GY>T-3/@VXX &`ƌ!7"n:۬={6RSS1[[[ \U:u* 1kJ(Vӆ!6l(=~rh6{Ϳ)7jDcUݬ*?%UH$jfvm%߽{[ o#""|c4h@Xjz\ UGDejb֭Xf(7R-Z$:k֬Qlð6 Sxnno Dqe4jRTME9ZSuTWWaa!͟~pTˍQ?sKLUm\^gT'-ՌO',? )SkZn}H9uG\6** o(ꫯ`̛7O3gz}0u(ښA sfWGVTT<""""""R NՒ*tW"""Rf2|5$\N]xгgO UH$VȳÕNAB߾}q A'N@fԖ;)) }AFF 兓'OƦFUeð\4[ՔLLLT_҇ެ%"""""©ZRvDNA 6vHT$d b]Ʃf{XZYbxPL 0+VLqDV7ksrrп\vMoҤ ;-[-۷ѧO ɓ'kuT5m*:y>5h_U! @nnn䷳SODDDDDmZIƮXSgktBm9wllj^V[>"""R/Ts'c01o`̙7nJJJ4T!6k>|@ 7Ʊc뫶ܩӧ޽+ԩSpvv5U,FFF *LMMBI~"""""_=O?$qMIi 6qLgH%܅DM~Ȟhh]>v>6NMDkwDTYҥKBCC秶C߾}"ԩSpuuuQgʕ_WTTY~TY]~"""""Oڊdh\|r{^,0jGDDDu.CέDk׮]=ʈY[PPAxÆ qQo^mЧO$'' 8uUU+C5סCD5 9D"v///!yվ6ESM9UK/⟟ 0:Y/-'=3 3|PHbС/4TiUѣGa399qqqPL_#F}Wjbb"V^-`J2e۷#22Rرc fcctVZ$۷gΜļEQ 5w}[n b RH?pj+BN b6 دrbAl؛`hdDDD]M1sH ;f͂\.PDF%%%5j?.Wk#//mllp 1ְaC|'\.ĉUެ, vh勇%بwGA0i6`%pBCCT TFT?H*:Ցm۶aҤIDͰС֯__ŋ1w\QjWñ`FϞ=QXX(Zd D׮]P\vMt>~UޣGbС(//5k h߾=*:{JQ{ $___aaa8vDd2DDDɩ?3,]TJ@`` d2N<Ǐ+mӦ /+ ;;;A,##/ǬY_ b_?ZR W\\ӟ <nvU쪹b7g;=9z8⫐lJ˰p9Z&Mw^J,}he$ >Çkuokk@FFFrkW?c׮]1bwbƍظqcR)֭[WF- 4k֬ԩSE D$&&Vo&&&سgOx `ɒ%+Wʕ+UoԨBBBjԨnܧb۶m\.GTTsd8tPDDDDD[8UKu.:NШn֨QF-SF-042Ļ_OCVرbF`` VZU k\ !""Z>7778qSLU7|pqq־6m ** 6-Ž;dV^peiӦVlق+W̬ZF˗/ٹVH]Tj>A/  tEjIDDD)hJ>\7 `ԩxw 5TXV"<UOsW/^ĪUСC*koݺ5.]+Wȁ~ʕ+Xx1|||ߩS'^ϟJ7qqqO^u۷/6oތSNM%{=`鰷:ccc 6 سgODDDDD=z$zj:=LA,7RߩMET ?|ImH_Y;Xي ׯ;Qi익T۷oڵkHOOGVV7n '''xyyEj#77666prrU۷ mڴ r/^ĝ;wp=NNNh׮ԒWX'""""W|W-FQ;|i .|FFy[Ճ|ZeLxyajGDDD_5/yzz"44M6@eT[?Z"-o"""""}#d2=5uǁoh*%+6gwGrUؔ`R[N"""r,܄N֬g_Q}oHI ,4j|W-)AVvܝGjy'-RVZQKDDDJJx`Wahd X{!j* UGج%""""""RwcptכCqI 6iLH%xfp&J0i#"""8~>cX4KKK1c &ʰYKDDDDDDNRm\Ш+X{gx-g {õr~kK.hFϞ=ʈtDDDDDDDZ/7\ah ń1ԚspANCLSDDDT;ζXv` Zwi%Z;{,ڴix TFج%""""""zNRm BLAo/8[-g\=L~ u {7{$"" Z>~f&;ATi,nނmoEEDR `ApӤ4C: .&2'{.sfk9e^;xZnoƚa-]99z,8WC_ֺ-Xg}|4tmn Jͦ۟]W>>Ƶ_'N;`X _`W-t,!wo@?;`dnl˺&(:BC<''G<.aQwcX @>U+*DG&nt:5Fujm^W/{QjVt6sLiFgΜ31 ŕ4cΞ4[oj-lثc'uVpXjHRDt}Q:6ua5mT۶m31 iiie;pիk,0$P趚𣏕P{˸viF ,;31 /jq^8^^CڹU;uY#1ZR=u|yjjqYyp4}FV"jQ0klYVj8pf# Q&_з=yFШQ4degg[9pv>8]{zFk׭ڸ7oUb9#}wo,ũVzTNeksQVt) :<Z]=G(<,m5sߍ,\z "z׿l٢&Mhy  kvredf齹K,<4@zpkK6lZv[9IP?yټk'NP5{lŰîZ\)_~T#ܻ|V\zadbtn p?6Xܹs:ty:ð3~xvğNgbdUԱ}}֝K2A W>3F0#w:z饗ԯ_?eees7p/w$egg?42vբ^t2l6y𧻓YZvn Pկ _R\-ZH\s;V`X )S(!!Eݠ$پ7Nnd+AnW딙cd>^:uҧ~jQw@`X (ϟ};p'ֲ_6h˞CFZP>ܭug[mn .7=_O<##Cw}F!iQw{PM0XhXu"[}mdܿ[O҆⌬5un N-4 _QDt;Nkݻ222,pRmڵڸqJ>ube:r*vk֝9w;K4n Pbj/V.ז.]X>|؂aX (ƌ?7#)ihFV>"X7tkֺqee㶍TQ (.Ae4QЯ˵]v)66V?Pj߿_uiD!u r| ߭Znyk6 cW-v=mĭQӧս{wM:բŰPj=Z99961$8t`duEUnǾڱ5\1bZ*}c&233Gnd`X (5|#k٠U,oQG()&~3cۺyk?ͮZpukֱ^*Eynn}]uUiiiu\9R_Թs!voߺS66ͪFu~H{ڞU)ʭu~9N a-T/uA#uMN%٠ֺ?ݭR!]T&(;JFmiq*_˵?P]wRRR,|*'N4etku Lf-]edU* Zpj7,L>2uJr14ax5lwիմiS޽ۂ΀cX (5~gmٲwn-_:BIƧ󔞕mdC^+{V:fd=[$zg}h7kqqqjٲ,YbAg@0/}7u;iڭF֬^E5nXŭusrzÂ2e>n Pl6;t ۸~߶;1 wO?dd׷n`D^\9ss/v/ u-ۢFv=.ptYÂ<;;[Ohd[Vn%?l3[\ ^SWWF^{MO]Ԟzc[XjFeʆ'OZ `bX *8NM2ȪFu4#xm?⌬SZWߣ#Re"넄|^ /cǎޝitꍙ_Y Xj]h^4 /o(̙3u o񆱎(׷x_s}o]O8kdn@("*ڤ$:L k%ҥKsN#ԵvESӴVYTxzu{méK6YpX>ESNJo6ҩQԟ5sFvSKm /n-a-y7u2שES5cefٰ^ >uYjܶxpu699٢NJEiѲC PlF֤N5i\XϘFKÞZ,cl,Dy饗upnȸk7+=m蠶Rڲ5lP*[d%sXÇjx0֯_od۷ES-_XWPFc"̹kt^X6 gW-"]ΤSrj\Q&HBT-&RMU֯)5P?"o@sc :cX 8FRNN΅f7#x{uQ{3ש?/٤s.3ˡcRtT68~syMypB utQeedY˖--`X 0gΜUڪ]΢P8lW]))˴c_|/QFq;FE~p'I&u0x+==rEݠ1?t}֬$w\o~B)l٣7>wV랭eيP4noWmZ'|bd(nMkBdSR>wl}zӟpV.WrD*Fbxu*)SqҴD~ݗ̜^7w w _+@9簱nذE1x~[gΜ1v$a!:%u+9kty]|1RGE)>9Cwk9}xkM[67\ߤry F-,8a-#8NM<*FU6u$<$XM4~Y3npjd@#]](azGHw-'6=5]?Q+k?VZO@;y/Q_ǎ-8a-#̞=[4nP a!l qk腡 yy9pNw6kGhx:GJ>Ңt6~GC[Vnі[TA5\[bcNcZXOꫯ밐 ړ_`"ʄs;8׻KH4Qwm^S7.⫧hҳ4UF#NLJY]=[*UǢn,?k˖-FֿSkx@(LdxcӧjҴ0Oܧyy=3Zdڰ74-]^mӭ=k뷻g۪yټ~OkΤzhJIL)P?,7vXc{onQ7(.֦gwUJyvmzZyS;?Ukyi䝍+C $ESҏ)3sm?h_iu (NX:.bg-Rvʕ+dQG(Gr=t"X*jP`~3ߧShUjĄ'[itǍ23ezǵp"9]\D&Y,a-RGḰmǐ^v("yڰ$)ldW OPN{=#\>R׷jjQG(&͜[w cm/WK63F6{ ogj"_y1WZpF:ccmԱcGL knƌ:z ٢nP-]^]Se9 ~S3W癕 C_N{W^4{bgUr䡓u-(ilk+TPL k^3ԵEݠwXVnnSQ ө,#ګ^5bB5ܭl^l~}1^))P4HRN:\1+Vh۶mF6Kx{[J4=6g?MN){f;# =7սM&lG1\S#z?M?m;wqcݬY3:\1q?_ݿEݠ$r:z|;lG%; |XIi~u_\"Z V \M<~CF} fiJPqr;wZ@5jN煵ݦ eaG(i,_ϗ1 _޹ uy}*5>8l6u5kB'U.rck, yRddE<s -^6 uf4Ff i(4O6/zRZ߾l-UVUi7[5L 쪾VVF4C/7Qii4ڵk[b1vXee]yIz5tR|m.9uյI$Keg3h28g>zkUg>vp dk\ߺw={~6 ޱM6畀58r/ :kWovS϶s8i>t (05kִ  knoȰ 9Qk[U%{{K9y..>Mk8cd-F)^evyzVz p݅MzHuߑ}Gu&M,Z,YDv2A]n[J,=:}%>ohRIwuF kY;r^T+MqձR}:V7Wa.5 r:y P29%5hݺEcX p_|Xξ]-%Ann7ѡwK˿?"$X絳vבmܟddc+nUa":2HO{בe\s:zbg(%KJ;┓m-ةS'kZ[lٲEk֬1Q_>w҇sъ_YD޹|~GvDNNw(71:eiWK#:wfCTtgjj (Z6O QZ,Z[=Zx,neFcaת\ǥg2/r IںF\f%OZV7unr-=-]?=Eo?2,h}Ql6b<Çkɒ%F!ʅ_ŕ9x􄞛~X꾁^4{bgUr䡓}-؂Za-Z1Ei!:1NN6kkF_76-eshkSP}w&%Gggek5JILAKj[ŽiO>]`d6/iרF"g^\t^JPMAO m(_k\g~;fQ;nРEðpE|r#Ԣʖ #xSgkӔuɎ;UF1E^/*4a77 "rw5*rvlq{пtwtJNX: O(+2f\ټy99zjWC=fw*EO*nWZխovUNUtqYY |D%Y (ud١C a-%''kΜ9FvMZmQG4TG,Z _yovO;M]/>B=ZAgm]=Rofn]vu Ze{饗t9#{p`Or-e -5O&͇]JQn󺵩즦uʺ\K>/Mᰠ;PZݲXߢnaX ,㏍Nj٨EmlyۼV춺/ުK~:9m5q^d>Q'os״܀9zY Pp kwUBBկE;o}94}gCSmu'jGv&)muqf?4'S\ʓң-zF6^#םt6;06M;v (_~XjXN41ub},"OoNv~Z/T%f>קczկr-1>Q_-aڽyPBCC-(8BYf6mdd}k%_:'if?mzcxk+ڛ$hþSFִzfXk }>_Gv9Iw:zbg(%K]u:u,(B=zzZ <#ܻg6Q%CSwxʾv=xcfFht+r8=YZڂ@Iwy$Ff,(۷o~#ҪYG_L#ܪ~;tƬݩSR3r#n2ZzZz~eep0(t%J4mZ P8 k6j(9^6MŽ`\t(6RVXzxm[=]ёe\^{:\I#h s=xuzݺׂ@Iʽ)/;wp ̙3Z`nT[UYsf9LP_j++Gd#{U(eCtiΤ'5UJ˵c°q7yNͻuriQ7@x[@^u!>^XԩS,bڵK{ѣGu+WN*URUjUG*%%E*TPLL7n˻ЦMt!;vL銎VLLbcc?~a׃oX۟o62&VA~nt:F૗*ILҲ/\?ȰU֗tӷ鳥sme|qEgkժeM#ea3gd-^X˗/י3g*TP5vXկ_p:9sOQyF0++KSN̙3~|_׸q rp{Of}ݵ^aÆO?q&O?\56M;wwܡaÆYmIڻw}]͞=['O5two,''|bd($!KҘ_)XzX&}+<$ºl1MH(PxvM#jT3 IDATгood =[hDu uVu <ձnjuӦM-(<&nI&iZfe=noرcsE8p@wyVZU{"##5e 0jKw>\۶mjժO>Qǎ?;S.=M4̙3ըQ+?g=C;N:O>Q*Uvnn{=9e_4hLe^QԩS*W||\||+ꫯjȑf]ѶE*g4xK:q&PKxq+Ku"g`I^Z6M~x@M=p+_y,=%R{I׽/#ߢ9(2tw{t\3M>]Ç+6O'Ng/r8Uڵ5cƌej֬YAiܸq][.]VZjP+Tr.]駟^QiӦ/ԠVnݪ-ZhŊWTԨQ 5~IM6~EN=装JܹscǎtjFV)*BݮmfQGc/2mS+JӤxicP+I le j%)L>snMx _|;=sW͋Ҫ5ӊyКG^cP+?R2eԠA5h@ 6TTTvޭ]viժU:~qOffx5jHwGgϞu]K.ڿ-[M6<yѪVeJ~Ӑ!Cr-&&FݺuSuV-[L;v0^t:uJ*ܹs/[L=K奚5k{jѢ֭[˗k벲4h /jذazߨQ#u] 4ʕ+|r퓓ջwo[Nх?jԨ<v]͛7W=TJXB+Vp(>|X{֪U,e :dd(Ͳ"~{ ˫XzX7)熺>u*"&d7zҵMW151@EOǦ:<<\+W,{ r\\jԨqa|P{_?QzzƏ_]/bW7*,,}mVk׮5@-X@ݺusyӧukF-[^zzAʕ7|kcǎiZfiϞ=(pxծ]eP]N}7YݻwO>ڻw׬YS;v(۶mS͕cڵӜ9sTB{6lؠ޽{+!!;t蠕+Wt~Gs^\A>PxyRnn; 0@9GÆ URW۶}\=5k~:,$H|=pVis,[3CĴ {u]zyI_\@/[ ӇK(:9N['gboU_BË!,o<~~u-a ;]mݠAM:Uرczj*7:p|מ7oˠnk̙yj%)""BfR֭<++K=\kKɓ]ZhQZIX.\2HMJJ Uܸq.ڨ(-Y$At~ߺ ۧP}YAmڵp<ԲeK-\PFj*-ZN~im۶9sˠV:۷gϞ1c/g͚e|y\-V\nNԖ2ګ?1lܢ4bzcP+Iú6sP+qg-|m5id\߹~F~Z~ڜjwdQcݤIˆaaaZdmۦ{W~~~E8qK~á%Wտ_-2vKW_}u~JJ&L`d^^^1cZjFFFoUٲewy4Y~8>pBU^/U.\㕚]_~E|EDDu6mh.{]κY.\#K 8Pg)PmwaQ]3C `E(%EKb4c-XbIb&F jEQ^Gy&̙ys쳖5YgMDDDdIM&lms}$BaF٣9"Z1Y?FT!>X8;O%Dǖ+&r3s1X5e5u&"")4 ;q_lGd$mݻJxѤI!T*q֭'΍7j|Ar{yyaʔ)z l޼YӈDa۶mz'm\u&qȑ2BL&U8pp+We΋ELF#zUzF}TT?~\hdgg 1U(I?_%5۾}vO=ܪZ"""".RSSc!%9xo7|Ǯ5;kӜYj/gp/]\=&ԭYK,,Gnغ'Gw UX?k~}ʒo@DDDURJ"K`TؓnHJ kGI 29s⛼mڴAz*՝O_R3W^>7^Rp ZhРXbb%homO>4%ꫯPPP ԥX!f%aK硿Xfm Z.4y8٪R>IՁ\.;vzwy9]O; 6:"""2g 2Y$m*JL8Q/>r'-6 KVetd26KʟL&+aJߏHjׯǃ~C&ⷝ8tAKvi>z/>"ة==]qF6WAض0}Bw*/)1Wr[l"""Kw0nذ!rm{Q fk'N{ 1// ݰ5I~+++ǏyV* cwuuyo_{"""""s7o{IjhxsqNtu@tA5k|OLrlSC;0a$q\ꈈ"bΊpe-Y,YY{}B~طoN:镮e l{=Im322*Og{)Wԝ;wefʔ)&V WŠ݌Sㅘ /jZ|4.-Lio# y|kEiq+$ ۿgK_k|,_[߄DUQYb ںj;4*[ijjjv&57ѬMIIAdd$ݻ'}}}qAԭ[B+fȆL&+a$4k,6TV&z^iIujUɘ Fe]3eTȳg=zݻw mZ022?OacBZ%$5Lz 1bZߧ C&{>.0.萝W7h|~Y{wAܕ8pZHT%&V0 @?~lJc駟ƭ[7OTR7 _-kBweǞUU{Ksf KWՍj3&0mh;4c1!˰gЫacW@ZFr*oHOlM$)_ߗ.<@DDDKNwam߾DUY7k322гgOZjh޼y[vm!vVVVpss3I0~ҶWe敕b[Mt:dff2ðuc'""""27Jk+Y̜\? B|tXo6ngz<=mB,=3yru¦o~z[5}VL1YiYUHDDD,qܮ]JT Qՙm6++ z•+W;8J߻&oev* C^@@ E)3 y-Z(sK\ m 4ՕRZ.eas"77W1DՐ1i4x <Ma&!zfo+?>}x_9玦e,B!1g媿#YX|N;#AuDDDoψ  d̲Y޽{… B QQQhӦM_-_uNgrf)DDDDDBb T[:/+>4@&2p.M_΢H+vjgf-,46/D6)sX,ʽMDDDUw)V׭[Wo%1fmnn.郿K`߾}we˖zJ?{nؓu%߳gOs3~~LV:R5 /Ĭдi ߋH WƣGبOKT }|s1p6z\g !ަ6~1=/ҳ٬ҹ8`]1qLkB:t$ܸ+MDDD5ݛ =2\U6//}ӧ3݋;$Oƍ'RSSqĉ k۶mzݻ9SNx"+X&=1XX^,** ʝ(!fooN:U8Gyܽ{/_buօ$Ǐsΰ6DDDDD`a,Q5d,cb1g#0{=Z p9J֍jc#  OWaS_^_DIZJb %v%AeDDD5ۣ¸m۶UBdfӬU*߿?N<)ĝgtŠh0##ǏbjBxxx󜜜УG*r XPP5jT&M>U*8p@8p@H8882DDDMݾ}׮]P~}'4hޗǎCVVV?sTvލ[n ]P(JA(%#-X"qE=#UW+YѰ5ˮ8+[R4-{bXdCȋ.Tb)DDDdPyYyNb<,Y4k?~gyǎ⎎ؽ{Qԫht׮]hgy\o;K|yR淲B%/**®]{Ul9s0v+TJP]q#=G){=[=H7VGBjrsᅣb_Lvsƙy&MJ/hEF7i }Qꈈj1X.&%YR0x`DGG qڵ O=QFDD-Ek~ZZO.d2xr6l݅Xtt4nZqqqXd+Rcƌۮ_~3g5ԩSظq,w7ԋ-^ѣBERRJo[|sD5HJ/^;~_p-eY)KƽB,_LV,j`)\M6; }Ji=ml򸲖*{/Zz]w`Ք(RIPQXaAdL6k 1dk֬r0bjJaРAO*2|U^8IBu1NF3hgˏuwz=YTE3S6; -%`nǵ+mV%w"""ǥ͛sXjj*<=oS$=\k.]Bn*46mRӧOe˖נAUjo(ԫWBZngVQ 3fƍQyݻwǥKԨ 6`ѢEм!CҥKiٳF-5DՐ!t:LXJQ۲/Iä5j_JF-ZN<'=#OU\d:ض0MNƜ7YKٌօ[ۛZ$kdW8r֮]%d24j9]!CTڶmcСiLVZaٸz*wn=zիW1sLSNXd /$?k׮aҤIhذaS(x駱n:>| 40HqxPvR?gccbؼysDDDDDRh4Xjk\5._w M!dcBameG9ړP&mϞ5jpi**릇aam%~?hoC>{n2&KT!eK+$Ȱ$=zW\Ç ggg߿+9#!!111x!???4i͚˔7o"66>Dff&<==@5Hre$$$ )) ]6кu2jqy$&&PTڵk777nȼ,]ƍbA% µJ5o< %'/.>&6N-}vF_.;? ^fOERck5i$!AeDDD"?;ɼ$R)VOH7lذּys4o\$\.G\LDDDDdi-Z$=\LXG!COL~`7jsUj^ 6F-x ㌬* (Fbw}ZA~VMY7oޱb2DB?Jb]t"♵DDDDDDܶm'ĆG>U/d+T ;7ij5H7W4 dowGD/||rul֒q*͇1k\K?tnHPeF+å)dNDDDDDT͚5K;;g2NL!޾& ngZ;kbn[7 k5eofI"V,ULWfc_jKMDDDpՓ"b;s Ν;'uymQK3f/|P2r,l6y5mU(3s٬%t3) ϨVU)HKJB"""vaܸqc*!2<6k/gjcm7"]ѧa1!foW֨컆m 1\#QsWUmga,r9> +t;0a$q\ꈈ¸uUBdxlUS 8x s)3'UWL& if~Fx:tK{^xQsBmw'af-I%$ E""WZ2'PDDDh@}SNUCdxlUSSNEQ? )\w"\J &㘧Чm}>x,4BFm(~.8OAaFjsƢ ]0ͶU]? >)IPys⿁#""$ج%"""""e!"u|$H{s#93Gwmw sS1iYhbvD y3Ҩ U/XJ1?&]%㫑ߡj%<<'&MHT YKDDDDDT ͘1qBA-G2hyPP$6"7Ij{8C^*5#_b6}4F o"/ۊi##+-K umaܨQ#*!26kZQ5kP!|m{G%ĜtL(79;o<\Z /A.publ֒yP(?5,\m^ŧ'3TGDD$qqV$8,ɊhٲeHOOb )Q5T9 1Lw@omS %Wƫ+ -W%Ѱ?c;ڴ,U)$Fhpml_`ok%'P]l""G"~رDDDDDDDՈVŋ;"L|` E!Nkgj ^(+#/ W';6Zbl֒qq0qLk{1B~w)Ȅ?@PKS YKDDDDDTlٲwb/-kkBu3;d Ȗxg Ui@<潌zޮFm 8=@J~uN$` _bǪ]TFDDd:7>U#_0vur~%*kq#!֤3f 2(9EZIv6V>yߖx:L6k5-{bXdCȋW_1}LdHS ]-6lQh"""""jĉxؽl%*?b+BKv5Y9nZ+&Fx[55o7Ys?Y[h|9]q>?/0Ȳ%& -[JT YKDDDDDTML6MX!%*ąXiV0od';%FŻ?!0<|!&fI"=ۿ-=fb jj$8RF "ašCXd^LXN/V{NB\!a1o[JmgaǦYw;2 6z:6p?>I 'n2B{wBDDDDDDԩSh7xwx +(TޜyÈ%VNMl\\%|k,%A&4fif8!'CL:6엠2"""É9{CؠCUCd)U놩C͛VŇNȍB\.[`\qZcnL.쉁! UX?k~}Ji $""+q¸~P(UCd2͛=iNܝa fi"٥-DpSk٘7X?k]>KNLWc:6k,… )^Kj"UhJ sSߍlv V翝þL~>~>9͍\.KdOw;5RVž QH\GfJ&X׮]%ȸج%"""""PZ˖-bukg`*bZMbݚzc͍o }QJN`LxQr#g[a*DG.XU|='~CKPQ]?# TmYKDDDDDd~$&ͷ}C.磞۸ΊgHaΈΐd̪)aBLW03gŷ*" jR ۿ]]+/)1Wre9K¸nݺ31!DODDDDDDjܹ~Jj%`/;B:~<* SFw7x>s*6a';[~ ƅJCIq{¸ys>Cdج%"""""@Ѹr{63'|FHط%2x[/3w⟍膯F<%tqƙ9UBd=Т޵̔L|e66ZVꈈJڵ"cM6Mg{KT NäEk%{ň7g%v^| ZŚylR鈍1fPS(VZ;Wç -)M l!*Q5Df-q=*";})3۾G.b<05w|"^/ΘfYow'a"Ӓpd+ gݹ '؎TGDD􏘳1X.#,,LjZ"""""" 3uTaJB"z7䷽BZ:ְ[W/u{#>8Kf-,!^ؾ(!zX>a*:'""i}Q\\\$ج%""""" رcu )Fv,\F<AhVݠG qlo-񋾜xV'0X4  '?Eܕ8 #"¸YfUBdlYiӦ@=DГt:_əBo:%E]÷o>>o\Slit湵TC -s{I}JiIjtwa7"""c{HKT iYKDDDDDd!O? V @Iߴg{9a!ͳ` ?xS/jXtW@Zc *!2|雧1_rpH˶bڈLɔB""IL*Q5Df-?>zWn _;kÝSß7] X@.\ao#n/Q5DAcPrջ{)㟙HP$ V'"""$4ج%"""""Z˗/b}ޡDQY2r0a:h}`4ec#zax6jKh#3٬%ؾ0zה9J,h O\uZꈈ&uA<UCdlYu!))I.M1T&N}} o%O pWfdI+&wÄ1a[l:t HqWZKҤI*!2>Yy cWg *Q5T?3>`9~:{CZϱQqzJJ׈~mN$` _bǪ]TFDDYm֭U|z#"""""2sQQQң  E)3H*_1'[+, +zm<y@WS;[sl:rtu9\YKTFu]e~O lL.Tb̑@""V UH.ĺt"Q5D'8""""""37c a`g"%Jϖ2_i+*cөxq Ŏ3]iب-'/7{aS(Q%DJ/hEF7i }Qꈈ:F+å)ȄGDDDDDdp !'JARjpwG6 cۘMQۯSx vYj*!C|۞SZnf.濻FHꈈ:y0vwwGz$t$GDDDDDdƾ[h]PоVD%&\}_b0s~WHc"(lV^j kVCcITJ )4GDDDDDdZ-6o,4DQI<}?G]쭱ptX)d*bg{%(|(gaf-QE}~z%~C'c߆TFDD~a$Q%D':""""""3~$%*IRr؈Qe2`ưuobT0eyh-hd|=]qNEG#5wօ=10>dK)Tb 9Pf+),FAڃ4!֩S'!2->e˖ cG^j8uQ>=rުNR"^6u}gJ|=]Ndfi"*3 ;ZWIr%s޹UCdZ|#"""""2Cyyy?XxH DQq_܈ h@pu%;bڮamU[U/f-QeEvm #CZvz6ysZF#AuDDd n%ѤI!2-6kˡR[9 \bHޣgY!h/w\^sj6ض]ЂѰaldg%3ج% Ow;5Vž QH,QDDdn_-5j$Q%Df-Z~0nTԕ{Ij&!&0rҽ'agЂ٨5 '[a*DT^r گa߉g89w_jJJLZ""""""3s5\~]  Q5oj5{(UB&ԧJ>z!> 5jC"z+?*جMfPo{w jj,x)Zm jH*:v(Q5Df-Yp!t*2E IDAT^OIXWB|ERZxO?y>pEbm~;vlZb3 $zU`G0k\sk2""27cːC$6k̈FۅXMXu</1/g[2Yϩ=ןFAFmp8Zcr-֬e zE#7kgΖ0jn c{{{nL5 DDDDDDfdHOOb/{ڭBJ.7/u]{6.=F%choS{S<]qVNa)$tLk;V풠"""2'qWn B.gjn'"""""2#0tsSm% _UD~Zݳ97}NŸ'*֨m'F(3s٬%2&\_B ;$"""s([l)Q%D`LĉB,sU.)nڹ[So֣EyN$ Fm8U8V~.Omga,AgxN]&K"CBB$H|'"""""2-ZMLQ#$~s\b>v3^w֜B9شN. *lL[KlfK$:b B$"&#_/ĺw.Q5D`L¸=ԭ)Q5y?b 9 g{J 8b6N, wgJKSE+ (,ҙbg>-CDD9{CؠCUC$ 6k'/wR(TMuZgLYB;N. O7Rf1k@zc *!yBk;Db CPHT 4ج%"""""2/vtI߮ƃ,!t^ެR <JL++ S)6j}檶0Rre-Yq7cۏIT c\\x LlI >>*"Y;rKTUqUuX9ZaPXaQ#9[5swᅣ q,PTZ") l(SCDDFw]aܬY3*!DDDDDDhѢ|||?F Gɦ1dd⳥?A/&0TbsUxyY4eNnNv8e5mHxKXJpvb{""2 q۶<j.6k$ ˱tRXYY]S #f32s%zjhr:5D}<F-FRƞ-6 KKDRGG?!sҲ$%_L!ֹsg!DDDDDD2dv܉?V`㾣dl6>-h͸_bvG3BQsG}3RfepwbY3F$B'4E^G(nrUC$=6k$ԧO\~[lAVJFոn+_1Sǡ*ٚqsƂ]`UlueiF 3[1XdN8=KU'9ecw!vbIhZ*"""cu0DIZ""""""30d\z[nEPPPSipF< O7g,#gQVZ˓)+6Be2`sw,=rbwq/-O;`#9!K&l'3 $>qVj:pNjbq&M)LYKDDDDDdF+W` \^c[ZSWne$8p"4 +6E ><WLʷ6'/TQhgݳ_BנwK^gYK$~Eܞ|O%!Ca,Q%DZ""""""34p@_HHHɓz pu|` ^Ʊ1:+7= р})RvJwƮY/",j%rulIKwSc UȐ UH.:w,Q5DZ""""""3V^=L>ׯ_Gll,>9'q>wg/GaҒu8k˾cg!`/w\sj*5,?!`k^@x[K&n-ʼn6fP(wVջ% %rpi!2lYƍcܹիWAWuf*9r1z9WbiKJƴ\&ìB]lے.8f q;+lL՛dMP]P]9Qc8xkv6Vաd9|*S6;ot8FlnYuoܕJeġFݷo_wA;̙3Zd&Oz9v:So.\nӚ"kogk`fklڟSvi?4}j;Fi=Z$3):R~aEWh-v]Gv1z;ugPIC( >*;;[~nFyOC'N땏˔[Pz/ѡc'?2zv!wlA~>+=8+-qR6O˪TUUi -Lu=zEi<&O *;;[oZ/sVUiz~"]}uۋ?WaIiXBlVLOv5zSVMLk]Y_^xXnX+$O^F/3}IdR{M+7gK@[g^ Qr2@;뫻K˗/WNNf͚#G׷{v( R8x¨{mQ0(,,L<֭[ =0`6WT okO/>K+m[%9tTxe9T~r:z]TU)=:W~WIAu2g 0ꜼrMi]J}Ě0&q::{" 2Ģ4{v)&&F?q'|RҬY4|&^ݚt*3@GNѱә:q&Kr݇ٹ^vym_xxIE.4WtOUڢ2[ P{75ꀀǢ4{aX h$=3zgsN͚5K-ѣG'Xl5 ^{z'%T_SPT'ةL8\SV^r _Tsxn5409^m[lxQZ fMH6ZJMF SFݵk=hKO>9sfΜ 6W^ŋu:-ǟ?~/"_O}0 !ưa-nVb`gUVgZh#8nԽ{( ~.ɐ!C4dIի5{l-[LYYYnSx1"XcB%6TWtGVOgby kwt˘;SGNk߷cp S챳FbQ044tjٲez״rJ_kl6\R?Lqb,c9-,u)o~/n4k}437w4a-vp p84|ZhQ*22R111ԩ㕐$S_}]bCų1ɢDD59%p1$*$-}sEi9b7ba-m͟?_3g4z~>Zm.Ba-|3۽a,J {[W^yEiİ;iӦt^c-JOVcX[pgw]6C,.V0Ν;ߢ4{bX p;C&LP~~ѿ㚾g RńunAEI4DN!-}kut1ٳEIŰv|Am۶9hQ"xsXWİpw]dyyڸbEi9uQ/ʁk_}m(<]lDQ( xEO^fQ@MEyE*.0zW]uEiŰV{99ffF%GY ..*Ĩ*-J~x;2OfZpvnWZZEiŰ6N>c?A_עDh/:FuqS:.MLV];N-|mr;v:/rJedd{( ړ1a.̜b h i8`{u=,J7 /uDnۢ4hOb#Bu37~hx늖eSX-( bJ$TyyFoxnaΜ9*.6}hPҠ= 3,vmn]^tk hY4zF& 0XjƌF諟\ǰn"@[s$?\"@}F.̢4cX Lee.]jƦ$ˢDh:Fu~9h _ mYEe%e%'}WQw͢$@`w}WF7Z]hsX[^Yb[ 5'v7r-oEi9y8ègQm`X ̜9sPhQwq5[bAb@(%v25_Uքvp(d2dEia-Znћ^6SjQ1]ԑڳi5aIߕs$FmQm`X Č3TYYY]m6rP m5zٹ ko>|u\ h.{65ꐐ%''[h,1ܢ4w":+̢$.Rގw8آD>L7ꤤ$mZ@۲eo~ө'2ސ!C,J ktj…FoDqzba-жM6Ѩ2mv UUQFYhCZegg'X0ņunaEI4[tUH[Rg۳qQO>V5{lqCY0u 6"@[mט! {gQ"\vubbvPj_Гmt 1b@[[fvsh%uqիEIwV3k, P_Nj] .sr:-J9$ąOբ64uJ :a-ռ;F;1FW$Xp):ԨN)¢4m%u^f6,hQm?آ4@":;'ҀFoݧp8,J1o}Z/lQ< 0ꜼrhnwfEyEZWϐs&GFoĈUZZ>xewyy)&ưa-)F)6<ܕϰ{֕W^iQ1E*))1z2Ģ4ńuNAEI47ݮ#:y8âDz;w,:PZ@z뭷:sdQb#:Ң$ZOn&o[u]UU.0mG3={ZhZ̑#Gyfw},J4LlDQ1Zhp[jM-f>w& Q0;*8g"fΜiԡA~~..*ԥSbA-qI1>>Ͷӧ5sL͝;W^c5zh'?ѝwlkK /י3gjW7pntMͺ>I>.,J4MLxQ0y(''5JotrIkWUU_o[6)Sh֬Y/XwY4@K۴iRSSތo/nbQ"i~c~]^ga"-iуZI:qxzM駟jȐ!J|W7lw3QZIھ}RRRrKZnF j%iժU߿vqIO6M=Pjȸ@;u~z@M!<*Ң$ZCpjfZ;1ѣG[htYM4IuE\s:tH;vu89|=z'a۶mJ7|k5bm߾]˗/ݻkN~K.MKpz\6MIII;vRRR~zXB6Д)SW_wލ^Μ9zꩧj]O>;vzիWkŊ:uq]~~Ə+..oҠAtuשK.ZrV\2P>~Ə5k4@[p8h"wD7#TbzMzuku_nӀ 5vQGDDs6O>JII|f}siԩ:y O<~gnܸQ>֬Yc?i\7|~ll/^ܓSoUVVj:pݰzԩ.GJw]/VRRRuoڴioVPPoYwnQ;wҿꪫ4oZVRSSrJ 4;N=s 0sL=zO>uP+I:u҂ A$駟nڒOhҥ.^ݵdCK/5j{LΙJNNNւ ARSS`5kO>iNS\Ç;c jϳlof_{wm۶xٳgu\TƦ^nQt se[@k뚡ޞ {ռI"_~%>KTPP-[hٺ4x`y{M6x GPw @ߌa!C7::ZK,Qdd饗tر_~kJLLnݺi3O?.|WZxы+[o20}s<6))I ,wP/I'O3ko|Eڜ|@{05w/{gEii!zj&ڵamϞ=J۷ooԼfvMׯ7.k\jU^ڥ7vFG2-++Ӻu~cwJRBBǏ_tX]K xkn:7>nla"yDRɯJˮ[(dE#ew77Ж6;s96}oڟyJBB˼h?j8ֵcO8I& Wy=䓵BCC>}lR>Hzg];88XuUUפf՚5UVVIC|9QZsXjMTU 8q3~Ei쯳uV^iXe=Ǖ{u.W\aQx@JJJZ u{eP;qDlo~飏>RHH饗zm΁0P^^^kcXga-fΜiaA~m4ϫg 7|@{t$?J]8{Ei6[,nXsNF/&&Fgn$$$좭zСKڎ0iw6Emշ~ddv\UUrss[eFKx X0||8 ):PtN~EIX)4WW 0eVCΙ#FX,nX_:EGG7nvcm={sXzc\Gݺul...Veeex7xCFIC-Jp9u\ MݨK˵ֵkn֕W^iQa޽{]z}mk)99:uJEEEuӫW/ޱcǚ~gD~UUUokVuoQ'v ו/& bjk_m߀QJd>kVYZ١ms.4M8pw);)t:z[dI^xKbߋ'o~UUUw8ZlVh222a7uXgif٤kڳiEiknzmciwڞ[2jVsK*???e?~k/Xez9rKo*/o1NZ| СV58zmf㕘Xu?FvZc &FV̘1Cd@gb kvֱ+|N9K-JF=h kk;xΝM~;vuxx:w\3fK۷ȑ#Fo߾/99eiqqVXѨWXRw*00;3zhÇ7{r-oĉ.^f.yI&55QѡZG\Tam9U9`5?/_Pq~E ]uU<ZI7nlk8p@FO>bcwwv}CV[~llCϝ;E5jv53'[n>l]ߢ4@f_XaQ`UVTj[vomv|h8ngy:UUUz5M:UFoժU5c iӦ5ӧ{i oܹs^tttwKסCtG}/]q1bD_rq]}YkV]ti@[T=H]?4@뉫1vYpjݰfZ@ۿQwQx&Nxg~o~cv7hm-r8Zdѻ6%I~>ux@?ϧ)( w1D/YZ=GG%p C WKKK5am޼{2224qDߠu;7n\;\8P/uq}'NSNzϦM4ao*M8kl6K믿]wݥ\9N-[LwqG?Ҁ(zg~~sEikY%.nU!A懖Q׺uԩSFOoKn+%%EcǎU||>s\]ڵkȇLg*&&_G֪U!:1ZYik?ۺkƿYҷwyy{i)<:TмJKuOόML~&Ola*w6O|X۵kW;v^f)==AGO>]o%>LoK,-2nݮٳgk鍾W^yES5 ,~Ev|d ݃-L=U \ 6~8ۭ l?=S]l6={VB{i;1C_]} Ҥ+==IZ黝֭S޽u_BB>KJ~3-_\]vYׯ6lpIZIzꩧ4w\EFF6꾴4mݺoS@@@l6Gߴc]Һ3F;vO?^zջСC5c mܸQ}moVܹS=vZu^^^o/PBBBCi۶mzաC:Մ `͛7f-;wQKURg-%&,Шs \}IF 7ZߑG:99ٚ dwtرc Ubb.r%$$˫E?rvޭSN)++KԩգG][ݫԩSUtt:u^z5ۀ>۶mӑ#GBuA:uR~6өoVǏɓ'UVV8uI Rxx=sӶ۱ci6Z%^T~w}u71XX pSWlr/G=w`a*h>e̪|A & RDJp7^^v]?<^s?=\;2Of*s1WQV9F+( ,?~N>7j@WZNǨ`/>gQhX5é.040=zha-ł iiEiku2w8t._5g[z9@>PΝ-Jx./QDŽjnUrrK -1.ɨfZ@Hߕnݺu(  EEEZzћ8vB} ueXHqPQϢ$g6/Ia,Z?KuD'j :p82zC(  QLVX!"רs@{V]WUUik -LM+]*QFYp k:۷( >"CM!2_Cnڴr*+*-JMg^ QrrEiưPg鬮}T !:[;nF]RX/}aQh;uRREIǰ Ir:裏> (>:a-WwPXͰ@sIӧEIǰ IZr222=Yp/%¢$Q F}|qޙnQh<ө'2^j*-a-@K/uDn'I 2"A vw&?Ǯ^[dQhN`ڨQ  k*++ӊ+޸am# I"a-ޖ[UVRfQ"h=u@@ -w*..6z2Ģ4 1s%LݨK˵u\ F(qRoaI"4W5a71:̨Kʝ*/gw- ;Xj@?x¨{mQ}`X ɓ'2:DŽrK-HeLW>}lSF;kԃ( >0v矗ᨮzxP '&Ky OcX &*_OV k\Lp}G{keX m¨ Zk ||;W/ܵz~ZZZRWW/|r8:{Ξ=m۶i…z'uWyCFTVVjRyצ\._>9&*4s *7D=:+#K[ܦ#[ jwi7( k ks %1]]*,%o.eX Qq~1bEia-ZTqqq[-((޹ZߑW<؝xwV SBP?(_၁ WD`"+^ee}9g3y:SX"s:w9͝;W|>=S XcΜ9Fу-J؈@-dg-뚡G{{6Q^V£-L]wJZTHc \TT2\---^p$zy>>Q|pP(_ Tx`"ӢyC5,1Q]vNSGrr)=sF2I>uJ5'z-=czG0oM@}bÃ:ho_8Тam0m:>>^m؅GUWX |p矵ʑ  W}p`5W!+]၁ TbGrs$ZH`4L{ƣG}<_[Nw&==]SLQJJf̘aÆY͝;רt렮qچF]Pr΢$ڢۮO뼬|mXQîRézРA%u8$/<];W/|] w$0'5V=lߦ?.Z#99.lڴIW]uz͘1C_~I۷[jFﮱ-Jq氶J (䆫̛ە[󮗽a-P[wUWYh_qH;V/<[H`W دnW֮?>LםN,Y+WwxwNދBaFaRwjO-j2TDPTDEJH @durN~I >.7 zrs߷w}2g fUgWy7M+d/xZ ׎_e]eȔ|И Dv+`Mi°hSSS#ʖ? W/y\eK`m1 ؤ96Y~,T[={OV\)<< ;l/RM/ + 77/ Kj$2Yᤘ}((kY/Ԝ <) \!|ܹS222j*[[x{xnI2Eb|* frMyy{ɛo)_oժURXX)IS .!j/ Kj4&`o|dxP{?%<@i:!!AS0풝-7n^RSSR^^~!'_wϮgV}&[ɓr 7UW]%-=zhH,Xrݘ>'S_%2,moɷ(}NUÇkJ8jkkW^+Vȱcl\WlSHG廌 yr9k:u;+;LyylݺUMߏm@+{Hمa-֚-!^_|Ǧ61MMU*qiJ8f/K~~~}̳;_}%UUʯ8qB.\(<򈦄g޼yRWWwv>V_ x+uqY$`'Vg\+'Oln34DDFIt`-Ul `d{H/W~mܹ k;вe˔z`p) `ƒ|+yG>X)ƆGeͻ_#/Zs2؏ǔ:<<\4ZpYGyDv5nąȀ(ѽL[Fvg ż=|X{MiԥFMIس@/=8Bv8g^g;ZR} 8)I555cɲehlCI)rQ 8MJl.5|ߙ={\Rc*+[yʣ7'iLاamZm }amMeldLoTQ^VR'&&jJ8'ȨQdMj|}o7${^8A--W֭['eee9,VRz`f^9J]Vɰ@یKukoV|) gU_[/E犕^rr4sbX 2vX9rկy&ɑ?I~"?JxzRʜ9s4q 7ns)) `ߢC- h6kJ<9Ns2s%IMi8lR2e4sVpsd̘1]DA~J:ePt[xټy:[nO) `ߺ*,RZ^) {w}]v5pF)upphJ8'@DDrrrd̘1Ùp6999n:Mi[MMlڴIݐW\]y;EŰVDZC]TzZM8G}єp^;r);vdgg+}_ɵiJY?jg-\PSpBVIO:FSx+*afNOP꺚:ٸkMi83'zС΋a-N.##CƎ+yyÿILS-WzwNaB疫z*S$mo0#'ؠ&O' 8 &MB Yc2GM࿒{It@{5Od2]\ ĭ5&Ch:-fX K|ܔڅ4,+WSy1 ڵK&O,J?W?ҍ1؆i[!oٲE5?+WT1b$2OSqx+uqߗLNRz?,UeUpYwޚ΍a-NfrWKYYҏ yBEFjJf%'+uUU,_\SuV9}қupMi% k Jm7ʺҔ3=~F) DkBw MO<)}5% JoٲeؗzK|<偫jJ8 .dX IJo4f(ѣ92Ёa-Nbڵr7JUV`ɧGHdpy۷o^U}}lذA|toqwwՔp,J]ZiԔx.+[S5r3sV},%%ES1 ZJn6QOIf^ Yn\[[+K.Ք>,[L˕oQ-4@֖3бfܔ 5eµp9J=`MI0!\R^~e dz~ӑC:55U4=%%%RPPjJ8@.fX EJd2iJQ>#GԔZ^vv򗿴ad/8njKB6LokLd[mۦ"2qhkK*5%RWVʶϷkJ/*7qDMi0L&曥L6l{} j8]r4gΝJ+^\ ="¨) G69)Z"B7}ISȮJ&ɚaX ]{ꩧJd,֭J}Q9uꔞ06&55UĄhJ8am%Z`0]2m2WXO@'>N}2o<)Μ%nnR@{`q,n, ,Иv;vLGjJ8He pt3n#n|(/`re+u5%ء<1c9V)t`ѽ[j4h4JNN= FSE*uuYX] xJoϦb{;{R>\S?aX 1r7KIIҿs')IS*Z[|!؆}Ѩ~h;ih4 hH;Ԕ޽#-w6۷oWO-XSEWkJэ!>JjJ^NWj ))) ZW_}%|8sxkJ]KFũF Milþ}gT$spuu 篢ZMi8Rԟ}r2s%IMiأJ-~~~ Zɤg|$DDjJ5bRgee5ѣJ='g-SL]G<=\ޚk4`NVꄄMI\a-vl6-"EEEJDŘR^w) ̟?_SNTW֌L) S=3֔Fݻw+u@qwwmj%"WK+|\Sϝ:'i{`72g*uxxiJR kQfYn6W7 ,)w77л[n4:tHjJ8paQSiM]|?hSGO)u߾}@cX ?m-˻ާ)ئ{ܹsesmxq'RSD)5Z]U&'E)C*M؃}hJ) kfY̙<\]e3,F`0U)M6dҔ뤧+MIc0HcX gcՕqiӤTS"̉<:t$°fUzg2@KbX.--/BSa2$''G%) B=a-3bpXLfZ}$yJo̘1h Zlī>>)S53W/Wz/۷O^Jb4s Ww@).Ӕ3}k٬)[sIi06(S % kٲ}vwKbiJ`0,B޼yFM:~һ[p3W aJ]R^̕9Rɮ {4`kǦK޽ kr<ͪZhcխ+**O>є۷O{F<+(uq9+ktk'JH%6hJdRj]<F㏕ޘx)د=%&0Ptcǎ)0MIԥm2 rq Ss5%`KrQkJ9 klRTT8IS Pm۶Imm4+//OƅkJ8am%Z]oM jpP7whLf)-PzG֔@sܹs[`\?h4`f%SjY|4l6KaaЃ@W W*^TvmحfJ}mKIIє@sс$55UݓyCcb$b+>@Ss 1աޑ+:,@˫l֔3:JvMi؂=iJ6Ȁ `4zWB&^TUUiJ9>ԮI mj%:T֚"upf%"Kmh4lYJM*++e +-qVuuuxb=a:IFFRx+oh+(֐3 rJ/I):[)r2szn6whoX픩cI>J?Ԕs^Sp<[Ŵ{n)))є9sFC4%ԅ k!#""޾^M_ =}VjԟI&N) aX 7n'N(cjJc҅%FA{Q k],eY @zO$tK۝lZ43gR{zʬq\ hPVX)M+*Rϟ) uZqY$]yѪQ 3:z\յİ.v9ټyһqrwהMC*KiiRDŽhJ <[ˬW@Whje_ o(;#G ) +aX @?)Fŧ]\\S5&539Y .7Cnhh~[ca6L Ԕ@xja-=*ՕWxxyhJ@:))ISW°.d6>Pz#wf^hn28O58РzEkJ "a-PQk4%Sb*Rz&LД0 }ᇒ~=3MOTB>x9sFSq ^n k]"-F˚83BŰ݃vgtdQvssdMi\ ZЛo~~rsxpXq3\|c6eӧM%2bX[̕йj?QJ+^^^ttٳgһs1:S$*>LSԁUό1) gfjs,[) aٳl6_]]S4&qaJ&YYYҴ_nnR{kJ@D[xR7HqI0Zu;Z)=}N`jkkVqMG"y#=0z^e޼yٳg:,GS""aVj I8ZVή\ʋʕ5 kϗr&OPC^ZS+((P Nn Kk5jjՕ k;] hJ%~m sbLIKKӔ}, ՔO}ՁH РA=9p63:::Z4 kd;v<0z4$^nnJ^B6Էr&+t/.*Ӕ3x5s%Gu:ROS-ŰNC}<<'hJSF֬Y'L;Y>_^P-,P@׫SAae-lNUaÆiJЉJJJdÆ Jϋ@FT'O5i;.הOԳzVZ{ΤFJK޸q4R kDs̑:NՔpϟ)M(5+kƒami%QzuZ_3Iߛ.jIIIј@K0fYd-FhJrwqz+uiJvJ]Z@`u{ $ϧ+8J.aaah)t/BrssCl=ݓԹcMi&((HjEX k+zuFR{ykJ@SGO)u߾}* k$RȽ@׻yTz4mc=e(%]Vݠ) gVoTW2K^VR'&&jJ5 N:eJa)'nn2O~z1ͼ(uMZ@0,:w@36? 2,k\KNN֔@k0̞=[LO4 ԩ.uߨJ}yټy4gyTM=+,""5pVF4/Zid8.fƔ)S4 k`FQ>c3^kJtn jJzJ]o4bC]+<(@\ .JZSΨz5o$t81 MiZ:{'3O) )A$$(nBUiH'. Kj5ʫz kgqIݻ$Za-l޼yJ$ ) 9EEEn:MiZa٢J I\*SJ@)oje-Zi9CՔ@k1߿_RSS=IIb0O.ؚ)}JE4iTB?fՔOBNSrdA<<4ЕL&(1c4s5['tW^yE/^nn1 rU~Jo~5 ga3u,nnjY:u4Za-R֬Y_}|4%\Ɍ1cL>3MiZo߾J}TA3W*2#TVZwOfhSjάZ:ȿoVzOO) %-QJoɒ%ҴD^$~n^R@שz0E,fP tsGFʨzZz˖-R_oQF)J36s5ԥujڃ=~F-`6n(YYS3&kJh וb MiZn„ Jm27uLS""~~RTR) gS[gRjO3QjVa-|rWzL) =P~rMiZf̘1J]\Q#t1\$O=#Tjja$sQFiJoRG˭adŋ BV[m?Ba-Q[k|R[1i$MiZ(--Mݫ>sANm?#MiZW",2I?) Pu8R\V) gSg9ee-v+$%%iJ46zWl6_]])S4& +޽{XSWCY\ +z+5Z]rX̕J'h+AmmZJMG"5%tr6;hLteT"MI(uIE$MbX̕Evzʀ4% khsJEE{2e0#$DFE)O?TS>|Rg);?ZJ]ZaԔ7 2gWj@ZrU|hLNHДБn2D/ΝӔ&M5u i_4 kta-+kV|Xʪބ 4 kh۷Kzz{`Mimd1\d~[c2d*|GSU ktzILFǰplGvUj777INN֔@{0(<“0"dhL[r4-3m4^+ԔpnaJ]^ f[\M mJ"q//?ZZX6nܨ8P|==5%tCÒ)͕=sb0\|{W`m֘p^d)Ӕ(iHdSߟ_SŰV3gթ==*Mi1clkLtyC c*_rM-<ЪWX\! gRĖ>~l 8s)#4%^ kh!,K,QzCcbdHn:K ]}ҴK/Ե @gOWWX\) gQa15CS\ʋ3Ǎ) bX @ ^ZΜ9apX钙)͕M6Mʭi]) IDATB#2 K\MS]!mwR IIIє@{1^uFiJl-W566V""rvm,O^c"9YkJ9@\Y) _}4::Z8W khSNɎ;ޭÆk3ػ//ISzW֔eƌ#W_}۴/KvєpN^J]\V) gQYk=<tiׯ$:ZZW^tv5SjL w'NÇkJ2o񆸻_(}յ@W QzMI8vZ:,p`_pFQ>c/݃5%tG/77[ 9!!Anv;=OqLS"Yk+\ZAVMU*qiJ#0 -Z$%%%J7R4t%o֬Y)M˽]fM ktZmYY 8xvqq>Z`޼yJ$?0@S@WkH>}ݻWSYf)"YnDs SJc3W@Ǩ5)W3Ww~Pp Ӕ@Gp%ΫARSS%//OΞ=+EEE$QQQ)zS$--M$??_BCC[n ~?Krq9sKDDȀ$..*N3gHyyDEEIll 2D"##;&I~GΖݻw7{ݐ!C{\eܹl29rH׍;V~yݽξ8w[ɓ'`0Ȕ)S￿-"rq#}?k<<@'裏Jqqq_3ydYxѣ]nllsHMMM_w%$$]TAADDD(| {hr e3fjL2sRe˖y3Fg^{IΊŗ7@>W,q;M";kԽ,O\\tr$~1P_[/₶Kv&9\|1o(ZN) @/w}WSsww_|QT ՔpaV&QSn˙#}=zUg>|XyO> 3={믗 +w}lٲkM ovY`_=C(6l[oUjk/NMM{LV[~ڳ&kJ k dӦM2m4`0ٳ[oЫ33s7n+G2-*mݎNuJ;oC:uT/$&&/K7oٳG***dϞ=2o< :tH?(=WWW_"֭VDW^Kjj$'CzKN>/hrP+"ҭ[7Yz7R{/ ._}UdݺuVC'NȼyZu{NY+WV5j^Z,۶m|-lVq| j"\s,]je˖Ijjj>J%3ǎՔ` ~>`( jJ:r$%%)gZw T:MI8z ۏa-ΜP:t$:a{)//;#eȑ ~d̙M6lJۖh鹵/[vҥ2z˾6,,L֭['!!!J޼yݢ)S(ے]N߾}'Tzuuux~ѢEVZ^cƍ{Gg}֢ϟ?ߪSOxˆo]RRRѣGe۶mmK/mIϞ=ކ Z<,`(\\.`0U4aꪫ'dwM/.cX sf3k5YeI*g|Z:͝;W <]rUVYnV}oTFYF鹸tWznnnr 7t>$˗/WzI=zHpMdR>,Y)MNFFkL8`uX[RQ) GWVY/ Ӥ;Y 8~VN>] M6)=>}zNbb΃JVVe_)GUz2mڴVڴi}qF;)++Sz2dȐVݿ?ЖV^m]ĉ%((gX 8˗KA#&jJ5A_}ʎ lРAr-(Gr]+"H}>O?Mΰp4Y{) ڦ|g޻vziaÆI[ZiyƢ-[XM?]U+"'J/''A&VK?oRG-omXu+rYr4kz3֔p\!갶a-NRl9ugdW/HKKeUHlll-ťo6رcVccl/l*OM1VM0Pnj-={#b25%OtPGuYjkY] 圯RjOdH}~czpN7ݳgՙ}{L6n(R]]-~z3g̚5KF!sM ,m^w:o4ĉiUz>>>agX دϕ}%ϯW٬d˗kJzO(ry[XUFCV|=) ΒWG ) tǏ[~3f+VO?m zmV_]\\:tXzdSθG76=z͝;W***ޓ4cXltVz|4m?I"##wH +'WXR) G*׎є@g9q0K{ɱ]q%%%Vnݺɣ>*~K#M~;C_YYikM,ʪnlZYڞ7:AǥKWw;zX{mR I}hJTd߱cTUU5s^xAȋp0.~PTbjٺ爺p w7[5%Yr2sUcqamiiUoٲe` u``[r! )//ݻw˟&׿-Y*b=0llllnnnߦ{Z==.k=,EUUUr[n:cxpy[!ҥK5i\zW Ky!깑El c䜭7QzkOT:|5j$@c°VDD?uԨQ~ydРA$ӟdǎ| ,>yt u b0aE||?-S}=B@x+uq9ZSUc_"gEuT:St𐤤$Mit^nglsbbb䩧[βk+**j=k=6u6߻_W'O6e̙#uu?4sNJ_ ]Ű@N:'L;kÈFnA͛5iɓ'KJJ[C<)- k,6I;!DŽkJeg(4%:3'67L0aB Dg6%==Wtd2IYYnjWWWlb[϶޿#W<3oZkժU8c?)M&s<&k Q#V5%`L&<p.O pd+5[ 醵M{ݦ.7u`Y.wXmgaM+-R=#GjJW"ԟ:)I 2DnI=nۡlٸD VwbX f<9{>R]\>*y%GP|X3'N) tA5am3D:ve땶@ywwwӧYI\UU%Fc,ʒ;w*[ @ QLMI_ڍy5_t:-nFYSl6˳mUuWoi5%U:nnn2vXMit8~PPP噵"ڷ5nӽsss[m]v䰺+l+j0oLј`{)uaa?m[=Pze٦-:T(U5 7/QA="3_!o)yRZ-H`nXj'ڶWVVUwaQ]ߙ;vlG%DcQ$I4iM&nM1ŎX ػ"fYR3yy|f7xSVCZٻu_UVQ&_&UEEEtɄ;vHjh֬YY_AA6m$h!^ނ= L&EEE J>cxxHWz/\ HTeuTji2$"x00PDdu7/H?{ "U뚵ЯSe5]+&oy[-@׮]Km+[nU[le2s9{.U۹s'to^;ۡk׮UʸqN<)aÆUoV8p6ݺucYW_}Uj'QyܜPG%m:tHP^RU? JDd<]t>z!"{lQY/C$""QRnH;v,V6kG]vsfŋqE;1`R6,O:ׯKjm۶EFʞMʧk׮*׮]zy+իWPדO>Yἑ#GB.k}߭+{zJ߃/$z7!4KXho.\Ia/>2x+%tM4DdtlX>bx'$""Q-ZIG%f#m۶ /_.gFvU__+X՝e]_f;88`ذaݻKj?tV5YKd?SMܥԋDDDUVd\N{7|SRKâ~KwRgqe-/MJOqD:+= w$"T+L+ DDDHjx+=T}}-=q߾}_+u˗/O?Ԝ0mڴJ͟>}zzG9rR>Kj~~~nVΞ=TmժU>7_~҇D-[D^-*qyVXk׮IjD+5Zxd"*4SRRj˹>K V M DT&_dή1DDT;*V|"iS1u1HKq$:u]P"Z٬zԶ^7oƆ *5>@||KV8WR᭷ޒf3&M!Cj,Ι3Fa֬Y`#*<ʕ+1bD3n͛W<CBVwnLL &M,ᇐdĉѦMI9rdgxW%5\^%x IDATCDIb֭-R ""yqcl6zrZN\[P""r٬%~w$%݆tìf ED]?C2n޼$DdiY{T}„ xa4˜V1rH,XVXQjly^xRU^#FĉeIJJȑ#K@+uyw!cСptRF瞫?#888#GDrrrs;#F`nճgO9dXdIC0ydh4R_3رc&N8?۷H+W"/OzFK JCDD5Kx!AiĉѶm[I;OVZDDU2j'`.ѩد#s<K.}NgD5dʆr~9Hjaaa^/ɰuVԩSRW_}UnݺChРΟ?x>|fސ!C_U޽{CӕZHH =z !!;wٳgK]舭[bU7l߾?8L&S5i BN;wJVVڵkܒڶmAe˖Ʈ]$$$111͛>T].s4hBBBg޽Uaaa8p@l?I--- p"zp5| Bk LDDD5ɠ՟cU;wbSZE<;2ي6-o.(0يcx_G`, צ{ksP(hz3DHm޼FvԴ~fmhh(n޼YmO&ڵkl6cԨQزes[[nœO>Y!]rk׮ӫ<K<쳥VV~w ɜ?>>鉨6+2yd47$$Fݺui.Df۶m`_䓈DDDTӼմehzB.Ch(P`*"O{ǡ} 6! tF"faoѡDTEae2'0iFUr /_WW׊'#88+~jwVĠuUנADFF>TxΝW^慅!66cҼ>} >>waʕpyFB|||5j-[&{:;cZxWAi&\dX{rJsWa"kA+lELtax7٨%"\8qQ2gެd}0w\$$$W_-q)vaܹ8{lJ-OpqZ ]to6m࣏>ӧѧO7 0O|VZ]vŧ~G:A; x뭷Zu Ǻuw^4hРk^ɓ'/"0J#F-[iӦ*7H$۷OR@DDT#jX2.,,DLL4իC6l/:ՌD#mj a6˹jײ܇g6j5 ;Alɍs7$f͚ JBD |d[uIܸqA&Mкuk4iUqu={Ȁ7ѴiS4oܢҥKHNNFѪUjkɓ'qu$%%!''FXX}l6un߾ :u 88;v]ӖْsbժUcL|F"UG^_<~0|ѼysZ&A8vTDt4oIm!ODd&j1(dJCEn*7AɈ}%(?s&"-5=5Ѯ];kNȽCCCZh-Zr9trDd_L&֯_/uiЀZ""~KU<zb„ okqS3ӯjǺ&jyl2)xfatF],qDTh0"3E-u]P" YtImN^QMW[22.] wwwI0+Qy\psRHjj}9WQMi C͗}} V JFDRRG&OP"6kF[zdRቶaQM*kZAI,s̑Ԯ&k-%"Nqf6DEfG {zb]L" nݺ5YKDD5֙3gpqImlN㏈,UiNN$h"JjDDϧd6++&1($I^y (g&f^4i"( Y VQxb ^W\ ""|\+ksss%ZF/(U9KlC9WQM`(LI[(XP2"u$Im۶Z""z=l"m~%#""Nnnq^^^9Wڷ^{ ՓVrZPDeQJ"Y &<0WnIwpvuƛ_-JFDd2!#)]Rڵ4Dd-lQgzž!"MR^_3ϥT(XhǛ_F JDdZ(( YZAEܵ,I]_&aΔwU8^YKDD5ڵk%&~3>KPPPNWmʔ)hժx$eh%"]^φAIȒL&3} N^TKJGj.Zvi)(ًs.H* 7Z""qpEIm !""+(UKIIr9>I`,˟vxKHg1xqaĞn_pPϢ]v=vdܨQ#AIȚج%"gҥR=!"ڤ65k`Ȑ!޽Ky^ )Wِ+,l6屈K 9">A႒I|[2nӦ$DdMlQ]vIjZR)(&ΎpT($4AicŊղdO LDd{$ r&"{$2L_0 F<"(ٌDK|ك6`jO>Fx,rQ(gddJbx$'ٞ`RL^@"n|ݔe7&b]*[0Z߾}ń!"bj ٌNRkVuJDDDkjzVZ{EE˟ȶ +Q BDg ظj/Щ1|珝]]]ѺukAiȚج%"FRt)UKDD$jAIq;vvB~>'(mqtP@*]u1JCDM͖#f='}B@""wWNI_ 7BD5QrJ c;vj+Zجe˖UR{0͂owgCfA&W~o*BI0e "حK$c%=ج%"ʕ+FB!(VΒFĺ1k,Ibk777꫘?~q-)3;SLF$A5 JBDbD Ktj;|ϕ$JK {p| L-RΝ;[9f-ٽk"++KR{.s[srr%7۷oזoOwR`2" 6kEԱd MFms٨% e$e`H}IW*gϞ<ڋa_|!-!"UڐD GGG,\5MoU/ H/w8+@P"؄4<Bެc3u6S\YYܸ;cx)E%.{1|VHHDZ""kHHH&v ssu:$̘1׿p…I~D5WD&B&^X|F 4_ G>Z%")F}n^ \ggg<#4iƏ6ڒ%K$cGG<׻4DDDofm^^$r|9rdq-/|?%08A*8[W(( UkYxÃKutf3þ_#fa\MVRDn0aL2...!Z""[ضm6EK%"""=F#Fc{C~ĈGlllqmӾX8H _Xo4`(DDTZZx{™g*5I&x'0g4n)b۷% kGX#""TJӈ|rlٲEr_ЮM &#.w'X]VDdY:}.icy u JFDf6qyyN8MN慆b3gZnmDT6ٝ%Kor /'0R)fmPPy|gŵ73xfhɈW"<ːoq喴A7z Z4,R%ٴ'DVFvԭ[Æ CDD:udDT۰YKDDvd2aZ OP"""<%cZ-(Xx1{dg} }: U2hQAE5iF_&aM%#"K~"7F".*n{ fBϞ=-j36kȮ|wȐԞKP"";K:h4wwwKZbo8&>"0xIƚ6kD0x8yQڰqT:UsѲKKAɈ:ݾ qbo+5 Œ3Я_?ADVf-ٕ{O #ۆ JCDDT6temVV$e޼yXf RRRkl< >3j@\6kl6%{FڸQ8(gѮW;AɈ:%a=8y )S*>(O!CAKDVf-ٍ?~\Rש&"""=2''+kGGG,X=\qMǂſ,0ux{HٹNf/Et\.W, :Uȍ8*ၾ}bڴi9r$ Z""/FQ߸ ^O`"""ljZAIlٳl2\|xs#/ꐨWYW"2|Hd >?]1IL.k''Z?AXܼx E;ի&O1c I*f-^-[Hj6v|r,YF* x틝X֓Y^_OɸyFx;3_}7E00PDT%l1ۏ0qrrB=0i$L0KDZ"" W.{n?%""*t:AIlSO=N:I7ذ NFYVRL=DlqTGc$"-:+ThpRDxx8ƍS IDDd+ɸ?z6n,( XYfmi˖-C߾}800m Y3/(3ˀF "tlX>bxrQE#q}Fp2"uw4kw">?' y7Λ"&IL&u `˯p\.GV0j(DDD 8/clZzz:v- m.JDDDD ,RRRج-ux'y[y2ui"0\d3*~0MDU?'a.Ѩ6f7SL("fGoq6 rΑdh֬x ̙3JDdElMO`4F"""/dX --M`"۵b 0.|8f-P~ΒZf-Quy8~qڎ:<\.(Qe6q&,nއӇNCԼFaĈxgѬY3 $"Z""Yf}>ZrU8\GGe[.N5k㻝'1yP;Ɉ,M2ʩL>"cx{16[OQKde_M{q2jmԯ_Æ Ü9sfDDZ""Y7oFrrjNhfddLcۖ,YCńmP:,^ZjזXhԛwl7ּB!(Qr5*vo܃OBԜ:u`С@xx6kf\R2ss؎!""7''dn񖙙)0m />v#57൱=&#~qVn$D5GL0&Iax7GDt-Dnĉ}qH}X8!mo*DDd\Imt|솻dV% ,ڵk%E/Yx &#^AqVn$D5kYxÃKutV JFT%HF=8RoVj73fGDDclG}Ur9^_`""")٬h* \mT*1o<̝;Ò&#^A*XW 9&ZZ|8DDe`lh͛%G7FDDDDUr> ~+pڵ[3 IDATE`2'm֚|xqHL3 ]b+qX%#Y2!!R"*>}`ʔ)xꩧ]RZ""9_~%%""3.ƋV~r|G7n\q-Go_o~1#3Z*HôK>>xPyʙIDhosb 7a6+^zaĉ?~>xgJy LFT=\psR@wj=5Ȏdf0m~i '|?ނٷ>>VHJDT{YKDD6#;;۶ml DDDDUڬ+J*˪UбcGLw50Z9Ml0jr)/&"7Vz'gWg 4h@P2"PXP]G[4|}/ rmFBDDvbla0HT4DDD]20͐ٗ0<ߊkѧn U H`2秒re-QY&뿧i5$_S:+IXAld‰=q~9r]/d2hO=fϞzY!)YKDD6l6oZש!!=Zٌ,nW+WĶmېUDWs#zH^35Z$D+T~~tvT:UsѲKKɈll郧(9|:I6m#GbΜ9hܸSQIlMغu+n޼)CP""Qfmԯ_&M_]\;y%$`\6=/Y9HT[5qq8EE9E^f?z{އSN#GSyx1{ln)~ج%""l2S!""zx~nnjiii|VEK.O?^1}[qKi[>χ,DX)d唽ڼ}aT>*+'#=/aϦ8$2+5'$$CŜ9sСC '$"bu%'с?~) 8;8PXX\KKK>y{{Ò%KkRףxqTWɈ\є+,Jl6L^'gy'3B/n@HECԜ@ 2޽уSp""nɒ%(A\&+ LDDDT=\JI6==]`믿FFFFq1gDg(= 69z MptT;q(SpEmn٫erzxޙ gWg+$ Wgn.Aa֬Yӧw#!"qlP& 6lԺDDDDU:/xVWnI9;;7kV\KQx_"Y#(_{&#D N^T#)=hP/?[Z'$ IHR9xG1}t 2 Z"";f- n:dfJzWoAid\gUK/O?7n(}K,^\mE%W{{U<6k08Gql'TAwoJ<61}i(\mN:Uȍu ܖ(iӦaȑ3CDdج%"">38۴zyhj4AIBￏɓ'״y痻+&#:G<]ww A`"wZvFlB:.Ȇ!T.n.h+ ^`? $-YYػibw[(2WܡuuuE^0yd3VHJDDf- s)Ij:uV=DDTcx8HYYY &M’%KP\[$ޙuTUY3ty8 O̕,d4_@h]vANwAt:yb0qrrB=0i$L0U&aYx19Hp } DDDT<]Ҳ%9ac/ށM>-0Q;Zjn83+_`iuFMBt\*N]T#%/CMѾO{t.n.O":=0V\T"<<ƍԩSέj*6kHN?CR߼|%"""~%VjZAIjAgϞ8x`qp- mLFT5~*: m)(4P|*OFܹL\O΅[KFm!0@ %=F8¥Ph,p:v숱cbƌBR""Z""b͕^OP"""qusrr%YV\pw ,41m\6Up2,ѬVm Yf6q$hB:.F~A۳ -u׷PZ"T`,@̶#8\8qFCş aaax1k,f"چZ""믿{FYwfNfԩ &٥couD>LFTy^e49l֒JŎۈ9sWs9ʕqٺh٥:]CPX(-m2L8GWrZ¨Q`+$%""[f-Y޽{q%ImjnYfmYbvNx8lɈ*'[[&уh }$ Rq險m-fCԯJ %]fq~9pdh֬|I̞=JDDvZ""KJNN٣4DDD.?ȣiԨƏuN\JQg1O+Ɉ*'[f-Y!߄q): HLѡ6F^a23,ƙf9eW%ƍdz>f͚Y8%#6kȪ) oΎYSXX\ӃOyfɊ~Oj\.0Qł|UqPPI 8r: 'Rw>WsPPXsg]]*mFpmYvplڋѧUk+5~>|8̙mZ8!;6kȪ> ;ҿDDDDQM HSaXlYqJk~?g"0QIzy%F>؄tD-Uk:(PiZvi.;i|j W{4McȐ!@xxQMf-Yl/u 恁YV`+hSSS٬F>֭[ڢc氎ptTLFt~j<ԯ[NԕKAy5.FJF^5efSh׻|I֥[␑Yx0sL bfӦMHIIf)( )T8hiZZD5 ^uŵu.>!N+.Qܡ`Ӗg s* Hll@o4 kڡpvD|#HZ98p f̘G}+щ衱YKDDVrJc:t:\0p===]`^իX\[9< *7gɈ'DFN~q-CmD'RpB&.\|8DDTج%""t9" '$"MD^_>߼wjΏvFFeed!rC$bwCDU"郩SbԨQ|~ADDf-Yŋa6r9'0u)$c6k-cԩXt)Ν;W\϶8̟A>l3m83;ɮ:N!)=zdh HBӃU:+Q'4 [7DH[UO@d-mB#y&&ssѫW/L8ǏRBR""ج%""3%^qQ$mj4AIj6\Kb5}~!^l;ֿ3Z`2t = p397S4b3H##uv>s Q{i&{$ 6 ?r:Dmq5ZNNNڵ+&Nɓ' Ib,n͚5Ζ^WL"""+S9K1lZΰaЭ[76G©hQO`2yJY9 .Uu7sq+E4R3HSe&m|tDP@4l-[]0xyY~D5!π-pør2 *3Ν;cܸq6mT*N'""qج%""[fd7ȺTҕ%_`b ѣBs?ݎK' NFTZd1 JRf$gq#9SlKGƀlsXX*U^4a=ڠipp: F0q/ ] :t1c`ƌBR""?""9sFR.( yHWJR;t ۋk_3нu=ɈJ .ѬeA LHLfH#53YPg#+Ljܼմ/q2z*xy7!M#akLDGv0*̔h۶-FZ!)QհYKDDd>KP"""v6kZ$ǪUкukYeSTz;|>Kp2"@8;PPۥFR.n"9-IyHS둑69F w>lU88*r;TO!~¯ϗ%z& '!8{]g{d2hO=fϞz66kb4dU <֪KGDDTl JR{4mO?4~Z$~FQ d;h Qd6C& Jd=fasq;=)zGֈl {?Jg%<ݠ'n#AցGy-f3N8}D!py״iS9sAƍ-YKDD|r ҷ^__P"""1ܤۜt:AIje˖~C^/ގE)0]AqyFxˍ&ӝao"1EwVæcVW+?.n.pr ^7b⏠jW׊UٌGcϦ}8}4r4;6aÆ>|8fϞ֭[[8%eYKDDa6Jjm>$DL """A|%c^/(I3gbժUŵk)Y:\ -)W7T-=SoZc 7uiyHN#U M>2d 'faU*x{'~uP/A3_ /aϦٕaÆaСYDDdnݺ%CP"""qJ50 p~@fffq${e~^4Mdypr# u5''7rqm^6ٹF ;n*7{ٖ8 >>zj:<\?w#uRs1dDDD{NHDDd]lE,[L2rq.]!""'ãT-%%S˸aƍ6lnA9T2x)u萌,}f< uדsޖXL=dfC͇6a=O_W~ulK\a~%^Ižq|OoWj/3gO><#j,6kݼyԞlCDDO`T6kdXn&Mz.>#O㻷DA$YήZd2#9=7Srq+Ut=R3󐮹ӈ1"GgDA2n*w|T·Ajajԛؽq9)`ƌcǎEVV^|EF]Gib΂Rm!=3*pC Rsq+%w%N葡1@}g[;Z;=zGχ s>l` Hu#n_ T=SNȑ#9:lQ2LظqֵaC4H<9+UٳgG?~<Μ9#V9= aSUdٹ\ PR96Ŧ!;ٖ堼s><}lKS~oDM37/B+z)S駟#_ "ڋZ""V| 233%z6::J%VumN… tR9uZN lFZV.ĕj\O­lAF>^R߹3G#"ZI2VՂiXh,Y=+[h=`F8dM\j\MFZ6nkEFuZ]>L޳r4b}=@|DT#q8K'/Xԩƌ3fӓ/>DDTm>#}x#0mP9Ih4нr9}]7Ǐ/Yވc8B%L\IZ7ӲM.ҳ#7a45 6? 99~~:u j v~? '.ٻ󸨫vd]ٔpI23w SRM[Y{iYv+ʭŲeVK"  (602od@f>,ѭs8s^v-bޟsN9ӧM!)Qb-5 LFGLX-))) iҽ{w$&&bժUXr.Wxk3"$MT*f!^ n_!^ % r-v邅lllЩS'8::]tvnݺܹs3fLU 6d&ITwWϦ@^q9R={Ĕ)Sh"!)Qb-5͛7C& %R""TxG$-T*1c t{ IDAT9 -G; ~Z>^.6"%m?Jn!5iEȸ[;/G^ (.CRVVV wwwxyyݺunΝ; 555(-x wqWPQVDL4 }P""6Z""jm ) QbAX-++) 5[n8s >|ᇨ|=>% y4mU6A!6 w ʑ[,Cai%JdĆر#ggg^^^֭<==adt /^~e 6L .k豬[ -HQ"&"""jy,,m\Blw^yxx 667nvٞO'mm$?Oן%c$Y>`_*9aFFFv ...pss;Ulll҂QkHLD\ ,s=O ""jX%"&ST駟}:wHZ&{ <ǒ%K0ydԩS:[_=)̬aX `gg'hk{$_yYydjF  %""j!X%"&۽{7޽+ }vHiZ.KK{XJu111LHz%H`aa[[[a===kw-Jԩ]^R.R"jɊs9;Yisss1s)SIDDDDDd7o;YX`j"!""jMM!HzC{k'b޼y8qŎ:u3ХKavڪmmYDޔ(>Q5\533Ð!C3fXIX%"&~:}ӟ|(i JafdYw剘KΝ䠦* 555VTGmh\cڷnggg!$$D$hWUZY {O"ӸyTJUsLLL0h !88/",QYCC020#Fe`lbmfmm kkkc4| vԎDXK+8Cv1 es燙3gbܹCR"""jn,Qr믂] ա"%!MЮWJ^cS]JԖUɫ{4N8n@q_ӧO… akkDDDK,Qmݺ7F' Q+aab"hXKDqwwW+WGwG.U+81O!UTɫ#J닩S"$$wQ[b-57|#h{aHiZKSSAH$DҸB"/?Z6BT\yD=3ɐ ΑH$ѣ&MPtYIH ,Q>}ɂEJCDDzX"%!:tL&+DJŘK-O_FEiVu@[)%`eݺuvcc>;D4DDD]RR"R"j,--ڢ\'jmT*=Q{ eEeZċ/PK)aVTT?SBs# ,֖i.;vݻwky|q"DžI(]777 44qB"""jX%""_rn7G) QbcA.//) D666viaHIH)PxWc1vX,Z<Qkb-iERa킾>.*N ""V\ffgg'hXKIFcH<(>7nFpC:t(̙iӦHIDDԠ5k@Tֶ $9rZ?/]R8;vs2"j\]]my\$D퇬D_#xd@}a6mBBBDDT . ))I7RTDDDD-ޤ V'6dlܸ c2"jR),,,PZZZWxPDDRę# 8~WϦ@^q9R={Ĕ)Sh"!)X%"zY555mCCcTZŬz}+++A8X4DJ -gQQVD'x'NDhh(<<BppQ[ұcGܹs]_"bKRˈ.^D>>:NIDDDkH͛7C&ȑ"!""ǝ"\{$foo~o& [[[ADӵk}c.jχ;___'$"""j<kHm۶ O8:bHi+΄ȫ5@XX>C9!EvvvvYQHIZoخH$E_@G`ܸq uXKDDj94A߼ADJCDD?U ;oNB\q1f͚O?Tmpppe%"%!WL 瑟]՜N:^/aÆ8!Qa|g) zF4DDDRS$5G M6C][() d bg$"{;W9666ǂ sA*8%Qc޽Ǐ S##oIC^XXCbӦM߿Q{"hWWH?+  Ⱦ+++5 ,@@@ DDDXKDD|  Em["QELDDD'SSU޽{>Q{*herT*M)/F8s,f91b̙)S@IZ""R? :wF:DZfsr4|N;1fϞdDuYV)U(/.HGiQ)B3ȸUZ333 21cHX%"Z;wDnnECy).²p|zvcҥXd w޹gXKT'iܼr *9&&&4h 333=$%"""DDT?,,0_?5J v&&~u1Xx1T Z»%b*"+8Cv1 es燙3gbCR"""Z""\zgϞMr""VJcG5&ekcllٳgO>@QQQm0W%BX:n@q_ӧO… =5kfTKed`7F1QӨT*l=u FC^y1?~<6l$"zkkkA(X4DU+8'cp5U ΑJԩSGGG=$%"""jX%""rݻW7kW8XZi~=wd;fذaظq#dDDر]_"R"!RĈD3ɐ ΑH$ѣ&MPtYIZk_5JKK}!N""&8wRvvc|}}駟_ɈN./*) у*.\BԯQwZ֭xbxye"""GauVA#||DJCDD99x{^Ĥ;+WĬYikX%=STH9s¥KZċ/PK)kڹSN!%%E~shSTT GD \?6l~53WWWA~}(Jڂ{plW$#q7Vs:vѣGc…3f DDDD:b-Q;R>&?X~9;2 5(H0tPlڴ s:""ܹ]SSb9ۉZc J4_.`ii#Gbܹ KDDDDzb-Q;~ܹsG2xHiu:=xӧO|gxHt֗b-i8D!ܾq5+:tСC1gL6 FFFzHJDDDDaJOOGJJ ;;;>>>:_իHMMEVV WWW:_?)) Bii)___8::tmRs!33٨3пtQӆ m0)8ufc<< &O$Bݻw DgQ{#W(l=5111App0֭[~"v<%"%!)#'먒W58}ԩS{{{=$%"""jQ 4?>j9q%%%a nnnx1tP\xGArr`J¢EХK99r/VH$?Gfê0uT:u zj۶mXH$ݻ7ѳgODGGѣ+))A@@}Z<3f tcǎرcj;o߾EK =Q*8s$'O+40T={bʔ)Xh_,'"""jEZd{vڅbQr,]iii~cĉ(//;88СC0`ڜlL6 OS(2e nܸܣbڴicPwm߼yׯ_Nj/گbĉHNNn/_FXXZ>={ɩog" _s^z%DGGk6`GBԩS7vo˨Ç1ydAIIIxWQi˖-[ǎӣHi}>wdiwQ"`ذaؼy3tDD-KNzFR[Rp>FdT=&'x'NDhh(<<BYYSN?ԞjEᴴ4OC-[j*ݺuCxxPz jwoooi;y?)3f >J^^乹joT=3f554LTDD][*yzzbժUx饗Ommg-1_ӵk}c.j}ĵ;___'$"""jZ=EԭTNNN,Ԋ_Wm^y<={9rH ؾ}Vmۦ5((HB-`̙3}yyyߴ_ZjG._399'Ol+VzKÇ ^* jqAAsݻPKDD*A7[upp B-΂v#qey&^{G`/2Ξ=t|,A<A1k֬i5ݫبghmT ZH"m\ /e}"1mڴ %#GښB څkЕPi8 222dODT777AJ^*yHi2ebۇ7|% Eyŏ`DGG#++ ~-OGRVV ֶ% [tСY8v옠O"`ĉzN߾}vw^x7qdA9?;rZ!(<==f_uCEǎ{}k{d{wGG|999aÆ)1P-?OQ ,ѣuxTU ߆ׯ:wgbӦM(xyy;'::Z߿Q}rqqqj6~cw~p۷/jn߾7o6B9kggCuu5 >(~+ޞK =`صk w^?Z"""v[eXYY5Z)))j}M6=%/H}UTvZs_SSݻI#ҕw"**J7 DDDVkx~$߽[~a{щ%dS[$bҢRgq55*233Ð!C3fXI5ao;wԶ_zz ~KS1ήI4bP(&傾:4%M7T&ڵkP(jRo>r""p֭zxzzb՘1c];v݇^)+1M%+!zI~7܂Jjp   LIj?}mSNu^^^Z_SZ=Q_H$Z,}EEEP*jkub{_&?HT*~'A߀.]еS'QkYXdj4rppꫯG"fdcc#hX/ 9N i]LEB###a̙?>,,,ڂv]-..FHH?orPj}M- }WTj;Kg}Mnmm D"]}Db駟+ 2T4DDZdX~;*k瑎DD:`oo/hM]W!P,N 7hpӧc…:<ڮv]}7]۞8q"O5 gg+`p2 <(ZZZ6imkkkHRTiG}-ydСCj=@Oݓ,-1o_QK'W(a|wdUUǘbΜ9XnT4YII@q2<WJ{äR)|}}1uTQI.L9-Y-8p?Cm[l(ުswXyyy#>%K^)<==< HIIABBoƓxD%Q* l:z~100ĉatY A[VZIZ=RĈD3ɐO K"G4iBCC}vZ-,,Dhho cwnkkti͚5Fx}Qsb">.*uD#F`ӦMs:"YЮ(cT*.\BԯQwZ}||0a,^^^^:NIDDDDU,־k{nm{̘1;w^677GII@0mݶMZzBk1~SܺuK+"mrݻW7k784s""j{"]û#.X~=Fe""suuJSTH9s¥K(_OOO?aaaѣSQSN,{I[vWݷovY۶֭[X[SSӬZDR] M*2@T6ƣb79&ꫯPV&0EJCDD-Iҝ;}süzjL>]Ɉannnve`ADÍ 7;I'/$ x: ߿ÚRh[δbmAA}k׮]#Y0T*jto``ܿ_mXZ\\&)DM ^#R""j 2 v>NI|r+㜈Hdj}w X =%"p> iwƎEaРA:NHDDDDTvU}[>|Zj>v횠CMG7td#233kC`C4~J;4kjjPTT[[[3t>ի9) @&s*_ރ&000hUSyKv'-?ù瑗{{{#$$Æ IDDDD"bmvv}-O޿3\]]q!޽;N8!kjPӼS޽XеkW\~]9U}055\=?2 FFF:_H_֭['hcѳϊRYUcXȪ4155ܹsvZX[[9!=T*9JKKk5V˼c"߹; 0fhiWں233 UTT"A?HճgO9Ν;Z=׏9"kkjjnݺ|}Tݻ… Lx{{|}"}(,,Tg|l^071)R'N`Hs!&N7U H[VVVrEL#c J8#Gļy0a>(QuB=իZޤgiڽP-/H~MMMW*8|>D駟K"!""}ۙ>WC5j% FcϞ=,p;vKG8{҉ pd>PۡC رc I&PKDDDD-^*J$C>O? :;Ο?۷o7cᏜ3l0#GwRYY{ O?{15_ס $%% cbbԎA4hPS&j.* }}]]YDDD/GO!睸S'|ǏGDDzDDu *-*)Jd8X>=cسd^D )Fm۶9*Xw^(&qqgΛ7Om͛7`aaѣw5`yE |}}yݺuS}*pFQTVV ytFf͛7qFפIv?ɓ'Q\χYɍzQs۷o}!MpSW8wFbbb/Qbgg'hbm<3!`?[P)U166СCW_!???>LMMb㪩b-h**6vw \gyFW]]6j}MY%mܸQжADJCDDt 3#7mD|/ׯ_Ǵi-+))IW!bw$>Wn@Yw!~ilذ8q/^ sss=&'"""" kE0m4w^槦?`޼yZ͟?ڑ@?㵚iܹSgoou244ToHKKjo'Nz聡C6y?PXlܸn=ҥVt!==)~&"6@&wugr2T^&{ｇLjQ$hJ#o U k0g6MAuUus ObժUA\\,Ykkk=&'""""=~r#+++,[LЧR0{l9ss1vXbXXB///r9&L# -- &LPw߅V2ƍcܸq(,,|ܸ8̞=*HUV5x__f͚vO[jj*w޽ R)֬YDf(n bȑ"&""TYUwʏSBJG CFF>C]Y^YȖGTX) Ö_reTɫ#JѻwoXHLLIJe`ooDDDDDbH^{5jee%&Lsi@;v{KKKA_^^ƍW7nh,{yyW^iWqNBB&L\.gk׮U돍Epp0ԾRpa̚5K83g_~ZO v-6 "BTbH#|q"* 1m4b˖-qDDԆ -JBb9>/~e/FK$tK.EZZ.]>@NDDDDV6?xWaÇX8p .]J2e -11/^=B-Q+tmڵk1e#""HwANhڛoخH$E_@Qrqqq ???'$""""j}D/J$H$fyVMMM=Kkغu+~={{YfW_E?zh\t _~%vء8p fϞE5̘1Ç;w"( 0|p̝;Ͳ6c/]p],xpB?'zk׮bjhWˊUUkWC''' 9DD팥\_tn2qlW$.DG~vVs / $$C qB""""McI]zz:|ݺuO<^7n ''EEE z;Ytdgg pqqA>}ӵU*qmdeeA....x'ѱcGֶ}{Ə~4:O~y9=p^8Rq5,YwyzNHDD-@xcNAH$D&"vVslmm_ƨQrL[yxxCwݻ~߾}ѷo_Q֖JxꩧSO>Qcl޼YP%#G3Z XyO|*cLMM`^VVVzNHDD-IݗKKtN^V"vE"!"7sceeѣGc` X%"jm&hprvQ(Jl(_ 1ydlذ...zNHDD-]VXl.-BH9rwRkWM<͖=b?q-A߂g) =JO XuOdh %H0zhlܸzsB""jz^iQ)B3ȸUZ333 21c'""""jF,BׯMM1o"!"NI{ڽ{Æ 0d=&#"֢]\RY!+!zI~7܂Jjp  Yfa033kDDDDD0kZlDEE &c##qLg32ӵkW]'Oc2""jmRG%dx b"RᩧK/¢IH{,2[lBmK%5j/78vT+V`ѢEJzNHDDM;++[%BX:n@q_Qؿ_~1c.\LDDDDDc9w ^u"""//ǻ Pj޹dmm%Kw߅OC ""- J9J% Պj\M*yUϔJӧLݻDDDDD?,2vzFqḙ,XUVJ ҥ([[Wnľ$I\&o9=zɓh"tYG1X%"jem_WzF(Jl:ϣQT@CCCL2ׯW;’H[j}o<}||0a,^^^^DDHzz:d2o@.&"R#,V>c$ {9lڴ =zsB""jk=ǏGXXp,"qqqT}) IDAT"!"j_$c[???lܸ>Q[8777 44q*"""""j.,"% r""]J}8Ynݺaڵ4iQ{ JƎEaРAzLEDDDDDͅ")))HIھԼ<, ߇c׮FgggX!!!09IRDDD`ذaCDDDDʱXKDԊ :9//Dz؛RqLǎobٲe022sB""jo6mڄׯcڴiQ{OZdAV$DDٌޝGWU 20$d eAD,/PZjJjZ"VBdœ0% $$9?xٜ$$!dY'%KP䙰0Fabgk74hNpA^,Lpp~n7CTEPI:uʰMМKݞ=w8 =Sn] o~#ooo;@UFX @BB233 {B|Yzc }c.zW'Oo-;kR4vuZp7Mt1;3=z͛F!kؽ{aݰV-UUpЂ(~*IO/n{),,WP ߿߰nNܿ_,[ <ӳgO͟?_}13xP a ;OЬ(&13aaa;w fbg@@%p)úcc>@wf-^,Lpp^{5M?ziRw@CX &::ڰOn$jne{C;wlRw@EX fuh`:Pm:4 M$'OjZyn;K/aÆ! gX N[AAnվbiȑmfRna-xDúc`7u]R ut.df{6$$DO<^x!BX "99YiiiMfsnV<"l6S3fШQdM@y"iX$u',ph`ftTg}||tW^QǎM@E"mX_n@E6nW;wlzzg7n{L3gT:uLkC߿߰nNTghڵZy`]ǏUE3[rS'ʓВؽ`&E'&tyF2d"""Խ{w!  򐙓7oւ:ubg4hG}TgV``Ip7ZJKK3uin܊ǵk|>e]Ro]>&N(///:)kl߾ݰIf5E8!G1:^ 9sw&v1ɿfM7u\G[u25سӄ 4{l5lФx2Z3𙕀';kj޽-l5m4MٶMmߦŞ иq&M!ʊ,--M7|eא7uSkt)'سaaa:u~iU^ݤTvfMRP4@[u|sժUS߾}5sL! 71TfM7uTMyǎH}:r|gk׮c*""B͛77ADX nkX9?[owRŞmٲz)M6M5 倰,..ΰnN#q]VWPP9///yz饗t}!,!!nߨ:@_ܩnсg=QF)""Bmڴ1CU a-QZZRSS {]CB `M)YYnVrVVg5k'xBӧOIkv!iѬewb]5JA~vz꥗^zIÇn7KUa-QLLa]OktS7@p8ML>ܺE{O.u!u뺩rK|Ymؠ;u.#س]<:kM @__qS7gq8ZiNtņըQC RDDzibpkkMbcc ^ (+7WݲE_TbZZgׯG}TodRP~kM PuKNܵk46VŞرMɓ'ˤ]ǏUPkD3C á_6nTtb׫Q u.j!>|ذnU:AUl֗;vŋŞmР}Q͚5KMp`'O5rS'ukXe]Ro]>&N(///:IlMV߯7( 9yq5h ͙3G{6CU`;wp~Ѭd_n?"djjgի &hjذI CX &ٹsa]G qy:tWo^ӦMɓmRIbcc &u뺩Tf)YYh}{{[ٳշo_:a-Æu *áo~ԏǎ)uC=5iĤ.EX &9yaݮQc7uxJߴIK̛ ԩSOz&t`,]pׅ'Q+yyZ/vОӧp:=W>}LPkr8͛x}yVٺU('?سժUS>}Ojܸq2K'"ԪU˰KX[I6mvK7=qSpp *Z0KXGX[߻w>>>{{1K@eBX &]ak+g͛b~]|:tĉ5uT_p=Z0a>"h?s֭aÆiƌܹ LPN:'/M8[-p.ߑz)SI]LPn]:'k= }y[ ii7=c^PӦMM`U`Z!6ág۷+q;ŞQiӦiРA{ Z0A kө脓ټ{?7mֲ}JʺmG[EX & SZqm[ kM/wFъM:-M׮][wyÔUa-nkp\On̚ ޽Z3JۏSM>#n{z5i$U `_|f]o߮׍]YІ8}uGRvMiԨF^-[4Ka-;TXX9rmoA[p5]3JQ7z۷~i 6Lv݄NpEX &4i̙sm}9$${h\h7)%+D׵mVƍӳ> 9N ]pA 40?^AAAn!33S 6e0 IDAT޹|t|e h7nÇgQǎ+K@EZ“`" $$DoMKW]rE|+EEEySN?~y[t… "[&LO7|szz=;ZrcW)k@{` 6tW}i劉QttN8qkl6 \3%%E-[ݻw5j3<9s(00̽\g=#ڷo_i޼>s򗿼6lФI Ƌҹsg}Wر-o5uT_WzKN׿j̙.uGG}T.*?͛[ xl5lPoo~=ٍ]\G̑$z-$$h(ZTJYYY~Q*33S~~~n(;V>> {ѩinb8}o|Ew4 Ԙ1cb ]pA׿4x`Zxkf~j߾ڷo:v:x:7*-kڵkBBBJ]sϞ=;v\޽{zj8ppphʔ) U~J]՚:u˾fSV=zhǎZf;f8w=Z?:tP}|BwQj߾6mڤ5k̙3s.]СCc5nܸ_}U-XeKݺuӠAkjڵ.rbb-[+Qu̜9S|󍮾 7?_>L}N*6뫝QZw˗.00P c=*#Ӏ5jcǎѣŋV+44TӦM#'AZ|wrMRRƌ۷_ӨQzꕸ5fWJijufN4Itw}t1B(o>=S.wy[5jc=&Iڹsk_;uƍM6ͅţGk{?NVZx@999׾g=/KU.];֭[&%i…rD7vVzQr)^q<`M8'gU[*no]'Oև~;w*##C;wԇ~ڵkWH^zoѱc4cƌbZ@?ŋ]W^yT}bI^n69~6|owiP{˖-Ӆ [C%T964ٳg5jyyJHHl.Oɓ'>}?}]^Kjٲᵺ6M~x^>}Q>Л^qm֬YSׯW޽ozm4`^ ѣGK:Qw}a^z,2(ޢEm߾bcc],\Phof?Q]3We}VFɓ'-*k?@ek^?3ZǏ_V ԇzȰw…B:.G}sϕ(~~Uq߾} {Ж-[\m_# /_j 5j^}UÞ_)7uu]/ '.,9r x2觟~*}KCaK~^^-[fسl?mfثV>SEyW5vXeԥKݻw ]ְg4bĈRտ]ܻw;Vuqqq:paOᥪ.իotƍu%^-ԩSR/Wa!,Y}wnݺ\ꫯrǺWƀBff~zYYZWkRtI(UmZAX[JO{ &>붬ae```_m6[OKKSAAK¾_싺699nK/)..N~鵽|CS>qݻVUv%$jqℎ&_,qݮ-Z讻ѣ h0 am)M:U7<־}{Mu:*4+,VjժUuԑn7(Fq_+ԓLjj^=|?8Zn֭[9sREJɓڳga/$$D-Z(un޽{ۻ@Tϫ:raQFZ~ڴiS5`{Չ' {:uiܦMOf͚R_f {oOu5;vL+Uy9 >\vql٢/rxT@V .h:taaÆZ~ڶm[!u KtgaK~j4tPoРcײeJU kPU6MMM~~PP֭[nj3F7+_bثQ&MT{1Ԏ;Jt_ׯ_'t{t]xbm޼ٰ׮];}eoָ( {Thhh*^tIڻwa~Znk[ڵ5{lÞЄ U^Zp9+Vh~_hݺuk{ի]^+֭[]T-O?WKRNvi˖-ZfMsH~DD~߻vC vBܹnZ⧊쭲l:~x~o[M4I}YYbFHnק~{^'hԩ*ԨQCK.\_o]kԩM6Џ<.\XkCBB}v5iҤPuY3k=. HuСT5kL֭VLիWiӦsΊV|M} ,u}O?t˯^`}]QF駟~"@=lnV^_,v{/~Ql;v{ƪo߾ey(66Voڷo_l^z/vܩN:K|Pٳռy"yyy/a5k֬\O6M{ѳ> yz6l,Yo3p=N8̙3JNNV@@զMm۶:tHqqq:sT~}}gϞ=:qℒ *88X;w.6L-C111JLLӧƍ+88XݺuSݺuoF~~:JNNVFF233)I'((HڵS&oo[(/3y0ϰVYyss+VjJ/R&MӹskJ}X +aa%3yvew[eBvmAS`` dZ*00ʕ+ڵkia-3y0ϰVcASZ4~x?^YYY6>y0ϰV<"S {G6ʹv]sa/))ɴV\RsssOp\kذw^aM3y0ϰVңGխ[ʕ+0~DD֭_Շ0ϰVqjmbI$3y&0J%2+V}988Ç/_.>}ZuO>-/_\>|Pg˗-aI$3y&0$י3///矗l"߸q}߸qC޲e^^^u֙;w>"D\?)S4XP3y&0$Drm9ww=חgF+W(UaÆ!11Xdg L"aI$8YKd… -ZƢE,ZlL"aI$3y&QTUU^I'Oڵk`Ѻ$6Dy&0$Dj#F]YY'NX>y&0$Dg L"aI$3H=c ÇWuDd(Dq^jW^űc|||,Vy&0$D׻,;;;ks\[ﾮ_}L`m"C1$D}YbI$3y&0ϼߏlݺ׮]3>[jHL>?IIdDBTTYOt/D}YC=7]vGy&0$DDy&0$D3y&0$Dr%o^qKÇ1 0@۶Caa!ڴic$DN#FX ,L"aI$3y&*ȳnMy&0$DvpqEuy&0$D}Z[nObaI$3y&0$N:)iiizv3"'"C1$DNȔ)Sl 42[Ç#$$X3y&0$D2i$E{ 1㹹 g L"aI$̳$˲lNG`` Ν;X޼ysL<QQQcܺu ;v@||<~ܼyS񾏏RSSMd D.\zws,IZl= A$ <wyǠDbI$3y&0wp 1aY6n܈#GZD0fYRW簰0^NDy&0$Dy&0$D?~\nݺ@b`I$3y&08YKdڱch?y&0$DrFXֿtIL3y&0$D燞={j۲,#22gΜ1{>>Pz/Ih^ضm~:;88СCӧ>"DSx̆y&0$D0fivDؾ};-['Nʕ+ڃF$>}Zq~mZJ{2d]$I /e˖yD o$!00cǎų>k+"Spv<اO8;;tWƥKtD YZuuڵkgϟ+8/ڎ)))cǐ[niO`۷/> 6mkjj|r|(--,X~ 8YKd&YYYxwo$ ???!¼yuV:uJ1{]>}SOo[(倇,UO>$$I, DJJAΙ3~iWbԨQoy{d2Ek0}X>~x:aÆ  2D h4XhVZ[n#^|E̝;dv111غu+? =z@~SOGir>}h768$K۲e ]"''ǠmWZzKcT*L>~)|6mnܸQ:$!** ^_~e\ҨÆ G>Df~zusÄcݺufEC '| ,(++Crr2M@$%%YDDbۻw/?OݦM'4is3f4iDرc1otɲW_}O?tw׍ǔ)Scbb]P zlAAAڵ+v튖-["//FVVVܼy3apV9`ƌXr%RSSk2VR[nǕ+Wk׮պwy .4DYnMբEZǃue6s=טn*55Ǐ7\\\b <3 o`LL▴ ,hp;֛;wnг7+**3g轧R]x077ׯ_u$'kL(..Ǐ=;;;<Èѣm-#n޼BψZ+$ĸq9;dҥ ~FcላC-ڎs2%LMqE󈂃qĉ4i6nܨXּys+3gZlY;v[ov!##w{UVVWoܬY3AAApssɓ'xΟ?>K..wssҥK1yd8::*3g]\xyyS-INNJSNҎj5aoo_())FюpZWTصkBCCMȆUTTk׮zF43<_uf.)q8YKd"#4h|e,..CBB/s+((7o^Jź555rjj-Zu@ _x)Y{@9J\\"Osipk*i֬|F n~=pСz3t}'SRR =fE;6Ӑ[xrvvjzŋVlF[lhu]&Oe{{ZxLc@Tyz7Fw2LR۷o7vUU}ŋvǎ&==]ў;w.רk׮E֭SN1hS,O~g={Vm7n.:u*zmp>Lq۶$}ڬ$.#<7x K?Q;wÇcK.FX @ll,q)ILqqq/7]pp083gb͚5$zp9m{ڵh4Eې tǃ&Lht:`РAعs';6?{,:u}:wjjj#""\\\o7T,5kQݻw3y|9d- ^зo_jW۷/?ΙzgΜd-5͛7QXXmn-2jsE`` L 3g 44{9ݻ#;;[>|0fϞ=P}Y$!44I#o ݁wmҴvpp@```e霬F9s挢mL6uו$ ݻw7ݻwNʲ4v! 9''(UUU(((жg}fW_wxb?pܹ+gLIV+&> GGG|ڵktRd$Ixg?rԩw̨Rܤ>k'keYFFFQ|A]W$݇=zh'k9Y ] 9 tk^p} 1g9l0(ܹSd~~>BCCNr 2_gVŜP̚5KqBP@;vc;:}tGK1Ku'T=<< \n ;x1z?f¦M6 ӻB\1fŲd?m/ ՘ཿ:ugg&W^vVVVGٳ1Au[jը 'kL@+8#_,2Đh7̤ $:wBCC} 9?jjjb vb {VJJJ۴o^BWܚ?]׮]S91LDLֶH;d;{t'k ;|rp%E$1hӦg6ʕ+ڏpڶmh߽|ޒݫhGt/dϟǰa m )|}ŢE{Rm磴m}{&_4 ];}CC}V8po1wIW{5pt޻ߒtk?Kj,J_)&wdx{{+!,,M!qvv,2k[n^aoo8$2^xzV]R,ӝp#2Խ'ѝؽ}vQ>J;vxm*++&sرcZlXaÆŋMAdm"!!|"n¼yпn-Kd ͛7ǨQmY1{zT;3,8rbq>"C5e<;ԛx5xwK'kLW^g![tcŭT*UnEbн$==$ڵ+_xaaa0I"C"55/b)S(It8q}v,Zx ų}F bW_U\ムFHwpsN O,F_X]]8X%I'ktDSXXǏ['PXXXԫ!vL5N0`vڥKpyÆ CQQI+_g8INT 2q)~-:ן`E;JNN6XݻʲG݇cǎi}?9YKd<ƌxNHH1cBCCnLd(s ΁;VNJJ;SҥK CZZj5_|vء8IAelذC"}GСC,XP#Zn/R۾vx ܼyӨj*2݉c"cxxx(nVPP˗7ÖkIDAT]NN֬Y|~WXB jթS' 0@,22Rs8rFXֿDawo߾ؽ{mvv6BCCqy"jHv?㫯R<Ǿ 1t"9?^Yfɓ?Զ/\FMJJ|X6n8M6ggeeaڵ nWTT+V-?>n߾mp+y;;;[k"2***0a,]l5-[qơBg1[MWvIIIU,/..Id.HMMԩSˋObرt钕zG/]]]#G駟_pBmѣ޽;֮]Oĉ1ydrgggKM$dtfYhQǔ)SPm.\yTbDzꩧ˗/#,, qqqfyf=駟6[MǃAAAسg<==O>aÆ]%Ndn?<X!C^3DG"s_|Xv b޼y!VUl߿?`R9r1cF> :IND;wsu5oÍ7@qBvv6>sjaΜ9eǩB&"*KڵkrMMMj$''˯[k!C4,˲6S*Jtk?^ѣ^=<<6m(YJbb㣗Kwwwo[?((Y%k`gg'7N>x|u6/ԩSŋ˱;䘘7ߔq:K.''*J-J%O6Ms׮]'/YDnٲb}ɡ:k㏲Zt\\?9֭[r``^T*./_\.((hr.ի|-|"U555"å&}uE{ɒK/___yϞ=zsHNOOeYsɱrǎ+?egϖo޼Yg̓ݺuӫ_[cIeoMD :s QVVV:xGZ$ FUPPDųiu~/**J<%K_7y/bʪsIgW_}ۯ>>AvuDEE!..N{E#QSL4 7nm燌 [==OOO< D@@Gl6d)))0`@9$ =ztƃ۶mj:t}1G$;wj۫Wֻ )deeaxb76YZAAa׮]* SNŒ%K A3f`յ'Ij5:tj|%IjK BRR"ݖ~$Yfprrµkrンg//xǏu<?}W!--d-xPTTdjH۶muVmt_ӵ: //,i]|u> :u*.\XË/9Y9slxcA #++ 4xرCEEE>ڵ aaaޞ^۷oĉQ^^nv';&tqqƍ_K˗/ٳm>^>|oAe7ԛի1rHɪdY+R;eȑشi-Vv 4Ȩm$Iƍ1~x\z~~~^֐X7ۛ X>}pad;6fv8|0'jd"##킂lڴ,{nsFBZZs0sLJ#^111/9QK&}}oj*󍜝O?E3pLjȑطo:vhvwǂvɜ%yLl߾,w{,'j I0c yzzb<;Zºu됐m*KJJBAAzF@:tVB``I9sӘ6mIMtWqq 2v트8<~~~ظq# _򞨩%KluZnO>N⭏ɤt???2s}}}lIDֹsg޽˖-Sΐeׯӧ;;F4[0O"++ }L"Σ>M%9ys^x,))yheb͚5bŊXrY/Vӧnj3bڴi>B̟??~֮]&M*؞/}KQYYwssswW\*֭[/7|3nzЪiӦٳ;NsԩX~}lذ!6l6m={ưaⳟl{|ݻ?AKn%KM#G/+VիW_%ꫯ;3f̘s̹O|ߏymٲ%FQ=;hui{<o 'OƲeb,Cć>fϞ3g0MCCC+g}>c6lNddɒYB'ʲ,jkk*jjj&8GmE|N(++}ưab1bĈ(OAWQ__[lɻosCCCg?ƈ '\+W#<ϻߓs.EGǎ;ߏCšC9kӧO1"V\~Xn]lݺ5o}QFSSjhhv=0aB'+صkWY&quUy>ȥ9Ç#GƐ!Cاs]o֭[OZ T__Oq"3Z". k']YeqhhhhD=gϞѣG(-kAϤDϤDϤDƳXD֭^{-*o'O_xH.kĄ b1y4iR7IIIIYsss:t(ѣqш(++k#")IIII=O>:\uuuW?gϞvoСz33)3)3)3)jjj_=֬YQSSo> 1dȐ>|x >[nr\ޭO>/~񋬱od'Ng{mغGͮV{ 0 ;zh!L====˗j[iii6ul…ٶm۲cǎǏ϶oߞ-\0:ujVZZz=JJJe˖'+3)3)3) k?odGi1~|&;|{o?Q?]IIIIɾ}޽{ꫢ"[xqV__a{g/Ά j޽{g^EפgRgRgR|A&OK>a„s~ly晼s=g}lccc6eʔ_yYMMM{~3I3)3)3)y'Z3ql٤IZ`{5虔虔虔9a-t嗻,۵kE):uj˱⊬w}2w܋Q@$EϤDϤDϤ"3gfǏ/Ǐf͚wEEE%mz&%z&%z&%zWR쿙 ),Z'NA]ԱJJJbΜ9-XtY߿ow/jo3i3)3)3)ٲeK?iYrx[n߻[nhѢr-ܹ36o\IIII[3gϞ1cuѣG筫/| ѽ{cǎ:.====O1v(//cر-,bΝ?i3)3)3)sk{ｼڮ92o]WWwǗĨQZYJϤDϤDϤDϤw[u]~y"LJLJLJܚa-t믿>oo߾voy릦~Fh9u陔虔虔虔9r$o}7w9tMy놆N?ҠgRgRgR k1"oqvoӦMy[nsNIIIII~֯ZÚ5k}s z&%z&%z&%znͰ:/_]ǫ[b֙\}y뫮]@ץgRgRgRgRr 7W^pzJϤDϤDϤDϭB޽{ ﾶ;䭋N._z&%z&%z&%z&%#G\.ײ>p@ꫝhY@u陔虔虔5Z ͫGv :ƱcF8q"شk׮u>}.ho$====믿>Mwߌ3bʕ{ժU1cƌnV3)3)3)skA,XW^yeԩSq]wܹs[3Yti5*nݚwӣw_l߿N@ϤDϤDϤDϤ{[;v,̙ .,؞=\̚5+;w}W=LJLJLJ/eYV쓀T,X ,X7C!CĠAQ[[;v숿[o={Fuuy/vׯ_' >CϤDϤDϤDϤ9MoF|կ~5OSLɻۅvXjUZ*jkk[=fʔ)_gҢgRgR|ЁN<cǎs>.E[~^|6׿uhKƃ>ئ755SO=MMM?Pc7IIIIݻk_Z_Ə/r 8Sk3)3)3)ѳa-xGyg}6z gFϤDϤDϤDϤd1o޼ضm[A?tg.LJLJLJrφP`QSSUUUQ]]UUUm۶ݻw 66rȸ}pNz&%z&%z&%z&%MMMf͚XbE\2Ӯ?O3f̈iӦEIII)IIIIWٰ,ڨѣG[n\sMEYYY7 Ç#FDEEE IIIIWٰ.$ΰ k k'ۻwo466o" IIIɥs.˲gE3fLl޼9,\.MMM>%hz&%z&%z&%Z. p˲,>z/s3)3)3)z6HD.+)@3)3)3)z6(Z"0(Z"0(b嬤$r\ϲh{====3hKh====e\#>Ou~{NۏEϤDϤDϤlX ׿/i=:6oi6====sdv7n\zÆ E:h?====3hn-ooǩSt6>z&%z&%z&%zgX tĉ]3)3)3)s>Zv8ŦrgRgRgR|ЫW馛/6qy3)3)3)s>Zv:]&.z&%z&%z&%za-@;7.o]]]'N蔽7xc KϤDϤDϤD+- \ػwo:+_ ]III?˲,+It5. PE`X PE`X PE`X PE`X PE`X PE`X PE`X PE`X PE`X PE`X PE`X PE`X PE`X PEN៺IENDB`geopandas-1.2.0/doc/source/gallery/overlays.ipynb000066400000000000000000000124641525650651600221100ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Overlays\n", "\n", "Spatial overlays allow you to compare two GeoDataFrames containing polygon or multipolygon geometries \n", "and create a new GeoDataFrame with the new geometries representing the spatial combination *and*\n", "merged properties. This allows you to answer questions like\n", "\n", "> What are the demographics of the census tracts within 1000 ft of the highway?\n", "\n", "The basic idea is demonstrated by the graphic below but keep in mind that overlays operate at the dataframe level, \n", "not on individual geometries, and the properties from both are retained\n", "\n", "![illustration](http://docs.qgis.org/testing/en/_images/overlay_operations.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can load up two GeoDataFrames containing (multi)polygon geometries..." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "from shapely.geometry import Point\n", "from geopandas import GeoDataFrame, read_file\n", "from geopandas.tools import overlay\n", "from geodatasets import get_path\n", "\n", "# NYC Boros\n", "zippath = get_path(\"nybb\")\n", "polydf = read_file(zippath)\n", "\n", "# Generate some circles\n", "b = [int(x) for x in polydf.total_bounds]\n", "N = 10\n", "polydf2 = GeoDataFrame(\n", " [\n", " {\"geometry\": Point(x, y).buffer(10000), \"value1\": x + y, \"value2\": x - y}\n", " for x, y in zip(\n", " range(b[0], b[2], int((b[2] - b[0]) / N)),\n", " range(b[1], b[3], int((b[3] - b[1]) / N)),\n", " )\n", " ]\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first dataframe contains multipolygons of the NYC boros" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "polydf.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And the second GeoDataFrame is a sequentially generated set of circles in the same geographic space. We'll plot these with a [different color palette](https://matplotlib.org/examples/color/colormaps_reference.html)." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "polydf2.plot(cmap=\"tab20b\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `geopandas.tools.overlay` function takes three arguments:\n", "\n", "* df1\n", "* df2\n", "* how\n", "\n", "Where `how` can be one of:\n", "\n", " ['intersection',\n", " 'union',\n", " 'identity',\n", " 'symmetric_difference',\n", " 'difference']\n", "\n", "So let's identify the areas (and attributes) where both dataframes intersect using the `overlay` method. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "newdf = polydf.overlay(polydf2, how=\"intersection\")\n", "newdf.plot(cmap=\"tab20b\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And take a look at the attributes; we see that the attributes from both of the original GeoDataFrames are retained. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "polydf.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "polydf2.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "newdf.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's look at the other `how` operations:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "newdf = polydf.overlay(polydf2, how=\"union\")\n", "newdf.plot(cmap=\"tab20b\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "newdf = polydf.overlay(polydf2, how=\"identity\")\n", "newdf.plot(cmap=\"tab20b\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "nbsphinx-thumbnail" ] }, "outputs": [], "source": [ "newdf = polydf.overlay(polydf2, how=\"symmetric_difference\")\n", "newdf.plot(cmap=\"tab20b\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "newdf = polydf.overlay(polydf2, how=\"difference\")\n", "newdf.plot(cmap=\"tab20b\")" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.0" } }, "nbformat": 4, "nbformat_minor": 4 } geopandas-1.2.0/doc/source/gallery/pandas_accessor.ipynb000066400000000000000000000053651525650651600233760ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Using GeoPandas via the pandas \"geo\" accessor\n", "\n", "GeoPandas can be used from pandas with the \"geo\" accessor, which is registered on `pandas.Series` when `geopandas.accessors` is first imported." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Import geopandas.accessors to register the \"geo\" accessor.\n", "import geopandas.accessors # noqa # pylint: disable=unused-import\n", "import pandas as pd\n", "from shapely import Point" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using the Series.geo accessor\n", "\n", "Construct a Series using the GeometryDtype." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 POINT (1 2)\n", "1 POINT (3 4)\n", "2 POINT (5 6)\n", "dtype: geometry" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s = pd.Series(\n", " [Point(1, 2), Point(3, 4), Point(5, 6)],\n", " dtype='geometry',\n", ")\n", "s" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "All GeoSeries methods and properties are available via the \"geo\" accessor." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 1.0\n", "1 3.0\n", "2 5.0\n", "dtype: float64" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.geo.x" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 4.472136\n", "1 10.000000\n", "2 15.620499\n", "dtype: float64" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s2 = pd.Series(\n", " [Point(-1, -2), Point(-3, -4), Point(-5, -6)],\n", " dtype='geometry',\n", ")\n", "s.geo.distance(s2)" ] } ], "metadata": { "kernelspec": { "display_name": "dev-3.11", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.2" } }, "nbformat": 4, "nbformat_minor": 2 } geopandas-1.2.0/doc/source/gallery/plot_clip.ipynb000066400000000000000000000215111525650651600222220ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Clip Vector Data with GeoPandas\n", "\n", "\n", "Learn how to clip geometries to the boundary of a polygon geometry\n", "using GeoPandas." ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "The example below shows you how to clip a set of vector geometries\n", "to the spatial extent / shape of another vector object. Both sets of geometries\n", "must be opened with GeoPandas as GeoDataFrames and be in the same Coordinate\n", "Reference System (CRS) for the `clip` function in GeoPandas to work.\n", "\n", "This example uses example data from ``geodatasets`` ``'geoda.chicago-health'`` and\n", "``'geoda.groceries'``, alongside a custom rectangle geometry made with\n", "shapely and then turned into a GeoDataFrame.\n", "\n", "
\n", " \n", "Note\n", "\n", "The object to be clipped will be clipped to the full extent of the clip\n", "object. If there are multiple polygons in clip object, the input data will\n", "be clipped to the total boundary of all polygons in clip object.\n", "
\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Import Packages\n", "---------------\n", "\n", "To begin, import the needed packages.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import geopandas\n", "from shapely.geometry import box\n", "import geodatasets" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Get or Create Example Data\n", "--------------------------\n", "\n", "Below, the example GeoPandas data is imported and opened as a GeoDataFrame.\n", "Additionally, a polygon is created with shapely and then converted into a\n", "GeoDataFrame with the same CRS as the GeoPandas Chicago dataset.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "chicago = geopandas.read_file(geodatasets.get_path(\"geoda.chicago_commpop\"))\n", "groceries = geopandas.read_file(geodatasets.get_path(\"geoda.groceries\")).to_crs(chicago.crs)\n", "\n", "# Create a subset of the Chicago data that is just the South American continent\n", "near_west_side = chicago[chicago[\"community\"] == \"NEAR WEST SIDE\"]\n", "\n", "# Create a custom polygon\n", "polygon = box(-87.8, 41.90, -87.5, 42)\n", "poly_gdf = geopandas.GeoDataFrame([1], geometry=[polygon], crs=chicago.crs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot the Unclipped Data\n", "-----------------------\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(12, 8))\n", "chicago.plot(ax=ax1)\n", "poly_gdf.boundary.plot(ax=ax1, color=\"red\")\n", "near_west_side.boundary.plot(ax=ax2, color=\"green\")\n", "groceries.plot(ax=ax2, color=\"purple\")\n", "ax1.set_title(\"All Unclipped Chicago Communities\", fontsize=20)\n", "ax2.set_title(\"All Unclipped Groceries\", fontsize=20)\n", "ax1.set_axis_off()\n", "ax2.set_axis_off()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Clip the Data\n", "--------------\n", "\n", "The object on which you call `clip` is the object that will\n", "be clipped. The object you pass is the clip extent. The returned output\n", "will be a new clipped GeoDataFrame. All of the attributes for each returned\n", "geometry will be retained when you clip.\n", "\n", "
\n", "\n", "Note\n", "\n", "Recall that the data must be in the same CRS in order to use the\n", "`clip` method. If the data are not in the same CRS, be sure to use\n", "the GeoPandas `GeoDataFrame.to_crs` method to ensure both datasets\n", "are in the same CRS.\n", "
\n", "\n" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Clip the Chicago Data\n", "--------------------\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "nbsphinx-thumbnail" ] }, "outputs": [], "source": [ "chicago_clipped = chicago.clip(polygon)\n", "\n", "# Plot the clipped data\n", "# The plot below shows the results of the clip function applied to the chicago\n", "# sphinx_gallery_thumbnail_number = 2\n", "fig, ax = plt.subplots(figsize=(12, 8))\n", "chicago_clipped.plot(ax=ax, color=\"purple\")\n", "chicago.boundary.plot(ax=ax)\n", "poly_gdf.boundary.plot(ax=ax, color=\"red\")\n", "ax.set_title(\"Chicago Clipped\", fontsize=20)\n", "ax.set_axis_off()\n", "plt.show()" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "
\n", " \n", "Note\n", "\n", "For historical reasons, the clip method is also available as a top-level function `geopandas.clip`.\n", "It is recommended to use the method as the function may be deprecated in the future.\n", "
\n", "\n", "Clip the groceries Data\n", "----------------------\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "groceries_clipped = groceries.clip(near_west_side)\n", "\n", "# Plot the clipped data\n", "# The plot below shows the results of the clip function applied to the capital cities\n", "fig, ax = plt.subplots(figsize=(12, 8))\n", "groceries_clipped.plot(ax=ax, color=\"purple\")\n", "near_west_side.boundary.plot(ax=ax, color=\"green\")\n", "ax.set_title(\"Groceries Clipped\", fontsize=20)\n", "ax.set_axis_off()\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.8" } }, "nbformat": 4, "nbformat_minor": 4 } geopandas-1.2.0/doc/source/gallery/plotting_basemap_background.ipynb000066400000000000000000000156571525650651600260020ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "# Adding a background map to plots\n", "\n", "This example shows how you can add a background basemap to plots created\n", "with the geopandas ``.plot()`` method beyond capabilities of the `tiles` keyword. This makes use of the\n", "[contextily](https://github.com/geopandas/contextily) package to retrieve\n", "web map tiles from several sources (OpenStreetMap, NASA). Also have a\n", "look at contextily's \n", "[introduction guide](https://contextily.readthedocs.io/en/latest/intro_guide.html#Using-transparent-layers)\n", "for possible new features not covered here.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import geopandas\n", "import geodatasets\n", "import contextily as cx" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's use the NYC borough boundary data that is available in geopandas\n", "datasets. Plotting this gives the following result:\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df = geopandas.read_file(geodatasets.get_path(\"nybb\"))\n", "ax = df.plot(figsize=(10, 10), alpha=0.5, edgecolor=\"k\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Matching coordinate systems \n", "\n", "\n", "Before adding web map tiles to this plot, we first need to ensure the\n", "coordinate reference systems (CRS) of the tiles and the data match.\n", "Web map tiles are typically provided in\n", "[Web Mercator](https://en.wikipedia.org/wiki/Web_Mercator_projection)\n", "([EPSG 3857](https://spatialreference.org/ref/epsg/3857/)), so let us first check what\n", "CRS our NYC boroughs are in:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df.crs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we know the CRS do not match, so we need to choose in which\n", "CRS we wish to visualize the data: either the CRS of the tiles,\n", "the one of the data, or even a different one.\n", "\n", "The first option to match CRS is to leverage the `to_crs` method\n", "of GeoDataFrames to convert the CRS of our data, here to Web Mercator:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df_wm = df.to_crs(epsg=3857)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can then use `add_basemap` function of contextily to easily add a\n", "background map to our plot:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "nbsphinx-thumbnail" ] }, "outputs": [], "source": [ "ax = df_wm.plot(figsize=(10, 10), alpha=0.5, edgecolor=\"k\")\n", "cx.add_basemap(ax)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we want to convert the CRS of the tiles instead, which might be advisable\n", "for large datasets, we can use the `crs` keyword argument of `add_basemap`\n", "as follows:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ax = df.plot(figsize=(10, 10), alpha=0.5, edgecolor=\"k\")\n", "cx.add_basemap(ax, crs=df.crs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This reprojects map tiles to a target CRS which may in some cases cause a\n", "loss of sharpness. See \n", "[contextily's guide on warping tiles](https://contextily.readthedocs.io/en/latest/warping_guide.html)\n", "for more information on the subject." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Controlling the level of detail" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can control the detail of the map tiles using the optional `zoom` keyword\n", "(be careful to not specify a too high `zoom` level,\n", "as this can result in a large download).:\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ax = df_wm.plot(figsize=(10, 10), alpha=0.5, edgecolor=\"k\")\n", "cx.add_basemap(ax, zoom=10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Choosing a different style" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By default, contextily uses the OpenStreetMap HOT style. We can specify a\n", "different style using ``cx.providers``:\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ax = df_wm.plot(figsize=(10, 10), alpha=0.5, edgecolor=\"k\")\n", "cx.add_basemap(ax, source=cx.providers.OpenTopoMap)\n", "ax.set_axis_off()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Adding labels as an overlay" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sometimes, when you plot data on a basemap, the data will obscure some important map elements, such as labels,\n", "that you would otherwise want to see unobscured. Some map tile providers offer multiple sets of partially\n", "transparent tiles to solve this, and `contextily` will do its best to auto-detect these transparent layers\n", "and put them on top." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ax = df_wm.plot(figsize=(10, 10), alpha=0.5, edgecolor=\"k\")\n", "cx.add_basemap(ax, source=cx.providers.OpenRailwayMap)\n", "cx.add_basemap(ax, source=cx.providers.OpenStreetMap.HOT)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By splitting the layers like this, you can also independently manipulate the level of zoom on each layer,\n", "for example to make labels larger while still showing a lot of detail." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ax = df_wm.plot(figsize=(10, 10), alpha=0.5, edgecolor=\"k\")\n", "cx.add_basemap(ax, source=cx.providers.OpenRailwayMap, zoom=10)\n", "cx.add_basemap(ax, source=cx.providers.OpenStreetMap.HOT, zoom=12)" ] } ], "metadata": { "kernelspec": { "display_name": "default", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.14.6" } }, "nbformat": 4, "nbformat_minor": 4 } geopandas-1.2.0/doc/source/gallery/plotting_with_folium.ipynb000066400000000000000000000151671525650651600245150ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Plotting with Folium\n", "\n", "__What is Folium?__\n", "\n", "[Folium](https://python-visualization.github.io/folium/) builds on the data wrangling strengths of the Python ecosystem and the mapping strengths of the leaflet.js library. This allows you to manipulate your data in Geopandas and visualize it on a Leaflet map via Folium.\n", "\n", "In this example, we will first use Geopandas to load the geometries (volcano point data), and then create the Folium map with markers representing the different types of volcanoes." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load geometries\n", "This example uses a freely available [volcano dataset](https://www.kaggle.com/texasdave/volcano-eruptions). We will be reading the csv file using pandas, and then convert the pandas `DataFrame` to a Geopandas `GeoDataFrame`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Import Libraries\n", "import pandas as pd\n", "import geopandas\n", "import folium\n", "import geodatasets\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df1 = pd.read_csv(\"volcano_data_2010.csv\")\n", "\n", "# Keep only relevant columns\n", "df = df1.loc[:, (\"Year\", \"Name\", \"Country\", \"Latitude\", \"Longitude\", \"Type\")]\n", "df.info()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Create point geometries\n", "geometry = geopandas.points_from_xy(df.Longitude, df.Latitude)\n", "geo_df = geopandas.GeoDataFrame(\n", " df[[\"Year\", \"Name\", \"Country\", \"Latitude\", \"Longitude\", \"Type\"]], geometry=geometry\n", ")\n", "\n", "geo_df.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "world = geopandas.read_file(geodatasets.get_path(\"naturalearth.land\"))\n", "df.Type.unique()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "nbsphinx-thumbnail" ] }, "outputs": [], "source": [ "fig, ax = plt.subplots(figsize=(24, 18))\n", "world.plot(ax=ax, alpha=0.4, color=\"grey\")\n", "geo_df.plot(column=\"Type\", ax=ax, legend=True)\n", "plt.title(\"Volcanoes\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create Folium map\n", "Folium has a number of built-in tilesets. For example:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# OpenTopoMap\n", "map = folium.Map(location=[13.406, 80.110], tiles=\"OpenTopoMap\", zoom_start=9)\n", "map" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# OpenStreetMap\n", "map = folium.Map(location=[13.406, 80.110], tiles=\"OpenStreetMap\", zoom_start=9)\n", "map" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Add markers\n", "To represent the different types of volcanoes, you can create Folium markers and add them to your map." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Create a geometry list from the GeoDataFrame\n", "geo_df_list = [[point.xy[1][0], point.xy[0][0]] for point in geo_df.geometry]\n", "\n", "# Iterate through list and add a marker for each volcano, color-coded by its type.\n", "i = 0\n", "for coordinates in geo_df_list:\n", " # assign a color marker for the type of volcano, Strato being the most common\n", " if geo_df.Type[i] == \"Stratovolcano\":\n", " type_color = \"green\"\n", " elif geo_df.Type[i] == \"Complex volcano\":\n", " type_color = \"blue\"\n", " elif geo_df.Type[i] == \"Shield volcano\":\n", " type_color = \"orange\"\n", " elif geo_df.Type[i] == \"Lava dome\":\n", " type_color = \"pink\"\n", " else:\n", " type_color = \"purple\"\n", "\n", " # Place the markers with the popup labels and data\n", " map.add_child(\n", " folium.Marker(\n", " location=coordinates,\n", " popup=\"Year: \"\n", " + str(geo_df.Year[i])\n", " + \"
\"\n", " + \"Name: \"\n", " + str(geo_df.Name[i])\n", " + \"
\"\n", " + \"Country: \"\n", " + str(geo_df.Country[i])\n", " + \"
\"\n", " + \"Type: \"\n", " + str(geo_df.Type[i])\n", " + \"
\"\n", " + \"Coordinates: \"\n", " + str(geo_df_list[i]),\n", " icon=folium.Icon(color=\"%s\" % type_color),\n", " )\n", " )\n", " i = i + 1" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "map" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Folium Heatmaps\n", "\n", "Folium is well known for its heatmaps, which create a heatmap layer. To plot a heatmap in Folium, you need a list of latitudes and longitudes." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# This example uses heatmaps to visualize the density of volcanoes\n", "# which is more in some parts of the world compared to others.\n", "\n", "from folium import plugins\n", "\n", "map = folium.Map(location=[15, 30], tiles=\"OpenTopoMap\", zoom_start=2)\n", "\n", "heat_data = [[point.xy[1][0], point.xy[0][0]] for point in geo_df.geometry]\n", "\n", "heat_data\n", "plugins.HeatMap(heat_data).add_to(map)\n", "\n", "map" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "default", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.14.6" } }, "nbformat": 4, "nbformat_minor": 4 } geopandas-1.2.0/doc/source/gallery/polygon_plotting_with_folium.ipynb000066400000000000000000000131201525650651600262470ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Plotting polygons with Folium\n", "This example demonstrates how to plot polygons on a Folium map." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import geopandas as gpd\n", "import geodatasets\n", "import folium\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load geometries\n", "This example uses the nybb dataset, which contains polygons of New York boroughs." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "path = geodatasets.get_path(\"nybb\")\n", "df = gpd.read_file(path)\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot from the original dataset" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "nbsphinx-thumbnail" ] }, "outputs": [], "source": [ "df.plot(figsize=(6, 6))\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice that the values of the polygon geometries do not directly represent the values of latitude or longitude in a geographic coordinate system.\n", "To view the coordinate reference system of the geometry column, access the `crs` attribute:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df.crs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The [EPSG:2263](https://spatialreference.org/ref/epsg/2263/) crs is a projected coordinate reference system with linear units (ft in this case).\n", "As folium (i.e. leaflet.js) by default accepts values of latitude and longitude (angular units) as input, we need to project the geometry to a geographic coordinate system first." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Use WGS 84 (epsg:4326) as the geographic coordinate system\n", "df = df.to_crs(epsg=4326)\n", "print(df.crs)\n", "df.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df.plot(figsize=(6, 6))\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create Folium map" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "m = folium.Map(location=[40.70, -73.94], zoom_start=10)\n", "m" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Add polygons to map\n", "Overlay the boundaries of boroughs on map with borough name as popup:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for _, r in df.iterrows():\n", " # Without simplifying the representation of each borough,\n", " # the map might not be displayed\n", " sim_geo = gpd.GeoSeries(r[\"geometry\"]).simplify(tolerance=0.001)\n", " geo_j = sim_geo.to_json()\n", " geo_j = folium.GeoJson(data=geo_j, style_function=lambda x: {\"fillColor\": \"orange\"})\n", " folium.Popup(r[\"BoroName\"]).add_to(geo_j)\n", " geo_j.add_to(m)\n", "m" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Add centroid markers\n", "In order to properly compute geometric properties, in this case centroids, of the geometries, we need to project the data to a projected coordinate system." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Project to NAD83 projected crs\n", "df = df.to_crs(epsg=2263)\n", "\n", "# Access the centroid attribute of each polygon\n", "df[\"centroid\"] = df.centroid" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since we're again adding a new geometry to the Folium map, we need to project the geometry back to a geographic coordinate system with latitude and longitude values." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Project to WGS84 geographic crs\n", "\n", "# geometry (active) column\n", "df = df.to_crs(epsg=4326)\n", "\n", "# Centroid column\n", "df[\"centroid\"] = df[\"centroid\"].to_crs(epsg=4326)\n", "\n", "df.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for _, r in df.iterrows():\n", " lat = r[\"centroid\"].y\n", " lon = r[\"centroid\"].x\n", " folium.Marker(\n", " location=[lat, lon],\n", " popup=\"length: {}
area: {}\".format(r[\"Shape_Leng\"], r[\"Shape_Area\"]),\n", " ).add_to(m)\n", "\n", "m" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "default", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.14.6" } }, "nbformat": 4, "nbformat_minor": 4 } geopandas-1.2.0/doc/source/gallery/s2a_l2a_fishbourne.tif000066400000000000000000035464421525650651600233670ustar00rootroot00000000000000II*-RS$ , D t Rr-B@SexR"b2"4GrZBmR"b)2<OatrBR" 0CbV2i|ҎrBR%"8J]pb2һrB,?RR"ewŠb2rB!4FYlR"·b2(r;BNasR"b 20BUrhB{R" $ b7 2J ] o r B  R " + > Q bd 2w  Ҝ r B  R3 "F X k ~ b 2  r B  ' : M R` "s ˜ b 2  r B/ B T g z R " b 2 #6rIB\o$@$@#A1sUA# v )#WGS 84 / UTM zone 30N|WGS 84|D=DC5C@D@LDCDC@,D D;DC.D@~D9DvC/D@|D@6DsC@.D D@:DC@0D~D3DC/DyD@2DC3D@|D4DC0D@D1DC-DzD(DC-DsD(DC(DpD#DvC&DsD%DC,DsDDqC@:DD?DC@=D@D@@DC7D D@=DC1D`D@DC4D D@ADC@4DDEDC3D@DCDC3DD@GDC@0D@DCDC6D`DEDC8D DCDC1DD@BDC@3D~D?DC1DDBDC3D`D@DC3DD@>DC@3DD9DC6D@D@5DC3D|D3DC,D@D3DC.DyD&DC@3D@tD@&DC1DsD@!DC+D@qD@D~C'DjD@DC:DDDDC@DDDDC@9DD?DC2D D=DC:D D>DC@7D`DBDC7D`D@CDC@8DDCDC/DDCDC3D`D@FDC2D DEDC0DDCDC-D}D>DC2D@DFDC6DD>DC.DD@?DC3DD@ADC0DD@?DC@3D@D@8DC8D`D8DC2DD@7DC0D`D8DC@4D~D4DC9D|D.DC+DyD@+DC1DxD-DC/D@tD@%D~C&DlD!DC@DjDDzC$DkDD{CEDDKDCCD`DODC@AD`D@MDC@=D DHDC@:D`DEDC8D@D@EDC7DD@GDC@:DDCDC@8DD@FDC8D D@GDC@2D`DHDC2DD@DDC1D D@CDC4DD@EDC2D@DBDC.DDIDC2DDDDC2D`DFDC0D`D@@DC.DD@;DC6D~D9DC1D`D@DC2D D9DCDDIDC@CD@DNDC;D`D@ODC@5DDEDC7DDBDC7D@D@DDC9DDGDC@8D D@BDC@.D~D@ADC&D@DBDC@2DD@JDC3DDIDC0D@DJDC.D`D@IDC5DD@HDC5D D@CDC0DD@DC@7D D;DC4DD@DC1D D9DC1D D=DC/DD@:DC@5DDDC;DDJDC@DC3DD=DC@6D@D=DC6DD@=DC3D~D@DC6DDGDyC@9DDQDC:DD@UDC2D DRDC@$D@zDDDD'DjD@:D@aDD\D0D]D"DlD5DD*DD@CDC5DDCDC/DDCDC3D`D@@DC@3DDDC(D~D@;D~C4D@}D@9DC/D}D5DC.D@D1DC2D|D@+DC3D}D(DC@-DmD@DmC)DnDDwC&DkD@D|C@$DcD@ DgCD@ZDDUCD[D@DjCDLDCHCD@GDCNCDBDC5CcCDeC EhCDkC02EnCDPC (EOC DRC0$ECDlC'EC DC@ECDC@ECDCDC3DDD DED,DlD@D@cD9DBDCADD@ZD@DRD@#DFD.DD_DD1D@DbDC5DD]DD@'D@uD@VD5D,DpDLDD-DsD@KD@D-DsDHD@D.DnD@=DC3D}DBDC@+D@D@ADC.DD?DC@4D`D@DDC5DDHDC7DDADC8D`D?DC8DD;DC3D|DDC6D D@8DC7DD8DC7D@|D@.DC6DyD,DC+DoD&DoC@1DpD!DC&D@mD@DxC@*DjD@DC"DeDDoC$DcDDjC!D\DCjCD@TDCZCD@LDCGCsCDcC[EtC@DoCXEqC@DhCOEyCDhCXEqC@DcC DEYCDwCFE[CDlC ]EsC@DCPLEC@DC>ECDCECDwC$EwC@D{C=ECDC"ECDCECDC`ECCC DCDC` ECDC@ECDCECGD2D`D@!DxDlDD4D D@jD@MD2DDVDC0DDODC@0DDIDC2D@}DEDC5DD@LDC@6D`D@ODC3DD@IDC>DDKDC9DD@CDCD~D1DC6D@{D,DC5DxD@&DC+D@wDDvC,D@rDDC)DkDDnC'DaD DsCD@]DC^C"DXDC\CDRDCXC|CD_CeEcCDZCpfEYCDHC`dE`CDKCkEcCDRCTE`CDqCQEMCDXC`tESC DHCUERC@DSCDEwCDCAEC$DChEC$DCpbEC@DC;ECDC %ECDC *EZCDqC6EzC@#DC`GECDCDECDC5ECDC%EC@3DD E@DOD@1D`DD@\DBD D@.DxDSD@+D2DDIDC-D@zDFDC,DDKDC4D`DLDC-DuDFDC/D@|D@BDC6DD?DCDC,D@{D7DC@/D@wD=DC+DzD:DC-D@{D9DC.DzD@7DC8D}D6DC@7D~D2DC7D~D@0DC5D{D+DC)D@vD DC@'DvDDlC)D@lDDgC#DeDD]C@ D@^D@DbCDZDC[C'D]D@DdC DTDC]C@DIDCWCC@D]CbEC@DqC hEiC@D\CkE]C'D^CpeESCDHCPkENCDKCphEOCD9CbE?CC:CLEiCC[C`AEC&DC@WEC@-DCaECDCIEwC@D}C=EC@ DC`=EC@%DCDEC"DCSEC%DCpLEC$DCpMEC@!DCCEC+DC 7EC DC`-EC@DCECDCEC9DDDDaDDDRD%D@vDSD$D+D}DPD@D;DDXDC/DwD@GD@#D$D@lD@6D@:D&D@iD@6D D@3D`DFDC@:DDJDC>D`DFDC@3D@D;DC6D@vD?DC,DuD7DC@+D|D:DC@(DxD8DC@/D@zD:DC3D|D5DC7D~D@8DC3D~D.DC/D}D@.DC+D@xD)DC1D@uD@(DC@)D@nDDqC@-DhDDjC'D@gD@DcC@(D@iDDiC!D\DCaC@'DVDCcCDWDCSCJCDNC`uE]CDNCtERCD_CWEeCDfCIEYC DOC`XEGCC7C\EFCC-C`ZECCCFC]EC DC NEC!DCLECDCSEzC@ DzC`HECDCGEC@$DCKEC,DCPSE|CDC`TEC&DCDEC@"DC9EC@DC07EC@#DCGEC@!DC@KECDC@DEC@DC *ECDCDC$D D@DC5D D`DC@CDDD DYD6DsDDWD@0DPD"D^D3DRDD@^D@1D@6D'DpD-DD@,D{D@=DC6DDJDC>DDGDC5DDDDC@2D}D:DC@.D~D:DC-DuD8DC0DuD:DC'D@wD:DC@2D}D7DC1D{D@4DC@/D{D@4D}C@-D@zD-DsC6D|D+DC-D@wD@)DyC1DqD'D{C/D@vD@!DpC@,DrDDtC(D@hD DZC D@bD@DTC@D[DCEC@DWDCVC@D@MDCFC^CDHC0rEQCD=CiEKCCUC GEkC DKCVErCD]CXEgC@D`C eE_C DSCPiEoCDkCNEC DCTEC"DC TExC D{CSEC D{CLECDC0bECDCpbEbCDnCWECDCVEC@DC0FEsC@D{C@KE}CDsC0^EC@%DCmEC.DCp{EC&DC0cEnC DC :EC DC!ECDCECDCEC DCECDC`DCDCDC=D@D@DD[D*D~D'D@fD;D\D(DcD3D&D5DDEDC@;DDKDCDC2DD;DC/D|D4DC@*D~D;DC&DwD7DC.DyD5DC)DvD@3DC@(DuD6DC@'DwD3DC1DyD/DC&DzD.DC2D@{D+DC2DtD&DC0D@uD#DC@(DsDDeC(DqDDpC@&D@eDDXC#D_DD_C#D[DCMC@"D[DCWCDPDCFC@D@PDCRCPCDRCrEgC DUCmEdCDJC`iEKCD^CqEFCD;CPsERC@ DLCEPCDXCvETC@DNC`nE\C@DYCEC DcCPbEcCCTCKE@CC3CPUEXCD_CwEpC@DjCEZC@ DbCEmCDCaEvCD{CTECD}C]EjC DZChEuCDvCjECDC0NEqC DoCNEoCDC@IEyC@DC>EoCDxC`SEC%DCpfEjC DCgECDCIECDCAEC@(DCVEC@.DCGEC@8D@ D E DLD.DD4D@yD@KD@D7D DRDC7DDPDC6DD@RDC9D`DODC4D`DHDC@7DDDDC3D}D@ADC7D@DDDC1D|D=DC@1D@wD@9DC)DzD4DC+DzD4DC-DzD@4DC@+D@yD6DC@)D~D3DC3D@|D,DC@2DsD)DC@1D@sD)DC*DuD@%DC@,DxD%DyC*DsDDgC)DnDDnC)DiDD`C(DhDDiC!D@dDDTC$DYDCYCD@TDC:C@D@MDCTCYC@DUC@gEYC@DRCrELC DICsEdCDdCqEaCDbC`lE^C@ DMCp{EKCCCC;CjESCC:C]ECD\CnEqCDOCEeC D\CpEUCC5CwE;CC=CPaEzC@DTC WE_CDgCPVE|C@DyCjEC DCcECDC@`EC@DC`iECDCpyECD|CEyCD{C@jECDC}E}CDCEC@DCeEC'DCkEC)DCsEC+DCPhEC+DCsEC DCpEC"DCPlEC?D@D8ECJD4D DCDCDC@ DCDD@BDDDC@DD~D!D@eD@DD@+D2D`D@XDC:DD@RDC:DD@PDC9DDGDC;DD@GDC@5DDBDC;DDADC*D@zD9DC@-D{DDC@(D}DEaCCXCBEYC@DTCtEDCCJCmEMCCKCKErC@DcC_EdCD[C [EfC DjCaEsC DCdECOD@DqEC+DCEgCDC bECDC0)EwCDC+EzCC}C>ECDCGECDC7ECDC6EC@@D@ D`)ED@ODEDD@D`D}DD@7D`DuD_D-D`D@]DD4DD@ODC@1D@DFDC5DDDDC7DD@BDC0DD=DC-D|D=DC@,DyDCDC,DzD=DC-D~D>DC@-D~D@8DC@2D}D?DC+D|D7DC@/DyD4DC@-D@wD,DC0DrD.DC.DhD!DC,DkDDsC%D@pDD\C&DrDDzC'DjDDgC'DkD@DcC*DmDDiC*DqDDqC,DlD@ DcC)D@nD DfC.DjD@ DgCCC@ D-C0tEhCDMC8EWC@DDCEKCDACXEZC@D>CEfCDWCxEMCDPChETC@D>C0EtC DdCpExCDeCvEiC DGC mEjCDRCPEBCDGC_ESCCRC0wEPCCMCPgEbCCMC ]E8CC7CpcElC@DWCuECDcCpxETCC?C^E.CC0CpIERCCWCSEECC;CPOE2CC"CKEHC@DFCPuE]CDbCEEC@DDCYEwCD_CcECDCiEC:DDYECDC`7EXCCyCCEoCDsC@2EsCC{C@:ECDC0VEC@DCptECDC RECDCZEC6DD1EC.DD EHD DDDoD DDDCD@}DuDD@1DzDbD;D4D@DWDC5D@DQDC6DD@GDC0DD@DC1D`DCDC,D DHDC-DD?DC*D@DHDC@,DD>DC@3DDADC@.DD>DC@.D~D=DC@,D@zD@3DC1DvD.DC0DxD(DC@,D@tD%DC@&DhD@DC@)DiDDwC)D@iD@DjC@)DgDDfC)D@lDDaC,DpDDdC@+D@mDDxC-DlD DcC@1DgD DlC3D@lD DgC,DfD DZCNC D=C`~EZC DRCyE]C DSC`EXC@ D]C(EJCD8CEXCD@CwEVCC;CkEwC DC`ECDCEEyC@DrC@TEYCDOCbEvCDZC`gE?C@ DHCnERCDPCrE9CC>C@fENCCJCgEGCDNC yEdCC[CPeEWCCgCBElCCKC9E;CCCFERC@ DVCpcEUCCGC`jERCC?C؂ERC@DJCELCCGCxETCCKCWECDCPcEC*DD3EC"DD EC DC@EnC@DC^ECDCp[ECDCXECDC[EC@DCjEC@DCHEC(DCFED3DDEC$D@DE/DID@XDDGDpDzDD)DLD4D D DCD>DD6D~DkDD8D DhD3D@2D~DRDC3DDHDC@9D DEDC@6DDODC1D`D@MDC4D@DHDC4DDEDC9D DIDC6DD@GDC3D DDDC@/DDADC@4DD@@DC2D{D:DC,DzD3DC@*DrD.DC2DvD+DC@-D@qDD|C(D@oD@DoC0DmDDnC1DlDDC+DmDDpC1DnD DlC@0D@kD DvC@/DpDDpC@*D@nD D`C.DkD DvC@5D@vDD}C0DuDD|C/DmD@DsC@2DnD@ DC4DxD@D{CGCD3CHEZC DKCXEJC@DDCE>CCFC@kE(CCCQE5CC"C]E=CC+ChEhCCqCpCEiCChC6EHCCHC`SE\C@DDCpE`C@D@C؁E?CD4C{ENCDRC}EIC DMCЂEUCDDC~E[CDOCpvE@CDQCdE?CCGCpUELCC0C@XE$CCC [E@CC;C}ED`D@DD@4DDpD-D5D DZDC*DDKDC%DDFDC0DDBDC@/D@D@BDC2DDBDC4D@D@BDC/DDGDC2D@DADC7DD?DC5DD=DC1D}D;DC5DxD5DC/D@}D4DC)DuD)DC#DrD%DC)DmDDrC.DkDD~C(DiD@D_C@0D@lD DkC@-DgD DoC@1DmDDlC'DqDD_C.DnD DlC8D@uD@DC@:DtDDC@1D@qDDC2DqDDmC4D@oD@ DaC5DuDDpC(D@mD@ DnC1DhDDxC1D@qD DwCUCC4CPYEfC@DJC0iEdCDMCYE4CCPC[E.CC CPiEBC@D3CyE.C@D"CPsECCC1C``EMCDKC@pEbC@DbC }EaCDnC0qECDCrE}CD|ClEC@DCYEC DrCXErC@DOCzE_C DTCwE\CDZC`kEWCDNCyEUC DVCuE8CCGCuENC@D8CENCCSCUEoCDhCuELC DICE`CCRC~EHCC.CUECDCpYECZDGDeECZDDQEC@!DC`YEC@3DCuEC+DCaEC@DCUEC)D D@ EC@-DDDuCCC ECCC*EGCC[C0E\CC}CPECCCpECDC*EhCDC)EC@DCpCEC@:DDPZE@DgDFD0GE,DtDqDE1DsDDELDDD DYDDDDCDDD!D9DDVDC4D D@JDC5D`DGDC7DD@FDC2DDFDC@5DD@BDC@5D@DADC1DDCDC4D@DBDC,D@}D=DC2DD@CDGCEDC D7CE0C@D.CprEGCD;C kEECCAC0dEbCD]CcEXC DtCpIE}CCCPEC@DCdEC>DCECAD@D lECLD)D>EDWD(DIE@DlD@>DAE;DD`DpETDD DDHDD`DD9DDbD>D@-DD@EDC4DDCDC;DDLDC9D D@FDC:D`D@FDC4DD@DC9DDEDC5DDADC5DDBDC0D|D7DC/DwD@8DC0DzD@6DC5D@zD5DC@.DtD*DC@2D@qD&DC+DxD@"DC,D@vD!DC@.D@oDDC@0DuD@DC3D@zDDC@CCCC;E6CC8C DEZCCCTECDC`5ECDCLE}CDC0yEiCD^C؁E}C@DmCP~EC@DlCsECDCcEiCDcCfEoCDVChEVCDfC EIC@DUCE`CD;CETCCQCEQCCHC@ECDCaECCCp6ExCDCP_EC@DC[ECDCPDE@"DjD\D.E@LDDXDPE@4D]D@`DE@DFD@6DP&EC@#DC]E{C@ DC`DE}C@DCUEC@?DC@gEC@MD@D@6EC?D$DpEC$DC"EC#DC`ECBDDP{EC@ED#DfE@ DJD3D0EC'DD E@ DxDcD0E)DD@DELD D`DDQDDDD=DD@zDD9D`D@XDC=DDPDCCD`D@NDC:DDIDCD@D*DC6DyD!DC@3DtDDzC@2DxDDC4D}D@DC3D}DDC:D{DDCCptE5C@D6CE8CC1CHEACC5C~E;CC4CpEXCCQCqERCDJC{EgCDGCEOC@D;C ~E?C@D/CЄEWCDCuE}CCnCJECCC:EC@DCLEcCD[C@jECD~CEjCDeC0EnCDgCiEXC@DZChEbC DHC{E[CDIC0EHC DDCESC@ DAC{E[CDAC@}E8CC,CiECC4CpQE8CC/C@REgC@DsC ^EsCDCMEC DC CEC)DC6ED^D@MDE@aD@DDEHD{DDEC@DC@ErCDC=EC%DC`ZEC-DC@E@D9DDPEC@'DDEC@'DCEDPD@D>E@ DYD0DPWE@ DUD3DJED7D&DECDC@EC@DC;ECDC@DDJDC7DD@EDC@6DDADC9DD@DC3DDCENC@ DmC~EIC@DNC aE\CDiCP]EeCDSCPLEyCDCPeEC@(DC E`C@ DaC0EGCDLCoE\CD_CvEUCDPC@E>CD;C@{EICD=CqE\CDWCyEgCDYClE?CC/CPZE?CC8CgENC@ DWCaEfC@ D}COEZCDbC`>EoCDeCOECDzC]EC8DD@YE9DDDLEED`D@D9EDqD@DD 7EC@ADCeECLDC]EC2DDP,E@#D_D7DPECDC3D@}D;DC@3D~D@DC0D D?DC6D~D;DC@4DD9DC;DD@D~D%DC;DD@'DC9DD@*DC^C DUChEC"DtCECDqCEWCD@ChE]CDcC0EC@,D{CECDC(EYCC_CXEZCDFCHEjCDPC(ESCDECPEVC D>CXEICC5C(EOCCFC@XEZC@ DNC`dEQCCTC cE}CDC`lEC%DC@xEsCDCeEIC@D6CpEMCDPCESC@DSCE=CD9C(EAC@D5CȅENC DPCxE[C@D_CfEKC@DPCgEHCCFCPhEBC@ DSCP^EYCDkCWEWC@ DVC`QEvC@DzCjEC,DCiECGD@(D@pE DSDJD9EDTDlD`"EDJDD%EC@8D"D@CE$D@tD8D`MED@D"D(E4DmD[DE3DdDYDEDEDBDE DUD8D`0E$DXD@:D@EC@(DD EC.DCEC6D+D` ED2D D5EC$DDp,EC'DCECDC8EC@!DxC0aE|CD|CFECDC ,EC$DCEC?D!DEDQD@?DD"DfD9DC3D@DNDC@4DDHDC@?D`DNDC@=DDDDC9D DDDC7DD@@DC?DD@CDC.DD>DC@5D`D@DC@6DD9DC@6DD=DC@4D D=DC5DD@BDC@5DD?DC8DD@BDC1D`D=DC7D@D;DC@9D D8DC@1D@|D7DC0D~D.DC/DD@*DC2D@yD,DC6D~D@,DC6D~D,DC0DD/DC5DD*DC@4D`D,DC@5D~D,DC5DD-DC:DD+DC9DD.DCEC#DCPOE@DdD?D9E D@XD>D0%EDJD?DDDDLDC9D D@ADC8DDCDC9DD@=DC9D`DADC7DDEDC5DD@=DC@:D@D=DCDC=D@D@9DC;DD3DC7DD,DC:DD@1DC@6DD@2DC4D D4DC>DD5DC@DD7DC9DD4DC8DD3DCoCDlC0EFCCaC@kE@CC0C`bE&CC3C~ELCDRCEyC@DtCȁE?CC:CwEMCDRCEgCDdC؂EbC@DbCEJC)DYCECC&DQChEGC@DQC0EkCDaCEvCDmCE~C+DC@EsC@DlCEgCDXCtEECCWCNE+CC-C`E-C D'CPEMCDCC|E;C@ D,CEUCDICHEaCD`CЁEoCDnCEcC@DNCEWCDFCxEXC DSCrE[C DNC sEiCCZCkECDC@ZE4DvDDp"ED_DKDEEC'DCtEC@ DC ZECDChECDCpbEC@DC5ED=D@D+E DBD-D0E"D@bD@TD E.D@_D@ODE(D@TD@KD D-DND=D DD@:D3DEC8D@"D'EDDD(D`E@#DSD>DECD@ DD}CCCDWCDZC?EaC@DWCtEjC DsCP\EmC@DCBEC+DCDEC@"DCpEEC#DC!EC@7D@DE DrD@`DD&DDpDD8DDZDC7D@DNDC9D D@FDCDC5D@D=DC;DDDDCDD@HDCBDDIDC@;D@DCDC9D`D>DC3D`D7DCDD:DC>D@D:DC@@DD:DC8DD7DC8D D9DC9D}D@3DC4D D3DC-D D,DwC3D|D@+DC9D D.DClCDpCEcCDjCEdCD^CpEYCDcCHESCDeCȌECDCtE2CC'CPlE/CC0CfEECC@CdEcC"DTC EgCDQCEgC@DPCEYC@DiC0|E]CDMC EeC@DmCXEmC@DUC0EbC@DaCtEmC@DbCtEfC DsC vErC@ DXClEfCDBC(EiC DaCЄENC@D;CpEqCD`CErC!DvCEzC$D~CEcCD]CuEMCDKCEAC DGCzE=C DKC|EHCC8CkEC@DCHE!DVDDD@'EC4D&D:EC@DClEC@DCOECDCREC DCPUEC@(DCbECDCNE@D@D@)D,E'DdD@]D%E@D@ID%DED@@D9DD@/D@WDDDD@ DJD@6D`DC!DDDCDC@D@D.D@DDC&D@ D`DCDCP)EoC@D}C`PEFCDJC`lEpCDbChECDC@UEC&DCp^EC(DC`]EjCDCP@ECDC$EC@"D@ DE@DRDbD D2DzDlDD5DDLD@$D;D DLDC?DDJDCBDDODCBDD@KDCBDDKDC=DDIDC@:D`DEDC@6DD>DCDD@EDC:DDEDC@D`DDDC@@DDCDC@DD@@DC:DDCDCCCJCgE%CC C0yEhCDLCE?C@DICEOC@DACECC-C]ECCC0jEKCC8C}EUC@ DDCHE9CC5C{E8CC*C@rEICCWC@pEnCDlC@rE[C@DoCprE+C@ DZCPrE[C$DYCExCD]CȕESCD5CEfCDkCЍEmC DqC ~EQCCQC\ELCDaCpQE`C!DnCEKCDRCPEOCDMCElCDCC@ECDC \EC/DD.ECDC03EC@DCHECDC'ECDC@!EC+D@D%EC5DCSETCDtC\EC*DCDEDWDFDEDADDECD@DDDED@(DDDCD2DED7D"DDC@DCEC0DD ECDCECDC0;ElC$DwC0[EZCDHCiEiCDhCrEtCDqC`rEC!DClECDCp]EkC@DsC@UEiC@DpC0NECDCEEC5DD +ECTD@9DDC@CD@%D DD@_D9D)D@/D`DDDC=D DMDC@D DNDC@DD@PDCD`DGDC@>D`D@BDCDC@2DD=DC:D`D@DC@7D`D@ADCD@D@IDC8DD@FDC@>DD@GDC:DDFDC@=DDIDC@D D@ODCADDNDCFD`DRDCCD`DPDCAD DODC@>D DKDC=DDGDC=DD@FDC=DD@CDC;DD@@DC@;D@DCDC?DD>DC?DD@@DC@8DDDDC>DD@IDC9DDFDC;DDDDC6D`D@FDC;D@D@MDC=D@DHDC?DDHDC?D D@CDC?D DEDC@ADDCDC;DDBDC:DD?DC=D`D@=DC=DD=DC9DD8DC>D D@6DC@BDD7DC5DD3DC2D@{D@%DC2D@yD!DC6DxD$DC*DuDDC@.DrDD|C@+DmDDC5D@sDDC0DiDDCC@DCEC6DCzE|CDChEsC$DeC yEWCDjC~E]C DRCEOCDYCEKC@DKC0ECDCPtEC@4DC0qECAD@"DcE~DD@D@LEPDsD2D0@EC5DD,EC?DC &EC-DDED9DDEDD@HDC@6D@D@BDC:D`DADC@D DFDCDC@9DD>DC5DD;DCD D@DC6DD@CXECDdC8ECD{C(ECDvCEkCDC0E~CDC`ErC DCE|C DCЙEC@DC0ECDChECDCxEC@DCE~CDCEwC DCEsC DC(EC+DCXEC@=DCECDCPESC D\CPEFC DUC0EIC@DHCxE]CDUCЄECDCiECDC`oEC8DC`PE@OD@iD@bD0ECUDDLEC3DCqErCDCjEC@%DeC@ExC'D~CE}C@'DtCxEC@)DC(EjCDiCЕEKCDmC؄EjCDgCzEC+DC WEDrD@JDP=E@'D`DIDE%DWDBD ECDC@EC+DD ED@OD*D`E D@WD5D@EC%D@DECDCpE}CDC*ErCDrC0UECDCdE`C@DbCeEUC@DYClEeCDSCwECDxCPE~C DyCEgC DOCEdC@DACxEkCDlCrE_C@DdCfEaC@D_C sEMC DICXEwCDtCPZEqCDCPREaCD|CPWEaC DC@:ECDCEC@BD)DDD@TDED`D@/DDTD*DDDMDCBDD@SDC?D@DXDC@EDD\DCBD@DSDCDC8DDDD:DC?D`D@8DC;DD3DC6DD@.DCCDQCЃE}C DCPECD}CEtC DsCEoCDTCE`CDgC(EfC@DrCPECD|C0ECDC0ECDCECDC ECDC EC@DC(ECDCECDC0EC@DCEtC DCPECDCEC@DCECDC`EpCDCE~C@&DCЦECDCPEsCDZC`kEC@3D@DJE@DD@Dp%E@ DODJDEvC3DCiEC.DCECDCEC3DCEC*DCECDCECDCEC$DCpEC@+DCEaCDC`ExC@ DC@EC"DCECAD@DfEDED"D EC@DCEC DC E@'DGD>DpE"D]DIDEC5DD@7EC+DCECDC+EC/DC\EC&DCmECDCmECDrCЇErC(DjCPEmCD~CEyCDlCEaC DVCpoERC@D\C@pEC DnCE^CDlCЅEyC@DlC؃EhCDCzEtCDCEtCDCEhC@DhCmESCCC@'EkC@D}CBEmC DnCMElCDCPMEC@JDD=ED`D@DE+DD|DD:DDgDDD@@DC9DD@=DC5DD@=DC7D D8DC4D}D6DC2DD/DC@/D@yD-DC/DyD'DC5DzD/DC2D{D1DC3D|D,DC.D@wD@+DC/DwD@(DC-D@yD#DC@.DxD(DC@-DzD,DC@/D@zD@(DC/D@xD)DC@2D@vD@.DC6DtD@(DC3DyD+DC/DwD+DvC4DwD"D~C.DpDDrC@+D@nDDC)DlDD]C+DkD@DvC'DdD@ DeC#D@fDDC#D@bDD|C%DbDDlC D[DC]CDODCYC#D@QDCoC@DQDC`CDGDCMCDDDCOCD;DCMC@DDCaCC+DCRCC@/DC\CC*DC]C@D,DCYCC$DCPCD(DCgCD&DCfCC$DCNCC DC@CCDCGCCDC@CC@DC:CCDC/CCDC0CCDC CCDC&CqC%DaC0EC DCElCDYCpErCDoChE\C@D=CEsC@D`CȥEtCDVC EdCCcCEnCDCEC@ DC ExC DCEC@ DCEC DCECDCECDCEC@DCEC DCؚECDCpEC@DCECDCErCDCEC@DC@EtCD}CPEdC@)DGCЌECADCEADDD`VE!D{DDUECdDC،ECKD@ D EC@BDCPEC)DCE~CDCEC!DCEsCDCpECDCxEC@DC xEeCCaC`E|C@DCoEC@3DC~EC0DCPgEC@DC3EC@DC4E|CDC%ED@9D%D EDND@@D DC&DD ECCCDYCDC.EC"DC0NECDCpTEC@DC`ECD}C lESCD]C`^E^CDwCuEsC@D\CHE^C DRCEwC@#DkCEC)D{CEQCDbCPrE\CDQC0kEPCC\C0cEC@DCaE{C DCqEkC@DuCnEXC@DC_ECDC xEC@DCExC.DCXEC0DC oECDC/E@ DKD@:DPEDcD@eDD)D@sD@XDqD8D@D@aD#D=D DXDC?DDZDC>DDiDC?DD@gDC@=DDQDCCD5DCFC D>DC[CD-DCmCD@+DCgCD&DCUCD+DCaCD@)DC[CD@(DCXCC@ DCLCD$DChCC@DCWCCDCSCCDC/CC@DC/CCDC+CCDC3CC@DC#CCDC"CCDCCCDCCC@-DChECDC8ECDbCEmCChC8EkCCVCEC@DgC EzC@DTCXEuCCcCEnC@DCECDCECDCEC@DC@EyCDCpECDC8E{CDC`ECDCEC@DCECDC8ECDCEC@DCECDCEC@DChEC!DC0ExC@DzC8ECLDDEDoDBDXECBD@DkECJDCpEC7DDgECGDCECDCuEC-DCxEC6DC`EC"DCȝECDCEnCDC0`E^CDIClECDCEC9DD[E D>DDD`EC-DD;ECDC`EbCDeCPMECDCECCCDCCCE\CCfC+EqC@ DCDEC@DCQEkC@DmC`YE]C DeC jERCCPCpgENCCKCdEqCDbCtECDrCxECDcCPEqC@D~CEC&DCEC$DCEC-DCErC@&DCЅE{C&DvCЁEzC!DCHEqCDzC`tE_C@'DC؅EC-DCE|C@D}CEfC%DzCE]C@DuC0oEeCDoCWEC#DC:ECDC%EC@7D@DDC;D/DDD@aD4D:D.D@{DPD)D@=DDhDFDQD D}D\DED D@`DDGD DTDC@DCHC@ D=DCECC+DC^CC'DCaCC@$DCVCC+DCbCC0DCbCD@.DCfCC(DCQCD+DC_CC&DCYCC&DCJCC DC)CCDC0CC@DC*CCDC5CCDC$CCDC,CC@"DCxE}CDCEC@DnCEdCDxCHExCD\CȡECDZCEhCCXCȟEnCCnCpEuCDtCPECDCECDCEC@DC`ECDC(E|CDCE{C@DCxEoCDCECDC ECDCECDCECDCEC@DC(E}C@DChEkCDCERCCOCXEC"DCtE@DeDGD}EC0DC vEC@DCpWEC@DCaEC(DChEC@ DCWEC@4DC}EC1DCEC+DCECDCmERCCCdEC!DdCpEC@(DCxEC8DC~ECDXD.D`E=DD^D]EDWDD}EC@"DC vEfCDtCXECDC oEC@:DChEC/DCECDC0ECDCtEbCCwC=E:CC:CKEzCDiCE~CD~CpEaCCdCGE^C@DuCQEOCDmCpECDvCoEC DrCp\ECDCPeEC@DC}ExCDdC {ETCDQChEkCD[C؉EtC@$DgCEC?DCІE D@WD1D`;E@DPDKDEDED-DE8DSDDCJC@ D@@DCIC D@=DCOCC,DCDCC&DCOCC%DC5CC%DC*CC@"DC&CCDCCCDCCCDCCCDC(ECDCОECDCE}CDnC`EfCC]C`EnCCZC`EkCC]C؜EYCCRC0ESCCICxEtCDsCEoCC`CHEbCCQCpEgCCuCHEC@DC(EvCDCEC@ DCxECDCEC@DCECDCECDChEwCDyCȕEaCDXCxEC!DC؉EC?DCECCD DPEC0DCPeEC>D'DlECWD@D@EDeD9DiE@vDD Dp,EDD`D+EDDD1E"D@zD_DECDCxEC@ DCkEpCD{CEC+DC`ECDCEdCDqCHEfCDC0pE4CCHCWE{C@!D}CЄECDqCEpC DgCEmCDqCvEuCD{CwE[CDLC|E6C@ DSCwECCDDMDC@4DD@?DC@/DzD-DC@3DxD@(DC@-DsD#DC'DpDDC#D@lDDuC@!DdD@DC@!D@]DDoC D[D DiCDZD@DaCDXD@DpCDSDCYC@DODCPCDPDCACDLDCMCDIDCCCD@GDCHC DIDCDCDKDCXC DPDCMC DJDCVCD@KDCOC@DEDCTC D?DCFCDBDCFC DCDCHCD@BDCVC@D@DCTCD@FDCQC@DCDCHC@D?DCVC D@ADCSCDBDCPCDDCFC@D@:DCBC D=DCQC@ D@:DCECD8DCGCD:DCJC D9DCfCC#DC?CC&DC3CC+DC9CC"DC-CC&DC,CC#DC(CC@!DC#C~CDCЅEC$DCECDCEwCDnC E\CC]CE\CDgChEVCCaC8EUCC^C8E^CCWC`EhCC[CEbCCXCEkCChCXEkCCjCEnCDCpEiCCCECDCECDCȝECDCEyCDuCpECD^C@EeC@ DCpECMD DEC@JDDECVDGD~E@D\D_D@XEC@QD9DYEDkDTDȁEDqDJDHECTD@DECbDDXECHDDXEC>DCPTEC(DCPIECDC@WEfC@DCEC@0DC ECADDEC$DCАEC@DCpECDCE\CDoC=EZCDgCzEdCDjCpqE^C DaCpEdC@DfCEC@DwCqEmC DzCfECDC0eEZCDeCdEbC DkC^ERC@DcCfEC)D|CECNDCPECEDD QEC@DC EqCCqC`6EdCDC]ECDCCElCCC2ECDCp9ECDCpECPDCzE@VDD{DP8EPDDD"E@?DzD~D0EHD D@D$E@DUDCD2EC8DD]ECDCXECDcCpEsCDiCpEyCDxCEVC DuC`EFCCqC WEmCD`CyEC&DCqECDCLE}C DCSE~CDC NEC@DvCQECDCGEC@DC EC9DDpE@DDLD ED@cDDDYDD`DD@^D@D D9DaDD@DCVD@D{DC@GDDaDCD@D~EC^D@/DECMDEDtED@EDED'ED@)D@DEC@4D@3D )ECaDDDvEC@_D%DEC_D!DXECdDD8ECZD DECZDDE%DzD]D0eE@DND@DAEC@DCyECDCpFECCC`aEC@DC EC#DC`ECDCEdCCPCjEfC$DtCXE]C DmCzElC@DqCqEC)DCEzC-DCEsCDCpEnC@DrCP]EhCDpC`cEC@DC{EC@D|CHEuCD|C`EC0DCyE@ D?D@/D (E!D@HD=D0 ECID"D*EC:D D@:E@D@JD6D`E@+D_DWD`!EDaDID-E D^D$DKEC-DD0HED@PD@UDE@5D@DtDp&E&D@hDD E@2DnD@DE@.D@aDEDpEC@DCpEC@!DCp,E!DTD1D"ECDClECBDCpiECDCdEzC@DvC|EC?DCECKD DTEC DDEC,DC7ECADChECD D*ECFDGD@EDqDQDDQDDD`DpD`DDvD@oDD D@&D@aD DDDFD@DiDC;DDODC@3DDADC@.D D7DC2DD2DC@/D D@5DC1D|D0DC2D}D.DC@/D~D'DC4D@~D'DC@2D@uD DC)DwD"DC0D@vD!DC3DrD DC.DnD@DC-DkDDC(D@gD DrC%D@_D@DpCD@WDCdCDRDCdCD@HDC^C DIDCWC DCDCGC@ D@CC@*DCKCC&DCFCC@)DCCCC@%DC0CC(DC4CC@'DC-CC&DCED?D4D` E@ D D@3D@E@ D]D@dDEECfD=DxEC\D&DECUDDECVDDECUD@'D`ECFD@"DgECDCPCE:CCXCVECCCDCp?EPCC[C0lErCDCECDC@EQCDxCbE]CC@C^EFCCSC_E{C@ DsCfEC@2DCEvC"DC EoCDnC8EnC DCkEdC DlC WEXC@ DXC ZEuCDCWEC%DClEC0DCpEC9DCpcEDkDjD,EDODMD EDRDVDE-D_DeDE*D]DWD` E@?DwD`DECDwDeDpE7DmD@\DPE(DmD@nD"E8D@wD DEDDnD@lDEBDzDDE@=D@D D0E@(D@dDSDEC#DC@VEC.DC~EC9D1D1EC@BD@DDC-DD3DC@.DD4DC1D}D/DC5DD/DC@7DzD/DC7DD/DC@0D@D)DC/DyD"DC-DqD@DC0DmDDC@+D@lD@DwC)DhD@DxC%D@_D DnC%D\DDsCDSDC`CDPDCVCDNDCPCDODCTC@DHDCGC D@?DCECDCD1DCMCD/DC?CC-DC?CC+DC1CD1DCGCD/DCPCD@4DCgCD2DCOCD1DCJC@D1DCRC@D7DCgC@ D6DC\C@ D9DCnCD@1DC^C@ D3DCzC@D1DCeCC.DC`CC+DCNC@D+DC@CC(DC6CC)DCECC@)DCD@mD@oD@E$DQD@cD@EDC9EC@#DCEECDC*EC;DCPlEC:D@D@&E@D@(D DD@DED6D E@DHD2DE&DJDIDD/DhD@qDD1D|D@mD0DC%EC&DCPWEwC@DC`HEC!DC0HEbCDC>ECDCp1E}C@DC1ECDC,EiC DCEC@DCECDCDCDC`DCAD@DDDDdDD=D@DDVDHDDD!DMD@DvDCODDnDC@PDDbDCMDDpDCGD DlDC>DDXDC5D@D@DC7DD:DC5DD@9DC8DD;DC@5DD9DC:D@D2DC4DD1DC6D~D.DC/DyD*DC@-D{D%DC@1DyD@"DC2D@sD@!DC&DoD@DC'D@hD@DC@)D@fDDC)DjDD|C@&DhDDwC@)DdD@DtC)D@`D DcC%D\DDoC@D@KDCZC@D@JDCkCDEDC\C D=DCRCD6DCCC@D2DCACD0DCECD.DCZC@D/DCJCD@1DCSC@D@2DCUCD6DCfC D8DCoCD9DCCD@7DCpC D@;DCyCD7DCuCD@3DCkCD0DCZCD.DCKCC,DC?CC,DCJCC&DCCCC(DCDtC8E|CNDCEC*D|CpEcC@DbCPWEHCCEC@UE@CCNCnEkCCECiEjCCeC[ECDXC _EyC@D|C`bECDCeEnCDCD`D&D[DYDE/DbDiDP EC@.DC@ EvCDCPEzCD{CIE|C(DC0`EzC%DCSEC%DCUEnC@DkCPLECDxCAE`C DxC HEwCDtC?ESCCJC.EIC@DCC03EmCDzCP#EC DC0ECDCDDND1D DD]DIDwD@DDJD9DDWD@6DD@$DzDSD@:D4DyDSDqD@/DqDXDJDD^D/D#D@ DGDD-D DDDD6D DSD D@5DD@SDD#D!D@oD@"DC-D@D3DC3D@D6DC7D}D5DC@5DD6DC6D D4DC1D`D2DC2D@yD&DC2DuD"DC(DvD@#DC)DvD!DC(DqDDzC@/D@qD@"DyC@+DsD#D|C7D@uD"DC@,DpD@DC,DrDDtC$D@dD@DxC"D`D@ DCDZDCxCDNDC^C DCDCMC D@8DCGCD@/DC=C@D@/DCD@DyECPDCXECODDXECVDDȌEC[DCFECCC[COEeCDRCnE}C!DwCEC4DCPECDC0EC'DCOE#DeD@bDEDfDVD0%E&D@YD@SDEBD@`DWDPE3DuDoDJE@DzD}D]E@D]DZDUECD D%EC.DBDD@3D{DDP E(DiDhD@#E@HDuDD`DEDqD|D`D2DjDoD E=DD D`E D@bDQD@&ECDC EvCCCECCCP9ECDC[ECDC@+EC2DC`:ECGDCPTEC?D@DPCEDID:D EDGD@@D@DC@C@ D5CEpC"D[CEsCD]C`EsCDLCEoC@"D_C EdCDbCE`CD`CpEC'DC oECDCQECDC_ECDEC8DC&EhCDCWEC@ DCWE@DBD@3DP-EDOD?D3EDSD1D@ZE D@XD.DAED`DGD0?E@D@ID@GDEC DDDCDC`DkCDC EeCD{CPHEtC@#DC@]EpCDCPJEeC@ D`C@UEuC#DdCTETCDgC`QEKCDMC@EvC@ D^C:ENC DECIESCCDCGE4CCD+D0EDXDHDE@,D`DWDE@6D@jD@\DEDBDGDEC#DC`ECDCP(ECDC;EuC)DC[ExCDCREdCDqC0AEC*DC WE_C@DqC0OE_CDeCUEaC@DiCpKE\CDNCVE?C@D.CQEICDNCPE9C@D>C [EKC DIC@\EPC DHCKESCCXC;EFCC]C6E>CCVC`>ESCDYC@BEjCDkC@?EoCDmCTEwCDpCpcEZCD]C`WErCDrC]EzC&DuCnEmC@D|CgEC&DC:EC@$DCFE{C@DCBEC@1DCkEZCDZCoEYCDmC4ExC(DCAEC1DCEECDC`;EC"DC0ECDCEC@ DCDC&DC D@ DPD@DDDYD-D@D D@TDDCDC.DC@ZDCDC@eDC#DC@lDC@ DCpDCCCqDCCCDCDCDC DCD@DBDDjD2DzD@7DC@D@D?DCFDDGDCGDDHDC@DD=DC*D@eDDCDEDC\CD7DCHCD@8DCCD5DCCD1DCvCD0DC\CC1DCjCC-DC?CC/DCJCC-DCGCC+DCICC!DC*CC@&DC-CC$DC!CC,DCEC8DC؈EC@DCؕEC@DCEC@5DCЛEC@1DC EC$DCEwCD}CȝE]CC]C E}CDjCpEcCDoC(EYCCHC8E6CC*C`pE0CCDD@GDCEDD@PDCIDDTDCID@DVDC@ADDJDC2D~D0DC@D[D@DnC@D@EDC}C D3DC{C@D2DCuCD/DCpCD@0DCgCC.DCICC-DCOCC*DCGCC+DCGCC%DC)CC)DC CC$DCCC$DCCC@DCEC@/DCEC@!DC(EC!DCPEC(DCECMDDXEC4DCECDCXEyCDlCȘECDCPEsC DnChEcCCHCȍEaC D]CHEC@,DCEC.DCEC4DCEC@/DCEC'DChECDoC`EqC@D[CEeC@DCEyC DsCqEeC!DZC}EzC@/DC0EC)DCECDCECDCPrECKD DE@DcDDDE DmDPDE@D@nDFD(E@YD D@D hE D@DDMEDLD9DaEC8D DEECD DDMD@8DDC(D@DECADC6EC9DCUEC@"DC0DECDC,ED)D.DD*DKDQD`D DMD3DEC,DCCECHDC_EmC@"DwC mEwC@DCP\EpCDlC0eEC5DCPeEC@DC TE\CDhCOENCDOCeEFCD5CbEHC@D3CaEECDECcEACD5C}EoC@D_CEeC#DFCEmC DjC8ECDvChEjCDgC0sEXC@ DICE\CC@CxEmCDyC@qEgC(DzC`EICDcCpyEUCDSCREJCDFC nExCDqC}EjCDnCqExCDsC0ElC(DqCEC#DuC`E`CDhCPECDuC`EC@DCAEC.DC[EgC#D|C(EwCDpCEMCCRC REFCC1C"EBCCEC@7EMCCjCp)E-CCIC EECCD@bD_DECDD`DCDCDCBD D/ECDnCxEC.DCpEC8DChEzC!D|C]EqC@DCPFECDCGECDCPVE\C DBCaE9CD9C `EHCD@CqEHCDE2CC(C@ESCC5CpEbCCqC5EC@'DC]EC@DcCaEfC DXCPEACCIC5EZCDhCPHEMCDOC0lE~CDC KECDCEsCCCDCCCBDCDC@lD@DfDAD@UD?D D@TDC>D`D^DC@CDD_DCBDD@VDC;DD@ODC+DpD.DCD@OD@DC@D,DCCC9C@EBCC?C EUCD\CEcCCoC2EC@DC@:EZCCvC#ECDC*ECDC*ECDC-ESC@ DCYEC@D~CWEMCCCpEGCClC DC@DC DCDCED@MD9DDDfDHD3DDwDLDD:DD@vD!DGD DmDDKD D`DC@9DD@DC)DaD#DCDD1E DWD@UD;ED=DBDEC@DDDC*DD@EDXDTD@,E=D\D@]DE@DaD@VD/E)DgD@hD@E DJD.DECDC\ECDCECDC0BEC2D*DpE!D@VD;DPECDC;EC DCECCCDCCC DCDC"EDUD&D =E/DiDmDPEDGDKDE@D0D'DD@DBD1DDC8D D0EC@4DC@GElC+DCOEaCDVCZE~CDkC\EC-DCkEC2DCtEC!DCgECDC/EC DC EUCDSCQE`CDJCEZCDOCEFCDFC8EHCC>C0YETCCICWERCCLC `EWCD5C _EIC@DRC}E\CDUC`EjC DfCvEC(DCuEC@%DCpECDCpEC@ DC fE;CC'C@@EWCDqCeEC&DCEC@DCE{CDC_EGCCHC*E7CC1C0^EMCDNC(EC1DCHECDCkE7CC5C E:CCGCBEQC@DPCsErC@ DkCgEQC DHC[EcCD^C@GEfC DxCPQEdCDzC06ECDCSEC@D~CRE;CClC0ESCCgC`ELCCsC`/EzCCtC@1E1CCcC ECDwCFEzCCC %ECCCEiCCCD)CCwC DoCCC`DCCC@cDCCCYDD@LD@:DDD]D=DD@D`D5D?DDYD(D;DD9DD?DD.DC0DDBDCC D>DCsCD*DCaCC@)DC:CC)DC8CCDC#CC&DCCC)DCCC#DC'CCDCCC@DCEC@DCyECDCPECDCECDCEC%DCEC@DD`QEC@ID5DKECMD@DP:EC!DDp#EC"D@+D@EC.DC%ECADD0oE@)D^D@MD`E@KDDD EFDDwD$E@(DODPD0ED@BDDDECED@"DZEC0DCpdECDC@YECHD6D`'E1D@^D]DPEC!D@D0ECDCP4ELCCCBE`CDpCNEC@DC`=EC@+DD E@ DQDQDpE D@BD@8DECD DDCD@ DDC(D@DDC8DC7EC@DCfExCDCPcEaCCNC@GE_CCLC@ElCDgCP_EZCClC\ECCC3EpCCC/EMCDDC \ETC@ DAC}EVCDKC`yENC@D.Cp~EoCDSCHEpCD[CEXCC]C@gENC@DBCEPC@D_CEC-DCEC,DC EC@DC`eEC@DCSEvCDC`uEC DCrEMCC3C@LEnCDCPrEC,DCEC@5DCECDCoERCC;C`]ECDCECBDCXEC@ADCE|CDC@EBCDQC`wExC@DC0ECFDCxEC-DCXEKC@ DKClEdCD`ClEvC DsCWECDCP5EC,DC`sEC-DCEYCCC4EECC@CPEiCCtC@GEC@DCpvECDCpRECDCvEC'DCsECDCPEkCCwC/EBCC>CECC2CEECC(C`D?CCtCPEC DCECCC@DC@DCDCDCDC%D DD@D@DD D`DD7DC@*D D4DCCC*DCqC@D.DCKCD,DCGCC$DC0CC@%DC#CC#DCCC@$DC+CC!DC$CC$DC)CCDCECDCEC@DCpECDCEC@DCEC@DC0ECDC aEFC DNCEC@DC`zEC*DChEC@$DCEC@$DCpEC'DC{EC@#DCtEC$DCsEC$DCECDCEC!DCȘECDC0ECDCHExCDCECDCEC!DC@ECDC8ECDCxEC"DCECDCECDCXECDCxECDCECDCEeC DwCXECDCEC4DC~EC3D D@WEC9D@DUECCD@'D`TEC@D)DQEC@6D@DHEC;DDREC3DC[EC%DCaEC%DCqEC@%DC rEoCDCrE}C(DCE{C@*DlCE]C@!DYC EWC@ DFCHE_CD|C ~EjCD_C0EoCDCP[ED?D(DEC$D D4EC6DD1ECED,D@3ECVD1D`_ED@MDFDp E%DWD@`D`D&DCD@8DDC.D4DDC1DD0ED6D"DE@D9D"DEC@3DD@#E,DfDaDE1D_DUDE@D;D$DDC@?D@D ECDCpGECDCpEtCDClE^CCsCECDCPECCCDCDCPEC'DCEC4DDDD@9D D`ECIDCWEC%DCXERCCKCpMEcCDvCQEbC@DCvEHCD]C`dEGCC?CME,CC-C0UEC؄EgCD[CEYCDBCElCDSCEfCDVC@EWCDXC0yENCD[C8E\C*DjCXEC+DCpEC!DCMECCCP*ECD{C08ExC DjC^EC@DCGEBCDNCXEC DC}ECADDHEC@OD@%DEC%DCyEbC D]CYEC@EDC_EQD@eDHDp7E(DID&D EED@FD DfEC@DCEC@2DCЕEnCDC0hEPCC]C [E=CDTCxEUC#DmCȀEMCCnCEECDC@7E~CDC kEC'DC0EaCCvCP7ELCClC`ECDCQEC@%DCaEC+DCVECE}CCCEC@DC D D@AD@DD@D@ID@DC@D*DCCC@DCkCC)DC>CC@"DC7CCDC'CC@#DC#CC@%DC-CC DC.CC#DC.CC*DC(EC*DCEC"DC0ECDC@ECDCEC DC{EC@+DC~EC3DC@EC@%DCEC0DC0}EC)DCPEC@DCECDCКEC&DCEC#DCEC%DCEC$DCEC@'DCXECDC0ECDC8ECDC ECDC`EC&DCȐEC*DCXEC&DCEC'DCEC%DC`EC6DCEC@'DCEC@DCEwCDxCEhCCC0fEC/DC WEC.DD[EC@*DDWEC@,DDdEC1DDhEC:D DPbEC@/DD VEC0D DTEC+DD`EC/DCpdEC9D)DLEC4DCSEC9DC(EC/DCPEC0DC@xEC-DC0~E{C@D}C@rEnCDcCErCDtCEC;DCSEC6DD(EC@3DD ED?D@,D E"DNDPDDD?D@ADD@DNDID E DXDVD`E#DFD@HD D@DFD@FDE,D@PDYDDDNDSD`DC@DC DCCC EC@DC!EC@3DD@ED5D:DDD0D0D D DAD(DEC5DDp5E@DD@iD@PD1E)D@WD>D GEC/DCP}EC@DC@E{CDrC ]EC@DCEC@DCDD@6DDLDP%E D@MDmD EC@)DD`ECDCFEC@(DCxEC!DCPECDCxEC@DCEfCC`CppE8CCaC9EC$DCEC@!DC`EC@5DC qEC(DC`]EuCDC@EIC@DKCP/EqC@DC#ECKDD`DDPDDrDD?DCCC#DCcCC@%DC;CC@ DC)CCDCCC@DCCC"DC*CC DC#CC&DC"CCDCCC&DC[EC1DCXEC-DCECDChEC@DvCEC@#DCEC@EDCE@ DrDGD8ECLD>D`fEC@?DDcEC@;DC@xEC@2DC8ECDChEC"DCEC1DC(EC+DCPEC&DC~EC@!DCEC@DCECDC`ECDCEC(DCpEC@&DCHEC@$DC`EC(DCEC@!DCECDCEC@DCECDCECDC@E\C@DxCeEyC@DC@EC5DCVEC@2D@ DVEC@5DDZEC@3DD gEC@2DCsEC@7D D@eEC2D DZEC6DD^EC)DCfEC@-DChEC6D@DpSEC3DCZEC-DChEC@/D@DeEC'DCiEyCDCbEQCC6C0dE[CD:C`pEECDTC^EC@.DC/EC'D$D0ECDC`ECCC@ EC!D@ DDD@=D@;DDD5D+DE@D>DAD`D DMDNDDDKDVD@E@-DXD@^DD@D:DE-DD@DPIEtD@DD"EDkDDD D:D@sD D@3DkDzDE@#D@D D~ECkD@"DEC@?DC8ECDCuEuCCC.ECDC@NEC"DC~EC;DCUElCCC#E`CCUC5ERC@DDC^E_CC`C@>EC'DCE@D@DD'DDD@DD@D D@D"DCjCC)DC:CC DC,CC!DC'CC DC-CC DC4CCDC'CC#DC$CCDCCC DCCC@DC`CEC$DC^EC!DC|ECDCwE[CC~CREC@2DC0fE DSDDptEDlD]DiE/DsD D0CEDTDbDpGE@DND9D@WEC@=DDwEC,DC@EC(DCEC@3DCЄEC@3DDvEC.DCzEC"DCEC%DCEC@%DCEC.DCДEC(DC؍EC@"DCВEC DCxEC@$DCEC+DCxEC(DCXEC"DCEmC DzCEaCCfCRESCDACpaEtC@DCEC&DC}EC/DDgEC@7D@DaEC:D@DjEC5D@DqEC2D DZEC.DD0ZEC@5D DbEC)DC`mEC)DCpkEC@4D@DSEC4DD]EC@-DCpaEC/DDYEC@"DCfEaCDC`XEHCD4ClEEC@DJC]EgCD\C@_ECDCIEC@DC ECCCEQCCmCExCCCECCC`DCCCDCDCD@D@:D1D@DDHD@RDEDDD9D`DCDD`DCDC@EC@7DC0kEDeDJD;E+DjD@jD E(D@VDTDDD5D+D D"DMDKDDD?DBD@D!DED@FDD5DqDvD`EFDgDkD$E#D@^D@PDE@DTDD=EC#DCJECDCPE@CC}C`!E]CCcCPE@CCNCCEC@DxC@fENCCRCWEHCD?CpqE7CDCCrEECDCD2C`rEC@%DCTE@DXD#DD@D6DD@PDC@"DCCC*DCNCCDC.CC!DC4CC)DC-CC DC/CC DC(CC#DC(CC#DC(CC!DCCCDD0FECDD TEC@DCZEC@!DC`ECDCP`EC8DChEC@ID D0{EC@LD@D(ED_D@SD]E@ D\DkDFE DTD@VDAEDRDKDLECID@3D^EC>DD`|EC:DD0EC@5D@D؂EC@.DCEC+DCEC(DCȔEC*DCEC0DCEC$DCEC@&DCؔEC@)DCxEC@%DC(EC2DCPEC)DCEpCD|CnE`CCVCUEYCClCP@E_CCACGEdCDC@tEC@!DChEC-DCpEC5DDkEC8DDsEC@7DDlEC5D D[EC0DD]EC8D@ DgEC)DCrEC@/DDlEC@4DDXEC0DDcEC.DD`^EC@3D D ]EC"DC`dEkCDClEkC@D_CEjCDCfEhCDiCiEMC@ DjC0]E[CCrC 8ElCCC'E}CCC(EnCCCEqCCCECCCpEhCCCECDCECCCDCCCD}CCCDC(DC(EDZD@7D0'EDDD@D`%E@1DuDzD$E,D@UDKD@D"DKDND D@/DED>D@DD@VD\DE4DZDWDEKDDD`E@#D@_D@D0.E D@PD@9D,EDJD@$D3ECADDP@ECBDClECDC|EbCDgC_EeCDPC@sE{CD|CpbEiCD|CAE|C@DlCmE[CDZCmEOCD\CPsEQC@D>CvEECDCC{EICDRCExCDYCnECDCfECADDaEC@D$D)E D%D@CD`ECDC #EC5DCiEC>DCEC@'DC`{ECDClECDC`zECDC0_ECDCPfECHDDECSD@-DEC@0DC~EC/DCmECD|CxEOCCfCRE4CCCD/CCGCDCDC;EC@FDCȈEC@GD@ DȊE@D@XD>D,EC0D@)DErCCC+EkCD`CTEmC@DCpDDdE@DKD@8DYEDLDYDHE@D@NDGD@TEC6D@ DxEC'DC0EC@)DCEC@'DCEC@$DCxECDCECDCECDCECDCEC@ DCECDCEC@DC{EECCOC`HE7CC@C`3EPCCKCPaEICCWC`_EhCDTC0aELCC[C0iEfC DC yEC.DCvEC5DCuEC@7DD`uEC@4D@ D gEC@1D@D_EC7DCeEC4DCkEC%DC~EC2DDrEC.DCpcEC1DCqEC.DCnEC1DChEC'DC`gEzCDsCEhC@ DgC(E}C@DoCqEtCDZC`qE]C@DZC@ZEJCDWC UEgCDiC@VEYCCFCDE?CCWC)EoCCxC'EwCDC@8EWCCCD/EDqD@lD0EDMD1D`E D@JD/D0EDFD@(D)EDHD@@DEDfD@aD@(EC9D@DMEC@$DCЃEjC%DjCE{CDzC`VEtC DlC5EVC DmCHEACDLCZEFC@DCC@qEGC@D.C`E9CC4C@~EFCC8C@vERC@DPCeEC(DC fECMDED@-E@DPDDE8DDDpEDFDMDECKDD0EC@GDDlEC@ DCPAEC@DzCQECCD@D@zECJDDOEC DC ;ECJD!DnECMD D(EC-DClE}C DC0JE8CC)C1EBCCP ECOCBDOCCMCDuC DCPEC@+DCEC@>DDpE DED;D ECDCEoC DzCPWEC@$DCsEC!DC aEC@ DCmEC DCZEC*DC0FEC*DD 7ECCC`DdCCCECDC@+ECDCFE{CDCSEC@/D@D@KEC@0DDPEC DC EC"DCHEC DCEC@7DCEC)DCuEJCC?CPECDlC`_EZC@)DwC0zEOCDCCP~E{CDCECKD@D00E@4D@zD4DrD@;DrDDCDCDCqCD(DCuCC!DC>CC DC3CC"DC+CC$DC>CCDC3CC!DC,CC@!DC'CC!DC+CDNDCICDRDCEC#D@RDC2C(DVDCMCC@DDQEC D DRECD D@QECDDNEC@DDpREC%DCgEC(DC xEC.DCEC"DChEC2DCEC3DCXEC;DC EC:DDpuECAD,DdEC@D'D]EC:D'D`bEC6DDwEC!DCECDCECDCECDCPEC@ DCECDCXEtC@DrCXEUCDC`FE\CCxCpPE6CCdCVE?CDZCWEACC>COEACDMCZE7CDNClEPCCCCpnEeC@D|CrEC&DC@|EC8DDyEC6DDpqEC4DDPiEC@2DD0jEC5DD lEC@+DC@eEC@DCpEC/DCnEC/DCiEC6D@DiEC@/DCPnEC@3DClECDCpaEVCDVC`tESCDdCEdCDkC}EcCDgC؀E}CDC0gE|CDC0MEvC@DCp[EVCDGCIEJCDKC`3EOCDRCBEVCDhCDEZCDoC`6E^C@ DzC9ESCDaC=EuCDC9EwC$DzCLErCDC0JECCCpECDCD@D!D@)D@DDD@DD@D@RDPDED>D@/DPE@ D7D!D@$EDGD4Dp7EDCPyE0CC-CEICDTC}EC/DCkE@DgDD$E.DsDDEDZDD DD2D@nD0ED/D@gDD@DTDaDEC@DCECDC2ED@QD@bDp/EDaD`DEC@!DC7E DRD@;D=ECHD@&DyEC'DC@AExCC_C CEVCDCCppEHCCEC ME?CC2C E9CC_CE?CCTCOEYCCCYEC@DC 4EC&DDECDCE6CDUCNEC@%DCgEC@ADCvEC0DCgEC3DDDECLD)DFEC(DDpECCCP ECDC/EVCCCP!ECDC#ECCC-ECCCECDC JECDC1ECDCMEC,DCPnEC@>DDEC@BDDECDCxEjCDCZEuCDCOEVCC?CgE?CDTCsE=CD/CjEC/DCPrEDWD@&DE2DzD-DD4DwDDC DFDCCD'DC^CC@ DC=CC#DC0CC DC(CCDC;CC"DC.CC#DC,CC DC-CC!DC0C$D@YDCCaE;C@D7CIE?CC;CYE6CCVCUEXCCGCPeEMCDCCE;CC;C2E;CCOC&E;CCCC8EACDDC@AE_C@ DmC0@E^C#DqC0PE[CDhC SE[C DcC2EaCDvCECCC DCCC`DCDCDCD D@DD@"D@DDD)D@%D@D@D0D+D`D@DFD@'DE@D]D@GDpE$D\D@TD0E#D@BD@@DD#DSD_DE@-DPDXD`ECDDEdCCMCQEGCCIC[E`CDRCjESCDWC@~EUCDICENC DDChE:CC2CEHC@D;C yECwEKCCNC}EC@&DCeE@ DKD|D'E D6DDED]DDE@`D`D D0 EPD}D@D DD$D$ECCCECDCECDC>EC DCP6EC(DCp/ECDCBEC@"DCFEC3DCEC@DCkEC@$DCEC9DCEC4DC EC=DCEC.DCEC DCPSEC@DC0>EgCDnCHEsC3DwCXEC7DrCEC@ND DcE@*D@pD@;D`DD EC@(DDEC5D@ADEDBDEDEC$D D+EjCDnCJEOCCOC`EmCDYCvE\C#DfCxEmC@'D^C E_CDSCE8CC.ChEKCC9C`ElC@DpCE}C@DCxEC@3DCE$DTD@`Dp;E/DmD D0E5D@|DDEHDDDpE@LDD@DECD@DD EC@5D DPDCEC*DCEC@1DC(EDEDC ECBDCEC@DDC@EC@DCEC@DC E"DxD@{D!ED4DD0ECDCLE{CDiCp{EC5DC ED@WD@D@/E@5D@sD.D=D@hD DaDDKD@DDCD4DCqCC@'DCeCC"DCBCC@DC.CC@!DC+CC$DC8CCDC(CCDC/CC!DC0CC%DC5CC"DC/C@&DiDDMC"D`DCQC"D]DCIC@!DZDCNC#DZDCCCDYDC9C@"DWDC>C@"D@^DCMC D]DC9C(D^DC4C%D@`DCJC&D@dDCVC#D]DCAC#DeDC@CCD@DMEC*DD`UECDDSECD@ DTECDD QECDD LEC@DCpNEC@#D@ DcED@YD@8DpnEC.DChEC@DCEC-DCPEC1DCEC7D@DEC8DCEC@2DCEC@-DCȊEC4DCEC)DCEC@*DCpEC-DC@ECDCEXC D`CwEPCDdC@{E`C@DeCXEC)DpCEgCDgC EZC@D`CyE_CDcC sEjC DoCxEkCDCEpCDxC@yEgCCeCXEC@DCwECDC EC@$DC}EC%DCxEC@*DCpEC.DC`rEC/DCyEC@,DC{EC*DCqEC&DC0pEC@5DCEC@2DCvECDCgEKCCeCvE\C@ DXCXEUC@DXCHESC)D`C(ESCDmC@bEUCCEC0AE3CC,C3E*CC:C%E8CC8C0GEMCCIC`QELCCQCLE_CCcC5E[CDNC0BEVC DlCJEICDUCZEYCD[CUEYC DTC^E`C@D_CkESCDtC ZE{CDsCMEgCDdCP^E`C@DsCBEyCDnC E[CCBC`ElCC\CPECCCDCCCDCDC`DCDCEC@DDp EC3DD@ED@_DUD *E@DTDbDEC6D@?D!ECDC0IE[C DRCoE@C@ DEC}E2CD=CE@C@DHCpEeCDTCESCD>CE[CDGC@EwCDrCPEuCDClEiCD|C aECDCECDD ECDCECDCDCPEC1D D5EC@3DDAE DODoDE@DWDVDEC(DCEC$DCFEmC@$DsCEVC DRCEEC DMCtE8CC0CtEDCC4CxEHCD+CECC@D6CE@CD=CEvCDzCE_C@DkC EuC0D|C{ECDC`[EDuD@{D#E@0DDmD(ELCCCECCCp ECCCECDC/E~CCC`FE;CCBC E7CCHC&ECDC0lEC+DCjEC!DC0PEC@IDD[EQD D9D0fECFD DPEC.DCEC5DCEC@.DCgECFDMDECCCEbCCaC6EMC@DFC@\EC+DC`XED@aD@D`D?DDC$DaDC5C)DcDCFC+DbDCKC@.DgDCHC"DeDC3CCDDpPEC%D@D@XECD DYECDDVECDDKEC@D@D`JECDCRECDDTEC2D@ D^EC0DCPEC0DCHEC@'DCEC0DCXEC8DCEC2DCEC8DCEC7DC EC8DCEC8DCEC5DDEC@'DC`ECDCiECDC nEbC@D{CȁEbC@D`CEvCDlC@E`C@DTCp{EJC@ DbCmEWCD\CPlEiC DC`mEC@ DC@TE{CDCmEzCDCEC,DCEC1DCEC2DCzEC@.DCPwEC1DC0uEC7D@ D@kEC9DCpEC,DCp|EC@ DC|EC#DCPyEC/DChEC*DCwEbC@DCpkECCCHCcEHC@DHCwERC@DQC@E^C D|CmESCDXCZEEC DGC_E/CC$C`_E@CC>CXEBCC4CXE@CC>C0VE^C@DUCJEnCDiCEEhC D[C@QE]CDcCP[EFC@DJC dE_CDYCmEYCC=CPfE+CCCC>CTESCCZCpKEPCDSC0REJC@ DXCaE?C DEC`EOCC2C`nEDCC.CoE,CCCCNE6CCACCERCCLCHEC(EOCC3CsEUCDIC^ERC@ DJCsEnC#DoCEaCDgCEfCDpCxEdC@"DUCPEyC%DCE`C@DVC@EiC@D~CEC+DC ECDC oEC)DC9ECDC?EqC#DCeEC@DCE|CCC`DCCGC0$E"CC7CEECvE$CDErCClCJEmC DCEEiCDdCpSEsCDC VECDC>E~CDCqEuCDCEuC DCEC@ D]CpECWDJD {ED]D(D|EDYD@9DaEC@DDC0wECCDCEC8DCxEC@8DCPEC@"DCtEC@dDGDAED@D@DNEC@XDD`uEC@!DChEC@ DyCxEC)DC0xEC)DC0tEC9DC؄E~C#DCiECDC3EC@DC9ECDC0!EnCC}C@#ECDCEC DrC@?E_C@DCcECDfC_EC DCQECDC.EC)DC-E#D@qD@4DD@EvC DCqEaCDhCErCDcCE\C@ D@C@~EQC DWC`vE[C@ DVCuEmCDMCEcC%DfChEC@DvCEmCDiCE\CDkC`EsC(DCEC@3DCErC@6DCECDCPsEhCCC XECDCP4EC@DC >ECDCREC@+DCPwEC;DCECDCxEC DC`GEC@DC;ECDC FECDCpJECDCzEC DCxEC@D}ChEC@DCEDjDiD{EC@RD DECWD1DEC>DC؀EClD@DDED@lD@oDpVEC@YD#D`XEC:DC QED@>DBD` E%D@LD{D`EC1DD`&EC@ DC\E[C@ DyCqEC@DC0'ED@!D*DDDBDD ,ECDC CEC@DCPTECDC^ELCDvCHE\CDfC[EXCDCfEC@DvCECDC0EjCDNCEfCDlCtECDCpNEC6DDpE(DuD2DD8DD,DC3D@tD@DC!DXDCxC D@4DCXCC@&DCACC&DC:CC!DC6CCDCDCC@!DC>CC(DCFCC%DC7CC@(DC.CC%DC+CC*DC:CC#DC.C@>D@D,DC9DD(DC@=DD*DC;D`D)DC?DD@*DC:DD/DC>DD,DC@D DPhEC@DDwECDD@D{EC@9DD`xEC@&DC[ECDC]ECDCMEiCCC9EC@DCPVEC"DCErC;DuC`E.CCCHEtCDdCyEeCDbCEDCDOCzEDC@D@CEJCC5C؅E,CC3Cp|E;CDNC@ELC DIC}EZC@D`CqEPC@ DNCeEC@DC0dECDiChEuC@#D`CEaCD]CEkC@DXChE|C@!DgClEC@$DCrEhCDpCVEaCDdCSElCDC hEfCDfC`ZEEC@DDC[E[CDlCYEHC DdCqEiC!DoCETCDVC؃ERC@DXCEzCDoCEC@+DCbE*D{DbD0?E9D{D`D`ECDC@EzC@DCUEOCDZCP~ElC@'DwC{EMCDLCxEgC#DqCPEuC@*DoC؃ECJDCEC@DCؚECXDDEC6DCEC*DC ECHDCȏE@DXD#DpoEC@=DCmECDC0tEC@DCXECDCKECDCpCECDC3EC@DC0kEtC@DCPSEZCCkCpLECDCCEC@DC@VECDC`ECDC hEC@.D@D`EC1DCEnCDCErCDbCzEC@DCBEC?DC0EC7DD@[ECDD9E~DD DiEDD`D/E*D@]DSD EC:DDp?EC@HD@DIEC#D@ D05E{CCC@%E{CDCZEvCD|C^EC@AD@D@.E@TDDD0EAD@DmDNEC@ND.DcEC9DC@kECDCQEC@DC@XEmCDWCnEpCDfC{EC@DC`[EvCDCbEC"DkChEC=DC`EC`DD0tE!D@hD2DD@;D@D:D@DD D@)DC@DD+DC9DD-DC>DD-DCAD@D2DC@D~D,DC@8D`D+DC6DD@,DC9D~D$DC8D@}D#DzC4D@D%D|C3D@yD&DiC1D@|D@ DfC0D|DDQC3D{DDdC*D@yDDgC0DxDDwC5DyD@DWC7DDD_C:DD@D`C;DyD!DnC@.DsDDWC@2D@uD@DUC.DrDDTC2DwDDWC4DsDDSC3D@uD@D]C-DnD@DUC1DrDDbC)DmDDMC@(DqD@DJC&DmD D@C0D@nDD_C@,DmD DKC(D@nD@ DOC&DsD DXC1DtD@DUC-DmD DIC@.D@oD@DJC.DjD@DMC@*DmDD[C@*DkDDLC*D@jDD@C-D@mD@DDC0D@oD@ D`C(DmD@DTC)DiD@DRC@DQDC0CDSDC=CDYDC=CD@PDC/CDRDC,C@D@;DC(C D5DCC D4DC"CD@D D{EC;DD`ENCDxC{E1CC.CE_CC[CE\CDGC@ECCDTC`~ELC DRC؄E6CC)CEHCC(C}EEC DACPrEACDCCbEmC@DlC`EJCC8C ^EACDCC0UE`CCyC=EWCDkCQEbCDvCEC@DC`SEC@ DC`ECDC[ExCDCPlE_C"DlCXE7CCCpEC-DC }ECDCFEC#DD(EiDD`DUEDDD`1ETDXDED EC@'DCWEuCDC@rEUCCCUECCD6CaE[C@DiCpxEeCD{CdECFD DWE6DD DtECgD$DvECDC]ECDC`RECDCZEtCDC hESC@D_C0xEoCDUCwECDC0kEYCDC ECCDC8ECgDDE.D D@CD@E;DDKD0DDDD9DC6DD%DC/DnDDCDSDC`CD2DCJCC%DC0CC@"DC-CCDCCCDC"CCDCCC@%DC/CC)DCCC@.DC;CD0DCUC DADCgCDCDCcC@C%D_DD?CD@ZDC.CDZDC3CDYDC3CDZDC3C@DRDC2CDRDC:CD@SDC5CD@ODC3CDNDC!C@DODC9CDPDCC D=DCCD@9DC$C@ D8DCC D9DCC D>DCCD>DCCD=DCC@D@DCCDADC(CDCDCC@DCDC#CDDDCCCD@D MEC@DD`LECD D DEC@DDCECDD`KECD D0OEC DCMEC!D@D PEC'DDNECD@DCЏE?CC=CkEOCCGCpjEXCDCCEC@$DfCEgC D4C ~E3CC>C@CE&CCCpTEICDCCE[CC>CP~ESCCeCZEuC@DmC^EyCDrCgE_CDaCp|E~C@&DpCEeCDbCEOCDCCE_CDiCXEeCDtC@mEfC#DdCEsC.DC8E{C@)D|CEkC@,DCECCDCECADC@EC?DDDEC;DD:EwCD_CECsD"DE@DDCDEC;DCEC%D DJECKD:D0E@D@\D@ZDEzD`D`D`2ECPD-D(EADD`DPSEOD@YDdD)ECDCP=ECDC@TEDZD@$D^E@DxD@5DhECDCJECDCPEC DCVE#DsD@_D1E5DJDTDECDC#EC@DC0IEnCDClE`CDIChEGCC0CyEsCCfC8EGCCdC@PE1CCBC-E/CCKC3EJC@DcC0pEhCDC؆EC#DCEC)DC`rEnCDC0jExC@ DCOEC@=D@ DLE5D@wDaD0(EC@-D@ D=EC!DCvErC@DCHEeCDbC@kEcC!D^C`EsC>DCE|C@DCEC@CDCECNDD،EC@DCVEoCCC!ECDCp:ECCDCREC2DC0YECQDCP{ECSDCЅECKDCHECUDCЎEDiD/DgE.D D]DD?DDQDNDCD@DBDC@ADD@+DC@3DuDDC D@XDCfCD@7DCSCC@(DCOCC$DC?CC@'DC=CCDC*CCDC/CC!DC'CC@%DC&CC$DCC@D,DC>CD@DC|CD=DCCD@;DCaC>D D=DCFD@DBDC@@DDBDC@=DD=DC@DD@-DC:DD-DC2DD,DC@DD1DCDDD/DCDC9CD@?DC)CD@?DC-C@D=DC#CDADC'CD@DC(CD@DDC%C@DGDC7C@DDDC&CD@FDCC@DFDCCDGDC2CDJDCCD@NDC1C@DIDC'CDLDC1CDMDC-CD@MDC#C@DODCCD@PDC"CD@NDCCDLDC%CCD D0PECD D`IEC@DDEEC DDKEC!D D`OECD DRECD@D@REC$D@DRECDDJEC@DD=ECDD`=ECD@DGEC1DCnECSD!D~EC"DCEC'DCErCDC@pEjC DCEC(DCEqC@DCȘEuCDC0ECDCEwC@ DvC(ErCCsC EoCDCHEvCDCEqCCCEoCD|C0EdCDyC0EC DC`ECDChEC@DCxEdC DCECDCECDCECDCE~CDC@ECDC eEC7DCpxEC@HDDЈEC@EDDȃECED DXECDDDECND@ DECID4D vEC@OD@DCEQCDOC0tEBCCKCSEJCC9C=ErC DChEC(DCE~CDtCE`CDVCExC"D^CPEC#DzCEC2DCEC@CC@%DC(CC!DC*CC"DC$CC&DC"CC#DC CC!DCCC,DC'CC@.DC6CD/DC(C?DD@.DC;DD/DC@AD`D@?DC@CD@D>DC=D D@@DC@DD9DCAD@D.DC2D D+DC6D@D.DC@8DD)DC>D@D-DC;DD.DC9D`D3DC=DD0DC@D`D0DC?D`D0DC@DD=DC>DD6DC@D`D7DC=DD6DC:DD@5DC7D|D+DxC8DzD@&DvC2D{D!D\C8D@{D#DcC5DzD@DaC4D|D$DkC@7D@}D"DuC5D|D"DuC2D|D#DiC;D}D@DeC-DxD@DTC0DwDD[C,DpD@DNC(DmDDPC/DmD@DNC0DoDDZC@*DkD@ DDC&DgD D5C%DeDD=C&DfDDBC$DgDD@C(DjD@D8C'DgDDHC)DmDDEC@*D@hDD>C@*DfD@D@C"D@dDD9C'DgD@DAC&DbDDFC'DlDDSC@'DmD DWC(D@qD@DSC-D@kD@ D\C/DkD DSC-DmDDHC(D@jD@ DLC&D@dD DDC@,DjD DJC+DkD DNC)DnDDeC*D@gDDRC@)D@jDDNC+DiDDHC%DlDDWC!DbDDAC DcD@DBC%DbDCEC$DfDD[CD_DCDC CDC@D]CPfEHC DJC@E:C D2CE.CDBCE5CC(CEBCC$CE@CC+CPE9CD4CwE7C D=C`rEGCDJCwEEC DEC{ErC@!DsCHE{C%DrCHE\CDfCXEkC0DsC@EC@5DChEkC@.DwCEC3DzChECEDCED{D1D0E@DD@D0cEDDD0RED@DD )EC#DCRE_C,D}C`EC4DCEC0DChEDgD D E9D D|D8EOD@mDoDE@"DBDNDpE0D@aDcD`E7DVDiD@E@D@LDPDEC:DDvECDCEC DCEiC@DnCxEcC@$DOCpElC DqC`wE\CCaCXEFCDNCyECSDCЋE@DfDLD؆EHD`D DP`EDD DZEC5DD0EECDC02E_CCCp-EC@DC{ECUD@DE@XDDDED`D[DoECYDCEC@DCEPCCC'D@gDD=C%DfDD=C@)DbDD:C-DgD@DNC,DgDD@C@#D@hD@DHC@!D@eDDAC'DkDDGC,DcDD@C@'DiD@ DFC@*D@hD@DPC+DjD DNC@(DnD DHC'DnD@ DKC'DdD D@C@*DgD DMC&DiD DNC*DkD@ DOC*D@jDD`C'DqDDkC'D@jD@ DRC@(D@pD DOC@&DkD@DMC%DgD DYC(DdDDGC*DiDDDC$DkD DJC@ DjDDjC(DbDDNC&D`DDRCD`DCGC@#D`DCTC#DYDCECDXDC8CDXDC-C$DXDCICDTDC=C@DSDC.CD@KDC'CDQDC*CD@QDC*CD@ODC,CD@MDC&CDODC4CDODC&C@DIDC#C@DKDCC@DMDC*CD@JDCCD@DDCC@DGDCCDGDC&CDCDC CD@GDC#CDBDCCDEDC$CDCDCCDDDC"C@DDDCCDIDC-CDCDC CDGDC)C@DCDCCDEDCCD@EDC C@DFDC$C@DDDCCDKDC'CD@LDC%CDJDC#CDLDCC@D@ODCCDNDCCDMDCCDKDCC@D@QDCCDLDCCDNDC.CD@KDC&CDNDCCDODCCDPDCCD@PDCCDWDC%C@DPDCCD@TDC"CC"DDPEC@)DDPNEC-DDNEC@7DDpVEC$DDQECD@ DOEC&D DTECD DKECDD >EC@'D@(DCEC%D@-DDEC DDSEC+D@DmECTD$DEC;DCІEC@DC@C1D@fD@D>C'D@fD@D>C#DdDDLC&D@eDD3C*DiDDLC@%DfDC9C$DdD@DAC@&D`DD;C'DjD@D@C-D@fDC(C,DfDDDC+D@kD@D:C0DjDDPC!D@fDDKC@'DjD@D4C@#DbDD8C*DdD@DUC*DhDDGC&D@lD@DGC$DhDDOC'D@nD DjC%D@gD DYC)DdDDYC"DeDDRC'DhD DSC'D@eD D:C%DiDDPC$DhDDNC'DfDD^C$DdD@D]C!D@eDDQC#DcDDMC@)DbDD`C"D]DCBC"D]DC3C"D[DC6C@'D]DCWC"DZDC@C@D@[DC7C@D@UDC4CDVDC1CDUDC/CDRDC9CDNDC)CDPDC:CDPDC)CDJDC/CD@KDCC@DKDC CD@EDCC@DEDC!CDJDCC@DHDCCDGDCCDDDCCD@CDCCD@FDC)CDEDCCDHDC"CDGDCC@DJDC/CD@HDC%C@DJDC&CDHDCCDHDC(CDHDC!C@D@HDC C@DIDCCDPDC%CDHDC CDLDCCDMDC!CDODC#CDODCC@DNDCCDLDCCDRDC2CDMDCC@DODC"CDODC&CD@RDC,CD@QDC(CDRDC!CD@RDCC@!DWDC(C@DRDC'CD@VDC1CC#D@DSEC*D@D@TEC2D@DUEC;D@DWEC@%D DPEC%DD0PEC!D D PECD D`AECD@D?EC0D/DKEC0D+DKEC@0D&DREC-DDPhEDkD@IDECXD@#DEC3DCEC,DCECDC`EyCCC`EhCCwCHECDuC0EwCDCpE|CDCxEoCDCEhC@D~CxEnCCC8EjCDChECCCEaCDvCxECDCxEC@DCEC@DCЎECDC`ECDC0ECDCECDCEC@DC(ECCC0CEC@D%D4ED@JD,DCEC+D2DDGDnD D E6DVD@TD`ETDD`D E@DDD`E@DqD@GD OEC DCpECDCuEC:D@D lEC(DC@mECDCElC@ DuCzEeCCaCptEKCDRCxE[C DCCxEcCDpCEuCDCEhCDCtEHCDBCȄERC DDD8DC?DD5DC@9DD@3DC9D`D3DC8DD4DC@D DFDC@7DD@8DC9D D1DC?D}D/DC@7DD'DC@6D@D.DyC0DvD@/DtC3DtDDrC;DyD"DtC8D@|DDgC8D@~D DhC@7DwDD[C;D@wDDhC3DtDDkC3DsDDoC1DoDDbC3D@uD@DhC3DsD@DcC0DrDD_C(DdD DJC.DfDDZC-DfDDIC-D@dDDRC@.DiDDPC2DmDDYC,D@kDDIC@&DjDDNC%DfDDQC-D@oD@DUC@+DlD@DGC@)DfDDDC@-DhDD?C/DlD DKC)D@gDDAC@,DgD@DQC#DgDDKC-DkD@D\C)DiDDKC(D@hDDUC,DaD@DKC@+D@hD DiC#DiDDTC'D@gDDLC#D@fDDRC@&DdD@ D[C*DgD@DVC)DbD@D[C'DgDDaC@-DkD@DoC@&D@iDD^C%DeDCWC@&DbDDRC&DbDDfC!DaDCRC%D`DCVC@$D]DCRC(D@aDC\C)D\DCJC@#D\DC`C!D\DCAC D@\DCWC@"D[DCBC@&D@[DCLC#DVDC>CDVDCXCDWDC4C!D@QDC8C@D@NDC3CDKDCECDDGEC@+D!DVEC-D'DMEC-D)DEEC@(DDPXE DjDRDEC@YDD EC@.DCРEC DChECDCECDCElCDvChEyC@DkCЪEkCDqCEaCDgCEcCCnCEXC@DoCEgCDoCEcCC^CXE{CCgC@ESCClCEcCD~C0EpCCwCEsCCCHECDCEC*DCЋECDC0ECDCEC@DCECDCeEC@DCEECHDD@ED8D@0D EDXDDD=ECTDDLESDdD[DEDD DP/EWDD@D^ECDC }ExCDCsEC5DDsEC3DCzEoC DCEjC DCuEkCD\CEUCC;CxE:CC?CE`C@DkCEwCDwCzE[CCZCwECCD.CpEDC@D9CE3CD-CEHC@DDC(E]C@DVCxEiC@4DmCxE~C?DCEC@=DCE|C@6DC EC5DCXECTDDE[DD DPME@dDD`D08EvDDD CEDD DpBED@DD`HE,D DD@REC(DCpjEHCDDC EnC@DhCENC D8CEfC DCxECCC[EC@DC kEC DC\ECDC>EC#DC `EC>D4Dp]E DmDkDHED@hDkDyEC!DC_EWC@ DMCfEvC@&DuCESCDVCEWC@DTChEeC@$DMC0EVC@D@C}EMCCGC`ECDCDE@DFD2DE@DD@DED`DD0 EDiD>DZEC$DC0xEC%DC8ECQD"DpHEC"D/D@DC(D9DDCD(DEC,D DLEC/DCxEC/DCРEQCCVCsEUCCCP2ECDC0@EC*DCxEuC@5DCEC@PDC`EC@@DCECEDC nEC@EDDHEDKD@D`ED@SD)DD+DcDbDDADuDnD DCADGD` ECCD"D_ECTD D@EC@4D D`]EC4D(DEDcD@RD DBD@~DJDDDmDVD!EyDDD@DADDODDAD@DDDC@DCGC D>DC0CD8DC'CC@3DC%CC0DC)CC'DC%CC-DC5CC1DC+C6D@xDDgC2D@zDDgC7DxD@DaC8DzDDlC@3D@zD@DlC9D@DDC>DD@0DC;DD@=DC6D`D@8DC6DD=DC7D@D9DC7DD8DC+D@D@3DC3D D3DC9DD3DC8D@D;DC:DD8DC@DD@2DC7DD.DC8D@D.DC=D D,DC8DD)DC@8DD)DlC0DxD&DtC1D@xDDtC6D{D&DxC@5DyD@DXC@5D{DDcC@1DwDDYC@1D@wDD]C0D@uDDaC@3DwDDcC0DpDDZC1DwDD_C(DmD@ DSC@1DpD DRC)DeDDMC+D`D@DBC)DcDD3C'DdDD=C,D@eDDMC@,DeDDQC%DgDCC!D@`DDOC(DcDD_C!D_DCPC@D@ZDCEC#D^DC9C@D]DCCCD^DC6C@D@]DC?C@!DZDCAC@%D^DCTC"D@\DC5CDYDC9C D@XDC5C@#DYDCJCDYDC8C@DZDC;CDWDC;C!D@XDCLC@DUDC*C@D@SDC*CDLDC"CDJDC2CDKDCC@DNDC%CD@JDCCDMDC&CDJDCCD@JDC+CD@JDCCDJDC!CDFDCCDJDCCD@KDC)CDLDC)CDJDC$CDMDC+CDJDC!CDJDCCDIDCCDLDC%CDLDC%C DPDC&CD@KDC*CDQDC"CDODC&CDSDC-C@DPDCCDSDC$CDPDC&CDWDC.CDQDCCD@PDCC@DRDC%C@D@XDC1CDQDCCDUDCCDZDC"C"DXDC%C@D@UDCCD@YDCCC$DDREC@$DDOEC%DDTEC@D@DSEC@D@ DQEC%DDQEC@DDNECD D`DECD DPEC@(DD\EC/D+DNEC,D3D@EC@(D@ DIECJD@!DyECjD3DEC@(DCEC@!DC8EC)DC ECDCEsCDCpE{C@ DCPEC DCEqCDrCpECDtCXEqCD}CEC@D{C8EmCC[CؚEmCCdCЕEVCCmC`EgCCzCE\CC{CEfCDCEC@ DCPEC@DCE|CDCE{C@ DCE}C@ DCEC@DCpECDCȂEC@CCjCPiE?CCECgE:CCNCjESCC[CpfEOCCMCQE?CC>CLE7CC&CuE:CD7CE9C@D/C@E@CDACEdC$DeCxEC@>DCEC8DCEC@-DCЦEC$DCECDCECND@D`E)DDsDPJEDaDXDAE@ DD@wDEEADD@D0AE!DDlD[EC2DCpoE|C DsC|EUCDPCp{E_CDcCfE[CDICECDC@TECDCEC=DCTECDCpYE\CCCPUEC DCkECDCHECAD@DEC DDRExCDC@JEGC@DXC yEWC!DbCpEQCD=CE\C!DPCXEC3DwChEC.D}C0EC'D}CEC@+DC8ED@*DC0GED@D@DE D DD0YEC(DCȊECDC {ECDClE&D@RD9DE@IDYD>DEDBD@%DPZECADDpEC@/DC}EC)DCEmCD|CEXC@DUC}E|CDC{EC DCEC*DCpECEDCE DhD DpeEDbD8DPEDOD D`D-D\D'DD2DmD6DlDD[D@D@ DD@XD@8D8D@ D@\DMDD@,D@yDDDDUD4D GECLD@&D@REDND@:DE)D@gDFDDED D@jD`DPD`DD@D>DDoD`DNDD^DDDDD9DCFDDCC+DC,CC@-DC4C@D@-DC)CD0DC,C@D7DC6CD>DCAC@D=DC%C@D7DC-CC/DC)CD.DC*CC)DC$CD@0DC7C4DlD@DiC@0DnDDYC0D@pD DaC6DqD D\C@4DtD DgC0D@tDDiC6DD$DyC>DD3DCDCC(DbDC?C@&DbD@DMC D@aDCC&D@jDDKC$D@gDDBC&DfD@DCDUDCC DTDC-C@D@\DC2CDYDCBC@DUDC1CD@RDC&CDODC%CD@ODC,CDMDCCDNDC"CDODC"CDSDC*CDPDCCD@RDC+CDPDC$C@DNDC&C@D@KDC!CDODCCDMDC#CD@MDC5C@DLDCCDIDC%CDKDCCDPDC)CDKDC!CDODC*CDMDC"C@DSDC%CDQDC%CDRDCCDQDCCDUDC(C@D@SDCCDUDCCDVDC"C@D@WDC-CD@UDC+CDRDC%C@DQDCCDUDC*CDSDCCDTDCCDXDCCD\DC%CDYDCCDXDCCC@4DDLEC*DDJEC@"D@ DJECD D0HECDDBEC(D DGEC(D@DHEC DDMEC@DDREC3D@8DJEC0D7D @EC4D@3DFEC#DD@GEC@DCpNECCDDhEC/DCECDnCxEvCDCEyCDlCpEC@DqC`EsC DlCECDCXE}CD~CHEC DCEiC@DvC8EkC@DCE[CCyC0E\CCiCpEeCC`CPEmCCmCEWCC{CEgCCgC@ExCCtCE~CDC`EnCDvCE_CDhCȜEsC DC8ECDC8ECDC0zECCCpAEC+DD[EC9D@ DPxECDC{ECDCyECDCp~EC@ DC@EoCDC@zEvC@DCxEC@DC8EC@)DCEC.DC[ECDCEC@DCxEC@DCElCCmCE%CC.C`GE:CCCTECCD@C}ECCDACEPCDKCpEC4DCXEC@1DCExC)DCHEC,DCpEC+DCEC(DC0E|CDC ECDCEC%DCEC^D@3DtECkD3DfEC@>DCzEC$DCEC@DC8E|C@DCrEoC@DC@xEPCDUClEMCC^C`QEPCCiCZECDC@8EC@D@ D%EC'DC@vEC+DC`EC>DCEC,DCHE|C DCpEeC@ DpC@yEaCCoC0_ECDkCpEnC'DoCȃEtC3D}CXEC@3DCЉEC1DCxECDCVEC@DC`^EC DCPoECDCiEC DCYEC@?D@DPoECeD^DEC@>DCPEqCDjCpE;CC;C^EBC@DYCkEC@/DC8ECBDCpEC@9DDXEC-DC|ECDCXERC@DfCaECWDDhE)D@gDDDTE DsD8D[E)DvD@1DP1EDKDDDD@SDD)D@D@jD)DC#DcD(DC(DjD1DC5D@wDADC@AD@zDCD=DC?CD>DCLCD@DC,CD>DC1CD@DC2CD:DC4CC6DC8C@-DuDDuC0DrDDjC.DnD@DoC*DjDC^C@-DnDDgC4DqD DfC7D@qD DuC.DtD@ DlC3D~D@D|C9DD+DCDD0D}C;DD@*DCDD-DC@:D`D)D{C@C$D\DCJC DYDCOC@$D@_DCFCD\DC8CDWDC;C@DZDC9C D\DC]C D@WDC=CD@[DC7CDWDC8C@DYDCQC@DWDCACDVDC+C@DTDC/CDRDC+C@DWDC,CDQDC-CDTDCC DUDC6C@DQDC,CDTDC-CDRDC&CDPDC,CDKDC0C$DNDC,CDQDC.C DXDCCCD@ODCEC@-D@#DDEC.D$DEEC'DDEEC%DDMEC%D@D IEDGD@OD`@EC;D@HD>EC1D@3DFEC@-DDREC@DCrEC@CD@ D8EC'DCErCD\CȳEC@DgC(ECDWC@EqCDoC8EqCDeCECDC(EnC DC؞EzCDCE\CC|ChElCDC8EbCCxCENCChCEICCWCE^CCaC@EPCCaCEdCCRCE^CCeCXEqCDxCЛE`CCcCXEPCCVCElCClCEoC DCE}C@ DCЉEXCC~C?EC@DC OECDCzEC DCrECDCpECDCuEqCDC`|EbCDCP~EnCCCEgC@DCECDCEC/DD`mEC@DC0kENCDuCkEpCDuCPuEICCBCiE8CC#C^EJCD.C؉E^C,D\C`EaC@8D]CئEnCD~C0ECDCHEC%DCEuC.D|CEwC1DChEiC/DCEC.DCEmC@!DCEYCD}ChECC@ DcCEC@"DCEC"DCEhC DvCEC D~CECDCEdCDcCrEHCC>CcETC DQC]EbCD`C0aE-CCIC [EC@DCHECCC UE2CCiCPEfCChC`FECCCXEzC@DC0TEtC@DyC}EjC DbCqElCDbCnE:CDOCȂEiC/DuCEzC@-DCEnCD|CECDbCPEUC DQCVEmC DKCyECDC}ECDCpPEiCCPClEC!DCЃECbDRD{EC=DCEaC@DC0oEYCDXCbE[CDjC~EC*DCEC@>DCEC3DCxEC@1DC(ECDCfEC;DC EC@_DDiE@.DiDMDpE@)DYD7D`D%D@gD@8DNDD^D$DC@$D@]D$DC@%DeD&DC$DfD'DC'DpD>DC)D@wDDC6C@D?DC9CD?DC)C@DEDC0CDADC2C@D@=DC6C+DrD(D|C/DvD@"DyC.DpDDoC1DjDDtC@.DeDD`C0DsD DgC)DrD DaC2DoDDrC,DpDDhC3D|DDqC5DD DxC:DD@+DC5D`D0DnC9DD@1DsC3DD,DzC:D@D,DC@3DD&DxC4DD*DzC7DD+DuC5DD@2DC7D@~D@2DC@6DD5DC5DD1DC@6D D@6DC4D@D8DC8DD@:DC:D`D?DC@>DDCDC;DD=DC=DD?DC@>D@DDD?DCDD0DCBDD1DC5D D&DpC@DD*DzC4DzD@!DjC@C@$DgD@DFC&D@bD@D4C)DeDDMC&DfDC4C@,D@kDC/C(DfDDHC@-DjDDIC@*DeDD?C%DgDDKC$D@iDDAC&D@gDDIC D@fD@DHC'DdDDZC)DdD@DJC*DhD DYC$DcDDEC@#D@dDDFC@%DXDDRC%DcDCJC D]DCDC@%D[DCRC!DXDCOC@(D]DCPC D@YDCECDUDCAC!DZDC2C D_DCBCDZDCBCD\DC-C@D[DC0C"D@[DCJCDWDC3CD@UDC%CD@TDC%CDVDC CD@WDCC@DRDCCDUDCCDYDC7CDRDC(CDSDC CD@UDCCDVDC%C@D@SDC(CD@SDC0CDLDC)C"DXDC5C DQDC.CDODC"C@DQDCCDVDC*C!DUDC+C"DSDC7C DQDC7C@DYDCACDUDC9CD[DC/C@D]DC$CD\DC4CDXDC)CD@[DC%C@D[DC/C D`DD9C!D@]DC%C@D]DDCDXDCCD[DD+CD]DD(CD]DD.CD@\DC-C@'D_DD4C$D@[D@D*C"D@_DD CC@)DD@?EC;D,DEC(DD:EC@2D DKEC+D@ D0OECDDLEC D@D0REC(D@"DMECCDBDFEC6D@4DCECDCYCD=DCIC D5DC3C@D5DCDC:DD?DCAD`D?DCD`DBDC>DD?DC@>DDC*DhDDFC)DiD DIC@*DdD@DNC%DdDD?C-DjDDHC@+DgDDAC*DlDDTCDhD@DOC@)DeDDaC@-DdDDVC@)D@gD D_C%DfDD?C&DbD@DBC$D[DDQC@ DeDDOC@"D@aDDHC@#D@^DDRC@D]DC?C'DbDCGC#D@]DCFC!D@[DCHC!D@]DC?C$D`DCIC D[DC>C!D]DC3C@"D]DC0C'D^DCFC@ D@[DC2CD@[DC7CDVDC9CDXDCCD_DD,C$D`DD0CD]DD&C!DaDD*C!DcDD)C#DbD@D1C!DaD@D,C@)DhD D&C@(DbDD,C@$DeDD2CC5D@DAEC3D@&D;EC)DDP:EC(DD>EC*D@D0MEC%D DSEC#DDXEC%DDZEC+D@DYEC@/D@"DSEC0D'D0KEDADED FECCDDDVDD=DDED@D7DD%DC:D{D@DC0DmDDC+DcD DC@D@^DC~CDSDCoCDRDCC@ D=DC_CD9DCXCD@D D@3DC3DD-DC=D D,DC@;D D2DC>DD@4DC>D@D3DC@4DD2DC,D`D2DC7D@D7DC6DD:DCADD=DC7D@D=DCDD@;DCD@DD~D(DC;DD(DC7DDDwC@5DD@D^C@3DyD@DeC@4DwDD^C0D@zD@DFC1DqDDcC-DoDD[C=DyDDkC2DuDDVC2D@sDDRC*DqD DdC2D@qD@DiC)DoD D^C@2DsD@DNC@1DlD@ DSC@1D@kD@DrC@)DlD@DYC+D@hD DTC+DmD DIC,DeDDMC*DhDD@C(DmD DSC+DlD@DEC.D@nDDYC@+DiDDOC1DhDDOC-DeDDCC@*DlD DRC@)D@jDDDC@(D@kDDJC@'DgDDLC(DlD@ DRC$DeD@DGC#DdDD:CDfDDPC$DgDD\C#DbDDAC"D@_D@DDC@!D`DCPC+DcDDaCDbDCDE@DZD,D@dDD_D@"D DDfD#DCD@_D$DCDbD'DC DeD.DCDaD)DC#D@nD@)DC$DqD)DC(DvD@0DC+DvD@(DCGDD@KDCDD D@DCDD@NDCDDyDD;D}D$DC6DxD&DC?D@|D'DC2D@|D$DC?D@D@:DC0D@D1DC@8D`D@%DC3DD@2DC8D`D1DC8D{D)DC7DvD$DC:D@zDDC7DxDDC1DyDDC@4D{D@DC1D|D@DC3D|DDC1DuD@DC@1DzDDC1D~DDC@;DD@(DC>DD@+DC@DD@7DCCDD@=DC:DD:DC?DD5DC=DD5DCDDD;DC@CD@D9DC?DD:DC@D D9DC@?D@D=DC=D@D=DC8DD=DC@DCDCDD1DC@:DD-DC8DD@'D~C@9D|D"DnC8D~D$DC5D@{D"DhC7D@xDDqC@;DyDDjC;D@|DDC2DvD@DrC4D@vDDdC-DyDDXC6DyDDoC1DxDDiC8D@vD@D\C3DxDD`C2DzDD{C1DvD@DdC.DtD@DbC1DrDDZC@-DlDDeC-DnD@ DXC(DpD DmC,D@pD DeC4D@mDD]C0D@mD@DaC3DoD@ DeC/DlD D\C,DnDDfC0DlD@ DPC@/DnD DHC/D@nD DYC,D@kD DiC*DdD DIC'DiDDHC&DlD DSC&DlD DdC&DgD@DGC&DeDDNC$D@fDDbC*DfDD`C$DcDDPC&DcDDLC@%DeDDFC*DfDDZC(D`DCHC%DaDDYC%D`DCVC@$D@^DDZC@D[DCUC D\DC8CDZDC8CDYDCFC#D]DC4C"D_DC;C@$D@bDC:C.DhDDRC#D@`DDNC@ D]DDHC DZD@D>C"DdDDJC@"D]DC@C!DdDC9C@D@^DCFC$DaDDRC$D\DCHC@!D[DC?CD@^DC:C@'D@bDCMC@$D\DCDC#DdDCFC@#DeDCGC#DfDDRC%DcDDHC@(D@dDD>C%DeDD=C(DeDDHC&DgDD8C&DjD D>C(DeDD?C@/DhDDAC'DhD D?C%DhD D4C(DjD D;C@)DhD DOC@(D@gD D7C*DlD@ DKC)DjDDNC.D@kD@D\C(DiDD8C@*D@mDD;CC4D@ D@MEC0DDHEC@+DD@EC&DDHEC$DCNEC DCRECDCWECDCaEC'DDcEC@-D D\EC'DDPWEC2DDpTEC@(D@DpLEC0DCpECGDDEC@&DCpEwC@DkCpECDCEC@DC8ECDzC0EhC DqCEuCDC0EfCDCEnCDjC0E]CCcC(ElCCwC@E]CCbCHEZCC]CE\CCeCE`CDxCEjCCmCpEhCCkCEaCCgCpEsCD{CECChCXEjCDnCxEmCCoCEfCDwC0EdCDoCEaCCnCuEQCC7CZE]CCdCIENCCFC`TELCCDCZEeCDlC0zEC@DCEC&DCEC1DCEC8DCȋEC*DCE~CDCEC6DCEC5DCEC$DCEC5DCEC@DD4DC8D D@0DC@8D D9DCDD9DCDD@D8DCBDD>DC@BDD;DCDCDC7DD@ADC9D`DBDC;DDCDC;DD@ADC9DD@FDC@@DDIDCADDODCJDDNDCIDDODC@FDD@HDC@CD`DFDC@:DD=DCDC=DD8DC;D`D2DC@;DD-DC@:D D(DC8D@}D&DC8D@D@'D}C9D|D%DpC@5D}D"DtC7D{D@DqC@8D@~DD{C6D@xDDkC@5DzD@D\C1D@wD@DWC@5D~DDhC@.DwDDXC-DuDDoC@2D@yDDgC0DyDDjC@4D@xDDXC0D@sDDXC0D@pD@DVC@+DqDD_C-DoD@DcC-DlD@DTC,D@lD@ DYC&DkDDdC@'DlD DQC1D@lD DPC.D@iD DKC+DoD@ DJC@,D@gD@DGC+DjDDSC@*DhD@ DTC(DgD@ DiC@'DdD DFC'D@gDDFC@)DeDDGC!DeD DNC%DhDDFC*D@dD@ DMC+D@eDDTC)DfDDOC$DdDDNC!D@eDCHC!D@bDD8C@#D`DD?C@$D@]DCGC@$D@^DCDC!D@]DC9C@ DaDCC@#DVDC5CDYDC6CD\DCBC!D@]DC7C$D]DCBC"D^DC9C@$DaDDBC@D[DD8C$D]DC:C"D@]DD0C%DcDD8C!D_DC@C$DhDC=C@DZDC9C"DaDD1CDaDC9C D]DC>CD]DC.C@ D@]DC,C!DZDC(C@!D_DC9C!DbDC=C@'DcDCPC"DgDC;CD`DD2CD@aDD0C#DdDD6C@$DgD@D.C)DiDD>C"D@eD@ D?C@0D@jDDBC*DjD D?C#DhD D4C'DeD@ D/C'DeD@D:C@ D@fD@D=C"DkDDAC!D@iDD;C(DiDDPC%DhDDAC&DjDDACC@6D@DLEC,DD`FEC"DDAECDCJEC#DC0UEC&DCXEC@DC ZEC@"DCp\EC)D@D_EC/DD`EC@(DCYECDCJEC@DDNEC@3DDprEC;DD@EC8DCE|C@DCEC"DC(ECDCEoC@DuCpEoC@DCآEC DCОEdCCCEfCCCȚEVCCrCEiCC~CPE]CCuCȝEZCDqCE]CDzCEaCDCPE]CDCE`CCC`EXCCjC`EjCDCECDwC(EzC@ DCE]CCSCECCCDBCEdCDXCEeC@DaCEoCDCȑEmC@DCE{CDwCE{CD{CEPCD;CEaCD:CESCD;CEC0DzCEC4DCEKCDmCnE:C@DKC8EPCDUCENCCKCgEuCDxCEiCDCECDC`yEC@DCpECDC kEC@DC?DD=DCCDDADC@DDPDC@CD@DPDC?DDODC>D DTDCGDDQDC@GDD@VDCJDD@MDCED DGDC@GDDDDCBDDADC6D@D@CDaDC3C'DcDDAC@"DdD@D;C*DcDDKC$DfDD-C"DeDD(C%DdDD1C@'DhD@D=C%DgDD4C)DgD@D>C!DhDD6C-DqDD0C@+DmD@ D,C#DgD@D4C(DeD@ D)C@(DjD@ D/C@(DjD D(C$D@iD@DAC"DjDD1C@(DlD@DEC)DlDD;C)DlDD2CC6DDKEC.DDKEC!DDpIEC@DC@OECDCXEC DC[ECDC]EC!DCaEC@$DD@_EC+D@D aEC$DC[ECDCQEC"DC_EC;DDpsEC@LD DjECCDDؓEC DCEC@DCPE}C@D~CxEeCDCEwC DCСE}CDCpEeCDC؜EkCCC(ENCC~C8E_CCCEfC@DxCEUCCwCEQCCzCEVCDCEZCCzCE]CCrCEvC@DCpEtC DCآEyCDCEqC@DhCE7CC'ClEJCCJC yEMCCPCEGCCLCECDC؞EC@*DChEC'DCHECDCXEC#DCEC%DCHECDCECDCECDC`EC%DCEC@DCECDC`EC+DCEC(DCEC@DCXE?CCNCgEKCCbCtEoCCJCEuCCbCuExC DC`oEIC@D^CE_C DXCЍEdC DxCp}EqCDCp~EC%DCHECDC|EtCD}CzEtCD{C`uEXCCKC\E0CDFC[EPCD`Cp|ETC@D]CȂE]CDjC0wERCDnC`\E0CD+C{ETCD\CȃEC&DCEC@1DD8EC=D-DEC@DChEC/DCxEC1DCؕEuCDC`}EFC@DeC`ExC@DCEC!DCECDDGDC@:DD@EDC:D`DHDC8DD@LDC?DDPDC@DD@QDCAD@D@TDC=D@DSDC?D DXDCBDD@TDCHDD@RDC@KDDODC@IDDQDCED`DKDC@EDD@JDC@>D DADC:D D8DC5D`D@1DC:DD1DC5D`D/D}C8DD@0DC5DD@/DC-D@}D@,DsC2DyD"DvC1D@~D'DvC2D{D@'DlC7DD(DbC@7D@~D'DhCC@ D`DD>C%D]D@DSC@#D@]DCNC@ D_DCFC#D`DC2CDeDD:C!D^DC1C@D]DC.CDZDC/CD]DCCC@D\DC*C#D@\DC&C DaDC1C!D@dDCBC&D]DC9C$D^DCBC$D^DC1C@(D@aDD8C#DbDC4C!D@`DD5C@!D`DD>C&DbDDIC&D@dDD8C!D@cDD1C#DeDD=C%D@gD@D8C!D@bDD5C@%DhDDFC"DfDDCC&DiD@DJC(D@gDD;C'DcD@D;C D@eD@D3C#DjDD=C@!D@gD@D-C&D@eD@ D6C%DhDD-C(DmDDAC'DnD@D7C@%D@fD@D*C%DgD@ D0C'D@mD@D;C$D@lD D"C@#D@mDD9C(D@nDD+C)DnD@D1C&DoDD?C@$D@oDD2CC@5DDBEC0D@ DIEC%DD`LECDCREC@DC@UECDCWECDCYEC DCp`EC DC_EC(D@ Dp]EC@DCpUEC@DCTEC)DDjEC'DC؂EC-DCEC@;DDPEC@*DCECDCEC@ DCECDC8EdCDC EgCDCEdC@D}CEeCDC EfCCChEtC@DCEfCCCPE^CCyC`E]C@DC`EoC@DCEoCCCEfCDCxE~CDCECDCE|C@DCEbCCCkELC@D`CEC=DCpEC!DCECDCEC@DCEC'DC(EC(DCؔEC)DCEC#DCEC@*DC0EC*DC EC@DC(ECDCPEC@+DCEC*DCؘEC"DCpECDCECDC0E[CCmC~E\CC^CsEUC@DC|EQCCNCpvEFCCfCtE_CDhCESC@ DQC`EBCCFC wECDC0pECDC0oEC0D@D0bEC/D@D@iECDCuECDCgEC@DCExC@DC~EC@DCPpECDC oETCCQCdElCCcC@fECDC`ECADCEC'DCqECDC'EC DD'ECDC^EmC@DpC@VECDC_EC@ DC\ECDCEC'DCEC@&DCECDC~ErCCCWEiCCXCLE~C@ D}CmEC,DC`E^CDPC~EOCDDCEC!DCECDCp'ECDCEC@ DCp@EC"DCp~EC0DCXE|CDlCECDCPEC@#DC@gEC*DCsEDmD:D6E@6DkDCDDOD`DcD@{DdDD@uD@~DD`D@DDDDDyD9DyDIDC[D|D@CDDD@nD%DCD@lD"DC+DsD@8DC+DqD7DC*DpDIDC+D|DKDC/D@D@=DC&DuD2DC'DpD.DC D@gD#DC"D^DDCDZDDnCDaD@DCDZDDC@VDD2DD@=DD>DC@,D@gDDCWD`D4D D@ADeDDCDVDCC&DfDDC&DiD DC#DhD"DC!DdDDC&DcDDC$D`DD{C-D@vD(DC@.DwD.DC@$D@hD@7D8DD[DUD@D@)DoDdDE@DiDMD`8EADD@D$E8D`DD`D$DDDED@DuDpED[DMD DC@ DCDC@ DCDCDC`DCDC`DC@D D@DCTDUDDDzDmDDD`DiDDDID@SDDC@D@ DZDD@DD@ D*D DHD D@DDPDDrDD@QD D@_DDcDDDD@bD@!DC;DDDDC;DDMDCDD DKDCDD0DC:D@D.DCBDD2DC@7D D/DC>DD@-DC7DD@1DC6DD0DC8DD@(DwC8D`D-D~C8DD@'DC7DD+DC6D|D@(DoC7D D$D{C4D~D"DqC1D@}D DlC4DxD@ DqC5D{D D|C-DwDDkC@/DzDD\C/DwDDZC4D@uD@DqC2DsDDWC+DoDDUC@,DlDDjC/DmD@DiC.DjD@ D^C+DgD@ DTC@"DdD DNC%DdD@ D^C#D@bDDTC$DeDDJC%D@gD@D=C%DeDDLC$D_DDFCD_DDBC!D]DC6C@ D@aDCDC@D_DC1C&D_DC5C DdDC9C%D@iDD>C$D@bDC;CDaDDDCDaD@D5C@&D@gDDLC%DdDC?C)D@`D@DAC'DhDDFC$DeD DNC#DiD@ D6C@%DfDD7C@'DfDD0C)DgD@ DEC&DgD D?C(D@kD DLC*D@jD DLC@*DoD@DLC'DmD@ DJC,DlD DQC'DlD D6C@'DkD@ DAC#DiD D8C&DiDDIC%D@lD@ DSC'DlDDQC@&DkD@D&C@,DmDD0C@*DoDD>C.D@pDDJC(D@nD@D'C@,DrDDGC.DrDDTC2D@rDDbC0DrDDNC0D@rD@DNCC'D D9EC,D DDEC DDGECDC@NECDC0MEC@DCTEC@DC`WEC@DCpZECDCXECDDREC@DC@PECDC YEC#DCPnEC'DC`zEC@7DCEC0DCxEC!DCECDCECDCECCCE[CCkC@E\CCgCxE`CCcCEfCCCE`CCtCȝEeCDC`ESCCCxEgCDCESC@DCEhC@DCE]CDCHEsCDC0ECDCECDCEjCDCnEkCDC`EoC"DChECHDCpECDCHEC@DCEC%DCEC@)DCECDC@EC@+DCEC%DCȡEC@-DC EC(DCEC@.DCpEC@$DCXEC&DCEC@&DCEC"DCpExC@DC؜EcCCdCȆE:CC7C0nEfCCTCEHCCTC E_CCKC`lEoCCgCpE]CCnCwEFCCCCE/CC)CЂEkC@DxCyEwCCC>ECDC IEC;DC@|EC@-DC@EC@9DC~EC5DCEC2DCpBEC@DCCC@C8EICCQC@ECLD@!DJED@JDEDP!EDqDgD0HECaD>DMEC8DD 0ECDC7EcCDCEiCCC`DhCCLCpEMCCXCE}CCCDCDCDCDC DVCCpCD^CCsC`DRCC|CDC@DCECCCDBCC_CDHCC]C@DCCC DKCCC@D^CC`C`DCCCXC_DCCCD|CCCDCCC DC@DCeDD@ODD@ADDiD%DC DRD@DC DSDDCCGDDCCLDDCCBD@ DC@D\D"DD#DpD5D!DDxD5D(D"D~D6DD/D|D;DC:DDJDC5DD@RDC:DD@UDC=DDSDCED DWDC=DDVDCC'D@mD D?C(DlDD@C@$DkD D-C*DkD@DGC@%D@mD D9C@%DnD@ DCC)DlDDCC*DoDDLC@)DnDD0C+D@nDD2C+D@nD@D%C@.DoDD=C*DmDD)C&DlD@DCC)DnDD@C4DoD@DAC@,DpDDPC,DrDDGCC@"D D:EC*DD@BEC#D DBECDDFECDCEECDCMEC@DCOECDCpOECDC@NEC@DDQECDCpVEC%D@D ^EC@%DC`uEC@2DCyECTDDEC)DCEC@DC@EC DCEYCCpCEMCCmCPE`CDCЙEjCDCE\CCmC؝EjCCtCEpCDzCExC DCEmCDCEyC@DCHEaC@DCEWCDCE]C@DCEzCDCEC DCECDC@EC@/DCEC:DCECDCHECDC0ECDChEC@DCEC$DCHEC)DCEC#DCEC@4DC(EC4DCXEC@8DCصEC@1DCEC1DCتEC&DChEC1DCEC0DCEzCDCؕE/CCjCPyE1CC:CpE-CC7C`qEDCC6C E3CC;C@{E_CCWCpEwCCfC~EiC DCEJCC[C0ERCC>C EQC@DaCqE]CClC3ECCCpnEC?DCpEC@DDDE$DzD@kDoEC4D,DE`CCCEyCCC*EkCDCTEC@DCVECDCFENCCgC!EICCtCKECCC[EnC@DsCvE6C@DfCPzECD|CE|CDpCXEZCDHCBEICC]CQEZCCnCp\EC DCpeECDC0BECCCDCCC@E5CCECNE@CDJCphE^CCWCZEiCDlCnEC@DC0~EC=DDnECDC0MEVCCfChE\CCPCbEyCDvCȂE`CDIC(EC@*DCEC.DCpEC*DCZE@DRDDE@@D|DODDDDDD@8DDD D*D`D@vDcDD]D:D@DD\D+DC D@dD@'DC@"D@bD"DC@"DfDDCD@eDDC@D@YD@ DuC1D`D@DC@BD@[DDC)DXDDCD@SDC}CDQDCCDQD@ DCD[DDZC!D@cDD}CD@jDDC@$D@gDDC$DbDDC%D@kD!DC@-DyD@2DC@4D D=DC4D@~DEICDPCJE_CD\CHEC@DpCp7EZCC\C E3CC+CPEICC6CEaCCbCE4CC@C`D/CC,CD:CC8CD\CCeCD"CC3CD)CCPC ETCCaC EC@DCDC@DC D~CCCDiCCCDgCCCDCCCD|CC~CDCDCDC@DCDC@ DCDC@"DC DC+DCDC6DCSDCDDTDC?DD@VDC@FDD@_DCBD@D\DCIDD@^DC@MDD\DCODD`DCOD DXDCBDDUDC@DDHDCAD`DFDC7DDBDC:DDADC;DDADC@DDADC8DD>DC:DD>DC@:D`DD@D@ADC@7D D;DC9DD>DC6DD@DC8DD9DC3DD4DC@2D@D/DC4DD@1DC4D}D-D|C8D@D@/DC5D@|D*DsC4D@~D.DwC2D@}D-DC@/DwD,DlC,D|D#DkC@-D|D@'DqC0DzD%DhC/D@wD@%DiC@.D@~D@"DqC5D{D$DwC)DuD@D|C0D@wDDiC4DuDD_C0D@uDDpC,DpDD_C3DmDDoC,D@sDDgC+DlDD\C&DiDDIC*DgD DDC+DhDD,C.DjDDBC,DjDD)C(DiDD>C&D@aDC7C)DeDDVC*D@iDDNC(D@gDD9C(DfD@D,C@+DeDD7C'DbDD9C@)D@gDD:C*D@hD@DCC,D@oDD@C,DnD D9C@)DjD D3C%DmD D6C@+D@qDDPC*D@nD D5C*DlDDBC*DoDDDC(DlDDVC%DoDD5C'DlDDLC+DoDD4C*DuDDOC+DkDD:C@/DnDD?C2D@qD@D5C/D@rD@DHC-DqD@DBC*D@oD@DDC+D@qD@DHC+D@tDDLC(DrDD;C.DpD@D?C*D@sDDDC@2DvDDUC@-DqDDHC'DqDD?CC(D@ DGEC@-DDEEC@(DDPDEC@ D DBECD@D=ECDDEEC@DDpJEC@DDPCEC@DDpIECDDREC"DCPTEC.DDgEC-DCECPDD@EC@ND-DxEC"DCECDCEvC DCEaCDCEdCCvCXEeC@DC@EpCDC`EjCDtC0EpCD~CE}C DCEC@DCEcC@DCXEcC@ DCxE]CDCȣEkCDCEuCDC0EkCDCEC@ DC{ECOD1DP}ECMD2DpEC@D DЎEC*DCHEC(DCEC#DCEC"DC EC@#DC@EC@'DCȓEC$DCEC@/DCȚEC0DCEC0DCEC-DCЧEC&DCEC@.DCEC@:DCEC1DCEyC@DC0E9CCFC0~EZCCQCEICCHCECCC7C`EACCDC/DxD>DC.DvD9DC*DtD7DC@(DsD@?DC-DqD@\D@ DBD D@D D@VDD`D D1DDDDDYD>D@DDZD#DC D]D$DC&DkD)DC#DiD%DC D@iDDC#DoD$DC"DgD@DC D[D@DCDQDCCDADDC@DPD@DgC@DVDDlCD]D@DfC D@eDDC"DdDDCD@iD@ DC#DiDDC%D@kD&DC0DD9DC;D@D@=DC,D|D8DD%D@oDGD@DDQD@D@.EC,DCSECBDCECEDC}E@DD@DD`JE@SD DD&EDMDHDE@ DBD@E@CCHC%EeCDdC@EhCDbCeEZCC~CVEDCCACGEJCCRCP6EqCD|C2ECDCP=EKCCKCBE1CCJC6ETCCQC@,EPCCxC ERCCSC`EZCCdC(EXCCRC E^CDmC:EyCDeCWECDCPOECDC@1ExCCCEdCCCEqCCC E}CCCDkCCwCDCCC@DC@DCDC@DCDC&DCDC+DCbDC6DDHD DLD#DND@#DpD@3D@&D1DDED@ DCDD]DC@JDDbDC@PDD@dDCKDDaDCLDD@dDC@FD@D[DCDDDWDC@D`D@MDCDC2DD@:DC@0DD@6DC4DD;DC2D}D3DC4DD@3DC6DD0DC4DD@3DC1D@D2DC.DxD@-DhC,DyD$DvC-D@D)D{C@.D{D(DvC.DwD)DC0D@{D(DsC5D}D)DtC@/DxD%DrC1D@|D"DzC@.D@tD"DYC3D@yD"DrC@/DtDDqC*DoDDeC-DqDDfC0DoDDjC@)DqDDRC/DoD@DOC+DmD D?C@1DlDDQC)D@nD D?C"DmD@DGC'DmDDAC&D@lD DNC#DmD DIC@#DiDDDC,D@jDD=C*DjD DKC$D@hDD9C#DjD D7C@)DkDDIC0DoD@DTC@(DmDDTC-DjDD1C'D@lDD8C*DoDDVC@*D@uDD@C*DtDDAC,DpDDJC.D@rDDNC*DsDD6C@.DpDDYC)DwDDQC+D@wD@DGC/DoDDEC@DDDEC D DEECD@DP@ECDDHEC@,D DPPEC@#DC0YEC5DDiE D7D@ DhE@DDiDvEDYDBDEC@'DChEC#DCECDCПECDCHEC DChE|CDCEvC DCExCDCФEyC DCECDC(ECDCEsCDCEbCDCEC DCEC DC E^CCvCwEYCC~CUEC0DDPdE@D_DYDp_EDFD1DaEC:DCpEC!DCȠEC DChEC0DCEC2DCEC/DCEC+DChEC$DCEC@%DC8EC'DCEC)DCPEC@"DCxEC@'DCxEC@:DCECADC8EC@)DCXEnC DsC؎EKCCUC@EtCDCȋEbCCwCEECCUCEcCCFC ERCCXChEqCDeCEeCDCE{CDCECDCpE_C@D^CdEmCC{ClEC@6DCEC@;D@D}ECD@DE@*D@VD@cDEC@AD DZEyCDCMECDCEEgCCfC TERCDeCp_EQCDkCpZEnC DC0aEC DCPnECDC 9ECDC7EC-DC.ECDCKENC@DQCpUEC@)D^C0EuC)DyCE_CDbCPENCDECE]CCQCKEMCD`ChEBCD@C`iECCC6C{EoCDtC0qE~CDqCjEkCDCpECDC"EC@ D DDCCCDC@DC`8EC3DCpECEDCEC[DCPECJDCРEC2DCYEDADD D@D@TDD@"D&D@iD(DD$DnD,DC'DuD,DC3DtD@@DC!DbDDCYDDvDDmD@DaD?D-DDADC=DD@DDC/D~DDDC7D D@DC4D@DDCxECIDDZE@,D@jD@rDEDBDUD`"ECD@DECCCDoCCCDFCCjCEuCDCNEWCCRC\EbC@DaC`VECDCEC(DCEC,DCpEC)D}C ErC@"D\CؑEuCDC@}ECD~C`BEWC D]C\EkC DfClEbCDeCp]EC@D}ClE~CDjC|EsCCiCp[EjC DCpUElCDCP=E=CCPCWE=CCdCPEbCCCPECDCfEC DdCUEOCCOC EECDC RECDCpqEjCDgClEeCDhCZEhC DrC;ECDC(EtC@DC8E]C@ DCDD@QDC@>DD@MDC?D@D@NDC=DD@LDCDDEDC7DD=DC6DD@DC@ECDDEC@DD0AEC&D D@HEC%DD0HEC)DDDED@ADDJE@DZD@+DQE D@oD@TD0fEDFD$DEC5DCEC2DCEC+DChECDCECDCXECDCأEoC DCEnCDCEvC DChElC@DCXEC@DCpE{CDCEeCDC`E}C DC8ECDCEpCCCuEC!DC~EC@4DC`LEDMD%DYEDUD1DeEC@.DC EC@DCHEC@DCxEC'DCHEC1DCEC5DCECDC1D@D;DC@6D@DC=EpC@!DcC jE{C@EDqCEICDcC {ENCDC@7EgCCpC]EC"DC`JEDKD@DD@&DbDD@D)DmD!DC(DnD&DC'DpD2DCUD DPDD3DDBDC+DD@7DC/DiD.DC+DvD@6DC*DxD@7DC+D}D@5DC/D~D@3DC@2D@D4DC2DzD.DC@/DyD+DC+D@uD+DC(DnD.DC(D@nD.DC,D@|D6DC-D}D@KDCDD@DD`D@DDDD@'DpD@ODD@"D@eD)DC(D@nD@&DC@'DoD+DC+DrD-DC@ DiD!DqC@*DqD@0DC@.DhD-DC'D@XD#DCD:DDC!DSD*DC#DcD'DCD@]DDC@D@^DD|CD\DDCD@`DDC$DjDDC@(DoD/DC@(D@lD.DC,DiD(DC@(D@jD)DC*D@iD.DCDVD)DDCKD@D@#EC=D#D?E=DDD"E@KD D@D0ED@OD@^D'ECDC1EnCDC5EC@KD%DUEDED*D.ECC CDkCCcC1EWCC_CuEqC@DbCEpCD_C8EdCCZCrEPCCZCOECDCEC*DCEC@%DCEoCDdCEsCDYCأEmCDeCHEmCDhCPEYC+DTCEVC@ D^CE]CD_CgEUCCXCaE=CC:CmE[C DYCxExC DC`EC DCiEcC D|C0oEhCD~CE`C@DYC@ENCDpCEFC DHCECCDVC{EXCDdCzEaCDbC|E:CC6ChEECCICpE>CDPCPgE_CD]C0qEVCDPCbEdCDjC\EhCDkCgEPC@ DeCdEHCDSC{EOC DLCKETCCHCp8E`C@D^C TEYCD\CprE[CDgCrESC D]C@kEnCDuCRE}CDCP1EC DCDCCCDC@&DC`DDpD@CDBCYERCDJCaE.CC.CPLE5CC=CpXEZC D@C}E6C@DKC0~EACDFCPEcCDlCEGCDNCxE[C DXC(EMCDEC EDDfDC@7DDfDC8DD]DC=DD_DC@6D`DYDC3DDRDC9DDUDC@@DD@\DC4DD@RDC9DDQDC@DCȘEC@8DCȟEC!DCxEC%DCE{C*DzChElCDQCEUCCCE=CC%C`\E5CCHC@jELCCFChEYCDICpvEoC@ DMC|EcC DPCpkEKCC?C UEUCC@CbEYC D3CP{E4C@ D8CEICDGCEfCD[CȋEICDMCE]CDZCERCDDDXDC?DDPDCD@'D`yEC.DCEC/DC؄ECDC`_EhC@D}CPqEuC,DC8E[C!DYCEC!DC@PEC DCHESCD=CHEEC@DQCELC@DLCE9C@ D:CE`CDEC؎EjCD]C`EC@DsCgEC@DCpQEDLD0DpEDPDDlD"D`DDC$DkD#DC(DnD@*DC@%DvD@2DCaDDfDCBDiD(DD@1DD3DC"DeD*DC%D@qD2DC&DvD5DC*D|DBDC'DsDD@|DlD@CC:CqECCD8CHEOCDHC(EACCKCjE4C DFCXE@C@D?CpsEVC@ DTC wEoC@ DiClEC@DC0ZEZCDqCPKEmCDnCYE9CC6C ?E6CCGCHEIC DDCSETC@DjC8EfCDC ECDCDC@DDDDDfDJDD!D`DdDSD:D@D@D@gDFD`DDtD@TD@DDaD@CDD`DiD#DD@dD=DD@\D@DD,D DZDD!D~D@XD:D DWD=D@XDC@6D DpDC@GD@D@=D@DlDDDD3DD@gD D@FDD@uDC@D@DqDC8DDaDC@9DD_DC1DD\DC8D`DZDC5D`D@TDC9D`DWDC@6D D@YDC8DDRDC@8D D@UDC8D@DSDC5DDKDC:D`DNDC5DDRDC6DDODC4DDQDC9D DKDC8DDBDC4D|D:DC@0DD@8DC7DD6DC5D@}D@-DC@2D|D0DC@3D}D)DC8D|D,DC4D~D@+DC4DD,DC@4DxD/D{C4D|D3DzC/DD0DtC2DD/D|C2DD2DxC8DD5DC0D D2DC@1DD@/DtC/D~D@-DaC/D~D(D]C@3D{D#DOC/DxD%DPC/DvD@#DOC2D@}D$DiC@1D@yD!DSC+D{D$DNC@-DtD"DQC0DvD$DVC.DwDDEC.DwD@DAC.DxD@DKC+D|D@!DbC,DyDDOC@0DtDDOC-DuDD6C/DwDDNC+DuDDUC,DuD@DIC'DtDDHC*D@yDDXC@0D@sD@DGC*D@oDDACC DC;ECDD09ECDC/ECDCECDC 8EC$DCgEC@1DCEC@DCEC@3DCXEC;DCpEC1DCEC@#DCxEC@DC8EC$DChEC"DCEC(DCЉEC5DCEC8DCEC+DC`EC)DCxEC@%DCEC@%DCEC@ DCEC@DCECeD@(DECDDCEC2DCECEvCCC`JEvCDCwEC2DC]ECFD6DpbE@DMDGDjED@LDbDpWECJD@DpyECYD$D0|EC@3DCPTEC"DC@kEC)DCyEyC)DC{EwC7DCuEC(DCEC@DCE{C@DVCEuCDSC@EKC@D?CE[C@D[CEbC@DeC}EvC DYCpjEC@3DC0\E DJD8DE DED@D@cDDYD@DC&DhD DC@)DkD(DC!DrD@,DC/D@xD6DC@#DcDDC!D@qD@.DC"D@eD@&DC@!D@rD@5DC0DtD@DC@3D}D@DC+DzD=DC@2DvD9DC$D@nD5DC%DnD,DC@D@[DD@D@D@?D"DDD@FD#D@D@DPDD@DDNDDGD@ DJD@/D@D-D@wDyDE;D@DDp)E@DED*D9EC@:D@D@ ED@bD2D+D/DvD@5DC:DDIDC*DsD@,DC@/DwD5DC?D@vDRDD8D@qD@HDSDDaD$DCD]DDC)DeD4DC-DuD2DCDaD@1DDDKD6DxDD;D6DD@MDPDdDD2DaDmD`DD@3D6DDDODEDDDZDEDZD@D@cD@6DD@DeD@&DCD@YDDC#D_D/DC$DrDODC@6DDqDMDQD D D D^DDDDND@DDEC#D D@EC@DC@>EDeDDD@DdDZDD'DDyD@DDmD`D DCDDnDCCCoDC@DDDCDCDCCCDcCCCDvCCC@DCDC DDWD+DD/DD@uD@nDFDDD8DKD@D|D DAD DkDC6DDfDC5DDcDC@0DD`DC@/DD]DC7D@DbDC@7D DeDC0DDeDC@4DDcDC0D`DZDC;DDcDC8DD@`DC@6D@DbDC2DDdDC5DD@`DC:DD]DC9DDQDC@9DD@LDC;D DADC8D~D5DC4DD7DC@4D`D@3DC6D@D@5DC3D D0DC@4D D/DC@4D@~D0DC5DD2DC-DD,DC3D~D@.DC5D@{D@3DC8D`D@8DC6DD3DC1D~D1DqC.D|D,DiC@1D`D(DkC+DD"DmC4D@D#DgC@2D D$DTC/D D&DoC1D{D$DRC,DzD'DNC0DzD!DWC6DyD@$DXC2DzDDTC@.DyD@!D]C/DyD"D[C7D{D)DfC7D`D@ DMC4D@|D@ D]C0D|D$DRC8D{D*DbC0DwD"D]C2DsD#DaC@2D@vD@ DJC0DyD"D[C,D@sDDSC.DrD@DYCC@ DC9ECD@DP=EC@DC6EC@"DCKEC1DCPyEC>DCEC9DC@EC6DC(EC@DCPECDC.D@rD>DC@.DwD=DC+D@{DBDC5DxD@@DC@'D@tD.DC$D@uD,DC@DUDDD@DBD'DD@DOD8D DDND$D DDDD1D DD]DDYDIDqD@cD`D DKD#D,DLDiD`DDD`DDDJDDD`D|DD`DDOD@wDwDD@DODLD` ED@WDQDE DHDQDD'DYDkD`D,DsD DD9DD`DD(DvDdD@DDsDjD DNDDD`D2DDxDp1E@D~D@hDPbEDXD,D?E DDD@+D)EDZDCD.E,D@]DZD0,E@DYD8DHEC-DC~EC7DCiEC DCPEC@DC`gED@XD4DpeEC1D@D`MEC@*DC(EPC DOC(E{CDCECDCE{C DC`ECDCECDC(E]CDuCpE`CDvCxECDCpEC)DCXEC@$DCEC1DCEC-DCEC@2DCEC:DCEC6DC8EC@/DC؝EC1DCEsC'DCEC@1DCEnC@!DaCКE]CDXCXE1CD;ChECC-C}ENC D[CvEpCDiC`~EYC D@CЃEdC DLCXEAC@ DHC|EsCDeCEXC D;C|E&CCAC`BEQCCTCMEVCDbC sENCDZC \EKCC>C`GECC.CVEHCDJCwETCDBCENCDJCjE-CC-C`E>C D9CiEVCDIC0tENC@ D9CsEDCC=CoE_C@DJCXEeCDXCEWCDHCdE-CCJC0UElCDiC@E8C DSCEaC@ DdC@jEICDDC]EECCFCQE#CC3C AE0CCMC08E>C D]C@*EQCCkCEkCDCpEtCCC DpCCCDC D D DCCCDCCCoCDDCȕEC>DCXEC9DC8EC@6DCPEC@6DCxEC@.DCEC*DCEC'DC`EC)DCxECDC(EvCDxCPEC7DChEC\DDЧEC8DCEC!DCEqC@DCxECDCEC(DCEC>DCȡECFDCEC@FD@DXEC;DDEC(DC ECDCEC DC@EC DCEC@DCECDCE}CCC E~CDCPEC@ DC@EC@DChECDC@tECDCECDCEC D}CșECD{C@rECDCxECDCECDCЗECDCECDCECDCEC@DCEC)DC0EC'DC0xEC@DCpEC:DDoEC+DCECDCECDCEC@ DCHECDCECDC@EC@DCECDCE}C@ DoCEzC1DCaEC@DCkEC@'D@DpxE DOD?DEC>DD[ECDCp/EC@ DC@iEyC@DC`qEC DCDC)DsDDC@5D{DD0 EJDDDD D@0D)D DDDDPED`DD`E@zDDDDLD@xDtDDDSD7DP'ECDDEC DCDC@DC ECDCE@D@UD@RD D*DmDDECED@/DPEC9DCHE@D6DDDE@DDjD^E@ADDzDPIE)DdD]DEDKD8DD@D6D!D`DCDC`DC-D@DEC@#D DBEC*DDECFD)D4EC@-DC0sECOD#DUEC@BDD WEC!DC@EUCCKCE{C@DC@EC@8DC`EC5DC`EC(DCEC@DCHECDCECDCPEC&DCEC@)DC`EC0DC(EC+DCКEC@(DCEC&DC8EC@+DCEC4DCE~C@.DC`EC&DCHEC DC0EC@(DCpEC@-DC EdCD]CE?CCHC{EYCCCC dEDCC8CeEKCD\C uEhCDCvEC@DCPmEPC DMCEpC@DjCE}CDlC{EXC@DqC0jE8CC:CeEQCC?CaE_CDWCtEmCDtC@zEFCCOC`TEHCCLC@D`D@[DC;DDPDCDD3DC7DD6DC3DD3DC7DD4DC5DD4DC@4D@D3DC1DD@2D~C6DD@1DC3D D@-DzC5D@D,DtC9D~D,DcC@>DD,DvC8D~D*DsC@8DD%DrC3DD'DmC@5DD(D}C4D D'DtC@3D}D@&D^C1D~D%DgC5D D@*DpC3D@|D(DjC2D}D%D_C2D{DD\C1D{D D`C+DyD!DmC0D`D#DVC/D~DD`C5D}D@!DcC1DtDDeC3DrDDWCCD@D0:ECDC8EC@DC[E@)D@OD9DoE8D}DD0UED@dD`DeECLDDHECEDDEC:DCEC8DC؜ECDCEC>DC ECDCȚECDC.D|DDC0NEC8DCbE@DhDKDBEPD`D D@DDSDND0E`D DDEDDDEDD`DD@ D@CD*D@ECDC@2EUCC{CPEGCCYC EjC@DeCBEICDC 5EC@DCCDECrEQCCRC@{EPC@DQCPExC DbC{ELCCXCp^EDCD`CREVC@DVCzE[CDQCE9CD6CqETCDHC|EKC D3C|EOCD7CzECC@DJCETCD`CpEDC D6CЇEHCCEC`}E=CC1CXEQCCHCpUE0CD4C sE.CC6ChE>CCDCМECBDC0EC@DCECEDCpECADC،EC@8DCEC7DCEC7DC8EC@=DCPEC;DCEC/DCEsCD|CHECDC@ECPDCEC@DC`EC>DCEC:DC0EC;DCEC/DCECDCEC@DCEC@1DCECGDCEC9DCECDCEC@DCEC@DCEtCCCECDC`ECDCxEC@DC ECDCEC@DC8E~C@DCECDCEC DCECDC`EC@DCECDChEC@DCУECDCEC DCPE{CDCEC@ DCECDCEC@DCEaC DC ~EUCDnCuE}CDCHEC(DCECDC@EC DCECDC؀EC@ DCp~ECDCEC@DC|ECDCpEWCDCJEE`C@DwCPZEDCCKC\EYCCJCvE_CCUCECDhCXEC;DCECIDC@ECBDCEC@4DChEC@,DCEC)DCEC7DCEC@+DCECDCECDCPECDCECD~CEC)DCEC"DCPEC%DC@ECDCEC@DC EzC@D{C8EC'DCXECDCЙEoCDZCEXCDMC EFC DC`mEMCD@CyECCDGCPoERCC?C`vEJCDNCE9CDEC@uE7CD9C dEPC@D\CpzEQCDXCuE[CD}CjElCDfCȄETCCKC(E.CC=C|EMCDKCERC@D,C8EYCDCC@~EUC@ DKCpEDC DQC EACDCCEMCDACvEFCCGC~EmC DXC{ELC DMCȃEDCDFCЀEEC D?CuESCD_C E5CC=C|ESC@DGCECCCKCESCCJCEECC'CE9CCFCsEKCCNCpERC@DMCPE6CC0C]EBCD>CaEPC D]CgEtC"DqC_E[CD]CrEIC@DRCEXC@DXC@nEqCD_CxEPCDJCuEHC D@C`gE@CDBCqEIC DRCuEDCDLC_EeCDeC@bEFC@ DGC kEKCDQCeEZCDjC NEyC@DCUEC3DChEC1DCEC@4DCPEC1DCHEC2DCEC@3DCEC$DCECDCbEC@DCQECDCEEC@DC`7EC DCBEC@+DC(EC#DC ECD@DEDrD]DE9DD`DD.D DDD0D@DD@wDCD@D DfD@=DDDHD@/D DiD%D/D`D@VDC;DDLDC4DD;DC5DD2DC3DD@2DC7DD5DC6DD3DC@9D@D5DC@6DD5DC8D@D5DC2DD1DC@4D}D0DC6D D2DCDCEC9DCEC=DC(EC:DCEC6DC(EC8DCEC;DC`EC@9DCEC6DCEC@&DCEC@ DCECDCECPDCEC9DChEC@@DCECED@DD7DE6DxDDEcD DD+EmD`DD@D@EDfDhDE@DQD>DBECDD0EGCCvCECDDEC2DDpHE~CCC`EOCCCZE6CC[CWEFCC>C{EXCCJCEdCCSCEC(D{C0ECCDCECDDCECADCEC9DCEC6DCEC@/DChEC6DC`EC1DCpEC@/DC ECDCiE.CC3Cp,EPCC2CaEC@%DCEC!DCXEC!DCECDCЕETCC;C0gEWCDXCzEC!DCȚEC@DCE}C$DC8E.CDZC8E1C@D6CXE2C@DPCHEYCDOC8EbCDYCEhCDtChEZCDeCE\C DECE2C DTCzE6CC=C`vERCCECyEMCDFCiE9C@DMCpwEVCDCC(E`C@DSChErCDCpwEYCD\C`vE9C@D=CzEC@D-CmE0CD2CEECDCJECID&DHE'DDoDpOE@9D D`D5E@:D`DDECD DDEKDDD D@=DDDD@/DDDuD@0DD@pD'D/DzDSDC5D@D@GDC@4D~D?DCDsDD E,DjD@DDWD DDE@vDDETED@DDKEC*DC`fEvC@=DCpvEC,DCpfECDC&EC!DDDCD DDC DD@DC@DCDCDCp)EC$DCsEC+DCpjECDCwEC#DCsEC?DDqEC[D+DDE8D@yDRDD1DdD(DD"DhD&DC"DmD'DC@"DpD(DC%DpD)DC#DjD(DCD@hD%DsC@D@dD)DyC@%DlD/DC#DhD@1DC"DgD2DC@'D@wD@5DC@2D@|D6DC+DtD/DC@DSD0D@ZD%DbDjDE@D]DYD:EC@DD0DPNEOD@uDbD0EuD{DeD`D@DD@DDDDPENE@$DDD7ERCC\CSEC DCjEXCCEC=ErC@ DC]EXCCYCKECDC gEC&DCyECDCEC@DC`DC@BDD0\EC@QD DpmEDNDDDE`D@rDD`D@DD`D`EDD@DDDDD@D@D@DDD DDDD@gD DDD)D@[D@_DEC?D@DYE D@FD3D0ECJD@0DED;D8D.E D&D5DEDKD1DE@"D]D@KDE@:D@D@D`!E9DDD@ E-DSDIDD?D DD ED`DrD EC@&DDEHCCXC9EC%DCMEC@2DDPXEC DC`SECDCxE;CCKC@eEMCCICEnC D}CEC,DCEC>DC8ECFDCEC@GDCECCDCاEC9DCPEC@1DCEC/DCHEC@DCECCYC QE&CC5C:EYCDJC}EC DCxEC$DCXECDCECDCEqCDiCXECDzCEC@/DC(EC"DCPEC@&DCHEpC@!DCpEjCDnCEqC@'DgCEkC)DvCEQCDfChEC4DCEvC@D~C0ETC@DdCpEVC DdCeEPC DqC_EPC@ DVCeEUC DUClE4C@ DICvERCDSCEkC@D~CERCDhC{E4C D7CnE8CC5CE/C@D0C@mE7C@D:C@EOCDPCEeC%DwCEHC DAC}ECCCBCE5CDHCE^CDZC`EACCDC0xEACCMC@[ENCCWCsEBCC8CoE3CD5CE;CDIC@sE)CC3CPdEICCFCxE@CDACXESCDCC@wELC DRCEdCDUCЃECCC1C؄E>CDNCPE6CCACE@C D5C؅EEC@D@C`oEYCDGClERC DdCyEC&D}C`EVCDcCpE_C@ DZClECCC0C`\EPC@D?CuEKC@ DEC`qE[CDNC`jE.CC"C0kE%CC-C0jEDD@?DC2D~D@7DC8DD6DC6DD1DC@5D D6DC7DD5DC0DD3DC@0D~D+DqC2D}D,DC/D~D@)DC@:D@D)DC3D|D*DpC@7D@}D+DC6DD-DC@1DD+DC0DxD%DpC0D|D)DCC6DC ECHDDEC@(DClECZDWDbECKDDEC@8DCECDCEC7DC8EC@8DCEC6DCEC9DCEC@=DCECDrD DD@D`D E.DgDD0E@DmDD+EC@%DD&EC DCAEcC@5DC0kE~C:DCEC$DC lECHDCpEEC@0DC-EC@DCECD@ DDC DCDCD@DEC1DDEC@,DCeEC3DCrEC@PDDcECLD*D8E'D@UD(D D DnD%D@ DD@fD DCDnD@#DC@ DnD&DC%DqD+DCDhD@)DCDlD&DtC@D@eD@"DtC DhD@.D|C DfD@1D|C DiD/DC)DqD3DC@.DyD5DC-DvD@/DCDJDDD0DnDfD@E@GDDD`BED]DqDP2E^DDDE D@D D DDD`DpE@LD DD;EC1DC`7EJCCBCaEC@DCYE~C DC0eEC!DCEC@.DCHEC9DC ECDC[ECCC EiCCCEC@+DCAECFDDrE@ODD|D9ED`DDE D`D`DE@CD@jD@bD`D6D[DbDD@DDDD@DD DD@DDDDuDDDD@=DxDpDE*D@[DQDEC@)DDPECD5D0 E D@PD_DEC6D+D/EC;D6DE!DiDDE1DaDwD`D D$D,DD2D@hDDE@FD`DD0E D;DD`&EC@&DCiEC@:DD^E@DZD@iDp/ECD DE'CC]C`@E$CC1CWEZCDUCXEC@*DCxECFDC0EC@ADC@EC@FDC`ECADCУECADCEC1DChEC'DgCȨEC*DmCHEC@9DCEC+DChE]C@DcCpGE.CC$CEUCCsC2EYCDSC`cEUCCKCjECDC\ElCCaC kECD~C@EC"D}CEC DChEC,DCEC'DCEC$DC(EC@$DCEC&DCШEC+DCEC'DCHEC@"DCEC@'DCEtCDmCEbCD\CxEvCDyCoECDC0TEoCD}CpsEVC@DECЀELC DKCHEKC DKC(EMC@DLC(EACD=CzECjE+CCCpxE7CC9C~E/CC:ClE7CDHCcE#CDBC@rEHC DGC@EHC DJCECC D=C0|EOC DcCHEiCDyCEMC DKCEFCDRCEICDECEPCDMCpEGC@D6CpE7CC0CE"CD>CE?CD@CiECCCREJCCPCp_EiCDXCkE2CCDCgEOCDIC uEMCDHC@wE/CC.CECC1CETCCICHEFCDGC EJCDICEC-DC`EC4DCxEC6DCEC0DCEC@2DCةEC-DCEC@.DCEC@$DCEC'DCHEC@DCSEC@DCrDCDCDC@DDDCDC3DC DC@DCDD0 EC9DUDEC6D D6EvCDC?ESCC{C8EVCDCDEpCDC``ECGD3DUE@JDDDEEdDDD4ElD@D@DE`DDDD[D@DD`D@GD DDpD4D D`DD@6D@D@CDC3DD7DC7D@D6DC/DD3DC5D`D7DC0D`D@5DC8DD@8DC4D`D@9DC@7D~D3DC.D@D4DC5D D4DC2D{D3DC0D{D@/DC0DD.DC0DD@0DC@0D}D-DC@0DD0DC/D@|D@1DC@3D@~D@-DC*DxD.D}C-D@{D*DC3D{D@*DC1D@{D-DC.DtD)DzC(DyD*DuCC@DCECDCXECDC EC DC`EC5DC0EC;DC@EC@9DCEC5DChEC@:DCȧEC?DCEC:DCEC=DC0EC@@DCРECIDCEC@DCEC@ADCبEC@DCEC.DCpE|C DCECDCPEC4DC0ECEC5DCOECTD@0DiEE{C@DCPNEC@)DC6EC3DC@QECFD DpVECODChE D DeDxECVD@ DqEC2DCNEC(DDECDCDCCCDCCCDC@ DC EC*DCCECYDD_EC(DD7E@DSDDD+DrD-DD%DcDDC@%D@pD)DC@ DoD)DC+DvD,DC&DpD(DCD@nD@&DCDfD$DxC!D@lD0DC@!DfD@5DC DiD3DC*DpD1DC*D@tD6DC@3DqD/DC%D@[D#DC@ADxDiDD DDD >E;D D@DPEPDD D SEqDD@D@VE@|DDDQECbDCD@cEBC#DoC@qEzC$DnCPuEtC#DCpVECDC`DECDC8ECDC?EwCCC*E>CCVCD0CC?CD,CCfC@DCDC`(EC@RD/DTEFDD D8ED D DE`DDDE@zDDDE7DIDYD D@DD@D@DDD`DDDD@DDDD`DD`DDD`D@RDkDfDDC@"D@DD@D1D6DE0DD`DPECLDD0REDSDPD@9E@`D`DD(E@&DaDYDED@5D@ADD\DDDD%D@YDdD@ECD@D@ EDJD@'D0;E@D^D@D`GECEHCD;CEIC@DICE+CC;CE"CCChECCDICeEmCDhCEfC#D[CEgCDkCȏEiCDsC؃E[C@DjCEVCCgCpEFCCQCHEXCD^CECDCEC>DC@EC@6DCEC1DCEC1DCEC1DCEC-DCEC5DC8EC&DC0EC,DCEC DCZECDCDCDDDC@DCDCDC@oDCD@ D@DC+D@2D EDMDnDEC"D Dp+EmCDC0:E`CDCP[EC$DCgECDvCgECDC0FEC@MDDBE@D@oDbD0?E DaDEDP'EDD@iDAE@KDD`D?E\DD DpENDDD@D@>DDDD@DvDDp$EC!DC-EC-DC`ECFDDVEC1D@D&ED=D@$D`E@DJD;DED@oD?D`PE5D DDXECWD@D LEC\DCfEC@DC0GEyC DC-EhC DCP$ECDC@DC@DC`D DWDqDEDzDJDWECDCLEDKDD0E/DiD)DADD@bDDC*DnD(DC*D@sD*DC(D@tD(DC$DkD(DC DlD*DC#DlD(DC(D@mD@2DC DeD2DC%DfD,DCDC*DzD@?DC.D D9DC.DD@3DC@,D`D3DC@,D{D0DC2DD4DC4D`D@6DC:D`D:DC4DD=DC;D`DCDC;DDDChEC8DCEC5DCxECFDC8EC.DC EoCD}CHEC@DDCECKD DEC2DCEC6DC8EC1DCEC/DCpEC)DCEC/DCطEC3DCEC3DCEC2DCEC1DC(EC8DCECDC@LE$DEDCDpED@UD@XD E'D]D@VD E@D_D@6D6E DsDPD IED\DTDECDDDC@"D DDC@0DC0PEC3DCEC6DCyEC@'DCVED@JDDD>E)DDD,EC@JDD WEC@DCcECHDDE*DzD7D@iD(DcDDC@.DpD%DC3D@qD/DC1DwD0DC+D@pD@.DC'DoD0DC@4DlD@0DCFDDSDC-D@uD@9DC.DjD.DCRDDUD8DDD D@kD{D DDKD@UD}D]D/D@=D@jD]DfD@D@D D E-DqDfDEECDCVECGD@:DpMECeDRD@VEDD@fDgEC@wDGDPEC~D@1DEDDXDECJDD]ECCC)EjCCCEvC DvCUEnCDClEC@DC0TEdD@DD0'EDD`D@E_D@DD`ElDDD0E@D DDP7E D DD0E DD0E"EDD@D`EtDDD`D@DD`DDD DDDD@D`DDD`D@D DmDD`DD;DXDaDED[DSD`KEADDqDP^EMDsD7D0EDYD@-D0>ED'D DDC@DCPED@D@>D-E@3DlDD E{D DD>ED D@DFE@6DeDtDDE@D@ID6DBERD~DDpLEC@ED@DsEC"DCplEC@DCEC3DCxECDDCةECHDCPEC@BDCEC=DCpEC5DC EC?DCEC@9DCEC@1DC(EC,DyC0EC@2DCЫEC=DC(ECDCPnECCCPMECDCtE^CC_CPpECC}CWE`CCSCYE?CCJCmEVC@DTCECDCEC@+DCEC1DCEC0DCEC-DC(EC@(DCXEC@+DCEC/DCEC0DC(EC-DCаEC@0DCEC@%DCEC$DCEC DCXEC'DCЦEC"DCpEC@#DC@EC*DCEC@)DC EC@'DCEC@$DCpEmC DrCE}CDC0ElC!DsCE^CDWCETC D@CEgCDXChEwCDpCEC+DxCE[C@ DCEMC DgC؈EoCDXCEiC$DbCEtC(DxCxEC"DC`EkCDuC0EC,D}CEC@-DC(EvC$DC8EWC@ DWCEeCDMCEbC*DkCxEC@DDHDC7DD=DC3DDADCDdDD@D@DeDDD2D DED D0D DFDC/DyDKDC3D`DKD@DC'DCEC)DCEC(DCEC@1DC8ECDCEC?DCХEC9DC8EC4DCEC@:DCEC@DC(ECDCPECCDDEC.DC`EC@2DCػEC@2DCXEC7DCEC1DCEC2DCpEC1DCEC6DCXEC4DCEC7DCEC7DCEC1DCEC!D{C@EC@)DCEC'DCpEC@$DCHEC$DsChEC.DCEC,DCEC4DCECAD D(EC@VDDECFDC`ECD@mDDDD DDD@DD@DDDXD DDD2DDD@EC9D@D0VEC@DC^EC?D5DEED@OD@KDKEDPDZDOECODGD`[ECOD@?DfE@D|D@rDPjEC@ZD5DTEGCDCaEFC DKCkEcCDoCmEVC@D@CrECDCiE@EDoDpD@E DDD0DED@oD`D5E'D^DQDP0EDDD@KE DDD2EDDD.E`DD`D E@^D`DDDDDD`D~D D@D@DlD@D@DDjDD@DD@lDD D DYDuDzDDDLDFDpEDTDEDRE@D@`DGD`QECCDD[ECDC HECDC LEZDD@D6E DDD`:EDDE9EDD`DP:E@7DDDp>ECGDD?EC@$DCPEBCCuCE[CCOC3EmCDfCEC@7DCECADChEC@DCXEC6DCEC1DCEC5DCEC=DCХEC6DCEC4DC`EC)DvCxEC0DCEC9DC0EC@DCEgC@DuCxEwCCCZEgCCC@-ECCCLEFCCmC~EDCCEC؉EHCDHCXEWC@D=C(EcCCYCp{EC@D|CHEC&DCEC@"DCPEC)DzC@EC@.DCHEC@)DC(EC-DChEC@2DCEC0DC`EC(DCEC/DCXEC@(DCآEC@'DCXEC"DCЩEC"DCEC$DCxEC*DC`EC(DC EC2DCEqC.DCEC7DCEC@5DCPEfC!DkCEkC%DfCEoCDjCEzC(DChEC@1DCEC>DCEC2DCEC0DyC8EmC%DCElC@DyCEC$DCEsCDzCEzC#DwCE`CDcCElC@(DqCE]CDjCE~CDoCPEC6DCxECADCEC>DCȭEC@BDCECCDCEC=DCEmC(DzCXEaC@)DCpEC@&DCPEC/DC EdCDtCXE}C$D^CEC8DCxEC5DCEXC@DLCE[CD]CEeCDlCEC5DCEC,DCؑEC$DC0EC/DCEC;DC0EC7DCEC2DCEC/DCEC5DCЩEC4DC`EC/DC@EC@(DC8EC@DCECDCDCDDCDCDD8DCDD`DC D@DECD@DDCCCEKC@DnC2EVC@"DUCmE[C@DmCPjEbCDZCmEC!DtCrEuC(DlCpEC1DCEC@:DCECDC$ECDCp4EC@,DCEC@'DCЗEC@%DCEzCDtC`EXCDyCkEC3DC0tEC-DD]EDmDND"EC)D@#D@DC@DCDC-DCp)ECeDPD0-EDoDzD`DC@ DCDlCCCDC DCDCDCDC D@ DD DLD7DlDD@HD@D*DC@9DCDCDCYDC#DCDCDCDCDCECDCEC/DCE]CDC EC@5DC؊E@ D@bD@EDED`D*DxECADChEC0DCxEC5DCHEC8DCEC@7DCEC5DCEC@7DCEC6DCxEC1DCxEC@6DCEC@0DCEC@%DCEC#D|CEC+DCEC@$DCEC@(DCȁEC)DC8EC6DCEC4DC8EC;DCECFDCECHDCXECFDCECPD@D8ECGDC@EC1DCEoCCCtEwCDC{EYC DC0EoCD~CXEC#DCEC#DCEC.DrCHE|C&DC`EC@DCECDCECDCp~ECDCpECDCqECDC@EC&DCtEC@DCpEC6DCoEC$DCyEvC%DC8EC4DC(ECECDCخEC@4DCEC@8DCEC9DCE~C@5DCEC5DCEC-DCȩEC@)DCE8C@DLC0EECDOCExCDyCؠEC-DCEC5DCpEC9DChEC7DCEC;DCHEC7DCEC7DCEC@2DC`EC/DCEC7DCEC@:DCEC*DCxEkC@DfCHEVCDWCEoCDpCElC@DgCPEaCDmCE[CDkCElCDCECDCEC#DCEC?DCEC>DCEC6DCأEC@3DCEC3DCEC0DCEC0DCثEC+DC ECDCECDC 1EC DCDC@D@D@'DCDD2DC@D D`DCD@(D`DCDCDICCoCE[CDvCUEC=DCtEwC@3DCpmEcCDcCkEC#DvCzEvC@2DwCE~C.DCEC@DC`sEC@ DC EC@!DC?ECDCbEC"DCEC.DCECDCEC#DCtEC&DCfErCCC$ECDC ECDCPECDCEC@$DCCECD!DEC DCpEDCCKC1EOC@ D_CPMEhC@ DiC;ElC DxCEbCCCEC@DCDCCC DOCC|C D?CC?CEmCCCP2EC%DCp6EzCDCEC@ DC ECDC*ERCDnC`@EaCDfC2EC/DCEC4DCEC@0DCEC@&DC(EC!DCEC@+DC8EC8DCEC;DCxEC>DCEC;DCEC7DC8EC4DC EC5DCЪEC/DCEC3DCEC7DCEC:DC EC(DChE{CD~C`EC@,DCYE@-DgD`D1E@DeD6DpEC@]D@DECADC8EC0DC8EC4DCxEC@5DChEC6DC8EC5DCEC@2DCPEC@*DCEC+DC EC'DCEC&DCEC@5DC8EC5DCxEC'DCgEC@DC4EC@DC}EC4DChEC5DC(ECGDCECDDCEC?DCECCDCECHDCEC8DCEC9DCECD,DHEC@BD7DDEC@;D5D@FECFD;DOEC@DC2EGCD`C@aELCDFCPjEsCDkCpYEZCDSCsEXC@ DUCyEC@DC`^ECDCPJECDCVEkCCCVEC)D@DPOE DpDKD0aECSD1D?E@DCD@CD@EDHD@=D`E@DpDdDEDDDD}DDDD@wDD@D@DwDD`DD@pD`D D`D]DzDDD@DRDZD` E.D~DD@(E%D}D@{DED@TD@AD0EC4D@D=ECTD:D0$EC#D8D DDBDbDD-D@DlD&E DFD@/DECD@Dp ECED Dp_EC0DCETCDcCEnCCdC}EC@4DCEC@?DCxECBDCEC.DCEC@2DCУEC:DCxEC7DCPEC2DCEC9DCPEC@4DCEC4DCEC@?DChEC;DCEC@%DlCExCD^CEC&DCPEC8DCEC0DC8EC1DCؤEC@6DCȥE~C@!DyCEUC D^CHEC@DoCECDCEC@!DxCEC@"D{C8EC'DoCEC@3DuCEC@;DCxEC@4DC0EC6DC(EC@7DCXEC2DCEC*DCHEC@*DCEC#DCECDsCECDuC0ECDCEC,DChEC@3DCEjCDaCEcCDaCXEcCDdC@EQCDKCEC!DC(EC9DCpEC@0DCФEC@-DC(EC0DCpEC/DCEC@/DCEC1DCEC6DCPEC6DCXEC4DCEC6DCHE}C DyC@EvC$DuCHEC@&DCPEC-DCpEC(DCEC,DCȷEC3DC8ECDCXEC@DChEC:DCEC/DCEC*DCEC@+DC0EC1DCEC1DCEC"DChEC DC=EC DCDCDC@9DCDDDC@DDjDCD DDCD DD=CCCE\CDMC@JE\C!DpCdEZC)DuC`nERC,DeC0uERCDNC0uEjCD[CpsE|C)DmC0mEQC"DCtECHDCpjEC4DCQEzC@ DCLEpC@$DCPEC@:DCECKDCEC@9DCxEC9DCECDC@{EC@DCkECDCP^E{C@DC 1EVCCCECCCECD&DDCDC*E3C@ DSCaEdC DpCERC'DgC qEcCDxCEE]CDxCE`C DPCUEpCDxCWECDC BEPCCxC@ECCC&EC DCGEBCCSC0FEOCDXCQEC(DC`EC2DCEC+DCEC*DCxEC DCEC$DCEC:DCEC@=DCEC7DCؗEC9DCXEC8DC EC6DC`EC@6DCPEC3DC8EC7DCEC8DCEC@&DC`EoCDCtEC DCEC8DCEC@DCkEC+DCqEC@?DCHEDeDDECODCEC@CDCECCDCEC>DCXEC5DChEC.DCHEC$DCHEC0DChEC+DC@EC2DChECEDCEC9DCECDC4ECDCPEC)DC0dECBDCpEC>DDECNDCECFDCpECBDCPECFDCECBDCEC@CDC@ECHDCECVD@ DHECQD DxEC@NDCECJDCEC!DCxE;CDSCxE8CC2CzEQCCKCtEqC@ DqCECDCECDCPEC@DCEC@DC{ECDC}ElCDC`EC@DCEDNDDPYEC@3DC0}EC(DCECDC8EC@ED)DEECDCp:EhC DsCeEzC$DwCPvEpC@D`CEC DC~EC"DC ZE@'D[DvD0/EiD DDEDDDD`DDD0 E@CD`DD E>D@aDtD`EDDDVE@DD`D0`E&D@D D,EmDDDD-DQD@MD@DC5D@D EC DC,E{CCC.E~CDCEEC#DCEC.DC@bEC*DCxEwC DCvE\C DmCTEC@DC`fEC0DCEC#DCxEC5DCE D@JD@DD@DTDD@.DD@`DDC&DfDDC@.DrD&DC+DsD(DC*D{D5DC@)D@wD@/D}C%DpD(DqC@%DhD&DaC@(DjD&DqC!DlD,D}C'DnD2DC+DoD7DC*D@xD4DChD D@bD@ D@DD D DD@DDD@DDD E:DnDtD`DCCCDCCC DCDC0EC#D@ D-EC@4DD0AEC9D$D0KECDCECDCECDCEC=DCEC:DCEC9DCECMDCxEC@1DCElC@DeCHEhCDcCEKC D[CERC@DMCxEeCDYCEmCD|C(EC@DC؍EC/DCؒEC@9DCEC@9DC(EC7DCEC@3DC0EC3DCEC@1DC@EC8DCЭEC0DCECDC^EC@ DC DC@D@D@?DCDC@DCDDDC@DC`DCDCPElCCCE0CCOC 4EfC!DkCPWEeC@DuCPgEmC/DoC@xEsC/DsC~ElCDhCEqC@DuCpzEtC#DkCqEC@@DCXE@DD;DpFE@=DbD D"ECDC?EC%DC iECECCCVCEXCDeC3EACCiC`5EJCCOC0*EECCYC2ETC@ DVCeEeCDiCyEeC@DvCERCDCXEjCDCDEgCDC*ENCCbC&ENCDhCWEC%DCEC'DC EC"DCEC+DCEC!DCEC&DCțEC8DCEC@=DC8EC@.DCPEC@5DCЛEC@/DC0EC3DCХEC7DCEC7DCPEC@3DCEC DCETCCPCmECDCiEC@;DCEC@?DCEC2DCEC@DCE{CDC0yECCDCECVD@ DECNDCEC@DCXEC8DCEC-DC`ECDC0ECDCEC4DCEC2DC8EC@5DCECEDCEC=DCبEC@ DCP(EC@DC@ECCDDtECJDDqECFD@DECLDC8ECCDCEC>DCEC;DCEC7DCpEC;DCЍEC@CDCxEC@LDDECMD@DECKDDECJDC0ECDC oETCDTC `E,CC-C|E1CDDCpEC@DCEC=DC`EC9DCEC4DC0EC(DCXEUCCCCPvE0CC-CprEXC DTC8EC@#DoCEC@,DCEC.DCشEC/DC EC@+D|CEC@2DCEC3DCEC@;DCEC4DCHEC7DC(EC3DCXEC@3DCEC*DxCECDCEC DCEC3DCECDCETC@DtCEC"DCECDCEC0DCEC@=DC؞EC6DCPEC5DC0EC@0DCEC6DCEC9DC8EC9DCEC@9DCEC9DCEC4DCEC0DChEC@"DCEC1DCEC8DCXEC8DCEC@:DCXECCDIC0iEEC#DMCqEjCDhCwEC'DmC@|EbC+D[CETC!D]CvEiC%D}CmEWC(DYCnEC>DC0vE DD7D0EIDQDCEdCDC=ECDC]EC!DC E@D:D@3DECDD`ECDCpOEC@9DCpEC_D"DȋECSDD`dEC9DC.E D@DCEC@FD DEC>DCȫEC#DC ECDCHEC:DCEC@KDDpEC=DCȭEC@DCECEDCEC@DC0EC!DCPjEC.D@DOECJD@5D`lECAD@ DEC?DCEC@CDCEC@>DCEC@9DCEC@3DCEC6DCECDChEDhD^D؁EC@;D@&DpdECD5DP!E*D[D@D1E5D@pDDE+D@DUDD@FDwD@{DDqDD@DD`DDDD(DiDUDED@dD@BD E@D0 EDIDID`D@D1D@&DED*D@.DDD@;D6D@DYD@yD`D DD`DDDD`D DDoDDD@DqD}D`DD@kDDD D@3DeDVDDC@DCECDDDCDDDCID/DEDaDKDDCxEC9DC`EC1DC(EC;DCECMDCEC@3DCXEeC@DnCEOCDSCE\CDYChEEC DFCEcC DyCECDCECDC`EC@%DCEC=DCEC@3DChEC=DCHEC=DCHEC7DCEC9DCEC@5DCECDCsEC@DC@DCDCDC@D@DDCD D`DC@ DC`DMCCC ,EYC DYCDE]C DVC]EBCD;CsEDD؈ECDC(EC@D@ D^E@4D@~DcDCE@D@D_DEC)DC;ECKD@DEC2DC0QECDCfEbC@DZCjEC@DClE@D[DKDQEDDDKEcDD@DEE&D@DhD1EDkD@ D`DDOD DCD[D@ DC*DhD@DC+D@pDDC1D@xD)D{C3D@}D2DC4DD7DC@/DD0DC)D{D-DC"D@jD'DbC#D@jD%D|CDkD'DxC D@fD DC@TD`D@iD@D DDDD5DD7DC.DxD.DCCDnD?D@WDD`DDDvDDdD DC@"D#D DCCCDCCCECC\C&ECDC@E4D@sDD@D@9D@nD@yDD D/D@,DDC DDDC DC@ EC DCECCCECDC]E}C-DCEC DCiECDCpuEC+DCPrECND@/DaEC,DChEC*DD-EC@$DDEC.DDAE@#DWD@HD EDJDADE'D@ZD@ODDDDD DDD DDyDD@DD@oDD@D@D@oD D DD@_DD~DDC@,DC9ECDC5ECD-DD:DrD@~DPEJDaD@DPECDC0E_CCkC`TE^C DC]EC DC@:EzCDC?ExC@DC6EC DCPKEC*DCsEC6DC`EC8DCEC5DCEC:DC`EC@DCpECDC`ECBDC@EC@BDCȠECDDCEzCD\C(EC3DCEC5DC0EC5DC0EC6DC ECADChECDCETCCjCPKECDCE~CDCpEC,DChEC9DCEC@>DCаEC8DCEC0DCPEC0DCEC,DCEC4DCEC@6DCXEC@4DCEC)DCEC'DC@EyCDxC~EC DCEC@DC EC5DCEC;DCpECGDChEC@9DC@EC2DCEC@-DCEC(DCEC#DC EC+DCبEC@/DCEC/DC8EyC(DCEWCDSC؉E-CC*C EC@DwCEC1DCEC3DCEC9DCEC3DCEC9DCEC9DC@EC4DCEC@DCЛEC)DCؙEC@&DCEC!DCECDCpEyCDCEC)DCEC>DDECRD+D`jEDNDBDQE@DSDKD@MEC:DC(EC@DCECCCE\CCJC@DSCC\C $EC!DCEC;DCȓEC2DChEC@)DC(EDKD(D~E#DDSDЁE@D{DBDEC@DCEC9DCEC8DCEC@/DCsEC2DC~EC@BDCE}C,DsC8EqC&DcCElCDcC8EJCDICxEfCDKCEC@"DCEC2DCnECDC0`EC@.DChEC$DCqEC@DC0WEC#D DEDFDD EC4DD0?EC@DCpaEC@DCqEC1DC:ED:DD0EDDDD^D@gDDPEC DC ECDCPbEC2DCRE@*D@iDOD`EWD DD ED@]D@ZD@!ECDCDC(DCE D7D!D05EC0DCprEC+DC`ECeD2D]EDD@DDED`DlD`OEDSD@'D&E"D@mD@*DDDSD DCDXD DC'D`DDC/DpD"DC4DD.DC@5D`D@3DC3D`DDCECDCECCDCȢECADC(EC@"DCECDClEyCDnC0tEKCCBCVEYCD[CiECDCpEC@$DCEC@.DCEC@2DC0EC2DCEC@2DCEC8DCECDCxEC+DCEC1DCxEC&DCECDC EEC*DC0QECYD"DpECUDDXECHDCEC@ADC؝EC6DCEC@1DCEC%DCEC@%DCEC#DC(EC@.DCEC@-DCEC@0DC EC@'DCE[CCECp~E2CC:CEC+DCEC@DCECDDCxEC>DCEC>DCEC=DCPEC=DCؿEC@0DC(EC6DCEC8DCxEC@EDCECJDCECPDC0EC4DCEKC@DiCEPCDOC|EFCC=CyE4CCFC[ECDzC0|ECDCEC1DCȅEC?DChEC?DCECDC8EC@/DCEC2DCEC:DCȧEC,DCEC1DCHEC@-DCEC)DC8E}CDCpEC@DCEC(DCECADC0ECLD@ DrE D@\DVDKEDVDuDP&ECJD:DSEC7DC@EsC-DCEkC@DxC@]EqCCCLECDCEC2DCȬEC.DCEC#DCECADCXE DaD@5DE'D@D@\D lE@DtDPDPTEC@2D@!D +EDKDIDoECeDDECSDDECYDDEDiDcDgECfD:DxECGDD EC;DCPEC@9DC8EC2DCКEC-DCEC2DCEC9DCEC2DCEC4DCPEC-DC0EC3DCEC*DCECDDD8EC@8DCEC$DCEC)DCXEC%DChEC@.DCEC@4DCEC@.DCEC@3DCEC'DCqEC@DDD{ECXDC0EvC;D`C@EPCDLCEZC@D=CؑEWCD0CERCDfCXEC)DCEZC@DlCgEjCD[C dEC'DC`pEXC DYC^EuCDnCP`E@D@6D@)D5ED7DDPEC@D-D0*EmC@)DC0rEC1DCEC@1DC1ED@:D1DDcD DD@DDXD@7DE~CDCPQEC4DCpgECDCP7ECDCE'DZDQD E/DxDeDECQD D@[EC)DCBECDC0ECDC+EC@DCPbEC@.DCPaE@CD DDPECeD@3DXECDDDp1E@DkDD@oDD@SD@DC@D@TDDC%DeD@DC+D@sD&DC@6D@~D1DC:DD@5DC@:D`D8DC/D}D-DpC$DqD*DqC#DjD%DaC$D@mD!DpC@'DkD@&DC@(DvD-D|CD D DD@ZDDMDC0D}D(DC'DtDDCID@eD@DC`DD6D D,DIDDEzCDCp.ECDRCoEC@7DC0zEdCDmCЂEC@DCECDC~ECDC06ECDC0EC8DD`EC@#DC`SECDtCpiEC DCAECDC}EC@/DC`ECDC`AEC DCPLE@DGD-DDEC^D^D D@DOD@>D`EC:DD EC)D@DpECDC'EC$DCPHED@+DDEC3DDPEAD@yDwD@EDD@DDD@DD DDDDD@D@DD@DDD D`D7DxD@dD0EC3DCaEC3DDEECDC EC@ DCIEC@DCSEDxD@bDeE@ D@WD@NDPEUCDCwE`CDTCȃEjCCbC9EpCCC)EECCgC ECCC@ EC'DCEC6DC EC6DCȜEC4DCEC@8DCEC"DChE#CCC`=E2CCC8ECDCfEC:DCEC@DsD~D?E+DaDeD`9EDaD7DECcDDECVDDE@D@hDCD{E(DtD|DXECbD2DECNDD(ECGDC EC@ADC`EC7DCpEC0DCHEC1DCEC5DC8EC2DCEC5DCEC9DCEC@2DCED@/DDbE:D@]DdDQECVD+DjEC.DCECFDCECIDCEC@?D@DHECCD@D،ECMDDECYD@ DEC@@DCpECDC7ECDC.ETCCNC@PEACCBC YErCDVCEhC@'DIC0EC@DC(EC&DC@EYC@DqCyEnC!DbCEhC!DxCxECC DDC yEmC@D~CECDCIECC@DEfCCsCpFEC7DCЀECDD@DdECDCECCDCгEC@/DCXEC6DCEC5DChEC9DCxEC8DCHEC2DCPEC3DCأEC0DCpElC DvChECDCE{C@DrC@EQCC_CPELCCNCEYCC@C`E2CCZCqERCC]C[EC DCEvC@DmC؁E(CDTC }ECDC ECDCwEC@ DC`OEC*DCPfEC@KD0D@xEC0D"DdEC3D@ D _EC@2DCiEmCDCiEC DCEC=DCEC$DCECDCPEC*DC@EC4DCEC,DCEC0DCxEC.DCxEC)DChEC@0DCEC5DCȹEC9DCEC@?DCEC=DC@ECEDCxECGDC(ECKDCECCDCEC>DCECCDChEC@ODCECLDC8ECPDCEC(DCHEGCC>CUECCCiC0pEVCDjC E`C@ D{C؅EC'DCИEC%DCECADCECBDCȢECHDCECFDCECDDCHEC;DC8EC@7DCEC4DCEC@3DCEC@ DxCEC DCpuEC!DCECDCE7CCFCUE@CC9CpTECCC7CqEOCDVC }EACD@C~EECDAC|E]C DUC{EYC*DpCEEC'DJCE?C D\CmEpC@DlCPqEwC/DCEC>DCE9D@dDD#ED`D@DE:DDdD5ECDCBEC@DC0LEVCCsC-EgCD}C HEC-DCpbEC,DCbEpCDCP@EC"DC4ECDC)EC@DCFE_C DC=EgC DqCOEC1DCplEC#DCSEC#DCp^ECCCpVENCDsCeEKC@DbCȂEJCC_CmEkCC`CrESCCCpECDCЊEC7DCE=C@DC E\C(DvC}EbC@DC gEC@DC0oEC=D@D`EC@-DC0EtCDCREyCDxCECDC`ECDyCEuCDC8EuCDCثEC#DCEC-DC`EC1DCEC?DCȜEC5DC8EC*DCECDC(ECDCEC5DCECBDD}ECQD*DkE@DXDEDVE@ D@[D@cD?EDODeD$E DHDUD-EC@HD:DJEC@KD@D0wEC;DCEC.DCPEC.DC0EC?DCEC4DCHEC&DCE~C DCxEC'DCECNDDXEDmDBDwE D@hD>DECVDDEC[DDECQD@ DEDtD~DVE/D@zDDpRECUD#DECLDCPECEDC@ECJDCECADC8EC;DC@EC8DCHEC5DCHEC@3DCEC=DCEC@?DC EC3DC8E@TD D@fD YE=DDD0JECTD@DpaEC)DCEC5DCHEC@>DCEC@=DCEC@@DC؍EC4DCECBDCXEC+DCPbECCC WEQCCsC9E7CC>C:E1CC'C@LEKCDNCpqE?C@DMCzECDC0gEC@*DCcEC@!DCELC@"DOCETCCOCeELCDICwEMC@ D`CHE7CDYC`cEDD8DD0D@sDDCDSD DCC@*DCDC DC8EZCD`CmE?CD>C{EICDFC|EhCDPCmEjCCCVECCCEC@ DCPECDDD@(DqDD#EC@ID DFECDCBECDC:EC DC FE@D@JDDPZEC3D DoEC4DC gE@D D@LDPME-D@~DTD@(E"D`D@:D 1E@$DTD@D"ED=D'DECDC9EC.D@ D*EC-D D@DC3D,D`E D@QD@VD E@qDDDEDDD@E D`D@DEDDD0EDDgD`ECBD DECDC`2ECDCpEC#DCEC%DD ME"DSD@ID0E@`D@DD@ECD@DDEC@:D@D7ECDC?ECDC :ECDCTEC&DC{EC)DC|EpCDCaEC&DCwEC@3DCExC@DCwE+CC2C4EPCC-C`%EPCCGCVENCCVCyE:CCFCpvEC@DChECEDCHEC?DCEC5DCEC?DCгEC5D}CPEC3DCȰEC@;DCPECDC0EC9DCHEC2DCpEC3DCEC6DCEC!DCPETC@DzCЋEDCCOC`EsCD|CEtCD~C EaCDZCEHCCVCErCDrCP}EoCDCȅEC3DCEC@@DCEC!DCЈE}CDC\EC)DC yEC=D@*D~ECNDD0wEC:D DjEC#DCZECDC@EC&DD KECLDDEC9DCPEC2DCEC@&DChEC/DCEC1DCEC(DCEC+DCEC(DCEC0DC0EC@4DCECCEQC@D[C_EC*DC^EC-DCECBDD@}E@oDD DJED D DGEkD`D@D1EC@$DC)E}CDC0NEaCDCXEWCDbCȂEbC@DtCjEpCDC@EWCDXCIETCDvCdEhC DCMEgCDC>EPCCC3EXCDeCZEC7DCEC8DCPEcC@DCTEFCCC2E;CCLC@8E0CC=C`_EZCCoC`eEnCDpC0lEC+D DE DDDE DD@DEBDDxDxEC2DC`EwCCCJESCCCYEC=DD@}EC4DCHECDC0\EC DqCECDCECDCECDCЬECDCECDC@ECDC8ECDCEC2DCEC4DC(EC%DCEC+DCEC@*DCxECJDDECFD@'DgECIDDtEDTD2DeED@XD@KD`PECJDFD09ECCD@5DGECED2DNECJD.DZECAD D~ECADCECBDC@ECEDCEC7DCHEC@%DCECDCxECDCE DWDDP`EHDkDbD0ZEND DD@`E@ DD@jDPtE@DuDdD}EDeDLDdED@jDrD`>ECbDND0YECKDDEC@IDCECDDCECNDChECIDCEC@JDCxECFDCEC?DCEC7DCECADCEC2DCȕE@DGDDЂE}D`DD =E1DvD@QD@XEC*DCzECDCEC@DC0EC#DCEC$DCEC.DC؄ECDCECDCkECDCHEC@MDC@EC>DCLEiCDNCdEOC@ D3C8ElCDDCPEUC DNCEmCDsC`gEC DC[EpCDRC`kEOCD7C|EkC DCCEfC$DoCEgCDaC@uE`CD`C8E_CD_CxElC'DCECDCME@D@:DD@ED@9D*D DD,D%DDC#DCDhC*DC^EsC+DCiEC@0DCp\EC$DC[ECKDDYECtD-D`EC@MDDp+ECLDD@ECJDCp=ECVDD08E@D@vD5D~EKDD`DvE`DDEbE`DD DIEDcDMDhED@jD3DSEDaDDDDZD@DC+D@hD&DC,DD4DC5D~D2DC9D@D8DC@3DD@4DC4DD3DC*D@tD-D{C$DpD$DpC!DjD&DiC DnD(DC#DnD*DC@*D@tD@0DC8DuD@4DCSDDQDDPDD@WD@ D@0D@xD-DDDND DDC@DCDXC@D`CEEhCDYCPrEWC@DXC pEvC@DgCuEkCDhCpE~CDCPEC.DC`cEC@%DC6EC@ DCECDC#EC DCD*D1EC@7D*DECDD#EC+DD-EC!DD@EC&DCDCEC8DCEC@=DCEC@DCEC@GDCECBDC@EC?DC EC>DCEC>DChEC@:DCPEC$DCXE~CDCECDCEoC DC8E\C@DPCxEkCDdCȃECCxCECCC`rEC8DC@EC@DCeECDCTEC%DD@gEC:D@DnE@D_D@IDpkED]DkDsED{DRDEC+D3D`CEC@9D D E D@zDTDdED@oDUD0ECFDCECEDChEC@9DCEC1DCEC@0DCEC&DCEC&DCPEsCDqCEzCDC EC,DCECFDCECEDCEC.DC@EC@&DCpECEDCتEC@>DCEC6DCEC5DCEC7DChEC5DCxEC2DCXEC@C`E}C@DCECD,D-EDED%D7EDCD&D1ED@5DKDED@CD>DDZD~D@}DDADhDfD`D@DHD(DGECHD D@E@]DDDpzEdDD`D=ED7D@KDDD@PDSDDD]DSD0+EC>D*D@EC@DC +EC@"DC GEC DCXEC"DC@ECDC@ EC&D@Dp(ECDCREPCDcCXEnCDC(EmCDC bE_CDsCIEMCC@C0_ENC DJCEbCDaC`EXCCC`E\CCbCEdCDC ECDCEC?DCPEC9DC0EC8DCEC8DCECBDCECADCECFDCطEC=DCECFDC`ECBDC8ECEDCEC@CDCEC@DCPECADCEC@DCpEC7DCpEC6DCEC@.DCpEC@DCEiC@ D\CEMC@DICEXCDxCuECDC@jEXCCgCPcEqCCCp\E:CCC@ExCCC)ECHD@D0oE@8D`DDwED@DD_EZD@D`DPKE@DyDkDGEC@#D+DECDCDCJD@DDECFD&D@PECDDC@EC@IDCȯEC@GDCEC-DCEC@#DCxEwCDCE{C@ DyCE.CCC6ECC C0E#CC;CTE`C DjCpWEPCD]CQECCC&E9CC4C0aEC)DCpEC,DCpEC%DCEC*DC EC@4DCECCDC@EC@UD DED@VD DECODCpEC#DC8EtC+D}CȧEC6DCpEC>DC0EC6DCEC(DChEC-DCXEC@UDCECSDCECJDCECGDCEC@3DCpEC"DCEyC DCpEyC)DCEvC#DCE~C DyCE>CCAC`VEGCCXCCE|CD|ChEdC@DwCyE[C@DbC|EqCDkCyEuC@%DCPEyC/DC8EwC1DvCxEiC2DmCE^C!DaCpEEC@D?CEDCD;ChE_CDiCyE\C@DgCfE&CC$ChECC(C0REC DjC0EE@CDC9EKCD[CdE_C@DuC@xEXCDeCEXCDXCpE2CD[CeE[C@DNC0iEMCDYCXEpC@DgCEGCD^C`EvCDC|E~CDCnEC DCqEC@JDCECEDDXE`C@ DC0kEKC D\CTE:CDFCE5C@ D@C(E_CD]CE\C DC{EBDDMDjEJDDxDHE@EDxDsD=EaD@DD[EC3DC >ENCDrC@mECDCsEC@%D@DcEC)DCkEC#DCpzEC DqCpECDyCECDmCEvCDdCEC@DsCȭECDCpECDCECDCECDCتElCDsCؓEC$DCECADDXE@DJDRDYEC@IDDDbEC?D@D pECED DvECDDDECHDC،EC@KDCECHDCECCDChECEDCАEC;DCEC4DCEC DCPxEC@DC_EC:DD>EDXD@:D*E[DoD@UD@EDDD&E E$EEGED@DDuECKDDpEC7DCEC=DCXECHDCИECADCEC?DCXEC>DCECADCEC;DCEC/DCXEC*DCXEC3DCЊEC@2DCEC@DC ECDCECDCEqCDCEC@DC@zEvC DCXElCDCphEiCD{C@E}C@DCEJC DhCpEECCdCp^EGC DWCmE`CDSC}EhC@D[C(EpC%DpCE_C D`CPzEVCDaCEAC@DECPEUCDJCEEC@ DJC@ERCD\CE[CDcCExC-D{CȔEC0DCEC+DCPdEC,DD"ECDD@DCDC ECDCp#EC+DC$ECDC@DyCCC`?E}C(DCyECDCDEC-DC \EDhD9D8ECVD6DEECRDD1ECDC&EACDhC`REC0DCptED@wDiD`CEED0E[E E *E)EPtEWD`DDppE@/DDD>EKDnDqD`DQD@|D@HD@?DBDwDRDC5D@D=DC,DzD/DC*D@{D-DeC@/D{D@0D~C5D{D-DxC@-D D0DyC)D@pD%DC,D@{D5DC:D@|D@ADC'DsD,DC*D@oD1DCIDDSD@DRD`DRDC8D`D;DC,D@sD@'DC%DoDDCDKD D+DC@DCE\CDZCqEtC$DWCEiC@!DQCEVCDPCEfC DjCE6C@DWCqECD@ DpSEDzDcDE@%DqDD EC2DD:ED@YDDD2DWD`DEC@BD@DcEC@'DC@rEC*DCMECDCpIECCCaECDC 8ECD@ DEC9D0D'EC:D"DiEwCDCEC@0DCECDC`bE)CCHC@WEFCD9C ElCDaC(EiCDhCȊEsCCbCEHCCYCEWCDoCEC-DCxEC@ODCEC@8DChEC8DCEC;DCxECGDCXEC@FDC8EC@JDCаEC@ADCECFDC@EC?DC(EC@IDCpECDDC`ECJDCECGDCЯEC=DCHEC9DCEC@9DCجEC3DCHEC DCEC@DCECDCnEjCDCCECDCSEXCCvChE:CCZCNE4CC9CMEC@DCZEDDDDVE`DDD LE@oD`D DP#E(DWDkDDC@"DDDC@DCD DD(DDCD D DCDD EC@KD@DIEC+DCEC@DCHEFCD[CjE*CC2C`AE7CCOC`REhC D}CpiETCCHCcE3CC.ClECCCPSECCC`6ECC C:ECCCWE=CCICEC*DCEC:DCЌEC@9DCEC@DCECGDC0ECNDCEC\DCECIDCEC>DCEC@4DC@EC4DCEC8DCEC@?DC8EC2DCpEC DC(EC@6DCECWDCECUDCECYDCȧEC[DCȤECDDC(EC*DCEC%DCEC!DCEC"DCECDCEkC@DcCPE|C#D|CExC!DCHEgC(DrCEC@.DC0EC?DqChE{C1DCEC@1DCE{CDCEzC)DCЉEmC.DwCHE\CDRC`E`CDACxESCDaC@EQCDUCpyE!CD=CEWC@DdCEICD]C؁E>C@DLC{EVCDmC`EMCDVC{EDCDUC@dEZCDgCUEkC5DmC@E^C(DPCELCDMCEzC&DwCzEoC$D{CHEC,DCECDCEC'DC}EC@2DCECDCfETCDsC_E]CDCgEIC@DiCE.C DTCELCDoCEC&DCHEC)DD{EC@.D@#Dp_EsC DCUECDCRECD}C`mEqC@%DCECDCgEC8D@D]EC+DC0oECDCpE|C DoCpEC@DvCEqCDYCHEvCDcCEqCDoCEC"DCECDCEcCCQCEpCCCECDCECDChEC9DCPEC@=DCECGDC8EC@BDCpEC*DCEC(DC EC@#DCEC@DChEC!DChECDCxECDCEhC@D~CERCCQCP_E?CDHCoEZCDXCrEeCDMCECDyCHEWCDbCuEC DmCPEtCD^CECDwCEC4DC ECLDCpEC6DCXEnC0DqCETCDACPEXC@D5CEPCD?CЂEzCDCmEtCDrChEwC'DCECDCTECD%D0EDXDECDDDCDCPEvCCCEmCCC)EtC@DCZEC DC MEC@>DCpiEDvDEDCE@DD`D3EC@?DD` EqCDxC0VEyC@!DC(EC@*DC0tECD*DjEfE@8E_EPEE`pE.EE=DQDmD ~E@D@D[D EHDxDSD{DbDD D DoDDDDGDD`D0D0DxD4DC1DzD@-DxC4D|D/D}C3DzD/DiC@.D{D@0DC&DlD@%DwC>D~DCDCSDyD@MDC@'DtD@+DC,DzD;DC@JDD@QD2DuDDcDVDADDHDD@;DsD@8DC-D@jD+DD@ DRDDDC+DCLEC&DC@uEC,DC\EvC(D~CwEvC@)DZC E{CDlCPEiCDqChEDECDC,EaCD~C@E_C DfCEC@DC{EC(D@D;ED@^D@]DPEC0D@EDDC@ DC@ECDC.EC DC%ECDCEC@%D DEC.DDIECHDDPE&DUDUDEC@'D@!D`ECDC0-ED7DDE@)DHDVD E&DUD@JDD@D?DD ECDGDD2D@@DMDD2DLDZD D>DuDdD E@D5D@-DDCDDEC@ODDTEC4DCzEdC@DSC]E;CC>CPOE2CCHC GENCCdC`EBCC\CP^E-CC+CVE4CC)CPmE)CC%CPnE=CC8CE7CD>C8EGC DDCEhCDkCEC1DC0EC@DCECKDC8ECADCpECEDCECDDC EC@9DCEC3DCEC5DC@EC@1DCpEC3DC`EC,DC0EC6DCxECDCPECDwCEC5DCpECNDCpECODC@EC\DCECUDCpEC=DCEC@,DC`EC@ DCEtC@DwCEsC@DCECDCE|CD`CpEeCD^CEwCDoCE}C.DwCȡECADCEC@BDCEC@FDCEC6DCEC@$DChEvC&DyCEzC@%D{CEVCDOCЋEQC!DCCEOC@DDChEYCDXC؇E1CD9CEGC"DbCERCD@CxE9C@DBCEoCD`C E@C DPC iE7CCIC@XElCDC`nEZCDXCEOCDGCȒEMC@DVCEC+DjC`EvC@+DCEC3DCEC@(DCvEC.DC@EC4DCEC"DCbEC@DCXEkCCC5E\CCCaETC D\C]ELCCfCYECDChEC.DCxEDkDD qECCC@PEZCDVCPEAC@DSC`VECCCdCYE}CDCPPEC6D@DpUEC@5D D_E{C DCPZEC@DCȸECD{CECDkCECDCECD~CEzCDCxEbC D{C@EC%DC`sEDcD?DrECJD2DE@D@QD@[D]EDMDyDPEDGDJD4ECKD0D0PEDPD@2DGE@DSD@=DpTEDUD@;D0]ECRD$D0oECJD DsEC@EDDvECEDDECJDD8ECHD@DHECHDDECFD Dp|EC@BD@D`~ECGDCxECGDDXEC>DCEC+DC؁EC@DC؀ECDC`=ECDC$ECDC@RECDCmE@ D@FD#D TE@D D>DXECTD@"DECYD@ DECMDC EC@GDChECFDCЍECIDC(ECBDCpECBDCECHDCEC=DCEC@.DCEC(DCEC@&DC EC@&DCPEC"DCEC!DCEC(DCȒESCDCuEICD@CwEYCDMCXEmCDiC8EfCDXCȊEEC@DaCpmEXC@D\CeEC@ DCEoC@!DmC؊EC-DC(EC@DCyED@_D5D`iEC^DCEC@4DCEC0DCEC4DlC8E{C4DbCXEC@&DCwECDCrEC'DC@EED@E{C(DhCpEC DmCxECDDC@VE D@SD5D3E@DPDBDPEC@+DC%EC@/DCsEC@.DCtEC5DC~E D@D,DE@DDDE@CDDDYE@D@*D-D ED|D,D@oD9D@sD@8DCID`DbDDWD`D DqD=DDmDD2D~DED`DUD=EC:DDVEzCCCfECDCGEC#DCrEC'DCxECDCpEaCCC 3ECCCNEC@!D D _ED[D^DED@lDiD2ECQD&DEECCcC0REuC@+DCpEUCCQC0EkCDcC@ECDC8ECDC0ECDCEC1DCEC1DCEC@9DC@EC@7DCEC=DCHEC@=DC@ECJDCXECODCECLDCECFDCثEC@HDCPECGDC@EC@QDCECODC`ECMDCECEDCXECJDCxEC;DCEC@0DC@EC.DCEC&DCEC@DChEC@DDbEC@:DC0xEC*DCuEC@DCxECED*DqE&DDDuEDD`DD]E@DTDYDENDxDrDD8D@JDZDD.DHDLDD/DjDYD&EDlD@;D0sE#DqDeDrE4DjD@xDPED@HD@'D$ECODCtE`CDC-ECCCECDCEpCD}CyExC D|CЀEFCC`C@DE-CC6C8E\CCGCPEICC@CEaCD`CXElC@.DeChEC1DC(EC.DCxEC?DChECDtCE\C@ DlCXE`CDbCE[CDcCȄEHC@DHCPzE;CDLC~E~CDCPvECDCzESC@DVCXE5CDiCECD`C@uElC/DC~ECID&D`EC%DC04EC@'DC6EC8D D'ECDCEC"DC ECDD*EXCDCQEXCC]CIEkCCC`_EC"DCEC@%DCEC*DCqE]CCtCMEfCD^CEPCCTCTESCCsC DECDCPHECDC@EEC;DD9EC@#DD ECDgCмECDwCEhC D^CECDCECDC0EC+DC E2D`D@XDeEWD~D@pDWEeD@DD TE%D~D{DXED@^DxD?E#D]DD@EDYDiD,ECGD1DPECLD(DP\EC@OD$D`cECMD4D\ECTD:D^EC@DD@oECJD D8ECKD@ DECMDDЅEC@9DC8ECCDCEC=DCPEC=DCEC@DDCXEC@DDC`EC@DCEC@CZECDCgEC@MD@DuE D@4DQD`ED@]DaDEDnDPDPEC@1D@DYED]D;DAEJD D@DEDWDQDEC?D@&D:ED@RD5D5EDLD*D 9E@/D@uDDP=ECDqDDDC"DD EC-DC-E9D@UDYDECDC`EC@DC:EC3DD0gECD Dp=ED1D8DEC,D;DE@>DbDDE@D@WDQDJEC6D DwEC;DCE{CDCPqEmCCC_ECDCHEC-DCȉEC8DCECDCEC DCZECDCsECGDDE\DDDLEDMD@]DEDCCC.EvCDsChEC9DCHEnC@DCECDC`EqCDCHEC@+DC0EC@CȃE/CCFC`EJCD*CoElC!DCuE,DxDND2ED$D'DDD)DADD@!D.D/DDCD@DDNDDgDDDgDoDEaCDWC RE4CDYCzE{CDCEC DC UEeCCCECCC4EgCDC}EcCDhCE=CCNCLEPCCkC07EkCDC]EC3DC@^E DLDDDED(D+DDCDCECDCECDCEC#DCȗEC.DCE DlD@@DsE@gDD@DiE0D@DDpZEDC EC3DC`EC2DC |EC@BDCPEC@>DCEC7DCEC'DCEC@DCcEC@/DC|EC@DDDDD3DDADD@*D@eD"D&DD>D@ D`DC DCEUCCGC%ECCC@2ECDCDlCCrC`5EC@'DC]ECDC#EvC DCp8EC DCME}CDjCpIERCDSC@\EsCDtCyEC0DDYECDCDEC3D@9Dp-EC&D DPkECDD3EC@QDOD`E*DoD`D`DD@jDUDED^D=D +EDPD@RD(E)DkD@VD0E@PDyD`D@$E@ DYDkD%ECADBD0$E@0D\D=D E@DnDfD DC,DDfECEDD mEQD DD_EDFDBD@%ECD@,DD DMDPDD@sDD@D,E@$D@DRDPHEDQD-DkEC?DCHECDCECDCEC-DCEC2DCȁEC7DC`EC,DC8ECDCEzCDCoECDCEC@5DCfECCC3EOCDYCEC6DCEC8DC`EC6DCEC0DCECDpCHECDiCxECDCnEC%DCEC*DCECDCP{ECDCgEC>DCEC@GDCpECJDCECLDC0EC@PDCECPDCEC@FDCXECKDCHECJDC8EC@MDCЯECKDCȪEeC D\CzE+CCCE;CC@CP9EyCDCIEC@DC ]EC)DCgECBD@D fEC\D D oECDD;D _EDECDCp[ED@D@D@EDOD@BD`EED]DLDDC@(DCECBDCECHDCȠEC@WDDE@8DDD0zEKDDDlECDDD {EGD D DgED@]DAD0TEDYD@LD;ED@YDGDGECZD@-DpkECMD&DkECTD@EDQE DWD[DGED[DVDRE@DfDgDpFE%DrDDp3EC@ZD4D\ECHDDXECGD@DHECEDD@ECCDC؋EC@ADCHECD3D0MEmDDD%E!D@/D@DDDDDkDDC8D@;DEC'D@)D`DC@DD ECAD*DE@D@^DbD !E D@UD@ND@9E'DjDVDEPDiDgDD@DRDD@"E@"D@CCKClECC\CoE]CDWCEC&DCEC4DCEC@;DC ECEMDDD 'ECD@D0EDJD4D0EC@5DC@1ECDCDC DDD@D@-D@CDD@ DDDDD DQDpD`D D:D]DDC!D@KDECCD EC DCErCCCGEpCDlCECDCEKCCFCUECCCUE,CC0CEVCCQCECDSCEhC$DgC`EC1DCШEC@3DCEC?DCECMDCECLDCEC@GDC EC@@DCEC;DCEC-DCPEC@ DCEC(DCE}C*DCEC9DCEC@CDC@EC?DCEC.DCEC3DCEC:DCHEC6DCXECTDC@ECPDCEC6DC(ECDC(EgCDlC0ExCDCEC@DCErCD}CxECD{C0EC DCqE{C DCoEC/DCȜEsC'DpCEgC@DKCبEWCD;CE]CDOCEeC@D3CERCDDDqE@D@ADDKE'D D~DVE!DfDmD0E;D@DqDBE7D`D D`SEDD@dD@oEC@FDC~EC DCKEC;D D@]EC@TDDECMDDECSD!D}EDTDRDP^E!D@iD@zDPEE6D@D D;E@'D@zD}DCECWD!DgECKD DECLDDECPDDxECGDD`ECGDCEC@FDC@ECFDCpEC?DCEC@DCXEC9DCHEC@8DCEC@DDzECJD@D`EC@MDC@ECNDCEC7DC0EC*DC@{EC*DCwEC'DC`}EC"DCxECDC`ECDCVEC@1DCpyECPDC؈EC@WD@D`EC@KDCEC3DCEC!DCECDCECDCEC'DCECDCHEsC DtCECsEdCDeCyEC8DC ECFDCEC!DCZEC@DC@aERC@ DRCmEpCDCoEcCDbCEQCD\C}EeCCC;ECDC0ECDC`QE@DQD@DE@D`DD-EDD`D3E DDyDPEC@DDEC D@4DDC DD@DCDDDD6D8D D@\D@jD@rDE+DbDGDDC@+DD EC6DD IEDKD8DKEDQD@JD#EC@1DAD EDADRDEC8D=DEC;DD@EEC&DC`SEC@.DC@fEC@BD DqEC=DC~EC@3DChEC6DCEC4DCЃEC5DCЄEC5DCЄEC3DCEC@1DCECDCtEkCDQC@}EzC@DCEC.DCEC8DC0ECBDCXEC@GDC8ECCDC(ECCDC`EC@7DCxEC5DCEC#DC0~ECDC0KEFCCGCE)CC@CEC@DCbECIDCECIDC8ECKDCECLDCECLDCЪEC@MDCحEC@NDCECNDC@ECODCEC8DCEFCC-C IECCCQE[CDFCEiC DVC@E_CC~C0gECCCjC>ECDCp=E DeDyDHEWDD@D&EC@%D@YDEC@9D,DED>DoDE;DZD@DEDD`DP0EwD DDpFECD@D)ECHDDHEC@%D%DECCCDDZDVDpEqD@DDAEpD`DDEE@DlD@D=ECSDDDFECDC@*E>CCXCp7E^CCoCbESCDIC@EaCCZC0oEBCCSC jE+CCC8EFC D3CtEQC D9CEFCCFCXEoC DRCEzC(DqCEC1DC@EC6DC(EC@JDCECLDCHECNDCHECIDC EC>DCxEC4DCXEC*DCEC@!DC EC0DCEC.DCECNDCHEC@FDCEC@5DC`EC@"DCEC6DCECYD!DrECTDDDkE@UDDdD0ECBDCpEC@DCȯECD|CPEbC@D[CE]C@DvCEuCDwCECDxC8EOCCHCXEaCDC`VEC#DC}EC5DCPExC@ D^CE\CDHCEWC D9CEsCDWC@E]CD9CEUCDACECC DCCE[CDIC0xEmC D_CuEpC@'DvCEiC+DXCEWC@ DVC`EHCDICE@CDRCHEKC"DLCЇEoC3DgCXEtC8DsC8EnC2DkCE]C#DWCE{C!DjCEECDXC@EgC#D[CؑEhC!DCEiCDCEC.DCECDD`HEC+DC:EC@DC6EC@(DC(EC@=DCEC$DCaEC@DCRE{C@ DC0>EC@DCpQEC DC@5EmCCCDEC DC "EC,D@DEC$DDECDC"E_CCC0E[CC[CDEC@DCP;EC@ DCP=EC@9DCdEC2DCECNDCED'D"D EQDzDDE`DDDBE:DD D0ED@ZD@QDp%E1DDD(EjD DD0EED@[D@5DmECDC~EC@0DC(ECDCEC;DDEC>D DnEC@D@DPnEC?DDrEC@D@DxEC>D@DwEC8DDtEC@;DC0xEC?D@D(EC4DChEC+DC@}EC$DCEC)DC@EC&DCkEC5DCqEC3DC@|EC@%DCE}C@DCEpCD|CEbC@DlCEpCDtCЂEVCCPC`EHCC@CyE9CC"ChEOCC6CvEXCDHCEyC2DCEC@D@DEDwDxDwE[D}DD.E@qDDD^E DkDJDEC@FDCEC@MDCEC@DC(EC@"DCrECDC]ECDCRECD&D@(EC"D1D EC@,DD EC@DC0EsCDC6ECDCECDC@EC@5DCcECDCTECGDDE@"DJD@/D`D@D@VD/DDC=D@D`(EC/DC@JEC+DC IEC@5DCuEDXDDEC@SDD EC@9DC8EC@VD@ D5ED\D@3DDD[D@ D@CD+DkD!DD,DnDDC,D@xD#DC@.DwD@(DC@3DyD.DC,DyD,DC*DxD/DC-DsD-DC'DyD@/DC#DrD,D}C(DrD*DrCDnD+D~C%DmD.DCFDDHDC@lDDqDC&DfD%DC'D@kD.DC@DhD*DCDkD4DDDcD@3DD&DcD,DC)DlD@,DC.DoD+DC1DsD4DCPD@yDNDDD@DFD@"D DC DC@EgCDgC[EC"DClEC@0DC0kE{C#DqCqEcC DsC0{ECDC_ECDC+EC@DCpEE?C D=CE]C@DcC zEvC@ DCE`C@DtCEBCCOCAErCCC DlCCC ECCCDCDD@$E'DtDzDOEC\D$D4ECDCp#ECDC ECD D`ECD,D`DD@FD6D D@-DgDbD DCDC EwCDC;EC@DC MEC@0DD0E"D!D@XD DD@9DuDDDFDdD D@D@>DPDEC(DDVEC&DC{EC(DCEC1DC8EC?DCHEC2DCPEC@/DCEC-DC0EC4DCEC2DCEC6DCECDCiEZC DCYEtCDCEC6DC`EC=DCxECEDCECEDCECKDCxECJDCECGDCEC>DC8EC@=DCEC2DCHECDCyEpCCC`C|EJCCUClERCCBCuEtC DCE"DvDD`]EPDD`DE DEDED`ED@:D@RDP E@4DjD@yD EC6D@ DECYDnDPDD@D_EfDDDlEC*DCE|C DmCE[C DeCE>CD?CEKCDZCXERCD]CELCDMCРE,CCC\E:CCECmEgC@D]C0ExC@D`CEpCDQCELC@ DCCEACD4C(EbC DGCESCD*CEQCD0CE9C@ D)CEiCDmC0}EvC@-DCE~C&DC؀ERCDXCELCDOCEYCDeCEnC#DkCEeC)DPCEjC(D^CEYC"D[C(EiC2DpCE8C@ D3CmE4CC?CtENCD@CEC?DC؍ECCDCEgCD|CErCDClE}CDC GECDC,E=CCuCp%ESCClC0TECCCCOEbCCCP EHCCC.EICChCBECC_C@EECCfC5E:CC5CPLECCC7EACC_C`,EFCCC'EhCCC7E~C@DC4EwCDC\EC@DC`kECDCXE1CCfC`LE_CCxCpnEC;DD`DE2DADWD DD@DDME DD EMEeDD DpAE@6DwDoDJE1DD{D$EgDD D E?D@|DbD&ECDCREC@DCEC&DCXEC,DCE@D_DDQEDcD@8DhECTDDECKDDhECHD D@ECJDD(EC@KDC ECFDCECDDCECKD@D ECCDCЊECBDCHEC=DCECFDDxEC@AD@DPECECDCIEC4DCpECADCECCDCECIDCXEC@MDCECHDCHECIDC؆ECJDCECIDCȇECDDCxECCDCEC@>DC EC5DCEC&DCECDC@JEUCClCBERCCC0OEC$DCECBDCEC@FDCEC@IDCxECIDCECEDCEC@NDCxECKDC8ECGDC8E_CDeCEQCC8CP~E2CC+CP{E9CC7C0eENCD[CrEYC DNCPExCDCXE/DzDDgEQDDD 'EDGD2DEDXD]DEDBD@9D0ECDCp%EC@ DCEEC3DD@KEDKDC0VEPC@!DZC`ERCDZChEECCCCgEuC@DYC@oEACCDCEC@9DCECQDCHECLDCECEDCEC1DCEC/DC`EC,DCEC@(DC(EC&DCEC@4DCEC@ADCECHDCЕEC0DCEC-DCEC@.DCЗECGDD~ECQDDpxEC@eD3DpmECND@D{ECDCECDC8EgC@ D_CEICC@C(EMC DTC ERC D^CEKCDHCE0CC1CEVCDSCELCDUCyEaCDUC؃ExC@DMCМE\C DECEGC@D4CPEUCDCCtC7ECC1CUE&CC?CbESCDRCmEaCD]CzECC@DNCEXCD[CЁEECDLCxEgC DsCPcEhCCCP'ElCCCEYCCC01EvCCC`+ECCC /E5CC;CHEC@D@ DE1D@yDgD0ED@DDjE DD@EAE{D DD@ED@DDPEMD@DD0+E@%D?DuDDDhDHD@ECADCEC4DCEC@,DC@EC DCEC@8DCEC@DCjEC@MDDjECQDDpvECWD@DpECTD@DECMD@DECLD@DECODDpECIDCEC@DCEC?DCECGDCxECGDCȉECHDDECGD@D|ECLDDtEC@BDCECDDDEC=DCECDC0~EC@DC`EC3DCЁEC@4DC`vEC@9DC`xEC.DCECDCFE_CCWCNEC@DChECEDC؊ECCDCЅECEDCECMDChECQDCECDDCpEC@EDC0EC@KDCECGDCPECEDCECCDC8ECBDCІEC@@DCEC:DCECDC{ECDC0iEC@DCrECDCEC>DC(ECADCECFDC ECFDCXECKDC8ECMDCECMDC8ECBDCEOCC\C0]E>CC4CGEGCCIC@UEC D{CpjEC@&DCEoC@DeC0EsCDCPpEC@DC@DCEC)DD0?ECDC"EC@ DC` ECDC#ECDCECDoCHE]CDFC8E8CDBC(EDC DbC`E\CD^CE^CDDC(EdCCCsECDCmE`C@DdCtElC@ DdC@EcCDSC0EkCDCCEZCDDCEC?DCEC@9DCnEC2D@D@gEC;DCPkEC:DD pEC@4DCnEC+DC0hEvC@DCPtEwCDgCXE{CD_CxEfC@ DoCEoCDxCȈECDCEC(DCEC@DCؒEpC DnCEC*DCEC(DC@EC DCPEcC@!DnCEfC@ DCxEC@&DCVE D2D$Dp%EC,DC4EC;DCjEC@1DCEC3DC@`ECDC^EC@DCwEC%DCEC4DCE.D@TD^D>EDJD5D DD@?D1DPEC@DC>EXCD]C0pECDC ^ECDC2EC$DC "E@ DD@@D"E@PDDnD`D1DD6DD@,D@wD-DC@&D@nDDC@)DpD$DC#DwD&DFD@!DsD1D D/DyD7DD@0DD@*DC2DyD%DC6DD(DC6DD@*DC@>D`D4DC=DD3DC2DD3DC,D@}D@3DC/D{D8DC.DzD0DC+D{D@.DC&D@uD4DC-DvD2DC2DD;DC@HD@DWDC#DmD0DC,DtD@3DC'D@rD*DC)D@nD-DC-DsD-DC@4D@yD4DC6DD/DC9D@D@/DC9D D2DC5DwD@+DC)DfD@DC D@_DD D@DWD+D`DC@.D@DDmCCCEmCDKCSEC6DC^ECDCp,ElCCrC9ECDCgEC+DCEC@-DCdECDC *EkCCaC8ECDC@IEC4DDMEC=DD&EC/DDEEC@DC@gECDCeEoCCCRECCCDC@EC8DCEC;DCwEC/DCdEC@DCPREC@DCgEC,DCEC@BDChECIDCEC@EDCXECJDCECJDCXECRDCXEC@MDCECJDCECIDCPECGDC8ECHDC`ECFDC ECGDCEC@KDCEC@?DCEC3DCEC%DCEC+DCpEvCDCPEC%DCHECCDCxEC@NDCECBDCECNDCECTDC8ECWDCECDDCPEtCDiCxEUCC]ChECDC YEC@&DCrEeC@DCEeCCkCHECDCEC@ DC`RECDCFEC5DC pEC/DC0EC-DC(EC1DCЕEC(DCEC!DCE@D@tDDEC`DEDE@ DvDDC8EC=DC(EC@9DC|EC5DCwEC2DCkEC0DC@eEC@5DC0aEC6DC@cEC+DChECDCplEgCDaC E[C D\C@~EZCDBCЃEWCDOCؑEcCDUC8EzCDtC؉EUCD]CEhCD^CEzCD`CxEC@ DC`}EC@ DClEnCDxChEaCDGCpESCDaCESC@DCtEjCDC0bESC@DRC0aEyC@ DCjEhC@D}CpEhCDrCEbCDSCEC@DkC EC'DCEC%DCEC;D DpOE DQD@DDECD1D ETCCC!ExC!D~CiEC"DC0mE\C@DJC0[ExC@D~CGEC@ DC(ED=D)DD3DPD6D^DDfD$DCD@XD@D7D DUDD@D@-D@vD@(DC2D@{D2DC@3D`D.DC7DD@,DC@6DD@*DC3D`D/DC3DD)DC;DD-DC3D D/DC-D{D2DC)DyD6DC@0DuD3DC@*D@pD@,DC+DpD@2DbC$D@rD-DbC,D@pD0D\C+DD9DC@DDFDC#DkD(DC(DmD-D{C@#D@gD&DeC*D@jD*DyC@.DvD,DtC7D~D-DC@-DzD@)DC1D{D(DC1D@{DDC/DtDDC%DpDDCDSDDDC7DD`DCDCp-EwCDeCVEtC-DC|ECRDCp}ECDDC`lEuCDC_ECDCCECDC 6ECDC*EC@DCEVCDzCHEgC DCrECDC?EC@DC`ECCC8ECDC@YEC@ DCEC@DC`rECDCaEC@DC+EC.D-D0EDMD@[D;ECDD`8EDDD@3DE6DkDXD@DCLD@Dp8EC;DDKE@&DbDDNECTDMDE@D@&DEDD D@GD@=D@EC=DDFEC$DCcECDClEC(DCEC3DC0EC@;DC{EC7DC}EC@7DCEC8DC؄EC:DC EC@7DCEC8DC@lEC DCNECDCXEC7DCECFDCEC@FDCECNDCECJDCxECLDCxECFDCECKDC`ECKDC؇ECJDCHECGDC؊ECFDC0EC@BDC`EC@>DCxEC?DC ECCDCEC@?DCEC@6DCEjCCCbEeCC:CFED2DPhED`D@VDJE8D DgDNEDJD@3D$ECCC DCDCETC DMC(EYC@D^CHEVC@ DRCE`C@DcCзEbCDhCPEQC DtCEC DCJE{CDCzEoC@DCEC@"DCPECDkC0E>CC=C}EVCCACE^C@DMC ECD^C`oEGCDGCEC+DCwEC@CD1D)ECDCD_C DxCZEqC@6D~CEC@4DCELCDRCEC>DCEDOD D`EC DC@gEC:DC^EC DCECDC1ExCDwC`EYCD~CpcEuC@DCdEzC@ DCbEbCDtC_EGCDoCgEVCCjCPEUCDkC@SEGCDVCEBCD?CpE6C@D1CEGCDHC؂EWCDeC0|EPCDLCXE5CDDCsEZCCnCME:CCCPE CC7CDCC!C"E8CC3CfEoCCCrECCC_E0D@dD`D@ E&DDlDp2E$DJDvD E$DcDDpEgD@DD ,E0D@zDxDpE@DPDbD EC@PD@DpEC:DC0EC@)DChECDCEC@,DCEC1DCșECCCdE^CCbChEC"DCȠEC-DCEC@DCXECGDCPEC@DCEC=DDuECADCECADCECMD@DECID DuECGDD`zECEDDyECJDDzEC@KDDECHD DECFDD8ECBD@DEC=DDEC@CD@D(EC:DC~EC7DC}EC9DC~EC@5DC{EC@6DCuEC:DCPqEC9DC0kEC@C0bECCECDCAECDC@LEvC DC `EC@.DC`ECDC(EC@>DCEC@5DCEC@4DCEC6DCECDCE~CCC@PESCCCPOECDC`cEC.DD\EC@1D@$D2EC/D@3D*EC@?DD?EC@D@DWEC,DD`%EC@DDEC@)DCEvCD~CHEvCDC@EC DCxEC%DCECDCE~C@ DCEC@DC``EC'DCEC@%DCECDCzECDvC`dE7CCHCVE_CCRCPfEeCCdCdEBCC@C@^EACDDC]EJC@ DJCiEaCDlC0EEC@D>CqE9CCDCsE9CCCClEhCDC7ECCC2EOC@D[Cp:ETCD^C hE}C-D}C`vEtC)DmC~E9C DTCEC9DD@tEDND@=DEC@DC*EC(DD !EC&D@D DCDC0.ENCD~C?EyCDC0^EnCDuCREaC DC^EnCDC0rEpCDClEnCDCNEmCD|CbEWC!D^CE8CCXCxEICD;CEWCDICȋE\C D`C`ETC)DYC(E\CDJCEEC@DPC`^ECC3CECbCCD CC*CJE=CDICE7CCD@DEC@CD@DPECEDDEC@GDD EC=DCECDD DC@DDDjCCzC`E#CC#C0IECCD:CpEQC@ D,CPnEGC DJCeE)CC-C`EE3CCCIEICCnCCEC DC`ECDCVEC@D+D@EC@(DC@\EC.DC`vED.D@7DE@OD`D@xDD@eD@D DE1D DD`DEDDDBE@%D DDPE,DqD@|D E;DDD EDIDDD+EC@DCHEC!DC)ECDCLEC!DChEC5DC0|EC4DCp~EC3DCEC/DCEC7DCЁEC@6DCEC@DCqEC4D@DMEC@DCVEC)DCEC@;DCEC=DC`ECGDCEC@HDCECKDCECKDCECTDC؊ECJDCECCDCECJDCECLDCECIDCPECJDCEC@EDCECDDCECADCpEC>DCEC4DC0yE{CCeC0gEcC@ DC`EC!DCEC@!DCEoCDpCERC@ DHCE=CCPCfEpCDsCuEC@#DCXEC7DCECJDCEC:DCECDCeE CCWCECC;CEvCCCTE"D{DDCEC@2DCВEC DC؀ECDC@uEC'DCxECADD pEC:DD@7EC1D D7ECVDKDRECJD)DpkE@ D^D=DUE DpD@DD@[EDdD8DEC@PDDECRDDEC=DCEC@DOD@%EC'DD-ECDDJEC@XD1D@LE@DHD\DPLECHD@%DYEC@?DC`rECGD@DEC_D=D ECODDEC,DCxEqC@DCEgCDfCPRERCC?CE>CC>C`,EqCDCpEC1DC`EC"DC@EC@DC@kEC@(DC^EC@6DCuECDDCECIDCECHDCEC@8DCkEC2DCaEC?DD8ECKDDsEC>DDyEC;DC@EC.DChEC@+DCEC*DCxEC4DCEC7DCXEC?DDECBDChEC?D@DEC9DC ~EC>DC}EC;DCzEC8DC wEC@DCrEhCDaCxEkCDeCEVCDRCEEC"DACE^C'DhCxEkCD]CEoC(DiCPEpC(DkC@EqC)DQC(EoCDQCEjCDeCERCDICE\C DICEYCD`CEdC@D`CpETC&DKCEWCDWCE]C@D1CEfC DECELC@D`CE`CDMCE^CDKCEqC$D`CEC@DgCEbCDjC؄EC@DqCPfEC9D DjECDC@SETCClC'EC@DC9EC"DC`kEaCDZCpE`C@DACEhC(DcCpEC@(DCsECDC OECWDCDE,DQDMD0E@>DDaDDCDD@KDAD0DD(DC0D|D"DC5D@|D@.DC9DD2DC@DD`D@JE2D D`D`XEWDDDHE@DNDVDPECD@jDD0ESD@D`D?E@DFD/DpXECDCdEC*DC0\EC.DCPiEC5DCPEC2DCzEC-DCPEC9DCHEC5DCEC9DCEC8DCECBD@DuE DODC3E_CCCIEC DCPVED@pDbDREAD D DP@E0DvD D2EC@;D@"D KEC@.D D@4EDTDQD)EDvDlDphEC@SDDEC5DC8EC@4DCEDD+DECBD(DEC,DC[EC DDDC.D@D`EC>DCXECJDDoECDCECTDCИECUDC EC@KDCECPDCHECBDC@EC:DC؟EC=DCPEC@GDCEC@JDCEC&DCEC9DC~EC@*DC0dEC7DCxEC=DD`\EDPD@9D1E@&D{D{DJEODDD0\EC@{D!DEC@fD@D8EC@fD DECgDC(EC]DCEC@TDCEC@9DChEC9DCEC@?DC(EC+DCEC'DCEXC DrChEPCDkCmEC+DCEC+DChEC&DCEC4DCEC:DChEC(DCE\C D]C0dE)CC*C0]EHCCKC`uEECD[C`rEsCDmC }EC*DCxEnCDiCpEgC!D\CzEjCDgC`EPC DbC]EsC)DwCEC@DCEC@2DDdEDFD'D ;EeCCCEsCCCpQECPD@DP@EC@DC-E[CDCkEC@8DCEC:DCyECDC`ECDCPqEwC DeCpErCCCYE\CDzC~EhCDxCHEC DCCECDvCsEfC*DCXEC@-DCEpC@DCcECDCcEC6DCECDCiEsCCCP0E]CCrC_EICDZCȑEC!DCECDCEuCDC[ECDC*ECDCpaED^DKDeEDRDCDFEC'DD?ECDC`PEC0D DSEDcD6DECcDJDECUDD0ECFDCЗEC+DCEC&DC`EoCDuCdE[CDoC ECBDChECUD+DEC=D@DhEwCDC{EC DCgEC2DCxEC7DCpzEC'DCfEC@ECDC0.EC DCEaCC}CEQC D>CjE_C@DJCEqC@DgCEUCD_CPETCDDC؇E^C D`CECDC(ECDCHEC$DCmEfCDCiECDCFED@/D.D:E@ADqDdDE@-DaDWD@ECDC@2EDyD_D@?E\D@DD@SEEDD`D*E_D`DDE@hDDDPECbD3D _ECDC@qECDClEC2DC@EC8DC yEC+DCwEC@/DCEC0DCEC5DCEC@6DCEC6DCEC5DCPEC@CC8C؁E*CC4C0ZEECCqC>E=CCEJCCVCZEKC@DUCyErCDC ECDC]EDPD@FDpSE@RDD D :E)D}D D@2EC7DDeECDCp,EC"DAD ED@fDWDcECTD!DECDCECMDCPECQDC@ECODCМECEDCEC@8DC EC@7DCPEC8DCECBDCEC>DCEC@DC(ECID D|EC@DDDsEC@DD@DpzEC@D.D@jE@'DxDnD`=E DaDBD3ECODDlEC@FDCPECXDCxECVDCECKDCXECQDCȓEC@LDC؏EC@*DCEiCDxCpE_C@ DC`~EpCDC^EeCDQCpuETCCdCEzC@ DlC kEC@3DCEC)DCEC@#DC0EC@!DC(EC)DCЖECDCE\CD\CcEGC D7C0sExC#DmC8E`C D_C؄EQC D=CȈETCD]CElCD_CEQCDhC(EEC DRCpELCDXCErC)DC@EC D/CXEC@$DChEC;DC:E@CCQC>ECDCOE@D8DDpEZCCCE]C DsCaEC7DD0iECXDDlEC@,DCgEtC DtCnE[C@D]CpoEgCD\CzEbCDhCEjC DCfECCC.EaCDhC0PEVCCzCbEC@DCrEC@!DCHEC@4DC`zECiD@8DyEC)DCgETCCCCEuC DC`dE_CD{CPvEC@DCE6CCfCDE2CC?C:EC@!DCxED_DRDEMD DD`;ED@HDKD ;ED@gDCD6E DMD@0DEC@DD؁EC@hD=DEC@;DD0EC@*DC0tECIDDECXD@DEC@ODDECADCؤEC@GDCxEC@^D#DEC@aD.D@EC@fD5D`ECLD DȕEC&DC(EC5DCE\CDCrE^CCgC@OEC@,DCZECDCEC@,DCEC4DCxECFDD0xECND@ DvECMD!DEC>DDXEC/DCXEC@'DCECDCEC-DCpsEC@+DC`uEC0DCwEC5DC wEC,DC`oEC@.DC|EC4DCpEC DC`fEmC D`CxEVC DKCPE~CDfChECDuChEqC@DkCEcC@DMChEaCDVCEmCDSC EAC@D=CؑEVC@!DfCEUCDUCEQCDICEzCDmCpEC@3DvCEmCDMC0EkC@-DVCEaCDDC8ENC@D5CENCDNC(ESC@D@CE6C@D0ChENC D:CE\CD@CEhC!DKC0EeC!DBCEmC*D^CECDCEC@@DCE~CCC0BE`CD[CGEC.DC tEeC@DpC0XEdCDWCbEdC!DVCErCDsCvECDCREC'DCE@D@XDDJE@_DnDD DND D^D`DpD`DD`D;D@D,DC4DxD)DC9D@D@.DC1DD/DyC:D@D0DC0D{D@3DrCqDDoD@D@UDDsDD/DqD@DCEC@=DC0|EC3DCEC6DCPEC5DC@EC4DCHEC6DCȂEC3DCEC5DCEC@;DCEC@;DCECED7DOEC$DC`]EC-DC`pEC2DCEC@DCXECBDCECODCPEC@NDC@ECLDCECMDCECMDC8ECIDC8ECKDChEC@ADCȔECADC8ECBDCPEC@CDCECCDCECDDCЍECADCEC?DCxECADCEC1DC0EC!DCErC(DtCȠE]C@D`C8ECDC0EqCDwC؞E\CDPCE_CDQCEeCC}CwEtCDyChEC)DCEC)DCДEiC DtCxEC%DCXED@UD5D@SE(DeDD$EC:DBD`ECDCP5ECDC@BEC@DCAECDC CECDCzEDzDDEDDxDEC`DCEC;DC`ECKDDE%DaD,D nEDFDID`EC@DDEC4DCEC/DCEC-DCPEC@.DCEC;DCВEC%DCECDCECDC@gExCDCjE]CDCqEbCD_CEZCC4CEYC D\CEsC,DC`EC8DCEC?DCECNDC0ECMDCECPDC8EC=DCPEC0DCXEC-DCEC4DCЕEC@CEiCDjCE]C@D^CEWCD;CEtC(DdCEuCDQCELC@ DCCETC DCCHE]CDBC؍EGCDBC0EWCDGC0ElC@"DMCPEoC@%DSCXESCDHCEMCDICEACC@CSEsCCC;ECDC JECHDC0rEoCDoCzELCDEC`nEUC DKC`|EYCDaCP}EcC@ D\CRECDC WEC@8DChEDlD8D EPD@xDYDD@ODDZD1DzD@DDID/DD"DC7D@zD/DC@5DD4DC*D~D3DC/DwD@0DC#DuD@/DCwD D@iD@DmD D@DC0DeD2DC@*DsD.DrC,DlD.DC)DpD)DyC$DrD/DyC"D@sD/DyC@&DsD4DC)D@qD@-DtC/DwD3DC@(DpD@1DC'DyD1DC-D@tD1DC)DjD+DC0DtD+DCDqDDCC@-DC!DCDCDCDC+EnCDbCgEZC@DUCPENCDFC8E]CDGCwEcC@ DGCxEpCDdC pECDC xEC@7DC^EC@2DChEeCDkC@hE>CC5C_E@CDIC0pEC2DCEC:DD|EC"DCWEC-DCEC@#DCEsCCCtEC@DCFEC@DCNEC2D@DED8D5D/EC@WD@$D DEODbDrD ED\D1DEC@(D@Dp:EC%DCFECKDDD >EEDD`D EYD`DDE*DsD@iD0KEDZDADrE-D@D@ODdE@D@CD@,DAEC3DC0oEC@C`6E.CC4C4E;CCSC.EaCClCEEsC@DC NECDCEEC@ DC"ECDCE.CC/C0E6CCAC0zE@}DDD ]E@DDD UE@)DXD]D E,D@XDTD ED@KD D}ECKD@D(ECDCTEC@+D D{EC_D@0DECZDDECSD DПECID@DȩEC@cD'DECUD@DPEC@SDDEC@MDDECSDDHECTDDECYD@DECVDD8EC'DCEzCDiCXEC@DC@EC@&DCEC.DCЦEC8DCEC)DC|EC@DC@ECDCEC@1DChECADC0ECLDDXECOD DEC@SDDECQDDECHDCEC;DCȗECC@=ECDCQEC@DC`oEcCDhCElCDjC`yE{C@DcCExC@DbCHEC,DC vEC=DC`}ED@QD@DEE5DrDDD`D.D@wD5DD4DD.DC3D@|D1DC4D`D-DC;D@~D6DC4D D6DC@DuD?DCwDDsD@"DfDDiDC(D@D0DC@)DyD4DC@*DrD/DC(DsD+DyC-DtD2DC)DuD@0DC@)DvD5DC.DyD1DC+D|D5DC1DwD4DC.D@yD7DC@+DuD2DC@DD@?DCsDDvDQD@JDDSD@)D"DcDDDC2DCuDrCC_CEzCDfCPEEC5DCtEC?DCEhC"DCvEICDTCqEqC@D]C@EC"DCXEC@3DC`UEC@DCpEDDD@D`EC=DDp)EYCD}C0EEbC@D]CP{ENCD:CXE@DGD@DHELD@kD@kD5EDFD@-Dp E@D@0D$DEEC DC{ECDC`EC@DCWECDC@BE@$D@UDFDD@DBDJD,EDKD3DE@aDwD@D@DC6DC8EC DC1EC DDDC@DDDCPD$D@*E+DDYD`^ECeD$D|EC6D@D@|E DD:D ~EC@8D DwEC?DC |EC@7DCȀEC;DC8EC;DCEC6DCEC@7DCEC:DCEC3DCEC3DC EC@9DCpEC7DC؆EC9DCEC?DC}ECCD DtECIDD nEC4DCZEC@DCfEC2DCppECADChECIDCECKDCЌECSDCȐEC\DCȐECQDCECKDC@ECJDCБECKDC(ECIDCؗECMDCȔEC@IDCXECLDCؑECTDCxECWDC EC@PDCECRDCECVDCEC;DCEC@)DCPEC=DCضECCCICLE;CC;C PE7CC&CPE9CCVCpKETCC|C1ECCC 3EnCCsCCEfCCmC]EICC7C~ESCDJCE@TDDDUEDiDUD;E@:DDDEDxD@`DPYEC:D@DEC5DC8E{C DCECDCEC@PD&DEC@PDDECLDCEC@GDCEC@LD(DآECVD@DEC@FDDEC@NDDpECPDDECKDD@EC>DCEC@.DCEiCD^CEMCD@CESCCC EeCDNC EUCDMCEiCDPC8E[CDDCEjC@DLCXEcC@D@CEmCD]CElC#D\CEXC@DNCEVC@DECEhCDTC؍ESC@DXCEgCDNCȀEACC*C@dE5CC-C oE\C DwCcE{CDCaEmCD^CPeE[CDeC_EyCDqCwEC(DwCEC5DC EC8DCEDWD%D@EDD0DoD@*DC@1DD.DC2D|D@0DC@4DD1DC@4DD/DC+DD2DxCbD`D`DC D D`DbD@bDDqDD%DyD@+DC+DrD3DC@&DmD*DC(DrD'DtC.DtD@/DC(D@rD@.DC@&DsD/DC@-DvD@0DC0D{D/DC@/DvD6DC'DwD4DC@-DsD0DCYDD@TDDDDDDD DDDC@DCdDOCCbCDDCC>CGEbCD}C@mEJC@DC-EbCCwC@ED$D`@EbC@DCEIC@D[CkEC3D-D~EC5DCYEKCCC EEC@DC KEC DC]EC@;DDECCCkEC DCuEC.D@ D4EC DDDDJD`D@DDCpZEgC@DCE{C@*DsCEC-DCECDCHEiCCCE]CCxC6ECCC5CDE;CC0C7EJCCSCIE^CCbCZEpC@DzC@yEqC DCEjCDCkEuCDhCECDCEzCDCЙEXCD^CENCDHC(EQCD\CEC(DCPEfC D}CxE;CCVCpHEICCC0PEMCDRC^EC)DCXECODCXEC=DCЛEC&DCجExC@1DCXExC)DC8ECDCȪECDCEC1DCEC.DCPEC1DCEC+DC`ECDCРEC@,DC(ECRDCEC@SDCXECTDC@EC@ODCECKDCpECLDCECDDC@EC#DC(ECDC@EnC DCtECDCPEC@DCEgCDyC`qEC-DC|EC@2DCEC)DC0EC$DCEC"DCEtC@DCrEACC CIEPC@DMCuEZCD]CXEFCDAC yE8CC6CE_CDECHESCDFC}EGCDTCnEJC@D;C@zENCDGCEHC DAC`EVCDWCPEpC%DC؍EC4DCnE@D&D(DD D@UD@=D=ED@KD@1D/EC@2DD@3EC+D@D2E@ D7D#D`%EC%DCZECDCIE5CCPC_EsC*DbCEQCDTCxEsC!DeCEnC%DYCE`CDXC0EJCCIC vEJC D=CȂESCC@CEOCDQCЍE/CD@CP~ECC1CsEDCpEC2DCEC)DChEC+DChEkC&DhCEvC#DrCEC$DCPECDC EpC@"DCfExCDCmE>CD;CEDCCSCTE?CC:C@TEMCDWCgETCDRCuEdCD[CzE\C@DGCEkC@DVCECDXCXEC'DCXEuC@4DCEC@D@D'D@mD!D-D.DyD&DC@)DyD'DC5DD2DC4D|D@4DC/DzD2DyC)D{D@3DC)DvD3DC@DuD+DC"DtD+DC@.DtD0DC+D@tD@8DC@*D@qD2DC)DsD2DC&DrD-DC#D@sD/DC%D@rD@3D}C*DpD/DC+DqD1DC,D@yD@6DC@*D@vD/DC+DsD@.DxC@&DwD$DC'DfD@DCC@1DC@9DCDCEC@%DCKECDCHEpCD{C^EiCD_CzENC@DXCkE^CDpCErC@D^CEPCDKCpEpCDkC`ECDC|E@CCgC`:EqCDC2ECCC0ESCCkCP$EC@DCdEC3DCtEC#D@DED*D/DEC=DC>EC&DC)ECDC SEC%DCqEC,DCxE D-D@D /ED%D@*DD D@:D@JDD9DWDaD`DLDDuD@DC=D@D;EC@$DCbED@D,DAED8DAD$EC@8D@ D0ZECDCRECCC`3EC DCoEC@-DCȆEC@+DCEC5DCEC2DCHEC0DChEC3DC@EC-DC@EC2DCpEC4DCEC3DCEC4DCEC4DC8EC5DCEC9DC8EC7DCxEC4DCEC4DCEC@8DC(EC@2DCEC8DCEC1DC(EC/DCؐEC;DCЏECCDC؉EC@BDCECADC(EC@DDCECBDCxECADC8ECDDCXECADCEC?DCECCDCEC@DCpECJDCPECWDCpEC@RDCxECLDCEC9DCEC=DCEC;DCECCzEPCDNC`EjCD[CЇE|CDgC}E\CCXC|EACCICXEPC DECE_C"DNCЁE?C D/C`tE+CC%CPuE/C@D+C؁E1C@D2C E;C D5C؀E>CC=CuELC DCxEC DC[EdCCC KEC@#DCREDZD~DCEHD@DDpEHDD@DHECJD@DЀEC5DDECBDCE D\DDwE,D@YDGDLEC4DDjEC4DCpEC@FD DhEC6DC@EC>DCECOD@DECRDDEC_D#DECTDDEC7DCXEC@8DC8EC@?DCEC+DCE`C DPCEOCCBCE[CCLC@EaCCVCPEyCDeC EC@DCEC$DCEC DC ECDCECDC(ECDCEsC@ DCEC@ DC8E{C@ DCEC DCEC DCECDCEC)DCЈEC@DC8E|C1DCEoCDjC{EC0DCE{C5DsCE[CDjCtEC'DCED`DD@:D&DjD@D@D@2D~D)DC1D|D@/DC@4DD/DC@0DD.DC-DzD@4DC*DyD@2DC*DzD1DC&DuD@,DxC+DsD4DC+DtD5DC0D{D9DC+DwD6DC)D|D6DC@-D@vD6DC/DzD5DC(DwD1DC(D{D1DC.D@wD/DC-DxD0DC)DvD,DC0D@wD%DC(D@pDDCD@ID@ D[DC@DCDgC@DC6E{CDC?ECDCp!ECDC!ExCD{CUEfCDC ?ECCC=EC*DCȅEC)DCEC@.DD YEC[D@DFEC,DC0:E|CDCSEnCDCWEiCDyCrEXCDsCxE~CDCKEC DCpECDCpE~CDC5ECDCUECDCbEC,DD`ZEC?D@D\EC@)DD.EC@"D DD D@ D8D0 EDED7DEDDnD@sDD DAD$DP=EDXD'DWE@DKD/DED>DADECLDDgEC"DCECDCnEC1DC EC/DCEC@-DCEC1DCEC5DCEC2DCXEC1DCEC3DCEC2DC@EC2DCEC2DCEC5DC(EC@7DCEC8DCEC9DCEC4DCEC3DCxEC@5DCEC7DCEC2DCEC@6DCEC@?DCEC=DCEC-DChEC@DC ECBDCEC@HDC8EC?DC@ECGDCEC@EDCECNDCECLDCEC]DCЙECJDCEC@>DCECDDP ED>D8D@D DOD@CDDDTDVD&EC8DCgEC'DC}EC@,DCEC1DCEC.DCEC/DCXEC@2DCXEC3DCEC0DC`EC9DCEC2DCEC/DCEC+DCЌEC0DCEC0DCEC4DCxEC-DCEC7DCxEC@5DC@EC-DCEC0DC`EC6DC`EC1DC@EC4DC@EC2DC EC=DCEC6DCPtEC>DCuECFDCECODCECBDC`EC@=DCECEDCECLDCECBDCECBDC(EC@EDC@ECHDCECPDC@ECODCEC=DCPEC4DCEC2DCxEC6DCEC9DCEC>DCPECFDCzECADCEC9DCEC;DCEC@;DCEC:DC8EC-DChECBDCEC8DC@ECDC(EC@DCEC@DCvEC DCsE D@oD@wDyEDDD@rDTE2D@LD@TD0+EED\DcDEZDyD`D@ ERDeD@hDE*DcDCDEC9DCEEC@`D@DhECgDD@EDtD DhECEDCEC7DCEpC,DpCEvC-DgC؜EeC#DlCElC"DmCE}CD^CpEC@!DC(E=C@DfCETCD3ChE,CC8C zE]C@DLCPEC/DCEC@FDC(EC5DCxEC@&DCECDnCEyC@DC@EC#DyCEC'DCȶEC@"DkCEhC@DbCEjCDVCEtC!DhCxEeCDmC0E]CDjCECDgCШEC@*DCHEC,DCEC)DC0E^CDhCPEcC DCЊEC5DCEC@5DCEC3DC(ECE-CC.CTE3CC4CfE.CC2CoE5CC7C[E.CC&CHE-CCDUEC4DCgECCC4E]C@DCpcECDCEqC"DCEC@!DCHECND$DȏEC@dDVDEC2D$DECD@EC9D@DQEC@DC|EC(DChEC4DCEC@9DC(EC0DCEC3DCEC@3DCEC1DCXEC2DCEC9DC EC1DCEC2DChEC@1DC؎EC@1DCEC0DCxEC2DCEC@0DCEC2DCEC3DCEC5DCEC2DCЌEC5DCEC@4DCEC5DChEC7DCEC@7DCEC4DC xEC@BD@DwECGDDpECIDC0ECHDCEC@DDC`ECHDC(EC@FDCECGDCECLDCؙECEDCEC@NDCpEC[DC(E@DaDDECDCxEC:DCEC@;DCEC=DCEC6DCEC@DCЎEC7DC8ECKDCpECFDC؋EC@4DCEC6DCEC@DCECFDCECqD;DED`DHDqECZDDeEDKDDLEUD}DzDFE%D@VD3D ;EC@DC@"EC'DCECMDCPECQDCБEDcD@D؂ED@bD2D |E@ DWDDPEC:DCEC@>DCETCDpCؙEoCDjCȌESCDeCEC%DChEBCD]C(EBCDCCEiCDTCPEC-DCجEC@,DCEC@!DC@E|C@DrCEC@%DCзEC$DCPEC DC(EC@&DCEC DCEC@DkCEmCDgC@EvCDfCpE}C$D}CEmC@DtCEeCDiC ECD~CEC@)DCEC'DCxEC#DCТEC@DCECDCwEC@0DCEC9DC@sEC2DCEC@0DCEC5DCECIDCECNDC(ECDCECDlCEoCDnC0EvCDsCE?C@DQC0dE>CCMC@7E{CDC`XEC'DCECDCHECD{CxEC@DCБEXCDHC~EaC DACiEUCDQCHEGC#DVC(E;C@D8CxE[CDBCwEcCDZCmEuC DrCZEKCCWCgEHC@DECEDCDGCyEMCDVCEAC@DOCEvCDCEC@4DCPECDD"EDIDADp E&D`D`D )ED`D@TDE@D3D+DE D*D;DD@.DpDND@ErCCCpOEECC#C \E:CC=CPeEJCD5CphE'CC0CXE%CC(COE"CC'CbE)CC>C`zE4CD;C~EYCDGChEACC4CyEVCC[CwE5CC@C sECCC0eECCYCPEC DCZEUCDrCEICC?C،ECDCwECDCEPCCCDDDD6EDED@[D EDgD@JD@6EC@DDjEC1DC dECCCPjEtC@ DpCEhCCwC0E}CDCEC@1DCPECODD؏E DbDoDEDmDnDPECQDEDECPD6DHEC;DC0EC@DID:EDOD5D8E%DGDD`+EC4D$D0:E|C@3DC]EC,DCbEC@ZD@ DTEC@DCPmEC)DCOEDJDDD1D@wD+DC@,D}D@+DC.DxD@0DC=DD>DC@DCDCDC$E}CDtC[EwC%DyC0jEpCDC`hE[C@DbC`ECDCNECDC`HEC4DCjEC'DC~EC@ DCPWECDDpDECCCEBCCeC -E`CDZCMEDCCMCPAEC#DCdE+D@rD@hD3E D@5D=DEC!DC9EC+DCp^ECDCpKEoCCC>ECDCPECEDCzECDCIECDDE@D%DIDDC@9D@ D EGDiDoDD_DDDDNDDzDDDDYDD@DC@9DD,EC@,DCpEC@>DCEC2DCEC1DCEC8DCEC2DC EC5DCxEC3DChEC5DCEC6DC@ECD DEC@?DCXEC@DDC0ECEDCECIDCPECHDCECQDCЗECHDCHECNDC0EC:DC@EC?DCEC@8DCEC?DCEC6DCPEC@=DCЍEC;DCEC@=DCEC7DCАEC@5DCHEC@8DCHEC=DCEC8DCEC>DCEC8DCEC?DC8ECCDCECIDCECGDCPED`D%DECCD:C8EGC DHChE\CDNC{E\C@DSC`aEFC DSC`pEGCDACE+C@D3C8EKCD>C0ETCD4CEuCD}C0EC4DC8EC@DCEDVDgD!E|DDD`OE>DD@{Dp ED\DbDEC@-D#DEC$DDECDC@.EACC=ClE6CC6ChEOCC;CpE-CC*C qE1CCC jE:C D,CEWCDDCED@ D ECGD&D8ECGDD EC@.DC0ECDnC@EnCDdCESCCRCxEICC@C eE8CDGC~EdCDkCyEgCC\CpaE]CDMC]EsCDC؋EC!DCEC@DCXECDCXECDCEC@DC ECDCECDCECDC@ECDCECDCxECDCXECDCECDCȀECDC}ECDC`~ECDCpECDCECDCECDCEC@"DCEC.DCEC=DCEC>DCEC8DC~EC8DCȀEC&DC EC&DCHEC@0DCECDCVECDCgECDCrECDCȃEiCDWC|EC(DCEC@DCEC#DCPiEC@?DC`EC6DChECMD*D;E'DSDaD0>E DCC=C `EFCC@CdEPCCYC@oE@CC'C@EEeC@ DoC@gErC@DCnE^C@DTCp{ERCDLC}EgC@!DeCxE^C@ DfCuE`C"DdC uEYCDHC`nETCDCCtEFCDZChE`CDVCxE:C@ DCCxERCCgCECDC@PEC@DCEC@?DD0ECFDBDECDDGDE DADJDEC4D#D`ECD#D`DCDC EOCD8C_EBC@DRCqENC DLCqERCD>C@wE8CC;CnE6CD/CmEYCDXC|EGC D>CfEBCC5CZESCCdCEECCCHC`6E7CC/CP9EWCC8CqE4CC,C`qE CC2CqE7CC>CEnC@DCECDC^E D=D`D-E.DgDWDAEC0DD@2ED4D*DEC3DDPQECD D(EC@-DC EC3DCpEC@7DDEC@9D DEC@(DCE~CDCE_CDWC|EWCD@C_E:CCCVEPCC3CfEPCDOCPELC@DQC(EfCC]C@xErC@DbCЃEnCDlCECDCxEC@DC8EC DCECDCECDCECDCECDCEC@DCȐECDC0ECDCEqCDCECDCECDC(ECDCpECDC uECDC|ECDC~ECDCECDCEqC DCECDCXEC DCEC@5DC`EC5DC{EC@6DCpEC3DCEC2DCȎEC/DC EuCCC`XEaCCVCUEC DxCdECDfC@yEkCDHCEC'DCEC@ADCECDC@ECMDCED@MD@6D@~E+DD@oDPNE@D]DkDPEDJDPD`EC+D@DED@YDLD*ECVD7DMEC@8DCVEC2DClEC0DCpzEBD{D\DP_E@/D`D9DD1D@rD(DC@0D~D.DC@2DwD@7DC+D|D@3DC2D{D@/DC7DD4DC@3DD,DC7DyD&DC2D@D@(DC3DD@+DC@4D{D&DpC0DvD'DC!DcDDC@?D@qD?D@ D@5D@wD-DCDWDDDC=DCEC2DDP8EC@KDD3ED[D.D@DDZDDC$D@bD@DC7D@mD+DC1DmD!DC,DkD(DC)DuD1DDC:DC0(EC-DChEC$DC0\EmC)DxCoEuC#D_CpxE^CD^CeEUCCCCNENCCeC ,ECDCFEC&DD$EC@D0DDCDC`EpCCeC ?EuCDlCpqEeCDoCzECDCЃE DTD8D SEgD`DwDErDD{D EDFD@D@KEC@'DCxECDC@xEC@ DCyEC@DCuEC@#DCPqEC;D@D8EC5DDEC*D D0EPDiDdDDiDD D@DD`DDDDE EKEDDD=EDGDDCEC%DC^EC0DCPEC@6DCHEC@1DCxEC1DChEC1DCEC.DCEC0DCEC;DCpEC@8DCEC4DCEC5DCEC@2DCȏEC2DCEC@4DCEC3DCEC,DCEC3DCEC@.DCHEC4DCEC@1DC@EC4DCEC@.DC8EC2DCEC@1DChEC7DCEC;D DfECDD8Dp=E DJD@VD /ED@D@ED@=EC8DCuEC>DCECGDChEC@LDChEC@HDCECIDCВEC@DDCEC@?DCEC5DCEC9DCPEC=DCECADCȐECADCEC@DCPECDC(EC;DCXEC=DC@EC5DC`EC,DCEC+DCEC@/DC8EyC5DChEfC!DCEvCDeC8EbC@D[CE(CCCmECCC7EuCDC0EC6DCECDaCEpCDbCEiCD]C0EkCDmCpEeCD_C0EuC)DzCEwC@$D[CEC-DCPECDCxECDCECDnCEC@*DCXEC6DCEC3DCE]C@DvCЛEeCDACE!CC4CoE,CC0C`]E[CD(CTEdC DnCE\C@DcCtEFC@DUCptE:C@D9CyEVCDUC8EPC DVCEPCD_CtEdCDMC0|EmC DrC؂EYC@DcCpEJC DPCoEC@ D1CE2CCAC0cEXCCNC:EC DC0EC DCPECCC`DCCCDCCC@DCDCDpCCC DXCCrCpEWCDBC_EMCDHCyERC@ D=CPzEJCDECPjE3CCAC\E$CCCZE?C@D7CPEKCCHCJE3CC&C`E%CC7CWECC3CFE%CC'CQE8CC5ChE*CCCpzE8CCDChECGDCEC@;DCEC@ADCHEC9DC8EC:DCEC@=DCEC@BDCEC@DCECKDCЙEC?DCEC@DCEC@BDCHEC>DCEC@ADCEC;DCECDCPEC@BDCECCDC8ECJDCEC@@DCEC%DCP|EC2DC7EcC DCppE}CDZCENCDYCEZC@DKChEUC@DgCEmCCxC@]EcCC]Cp[ESCDBCHEiCDLCHEnC$DCEC1DCEC@DCECLDCEC?DCEzC7DCEC@4DCxEC@ADCEyC;DCErC)DCExCDqC0ELCC=CЎE CCC*E7CCHCPLExC@DCEC'DiCXEtC@DWCErCDKC ElCDWC0EhC@DgC EuCDUCpEC.D{CEC%DfCEC'DCECDCECDCEpC@DCEC DCخEC*DCEgC@"D|CEkCDWChEzC@DpCEKCDfCEhCDPCpErC"D]C@sEgC@DzCyElC@ DqCPEHCD^C]E=C@D:CE_C-D[CHEPCDRC؄ECC@DRChEYC#D\CEeC!D}C~EJC D]CjEMC@DPC``EGCD0CwE7CD/CwE=CD2C gE3C@DRCDE6CChC)E\CCuCEZCCCD`CCCD\CCvC@ E1CC2C8E.CC/C@EZC@DXC^E CD4CgEBCDFCxE-CC*CTE0CC5CRE(CC/CEIC@D5C}E,CC6C cE9CC,C0lE'CC#CjE7CDECeE2CD?C0kECEIC D>CEeC@DbCEC@DChEC%DD[EC!D@D .EC'D DP.EC.DDEC@*D@DECJD@DJEC@BD DZEC@D@DDC8D@D+EC@)DC/EC-DCiEC2DCzEC8DC0iECADD ED@HD@DXDD`DDhDgDD]DPD8DmD@,DCOD`D?D-D@D@GD@D DC!DC CDfCUE8CCNC)EfCDC8E]CDCDEhC DfC؇EC2DCECPDC gEC@8DCppEC@5DDp:EC&DD@&EC DCEC DCxEC@7DCEC6DDEC%D@D`8EC1DDpBEC@?DDOEC@.DDGECDYDEDCD@YD`E@bDmD@zDD@DD`DDDD`D@DDDDE)D@CDVD6EDSDRD`mEC?DDyEC%DC{EC6DC8EC@1DCEC.DChEC.DCPEC-DCEC@4DCEC/DChEC0DCЎEC@,DCEC0DC EC-DCEC/DCEC@/DCEC3DCEC1DCpEC/DCHEC0DCEC2DC؎EC+DC`EC2DCEC3DCEC0DCEC@1DCEC9DC0yEC;DDTEDPD@bD#EDKDuDpE D;D`DP EC/DDWEC>DCrEDfDD{EC@BDDmEC5DD_ECBDDnEC@?DD0oEC>DChEC9DCEC:DCECBDCECJDCXEC;DCАEC>DCXEC=DCEC=DCEC8DC EC@=DCEC@>DCHECEDCECDCECSDCECGDCE|C@@DCpEC@=DCEnC;DCȬE_C+DCEGCDCEtC@D|CpELCCCC@ECC(CMEaC DgCXE{C+DuCEC*D[CEpCDMCxEtC@DICEpCDJCHEvCD_CذEvC@*DnCةE{C)DqC0EuC$DkCEC+DCECDCEZC@DvCPEuCDwCEC#DCEwCD{CEqCDrC@EnC@D_CEyC@ DCEVC DZCE(CCCXECCD0CgEGCDSCkEBCDUCUEPC DVCiEKC@DBC`ELC@D^CpEEC@DICzEDC D:C؂EBCD>CEmCD\CsE=CDDC@rEESCC[CP/EICCDC:E.CC#CWE0C DBCjEKCDPCphE:CD;CwE9CDCCoE-CC+CSE*CC2CcE!CD2C0E/CD8CpuE/CC.C0tENCC,C؄E+CC-C|EHC@ DFCPsE4C@DD@DCP`EDD EC.D D@>E&DUDEDPEBD^DZD@DCDC`CECDC{EC2DCmED[DaDEDFDQD'EC(DC0"EC@"DCDECDCEC'D3DD@ DJD@]DDZD@uD{DDPDlDDDRDDD@EcD@DDpED@HDHD7EDED=DdECEDDPEC/DCEC=DCEC9DC`EC4DC@EC6DChEC1DCEC@;DChEC0DC(EC@4DCEC,DC`EC,DCEC@&DChEC@,DCEC)DCБEC1DCEC2DChEC/DCPEC5DCЎEC0DCPEC,DCEC@5DCEC1DC؋EC1DCEC3DCHEC=DD0vEDID>DAE@D@UDgDE(DXDDE@DPDDEC?D4DDEC/D@D5E@ D>D3DaED@QD@8D nED@ODMD`CEDWD@=DDEC@LD%DbEC@HDD`}ECCDCECGDC(ECFDC؋ECDDCHEC@ADCEC@?DCEC@DDCИEC@DCEC7DCxEC=DCHEC@ADC ECBDCEC?DCEC?DCXEC@:DCEC:DCEC@>DCPEC7DCEC9DCEC/DCEC@DC`ECDCEC@DC0ECDChEcCDCEC@C D=C`wE>C D?CuE\C@DYClEXC@DVCȃENCDSCEWCD:ChESC DgCXEQCDEC`xEBC@DQCzEKCDbCEgCDCxEfCD{C0jE^CDeCPE4CDFCiEBC@ D/CELCDRCE\C@DjC|EbCDeCEnCDYC0qEBC@DTC `EOCDaCxEDCDVCxE?CCHCp[E@C DQCEWCDDHD@=DD.DC@5D}D,DC@+DzD@1DC1D@yD,DC*DzD-DC0DD2DC.DD0DtC@8DD0DC9D`D0DC@/DD0DC@3D@zD*DC-D@qDDCD@]DDC@DADD3DC(DCDC9DCZECNDCyEC&DD IECDD)EC@DC`EC=DCEC1DC@ECDCp`ECDC&EC+DCED@8DDDDQD@D@NDJDDSD`DDDDDC@DDCEyCDfCvEeC DkCP\ECDC,E@D(D/DDCDD@E|C!DCp7EGCD*C0\EQC@DRCEC:DC{ECTD@DoEDZD@DOEC)DCEC D DDCDCE@D6DD0 E?DUD@NDDC DC*EfC DCpmECKDD`DCEC@DCEC;DCEC5DCЙEC@7DC8EC@8DCEC@DCEC;DCEC8DC`EC@5DC8EC@7DCEC:DCМEC6DCXEC/DCEC@)DCȢEC!DCEC@ DCECDCXEvC@ DqCE~CDCECNDCECSDCHEC@YDCECLDCECQDCEC@ODChEC@FDCpEC@EDC`EC@GDCEC@DCНECADCEtC@DCEC@ DC|EKC D;C0EC@$DCEC>DC@EC@DDCXEC@=DCEqC.DxCEbCDHCpE@CDTCEfC&DnCEC,D~C8EZCDNC(EZCDlCEpC DtC8E[CDC@oE/CC.CmEDCC8CE6CC>Cp{E>CCUC{EICDD`DC>DD@YEC2DCEC DCxE~C@DC`EC2DC`EC@-DC`EeC@D]CxElCDnCprEnCCbC\ENCCNCpNEYCChC`bEmC DCP~EC DCE{CDCE}CDCEC@DC(ECDCECDC(EC%DCEC%DCEC DCECDCHECDC0ECDCxECDCECDC{ECDCpyEiCDC0JEkCC~C`VECDC@bECDCUEoCDrCUEC'DCЂEC;DCEC@HDCEC@BD@DE@ DvDiDYE@KD`DD0$E@kDD Dp)E2D DD_EC@D DEC=DCHEC;DC EC@HDC@rEC0DDDEC@DC=ED@MD@%DD6D@nD3DDJDDGD*D;DD5DC0D{D-DC-D}D@2DC-D@xD0DC(D@{D@/DC5DD2DC4D@D.DC7DD-DC@5D D0DC2D@|DHD5D.D@wD$DC@ DkD@DC@D@KDD DC*DCDC DC'EC/DCcEC4DDpYEC@PD@DC`yEC@@DD WEDOD]D(E$D@NDyD`EDND|DE!DMDDE7DeDD$EC@1D@DD4EC!DC`8ED4D^D ECBD?DEDADJD@E%D@]DD E@DcDODNECQD D{EC@GDCECBDCpEC=DCXEC>DCEC@=DCЖEC@=DC`EC=DCEC9DChEC@:DCEC@;DCEC8DC`EC9DCEC:DCEC>DCECClEACDdC E{CDqCXE_C@DrCEC"DCEoC#DC8EtC*DC8E{C#DCXEzC"DCEECCVCaEZCD?C0nE=CDJCERCDZC~EXCD[C@qEbC DPCtEACC?C0lE_C@DGCyEUCDOCERCD]CPEfC@"D^CEtCDoCxE[C DHCE\CD>CEKCD1CyEPCDTCECC@DaC0xEbC@DCPE|C DCEcCDC0}EJCCiCqEaC DyCE]C D^CȕE]CDpCEKCDRCȂEOCDRCEjCDrC`yEoC'DrCxEOCDUC0ETCDJC@E?C DECpEECDECVE3CDHC@ZE;CDQC\E0CC9CpUEICCNC`rEFCDMCpqE&CCC^EeC@DCqEDkD@$D`TEC,DEDEDID+D@#EC!DCDeDuD8E@1DnDfD DEDTDjDE@D>D@DcE|CCCiEC@DCqEC4DCpwECDDwECDD@DPQE D@QDXD%EDMDkDE@DADpD@E DADwD@E@DUD@oD@ECD5D@-E DD@)D*E DID@sD`ED?D@kD` E#DOD{D E@0DjDD0E@DbD[DFEC@KDD`xEC;DCEC=DCPECDCEC=DC`EC=DCECC DECpEPCDLC0{ENCDXCEoCDiCEkC@DOCE>CD6C8EFCD/CzEICDGCPEJC DPCpEfC@DCEC@0DCEC#DCECDC{EC@!DCE~CDCHECDCE`CDfCEXC DUCEgCD]C؆EeC.DpCpE\C@#DYCpE^C@DBCEQC@ DHCuEaC D`C``EKCD\CcE[C DTCwEJC D?CzEBCCKCwE8CC)CpYE#CC"C`ZECDCyED@tD@DDE@DZDoDE@$D@gDZDEC@DDPECDC#ECCCECDC(EC7DCECDChE#D;D?D 4E6DtDcD E5DbDrDE DNDKDEC9D1DBEC@9DDnECND8D`XE DBDKD EHDWD}D` E7DvDXD.E#D@TDWD+E:D@hDkD0EMD|DDpE5D@cD\DED1D@&D EC=D@D VECCD DEDSD,DdEC9D@ D`YECADD(E@DsDND]E-DnDxDE@DfD^D@D@D7D@D7DC5DD:DC@5D~D@:DC@2D|D2DC:D@D8DC8D@D6DC6DD5DC4DD/DC>D@D@@DC@1D D.DC3D}D,DC@(DwD@ DC@2D@aDHDLDoDDD DD@DDEDD D@D E:DDD.E@D@=D#D#E D4D/D@E%DTDIDDCD@nD`DE@DLD@UD ED@7DJD`ED@cDkD&EC@UD@ DJECVD"DvEC]D@DECLDD`yEC9DCXECXDDE@DcD\D `EC@1DCpEiCD^CIEhCDeC_EJC@DKCzEOCDVCpE[CDLCEhCDcCEC2DzC؍ECEDD`sEqDDDDE@YDDDPKEC*DD iEC@DD"DhE@D`DlDEC@D@D@EC@*DCZEC:DDEC7D@$DNEC@&D?DEDDD9D0(ECPD@6DQEC@DD`/EC DCpHEC,D)D0EDEDFDED@DC@ECDDCECCDCECDC@ECCDCECDDCEC>DCEC>DCEC>DCECChEPC@!DUCEnCDCP}EC@ DCPjEC@-DC nEC.DCPEC-DCtEC8DCrEC(DC`pEC9DCEXCDC`|EdCDYCEoC,DbCXECCDPCEJC@ D?CoERC@ DZCnExC@DCfEVCDtCxEeCDPCEfC@DbC`yE^C DCmERCDICkE3CDbChEC5DC8ED@qD`DBE!D@mDuD@5E"D~D^D ,EDDD@0DEC$DC-ECD@ DDCDC0 ECDCEC@;DCEDJDKD0E`D@DDEZDD`D E@%DaDiDDC=D@/Dp4EC.DD `E@D4D8D0/EDLD@_DD@|DD D EFDDfDE@D@0D@/D E@8D@oD@iDE\DD D E@'DzDYD@/E DSDAD0-E5DgD@mDED@WDKD2EDAD@+DpE4D@SD@eDPE@3DxD`DTEDXD@/D+EC@;D@&D /EC@ED&D`ECUD.D`WED?DSD E4D@hDD EEDD DE@4DSDUDpE@ D:D"DEC DC`\EnCDCXEuCDCHEpCDCHEgCDiC]EfCDxCVEQCCVC0[EKCCVC RE`CCiCVEVCCvC[EvCDCdErC DCeECDC@xEC@#DCjECDCiEC@DCvEC@DCoECDCcEC"DCpaECDCP{E\C@DyC[ECCCeEC0DCECDCp}E|CCCfEoC@DCEC2DD0E DZD@HD \E@$DtD@gD8ED6DLD@ EDRD`D>E@D@hDUDdED@ZD2D`OEC,DDQEC9DC^EC@aD@ D0eEC@VDDtE@D[DDBEDbD#DDCDD@DDD6E@6D{DkD%EID@D D E@,DD`DED`DhD@DC#DDEC#D'DECQD-DYECXDDECFDCEC3DDECkD&D0tE D;D@FDDEiCCwC@8EZCCMC(E\C@ DdCPYENC D@CPsE>CDCCEICDEC~E_CDaCxEC@4DC8E$D@vDDYE@*D`DD E3DDD EDODTD/ECDXDpEC!D@DpIEC0DC0uECDD`GEC?D3D4ECFDDIED,D6DE5DpD@`D0ECDD@EC@+DD UEFDDD;E9DXD@vD#EC@cD;D%E&DdDoD%E+D@SDVD EC6DDLEC@FD@D wE@DBD3D EECDCEECDCpEC?DChEC@@DCEC@DC0EC>DCEC:DCHEC@DCECADCEC@KDCECLDCpEC@TDCECQDCpEC@MD DuEC@=DCyECODCECKDCxECCDC`ECHDCؗECEDCECKDCpECJDCȔECIDCГECHDC،ECDC0iE8CC%C`4E^CCTCPpEC5DC8ECLDCECMDCXECBDCȟEsC+DCEC@,DCE|CDC(E[CDcCEmCDfCPEC1DChEC&DnCEC%DCHECDkC@EC@/DCEC/DCPEC@"DC؜EC#D~CПEC@*DCEC1DC EC-DCEdCDhC XEKCCrC `EdC%DRCEmC@DeCЪEbC@DoCE}CDyC EzC@DCxEsC)DCEdC@DCEtC DOCZEEC DOCgEWC DAC0rE>CD,C|EUC@DSCPlEmCDUC`mE[CDTC`uEJCDKCtE4CCCCsEDCD8C@hEVC DQChE@CDLCqERCDBC~ENC@DOCsEcC DTCEYCDYCEGC"D_CPE]C(DICEC+DC xETCCCGECDC^EC7DCplEC-DC0gEC%DC\EC DC-EC0D#DP!EC@DCNEMCD>CxEOCDYChE,CD:CHE:C@D/CHEEC@DBCЀEVC!DhC{EAC@DGCE1C D;C0EUC@DRC`uE\C@DuClELCCBC pEJCDCEC@D DpEC=D@Dp*E;DmDDEDD@DEDD@{DdD0EC3D@D1EC D@)DECCC@EC-DC~EC@RD@DRED=DWDErDD`D EFD@DDD(D@ED@\DEC3D D0LEC+DC yE@D;D@5D%E@&D@YD_DpE@+DSDLD ED@7D@3DE D@9D@AD E=D@WD@uD@DDVDQDE#DMDFD E2DDjDp3EFD@DD EDUD@D,E#D@FD@BD EWDiD D EcDD@DE4D`DeDECKD@,DPECGDDhED@ID@6D0E$DED@pD DVDD DDDDxDD E#DCD@NDDDLD;DED>D@0DbECaDDwEC@(DC@{EC"DCEtCDpChE|CDmCcEFCC;CpdENCCgCDC؛EC>DCEC@>DCECDCEC@:DCXECCDKC@yEMC DYCuErC@ DQC`fEHCDRCyEDCDDCwE]CC9C0wE1CD4Cp~E=C D@CuESCDRCqEjCD_CEQC@DPCyE^CDFC(EXC@ D]C`EpCDzC؍EXC@DiChE|C8DCELCD`C YECDC sEC-DCgEC@&DCaECDC EC@DC DC/D]D DCDC@@EZCDhCpsEjC@ DyCЁEJC DVCxEOCDICEJCDFCpE5C DDC0E>CD;CHE;C@ DGCPE+CDECEECCuCmEOC@DRC@pEICCqCfECDC-ED0DHD0 EFD@uDrDD%D@\DD D@D3D@>D@ECDCP6EICCCE CC1C EC@AD@DuEC>D@(D@&ED D9DCGDD=DC0D|D%DC@DdD!DD@DRD@!D@D@0D@DD0;EeD@DD5E@DDDpJE@DDE@1E}D`DD ESD D`DEDDD0EOD`DDE}DDD0E2DDDECD DDp7E4D~DD*EC4D@*D$ECDC3ECDCkECDCpsEC DCpQEC@CDECtEACD?CoE_C@ DTC@tE~CDCȁEC"DCSED@ID@DCEC9DC`EC6DCEC4DCxEC8DCEC8DC0EC>DCEC@=DCEC@DCEC8DChEC)DCECDC]EC@DC$ECCCECDC^EC,DCxECGDCXEC@KDC0EC@RDC(ECRDC؜ECZDChECSDCEC[DCpEC@QDCPECADCEC2DCPEC2DCECADCEC=DC`EC@'DCkEC8DC@ECNDCȜECADC0EC:DCȖECDC0EoC@ DCPE]CCZCP*EpCCxCIEfC DRCPrEYCDLCkEBC DJCtErCDmCІE^CDoCPEC)DCuEC@)DC`HECCCDC}EC2DCPEC8DCXEC=DCEC@7DCEC@6DC EC8DCXEC8DCHEC5DCКEC@7DCEC6DCȚEC?DC0EC?DC8EC@9DC(EC6DCEC7DCpEC6DCEC@=DCEC@DC(EC>DC(ECADChEC?DCPEC:DCPEC#DChEC@DC@PE`CCCREC DC.E@D@D@NDECDCPE\CCnCBEgCDCfEC!DC`{EC-DCPEC:DCEC?DCpECIDCEC@SDChECQDChECPDC EC@=DCEC@.DCEC9DCEC>DCEC3DCEC1DCXEC?DC8EC@JDCECIDChECCDCEC@CDCECGDCxECODC`EC@NDCECMDC8ECIDC8ECPDChECJDCEC@JDCEC@ODCxECMDCEC@ODCXEC@ADCE]CDuC؎EHCC4CtELC D]C0pEC@$DCȑEC.DCXEC#DxCEC!DtC(ECDCECDvC`EC DtCECDzCEC(DCEC%DC0EC*DCEC@0DC EZCCHC8EfCCcCECDCEC(DCEC%DCEiCDcCxEfCD|CxE{C#DC`E=CCWC \E"CCC`3EiCDCSEOCDbC_EKCDECnEpCDiC uEeC@D`CXE3CC:C@E>CD6C0ENC@$DHCPEcC"DbCEHCDTC0EeC@DZCEVCD?CpECD\C8EwC@&DkCprEgCD\C8E?C@DHCEtCDC0EC@8DCECUDDzEC+DCvEgCD_CHEiCDlC`EC"DDKEaDD DE6DDDECDC1EfCDC0fEpCDC`TE4CC^C/EACC2CUEUC@D]CEC)DCȇEC/DC0EZCDCECEDC@EC8D DnE]CCbCAEQCCaC-EfCDC0WENCCyCIEKC@D}ChEhCDCNEsCCCP&ECCC >EBCCkC1E$CCCC JEECCECNEC+DCKE@D@.DFDD4DcDqDE@DD{DD D@D@ZD@rDPE3D@eDvDD DSD@BDP$EDCDRDD,DZD~D E*D`D]D DDEDLD EDKDgDD@D9D-D%E D)D=D E DCD8DE!DKD@ID E DAD0D@DD5D@YDE D@DD@6Dp+EC*D@DED*D@)DDDeDBD1ED\DSD2E@D@]DKDD E@DcDSD4EC@7D@+DECDCEfCDCDECDCsECADD@TED@GDcDPE@/DD`DEDmDXD1E}CDC0CCC@[ECDCPEeC DCnEICDCCESCDxCЎEC@,DCEC@&DCECDC8EC,DCEC@=DC@pEfCCC0QElCCC0ECDC fEC"DCYECDCuEC@DC؁EYC@DvC0`ECCCuCMEfCCC@'EKCCaCuEVC D[C\EDBD*D3ED6DKDpE@4DTDlDE@:DqDhD EC@AD@$D`LE@DFDCDE DIDHDE5D@\DDDZD`DDE^D D@DDkD@mDDE:DsDqDDC@DDGED@?DADEDUD@YDP#E@=DSD@`D D D:D2DD$DRDpDEDGD4D.E D@%D D@D@ DBD@9DE#DmDTDP6E@DD9D~DMDCADDBDC@JD`DDDCMD`D@KD@D3DD>DC.D~D8DC8DD?DC@;D D;DC7D`D.DC@6D D.DC3D D.DC3D~D(DC0D{D@,DC0DxD.DC'D@tDDCC'DCCC@ DCDC#DC]ECE@.DPDpDE$D_DsD D@D DD@2E0D@DtD/EC5DHD&EDqD3DDEC3D,DP6EC;D@D/EC-DCNEC=DCpwEC9DD RED8D@KDEC$D@Dp EiCCC E"CC*CD4CCCEFCC6CVEPCDFCfEKCCACOEtC@DwC[EC@DCrECDrCHEC'DCE@>DDuDp]E@YDzDDEC@/D@D0dEC DCED@D&DjD\D`DbDDDEIDiDjDD3DNDRDDGDiD@sDDDD`D D@fD`DDD@dDD@DD@MDaD@{DD@%D@KDSDD4DpDDEDqDEDaECCDDEE@D@3D-DEC3D@ DYECADC EC,DC`EC#DCzEZCDjCiEoC D^CeECDC]EC@DCjECDC{EC$DCphEC DCpWECDCpiEC'DCfEC7DCDEC@:DD@HECDC%EC DCD'DEC DC1EaCCwCPMEC DC@ECDCȑECDC@EC#DCErC$DCPE}CDCPECDCpECMDCEVDzD@qD4EhD~DD$E9D D`Dp>E.DsD_D EDOD@ DEC6D@DBEDkD@ND@UE,DpDD*E;D@BDHD0 E)DPDD@EC?D@ DmEC8DCEC9DCPECDD@GDC;DDCDC5DzD:DC9DD3DC@D D5DC6DD4DC6DD0DC;DD@3DC;DD1DC9DD*DC9DD@4DC.DtD0D/DDEDDDC@DCOEC@DDCEC@$DC0uECDCpEC@DC`{ECRD@0D\E@BDhD@D EjDD@DE@PD@DDCECWDC(EC4D DeE:D D@wD05EGD{D D(E@ DDZD?E@CD@DyD-E+DdD[D)EC@#DDP0EC@DC?E>CC:CDEJCC4C@YEFCC,CfEPCCMC@}ENC DDCrE_CDcCxE`C@ DXCEC@DCqECDCECDyCEuCCYCprECDC cED2D;DpEC9D D@ECCCKEnCC~CJEWCCXC:ELCCRCDCEC=DCECGDCЋECHDCEC&DCEcC@DsC8E{CD]Cp~EC!DC`nEC@(DCE~CDnCEC@#DpCEC DwCEwC$DqChEC%DC`EC(DCEC1DCEC-DCxEC,DC(EC,DC@EC;DCEC0DCECD~CEDCDpCESCDxCE~C@DCECDCEC!DtCxE}CDyCESCDFCPLE^CD`CnEzC.DtCyErC#DzCERC@DTC@EfC@D}C}EC@'DCEC@DfCpvE[CD_CxEvC)DCEUCD[CnE^C@ DJCiE[CDkC{E_CDXC`rE^CDVCEhCDjChELCD9C xEICDQCdECDCWEC>DCsED@iD@EDTE@D6DD E#D`DD@ED@3DD EC@ DCLEpC@DC`qEtCDCPsEC@DC0EC0DCxEC@'DCXEC(DCHEC!DC@ECDC BEECCHCgE-CCFC8EjCDC`1E@-D_D]D@E@DDkDEC3D@#D@ E`CDhCPEC@-DCGEC@2D@;DPE@@DD`DAE8DD D@EC@DC@DCCCPECDC5ECDCmEwCDC``E{CDCTE@>DuDlDD D@D[D DD@NDUD&E#DmDODp2E@SDtDvD`D3DdD@pDE$DODpDE@DDDDE=DD{DE9D`D@xDE8DWDsDDDEDD:DPEED@fD@tDD@)DWDhD DD=DXDD6DZD@pDDID^D~D`DD@SDED2ECDAD06ECDCbECDC8EgC@ DSC0eECDC vEC@&DCp{EC;DCPEC@LDC@EC9DCHECDC_ECDCMECCCdCdECDCLEC@5DDBED@DDVDEC#DBD DC DC`DCDC$ETCCC 5EjC@DC@XED@]D(DeEDDDbECCDD hE}CDCfECDC@UEeCCeCVEACC@C`ECC/C@LEdCDCSE@2D}DuD QE@LD@D DAE}CD D ECD`CpTECDCIEC@?D@/DEFD@DWD]EDiD`DEE@3DiDcD`E@DZDmDEC7DCJE@ D@DD1DPLEC@D@D=E{CCC2E=DZDlDD'DXDdDP E!D`DMDKEC@@D@.D5ED*D@6D`D3DWD@^D ENDoD}D`DED@eDfDDD@/D(DECDD'D0?E!D@_D@VD9ED@ED'D)E(DVD`D`DIDDrDE@#DMDbDD@ D@DED@DMDpE8DlDoDEWDDD ELDDyDEDTDPD0#E3DOD@qD E@dD DDE5DhDgDDD>DBDDDBDTDE2DDfD@ECIDCECDChEvC@ DdCErCDsCxEC@ DCZEC@ DC`CECHDDEDQD*DlEC-DCdEC/DCPQE@DRDXD&ED>DYDEC@DC4EiCCHC@iECDCEC'DC@^EC@4DCYEDlD=DCEDjD4D ED`D@*D@D"DvDDCSD DPD DVDDOD@ D>DD9DC@2DD0DCEDD;DCDD ECDCEC*DD&E DKD@VD EC@DCE(CCCpEBCD?CnETCD7C؄EkC@DgCEC&DChEC@5DCEC%DC0|EC@"DC~ECCCgE+CCNC?E`CCtCME[CCYCp2E'CC*CE|CCC`1EC@/D,DECDC`"EPCCECPeEYCDUCvESCDPCfEbCC\C`JESCDC>ECDC YE@D[D6D0SE@=DqDQDUEC@(DCpEC(DCEC$DC ECDCECDC@EC@$DCEC*DCؓENC@DkCECDC0pEFCECDD`gEzCDCEC,DCPEC7DCEC7DCPEC8DCEC9DCEC@;DCEC3DCEC3DC`EC!DC~EwCDC@qE\CCvC[EC@ DCmEnCDrCxEsCDuCEsCD{CpElC@!DCpEC@DC8ECDCEC@)DC`iEC2DCBECDC,EC DCEC DC *ECDC/ECDCPRECDDNE7DfD@hD :ED@OD=D`EC'DD'EC1D@DHEC@DCPEPCC_C0RESCCFC_EVC@D^CESCDFC`sE[CDqCPrElCDdC]EFCCDCDE@CCUCETCCjCECCCEC DC 3EC DC6EC DCCEC'DCAEC=DC@NEC&DCEC5DCEC!DC0ECDCEmCDCHE{CDC`EmC%DrCEC@'DCEyC@%DrC`EqCD~CEsC&DmCEC@DuCEVC@DtCEeC DtCXEUCD[CpE@CD_CxEkCDC\EC@&DCfEsC DwCEYCD^ChEOCD[CPEiC DlChEXC D_CȋE[C@ DRCȌEMCDYCEiCDeCEiCDVCEaC@DrCPEoCDyC EwC DChEqCDlCEiC DfCECDCEC@.DCESCDaC {EfCCTC`oE`CCUC aE;CC[CJEqCDeCREoCDmCeEaC DPC tEbC#DgCoElCDCnEbCD_C@EoC@!DjCEzC!DCpEXC"DkCPwEC*DpCEzC@8DnCEhCDeCETCD;CE9CD:ChEQCDXCEhCDiC0|EJCD;CE>CDAC`E%CCDDWDD`DEE%DD DYELD D@D8E@$DmDDECAD@D0E@"DRDZD DD6D=D`EC$D D@EC?D.DED@ND1DAEC@?D#DIEC@*D4DEDKDPDE@-DZD@]D E:D@dD@hD`DIDlDbD`D@D4D*DEC#DCpfEC@1DD ]ECIDDKE"D\D[DP!E8DhDfDED;DUD`D@D;D@5DDDJD@KDEIDDD E^DD@D DxDDD`E@!DdDXDE@ DDD?DDDGD@DDEC8DDE@D9DND D*D[D^D@E@1DTDcDE DOD@BD D@DCD@ID`ECPD1D3EDGD?DE@"D[DiD`E)D_D@^D E DLD@;D"ED@SDWD$E0D]DzDE*DdD@kD@ E@@DvDDE5DpDpD`D*D?DXDDDYD@KD&EC[D(DeECFD@D@SEC#DCPEC DCvEtCD~CE_CCeCaEZC DC kEC1DCfECGD=D$EC0DDpDEC DCEC@DCEC DCEjCDdCP[EC@IDCwEC%DCREC@9DCPE DZD,D >ED@dD;DDDRDD@kD=D@DGDXD@eDDlD@:DYDDZDCLDDHDC@=D|D4DC9DD/DCCDDCDC>DD:DC@2DD/DC/D{D@,DC@1DD*DC5DzD)DC?DD@3DC@;DD=DC@6DD@1DCVDD@QDDyD DtDD D@ODD`DCDC`DC2DCPEEC:DCeEC7DCcEC@#DDhECDC^ECDDECDCEC@4D@DE3D@D D8E$DD|D7E@DBDECDCP)EC@D@D (EYCCICSE\C DjCjE`CC_CYEhCCCKEPC@D_CQECDCyEC@CDDvECD@~D@eD@hECEDC0jEC&DC@}EC&DCrEC@ DCHEC DCECDCPEC@.DCECDCECDCzECVDCPEEDLEE DDE]EC@BD@4D@DwD`DDDDGD5D EC@D&D 9E@MDDD@OE@1DwDDE/DeD@uD`DDBD#Dp;ECDCZECDCcEC@"DClEC"DCcEC#DC`]EC DCBEC@DC@#EC D DECCC0ECDCPECCD DlEC,D@D E D@=D\D@DDADXDpDENCCC.E/CCVCfERCC_C|EnC@DPCЃECDkCPEzC(D~CEuC!DeC~EMCDXCE?CD.C`VEWCCKCPOE.CCOC6E:CCEC E$CC C@DKCCSCEkCCC#ECCC`EC@DCECDCP,ECDC`7ECDCp=EC@DCWECDCkECDCiEC"DCdEC+DC`|EC'DCEC@DCEC"DCyECDCECDCE~CDCsECDC`uEC8DCTEC6DCcECDC`EC"DCECDCECDC@EC DCEC%DCEC@DCEC!DCЇEC!DCEC&DC`ECDC0EC@DC@EsCDCEbCDeCECDC~EC@"DClEC DaC EFC@ D_CȅENCDWCrE\C@D^CaExC@D`C@jEnC!D[CrEUCDQC(EpC!DsCEtC$D~CEFCDZCEcC DaC|EhCDsCuE\C@5DbCEZC-DYCEaC*DCEcC@#DpC(E8C D9C؄ENC@ DCCEiCDgCEC(DCЀE^C#DxCE?CC?CE@CD3C8EJCDTCE@CC@CvELCCbCHE?CDdCJEYC DjCwEeCDtCdEZC DC_ECDC`3EC DC`DZCDCEC@DCvECDC`EiCDCvECDC%ECCcCP*E CC"C@pECCLCP_ECDCAEC(DD ECCyC RE^CDC`UECCC`EC"D@DD!DDD$EDYDAD0EC&DDDCDCDC(DD E5DQDfD@D@(DMD@dDD?D@ID@OD DC>D@D`AEC9DD`oEC.D@D`(EZDYD@D@DAD@zDD E@DCD@D`EC9D3DED@YD@IDEXDrDoD EDLD3D?ECDCXEC@5DCE@DSD4DgE,DfDVD`E(DRDZDDDSDNDD@'D?D@]DE1D@jDiDE2D@uDkD0'EHDjDxDDDCDIDEDAD?DPEC9DD`2ED@&D@0DE,DlDdDE!D^D@MDED8D@DE DODHD`D@ D=D@5DP6EC@FD#D)E@D@`DxDE.D[DoDPE(DHDLDDD=D-DPE@ D@JDED`E=D@pDDDODwDDD@RD@{D`D@D0D@^DD`DD@RD@XD&E@D@ZD6D@WEC@2D@DEED@=D@ADEDLDMDEC@%DCPQEC@DCgECDClECDCzEC*D(DPD@'DDXD#DDD[DD@EC$DCEC@'DC-EC#DCcECHDC0oEC(DC=ECD@ DP0EC%D@Dp5ED.D=DED@ D4DDC/D@D#EADDDQE8D DD0 ECDCD*CC@C@EIC@D`C\ECDCІECDC7EC DC0#E6CC7C _EECC.C@E CCC E.CC C.EJC DGC}EgC@"DrCECDC0EC'DCpnEC@DCREyCCCPERCCCDvCCtCSEGCCNCWEfCCsCNEfCC[CPEXC@DrCPZEeC@DbCcElCDXC0hEHCCiCAE2CCcC E;CC@CE[CCCAEWCCRCPPEsC@D]C`rEC0DCHECFDC(ECDCsECEDDfE@ D@D@D0wEkC DCbEC@DCpzECDCECDC@ECDCwEC,DCEC)DC(EC@2DCHECAD DEC\DCED`D@GDGE DLDDD,D\DwDDpD DDD!D9DDDED@D9E"DjDbDKECMDDE=CCJCEQCDVC@zEC0DCEC@6DCTEC,DDDC.DDEMCD@C@rErCCaC}ECC&C`WE,CC.CEWC D`CEC/DC(EC DC`XECC@DECCC0 E8CC2C$E6CC/CpNEHCCKC^E>CC\C`QE*CCIC8E(CC:C@FE[CCHCpkETCC)CpqEeCDWCiE9CC)CRECC C*E9CCD`'EbD@DDEDQD@MD%E&DYDBDE D9D@.D0#E@DDD2DCEDKD;D06E@DHDADED\D6DpUEDKD@2D@:ED@0D0DD,DbDcDPE@QDkD@oDD@DiDWD %EZD`D{D D@;D@uDiD EDYDJDE@DyDvDE9DoDfDE:D_DuDPED@RDCD&EDHDFDPE@D=DIDD@D@D@ID DD@>D>DEDKD@OD E7D@nDqD@ E&DfDPD`E+DIDYDp EDmD@1D0=E@#DoD_D\EMDzDDP E-DaD@mDp EHD@D DE4D@dDYD` EDODJDEDTDOD"E@WD@zDDEDDzDDETD D`D0 E@cD}D@DEC(D DEED>D8DE#D@VD^DDDAD@,D&E DLDDPEC@@DCEC@/DCP|EC@"DC4EC,D D>EC8D@)D4E DCDJDE@DYDND ED_DAD7ED9D$D@EC@DC0:ECDCoE@2D\DeDp?E4D`DTDD$D@UDD@OD-DrD DC0D@iDDC=D D/DC@RDDBDC>DD0DC6D@D)DC6DD@)DC6D D-DC5D@D%DC0D@D$DC@/D@D'DC@0DD-DC@*D@zD%DC@)DnD@$DCD@fD D@=D@DYDD{DC-DCDCDDDC@*D@D9EC4DCfEC@3DCPEEC@D@ D0KEC@DC %EC0DDEC,DD@#ECDC ECDC`0EC$DDKEDKD@PD 1E@ D\D?D@ECHDD`ECEDDPEEC&DCsEC6DCȍEC-DD`REC1DDECDD@DCCCDBCCmC5E{CDC ~ExC@DCXECDCEC@+DCEC DCiEC@ DC0!EC@DC EwCCCpE`CD[CEUCC`C!E/CC7C@#ECC>CP1E:CCGC#EsCCC:EC-DC@\EC@!DCpeECDC0aEC2D@D @ECDD $E D3D5D@+E D@VDLDEC DC!E`CCCp+ECDCFECDC05EC@DCEDDDD DCDDKED@4DID"EDGD@[D(EC*D@DErCCC.EQCCpC`QEC#D@D bECDD#EC@DCEC DCZE`C@DCvEYCCgCuESCCYCPvENCC[CEjCDfCEvCDuCHECDCEC.DC}EC'DCTEC@DCp/EC,DDE@ D7D@ DEC+DC7EC"DDTECADD`EC'DC~ECND@%D`EC@ED!DZEDqD_DBEC2DJD@E@CC:CPhE7CC@CuE:CDKCzEWCDUC0kEUCDMCfEKC D\CfEhCDbC lEqC@DsCqE^CDiC ~EC@%DC@EC6DCEC;DC0EC2DCEC'DCECDCXE}C@DCEyCDC8ECDCECDCEvCDCXEC DCЎEC@&DCXEC"DCEC@&DC EC DCEC@!DCnEC3DCnEC&DCEC@DCEC-DCEC$DCxE~C@DC0EqCDC0ECDCECDCpECDCECDCECDCEC@*DCEC@DCECDCEC%DCEC(DCEC'DCȒEC&DCEC/DCEC-DCEC@(DCEC%DCxEC#DCEC!DCEC*DCXEC+DC EC4DC8EC;DC(EC8DCEC1DCEC"DCECDC}EC@DCpcEC@DCJEC@DCPMECDC0TECCCPECCCFECCC`DECCCMECDC`IECDCpCEC DC:EC DCP=ECDCAEC DD0:EC'DDE@D@*D@DDC+DDDC@2DD DCDCED-D DpED@7DLDD,DlD@^D-E6DhD@qD`E@1D_DrDE@=DhDrD EDGD=D)ECDD@D5EC4DCPQE@D0D0DE3DXDjDDMDxDDE@.DcDTDPEDZD;D/E8DpD@bDEADiDgDpE'DSDLD E@DTDHDE@DNDBDp E@D@JD^D0ED@PD?Dp4E DOD@?D#E@D?DLDDD5D;D0 EC/D@D` E DIDIDPE@,D^DrDPEGD@DD E8D@]D_D EDCD"D E@DZDUD 'E8DzD@~D E@2D@hD@nDp!E-D]D_DE#DJDGDD)D@JDVDEDkDJD=EMD@D D#EMD DD@EBD DDE?D~D@~DEC@)D&D0E@DODKD E+DZDpD`DC?D@D6EC;DC|EC9DCEC@/DCDED%D"DD/D@cD@tD$EcD DD3E-DzDjD E=DcDrD`D3D_D`DD@D@MDdDD@ D0D/D-EC?DDPE(DSDdDE@}D@D@D D@KD@DHD@]D@UD`D@SDiDLD D@=DD=D@D@3DC@FDD6DC@:DD@(DC@0DD%DC.DD@(DC6DD&DC@1D{D!DC*DvD!DC0D@zD1D@D:D D:DD"DiD@D$DDPD D DDJD@DDC@DCpECCC'EfC DmC3EC@+DCZEC%DClEC@DCdEC5D Dp;EC&D@DDC+D$DD@D:D4D3EC@>DD@:EDRDCD>E DPDAD`AEC/DDP/EC*DDDC@=D@DQECkD)DpaEC_DD{EC5D DWEC@D!D0 ECD@DECD@DEdCDC^EUCDCȄEC@ DCEC DCpzECDCEC,DCEC!DC1EC@DD0EC@DC E'CCDCEKCDFC@:EWCCAC`XEECDHCqE\C@DYCEnC DrC(E;C@D\CPE]C@DNCENC DCCE^CDlCpEnC@D^CPzE2CC-CqE4CC-CjEoC DpCpECDCuELCCOClEHCCRCiEBCC3CjE)CC7CPE,CC2C2E7CD@CfE$CC&CRE5CC)CDE`CC?C?EDCCECP@EPCC1CBEDCC.C UEgCD`C \EC@DCkEC0DC@YEC!DCcEC(DCsEC@%DD2E@ D#D#DD]D DDEXD@wD}DED@EC+DChEC@D D.ED@%D@ODEC=DID'ECCCAENC@DHCpEZC@DiCElCDsCDE_CCjCpCDAC@E:CDWC`qEoC@D_CdEbC@$DaCnEZCDnCyE`CD`CE~C/DC@EC:DCXEC,DC {EC@DCWECDCwEC9DCEC@2DCEC DC0E|C@DCxECDCEwC@"DCE{CDCElCDC@ECDC(EC$DCEC$DCȓEC(DC EfCDC|EC@DC0UEC(DCwEC@$DChECDC`EC@DCECDCHECDCxEgCDCEC@!DChEC&DC(ECDCECDCEC DCECDCECDCECDC EC&DCpEC@+DCEC@3DCEC=DCXECFDDEC@ODDEC>D@ D|EC.DC}EC@2DCpxEC=D@D`ECBD@ DECCDD@yECGDDxECEDD0}EC:DCHEC4DC`EC#DCXEmCDC ECDCkECCCPRECCCDECCC@EEkCCCIEtCCCHEsCCC@GEnCCC`BEzCCCFEnCCC`OEtCCCRECCCNECCCLECDDJEC!D D@/E D@7D@9D DD&D1D`DD@AD@ADEC2DDECDCECDC`E6D`D_DE8DlDDPEIDDDE@oD D`DpEMD~DD0ED@^DID0.EC.DDpKE@D>D@=DEHDsDqD0ETD|DyDE&DLD@UDEDKDKDEDZDJDEDDD.DEC?D+D !E@ DCDCDp!EDFD@DE D@ED@D@EDUD?DP#EDLD@D4EDHD7DDCDDp ECD DE@!DODZDD/D`D@wDDPDDDD@LDqDD DCDpDsDE1DfDbDDGDhD@DPERD~DD`E"D`D]DEDFDLD@D@!DXD@VD%ED@rDHDNE]D DDE@dD@D D"ELD@DD!E?D DzDED3D;DED:DSDE.D^D@{D D D@ZD@3DBECXDDEC@SDDeEC&DDED@3D/DE@'D@uDjD#EeD D`D0E@BDuDxDD@D'D@3D DD?D@DD0E@8D|DDESDDD@EADRDlDDKD|DDDD{D?D8D]DDZDDKDDD'D +ECD;DDDbD]D@1ECXDCEC@4DCPEC*DCElCCCMEtCCC*EC@DCpUEC'D D&ECCC EyCC~CEZCCdC0YEqC DmCwEyC D}C@xEcCD]CȈEzCDpCEvC DhCEpCDWCPEqC@ DWC}E]CCWC0E|CDrCEC DaCECDCrE^C@DvC`wEC DCExCDxC EmCDXCEXCD]C{E2CC/CIE:CCBC@EXCCKCZEcCDFCcEiC@DJC cEsC DZC~EpCDgCvEqCD}CpNEoCDPCbECDCEC8DC@EC+DC |ECDCPAEC@/DCaE@ D@aD@?D]E%D@_D@CDE@/D@KD8DDHD DD`DDaD@DE.DNDODDC$DCECCC5EC@*DCzEC@6DDdECKD-DaEC'DDRECCCp;ECDCuEC@DCEcCD^C`hEaCCRCUE}CDCRECCCpKECDCDEC(DC@EC'D(D0%E@D-D8DEC$DD0EC"DC,EC"DDFEDTDMDP#E@D@CD2DEC@D%DDC DC/EhCDC0eEhC DC@EC"DCȈECDC0[ECDC(ECCCDbCCCEXCCMCPFECDCbEC,DCPSEC@DCWEDMDID ;EVD@D@DExCDC@EEKCCnCWELCDXC3EGCDMC@OErCDC_EvC DlClEgCDMCREqCDC ^ECDCnEYC@DcC EECDWCkEOCDBCpmEsCDgC@gEcCDpCbEMCDWCfE[C@DbCkEzCDzC`uEC"DCECGDCEC2DChExCDCYERCCVCDEhCCC`eEC@DC؏EC?DC(EC,DCEC@$DCpEC DCEC'DCHECDCECDC@ECDCECDCEC'DC EC+DCEC@DCpEC+DClEC4DC؎EC)DCEC@DCpECDCECDCEC@DC(EnC@DC@EC@"DCEC$DC EC@DCECDCEC"DCȘEC)DCEC@'DCXEC@DCEC+DCEC4DCECD~DDD ECVD$DlEDWD@CDFE@D@IDBDE@DKD@WDE@ D@ID6D8EPDlD`DpE2D@xDmDPEDDD7DD'D@RDvDE@ DyDpD!EDJDQDE7D~D`DDcD`DDDHDDYDD$DoD!D%D@)DvD%DD%DuDDC@7DD*DC7DD(DC7DD)DC0DD@#DC@/D{DDxC@-DwDDC@*DxDDC@*DiD(DC@$D@dD$DC@D^D DCD@VDD@>DC/D@D ECDCHECDCdEC;DCpECCC@C mEaCDgCp{ExC#D{C@lEpCDlC@zEOC@ DC`pESCDC'E.CC=C0E2CC6CE[CCoC*ECCC0EC@DC DCCC@DCDCECDCPECDC`DCCC0E/CC0C D@ DEC8D DmED@D[DhE@ DXDhDE@7DtD|Dp-E@4DsDfD EDFD4DD'DGD[D@D@TDDDD@D@0D+D DD(D@D EC@ DC4EC,D@DE(DdD@D EC@/D@ DDCDCEC/D@D8EDDD>DE@D@=D2D`,EC@8DD`ZECTD(DVEDKD@3D`OEC5DC0DEC@1DCuE@D)D DFEC@DCp@EC!DCQEC4DD4ED@hD\D4E@4DDDED@WD@ODDD@ZDDDDDcD@MDE>DgD{DD6DyDiD`D9DyD@kDE=DfD_DE>DjD|D`9EADD@}D02E$DcDcD0%E\D`DD+E@TDDD E(DbD@ND@D@ D?D.DpEC@DC@.ECOD@'DXE@0DLDfDPED@7DGDD#DWDaD E DbDCD`9EEDoDD ED@-D@;D DD7D4DElDDD$E@PDDDpE@DZD@CDD-DjDfD DZD@DDD@5D D0D@(D$D@lD@DC3DwD!DC.D@tD#DC=DzD7DC5D@D*DC.DyDDC-DwDD~C'DuDDC"D@sD@D{C,DxD'DC@0DoD4D D.DvD.DD@ D@OD@DDC(DCDC DCpEEC@-DCqEC@=DDiEC@YD7D@oECUD@CDgECQD6DnECKD?D}ECRDDuEC'DCmEC@DCEC@DCxEC@DCWECDC~EC@DCP-E6D@@D[DEC@!DC#EUCD]CSEwCD|C EzCDCXECDCqEyCCC)ECDC`EEC@4D@DLEDcD@RDp1EC@JDD?EC+DC bEC1DC0mEC@+DCHEC@1DCuECDCQEC$DC8E@D@FDRDDD@_DVDECCC,EeCCMChEC3DC8EC)DCءECDqCEvCCeCNECDCuEC%DC`EC@2DC(ECDCЎEC%DCEC@DCЀE^C DrC(EC%DCXECDC8EFCC4CPOEcCD@C_EUC DLCsE|C@DdCEMC@DICEHCDLC EZCDSC8E;CC,CpdEMCC-CcEOCDMC0XEHCCHCPNETC DOClEfCCUCVE6CCKC ,ECC C4EGCC5C@TE4CC4CJE3CC/C@%EAC@DVC0&EhCCC1E^CDrC]EfC@D{CbEdCC_CqECDC[EkCDuCnECDCECDC@EC@*DCEC&DCgEC#DC0hEC@DC`RECCCECDC EC@DC4EC@DC`PECDCBEC6DCxED[DkD`=ED@LDmD EC@CDD0HEC,D@"D`EDBD;DEC@&D@(DEC@DC7EkCDC?ECCC#ECCCE4CC@C!EdCCCP1E`CC`C0EGCD|Cp`EcC@DgCdEqCD_CpnElC DcCE_C@ DJCEC6DCE9D@DDaE?DD~D3E DODZD E3D@EDmDE/DrDSD+E D@QDFD@FEDwDyDPME.DDqDlEC@,DCXEC9DC8E D\D DoE:D@wDnDp?E#DrDqD9ED@^DC0dECD;CoEHCDDCnEmCDCkEtCDC0yEDC@ DYCgEGC@DfCPEkCDuC@VEoC@D`C dEbC$DoCE_C@/D|CEC@D'DIED|D@`DBE@DNDKDPED@JDBD@:E@DADBD-ECD D0ECDC`ED-D@"D`DCDDDCDCE[CDCpFECDCXEC@"DC^ECDC`HEC@$DC@@EC3D%Dp*ECTD8D &ED@D@=DpE@D8D1D"EDGD@CDED;D@6D#EC6D*D@ECED$DP#E@D@ND9DED/D*DE@DCD@;DEDAD@=DDDID@DD ED.D@=DECDD EC>D#DP0ECyEZCD}CEC@DCP~ECDCpvETCCXCgEOCCDCE6CC)C}ENCD@CpcEXC@DKC[E^C D~CXEmC@DgC{E^CDZCЂEjC DZCEC'DCHEC@DDWEC@DCcERCC^CEECDC\EC-DCzEC,DCPkEC DCECCDDC*DD`DC D@DDEC(DC`TEC,DCUEC;DC@oE@D@8DADGEDUDtD!ECRD"DZEC!DDP(EC DDEC@DDDCCC0ECCC`EnCCCPE,CC:C0E9CC(C_E0CCD@jD@D@/E\DDD7EC@D@D1ECEDD`]EC4DDnECKD@"DfECGD,D PECGDDpiECCD DXECLDDHECZD@$Dp~EC[D&DP{ECHDDECFD@D`EC?DCEC4DCEC@/DCXEC6DCEC9DC ECE|C@DCQE@CD^CdE`C'DzC8E_C%DCECODDБEADD@D`\EdDDDp8EMD`D`DEE@DoDEDpeEC.DCtEC"DCcEoC D}CfEtCDC`E{C DC~EC@)DCEC(DCEC@DCEpC@ DCEC&DCțEC@"DC0EC@DCxEC@#DCМEC@!DCȚEC@DcCdEC@DC0ECDC0EmCD}CHEvC@DC`E~C DCECDCECDCE_CDwCE(CC&C0_E*CC+COE CCC-E$CCBC@&E9CC.CEPCCiCFECDCPPEjCDCiEC@,DC8EC6DCECFD@DpEC@ID@D ED@YD@3DpyECQD)DuEDfD@:DqEDaDJDqE@D_DCDPvED@fD:DwEDfD@:DyE@D\DD-DBE,D D D0E@"DyD@`Dp9EDED5DE'DhD@GD`/EC@ODDPECDC0IECDC`TEC)DDBEC@DC0GEC DCbECDCiEC$DCYEC6DDKE DOD@*D0;ECFD+D@*ED@>DHD E DRDaDD@/DfDuDE:D@iDDE=D_D D@ E@ DTDaD ED@]DLDP&E1DVDoD`EZD}DD@DDkDkD@(E#DMDCD` EC4DD*EDCDJDD D[DODEVDD`D EDeDTD`.EDHDBD E@ DBD/D`E8DaDGD ,E/D`DMD !EDRD3D@ECADDWEDTD1DKEQDyDDEND`DxD DYD@iDDD1D@zDYD"EDLD*DCE2DDkD` E%D_D9D_DD_DDCDcDDC>DDJD/DED@DDDD/D{D'DC@PD DLDC,DD#DC+DyD@DC)D|D#DC&DxDDyC@*DqD%DCIDDGDDODDfDRD@DaDDC DQDD D DHDDDC@AD D@HEC4DDZEC&DC:EC1D$DFECJDDgEC@HD"DqE@D^D@TDjED@_DoD]E@DXD@ODp\EC@5D D0qECDC@bEC@DC^ECDC`aEC@ DCDEC@'DC7EC@CDCcEC)DC8EC(DCEC@DCECDCECDC EC=DCE@-D`DdDjE[DDD'EDDTDD)D`DDEEC0DCpEC@DCmECDC\ECDDE@DDD;D`DC:DD/EC(DDD'DXDqDp'EC@DDC?EC@ DC wEC@ADC ECDC@`EXCCsC'ECDCCECDC lEC-DC]EC>DDbEC>DCEDVD@ D@oEC!DCp>E{C DCmEDED@DZEC@0D@D@JEzC@ DC{EbC@D[C{EpCDMCPyEcC DNC~E?C@DEClEWC@DDCPRECC3C`E1CC*CWENC@ D5CEcCDICEzC@'DeCЖEC@BDC(ED.DD2ECDD@EsCCqC%EkCCbCVE1CC6C eEMCD=CHEqC@$DYCPECDCEjCCYCvEsC@DvCxEtCDoCEC@'DCrE*DSD=DPEC.DC%ECDC KEC@DC4ECDCCCVC` EhCC{C E|CCCDdCCC "EOCC\CNEHCD[CME]C@DBCpaEfC@ DCC`EVC@DcC@EfC#DgCHEcC@ DoC؀EkC@ DjC~ElC,DuCEC@5DCEC8DCE@!D}DD\E@IDDD0QEnDD@D@fE DQD1D\EC@1DCvEC7DDgE@DTD@/DeECOD/DgECOD@ DpECVD@DȉECCD DEC@>DC(EC9DCHECCCOCEaC DgC`EE`CD`C0lERCDUCE`CDpC~E4CD\CmEWCCSCbERCCGCUE[CDeCYEOCD`CPXETC@DHCbEiCDC`"EqCCC-EVCCZC08EHCCvC'E0CC9CFE9CC6CkEECC8C~EOCDGC@xE]CDaC`yE~CDC0iEjCCuCAEYCDHC`ZE_CDaClEgCDgCuE[CDcC |EwC!DCEC@LDCEDDzDxEdDDDp=E}D@DD:EiD`D@D6E*D@D`DCECED DPcEC.DCjErC!DC^EtCDCNE>CDOC`TECDC yEC@DC8EC@$DCECDC8ECDCEwCDCECDCECDC`ECDC EC@D|CgECDC{EC DCE~CD}CEC@DCPECDCE~CDCECDC0EC D~CE]CDSCHERCC?CiEMCCNCPkEVC DYCmE3CC8CFE!CC'CJEGCCQC:EWCDkCcEC"DCXEC3DCECADDhECHD DECTD'DECUD@"DxECUD*DrEC\D:DpEDaD@GD`uEDdDEDPxE@D@hD=D@vEDSD@+DvEC@GDDpwEC@6D@D~EC6DCEC@DCEC@DCXECCCPUEGCCCTE2CCXCOE9CCfCQE,CCeCQE>CCqCPRE.CCmCREHCCmCSE,CCWCpVE8CCCSEFCCCPQEPCCC0TEkCCCRECCCEEOCCC ECD@Dp;EC@0D@D0>EC@D DCECAD@D .EDYD@D(EDsD@D01ECD@DEC@ID%DtEDXDTDgEC@UDED^ECCD0DpcEC@DCdExCDCRECDC@CEC@(DCMECDCSEVCC~CmEC DCECDCEC@ DC ECDC8EC@ DC qEtCCC^ED@;D@AD@NE=D@DD8EDED@zDE@ DDUD3EC@DC^EC@DCPUEC@DCXEC#DC+ED)D.DDC,DDEC@ D DEC@ DC@DVCCC'ETCClC0jEC@.DCEC@6DCpvECDCFEC0DCrEC@DCEC@+DC }EC@3D@DEC DC`EC)DC@}EC@&DC`|EC@'DCEDJD@2DTECOD@(DpIEC@DC`UERCCaC@YE>CC8CsEAC DAC|E?CD8CnESCD%CeECCD*CrEDC@D4CsE4CC&CESCD7CȊEC8DCECPDDȋE@ DOD/DP$EC#DCPElC@ DC_EC@DCaE?CD=CkETCD@ChEpC@!DKCКEZCC/CtE:CC;C]EgC@DSCyEeCDHCPsEsC@ DlCtECJDD0E DGD)DE{CDCqECDC#EC DCDC@DC@&ECDCQECDC EC@DC 9EC"DC GEC@DC(EYC@DC?ECDCEC DCDCCC`DoCCvCD(CCCp&EDCEC3DCECDC0EUCChCD-CCCC0EGC@ DVC0LEcC@DZCuEUCDMCȆEWCDSCvEFC DECEaCDfCEBC DRC0rETC D[C_ETCDCcEkC@DuCtEbC@DoCWECCKC2E/CCVC+EmCDC05EICCFC\E`CCKCEeCCRCvEqCDrCEPC DlCPuE&CCHCKEeCCCEGCCC0EACCBCBEWC@DbCEYCDcChE|C"DC|ED~DdDzEOD@DD IEzDDDp0E@tDDD:EPD`DD+E;DDD,EDsDdDDEC=DCvExC'DCnEyC@%D{CpaE[CDlCmE7CDQCFEMCChCQECDCeEC@DCPECDC`EC@DCECDCEpCDCXEZC DCPwE]CCQCPE}C"DC@kEC)DCEsCDC`EzC@ DC0EC@DCE|C@D~ChEeCDCExCDChE\CCXCptE_CC:CpbEjCDlCmE]C@!DfC@EOCDJCvEGCC9C}EDCCSC`ENCC^CjECDCEC.DCxEC;D DPECDDDECID DECOD@DECTD+DrEC\D@4D oEDnDGD`rE@ DiD@LDtE DhDGDrECMD@)DrECADDqEC@D DwEC4DD~ECDCECDCppECCCLE6CCnC0UE;CCSCSE5CC`CPECC5CQE'CC9C`SECCICUE&CC]CTE(CCPCRE:CCMC@RECC\CPRE3CCbC0WECCCMECCC4EhCCC4EC$DC ?ECDD*EDIDD{DDE=D@xDD E-DaDiDE@DTDKD .EDD`D(DC+D`D#DCCC=ChEMC@D;CzECC D@C@EECD?CE=CD4C{EEC@DDC~EC*DCxEC1DCEC:D+DPECDD5EC@,DC8EC>DCxEC$DCnElC DaC(EJCC?C~EECC5CgEBCDHCuESCD;C0sE[C@D;CEtCDjCXECDCQEDND0D#ECFDC`eECDC %ECCCDCCC0ECCCPECDCECDCEC@DCECDC`DCD|C'E=CCPC`EPCC\C D~CCC`EdCCZC EE5CD>CoE`C DkC^EcC D[C0hEmCDeCPEUC@DPCxEC@DCxExC&DC،ERC DiC ECDCpECDC{E_CDCEC@DC8ECNDD pE@DOD1D LECID@$D\EC@eD'D~E@ D@lDQDEDzD@\DE@$D}DfDED_D4DECNDDEC@DCؗEC@9DCȗECDDCECDC0EC9DCEC5DC(EC8DCEC9DC EC6DCEC@=DCEC@DC`ECBDCxECsc7zkr^W 5=P> 9A HN$'H$  @r 9A HN$'H$  @r 9A HN$'H$  @r 9A HN$'HǏ͛K̙3U=V?q?ufXΝ;cӦMuزeK_,!VƟ %Hz(V^Pw bܸq1s̚|x\s5`ǰao_ZA %Aw}rhnn{+ 2eJ#@]j{| @r 9Aܰ`h⋱bŊx'=:;;cرqUWU,XSL{\\ݝ58p,ܹs9NW#F|;bŊ1b L@#$ PقwjժXdI޽{+ƏMMԷ8~xW\Æ5 'o@YŰabԩIiooKƝw޽q5$ ꫯ{_K*˲Ȳ,̙<@?18tP1cb1cƌʹk.wH;$PJַ#DDqcǎɓq]w%رccݺuq-ĻoO͛[oyWĉkHԩSqGDcԨQ9O r}BF\PlQ(GV1H * .e˖ի+ַm6 'NI&ռ'}:uA}%Hʽ{X1bDp 9M@$… _~_ɓa*Fy r}BF\PlQ^xd8$ F eY#ގW׾s̉~:zxg !,={v,]4-[ÇY/J;v,=ǎYfٳc1rA3ZZZ*֎9&Ms &Vs0s̘9sfc@(A HN$'H$  @r 9A HN$'H$  @r 9A HN$'H$  @r 9A HN$'H$  @r 9A HN$'H$  @r 9A HN$'H$  @r 9A HN$'H$  @r 9A HN$'H$  @r 9A HN$'H$  @r 9A HN$'H$  @r 9A HN$'H$  @r 9A HN$'H$  @r 9A HN$'H$  @r 9A HN$'H$  @r 9A HN$'H$  @r 9A HN$'H$  @r 9A HN$'H$  @r 9A HN$'H$  @r 9A HN$'H$  @r 9A HN$'H$  @r 9A HN$'H$  @r 9A HN$'H$  @r 9A HN$'H$  @r 9A HN$'H$  @r ayjoo]vEGGG9r$&LӦM+2ʼ$ }tزe֭[Ua ;sL7Gy$y^7o޼7cC8!euwww=Dܹ36m[n-[D{{Eɲ,Ν;7ömK_RܹǴƺuoL:KgggT9r$&M<C!=P^:1ŋٳgu\{{{|/y %m۶;zOԧn۷͛=]]]=f̘7|P @I9Ν ,lj'[oK,_b%6mЇo=zkgϞ}swޘ0a  @y4=@QeYÇWXfMk֬1c=f~_/\6r_c1mڴxGc'N6+uwww=D+1nܸhnn=q_gYΝzuɘ5kV?~ZeaÆXtE߻wo|c89^xᅘ1cƀf쌖#GĤI/@|Zz1eʔ EaÆ "oSqW+zXn]hٸqcŋGOӟTL4ABkb-˲XdI?~Vm߾=^z饁@$ȿ8sLڵ^]vY. O}zJ``„ 5qwi/ϰ ΪZi\Μ9yuiڴiq%=@ ABUk Æ 1cɓ'ϯ>}ـ{7|3Q.]r%|O~(P 3bĈ~WYwwwwCԫ8p,s6ĉhYM74\,F ?=ztr-U .f^6o޼5k@!F$mV?_{K<1!H(o=ƍWOƍt//*֚㮻J5"$… 1cw݊<=zh|3cǎU߈3f:#F u"˲>wٲeUogwtttŋc߾}^zi|PGް(EEGGG?|pq,S&'ͱqƸԩS׏9 ,ӧǭcΝyhkkgaÆ0a  y#$\@[[[8p_l߾eYgϞX}run:Cbڵv^ojj_q- 1 lBCzEO?s/QgΜ+_ M#$\@e^Wykuu֭[c;wnG?;vM7T90<ߟy$K.;hoo8|p=z4ƍӦM+"ꪤ"#$4hmm{ ȅG6 ACK (51C#HJI$#F R#@1#@qR#@'F$uM$HK%1 ;b(>APWP @#@$uAE (41'APXb_P @ B#@9#@yB#@܉| @PN 7b(/A 1 rb(?A0 #@$CBE :14A0И #@$BM$'F @Rb B$$F!H#&HL 1AP31AP1p!~#}!HL 1@  #$#$=#!H$@ <1 1 #  H&F Lh@b`  H"F Jhb` @ Pbb /()1'Aț JF JD JB J@ ("A11PTSb P@ Έz H:"F 'b@=$@z%H#L$F  F  D  B  @ r&FH9#e%H2$@@ `F H!$F h$b( IDAT`F$HA$F hdb  11 #M #T$ `=$@$@ &H~#\ A7#1@ #H# .@PABP;A@00x1muyd%la$#ZЉ*M@T;v.iә8t`JZ3:M4,C#&H$n5IvCIsvs< =߹x~o&N#$CGA i y< 4,1@hHb$p'H*C@#T  F,A'FA"F RCP; {b#Hj %F] ;b'H !Fyb#H %F_j 9b'HA@#  FHAU'FHAU%FH'AU#FH/AU!FH7A'FH?A%Fh *F8 T(;1@$PVb$HlK@Y ĉ$(1$FM!Ft&Lh&D@>J&F`,J"F`<&FL E@! L@ D@1 b RR KD!F` d#Ao#A!F Y$N (A@#PN$F F@%JRաC矏_WMMMs΍ F&YT H[l{,ƩS~fΜ98>Oży*᯵Gooo"w_>VZ]@u4l(Pr-bŊַ5f_+/ܲ<<j$7.`}O|LO@xe>|8>s~v9Ē%K#c֭ӓ<.Xvm1s̘;wnIggϞ6@%&A|򓟌;wf&O7n|#1io2v8rH#dYn]\wutҊ=Zgggb#P!7M4H8H|_9o_u\z饱yj"F^x7rfI G)Re2:ujE{{{L<92L۷/n;+%01+D;d"ĥ^~{,]4bΜ9oE'N={+sej2+z. y!H?˖- :+,X ,[n%og>/+Wƒ%K-=/΍$ .1@̘1+W6hWlV\9nm/RNxp=!z y{@;[U͹7nKf=`m۶H^{5kVw@Z*ԧ=-Z5;uT|.͛7̮wW_}5;5{{Sm  a +Vșmٲ;r")SDggv'9/ @zH3A [nǏ|Ǐ~Ț-^8ZZZ&_9=z4֭[3$@r a Ϗ%Kdc?vXZ*g~]w%_9w},k6}xW ]4A8֯_3{衇bÆ ~ӓ5ꪫo/oᆘ4iR֟'x׾8qDAsx衇K_Ro&ۋ&FQq5ĭޚ3_relڴ)r~vȑXretwwg3L|>L挟ٽ{wq[[|&So}{r~{{q/M@#Rjݣ>;w]v5;3XdIx100۶mgy&N:sϺubٲe{`` |xc1whkk袋"Ğ={bϞ=+Ν;sƍ+7F#H8s=7|ɸ뢯/g͛7͛ǽcX_U9޽{c޽E{{wg.V84"l(@[[[Nj^*n~ @ňq#@c$e'F#HJI ( 146A81 H%F" @ @"  | @X @Ix @BB b3#$#$c#$y$9D ,b -b ) "@ H (A401P.hPb Ѐ@ J$@#"H!F*I @T RNT RLT RJT RHT RFAH 1PK bΉZ$H:&Fj eCb Έz H:"F 'bƉz$H&F jgAb ƈ4$@ #i!H!FD5@ L H D @ *L4ATh1H Pb@#$@F%H2#Le F &F$@&H~C #d$r ` Db b Hb3$@$@$@$'HqJ#H1J'H<#HQ'Hӈ!H#FH B4A Ow^K_R&+PB޽{㩧ʚ577lJ甼 IDAT5̙%$D|X}ƺHѯY=iZٮh=+?P  P{h%Po 9$#G|x}ʔ)q9Çߚ=z݀ڶ3~sIK3A(i BkI_|qIFFF8ydWd2^1}hmm:3f̨ 1 ԃjzSV{HsV{㳟luY^`L0J``-WQ?s)JkŬY(ޢEs\_eּJ@d2XU-Z(~YkkkLBLJ}K~B|Z F z f̘$$$d2D_";uYqw_xQH:+/^\5 Ryф7Q=H( 14444ۿ g2{YAB&)+ СC93VPQw̒ >hkk+${sۿ]]4A(^ziά;|@ReA@>Q.䒘}z,Z(kvԩ]]7oΙ]%v&^{mL6-k$?軶lْיL.gsV@ aL\sMz9+WƦMbhh(gG+W< ? d lWWWN@pر?رcG3ػwoo{]hhS@-{GcΝk׮fqw=K,o1b۶m3ĩSrYn],[b{O6-wwcppꫯ /0:::w~w?il۶-MSNo~RHHequE___I??^ԙn!~d6nGT=O>dX"Ng}x_Hg H qHg H qHg H qHg H qHg H qHg H qHg H qHg H qHg H qHg H qHg H qHg H qHg H qHg H qHg H qHg H qHg H qHg H qHg H qHg H qHg H qHg H qHg H qHg H qHg H qHg H qHg H qHg H qHg H qHg H qHg H qHg H qHg H qHg H qHg H qHg H qHg H qHg H qHg H qHg H qNX]6^}Xzu[.:vO 0  $CMMMw}q=ij>t: w^\r%ѣGf%wx 9ydC[1jԨ gu!"7ߌ'ưan,N*w @3ؼysRHRy||¦FpO(lj ¦Fba ,XtP(}{ｸbs;;$g;zئMK. ޳RTn:gضm[Y&t.Z(>cĉ>vMMMVټy~ I ¥>Q(\ ”gk-b tc4T*#Fs=7:ݻwc7oK.ŋ-J7xc|_QF5* 6dņ Ҩ=ӿ|B}BaSP'65 K}BaSP6lؐq}Ȇ>=,JHu0qXlY̙3'&L#Feo߾} >}z{<+V, ֭w}7#V,_r8p`+#~^ｦN;ZpaVC& 8ӲbĀbذaZ 6/WkFvW^ѷo8CbŊxc͚5oG׮]W^1hР4hPӃDsI _{ｨQQQ[lѡC|K(jJ֭[cQYYkÆ ѹs;WzꡔRb N{1s̨zM*ǹǏګ߿#}]>wމ>fϞ/B̝;7GI|RYYY+cƌq;ߚ{g} 0 ;ＸKG>NRs9'`Gy$x'?={9wb͜an~ayq}ŋ/X FO1*+Ld/h 6l^zi׼ybʕY^>OaÆ Oƴi⩧ 6{wuQqg?frQJU֨ϡx㥗^sK/jTWWrmnbر~7z J)-j ֭[ .k?c=W^ѿ8^t:˗/GeeeTUUEeeenzu >[o^xa;2oĔ)S꼦}q77&l^y啸 W_~ɓw9pj47?O+Hd/H«?x̛7/^zXb.פR:}mN'-E1 I n֭qmŴi^hP͵j*ƌ_}Me飔RQC)eK,!C4j֭[\W^ye~ eVz(k{mq1ĦM믏L8]3@#<2֬YӠƍ gU7SO>kⷿm; 2cXn„ 1iҤz)RVCC'lŊݽM6qw\~RJFQJY;رc̚5+8D=RU5Z=< PVZ'|raݻDZG7|3Ozu]zۯt9lh6n_s^رc=:?/1gΜwJ;"5M{g|qc…1}Xxqkjjj/>}ĨQ+mbOl㖠韔7'JŞ{C !CСC{tXdIgVIDAT=+++gqF֚6mڤ_f9\z|߳~7w_:7nL;ZJO?tut~rѣG^ʹf#<2kMΝӕ͖;Gh߾}3=nܸf;64.,gm6=bĈ7tΝ3~VVVIP[PjK.Ç7kρ oV^{핾k;]t۶mjwwy QJ[)Ԩϡ7x#=p׿ڵkn͚56md8C)u^C $`?̔)Svn˖-?蠃]tQWU^JG;^('-A1 韔;뮻[ns sG~z(\k{hY@qk&+~7iӵm۶G}49hSN9s$'Yw.JvXF|&c.7no1+{7+O֭[<ѥKwܑ3 )ZPꮼظqc,X 7rHOZT*[|s;#Ν6msw;vwt)|xW㢋.6mk#⦛nʊ?IS(kع;/^xɏ®FKdƌtҌ؀b„ uZ߽{kkF}__uZ߮]mz衇bÆ QFgQ\zm۶ɓJkJ{]@(֡!(u:uN8Q{\veg6ooF }RW5 ډ'={vWW* 4Ȕ)Sbzj8䓣UV{,\5uQG]v͈=sQUUv&^78'HZ1(POZbC>NǼy(MjصOgD4 u-Ut:SN͊Q ֥K8ꨣ2bUUU1sF(TYYY|xGlڴ)~X*w_~ ƛo!Z@a?(tPVlMzL}.5 ͧRYN u-e˗ǚ5k2bݺu.\nƌ M ҶmbΜ9 u55gΜ3btPݻ{嚬BP5 &R~XN(]>j_|1+6dȐ&= u-e^{-+v 'D*^'tRVl Ւ%Krܹs:Ҕ5A (T\@a?(d|va Iڵk]tɘ_tisەR{T5Ze˖UW]?|X"֯_{gV1bD͞:R{e=J!ҤGnU9e۶m1f̘q^r%1t&= M)P [ҵkծ]h۶mlݺuGlQSSee{G}4+zXzu4{?яK.i)MIعy樨>}4x${uK,i^b̚5+f͚{ߎ{nGm۶qWUW]m۶mPJ$$'̺X޽[nMz\}&_5ZCiɶmqwܑ8/}K1q&C FkS =@c0zGF.]gSz.7_">;%\S'騬lI枫sͭkn?я{[o5N;|%AT ' )7o*+}ɏ®FPّG|﫫cݺuQUUqW4ۅN=k ￟KLDĦM_cRhӦM 0 gHRvѢEqGf̐RT5dѱcnj'@sL*v-VZVXgqF\p͜%&J@C S;hW]uUTTTdĺwhQص|J[pakBA⦛nfЩzLP [S_I9RTzq뭷Ƌ/k֬-[IJebʕqƘ7o^{}.7xc<͒/9.v6UM}zT*߃͛77x?HB1vT*RT :4nxcʕuXbE曱yxW⡇c9&>LXhQ'#qƸ (+I he˖ũ۶mˈ2$n=OYAkWI hUVű?3}?Ѯ]fElTs(u1x8;N̙3'?3^o16mj|PTh5XC $PoJM+J|3fLr)YW^y%P욺j;F>oJT*ehN\I8cbYq=NV'!~8ѳgxs)飐j4 >RN?xX~馛z(Z>k4 C $'.j{IS|};/lwYge-ZL(v\SI^]]^F&)]\I Y'B= UYYG7x##^{_o='}O!hR|u)|(//ψ?k6qP|hR %äI?q^ݱcǝGY$/̔GN_s6lXV,MqjJRѽ{3=z7lؐk>Fvz4 -Yg?baÆ8cc]SO=u+梏¿j&PJȑ#sΉGl˖-1cƌ8sz(]>j4)C $={FϞ=FN6imݺ5lْoUV گ9 <8Zj;b.hS5ku{ <8ukg?I*MJNb}UV-Y$jjj Oh $ջ|,\0#޹sxꩧr#梏BahR|%83.vFD@PѤbչ!Cd>d1lBԦMc=2b@E˕dMmٲ%)'=%(MRn2cӦMy -@$7 'ψw)OtP2}k4I>RrҥKxz(Osh Hކ>}zN8_QZbѤJK+dņdC~FR=@ rie>݀veÆ 1k֬X׮]ciLjfYRx>qg[SYHSOmTn;ӡCo .+VdĆ  hLzbѤy,ZB< ټys|_>#ޡCx'#SfQZbѤJK/g>&; 5Bh$.L6-&_W-׭[rKV<MUS_b۶mu{ֳ:ڵkۙ\W;gqFss6|^{mV\σ?I >|+se۷o?xۿ[2>JKR5X>|OgRTk=&_5XC /iԨQ1hРnʸ2bT*.rlJ]tQEnݺ" c͛7UW]U[l+2+5կ~5:w1cFL2N-[?3bmڴ /0ъF~oFm۶>OAi?I [i3fȈ1mڴ}ϧRk6ϡ_~y,^8#֫W޽{WW6V44C=NR_ZJO:ulْ>#֞vi:~߾}1c.H?zo{W\qEqҏdCz۶mu>vqg?YnA>}}cYYY"Ꟑ|UڵK[N vضm[N]m߾}gi룰sZ>R͛ꪫ 㮻y{z=jW5Z=@2rȜoO3<3kM6mүz̙3w.KRt޽Ut~8ݿ|+wgժU}ݗ㏳^qs6ǧz y֭[s #=o޼kV^>#t9]YYYcCs*ONRnݺx:oƌ<0gҗT缡P4@ WhCC|裏ҧzjiv>1Q]1רϡg}6JO?=Oyc=>s߮>z(Ԯk{hy~@{衇bȑnݺ> N8޽{{q1Ċ+b1H{RnT.wWVZ_}L81ݻw7}m۶oK._=-[sA~Fyke &.KbqqEUUUL>=fϞq>^{m{͖w6mbʔ)/|!6o޼#oLj#b}?>Jŵ^cǎmTkU]]K,%K{ѣov5AfD{g<qGd]<ٸq3cǎ뮻)i駟}QVZw}w}ݵ/++_W/~S+n|z;o=z4AV8/zYpa?KRY蟴:4T'`ܻnҥKׇ>J*PJ]UUUTUUK/TGqDxcر1k֬:֭[qqg6av/bveذa$N<2L}_~9.xG꼮}qM7ŷ&nN:餘={v37&OG}tf(M/&Nw^Aqh̿8kRWCCەI (E(C)uڵwĬ#8"8ʬPJQ1h)P $1s̸{}̜93jjj^Jqcj1STsmgϞ/B̝;7x.++^zc̘11nܸ uѽ{2eJ<#6zҵgϞqgc :^C)ÇǼy;{7Ν[1~\r%ѡCD͡Xj.Cy/blذa=/J^{38#Ə[dKuKhꡡR> m=O54}r+9R7rxwb֬Y1cƌ1cF̟??>ZSTj*zC /<;砇B특FKh֭[ .k?c=W^ѿ8^t:˗/GeeeTUUEeeen:D֭*-;Ϗkڵk]vѫWӧOr!y++Vŋڵk2:wz?>;=HD1?Xnݎ~WUU <O߿wHz+׎5*e~Ř1cvn /I h)Q(,>Rn?q{wQVVTwIsJ5r qw Hg H qHg H qHg H qHg H qHg H qHg H qHg H q;-)xIENDB`geopandas-1.2.0/doc/source/gallery/test_buffer.png000066400000000000000000002204301525650651600222110ustar00rootroot00000000000000PNG  IHDR$%dЎksBIT|d pHYs.#.#x?v IDATxwtTk&tB(BU@EzDHP""E@@)**(E(( ҉^R&=LBd$~b=lǔ*Ld6:yHHHHHHHHHHHHHHHW^֭[5c kNŊl f_3g4d3Fpe͛?맟~RLL]_c2>ğ HHҥK5oW֊+er-Ν&MbŊ~ڵk /6uݻWaaa? r 矿aIԩg7xpM3r:whŊjܸ|nݪf͚{8~O9rD۷oÇu :uJ.\P\\ӳL&yxxb<<=)///yzyK^>xZg[%IOtn/wwӳp,99I?X'r[tnݼ[n)VnVrR!ydvwrswfqLfNSRRS$'+%%EVQ驀jժ0`+ft, Kũx⒤'ND@q>b]E-<~_?_~y<ʛ72on:O 'r3Ok?jotL3PBB (@}-o?z[~||GzuIz ]{Suu]:{AW.]tuݼvK7ovmݎםߎ׭pGV]]jڴƎ \ $)66(C-?~˗O>>>?+___ɓG~~~XN]6ï8?v?jU||n޼8߿/ʕ+qFfYŋWڵլY3iFgF~=FPgP5e?nnn Z#UtiPN n;nc& +@B PZ,TEUR$]C\ޞv'>^|~bK$=ȼyrrO-]TK.g@y3f  5ʛ7˧ *44T*YC!f?C"))I'OuI={V.\իW{_YV;cǎi͚56 -ZP֭PrPŊj $ Z{ŋL&R D*x1(URe˗UDrssQݧ 0`2BTH!+Ј" +BE ʜ zI]=ܤKWt`:}^]c9PXx/^,ŢP5kLƍSXXXf8tdv|@*\0P&Ţ%JDvkqqq:q?E;wN/^Եkה? nnn (4o\mڴQ@@@fX87o^)RDOvAYVk9rD֭{~<<=Tp!*\XEx*SJ+#ȡΞ9|@^^*VXTUxSш6 QP|jq5ڟotYݸz]eMc7ÀdC-BBB?j.^"""x:tX/^rEǏWLLN:˺r]𗒒GѣZz5 @6@l3[nѶm/B%TFumj~H% :Ξ9Џ¹^^*QUWUJeNPv gkO=|34G EQ/2}bQXXʗ/5kbŊ%˗OC=dSy~GٳGօ k@!""H_.OjաClj /s(VU},YݻwBDZ6u@|VmS=QW*uteR y_8s^N}.|O 'NТEh"Y,-[V/ZlU [Ɍ>rʩf͚T}(Vոqc5nX_ ~W9r$ Gё#Gjժ5kAlٲQdG*>>ަVre맟~… yf]t^ 7;r֯X_Mqo~Ym\} (۷OZ~a͟?_*Uʪetjڴ6m*x +WN;vTU@g'phϞ=v5>8Dv ?։'2:/顇k WxN_|U'<<QW+*uL dS (lX^}._䔻Gbbo߮ʕ+O[… PۇZ,-Zʕ+Ӈ\%={ȑ#xb :p^z%M>]=wﮞ={Y?'smM8Ѯ@7nhѢEzw^YֻqFmٸE5ܣPXa=;n?Xz={*::e }2]vi׮]zT~}GQQQ21\O|`gȑ:rMK=AKLLԚ5kb ڵKw}MzcjqVRRj6ߨw~;w5Av:z b>yg~fn^k/_W_}UgVb4j(?#@>_5jPz@Gvޭ:pڲel٢|E߿}Qgr>1tQ 4з~kS[|zڵkj߾<<| =z_~*]]_}v 2D[k޼ r_g-\P7nŋz*VOQ6儔cGjup]wXTVe5hPՑ_{ϧf$=pT;4_~M͛7֭ L2Ί i>MeʔQFT~}yzz:!%@뫨(EEEڼyvܩsΥ'Oj޼y?*TΝ;Spp9 wѲeK={6Uydg}B eZFG\9shΜ9*Q+0hѢ2L:|>Cw>ZbEfũSpB}G:v]7L*Q=\tT``Rz._O>Z?ױGzdRJehT sBJd%˗%ђ_oS'N*xOIs玾+-[VyQdd.\y:+6WZX1խ[W-ZR߂գGCǏ͛?OMM~M2EjRգGyGྙRSS ɓ'3L&N8tseÊ+OsjSW7xCjʔtBBBlj/^T3رCǏ׎;r V&.*Zۯ,Үo544iZWM6QpS*URӦMUvmP ..N[nuĉt﫹Yf>}z۸#w~=r?Sϟו+Wte]rE%JD-\PsџqnnnXZEEUHyyw wZSaݧzw y<&-Xj:B#Z1m8o- @/t{CK*FA! ]tI7oΝ;u43ͪ[L;1!\C1DZ5k,pByzzq0l f?N5wVٴd//5lXzwTP '&2׆U%vtСCdzGԽ{w>ȅ٣{OLիk„ rR2*c N qㆦLe˖iVm#oH:1!v\}}f]qyij 91!xmݸz#<<<Խ{w-ZHNJeVÆ յkW;1!\Çj*ݻWV5ʔ)1c駟fk\HpogϞĉ+...UubB\+W쭷Ow+6S'[eVdk#`EoN[K.{Zn5kIj޼:t->}Z+Wԏ?aaa6l(Ą0C1đ#G4n8mذAwI!zG7uy<<<t-Y>IHHE SQjԶ-|G5o9q:L&{1_^AAl_C˧HEFF1Zjvء4 Q5zh^s c N {ٳGcǎ՗_~`0=ݷvxG ѣzk^CK=fue\™3f8rcVu֩xNH2ڇ*TH۷W&MCpn޼_۶m۷<. @={ԤIؚ:~=47| &SzՔ.[F&7sb:\_;ӽRNzwT5p}7М}CTT){QJCթS'խ[׉S%$$?֖-[t4QΝ5uT:1!Hp'OT>}_dRjUH''5]xQ3D/{ R:鬲U99!܉Iov˚bM:u諯LC˔)]jժNN ))I7oֆ t4СC5e aj8 '`Η'_W||cnfz*89!)99Y,]oqZc*V*ܹl.%9YKgm)9))/jԩNLAd5UJ=S*U 7Zꫯn:9s&´`EFF:1C16nܨ?tnXTQ} >DŊ;5l]zhy"C5}:@hrXk6N|BO?Q[Zj驧R"EΝ;vZ?~[Fвej*Z}vۺ~0͟?_mڴqr2dc N slܸQ ɓ' ְ1#xVNNk۽sf4C'O8~tB!BjPɀ--q4caq r>4o޼zgT~}''ݝ;wb }JNNvxL&Mdqfj8 '`~=krr:\ 4sJbu"UǾt< IDAT_4{,݊KZj믿fIևYfٳC>}Z ,Ё{yyiȐ!6m8d@0JJJ҄ 4o޼tg 3&xDq'u%''kŒinP푿g(PWbFY0LFri*RkZl]pmc N =mذAN{GHgnfNI{g^談vr26qև=r"qj8 '`}/tnXԮs 5L<= }=<=٭:,W{߫WǼi6@[͛gϞd#Pl㐽q? pN!))IǏ׼y󔐐5itg%''ke= 3w64mFh''˗נA؞Ʒ~~;m-y)**ɐc N 6mڤԩSְ1lk'MǏ)\@Ga{ uKSLG<&00P}ԩd@u>400P{f{Jw;Ｃ?\IIIiܸ-[0'#@uhZto|=Y)z}kZ|k(39SFq2d^u'lw_/VΝ P{ $i8]նm[߿z5ad+\gi7TϪ@h'',ﾾBW}6Jү*''\Pgol2]znd2W^Zh, q?4gժU0`nݺe?~܀d~i&捛vk! }PZ$lnit=<Z5e>3j)JpO>ZdSn}hFsϱ=AgΜQttNyJ#?٭Af HU$%&jtŊ`C}||4`܇-ZH[nu8ۥK-_\|yijltγdլY@˖>dlڬOtv8PJ9yo.dМ}~?TPP>S ^y1'777 8PGZjj֬Y *7(!DL'!!AݻwG}df6Ɏ9a HJJ铦i7f>N]tk(9z&O>{=''>d2Eׯ}(I._iӦvk>>>={{9.@{UTTN8a_^M vq}4~xɓn}*Wc@:Vpk׮iƌ:tݚf̘Ç,g~=4?C:uZ:kW׀di:sn-(>=?}*Ԩh@29EJr&p=ڻw ,dIװaTF jʕ+~z[8lR| H3q?c@2sL=c?R> 8ԳS=R]s>0KkD_[8\tIZx^Z\9-\a fY=zK/>Le˖ծ] H܂'$i:7nPǎ_ح!x4&r;.VF7_l[sKԶ{[4tΜר+f&M8{UܭСtB 7o*::Zn>M8Qƍ3 YP{ $i8ƞ={ԪU+;wn-_p羢j vQ=oΞ>kPF@)))vM4ц m@S/Rw!CG}0=ӥa h3 ˹k‚?~o_vM:vCT 2(٬'|RӧOw8_vںzS1dSk֬Q֭u-bC%oP:\7_~ӭ7mO=3F+?_ȍȀd_C; K/={f-&&Fk޽{ J =znOxDVVU1(moiƄJING-ﾪe" JߨW_ԓSSSձcG͙3ǀTCCCCkL9v_Mŋz_ 9 @6aZOW^QjjZ*)_p>RSzZ4w5r*4Zy اO*Lvk#Fаa H(>tҚ3g J ԪUKӦM67oT˖-vZ!'` ղeK\nzut 4z pvk6{LO } "kI2sCBn^ZF EGGۀdB2e4k,ԭ[7͟?ߠdH\\ll}Q}vkmGiX,$k@g[kdM!www@TS]#wG5 r&Mh„ @TH͞=[ '''kС7nAɐ1ϫF駟l&I}F8ݵɧnȾ2q  +77.7wܩҥK+99ـdȩC;t!CЇ9X|4{l,YҦ3fw޲ZCvD ÇF:|MM#ƍҠC Jk;sztij׻AϧYvkGQhhn߾m@24ifY}O?mP2뫙3gZjvk˗/W6md@2dG $.W:utY:C]fP2\ۡ?g:sMSfUM J[K,Sbvk/^Tty!HX,>|"## JE&MR 6oެz)660M6qƺvMW/^7(]޺zM?&1E5=dP2nMRn-66VŊӾ} H.>['NTz JHfYÇO}(11Ѧ^(hʔ/kP2\+h܈1Jc{ /W"a%1jݵ]=%%EM4ъ+ NZ}hHHfϞp5QQQ6l,MӪ]~1CjرZ6KjT>@Z8wfM}Ykhe4c+ 6(d^#ǰvTKӦM3 bŊiΜ9 1(WU~}?^6+WQFcP22YVuYͳ[Vp זjՔq%vk)/πd\mҰ2Lvk&Lг>k@*k֬Y7 Zj>}]mjʕ%b 0P.]vZz&ʷe@*\[jUc֧[ߪƾ6^$cm.!ŋ5tPRUՇ֩SGӧO}(,YRfRpIջwo}%+b 0Ȑ!C~ԡk'Z0Gn>L {a֢zCސ\ v<<=͛9s &>ט1cCdXBkhѢ6dS_AjH L&2@/N/SKkd6>L\Cbzk[1b>#RUՇvUp4{l-[֦(ݻנdp%t-_\Ǐ_=g@"\_zώyNOE \P\^>vk:uҎ; HՇvEO>^^^>}J,iSy6mce08ѦMsj?ّapa[ \{Cwzjj6lcǎ FHmѢ2bԩSUP!ŋհaC]zՠdp $N{n=JLL7hHc&3(a?Aw_{ rr*TADZ}h:us <1cl111_ J18ÇղeKԫ֨WfN`L?d2ܹYC˗/ѣGӇtӴikS߿ZhHt@;6lk׮K*ޒAp] #@RMlWqÕl@*dа0M<>@)R&M$OOO~;m' ݼySٳgmB Uoˠd. s5lDOfW?wVj@"dА͘1C^^Vٲe5rHak`yl $Y$))I7ÇmAZr JbF>լ]sƍY!>4 @ӧOA6jҀd6oNjP* XVEFF꧟~jᲷTHF츁^]}۶mzꩧ H̔V^zI 0(ܪiӦ}'Oŋ H#0d^zmoxxxh¹*SAp] #s{}"ʕ^Zƍ3 2>bhر0(ܮSNjժMjjРAڰaAL $lzwmjnnnp@mڴɀD_i<իg@"7rHUXѦvmnZA $d͚5zjSuy~r;8-t<̀D7G}hPPf̘!oooRXn^4(2 }4qDb+(_p>RFURO<۷osԇ_T``A<#)**JIIIBfb GqqqСmWjU JkbiKcmDU\٠DHҥʔ W^*YMСC۷AHQ׮]u)CV0dOn$Z2lǎ J9CT;2lرcDwzjR!0܃ hÆ 6|z%51[xu]7з~k@Q7o^5ʠD5tPg8>55UWLLqH2h߾}9rMM3^)09Cgxzf@H9rcP*uQ޺uKJII1( @$$$(**J 6zf JarӦvUZՠDGZ}hvTR%R@ׯmjg l zǏԪTcr&-jW?쾹-WufP"znnn?~mK,Ѻu J@pK.ڵkmjyjl@br2ʪM'g?l@Qﯱc҇BBB4p@LjVU<Μ9c`2X ?oS35՗dP*\ xB^zJNN6(U55|p ^zjԨMj۶VAp?HҐm*..ΦޥSzG JkarY+gaݾ}[j2(QΓVի 1`)b;?kĈ%` HC߾}uaZJ%0ΝdW߳gLb@QZdIˠD`Ţc˦>|mٲŠTW $^Z+W7_gN0VkWVsOjxb>yH[w{>1@|s 3gL^{p)K!>d˖-awȈ;p_,R*HeB\m IDAT範|CbaÆ0k֬HWw{GBy睗xL. KQ;GL)#Н tah681gώzt{aС׼y o/ /0R">B,Y֮]G)2_ꩧ¿ۿEJTvX5kVD|2dHb~7\2R"E!f͚p'fmCˮ"R"HeGZç.<`~iEHS sNDգ9,^8rpgFLEwyg}vx)ħ8c !mٻﾫЍb{g"%.SL ӦMKyp7GJDwi?ýޛMoj8GEJ)#K~U{^X~}4Uls=ٳ#%N~hK/4lܸ1R"QHfŋ'f|>, )@O}Lr~Yggg9sfDSlmhh(=W_ ^xaD@ZdIXvmb®vRFBCCCbSO[o5Rt)Ν;75*R"vLJÇ'fw?)])$P֮]կ&fmC /|D2ol ,TN&] ~zDկ>s9!m3<3b*>L!tYg{/1.M]3@R:fт0hȠlƍ3Έ(gqFhjj NM=[nSHy{M&7%"%x(+zelҥaWl8qb={vDr._K*R"Gre¸0Yggg5kVDC\)@ $fj袋"%/)W^yexgvkD2vwyhhhH?[o5R8s F N<0|lҥ!k׮ _}b6xp#%8|cc8S 槝vZ4qC N?H>| \n묽=uYS@8묳{ｗ]wn٣@%?ua8xPbqpgDJTY3<๦~졇 #%B!pw/1wp1GEJ@ KvelҥaTN=tĉaّԆ;/455%f\rIظqcDM!koo/[g|>,e S0k֬Hʯ/^1@mhkk .L^ypEEJTȼ/~gM0m\DPYv"7'fòe"%*b{9sѣ#%-'xb6lXbtODJTȴk׆믿>14-\t)T2iol !MyC N?HjO}}}8spYgEJTȴ|3wM.)T2i2ԅaƍ3ˠb{駟~?1{ҥKQ d/p&fNtDP9`o;lڴ)B+N81vaԶ;/455%f^ziزeKDG!̺+Cggs>K*b" ejqap'GJTZ]Іx∉j ^z)x㍑2??ïlQGq*C%.;#l޼9R(Μ93=:R"Boo A뮻.tttDJT[Ȥ/{omT|cc͛N]ІpꩧFL@!ׇN>}͚5?ADE!yÊ+C)2sC.KnHiz:}0xH'f\sM4E!̹ ~iۢ@iWbiӦ/~1R).Z(b">,χs&fV wuWDC!Ly饗ߞpЌ0nq@y)#P.o f_W"$b{F)Ŝp %1ꪫ" dʒ%K|912l@ƍM7)QC }ZZZ¡=7M@5B!x뭷¿˿$f<)R"(e].Iz{ &DJr)11[dI4A!җ6nܘHi|ȊcFcF&fof[#%1ГN:)R>Jkkk8_(Ȅ?|N1!tݼ2YK=IvL=tرaԩ=-Z뷞;::_1Q)$ _oyg"PF 41 mK֯_~_EJ}'pB4lCiӦ%f?OÚ5k8ޖ-[7ޘ3:~ĜHȲ/+#§>IO=tĈ -Zr͛%Lz7xcx饗OPW7٠@M9=hM̞}OFJmЅ C.&MvmW^(lTpu%fC O8.R"(-ejŧ>'?AsFJ@O,Z(q~ÕW^)Mv)$Pվh{(@8qb?~|b?ۑemv5$; Nn^ CZtv{h#7N:w W_}u4٤@Z|yD=)2j?;C=6l)QR=xӦM cƌIniӦHG!uUW%} Hi4u䑉sggg8"I꺇/գ^k|+dBU׿uxG)2pb{衇ZZZ"%fΜ }_[l([JK,Iܹ@)#oh5wfbN;H[=1r)P*uuua /oEJ- TuօO_FJI.9?%elٲHi|phmmR:# ] 7)M($Pun-R91276MM>{y/8B=t„ aСQPG}thhhz _"&~?91wё@)# 68۩bW9|@(#-[7͊f躇655}c@e~>xHiA!/_)8e~^pZkCglȢYfo&+V/rb6c#;% >$1[fMؼysE>:gΜ|6Ô)S#R꧐@z[֝v qL2'8wvv+"uׯ_8+1w^ wqG4O!iӦp}%f5PW0駌=w)DŽ\].1[ qSN  HnHi- ;'O>!R~; acW^y%eb{GY 0}^DM!|'q>bxHi`(#@i?N*wye̮{!C^{U :*w}`Od($zoV9GJGJ leb{X ]ƍF}ߍ)$z7æMs\8񔿉MJo>{$6lO>dY>:o޼|t'?pxW"^ ~y1) l2ǧ?f\m躇=:e,騣 \nyᦛn:)$jk֬ +WL_NP>{hӜ7)CgΜY ƍ?z)$j7pCznhhǝt|DP2ԃK7mn^3]pG3gN|SOEJSHn-q{Ia@qPg|3/| %{ BkkkI?0gΜ緞;;;׾B裏gy&1;jё@qP9rUJvb{aVP]s;:)$Z7pC^Hi2T!G$oe˖};ԤP?yݺuWU4G!T˗/O̦͘#%$ec fC'Mz}m!ZZZo|TR?ixW') $)#@SNMe˖HBt7'; f(YÔ O>&q ]vYu߿8zuM#}7)MuQH xfq .eH#OW#vmf=tBa} L͚y/}( = szYfzdK]]]{|0Rꢐ@,[,q76>0RPF4qo)I)$ ˖-KfDJ@)#@L͛ի{h>oɳ=S$իW7x#RtSH ~_$λ>s;( eȞ[0꫷{ѣl3f\.~GL^ ~y~S#% ˔ <<444$f{6us=K ljmm Æ KVX)M)$]vrHi*eȶ'?|-N6,Ȧ'>`$馐@t]4#RHo;7oV^]]|>w߲e {LIWof4饐@t]۹<QF0c fW_}uvCGm`̘1#r玎pwGLN DS/RFjǰ{-ڮ{^{U\dSkkk6lXbv b DOsGGG$饐@Tl-s"/83Ȯ!CĎj DaÆyj|؀o[[W_ ovkErMm ʄ='Ǝ@QFyQMc=Vqƕ=5jԨ~Q SH 5vSӧOT e=&'ᥗ^ !>e {Ǝ;B)$C *t3 f?яB!<䓉y[[[E2]{@jy3`_2O?@ﴶ1RK!e]bG o'3ڵk#  ;Bj)$v-vRL^}M_|^z|ѕ@F_H=;)숶am ڵk￟ﶛb<7bĈRK!T E5z\GM!3fL@* 4(~!5EI){ｉN;d$ƍ;Bj)$ ÆQFzj f綶@O;6r1RI!TyԨHe7Zu%Çd$29?vTRH v76vRB(Ʀyƍ;\8dܠAbGH%Ra#@)͔-[$λK%qÆyD}1 ĽȔR:b6nU( SH 0r1H(]p}sr'mPW"@RFaASԸqbGH%7 17vQH ~(#7SS$ԂRG!;TJ]}_{T8 ǎ: d IDATDǭKj2PIE@9455Ŏ: PYk֬ O>dxC[[[9rd0aB0aBL!&(#Vߠ@($RHNzxG裏u#Bm۴iS79?xK??KYfU aZWsCx!(RG!7|sW;F}a}޷f͚},?S8ʔ_! fJ߾}+Z 2}8F̙fΜV\VXVZxMGGG8Ø1caV֬e>q(#5)wH!Bjjj O6mZ|ؑ |aaÆ СC]w ʡ%vԩ r\a3 7tSx;~pM7_ó>E!0rpwv-17ߗ-k!2}y4;$P}!u!\rIk9ovk\.8`# CB7^я~(a=z\}aҥC]?YM;C(ܩ3fٲe5nqm7r]*KHEC(w/!N\.,XCי`شiSb6eʔ0zV$=֭ƒ_QFҬmha!>BjA~bGH P07o^U}Vѵ2k:`VWW!G߾}cGH[x<!F]&wHF@52bH,EH@-PH(! mmm=VW-P}jҷ%vjH>!u2W^)0hPs6]ZZr(#r2dÆ BkkkbZG!Z(#Y;Ԍ(\.BB]fx;;;ƍK|>AѡOe)#Y;)}|AT? )իW|F) ?=ʒe`۔,QHࣜp #dG6d\o鹚(#Ycq ۷ox뭷^{-1G{ fѵcŗmI @i(#P_G{YgU4M!!C($tvvJBhhȇ>}JzMzOZܣ}%NR 801òe˶׾Ĭ)|ӟ.UD"SF*A!!=PWW;v5\z饉YGGGXhQx衇W_}5y^K?φ1cRI*/.({c =X a駟N  @(#;@͛7/ _L;;;Ô)S}}. ?Oˆ#Jlٲ0k֬qƭׇiӦQFs熃>8<aŊaժUG?Qhkk+k^O4mXjUXvgʕ,˅.SN ?… > rK[}]]]7?rG̔<:;;+y o{Ce]~p駗)Ģ \dz=ܞ:ujxGW0}m~>㏇ٳg3He &l؆իWG_%^ccc8֬YVZ^|ꫯ#GLJ=أ @<@l 5fرaرcPF@xdd2 @($@(#iUNH#b@Z)$@RFL!2v PejUD P%jU@6 r@5RHSFB2P j)dB2 @($@ (#Y)#Y)#Y(#Y(#Y eV($@(#D!*@5 Pf@-RH2RFjB2P Z%%D(e$%eB )$@)#tO!z@``)#|4G!2SHc#(#8eQHn(#B; Ѕ2@)$(#BoAy@MSF(j2@($PK!P~ ePHf(#TB5Azf@)2HA*b@)3H:<3F2Fd'_  $t1fEs ]`3H$c5N0F1H c80F1Hc<v#@$1tA@= ;0F3H 1A@#@$% (i3 e= (Iг c= (); a ( л >zAЧ#@: >cI.1F]Xmذ!^xxw&*++c1v8##ɤ]J1NZhQu]_*sΨQ⬳Ί/8Ə Ӹqby9k޼yqW,IP8֯_gqFvi_bcw&Mo[ O{#@a1HիW ,}q?jD#@!ʆ]شiS|c5kdlwiӦ̙3cҥKpw~\sMo2lذ;vl9rd@#@a2H؅ . ^yDֿ?~|򓟌~!\sMW_v[k6NӧO;5kVu]|6c(\^Ўg}6ﾬns9'1Fv-n喘={vϮ  AB;ꪬlܹ1wvd2qƔ)Ss=st1>Xre<#lq7vʜ{w*c( ;`l̙Q^^3;JdO 9kښȎ96lXϚ9sf?-_[#@q*K@!zW}ݷKgUVVƞ{֭۞Xݺ7ߌ+2~ŪUbݺu1t8uQ{z?AB UUU]>*1HիWGuuu'|2|DvXvm<۳WW^yeTTTZ?ؑ17loAjkkwuָ㠃 ]e !͛7ge$DDud21`qE#UV駟{n/#@ 9 !gS&L&tP}1}QFm!l۶-VXzvmces3Llnn-[:}_A=І#[v<7)~ 9A¥^3f̈C9d3p8CC38#Ne]ӦM\pb?}XR޻.c 9sҮgxeC=}oW]ver3gN,[,kxiӦ{BD#@_ 9 <8+y2dHICӣm{cŋc̙y_y=b@!tߦMsfC@ ""N<Ę;wnr-_ l@T3).ؘ&+r(ABD'>lٲe)42F !#Gfe$d2>|x rHVf@#@gĉ| ]>71f̘DkEkkkJ+4$pAeeWY Y\B^{%muuu)/0FaOdK.7vŋGKKK"5x(4';R#1***bʔ)9~_v fe'pB7|3aR18ӲEuꌖCYYY̚5[z?z)4#@i2H؉\~8;|SO=㎋nI[lk6+7Ht$Ą bڴi>W\qEV~_OWoF"?>F#c(m 7o^Vv7ǭ}O}*.]'Og}v>O~%{vyC=?c۶m7|s|{ٷ7n\Ϥ4# 83/첸{%guuuqeł y&n[}2.^s9'gcŊ:#8"/f#H\r%Ki2F"".PxWb۳8s㢋.iӦ'xXdI477gsƌ3zwmmmtMqM7ń bرQ]]ƍ}72LX"VXz+9kb֛fA. :4|:uj$~iӦXpa,\3>Ou]ד5۵rXre;#n9Z#+::^xᅘ5kVSLC+:c`GAÇ ĢE?A<ԔwGs̉syLK;/>XdI<3Ć 1bDL0!N?;wn 0K)=@. 4k֬5kVlܸ1{ѣGGuuuy~R>ڥ3f̈3flzxw6֯_$btAa@G$btAK@g$2F )c 0H#e$#`lgA@~$@$@3FzA0c$@2FzA c$@1FzAc$@0FzAc$@g 0c - G#i2H>HA1@!0H>( G# c$@3F A1cP$@2F A!c$@1FAcX$@0FAc$@3FA0cX$@2FA c$@1F( @_aK #}MYmkl3>:+ּ&# RS=:+7FW6@JZ艓L&c=Hy|6g6FAgʏsA_?poݘO<_B#3H^%KگhmiMNSjeiRW}15nK'~lZ\KRj3}~lX_?x2R3c=h˖=ۉ|qc[)5Y C ƊWW${u߽> R3g=5W㹥&!WJzA1/y7<51f|uJzA{GG%eey_L@2H0Wn{R+auָ/Gccc"K>0>V :|-VZȎ98SSjPx ~LdG~iJ AtЪ?)n~^"+P_WbА)(L M+s ['RjP }[7V&:"f{ZJ AC|0dA"sxU\?~ŷЎW9uLduy6tZ>؉/E}DϟqpJAwcxDvQƙI@0H#{OdC#Dmv7wW_mmm۳L~q7CH@0HW\zylzS"}iqSjP| o߉_\8lbEJ@q2HĿD7|5R+dhmmݞe2Kc{ 8$@DbJd=8G$P־^̿D6bK?R#g@ɻyѰ!}ŀSjP (iKz:oGxLJJVsssp߯ F}A%kV";f1Rjw$P֭]yw"1zd9- [$}0p`JJΒ=.~$>8cRj$PRƯKdW^R# rϿoiU";cFS2Hd[6D6|ԈsY)5 (7\?/oIdX^R# wOcm"tr)5 󚛛Ưϋـq_H@f@wϿoiU";쿏N!`@nژ]lcg4$ЧoǖD/SjP 賖R0ο )6( I|se,ywlc}G$'yںz@8ssRlPZ sj֬G?Ȏy| kXJJA}ο~hnn~ݯ=7$Чlܰ1~ٔR#d@rߏƭ[_g2sY)6(M [7Ǣ.Ldsx.ns";9)(m Mv";ఉqSjP bCDvL  ֖=&Gݑ)5 ?wߩId?szd -l1{p:o?V)9= e}{3f636AE뙧ī//Oduj (Ke@wL\}8SSj2H(-_rurJmhAE)옓F os) =D6}֌+ "q=z>1]1Hm<4}hq)# (x,z _g2>+ _?FR: b/%ۇSj@G$PtAn.0 N8 _?pУ!C2H`+هO912tAk4xPL GLa@Azvc%>>=6tAiѿ/J\9|Ϙ4eRJm, 6q}IS#75k5&cMM ]a@y'1Rj@W$Pp]Lzl@YJm  β^J\E w⽚wR O&+c'$ >?qB (K ]e@Aye'R w⽚w٤cO a@x'1qҁ); (_Lz Q6,6tA%|pJM. ;oռ:96tAǟH\W&2H <~ Rj@w$P^~aY|0H uv{lұSj@>$"q]^QLR =~ Rj@>$_X>Sj@$͛wKdR T[.q]QYv@JmRMQ/ js]]z䘑)5 H͛ףR Tmټ%q~|2H UMMM}'T|2Hpd2Q= ưa1<AciW O (#; AciW O (v Q+' ecjl+ (U#k' G]<2H =fT# ]<2H o# Myd@+ʣjDU5#R#}oI}Nyf@Fz]@_c@ v ULyf@*yB@_SvbaÆxwމѣGرc#L&vŝZjU꫱f͚XvmTUUѣc?N8㥗^ŋ~TWWI'ԣ]ԣ q+ϏO~ѯ=`knKsԩScһ1fϞ7oN#Fx <Ȭ{֬Ygyf<۳837ވ;h!=v6ʆxgo8scm[nٳgg+葞z[o%s1zXhQL0!/o~=5"ꪫsܹs۽/ĽSLI=\΁Cmڴ)͛?aV^ƞ{ȿի챳HAB+WGy$ <8n_YYw㎼kO~ذaC";餓OK.Id1|UL׿_dr+SzA@d^{-Z[[1Æ Y3g}מW^y%+;StVzW6M ;xWf̘]~&3HA/ݫ|ގի|^{=}ZjOW6I ;Xn]VAB[[[_'w|~^'$`YY> uuu]>=^VV{"۲eKjOKSKijjJ $|L&6m~1x.Muak﫨[JGCCC(p[vˈ<7)~ ;K}~GUUUş aksܜNy:P:Ү@3_W6t@&I|(o {#ZZZPYY٥Ӵm[zB= ;9rdVAB&Çw Ԓ3 "9+kkkK 5  a'N9H4hPTWWw{<.՞zFuiWlt, ;8蠃իWw鬆/s%~] mzJ}}} A߿"[tilܸg-^8Zv½'>8+{tYz@uYYss6# $ļy󲲛o9nvkmmO}StD>y8;'xb/=ӡ{w}ꪫrvzgڽwя~4_Cm豳(][6~TGhҮPp v裏3<3+{).zA&n[}2LwܹY?=\{֬Yf͊+W&=#(M[sAA= ![Y ٝwJ,_|{{n\tE1mڴ8䓣6/^K,sژ1cF.// ć?]6:3fL̜93>//N;Հ'?ITUUhjwKI;5jT/kllLB1HhСCSFMMMg6m … =ӟt\wu=Y3ɓ'}vZ455%~o]wwuNׯ_~1}k7gPz6͛7rJ-[ҮPpa^Yfuǭw}w5۵SN9%,Y|p;vl<#~%m~|e[㶜? 'xBB6>|x,X ,Xzj 0`;jԨKLKǕ IDAT_ /Kdɓ'[裏n39䐘7o^,[,N8.fgeS}y[S ۶Ɨ2lYfŬYbƍsEMMMDeee=:#ր "GS4p/UVˣ&֯_Æ ѣG?8U_Wз555z `֭iW(8 ]0lذ>}z5dܸq1nܸk$$Z[Zr^@Ol^@)_A1  56kmm͙{e=)  m7Sȿֶ͛{ $M Kƈ܃ur Y oۮ g배0c:fOb >!D RXPh/:wi| ۮ)W?5rPRN>\5| +I&:33[u.))v@|ǣS|~wl].x#Vvv7kÇmWY $g$d 7ʕ{풓U =jb >clW3 *bŊ6Cy @8qv@|Ʊd&/B$r-F/X%!##v@|S+PEn{ｒ;SSSU ti5| /خsؤII]we^\*,,]g1QX p}mȞ|IIRڵ)>III+4SK]ǒ~:h&n<\#GخHOٜv; c?׭[WAAG! mWi $ ?vL馛~˫rÇQ N"5| {5E"<ίSWeoSNk5ٹsw1;SYhw~XGDD($$Ȟx xI&]Vj/7sqd]t}.~0 \|_xx}QKm| zrd.oo#""v4k8hӦMXvmk׮m` >!&&FFI5k֝wid;v>%''|R@|ƭj3OfXj6Yͯ 40-/)))#6mjc >q]-es9Ν;_}/~gϖH/@`p=#v3zB MuDD®͚53x/""B<2~^z9Xhu4fzf͚g}z<LHݻuڵpb >%&&FF뇝|ٹ3Yƺf͚WѪQm߾zSrr~衇,5 $z:3-Rfy<#kѢ5=V .qK@Yu;wȚ6mj` >/vuqKmhecǎX76:{5= 0]|_DD5jd` >Gla< M:22RaaaXM65x<ڸqu]vڵk%Q<sbbbjd'^,e25k^EGGFFw]Knngd=6Zjsg)d6_2 W^۰w^egg_cêUodZƿ0G9a lY ?]ٱcG\_ׅo1aڵRݺu3 $'5nXʕ3G Fv_귿[N>ŗ% $gunNt]=o%?c}!cӲeTTT:88X:tȿ05vXG6哉|O2W^<o_G_u<6?}Sj,? $gժUK7pسRm;7mSA~=%7|ׯod6l(cǎѣF֢E Km iO>.,,ƕ lr4GWѲeKc} %%%sÒ%KurԾ}{Km i0{, MXWXQUV-hժ"""clJLL4=,O $UTIUT1c]Xhs]dd͛7/ ף>jd7oVQQ%D{QffiR@|/lǣ_.`i و#JڵkgϞ=m۶s|Ӳeˌutt^|EKm y2e_-m:qz +{uM7Kv;ӟx [jH wad'3cf͐jc ~^s\ %Y=un>ۍl՗5 P4zJAA|~-QNc}>~9i &}#8pWg1?Ν;رb Y, $o|'l?&Xh-m6aaazgܝ:uu]XX˗{v\|\_͚5u=)HӟٮvXj(m)ScdרQw}xӧdd/6|X]{-Iƍ/>|N@DK.UQQѯ`kN $9-4};HթSǫx xynj t̙3X/_^m۶&p0駟:yP&ՉFvM7YLf˖-u 7̙3tӧOkƍF+**R@N twYNYR#@IlС˫yF~۷R#@I9s @˥?^@W_90uS:EFF}v \rF6}tKm%%;;[k׮5aÆv ԩ#:m -5=WGٳ&7zgl׮]JIIP̙\#%xG73 M%!';Gv5r޳4h x<6mFQPPo5iR@#<*UY 逥F1x&W_k˥[jseksrr4gKmWo*UomM`c a_urs,5\󕟗odeѕi֬jժed˗/WAAF+uV9rڷoWw $ `+GXj&~ͳXgeeiŖ̙3uTTcMc >s%/:,^ FVV-z뭖]-[o7%KR#۷O7-Zo(1~ȊE?xF˙:j#={\ u233Zjӧrʩ_~ڔ $ ̜9SAA_uK)m u&bbbTNKM,X` RRRRk.#_xO@JXX~a#+,(ԗ-5'nglڴi\`FKř6m<ϯ됐 #Ӓ-5oƎkIW۶mڱcFmڴirݿ4h ;sVt M}q#?oQ۷"##@ 6C=uZjT0X#ď I;sd ФdEGG륗^2h͚5"_~~Rs:es, m ti#RN,5*YTDDMR~RJFvM@2sGֺukURB״iS=cFo>YR#([ƍ<#6l"##-5*H@_;e_-Y@ٱh8ed4iF1aɓk ۶mӖ-[~jӦFe (6l ȣk|3͑OMLL:wldgΜɓ-5vgd8qF` eƪUld~N ,56@Y͚5Zj]nd+VѣG-56{l8qZn{R#02#""Bzr#zBlG'kg#s\Z~FѣGYaa#\L-Xnf1R#H $0`*Udd5 @`VGֺukURB{5kۧuYjiܸq3C*22R#H $ 믝٬%:6B<\S駌,22R&MȮ &(<<&N\K l߾][l1?j۶FP4lP 642OQuR#>z#7ĨsFvM2N! n[}<ϯYhh&NhIVRpp%MM,50 Yll{9K|Ccd~;fٳgĉF֪U+թSR#o $LP='=a #sv#s\,5=z:2… r9rF ( J*YlM[Z |#kݺTbyꩧ޽{n:K7NF6l0EFFZj12mѢElsZBJ?ed4iF?Wlĉ˳۵e#նm[KPP=jРy)Sy||6;PΝ̙33y<_PMȕ}  (V^`#KޛYjeh*,,4X=(&&Ⱦ[;vR#/sщ'W^Qݺu-5¥02/""B=zpCV^NF?6LЎ@Һu,5}=zjȐ!*(( CRR̙cd+Wȑ#-50H4h*VhdK?}>q-[TժU-4O={1#;z1@ IDAT,5ߗ[0!Ce.-[̑MJѴOx [tmdъȿL4IFrJ%$$XjmĈJKK3F}a o 4P׮]: w`SӍr)11R#sm/5x<3f/sO({.]77߬yYj+@ >\k}Tom v*G>tPլYB# /]vFv 4Hn%%%E'O6͘1C+W W"۷oWXXw۞Ѱ8wߵ7No,99Y&L|GAA <#֭{1KpH.e˖95gl wrWYdd{K_hh/^(#_t~K7|JMM5 h<| @1?^sfd ┚򋑹\.mܸQ!!!Z5jhܸqr\fG#GTfff`ϪUn:#T.\ )0~xqFxԧ}/ Ѳ:_uԱ(lRߌ>}ZÆ JNffF)kr4vXըQb3ksNSg5 K է}/C I^Əo$i֬Y[lǎ;wFp4x`?[h-[Zj@p V?ܑo^9ϴ_חRNvj׮]_4w\ӕh\O?T0XM8R#4kԶm[=|ZpFpkKsWTTFX ~N)FZzEDDh…al/|rUTɑ6Sm߰B#z{~t-ZPӦM-4¥K?i:zVpu-Zy҈#TV-KPHSRRʗ/oip:?N)B#' aÆ/-4¿xbխ[Ȳ{)33R+2ׯɓy=kYh@p*T={($$ȋ+oT:MKf,i y5qF p%ʗ/5k֨zF~i[ٖرCFRQQjJC ԨQC}̗TaA: dXj۸2A?+W}YhQR%]VUT1T{*(( !C8ޯ4iD&M 4lP_}#ϽO&waV?ydd9|SLLV\oȓ4`/'O>PN#kŎ"p' {NÇwgOU:ZhԔ_~x\/#y>!( W~^\.-]TuԱ %^Ј#r|ѢEZ`V2ۭ>@)))F^R%^2|W7ި}a]XO]{&LPƍ-BIرzf UEEE:tk䑑Ztbcc-51bt?hP̴LG޷o_mB#]vFVTTѣGk۶mZ(k┘hdaaa={4h`l` (e?Tȧ-4P?۵k~yJMYaaXj1c# OZj[HJYHH:(>觭{,P`vnț4i &Xho ynn>CZj -]TgvV֭_1xADDfG>n@5+n.Y^-[B#xShhVXZjsԧO={R3jƍ/xsѣVKT;v(88ȋE-e %hyLLow^1)22R֭mfիrss-5h٣O>Dn_z%9R+/UV\)ez+';R3bD<ΑWXQIIIoYk׮UʕرczUPP`@q! 4HF?YӧO yM:Ցd_P&t*=B+hۃ$ WJJBBBUwyEEE?w}Wٖw{Q޽+>-]TAA|]7_֐!Cy\Uٟl6+nF Ѿ}_FlW͛ryRRuL\7?t #ԨQCk֬QXXf% $So#/,(-imZG?9_rAAAڴibbb,y'4qDǕ2RSSխ[7;vR3fҥ裏4n:EGG[j_@`رcյkWG^TTiZ';|KV9mذAճ E1cʗ/oO;Ｃ}Yj_̘1CǏ6;SVfE $ >\Çwnx<S͙0aÆڿVrW Ԕ__*\c{Ց#Gr.%&&F[nUݺuh銋 /o{::wYf{p_ի+55$MM۫ ?B36k:?AyM4-B XըQ#ҥK5tPn tIuU)))FCjȑ0 *(--MժUs쥧}:flظ2A},KviٲeZ!/_^Ws=۰a{=Yh$u]F'G00;vLuq:ך',4MKf,=>qW&L (88XsU{?yeeeYhvء޽{;?22RW֭_b qvc=roO, #'k'΁˥'_~Z!Ѝ3Ft{au )c0`rrrRJZj|IKH++8rw[=_y[&Zh4 O[ȃl2mB+}jiii֭:dҲh" >\F^Z5mܸQ 4 OsGCҲ @ix^~E# Վ;Ըqc Pִo^SDD={Vzݻ-5P5qDylll٢XKHȀ4~x\.ބax wazMG^ddRRRTN PV5kL+WTŊ… ?4rH͛7ϱW~}mݺUUV ϼkZd,OIZ0ez܅G'գ-t1^ʕƇ@І t-yAA>cM0+*/33SV^kҤe  Omٲq$q^yo:/B3b3o]s!DZWF :.xw߭͛7??H/VݕaN:VZ믿.{(j1O~({!W_㾴 *;/wլ3x 6b`[uVիWϱN:iÆ Rn[qqq""BǏא!C,Cb sQQQ:rڴiS#?zKh]yyPԩS-40͜9S&MRddx7ߨK.JMMԩS':tȱ-[W_ )@)HOOW*U,--M7|Fgzꩧ+"4Sjӽ[h&_zuڵKQQQ^n\g޽{{z7[h@ +W:r/k„ 0}WHHƍuiu]/Xo x=w՟Z^r_Wrr2+ڹsڴi#eO>ш#TPP`!Pv?~\]tъ+7pNS2R@`"""~uԩG՟^Mۼ (& bfV\9YF}f դI4c x㍎5k護ұc,ʦUVk׮:zcvڱc^~e PpdKxոqc{ (;rsv.:q45kj׮]z駵k.^rꫯ' ( 4j(_ޱrԾ}{}g .}WHXFtYծ] fs^nm -.9н{w۷a;C۶mo c<3FÆ S^^@JIIQǎF֜9s3b paaaڳgz8S~9vk6YhҠTXP צM[h`7NSŊ:uCYhK{JMuw}ڽ{{9 PqdKطuV5jH.\(v&e^ngo]q"wڲeBBB zge^XXZjf͚Yh\1B7ntC1b f(|< WΞ=z7j6:yqe>Ѫa˥}jǎ #LVխ[7ea_hɱ_IIIرc7t.\QF1B 2p@W' (8X/wzEO|B3kHAڹi{QQQڰaԩfooԲeKedd8*W;fqݚ={͛~Æ `UZBCH={U tܹb+VF};h3M7h@ \ozgaÆbի:r^nm۶)..N'Np셄{o˖-7߬aÆM6^nN $kxq?;A 2@g2/}}*,,̅&:|/^}{wij XHEEEnZL(:+/kjsF[.UQQQzt}(:VZZ>Lf`0}zծ];FoWRR^˖-SFFFժUKwufΜ;0BC-ѐ!!I)??jk4r(Eot1/Ox-ٲDž?xիWkƌڶm[-[T]MQQj*;w킂t}i̙q]p+Z!.CZ|{ $ Н?ߟ=\FIj׮+x.'ڲe^z%[MjȑӧF #'_|5kŋnWN=Z j Z!.Cše˖WzzzםꉗTEOs)lSmذAnנA=zTn]FOPK4$eH@TdӦM;vN8QvA7߿FG(:ێ7?TYF Z.7JIIK/u=N:.tI}ںukz'O+?j*:{߯4nݺ;;[顇ɓ徿vs.]xZkGzzJ ,I 55lp B8sZ|ڴC *>00P'N[o%>/^Ӈ~ 7߬AK.=*((К5ka%'';t_|QFs ,p= 6:qymܸ9z5b'FV>G ~g5jT Q/^߯J?H>+&ߪ\o),(޿M_((8<9Z=W._֧shu愆g՟g'Go_/*$$]|Yo̙3gT}zԽ{w}jٲ "-e眾 QCOA~m޼Yk׮Ձ*|$뵇x~Jo??[|mlJc6ۺ0jbieEU>""Bf҄ + @>??{ｧ{ .hڵZv5j^z7IH߾}zj޽[n}_VϞ=]!{hHӧ5d0`Ǐ+11Qv8@}1cƸ*lԸq* sp4}dIҏ??ڷo˝S\\{GQ`p:QEDE*l\gצt_Mw ݻm[I4}tpk׮]RRRkI**(TҺ-JZEAi̓쪰jcZu烺r`^=zhΜ9ܹ#hBo~mmݺUsժUmu{ɤǏZxڶm;C~jժjsiժUڼyN>mӜ6mפI |ںu٘/^lьpUxx,Y~)))lz785f0a&LbKZ`222*sBVJXF<7w-Qrs.h?Ԏ;Tɦ9FQm۶̙35|p'GUӳgOSZbϟu%uڿ߯>@:uչsgFG fmذAǎ|Ѻ{5yduQ5WQRR_|b|6lXsb KǏ/OHHPRR/Pk֬Y5kΟ?ɓ'okegjZNJlP7YbnpQc$j;?坯sص4izJ8h԰a4l0?֢ET emٲEWչsgǫ^z. ߉'}ȑ#|rsիjҤIׯ j6ʱ~z=zl,..NӦMi~ddOGyl_ կ__K,$:tHO<6oެrJM8&$jmB0 \m@H>xLy*|4h={j׮(cSff}}:p@ϟ͛yf{TVhPxMuGc!˖-:t]k 2D~~~*)))oURR"??jr7xH~=3:pY^ZSR\b٠{tбոϏ5ώ|v7 HRڵ5h ͝;W5rR^z饗K/ѣzw|r;d2)##Cf [VNԳgOխ[ׅ?g8ynZրP]Ν;kњ0aBCC)А`dRBBxej^ Իwomذl,;;[6lP' 4H $͛7OtIN(#Y,AS|';vmڮ_ֱGԀ IjR5gu Qjݺ̙9sh͚7oV^ ]۠i&wPATmݺU֭[kȑzu p7HNN֙3g"""Իwo6lYC hHkҤI4i믿ӧu_mPH%~kBDTnOysԀ I!!!jӦy9Qw[u뭷T JHHPRR+ 6lh7rDBڵաC[ƍM7HT V8pblРA2 vuw멧2;x`cX={fϞ-?oV^mw/iJ&] E4M=nwUm[.ھ!I7ʭfB۶m5zhM*($XfM'^7p(%:vo٩Cǔ窨HҪ5HwѣG'^z=zF-oA65(+==]7n#sƍI&Q˖-U^=\f*--ՙ3gtqSҀnt}¹sp UPP9sF?sٸ`PhhհaCEGGy抋S&MTV-lĉ:qN:g*##CΝ3C Vջwo 2D}!4$XqE1G6$HR^^[ t 7HgΜ)9S>} /h̙޷deeU{]BCC5s̲9y̙̙3vdRQa #{/W}`4h4(?jRPPNXz@aQ -[߹rUTX$<[y9ZZ\ֹ]8wA9w!O.^R|^*Taa^WteX%*---3E1b&NhրppgffҥKnpk+11Q֭Ν;uԩ*L&)//O)))f3 Q@@jժ@z@-v VڵRߐ[TtM 5{g+/GKKKUXXK.ŋtuҥWaǡ****{]|εkVV[o5k@q~Wv**%ly.]uKҦMt-Ti;C֭3۷oڷo_ma4e0ԭ[7=ݻ6mmuQ s=zvܩ{ &IǏWnnc5$9RV~?x C̭^xA6oo45i$ܹSu5^jj|IGQ5 5}m2]Yz3=M5gO>DGuCDo}ڦ=Q``ԩc6V;Ejh߾XbbJKK^ﾳ󖆄t?l[b VhBfcڼyk%$$Xӧʱ޽{-n&7D64$cذac˗/kmڴl,<<\}Nh.{n;!!Ç!aʕ*))1{d4zt4$lWݤ_~jݺXrrygeeW_53 8qbt &(;;l,""B;wvSDoBCB9FfΜi1OWzB{JNN6{ճgOch4{mذyK.շ~Rum>VZe6n04|ڽ&!#GT=JKK5zhYl=ںux@@f͚Ux  C%%%iСь3f˖-Sv4{lM0A{1j&d2;Ov)+=={0`%&&j׮]`Ђ 4n8Top޴i4gΜԪU+5mT͛7WլY3ȑ#:z:cǎY]uڽ{Ps;O״iS\RS^^233tR-]AӧOJJJta>|ܹsiF؅tE?ƍM6?4={VVյkW佳СC:s222hnZ[vwxCxsLO\pA:yRSSUPP-ZEUhhCurEJNNVVVV+''GaaafwWJz+`ɤE?ֆ TZZj`Pll|AM2E 6bcc-ٰnݺJp9Oںul٢۷ŋhTttZhcjv[ZZŋO>Ѻu_\\z!M35aYYYzǴl2焄h*g3FyNLL.\Xi)-G!_3<㐵Gؿ;ܹS;vPJJJs MDDMMMCxPV\-[T)4vX͘1CM4qBBr x IDAT_vak׮Zkh̘1zgղeKEf?j(|/PPmwV߾}W3fK/*GCQСCuF}G7n"s?WM6Ms̱k5$P?˼i _ⰻw>И1cEQ2G\켪nݺڴi:tA N>!CXmFԀԿ?~\ڵkţ^~eĸ*~%,,Ѡo~:֭k?$Vll?*lIvӎ5&M;mݦ{*11Q4ۦT'NTfԯ_?W śsZWV|uϷi իWOڵSvԾ}{EFFѣ:|6nܨgϚ)**c=:s.:rZ— nZ۷WFԸqc5nXJKKSZZ;իW[Ԭ皶nSCQxc^'j ^z +$$Ĕhu,ӈ#,:دaƌ.?`o[MŋM%%%暞x 9vZ]XXh!**ʴcsLrŜ0SVVb9ڼys}?emԩVs,((ԭ[7{SXXF6LO hE/Og L;wk…q3'N0nذi ]t/,r7..t9Q6_QCˎ9bjѢG5}駦g4̙3h]t1]|ɑ _95TW_}e̲e*WPP`ٳÇ(_ѐO}v'd̙SR-uELok׮mJJJp^ffe˖sz)EΛs?WM6`ԩi„ ߴcӕ+Wʶ>Nur: oɒ%V?#O.?5skwhw*))ы/h1>{l 6¹AAAZbs8o[M2ESLp`В%KԣG]v?whj֬Y1-Z"EDDhժUjРy$Q5G_*77Wwև~{L]vg?`0VZӼy}vi7o֭0~Zz߯ &(00Юݺuoa1j*GX!(|(=CV'mٲsg*P%ׯѣG4m4GFFj׿ml,$$Dllu[Wԗ_~~iĈ6oժ|I"-\Q!9 F}P?QSxkׯAUkSUVfc:rHֵus@.[:}P6Q_ECdٲecCk!CloUIIIb8p]Xzp7*;;Uֶohޜ<56 M;6LڹsKlPSKmܕd2)!!bG5\AݻXvv6lPod탠9e45d-_ZU$//Ok׮53 v~7+&&ll޽:~xuC[sg~|EN,v}RG۹#GHx̵QC۹#G} [rrΜ9c6a\` knU JEEEJJJ2pi WTRR._l6֩S'5mu~O9 3Q?"33b~N'u;rmmf1֮]; 9hH8`16h  ׺-ܑ*WXXw}l_=SK leСCcU頓&Mش+ڵKO=l٢SN);;[jӦ٫GUǑ9 u=zʱUWkysرczO?)%%EWE֭5j_G .X`0k׮N/ur<***رc-9ydoީsgj( ^²dht |Jk9ZM6iӦMfcƍƂ3SPP|NppիW[IiӦp~mܕ85YQQRSSd͛7q*̙35Srዼ9֭k6v]9G+b0TV-5kL111+hĈ3f|mWOkN p{NfcV/:u;s"ݻu:[n7xC.I EM9Zo4$nξ0c2 w﮷zK۶mә3gTPPcǎԩSΝ;h"|Vט5k֭[x\qY9uU^poѫ  ڷoW_}U?N:Bמ={_o߾VYd.\X x/o? 8ŋ{YWhhO*P|JIIѩS\_j(`wUXCiHݜ]fΜcǎ)))IӦMSneMHH:wѣGkΝz,nEd24~x:=f&oͱSjϞ=ڷo^|ESfPԡCw}gI&kM6/mWOյm6=ƍs٭੣@QycN9bO6M:tkΨQo>5kl<77W< y PI :;Cl]v;wu85Q^^JJJ^:p;͙3Gݻwan[rk( PY 1d2U;\W>^͛7ל9s,?=z ]S+sʛclUի Z|6X~j M|$PUOր/7kL] uI9Z]—m۶뮻O())I_ڶmkշo_95j(`r<ҐY+(,ZرcuZٳ 9;ۇ#\ ʀ+ys:B߾}5eqjPuOyI *222twZqZvջ8u0i9— >\?EkFFx  TΝ9ZCiHPWO\<goEjFi1o>7Do9KJJt1oujXOyI ^YYY߿9b6ްaC?Q˖-]u?q(|Ylٳg_j(`w娣xb |Ü9skeu֭QQQc0Wy=WرŘ9e0Y*eq[srr,Ɯ7`oQGvjjPuOf9r&9990`}85'<$`ͥKt=hƍf!!!t뭷)QGQxcVǡ ._kך 55r|XOjܸq8q# ٰa|I۷{ ғO>Ygϵb O˵w?:ل: _ 9q(|]IIp*&&F111j޼u*55UU=zq؎@ EM95TKӦMrJOyyyfҥKtr M>]ƍsv*))Çua_sUpp"CMT^=ZJzx[V@+2n8 ӪΝ;?u>}Z ,Ђ ʝo4;pv@ys^duEsUTTJMMk޽{`eBDMMCx:wy?Zk7hРZAPleggkǎvի>sr557URCiH@uE?ƍM6 Cmnݪ-[hxb'FբE ;VǏr܀-"##l2-_\}~rO6nXFҔ)SWU5ܹvܩ>@-g5zhM Introduction to GeoPandas Examples Gallery ``` ## Installation GeoPandas is written in pure Python, but has several dependencies written in C ([GEOS](https://geos.osgeo.org), [GDAL](https://www.gdal.org/), [PROJ](https://proj.org/)). Those base C libraries can sometimes be a challenge to install. Therefore, we advise you to closely follow the recommendations below to avoid installation problems. ### Easy way The best way to install GeoPandas is using ``conda`` and ``conda-forge`` channel: ``` conda install -c conda-forge geopandas ``` ### Detailed instructions Do you prefer ``pip install`` or installation from source? Or want to install a specific version? See {doc}`detailed instructions `. ### What now? - If you don't have GeoPandas yet, check {doc}`Installation `. - If you have never used GeoPandas and want to get familiar with it and its core functionality quickly, see {doc}`Getting Started Tutorial `. - Detailed illustration of how to work with different parts of GeoPandas, make maps, manage projections, spatially merge data or geocode are part of our {doc}`User Guide `. - And if you are interested in the complete documentation of all classes, functions, method and attributes GeoPandas offers, {doc}`API Reference ` is here for you. ```{container} button {doc}`Installation ` {doc}`Introduction ` {doc}`User Guide ` {doc}`API Reference ` ``` ## Get in touch Haven't found what you were looking for? - Ask usage questions ("How do I?") on [StackOverflow](https://stackoverflow.com/questions/tagged/geopandas) or [GIS StackExchange](https://gis.stackexchange.com/questions/tagged/geopandas). - Get involved in [discussions on GitHub](https://github.com/geopandas/geopandas/discussions) - Report bugs, suggest features or view the source code on [GitHub](https://github.com/geopandas/geopandas). - For a quick question about a bug report or feature request, or Pull Request, head over to the [gitter channel](https://gitter.im/geopandas/geopandas). - For less well defined questions or ideas, or to announce other projects of interest to GeoPandas users, ... use the [mailing list](https://groups.google.com/forum/#!forum/geopandas). geopandas-1.2.0/doc/source/getting_started/000077500000000000000000000000001525650651600207225ustar00rootroot00000000000000geopandas-1.2.0/doc/source/getting_started/install.rst000066400000000000000000000156741525650651600231370ustar00rootroot00000000000000Installation ============ GeoPandas depends for its spatial functionality on a large geospatial, open source stack of libraries (`GEOS`_, `GDAL`_, `PROJ`_). See the :ref:`dependencies` section below for more details. Those base C libraries can sometimes be a challenge to install. Therefore, we advise you to closely follow the recommendations below to avoid installation problems. .. _install-conda: Installing with Anaconda / conda -------------------------------- To install GeoPandas and all its dependencies, we recommend to use the `conda`_ package manager. This can be obtained by installing the `Anaconda Distribution`_ (a free Python distribution for data science), or through `miniconda`_ (minimal distribution only containing Python and the `conda`_ package manager). See also the `Conda installation docs `__ for more information on how to install Anaconda or miniconda locally. The advantage of using the `conda`_ package manager is that it provides pre-built binaries for all the required and optional dependencies of GeoPandas for all platforms (Windows, Mac, Linux). To install the latest version of GeoPandas, you can then do:: conda install geopandas Using the conda-forge channel ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ `conda-forge`_ is a community effort that provides conda packages for a wide range of software. It provides the *conda-forge* package channel for conda from which packages can be installed, in addition to the "*defaults*" channel provided by Anaconda. Depending on what other packages you are working with, the *defaults* channel or *conda-forge* channel may be better for your needs (e.g. some packages are available on *conda-forge* and not on *defaults*). Generally, *conda-forge* is more up to date with the latest versions of geospatial python packages. GeoPandas and all its dependencies are available on the *conda-forge* channel, and can be installed as:: conda install --channel conda-forge geopandas .. note:: We strongly recommend to either install everything from the *defaults* channel, or everything from the *conda-forge* channel. Ending up with a mixture of packages from both channels for the dependencies of GeoPandas can lead to import problems. See the `conda-forge section on using multiple channels `__ for more details. Creating a new environment ^^^^^^^^^^^^^^^^^^^^^^^^^^ Creating a new environment is not strictly necessary, but given that installing other geospatial packages from different channels may cause dependency conflicts (as mentioned in the note above), it can be good practice to install the geospatial stack in a clean environment starting fresh. The following commands create a new environment with the name ``geo_env``, configures it to install packages always from conda-forge, and installs GeoPandas in it:: conda create -n geo_env conda activate geo_env conda config --env --add channels conda-forge conda config --env --set channel_priority strict conda install python=3 geopandas .. _install-pip: Installing with pip ------------------- GeoPandas can also be installed with pip, if all dependencies can be installed as well:: pip install geopandas .. _install-deps: .. warning:: When using pip to install GeoPandas, you need to make sure that all dependencies are installed correctly. Our main dependencies (`shapely`_, `pyproj`_, `pyogrio`_) provide binary wheels with dependencies included for Mac, Linux, and Windows. However, depending on your platform or Python version, there might be no pre-compiled wheels available, and then you need to compile and install their C dependencies manually. We refer to the individual packages for more details on installing those. Using conda (see above) avoids the need to compile the dependencies yourself. Optional runtime dependencies can also be installed all at once:: pip install 'geopandas[all]' Installing from source ---------------------- You may install the latest development version by cloning the `GitHub` repository and using pip to install from the local directory:: git clone https://github.com/geopandas/geopandas.git cd geopandas pip install . Development dependencies can be installed using the ``dev`` dependency group:: pip install -e . --group dev It is also possible to install the latest development version directly from the GitHub repository with:: pip install git+git://github.com/geopandas/geopandas.git For installing GeoPandas from source, the same :ref:`note ` on the need to have all dependencies correctly installed applies. But, those dependencies can also be installed independently with conda before installing GeoPandas from source:: conda install pandas pyogrio shapely pyproj See the :ref:`section on conda ` above for more details on getting running with Anaconda. .. _dependencies: Dependencies ------------ Required dependencies: - `numpy`_ - `pandas`_ (version 2.2 or later) - `shapely`_ (interface to `GEOS`_; version 2.1 or later) - `pyogrio`_ (interface to `GDAL`_; version 0.8 or later) - `pyproj`_ (interface to `PROJ`_; version 3.7.0 or later) - `packaging`_ Further, optional dependencies are: - `fiona`_ (optional; slower alternative to `pyogrio`) - `psycopg`_ (optional; for PostGIS connection) - `psycopg2`_ (optional; for PostGIS connection - older version of `psycopg` library) - `GeoAlchemy2`_ (optional; for writing to PostGIS) - `geopy`_ (optional; for geocoding) - `pointpats`_ (optional; for advanced point sampling) - `scipy`_ (optional; for sparse output of spatial indexing) For plotting, these additional packages may be used: - `matplotlib`_ (>= 3.9.0) - `mapclassify`_ (>= 2.7.0) - `folium`_ (for interactive plotting) .. _PyPI: https://pypi.python.org/pypi/geopandas .. _GitHub: https://github.com/geopandas/geopandas .. _numpy: http://www.numpy.org .. _pandas: http://pandas.pydata.org .. _shapely: https://shapely.readthedocs.io .. _fiona: https://fiona.readthedocs.io .. _pyogrio: https://pyogrio.readthedocs.io .. _matplotlib: http://matplotlib.org .. _geopy: https://github.com/geopy/geopy .. _psycopg: https://pypi.python.org/pypi/psycopg .. _psycopg2: https://pypi.python.org/pypi/psycopg2 .. _GeoAlchemy2: https://geoalchemy-2.readthedocs.io/ .. _mapclassify: http://pysal.org/mapclassify .. _pyproj: https://github.com/pyproj4/pyproj .. _conda: https://conda.io/en/latest/ .. _Anaconda distribution: https://www.anaconda.com/distribution/ .. _miniconda: https://docs.conda.io/en/latest/miniconda.html .. _conda-forge: https://conda-forge.org/ .. _GDAL: https://www.gdal.org/ .. _GEOS: https://geos.osgeo.org .. _PROJ: https://proj.org/ .. _packaging: https://packaging.pypa.io/en/latest/ .. _pointpats: https://pysal.org/pointpats/ .. _folium: https://python-visualization.github.io/folium/latest/ .. _scipy: https://docs.scipy.org/doc/scipy/ geopandas-1.2.0/doc/source/getting_started/introduction.ipynb000066400000000000000000000435721525650651600245210ustar00rootroot00000000000000{ "cells": [ { "attachments": {}, "cell_type": "markdown", "metadata": { "tags": [] }, "source": [ "# Introduction to GeoPandas\n", "\n", "This quick tutorial introduces the key concepts and basic features of GeoPandas to help you get started with your projects.\n", "\n", "## Concepts\n", "\n", "GeoPandas, as the name suggests, extends the popular data science library [pandas](https://pandas.pydata.org) by adding support for geospatial data. If you are not familiar with `pandas`, we recommend taking a quick look at its [Getting started documentation](https://pandas.pydata.org/docs/getting_started/index.html#getting-started) before proceeding.\n", "\n", "The core data structure in GeoPandas is the `geopandas.GeoDataFrame`, a subclass of `pandas.DataFrame`, that can store geometry columns and perform spatial operations. The `geopandas.GeoSeries`, a subclass of `pandas.Series`, handles the geometries. Therefore, your `GeoDataFrame` is a combination of `pandas.Series`, with traditional data (numerical, boolean, text etc.), and `geopandas.GeoSeries`, with geometries (points, polygons etc.). You can have as many columns with geometries as you wish; unlike in some typical desktop GIS software.\n", "\n", "![geodataframe schema](../_static/dataframe.svg)\n", "\n", "Each `GeoSeries` can contain any geometry type (you can even mix them within a single array) and has a `GeoSeries.crs` attribute, which stores information about the projection (CRS stands for Coordinate Reference System). Therefore, each `GeoSeries` in a `GeoDataFrame` can be in a different projection, allowing you to have, for example, multiple versions (different projections) of the same geometry.\n", "\n", "Only one `GeoSeries` in a `GeoDataFrame` is considered the _active geometry column_, which means that all geometric operations applied to a `GeoDataFrame` operate on this column.\n", "The active geometry column is accessed via the `GeoDataFrame.geometry` attribute.\n", "\n", "\n", "
\n", "User guide\n", " \n", "See more on [data structures](../docs/user_guide/data_structures.rst) in the user guide.\n", "
\n", "\n", "\n", "Let's see how some of these concepts work in practice.\n", "\n", "## Reading and writing files\n", "\n", "First, we need to read some data.\n", "\n", "### Reading files\n", "\n", "Assuming you have a file containing both data and geometry (e.g. GeoPackage, GeoJSON, Shapefile), you can read it using `geopandas.read_file()`, which automatically detects the filetype and creates a `GeoDataFrame`. This tutorial uses the `\"nybb\"` dataset, a map of New York boroughs, which is available through the `geodatasets` package. Therefore, we use `geodatasets.get_path()` to download the dataset and retrieve the path to the local copy." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import geopandas\n", "from geodatasets import get_path\n", "\n", "path_to_data = get_path(\"nybb\")\n", "gdf = geopandas.read_file(path_to_data)\n", "\n", "gdf" ] }, { "cell_type": "markdown", "metadata": { "tags": [] }, "source": [ "### Writing files\n", "\n", "To write a `GeoDataFrame` back to file use `GeoDataFrame.to_file()`. By default, the file format is inferred by the file extension (e.g. `.shp` for Shapefile, `.geojson` for GeoJSON), but you can specify this explicitly with the `driver` keyword." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "gdf.to_file(\"my_file.geojson\", driver=\"GeoJSON\")" ] }, { "cell_type": "markdown", "metadata": { "tags": [] }, "source": [ "
\n", "User guide\n", " \n", "See more on [reading and writing files](../docs/user_guide/io.rst) in the user guide.\n", "
\n", "\n", "\n", "\n", "## Simple accessors and methods\n", "\n", "Now we have our `GeoDataFrame` and can start working with its geometry. \n", "\n", "Since there was only one geometry column in the New York Boroughs dataset, this column automatically becomes the _active_ geometry and spatial methods used on the `GeoDataFrame` will be applied to the `\"geometry\"` column.\n", "\n", "### Measuring area\n", "\n", "To measure the area of each polygon (or MultiPolygon in this specific case), access the `GeoDataFrame.area` attribute, which returns a `pandas.Series`. Note that `GeoDataFrame.area` is just `GeoSeries.area` applied to the _active_ geometry column.\n", "\n", "But first, to make the results easier to read, set the names of the boroughs as the index:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "gdf = gdf.set_index(\"BoroName\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "gdf[\"area\"] = gdf.area\n", "gdf[\"area\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Getting polygon boundary and centroid\n", "\n", "To get the boundary of each polygon (LineString), access the `GeoDataFrame.boundary`:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "gdf[\"boundary\"] = gdf.boundary\n", "gdf[\"boundary\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since we have saved boundary as a new column, we now have two geometry columns in the same `GeoDataFrame`.\n", "\n", "We can also create new geometries, which could be, for example, a buffered version of the original one (i.e., `GeoDataFrame.buffer(10)`) or its centroid:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "gdf[\"centroid\"] = gdf.centroid\n", "gdf[\"centroid\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Measuring distance\n", "\n", "We can also measure how far each centroid is from the first centroid location." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "first_point = gdf[\"centroid\"].iloc[0]\n", "gdf[\"distance\"] = gdf[\"centroid\"].distance(first_point)\n", "gdf[\"distance\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that `geopandas.GeoDataFrame` is a subclass of `pandas.DataFrame`, so we have all the pandas functionality available to use on the geospatial dataset — we can even perform data manipulations with the attributes and geometry information together.\n", "\n", "For example, to calculate the average of the distances measured above, access the 'distance' column and call the mean() method on it:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "gdf[\"distance\"].mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Making maps\n", "\n", "GeoPandas can also plot maps, so we can check how the geometries appear in space. To plot the active geometry, call `GeoDataFrame.plot()`. To color code by another column, pass in that column as the first argument. In the example below, we plot the active geometry column and color code by the `\"area\"` column. We also want to show a legend (`legend=True`)." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "gdf.plot(\"area\", legend=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can also explore your data interactively using `GeoDataFrame.explore()`, which behaves in the same way `plot()` does but returns an interactive map instead." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "gdf.explore(\"area\", legend=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Switching the active geometry (`GeoDataFrame.set_geometry`) to centroids, we can plot the same data using point geometry." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "gdf = gdf.set_geometry(\"centroid\")\n", "gdf.plot(\"area\", legend=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And we can also layer both `GeoSeries` on top of each other. We just need to use one plot as an axis for the other." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ax = gdf[\"geometry\"].plot()\n", "gdf[\"centroid\"].plot(ax=ax, color=\"black\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we set the active geometry back to the original `GeoSeries`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "gdf = gdf.set_geometry(\"geometry\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "User guide\n", " \n", "See more on [mapping](../docs/user_guide/mapping.rst) in the user guide.\n", "
\n", "\n", "## Geometry creation\n", "\n", "We can further work with the geometry and create new shapes based on those we already have. \n", "\n", "### Convex hull\n", "\n", "If we are interested in the convex hull of our polygons, we can access `GeoDataFrame.convex_hull`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "gdf[\"convex_hull\"] = gdf.convex_hull" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# saving the first plot as an axis and setting alpha (transparency) to 0.5\n", "ax = gdf[\"convex_hull\"].plot(alpha=0.5)\n", "# passing the first plot and setting linewidth to 0.5\n", "gdf[\"boundary\"].plot(ax=ax, color=\"white\", linewidth=0.5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Buffer\n", "\n", "In other cases, we may need to buffer the geometry using `GeoDataFrame.buffer()`. Geometry methods are automatically applied to the active geometry, but we can apply them directly to any `GeoSeries` as well. Let's buffer the boroughs and their centroids and plot both on top of each other." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# buffering the active geometry by 10 000 feet (geometry is already in feet)\n", "gdf[\"buffered\"] = gdf.buffer(10000)\n", "\n", "# buffering the centroid geometry by 10 000 feet (geometry is already in feet)\n", "gdf[\"buffered_centroid\"] = gdf[\"centroid\"].buffer(10000)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# saving the first plot as an axis and setting alpha (transparency) to 0.5\n", "ax = gdf[\"buffered\"].plot(alpha=0.5)\n", "# passing the first plot as an axis to the second\n", "gdf[\"buffered_centroid\"].plot(ax=ax, color=\"red\", alpha=0.5)\n", "# passing the first plot and setting linewidth to 0.5\n", "gdf[\"boundary\"].plot(ax=ax, color=\"white\", linewidth=0.5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "User guide\n", " \n", "See more on [geometry manipulations](../docs/user_guide/geometric_manipulations.rst) in the user guide.\n", "
\n", "\n", "## Geometry relations\n", "\n", "We can also ask about the spatial relations of different geometries. Using the geometries above, we can check which of the buffered boroughs intersect the original geometry of Brooklyn, i.e., is within 10 000 feet from Brooklyn.\n", "\n", "First, we get a polygon of Brooklyn." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "brooklyn = gdf.loc[\"Brooklyn\", \"geometry\"]\n", "brooklyn" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The polygon is a [shapely geometry object](https://shapely.readthedocs.io/en/stable/manual.html#geometric-objects), as any other geometry used in GeoPandas." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "type(brooklyn)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we can check which of the geometries in `gdf[\"buffered\"]` intersects it." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "gdf[\"buffered\"].intersects(brooklyn)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Only Bronx (on the north) is more than 10 000 feet away from Brooklyn. All the others are closer and intersect our polygon.\n", "\n", "Alternatively, we can check which buffered centroids are entirely within the original boroughs polygons. In this case, both `GeoSeries` are aligned, and the check is performed for each row." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "gdf[\"within\"] = gdf[\"buffered_centroid\"].within(gdf)\n", "gdf[\"within\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can plot the results on the map to confirm the finding." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [] }, "outputs": [], "source": [ "gdf = gdf.set_geometry(\"buffered_centroid\")\n", "# using categorical plot and setting the position of the legend\n", "ax = gdf.plot(\n", " \"within\", legend=True, categorical=True, legend_kwds={\"loc\": \"upper left\"}\n", ")\n", "# passing the first plot and setting linewidth to 0.5\n", "gdf[\"boundary\"].plot(ax=ax, color=\"black\", linewidth=0.5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Projections\n", "\n", "Each `GeoSeries` has its Coordinate Reference System (CRS) accessible at `GeoSeries.crs`. The CRS tells GeoPandas where the coordinates of the geometries are located on the earth's surface. In some cases, the CRS is geographic, which means that the coordinates are in latitude and longitude. In those cases, its CRS is WGS84, with the authority code `EPSG:4326`. Let's see the projection of our NY boroughs `GeoDataFrame`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "gdf.crs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Geometries are in `EPSG:2263` with coordinates in feet. We can easily re-project a `GeoSeries` to another CRS, like `EPSG:4326` using `GeoSeries.to_crs()`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "gdf = gdf.set_geometry(\"geometry\")\n", "boroughs_4326 = gdf.to_crs(\"EPSG:4326\")\n", "boroughs_4326.plot()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "boroughs_4326.crs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice the difference in coordinates along the axes of the plot. Where we had 120 000 - 280 000 (feet) before, we now have 40.5 - 40.9 (degrees). In this case, `boroughs_4326` has a `\"geometry\"` column in WGS84 but all the other (with centroids etc.) remain in the original CRS.\n", "\n", "
\n", "Warning\n", " \n", "For operations that rely on distance or area, you always need to use a projected CRS (in meters, feet, kilometers etc.) not a geographic one (in degrees). GeoPandas operations are planar, whereas degrees reflect the position on a sphere. Therefore, spatial operations using degrees may not yield correct results. For example, the result of `gdf.area.sum()` (projected CRS) is 8 429 911 572 ft2 but the result of `boroughs_4326.area.sum()` (geographic CRS) is 0.083.\n", "
\n", "\n", "
\n", "User guide\n", " \n", "See more on [projections](../docs/user_guide/projections.rst) in the user guide.\n", "
\n", "\n", "## What next?\n", "\n", "With GeoPandas we can do much more than what has been introduced so far, from [aggregations](../docs/user_guide/aggregation_with_dissolve.rst), to [spatial joins](../docs/user_guide/mergingdata.rst), to [geocoding](../docs/user_guide/geocoding.rst), and [much more](../gallery/index.rst).\n", "\n", "Head over to the [user guide](../docs/user_guide.rst) to learn more about the different features of GeoPandas, the [Examples](../gallery/index.rst) to see how they can be used, or to the [API reference](../docs/reference.rst) for the details." ] } ], "metadata": { "kernelspec": { "display_name": "geopandas_dev", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.0" }, "vscode": { "interpreter": { "hash": "b1fe2ae8565152c84d3dbd08488d3746f754c9bdf2de9b61cf939da5306d3793" } } }, "nbformat": 4, "nbformat_minor": 4 } geopandas-1.2.0/doc/source/index.rst000066400000000000000000000064011525650651600173750ustar00rootroot00000000000000:html_theme.sidebar_secondary.remove: GeoPandas |version| =================== GeoPandas is an open source project to make working with geospatial data in python easier. GeoPandas extends the datatypes used by `pandas`_ to allow spatial operations on geometric types. Geometric operations are performed by `shapely`_. Geopandas further depends on `pyogrio`_ for file access and `matplotlib`_ for plotting. .. _pandas: http://pandas.pydata.org .. _shapely: https://shapely.readthedocs.io .. _pyogrio: https://pyogrio.readthedocs.io .. _matplotlib: http://matplotlib.org Description ----------- The goal of GeoPandas is to make working with geospatial data in python easier. It combines the capabilities of pandas and shapely, providing geospatial operations in pandas and a high-level interface to multiple geometries to shapely. GeoPandas enables you to easily do operations in python that would otherwise require a spatial database such as PostGIS. .. toctree:: :hidden: Home About Getting started Documentation Community .. container:: button :doc:`Getting started ` :doc:`Documentation ` :doc:`About GeoPandas ` :doc:`Community ` Useful links ------------ `Binary Installers (PyPI) `_ | `Source Repository (GitHub) `_ | `Issues & Ideas `_ | `Q&A Support `_ |pypi| |Actions Status| |Coverage Status| |Join the chat at https://gitter.im/geopandas/geopandas| |Binder| |DOI| |Powered by NumFOCUS| .. |pypi| image:: https://img.shields.io/pypi/v/geopandas.svg :target: https://pypi.python.org/pypi/geopandas/ .. |Actions Status| image:: https://github.com/geopandas/geopandas/workflows/Tests/badge.svg :target: https://github.com/geopandas/geopandas/actions?query=workflow%3ATests .. |Coverage Status| image:: https://codecov.io/gh/geopandas/geopandas/branch/main/graph/badge.svg :target: https://codecov.io/gh/geopandas/geopandas .. |Join the chat at https://gitter.im/geopandas/geopandas| image:: https://badges.gitter.im/Join%20Chat.svg :target: https://gitter.im/geopandas/geopandas?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge .. |Binder| image:: https://mybinder.org/badge.svg :target: https://mybinder.org/v2/gh/geopandas/geopandas/main .. |DOI| image:: https://zenodo.org/badge/11002815.svg :target: https://zenodo.org/badge/latestdoi/11002815 .. |Powered by NumFOCUS| image:: https://img.shields.io/badge/powered%20by-NumFOCUS-orange.svg?style=flat&colorA=E1523D&colorB=007D8A :target: https://numfocus.org Supported by ------------ The GeoPandas project uses an `open governance model `__ and is fiscally sponsored by `NumFOCUS `__. Consider making a `tax-deductible donation `__ to help the project pay for developer time, professional services, travel, workshops, and a variety of other needs. .. image:: _static/SponsoredProject.svg :alt: numfocus :width: 400 :target: https://numfocus.org/project/geopandas geopandas-1.2.0/environment-dev.yml000066400000000000000000000014201525650651600173260ustar00rootroot00000000000000name: geopandas-dev channels: - conda-forge dependencies: - python # required - pyogrio>=0.7.2 - pandas>=2.0 - pyproj>=3.5.0 - shapely>=2.0.0 - packaging # testing - pytest>=3.1.0 - pytest-cov - pytest-xdist - fsspec - codecov - geodatasets - pytz - geoarrow-pyarrow # styling - pre-commit - ruff==0.13.3 # optional - fiona - folium - xyzservices - contextily - scipy - libspatialite - geoalchemy2 - pyarrow # doctest testing - pytest-doctestplus # geocoding - geopy # geodatabase access - psycopg2>=2.8.0 - psycopg>=3.1.0 - sqlalchemy>=2.0 # plotting - matplotlib>=3.7 - mapclassify # point sampling - pointpats geopandas-1.2.0/environment.yml000066400000000000000000000003531525650651600165560ustar00rootroot00000000000000name: geopandas channels: - conda-forge dependencies: - geopandas - shapely - pyogrio - pyproj - mapclassify - libpysal - matplotlib - geopy - cartopy - pyepsg - contextily - rasterio - folium - packaging geopandas-1.2.0/examples/000077500000000000000000000000001525650651600153045ustar00rootroot00000000000000geopandas-1.2.0/examples/README.md000066400000000000000000000003771525650651600165720ustar00rootroot00000000000000# Examples Gallery Examples are available in the [documentation](https://geopandas.readthedocs.io/en/latest/gallery/index.html). Source Jupyter notebooks are in [`doc/source/gallery`](https://github.com/geopandas/geopandas/tree/main/doc/source/gallery). geopandas-1.2.0/geopandas/000077500000000000000000000000001525650651600154275ustar00rootroot00000000000000geopandas-1.2.0/geopandas/__init__.py000066400000000000000000000016601525650651600175430ustar00rootroot00000000000000from geopandas._config import options from geopandas.geoseries import GeoSeries from geopandas.geodataframe import GeoDataFrame from geopandas.array import points_from_xy from geopandas.io.file import _read_file as read_file from geopandas.io.file import _list_layers as list_layers from geopandas.io.file import _read_file_info as read_file_info from geopandas.io.arrow import _read_parquet as read_parquet from geopandas.io.arrow import _read_feather as read_feather from geopandas.io.sql import _read_postgis as read_postgis from geopandas.tools import ( sjoin, sjoin_nearest, overlay, clip, geocode, make_grid, reverse_geocode, ) from geopandas.tools._show_versions import show_versions import geopandas.datasets # make the interactive namespace easier to use # for `from geopandas import *` demos. import geopandas as gpd import pandas as pd import numpy as np from geopandas._version import __version__ geopandas-1.2.0/geopandas/_compat.py000066400000000000000000000046341525650651600174320ustar00rootroot00000000000000import importlib from packaging.version import Version import pandas as pd import shapely # ----------------------------------------------------------------------------- # pandas compat # ----------------------------------------------------------------------------- PANDAS_GE_23 = Version(pd.__version__) >= Version("2.3.0") PANDAS_GE_30 = Version(pd.__version__) >= Version("3.0.0") PANDAS_GE_31 = Version(pd.__version__) >= Version("3.1.0.dev0") if PANDAS_GE_30: PANDAS_INFER_STR = True else: PANDAS_INFER_STR = PANDAS_GE_23 and pd.options.future.infer_string # ----------------------------------------------------------------------------- # Shapely / GEOS compat # ----------------------------------------------------------------------------- GEOS_GE_390 = shapely.geos_version >= (3, 9, 0) GEOS_GE_310 = shapely.geos_version >= (3, 10, 0) GEOS_GE_312 = shapely.geos_version >= (3, 12, 0) def import_optional_dependency(name: str, extra: str = ""): """ Import an optional dependency. Adapted from pandas.compat._optional::import_optional_dependency Raises a formatted ImportError if the module is not present. Parameters ---------- name : str The module name. extra : str Additional text to include in the ImportError message. Returns ------- module """ msg = f"""Missing optional dependency '{name}'. {extra} " "Use pip or conda to install {name}.""" if not isinstance(name, str): raise ValueError(f"Invalid module name: '{name}'; must be a string") try: module = importlib.import_module(name) except ImportError: raise ImportError(msg) from None return module # ----------------------------------------------------------------------------- # pyproj compat # ----------------------------------------------------------------------------- try: import pyproj # noqa: F401 HAS_PYPROJ = True except ImportError as err: HAS_PYPROJ = False pyproj_import_error = str(err) def requires_pyproj(func): def wrapper(*args, **kwargs): if not HAS_PYPROJ: raise ImportError( f"The 'pyproj' package is required for {func.__name__} to work. " "Install it and initialize the object with a CRS before using it." f"\nImporting pyproj resulted in: {pyproj_import_error}" ) return func(*args, **kwargs) return wrapper geopandas-1.2.0/geopandas/_config.py000066400000000000000000000070671525650651600174170ustar00rootroot00000000000000""" Lightweight options machinery. Based on https://github.com/topper-123/optioneer, but simplified (don't deal with nested options, deprecated options, ..), just the attribute-style dict like holding the options and giving a nice repr. """ import textwrap import warnings from collections import namedtuple Option = namedtuple("Option", "key default_value doc validator callback") class Options: """Provide attribute-style access to configuration dict.""" def __init__(self, options: dict[str, Option]) -> None: super().__setattr__("_options", options) # populate with default values config = {} for key, option in options.items(): config[key] = option.default_value super().__setattr__("_config", config) def __setattr__(self, key, value) -> None: # you can't set new keys if key in self._config: option = self._options[key] if option.validator: option.validator(value) self._config[key] = value if option.callback: option.callback(key, value) else: msg = "You can only set the value of existing options" raise AttributeError(msg) def __getattr__(self, key): try: return self._config[key] except KeyError: raise AttributeError("No such option") def __dir__(self) -> list[str]: return list(self._config.keys()) def __repr__(self) -> str: cls = self.__class__.__name__ description = "" for key, option in self._options.items(): descr = ( f"{key}: {self._config[key]!r} [default: {option.default_value!r}]\n" ) description += descr if option.doc: doc_text = "\n".join(textwrap.wrap(option.doc, width=70)) else: doc_text = "No description available." doc_text = textwrap.indent(doc_text, prefix=" ") description += doc_text + "\n" space = "\n " description = description.replace("\n", space) return f"{cls}({space}{description})" def _validate_display_precision(value: int) -> None: if value is not None: if not isinstance(value, int) or not (0 <= value <= 16): raise ValueError("Invalid value, needs to be an integer [0-16]") display_precision = Option( key="display_precision", default_value=None, doc=( "The precision (maximum number of decimals) of the coordinates in " "the WKT representation in the Series/DataFrame display. " "By default (None), it tries to infer and use 3 decimals for projected " "coordinates and 5 decimals for geographic coordinates." ), validator=_validate_display_precision, callback=None, ) def _warn_use_pygeos_deprecated(_value) -> None: warnings.warn( "pygeos support was removed in 1.0. " "geopandas.use_pygeos is a no-op and will be removed in geopandas 1.1.", stacklevel=3, ) def _validate_io_engine(value: str) -> None: if value is not None: if value not in ("pyogrio", "fiona"): raise ValueError(f"Expected 'pyogrio' or 'fiona', got '{value}'") io_engine = Option( key="io_engine", default_value=None, doc=( "The default engine for ``read_file`` and ``to_file``. " "Options are 'pyogrio' and 'fiona'." ), validator=_validate_io_engine, callback=None, ) options = Options( { "display_precision": display_precision, "io_engine": io_engine, } ) geopandas-1.2.0/geopandas/_decorator.py000066400000000000000000000037431525650651600201310ustar00rootroot00000000000000from collections.abc import Callable from textwrap import dedent # doc decorator function ported with modifications from Pandas # https://github.com/pandas-dev/pandas/blob/master/pandas/util/_decorators.py def doc(*docstrings: str | Callable, **params) -> Callable: """Append docstrings to a callable. A decorator take docstring templates, concatenate them and perform string substitution on it. This decorator will add a variable "_docstring_components" to the wrapped callable to keep track the original docstring template for potential usage. If it should be consider as a template, it will be saved as a string. Otherwise, it will be saved as callable, and later user __doc__ and dedent to get docstring. Parameters ---------- *docstrings : str or callable The string / docstring / docstring template to be appended in order after default docstring under callable. **params The string which would be used to format docstring template. """ def decorator(decorated: Callable) -> Callable: # collecting docstring and docstring templates docstring_components: list[str | Callable] = [] if decorated.__doc__: docstring_components.append(dedent(decorated.__doc__)) for docstring in docstrings: if hasattr(docstring, "_docstring_components"): docstring_components.extend(docstring._docstring_components) elif isinstance(docstring, str) or docstring.__doc__: docstring_components.append(docstring) # formatting templates and concatenating docstring decorated.__doc__ = "".join( ( component.format(**params) if isinstance(component, str) else dedent(component.__doc__ or "") ) for component in docstring_components ) decorated._docstring_components = docstring_components return decorated return decorator geopandas-1.2.0/geopandas/accessors.py000066400000000000000000000013561525650651600177730ustar00rootroot00000000000000"""Accessors for accessing GeoPandas functionality via pandas extension dtypes.""" import pandas.api.extensions from geopandas import GeoSeries from geopandas.array import GeometryDtype @pandas.api.extensions.register_series_accessor("geo") class GeoSeriesAccessor: """Series.geo accessor to expose GeoSeries methods on pandas Series. Parameters ---------- series : pandas.Series A Series with geometry dtype. """ def __init__(self, series): if not isinstance(series.dtype, GeometryDtype): raise AttributeError("Can only use .geo accessor with GeometryDtype values") self._geoseries = GeoSeries(series) def __getattr__(self, name): return getattr(self._geoseries, name) geopandas-1.2.0/geopandas/array.py000066400000000000000000002070131525650651600171220ustar00rootroot00000000000000from __future__ import annotations import inspect import numbers import operator import typing import warnings from functools import lru_cache from typing import Any, ClassVar, Literal import numpy as np import pandas as pd from pandas.api.extensions import ( ExtensionArray, ExtensionDtype, register_extension_dtype, ) import shapely import shapely.affinity import shapely.geometry import shapely.ops import shapely.wkt from shapely.geometry.base import BaseGeometry from ._compat import ( GEOS_GE_312, HAS_PYPROJ, requires_pyproj, ) from .sindex import SpatialIndex if typing.TYPE_CHECKING: import numpy.typing as npt from numpy._typing import ArrayLike from .base import GeoPandasBase if HAS_PYPROJ: from pyproj import CRS else: CRS = Any if HAS_PYPROJ: from pyproj import Transformer TransformerFromCRS = lru_cache(Transformer.from_crs) _names = { "MISSING": None, "NAG": None, "POINT": "Point", "LINESTRING": "LineString", "LINEARRING": "LinearRing", "POLYGON": "Polygon", "MULTIPOINT": "MultiPoint", "MULTILINESTRING": "MultiLineString", "MULTIPOLYGON": "MultiPolygon", "GEOMETRYCOLLECTION": "GeometryCollection", } POLYGON_GEOM_TYPES = {"Polygon", "MultiPolygon"} LINE_GEOM_TYPES = {"LineString", "MultiLineString", "LinearRing"} POINT_GEOM_TYPES = {"Point", "MultiPoint"} type_mapping = {p.value: _names[p.name] for p in shapely.GeometryType} geometry_type_ids = list(type_mapping.keys()) geometry_type_values = np.array(list(type_mapping.values()), dtype=object) class GeometryDtype(ExtensionDtype): type = BaseGeometry name = "geometry" na_value = None @classmethod def construct_from_string(cls, string): if not isinstance(string, str): raise TypeError( f"'construct_from_string' expects a string, got {type(string)}" ) elif string == cls.name: return cls() else: raise TypeError(f"Cannot construct a '{cls.__name__}' from '{string}'") @classmethod def construct_array_type(cls): return GeometryArray register_extension_dtype(GeometryDtype) def _check_crs( left: GeoPandasBase, right: GeoPandasBase, allow_none: bool = False ) -> bool: """ Check if the projection of both arrays is the same. If allow_none is True, empty CRS is treated as the same. """ if allow_none: if not left.crs or not right.crs: return True if not left.crs == right.crs: return False return True def _crs_mismatch_warn( left: GeoPandasBase, right: GeoPandasBase, stacklevel: int = 3 ) -> None: """Raise a CRS mismatch warning with the information on the assigned CRS.""" if left.crs: left_srs = left.crs.to_string() left_srs = left_srs if len(left_srs) <= 50 else " ".join([left_srs[:50], "..."]) else: left_srs = None if right.crs: right_srs = right.crs.to_string() right_srs = ( right_srs if len(right_srs) <= 50 else " ".join([right_srs[:50], "..."]) ) else: right_srs = None warnings.warn( "CRS mismatch between the CRS of left geometries " "and the CRS of right geometries.\n" "Use `to_crs()` to reproject one of " "the input geometries to match the CRS of the other.\n\n" f"Left CRS: {left_srs}\n" f"Right CRS: {right_srs}\n", UserWarning, stacklevel=stacklevel, ) def isna(value: None | float | pd.NA) -> bool: """ Check if scalar value is NA-like (None, np.nan or pd.NA). Custom version that only works for scalars (returning True or False), as `pd.isna` also works for array-like input returning a boolean array. """ if value is None: return True elif isinstance(value, float) and np.isnan(value): return True elif value is pd.NA: return True else: return False # ----------------------------------------------------------------------------- # Constructors / converters to other formats # ----------------------------------------------------------------------------- def _is_scalar_geometry(geom) -> bool: return isinstance(geom, BaseGeometry) def from_shapely(data, crs: CRS | None = None) -> GeometryArray: """ Convert a list or array of shapely objects to a GeometryArray. Validates the elements. Parameters ---------- data : array-like list or array of shapely objects crs : value, optional Coordinate Reference System of the geometry objects. Can be anything accepted by :meth:`pyproj.CRS.from_user_input() `, such as an authority string (eg "EPSG:4326") or a WKT string. """ if not isinstance(data, np.ndarray): arr = np.empty(len(data), dtype=object) arr[:] = data elif len(data) == 0 and data.dtype == "float64": arr = data.astype(object) else: arr = data if not shapely.is_valid_input(arr).all(): out = [] for geom in data: if isinstance(geom, BaseGeometry): out.append(geom) elif hasattr(geom, "__geo_interface__"): geom = shapely.geometry.shape(geom) out.append(geom) elif isna(geom): out.append(None) else: raise TypeError(f"Input must be valid geometry objects: {geom}") arr = np.array(out, dtype=object) return GeometryArray(arr, crs=crs) def to_shapely(geoms: GeometryArray) -> np.ndarray: """Convert GeometryArray to numpy object array of shapely objects.""" if not isinstance(geoms, GeometryArray): raise ValueError("'geoms' must be a GeometryArray") return geoms._data def from_wkb( data, crs: Any | None = None, on_invalid: Literal["raise", "warn", "ignore"] = "raise", ) -> GeometryArray: """ Convert a list or array of WKB objects to a GeometryArray. Parameters ---------- data : array-like list or array of WKB objects crs : value, optional Coordinate Reference System of the geometry objects. Can be anything accepted by :meth:`pyproj.CRS.from_user_input() `, such as an authority string (eg "EPSG:4326") or a WKT string. on_invalid: {"raise", "warn", "ignore"}, default "raise" - raise: an exception will be raised if a WKB input geometry is invalid. - warn: a warning will be raised and invalid WKB geometries will be returned as None. - ignore: invalid WKB geometries will be returned as None without a warning. - fix: an effort is made to fix invalid input geometries (e.g. close unclosed rings). If this is not possible, they are returned as ``None`` without a warning. Requires GEOS >= 3.11 and shapely >= 2.1. """ if isinstance(data, ExtensionArray): data = data.to_numpy(na_value=None) return GeometryArray(shapely.from_wkb(data, on_invalid=on_invalid), crs=crs) def to_wkb(geoms: GeometryArray, hex: bool = False, **kwargs): """Convert GeometryArray to a numpy object array of WKB objects.""" if not isinstance(geoms, GeometryArray): raise ValueError("'geoms' must be a GeometryArray") return shapely.to_wkb(geoms, hex=hex, **kwargs) def from_wkt( data, crs: Any | None = None, on_invalid: Literal["raise", "warn", "ignore"] = "raise", ) -> GeometryArray: """ Convert a list or array of WKT objects to a GeometryArray. Parameters ---------- data : array-like list or array of WKT objects crs : value, optional Coordinate Reference System of the geometry objects. Can be anything accepted by :meth:`pyproj.CRS.from_user_input() `, such as an authority string (eg "EPSG:4326") or a WKT string. on_invalid : {"raise", "warn", "ignore"}, default "raise" - raise: an exception will be raised if a WKT input geometry is invalid. - warn: a warning will be raised and invalid WKT geometries will be returned as ``None``. - ignore: invalid WKT geometries will be returned as ``None`` without a warning. - fix: an effort is made to fix invalid input geometries (e.g. close unclosed rings). If this is not possible, they are returned as ``None`` without a warning. Requires GEOS >= 3.11 and shapely >= 2.1. """ if isinstance(data, ExtensionArray): data = data.to_numpy(na_value=None) return GeometryArray(shapely.from_wkt(data, on_invalid=on_invalid), crs=crs) def to_wkt(geoms: GeometryArray, **kwargs): """Convert GeometryArray to a numpy object array of WKT objects.""" if not isinstance(geoms, GeometryArray): raise ValueError("'geoms' must be a GeometryArray") return shapely.to_wkt(geoms, **kwargs) def points_from_xy( x: npt.ArrayLike, y: npt.ArrayLike, z: npt.ArrayLike = None, crs: Any | None = None ) -> GeometryArray: """ Generate GeometryArray of shapely Point geometries from x, y(, z) coordinates. In case of geographic coordinates, it is assumed that longitude is captured by ``x`` coordinates and latitude by ``y``. Parameters ---------- x, y, z : iterable crs : value, optional Coordinate Reference System of the geometry objects. Can be anything accepted by :meth:`pyproj.CRS.from_user_input() `, such as an authority string (eg "EPSG:4326") or a WKT string. Examples -------- >>> import pandas as pd >>> df = pd.DataFrame({'x': [0, 1, 2], 'y': [0, 1, 2], 'z': [0, 1, 2]}) >>> df x y z 0 0 0 0 1 1 1 1 2 2 2 2 >>> geometry = geopandas.points_from_xy(x=[1, 0], y=[0, 1]) >>> geometry = geopandas.points_from_xy(df['x'], df['y'], df['z']) >>> gdf = geopandas.GeoDataFrame( ... df, geometry=geopandas.points_from_xy(df['x'], df['y'])) Having geographic coordinates: >>> df = pd.DataFrame({'longitude': [-140, 0, 123], 'latitude': [-65, 1, 48]}) >>> df longitude latitude 0 -140 -65 1 0 1 2 123 48 >>> geometry = geopandas.points_from_xy(df.longitude, df.latitude, crs="EPSG:4326") Returns ------- output : GeometryArray """ x = np.asarray(x, dtype="float64") y = np.asarray(y, dtype="float64") if z is not None: z = np.asarray(z, dtype="float64") return GeometryArray(shapely.points(x, y, z), crs=crs) class GeometryArray(ExtensionArray): """Class wrapping a numpy array of Shapely objects. It also holds the array-based implementations. """ _dtype = GeometryDtype() def __init__(self, data, crs: Any | None = None): if isinstance(data, self.__class__): if not crs: crs = data.crs data = data._data elif not isinstance(data, np.ndarray): raise TypeError( "'data' should be array of geometry objects. Use from_shapely, " "from_wkb, from_wkt functions to construct a GeometryArray." ) elif not data.ndim == 1: raise ValueError( "'data' should be a 1-dimensional array of geometry objects." ) self._data = data self._crs = None self.crs = crs self._sindex = None @property def sindex(self) -> SpatialIndex: """Spatial index for the geometries in this array.""" if self._sindex is None: self._sindex = SpatialIndex(self._data) return self._sindex @property def has_sindex(self) -> bool: """Check the existence of the spatial index without generating it. Use the `.sindex` attribute on a GeoDataFrame or GeoSeries to generate a spatial index if it does not yet exist, which may take considerable time based on the underlying index implementation. Note that the underlying spatial index may not be fully initialized until the first use. See Also -------- GeoDataFrame.has_sindex Returns ------- bool `True` if the spatial index has been generated or `False` if not. """ return self._sindex is not None @property def crs(self) -> CRS: """The Coordinate Reference System (CRS) represented as a ``pyproj.CRS`` object. Returns a ``pyproj.CRS`` or None. When setting, the value Coordinate Reference System of the geometry objects. Can be anything accepted by :meth:`pyproj.CRS.from_user_input() `, such as an authority string (eg "EPSG:4326") or a WKT string. """ return self._crs @crs.setter def crs(self, value: Any) -> None: """Set the value of the crs.""" if HAS_PYPROJ: from pyproj import CRS self._crs = None if not value else CRS.from_user_input(value) else: if value is not None: warnings.warn( "Cannot set the CRS, falling back to None. The CRS support requires" " the 'pyproj' package, but it is not installed or does not import" " correctly. The functions depending on CRS will raise an error or" " may produce unexpected results.", UserWarning, stacklevel=2, ) self._crs = None def check_geographic_crs(self, stacklevel: int) -> None: """Check CRS and warn if the planar operation is done in a geographic CRS.""" if self.crs and self.crs.is_geographic: warnings.warn( "Geometry is in a geographic CRS. Results from " f"'{inspect.stack()[1].function}' are likely incorrect. " "Use 'GeoSeries.to_crs()' to re-project geometries to a " "projected CRS before this operation.\n", UserWarning, stacklevel=stacklevel, ) @property def dtype(self) -> GeometryDtype: return self._dtype def __len__(self) -> int: return self.shape[0] def __getitem__(self, idx) -> GeometryArray: if isinstance(idx, numbers.Integral): return self._data[idx] elif ( isinstance(idx, slice) and idx.start is None and idx.stop is None and idx.step is None ): # special case of a full slice -> preserve the sindex # (to ensure view() preserves it as well) result = GeometryArray(self._data[idx], crs=self.crs) result._sindex = self._sindex return result # array-like, slice # validate and convert IntegerArray/BooleanArray # to numpy array, pass-through non-array-like indexers idx = pd.api.indexers.check_array_indexer(self, idx) return GeometryArray(self._data[idx], crs=self.crs) def __setitem__(self, key, value): # validate and convert IntegerArray/BooleanArray # keys to numpy array, pass-through non-array-like indexers key = pd.api.indexers.check_array_indexer(self, key) if isinstance(value, pd.Series): value = value.values if isinstance(value, pd.DataFrame): value = value.values.flatten() if isinstance(value, list | np.ndarray): value = from_shapely(value) if isinstance(value, GeometryArray): if isinstance(key, numbers.Integral): raise ValueError("cannot set a single element with an array") self._data[key] = value._data elif isinstance(value, BaseGeometry) or isna(value): if isna(value): # internally only use None as missing value indicator # but accept others value = None elif isinstance(value, BaseGeometry): value = from_shapely([value])._data[0] else: raise TypeError("should be valid geometry") if isinstance(key, slice | list | np.ndarray): value_array = np.empty(1, dtype=object) value_array[:] = [value] self._data[key] = value_array else: self._data[key] = value else: raise TypeError( f"Value should be either a BaseGeometry or None, got {value!s}" ) # invalidate spatial index self._sindex = None # TODO: use this once pandas-dev/pandas#33457 is fixed # if hasattr(value, "crs"): # if value.crs and (value.crs != self.crs): # raise ValueError( # "CRS mismatch between CRS of the passed geometries " # "and CRS of existing geometries." # ) def __getstate__(self): return (shapely.to_wkb(self._data), self._crs) def __setstate__(self, state): if not isinstance(state, dict): # pickle file saved with pygeos geoms = shapely.from_wkb(state[0]) self._crs = state[1] self._sindex = None # pygeos.STRtree could not be pickled yet self._data = geoms self.base = None else: if "data" in state: state["_data"] = state.pop("data") if "_crs" not in state: state["_crs"] = None self.__dict__.update(state) # ------------------------------------------------------------------------- # Geometry related methods # ------------------------------------------------------------------------- @property def is_valid(self): return shapely.is_valid(self._data) def is_valid_reason(self): return shapely.is_valid_reason(self._data) def is_valid_coverage(self, gap_width: float = 0.0): if not GEOS_GE_312: raise ImportError("Method 'is_valid_coverage' requires GEOS>=3.12.") return bool(shapely.coverage_is_valid(self._data, gap_width=gap_width)) def invalid_coverage_edges(self, gap_width: float = 0.0): if not GEOS_GE_312: raise ImportError( "Method 'invalid_coverage_edges' requires and GEOS>=3.12." ) return shapely.coverage_invalid_edges(self._data, gap_width=gap_width) @property def is_empty(self): return shapely.is_empty(self._data) @property def is_simple(self): return shapely.is_simple(self._data) @property def is_ring(self): return shapely.is_ring(self._data) @property def is_closed(self): return shapely.is_closed(self._data) @property def is_ccw(self): return shapely.is_ccw(self._data) @property def has_z(self): return shapely.has_z(self._data) @property def has_m(self): return shapely.has_m(self._data) @property def geom_type(self): res = shapely.get_type_id(self._data) return geometry_type_values[np.searchsorted(geometry_type_ids, res)] @property def area(self): """Return the area of the geometries in this array. Raises a UserWarning if the CRS is geographic, as the area calculation is not accurate in that case. Note that the area is calculated in the units of the CRS. Returns ------- np.ndarray of float Area of the geometries. """ self.check_geographic_crs(stacklevel=5) return shapely.area(self._data) @property def length(self): self.check_geographic_crs(stacklevel=5) return shapely.length(self._data) def count_coordinates(self): return shapely.get_num_coordinates(self._data) def count_geometries(self): return shapely.get_num_geometries(self._data) def count_interior_rings(self): return shapely.get_num_interior_rings(self._data) def get_precision(self): return shapely.get_precision(self._data) def get_geometry(self, index): return shapely.get_geometry(self._data, index=index) # # Unary operations that return new geometries # @property def boundary(self) -> GeometryArray: return GeometryArray(shapely.boundary(self._data), crs=self.crs) @property def centroid(self) -> GeometryArray: self.check_geographic_crs(stacklevel=5) return GeometryArray(shapely.centroid(self._data), crs=self.crs) def concave_hull(self, ratio, allow_holes): return shapely.concave_hull(self._data, ratio=ratio, allow_holes=allow_holes) def constrained_delaunay_triangles(self) -> GeometryArray: return GeometryArray( shapely.constrained_delaunay_triangles(self._data), crs=self.crs ) @property def convex_hull(self) -> GeometryArray: """Return the convex hull of the geometries in this array.""" return GeometryArray(shapely.convex_hull(self._data), crs=self.crs) @property def envelope(self) -> GeometryArray: """Return the envelope of the geometries in this array.""" return GeometryArray(shapely.envelope(self._data), crs=self.crs) def minimum_rotated_rectangle(self): """Return the minimum rotated rectangle of the geometries in this array.""" return GeometryArray(shapely.oriented_envelope(self._data), crs=self.crs) @property def exterior(self) -> GeometryArray: return GeometryArray(shapely.get_exterior_ring(self._data), crs=self.crs) def extract_unique_points(self) -> GeometryArray: return GeometryArray(shapely.extract_unique_points(self._data), crs=self.crs) def offset_curve(self, distance, quad_segs=8, join_style="round", mitre_limit=5.0): return GeometryArray( shapely.offset_curve( self._data, distance, quad_segs=quad_segs, join_style=join_style, mitre_limit=mitre_limit, ), crs=self.crs, ) @property def interiors(self) -> np.ndarray: # no GeometryArray as result has_non_poly = False inner_rings = [] for geom in self._data: interior_ring_seq = getattr(geom, "interiors", None) # polygon case if interior_ring_seq is not None: inner_rings.append(list(interior_ring_seq)) # non-polygon case else: has_non_poly = True inner_rings.append(None) if has_non_poly: warnings.warn( "Only Polygon objects have interior rings. For other " "geometry types, None is returned.", stacklevel=2, ) # need to allocate empty first in case of all empty lists in inner_rings data = np.empty(len(inner_rings), dtype=object) data[:] = inner_rings return data def remove_repeated_points(self, tolerance=0.0) -> GeometryArray: return GeometryArray( shapely.remove_repeated_points(self._data, tolerance=tolerance), crs=self.crs, ) def representative_point(self) -> GeometryArray: return GeometryArray(shapely.point_on_surface(self._data), crs=self.crs) def minimum_bounding_circle(self) -> GeometryArray: return GeometryArray(shapely.minimum_bounding_circle(self._data), crs=self.crs) def maximum_inscribed_circle(self, tolerance) -> GeometryArray: return GeometryArray( shapely.maximum_inscribed_circle(self._data, tolerance=tolerance), crs=self.crs, ) def minimum_bounding_radius(self): return shapely.minimum_bounding_radius(self._data) def minimum_clearance(self): return shapely.minimum_clearance(self._data) def minimum_clearance_line(self) -> GeometryArray: return GeometryArray(shapely.minimum_clearance_line(self._data), crs=self.crs) def normalize(self) -> GeometryArray: return GeometryArray(shapely.normalize(self._data), crs=self.crs) def orient_polygons(self, exterior_cw: bool = False) -> GeometryArray: return GeometryArray( shapely.orient_polygons(self._data, exterior_cw=exterior_cw), crs=self.crs ) def make_valid( self, method: Literal["linework", "structure"] = "linework", keep_collapsed: bool = True, ) -> GeometryArray: return GeometryArray( shapely.make_valid( self._data, method=method, keep_collapsed=keep_collapsed ), crs=self.crs, ) def reverse(self) -> GeometryArray: return GeometryArray(shapely.reverse(self._data), crs=self.crs) def segmentize(self, max_segment_length) -> GeometryArray: return GeometryArray( shapely.segmentize(self._data, max_segment_length), crs=self.crs, ) def force_2d(self) -> GeometryArray: return GeometryArray(shapely.force_2d(self._data), crs=self.crs) def force_3d(self, z=0) -> GeometryArray: return GeometryArray(shapely.force_3d(self._data, z=z), crs=self.crs) def transform(self, transformation, include_z: bool = False) -> GeometryArray: return GeometryArray( shapely.transform(self._data, transformation, include_z=include_z), crs=self.crs, ) def line_merge(self, directed: bool = False) -> GeometryArray: return GeometryArray( shapely.line_merge(self._data, directed=directed), crs=self.crs ) def set_precision(self, grid_size: float, mode="valid_output"): return GeometryArray( shapely.set_precision(self._data, grid_size=grid_size, mode=mode), crs=self.crs, ) # # Binary predicates # @staticmethod def _binary_method(op, left, right, **kwargs): if isinstance(right, GeometryArray): if len(left) != len(right): msg = ( "Lengths of inputs do not match. " f"Left: {len(left)}, Right: {len(right)}" ) raise ValueError(msg) if not _check_crs(left, right): _crs_mismatch_warn(left, right, stacklevel=7) right = right._data return getattr(shapely, op)(left._data, right, **kwargs) def covers(self, other): return self._binary_method("covers", self, other) def covered_by(self, other): return self._binary_method("covered_by", self, other) def contains(self, other): return self._binary_method("contains", self, other) def contains_properly(self, other): return self._binary_method("contains_properly", self, other) def crosses(self, other): return self._binary_method("crosses", self, other) def disjoint(self, other): return self._binary_method("disjoint", self, other) def geom_equals(self, other): return self._binary_method("equals", self, other) def intersects(self, other): return self._binary_method("intersects", self, other) def overlaps(self, other): return self._binary_method("overlaps", self, other) def touches(self, other): return self._binary_method("touches", self, other) def within(self, other): return self._binary_method("within", self, other) def dwithin(self, other, distance): self.check_geographic_crs(stacklevel=6) return self._binary_method("dwithin", self, other, distance=distance) def geom_equals_exact(self, other, tolerance): return self._binary_method("equals_exact", self, other, tolerance=tolerance) def geom_equals_identical(self, other): return self._binary_method("equals_identical", self, other) # # Binary operations that return new geometries # def clip_by_rect(self, xmin, ymin, xmax, ymax) -> GeometryArray: return GeometryArray( shapely.clip_by_rect(self._data, xmin, ymin, xmax, ymax), crs=self.crs ) def difference(self, other, grid_size=None) -> GeometryArray: return GeometryArray( self._binary_method("difference", self, other, grid_size=grid_size), crs=self.crs, ) def intersection(self, other, grid_size=None) -> GeometryArray: return GeometryArray( self._binary_method("intersection", self, other, grid_size=grid_size), crs=self.crs, ) def symmetric_difference(self, other, grid_size=None) -> GeometryArray: return GeometryArray( self._binary_method( "symmetric_difference", self, other, grid_size=grid_size ), crs=self.crs, ) def union(self, other, grid_size=None) -> GeometryArray: return GeometryArray( self._binary_method("union", self, other, grid_size=grid_size), crs=self.crs ) def shortest_line(self, other) -> GeometryArray: return GeometryArray( self._binary_method("shortest_line", self, other), crs=self.crs ) def snap(self, other, tolerance) -> GeometryArray: return GeometryArray( self._binary_method("snap", self, other, tolerance=tolerance), crs=self.crs ) def shared_paths(self, other) -> GeometryArray: return GeometryArray( self._binary_method("shared_paths", self, other), crs=self.crs ) # # Other operations # def distance(self, other): self.check_geographic_crs(stacklevel=6) return self._binary_method("distance", self, other) def hausdorff_distance(self, other, **kwargs): self.check_geographic_crs(stacklevel=6) return self._binary_method("hausdorff_distance", self, other, **kwargs) def frechet_distance(self, other, **kwargs): self.check_geographic_crs(stacklevel=6) return self._binary_method("frechet_distance", self, other, **kwargs) def buffer(self, distance, quad_segs: int | None = None, **kwargs): if not (isinstance(distance, int | float) and distance == 0): self.check_geographic_crs(stacklevel=5) if "resolution" in kwargs: if quad_segs is not None: msg = ( "`buffer` received both `quad_segs` and `resolution` but these are " "aliases for the same parameter. Use `quad_segs` only instead." ) raise ValueError(msg) msg = ( "The `resolution` argument to `buffer` is deprecated, `quad_segs` " "should be used instead to align with shapely." ) warnings.warn( msg, category=DeprecationWarning, stacklevel=4, ) quad_segs = kwargs.pop("resolution") if quad_segs is None: quad_segs = 16 # note shapely default is 8, 16 is historical choice return GeometryArray( shapely.buffer(self._data, distance, quad_segs=quad_segs, **kwargs), crs=self.crs, ) def interpolate(self, distance, normalized: bool = False) -> GeometryArray: self.check_geographic_crs(stacklevel=5) return GeometryArray( shapely.line_interpolate_point(self._data, distance, normalized=normalized), crs=self.crs, ) def simplify(self, tolerance, preserve_topology: bool = True) -> GeometryArray: return GeometryArray( shapely.simplify( self._data, tolerance, preserve_topology=preserve_topology ), crs=self.crs, ) def simplify_coverage( self, tolerance, simplify_boundary: bool = True ) -> GeometryArray: if not GEOS_GE_312: raise ImportError( "'simplify_coverage' requires shapely>=2.1 and GEOS>=3.12." ) return GeometryArray( shapely.coverage_simplify( self._data, tolerance, simplify_boundary=simplify_boundary ), crs=self.crs, ) def project(self, other, normalized=False): if isinstance(other, GeometryArray): other = other._data return shapely.line_locate_point(self._data, other, normalized=normalized) def relate(self, other): if isinstance(other, GeometryArray): other = other._data return shapely.relate(self._data, other) def relate_pattern(self, other, pattern): if isinstance(other, GeometryArray): other = other._data return shapely.relate_pattern(self._data, other, pattern) # # Reduction operations that return a Shapely geometry # def unary_union(self): warnings.warn( "The 'unary_union' attribute is deprecated, " "use the 'union_all' method instead.", DeprecationWarning, stacklevel=2, ) return self.union_all() def union_all(self, method="unary", grid_size=None): if method != "unary" and grid_size is not None: raise ValueError(f"grid_size is not supported for method '{method}'.") if method == "coverage": return shapely.coverage_union_all(self._data) elif method == "unary": return shapely.union_all(self._data, grid_size=grid_size) elif method == "disjoint_subset": if not GEOS_GE_312: raise ImportError( "Method 'disjoin_subset' requires shapely>=2.1 and GEOS>=3.12." ) return shapely.disjoint_subset_union_all(self._data) else: raise ValueError( f"Method '{method}' not recognized. Use 'coverage', 'unary' or " "'disjoint_subset'." ) def intersection_all(self): return shapely.intersection_all(self._data) # # Affinity operations # @staticmethod def _affinity_method(op, left, *args, **kwargs): # not all shapely.affinity methods can handle empty geometries: # affine_transform itself works (as well as translate), but rotate, scale # and skew fail (they try to unpack the bounds). # Here: consistently returning empty geom for input empty geom out = [] for geom in left: if geom is None or geom.is_empty: res = geom else: res = getattr(shapely.affinity, op)(geom, *args, **kwargs) out.append(res) data = np.empty(len(left), dtype=object) data[:] = out return data def affine_transform(self, matrix) -> GeometryArray: return GeometryArray( self._affinity_method("affine_transform", self._data, matrix), crs=self.crs, ) def translate( self, xoff: float = 0.0, yoff: float = 0.0, zoff: float = 0.0 ) -> GeometryArray: return GeometryArray( self._affinity_method("translate", self._data, xoff, yoff, zoff), crs=self.crs, ) def rotate( self, angle, origin="center", use_radians: bool = False ) -> GeometryArray: return GeometryArray( self._affinity_method( "rotate", self._data, angle, origin=origin, use_radians=use_radians ), crs=self.crs, ) def scale( self, xfact: float = 1.0, yfact: float = 1.0, zfact: float = 1.0, origin="center", ) -> GeometryArray: return GeometryArray( self._affinity_method( "scale", self._data, xfact, yfact, zfact, origin=origin ), crs=self.crs, ) def skew( self, xs: float = 0.0, ys: float = 0.0, origin="center", use_radians: bool = False, ) -> GeometryArray: return GeometryArray( self._affinity_method( "skew", self._data, xs, ys, origin=origin, use_radians=use_radians ), crs=self.crs, ) @requires_pyproj def to_crs(self, crs: Any | None = None, epsg: int | None = None) -> GeometryArray: """Transform all geometries to a different coordinate reference system. Transform all geometries in a GeometryArray to a different coordinate reference system. The ``crs`` attribute on the current GeometryArray must be set. Either ``crs`` or ``epsg`` may be specified for output. This method will transform all points in all objects. It has no notion of projecting entire geometries. All segments joining points are assumed to be lines in the current projection, not geodesics. Objects crossing the dateline (or other projection boundary) will have undesirable behavior. Parameters ---------- crs : pyproj.CRS, optional if `epsg` is specified The value can be anything accepted by :meth:`pyproj.CRS.from_user_input() `, such as an authority string (eg "EPSG:4326") or a WKT string. epsg : int, optional if `crs` is specified EPSG code specifying output projection. Returns ------- GeometryArray Examples -------- >>> from shapely.geometry import Point >>> from geopandas.array import from_shapely, to_wkt >>> a = from_shapely([Point(1, 1), Point(2, 2), Point(3, 3)], crs=4326) >>> to_wkt(a) array(['POINT (1 1)', 'POINT (2 2)', 'POINT (3 3)'], dtype=object) >>> a.crs # doctest: +SKIP Name: WGS 84 Axis Info [ellipsoidal]: - Lat[north]: Geodetic latitude (degree) - Lon[east]: Geodetic longitude (degree) Area of Use: - name: World - bounds: (-180.0, -90.0, 180.0, 90.0) Datum: World Geodetic System 1984 - Ellipsoid: WGS 84 - Prime Meridian: Greenwich >>> a = a.to_crs(3857) >>> to_wkt(a) array(['POINT (111319.490793 111325.142866)', 'POINT (222638.981587 222684.208506)', 'POINT (333958.47238 334111.171402)'], dtype=object) >>> a.crs # doctest: +SKIP Name: WGS 84 / Pseudo-Mercator Axis Info [cartesian]: - X[east]: Easting (metre) - Y[north]: Northing (metre) Area of Use: - name: World - 85°S to 85°N - bounds: (-180.0, -85.06, 180.0, 85.06) Coordinate Operation: - name: Popular Visualisation Pseudo-Mercator - method: Popular Visualisation Pseudo Mercator Datum: World Geodetic System 1984 - Ellipsoid: WGS 84 - Prime Meridian: Greenwich """ from pyproj import CRS if self.crs is None: raise ValueError( "Cannot transform naive geometries. " "Please set a crs on the object first." ) if crs is not None: crs = CRS.from_user_input(crs) elif epsg is not None: crs = CRS.from_epsg(epsg) else: raise ValueError("Must pass either crs or epsg.") # skip if the input CRS and output CRS are the exact same if self.crs.is_exact_same(crs): return self transformer = TransformerFromCRS(self.crs, crs, always_xy=True) new_data = transform(self._data, transformer.transform) return GeometryArray(new_data, crs=crs) @requires_pyproj def estimate_utm_crs(self, datum_name: str = "WGS 84") -> CRS: """Return the estimated UTM CRS based on the bounds of the dataset. .. versionadded:: 0.9 .. note:: Requires pyproj 3+ Parameters ---------- datum_name : str, optional The name of the datum to use in the query. Default is WGS 84. Returns ------- pyproj.CRS Examples -------- >>> import geodatasets >>> df = geopandas.read_file( ... geodatasets.get_path("geoda.chicago_commpop") ... ) >>> df.geometry.values.estimate_utm_crs() # doctest: +SKIP Name: WGS 84 / UTM zone 16N Axis Info [cartesian]: - E[east]: Easting (metre) - N[north]: Northing (metre) Area of Use: - name: Between 90°W and 84°W, northern hemisphere between equator and 84°N,... - bounds: (-90.0, 0.0, -84.0, 84.0) Coordinate Operation: - name: UTM zone 16N - method: Transverse Mercator Datum: World Geodetic System 1984 ensemble - Ellipsoid: WGS 84 - Prime Meridian: Greenwich """ from pyproj import CRS from pyproj.aoi import AreaOfInterest from pyproj.database import query_utm_crs_info if not self.crs: raise RuntimeError("crs must be set to estimate UTM CRS.") minx, miny, maxx, maxy = self.total_bounds if self.crs.is_geographic: x_center = np.mean([minx, maxx]) y_center = np.mean([miny, maxy]) # ensure using geographic coordinates else: transformer = TransformerFromCRS(self.crs, "EPSG:4326", always_xy=True) minx, miny, maxx, maxy = transformer.transform_bounds( minx, miny, maxx, maxy ) y_center = np.mean([miny, maxy]) # crossed the antimeridian if minx > maxx: # shift maxx from [-180,180] to [0,360] # so both numbers are positive for center calculation # Example: -175 to 185 maxx += 360 x_center = np.mean([minx, maxx]) # shift back to [-180,180] x_center = ((x_center + 180) % 360) - 180 else: x_center = np.mean([minx, maxx]) utm_crs_list = query_utm_crs_info( datum_name=datum_name, area_of_interest=AreaOfInterest( west_lon_degree=x_center, south_lat_degree=y_center, east_lon_degree=x_center, north_lat_degree=y_center, ), ) try: return CRS.from_epsg(utm_crs_list[0].code) except IndexError: raise RuntimeError("Unable to determine UTM CRS") # # Coordinate related properties # @property def x(self): """Return the x location of point geometries in a GeoSeries.""" if (self.geom_type[~self.isna()] == "Point").all(): empty = self.is_empty if empty.any(): nonempty = ~empty coords = np.full_like(nonempty, dtype=float, fill_value=np.nan) coords[nonempty] = shapely.get_x(self._data[nonempty]) return coords else: return shapely.get_x(self._data) else: message = "x attribute access only provided for Point geometries" raise ValueError(message) @property def y(self): """Return the y location of point geometries in a GeoSeries.""" if (self.geom_type[~self.isna()] == "Point").all(): empty = self.is_empty if empty.any(): nonempty = ~empty coords = np.full_like(nonempty, dtype=float, fill_value=np.nan) coords[nonempty] = shapely.get_y(self._data[nonempty]) return coords else: return shapely.get_y(self._data) else: message = "y attribute access only provided for Point geometries" raise ValueError(message) @property def z(self): """Return the z location of point geometries in a GeoSeries.""" if (self.geom_type[~self.isna()] == "Point").all(): empty = self.is_empty if empty.any(): nonempty = ~empty coords = np.full_like(nonempty, dtype=float, fill_value=np.nan) coords[nonempty] = shapely.get_z(self._data[nonempty]) return coords else: return shapely.get_z(self._data) else: message = "z attribute access only provided for Point geometries" raise ValueError(message) @property def m(self): """Return the m coordinate of point geometries in a GeoSeries.""" if (self.geom_type[~self.isna()] == "Point").all(): empty = self.is_empty if empty.any(): nonempty = ~empty coords = np.full_like(nonempty, dtype=float, fill_value=np.nan) coords[nonempty] = shapely.get_m(self._data[nonempty]) return coords else: return shapely.get_m(self._data) else: message = "m attribute access only provided for Point geometries" raise ValueError(message) @property def bounds(self): return shapely.bounds(self._data) @property def total_bounds(self): if len(self) == 0: # numpy 'min' cannot handle empty arrays # TODO with numpy >= 1.15, the 'initial' argument can be used return np.array([np.nan, np.nan, np.nan, np.nan]) b = self.bounds with warnings.catch_warnings(): # if all rows are empty geometry / none, nan is expected warnings.filterwarnings( "ignore", r"All-NaN slice encountered", RuntimeWarning ) return np.array( ( np.nanmin(b[:, 0]), # minx np.nanmin(b[:, 1]), # miny np.nanmax(b[:, 2]), # maxx np.nanmax(b[:, 3]), # maxy ) ) # ------------------------------------------------------------------------- # general array like compat # ------------------------------------------------------------------------- @property def size(self): return self._data.size @property def shape(self) -> tuple: return (self.size,) @property def ndim(self) -> int: return len(self.shape) def copy(self, *args, **kwargs) -> GeometryArray: # still taking args/kwargs for compat with pandas 0.24 return GeometryArray(self._data.copy(), crs=self._crs) def take(self, indices, allow_fill: bool = False, fill_value=None) -> GeometryArray: from pandas.api.extensions import take if allow_fill: if fill_value is None or pd.isna(fill_value): fill_value = None elif not _is_scalar_geometry(fill_value): raise TypeError("provide geometry or None as fill value") result = take(self._data, indices, allow_fill=allow_fill, fill_value=fill_value) if allow_fill and fill_value is None: result[~shapely.is_valid_input(result)] = None return GeometryArray(result, crs=self.crs) # compat for pandas < 3.0 def _pad_or_backfill( self, method, limit=None, limit_area=None, copy=True, **kwargs ): kwargs["limit_area"] = limit_area return super()._pad_or_backfill(method=method, limit=limit, copy=copy, **kwargs) def fillna( self, value=None, method=None, limit: int | None = None, copy: bool = True ) -> GeometryArray: """ Fill NA values with geometry (or geometries) or using the specified method. Parameters ---------- value : shapely geometry object or GeometryArray If a geometry value is passed it is used to fill all missing values. Alternatively, a GeometryArray 'value' can be given. It's expected that the GeometryArray has the same length as 'self'. method : {'backfill', 'bfill', 'pad', 'ffill', None}, default None Method to use for filling holes in reindexed Series pad / ffill: propagate last valid observation forward to next valid backfill / bfill: use NEXT valid observation to fill gap limit : int, default None The maximum number of entries where NA values will be filled. copy : bool, default True Whether to make a copy of the data before filling. If False, then the original should be modified and no new memory should be allocated. Returns ------- GeometryArray """ if method is not None: raise NotImplementedError("fillna with a method is not yet supported") if isinstance(value, dict): # to match upstream pandas specifically raising for dict raise TypeError( "ExtensionArray.fillna does not support filling with a dict. " "Use Series.fillna instead." ) mask = self.isna() if copy: new_values = self.copy() else: new_values = self if not mask.any(): return new_values if limit is not None and limit < len(self): modify = mask.cumsum() > limit if modify.any(): mask[modify] = False if isna(value): value = [None] elif _is_scalar_geometry(value): value = [value] elif isinstance(value, GeometryArray): value = value[mask] else: raise TypeError( "'value' parameter must be None, a scalar geometry, or a GeoSeries, " f"but you passed a {type(value).__name__!r}" ) value_arr = np.asarray(value, dtype=object) new_values._data[mask] = value_arr return new_values def astype(self, dtype, copy: bool = True) -> np.ndarray: """ Cast to a NumPy array with 'dtype'. Parameters ---------- dtype : str or dtype Typecode or data-type to which the array is cast. copy : bool, default True Whether to copy the data, even if not necessary. If False, a copy is made only if the old dtype does not match the new dtype. Returns ------- array : ndarray NumPy ndarray with 'dtype' for its dtype. """ if isinstance(dtype, GeometryDtype): if copy: return self.copy() else: return self elif pd.api.types.is_string_dtype(dtype) and not pd.api.types.is_object_dtype( dtype ): string_values = to_wkt(self) pd_dtype = pd.api.types.pandas_dtype(dtype) if isinstance(pd_dtype, pd.StringDtype): # ensure to return a pandas string array instead of numpy array return pd.array(string_values, dtype=pd_dtype) return string_values.astype(dtype, copy=False) else: # numpy 2.0 makes copy=False case strict (errors if cannot avoid the copy) # -> in that case use `np.asarray` as backwards compatible alternative # for `copy=None` (when requiring numpy 2+, this can be cleaned up) if not copy: return np.asarray(self, dtype=dtype) else: return np.array(self, dtype=dtype, copy=copy) def isna(self) -> np.ndarray: """Boolean NumPy array indicating if each value is missing.""" return shapely.is_missing(self._data) def value_counts( self, dropna: bool = True, ) -> pd.Series: """ Compute a histogram of the counts of non-null values. Parameters ---------- dropna : bool, default True Don't include counts of NaN Returns ------- pd.Series """ # note ExtensionArray usage of value_counts only specifies dropna, # so sort, normalize and bins are not arguments values = to_wkb(self) from pandas import Index, Series result = Series(values).value_counts(dropna=dropna) # value_counts converts None to nan, need to convert back for from_wkb to work # note result.index already has object dtype, not geometry # Can't use fillna(None) or Index.putmask, as this gets converted back to nan # for object dtypes result.index = Index( from_wkb(np.where(result.index.isna(), None, result.index), crs=self.crs) ) return result def unique(self) -> ExtensionArray: """Compute the ExtensionArray of unique values. Returns ------- uniques : ExtensionArray """ from pandas import factorize _, uniques = factorize(self) return uniques @property def nbytes(self): return self._data.nbytes def shift(self, periods: int = 1, fill_value: Any | None = None) -> GeometryArray: """ Shift values by desired number. Newly introduced missing values are filled with ``self.dtype.na_value``. Parameters ---------- periods : int, default 1 The number of periods to shift. Negative values are allowed for shifting backwards. fill_value : object, optional (default None) The scalar value to use for newly introduced missing values. The default is ``self.dtype.na_value``. Returns ------- GeometryArray Shifted. Notes ----- If ``self`` is empty or ``periods`` is 0, a copy of ``self`` is returned. If ``periods > len(self)``, then an array of size len(self) is returned, with all values filled with ``self.dtype.na_value``. """ shifted = super().shift(periods, fill_value) shifted.crs = self.crs return shifted # ------------------------------------------------------------------------- # ExtensionArray specific # ------------------------------------------------------------------------- @classmethod def _from_sequence(cls, scalars, dtype=None, copy: bool = False) -> ExtensionArray: """ Construct a new ExtensionArray from a sequence of scalars. Parameters ---------- scalars : Sequence Each element will be an instance of the scalar type for this array, ``cls.dtype.type``. dtype : dtype, optional Construct for this particular dtype. This should be a Dtype compatible with the ExtensionArray. copy : boolean, default False If True, copy the underlying data. Returns ------- ExtensionArray """ # GH 1413 if isinstance(scalars, BaseGeometry): scalars = [scalars] return from_shapely(scalars) def _cast_pointwise_result(self, values) -> ArrayLike: """ Construct an ExtensionArray after a pointwise operation. Cast the result of a pointwise operation (e.g. Series.map) to an array. This is not required to return an ExtensionArray of the same type as self or of the same dtype. It can also return another ExtensionArray of the same "family" if you implement multiple ExtensionArrays/Dtypes that are interoperable (e.g. if you have float array with units, this method can return an int array with units). If converting to your own ExtensionArray is not possible, this method falls back to returning an array with the default type inference. If you only need to cast to `self.dtype`, it is recommended to override `_from_scalars` instead of this method. Parameters ---------- values : sequence Returns ------- ExtensionArray or ndarray """ # If crs was part of the dtype, could take above advice and # override _from_scalars instead try: if isinstance(values, self.__class__): return GeometryArray(values, crs=self.crs) else: # pd.Series setitem boxes scalar into a list return from_shapely(values, crs=self.crs) except (ValueError, TypeError): return super()._cast_pointwise_result(values) @classmethod def _from_sequence_of_strings(cls, strings, *, dtype=None, copy=False): """ Construct a new ExtensionArray from a sequence of strings. Parameters ---------- strings : Sequence Each element will be an instance of the scalar type for this array, ``cls.dtype.type``. dtype : dtype, optional Construct for this particular dtype. This should be a Dtype compatible with the ExtensionArray. copy : bool, default False If True, copy the underlying data. Returns ------- ExtensionArray """ # GH 3099 return from_wkt(strings) def _values_for_factorize(self) -> tuple[np.ndarray, Any]: """Return an array and missing value suitable for factorization. Returns ------- values : ndarray An array suitable for factorization. This should maintain order and be a supported dtype (Float64, Int64, UInt64, String, Object). By default, the extension array is cast to object dtype. na_value : object The value in `values` to consider missing. This will be treated as NA in the factorization routines, so it will be coded as `na_sentinal` and not included in `uniques`. By default, ``np.nan`` is used. """ vals = to_wkb(self) return vals, None @classmethod def _from_factorized(cls, values: np.ndarray, original: ExtensionArray): """ Reconstruct an ExtensionArray after factorization. Parameters ---------- values : ndarray An integer ndarray with the factorized values. original : ExtensionArray The original ExtensionArray that factorize was called on. See Also -------- pandas.factorize ExtensionArray.factorize """ return from_wkb(values, crs=original.crs) def _values_for_argsort(self) -> np.ndarray: """Return values for sorting. Returns ------- ndarray The transformed values should maintain the ordering between values within the array. See Also -------- ExtensionArray.argsort """ # Note: this is used in `ExtensionArray.argsort`. from geopandas.tools.hilbert_curve import _hilbert_distance if self.size == 0: # TODO _hilbert_distance fails for empty array return np.array([], dtype="uint32") mask_empty = self.is_empty has_empty = mask_empty.any() mask = self.isna() | mask_empty if mask.any(): # if there are missing or empty geometries, we fill those with # a dummy geometry so that the _hilbert_distance function can # process those. The missing values are handled separately by # pandas regardless of the values we return here (to sort # first/last depending on 'na_position'), the distances for the # empty geometries are replaced below with an appropriate value geoms = self.copy() indices = np.nonzero(~mask)[0] if indices.size: geom = self[indices[0]] else: # for all-empty/NA, just take random geometry geom = shapely.geometry.Point(0, 0) geoms[mask] = geom else: geoms = self if has_empty: # in case we have empty geometries, we need to expand the total # bounds with a small percentage, so the empties can be # deterministically sorted first total_bounds = geoms.total_bounds xoff = (total_bounds[2] - total_bounds[0]) * 0.01 yoff = (total_bounds[3] - total_bounds[1]) * 0.01 total_bounds += np.array([-xoff, -yoff, xoff, yoff]) else: total_bounds = None distances = _hilbert_distance(geoms, total_bounds=total_bounds) if has_empty: # empty geometries are sorted first ("smallest"), so fill in # smallest possible value for uints distances[mask_empty] = 0 return distances def argmin(self, skipna: bool = True) -> int: raise TypeError("geometries have no minimum or maximum") def argmax(self, skipna: bool = True) -> int: raise TypeError("geometries have no minimum or maximum") def _formatter(self, boxed: bool = False): """Return a formatting function for scalar values. This is used in the default '__repr__'. The returned formatting function receives instances of your scalar type. Parameters ---------- boxed: bool, default False An indicated for whether or not your array is being printed within a Series, DataFrame, or Index (True), or just by itself (False). This may be useful if you want scalar values to appear differently within a Series versus on its own (e.g. quoted or not). Returns ------- Callable[[Any], str] A callable that gets instances of the scalar type and returns a string. By default, :func:`repr` is used when ``boxed=False`` and :func:`str` is used when ``boxed=True``. """ if boxed: import geopandas precision = geopandas.options.display_precision if precision is None: if self.crs: if self.crs.is_projected: precision = 3 else: precision = 5 else: # fallback # dummy heuristic based on 10 first geometries that should # work in most cases with warnings.catch_warnings(): warnings.simplefilter("ignore", category=RuntimeWarning) xmin, ymin, xmax, ymax = self[~self.isna()][:10].total_bounds if ( (-180 <= xmin <= 180) and (-180 <= xmax <= 180) and (-90 <= ymin <= 90) and (-90 <= ymax <= 90) ): # geographic coordinates precision = 5 else: # typically projected coordinates # (in case of unit meter: mm precision) precision = 3 return lambda geom: shapely.to_wkt(geom, rounding_precision=precision) return repr @classmethod def _concat_same_type(cls, to_concat): """Concatenate multiple array. Parameters ---------- to_concat : sequence of this type Returns ------- ExtensionArray """ data = np.concatenate([ga._data for ga in to_concat]) return GeometryArray(data, crs=_get_common_crs(to_concat)) def _reduce( self, name: str, *, skipna: bool = True, keepdims: bool = False, **kwargs ): # ensure the base class version does not call our non-reduction skew method if name == "skew": raise TypeError( f"'{type(self).__name__}' with dtype {self.dtype} " f"does not support operation '{name}'" ) return super()._reduce(name, skipna=skipna, keepdims=keepdims, **kwargs) def all(self, *, skipna: bool = True) -> bool: """Return whether all elements are truthy. Returns ------- bool """ # TODO add handling for skipna return self._data.all() def any(self, *, skipna: bool = True) -> bool: """Return whether any element is truthy. Returns ------- bool """ # TODO add handling for skipna return self._data.any() def __array__(self, dtype=None, copy=None) -> np.ndarray: """Return the data as a numpy array. This is the numpy array interface. Returns ------- values : numpy array """ if copy and (dtype is None or dtype == np.dtype("object")): return self._data.copy() return self._data def _binop(self, other, op): def convert_values(param): if not _is_scalar_geometry(param) and ( isinstance(param, ExtensionArray) or pd.api.types.is_list_like(param) ): ovalues = param else: # Assume its an object ovalues = [param] * len(self) return ovalues if isinstance(other, pd.Series | pd.Index | pd.DataFrame): # rely on pandas to unbox and dispatch to us return NotImplemented lvalues = self rvalues = convert_values(other) if len(lvalues) != len(rvalues): raise ValueError("Lengths must match to compare") # If the operator is not defined for the underlying objects, # a TypeError should be raised res = [op(a, b) for (a, b) in zip(lvalues, rvalues)] res = np.asarray(res, dtype=bool) return res def __eq__(self, other): return self._binop(other, operator.eq) # https://github.com/python/typeshed/issues/2148#issuecomment-520783318 # Incompatible types in assignment (expression has type "None", base class # "object" defined the type as "Callable[[object], int]") # (Explicitly mirrored from pandas to declare non hashable) __hash__: ClassVar[None] # type: ignore[assignment] def __ne__(self, other): return self._binop(other, operator.ne) def __contains__(self, item) -> bool: """Return for `item in self`.""" if isna(item): if ( item is self.dtype.na_value or isinstance(item, self.dtype.type) or item is None ): return self.isna().any() else: return False return (self == item).any() def _get_common_crs(arr_seq) -> CRS: # mask out all None arrays with no crs (most likely auto generated by pandas # from concat with missing column) arr_seq = [ga for ga in arr_seq if not (ga.isna().all() and ga.crs is None)] # determine unique crs without using a set, because CRS hash can be different # for objects with the same CRS unique_crs = [] for arr in arr_seq: if arr.crs not in unique_crs: unique_crs.append(arr.crs) crs_not_none = [crs for crs in unique_crs if crs is not None] names = [crs.name for crs in crs_not_none] if len(crs_not_none) == 0: return None if len(crs_not_none) == 1: if len(unique_crs) != 1: warnings.warn( "CRS not set for some of the concatenation inputs. " f"Setting output's CRS as {names[0]} " "(the single non-null crs provided).", stacklevel=2, ) return crs_not_none[0] raise ValueError( f"Cannot determine common CRS for concatenation inputs, got {names}. " "Use `to_crs()` to transform geometries to the same CRS before merging." ) def transform(data, func) -> np.ndarray: has_z = shapely.has_z(data) result = np.empty_like(data) coords = shapely.get_coordinates(data[~has_z], include_z=False) new_coords_z = func(coords[:, 0], coords[:, 1]) result[~has_z] = shapely.set_coordinates( data[~has_z].copy(), np.array(new_coords_z).T ) coords_z = shapely.get_coordinates(data[has_z], include_z=True) new_coords_z = func(coords_z[:, 0], coords_z[:, 1], coords_z[:, 2]) result[has_z] = shapely.set_coordinates( data[has_z].copy(), np.array(new_coords_z).T ) return result geopandas-1.2.0/geopandas/base.py000066400000000000000000007263641525650651600167350ustar00rootroot00000000000000from typing import TYPE_CHECKING, Literal from warnings import warn import numpy as np import pandas as pd from pandas import DataFrame, Series import shapely from shapely.geometry import MultiPoint, box from shapely.geometry.base import BaseGeometry from . import _compat as compat from .array import GeometryArray, GeometryDtype if TYPE_CHECKING: from .geoseries import GeoSeries def is_geometry_type(data): """Check if the data is of geometry dtype. Does not include object array of shapely scalars. """ if isinstance(getattr(data, "dtype", None), GeometryDtype): # GeometryArray, GeoSeries and Series[GeometryArray] return True else: return False def _delegate_binary_method(op, this, other, align, *args, **kwargs): # type: (str, GeoSeries, GeoSeries) -> GeoSeries/Series if align is None: align = True maybe_warn = True else: maybe_warn = False this = this.geometry # Use same alignment logic, regardless of if `other` is Series or GeoSeries. if ( not isinstance(other, GeoPandasBase) and isinstance(other, pd.Series) and isinstance(other.dtype, GeometryDtype) ): # Avoid circular imports by importing here. import geopandas.geoseries other = geopandas.geoseries.GeoSeries(other) if isinstance(other, GeoPandasBase): if align and not this.index.equals(other.index): if maybe_warn: warn( "The indices of the left and right GeoSeries' are not equal, and " "therefore they will be aligned (reordering and/or introducing " "missing values) before executing the operation. If this alignment " "is the desired behaviour, you can silence this warning by passing " "'align=True'. If you don't want alignment and protect yourself of " "accidentally aligning, you can pass 'align=False'.", stacklevel=4, ) this, other = this.align(other.geometry) else: other = other.geometry a_this = GeometryArray(this.values) other = GeometryArray(other.values) elif isinstance(other, BaseGeometry): a_this = GeometryArray(this.values) else: raise TypeError(type(this), type(other)) data = getattr(a_this, op)(other, *args, **kwargs) return data, this.index def _binary_geo(op, this, other, align, *args, **kwargs): # type: (str, GeoSeries, GeoSeries) -> GeoSeries """Binary operation on GeoSeries objects that returns a GeoSeries.""" from .geoseries import GeoSeries geoms, index = _delegate_binary_method(op, this, other, align, *args, **kwargs) return GeoSeries(geoms, index=index, crs=this.crs) def _binary_op(op, this, other, align, *args, **kwargs): # type: (str, GeoSeries, GeoSeries, args/kwargs) -> Series[bool/float] """Binary operation on GeoSeries objects that returns a Series.""" data, index = _delegate_binary_method(op, this, other, align, *args, **kwargs) return Series(data, index=index) def _delegate_property(op, this): # type: (str, GeoSeries) -> GeoSeries/Series a_this = GeometryArray(this.geometry.values) data = getattr(a_this, op) if isinstance(data, GeometryArray): from .geoseries import GeoSeries return GeoSeries(data, index=this.index, crs=this.crs) else: return Series(data, index=this.index) def _delegate_geo_method(op, this, **kwargs): # type: (str, GeoSeries) -> GeoSeries """Unary operation that returns a GeoSeries.""" from .geodataframe import GeoDataFrame from .geoseries import GeoSeries if isinstance(this, GeoSeries): klass, var_name = "GeoSeries", "gs" elif isinstance(this, GeoDataFrame): klass, var_name = "GeoDataFrame", "gdf" else: klass, var_name = this.__class__.__name__, "this" for key, val in kwargs.items(): if isinstance(val, pd.Series): if not val.index.equals(this.index): raise ValueError( f"Index of the Series passed as '{key}' does not match index of " f"the {klass}. If you want both Series to be aligned, align them " f"before passing them to this method as " f"`{var_name}, {key} = {var_name}.align({key})`. If " f"you want to ignore the index, pass the underlying array as " f"'{key}' using `{key}.values`." ) kwargs[key] = np.asarray(val) a_this = GeometryArray(this.geometry.values) data = getattr(a_this, op)(**kwargs) return GeoSeries(data, index=this.index, crs=this.crs) class GeoPandasBase: @property def area(self): """Return a ``Series`` containing the area of each geometry in the ``GeoSeries`` expressed in the units of the CRS. Examples -------- >>> from shapely.geometry import Polygon, LineString, Point >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (1, 1), (0, 1)]), ... Polygon([(10, 0), (10, 5), (0, 0)]), ... Polygon([(0, 0), (2, 2), (2, 0)]), ... LineString([(0, 0), (1, 1), (0, 1)]), ... Point(0, 1) ... ] ... ) >>> s 0 POLYGON ((0 0, 1 1, 0 1, 0 0)) 1 POLYGON ((10 0, 10 5, 0 0, 10 0)) 2 POLYGON ((0 0, 2 2, 2 0, 0 0)) 3 LINESTRING (0 0, 1 1, 0 1) 4 POINT (0 1) dtype: geometry >>> s.area 0 0.5 1 25.0 2 2.0 3 0.0 4 0.0 dtype: float64 See Also -------- GeoSeries.length : measure length Notes ----- Area may be invalid for a geographic CRS using degrees as units; use :meth:`GeoSeries.to_crs` to project geometries to a planar CRS before using this function. Every operation in GeoPandas is planar, i.e. the potential third dimension is not taken into account. """ return _delegate_property("area", self) @property def crs(self): """The Coordinate Reference System (CRS) as a ``pyproj.CRS`` object. Returns ------- ``pyproj.CRS`` | None CRS assigned to a GeoSeries Examples -------- >>> s.crs # doctest: +SKIP Name: WGS 84 Axis Info [ellipsoidal]: - Lat[north]: Geodetic latitude (degree) - Lon[east]: Geodetic longitude (degree) Area of Use: - name: World - bounds: (-180.0, -90.0, 180.0, 90.0) Datum: World Geodetic System 1984 - Ellipsoid: WGS 84 - Prime Meridian: Greenwich See Also -------- GeoSeries.set_crs : assign CRS GeoSeries.to_crs : re-project to another CRS """ return self.geometry.values.crs @crs.setter def crs(self, value): """Set the value of the crs.""" self.geometry.values.crs = value @property def geom_type(self): """ Returns a ``Series`` of strings specifying the `Geometry Type` of each object. Examples -------- >>> from shapely.geometry import Point, Polygon, LineString >>> d = {'geometry': [Point(2, 1), Polygon([(0, 0), (1, 1), (1, 0)]), ... LineString([(0, 0), (1, 1)])]} >>> gdf = geopandas.GeoDataFrame(d, crs="EPSG:4326") >>> gdf.geom_type 0 Point 1 Polygon 2 LineString dtype: str """ return _delegate_property("geom_type", self) @property def type(self): """Return the geometry type of each geometry in the GeoSeries.""" return self.geom_type @property def length(self): """Return a ``Series`` containing the length of each geometry expressed in the units of the CRS. In the case of a (Multi)Polygon it measures the length of its exterior (i.e. perimeter). Examples -------- >>> from shapely.geometry import Polygon, LineString, MultiLineString, Point, \ GeometryCollection >>> s = geopandas.GeoSeries( ... [ ... LineString([(0, 0), (1, 1), (0, 1)]), ... LineString([(10, 0), (10, 5), (0, 0)]), ... MultiLineString([((0, 0), (1, 0)), ((-1, 0), (1, 0))]), ... Polygon([(0, 0), (1, 1), (0, 1)]), ... Point(0, 1), ... GeometryCollection([Point(1, 0), LineString([(10, 0), (10, 5), (0,\ 0)])]) ... ] ... ) >>> s 0 LINESTRING (0 0, 1 1, 0 1) 1 LINESTRING (10 0, 10 5, 0 0) 2 MULTILINESTRING ((0 0, 1 0), (-1 0, 1 0)) 3 POLYGON ((0 0, 1 1, 0 1, 0 0)) 4 POINT (0 1) 5 GEOMETRYCOLLECTION (POINT (1 0), LINESTRING (1... dtype: geometry >>> s.length 0 2.414214 1 16.180340 2 3.000000 3 3.414214 4 0.000000 5 16.180340 dtype: float64 See Also -------- GeoSeries.area : measure area of a polygon Notes ----- Length may be invalid for a geographic CRS using degrees as units; use :meth:`GeoSeries.to_crs` to project geometries to a planar CRS before using this function. Every operation in GeoPandas is planar, i.e. the potential third dimension is not taken into account. """ return _delegate_property("length", self) @property def is_valid(self): """Return a ``Series`` of ``dtype('bool')`` with value ``True`` for geometries that are valid. Examples -------- An example with one invalid polygon (a bowtie geometry crossing itself) and one missing geometry: >>> from shapely.geometry import Polygon >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (1, 1), (0, 1)]), ... Polygon([(0,0), (1, 1), (1, 0), (0, 1)]), # bowtie geometry ... Polygon([(0, 0), (2, 2), (2, 0)]), ... None ... ] ... ) >>> s 0 POLYGON ((0 0, 1 1, 0 1, 0 0)) 1 POLYGON ((0 0, 1 1, 1 0, 0 1, 0 0)) 2 POLYGON ((0 0, 2 2, 2 0, 0 0)) 3 None dtype: geometry >>> s.is_valid 0 True 1 False 2 True 3 False dtype: bool See Also -------- GeoSeries.is_valid_reason : reason for invalidity """ return _delegate_property("is_valid", self) def is_valid_reason(self): """Return a ``Series`` of strings with the reason for invalidity of each geometry. Examples -------- An example with one invalid polygon (a bowtie geometry crossing itself) and one missing geometry: >>> from shapely.geometry import Polygon >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (1, 1), (0, 1)]), ... Polygon([(0,0), (1, 1), (1, 0), (0, 1)]), # bowtie geometry ... Polygon([(0, 0), (2, 2), (2, 0)]), ... None ... ] ... ) >>> s 0 POLYGON ((0 0, 1 1, 0 1, 0 0)) 1 POLYGON ((0 0, 1 1, 1 0, 0 1, 0 0)) 2 POLYGON ((0 0, 2 2, 2 0, 0 0)) 3 None dtype: geometry >>> s.is_valid_reason() 0 Valid Geometry 1 Self-intersection[0.5 0.5] 2 Valid Geometry 3 NaN dtype: str See Also -------- GeoSeries.is_valid : detect invalid geometries GeoSeries.make_valid : fix invalid geometries """ return Series(self.geometry.values.is_valid_reason(), index=self.index) def is_valid_coverage(self, *, gap_width=0.0): """Return a ``bool`` indicating whether a ``GeoSeries`` forms a valid coverage. A ``GeoSeries`` of valid polygons is considered a coverage if the polygons are: * **Non-overlapping** - polygons do not overlap (their interiors do not intersect) * **Edge-Matched** - vertices along shared edges are identical A valid coverage may contain holes (regions of no coverage). However, sometimes it might be desirable to detect narrow gaps as invalidities in the coverage. The ``gap_width`` parameter allows to specify the maximum width of gaps to detect. When gaps are detected, this method will return ``False`` and the :meth:`invalid_coverage_edges` method can be used to find the edges of those gaps. Geometries that are not Polygon or MultiPolygon are ignored and an empty LineString is returned. Requires Shapely >= 2.1. .. versionadded:: 1.1.0 Parameters ---------- gap_width : float, optional The maximum width of gaps to detect, by default 0.0 Returns ------- bool Examples -------- >>> from shapely import Polygon >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (1, 1), (1, 0), (0, 0)]), ... Polygon([(0, 0), (1, 1), (0, 1), (0, 0)]), ... ] ... ) >>> s 0 POLYGON ((0 0, 1 1, 1 0, 0 0)) 1 POLYGON ((0 0, 1 1, 0 1, 0 0)) dtype: geometry >>> s.is_valid_coverage() True Violation of edge-matching: >>> s2 = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (1, 1), (1, 0), (0, 0)]), ... Polygon([(0, 0), (0.5, 0.5), (1, 1), (0, 1), (0, 0)]) ... ] ... ) >>> s2 0 POLYGON ((0 0, 1 1, 1 0, 0 0)) 1 POLYGON ((0 0, 0.5 0.5, 1 1, 0 1, 0 0)) dtype: geometry >>> s2.is_valid_coverage() False See Also -------- GeoSeries.invalid_coverage_edges GeoSeries.simplify_coverage """ return self.geometry.values.is_valid_coverage(gap_width=gap_width) def invalid_coverage_edges(self, *, gap_width=0.0): """Return a ``GeoSeries`` containing edges causing invalid polygonal coverage. This method returns (Multi)LineStrings showing the location of edges violating polygonal coverage (if any) in each polygon in the input ``GeoSeries``. A ``GeoSeries`` of valid polygons is considered a coverage if the polygons are: * **Non-overlapping** - polygons do not overlap (their interiors do not intersect) * **Edge-Matched** - vertices along shared edges are identical A valid coverage may contain holes (regions of no coverage). However, sometimes it might be desirable to detect narrow gaps as invalidities in the coverage. The ``gap_width`` parameter allows to specify the maximum width of gaps to detect. When gaps are detected, the :meth:`is_valid_coverage` method will return ``False`` and this method can be used to find the edges of those gaps. Geometries that are not Polygon or MultiPolygon are ignored. Requires Shapely >= 2.1. .. versionadded:: 1.1.0 Parameters ---------- gap_width : float, optional The maximum width of gaps to detect, by default 0.0 Returns ------- GeoSeries Examples -------- Violation of edge-matching: >>> from shapely import Polygon >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (1, 1), (1, 0), (0, 0)]), ... Polygon([(0, 0), (0.5, 0.5), (1, 1), (0, 1), (0, 0)]) ... ] ... ) >>> s 0 POLYGON ((0 0, 1 1, 1 0, 0 0)) 1 POLYGON ((0 0, 0.5 0.5, 1 1, 0 1, 0 0)) dtype: geometry >>> s.invalid_coverage_edges() 0 LINESTRING (0 0, 1 1) 1 LINESTRING (0 0, 0.5 0.5, 1 1) dtype: geometry See Also -------- GeoSeries.is_valid_coverage GeoSeries.simplify_coverage """ return _delegate_geo_method("invalid_coverage_edges", self, gap_width=gap_width) @property def is_empty(self): """ Returns a ``Series`` of ``dtype('bool')`` with value ``True`` for empty geometries. Examples -------- An example of a GeoDataFrame with one empty point, one point and one missing value: >>> from shapely.geometry import Point >>> d = {'geometry': [Point(), Point(2, 1), None]} >>> gdf = geopandas.GeoDataFrame(d, crs="EPSG:4326") >>> gdf geometry 0 POINT EMPTY 1 POINT (2 1) 2 None >>> gdf.is_empty 0 True 1 False 2 False dtype: bool See Also -------- GeoSeries.isna : detect missing values """ return _delegate_property("is_empty", self) def count_coordinates(self): """Return a ``Series`` containing the count of the number of coordinate pairs in each geometry. Examples -------- An example of a GeoDataFrame with two line strings, one point and one None value: >>> from shapely.geometry import Polygon, LineString, Point >>> s = geopandas.GeoSeries( ... [ ... LineString([(0, 0), (1, 1), (1, -1), (0, 1)]), ... LineString([(0, 0), (1, 1), (1, -1)]), ... Point(0, 0), ... Polygon([(10, 10), (10, 20), (20, 20), (20, 10), (10, 10)]), ... None ... ] ... ) >>> s 0 LINESTRING (0 0, 1 1, 1 -1, 0 1) 1 LINESTRING (0 0, 1 1, 1 -1) 2 POINT (0 0) 3 POLYGON ((10 10, 10 20, 20 20, 20 10, 10 10)) 4 None dtype: geometry >>> s.count_coordinates() 0 4 1 3 2 1 3 5 4 0 dtype: int32 See Also -------- GeoSeries.get_coordinates : extract coordinates as a :class:`~pandas.DataFrame` GoSeries.count_geometries : count the number of geometries in a collection """ return Series(self.geometry.values.count_coordinates(), index=self.index) def count_geometries(self): """Return a ``Series`` containing the count of geometries in each multi-part geometry. For single-part geometry objects, this is always 1. For multi-part geometries, like ``MultiPoint`` or ``MultiLineString``, it is the number of parts in the geometry. For ``GeometryCollection``, it is the number of geometries direct parts of the collection (the method does not recurse into collections within collections). Examples -------- >>> from shapely.geometry import Point, MultiPoint, LineString, MultiLineString >>> s = geopandas.GeoSeries( ... [ ... MultiPoint([(0, 0), (1, 1), (1, -1), (0, 1)]), ... MultiLineString([((0, 0), (1, 1)), ((-1, 0), (1, 0))]), ... LineString([(0, 0), (1, 1), (1, -1)]), ... Point(0, 0), ... ] ... ) >>> s 0 MULTIPOINT ((0 0), (1 1), (1 -1), (0 1)) 1 MULTILINESTRING ((0 0, 1 1), (-1 0, 1 0)) 2 LINESTRING (0 0, 1 1, 1 -1) 3 POINT (0 0) dtype: geometry >>> s.count_geometries() 0 4 1 2 2 1 3 1 dtype: int32 See Also -------- GeoSeries.count_coordinates : count the number of coordinates in a geometry GeoSeries.count_interior_rings : count the number of interior rings """ return Series(self.geometry.values.count_geometries(), index=self.index) def count_interior_rings(self): """Return a ``Series`` containing the count of the number of interior rings in a polygonal geometry. For non-polygonal geometries, this is always 0. Examples -------- >>> from shapely.geometry import Polygon, Point >>> s = geopandas.GeoSeries( ... [ ... Polygon( ... [(0, 0), (0, 5), (5, 5), (5, 0)], ... [[(1, 1), (1, 4), (4, 4), (4, 1)]], ... ), ... Polygon( ... [(0, 0), (0, 5), (5, 5), (5, 0)], ... [ ... [(1, 1), (1, 2), (2, 2), (2, 1)], ... [(3, 2), (3, 3), (4, 3), (4, 2)], ... ], ... ), ... Point(0, 1), ... ] ... ) >>> s 0 POLYGON ((0 0, 0 5, 5 5, 5 0, 0 0), (1 1, 1 4,... 1 POLYGON ((0 0, 0 5, 5 5, 5 0, 0 0), (1 1, 1 2,... 2 POINT (0 1) dtype: geometry >>> s.count_interior_rings() 0 1 1 2 2 0 dtype: int32 See Also -------- GeoSeries.count_coordinates : count the number of coordinates in a geometry GeoSeries.count_geometries : count the number of geometries in a collection """ return Series(self.geometry.values.count_interior_rings(), index=self.index) @property def is_simple(self): """Return a ``Series`` of ``dtype('bool')`` with value ``True`` for geometries that do not cross themselves. This is meaningful only for `LineStrings` and `LinearRings`. Examples -------- >>> from shapely.geometry import LineString >>> s = geopandas.GeoSeries( ... [ ... LineString([(0, 0), (1, 1), (1, -1), (0, 1)]), ... LineString([(0, 0), (1, 1), (1, -1)]), ... ] ... ) >>> s 0 LINESTRING (0 0, 1 1, 1 -1, 0 1) 1 LINESTRING (0 0, 1 1, 1 -1) dtype: geometry >>> s.is_simple 0 False 1 True dtype: bool """ return _delegate_property("is_simple", self) @property def is_ring(self): """Return a ``Series`` of ``dtype('bool')`` with value ``True`` for features that are closed. When constructing a LinearRing, the sequence of coordinates may be explicitly closed by passing identical values in the first and last indices. Otherwise, the sequence will be implicitly closed by copying the first tuple to the last index. Examples -------- >>> from shapely.geometry import LineString, LinearRing >>> s = geopandas.GeoSeries( ... [ ... LineString([(0, 0), (1, 1), (1, -1)]), ... LineString([(0, 0), (1, 1), (1, -1), (0, 0)]), ... LinearRing([(0, 0), (1, 1), (1, -1)]), ... ] ... ) >>> s 0 LINESTRING (0 0, 1 1, 1 -1) 1 LINESTRING (0 0, 1 1, 1 -1, 0 0) 2 LINEARRING (0 0, 1 1, 1 -1, 0 0) dtype: geometry >>> s.is_ring 0 False 1 True 2 True dtype: bool """ return _delegate_property("is_ring", self) @property def is_ccw(self): """Return a ``Series`` of ``dtype('bool')`` with value ``True`` if a LineString or LinearRing is counterclockwise. Note that there are no checks on whether lines are actually closed and not self-intersecting, while this is a requirement for ``is_ccw``. The recommended usage of this property for LineStrings is ``GeoSeries.is_ccw & GeoSeries.is_simple`` and for LinearRings ``GeoSeries.is_ccw & GeoSeries.is_valid``. This property will return False for non-linear geometries and for lines with fewer than 4 points (including the closing point). Examples -------- >>> from shapely.geometry import LineString, LinearRing, Point >>> s = geopandas.GeoSeries( ... [ ... LinearRing([(0, 0), (0, 1), (1, 1), (0, 0)]), ... LinearRing([(0, 0), (1, 1), (0, 1), (0, 0)]), ... LineString([(0, 0), (1, 1), (0, 1)]), ... Point(3, 3) ... ] ... ) >>> s 0 LINEARRING (0 0, 0 1, 1 1, 0 0) 1 LINEARRING (0 0, 1 1, 0 1, 0 0) 2 LINESTRING (0 0, 1 1, 0 1) 3 POINT (3 3) dtype: geometry >>> s.is_ccw 0 False 1 True 2 False 3 False dtype: bool """ return _delegate_property("is_ccw", self) @property def is_closed(self): """Return a ``Series`` of ``dtype('bool')`` with value ``True`` if a LineString's or LinearRing's first and last points are equal. Returns False for any other geometry type. Examples -------- >>> from shapely.geometry import LineString, Point, Polygon >>> s = geopandas.GeoSeries( ... [ ... LineString([(0, 0), (1, 1), (0, 1), (0, 0)]), ... LineString([(0, 0), (1, 1), (0, 1)]), ... Polygon([(0, 0), (0, 1), (1, 1), (0, 0)]), ... Point(3, 3) ... ] ... ) >>> s 0 LINESTRING (0 0, 1 1, 0 1, 0 0) 1 LINESTRING (0 0, 1 1, 0 1) 2 POLYGON ((0 0, 0 1, 1 1, 0 0)) 3 POINT (3 3) dtype: geometry >>> s.is_closed 0 True 1 False 2 False 3 False dtype: bool """ return _delegate_property("is_closed", self) @property def has_z(self): """Return a ``Series`` of ``dtype('bool')`` with value ``True`` for features that have a z-component. Notes ----- Every operation in GeoPandas is planar, i.e. the potential third dimension is not taken into account. Examples -------- >>> from shapely.geometry import Point >>> s = geopandas.GeoSeries( ... [ ... Point(0, 1), ... Point(0, 1, 2), ... ] ... ) >>> s 0 POINT (0 1) 1 POINT Z (0 1 2) dtype: geometry >>> s.has_z 0 False 1 True dtype: bool """ return _delegate_property("has_z", self) @property def has_m(self): """Return a ``Series`` of ``dtype('bool')`` with value ``True`` for features that have a m-component. Requires Shapely >= 2.1. .. versionadded:: 1.1.0 Examples -------- >>> from shapely.geometry import Point >>> s = geopandas.GeoSeries.from_wkt( ... [ ... "POINT M (2 3 5)", ... "POINT Z (1 2 3)", ... "POINT (0 0)", ... ] ... ) >>> s 0 POINT M (2 3 5) 1 POINT Z (1 2 3) 2 POINT (0 0) dtype: geometry >>> s.has_m 0 True 1 False 2 False dtype: bool """ return _delegate_property("has_m", self) def get_precision(self): """Return a ``Series`` of the precision of each geometry. If a precision has not been previously set, it will be 0, indicating regular double precision coordinates are in use. Otherwise, it will return the precision grid size that was set on a geometry. Returns NaN for not-a-geometry values. Examples -------- >>> from shapely.geometry import Point >>> s = geopandas.GeoSeries( ... [ ... Point(0, 1), ... Point(0, 1, 2), ... Point(0, 1.5, 2), ... ] ... ) >>> s 0 POINT (0 1) 1 POINT Z (0 1 2) 2 POINT Z (0 1.5 2) dtype: geometry >>> s.get_precision() 0 0.0 1 0.0 2 0.0 dtype: float64 >>> s1 = s.set_precision(1) >>> s1 0 POINT (0 1) 1 POINT Z (0 1 2) 2 POINT Z (0 2 2) dtype: geometry >>> s1.get_precision() 0 1.0 1 1.0 2 1.0 dtype: float64 See Also -------- GeoSeries.set_precision : set precision grid size """ return Series(self.geometry.values.get_precision(), index=self.index) def get_geometry(self, index): """Return the n-th geometry from a collection of geometries. Parameters ---------- index : int or array_like Position of a geometry to be retrieved within its collection Returns ------- GeoSeries Notes ----- Simple geometries act as collections of length 1. Any out-of-range index value returns None. Examples -------- >>> from shapely.geometry import Point, MultiPoint, GeometryCollection >>> s = geopandas.GeoSeries( ... [ ... Point(0, 0), ... MultiPoint([(0, 0), (1, 1), (0, 1), (1, 0)]), ... GeometryCollection( ... [MultiPoint([(0, 0), (1, 1), (0, 1), (1, 0)]), Point(0, 1)] ... ), ... ] ... ) >>> s 0 POINT (0 0) 1 MULTIPOINT ((0 0), (1 1), (0 1), (1 0)) 2 GEOMETRYCOLLECTION (MULTIPOINT ((0 0), (1 1), ... dtype: geometry >>> s.get_geometry(0) 0 POINT (0 0) 1 POINT (0 0) 2 MULTIPOINT ((0 0), (1 1), (0 1), (1 0)) dtype: geometry >>> s.get_geometry(1) 0 None 1 POINT (1 1) 2 POINT (0 1) dtype: geometry >>> s.get_geometry(-1) 0 POINT (0 0) 1 POINT (1 0) 2 POINT (0 1) dtype: geometry """ return _delegate_geo_method("get_geometry", self, index=index) # # Unary operations that return a GeoSeries # @property def boundary(self): """Return a ``GeoSeries`` of lower dimensional objects representing each geometry's set-theoretic `boundary`. Examples -------- >>> from shapely.geometry import Polygon, LineString, Point >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (1, 1), (0, 1)]), ... LineString([(0, 0), (1, 1), (1, 0)]), ... Point(0, 0), ... ] ... ) >>> s 0 POLYGON ((0 0, 1 1, 0 1, 0 0)) 1 LINESTRING (0 0, 1 1, 1 0) 2 POINT (0 0) dtype: geometry >>> s.boundary 0 LINESTRING (0 0, 1 1, 0 1, 0 0) 1 MULTIPOINT ((0 0), (1 0)) 2 GEOMETRYCOLLECTION EMPTY dtype: geometry See Also -------- GeoSeries.exterior : outer boundary (without interior rings) """ return _delegate_property("boundary", self) @property def centroid(self): """Return a ``GeoSeries`` of points representing the centroid of each geometry. Note that centroid does not have to be on or within original geometry. Examples -------- >>> from shapely.geometry import Polygon, LineString, Point >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (1, 1), (0, 1)]), ... LineString([(0, 0), (1, 1), (1, 0)]), ... Point(0, 0), ... ] ... ) >>> s 0 POLYGON ((0 0, 1 1, 0 1, 0 0)) 1 LINESTRING (0 0, 1 1, 1 0) 2 POINT (0 0) dtype: geometry >>> s.centroid 0 POINT (0.33333 0.66667) 1 POINT (0.70711 0.5) 2 POINT (0 0) dtype: geometry See Also -------- GeoSeries.representative_point : point guaranteed to be within each geometry """ return _delegate_property("centroid", self) def concave_hull(self, ratio=0.0, allow_holes=False): """Return a ``GeoSeries`` of geometries representing the concave hull of vertices of each geometry. The concave hull of a geometry is the smallest concave `Polygon` containing all the points in each geometry, unless the number of points in the geometric object is less than three. For two points, the concave hull collapses to a `LineString`; for 1, a `Point`. The hull is constructed by removing border triangles of the Delaunay Triangulation of the points as long as their "size" is larger than the maximum edge length ratio and optionally allowing holes. The edge length factor is a fraction of the length difference between the longest and shortest edges in the Delaunay Triangulation of the input points. For further information on the algorithm used, see https://libgeos.org/doxygen/classgeos_1_1algorithm_1_1hull_1_1ConcaveHull.html Parameters ---------- ratio : float, (optional, default 0.0) Number in the range [0, 1]. Higher numbers will include fewer vertices in the hull. allow_holes : bool, (optional, default False) If set to True, the concave hull may have holes. Examples -------- >>> from shapely.geometry import Polygon, LineString, Point, MultiPoint >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (1, 1), (0, 1)]), ... LineString([(0, 0), (1, 1), (1, 0)]), ... MultiPoint([(0, 0), (1, 1), (0, 1), (1, 0), (0.5, 0.5)]), ... MultiPoint([(0, 0), (1, 1)]), ... Point(0, 0), ... ], ... crs=3857 ... ) >>> s 0 POLYGON ((0 0, 1 1, 0 1, 0 0)) 1 LINESTRING (0 0, 1 1, 1 0) 2 MULTIPOINT ((0 0), (1 1), (0 1), (1 0), (0.5 0... 3 MULTIPOINT ((0 0), (1 1)) 4 POINT (0 0) dtype: geometry >>> s.concave_hull() 0 POLYGON ((0 1, 1 1, 0 0, 0 1)) 1 POLYGON ((0 0, 1 1, 1 0, 0 0)) 2 POLYGON ((0.5 0.5, 0 1, 1 1, 1 0, 0 0, 0.5 0.5)) 3 LINESTRING (0 0, 1 1) 4 POINT (0 0) dtype: geometry See Also -------- GeoSeries.convex_hull : convex hull geometry Notes ----- The algorithms considers only vertices of each geometry. As a result the hull may not fully enclose input geometry. If that happens, increasing ``ratio`` should resolve the issue. """ return _delegate_geo_method( "concave_hull", self, ratio=ratio, allow_holes=allow_holes ) def constrained_delaunay_triangles(self): """Return a :class:`~geopandas.GeoSeries` with the constrained Delaunay triangulation of polygons. A constrained Delaunay triangulation requires the edges of the input polygon(s) to be in the set of resulting triangle edges. An unconstrained delaunay triangulation only triangulates based on the vertices, hence triangle edges could cross polygon boundaries. Requires Shapely >= 2.1. .. versionadded:: 1.1.0 Examples -------- >>> from shapely.geometry import Polygon >>> s = geopandas.GeoSeries([Polygon([(0, 0), (1, 1), (0, 1)])]) >>> s 0 POLYGON ((0 0, 1 1, 0 1, 0 0)) dtype: geometry >>> s.constrained_delaunay_triangles() 0 GEOMETRYCOLLECTION (POLYGON ((0 0, 0 1, 1 1, 0... dtype: geometry See Also -------- GeoSeries.delaunay_triangles : Delaunay triangulation """ return _delegate_geo_method("constrained_delaunay_triangles", self) @property def convex_hull(self): """Return a ``GeoSeries`` of geometries representing the convex hull of each geometry. The convex hull of a geometry is the smallest convex `Polygon` containing all the points in each geometry, unless the number of points in the geometric object is less than three. For two points, the convex hull collapses to a `LineString`; for 1, a `Point`. Examples -------- >>> from shapely.geometry import Polygon, LineString, Point, MultiPoint >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (1, 1), (0, 1)]), ... LineString([(0, 0), (1, 1), (1, 0)]), ... MultiPoint([(0, 0), (1, 1), (0, 1), (1, 0), (0.5, 0.5)]), ... MultiPoint([(0, 0), (1, 1)]), ... Point(0, 0), ... ] ... ) >>> s 0 POLYGON ((0 0, 1 1, 0 1, 0 0)) 1 LINESTRING (0 0, 1 1, 1 0) 2 MULTIPOINT ((0 0), (1 1), (0 1), (1 0), (0.5 0... 3 MULTIPOINT ((0 0), (1 1)) 4 POINT (0 0) dtype: geometry >>> s.convex_hull 0 POLYGON ((0 0, 0 1, 1 1, 0 0)) 1 POLYGON ((0 0, 1 1, 1 0, 0 0)) 2 POLYGON ((0 0, 0 1, 1 1, 1 0, 0 0)) 3 LINESTRING (0 0, 1 1) 4 POINT (0 0) dtype: geometry See Also -------- GeoSeries.concave_hull : concave hull geometry GeoSeries.envelope : bounding rectangle geometry """ return _delegate_property("convex_hull", self) def delaunay_triangles(self, tolerance=0.0, only_edges=False): """Return a ``GeoSeries`` consisting of objects representing the computed Delaunay triangulation between the vertices of an input geometry. All geometries within the GeoSeries are considered together within a single Delaunay triangulation. The resulting geometries therefore do not map 1:1 to input geometries. Note that each vertex of a geometry is considered a site for the triangulation, so the triangles will be constructed between the vertices of each geometry. Notes ----- If you want to generate Delaunay triangles for each geometry separately, use :func:`shapely.delaunay_triangles` instead. Parameters ---------- tolerance : float, default 0.0 Snap input vertices together if their distance is less than this value. only_edges : bool (optional, default False) If set to True, the triangulation will return linestrings instead of polygons. Examples -------- >>> from shapely import LineString, MultiPoint, Point, Polygon >>> s = geopandas.GeoSeries( ... [ ... Point(1, 1), ... Point(2, 2), ... Point(1, 3), ... Point(0, 2), ... ] ... ) >>> s 0 POINT (1 1) 1 POINT (2 2) 2 POINT (1 3) 3 POINT (0 2) dtype: geometry >>> s.delaunay_triangles() 0 POLYGON ((0 2, 1 1, 1 3, 0 2)) 1 POLYGON ((1 3, 1 1, 2 2, 1 3)) dtype: geometry >>> s.delaunay_triangles(only_edges=True) 0 LINESTRING (1 3, 2 2) 1 LINESTRING (0 2, 1 3) 2 LINESTRING (0 2, 1 1) 3 LINESTRING (1 1, 2 2) 4 LINESTRING (1 1, 1 3) dtype: geometry The method supports any geometry type but keep in mind that the underlying algorithm is based on the vertices of the input geometries only and does not consider edge segments between vertices. >>> s2 = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (1, 1), (0, 1)]), ... LineString([(1, 0), (2, 1), (1, 2)]), ... MultiPoint([(2, 3), (2, 0), (3, 1)]), ... ] ... ) >>> s2 0 POLYGON ((0 0, 1 1, 0 1, 0 0)) 1 LINESTRING (1 0, 2 1, 1 2) 2 MULTIPOINT ((2 3), (2 0), (3 1)) dtype: geometry >>> s2.delaunay_triangles() 0 POLYGON ((0 1, 0 0, 1 0, 0 1)) 1 POLYGON ((0 1, 1 0, 1 1, 0 1)) 2 POLYGON ((0 1, 1 1, 1 2, 0 1)) 3 POLYGON ((1 2, 1 1, 2 1, 1 2)) 4 POLYGON ((1 2, 2 1, 2 3, 1 2)) 5 POLYGON ((2 3, 2 1, 3 1, 2 3)) 6 POLYGON ((3 1, 2 1, 2 0, 3 1)) 7 POLYGON ((2 0, 2 1, 1 1, 2 0)) 8 POLYGON ((2 0, 1 1, 1 0, 2 0)) dtype: geometry See Also -------- GeoSeries.voronoi_polygons : Voronoi diagram around vertices GeoSeries.constrained_delaunay_triangles : constrained Delaunay triangulation """ from .geoseries import GeoSeries geometry_input = shapely.geometrycollections(self.geometry.values._data) delaunay = shapely.delaunay_triangles( geometry_input, tolerance=tolerance, only_edges=only_edges, ) return GeoSeries(delaunay, crs=self.crs).explode(ignore_index=True) def voronoi_polygons(self, tolerance=0.0, extend_to=None, only_edges=False): """Return a ``GeoSeries`` consisting of objects representing the computed Voronoi diagram around the vertices of an input geometry. All geometries within the GeoSeries are considered together within a single Voronoi diagram. The resulting geometries therefore do not necessarily map 1:1 to input geometries. Note that each vertex of a geometry is considered a site for the Voronoi diagram, so the diagram will be constructed around the vertices of each geometry. Notes ----- The order of polygons in the output currently does not correspond to the order of input vertices. If you want to generate a Voronoi diagram for each geometry separately, use :func:`shapely.voronoi_polygons` instead. Parameters ---------- tolerance : float, default 0.0 Snap input vertices together if their distance is less than this value. extend_to : shapely.Geometry, default None If set, the Voronoi diagram will be extended to cover the envelope of this geometry (unless this envelope is smaller than the input geometry). only_edges : bool (optional, default False) If set to True, the diagram will return LineStrings instead of Polygons. Examples -------- The most common use case is to generate polygons representing the Voronoi diagram around a set of points: >>> from shapely import LineString, MultiPoint, Point, Polygon >>> s = geopandas.GeoSeries( ... [ ... Point(1, 1), ... Point(2, 2), ... Point(1, 3), ... Point(0, 2), ... ] ... ) >>> s 0 POINT (1 1) 1 POINT (2 2) 2 POINT (1 3) 3 POINT (0 2) dtype: geometry By default, you get back a GeoSeries of polygons: >>> s.voronoi_polygons() 0 POLYGON ((-2 5, 1 2, -2 -1, -2 5)) 1 POLYGON ((4 5, 1 2, -2 5, 4 5)) 2 POLYGON ((-2 -1, 1 2, 4 -1, -2 -1)) 3 POLYGON ((4 -1, 4 -1, 1 2, 4 5, 4 -1)) dtype: geometry If you set only_edges to True, you get back LineStrings representing the edges of the Voronoi diagram: >>> s.voronoi_polygons(only_edges=True) 0 LINESTRING (-2 5, 1 2) 1 LINESTRING (1 2, -2 -1) 2 LINESTRING (4 5, 1 2) 3 LINESTRING (1 2, 4 -1) dtype: geometry You can also extend each diagram to a given geometry: >>> limit = Polygon([(-10, -10), (0, 15), (15, 15), (15, 0)]) >>> s.voronoi_polygons(extend_to=limit) 0 POLYGON ((-10 13, 1 2, -10 -9, -10 13)) 1 POLYGON ((15 15, 15 -10, 13 -10, 1 2, 14 15, 1... 2 POLYGON ((-10 -10, -10 -9, 1 2, 13 -10, -10 -10)) 3 POLYGON ((-10 15, 14 15, 1 2, -10 13, -10 15)) dtype: geometry The method supports any geometry type but keep in mind that the underlying algorithm is based on the vertices of the input geometries only and does not consider edge segments between vertices. >>> s2 = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (1, 1), (0, 1)]), ... LineString([(1, 0), (2, 1), (1, 2)]), ... MultiPoint([(2, 3), (2, 0), (3, 1)]), ... ] ... ) >>> s2 0 POLYGON ((0 0, 1 1, 0 1, 0 0)) 1 LINESTRING (1 0, 2 1, 1 2) 2 MULTIPOINT ((2 3), (2 0), (3 1)) dtype: geometry >>> s2.voronoi_polygons() 0 POLYGON ((1.5 1.5, 1.5 0.5, 0.5 0.5, 0.5 1.5, ... 1 POLYGON ((1.5 0.5, 1.5 1.5, 2 2, 2.5 2, 2.5 0.... 2 POLYGON ((-3 -3, -3 0.5, 0.5 0.5, 0.5 -3, -3 -3)) 3 POLYGON ((0.5 -3, 0.5 0.5, 1.5 0.5, 1.5 -3, 0.... 4 POLYGON ((-3 5, 0.5 1.5, 0.5 0.5, -3 0.5, -3 5)) 5 POLYGON ((-3 6, -2 6, 2 2, 1.5 1.5, 0.5 1.5, -... 6 POLYGON ((1.5 -3, 1.5 0.5, 2.5 0.5, 6 -3, 1.5 ... 7 POLYGON ((6 6, 6 3.75, 2.5 2, 2 2, -2 6, 6 6)) 8 POLYGON ((6 -3, 2.5 0.5, 2.5 2, 6 3.75, 6 -3)) dtype: geometry See Also -------- GeoSeries.delaunay_triangles : Delaunay triangulation around vertices """ from .geoseries import GeoSeries geometry_input = shapely.geometrycollections(self.geometry.values._data) voronoi = shapely.voronoi_polygons( geometry_input, tolerance=tolerance, extend_to=extend_to, only_edges=only_edges, ) return GeoSeries(voronoi, crs=self.crs).explode(ignore_index=True) @property def envelope(self): """Return a ``GeoSeries`` of geometries representing the envelope of each geometry. The envelope of a geometry is the bounding rectangle. That is, the point or smallest rectangular polygon (with sides parallel to the coordinate axes) that contains the geometry. Examples -------- >>> from shapely.geometry import Polygon, LineString, Point, MultiPoint >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (1, 1), (0, 1)]), ... LineString([(0, 0), (1, 1), (1, 0)]), ... MultiPoint([(0, 0), (1, 1)]), ... Point(0, 0), ... ] ... ) >>> s 0 POLYGON ((0 0, 1 1, 0 1, 0 0)) 1 LINESTRING (0 0, 1 1, 1 0) 2 MULTIPOINT ((0 0), (1 1)) 3 POINT (0 0) dtype: geometry >>> s.envelope 0 POLYGON ((0 0, 1 0, 1 1, 0 1, 0 0)) 1 POLYGON ((0 0, 1 0, 1 1, 0 1, 0 0)) 2 POLYGON ((0 0, 1 0, 1 1, 0 1, 0 0)) 3 POINT (0 0) dtype: geometry See Also -------- GeoSeries.convex_hull : convex hull geometry """ return _delegate_property("envelope", self) def minimum_rotated_rectangle(self): """Return a ``GeoSeries`` of the general minimum bounding rectangle that contains the object. Unlike envelope this rectangle is not constrained to be parallel to the coordinate axes. If the convex hull of the object is a degenerate (line or point) this degenerate is returned. Examples -------- >>> from shapely.geometry import Polygon, LineString, Point, MultiPoint >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (1, 1), (0, 1)]), ... LineString([(0, 0), (1, 1), (1, 0)]), ... MultiPoint([(0, 0), (1, 1)]), ... Point(0, 0), ... ] ... ) >>> s 0 POLYGON ((0 0, 1 1, 0 1, 0 0)) 1 LINESTRING (0 0, 1 1, 1 0) 2 MULTIPOINT ((0 0), (1 1)) 3 POINT (0 0) dtype: geometry >>> s.minimum_rotated_rectangle() 0 POLYGON ((0 0, 0 1, 1 1, 1 0, 0 0)) 1 POLYGON ((1 1, 1 0, 0 0, 0 1, 1 1)) 2 LINESTRING (0 0, 1 1) 3 POINT (0 0) dtype: geometry See Also -------- GeoSeries.envelope : bounding rectangle """ return _delegate_geo_method("minimum_rotated_rectangle", self) @property def exterior(self): """Return a ``GeoSeries`` of LinearRings representing the outer boundary of each polygon in the GeoSeries. Applies to GeoSeries containing only Polygons. Returns ``None``` for other geometry types. Examples -------- >>> from shapely.geometry import Polygon, Point >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (1, 1), (0, 1)]), ... Polygon([(1, 0), (2, 1), (0, 0)]), ... Point(0, 1) ... ] ... ) >>> s 0 POLYGON ((0 0, 1 1, 0 1, 0 0)) 1 POLYGON ((1 0, 2 1, 0 0, 1 0)) 2 POINT (0 1) dtype: geometry >>> s.exterior 0 LINEARRING (0 0, 1 1, 0 1, 0 0) 1 LINEARRING (1 0, 2 1, 0 0, 1 0) 2 None dtype: geometry See Also -------- GeoSeries.boundary : complete set-theoretic boundary GeoSeries.interiors : list of inner rings of each polygon """ # TODO: return empty geometry for non-polygons return _delegate_property("exterior", self) def extract_unique_points(self): """Return a ``GeoSeries`` of MultiPoints representing all distinct vertices of an input geometry. Examples -------- >>> from shapely import LineString, Polygon >>> s = geopandas.GeoSeries( ... [ ... LineString([(0, 0), (0, 0), (1, 1), (1, 1)]), ... Polygon([(0, 0), (0, 0), (1, 1), (1, 1)]) ... ], ... ) >>> s 0 LINESTRING (0 0, 0 0, 1 1, 1 1) 1 POLYGON ((0 0, 0 0, 1 1, 1 1, 0 0)) dtype: geometry >>> s.extract_unique_points() 0 MULTIPOINT ((0 0), (1 1)) 1 MULTIPOINT ((0 0), (1 1)) dtype: geometry See Also -------- GeoSeries.get_coordinates : extract coordinates as a :class:`~pandas.DataFrame` """ return _delegate_geo_method("extract_unique_points", self) def offset_curve(self, distance, quad_segs=8, join_style="round", mitre_limit=5.0): """Return a ``LineString`` or ``MultiLineString`` geometry at a distance from the object on its right or its left side. Parameters ---------- distance : float | array-like Specifies the offset distance from the input geometry. Negative for right side offset, positive for left side offset. quad_segs : int (optional, default 8) Specifies the number of linear segments in a quarter circle in the approximation of circular arcs. join_style : {'round', 'bevel', 'mitre'}, (optional, default 'round') Specifies the shape of outside corners. 'round' results in rounded shapes. 'bevel' results in a beveled edge that touches the original vertex. 'mitre' results in a single vertex that is beveled depending on the ``mitre_limit`` parameter. mitre_limit : float (optional, default 5.0) Crops of 'mitre'-style joins if the point is displaced from the buffered vertex by more than this limit. See http://shapely.readthedocs.io/en/latest/manual.html#object.offset_curve for details. Examples -------- >>> from shapely.geometry import LineString >>> s = geopandas.GeoSeries( ... [ ... LineString([(0, 0), (0, 1), (1, 1)]), ... ], ... crs=3857 ... ) >>> s 0 LINESTRING (0 0, 0 1, 1 1) dtype: geometry >>> s.offset_curve(1) 0 LINESTRING (-1 0, -1 1, -0.981 1.195, -0.924 1... dtype: geometry """ return _delegate_geo_method( "offset_curve", self, distance=distance, quad_segs=quad_segs, join_style=join_style, mitre_limit=mitre_limit, ) @property def interiors(self): """Return a ``Series`` of List representing the inner rings of each polygon in the GeoSeries. Applies to GeoSeries containing only Polygons. Returns ------- inner_rings: Series of List Inner rings of each polygon in the GeoSeries. Examples -------- >>> from shapely.geometry import Polygon >>> s = geopandas.GeoSeries( ... [ ... Polygon( ... [(0, 0), (0, 5), (5, 5), (5, 0)], ... [[(1, 1), (2, 1), (1, 2)], [(1, 4), (2, 4), (2, 3)]], ... ), ... Polygon([(1, 0), (2, 1), (0, 0)]), ... ] ... ) >>> s 0 POLYGON ((0 0, 0 5, 5 5, 5 0, 0 0), (1 1, 2 1,... 1 POLYGON ((1 0, 2 1, 0 0, 1 0)) dtype: geometry >>> s.interiors 0 [LINEARRING (1 1, 2 1, 1 2, 1 1), LINEARRING (... 1 [] dtype: object See Also -------- GeoSeries.exterior : outer boundary """ return _delegate_property("interiors", self) def remove_repeated_points(self, tolerance=0.0): """Return a ``GeoSeries`` containing a copy of the input geometry with repeated points removed. From the start of the coordinate sequence, each next point within the tolerance is removed. Removing repeated points with a non-zero tolerance may result in an invalid geometry being returned. Parameters ---------- tolerance : float, default 0.0 Remove all points within this distance of each other. Use 0.0 to remove only exactly repeated points (the default). Examples -------- >>> from shapely import LineString, Polygon >>> s = geopandas.GeoSeries( ... [ ... LineString([(0, 0), (0, 0), (1, 0)]), ... Polygon([(0, 0), (0, 0.5), (0, 1), (0.5, 1), (0,0)]), ... ], ... ) >>> s 0 LINESTRING (0 0, 0 0, 1 0) 1 POLYGON ((0 0, 0 0.5, 0 1, 0.5 1, 0 0)) dtype: geometry >>> s.remove_repeated_points(tolerance=0.0) 0 LINESTRING (0 0, 1 0) 1 POLYGON ((0 0, 0 0.5, 0 1, 0.5 1, 0 0)) dtype: geometry """ return _delegate_geo_method("remove_repeated_points", self, tolerance=tolerance) def set_precision(self, grid_size, mode="valid_output"): """Return a ``GeoSeries`` with the precision set to a precision grid size. By default, geometries use double precision coordinates (``grid_size=0``). Coordinates will be rounded if a precision grid is less precise than the input geometry. Duplicated vertices will be dropped from lines and polygons for grid sizes greater than 0. Line and polygon geometries may collapse to empty geometries if all vertices are closer together than ``grid_size``. Spikes or sections in Polygons narrower than ``grid_size`` after rounding the vertices will be removed, which can lead to MultiPolygons or empty geometries. Z values, if present, will not be modified. Parameters ---------- grid_size : float Precision grid size. If 0, will use double precision (will not modify geometry if precision grid size was not previously set). If this value is more precise than input geometry, the input geometry will not be modified. mode : {'valid_output', 'pointwise', 'keep_collapsed'}, default 'valid_output' This parameter determines the way a precision reduction is applied on the geometry. There are three modes: * ``'valid_output'`` (default): The output is always valid. Collapsed geometry elements (including both polygons and lines) are removed. Duplicate vertices are removed. * ``'pointwise'``: Precision reduction is performed pointwise. Output geometry may be invalid due to collapse or self-intersection. Duplicate vertices are not removed. * ``'keep_collapsed'``: Like the default mode, except that collapsed linear geometry elements are preserved. Collapsed polygonal input elements are removed. Duplicate vertices are removed. Examples -------- >>> from shapely import LineString, Point >>> s = geopandas.GeoSeries( ... [ ... Point(0.9, 0.9), ... Point(0.9, 0.9, 0.9), ... LineString([(0, 0), (0, 0.1), (0, 1), (1, 1)]), ... LineString([(0, 0), (0, 0.1), (0.1, 0.1)]) ... ], ... ) >>> s 0 POINT (0.9 0.9) 1 POINT Z (0.9 0.9 0.9) 2 LINESTRING (0 0, 0 0.1, 0 1, 1 1) 3 LINESTRING (0 0, 0 0.1, 0.1 0.1) dtype: geometry >>> s.set_precision(1) 0 POINT (1 1) 1 POINT Z (1 1 0.9) 2 LINESTRING (0 0, 0 1, 1 1) 3 LINESTRING EMPTY dtype: geometry >>> s.set_precision(1, mode="pointwise") 0 POINT (1 1) 1 POINT Z (1 1 0.9) 2 LINESTRING (0 0, 0 0, 0 1, 1 1) 3 LINESTRING (0 0, 0 0, 0 0) dtype: geometry >>> s.set_precision(1, mode="keep_collapsed") 0 POINT (1 1) 1 POINT Z (1 1 0.9) 2 LINESTRING (0 0, 0 1, 1 1) 3 LINESTRING (0 0, 0 0) dtype: geometry Notes ----- Subsequent operations will always be performed in the precision of the geometry with higher precision (smaller ``grid_size``). That same precision will be attached to the operation outputs. Input geometries should be geometrically valid; unexpected results may occur if input geometries are not. You can check the validity with :meth:`~GeoSeries.is_valid` and fix invalid geometries with :meth:`~GeoSeries.make_valid` methods. """ return _delegate_geo_method( "set_precision", self, grid_size=grid_size, mode=mode ) def representative_point(self): """Return a ``GeoSeries`` of (cheaply computed) points that are guaranteed to be within each geometry. Examples -------- >>> from shapely.geometry import Polygon, LineString, Point >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (1, 1), (0, 1)]), ... LineString([(0, 0), (1, 1), (1, 0)]), ... Point(0, 0), ... ] ... ) >>> s 0 POLYGON ((0 0, 1 1, 0 1, 0 0)) 1 LINESTRING (0 0, 1 1, 1 0) 2 POINT (0 0) dtype: geometry >>> s.representative_point() 0 POINT (0.25 0.5) 1 POINT (1 1) 2 POINT (0 0) dtype: geometry See Also -------- GeoSeries.centroid : geometric centroid """ return _delegate_geo_method("representative_point", self) def minimum_bounding_circle(self): """Return a ``GeoSeries`` of geometries representing the minimum bounding circle that encloses each geometry. Examples -------- >>> from shapely.geometry import Polygon, LineString, Point >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (1, 1), (0, 1), (0, 0)]), ... LineString([(0, 0), (1, 1), (1, 0)]), ... Point(0, 0), ... ] ... ) >>> s 0 POLYGON ((0 0, 1 1, 0 1, 0 0)) 1 LINESTRING (0 0, 1 1, 1 0) 2 POINT (0 0) dtype: geometry >>> s.minimum_bounding_circle() 0 POLYGON ((1.20711 0.5, 1.19352 0.36205, 1.1532... 1 POLYGON ((1.20711 0.5, 1.19352 0.36205, 1.1532... 2 POINT (0 0) dtype: geometry See Also -------- GeoSeries.convex_hull : convex hull geometry GeoSeries.maximum_inscribed_circle : the largest circle within the geometry """ return _delegate_geo_method("minimum_bounding_circle", self) def maximum_inscribed_circle(self, *, tolerance=None): """Return a ``GeoSeries`` of geometries representing the largest circle that is fully contained within the input geometry. Constructs the “maximum inscribed circle” (MIC) for a polygonal geometry, up to a specified tolerance. The MIC is determined by a point in the interior of the area which has the farthest distance from the area boundary, along with a boundary point at that distance. In the context of geography the center of the MIC is known as the “pole of inaccessibility”. A cartographic use case is to determine a suitable point to place a map label within a polygon. The radius length of the MIC is a measure of how “narrow” a polygon is. It is the distance at which the negative buffer becomes empty. The method supports polygons with holes and multipolygons but will raise an error for any other geometry type. Returns a GeoSeries with two-point linestrings rows, with the first point at the center of the inscribed circle and the second on the boundary of the inscribed circle. Requires Shapely >= 2.1. .. versionadded:: 1.1.0 Parameters ---------- tolerance : float, np.array, pd.Series Stop the algorithm when the search area is smaller than this tolerance. When not specified, uses ``max(width, height) / 1000`` per geometry as the default. If np.array or pd.Series are used then it must have same length as the GeoSeries. Examples -------- >>> from shapely.geometry import Polygon, LineString, Point >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (1, 1), (0, 1), (0, 0)]), ... Polygon([(0, 0), (0.5, -1), (1, 0), (1, 1), (-0.5, 0.5)]), ... ] ... ) >>> s 0 POLYGON ((0 0, 1 1, 0 1, 0 0)) 1 POLYGON ((0 0, 0.5 -1, 1 0, 1 1, -0.5 0.5, 0 0)) dtype: geometry >>> s.maximum_inscribed_circle() 0 LINESTRING (0.29289 0.70711, 0.5 0.5) 1 LINESTRING (0.4668 0.25977, 1 0.25977) dtype: geometry >>> s.maximum_inscribed_circle(tolerance=2) 0 LINESTRING (0.29289 0.70711, 0.5 0.5) 1 LINESTRING (0.375 0.25, 0 0) dtype: geometry See Also -------- minimum_bounding_circle """ return _delegate_geo_method( "maximum_inscribed_circle", self, tolerance=tolerance ) def minimum_bounding_radius(self): """Return a `Series` of the radii of the minimum bounding circles that enclose each geometry. Examples -------- >>> from shapely.geometry import Point, LineString, Polygon >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (1, 1), (0, 1), (0, 0)]), ... LineString([(0, 0), (1, 1), (1, 0)]), ... Point(0,0), ... ] ... ) >>> s 0 POLYGON ((0 0, 1 1, 0 1, 0 0)) 1 LINESTRING (0 0, 1 1, 1 0) 2 POINT (0 0) dtype: geometry >>> s.minimum_bounding_radius() 0 0.707107 1 0.707107 2 0.000000 dtype: float64 See Also -------- GeoSeries.minumum_bounding_circle : minimum bounding circle (geometry) """ return Series(self.geometry.values.minimum_bounding_radius(), index=self.index) def minimum_clearance(self): """Return a ``Series`` containing the minimum clearance distance, which is the smallest distance by which a vertex of the geometry could be moved to produce an invalid geometry. If no minimum clearance exists for a geometry (for example, a single point, or an empty geometry), infinity is returned. Examples -------- >>> from shapely.geometry import Polygon, LineString, Point >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (1, 1), (0, 1), (0, 0)]), ... LineString([(0, 0), (1, 1), (3, 2)]), ... Point(0, 0), ... ] ... ) >>> s 0 POLYGON ((0 0, 1 1, 0 1, 0 0)) 1 LINESTRING (0 0, 1 1, 3 2) 2 POINT (0 0) dtype: geometry >>> s.minimum_clearance() 0 0.707107 1 1.414214 2 inf dtype: float64 See Also -------- minimum_clearance_line """ return Series(self.geometry.values.minimum_clearance(), index=self.index) def minimum_clearance_line(self): """Return a ``GeoSeries`` of linestrings whose endpoints define the minimum clearance. A geometry's “minimum clearance” is the smallest distance by which a vertex of the geometry could be moved to produce an invalid geometry. If the geometry has no minimum clearance, an empty LineString will be returned. Requires Shapely >= 2.1. .. versionadded:: 1.1.0 Examples -------- >>> from shapely.geometry import Polygon, LineString, Point >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (1, 1), (0, 1), (0, 0)]), ... LineString([(0, 0), (1, 1), (3, 2)]), ... Point(0, 0), ... ] ... ) >>> s 0 POLYGON ((0 0, 1 1, 0 1, 0 0)) 1 LINESTRING (0 0, 1 1, 3 2) 2 POINT (0 0) dtype: geometry >>> s.minimum_clearance_line() 0 LINESTRING (0 1, 0.5 0.5) 1 LINESTRING (0 0, 1 1) 2 LINESTRING EMPTY dtype: geometry See Also -------- minimum_clearance """ return _delegate_geo_method("minimum_clearance_line", self) def normalize(self): """Return a ``GeoSeries`` of normalized geometries to normal form (or canonical form). This method orders the coordinates, rings of a polygon and parts of multi geometries consistently. Typically useful for testing purposes (for example in combination with `equals_exact`). Examples -------- >>> from shapely.geometry import Polygon, LineString, Point >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (1, 1), (0, 1)]), ... LineString([(0, 0), (1, 1), (1, 0)]), ... Point(0, 0), ... ], ... ) >>> s 0 POLYGON ((0 0, 1 1, 0 1, 0 0)) 1 LINESTRING (0 0, 1 1, 1 0) 2 POINT (0 0) dtype: geometry >>> s.normalize() 0 POLYGON ((0 0, 0 1, 1 1, 0 0)) 1 LINESTRING (0 0, 1 1, 1 0) 2 POINT (0 0) dtype: geometry """ return _delegate_geo_method("normalize", self) def orient_polygons(self, *, exterior_cw=False): """Return a ``GeoSeries`` of geometries with enforced ring orientation. Enforce a ring orientation on all polygonal elements in the ``GeoSeries``. Forces (Multi)Polygons to use a counter-clockwise orientation for their exterior ring, and a clockwise orientation for their interior rings (or the oppposite if ``exterior_cw=True``). Also processes geometries inside a GeometryCollection in the same way. Other geometries are returned unchanged. Requires Shapely >= 2.1. .. versionadded:: 1.1.0 Parameters ---------- exterior_cw : bool If ``True``, exterior rings will be clockwise and interior rings will be counter-clockwise. Examples -------- >>> from shapely.geometry import Polygon, LineString, Point >>> s = geopandas.GeoSeries( ... [ ... Polygon( ... [(0, 0), (0, 10), (10, 10), (10, 0), (0, 0)], ... holes=[[(2, 2), (2, 4), (4, 4), (4, 2), (2, 2)]], ... ), ... LineString([(0, 0), (1, 1), (1, 0)]), ... Point(0, 0), ... ], ... ) >>> s 0 POLYGON ((0 0, 0 10, 10 10, 10 0, 0 0), (2 2, ... 1 LINESTRING (0 0, 1 1, 1 0) 2 POINT (0 0) dtype: geometry >>> s.orient_polygons() 0 POLYGON ((0 0, 10 0, 10 10, 0 10, 0 0), (2 2, ... 1 LINESTRING (0 0, 1 1, 1 0) 2 POINT (0 0) dtype: geometry >>> s.orient_polygons(exterior_cw=True) 0 POLYGON ((0 0, 0 10, 10 10, 10 0, 0 0), (2 2, ... 1 LINESTRING (0 0, 1 1, 1 0) 2 POINT (0 0) dtype: geometry """ return _delegate_geo_method("orient_polygons", self, exterior_cw=exterior_cw) def make_valid(self, *, method="linework", keep_collapsed=True): """Repairs invalid geometries. Returns a ``GeoSeries`` with valid geometries. If the input geometry is already valid, then it will be preserved. In many cases, in order to create a valid geometry, the input geometry must be split into multiple parts or multiple geometries. If the geometry must be split into multiple parts of the same type to be made valid, then a multi-part geometry will be returned (e.g. a MultiPolygon). If the geometry must be split into multiple parts of different types to be made valid, then a GeometryCollection will be returned. Two ``methods`` are available: * the 'linework' algorithm tries to preserve every edge and vertex in the input. It combines all rings into a set of noded lines and then extracts valid polygons from that linework. An alternating even-odd strategy is used to assign areas as interior or exterior. A disadvantage is that for some relatively simple invalid geometries this produces rather complex results. * the 'structure' algorithm tries to reason from the structure of the input to find the 'correct' repair: exterior rings bound area, interior holes exclude area. It first makes all rings valid, then shells are merged and holes are subtracted from the shells to generate valid result. It assumes that holes and shells are correctly categorized in the input geometry. Parameters ---------- method : {'linework', 'structure'}, default 'linework' Algorithm to use when repairing geometry. 'structure' requires GEOS >= 3.10 and shapely >= 2.1. .. versionadded:: 1.1.0 keep_collapsed : bool, default True For the 'structure' method, True will keep components that have collapsed into a lower dimensionality. For example, a ring collapsing to a line, or a line collapsing to a point. Must be True for the 'linework' method. .. versionadded:: 1.1.0 Examples -------- >>> from shapely.geometry import MultiPolygon, Polygon, LineString, Point >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (0, 2), (1, 1), (2, 2), (2, 0), (1, 1), (0, 0)]), ... Polygon([(0, 2), (0, 1), (2, 0), (0, 0), (0, 2)]), ... LineString([(0, 0), (1, 1), (1, 0)]), ... ], ... ) >>> s 0 POLYGON ((0 0, 0 2, 1 1, 2 2, 2 0, 1 1, 0 0)) 1 POLYGON ((0 2, 0 1, 2 0, 0 0, 0 2)) 2 LINESTRING (0 0, 1 1, 1 0) dtype: geometry >>> s.make_valid() 0 MULTIPOLYGON (((1 1, 0 0, 0 2, 1 1)), ((2 0, 1... 1 GEOMETRYCOLLECTION (POLYGON ((2 0, 0 0, 0 1, 2... 2 LINESTRING (0 0, 1 1, 1 0) dtype: geometry """ return _delegate_geo_method( "make_valid", self, method=method, keep_collapsed=keep_collapsed ) def reverse(self): """Return a ``GeoSeries`` with the order of coordinates reversed. Examples -------- >>> from shapely.geometry import Polygon, LineString, Point >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (1, 1), (0, 1)]), ... LineString([(0, 0), (1, 1), (1, 0)]), ... Point(0, 0), ... ] ... ) >>> s 0 POLYGON ((0 0, 1 1, 0 1, 0 0)) 1 LINESTRING (0 0, 1 1, 1 0) 2 POINT (0 0) dtype: geometry >>> s.reverse() 0 POLYGON ((0 0, 0 1, 1 1, 0 0)) 1 LINESTRING (1 0, 1 1, 0 0) 2 POINT (0 0) dtype: geometry See Also -------- GeoSeries.normalize : normalize order of coordinates """ return _delegate_geo_method("reverse", self) def segmentize(self, max_segment_length): """Return a ``GeoSeries`` with vertices added to line segments based on maximum segment length. Additional vertices will be added to every line segment in an input geometry so that segments are no longer than the provided maximum segment length. New vertices will evenly subdivide each segment. Only linear components of input geometries are densified; other geometries are returned unmodified. Parameters ---------- max_segment_length : float | array-like Additional vertices will be added so that all line segments are no longer than this value. Must be greater than 0. Returns ------- GeoSeries Examples -------- >>> from shapely.geometry import Polygon, LineString >>> s = geopandas.GeoSeries( ... [ ... LineString([(0, 0), (0, 10)]), ... Polygon([(0, 0), (10, 0), (10, 10), (0, 10), (0, 0)]), ... ], ... ) >>> s 0 LINESTRING (0 0, 0 10) 1 POLYGON ((0 0, 10 0, 10 10, 0 10, 0 0)) dtype: geometry >>> s.segmentize(max_segment_length=5) 0 LINESTRING (0 0, 0 5, 0 10) 1 POLYGON ((0 0, 5 0, 10 0, 10 5, 10 10, 5 10, 0... dtype: geometry """ return _delegate_geo_method( "segmentize", self, max_segment_length=max_segment_length ) def transform(self, transformation, include_z=False): """Return a ``GeoSeries`` with the transformation function applied to the geometry coordinates. Parameters ---------- transformation : Callable A function that transforms a (N, 2) or (N, 3) ndarray of float64 to another (N,2) or (N, 3) ndarray of float64 include_z : bool, default False If True include the third dimension in the coordinates array that is passed to the ``transformation`` function. If a geometry has no third dimension, the z-coordinates passed to the function will be NaN. Returns ------- GeoSeries Examples -------- >>> from shapely import Point, Polygon >>> s = geopandas.GeoSeries([Point(0, 0)]) >>> s.transform(lambda x: x + 1) 0 POINT (1 1) dtype: geometry >>> s = geopandas.GeoSeries([Polygon([(0, 0), (1, 1), (0, 1)])]) >>> s.transform(lambda x: x * [2, 3]) 0 POLYGON ((0 0, 2 3, 0 3, 0 0)) dtype: geometry By default the third dimension is ignored and you need explicitly include it: >>> s = geopandas.GeoSeries([Point(0, 0, 0)]) >>> s.transform(lambda x: x + 1, include_z=True) 0 POINT Z (1 1 1) dtype: geometry """ return _delegate_geo_method( "transform", self, transformation=transformation, include_z=include_z ) def force_2d(self): """Force the dimensionality of a geometry to 2D. Removes the additional Z coordinate dimension from all geometries. Returns ------- GeoSeries Examples -------- >>> from shapely import Polygon, LineString, Point >>> s = geopandas.GeoSeries( ... [ ... Point(0.5, 2.5, 0), ... LineString([(1, 1, 1), (0, 1, 3), (1, 0, 2)]), ... Polygon([(0, 0, 0), (0, 10, 0), (10, 10, 0)]), ... ], ... ) >>> s 0 POINT Z (0.5 2.5 0) 1 LINESTRING Z (1 1 1, 0 1 3, 1 0 2) 2 POLYGON Z ((0 0 0, 0 10 0, 10 10 0, 0 0 0)) dtype: geometry >>> s.force_2d() 0 POINT (0.5 2.5) 1 LINESTRING (1 1, 0 1, 1 0) 2 POLYGON ((0 0, 0 10, 10 10, 0 0)) dtype: geometry """ return _delegate_geo_method("force_2d", self) def force_3d(self, z=0): """Force the dimensionality of a geometry to 3D. 2D geometries will get the provided Z coordinate; 3D geometries are unchanged (unless their Z coordinate is ``np.nan``). Note that for empty geometries, 3D is only supported since GEOS 3.9 and then still only for simple geometries (non-collections). Parameters ---------- z : float | array_like (default 0) Z coordinate to be assigned Returns ------- GeoSeries Examples -------- >>> from shapely import Polygon, LineString, Point >>> s = geopandas.GeoSeries( ... [ ... Point(1, 2), ... Point(0.5, 2.5, 2), ... LineString([(1, 1), (0, 1), (1, 0)]), ... Polygon([(0, 0), (0, 10), (10, 10)]), ... ], ... ) >>> s 0 POINT (1 2) 1 POINT Z (0.5 2.5 2) 2 LINESTRING (1 1, 0 1, 1 0) 3 POLYGON ((0 0, 0 10, 10 10, 0 0)) dtype: geometry >>> s.force_3d() 0 POINT Z (1 2 0) 1 POINT Z (0.5 2.5 2) 2 LINESTRING Z (1 1 0, 0 1 0, 1 0 0) 3 POLYGON Z ((0 0 0, 0 10 0, 10 10 0, 0 0 0)) dtype: geometry Z coordinate can be specified as scalar: >>> s.force_3d(4) 0 POINT Z (1 2 4) 1 POINT Z (0.5 2.5 2) 2 LINESTRING Z (1 1 4, 0 1 4, 1 0 4) 3 POLYGON Z ((0 0 4, 0 10 4, 10 10 4, 0 0 4)) dtype: geometry Or as an array-like (one value per geometry): >>> s.force_3d(range(4)) 0 POINT Z (1 2 0) 1 POINT Z (0.5 2.5 2) 2 LINESTRING Z (1 1 2, 0 1 2, 1 0 2) 3 POLYGON Z ((0 0 3, 0 10 3, 10 10 3, 0 0 3)) dtype: geometry """ return _delegate_geo_method("force_3d", self, z=z) def line_merge(self, directed=False): """Return (Multi)LineStrings formed by combining the lines in a MultiLineString. Lines are joined together at their endpoints in case two lines are intersecting. Lines are not joined when 3 or more lines are intersecting at the endpoints. Line elements that cannot be joined are kept as is in the resulting MultiLineString. The direction of each merged LineString will be that of the majority of the LineStrings from which it was derived. Except if ``directed=True`` is specified, then the operation will not change the order of points within lines and so only lines which can be joined with no change in direction are merged. Non-linear geometeries result in an empty GeometryCollection. Parameters ---------- directed : bool, default False Only combine lines if possible without changing point order. Requires GEOS >= 3.11.0 Returns ------- GeoSeries Examples -------- >>> from shapely.geometry import MultiLineString, Point >>> s = geopandas.GeoSeries( ... [ ... MultiLineString([[(0, 2), (0, 10)], [(0, 10), (5, 10)]]), ... MultiLineString([[(0, 2), (0, 10)], [(0, 11), (5, 10)]]), ... MultiLineString(), ... MultiLineString([[(0, 0), (1, 0)], [(0, 0), (3, 0)]]), ... Point(0, 0), ... ] ... ) >>> s 0 MULTILINESTRING ((0 2, 0 10), (0 10, 5 10)) 1 MULTILINESTRING ((0 2, 0 10), (0 11, 5 10)) 2 MULTILINESTRING EMPTY 3 MULTILINESTRING ((0 0, 1 0), (0 0, 3 0)) 4 POINT (0 0) dtype: geometry >>> s.line_merge() 0 LINESTRING (0 2, 0 10, 5 10) 1 MULTILINESTRING ((0 2, 0 10), (0 11, 5 10)) 2 GEOMETRYCOLLECTION EMPTY 3 LINESTRING (1 0, 0 0, 3 0) 4 GEOMETRYCOLLECTION EMPTY dtype: geometry With ``directed=True``, you can avoid changing the order of points within lines and merge only lines where no change of direction is required: >>> s.line_merge(directed=True) 0 LINESTRING (0 2, 0 10, 5 10) 1 MULTILINESTRING ((0 2, 0 10), (0 11, 5 10)) 2 GEOMETRYCOLLECTION EMPTY 3 MULTILINESTRING ((0 0, 1 0), (0 0, 3 0)) 4 GEOMETRYCOLLECTION EMPTY dtype: geometry """ return _delegate_geo_method("line_merge", self, directed=directed) # # Reduction operations that return a Shapely geometry # @property def unary_union(self): """Return a geometry containing the union of all geometries in the ``GeoSeries``. The ``unary_union`` attribute is deprecated. Use :meth:`union_all` instead. Examples -------- >>> from shapely.geometry import box >>> s = geopandas.GeoSeries([box(0,0,1,1), box(0,0,2,2)]) >>> s 0 POLYGON ((1 0, 1 1, 0 1, 0 0, 1 0)) 1 POLYGON ((2 0, 2 2, 0 2, 0 0, 2 0)) dtype: geometry >>> union = s.unary_union >>> print(union) POLYGON ((0 1, 0 2, 2 2, 2 0, 1 0, 0 0, 0 1)) See Also -------- GeoSeries.union_all """ warn( "The 'unary_union' attribute is deprecated, " "use the 'union_all()' method instead.", DeprecationWarning, stacklevel=2, ) return self.geometry.values.union_all() def union_all(self, method="unary", *, grid_size=None): """Return a geometry containing the union of all geometries in the ``GeoSeries``. By default, the unary union algorithm is used. If the geometries are non-overlapping (forming a coverage), GeoPandas can use a significantly faster algorithm to perform the union using the ``method="coverage"`` option. Alternatively, for situations which can be divided into many disjoint subsets, ``method="disjoint_subset"`` may be preferable. Parameters ---------- method : str (default ``"unary"``) The method to use for the union. Options are: * ``"unary"``: use the unary union algorithm. This option is the most robust but can be slow for large numbers of geometries (default). * ``"coverage"``: use the coverage union algorithm. This option is optimized for non-overlapping polygons and can be significantly faster than the unary union algorithm. However, it can produce invalid geometries if the polygons overlap. * ``"disjoint_subset:``: use the disjoint subset union algorithm. This option is optimized for inputs that can be divided into subsets that do not intersect. If there is only one such subset, performance can be expected to be worse than ``"unary"``. Requires Shapely >= 2.1. grid_size : float, default None When grid size is specified, a fixed-precision space is used to perform the union operations. This can be useful when unioning geometries that are not perfectly snapped or to avoid geometries not being unioned because of `robustness issues `_. The inputs are first snapped to a grid of the given size. When a line segment of a geometry is within tolerance off a vertex of another geometry, this vertex will be inserted in the line segment. Finally, the result vertices are computed on the same grid. Is only supported for ``method`` ``"unary"``. If None, the highest precision of the inputs will be used. Defaults to None. .. versionadded:: 1.1.0 Examples -------- >>> from shapely.geometry import box >>> s = geopandas.GeoSeries([box(0, 0, 1, 1), box(0, 0, 2, 2)]) >>> s 0 POLYGON ((1 0, 1 1, 0 1, 0 0, 1 0)) 1 POLYGON ((2 0, 2 2, 0 2, 0 0, 2 0)) dtype: geometry >>> s.union_all() """ return self.geometry.values.union_all(method=method, grid_size=grid_size) def intersection_all(self): """Return a geometry containing the intersection of all geometries in the ``GeoSeries``. This method ignores None values when other geometries are present. If all elements of the GeoSeries are None, an empty GeometryCollection is returned. Examples -------- >>> from shapely.geometry import box >>> s = geopandas.GeoSeries( ... [box(0, 0, 2, 2), box(1, 1, 3, 3), box(0, 0, 1.5, 1.5)] ... ) >>> s 0 POLYGON ((2 0, 2 2, 0 2, 0 0, 2 0)) 1 POLYGON ((3 1, 3 3, 1 3, 1 1, 3 1)) 2 POLYGON ((1.5 0, 1.5 1.5, 0 1.5, 0 0, 1.5 0)) dtype: geometry >>> s.intersection_all() """ return self.geometry.values.intersection_all() # # Binary operations that return a pandas Series # def contains(self, other, align=None): """Return a ``Series`` of ``dtype('bool')`` with value ``True`` for each aligned geometry that contains `other`. An object is said to contain `other` if at least one point of `other` lies in the interior and no points of `other` lie in the exterior of the object. (Therefore, any given polygon does not contain its own boundary - there is not any point that lies in the interior.) If either object is empty, this operation returns ``False``. This is the inverse of :meth:`within` in the sense that the expression ``a.contains(b) == b.within(a)`` always evaluates to ``True``. The operation works on a 1-to-1 row-wise manner: .. image:: ../../../_static/binary_op-01.svg :align: center Parameters ---------- other : GeoSeries or geometric object The GeoSeries (elementwise) or geometric object to test if it is contained. align : bool | None (default None) If True, automatically aligns GeoSeries based on their indices. If False, the order of elements is preserved. None defaults to True. Returns ------- Series (bool) Examples -------- >>> from shapely.geometry import Polygon, LineString, Point >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (1, 1), (0, 1)]), ... LineString([(0, 0), (0, 2)]), ... LineString([(0, 0), (0, 1)]), ... Point(0, 1), ... ], ... index=range(0, 4), ... ) >>> s2 = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (2, 2), (0, 2)]), ... Polygon([(0, 0), (1, 2), (0, 2)]), ... LineString([(0, 0), (0, 2)]), ... Point(0, 1), ... ], ... index=range(1, 5), ... ) >>> s 0 POLYGON ((0 0, 1 1, 0 1, 0 0)) 1 LINESTRING (0 0, 0 2) 2 LINESTRING (0 0, 0 1) 3 POINT (0 1) dtype: geometry >>> s2 1 POLYGON ((0 0, 2 2, 0 2, 0 0)) 2 POLYGON ((0 0, 1 2, 0 2, 0 0)) 3 LINESTRING (0 0, 0 2) 4 POINT (0 1) dtype: geometry We can check if each geometry of GeoSeries contains a single geometry: .. image:: ../../../_static/binary_op-03.svg :align: center >>> point = Point(0, 1) >>> s.contains(point) 0 False 1 True 2 False 3 True dtype: bool We can also check two GeoSeries against each other, row by row. The GeoSeries above have different indices. We can either align both GeoSeries based on index values and compare elements with the same index using ``align=True`` or ignore index and compare elements based on their matching order using ``align=False``: .. image:: ../../../_static/binary_op-02.svg >>> s2.contains(s, align=True) 0 False 1 False 2 False 3 True 4 False dtype: bool >>> s2.contains(s, align=False) 1 True 2 False 3 True 4 True dtype: bool Notes ----- This method works in a row-wise manner. It does not check if an element of one GeoSeries ``contains`` *any* element of the other one. See Also -------- GeoSeries.contains_properly GeoSeries.within """ return _binary_op("contains", self, other, align) def contains_properly(self, other, align=None): """Return a ``Series`` of ``dtype('bool')`` with value ``True`` for each aligned geometry that is completely inside ``other``, with no common boundary points. Geometry A contains geometry B properly if B intersects the interior of A but not the boundary (or exterior). This means that a geometry A does not “contain properly” itself, which contrasts with the :meth:`~GeoSeries.contains` method, where common points on the boundary are allowed. The operation works on a 1-to-1 row-wise manner: .. image:: ../../../_static/binary_op-01.svg :align: center Parameters ---------- other : GeoSeries or geometric object The GeoSeries (elementwise) or geometric object to test if it is contained. align : bool | None (default None) If True, automatically aligns GeoSeries based on their indices. If False, the order of elements is preserved. None defaults to True. Returns ------- Series (bool) Examples -------- >>> from shapely.geometry import Polygon, LineString, Point >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (1, 1), (0, 1)]), ... LineString([(0, 0), (0, 2)]), ... LineString([(0, 0), (0, 1)]), ... Point(0, 1), ... ], ... index=range(0, 4), ... ) >>> s2 = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (2, 2), (0, 2)]), ... Polygon([(0, 0), (1, 2), (0, 2)]), ... LineString([(0, 0), (0, 2)]), ... Point(0, 1), ... ], ... index=range(1, 5), ... ) >>> s 0 POLYGON ((0 0, 1 1, 0 1, 0 0)) 1 LINESTRING (0 0, 0 2) 2 LINESTRING (0 0, 0 1) 3 POINT (0 1) dtype: geometry >>> s2 1 POLYGON ((0 0, 2 2, 0 2, 0 0)) 2 POLYGON ((0 0, 1 2, 0 2, 0 0)) 3 LINESTRING (0 0, 0 2) 4 POINT (0 1) dtype: geometry We can check if each geometry of GeoSeries contains a single geometry: .. image:: ../../../_static/binary_op-03.svg :align: center >>> point = Point(0, 1) >>> s.contains_properly(point) 0 False 1 True 2 False 3 True dtype: bool We can also check two GeoSeries against each other, row by row. The GeoSeries above have different indices. We can either align both GeoSeries based on index values and compare elements with the same index using ``align=True`` or ignore index and compare elements based on their matching order using ``align=False``: .. image:: ../../../_static/binary_op-02.svg >>> s2.contains_properly(s, align=True) 0 False 1 False 2 False 3 True 4 False dtype: bool >>> s2.contains_properly(s, align=False) 1 False 2 False 3 False 4 True dtype: bool Compare it to the result of :meth:`~GeoSeries.contains`: >>> s2.contains(s, align=False) 1 True 2 False 3 True 4 True dtype: bool Notes ----- This method works in a row-wise manner. It does not check if an element of one GeoSeries ``contains_properly`` *any* element of the other one. See Also -------- GeoSeries.contains """ return _binary_op("contains_properly", self, other, align) def dwithin(self, other, distance, align=None): """Return a ``Series`` of ``dtype('bool')`` with value ``True`` for each aligned geometry that is within a set distance from ``other``. The operation works on a 1-to-1 row-wise manner: .. image:: ../../../_static/binary_op-01.svg :align: center Parameters ---------- other : GeoSeries or geometric object The GeoSeries (elementwise) or geometric object to test for equality. distance : float, np.array, pd.Series Distance(s) to test if each geometry is within. A scalar distance will be applied to all geometries. An array or Series will be applied elementwise. If np.array or pd.Series are used then it must have same length as the GeoSeries. align : bool | None (default None) If True, automatically aligns GeoSeries based on their indices. If False, the order of elements is preserved. None defaults to True. Returns ------- Series (bool) Examples -------- >>> from shapely.geometry import Polygon, LineString, Point >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (1, 1), (0, 1)]), ... LineString([(0, 0), (0, 2)]), ... LineString([(0, 0), (0, 1)]), ... Point(0, 1), ... ], ... index=range(0, 4), ... ) >>> s2 = geopandas.GeoSeries( ... [ ... Polygon([(1, 0), (4, 2), (2, 2)]), ... Polygon([(2, 0), (3, 2), (2, 2)]), ... LineString([(2, 0), (2, 2)]), ... Point(1, 1), ... ], ... index=range(1, 5), ... ) >>> s 0 POLYGON ((0 0, 1 1, 0 1, 0 0)) 1 LINESTRING (0 0, 0 2) 2 LINESTRING (0 0, 0 1) 3 POINT (0 1) dtype: geometry >>> s2 1 POLYGON ((1 0, 4 2, 2 2, 1 0)) 2 POLYGON ((2 0, 3 2, 2 2, 2 0)) 3 LINESTRING (2 0, 2 2) 4 POINT (1 1) dtype: geometry We can check if each geometry of GeoSeries contains a single geometry: .. image:: ../../../_static/binary_op-03.svg :align: center >>> point = Point(0, 1) >>> s2.dwithin(point, 1.8) 1 True 2 False 3 False 4 True dtype: bool We can also check two GeoSeries against each other, row by row. The GeoSeries above have different indices. We can either align both GeoSeries based on index values and compare elements with the same index using ``align=True`` or ignore index and compare elements based on their matching order using ``align=False``: .. image:: ../../../_static/binary_op-02.svg >>> s.dwithin(s2, distance=1, align=True) 0 False 1 True 2 False 3 False 4 False dtype: bool >>> s.dwithin(s2, distance=1, align=False) 0 True 1 False 2 False 3 True dtype: bool Notes ----- This method works in a row-wise manner. It does not check if an element of one GeoSeries is within the set distance of *any* element of the other one. See Also -------- GeoSeries.within """ return _binary_op("dwithin", self, other, distance=distance, align=align) def geom_equals(self, other, align=None): """Return a ``Series`` of ``dtype('bool')`` with value ``True`` for each aligned geometry equal to `other`. An object is said to be equal to `other` if its set-theoretic `boundary`, `interior`, and `exterior` coincides with those of the other. The operation works on a 1-to-1 row-wise manner: .. image:: ../../../_static/binary_op-01.svg :align: center Parameters ---------- other : GeoSeries or geometric object The GeoSeries (elementwise) or geometric object to test for equality. align : bool | None (default None) If True, automatically aligns GeoSeries based on their indices. If False, the order of elements is preserved. None defaults to True. Returns ------- Series (bool) Examples -------- >>> from shapely.geometry import Polygon, LineString, Point >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (2, 2), (0, 2)]), ... Polygon([(0, 0), (1, 2), (0, 2)]), ... LineString([(0, 0), (0, 2)]), ... Point(0, 1), ... ], ... ) >>> s2 = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (2, 2), (0, 2)]), ... Polygon([(0, 0), (1, 2), (0, 2)]), ... Point(0, 1), ... LineString([(0, 0), (0, 2)]), ... ], ... index=range(1, 5), ... ) >>> s 0 POLYGON ((0 0, 2 2, 0 2, 0 0)) 1 POLYGON ((0 0, 1 2, 0 2, 0 0)) 2 LINESTRING (0 0, 0 2) 3 POINT (0 1) dtype: geometry >>> s2 1 POLYGON ((0 0, 2 2, 0 2, 0 0)) 2 POLYGON ((0 0, 1 2, 0 2, 0 0)) 3 POINT (0 1) 4 LINESTRING (0 0, 0 2) dtype: geometry We can check if each geometry of GeoSeries contains a single geometry: .. image:: ../../../_static/binary_op-03.svg :align: center >>> polygon = Polygon([(0, 0), (2, 2), (0, 2)]) >>> s.geom_equals(polygon, align=True) 0 True 1 False 2 False 3 False dtype: bool We can also check two GeoSeries against each other, row by row. The GeoSeries above have different indices. We can either align both GeoSeries based on index values and compare elements with the same index using ``align=True`` or ignore index and compare elements based on their matching order using ``align=False``: .. image:: ../../../_static/binary_op-02.svg >>> s.geom_equals(s2) 0 False 1 False 2 False 3 True 4 False dtype: bool >>> s.geom_equals(s2, align=False) 0 True 1 True 2 False 3 False dtype: bool Notes ----- This method works in a row-wise manner. It does not check if an element of one GeoSeries is equal to *any* element of the other one. See Also -------- GeoSeries.geom_equals_exact GeoSeries.geom_equals_identical """ return _binary_op("geom_equals", self, other, align) def geom_equals_exact(self, other, tolerance, align=None): """Return True for all geometries that equal aligned *other* to a given tolerance, else False. The operation works on a 1-to-1 row-wise manner: .. image:: ../../../_static/binary_op-01.svg :align: center Parameters ---------- other : GeoSeries or geometric object The GeoSeries (elementwise) or geometric object to compare to. tolerance : float Decimal place precision used when testing for approximate equality. align : bool | None (default None) If True, automatically aligns GeoSeries based on their indices. If False, the order of elements is preserved. None defaults to True. Returns ------- Series (bool) Examples -------- >>> from shapely.geometry import Point >>> s = geopandas.GeoSeries( ... [ ... Point(0, 1.1), ... Point(0, 1.0), ... Point(0, 1.2), ... ] ... ) >>> s 0 POINT (0 1.1) 1 POINT (0 1) 2 POINT (0 1.2) dtype: geometry >>> s.geom_equals_exact(Point(0, 1), tolerance=0.1) 0 False 1 True 2 False dtype: bool >>> s.geom_equals_exact(Point(0, 1), tolerance=0.15) 0 True 1 True 2 False dtype: bool Notes ----- This method works in a row-wise manner. It does not check if an element of one GeoSeries is equal to *any* element of the other one. See Also -------- GeoSeries.geom_equals GeoSeries.geom_equals_identical """ return _binary_op( "geom_equals_exact", self, other, tolerance=tolerance, align=align ) def geom_equals_identical(self, other, align=None): """Return True for all geometries that are identical aligned *other*, else False. This function verifies whether geometries are pointwise equivalent by checking that the structure, ordering, and values of all vertices are identical in all dimensions. Similarly to :meth:`geom_equals_exact`, this function uses exact coordinate equality and requires coordinates to be in the same order for all components (vertices, rings, or parts) of a geometry. However, in contrast to :meth:`geom_equals_exact`, this function does not allow specifying specify a tolerance, and additionally requires all dimensions to be the same (:meth:`geom_equals_exact` ignores the Z and M dimensions), where NaN values are considered to be equal to other NaN values. This function is the vectorized equivalent of scalar equality of geometry objects (``a == b``, i.e. ``__eq__``). The operation works on a 1-to-1 row-wise manner: .. image:: ../../../_static/binary_op-01.svg :align: center Requires Shapely >= 2.1. .. versionadded:: 1.1.0 Parameters ---------- other : GeoSeries or geometric object The GeoSeries (elementwise) or geometric object to compare to. align : bool | None (default None) If True, automatically aligns GeoSeries based on their indices. If False, the order of elements is preserved. None defaults to True. Returns ------- Series (bool) Examples -------- >>> from shapely.geometry import Point >>> s = geopandas.GeoSeries( ... [ ... Point(0, 1.1), ... Point(0, 1.0), ... Point(0, 1.2), ... ] ... ) >>> s 0 POINT (0 1.1) 1 POINT (0 1) 2 POINT (0 1.2) dtype: geometry >>> s.geom_equals_identical(Point(0, 1)) 0 False 1 True 2 False dtype: bool Notes ----- This method works in a row-wise manner. It does not check if an element of one GeoSeries is equal to *any* element of the other one. See Also -------- GeoSeries.geom_equals GeoSeries.geom_equals_exact """ return _binary_op("geom_equals_identical", self, other, align=align) def crosses(self, other, align=None): """Return a ``Series`` of ``dtype('bool')`` with value ``True`` for each aligned geometry that cross `other`. An object is said to cross `other` if its `interior` intersects the `interior` of the other but does not contain it, and the dimension of the intersection is less than the dimension of the one or the other. The operation works on a 1-to-1 row-wise manner: .. image:: ../../../_static/binary_op-01.svg :align: center Parameters ---------- other : GeoSeries or geometric object The GeoSeries (elementwise) or geometric object to test if is crossed. align : bool | None (default None) If True, automatically aligns GeoSeries based on their indices. If False, the order of elements is preserved. None defaults to True. Returns ------- Series (bool) Examples -------- >>> from shapely.geometry import Polygon, LineString, Point >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (2, 2), (0, 2)]), ... LineString([(0, 0), (2, 2)]), ... LineString([(2, 0), (0, 2)]), ... Point(0, 1), ... ], ... ) >>> s2 = geopandas.GeoSeries( ... [ ... LineString([(1, 0), (1, 3)]), ... LineString([(2, 0), (0, 2)]), ... Point(1, 1), ... Point(0, 1), ... ], ... index=range(1, 5), ... ) >>> s 0 POLYGON ((0 0, 2 2, 0 2, 0 0)) 1 LINESTRING (0 0, 2 2) 2 LINESTRING (2 0, 0 2) 3 POINT (0 1) dtype: geometry >>> s2 1 LINESTRING (1 0, 1 3) 2 LINESTRING (2 0, 0 2) 3 POINT (1 1) 4 POINT (0 1) dtype: geometry We can check if each geometry of GeoSeries crosses a single geometry: .. image:: ../../../_static/binary_op-03.svg :align: center >>> line = LineString([(-1, 1), (3, 1)]) >>> s.crosses(line) 0 True 1 True 2 True 3 False dtype: bool We can also check two GeoSeries against each other, row by row. The GeoSeries above have different indices. We can either align both GeoSeries based on index values and compare elements with the same index using ``align=True`` or ignore index and compare elements based on their matching order using ``align=False``: .. image:: ../../../_static/binary_op-02.svg >>> s.crosses(s2, align=True) 0 False 1 True 2 False 3 False 4 False dtype: bool >>> s.crosses(s2, align=False) 0 True 1 True 2 False 3 False dtype: bool Notice that a line does not cross a point that it contains. Notes ----- This method works in a row-wise manner. It does not check if an element of one GeoSeries ``crosses`` *any* element of the other one. See Also -------- GeoSeries.disjoint GeoSeries.intersects """ return _binary_op("crosses", self, other, align) def disjoint(self, other, align=None): """Return a ``Series`` of ``dtype('bool')`` with value ``True`` for each aligned geometry disjoint to `other`. An object is said to be disjoint to `other` if its `boundary` and `interior` does not intersect at all with those of the other. The operation works on a 1-to-1 row-wise manner: .. image:: ../../../_static/binary_op-01.svg :align: center Parameters ---------- other : GeoSeries or geometric object The GeoSeries (elementwise) or geometric object to test if is disjoint. align : bool | None (default None) If True, automatically aligns GeoSeries based on their indices. If False, the order of elements is preserved. None defaults to True. Returns ------- Series (bool) Examples -------- >>> from shapely.geometry import Polygon, LineString, Point >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (2, 2), (0, 2)]), ... LineString([(0, 0), (2, 2)]), ... LineString([(2, 0), (0, 2)]), ... Point(0, 1), ... ], ... ) >>> s2 = geopandas.GeoSeries( ... [ ... Polygon([(-1, 0), (-1, 2), (0, -2)]), ... LineString([(0, 0), (0, 1)]), ... Point(1, 1), ... Point(0, 0), ... ], ... ) >>> s 0 POLYGON ((0 0, 2 2, 0 2, 0 0)) 1 LINESTRING (0 0, 2 2) 2 LINESTRING (2 0, 0 2) 3 POINT (0 1) dtype: geometry >>> s2 0 POLYGON ((-1 0, -1 2, 0 -2, -1 0)) 1 LINESTRING (0 0, 0 1) 2 POINT (1 1) 3 POINT (0 0) dtype: geometry We can check each geometry of GeoSeries to a single geometry: .. image:: ../../../_static/binary_op-03.svg :align: center >>> line = LineString([(0, 0), (2, 0)]) >>> s.disjoint(line) 0 False 1 False 2 False 3 True dtype: bool We can also check two GeoSeries against each other, row by row. We can either align both GeoSeries based on index values and compare elements with the same index using ``align=True`` or ignore index and compare elements based on their matching order using ``align=False``: .. image:: ../../../_static/binary_op-02.svg >>> s.disjoint(s2) 0 True 1 False 2 False 3 True dtype: bool Notes ----- This method works in a row-wise manner. It does not check if an element of one GeoSeries is equal to *any* element of the other one. See Also -------- GeoSeries.intersects GeoSeries.touches """ return _binary_op("disjoint", self, other, align) def intersects(self, other, align=None): """Return a ``Series`` of ``dtype('bool')`` with value ``True`` for each aligned geometry that intersects `other`. An object is said to intersect `other` if its `boundary` and `interior` intersects in any way with those of the other. The operation works on a 1-to-1 row-wise manner: .. image:: ../../../_static/binary_op-01.svg :align: center Parameters ---------- other : GeoSeries or geometric object The GeoSeries (elementwise) or geometric object to test if is intersected. align : bool | None (default None) If True, automatically aligns GeoSeries based on their indices. If False, the order of elements is preserved. None defaults to True. Returns ------- Series (bool) Examples -------- >>> from shapely.geometry import Polygon, LineString, Point >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (2, 2), (0, 2)]), ... LineString([(0, 0), (2, 2)]), ... LineString([(2, 0), (0, 2)]), ... Point(0, 1), ... ], ... ) >>> s2 = geopandas.GeoSeries( ... [ ... LineString([(1, 0), (1, 3)]), ... LineString([(2, 0), (0, 2)]), ... Point(1, 1), ... Point(0, 1), ... ], ... index=range(1, 5), ... ) >>> s 0 POLYGON ((0 0, 2 2, 0 2, 0 0)) 1 LINESTRING (0 0, 2 2) 2 LINESTRING (2 0, 0 2) 3 POINT (0 1) dtype: geometry >>> s2 1 LINESTRING (1 0, 1 3) 2 LINESTRING (2 0, 0 2) 3 POINT (1 1) 4 POINT (0 1) dtype: geometry We can check if each geometry of GeoSeries crosses a single geometry: .. image:: ../../../_static/binary_op-03.svg :align: center >>> line = LineString([(-1, 1), (3, 1)]) >>> s.intersects(line) 0 True 1 True 2 True 3 True dtype: bool We can also check two GeoSeries against each other, row by row. The GeoSeries above have different indices. We can either align both GeoSeries based on index values and compare elements with the same index using ``align=True`` or ignore index and compare elements based on their matching order using ``align=False``: .. image:: ../../../_static/binary_op-02.svg >>> s.intersects(s2, align=True) 0 False 1 True 2 True 3 False 4 False dtype: bool >>> s.intersects(s2, align=False) 0 True 1 True 2 True 3 True dtype: bool Notes ----- This method works in a row-wise manner. It does not check if an element of one GeoSeries ``crosses`` *any* element of the other one. See Also -------- GeoSeries.disjoint GeoSeries.crosses GeoSeries.touches GeoSeries.intersection """ return _binary_op("intersects", self, other, align) def overlaps(self, other, align=None): """Return True for all aligned geometries that overlap *other*, else False. Geometries overlaps if they have more than one but not all points in common, have the same dimension, and the intersection of the interiors of the geometries has the same dimension as the geometries themselves. The operation works on a 1-to-1 row-wise manner: .. image:: ../../../_static/binary_op-01.svg :align: center Parameters ---------- other : GeoSeries or geometric object The GeoSeries (elementwise) or geometric object to test if overlaps. align : bool | None (default None) If True, automatically aligns GeoSeries based on their indices. If False, the order of elements is preserved. None defaults to True. Returns ------- Series (bool) Examples -------- >>> from shapely.geometry import Polygon, LineString, MultiPoint, Point >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (2, 2), (0, 2)]), ... Polygon([(0, 0), (2, 2), (0, 2)]), ... LineString([(0, 0), (2, 2)]), ... MultiPoint([(0, 0), (0, 1)]), ... ], ... ) >>> s2 = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (2, 0), (0, 2)]), ... LineString([(0, 1), (1, 1)]), ... LineString([(1, 1), (3, 3)]), ... Point(0, 1), ... ], ... index=range(1, 5), ... ) >>> s 0 POLYGON ((0 0, 2 2, 0 2, 0 0)) 1 POLYGON ((0 0, 2 2, 0 2, 0 0)) 2 LINESTRING (0 0, 2 2) 3 MULTIPOINT ((0 0), (0 1)) dtype: geometry >>> s2 1 POLYGON ((0 0, 2 0, 0 2, 0 0)) 2 LINESTRING (0 1, 1 1) 3 LINESTRING (1 1, 3 3) 4 POINT (0 1) dtype: geometry We can check if each geometry of GeoSeries overlaps a single geometry: .. image:: ../../../_static/binary_op-03.svg :align: center >>> polygon = Polygon([(0, 0), (1, 0), (1, 1), (0, 1)]) >>> s.overlaps(polygon, align=True) 0 True 1 True 2 False 3 False dtype: bool We can also check two GeoSeries against each other, row by row. The GeoSeries above have different indices. We can either align both GeoSeries based on index values and compare elements with the same index using ``align=True`` or ignore index and compare elements based on their matching order using ``align=False``: .. image:: ../../../_static/binary_op-02.svg >>> s.overlaps(s2) 0 False 1 True 2 False 3 False 4 False dtype: bool >>> s.overlaps(s2, align=False) 0 True 1 False 2 True 3 False dtype: bool Notes ----- This method works in a row-wise manner. It does not check if an element of one GeoSeries ``overlaps`` *any* element of the other one. See Also -------- GeoSeries.crosses GeoSeries.intersects """ return _binary_op("overlaps", self, other, align) def touches(self, other, align=None): """Return a ``Series`` of ``dtype('bool')`` with value ``True`` for each aligned geometry that touches `other`. An object is said to touch `other` if it has at least one point in common with `other` and its interior does not intersect with any part of the other. Overlapping features therefore do not touch. The operation works on a 1-to-1 row-wise manner: .. image:: ../../../_static/binary_op-01.svg :align: center Parameters ---------- other : GeoSeries or geometric object The GeoSeries (elementwise) or geometric object to test if is touched. align : bool | None (default None) If True, automatically aligns GeoSeries based on their indices. If False, the order of elements is preserved. None defaults to True. Returns ------- Series (bool) Examples -------- >>> from shapely.geometry import Polygon, LineString, MultiPoint, Point >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (2, 2), (0, 2)]), ... Polygon([(0, 0), (2, 2), (0, 2)]), ... LineString([(0, 0), (2, 2)]), ... MultiPoint([(0, 0), (0, 1)]), ... ], ... ) >>> s2 = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (-2, 0), (0, -2)]), ... LineString([(0, 1), (1, 1)]), ... LineString([(1, 1), (3, 0)]), ... Point(0, 1), ... ], ... index=range(1, 5), ... ) >>> s 0 POLYGON ((0 0, 2 2, 0 2, 0 0)) 1 POLYGON ((0 0, 2 2, 0 2, 0 0)) 2 LINESTRING (0 0, 2 2) 3 MULTIPOINT ((0 0), (0 1)) dtype: geometry >>> s2 1 POLYGON ((0 0, -2 0, 0 -2, 0 0)) 2 LINESTRING (0 1, 1 1) 3 LINESTRING (1 1, 3 0) 4 POINT (0 1) dtype: geometry We can check if each geometry of GeoSeries touches a single geometry: .. image:: ../../../_static/binary_op-03.svg :align: center >>> line = LineString([(0, 0), (-1, -2)]) >>> s.touches(line) 0 True 1 True 2 True 3 True dtype: bool We can also check two GeoSeries against each other, row by row. The GeoSeries above have different indices. We can either align both GeoSeries based on index values and compare elements with the same index using ``align=True`` or ignore index and compare elements based on their matching order using ``align=False``: .. image:: ../../../_static/binary_op-02.svg >>> s.touches(s2, align=True) 0 False 1 True 2 True 3 False 4 False dtype: bool >>> s.touches(s2, align=False) 0 True 1 False 2 True 3 False dtype: bool Notes ----- This method works in a row-wise manner. It does not check if an element of one GeoSeries ``touches`` *any* element of the other one. See Also -------- GeoSeries.overlaps GeoSeries.intersects """ return _binary_op("touches", self, other, align) def within(self, other, align=None): """Return a ``Series`` of ``dtype('bool')`` with value ``True`` for each aligned geometry that is within `other`. An object is said to be within `other` if at least one of its points is located in the `interior` and no points are located in the `exterior` of the other. If either object is empty, this operation returns ``False``. This is the inverse of :meth:`contains` in the sense that the expression ``a.within(b) == b.contains(a)`` always evaluates to ``True``. The operation works on a 1-to-1 row-wise manner: .. image:: ../../../_static/binary_op-01.svg :align: center Parameters ---------- other : GeoSeries or geometric object The GeoSeries (elementwise) or geometric object to test if each geometry is within. align : bool | None (default None) If True, automatically aligns GeoSeries based on their indices. If False, the order of elements is preserved. None defaults to True. Returns ------- Series (bool) Examples -------- >>> from shapely.geometry import Polygon, LineString, Point >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (2, 2), (0, 2)]), ... Polygon([(0, 0), (1, 2), (0, 2)]), ... LineString([(0, 0), (0, 2)]), ... Point(0, 1), ... ], ... ) >>> s2 = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (1, 1), (0, 1)]), ... LineString([(0, 0), (0, 2)]), ... LineString([(0, 0), (0, 1)]), ... Point(0, 1), ... ], ... index=range(1, 5), ... ) >>> s 0 POLYGON ((0 0, 2 2, 0 2, 0 0)) 1 POLYGON ((0 0, 1 2, 0 2, 0 0)) 2 LINESTRING (0 0, 0 2) 3 POINT (0 1) dtype: geometry >>> s2 1 POLYGON ((0 0, 1 1, 0 1, 0 0)) 2 LINESTRING (0 0, 0 2) 3 LINESTRING (0 0, 0 1) 4 POINT (0 1) dtype: geometry We can check if each geometry of GeoSeries is within a single geometry: .. image:: ../../../_static/binary_op-03.svg :align: center >>> polygon = Polygon([(0, 0), (2, 2), (0, 2)]) >>> s.within(polygon, align=True) 0 True 1 True 2 False 3 False dtype: bool We can also check two GeoSeries against each other, row by row. The GeoSeries above have different indices. We can either align both GeoSeries based on index values and compare elements with the same index using ``align=True`` or ignore index and compare elements based on their matching order using ``align=False``: .. image:: ../../../_static/binary_op-02.svg >>> s2.within(s) 0 False 1 False 2 True 3 False 4 False dtype: bool >>> s2.within(s, align=False) 1 True 2 False 3 True 4 True dtype: bool Notes ----- This method works in a row-wise manner. It does not check if an element of one GeoSeries is ``within`` *any* element of the other one. See Also -------- GeoSeries.contains """ return _binary_op("within", self, other, align) def covers(self, other, align=None): """Return a ``Series`` of ``dtype('bool')`` with value ``True`` for each aligned geometry that is entirely covering `other`. An object A is said to cover another object B if no points of B lie in the exterior of A. If either object is empty, this operation returns ``False``. The operation works on a 1-to-1 row-wise manner: .. image:: ../../../_static/binary_op-01.svg :align: center See https://lin-ear-th-inking.blogspot.com/2007/06/subtleties-of-ogc-covers-spatial.html for reference. Parameters ---------- other : Geoseries or geometric object The Geoseries (elementwise) or geometric object to check is being covered. align : bool | None (default None) If True, automatically aligns GeoSeries based on their indices. If False, the order of elements is preserved. None defaults to True. Returns ------- Series (bool) Examples -------- >>> from shapely.geometry import Polygon, LineString, Point >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (2, 0), (2, 2), (0, 2)]), ... Polygon([(0, 0), (2, 2), (0, 2)]), ... LineString([(0, 0), (2, 2)]), ... Point(0, 0), ... ], ... ) >>> s2 = geopandas.GeoSeries( ... [ ... Polygon([(0.5, 0.5), (1.5, 0.5), (1.5, 1.5), (0.5, 1.5)]), ... Polygon([(0, 0), (2, 0), (2, 2), (0, 2)]), ... LineString([(1, 1), (1.5, 1.5)]), ... Point(0, 0), ... ], ... index=range(1, 5), ... ) >>> s 0 POLYGON ((0 0, 2 0, 2 2, 0 2, 0 0)) 1 POLYGON ((0 0, 2 2, 0 2, 0 0)) 2 LINESTRING (0 0, 2 2) 3 POINT (0 0) dtype: geometry >>> s2 1 POLYGON ((0.5 0.5, 1.5 0.5, 1.5 1.5, 0.5 1.5, ... 2 POLYGON ((0 0, 2 0, 2 2, 0 2, 0 0)) 3 LINESTRING (1 1, 1.5 1.5) 4 POINT (0 0) dtype: geometry We can check if each geometry of GeoSeries covers a single geometry: .. image:: ../../../_static/binary_op-03.svg :align: center >>> poly = Polygon([(0, 0), (2, 0), (2, 2), (0, 2)]) >>> s.covers(poly) 0 True 1 False 2 False 3 False dtype: bool We can also check two GeoSeries against each other, row by row. The GeoSeries above have different indices. We can either align both GeoSeries based on index values and compare elements with the same index using ``align=True`` or ignore index and compare elements based on their matching order using ``align=False``: .. image:: ../../../_static/binary_op-02.svg >>> s.covers(s2, align=True) 0 False 1 False 2 False 3 False 4 False dtype: bool >>> s.covers(s2, align=False) 0 True 1 False 2 True 3 True dtype: bool Notes ----- This method works in a row-wise manner. It does not check if an element of one GeoSeries ``covers`` *any* element of the other one. See Also -------- GeoSeries.covered_by GeoSeries.overlaps """ return _binary_op("covers", self, other, align) def covered_by(self, other, align=None): """Return a ``Series`` of ``dtype('bool')`` with value ``True`` for each aligned geometry that is entirely covered by `other`. An object A is said to cover another object B if no points of B lie in the exterior of A. The operation works on a 1-to-1 row-wise manner: .. image:: ../../../_static/binary_op-01.svg :align: center See https://lin-ear-th-inking.blogspot.com/2007/06/subtleties-of-ogc-covers-spatial.html for reference. Parameters ---------- other : Geoseries or geometric object The Geoseries (elementwise) or geometric object to check is being covered. align : bool | None (default None) If True, automatically aligns GeoSeries based on their indices. If False, the order of elements is preserved. None defaults to True. Returns ------- Series (bool) Examples -------- >>> from shapely.geometry import Polygon, LineString, Point >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0.5, 0.5), (1.5, 0.5), (1.5, 1.5), (0.5, 1.5)]), ... Polygon([(0, 0), (2, 0), (2, 2), (0, 2)]), ... LineString([(1, 1), (1.5, 1.5)]), ... Point(0, 0), ... ], ... ) >>> s2 = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (2, 0), (2, 2), (0, 2)]), ... Polygon([(0, 0), (2, 2), (0, 2)]), ... LineString([(0, 0), (2, 2)]), ... Point(0, 0), ... ], ... index=range(1, 5), ... ) >>> s 0 POLYGON ((0.5 0.5, 1.5 0.5, 1.5 1.5, 0.5 1.5, ... 1 POLYGON ((0 0, 2 0, 2 2, 0 2, 0 0)) 2 LINESTRING (1 1, 1.5 1.5) 3 POINT (0 0) dtype: geometry >>> >>> s2 1 POLYGON ((0 0, 2 0, 2 2, 0 2, 0 0)) 2 POLYGON ((0 0, 2 2, 0 2, 0 0)) 3 LINESTRING (0 0, 2 2) 4 POINT (0 0) dtype: geometry We can check if each geometry of GeoSeries is covered by a single geometry: .. image:: ../../../_static/binary_op-03.svg :align: center >>> poly = Polygon([(0, 0), (2, 0), (2, 2), (0, 2)]) >>> s.covered_by(poly) 0 True 1 True 2 True 3 True dtype: bool We can also check two GeoSeries against each other, row by row. The GeoSeries above have different indices. We can either align both GeoSeries based on index values and compare elements with the same index using ``align=True`` or ignore index and compare elements based on their matching order using ``align=False``: .. image:: ../../../_static/binary_op-02.svg >>> s.covered_by(s2, align=True) 0 False 1 True 2 True 3 True 4 False dtype: bool >>> s.covered_by(s2, align=False) 0 True 1 False 2 True 3 True dtype: bool Notes ----- This method works in a row-wise manner. It does not check if an element of one GeoSeries is ``covered_by`` *any* element of the other one. See Also -------- GeoSeries.covers GeoSeries.overlaps """ return _binary_op("covered_by", self, other, align) def distance(self, other, align=None): """Return a ``Series`` containing the distance to aligned `other`. The operation works on a 1-to-1 row-wise manner: .. image:: ../../../_static/binary_op-01.svg :align: center Parameters ---------- other : Geoseries or geometric object The Geoseries (elementwise) or geometric object to find the distance to. align : bool | None (default None) If True, automatically aligns GeoSeries based on their indices. If False, the order of elements is preserved. None defaults to True. Returns ------- Series (float) Examples -------- >>> from shapely.geometry import Polygon, LineString, Point >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (1, 0), (1, 1)]), ... Polygon([(0, 0), (-1, 0), (-1, 1)]), ... LineString([(1, 1), (0, 0)]), ... Point(0, 0), ... ], ... ) >>> s2 = geopandas.GeoSeries( ... [ ... Polygon([(0.5, 0.5), (1.5, 0.5), (1.5, 1.5), (0.5, 1.5)]), ... Point(3, 1), ... LineString([(1, 0), (2, 0)]), ... Point(0, 1), ... ], ... index=range(1, 5), ... ) >>> s 0 POLYGON ((0 0, 1 0, 1 1, 0 0)) 1 POLYGON ((0 0, -1 0, -1 1, 0 0)) 2 LINESTRING (1 1, 0 0) 3 POINT (0 0) dtype: geometry >>> s2 1 POLYGON ((0.5 0.5, 1.5 0.5, 1.5 1.5, 0.5 1.5, ... 2 POINT (3 1) 3 LINESTRING (1 0, 2 0) 4 POINT (0 1) dtype: geometry We can check the distance of each geometry of GeoSeries to a single geometry: .. image:: ../../../_static/binary_op-03.svg :align: center >>> point = Point(-1, 0) >>> s.distance(point) 0 1.0 1 0.0 2 1.0 3 1.0 dtype: float64 We can also check two GeoSeries against each other, row by row. The GeoSeries above have different indices. We can either align both GeoSeries based on index values and use elements with the same index using ``align=True`` or ignore index and use elements based on their matching order using ``align=False``: .. image:: ../../../_static/binary_op-02.svg >>> s.distance(s2, align=True) 0 NaN 1 0.707107 2 2.000000 3 1.000000 4 NaN dtype: float64 >>> s.distance(s2, align=False) 0 0.000000 1 3.162278 2 0.707107 3 1.000000 dtype: float64 """ return _binary_op("distance", self, other, align) def hausdorff_distance(self, other, align=None, densify=None): """Return a ``Series`` containing the Hausdorff distance to aligned `other`. The Hausdorff distance is the largest distance consisting of any point in `self` with the nearest point in `other`. The operation works on a 1-to-1 row-wise manner: .. image:: ../../../_static/binary_op-01.svg :align: center Parameters ---------- other : GeoSeries or geometric object The Geoseries (elementwise) or geometric object to find the distance to. align : bool | None (default None) If True, automatically aligns GeoSeries based on their indices. If False, the order of elements is preserved. None defaults to True. densify : float (default None) A value between 0 and 1, that splits each subsegment of a line string into equal length segments, making the approximation less coarse. A densify value of 0.5 will add a point halfway between each pair of points. A densify value of 0.25 will add a point a quarter of the way between each pair of points. Returns ------- Series (float) Examples -------- >>> from shapely.geometry import Polygon, LineString, Point >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (1, 0), (1, 1)]), ... Polygon([(0, 0), (-1, 0), (-1, 1)]), ... LineString([(1, 1), (0, 0)]), ... Point(0, 0), ... ], ... ) >>> s2 = geopandas.GeoSeries( ... [ ... Polygon([(0.5, 0.5), (1.5, 0.5), (1.5, 1.5), (0.5, 1.5)]), ... Point(3, 1), ... LineString([(1, 0), (2, 0)]), ... Point(0, 1), ... ], ... index=range(1, 5), ... ) >>> s 0 POLYGON ((0 0, 1 0, 1 1, 0 0)) 1 POLYGON ((0 0, -1 0, -1 1, 0 0)) 2 LINESTRING (1 1, 0 0) 3 POINT (0 0) dtype: geometry >>> s2 1 POLYGON ((0.5 0.5, 1.5 0.5, 1.5 1.5, 0.5 1.5, ... 2 POINT (3 1) 3 LINESTRING (1 0, 2 0) 4 POINT (0 1) dtype: geometry We can check the hausdorff distance of each geometry of GeoSeries to a single geometry: >>> point = Point(-1, 0) >>> s.hausdorff_distance(point) 0 2.236068 1 1.000000 2 2.236068 3 1.000000 dtype: float64 We can also check two GeoSeries against each other, row by row. The GeoSeries above have different indices. We can either align both GeoSeries based on index values and use elements with the same index using ``align=True`` or ignore index and use elements based on their matching order using ``align=False``: .. image:: ../../../_static/binary_op-02.svg >>> s.hausdorff_distance(s2, align=True) 0 NaN 1 2.121320 2 3.162278 3 2.000000 4 NaN dtype: float64 >>> s.hausdorff_distance(s2, align=False) 0 0.707107 1 4.123106 2 1.414214 3 1.000000 dtype: float64 We can also set a densify value, which is a float between 0 and 1 and signifies the fraction of the distance between each pair of points that will be used as the distance between the points when densifying. >>> l1 = geopandas.GeoSeries([LineString([(130, 0), (0, 0), (0, 150)])]) >>> l2 = geopandas.GeoSeries([LineString([(10, 10), (10, 150), (130, 10)])]) >>> l1.hausdorff_distance(l2) 0 14.142136 dtype: float64 >>> l1.hausdorff_distance(l2, densify=0.25) 0 70.0 dtype: float64 """ return _binary_op("hausdorff_distance", self, other, align, densify=densify) def frechet_distance(self, other, align=None, densify=None): """Return a ``Series`` containing the Frechet distance to aligned `other`. The Fréchet distance is a measure of similarity: it is the greatest distance between any point in A and the closest point in B. The discrete distance is an approximation of this metric: only vertices are considered. The parameter ``densify`` makes this approximation less coarse by splitting the line segments between vertices before computing the distance. Fréchet distance sweep continuously along their respective curves and the direction of curves is significant. This makes it a better measure of similarity than Hausdorff distance for curve or surface matching. The operation works on a 1-to-1 row-wise manner: .. image:: ../../../_static/binary_op-01.svg :align: center Parameters ---------- other : GeoSeries or geometric object The Geoseries (elementwise) or geometric object to find the distance to. align : bool | None (default None) If True, automatically aligns GeoSeries based on their indices. If False, the order of elements is preserved. None defaults to True. densify : float (default None) A value between 0 and 1, that splits each subsegment of a line string into equal length segments, making the approximation less coarse. A densify value of 0.5 will add a point halfway between each pair of points. A densify value of 0.25 will add a point every quarter of the way between each pair of points. Returns ------- Series (float) Examples -------- >>> from shapely.geometry import Polygon, LineString, Point >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (1, 0), (1, 1)]), ... Polygon([(0, 0), (-1, 0), (-1, 1)]), ... LineString([(1, 1), (0, 0)]), ... Point(0, 0), ... ], ... ) >>> s2 = geopandas.GeoSeries( ... [ ... Polygon([(0.5, 0.5), (1.5, 0.5), (1.5, 1.5), (0.5, 1.5)]), ... Point(3, 1), ... LineString([(1, 0), (2, 0)]), ... Point(0, 1), ... ], ... index=range(1, 5), ... ) >>> s 0 POLYGON ((0 0, 1 0, 1 1, 0 0)) 1 POLYGON ((0 0, -1 0, -1 1, 0 0)) 2 LINESTRING (1 1, 0 0) 3 POINT (0 0) dtype: geometry >>> s2 1 POLYGON ((0.5 0.5, 1.5 0.5, 1.5 1.5, 0.5 1.5, ... 2 POINT (3 1) 3 LINESTRING (1 0, 2 0) 4 POINT (0 1) dtype: geometry We can check the frechet distance of each geometry of GeoSeries to a single geometry: >>> point = Point(-1, 0) >>> s.frechet_distance(point) 0 2.236068 1 1.000000 2 2.236068 3 1.000000 dtype: float64 We can also check two GeoSeries against each other, row by row. The GeoSeries above have different indices. We can either align both GeoSeries based on index values and use elements with the same index using ``align=True`` or ignore index and use elements based on their matching order using ``align=False``: .. image:: ../../../_static/binary_op-02.svg >>> s.frechet_distance(s2, align=True) 0 NaN 1 2.121320 2 3.162278 3 2.000000 4 NaN dtype: float64 >>> s.frechet_distance(s2, align=False) 0 0.707107 1 4.123106 2 2.000000 3 1.000000 dtype: float64 We can also set a ``densify`` value, which is a float between 0 and 1 and signifies the fraction of the distance between each pair of points that will be used as the distance between the points when densifying. >>> l1 = geopandas.GeoSeries([LineString([(0, 0), (10, 0), (0, 15)])]) >>> l2 = geopandas.GeoSeries([LineString([(0, 0), (20, 15), (9, 11)])]) >>> l1.frechet_distance(l2) 0 18.027756 dtype: float64 >>> l1.frechet_distance(l2, densify=0.25) 0 16.77051 dtype: float64 """ return _binary_op("frechet_distance", self, other, align, densify=densify) # # Binary operations that return a GeoSeries # def difference(self, other, align=None, grid_size=None): """Return a ``GeoSeries`` of the points in each aligned geometry that are not in `other`. .. image:: ../../../_static/binary_geo-difference.svg :align: center The operation works on a 1-to-1 row-wise manner: .. image:: ../../../_static/binary_op-01.svg :align: center Parameters ---------- other : Geoseries or geometric object The Geoseries (elementwise) or geometric object to find the difference to. align : bool | None (default None) If True, automatically aligns GeoSeries based on their indices. If False, the order of elements is preserved. None defaults to True. grid_size : float | None (default None) The cell size of the precision grid to use. All the vertices of the output geometry will fall on the grid defined by the grid size. If None, the highest precision (smallest grid size) of the inputs is used. Returns ------- GeoSeries Examples -------- >>> from shapely.geometry import Polygon, LineString, Point >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (2, 2), (0, 2)]), ... Polygon([(0, 0), (2, 2), (0, 2)]), ... LineString([(0, 0), (2, 2)]), ... LineString([(2, 0), (0, 2)]), ... Point(0, 1), ... ], ... ) >>> s2 = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (1, 1), (0, 1)]), ... LineString([(1, 0), (1, 3)]), ... LineString([(2, 0), (0, 2)]), ... Point(1, 1), ... Point(0, 1), ... ], ... index=range(1, 6), ... ) >>> s 0 POLYGON ((0 0, 2 2, 0 2, 0 0)) 1 POLYGON ((0 0, 2 2, 0 2, 0 0)) 2 LINESTRING (0 0, 2 2) 3 LINESTRING (2 0, 0 2) 4 POINT (0 1) dtype: geometry >>> s2 1 POLYGON ((0 0, 1 1, 0 1, 0 0)) 2 LINESTRING (1 0, 1 3) 3 LINESTRING (2 0, 0 2) 4 POINT (1 1) 5 POINT (0 1) dtype: geometry We can do difference of each geometry and a single shapely geometry: .. image:: ../../../_static/binary_op-03.svg :align: center >>> s.difference(Polygon([(0, 0), (1, 1), (0, 1)])) 0 POLYGON ((0 2, 2 2, 1 1, 0 1, 0 2)) 1 POLYGON ((0 2, 2 2, 1 1, 0 1, 0 2)) 2 LINESTRING (1 1, 2 2) 3 MULTILINESTRING ((2 0, 1 1), (1 1, 0 2)) 4 POINT EMPTY dtype: geometry We can also check two GeoSeries against each other, row by row. The GeoSeries above have different indices. We can either align both GeoSeries based on index values and compare elements with the same index using ``align=True`` or ignore index and compare elements based on their matching order using ``align=False``: .. image:: ../../../_static/binary_op-02.svg >>> s.difference(s2, align=True) 0 None 1 POLYGON ((0 2, 2 2, 1 1, 0 1, 0 2)) 2 MULTILINESTRING ((0 0, 1 1), (1 1, 2 2)) 3 LINESTRING EMPTY 4 POINT (0 1) 5 None dtype: geometry >>> s.difference(s2, align=False) 0 POLYGON ((0 2, 2 2, 1 1, 0 1, 0 2)) 1 POLYGON ((0 0, 0 2, 1 2, 2 2, 1 1, 0 0)) 2 MULTILINESTRING ((0 0, 1 1), (1 1, 2 2)) 3 LINESTRING (2 0, 0 2) 4 POINT EMPTY dtype: geometry See Also -------- GeoSeries.symmetric_difference GeoSeries.union GeoSeries.intersection """ return _binary_geo("difference", self, other, align, grid_size=grid_size) def symmetric_difference(self, other, align=None, grid_size=None): """Return a ``GeoSeries`` of the symmetric difference of points in each aligned geometry with `other`. For each geometry, the symmetric difference consists of points in the geometry not in `other`, and points in `other` not in the geometry. .. image:: ../../../_static/binary_geo-symm_diff.svg :align: center The operation works on a 1-to-1 row-wise manner: .. image:: ../../../_static/binary_op-01.svg :align: center Parameters ---------- other : Geoseries or geometric object The Geoseries (elementwise) or geometric object to find the symmetric difference to. align : bool | None (default None) If True, automatically aligns GeoSeries based on their indices. If False, the order of elements is preserved. None defaults to True. grid_size : float | None (default None) The cell size of the precision grid to use. All the vertices of the output geometry will fall on the grid defined by the grid size. If None, the highest precision (smallest grid size) of the inputs is used. Returns ------- GeoSeries Examples -------- >>> from shapely.geometry import Polygon, LineString, Point >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (2, 2), (0, 2)]), ... Polygon([(0, 0), (2, 2), (0, 2)]), ... LineString([(0, 0), (2, 2)]), ... LineString([(2, 0), (0, 2)]), ... Point(0, 1), ... ], ... ) >>> s2 = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (1, 1), (0, 1)]), ... LineString([(1, 0), (1, 3)]), ... LineString([(2, 0), (0, 2)]), ... Point(1, 1), ... Point(0, 1), ... ], ... index=range(1, 6), ... ) >>> s 0 POLYGON ((0 0, 2 2, 0 2, 0 0)) 1 POLYGON ((0 0, 2 2, 0 2, 0 0)) 2 LINESTRING (0 0, 2 2) 3 LINESTRING (2 0, 0 2) 4 POINT (0 1) dtype: geometry >>> s2 1 POLYGON ((0 0, 1 1, 0 1, 0 0)) 2 LINESTRING (1 0, 1 3) 3 LINESTRING (2 0, 0 2) 4 POINT (1 1) 5 POINT (0 1) dtype: geometry We can do symmetric difference of each geometry and a single shapely geometry: .. image:: ../../../_static/binary_op-03.svg :align: center >>> s.symmetric_difference(Polygon([(0, 0), (1, 1), (0, 1)])) 0 POLYGON ((0 2, 2 2, 1 1, 0 1, 0 2)) 1 POLYGON ((0 2, 2 2, 1 1, 0 1, 0 2)) 2 GEOMETRYCOLLECTION (POLYGON ((0 0, 0 1, 1 1, 0... 3 GEOMETRYCOLLECTION (POLYGON ((0 0, 0 1, 1 1, 0... 4 POLYGON ((0 1, 1 1, 0 0, 0 1)) dtype: geometry We can also check two GeoSeries against each other, row by row. The GeoSeries above have different indices. We can either align both GeoSeries based on index values and compare elements with the same index using ``align=True`` or ignore index and compare elements based on their matching order using ``align=False``: .. image:: ../../../_static/binary_op-02.svg >>> s.symmetric_difference(s2, align=True) 0 None 1 POLYGON ((0 2, 2 2, 1 1, 0 1, 0 2)) 2 MULTILINESTRING ((0 0, 1 1), (1 1, 2 2), (1 0,... 3 LINESTRING EMPTY 4 MULTIPOINT ((0 1), (1 1)) 5 None dtype: geometry >>> s.symmetric_difference(s2, align=False) 0 POLYGON ((0 2, 2 2, 1 1, 0 1, 0 2)) 1 GEOMETRYCOLLECTION (POLYGON ((0 0, 0 2, 1 2, 2... 2 MULTILINESTRING ((0 0, 1 1), (1 1, 2 2), (2 0,... 3 LINESTRING (2 0, 0 2) 4 POINT EMPTY dtype: geometry See Also -------- GeoSeries.difference GeoSeries.union GeoSeries.intersection """ return _binary_geo( "symmetric_difference", self, other, align, grid_size=grid_size ) def union(self, other, align=None, grid_size=None): """Return a ``GeoSeries`` of the union of points in each aligned geometry with `other`. .. image:: ../../../_static/binary_geo-union.svg :align: center The operation works on a 1-to-1 row-wise manner: .. image:: ../../../_static/binary_op-01.svg :align: center Parameters ---------- other : Geoseries or geometric object The Geoseries (elementwise) or geometric object to find the union with. align : bool | None (default None) If True, automatically aligns GeoSeries based on their indices. If False, the order of elements is preserved. None defaults to True. grid_size : float | None (default None) The cell size of the precision grid to use. All the vertices of the output geometry will fall on the grid defined by the grid size. If None, the highest precision (smallest grid size) of the inputs is used. Returns ------- GeoSeries Examples -------- >>> from shapely.geometry import Polygon, LineString, Point >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (2, 2), (0, 2)]), ... Polygon([(0, 0), (2, 2), (0, 2)]), ... LineString([(0, 0), (2, 2)]), ... LineString([(2, 0), (0, 2)]), ... Point(0, 1), ... ], ... ) >>> s2 = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (1, 1), (0, 1)]), ... LineString([(1, 0), (1, 3)]), ... LineString([(2, 0), (0, 2)]), ... Point(1, 1), ... Point(0, 1), ... ], ... index=range(1, 6), ... ) >>> s 0 POLYGON ((0 0, 2 2, 0 2, 0 0)) 1 POLYGON ((0 0, 2 2, 0 2, 0 0)) 2 LINESTRING (0 0, 2 2) 3 LINESTRING (2 0, 0 2) 4 POINT (0 1) dtype: geometry >>> >>> s2 1 POLYGON ((0 0, 1 1, 0 1, 0 0)) 2 LINESTRING (1 0, 1 3) 3 LINESTRING (2 0, 0 2) 4 POINT (1 1) 5 POINT (0 1) dtype: geometry We can do union of each geometry and a single shapely geometry: .. image:: ../../../_static/binary_op-03.svg :align: center >>> s.union(Polygon([(0, 0), (1, 1), (0, 1)])) 0 POLYGON ((0 0, 0 1, 0 2, 2 2, 1 1, 0 0)) 1 POLYGON ((0 0, 0 1, 0 2, 2 2, 1 1, 0 0)) 2 GEOMETRYCOLLECTION (POLYGON ((0 0, 0 1, 1 1, 0... 3 GEOMETRYCOLLECTION (POLYGON ((0 0, 0 1, 1 1, 0... 4 POLYGON ((0 1, 1 1, 0 0, 0 1)) dtype: geometry We can also check two GeoSeries against each other, row by row. The GeoSeries above have different indices. We can either align both GeoSeries based on index values and compare elements with the same index using ``align=True`` or ignore index and compare elements based on their matching order using ``align=False``: .. image:: ../../../_static/binary_op-02.svg >>> s.union(s2, align=True) 0 None 1 POLYGON ((0 0, 0 1, 0 2, 2 2, 1 1, 0 0)) 2 MULTILINESTRING ((0 0, 1 1), (1 1, 2 2), (1 0,... 3 LINESTRING (2 0, 0 2) 4 MULTIPOINT ((0 1), (1 1)) 5 None dtype: geometry >>> s.union(s2, align=False) 0 POLYGON ((0 0, 0 1, 0 2, 2 2, 1 1, 0 0)) 1 GEOMETRYCOLLECTION (POLYGON ((0 0, 0 2, 1 2, 2... 2 MULTILINESTRING ((0 0, 1 1), (1 1, 2 2), (2 0,... 3 LINESTRING (2 0, 0 2) 4 POINT (0 1) dtype: geometry See Also -------- GeoSeries.symmetric_difference GeoSeries.difference GeoSeries.intersection """ return _binary_geo("union", self, other, align, grid_size=grid_size) def intersection(self, other, align=None, grid_size=None): """Return a ``GeoSeries`` of the intersection of points in each aligned geometry with `other`. .. image:: ../../../_static/binary_geo-intersection.svg :align: center The operation works on a 1-to-1 row-wise manner: .. image:: ../../../_static/binary_op-01.svg :align: center Parameters ---------- other : Geoseries or geometric object The Geoseries (elementwise) or geometric object to find the intersection with. align : bool | None (default None) If True, automatically aligns GeoSeries based on their indices. If False, the order of elements is preserved. None defaults to True. grid_size : float | None (default None) The cell size of the precision grid to use. All the vertices of the output geometry will fall on the grid defined by the grid size. If None, the highest precision (smallest grid size) of the inputs is used. Returns ------- GeoSeries Examples -------- >>> from shapely.geometry import Polygon, LineString, Point >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (2, 2), (0, 2)]), ... Polygon([(0, 0), (2, 2), (0, 2)]), ... LineString([(0, 0), (2, 2)]), ... LineString([(2, 0), (0, 2)]), ... Point(0, 1), ... ], ... ) >>> s2 = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (1, 1), (0, 1)]), ... LineString([(1, 0), (1, 3)]), ... LineString([(2, 0), (0, 2)]), ... Point(1, 1), ... Point(0, 1), ... ], ... index=range(1, 6), ... ) >>> s 0 POLYGON ((0 0, 2 2, 0 2, 0 0)) 1 POLYGON ((0 0, 2 2, 0 2, 0 0)) 2 LINESTRING (0 0, 2 2) 3 LINESTRING (2 0, 0 2) 4 POINT (0 1) dtype: geometry >>> s2 1 POLYGON ((0 0, 1 1, 0 1, 0 0)) 2 LINESTRING (1 0, 1 3) 3 LINESTRING (2 0, 0 2) 4 POINT (1 1) 5 POINT (0 1) dtype: geometry We can also do intersection of each geometry and a single shapely geometry: .. image:: ../../../_static/binary_op-03.svg :align: center >>> s.intersection(Polygon([(0, 0), (1, 1), (0, 1)])) 0 POLYGON ((0 0, 0 1, 1 1, 0 0)) 1 POLYGON ((0 0, 0 1, 1 1, 0 0)) 2 LINESTRING (0 0, 1 1) 3 POINT (1 1) 4 POINT (0 1) dtype: geometry We can also check two GeoSeries against each other, row by row. The GeoSeries above have different indices. We can either align both GeoSeries based on index values and compare elements with the same index using ``align=True`` or ignore index and compare elements based on their matching order using ``align=False``: .. image:: ../../../_static/binary_op-02.svg >>> s.intersection(s2, align=True) 0 None 1 POLYGON ((0 0, 0 1, 1 1, 0 0)) 2 POINT (1 1) 3 LINESTRING (2 0, 0 2) 4 POINT EMPTY 5 None dtype: geometry >>> s.intersection(s2, align=False) 0 POLYGON ((0 0, 0 1, 1 1, 0 0)) 1 LINESTRING (1 1, 1 2) 2 POINT (1 1) 3 POINT (1 1) 4 POINT (0 1) dtype: geometry See Also -------- GeoSeries.difference GeoSeries.symmetric_difference GeoSeries.union """ return _binary_geo("intersection", self, other, align, grid_size=grid_size) def clip_by_rect(self, xmin, ymin, xmax, ymax): """Return a ``GeoSeries`` of the portions of geometry within the given rectangle. Note that the results are not exactly equal to :meth:`~GeoSeries.intersection()`. E.g. in edge cases, :meth:`~GeoSeries.clip_by_rect()` will not return a point just touching the rectangle. Check the examples section below for some of these exceptions. The geometry is clipped in a fast but possibly dirty way. The output is not guaranteed to be valid. No exceptions will be raised for topological errors. Note: empty geometries or geometries that do not overlap with the specified bounds will result in ``GEOMETRYCOLLECTION EMPTY``. Parameters ---------- xmin: float Minimum x value of the rectangle ymin: float Minimum y value of the rectangle xmax: float Maximum x value of the rectangle ymax: float Maximum y value of the rectangle Returns ------- GeoSeries Examples -------- >>> from shapely.geometry import Polygon, LineString, Point >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (2, 2), (0, 2)]), ... Polygon([(0, 0), (2, 2), (0, 2)]), ... LineString([(0, 0), (2, 2)]), ... LineString([(2, 0), (0, 2)]), ... Point(0, 1), ... ], ... ) >>> bounds = (0, 0, 1, 1) >>> s 0 POLYGON ((0 0, 2 2, 0 2, 0 0)) 1 POLYGON ((0 0, 2 2, 0 2, 0 0)) 2 LINESTRING (0 0, 2 2) 3 LINESTRING (2 0, 0 2) 4 POINT (0 1) dtype: geometry >>> s.clip_by_rect(*bounds) 0 POLYGON ((0 0, 0 1, 1 1, 0 0)) 1 POLYGON ((0 0, 0 1, 1 1, 0 0)) 2 LINESTRING (0 0, 1 1) 3 GEOMETRYCOLLECTION EMPTY 4 GEOMETRYCOLLECTION EMPTY dtype: geometry See Also -------- GeoSeries.intersection """ from .geoseries import GeoSeries geometry_array = GeometryArray(self.geometry.values) clipped_geometry = geometry_array.clip_by_rect(xmin, ymin, xmax, ymax) return GeoSeries(clipped_geometry, index=self.index, crs=self.crs) def shortest_line(self, other, align=None): """Return the shortest two-point line between two geometries. The resulting line consists of two points, representing the nearest points between the geometry pair. The line always starts in the first geometry a and ends in he second geometry b. The endpoints of the line will not necessarily be existing vertices of the input geometries a and b, but can also be a point along a line segment. The operation works on a 1-to-1 row-wise manner: .. image:: ../../../_static/binary_op-01.svg :align: center Parameters ---------- other : Geoseries or geometric object The Geoseries (elementwise) or geometric object to find the shortest line with. align : bool | None (default None) If True, automatically aligns GeoSeries based on their indices. If False, the order of elements is preserved. None defaults to True. Returns ------- GeoSeries Examples -------- >>> from shapely.geometry import Polygon, LineString, Point >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (2, 2), (0, 2)]), ... Polygon([(0, 0), (2, 2), (0, 2)]), ... LineString([(0, 0), (2, 2)]), ... LineString([(2, 0), (0, 2)]), ... Point(0, 1), ... ], ... ) >>> s 0 POLYGON ((0 0, 2 2, 0 2, 0 0)) 1 POLYGON ((0 0, 2 2, 0 2, 0 0)) 2 LINESTRING (0 0, 2 2) 3 LINESTRING (2 0, 0 2) 4 POINT (0 1) dtype: geometry We can also do intersection of each geometry and a single shapely geometry: .. image:: ../../../_static/binary_op-03.svg :align: center >>> p = Point(3, 3) >>> s.shortest_line(p) 0 LINESTRING (2 2, 3 3) 1 LINESTRING (2 2, 3 3) 2 LINESTRING (2 2, 3 3) 3 LINESTRING (1 1, 3 3) 4 LINESTRING (0 1, 3 3) dtype: geometry We can also check two GeoSeries against each other, row by row. The GeoSeries above have different indices than the one below. We can either align both GeoSeries based on index values and compare elements with the same index using ``align=True`` or ignore index and compare elements based on their matching order using ``align=False``: .. image:: ../../../_static/binary_op-02.svg >>> s2 = geopandas.GeoSeries( ... [ ... Polygon([(0.5, 0.5), (1.5, 0.5), (1.5, 1.5), (0.5, 1.5)]), ... Point(3, 1), ... LineString([(1, 0), (2, 0)]), ... Point(10, 15), ... Point(0, 1), ... ], ... index=range(1, 6), ... ) >>> s.shortest_line(s2, align=True) 0 None 1 LINESTRING (0.5 0.5, 0.5 0.5) 2 LINESTRING (2 2, 3 1) 3 LINESTRING (2 0, 2 0) 4 LINESTRING (0 1, 10 15) 5 None dtype: geometry >>> >>> s.shortest_line(s2, align=False) 0 LINESTRING (0.5 0.5, 0.5 0.5) 1 LINESTRING (2 2, 3 1) 2 LINESTRING (0.5 0.5, 1 0) 3 LINESTRING (0 2, 10 15) 4 LINESTRING (0 1, 0 1) dtype: geometry """ return _binary_geo("shortest_line", self, other, align) def snap(self, other, tolerance, align=None): """Snap the vertices and segments of the geometry to vertices of the reference. Vertices and segments of the input geometry are snapped to vertices of the reference geometry, returning a new geometry; the input geometries are not modified. The result geometry is the input geometry with the vertices and segments snapped. If no snapping occurs then the input geometry is returned unchanged. The tolerance is used to control where snapping is performed. Where possible, this operation tries to avoid creating invalid geometries; however, it does not guarantee that output geometries will be valid. It is the responsibility of the caller to check for and handle invalid geometries. Because too much snapping can result in invalid geometries being created, heuristics are used to determine the number and location of snapped vertices that are likely safe to snap. These heuristics may omit some potential snaps that are otherwise within the tolerance. The operation works in a 1-to-1 row-wise manner: .. image:: ../../../_static/binary_op-01.svg :align: center Parameters ---------- other : GeoSeries or geometric object The Geoseries (elementwise) or geometric object to snap to. tolerance : float or array like Maximum distance between vertices that shall be snapped align : bool | None (default None) If True, automatically aligns GeoSeries based on their indices. If False, the order of elements is preserved. None defaults to True. Returns ------- GeoSeries Examples -------- >>> from shapely import Polygon, LineString, Point >>> s = geopandas.GeoSeries( ... [ ... Point(0.5, 2.5), ... LineString([(0.1, 0.1), (0.49, 0.51), (1.01, 0.89)]), ... Polygon([(0, 0), (0, 10), (10, 10), (10, 0), (0, 0)]), ... ], ... ) >>> s 0 POINT (0.5 2.5) 1 LINESTRING (0.1 0.1, 0.49 0.51, 1.01 0.89) 2 POLYGON ((0 0, 0 10, 10 10, 10 0, 0 0)) dtype: geometry >>> s2 = geopandas.GeoSeries( ... [ ... Point(0, 2), ... LineString([(0, 0), (0.5, 0.5), (1.0, 1.0)]), ... Point(8, 10), ... ], ... index=range(1, 4), ... ) >>> s2 1 POINT (0 2) 2 LINESTRING (0 0, 0.5 0.5, 1 1) 3 POINT (8 10) dtype: geometry We can snap each geometry to a single shapely geometry: .. image:: ../../../_static/binary_op-03.svg :align: center >>> s.snap(Point(0, 2), tolerance=1) 0 POINT (0 2) 1 LINESTRING (0.1 0.1, 0.49 0.51, 1.01 0.89) 2 POLYGON ((0 0, 0 2, 0 10, 10 10, 10 0, 0 0)) dtype: geometry We can also snap two GeoSeries to each other, row by row. The GeoSeries above have different indices. We can either align both GeoSeries based on index values and snap elements with the same index using ``align=True`` or ignore index and snap elements based on their matching order using ``align=False``: .. image:: ../../../_static/binary_op-02.svg >>> s.snap(s2, tolerance=1, align=True) 0 None 1 LINESTRING (0.1 0.1, 0.49 0.51, 1.01 0.89) 2 POLYGON ((0.5 0.5, 1 1, 0 10, 10 10, 10 0, 0.5... 3 None dtype: geometry >>> s.snap(s2, tolerance=1, align=False) 0 POINT (0 2) 1 LINESTRING (0 0, 0.5 0.5, 1 1) 2 POLYGON ((0 0, 0 10, 8 10, 10 10, 10 0, 0 0)) dtype: geometry """ return _binary_geo("snap", self, other, align, tolerance=tolerance) def shared_paths(self, other, align=None): """Return the shared paths between two geometries. Geometries within the GeoSeries should be only (Multi)LineStrings or LinearRings. A GeoSeries of GeometryCollections is returned with two elements in each GeometryCollection. The first element is a MultiLineString containing shared paths with the same direction for both inputs. The second element is a MultiLineString containing shared paths with the opposite direction for the two inputs. You can extract individual geometries of the resulting GeometryCollection using the :meth:`GeoSeries.get_geometry` method. The operation works on a 1-to-1 row-wise manner: .. image:: ../../../_static/binary_op-01.svg :align: center Parameters ---------- other : Geoseries or geometric object The Geoseries (elementwise) or geometric object to find the shared paths with. Has to contain only (Multi)LineString or LinearRing geometry types. align : bool | None (default None) If True, automatically aligns GeoSeries based on their indices. If False, the order of elements is preserved. None defaults to True. Returns ------- GeoSeries Examples -------- >>> from shapely.geometry import LineString, MultiLineString >>> s = geopandas.GeoSeries( ... [ ... LineString([(0, 0), (1, 0), (1, 1), (0, 1), (0, 0)]), ... LineString([(1, 0), (2, 0), (2, 1), (1, 1), (1, 0)]), ... MultiLineString([[(1, 0), (2, 0)], [(2, 1), (1, 1), (1, 0)]]), ... ], ... ) >>> s 0 LINESTRING (0 0, 1 0, 1 1, 0 1, 0 0) 1 LINESTRING (1 0, 2 0, 2 1, 1 1, 1 0) 2 MULTILINESTRING ((1 0, 2 0), (2 1, 1 1, 1 0)) dtype: geometry We can find the shared paths between each geometry and a single shapely geometry: .. image:: ../../../_static/binary_op-03.svg :align: center >>> l = LineString([(1, 1), (0, 1)]) >>> s.shared_paths(l) 0 GEOMETRYCOLLECTION (MULTILINESTRING ((1 1, 0 1... 1 GEOMETRYCOLLECTION (MULTILINESTRING EMPTY, MUL... 2 GEOMETRYCOLLECTION (MULTILINESTRING EMPTY, MUL... dtype: geometry We can also check two GeoSeries against each other, row by row. The GeoSeries above have different indices than the one below. We can either align both GeoSeries based on index values and compare elements with the same index using ``align=True`` or ignore index and compare elements based on their matching order using ``align=False``: .. image:: ../../../_static/binary_op-02.svg >>> s2 = geopandas.GeoSeries( ... [ ... LineString([(1, 1), (0, 1)]), ... LineString([(0, 0), (1, 0), (1, 1), (0, 1), (0, 0)]), ... LineString([(1, 0), (2, 0), (2, 1), (1, 1), (1, 0)]), ... ], ... index=[1, 2, 3] ... ) >>> s.shared_paths(s2, align=True) 0 None 1 GEOMETRYCOLLECTION (MULTILINESTRING EMPTY, MUL... 2 GEOMETRYCOLLECTION (MULTILINESTRING EMPTY, MUL... 3 None dtype: geometry >>> >>> s.shared_paths(s2, align=False) 0 GEOMETRYCOLLECTION (MULTILINESTRING ((1 1, 0 1... 1 GEOMETRYCOLLECTION (MULTILINESTRING EMPTY, MUL... 2 GEOMETRYCOLLECTION (MULTILINESTRING ((1 0, 2 0... dtype: geometry See Also -------- GeoSeries.get_geometry """ return _binary_geo("shared_paths", self, other, align) # # Other operations # @property def bounds(self): """Return a ``DataFrame`` with columns ``minx``, ``miny``, ``maxx``, ``maxy`` values containing the bounds for each geometry. See ``GeoSeries.total_bounds`` for the limits of the entire series. Examples -------- >>> from shapely.geometry import Point, Polygon, LineString >>> d = {'geometry': [Point(2, 1), Polygon([(0, 0), (1, 1), (1, 0)]), ... LineString([(0, 1), (1, 2)])]} >>> gdf = geopandas.GeoDataFrame(d, crs="EPSG:4326") >>> gdf.bounds minx miny maxx maxy 0 2.0 1.0 2.0 1.0 1 0.0 0.0 1.0 1.0 2 0.0 1.0 1.0 2.0 You can assign the bounds to the ``GeoDataFrame`` as: >>> import pandas as pd >>> gdf = pd.concat([gdf, gdf.bounds], axis=1) >>> gdf geometry minx miny maxx maxy 0 POINT (2 1) 2.0 1.0 2.0 1.0 1 POLYGON ((0 0, 1 1, 1 0, 0 0)) 0.0 0.0 1.0 1.0 2 LINESTRING (0 1, 1 2) 0.0 1.0 1.0 2.0 """ bounds = GeometryArray(self.geometry.values).bounds return DataFrame( bounds, columns=["minx", "miny", "maxx", "maxy"], index=self.index ) @property def total_bounds(self): """Return a tuple containing ``minx``, ``miny``, ``maxx``, ``maxy`` values for the bounds of the series as a whole. See ``GeoSeries.bounds`` for the bounds of the geometries contained in the series. Examples -------- >>> from shapely.geometry import Point, Polygon, LineString >>> d = {'geometry': [Point(3, -1), Polygon([(0, 0), (1, 1), (1, 0)]), ... LineString([(0, 1), (1, 2)])]} >>> gdf = geopandas.GeoDataFrame(d, crs="EPSG:4326") >>> gdf.total_bounds array([ 0., -1., 3., 2.]) """ return GeometryArray(self.geometry.values).total_bounds @property def sindex(self): """Generate the spatial index. Creates R-tree spatial index based on ``shapely.STRtree``. Note that the spatial index may not be fully initialized until the first use. Examples -------- >>> from shapely.geometry import box >>> s = geopandas.GeoSeries(geopandas.points_from_xy(range(5), range(5))) >>> s 0 POINT (0 0) 1 POINT (1 1) 2 POINT (2 2) 3 POINT (3 3) 4 POINT (4 4) dtype: geometry Query the spatial index with a single geometry based on the bounding box: >>> s.sindex.query(box(1, 1, 3, 3)) array([1, 2, 3]) Query the spatial index with a single geometry based on the predicate: >>> s.sindex.query(box(1, 1, 3, 3), predicate="contains") array([2]) Query the spatial index with an array of geometries based on the bounding box: >>> s2 = geopandas.GeoSeries([box(1, 1, 3, 3), box(4, 4, 5, 5)]) >>> s2 0 POLYGON ((3 1, 3 3, 1 3, 1 1, 3 1)) 1 POLYGON ((5 4, 5 5, 4 5, 4 4, 5 4)) dtype: geometry >>> s.sindex.query(s2) array([[0, 0, 0, 1], [1, 2, 3, 4]]) Query the spatial index with an array of geometries based on the predicate: >>> s.sindex.query(s2, predicate="contains") array([[0], [2]]) """ return self.geometry.values.sindex @property def has_sindex(self): """Check the existence of the spatial index without generating it. Use the `.sindex` attribute on a GeoDataFrame or GeoSeries to generate a spatial index if it does not yet exist, which may take considerable time based on the underlying index implementation. Note that the underlying spatial index may not be fully initialized until the first use. Examples -------- >>> from shapely.geometry import Point >>> d = {'geometry': [Point(1, 2), Point(2, 1)]} >>> gdf = geopandas.GeoDataFrame(d) >>> gdf.has_sindex False >>> index = gdf.sindex >>> gdf.has_sindex True Returns ------- bool `True` if the spatial index has been generated or `False` if not. """ return self.geometry.values.has_sindex def buffer( self, distance, quad_segs=None, cap_style="round", join_style="round", mitre_limit=5.0, single_sided=False, **kwargs, ): """Return a ``GeoSeries`` of geometries representing all points within a given ``distance`` of each geometric object. Computes the buffer of a geometry for positive and negative buffer distance. The buffer of a geometry is defined as the Minkowski sum (or difference, for negative distance) of the geometry with a circle with radius equal to the absolute value of the buffer distance. The buffer operation always returns a polygonal result. The negative or zero-distance buffer of lines and points is always empty. Parameters ---------- distance : float, np.array, pd.Series The radius of the buffer in the Minkowski sum (or difference). If np.array or pd.Series are used then it must have same length as the GeoSeries. quad_segs : int (optional, default 16) The resolution of the buffer around each vertex. Specifies the number of linear segments in a quarter circle in the approximation of circular arcs. cap_style : {'round', 'square', 'flat'}, default 'round' Specifies the shape of buffered line endings. ``'round'`` results in circular line endings (see ``quad_segs``). Both ``'square'`` and ``'flat'`` result in rectangular line endings, ``'flat'`` will end at the original vertex, while ``'square'`` involves adding the buffer width. join_style : {'round', 'mitre', 'bevel'}, default 'round' Specifies the shape of buffered line midpoints. ``'round'`` results in rounded shapes. ``'bevel'`` results in a beveled edge that touches the original vertex. ``'mitre'`` results in a single vertex that is beveled depending on the ``mitre_limit`` parameter. mitre_limit : float, default 5.0 Crops of ``'mitre'``-style joins if the point is displaced from the buffered vertex by more than this limit. single_sided : bool, default False Only buffer at one side of the geometry. Examples -------- >>> from shapely.geometry import Point, LineString, Polygon >>> s = geopandas.GeoSeries( ... [ ... Point(0, 0), ... LineString([(1, -1), (1, 0), (2, 0), (2, 1)]), ... Polygon([(3, -1), (4, 0), (3, 1)]), ... ] ... ) >>> s 0 POINT (0 0) 1 LINESTRING (1 -1, 1 0, 2 0, 2 1) 2 POLYGON ((3 -1, 4 0, 3 1, 3 -1)) dtype: geometry >>> s.buffer(0.2) 0 POLYGON ((0.2 0, 0.19904 -0.0196, 0.19616 -0.0... 1 POLYGON ((0.8 0, 0.80096 0.0196, 0.80384 0.039... 2 POLYGON ((2.8 -1, 2.8 1, 2.80096 1.0196, 2.803... dtype: geometry Further specification as ``join_style`` and ``cap_style`` are shown in the following illustration: .. plot:: _static/code/buffer.py """ return _delegate_geo_method( "buffer", self, distance=distance, quad_segs=quad_segs, cap_style=cap_style, join_style=join_style, mitre_limit=mitre_limit, single_sided=single_sided, **kwargs, ) def simplify(self, tolerance, preserve_topology=True): """Return a ``GeoSeries`` containing a simplified representation of each geometry. The algorithm (Douglas-Peucker) recursively splits the original line into smaller parts and connects these parts' endpoints by a straight line. Then, it removes all points whose distance to the straight line is smaller than `tolerance`. It does not move any points and it always preserves endpoints of the original line or polygon. See https://shapely.readthedocs.io/en/latest/manual.html#object.simplify for details Simplifies individual geometries independently, without considering the topology of a potential polygonal coverage. If you would like to treat the ``GeoSeries`` as a coverage and simplify its edges, while preserving the coverage topology, see :meth:`simplify_coverage`. Parameters ---------- tolerance : float All parts of a simplified geometry will be no more than `tolerance` distance from the original. It has the same units as the coordinate reference system of the GeoSeries. For example, using `tolerance=100` in a projected CRS with meters as units means a distance of 100 meters in reality. preserve_topology: bool (default True) False uses a quicker algorithm, but may produce self-intersecting or otherwise invalid geometries. Notes ----- Invalid geometric objects may result from simplification that does not preserve topology and simplification may be sensitive to the order of coordinates: two geometries differing only in order of coordinates may be simplified differently. See Also -------- simplify_coverage : simplify geometries using coverage simplification Examples -------- >>> from shapely.geometry import Point, LineString >>> s = geopandas.GeoSeries( ... [Point(0, 0).buffer(1), LineString([(0, 0), (1, 10), (0, 20)])] ... ) >>> s 0 POLYGON ((1 0, 0.99518 -0.09802, 0.98079 -0.19... 1 LINESTRING (0 0, 1 10, 0 20) dtype: geometry >>> s.simplify(1) 0 POLYGON ((0 1, 0 -1, -1 0, 0 1)) 1 LINESTRING (0 0, 0 20) dtype: geometry """ return _delegate_geo_method( "simplify", self, tolerance=tolerance, preserve_topology=preserve_topology ) def simplify_coverage(self, tolerance, *, simplify_boundary=True): """Return a ``GeoSeries`` containing a simplified representation of polygonal coverage. Assumes that the ``GeoSeries`` forms a polygonal coverage. Under this assumption, the method simplifies the edges using the Visvalingam-Whyatt algorithm, while preserving a valid coverage. In the most simplified case, polygons are reduced to triangles. A ``GeoSeries`` of valid polygons is considered a coverage if the polygons are: * **Non-overlapping** - polygons do not overlap (their interiors do not intersect) * **Edge-Matched** - vertices along shared edges are identical The method allows simplification of all edges including the outer boundaries of the coverage or simplification of only the inner (shared) edges. If there are other geometry types than Polygons or MultiPolygons present, the method will raise an error. If the geometry is polygonal but does not form a valid coverage due to overlaps, it will be simplified but it may result in invalid coverage topology. Requires Shapely >= 2.1. .. versionadded:: 1.1.0 Parameters ---------- tolerance : float The degree of simplification roughly equal to the square root of the area of triangles that will be removed. It has the same units as the coordinate reference system of the GeoSeries. For example, using `tolerance=100` in a projected CRS with meters as units means a distance of 100 meters in reality. simplify_boundary: bool (default True) By default (True), simplifies both internal edges of the coverage as well as its boundary. If set to False, only simplifies internal edges. See Also -------- simplify : simplification of individual geometries Examples -------- >>> from shapely.geometry import Polygon >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (1, 1.1), (2, 0), (1.5, 1), (2, 2), (0, 2)]), ... Polygon([(2, 0), (4, 0), (4, 2), (2, 2), (1.5, 1)]), ... ] ... ) >>> s 0 POLYGON ((0 0, 1 1.1, 2 0, 1.5 1, 2 2, 0 2, 0 0)) 1 POLYGON ((2 0, 4 0, 4 2, 2 2, 1.5 1, 2 0)) dtype: geometry >>> s.simplify_coverage(1) 0 POLYGON ((2 0, 2 2, 0 2, 2 0)) 1 POLYGON ((2 0, 4 0, 4 2, 2 2, 2 0)) dtype: geometry >>> s.simplify_coverage(1, simplify_boundary=False) 0 POLYGON ((2 0, 2 2, 0 2, 0 0, 1 1.1, 2 0)) 1 POLYGON ((2 0, 4 0, 4 2, 2 2, 2 0)) dtype: geometry """ return _delegate_geo_method( "simplify_coverage", self, tolerance=tolerance, simplify_boundary=simplify_boundary, ) def relate(self, other, align=None): """Return the DE-9IM intersection matrices for the geometries. The operation works on a 1-to-1 row-wise manner: .. image:: ../../../_static/binary_op-01.svg :align: center Parameters ---------- other : BaseGeometry or GeoSeries The other geometry to computed the DE-9IM intersection matrices from. align : bool | None (default None) If True, automatically aligns GeoSeries based on their indices. If False, the order of elements is preserved. None defaults to True. Returns ------- spatial_relations: Series of strings The DE-9IM intersection matrices which describe the spatial relations of the other geometry. Examples -------- >>> from shapely.geometry import Polygon, LineString, Point >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (2, 2), (0, 2)]), ... Polygon([(0, 0), (2, 2), (0, 2)]), ... LineString([(0, 0), (2, 2)]), ... LineString([(2, 0), (0, 2)]), ... Point(0, 1), ... ], ... ) >>> s2 = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (1, 1), (0, 1)]), ... LineString([(1, 0), (1, 3)]), ... LineString([(2, 0), (0, 2)]), ... Point(1, 1), ... Point(0, 1), ... ], ... index=range(1, 6), ... ) >>> s 0 POLYGON ((0 0, 2 2, 0 2, 0 0)) 1 POLYGON ((0 0, 2 2, 0 2, 0 0)) 2 LINESTRING (0 0, 2 2) 3 LINESTRING (2 0, 0 2) 4 POINT (0 1) dtype: geometry >>> s2 1 POLYGON ((0 0, 1 1, 0 1, 0 0)) 2 LINESTRING (1 0, 1 3) 3 LINESTRING (2 0, 0 2) 4 POINT (1 1) 5 POINT (0 1) dtype: geometry We can relate each geometry and a single shapely geometry: .. image:: ../../../_static/binary_op-03.svg :align: center >>> s.relate(Polygon([(0, 0), (1, 1), (0, 1)])) 0 212F11FF2 1 212F11FF2 2 F11F00212 3 F01FF0212 4 F0FFFF212 dtype: str We can also check two GeoSeries against each other, row by row. The GeoSeries above have different indices. We can either align both GeoSeries based on index values and compare elements with the same index using ``align=True`` or ignore index and compare elements based on their matching order using ``align=False``: .. image:: ../../../_static/binary_op-02.svg >>> s.relate(s2, align=True) 0 NaN 1 212F11FF2 2 0F1FF0102 3 1FFF0FFF2 4 FF0FFF0F2 5 NaN dtype: str >>> s.relate(s2, align=False) 0 212F11FF2 1 1F20F1102 2 0F1FF0102 3 0F1FF0FF2 4 0FFFFFFF2 dtype: str """ return _binary_op("relate", self, other, align) def relate_pattern(self, other, pattern, align=None): """Return True if the DE-9IM string code for the relationship between the geometries satisfies the pattern, else False. This function compares the DE-9IM code string for two geometries against a specified pattern. If the string matches the pattern then ``True`` is returned, otherwise ``False``. The pattern specified can be an exact match (``0``, ``1`` or ``2``), a boolean match (uppercase ``T`` or ``F``), or a wildcard (``*``). For example, the pattern for the ``within`` predicate is ``'T*F**F***'`` The operation works on a 1-to-1 row-wise manner: .. image:: ../../../_static/binary_op-01.svg :align: center Parameters ---------- other : BaseGeometry or GeoSeries The other geometry to be tested against the pattern. pattern : str The DE-9IM pattern to test against. align : bool | None (default None) If True, automatically aligns GeoSeries based on their indices. If False, the order of elements is preserved. None defaults to True. Returns ------- Series Examples -------- >>> from shapely.geometry import Polygon, LineString, Point >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (2, 2), (0, 2)]), ... Polygon([(0, 0), (2, 2), (0, 2)]), ... LineString([(0, 0), (2, 2)]), ... LineString([(2, 0), (0, 2)]), ... Point(0, 1), ... ], ... ) >>> s2 = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (1, 1), (0, 1)]), ... LineString([(1, 0), (1, 3)]), ... LineString([(2, 0), (0, 2)]), ... Point(1, 1), ... Point(0, 1), ... ], ... index=range(1, 6), ... ) >>> s 0 POLYGON ((0 0, 2 2, 0 2, 0 0)) 1 POLYGON ((0 0, 2 2, 0 2, 0 0)) 2 LINESTRING (0 0, 2 2) 3 LINESTRING (2 0, 0 2) 4 POINT (0 1) dtype: geometry >>> s2 1 POLYGON ((0 0, 1 1, 0 1, 0 0)) 2 LINESTRING (1 0, 1 3) 3 LINESTRING (2 0, 0 2) 4 POINT (1 1) 5 POINT (0 1) dtype: geometry We can check the relate pattern of each geometry and a single shapely geometry: .. image:: ../../../_static/binary_op-03.svg :align: center >>> s.relate_pattern(Polygon([(0, 0), (1, 1), (0, 1)]), "2*T***F**") 0 True 1 True 2 False 3 False 4 False dtype: bool We can also check two GeoSeries against each other, row by row. The GeoSeries above have different indices. We can either align both GeoSeries based on index values and compare elements with the same index using ``align=True`` or ignore index and compare elements based on their matching order using ``align=False``: .. image:: ../../../_static/binary_op-02.svg >>> s.relate_pattern(s2, "TF******T", align=True) 0 False 1 False 2 True 3 True 4 False 5 False dtype: bool >>> s.relate_pattern(s2, "TF******T", align=False) 0 False 1 True 2 True 3 True 4 True dtype: bool """ return _binary_op("relate_pattern", self, other, pattern=pattern, align=align) def project(self, other, normalized=False, align=None): """Return the distance along each geometry nearest to *other*. The operation works on a 1-to-1 row-wise manner: .. image:: ../../../_static/binary_op-01.svg :align: center The project method is the inverse of interpolate. In shapely, this is equal to ``line_locate_point``. Parameters ---------- other : BaseGeometry or GeoSeries The *other* geometry to computed projected point from. normalized : boolean If normalized is True, return the distance normalized to the length of the object. align : bool | None (default None) If True, automatically aligns GeoSeries based on their indices. If False, the order of elements is preserved. None defaults to True. Returns ------- Series Examples -------- >>> from shapely.geometry import LineString, Point >>> s = geopandas.GeoSeries( ... [ ... LineString([(0, 0), (2, 0), (0, 2)]), ... LineString([(0, 0), (2, 2)]), ... LineString([(2, 0), (0, 2)]), ... ], ... ) >>> s2 = geopandas.GeoSeries( ... [ ... Point(1, 0), ... Point(1, 0), ... Point(2, 1), ... ], ... index=range(1, 4), ... ) >>> s 0 LINESTRING (0 0, 2 0, 0 2) 1 LINESTRING (0 0, 2 2) 2 LINESTRING (2 0, 0 2) dtype: geometry >>> s2 1 POINT (1 0) 2 POINT (1 0) 3 POINT (2 1) dtype: geometry We can project each geometry on a single shapely geometry: .. image:: ../../../_static/binary_op-03.svg :align: center >>> s.project(Point(1, 0)) 0 1.000000 1 0.707107 2 0.707107 dtype: float64 We can also check two GeoSeries against each other, row by row. The GeoSeries above have different indices. We can either align both GeoSeries based on index values and project elements with the same index using ``align=True`` or ignore index and project elements based on their matching order using ``align=False``: .. image:: ../../../_static/binary_op-02.svg >>> s.project(s2, align=True) 0 NaN 1 0.707107 2 0.707107 3 NaN dtype: float64 >>> s.project(s2, align=False) 0 1.000000 1 0.707107 2 0.707107 dtype: float64 See Also -------- GeoSeries.interpolate """ return _binary_op("project", self, other, normalized=normalized, align=align) def interpolate(self, distance, normalized=False): """Return a point at the specified distance along each geometry. Parameters ---------- distance : float or Series of floats Distance(s) along the geometries at which a point should be returned. If np.array or pd.Series are used then it must have same length as the GeoSeries. normalized : boolean If normalized is True, distance will be interpreted as a fraction of the geometric object's length. Examples -------- >>> from shapely.geometry import LineString, Point >>> s = geopandas.GeoSeries( ... [ ... LineString([(0, 0), (2, 0), (0, 2)]), ... LineString([(0, 0), (2, 2)]), ... LineString([(2, 0), (0, 2)]), ... ], ... ) >>> s 0 LINESTRING (0 0, 2 0, 0 2) 1 LINESTRING (0 0, 2 2) 2 LINESTRING (2 0, 0 2) dtype: geometry >>> s.interpolate(1) 0 POINT (1 0) 1 POINT (0.70711 0.70711) 2 POINT (1.29289 0.70711) dtype: geometry >>> s.interpolate([1, 2, 3]) 0 POINT (1 0) 1 POINT (1.41421 1.41421) 2 POINT (0 2) dtype: geometry """ return _delegate_geo_method( "interpolate", self, distance=distance, normalized=normalized ) def affine_transform(self, matrix): """Return a ``GeoSeries`` with translated geometries. See http://shapely.readthedocs.io/en/stable/manual.html#shapely.affinity.affine_transform for details. Parameters ---------- matrix: List or tuple 6 or 12 items for 2D or 3D transformations respectively. For 2D affine transformations, the 6 parameter matrix is ``[a, b, d, e, xoff, yoff]`` For 3D affine transformations, the 12 parameter matrix is ``[a, b, c, d, e, f, g, h, i, xoff, yoff, zoff]`` Examples -------- >>> from shapely.geometry import Point, LineString, Polygon >>> s = geopandas.GeoSeries( ... [ ... Point(1, 1), ... LineString([(1, -1), (1, 0)]), ... Polygon([(3, -1), (4, 0), (3, 1)]), ... ] ... ) >>> s 0 POINT (1 1) 1 LINESTRING (1 -1, 1 0) 2 POLYGON ((3 -1, 4 0, 3 1, 3 -1)) dtype: geometry >>> s.affine_transform([2, 3, 2, 4, 5, 2]) 0 POINT (10 8) 1 LINESTRING (4 0, 7 4) 2 POLYGON ((8 4, 13 10, 14 12, 8 4)) dtype: geometry """ # (E501 link is longer than max line length) return _delegate_geo_method("affine_transform", self, matrix=matrix) def translate(self, xoff=0.0, yoff=0.0, zoff=0.0): """Return a ``GeoSeries`` with translated geometries. See http://shapely.readthedocs.io/en/latest/manual.html#shapely.affinity.translate for details. Parameters ---------- xoff, yoff, zoff : float, float, float Amount of offset along each dimension. xoff, yoff, and zoff for translation along the x, y, and z dimensions respectively. Examples -------- >>> from shapely.geometry import Point, LineString, Polygon >>> s = geopandas.GeoSeries( ... [ ... Point(1, 1), ... LineString([(1, -1), (1, 0)]), ... Polygon([(3, -1), (4, 0), (3, 1)]), ... ] ... ) >>> s 0 POINT (1 1) 1 LINESTRING (1 -1, 1 0) 2 POLYGON ((3 -1, 4 0, 3 1, 3 -1)) dtype: geometry >>> s.translate(2, 3) 0 POINT (3 4) 1 LINESTRING (3 2, 3 3) 2 POLYGON ((5 2, 6 3, 5 4, 5 2)) dtype: geometry """ # (E501 link is longer than max line length) return _delegate_geo_method("translate", self, xoff=xoff, yoff=yoff, zoff=zoff) def rotate(self, angle, origin="center", use_radians=False): """Return a ``GeoSeries`` with rotated geometries. See http://shapely.readthedocs.io/en/latest/manual.html#shapely.affinity.rotate for details. Parameters ---------- angle : float The angle of rotation can be specified in either degrees (default) or radians by setting use_radians=True. Positive angles are counter-clockwise and negative are clockwise rotations. origin : string, Point, or tuple (x, y) The point of origin can be a keyword 'center' for the bounding box center (default), 'centroid' for the geometry's centroid, a Point object or a coordinate tuple (x, y). use_radians : boolean Whether to interpret the angle of rotation as degrees or radians Examples -------- >>> from shapely.geometry import Point, LineString, Polygon >>> s = geopandas.GeoSeries( ... [ ... Point(1, 1), ... LineString([(1, -1), (1, 0)]), ... Polygon([(3, -1), (4, 0), (3, 1)]), ... ] ... ) >>> s 0 POINT (1 1) 1 LINESTRING (1 -1, 1 0) 2 POLYGON ((3 -1, 4 0, 3 1, 3 -1)) dtype: geometry >>> s.rotate(90) 0 POINT (1 1) 1 LINESTRING (1.5 -0.5, 0.5 -0.5) 2 POLYGON ((4.5 -0.5, 3.5 0.5, 2.5 -0.5, 4.5 -0.5)) dtype: geometry >>> s.rotate(90, origin=(0, 0)) 0 POINT (-1 1) 1 LINESTRING (1 1, 0 1) 2 POLYGON ((1 3, 0 4, -1 3, 1 3)) dtype: geometry """ return _delegate_geo_method( "rotate", self, angle=angle, origin=origin, use_radians=use_radians ) def scale(self, xfact=1.0, yfact=1.0, zfact=1.0, origin="center"): """Return a ``GeoSeries`` with scaled geometries. The geometries can be scaled by different factors along each dimension. Negative scale factors will mirror or reflect coordinates. See http://shapely.readthedocs.io/en/latest/manual.html#shapely.affinity.scale for details. Parameters ---------- xfact, yfact, zfact : float, float, float Scaling factors for the x, y, and z dimensions respectively. origin : string, Point, or tuple The point of origin can be a keyword 'center' for the 2D bounding box center (default), 'centroid' for the geometry's 2D centroid, a Point object or a coordinate tuple (x, y, z). Examples -------- >>> from shapely.geometry import Point, LineString, Polygon >>> s = geopandas.GeoSeries( ... [ ... Point(1, 1), ... LineString([(1, -1), (1, 0)]), ... Polygon([(3, -1), (4, 0), (3, 1)]), ... ] ... ) >>> s 0 POINT (1 1) 1 LINESTRING (1 -1, 1 0) 2 POLYGON ((3 -1, 4 0, 3 1, 3 -1)) dtype: geometry >>> s.scale(2, 3) 0 POINT (1 1) 1 LINESTRING (1 -2, 1 1) 2 POLYGON ((2.5 -3, 4.5 0, 2.5 3, 2.5 -3)) dtype: geometry >>> s.scale(2, 3, origin=(0, 0)) 0 POINT (2 3) 1 LINESTRING (2 -3, 2 0) 2 POLYGON ((6 -3, 8 0, 6 3, 6 -3)) dtype: geometry """ return _delegate_geo_method( "scale", self, xfact=xfact, yfact=yfact, zfact=zfact, origin=origin ) def skew(self, xs=0.0, ys=0.0, origin="center", use_radians=False): """Return a ``GeoSeries`` with skewed geometries. The geometries are sheared by angles along the x and y dimensions. See http://shapely.readthedocs.io/en/latest/manual.html#shapely.affinity.skew for details. Parameters ---------- xs, ys : float, float The shear angle(s) for the x and y axes respectively. These can be specified in either degrees (default) or radians by setting use_radians=True. origin : string, Point, or tuple (x, y) The point of origin can be a keyword 'center' for the bounding box center (default), 'centroid' for the geometry's centroid, a Point object or a coordinate tuple (x, y). use_radians : boolean Whether to interpret the shear angle(s) as degrees or radians Examples -------- >>> from shapely.geometry import Point, LineString, Polygon >>> s = geopandas.GeoSeries( ... [ ... Point(1, 1), ... LineString([(1, -1), (1, 0)]), ... Polygon([(3, -1), (4, 0), (3, 1)]), ... ] ... ) >>> s 0 POINT (1 1) 1 LINESTRING (1 -1, 1 0) 2 POLYGON ((3 -1, 4 0, 3 1, 3 -1)) dtype: geometry >>> s.skew(45, 30) 0 POINT (1 1) 1 LINESTRING (0.5 -1, 1.5 0) 2 POLYGON ((2 -1.28868, 4 0.28868, 4 0.71132, 2 ... dtype: geometry >>> s.skew(45, 30, origin=(0, 0)) 0 POINT (2 1.57735) 1 LINESTRING (1.11022e-16 -0.42265, 1 0.57735) 2 POLYGON ((2 0.73205, 4 2.3094, 4 2.73205, 2 0.... dtype: geometry """ return _delegate_geo_method( "skew", self, xs=xs, ys=ys, origin=origin, use_radians=use_radians ) @property def cx(self): """ Coordinate based indexer to select by intersection with bounding box. Format of input should be ``.cx[xmin:xmax, ymin:ymax]``. Any of ``xmin``, ``xmax``, ``ymin``, and ``ymax`` can be provided, but input must include a comma separating x and y slices. That is, ``.cx[:, :]`` will return the full series/frame, but ``.cx[:]`` is not implemented. Examples -------- >>> from shapely.geometry import LineString, Point >>> s = geopandas.GeoSeries( ... [Point(0, 0), Point(1, 2), Point(3, 3), LineString([(0, 0), (3, 3)])] ... ) >>> s 0 POINT (0 0) 1 POINT (1 2) 2 POINT (3 3) 3 LINESTRING (0 0, 3 3) dtype: geometry >>> s.cx[0:1, 0:1] 0 POINT (0 0) 3 LINESTRING (0 0, 3 3) dtype: geometry >>> s.cx[:, 1:] 1 POINT (1 2) 2 POINT (3 3) 3 LINESTRING (0 0, 3 3) dtype: geometry """ return _CoordinateIndexer(self) def get_coordinates( self, include_z=False, ignore_index=False, index_parts=False, *, include_m=False ): """Get coordinates from a :class:`GeoSeries` as a :class:`~pandas.DataFrame` of floats. The shape of the returned :class:`~pandas.DataFrame` is (N, 2), with N being the number of coordinate pairs. With the default of ``include_z=False``, three-dimensional data is ignored. When specifying ``include_z=True``, the shape of the returned :class:`~pandas.DataFrame` is (N, 3). Parameters ---------- include_z : bool, default False Include Z coordinates ignore_index : bool, default False If True, the resulting index will be labelled 0, 1, …, n - 1, ignoring ``index_parts``. index_parts : bool, default False If True, the resulting index will be a :class:`~pandas.MultiIndex` (original index with an additional level indicating the ordering of the coordinate pairs: a new zero-based index for each geometry in the original GeoSeries). include_m : bool, default False Include M coordinates. Requires shapely >= 2.1. Returns ------- pandas.DataFrame Examples -------- >>> from shapely.geometry import Point, LineString, Polygon >>> s = geopandas.GeoSeries( ... [ ... Point(1, 1), ... LineString([(1, -1), (1, 0)]), ... Polygon([(3, -1), (4, 0), (3, 1)]), ... ] ... ) >>> s 0 POINT (1 1) 1 LINESTRING (1 -1, 1 0) 2 POLYGON ((3 -1, 4 0, 3 1, 3 -1)) dtype: geometry >>> s.get_coordinates() x y 0 1.0 1.0 1 1.0 -1.0 1 1.0 0.0 2 3.0 -1.0 2 4.0 0.0 2 3.0 1.0 2 3.0 -1.0 >>> s.get_coordinates(ignore_index=True) x y 0 1.0 1.0 1 1.0 -1.0 2 1.0 0.0 3 3.0 -1.0 4 4.0 0.0 5 3.0 1.0 6 3.0 -1.0 >>> s.get_coordinates(index_parts=True) x y 0 0 1.0 1.0 1 0 1.0 -1.0 1 1.0 0.0 2 0 3.0 -1.0 1 4.0 0.0 2 3.0 1.0 3 3.0 -1.0 """ if include_m: # can be merged with the one below once min requirement is shapely 2.1 coords, outer_idx = shapely.get_coordinates( self.geometry.values._data, include_z=include_z, include_m=include_m, return_index=True, ) else: coords, outer_idx = shapely.get_coordinates( self.geometry.values._data, include_z=include_z, return_index=True ) column_names = ["x", "y"] if include_z: column_names.append("z") if include_m: column_names.append("m") index = _get_index_for_parts( self.index, outer_idx, ignore_index=ignore_index, index_parts=index_parts, ) return pd.DataFrame(coords, index=index, columns=column_names) def hilbert_distance(self, total_bounds=None, level=16): """ Calculate the distance along a Hilbert curve. The distances are calculated for the midpoints of the geometries in the GeoDataFrame, and using the total bounds of the GeoDataFrame. The Hilbert distance can be used to spatially sort GeoPandas objects, by mapping two dimensional geometries along the Hilbert curve. Parameters ---------- total_bounds : 4-element array, optional The spatial extent in which the curve is constructed (used to rescale the geometry midpoints). By default, the total bounds of the full GeoDataFrame or GeoSeries will be computed. If known, you can pass the total bounds to avoid this extra computation. level : int (1 - 16), default 16 Determines the precision of the curve (points on the curve will have coordinates in the range [0, 2^level - 1]). Returns ------- Series Series containing distance along the curve for geometry """ from geopandas.tools.hilbert_curve import _hilbert_distance distances = _hilbert_distance( self.geometry.values, total_bounds=total_bounds, level=level ) return pd.Series(distances, index=self.index, name="hilbert_distance") def sample_points(self, size, method="uniform", rng=None, **kwargs): """ Sample points from each geometry. Generate a MultiPoint per each geometry containing points sampled from the geometry. You can either sample randomly from a uniform distribution or use an advanced sampling algorithm from the ``pointpats`` package. For polygons, this samples within the area of the polygon. For lines, this samples along the length of the linestring. For multi-part geometries, the weights of each part are selected according to their relevant attribute (area for Polygons, length for LineStrings), and then points are sampled from each part. Any other geometry type (e.g. Point, GeometryCollection) is ignored, and an empty MultiPoint geometry is returned. Parameters ---------- size : int | array-like The size of the sample requested. Indicates the number of samples to draw from each geometry. If an array of the same length as a GeoSeries is passed, it denotes the size of a sample per geometry. method : str, default "uniform" The sampling method. ``uniform`` samples uniformly at random from a geometry using ``numpy.random.uniform``. Other allowed strings (e.g. ``"cluster_poisson"``) denote sampling function name from the ``pointpats.random`` module (see http://pysal.org/pointpats/api.html#random-distributions). Pointpats methods are implemented for (Multi)Polygons only and will return an empty MultiPoint for other geometry types. rng : {None, int, array_like[ints], SeedSequence, BitGenerator, Generator}, optional A random generator or seed to initialize the numpy BitGenerator. If None, then fresh, unpredictable entropy will be pulled from the OS. **kwargs : dict Options for the pointpats sampling algorithms. Returns ------- GeoSeries Points sampled within (or along) each geometry. Examples -------- >>> from shapely.geometry import Polygon >>> s = geopandas.GeoSeries( ... [ ... Polygon([(1, -1), (1, 0), (0, 0)]), ... Polygon([(3, -1), (4, 0), (3, 1)]), ... ] ... ) >>> s.sample_points(size=10) # doctest: +SKIP 0 MULTIPOINT ((0.1045 -0.10294), (0.35249 -0.264... 1 MULTIPOINT ((3.03261 -0.43069), (3.10068 0.114... Name: sampled_points, dtype: geometry """ # noqa: E501 from .geoseries import GeoSeries from .tools._random import uniform if method == "uniform": if pd.api.types.is_list_like(size): result = [uniform(geom, s, rng) for geom, s in zip(self.geometry, size)] else: result = self.geometry.apply(uniform, size=size, rng=rng) else: pointpats = compat.import_optional_dependency( "pointpats", f"For complex sampling methods, the pointpats module is required. " f"Your requested method, '{method}' was not a supported option " f"and the pointpats package was not able to be imported.", ) if not hasattr(pointpats.random, method): raise AttributeError( f"pointpats.random module has no sampling method {method}." f"Consult the pointpats.random module documentation for" f" available random sampling methods." ) sample_function = getattr(pointpats.random, method) if pd.api.types.is_list_like(size): result = [ ( shapely.multipoints( sample_function(x, size=s, rng=rng, **kwargs) ) if not (x.is_empty or x is None or "Polygon" not in x.geom_type) else MultiPoint() ) for x, s in zip(self.geometry, size) ] else: result = self.geometry.apply( lambda x: ( shapely.multipoints( sample_function(x, size=size, rng=rng, **kwargs) ) if not (x.is_empty or x is None or "Polygon" not in x.geom_type) else MultiPoint() ), ) return GeoSeries(result, name="sampled_points", crs=self.crs, index=self.index) def build_area(self, node=True): """Create an areal geometry formed by the constituent linework. Builds areas from the GeoSeries that contain linework which represents the edges of a planar graph. Any geometry type may be provided as input; only the constituent lines and rings will be used to create the output polygons. All geometries within the GeoSeries are considered together and the resulting polygons therefore do not map 1:1 to input geometries. This function converts inner rings into holes. To turn inner rings into polygons as well, use polygonize. Unless you know that the input GeoSeries represents a planar graph with a clean topology (e.g. there is a node on both lines where they intersect), it is recommended to use ``node=True`` which performs noding prior to building areal geometry. Using ``node=False`` will provide performance benefits but may result in incorrect polygons if the input is not of the proper topology. If the input linework crosses, this function may produce invalid polygons. Use :meth:`GeoSeries.make_valid` to ensure valid geometries. Parameters ---------- node : bool, default True Perform noding prior to building the areas, by default True. Returns ------- GeoSeries GeoSeries with polygons Examples -------- >>> from shapely.geometry import LineString, Polygon >>> s = geopandas.GeoSeries([ ... LineString([(18, 4), (4, 2), (2, 9)]), ... LineString([(18, 4), (16, 16)]), ... LineString([(16, 16), (8, 19), (8, 12), (2, 9)]), ... LineString([(8, 6), (12, 13), (15, 8)]), ... LineString([(8, 6), (15, 8)]), ... LineString([(0, 0), (0, 3), (3, 3), (3, 0), (0, 0)]), ... Polygon([(1, 1), (2, 2), (1, 2), (1, 1)]), ... ]) >>> s.build_area() 0 POLYGON ((0 3, 3 3, 3 0, 0 0, 0 3), (1 1, 2 2,... 1 POLYGON ((4 2, 2 9, 8 12, 8 19, 16 16, 18 4, 4... Name: polygons, dtype: geometry """ from .geoseries import GeoSeries if node: geometry_input = self.geometry.union_all() else: geometry_input = shapely.geometrycollections(self.geometry.values._data) polygons = shapely.build_area(geometry_input) return GeoSeries(polygons, crs=self.crs, name="polygons").explode( ignore_index=True ) def polygonize(self, node=True, full=False): """Create polygons formed from the linework of a GeoSeries. Polygonizes the GeoSeries that contain linework which represents the edges of a planar graph. Any geometry type may be provided as input; only the constituent lines and rings will be used to create the output polygons. Lines or rings that when combined do not completely close a polygon will be ignored. Duplicate segments are ignored. Unless you know that the input GeoSeries represents a planar graph with a clean topology (e.g. there is a node on both lines where they intersect), it is recommended to use ``node=True`` which performs noding prior to polygonization. Using ``node=False`` will provide performance benefits but may result in incorrect polygons if the input is not of the proper topology. When ``full=True``, the return value is a 4-tuple containing output polygons, along with lines which could not be converted to polygons. The return value consists of 4 elements or varying lengths: - GeoSeries of the valid polygons (same as with ``full=False``) - GeoSeries of cut edges: edges connected on both ends but not part of polygonal output - GeoSeries of dangles: edges connected on one end but not part of polygonal output - GeoSeries of invalid rings: polygons that are formed but are not valid (bowties, etc) Parameters ---------- node : bool, default True Perform noding prior to polygonization, by default True. full : bool, default False Return the full output composed of a tuple of GeoSeries, by default False. Returns ------- GeoSeries | tuple(GeoSeries, GeoSeries, GeoSeries, GeoSeries) GeoSeries with the polygons or a tuple of four GeoSeries as ``(polygons, cuts, dangles, invalid)`` Examples -------- >>> from shapely.geometry import LineString >>> s = geopandas.GeoSeries([ ... LineString([(0, 0), (1, 1)]), ... LineString([(0, 0), (0, 1), (1, 1), (1, 0), (0, 0)]), ... LineString([(0.5, 0.2), (0.5, 0.8)]), ... ]) >>> s.polygonize() 0 POLYGON ((0 0, 0.5 0.5, 1 1, 1 0, 0 0)) 1 POLYGON ((0.5 0.5, 0 0, 0 1, 1 1, 0.5 0.5)) Name: polygons, dtype: geometry >>> polygons, cuts, dangles, invalid = s.polygonize(full=True) """ from .geoseries import GeoSeries if node: geometry_input = [self.geometry.union_all()] else: geometry_input = self.geometry.values if full: polygons, cuts, dangles, invalid = shapely.polygonize_full(geometry_input) cuts = GeoSeries(cuts, crs=self.crs, name="cut_edges").explode( ignore_index=True ) dangles = GeoSeries(dangles, crs=self.crs, name="dangles").explode( ignore_index=True ) invalid = GeoSeries(invalid, crs=self.crs, name="invalid_rings").explode( ignore_index=True ) polygons = GeoSeries(polygons, crs=self.crs, name="polygons").explode( ignore_index=True ) return (polygons, cuts, dangles, invalid) polygons = shapely.polygonize(geometry_input) return GeoSeries(polygons, crs=self.crs, name="polygons").explode( ignore_index=True ) def make_grid( self, cell_size: float, grid_type: Literal["square", "hexagon"] = "square", feature_type: Literal["centers", "corners", "polygons"] = "polygons", offset: tuple[float, float] | None = None, intersect: bool = True, flat_topped: bool = False, ) -> "GeoSeries": """Provide the centers, corners, or polygons of a square or hexagonal grid. The grid covers the area of the GeoSeries' total bounds. By default, only grid elements that spatially overlap with the GeoSeries geometries are returned. This filtering can be disabled by setting the ``intersect`` parameter to ``False``. The origin of the grid is at the lower left corner of the bounding box of the GeoSeries. .. versionadded:: 1.2 Parameters ---------- cell_size : float Side length of the square. For hexagonal cells the distance between opposite edges (edge length is ``cellsize/sqrt(3)``). grid_type : str, one of "square", "hexagon", default "square" Grid type that is returned. All cell types reflect naive tiling of a plane, not a tiling of the globe (like H3 or S2). feature_type : str, one of "centers", "corners", "polygons", default "polygons" Grid feature that is returned. ``"centers"`` returns points at the center of each grid cell. ``"corners"`` returns points at all unique vertices of the grid cells (i.e., the points where cell edges meet). ``"polygons"`` returns the grid cell polygons. offset : tuple, optional Lower left corner coordinates (x, y) of the grid. By default uses the lower left corner of the bounding box of the input geometry. intersect : bool, default True If False, the grid is not filtered by the geometries and the full grid covering the bounding box is returned. flat_topped : bool, default False If True, generate flat topped hexagons. By default (False), the orientation of the hexagonal cells is such that a corner points upwards. Returns ------- GeoSeries The returned GeoSeries contains the grid-cell centers, corners, or polygons. See Also -------- make_grid : equivalent top-level function Notes ----- When ``intersect=True``, the grid is filtered using the ``"intersects"`` spatial predicate. The filtering behavior depends on ``feature_type``: - ``"polygons"``: All grid cell polygons that share any area or boundary with the input geometries are returned. - ``"centers"``: Only center points that fall within or on the boundary of the input geometries are returned. - ``"corners"``: Only corner points that fall within or on the boundary of the input geometries are returned. As a result, the set of corners returned when ``feature_type="corners"`` may not correspond exactly to the vertices of the polygons returned when ``feature_type="polygons"``. Examples -------- >>> import geopandas >>> import geodatasets >>> world = geopandas.read_file( ... geodatasets.get_path('naturalearth land')) >>> madagascar = world.cx[45:50, -25:-15] >>> sq_grid = madagascar.geometry.make_grid(cell_size=1) >>> sq_grid.head(3) 0 POLYGON ((43.25419 -25.60143, 44.25419 -25.601... 1 POLYGON ((43.25419 -24.60143, 44.25419 -24.601... 2 POLYGON ((43.25419 -23.60143, 44.25419 -23.601... dtype: geometry By default, grid is aligned with the bounding box of the input geometries. If instead you want to a fixed grid aligning with specific coordinates, use the ``offset`` keyword. For example, to have a grid of round degrees: >>> sq_grid2 = madagascar.geometry.make_grid(cell_size=1, offset=(30, -30)) >>> sq_grid2.head(3) 0 POLYGON ((43 -25, 44 -25, 44 -24, 43 -24, 43 -... 1 POLYGON ((43 -24, 44 -24, 44 -23, 43 -23, 43 -... 2 POLYGON ((43 -23, 44 -23, 44 -22, 43 -22, 43 -... dtype: geometry .. plot:: _static/code/make_grid.py Specify the ``feature_type`` keyword to get the centers or corners of the grid cells, instead of the polygons: >>> sq_grid_centers = madagascar.geometry.make_grid( ... cell_size=1, feature_type="centers" ... ) >>> sq_grid_centers.head(3) 0 POINT (43.75419 -24.10143) 1 POINT (43.75419 -23.10143) 2 POINT (43.75419 -22.10143) dtype: geometry Specify the ``grid_type="hexagon`` keyword to get hexagons instead of the default squares. .. plot:: _static/code/make_grid_types.py """ from .tools.make_grid import make_grid return make_grid( self.geometry, cell_size=cell_size, grid_type=grid_type, feature_type=feature_type, offset=offset, intersect=intersect, flat_topped=flat_topped, ) def _get_index_for_parts(orig_idx, outer_idx, ignore_index, index_parts): """Handle index when geometries get exploded to parts. Helper function used in get_coordinates and explode. Parameters ---------- orig_idx : pandas.Index original index outer_idx : array the index of each returned geometry as a separate ndarray of integers ignore_index : bool index_parts : bool Returns ------- pandas.Index index or multiindex """ if ignore_index: return None else: if len(outer_idx): # Generate inner index as a range per value of outer_idx # 1. identify the start of each run of values in outer_idx # 2. count number of values per run # 3. use cumulative sums to create an incremental range # starting at 0 in each run run_start = np.r_[True, outer_idx[:-1] != outer_idx[1:]] counts = np.diff(np.r_[np.nonzero(run_start)[0], len(outer_idx)]) inner_index = (~run_start).cumsum(dtype=np.int64) inner_index -= np.repeat(inner_index[run_start], counts) else: inner_index = [] # extract original index values based on integer index outer_index = orig_idx.take(outer_idx) if index_parts: nlevels = outer_index.nlevels index_arrays = [outer_index.get_level_values(lvl) for lvl in range(nlevels)] index_arrays.append(inner_index) index = pd.MultiIndex.from_arrays( index_arrays, names=list(orig_idx.names) + [None] ) else: index = outer_index return index class _CoordinateIndexer: # see docstring GeoPandasBase.cx property above def __init__(self, obj): self.obj = obj def __getitem__(self, key): obj = self.obj xs, ys = key # handle numeric values as x and/or y coordinate index if type(xs) is not slice: xs = slice(xs, xs) if type(ys) is not slice: ys = slice(ys, ys) # don't know how to handle step; should this raise? if xs.step is not None or ys.step is not None: warn( "Ignoring step - full interval is used.", stacklevel=2, ) if xs.start is None or xs.stop is None or ys.start is None or ys.stop is None: xmin, ymin, xmax, ymax = obj.total_bounds bbox = box( xs.start if xs.start is not None else xmin, ys.start if ys.start is not None else ymin, xs.stop if xs.stop is not None else xmax, ys.stop if ys.stop is not None else ymax, ) idx = obj.intersects(bbox) return obj[idx] geopandas-1.2.0/geopandas/conftest.py000066400000000000000000000025361525650651600176340ustar00rootroot00000000000000import os.path import geopandas import pytest from geopandas.tests.util import _NATURALEARTH_CITIES, _NATURALEARTH_LOWRES, _NYBB @pytest.fixture(autouse=True) def add_geopandas(doctest_namespace): doctest_namespace["geopandas"] = geopandas # Datasets used in our tests @pytest.fixture(scope="session") def naturalearth_lowres() -> str: # skip if data missing, unless on github actions if os.path.isfile(_NATURALEARTH_LOWRES) or os.getenv("GITHUB_ACTIONS"): return _NATURALEARTH_LOWRES else: pytest.skip("Naturalearth lowres dataset not found") @pytest.fixture(scope="session") def naturalearth_cities() -> str: # skip if data missing, unless on github actions if os.path.isfile(_NATURALEARTH_CITIES) or os.getenv("GITHUB_ACTIONS"): return _NATURALEARTH_CITIES else: pytest.skip("Naturalearth cities dataset not found") @pytest.fixture(scope="session") def nybb_filename() -> str: # skip if data missing, unless on github actions if os.path.isfile(_NYBB[len("zip://") :]) or os.getenv("GITHUB_ACTIONS"): return _NYBB else: pytest.skip("NYBB dataset not found") @pytest.fixture(scope="class") def _setup_class_nybb_filename(nybb_filename, request): """Attach nybb_filename class attribute for unittest style setup_method""" request.cls.nybb_filename = nybb_filename geopandas-1.2.0/geopandas/datasets/000077500000000000000000000000001525650651600172375ustar00rootroot00000000000000geopandas-1.2.0/geopandas/datasets/__init__.py000066400000000000000000000017641525650651600213600ustar00rootroot00000000000000__all__ = [] available = [] # previously part of __all__ _prev_available = ["naturalearth_cities", "naturalearth_lowres", "nybb"] def get_path(dataset): ne_message = "https://www.naturalearthdata.com/downloads/110m-cultural-vectors/." nybb_message = ( "the geodatasets package.\n\nfrom geodatasets import get_path\n" "path_to_file = get_path('nybb')\n" ) error_msg = ( "The geopandas.dataset has been deprecated and was removed in GeoPandas " f"1.0. You can get the original '{dataset}' data from " f"{ne_message if 'natural' in dataset else nybb_message}" ) if dataset in _prev_available: raise AttributeError(error_msg) else: error_msg = ( "The geopandas.dataset has been deprecated and " "was removed in GeoPandas 1.0. New sample datasets are now available " "in the geodatasets package (https://geodatasets.readthedocs.io/en/latest/)" ) raise AttributeError(error_msg) geopandas-1.2.0/geopandas/explore.py000066400000000000000000001113121525650651600174560ustar00rootroot00000000000000import warnings from statistics import mean import numpy as np import pandas as pd from pandas.api.types import is_datetime64_any_dtype from shapely.geometry import LineString import geopandas _MAP_KWARGS = [ "location", "prefer_canvas", "no_touch", "disable_3d", "png_enabled", "zoom_control", "crs", "zoom_start", "left", "top", "position", "min_zoom", "max_zoom", "min_lat", "max_lat", "min_lon", "max_lon", "max_bounds", ] def _explore( df, column=None, cmap=None, color=None, m=None, tiles="OpenStreetMap", attr=None, tooltip=True, popup=False, highlight=True, categorical=False, legend=True, scheme=None, k=5, vmin=None, vmax=None, width="100%", height="100%", categories=None, classification_kwds=None, control_scale=True, marker_type=None, marker_kwds={}, style_kwds={}, highlight_kwds={}, missing_kwds={}, tooltip_kwds={}, popup_kwds={}, legend_kwds={}, map_kwds={}, **kwargs, ): """Explore data in interactive map based on GeoPandas and folium/leaflet.js. Generate an interactive leaflet map based on :class:`~geopandas.GeoDataFrame` Parameters ---------- column : str, np.array, pd.Series (default None) The name of the dataframe column, :class:`numpy.array`, or :class:`pandas.Series` to be plotted. If :class:`numpy.array` or :class:`pandas.Series` are used then it must have same length as dataframe. cmap : str, matplotlib.Colormap, branca.colormap or function (default None) The name of a colormap recognized by ``matplotlib``, a list-like of colors, :class:`matplotlib.colors.Colormap`, a :class:`branca.colormap.ColorMap` or function that returns a named color or hex based on the column value, e.g.:: def my_colormap(value): # scalar value defined in 'column' if value > 1: return "green" return "red" color : str, array-like (default None) Named color or a list-like of colors (named or hex). m : folium.Map (default None) Existing map instance on which to draw the plot. tiles : str, xyzservices.TileProvider (default 'OpenStreetMap Mapnik') Map tileset to use. Can choose from the list supported by folium, query a :class:`xyzservices.TileProvider` by a name from ``xyzservices.providers``, pass :class:`xyzservices.TileProvider` object or pass custom XYZ URL. When ``xyzservices`` is not available, it defaults to OpenStreetMap Mapnik tiles. You can pass a custom tileset to Folium by passing a Leaflet-style URL to the tiles parameter: ``http://{s}.yourtiles.com/{z}/{x}/{y}.png``. Be sure to check their terms and conditions and to provide attribution with the ``attr`` keyword. attr : str (default None) Map tile attribution; only required if passing custom tile URL. tooltip : bool, str, int, list (default True) Display GeoDataFrame attributes when hovering over the object. ``True`` includes all columns. ``False`` removes tooltip. Pass string or list of strings to specify a column(s). Integer specifies first n columns to be included. Defaults to ``True``. popup : bool, str, int, list (default False) Input GeoDataFrame attributes for object displayed when clicking. ``True`` includes all columns. ``False`` removes popup. Pass string or list of strings to specify a column(s). Integer specifies first n columns to be included. Defaults to ``False``. highlight : bool (default True) Enable highlight functionality when hovering over a geometry. categorical : bool (default False) If ``False``, ``cmap`` will reflect numerical values of the column being plotted. For non-numerical columns, this will be set to True. legend : bool (default True) Plot a legend in choropleth plots. Ignored if no ``column`` is given. scheme : str (default None) Name of a choropleth classification scheme (requires ``mapclassify`` >= 2.4.0). A :func:`mapclassify.classify` will be used under the hood. Supported are all schemes provided by ``mapclassify`` (e.g. ``'BoxPlot'``, ``'EqualInterval'``, ``'FisherJenks'``, ``'FisherJenksSampled'``, ``'HeadTailBreaks'``, ``'JenksCaspall'``, ``'JenksCaspallForced'``, ``'JenksCaspallSampled'``, ``'MaxP'``, ``'MaximumBreaks'``, ``'NaturalBreaks'``, ``'Quantiles'``, ``'Percentiles'``, ``'StdMean'``, ``'UserDefined'``). Arguments can be passed in ``classification_kwds``. k : int (default 5) Number of classes vmin : None or float (default None) Minimum value of ``cmap``. If ``None``, the minimum data value in the column to be plotted is used. vmax : None or float (default None) Maximum value of ``cmap``. If ``None``, the maximum data value in the column to be plotted is used. width : pixel int or percentage string (default: '100%') Width of the folium :class:`~folium.folium.Map`. If the argument m is given explicitly, width is ignored. height : pixel int or percentage string (default: '100%') Height of the folium :class:`~folium.folium.Map`. If the argument m is given explicitly, height is ignored. categories : list-like Ordered list-like object of categories to be used for categorical plot. classification_kwds : dict (default None) Keyword arguments to pass to mapclassify control_scale : bool, (default True) Whether to add a control scale on the map. marker_type : str, folium.Circle, folium.CircleMarker, folium.Marker (default None) Allowed string options are ('marker', 'circle', 'circle_marker'). Defaults to folium.CircleMarker. marker_kwds: dict (default {}) Additional keywords to be passed to the selected ``marker_type``, e.g.: radius : float (default 2 for ``circle_marker`` and 50 for ``circle``)) Radius of the circle, in meters (for ``circle``) or pixels (for ``circle_marker``). fill : bool (default True) Whether to fill the ``circle`` or ``circle_marker`` with color. icon : folium.map.Icon the :class:`folium.map.Icon` object to use to render the marker. draggable : bool (default False) Set to True to be able to drag the marker around the map. style_kwds : dict (default {}) Additional style to be passed to folium ``style_function``: stroke : bool (default True) Whether to draw stroke along the path. Set it to ``False`` to disable borders on polygons or circles. color : str Stroke color weight : int Stroke width in pixels opacity : float (default 1.0) Stroke opacity fill : boolean (default True) Whether to fill the path with color. Set it to ``False`` to disable filling on polygons or circles. fillColor : str Fill color. Defaults to the value of the color option fillOpacity : float (default 0.5) Fill opacity. style_function : callable Function mapping a GeoJson Feature to a style ``dict``. * Style properties :func:`folium.vector_layers.path_options` * GeoJson features :class:`GeoDataFrame.__geo_interface__` e.g.:: lambda x: {"color":"red" if x["properties"]["gdp_md_est"]<10**6 else "blue"} Plus all supported by :func:`folium.vector_layers.path_options`. See the documentation of :class:`folium.features.GeoJson` for details. highlight_kwds : dict (default {}) Style to be passed to folium highlight_function. Uses the same keywords as ``style_kwds``. When empty, defaults to ``{"fillOpacity": 0.75}``. missing_kwds : dict (default {}) Additional style for missing values: color : str Color of missing values. Defaults to ``None``, which uses Folium's default. label : str (default "NaN") Legend entry for missing values. tooltip_kwds : dict (default {}) Additional keywords to be passed to :class:`folium.features.GeoJsonTooltip`, e.g. ``aliases``, ``labels``, or ``sticky``. popup_kwds : dict (default {}) Additional keywords to be passed to :class:`folium.features.GeoJsonPopup`, e.g. ``aliases`` or ``labels``. legend_kwds : dict (default {}) Additional keywords to be passed to the legend. Currently supported customisation: caption : string Custom caption of the legend. Defaults to the column name. labels : list-like A list of legend labels to override the auto-generated labels. Needs to have the same number of elements as the number of unique categories (for categorical or boolean columns) or the number of classes ``k`` (for ``scheme``-based classification). For ``scheme``-based classification it applies only if ``colorbar=False``. Additional accepted keywords when ``scheme`` is specified: colorbar : bool (default True) An option to control the style of the legend. If True, continuous colorbar will be used. If False, categorical legend will be used for bins. scale : bool (default True) Scale bins along the colorbar axis according to the bin edges (True) or use the equal length for each bin (False) fmt : string (default "{:.2f}") A formatting specification for the bin edges of the classes in the legend. For example, to have no decimals: ``{"fmt": "{:.0f}"}``. Applies if ``colorbar=False``. interval : boolean (default False) An option to control brackets from mapclassify legend. If True, open/closed interval brackets are shown in the legend. Applies if ``colorbar=False``. max_labels : int, default 10 Maximum number of colorbar tick labels (requires branca>=0.5.0) map_kwds : dict (default {}) Additional keywords to be passed to folium :class:`~folium.folium.Map`, e.g. ``dragging``, or ``scrollWheelZoom``. **kwargs : dict Additional options to be passed on to the folium object. Returns ------- m : folium.folium.Map folium :class:`~folium.folium.Map` instance Examples -------- >>> import geodatasets >>> df = geopandas.read_file( ... geodatasets.get_path("geoda.chicago_health") ... ) >>> df.head(2) # doctest: +SKIP ComAreaID ... geometry 0 35 ... POLYGON ((-87.60914 41.84469, -87.60915 41.844... 1 36 ... POLYGON ((-87.59215 41.81693, -87.59231 41.816... [2 rows x 87 columns] >>> df.explore("Pop2012", cmap="Blues") # doctest: +SKIP """ def _colormap_helper(_cmap, n_resample=None, idx=None): """Return the color map specified. Helper function for MPL deprecation - GH#2596. """ if not n_resample: return cm.get_cmap(_cmap) else: return cm.get_cmap(_cmap).resampled(n_resample)(idx) try: import re import branca as bc import folium import matplotlib.pyplot as plt from mapclassify import classify from matplotlib import colormaps as cm from matplotlib import colors except (ImportError, ModuleNotFoundError): raise ImportError( "The 'folium>=0.12', 'matplotlib' and 'mapclassify' packages " "are required for 'explore()'. You can install them using " "'conda install -c conda-forge \"folium>=0.12\" matplotlib mapclassify' " "or 'pip install \"folium>=0.12\" matplotlib mapclassify'." ) # xyservices is an optional dependency try: import xyzservices HAS_XYZSERVICES = True except (ImportError, ModuleNotFoundError): HAS_XYZSERVICES = False gdf = df.copy() # convert LinearRing to LineString rings_mask = df.geom_type == "LinearRing" if rings_mask.any(): gdf.geometry[rings_mask] = gdf.geometry[rings_mask].apply(LineString) if isinstance(gdf, geopandas.GeoSeries): gdf = gdf.to_frame() if gdf.crs is None: kwargs["crs"] = "Simple" tiles = None elif not gdf.crs.equals(4326): gdf = gdf.to_crs(4326) # Fields which are not JSON serializable are coerced to strings json_not_supported_cols = gdf.columns[ [is_datetime64_any_dtype(gdf[c]) for c in gdf.columns] ].union(gdf.columns[gdf.dtypes == "object"]) if len(json_not_supported_cols) > 0: gdf = gdf.astype({c: "string" for c in json_not_supported_cols}) if not isinstance(gdf.index, pd.MultiIndex) and ( is_datetime64_any_dtype(gdf.index) or (gdf.index.dtype == "object") ): gdf.index = gdf.index.astype("string") # create folium.Map object if m is None: # Get bounds to specify location and map extent bounds = gdf.total_bounds location = kwargs.pop("location", None) if location is None and not np.isnan(bounds).all(): x = mean([bounds[0], bounds[2]]) y = mean([bounds[1], bounds[3]]) location = (y, x) if "zoom_start" in kwargs.keys(): fit = False else: fit = True else: fit = False # get a subset of kwargs to be passed to folium.Map for i in _MAP_KWARGS: if i in map_kwds: raise ValueError( f"'{i}' cannot be specified in 'map_kwds'. " f"Use the '{i}={map_kwds[i]}' argument instead." ) map_kwds = { **map_kwds, **{i: kwargs[i] for i in kwargs.keys() if i in _MAP_KWARGS}, } if HAS_XYZSERVICES: # match provider name string to xyzservices.TileProvider if isinstance(tiles, str): try: tiles = xyzservices.providers.query_name(tiles) except ValueError: pass if isinstance(tiles, xyzservices.TileProvider): attr = attr if attr else tiles.html_attribution if "min_zoom" not in map_kwds: map_kwds["min_zoom"] = tiles.get("min_zoom", 0) if "max_zoom" not in map_kwds: map_kwds["max_zoom"] = tiles.get("max_zoom", 18) tiles = tiles.build_url(scale_factor="{r}") m = folium.Map( location=location, control_scale=control_scale, tiles=tiles, attr=attr, width=width, height=height, **map_kwds, ) # fit bounds to get a proper zoom level if fit: m.fit_bounds([[bounds[1], bounds[0]], [bounds[3], bounds[2]]]) if gdf.is_empty.all(): warnings.warn( "The GeoSeries you are attempting to plot is " "composed of empty geometries. Nothing has been displayed.", UserWarning, stacklevel=3, ) return m for map_kwd in _MAP_KWARGS: kwargs.pop(map_kwd, None) nan_idx = None if column is not None: if pd.api.types.is_list_like(column): if len(column) != gdf.shape[0]: raise ValueError( "The GeoDataFrame and given column have different number of rows." ) else: column_name = "__plottable_column" gdf[column_name] = column column = column_name elif isinstance(gdf[column].dtype, pd.CategoricalDtype): if categories is not None: raise ValueError( "Cannot specify 'categories' when column has categorical dtype" ) categorical = True elif ( pd.api.types.is_object_dtype(gdf[column]) or pd.api.types.is_bool_dtype(gdf[column]) or pd.api.types.is_string_dtype(gdf[column]) or categories ): categorical = True nan_idx = pd.isna(gdf[column]) if categorical: cat = pd.Categorical(gdf[column][~nan_idx], categories=categories) N = len(cat.categories) cmap = cmap if cmap else "tab20" # colormap exists in matplotlib if cmap in plt.colormaps(): color = np.apply_along_axis( colors.to_hex, 1, _colormap_helper(cmap, n_resample=N, idx=cat.codes), ) legend_colors = np.apply_along_axis( colors.to_hex, 1, _colormap_helper(cmap, n_resample=N, idx=range(N)) ) # colormap is matplotlib.Colormap elif isinstance(cmap, colors.Colormap): color = np.apply_along_axis(colors.to_hex, 1, cmap(cat.codes)) legend_colors = np.apply_along_axis(colors.to_hex, 1, cmap(range(N))) # custom list of colors elif pd.api.types.is_list_like(cmap): if N > len(cmap): cmap = cmap * (N // len(cmap) + 1) color = np.take(cmap, cat.codes) legend_colors = np.take(cmap, range(N)) else: raise ValueError( "'cmap' is invalid. For categorical plots, pass either valid " "named matplotlib colormap or a list-like of colors." ) elif callable(cmap): # List of colors based on Branca colormaps or self-defined functions color = [cmap(x) for x in df[column]] else: vmin = gdf[column].min() if vmin is None else vmin vmax = gdf[column].max() if vmax is None else vmax # get bins if scheme is not None: if classification_kwds is None: classification_kwds = {} if "k" not in classification_kwds: classification_kwds["k"] = k binning = classify( np.asarray(gdf[column][~nan_idx]), scheme, **classification_kwds ) color = np.apply_along_axis( colors.to_hex, 1, _colormap_helper(cmap, n_resample=binning.k, idx=binning.yb), ) else: bins = np.linspace(vmin, vmax, 257)[1:] binning = classify( np.asarray(gdf[column][~nan_idx]), "UserDefined", bins=bins ) color = np.apply_along_axis( colors.to_hex, 1, _colormap_helper(cmap, n_resample=256, idx=binning.yb), ) # set default style if "fillOpacity" not in style_kwds: style_kwds["fillOpacity"] = 0.5 if "weight" not in style_kwds: style_kwds["weight"] = 2 if "style_function" in style_kwds: style_kwds_function = style_kwds["style_function"] if not callable(style_kwds_function): raise ValueError("'style_function' has to be a callable") style_kwds.pop("style_function") else: def _no_style(x): return {} style_kwds_function = _no_style # specify color if color is not None: if ( isinstance(color, str) and isinstance(gdf, geopandas.GeoDataFrame) and color in gdf.columns ): # use existing column def _style_color(x): base_style = { "fillColor": x["properties"][color], **style_kwds, } return { **base_style, **style_kwds_function(x), } style_function = _style_color else: # assign new column if isinstance(gdf, geopandas.GeoSeries): gdf = geopandas.GeoDataFrame(geometry=gdf) if nan_idx is not None and nan_idx.any(): nan_color = missing_kwds.pop("color", None) gdf["__folium_color"] = nan_color gdf.loc[~nan_idx, "__folium_color"] = color else: gdf["__folium_color"] = color stroke_color = style_kwds.pop("color", None) if not stroke_color: def _style_column(x): base_style = { "fillColor": x["properties"]["__folium_color"], "color": x["properties"]["__folium_color"], **style_kwds, } return { **base_style, **style_kwds_function(x), } style_function = _style_column else: def _style_stroke(x): base_style = { "fillColor": x["properties"]["__folium_color"], "color": stroke_color, **style_kwds, } return { **base_style, **style_kwds_function(x), } style_function = _style_stroke else: # use folium default def _style_default(x): return {**style_kwds, **style_kwds_function(x)} style_function = _style_default if highlight: if "fillOpacity" not in highlight_kwds: highlight_kwds["fillOpacity"] = 0.75 def _style_highlight(x): return {**highlight_kwds} highlight_function = _style_highlight else: highlight_function = None # define default for points if marker_type is None: marker_type = "circle_marker" marker = marker_type if isinstance(marker_type, str): if marker_type == "marker": marker = folium.Marker(**marker_kwds) elif marker_type == "circle": marker = folium.Circle(**marker_kwds) elif marker_type == "circle_marker": marker_kwds["radius"] = marker_kwds.get("radius", 2) marker_kwds["fill"] = marker_kwds.get("fill", True) marker = folium.CircleMarker(**marker_kwds) else: raise ValueError( "Only 'marker', 'circle', and 'circle_marker' are " "supported as marker values" ) # remove additional geometries if isinstance(gdf, geopandas.GeoDataFrame): non_active_geoms = [ name for name, val in (gdf.dtypes == "geometry").items() if val and name != gdf.geometry.name ] gdf = gdf.drop(columns=non_active_geoms) # prepare tooltip and popup if isinstance(gdf, geopandas.GeoDataFrame): # add named index to the tooltip if gdf.index.name is not None: gdf = gdf.reset_index() # specify fields to show in the tooltip tooltip = _tooltip_popup("tooltip", tooltip, gdf, **tooltip_kwds) popup = _tooltip_popup("popup", popup, gdf, **popup_kwds) else: tooltip = None popup = None # escape the curly braces {{}} for jinja2 templates feature_collection = gdf[ ~(gdf.geometry.isna() | gdf.geometry.is_empty) # drop missing or empty geoms ].__geo_interface__ for feature in feature_collection["features"]: for prop in feature["properties"]: # escape the curly braces in values if isinstance(feature["properties"][prop], str): feature["properties"][prop] = re.sub( r"\{{2,}", lambda x: "{% raw %}" + x.group(0) + "{% endraw %}", feature["properties"][prop], ) # add dataframe to map folium.GeoJson( feature_collection, tooltip=tooltip, popup=popup, marker=marker, style_function=style_function, highlight_function=highlight_function, **kwargs, ).add_to(m) if legend: # NOTE: overlaps will be resolved in branca #88 caption = column if not column == "__plottable_column" else "" caption = legend_kwds.pop("caption", caption) if categorical: categories = cat.categories.to_list() legend_colors = legend_colors.tolist() labels = legend_kwds.pop("labels", None) if labels is not None: if len(labels) != len(categories): raise ValueError( f"The number of legend labels ({len(labels)}) does not " f"match the number of categories ({len(categories)})." ) categories = list(labels) if nan_idx.any() and nan_color: categories.append(missing_kwds.pop("label", "NaN")) legend_colors.append(nan_color) _categorical_legend(m, caption, categories, legend_colors) elif column is not None: cbar = legend_kwds.pop("colorbar", True) colormap_kwds = {} if "max_labels" in legend_kwds: colormap_kwds["max_labels"] = legend_kwds.pop("max_labels") if scheme: cb_colors = np.apply_along_axis( colors.to_hex, 1, _colormap_helper(cmap, n_resample=binning.k, idx=range(binning.k)), ) if cbar: if legend_kwds.pop("scale", True): index = [vmin] + binning.bins.tolist() else: index = None colorbar = bc.colormap.StepColormap( cb_colors, vmin=vmin, vmax=vmax, caption=caption, index=index, **colormap_kwds, ) else: fmt = legend_kwds.pop("fmt", "{:.2f}") if "labels" in legend_kwds: categories = legend_kwds.pop("labels") else: categories = binning.get_legend_classes(fmt) show_interval = legend_kwds.pop("interval", False) if not show_interval: categories = [c[1:-1] for c in categories] if nan_idx.any() and nan_color: categories.append(missing_kwds.pop("label", "NaN")) cb_colors = np.append(cb_colors, nan_color) _categorical_legend(m, caption, categories, cb_colors) else: if isinstance(cmap, bc.colormap.ColorMap): colorbar = cmap else: mp_cmap = _colormap_helper(cmap) cb_colors = np.apply_along_axis( colors.to_hex, 1, mp_cmap(range(mp_cmap.N)) ) # linear legend if mp_cmap.N > 20: colorbar = bc.colormap.LinearColormap( cb_colors, vmin=vmin, vmax=vmax, caption=caption, **colormap_kwds, ) # steps else: colorbar = bc.colormap.StepColormap( cb_colors, vmin=vmin, vmax=vmax, caption=caption, **colormap_kwds, ) if cbar: if nan_idx.any() and nan_color: _categorical_legend( m, "", [missing_kwds.pop("label", "NaN")], [nan_color] ) m.add_child(colorbar) return m def _tooltip_popup(type, fields, gdf, **kwds): """Get tooltip or popup.""" import folium # specify fields to show in the tooltip if fields is False or fields is None or fields == 0: return None else: if fields is True: fields = gdf.columns.drop(gdf.geometry.name).to_list() elif isinstance(fields, int): fields = gdf.columns.drop(gdf.geometry.name).to_list()[:fields] elif isinstance(fields, str): fields = [fields] for field in ["__plottable_column", "__folium_color"]: if field in fields: fields.remove(field) # Cast fields to str fields = list(map(str, fields)) if type == "tooltip": return folium.GeoJsonTooltip(fields, **kwds) elif type == "popup": return folium.GeoJsonPopup(fields, **kwds) def _categorical_legend(m, title, categories, colors): """Add categorical legend to a map. The implementation is using the code originally written by Michel Metran (@michelmetran) and released on GitHub (https://github.com/michelmetran/package_folium) under MIT license. Copyright (c) 2020 Michel Metran Parameters ---------- m : folium.Map Existing map instance on which to draw the plot title : str title of the legend (e.g. column name) categories : list-like list of categories colors : list-like list of colors (in the same order as categories) """ # Header to Add head = """ {% macro header(this, kwargs) %} {% endmacro %} """ import branca as bc # Add CSS (on Header) macro = bc.element.MacroElement() macro._template = bc.element.Template(head) m.get_root().add_child(macro) body = f"""
{title}
    """ # Loop Categories for label, color in zip(categories, colors): body += f"""
  • {label}
    • """ body += """
""" # Add Body body = bc.element.Element(body, "legend") m.get_root().html.add_child(body) def _explore_geoseries( s, color=None, m=None, tiles="OpenStreetMap", attr=None, highlight=True, width="100%", height="100%", control_scale=True, marker_type=None, marker_kwds={}, style_kwds={}, highlight_kwds={}, map_kwds={}, **kwargs, ): """Interactive map based on GeoPandas and folium/leaflet.js. Generate an interactive leaflet map based on :class:`~geopandas.GeoSeries` Parameters ---------- color : str, array-like (default None) Named color or a list-like of colors (named or hex). m : folium.Map (default None) Existing map instance on which to draw the plot. tiles : str, xyzservices.TileProvider (default 'OpenStreetMap Mapnik') Map tileset to use. Can choose from the list supported by folium, query a :class:`xyzservices.TileProvider` by a name from ``xyzservices.providers``, pass :class:`xyzservices.TileProvider` object or pass custom XYZ URL. When ``xyzservices`` is not available, it defaults to OpenStreetMap Mapnik tiles. You can pass a custom tileset to Folium by passing a Leaflet-style URL to the tiles parameter: ``http://{s}.yourtiles.com/{z}/{x}/{y}.png``. Be sure to check their terms and conditions and to provide attribution with the ``attr`` keyword. attr : str (default None) Map tile attribution; only required if passing custom tile URL. highlight : bool (default True) Enable highlight functionality when hovering over a geometry. width : pixel int or percentage string (default: '100%') Width of the folium :class:`~folium.folium.Map`. If the argument m is given explicitly, width is ignored. height : pixel int or percentage string (default: '100%') Height of the folium :class:`~folium.folium.Map`. If the argument m is given explicitly, height is ignored. control_scale : bool, (default True) Whether to add a control scale on the map. marker_type : str, folium.Circle, folium.CircleMarker, folium.Marker (default None) Allowed string options are ('marker', 'circle', 'circle_marker'). Defaults to folium.Marker. marker_kwds: dict (default {}) Additional keywords to be passed to the selected ``marker_type``, e.g.: radius : float Radius of the circle, in meters (for ``'circle'``) or pixels (for ``circle_marker``). icon : folium.map.Icon the :class:`folium.map.Icon` object to use to render the marker. draggable : bool (default False) Set to True to be able to drag the marker around the map. style_kwds : dict (default {}) Additional style to be passed to folium ``style_function``: stroke : bool (default True) Whether to draw stroke along the path. Set it to ``False`` to disable borders on polygons or circles. color : str Stroke color weight : int Stroke width in pixels opacity : float (default 1.0) Stroke opacity fill : boolean (default True) Whether to fill the path with color. Set it to ``False`` to disable filling on polygons or circles. fillColor : str Fill color. Defaults to the value of the color option fillOpacity : float (default 0.5) Fill opacity. style_function : callable Function mapping a GeoJson Feature to a style ``dict``. * Style properties :func:`folium.vector_layers.path_options` * GeoJson features :class:`GeoSeries.__geo_interface__` e.g.:: lambda x: {"color":"red" if x["properties"]["gdp_md_est"]<10**6 else "blue"} Plus all supported by :func:`folium.vector_layers.path_options`. See the documentation of :class:`folium.features.GeoJson` for details. highlight_kwds : dict (default {}) Style to be passed to folium highlight_function. Uses the same keywords as ``style_kwds``. When empty, defaults to ``{"fillOpacity": 0.75}``. map_kwds : dict (default {}) Additional keywords to be passed to folium :class:`~folium.folium.Map`, e.g. ``dragging``, or ``scrollWheelZoom``. **kwargs : dict Additional options to be passed on to the folium. Returns ------- m : folium.folium.Map folium :class:`~folium.folium.Map` instance """ return _explore( s, color=color, m=m, tiles=tiles, attr=attr, highlight=highlight, width=width, height=height, control_scale=control_scale, marker_type=marker_type, marker_kwds=marker_kwds, style_kwds=style_kwds, highlight_kwds=highlight_kwds, map_kwds=map_kwds, **kwargs, ) geopandas-1.2.0/geopandas/geodataframe.py000066400000000000000000003456601525650651600204360ustar00rootroot00000000000000from __future__ import annotations import json import typing import warnings from typing import Any, Literal import numpy as np import pandas as pd from pandas import DataFrame, Series import shapely.errors from shapely.geometry import mapping, shape from shapely.geometry.base import BaseGeometry import geopandas from geopandas.array import GeometryArray, GeometryDtype, from_shapely, to_wkb, to_wkt from geopandas.base import GeoPandasBase, is_geometry_type from geopandas.explore import _explore from geopandas.geoseries import GeoSeries from ._compat import HAS_PYPROJ, PANDAS_GE_30 from ._decorator import doc if PANDAS_GE_30: from pandas.core.accessor import Accessor else: from pandas.core.accessor import CachedAccessor as Accessor if typing.TYPE_CHECKING: import os from collections.abc import Iterable import folium import sqlalchemy.text from pyproj import CRS from geopandas.io.arrow import ( PARQUET_GEOMETRY_ENCODINGS, SUPPORTED_VERSIONS_LITERAL, ) def _ensure_geometry(data, crs: Any | None = None) -> GeoSeries | GeometryArray: """ Ensure the data is of geometry dtype or converted to it. If input is a (Geo)Series, output is a GeoSeries, otherwise output is GeometryArray. If the input is a GeometryDtype with a set CRS, `crs` is ignored. """ if is_geometry_type(data): if isinstance(data, Series): data = GeoSeries(data) if data.crs is None and crs is not None: # Avoids caching issues/crs sharing issues data = data.copy() if isinstance(data, GeometryArray): data.crs = crs else: data.array.crs = crs return data else: if isinstance(data, Series): out = from_shapely(np.asarray(data), crs=crs) return GeoSeries(out, index=data.index, name=data.name) else: out = from_shapely(data, crs=crs) return out crs_mismatch_error = ( "CRS mismatch between CRS of the passed geometries " "and 'crs'. Use 'GeoDataFrame.set_crs(crs, " "allow_override=True)' to overwrite CRS or " "'GeoDataFrame.to_crs(crs)' to reproject geometries. " ) class GeoDataFrame(GeoPandasBase, DataFrame): """A GeoDataFrame object is a pandas.DataFrame that has one or more columns containing geometry. In addition to the standard DataFrame constructor arguments, GeoDataFrame also accepts the following keyword arguments: Parameters ---------- crs : value (optional) Coordinate Reference System of the geometry objects. Can be anything accepted by :meth:`pyproj.CRS.from_user_input() `, such as an authority string (eg "EPSG:4326") or a WKT string. geometry : str or array-like (optional) Value to use as the active geometry column. If str, treated as column name to use. If array-like, it will be added as new column named 'geometry' on the GeoDataFrame and set as the active geometry column. Note that if ``geometry`` is a (Geo)Series with a name, the name will not be used, a column named "geometry" will still be added. To preserve the name, you can use :meth:`~GeoDataFrame.rename_geometry` to update the geometry column name. Examples -------- Constructing GeoDataFrame from a dictionary. >>> from shapely.geometry import Point >>> d = {'col1': ['name1', 'name2'], 'geometry': [Point(1, 2), Point(2, 1)]} >>> gdf = geopandas.GeoDataFrame(d, crs="EPSG:4326") >>> gdf col1 geometry 0 name1 POINT (1 2) 1 name2 POINT (2 1) Notice that the inferred dtype of 'geometry' columns is geometry. >>> gdf.dtypes col1 str geometry geometry dtype: object Constructing GeoDataFrame from a pandas DataFrame with a column of WKT geometries: >>> import pandas as pd >>> d = {'col1': ['name1', 'name2'], 'wkt': ['POINT (1 2)', 'POINT (2 1)']} >>> df = pd.DataFrame(d) >>> gs = geopandas.GeoSeries.from_wkt(df['wkt']) >>> gdf = geopandas.GeoDataFrame(df, geometry=gs, crs="EPSG:4326") >>> gdf col1 wkt geometry 0 name1 POINT (1 2) POINT (1 2) 1 name2 POINT (2 1) POINT (2 1) See Also -------- GeoSeries : Series object designed to store shapely geometry objects """ _metadata = ["_geometry_column_name"] _internal_names = DataFrame._internal_names + ["geometry"] _internal_names_set = set(_internal_names) _geometry_column_name = None def __init__( self, data=None, *args, geometry: Any | None = None, crs: Any | None = None, **kwargs, ): if ( kwargs.get("copy") is None and isinstance(data, DataFrame) and not isinstance(data, GeoDataFrame) ): kwargs.update(copy=True) if data is None and "columns" not in kwargs: # pandas will interpret "str" as object dtype for pandas < 3 and # as string dtype for pandas >= 3. This ensures we still get string # columns when doing GeoDataFrame(geometry=[..]) kwargs["columns"] = pd.Index([], dtype="str") super().__init__(data, *args, **kwargs) if isinstance(data, DataFrame) and data.attrs: self.attrs = data.attrs # set_geometry ensures the geometry data have the proper dtype, # but is not called if `geometry=None` ('geometry' column present # in the data), so therefore need to ensure it here manually # but within a try/except because currently non-geometries are # allowed in that case # TODO do we want to raise / return normal DataFrame in this case? # if gdf passed in and geo_col is set, we use that for geometry if geometry is None and isinstance(data, GeoDataFrame): self._geometry_column_name = data._geometry_column_name if crs is not None and data.crs != crs: raise ValueError(crs_mismatch_error) if ( geometry is None and self.columns.nlevels == 1 and "geometry" in self.columns ): # Check for multiple columns with name "geometry". If there are, # self["geometry"] is a gdf and constructor gets recursively recalled # by pandas internals trying to access this if (self.columns == "geometry").sum() > 1: raise ValueError( "GeoDataFrame does not support multiple columns " "using the geometry column name 'geometry'." ) # only if we have actual geometry values -> call set_geometry if ( hasattr(self["geometry"].values, "crs") and self["geometry"].values.crs and crs and not self["geometry"].values.crs == crs ): raise ValueError(crs_mismatch_error) # If "geometry" is potentially coercible to geometry, we try and convert it geom_dtype = self["geometry"].dtype if ( geom_dtype == "geometry" # noqa: PLR1714 or geom_dtype == "object" # special case for geometry = [], has float dtype or (len(self) == 0 and geom_dtype == "float") # special case for geometry = [np.nan] or ((not self.empty) and self["geometry"].isna().all()) ): try: self["geometry"] = _ensure_geometry(self["geometry"].values, crs) except TypeError: pass else: # feed through to call set geometry below geometry = "geometry" if geometry is not None: if ( hasattr(geometry, "crs") and geometry.crs and crs and not geometry.crs == crs ): raise ValueError(crs_mismatch_error) if isinstance(geometry, pd.Series) and geometry.name not in ( "geometry", None, ): # __init__ always creates geometry col named "geometry" # rename as `set_geometry` respects the given series name geometry = geometry.rename("geometry") self.set_geometry(geometry, inplace=True, crs=crs) if geometry is None and crs: raise ValueError( "Assigning CRS to a GeoDataFrame without a geometry column is not " "supported. Supply geometry using the 'geometry=' keyword argument, " "or by providing a DataFrame with column name 'geometry'", ) def __setattr__(self, attr, val): # have to special case geometry b/c pandas tries to use as column... if attr == "geometry": object.__setattr__(self, attr, val) else: super().__setattr__(attr, val) def _get_geometry(self) -> GeoSeries: if self._geometry_column_name not in self: if self._geometry_column_name is None: msg = ( "You are calling a geospatial method on the GeoDataFrame, " "but the active geometry column to use has not been set. " ) else: msg = ( "You are calling a geospatial method on the GeoDataFrame, " f"but the active geometry column ('{self._geometry_column_name}') " "is not present. " ) geo_cols = list(self.columns[self.dtypes == "geometry"]) if len(geo_cols) > 0: msg += ( f"\nThere are columns with geometry data type ({geo_cols}), and " "you can either set one as the active geometry with " 'df.set_geometry("name") or access the column as a ' 'GeoSeries (df["name"]) and call the method directly on it.' ) else: msg += ( "\nThere are no existing columns with geometry data type. You can " "add a geometry column as the active geometry column with " "df.set_geometry. " ) raise AttributeError(msg) return self[self._geometry_column_name] def _set_geometry(self, col): if not pd.api.types.is_list_like(col): raise ValueError("Must use a list-like to set the geometry property") self._persist_old_default_geometry_colname() self.set_geometry(col, inplace=True) geometry = property( fget=_get_geometry, fset=_set_geometry, doc="Geometry data for GeoDataFrame" ) @typing.overload def set_geometry( self, col, drop: bool | None = ..., inplace: Literal[True] = ..., crs: Any | None = ..., ) -> None: ... @typing.overload def set_geometry( self, col, drop: bool | None = ..., inplace: Literal[False] = ..., crs: Any | None = ..., ) -> GeoDataFrame: ... def set_geometry( self, col, drop: bool | None = None, inplace: bool = False, crs: Any | None = None, ) -> GeoDataFrame | None: """ Set the GeoDataFrame geometry using either an existing column or the specified input. By default yields a new object. The original geometry column is replaced with the input. Parameters ---------- col : column label or array-like An existing column name or values to set as the new geometry column. If values (array-like, (Geo)Series) are passed, then if they are named (Series) the new geometry column will have the corresponding name, otherwise the existing geometry column will be replaced. If there is no existing geometry column, the new geometry column will use the default name "geometry". drop : boolean, default False When specifying a named Series or an existing column name for `col`, controls if the previous geometry column should be dropped from the result. The default of False keeps both the old and new geometry column. .. deprecated:: 1.0.0 inplace : boolean, default False Modify the GeoDataFrame in place (do not create a new object) crs : pyproj.CRS, optional Coordinate system to use. The value can be anything accepted by :meth:`pyproj.CRS.from_user_input() `, such as an authority string (eg "EPSG:4326") or a WKT string. If passed, overrides both DataFrame and col's crs. Otherwise, tries to get crs from passed col values or DataFrame. Examples -------- >>> from shapely.geometry import Point >>> d = {'col1': ['name1', 'name2'], 'geometry': [Point(1, 2), Point(2, 1)]} >>> gdf = geopandas.GeoDataFrame(d, crs="EPSG:4326") >>> gdf col1 geometry 0 name1 POINT (1 2) 1 name2 POINT (2 1) Passing an array: >>> df1 = gdf.set_geometry([Point(0,0), Point(1,1)]) >>> df1 col1 geometry 0 name1 POINT (0 0) 1 name2 POINT (1 1) Using existing column: >>> gdf["buffered"] = gdf.buffer(2) >>> df2 = gdf.set_geometry("buffered") >>> df2.geometry 0 POLYGON ((3 2, 2.99037 1.80397, 2.96157 1.6098... 1 POLYGON ((4 1, 3.99037 0.80397, 3.96157 0.6098... Name: buffered, dtype: geometry Returns ------- GeoDataFrame See Also -------- GeoDataFrame.rename_geometry : rename an active geometry column """ # Most of the code here is taken from DataFrame.set_index() if inplace: frame = self else: if PANDAS_GE_30: frame = self.copy(deep=False) else: frame = self.copy() geo_column_name = self._geometry_column_name if geo_column_name is None: geo_column_name = "geometry" if isinstance(col, Series | list | np.ndarray | GeometryArray): if drop: msg = ( "The `drop` keyword argument is deprecated and has no effect when " "`col` is an array-like value. You should stop passing `drop` to " "`set_geometry` when this is the case." ) warnings.warn(msg, category=FutureWarning, stacklevel=2) if isinstance(col, Series) and col.name is not None: geo_column_name = col.name level = col elif hasattr(col, "ndim") and col.ndim > 1: raise ValueError("Must pass array with one dimension only.") else: # should be a colname try: level = frame[col] except KeyError: raise ValueError(f"Unknown column {col}") if isinstance(level, DataFrame): raise ValueError( "GeoDataFrame does not support setting the geometry column where " "the column name is shared by multiple columns." ) given_colname_drop_msg = ( "The `drop` keyword argument is deprecated and in future the only " "supported behaviour will match drop=False. To silence this " "warning and adopt the future behaviour, stop providing " "`drop` as a keyword to `set_geometry`. To replicate the " "`drop=True` behaviour you should update " "your code to\n`geo_col_name = gdf.active_geometry_name;" " gdf.set_geometry(new_geo_col).drop(" "columns=geo_col_name).rename_geometry(geo_col_name)`." ) if drop is False: # specifically False, not falsy i.e. None # User supplied False explicitly, but arg is deprecated warnings.warn( given_colname_drop_msg, category=FutureWarning, stacklevel=2, ) if drop: del frame[col] frame.__class__ = GeoDataFrame # revert the casting done in __delitem__, keep gdf warnings.warn( given_colname_drop_msg, category=FutureWarning, stacklevel=2, ) else: # if not dropping, set the active geometry name to the given col name geo_column_name = col if not crs: crs = getattr(level, "crs", None) # Check that we are using a listlike of geometries level = _ensure_geometry(level, crs=crs) # ensure_geometry only sets crs on level if it has crs==None if isinstance(level, GeoSeries): level.array.crs = crs else: level.crs = crs # update _geometry_column_name prior to assignment # to avoid default is None warning frame._geometry_column_name = geo_column_name if isinstance(frame.columns, pd.MultiIndex) and not isinstance( geo_column_name, tuple ): # when setting a single column to dataframe with multi-index columns, # pandas does not like setting it with only the first level name geo_column_name = (geo_column_name,) + ("",) * (frame.columns.nlevels - 1) frame[geo_column_name] = level if not inplace: return frame @typing.overload def rename_geometry( self, col: str, inplace: Literal[True] = ..., ) -> None: ... @typing.overload def rename_geometry( self, col: str, inplace: Literal[False] = ..., ) -> GeoDataFrame: ... def rename_geometry(self, col: str, inplace: bool = False) -> GeoDataFrame | None: """Rename the GeoDataFrame geometry column to the specified name. By default yields a new object. The original geometry column is replaced with the input. Parameters ---------- col : new geometry column label inplace : boolean, default False Modify the GeoDataFrame in place (do not create a new object) Examples -------- >>> from shapely.geometry import Point >>> d = {'col1': ['name1', 'name2'], 'geometry': [Point(1, 2), Point(2, 1)]} >>> df = geopandas.GeoDataFrame(d, crs="EPSG:4326") >>> df1 = df.rename_geometry('geom1') >>> df1.geometry.name 'geom1' >>> df.rename_geometry('geom1', inplace=True) >>> df.geometry.name 'geom1' See Also -------- GeoDataFrame.set_geometry : set the active geometry """ geometry_col = self.geometry.name if col in self.columns: raise ValueError(f"Column named {col} already exists") else: if not inplace: return self.rename(columns={geometry_col: col}).set_geometry( col, inplace=inplace ) with warnings.catch_warnings(): # TODO: pandas >= 3.1 triggers a warning for inplace rename here # suppresing this for now, but we have to replace this with our own # warning specifically for rename_geometry warnings.filterwarnings("ignore", ".*inplace keyword.*") self.rename(columns={geometry_col: col}, inplace=inplace) self.set_geometry(col, inplace=inplace) @property def active_geometry_name(self) -> Any: """Return the name of the active geometry column. Returns a name if a GeoDataFrame has an active geometry column set, otherwise returns None. The return type is usually a string, but may be an integer, tuple or other hashable, depending on the contents of the dataframe columns. You can also access the active geometry column using the ``.geometry`` property. You can set a GeoSeries to be an active geometry using the :meth:`~GeoDataFrame.set_geometry` method. Returns ------- str or other index label supported by pandas name of an active geometry column or None See Also -------- GeoDataFrame.set_geometry : set the active geometry """ return self._geometry_column_name @property def crs(self) -> CRS: """ The Coordinate Reference System (CRS) represented as a ``pyproj.CRS`` object. Returns ------- ``pyproj.CRS`` | None CRS assigned to an active geometry column Examples -------- >>> gdf.crs # doctest: +SKIP Name: WGS 84 Axis Info [ellipsoidal]: - Lat[north]: Geodetic latitude (degree) - Lon[east]: Geodetic longitude (degree) Area of Use: - name: World - bounds: (-180.0, -90.0, 180.0, 90.0) Datum: World Geodetic System 1984 - Ellipsoid: WGS 84 - Prime Meridian: Greenwich See Also -------- GeoDataFrame.set_crs : assign CRS GeoDataFrame.to_crs : re-project to another CRS """ try: return self.geometry.crs except AttributeError: raise AttributeError( "The CRS attribute of a GeoDataFrame without an active " "geometry column is not defined. Use GeoDataFrame.set_geometry " "to set the active geometry column." ) @crs.setter def crs(self, value) -> None: """Set the value of the crs.""" if self._geometry_column_name is None: raise ValueError( "Assigning CRS to a GeoDataFrame without a geometry column is not " "supported. Use GeoDataFrame.set_geometry to set the active " "geometry column.", ) if hasattr(self.geometry.values, "crs"): if self.crs is not None: warnings.warn( "Overriding the CRS of a GeoDataFrame that already has CRS. " "This unsafe behavior will be deprecated in future versions. " "Use GeoDataFrame.set_crs method instead", stacklevel=2, category=FutureWarning, ) self.geometry.values.crs = value else: # column called 'geometry' without geometry raise ValueError( "Assigning CRS to a GeoDataFrame without an active geometry " "column is not supported. Use GeoDataFrame.set_geometry to set " "the active geometry column.", ) def __setstate__(self, state) -> None: # overriding DataFrame method for compat with older pickles (CRS handling) crs = None if isinstance(state, dict): if "crs" in state and "_crs" not in state: crs = state.pop("crs", None) else: crs = state.pop("_crs", None) if crs is not None and not HAS_PYPROJ: raise ImportError( "Unpickling a GeoDataFrame with CRS requires the 'pyproj' package, " "but it is not installed or does not import correctly. " ) elif crs is not None: from pyproj import CRS crs = CRS.from_user_input(crs) super().__setstate__(state) # for some versions that didn't yet have CRS at array level -> crs is set # at GeoDataFrame level with '_crs' (and not 'crs'), so without propagating # to the GeoSeries/GeometryArray try: if crs is not None: if self.geometry.values.crs is None: self.crs = crs except Exception: pass @classmethod def from_dict( cls, data: dict, geometry=None, crs: Any | None = None, **kwargs, ) -> GeoDataFrame: """Construct GeoDataFrame from dict of array-like or dicts by overriding DataFrame.from_dict method with geometry and crs. Parameters ---------- data : dict Of the form {field : array-like} or {field : dict}. geometry : str or array (optional) If str, column to use as geometry. If array, will be set as 'geometry' column on GeoDataFrame. crs : str or dict (optional) Coordinate reference system to set on the resulting frame. kwargs : key-word arguments These arguments are passed to DataFrame.from_dict Returns ------- GeoDataFrame """ dataframe = DataFrame.from_dict(data, **kwargs) return cls(dataframe, geometry=geometry, crs=crs) @classmethod def from_file(cls, filename: os.PathLike | typing.IO, **kwargs) -> GeoDataFrame: """Alternate constructor to create a ``GeoDataFrame`` from a file. It is recommended to use :func:`geopandas.read_file` instead. Can load a ``GeoDataFrame`` from a file in any format recognized by `pyogrio`. See http://pyogrio.readthedocs.io/ for details. Parameters ---------- filename : str File path or file handle to read from. Depending on which kwargs are included, the content of filename may vary. See :func:`pyogrio.read_dataframe` for usage details. kwargs : key-word arguments These arguments are passed to :func:`pyogrio.read_dataframe`, and can be used to access multi-layer data, data stored within archives (zip files), etc. Examples -------- >>> import geodatasets >>> path = geodatasets.get_path('nybb') >>> gdf = geopandas.GeoDataFrame.from_file(path) >>> gdf # doctest: +SKIP BoroCode BoroName Shape_Leng Shape_Area \ geometry 0 5 Staten Island 330470.010332 1.623820e+09 MULTIPOLYGON ((\ (970217.022 145643.332, 970227.... 1 4 Queens 896344.047763 3.045213e+09 MULTIPOLYGON ((\ (1029606.077 156073.814, 102957... 2 3 Brooklyn 741080.523166 1.937479e+09 MULTIPOLYGON ((\ (1021176.479 151374.797, 102100... 3 1 Manhattan 359299.096471 6.364715e+08 MULTIPOLYGON ((\ (981219.056 188655.316, 980940.... 4 2 Bronx 464392.991824 1.186925e+09 MULTIPOLYGON ((\ (1012821.806 229228.265, 101278... The recommended method of reading files is :func:`geopandas.read_file`: >>> gdf = geopandas.read_file(path) See Also -------- read_file : read file to GeoDataFrame GeoDataFrame.to_file : write GeoDataFrame to file """ return geopandas.io.file._read_file(filename, **kwargs) @classmethod def from_features( cls, features, crs: Any | None = None, columns: Iterable[str] | None = None ) -> GeoDataFrame: """ Alternate constructor to create GeoDataFrame from an iterable of features or a feature collection. Parameters ---------- features - Iterable of features, where each element must be a feature dictionary or implement the __geo_interface__. - Feature collection, where the 'features' key contains an iterable of features. - Object holding a feature collection that implements the ``__geo_interface__``. crs : str or dict (optional) Coordinate reference system to set on the resulting frame. columns : list of column names, optional Optionally specify the column names to include in the output frame. This does not overwrite the property names of the input, but can ensure a consistent output format. Returns ------- GeoDataFrame Notes ----- For more information about the ``__geo_interface__``, see https://gist.github.com/sgillies/2217756 Examples -------- >>> feature_coll = { ... "type": "FeatureCollection", ... "features": [ ... { ... "id": "0", ... "type": "Feature", ... "properties": {"col1": "name1"}, ... "geometry": {"type": "Point", "coordinates": (1.0, 2.0)}, ... "bbox": (1.0, 2.0, 1.0, 2.0), ... }, ... { ... "id": "1", ... "type": "Feature", ... "properties": {"col1": "name2"}, ... "geometry": {"type": "Point", "coordinates": (2.0, 1.0)}, ... "bbox": (2.0, 1.0, 2.0, 1.0), ... }, ... ], ... "bbox": (1.0, 1.0, 2.0, 2.0), ... } >>> df = geopandas.GeoDataFrame.from_features(feature_coll) >>> df geometry col1 0 POINT (1 2) name1 1 POINT (2 1) name2 """ # Handle feature collections if hasattr(features, "__geo_interface__"): fs = features.__geo_interface__ else: fs = features if isinstance(fs, dict) and fs.get("type") == "FeatureCollection": features_lst = fs["features"] else: features_lst = features rows = [] for feature in features_lst: # load geometry if hasattr(feature, "__geo_interface__"): feature = feature.__geo_interface__ row = { "geometry": shape(feature["geometry"]) if feature["geometry"] else None } # load properties properties = feature.get("properties") or {} row.update(properties) rows.append(row) if not rows: return cls(geometry=[], columns=columns, crs=crs) return cls(rows, columns=columns, crs=crs) @classmethod def from_postgis( cls, sql: str | sqlalchemy.text, con, geom_col: str = "geom", crs: Any | None = None, index_col: str | list[str] | None = None, coerce_float: bool = True, parse_dates: list | dict | None = None, params: list | tuple | dict | None = None, chunksize: int | None = None, ) -> GeoDataFrame: """ Alternate constructor to create a ``GeoDataFrame`` from a sql query containing a geometry column in WKB representation. Parameters ---------- sql : string con : sqlalchemy.engine.Connection or sqlalchemy.engine.Engine geom_col : string, default 'geom' column name to convert to shapely geometries crs : optional Coordinate reference system to use for the returned GeoDataFrame index_col : string or list of strings, optional, default: None Column(s) to set as index(MultiIndex) coerce_float : boolean, default True Attempt to convert values of non-string, non-numeric objects (like decimal.Decimal) to floating point, useful for SQL result sets parse_dates : list or dict, default None - List of column names to parse as dates. - Dict of ``{column_name: format string}`` where format string is strftime compatible in case of parsing string times, or is one of (D, s, ns, ms, us) in case of parsing integer timestamps. - Dict of ``{column_name: arg dict}``, where the arg dict corresponds to the keyword arguments of :func:`pandas.to_datetime`. Especially useful with databases without native Datetime support, such as SQLite. params : list, tuple or dict, optional, default None List of parameters to pass to execute method. chunksize : int, default None If specified, return an iterator where chunksize is the number of rows to include in each chunk. Examples -------- PostGIS >>> from sqlalchemy import create_engine # doctest: +SKIP >>> db_connection_url = "postgresql://myusername:mypassword@myhost:5432/mydb" >>> con = create_engine(db_connection_url) # doctest: +SKIP >>> sql = "SELECT geom, highway FROM roads" >>> df = geopandas.GeoDataFrame.from_postgis(sql, con) # doctest: +SKIP SpatiaLite >>> sql = "SELECT ST_Binary(geom) AS geom, highway FROM roads" >>> df = geopandas.GeoDataFrame.from_postgis(sql, con) # doctest: +SKIP The recommended method of reading from PostGIS is :func:`geopandas.read_postgis`: >>> df = geopandas.read_postgis(sql, con) # doctest: +SKIP See Also -------- geopandas.read_postgis : read PostGIS database to GeoDataFrame """ df = geopandas.io.sql._read_postgis( sql, con, geom_col=geom_col, crs=crs, index_col=index_col, coerce_float=coerce_float, parse_dates=parse_dates, params=params, chunksize=chunksize, ) return df @classmethod def from_arrow( cls, table, geometry: str | None = None, to_pandas_kwargs: dict | None = None ): """ Construct a GeoDataFrame from an Arrow table object based on GeoArrow extension types. See https://geoarrow.org/ for details on the GeoArrow specification. This functions accepts any tabular Arrow object implementing the `Arrow PyCapsule Protocol`_ (i.e. having an ``__arrow_c_array__`` or ``__arrow_c_stream__`` method). .. _Arrow PyCapsule Protocol: https://arrow.apache.org/docs/format/CDataInterface/PyCapsuleInterface.html .. versionadded:: 1.0 Parameters ---------- table : pyarrow.Table or Arrow-compatible table Any tabular object implementing the Arrow PyCapsule Protocol (i.e. has an ``__arrow_c_array__`` or ``__arrow_c_stream__`` method). This table should have at least one column with a geoarrow geometry type. geometry : str, default None The name of the geometry column to set as the active geometry column. If None, the first geometry column found will be used. to_pandas_kwargs : dict, optional Arguments passed to the `pa.Table.to_pandas` method for non-geometry columns. This can be used to control the behavior of the conversion of the non-geometry columns to a pandas DataFrame. For example, you can use this to control the dtype conversion of the columns. By default, the `to_pandas` method is called with no additional arguments. Returns ------- GeoDataFrame See Also -------- GeoDataFrame.to_arrow GeoSeries.from_arrow Examples -------- >>> import geoarrow.pyarrow as ga >>> import pyarrow as pa >>> table = pa.Table.from_arrays([ ... ga.as_geoarrow( ... [None, "POLYGON ((0 0, 1 1, 0 1, 0 0))", "LINESTRING (0 0, -1 1, 0 -1)"] ... ), ... pa.array([1, 2, 3]), ... pa.array(["a", "b", "c"]), ... ], names=["geometry", "id", "value"]) >>> gdf = geopandas.GeoDataFrame.from_arrow(table) >>> gdf geometry id value 0 None 1 a 1 POLYGON ((0 0, 1 1, 0 1, 0 0)) 2 b 2 LINESTRING (0 0, -1 1, 0 -1) 3 c """ from geopandas.io._geoarrow import arrow_to_geopandas return arrow_to_geopandas( table, geometry=geometry, to_pandas_kwargs=to_pandas_kwargs ) def to_json( self, na: Literal["null", "drop", "keep"] = "null", show_bbox: bool = False, drop_id: bool = False, to_wgs84: bool = False, **kwargs, ) -> str: """Return a GeoJSON representation of the ``GeoDataFrame`` as a string. Parameters ---------- na : {'null', 'drop', 'keep'}, default 'null' Indicates how to output missing (NaN) values in the GeoDataFrame. See below. show_bbox : bool, optional, default: False Include bbox (bounds) in the geojson drop_id : bool, default: False Whether to retain the index of the GeoDataFrame as the id property in the generated GeoJSON. Default is False, but may want True if the index is just arbitrary row numbers. to_wgs84: bool, optional, default: False If the CRS is set on the active geometry column it is exported as WGS84 (EPSG:4326) to meet the `2016 GeoJSON specification `_. Set to True to force re-projection and set to False to ignore CRS. False by default. Notes ----- The remaining *kwargs* are passed to json.dumps(). Missing (NaN) values in the GeoDataFrame can be represented as follows: - ``null``: output the missing entries as JSON null. - ``drop``: remove the property from the feature. This applies to each feature individually so that features may have different properties. - ``keep``: output the missing entries as NaN. If the GeoDataFrame has a defined CRS, its definition will be included in the output unless it is equal to WGS84 (default GeoJSON CRS) or not possible to represent in the URN OGC format, or unless ``to_wgs84=True`` is specified. Examples -------- >>> from shapely.geometry import Point >>> d = {'col1': ['name1', 'name2'], 'geometry': [Point(1, 2), Point(2, 1)]} >>> gdf = geopandas.GeoDataFrame(d, crs="EPSG:3857") >>> gdf col1 geometry 0 name1 POINT (1 2) 1 name2 POINT (2 1) >>> gdf.to_json() '{"type": "FeatureCollection", "features": [{"id": "0", "type": "Feature", \ "properties": {"col1": "name1"}, "geometry": {"type": "Point", "coordinates": [1.0,\ 2.0]}}, {"id": "1", "type": "Feature", "properties": {"col1": "name2"}, "geometry"\ : {"type": "Point", "coordinates": [2.0, 1.0]}}], "crs": {"type": "name", "properti\ es": {"name": "urn:ogc:def:crs:EPSG::3857"}}}' Alternatively, you can write GeoJSON to file: >>> gdf.to_file(path, driver="GeoJSON") # doctest: +SKIP See Also -------- GeoDataFrame.to_file : write GeoDataFrame to file """ if to_wgs84: if self.crs: df = self.to_crs(epsg=4326) else: raise ValueError( "CRS is not set. Cannot re-project to WGS84 (EPSG:4326)." ) else: df = self geo = df.to_geo_dict(na=na, show_bbox=show_bbox, drop_id=drop_id) # if the geometry is not in WGS84, include CRS in the JSON if df.crs is not None and not df.crs.equals("epsg:4326"): auth_crsdef = self.crs.to_authority() allowed_authorities = ["EDCS", "EPSG", "OGC", "SI", "UCUM"] if auth_crsdef is None or auth_crsdef[0] not in allowed_authorities: warnings.warn( "GeoDataFrame's CRS is not representable in URN OGC " "format. Resulting JSON will contain no CRS information.", stacklevel=2, ) else: authority, code = auth_crsdef ogc_crs = f"urn:ogc:def:crs:{authority}::{code}" geo["crs"] = {"type": "name", "properties": {"name": ogc_crs}} return json.dumps(geo, **kwargs) @property def __geo_interface__(self) -> dict: """Returns a ``GeoDataFrame`` as a python feature collection. Implements the `geo_interface`. The returned python data structure represents the ``GeoDataFrame`` as a GeoJSON-like ``FeatureCollection``. This differs from :meth:`to_geo_dict` only in that it is a property with default args instead of a method. CRS of the dataframe is not passed on to the output, unlike :meth:`~GeoDataFrame.to_json()`. Examples -------- >>> from shapely.geometry import Point >>> d = {'col1': ['name1', 'name2'], 'geometry': [Point(1, 2), Point(2, 1)]} >>> gdf = geopandas.GeoDataFrame(d, crs="EPSG:4326") >>> gdf col1 geometry 0 name1 POINT (1 2) 1 name2 POINT (2 1) >>> gdf.__geo_interface__ {'type': 'FeatureCollection', 'features': [{'id': '0', 'type': 'Feature', \ 'properties': {'col1': 'name1'}, 'geometry': {'type': 'Point', 'coordinates': (1.0\ , 2.0)}, 'bbox': (1.0, 2.0, 1.0, 2.0)}, {'id': '1', 'type': 'Feature', 'properties\ ': {'col1': 'name2'}, 'geometry': {'type': 'Point', 'coordinates': (2.0, 1.0)}, 'b\ box': (2.0, 1.0, 2.0, 1.0)}], 'bbox': (1.0, 1.0, 2.0, 2.0)} """ return self.to_geo_dict(na="null", show_bbox=True, drop_id=False) def iterfeatures( self, na: str = "null", show_bbox: bool = False, drop_id: bool = False ) -> typing.Generator[dict]: """Return an iterator that yields feature dictionaries that comply with __geo_interface__. Parameters ---------- na : str, optional Options are {'null', 'drop', 'keep'}, default 'null'. Indicates how to output missing (NaN) values in the GeoDataFrame - null: output the missing entries as JSON null - drop: remove the property from the feature. This applies to each feature \ individually so that features may have different properties - keep: output the missing entries as NaN show_bbox : bool, optional Include bbox (bounds) in the geojson. Default False. drop_id : bool, default: False Whether to retain the index of the GeoDataFrame as the id property in the generated GeoJSON. Default is False, but may want True if the index is just arbitrary row numbers. Examples -------- >>> from shapely.geometry import Point >>> d = {'col1': ['name1', 'name2'], 'geometry': [Point(1, 2), Point(2, 1)]} >>> gdf = geopandas.GeoDataFrame(d, crs="EPSG:4326") >>> gdf col1 geometry 0 name1 POINT (1 2) 1 name2 POINT (2 1) >>> feature = next(gdf.iterfeatures()) >>> feature {'id': '0', 'type': 'Feature', 'properties': {'col1': 'name1'}, 'geometry': {\ 'type': 'Point', 'coordinates': (1.0, 2.0)}} """ if na not in ["null", "drop", "keep"]: raise ValueError(f"Unknown na method {na}") ids = np.asarray(self.index) geometries = np.asarray(self.geometry) if not self.columns.is_unique: raise ValueError("GeoDataFrame cannot contain duplicated column names.") properties_cols = self.columns.drop(self._geometry_column_name) if len(properties_cols) > 0: # convert to object to get python scalars. properties_cols = self[properties_cols] properties = properties_cols.astype(object) na_mask = pd.isna(properties_cols).values if na == "null": properties[na_mask] = None for i, row in enumerate(properties.values): geom = geometries[i] if na == "drop": na_mask_row = na_mask[i] properties_items = { k: v for k, v, na in zip(properties_cols, row, na_mask_row) if not na } else: properties_items = dict(zip(properties_cols, row)) if drop_id: feature = {} else: feature = {"id": str(ids[i])} feature["type"] = "Feature" feature["properties"] = properties_items feature["geometry"] = mapping(geom) if geom else None if show_bbox: feature["bbox"] = geom.bounds if geom else None yield feature else: for fid, geom in zip(ids, geometries): if drop_id: feature = {} else: feature = {"id": str(fid)} feature["type"] = "Feature" feature["properties"] = {} feature["geometry"] = mapping(geom) if geom else None if show_bbox: feature["bbox"] = geom.bounds if geom else None yield feature def to_geo_dict( self, na: str | None = "null", show_bbox: bool = False, drop_id: bool = False ) -> dict: """Return a python feature collection representation of the GeoDataFrame as a dictionary with a list of features based on the ``__geo_interface__`` GeoJSON-like specification. Parameters ---------- na : str, optional Options are {'null', 'drop', 'keep'}, default 'null'. Indicates how to output missing (NaN) values in the GeoDataFrame - null: output the missing entries as JSON null - drop: remove the property from the feature. This applies to each feature \ individually so that features may have different properties - keep: output the missing entries as NaN show_bbox : bool, optional Include bbox (bounds) in the geojson. Default False. drop_id : bool, default: False Whether to retain the index of the GeoDataFrame as the id property in the generated dictionary. Default is False, but may want True if the index is just arbitrary row numbers. Examples -------- >>> from shapely.geometry import Point >>> d = {'col1': ['name1', 'name2'], 'geometry': [Point(1, 2), Point(2, 1)]} >>> gdf = geopandas.GeoDataFrame(d) >>> gdf col1 geometry 0 name1 POINT (1 2) 1 name2 POINT (2 1) >>> gdf.to_geo_dict() {'type': 'FeatureCollection', 'features': [{'id': '0', 'type': 'Feature', '\ properties': {'col1': 'name1'}, 'geometry': {'type': 'Point', 'coordinates': (1.0, \ 2.0)}}, {'id': '1', 'type': 'Feature', 'properties': {'col1': 'name2'}, 'geometry':\ {'type': 'Point', 'coordinates': (2.0, 1.0)}}]} See Also -------- GeoDataFrame.to_json : return a GeoDataFrame as a GeoJSON string """ geo = { "type": "FeatureCollection", "features": list( self.iterfeatures(na=na, show_bbox=show_bbox, drop_id=drop_id) ), } if show_bbox: geo["bbox"] = tuple(self.total_bounds.tolist()) # tolist to avoid np dtypes return geo def to_wkb(self, hex: bool = False, **kwargs) -> pd.DataFrame: """ Encode all geometry columns in the GeoDataFrame to WKB. Parameters ---------- hex : bool If true, export the WKB as a hexadecimal string. The default is to return a binary bytes object. kwargs Additional keyword args will be passed to :func:`shapely.to_wkb`. Returns ------- DataFrame geometry columns are encoded to WKB Notes ----- The Well-Known Binary (WKB) specification does not support all variations of geometry types that GeoPandas does, and some geometries may not be serialised without information loss. Notably: - LinearRing will be converted to LineString - Empty Points will be converted to Points with NaN as coordinates (but will be read by :meth:`~geopandas.GeoSeries.from_wkb` as empty Points). """ df = DataFrame(self.copy(deep=not PANDAS_GE_30)) # Encode all geometry columns to WKB for col in df.columns[df.dtypes == "geometry"]: df[col] = to_wkb(df[col].values, hex=hex, **kwargs) return df def to_wkt(self, **kwargs) -> pd.DataFrame: """ Encode all geometry columns in the GeoDataFrame to WKT. Parameters ---------- kwargs Keyword args will be passed to :func:`shapely.to_wkt`. Returns ------- DataFrame geometry columns are encoded to WKT """ df = DataFrame(self.copy(deep=not PANDAS_GE_30)) # Encode all geometry columns to WKT for col in df.columns[df.dtypes == "geometry"]: df[col] = to_wkt(df[col].values, **kwargs) return df def to_arrow( self, *, index: bool | None = None, geometry_encoding: PARQUET_GEOMETRY_ENCODINGS = "WKB", interleaved: bool = True, include_z: bool | None = None, ): """Encode a GeoDataFrame to GeoArrow format. See https://geoarrow.org/ for details on the GeoArrow specification. This function returns a generic Arrow data object implementing the `Arrow PyCapsule Protocol`_ (i.e. having an ``__arrow_c_stream__`` method). This object can then be consumed by your Arrow implementation of choice that supports this protocol. .. _Arrow PyCapsule Protocol: https://arrow.apache.org/docs/format/CDataInterface/PyCapsuleInterface.html .. versionadded:: 1.0 Parameters ---------- index : bool, default None If ``True``, always include the dataframe's index(es) as columns in the file output. If ``False``, the index(es) will not be written to the file. If ``None``, the index(ex) will be included as columns in the file output except `RangeIndex` which is stored as metadata only. geometry_encoding : {'WKB', 'geoarrow' }, default 'WKB' The GeoArrow encoding to use for the data conversion. interleaved : bool, default True Only relevant for 'geoarrow' encoding. If True, the geometries' coordinates are interleaved in a single fixed size list array. If False, the coordinates are stored as separate arrays in a struct type. include_z : bool, default None Only relevant for 'geoarrow' encoding (for WKB, the dimensionality of the individual geometries is preserved). If False, return 2D geometries. If True, include the third dimension in the output (if a geometry has no third dimension, the z-coordinates will be NaN). By default, will infer the dimensionality from the input geometries. Note that this inference can be unreliable with empty geometries (for a guaranteed result, it is recommended to specify the keyword). Returns ------- ArrowTable A generic Arrow table object with geometry columns encoded to GeoArrow. Examples -------- >>> from shapely.geometry import Point >>> data = {'col1': ['name1', 'name2'], 'geometry': [Point(1, 2), Point(2, 1)]} >>> gdf = geopandas.GeoDataFrame(data) >>> gdf col1 geometry 0 name1 POINT (1 2) 1 name2 POINT (2 1) >>> arrow_table = gdf.to_arrow() >>> arrow_table The returned data object needs to be consumed by a library implementing the Arrow PyCapsule Protocol. For example, wrapping the data as a pyarrow.Table (requires pyarrow >= 14.0): >>> import pyarrow as pa >>> table = pa.table(arrow_table) >>> table pyarrow.Table col1: large_string geometry: extension> ---- col1: [["name1","name2"]] geometry: [[0101000000000000000000F03F0000000000000040,\ 01010000000000000000000040000000000000F03F]] """ from geopandas.io._geoarrow import ArrowTable, geopandas_to_arrow table, _ = geopandas_to_arrow( self, index=index, geometry_encoding=geometry_encoding, interleaved=interleaved, include_z=include_z, ) return ArrowTable(table) def to_parquet( self, path: os.PathLike | typing.IO, index: bool | None = None, compression: str = "snappy", geometry_encoding: PARQUET_GEOMETRY_ENCODINGS = "WKB", write_covering_bbox: bool = False, schema_version: SUPPORTED_VERSIONS_LITERAL | None = None, **kwargs, ) -> None: """Write a GeoDataFrame to the Parquet format. By default, all geometry columns present are serialized to WKB format in the file. Requires 'pyarrow'. .. versionadded:: 0.8 Parameters ---------- path : str, path object index : bool, default None If ``True``, always include the dataframe's index(es) as columns in the file output. If ``False``, the index(es) will not be written to the file. If ``None``, the index(ex) will be included as columns in the file output except `RangeIndex` which is stored as metadata only. compression : {'snappy', 'gzip', 'brotli', 'lz4', 'zstd', None}, \ default 'snappy' Name of the compression to use. Use ``None`` for no compression. geometry_encoding : {'WKB', 'geoarrow'}, default 'WKB' The encoding to use for the geometry columns. Defaults to "WKB" for maximum interoperability. Specify "geoarrow" to use one of the native GeoArrow-based single-geometry type encodings. Note: the "geoarrow" option is part of the newer GeoParquet 1.1 specification, should be considered as experimental, and may not be supported by all readers. write_covering_bbox : bool, default False Writes the bounding box column for each row entry with column name 'bbox'. Writing a bbox column can be computationally expensive, but allows you to specify a `bbox` in :func:`read_parquet` for filtered reading. schema_version : {'0.1.0', '0.4.0', '1.0.0', '1.1.0', '2.0.0', None} GeoParquet specification version; if not provided, will default to latest supported stable version (1.1.0). kwargs Additional keyword arguments passed to :func:`pyarrow.parquet.write_table`. Examples -------- >>> gdf.to_parquet('data.parquet') # doctest: +SKIP See Also -------- GeoDataFrame.to_feather : write GeoDataFrame to feather GeoDataFrame.to_file : write GeoDataFrame to file """ # Accept engine keyword for compatibility with pandas.DataFrame.to_parquet # The only engine currently supported by GeoPandas is pyarrow, so no # other engine should be specified. engine = kwargs.pop("engine", "auto") if engine not in ("auto", "pyarrow"): raise ValueError( "GeoPandas only supports using pyarrow as the engine for " f"to_parquet: {engine!r} passed instead." ) from geopandas.io.arrow import _to_parquet _to_parquet( self, path, compression=compression, geometry_encoding=geometry_encoding, index=index, schema_version=schema_version, write_covering_bbox=write_covering_bbox, **kwargs, ) def to_feather( self, path: os.PathLike, index: bool | None = None, compression: str | None = None, schema_version: SUPPORTED_VERSIONS_LITERAL | None = None, **kwargs, ): """Write a GeoDataFrame to the Feather format. Any geometry columns present are serialized to WKB format in the file. Requires 'pyarrow' >= 0.17. .. versionadded:: 0.8 Parameters ---------- path : str, path object index : bool, default None If ``True``, always include the dataframe's index(es) as columns in the file output. If ``False``, the index(es) will not be written to the file. If ``None``, the index(ex) will be included as columns in the file output except `RangeIndex` which is stored as metadata only. compression : {'zstd', 'lz4', 'uncompressed'}, optional Name of the compression to use. Use ``"uncompressed"`` for no compression. By default uses LZ4 if available, otherwise uncompressed. schema_version : {'0.1.0', '0.4.0', '1.0.0', '1.1.0' None} GeoParquet specification version; if not provided will default to latest supported stable version (1.1.0). kwargs Additional keyword arguments passed to :func:`pyarrow.feather.write_feather`. Examples -------- >>> gdf.to_feather('data.feather') # doctest: +SKIP See Also -------- GeoDataFrame.to_parquet : write GeoDataFrame to parquet GeoDataFrame.to_file : write GeoDataFrame to file """ from geopandas.io.arrow import _to_feather _to_feather( self, path, index=index, compression=compression, schema_version=schema_version, **kwargs, ) def to_file( self, filename: os.PathLike | typing.IO, driver: str | None = None, schema: dict | None = None, index: bool | None = None, **kwargs, ): """Write the ``GeoDataFrame`` to a file. By default, an ESRI shapefile is written, but any OGR data source supported by Pyogrio or Fiona can be written. A dictionary of supported OGR providers is available via: >>> import pyogrio >>> pyogrio.list_drivers() # doctest: +SKIP Parameters ---------- filename : string File path or file handle to write to. The path may specify a GDAL VSI scheme. driver : string, default None The OGR format driver used to write the vector file. If not specified, it attempts to infer it from the file extension. If no extension is specified, it saves ESRI Shapefile to a folder. schema : dict, default None If specified, the schema dictionary is passed to Fiona to better control how the file is written. If None, GeoPandas will determine the schema based on each column's dtype. Not supported for the "pyogrio" engine. index : bool, default None If True, write index into one or more columns (for MultiIndex). Default None writes the index into one or more columns only if the index is named, is a MultiIndex, or has a non-integer data type. If False, no index is written. .. versionadded:: 0.7 Previously the index was not written. mode : string, default 'w' The write mode, 'w' to overwrite the existing file and 'a' to append. Not all drivers support appending. The drivers that support appending are listed in fiona.supported_drivers or https://github.com/Toblerity/Fiona/blob/master/fiona/drvsupport.py crs : pyproj.CRS, default None If specified, the CRS is passed to Fiona to better control how the file is written. If None, GeoPandas will determine the crs based on crs df attribute. The value can be anything accepted by :meth:`pyproj.CRS.from_user_input() `, such as an authority string (eg "EPSG:4326") or a WKT string. The keyword is not supported for the "pyogrio" engine. engine : str, "pyogrio" or "fiona" The underlying library that is used to write the file. Currently, the supported options are "pyogrio" and "fiona". Defaults to "pyogrio" if installed, otherwise tries "fiona". metadata : dict[str, str], default None Optional metadata to be stored in the file. Keys and values must be strings. Supported only for "GPKG" driver. **kwargs : Keyword args to be passed to the engine, and can be used to write to multi-layer data, store data within archives (zip files), etc. In case of the "pyogrio" engine, the keyword arguments are passed to `pyogrio.write_dataframe`. In case of the "fiona" engine, the keyword arguments are passed to fiona.open`. For more information on possible keywords, type: ``import pyogrio; help(pyogrio.write_dataframe)``. Notes ----- The format drivers will attempt to detect the encoding of your data, but may fail. In this case, the proper encoding can be specified explicitly by using the encoding keyword parameter, e.g. ``encoding='utf-8'``. See Also -------- GeoSeries.to_file GeoDataFrame.to_postgis : write GeoDataFrame to PostGIS database GeoDataFrame.to_parquet : write GeoDataFrame to parquet GeoDataFrame.to_feather : write GeoDataFrame to feather Examples -------- >>> gdf.to_file('dataframe.shp') # doctest: +SKIP >>> gdf.to_file('dataframe.gpkg', driver='GPKG', layer='name') # doctest: +SKIP >>> gdf.to_file('dataframe.geojson', driver='GeoJSON') # doctest: +SKIP With selected drivers you can also append to a file with `mode="a"`: >>> gdf.to_file('dataframe.shp', mode="a") # doctest: +SKIP Using the engine-specific keyword arguments it is possible to e.g. create a spatialite file with a custom layer name: >>> gdf.to_file( ... 'dataframe.sqlite', driver='SQLite', spatialite=True, layer='test' ... ) # doctest: +SKIP """ from geopandas.io.file import _to_file _to_file(self, filename, driver, schema, index, **kwargs) @typing.overload def set_crs( self, crs: Any | None = ..., epsg: int | None = ..., inplace: Literal[True] = ..., allow_override: bool = ..., ) -> None: ... @typing.overload def set_crs( self, crs: Any | None = ..., epsg: int | None = ..., inplace: Literal[False] = ..., allow_override: bool = ..., ) -> GeoDataFrame: ... def set_crs( self, crs: Any | None = None, epsg: int | None = None, inplace: bool = False, allow_override: bool = False, ) -> GeoDataFrame | None: """ Set the Coordinate Reference System (CRS) of the ``GeoDataFrame``. If there are multiple geometry columns within the GeoDataFrame, only the CRS of the active geometry column is set. Pass ``None`` to remove CRS from the active geometry column. Notes ----- The underlying geometries are not transformed to this CRS. To transform the geometries to a new CRS, use the ``to_crs`` method. Parameters ---------- crs : pyproj.CRS | None, optional The value can be anything accepted by :meth:`pyproj.CRS.from_user_input() `, such as an authority string (eg "EPSG:4326") or a WKT string. epsg : int, optional EPSG code specifying the projection. inplace : bool, default False If True, the CRS of the GeoDataFrame will be changed in place (while still returning the result) instead of making a copy of the GeoDataFrame. allow_override : bool, default False If the the GeoDataFrame already has a CRS, allow to replace the existing CRS, even when both are not equal. Examples -------- >>> from shapely.geometry import Point >>> d = {'col1': ['name1', 'name2'], 'geometry': [Point(1, 2), Point(2, 1)]} >>> gdf = geopandas.GeoDataFrame(d) >>> gdf col1 geometry 0 name1 POINT (1 2) 1 name2 POINT (2 1) Setting CRS to a GeoDataFrame without one: >>> gdf.crs is None True >>> gdf = gdf.set_crs('epsg:3857') >>> gdf.crs # doctest: +SKIP Name: WGS 84 / Pseudo-Mercator Axis Info [cartesian]: - X[east]: Easting (metre) - Y[north]: Northing (metre) Area of Use: - name: World - 85°S to 85°N - bounds: (-180.0, -85.06, 180.0, 85.06) Coordinate Operation: - name: Popular Visualisation Pseudo-Mercator - method: Popular Visualisation Pseudo Mercator Datum: World Geodetic System 1984 - Ellipsoid: WGS 84 - Prime Meridian: Greenwich Overriding existing CRS: >>> gdf = gdf.set_crs(4326, allow_override=True) Without ``allow_override=True``, ``set_crs`` returns an error if you try to override CRS. See Also -------- GeoDataFrame.to_crs : re-project to another CRS """ if not inplace: df = self.copy(deep=not PANDAS_GE_30) else: df = self df.geometry = df.geometry.set_crs( crs=crs, epsg=epsg, allow_override=allow_override, inplace=True ) return df @typing.overload def to_crs( self, crs: Any | None = ..., epsg: int | None = ..., inplace: Literal[False] = ..., ) -> GeoDataFrame: ... @typing.overload def to_crs( self, crs: Any | None = ..., epsg: int | None = ..., inplace: Literal[True] = ..., ) -> None: ... def to_crs( self, crs: Any | None = None, epsg: int | None = None, inplace: bool = False, ) -> GeoDataFrame | None: """Transform geometries to a new coordinate reference system. Transform all geometries in an active geometry column to a different coordinate reference system. The ``crs`` attribute on the current GeoSeries must be set. Either ``crs`` or ``epsg`` may be specified for output. This method will transform all points in all objects. It has no notion of projecting entire geometries. All segments joining points are assumed to be lines in the current projection, not geodesics. Objects crossing the dateline (or other projection boundary) will have undesirable behavior. Parameters ---------- crs : pyproj.CRS, optional if `epsg` is specified The value can be anything accepted by :meth:`pyproj.CRS.from_user_input() `, such as an authority string (eg "EPSG:4326") or a WKT string. epsg : int, optional if `crs` is specified EPSG code specifying output projection. inplace : bool, optional, default: False Whether to return a new GeoDataFrame or do the transformation in place. Returns ------- GeoDataFrame Examples -------- >>> from shapely.geometry import Point >>> d = {'col1': ['name1', 'name2'], 'geometry': [Point(1, 2), Point(2, 1)]} >>> gdf = geopandas.GeoDataFrame(d, crs=4326) >>> gdf col1 geometry 0 name1 POINT (1 2) 1 name2 POINT (2 1) >>> gdf.crs # doctest: +SKIP Name: WGS 84 Axis Info [ellipsoidal]: - Lat[north]: Geodetic latitude (degree) - Lon[east]: Geodetic longitude (degree) Area of Use: - name: World - bounds: (-180.0, -90.0, 180.0, 90.0) Datum: World Geodetic System 1984 - Ellipsoid: WGS 84 - Prime Meridian: Greenwich >>> gdf = gdf.to_crs(3857) >>> gdf col1 geometry 0 name1 POINT (111319.491 222684.209) 1 name2 POINT (222638.982 111325.143) >>> gdf.crs # doctest: +SKIP Name: WGS 84 / Pseudo-Mercator Axis Info [cartesian]: - X[east]: Easting (metre) - Y[north]: Northing (metre) Area of Use: - name: World - 85°S to 85°N - bounds: (-180.0, -85.06, 180.0, 85.06) Coordinate Operation: - name: Popular Visualisation Pseudo-Mercator - method: Popular Visualisation Pseudo Mercator Datum: World Geodetic System 1984 - Ellipsoid: WGS 84 - Prime Meridian: Greenwich See Also -------- GeoDataFrame.set_crs : assign CRS without re-projection """ if inplace: df = self else: df = self.copy(deep=not PANDAS_GE_30) geom = df.geometry.to_crs(crs=crs, epsg=epsg) df.geometry = geom if not inplace: return df def estimate_utm_crs(self, datum_name: str = "WGS 84") -> CRS: """Return the estimated UTM CRS based on the bounds of the dataset. .. versionadded:: 0.9 Parameters ---------- datum_name : str, optional The name of the datum to use in the query. Default is WGS 84. Returns ------- pyproj.CRS Examples -------- >>> import geodatasets >>> df = geopandas.read_file( ... geodatasets.get_path("geoda.chicago_health") ... ) >>> df.estimate_utm_crs() # doctest: +SKIP Name: WGS 84 / UTM zone 16N Axis Info [cartesian]: - E[east]: Easting (metre) - N[north]: Northing (metre) Area of Use: - name: Between 90°W and 84°W, northern hemisphere between equator and 84°N... - bounds: (-90.0, 0.0, -84.0, 84.0) Coordinate Operation: - name: UTM zone 16N - method: Transverse Mercator Datum: World Geodetic System 1984 ensemble - Ellipsoid: WGS 84 - Prime Meridian: Greenwich """ return self.geometry.estimate_utm_crs(datum_name=datum_name) def __getitem__(self, key): """ If the result is a column containing only 'geometry', return a GeoSeries. If it's a DataFrame with any columns of GeometryDtype, return a GeoDataFrame. """ result = super().__getitem__(key) # Custom logic to avoid waiting for pandas GH51895 # result is not geometry dtype for multi-indexes if ( pd.api.types.is_scalar(key) and key == "" and isinstance(self.columns, pd.MultiIndex) and isinstance(result, Series) and not is_geometry_type(result) ): loc = self.columns.get_loc(key) # squeeze stops multilevel columns from returning a gdf result = self.iloc[:, loc].squeeze(axis="columns") geo_col = self._geometry_column_name if isinstance(result, Series) and isinstance(result.dtype, GeometryDtype): result.__class__ = GeoSeries elif isinstance(result, DataFrame): if (result.dtypes == "geometry").sum() > 0: result.__class__ = type(self) if geo_col in result: result._geometry_column_name = geo_col else: result.__class__ = DataFrame return result def __delitem__(self, key) -> None: """If the last geometry column is removed, downcast to a dataframe.""" super().__delitem__(key) if (self.dtypes == "geometry").sum() == 0: self.__class__ = DataFrame def _persist_old_default_geometry_colname(self) -> None: """Persist the default geometry column name of 'geometry' temporarily for backwards compatibility. """ # self.columns check required to avoid this warning in __init__ if self._geometry_column_name is None and "geometry" not in self.columns: msg = ( "You are adding a column named 'geometry' to a GeoDataFrame " "constructed without an active geometry column. Currently, " "this automatically sets the active geometry column to 'geometry' " "but in the future that will no longer happen. Instead, either " "provide geometry to the GeoDataFrame constructor " "(GeoDataFrame(... geometry=GeoSeries()) or use " "`set_geometry('geometry')` " "to explicitly set the active geometry column." ) warnings.warn(msg, category=FutureWarning, stacklevel=3) self._geometry_column_name = "geometry" def __setitem__(self, key, value): """Overridden to preserve CRS of GeometryArray. Important for cases like df['geometry'] = [geom... for geom in df.geometry] """ if not pd.api.types.is_list_like(key) and ( key == self._geometry_column_name or (key == "geometry" and self._geometry_column_name is None) ): if pd.api.types.is_scalar(value) or isinstance(value, BaseGeometry): value = [value] * self.shape[0] crs = getattr(self, "crs", None) # if we don't have a GeoDataFrame yet and there is a column named crs, # don't try to use that as a crs if isinstance(crs, pd.Series | pd.DataFrame): crs = None try: value = _ensure_geometry(value, crs=crs) except TypeError: warnings.warn( "Geometry column does not contain geometry.", stacklevel=2, ) else: if key == "geometry": self._persist_old_default_geometry_colname() super().__setitem__(key, value) # # Implement pandas methods # @doc(pd.DataFrame) def copy(self, deep: bool = True) -> GeoDataFrame: copied = super().copy(deep=deep) if type(copied) is pd.DataFrame: copied.__class__ = type(self) copied._geometry_column_name = self._geometry_column_name return copied @doc(pd.DataFrame) def apply( self, func, axis=0, raw: bool = False, result_type=None, args=(), **kwargs, ): result = super().apply( func, axis=axis, raw=raw, result_type=result_type, args=args, **kwargs ) # Reconstruct gdf if it was lost by apply if ( isinstance(result, DataFrame) and self._geometry_column_name in result.columns ): # axis=1 apply will split GeometryDType to object, try and cast back try: result = result.set_geometry(self._geometry_column_name) except TypeError: pass else: if self.crs is not None and result.crs is None: if PANDAS_GE_30: result = result.set_crs(self.crs) else: result.set_crs(self.crs, inplace=True) elif isinstance(result, Series) and result.dtype == "object": # Try reconstruct series GeometryDtype if lost by apply # If all none and object dtype assert list of nones is more likely # intended than list of null geometry. if not result.isna().all(): try: # not enough info about func to preserve CRS result = _ensure_geometry(result) except (TypeError, shapely.errors.GeometryTypeError): pass return result @classmethod def _geodataframe_constructor_with_fallback( cls, *args, **kwargs ) -> pd.DataFrame | GeoDataFrame: """A flexible constructor for GeoDataFrame._constructor, which falls back to returning a DataFrame (if a certain operation does not preserve the geometry column). """ # noqa: D401 df = cls(*args, **kwargs) geometry_cols_mask = df.dtypes == "geometry" if len(geometry_cols_mask) == 0 or geometry_cols_mask.sum() == 0: df = pd.DataFrame(df) return df @property def _constructor(self) -> DataFrame | GeoDataFrame: return self._geodataframe_constructor_with_fallback def _constructor_from_mgr(self, mgr, axes) -> DataFrame | GeoDataFrame: # replicate _geodataframe_constructor_with_fallback behaviour # unless safe to skip if not any(isinstance(block.dtype, GeometryDtype) for block in mgr.blocks): return self._geodataframe_constructor_with_fallback( pd.DataFrame._from_mgr(mgr, axes) ) gdf = self._from_mgr(mgr, axes) # _from_mgr doesn't preserve metadata (expect __finalize__ to be called) # still need to mimic __init__ behaviour with geometry=None if (gdf.columns == "geometry").sum() == 1: # only if "geometry" is single col gdf._geometry_column_name = "geometry" return gdf @property def _constructor_sliced(self) -> Series | GeoSeries: def _geodataframe_constructor_sliced(*args, **kwargs): """A specialized (Geo)Series constructor which can fall back to a Series if a certain operation does not produce geometries. Note: - We only return a GeoSeries if the data is actually of geometry dtype (and so we don't try to convert geometry objects such as the normal GeoSeries(..) constructor does with `_ensure_geometry`). - When we get here from obtaining a row or column from a GeoDataFrame, the goal is to only return a GeoSeries for a geometry column, and not return a GeoSeries for a row that happened to come from a DataFrame with only geometry dtype columns (and thus could have a geometry dtype). Therefore, we don't return a GeoSeries if we are sure we are in a row selection case (by checking the identity of the index) """ # noqa: D401 srs = pd.Series(*args, **kwargs) is_row_proxy = srs.index.is_(self.columns) if is_geometry_type(srs) and not is_row_proxy: srs = GeoSeries(srs) return srs return _geodataframe_constructor_sliced def _constructor_sliced_from_mgr(self, mgr, axes) -> Series | GeoSeries: is_row_proxy = mgr.index.is_(self.columns) if isinstance(mgr.blocks[0].dtype, GeometryDtype) and not is_row_proxy: return GeoSeries._from_mgr(mgr, axes) return Series._from_mgr(mgr, axes) def __finalize__( self, other, method: str | None = None, **kwargs ) -> GeoDataFrame | GeoSeries: """Propagate metadata from other to self.""" self = super().__finalize__(other, method=method, **kwargs) # noqa: PLW0642 # merge operation: using metadata of the left object if method == "merge": # pandas-dev/pandas#60357 : merge/concat use input_objs if PANDAS_GE_30: # other is a types.SimpleNameSpace left_obj = other.input_objs[0] else: # other is a _MergeOperation left_obj = other.left for name in self._metadata: object.__setattr__(self, name, getattr(left_obj, name, None)) # concat operation: using metadata of the first object elif method == "concat": # pandas-dev/pandas#60357 : merge/concat use input_objs if PANDAS_GE_30: first_obj = other.input_objs[0] else: first_obj = other.objs[0] for name in self._metadata: object.__setattr__(self, name, getattr(first_obj, name, None)) if ( self.columns.nlevels == 1 and (self.columns == self._geometry_column_name).sum() > 1 ) or ( self.columns.nlevels > 1 and ( self.columns.get_level_values(0) == self._geometry_column_name ).sum() > 1 ): raise ValueError( "Concat operation has resulted in multiple columns using " f"the geometry column name '{self._geometry_column_name}'.\n" "Please ensure this column from the first DataFrame is not " "repeated." ) elif method == "unstack": # unstack adds multiindex columns and reshapes data. # it never makes sense to retain geometry column self._geometry_column_name = None self._crs = None return self def dissolve( self, by: str | None = None, aggfunc="first", as_index: bool = True, level=None, sort: bool = True, observed: bool = False, dropna: bool = True, method: Literal["unary", "coverage", "disjoint_subset"] = "unary", grid_size: float | None = None, **kwargs, ) -> GeoDataFrame: """ Dissolve geometries within `groupby` into single observation. This is accomplished by applying the `union_all` method to all geometries within a groupself. Observations associated with each `groupby` group will be aggregated using the `aggfunc`. Parameters ---------- by : str or list-like, default None Column(s) whose values define the groups to be dissolved. If None, the entire GeoDataFrame is considered as a single group. If a list-like object is provided, the values in the list are treated as categorical labels, and polygons will be combined based on the equality of these categorical labels. aggfunc : function or string, default "first" Aggregation function for manipulation of data associated with each group. Passed to pandas `groupby.agg` method. Accepted combinations are: - function - string function name - list of functions and/or function names, e.g. [np.sum, 'mean'] - dict of axis labels -> functions, function names or list of such. as_index : boolean, default True If true, groupby columns become index of result. level : int or str or sequence of int or sequence of str, default None If the axis is a MultiIndex (hierarchical), group by a particular level or levels. sort : bool, default True Sort group keys. Get better performance by turning this off. Note this does not influence the order of observations within each group. Groupby preserves the order of rows within each group. observed : bool, default False This only applies if any of the groupers are Categoricals. If True: only show observed values for categorical groupers. If False: show all values for categorical groupers. dropna : bool, default True If True, and if group keys contain NA values, NA values together with row/column will be dropped. If False, NA values will also be treated as the key in groups. method : str (default ``"unary"``) The method to use for the union. Options are: * ``"unary"``: use the unary union algorithm. This option is the most robust but can be slow for large numbers of geometries (default). * ``"coverage"``: use the coverage union algorithm. This option is optimized for non-overlapping polygons and can be significantly faster than the unary union algorithm. However, it can produce invalid geometries if the polygons overlap. * ``"disjoint_subset:``: use the disjoint subset union algorithm. This option is optimized for inputs that can be divided into subsets that do not intersect. If there is only one such subset, performance can be expected to be worse than ``"unary"``. Requires Shapely >= 2.1. grid_size : float, default None When grid size is specified, a fixed-precision space is used to perform the union operations. This can be useful when unioning geometries that are not perfectly snapped or to avoid geometries not being unioned because of `robustness issues `_. The inputs are first snapped to a grid of the given size. When a line segment of a geometry is within tolerance off a vertex of another geometry, this vertex will be inserted in the line segment. Finally, the result vertices are computed on the same grid. Is only supported for ``method`` ``"unary"``. If None, the highest precision of the inputs will be used. Defaults to None. .. versionadded:: 1.1.0 **kwargs : Keyword arguments to be passed to the pandas `DataFrameGroupby.agg` method which is used by `dissolve`. In particular, `numeric_only` may be supplied, which will be required in pandas 2.0 for certain aggfuncs. .. versionadded:: 0.13.0 Returns ------- GeoDataFrame Examples -------- >>> from shapely.geometry import Point >>> d = { ... "col1": ["name1", "name2", "name1"], ... "geometry": [Point(1, 2), Point(2, 1), Point(0, 1)], ... } >>> gdf = geopandas.GeoDataFrame(d, crs=4326) >>> gdf col1 geometry 0 name1 POINT (1 2) 1 name2 POINT (2 1) 2 name1 POINT (0 1) >>> dissolved = gdf.dissolve('col1') >>> dissolved # doctest: +SKIP geometry col1 name1 MULTIPOINT ((0 1), (1 2)) name2 POINT (2 1) See Also -------- GeoDataFrame.explode : explode multi-part geometries into single geometries """ if by is None and level is None: by = np.zeros(len(self), dtype="int64") # type: ignore [assignment] groupby_kwargs = { "by": by, "level": level, "sort": sort, "observed": observed, "dropna": dropna, } # Process non-spatial component data = self.drop(labels=self.geometry.name, axis=1) aggregated_data = data.groupby(**groupby_kwargs).agg(aggfunc, **kwargs) aggregated_data.columns = aggregated_data.columns.to_flat_index() # Process spatial component def merge_geometries(block): merged_geom = block.union_all(method=method, grid_size=grid_size) return merged_geom g = self.groupby(group_keys=False, **groupby_kwargs)[self.geometry.name].agg( merge_geometries ) # Aggregate aggregated_geometry = type(self)(g, geometry=self.geometry.name, crs=self.crs) # Recombine aggregated = aggregated_geometry.join(aggregated_data) # Reset if requested if not as_index: aggregated = aggregated.reset_index() return aggregated # overrides the pandas native explode method to break up features geometrically def explode( self, column: str | None = None, ignore_index: bool = False, index_parts: bool = False, **kwargs, ) -> GeoDataFrame | DataFrame: """ Explode multi-part geometries into multiple component geometries. Each row containing a multi-part geometry will be split into multiple rows with each component geometry, thereby increasing the vertical size of the GeoDataFrame. GeometryCollections are split into their direct components. If a GeometryCollection contains a multi-part geometry, that component is returned as a multi-part geometry. To further split multi-part geometries within a GeometryCollection, call ``explode`` a second time. Parameters ---------- column : string or list of strings, default None Column(s) to explode. In the case of a geometry column, multi-part geometries are converted to their component geometries. If None, the active geometry column is used. A list of multiple (non-geometry) columns may be passed to use the pandas multi-column explode. ignore_index : bool, default False If True, the resulting index will be labelled 0, 1, …, n - 1, ignoring `index_parts`. index_parts : boolean, default False If True, the resulting index will be a multi-index (original index with an additional level indicating the multiple geometries: a new zero-based index for each component geometry per input geometry). Returns ------- GeoDataFrame Exploded geodataframe with each component geometry as a separate entry in the geodataframe. Examples -------- >>> from shapely.geometry import MultiPoint >>> d = { ... "col1": ["name1", "name2"], ... "geometry": [ ... MultiPoint([(1, 2), (3, 4)]), ... MultiPoint([(2, 1), (0, 0)]), ... ], ... } >>> gdf = geopandas.GeoDataFrame(d, crs=4326) >>> gdf col1 geometry 0 name1 MULTIPOINT ((1 2), (3 4)) 1 name2 MULTIPOINT ((2 1), (0 0)) >>> exploded = gdf.explode(index_parts=True) >>> exploded col1 geometry 0 0 name1 POINT (1 2) 1 name1 POINT (3 4) 1 0 name2 POINT (2 1) 1 name2 POINT (0 0) >>> exploded = gdf.explode(index_parts=False) >>> exploded col1 geometry 0 name1 POINT (1 2) 0 name1 POINT (3 4) 1 name2 POINT (2 1) 1 name2 POINT (0 0) >>> exploded = gdf.explode(ignore_index=True) >>> exploded col1 geometry 0 name1 POINT (1 2) 1 name1 POINT (3 4) 2 name2 POINT (2 1) 3 name2 POINT (0 0) A single ``explode`` call splits a GeometryCollection into its direct components, which may themselves be multi-part geometries: >>> from shapely.geometry import GeometryCollection >>> gdf = geopandas.GeoDataFrame( ... { ... "geometry": [ ... GeometryCollection( ... [ ... MultiPoint([(1, 2), (3, 4)]), ... MultiPoint([(2, 1), (0, 0)]), ... ] ... ) ... ] ... }, ... crs=4326, ... ) >>> gdf.explode(index_parts=False) geometry 0 MULTIPOINT ((1 2), (3 4)) 0 MULTIPOINT ((2 1), (0 0)) See Also -------- GeoDataFrame.dissolve : dissolve geometries into a single observation. """ # If no column is specified then default to the active geometry column if column is None: column = self.geometry.name # ``self[column].dtypes`` is a scalar dtype when a single column is # selected and a Series of dtypes when multiple columns are selected. dtypes = self[column].dtypes if isinstance(dtypes, Series): # Multiple columns: only the pandas (non-geometry) multi-column # explode is supported. if any(isinstance(dtype, GeometryDtype) for dtype in dtypes): raise ValueError( "Exploding multiple columns including a geometry column is " "not supported." ) return super().explode(column, ignore_index=ignore_index, **kwargs) # If the specified column is not a geometry dtype use pandas explode if not isinstance(dtypes, GeometryDtype): return super().explode(column, ignore_index=ignore_index, **kwargs) exploded_geom = self.geometry.reset_index(drop=True).explode(index_parts=True) df = self.drop(self._geometry_column_name, axis=1).take( exploded_geom.index.droplevel(-1) ) df[exploded_geom.name] = exploded_geom.values df = df.set_geometry(self._geometry_column_name).__finalize__(self) if ignore_index: if PANDAS_GE_30: df = df.reset_index(drop=True) else: df.reset_index(inplace=True, drop=True) elif index_parts: # reset to MultiIndex, otherwise df index is only first level of # exploded GeoSeries index. df = df.set_index( exploded_geom.index.droplevel( list(range(exploded_geom.index.nlevels - 1)) ), append=True, ) return df def to_postgis( self, name: str, con, schema: str | None = None, if_exists: Literal["fail", "replace", "append"] = "fail", index: bool = False, index_label: Iterable[str] | str | None = None, chunksize: int | None = None, dtype=None, ) -> None: """ Upload GeoDataFrame into PostGIS database. This method requires SQLAlchemy and GeoAlchemy2, and a PostgreSQL Python driver (psycopg or psycopg2) to be installed. It is also possible to use :meth:`~GeoDataFrame.to_file` to write to a database. Especially for file geodatabases like GeoPackage or SpatiaLite this can be easier. Parameters ---------- name : str Name of the target table. con : sqlalchemy.engine.Connection or sqlalchemy.engine.Engine Active connection to the PostGIS database. if_exists : {'fail', 'replace', 'append'}, default 'fail' How to behave if the table already exists: - fail: Raise a ValueError. - replace: Drop the table before inserting new values. - append: Insert new values to the existing table. schema : string, optional Specify the schema. If None, use default schema: 'public'. index : bool, default False Write DataFrame index as a column. Uses *index_label* as the column name in the table. index_label : string or sequence, default None Column label for index column(s). If None is given (default) and index is True, then the index names are used. chunksize : int, optional Rows will be written in batches of this size at a time. By default, all rows will be written at once. dtype : dict of column name to SQL type, default None Specifying the datatype for columns. The keys should be the column names and the values should be the SQLAlchemy types. Examples -------- >>> from sqlalchemy import create_engine >>> engine = create_engine("postgresql://myusername:mypassword@myhost:5432\ /mydatabase") # doctest: +SKIP >>> gdf.to_postgis("my_table", engine) # doctest: +SKIP See Also -------- GeoDataFrame.to_file : write GeoDataFrame to file read_postgis : read PostGIS database to GeoDataFrame """ geopandas.io.sql._write_postgis( self, name, con, schema, if_exists, index, index_label, chunksize, dtype ) plot = Accessor("plot", geopandas.plotting.GeoplotAccessor) @doc(_explore) def explore(self, *args, **kwargs) -> folium.Map: return _explore(self, *args, **kwargs) def sjoin( self, df: GeoDataFrame, how: Literal["left", "right", "inner"] = "inner", predicate: str = "intersects", lsuffix: str = "left", rsuffix: str = "right", **kwargs, ) -> GeoDataFrame: """Spatial join of two GeoDataFrames. See the User Guide page :doc:`../../user_guide/mergingdata` for details. Parameters ---------- df : GeoDataFrame how : string, default 'inner' The type of join: * 'left': use keys from left_df; retain only left_df geometry column * 'right': use keys from right_df; retain only right_df geometry column * 'inner': use intersection of keys from both dfs; retain only left_df geometry column predicate : string, default 'intersects' Binary predicate. Valid values are determined by the spatial index used. You can check the valid values in left_df or right_df as ``left_df.sindex.valid_query_predicates`` or ``right_df.sindex.valid_query_predicates`` Available predicates include: * ``'intersects'``: True if geometries intersect (boundaries and interiors) * ``'within'``: True if left geometry is completely within right geometry * ``'contains'``: True if left geometry completely contains right geometry * ``'contains_properly'``: True if left geometry contains right geometry and their boundaries do not touch * ``'overlaps'``: True if geometries overlap but neither contains the other * ``'crosses':`` True if geometries cross (interiors intersect but neither contains the other, with intersection dimension less than max dimension) * ``'touches'``: True if geometries touch at boundaries but interiors don't * ``'covers'``: True if left geometry covers right geometry (every point of right is a point of left) * ``'covered_by'``: True if left geometry is covered by right geometry * ``'dwithin'``: True if geometries are within specified distance (requires distance parameter) lsuffix : string, default 'left' Suffix to apply to overlapping column names (left GeoDataFrame). rsuffix : string, default 'right' Suffix to apply to overlapping column names (right GeoDataFrame). distance : number or array_like, optional Distance(s) around each input geometry within which to query the tree for the 'dwithin' predicate. If array_like, must be one-dimesional with length equal to length of left GeoDataFrame. Required if ``predicate='dwithin'``. on_attribute : string, list or tuple Column name(s) to join on as an additional join restriction on top of the spatial predicate. These must be found in both DataFrames. If set, observations are joined only if the predicate applies and values in specified columns match. Examples -------- >>> import geodatasets >>> chicago = geopandas.read_file( ... geodatasets.get_path("geoda.chicago_commpop") ... ) >>> groceries = geopandas.read_file( ... geodatasets.get_path("geoda.groceries") ... ).to_crs(chicago.crs) >>> chicago.head() # doctest: +SKIP community ... geometry 0 DOUGLAS ... MULTIPOLYGON (((-87.60914 41.84469, -87.60915 ... 1 OAKLAND ... MULTIPOLYGON (((-87.59215 41.81693, -87.59231 ... 2 FULLER PARK ... MULTIPOLYGON (((-87.62880 41.80189, -87.62879 ... 3 GRAND BOULEVARD ... MULTIPOLYGON (((-87.60671 41.81681, -87.60670 ... 4 KENWOOD ... MULTIPOLYGON (((-87.59215 41.81693, -87.59215 ... [5 rows x 9 columns] >>> groceries.head() # doctest: +SKIP OBJECTID Ycoord ... Category geometry 0 16 41.973266 ... NaN MULTIPOINT ((-87.65661 41.97321)) 1 18 41.696367 ... NaN MULTIPOINT ((-87.68136 41.69713)) 2 22 41.868634 ... NaN MULTIPOINT ((-87.63918 41.86847)) 3 23 41.877590 ... new MULTIPOINT ((-87.65495 41.87783)) 4 27 41.737696 ... NaN MULTIPOINT ((-87.62715 41.73623)) [5 rows x 8 columns] >>> groceries_w_communities = groceries.sjoin(chicago) >>> groceries_w_communities[["OBJECTID", "community", "geometry"]].head() OBJECTID community geometry 0 16 UPTOWN MULTIPOINT ((-87.65661 41.97321)) 1 18 MORGAN PARK MULTIPOINT ((-87.68136 41.69713)) 2 22 NEAR WEST SIDE MULTIPOINT ((-87.63918 41.86847)) 3 23 NEAR WEST SIDE MULTIPOINT ((-87.65495 41.87783)) 4 27 CHATHAM MULTIPOINT ((-87.62715 41.73623)) Notes ----- Every operation in GeoPandas is planar, i.e. the potential third dimension is not taken into account. See Also -------- GeoDataFrame.sjoin_nearest : nearest neighbor join sjoin : equivalent top-level function """ return geopandas.sjoin( left_df=self, right_df=df, how=how, predicate=predicate, lsuffix=lsuffix, rsuffix=rsuffix, **kwargs, ) def sjoin_nearest( self, right: GeoDataFrame, how: Literal["left", "right", "inner"] = "inner", max_distance: float | None = None, lsuffix: str = "left", rsuffix: str = "right", distance_col: str | None = None, exclusive: bool = False, ) -> GeoDataFrame: """ Spatial join of two GeoDataFrames based on the distance between their geometries. Results will include multiple output records for a single input record where there are multiple equidistant nearest or intersected neighbors. See the User Guide page https://geopandas.readthedocs.io/en/latest/docs/user_guide/mergingdata.html for more details. Parameters ---------- right : GeoDataFrame how : string, default 'inner' The type of join: * 'left': use keys from left_df; retain only left_df geometry column * 'right': use keys from right_df; retain only right_df geometry column * 'inner': use intersection of keys from both dfs; retain only left_df geometry column max_distance : float, default None Maximum distance within which to query for nearest geometry. Must be greater than 0. The max_distance used to search for nearest items in the tree may have a significant impact on performance by reducing the number of input geometries that are evaluated for nearest items in the tree. lsuffix : string, default 'left' Suffix to apply to overlapping column names (left GeoDataFrame). rsuffix : string, default 'right' Suffix to apply to overlapping column names (right GeoDataFrame). distance_col : string, default None If set, save the distances computed between matching geometries under a column of this name in the joined GeoDataFrame. exclusive : bool, optional, default False If True, the nearest geometries that are equal to the input geometry will not be returned, default False. Examples -------- >>> import geodatasets >>> groceries = geopandas.read_file( ... geodatasets.get_path("geoda.groceries") ... ) >>> chicago = geopandas.read_file( ... geodatasets.get_path("geoda.chicago_health") ... ).to_crs(groceries.crs) >>> chicago.head() # doctest: +SKIP ComAreaID ... geometry 0 35 ... POLYGON ((-87.60914 41.84469, -87.60915 41.844... 1 36 ... POLYGON ((-87.59215 41.81693, -87.59231 41.816... 2 37 ... POLYGON ((-87.62880 41.80189, -87.62879 41.801... 3 38 ... POLYGON ((-87.60671 41.81681, -87.60670 41.816... 4 39 ... POLYGON ((-87.59215 41.81693, -87.59215 41.816... [5 rows x 87 columns] >>> groceries.head() # doctest: +SKIP OBJECTID Ycoord ... Category geometry 0 16 41.973266 ... NaN MULTIPOINT ((-87.65661 41.97321)) 1 18 41.696367 ... NaN MULTIPOINT ((-87.68136 41.69713)) 2 22 41.868634 ... NaN MULTIPOINT ((-87.63918 41.86847)) 3 23 41.877590 ... new MULTIPOINT ((-87.65495 41.87783)) 4 27 41.737696 ... NaN MULTIPOINT ((-87.62715 41.73623)) [5 rows x 8 columns] >>> groceries_w_communities = groceries.sjoin_nearest(chicago) >>> groceries_w_communities[["Chain", "community", "geometry"]].head(2) Chain community geometry 0 VIET HOA PLAZA UPTOWN MULTIPOINT ((1168268.672 1933554.35)) 1 COUNTY FAIR FOODS MORGAN PARK MULTIPOINT ((1162302.618 1832900.224)) To include the distances: >>> groceries_w_communities = groceries.sjoin_nearest(chicago, \ distance_col="distances") >>> groceries_w_communities[["Chain", "community", \ "distances"]].head(2) Chain community distances 0 VIET HOA PLAZA UPTOWN 0.0 1 COUNTY FAIR FOODS MORGAN PARK 0.0 In the following example, we get multiple groceries for Uptown because all results are equidistant (in this case zero because they intersect). In fact, we get 4 results in total: >>> chicago_w_groceries = groceries.sjoin_nearest(chicago, \ distance_col="distances", how="right") >>> uptown_results = \ chicago_w_groceries[chicago_w_groceries["community"] == "UPTOWN"] >>> uptown_results[["Chain", "community"]] Chain community 30 VIET HOA PLAZA UPTOWN 30 JEWEL OSCO UPTOWN 30 TARGET UPTOWN 30 Mariano's UPTOWN See Also -------- GeoDataFrame.sjoin : binary predicate joins sjoin_nearest : equivalent top-level function Notes ----- Since this join relies on distances, results will be inaccurate if your geometries are in a geographic CRS. Every operation in GeoPandas is planar, i.e. the potential third dimension is not taken into account. """ return geopandas.sjoin_nearest( self, right, how=how, max_distance=max_distance, lsuffix=lsuffix, rsuffix=rsuffix, distance_col=distance_col, exclusive=exclusive, ) def clip( self, mask, keep_geom_type: bool = False, sort: bool = False ) -> GeoDataFrame: """Clip points, lines, or polygon geometries to the mask extent. Both layers must be in the same Coordinate Reference System (CRS). The GeoDataFrame will be clipped to the full extent of the ``mask`` object. If there are multiple polygons in mask, data from the GeoDataFrame will be clipped to the total boundary of all polygons in mask. Parameters ---------- mask : GeoDataFrame, GeoSeries, (Multi)Polygon, list-like Polygon vector layer used to clip the GeoDataFrame. The mask's geometry is dissolved into one geometric feature and intersected with GeoDataFrame. If the mask is list-like with four elements ``(minx, miny, maxx, maxy)``, ``clip`` will use a faster rectangle clipping (:meth:`~GeoSeries.clip_by_rect`), possibly leading to slightly different results. keep_geom_type : boolean, default False If True, return only geometries of original type in case of intersection resulting in multiple geometry types or GeometryCollections. If False, return all resulting geometries (potentially mixed types). sort : boolean, default False If True, the order of rows in the clipped GeoDataFrame will be preserved at small performance cost. If False the order of rows in the clipped GeoDataFrame will be random. Returns ------- GeoDataFrame Vector data (points, lines, polygons) from the GeoDataFrame clipped to polygon boundary from mask. See Also -------- clip : equivalent top-level function Examples -------- Clip points (grocery stores) with polygons (the Near West Side community): >>> import geodatasets >>> chicago = geopandas.read_file( ... geodatasets.get_path("geoda.chicago_health") ... ) >>> near_west_side = chicago[chicago["community"] == "NEAR WEST SIDE"] >>> groceries = geopandas.read_file( ... geodatasets.get_path("geoda.groceries") ... ).to_crs(chicago.crs) >>> groceries.shape (148, 8) >>> nws_groceries = groceries.clip(near_west_side) >>> nws_groceries.shape (7, 8) """ return geopandas.clip(self, mask=mask, keep_geom_type=keep_geom_type, sort=sort) def overlay( self, right: GeoDataFrame, how: Literal[ "intersection", "union", "identity", "symmetric_difference", "difference" ] = "intersection", keep_geom_type: bool | None = None, make_valid: bool = True, ): """Perform spatial overlay between GeoDataFrames. Currently only supports data GeoDataFrames with uniform geometry types, i.e. containing only (Multi)Polygons, or only (Multi)Points, or a combination of (Multi)LineString and LinearRing shapes. Implements several methods that are all effectively subsets of the union. See the User Guide page :doc:`../../user_guide/set_operations` for details. Parameters ---------- right : GeoDataFrame how : string Method of spatial overlay: 'intersection', 'union', 'identity', 'symmetric_difference' or 'difference'. keep_geom_type : bool If True, return only geometries of the same geometry type the GeoDataFrame has, if False, return all resulting geometries. Default is None, which will set keep_geom_type to True but warn upon dropping geometries. make_valid : bool, default True If True, any invalid input geometries are corrected with a call to make_valid(), if False, a `ValueError` is raised if any input geometries are invalid. Returns ------- df : GeoDataFrame GeoDataFrame with new set of polygons and attributes resulting from the overlay Examples -------- >>> from shapely.geometry import Polygon >>> polys1 = geopandas.GeoSeries([Polygon([(0,0), (2,0), (2,2), (0,2)]), ... Polygon([(2,2), (4,2), (4,4), (2,4)])]) >>> polys2 = geopandas.GeoSeries([Polygon([(1,1), (3,1), (3,3), (1,3)]), ... Polygon([(3,3), (5,3), (5,5), (3,5)])]) >>> df1 = geopandas.GeoDataFrame({'geometry': polys1, 'df1_data':[1,2]}) >>> df2 = geopandas.GeoDataFrame({'geometry': polys2, 'df2_data':[1,2]}) >>> df1.overlay(df2, how='union') df1_data df2_data geometry 0 1.0 1.0 POLYGON ((2 2, 2 1, 1 1, 1 2, 2 2)) 1 2.0 1.0 POLYGON ((2 2, 2 3, 3 3, 3 2, 2 2)) 2 2.0 2.0 POLYGON ((4 4, 4 3, 3 3, 3 4, 4 4)) 3 1.0 NaN POLYGON ((2 0, 0 0, 0 2, 1 2, 1 1, 2 1, 2 0)) 4 2.0 NaN MULTIPOLYGON (((3 4, 3 3, 2 3, 2 4, 3 4)), ((4... 5 NaN 1.0 MULTIPOLYGON (((2 3, 2 2, 1 2, 1 3, 2 3)), ((3... 6 NaN 2.0 POLYGON ((3 5, 5 5, 5 3, 4 3, 4 4, 3 4, 3 5)) >>> df1.overlay(df2, how='intersection') df1_data df2_data geometry 0 1 1 POLYGON ((2 2, 2 1, 1 1, 1 2, 2 2)) 1 2 1 POLYGON ((2 2, 2 3, 3 3, 3 2, 2 2)) 2 2 2 POLYGON ((4 4, 4 3, 3 3, 3 4, 4 4)) >>> df1.overlay(df2, how='symmetric_difference') df1_data df2_data geometry 0 1.0 NaN POLYGON ((2 0, 0 0, 0 2, 1 2, 1 1, 2 1, 2 0)) 1 2.0 NaN MULTIPOLYGON (((3 4, 3 3, 2 3, 2 4, 3 4)), ((4... 2 NaN 1.0 MULTIPOLYGON (((2 3, 2 2, 1 2, 1 3, 2 3)), ((3... 3 NaN 2.0 POLYGON ((3 5, 5 5, 5 3, 4 3, 4 4, 3 4, 3 5)) >>> df1.overlay(df2, how='difference') geometry df1_data 0 POLYGON ((2 0, 0 0, 0 2, 1 2, 1 1, 2 1, 2 0)) 1 1 MULTIPOLYGON (((3 4, 3 3, 2 3, 2 4, 3 4)), ((4... 2 >>> df1.overlay(df2, how='identity') df1_data df2_data geometry 0 1 1.0 POLYGON ((2 2, 2 1, 1 1, 1 2, 2 2)) 1 2 1.0 POLYGON ((2 2, 2 3, 3 3, 3 2, 2 2)) 2 2 2.0 POLYGON ((4 4, 4 3, 3 3, 3 4, 4 4)) 3 1 NaN POLYGON ((2 0, 0 0, 0 2, 1 2, 1 1, 2 1, 2 0)) 4 2 NaN MULTIPOLYGON (((3 4, 3 3, 2 3, 2 4, 3 4)), ((4... See Also -------- GeoDataFrame.sjoin : spatial join overlay : equivalent top-level function Notes ----- Every operation in GeoPandas is planar, i.e. the potential third dimension is not taken into account. """ return geopandas.overlay( self, right, how=how, keep_geom_type=keep_geom_type, make_valid=make_valid ) def _dataframe_set_geometry( self, col, drop: bool | None = None, inplace: Literal[False] = False, crs: Any | None = None, ) -> GeoDataFrame: if inplace: raise ValueError( "Can't do inplace setting when converting from DataFrame to GeoDataFrame" ) gf = GeoDataFrame(self) # this will copy so that BlockManager gets copied return gf.set_geometry(col, drop=drop, inplace=False, crs=crs) DataFrame.set_geometry = _dataframe_set_geometry geopandas-1.2.0/geopandas/geoseries.py000066400000000000000000001542231525650651600177750ustar00rootroot00000000000000from __future__ import annotations import typing import warnings from typing import Any import numpy as np import pandas as pd from pandas import Series from pandas.core.internals import SingleBlockManager import shapely from shapely.geometry import GeometryCollection from shapely.geometry.base import BaseGeometry import geopandas from geopandas.base import GeoPandasBase, _delegate_property from geopandas.explore import _explore_geoseries from geopandas.plotting import plot_series from . import _compat as compat from ._decorator import doc from .array import ( GeometryDtype, from_shapely, from_wkb, from_wkt, points_from_xy, to_wkb, to_wkt, ) from .base import is_geometry_type if typing.TYPE_CHECKING: import os from collections.abc import Callable def _geoseries_constructor_with_fallback( data=None, index=None, crs: Any | None = None, **kwargs ): """A flexible constructor for GeoSeries._constructor, which needs to be able to fall back to a Series (if a certain operation does not produce geometries). """ # noqa: D401 try: return GeoSeries(data=data, index=index, crs=crs, **kwargs) except TypeError: return Series(data=data, index=index, **kwargs) def _expanddim_logic(df): """Shared logic for _constructor_expanddim and _constructor_from_mgr_expanddim.""" from geopandas import GeoDataFrame if (df.dtypes == "geometry").sum() > 0: if df.shape[1] == 1: geo_col_name = df.columns[0] else: geo_col_name = None if geo_col_name is None or not is_geometry_type(df[geo_col_name]): df = GeoDataFrame(df) df._geometry_column_name = None else: df = df.set_geometry(geo_col_name) return df def _geoseries_expanddim(data=None, *args, **kwargs): # pd.Series._constructor_expanddim == pd.DataFrame, we start # with that then specialize. df = pd.DataFrame(data, *args, **kwargs) return _expanddim_logic(df) class GeoSeries(GeoPandasBase, Series): """ A Series object designed to store shapely geometry objects. Parameters ---------- data : array-like, dict, scalar value The geometries to store in the GeoSeries. index : array-like or Index The index for the GeoSeries. crs : value (optional) Coordinate Reference System of the geometry objects. Can be anything accepted by :meth:`pyproj.CRS.from_user_input() `, such as an authority string (eg "EPSG:4326") or a WKT string. kwargs Additional arguments passed to the Series constructor, e.g. ``name``. Examples -------- >>> from shapely.geometry import Point >>> s = geopandas.GeoSeries([Point(1, 1), Point(2, 2), Point(3, 3)]) >>> s 0 POINT (1 1) 1 POINT (2 2) 2 POINT (3 3) dtype: geometry >>> s = geopandas.GeoSeries( ... [Point(1, 1), Point(2, 2), Point(3, 3)], crs="EPSG:3857" ... ) >>> s.crs # doctest: +SKIP Name: WGS 84 / Pseudo-Mercator Axis Info [cartesian]: - X[east]: Easting (metre) - Y[north]: Northing (metre) Area of Use: - name: World - 85°S to 85°N - bounds: (-180.0, -85.06, 180.0, 85.06) Coordinate Operation: - name: Popular Visualisation Pseudo-Mercator - method: Popular Visualisation Pseudo Mercator Datum: World Geodetic System 1984 - Ellipsoid: WGS 84 - Prime Meridian: Greenwich >>> s = geopandas.GeoSeries( ... [Point(1, 1), Point(2, 2), Point(3, 3)], index=["a", "b", "c"], crs=4326 ... ) >>> s a POINT (1 1) b POINT (2 2) c POINT (3 3) dtype: geometry >>> s.crs Name: WGS 84 Axis Info [ellipsoidal]: - Lat[north]: Geodetic latitude (degree) - Lon[east]: Geodetic longitude (degree) Area of Use: - name: World. - bounds: (-180.0, -90.0, 180.0, 90.0) Datum: World Geodetic System 1984 ensemble - Ellipsoid: WGS 84 - Prime Meridian: Greenwich See Also -------- GeoDataFrame pandas.Series """ def __init__(self, data=None, index=None, crs: Any | None = None, **kwargs): if ( hasattr(data, "crs") or (isinstance(data, pd.Series) and hasattr(data.array, "crs")) ) and crs: data_crs = data.crs if hasattr(data, "crs") else data.array.crs if not data_crs: # make a copy to avoid setting CRS to passed GeometryArray data = data.copy() else: if not data_crs == crs: raise ValueError( "CRS mismatch between CRS of the passed geometries " "and 'crs'. Use 'GeoSeries.set_crs(crs, " "allow_override=True)' to overwrite CRS or " "'GeoSeries.to_crs(crs)' to reproject geometries. " ) if isinstance(data, SingleBlockManager): if not isinstance(data.blocks[0].dtype, GeometryDtype): raise TypeError( "Non geometry data passed to GeoSeries constructor, " f"received data of dtype '{data.blocks[0].dtype}'" ) if isinstance(data, BaseGeometry): # fix problem for scalar geometries passed, ensure the list of # scalars is of correct length if index is specified n = len(index) if index is not None else 1 data = [data] * n name = kwargs.pop("name", None) if not is_geometry_type(data): # if data is None and dtype is specified (eg from empty overlay # test), specifying dtype raises an error: # https://github.com/pandas-dev/pandas/issues/26469 kwargs.pop("dtype", None) # Use Series constructor to handle input data with warnings.catch_warnings(): # suppress additional warning from pandas for empty data # (will always give object dtype instead of float dtype in the future, # making the `if s.empty: s = s.astype(object)` below unnecessary) empty_msg = "The default dtype for empty Series" warnings.filterwarnings("ignore", empty_msg, DeprecationWarning) warnings.filterwarnings("ignore", empty_msg, FutureWarning) s = pd.Series(data, index=index, name=name, **kwargs) # prevent trying to convert non-geometry objects if s.dtype != object: if (s.empty and s.dtype == "float64") or data is None: # pd.Series with empty data gives float64 for older pandas versions s = s.astype(object) else: raise TypeError( "Non geometry data passed to GeoSeries constructor, " f"received data of dtype '{s.dtype}'" ) # extract object-dtype numpy array from pandas Series; with CoW this # gives a read-only array, so we try to set the flag back to writeable data = s.to_numpy() try: data.flags.writeable = True except ValueError: pass # try to convert to GeometryArray try: data = from_shapely(data, crs) except TypeError: raise TypeError( "Non geometry data passed to GeoSeries constructor, " f"received data of dtype '{s.dtype}'" ) index = s.index name = s.name super().__init__(data, index=index, name=name, **kwargs) if not self.crs: self.crs = crs @GeoPandasBase.crs.setter def crs(self, value): if self.crs is not None: warnings.warn( "Overriding the CRS of a GeoSeries that already has CRS. " "This unsafe behavior will be deprecated in future versions. " "Use GeoSeries.set_crs method instead.", stacklevel=2, category=FutureWarning, ) self.geometry.values.crs = value @property def geometry(self) -> GeoSeries: return self @property def x(self) -> Series: """Return the x location of point geometries in a GeoSeries. Returns ------- pandas.Series Examples -------- >>> from shapely.geometry import Point >>> s = geopandas.GeoSeries([Point(1, 1), Point(2, 2), Point(3, 3)]) >>> s.x 0 1.0 1 2.0 2 3.0 dtype: float64 See Also -------- GeoSeries.y GeoSeries.z """ return _delegate_property("x", self) @property def y(self) -> Series: """Return the y location of point geometries in a GeoSeries. Returns ------- pandas.Series Examples -------- >>> from shapely.geometry import Point >>> s = geopandas.GeoSeries([Point(1, 1), Point(2, 2), Point(3, 3)]) >>> s.y 0 1.0 1 2.0 2 3.0 dtype: float64 See Also -------- GeoSeries.x GeoSeries.z GeoSeries.m """ return _delegate_property("y", self) @property def z(self) -> Series: """Return the z location of point geometries in a GeoSeries. Returns ------- pandas.Series Examples -------- >>> from shapely.geometry import Point >>> s = geopandas.GeoSeries([Point(1, 1, 1), Point(2, 2, 2), Point(3, 3, 3)]) >>> s.z 0 1.0 1 2.0 2 3.0 dtype: float64 See Also -------- GeoSeries.x GeoSeries.y GeoSeries.m """ return _delegate_property("z", self) @property def m(self) -> Series: """Return the m coordinate of point geometries in a GeoSeries. Requires Shapely >= 2.1. .. versionadded:: 1.1.0 Returns ------- pandas.Series Examples -------- >>> from shapely.geometry import Point >>> s = geopandas.GeoSeries.from_wkt( ... [ ... "POINT M (2 3 5)", ... "POINT M (1 2 3)", ... ] ... ) >>> s 0 POINT M (2 3 5) 1 POINT M (1 2 3) dtype: geometry >>> s.m 0 5.0 1 3.0 dtype: float64 See Also -------- GeoSeries.x GeoSeries.y GeoSeries.z """ return _delegate_property("m", self) @classmethod def from_file(cls, filename: os.PathLike | typing.IO, **kwargs) -> GeoSeries: """Alternate constructor to create a ``GeoSeries`` from a file. Can load a ``GeoSeries`` from a file from any format recognized by `pyogrio`. See http://pyogrio.readthedocs.io/ for details. From a file with attributes loads only geometry column. Note that to do that, GeoPandas first loads the whole GeoDataFrame. Parameters ---------- filename : str File path or file handle to read from. Depending on which kwargs are included, the content of filename may vary. See :func:`pyogrio.read_dataframe` for usage details. kwargs : key-word arguments These arguments are passed to :func:`pyogrio.read_dataframe`, and can be used to access multi-layer data, data stored within archives (zip files), etc. Examples -------- >>> import geodatasets >>> path = geodatasets.get_path('nybb') >>> s = geopandas.GeoSeries.from_file(path) >>> s 0 MULTIPOLYGON (((970217.022 145643.332, 970227.... 1 MULTIPOLYGON (((1029606.077 156073.814, 102957... 2 MULTIPOLYGON (((1021176.479 151374.797, 102100... 3 MULTIPOLYGON (((981219.056 188655.316, 980940.... 4 MULTIPOLYGON (((1012821.806 229228.265, 101278... Name: geometry, dtype: geometry See Also -------- read_file : read file to GeoDataFrame """ from geopandas import GeoDataFrame df = GeoDataFrame.from_file(filename, **kwargs) return GeoSeries(df.geometry, crs=df.crs) @classmethod def from_wkb( cls, data, index=None, crs: Any | None = None, on_invalid="raise", **kwargs ) -> GeoSeries: r"""Alternate constructor to create a ``GeoSeries`` from a list or array of WKB objects. Parameters ---------- data : array-like or Series Series, list or array of WKB objects index : array-like or Index The index for the GeoSeries. crs : value, optional Coordinate Reference System of the geometry objects. Can be anything accepted by :meth:`pyproj.CRS.from_user_input() `, such as an authority string (eg "EPSG:4326") or a WKT string. on_invalid: {"raise", "warn", "ignore"}, default "raise" - raise: an exception will be raised if a WKB input geometry is invalid. - warn: a warning will be raised and invalid WKB geometries will be returned as None. - ignore: invalid WKB geometries will be returned as None without a warning. - fix: an effort is made to fix invalid input geometries (e.g. close unclosed rings). If this is not possible, they are returned as ``None`` without a warning. Requires GEOS >= 3.11 and shapely >= 2.1. kwargs Additional arguments passed to the Series constructor, e.g. ``name``. Returns ------- GeoSeries See Also -------- GeoSeries.from_wkt Examples -------- >>> wkbs = [ ... ( ... b"\x01\x01\x00\x00\x00\x00\x00\x00\x00" ... b"\x00\x00\xf0?\x00\x00\x00\x00\x00\x00\xf0?" ... ), ... ( ... b"\x01\x01\x00\x00\x00\x00\x00\x00\x00" ... b"\x00\x00\x00@\x00\x00\x00\x00\x00\x00\x00@" ... ), ... ( ... b"\x01\x01\x00\x00\x00\x00\x00\x00\x00\x00" ... b"\x00\x08@\x00\x00\x00\x00\x00\x00\x08@" ... ), ... ] >>> s = geopandas.GeoSeries.from_wkb(wkbs) >>> s 0 POINT (1 1) 1 POINT (2 2) 2 POINT (3 3) dtype: geometry """ return cls._from_wkb_or_wkt( from_wkb, data, index=index, crs=crs, on_invalid=on_invalid, **kwargs ) @classmethod def from_wkt( cls, data, index=None, crs: Any | None = None, on_invalid="raise", **kwargs ) -> GeoSeries: """Alternate constructor to create a ``GeoSeries`` from a list or array of WKT objects. Parameters ---------- data : array-like, Series Series, list, or array of WKT objects index : array-like or Index The index for the GeoSeries. crs : value, optional Coordinate Reference System of the geometry objects. Can be anything accepted by :meth:`pyproj.CRS.from_user_input() `, such as an authority string (eg "EPSG:4326") or a WKT string. on_invalid : {"raise", "warn", "ignore"}, default "raise" - raise: an exception will be raised if a WKT input geometry is invalid. - warn: a warning will be raised and invalid WKT geometries will be returned as ``None``. - ignore: invalid WKT geometries will be returned as ``None`` without a warning. - fix: an effort is made to fix invalid input geometries (e.g. close unclosed rings). If this is not possible, they are returned as ``None`` without a warning. Requires GEOS >= 3.11 and shapely >= 2.1. kwargs Additional arguments passed to the Series constructor, e.g. ``name``. Returns ------- GeoSeries See Also -------- GeoSeries.from_wkb Examples -------- >>> wkts = [ ... 'POINT (1 1)', ... 'POINT (2 2)', ... 'POINT (3 3)', ... ] >>> s = geopandas.GeoSeries.from_wkt(wkts) >>> s 0 POINT (1 1) 1 POINT (2 2) 2 POINT (3 3) dtype: geometry """ return cls._from_wkb_or_wkt( from_wkt, data, index=index, crs=crs, on_invalid=on_invalid, **kwargs ) @classmethod def from_xy(cls, x, y, z=None, index=None, crs=None, **kwargs) -> GeoSeries: """Alternate constructor to create a :class:`~geopandas.GeoSeries` of Point geometries from lists or arrays of x, y(, z) coordinates. In case of geographic coordinates, it is assumed that longitude is captured by ``x`` coordinates and latitude by ``y``. Parameters ---------- x, y, z : iterable index : array-like or Index, optional The index for the GeoSeries. If not given and all coordinate inputs are Series with an equal index, that index is used. crs : value, optional Coordinate Reference System of the geometry objects. Can be anything accepted by :meth:`pyproj.CRS.from_user_input() `, such as an authority string (eg "EPSG:4326") or a WKT string. **kwargs Additional arguments passed to the Series constructor, e.g. ``name``. Returns ------- GeoSeries See Also -------- GeoSeries.from_wkt points_from_xy Examples -------- >>> x = [2.5, 5, -3.0] >>> y = [0.5, 1, 1.5] >>> s = geopandas.GeoSeries.from_xy(x, y, crs="EPSG:4326") >>> s 0 POINT (2.5 0.5) 1 POINT (5 1) 2 POINT (-3 1.5) dtype: geometry """ if index is None: if ( isinstance(x, Series) and isinstance(y, Series) and x.index.equals(y.index) and (z is None or (isinstance(z, Series) and x.index.equals(z.index))) ): # check if we can reuse index index = x.index return cls(points_from_xy(x, y, z, crs=crs), index=index, crs=crs, **kwargs) @classmethod def _from_wkb_or_wkt( cls, from_wkb_or_wkt_function: Callable, data, index=None, crs: Any | None = None, on_invalid: str = "raise", **kwargs, ) -> GeoSeries: """Create a GeoSeries from either WKT or WKB values.""" if isinstance(data, Series): if index is not None: data = data.reindex(index) else: index = data.index data = data.to_numpy(na_value=None) return cls( from_wkb_or_wkt_function(data, crs=crs, on_invalid=on_invalid), index=index, **kwargs, ) @classmethod def from_arrow(cls, arr, **kwargs) -> GeoSeries: """ Construct a GeoSeries from an Arrow array object with a GeoArrow extension type. See https://geoarrow.org/ for details on the GeoArrow specification. This functions accepts any Arrow array object implementing the `Arrow PyCapsule Protocol`_ (i.e. having an ``__arrow_c_array__`` method). .. _Arrow PyCapsule Protocol: https://arrow.apache.org/docs/format/CDataInterface/PyCapsuleInterface.html .. versionadded:: 1.0 Parameters ---------- arr : pyarrow.Array, Arrow array Any array object implementing the Arrow PyCapsule Protocol (i.e. has an ``__arrow_c_array__`` or ``__arrow_c_stream__`` method). The type of the array should be one of the geoarrow geometry types. **kwargs Other parameters passed to the GeoSeries constructor. Returns ------- GeoSeries See Also -------- GeoSeries.to_arrow Examples -------- >>> import geoarrow.pyarrow as ga >>> array = ga.as_geoarrow( ... [None, "POLYGON ((0 0, 1 1, 0 1, 0 0))", "LINESTRING (0 0, -1 1, 0 -1)"]) >>> geoseries = geopandas.GeoSeries.from_arrow(array) >>> geoseries 0 None 1 POLYGON ((0 0, 1 1, 0 1, 0 0)) 2 LINESTRING (0 0, -1 1, 0 -1) dtype: geometry """ from geopandas.io._geoarrow import arrow_to_geometry_array return cls(arrow_to_geometry_array(arr), **kwargs) @property def __geo_interface__(self) -> dict: """Returns a ``GeoSeries`` as a python feature collection. Implements the `geo_interface`. The returned python data structure represents the ``GeoSeries`` as a GeoJSON-like ``FeatureCollection``. Note that the features will have an empty ``properties`` dict as they don't have associated attributes (geometry only). Examples -------- >>> from shapely.geometry import Point >>> s = geopandas.GeoSeries([Point(1, 1), Point(2, 2), Point(3, 3)]) >>> s.__geo_interface__ {'type': 'FeatureCollection', 'features': [{'id': '0', 'type': 'Feature', \ 'properties': {}, 'geometry': {'type': 'Point', 'coordinates': (1.0, 1.0)}, \ 'bbox': (1.0, 1.0, 1.0, 1.0)}, {'id': '1', 'type': 'Feature', \ 'properties': {}, 'geometry': {'type': 'Point', 'coordinates': (2.0, 2.0)}, \ 'bbox': (2.0, 2.0, 2.0, 2.0)}, {'id': '2', 'type': 'Feature', 'properties': \ {}, 'geometry': {'type': 'Point', 'coordinates': (3.0, 3.0)}, 'bbox': (3.0, \ 3.0, 3.0, 3.0)}], 'bbox': (1.0, 1.0, 3.0, 3.0)} """ from geopandas import GeoDataFrame return GeoDataFrame({"geometry": self}).__geo_interface__ def to_file( self, filename: os.PathLike | typing.IO, driver: str | None = None, index: bool | None = None, **kwargs, ): """Write the ``GeoSeries`` to a file. By default, an ESRI shapefile is written, but any OGR data source supported by Pyogrio or Fiona can be written. Parameters ---------- filename : string File path or file handle to write to. The path may specify a GDAL VSI scheme. driver : string, default None The OGR format driver used to write the vector file. If not specified, it attempts to infer it from the file extension. If no extension is specified, it saves ESRI Shapefile to a folder. index : bool, default None If True, write index into one or more columns (for MultiIndex). Default None writes the index into one or more columns only if the index is named, is a MultiIndex, or has a non-integer data type. If False, no index is written. .. versionadded:: 0.7 Previously the index was not written. mode : string, default 'w' The write mode, 'w' to overwrite the existing file and 'a' to append. Not all drivers support appending. The drivers that support appending are listed in fiona.supported_drivers or https://github.com/Toblerity/Fiona/blob/master/fiona/drvsupport.py crs : pyproj.CRS, default None If specified, the CRS is passed to Fiona to better control how the file is written. If None, GeoPandas will determine the crs based on crs df attribute. The value can be anything accepted by :meth:`pyproj.CRS.from_user_input() `, such as an authority string (eg "EPSG:4326") or a WKT string. The keyword is not supported for the "pyogrio" engine. engine : str, "pyogrio" or "fiona" The underlying library that is used to write the file. Currently, the supported options are "pyogrio" and "fiona". Defaults to "pyogrio" if installed, otherwise tries "fiona". **kwargs : Keyword args to be passed to the engine, and can be used to write to multi-layer data, store data within archives (zip files), etc. In case of the "pyogrio" engine, the keyword arguments are passed to `pyogrio.write_dataframe`. In case of the "fiona" engine, the keyword arguments are passed to fiona.open`. For more information on possible keywords, type: ``import pyogrio; help(pyogrio.write_dataframe)``. See Also -------- GeoDataFrame.to_file : write GeoDataFrame to file read_file : read file to GeoDataFrame Examples -------- >>> s.to_file('series.shp') # doctest: +SKIP >>> s.to_file('series.gpkg', driver='GPKG', layer='name1') # doctest: +SKIP >>> s.to_file('series.geojson', driver='GeoJSON') # doctest: +SKIP """ from geopandas import GeoDataFrame data = GeoDataFrame({"geometry": self}, index=self.index) data.to_file(filename, driver, index=index, **kwargs) # # Implement pandas methods # @property def _constructor(self): return _geoseries_constructor_with_fallback def _constructor_from_mgr(self, mgr, axes): assert isinstance(mgr, SingleBlockManager) if not isinstance(mgr.blocks[0].dtype, GeometryDtype): return Series._from_mgr(mgr, axes) return GeoSeries._from_mgr(mgr, axes) @property def _constructor_expanddim(self): return _geoseries_expanddim def _constructor_expanddim_from_mgr(self, mgr, axes): df = pd.DataFrame._from_mgr(mgr, axes) return _expanddim_logic(df) def _wrapped_pandas_method(self, mtd, *args, **kwargs): """Wrap a generic pandas method to ensure it returns a GeoSeries.""" val = getattr(super(), mtd)(*args, **kwargs) if type(val) is Series: val.__class__ = GeoSeries val.crs = self.crs return val def __getitem__(self, key): return self._wrapped_pandas_method("__getitem__", key) @doc(pd.Series) def sort_index(self, *args, **kwargs): return self._wrapped_pandas_method("sort_index", *args, **kwargs) @doc(pd.Series) def take(self, *args, **kwargs): return self._wrapped_pandas_method("take", *args, **kwargs) @doc(pd.Series) def apply(self, func, convert_dtype: bool | None = None, args=(), **kwargs): if convert_dtype is not None: kwargs["convert_dtype"] = convert_dtype # to avoid warning result = super().apply(func, args=args, **kwargs) if isinstance(result, GeoSeries): if self.crs is not None: result.set_crs(self.crs, inplace=True) return result def isna(self) -> Series: """ Detect missing values. Historically, NA values in a GeoSeries could be represented by empty geometric objects, in addition to standard representations such as None and np.nan. This behaviour is changed in version 0.6.0, and now only actual missing values return True. To detect empty geometries, use ``GeoSeries.is_empty`` instead. Returns ------- A boolean pandas Series of the same size as the GeoSeries, True where a value is NA. Examples -------- >>> from shapely.geometry import Polygon >>> s = geopandas.GeoSeries( ... [Polygon([(0, 0), (1, 1), (0, 1)]), None, Polygon([])] ... ) >>> s 0 POLYGON ((0 0, 1 1, 0 1, 0 0)) 1 None 2 POLYGON EMPTY dtype: geometry >>> s.isna() 0 False 1 True 2 False dtype: bool See Also -------- GeoSeries.notna : inverse of isna GeoSeries.is_empty : detect empty geometries """ return super().isna() def isnull(self) -> Series: """Alias for `isna` method. See `isna` for more detail.""" return self.isna() def notna(self) -> Series: """ Detect non-missing values. Historically, NA values in a GeoSeries could be represented by empty geometric objects, in addition to standard representations such as None and np.nan. This behaviour is changed in version 0.6.0, and now only actual missing values return False. To detect empty geometries, use ``~GeoSeries.is_empty`` instead. Returns ------- A boolean pandas Series of the same size as the GeoSeries, False where a value is NA. Examples -------- >>> from shapely.geometry import Polygon >>> s = geopandas.GeoSeries( ... [Polygon([(0, 0), (1, 1), (0, 1)]), None, Polygon([])] ... ) >>> s 0 POLYGON ((0 0, 1 1, 0 1, 0 0)) 1 None 2 POLYGON EMPTY dtype: geometry >>> s.notna() 0 True 1 False 2 True dtype: bool See Also -------- GeoSeries.isna : inverse of notna GeoSeries.is_empty : detect empty geometries """ if self.is_empty.any(): warnings.warn( "GeoSeries.notna() previously returned False for both missing (None) " "and empty geometries. Now, it only returns False for missing values. " "Since the calling GeoSeries contains empty geometries, the result " "has changed compared to previous versions of GeoPandas.\n" "Given a GeoSeries 's', you can use '~s.is_empty & s.notna()' to get " "back the old behaviour.\n\n" "To further ignore this warning, you can do: \n" "import warnings; warnings.filterwarnings('ignore', " "'GeoSeries.notna', UserWarning)", UserWarning, stacklevel=2, ) return super().notna() def notnull(self) -> Series: """Alias for `notna` method. See `notna` for more detail.""" return self.notna() def fillna(self, value=None, inplace: bool = False, limit=None, **kwargs): """ Fill NA values with geometry (or geometries). Parameters ---------- value : shapely geometry or GeoSeries, default None If None is passed, NA values will be filled with GEOMETRYCOLLECTION EMPTY. If a shapely geometry object is passed, it will be used to fill all missing values. If a ``GeoSeries`` or ``GeometryArray`` are passed, missing values will be filled based on the corresponding index locations. If pd.NA or np.nan are passed, values will be filled with ``None`` (not GEOMETRYCOLLECTION EMPTY). limit : int, default None This is the maximum number of entries along the entire axis where NaNs will be filled. Must be greater than 0 if not None. Returns ------- GeoSeries Examples -------- >>> from shapely.geometry import Polygon >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (1, 1), (0, 1)]), ... None, ... Polygon([(0, 0), (-1, 1), (0, -1)]), ... ] ... ) >>> s 0 POLYGON ((0 0, 1 1, 0 1, 0 0)) 1 None 2 POLYGON ((0 0, -1 1, 0 -1, 0 0)) dtype: geometry Filled with an empty polygon. >>> s.fillna() 0 POLYGON ((0 0, 1 1, 0 1, 0 0)) 1 GEOMETRYCOLLECTION EMPTY 2 POLYGON ((0 0, -1 1, 0 -1, 0 0)) dtype: geometry Filled with a specific polygon. >>> s.fillna(Polygon([(0, 1), (2, 1), (1, 2)])) 0 POLYGON ((0 0, 1 1, 0 1, 0 0)) 1 POLYGON ((0 1, 2 1, 1 2, 0 1)) 2 POLYGON ((0 0, -1 1, 0 -1, 0 0)) dtype: geometry Filled with another GeoSeries. >>> from shapely.geometry import Point >>> s_fill = geopandas.GeoSeries( ... [ ... Point(0, 0), ... Point(1, 1), ... Point(2, 2), ... ] ... ) >>> s.fillna(s_fill) 0 POLYGON ((0 0, 1 1, 0 1, 0 0)) 1 POINT (1 1) 2 POLYGON ((0 0, -1 1, 0 -1, 0 0)) dtype: geometry See Also -------- GeoSeries.isna : detect missing values """ if value is None: value = GeometryCollection() return super().fillna(value=value, limit=limit, inplace=inplace, **kwargs) def __contains__(self, other) -> bool: """Allow tests of the form "geom in s". Tests whether a GeoSeries contains a geometry. Note: This is not the same as the geometric method "contains". """ if isinstance(other, BaseGeometry): return np.any(self.geom_equals(other)) else: return False @doc(plot_series) def plot(self, *args, **kwargs): return plot_series(self, *args, **kwargs) @doc(_explore_geoseries) def explore(self, *args, **kwargs): """Explore with an interactive map based on folium/leaflet.js.""" return _explore_geoseries(self, *args, **kwargs) def explode(self, ignore_index=False, index_parts=False) -> GeoSeries: """ Explode multi-part geometries into multiple component geometries. Single rows can become multiple rows. This is analogous to PostGIS's ST_Dump(). The 'path' index is the second level of the returned MultiIndex GeometryCollections are split into their direct components. If a GeometryCollection contains a multi-part geometry, that component is returned as a multi-part geometry. To further split multi-part geometries within a GeometryCollection, call ``explode`` a second time. Parameters ---------- ignore_index : bool, default False If True, the resulting index will be labelled 0, 1, …, n - 1, ignoring `index_parts`. index_parts : boolean, default False If True, the resulting index will be a multi-index (original index with an additional level indicating the multiple geometries: a new zero-based index for each component geometry per input geometry). Returns ------- GeoSeries Exploded GeoSeries with each component geometry as a separate entry. If ``index_parts`` is True, the resulting index will be a MultiIndex with the original index and a zero-based integer index that counts the component geometries within each input geometry. Examples -------- >>> from shapely.geometry import MultiPoint >>> s = geopandas.GeoSeries( ... [MultiPoint([(0, 0), (1, 1)]), MultiPoint([(2, 2), (3, 3), (4, 4)])] ... ) >>> s 0 MULTIPOINT ((0 0), (1 1)) 1 MULTIPOINT ((2 2), (3 3), (4 4)) dtype: geometry >>> s.explode(index_parts=True) 0 0 POINT (0 0) 1 POINT (1 1) 1 0 POINT (2 2) 1 POINT (3 3) 2 POINT (4 4) dtype: geometry See Also -------- GeoDataFrame.explode """ from .base import _get_index_for_parts geometries, outer_idx = shapely.get_parts(self.values._data, return_index=True) index = _get_index_for_parts( self.index, outer_idx, ignore_index=ignore_index, index_parts=index_parts, ) return GeoSeries(geometries, index=index, crs=self.crs).__finalize__(self) # # Additional methods # @compat.requires_pyproj def set_crs( self, crs: Any | None = None, epsg: int | None = None, inplace: bool = False, allow_override: bool = False, ): """ Set the Coordinate Reference System (CRS) of a ``GeoSeries``. Pass ``None`` to remove CRS from the ``GeoSeries``. Notes ----- The underlying geometries are not transformed to this CRS. To transform the geometries to a new CRS, use the ``to_crs`` method. Parameters ---------- crs : pyproj.CRS | None, optional The value can be anything accepted by :meth:`pyproj.CRS.from_user_input() `, such as an authority string (eg "EPSG:4326") or a WKT string. epsg : int, optional if `crs` is specified EPSG code specifying the projection. inplace : bool, default False If True, the CRS of the GeoSeries will be changed in place (while still returning the result) instead of making a copy of the GeoSeries. allow_override : bool, default False If the the GeoSeries already has a CRS, allow to replace the existing CRS, even when both are not equal. Returns ------- GeoSeries Examples -------- >>> from shapely.geometry import Point >>> s = geopandas.GeoSeries([Point(1, 1), Point(2, 2), Point(3, 3)]) >>> s 0 POINT (1 1) 1 POINT (2 2) 2 POINT (3 3) dtype: geometry Setting CRS to a GeoSeries without one: >>> s.crs is None True >>> s = s.set_crs('epsg:3857') >>> s.crs # doctest: +SKIP Name: WGS 84 / Pseudo-Mercator Axis Info [cartesian]: - X[east]: Easting (metre) - Y[north]: Northing (metre) Area of Use: - name: World - 85°S to 85°N - bounds: (-180.0, -85.06, 180.0, 85.06) Coordinate Operation: - name: Popular Visualisation Pseudo-Mercator - method: Popular Visualisation Pseudo Mercator Datum: World Geodetic System 1984 - Ellipsoid: WGS 84 - Prime Meridian: Greenwich Overriding existing CRS: >>> s = s.set_crs(4326, allow_override=True) Without ``allow_override=True``, ``set_crs`` returns an error if you try to override CRS. See Also -------- GeoSeries.to_crs : re-project to another CRS """ from pyproj import CRS if crs is not None: crs = CRS.from_user_input(crs) elif epsg is not None: crs = CRS.from_epsg(epsg) if not allow_override and self.crs is not None and not self.crs == crs: raise ValueError( "The GeoSeries already has a CRS which is not equal to the passed " "CRS. Specify 'allow_override=True' to allow replacing the existing " "CRS without doing any transformation. If you actually want to " "transform the geometries, use 'GeoSeries.to_crs' instead." ) if not inplace: result = self.copy(deep=not compat.PANDAS_GE_30) else: result = self result.array.crs = crs return result def to_crs(self, crs: Any | None = None, epsg: int | None = None) -> GeoSeries: """Return a ``GeoSeries`` with all geometries transformed to a new coordinate reference system. Transform all geometries in a GeoSeries to a different coordinate reference system. The ``crs`` attribute on the current GeoSeries must be set. Either ``crs`` or ``epsg`` may be specified for output. This method will transform all points in all objects. It has no notion of projecting entire geometries. All segments joining points are assumed to be lines in the current projection, not geodesics. Objects crossing the dateline (or other projection boundary) will have undesirable behavior. Parameters ---------- crs : pyproj.CRS, optional if `epsg` is specified The value can be anything accepted by :meth:`pyproj.CRS.from_user_input() `, such as an authority string (eg "EPSG:4326") or a WKT string. epsg : int, optional if `crs` is specified EPSG code specifying output projection. Returns ------- GeoSeries Examples -------- >>> from shapely.geometry import Point >>> s = geopandas.GeoSeries([Point(1, 1), Point(2, 2), Point(3, 3)], crs=4326) >>> s 0 POINT (1 1) 1 POINT (2 2) 2 POINT (3 3) dtype: geometry >>> s.crs # doctest: +SKIP Name: WGS 84 Axis Info [ellipsoidal]: - Lat[north]: Geodetic latitude (degree) - Lon[east]: Geodetic longitude (degree) Area of Use: - name: World - bounds: (-180.0, -90.0, 180.0, 90.0) Datum: World Geodetic System 1984 - Ellipsoid: WGS 84 - Prime Meridian: Greenwich >>> s = s.to_crs(3857) >>> s 0 POINT (111319.491 111325.143) 1 POINT (222638.982 222684.209) 2 POINT (333958.472 334111.171) dtype: geometry >>> s.crs # doctest: +SKIP Name: WGS 84 / Pseudo-Mercator Axis Info [cartesian]: - X[east]: Easting (metre) - Y[north]: Northing (metre) Area of Use: - name: World - 85°S to 85°N - bounds: (-180.0, -85.06, 180.0, 85.06) Coordinate Operation: - name: Popular Visualisation Pseudo-Mercator - method: Popular Visualisation Pseudo Mercator Datum: World Geodetic System 1984 - Ellipsoid: WGS 84 - Prime Meridian: Greenwich See Also -------- GeoSeries.set_crs : assign CRS """ return GeoSeries( self.values.to_crs(crs=crs, epsg=epsg), index=self.index, name=self.name ) def estimate_utm_crs(self, datum_name: str = "WGS 84"): """Return the estimated UTM CRS based on the bounds of the dataset. .. versionadded:: 0.9 Parameters ---------- datum_name : str, optional The name of the datum to use in the query. Default is WGS 84. Returns ------- pyproj.CRS Examples -------- >>> import geodatasets >>> df = geopandas.read_file( ... geodatasets.get_path("geoda.chicago_health") ... ) >>> df.geometry.estimate_utm_crs() # doctest: +SKIP Name: WGS 84 / UTM zone 16N Axis Info [cartesian]: - E[east]: Easting (metre) - N[north]: Northing (metre) Area of Use: - name: Between 90°W and 84°W, northern hemisphere between equator and 84°N, ... - bounds: (-90.0, 0.0, -84.0, 84.0) Coordinate Operation: - name: UTM zone 16N - method: Transverse Mercator Datum: World Geodetic System 1984 ensemble - Ellipsoid: WGS 84 - Prime Meridian: Greenwich """ return self.values.estimate_utm_crs(datum_name) def to_json( self, show_bbox: bool = True, drop_id: bool = False, to_wgs84: bool = False, **kwargs, ) -> str: """Return a GeoJSON string representation of the GeoSeries. Parameters ---------- show_bbox : bool, optional, default: True Include bbox (bounds) in the geojson drop_id : bool, default: False Whether to retain the index of the GeoSeries as the id property in the generated GeoJSON. Default is False, but may want True if the index is just arbitrary row numbers. to_wgs84: bool, optional, default: False If the CRS is set on the active geometry column it is exported as WGS84 (EPSG:4326) to meet the `2016 GeoJSON specification `_. Set to True to force re-projection and set to False to ignore CRS. False by default. *kwargs* that will be passed to json.dumps(). Returns ------- JSON string Examples -------- >>> from shapely.geometry import Point >>> s = geopandas.GeoSeries([Point(1, 1), Point(2, 2), Point(3, 3)]) >>> s 0 POINT (1 1) 1 POINT (2 2) 2 POINT (3 3) dtype: geometry >>> s.to_json() '{"type": "FeatureCollection", "features": [{"id": "0", "type": "Feature", "pr\ operties": {}, "geometry": {"type": "Point", "coordinates": [1.0, 1.0]}, "bbox": [1.0,\ 1.0, 1.0, 1.0]}, {"id": "1", "type": "Feature", "properties": {}, "geometry": {"type"\ : "Point", "coordinates": [2.0, 2.0]}, "bbox": [2.0, 2.0, 2.0, 2.0]}, {"id": "2", "typ\ e": "Feature", "properties": {}, "geometry": {"type": "Point", "coordinates": [3.0, 3.\ 0]}, "bbox": [3.0, 3.0, 3.0, 3.0]}], "bbox": [1.0, 1.0, 3.0, 3.0]}' See Also -------- GeoSeries.to_file : write GeoSeries to file """ return self.to_frame("geometry").to_json( na="null", show_bbox=show_bbox, drop_id=drop_id, to_wgs84=to_wgs84, **kwargs ) def to_wkb(self, hex: bool = False, **kwargs) -> Series: r"""Convert GeoSeries geometries to WKB. Parameters ---------- hex : bool If true, export the WKB as a hexadecimal string. The default is to return a binary bytes object. kwargs Additional keyword args will be passed to :func:`shapely.to_wkb`. Returns ------- Series WKB representations of the geometries See Also -------- GeoSeries.to_wkt Examples -------- >>> from shapely.geometry import Point, Polygon >>> s = geopandas.GeoSeries( ... [ ... Point(0, 0), ... Polygon(), ... Polygon([(0, 0), (1, 1), (1, 0)]), ... None, ... ] ... ) >>> s.to_wkb() 0 b'\x01\x01\x00\x00\x00\x00\x00\x00\x00\x00\x00... 1 b'\x01\x03\x00\x00\x00\x00\x00\x00\x00' 2 b'\x01\x03\x00\x00\x00\x01\x00\x00\x00\x04\x00... 3 None dtype: object >>> s.to_wkb(hex=True) 0 010100000000000000000000000000000000000000 1 010300000000000000 2 0103000000010000000400000000000000000000000000... 3 NaN dtype: str Notes ----- The Well-Known Binary (WKB) specification does not support all variations of geometry types that GeoPandas does, and some geometries may not be serialised without information loss. Notably: - LinearRing will be converted to LineString - Empty Points will be converted to Points with NaN as coordinates (but will be read by :meth:`from_wkb` as empty Points). """ return Series(to_wkb(self.array, hex=hex, **kwargs), index=self.index) def to_wkt(self, **kwargs) -> Series: """Convert GeoSeries geometries to WKT. Parameters ---------- kwargs Keyword args will be passed to :func:`shapely.to_wkt`. Returns ------- Series WKT representations of the geometries Examples -------- >>> from shapely.geometry import Point >>> s = geopandas.GeoSeries([Point(1, 1), Point(2, 2), Point(3, 3)]) >>> s 0 POINT (1 1) 1 POINT (2 2) 2 POINT (3 3) dtype: geometry >>> s.to_wkt() 0 POINT (1 1) 1 POINT (2 2) 2 POINT (3 3) dtype: str See Also -------- GeoSeries.to_wkb """ return Series(to_wkt(self.array, **kwargs), index=self.index) def to_arrow(self, geometry_encoding="WKB", interleaved=True, include_z=None): """Encode a GeoSeries to GeoArrow format. See https://geoarrow.org/ for details on the GeoArrow specification. This functions returns a generic Arrow array object implementing the `Arrow PyCapsule Protocol`_ (i.e. having an ``__arrow_c_array__`` method). This object can then be consumed by your Arrow implementation of choice that supports this protocol. .. _Arrow PyCapsule Protocol: https://arrow.apache.org/docs/format/CDataInterface/PyCapsuleInterface.html .. versionadded:: 1.0 Parameters ---------- geometry_encoding : {'WKB', 'geoarrow' }, default 'WKB' The GeoArrow encoding to use for the data conversion. interleaved : bool, default True Only relevant for 'geoarrow' encoding. If True, the geometries' coordinates are interleaved in a single fixed size list array. If False, the coordinates are stored as separate arrays in a struct type. include_z : bool, default None Only relevant for 'geoarrow' encoding (for WKB, the dimensionality of the individual geometries is preserved). If False, return 2D geometries. If True, include the third dimension in the output (if a geometry has no third dimension, the z-coordinates will be NaN). By default, will infer the dimensionality from the input geometries. Note that this inference can be unreliable with empty geometries (for a guaranteed result, it is recommended to specify the keyword). Returns ------- GeoArrowArray A generic Arrow array object with geometry data encoded to GeoArrow. Examples -------- >>> from shapely.geometry import Point >>> gser = geopandas.GeoSeries([Point(1, 2), Point(2, 1)]) >>> gser 0 POINT (1 2) 1 POINT (2 1) dtype: geometry >>> arrow_array = gser.to_arrow() >>> arrow_array The returned array object needs to be consumed by a library implementing the Arrow PyCapsule Protocol. For example, wrapping the data as a pyarrow.Array (requires pyarrow >= 14.0): >>> import pyarrow as pa >>> array = pa.array(arrow_array) >>> array GeometryExtensionArray:WkbType(geoarrow.wkb)[2] """ from geopandas.io._geoarrow import ( GeoArrowArray, construct_geometry_array, construct_wkb_array, ) field_name = self.name if self.name is not None else "" if geometry_encoding.lower() == "geoarrow": field, geom_arr = construct_geometry_array( np.array(self.array), include_z=include_z, field_name=field_name, crs=self.crs, interleaved=interleaved, ) elif geometry_encoding.lower() == "wkb": field, geom_arr = construct_wkb_array( np.asarray(self.array), field_name=field_name, crs=self.crs ) else: raise ValueError( "Expected geometry encoding 'WKB' or 'geoarrow' " f"got {geometry_encoding}" ) return GeoArrowArray(field, geom_arr) def clip(self, mask, keep_geom_type: bool = False, sort=False) -> GeoSeries: """Clip points, lines, or polygon geometries to the mask extent. Both layers must be in the same Coordinate Reference System (CRS). The GeoSeries will be clipped to the full extent of the `mask` object. If there are multiple polygons in mask, data from the GeoSeries will be clipped to the total boundary of all polygons in mask. Parameters ---------- mask : GeoDataFrame, GeoSeries, (Multi)Polygon, list-like Polygon vector layer used to clip `gdf`. The mask's geometry is dissolved into one geometric feature and intersected with GeoSeries. If the mask is list-like with four elements ``(minx, miny, maxx, maxy)``, ``clip`` will use a faster rectangle clipping (:meth:`~GeoSeries.clip_by_rect`), possibly leading to slightly different results. keep_geom_type : boolean, default False If True, return only geometries of original type in case of intersection resulting in multiple geometry types or GeometryCollections. If False, return all resulting geometries (potentially mixed-types). sort : boolean, default False If True, the order of rows in the clipped GeoSeries will be preserved at small performance cost. If False the order of rows in the clipped GeoSeries will be random. Returns ------- GeoSeries Vector data (points, lines, polygons) from `gdf` clipped to polygon boundary from mask. See Also -------- clip : top-level function for clip Examples -------- Clip points (grocery stores) with polygons (the Near West Side community): >>> import geodatasets >>> chicago = geopandas.read_file( ... geodatasets.get_path("geoda.chicago_health") ... ) >>> near_west_side = chicago[chicago["community"] == "NEAR WEST SIDE"] >>> groceries = geopandas.read_file( ... geodatasets.get_path("geoda.groceries") ... ).to_crs(chicago.crs) >>> groceries.shape (148, 8) >>> nws_groceries = groceries.geometry.clip(near_west_side) >>> nws_groceries.shape (7,) """ return geopandas.clip(self, mask=mask, keep_geom_type=keep_geom_type, sort=sort) geopandas-1.2.0/geopandas/io/000077500000000000000000000000001525650651600160365ustar00rootroot00000000000000geopandas-1.2.0/geopandas/io/__init__.py000066400000000000000000000000001525650651600201350ustar00rootroot00000000000000geopandas-1.2.0/geopandas/io/_geoarrow.py000066400000000000000000000503301525650651600203750ustar00rootroot00000000000000import json import numpy as np import pandas as pd import pyarrow as pa from numpy.typing import NDArray import shapely from shapely import GeometryType from geopandas import GeoDataFrame from geopandas.array import from_shapely, from_wkb GEOARROW_ENCODINGS = [ "point", "linestring", "polygon", "multipoint", "multilinestring", "multipolygon", ] ## GeoPandas -> GeoArrow class ArrowTable: """ Wrapper class for Arrow data. This class implements the `Arrow PyCapsule Protocol`_ (i.e. having an ``__arrow_c_stream__`` method). This object can then be consumed by your Arrow implementation of choice that supports this protocol. .. _Arrow PyCapsule Protocol: https://arrow.apache.org/docs/format/CDataInterface/PyCapsuleInterface.html Example ------- >>> import pyarrow as pa >>> pa.table(gdf.to_arrow()) # doctest: +SKIP """ def __init__(self, pa_table): self._pa_table = pa_table def __arrow_c_stream__(self, requested_schema=None): return self._pa_table.__arrow_c_stream__(requested_schema=requested_schema) class GeoArrowArray: """ Wrapper class for a geometry array as Arrow data. This class implements the `Arrow PyCapsule Protocol`_ (i.e. having an ``__arrow_c_array/stream__`` method). This object can then be consumed by your Arrow implementation of choice that supports this protocol. .. _Arrow PyCapsule Protocol: https://arrow.apache.org/docs/format/CDataInterface/PyCapsuleInterface.html Example ------- >>> import pyarrow as pa >>> pa.array(ser.to_arrow()) # doctest: +SKIP """ def __init__(self, pa_field, pa_array): self._pa_array = pa_array self._pa_field = pa_field def __arrow_c_array__(self, requested_schema=None): if requested_schema is not None: raise NotImplementedError( "Requested schema is not supported for geometry arrays" ) return ( self._pa_field.__arrow_c_schema__(), self._pa_array.__arrow_c_array__()[1], ) def geopandas_to_arrow( df, index=None, geometry_encoding="WKB", interleaved=True, include_z=None, ): """ Convert GeoDataFrame to a pyarrow.Table. Parameters ---------- df : GeoDataFrame The GeoDataFrame to convert. index : bool, default None If ``True``, always include the dataframe's index(es) as columns in the file output. If ``False``, the index(es) will not be written to the file. If ``None``, the index(ex) will be included as columns in the file output except `RangeIndex` which is stored as metadata only. geometry_encoding : {'WKB', 'geoarrow' }, default 'WKB' The GeoArrow encoding to use for the data conversion. interleaved : bool, default True Only relevant for 'geoarrow' encoding. If True, the geometries' coordinates are interleaved in a single fixed size list array. If False, the coordinates are stored as separate arrays in a struct type. include_z : bool, default None Only relevant for 'geoarrow' encoding (for WKB, the dimensionality of the individual geometries is preserved). If False, return 2D geometries. If True, include the third dimension in the output (if a geometry has no third dimension, the z-coordinates will be NaN). By default, will infer the dimensionality from the input geometries. Note that this inference can be unreliable with empty geometries (for a guaranteed result, it is recommended to specify the keyword). """ mask = df.dtypes == "geometry" geometry_columns = df.columns[mask] geometry_indices = np.asarray(mask).nonzero()[0] df_attr = pd.DataFrame(df.copy(deep=False)) # replace geometry columns with dummy values -> will get converted to # Arrow null column (not holding any memory), so we can afterwards # fill the resulting table with the correct geometry fields for col in geometry_columns: df_attr[col] = None table = pa.Table.from_pandas(df_attr, preserve_index=index) geometry_encoding_dict = {} if geometry_encoding.lower() == "geoarrow": # Encode all geometry columns to GeoArrow for i, col in zip(geometry_indices, geometry_columns): field, geom_arr = construct_geometry_array( np.array(df[col].array), include_z=include_z, field_name=col, crs=df[col].crs, interleaved=interleaved, ) table = table.set_column(i, field, geom_arr) geometry_encoding_dict[col] = ( field.metadata[b"ARROW:extension:name"] .decode() .removeprefix("geoarrow.") ) elif geometry_encoding.lower() == "wkb": # Encode all geometry columns to WKB for i, col in zip(geometry_indices, geometry_columns): field, wkb_arr = construct_wkb_array( np.asarray(df[col].array), field_name=col, crs=df[col].crs ) table = table.set_column(i, field, wkb_arr) geometry_encoding_dict[col] = "WKB" else: raise ValueError( f"Expected geometry encoding 'WKB' or 'geoarrow' got {geometry_encoding}" ) return table, geometry_encoding_dict def construct_wkb_array( shapely_arr: NDArray[np.object_], *, field_name: str = "geometry", crs: str | None = None, ) -> tuple[pa.Field, pa.Array]: if shapely.geos_version > (3, 10, 0): kwargs = {"flavor": "iso"} else: if shapely.has_z(shapely_arr).any(): raise ValueError("Cannot write 3D geometries with GEOS<3.10") kwargs = {} wkb_arr = shapely.to_wkb(shapely_arr, **kwargs) extension_metadata = {"ARROW:extension:name": "geoarrow.wkb"} if crs is not None: extension_metadata["ARROW:extension:metadata"] = json.dumps( {"crs": crs.to_json_dict()} ) else: # In theory this should not be needed, but otherwise pyarrow < 17 # crashes on receiving such data through C Data Interface # https://github.com/apache/arrow/issues/41741 extension_metadata["ARROW:extension:metadata"] = "{}" field = pa.field( field_name, type=pa.binary(), nullable=True, metadata=extension_metadata ) parr = pa.array(np.asarray(wkb_arr), pa.binary()) return field, parr def _convert_inner_coords(coords, interleaved, dims, mask=None): if interleaved: coords_field = pa.field(dims, pa.float64(), nullable=False) typ = pa.list_(coords_field, len(dims)) if mask is None: # mask keyword only added in pyarrow 15.0.0 parr = pa.FixedSizeListArray.from_arrays(coords.ravel(), type=typ) else: parr = pa.FixedSizeListArray.from_arrays( coords.ravel(), type=typ, mask=mask ) else: if dims == "xy": fields = [ pa.field("x", pa.float64(), nullable=False), pa.field("y", pa.float64(), nullable=False), ] parr = pa.StructArray.from_arrays( [coords[:, 0].copy(), coords[:, 1].copy()], fields=fields, mask=mask ) else: fields = [ pa.field("x", pa.float64(), nullable=False), pa.field("y", pa.float64(), nullable=False), pa.field("z", pa.float64(), nullable=False), ] parr = pa.StructArray.from_arrays( [coords[:, 0].copy(), coords[:, 1].copy(), coords[:, 2].copy()], fields=fields, mask=mask, ) return parr def _linestring_type(point_type): return pa.list_(pa.field("vertices", point_type, nullable=False)) def _polygon_type(point_type): return pa.list_( pa.field( "rings", pa.list_(pa.field("vertices", point_type, nullable=False)), nullable=False, ) ) def _multipoint_type(point_type): return pa.list_(pa.field("points", point_type, nullable=False)) def _multilinestring_type(point_type): return pa.list_( pa.field("linestrings", _linestring_type(point_type), nullable=False) ) def _multipolygon_type(point_type): return pa.list_(pa.field("polygons", _polygon_type(point_type), nullable=False)) def construct_geometry_array( shapely_arr: NDArray[np.object_], include_z: bool | None = None, *, field_name: str = "geometry", crs: str | None = None, interleaved: bool = True, ) -> tuple[pa.Field, pa.Array]: # NOTE: this implementation returns a (field, array) pair so that it can set the # extension metadata on the field without instantiating extension types into the # global pyarrow registry mask = shapely.is_missing(shapely_arr) if len(shapely_arr) == 0 or mask.all(): raise NotImplementedError( "Converting an empty or all-missing GeoDataFrame to the 'geoarrow' " "encoding is not yet supported." ) geom_type, coords, offsets = shapely.to_ragged_array( shapely_arr, include_z=include_z ) if mask.any(): mask = pa.array(mask, type=pa.bool_()) else: mask = None if coords.shape[-1] == 2: dims = "xy" elif coords.shape[-1] == 3: dims = "xyz" else: raise ValueError(f"Unexpected coords dimensions: {coords.shape}") extension_metadata: dict[str, str] = {} if crs is not None: extension_metadata["ARROW:extension:metadata"] = json.dumps( {"crs": crs.to_json_dict()} ) else: # In theory this should not be needed, but otherwise pyarrow < 17 # crashes on receiving such data through C Data Interface # https://github.com/apache/arrow/issues/41741 extension_metadata["ARROW:extension:metadata"] = "{}" if geom_type == GeometryType.POINT: parr = _convert_inner_coords(coords, interleaved, dims, mask=mask) extension_metadata["ARROW:extension:name"] = "geoarrow.point" field = pa.field( field_name, parr.type, nullable=True, metadata=extension_metadata, ) return field, parr elif geom_type == GeometryType.LINESTRING: assert len(offsets) == 1, "Expected one offsets array" (geom_offsets,) = offsets _parr = _convert_inner_coords(coords, interleaved, dims) parr = pa.ListArray.from_arrays( pa.array(geom_offsets), _parr, _linestring_type(_parr.type), mask=mask ) extension_metadata["ARROW:extension:name"] = "geoarrow.linestring" field = pa.field( field_name, parr.type, nullable=True, metadata=extension_metadata, ) return field, parr elif geom_type == GeometryType.POLYGON: assert len(offsets) == 2, "Expected two offsets arrays" ring_offsets, geom_offsets = offsets _parr = _convert_inner_coords(coords, interleaved, dims) _parr1 = pa.ListArray.from_arrays(pa.array(ring_offsets), _parr) parr = pa.ListArray.from_arrays(pa.array(geom_offsets), _parr1, mask=mask) parr = parr.cast(_polygon_type(_parr.type)) extension_metadata["ARROW:extension:name"] = "geoarrow.polygon" field = pa.field( field_name, parr.type, nullable=True, metadata=extension_metadata, ) return field, parr elif geom_type == GeometryType.MULTIPOINT: assert len(offsets) == 1, "Expected one offsets array" (geom_offsets,) = offsets _parr = _convert_inner_coords(coords, interleaved, dims) parr = pa.ListArray.from_arrays( pa.array(geom_offsets), _parr, type=_multipoint_type(_parr.type), mask=mask ) extension_metadata["ARROW:extension:name"] = "geoarrow.multipoint" field = pa.field( field_name, parr.type, nullable=True, metadata=extension_metadata, ) return field, parr elif geom_type == GeometryType.MULTILINESTRING: assert len(offsets) == 2, "Expected two offsets arrays" ring_offsets, geom_offsets = offsets _parr = _convert_inner_coords(coords, interleaved, dims) _parr1 = pa.ListArray.from_arrays(pa.array(ring_offsets), _parr) parr = pa.ListArray.from_arrays(pa.array(geom_offsets), _parr1, mask=mask) parr = parr.cast(_multilinestring_type(_parr.type)) extension_metadata["ARROW:extension:name"] = "geoarrow.multilinestring" field = pa.field( field_name, parr.type, nullable=True, metadata=extension_metadata, ) return field, parr elif geom_type == GeometryType.MULTIPOLYGON: assert len(offsets) == 3, "Expected three offsets arrays" ring_offsets, polygon_offsets, geom_offsets = offsets _parr = _convert_inner_coords(coords, interleaved, dims) _parr1 = pa.ListArray.from_arrays(pa.array(ring_offsets), _parr) _parr2 = pa.ListArray.from_arrays(pa.array(polygon_offsets), _parr1) parr = pa.ListArray.from_arrays(pa.array(geom_offsets), _parr2, mask=mask) parr = parr.cast(_multipolygon_type(_parr.type)) extension_metadata["ARROW:extension:name"] = "geoarrow.multipolygon" field = pa.field( field_name, parr.type, nullable=True, metadata=extension_metadata, ) return field, parr else: raise ValueError(f"Unsupported type for geoarrow: {geom_type}") ## GeoArrow -> GeoPandas def _get_arrow_geometry_field(field): if (meta := field.metadata) is not None: if (ext_name := meta.get(b"ARROW:extension:name", None)) is not None: if ext_name.startswith(b"geoarrow."): if ( ext_meta := meta.get(b"ARROW:extension:metadata", None) ) is not None: ext_meta = json.loads(ext_meta.decode()) return ext_name.decode(), ext_meta if isinstance(field.type, pa.ExtensionType): ext_name = field.type.extension_name if ext_name.startswith("geoarrow."): ext_meta_ser = field.type.__arrow_ext_serialize__() if ext_meta_ser: ext_meta = json.loads(ext_meta_ser.decode()) else: ext_meta = None return ext_name, ext_meta return None def arrow_to_geopandas(table, geometry=None, to_pandas_kwargs=None): """ Convert Arrow table object to a GeoDataFrame based on GeoArrow extension types. Parameters ---------- table : pyarrow.Table The Arrow table to convert. geometry : str, default None The name of the geometry column to set as the active geometry column. If None, the first geometry column found will be used. to_pandas_kwargs : dict, optional Arguments passed to the `pa.Table.to_pandas` method for non-geometry columns. This can be used to control the behavior of the conversion of the non-geometry columns to a pandas DataFrame. For example, you can use this to control the dtype conversion of the columns. By default, the `to_pandas` method is called with no additional arguments. Returns ------- GeoDataFrame """ if not isinstance(table, pa.Table): table = pa.table(table) geom_fields = [] for i, field in enumerate(table.schema): geom = _get_arrow_geometry_field(field) if geom is not None: geom_fields.append((i, field.name, *geom)) if len(geom_fields) == 0: raise ValueError("No geometry column found in the Arrow table.") table_attr = table.drop([f[1] for f in geom_fields]) if to_pandas_kwargs is None: to_pandas_kwargs = {} df = table_attr.to_pandas(**to_pandas_kwargs) for i, col, ext_name, ext_meta in geom_fields: crs = None if ext_meta is not None and "crs" in ext_meta: crs = ext_meta["crs"] if ext_name == "geoarrow.wkb": geom_arr = from_wkb(np.array(table[col]), crs=crs) elif ext_name.split(".")[1] in GEOARROW_ENCODINGS: geom_arr = from_shapely( construct_shapely_array(table[col].combine_chunks(), ext_name), crs=crs ) else: raise TypeError(f"Unknown GeoArrow extension type: {ext_name}") df.insert(i, col, geom_arr) return GeoDataFrame(df, geometry=geometry or geom_fields[0][1]) def arrow_to_geometry_array(arr): """ Convert Arrow array object (representing single GeoArrow array) to a geopandas GeometryArray. Specifically for GeoSeries.from_arrow. """ schema_capsule, array_capsule = arr.__arrow_c_array__() field = pa.Field._import_from_c_capsule(schema_capsule) pa_arr = pa.Array._import_from_c_capsule(field.__arrow_c_schema__(), array_capsule) geom_info = _get_arrow_geometry_field(field) if geom_info is None: raise ValueError("No GeoArrow geometry field found.") ext_name, ext_meta = geom_info crs = None if ext_meta is not None and "crs" in ext_meta: crs = ext_meta["crs"] if ext_name == "geoarrow.wkb": geom_arr = from_wkb(np.array(pa_arr), crs=crs) elif ext_name.split(".")[1] in GEOARROW_ENCODINGS: geom_arr = from_shapely(construct_shapely_array(pa_arr, ext_name), crs=crs) else: raise ValueError(f"Unknown GeoArrow extension type: {ext_name}") return geom_arr def _get_inner_coords(arr): if pa.types.is_struct(arr.type): if arr.type.num_fields == 2: coords = np.column_stack( [np.asarray(arr.field("x")), np.asarray(arr.field("y"))] ) else: coords = np.column_stack( [ np.asarray(arr.field("x")), np.asarray(arr.field("y")), np.asarray(arr.field("z")), ] ) return coords else: # fixed size list return np.asarray(arr.values).reshape(len(arr), -1) def construct_shapely_array(arr: pa.Array, extension_name: str): """ Construct a NumPy array of shapely geometries from a pyarrow.Array with GeoArrow extension type. """ if isinstance(arr, pa.ExtensionArray): arr = arr.storage if extension_name == "geoarrow.point": coords = _get_inner_coords(arr) result = shapely.from_ragged_array(GeometryType.POINT, coords, None) elif extension_name == "geoarrow.linestring": coords = _get_inner_coords(arr.values) offsets1 = np.asarray(arr.offsets) offsets = (offsets1,) result = shapely.from_ragged_array(GeometryType.LINESTRING, coords, offsets) elif extension_name == "geoarrow.polygon": coords = _get_inner_coords(arr.values.values) offsets2 = np.asarray(arr.offsets) offsets1 = np.asarray(arr.values.offsets) offsets = (offsets1, offsets2) result = shapely.from_ragged_array(GeometryType.POLYGON, coords, offsets) elif extension_name == "geoarrow.multipoint": coords = _get_inner_coords(arr.values) offsets1 = np.asarray(arr.offsets) offsets = (offsets1,) result = shapely.from_ragged_array(GeometryType.MULTIPOINT, coords, offsets) elif extension_name == "geoarrow.multilinestring": coords = _get_inner_coords(arr.values.values) offsets2 = np.asarray(arr.offsets) offsets1 = np.asarray(arr.values.offsets) offsets = (offsets1, offsets2) result = shapely.from_ragged_array( GeometryType.MULTILINESTRING, coords, offsets ) elif extension_name == "geoarrow.multipolygon": coords = _get_inner_coords(arr.values.values.values) offsets3 = np.asarray(arr.offsets) offsets2 = np.asarray(arr.values.offsets) offsets1 = np.asarray(arr.values.values.offsets) offsets = (offsets1, offsets2, offsets3) result = shapely.from_ragged_array(GeometryType.MULTIPOLYGON, coords, offsets) else: raise ValueError(extension_name) # apply validity mask if arr.null_count: mask = np.asarray(arr.is_null()) result = np.where(mask, None, result) return result geopandas-1.2.0/geopandas/io/arrow.py000066400000000000000000001211241525650651600175430ustar00rootroot00000000000000import contextlib import json import warnings from packaging.version import Version from typing import Literal, get_args import numpy as np from pandas import DataFrame, Series import shapely import geopandas from geopandas import GeoDataFrame from geopandas._compat import import_optional_dependency from geopandas.array import from_shapely, from_wkb from .file import _expand_user METADATA_VERSION = "1.1.0" SUPPORTED_VERSIONS_LITERAL = Literal[ "0.1.0", "0.4.0", "1.0.0-beta.1", "1.0.0", "1.1.0", "2.0.0" ] SUPPORTED_VERSIONS = list(get_args(SUPPORTED_VERSIONS_LITERAL)) GEOARROW_ENCODINGS = [ "point", "linestring", "polygon", "multipoint", "multilinestring", "multipolygon", ] SUPPORTED_ENCODINGS = ["WKB"] + GEOARROW_ENCODINGS PARQUET_GEOMETRY_ENCODINGS = Literal["WKB", "geoarrow"] # reference: https://github.com/opengeospatial/geoparquet # Metadata structure: # { # "geo": { # "columns": { # "": { # "encoding": "WKB" # "geometry_types": # "crs": "", # "orientation": "<'counterclockwise' or None: OPTIONAL>" # "edges": "planar" # "bbox": # "epoch": # } # }, # "primary_column": "", # "version": "", # # # Additional GeoPandas specific metadata (not in metadata spec) # "creator": { # "library": "geopandas", # "version": "" # } # } # } def _is_fsspec_url(url): return isinstance(url, str) and "://" in url def _remove_id_from_member_of_ensembles(json_dict): """ Older PROJ versions will not recognize IDs of datum ensemble members that were added in more recent PROJ database versions. Cf https://github.com/opengeospatial/geoparquet/discussions/110 and https://github.com/OSGeo/PROJ/pull/3221 Mimicking the patch to GDAL from https://github.com/OSGeo/gdal/pull/5872 """ for key, value in json_dict.items(): if isinstance(value, dict): _remove_id_from_member_of_ensembles(value) elif key == "members" and isinstance(value, list): for member in value: member.pop("id", None) # type ids 0 to 7 _geometry_type_names = [ "Point", "LineString", "LineString", "Polygon", "MultiPoint", "MultiLineString", "MultiPolygon", "GeometryCollection", ] _geometry_type_names += [geom_type + " Z" for geom_type in _geometry_type_names] def _get_geometry_types(series): """Get unique geometry types from a GeoSeries.""" arr_geometry_types = shapely.get_type_id(series.array._data) # ensure to include "... Z" for 3D geometries has_z = shapely.has_z(series.array._data) arr_geometry_types[has_z] += 8 geometry_types = Series(arr_geometry_types).unique().tolist() # drop missing values (shapely.get_type_id returns -1 for those) if -1 in geometry_types: geometry_types.remove(-1) return sorted([_geometry_type_names[idx] for idx in geometry_types]) def _create_metadata( df, schema_version=None, geometry_encoding=None, write_covering_bbox=False ): """Create and encode geo metadata dict. Parameters ---------- df : GeoDataFrame schema_version : {'0.1.0', '0.4.0', '1.0.0-beta.1', '1.0.0', '1.1.0', '2.0.0', None} GeoParquet specification version; if not provided will default to latest supported version. geometry_encoding : dict, default None GeoParquet encoding per geometry column. Defaults to "WKB" for columns that are not present in the dictionary. write_covering_bbox : bool, default False Writes the bounding box column for each row entry with column name 'bbox'. Writing a bbox column can be computationally expensive, hence is default setting is False. Returns ------- dict """ if schema_version is None: schema_version = METADATA_VERSION if schema_version not in SUPPORTED_VERSIONS: raise ValueError( f"schema_version must be one of: {', '.join(SUPPORTED_VERSIONS)}" ) if write_covering_bbox and schema_version < "1.1.0": raise ValueError( "Writing a bounding box column is only supported since GeoParquet 1.1.0, " f"while a {schema_version=} is specified." ) # Construct metadata for each geometry column_metadata = {} for col in df.columns[df.dtypes == "geometry"]: series = df[col] geometry_types = _get_geometry_types(series) if schema_version[0] == "0": geometry_types_name = "geometry_type" if len(geometry_types) == 1: geometry_types = geometry_types[0] else: geometry_types_name = "geometry_types" crs = None if series.crs: if schema_version == "0.1.0": crs = series.crs.to_wkt() else: # version >= 0.4.0 crs = series.crs.to_json_dict() _remove_id_from_member_of_ensembles(crs) encoding = ( geometry_encoding[col] if geometry_encoding and col in geometry_encoding else "WKB" ) if schema_version == "2.0.0" and encoding != "WKB": raise ValueError( "When writing GeoParquet 2.0.0 files, only the 'WKB' encoding is " "supported. Specify `schema_version='1.1.0'` to use the 'geoarrow' " "encoding." ) column_metadata[col] = { "encoding": encoding, "crs": crs, geometry_types_name: geometry_types, } bbox = series.total_bounds.tolist() if np.isfinite(bbox).all(): # don't add bbox with NaNs for empty / all-NA geometry column column_metadata[col]["bbox"] = bbox if write_covering_bbox: column_metadata[col]["covering"] = { "bbox": { "xmin": ["bbox", "xmin"], "ymin": ["bbox", "ymin"], "xmax": ["bbox", "xmax"], "ymax": ["bbox", "ymax"], }, } return { "primary_column": df._geometry_column_name, "columns": column_metadata, "version": schema_version, "creator": {"library": "geopandas", "version": geopandas.__version__}, } def _encode_metadata(metadata): """Encode metadata dict to UTF-8 JSON string. Parameters ---------- metadata : dict Returns ------- UTF-8 encoded JSON string """ return json.dumps(metadata).encode("utf-8") def _decode_metadata(metadata_str): """Decode a UTF-8 encoded JSON string to dict. Parameters ---------- metadata_str : string (UTF-8 encoded) Returns ------- dict """ if metadata_str is None: return None return json.loads(metadata_str.decode("utf-8")) def _validate_dataframe(df): """Validate that the GeoDataFrame conforms to requirements for writing to Parquet format. Raises `ValueError` if the GeoDataFrame is not valid. copied from `pandas.io.parquet` Parameters ---------- df : GeoDataFrame """ if not isinstance(df, DataFrame): raise ValueError("Writing to Parquet/Feather only supports IO with DataFrames") # must have value column names (strings only) if df.columns.inferred_type not in {"string", "unicode", "empty"}: raise ValueError("Writing to Parquet/Feather requires string column names") # index level names must be strings valid_names = all( isinstance(name, str) for name in df.index.names if name is not None ) if not valid_names: raise ValueError("Index level names must be strings") def _validate_geo_metadata(metadata): """Validate geo metadata. Must not be empty, and must contain the structure specified above. Raises ValueError if metadata is not valid. Parameters ---------- metadata : dict """ if not metadata: raise ValueError("Missing or malformed geo metadata in Parquet/Feather file") # version was schema_version in 0.1.0 version = metadata.get("version", metadata.get("schema_version")) if not version: raise ValueError( "'geo' metadata in Parquet/Feather file is missing required key: 'version'" ) required_keys = ("primary_column", "columns") for key in required_keys: if metadata.get(key, None) is None: raise ValueError( "'geo' metadata in Parquet/Feather file is missing required key: " f"'{key}'" ) if not isinstance(metadata["columns"], dict): raise ValueError("'columns' in 'geo' metadata must be a dict") # Validate that geometry columns have required metadata and values # leaving out "geometry_type" for compatibility with 0.1 required_col_keys = ("encoding",) for col, column_metadata in metadata["columns"].items(): for key in required_col_keys: if key not in column_metadata: raise ValueError( "'geo' metadata in Parquet/Feather file is missing required key " f"'{key}' for column '{col}'" ) if column_metadata["encoding"] not in SUPPORTED_ENCODINGS: raise ValueError( "Only WKB geometry encoding or one of the native encodings " f"({GEOARROW_ENCODINGS!r}) are supported, " f"got: {column_metadata['encoding']}" ) if column_metadata.get("edges", "planar") == "spherical": warnings.warn( f"The geo metadata indicate that column '{col}' has spherical edges, " "but because GeoPandas currently does not support spherical " "geometry, it ignores this metadata and will interpret the edges of " "the geometries as planar.", UserWarning, stacklevel=4, ) if "covering" in column_metadata: covering = column_metadata["covering"] if "bbox" in covering: bbox = covering["bbox"] for var in ["xmin", "ymin", "xmax", "ymax"]: if var not in bbox.keys(): raise ValueError("Metadata for bbox column is malformed.") @contextlib.contextmanager def _ensure_geoarrow_wkb_ext_type_registered(): """Ensure that the GeoArrow WKB extension type is registered. Either uses an already registered extension type, or temporarily registers a small dummy one with enough functionality for the writing to succeed. This is needed for writing GeoParquet 2.0 files to ensure the pyarrow.parquet writer will use the Geometry logical type. It does only do that for input that actually uses an extension type (just a binary field with the extension type metadata is not sufficient to trigger the logical type). """ import pyarrow class _WkbExtensionType(pyarrow.ExtensionType): def __init__(self, metadata=b""): self._metadata = metadata super().__init__( pyarrow.binary(), "geoarrow.wkb", ) def __arrow_ext_serialize__(self) -> bytes: return self._metadata @classmethod def __arrow_ext_deserialize__(cls, storage_type, serialized): return _WkbExtensionType(serialized) try: pyarrow.register_extension_type(_WkbExtensionType()) except Exception: # already registered by other package -> use that one yield return # otherwise we have registered our dummy -> unregister at the end try: yield finally: pyarrow.unregister_extension_type("geoarrow.wkb") def _geopandas_to_arrow( df, index=None, geometry_encoding="WKB", schema_version=None, write_covering_bbox=None, ): """Convert a GeoDataFrame to a pyarrow Table. Helper function with main, shared logic for to_parquet/to_feather. """ import pyarrow as pa from pyarrow import StructArray from geopandas.io._geoarrow import geopandas_to_arrow _validate_dataframe(df) if schema_version is not None: if geometry_encoding != "WKB" and schema_version != "1.1.0": raise ValueError( "'geoarrow' encoding is only supported with schema version >= 1.1.0" ) if schema_version == "2.0.0" and Version(pa.__version__) < Version("21.0.0"): raise ValueError( "Writing GeoParquet 2.0 files requires pyarrow>=21.0, while pyarrow " f"{pa.__version__} is installed." ) table, geometry_encoding_dict = geopandas_to_arrow( df, geometry_encoding=geometry_encoding, index=index, interleaved=False ) geo_metadata = _create_metadata( df, schema_version=schema_version, geometry_encoding=geometry_encoding_dict, write_covering_bbox=write_covering_bbox, ) if schema_version == "2.0.0": # ensure `table` uses extension types and not just extension field metadata # such that pyarrow.parquet will write the Geometry logical type with _ensure_geoarrow_wkb_ext_type_registered(): for col, encoding in geometry_encoding_dict.items(): if encoding == "WKB": geom_ext = pa.table(table.select([col])) table = table.set_column( table.schema.get_field_index(col), geom_ext.schema.field(col), geom_ext[col], ) if write_covering_bbox: if "bbox" in df.columns: raise ValueError( "An existing column 'bbox' already exists in the dataframe. " "Please rename to write covering bbox." ) bounds = df.bounds bbox_array = StructArray.from_arrays( [bounds["minx"], bounds["miny"], bounds["maxx"], bounds["maxy"]], names=["xmin", "ymin", "xmax", "ymax"], ) table = table.append_column("bbox", bbox_array) # Store geopandas specific file-level metadata # This must be done AFTER creating the table or it is not persisted metadata = table.schema.metadata metadata.update({b"geo": _encode_metadata(geo_metadata)}) # Store attributes in metadata if exists if df.attrs: df_metadata = {"PANDAS_ATTRS": json.dumps(df.attrs)} metadata |= df_metadata return table.replace_schema_metadata(metadata) def _to_parquet( df, path, index=None, compression="snappy", geometry_encoding="WKB", schema_version=None, write_covering_bbox=False, **kwargs, ): """ Write a GeoDataFrame to the Parquet format. Any geometry columns present are serialized to WKB format in the file. Requires 'pyarrow'. This is tracking version 1.1.0 of the GeoParquet specification at: https://github.com/opengeospatial/geoparquet. Writing older versions is supported using the `schema_version` keyword. .. versionadded:: 0.8 Parameters ---------- path : str, path object or file-like object String, path object (implementing os.PathLike[str]) or file-like object implementing a binary read() function. index : bool, default None If ``True``, always include the dataframe's index(es) as columns in the file output. If ``False``, the index(es) will not be written to the file. If ``None``, the index(ex) will be included as columns in the file output except `RangeIndex` which is stored as metadata only. compression : {'snappy', 'gzip', 'brotli', None}, default 'snappy' Name of the compression to use. Use ``None`` for no compression. geometry_encoding : {'WKB', 'geoarrow'}, default 'WKB' The encoding to use for the geometry columns. Defaults to "WKB" for maximum interoperability. Specify "geoarrow" to use one of the native GeoArrow-based single-geometry type encodings. schema_version : {'0.1.0', '0.4.0', '1.0.0', '1.1.0', '2.0.0', None} GeoParquet specification version; if not provided will default to latest supported version. write_covering_bbox : bool, default False Writes the bounding box column for each row entry with column name 'bbox'. Writing a bbox column can be computationally expensive, hence is default setting is False. **kwargs Additional keyword arguments passed to pyarrow.parquet.write_table(). """ parquet = import_optional_dependency( "pyarrow.parquet", extra="pyarrow is required for Parquet support." ) path = _expand_user(path) table = _geopandas_to_arrow( df, index=index, geometry_encoding=geometry_encoding, schema_version=schema_version, write_covering_bbox=write_covering_bbox, ) parquet.write_table(table, path, compression=compression, **kwargs) def _to_feather(df, path, index=None, compression=None, schema_version=None, **kwargs): """ Write a GeoDataFrame to the Feather format. Any geometry columns present are serialized to WKB format in the file. Requires 'pyarrow' >= 0.17. This is tracking version 1.1.0 of the GeoParquet specification for the metadata at: https://github.com/opengeospatial/geoparquet. Writing older versions is supported using the `schema_version` keyword. .. versionadded:: 0.8 Parameters ---------- path : str, path object or file-like object String, path object (implementing os.PathLike[str]) or file-like object implementing a binary read() function. index : bool, default None If ``True``, always include the dataframe's index(es) as columns in the file output. If ``False``, the index(es) will not be written to the file. If ``None``, the index(ex) will be included as columns in the file output except `RangeIndex` which is stored as metadata only. compression : {'zstd', 'lz4', 'uncompressed'}, optional Name of the compression to use. Use ``"uncompressed"`` for no compression. By default uses LZ4 if available, otherwise uncompressed. schema_version : {'0.1.0', '0.4.0', '1.0.0', '1.1.0', '2.0.0', None} GeoParquet specification version for the metadata; if not provided will default to latest supported version. kwargs Additional keyword arguments passed to pyarrow.feather.write_feather(). """ feather = import_optional_dependency( "pyarrow.feather", extra="pyarrow is required for Feather support." ) path = _expand_user(path) table = _geopandas_to_arrow(df, index=index, schema_version=schema_version) feather.write_feather(table, path, compression=compression, **kwargs) def _arrow_to_geopandas(table, geo_metadata=None, to_pandas_kwargs=None, df_attrs=None): """Convert a pyarrow Table to a GeoDataFrame. Helper function with main, shared logic for read_parquet/read_feather. """ if geo_metadata is None: # Note: this path of not passing metadata is also used by dask-geopandas geo_metadata = _validate_and_decode_metadata(table.schema.metadata) if to_pandas_kwargs is None: to_pandas_kwargs = {} # Find all geometry columns that were read from the file. May # be a subset if 'columns' parameter is used. geometry_columns = [ col for col in geo_metadata["columns"] if col in table.column_names ] result_column_names = list(table.slice(0, 0).to_pandas(**to_pandas_kwargs).columns) geometry_columns.sort(key=result_column_names.index) if not len(geometry_columns): raise ValueError( """No geometry columns are included in the columns read from the Parquet/Feather file. To read this file without geometry columns, use pandas.read_parquet/read_feather() instead.""" ) geometry = geo_metadata["primary_column"] # Missing geometry likely indicates a subset of columns was read; # promote the first available geometry to the primary geometry. if len(geometry_columns) and geometry not in geometry_columns: geometry = geometry_columns[0] # if there are multiple non-primary geometry columns, raise a warning if len(geometry_columns) > 1: warnings.warn( "Multiple non-primary geometry columns read from Parquet/Feather " "file. The first column read was promoted to the primary geometry.", stacklevel=3, ) table_attr = table.drop(geometry_columns) df = table_attr.to_pandas(**to_pandas_kwargs) # Convert the WKB columns that are present back to geometry. for col in geometry_columns: col_metadata = geo_metadata["columns"][col] if "crs" in col_metadata: crs = col_metadata["crs"] if isinstance(crs, dict): _remove_id_from_member_of_ensembles(crs) else: # per the GeoParquet spec, missing CRS is to be interpreted as # OGC:CRS84 crs = "OGC:CRS84" if col_metadata["encoding"] == "WKB": geom_arr = from_wkb(np.array(table[col]), crs=crs) else: from geopandas.io._geoarrow import construct_shapely_array geom_arr = from_shapely( construct_shapely_array( table[col].combine_chunks(), "geoarrow." + col_metadata["encoding"] ), crs=crs, ) df.insert(result_column_names.index(col), col, geom_arr) # Add dataframe attrs if df_attrs: df.attrs = json.loads(df_attrs) return GeoDataFrame(df, geometry=geometry) def _get_filesystem_path(path, filesystem=None, storage_options=None): """ Get the filesystem and path for a given filesystem and path. If the filesystem is not None then it's just returned as is. """ if isinstance(path, str) and storage_options is None and filesystem is None: # Use the native pyarrow filesystem if possible. try: from pyarrow.fs import FileSystem filesystem, path = FileSystem.from_uri(path) except Exception: # fallback to use get_handle / fsspec for filesystems # that pyarrow doesn't support pass if _is_fsspec_url(path) and filesystem is None: fsspec = import_optional_dependency( "fsspec", extra="fsspec is required for 'storage_options'." ) filesystem, path = fsspec.core.url_to_fs(path, **(storage_options or {})) if filesystem is None and storage_options: raise ValueError( f"Cannot provide 'storage_options' with non-fsspec path '{path}'" ) return filesystem, path def _ensure_arrow_fs(filesystem): """Check if ``filesystem`` is a valid filesystem. Simplified version of pyarrow.fs._ensure_filesystem. This is only needed below because `pyarrow.parquet.read_metadata` does not yet accept a filesystem keyword (https://issues.apache.org/jira/browse/ARROW-16719) """ from pyarrow import fs if isinstance(filesystem, fs.FileSystem): return filesystem # handle fsspec-compatible filesystems try: import fsspec except ImportError: pass else: if isinstance(filesystem, fsspec.AbstractFileSystem): return fs.PyFileSystem(fs.FSSpecHandler(filesystem)) return filesystem def _validate_and_decode_metadata(metadata): if metadata is None or b"geo" not in metadata: raise ValueError( """Missing geo metadata in Parquet/Feather file. Use pandas.read_parquet/read_feather() instead.""" ) # check for malformed metadata try: decoded_geo_metadata = _decode_metadata(metadata.get(b"geo", b"")) except (TypeError, json.decoder.JSONDecodeError): raise ValueError("Missing or malformed geo metadata in Parquet/Feather file") _validate_geo_metadata(decoded_geo_metadata) return decoded_geo_metadata def _open_parquet_dataset(path, filesystem, kwargs, bbox): """Open the Parquet file/dataset and get the schema and metadata. This has a fallback ParquetFile in case pyarrow.dataset is not available, mimicking the logic of pyarrow.parquet.read_table(). TODO: unless pyarrow.parquet starts to provide more of this functionality out of the box, we should deprecate the fallback here (indicating in the warning / error that you should ensure to have pyarrow.dataset available) """ import pyarrow from pyarrow import parquet try: try: dataset = parquet.ParquetDataset(path, filesystem=filesystem, **kwargs) schema = dataset.schema except ImportError: # the above can fail if pyarrow.dataset submodule is not available if kwargs.get("filters") is not None: raise ValueError( "the 'filters' keyword is not supported when the " "pyarrow.dataset module is not available" ) if bbox is not None: raise ValueError( "the 'bbox' keyword is not supported when the " "pyarrow.dataset module is not available" ) if filesystem is not None: pa_filesystem = _ensure_arrow_fs(filesystem) source = pa_filesystem.open_input_file(path) else: source = path dataset = parquet.ParquetFile(source, **kwargs) schema = dataset.schema_arrow except OSError as exc: if "Thrift LogicalType that is not recognized" in str(exc): raise OSError( "Reading GeoParquet 2.0 files with Parquet Geometry/Geography logical " "types requires pyarrow>=20.0, while pyarrow " f"{pyarrow.__version__} is installed." ) from exc raise metadata = schema.metadata or {} # read metadata separately to get the raw Parquet FileMetaData metadata # (pyarrow doesn't properly exposes those in schema.metadata for files # created by GDAL - https://issues.apache.org/jira/browse/ARROW-16688) if b"geo" not in metadata: try: metadata = parquet.read_metadata(path, filesystem=filesystem).metadata except Exception: pass return dataset, schema, metadata def _add_index_columns_from_pandas_metadata(columns, metadata): """ When a user specifies a subset of columns to read, we by default still include the index columns in the read as well to be able to restore the index. Adapted from pyarrow.parquet.core.ParquetDataset.read() """ if metadata and b"pandas" in metadata: pandas_metadata = _decode_metadata(metadata[b"pandas"]) # RangeIndex can be represented as dict instead of column name index_columns = [ col for col in pandas_metadata["index_columns"] if not isinstance(col, dict) ] columns = list(columns) + list(set(index_columns) - set(columns)) return columns def _restore_pandas_metadata(table, metadata): """ If use_pandas_metadata, restore the pandas metadata (which gets lost if doing a specific `columns` selection in to_table). Adapted from pyarrow.parquet.core.ParquetDataset.read() """ if b"pandas" in metadata: new_metadata = table.schema.metadata or {} new_metadata.update({b"pandas": metadata[b"pandas"]}) table = table.replace_schema_metadata(new_metadata) return table def _read_parquet( path, columns=None, storage_options=None, bbox=None, to_pandas_kwargs=None, **kwargs, ): """ Load a Parquet object from the file path, returning a GeoDataFrame. You can read a subset of columns in the file using the ``columns`` parameter. However, the structure of the returned GeoDataFrame will depend on which columns you read: * if no geometry columns are read, this will raise a ``ValueError`` - you should use the pandas `read_parquet` method instead. * if the primary geometry column saved to this file is not included in columns, the first available geometry column will be set as the geometry column of the returned GeoDataFrame. Supports versions 0.1.0, 0.4.0, 1.0.0, 1.1.0 and 2.0.0 of the GeoParquet specification at: https://github.com/opengeospatial/geoparquet If 'crs' key is not present in the GeoParquet metadata associated with the Parquet object, it will default to "OGC:CRS84" according to the specification. Requires 'pyarrow'. .. versionadded:: 0.8 Parameters ---------- path : str, path object or file-like object String, path object (implementing os.PathLike[str]) or file-like object implementing a binary read() function. columns : list-like of strings, default=None If not None, only these columns will be read from the file. If the primary geometry column is not included, the first secondary geometry read from the file will be set as the geometry column of the returned GeoDataFrame. If no geometry columns are present, a ``ValueError`` will be raised. storage_options : dict, optional Extra options that make sense for a particular storage connection, e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to urllib as header options. For other URLs (e.g. starting with "s3://", and "gcs://") the key-value pairs are forwarded to fsspec. Please see fsspec and urllib for more details. When no storage options are provided and a filesystem is implemented by both ``pyarrow.fs`` and ``fsspec`` (e.g. "s3://") then the ``pyarrow.fs`` filesystem is preferred. Provide the instantiated fsspec filesystem using the ``filesystem`` keyword if you wish to use its implementation. bbox : tuple, optional Bounding box to be used to filter selection from geoparquet data. This is only usable if the data was saved with the bbox covering metadata or with GeoParquet >= 2.0 files. Input is of the tuple format (xmin, ymin, xmax, ymax). Filtering on Z or M coordinates is not supported. to_pandas_kwargs : dict, optional Arguments passed to the `pa.Table.to_pandas` method for non-geometry columns. This can be used to control the behavior of the conversion of the non-geometry columns to a pandas DataFrame. For example, you can use this to control the dtype conversion of the columns. By default, the `to_pandas` method is called with no additional arguments. **kwargs Any additional kwargs passed to :func:`pyarrow.parquet.read_table`. Returns ------- GeoDataFrame Examples -------- >>> df = geopandas.read_parquet("data.parquet") # doctest: +SKIP Specifying columns to read: >>> df = geopandas.read_parquet( ... "data.parquet", ... columns=["geometry", "pop_est"] ... ) # doctest: +SKIP From bytes: >>> p_data = client.get_bytes("bucket/geodata.parquet") # doctest: +SKIP >>> with BytesIO(p_data) as geo_bytes: # doctest: +SKIP >>> df = gpd.read_parquet(geo_bytes) # doctest: +SKIP """ parquet = import_optional_dependency( "pyarrow.parquet", extra="pyarrow is required for Parquet support." ) # TODO(https://github.com/pandas-dev/pandas/pull/41194): see if pandas # adds filesystem as a keyword and match that. filesystem = kwargs.pop("filesystem", None) filesystem, path = _get_filesystem_path( path, filesystem=filesystem, storage_options=storage_options ) path = _expand_user(path) # those keywords are used at read() time use_pandas_metadata = kwargs.pop("use_pandas_metadata", True) use_threads = kwargs.pop("use_threads", True) dataset, schema, metadata = _open_parquet_dataset(path, filesystem, kwargs, bbox) geo_metadata = _validate_and_decode_metadata(metadata) if len(geo_metadata["columns"]) == 0: raise ValueError( """No geometry columns are included in the columns read from the Parquet/Feather file. To read this file without geometry columns, use pandas.read_parquet/read_feather() instead.""" ) bbox_filter = ( _get_parquet_bbox_filter(geo_metadata, bbox) if bbox is not None else None ) if_bbox_column_exists = _check_if_covering_in_geo_metadata(geo_metadata) # by default, bbox column is not read in, so must specify which # columns are read in if it exists. if not columns and if_bbox_column_exists: columns = _get_non_bbox_columns(schema, geo_metadata) elif columns is not None and use_pandas_metadata: columns = _add_index_columns_from_pandas_metadata(columns, metadata) filters = kwargs.pop("filters", None) if filters is not None: # support both old-style [(..)] filters and pyarrow expressions filters = parquet.filters_to_expression(filters) # if both bbox and filters kwargs are used, must splice together. if bbox_filter is not None: if filters is not None: filters = bbox_filter & filters else: filters = bbox_filter if isinstance(dataset, parquet.ParquetFile): table = dataset.read(columns=columns, use_threads=use_threads) else: dsdataset = dataset._dataset if bbox is not None and bbox_filter is None: # if bbox is specified, but the dataset does not have a bbox column, # we need to filter the row groups manually. import pyarrow.dataset as ds geometry = geo_metadata["primary_column"] filtered_fragments = [] for fragment in dataset.fragments: geometry_idx = fragment.metadata.schema.names.index(geometry) row_group_ids = [ rg.id for rg in fragment.row_groups if _bbox_intersects( rg.metadata.column(geometry_idx).geo_statistics, bbox ) ] fragment_filtered = fragment.subset(row_group_ids=row_group_ids) filtered_fragments.append(fragment_filtered) dsdataset = ds.FileSystemDataset( filtered_fragments, dsdataset.schema, dsdataset.format, filesystem=dsdataset.filesystem, root_partition=dsdataset.partition_expression, ) table = dsdataset.to_table( columns=columns, filter=filters, use_threads=use_threads ) if use_pandas_metadata: table = _restore_pandas_metadata(table, metadata) if metadata and b"PANDAS_ATTRS" in metadata: df_attrs = metadata[b"PANDAS_ATTRS"] else: df_attrs = None result = _arrow_to_geopandas(table, geo_metadata, to_pandas_kwargs, df_attrs) if bbox is not None and bbox_filter is None: # post-filtering step: manual filtering above only filtered up to the row group # level -> further filter at the row level to give consistent behaviour bbox_bounds = result.bounds result_bbox = shapely.box( bbox_bounds["minx"], bbox_bounds["miny"], bbox_bounds["maxx"], bbox_bounds["maxy"], ) result = result[ shapely.intersects(result_bbox, shapely.box(*bbox)) ].reset_index(drop=True) return result def _read_feather(path, columns=None, to_pandas_kwargs=None, **kwargs): """ Load a Feather object from the file path, returning a GeoDataFrame. You can read a subset of columns in the file using the ``columns`` parameter. However, the structure of the returned GeoDataFrame will depend on which columns you read: * if no geometry columns are read, this will raise a ``ValueError`` - you should use the pandas `read_feather` method instead. * if the primary geometry column saved to this file is not included in columns, the first available geometry column will be set as the geometry column of the returned GeoDataFrame. Supports versions 0.1.0, 0.4.0, 1.0.0 and 1.1.0 of the GeoParquet specification at: https://github.com/opengeospatial/geoparquet If 'crs' key is not present in the Feather metadata associated with the Parquet object, it will default to "OGC:CRS84" according to the specification. Requires 'pyarrow' >= 0.17. .. versionadded:: 0.8 Parameters ---------- path : str, path object or file-like object String, path object (implementing os.PathLike[str]) or file-like object implementing a binary read() function. columns : list-like of strings, default=None If not None, only these columns will be read from the file. If the primary geometry column is not included, the first secondary geometry read from the file will be set as the geometry column of the returned GeoDataFrame. If no geometry columns are present, a ``ValueError`` will be raised. to_pandas_kwargs : dict, optional Arguments passed to the `pa.Table.to_pandas` method for non-geometry columns. This can be used to control the behavior of the conversion of the non-geometry columns to a pandas DataFrame. For example, you can use this to control the dtype conversion of the columns. By default, the `to_pandas` method is called with no additional arguments. **kwargs Any additional kwargs passed to pyarrow.feather.read_table(). Returns ------- GeoDataFrame Examples -------- >>> df = geopandas.read_feather("data.feather") # doctest: +SKIP Specifying columns to read: >>> df = geopandas.read_feather( ... "data.feather", ... columns=["geometry", "pop_est"] ... ) # doctest: +SKIP See the `read_parquet` docs for examples of reading and writing to/from bytes objects. """ feather = import_optional_dependency( "pyarrow.feather", extra="pyarrow is required for Feather support." ) path = _expand_user(path) table = feather.read_table(path, columns=columns, **kwargs) return _arrow_to_geopandas(table, to_pandas_kwargs=to_pandas_kwargs) def _get_parquet_bbox_filter(geo_metadata, bbox): primary_column = geo_metadata["primary_column"] if _check_if_covering_in_geo_metadata(geo_metadata): bbox_column_name = _get_bbox_encoding_column_name(geo_metadata) return _convert_bbox_to_parquet_filter(bbox, bbox_column_name) elif geo_metadata["columns"][primary_column]["encoding"] == "point": import pyarrow.compute as pc return ( (pc.field((primary_column, "x")) >= bbox[0]) & (pc.field((primary_column, "x")) <= bbox[2]) & (pc.field((primary_column, "y")) >= bbox[1]) & (pc.field((primary_column, "y")) <= bbox[3]) ) elif geo_metadata["version"] == "2.0.0": # this case gets handled later manually return None else: raise ValueError( "Specifying 'bbox' not supported for this Parquet file (it should either " "have a bbox covering column, use 'point' encoding, or use GeoParquet 2.0 " "logical geometry types)." ) def _convert_bbox_to_parquet_filter(bbox, bbox_column_name): import pyarrow.compute as pc return ~( (pc.field((bbox_column_name, "xmin")) > bbox[2]) | (pc.field((bbox_column_name, "ymin")) > bbox[3]) | (pc.field((bbox_column_name, "xmax")) < bbox[0]) | (pc.field((bbox_column_name, "ymax")) < bbox[1]) ) def _check_if_covering_in_geo_metadata(geo_metadata): primary_column = geo_metadata["primary_column"] return "covering" in geo_metadata["columns"][primary_column].keys() def _get_bbox_encoding_column_name(geo_metadata): primary_column = geo_metadata["primary_column"] return geo_metadata["columns"][primary_column]["covering"]["bbox"]["xmin"][0] def _get_non_bbox_columns(schema, geo_metadata): bbox_column_name = _get_bbox_encoding_column_name(geo_metadata) columns = schema.names if bbox_column_name in columns: columns.remove(bbox_column_name) return columns def _bbox_intersects(geo_stats, bbox): stats_xmin, stats_ymin, stats_xmax, stats_ymax = ( geo_stats.xmin, geo_stats.ymin, geo_stats.xmax, geo_stats.ymax, ) xmin, ymin, xmax, ymax = bbox return ( (stats_xmin <= xmax) and (stats_xmax >= xmin) and (stats_ymin <= ymax) and (stats_ymax >= ymin) ) geopandas-1.2.0/geopandas/io/file.py000066400000000000000000001022721525650651600173330ustar00rootroot00000000000000from __future__ import annotations import os import urllib.request import warnings from http import HTTPStatus from io import IOBase from packaging.version import Version from pathlib import Path from typing import Any # Adapted from pandas.io.common from urllib.parse import urlparse as parse_url from urllib.parse import uses_netloc, uses_params, uses_relative from urllib.request import Request import numpy as np import pandas as pd from pandas.api.types import ( is_datetime64_any_dtype, is_integer_dtype, is_object_dtype, is_string_dtype, ) import shapely from shapely.geometry import mapping from shapely.geometry.base import BaseGeometry from geopandas import GeoDataFrame, GeoSeries from geopandas._compat import HAS_PYPROJ from geopandas.io.util import vsi_path _VALID_URLS = set(uses_relative + uses_netloc + uses_params) _VALID_URLS.discard("") # file:// URIs are supported by fiona/pyogrio -> don't already open + read the file here _VALID_URLS.discard("file") fiona = None fiona_env = None fiona_import_error = None FIONA_GE_19 = False def _import_fiona(): global fiona global fiona_env global fiona_import_error global FIONA_GE_19 if fiona is None: try: import fiona # only try to import fiona.Env if the main fiona import succeeded # (otherwise you can get confusing "AttributeError: module 'fiona' # has no attribute '_loading'" / partially initialized module errors) try: from fiona import Env as fiona_env except ImportError: try: from fiona import drivers as fiona_env except ImportError: fiona_env = None FIONA_GE_19 = Version(Version(fiona.__version__).base_version) >= Version( "1.9.0" ) except ImportError as err: fiona = False fiona_import_error = str(err) pyogrio = None pyogrio_import_error = None def _import_pyogrio(): global pyogrio global pyogrio_import_error if pyogrio is None: try: import pyogrio except ImportError as err: pyogrio = False pyogrio_import_error = str(err) def _check_fiona(func): if not fiona: raise ImportError( f"the {func} requires the 'fiona' package, but it is not installed or does " f"not import correctly.\nImporting fiona resulted in: {fiona_import_error}" ) def _check_pyogrio(func): if not pyogrio: raise ImportError( f"the {func} requires the 'pyogrio' package, but it is not installed " "or does not import correctly." f"\nImporting pyogrio resulted in: {pyogrio_import_error}" ) def _check_metadata_supported(metadata: str | None, engine: str, driver: str) -> None: if metadata is None: return if driver != "GPKG": raise NotImplementedError( "The 'metadata' keyword is only supported for the GPKG driver." ) if engine == "fiona" and not FIONA_GE_19: raise NotImplementedError( "The 'metadata' keyword is only supported for Fiona >= 1.9." ) def _check_engine(engine, func): # if not specified through keyword or option, then default to "pyogrio" if # installed, otherwise try fiona if engine is None: import geopandas engine = geopandas.options.io_engine if engine is None: _import_pyogrio() if pyogrio: engine = "pyogrio" else: _import_fiona() if fiona: engine = "fiona" if engine == "pyogrio": _import_pyogrio() _check_pyogrio(func) elif engine == "fiona": _import_fiona() _check_fiona(func) elif engine is None: raise ImportError( f"The {func} requires the 'pyogrio' or 'fiona' package, " "but neither is installed or imports correctly." f"\nImporting pyogrio resulted in: {pyogrio_import_error}" f"\nImporting fiona resulted in: {fiona_import_error}" ) return engine _EXTENSION_TO_DRIVER = { ".bna": "BNA", ".dxf": "DXF", ".csv": "CSV", ".shp": "ESRI Shapefile", ".dbf": "ESRI Shapefile", ".json": "GeoJSON", ".geojson": "GeoJSON", ".geojsonl": "GeoJSONSeq", ".geojsons": "GeoJSONSeq", ".gpkg": "GPKG", ".gml": "GML", ".xml": "GML", ".gpx": "GPX", ".gtm": "GPSTrackMaker", ".gtz": "GPSTrackMaker", ".tab": "MapInfo File", ".mif": "MapInfo File", ".mid": "MapInfo File", ".dgn": "DGN", ".fgb": "FlatGeobuf", } def _expand_user(path): """Expand paths that use ~.""" if isinstance(path, str): path = os.path.expanduser(path) elif isinstance(path, Path): path = path.expanduser() return path def _is_url(url): """Check to see if *url* has a valid protocol.""" try: return parse_url(url).scheme in _VALID_URLS except Exception: return False def _read_file( filename, bbox=None, mask=None, columns=None, rows=None, engine=None, **kwargs ): """Return a GeoDataFrame from a file or URL. Parameters ---------- filename : str, path object or file-like object Either the absolute or relative path to the file or URL to be opened, or any object with a read() method (such as an open file or StringIO) bbox : tuple | GeoDataFrame or GeoSeries | shapely Geometry, default None Filter features by given bounding box, GeoSeries, GeoDataFrame or a shapely geometry. With engine="fiona", CRS mis-matches are resolved if given a GeoSeries or GeoDataFrame. With engine="pyogrio", bbox must be in the same CRS as the dataset. Tuple is (minx, miny, maxx, maxy) to match the bounds property of shapely geometry objects. Cannot be used with mask. mask : dict | GeoDataFrame or GeoSeries | shapely Geometry, default None Filter for features that intersect with the given dict-like geojson geometry, GeoSeries, GeoDataFrame or shapely geometry. CRS mis-matches are resolved if given a GeoSeries or GeoDataFrame. Cannot be used with bbox. If multiple geometries are passed, this will first union all geometries, which may be computationally expensive. columns : list, optional List of column names to import from the data source. Column names must exactly match the names in the data source. To avoid reading any columns (besides the geometry column), pass an empty list-like. By default reads all columns. rows : int or slice, default None Load in specific rows by passing an integer (first `n` rows) or a slice() object. engine : str, "pyogrio" or "fiona" The underlying library that is used to read the file. Currently, the supported options are "pyogrio" and "fiona". Defaults to "pyogrio" if installed, otherwise tries "fiona". Engine can also be set globally with the ``geopandas.options.io_engine`` option. **kwargs : Keyword args to be passed to the engine, and can be used to write to multi-layer data, store data within archives (zip files), etc. In case of the "pyogrio" engine, the keyword arguments are passed to `pyogrio.read_dataframe`. In case of the "fiona" engine, the keyword arguments are passed to fiona.open`. For more information on possible keywords, type: ``import pyogrio; help(pyogrio.read_dataframe)``. Examples -------- >>> df = geopandas.read_file("nybb.shp") # doctest: +SKIP Specifying layer of GPKG: >>> df = geopandas.read_file("file.gpkg", layer='cities') # doctest: +SKIP Reading only first 10 rows: >>> df = geopandas.read_file("nybb.shp", rows=10) # doctest: +SKIP Reading only geometries intersecting ``mask``: >>> df = geopandas.read_file("nybb.shp", mask=polygon) # doctest: +SKIP Reading only geometries intersecting ``bbox``: >>> df = geopandas.read_file("nybb.shp", bbox=(0, 0, 10, 20)) # doctest: +SKIP Returns ------- :obj:`geopandas.GeoDataFrame` or :obj:`pandas.DataFrame` : If `ignore_geometry=True` a :obj:`pandas.DataFrame` will be returned. Notes ----- The format drivers will attempt to detect the encoding of your data, but may fail. In this case, the proper encoding can be specified explicitly by using the encoding keyword parameter, e.g. ``encoding='utf-8'``. For faster data reading with the default pyogrio engine when pyarrow is installed, pass ``use_arrow=True`` as an argument. See the User Guide page :doc:`../../user_guide/io` for details. Some formats (e.g. GeoPackage) store a feature ID (``fid``) that is not included in the columns by default. With the pyogrio engine, pass ``fid_as_index=True`` to use it as the index of the returned GeoDataFrame. See the User Guide page :doc:`../../user_guide/io` for details. When specifying a URL, geopandas will check if the server supports reading partial data and in that case pass the URL as is to the underlying engine, which will then use the network file system handler of GDAL to read from the URL. Otherwise geopandas will download the data from the URL and pass all data in-memory to the underlying engine. If you need more control over how the URL is read, you can specify the GDAL virtual filesystem manually (e.g. ``/vsicurl/https://...``). See the GDAL documentation on filesystems for more details (https://gdal.org/user/virtual_file_systems.html#vsicurl-http-https-ftp-files-random-access). """ engine = _check_engine(engine, "'read_file' function") filename = _expand_user(filename) from_bytes = False if _is_url(filename): # if it is a url that supports random access -> pass through to # pyogrio/fiona as is (to support downloading only part of the file) # otherwise still download manually because pyogrio/fiona don't support # all types of urls (https://github.com/geopandas/geopandas/issues/2908) try: with urllib.request.urlopen( Request(filename, headers={"Range": "bytes=0-1"}) ) as response: if ( response.headers.get("Accept-Ranges") == "none" or response.status != HTTPStatus.PARTIAL_CONTENT ): from_bytes = True except ConnectionError: from_bytes = True if from_bytes: with urllib.request.urlopen(filename) as response: filename = response.read() if engine == "pyogrio": return _read_file_pyogrio( filename, bbox=bbox, mask=mask, columns=columns, rows=rows, **kwargs ) elif engine == "fiona": if pd.api.types.is_file_like(filename): data = filename.read() path_or_bytes = data.encode("utf-8") if isinstance(data, str) else data from_bytes = True else: path_or_bytes = filename return _read_file_fiona( path_or_bytes, from_bytes, bbox=bbox, mask=mask, columns=columns, rows=rows, **kwargs, ) else: raise ValueError(f"unknown engine '{engine}'") def _read_file_fiona( path_or_bytes, from_bytes, bbox=None, mask=None, columns=None, rows=None, where=None, **kwargs, ): if where is not None and not FIONA_GE_19: raise NotImplementedError("where requires fiona 1.9+") if columns is not None: if "include_fields" in kwargs: raise ValueError( "Cannot specify both 'include_fields' and 'columns' keywords" ) if not FIONA_GE_19: raise NotImplementedError("'columns' keyword requires fiona 1.9+") kwargs["include_fields"] = columns elif "include_fields" in kwargs: # alias to columns, as this variable is used below to specify column order # in the dataframe creation columns = kwargs["include_fields"] if not from_bytes: # Opening a file via URL or file-like-object above automatically detects a # zipped file. In order to match that behavior, attempt to add a zip scheme # if missing. path_or_bytes = vsi_path(str(path_or_bytes)) if from_bytes: reader = fiona.BytesCollection else: reader = fiona.open with fiona_env(): with reader(path_or_bytes, **kwargs) as features: crs = features.crs_wkt # returns "" if empty crs = crs or None # attempt to get EPSG code try: # fiona 1.9+ epsg = features.crs.to_epsg(confidence_threshold=100) if epsg is not None: crs = epsg except AttributeError: # fiona <= 1.8 try: crs = features.crs["init"] except (TypeError, KeyError): pass # handle loading the bounding box if bbox is not None: if isinstance(bbox, GeoDataFrame | GeoSeries): bbox = tuple(bbox.to_crs(crs).total_bounds) elif isinstance(bbox, BaseGeometry): bbox = bbox.bounds assert len(bbox) == 4 # handle loading the mask elif isinstance(mask, GeoDataFrame | GeoSeries): if crs is not None and mask.crs is not None: mask = mask.to_crs(crs) else: _warn_missing_crs_of_dataframe_and_mask(crs, mask) mask = mapping(mask.union_all()) elif isinstance(mask, BaseGeometry): mask = mapping(mask) filters = {} if bbox is not None: filters["bbox"] = bbox if mask is not None: filters["mask"] = mask if where is not None: filters["where"] = where # setup the data loading filter if rows is not None: if isinstance(rows, int): rows = slice(rows) elif not isinstance(rows, slice): raise TypeError("'rows' must be an integer or a slice.") f_filt = features.filter(rows.start, rows.stop, rows.step, **filters) elif filters: f_filt = features.filter(**filters) else: f_filt = features # get list of columns columns = columns or list(features.schema["properties"]) datetime_fields = [ k for (k, v) in features.schema["properties"].items() if v == "datetime" ] if ( kwargs.get("ignore_geometry", False) or features.schema["geometry"] == "None" ): df = pd.DataFrame( [record["properties"] for record in f_filt], columns=columns ) else: df = GeoDataFrame.from_features( f_filt, crs=crs, columns=columns + ["geometry"] ) for k in datetime_fields: as_dt = None # plain try catch for when pandas will raise in the future # TODO we can tighten the exception type in future when it does try: with warnings.catch_warnings(): # pandas 2.x does not yet enforce this behaviour but raises a # warning -> we want to to suppress this warning for our users, # and do this by turning it into an error so we take the # `except` code path to try again with utc=True warnings.filterwarnings( "error", "In a future version of pandas, parsing datetimes with " "mixed time zones will raise an error", FutureWarning, ) as_dt = pd.to_datetime(df[k]) except Exception: pass if as_dt is None or as_dt.dtype == "object": # if to_datetime failed, try again for mixed timezone offsets # This can still fail if there are invalid datetimes try: as_dt = pd.to_datetime(df[k], utc=True) except Exception: pass # if to_datetime succeeded, round datetimes as # fiona only supports up to ms precision (any microseconds are # floating point rounding error) if as_dt is not None and not (as_dt.dtype == "object"): df[k] = as_dt.dt.as_unit("ms") return df def _read_file_pyogrio(path_or_bytes, bbox=None, mask=None, rows=None, **kwargs): import pyogrio if rows is not None: if isinstance(rows, int): kwargs["max_features"] = rows elif isinstance(rows, slice): if rows.start is not None: if rows.start < 0: raise ValueError( "Negative slice start not supported with the 'pyogrio' engine." ) kwargs["skip_features"] = rows.start if rows.stop is not None: kwargs["max_features"] = rows.stop - (rows.start or 0) if rows.step is not None: raise ValueError("slice with step is not supported") else: raise TypeError("'rows' must be an integer or a slice.") if bbox is not None and mask is not None: # match error message from Fiona raise ValueError("mask and bbox can not be set together") if bbox is not None: if isinstance(bbox, GeoDataFrame | GeoSeries): crs = pyogrio.read_info(path_or_bytes, layer=kwargs.get("layer")).get("crs") if isinstance(path_or_bytes, IOBase): path_or_bytes.seek(0) bbox = tuple(bbox.to_crs(crs).total_bounds) elif isinstance(bbox, BaseGeometry): bbox = bbox.bounds if len(bbox) != 4: raise ValueError("'bbox' should be a length-4 tuple.") if mask is not None: # NOTE: mask cannot be used at same time as bbox keyword if isinstance(mask, GeoDataFrame | GeoSeries): crs = pyogrio.read_info(path_or_bytes, layer=kwargs.get("layer")).get("crs") if crs is not None and mask.crs is not None: mask = mask.to_crs(crs) else: _warn_missing_crs_of_dataframe_and_mask(crs, mask) if isinstance(path_or_bytes, IOBase): path_or_bytes.seek(0) mask = shapely.unary_union(mask.geometry.values) elif isinstance(mask, BaseGeometry): mask = shapely.unary_union(mask) elif isinstance(mask, dict) or hasattr(mask, "__geo_interface__"): # convert GeoJSON to shapely geometry mask = shapely.geometry.shape(mask) kwargs["mask"] = mask if kwargs.pop("ignore_geometry", False): kwargs["read_geometry"] = False # translate `ignore_fields`/`include_fields` keyword for back compat with fiona if "ignore_fields" in kwargs and "include_fields" in kwargs: raise ValueError("Cannot specify both 'ignore_fields' and 'include_fields'") elif "ignore_fields" in kwargs: if kwargs.get("columns", None) is not None: raise ValueError( "Cannot specify both 'columns' and 'ignore_fields' keywords" ) warnings.warn( "The 'include_fields' and 'ignore_fields' keywords are deprecated, and " "will be removed in a future release. You can use the 'columns' keyword " "instead to select which columns to read.", DeprecationWarning, stacklevel=3, ) ignore_fields = kwargs.pop("ignore_fields") fields = pyogrio.read_info(path_or_bytes, layer=kwargs.get("layer"))["fields"] include_fields = [col for col in fields if col not in ignore_fields] kwargs["columns"] = include_fields elif "include_fields" in kwargs: # translate `include_fields` keyword for back compat with fiona engine if kwargs.get("columns", None) is not None: raise ValueError( "Cannot specify both 'columns' and 'include_fields' keywords" ) warnings.warn( "The 'include_fields' and 'ignore_fields' keywords are deprecated, and " "will be removed in a future release. You can use the 'columns' keyword " "instead to select which columns to read.", DeprecationWarning, stacklevel=3, ) kwargs["columns"] = kwargs.pop("include_fields") return pyogrio.read_dataframe(path_or_bytes, bbox=bbox, **kwargs) def _warn_missing_crs_of_dataframe_and_mask(source_dataset_crs, mask): """ Warn if one, or both, of the source dataset or mask does not have a crs. """ if (source_dataset_crs is None) and (mask.crs is None): msg = "There is no CRS defined in the source dataset nor mask. " elif (source_dataset_crs is None) and (mask.crs is not None): msg = "There is no CRS defined in the source dataset. " else: # crs not None and mask.crs is None msg = "There is no CRS defined in the mask. " msg += ( "This may lead to a misalignment of the mask and the " "source dataset, leading to incorrect masking. Ensure " "both inputs share the same CRS." ) warnings.warn(msg, UserWarning, stacklevel=3) def _detect_driver(path): """Attempt to auto-detect driver based on the extension.""" try: # in case the path is a file handle path = path.name except AttributeError: pass try: return _EXTENSION_TO_DRIVER[Path(path).suffix.lower()] except KeyError: # Assume it is a shapefile folder for now. In the future, # will likely raise an exception when the expected # folder writing behavior is more clearly defined. return "ESRI Shapefile" def _to_file( df, filename, driver=None, schema=None, index=None, mode="w", crs=None, engine=None, metadata=None, **kwargs, ): """Write this GeoDataFrame to an OGR data source. A dictionary of supported OGR providers is available via: >>> import pyogrio >>> pyogrio.list_drivers() # doctest: +SKIP Parameters ---------- df : GeoDataFrame to be written filename : string File path or file handle to write to. The path may specify a GDAL VSI scheme. driver : string, default None The OGR format driver used to write the vector file. If not specified, it attempts to infer it from the file extension. If no extension is specified, it saves ESRI Shapefile to a folder. schema : dict, default None If specified, the schema dictionary is passed to Fiona to better control how the file is written. If None, GeoPandas will determine the schema based on each column's dtype. Not supported for the "pyogrio" engine. index : bool, default None If True, write index into one or more columns (for MultiIndex). Default None writes the index into one or more columns only if the index is named, is a MultiIndex, or has a non-integer data type. If False, no index is written. .. versionadded:: 0.7 Previously the index was not written. mode : string, default 'w' The write mode, 'w' to overwrite the existing file and 'a' to append; when using the pyogrio engine, you can also pass ``append=True``. Not all drivers support appending. For the fiona engine, the drivers that support appending are listed in fiona.supported_drivers or https://github.com/Toblerity/Fiona/blob/master/fiona/drvsupport.py. For the pyogrio engine, you should be able to use any driver that is available in your installation of GDAL that supports append capability; see the specific driver entry at https://gdal.org/drivers/vector/index.html for more information. crs : pyproj.CRS, default None If specified, the CRS is passed to Fiona to better control how the file is written. If None, GeoPandas will determine the crs based on crs df attribute. The value can be anything accepted by :meth:`pyproj.CRS.from_user_input() `, such as an authority string (eg "EPSG:4326") or a WKT string. engine : str, "pyogrio" or "fiona" The underlying library that is used to read the file. Currently, the supported options are "pyogrio" and "fiona". Defaults to "pyogrio" if installed, otherwise tries "fiona". Engine can also be set globally with the ``geopandas.options.io_engine`` option. metadata : dict[str, str], default None Optional metadata to be stored in the file. Keys and values must be strings. Only supported for the "GPKG" driver (requires Fiona >= 1.9 or pyogrio >= 0.6). **kwargs : Keyword args to be passed to the engine, and can be used to write to multi-layer data, store data within archives (zip files), etc. In case of the "fiona" engine, the keyword arguments are passed to fiona.open`. For more information on possible keywords, type: ``import fiona; help(fiona.open)``. In case of the "pyogrio" engine, the keyword arguments are passed to `pyogrio.write_dataframe`. Notes ----- The format drivers will attempt to detect the encoding of your data, but may fail. In this case, the proper encoding can be specified explicitly by using the encoding keyword parameter, e.g. ``encoding='utf-8'``. """ engine = _check_engine(engine, "'to_file' method") filename = _expand_user(filename) if index is None: # Determine if index attribute(s) should be saved to file # (only if they are named or are non-integer) index = list(df.index.names) != [None] or not is_integer_dtype(df.index.dtype) if index: df = df.reset_index(drop=False) if driver is None: driver = _detect_driver(filename) if driver == "ESRI Shapefile" and any(len(c) > 10 for c in df.columns.tolist()): warnings.warn( "Column names longer than 10 characters will be truncated when saved to " "ESRI Shapefile.", stacklevel=3, ) if (df.dtypes == "geometry").sum() > 1: raise ValueError( "GeoDataFrame contains multiple geometry columns but GeoDataFrame.to_file " "supports only a single geometry column. Use a GeoDataFrame.to_parquet or " "GeoDataFrame.to_feather, drop additional geometry columns or convert them " "to a supported format like a well-known text (WKT) using " "`GeoSeries.to_wkt()`.", ) _check_metadata_supported(metadata, engine, driver) if mode not in ("w", "a"): raise ValueError(f"'mode' should be one of 'w' or 'a', got '{mode}' instead") if engine == "pyogrio": _to_file_pyogrio(df, filename, driver, schema, crs, mode, metadata, **kwargs) elif engine == "fiona": _to_file_fiona(df, filename, driver, schema, crs, mode, metadata, **kwargs) else: raise ValueError(f"unknown engine '{engine}'") def _to_file_fiona(df, filename, driver, schema, crs, mode, metadata, **kwargs): if not HAS_PYPROJ and crs: raise ImportError( "The 'pyproj' package is required to write a file with a CRS, but it is not" " installed or does not import correctly." ) if schema is None: schema = infer_schema(df) if crs: from pyproj import CRS crs = CRS.from_user_input(crs) else: crs = df.crs with fiona_env(): crs_wkt = None try: gdal_version = Version( fiona.env.get_gdal_release_name().strip("e") ) # GH3147 except (AttributeError, ValueError): gdal_version = Version("2.0.0") # just assume it is not the latest if gdal_version >= Version("3.0.0") and crs: crs_wkt = crs.to_wkt() elif crs: crs_wkt = crs.to_wkt("WKT1_GDAL") with fiona.open( filename, mode=mode, driver=driver, crs_wkt=crs_wkt, schema=schema, **kwargs ) as colxn: if metadata is not None: colxn.update_tags(metadata) colxn.writerecords(df.iterfeatures()) def _to_file_pyogrio(df, filename, driver, schema, crs, mode, metadata, **kwargs): import pyogrio if schema is not None: raise ValueError( "The 'schema' argument is not supported with the 'pyogrio' engine." ) if mode == "a": kwargs["append"] = True if crs is not None: raise ValueError("Passing 'crs' is not supported with the 'pyogrio' engine.") # for the fiona engine, this check is done in gdf.iterfeatures() if not df.columns.is_unique: raise ValueError("GeoDataFrame cannot contain duplicated column names.") pyogrio.write_dataframe(df, filename, driver=driver, metadata=metadata, **kwargs) def infer_schema(df): from collections import OrderedDict # TODO: test pandas string type and boolean type once released types = { "Int32": "int32", "int32": "int32", "Int64": "int", "string": "str", "boolean": "bool", } def convert_type(column, in_type): if is_object_dtype(in_type) or is_string_dtype(in_type): return "str" if is_datetime64_any_dtype(in_type): # numpy datetime type regardless of frequency return "datetime" if str(in_type) in types: out_type = types[str(in_type)] else: out_type = type(np.zeros(1, in_type).item()).__name__ if out_type == "long": out_type = "int" return out_type properties = OrderedDict( [ (col, convert_type(col, _type)) for col, _type in zip(df.columns, df.dtypes) if col != df._geometry_column_name ] ) if df.empty: warnings.warn( "You are attempting to write an empty DataFrame to file. " "For some drivers, this operation may fail.", UserWarning, stacklevel=3, ) # Since https://github.com/Toblerity/Fiona/issues/446 resolution, # Fiona allows a list of geometry types geom_types = _geometry_types(df) schema = {"geometry": geom_types, "properties": properties} return schema def _geometry_types(df): """Determine the geometry types in the GeoDataFrame for the schema.""" geom_types_2D = df[~df.geometry.has_z].geometry.geom_type.unique() geom_types_2D = list(geom_types_2D[pd.notna(geom_types_2D)]) geom_types_3D = df[df.geometry.has_z].geometry.geom_type.unique() geom_types_3D = list(geom_types_3D[pd.notna(geom_types_3D)]) geom_types_3D = ["3D " + gtype for gtype in geom_types_3D] geom_types = geom_types_3D + geom_types_2D if len(geom_types) == 0: # Default geometry type supported by Fiona # (Since https://github.com/Toblerity/Fiona/issues/446 resolution) return "Unknown" if len(geom_types) == 1: geom_types = geom_types[0] return geom_types def _list_layers(filename) -> pd.DataFrame: """List layers available in a file. Provides an overview of layers available in a file or URL together with their geometry types. When supported by the data source, this includes both spatial and non-spatial layers. Non-spatial layers are indicated by the ``"geometry_type"`` column being ``None``. GeoPandas will not read such layers but they can be read into a pd.DataFrame using :func:`pyogrio.read_dataframe`. Parameters ---------- filename : str, path object or file-like object Either the absolute or relative path to the file or URL to be opened, or any object with a read() method (such as an open file or StringIO) Returns ------- pandas.DataFrame A DataFrame with columns "name" and "geometry_type" and one row per layer. """ _import_pyogrio() _check_pyogrio("list_layers") import pyogrio return pd.DataFrame( pyogrio.list_layers(filename), columns=["name", "geometry_type"] ) def _read_file_info(filename, layer=None) -> dict[str, Any]: """Read layer information from a file. Parameters ---------- filename : str, path object or file-like object Either the absolute or relative path to the file or URL to be opened, or any object with a read() method (such as an open file or StringIO) layer : str or int, optional If the file contains multiple layers, the name or number of the layer to be read. If None, the first layer is read. Returns ------- dict A dictionary with layer metadata. """ _import_pyogrio() _check_pyogrio("read_info") import pyogrio return pyogrio.read_info(filename, layer=layer) geopandas-1.2.0/geopandas/io/sql.py000066400000000000000000000365701525650651600172220ustar00rootroot00000000000000import warnings from contextlib import contextmanager from functools import lru_cache import pandas as pd import shapely import shapely.wkb from geopandas import GeoDataFrame @contextmanager def _get_conn(conn_or_engine): """ Yield a connection within a transaction context. Engine.begin() returns a Connection with an implicit Transaction while Connection.begin() returns the Transaction. This helper will always return a Connection with an implicit (possibly nested) Transaction. Parameters ---------- conn_or_engine : Connection or Engine A sqlalchemy Connection or Engine instance Returns ------- Connection """ from sqlalchemy.engine.base import Connection, Engine if isinstance(conn_or_engine, Connection): if not conn_or_engine.in_transaction(): with conn_or_engine.begin(): yield conn_or_engine else: yield conn_or_engine elif isinstance(conn_or_engine, Engine): with conn_or_engine.begin() as conn: yield conn else: raise ValueError(f"Unknown Connectable: {conn_or_engine}") def _df_to_geodf(df, geom_col="geom", crs=None, con=None): """Transform a pandas DataFrame into a GeoDataFrame. The column 'geom_col' must be a geometry column in WKB representation. To be used to convert df based on pd.read_sql to gdf. Parameters ---------- df : DataFrame pandas DataFrame with geometry column in WKB representation. geom_col : string, default 'geom' column name to convert to shapely geometries crs : pyproj.CRS, optional CRS to use for the returned GeoDataFrame. The value can be anything accepted by :meth:`pyproj.CRS.from_user_input() `, such as an authority string (eg "EPSG:4326") or a WKT string. If not set, tries to determine CRS from the SRID associated with the first geometry in the database, and assigns that to all geometries. con : sqlalchemy.engine.Connection or sqlalchemy.engine.Engine Active connection to the database to query. Returns ------- GeoDataFrame """ if geom_col not in df: raise ValueError(f"Query missing geometry column '{geom_col}'") if df.columns.to_list().count(geom_col) > 1: raise ValueError( f"Duplicate geometry column '{geom_col}' detected in SQL query output. Only" "one geometry column is allowed." ) geoms = df[geom_col].dropna() if not geoms.empty: load_geom_bytes = shapely.wkb.loads """Load from Python 3 binary.""" def load_geom_text(x): """Load from binary encoded as text.""" return shapely.wkb.loads(str(x), hex=True) if isinstance(geoms.iat[0], bytes): load_geom = load_geom_bytes else: load_geom = load_geom_text df[geom_col] = geoms = geoms.apply(load_geom) if crs is None: srid = int(shapely.get_srid(geoms.iat[0])) # if no defined SRID in geodatabase, returns SRID of 0 if srid != 0: try: spatial_ref_sys_df = _get_spatial_ref_sys_df(con, srid) except pd.errors.DatabaseError: warning_msg = ( f"Could not find the spatial reference system table " f"(spatial_ref_sys) in PostGIS." f"Trying epsg:{srid} as a fallback." ) warnings.warn(warning_msg, UserWarning, stacklevel=3) crs = f"epsg:{srid}" else: if not spatial_ref_sys_df.empty: auth_name = spatial_ref_sys_df["auth_name"].item() crs = f"{auth_name}:{srid}" else: warning_msg = ( f"Could not find srid {srid} in the " f"spatial_ref_sys table. " f"Trying epsg:{srid} as a fallback." ) warnings.warn(warning_msg, UserWarning, stacklevel=3) crs = f"epsg:{srid}" return GeoDataFrame(df, crs=crs, geometry=geom_col) def _read_postgis( sql, con, geom_col="geom", crs=None, index_col=None, coerce_float=True, parse_dates=None, params=None, chunksize=None, ): """Return a GeoDataFrame corresponding to the result of the query string, which must contain a geometry column in WKB representation. It is also possible to use :meth:`~GeoDataFrame.read_file` to read from a database. Especially for file geodatabases like GeoPackage or SpatiaLite this can be easier. Parameters ---------- sql : string SQL query to execute in selecting entries from database, or name of the table to read from the database. con : sqlalchemy.engine.Connection or sqlalchemy.engine.Engine Active connection to the database to query. geom_col : string, default 'geom' column name to convert to shapely geometries crs : dict or str, optional CRS to use for the returned GeoDataFrame; if not set, tries to determine CRS from the SRID associated with the first geometry in the database, and assigns that to all geometries. chunksize : int, default None If specified, return an iterator where chunksize is the number of rows to include in each chunk. See the documentation for pandas.read_sql for further explanation of the following parameters: index_col, coerce_float, parse_dates, params, chunksize Returns ------- GeoDataFrame Examples -------- PostGIS >>> from sqlalchemy import create_engine # doctest: +SKIP >>> db_connection_url = "postgresql://myusername:mypassword@myhost:5432/mydatabase" >>> con = create_engine(db_connection_url) # doctest: +SKIP >>> sql = "SELECT geom, highway FROM roads" >>> df = geopandas.read_postgis(sql, con) # doctest: +SKIP SpatiaLite >>> sql = "SELECT ST_AsBinary(geom) AS geom, highway FROM roads" >>> df = geopandas.read_postgis(sql, con) # doctest: +SKIP """ if chunksize is None: # read all in one chunk and return a single GeoDataFrame df = pd.read_sql( sql, con, index_col=index_col, coerce_float=coerce_float, parse_dates=parse_dates, params=params, chunksize=chunksize, ) return _df_to_geodf(df, geom_col=geom_col, crs=crs, con=con) else: # read data in chunks and return a generator df_generator = pd.read_sql( sql, con, index_col=index_col, coerce_float=coerce_float, parse_dates=parse_dates, params=params, chunksize=chunksize, ) return ( _df_to_geodf(df, geom_col=geom_col, crs=crs, con=con) for df in df_generator ) def _get_geometry_type(gdf): """Get basic geometry type of a GeoDataFrame. See more info from: https://geoalchemy-2.readthedocs.io/en/latest/types.html#geoalchemy2.types._GISType Following rules apply: - if geometries all share the same geometry-type, geometries are inserted with the given GeometryType with following types: - Point, LineString, Polygon, MultiPoint, MultiLineString, MultiPolygon, GeometryCollection. - LinearRing geometries will be converted into LineString -objects. - in all other cases, geometries will be inserted with type GEOMETRY: - a mix of Polygons and MultiPolygons in GeoSeries - a mix of Points and LineStrings in GeoSeries - geometry is of type GeometryCollection, such as GeometryCollection([Point, LineStrings]) - if any of the geometries has Z-coordinate, all records will be written with 3D. """ geom_types = list(gdf.geometry.geom_type.unique()) has_curve = False for gt in geom_types: if pd.isna(gt): continue elif "LinearRing" in gt: has_curve = True if len(geom_types) == 1: if has_curve: target_geom_type = "LINESTRING" else: if pd.isna(geom_types[0]): raise ValueError("No valid geometries in the data.") else: target_geom_type = geom_types[0].upper() else: target_geom_type = "GEOMETRY" # Check for 3D-coordinates if any(gdf.geometry.has_z): target_geom_type += "Z" return target_geom_type, has_curve def _get_srid_from_crs(gdf): """Get EPSG code from CRS if available. If not, return 0.""" # Use geoalchemy2 default for srid # Note: undefined srid in PostGIS is 0 srid = None warning_msg = ( "Could not parse CRS from the GeoDataFrame. Inserting data without defined CRS." ) if gdf.crs is not None: try: for confidence in (100, 70, 25): srid = gdf.crs.to_epsg(min_confidence=confidence) if srid is not None: break auth_srid = gdf.crs.to_authority( auth_name="ESRI", min_confidence=confidence ) if auth_srid is not None: srid = int(auth_srid[1]) break except Exception: warnings.warn(warning_msg, UserWarning, stacklevel=2) if srid is None: srid = 0 warnings.warn(warning_msg, UserWarning, stacklevel=2) return srid def _convert_linearring_to_linestring(gdf, geom_name): from shapely.geometry import LineString # Todo: Use shapely function once it's implemented: # https://github.com/shapely/shapely/issues/1617 mask = gdf.geom_type == "LinearRing" gdf.loc[mask, geom_name] = gdf.loc[mask, geom_name].apply(LineString) return gdf def _convert_to_ewkb(gdf, geom_name, srid): """Convert geometries to ewkb.""" geoms = shapely.to_wkb( shapely.set_srid(gdf[geom_name].values._data, srid=srid), hex=True, include_srid=True, ) # The gdf will warn that the geometry column doesn't hold in-memory geometries # now that they are EWKB, so convert back to a regular dataframe to avoid warning # the user that the dtypes are unexpected. df = pd.DataFrame(gdf, copy=False) df[geom_name] = geoms return df def _psql_insert_copy(tbl, conn, keys, data_iter): import csv import io s_buf = io.StringIO() writer = csv.writer(s_buf) writer.writerows(data_iter) s_buf.seek(0) preparer = conn.dialect.identifier_preparer columns = ", ".join(preparer.quote(k) for k in keys) table_name = preparer.quote(tbl.table.name) if tbl.table.schema: table_name = f"{preparer.quote_schema(tbl.table.schema)}.{table_name}" dbapi_conn = conn.connection sql = f"COPY {table_name} ({columns}) FROM STDIN WITH CSV" with dbapi_conn.cursor() as cur: # Use psycopg method if it's available if hasattr(cur, "copy") and callable(cur.copy): with cur.copy(sql) as copy: copy.write(s_buf.read()) else: # otherwise use psycopg2 method cur.copy_expert(sql, s_buf) def _write_postgis( gdf, name, con, schema=None, if_exists="fail", index=False, index_label=None, chunksize=None, dtype=None, ): """ Upload GeoDataFrame into PostGIS database. This method requires SQLAlchemy and GeoAlchemy2, and a PostgreSQL Python driver (e.g. psycopg2) to be installed. Parameters ---------- name : str Name of the target table. con : sqlalchemy.engine.Connection or sqlalchemy.engine.Engine Active connection to the PostGIS database. if_exists : {'fail', 'replace', 'append'}, default 'fail' How to behave if the table already exists: - fail: Raise a ValueError. - replace: Drop the table before inserting new values. - append: Insert new values to the existing table. schema : string, optional Specify the schema. If None, use default schema: 'public'. index : bool, default True Write DataFrame index as a column. Uses *index_label* as the column name in the table. index_label : string or sequence, default None Column label for index column(s). If None is given (default) and index is True, then the index names are used. chunksize : int, optional Rows will be written in batches of this size at a time. By default, all rows will be written at once. dtype : dict of column name to SQL type, default None Specifying the datatype for columns. The keys should be the column names and the values should be the SQLAlchemy types. Examples -------- >>> from sqlalchemy import create_engine # doctest: +SKIP >>> engine = create_engine("postgresql://myusername:mypassword@myhost:5432\ /mydatabase";) # doctest: +SKIP >>> gdf.to_postgis("my_table", engine) # doctest: +SKIP """ try: from geoalchemy2 import Geometry from sqlalchemy import text except ImportError: raise ImportError("'to_postgis()' requires geoalchemy2 package.") gdf = gdf.copy() geom_name = gdf.geometry.name # Get srid srid = _get_srid_from_crs(gdf) # Get geometry type and info whether data contains LinearRing. geometry_type, has_curve = _get_geometry_type(gdf) # Build dtype with Geometry if dtype is not None: dtype[geom_name] = Geometry(geometry_type=geometry_type, srid=srid) else: dtype = {geom_name: Geometry(geometry_type=geometry_type, srid=srid)} # Convert LinearRing geometries to LineString if has_curve: gdf = _convert_linearring_to_linestring(gdf, geom_name) # Convert geometries to EWKB gdf = _convert_to_ewkb(gdf, geom_name, srid) if schema is not None: schema_name = schema else: schema_name = "public" if if_exists == "append": # Check that the geometry srid matches with the current GeoDataFrame with _get_conn(con) as connection: # Only check SRID if table exists if connection.dialect.has_table(connection, name, schema): target_srid = connection.execute( text( "SELECT Find_SRID(:schema_name, :name, :geom_name);" ).bindparams( schema_name=schema_name, name=name, geom_name=geom_name ) ).fetchone()[0] if target_srid != srid: msg = ( f"The CRS of the target table (EPSG:{target_srid}) differs " f"from the CRS of current GeoDataFrame (EPSG:{srid})." ) raise ValueError(msg) with _get_conn(con) as connection: gdf.to_sql( name, connection, schema=schema_name, if_exists=if_exists, index=index, index_label=index_label, chunksize=chunksize, dtype=dtype, method=_psql_insert_copy, ) @lru_cache def _get_spatial_ref_sys_df(con, srid: int): spatial_ref_sys_sql = ( f"SELECT srid, auth_name FROM spatial_ref_sys WHERE srid = {srid}" ) return pd.read_sql(spatial_ref_sys_sql, con) geopandas-1.2.0/geopandas/io/tests/000077500000000000000000000000001525650651600172005ustar00rootroot00000000000000geopandas-1.2.0/geopandas/io/tests/__init__.py000066400000000000000000000000001525650651600212770ustar00rootroot00000000000000geopandas-1.2.0/geopandas/io/tests/data/000077500000000000000000000000001525650651600201115ustar00rootroot00000000000000geopandas-1.2.0/geopandas/io/tests/data/arrow/000077500000000000000000000000001525650651600212435ustar00rootroot00000000000000geopandas-1.2.0/geopandas/io/tests/data/arrow/geoparquet/000077500000000000000000000000001525650651600234175ustar00rootroot00000000000000geopandas-1.2.0/geopandas/io/tests/data/arrow/geoparquet/1.1.0/000077500000000000000000000000001525650651600240545ustar00rootroot00000000000000data-linestring-encoding_native.parquet000066400000000000000000000037471525650651600336320ustar00rootroot00000000000000geopandas-1.2.0/geopandas/io/tests/data/arrow/geoparquet/1.1.0PAR10,L <,( $$&5col&P&(,0,L>@($@D@"&, D@$@(D@$@@j$& 5Hgeometrylistelementx &&D@$@(D@$@,0,L$@(>@D@"&, D@$@(D@$@@j$& 5Hgeometrylistelementy &&D@$@(D@$@,|5schema%col5geometryL<5list5element %x %y<&5col&P&(,& 5Hgeometrylistelementx &&D@$@(D@$@,& 5Hgeometrylistelementy &&D@$@(D@$@,&,geo{"version": "1.1.0", "primary_column": "geometry", "columns": {"geometry": {"encoding": "linestring", "geometry_types": ["LineString"]}}} ARROW: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 parquet-cpp-arrow version 15.0.2<CPAR1geopandas-1.2.0/geopandas/io/tests/data/arrow/geoparquet/1.1.0/data-linestring-encoding_wkb.parquet000066400000000000000000000027021525650651600331740ustar00rootroot00000000000000PAR10,L <,( $$&5col&P&(,^LJ(9>@ $:D 8D@ ,6(9>@$@$@>@D@D@  & 5geometry&&6(9>@$@$@>@D@D@ ,<5schema%col %geometry,&5col&P&(,& 5geometry&&6(9>@$@$@>@D@D@ ,&,geo{"version": "1.1.0", "primary_column": "geometry", "columns": {"geometry": {"encoding": "WKB", "geometry_types": ["LineString"]}}} ARROW:schema/////2ABAAAQAAAAAAAKAA4ABgAFAAgACgAAAAABBAAQAAAAAAAKAAwAAAAEAAgACgAAALQAAAAEAAAAAQAAAAwAAAAIAAwABAAIAAgAAAAIAAAADAAAAAMAAABnZW8AggAAAHsidmVyc2lvbiI6ICIxLjEuMCIsICJwcmltYXJ5X2NvbHVtbiI6ICJnZW9tZXRyeSIsICJjb2x1bW5zIjogeyJnZW9tZXRyeSI6IHsiZW5jb2RpbmciOiAiV0tCIiwgImdlb21ldHJ5X3R5cGVzIjogWyJMaW5lU3RyaW5nIl19fX0AAAIAAABIAAAABAAAAND///8AAAEEEAAAACAAAAAEAAAAAAAAAAgAAABnZW9tZXRyeQAAAAAEAAQABAAAABAAFAAIAAYABwAMAAAAEAAQAAAAAAABAhAAAAAcAAAABAAAAAAAAAADAAAAY29sAAgADAAIAAcACAAAAAAAAAFAAAAA parquet-cpp-arrow version 15.0.2,PAR1data-multilinestring-encoding_native.parquet000066400000000000000000000042521525650651600346750ustar00rootroot00000000000000geopandas-1.2.0/geopandas/io/tests/data/arrow/geoparquet/1.1.0PAR1@4L  <,( $&5col&X&(,@4L >@$ $D@4@04,D@$@(D@$@\( d'& 5hgeometrylistelementlistelementx&&D@$@(D@$@,@4L $@> $D@4@04,D@$@(D@$@\( $k&  5hgeometrylistelementlistelementy&&D@$@(D@$@,5schema%col5geometryL<5list5elementL<5list5element %x %y<&5col&X&(,& 5hgeometrylistelementlistelementx&&D@$@(D@$@,&  5hgeometrylistelementlistelementy&&D@$@(D@$@,&,geo{"version": "1.1.0", "primary_column": "geometry", "columns": {"geometry": {"encoding": "multilinestring", "geometry_types": ["MultiLineString"]}}} ARROW: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 parquet-cpp-arrow version 15.0.2<PAR1data-multilinestring-encoding_wkb.parquet000066400000000000000000000034151525650651600341720ustar00rootroot00000000000000geopandas-1.2.0/geopandas/io/tests/data/arrow/geoparquet/1.1.0PAR1@4L  <,( $&5col&X&(,LB>@ $:D  D@ FA:F>>4 6 D@9 t_w I8$@ ,6($@$@4@4@$@D@D@D@>@>@D@4@>@$@  $$& 5geometry&&6($@$@4@4@$@D@D@D@>@>@D@4@>@$@ ,<5schema%col %geometry,&5col&X&(,& 5geometry&&6($@$@4@4@$@D@D@D@>@>@D@4@>@$@ ,&,geo{"version": "1.1.0", "primary_column": "geometry", "columns": {"geometry": {"encoding": "WKB", "geometry_types": ["MultiLineString"]}}} ARROW:schema/////2gBAAAQAAAAAAAKAA4ABgAFAAgACgAAAAABBAAQAAAAAAAKAAwAAAAEAAgACgAAALgAAAAEAAAAAQAAAAwAAAAIAAwABAAIAAgAAAAIAAAADAAAAAMAAABnZW8AhwAAAHsidmVyc2lvbiI6ICIxLjEuMCIsICJwcmltYXJ5X2NvbHVtbiI6ICJnZW9tZXRyeSIsICJjb2x1bW5zIjogeyJnZW9tZXRyeSI6IHsiZW5jb2RpbmciOiAiV0tCIiwgImdlb21ldHJ5X3R5cGVzIjogWyJNdWx0aUxpbmVTdHJpbmciXX19fQACAAAASAAAAAQAAADQ////AAABBBAAAAAgAAAABAAAAAAAAAAIAAAAZ2VvbWV0cnkAAAAABAAEAAQAAAAQABQACAAGAAcADAAAABAAEAAAAAAAAQIQAAAAHAAAAAQAAAAAAAAAAwAAAGNvbAAIAAwACAAHAAgAAAAAAAABQAAAAAAAAAA= parquet-cpp-arrow version 15.0.2,*PAR1data-multipoint-encoding_native.parquet000066400000000000000000000037531525650651600336550ustar00rootroot00000000000000geopandas-1.2.0/geopandas/io/tests/data/arrow/geoparquet/1.1.0PAR1@4L  <,( $&5col&X&(,@4L >@$ $D@4@"&,D@$@(D@$@@& 5Hgeometrylistelementx&&D@$@(D@$@,@4L $@D $>@4@"&,D@$@(D@$@@& 5Hgeometrylistelementy&&D@$@(D@$@,|5schema%col5geometryL<5list5element %x %y<&5col&X&(,& 5Hgeometrylistelementx&&D@$@(D@$@,& 5Hgeometrylistelementy&&D@$@(D@$@,&,geo{"version": "1.1.0", "primary_column": "geometry", "columns": {"geometry": {"encoding": "multipoint", "geometry_types": ["MultiPoint"]}}} ARROW: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 parquet-cpp-arrow version 15.0.2<;PAR1geopandas-1.2.0/geopandas/io/tests/data/arrow/geoparquet/1.1.0/data-multipoint-encoding_wkb.parquet000066400000000000000000000031261525650651600332230ustar00rootroot00000000000000PAR1@4L  <,( $&5col&X&(,L >@ $@]""$ "D@D >.4 4.> 8$@ ,6(]$@D@D@>@4@4@>@$@  $$& 5geometry&&6(]$@D@D@>@4@4@>@$@ ,<5schema%col %geometry,&5col&X&(,& 5geometry&&6(]$@D@D@>@4@4@>@$@ ,&,geo{"version": "1.1.0", "primary_column": "geometry", "columns": {"geometry": {"encoding": "WKB", "geometry_types": ["MultiPoint"]}}} ARROW:schema/////2ABAAAQAAAAAAAKAA4ABgAFAAgACgAAAAABBAAQAAAAAAAKAAwAAAAEAAgACgAAALQAAAAEAAAAAQAAAAwAAAAIAAwABAAIAAgAAAAIAAAADAAAAAMAAABnZW8AggAAAHsidmVyc2lvbiI6ICIxLjEuMCIsICJwcmltYXJ5X2NvbHVtbiI6ICJnZW9tZXRyeSIsICJjb2x1bW5zIjogeyJnZW9tZXRyeSI6IHsiZW5jb2RpbmciOiAiV0tCIiwgImdlb21ldHJ5X3R5cGVzIjogWyJNdWx0aVBvaW50Il19fX0AAAIAAABIAAAABAAAAND///8AAAEEEAAAACAAAAAEAAAAAAAAAAgAAABnZW9tZXRyeQAAAAAEAAQABAAAABAAFAAIAAYABwAMAAAAEAAQAAAAAAABAhAAAAAcAAAABAAAAAAAAAADAAAAY29sAAgADAAIAAcACAAAAAAAAAFAAAAA parquet-cpp-arrow version 15.0.2,PAR1data-multipolygon-encoding_native.parquet000066400000000000000000000045651525650651600342150ustar00rootroot00000000000000geopandas-1.2.0/geopandas/io/tests/data/arrow/geoparquet/1.1.0PAR1P@D 4 $ F (.@@JN,<F@@(F@@% 8 phF6P& 5geometrylistelementlistelementlistelementx<&&F@@(F@@,dLH$@D 4  F > A (.@9@RV,<F@@(F@@) 8 ! 2 0AV0br(&  5geometrylistelementlistelementlistelementy<&&F@@(F@@,5schema%col5geometryL<5list5elementL<5list5elementL<5list5element %x %y <&5col &`&(,& 5geometrylistelementlistelementlistelementx<&&F@@(F@@,&  5geometrylistelementlistelementlistelementy<&&F@@(F@@, &,geo{"version": "1.1.0", "primary_column": "geometry", "columns": {"geometry": {"encoding": "multipolygon", "geometry_types": ["MultiPolygon"]}}} ARROW: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 parquet-cpp-arrow version 15.0.2<9PAR1data-multipolygon-encoding_wkb.parquet000066400000000000000000000043441525650651600335050ustar00rootroot00000000000000geopandas-1.2.0/geopandas/io/tests/data/arrow/geoparquet/1.1.0PAR1P@ $ D:4:(@\j j _* :FZZZ4@M. = 5 m(@-!ZZ R(D V! =A  m HA@bh$<9 d84@ , 6(D@D@4@F@F@>@D@D@4@A@$@>@$@$@>@@F@4@4@A@>@4@4@.@4@9@>@4@  $&  5geometry  &&6(D@D@4@F@F@>@D@D@4@A@$@>@$@$@>@@F@4@4@A@>@4@4@.@4@9@>@4@ ,<5schema%col %geometry ,&5col &`&(,&  5geometry  &&6(D@D@4@F@F@>@D@D@4@A@$@>@$@$@>@@F@4@4@A@>@4@4@.@4@9@>@4@ , & ,geo{"version": "1.1.0", "primary_column": "geometry", "columns": {"geometry": {"encoding": "WKB", "geometry_types": ["MultiPolygon"]}}} ARROW:schema/////2gBAAAQAAAAAAAKAA4ABgAFAAgACgAAAAABBAAQAAAAAAAKAAwAAAAEAAgACgAAALgAAAAEAAAAAQAAAAwAAAAIAAwABAAIAAgAAAAIAAAADAAAAAMAAABnZW8AhAAAAHsidmVyc2lvbiI6ICIxLjEuMCIsICJwcmltYXJ5X2NvbHVtbiI6ICJnZW9tZXRyeSIsICJjb2x1bW5zIjogeyJnZW9tZXRyeSI6IHsiZW5jb2RpbmciOiAiV0tCIiwgImdlb21ldHJ5X3R5cGVzIjogWyJNdWx0aVBvbHlnb24iXX19fQAAAAACAAAASAAAAAQAAADQ////AAABBBAAAAAgAAAABAAAAAAAAAAIAAAAZ2VvbWV0cnkAAAAABAAEAAQAAAAQABQACAAGAAcADAAAABAAEAAAAAAAAQIQAAAAHAAAAAQAAAAAAAAAAwAAAGNvbAAIAAwACAAHAAgAAAAAAAABQAAAAAAAAAA= parquet-cpp-arrow version 15.0.2,PAR1geopandas-1.2.0/geopandas/io/tests/data/arrow/geoparquet/1.1.0/data-point-encoding_native.parquet000066400000000000000000000034531525650651600326560ustar00rootroot00000000000000PAR1@4L  <,( $&5col&X&(,0,L>@(D@,D@>@(D@>@ $ $& 5(geometryx&&D@>@(D@>@,0,L$@(D@,D@$@(D@$@ $ $& 5(geometryy&&D@$@(D@$@,\5schema%col5geometry %x %y<&5col&X&(,& 5(geometryx&&D@>@(D@>@,& 5(geometryy&&D@$@(D@$@,&,geo{"version": "1.1.0", "primary_column": "geometry", "columns": {"geometry": {"encoding": "point", "geometry_types": ["Point"]}}} ARROW: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 parquet-cpp-arrow version 15.0.2<PAR1geopandas-1.2.0/geopandas/io/tests/data/arrow/geoparquet/1.1.0/data-point-encoding_wkb.parquet000066400000000000000000000025661525650651600321570ustar00rootroot00000000000000PAR1@4L  <,( $&5col&X&(,XLK>@ $@: :$D@D@,6(>@$@ $ $& 5geometry&&6(>@$@,<5schema%col %geometry,&5col&X&(,& 5geometry&&6(>@$@,&,geo}{"version": "1.1.0", "primary_column": "geometry", "columns": {"geometry": {"encoding": "WKB", "geometry_types": ["Point"]}}} ARROW:schema/////2ABAAAQAAAAAAAKAA4ABgAFAAgACgAAAAABBAAQAAAAAAAKAAwAAAAEAAgACgAAALAAAAAEAAAAAQAAAAwAAAAIAAwABAAIAAgAAAAIAAAADAAAAAMAAABnZW8AfQAAAHsidmVyc2lvbiI6ICIxLjEuMCIsICJwcmltYXJ5X2NvbHVtbiI6ICJnZW9tZXRyeSIsICJjb2x1bW5zIjogeyJnZW9tZXRyeSI6IHsiZW5jb2RpbmciOiAiV0tCIiwgImdlb21ldHJ5X3R5cGVzIjogWyJQb2ludCJdfX19AAAAAgAAAEgAAAAEAAAA0P///wAAAQQQAAAAIAAAAAQAAAAAAAAACAAAAGdlb21ldHJ5AAAAAAQABAAEAAAAEAAUAAgABgAHAAwAAAAQABAAAAAAAAECEAAAABwAAAAEAAAAAAAAAAMAAABjb2wACAAMAAgABwAIAAAAAAAAAUAAAAAAAAAA parquet-cpp-arrow version 15.0.2,PAR1geopandas-1.2.0/geopandas/io/tests/data/arrow/geoparquet/1.1.0/data-polygon-encoding_native.parquet000066400000000000000000000042251525650651600332120ustar00rootroot00000000000000PAR1@4L  <,( $&5col&X&(,pNL8>@D 4 $ A $F@.@48, F@$@(F@$@d ֣& 5hgeometrylistelementlistelementx &&F@$@(F@$@,`HL 0$@D 4 F (>@A@48, F@$@(F@$@d H,+&  5hgeometrylistelementlistelementy &&F@$@(F@$@,5schema%col5geometryL<5list5elementL<5list5element %x %y<&5col&X&(,& 5hgeometrylistelementlistelementx &&F@$@(F@$@,&  5hgeometrylistelementlistelementy &&F@$@(F@$@,&,geo{"version": "1.1.0", "primary_column": "geometry", "columns": {"geometry": {"encoding": "polygon", "geometry_types": ["Polygon"]}}} ARROW: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 parquet-cpp-arrow version 15.0.2<PAR1geopandas-1.2.0/geopandas/io/tests/data/arrow/geoparquet/1.1.0/data-polygon-encoding_wkb.parquet000066400000000000000000000035051525650651600325070ustar00rootroot00000000000000PAR1@4L  <,( $&5col&X&(,L]>@ $ D:4:(@ aaA A 9F . y (a@ }$D8>@ ,6(A@$@F@F@.@D@$@4@A@$@4@>@A@A@>@4@4@>@  $$& 5geometry&&6(A@$@F@F@.@D@$@4@A@$@4@>@A@A@>@4@4@>@ ,<5schema%col %geometry,&5col&X&(,& 5geometry&&6(A@$@F@F@.@D@$@4@A@$@4@>@A@A@>@4@4@>@ , &,geo{"version": "1.1.0", "primary_column": "geometry", "columns": {"geometry": {"encoding": "WKB", "geometry_types": ["Polygon"]}}} ARROW:schema/////2ABAAAQAAAAAAAKAA4ABgAFAAgACgAAAAABBAAQAAAAAAAKAAwAAAAEAAgACgAAALAAAAAEAAAAAQAAAAwAAAAIAAwABAAIAAgAAAAIAAAADAAAAAMAAABnZW8AfwAAAHsidmVyc2lvbiI6ICIxLjEuMCIsICJwcmltYXJ5X2NvbHVtbiI6ICJnZW9tZXRyeSIsICJjb2x1bW5zIjogeyJnZW9tZXRyeSI6IHsiZW5jb2RpbmciOiAiV0tCIiwgImdlb21ldHJ5X3R5cGVzIjogWyJQb2x5Z29uIl19fX0AAgAAAEgAAAAEAAAA0P///wAAAQQQAAAAIAAAAAQAAAAAAAAACAAAAGdlb21ldHJ5AAAAAAQABAAEAAAAEAAUAAgABgAHAAwAAAAQABAAAAAAAAECEAAAABwAAAAEAAAAAAAAAAMAAABjb2wACAAMAAgABwAIAAAAAAAAAUAAAAAAAAAA parquet-cpp-arrow version 15.0.2,+PAR1geopandas-1.2.0/geopandas/io/tests/data/arrow/geoparquet/2.0.0/000077500000000000000000000000001525650651600240545ustar00rootroot00000000000000geopandas-1.2.0/geopandas/io/tests/data/arrow/geoparquet/2.0.0/data-linestring-encoding_wkb.parquet000066400000000000000000000025401525650651600331740ustar00rootroot00000000000000PAR10,L <,( $$^LJ(9>@ $:D 8D@ , <5schema%col %geometryl ",&5col&P&(,<)&& 5geometry&&),<&$@D@$@D@&,geo{"version": "2.0.0", "primary_column": "geometry", "columns": {"geometry": {"encoding": "WKB", "geometry_types": ["LineString"]}}} ARROW: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 parquet-cpp-arrow version 23.0.1,PAR1data-multilinestring-encoding_wkb.parquet000066400000000000000000000026461525650651600341770ustar00rootroot00000000000000geopandas-1.2.0/geopandas/io/tests/data/arrow/geoparquet/2.0.0PAR1@4L  <,( $LB>@ $:D  D@ FA:F>>4 6 D@9 t_w I8$@ , $$<5schema%col %geometryl ",&5col&X&(,<)&& 5geometry&&),<&$@D@$@D@ &,geo{"version": "2.0.0", "primary_column": "geometry", "columns": {"geometry": {"encoding": "WKB", "geometry_types": ["MultiLineString"]}}} ARROW: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 parquet-cpp-arrow version 23.0.1,PAR1geopandas-1.2.0/geopandas/io/tests/data/arrow/geoparquet/2.0.0/data-multipoint-encoding_wkb.parquet000066400000000000000000000026101525650651600332200ustar00rootroot00000000000000PAR1@4L  <,( $L >@ $@]""$ "D@D >.4 4.> 8$@ , $$<5schema%col %geometryl ",&5col&X&(,<)&& 5geometry&&),<&$@D@$@D@&,geo{"version": "2.0.0", "primary_column": "geometry", "columns": {"geometry": {"encoding": "WKB", "geometry_types": ["MultiPoint"]}}} ARROW: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 parquet-cpp-arrow version 23.0.1,PAR1data-multipolygon-encoding_wkb.parquet000066400000000000000000000030111525650651600334730ustar00rootroot00000000000000geopandas-1.2.0/geopandas/io/tests/data/arrow/geoparquet/2.0.0PAR1P@ $ D:4:(@\j j _* :FZZZ4@M. = 5 m(@-!ZZ R(D V! =A  m HA@bh$<9 d84@ ,  $<5schema%col %geometryl " ,&5col &`&(,<)& & 5geometry  &&),<&@F@@F@   &,geo{"version": "2.0.0", "primary_column": "geometry", "columns": {"geometry": {"encoding": "WKB", "geometry_types": ["MultiPolygon"]}}} ARROW: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 parquet-cpp-arrow version 23.0.1,PAR1geopandas-1.2.0/geopandas/io/tests/data/arrow/geoparquet/2.0.0/data-point-encoding_wkb.parquet000066400000000000000000000025171525650651600321530ustar00rootroot00000000000000PAR1@4L  <,( $XLK>@ $@: :$D@D@, $ $<5schema%col %geometryl ",&5col&X&(,<)&& 5geometry&&),<~&>@D@$@D@&,geo}{"version": "2.0.0", "primary_column": "geometry", "columns": {"geometry": {"encoding": "WKB", "geometry_types": ["Point"]}}} ARROW: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 parquet-cpp-arrow version 23.0.1,vPAR1geopandas-1.2.0/geopandas/io/tests/data/arrow/geoparquet/2.0.0/data-polygon-encoding_wkb.parquet000066400000000000000000000026341525650651600325110ustar00rootroot00000000000000PAR1@4L  <,( $L]>@ $ D:4:(@ aaA A 9F . y (a@ }$D8>@ , $$<5schema%col %geometryl ",&5col&X&(,<)&& 5geometry&&),<&$@F@$@F@&,geo{"version": "2.0.0", "primary_column": "geometry", "columns": {"geometry": {"encoding": "WKB", "geometry_types": ["Polygon"]}}} ARROW: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 parquet-cpp-arrow version 23.0.1,yPAR1geopandas-1.2.0/geopandas/io/tests/data/arrow/geoparquet/README.md000066400000000000000000000001661525650651600247010ustar00rootroot00000000000000The data included in this directory are copied from the `test-data` from https://github.com/opengeospatial/geoparquet geopandas-1.2.0/geopandas/io/tests/data/arrow/test_data_gdal350.arrow000066400000000000000000000107321525650651600255110ustar00rootroot00000000000000ARROW1x     geo{"schema_version":"0.1.0","primary_column":"geometry","columns":{"geometry":{"encoding":"WKB","crs":"GEOGCRS[\"WGS 84\",ENSEMBLE[\"World Geodetic System 1984 ensemble\",MEMBER[\"World Geodetic System 1984 (Transit)\"],MEMBER[\"World Geodetic System 1984 (G730)\"],MEMBER[\"World Geodetic System 1984 (G873)\"],MEMBER[\"World Geodetic System 1984 (G1150)\"],MEMBER[\"World Geodetic System 1984 (G1674)\"],MEMBER[\"World Geodetic System 1984 (G1762)\"],MEMBER[\"World Geodetic System 1984 (G2139)\"],ELLIPSOID[\"WGS 84\",6378137,298.257223563,LENGTHUNIT[\"metre\",1]],ENSEMBLEACCURACY[2.0]],PRIMEM[\"Greenwich\",0,ANGLEUNIT[\"degree\",0.0174532925199433]],CS[ellipsoidal,2],AXIS[\"geodetic latitude (Lat)\",north,ORDER[1],ANGLEUNIT[\"degree\",0.0174532925199433]],AXIS[\"geodetic longitude (Lon)\",east,ORDER[2],ANGLEUNIT[\"degree\",0.0174532925199433]],USAGE[SCOPE[\"Horizontal component of 3D system.\"],AREA[\"World.\"],BBOX[-90,-180,90,180]],ID[\"EPSG\",4326]]","gdal:geometry_type":"mixed"}}} `geometry   ARROW::extension:nameWKB\  col_float col_int @ col_strH  P   #(HH%pp'# "M`@ "M`@ab"M`@""M`@?? "M`@  "M`@tT$?  ]#@' (]= @@ 4xgdal:geo{"primary_column":"geometry","columns":{"geometry":{"encoding":"WKB","crs":"GEOGCRS[\"WGS 84\",ENSEMBLE[\"World Geodetic System 1984 ensemble\",MEMBER[\"World Geodetic System 1984 (Transit)\"],MEMBER[\"World Geodetic System 1984 (G730)\"],MEMBER[\"World Geodetic System 1984 (G873)\"],MEMBER[\"World Geodetic System 1984 (G1150)\"],MEMBER[\"World Geodetic System 1984 (G1674)\"],MEMBER[\"World Geodetic System 1984 (G1762)\"],MEMBER[\"World Geodetic System 1984 (G2139)\"],ELLIPSOID[\"WGS 84\",6378137,298.257223563,LENGTHUNIT[\"metre\",1]],ENSEMBLEACCURACY[2.0]],PRIMEM[\"Greenwich\",0,ANGLEUNIT[\"degree\",0.0174532925199433]],CS[ellipsoidal,2],AXIS[\"geodetic latitude (Lat)\",north,ORDER[1],ANGLEUNIT[\"degree\",0.0174532925199433]],AXIS[\"geodetic longitude (Lon)\",east,ORDER[2],ANGLEUNIT[\"degree\",0.0174532925199433]],USAGE[SCOPE[\"Horizontal component of 3D system.\"],AREA[\"World.\"],BBOX[-90,-180,90,180]],ID[\"EPSG\",4326]]","bbox":[0.0,0.0,5.0,5.0]}}}PP   geo{"schema_version":"0.1.0","primary_column":"geometry","columns":{"geometry":{"encoding":"WKB","crs":"GEOGCRS[\"WGS 84\",ENSEMBLE[\"World Geodetic System 1984 ensemble\",MEMBER[\"World Geodetic System 1984 (Transit)\"],MEMBER[\"World Geodetic System 1984 (G730)\"],MEMBER[\"World Geodetic System 1984 (G873)\"],MEMBER[\"World Geodetic System 1984 (G1150)\"],MEMBER[\"World Geodetic System 1984 (G1674)\"],MEMBER[\"World Geodetic System 1984 (G1762)\"],MEMBER[\"World Geodetic System 1984 (G2139)\"],ELLIPSOID[\"WGS 84\",6378137,298.257223563,LENGTHUNIT[\"metre\",1]],ENSEMBLEACCURACY[2.0]],PRIMEM[\"Greenwich\",0,ANGLEUNIT[\"degree\",0.0174532925199433]],CS[ellipsoidal,2],AXIS[\"geodetic latitude (Lat)\",north,ORDER[1],ANGLEUNIT[\"degree\",0.0174532925199433]],AXIS[\"geodetic longitude (Lon)\",east,ORDER[2],ANGLEUNIT[\"degree\",0.0174532925199433]],USAGE[SCOPE[\"Horizontal component of 3D system.\"],AREA[\"World.\"],BBOX[-90,-180,90,180]],ID[\"EPSG\",4326]]","gdal:geometry_type":"mixed"}}} `geometry   ARROW::extension:nameWKB\  col_float col_int @ col_str ARROW1geopandas-1.2.0/geopandas/io/tests/data/arrow/test_data_gdal350.parquet000066400000000000000000000070001525650651600260320ustar00rootroot00000000000000PAR1L $ab,6(ba & 5col_strx&<&6(ba, $L<,( &5col_int&&(, $LJ]@ ' >(]F Y >$@@,6(?????@@@@@@@@@@]@@@@@@@@@@ & 5geometry & &6(?????@@@@@@@@@@]@@@@@@@@@@,\5schema %col_str%L%col_int % col_float %geometryL& 5col_strx&<&6(ba,&5col_int&&(,& 5 col_float&&??(??,& 5geometry & &6(?????@@@@@@@@@@]@@@@@@@@@@, & , ARROW:schema/////xgBAAAQAAAAAAAKAAwABgAFAAgACgAAAAABBAAMAAAACAAIAAAABAAIAAAABAAAAAQAAAC4AAAAbAAAADQAAAAEAAAAaP///wAAAQQQAAAAHAAAAAQAAAAAAAAACAAAAGdlb21ldHJ5AAAAAFz///+U////AAABAxAAAAAgAAAABAAAAAAAAAAJAAAAY29sX2Zsb2F0AAYACAAGAAYAAAAAAAIAyP///wAAAQIQAAAAIAAAAAQAAAAAAAAABwAAAGNvbF9pbnQACAAMAAgABwAIAAAAAAAAAUAAAAAQABQACAAGAAcADAAAABAAEAAAAAAAAQUQAAAAHAAAAAQAAAAAAAAABwAAAGNvbF9zdHIABAAEAAQAAAAAAAAAgeo{"version":"0.3.0","primary_column":"geometry","columns":{"geometry":{"encoding":"WKB","crs":"GEOGCRS[\"WGS 84\",ENSEMBLE[\"World Geodetic System 1984 ensemble\",MEMBER[\"World Geodetic System 1984 (Transit)\"],MEMBER[\"World Geodetic System 1984 (G730)\"],MEMBER[\"World Geodetic System 1984 (G873)\"],MEMBER[\"World Geodetic System 1984 (G1150)\"],MEMBER[\"World Geodetic System 1984 (G1674)\"],MEMBER[\"World Geodetic System 1984 (G1762)\"],MEMBER[\"World Geodetic System 1984 (G2139)\"],ELLIPSOID[\"WGS 84\",6378137,298.257223563,LENGTHUNIT[\"metre\",1]],ENSEMBLEACCURACY[2.0]],PRIMEM[\"Greenwich\",0,ANGLEUNIT[\"degree\",0.0174532925199433]],CS[ellipsoidal,2],AXIS[\"geodetic latitude (Lat)\",north,ORDER[1],ANGLEUNIT[\"degree\",0.0174532925199433]],AXIS[\"geodetic longitude (Lon)\",east,ORDER[2],ANGLEUNIT[\"degree\",0.0174532925199433]],USAGE[SCOPE[\"Horizontal component of 3D system.\"],AREA[\"World.\"],BBOX[-90,-180,90,180]],ID[\"EPSG\",4326]]","bbox":[0.0,0.0,5.0,5.0],"orientation":"counterclockwise","geometry_type":["Polygon","MultiPolygon"]}}}parquet-cpp-arrow version 7.0.0LPAR1geopandas-1.2.0/geopandas/io/tests/data/arrow/test_data_gdal390.parquet000066400000000000000000000076301525650651600260470ustar00rootroot00000000000000PAR1L $ab,6(ba & 5col_strx&<&6(ba, $L<,( &5col_int&&(, $LJ]@ ' >(f Jj b >$@@, &  5geometry& &),L@,@(@ & 5( geometry_bboxxmin& & @(@,L@,@(@ &5( geometry_bboxymin&&@(@,L@@@,@@@(@@@ &5( geometry_bboxxmax&&@@@(@@@,L@@@,@@@(@@@ &5( geometry_bboxymax&&@@@(@@@,5schema  %col_str%L%col_int % col_float %geometry5 geometry_bbox%xmin%ymin%xmax%ymax& 5col_strx&<&6(ba,&5col_int&&(,& 5 col_float&&??(??,&  5geometry& &),& 5( geometry_bboxxmin& & @(@,&5( geometry_bboxymin&&@(@,&5( geometry_bboxxmax&&@@@(@@@,&5( geometry_bboxymax&&@@@(@@@,& ,geo{"version":"1.0.0","primary_column":"geometry","columns":{"geometry":{"encoding":"WKB","bbox":[0.0,0.0,5.0,5.0],"covering":{"bbox":{"xmin":["geometry_bbox","xmin"],"ymin":["geometry_bbox","ymin"],"xmax":["geometry_bbox","xmax"],"ymax":["geometry_bbox","ymax"]}},"orientation":"counterclockwise","geometry_types":["MultiPolygon"]}}} ARROW:schema 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2GDAL 3.9.0, using parquet-cpp-arrow version 16.1.0 PAR1geopandas-1.2.0/geopandas/io/tests/data/arrow/test_data_v0.1.0.feather000066400000000000000000000120521525650651600254550ustar00rootroot00000000000000ARROW1    P geo'{"primary_column": "geometry", "columns": {"geometry": {"crs": "GEOGCRS[\"WGS 84\",ENSEMBLE[\"World Geodetic System 1984 ensemble\",MEMBER[\"World Geodetic System 1984 (Transit)\"],MEMBER[\"World Geodetic System 1984 (G730)\"],MEMBER[\"World Geodetic System 1984 (G873)\"],MEMBER[\"World Geodetic System 1984 (G1150)\"],MEMBER[\"World Geodetic System 1984 (G1674)\"],MEMBER[\"World Geodetic System 1984 (G1762)\"],MEMBER[\"World Geodetic System 1984 (G2139)\"],ELLIPSOID[\"WGS 84\",6378137,298.257223563,LENGTHUNIT[\"metre\",1]],ENSEMBLEACCURACY[2.0]],PRIMEM[\"Greenwich\",0,ANGLEUNIT[\"degree\",0.0174532925199433]],CS[ellipsoidal,2],AXIS[\"geodetic latitude (Lat)\",north,ORDER[1],ANGLEUNIT[\"degree\",0.0174532925199433]],AXIS[\"geodetic longitude (Lon)\",east,ORDER[2],ANGLEUNIT[\"degree\",0.0174532925199433]],USAGE[SCOPE[\"Horizontal component of 3D system.\"],AREA[\"World.\"],BBOX[-90,-180,90,180]],ID[\"EPSG\",4326]]", "encoding": "WKB", "bbox": [0.0, 0.0, 5.0, 5.0]}}, "schema_version": "0.1.0", "creator": {"library": "geopandas", "version": "0.10.2"}} pandas {"index_columns": [{"kind": "range", "name": null, "start": 0, "stop": 2, "step": 1}], "column_indexes": [{"name": null, "field_name": null, "pandas_type": "unicode", "numpy_type": "object", "metadata": {"encoding": "UTF-8"}}], "columns": [{"name": "col_str", "field_name": "col_str", "pandas_type": "unicode", "numpy_type": "object", "metadata": null}, {"name": "col_int", "field_name": "col_int", "pandas_type": "int64", "numpy_type": "int64", "metadata": null}, {"name": "col_float", "field_name": "col_float", "pandas_type": "float64", "numpy_type": "float64", "metadata": null}, {"name": "geometry", "field_name": "geometry", "pandas_type": "bytes", "numpy_type": "object", "metadata": null}], "creator": {"library": "pyarrow", "version": "6.0.1"}, "pandas_version": "1.3.5"}l4hgeometry\  col_float col_int @ col_strH  P   #(HH%pp'# "M`@ "M`@ab"M`@""M`@?? "M`@  "M`@tT$?  ]#@' (]= @@   <(PP  P geo'{"primary_column": "geometry", "columns": {"geometry": {"crs": "GEOGCRS[\"WGS 84\",ENSEMBLE[\"World Geodetic System 1984 ensemble\",MEMBER[\"World Geodetic System 1984 (Transit)\"],MEMBER[\"World Geodetic System 1984 (G730)\"],MEMBER[\"World Geodetic System 1984 (G873)\"],MEMBER[\"World Geodetic System 1984 (G1150)\"],MEMBER[\"World Geodetic System 1984 (G1674)\"],MEMBER[\"World Geodetic System 1984 (G1762)\"],MEMBER[\"World Geodetic System 1984 (G2139)\"],ELLIPSOID[\"WGS 84\",6378137,298.257223563,LENGTHUNIT[\"metre\",1]],ENSEMBLEACCURACY[2.0]],PRIMEM[\"Greenwich\",0,ANGLEUNIT[\"degree\",0.0174532925199433]],CS[ellipsoidal,2],AXIS[\"geodetic latitude (Lat)\",north,ORDER[1],ANGLEUNIT[\"degree\",0.0174532925199433]],AXIS[\"geodetic longitude (Lon)\",east,ORDER[2],ANGLEUNIT[\"degree\",0.0174532925199433]],USAGE[SCOPE[\"Horizontal component of 3D system.\"],AREA[\"World.\"],BBOX[-90,-180,90,180]],ID[\"EPSG\",4326]]", "encoding": "WKB", "bbox": [0.0, 0.0, 5.0, 5.0]}}, "schema_version": "0.1.0", "creator": {"library": "geopandas", "version": "0.10.2"}} pandas {"index_columns": [{"kind": "range", "name": null, "start": 0, "stop": 2, "step": 1}], "column_indexes": [{"name": null, "field_name": null, "pandas_type": "unicode", "numpy_type": "object", "metadata": {"encoding": "UTF-8"}}], "columns": [{"name": "col_str", "field_name": "col_str", "pandas_type": "unicode", "numpy_type": "object", "metadata": null}, {"name": "col_int", "field_name": "col_int", "pandas_type": "int64", "numpy_type": "int64", "metadata": null}, {"name": "col_float", "field_name": "col_float", "pandas_type": "float64", "numpy_type": "float64", "metadata": null}, {"name": "geometry", "field_name": "geometry", "pandas_type": "bytes", "numpy_type": "object", "metadata": null}], "creator": {"library": "pyarrow", "version": "6.0.1"}, "pandas_version": "1.3.5"}l4hgeometry\  col_float col_int @ col_strARROW1geopandas-1.2.0/geopandas/io/tests/data/arrow/test_data_v0.1.0.parquet000066400000000000000000000154471525650651600255330ustar00rootroot00000000000000PAR1L $ab,6(ba & 5col_strx&<&6(ba, $L<,( &5col_int&&(, $LJ]@ ' >(]F Y >$@@,6(?????@@@@@@@@@@]@@@@@@@@@@ & 5geometry & &6(?????@@@@@@@@@@]@@@@@@@@@@,\5schema %col_str%L%col_int % col_float %geometryL& 5col_strx&<&6(ba,&5col_int&&(,& 5 col_float&&??(??,& 5geometry & &6(?????@@@@@@@@@@]@@@@@@@@@@, & <pandas{"index_columns": [{"kind": "range", "name": null, "start": 0, "stop": 2, "step": 1}], "column_indexes": [{"name": null, "field_name": null, "pandas_type": "unicode", "numpy_type": "object", "metadata": {"encoding": "UTF-8"}}], "columns": [{"name": "col_str", "field_name": "col_str", "pandas_type": "unicode", "numpy_type": "object", "metadata": null}, {"name": "col_int", "field_name": "col_int", "pandas_type": "int64", "numpy_type": "int64", "metadata": null}, {"name": "col_float", "field_name": "col_float", "pandas_type": "float64", "numpy_type": "float64", "metadata": null}, {"name": "geometry", "field_name": "geometry", "pandas_type": "bytes", "numpy_type": "object", "metadata": null}], "creator": {"library": "pyarrow", "version": "6.0.1"}, "pandas_version": "1.3.5"}geo{"primary_column": "geometry", "columns": {"geometry": {"crs": "GEOGCRS[\"WGS 84\",ENSEMBLE[\"World Geodetic System 1984 ensemble\",MEMBER[\"World Geodetic System 1984 (Transit)\"],MEMBER[\"World Geodetic System 1984 (G730)\"],MEMBER[\"World Geodetic System 1984 (G873)\"],MEMBER[\"World Geodetic System 1984 (G1150)\"],MEMBER[\"World Geodetic System 1984 (G1674)\"],MEMBER[\"World Geodetic System 1984 (G1762)\"],MEMBER[\"World Geodetic System 1984 (G2139)\"],ELLIPSOID[\"WGS 84\",6378137,298.257223563,LENGTHUNIT[\"metre\",1]],ENSEMBLEACCURACY[2.0]],PRIMEM[\"Greenwich\",0,ANGLEUNIT[\"degree\",0.0174532925199433]],CS[ellipsoidal,2],AXIS[\"geodetic latitude (Lat)\",north,ORDER[1],ANGLEUNIT[\"degree\",0.0174532925199433]],AXIS[\"geodetic longitude (Lon)\",east,ORDER[2],ANGLEUNIT[\"degree\",0.0174532925199433]],USAGE[SCOPE[\"Horizontal component of 3D system.\"],AREA[\"World.\"],BBOX[-90,-180,90,180]],ID[\"EPSG\",4326]]", "encoding": "WKB", "bbox": [0.0, 0.0, 5.0, 5.0]}}, "schema_version": "0.1.0", "creator": {"library": "geopandas", "version": "0.10.2"}} ARROW: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parquet-cpp-arrow version 6.0.1LPAR1geopandas-1.2.0/geopandas/io/tests/data/arrow/test_data_v0.4.0.feather000066400000000000000000000116521525650651600254650ustar00rootroot00000000000000ARROW1`    H geo{"primary_column": "geometry", "columns": {"geometry": {"encoding": "WKB", "crs": {"$schema": "https://proj.org/schemas/v0.2/projjson.schema.json", "type": "GeographicCRS", "name": "WGS 84", "datum": {"type": "GeodeticReferenceFrame", "name": "World Geodetic System 1984", "ellipsoid": {"name": "WGS 84", "semi_major_axis": 6378137, "inverse_flattening": 298.257223563}}, "coordinate_system": {"subtype": "ellipsoidal", "axis": [{"name": "Geodetic latitude", "abbreviation": "Lat", "direction": "north", "unit": "degree"}, {"name": "Geodetic longitude", "abbreviation": "Lon", "direction": "east", "unit": "degree"}]}, "scope": "Horizontal component of 3D system.", "area": "World.", "bbox": {"south_latitude": -90, "west_longitude": -180, "north_latitude": 90, "east_longitude": 180}, "id": {"authority": "EPSG", "code": 4326}}, "geometry_type": ["MultiPolygon", "Polygon"], "bbox": [0.0, 0.0, 5.0, 5.0]}}, "version": "0.4.0", "creator": {"library": "geopandas", "version": "0.10.2+89.g6b848f1c"}} pandas {"index_columns": [{"kind": "range", "name": null, "start": 0, "stop": 2, "step": 1}], "column_indexes": [{"name": null, "field_name": null, "pandas_type": "unicode", "numpy_type": "object", "metadata": {"encoding": "UTF-8"}}], "columns": [{"name": "col_str", "field_name": "col_str", "pandas_type": "unicode", "numpy_type": "object", "metadata": null}, {"name": "col_int", "field_name": "col_int", "pandas_type": "int64", "numpy_type": "int64", "metadata": null}, {"name": "col_float", "field_name": "col_float", "pandas_type": "float64", "numpy_type": "float64", "metadata": null}, {"name": "geometry", "field_name": "geometry", "pandas_type": "bytes", "numpy_type": "object", "metadata": null}], "creator": {"library": "pyarrow", "version": "4.0.0"}, "pandas_version": "1.4.2"}l4hgeometry\  col_float col_int @ col_strH  P   #(HH%pp'# "M`@ "M`@ab"M`@""M`@?? "M`@  "M`@tT$?  ]#@' (]= @@   <(pPP  H geo{"primary_column": "geometry", "columns": {"geometry": {"encoding": "WKB", "crs": {"$schema": "https://proj.org/schemas/v0.2/projjson.schema.json", "type": "GeographicCRS", "name": "WGS 84", "datum": {"type": "GeodeticReferenceFrame", "name": "World Geodetic System 1984", "ellipsoid": {"name": "WGS 84", "semi_major_axis": 6378137, "inverse_flattening": 298.257223563}}, "coordinate_system": {"subtype": "ellipsoidal", "axis": [{"name": "Geodetic latitude", "abbreviation": "Lat", "direction": "north", "unit": "degree"}, {"name": "Geodetic longitude", "abbreviation": "Lon", "direction": "east", "unit": "degree"}]}, "scope": "Horizontal component of 3D system.", "area": "World.", "bbox": {"south_latitude": -90, "west_longitude": -180, "north_latitude": 90, "east_longitude": 180}, "id": {"authority": "EPSG", "code": 4326}}, "geometry_type": ["MultiPolygon", "Polygon"], "bbox": [0.0, 0.0, 5.0, 5.0]}}, "version": "0.4.0", "creator": {"library": "geopandas", "version": "0.10.2+89.g6b848f1c"}} pandas {"index_columns": [{"kind": "range", "name": null, "start": 0, "stop": 2, "step": 1}], "column_indexes": [{"name": null, "field_name": null, "pandas_type": "unicode", "numpy_type": "object", "metadata": {"encoding": "UTF-8"}}], "columns": [{"name": "col_str", "field_name": "col_str", "pandas_type": "unicode", "numpy_type": "object", "metadata": null}, {"name": "col_int", "field_name": "col_int", "pandas_type": "int64", "numpy_type": "int64", "metadata": null}, {"name": "col_float", "field_name": "col_float", "pandas_type": "float64", "numpy_type": "float64", "metadata": null}, {"name": "geometry", "field_name": "geometry", "pandas_type": "bytes", "numpy_type": "object", "metadata": null}], "creator": {"library": "pyarrow", "version": "4.0.0"}, "pandas_version": "1.4.2"}l4hgeometry\  col_float col_int @ col_strARROW1geopandas-1.2.0/geopandas/io/tests/data/arrow/test_data_v0.4.0.parquet000066400000000000000000000152231525650651600255260ustar00rootroot00000000000000PAR1L $ab,6(ba & 5col_strx&<&6(ba, $L<,( &5col_int&&(, $LJ]@ ' >(]F Y >$@@,6(?????@@@@@@@@@@]@@@@@@@@@@ & 5geometry & &6(?????@@@@@@@@@@]@@@@@@@@@@,\5schema %col_str%L%col_int % col_float %geometryL& 5col_strx&<&6(ba,&5col_int&&(,& 5 col_float&&??(??,& 5geometry & &6(?????@@@@@@@@@@]@@@@@@@@@@, & <pandas{"index_columns": [{"kind": "range", "name": null, "start": 0, "stop": 2, "step": 1}], "column_indexes": [{"name": null, "field_name": null, "pandas_type": "unicode", "numpy_type": "object", "metadata": {"encoding": "UTF-8"}}], "columns": [{"name": "col_str", "field_name": "col_str", "pandas_type": "unicode", "numpy_type": "object", "metadata": null}, {"name": "col_int", "field_name": "col_int", "pandas_type": "int64", "numpy_type": "int64", "metadata": null}, {"name": "col_float", "field_name": "col_float", "pandas_type": "float64", "numpy_type": "float64", "metadata": null}, {"name": "geometry", "field_name": "geometry", "pandas_type": "bytes", "numpy_type": "object", "metadata": null}], "creator": {"library": "pyarrow", "version": "4.0.0"}, "pandas_version": "1.4.2"}geo{"primary_column": "geometry", "columns": {"geometry": {"encoding": "WKB", "crs": {"$schema": "https://proj.org/schemas/v0.2/projjson.schema.json", "type": "GeographicCRS", "name": "WGS 84", "datum": {"type": "GeodeticReferenceFrame", "name": "World Geodetic System 1984", "ellipsoid": {"name": "WGS 84", "semi_major_axis": 6378137, "inverse_flattening": 298.257223563}}, "coordinate_system": {"subtype": "ellipsoidal", "axis": [{"name": "Geodetic latitude", "abbreviation": "Lat", "direction": "north", "unit": "degree"}, {"name": "Geodetic longitude", "abbreviation": "Lon", "direction": "east", "unit": "degree"}]}, "scope": "Horizontal component of 3D system.", "area": "World.", "bbox": {"south_latitude": -90, "west_longitude": -180, "north_latitude": 90, "east_longitude": 180}, "id": {"authority": "EPSG", "code": 4326}}, "geometry_type": ["MultiPolygon", "Polygon"], "bbox": [0.0, 0.0, 5.0, 5.0]}}, "version": "0.4.0", "creator": {"library": "geopandas", "version": "0.10.2+89.g6b848f1c"}} ARROW:schema/////2AIAAAQAAAAAAAKAA4ABgAFAAgACgAAAAABBAAQAAAAAAAKAAwAAAAEAAgACgAAAEgHAAAEAAAAAgAAABAEAAAEAAAAAPz//wgAAAAMAAAAAwAAAGdlbwDmAwAAeyJwcmltYXJ5X2NvbHVtbiI6ICJnZW9tZXRyeSIsICJjb2x1bW5zIjogeyJnZW9tZXRyeSI6IHsiZW5jb2RpbmciOiAiV0tCIiwgImNycyI6IHsiJHNjaGVtYSI6ICJodHRwczovL3Byb2oub3JnL3NjaGVtYXMvdjAuMi9wcm9qanNvbi5zY2hlbWEuanNvbiIsICJ0eXBlIjogIkdlb2dyYXBoaWNDUlMiLCAibmFtZSI6ICJXR1MgODQiLCAiZGF0dW0iOiB7InR5cGUiOiAiR2VvZGV0aWNSZWZlcmVuY2VGcmFtZSIsICJuYW1lIjogIldvcmxkIEdlb2RldGljIFN5c3RlbSAxOTg0IiwgImVsbGlwc29pZCI6IHsibmFtZSI6ICJXR1MgODQiLCAic2VtaV9tYWpvcl9heGlzIjogNjM3ODEzNywgImludmVyc2VfZmxhdHRlbmluZyI6IDI5OC4yNTcyMjM1NjN9fSwgImNvb3JkaW5hdGVfc3lzdGVtIjogeyJzdWJ0eXBlIjogImVsbGlwc29pZGFsIiwgImF4aXMiOiBbeyJuYW1lIjogIkdlb2RldGljIGxhdGl0dWRlIiwgImFiYnJldmlhdGlvbiI6ICJMYXQiLCAiZGlyZWN0aW9uIjogIm5vcnRoIiwgInVuaXQiOiAiZGVncmVlIn0sIHsibmFtZSI6ICJHZW9kZXRpYyBsb25naXR1ZGUiLCAiYWJicmV2aWF0aW9uIjogIkxvbiIsICJkaXJlY3Rpb24iOiAiZWFzdCIsICJ1bml0IjogImRlZ3JlZSJ9XX0sICJzY29wZSI6ICJIb3Jpem9udGFsIGNvbXBvbmVudCBvZiAzRCBzeXN0ZW0uIiwgImFyZWEiOiAiV29ybGQuIiwgImJib3giOiB7InNvdXRoX2xhdGl0dWRlIjogLTkwLCAid2VzdF9sb25naXR1ZGUiOiAtMTgwLCAibm9ydGhfbGF0aXR1ZGUiOiA5MCwgImVhc3RfbG9uZ2l0dWRlIjogMTgwfSwgImlkIjogeyJhdXRob3JpdHkiOiAiRVBTRyIsICJjb2RlIjogNDMyNn19LCAiZ2VvbWV0cnlfdHlwZSI6IFsiTXVsdGlQb2x5Z29uIiwgIlBvbHlnb24iXSwgImJib3giOiBbMC4wLCAwLjAsIDUuMCwgNS4wXX19LCAidmVyc2lvbiI6ICIwLjQuMCIsICJjcmVhdG9yIjogeyJsaWJyYXJ5IjogImdlb3BhbmRhcyIsICJ2ZXJzaW9uIjogIjAuMTAuMis4OS5nNmI4NDhmMWMifX0AAAgADAAEAAgACAAAAAgAAAAQAAAABgAAAHBhbmRhcwAADAMAAHsiaW5kZXhfY29sdW1ucyI6IFt7ImtpbmQiOiAicmFuZ2UiLCAibmFtZSI6IG51bGwsICJzdGFydCI6IDAsICJzdG9wIjogMiwgInN0ZXAiOiAxfV0sICJjb2x1bW5faW5kZXhlcyI6IFt7Im5hbWUiOiBudWxsLCAiZmllbGRfbmFtZSI6IG51bGwsICJwYW5kYXNfdHlwZSI6ICJ1bmljb2RlIiwgIm51bXB5X3R5cGUiOiAib2JqZWN0IiwgIm1ldGFkYXRhIjogeyJlbmNvZGluZyI6ICJVVEYtOCJ9fV0sICJjb2x1bW5zIjogW3sibmFtZSI6ICJjb2xfc3RyIiwgImZpZWxkX25hbWUiOiAiY29sX3N0ciIsICJwYW5kYXNfdHlwZSI6ICJ1bmljb2RlIiwgIm51bXB5X3R5cGUiOiAib2JqZWN0IiwgIm1ldGFkYXRhIjogbnVsbH0sIHsibmFtZSI6ICJjb2xfaW50IiwgImZpZWxkX25hbWUiOiAiY29sX2ludCIsICJwYW5kYXNfdHlwZSI6ICJpbnQ2NCIsICJudW1weV90eXBlIjogImludDY0IiwgIm1ldGFkYXRhIjogbnVsbH0sIHsibmFtZSI6ICJjb2xfZmxvYXQiLCAiZmllbGRfbmFtZSI6ICJjb2xfZmxvYXQiLCAicGFuZGFzX3R5cGUiOiAiZmxvYXQ2NCIsICJudW1weV90eXBlIjogImZsb2F0NjQiLCAibWV0YWRhdGEiOiBudWxsfSwgeyJuYW1lIjogImdlb21ldHJ5IiwgImZpZWxkX25hbWUiOiAiZ2VvbWV0cnkiLCAicGFuZGFzX3R5cGUiOiAiYnl0ZXMiLCAibnVtcHlfdHlwZSI6ICJvYmplY3QiLCAibWV0YWRhdGEiOiBudWxsfV0sICJjcmVhdG9yIjogeyJsaWJyYXJ5IjogInB5YXJyb3ciLCAidmVyc2lvbiI6ICI0LjAuMCJ9LCAicGFuZGFzX3ZlcnNpb24iOiAiMS40LjIifQAAAAAEAAAAuAAAAGwAAAA0AAAABAAAAGj///8AAAEEEAAAABwAAAAEAAAAAAAAAAgAAABnZW9tZXRyeQAAAABc////lP///wAAAQMQAAAAIAAAAAQAAAAAAAAACQAAAGNvbF9mbG9hdAAGAAgABgAGAAAAAAACAMj///8AAAECEAAAACAAAAAEAAAAAAAAAAcAAABjb2xfaW50AAgADAAIAAcACAAAAAAAAAFAAAAAEAAUAAgABgAHAAwAAAAQABAAAAAAAAEFEAAAABwAAAAEAAAAAAAAAAcAAABjb2xfc3RyAAQABAAEAAAAAAAAAA==parquet-cpp-arrow version 4.0.0LPPAR1geopandas-1.2.0/geopandas/io/tests/data/arrow/test_data_v1.0.0-beta.1.feather000066400000000000000000000133321525650651600265270ustar00rootroot00000000000000ARROW1     h geo{"primary_column": "geometry", "columns": {"geometry": {"encoding": "WKB", "crs": {"$schema": "https://proj.org/schemas/v0.4/projjson.schema.json", "type": "GeographicCRS", "name": "WGS 84", "datum_ensemble": {"name": "World Geodetic System 1984 ensemble", "members": [{"name": "World Geodetic System 1984 (Transit)"}, {"name": "World Geodetic System 1984 (G730)"}, {"name": "World Geodetic System 1984 (G873)"}, {"name": "World Geodetic System 1984 (G1150)"}, {"name": "World Geodetic System 1984 (G1674)"}, {"name": "World Geodetic System 1984 (G1762)"}, {"name": "World Geodetic System 1984 (G2139)"}], "ellipsoid": {"name": "WGS 84", "semi_major_axis": 6378137, "inverse_flattening": 298.257223563}, "accuracy": "2.0", "id": {"authority": "EPSG", "code": 6326}}, "coordinate_system": {"subtype": "ellipsoidal", "axis": [{"name": "Geodetic latitude", "abbreviation": "Lat", "direction": "north", "unit": "degree"}, {"name": "Geodetic longitude", "abbreviation": "Lon", "direction": "east", "unit": "degree"}]}, "scope": "Horizontal component of 3D system.", "area": "World.", "bbox": {"south_latitude": -90, "west_longitude": -180, "north_latitude": 90, "east_longitude": 180}, "id": {"authority": "EPSG", "code": 4326}}, "geometry_types": ["MultiPolygon", "Polygon"], "bbox": [0.0, 0.0, 5.0, 5.0]}}, "version": "1.0.0-beta.1", "creator": {"library": "geopandas", "version": "0.12.1+47.g69816130.dirty"}} pandas {"index_columns": [{"kind": "range", "name": null, "start": 0, "stop": 2, "step": 1}], "column_indexes": [{"name": null, "field_name": null, "pandas_type": "unicode", "numpy_type": "object", "metadata": {"encoding": "UTF-8"}}], "columns": [{"name": "col_str", "field_name": "col_str", "pandas_type": "unicode", "numpy_type": "object", "metadata": null}, {"name": "col_int", "field_name": "col_int", "pandas_type": "int64", "numpy_type": "int64", "metadata": null}, {"name": "col_float", "field_name": "col_float", "pandas_type": "float64", "numpy_type": "float64", "metadata": null}, {"name": "geometry", "field_name": "geometry", "pandas_type": "bytes", "numpy_type": "object", "metadata": null}], "creator": {"library": "pyarrow", "version": "9.0.0"}, "pandas_version": "1.5.2"}l4hgeometry\  col_float col_int @ col_strH  P   #(HH%pp'# "M`@ "M`@ab"M`@""M`@?? "M`@  "M`@tT$?  ]#@' (]= @@   <( PP  h geo{"primary_column": "geometry", "columns": {"geometry": {"encoding": "WKB", "crs": {"$schema": "https://proj.org/schemas/v0.4/projjson.schema.json", "type": "GeographicCRS", "name": "WGS 84", "datum_ensemble": {"name": "World Geodetic System 1984 ensemble", "members": [{"name": "World Geodetic System 1984 (Transit)"}, {"name": "World Geodetic System 1984 (G730)"}, {"name": "World Geodetic System 1984 (G873)"}, {"name": "World Geodetic System 1984 (G1150)"}, {"name": "World Geodetic System 1984 (G1674)"}, {"name": "World Geodetic System 1984 (G1762)"}, {"name": "World Geodetic System 1984 (G2139)"}], "ellipsoid": {"name": "WGS 84", "semi_major_axis": 6378137, "inverse_flattening": 298.257223563}, "accuracy": "2.0", "id": {"authority": "EPSG", "code": 6326}}, "coordinate_system": {"subtype": "ellipsoidal", "axis": [{"name": "Geodetic latitude", "abbreviation": "Lat", "direction": "north", "unit": "degree"}, {"name": "Geodetic longitude", "abbreviation": "Lon", "direction": "east", "unit": "degree"}]}, "scope": "Horizontal component of 3D system.", "area": "World.", "bbox": {"south_latitude": -90, "west_longitude": -180, "north_latitude": 90, "east_longitude": 180}, "id": {"authority": "EPSG", "code": 4326}}, "geometry_types": ["MultiPolygon", "Polygon"], "bbox": [0.0, 0.0, 5.0, 5.0]}}, "version": "1.0.0-beta.1", "creator": {"library": "geopandas", "version": "0.12.1+47.g69816130.dirty"}} pandas {"index_columns": [{"kind": "range", "name": null, "start": 0, "stop": 2, "step": 1}], "column_indexes": [{"name": null, "field_name": null, "pandas_type": "unicode", "numpy_type": "object", "metadata": {"encoding": "UTF-8"}}], "columns": [{"name": "col_str", "field_name": "col_str", "pandas_type": "unicode", "numpy_type": "object", "metadata": null}, {"name": "col_int", "field_name": "col_int", "pandas_type": "int64", "numpy_type": "int64", "metadata": null}, {"name": "col_float", "field_name": "col_float", "pandas_type": "float64", "numpy_type": "float64", "metadata": null}, {"name": "geometry", "field_name": "geometry", "pandas_type": "bytes", "numpy_type": "object", "metadata": null}], "creator": {"library": "pyarrow", "version": "9.0.0"}, "pandas_version": "1.5.2"}l4hgeometry\  col_float col_int @ col_str ARROW1geopandas-1.2.0/geopandas/io/tests/data/arrow/test_data_v1.0.0-beta.1.parquet000066400000000000000000000171131525650651600265730ustar00rootroot00000000000000PAR1L $ab,6(ba & 5col_strx&<&6(ba, $L<,( &5col_int&&(, $LJ]@ ' >(]F Y >$@@,6(?????@@@@@@@@@@]@@@@@@@@@@ & 5geometry & &6(?????@@@@@@@@@@]@@@@@@@@@@,\5schema %col_str%L%col_int % col_float %geometryL& 5col_strx&<&6(ba,&5col_int&&(,& 5 col_float&&??(??,& 5geometry & &6(?????@@@@@@@@@@]@@@@@@@@@@, & <pandas{"index_columns": [{"kind": "range", "name": null, "start": 0, "stop": 2, "step": 1}], "column_indexes": [{"name": null, "field_name": null, "pandas_type": "unicode", "numpy_type": "object", "metadata": {"encoding": "UTF-8"}}], "columns": [{"name": "col_str", "field_name": "col_str", "pandas_type": "unicode", "numpy_type": "object", "metadata": null}, {"name": "col_int", "field_name": "col_int", "pandas_type": "int64", "numpy_type": "int64", "metadata": null}, {"name": "col_float", "field_name": "col_float", "pandas_type": "float64", "numpy_type": "float64", "metadata": null}, {"name": "geometry", "field_name": "geometry", "pandas_type": "bytes", "numpy_type": "object", "metadata": null}], "creator": {"library": "pyarrow", "version": "9.0.0"}, "pandas_version": "1.5.2"}geo {"primary_column": "geometry", "columns": {"geometry": {"encoding": "WKB", "crs": {"$schema": "https://proj.org/schemas/v0.4/projjson.schema.json", "type": "GeographicCRS", "name": "WGS 84", "datum_ensemble": {"name": "World Geodetic System 1984 ensemble", "members": [{"name": "World Geodetic System 1984 (Transit)"}, {"name": "World Geodetic System 1984 (G730)"}, {"name": "World Geodetic System 1984 (G873)"}, {"name": "World Geodetic System 1984 (G1150)"}, {"name": "World Geodetic System 1984 (G1674)"}, {"name": "World Geodetic System 1984 (G1762)"}, {"name": "World Geodetic System 1984 (G2139)"}], "ellipsoid": {"name": "WGS 84", "semi_major_axis": 6378137, "inverse_flattening": 298.257223563}, "accuracy": "2.0", "id": {"authority": "EPSG", "code": 6326}}, "coordinate_system": {"subtype": "ellipsoidal", "axis": [{"name": "Geodetic latitude", "abbreviation": "Lat", "direction": "north", "unit": "degree"}, {"name": "Geodetic longitude", "abbreviation": "Lon", "direction": "east", "unit": "degree"}]}, "scope": "Horizontal component of 3D system.", "area": "World.", "bbox": {"south_latitude": -90, "west_longitude": -180, "north_latitude": 90, "east_longitude": 180}, "id": {"authority": "EPSG", "code": 4326}}, "geometry_types": ["MultiPolygon", "Polygon"], "bbox": [0.0, 0.0, 5.0, 5.0]}}, "version": "1.0.0-beta.1", "creator": {"library": "geopandas", "version": "0.12.1+47.g69816130.dirty"}} ARROW: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parquet-cpp-arrow version 9.0.0LPAR1geopandas-1.2.0/geopandas/io/tests/data/arrow/test_data_v1.0.0.feather000066400000000000000000000133221525650651600254560ustar00rootroot00000000000000ARROW1     l geox{"primary_column": "geometry", "columns": {"geometry": {"encoding": "WKB", "crs": {"$schema": "https://proj.org/schemas/v0.5/projjson.schema.json", "type": "GeographicCRS", "name": "WGS 84", "datum_ensemble": {"name": "World Geodetic System 1984 ensemble", "members": [{"name": "World Geodetic System 1984 (Transit)"}, {"name": "World Geodetic System 1984 (G730)"}, {"name": "World Geodetic System 1984 (G873)"}, {"name": "World Geodetic System 1984 (G1150)"}, {"name": "World Geodetic System 1984 (G1674)"}, {"name": "World Geodetic System 1984 (G1762)"}, {"name": "World Geodetic System 1984 (G2139)"}], "ellipsoid": {"name": "WGS 84", "semi_major_axis": 6378137, "inverse_flattening": 298.257223563}, "accuracy": "2.0", "id": {"authority": "EPSG", "code": 6326}}, "coordinate_system": {"subtype": "ellipsoidal", "axis": [{"name": "Geodetic latitude", "abbreviation": "Lat", "direction": "north", "unit": "degree"}, {"name": "Geodetic longitude", "abbreviation": "Lon", "direction": "east", "unit": "degree"}]}, "scope": "Horizontal component of 3D system.", "area": "World.", "bbox": {"south_latitude": -90, "west_longitude": -180, "north_latitude": 90, "east_longitude": 180}, "id": {"authority": "EPSG", "code": 4326}}, "geometry_types": ["MultiPolygon", "Polygon"], "bbox": [0.0, 0.0, 5.0, 5.0]}}, "version": "1.0.0", "creator": {"library": "geopandas", "version": "0.14.0+15.g8f2b4cc1.dirty"}} pandas {"index_columns": [{"kind": "range", "name": null, "start": 0, "stop": 2, "step": 1}], "column_indexes": [{"name": null, "field_name": null, "pandas_type": "unicode", "numpy_type": "object", "metadata": {"encoding": "UTF-8"}}], "columns": [{"name": "col_str", "field_name": "col_str", "pandas_type": "unicode", "numpy_type": "object", "metadata": null}, {"name": "col_int", "field_name": "col_int", "pandas_type": "int64", "numpy_type": "int64", "metadata": null}, {"name": "col_float", "field_name": "col_float", "pandas_type": "float64", "numpy_type": "float64", "metadata": null}, {"name": "geometry", "field_name": "geometry", "pandas_type": "bytes", "numpy_type": "object", "metadata": null}], "creator": {"library": "pyarrow", "version": "10.0.1"}, "pandas_version": "2.1.2"}l4hgeometry\  col_float col_int @ col_strH  P   #(HH%pp'# "M`@ "M`@ab"M`@""M`@?? "M`@  "M`@tT$?  ]#@' (]= @@   @( PP  l geox{"primary_column": "geometry", "columns": {"geometry": {"encoding": "WKB", "crs": {"$schema": "https://proj.org/schemas/v0.5/projjson.schema.json", "type": "GeographicCRS", "name": "WGS 84", "datum_ensemble": {"name": "World Geodetic System 1984 ensemble", "members": [{"name": "World Geodetic System 1984 (Transit)"}, {"name": "World Geodetic System 1984 (G730)"}, {"name": "World Geodetic System 1984 (G873)"}, {"name": "World Geodetic System 1984 (G1150)"}, {"name": "World Geodetic System 1984 (G1674)"}, {"name": "World Geodetic System 1984 (G1762)"}, {"name": "World Geodetic System 1984 (G2139)"}], "ellipsoid": {"name": "WGS 84", "semi_major_axis": 6378137, "inverse_flattening": 298.257223563}, "accuracy": "2.0", "id": {"authority": "EPSG", "code": 6326}}, "coordinate_system": {"subtype": "ellipsoidal", "axis": [{"name": "Geodetic latitude", "abbreviation": "Lat", "direction": "north", "unit": "degree"}, {"name": "Geodetic longitude", "abbreviation": "Lon", "direction": "east", "unit": "degree"}]}, "scope": "Horizontal component of 3D system.", "area": "World.", "bbox": {"south_latitude": -90, "west_longitude": -180, "north_latitude": 90, "east_longitude": 180}, "id": {"authority": "EPSG", "code": 4326}}, "geometry_types": ["MultiPolygon", "Polygon"], "bbox": [0.0, 0.0, 5.0, 5.0]}}, "version": "1.0.0", "creator": {"library": "geopandas", "version": "0.14.0+15.g8f2b4cc1.dirty"}} pandas {"index_columns": [{"kind": "range", "name": null, "start": 0, "stop": 2, "step": 1}], "column_indexes": [{"name": null, "field_name": null, "pandas_type": "unicode", "numpy_type": "object", "metadata": {"encoding": "UTF-8"}}], "columns": [{"name": "col_str", "field_name": "col_str", "pandas_type": "unicode", "numpy_type": "object", "metadata": null}, {"name": "col_int", "field_name": "col_int", "pandas_type": "int64", "numpy_type": "int64", "metadata": null}, {"name": "col_float", "field_name": "col_float", "pandas_type": "float64", "numpy_type": "float64", "metadata": null}, {"name": "geometry", "field_name": "geometry", "pandas_type": "bytes", "numpy_type": "object", "metadata": null}], "creator": {"library": "pyarrow", "version": "10.0.1"}, "pandas_version": "2.1.2"}l4hgeometry\  col_float col_int @ col_str ARROW1geopandas-1.2.0/geopandas/io/tests/data/arrow/test_data_v1.0.0.parquet000066400000000000000000000170721525650651600255270ustar00rootroot00000000000000PAR1L $ab,6(ba & 5col_strx&<&6(ba, $L<,( &5col_int&&(, $LJ]@ ' >(]F Y >$@@,6(?????@@@@@@@@@@]@@@@@@@@@@ & 5geometry & &6(?????@@@@@@@@@@]@@@@@@@@@@,\5schema %col_str%L%col_int % col_float %geometryL& 5col_strx&<&6(ba,&5col_int&&(,& 5 col_float&&??(??,& 5geometry & &6(?????@@@@@@@@@@]@@@@@@@@@@, & <pandas{"index_columns": [{"kind": "range", "name": null, "start": 0, "stop": 2, "step": 1}], "column_indexes": [{"name": null, "field_name": null, "pandas_type": "unicode", "numpy_type": "object", "metadata": {"encoding": "UTF-8"}}], "columns": [{"name": "col_str", "field_name": "col_str", "pandas_type": "unicode", "numpy_type": "object", "metadata": null}, {"name": "col_int", "field_name": "col_int", "pandas_type": "int64", "numpy_type": "int64", "metadata": null}, {"name": "col_float", "field_name": "col_float", "pandas_type": "float64", "numpy_type": "float64", "metadata": null}, {"name": "geometry", "field_name": "geometry", "pandas_type": "bytes", "numpy_type": "object", "metadata": null}], "creator": {"library": "pyarrow", "version": "10.0.1"}, "pandas_version": "2.1.2"}geo {"primary_column": "geometry", "columns": {"geometry": {"encoding": "WKB", "crs": {"$schema": "https://proj.org/schemas/v0.5/projjson.schema.json", "type": "GeographicCRS", "name": "WGS 84", "datum_ensemble": {"name": "World Geodetic System 1984 ensemble", "members": [{"name": "World Geodetic System 1984 (Transit)"}, {"name": "World Geodetic System 1984 (G730)"}, {"name": "World Geodetic System 1984 (G873)"}, {"name": "World Geodetic System 1984 (G1150)"}, {"name": "World Geodetic System 1984 (G1674)"}, {"name": "World Geodetic System 1984 (G1762)"}, {"name": "World Geodetic System 1984 (G2139)"}], "ellipsoid": {"name": "WGS 84", "semi_major_axis": 6378137, "inverse_flattening": 298.257223563}, "accuracy": "2.0", "id": {"authority": "EPSG", "code": 6326}}, "coordinate_system": {"subtype": "ellipsoidal", "axis": [{"name": "Geodetic latitude", "abbreviation": "Lat", "direction": "north", "unit": "degree"}, {"name": "Geodetic longitude", "abbreviation": "Lon", "direction": "east", "unit": "degree"}]}, "scope": "Horizontal component of 3D system.", "area": "World.", "bbox": {"south_latitude": -90, "west_longitude": -180, "north_latitude": 90, "east_longitude": 180}, "id": {"authority": "EPSG", "code": 4326}}, "geometry_types": ["MultiPolygon", "Polygon"], "bbox": [0.0, 0.0, 5.0, 5.0]}}, "version": "1.0.0", "creator": {"library": "geopandas", "version": "0.14.0+15.g8f2b4cc1.dirty"}} ARROW: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 parquet-cpp-arrow version 10.0.1LPAR1geopandas-1.2.0/geopandas/io/tests/data/geoarrow/000077500000000000000000000000001525650651600217365ustar00rootroot00000000000000geopandas-1.2.0/geopandas/io/tests/data/geoarrow/README.md000066400000000000000000000002141525650651600232120ustar00rootroot00000000000000The data included in this directory are copied from the `geoarrow-data` project https://github.com/geoarrow/geoarrow-data/tree/main/example geopandas-1.2.0/geopandas/io/tests/data/geoarrow/example-linestring-interleaved.arrow000066400000000000000000000025121525650651600311210ustar00rootroot00000000000000ARROW1   d  geometryD$ARROW:extension:metadata{}  ARROW:extension:namegeoarrow.linestring$0vertices xy $ row_number    P     0>@$@$@>@D@D@   4$Pd  geometryD$ARROW:extension:metadata{}  ARROW:extension:namegeoarrow.linestring$0vertices xy $ row_number  ARROW1geopandas-1.2.0/geopandas/io/tests/data/geoarrow/example-linestring-wkb.arrow000066400000000000000000000020521525650651600274010ustar00rootroot00000000000000ARROW1P    geometryD$ARROW:extension:metadata{}  ARROW:extension:name geoarrow.wkb $ row_number    p   l  K9BK>@$@$@>@D@D@   4$`p geometryD$ARROW:extension:metadata{}  ARROW:extension:name geoarrow.wkb $ row_number  xARROW1geopandas-1.2.0/geopandas/io/tests/data/geoarrow/example-linestring.arrow000066400000000000000000000026721525650651600266300ustar00rootroot00000000000000ARROW1     geometryD$ARROW:extension:metadata{}  ARROW:extension:namegeoarrow.linestring (Xverticesy x $ row_number  8  P     88>@$@D@$@>@D@   4$@P  geometryD$ARROW:extension:metadata{}  ARROW:extension:namegeoarrow.linestring (Xverticesy x $ row_number  ARROW1geopandas-1.2.0/geopandas/io/tests/data/geoarrow/example-linestring_z-interleaved.arrow000066400000000000000000000025421525650651600314550ustar00rootroot00000000000000ARROW1   d  geometryD$ARROW:extension:metadata{}  ARROW:extension:namegeoarrow.linestring$0vertices xyz $ row_number    h     H >@$@D@$@>@D@D@D@T@   4$hd  geometryD$ARROW:extension:metadata{}  ARROW:extension:namegeoarrow.linestring$0vertices xyz $ row_number  ARROW1geopandas-1.2.0/geopandas/io/tests/data/geoarrow/example-linestring_z-wkb.arrow000066400000000000000000000021021525650651600277260ustar00rootroot00000000000000ARROW1P    geometryD$ARROW:extension:metadata{}  ARROW:extension:name geoarrow.wkb $ row_number       l  cQZc>@$@D@$@>@D@D@D@T@   4$` geometryD$ARROW:extension:metadata{}  ARROW:extension:name geoarrow.wkb $ row_number  xARROW1geopandas-1.2.0/geopandas/io/tests/data/geoarrow/example-linestring_z.arrow000066400000000000000000000031421525650651600271520ustar00rootroot00000000000000ARROW1      geometryD$ARROW:extension:metadata{}  ARROW:extension:namegeoarrow.linestringt ,Hverticeszy x $ row_number  h  h     88PP>@$@D@$@>@D@D@D@T@   8(0ph  geometryD$ARROW:extension:metadata{}  ARROW:extension:namegeoarrow.linestringt ,Hverticeszy x $ row_number  HARROW1geopandas-1.2.0/geopandas/io/tests/data/geoarrow/example-multilinestring-interleaved.arrow000066400000000000000000000031621525650651600321760ustar00rootroot00000000000000ARROW1     geometryD$ARROW:extension:metadata{}  ARROW:extension:namegeoarrow.multilinestring $ linestrings$0vertices xy $ row_number  8      ((888  >@$@$@>@D@D@$@$@4@4@$@D@D@D@>@>@D@4@>@$@   4$@  geometryD$ARROW:extension:metadata{}  ARROW:extension:namegeoarrow.multilinestring $ linestrings$0vertices xy $ row_number  0ARROW1geopandas-1.2.0/geopandas/io/tests/data/geoarrow/example-multilinestring-wkb.arrow000066400000000000000000000023021525650651600304520ustar00rootroot00000000000000ARROW1P    geometryD$ARROW:extension:metadata{}  ARROW:extension:name geoarrow.wkb $ row_number       l(B>@$@$@>@D@D@$@$@4@4@$@D@D@D@>@>@D@4@>@$@   4$` geometryD$ARROW:extension:metadata{}  ARROW:extension:name geoarrow.wkb $ row_number  xARROW1geopandas-1.2.0/geopandas/io/tests/data/geoarrow/example-multilinestring.arrow000066400000000000000000000033421525650651600276760ustar00rootroot00000000000000ARROW1(     geometryD$ARROW:extension:metadata{}  ARROW:extension:namegeoarrow.multilinestringp   linestrings (Xverticesy x $ row_number  h      ((888PP  >@$@D@$@4@$@D@>@D@>@$@>@D@$@4@D@D@>@4@$@   4$8p  geometryD$ARROW:extension:metadata{}  ARROW:extension:namegeoarrow.multilinestringp   linestrings (Xverticesy x $ row_number  PARROW1geopandas-1.2.0/geopandas/io/tests/data/geoarrow/example-multilinestring_z-interleaved.arrow000066400000000000000000000033021525650651600325230ustar00rootroot00000000000000ARROW1     geometryD$ARROW:extension:metadata{}  ARROW:extension:namegeoarrow.multilinestring $ linestrings$0vertices xyz $ row_number  8  (    ((888  >@$@D@$@>@D@D@D@T@$@$@4@4@4@D@$@D@I@D@D@T@>@>@N@D@4@N@>@$@D@   4$@(  geometryD$ARROW:extension:metadata{}  ARROW:extension:namegeoarrow.multilinestring $ linestrings$0vertices xyz $ row_number  0ARROW1geopandas-1.2.0/geopandas/io/tests/data/geoarrow/example-multilinestring_z-wkb.arrow000066400000000000000000000024221525650651600310060ustar00rootroot00000000000000ARROW1P    geometryD$ARROW:extension:metadata{}  ARROW:extension:name geoarrow.wkb $ row_number    X   l(/Z&/>@$@D@$@>@D@D@D@T@$@$@4@4@4@D@$@D@I@D@D@T@>@>@N@D@4@N@>@$@D@   4$`X geometryD$ARROW:extension:metadata{}  ARROW:extension:name geoarrow.wkb $ row_number  xARROW1geopandas-1.2.0/geopandas/io/tests/data/geoarrow/example-multilinestring_z.arrow000066400000000000000000000037021525650651600302270ustar00rootroot00000000000000ARROW1X     geometryD$ARROW:extension:metadata{}  ARROW:extension:namegeoarrow.multilinestringD   linestringst ,Hverticeszy x $ row_number    (    ((888PPP  >@$@D@$@4@$@D@>@D@>@$@>@D@$@4@D@D@>@4@$@D@D@T@4@D@I@T@N@N@D@   8(h(  geometryD$ARROW:extension:metadata{}  ARROW:extension:namegeoarrow.multilinestringD   linestringst ,Hverticeszy x $ row_number  ARROW1geopandas-1.2.0/geopandas/io/tests/data/geoarrow/example-multipoint-interleaved.arrow000066400000000000000000000026721525650651600311560ustar00rootroot00000000000000ARROW1   d  geometryD$ARROW:extension:metadata{}  ARROW:extension:namegeoarrow.multipoint$0points xy $ row_number       000  >@$@$@D@D@>@4@4@>@$@$@D@D@>@4@4@>@$@   4$d  geometryD$ARROW:extension:metadata{}  ARROW:extension:namegeoarrow.multipoint$0points xy $ row_number  ARROW1geopandas-1.2.0/geopandas/io/tests/data/geoarrow/example-multipoint-wkb.arrow000066400000000000000000000023321525650651600274300ustar00rootroot00000000000000ARROW1P    geometryD$ARROW:extension:metadata{}  ARROW:extension:name geoarrow.wkb $ row_number        l0{>@$@$@D@D@>@4@4@>@$@$@D@D@>@4@4@>@$@   4$`  geometryD$ARROW:extension:metadata{}  ARROW:extension:name geoarrow.wkb $ row_number  xARROW1geopandas-1.2.0/geopandas/io/tests/data/geoarrow/example-multipoint.arrow000066400000000000000000000030521525650651600266470ustar00rootroot00000000000000ARROW1     geometryD$ARROW:extension:metadata{}  ARROW:extension:namegeoarrow.multipoint $Tpointsy x $ row_number  8      000HxxH  >@$@D@4@>@$@D@4@>@$@D@>@4@$@D@>@4@$@   8(@  geometryD$ARROW:extension:metadata{}  ARROW:extension:namegeoarrow.multipoint $Tpointsy x $ row_number  ARROW1geopandas-1.2.0/geopandas/io/tests/data/geoarrow/example-multipoint_z-interleaved.arrow000066400000000000000000000030021525650651600314730ustar00rootroot00000000000000ARROW1   d  geometryD$ARROW:extension:metadata{}  ARROW:extension:namegeoarrow.multipoint$0points xyz $ row_number       000  >@$@D@$@D@I@D@>@Q@4@4@D@>@$@D@$@D@I@D@>@Q@4@4@D@>@$@D@   4$d  geometryD$ARROW:extension:metadata{}  ARROW:extension:namegeoarrow.multipoint$0points xyz $ row_number  ARROW1geopandas-1.2.0/geopandas/io/tests/data/geoarrow/example-multipoint_z-wkb.arrow000066400000000000000000000024421525650651600277630ustar00rootroot00000000000000ARROW1P    geometryD$ARROW:extension:metadata{}  ARROW:extension:name geoarrow.wkb $ row_number    h   l02& )2>@$@D@$@D@I@D@>@Q@4@4@D@>@$@D@$@D@I@D@>@Q@4@4@D@>@$@D@   4$`h geometryD$ARROW:extension:metadata{}  ARROW:extension:name geoarrow.wkb $ row_number  xARROW1geopandas-1.2.0/geopandas/io/tests/data/geoarrow/example-multipoint_z.arrow000066400000000000000000000033621525650651600272040ustar00rootroot00000000000000ARROW1     geometryD$ARROW:extension:metadata{}  ARROW:extension:namegeoarrow.multipointx (Dpointszy x $ row_number  h      000HxxHH  >@$@D@4@>@$@D@4@>@$@D@>@4@$@D@>@4@$@D@I@Q@D@D@I@Q@D@D@   4$(p  geometryD$ARROW:extension:metadata{}  ARROW:extension:namegeoarrow.multipointx (Dpointszy x $ row_number  @ARROW1geopandas-1.2.0/geopandas/io/tests/data/geoarrow/example-multipolygon-interleaved.arrow000066400000000000000000000041021525650651600315020ustar00rootroot00000000000000ARROW10     geometryD$ARROW:extension:metadata{}  ARROW:extension:namegeoarrow.multipolygonh  polygons  rings$0vertices xy $ row_number  h  (    00HHhhh8 >@$@D@D@4@D@$@4@>@$@>@4@F@D@$@D@>@4@.@@D@$@$@4@@$@.@@D@D@4@F@F@>@D@D@4@A@$@>@$@$@>@@F@4@4@A@>@4@4@.@4@9@>@4@   4$@p(  geometryD$ARROW:extension:metadata{}  ARROW:extension:namegeoarrow.multipolygonh  polygons  rings$0vertices xy $ row_number  XARROW1geopandas-1.2.0/geopandas/io/tests/data/geoarrow/example-multipolygon-wkb.arrow000066400000000000000000000030421525650651600277650ustar00rootroot00000000000000ARROW1P    geometryD$ARROW:extension:metadata{}  ARROW:extension:name geoarrow.wkb $ row_number    h   l02f )2>@$@D@D@4@D@$@4@>@$@>@4@F@D@$@D@>@4@.@@D@$@$@4@@$@.@@D@D@4@F@F@>@D@D@4@A@$@>@$@$@>@@F@4@4@A@>@4@4@.@4@9@>@4@   4$`h geometryD$ARROW:extension:metadata{}  ARROW:extension:name geoarrow.wkb $ row_number  xARROW1geopandas-1.2.0/geopandas/io/tests/data/geoarrow/example-multipolygon.arrow000066400000000000000000000042621525650651600272110ustar00rootroot00000000000000ARROW1P     geometryD$ARROW:extension:metadata{}  ARROW:extension:namegeoarrow.multipolygonpD  polygonst rings (Xverticesy x $ row_number    (    00HHhhhHH >@D@4@$@>@>@F@$@>@.@D@$@@.@D@4@F@D@4@$@$@>@F@4@>@4@4@>@$@D@D@4@$@4@D@D@4@@$@4@$@@D@F@>@D@A@>@$@@4@A@4@.@9@4@   4$`(  geometryD$ARROW:extension:metadata{}  ARROW:extension:namegeoarrow.multipolygonpD  polygonst rings (Xverticesy x $ row_number  xARROW1geopandas-1.2.0/geopandas/io/tests/data/geoarrow/example-multipolygon_z-interleaved.arrow000066400000000000000000000044421525650651600320420ustar00rootroot00000000000000ARROW10     geometryD$ARROW:extension:metadata{}  ARROW:extension:namegeoarrow.multipolygonh  polygons  rings$0vertices xyz $ row_number  h      00HHhhhT >@$@D@D@D@T@4@D@N@$@4@>@>@$@D@>@4@I@F@D@@U@$@D@I@>@4@I@.@@4@D@$@I@$@4@>@@$@.@.@@4@D@D@T@4@F@@P@F@>@R@D@D@T@4@A@K@$@>@D@$@$@4@>@@A@F@4@@P@4@A@K@>@4@I@4@.@A@4@9@F@>@4@I@   4$@p  geometryD$ARROW:extension:metadata{}  ARROW:extension:namegeoarrow.multipolygonh  polygons  rings$0vertices xyz $ row_number  XARROW1geopandas-1.2.0/geopandas/io/tests/data/geoarrow/example-multipolygon_z-wkb.arrow000066400000000000000000000034021525650651600303160ustar00rootroot00000000000000ARROW1P    geometryD$ARROW:extension:metadata{}  ARROW:extension:name geoarrow.wkb $ row_number    H   l0 >@$@D@D@D@T@4@D@N@$@4@>@>@$@D@>@4@I@F@D@@U@$@D@I@>@4@I@.@@4@D@$@I@$@4@>@@$@.@.@@4@D@D@T@4@F@@P@F@>@R@D@D@T@4@A@K@$@>@D@$@$@4@>@@A@F@4@@P@4@A@K@>@4@I@4@.@A@4@9@F@>@4@I@   4$`H geometryD$ARROW:extension:metadata{}  ARROW:extension:name geoarrow.wkb $ row_number  xARROW1geopandas-1.2.0/geopandas/io/tests/data/geoarrow/example-multipolygon_z.arrow000066400000000000000000000050421525650651600275370ustar00rootroot00000000000000ARROW1     geometryD$ARROW:extension:metadata{}  ARROW:extension:namegeoarrow.multipolygonl  polygonsH ringst ,Hverticeszy x $ row_number       00HHhhhHH(( >@D@4@$@>@>@F@$@>@.@D@$@@.@D@4@F@D@4@$@$@>@F@4@>@4@4@>@$@D@D@4@$@4@D@D@4@@$@4@$@@D@F@>@D@A@>@$@@4@A@4@.@9@4@D@T@N@>@D@I@@U@I@I@4@I@>@.@4@T@@P@R@T@K@D@4@A@@P@K@I@A@F@I@   8(  geometryD$ARROW:extension:metadata{}  ARROW:extension:namegeoarrow.multipolygonl  polygonsH ringst ,Hverticeszy x $ row_number  ARROW1geopandas-1.2.0/geopandas/io/tests/data/geoarrow/example-point-interleaved.arrow000066400000000000000000000022521525650651600300750ustar00rootroot00000000000000ARROW1   0 geometryD$ARROW:extension:metadata{}  ARROW:extension:namegeoarrow.point xy $ row_number    @   l 0>@$@   8(@0 geometryD$ARROW:extension:metadata{}  ARROW:extension:namegeoarrow.point xy $ row_number  ARROW1geopandas-1.2.0/geopandas/io/tests/data/geoarrow/example-point-wkb.arrow000066400000000000000000000020321525650651600263520ustar00rootroot00000000000000ARROW1P    geometryD$ARROW:extension:metadata{}  ARROW:extension:name geoarrow.wkb $ row_number    `   l  ?*?>@$@   4$`` geometryD$ARROW:extension:metadata{}  ARROW:extension:name geoarrow.wkb $ row_number  xARROW1geopandas-1.2.0/geopandas/io/tests/data/geoarrow/example-point.arrow000066400000000000000000000024321525650651600255750ustar00rootroot00000000000000ARROW1   P   geometryD$ARROW:extension:metadata{}  ARROW:extension:namegeoarrow.pointy x $ row_number    @    ((>@$@   8(@P   geometryD$ARROW:extension:metadata{}  ARROW:extension:namegeoarrow.pointy x $ row_number  ARROW1geopandas-1.2.0/geopandas/io/tests/data/geoarrow/example-point_z-interleaved.arrow000066400000000000000000000023021525650651600304220ustar00rootroot00000000000000ARROW1   0 geometryD$ARROW:extension:metadata{}  ARROW:extension:namegeoarrow.point xyz $ row_number    X   l H >@$@D@   8(X0 geometryD$ARROW:extension:metadata{}  ARROW:extension:namegeoarrow.point xyz $ row_number  ARROW1geopandas-1.2.0/geopandas/io/tests/data/geoarrow/example-point_z-wkb.arrow000066400000000000000000000020621525650651600267060ustar00rootroot00000000000000ARROW1P    geometryD$ARROW:extension:metadata{}  ARROW:extension:name geoarrow.wkb $ row_number    x   l  W:W>@$@D@   4$`x geometryD$ARROW:extension:metadata{}  ARROW:extension:name geoarrow.wkb $ row_number  xARROW1geopandas-1.2.0/geopandas/io/tests/data/geoarrow/example-point_z.arrow000066400000000000000000000026621525650651600261330ustar00rootroot00000000000000ARROW1   |  $geometryD$ARROW:extension:metadata{}  ARROW:extension:namegeoarrow.pointzy x $ row_number  8  X    ((@@>@$@D@   4$@X|  $geometryD$ARROW:extension:metadata{}  ARROW:extension:namegeoarrow.pointzy x $ row_number  ARROW1geopandas-1.2.0/geopandas/io/tests/data/geoarrow/example-polygon-interleaved.arrow000066400000000000000000000032421525650651600304330ustar00rootroot00000000000000ARROW1     geometryD$ARROW:extension:metadata{}  ARROW:extension:namegeoarrow.polygon  rings$0vertices xy $ row_number  8      ((888 >@$@D@D@4@D@$@4@>@$@A@$@F@F@.@D@$@4@A@$@4@>@A@A@>@4@4@>@   8(@  geometryD$ARROW:extension:metadata{}  ARROW:extension:namegeoarrow.polygon  rings$0vertices xy $ row_number  (ARROW1geopandas-1.2.0/geopandas/io/tests/data/geoarrow/example-polygon-wkb.arrow000066400000000000000000000023621525650651600267160ustar00rootroot00000000000000ARROW1P    geometryD$ARROW:extension:metadata{}  ARROW:extension:name geoarrow.wkb $ row_number    8   l(]>@$@D@D@4@D@$@4@>@$@A@$@F@F@.@D@$@4@A@$@4@>@A@A@>@4@4@>@   4$`8 geometryD$ARROW:extension:metadata{}  ARROW:extension:name geoarrow.wkb $ row_number  xARROW1geopandas-1.2.0/geopandas/io/tests/data/geoarrow/example-polygon.arrow000066400000000000000000000034221525650651600261330ustar00rootroot00000000000000ARROW1      geometryD$ARROW:extension:metadata{}  ARROW:extension:namegeoarrow.polygont rings (Xverticesy x $ row_number  h      ((888pp >@D@4@$@>@A@F@.@$@A@4@A@>@4@$@D@D@4@$@$@F@D@4@$@>@A@4@>@   8(0p  geometryD$ARROW:extension:metadata{}  ARROW:extension:namegeoarrow.polygont rings (Xverticesy x $ row_number  HARROW1geopandas-1.2.0/geopandas/io/tests/data/geoarrow/example-polygon_z-interleaved.arrow000066400000000000000000000034221525650651600307640ustar00rootroot00000000000000ARROW1     geometryD$ARROW:extension:metadata{}  ARROW:extension:namegeoarrow.polygon  rings$0vertices xyz $ row_number  8      ((888P* >@$@D@D@D@T@4@D@N@$@4@>@>@$@D@A@$@F@F@F@V@.@D@K@$@4@>@A@$@F@4@>@I@A@A@Q@>@4@I@4@>@I@   8(@  geometryD$ARROW:extension:metadata{}  ARROW:extension:namegeoarrow.polygon  rings$0vertices xyz $ row_number  (ARROW1geopandas-1.2.0/geopandas/io/tests/data/geoarrow/example-polygon_z-wkb.arrow000066400000000000000000000025421525650651600272470ustar00rootroot00000000000000ARROW1P    geometryD$ARROW:extension:metadata{}  ARROW:extension:name geoarrow.wkb $ row_number       l(nw>@$@D@D@D@T@4@D@N@$@4@>@>@$@D@A@$@F@F@F@V@.@D@K@$@4@>@A@$@F@4@>@I@A@A@Q@>@4@I@4@>@I@   4$` geometryD$ARROW:extension:metadata{}  ARROW:extension:name geoarrow.wkb $ row_number  xARROW1geopandas-1.2.0/geopandas/io/tests/data/geoarrow/example-polygon_z.arrow000066400000000000000000000040021525650651600264570ustar00rootroot00000000000000ARROW1H     geometryD$ARROW:extension:metadata{}  ARROW:extension:namegeoarrow.polygonH ringst ,Hverticeszy x $ row_number        ((888ppp >@D@4@$@>@A@F@.@$@A@4@A@>@4@$@D@D@4@$@$@F@D@4@$@>@A@4@>@D@T@N@>@D@F@V@K@>@F@I@Q@I@I@   4$X  geometryD$ARROW:extension:metadata{}  ARROW:extension:namegeoarrow.polygonH ringst ,Hverticeszy x $ row_number  pARROW1geopandas-1.2.0/geopandas/io/tests/data/pickle/000077500000000000000000000000001525650651600213605ustar00rootroot00000000000000geopandas-1.2.0/geopandas/io/tests/data/pickle/0.6.3_pd-0.25.3_py-3.8.0_x86_64_linux.pickle000066400000000000000000000030721525650651600300360ustar00rootroot00000000000000/}( gdf_the_geomgeopandas.geodataframe GeoDataFrame)}(_datapandas.core.internals.managers BlockManager)(](pandas.core.indexes.base _new_Indexh Index}(datanumpy.core.multiarray _reconstructnumpyndarrayKCbR(KKhdtypeO8KKR(K|NNNJJK?tb](athe_geometbnameNuRhpandas.core.indexes.range RangeIndex}(h*NstartKstopKstepKuRe](hhKhR(KKKh i8KKR(KNNNJJKtbC??333333?tbhD)}hhhKhR(KKh#](hN)RC??bhN)RC@@bhN)RC@@betbsbe](hh}(hhhKhR(KKh#]h'atbh*NuRhh}(hhhKhR(KKh#]hatbh*NuRe}hj}(hlhhm](}(hphhqhtKKKRu}(hphhqhtKKKRueustbh{h|h}h~h EPSG:4326hhubu.geopandas-1.2.0/geopandas/io/tests/data/pickle/0.7.0_pd-1.0.4_py-3.7.6_x86_64_linux.pickle000066400000000000000000000031461525650651600277560ustar00rootroot00000000000000[}( gdf_the_geomgeopandas.geodataframe GeoDataFrame)}(_datapandas.core.internals.managers BlockManager)(](pandas.core.indexes.base _new_Indexh Index}(datanumpy.core.multiarray _reconstructnumpyndarrayKCbR(KKhdtypeO8KKR(K|NNNJJK?tb](athe_geometbnameNuRhpandas.core.indexes.range RangeIndex}(h*NstartKstopKstepKuRe](hhKhR(KKKh i8KKR(KNNNJJKtbC??333333?tbhD)}hhhKhR(KKh#](hN)RC??bhN)RC@@bhN)RC@@betbsbe](hh}(hhhKhR(KKh#]h'atbh*NuRhh}(hhhKhR(KKh#]hatbh*NuRe}hj}(hlhhm](}(hphhqhtKKKRu}(hphhqhtKKKRueustbh{h|h}h~h}hpyproj.crs.crsCRS EPSG:4326Rhhubu.geopandas-1.2.0/geopandas/io/tests/data/pickle/0.8.0_pd-1.0.5_py-3.8.3_x86_64_linux.pickle000066400000000000000000000031571525650651600277600ustar00rootroot00000000000000d}( gdf_the_geomgeopandas.geodataframe GeoDataFrame)}(_datapandas.core.internals.managers BlockManager)(](pandas.core.indexes.base _new_Indexh Index}(datanumpy.core.multiarray _reconstructnumpyndarrayKCbR(KKhdtypeO8KKR(K|NNNJJK?tb](athe_geometbnameNuRhpandas.core.indexes.range RangeIndex}(h*NstartKstopKstepKuRe](hhKhR(KKKh i8KKR(KNNNJJKtbC??333333?tbhD)}(hhhKhR(KKh#](hN)RC??bhN)RC@@bhN)RC@@betbhVpyproj.crs.crsCRS EPSG:4326Rube](hh}(hhhKhR(KKh#]h'atbh*NuRhh}(hhhKhR(KKh#]hatbh*NuRe}hk}(hmhhn](}(hqhhrhuKKKRu}(hqhhrhuKKKRueustbh|h}h~hh}hVhhhubu.geopandas-1.2.0/geopandas/io/tests/generate_legacy_storage_files.py000066400000000000000000000052421525650651600256010ustar00rootroot00000000000000""" Script to create the data and write legacy storage (pickle) files. Based on pandas' generate_legacy_storage_files.py script. To use this script, create an environment for which you want to generate pickles, activate the environment, and run this script as: $ python geopandas/geopandas/io/tests/generate_legacy_storage_files.py \ geopandas/geopandas/io/tests/data/pickle/ pickle This script generates a storage file for the current arch, system, The idea here is you are using the *current* version of the generate_legacy_storage_files with an *older* version of geopandas to generate a pickle file. We will then check this file into a current branch, and test using test_pickle.py. This will load the *older* pickles and test versus the current data that is generated (with master). These are then compared. """ import os import pickle import platform import sys import pandas as pd from shapely.geometry import Point import geopandas def create_pickle_data(): """create the pickle data""" # custom geometry column name gdf_the_geom = geopandas.GeoDataFrame( {"a": [1, 2, 3], "the_geom": [Point(1, 1), Point(2, 2), Point(3, 3)]}, geometry="the_geom", ) # with crs gdf_crs = geopandas.GeoDataFrame( {"a": [0.1, 0.2, 0.3], "geometry": [Point(1, 1), Point(2, 2), Point(3, 3)]}, crs="EPSG:4326", ) return {"gdf_the_geom": gdf_the_geom, "gdf_crs": gdf_crs} def platform_name(): return "_".join( [ str(geopandas.__version__), "pd-" + str(pd.__version__), "py-" + str(platform.python_version()), str(platform.machine()), str(platform.system().lower()), ] ) def write_legacy_pickles(output_dir): print( "This script generates a storage file for the current arch, system, " "and python version" ) print("geopandas version: {}").format(geopandas.__version__) print(f" output dir : {output_dir}") print(" storage format: pickle") pth = f"{platform_name()}.pickle" fh = open(os.path.join(output_dir, pth), "wb") pickle.dump(create_pickle_data(), fh, pickle.DEFAULT_PROTOCOL) fh.close() print(f"created pickle file: {pth}") def main(): if len(sys.argv) != 3: sys.exit( "Specify output directory and storage type: generate_legacy_" "storage_files.py " ) output_dir = str(sys.argv[1]) storage_type = str(sys.argv[2]) if storage_type == "pickle": write_legacy_pickles(output_dir=output_dir) else: sys.exit("storage_type must be one of {'pickle'}") if __name__ == "__main__": main() geopandas-1.2.0/geopandas/io/tests/test_arrow.py000066400000000000000000001664541525650651600217630ustar00rootroot00000000000000import contextlib import json import os import pathlib import re import sys from io import BytesIO from itertools import product from packaging.version import Version from unittest.mock import patch import numpy as np import pandas as pd from pandas import ArrowDtype, DataFrame, Index, Series from pandas import read_parquet as pd_read_parquet import shapely from shapely.geometry import LineString, MultiPolygon, Point, Polygon, box import geopandas from geopandas import GeoDataFrame, read_feather, read_file, read_parquet from geopandas._compat import HAS_PYPROJ from geopandas.array import to_wkb from geopandas.io.arrow import ( METADATA_VERSION, SUPPORTED_VERSIONS, _convert_bbox_to_parquet_filter, _create_metadata, _decode_metadata, _encode_metadata, _geopandas_to_arrow, _get_filesystem_path, _remove_id_from_member_of_ensembles, _validate_dataframe, _validate_geo_metadata, ) import pytest from geopandas.testing import assert_geodataframe_equal, assert_geoseries_equal from geopandas.tests.util import mock from pandas.testing import assert_frame_equal DATA_PATH = pathlib.Path(os.path.dirname(__file__)) / "data" # Skip all tests in this module if pyarrow is not available pyarrow = pytest.importorskip("pyarrow") import pyarrow.compute as pc import pyarrow.parquet as pq from pyarrow import feather @pytest.fixture(params=["parquet", pytest.param("feather")]) def file_format(request): if request.param == "parquet": return read_parquet, GeoDataFrame.to_parquet elif request.param == "feather": return read_feather, GeoDataFrame.to_feather def test_create_metadata(naturalearth_lowres): df = read_file(naturalearth_lowres) metadata = _create_metadata(df, geometry_encoding={"geometry": "WKB"}) assert isinstance(metadata, dict) assert metadata["version"] == METADATA_VERSION assert metadata["primary_column"] == "geometry" assert "geometry" in metadata["columns"] if HAS_PYPROJ: crs_expected = df.crs.to_json_dict() _remove_id_from_member_of_ensembles(crs_expected) assert metadata["columns"]["geometry"]["crs"] == crs_expected assert metadata["columns"]["geometry"]["encoding"] == "WKB" assert metadata["columns"]["geometry"]["geometry_types"] == [ "MultiPolygon", "Polygon", ] assert np.array_equal( metadata["columns"]["geometry"]["bbox"], df.geometry.total_bounds ) assert metadata["creator"]["library"] == "geopandas" assert metadata["creator"]["version"] == geopandas.__version__ # specifying non-WKB encoding sets default schema to 1.1.0 metadata = _create_metadata(df, geometry_encoding={"geometry": "point"}) assert metadata["version"] == "1.1.0" assert metadata["columns"]["geometry"]["encoding"] == "point" # check that providing no geometry encoding defaults to WKB metadata = _create_metadata(df) assert metadata["columns"]["geometry"]["encoding"] == "WKB" # specifying write_covering_bbox sets default schema to 1.1.0 metadata = _create_metadata(df, write_covering_bbox=True) assert metadata["version"] == "1.1.0" with pytest.raises( ValueError, match="Writing a bounding box column is only supported since" ): _create_metadata(df, write_covering_bbox=True, schema_version="1.0.0") def test_create_metadata_with_z_geometries(): geometry_types = [ "Point", "Point Z", "LineString", "LineString Z", "Polygon", "Polygon Z", "MultiPolygon", "MultiPolygon Z", ] df = geopandas.GeoDataFrame( { "geo_type": geometry_types, "geometry": [ Point(1, 2), Point(1, 2, 3), LineString([(0, 0), (1, 1), (2, 2)]), LineString([(0, 0, 1), (1, 1, 2), (2, 2, 3)]), Polygon([(0, 0), (0, 1), (1, 1), (1, 0)]), Polygon([(0, 0, 0), (0, 1, 0.5), (1, 1, 1), (1, 0, 0.5)]), MultiPolygon( [ Polygon([(0, 0), (0, 1), (1, 1), (1, 0)]), Polygon([(0.5, 0.5), (0.5, 1.5), (1.5, 1.5), (1.5, 0.5)]), ] ), MultiPolygon( [ Polygon([(0, 0, 0), (0, 1, 0.5), (1, 1, 1), (1, 0, 0.5)]), Polygon( [ (0.5, 0.5, 1), (0.5, 1.5, 1.5), (1.5, 1.5, 2), (1.5, 0.5, 1.5), ] ), ] ), ], }, ) metadata = _create_metadata(df, geometry_encoding={"geometry": "WKB"}) assert sorted(metadata["columns"]["geometry"]["geometry_types"]) == sorted( geometry_types ) # only 3D geometries metadata = _create_metadata(df.iloc[1::2], geometry_encoding={"geometry": "WKB"}) assert all( geom_type.endswith(" Z") for geom_type in metadata["columns"]["geometry"]["geometry_types"] ) metadata = _create_metadata(df.iloc[5:7], geometry_encoding={"geometry": "WKB"}) assert metadata["columns"]["geometry"]["geometry_types"] == [ "MultiPolygon", "Polygon Z", ] def test_crs_metadata_datum_ensemble(): pyproj = pytest.importorskip("pyproj") # compatibility for older PROJ versions using PROJJSON with datum ensembles # https://github.com/geopandas/geopandas/pull/2453 crs = pyproj.CRS("EPSG:4326") crs_json = crs.to_json_dict() check_ensemble = False if "datum_ensemble" in crs_json: # older version of PROJ don't yet have datum ensembles check_ensemble = True assert "id" in crs_json["datum_ensemble"]["members"][0] _remove_id_from_member_of_ensembles(crs_json) if check_ensemble: assert "id" not in crs_json["datum_ensemble"]["members"][0] # ensure roundtrip still results in an equivalent CRS assert pyproj.CRS(crs_json) == crs def test_write_metadata_invalid_spec_version(tmp_path): gdf = geopandas.GeoDataFrame(geometry=[box(0, 0, 10, 10)], crs="EPSG:4326") with pytest.raises(ValueError, match="schema_version must be one of"): _create_metadata(gdf, schema_version="invalid") with pytest.raises( ValueError, match=re.escape( "'geoarrow' encoding is only supported with schema version >= 1.1.0" ), ): gdf.to_parquet(tmp_path, schema_version="1.0.0", geometry_encoding="geoarrow") def test_encode_metadata(): metadata = {"a": "b"} expected = b'{"a": "b"}' assert _encode_metadata(metadata) == expected def test_decode_metadata(): metadata_str = b'{"a": "b"}' expected = {"a": "b"} assert _decode_metadata(metadata_str) == expected assert _decode_metadata(None) is None def test_validate_dataframe(naturalearth_lowres): df = read_file(naturalearth_lowres) # valid: should not raise ValueError _validate_dataframe(df) _validate_dataframe(df.set_index("iso_a3")) # add column with non-string type df[0] = 1 # invalid: should raise ValueError with pytest.raises(ValueError): _validate_dataframe(df) with pytest.raises(ValueError): _validate_dataframe(df.set_index(0)) # not a DataFrame: should raise ValueError with pytest.raises(ValueError): _validate_dataframe("not a dataframe") def test_validate_geo_metadata_valid(): _validate_geo_metadata( { "primary_column": "geometry", "columns": {"geometry": {"crs": None, "encoding": "WKB"}}, "schema_version": "0.1.0", } ) _validate_geo_metadata( { "primary_column": "geometry", "columns": {"geometry": {"crs": None, "encoding": "WKB"}}, "version": "", } ) _validate_geo_metadata( { "primary_column": "geometry", "columns": { "geometry": { "crs": { # truncated PROJJSON for testing, as PROJJSON contents # not validated here "id": {"authority": "EPSG", "code": 4326}, }, "encoding": "point", } }, "version": "0.4.0", } ) @pytest.mark.parametrize( "metadata,error", [ (None, "Missing or malformed geo metadata in Parquet/Feather file"), ({}, "Missing or malformed geo metadata in Parquet/Feather file"), # missing "version" key: ( {"primary_column": "foo", "columns": None}, "'geo' metadata in Parquet/Feather file is missing required key", ), # missing "columns" key: ( {"primary_column": "foo", "version": ""}, "'geo' metadata in Parquet/Feather file is missing required key:", ), # missing "primary_column" ( {"columns": [], "version": ""}, "'geo' metadata in Parquet/Feather file is missing required key:", ), ( {"primary_column": "foo", "columns": [], "version": ""}, "'columns' in 'geo' metadata must be a dict", ), # missing "encoding" for column ( {"primary_column": "foo", "columns": {"foo": {}}, "version": ""}, ( "'geo' metadata in Parquet/Feather file is missing required key " "'encoding' for column 'foo'" ), ), # invalid column encoding ( { "primary_column": "foo", "columns": {"foo": {"crs": None, "encoding": None}}, "version": "", }, "Only WKB geometry encoding", ), ( { "primary_column": "foo", "columns": {"foo": {"crs": None, "encoding": "BKW"}}, "version": "", }, "Only WKB geometry encoding", ), ], ) def test_validate_geo_metadata_invalid(metadata, error): with pytest.raises(ValueError, match=error): _validate_geo_metadata(metadata) def test_validate_geo_metadata_edges(): metadata = { "primary_column": "geometry", "columns": {"geometry": {"crs": None, "encoding": "WKB", "edges": "spherical"}}, "version": "1.0.0-beta.1", } with pytest.warns( UserWarning, match="The geo metadata indicate that column 'geometry' has spherical edges", ): _validate_geo_metadata(metadata) def test_to_parquet_fails_on_invalid_engine(tmpdir): df = GeoDataFrame(data=[[1, 2, 3]], columns=["a", "b", "a"], geometry=[Point(1, 1)]) with pytest.raises( ValueError, match=( "GeoPandas only supports using pyarrow as the engine for " "to_parquet: 'fastparquet' passed instead" ), ): df.to_parquet(tmpdir / "test.parquet", engine="fastparquet") @mock.patch("geopandas.io.arrow._to_parquet") def test_to_parquet_does_not_pass_engine_along(mock_to_parquet): df = GeoDataFrame(data=[[1, 2, 3]], columns=["a", "b", "a"], geometry=[Point(1, 1)]) df.to_parquet("", engine="pyarrow") # assert that engine keyword is not passed through to _to_parquet (and thus # parquet.write_table) mock_to_parquet.assert_called_with( df, "", compression="snappy", geometry_encoding="WKB", index=None, schema_version=None, write_covering_bbox=False, ) # TEMPORARY: used to determine if pyarrow fails for roundtripping pandas data # without geometries def test_pandas_parquet_roundtrip1(tmpdir): df = DataFrame({"a": [1, 2, 3], "b": ["a", "b", "c"]}) filename = os.path.join(str(tmpdir), "test.pq") df.to_parquet(filename) pq_df = pd_read_parquet(filename) assert_frame_equal(df, pq_df) @pytest.mark.parametrize( "test_dataset", ["naturalearth_lowres", "naturalearth_cities", "nybb_filename"] ) def test_pandas_parquet_roundtrip2(test_dataset, tmpdir, request): path = request.getfixturevalue(test_dataset) df = DataFrame(read_file(path).drop(columns=["geometry"])) filename = os.path.join(str(tmpdir), "test.pq") df.to_parquet(filename) pq_df = pd_read_parquet(filename) assert_frame_equal(df, pq_df) @pytest.mark.parametrize("schema_version", [None, "1.0.0", "1.1.0", "2.0.0"]) @pytest.mark.parametrize( "test_dataset", ["naturalearth_lowres", "naturalearth_cities", "nybb_filename"] ) def test_roundtrip(tmpdir, file_format, test_dataset, schema_version, request): """Writing to parquet should not raise errors, and should not alter original GeoDataFrame """ if schema_version == "2.0.0" and Version(pyarrow.__version__) < Version("21.0.0"): pytest.skip("Writing GeoParquet 2.0 files requires pyarrow>=21.0") path = request.getfixturevalue(test_dataset) reader, writer = file_format df = read_file(path) orig = df.copy() filename = os.path.join(str(tmpdir), "test.pq") writer(df, filename, schema_version=schema_version) assert os.path.exists(filename) # make sure that the original data frame is unaltered assert_geodataframe_equal(df, orig) # make sure that we can roundtrip the data frame pq_df = reader(filename) assert isinstance(pq_df, GeoDataFrame) assert_geodataframe_equal(df, pq_df) @pytest.mark.parametrize( "test_dataset", ["naturalearth_lowres", "naturalearth_cities", "nybb_filename"] ) def test_roundtrip_bytesio(file_format, test_dataset, request): """Should be able to roundtrip in BytesIO.""" path = request.getfixturevalue(test_dataset) reader, writer = file_format df = read_file(path) orig = df.copy() buf = BytesIO() writer(df, buf) buf.seek(0) # make sure that the original data frame is unaltered assert_geodataframe_equal(df, orig) # make sure that we can roundtrip the data frame pq_df = reader(BytesIO(buf.getvalue())) assert isinstance(pq_df, GeoDataFrame) assert_geodataframe_equal(df, pq_df) def test_index(tmpdir, file_format, naturalearth_lowres): """Setting index=`True` should preserve index in output, and setting index=`False` should drop index from output. """ reader, writer = file_format df = read_file(naturalearth_lowres).set_index("iso_a3") filename = os.path.join(str(tmpdir), "test_with_index.pq") writer(df, filename, index=True) pq_df = reader(filename) assert_geodataframe_equal(df, pq_df) filename = os.path.join(str(tmpdir), "drop_index.pq") writer(df, filename, index=False) pq_df = reader(filename) assert_geodataframe_equal(df.reset_index(drop=True), pq_df) def test_column_order(tmpdir, file_format, naturalearth_lowres): """The order of columns should be preserved in the output.""" reader, _writer = file_format df = read_file(naturalearth_lowres) df = df.set_index("iso_a3") df["geom2"] = df.geometry.representative_point() table = _geopandas_to_arrow(df) custom_column_order = [ "iso_a3", "geom2", "pop_est", "continent", "name", "geometry", "gdp_md_est", ] table = table.select(custom_column_order) if reader is read_parquet: filename = os.path.join(str(tmpdir), "test_column_order.pq") pq.write_table(table, filename) else: filename = os.path.join(str(tmpdir), "test_column_order.feather") feather.write_feather(table, filename) result = reader(filename) assert list(result.columns) == custom_column_order[1:] assert_geodataframe_equal(result, df[custom_column_order[1:]]) @pytest.mark.parametrize( "compression", ["snappy", "gzip", "brotli", "lz4", "zstd", None] ) def test_parquet_compression(compression, tmpdir, naturalearth_lowres): """Using compression options should not raise errors, and should return identical GeoDataFrame. """ df = read_file(naturalearth_lowres) filename = os.path.join(str(tmpdir), "test.pq") df.to_parquet(filename, compression=compression) pq_df = read_parquet(filename) assert isinstance(pq_df, GeoDataFrame) assert_geodataframe_equal(df, pq_df) @pytest.mark.parametrize("compression", ["uncompressed", "lz4", "zstd"]) def test_feather_compression(compression, tmpdir, naturalearth_lowres): """Using compression options should not raise errors, and should return identical GeoDataFrame. """ df = read_file(naturalearth_lowres) filename = os.path.join(str(tmpdir), "test.feather") df.to_feather(filename, compression=compression) pq_df = read_feather(filename) assert isinstance(pq_df, GeoDataFrame) assert_geodataframe_equal(df, pq_df) def test_parquet_multiple_geom_cols(tmpdir, file_format, naturalearth_lowres): """If multiple geometry columns are present when written to parquet, they should all be returned as such when read from parquet. """ reader, writer = file_format df = read_file(naturalearth_lowres) df["geom2"] = df.geometry.copy() filename = os.path.join(str(tmpdir), "test.pq") writer(df, filename) assert os.path.exists(filename) pq_df = reader(filename) assert isinstance(pq_df, GeoDataFrame) assert_geodataframe_equal(df, pq_df) assert_geoseries_equal(df.geom2, pq_df.geom2, check_geom_type=True) def test_parquet_missing_metadata(tmpdir, naturalearth_lowres): """Missing geo metadata, such as from a parquet file created from a pandas DataFrame, will raise a ValueError. """ df = read_file(naturalearth_lowres) # convert to DataFrame df = DataFrame(df) # convert the geometry column so we can extract later df["geometry"] = to_wkb(df["geometry"].values) filename = os.path.join(str(tmpdir), "test.pq") # use pandas to_parquet (no geo metadata) df.to_parquet(filename) # missing metadata will raise ValueError with pytest.raises( ValueError, match="Missing geo metadata in Parquet/Feather file" ): read_parquet(filename) def test_parquet_missing_metadata2(tmpdir): """Missing geo metadata, such as from a parquet file created from a pyarrow Table (which will also not contain pandas metadata), will raise a ValueError. """ import pyarrow.parquet as pq table = pyarrow.table({"a": [1, 2, 3]}) filename = os.path.join(str(tmpdir), "test.pq") # use pyarrow.parquet write_table (no geo metadata, but also no pandas metadata) pq.write_table(table, filename) # missing metadata will raise ValueError with pytest.raises( ValueError, match="Missing geo metadata in Parquet/Feather file" ): read_parquet(filename) @pytest.mark.parametrize( "geo_meta,error", [ ({"geo": b""}, "Missing or malformed geo metadata in Parquet/Feather file"), ( {"geo": _encode_metadata({})}, "Missing or malformed geo metadata in Parquet/Feather file", ), ( {"geo": _encode_metadata({"foo": "bar"})}, "'geo' metadata in Parquet/Feather file is missing required key", ), ], ) def test_parquet_invalid_metadata(tmpdir, geo_meta, error, naturalearth_lowres): """Has geo metadata with missing required fields will raise a ValueError. This requires writing the parquet file directly below, so that we can control the metadata that is written for this test. """ from pyarrow import Table, parquet df = read_file(naturalearth_lowres) # convert to DataFrame and encode geometry to WKB df = DataFrame(df) df["geometry"] = to_wkb(df["geometry"].values) table = Table.from_pandas(df) metadata = table.schema.metadata metadata.update(geo_meta) table = table.replace_schema_metadata(metadata) filename = os.path.join(str(tmpdir), "test.pq") parquet.write_table(table, filename) with pytest.raises(ValueError, match=error): read_parquet(filename) def test_subset_columns(tmpdir, file_format, naturalearth_lowres): """Reading a subset of columns should correctly decode selected geometry columns. """ reader, writer = file_format df = read_file(naturalearth_lowres) filename = os.path.join(str(tmpdir), "test.pq") writer(df, filename) pq_df = reader(filename, columns=["name", "geometry"]) assert_geodataframe_equal(df[["name", "geometry"]], pq_df) with pytest.raises( ValueError, match="No geometry columns are included in the columns read" ): reader(filename, columns=["name"]) def test_promote_secondary_geometry(tmpdir, file_format, naturalearth_lowres): """Reading a subset of columns that does not include the primary geometry column should promote the first geometry column present. """ reader, writer = file_format df = read_file(naturalearth_lowres) df["geom2"] = df.geometry.copy() filename = os.path.join(str(tmpdir), "test.pq") writer(df, filename) pq_df = reader(filename, columns=["name", "geom2"]) assert_geodataframe_equal(df.set_geometry("geom2")[["name", "geom2"]], pq_df) df["geom3"] = df.geometry.copy() writer(df, filename) with pytest.warns( UserWarning, match="Multiple non-primary geometry columns read from Parquet/Feather file.", ): pq_df = reader(filename, columns=["name", "geom2", "geom3"]) assert_geodataframe_equal( df.set_geometry("geom2")[["name", "geom2", "geom3"]], pq_df ) def test_columns_no_geometry(tmpdir, file_format, naturalearth_lowres): """Reading a parquet file that is missing all of the geometry columns should raise a ValueError""" reader, writer = file_format df = read_file(naturalearth_lowres) filename = os.path.join(str(tmpdir), "test.pq") writer(df, filename) with pytest.raises(ValueError): reader(filename, columns=["name"]) def test_missing_crs(tmpdir, file_format, naturalearth_lowres): """If CRS is `None`, it should be properly handled and remain `None` when read from parquet`. """ reader, writer = file_format df = read_file(naturalearth_lowres) df.geometry.array.crs = None filename = os.path.join(str(tmpdir), "test.pq") writer(df, filename) pq_df = reader(filename) assert pq_df.crs is None assert_geodataframe_equal(df, pq_df, check_crs=True) def test_default_geo_col_writes(tmp_path): # edge case geo col name None writes successfully df = GeoDataFrame({"a": [1, 2]}) df.to_parquet(tmp_path / "test.pq") # cannot be round tripped as gdf due to invalid geom col pq_df = pd_read_parquet(tmp_path / "test.pq") assert_frame_equal(df, pq_df) def test_fsspec_url(naturalearth_lowres): _ = pytest.importorskip("fsspec") import fsspec.implementations.memory class MyMemoryFileSystem(fsspec.implementations.memory.MemoryFileSystem): # Simple fsspec filesystem that adds a required keyword. # Attempting to use this filesystem without the keyword will raise an exception. def __init__(self, is_set, *args, **kwargs): self.is_set = is_set super().__init__(*args, **kwargs) fsspec.register_implementation("memory", MyMemoryFileSystem, clobber=True) memfs = MyMemoryFileSystem(is_set=True) df = read_file(naturalearth_lowres) with memfs.open("data.parquet", "wb") as f: df.to_parquet(f) result = read_parquet("memory://data.parquet", storage_options={"is_set": True}) assert_geodataframe_equal(result, df) result = read_parquet("memory://data.parquet", filesystem=memfs) assert_geodataframe_equal(result, df) # reset fsspec registry fsspec.register_implementation( "memory", fsspec.implementations.memory.MemoryFileSystem, clobber=True ) def test_non_fsspec_url_with_storage_options_raises(naturalearth_lowres): with pytest.raises(ValueError, match="storage_options"): read_parquet(naturalearth_lowres, storage_options={"foo": "bar"}) def test_prefers_pyarrow_fs(): filesystem, _ = _get_filesystem_path("file:///data.parquet") assert isinstance(filesystem, pyarrow.fs.LocalFileSystem) def test_write_read_parquet_expand_user(): gdf = geopandas.GeoDataFrame(geometry=[box(0, 0, 10, 10)], crs="epsg:4326") test_file = "~/test_file.parquet" gdf.to_parquet(test_file) pq_df = geopandas.read_parquet(test_file) assert_geodataframe_equal(gdf, pq_df, check_crs=True) os.remove(os.path.expanduser(test_file)) def test_write_read_feather_expand_user(): gdf = geopandas.GeoDataFrame(geometry=[box(0, 0, 10, 10)], crs="epsg:4326") test_file = "~/test_file.feather" gdf.to_feather(test_file) f_df = geopandas.read_feather(test_file) assert_geodataframe_equal(gdf, f_df, check_crs=True) os.remove(os.path.expanduser(test_file)) @pytest.mark.parametrize("geometry", [[], [None]]) def test_write_empty_bbox(tmpdir, geometry): # empty dataframe or all missing geometries -> avoid bbox with NaNs gdf = geopandas.GeoDataFrame({"col": [1] * len(geometry)}, geometry=geometry) gdf.to_parquet(tmpdir / "test.parquet") from pyarrow.parquet import read_table table = read_table(tmpdir / "test.parquet") metadata = json.loads(table.schema.metadata[b"geo"]) assert "encoding" in metadata["columns"]["geometry"] assert "bbox" not in metadata["columns"]["geometry"] @pytest.mark.skipif( Version(pyarrow.__version__) < Version("19.0.0"), reason="This version of pyarrow does not support reading complex types", ) @pytest.mark.parametrize("format", ["feather", "parquet"]) def test_write_read_to_pandas_kwargs(tmpdir, format): filename = os.path.join(str(tmpdir), f"test.{format}") # Use arrow types to ensure that we can assert the roundtrip was successful int_type = ArrowDtype(pyarrow.int64()) str_type = ArrowDtype(pyarrow.string()) complex_type = ArrowDtype(pyarrow.struct([pyarrow.field("foo", pyarrow.string())])) index = Index([0], dtype=ArrowDtype(pyarrow.int64())) gdf = geopandas.GeoDataFrame( { "geometry": [box(0, 0, 10, 10)], "i": Series([1], index=index, dtype=int_type), "s": Series(["a"], index=index, dtype=str_type), "c": Series([{"foo": "bar"}], index=index, dtype=complex_type), }, index=index, ) if format == "feather": gdf.to_feather(filename) read_func = read_feather else: gdf.to_parquet(filename) read_func = read_parquet # simulate the `dtype_backend="pyarrow"` option in `pandas.read_parquet` gdf_roundtrip = read_func(filename, to_pandas_kwargs={"types_mapper": ArrowDtype}) assert isinstance(gdf_roundtrip, geopandas.GeoDataFrame) assert gdf_roundtrip.dtypes["i"] == int_type assert gdf_roundtrip.dtypes["s"] == str_type assert gdf_roundtrip.dtypes["c"] == complex_type assert_geodataframe_equal(gdf_roundtrip, gdf, check_dtype=True) @pytest.mark.parametrize("format", ["feather", "parquet"]) def test_read_complex_type_with_numpy_backend_xfail(tmpdir, format): filename = os.path.join(str(tmpdir), f"test.{format}") complex_type = ArrowDtype(pyarrow.struct([pyarrow.field("foo", pyarrow.string())])) index = Index([0], dtype=ArrowDtype(pyarrow.int64())) gdf = geopandas.GeoDataFrame( { "geometry": [box(0, 0, 10, 10)], "c": Series([{"foo": "bar"}], index=index, dtype=complex_type), }, index=index, ) if format == "feather": gdf.to_feather(filename) read_func = read_feather else: gdf.to_parquet(filename) read_func = read_parquet # Note: due to bugs in pyarrow, we can't read complex types without using # the types mapper. This is a long standing pandas issue as noted here: # - https://github.com/pandas-dev/pandas/issues/53011 # - https://github.com/apache/arrow/issues/39914 match = re.escape("data type 'struct[pyarrow]' not understood") with pytest.raises(TypeError, match=match): read_func(filename) @pytest.mark.parametrize("format", ["feather", "parquet"]) def test_write_read_default_crs(tmpdir, format): pyproj = pytest.importorskip("pyproj") if format == "feather": from pyarrow.feather import write_feather as write else: from pyarrow.parquet import write_table as write filename = os.path.join(str(tmpdir), f"test.{format}") gdf = geopandas.GeoDataFrame(geometry=[box(0, 0, 10, 10)]) table = _geopandas_to_arrow(gdf) # update the geo metadata to strip 'crs' entry metadata = table.schema.metadata geo_metadata = _decode_metadata(metadata[b"geo"]) del geo_metadata["columns"]["geometry"]["crs"] metadata.update({b"geo": _encode_metadata(geo_metadata)}) table = table.replace_schema_metadata(metadata) write(table, filename) read = getattr(geopandas, f"read_{format}") df = read(filename) assert df.crs.equals(pyproj.CRS("OGC:CRS84")) @pytest.mark.skipif(shapely.geos_version < (3, 10, 0), reason="requires GEOS>=3.10") def test_write_iso_wkb(tmpdir): gdf = geopandas.GeoDataFrame( geometry=geopandas.GeoSeries.from_wkt(["POINT Z (1 2 3)"]) ) gdf.to_parquet(tmpdir / "test.parquet") from pyarrow.parquet import read_table table = read_table(tmpdir / "test.parquet") wkb = table["geometry"][0].as_py().hex() # correct ISO flavor assert wkb == "01e9030000000000000000f03f00000000000000400000000000000840" @pytest.mark.skipif(shapely.geos_version >= (3, 10, 0), reason="tests GEOS<3.10") def test_write_iso_wkb_old_geos(tmpdir): gdf = geopandas.GeoDataFrame( geometry=geopandas.GeoSeries.from_wkt(["POINT Z (1 2 3)"]) ) with pytest.raises(ValueError, match="Cannot write 3D"): gdf.to_parquet(tmpdir / "test.parquet") @pytest.mark.parametrize( "format,schema_version", list(product(["feather", "parquet"], [None] + SUPPORTED_VERSIONS)), ) def test_write_spec_version(tmpdir, format, schema_version): if format == "feather": from pyarrow.feather import read_table else: from pyarrow.parquet import read_table if schema_version == "2.0.0" and Version(pyarrow.__version__) < Version("21.0.0"): pytest.skip("Writing GeoParquet 2.0 files requires pyarrow>=21.0") filename = os.path.join(str(tmpdir), f"test.{format}") gdf = geopandas.GeoDataFrame(geometry=[box(0, 0, 10, 10)], crs="EPSG:4326") write = getattr(gdf, f"to_{format}") write(filename, schema_version=schema_version) # ensure that we can roundtrip data regardless of version read = getattr(geopandas, f"read_{format}") df = read(filename) assert_geodataframe_equal(df, gdf) # verify the correct version is written in the metadata schema_version = schema_version or METADATA_VERSION table = read_table(filename) metadata = json.loads(table.schema.metadata[b"geo"]) assert metadata["version"] == schema_version # verify that CRS is correctly handled between versions if HAS_PYPROJ: if schema_version == "0.1.0": assert metadata["columns"]["geometry"]["crs"] == gdf.crs.to_wkt() else: crs_expected = gdf.crs.to_json_dict() _remove_id_from_member_of_ensembles(crs_expected) assert metadata["columns"]["geometry"]["crs"] == crs_expected # verify that geometry_type(s) is correctly handled between versions if Version(schema_version) <= Version("0.4.0"): assert "geometry_type" in metadata["columns"]["geometry"] assert metadata["columns"]["geometry"]["geometry_type"] == "Polygon" else: assert "geometry_types" in metadata["columns"]["geometry"] assert metadata["columns"]["geometry"]["geometry_types"] == ["Polygon"] @pytest.mark.parametrize("version", ["0.1.0", "0.4.0", "1.0.0-beta.1"]) def test_read_versioned_file(version): """ Verify that files for different metadata spec versions can be read created for each supported version: # small dummy test dataset (not naturalearth_lowres, as this can change over time) from shapely.geometry import box, MultiPolygon df = geopandas.GeoDataFrame( {"col_str": ["a", "b"], "col_int": [1, 2], "col_float": [0.1, 0.2]}, geometry=[MultiPolygon([box(0, 0, 1, 1), box(2, 2, 3, 3)]), box(4, 4, 5,5)], crs="EPSG:4326", ) df.to_feather(DATA_PATH / 'arrow' / f'test_data_v{METADATA_VERSION}.feather') df.to_parquet(DATA_PATH / 'arrow' / f'test_data_v{METADATA_VERSION}.parquet') """ expected = geopandas.GeoDataFrame( {"col_str": ["a", "b"], "col_int": [1, 2], "col_float": [0.1, 0.2]}, geometry=[MultiPolygon([box(0, 0, 1, 1), box(2, 2, 3, 3)]), box(4, 4, 5, 5)], crs="EPSG:4326", ) df = geopandas.read_feather(DATA_PATH / "arrow" / f"test_data_v{version}.feather") assert_geodataframe_equal(df, expected, check_crs=True) df = geopandas.read_parquet(DATA_PATH / "arrow" / f"test_data_v{version}.parquet") assert_geodataframe_equal(df, expected, check_crs=True) def test_read_gdal_files(): """ Verify that files written by GDAL can be read by geopandas. Since it is currently not yet straightforward to install GDAL with Parquet/Arrow enabled in our conda setup, we are testing with some generated files included in the repo (using GDAL 3.5.0): # small dummy test dataset (not naturalearth_lowres, as this can change over time) from shapely.geometry import box, MultiPolygon df = geopandas.GeoDataFrame( {"col_str": ["a", "b"], "col_int": [1, 2], "col_float": [0.1, 0.2]}, geometry=[MultiPolygon([box(0, 0, 1, 1), box(2, 2, 3, 3)]), box(4, 4, 5,5)], crs="EPSG:4326", ) df.to_file("test_data.gpkg", GEOMETRY_NAME="geometry") and then the gpkg file is converted to Parquet/Arrow with: $ ogr2ogr -f Parquet -lco FID= test_data_gdal350.parquet test_data.gpkg $ ogr2ogr -f Arrow -lco FID= -lco GEOMETRY_ENCODING=WKB test_data_gdal350.arrow test_data.gpkg Repeated for GDAL 3.9 which adds a bbox covering column: $ ogr2ogr -f Parquet -lco FID= test_data_gdal390.parquet test_data.gpkg """ # noqa: E501 pytest.importorskip("pyproj") expected = geopandas.GeoDataFrame( {"col_str": ["a", "b"], "col_int": [1, 2], "col_float": [0.1, 0.2]}, geometry=[MultiPolygon([box(0, 0, 1, 1), box(2, 2, 3, 3)]), box(4, 4, 5, 5)], crs="EPSG:4326", ) df = geopandas.read_parquet(DATA_PATH / "arrow" / "test_data_gdal350.parquet") assert_geodataframe_equal(df, expected, check_crs=True) df = geopandas.read_feather(DATA_PATH / "arrow" / "test_data_gdal350.arrow") assert_geodataframe_equal(df, expected, check_crs=True) df = geopandas.read_parquet(DATA_PATH / "arrow" / "test_data_gdal390.parquet") # recent GDAL no longer writes CRS in metadata in case of EPSG:4326, so comes back # as default OGC:CRS84 expected = expected.to_crs("OGC:CRS84") assert_geodataframe_equal(df, expected, check_crs=True) df = geopandas.read_parquet( DATA_PATH / "arrow" / "test_data_gdal390.parquet", bbox=(0, 0, 2, 2) ) assert len(df) == 1 def test_parquet_read_partitioned_dataset(tmpdir, naturalearth_lowres): # we don't yet explicitly support this (in writing), but for Parquet it # works for reading (by relying on pyarrow.read_table) df = read_file(naturalearth_lowres) # manually create partitioned dataset basedir = tmpdir / "partitioned_dataset" basedir.mkdir() df[:100].to_parquet(basedir / "data1.parquet") df[100:].to_parquet(basedir / "data2.parquet") result = read_parquet(basedir) assert_geodataframe_equal(result, df) def test_parquet_read_partitioned_dataset_fsspec(tmpdir, naturalearth_lowres): fsspec = pytest.importorskip("fsspec") df = read_file(naturalearth_lowres) # manually create partitioned dataset memfs = fsspec.filesystem("memory") memfs.mkdir("partitioned_dataset") with memfs.open("partitioned_dataset/data1.parquet", "wb") as f: df[:100].to_parquet(f) with memfs.open("partitioned_dataset/data2.parquet", "wb") as f: df[100:].to_parquet(f) result = read_parquet("memory://partitioned_dataset") assert_geodataframe_equal(result, df) def test_parquet_read_partitioned_dataset_partitioning_none( tmpdir, naturalearth_lowres ): # https://github.com/geopandas/geopandas/issues/3459 # passing partitioning=None should omit the hive partition columns, also # when geopandas auto-detects the columns to read (triggered here by the # presence of a bbox covering column) df = read_file(naturalearth_lowres) # manually create hive-partitioned dataset with a bbox covering column basedir = tmpdir / "partitioned_dataset" basedir.mkdir() (basedir / "key=1").mkdir() (basedir / "key=2").mkdir() df[:100].to_parquet(basedir / "key=1" / "data.parquet", write_covering_bbox=True) df[100:].to_parquet(basedir / "key=2" / "data.parquet", write_covering_bbox=True) result = read_parquet(basedir, partitioning=None) assert "key" not in result.columns assert len(result) == len(df) @pytest.fixture def pyarrow_dataset_unavailable(): import pyarrow.dataset as ds sys.modules["pyarrow.dataset"] = None yield sys.modules["pyarrow.dataset"] = ds def test_parquet_dataset_availability( tmp_path, naturalearth_lowres, pyarrow_dataset_unavailable ): # basic roundtrip test (subset of test_roundtrip) to ensure basic files # can be read if pyarrow.dataset is not available df = read_file(naturalearth_lowres) orig = df.copy() filename = str(tmp_path / "test.parquet") # Write to single file df.to_parquet(filename) assert os.path.exists(filename) assert_geodataframe_equal(df, orig) # Read from single file pq_df = geopandas.read_parquet(filename) assert_geodataframe_equal(pq_df, df) pq_df = geopandas.read_parquet(filename, columns=["name", "geometry"]) assert_geodataframe_equal(pq_df, df[["name", "geometry"]]) # and with file-like object buf = BytesIO() df.to_parquet(buf) buf.seek(0) assert_geodataframe_equal(df, orig) pq_df = geopandas.read_parquet(BytesIO(buf.getvalue())) assert_geodataframe_equal(pq_df, df) def test_parquet_dataset_availability_partitioned( tmpdir, naturalearth_lowres, pyarrow_dataset_unavailable ): # reading a directory is not supported in this case # manually create partitioned dataset df = read_file(naturalearth_lowres) basedir = tmpdir / "partitioned_dataset" basedir.mkdir() df[:100].to_parquet(basedir / "data1.parquet") df[100:].to_parquet(basedir / "data2.parquet") with pytest.raises(Exception, match=r"is a directory|Failed to open local file"): geopandas.read_parquet(str(basedir)) def test_parquet_dataset_availability_filters( tmpdir, naturalearth_lowres, pyarrow_dataset_unavailable ): # reading with filters is not supported in this case df = read_file(naturalearth_lowres) filename = os.path.join(str(tmpdir), "test.pq") df.to_parquet(filename, write_covering_bbox=True) with pytest.raises(ValueError, match="not supported"): geopandas.read_parquet(filename, bbox=(0, 0, 1, 1)) with pytest.raises(ValueError, match="not supported"): geopandas.read_parquet(filename, filters=[("gdp_md_est", ">", 20000)]) @pytest.mark.parametrize( "index", [ pd.Index(["a", "b", "c"], name="named_index"), pd.RangeIndex(1, 4, name="named_index"), ], ) def test_read_parquet_pandas_metadata(tmp_path, index): gdf = GeoDataFrame( { "col1": pd.array([1, 2, 3], dtype="Int64"), "col2": pd.period_range("2012-01-01", periods=3, freq="D"), }, index=index, geometry=geopandas.points_from_xy([1, 2, 3], [1, 2, 3]), crs="EPSG:4326", ) gdf.to_parquet(tmp_path / "test.parquet") result = geopandas.read_parquet(tmp_path / "test.parquet") # index and dtypes should be preserved assert result.index.name == "named_index" assert result["col1"].dtype == "Int64" assert_geodataframe_equal(result, gdf) # also when reading a subset of columns, the index gets preserved result = geopandas.read_parquet( tmp_path / "test.parquet", columns=["col1", "geometry"] ) assert result.index.name == "named_index" assert result["col1"].dtype == "Int64" assert_geodataframe_equal(result, gdf[["col1", "geometry"]]) @pytest.mark.parametrize( "geometry_type", ["point", "linestring", "polygon", "multipoint", "multilinestring", "multipolygon"], ) def test_read_parquet_geoarrow(geometry_type): data_dir = DATA_PATH / "arrow" / "geoparquet" / "1.1.0" result = geopandas.read_parquet( data_dir / f"data-{geometry_type}-encoding_native.parquet" ) expected = geopandas.read_parquet( data_dir / f"data-{geometry_type}-encoding_wkb.parquet" ) assert_geodataframe_equal(result, expected, check_crs=True) @pytest.mark.parametrize( "geometry_type", ["point", "linestring", "polygon", "multipoint", "multilinestring", "multipolygon"], ) def test_geoarrow_roundtrip(tmp_path, geometry_type): data_dir = DATA_PATH / "arrow" / "geoparquet" / "1.1.0" df = geopandas.read_parquet(data_dir / f"data-{geometry_type}-encoding_wkb.parquet") df.to_parquet(tmp_path / "test.parquet", geometry_encoding="geoarrow") result = geopandas.read_parquet(tmp_path / "test.parquet") assert_geodataframe_equal(result, df, check_crs=True) def test_to_parquet_bbox_structure_and_metadata(tmpdir, naturalearth_lowres): # check metadata being written for covering. from pyarrow import parquet df = read_file(naturalearth_lowres) filename = os.path.join(str(tmpdir), "test.pq") df.to_parquet(filename, write_covering_bbox=True) table = parquet.read_table(filename) metadata = json.loads(table.schema.metadata[b"geo"].decode("utf-8")) assert metadata["columns"]["geometry"]["covering"] == { "bbox": { "xmin": ["bbox", "xmin"], "ymin": ["bbox", "ymin"], "xmax": ["bbox", "xmax"], "ymax": ["bbox", "ymax"], } } assert "bbox" in table.schema.names assert [field.name for field in table.schema.field("bbox").type] == [ "xmin", "ymin", "xmax", "ymax", ] @pytest.mark.parametrize( "geometry, expected_bbox", [ (Point(1, 3), {"xmin": 1.0, "ymin": 3.0, "xmax": 1.0, "ymax": 3.0}), ( LineString([(1, 1), (3, 3)]), {"xmin": 1.0, "ymin": 1.0, "xmax": 3.0, "ymax": 3.0}, ), ( Polygon([(2, 1), (1, 2), (2, 3), (3, 2)]), {"xmin": 1.0, "ymin": 1.0, "xmax": 3.0, "ymax": 3.0}, ), ( MultiPolygon([box(0, 0, 1, 1), box(2, 2, 3, 3), box(4, 4, 5, 5)]), {"xmin": 0.0, "ymin": 0.0, "xmax": 5.0, "ymax": 5.0}, ), ], ids=["Point", "LineString", "Polygon", "Multipolygon"], ) def test_to_parquet_bbox_values(tmpdir, geometry, expected_bbox): # check bbox bounds being written for different geometry types. import pyarrow.parquet as pq df = GeoDataFrame(data=[[1, 2]], columns=["a", "b"], geometry=[geometry]) filename = os.path.join(str(tmpdir), "test.pq") df.to_parquet(filename, write_covering_bbox=True) result = pq.read_table(filename).to_pandas() assert result["bbox"][0] == expected_bbox bbox_write_kwargs = pytest.mark.parametrize( "write_kwargs", [ {"write_covering_bbox": True, "schema_version": "1.1.0"}, pytest.param( {"schema_version": "2.0.0"}, marks=pytest.mark.skipif( Version(pyarrow.__version__) < Version("21.0.0"), reason="Writing GeoParquet 2.0 files requires pyarrow>=21.0", ), ), ], ) @bbox_write_kwargs def test_read_parquet_bbox_single_point(tmpdir, write_kwargs): # confirm that on a single point, bbox will pick it up. df = GeoDataFrame(data=[[1, 2]], columns=["a", "b"], geometry=[Point(1, 1)]) filename = os.path.join(str(tmpdir), "test.pq") df.to_parquet(filename, **write_kwargs) pq_df = read_parquet(filename, bbox=(1, 1, 1, 1)) assert len(pq_df) == 1 assert pq_df.geometry[0] == Point(1, 1) @bbox_write_kwargs @pytest.mark.parametrize("geometry_name", ["geometry", "custum_geom_col"]) def test_read_parquet_bbox(tmpdir, naturalearth_lowres, geometry_name, write_kwargs): # check bbox is being used to filter results. df = read_file(naturalearth_lowres) if geometry_name != "geometry": df = df.rename_geometry(geometry_name) filename = os.path.join(str(tmpdir), "test.pq") df.to_parquet(filename, **write_kwargs) pq_df = read_parquet(filename, bbox=(0, 0, 10, 10)) assert pq_df["name"].values.tolist() == [ "France", "Benin", "Nigeria", "Cameroon", "Togo", "Ghana", "Burkina Faso", "Gabon", "Eq. Guinea", ] @bbox_write_kwargs @pytest.mark.parametrize("geometry_name", ["geometry", "custum_geom_col"]) def test_read_parquet_bbox_partitioned( tmpdir, naturalearth_lowres, geometry_name, write_kwargs ): # check bbox is being used to filter results on partitioned data. df = read_file(naturalearth_lowres) if geometry_name != "geometry": df = df.rename_geometry(geometry_name) # manually create partitioned dataset basedir = tmpdir / "partitioned_dataset" basedir.mkdir() df[:100].to_parquet(basedir / "data1.parquet", **write_kwargs) df[100:].to_parquet(basedir / "data2.parquet", **write_kwargs) pq_df = read_parquet(basedir, bbox=(0, 0, 10, 10)) assert pq_df["name"].values.tolist() == [ "France", "Benin", "Nigeria", "Cameroon", "Togo", "Ghana", "Burkina Faso", "Gabon", "Eq. Guinea", ] @pytest.mark.skipif( Version(pyarrow.__version__) < Version("21.0.0"), reason="Writing GeoParquet 2.0 files requires pyarrow>=21.0", ) @pytest.mark.parametrize("geometry_name", ["geometry", "custum_geom_col"]) def test_read_parquet_bbox_partitioned_inconsistent_schema( tmpdir, naturalearth_lowres, geometry_name ): # in the case of a GeoParquet 2.0 file with statistics, filtering a # partitioned dataset should work fine if the geometry column is not the # same index in the schema in each of the files df = read_file(naturalearth_lowres) if geometry_name != "geometry": df = df.rename_geometry(geometry_name) # manually create partitioned dataset basedir = tmpdir / "partitioned_dataset" basedir.mkdir() df[:100].to_parquet(basedir / "data1.parquet", schema_version="2.0.0") df[100:][["name", geometry_name]].to_parquet( basedir / "data2.parquet", schema_version="2.0.0" ) pq_df = read_parquet(basedir, bbox=(0, 0, 10, 10)) assert pq_df["name"].values.tolist() == [ "France", "Benin", "Nigeria", "Cameroon", "Togo", "Ghana", "Burkina Faso", "Gabon", "Eq. Guinea", ] @bbox_write_kwargs @pytest.mark.parametrize( "geometry, bbox", [ (LineString([(1, 1), (3, 3)]), (1.5, 1.5, 3.5, 3.5)), (LineString([(1, 1), (3, 3)]), (3, 3, 3, 3)), (LineString([(1, 1), (3, 3)]), (1.5, 1.5, 2.5, 2.5)), (Polygon([(0, 0), (4, 0), (4, 4), (0, 4)]), (1, 1, 3, 3)), (Polygon([(0, 0), (4, 0), (4, 4), (0, 4)]), (1, 1, 5, 5)), (Polygon([(0, 0), (4, 0), (4, 4), (0, 4)]), (2, 2, 4, 4)), (Polygon([(0, 0), (4, 0), (4, 4), (0, 4)]), (4, 4, 4, 4)), (Polygon([(0, 0), (4, 0), (4, 4), (0, 4)]), (1, 1, 5, 3)), ], ) def test_read_parquet_bbox_partial_overlap_of_geometry( tmpdir, geometry, bbox, write_kwargs ): df = GeoDataFrame(data=[[1, 2]], columns=["a", "b"], geometry=[geometry]) filename = os.path.join(str(tmpdir), "test.pq") df.to_parquet(filename, **write_kwargs) pq_df = read_parquet(filename, bbox=bbox) assert len(pq_df) == 1 def test_read_parquet_no_bbox(tmpdir, naturalearth_lowres): # check error message when parquet lacks a bbox column but # want to use bbox kwarg in read_parquet. df = read_file(naturalearth_lowres) filename = os.path.join(str(tmpdir), "test.pq") df.to_parquet(filename) with pytest.raises(ValueError, match="Specifying 'bbox' not supported"): read_parquet(filename, bbox=(0, 0, 20, 20)) def test_read_parquet_no_bbox_partitioned(tmpdir, naturalearth_lowres): # check error message when partitioned parquet data does not have # a bbox column but want to use kwarg to read_parquet. df = read_file(naturalearth_lowres) # manually create partitioned dataset basedir = tmpdir / "partitioned_dataset" basedir.mkdir() df[:100].to_parquet(basedir / "data1.parquet") df[100:].to_parquet(basedir / "data2.parquet") with pytest.raises(ValueError, match="Specifying 'bbox' not supported"): read_parquet(basedir, bbox=(0, 0, 20, 20)) def test_convert_bbox_to_parquet_filter(): # check conversion of bbox to parquet filter expression import pyarrow.compute as pc bbox = (0, 0, 25, 35) expected = ~( (pc.field(("bbox", "xmin")) > 25) | (pc.field(("bbox", "ymin")) > 35) | (pc.field(("bbox", "xmax")) < 0) | (pc.field(("bbox", "ymax")) < 0) ) assert expected.equals(_convert_bbox_to_parquet_filter(bbox, "bbox")) def test_read_parquet_bbox_column_default_behaviour(tmpdir, naturalearth_lowres): # check that bbox column is not read in by default df = read_file(naturalearth_lowres) filename = os.path.join(str(tmpdir), "test.pq") df.to_parquet(filename, write_covering_bbox=True) result1 = read_parquet(filename) assert "bbox" not in result1 result2 = read_parquet(filename, columns=["name", "geometry"]) assert "bbox" not in result2 assert list(result2.columns) == ["name", "geometry"] @bbox_write_kwargs @pytest.mark.parametrize( "filters", [ [("gdp_md_est", ">", 20000)], pc.field("gdp_md_est") > 20000, ], ) def test_read_parquet_filters_and_bbox( tmpdir, naturalearth_lowres, filters, write_kwargs ): df = read_file(naturalearth_lowres) filename = os.path.join(str(tmpdir), "test.pq") df.to_parquet(filename, **write_kwargs) result = read_parquet(filename, filters=filters, bbox=(0, 0, 20, 20)) assert result["name"].values.tolist() == [ "Dem. Rep. Congo", "France", "Nigeria", "Cameroon", "Ghana", "Algeria", "Libya", ] @bbox_write_kwargs @pytest.mark.parametrize( "filters", [ ([("gdp_md_est", ">", 15000), ("gdp_md_est", "<", 16000)]), ((pc.field("gdp_md_est") > 15000) & (pc.field("gdp_md_est") < 16000)), ], ) def test_read_parquet_filters_without_bbox( tmpdir, naturalearth_lowres, filters, write_kwargs ): df = read_file(naturalearth_lowres) filename = os.path.join(str(tmpdir), "test.pq") df.to_parquet(filename, **write_kwargs) result = read_parquet(filename, filters=filters) assert result["name"].values.tolist() == ["Burkina Faso", "Mozambique", "Albania"] def test_read_parquet_file_with_custom_bbox_encoding_fieldname(tmpdir): import pyarrow.parquet as pq data = { "name": ["point1", "point2", "point3"], "geometry": [Point(1, 1), Point(2, 2), Point(3, 3)], } df = GeoDataFrame(data) filename = os.path.join(str(tmpdir), "test.pq") table = _geopandas_to_arrow( df, schema_version="1.1.0", write_covering_bbox=True, ) metadata = table.schema.metadata # rename_columns results in wiping of metadata table = table.rename_columns(["name", "geometry", "custom_bbox_name"]) geo_metadata = json.loads(metadata[b"geo"]) geo_metadata["columns"]["geometry"]["covering"]["bbox"] = { "xmin": ["custom_bbox_name", "xmin"], "ymin": ["custom_bbox_name", "ymin"], "xmax": ["custom_bbox_name", "xmax"], "ymax": ["custom_bbox_name", "ymax"], } metadata.update({b"geo": _encode_metadata(geo_metadata)}) table = table.replace_schema_metadata(metadata) pq.write_table(table, filename) pq_table = pq.read_table(filename) assert "custom_bbox_name" in pq_table.schema.names pq_df = read_parquet(filename, bbox=(1.5, 1.5, 2.5, 2.5)) assert pq_df["name"].values.tolist() == ["point2"] def test_to_parquet_with_existing_bbox_column(tmpdir, naturalearth_lowres): df = read_file(naturalearth_lowres) df = df.assign(bbox=[0] * len(df)) filename = os.path.join(str(tmpdir), "test.pq") with pytest.raises( ValueError, match="An existing column 'bbox' already exists in the dataframe" ): df.to_parquet(filename, write_covering_bbox=True) def test_read_parquet_bbox_points(tmp_path): # check bbox filtering on point geometries df = geopandas.GeoDataFrame( {"col": range(10)}, geometry=[Point(i, i) for i in range(10)] ) df.to_parquet(tmp_path / "test.parquet", geometry_encoding="geoarrow") result = geopandas.read_parquet(tmp_path / "test.parquet", bbox=(0, 0, 10, 10)) assert len(result) == 10 result = geopandas.read_parquet(tmp_path / "test.parquet", bbox=(3, 3, 5, 5)) assert len(result) == 3 @pytest.mark.skipif( Version(pyarrow.__version__) < Version("21.0.0"), reason="Writing GeoParquet 2.0 files requires pyarrow>=21.0", ) @patch("geopandas.io.arrow._bbox_intersects") def test_read_parquet_bbox_column_and_statistics(bbox_intersects, tmp_path): # if a GeoParquet file has both a bbox column and geospatial statistics, # reading the file with a bbox filter should work fine, but we prefer # using the bbox column (slighty more efficient) df = geopandas.GeoDataFrame( {"col": range(10)}, geometry=[Point(i, i) for i in range(10)] ) df.to_parquet( tmp_path / "test.parquet", write_covering_bbox=True, schema_version="2.0.0" ) result = geopandas.read_parquet(tmp_path / "test.parquet", bbox=(3, 3, 5, 5)) assert len(result) == 3 # filtering manually based on the statistics would use the _bbox_intersects function bbox_intersects.assert_not_called() def test_non_geo_parquet_read_with_proper_error(tmp_path): # https://github.com/geopandas/geopandas/issues/3556 gdf = GeoDataFrame( {"col": [1, 2, 3]}, geometry=geopandas.points_from_xy([1, 2, 3], [1, 2, 3]), crs="EPSG:4326", ) del gdf["geometry"] # for the purposes of testing, force a situation where gdf will be written without # a geometry column (but with the other metadata) gdf.__class__ = GeoDataFrame gdf.to_parquet(tmp_path / "test_no_geometry.parquet") with pytest.raises( ValueError, match="No geometry columns are included in the columns read" ): geopandas.read_parquet(tmp_path / "test_no_geometry.parquet") def test_save_df_attrs_to_parquet_metadata(tmp_path): gdf = geopandas.GeoDataFrame({"a": ["a"], "b": ["1"], "geometry": [None]}) gdf.attrs["test_key"] = "test_value" gdf.to_parquet(tmp_path / "gdf.parquet") gdf2 = geopandas.read_parquet(tmp_path / "gdf.parquet") assert gdf2.attrs == {"test_key": "test_value"} def test_read_parquet_from_https(): _ = pytest.importorskip("fsspec") _ = pytest.importorskip("requests") _ = pytest.importorskip("aiohttp") path = "https://github.com/opengeospatial/geoparquet/raw/refs/tags/v1.1.0/test_data/data-polygon-encoding_wkb.parquet" df = geopandas.read_parquet(path) assert df.shape == (4, 2) @contextlib.contextmanager def with_geoarrow_extension_types(): gp = pytest.importorskip("geoarrow.pyarrow") gp.register_extension_types() try: yield finally: gp.unregister_extension_types() @pytest.mark.skipif( Version(pyarrow.__version__) < Version("20.0.0"), reason="Reading GeoParquet 2.0 files requires pyarrow>=20.0.0", ) @pytest.mark.parametrize("extension_type_registered", [False, True]) @pytest.mark.parametrize( "geometry_type", ["point", "linestring", "polygon", "multipoint", "multilinestring", "multipolygon"], ) def test_read_parquet_2_0_native(geometry_type, extension_type_registered): data_dir = DATA_PATH / "arrow" / "geoparquet" if extension_type_registered: context = with_geoarrow_extension_types else: context = contextlib.nullcontext with context(): result = geopandas.read_parquet( data_dir / "2.0.0" / f"data-{geometry_type}-encoding_wkb.parquet" ) expected = geopandas.read_parquet( data_dir / "1.1.0" / f"data-{geometry_type}-encoding_wkb.parquet" ) assert_geodataframe_equal(result, expected, check_crs=True) @pytest.mark.skipif( Version(pyarrow.__version__) >= Version("20.0.0"), reason="Reading GeoParquet 2.0 files requires pyarrow>=20.0.0", ) def test_read_parquet_2_0_error_old_pyarrow(): data_dir = DATA_PATH / "arrow" / "geoparquet" with pytest.raises(OSError, match=r"Reading GeoParquet 2\.0 files"): geopandas.read_parquet(data_dir / "2.0.0" / "data-point-encoding_wkb.parquet") @pytest.mark.skipif( Version(pyarrow.__version__) < Version("21.0.0"), reason="Writing GeoParquet 2.0 files requires pyarrow>=21.0", ) @pytest.mark.parametrize("extension_type_registered", [False, True]) def test_write_parquet_2_0(tmp_path, extension_type_registered): if extension_type_registered: context = with_geoarrow_extension_types else: context = contextlib.nullcontext gdf = GeoDataFrame( {"col": [1, 2, 3]}, geometry=geopandas.points_from_xy([1, 2, 3], [1, 2, 3]), crs="EPSG:4326", ) with context(): gdf.to_parquet(tmp_path / "test-2_0.parquet", schema_version="2.0.0") meta = pq.read_metadata(tmp_path / "test-2_0.parquet") metadata = json.loads(meta.metadata[b"geo"]) assert metadata["version"] == "2.0.0" # verify the Parquet file is indeed using the Parquet logical types geo_col = meta.schema.column(1) assert geo_col.name == "geometry" assert geo_col.physical_type == "BYTE_ARRAY" assert str(geo_col.logical_type).startswith("Geometry") # also verify the statistics were written by default col_chunk = meta.row_group(0).column(1) assert col_chunk.is_geo_stats_set assert col_chunk.geo_statistics is not None @pytest.mark.skipif( Version(pyarrow.__version__) >= Version("21.0.0"), reason="Writing GeoParquet 2.0 files requires pyarrow>=21.0", ) def test_write_parquet_2_0_old_pyarrow(tmp_path): gdf = GeoDataFrame( {"col": [1, 2, 3]}, geometry=geopandas.points_from_xy([1, 2, 3], [1, 2, 3]), crs="EPSG:4326", ) with pytest.raises( ValueError, match=re.escape("Writing GeoParquet 2.0 files requires pyarrow>=21.0"), ): gdf.to_parquet(tmp_path / "test-2_0.parquet", schema_version="2.0.0") @pytest.mark.filterwarnings("ignore:.*proxy.*:RuntimeWarning") @pytest.mark.skipif( Version(pyarrow.__version__) < Version("21.0.0"), reason="Writing GeoParquet 2.0 files requires pyarrow>=21.0", ) def test_write_parquet_2_0_gdal_readable(tmp_path): pyogrio = pytest.importorskip("pyogrio") if "Parquet" not in pyogrio.list_drivers(): pytest.skip("Test needs pyogrio/GDAL with Parquet support") gdf = GeoDataFrame( {"col": [1, 2, 3]}, geometry=geopandas.points_from_xy([1, 2, 3], [1, 2, 3]), crs="EPSG:4326", ) gdf.to_parquet(tmp_path / "test-2_0.parquet", schema_version="2.0.0") result = geopandas.read_file(tmp_path / "test-2_0.parquet") assert_geodataframe_equal(result, gdf) geopandas-1.2.0/geopandas/io/tests/test_file.py000066400000000000000000001616401525650651600215400ustar00rootroot00000000000000import datetime import io import json import os import pathlib import shutil import tempfile import warnings import zoneinfo from collections import OrderedDict from packaging.version import Version import numpy as np import pandas as pd from pandas.api.types import is_datetime64_any_dtype from shapely.geometry import Point, Polygon, box, mapping import geopandas from geopandas import GeoDataFrame, GeoSeries, points_from_xy, read_file from geopandas._compat import HAS_PYPROJ, PANDAS_GE_30, PANDAS_INFER_STR from geopandas.io.file import _EXTENSION_TO_DRIVER, _detect_driver import pytest from geopandas.testing import assert_geodataframe_equal, assert_geoseries_equal from geopandas.tests.util import PACKAGE_DIR, validate_boro_df from pandas.testing import assert_frame_equal, assert_series_equal try: import pyogrio # those version checks have to be defined here instead of imported from # geopandas.io.file (those are only initialized lazily on first usage) PYOGRIO_GE_090 = Version(Version(pyogrio.__version__).base_version) >= Version( "0.9.0" ) PYOGRIO_GE_012 = Version(Version(pyogrio.__version__).base_version) >= Version( "0.12.0" ) PYOGRIO_GE_0121 = Version(Version(pyogrio.__version__).base_version) >= Version( "0.12.1" ) except ImportError: pyogrio = False PYOGRIO_GE_090 = False PYOGRIO_GE_012 = False PYOGRIO_GE_0121 = False try: import fiona FIONA_GE_19 = Version(Version(fiona.__version__).base_version) >= Version("1.9.0") except ImportError: fiona = False FIONA_GE_19 = False PYOGRIO_MARK = pytest.mark.skipif(not pyogrio, reason="pyogrio not installed") FIONA_MARK = pytest.mark.skipif(not fiona, reason="fiona not installed") _CRS = "epsg:4326" pytestmark = pytest.mark.filterwarnings("ignore:Value:RuntimeWarning:pyogrio") @pytest.fixture( params=[ pytest.param("fiona", marks=FIONA_MARK), pytest.param("pyogrio", marks=PYOGRIO_MARK), ] ) def engine(request): return request.param def skip_pyogrio_not_supported(engine): if engine == "pyogrio": pytest.skip("not supported for the pyogrio engine") @pytest.fixture def df_nybb(engine, nybb_filename): df = read_file(nybb_filename, engine=engine) return df @pytest.fixture def df_null(): return read_file( os.path.join(PACKAGE_DIR, "geopandas", "tests", "data", "null_geom.geojson") ) @pytest.fixture def file_path(): return os.path.join(PACKAGE_DIR, "geopandas", "tests", "data", "null_geom.geojson") @pytest.fixture def df_points(): N = 10 crs = _CRS df = GeoDataFrame( [ {"geometry": Point(x, y), "value1": x + y, "value2": x * y} for x, y in zip(range(N), range(N)) ], crs=crs, ) return df # ----------------------------------------------------------------------------- # to_file tests # ----------------------------------------------------------------------------- driver_ext_pairs = [ ("ESRI Shapefile", ".shp"), ("GeoJSON", ".geojson"), ("GPKG", ".gpkg"), (None, ".shp"), (None, ""), (None, ".geojson"), (None, ".gpkg"), ] def assert_correct_driver(file_path, ext, engine): # check the expected driver expected_driver = "ESRI Shapefile" if ext == "" else _EXTENSION_TO_DRIVER[ext] if engine == "fiona": with fiona.open(str(file_path)) as fds: assert fds.driver == expected_driver else: # TODO pyogrio doesn't yet provide a way to check the driver of a file return @pytest.mark.parametrize("driver,ext", driver_ext_pairs) def test_to_file(tmpdir, df_nybb, df_null, driver, ext, engine): """Test to_file and from_file""" tempfilename = os.path.join(str(tmpdir), "boros." + ext) df_nybb.to_file(tempfilename, driver=driver, engine=engine) # Read layer back in df = GeoDataFrame.from_file(tempfilename, engine=engine) assert "geometry" in df assert len(df) == 5 assert np.all(df["BoroName"].values == df_nybb["BoroName"]) # Write layer with null geometry out to file tempfilename = os.path.join(str(tmpdir), "null_geom" + ext) df_null.to_file(tempfilename, driver=driver, engine=engine) # Read layer back in df = GeoDataFrame.from_file(tempfilename, engine=engine) assert "geometry" in df assert len(df) == 2 assert np.all(df["Name"].values == df_null["Name"]) # check the expected driver assert_correct_driver(tempfilename, ext, engine) @pytest.mark.parametrize("driver,ext", driver_ext_pairs) def test_to_file_pathlib(tmpdir, df_nybb, driver, ext, engine): """Test to_file and from_file""" temppath = pathlib.Path(os.path.join(str(tmpdir), "boros." + ext)) df_nybb.to_file(temppath, driver=driver, engine=engine) # Read layer back in df = GeoDataFrame.from_file(temppath, engine=engine) assert "geometry" in df assert len(df) == 5 assert np.all(df["BoroName"].values == df_nybb["BoroName"]) # check the expected driver assert_correct_driver(temppath, ext, engine) @pytest.mark.parametrize("driver,ext", driver_ext_pairs) def test_to_file_bool(tmpdir, driver, ext, engine): """Test error raise when writing with a boolean column (GH #437).""" tempfilename = os.path.join(str(tmpdir), f"temp.{ext}") df = GeoDataFrame( { "col": [True, False, True], "geometry": [Point(0, 0), Point(1, 1), Point(2, 2)], }, crs=4326, ) df.to_file(tempfilename, driver=driver, engine=engine) result = read_file(tempfilename, engine=engine) if ext in (".shp", ""): # Shapefile does not support boolean, so is read back as int # but since GDAL 3.9 supports boolean fields in SHP if engine == "fiona" and fiona.gdal_version.minor < 9: df["col"] = df["col"].astype("int64") elif engine == "pyogrio" and pyogrio.__gdal_version__ < (3, 9): df["col"] = df["col"].astype("int32") assert_geodataframe_equal(result, df) # check the expected driver assert_correct_driver(tempfilename, ext, engine) TEST_DATE = datetime.datetime(2021, 11, 21, 1, 7, 43, 17500) # from pandas 2.0, utc equality checks less stringent, forward compat with zoneinfo utc = datetime.UTC eastern = zoneinfo.ZoneInfo("America/New_York") test_date_eastern = TEST_DATE.replace(tzinfo=eastern) datetime_type_tests = (TEST_DATE, test_date_eastern) @pytest.mark.filterwarnings( "ignore:Non-conformant content for record 1 in column b:RuntimeWarning" ) # for GPKG, GDAL writes the tz data but warns on reading (see DATETIME_FORMAT option) @pytest.mark.parametrize( "time", datetime_type_tests, ids=("naive_datetime", "datetime_with_timezone") ) @pytest.mark.parametrize("driver,ext", driver_ext_pairs) def test_to_file_datetime(tmpdir, driver, ext, time, engine): """Test writing a data file with the datetime column type""" if ext in (".shp", ""): pytest.skip(f"Driver corresponding to ext {ext} doesn't support dt fields") tempfilename = os.path.join(str(tmpdir), f"test_datetime{ext}") point = Point(0, 0) df = GeoDataFrame( {"a": [1.0, 2.0], "b": [time, time]}, geometry=[point, point], crs=4326 ) df["b"] = df["b"].dt.round(freq="ms") df.to_file(tempfilename, driver=driver, engine=engine) df_read = read_file(tempfilename, engine=engine) assert_geodataframe_equal(df.drop(columns=["b"]), df_read.drop(columns=["b"])) # Check datetime column expected = df["b"] expected = df["b"].dt.as_unit("ms") actual = df_read["b"] if df["b"].dt.tz is not None: # US/Eastern becomes a UTC-5 fixed offset when read from file # as GDAL only models offsets, not timezones. # Compare fair result in terms of UTC instead expected = expected.dt.tz_convert(utc) actual = actual.dt.tz_convert(utc) assert_series_equal(expected, actual) dt_exts = ["gpkg", "geojson"] def write_invalid_date_file(date_str, tmpdir, ext, engine): tempfilename = os.path.join(str(tmpdir), f"test_invalid_datetime.{ext}") df = GeoDataFrame( { "date": ["2014-08-26T10:01:23", "2014-08-26T10:01:23", date_str], "geometry": [Point(1, 1), Point(1, 1), Point(1, 1)], } ) with warnings.catch_warnings(): warnings.simplefilter("ignore") # suppress warnings from writing file # Schema not required for GeoJSON since not typed, but needed for GPKG if ext == "geojson": df.to_file(tempfilename, engine=engine) else: schema = {"geometry": "Point", "properties": {"date": "datetime"}} if engine == "pyogrio" and not fiona: # (use schema to write the invalid date without pandas datetimes pytest.skip("test requires fiona kwarg schema") df.to_file(tempfilename, schema=schema, engine="fiona") return tempfilename @pytest.mark.parametrize("ext", dt_exts) @pytest.mark.filterwarnings( "ignore:Invalid content for record 3 in column date.*:RuntimeWarning" ) def test_read_file_datetime_invalid(tmpdir, ext, engine): # https://github.com/geopandas/geopandas/issues/2502 date_str = "9999-99-99T00:00:00" # invalid date handled by GDAL tempfilename = write_invalid_date_file(date_str, tmpdir, ext, engine) res = read_file(tempfilename, engine=engine) if ext == "gpkg": assert is_datetime64_any_dtype(res["date"]) assert pd.isna(res["date"].iloc[-1]) else: assert res["date"].dtype == "str" if PANDAS_INFER_STR else object assert isinstance(res["date"].iloc[-1], str) @pytest.mark.parametrize("ext", dt_exts) def test_read_file_datetime_out_of_bounds_ns(tmpdir, ext, engine): # https://github.com/geopandas/geopandas/issues/2502 date_str = "9999-12-31T00:00:00" # valid to GDAL, not to [ns] format tempfilename = write_invalid_date_file(date_str, tmpdir, ext, engine) if engine == "pyogrio" and PYOGRIO_GE_012 and not PANDAS_GE_30: with pytest.warns( UserWarning, match="Error parsing datetimes, original strings are returned" ): res = read_file(tempfilename, engine=engine) else: res = read_file(tempfilename, engine=engine) if PANDAS_GE_30: assert res["date"].dtype == "datetime64[ms]" assert res["date"].iloc[-1] == pd.Timestamp("9999-12-31 00:00:00") else: # Pandas invalid datetimes are read in as object dtype (strings) assert res["date"].dtype == "str" if PANDAS_INFER_STR else "object" assert isinstance(res["date"].iloc[0], str) @pytest.mark.skipif(not HAS_PYPROJ, reason="pyproj not installed") def test_read_file_datetime_mixed_offsets(tmpdir): # https://github.com/geopandas/geopandas/issues/2478 tempfilename = os.path.join(str(tmpdir), "test_mixed_datetime.geojson") df = GeoDataFrame( { "date": [ "2014-08-26 10:01:23.040001+02:00", "2019-03-07 17:31:43.118999+01:00", ], "geometry": [Point(1, 1), Point(1, 1)], } ).set_crs("EPSG:4326") df.to_file(tempfilename) # check mixed tz don't crash GH2478 res = read_file(tempfilename) # Convert mixed timezones to UTC equivalent assert is_datetime64_any_dtype(res["date"]) assert res["date"].dt.tz == utc @pytest.mark.parametrize("driver,ext", driver_ext_pairs) def test_to_file_with_point_z(tmpdir, ext, driver, engine): """Test that 3D geometries are retained in writes (GH #612).""" tempfilename = os.path.join(str(tmpdir), "test_3Dpoint" + ext) point3d = Point(0, 0, 500) point2d = Point(1, 1) df = GeoDataFrame({"a": [1, 2]}, geometry=[point3d, point2d], crs=_CRS) df.to_file(tempfilename, driver=driver, engine=engine) df_read = GeoDataFrame.from_file(tempfilename, engine=engine) assert_geoseries_equal(df.geometry, df_read.geometry) # check the expected driver assert_correct_driver(tempfilename, ext, engine) @pytest.mark.parametrize("driver,ext", driver_ext_pairs) def test_to_file_with_poly_z(tmpdir, ext, driver, engine): """Test that 3D geometries are retained in writes (GH #612).""" tempfilename = os.path.join(str(tmpdir), "test_3Dpoly" + ext) poly3d = Polygon([[0, 0, 5], [0, 1, 5], [1, 1, 5], [1, 0, 5]]) poly2d = Polygon([[0, 0], [0, 1], [1, 1], [1, 0]]) df = GeoDataFrame({"a": [1, 2]}, geometry=[poly3d, poly2d], crs=_CRS) df.to_file(tempfilename, driver=driver, engine=engine) df_read = GeoDataFrame.from_file(tempfilename, engine=engine) assert_geoseries_equal(df.geometry, df_read.geometry) # check the expected driver assert_correct_driver(tempfilename, ext, engine) def test_to_file_types(tmpdir, df_points, engine): """Test various integer type columns (GH#93)""" tempfilename = os.path.join(str(tmpdir), "int.shp") int_types = [ np.int8, np.int16, np.int32, np.int64, np.intp, np.uint8, np.uint16, np.uint32, np.uint64, ] geometry = df_points.geometry data = { str(i): np.arange(len(geometry), dtype=dtype) for i, dtype in enumerate(int_types) } df = GeoDataFrame(data, geometry=geometry) df.to_file(tempfilename, engine=engine) @pytest.mark.parametrize("driver,ext", driver_ext_pairs + [("OGR_GMT", ".gmt")]) def test_to_file_int32(tmpdir, df_points, engine, driver, ext): tempfilename = os.path.join(str(tmpdir), f"int32.{ext}") geometry = df_points.geometry df = GeoDataFrame(geometry=geometry) df["data"] = pd.array([1, np.nan] * 5, dtype=pd.Int32Dtype()) df.to_file(tempfilename, driver=driver, engine=engine) df_read = GeoDataFrame.from_file(tempfilename, engine=engine) # the int column with missing values comes back as float expected = df.copy() expected["data"] = expected["data"].astype("float64") assert_geodataframe_equal(df_read, expected, check_like=True) tempfilename2 = os.path.join(str(tmpdir), f"int32_2.{ext}") df2 = df.dropna() df2.to_file(tempfilename2, driver=driver, engine=engine) df2_read = GeoDataFrame.from_file(tempfilename2, engine=engine) if engine == "pyogrio": assert df2_read["data"].dtype == "int32" else: # with the fiona engine the 32 bitwidth is not preserved assert df2_read["data"].dtype == "int64" @pytest.mark.parametrize("driver,ext", driver_ext_pairs) def test_to_file_int64(tmpdir, df_points, engine, driver, ext): tempfilename = os.path.join(str(tmpdir), f"int64.{ext}") geometry = df_points.geometry df = GeoDataFrame(geometry=geometry) df["data"] = pd.array([1, np.nan] * 5, dtype=pd.Int64Dtype()) df.to_file(tempfilename, driver=driver, engine=engine) df_read = GeoDataFrame.from_file(tempfilename, engine=engine) # the int column with missing values comes back as float expected = df.copy() expected["data"] = expected["data"].astype("float64") assert_geodataframe_equal(df_read, expected, check_like=True) def test_to_file_empty(tmpdir, engine): input_empty_df = GeoDataFrame(columns=["geometry"]) tempfilename = os.path.join(str(tmpdir), "test.shp") with pytest.warns(UserWarning): input_empty_df.to_file(tempfilename, engine=engine) def test_to_file_schema(tmpdir, df_nybb, engine): """ Ensure that the file is written according to the schema if it is specified """ tempfilename = os.path.join(str(tmpdir), "test.shp") properties = OrderedDict( [ ("Shape_Leng", "float:19.11"), ("BoroName", "str:40"), ("BoroCode", "int:10"), ("Shape_Area", "float:19.11"), ] ) schema = {"geometry": "Polygon", "properties": properties} if engine == "pyogrio": with pytest.raises(ValueError): df_nybb.iloc[:2].to_file(tempfilename, schema=schema, engine=engine) else: # Take the first 2 features to speed things up a bit df_nybb.iloc[:2].to_file(tempfilename, schema=schema, engine=engine) import fiona with fiona.open(tempfilename) as f: result_schema = f.schema assert result_schema == schema @pytest.mark.skipif(not HAS_PYPROJ, reason="pyproj not installed") def test_to_file_crs(tmpdir, engine, nybb_filename): """ Ensure that the file is written according to the crs if it is specified """ df = read_file(nybb_filename, engine=engine) tempfilename = os.path.join(str(tmpdir), "crs.shp") # save correct CRS df.to_file(tempfilename, engine=engine) result = GeoDataFrame.from_file(tempfilename, engine=engine) assert result.crs == df.crs if engine == "pyogrio": with pytest.raises(ValueError, match="Passing 'crs' is not supported"): df.to_file(tempfilename, crs=3857, engine=engine) return # overwrite CRS df.to_file(tempfilename, crs=3857, engine=engine) result = GeoDataFrame.from_file(tempfilename, engine=engine) assert result.crs == "epsg:3857" # specify CRS for gdf without one df2 = df.set_crs(None, allow_override=True) df2.to_file(tempfilename, crs=2263, engine=engine) df = GeoDataFrame.from_file(tempfilename, engine=engine) assert df.crs == "epsg:2263" def test_to_file_column_len(tmpdir, df_points, engine): """ Ensure that a warning about truncation is given when a geodataframe with column names longer than 10 characters is saved to shapefile """ tempfilename = os.path.join(str(tmpdir), "test.shp") df = df_points.iloc[:1].copy() df["0123456789A"] = ["the column name is 11 characters"] with pytest.warns() as captured: df.to_file(tempfilename, driver="ESRI Shapefile", engine=engine) column_names_warning = [ w for w in captured if w.category is UserWarning and "Column names longer than 10 characters will be truncated" in str(w.message) ] assert len(column_names_warning) == 1 def test_to_file_with_duplicate_columns(tmpdir, engine): df = GeoDataFrame(data=[[1, 2, 3]], columns=["a", "b", "a"], geometry=[Point(1, 1)]) tempfilename = os.path.join(str(tmpdir), "duplicate.shp") with pytest.raises( ValueError, match="GeoDataFrame cannot contain duplicated column names" ): df.to_file(tempfilename, engine=engine) @pytest.mark.parametrize("driver,ext", driver_ext_pairs) def test_append_file(tmpdir, df_nybb, df_null, driver, ext, engine): """Test to_file with append mode and from_file""" tempfilename = os.path.join(str(tmpdir), "boros" + ext) driver = driver if driver else _detect_driver(tempfilename) df_nybb.to_file(tempfilename, driver=driver, engine=engine) df_nybb.to_file(tempfilename, mode="a", driver=driver, engine=engine) # Read layer back in df = GeoDataFrame.from_file(tempfilename, engine=engine) assert "geometry" in df assert len(df) == (5 * 2) expected = pd.concat([df_nybb] * 2, ignore_index=True) assert_geodataframe_equal(df, expected, check_less_precise=True) if engine == "pyogrio": # for pyogrio also ensure append=True works tempfilename = os.path.join(str(tmpdir), "boros2" + ext) df_nybb.to_file(tempfilename, driver=driver, engine=engine) df_nybb.to_file(tempfilename, append=True, driver=driver, engine=engine) # Read layer back in df = GeoDataFrame.from_file(tempfilename, engine=engine) assert len(df) == (len(df_nybb) * 2) # Write layer with null geometry out to file tempfilename = os.path.join(str(tmpdir), "null_geom" + ext) df_null.to_file(tempfilename, driver=driver, engine=engine) df_null.to_file(tempfilename, mode="a", driver=driver, engine=engine) # Read layer back in df = GeoDataFrame.from_file(tempfilename, engine=engine) assert "geometry" in df assert len(df) == (2 * 2) expected = pd.concat([df_null] * 2, ignore_index=True) assert_geodataframe_equal(df, expected, check_less_precise=True) def test_mode_unsupported(tmpdir, df_nybb, engine): tempfilename = os.path.join(str(tmpdir), "data.shp") with pytest.raises(ValueError, match="'mode' should be one of 'w' or 'a'"): df_nybb.to_file(tempfilename, mode="r", engine=engine) @pytest.mark.parametrize("driver,ext", driver_ext_pairs) @pytest.mark.filterwarnings("ignore:'crs' was not provided.*:UserWarning") def test_empty_crs(tmpdir, driver, ext, engine): """Test handling of undefined CRS with GPKG driver (GH #1975).""" if ext == ".gpkg": pytest.xfail("GPKG is read with Undefined geographic SRS.") tempfilename = os.path.join(str(tmpdir), "boros" + ext) df = GeoDataFrame( { "a": [1.0, 2.0, 3.0], "geometry": [Point(0, 0), Point(1, 1), Point(2, 2)], }, ) df.to_file(tempfilename, driver=driver, engine=engine) result = read_file(tempfilename, engine=engine) if ext == ".geojson": # geojson by default assumes epsg:4326 df.geometry.array.crs = "EPSG:4326" assert_geodataframe_equal(result, df) # ----------------------------------------------------------------------------- # read_file tests # ----------------------------------------------------------------------------- NYBB_CRS = "epsg:2263" def test_read_file(engine, nybb_filename): df = read_file(nybb_filename, engine=engine) validate_boro_df(df) if HAS_PYPROJ: assert df.crs == NYBB_CRS expected_columns = ["BoroCode", "BoroName", "Shape_Leng", "Shape_Area"] assert (df.columns[:-1] == expected_columns).all() @pytest.mark.web @pytest.mark.parametrize( "url", [ # geojson url "https://raw.githubusercontent.com/geopandas/geopandas/" "main/geopandas/tests/data/null_geom.geojson", # url to zip file "https://raw.githubusercontent.com/geopandas/geopandas/" "main/geopandas/tests/data/nybb_16a.zip", # url to web service "https://demo.pygeoapi.io/stable/collections/obs/items", ], ) def test_read_file_url(engine, url): gdf = read_file(url, engine=engine) assert isinstance(gdf, geopandas.GeoDataFrame) @pytest.mark.web @pytest.mark.parametrize( "url", [ # url to zipfile without extension "https://geonode.goosocean.org/download/480", ], ) # Marked strict=False as state is not stable see # #3584, #3585, #3644 @pytest.mark.xfail(reason="SSL certificate issue", strict=False) def test_read_file_url_flaky(engine, url): gdf = read_file(url, engine=engine) assert isinstance(gdf, geopandas.GeoDataFrame) def test_read_file_local_uri(file_path, engine): local_uri = "file://" + file_path gdf = read_file(local_uri, engine=engine) assert isinstance(gdf, geopandas.GeoDataFrame) @pytest.mark.skipif(not HAS_PYPROJ, reason="pyproj not installed") def test_read_file_geojson_string_path(engine): if engine == "pyogrio" and not PYOGRIO_GE_090: pytest.skip("fixed in pyogrio 0.9.0") expected = GeoDataFrame({"val_with_hash": ["row # 0"], "geometry": [Point(0, 1)]}) features = { "type": "FeatureCollection", "features": [ { "type": "Feature", "properties": {"val_with_hash": "row # 0"}, "geometry": {"type": "Point", "coordinates": [0.0, 1.0]}, } ], } df_read = read_file(json.dumps(features), engine=engine) assert_geodataframe_equal(expected.set_crs("EPSG:4326"), df_read) def test_read_file_textio(file_path, engine): with open(file_path) as file_text_stream: file_stringio = io.StringIO(file_text_stream.read()) file_text_stream.seek(0) gdf_text_stream = read_file(file_text_stream, engine=engine) gdf_stringio = read_file(file_stringio, engine=engine) assert isinstance(gdf_text_stream, geopandas.GeoDataFrame) assert isinstance(gdf_stringio, geopandas.GeoDataFrame) def test_read_file_bytesio(file_path, engine): with open(file_path, "rb") as file_binary_stream: gdf_binary_stream = read_file(file_binary_stream, engine=engine) file_binary_stream.seek(0) file_bytesio = io.BytesIO(file_binary_stream.read()) gdf_bytesio = read_file(file_bytesio, engine=engine) assert isinstance(gdf_binary_stream, geopandas.GeoDataFrame) assert isinstance(gdf_bytesio, geopandas.GeoDataFrame) def test_read_file_raw_stream(file_path, engine): with open(file_path, "rb", buffering=0) as file_raw_stream: gdf_raw_stream = read_file(file_raw_stream, engine=engine) assert isinstance(gdf_raw_stream, geopandas.GeoDataFrame) def test_read_file_pathlib(file_path, engine): path_object = pathlib.Path(file_path) gdf_path_object = read_file(path_object, engine=engine) assert isinstance(gdf_path_object, geopandas.GeoDataFrame) def test_read_file_tempfile(engine): temp = tempfile.TemporaryFile() temp.write( b""" { "type": "Feature", "geometry": { "type": "Point", "coordinates": [0, 0] }, "properties": { "name": "Null Island" } } """ ) temp.seek(0) gdf_tempfile = geopandas.read_file(temp, engine=engine) assert isinstance(gdf_tempfile, geopandas.GeoDataFrame) temp.close() def test_read_binary_file_fsspec(engine, nybb_filename): fsspec = pytest.importorskip("fsspec") # Remove the zip scheme so fsspec doesn't open as a zipped file, # instead we want to read as bytes and let fiona decode it. path = nybb_filename[6:] with fsspec.open(path, "rb") as f: gdf = read_file(f, engine=engine) assert isinstance(gdf, geopandas.GeoDataFrame) def test_read_text_file_fsspec(file_path, engine): fsspec = pytest.importorskip("fsspec") with fsspec.open(file_path, "r") as f: gdf = read_file(f, engine=engine) assert isinstance(gdf, geopandas.GeoDataFrame) @pytest.mark.skipif(not HAS_PYPROJ, reason="pyproj not installed") def test_read_file_crs_warning_messages(engine): """ Testing warning messages when one of, or both of, the file or mask is missing a crs. """ file_with_crs = os.path.join(tempfile.mkdtemp(), "file_with_crs.shp") file_without_crs = os.path.join(tempfile.mkdtemp(), "file_without_crs.shp") df = pd.DataFrame( { "Latitude": [0, 1, 2, 3, 4], "Longitude": [0, 1, 2, 3, 4], } ) gdf_with_crs = GeoDataFrame( df, geometry=points_from_xy(df.Longitude, df.Latitude), crs="EPSG:4326" ) gdf_without_crs = GeoDataFrame( df, geometry=points_from_xy(df.Longitude, df.Latitude) ) gdf_with_crs.to_file(file_with_crs) with pytest.warns(UserWarning, match="""\'crs\' was not provided"""): gdf_without_crs.to_file(file_without_crs) with pytest.warns(UserWarning, match="""There is no CRS defined in the mask."""): data = read_file(file_with_crs, mask=gdf_without_crs, engine=engine) assert_geodataframe_equal(data, gdf_with_crs) with pytest.warns( UserWarning, match="""There is no CRS defined in the source dataset.""" ): data = read_file(file_without_crs, mask=gdf_with_crs, engine=engine) assert_geodataframe_equal(data, gdf_without_crs) with pytest.warns( UserWarning, match="""There is no CRS defined in the source dataset nor mask.""", ): data = read_file(file_without_crs, mask=gdf_without_crs, engine=engine) assert_geodataframe_equal(data, gdf_without_crs) with warnings.catch_warnings(): warnings.simplefilter("error") data = read_file(file_with_crs, mask=gdf_with_crs, engine=engine) assert_geodataframe_equal(data, gdf_with_crs) # Check with GeoSeries as a mask series_with_crs = GeoSeries( points_from_xy(df.Longitude, df.Latitude), crs="EPSG:4326" ) series_without_crs = GeoSeries(points_from_xy(df.Longitude, df.Latitude)) with pytest.warns(UserWarning, match="""There is no CRS defined in the mask."""): data = read_file(file_with_crs, mask=series_without_crs, engine=engine) assert_geodataframe_equal(data, gdf_with_crs) with pytest.warns( UserWarning, match="""There is no CRS defined in the source dataset.""" ): data = read_file(file_without_crs, mask=series_with_crs, engine=engine) assert_geodataframe_equal(data, gdf_without_crs) with pytest.warns( UserWarning, match="""There is no CRS defined in the source dataset nor mask.""", ): data = read_file(file_without_crs, mask=series_without_crs, engine=engine) assert_geodataframe_equal(data, gdf_without_crs) with warnings.catch_warnings(): warnings.simplefilter("error") data = read_file(file_with_crs, mask=series_with_crs, engine=engine) assert_geodataframe_equal(data, gdf_with_crs) def test_infer_zipped_file(engine, nybb_filename): # Remove the zip scheme so that the test for a zipped file can # check it and add it back. path = nybb_filename[6:] gdf = read_file(path, engine=engine) assert isinstance(gdf, geopandas.GeoDataFrame) # Check that it can successfully add a zip scheme to a path that already has a # scheme gdf = read_file("file+file://" + path, engine=engine) assert isinstance(gdf, geopandas.GeoDataFrame) # Check that it can add a zip scheme for a path that includes a subpath # within the archive. gdf = read_file(path + "!nybb.shp", engine=engine) assert isinstance(gdf, geopandas.GeoDataFrame) def test_allow_legacy_gdal_path(engine, nybb_filename): # Construct a GDAL-style zip path. path = "/vsizip/" + nybb_filename[6:] gdf = read_file(path, engine=engine) assert isinstance(gdf, geopandas.GeoDataFrame) @pytest.mark.skipif(not PYOGRIO_GE_090, reason="bug fixed in pyogrio 0.9.0") def test_read_file_with_hash_in_path(engine, nybb_filename, tmp_path): folder_with_hash = tmp_path / "path with # present" folder_with_hash.mkdir(exist_ok=True, parents=True) read_path = folder_with_hash / "nybb.zip" shutil.copy(nybb_filename[6:], read_path) gdf = read_file(read_path, engine=engine) assert isinstance(gdf, geopandas.GeoDataFrame) def test_read_file_bbox_tuple(df_nybb, engine, nybb_filename): bbox = ( 1031051.7879884212, 224272.49231459625, 1047224.3104931959, 244317.30894023244, ) filtered_df = read_file(nybb_filename, bbox=bbox, engine=engine) expected = df_nybb[df_nybb["BoroName"].isin(["Bronx", "Queens"])] assert_geodataframe_equal(filtered_df, expected.reset_index(drop=True)) def test_read_file_bbox_polygon(df_nybb, engine, nybb_filename): bbox = box( 1031051.7879884212, 224272.49231459625, 1047224.3104931959, 244317.30894023244 ) filtered_df = read_file(nybb_filename, bbox=bbox, engine=engine) expected = df_nybb[df_nybb["BoroName"].isin(["Bronx", "Queens"])] assert_geodataframe_equal(filtered_df, expected.reset_index(drop=True)) def test_read_file_filtered__rows(df_nybb, engine, nybb_filename): filtered_df = read_file(nybb_filename, rows=1, engine=engine) assert_geodataframe_equal(filtered_df, df_nybb.iloc[[0], :]) def test_read_file_filtered__rows_slice(df_nybb, engine, nybb_filename): filtered_df = read_file(nybb_filename, rows=slice(1, 3), engine=engine) assert_geodataframe_equal(filtered_df, df_nybb.iloc[1:3, :].reset_index(drop=True)) @pytest.mark.filterwarnings( "ignore:Layer does not support OLC_FASTFEATURECOUNT:RuntimeWarning" ) # for the slice with -1 def test_read_file_filtered__rows_bbox(df_nybb, engine, nybb_filename): bbox = ( 1031051.7879884212, 224272.49231459625, 1047224.3104931959, 244317.30894023244, ) if engine == "fiona": # combination bbox and rows (rows slice applied after bbox filtering!) filtered_df = read_file( nybb_filename, bbox=bbox, rows=slice(4, None), engine=engine ) assert filtered_df.empty if engine == "pyogrio": # TODO: support negative rows in pyogrio with pytest.raises( ValueError, match=r"'skip_features' must be between 0 and 1|Negative slice start", ): filtered_df = read_file( nybb_filename, bbox=bbox, rows=slice(-1, None), engine=engine ) else: filtered_df = read_file( nybb_filename, bbox=bbox, rows=slice(-1, None), engine=engine ) filtered_df["BoroCode"] = filtered_df["BoroCode"].astype("int64") assert_geodataframe_equal( filtered_df, df_nybb.iloc[4:, :].reset_index(drop=True) ) def test_read_file_filtered_rows_invalid(engine, nybb_filename): with pytest.raises(TypeError): read_file(nybb_filename, rows="not_a_slice", engine=engine) def test_read_file__ignore_geometry(engine, naturalearth_lowres): pdf = geopandas.read_file( naturalearth_lowres, ignore_geometry=True, engine=engine, ) assert "geometry" not in pdf.columns assert isinstance(pdf, pd.DataFrame) and not isinstance(pdf, geopandas.GeoDataFrame) @pytest.mark.filterwarnings( "ignore:The 'include_fields' and 'ignore_fields' keywords:DeprecationWarning" ) def test_read_file__ignore_fields(engine, naturalearth_lowres): gdf = geopandas.read_file( naturalearth_lowres, ignore_fields=["pop_est", "continent", "iso_a3", "gdp_md_est"], engine=engine, ) assert gdf.columns.tolist() == ["name", "geometry"] @pytest.mark.filterwarnings( "ignore:The 'include_fields' and 'ignore_fields' keywords:DeprecationWarning" ) def test_read_file__ignore_all_fields(engine, naturalearth_lowres): gdf = geopandas.read_file( naturalearth_lowres, ignore_fields=["pop_est", "continent", "name", "iso_a3", "gdp_md_est"], engine=engine, ) assert gdf.columns.tolist() == ["geometry"] def test_read_file_missing_geometry(tmpdir, engine): filename = str(tmpdir / "test.csv") expected = pd.DataFrame( {"col1": np.array([1, 2, 3], dtype="int64"), "col2": ["a", "b", "c"]} ) expected.to_csv(filename, index=False) df = geopandas.read_file(filename, engine=engine) # both engines read integers as strings; force back to original type df["col1"] = df["col1"].astype("int64") assert isinstance(df, pd.DataFrame) assert not isinstance(df, geopandas.GeoDataFrame) assert_frame_equal(df, expected) def test_read_file_None_attribute(tmp_path, engine): # Test added in context of https://github.com/geopandas/geopandas/issues/2901 test_path = tmp_path / "test.gpkg" gdf = GeoDataFrame( {"a": [None, None]}, geometry=[Point(1, 2), Point(3, 4)], crs=4326 ) gdf.to_file(test_path, engine=engine) read_gdf = read_file(test_path, engine=engine) assert_geodataframe_equal(gdf, read_gdf) def test_read_csv_dtype(tmpdir, df_nybb): filename = str(tmpdir / "test.csv") df_nybb.to_csv(filename, index=False) pdf = pd.read_csv(filename, dtype={"geometry": "geometry"}) assert pdf.geometry.dtype == "geometry" def test_read_file__where_filter(engine, naturalearth_lowres): if FIONA_GE_19 or engine == "pyogrio": gdf = geopandas.read_file( naturalearth_lowres, where="continent='Africa'", engine=engine, ) assert gdf.continent.unique().tolist() == ["Africa"] else: with pytest.raises(NotImplementedError): geopandas.read_file( naturalearth_lowres, where="continent='Africa'", engine="fiona", ) def test_read_file__columns(engine, naturalearth_lowres): if engine == "fiona" and not FIONA_GE_19: pytest.skip("columns requires fiona 1.9+") gdf = geopandas.read_file( naturalearth_lowres, columns=["name", "pop_est"], engine=engine ) expected = ( ["pop_est", "name", "geometry"] if engine == "pyogrio" and PYOGRIO_GE_0121 else ["name", "pop_est", "geometry"] ) assert gdf.columns.tolist() == expected def test_read_file__columns_empty(engine, naturalearth_lowres): if engine == "fiona" and not FIONA_GE_19: pytest.skip("columns requires fiona 1.9+") gdf = geopandas.read_file(naturalearth_lowres, columns=[], engine=engine) assert gdf.columns.tolist() == ["geometry"] @pytest.mark.skipif(FIONA_GE_19 or not fiona, reason="test for fiona < 1.9") def test_read_file__columns_old_fiona(naturalearth_lowres): with pytest.raises(NotImplementedError): geopandas.read_file( naturalearth_lowres, columns=["name", "pop_est"], engine="fiona" ) @pytest.mark.filterwarnings( "ignore:The 'include_fields' and 'ignore_fields' keywords:DeprecationWarning" ) def test_read_file__include_fields(engine, naturalearth_lowres): if engine == "fiona" and not FIONA_GE_19: pytest.skip("columns requires fiona 1.9+") gdf = geopandas.read_file( naturalearth_lowres, include_fields=["name", "pop_est"], engine=engine ) expected = ( ["pop_est", "name", "geometry"] if engine == "pyogrio" and PYOGRIO_GE_0121 else ["name", "pop_est", "geometry"] ) assert gdf.columns.tolist() == expected @pytest.mark.skipif(not FIONA_GE_19, reason="columns requires fiona 1.9+") def test_read_file__columns_conflicting_keywords(engine, naturalearth_lowres): path = naturalearth_lowres with pytest.raises(ValueError, match="Cannot specify both"): geopandas.read_file( path, include_fields=["name"], ignore_fields=["pop_est"], engine=engine ) with pytest.raises(ValueError, match="Cannot specify both"): geopandas.read_file( path, columns=["name"], include_fields=["pop_est"], engine=engine ) with pytest.raises(ValueError, match="Cannot specify both"): geopandas.read_file( path, columns=["name"], ignore_fields=["pop_est"], engine=engine ) @pytest.mark.skipif(not HAS_PYPROJ, reason="pyproj not installed") @pytest.mark.parametrize("file_like", [False, True]) def test_read_file_bbox_gdf(df_nybb, engine, nybb_filename, file_like): full_df_shape = df_nybb.shape bbox = geopandas.GeoDataFrame( geometry=[ box( 1031051.7879884212, 224272.49231459625, 1047224.3104931959, 244317.30894023244, ) ], crs=NYBB_CRS, ) infile = ( open(nybb_filename.replace("zip://", ""), "rb") if file_like else nybb_filename ) filtered_df = read_file(infile, bbox=bbox, engine=engine) filtered_df_shape = filtered_df.shape assert full_df_shape != filtered_df_shape assert filtered_df_shape == (2, 5) if file_like: infile.close() @pytest.mark.skipif(not HAS_PYPROJ, reason="pyproj not installed") @pytest.mark.parametrize("file_like", [False, True]) def test_read_file_mask_gdf(df_nybb, engine, nybb_filename, file_like): full_df_shape = df_nybb.shape mask = geopandas.GeoDataFrame( geometry=[ box( 1031051.7879884212, 224272.49231459625, 1047224.3104931959, 244317.30894023244, ) ], crs=NYBB_CRS, ) infile = ( open(nybb_filename.replace("zip://", ""), "rb") if file_like else nybb_filename ) filtered_df = read_file(infile, mask=mask, engine=engine) filtered_df_shape = filtered_df.shape assert full_df_shape != filtered_df_shape assert filtered_df_shape == (2, 5) if file_like: infile.close() def test_read_file_mask_polygon(df_nybb, engine, nybb_filename): full_df_shape = df_nybb.shape mask = box( 1031051.7879884212, 224272.49231459625, 1047224.3104931959, 244317.30894023244 ) filtered_df = read_file(nybb_filename, mask=mask, engine=engine) filtered_df_shape = filtered_df.shape assert full_df_shape != filtered_df_shape assert filtered_df_shape == (2, 5) def test_read_file_mask_geojson(df_nybb, nybb_filename, engine): full_df_shape = df_nybb.shape mask = mapping( box( 1031051.7879884212, 224272.49231459625, 1047224.3104931959, 244317.30894023244, ) ) filtered_df = read_file(nybb_filename, mask=mask, engine=engine) filtered_df_shape = filtered_df.shape assert full_df_shape != filtered_df_shape assert filtered_df_shape == (2, 5) @pytest.mark.skipif(not HAS_PYPROJ, reason="pyproj not installed") def test_read_file_bbox_gdf_mismatched_crs(df_nybb, engine, nybb_filename): full_df_shape = df_nybb.shape bbox = geopandas.GeoDataFrame( geometry=[ box( 1031051.7879884212, 224272.49231459625, 1047224.3104931959, 244317.30894023244, ) ], crs=NYBB_CRS, ) bbox.to_crs(epsg=4326, inplace=True) filtered_df = read_file(nybb_filename, bbox=bbox, engine=engine) filtered_df_shape = filtered_df.shape assert full_df_shape != filtered_df_shape assert filtered_df_shape == (2, 5) @pytest.mark.skipif(not HAS_PYPROJ, reason="pyproj not installed") def test_read_file_mask_gdf_mismatched_crs(df_nybb, engine, nybb_filename): full_df_shape = df_nybb.shape mask = geopandas.GeoDataFrame( geometry=[ box( 1031051.7879884212, 224272.49231459625, 1047224.3104931959, 244317.30894023244, ) ], crs=NYBB_CRS, ) mask.to_crs(epsg=4326, inplace=True) filtered_df = read_file(nybb_filename, mask=mask.geometry, engine=engine) filtered_df_shape = filtered_df.shape assert full_df_shape != filtered_df_shape assert filtered_df_shape == (2, 5) @pytest.mark.skipif(not HAS_PYPROJ, reason="pyproj not installed") def test_read_file_multi_layer_with_layer_arg_no_warning(tmp_path, engine): # While reading a file with multiple layers, if the layer is properly # specified, a "Specify layer" warning should not be emitted data1 = {"geometry": [Point(1, 2), Point(2, 1)]} gdf = geopandas.GeoDataFrame(data1, crs="EPSG:4326") file_path = tmp_path / "out.gpkg" gdf.to_file(file_path, layer="layer1", engine=engine) gdf.to_file(file_path, layer="layer2", engine=engine) with warnings.catch_warnings(record=True) as captured: warnings.simplefilter("always") read_file(file_path, bbox=gdf, layer="layer1", engine=engine) read_file(file_path, mask=gdf, layer="layer1", engine=engine) specify_layer_warnings = [ warning for warning in captured if warning.category is UserWarning and "specify layer parameter" in str(warning.message).lower() ] assert len(specify_layer_warnings) == 0, ( "'Specify layer parameter' warning was raised, but the layer was specified." ) def test_read_file_bbox_mask_not_allowed(engine, nybb_filename): bbox = ( 1031051.7879884212, 224272.49231459625, 1047224.3104931959, 244317.30894023244, ) mask = box(*bbox) with pytest.raises(ValueError, match="mask and bbox can not be set together"): read_file(nybb_filename, bbox=bbox, mask=mask) @pytest.mark.filterwarnings( "ignore:Layer 'b'test_empty'' does not have any features:UserWarning" ) def test_read_file_empty_shapefile(tmpdir, engine): if engine == "pyogrio" and not fiona: pytest.skip("test requires fiona to work") from geopandas.io.file import fiona_env # create empty shapefile meta = { "crs": {}, "crs_wkt": "", "driver": "ESRI Shapefile", "schema": { "geometry": "Point", "properties": OrderedDict([("A", "int:9"), ("Z", "float:24.15")]), }, } fname = str(tmpdir.join("test_empty.shp")) with fiona_env(): with fiona.open(fname, "w", **meta) as _: pass empty = read_file(fname, engine=engine) assert isinstance(empty, geopandas.GeoDataFrame) assert all(empty.columns == ["A", "Z", "geometry"]) class FileNumber: def __init__(self, tmpdir, base, ext): self.tmpdir = str(tmpdir) self.base = base self.ext = ext self.fileno = 0 def __repr__(self): filename = f"{self.base}{self.fileno:02d}.{self.ext}" return os.path.join(self.tmpdir, filename) def __next__(self): self.fileno += 1 return repr(self) @pytest.mark.parametrize( "driver,ext", [("ESRI Shapefile", "shp"), ("GeoJSON", "geojson")] ) def test_write_index_to_file(tmpdir, df_points, driver, ext, engine): fngen = FileNumber(tmpdir, "check", ext) def do_checks(df, index_is_used): # check combinations of index=None|True|False on GeoDataFrame/GeoSeries other_cols = list(df.columns) other_cols.remove("geometry") if driver == "ESRI Shapefile": # ESRI Shapefile will add FID if no other columns exist driver_col = ["FID"] else: driver_col = [] if index_is_used: index_cols = list(df.index.names) else: index_cols = [None] * len(df.index.names) # replicate pandas' default index names for regular and MultiIndex if index_cols == [None]: index_cols = ["index"] elif len(index_cols) > 1 and not all(index_cols): for level, index_col in enumerate(index_cols): if index_col is None: index_cols[level] = "level_" + str(level) # check GeoDataFrame with default index=None to autodetect tempfilename = next(fngen) df.to_file(tempfilename, driver=driver, index=None, engine=engine) df_check = read_file(tempfilename, engine=engine) if len(other_cols) == 0: expected_cols = driver_col[:] else: expected_cols = [] if index_is_used: expected_cols += index_cols expected_cols += other_cols + ["geometry"] assert list(df_check.columns) == expected_cols # similar check on GeoSeries with index=None tempfilename = next(fngen) df.geometry.to_file(tempfilename, driver=driver, index=None, engine=engine) df_check = read_file(tempfilename, engine=engine) if index_is_used: expected_cols = index_cols + ["geometry"] else: expected_cols = driver_col + ["geometry"] assert list(df_check.columns) == expected_cols # check GeoDataFrame with index=True tempfilename = next(fngen) df.to_file(tempfilename, driver=driver, index=True, engine=engine) df_check = read_file(tempfilename, engine=engine) assert list(df_check.columns) == index_cols + other_cols + ["geometry"] # similar check on GeoSeries with index=True tempfilename = next(fngen) df.geometry.to_file(tempfilename, driver=driver, index=True, engine=engine) df_check = read_file(tempfilename, engine=engine) assert list(df_check.columns) == index_cols + ["geometry"] # check GeoDataFrame with index=False tempfilename = next(fngen) df.to_file(tempfilename, driver=driver, index=False, engine=engine) df_check = read_file(tempfilename, engine=engine) if len(other_cols) == 0: expected_cols = driver_col + ["geometry"] else: expected_cols = other_cols + ["geometry"] assert list(df_check.columns) == expected_cols # similar check on GeoSeries with index=False tempfilename = next(fngen) df.geometry.to_file(tempfilename, driver=driver, index=False, engine=engine) df_check = read_file(tempfilename, engine=engine) assert list(df_check.columns) == driver_col + ["geometry"] # # Checks where index is not used/saved # # index is a default RangeIndex df_p = df_points.copy() df = GeoDataFrame(df_p["value1"], geometry=df_p.geometry) do_checks(df, index_is_used=False) # index is a RangeIndex, starting from 1 df.index += 1 do_checks(df, index_is_used=False) # index is an Int64Index regular sequence from 1 df_p.index = list(range(1, len(df) + 1)) df = GeoDataFrame(df_p["value1"], geometry=df_p.geometry) do_checks(df, index_is_used=False) # index was a default RangeIndex, but delete one row to make an Int64Index df_p = df_points.copy() df = GeoDataFrame(df_p["value1"], geometry=df_p.geometry).drop(5, axis=0) do_checks(df, index_is_used=False) # no other columns (except geometry) df = GeoDataFrame(geometry=df_p.geometry) do_checks(df, index_is_used=False) # # Checks where index is used/saved # # named index df_p = df_points.copy() df = GeoDataFrame(df_p["value1"], geometry=df_p.geometry) df.index.name = "foo_index" do_checks(df, index_is_used=True) # named index, same as pandas' default name after .reset_index(drop=False) df.index.name = "index" do_checks(df, index_is_used=True) # named MultiIndex df_p = df_points.copy() df_p["value3"] = df_p["value2"] - df_p["value1"] df_p = df_p.set_index(["value1", "value2"]) df = GeoDataFrame(df_p, geometry=df_p.geometry) do_checks(df, index_is_used=True) # partially unnamed MultiIndex df.index.names = ["first", None] do_checks(df, index_is_used=True) # unnamed MultiIndex df.index.names = [None, None] do_checks(df, index_is_used=True) # unnamed Float64Index df_p = df_points.copy() df = GeoDataFrame(df_p["value1"], geometry=df_p.geometry) df.index = df_p.index.astype(float) / 10 do_checks(df, index_is_used=True) # named Float64Index df.index.name = "centile" do_checks(df, index_is_used=True) # index as string df_p = df_points.copy() df = GeoDataFrame(df_p["value1"], geometry=df_p.geometry) df.index = pd.to_timedelta(range(len(df)), unit="days") # TODO: TimedeltaIndex is an invalid field type df.index = df.index.astype(str) do_checks(df, index_is_used=True) # unnamed DatetimeIndex df_p = df_points.copy() df = GeoDataFrame(df_p["value1"], geometry=df_p.geometry) df.index = pd.to_timedelta(range(len(df)), unit="days") + pd.to_datetime( ["1999-12-27"] * len(df) ) if driver == "ESRI Shapefile": # Shapefile driver does not support datetime fields df.index = df.index.astype(str) do_checks(df, index_is_used=True) # named DatetimeIndex df.index.name = "datetime" do_checks(df, index_is_used=True) def test_to_file__undetermined_driver(tmp_path, df_nybb): shpdir = tmp_path / "boros.invalid" df_nybb.to_file(shpdir) assert shpdir.is_dir() assert list(shpdir.glob("*.shp")) @pytest.mark.parametrize( "test_file", [(pathlib.Path("~/test_file.geojson")), "~/test_file.geojson"] ) def test_write_read_file(test_file, engine): gdf = geopandas.GeoDataFrame(geometry=[box(0, 0, 10, 10)], crs=_CRS) gdf.to_file(test_file, driver="GeoJSON") df_json = geopandas.read_file(test_file, engine=engine) assert_geodataframe_equal(gdf, df_json, check_crs=True) os.remove(os.path.expanduser(test_file)) @pytest.mark.skipif(fiona is False, reason="Fiona not available") @pytest.mark.skipif(FIONA_GE_19, reason="Fiona >= 1.9 supports metadata") def test_to_file_metadata_unsupported_fiona_version(tmp_path, df_points): metadata = {"title": "test"} tmp_file = tmp_path / "test.gpkg" match = "'metadata' keyword is only supported for Fiona >= 1.9" with pytest.raises(NotImplementedError, match=match): df_points.to_file(tmp_file, driver="GPKG", engine="fiona", metadata=metadata) @pytest.mark.skipif(not FIONA_GE_19, reason="only Fiona >= 1.9 supports metadata") def test_to_file_metadata_supported_fiona_version(tmp_path, df_points): metadata = {"title": "test"} tmp_file = tmp_path / "test.gpkg" df_points.to_file(tmp_file, driver="GPKG", engine="fiona", metadata=metadata) # Check that metadata is written to the file with fiona.open(tmp_file) as src: tags = src.tags() assert tags == metadata @pytest.mark.skipif(pyogrio is False, reason="Pyogrio not available") def test_to_file_metadata_pyogrio(tmp_path, df_points): metadata = {"title": "test"} tmp_file = tmp_path / "test.gpkg" df_points.to_file(tmp_file, driver="GPKG", engine="pyogrio", metadata=metadata) # Check that metadata is written to the file info = pyogrio.read_info(tmp_file) layer_metadata = info["layer_metadata"] assert layer_metadata == metadata @pytest.mark.skipif(pyogrio is False, reason="Pyogrio not available") def test_read_file_info(tmp_path, df_points): metadata = {"title": "test"} tmp_file = tmp_path / "test.gpkg" df_points.to_file(tmp_file, driver="GPKG", engine="pyogrio", metadata=metadata) # Check that metadata is written and read correctly layer_info = geopandas.read_file_info(tmp_file) assert layer_info["layer_metadata"] == metadata # basic check other properties are available as expected assert layer_info["driver"] == "GPKG" assert layer_info["crs"] == df_points.crs assert layer_info["features"] == len(df_points) @pytest.mark.parametrize( "driver, ext", [("ESRI Shapefile", ".shp"), ("GeoJSON", ".geojson")] ) def test_to_file_metadata_unsupported_driver(driver, ext, tmpdir, df_points, engine): metadata = {"title": "Test"} tempfilename = os.path.join(str(tmpdir), "test" + ext) with pytest.raises( NotImplementedError, match="'metadata' keyword is only supported for" ): df_points.to_file(tempfilename, driver=driver, metadata=metadata) def test_multiple_geom_cols_error(tmpdir, df_nybb): df_nybb["geom2"] = df_nybb.geometry with pytest.raises(ValueError, match="GeoDataFrame contains multiple geometry"): df_nybb.to_file(os.path.join(str(tmpdir), "boros.gpkg")) @PYOGRIO_MARK @FIONA_MARK def test_option_io_engine(nybb_filename): try: geopandas.options.io_engine = "pyogrio" # disallowing to read a Shapefile with fiona should ensure we are # actually reading with pyogrio import fiona orig = fiona.supported_drivers["ESRI Shapefile"] fiona.supported_drivers["ESRI Shapefile"] = "w" _ = geopandas.read_file(nybb_filename) finally: fiona.supported_drivers["ESRI Shapefile"] = orig geopandas.options.io_engine = None @pytest.mark.skipif(pyogrio, reason="test for pyogrio not installed") def test_error_engine_unavailable_pyogrio(tmp_path, df_points, file_path): with pytest.raises(ImportError, match="the 'read_file' function requires"): geopandas.read_file(file_path, engine="pyogrio") with pytest.raises(ImportError, match="the 'to_file' method requires"): df_points.to_file(tmp_path / "test.gpkg", engine="pyogrio") @pytest.mark.skipif(fiona, reason="test for fiona not installed") def test_error_engine_unavailable_fiona(tmp_path, df_points, file_path): with pytest.raises(ImportError, match="the 'read_file' function requires"): geopandas.read_file(file_path, engine="fiona") with pytest.raises(ImportError, match="the 'to_file' method requires"): df_points.to_file(tmp_path / "test.gpkg", engine="fiona") def test_error_monkeypatch_engine_unavailable_pyogrio( monkeypatch, tmp_path, df_points, file_path ) -> None: # monkeypatch to make pyogrio unimportable monkeypatch.setattr(geopandas.io.file, "_import_pyogrio", lambda: None) monkeypatch.setattr(geopandas.io.file, "pyogrio", None) monkeypatch.setattr( geopandas.io.file, "pyogrio_import_error", "No module named 'pyogrio'" ) with pytest.raises(ImportError, match="No module named 'pyogrio'"): geopandas.read_file(file_path, engine="pyogrio") with pytest.raises(ImportError, match="No module named 'pyogrio'"): df_points.to_file(tmp_path / "test.gpkg", engine="pyogrio") def test_error_monkeypatch_engine_unavailable_fiona( monkeypatch, tmp_path, df_points, file_path ) -> None: # monkeypatch to make fiona unimportable monkeypatch.setattr(geopandas.io.file, "_import_fiona", lambda: None) monkeypatch.setattr(geopandas.io.file, "fiona", None) monkeypatch.setattr( geopandas.io.file, "fiona_import_error", "No module named 'fiona'" ) with pytest.raises(ImportError, match="No module named 'fiona'"): geopandas.read_file(file_path, engine="fiona") with pytest.raises(ImportError, match="No module named 'fiona'"): df_points.to_file(tmp_path / "test.gpkg", engine="fiona") @PYOGRIO_MARK @pytest.mark.filterwarnings("ignore:Geometry is in a geographic CRS") def test_list_layers(df_points, tmpdir): tempfilename = os.path.join(str(tmpdir), "dataset.gpkg") df_points.to_file(tempfilename, layer="original") df_points.set_geometry(df_points.buffer(1)).to_file(tempfilename, layer="buffered") df_points.set_geometry(df_points.buffer(2).boundary).to_file( tempfilename, layer="boundary" ) pyogrio.write_dataframe( df_points[["value1", "value2"]], tempfilename, layer="non-spatial" ) layers = geopandas.list_layers(tempfilename) expected = pd.DataFrame( { "name": ["original", "buffered", "boundary", "non-spatial"], "geometry_type": ["Point", "Polygon", "LineString", None], } ) assert_frame_equal(layers, expected) geopandas-1.2.0/geopandas/io/tests/test_file_geom_types_drivers.py000066400000000000000000000221401525650651600255200ustar00rootroot00000000000000import os from shapely.geometry import ( LineString, MultiLineString, MultiPoint, MultiPolygon, Point, Polygon, ) import geopandas from geopandas import GeoDataFrame from .test_file import FIONA_MARK, PYOGRIO_MARK import pytest from geopandas.testing import assert_geodataframe_equal # Credit: Polygons below come from Montreal city Open Data portal # http://donnees.ville.montreal.qc.ca/dataset/unites-evaluation-fonciere city_hall_boundaries = Polygon( ( (-73.5541107525234, 45.5091983609661), (-73.5546126200639, 45.5086813829106), (-73.5540185061397, 45.5084409343852), (-73.5539986525799, 45.5084323044531), (-73.5535801792994, 45.5089539203786), (-73.5541107525234, 45.5091983609661), ) ) vauquelin_place = Polygon( ( (-73.5542465586147, 45.5081555487952), (-73.5540185061397, 45.5084409343852), (-73.5546126200639, 45.5086813829106), (-73.5548825850032, 45.5084033554357), (-73.5542465586147, 45.5081555487952), ) ) city_hall_walls = [ LineString( ( (-73.5541107525234, 45.5091983609661), (-73.5546126200639, 45.5086813829106), (-73.5540185061397, 45.5084409343852), ) ), LineString( ( (-73.5539986525799, 45.5084323044531), (-73.5535801792994, 45.5089539203786), (-73.5541107525234, 45.5091983609661), ) ), ] city_hall_entrance = Point(-73.553785, 45.508722) city_hall_balcony = Point(-73.554138, 45.509080) city_hall_council_chamber = Point(-73.554246, 45.508931) point_3D = Point(-73.553785, 45.508722, 300) # ***************************************** # TEST TOOLING class _ExpectedError: def __init__(self, error_type, error_message_match): self.type = error_type self.match = error_message_match class _ExpectedErrorBuilder: def __init__(self, composite_key): self.composite_key = composite_key def to_raise(self, error_type, error_match): _expected_exceptions[self.composite_key] = _ExpectedError( error_type, error_match ) def _expect_writing(gdf, ogr_driver): return _ExpectedErrorBuilder(_composite_key(gdf, ogr_driver)) def _composite_key(gdf, ogr_driver): return frozenset([id(gdf), ogr_driver]) def _expected_error_on(gdf, ogr_driver): composite_key = _composite_key(gdf, ogr_driver) return _expected_exceptions.get(composite_key, None) # ***************************************** # TEST CASES _geodataframes_to_write = [] _expected_exceptions = {} _CRS = "epsg:4326" # ------------------ # gdf with Points gdf = GeoDataFrame( {"a": [1, 2]}, crs=_CRS, geometry=[city_hall_entrance, city_hall_balcony] ) _geodataframes_to_write.append(gdf) # ------------------ # gdf with MultiPoints gdf = GeoDataFrame( {"a": [1, 2]}, crs=_CRS, geometry=[ MultiPoint([city_hall_balcony, city_hall_council_chamber]), MultiPoint([city_hall_entrance, city_hall_balcony, city_hall_council_chamber]), ], ) _geodataframes_to_write.append(gdf) # ------------------ # gdf with Points and MultiPoints gdf = GeoDataFrame( {"a": [1, 2]}, crs=_CRS, geometry=[MultiPoint([city_hall_entrance, city_hall_balcony]), city_hall_balcony], ) _geodataframes_to_write.append(gdf) # 'ESRI Shapefile' driver supports writing LineString/MultiLinestring and # Polygon/MultiPolygon but does not mention Point/MultiPoint # see https://www.gdal.org/drv_shapefile.html _expect_writing(gdf, "ESRI Shapefile").to_raise(RuntimeError, "Failed to write record") # ------------------ # gdf with LineStrings gdf = GeoDataFrame({"a": [1, 2]}, crs=_CRS, geometry=city_hall_walls) _geodataframes_to_write.append(gdf) # ------------------ # gdf with MultiLineStrings gdf = GeoDataFrame( {"a": [1, 2]}, crs=_CRS, geometry=[MultiLineString(city_hall_walls), MultiLineString(city_hall_walls)], ) _geodataframes_to_write.append(gdf) # ------------------ # gdf with LineStrings and MultiLineStrings gdf = GeoDataFrame( {"a": [1, 2]}, crs=_CRS, geometry=[MultiLineString(city_hall_walls), city_hall_walls[0]], ) _geodataframes_to_write.append(gdf) # ------------------ # gdf with Polygons gdf = GeoDataFrame( {"a": [1, 2]}, crs=_CRS, geometry=[city_hall_boundaries, vauquelin_place] ) _geodataframes_to_write.append(gdf) # ------------------ # gdf with MultiPolygon gdf = GeoDataFrame( {"a": [1]}, crs=_CRS, geometry=[MultiPolygon((city_hall_boundaries, vauquelin_place))], ) _geodataframes_to_write.append(gdf) # ------------------ # gdf with Polygon and MultiPolygon gdf = GeoDataFrame( {"a": [1, 2]}, crs=_CRS, geometry=[ MultiPolygon((city_hall_boundaries, vauquelin_place)), city_hall_boundaries, ], ) _geodataframes_to_write.append(gdf) # ------------------ # gdf with null geometry and Point gdf = GeoDataFrame({"a": [1, 2]}, crs=_CRS, geometry=[None, city_hall_entrance]) _geodataframes_to_write.append(gdf) # ------------------ # gdf with null geometry and 3D Point gdf = GeoDataFrame({"a": [1, 2]}, crs=_CRS, geometry=[None, point_3D]) _geodataframes_to_write.append(gdf) # ------------------ # gdf with null geometries only gdf = GeoDataFrame({"a": [1, 2]}, crs=_CRS, geometry=[None, None]) _geodataframes_to_write.append(gdf) # ------------------ # gdf with all shape types mixed together gdf = GeoDataFrame( {"a": [1, 2, 3, 4, 5, 6]}, crs=_CRS, geometry=[ MultiPolygon((city_hall_boundaries, vauquelin_place)), city_hall_entrance, MultiLineString(city_hall_walls), city_hall_walls[0], MultiPoint([city_hall_entrance, city_hall_balcony]), city_hall_balcony, ], ) _geodataframes_to_write.append(gdf) # Not supported by 'ESRI Shapefile' driver _expect_writing(gdf, "ESRI Shapefile").to_raise(RuntimeError, "Failed to write record") # ------------------ # gdf with all 2D shape types and 3D Point mixed together gdf = GeoDataFrame( {"a": [1, 2, 3, 4, 5, 6, 7]}, crs=_CRS, geometry=[ MultiPolygon((city_hall_boundaries, vauquelin_place)), city_hall_entrance, MultiLineString(city_hall_walls), city_hall_walls[0], MultiPoint([city_hall_entrance, city_hall_balcony]), city_hall_balcony, point_3D, ], ) _geodataframes_to_write.append(gdf) # Not supported by 'ESRI Shapefile' driver _expect_writing(gdf, "ESRI Shapefile").to_raise(RuntimeError, "Failed to write record") @pytest.fixture(params=_geodataframes_to_write) def geodataframe(request): return request.param @pytest.fixture( params=[ ("GeoJSON", ".geojson"), ("ESRI Shapefile", ".shp"), ("GPKG", ".gpkg"), ("SQLite", ".sqlite"), ] ) def ogr_driver(request): return request.param @pytest.fixture( params=[ pytest.param("fiona", marks=FIONA_MARK), pytest.param("pyogrio", marks=PYOGRIO_MARK), ] ) def engine(request): return request.param @pytest.mark.filterwarnings( "ignore:Unexpected data type for geometry column:RuntimeWarning" ) # https://github.com/OSGeo/gdal/issues/15096 def test_to_file_roundtrip(tmpdir, geodataframe, ogr_driver, engine): driver, ext = ogr_driver output_file = os.path.join(str(tmpdir), "output_file" + ext) write_kwargs = {} if driver == "SQLite": write_kwargs["spatialite"] = True # If only 3D Points, geometry_type needs to be specified for spatialite at the # moment. This if can be removed once the following PR is released: # https://github.com/geopandas/pyogrio/pull/223 if ( engine == "pyogrio" and len(geodataframe == 2) and geodataframe.geometry[0] is None and geodataframe.geometry[1] is not None and geodataframe.geometry[1].has_z ): write_kwargs["geometry_type"] = "Point Z" expected_error = _expected_error_on(geodataframe, driver) if expected_error: with pytest.raises( RuntimeError, match=r"Failed to write record|Could not add feature to layer" ): geodataframe.to_file( output_file, driver=driver, engine=engine, **write_kwargs ) else: if driver == "SQLite" and engine == "pyogrio": try: geodataframe.to_file( output_file, driver=driver, engine=engine, **write_kwargs ) except ValueError as e: if "unrecognized option 'SPATIALITE'" in str(e): pytest.xfail( "pyogrio wheels from PyPI do not come with SpatiaLite support. " f"Error: {e}" ) raise else: geodataframe.to_file( output_file, driver=driver, engine=engine, **write_kwargs ) reloaded = geopandas.read_file(output_file, engine=engine) if driver == "GeoJSON" and engine == "pyogrio": # For GeoJSON files, the int64 column comes back as int32 reloaded["a"] = reloaded["a"].astype("int64") assert_geodataframe_equal(geodataframe, reloaded, check_column_type="equiv") geopandas-1.2.0/geopandas/io/tests/test_geoarrow.py000066400000000000000000000463371525650651600224530ustar00rootroot00000000000000import contextlib import json import os import pathlib import numpy as np from pandas import ArrowDtype import shapely from shapely import MultiPoint, Point, box from geopandas import GeoDataFrame, GeoSeries import pytest from geopandas.testing import assert_geodataframe_equal, assert_geoseries_equal pytest.importorskip("pyarrow") import pyarrow as pa import pyarrow.compute as pc from pyarrow import feather DATA_PATH = pathlib.Path(os.path.dirname(__file__)) / "data" def pa_table(table): return pa.table(table) def pa_array(array): return pa.array(array) def assert_table_equal(left, right, check_metadata=True): geom_type = left["geometry"].type # in case of Points (directly the inner fixed_size_list or struct type) # -> there are NaNs for empties -> we need to compare them separately # and then fill, because pyarrow.Table.equals considers NaNs as not equal if pa.types.is_fixed_size_list(geom_type): left_values = left["geometry"].chunk(0).values right_values = right["geometry"].chunk(0).values assert pc.is_nan(left_values).equals(pc.is_nan(right_values)) left_geoms = pa.FixedSizeListArray.from_arrays( pc.replace_with_mask(left_values, pc.is_nan(left_values), 0.0), type=left["geometry"].type, ) right_geoms = pa.FixedSizeListArray.from_arrays( pc.replace_with_mask(right_values, pc.is_nan(right_values), 0.0), type=right["geometry"].type, ) left = left.set_column(1, left.schema.field("geometry"), left_geoms) right = right.set_column(1, right.schema.field("geometry"), right_geoms) elif pa.types.is_struct(geom_type): left_arr = left["geometry"].chunk(0) right_arr = right["geometry"].chunk(0) for i in range(left_arr.type.num_fields): assert pc.is_nan(left_arr.field(i)).equals(pc.is_nan(right_arr.field(i))) left_geoms = pa.StructArray.from_arrays( [ pc.replace_with_mask( left_arr.field(i), pc.is_nan(left_arr.field(i)), 0.0 ) for i in range(left_arr.type.num_fields) ], fields=list(left["geometry"].type), ) right_geoms = pa.StructArray.from_arrays( [ pc.replace_with_mask( right_arr.field(i), pc.is_nan(right_arr.field(i)), 0.0 ) for i in range(right_arr.type.num_fields) ], fields=list(right["geometry"].type), ) left = left.set_column(1, left.schema.field("geometry"), left_geoms) right = right.set_column(1, right.schema.field("geometry"), right_geoms) if left.equals(right, check_metadata=check_metadata): return if not left.schema.equals(right.schema): raise AssertionError( f"Schema not equal\nLeft:\n{left.schema}\nRight:\n{right.schema}" ) if check_metadata: if not left.schema.equals(right.schema, check_metadata=True): if not left.schema.metadata == right.schema.metadata: raise AssertionError( f"Metadata not equal\nLeft:\n{left.schema.metadata}\n" f"Right:\n{right.schema.metadata}" ) for col in left.schema.names: assert left.schema.field(col).equals( right.schema.field(col), check_metadata=True ) for col in left.column_names: a_left = pa.concat_arrays(left.column(col).chunks) a_right = pa.concat_arrays(right.column(col).chunks) if not a_left.equals(a_right): raise AssertionError(f"Column '{col}' not equal:\n{a_left.diff(a_right)}") raise AssertionError("Tables not equal for unknown reason") @pytest.mark.skipif( shapely.geos_version < (3, 9, 0), reason="Checking for empty is buggy with GEOS<3.9", ) # an old GEOS is installed in the CI builds with the defaults channel @pytest.mark.parametrize( "dim", [ "xy", pytest.param( "xyz", marks=pytest.mark.skipif( shapely.geos_version < (3, 10, 0), reason="Cannot write 3D geometries with GEOS<3.10", ), ), ], ) @pytest.mark.parametrize( "geometry_type", ["point", "linestring", "polygon", "multipoint", "multilinestring", "multipolygon"], ) @pytest.mark.parametrize( "geometry_encoding, interleaved", [("WKB", None), ("geoarrow", True), ("geoarrow", False)], ids=["WKB", "geoarrow-interleaved", "geoarrow-separated"], ) def test_geoarrow_export(geometry_type, dim, geometry_encoding, interleaved): base_path = DATA_PATH / "geoarrow" suffix = geometry_type + ("_z" if dim == "xyz" else "") # Read the example data df = feather.read_feather(base_path / f"example-{suffix}-wkb.arrow") df["geometry"] = GeoSeries.from_wkb(df["geometry"]) df["row_number"] = df["row_number"].astype("int32") df = GeoDataFrame(df) df.geometry.array.crs = None # Read the expected data if geometry_encoding == "WKB": filename = f"example-{suffix}-wkb.arrow" else: filename = f"example-{suffix}{'-interleaved' if interleaved else ''}.arrow" expected = feather.read_table(base_path / filename) # GeoDataFrame -> Arrow Table result = pa_table( df.to_arrow(geometry_encoding=geometry_encoding, interleaved=interleaved) ) # remove the "pandas" metadata result = result.replace_schema_metadata(None) mask_nonempty = None if ( geometry_encoding == "WKB" and dim == "xyz" and geometry_type.startswith("multi") ): # for collections with z dimension, drop the empties because those don't # roundtrip correctly to WKB # (https://github.com/libgeos/geos/issues/888) mask_nonempty = pa.array(np.asarray(~df.geometry.is_empty)) result = result.filter(mask_nonempty) expected = expected.filter(mask_nonempty) assert_table_equal(result, expected) # GeoSeries -> Arrow array if geometry_encoding != "WKB" and geometry_type == "point": # for points, we again have to handle NaNs separately, we already did that # for table so let's just skip this part return result_arr = pa_array( df.geometry.to_arrow( geometry_encoding=geometry_encoding, interleaved=interleaved ) ) if mask_nonempty is not None: result_arr = result_arr.filter(mask_nonempty) assert result_arr.equals(expected["geometry"].chunk(0)) @pytest.mark.parametrize("encoding", ["WKB", "geoarrow"]) def test_geoarrow_to_pandas_kwargs(encoding): g = box(0, 0, 10, 10) gdf = GeoDataFrame({"geometry": [g], "i": [1], "s": ["a"]}) table = pa_table(gdf.to_arrow(geometry_encoding=encoding)) # simulate the `dtype_backend="pyarrow"` option in `pandas.read_parquet` gdf_roundtrip = GeoDataFrame.from_arrow( table, to_pandas_kwargs={"types_mapper": ArrowDtype} ) assert isinstance(gdf_roundtrip, GeoDataFrame) assert isinstance(gdf_roundtrip.dtypes["i"], ArrowDtype) assert isinstance(gdf_roundtrip.dtypes["s"], ArrowDtype) @pytest.mark.parametrize("encoding", ["WKB", "geoarrow"]) def test_geoarrow_multiple_geometry_crs(encoding): pytest.importorskip("pyproj") # ensure each geometry column has its own crs gdf = GeoDataFrame(geometry=[box(0, 0, 10, 10)], crs="epsg:4326") gdf["geom2"] = gdf.geometry.to_crs("epsg:3857") result = pa_table(gdf.to_arrow(geometry_encoding=encoding)) meta1 = json.loads( result.schema.field("geometry").metadata[b"ARROW:extension:metadata"] ) assert meta1["crs"]["id"]["code"] == 4326 meta2 = json.loads( result.schema.field("geom2").metadata[b"ARROW:extension:metadata"] ) assert meta2["crs"]["id"]["code"] == 3857 roundtripped = GeoDataFrame.from_arrow(result) assert_geodataframe_equal(gdf, roundtripped) assert gdf.geometry.crs == "epsg:4326" assert gdf.geom2.crs == "epsg:3857" @pytest.mark.parametrize("encoding", ["WKB", "geoarrow"]) def test_geoarrow_series_name_crs(encoding): pytest.importorskip("pyproj") pytest.importorskip("pyarrow", minversion="14.0.0") gser = GeoSeries([box(0, 0, 10, 10)], crs="epsg:4326", name="geom") schema_capsule, _ = gser.to_arrow(geometry_encoding=encoding).__arrow_c_array__() field = pa.Field._import_from_c_capsule(schema_capsule) assert field.name == "geom" assert ( field.metadata[b"ARROW:extension:name"] == b"geoarrow.wkb" if encoding == "WKB" else b"geoarrow.polygon" ) meta = json.loads(field.metadata[b"ARROW:extension:metadata"]) assert meta["crs"]["id"]["code"] == 4326 # ensure it also works without a name gser = GeoSeries([box(0, 0, 10, 10)]) schema_capsule, _ = gser.to_arrow(geometry_encoding=encoding).__arrow_c_array__() field = pa.Field._import_from_c_capsule(schema_capsule) assert field.name == "" def test_geoarrow_unsupported_encoding(): gdf = GeoDataFrame(geometry=[box(0, 0, 10, 10)], crs="epsg:4326") with pytest.raises(ValueError, match="Expected geometry encoding"): gdf.to_arrow(geometry_encoding="invalid") with pytest.raises(ValueError, match="Expected geometry encoding"): gdf.geometry.to_arrow(geometry_encoding="invalid") def test_geoarrow_mixed_geometry_types(): gdf = GeoDataFrame( {"geometry": [Point(0, 0), box(0, 0, 10, 10)]}, crs="epsg:4326", ) with pytest.raises(ValueError, match="Geometry type combination is not supported"): gdf.to_arrow(geometry_encoding="geoarrow") gdf = GeoDataFrame( {"geometry": [Point(0, 0), MultiPoint([(0, 0), (1, 1)])]}, crs="epsg:4326", ) result = pa_table(gdf.to_arrow(geometry_encoding="geoarrow")) assert ( result.schema.field("geometry").metadata[b"ARROW:extension:name"] == b"geoarrow.multipoint" ) @pytest.mark.parametrize("geom_type", ["point", "polygon"]) @pytest.mark.parametrize( "encoding, interleaved", [("WKB", True), ("geoarrow", True), ("geoarrow", False)] ) def test_geoarrow_missing(encoding, interleaved, geom_type): # dummy test for single geometry type until missing values are included # in the test data for test_geoarrow_export gdf = GeoDataFrame( geometry=[Point(0, 0) if geom_type == "point" else box(0, 0, 10, 10), None], crs="epsg:4326", ) result = pa_table(gdf.to_arrow(geometry_encoding=encoding, interleaved=interleaved)) assert result["geometry"].null_count == 1 assert result["geometry"].is_null().to_pylist() == [False, True] def test_geoarrow_include_z(): gdf = GeoDataFrame({"geometry": [Point(0, 0), Point(1, 1), Point()]}) table = pa_table(gdf.to_arrow(geometry_encoding="geoarrow")) assert table["geometry"].type.value_field.name == "xy" assert table["geometry"].type.list_size == 2 table = pa_table(gdf.to_arrow(geometry_encoding="geoarrow", include_z=True)) assert table["geometry"].type.value_field.name == "xyz" assert table["geometry"].type.list_size == 3 assert np.isnan(table["geometry"].chunk(0).values.to_numpy()[2::3]).all() gdf = GeoDataFrame({"geometry": [Point(0, 0, 0), Point(1, 1, 1), Point()]}) table = pa_table(gdf.to_arrow(geometry_encoding="geoarrow")) assert table["geometry"].type.value_field.name == "xyz" assert table["geometry"].type.list_size == 3 table = pa_table(gdf.to_arrow(geometry_encoding="geoarrow", include_z=False)) assert table["geometry"].type.value_field.name == "xy" assert table["geometry"].type.list_size == 2 @contextlib.contextmanager def with_geoarrow_extension_types(): gp = pytest.importorskip("geoarrow.pyarrow") gp.register_extension_types() try: yield finally: gp.unregister_extension_types() @pytest.mark.parametrize("dim", ["xy", "xyz"]) @pytest.mark.parametrize( "geometry_type", ["point", "linestring", "polygon", "multipoint", "multilinestring", "multipolygon"], ) def test_geoarrow_export_with_extension_types(geometry_type, dim): # ensure the exported data can be imported by geoarrow-pyarrow and are # recognized as extension types base_path = DATA_PATH / "geoarrow" suffix = geometry_type + ("_z" if dim == "xyz" else "") # Read the example data df = feather.read_feather(base_path / f"example-{suffix}-wkb.arrow") df["geometry"] = GeoSeries.from_wkb(df["geometry"]) df["row_number"] = df["row_number"].astype("int32") df = GeoDataFrame(df) df.geometry.array.crs = None pytest.importorskip("geoarrow.pyarrow") with with_geoarrow_extension_types(): result1 = pa_table(df.to_arrow(geometry_encoding="WKB")) assert isinstance(result1["geometry"].type, pa.ExtensionType) result2 = pa_table(df.to_arrow(geometry_encoding="geoarrow")) assert isinstance(result2["geometry"].type, pa.ExtensionType) result3 = pa_table(df.to_arrow(geometry_encoding="geoarrow", interleaved=False)) assert isinstance(result3["geometry"].type, pa.ExtensionType) def test_geoarrow_export_empty(): gdf_empty = GeoDataFrame(columns=["col", "geometry"], geometry="geometry") gdf_all_missing = GeoDataFrame( {"col": [1], "geometry": [None]}, geometry="geometry" ) # no geometries to infer the geometry type -> raise error for now with pytest.raises(NotImplementedError): gdf_empty.to_arrow(geometry_encoding="geoarrow") with pytest.raises(NotImplementedError): gdf_all_missing.to_arrow(geometry_encoding="geoarrow") # with WKB encoding it roundtrips fine result = pa_table(gdf_empty.to_arrow(geometry_encoding="WKB")) roundtripped = GeoDataFrame.from_arrow(result) assert_geodataframe_equal(gdf_empty, roundtripped) result = pa_table(gdf_all_missing.to_arrow(geometry_encoding="WKB")) roundtripped = GeoDataFrame.from_arrow(result) assert_geodataframe_equal(gdf_all_missing, roundtripped) @pytest.mark.parametrize("dim", ["xy", "xyz"]) @pytest.mark.parametrize( "geometry_type", [ "point", "linestring", "polygon", "multipoint", "multilinestring", "multipolygon", ], ) def test_geoarrow_import(geometry_type, dim): base_path = DATA_PATH / "geoarrow" suffix = geometry_type + ("_z" if dim == "xyz" else "") # Read the example data df = feather.read_feather(base_path / f"example-{suffix}-wkb.arrow") df["geometry"] = GeoSeries.from_wkb(df["geometry"]) df = GeoDataFrame(df) df.geometry.crs = None table1 = feather.read_table(base_path / f"example-{suffix}-wkb.arrow") result1 = GeoDataFrame.from_arrow(table1) assert_geodataframe_equal(result1, df) table2 = feather.read_table(base_path / f"example-{suffix}-interleaved.arrow") result2 = GeoDataFrame.from_arrow(table2) assert_geodataframe_equal(result2, df) table3 = feather.read_table(base_path / f"example-{suffix}.arrow") result3 = GeoDataFrame.from_arrow(table3) assert_geodataframe_equal(result3, df) @pytest.mark.parametrize("encoding", ["WKB", "geoarrow"]) def test_geoarrow_import_geometry_column(encoding): pytest.importorskip("pyproj") # ensure each geometry column has its own crs gdf = GeoDataFrame(geometry=[box(0, 0, 10, 10)]) gdf["centroid"] = gdf.geometry.centroid result = GeoDataFrame.from_arrow(pa_table(gdf.to_arrow(geometry_encoding=encoding))) assert_geodataframe_equal(result, gdf) assert result.active_geometry_name == "geometry" result = GeoDataFrame.from_arrow( pa_table(gdf[["centroid"]].to_arrow(geometry_encoding=encoding)) ) assert result.active_geometry_name == "centroid" result = GeoDataFrame.from_arrow( pa_table(gdf.to_arrow(geometry_encoding=encoding)), geometry="centroid" ) assert result.active_geometry_name == "centroid" assert_geodataframe_equal(result, gdf.set_geometry("centroid")) def test_geoarrow_import_missing_geometry(): pytest.importorskip("pyarrow", minversion="14.0.0") table = pa.table({"a": [0, 1, 2], "b": [0.1, 0.2, 0.3]}) with pytest.raises(ValueError, match="No geometry column found"): GeoDataFrame.from_arrow(table) with pytest.raises(ValueError, match="No GeoArrow geometry field found"): GeoSeries.from_arrow(table["a"].chunk(0)) def test_geoarrow_import_capsule_interface(): # ensure we can import non-pyarrow object pytest.importorskip("pyarrow", minversion="14.0.0") gdf = GeoDataFrame({"col": [1]}, geometry=[box(0, 0, 10, 10)]) result = GeoDataFrame.from_arrow(gdf.to_arrow()) assert_geodataframe_equal(result, gdf) @pytest.mark.parametrize("dim", ["xy", "xyz"]) @pytest.mark.parametrize( "geometry_type", ["point", "linestring", "polygon", "multipoint", "multilinestring", "multipolygon"], ) def test_geoarrow_import_from_extension_types(geometry_type, dim): # ensure the exported data can be imported by geoarrow-pyarrow and are # recognized as extension types pytest.importorskip("pyproj") base_path = DATA_PATH / "geoarrow" suffix = geometry_type + ("_z" if dim == "xyz" else "") # Read the example data df = feather.read_feather(base_path / f"example-{suffix}-wkb.arrow") df["geometry"] = GeoSeries.from_wkb(df["geometry"]) df = GeoDataFrame(df, crs="EPSG:3857") pytest.importorskip("geoarrow.pyarrow") with with_geoarrow_extension_types(): result1 = GeoDataFrame.from_arrow( pa_table(df.to_arrow(geometry_encoding="WKB")) ) assert_geodataframe_equal(result1, df) result2 = GeoDataFrame.from_arrow( pa_table(df.to_arrow(geometry_encoding="geoarrow")) ) assert_geodataframe_equal(result2, df) result3 = GeoDataFrame.from_arrow( pa_table(df.to_arrow(geometry_encoding="geoarrow", interleaved=False)) ) assert_geodataframe_equal(result3, df) def test_geoarrow_import_geoseries(): pytest.importorskip("pyproj") gp = pytest.importorskip("geoarrow.pyarrow") ser = GeoSeries.from_wkt(["POINT (1 1)", "POINT (2 2)"], crs="EPSG:3857") with with_geoarrow_extension_types(): arr = gp.array(ser.to_arrow(geometry_encoding="WKB")) result = GeoSeries.from_arrow(arr) assert_geoseries_equal(result, ser) arr = gp.array(ser.to_arrow(geometry_encoding="geoarrow")) result = GeoSeries.from_arrow(arr) assert_geoseries_equal(result, ser) # the name is lost when going through a pyarrow.Array ser.name = "name" arr = gp.array(ser.to_arrow()) result = GeoSeries.from_arrow(arr) assert result.name is None # we can specify the name as one of the kwargs result = GeoSeries.from_arrow(arr, name="test") assert_geoseries_equal(result, ser) def test_geoarrow_import_unknown_geoarrow_type(): gdf = GeoDataFrame({"col": [1]}, geometry=[box(0, 0, 10, 10)]) table = pa_table(gdf.to_arrow()) schema = table.schema new_field = schema.field("geometry").with_metadata( { b"ARROW:extension:name": b"geoarrow.unknown", b"ARROW:extension:metadata": b"{}", } ) new_schema = pa.schema([schema.field(0), new_field]) new_table = table.cast(new_schema) with pytest.raises(TypeError, match="Unknown GeoArrow extension type"): GeoDataFrame.from_arrow(new_table) geopandas-1.2.0/geopandas/io/tests/test_infer_schema.py000066400000000000000000000204651525650651600232430ustar00rootroot00000000000000from collections import OrderedDict import numpy as np import pandas as pd from shapely.geometry import ( LineString, MultiLineString, MultiPoint, MultiPolygon, Point, Polygon, ) from geopandas import GeoDataFrame from geopandas.io.file import infer_schema import pytest # Credit: Polygons below come from Montreal city Open Data portal # http://donnees.ville.montreal.qc.ca/dataset/unites-evaluation-fonciere city_hall_boundaries = Polygon( ( (-73.5541107525234, 45.5091983609661), (-73.5546126200639, 45.5086813829106), (-73.5540185061397, 45.5084409343852), (-73.5539986525799, 45.5084323044531), (-73.5535801792994, 45.5089539203786), (-73.5541107525234, 45.5091983609661), ) ) vauquelin_place = Polygon( ( (-73.5542465586147, 45.5081555487952), (-73.5540185061397, 45.5084409343852), (-73.5546126200639, 45.5086813829106), (-73.5548825850032, 45.5084033554357), (-73.5542465586147, 45.5081555487952), ) ) city_hall_walls = [ LineString( ( (-73.5541107525234, 45.5091983609661), (-73.5546126200639, 45.5086813829106), (-73.5540185061397, 45.5084409343852), ) ), LineString( ( (-73.5539986525799, 45.5084323044531), (-73.5535801792994, 45.5089539203786), (-73.5541107525234, 45.5091983609661), ) ), ] city_hall_entrance = Point(-73.553785, 45.508722) city_hall_balcony = Point(-73.554138, 45.509080) city_hall_council_chamber = Point(-73.554246, 45.508931) point_3D = Point(-73.553785, 45.508722, 300) linestring_3D = LineString( ( (-73.5541107525234, 45.5091983609661, 300), (-73.5546126200639, 45.5086813829106, 300), (-73.5540185061397, 45.5084409343852, 300), ) ) polygon_3D = Polygon( ( (-73.5541107525234, 45.5091983609661, 300), (-73.5535801792994, 45.5089539203786, 300), (-73.5541107525234, 45.5091983609661, 300), ) ) def test_infer_schema_only_points(): df = GeoDataFrame(geometry=[city_hall_entrance, city_hall_balcony]) assert infer_schema(df) == {"geometry": "Point", "properties": OrderedDict()} def test_infer_schema_points_and_multipoints(): df = GeoDataFrame( geometry=[ MultiPoint([city_hall_entrance, city_hall_balcony]), city_hall_balcony, ] ) assert infer_schema(df) == { "geometry": ["MultiPoint", "Point"], "properties": OrderedDict(), } def test_infer_schema_only_multipoints(): df = GeoDataFrame( geometry=[ MultiPoint( [city_hall_entrance, city_hall_balcony, city_hall_council_chamber] ) ] ) assert infer_schema(df) == {"geometry": "MultiPoint", "properties": OrderedDict()} def test_infer_schema_only_linestrings(): df = GeoDataFrame(geometry=city_hall_walls) assert infer_schema(df) == {"geometry": "LineString", "properties": OrderedDict()} def test_infer_schema_linestrings_and_multilinestrings(): df = GeoDataFrame(geometry=[MultiLineString(city_hall_walls), city_hall_walls[0]]) assert infer_schema(df) == { "geometry": ["MultiLineString", "LineString"], "properties": OrderedDict(), } def test_infer_schema_only_multilinestrings(): df = GeoDataFrame(geometry=[MultiLineString(city_hall_walls)]) assert infer_schema(df) == { "geometry": "MultiLineString", "properties": OrderedDict(), } def test_infer_schema_only_polygons(): df = GeoDataFrame(geometry=[city_hall_boundaries, vauquelin_place]) assert infer_schema(df) == {"geometry": "Polygon", "properties": OrderedDict()} def test_infer_schema_polygons_and_multipolygons(): df = GeoDataFrame( geometry=[ MultiPolygon((city_hall_boundaries, vauquelin_place)), city_hall_boundaries, ] ) assert infer_schema(df) == { "geometry": ["MultiPolygon", "Polygon"], "properties": OrderedDict(), } def test_infer_schema_only_multipolygons(): df = GeoDataFrame(geometry=[MultiPolygon((city_hall_boundaries, vauquelin_place))]) assert infer_schema(df) == {"geometry": "MultiPolygon", "properties": OrderedDict()} def test_infer_schema_multiple_shape_types(): df = GeoDataFrame( geometry=[ MultiPolygon((city_hall_boundaries, vauquelin_place)), city_hall_boundaries, MultiLineString(city_hall_walls), city_hall_walls[0], MultiPoint([city_hall_entrance, city_hall_balcony]), city_hall_balcony, ] ) assert infer_schema(df) == { "geometry": [ "MultiPolygon", "Polygon", "MultiLineString", "LineString", "MultiPoint", "Point", ], "properties": OrderedDict(), } def test_infer_schema_mixed_3D_shape_type(): df = GeoDataFrame( geometry=[ MultiPolygon((city_hall_boundaries, vauquelin_place)), city_hall_boundaries, MultiLineString(city_hall_walls), city_hall_walls[0], MultiPoint([city_hall_entrance, city_hall_balcony]), city_hall_balcony, point_3D, ] ) assert infer_schema(df) == { "geometry": [ "3D Point", "MultiPolygon", "Polygon", "MultiLineString", "LineString", "MultiPoint", "Point", ], "properties": OrderedDict(), } def test_infer_schema_mixed_3D_Point(): df = GeoDataFrame(geometry=[city_hall_balcony, point_3D]) assert infer_schema(df) == { "geometry": ["3D Point", "Point"], "properties": OrderedDict(), } def test_infer_schema_only_3D_Points(): df = GeoDataFrame(geometry=[point_3D, point_3D]) assert infer_schema(df) == {"geometry": "3D Point", "properties": OrderedDict()} def test_infer_schema_mixed_3D_linestring(): df = GeoDataFrame(geometry=[city_hall_walls[0], linestring_3D]) assert infer_schema(df) == { "geometry": ["3D LineString", "LineString"], "properties": OrderedDict(), } def test_infer_schema_only_3D_linestrings(): df = GeoDataFrame(geometry=[linestring_3D, linestring_3D]) assert infer_schema(df) == { "geometry": "3D LineString", "properties": OrderedDict(), } def test_infer_schema_mixed_3D_Polygon(): df = GeoDataFrame(geometry=[city_hall_boundaries, polygon_3D]) assert infer_schema(df) == { "geometry": ["3D Polygon", "Polygon"], "properties": OrderedDict(), } def test_infer_schema_only_3D_Polygons(): df = GeoDataFrame(geometry=[polygon_3D, polygon_3D]) assert infer_schema(df) == {"geometry": "3D Polygon", "properties": OrderedDict()} def test_infer_schema_null_geometry_and_2D_point(): df = GeoDataFrame(geometry=[None, city_hall_entrance]) # None geometry type is then omitted assert infer_schema(df) == {"geometry": "Point", "properties": OrderedDict()} def test_infer_schema_null_geometry_and_3D_point(): df = GeoDataFrame(geometry=[None, point_3D]) # None geometry type is then omitted assert infer_schema(df) == {"geometry": "3D Point", "properties": OrderedDict()} def test_infer_schema_null_geometry_all(): df = GeoDataFrame(geometry=[None, None]) # None geometry type in then replaced by 'Unknown' # (default geometry type supported by Fiona) assert infer_schema(df) == {"geometry": "Unknown", "properties": OrderedDict()} @pytest.mark.parametrize( "array_data,dtype", [([1, 2**31 - 1], np.int32), ([1, np.nan], pd.Int32Dtype())] ) def test_infer_schema_int32(array_data, dtype): int32col = pd.array(data=array_data, dtype=dtype) df = GeoDataFrame(geometry=[city_hall_entrance, city_hall_balcony]) df["int32_column"] = int32col assert infer_schema(df) == { "geometry": "Point", "properties": OrderedDict([("int32_column", "int32")]), } def test_infer_schema_int64(): int64col = pd.array([1, np.nan], dtype=pd.Int64Dtype()) df = GeoDataFrame(geometry=[city_hall_entrance, city_hall_balcony]) df["int64_column"] = int64col assert infer_schema(df) == { "geometry": "Point", "properties": OrderedDict([("int64_column", "int")]), } geopandas-1.2.0/geopandas/io/tests/test_pickle.py000066400000000000000000000025671525650651600220720ustar00rootroot00000000000000""" See generate_legacy_storage_files.py for the creation of the legacy files. """ import glob import os import pathlib import pandas as pd import pytest from geopandas.testing import assert_geodataframe_equal DATA_PATH = pathlib.Path(os.path.dirname(__file__)) / "data" @pytest.fixture(scope="module") def current_pickle_data(): # our current version pickle data from .generate_legacy_storage_files import create_pickle_data return create_pickle_data() files = glob.glob(str(DATA_PATH / "pickle" / "*.pickle")) @pytest.fixture(params=files, ids=[p.split("/")[-1] for p in files]) def legacy_pickle(request): return request.param @pytest.mark.skip( reason=( "shapely 2.0/pygeos-based unpickling currently only works for " "shapely-2.0/pygeos-written files" ), ) def test_legacy_pickles(current_pickle_data, legacy_pickle): result = pd.read_pickle(legacy_pickle) for name, value in result.items(): expected = current_pickle_data[name] assert_geodataframe_equal(value, expected) def test_round_trip_current(tmpdir, current_pickle_data): data = current_pickle_data for name, value in data.items(): path = str(tmpdir / f"{name}.pickle") value.to_pickle(path) result = pd.read_pickle(path) assert_geodataframe_equal(result, value) assert isinstance(result.has_sindex, bool) geopandas-1.2.0/geopandas/io/tests/test_sql.py000066400000000000000000001124061525650651600214140ustar00rootroot00000000000000""" Tests here include reading/writing to different types of spatial databases. The spatial database tests may not work without additional system configuration. postGIS tests require a test database to have been setup; see geopandas.tests.util for more information. """ import os import uuid import warnings from importlib.util import find_spec import pandas as pd import geopandas from geopandas import GeoDataFrame, read_file, read_postgis from geopandas._compat import HAS_PYPROJ from geopandas.io.sql import _get_conn as get_conn from geopandas.io.sql import _write_postgis as write_postgis import pytest from geopandas.tests.util import ( create_postgis, create_spatialite, mock, validate_boro_df, ) try: from sqlalchemy import inspect, text except ImportError: # Avoid local imports for text in all sqlalchemy tests # all tests using text use engine_postgis, which ensures sqlalchemy is available text = str inspect = None @pytest.fixture def df_nybb(nybb_filename): df = read_file(nybb_filename) return df def check_available_postgis_drivers() -> list[str]: """Work out which of psycopg2 and psycopg are available. This prevents tests running if the relevant package isn't installed (rather than being skipped, as skips are treated as failures during postgis CI) """ drivers = [] if find_spec("psycopg"): drivers.append("psycopg") if find_spec("psycopg2"): drivers.append("psycopg2") return drivers POSTGIS_DRIVERS = check_available_postgis_drivers() POSTGIS_AVAILABLE = None def check_postgis_available(driver) -> bool: """ Check if a PostGIS database is available for testing. Check this once and cache the result, because this can be slow on Windows. """ global POSTGIS_AVAILABLE if POSTGIS_AVAILABLE is not None: return POSTGIS_AVAILABLE psycopg = pytest.importorskip(driver) try: con = psycopg.connect(**prepare_database_credentials()) except psycopg.OperationalError: POSTGIS_AVAILABLE = False else: con.close() POSTGIS_AVAILABLE = True return POSTGIS_AVAILABLE def prepare_database_credentials() -> dict: """Gather postgres connection credentials from environment variables.""" return { "dbname": "test_geopandas", "user": os.environ.get("PGUSER"), "password": os.environ.get("PGPASSWORD"), "host": os.environ.get("PGHOST"), "port": os.environ.get("PGPORT"), } @pytest.fixture() def connection_postgis(request): """Create a postgres connection using either psycopg2 or psycopg. Use this as an indirect fixture, where the request parameter is POSTGIS_DRIVERS.""" psycopg = pytest.importorskip(request.param) if not check_postgis_available(request.param): pytest.skip("Cannot connect with postgresql database") try: con = psycopg.connect(**prepare_database_credentials()) except psycopg.OperationalError: pytest.skip("Cannot connect with postgresql database") with warnings.catch_warnings(): warnings.filterwarnings( "ignore", message="pandas only supports SQLAlchemy connectable.*" ) yield con con.close() @pytest.fixture() def engine_postgis(request): """ Initiate a sqlalchemy connection engine using either psycopg2 or psycopg. Use this as an indirect fixture, where the request parameter is POSTGIS_DRIVERS. """ sqlalchemy = pytest.importorskip("sqlalchemy") from sqlalchemy.engine.url import URL if not check_postgis_available(request.param): pytest.skip("Cannot connect with postgresql database") credentials = prepare_database_credentials() try: con = sqlalchemy.create_engine( URL.create( drivername=f"postgresql+{request.param}", username=credentials["user"], database=credentials["dbname"], password=credentials["password"], host=credentials["host"], port=credentials["port"], ) ) con.connect() except Exception: pytest.skip("Cannot connect with postgresql database") yield con con.dispose() @pytest.fixture() def connection_spatialite(): """ Return a memory-based SQLite3 connection with SpatiaLite enabled & initialized. `The sqlite3 module must be built with loadable extension support `_ and `SpatiaLite `_ must be available on the system as a SQLite module. Packages available on Anaconda meet requirements. Exceptions ---------- ``AttributeError`` on missing support for loadable SQLite extensions ``sqlite3.OperationalError`` on missing SpatiaLite """ sqlite3 = pytest.importorskip("sqlite3") try: with sqlite3.connect(":memory:") as con: con.enable_load_extension(True) con.load_extension("mod_spatialite") con.execute("SELECT InitSpatialMetaData(TRUE)") except Exception: con.close() pytest.skip("Cannot setup spatialite database") yield con con.close() def drop_table_if_exists(conn_or_engine, table): sqlalchemy = pytest.importorskip("sqlalchemy") if sqlalchemy.inspect(conn_or_engine).has_table(table): metadata = sqlalchemy.MetaData() with warnings.catch_warnings(): warnings.filterwarnings( "ignore", message="Did not recognize type 'geometry' of column.*" ) metadata.reflect(conn_or_engine) table = metadata.tables.get(table) if table is not None: table.drop(conn_or_engine, checkfirst=True) @pytest.fixture def df_mixed_single_and_multi(): from shapely.geometry import LineString, MultiLineString, Point df = geopandas.GeoDataFrame( { "geometry": [ LineString([(0, 0), (1, 1)]), MultiLineString([[(0, 0), (1, 1)], [(2, 2), (3, 3)]]), Point(0, 1), ] }, crs="epsg:4326", ) return df @pytest.fixture def df_mixed_none_geometry(): from shapely.geometry import MultiPolygon df = geopandas.GeoDataFrame( { "geometry": [ None, MultiPolygon([[[[0, 0], [0, 1], [1, 1], [1, 0]]]]), ], "id": [0, 1], }, crs="epsg:4326", ) return df @pytest.fixture def df_geom_collection(): from shapely.geometry import GeometryCollection, LineString, Point, Polygon df = geopandas.GeoDataFrame( { "geometry": [ GeometryCollection( [ Polygon([(0, 0), (1, 1), (0, 1)]), LineString([(0, 0), (1, 1)]), Point(0, 0), ] ) ] }, crs="epsg:4326", ) return df @pytest.fixture def df_linear_ring(): from shapely.geometry import LinearRing df = geopandas.GeoDataFrame( {"geometry": [LinearRing(((0, 0), (0, 1), (1, 1), (1, 0)))]}, crs="epsg:4326" ) return df @pytest.fixture def df_3D_geoms(): from shapely.geometry import LineString, Point, Polygon df = geopandas.GeoDataFrame( { "geometry": [ LineString([(0, 0, 0), (1, 1, 1)]), Polygon([(0, 0, 0), (1, 1, 1), (0, 1, 1)]), Point(0, 1, 2), ] }, crs="epsg:4326", ) return df class TestIO: @pytest.mark.parametrize("engine_postgis", POSTGIS_DRIVERS, indirect=True) def test_get_conn(self, engine_postgis): Connection = pytest.importorskip("sqlalchemy.engine.base").Connection engine = engine_postgis with get_conn(engine) as output: assert isinstance(output, Connection) with engine.connect() as conn: with get_conn(conn) as output: assert isinstance(output, Connection) with pytest.raises(ValueError): with get_conn(object()): pass @pytest.mark.parametrize("connection_postgis", POSTGIS_DRIVERS, indirect=True) def test_read_postgis_default(self, connection_postgis, df_nybb): con = connection_postgis create_postgis(con, df_nybb) sql = "SELECT * FROM nybb;" df = read_postgis(sql, con) validate_boro_df(df) # no crs defined on the created geodatabase, and none specified # by user; should not be set to 0, as from get_srid failure assert df.crs is None @pytest.mark.parametrize("connection_postgis", POSTGIS_DRIVERS, indirect=True) def test_read_postgis_custom_geom_col(self, connection_postgis, df_nybb): con = connection_postgis geom_col = "the_geom" create_postgis(con, df_nybb, geom_col=geom_col) sql = "SELECT * FROM nybb;" df = read_postgis(sql, con, geom_col=geom_col) validate_boro_df(df) @pytest.mark.parametrize("connection_postgis", POSTGIS_DRIVERS, indirect=True) def test_read_postgis_select_geom_as(self, connection_postgis, df_nybb): """Tests that a SELECT {geom} AS {some_other_geom} works.""" con = connection_postgis orig_geom = "geom" out_geom = "the_geom" create_postgis(con, df_nybb, geom_col=orig_geom) sql = f"""SELECT borocode, boroname, shape_leng, shape_area, {orig_geom} as {out_geom} FROM nybb;""" df = read_postgis(sql, con, geom_col=out_geom) validate_boro_df(df) @pytest.mark.parametrize("connection_postgis", POSTGIS_DRIVERS, indirect=True) def test_read_postgis_get_srid(self, connection_postgis, df_nybb): """Tests that an SRID can be read from a geodatabase (GH #451).""" con = connection_postgis crs = "epsg:4269" df_reproj = df_nybb.to_crs(crs) create_postgis(con, df_reproj, srid=4269) sql = "SELECT * FROM nybb;" df = read_postgis(sql, con) validate_boro_df(df) assert df.crs == crs @pytest.mark.parametrize("connection_postgis", POSTGIS_DRIVERS, indirect=True) def test_read_postgis_override_srid(self, connection_postgis, df_nybb): """Tests that a user specified CRS overrides the geodatabase SRID.""" con = connection_postgis orig_crs = df_nybb.crs create_postgis(con, df_nybb, srid=4269) sql = "SELECT * FROM nybb;" df = read_postgis(sql, con, crs=orig_crs) validate_boro_df(df) assert df.crs == orig_crs @pytest.mark.parametrize("connection_postgis", POSTGIS_DRIVERS, indirect=True) def test_from_postgis_default(self, connection_postgis, df_nybb): con = connection_postgis create_postgis(con, df_nybb) sql = "SELECT * FROM nybb;" df = GeoDataFrame.from_postgis(sql, con) validate_boro_df(df, case_sensitive=False) @pytest.mark.parametrize("connection_postgis", POSTGIS_DRIVERS, indirect=True) def test_from_postgis_custom_geom_col(self, connection_postgis, df_nybb): con = connection_postgis geom_col = "the_geom" create_postgis(con, df_nybb, geom_col=geom_col) sql = "SELECT * FROM nybb;" df = GeoDataFrame.from_postgis(sql, con, geom_col=geom_col) validate_boro_df(df, case_sensitive=False) def test_read_postgis_null_geom(self, connection_spatialite, df_nybb): """Tests that geometry with NULL is accepted.""" con = connection_spatialite geom_col = df_nybb.geometry.name df_nybb.loc[0, "geometry"] = None create_spatialite(con, df_nybb) sql = ( "SELECT ogc_fid, borocode, boroname, shape_leng, shape_area, " f'AsEWKB("{geom_col}") AS "{geom_col}" FROM nybb' ) df = read_postgis(sql, con, geom_col=geom_col) validate_boro_df(df) def test_read_postgis_binary(self, connection_spatialite, df_nybb): """Tests that geometry read as binary is accepted.""" con = connection_spatialite geom_col = df_nybb.geometry.name create_spatialite(con, df_nybb) sql = ( "SELECT ogc_fid, borocode, boroname, shape_leng, shape_area, " f'ST_AsBinary("{geom_col}") AS "{geom_col}" FROM nybb' ) df = read_postgis(sql, con, geom_col=geom_col) validate_boro_df(df) @pytest.mark.parametrize("connection_postgis", POSTGIS_DRIVERS, indirect=True) def test_read_postgis_chunksize(self, connection_postgis, df_nybb): """Test chunksize argument""" chunksize = 2 con = connection_postgis create_postgis(con, df_nybb) sql = "SELECT * FROM nybb;" df = pd.concat(read_postgis(sql, con, chunksize=chunksize)) validate_boro_df(df) # no crs defined on the created geodatabase, and none specified # by user; should not be set to 0, as from get_srid failure assert df.crs is None @pytest.mark.parametrize("engine_postgis", POSTGIS_DRIVERS, indirect=True) def test_write_postgis_default(self, engine_postgis, df_nybb): """Tests that GeoDataFrame can be written to PostGIS with defaults.""" engine = engine_postgis table = "nybb" # If table exists, delete it before trying to write with defaults drop_table_if_exists(engine, table) # Write to db write_postgis(df_nybb, con=engine, name=table, if_exists="fail") # Validate sql = text(f"SELECT * FROM {table};") df = read_postgis(sql, engine, geom_col="geometry") validate_boro_df(df) @pytest.mark.parametrize("engine_postgis", POSTGIS_DRIVERS, indirect=True) def test_write_postgis_uppercase_tablename(self, engine_postgis, df_nybb): """Tests writing GeoDataFrame to PostGIS with uppercase tablename.""" engine = engine_postgis table = "aTestTable" # If table exists, delete it before trying to write with defaults drop_table_if_exists(engine, table) # Write to db write_postgis(df_nybb, con=engine, name=table, if_exists="fail") # Validate sql = text(f'SELECT * FROM "{table}";') df = read_postgis(sql, engine, geom_col="geometry") validate_boro_df(df) @pytest.mark.parametrize("engine_postgis", POSTGIS_DRIVERS, indirect=True) def test_write_postgis_sqlalchemy_connection(self, engine_postgis, df_nybb): """Tests that GeoDataFrame can be written to PostGIS with defaults.""" with engine_postgis.begin() as con: table = "nybb_con" # If table exists, delete it before trying to write with defaults drop_table_if_exists(con, table) # Write to db write_postgis(df_nybb, con=con, name=table, if_exists="fail") # Validate sql = text(f"SELECT * FROM {table};") df = read_postgis(sql, con, geom_col="geometry") validate_boro_df(df) @pytest.mark.parametrize("engine_postgis", POSTGIS_DRIVERS, indirect=True) def test_write_postgis_fail_when_table_exists(self, engine_postgis, df_nybb): """ Tests that uploading the same table raises error when: if_replace='fail'. """ engine = engine_postgis table = "nybb" # Ensure table exists write_postgis(df_nybb, con=engine, name=table, if_exists="replace") try: write_postgis(df_nybb, con=engine, name=table, if_exists="fail") except ValueError as e: if "already exists" in str(e): pass else: raise e @pytest.mark.parametrize("engine_postgis", POSTGIS_DRIVERS, indirect=True) def test_write_postgis_replace_when_table_exists(self, engine_postgis, df_nybb): """ Tests that replacing a table is possible when: if_replace='replace'. """ engine = engine_postgis table = "nybb" # Ensure table exists write_postgis(df_nybb, con=engine, name=table, if_exists="replace") # Overwrite write_postgis(df_nybb, con=engine, name=table, if_exists="replace") # Validate sql = text(f"SELECT * FROM {table};") df = read_postgis(sql, engine, geom_col="geometry") validate_boro_df(df) @pytest.mark.parametrize("engine_postgis", POSTGIS_DRIVERS, indirect=True) def test_write_postgis_append_when_table_exists(self, engine_postgis, df_nybb): """ Tests that appending to existing table produces correct results when: if_replace='append'. """ engine = engine_postgis table = "nybb" orig_rows, orig_cols = df_nybb.shape write_postgis(df_nybb, con=engine, name=table, if_exists="replace") write_postgis(df_nybb, con=engine, name=table, if_exists="append") # Validate sql = text(f"SELECT * FROM {table};") df = read_postgis(sql, engine, geom_col="geometry") new_rows, new_cols = df.shape # There should be twice as many rows in the new table assert new_rows == orig_rows * 2, ( f"There should be {orig_rows * 2} rows,found: {new_rows}", ) # Number of columns should stay the same assert new_cols == orig_cols, ( f"There should be {orig_cols} columns,found: {new_cols}", ) @pytest.mark.parametrize("engine_postgis", POSTGIS_DRIVERS, indirect=True) def test_write_postgis_without_crs(self, engine_postgis, df_nybb): """ Tests that GeoDataFrame can be written to PostGIS without CRS information. """ engine = engine_postgis table = "nybb" # Write to db df_nybb.geometry.array.crs = None with pytest.warns(UserWarning, match="Could not parse CRS from the GeoDataF"): write_postgis(df_nybb, con=engine, name=table, if_exists="replace") # Validate that srid is -1 sql = text( "SELECT Find_SRID('{schema}', '{table}', '{geom_col}');".format( schema="public", table=table, geom_col="geometry" ) ) with engine.connect() as conn: target_srid = conn.execute(sql).fetchone()[0] assert target_srid == 0, f"SRID should be 0, found {target_srid}" @pytest.mark.parametrize("engine_postgis", POSTGIS_DRIVERS, indirect=True) def test_write_postgis_with_esri_authority(self, engine_postgis, df_nybb): """ Tests that GeoDataFrame can be written to PostGIS with ESRI Authority CRS information (GH #2414). """ engine = engine_postgis table = "nybb" # Write to db df_nybb_esri = df_nybb.to_crs("ESRI:102003") write_postgis(df_nybb_esri, con=engine, name=table, if_exists="replace") # Validate that srid is 102003 sql = text( "SELECT Find_SRID('{schema}', '{table}', '{geom_col}');".format( schema="public", table=table, geom_col="geometry" ) ) with engine.connect() as conn: target_srid = conn.execute(sql).fetchone()[0] assert target_srid == 102003, f"SRID should be 102003, found {target_srid}" @pytest.mark.parametrize("engine_postgis", POSTGIS_DRIVERS, indirect=True) def test_write_postgis_geometry_collection( self, engine_postgis, df_geom_collection ): """ Tests that writing a mix of different geometry types is possible. """ engine = engine_postgis table = "geomtype_tests" write_postgis(df_geom_collection, con=engine, name=table, if_exists="replace") # Validate geometry type sql = text(f"SELECT DISTINCT(GeometryType(geometry)) FROM {table} ORDER BY 1;") with engine.connect() as conn: geom_type = conn.execute(sql).fetchone()[0] sql = text(f"SELECT * FROM {table};") df = read_postgis(sql, engine, geom_col="geometry") assert geom_type.upper() == "GEOMETRYCOLLECTION" assert df.geom_type.unique()[0] == "GeometryCollection" @pytest.mark.parametrize("engine_postgis", POSTGIS_DRIVERS, indirect=True) def test_write_postgis_none_geometry_types( self, engine_postgis, df_mixed_none_geometry ): """ Tests that writing a mix of None and MulitPolygons is possible. """ engine = engine_postgis table = "geomtype_tests" write_postgis( df_mixed_none_geometry, con=engine, name=table, if_exists="replace" ) # Validate geometry type sql = text(f"SELECT DISTINCT GeometryType(geometry) FROM {table} ORDER BY 1;") with engine.connect() as conn: res = conn.execute(sql).fetchall() assert res[0][0].upper() == "MULTIPOLYGON" assert pd.isna(res[1][0]) @pytest.mark.parametrize("engine_postgis", POSTGIS_DRIVERS, indirect=True) def test_write_postgis_mixed_geometry_types( self, engine_postgis, df_mixed_single_and_multi ): """ Tests that writing a mix of single and MultiGeometries is possible. """ engine = engine_postgis table = "geomtype_tests" write_postgis( df_mixed_single_and_multi, con=engine, name=table, if_exists="replace" ) # Validate geometry type sql = text(f"SELECT DISTINCT GeometryType(geometry) FROM {table} ORDER BY 1;") with engine.connect() as conn: res = conn.execute(sql).fetchall() assert res[0][0].upper() == "LINESTRING" assert res[1][0].upper() == "MULTILINESTRING" assert res[2][0].upper() == "POINT" @pytest.mark.parametrize("engine_postgis", POSTGIS_DRIVERS, indirect=True) def test_write_postgis_linear_ring(self, engine_postgis, df_linear_ring): """ Tests that writing a LinearRing. """ engine = engine_postgis table = "geomtype_tests" write_postgis(df_linear_ring, con=engine, name=table, if_exists="replace") # Validate geometry type sql = text(f"SELECT DISTINCT(GeometryType(geometry)) FROM {table} ORDER BY 1;") with engine.connect() as conn: geom_type = conn.execute(sql).fetchone()[0] assert geom_type.upper() == "LINESTRING" @pytest.mark.parametrize("engine_postgis", POSTGIS_DRIVERS, indirect=True) def test_write_postgis_in_chunks(self, engine_postgis, df_mixed_single_and_multi): """ Tests writing a LinearRing works. """ engine = engine_postgis table = "geomtype_tests" write_postgis( df_mixed_single_and_multi, con=engine, name=table, if_exists="replace", chunksize=1, ) # Validate row count sql = text(f"SELECT COUNT(geometry) FROM {table};") with engine.connect() as conn: row_cnt = conn.execute(sql).fetchone()[0] assert row_cnt == 3 # Validate geometry type sql = text(f"SELECT DISTINCT GeometryType(geometry) FROM {table} ORDER BY 1;") with engine.connect() as conn: res = conn.execute(sql).fetchall() assert res[0][0].upper() == "LINESTRING" assert res[1][0].upper() == "MULTILINESTRING" assert res[2][0].upper() == "POINT" @pytest.mark.parametrize("engine_postgis", POSTGIS_DRIVERS, indirect=True) def test_write_postgis_to_different_schema(self, engine_postgis, df_nybb): """ Tests writing data to alternative schema. """ engine = engine_postgis table = "nybb" schema_to_use = "test" sql = text(f"CREATE SCHEMA IF NOT EXISTS {schema_to_use};") with engine.begin() as conn: conn.execute(sql) write_postgis( df_nybb, con=engine, name=table, if_exists="replace", schema=schema_to_use ) # Validate sql = text(f"SELECT * FROM {schema_to_use}.{table};") df = read_postgis(sql, engine, geom_col="geometry") validate_boro_df(df) @pytest.mark.parametrize("engine_postgis", POSTGIS_DRIVERS, indirect=True) def test_write_postgis_to_different_schema_when_table_exists( self, engine_postgis, df_nybb ): """ Tests writing data to alternative schema. """ engine = engine_postgis table = "nybb" schema_to_use = "test" sql = text(f"CREATE SCHEMA IF NOT EXISTS {schema_to_use};") with engine.begin() as conn: conn.execute(sql) try: write_postgis( df_nybb, con=engine, name=table, if_exists="fail", schema=schema_to_use ) # Validate sql = text(f"SELECT * FROM {schema_to_use}.{table};") df = read_postgis(sql, engine, geom_col="geometry") validate_boro_df(df) # Should raise a ValueError when table exists except ValueError: pass # Try with replace flag on write_postgis( df_nybb, con=engine, name=table, if_exists="replace", schema=schema_to_use ) # Validate sql = text(f"SELECT * FROM {schema_to_use}.{table};") df = read_postgis(sql, engine, geom_col="geometry") validate_boro_df(df) @pytest.mark.parametrize("engine_postgis", POSTGIS_DRIVERS, indirect=True) def test_write_postgis_3D_geometries(self, engine_postgis, df_3D_geoms): """ Tests writing a geometries with 3 dimensions works. """ engine = engine_postgis table = "geomtype_tests" write_postgis(df_3D_geoms, con=engine, name=table, if_exists="replace") # Check that all geometries have 3 dimensions sql = text(f"SELECT * FROM {table};") df = read_postgis(sql, engine, geom_col="geometry") assert list(df.geometry.has_z) == [True, True, True] @pytest.mark.parametrize("engine_postgis", POSTGIS_DRIVERS, indirect=True) def test_row_order(self, engine_postgis, df_nybb): """ Tests that the row order in db table follows the order of the original frame. """ engine = engine_postgis table = "row_order_test" correct_order = df_nybb["BoroCode"].tolist() write_postgis(df_nybb, con=engine, name=table, if_exists="replace") # Check that the row order matches sql = text(f"SELECT * FROM {table};") df = read_postgis(sql, engine, geom_col="geometry") assert df["BoroCode"].tolist() == correct_order @pytest.mark.parametrize("engine_postgis", POSTGIS_DRIVERS, indirect=True) def test_append_before_table_exists(self, engine_postgis, df_nybb): """ Tests that insert works with if_exists='append' when table does not exist yet. """ engine = engine_postgis table = "nybb" # If table exists, delete it before trying to write with defaults drop_table_if_exists(engine, table) write_postgis(df_nybb, con=engine, name=table, if_exists="append") # Check that the row order matches sql = text(f"SELECT * FROM {table};") df = read_postgis(sql, engine, geom_col="geometry") validate_boro_df(df) @pytest.mark.parametrize("engine_postgis", POSTGIS_DRIVERS, indirect=True) def test_append_with_different_crs(self, engine_postgis, df_nybb): """ Tests that the warning is raised if table CRS differs from frame. """ engine = engine_postgis table = "nybb" write_postgis(df_nybb, con=engine, name=table, if_exists="replace") # Reproject df_nybb2 = df_nybb.to_crs(epsg=4326) # Should raise error when appending with pytest.raises(ValueError, match="CRS of the target table"): write_postgis(df_nybb2, con=engine, name=table, if_exists="append") @pytest.mark.parametrize("engine_postgis", POSTGIS_DRIVERS, indirect=True) def test_append_without_crs(self, engine_postgis, df_nybb): # This test was included in #3328 when the default value for no # CRS was changed from an SRID of -1 to 0. This resolves issues # of appending dataframes to postgis that have no CRS as postgis # no CRS value is 0. engine = engine_postgis df_nybb = df_nybb.set_crs(None, allow_override=True) table = "nybb" write_postgis(df_nybb, con=engine, name=table, if_exists="replace") # append another dataframe with no crs df_nybb2 = df_nybb write_postgis(df_nybb2, con=engine, name=table, if_exists="append") @pytest.mark.parametrize("engine_postgis", POSTGIS_DRIVERS, indirect=True) def test_append_with_custom_geom_col(self, engine_postgis, df_nybb): engine = engine_postgis table = "test_custom_geom" # Create GeoDataFrame with custom geometry column name gdf = df_nybb.copy() gdf = gdf.rename_geometry("custom_geom_123") write_postgis(gdf, con=engine, name=table, if_exists="replace") write_postgis(gdf, con=engine, name=table, if_exists="append") # Verify data was appended correctly sql = text(f'SELECT * FROM "{table}";') df = read_postgis(sql, engine, geom_col="custom_geom_123") assert len(df) == len(gdf) * 2 @pytest.mark.parametrize("engine_postgis", POSTGIS_DRIVERS, indirect=True) def test_duplicate_geometry_column_fails(self, engine_postgis): """ Tests that a ValueError is raised if an SQL query returns two geometry columns. """ engine = engine_postgis sql = "select ST_MakePoint(0, 0) as geom, ST_MakePoint(0, 0) as geom;" with pytest.raises(ValueError): read_postgis(sql, engine, geom_col="geom") @pytest.mark.parametrize("connection_postgis", POSTGIS_DRIVERS, indirect=True) def test_read_non_epsg_crs(self, connection_postgis, df_nybb): con = connection_postgis df_nybb = df_nybb.to_crs(crs="esri:54052") create_postgis(con, df_nybb, srid=54052) sql = "SELECT * FROM nybb;" df = read_postgis(sql, con) validate_boro_df(df) assert df.crs == "ESRI:54052" @pytest.mark.skipif(not HAS_PYPROJ, reason="pyproj not installed") @mock.patch("shapely.get_srid") @pytest.mark.parametrize("connection_postgis", POSTGIS_DRIVERS, indirect=True) def test_read_srid_not_in_table(self, mock_get_srid, connection_postgis, df_nybb): # mock a non-existent srid for edge case if shapely has an srid # not present in postgis table. pyproj = pytest.importorskip("pyproj") mock_get_srid.return_value = 99999 con = connection_postgis df_nybb = df_nybb.to_crs(crs="epsg:4326") create_postgis(con, df_nybb) sql = "SELECT * FROM nybb;" with pytest.raises(pyproj.exceptions.CRSError, match="crs not found"): with pytest.warns(UserWarning, match="Could not find srid 99999"): read_postgis(sql, con) @mock.patch("geopandas.io.sql._get_spatial_ref_sys_df") @pytest.mark.parametrize("connection_postgis", POSTGIS_DRIVERS, indirect=True) def test_read_no_spatial_ref_sys_table_in_postgis( self, mock_get_spatial_ref_sys_df, connection_postgis, df_nybb ): # mock for a non-existent spatial_ref_sys database mock_get_spatial_ref_sys_df.side_effect = pd.errors.DatabaseError con = connection_postgis df_nybb = df_nybb.to_crs(crs="epsg:4326") create_postgis(con, df_nybb, srid=4326) sql = "SELECT * FROM nybb;" with pytest.warns( UserWarning, match="Could not find the spatial reference system table" ): df = read_postgis(sql, con) assert df.crs == "EPSG:4326" @pytest.mark.parametrize("connection_postgis", POSTGIS_DRIVERS, indirect=True) def test_read_non_epsg_crs_chunksize(self, connection_postgis, df_nybb): """Test chunksize argument with non epsg crs""" chunksize = 2 con = connection_postgis df_nybb = df_nybb.to_crs(crs="esri:54052") create_postgis(con, df_nybb, srid=54052) sql = "SELECT * FROM nybb;" df = pd.concat(read_postgis(sql, con, chunksize=chunksize)) validate_boro_df(df) assert df.crs == "ESRI:54052" @pytest.mark.parametrize("engine_postgis", POSTGIS_DRIVERS, indirect=True) def test_to_postgis_sql_injection_prevention_geometry_column( self, engine_postgis, df_nybb ): """ Tests that SQL injection attempts via geometry column name are prevented. This test verifies that malicious geometry column names are treated as literal strings and not executed as SQL code in the Find_SRID query when using to_postgis with if_exists='append'. https://github.com/geopandas/geopandas/issues/3679 """ engine = engine_postgis table = "test_sql_injection" # Create a test table first to ensure append mode triggers potential issue gdf_normal = df_nybb.copy() write_postgis(gdf_normal, con=engine, name=table, if_exists="replace") # Create a test table that would be targeted by SQL injection # If SQL injection works, this table would be dropped test_target_table = f"test_target_{uuid.uuid4().hex[:12]}" with engine.begin() as conn: conn.execute( text(f"CREATE TABLE IF NOT EXISTS {test_target_table} (id INTEGER);") ) conn.execute(text(f"INSERT INTO {test_target_table} (id) VALUES (1);")) # Create GeoDataFrame with malicious geometry column name gdf_malicious = df_nybb.copy() malicious_geom_name = f"geometry'); DROP TABLE {test_target_table}; --" gdf_malicious = gdf_malicious.rename_geometry(malicious_geom_name) # First, verify both tables exist sql_check = text(f"SELECT COUNT(*) FROM {table};") sql_check_target = text(f"SELECT COUNT(*) FROM {test_target_table};") with engine.connect() as conn: initial_count = conn.execute(sql_check).fetchone()[0] assert conn.execute(sql_check_target).fetchone()[0] == 1 # Attempt append - this should fail because the geometry column name # doesn't exist, but it should NOT execute the injected SQL try: write_postgis(gdf_malicious, con=engine, name=table, if_exists="append") except (ValueError, Exception): pass # Verify both tables still exist and weren't dropped by SQL injection assert inspect(engine).has_table(table), ( "Test table should still exist after SQL injection attempt" ) assert inspect(engine).has_table(test_target_table), ( f"Target table '{test_target_table}' should not be dropped by SQL injection" ) with engine.connect() as conn: assert conn.execute(sql_check).fetchone()[0] == initial_count assert conn.execute(sql_check_target).fetchone()[0] == 1 @pytest.mark.parametrize("engine_postgis", POSTGIS_DRIVERS, indirect=True) def test_to_postgis_sql_injection_prevention_geometry_column_union( self, engine_postgis, df_nybb ): """ Similar test as above, using another SQL injection pattern: UNION SELECT attack via geometry column name. """ engine = engine_postgis table = "test_sql_injection_union" # Create initial table gdf_normal = df_nybb.copy() write_postgis(gdf_normal, con=engine, name=table, if_exists="replace") # Create a test table that would be targeted by UNION SELECT injection test_target_table = f"test_target_{uuid.uuid4().hex[:12]}" with engine.begin() as conn: conn.execute( text(f"CREATE TABLE IF NOT EXISTS {test_target_table} (password TEXT);") ) conn.execute( text(f"INSERT INTO {test_target_table} (password) VALUES ('secret');") ) # Create GeoDataFrame with UNION-based SQL injection attempt gdf_malicious = df_nybb.copy() malicious_geom_name = ( f"geometry')::text UNION SELECT password FROM {test_target_table}" "WHERE '1'='1'; --" ) gdf_malicious = gdf_malicious.rename_geometry(malicious_geom_name) # Verify tables exist before injection attempt sql_check = text(f"SELECT COUNT(*) FROM {table};") sql_check_target = text(f"SELECT COUNT(*) FROM {test_target_table};") with engine.connect() as conn: initial_count = conn.execute(sql_check).fetchone()[0] assert conn.execute(sql_check_target).fetchone()[0] == 1 # Attempt append - should not execute the UNION SELECT (but will still fail # not finding the SRID) with pytest.raises(Exception) as excinfo: write_postgis(gdf_malicious, con=engine, name=table, if_exists="append") assert "secret" not in str(excinfo.value), ( "SQL injection should not have been executed; " "'secret' should not be in the error" ) # Verify table integrity maintained assert inspect(engine).has_table(table), ( "Test table should still exist after SQL injection attempt" ) assert inspect(engine).has_table(test_target_table), ( f"Target table '{test_target_table}' should not be dropped by SQL injection" ) with engine.connect() as conn: assert conn.execute(sql_check).fetchone()[0] == initial_count assert conn.execute(sql_check_target).fetchone()[0] == 1 geopandas-1.2.0/geopandas/io/util.py000066400000000000000000000056411525650651600173730ustar00rootroot00000000000000"""Vendored, cut down version of pyogrio/util.py for use with fiona.""" import re import sys from urllib.parse import urlparse def vsi_path(path: str) -> str: """Ensure path is a local path or a GDAL-compatible vsi path.""" # path is already in GDAL format if path.startswith("/vsi"): return path # Windows drive letters (e.g. "C:\") confuse `urlparse` as they look like # URL schemes if sys.platform == "win32" and re.match("^[a-zA-Z]\\:", path): if not path.split("!", maxsplit=1)[0].endswith(".zip"): return path # prefix then allow to proceed with remaining parsing path = f"zip://{path}" path, archive, scheme = _parse_uri(path) if scheme or archive or path.endswith(".zip"): return _construct_vsi_path(path, archive, scheme) return path # Supported URI schemes and their mapping to GDAL's VSI suffix. SCHEMES = { "file": "file", "zip": "zip", "tar": "tar", "gzip": "gzip", "http": "curl", "https": "curl", "ftp": "curl", "s3": "s3", "gs": "gs", "az": "az", "adls": "adls", "adl": "adls", # fsspec uses this "hdfs": "hdfs", "webhdfs": "webhdfs", # GDAL additionally supports oss and swift for remote filesystems, but # those are for now not added as supported URI } CURLSCHEMES = {k for k, v in SCHEMES.items() if v == "curl"} def _parse_uri(path: str): """Parse a URI. Returns a tuples of (path, archive, scheme) path : str Parsed path. Includes the hostname and query string in the case of a URI. archive : str Parsed archive path. scheme : str URI scheme such as "https" or "zip+s3". """ parts = urlparse(path, allow_fragments=False) # if the scheme is not one of GDAL's supported schemes, return raw path if parts.scheme and not all(p in SCHEMES for p in parts.scheme.split("+")): return path, "", "" # we have a URI path = parts.path scheme = parts.scheme or "" if parts.query: path += "?" + parts.query if parts.scheme and parts.netloc: path = parts.netloc + path parts = path.split("!") path = parts.pop() if parts else "" archive = parts.pop() if parts else "" return (path, archive, scheme) def _construct_vsi_path(path, archive, scheme) -> str: """Convert a parsed path to a GDAL VSI path.""" prefix = "" suffix = "" schemes = scheme.split("+") if "zip" not in schemes and (archive.endswith(".zip") or path.endswith(".zip")): schemes.insert(0, "zip") if schemes: prefix = "/".join(f"vsi{SCHEMES[p]}" for p in schemes if p and p != "file") if schemes[-1] in CURLSCHEMES: suffix = f"{schemes[-1]}://" if prefix: if archive: return "/{}/{}{}/{}".format(prefix, suffix, archive, path.lstrip("/")) else: return f"/{prefix}/{suffix}{path}" return path geopandas-1.2.0/geopandas/plotting.py000066400000000000000000001527461525650651600176600ustar00rootroot00000000000000from __future__ import annotations import warnings from collections.abc import Collection, Iterable, Sequence from typing import TYPE_CHECKING, Any, Literal, TypeAlias import numpy as np import pandas as pd from numpy.typing import NDArray from pandas import CategoricalDtype from pandas.core.dtypes.cast import coerce_indexer_dtype from pandas.plotting import PlotAccessor import shapely import geopandas from ._decorator import doc if TYPE_CHECKING: import os from matplotlib.axes import Axes from matplotlib.collections import ( LineCollection, PatchCollection, PathCollection, ) from matplotlib.colors import Colormap from matplotlib.markers import MarkerStyle from matplotlib.patches import PathPatch from matplotlib.path import Path from rasterio.io import MemoryFile from xyzservices import TileProvider ColumnLike: TypeAlias = ( str | NDArray | pd.Series | pd.Index | pd.Categorical | Sequence | None ) def _set_aspect( aspect: float | Literal["auto", "equal", None], s: geopandas.GeoSeries | geopandas.GeoDataFrame, ax: Axes, ) -> None: """Set the aspect ratio of the axis. - If `aspect` is "auto" and the CRS is geographic, the aspect ratio is adjusted to account for latitude distortion, using a formula ported from the R package 'sp'. - If the CRS is not geographic, the aspect is set to "equal". - If `aspect` is not None or "auto", its value is passed directly to `ax.set_aspect`. Parameters ---------- aspect : str or float or None The aspect ratio to set. If "auto", the aspect is determined based on the geometry's CRS. If a float or other valid matplotlib aspect value, it is passed directly to `ax.set_aspect`. s : GeoSeries or GeoDataFrame The spatial data whose CRS and bounds are used to determine the aspect ratio if `aspect` is "auto". ax : matplotlib.axes.Axes The axes on which to set the aspect ratio. """ if aspect == "auto": if s.crs and s.crs.is_geographic: bounds = s.total_bounds y_coord = np.mean([bounds[1], bounds[3]]) ax.set_aspect(1 / np.cos(y_coord * np.pi / 180)) # formula ported from R package sp # https://github.com/edzer/sp/blob/master/R/mapasp.R else: ax.set_aspect("equal") elif aspect is not None: ax.set_aspect(aspect) def _sanitize_geoms( geoms: geopandas.GeoSeries, ) -> tuple[geopandas.GeoSeries, np.ndarray]: """Return sanitized geometry with the indices of original geometry. 1. Normalize all geometry to ensure holes are correctly plotted. 2. Explode GeometryCollections to individual components. This generates an index where values are repeated for all components in the same collection. 3. Filter out missing and empty geometry. The resulting index does not contain their IDs. Series like geoms and index, except that any GeometryCollections are split into their components and indices are repeated for all component in the same collection. At the same time, empty or missing geometries are filtered out. The index then maintains 1:1 matching of geometry to value. Returns ------- components : list of geometry component_index : index array indices are repeated for all components in the same collection """ # TODO(shapely) look into simplifying this with # shapely.get_parts(geoms, return_index=True) from shapely 2.0 geoms = geoms.normalize() components, component_index = [], [] if ( not geoms.geom_type.str.startswith("Geom").any() and not geoms.is_empty.any() and not geoms.isna().any() ): return geoms, np.arange(len(geoms)) for ix, geom in enumerate(geoms): if geom is not None and geom.geom_type.startswith("Geom") and not geom.is_empty: for poly in geom.geoms: components.append(poly) component_index.append(ix) elif geom is None or geom.is_empty: continue else: components.append(geom) component_index.append(ix) return geopandas.GeoSeries(components, crs=geoms.crs), np.array(component_index) def _expand_kwargs(kwargs: dict, multiindex: np.ndarray) -> None: """ Most arguments to the plot functions must be a (single) value, or a sequence of values. This function checks each key-value pair in 'kwargs' and expands it (in place) to the correct length/formats with help of 'multiindex', unless the value appears to already be a valid (single) value for the key. """ from matplotlib.colors import is_color_like scalar_kwargs = ["marker", "path_effects"] for att, value in kwargs.items(): if "color" in att: # color(s), edgecolor(s), facecolor(s) if is_color_like(value): continue elif "linestyle" in att: # linestyle(s) # A single linestyle can be 2-tuple of a number and an iterable. if ( isinstance(value, tuple) and len(value) == 2 and isinstance(value[1], Iterable) ): continue elif att in scalar_kwargs: # For these attributes, only a single value is allowed, so never expand. continue if pd.api.types.is_list_like(value): kwargs[att] = np.take(value, multiindex, axis=0) def _subset_kwds(kwds: dict, index: np.ndarray) -> dict: """Subsets list-like keyword arguments based on a given index array. Parameters ---------- kwds : dict Dictionary of keyword arguments to be subsetted. index : np.ndarray Array of bools used to subset list-like values in `kwds`. Returns ------- dict Dictionary with values subsetted according to `index` where applicable. """ subset_kwds = {} for key, val in kwds.items(): if isinstance(val, (np.ndarray, pd.Series, pd.Index, pd.DataFrame)) and ( len(val) == index.shape[0] ): subsetted = val[index] # if only one remains, extract scalar if len(subsetted) == 1: subset_kwds[key] = subsetted[0] else: subset_kwds[key] = subsetted else: # scalar subset_kwds[key] = val return subset_kwds def _plot_empty_collection(ax, majority_geom_type, color, label, **group_style_kwds): """Plot empty collections using correct handle and label.""" from matplotlib import collections if majority_geom_type.endswith("Polygon"): ax.add_collection( collections.PolyCollection( [], color=color, **group_style_kwds, label=label, ) ) elif majority_geom_type.endswith("Point"): ax.scatter( [], [], color=color, **group_style_kwds, label=label, ) else: ax.plot( [], [], color=color, **group_style_kwds, label=label, ) def _PolygonPatch(polygon: shapely.Geometry, **kwargs) -> PathPatch: """Construct a matplotlib patch from a (Multi)Polygon geometry. The `kwargs` are those supported by the matplotlib.patches.PathPatch class constructor. Returns an instance of matplotlib.patches.PathPatch. To ensure proper rendering on the matplotlib side, winding order of individual rings needs to be normalized as the order is what matplotlib uses to determine if a Path represents a patch or a hole. Example (using Shapely Point and a matplotlib axes):: b = shapely.geometry.Point(0, 0).buffer(1.0) patch = _PolygonPatch(b, fc='blue', ec='blue', alpha=0.5) ax.add_patch(patch) GeoPandas originally relied on the descartes package by Sean Gillies (BSD license, https://pypi.org/project/descartes) for PolygonPatch, but this dependency was removed in favor of the below matplotlib code. """ from matplotlib.patches import PathPatch from matplotlib.path import Path if polygon.geom_type == "Polygon": parts = [polygon] else: parts = polygon.geoms paths = [] for part in parts: # exteriors paths.append(Path(np.asarray(part.exterior.coords)[:, :2], closed=True)) # interiors paths.extend( Path(np.asarray(ring.coords)[:, :2], closed=True) for ring in part.interiors ) path = Path.make_compound_path(*paths) return PathPatch(path, **kwargs) def _plot_polygon_collection( ax: Axes, geoms: geopandas.GeoSeries, values: np.ndarray | None = None, cmap: str | Colormap | None = None, vmin: float | int | None = None, vmax: float | int | None = None, autolim: bool = True, **kwargs, ) -> PatchCollection: """Plot a collection of Polygon and MultiPolygon geometries to `ax`. Note that all style keywords, like ``color`` that can be set as an array in matplotlib shall be passed directly via kwargs. No need to explode geometries to single-parts as _PolygonPatch supports MultiPolygons, Parameters ---------- ax : matplotlib.axes.Axes Axes on which to add the collection geoms : GeoSeries GeoSeries of (Multi)Polygons values : np.ndarray, optional Values will be mapped to colors using vmin/vmax/norm/cmap. They should have 1:1 correspondence with the geometries (not their components). Otherwise follows `color` / `facecolor` kwargs, by default None cmap : str or Colormap, optional The colormap recognized by matplotlib, by default None vmin : float, optional Minimum value of cmap, by default None vmax : float, optional Maximum value of cmap, by default None autolim : bool, optional Update axes data limits to contain the new geometries, by default True Returns ------- _GeoPandasPolyCollection matplotlib.collections.Collection that was plotted """ from matplotlib.collections import PatchCollection from matplotlib.legend import Legend from matplotlib.legend_handler import HandlerPolyCollection class _GeoPandasPolyCollection(PatchCollection): """Subclass to assign handler without overriding one for PatchCollection.""" # PatchCollection is not supported by Legend but we can use PolyCollection handler # instead in our specific case. Define a subclass and assign a handler. Legend.update_default_handler_map( {_GeoPandasPolyCollection: HandlerPolyCollection()} ) # _GeoPandasPolyCollection does not accept some kwargs. kwargs.pop("markersize", None) kwargs.pop("marker", None) collection = _GeoPandasPolyCollection( [_PolygonPatch(poly) for poly in geoms], **kwargs ) if cmap and not isinstance(cmap, dict): collection.set_cmap(cmap) if values is not None: collection.set_array(np.asarray(values)) if "norm" not in kwargs: collection.set_clim(vmin, vmax) ax.add_collection(collection, autolim=autolim) ax.autoscale_view() return collection def _plot_linestring_collection( ax: Axes, geoms: geopandas.GeoSeries, values: np.ndarray | None = None, color: str | Sequence | None = None, cmap: str | Colormap | None = None, vmin: float | int | None = None, vmax: float | int | None = None, autolim: bool = True, **kwargs, ) -> LineCollection: """Plot a collection of LineString and MultiLineString geometries to `ax`. Parameters ---------- ax : matplotlib.axes.Axes Axes on which to add the collection geoms : GeoSeries GeoSeries of (Multi)Polygons values : np.ndarray, optional Values will be mapped to colors using vmin/vmax/norm/cmap. They should have 1:1 correspondence with the geometries (not their components). Otherwise follows `color` / `facecolor` kwargs, by default None color : single color or sequence of `N` colors Color definition understood by matplotlib. Cannot be used together with `values`. cmap : str or Colormap, optional The colormap recognized by matplotlib, by default None vmin : float, optional Minimum value of cmap, by default None vmax : float, optional Maximum value of cmap, by default None autolim : bool, optional Update axes data limits to contain the new geometries, by default True Returns ------- LineCollection matplotlib.collections.Collection that was plotted """ from matplotlib.collections import LineCollection geoms, multiindex = shapely.get_parts(geoms.values, return_index=True) if values is not None: values = np.take(values, multiindex, axis=0) # LineCollection does not accept some kwargs. kwargs.pop("markersize", None) kwargs.pop("marker", None) # Add to kwargs for easier checking below. if color is not None: kwargs["color"] = color _expand_kwargs(kwargs, multiindex) segments = [np.array(linestring.coords)[:, :2] for linestring in geoms] collection = LineCollection(segments, **kwargs) if values is not None: collection.set_array(np.asarray(values)) if cmap: collection.set_cmap(cmap) if "norm" not in kwargs: collection.set_clim(vmin, vmax) ax.add_collection(collection, autolim=autolim) ax.autoscale_view() return collection def _plot_point_collection( ax: Axes, geoms: geopandas.GeoSeries, values: np.ndarray | None = None, color: str | Sequence | None = None, cmap: str | Colormap | None = None, vmin: float | int | None = None, vmax: float | int | None = None, marker: str | MarkerStyle | Path = "o", markersize: float | Sequence[float] | None = None, **kwargs, ) -> PathCollection: """Plot a collection of Point and MultiPoint geometries to `ax`. Parameters ---------- ax : matplotlib.axes.Axes Axes on which to add the collection geoms : GeoSeries GeoSeries of (Multi)Polygons values : np.ndarray, optional Values will be mapped to colors using vmin/vmax/norm/cmap. They should have 1:1 correspondence with the geometries (not their components). Otherwise follows `color` / `facecolor` kwargs, by default None color : single color or sequence of `N` colors Color definition understood by matplotlib. Cannot be used together with `values`. cmap : str or Colormap, optional The colormap recognized by matplotlib, by default None vmin : float, optional Minimum value of cmap, by default None vmax : float, optional Maximum value of cmap, by default None marker : str, MarkerStyle, Path Style of the marker to be used. markersize : scalar or array-like, optional Size of the markers. Note that under the hood ``scatter`` is used, so the specified value will be proportional to the area of the marker (size in points^2). Returns ------- collection : matplotlib.collections.Collection that was plotted """ geoms, multiindex = shapely.get_parts(geoms.values, return_index=True) xy = shapely.get_coordinates(geoms) # Add to kwargs for easier checking below. if values is not None: kwargs["c"] = values if markersize is not None: kwargs["s"] = markersize if color is not None: kwargs["color"] = color if marker is not None: kwargs["marker"] = marker _expand_kwargs(kwargs, multiindex) _cmap = cmap if values is not None else None # norm cannot be passed alongside vmin and vmax if "norm" not in kwargs: collection = ax.scatter( xy[:, 0], xy[:, 1], vmin=vmin, vmax=vmax, cmap=_cmap, **kwargs ) else: collection = ax.scatter(xy[:, 0], xy[:, 1], cmap=cmap, **kwargs) # ensure that cmap is assigned when used categorical plotting for a reference if values is None and cmap is not None: collection.set_cmap(cmap) return collection def plot_series( s: geopandas.GeoSeries, cmap: str | Colormap | None = None, color: str | Sequence | None = None, ax: Axes | None = None, figsize: tuple[float, float] | None = None, aspect: float | Literal["auto", "equal", None] = "auto", autolim: bool = True, tiles: bool | str | TileProvider | os.PathLike | MemoryFile = False, attr: str | None = None, add_labels: bool = True, **style_kwds, ) -> Axes: """ Plot a GeoSeries. Generate a plot of a GeoSeries geometry with matplotlib. Parameters ---------- s : Series The GeoSeries to be plotted. Currently Polygon, MultiPolygon, LineString, MultiLineString, Point and MultiPoint geometries can be plotted. cmap : ``str`` | ``Colormap`` (default ``None``) The name of a colormap recognized by matplotlib, or a :class:`matplotlib.colors.Colormap`. Colors will be mapped to individual geometries along their order of appearance in the GeoSeries. Any colormap will work, but categorical colormaps are generally recommended. Examples of useful discrete colormaps include: "tab10", "tab20", "Accent", "Dark2", "Paired", "Pastel1", "Set1", "Set2" color : ``str``, ``np.array``, ``pd.Series`` (default ``None``) Color of the geometry. If specified as scalar matplotlib understands as a color (``str``, ``tuple`` or RGBA etc.), all objects will be colored uniformly. If specifies as array-like of the same length as GeoDataFrame, individual colors will be mapped to respective geometries.. ax : ``matplotlib.axes.Axes`` (default ``None``) :class:`matplotlib.axes.Axes` axes on which to draw the plot figsize : ``tuple`` of integers (default None) Size of the resulting :class:`matplotlib.figure.Figure`. If the argument ``ax`` is given explicitly, ``figsize`` is ignored. aspect : `'auto'`, `'equal'`, ``None`` or ``float`` (default ``'auto'``) Set aspect of axis. If ``'auto'``, the default aspect for map plots is ``'equal'``; if however data are not projected (coordinates are long/lat), the aspect is by default set to ``1/cos(df_y * pi/180)`` with ``df_y`` the y coordinate of the middle of the GeoDataFrame (the mean of the y range of bounding box) so that a long/lat square appears square in the middle of the plot. This implies an Equirectangular projection. If ``None``, the aspect of ``ax`` won't be changed. It can also be set manually (float) as the ratio of y-unit to x-unit. autolim : ``bool`` (default ``True``) Update axes data limits to contain the new geometries. tiles : bool, str, xyzservices.TileProvider, os.PathLike, file-like, or rasterio.io.MemoryFile (default False) Add contextual background tiles. Can be either a boolean, :class:`xyzservices.TileProvider`, any string that can be resolved by :func:`xyzservices.providers.query_name`, URL, or a path to a local file. The placeholders for the XYZ in the URL need to be `{x}`, `{y}`, `{z}`, respectively. For local file paths, the file is read with `rasterio` and all bands are loaded into the basemap. The tiles are automatically warped to the CRS of the geometry. Note that this can result in suboptimal rendering. To avoid warping, geometry needs to be in EPSG:3857 (Web Mercator) or a CRS of the tiles if other projection is used. Default basemap when `True` follows the default of the underlying :func:`contextily.add_basemap`, which is OpenStreetMap Humanitarian. attr : str (default None) Attribution text passed to :func:`contextily.add_basemap` as ``attribution``. When not provided, the default attribution of the selected tile source is used. add_labels : bool (default True) Use CRS metadata to label the axes. **style_kwds : dict Style options to be passed on to the actual plot function, such as ``edgecolor``, ``facecolor``, ``linewidth``, ``markersize``, ``alpha``. These can be scalar, which are uniformly mapped to all geometries, or array-likes of the same length as GeoSeries, which are mapped to their respective geometries. Returns ------- ax : matplotlib axes instance """ # noqa: E501 try: import matplotlib # noqa: F401 from matplotlib.colors import Colormap except ImportError as err: raise ImportError( "The matplotlib package is required for plotting in geopandas. " "You can install it using 'conda install -c conda-forge matplotlib' or " "'pip install matplotlib'." ) from err import matplotlib.pyplot as plt if ax is None: _, ax = plt.subplots(figsize=figsize) if add_labels: _set_axis_labels(ax, s.crs) if s.empty: warnings.warn( "The GeoSeries you are attempting to plot is " "empty. Nothing has been displayed.", UserWarning, stacklevel=3, ) return ax if s.is_empty.all(): warnings.warn( "The GeoSeries you are attempting to plot is " "composed of empty geometries. Nothing has been displayed.", UserWarning, stacklevel=3, ) return ax # set correct aspect to preserve proportions in geographic CRS _set_aspect(aspect, s, ax) # decompose GeometryCollections geoms, multiindex = _sanitize_geoms(s) _expand_kwargs(style_kwds, multiindex) values = None color_given = False # the order here matters to ensure that categorical plots which specify both # color and cmap are properly plotted - see #3763 if color is not None: # if color is as a list-like, ensure it is properly mapped to components color_given = pd.api.types.is_list_like(color) and len(color) == len(s) # have colors been given for all geometries? if color_given: # ensure indexes are consistent if isinstance(color, pd.Series): color = color.reindex(s.index) color = np.take(color, multiindex, axis=0) elif cmap is not None: # if cmap is specified, create range of colors based on cmap values = np.arange(len(s)) if isinstance(cmap, Colormap) and hasattr(cmap, "N"): # repeat for cmap with limited number of colors values = values % cmap.N style_kwds["vmin"] = values.min() style_kwds["vmax"] = values.max() # ensure proper mapping of values to components of GeometryCollections values = np.take(values, multiindex, axis=0) # subdivide by geometry type - each has its own collection geom_types = geoms.geom_type poly_idx = np.asarray((geom_types == "Polygon") | (geom_types == "MultiPolygon")) line_idx = np.asarray( (geom_types == "LineString") | (geom_types == "MultiLineString") | (geom_types == "LinearRing") ) point_idx = np.asarray((geom_types == "Point") | (geom_types == "MultiPoint")) # plot all Polygons and all MultiPolygon components in the same collection polys = geoms[poly_idx] if not polys.empty: # color overrides both face and edgecolor. As we want people to be # able to use edgecolor as well, pass color to facecolor facecolor = style_kwds.pop("facecolor", None) if color is not None: facecolor = color[poly_idx] if color_given else color values_ = values[poly_idx] if values is not None else None poly_kwds = _subset_kwds(style_kwds, poly_idx) _plot_polygon_collection( ax, polys, values_, facecolor=facecolor, cmap=cmap, autolim=autolim, **poly_kwds, ) # plot all LineStrings and MultiLineString components in same collection lines = geoms[line_idx] if not lines.empty: values_ = values[line_idx] if values is not None else None color_ = color[line_idx] if color_given else color lines_kwds = _subset_kwds(style_kwds, line_idx) _plot_linestring_collection( ax, lines, values_, color=color_, cmap=cmap, autolim=autolim, **lines_kwds ) # plot all Points in the same collection points = geoms[point_idx] if not points.empty: values_ = values[point_idx] if values is not None else None color_ = color[point_idx] if color_given else color points_kwds = _subset_kwds(style_kwds, point_idx) _plot_point_collection( ax, points, values_, color=color_, cmap=cmap, **points_kwds ) _add_basemap(ax, tiles, s.crs, attr=attr) ax.figure.canvas.draw_idle() return ax def plot_dataframe( df: geopandas.GeoDataFrame, column: ColumnLike = None, cmap: str | Colormap | dict | None = None, color: str | Sequence | None = None, ax: Axes | None = None, cax: Axes | None = None, categorical: bool = False, legend: bool = False, scheme: str | None = None, k: int = 5, vmin: float | None = None, vmax: float | None = None, markersize: str | float | Sequence | dict | None = None, figsize: tuple[float, float] | None = None, legend_kwds: dict | None = None, categories: Sequence | None = None, classification_kwds: dict | None = None, missing_kwds: dict | None = None, aspect: float | Literal["auto", "equal", None] = "auto", autolim: bool = True, tiles: bool | str | TileProvider | os.PathLike | MemoryFile = False, attr: str | None = None, add_labels: bool = True, **style_kwds, ) -> Axes: """ Plot a GeoDataFrame. Generate a plot of a GeoDataFrame with matplotlib. If a column is specified, the plot coloring will be based on values in that column. Parameters ---------- column : ``str``, ``np.array``, ``pd.Series``, ``pd.Index`` (default ``None``) The name of the GeoDataFrame column, ``np.array``, ``pd.Series``, or ``pd.Index`` to be plotted. If ``np.array``, ``pd.Series``, or ``pd.Index`` are used then it must have same length as GeoDataFrame. Values are used to color the plot. Ignored if ``color`` is also set. kind: str The kind of plots to produce. The default is to create a map (``"geo"``). Other supported kinds of plots from pandas: - ``'line'`` : line plot - ``'bar'`` : vertical bar plot - ``'barh'`` : horizontal bar plot - ``'hist'`` : histogram - ``'box'`` : BoxPlot - ``'kde'`` : Kernel Density Estimation plot - ``'density'`` : same as ``'kde'`` - ``'area'`` : area plot - ``'pie'`` : pie plot - ``'scatter'`` : scatter plot - ``'hexbin'`` : hexbin plot. cmap : ``str`` | ``Colormap`` | ``dict`` (default ``None``) The name of a colormap recognized by matplotlib, a :class:`matplotlib.colors.Colormap` or a dictionary of ``value: color`` mapping for categorical plots. color : ``str``, ``np.array``, ``pd.Series`` (default ``None``) Color of the geometry. If specified as scalar matplotlib understands as a color (``str``, ``tuple`` or RGBA etc.), all objects will be colored uniformly. If specifies as array-like of the same length as GeoDataFrame, individual colors will be mapped to respective geometries. ax : ``matplotlib.axes.Axes`` (default ``None``) :class:`matplotlib.axes.Axes` axes on which to draw the plot cax : ``matplotlib.axes.Axes`` (default ``None``) :class:`matplotlib.axes.Axes` on which to draw the legend in case of a colorbar. categorical : ``bool`` (default ``False``) Determine whether values of ``column`` shall be treated as a categorical variable or as a continuous one. For non-numerical columns, this will be set to ``True`` automatically. legend : ``bool`` (default ``False``) Plot a legend. Ignored if no ``column`` is given, or if ``color`` is given. This can be a :class:`matplotlib.legend.Legend` in case of categorical variables or those with set ``scheme`` and a categorical legend, or a :class`:matplotlib.colorbar.Colorbar` in case of continuous variables or those with set ``scheme`` and a colorbar legend. scheme : str (default None) Name of a choropleth classification scheme (requires ``mapclassify``). A :class:`mapclassify.classifiers.MapClassifier` object will be used under the hood. Supported are all schemes provided by ``mapclassify`` (e.g. ``'BoxPlot'``, ``'EqualInterval'``, ``'FisherJenks'``, ``'FisherJenksSampled'``, ``'HeadTailBreaks'``, ``'JenksCaspall'``, ``'JenksCaspallForced'``, ``'JenksCaspallSampled'``, ``'MaxP'``, ``'MaximumBreaks'``, ``'NaturalBreaks'``, ``'Quantiles'``, ``'Percentiles'``, ``'StdMean'``, ``'UserDefined'``). Arguments can be passed in ``classification_kwds``. This requires continuous variable and can result in a categorical plot where each classification bin is mapped to a category (default behavior) or in a continuous plot where bins are used to define :class:`matplotlib.colors.BoundaryNorm`. The latter can be enabled by specifying ``colorbar=True`` within ``legend_kwds`` and yields colorbar legend. In addition, ``scheme='greedy'`` uses :func:`mapclassify.greedy` to derive greedy (topological) coloring which attempts to color a GeoDataFrame using as few colors as possible, where no neighbours can have same color as the feature itself. This cannot be specified together with ``column`` as each geometry is treated as unique with no relation to its attributes. k : ``int`` (default ``5``) Number of classes (ignored if ``scheme`` is ``None``) vmin : ``None`` or ``float`` (default ``None``) Minimum value of ``cmap`` or ``scheme``. If ``None``, the minimum data value in the column to be plotted is used. vmax : ``None`` or ``float`` (default ``None``) Maximum value of ``cmap`` or ``scheme``. If ``None``, the maximum data value in the column to be plotted is used. markersize : ``str`` or ``float`` or ``Sequence`` (default None) Only applies to point geometries within a GeoDataFrame. If a ``str``, will use the values in the column of the GeoDataFrame specified by ``markersize`` to set the size of markers. Otherwise can be a value to apply to all points, or a sequence of the same length as the number of points. figsize : ``tuple`` of integers (default None) Size of the resulting :class:`matplotlib.figure.Figure`. If the argument ``ax`` is given explicitly, ``figsize`` is ignored. legend_kwds : dict (default None) Keyword arguments to pass to :func:`matplotlib.pyplot.legend` (e.g. ``labels``, or ``frameon``) or :func:`matplotlib.pyplot.colorbar` (e.g. ``orientation``). Additional accepted keywords when ``scheme`` is specified: ``fmt`` : string A formatting specification for the bin edges of the classes in the legend. For example, to have no decimals: ``{"fmt": "{:.0f}"}``. ``interval`` : boolean (default False) An option to control brackets from mapclassify legend. If True, open/closed interval brackets are shown in the legend. ``colorbar`` : boolean (default False) An option to control whether the legend should be treated as categorical or as a colorbar. When set to True, ``fmt`` and ``interval`` shall not be used. categories : ``list-like`` Ordered list-like object of categories to be used for categorical plot. classification_kwds : ``dict`` (default ``None``) Keyword arguments to pass to ``mapclassify`` when ``scheme`` is set. missing_kwds : ``dict`` (default None) Keyword arguments specifying style options (e.g. color, hatch, linewidth) to be passed on to geometries with missing values in addition to or overwriting other ``style_kwds``. If ``None``, geometries with missing values are not plotted. aspect : `'auto'`, `'equal'`, ``None`` or ``float`` (default ``'auto'``) Set aspect of axis. If ``'auto'``, the default aspect for map plots is ``'equal'``; if however data are not projected (coordinates are long/lat), the aspect is by default set to ``1/cos(df_y * pi/180)`` with ``df_y`` the y coordinate of the middle of the GeoDataFrame (the mean of the y range of bounding box) so that a long/lat square appears square in the middle of the plot. This implies an Equirectangular projection. If ``None``, the aspect of ``ax`` won't be changed. It can also be set manually (float) as the ratio of y-unit to x-unit. autolim : ``bool`` (default ``True``) Update axes data limits to contain the new geometries. tiles : bool, str, xyzservices.TileProvider, os.PathLike, file-like, or rasterio.io.MemoryFile (default False) Add contextual background tiles. Can be either a boolean, :class:`xyzservices.TileProvider`, any string that can be resolved by :func:`xyzservices.providers.query_name`, URL, or a path to a local file. The placeholders for the XYZ in the URL need to be `{x}`, `{y}`, `{z}`, respectively. For local file paths, the file is read with `rasterio` and all bands are loaded into the basemap. The tiles are automatically warped to the CRS of the geometry. Note that this can result in suboptimal rendering. To avoid warping, geometry needs to be in EPSG:3857 (Web Mercator) or a CRS of the tiles if other projection is used. Default basemap when `True` follows the default of the underlying :func:`contextily.add_basemap`, which is OpenStreetMap Humanitarian. attr : str (default None) Attribution text passed to :func:`contextily.add_basemap` as ``attribution``. When not provided, the default attribution of the selected tile source is used. add_labels : bool (default True) Use CRS metadata to label the axes. **style_kwds : dict Style options to be passed on to the actual plot function, such as ``edgecolor``, ``facecolor``, ``linewidth``, ``markersize``, ``alpha``. These can be scalar, which are uniformly mapped to all geometries, array-likes of the same length as GeoDataFrame, which are mapped to their respective geometries, or dictionaries mapping styles to values when dealing with categorical plots. Returns ------- ax : matplotlib axes instance Examples -------- >>> import geodatasets >>> df = geopandas.read_file(geodatasets.get_path("nybb")) >>> df.head() # doctest: +SKIP BoroCode ... geometry 0 5 ... MULTIPOLYGON (((970217.022 145643.332, 970227.... 1 4 ... MULTIPOLYGON (((1029606.077 156073.814, 102957... 2 3 ... MULTIPOLYGON (((1021176.479 151374.797, 102100... 3 1 ... MULTIPOLYGON (((981219.056 188655.316, 980940.... 4 2 ... MULTIPOLYGON (((1012821.806 229228.265, 101278... >>> df.plot("BoroName", cmap="Set1") # doctest: +SKIP See the User Guide page :doc:`../../user_guide/mapping` for details. """ # noqa: E501 try: import matplotlib.pyplot as plt from matplotlib import cm, colormaps, colors except ImportError: raise ImportError( "The matplotlib package is required for plotting in geopandas. " "You can install it using 'conda install -c conda-forge matplotlib' or " "'pip install matplotlib'." ) if column is not None and color is not None: warnings.warn( "Only specify one of 'column' or 'color'. Using 'color'.", UserWarning, stacklevel=3, ) column = None # Process polymorphic markersize if isinstance(markersize, str): markersize = df[markersize].values # add to style_kwds so it can be mapped to groups and masked for NaN if markersize is not None: style_kwds["markersize"] = markersize if classification_kwds is None: classification_kwds = {} if scheme: try: import mapclassify except ImportError: raise ImportError( "The 'mapclassify' package is required to use the 'scheme' keyword." ) if scheme == "greedy": if column is not None: raise ValueError( "The `scheme='greedy'` cannot be specified together with `column`." ) categorical = True scheme = None codes = mapclassify.greedy(df, **classification_kwds) column = pd.Categorical(codes) # if column is not set, we're showing just geometries -> plot_series if column is None: return plot_series( df.geometry, cmap=cmap, color=color, ax=ax, figsize=figsize, aspect=aspect, autolim=autolim, tiles=tiles, attr=attr, add_labels=add_labels, **style_kwds, ) if ax is None: if cax is not None: raise ValueError("'ax' can not be None if 'cax' is not.") _fig, ax = plt.subplots(figsize=figsize) if add_labels: _set_axis_labels(ax, df.crs) if df.empty: warnings.warn( "The GeoDataFrame you are attempting to plot is " "empty. Nothing has been displayed.", UserWarning, stacklevel=3, ) return ax # set correct aspect to preserve proportions in geographic CRS _set_aspect(aspect, df, ax) # Process polymorphic column argument (column name or array-like) if pd.api.types.is_list_like(column): # column name is a tuple or similar if pd.api.types.is_hashable(column) and column in df.columns: values = df[column] elif len(column) != df.shape[0]: raise ValueError( "The dataframe and given column have different number of rows." ) elif isinstance(column, pd.Series): values = column.reindex(df.index) elif isinstance(column, pd.Index): values = column.values else: values = np.asarray(column) else: values = df[column] # Infer categorical variable if isinstance(values.dtype, CategoricalDtype): if categories is not None: raise ValueError( "Cannot specify 'categories' when column has categorical dtype" ) categorical = True elif ( pd.api.types.is_object_dtype(values.dtype) or pd.api.types.is_bool_dtype(values.dtype) or pd.api.types.is_string_dtype(values.dtype) or categories ): categorical = True if legend_kwds is None: legend_kwds = {} else: # if legend_kwds set, copy so we don't update it in place. GH1555 legend_kwds = legend_kwds.copy() nan_idx = np.asarray(pd.isna(values), dtype="bool") if scheme: if "k" not in classification_kwds: classification_kwds["k"] = k mask = ~nan_idx if vmin is not None: mask = mask & (values >= vmin) if vmax is not None: mask = mask & (values <= vmax) binning = mapclassify.classify(values[mask], scheme, **classification_kwds) # if legend should not be a colorbar we need to treat this as # a categorical plot if not legend_kwds.pop("colorbar", False): categorical = True # use bin labels generated by mapclassify unless user passes their own if "labels" not in legend_kwds: classes = binning.get_legend_classes( fmt=legend_kwds.pop("fmt", "{:.2f}") ) if not legend_kwds.pop("interval", False): classes = [c[1:-1] for c in classes] legend_kwds["labels"] = classes codes = binning.find_bin(values[~nan_idx]) values = pd.Categorical( [np.nan] * len(values), categories=binning.bins, ordered=True ) values[~nan_idx] = pd.Categorical.from_codes( codes, categories=binning.bins, ordered=True, ) # Plot categorical values via groupby - each category is a group plotted using # plot_series if categorical: if categories is not None: values = _check_invalid_categories(categories, values) grouped = df.groupby(values, observed=False) ngroups = grouped.ngroups if cmap is None: if scheme: cmap = colormaps["viridis"] else: cmap = colormaps["tab20"] if ngroups > 10 else colormaps["tab10"] elif isinstance(cmap, str): cmap = colormaps[cmap] def _color(i, name, ngroups, cmap): """Pull the color from the cmap for group.""" if isinstance(cmap, colors.Colormap): if cmap.N < 32: # For categorical cmaps, iterate over colours for the optimal # contrast. Categorical cmaps have generally lower number of colors. # There's no way of pulling the info on the cmap type directly from # matplotlib. return cmap(i) else: # For continuous cmaps, stretch alongside whole range # (a single group has no range to stretch over) return cmap(i / (ngroups - 1) if ngroups > 1 else 0.0) elif isinstance(cmap, dict): return cmap[name] else: raise ValueError( "`cmap` type is not supported. Provide a string mappable " "to matplotlib colormap, `matplotlib.colors.Colormap` or a " "dictionary mapping values to colors." ) # get majority geom type to know how to indicate empty value in the legend majority_geom_type = df.geom_type.mode().iloc[0] # process custom labels if they are provided if "labels" in legend_kwds: if len(legend_kwds["labels"]) != ngroups: raise ValueError( "Number of labels must match number of categories, " f"received {len(legend_kwds['labels'])} labels " f"for {ngroups} categories." ) custom_labels = legend_kwds.pop("labels", None) else: custom_labels = None # looping over groups and adding them to the Axes one by one, each with its # own collection and label for i, (name, group) in enumerate(grouped): # this ensures that any style kwd can be mapped to a value and that # list-like kwds are properly split to groups group_style_kwds = {} for key, val in style_kwds.items(): if isinstance(val, dict): group_style_kwds[key] = val.get(name) elif ( pd.api.types.is_list_like(val) and len(val) == len(df) and not colors.is_color_like(val) ): group_style_kwds[key] = np.take( np.asarray(val), grouped.indices[name] ) else: group_style_kwds[key] = val # extract potential custom label label = custom_labels[i] if custom_labels else name # categoricals with more categories than observed values might be empty # plot nothing to get an item for legend. Determine how to plot nothing # based on a majority geom type to get matching handle in the legend if group.empty: _plot_empty_collection( ax, majority_geom_type, color=_color(i, name, ngroups, cmap), label=label, **group_style_kwds, ) else: plot_series( group.geometry, label=label, color=_color(i, name, ngroups, cmap), cmap=cmap, ax=ax, aspect=None, autolim=autolim, **group_style_kwds, ) missing_geoms = df.geometry[nan_idx] missing_data = not missing_geoms.empty else: values_min = values[~nan_idx].min() values_max = values[~nan_idx].max() mn = values_min if vmin is None else vmin mx = values_max if vmax is None else vmax # classification scheme sets boundary norm for segmented colorbar if scheme: if "norm" in style_kwds: raise ValueError("Cannot set `norm` and `scheme` at the same time.") if vmin is not None: lowest = vmin elif getattr(binning, "lowest", None) is not None: lowest = binning.lowest elif values_min > binning.bins[0]: # we don't know the real lowest value for this scheme # e.g. incorrect user_defined scheme without lowest # the first bin is zero length to preserve colour mapping lowest = binning.bins[0] else: lowest = values_min style_kwds["norm"] = colors.BoundaryNorm( boundaries=[lowest] + list(binning.bins), ncolors=256, ) # default to proportional spacing of the colorbar when using a scheme if "spacing" not in legend_kwds: legend_kwds["spacing"] = "proportional" # decompose GeometryCollections expl_series, multiindex = _sanitize_geoms(df.geometry) values = np.take(values, multiindex, axis=0) nan_idx = np.take(nan_idx, multiindex, axis=0) _expand_kwargs(style_kwds, multiindex) geom_types = expl_series.geom_type poly_idx = np.asarray( (geom_types == "Polygon") | (geom_types == "MultiPolygon") ) line_idx = np.asarray( (geom_types == "LineString") | (geom_types == "MultiLineString") | (geom_types == "LinearRing") ) point_idx = np.asarray((geom_types == "Point") | (geom_types == "MultiPoint")) # plot all Polygons and all MultiPolygon components in the same collection polys_notna = poly_idx & np.invert(nan_idx) polys = expl_series[polys_notna] if not polys.empty: subset = values[polys_notna] poly_kwds = _subset_kwds(style_kwds, polys_notna) _plot_polygon_collection( ax, polys, subset, vmin=mn, vmax=mx, cmap=cmap, autolim=autolim, **poly_kwds, ) # plot all LineStrings and MultiLineString components in same collection lines_notna = line_idx & np.invert(nan_idx) lines = expl_series[lines_notna] if not lines.empty: subset = values[lines_notna] lines_kwds = _subset_kwds(style_kwds, lines_notna) _plot_linestring_collection( ax, lines, subset, vmin=mn, vmax=mx, cmap=cmap, autolim=autolim, **lines_kwds, ) # plot all Points in the same collection points_notna = point_idx & np.invert(nan_idx) points = expl_series[points_notna] if not points.empty: subset = values[point_idx & np.invert(nan_idx)] points_kwds = _subset_kwds(style_kwds, points_notna) _plot_point_collection( ax, points, subset, vmin=mn, vmax=mx, cmap=cmap, **points_kwds, ) if legend: # check if the colorbar needs to show value truncation if "extend" not in legend_kwds: if (mn > values_min) & (mx < values_max): legend_kwds["extend"] = "both" elif mn > values_min: legend_kwds["extend"] = "min" elif mx < values_max: legend_kwds["extend"] = "max" # shrink the colorbar based on the new apect ratio - that way we ensure # that it is never much larger than the axis without complicated hacks bbox = ax.get_position() bbox_orig = ax.get_position(original=True) if "shrink" not in legend_kwds: if ( legend_kwds.get("location", "right") in [ "top", "bottom", ] or legend_kwds.get("orientation", "vertical") == "horizontal" ): ratio = bbox.width / bbox_orig.width else: ratio = bbox.height / bbox_orig.height legend_kwds["shrink"] = ratio legend_kwds["aspect"] = ratio * 20 mappable = cm.ScalarMappable( norm=style_kwds.get("norm", colors.Normalize(vmin=mn, vmax=mx)), cmap=cmap, ) ax.figure.colorbar( mappable, ax=ax, cax=cax, **legend_kwds, ) missing_geoms = expl_series[nan_idx] missing_data = not missing_geoms.empty if missing_kwds is not None and missing_data: merged_kwds = style_kwds.copy() merged_kwds.update(missing_kwds) # ensure we take proper subset of list-like inputs related to missing # and clear all the dicts mapping to categories - user shall specify # style of missing in missing_kwds for key, val in merged_kwds.items(): if isinstance(val, dict): merged_kwds[key] = None elif pd.api.types.is_list_like(val) and len(val) == len(df): merged_kwds[key] = val[nan_idx] plot_series( missing_geoms, ax=ax, aspect=None, label=merged_kwds.pop("label", "NaN"), **merged_kwds, ) if categorical and legend: ax.legend(**legend_kwds) # if there is already a colorbar but we want a legend for missing data, # user can simply call `ax.legend()` with any custom keywords. _add_basemap(ax, tiles, df.crs, attr=attr) ax.figure.canvas.draw_idle() return ax def _check_invalid_categories(categories: Collection[Any], values) -> pd.Categorical: """ Pandas 4 compat https://github.com/pandas-dev/pandas/pull/62142 Could potentially be replaced with a try/except on the above once the warning becomes an exception. This logic is derived from pandas/core/arrays/categorical.py::_get_codes_for_values. """ dtype = CategoricalDtype._from_values_or_dtype(values, categories) categories = dtype.categories codes = categories.get_indexer_for(values) wrong = (codes == -1) & ~pd.isna(values) if wrong.any(): missing = list(np.unique(values[wrong])) raise ValueError( "Column contains values not listed in categories. " f"Missing categories: {missing}." ) else: codes_downcast = coerce_indexer_dtype(codes, categories) cat = pd.Categorical.from_codes(codes_downcast, categories) return cat def _add_basemap(ax, tiles, crs, attr=None): """Optionally add basemap via contextily.""" if tiles: try: import contextily except ImportError: raise ImportError( "Contextily package is required for plotting background tiles. " "You can install it using 'conda install -c conda-forge contextily' or " "'pip install contextily'." ) if tiles is True: tiles = None contextily.add_basemap(source=tiles, ax=ax, crs=crs, attribution=attr) def _set_axis_labels(ax, crs): """Set labels for Axes based on CRS.""" # taken from xarray-contrib/xvec if crs: x_label = f"{crs.axis_info[0].name} [{crs.axis_info[0].unit_name}]" y_label = f"{crs.axis_info[1].name} [{crs.axis_info[1].unit_name}]" if crs.axis_info[0].direction == "north": x_label, y_label = y_label, x_label else: x_label, y_label = "x", "y" if ax.get_xlabel() == "": ax.set_xlabel(x_label, fontsize="small") if ax.get_ylabel() == "": ax.set_ylabel(y_label, fontsize="small") @doc(plot_dataframe) class GeoplotAccessor(PlotAccessor): _pandas_kinds = PlotAccessor._all_kinds def __call__(self, *args, **kwargs): data = self._parent.copy() kind = kwargs.pop("kind", "geo") if kind == "geo": return plot_dataframe(data, *args, **kwargs) if kind in self._pandas_kinds: # Access pandas plots return PlotAccessor(data)(kind=kind, **kwargs) else: # raise error raise ValueError(f"{kind} is not a valid plot kind") def geo(self, *args, **kwargs): return self(kind="geo", *args, **kwargs) # noqa: B026 geopandas-1.2.0/geopandas/sindex.py000066400000000000000000000527601525650651600173050ustar00rootroot00000000000000import numpy as np import shapely from shapely.geometry.base import BaseGeometry from . import _compat as compat from . import array, geoseries PREDICATES = {p.name for p in shapely.strtree.BinaryPredicate} | {None} if compat.GEOS_GE_310: PREDICATES.update(["dwithin"]) class SpatialIndex: """A simple wrapper around Shapely's STRTree. Parameters ---------- geometry : np.array of Shapely geometries Geometries from which to build the spatial index. """ def __init__(self, geometry): # set empty geometries to None to avoid segfault on GEOS <= 3.6 # see: # https://github.com/pygeos/pygeos/issues/146 # https://github.com/pygeos/pygeos/issues/147 non_empty = geometry.copy() non_empty[shapely.is_empty(non_empty)] = None # set empty geometries to None to maintain indexing self._tree = shapely.STRtree(non_empty) # store geometries, including empty geometries for user access self.geometries = geometry.copy() @property def valid_query_predicates(self): """Returns valid predicates for the spatial index. Returns ------- set Set of valid predicates for this spatial index. Examples -------- >>> from shapely.geometry import Point >>> s = geopandas.GeoSeries([Point(0, 0), Point(1, 1)]) >>> s.sindex.valid_query_predicates # doctest: +SKIP {None, "contains", "contains_properly", "covered_by", "covers", \ "crosses", "dwithin", "intersects", "overlaps", "touches", "within"} """ return PREDICATES def query( self, geometry, predicate=None, sort=False, distance=None, output_format="indices", ): """ Return the indices of tree geometries that satisfy the given query. When no predicate is provided, this returns indices of tree geometries whose bounding box intersects the bounding box of the input geometry. When a predicate is provided, the tree geometries are first queried based on bounding box intersection, and then further filtered to those that satisfy the predicate when comparing the input geometry to the tree geometry using the actual geometry (not the bounding box): ``predicate(input_geometry, tree_geometry)``. The result can be returned as an array of 'indices' or a boolean 'sparse' or 'dense' array. This can be controlled using the ``output_format`` keyword. Options are as follows. ``'indices'`` If the input geometry is a scalar, this returns an array of shape (n, ) with the indices of the matching tree geometries. If the input geometry is an array_like, this returns an array with shape (2,n) where the subarrays correspond to the indices of the input geometries and indices of the tree geometries associated with each. To generate an array of pairs of input geometry index and tree geometry index, simply transpose the result. ``'sparse'`` If the input geometry is a scalar, this returns a boolean scipy.sparse COO array of shape (len(tree), ) with boolean values marking whether the bounding box of a geometry in the tree intersects a bounding box of a given scalar. If the input geometry is an array_like, this returns a boolean scipy.sparse COO array with shape (len(tree), n) with boolean values marking whether the bounding box of a geometry in the tree intersects a bounding box of a given scalar. ``'dense'`` If the input geometry is a scalar, this returns a boolean numpy array of shape (len(tree), ) with boolean values marking whether the bounding box of a geometry in the tree intersects a bounding box of a given scalar. If the input geometry is an array_like, this returns a boolean numpy array with shape (len(tree), n) with boolean values marking whether the bounding box of a geometry in the tree intersects a bounding box of a given scalar. The 'dwithin' predicate requires GEOS >= 3.10. Bounding boxes are limited to two dimensions and are axis-aligned (equivalent to the ``bounds`` property of a geometry); any Z values present in input geometries are ignored when querying the tree. Any input geometry that is None or empty will never match geometries in the tree. See the User Guide page :doc:`../../user_guide/spatial_indexing` for more. Parameters ---------- geometry : shapely.Geometry or array-like of geometries \ (numpy.ndarray, GeoSeries, GeometryArray) A single shapely geometry or array of geometries to query against the spatial index. For array-like, accepts both GeoPandas geometry iterables (GeoSeries, GeometryArray) or a numpy array of Shapely geometries. predicate : {None, "contains", "contains_properly", "covered_by", "covers", \ "crosses", "intersects", "overlaps", "touches", "within", "dwithin"}, optional If predicate is provided, the input geometries are tested using the predicate function against each item in the tree whose extent intersects the envelope of the input geometry: ``predicate(input_geometry, tree_geometry)``. If possible, prepared geometries are used to help speed up the predicate operation. sort : bool, default False If True, the results will be sorted in ascending order. In case of 2D array, the result is sorted lexicographically using the geometries' indexes as the primary key and the sindex's indexes as the secondary key. If False, no additional sorting is applied (results are often sorted but there is no guarantee). Applicable only if output_format="indices". distance : number or array_like, optional Distances around each input geometry within which to query the tree for the 'dwithin' predicate. If array_like, shape must be broadcastable to shape of geometry. Required if ``predicate='dwithin'``. output_format : {"indices", "sparse", "dense"}, default "indices" Type of the output format representing the result of the query. Returns ------- `If geometry is a scalar:` ndarray with shape (n,) Integer indices for matching geometries from the spatial index tree geometries. If ``output_format="indices"``. OR scipy.sparse COO array with shape (len(tree), ) Boolean array aligned with array of geometries in the tree. If ``output_format="sparse"``. OR ndarray with shape (len(tree), ) Boolean array aligned with array of geometries in the tree. If ``output_format="dense"``. `If geometry is an array_like:` ndarray with shape (2, n) The first subarray contains input geometry integer indices. The second subarray contains tree geometry integer indices. If ``output_format="indices"``. OR scipy.sparse COO array with shape (len(tree), n) Boolean array aligned with array of geometries in the tree along axis 0 and with ``geometry`` along axis 1. If ``output_format="sparse"``. OR ndarray with shape (len(tree), n) Boolean array aligned with array of geometries in the tree along axis 0 and with ``geometry`` along axis 1. If ``output_format="dense"``. Examples -------- >>> from shapely.geometry import Point, box >>> s = geopandas.GeoSeries(geopandas.points_from_xy(range(10), range(10))) >>> s 0 POINT (0 0) 1 POINT (1 1) 2 POINT (2 2) 3 POINT (3 3) 4 POINT (4 4) 5 POINT (5 5) 6 POINT (6 6) 7 POINT (7 7) 8 POINT (8 8) 9 POINT (9 9) dtype: geometry Querying the tree with a scalar geometry: >>> s.sindex.query(box(1, 1, 3, 3)) array([1, 2, 3]) >>> s.sindex.query(box(1, 1, 3, 3), predicate="contains") array([2]) Querying the tree with an array of geometries: >>> s2 = geopandas.GeoSeries([box(2, 2, 4, 4), box(5, 5, 6, 6)]) >>> s2 0 POLYGON ((4 2, 4 4, 2 4, 2 2, 4 2)) 1 POLYGON ((6 5, 6 6, 5 6, 5 5, 6 5)) dtype: geometry >>> s.sindex.query(s2) array([[0, 0, 0, 1, 1], [2, 3, 4, 5, 6]]) >>> s.sindex.query(s2, predicate="contains") array([[0], [3]]) >>> s.sindex.query(box(1, 1, 3, 3), predicate="dwithin", distance=0) array([1, 2, 3]) >>> s.sindex.query(box(1, 1, 3, 3), predicate="dwithin", distance=2) array([0, 1, 2, 3, 4]) Returning boolean arrays: >>> s.sindex.query(box(1, 1, 3, 3), output_format="sparse") >>> s.sindex.query(box(1, 1, 3, 3), output_format="dense") array([False, True, True, True, False, False, False, False, False, False]) >>> s.sindex.query(s2, output_format="sparse") >>> s.sindex.query(s2, output_format="dense") array([[False, False], [False, False], [ True, False], [ True, False], [ True, False], [False, True], [False, True], [False, False], [False, False], [False, False]]) Notes ----- In the context of a spatial join, input geometries are the "left" geometries that determine the order of the results, and tree geometries are "right" geometries that are joined against the left geometries. This effectively performs an inner join, where only those combinations of geometries that can be joined based on overlapping bounding boxes or optional predicate are returned. """ if predicate not in self.valid_query_predicates: if predicate == "dwithin": raise ValueError("predicate = 'dwithin' requires GEOS >= 3.10.0") raise ValueError( f"Got predicate='{predicate}'; " f"`predicate` must be one of {self.valid_query_predicates}" ) # distance argument requirement of predicate `dwithin` # and only valid for predicate `dwithin` kwargs = {} if predicate == "dwithin": if distance is None: # the distance parameter is needed raise ValueError( "'distance' parameter is required for 'dwithin' predicate" ) # add distance to kwargs kwargs["distance"] = distance elif distance is not None: # distance parameter is invalid raise ValueError( "'distance' parameter is only supported in combination with " "'dwithin' predicate" ) geometry = self._as_geometry_array(geometry) indices = self._tree.query(geometry, predicate=predicate, **kwargs) if output_format == "indices" and sort: if indices.ndim == 1: indices = np.sort(indices) else: # sort by first array (geometry) and then second (tree) geo_idx, tree_idx = indices sort_indexer = np.lexsort((tree_idx, geo_idx)) indices = np.vstack((geo_idx[sort_indexer], tree_idx[sort_indexer])) if output_format == "sparse": scipy = compat.import_optional_dependency("scipy") if indices.ndim == 1: return scipy.sparse.coo_array( (np.ones(len(indices), dtype=np.bool_), indices.reshape(1, -1)), shape=(len(self.geometries),), dtype=np.bool_, ) return scipy.sparse.coo_array( (np.ones(len(indices[0]), dtype=np.bool_), indices[::-1]), shape=(len(self.geometries), len(geometry)), dtype=np.bool_, ) if output_format == "dense": if indices.ndim == 1: dense = np.zeros(len(self.geometries), dtype=bool) dense[indices] = True else: dense = np.zeros((len(self.geometries), len(geometry)), dtype=bool) tree, other = indices[::-1] dense[tree, other] = True return dense if output_format == "indices": return indices raise ValueError( f"Invalid output_format: '{output_format}'. " "Use one of 'indices', 'sparse', 'dense'." ) @staticmethod def _as_geometry_array(geometry): """Convert geometry into a numpy array of Shapely geometries. Parameters ---------- geometry An array-like of Shapely geometries, a GeoPandas GeoSeries/GeometryArray, shapely.geometry or list of shapely geometries. Returns ------- np.ndarray A numpy array of Shapely geometries. """ if isinstance(geometry, np.ndarray): return array.from_shapely(geometry)._data elif isinstance(geometry, geoseries.GeoSeries): return geometry.values._data elif isinstance(geometry, array.GeometryArray): return geometry._data elif isinstance(geometry, BaseGeometry): return geometry elif geometry is None: return None else: return np.asarray(geometry) def nearest( self, geometry, return_all=True, max_distance=None, return_distance=False, exclusive=False, ): """ Return the nearest geometry in the tree for each input geometry in ``geometry``. If multiple tree geometries have the same distance from an input geometry, multiple results will be returned for that input geometry by default. Specify ``return_all=False`` to only get a single nearest geometry (non-deterministic which nearest is returned). In the context of a spatial join, input geometries are the "left" geometries that determine the order of the results, and tree geometries are "right" geometries that are joined against the left geometries. If ``max_distance`` is not set, this will effectively be a left join because every geometry in ``geometry`` will have a nearest geometry in the tree. However, if ``max_distance`` is used, this becomes an inner join, since some geometries in ``geometry`` may not have a match in the tree. For performance reasons, it is highly recommended that you set the ``max_distance`` parameter. Parameters ---------- geometry : {shapely.geometry, GeoSeries, GeometryArray, numpy.array of Shapely \ geometries} A single shapely geometry, one of the GeoPandas geometry iterables (GeoSeries, GeometryArray), or a numpy array of Shapely geometries to query against the spatial index. return_all : bool, default True If there are multiple equidistant or intersecting nearest geometries, return all those geometries instead of a single nearest geometry. max_distance : float, optional Maximum distance within which to query for nearest items in tree. Must be greater than 0. By default None, indicating no distance limit. return_distance : bool, optional If True, will return distances in addition to indexes. By default False exclusive : bool, optional if True, the nearest geometries that are equal to the input geometry will not be returned. By default False. Requires Shapely >= 2.0. Returns ------- Indices or tuple of (indices, distances) Indices is an ndarray of shape (2,n) and distances (if present) an ndarray of shape (n). The first subarray of indices contains input geometry indices. The second subarray of indices contains tree geometry indices. Examples -------- >>> from shapely.geometry import Point, box >>> s = geopandas.GeoSeries(geopandas.points_from_xy(range(10), range(10))) >>> s.head() 0 POINT (0 0) 1 POINT (1 1) 2 POINT (2 2) 3 POINT (3 3) 4 POINT (4 4) dtype: geometry >>> s.sindex.nearest(Point(1, 1)) array([[0], [1]]) >>> s.sindex.nearest([box(4.9, 4.9, 5.1, 5.1)]) array([[0], [5]]) >>> s2 = geopandas.GeoSeries(geopandas.points_from_xy([7.6, 10], [7.6, 10])) >>> s2 0 POINT (7.6 7.6) 1 POINT (10 10) dtype: geometry >>> s.sindex.nearest(s2) array([[0, 1], [8, 9]]) """ geometry = self._as_geometry_array(geometry) if isinstance(geometry, BaseGeometry) or geometry is None: geometry = [geometry] result = self._tree.query_nearest( geometry, max_distance=max_distance, return_distance=return_distance, all_matches=return_all, exclusive=exclusive, ) if return_distance: indices, distances = result else: indices = result if return_distance: return indices, distances else: return indices def intersection(self, coordinates): """Compatibility wrapper for rtree.index.Index.intersection, use ``query`` instead. Parameters ---------- coordinates : sequence or array Sequence of the form (min_x, min_y, max_x, max_y) to query a rectangle or (x, y) to query a point. Examples -------- >>> from shapely.geometry import Point, box >>> s = geopandas.GeoSeries(geopandas.points_from_xy(range(10), range(10))) >>> s 0 POINT (0 0) 1 POINT (1 1) 2 POINT (2 2) 3 POINT (3 3) 4 POINT (4 4) 5 POINT (5 5) 6 POINT (6 6) 7 POINT (7 7) 8 POINT (8 8) 9 POINT (9 9) dtype: geometry >>> s.sindex.intersection(box(1, 1, 3, 3).bounds) array([1, 2, 3]) Alternatively, you can use ``query``: >>> s.sindex.query(box(1, 1, 3, 3)) array([1, 2, 3]) """ # TODO: we should deprecate this # convert bounds to geometry # the old API uses tuples of bound, but Shapely uses geometries try: iter(coordinates) except TypeError: # likely not an iterable # this is a check that rtree does, we mimic it # to ensure a useful failure message raise TypeError( "Invalid coordinates, must be iterable in format " "(minx, miny, maxx, maxy) (for bounds) or (x, y) (for points). " f"Got `coordinates` = {coordinates}." ) # need to convert tuple of bounds to a geometry object if len(coordinates) == 4: indexes = self._tree.query(shapely.box(*coordinates)) elif len(coordinates) == 2: indexes = self._tree.query(shapely.points(*coordinates)) else: raise TypeError( "Invalid coordinates, must be iterable in format " "(minx, miny, maxx, maxy) (for bounds) or (x, y) (for points). " f"Got `coordinates` = {coordinates}." ) return indexes @property def size(self): """Size of the spatial index. Number of leaves (input geometries) in the index. Examples -------- >>> from shapely.geometry import Point >>> s = geopandas.GeoSeries(geopandas.points_from_xy(range(10), range(10))) >>> s 0 POINT (0 0) 1 POINT (1 1) 2 POINT (2 2) 3 POINT (3 3) 4 POINT (4 4) 5 POINT (5 5) 6 POINT (6 6) 7 POINT (7 7) 8 POINT (8 8) 9 POINT (9 9) dtype: geometry >>> s.sindex.size 10 """ return len(self._tree) @property def is_empty(self): """Check if the spatial index is empty. Examples -------- >>> from shapely.geometry import Point >>> s = geopandas.GeoSeries(geopandas.points_from_xy(range(10), range(10))) >>> s 0 POINT (0 0) 1 POINT (1 1) 2 POINT (2 2) 3 POINT (3 3) 4 POINT (4 4) 5 POINT (5 5) 6 POINT (6 6) 7 POINT (7 7) 8 POINT (8 8) 9 POINT (9 9) dtype: geometry >>> s.sindex.is_empty False >>> s2 = geopandas.GeoSeries() >>> s2.sindex.is_empty True """ return len(self._tree) == 0 def __len__(self): return len(self._tree) geopandas-1.2.0/geopandas/testing.py000066400000000000000000000255001525650651600174600ustar00rootroot00000000000000"""Testing functionality for geopandas objects.""" import warnings import pandas as pd from geopandas import GeoDataFrame, GeoSeries from geopandas.array import GeometryDtype def _isna(this): """Version of isna that works for both scalars and (Geo)Series.""" with warnings.catch_warnings(): # GeoSeries.isna will raise a warning about no longer returning True # for empty geometries. This helper is used below always in combination # with an is_empty check to preserve behaviour, and thus we ignore the # warning here to avoid it bubbling up to the user warnings.filterwarnings( "ignore", r"GeoSeries.isna\(\) previously returned", UserWarning ) if hasattr(this, "isna"): return this.isna() elif hasattr(this, "isnull"): return this.isnull() else: return pd.isnull(this) def _geom_equals_mask(this, that): """ Test for geometric equality. Empty or missing geometries are considered equal. Parameters ---------- this, that : arrays of Geo objects (or anything that has an `is_empty` attribute) Returns ------- Series boolean Series, True if geometries in left equal geometries in right """ return ( this.geom_equals(that) | (this.is_empty & that.is_empty) | (_isna(this) & _isna(that)) ) def geom_equals(this, that): """ Test for geometric equality. Empty or missing geometries are considered equal. Parameters ---------- this, that : arrays of Geo objects (or anything that has an `is_empty` attribute) Returns ------- bool True if all geometries in left equal geometries in right """ return _geom_equals_mask(this, that).all() def _geom_almost_equals_mask(this, that): """ Test for 'almost' geometric equality. Empty or missing geometries considered equal. This method allows small difference in the coordinates, but this requires coordinates be in the same order for all components of a geometry. Parameters ---------- this, that : arrays of Geo objects Returns ------- Series boolean Series, True if geometries in left almost equal geometries in right """ return ( this.geom_equals_exact(that, tolerance=0.5 * 10 ** (-6)) | (this.is_empty & that.is_empty) | (_isna(this) & _isna(that)) ) def geom_almost_equals(this, that): """ Test for 'almost' geometric equality. Empty or missing geometries considered equal. This method allows small difference in the coordinates, but this requires coordinates be in the same order for all components of a geometry. Parameters ---------- this, that : arrays of Geo objects (or anything that has an `is_empty` property) Returns ------- bool True if all geometries in left almost equal geometries in right """ if isinstance(this, GeoDataFrame) and isinstance(that, GeoDataFrame): this = this.geometry that = that.geometry return _geom_almost_equals_mask(this, that).all() def assert_geoseries_equal( left, right, check_dtype=True, check_index_type=False, check_series_type=True, check_less_precise=False, check_geom_type=False, check_crs=True, normalize=False, ): """ Test util for checking that two GeoSeries are equal. Parameters ---------- left, right : two GeoSeries check_dtype : bool, default False If True, check geo dtype [only included so it's a drop-in replacement for assert_series_equal]. check_index_type : bool, default False Check that index types are equal. check_series_type : bool, default True Check that both are same type (*and* are GeoSeries). If False, will attempt to convert both into GeoSeries. check_less_precise : bool, default False If True, use geom_equals_exact with relative error of 0.5e-6. If False, use geom_equals. check_geom_type : bool, default False If True, check that all the geom types are equal. check_crs: bool, default True If `check_series_type` is True, then also check that the crs matches. normalize: bool, default False If True, normalize the geometries before comparing equality. Typically useful with ``check_less_precise=True``, which uses ``geom_equals_exact`` and requires exact coordinate order. """ assert len(left) == len(right), f"{len(left)} != {len(right)}" if check_dtype: msg = "dtype should be a GeometryDtype, got {0}" assert isinstance(left.dtype, GeometryDtype), msg.format(left.dtype) assert isinstance(right.dtype, GeometryDtype), msg.format(left.dtype) if check_index_type: assert isinstance(left.index, type(right.index)) if check_series_type: assert isinstance(left, GeoSeries) assert isinstance(left, type(right)) if check_crs: assert left.crs == right.crs else: if not isinstance(left, GeoSeries): left = GeoSeries(left) if not isinstance(right, GeoSeries): right = GeoSeries(right, index=left.index) assert left.index.equals(right.index), f"index: {left.index} != {right.index}" if check_geom_type: assert (left.geom_type == right.geom_type).all(), ( f"type: {left.geom_type} != {right.geom_type}" ) if normalize: left = GeoSeries(left.array.normalize()) right = GeoSeries(right.array.normalize()) if not check_crs: with warnings.catch_warnings(): warnings.filterwarnings("ignore", "CRS mismatch", UserWarning) _check_equality(left, right, check_less_precise) else: _check_equality(left, right, check_less_precise) def _truncated_string(geom): """Truncate WKT repr of geom.""" s = str(geom) if len(s) > 100: return s[:100] + "..." else: return s def _check_equality(left, right, check_less_precise): assert_error_message = ( "{0} out of {1} geometries are not {3}equal.\n" "Indices where geometries are not {3}equal: {2} \n" "The first not {3}equal geometry:\n" "Left: {4}\n" "Right: {5}\n" ) if check_less_precise: precise = "almost " equal = _geom_almost_equals_mask(left, right) else: precise = "" equal = _geom_equals_mask(left, right) if not equal.all(): unequal_left_geoms = left[~equal] unequal_right_geoms = right[~equal] raise AssertionError( assert_error_message.format( len(unequal_left_geoms), len(left), unequal_left_geoms.index.to_list(), precise, _truncated_string(unequal_left_geoms.iloc[0]), _truncated_string(unequal_right_geoms.iloc[0]), ) ) def assert_geodataframe_equal( left, right, check_dtype=True, check_index_type="equiv", check_column_type="equiv", check_frame_type=True, check_like=False, check_less_precise=False, check_geom_type=False, check_crs=True, normalize=False, ): """Check that two GeoDataFrames are equal. Parameters ---------- left, right : two GeoDataFrames check_dtype : bool, default True Whether to check the DataFrame dtype is identical. check_index_type, check_column_type : bool, default 'equiv' Check that index types are equal. check_frame_type : bool, default True Check that both are same type (*and* are GeoDataFrames). If False, will attempt to convert both into GeoDataFrame. check_like : bool, default False If true, ignore the order of rows & columns check_less_precise : bool, default False If True, use geom_equals_exact. if False, use geom_equals. check_geom_type : bool, default False If True, check that all the geom types are equal. check_crs: bool, default True If `check_frame_type` is True, then also check that the crs matches. normalize: bool, default False If True, normalize the geometries before comparing equality. Typically useful with ``check_less_precise=True``, which uses ``geom_equals_exact`` and requires exact coordinate order. """ try: # added from pandas 0.20 from pandas.testing import assert_frame_equal, assert_index_equal except ImportError: from pandas.util.testing import assert_frame_equal, assert_index_equal # instance validation if check_frame_type: assert isinstance(left, GeoDataFrame) assert isinstance(left, type(right)) if check_crs: # allow if neither left and right has an active geometry column if ( left._geometry_column_name is None and right._geometry_column_name is None ): pass elif ( left._geometry_column_name not in left.columns and right._geometry_column_name not in right.columns ): pass # no crs can be either None or {} elif not left.crs and not right.crs: pass else: assert left.crs == right.crs else: if not isinstance(left, GeoDataFrame): left = GeoDataFrame(left) if not isinstance(right, GeoDataFrame): right = GeoDataFrame(right) # shape comparison assert left.shape == right.shape, ( f"GeoDataFrame shape mismatch, left: {left.shape!r}, right: {right.shape!r}.\n" f"Left columns: {left.columns!r}, right columns: {right.columns!r}" ) if check_like: left = left.reindex_like(right) # column comparison assert_index_equal( left.columns, right.columns, exact=check_column_type, obj="GeoDataFrame.columns" ) # geometry comparison for col, dtype in left.dtypes.items(): if isinstance(dtype, GeometryDtype): assert_geoseries_equal( left[col], right[col], normalize=normalize, check_dtype=check_dtype, check_less_precise=check_less_precise, check_geom_type=check_geom_type, check_crs=check_crs, ) # ensure the active geometry column is the same assert left._geometry_column_name == right._geometry_column_name # drop geometries and check remaining columns left2 = left.select_dtypes(exclude="geometry") right2 = right.select_dtypes(exclude="geometry") assert_frame_equal( left2, right2, check_dtype=check_dtype, check_index_type=check_index_type, check_column_type=check_column_type, obj="GeoDataFrame", ) geopandas-1.2.0/geopandas/tests/000077500000000000000000000000001525650651600165715ustar00rootroot00000000000000geopandas-1.2.0/geopandas/tests/__init__.py000066400000000000000000000000001525650651600206700ustar00rootroot00000000000000geopandas-1.2.0/geopandas/tests/baseline_images/000077500000000000000000000000001525650651600217005ustar00rootroot00000000000000geopandas-1.2.0/geopandas/tests/baseline_images/test_plotting/000077500000000000000000000000001525650651600245775ustar00rootroot00000000000000geopandas-1.2.0/geopandas/tests/baseline_images/test_plotting/multipolygon_with_holes.pdf000066400000000000000000000072561525650651600322730ustar00rootroot00000000000000%PDF-1.4 % 1 0 obj << /Type /Catalog /Pages 2 0 R >> endobj 8 0 obj << /Font 3 0 R /XObject 7 0 R /ExtGState 4 0 R /Pattern 5 0 R /Shading 6 0 R /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 11 0 obj << /Type /Page /Parent 2 0 R /Resources 8 0 R /MediaBox [ 0 0 378.65625 378.65625 ] /Contents 9 0 R /Annots 10 0 R >> endobj 9 0 obj << /Length 12 0 R /Filter /FlateDecode >> stream x0E)biQO\a`;grr )@}(ܡ!^_?v/?\g~d>i7UĮ@EKpz|˿ujh˧"km8,3=3cQ\s%a$<;*14ny&i-5D,+魉z"jͭIB0,:A ~' [D͎l65ڋARfӈذi5e#zNxٺ0UY6i@MKIu]r:w~nSK[,sj 3#pR\N\1%E Ș- ]pG}͆l`tPh$JÝVrxS/m9v?;q鳝G'?f. endstream endobj 12 0 obj 552 endobj 10 0 obj [ ] endobj 17 0 obj << /Length 89 /Filter /FlateDecode >> stream x5M0 =GHQ[4Nk/!/ *HtD g$ï,ms%YLAo?ݎ endstream endobj 18 0 obj << /Length 141 /Filter /FlateDecode >> stream x=0C @)|Oj#K zcÆ`%Tk@%7ș`Gzb8\f}B%h{Siܦq5)ꜣg44s {Sw. endstream endobj 15 0 obj << /Type /Font /BaseFont /BMQQDV+DejaVuSans /FirstChar 0 /LastChar 255 /FontDescriptor 14 0 R /Subtype /Type3 /Name /BMQQDV+DejaVuSans /FontBBox [ -1021 -463 1794 1233 ] /FontMatrix [ 0.001 0 0 0.001 0 0 ] /CharProcs 16 0 R /Encoding << /Type /Encoding /Differences [ 120 /x /y ] >> /Widths 13 0 R >> endobj 14 0 obj << /Type /FontDescriptor /FontName /BMQQDV+DejaVuSans /Flags 32 /FontBBox [ -1021 -463 1794 1233 ] /Ascent 929 /Descent -236 /CapHeight 0 /XHeight 0 /ItalicAngle 0 /StemV 0 /MaxWidth 1342 >> endobj 13 0 obj [ 600 600 600 600 600 600 600 600 600 600 600 600 600 600 600 600 600 600 600 600 600 600 600 600 600 600 600 600 600 600 600 600 318 401 460 838 636 950 780 275 390 390 500 838 318 361 318 337 636 636 636 636 636 636 636 636 636 636 337 337 838 838 838 531 1000 684 686 698 770 632 575 775 752 295 295 656 557 863 748 787 603 787 695 635 611 732 684 989 685 611 685 390 337 390 838 500 500 613 635 550 635 615 352 635 634 278 278 579 278 974 634 612 635 635 411 521 392 634 592 818 592 592 525 636 337 636 838 600 636 600 318 352 518 1000 500 500 500 1342 635 400 1070 600 685 600 600 318 318 518 518 590 500 1000 500 1000 521 400 1023 600 525 611 318 401 636 636 636 636 337 500 500 1000 471 612 838 361 1000 500 500 838 401 401 500 636 636 318 500 401 471 612 969 969 969 531 684 684 684 684 684 684 974 698 632 632 632 632 295 295 295 295 775 748 787 787 787 787 787 838 787 732 732 732 732 611 605 630 613 613 613 613 613 613 982 550 615 615 615 615 278 278 278 278 612 634 612 612 612 612 612 838 612 634 634 634 634 592 635 592 ] endobj 16 0 obj << /x 17 0 R /y 18 0 R >> endobj 3 0 obj << /F1 15 0 R >> endobj 4 0 obj << /A1 << /Type /ExtGState /CA 0 /ca 1 >> /A2 << /Type /ExtGState /CA 1 /ca 1 >> >> endobj 5 0 obj << >> endobj 6 0 obj << >> endobj 7 0 obj << >> endobj 2 0 obj << /Type /Pages /Kids [ 11 0 R ] /Count 1 >> endobj 19 0 obj << >> endobj xref 0 20 0000000000 65535 f 0000000016 00000 n 0000003197 00000 n 0000003003 00000 n 0000003035 00000 n 0000003134 00000 n 0000003155 00000 n 0000003176 00000 n 0000000065 00000 n 0000000342 00000 n 0000000989 00000 n 0000000208 00000 n 0000000969 00000 n 0000001908 00000 n 0000001701 00000 n 0000001384 00000 n 0000002961 00000 n 0000001009 00000 n 0000001170 00000 n 0000003257 00000 n trailer << /Size 20 /Root 1 0 R /Info 19 0 R >> startxref 3279 %%EOF geopandas-1.2.0/geopandas/tests/baseline_images/test_plotting/multipolygon_with_holes.png000066400000000000000000000404631525650651600323030ustar00rootroot00000000000000PNG  IHDR))#:tEXtSoftwareMatplotlib version3.10.8, https://matplotlib.org/[u pHYs.#.#x?v@IDATxMWt2S So7J,W)Ъh+Q… ڝuՅOEQDA:[ꈟ5: Nos&y3ޙ^΄g3OLgslnnn]vp_`"R H@B")H$D !R H@B")H$D !R H@B")H$D м,Wŋ'}GjLxg.y߱cbLbuuSw*>)H$I1Mݚ`GM\|mmm,--90'\0͞n?F\ c|?cE'ӓc.{Oz):e x?TM׭H {AWdyk|`~~a/ߓ^,$D !R H@B")H$D !R H@byYciiilnnf0\3H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B 'g[vH1666L.--x`g<6x`q uhz lx܅Q)nnnX`fP0ZGU'px2ZlpyfR̩\;=81*"Ŝ{ݩSѣGa>VWW'H-xl{aMJwٓH@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H,4/ pyN*0+bNmllř]ZZ0J"ŜZYY [<'p8<8TyM03)X^^;|1cs,ueawc+Ŝ{}c{g3fPoifxj`V@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B"#ӧ!1R7yӦz%{r6&858nd;\EmO=xGkkk̮~>LJ>q᱗~Ț>Y#YZZڡD J3̮w;׿/~ozӛ~!k\1H1I۞]_?ή؞]qС1D rҰGΝs`"5я~۳+?{`zoɓ'w1nd{cx@;x"?ύ|+ͤoxg>wk܈l ͨ8{S/`}}}fbaQ?W}@{Wy{}~k?X98?㡇vx;1ooԶ)HL<//Q1=~5?yY0b ǻWj<׿%{PLϏ;6/w[կ~1m*ǟq=g}6`H~kq]wmm=kb{)niki#ӧOO|/}^L31o=_H1ʼnǏ|/yKkM{RL`GiVÃ>8CS'?YN^ BD !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B"Xh^k1g:vii?gbN|掎-2{fR̩\g:̙3״rϵ)Դ%z… 3S,$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D м,j}}I9sᱰ.lslNCҡC?<>3gΌ[oՙ=*PfT8c駟c7ؓ`;wn={9=;+=)O׃.k2\_KKK3w…Q)֑1 ׃=)H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B"Ūrw$˨H¨!P !2 EEPV]xct.0,B/\tGEl2i9ur)))))))))))))))))))))))))))))))))))))))))vLO#Uu*=ܵnK`)UճUuڌUfU=a)vOh@qiN>W9D zœ3W>L "9R_ٯ0#R 7Ә))))))))))))))))))))))))))))))))vLwCCC!\50]D yivO@HA\[Us%Lm"=UMEgL} GUuʌ %0=6soh )))))))))))))).bxxZV_____r@HD@HD@HD@HD@HD@HD@D;sYI$B_89Pd$?7<6`rVRS֭[o^|kݺuuĉ?{&Dt̞=⋺kժU{yjG tXIǏ뻁^kEǮ]j>ڶm[dxVR}kllYv={ߡC소Yf5.]1c%з:+&(nz뭷&-[tZvm>}gc&Ke˖Hsn>C=Tqիw٘ΩSE/|@1cƌzoۻwoر㊏@y?[~bŊfeEǜ9sU/c&+oƸ@q7ݻ/)Pt ͛ǿkY~+2^⬤""D B"D B"D B"D B"D B"D B"D B"D B"D B"D B"D B"D B"D B"D B"D B"D B"D B"D B*?0HIENDB`geopandas-1.2.0/geopandas/tests/baseline_images/test_plotting/polygon_with_holes.pdf000066400000000000000000000071701525650651600312130ustar00rootroot00000000000000%PDF-1.4 % 1 0 obj << /Type /Catalog /Pages 2 0 R >> endobj 8 0 obj << /Font 3 0 R /XObject 7 0 R /ExtGState 4 0 R /Pattern 5 0 R /Shading 6 0 R /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 11 0 obj << /Type /Page /Parent 2 0 R /Resources 8 0 R /MediaBox [ 0 0 378.65625 378.65625 ] /Contents 9 0 R /Annots 10 0 R >> endobj 9 0 obj << /Length 12 0 R /Filter /FlateDecode >> stream xTMO0ϯ#{[[!О%*Ej+mc'g&#˳2a#dTm`pG~\ʏ U-}ճfgj K7+[.NQ{Ҭ98,ryQy/|liǽhS{?.as#.]&ʚPP}^[J_L;pru(|Hw5[f>:%Cm1e =i\"(kkUOb-פ#i f s].fP&K3Tj13nD\AffK&\,M8?+Us1eElд4ryhx«{ka CґsSldq+.BqIW.aQ7,:^ɋnky fvH:18Li,'t/pȑ i3Ǡfgn  endstream endobj 12 0 obj 498 endobj 10 0 obj [ ] endobj 17 0 obj << /Length 89 /Filter /FlateDecode >> stream x5M0 =GHQ[4Nk/!/ *HtD g$ï,ms%YLAo?ݎ endstream endobj 18 0 obj << /Length 141 /Filter /FlateDecode >> stream x=0C @)|Oj#K zcÆ`%Tk@%7ș`Gzb8\f}B%h{Siܦq5)ꜣg44s {Sw. endstream endobj 15 0 obj << /Type /Font /BaseFont /BMQQDV+DejaVuSans /FirstChar 0 /LastChar 255 /FontDescriptor 14 0 R /Subtype /Type3 /Name /BMQQDV+DejaVuSans /FontBBox [ -1021 -463 1794 1233 ] /FontMatrix [ 0.001 0 0 0.001 0 0 ] /CharProcs 16 0 R /Encoding << /Type /Encoding /Differences [ 120 /x /y ] >> /Widths 13 0 R >> endobj 14 0 obj << /Type /FontDescriptor /FontName /BMQQDV+DejaVuSans /Flags 32 /FontBBox [ -1021 -463 1794 1233 ] /Ascent 929 /Descent -236 /CapHeight 0 /XHeight 0 /ItalicAngle 0 /StemV 0 /MaxWidth 1342 >> endobj 13 0 obj [ 600 600 600 600 600 600 600 600 600 600 600 600 600 600 600 600 600 600 600 600 600 600 600 600 600 600 600 600 600 600 600 600 318 401 460 838 636 950 780 275 390 390 500 838 318 361 318 337 636 636 636 636 636 636 636 636 636 636 337 337 838 838 838 531 1000 684 686 698 770 632 575 775 752 295 295 656 557 863 748 787 603 787 695 635 611 732 684 989 685 611 685 390 337 390 838 500 500 613 635 550 635 615 352 635 634 278 278 579 278 974 634 612 635 635 411 521 392 634 592 818 592 592 525 636 337 636 838 600 636 600 318 352 518 1000 500 500 500 1342 635 400 1070 600 685 600 600 318 318 518 518 590 500 1000 500 1000 521 400 1023 600 525 611 318 401 636 636 636 636 337 500 500 1000 471 612 838 361 1000 500 500 838 401 401 500 636 636 318 500 401 471 612 969 969 969 531 684 684 684 684 684 684 974 698 632 632 632 632 295 295 295 295 775 748 787 787 787 787 787 838 787 732 732 732 732 611 605 630 613 613 613 613 613 613 982 550 615 615 615 615 278 278 278 278 612 634 612 612 612 612 612 838 612 634 634 634 634 592 635 592 ] endobj 16 0 obj << /x 17 0 R /y 18 0 R >> endobj 3 0 obj << /F1 15 0 R >> endobj 4 0 obj << /A1 << /Type /ExtGState /CA 0 /ca 1 >> /A2 << /Type /ExtGState /CA 1 /ca 1 >> >> endobj 5 0 obj << >> endobj 6 0 obj << >> endobj 7 0 obj << >> endobj 2 0 obj << /Type /Pages /Kids [ 11 0 R ] /Count 1 >> endobj 19 0 obj << >> endobj xref 0 20 0000000000 65535 f 0000000016 00000 n 0000003143 00000 n 0000002949 00000 n 0000002981 00000 n 0000003080 00000 n 0000003101 00000 n 0000003122 00000 n 0000000065 00000 n 0000000342 00000 n 0000000935 00000 n 0000000208 00000 n 0000000915 00000 n 0000001854 00000 n 0000001647 00000 n 0000001330 00000 n 0000002907 00000 n 0000000955 00000 n 0000001116 00000 n 0000003203 00000 n trailer << /Size 20 /Root 1 0 R /Info 19 0 R >> startxref 3225 %%EOF geopandas-1.2.0/geopandas/tests/baseline_images/test_plotting/polygon_with_holes.png000066400000000000000000000400031525650651600312160ustar00rootroot00000000000000PNG  IHDR))#:tEXtSoftwareMatplotlib version3.10.8, https://matplotlib.org/[u pHYs.#.#x?v?oIDATxK]g J"֖ [>͡%Hx[TUA"ꤠu$^NH2Ђ &%z>֡ ׯ$e3_;1R}~CvRX~7wq\uU[b?׾6zի}nxzG3?7yӛ޴5bq_smsy;1?O+)N׼fo_W뮛0>`=t`#œͮؽ{fW>|x>oͮ[?i5W_}fWk^Wε^;wy;v:']y}l~_~yKX~g?{w/~zvL8ݫ_75VXB|e^^]ʎ}{g/ڧt]̦Xf^_Wh`H?i㎹ꪫ|;ߙ'N%H\r%󖷼ek3w~yK^2?ïG%.|ߝ7?no{*@;~?aoo~󛭈׾1Aooqyne#'?9W_}p sw5qr)/xksЇ>4x3l׮][-e垮SaVΝݳBDe_YYrz߽xֳ?{ٚm׿uc˒Oo}[Wk%"ŲDӗ2aqM'C_?z oؚET\~[{U|K_G9'_ bIgM\vews+N/=-N:}m`"'>Y/}Vx6\rY9;Jܕ?'37tӼ}Eg1)H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R HnN j913փe޶G5Hgx޳<`gkf>03kҙsfnz۸)VmyP;Fo_fٓ',dp!XEs .ڥ^v63)Vֽ3sM=cĜ٤ܥ[wSМ\g{\V{KCW\&ր:7?Ĝ\@eAp"^5w\xJ@o>-?.{pٓH@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R HnN :zh=` be=<3ؽgy,:بƽHnͳ8zOUdO e3sW\5 Lu\SX&̱z0ZZ+{hmܳ=~[Y{-'7̍3W,3(m87{/;~bW ʞ@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B")H$D !R H@B"w/6w 0RqD~t) %YR\Hn(r(!BnDQNpWHxfN{ccFdy^Z{jO}m}!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!R"!RoJ)M|P/?@P/"!R"!R"!R"!R"!R"!R"ќy[C/g)o>y%ߤ<Vw5~^nD@HD4eޚrvs}-ݺiLyC>yȑ>GZ@D@HD@HD@HD@HD@HD@HD@HD@HD@HD@HD@HD@HD@HD@HD@HD@HD@HD@HD@HD@HD@HD4g[n]sNm{YYdIvNyqYlY-PTڵK hX{-G^Z*a(*ⳅ : hhw-&M*o߾Oqܹm>ۼysٸqcm%KgϞV|N ]|TCDEϞ=˼yʩSjϝ;wnu\1Pa'О>}Z.]Z ;w!C;sLٵkWd@{vR 3ٳǏ{իWk!GչI&}I4ʎ/СCˁ:ڭ@BֵE@ ke@K7i? JGQE@t[h' 'l?^`AE@ B.Nw,[M%@ L|>@nD9 5""?@@Q: Ǝ|?@Q@ *p@ J@ \J-@,G@ B&t1@&3H@ (J8I@x[I9@ 2b,D3@*;E@ tpS@S@ 9y$^@n0@ 2A@"  "R.@!gUuG@ _,gf*zq=e&;@ C&t}*5@{VUE@ =YN+{.@ O>E@J0'@ Gj*~{lY@Ju@! & `@3A@" ~߿y0'b*@# :6Un5@w+3E@$ /XM6N94*@%  Y@/0+@& J\)\=@: ' ?0NP@O*@( ϊN\A#,@) 6[OQM1@* YL@P@Ө*4+ *NZ(@, H'pJI@ms<:@- OUN;ۣ71@. L._sA/ [enq?|S@0 f[$@nfB@1 S.K@]xw8@2  1M@nnB@3 a1H<@5hCYS.4 u' ?@#15 Z2e_@(h!6 w! e@.ڼ17  Uu+:H>5,@8 ?s1aM*85@9 Az5p. P@: ,xRY1\2@; jJ@@Ocd?< uDaKӳj!W@= $l.@ +@> v=1Q@M;GC@? 2 RLfgiJ9@ +NU'8P;WO(@A _$Y,x*7!c @B yz )ER@ 59=@@C Њ^#TU@];@D mb::@18=E cV<:<@8y9F KO(fb@hdF."G  {dc@8@"H :V)=S!@I l$d{XE=A@J c<@G@K s)K@@L>, 9L gF@j@M D2{l0M@JΕ7@N bS@@>U@O ȞZ@|5QG@P V5Fc1@@v6Q Y@@34"@R jX\2/Ff'@S m?t6A@<`?@T XBQ9@"xWK@U GU8@?ayL@V G TR@V;pE@W [/ɷ{;@zϒQ=X @G@m8)2Y +l SKWD˿Z UI UcSY#@[ QuMVQYh+@\ J<1Sׯ*!1@] 1*.@Nc:(@^ b !@=@_ L$wwC@mr.@` X)N/@ZfF@a .`8@mKM@b rl=C@@Ttr+c ^3١VDh?-@d lscz"@P4?e Xw"Nf@Y|T "2f ]l[}QR@g d _/}r 2@h E*- OCM)@i [A@$@@j 9dF@Q= D@k  `Q@5#/I@l CfY@-;,y1@m ~NÐ.@vin eF]+7"#@o ڃZͫ:F@p CRŚF@q xFNw4@p" iF@r ;\@(@s QQPPI} 3t A)Z1VwD@1@u > 2@+wqw@v .f'@gG@w @P3@MD@x zI|AF@ oD@y ^H@b@ 2D@z  B:Z@Z('@{ < Q&芭~0| V]<@ !s@} trP7@͢WE@~ )$;@ 9rJ@ hV@y;@ 8iu9@v1t8 x b@#G:A (<=mA(@ ?f b2@oE@ ŐwX@a&3@ EHe)tu#5  \F@ A#@ LD DK@!Lnw ؝Q)@K_ ? \xe뚼+ s.)-@R?o*A@ ӑ`R@QGw*@ ׀rA@˻E?@ ʴGE7pY-@ JgVSgO Y9@ ?t@@AҧUA@ z+e@ҙ{bD QCFvZ@j 5@ Jɼdn@@C@ 3@ߓ=?G@ ڧ1F@S.@ Z:@Zo}7F@ RI&&B@ߚ@@@ N*K"w\C@ ak[9 D@@$./@ N8~%@f6#ޕM@ O_g^5@${6J@ ,p_@C@ CC@He81 yy%!qaJ@ Ѓm%ϭ!B@ 0lY@B2M @ "nTGG"7@ :,@40! I@ ]c5G@V%_=@ {QGx2@ d rr˿qN`:5@ ɳ*\*@ ףp=r@@ ObA@I| @@  8@?HN@ Xc()@LK@ 8%J@ #4GB/ i=RS@ jI@ ]HV@j7@ ޝw*@V4! c@aB@ g? X@~k 0@  ^bB@ ?Z}:yC@ 1וLWOCjĽ=@ lcwTIWa 9@ jxqUQI@@ ]T7UZ)D@ ~кPi%@ ,V4ƒ>@1W7I@ DK@Cw79@ $e4QQ@D@ >tA}{ ų_s3D@ c@5G@ 0? 2@_z}M@ R< Y@`ƹ+@ X\TCSN( Py1Mo-@ 2/<@[+: Ya@}>ʈ -J@ 3('y @@ ; ۿ_@{B@ Fü>^@K^S6-@ Y>YoKS9@ *@$' CJ@ U@_,gJE@ %n_Q[Z@i.>@ Mq`@uXA@ lD.@]O xLS@ y<@ QcS@wH1@)@ g;7@keB@ *2F@蘘@@ YYC@d "@ q$I@MAA@ gs^'F;?H@ SXE@ Od\7MJN+NA :ߦKQ@3bBA@ \]0@7H@ d#b@yB ._d^@_, 9@ ^je@5[lB ؎]LA@ 1@S 1dsC@ xֹR"\\D@ oan?I@ N74j<@>@D@ 88\G@/mn8@ H4n2@ >(@ IB@-K@ |X9nq3@  h @Vy@ 3Nn(@ôoD@ sa:]@4kC@ X6sHhB@ : S*Z@r kYcuRed@ `e~??@uI >@ [^@t7?@ swa@ȣeA@ a 8R@%73@ ]0@mH@ ۡQãX@ kV@"eϑ6@ f{G&E?WE6 ZSj@PG7 0b@)@ :8}Y@6 L Z h v             , : H V d r             ( 6 D R ` n |            $ 2 @ N \ j x             . < J X f t            * 8 F T b p ~  & 4 B P ^ l z   " 0 > L Z h v    , : H V d r  ( 6 D R ` n geopandas-1.2.0/geopandas/tests/data/naturalearth_lowres/000077500000000000000000000000001525650651600235675ustar00rootroot00000000000000geopandas-1.2.0/geopandas/tests/data/naturalearth_lowres/naturalearth_lowres.cpg000066400000000000000000000000121525650651600303400ustar00rootroot00000000000000ISO-8859-1geopandas-1.2.0/geopandas/tests/data/naturalearth_lowres/naturalearth_lowres.dbf000066400000000000000000001421551525650651600303410ustar00rootroot00000000000000z pop_estNcontinentCPnameCPiso_a3CPgdp_md_estN 889953.000000000000000Oceania Fiji FJI 5496 58005463.000000000000000Africa Tanzania TZA 63177 603253.000000000000000Africa W. Sahara ESH 907 37589262.000000000000000North America Canada CAN 1736425 328239523.00000000000000North America United States of America USA 21433226 18513930.000000000000000Asia Kazakhstan KAZ 181665 33580650.000000000000000Asia Uzbekistan UZB 57921 8776109.000000000000000Oceania Papua New Guinea PNG 24829 270625568.00000000000000Asia Indonesia IDN 1119190 44938712.000000000000000South America Argentina ARG 445445 18952038.000000000000000South America Chile CHL 282318 86790567.000000000000000Africa Dem. Rep. Congo COD 50400 10192317.300000000745058Africa Somalia SOM 4719 52573973.000000000000000Africa Kenya KEN 95503 42813238.000000000000000Africa Sudan SDN 30513 15946876.000000000000000Africa Chad TCD 11314 11263077.000000000000000North America Haiti HTI 14332 10738958.000000000000000North America Dominican Rep. DOM 88941 144373535.00000000000000Europe Russia RUS 1699876 389482.000000000000000North America Bahamas BHS 13578 3398.000000000000000South America Falkland Is. FLK 282 5347896.000000000000000Europe Norway NOR 403336 56225.000000000000000North America Greenland GRL 3051 140.000000000000000Seven seas (open ocean) Fr. S. Antarctic Lands ATF 16 1293119.000000000000000Asia Timor-Leste TLS 2017 58558270.000000000000000Africa South Africa ZAF 351431 2125268.000000000000000Africa Lesotho LSO 2376 127575529.00000000000000North America Mexico MEX 1268870 3461734.000000000000000South America Uruguay URY 56045 211049527.00000000000000South America Brazil BRA 1839758 11513100.000000000000000South America Bolivia BOL 40895 32510453.000000000000000South America Peru PER 226848 50339443.000000000000000South America Colombia COL 323615 4246439.000000000000000North America Panama PAN 66800 5047561.000000000000000North America Costa Rica CRI 61801 6545502.000000000000000North America Nicaragua NIC 12520 9746117.000000000000000North America Honduras HND 25095 6453553.000000000000000North America El Salvador SLV 27022 16604026.000000000000000North America Guatemala GTM 76710 390353.000000000000000North America Belize BLZ 1879 28515829.000000000000000South America Venezuela VEN 482359 782766.000000000000000South America Guyana GUY 5173 581363.000000000000000South America Suriname SUR 3697 67059887.000000000000000Europe France FRA 2715518 17373662.000000000000000South America Ecuador ECU 107435 3193694.000000000000000North America Puerto Rico PRI 104988 2948279.000000000000000North America Jamaica JAM 16458 11333483.000000000000000North America Cuba CUB 100023 14645468.000000000000000Africa Zimbabwe ZWE 21440 2303697.000000000000000Africa Botswana BWA 18340 2494530.000000000000000Africa Namibia NAM 12366 16296364.000000000000000Africa Senegal SEN 23578 19658031.000000000000000Africa Mali MLI 17279 4525696.000000000000000Africa Mauritania MRT 7600 11801151.000000000000000Africa Benin BEN 14390 23310715.000000000000000Africa Niger NER 12911 200963599.00000000000000Africa Nigeria NGA 448120 25876380.000000000000000Africa Cameroon CMR 39007 8082366.000000000000000Africa Togo TGO 5490 30417856.000000000000000Africa Ghana GHA 66983 25716544.000000000000000Africa Cte d'Ivoire CIV 58539 12771246.000000000000000Africa Guinea GIN 12296 1920922.000000000000000Africa Guinea-Bissau GNB 1339 4937374.000000000000000Africa Liberia LBR 3070 7813215.000000000000000Africa Sierra Leone SLE 4121 20321378.000000000000000Africa Burkina Faso BFA 15990 4745185.000000000000000Africa Central African Rep. CAF 2220 5380508.000000000000000Africa Congo COG 12267 2172579.000000000000000Africa Gabon GAB 16874 1355986.000000000000000Africa Eq. Guinea GNQ 11026 17861030.000000000000000Africa Zambia ZMB 23309 18628747.000000000000000Africa Malawi MWI 7666 30366036.000000000000000Africa Mozambique MOZ 15291 1148130.000000000000000Africa eSwatini SWZ 4471 31825295.000000000000000Africa Angola AGO 88815 11530580.000000000000000Africa Burundi BDI 3012 9053300.000000000000000Asia Israel ISR 394652 6855713.000000000000000Asia Lebanon LBN 51991 26969307.000000000000000Africa Madagascar MDG 14114 4685306.000000000000000Asia Palestine PSE 16276 2347706.000000000000000Africa Gambia GMB 1826 11694719.000000000000000Africa Tunisia TUN 38796 43053054.000000000000000Africa Algeria DZA 171091 10101694.000000000000000Asia Jordan JOR 44502 9770529.000000000000000Asia United Arab Emirates ARE 421142 2832067.000000000000000Asia Qatar QAT 175837 4207083.000000000000000Asia Kuwait KWT 134628 39309783.000000000000000Asia Iraq IRQ 234094 4974986.000000000000000Asia Oman OMN 76331 299882.000000000000000Oceania Vanuatu VUT 934 16486542.000000000000000Asia Cambodia KHM 27089 69625582.000000000000000Asia Thailand THA 543548 7169455.000000000000000Asia Laos LAO 18173 54045420.000000000000000Asia Myanmar MMR 76085 96462106.000000000000000Asia Vietnam VNM 261921 25666161.000000000000000Asia North Korea PRK 40000 51709098.000000000000000Asia South Korea KOR 1646739 3225167.000000000000000Asia Mongolia MNG 13996 1366417754.0000000000000Asia India IND 2868929 163046161.00000000000000Asia Bangladesh BGD 302571 763092.000000000000000Asia Bhutan BTN 2530 28608710.000000000000000Asia Nepal NPL 30641 216565318.00000000000000Asia Pakistan PAK 278221 38041754.000000000000000Asia Afghanistan AFG 19291 9321018.000000000000000Asia Tajikistan TJK 8116 6456900.000000000000000Asia Kyrgyzstan KGZ 8454 5942089.000000000000000Asia Turkmenistan TKM 40761 82913906.000000000000000Asia Iran IRN 453996 17070135.000000000000000Asia Syria SYR 98830 2957731.000000000000000Asia Armenia ARM 13672 10285453.000000000000000Europe Sweden SWE 530883 9466856.000000000000000Europe Belarus BLR 63080 44385155.000000000000000Europe Ukraine UKR 153781 37970874.000000000000000Europe Poland POL 595858 8877067.000000000000000Europe Austria AUT 445075 9769949.000000000000000Europe Hungary HUN 163469 2657637.000000000000000Europe Moldova MDA 11968 19356544.000000000000000Europe Romania ROU 250077 2786844.000000000000000Europe Lithuania LTU 54627 1912789.000000000000000Europe Latvia LVA 34102 1326590.000000000000000Europe Estonia EST 31471 83132799.000000000000000Europe Germany DEU 3861123 6975761.000000000000000Europe Bulgaria BGR 68558 10716322.000000000000000Europe Greece GRC 209852 83429615.000000000000000Asia Turkey TUR 761425 2854191.000000000000000Europe Albania ALB 15279 4067500.000000000000000Europe Croatia HRV 60752 8574832.000000000000000Europe Switzerland CHE 703082 619896.000000000000000Europe Luxembourg LUX 71104 11484055.000000000000000Europe Belgium BEL 533097 17332850.000000000000000Europe Netherlands NLD 907050 10269417.000000000000000Europe Portugal PRT 238785 47076781.000000000000000Europe Spain ESP 1393490 4941444.000000000000000Europe Ireland IRL 388698 287800.000000000000000Oceania New Caledonia NCL 10770 669823.000000000000000Oceania Solomon Is. SLB 1589 4917000.000000000000000Oceania New Zealand NZL 206928 25364307.000000000000000Oceania Australia AUS 1396567 21803000.000000000000000Asia Sri Lanka LKA 84008 1397715000.0000000000000Asia China CHN 14342903 23568378.000000000000000Asia Taiwan TWN 1127000 60297396.000000000000000Europe Italy ITA 2003576 5818553.000000000000000Europe Denmark DNK 350104 66834405.000000000000000Europe United Kingdom GBR 2829108 361313.000000000000000Europe Iceland ISL 24188 10023318.000000000000000Asia Azerbaijan AZE 48047 3720382.000000000000000Asia Georgia GEO 17477 108116615.00000000000000Asia Philippines PHL 376795 31949777.000000000000000Asia Malaysia MYS 364681 433285.000000000000000Asia Brunei BRN 13469 2087946.000000000000000Europe Slovenia SVN 54174 5520314.000000000000000Europe Finland FIN 269296 5454073.000000000000000Europe Slovakia SVK 105079 10669709.000000000000000Europe Czechia CZE 250680 6081196.000000000000000Africa Eritrea ERI 2065 126264931.00000000000000Asia Japan JPN 5081769 7044636.000000000000000South America Paraguay PRY 38145 29161922.000000000000000Asia Yemen YEM 22581 34268528.000000000000000Asia Saudi Arabia SAU 792966 4490.000000000000000Antarctica Antarctica ATA 898 326000.000000000000000Asia N. Cyprus CYN 3600 1198575.000000000000000Asia Cyprus CYP 24948 36471769.000000000000000Africa Morocco MAR 119700 100388073.00000000000000Africa Egypt EGY 303092 6777452.000000000000000Africa Libya LBY 52091 112078730.00000000000000Africa Ethiopia ETH 95912 973560.000000000000000Africa Djibouti DJI 3324 5096159.000000000000000Africa Somaliland SOL 17836 44269594.000000000000000Africa Uganda UGA 35165 12626950.000000000000000Africa Rwanda RWA 10354 3301000.000000000000000Europe Bosnia and Herz. BIH 20164 2083459.000000000000000Europe North Macedonia MKD 12547 6944975.000000000000000Europe Serbia SRB 51475 622137.000000000000000Europe Montenegro MNE 5542 1794248.000000000000000Europe Kosovo -99 7926 1394973.000000000000000North America Trinidad and Tobago TTO 24269 11062113.000000000000000Africa S. Sudan SSD 11998geopandas-1.2.0/geopandas/tests/data/naturalearth_lowres/naturalearth_lowres.prj000066400000000000000000000002211525650651600303640ustar00rootroot00000000000000GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]]geopandas-1.2.0/geopandas/tests/data/naturalearth_lowres/naturalearth_lowres.shp000066400000000000000000005410101525650651600303710ustar00rootroot00000000000000' afVf@VIT@f6oI2f@.!X0f@r2/0f@C"0kf@q%0fw3Wf@)1@MSf@ =U0фlcf@V o02w;mf@y} a0f@r2/0{k`Df@h:;1$Kf@ACW1XVf@*1̯Qf@&23Y=f@6oI2n4,f@'"*24 )f@ 1û\w5f@2da1{k`Df@h:;1sbyf.!X0e= [}fjL t0fC"0fr2/0sbyf.!X09 W=@+q'h(D@gfffff4Kά@@gfffffK A@X`nB@Opy)B@|j nLOXC@ۧ1^C@â@)^fC@@jQC@(\p(C@@fffffmC@^)8߄BC@) p*ߗC@GĔ h C@ 9"$(~C@`|a2$2(D@Z$h(D@@cZ$ C@3%S,6C@ &@nB@מ&@w;SB@4#'%8cB@e6g0'ƒmAB@+q'ؓA@M&@GA@@ 'p= #A@`Q$8^m@@7)c#H%@@ O"063a@@NCv"R@@:2X!x3l?@| D+!Adb(?@?%l0!-Gq>@mA 9ԧq>@ s )23>@`=Q`m=@~cjk=@ر=@'S9 W=@aѦ@\0=@+?%>@jH\k>@Fɫs P̒>@ B5>@g?RDFPh˹>@|yviV4@Z8O K@`OH >@ILib07>@?Ɇ.tJk>@,P'>@ǘ;.Q?@~ pKά@@gfffff["<1X4@ 2T! ;@_'T! ;@ 2T!;@wi^!8|Oe;@,_!,ʂ9@H5W'K59@h8'@7h_7@!)`H7@Za<*n6@6?)$S5@tp^0TU5@["<1X4@@@::1l5@ 1 ;k5@$}-S'5@蹏B-Íf5@Q@/ WNlQ@鷯V!T_Q@ZKVUt:uP@n/iUCԷP@YU$]3P@0dudUo%;62Q@gaU_^}txQ@GɫsUsQ@2YاT jQ@OQTS"^JQ@NTjWV*Q@d}TJ_{Q@q $ PT7P@;SXTBCP@bE T?ޫVP@4 /UPkwP@SW>qU P@kMSUP@@UALMP@0U1P@QV@RDUP@߉Y/zVG?P@pB!V O@)'UHVfI)zO@[='VjO@ JWD )O@/>:uWtFsN@BsFW|\N@ݵ|ЫW6 ryM@>MWdM@w0W89@L@W%L@sVdpL@M֨BV4)mL@tqSq%;6aN@DֆS*?(O@jHZSû\wFO@|IcRkw#O@êRB}˜.O@!$ uR:p8O@[[%:R6 r O@1ZGUQz6>N@5vQ[ɎN@peQjH܇N@|gQQN@.!lRQ?ŊzM@-QS"fM@Pe6$M@)?Pg67bM@vݰOPl&lsM@#EdX%PI*N@_I O՚M@hUM@OhbM@J&²NAf{L@O}:NHPs+L@;NYBK@NM <K@}kLXyK@{/LLx&1PK@wL5J@Օ<LX>J@ƧK(J@^xK$zJ@kjZ_4L|/LI@=>LbI@$@M-cM~:I@ K<N9]I@1NnQfL I@H@3.}QS:XG@4OpQe-iG@?RDUQf67~G@)Qfffff&H@[[P~k H@ÙCPڔ+˝H@[| P _H@ecGPu H@#G3P^G@g\8P'6׆G@\].O5F@/N͗`F@6 ףp}E@!ψT0KeE@`a:靕TQ8F@>uYBTml{F@TDTPI`F@ T80QF@ }nTavG@/sLTavG@Up @#G@rq| U =읐AG@ $(~UJR4G@pV}&U(D8G@"UlDG@ 1UpQG@Y8U"5sG@q-QiUV!0G@XAUvG@1%UQG@3V@&H@zwQVϮH@hfffffVzGH@QV(\"H@)\VRH@أp= 'W9H@ā,`hWhaO;MH@7W=UH@)\WQkH@Hw;SW Ac̱H@N`Wx/H@1.WH@5YNXH@)YH@ mZJiH@43333ZH@43333[H@@\H@a]H@S:XA]H@^H@(\µ^H@ AJT|гY9O@UDT̯uO@: xTNGsO@sh|yT~ZO@'lscT@o*RaO@_TUO@ AJT|гY9O@@Sb3R@8TRUR@`^5T ]lR@T@pR@S@iR@(\ShdFR@S4R@TS@6R@ S 8R@S@/R@@Sb3R@n/TUW> O@1 {S6>W[1O@QHS5 .O@ۧ1S_vOO@S7N@Y0_TgN@ŏ1w-T[%XO@n/TUW> O@^/d7gWUmFR@_ WNR@>ynWz歪R@'4X-^R@|+S7xXRV.R@1sWlR@Bf~W=xcR@^/d7gWUmFR@uW㭖GaS@=?W9ڏ(r_S@ߜ XcS@iX)juS@ Wc\ztS@l;nWhS@uW㭖GaS@9H0XK:S@B5W[DŚS@^#W줃S@,SwSX {c/pvS@7*[X,HMS@~/bXU3QS@nrX}4ͷS@_3UXU;?S@9H0XK:S@V V7=R@&4pV' R@$E W:ӝR@+1W׏R@͛-88Wl}R@i4yW7 }nS@Wɕ?S@G;GXiُ0S@f/Xeb1TJS@DOȫWy!qCFS@wndW7lԮ1S@V =1S@_gzV14S@V=8S@΋tVAR@V*KVټ R@/QU=pA XH@8MzJ.~H@wnRJٌG@ރ-ڈJ0bSG@B1JkN.$OG@0[K"MgG@* J=G@|]oK9JG@?LK3zGqG@sKReuG@aFKP.ݱG@, LBH@\a&2M`H@*kLBOg[I@r^L. ʤI@,|KmvI@rKRLI@ϵKFDO畨I@|TQ{GP@gѨgT 1P@iT{ P@kcTO>cO@ZgO4Tq.YحP@^a4TjO@l?TѵO@`ST# kO@*T+!P@>z9mU֕QO@xvaUV][O@̲#ywU耑O@ 4UZKJGO@U]~KP@dU4P@xxU%%HoP@RJU籧jP@1ޫr>UKMP@ln_UC\RWP@|TQ{GP@ $RSNR@tS/R@_(RnjmR@gRKQ@ʔRXR@0|,Q@HRڦQ@Hق Q9@0|Q@0xN2Qٳ Q@HPg ͇Q@P=KQ@!3QU.Q@*ʜP[gLQ@T(07P=`>P@ѓ@dOlP@ｹ N;0,P@dO+4AP@<敏Olc/?P@IPQ\RD[P@:s(PD՘P@MQQ АP@O QN%lP@dEüP턮FP@gnP9Q{z)P@H}TPM~P@҂*P&nlKO@>ȫ)@PAgKVO@Y0UPV4xO@X2QcrO@-u٨P %qO@?P%}#O@`PZ 'N@@&8QQcTB*O@!&QctO@YqRKJO@ƕ>ɸQB O@`+6XRWKi P@XgR5ݠu+P@XbRWP@7OpmSA`ҰP@0KSu $P@ myS-SP@Im}RKw]P@qΒRקsP@Z4ry|RP@)R;<0|6P@eD;R/P@͋SRϏqQ@b(R #Q@D8uY7Sѧ3YC9Q@/SYOA uIQ@rWDdRS&%@qQ@~BʊS+tQ@DtCSGD*Q@aS}BͲwQ@l ֌ST4YXoQ@vQ@ҚX!V?\u R@trn[V)K$IHR@nY# V?lbR@kѨtU!qsR@ eU|JR@BuLqU#dR/"R@U&>h6UeRUR@R#2TpR@'l g&T -R@~eѨ/T-&HGR@ $RSNR@;WOR@qzWfR@l4rV.vR@&S@W=R@S2*LW:Sh1R@78WqA0R@:;WgR@a#(X~-Y(yuQ@wgEYgeQ@( PSˮY!A`Q@žvYڧGQ@[Z Y3.0Q@(\Zףp=:Q@@ ףp}ZQKQ@ZALGQ@?[T1Q@~/[ER(Q@Fx T\lIF"Q@%v\+y@Q@Gz\XQQ@{,}]7JQ@(\U]D ףp}Q@P.+] cZBQ@#Ed\V/Q@x@ٔ+n\خBOQ@ha\$zrQ@hffff\hffffQ@b(]M OQ@ZB>y]aQ@t)]a2U0Q@p1=]$Q@)?i]qQ@lC4]wEQ@,]|гR@h&lsw]<.-R@ \i>"TR@з鲊\`8GR@P\s)R@l89\T F%=R@9[ZR@䷯z[Mg'=R@h&k@[:pΈ(R@( [҇.Q@ؙBZ0R@)t^[ ܺER@<M ZKOR@SrZ YDR@YN#--R@(\bYףp= WR@ԕY3KvR@겘|XC5vhR@QXXq= ףpR@HzGXGz^R@!YnX,d?R@(\"Xp= #R@Gz.X ףp=Q@{k`X_vQ@KXƧQ@ԕYg?Q@Yq= ף R@ QYQ5R@YN#--R@fffffZgfffffR@q= ףPZ)\hR@ Z{GZR@QXZq= ף0R@\(Z> ףp]R@fffffZgfffffR@XGz.S@goX]zkS@"mXs]R@ ףp=XR@jeXӇ.oR@"28YOGɫR@:7Y^I R@i>"Y_LR@Nz1YZڊS@wW_Y}˜.S@[| X/r])S@wgX+0du%S@XGz.S@)[Z X &T@ gW9 :T@L$zW*Wx>T@4 W6MT@%:WSyPT@EV:M.T@\Vnq T@p= UGzT@zGU ףp=S@_vOtUoŏS@jU%S@yz,CBVaS@z]zVAzǍS@_8WodS@P1|Wg\8S@c*{W1ZGS@KIWjMSS@uVWZS@zNzX4 S@LTo l-XW T@)[Z X &T@EUV S@9LzSn/iAS@"-xS1S@R#TްmQf S@4)TYS@b(UT"S@gffffU{GS@H3Mg_VG=D;S@3ı.ngV⪲UdzbS@(\•UQS@WUٔ+˗S@ZiR U4 S@ KuYBTml{F@`a:靕TQ8F@!ψT0KeE@QT> ףp}E@Tףp= 7E@HzT ףp= E@ԅT&3D@hhT! D@7;l*T0iD@H T0iD@=uQT]E@P.TR .E@jSLhъnE@{GSQ{E@q= ףS(\E@eSE@ıS"E@j9oS5׿E@up{4S|E@ Sl\F@S#S F@y]RhohF@th|R&F@UBVR~RF@#-RQ镲 F@RQp= ףF@ݰmQ@F@ ףp=Q{GF@QQQF@˾+Q[닄XG@.OQbGPG@T9QPzG@7QHTo lG@2P|~G@PPF@- PSPBF@KP0?gF@Q`TR')F@p= CQH ףpE@KToQ(jME@`yOQ"E@b('Q@&nE@̴QxG*E@DzQأp= D@QQp= D@p= ׋Q(\E@85YxQPKD@(j0 }Q[ɎD@(\Q̼D@1BxQ9#JD@Уp= Q0\D@|GR(\D@m8RhZB>D@@ ףpmRʢ\.wD@LupRhCKD@ GzQp= wD@GzVRx= ףPD@+~RMbPD@p|Rpt`D@mR *S=~gA@/rYSȜ.AA@vS u@@`"lxzS8@@@LvlS9@@wJSF@@HTm2A@@l[7Tz,C@@4}ƅUT7p?@xS c_TPh>@hTTZ >@Q>TGz.=@NG"Tux<@H!T ףp= <@VT{G:@ TP4:@]zkT0*9@2bTP49@Q+T Gz9@D*-KTP}ƅ39@QUTp= ף9@4 ףpmT Q9@(\TG:@1w-!TP6~;@dpT@h;@Ť<@QT=@d,iTje=@dffffUp= >@I)FUYڢ=@ĞvkRU!q=@h"lxzqU r'>@Ufffff>@kUO}:F>@TV;pΈb>@L%KV0P>@ |TfVT](>@x]zZViƢ=@Q[Vpӂ}=@&NMV8J=@ϸp ZV`r3(=@P8qV@KN=@>:uVБy=@=UVOc&=@'>Vd;O=@xZWec=@貘|NW= ף=@lQfLvWqt=@\(WGz=@4FjW@ <@!Y&X,`N<@ \HX Gz;@DzWXzGa;@QXX= ף:@QUX)\5:@ \HX Q9@LaXp= 9@(\X(\:@zGX0Q^:@43333Xp= :@zGXףp=;@ԣp= Y(<@\ {,Y4<@QI=YB5va=@7djYm4=@ QY(\=@أp= YQ<@\(YQE=@,P>ZОvk=@I +-Z0)>@#EdBZ@~ޤ>@ĞvkhZlu?@L F%Z cf?@qZ|Z-9(?@(\[>?@{[ ܺW?@ ףp=B[1=aW?@vӂ[ Q5U?@{ʄS\@@\(\`:c4@=\rc 4@PCcbE-5@0Bc AJ5@Jhcht5@p%;6c e685@PCcbE-5@:pcHȰ5@rK!cqm5@!{cyR5@O`chtC5@MJc9zF5@Ml> cU]O5@2w-!cT[5@ec]cuT5A5@:pcHȰ5@-σc,N76@B&9 c Zd5@ PSc 5@%ǝcrw6@;ǀcN@a#6@c"Jv<6@-σc,N76@ .'dcQz,1N@A픵d @%N@RWdAszM@7M,+dRM@h%d4M@95dEN@ .'dcQz,1N@W3[Q'c)(L@0cc`aL@,.GcjϧL@˒3 c=!ӎL@,!)@c ^L@_PcheL@{&xUczvL@Бh8caRL@W3[Q'c)(L@p5aH1amQ@d;Oa!rhmQ@(\Ÿa6A}P@KRa&7'N@#~ja{pwn#N@i|?aaN@1%Ja`q8M@Gz.ap= sM@Yax]`M@9`8M@ ףp=`ǵM@` !<8nM@x]``4M@Ù_`ܘL@9(av`ػ?ޫFL@׻?@`Y9K@(\?`GzK@('Q`~fK@c'Q`IfK@Z'Q`x7fK@b`deK@(vve~`h7K@^`qq \/L@}@`PL@v6}`HM@W9`uq M@b#!aH)M@Ħ9a8?M@^XT{a^M@ڢhaKJM@haap$ N@|82'a@$M@[T)=b::N@-ccbxAiIK@^%{dh K@q( [dyHK@(ͻ9dK@%ʺ d"L@Bdh>>ւ5L@c8i"L@&%JOcěL@$J cL@C[ٛcО͘*M@՛fUc֝uM@e<;co7NM@ɺ cdM@$c(K6M@cP7wM@ҷfcxKJIIM@Va^ dM@˚$0\+d0֝UM@(-?dUM@]1!sAdq7*M@_4;d(M@uՑPdMxCM@ b/zdS&M@ȕ0dX"<"N@d[@N@7 :dqeuN@Vd8FN@C5dx 4 O@TPid0tQO@֑dXO@˺Lxd^OO@&bd 5WO@?xVHd t&^O@K61d5XO@v_dxfO@vodl^TBP@&B20d5EP@Vxd|90|x2P@]㨊,d<71P@bNdT#P@v?Xd 7P@a|qdH#P@9d\ P@dlco+P@2gTF dwEP@VCepU;jP@֔d$ P@F0d P@9[`td P@9ໃJU@fqQG@j T@dv>G@+b _T@茤<F@ĦΜ|T@W\[F@pS@g2quF@_o7T@eE@L T@ߖuE@9T@,E@:=|1S@FM?E@oS@4omE@ #jS@VoYzE@ <S@ƛRw~E@R@_pE@ R@/E@LeLiR@̮E@i/X_R@djO@E@E̍Q@5lE@vCQ@4-E&ZE@6Q@jW"E@Q@&KJh E@oS{DQ@.D@z(Q@3TUD@#G"Q@0 TD@\BP@ѿ$hD@^}P@gD@xvwP@XkD@hJ?P@xPD@SzEP@PRE@~s9P@qY1E@'ǗO@x)5E@TaӳO@/E@pwN@j3F@VfN@lQ[dF@QXM@HAsF@cwf@M@7OgF@BK@KJxF@1ȱK@D@YEK@DCD@?)`K@"\۠E@mr\* K@j|)E@yxJ@5E@xP@J@CCD@X !9J@8\HyE@l"XJ@PD8E@.@J@XRjeE@ ԫI@hQE@`rI@hF@ȣyh+I@H Z$F@@N'I@NF@II@rYAF@%I@`|F@~mJ@{+FF@pp;J@o(F@e5REJ@{G@FfxJ@࠵M/mG@8IaJ@`WfG@I@{;G@0T\I@0bMG@ԕH@'>3G@(-KH@ߡ(GG@@wXH@xO.G@SH@3G@ 2YG@k G@~;G@cs2H@sG@8vH@xN4`G@ ͘H@P_UG@3:I@1IH@,H@ YH@tMI@c҈!bI@oI@HM*J@xI@xr\5DK@`b[I@K@hz1OI@x@ٔcL@ I@E.M@[[%I@أN6M@4 EI@0P:fM@RkI@0ޛ0N@"GfI@H>ȱCN@%{I@%M@0 I@xvN@VW-K@+jP@8\LK@xqb Q@5|K@`'\DQ@{OK@@`Q@SԹK@hHiDQ@}K@dQyXR@pY>60K@$`R@HK@R>[R@@|J@<䠘R@H.J@9S@>K@zb!S@< K@>X3BsS@hóJ@sFT@`)(nI@doa$T@(ƝI@d|T@Peq gI@3G"T@%ڈI@0T@qI@_U@'I@SeGU@I@*bU@H@49.U@@:zH@ U@ }H@BK@D)B@r2CR@7OgF@61ȱK@D@BK@KJxF@cwf@M@7OgF@QXM@HAsF@VfN@lQ[dF@pwN@j3F@TaӳO@/E@'ǗO@x)5E@~s9P@qY1E@SzEP@PRE@hJ?P@xPD@xvwP@XkD@^}P@gD@\BP@ѿ$hD@#G"Q@0 TD@z(Q@3TUD@oS{DQ@.D@Q@&KJh E@6Q@jW"E@nԎQ@d:&wE@Q@#MD@ZQ@vDaD@@Q@ یJD@r2CR@q+nD@E̍Q@eD@UݞQ@d^GD@}r}Q@D@ 3|RQ@Rп$?D@Q@^ -EzD@$pUQ@"Y)]D@> @Q@Θ: D@T"Q@>.HC@ 5P@-MC@@mSMP@:9C@pC Q@,\fsC@*Q@V'OgC@¶P@D)B@ni(P@B@ 0P@…oB@9 P@ zB@f_MP@3C@\ P@p0b:rC@SNNO@&0C@Q/O@hYD@1 CN@܊D@q N@~mlD@@ Y;N@:ީ3D@`>e N@+0kD@VfM@AE@|mPM@(P2`E@ L@\HE@(VRwL@:9D@ B-_b@m$#fb@-6VuBVb@tX XP}b@?w5"^ѩb@\oe6$"P=b@xC9`#}|C=b@|0!^^# ݨa@,T!;a@U"ޯL-/a@%然Q"|EIPa@dClp\<"$󺋠a@hv4po?a@ V{c@ +G uɣ c@_.,0{$c@aѦȵL-yc@hC pc@ZV) c@p~dQ$Yb@ M nVLb@ͬ$H /b@PeCmk2b@p\b@0h=jKI=b@,]c@a V{c@ +G Qb@Ƚ\('n$b@!BU4b@2aEVb@ DZoi{b@C(4b@یb@(hCtb@OXU^b@@5Hb@C[Zxb@`iaz98b@|Q *f1b@нOX~_b@ pnktZpb@|Q>)6b@ի ј`c@`iE\lj c@pCz@\2 c@Y/|xKIsb@H`iB"b@\(=Qb@Ƚ\aQXc@$\Brlbc@ eDF#qc@Lsxec@`i(Zϗ+)|c@X$k@G 3sc@7 VXec@~9$uUWc@0rusPc@0tnOTc@9as+aQXc@$\ W@ċvGP$|EIPa@e)V@ &/6]fly?a@ $󺋠a@hv4po|EIPa@dClp\<")aۖa@Z[& da@,1 2\5\a@T ^1ũ3a@4w sL@Aa@9;dXeUa@&GUZ Ma@`Fdpݰ=a@lΒ`@x2/b4bD`@ |#Q6`@SeO Ԩ<ī`@7 jAI|`@P+;s;8`@e^ c u `@8J~ wz`@1}#`@0t|`@`rN]G`@@aJo`@ <@Tz`@&H8Һ]+^`@AB6d8P`@$`Z{`@7AT)`@&׿\;`@{4dx`@z"y nt)`@8JJ'X`@^( &b a@+$t .a@_K#A Ja@7'4>`ea@ i6e}a@ӔټE?a@ =_@,7!Z 5{D_@."m[PE_@^M"hs_@2G$w\k^@ċvGP$up^@bz$`KZ3^@#w^@|i~"=_@,7!dOj`@2`&aۛ`@'nJ`@xۚ!'`@IuјC`@ɉHj0`@n݋dOj`@2Bsx]@TDڌ@6% T]@ @z]@'{0M@cF x]@b*>?T ʿ]@g)?2g|s]@=\?о^]@;?̸sba]@и^z#]@ nu ,)"]@W>P 5z ]@! Fv ]@ٜA #X\@j@m!c\@cv3 9_\p\@`I[k rP\@p)@9.\\@0T2 4¶[@B! ^[@o9eFp~S[@@xx4J 5[@ i|i| =˚d[@@th BE[@~ hݿk/W<[@ ?l,lD[@@ӯx? @o"u[@ i?\_[@~?[P/[@ )O??T [@л.?0 V\@Uڏ?ț(7\@3j?b s\@:Ra{?|0Iħ\@mI?E6\@e@,8\@` Z @hWGd\@G9@bF@]@p9@Bsx]@TDڌ@z6+`@+j@O`@?~vZ`@˩&NW?`@< $`@C! OZ`@hm q1o_@ D% ZgY`@@у˿z6+`@+j_@P"SG\4 9^@C҉U!^@o2![k ^@#O@'w8?o^@d4pgR^S_^@}WL;%g^@OX{9^@ Όݞ;>^@aiʪb^@s­^@c 0Nܐ^@0m9J6]@ 9Ϗ5la{]@"l]@?qW]@ C! L ]@k |r #]@!|~j2]@HFD-h|]@ 3}K*]@6?H^@6" ?s8^@@3ކ?u j^@9?@]^@Nm?Y ^@Ģm$1]@bT%#z̄]@P˜"?^@h{T#ʿҡ1^@XJy#"í-^@Ԭiz$Y ^@Ģm$U^@P!x^@0i }̍ӹ^@z?0 Y@hr^@1L!]4IP^@R} !@%w8)]@\!Qp~]@, K6-^@lkmSy U^@P!]@JN 8]@s ؛]@ai!`~]@Xo 3!Dt/WQ]@t',"w^/]@LC"2[E]@$b Ush]@To 靋y]@!0 ]@JN \|([@1z&'[@8VbX[@ 7 j!㜰[@yYB6Y'\@VV<#>\@`iy`ߦ\@8 *BZ'\@[[ڽ 2 \@|[T%!}1]\@l 9= iĄ#\@hb+ɚ 2ti[@a Qy[@< > ^[[@ k;pRd,[@x[@)gZ@]jkJZ@&c dWZ@g&*NZ@0k6Z@:*6#[@п}b\|([@1Z@]["Z@^A;8Z@p<0ȼgZ@_Enu1Z@@hF~dvZ@܂8uTtZ@hv-Z@$|Q>~ܦΐY@8B5&dY@HۂʕY@b YY@+es9Y@ *fd'6 Y@zԌ(X@L.q?@X@@?cr?b|X@r;-?< 5lX@'@ CSKX@74gx @HT#X@l@KF`W@ ¸@W@e)V@PHW@@զN_X@|b;@wX@@@9X@`: @ٲujX@$Ye @zJ? )Y@@r`@jY@1Ҍi@YY@={a?v2Y@Ot,?LN=?CM|;MAK@PMrK1@=,MW,-@]6Mi@DCj?Mt;7AAML tޤA>aL"A@Me^LNO4B?dLVKfsB@I_L"Co.~Mc,-0\CdN:НvC'+OcNOiClOICZN*OXNODOJ-VD.wv_O|tD}O,YYDG7.P&fDGPLDx;D`q>PВlEo> kP|=>-EO녗E˭O4'HER>P5\oEKP䣤hEYq UP9\,@Fp]-dPRtcFGPwFtLPgUF9';'P&G\ V6P죤WGsViPm 9G PNНHҚP YBYH0:PNOQHj£.Q#8!Id|HQh~]I^X(24Q;GIX QA,J3$Q-T&J:A*_QLvUEJg%QtP'J@R꧈I\*R^AVI0آr>R(^I[>TRO|0IޚZRߟ¨H60|)R)vpH=L1Rd4CHR-xR=>G\Q,_`BqG^nTQȐFl92Q:Kmԡ|FjBQbdFtQkw)4FxQ FНFDQnpqE N]NQKE8͋ Rj,- E*QDmԓEg/bQbjDtJQ[QqCM]wQi7@uC^'u#Q(FC2قQh{BQ23g=TBE#aXQ8VBd؂՘QMRA{NtQp%AK]N%tQ\K@&„Q \ì@Ǒ>Q 5Pq]?dzQSV>IjހQVb10^=_ iQEuQ<;]/:|ӺQb8#5PEz8PV67LPe6Nd~P:L5Dv=P1ڨk6!PuO6i$TPOby_51YlO#f6bWO,a?6r;\lN1%,av7ڬ(N8Ve8dEMOgMXZ~8 +{LX~)9g?Ll:;94O OM m; LE3Ye;X'=L>L,vnj;:.wK GIIc;r;dKV-:׬ PKԤ?9Ȃ.wKG29'mJn[%':lfF Jh:>KzIڨy;dڃŔK4;EC2<%L3]NN =R[rPKKToPڨ{1r+3(QӀRtQJ:pΈ(QKoK}WP|\oKKToP\rK KKYNQ^2K'XQ}QE|'fKp]QKZӼRx]`?Kl4@RRJ:fR]P2kJı.nuRl/J$Rx):J^ Q@; KH8ߥQvJ`odQm2wJ,w-!VQ †WBJ+3(QӀRtQJ eQڨ{1DU4rjFQ?_B2OZ=QD2O2@jMQ~tg3mu0Q7Nf_4J]Ql~5APb2w67LPe6PV6#5PEz8|ӺQb8:&>Q<;]/:3&Q$ā:o'QiQ ;:Q;@Q}En;_ iQEuǑ>Q 5Pq]?&„Q \ì@K]N%tQ\K@{NtQp%Ad؂՘QMRAE#aXQ8VBQ23g=TB2قQh{B^'u#Q(FCM]wQi7@uCtJQ[QqCg/bQbjD*QDmԓE8͋ Rj,- E N]NQKEDQnpqExQ FНFtQkw)4FjBQbdFl92Q:Kmԡ|F^nTQȐF\Q,_`BqGR-xR=>G=L1Rd4CH60|)R)vpHޚZRߟ¨H[>TRO|0I0آr>R(^I\*R^AVI@R꧈Ig%QtP'J:A*_QLvUEJ3$Q-T&Jtʮ]QQ^%J V|QDJcg%Q,HsJ*$gQ:JJʀQ/@OJOc V#Rg/>JSlRHjJSlR4AjJŗRH!J)DR7KIg%R"cIԁJR1{n0Ï-RI0>VHϜRv,- GvRt9xG=R@ARG8(?LRM?FggR40 F&aOR5X&<:F-R1EqXRhK EʮlR6\ѮEP]>Rn˜E*5&RnDUkRnC<> MR촵CZd [`R-5$C ʢeR"B|&~JR(m؏Bwf#Rsw!A듞&QX&]@ޑQG5@QQs]>nQ^䜞~>mZQy=r9|ڙQ|7օQ=}d5OڅQ^J3&k9ؗQdY2:LvQ"rǺ2 eQڨ{1  B[](@,e6*8 ,?@N;@|9 W=@aѦ=@'Scjk=@ر`m=@~)23>@`=Q 9ԧq>@ s -Gq>@mA hxX>@Wy d۾=@?wf ?Y07 <@$ʑ/ !฿*s<@',pT"fv<@p=;6#[m~<@Lk%гK_<@aX' xu<@,sq'`ӧoW=@m(D=@X[(`D=@,e6*wL\-<@`ةzw*w"<@өze)3'<@H(~Rc;@ΩzC(cj*;@Y07'>&:@(,P'x>\9@B!'C k9@Vlpp&y}8@ 6z&d"P8@4/&1A8@Ek%/7@%x>t7@XbX%(D\6@h &6g6@bt%Yԧ'6@o k+&Fp56@."#`V 5@vm# #EB5@L^A!>5@o N5@* xe5@@rz)8ƃ4@n2 C4@xEHt4@`i307 4@Kb'w7qj3@`;(,*3@@lhI3@P,07v2@xWcLRY\"2@La@My1@@!# 0 1@nu. x50@87 ?컒0@Y5}\S0@H9*@ >LN*@yѦt`: *@|Ѧ`hx)@hqw(@حg~f B[](@(񵿚(@^(q>(@OqzRч)bC)@ -(`n)@D_d8*@0% RQ3+@(lJ$0:7J,@x? )k,@(OX ,Пc K*-@|Q77W.@@OX_9&Ё/@PC`؀\00@g5G pB/@r`,7h0@`@YI0@ڗhD1@}:up(07~1@  q0ۿ–ީ1@/H"1@K}?6q11@Na?x1@4?HΠt"2@@j@!Yd2@`p4@ s2@@Wx @2@>iƠ@(f2@Fiƈ@=w3@pH @iJ4@,G@Pv4@pKJ@zD5@P!@\0g6@0"@X7qA6@`Q{>@ o3k6@oU+@XA07L7@lcSp@ki8@Teo@ &`8@຦ǖ@: 9@@U_G9@:~@ >9@N;@Ug:@0~@G,qG ;@@XdAH_;@u(@O;@0g(7@m<@M &@ xY<@@r@xM\(=@0@ xK=@\9g@$Ö=@e)ޱ@J0Zw>@ @w>@y @:>@m@8 ,?@@(ÖH>@- ?0w>@TRaDW?X)>@,;?p 3=@Zc?U=@$fGʿ |=@DW=@` v xJ=@UeW{F@\@wg G@K7 @ r\M>H@(WXݬ!@`xH@@AK"@xH@0љn#@+{ xH@H;%@dxH@=&@^yH@^!m<&@^yH@b!m<&@EH@6T&@kCH@Oe('@m!I@yZ['@KԲ]I@z6 (@X5;I@X4 (@0"I@;M '@PI@x@U&@DԷ̅I@Gx $H%@}ijI@u7$@Q,FI@h?e"@P I@D4) @sH@7@6LH@{ަ?[@4G@8d@c AJHG@HĔH@0F@=;k]@w#F@#Ed?ZwgE@l g?T?o*RE@Ry;iyX5D@jR $I2D@e7@d"@]짎7@s\#@ 7@y޲-$@8!K@6@u m%@(96@H&@4O6@ n&@`96@k['@9d6@9b->(@I6@@)J)@А5@R%-)@ 6@E 2)@ХK6@K*@.6@ꭁ+@"6@@HI/,@@ԲM6@[wT,@+e6@y-@ ϽK7@wT\/@`&9 7@`->8/@+p7@@O3@7@4@ 9@:4@9@6@Q=@6@03333s@@6@0hnB@6@ 'B@Z5@ 5{|B@\wb4@mV}B@zpw3@(rKB@͒52@&pnB@,^2@-}4C@0:1@ʡEB@`Bsm1@.oB@ E$]C1@nmB@ u0@dw`B@CVJ0@E_)B@h-@/6B@ J,@\"B@`-l +@mA@P()@wWA@ K"@Z@@샽#@)i@@HU$@x|@@yp%@b)@@tD&@x|@@`9[(@8Z(_@@ :l~(@v1^V@@Ķ (@Gu @@`HX'@8(@@R\'@P ȋ53@@'G)&@Հ?@ `%@H=(UZ?@Fמ#@ |>@ z2j#@@!#=@~m$@pˁs=@ir|z+$@>=@L #@d==@@]5#@`„r<@ "@cS8;@ "@ۤc;@@]5#@,;@`GF#@fy:@O "@U.2z:@ix#@ :Y9@`PE$@` f9@ro{$@\L9@`aD*$@K@8@Fמ#@ H 8@V{!@еl18@^;u!@07@`Ђ6M=!@w?8@(~Xu @3u+@z @`&9 7@`hh7@:+p7@@O3@`&9 7@`->8/@ ϽK7@wT\/@+e6@y-@@ԲM6@[wT,@"6@@HI/,@.6@ꭁ+@ХK6@K*@ 6@E 2)@А5@R%-)@I6@@)J)@9d6@9b->(@`96@k['@4O6@ n&@(96@H&@MAH+;6@f%@%`L5@†U"%@X85@U"@pG4@f"@y3@UR&"@q2@,@!@(82@iC!@8q޹c2@  @`1@@K@`ӧ0@@Ht0@7%h@>bJ0@C%i@@20@@s@` q.@z @@mjG.@sp@ =.@0 @?R-@Аd!@S-@_!@ާO+@@fW#@gW,@ $@`6`n A-@#@QԖ-@4c#@ .@`yB#@Jq-@p1[%@-@|k3t'@_{f-@`TR'p(@k0,@Q)@ Ӗ> 1-@D-*@-+@ =*@0`n+@NN+@3u+@!,@N(D+@@e^/@)~.@Ф0@`ljә.@z 1@"d_/@ 3@``Yv/@cg:c4@xk.@P ur4@..@}8g 5@B1.@0LN5@+ݳ-@pJ6@ T/@`hh7@ ui3@7~5@+p7@@O3@.;lPR|K#2@+;QBj3@FzVTQ)3@+;Q@Fz+3@F$Q L2@S|Q|*2@BQd>Q2@vTQ HV 2@BR{72@0 6R`F%2@m]RxE72@ōg% {R|K#2@.;lPRDPZW2@*۬Rca72@gL\R7h2@3uy,Ryr2@nR{2@^0b2Rߵ3@.2R9{3@~n⹏ZRj3@%LRBj3@\%R 3@FzVTQ)3@S|Q;1@2.YQdI3@vTQ HV 2@BQd>Q2@S|Q|*2@F$Q L2@+;Q@Fz+3@FzVTQ)3@4geQdI3@,QdZ3@'Q7hu3@'|QS3@&d;qQÍK3@77O7NQ.P3@4GPQ3@Og3QTԞ2@2.YQx2@ ,QP]542@yJQ2l2@PͫigQH7hva2@|Qhm2@ㆈQ>I>2@8Q-/2@n>ժQDdHm2@$.Q;H2@J  Q;1@!Q<I1@vTQ HV 2@(f aaD@f@ǺPT@ _)?Wf@lBvQ@f@xBQ@p\f@=f2Q@>yX5Wf@6C@G@0xh C@ȔAsG@ bC@PvG@8qC@8^G@xC@bH@cZC@6TdH@aKW D@@YH@MD@`4H@ȹ(LC@MH@r\\C@ Ye4H@l\B@0b%1I@D&PB@)I@6A@uII@hjZ_A@FwcI@7ւA@HﺑI@(A@ȋ0bĠI@pY,A@8?@^J@0!N?@,gxJ@p.Ue?@X/XnJ@ʔ~&@@+J@PaKX@@\[J@t3@@d7J@ 4?@`WJ@ƚ?@J@lb?@nlK@wO>@Dg7gK@>>@)(pK@WGH>@yeeK@s=@!V! K@U_=@߇K@0c:=@ʹMK@3<-<@HA L@ ";@k71aL@";@tCL@(t{I;@(WL@@AN@t#?@"{-O@˖?@Z0boO@J. >@F͘O@kq>@SM P@ O_=@u@N#-sP@ =@oc/oP@ Ŗ8=@TsP@XJ\)r<@UQ@P<@ON%DQ@Hf=@/ JQ@0?@DcQ@G`?@dcQ@P @@syQ@ؙB@@wJSQ@pAB@hzDQ@ek%D@ qP@ѩD@X0AP@@w-!D@ )?P@%D@d P@ho1C@WLP@ AJ@@BsP@ ܺ@@H_{P@#bJhA@߾yP@X upA@Aj[P@/ xA@p P@($B@:P@"B@:O@ȁ,B@@}kP@EB@lƢ0P@dzB@8*IP@j+C@lT!P@7D@lƢ0P@uC@_P@ʄ_ E@dzP@жE E@ʚP@0rhE@3kP@|~!DF@*gP@HdE@lqP@X߾F@`!'P@|yE@҇$Q@ G@Q@rK!iG@,Nw(P@8܀F@!AP@hwEF@TP@{,G@TP@0sG@XkwP@ >H@lnP@M#I@_5P@ J@]P6Q@(G`S@(\Z@ QS@ [@c]K.S@R[@@ ףp-S@u7U\@<.9S@=\@׻?R@(:x\@8}R@l1\@D R@Pz[@lR@Y[@QR@(\[@(\R@Ϡ\@pUjrR@=$@A\@h~R@x'a\@!uqUR@h~\@BfR@)W\@2ı.pR@9d]@ X4eR@zG]@LzGR@hg ^@,Nw(>R@M^@D oR@QX_@p= cR@h2}_@ 0dR@m`@9zBR@ v!`@䠄R@ Q`@ QQ@pHc6`@qZQ@ P@$zf@`7l[>P@paVf@ K<"P@4)-f@lƢ&P@tJf@D P@Mb]f@hɎ@O@ЌEkf@2}}O@Bof@pHO@Mgf@p[&O@=yX+f@0䃞BO@~7e@ׁsN@`õe@wN@̄e@yN@4ZVe@<+N@@:umK@7 d@d,K@d@XRJ@puc@@zJ@oec@"I@Ec@OjI@@ ףpc@I@Kc@=yXJ@mc@eNŰK@YB}c@3KbL@Bc@lV}L@ cc@`{fIL@x^cc@H`"M@/ d@#bJM@n;d@Cl+N@nud@ N@`TR'd@lsczFO@@Dhd@@;O@ĶE Ud@ףp=N@c*d@P!TEN@c@4 N@4܀c@@]N@$XFc@= M@hffffac@ǺM@@٬c@H.!qM@$r3܀b@4cM@b@?M@q&b@M@aob@Pl> M@eN/b@,M@ϛTa@P?M@?ޫ^a@48EL@b `@4c]K@بhta@0[ AMK@#0&a@^J@48EEa@*J@,iYa@8* K@+|a@XZ*oGK@l a@ZwJ@0"a@J@D?a@ꭁI@h&ka@h~RI@Ta@e-9H@N(DQa@ G@TDGa@fj'G@ a@}?5^F@H}`@HOE@ / `@`xE@ .`@׻?gE@k)`@4fE@`@JvlE@i]`@//FE@X`@ЬL)E@(U/^@gI@hP2 ^@_I@5]@`JI@FC#v]@"II@bEx]@M@OgH@Qyq+]@YeH@VG\@`3 H@O 5\@7-I@hc|2\@0bI@C6t9\@2یH@!2[@5xCaH@la^[@aq H@,Y[@H@b7#i[@*H@w1Z@V!H@ *Z@V!#I@`xZ@o3I@@z'Z@=#I@0CLY@HÝ I@Ws`Y@RZAI@Wf,Y@DI@J?8Y@KT(I@VXX@aH'I@""X@ݨJ@'@tX@KJhI@œ9.ՎX@n:96I@3hPX@h4H@WW@hH@V8W@U.I@+qW@:9=I@zk`FW@e?I@뎃W@,}fI@?+V@jx*I@f@h3V@!:H@¶U@c H@ U@ }H@49.U@@:zH@*bU@H@SeGU@I@_U@'I@0T@qI@3G"T@%ڈI@d|T@Peq gI@doa$T@(ƝI@sFT@`)(nI@>X3BsS@hóJ@zb!S@< K@9S@>K@<䠘R@H.J@R>[R@@|J@$`R@HK@dQyXR@pY>60K@hHiDQ@}K@@`Q@SԹK@`'\DQ@{OK@xqb Q@5|K@+jP@8\LK@ mKP@9>VW-K@@N@K@PG1}N@J@%N@ؙp}J@آ.^N@0R(\J@xvN@ȱCN@%{I@0ޛ0N@"GfI@0P:fM@RkI@أN6M@4 EI@E.M@[[%I@x@ٔcL@ I@K@hz1OI@xr\5DK@`b[I@HM*J@xI@c҈!bI@oI@ YH@tMI@1IH@,H@P_UG@3:I@xN4`G@ ͘H@sG@8vH@~;G@cs2H@ 2YG@k G@SH@3G@@wXH@xO.G@(-KH@ߡ(GG@ԕH@'>3G@A}˜RH@Ћ4F@~7W@ǺPT@t$xX@:p/T@(' Y@YS@ %X@?o*RaS@#pX@ OeS@ >W@6!S@9TW@KS@Bf"W@{3 T@[V@9 {T@r.qW@ AT@~7W@ǺPT@(WZ@o\S@uDZ@ ^S@rZ| X@mzS@w-!PY@e1S@&†Y@y@ٔ+S@JFžY@GŧS@(WZ@o\S@)Wxa@S@U"b@^I R@ b@GzR@~Ta@J^c@R@J)^a@}"R@3.a@ 3R@V`0a@ 3R@0*Za@S@)Wxa@S@.4ib@1=aR@]Cmb@cCR@dGb@fk}бR@TgCb@,R@i㈵xKb@R@b@ S"R@.4ib@1=aR@?a@7pR@9za@ m9vR@pWsa@#~j^R@6I@}za@~^_I@Jb@^|H@#ѐa@Ү=H@;a@HG@^a@l:OkG@:+a@yG@)a@0b^G@ka@> F@na@fH(gG@Xga@dTeG@P85a@nmH@?yXa@ީH@;rla@\[yI@Ǘa@vLTI@~Sj׵a@7>֦J@dla@Z`J@Vy|Ca@beK@a@v_..K@Da@͘/J@Xa@<^J@~jteڊeP@e*e!vyP@\tYLeP@ao{enoP@X["bP@e%\P@(\‘eW}P@h\8e,P@x( e`vOP@jƢegffff(P@Qye{Fw;P@B!f]hVP@D 'fKaP@.[닄KfGXP@U:X\f)t^coP@nUf~k,P@G|fwP@D4mf$0YP@f >P@f}7=Q@1f Q@cX9e$@M-P@~jteڊeP@fxBQ@fkBvQ@ffff{f Q@IO`f|͍Q@a<~2fE )?Q@/n<5fMQ@Sr3Vf"[='Q@fxBQ@QS(\µ7@@S ףp= ;@ QS ףp=:@q= ףS Q:@gffffvSףp= :@GztSGz:@ ףp=SQk:@QS ףp=:@(\rS ףp= ;@@Sףp= :@0' KSL9@}kVS':@(\USHz:@&pnrS>'I:@(\rS ףp= ;@g67SLY859@)\xSQ+9@(\bSףp= W8@7bS"~7@RqS(\µ7@r -S[ AI8@|DL$SD$]8@g67SLY859@hNgffff&JĽI NINI33333MIgffffFM̌ILfffffIgffffM43333I43333MJMIYNgffff&JNIjP@{ M@ xK?@ڦ*T@XDPmI.@iW!'S@ q /@htT@=yX0@,+MJAT@&Nw@2@xS@l`q85@:X0S@;f3@.nS@Tx2@{,}tS@Uj@+1@ .VhS@ cZB1@Ù_3S@Oe/@M O1S@fL2r+@'†WXS@gk}V-@[ӼoS@,eXW*@sS@%ǝq&@kwS@.o$@2S@FZ*oW*@zk`T@9 {o+@&kCS@mI.@iW!'S@0?@DcQ@Hf=@/ JQ@P<@ON%DQ@x Ö=@CZ1qQ@Pi~w;@q#Q@e{-:@oBͲtQ@H>p9@Y1EQ@ xU8@cKV)Q@ |7@iA2 9Q@0f2[6@~5Q@W">5@-LVWQ@p+YE4@NFQ@fx4@#U;+DQ@`CAH3@AmxQ@()n1@q#P$Q@ӧ1@_/Q@:0@JVQ@@G0@en[P@ O7.@OPhP@O]+@O@j^2P@_\+@5ŁP@@0$+@kc$P@2H()@<P@s'@*ѵlO@U0'@N!FrN@f%C)@gN@sə(@nlN@ i{V&@IީXM@B&@BMmM@s)$@ "M@`Q @B>(M@`01@{ M@`MqЩ@HHKM@@Xҡ;@M@jP@޷ީIN@t^Ϧ@xO NO@@EX!@όO@ω/%@k}t@P@?,;y(@DžmMxP@@A鴅-@MAP@o0@6 $Q@st{/3@UNQtQ@ȳD`5@TQ@H7@Q@D8@ӏQ@DY^:@!GjQ@?Oa*<@Q@ xK?@ Q@h\0d>@MDQ@0?@DcQ@4KRh;@&T@D9@d1$S@ҏcC7@uʙS@?4@IoFS@mA3@iv)BS@(wVv2@.HGS@>6^1@shT@Dt4@ .H&T@Ho5@W7T@h^"T6@ڦ*T@@Yr9@wT@4KRh;@&T@)a_8@"lxzvS@"}6@y\S@۹4@^TkS@Y/j5@{S@V4@'KS@4)^6@iS@H7@S.S@)a_8@"lxzvS@8^I SR:N@_)j(VIT@aG#0T@9E}iT@VCC+ٱT@HĔHOCw#T@W&R?AVIT@a;BBT@]zk4"T@6k+ݕT@겘|:镲 T@gffff?̌T@,g~e?N`T@N;aoT@G ^/8;k]rT@![=6MT@|?5^6MOXoT@k+7k)IT@674_aT@N/OezT@ pU).nnT@_)j(t^cRT@T c I0̗`%T@0fffffT@]wb 41ZGU T@471PCT@gffff2S@9z3䈵S@Jvl3_chS@镲 y2[Ӽ?S@`4 p:uR@^/ 49EGrR@,yp_34FR@J55 SR@p>o4'KtR@nض(4d]R@N ^,6ͪSR@F7=~oӟSR@ 'P6F(R@GL6Ral! R@-+MJAG8yC&R@B 3m8H.!R@oeq7hW!R@!6Q@N5+yQ@ŬC97/$Q@7ܘN8xnжQ@Q9r]Q@ q39 %Q@L\:vR~Q@tv287ƋQ@b_YY6IQ@=~9 -PQ@S;BQ@0Bx>5A}Q@;pΈ? ܺQ@ݵ|g@LP@ҌEAr3܀P@>\r)-B{/L~P@BB@|P@.n0C'KlP@0CwW]P@vUD5P@ްmQfWD[_$P@`TR'Dg?O@qhE3VWO@hU5EQN@]PnEEL$zN@8gDioE# N@dF:N@o!GZ*oG8mN@g!HmN@՝HמYN@?HL1O@VI=>O@CnJYP@H3Mg#JM OKP@"^FJjjZ_P@(JK7P@ JYQiP@H}}J*Q@鲘ؼI5Op.Q@"JIeuIQ@#oI_v{Q@[='J):dQ@e,iGJA4F[Q@gJx&RQ@)WxWK D gQ@#S`K 8Q@?x-K-lQ@uT5J}Q@DnIzFwQ@J{,CQ@GK4)Q@K?àQ@ PSKoLOXQ@:(a[KQ%R@6UũKY=R@D$]LԕiR@5lL [tR@8J^cLMCQOR@vۅJMLR@uaNdS@{ 81@c$~[<(3c1@:,a9.ɝ4@0,a:z:icj4@^d:x65@p:e 6@lw ܾG:\0\6@y97q6@2@9(7YO7@r D9x:˻7@,c9 ;Y68@$sB9>Yq9@LL<9AY'9@|9q=9@D,9@9@ E8~RZ|:@[_ǝ8h Q:@J=8;@97KYi<@ 5ڨ61Dn=@E`6K=@ e *6t{R>@}XZE6?M>@Q &6\01?@Fb@63?@;b17t;?@B^8ם?@m{9Q͖x?@<1ɗ9HIU?@ ?9,`H ?@u@9XU\>@S:a076>@ue:3>@ vpl:cH?@bd/I;і39?@wp-;U, @@ʥۻ:JFcAj@@E:LFJ@@ ?\x;u?)';@@M<ȼ @@>3@h=?p>@|l>@cjC>@>#?&<@pq@GrA8<@Yb@8yv;@X& @G:ak:@K@"9@Na@mA9@M)@=@,9@ :@@ɇ8@5[@:7@"V@6@H_fM@P6@9A@H5@v:!!A"e4@f5AH)>4@@8 eA@̝3@pqhAvz13@pq6;AD2@8A_cjl2@K@H(`2@EyA?~>2@@ {@țmA@2@Q@:AH1@/>N@w?2@6@X\82@ѭ`?ݱ!1@ ȹ>Y}1@j㜞=@\1@aN>=fPX0@b=8:@aN>=0t{ؿ;@lWڨ%>@"q<@.~>8 J<@vV9>A0<@9>d۶=@ikg=(>S=@A=cjo<@<x8:@lWڨ%>(>S=@D¥< cjo<@<(>S=@A=d۶=@ikg=A0<@9>8 J<@vV9>@"q<@.~>0t{ؿ;@lWڨ%>8:@aN>=ZjR;@}[L">=`<@?@qZ|Z-9(?@L F%Z cf?@ĞvkhZlu?@#EdBZ@~ޤ>@I +-Z0)>@,P>ZОvk=@\(YQE=@أp= YQ<@ QY(\=@7djYm4=@QI=YB5va=@\ {,Y4<@ԣp= Y(<@zGXףp=;@43333Xp= :@zGX0Q^:@(\X(\:@LaXp= 9@LgHX >]'9@laX`:]'8@7lXFE8@\`qXÍ6@t7*wXJEܷq6@K"lX"5@xXXY8i5@% LX4@ȉ!X  3@lx6X;,^VJV|2@DK6VlF1@.AVܙ c2@+IVT1@:(IV@Xg1@YVMZ1@VM1@)-%VA?1A1@r V̠寎@1@1:V8I!0@QL}V0@$EtVРx0@geV\)h0@U`빝V0@PVVd 0@ kW;.@n4W7 =!.@x W) =-@tWPX-@tWW6h;/@xWs6]/@KuDWs30@d-W`u 0@ߜjX@/@P#X N/@DPX`/@q{ՀXPMx0@p-X@&n80@Pz(X*Ǵ0@,k5YpRP+1@y42jY&1@)zY1@H2SY%41@Xx6YJ2@tĸYd2@ }?Zp!]'P3@< Z_Z&W_3@| Z1nZL!o4@'uYZO4@\+| `Z ֤ 4@TQZ07h5@H$B&QZl5@L0fZpk5@<`lZurD6@bL}ցZPx>6@=Z@PE܌7@4xZpa8@L7ϸ[`,9@P[Ң9@Qk\[+9@JR[@ dq:@81Ks[:@![`G]);@ks[q ;@gs[ w#;@p+Bf[w<@ge\P T<@7e\0OD=@ge3\0Og>@n{J\1;q>@\̃I\k+?@-Bw\`N?@X8*\'?@`I`\0#?@Ҳ\7hd?@$2[\>@r*!\@Hy)>@*.\ &W=@u#!e\`=@)#[\<@ԅ4gQ\@_ o9<@ gH\ ˫BJi<@K=\pFl<@w׽0\hI;@LgN\>#;@\g:@ :1\@fЈ]">@Dq|-.]?@ oG8-H]hD@@]6MMRyANӚJg>I_L,~_7>0Hi|Lg>#eGKsB>$ IKyzʗ>ȽFIKfeL~?*PJ52 @NӚJ)$]@EJ`Eۙ@B%ԯJ3Z@.8J3m2AtyuwKMRyA}HKpW`A+ڎL?ynA#E2L)07AլLR4;A]6Mi@=,MW,-@PMrK1@CM|;MAK@LN=?I_L,~_7>p\y.R3Z@zlp]A@.GZ@B%ԯJ3Z@EJ`Eۙ@NӚJ)$]@*PJ52 @ȽFIKfeL~?$ IKyzʗ>#eGKsB>0Hi|Lg>I_L,~_7>EC2<%L3]NNKzIڨy;lfF Jh:'mJn[%':Ȃ.wKG29׬ PKԤ?9H'6KS)9-١%K8>%K-a8}SKl(ɗ7 IK6yS8qKKz7zkBKdnn79+*K~D֧6&~#K@U_[6#arLik49vXLM0E<-4|I_IL2Q3 L,Cff3zyuLQ ;2L$ʿLܞ,2ZkLXln1b#MfΎE1{1MC680JM9հL0dFNB0ENXA0. C% ND9='.r!N\ŽJ-Xl:Ntqlp@,]Cl@NCI+fJM|ĊNq*z0eGJNx*/DOle*F˭fOT˜U* Q!9=y?&aBQHy&>1uYמQW"ZQx<ګ($? Rב/q$U$RTV #;yNR1("讉@R$P"q⠲dQmRQ3ǿQ%DS?jK]\QE@ژ?^D#'PQ:wH?X(MQb?gE~sQSl?JItQo?ǑPK?ܼzkP L@`̣P@W?ۿ)PG?@P2{ ?u\ VٔP i.?:/cPڒA?WnVPWF?N'PAUzB?m PAػ?EQPPjr?r4O:q@Մ O@I@z;GP@q<*P E{ @ǫiPRׯ`@d:4Pp;i9@;(Plc @L 8OKz@nO:!*@pZfOKz!@[ O@,X@ܬ({NsW%@ZpLN!!@uF]Nw@``YN@.GZ@ Y^ M`f@WS5NCL@^%+Z:M@/~Me^@.yu_M @r;M'j @8NM`P>>@i@M??xBEM#J?^C%EMgJ?z6M@%hNl?M?x?HF겚L@1?P>L5-?/dL@Iu?› ELAqd??sK#*?*K@X. -@SCc L )0@~ѕK@ 9@9rK@>_@dܾ}KUԢ0@B%+CKJ㝪~@\-١E Kz.@ȐJo|@iJ`P>ޭ@NCJaOWm@kFxJ`pc@8GJ @JJ` @1!2Ilc@0y@I_@HCI4 @pW"AI1l?ՄH@G?P:Hd1?YIZY?bh1ItrnOHc$οJH\ :,G6⿳υHGP1sF\Nt5Fpɔ6~qJḞEp ΧDOeCK:DCSǐ @C, BH*9B}p|A0xd9!AgbrA\zlp]A-k@u_U@iAU(!4oA 5nL#RWSB.f&敀B֦W(*A6CPvG*BإAVC(b*ZzC<4+"B%pC U/He˭Cd#Z51h},8C$1@ հC%C2FbCFd_3@ *cDg34xDV5[sD!ɗ^68PD!V6Lqh䏉E%6vRF\Z7F7U;^m.eIU;?3E J|zHh@&[J;Dx)@B%ԯJ3Z@ eQb2w6L$ʿLH D##<"?aQhI%HgP2QCTlp&-8\Q(Qo&L0Q`zl%7P /$R4fP0z#$;UPH D##v!5x\PTM%5LTP%ټYP2!'H ?Pb(*A*l\QSQ1yQW18Qr!SrTQ`hf QqQp+;,yQ0S2Qpr[R {MGydO]NDR1x P]N5jRG*єRՇRIc .CR9z࿶RH JBŖuÿS7KRbY?GS<{?g$S仡q?^Y2[SұR?fjS&YDm? |SvS.0?7SA8ކC?\{kSD?cmS*0C?$`S@SA#@-]SnT @ d{S@`S씂 @$,HS`@`_S@F@jY@BLSS@C@>I bST@SwgTS%a@^SC@rJkxS+K$@7pS0=@E[Sl_@o.5OSL6@a^SҨ=o !@xvVSP4cLW!@5S—, G!@:PS]s"@B,R=rm"@mER0,%J#@iLR0N<%@!Ǒ RȂBׄ*&@PR'u >4&@yKR[G&@²&~ZR'lX;t&@@As.(Rdv'@b >R='@@iECQxk3(@oQXj(@Ϋ)Q9(@>C8Q(͆u'@UTQͣ7'@R`ͧ7&@ڂV'Rh٤%@49RP$@DAR9y#@|SSRSM"@fz2RL+"@͋E*R@%@!@ A&RءA @%RxY @7Rp(@zzrR@oN]α R !\@QsQ!i@%&Q@=Y@Q 1_l@H:L=XQpzAf@Z%u?Q@瓞Q[R@7O|PҦл@g%P`a@ݨ`PP=<:@dPP|bo@sP;P9"@W(Py0B@gP`V @/k0gP 1 @!5P륓@'P+`@=|SPPSXXE+!@ ܆ TX4c @Pq|}T,8ɘ @r ߜT'<. @jt<Tf)0@TCԭ@@T2@Ƈʺ8Tݠ@pCTUE@DM$LT@w̏t@t뼺?aTH6@jx)nT7 @,diT Y @)T V @x6|TH @ʚvT`% @cϽT 6s @ a{qTѨ= @xTͩ@!@VTR!@ʚTHJ~!@mVTHљ&"@q|TR "@R#Tnj!#@T(P 6j"@ʚITu !@7sTp֨=!@1mTȪw\"@*G\Thu|D!@喠U(df,$@SS:U8mk3#@^GUN4#@ή-UUI#@|SJjU&#@q V sU(E$@-**[rUn$@zg422jU 7%@R:E|U2'a%@xECmUyH-&@[acUA*o&@g\]9U!N%@BD+Uu R*&@,ǑU\~%@<+ U$?%@HTGA$t%@ҊT0%@'*T(v$@ʚT .%h#@R#Tnj!#@$КUGA$t%@(> lT,T.@4ҊT0%@HTGA$t%@<+ U$?%@,ǑU\~%@BD+Uu R*&@g\]9U!N%@[acUA*o&@xECmUyH-&@%EUH0큏&@IUpj'@|SU_I(@as UNɠ(@КU@)@2dUu !*@+*Uk3)@8DU()@!b^U(u4 *@l [U@ '*@ UiI*@VSUކ{+@1dHSUS蕎+@=?"U0+@ /)UDPK,@߫iHsUÞf+@PlUĞf+@x&$`U3?:t(,@ 0UJUQ'o,@U#!IUߵ-@`CU@-hc-@_+;U{-@$c{{4UDޠ-@H)U@MgU-@`U 8?:D>-@( B&U5^-@-8rTvI-@q:T -@y[T,T.@(> lT#R-@BPT =-@L/T lTdFb0@9(> lT#R-@y[T,T.@q:T -@-8rTvI-@( B&U5^-@`U 8?:D>-@H)U@MgU-@$c{{4UDޠ-@_+;U{-@`CU@-hc-@U#!IUߵ-@ 0UJUQ'o,@x&$`U3?:t(,@PlUĞf+@߫iHsUÞf+@ /)UDPK,@=?"U0+@1dHSUS蕎+@VSUކ{+@ UiI*@l [U@ '*@!b^U(u4 *@8DU()@ yRUp~V*@1UU*@pnMU+@wLU`A`+@%.VFt+@A V0+@K"VGB+@V5V)x~G,@nѨCVWU,@*VVQ'',@Pk4rPIV@#R%[-@]jNV=p-@>;lIV".@“A+VOPKG.@fVu t/@ VԞf`/@KUG/@PMgU/@>wU.4/@`kU@/@?͹U`o/@pPs8U;ϐ/@PUr/@a UdFb0@ykU E/@ 7j\U/@L'KU s6}/@`tL>Uz/@> !U?/@U<͙/@o  U@HyK/@0ՈTZ'.@CT1Ƴ.@(> lT#R-@&FVxF'KL*@pnMUQ'',@*VVQ'',@nѨCVWU,@V5V)x~G,@K"VGB+@A V0+@%.VFt+@wLU`A`+@pnMU+@1UU*@` UxF'KL*@ iV^rmS*@j4r5VP%*@ ynPV@n*@AsV`d +@FVx,~x+@ʮ#VF큑+@vW*4nVD,@B0bVدwX},@eV䤹,@*VVQ'',@'0 kWx,~x+@fVMZ1@#tWPX-@x W) =-@n4W7 =!.@ kW;.@PVVd 0@U`빝V0@geV\)h0@$EtVРx0@QL}V0@1:V8I!0@r V̠寎@1@)-%VA?1A1@VM1@YVMZ1@:(IV@Xg1@a]ΦIVq1@"'NVL/@|n(;V H/@5&VxHyi/@H,!VH/@fVu t/@“A+VOPKG.@>;lIV".@]jNV=p-@Pk4rPIV@#R%[-@*VVQ'',@eV䤹,@B0bVدwX},@vW*4nVD,@ʮ#VF큑+@FVx,~x+@l4V0+@k7VћB +@7$V 㪟@,@tWPX-@("'NVL/@eVq2@:(IV@Xg1@+IVT1@.AVܙ c2@DK6VlF1@7>;,^VJV|2@:3Vq2@>;,V LpZ2@eVh{BY2@6V)2@SACVdÍ1@Du٩ VBN}1@"Sw^V {!1@a{ECTV4/; 1@t VVĿ0@]Q#V@{C0@Y1.V i|;0@|n(;V H/@"'NVL/@a]ΦIVq1@:(IV@Xg1@)|SSR i.?__zMXSS(@\uF]Nw@ZpLN!!@ܬ({NsW%@[ O@,X@pZfOKz!@nO:!*@L 8OKz@;(Plc @d:4Pp;i9@ǫiPRׯ`@q<*P E{ @z;GP@Մ O@I@r4O:q@EQPPjr?m PAػ?N'PAUzB?WnVPWF?:/cPڒA?u\ VٔP i.?@P2{ ?=|SPC8Q(͆u'@0U Qu z'@SwQпw&@M]NQ8 ]%@Qx^$@DRy6#@nQX$"@$ Q*a\>F"@1uYQ(dP#@gQؙ‚l$@5УQ_ %@AjQjZ"?&@xVTΒQ`g '@^|QXSS(@m);,eeQR&@׾78Q9-PK&@`QA%@>Ǒl Q(%@qPHu |%@0|SPnL%@ȫiiPOf$@6`+8P`L'$@ -P{y$@b|-8ZPh0'gH%@'='OxjHg%@YN'pfn%@t]OEt-$@yFº1O8| {#@tr\Nt#@1PjNd>"@NPUNљ )!@m U6NH*4!@__zM@] @ayFNq=P@ ˛QN,@"s%N(,@]áEN}t$m@L5-?HF겚L@1?M?x?z6M@%hNl?^C%EMgJ?xBEM#J?i@M??8NM`P>>@r;M'j @.yu_M @/~Me^@^%+Z:M@WS5NCL@ Y^ M`f@``YN@.GZ@uF]Nw@<8΄N@b(+ZؑNPM~@@^T6L`;j_ @v L @w`g6L@k&&@› ELAqd?+lM#*?@&4J1p@B%+CKJ㝪~@dܾ}KUԢ0@9rK@>_@~ѕK@ 9@SCc L )0@*K@X. -@?sK#*?› ELAqd?w`g6L@k&&@v L @^T6L`;j_ @lM`S>@9vXL0_N@\2겅"K!)@B%+CKJ㝪~@,lB%+CKaOWm@@3u#@pi@I@JC1!2Ilc@JJ` @8GJ @kFxJ`pc@NCJaOWm@iJ`P>ޭ@ȐJo|@\-١E Kz.@B%+CKJ㝪~@>겅"K!)@<؃BKsA @PK`4Y @?2$3K<Ͷ@1vC=Ko(G@@&4JȔ@.w)J`FX @kpJp@ ӃLcI¸XC@1!2Ilc@Dʾ@p]H@:@~-ٙH@`2 @rFH@.ʳ_@.^*H@@`:oG@"@ ꑹG@@O`?@jyYYG@@3)@&ӤG@II&@pQ\G@:&@@T"G@@m$@XR6G@`C_@[hF@{5@buYF@`b@fF@F@/zF@?h@pg2 F@xHe2@D_F@\@E@wYv@<&E@taT:@(x'E@i>@jv+E@@[gnS@{>#S */'TW4{=T1Vl 0T?Ip@(;TnUyU%TZza">T1(ҿzgeVT_?oT+?j ߜSe9t?7SA8ކC? |SvS.0?fjS&YDm?^Y2[SұR?g$S仡q?GS<{?S7KRbY?BŖuÿ.`PSQ1@^zeP4F2@ gPbkǃ2@2]qPh:m2@^zePB`:2@z7vP@)1@|SeP#X1@PPSQ1@PԞ_2@Y4qP4F2@gPbkǃ2@/p_Y1SpMZ|1@<4 SH0.32@ VtdSp<}2@3b9S,^5f2@΂ bS{#)2@<4 S<# 1@į9SܕqD1@.^44MSpMZ|1@WqS21@_Y1Sʤ92@[;SDB\t2@sSH0.32@VtdSp<}2@0hYd>U-43@jdR<4H07@*׮c)T<4H07@HYTW7@K'T'!7@ҲP}S6@.SM2f6@(~]P4@hgeɈSIIm4@Si5S5@dJSeȓ5@\xp=S&5@6 T\5@C>I!T:~ 6@~m VtTsԞ*16@֖g%Tc6@ 7OrT 6@+BPTԞ*6@\x6TTkg#+6@>)T'6@}pVUuur5@̋#UB5@Yd>Ub5@QUw46@z`+U!LՐ6@2ʮT0 6@T(y>6@qTG(7@׮c)T<4H07@1@H+KC9@}XZE6W=l@@ /%\01?@Fb@6?M>@Q &6t{R>@}XZE6K=@ e *61Dn=@E`6Ön<@jsB5:Hx<@Vb h|5,+;@̭4 ;@ 5L4@ OK;@Zp:d4GO/*:@~%ڨK38mA9@(2pAY/9@~w/82H+KC9@"wߍ13Ha:@H681hw:@}1GY_ ;@Œz|"1t{&;@ wsJ1w<@Ow0`x"l<@:gc0 U<@\sik 0XmAO=@0e6J/5F>@ /OV>@d͇X/IM,?@{̜͸/X?@m0U?@PQ0n*@@[^d0̺ml@@sB0W=l@@&]1Yq9@LL<9 ;Y68@$sB9x:˻7@,c9(7YO7@r D97q6@2@9\0\6@y9e 6@lw ܾG:x65@p:icj4@^d:.ɝ4@0,a:z:(60744@b1>9_)l*4@8 Q3@jJ8x<3@{XZb5H34@Uw504@}ik@28"帰5@v82`Oe27@BYGIy1xZ{97@2H2'u378@ ?1@2L8@1'9@Ͼl1H+KC9@"wߍ1pAY/9@~w/828mA9@(2GO/*:@~%ڨK3@ OK;@Zp:d4 ;@ 5L4,+;@̭4:Hx<@Vb h|5Ön<@jsB51Dn=@E`63xdAw'@ d ='9@"~0, Q3@jJ8O 3@\ v{ 81@c$~[<@_0@:!hC2@@1XO1=2@"1H`5@b1>10tcj 77@0213&8@:,aK1Jm8@nQ yZ19@(-1'9@Ͼl1@2L8@1'u378@ ?1xZ{97@2H2`Oe27@BYGIy18"帰5@v8204@}ik@2H34@Uw5x<3@{XZb5 Q3@jJ84xB:1q(@y&'0@,a\0j0+@hH 1 Ft=,@B:1Uu-@}g/1R-@艸}a0B8>/@ v0 #"0@4_0Lt0@Pm:+Q?/ߌ^0@[E. E#X0@S)'-'0@y2,M0@D*  0@ L)Ft}.@ lW(;-@/??(}L+@St'-?:*@FV'.)MH*@y&NP)@n#'|D(@${ Q',(@9h(_i(@Ec(.)y(@8(q(@@܅o*&)@hˤf+Y, ,)@p9/;큟A)@|/j)@f%0pk3t)@̉}m0|D (@05c(n0?큃M*@wJ{/љB*@WH{a/ Dk*@@I /L*@@6FH.u +@p$l-**@., Bר*@; +nД+@ba,HnI+@ٱ,H.)Y@+@h_-N%B+@h8).x+@H+.A+@?/(BF?+@a\0j0+@5x lW(ok3V1$@@}8@Ln#'|D(@y&NP)@FV'.)MH*@St'-?:*@/??(}L+@ lW(;-@@-?' =#-@ w5U'=.@P~ &]%.@P7c4M%@|DC.@0f$w,$qrm5.@Շf#hK9.@Nٸ#.@q&{/@@\ B,寬30@v>?9S0@p{oH 4@Xn 8@Y}8@\9]0]'6@+?4^4@|@7hi$4@>w@13@ƚ\, @l&]3@SC @xdk3@@h@u'3@@T+0@SEL @$n8-/0@|' @hҙ"/@ߌ-@@=.@+?x7ͩ.@Z@?1h-@r;?tڙ-@0@\ ѿ ?-@oS5@ y;.@*-@@v@U6-@FIۈ88p*~,@mֽ.)+@?dy +@.. *@6L0;*@i"*t*@`kq\)@g>i~m'@q>Ɇ&@B %@p B6ѽ$@ODLq$@'3ok3V1$@n%Q %@tSG$@pڼtC6$@M0f@pk3*G$@䓴}*bK$@q0.絯wB$@T i$@`6 oW$@ #Rϕ%@  %@p,=!A$-%@@N٠)!uE&@p  p&@{!{'@M"~!y|D=-(@Фw5DA"(@/T"Py'(@/T"#Xsc(@w50#ю (@T$7+'@ /%p1PK'@Faݽ%`I[(@)Rv&YA(l(@[&X'(@Ф&nV'(@n#'|D(@6P["<1;-@Y8|Oe;@'["<1X4@tp^0TU5@6?)$S5@Za<*n6@!)`H7@h8'@7h_7@H5W'K59@,_!,ʂ9@wi^!8|Oe;@Y}8@Xn 8@p{oH 4@v>?9S0@@\ B,寬30@q&{/@Nٸ#.@Շf#hK9.@0f$w,$qrm5.@P7c4M%@|DC.@P~ &]%.@ w5U'=.@@-?' =#-@ lW(;-@ L)Ft}.@D*  0@y2,M0@S)'-'0@[E. E#X0@Pm:+Q?/ߌ^0@4_0Lt0@ v0 #"0@K0\10@0BE06*1@Aw%0ur2@A0#]3@`07h3@e G0<9 o4@dY"L0`_4@["<1X4@7>?`0ɑ@@*d|`@|Dx(@嚈@lc @ao?`0ɑ@uS8?<T@a?Y$@>?Xl1 $@Fd8?|D%@gv@?x#ޔ8&@!rM'?#yn'@?BDH'@ƚ\<@prX['@@@љRw(@tY@|Dx(@c @AQrmR'@@`4 @/&@@*d|`@y;0x%@@R @N$@ dѼ @] $@G @Hg"@@VhL@*tF"@YS@Ҩ=!@dx@Nɡ{@嚈@lc @8?AQrmR'@``Yv/@TBx7@:+ݳ-@pJ6@B1.@0LN5@..@}8g 5@xk.@P ur4@``Yv/@cg:c4@"d_/@ 3@`ljә.@z 1@)~.@Ф0@N(D+@@e^/@3u+@!,@0`n+@NN+@-+@ =*@ Ӗ> 1-@D-*@k0,@Q)@@Sm,@4c)@_t\,@(@m+@eGR(@1,*@+@`+*@k3:1+@1,7(@-p *@7<'@0jIp*@~%@`.)O*@ff%@(l~*@@:$@*@pN] #@ó)@S{V"@?)@7@g*@R&CR@u 22*@!H@=:*@`+@L%L*@\@4cZ+@ /y@S~+@@R%!@[#ϐ5@KY4'@TBx7@ n|)+@j^ 7@qI,@}6@+ݳ-@pJ6@9嚈@p[R^@l0؃'-@4cZ+@:嚈@lc @dx@Nɡ{@YS@Ҩ=!@@VhL@*tF"@G @Hg"@ dѼ @] $@@R @N$@@*d|`@y;0x%@@`4 @/&@c @AQrmR'@=q @D%)@2@7)@@?Xl1 $@"Z>?Y$@h ? ?:#@h?S"@a0:H_@TEP @p-polc@*XpMW@Az Q|qc@;UMx P {@JaP?\ф@@rp5]m sp @@arH#@ D<p#Ri$@@P@]%@z@ &@Æh^=%@ܿo]Y2&@__?@y &@=H,4!KhZ@@rp5]Q %@.T i$@q0.絯wB$@䓴}*bK$@M0f@pk3*G$@pڼtC6$@tSG$@n%Q %@'3ok3V1$@ODLq$@p B6ѽ$@V0N$@@͙@0ۤ#@2A,R]X8#@,0-)#@}^ c#@@arH#@@rp5]m sp @JaP?\ф@;UMx P {@Az Q|qc@*XpMW@V} 0,@ R @ NM@k~-V@@u@{ SeKhZ@O@@iPu@`OiƬ@Pؙ(`6@@0G@N"T@W`N7@)h `|Q{@H,4!+%@_Y `R@ P ?\@W Np@_m^ `C@Ďq x$? @ P @xiD1h -)1 @NTJ &!@1t( lj"@ہ =#@`ku B$@T i$@>H'B.-Kt<@NTY, ,)@Fhˤf+Y, ,)@@܅o*&)@8(q(@Ec(.)y(@9h(_i(@${ Q',(@n#'|D(@Ф&nV'(@[&X'(@)Rv&YA(l(@Faݽ%`I[(@ /%p1PK'@T$7+'@w50#ю (@/T"#Xsc(@/T"Py'(@Фw5DA"(@M"~!y|D=-(@{!{'@p  p&@@N٠)!uE&@p,=!A$-%@  %@ #Rϕ%@`6 oW$@T i$@`ku B$@ہ =#@1t( lj"@NTJ &!@xiD1h -)1 @ P @Ďq x$? @_m^ `C@W Np@.q!A%@f$%!-Kt<@ j"}ttA@H~" @`b3",Ѷ@Pw5#0!@ߪh{$ d @P{u$, @8_%* @["$VX[n!@U>O%P!@w >%+"@0T%a`#@&<&y|$@ Pa'PG$@ L(U #@Ps\M(hp#@R:+1)#R=#@8³l)Sw"@;~*`4c\!@^+@x O"@8%,⪷#@w³,rj $@I(-(ym$@?\Mb-yP%@٭-Os%@'B.hBװ&@8Ae_-h>'@p9ޛ,-PK'@p(>,+Z'@gH5+p ['@(à|+9&y^'@>?+ )yI(@\:+o+Pц~(@hˤf+Y, ,)@?̉}m0hBװ&@hˤf+;큟A)@̉}m0|D (@f%0pk3t)@|/j)@p9/;큟A)@hˤf+Y, ,)@\:+o+Pц~(@>?+ )yI(@(à|+9&y^'@gH5+p ['@p(>,+Z'@p9ޛ,-PK'@8Ae_-h>'@'B.hBװ&@`RRvT/aʆ&@0h '@,NKݕP03'@+O0d'@Y"$0⪁W(@̉}m0|D (@@³&p%Kl@Pؙ(0!@W Np@ P ?\@_Y `R@H,4!+%@)h `|Q{@W`N7@0G@N"T@Pؙ(`6@@`OiƬ@O@@iPu@pG]|p%Kl@p%Pt"ՎeT@ի# `_@/Ae%p{Q{@³&Qlc$@olLf&=bl@` {K&@M 7:@hD%d%a@P{u$, @ߪh{$ d @Pw5#0!@`b3",Ѷ@H~" @ j"}ttA@f$%!-Kt<@.q!A%@W Np@A;~*Qlc$@P{u$PG$@;~*`4c\!@8³l)Sw"@R:+1)#R=#@Ps\M(hp#@ L(U #@ Pa'PG$@&<&y|$@0T%a`#@w >%+"@U>O%P!@["$VX[n!@8_%* @P{u$, @hD%d%a@` {K&@M 7:@olLf&=bl@³&Qlc$@yj'pzݠp@=/(0@ @(j)901@C?*@(S @;~*`4c\!@BP]X8#@|j@@ y;.@'p B6ѽ$@B %@q>Ɇ&@g>i~m'@`kq\)@i"*t*@6L0;*@.. *@?dy +@mֽ.)+@FIۈ88p*~,@@v@U6-@*-@oS5@ y;.@0@\ ѿ ?-@r;?tڙ-@?r,@C?Pb6;+@o?(_*@_b?l~")@|j@?U@)@ƚ\<@prX['@?BDH'@!rM'?#yn'@gv@?x#ޔ8&@Fd8?|D%@__?@y &@ܿo]Y2&@Æh^=%@z@ &@@P@]%@ D<p#Ri$@@arH#@}^ c#@,0-)#@2A,R]X8#@@͙@0ۤ#@V0N$@p B6ѽ$@Cdj7,@#$E $@XdAH_;@H&@>XdAH_;@u(@G,qG ;@@Ug:@0~@ >9@N;@U_G9@:~@: 9@@ &`8@຦ǖ@ki8@Teo@XA07L7@lcSp@ o3k6@oU+@X7qA6@`Q{>@\0g6@0"@zD5@P!@Pv4@pKJ@iJ4@,G@=w3@pH @(f2@Fiƈ@2@>iƠ@ s2@@Wx @_U1@P$EH{ @3"1@]^X @p*j|0@(< @KJ0@#$E $@@>7/@u@Q{/@Ѓq@@w.@ Z @(O].@` @0(O]-@Kp@-;,@ ="0@<,-@PT@dj7,@[R@.&-@ Ng@&O]-@`NM@` q.@z @@20@@s@>bJ0@C%i@Ht0@7%h@`ӧ0@@`1@@K@8q޹c2@  @(82@iC!@q2@,@!@y3@UR&"@pG4@f"@X85@U"@%`L5@†U"%@MAH+;6@f%@(96@H&@8!K@6@u m%@ 7@y޲-$@]짎7@s\#@0>e7@d"@qށu7@$%!@@mA Adb(?@?%l0!x3l?@| D+!R@@:2X!063a@@NCv"@@ D{Z#+@@`  %|f.\@@.f՗%T@@ ܭ6'hS8@@t>JN(lk"~@@HԩzW)lX@@Fl+Tw?)e@@L+t{->@4SJN-5F>@ /XmAO=@0e6J/ U<@\sik 0`x"l<@:gc0w<@Ow0t{&;@ wsJ1GY_ ;@Œz|"1hw:@}13Ha:@H681H+KC9@"wߍ1'9@Ͼl19@(-1Jm8@nQ yZ13&8@:,aK10tcj 77@021Ö6@0/ԧI5@p40,5@)h!8@,h t)8P7@C+ɶ!)=t8@2sa(hv7@zq'=8@A(y&/7@%1A8@Ek%d"P8@4/&y}8@ 6z&C k9@Vlpp&x>\9@B!'>&:@(,P'cj*;@Y07'~Rc;@ΩzC(3'<@H(w"<@өze)wL\-<@`ةzw*`D=@,e6*D=@X[(`ӧoW=@m( xu<@,sq'гK_<@aX'[m~<@Lk%fv<@p=;6#฿*s<@',pT"?Y07 <@$ʑ/ !d۾=@?wf hxX>@Wy -Gq>@mA HlX@@!0BA@NCv"063a@@NCv"H%@@ O"8^m@@7)c#p= #A@`Q$@GA@@ 'Z=#A@ϩz_(@GA@:s(+A҈tA@tY[_!+xOFcLA@2+JN(T@@ ܭ6'|f.\@@.f՗%+@@`  %@@ D{Z#063a@@NCv"It{->@E:BBcD@iZ$O@GA@@ 'ؓA@M&ƒmAB@+q'%8cB@e6g0'@w;SB@4#'@nB@מ&S,6C@ & C@3%h(D@@cZ$2(D@Z$#v(D@iZ$;=D@<%(e7D@B!'oGD@}T%BG)ĺr\LD@}Jig,BBcD@\!.b-}<=D@i.Im D@„S0pC@ĄS0\;^DC@Kp1IB@F681J $B@\GI2!fA@,a2{?)eA@Ҽ 36dA@|/3!MYA@S68>4lUA@A54+{̯A@~A5qycA@p= #6}A@أp= 6tvWA@N%'74aKA@-rGI 7lyA@]P7|L,A@ V]c86A@-uz8A@84'ް@@n[9 u@@zC7:JFcAj@@E:U, @@ʥۻ:g\?@KJ:Q͖x?@<1ɗ9ם?@m{9t;?@B^83?@;b17\01?@Fb@6B[@@ b15A@@-w1e4+wrT@@M4Le.b@@ 13 UN@@BC\k3@d͇X/5F>@ /t{->@4SJN-Tw?)e@@L+T4@@[V,|3LA@p`[_K,D:A@2b9-eBBA@<=.L<5RU'A@4~.J,0A@(.07SA@!0jfA@:p40BA@DC1/@bd/I;U, @@ ?9 U, @@ʥۻ:і39?@wp-;cH?@bd/I;3>@ vpl:a076>@ue:XU\>@S:,`H ?@u@9HIU?@ ?9Q͖x?@<1ɗ9g\?@KJ:U, @@ʥۻ:KrЫG'@b1>1=t8@2K (`n)@Dч)bC)@ -q>(@OqzR񵿚(@^( B[](@( v'@&У (@"*lP!7=)@2(`n)@Dqw(@حg~f`hx)@h`: *@|Ѧ@ >LN*@yѦt\S0@H9*?컒0@Y5} x50@87 0 1@nu.@My1@@!# LRY\"2@La,07v2@xWchI3@P,*3@@l7qj3@`;(307 4@Kb'wHt4@`i C4@xE8ƃ4@n2 xe5@@rz)N5@*>5@o EB5@L^A!`V 5@vm# #Fp56@."#Yԧ'6@o k+&6g6@bt%(D\6@h &x>t7@XbX%/7@%=8@A(y&hv7@zq'=t8@2sa(8P7@C+ɶ!)h!8@,h t),5@)/ԧI5@p40Ö6@00tcj 77@021H`5@b1>1=2@"1>hC2@@1XO1Ы^k,@NfdZ1,@zCbl1x*@(V0BϠ)@"~0Pn(@"&L1dAw'@HM1ЫG'@( S0O`n'@o o/ yRE?(@ݭ-xY(@T%,d| )@;l+z)@YF*ǖ>6*@i(P wD+@)9=(ۂ:z+@g}&?ym_+@COv% b0O*@"sE$=*@-#'`n0)@UxU"`?)@^ !> y*@  ! x)@)b0Dt)@}_ft(@xѦ-qw(@حg~fLHPYa=@aѦP̒>@:v iV4@Z8OPh˹>@|yvB5>@g?RDFP̒>@ k>@Fɫs %>@jH\@\0=@+?9 W=@aѦ xF=@ Y HPYa=@ ̶x>K֡=@@tWW?K8=@:viV4@Z8OM!A@LV=@d A@е@@G&A@QZ@@r\A@|!Tn2@@YA@xijO)D@@Y|A@&W?@hA@}?@᥏9|A@Ț1?@=5RvA@zZ?@DA@E 4}?@A@T?@SvA@LV=@.< `iA@1U0=@o!A@ '8?@!!A@@'8?@!A@(8?@07GA@TP#?@DaKz>A@p.?@BT`A@tU @@P:KzA@,`xi@@,$AA@z77O@@Д~"A@@@rhA@Zxީe@@* A@:*ӡ@@'A@е@@d A@wީo@@0FoA@L >V[@@G&A@QZ@@NpД~"A@Zxީe@@x)MNB@)&RA@ 'A@е@@* A@:*ӡ@@rhA@Zxީe@@Д~"A@@@xA@,K@@TGcA@ROcNA@XA@)&RA@ƒm^9B@*LA@x)MNB@5A@lBƁB@ﺖ@@'A@е@@O |3E@b39L?)E@"-6 |3E@6aKvE@';#V5s|3dE@_;N)5O@$AE@/4K/F@ɗ4|_q;F@bjyo3IF@^GIE2lp=F@dCT2YFE@d#h1,v'F@!0'+{'9F@_#Z70tI^xF@b=r-0hYF@C/D$AF@P?/y'G@ ^/r\pG@`Bk.ȱAG@slpH0-`H@ .,$@9YB&@(̬B@rc "@ gN>@~w"@% @@pڠ @@@@f @@R_@@h7TXs@] @@3@<%v A@w.H @Z>SA@lB @8lA@Ыo @Wr7B@@ @^$yB@p#@(̬B@qk$@ gpB@pk0~\$@ -E\B@Њ:&@ Gq ʋB@963&@<3sB@33%@,k0z4B@!B/%@\EA@U%@ppjOxA@ 0H#%@b]jA@>jL$@,V*A@$@42.@@pB%@tf@@Ϧ"d7&@J*A@@9YB&@ @@SP&@78/@@x%@Ox @@~F%@4Hy?@#@F`?@=j$@P[:>@#@XÚ1>@rc "@ gN>@Swi^!xdk3@KY4'@$p'B@>wi^!8|Oe;@ 2T!;@_'T! ;@i%Y!4t^<@~-< SPH=@?sd>x|P=@`ڼl>@ GpmqM>@& Ԟ>@@9e. HO%?@? \EOx?@}(֒CA@L%[xA@oV ~)A@`lPA@QC?>7&B@x?ׅMB@(K @3VdB@VC@^nB@ >G@S[B@?sYv @)B@oPR@$p'B@1ʳ@^qB@@ @^$yB@Ыo @Wr7B@lB @8lA@w.H @Z>SA@3@<%v A@h7TXs@] @@f @@R_@@pڠ @@@@~w"@% @@rc "@ gN>@t|#@ l=@fQ#@<@@&H#&^#@N$<@xB##@0o1;@=B#@ $;@`ԏn#@ *:@\щ"@x{%:@=LF#@P o]9@ڂ#@18@\ё$@{a8@0*%@8@oU'@4=8@KY4'@TBx7@0>R%!@[#ϐ5@ Uӵ@X{O3@@h@u'3@SC @xdk3@ƚ\, @l&]3@>w@13@|@7hi$4@+?4^4@\9]0]'6@Y}8@wi^!8|Oe;@TSvA@tZ2=@CC@x@@r\A@|!Tn2@@G&A@QZ@@|V(@@jkeC@x@@CC@@@s.C@KN@@xL FB@,Z'?@GB@ ->@}B@`IJV>@,_uB@wHy>@X&M^B@5=@'@B@@LX=@ B@tZ2=@lm_zA@*G[=@SvA@LV=@A@T?@DA@E 4}?@VA@t9;R?@r\A@|!Tn2@@Uԇ-I@#)76@ f2L@ʂ2:@ԇ-I@‚>8@zI@:]HK8@`68@V`Rw8_I@#Ok8@r\I@A?U:@ gI@68@Rw8_I@y<]'{9@I@5:@CƪI@A?U:@8fI@ 7#9@r\I@0.679@hޱI@ <]8@(cI@#Ok8@gI@68@W`kHG@$ <@`B5H@t5>@ E G@[rI=@8rH@1uӈ=@*U H@>iN=@`B5H@I]'P<@x>MG@$ <@$pۺG@P=@kHG@TuY=@X5RG@t5>@E G@[rI=@XjkeC@TuY=@GHH@@\QPB@CC@@@jkeC@x@@bɀD@~5A@B%D@&NHlA@k"D@ -B@t$D@V'음MB@D/f,E@BNvlB@`FccE@@\QPB@L8E@D̠B@\:ӏ%F@P1B@PbF@ǎЕB@lֵF@:9 A@ah G@`3A@ͬmG@ OA@F@_A@!VF@j@@ G@-`x6@@60֪G@ȖH <@@D1G@@Í?@{WtG@&W>@oH@1>@L_H@,5s>@GHH@&WA=@E G@[rI=@X5RG@t5>@kHG@TuY=@۽ZF@-'-=@$FD@7h0?@Ȕ33D@1~?@CC@@@Y4c5RJ@7I0@hnM@H *[e:@/)몚K@U6@$qoK@;j7@BNK@q]7@ }3K@{7@tjK@8Bk!8@8r\K@(qE8@ZpK@cÍ8@ f2L@A?8@-lL@#YI=8@M?UL@l-7@M@xdax7@V]M@Hjϐ7@h^M@Ԟ 6@C͜M@%6@hnM@94˚6@ '/M@Í~O6@ٛM@x 5@8%M@oZ#o5@@9nM@,]15@xq5Rv>M@;m4@D4@5ȱ7L@j3@ l"L@`BIL3@hL@ u|2@\r\L@Y2@X NL@L*2@jAL@pjxM2@ jJ$L@,wE1@LB«K@T;V1@@_K@'ߡ1@88/K@, ou:1@0 ?eK@t`0@t TןK@tb؃ 1@(jJ@ca)0@1J@7I0@IMdJ@AY1@4c5RJ@1n83@KfK@j3@.UK@K6@몚K@U6@xi!L@,"ur9@| L@ʂ2:@yV.L@H *[e:@*>L@MsZ"O:@<.2L@,V_9@xi!L@,"ur9@Zz!d@C 0: d@qp9@- tu d@/: d@42aw0 ]|d@C 0|d@L=r(0tu d@/pߌad@[nV/訞d@zlp.!d@qp9@-dZard@+*-<d@v2z/ d@!:/pߌad@[nV/[BGY@'A$@@TZ@#$-@4oY@__(@BGY@HV*@ܦNBY@Ls,@Z@7X_,@pNZ@j,@ЂZ@Sa\+@@ ğZ@#$-@s~Z@ Ug,@@TZ@8<1+@h%sZ@pA(@"|ݞsZ@4c"'@0 Z@љr%@ x@hLZ@x;S%@n̕eZ@'A$@oY@PNC%@NY@љN&@4oY@__(@\XX@@/ 7@ަβeZ@Ӗ1j4@@pNZ@j,@Z@7X_,@ܦNBY@Ls,@BGY@HV*@4oY@__(@dkY@ *pJ)@^<5Y@0|DA)@4@ 5>Y@AP*@[PBY@x{O*@vZ2Y@=/(@bEGX@آdV%@fq@hX@4c#@r7;X@8z"@rNX@(lj"@Y@PAG @FJdY@!@YAY@2nm@ gY@+e@}A 5 Y@ ^̏@p1tY@V̏D>@wIY@@/ 7@J?DY@@+:Y@@@@b }Y@`(@OF4X@!d@4ئAX@`_@??X@A_ȡ@>X@큽 @RCX@ }t-@ؕX@,4 @X镐X@!@4_mX@Y#@36pX@y%@ X@k3'@KX@G)@X@q6*@tݞAX@j+@bX@jz>-@JX@W'U?.@^dX@0_.@uйX@D-0@6ǙX@Hd|0@VvX@ud1@XX@\^5r2@`sX@L&W2@=X@P-4L3@SkX@@&W3@X@|@E/4@da/WlY@Ӗ1j4@L #Y@;4@&Y@=E"3@ RY@kv3@)XXLBY@Th2@_CY@H:31@TEY@ L2@@nY@HU1@ =Y@ܒԞ11@~}Y@4 DO2@^6Y@*=2@^u-Z@qOm1@D!c1Z@4cxq0@ަβeZ@Iw$/@pbZ@8lrmr-@pNZ@j,@]@da/WlY@Sa\+@9.!Z@}xv6@%s~Z@ Ug,@@ ğZ@#$-@ЂZ@Sa\+@pNZ@j,@pbZ@8lrmr-@ަβeZ@Iw$/@D!c1Z@4cxq0@^u-Z@qOm1@^6Y@*=2@~}Y@4 DO2@ =Y@ܒԞ11@@nY@HU1@TEY@ L2@_CY@H:31@)XXLBY@Th2@ RY@kv3@&Y@=E"3@L #Y@;4@da/WlY@Ӗ1j4@=|1@*MGZ@HZ0@_Z@^'+/@9.!Z@p =g.@s~Z@ Ug,@^Htf1hW@Y#@o@ 5KY@nV<@Fda/WlY@Ӗ1j4@X@|@E/4@SkX@@&W3@=X@P-4L3@`sX@L&W2@XX@\^5r2@VvX@ud1@6ǙX@Hd|0@uйX@D-0@^dX@0_.@JX@W'U?.@bX@jz>-@tݞAX@j+@X@q6*@KX@G)@ X@k3'@36pX@y%@4_mX@Y#@_/WBX@9wY%@{[PX@d&@iX@W(@NܜX@kG>*@}rX@0UG+@@^qX@"-@?k6fX@ܽҾ0@ԥ҇JX@Y0@2^ X@P\>_m0@3wW@h&Wm/@漳W@da\^/@L^W@X0. 0@?G$W@p)n8F1@F=ɔW@gب62@]̍bW@xu]3@ airjW@(#]'3@6%EW@]3@laW@44@tf1hW@7hy5@)W@4dR5@N +W@u> 6@qzաJW@ IG6@NCW@~x6@R9.SRW@`1- 7@wTW@4F8@NԆW@H5]'7@ȩJ^W@1}kܬ8@@GW@h-L)9@W@{U:@i1W@ԞՒ:@!X@ࣉ C;@pHX@0.r;@5SCX@0 ;@26YX@;@oTX@7LB<@ ^zX@nV<@حJ?X@`҂I;@ym1X@#;A;@}X@mE]X:@dX@P-L09@99p_nX@4A?i9@+fX@ RP7@0ƕAX@+38@RsX@F o$7@) X@+r56@,!jX@xI6@S|X@(15@"e̍Y@\5@U/#IY@P35@o@ 5KY@'A?o5@=|1@ԏ FZ@iؾ2@<`Y@C3@ub Z@0A?3@ 4Z@)3@s Z@&W<4@NP Y@kk=4@`w8PY@54լ5@DkY@}xv6@N\?Y@Ps6@$IY@d)6@LN؄Z@lҳ6@miUZ@ Z7@ʉQysZ@Ԟ6@TmZ@l`#U6@ZF 5NZ@F76@ܩdZ@j5@"f'6[@`h5@íZ@d4@q{mxZ@ca3@L^jZ@`|3@\PZ@L:2@ݷJ?*Z@ 0@l{@h?[@Pgi0@"8[@p5c.@ WuU[@(4c *@^L[@CnU'@Ϳn[@HљB&@v#Z@HI$@^Z@P ?:#@dAp JZ@z3!@2Z@ _i{"@2Y|DZ@H4cD#@n̕eZ@'A$@`hVp_@vYB@'X`@)(!~E@0(U`@mY~D@[5`@L~D@d6`@qD@u &`@B0TD@1U `@Q -">D@⹍D`@+OLD@d(_@tȅ@D@#_@SvC@,"_@vDvC@12_@8DSC@>I"_@<>փC@0 `@!]NC@dx`@.TH-i/C@۾_@y&C@ U_@^ C@_@OrB@b -_@NՖH C@?_@2(Og4FC@@h4N_@vj:UC@H_@؋lC@!X_@>]C@;)T_@D-œC@@2/_@̆)C@hVp_@C@iE_@ HD@z_@h*0D@Tj_@lWD@b_@FgD@(]{`@r+D@H-8`@̅DD@d3`@e6E@u ?`@)(!~E@ TzT`@'2E@a}r_@p1A@;Y.`@!]NC@J?/_@<B@i0_@<耑B@ 6@S=GX W@7@-V@*՟7@!6V@/B:6@[2t,V@c#7@IV@[8@lV@@4b!8@n~W@lY8@,V@)L%9@LҷV@H!9@RzV@LμSE9@^GuV@ Ԟ9@ NܹVV@p1:@@% $V@@fOr:@y/m V@ *9@;V@7=9@sV@_ da8@~,gV@hjl8@,V@\;8@(J!V@*7@|/p~8V@g6@8e BV@XB6@>b8V@ٟ7hʰ5@.CX V@Xzx5@qU@ ~5@oU@kkI4@^U@ _&4@dCU@Q#z3@4p9T@PM2@y3G"T@T*ի1@+ VT@BB1@&ŦN=T@dY0@ߎUlT@jO0@x(2T@莽h/@ T@a/@໚T@,E.@X2T@O%+@H0GRT@љ4*@&3S@A(@ uS@ $@&S@0%"$@ %S@YZ"#@^$S@nn"@l ɑS@XN!@lL <|S@ :ڃ @nbS@0F%@^%S@!'m!@h_RS@(i$@[R@ ӝ&@JvYR@qX'@l#YR@S8{)@ o#[5@97Q@{4@@pQ@񌽘4@sJQ@;6@Vp~FiQ@#̓es6@<_VQ@TԞ6@He'N Q@Ԟ$7@%5Q@0.[8@"}rQ@7]'E[8@D̍Q@gV79@ Q@9@NϊQ@@3L}:@P[`Q@u :@JtQ@;;@*1FQ@0.;@|+X4R@I7*<@bB\R@1;=@R@p#>@R@tP?@TR@Dc"@@F-XR@:a@@LJR@g2@@je'oR@$\[(A@.O{_R@Y_A@(lsR@M@A@I7S@t&TSA@&<˘uS@F;A@rv(bS@55)A@I/S@2h@@T{^S@:RH@@XES@Z3=@@ o/WS@ܜ O@@2e JS@Dk?@ӷ*S@?+>@,GT@ej.>@T@3urغ=@JT@db<@M* 5CT@pZn8j<@<T@5;@z[xT@ 6@)W@4dR5@tf1hW@7hy5@laW@44@:NW@XO315@U W@J5@(nV@y.6@?[̍V@RI6@p*6V@Kg-6@LXV@t d6@&ZxV@15@ 6@e 4V@$L4*:@6W@dK<@ CV@ 2ߐ;@6W@:s;@ $W@08:@+V@x-:@V@u/:@U/WoV@$L4*:@6*{5V@^EU;@ 4V@xL;@s^V@7A? <@HV/WV@dK<@xV@|Ҡ<@,CV@xOe <@CV@ 2ߐ;@fJT@dXe:@.p/ V@0.7l>@LV@e;@yV@P*!r;@.p/ V@Dkv:@V@!;$j:@.|U@dXe:@eU@`5:@#PU@q#:@*p~3+U@|"<;@z[xT@@#ǦaT@0.7l>@݇T@5>@2@bB\R@1;=@|+X4R@I7*<@*1FQ@0.;@JtQ@;;@P[`Q@u :@NϊQ@@3L}:@ Q@9@D̍Q@gV79@"}rQ@7]'E[8@%5Q@0.[8@He'N Q@Ԟ$7@eP@F7@jNP@}k8@ZM@hܗP@Ll9@0X3!P@r-<9@IsO@{79@[yN@&W9@ȹ_N@<}gn=:@(FcO@l:@r\O@d7;@Tɱ`O@_ a;@lN<)]O@7LxB<@N@Y<@EN@CM=@_oN@<]I=@2aFO@XQ=@nO@w=@Dx P@yV?W=@XBmP@*^=@BP@qx=@s,P@ѫBP=@iP@ y(>@*<P@0>N?@vP@KM?@ kP@V:?@-Ƥ#Q@3|?@nN;Q@lĞ?@J?VTQ@%*?@)PQ@?@@@v8kQ@~@@vGiĵQ@4e@@C{Q@LA@*uoQ@$Ԓ@@HQ@U!,A@{w\Q@^D]A@>C@E 0P@…oB@ni(P@B@¶P@D)B@A Q@`oB@$)7Q@> g2B@໏LQ@diXB@3aQ@TYB@HCvQ@m0JB@TQQ@{B@^Q@&hC@}Q@>C@GQ@vj#!C@~dRQ@B@ǮQ@jB@dQ@H gB@E̍Q@N?@iP@ y(>@s,P@ѫBP=@BP@qx=@XBmP@*^=@Dx P@yV?W=@nO@w=@2aFO@XQ=@_oN@<]I=@[N@`>@l"N@SP'a?@ xN@ԞN?@m=nN@Ni@@$}3DN@7}@@Sz҈Z{N@ ư@@.HC@> @Q@Θ: D@$pUQ@"Y)]D@Q@^ -EzD@ 3|RQ@Rп$?D@}r}Q@D@UݞQ@d^GD@ #yQ@TٖC@cQ@e6 D@ ]Q@\[jC@zw%Q@\[VC@#k8Q@AsţC@~=i9kR@-2C@,[Pr{R@@C@Tq({R@"RMC@l#YR@Iq ~0C@7|R@PیB@M\R@ {<µB@o|R@fm0B@zzPR@dB@(R@l0bB@Z R@~}ayB@E̍Q@>C@^Q@&hC@TQQ@{B@HCvQ@m0JB@3aQ@TYB@໏LQ@diXB@$)7Q@> g2B@A Q@`oB@¶P@D)B@j0 ]Q@AsţC@9T@/E@#6Q@jW"E@vCQ@4-E&ZE@E̍Q@5lE@i/X_R@djO@E@LeLiR@̮E@ R@/E@R@_pE@ <S@ƛRw~E@ #jS@VoYzE@oS@4omE@:=|1S@FM?E@9T@,E@JT@>KJE@I/WˢS@0mD@`S@k"nD@9S@ I7D@:ڎ!S@ݴ6D@$R@ GD@q(R@~.D@HtR@F:mC@p}R@)({C@~=i9kR@-2C@#k8Q@AsţC@zw%Q@\[VC@ ]Q@\[jC@cQ@e6 D@ #yQ@TٖC@UݞQ@d^GD@E̍Q@eD@r2CR@q+nD@@Q@ یJD@ZQ@vDaD@Q@#MD@nԎQ@d:&wE@6Q@jW"E@kxP@J@f$A@9 P@(P2`E@6xP@J@CCD@yxJ@5E@mr\* K@j|)E@?)`K@"\۠E@YEK@DCD@1ȱK@D@ Be N@+0kD@@ Y;N@:ީ3D@q N@~mlD@1 CN@܊D@Q/O@hYD@SNNO@&0C@\ P@p0b:rC@f_MP@3C@9 P@ zB@ 0P@…oB@ GP@…gB@4ioP@*xB@ReP@WB@ `//P@F/& PB@͎"P@R'B@҈O@(HB@'ŘO@zUA@,j ~O@<7A@`$O@f$A@03 N@|5A@CN@)>B@pV0N@KJCB@h^ M@KܴB@:e7M@Hq B@CĊKL@>KʽC@p~FcGOL@fىC@hL@:B@4Lf{K@~#0hB@r\pfK@:B@J@H)(vB@P:%J@-EB@|&DJ@=.yC@0(+{J@R1C@̭J@?.C@.XJ@PlMD@@&uJ@1pD@GJ@PD@@P^K@\ۺyD@7UK@D@caJ@eE@ XuJ@"qD@d˛GhJ@LSD@xP@J@CCD@lx9~. F@&W9@(FcO@oCC@LGHH@&WA=@L_H@,5s>@oH@1>@{WtG@&W>@D1G@@Í?@60֪G@ȖH <@@ G@-`x6@@!VF@j@@F@_A@ͬmG@ OA@ah G@`3A@lֵF@:9 A@PbF@ǎЕB@LbF@6;NѕB@F@{\B@h5F@y$C@9~. F@fn+ͶC@|uWteF@oCC@xI^yF@>VC@ F@KoC@ u=bG@4%^C@؃m@G@` g2bC@xO^G@C@}3H@'C@-H@^O@C@_Z`H@FeC@63QH@Дﺛ"C@$NqH@(C@9V䌙H@B@l,I@B@DkI@{`oB@!J@XjYB@)+{J@VU!{B@J@H)(vB@r\pfK@:B@4Lf{K@~#0hB@hL@:B@p~FcGOL@fىC@CĊKL@>KʽC@:e7M@Hq B@h^ M@KܴB@pV0N@KJCB@CN@)>B@03 N@|5A@b fN@b:73A@@_oN@<]I=@EN@CM=@N@Y<@lN<)]O@7LxB<@Tɱ`O@_ a;@r\O@d7;@(FcO@l:@ȹ_N@<}gn=:@[yN@&W9@6+{M@8Qa9@%LCM@!ur&9@ l"ٲL@Nn8j9@) B|L@` R:@\Nf>L@<Ԟ$;@n҈K@ Tn8:@$ [K@d {:@,J@`7h:@g҈=J@ ;@ YI@q՝;@Af-mI@dk<@s㙸I@@k%>@`P:H@(qW=@,AM}xH@q,Q>@GHH@&WA=@m0FoA@j >V(@@D/f,E@BNvlB@G&A@QZ@@0FoA@L >V[@@d A@wީo@@'A@е@@lBƁB@ﺖ@@x)MNB@5A@ƒm^9B@*LA@XA@)&RA@|A@0{A@ ƒm+B@ (A@LGr5B@62B@)ӺWB@A=!B@xHo^B@hB@DB@tOB@(q.xC@ZsB@x1 YC@4A[B@(eC@t[B@r\-VD@B@ORo%D@{`B@D/f,E@BNvlB@t$D@V'음MB@k"D@ -B@B%D@&NHlA@bɀD@~5A@jkeC@x@@|V(@@G&A@QZ@@n/jE@4%^C@؃m@G@ZHŸD@؃m@G@` g2bC@ u=bG@4%^C@e F@XC@ :MF@C@'9M)F@DWbC@ AF@ZoC@|uWteF@oCC@ +{33F@٣D@pME@ږt D@p}.WE@-O^D@/jE@YˋD@@P:z|F@ZHŸD@F@T ~D@PyF@``gD@pF@\[GD@ܠ*F@D@~F@32C@:MkG@ bC@aK=G@mliC@؃m@G@` g2bC@oXB&@F5iMK@C7@NFQ@(B&@BMmM@ i{V&@IީXM@sə(@nlN@f%C)@gN@U0'@N!FrN@s'@*ѵlO@2H()@<P@@0$+@kc$P@_\+@5ŁP@O]+@O@j^2P@ O7.@OPhP@@G0@en[P@:0@JVQ@ӧ1@_/Q@()n1@q#P$Q@`CAH3@AmxQ@fx4@#U;+DQ@p+YE4@NFQ@0mA5@Otfz'Q@7@9P@W~ݐ7@qXP@C7@ qP@Ht.6@NQnP@h 65@!`AP@)^5@ܠ9xP@ؔfd3@{O@f:1@v4W_O@&1@QN@`w1@TE{QN@8L"2@қ+| N@`ޱ1@֝zM@ xE0@KJ#\M@Hwr0@\[CL@Bs/@DxY L@BWU-@8L@?F3,@+2K@-)@F5iMK@- @)@@OOgN'L@`-m'@zvL@B&@BMmM@pf37@ }I@PaKX@@HA L@-3<-<@HA L@0c:=@ʹMK@U_=@߇K@s=@!V! K@WGH>@yeeK@>>@)(pK@wO>@Dg7gK@lb?@nlK@ƚ?@J@ 4?@`WJ@t3@@d7J@PaKX@@\[J@ʔ~&@@+J@p.Ue?@X/XnJ@0!N?@,gxJ@hlj>?@^J@0uT5?@0T J@'6?@[F J@X=qs>@4>kJ@>@XеQI@h )>@ }I@ߋ)H(>@jDI@CA=@ȪM"I@8t*<@KI@13q<@XKTI@Xt{=<@+>I@˧=t;@͘I@tV:@oI@'DS9@({`I@h &`8@I@`mL8@DOgI@7@ I@Em 7@J@f37@ FU>J@$K7@uXJ@C077@~Q|J@PÖ 7@,J@X;{7@:J@B`s8@P< J@pt{N9@As&$K@H9@)-EilK@ x:@rΝfK@4H~:@rlһK@x`:;@9CK@3<-<@HA L@q8h?m6@x"D.F@aKW D@:*J@d`֨h@@p= J@(85R4@@H$J@`$ [@@xhq J@x-wX@@:*J@P:$2A@XⅷjI@pY,A@8C@G@H@؋JG@pU`>@R&G@L&q=@o:F@&`T&=@cHF@ f<@U{F@0);<@DF@:|<@rAsgF@pD<@}QF@`<@p\[!!G@"<@1G 8G@,u=@{/xCCBG@@+=@}0G@=@>Qe,G@fP>@C6G@I=@O{=CG@ CH=@ВAsQVG@H&`F=@vG@=KFj=@^G@ҧ =@P?OG@I&i=@t&G@ <@}H@0.ԧqB<@HfvH@ Ņ;@;H@~R:@w!/H@0Zی:@K@H@`2:@f,>EH@|3)9@lZG@=.59@z #G@0U8@yY>gG@ 5&f8@4+G@)7@(G@,i$7@3=OT H@яc6@G@`g 6@/ ;H@h?m6@K 6H@mAG6@AsiH@`6@ThvH@яc6@;H@,&6@pH@ԧ/m7@xE}'I@97@b6I@3&8@8xJZI@7@ I@`mL8@DOgI@h &`8@I@'DS9@({`I@tV:@oI@˧=t;@͘I@Xt{=<@+>I@13q<@XKTI@8t*<@KI@CA=@ȪM"I@ߋ)H(>@jDI@h )>@ }I@>@XеQI@X=qs>@4>kJ@'6?@[F J@`֨h@@p= J@rd'&,@;H@3&8@V!lK@-X;{7@:J@PÖ 7@,J@C077@~Q|J@$K7@uXJ@f37@ FU>J@Em 7@J@7@ I@3&8@8xJZI@97@b6I@ԧ/m7@xE}'I@,&6@pH@яc6@;H@`6@ThvH@hO5@b9z,H@ Q4@5H@ؠttj4@|9H@y43@j5xʛH@/07R3@_/>V)H@D2@mɷH@ԧg2@ tH@X(d2@0bH@fB1@FqFI@`1@xZ.I@ӧg0@+7@;K@X;{7@:J@s@@+"@KE7G@06q0@0bH@%06q0@bH@(m\0@^مG@ا0W0@г^@G@X|"ň0@uG@30@mG@Hf0@WG@A0F.@^PTG@OC-@KE7G@MY+@X0AG@pЫ(@ؙ{`?bG@raN(@4ŎG@@SdT&@gHxG@0S&@ R,`G@fé$@:_rG@ i#@ juG@@+"@{(G@=D#@> g2~G@ig>0#@"o5G@q#@kCG@ )$@ 귦G@@,;%@-G@`R&@- G@&`H(@ G@P!7=)@G@5H)@PjۻG@q`< *@+>VG@ r)@Ъ%H@x{V|*@rc@C5H@#1+@.+GpH@-&,@{Y>GH@ۂ-@Rq{H@:.@0bH@PU0@>Og]H@O)0@\[dH@6q0@iLH@JF0@Ue)G@nDp2@kVF@eo2@UF@Ht2@pdH6F@pfL1@$F@ H0@C&0G@4AH0@!z@G@Vcj^0@U7kG@30@mG@X|"ň0@uG@ا0W0@г^@G@(m\0@^مG@06q0@bH@XM }1@OV! G@ m1@$!g2G@X=N2@G@w2@؄0bw H@,,3@bީAH@KO3@o "H@f3@H@!=2=4@:)H@@'\;y4@HO`x HH@,!4@AsOH@J5@(H@h?m6@K 6H@u0Zی:@DF@fP>@;H@0Zی:@K@H@~R:@w!/H@ Ņ;@;H@0.ԧqB<@HfvH@ <@}H@I&i=@t&G@ҧ =@P?OG@=KFj=@^G@H&`F=@vG@ CH=@ВAsQVG@I=@O{=CG@fP>@C6G@=@>Qe,G@@+=@}0G@,u=@{/xCCBG@"<@1G 8G@`<@p\[!!G@pD<@}QF@:|<@rAsgF@0);<@DF@h <@(g2F@@B(<@]/G@Ö <@6gG@@$:;@>{ڳG@x;;@:VG@`͖:@V!H@0Zی:@K@H@vx^84@ZtE@L&e=@f,>EH@,0);<@DF@ f<@U{F@&`T&=@cHF@L&q=@o:F@L&e=@ 3F@I>@$=@thF@}<@ltF@[mގ<@fE@iAX;@tE@D >;@{еF@>:@4OgE@Dɻ9@ZtE@(8@|-E@@"U7@Y@E@6@qE@xU:6@uF@!Xy6@a4F@(Bm6@3IF@t{u6@cYF@t{$%6@ौ<=F@!5@<lbF@%`{5@c0F@U4@|KKF@D4@h}F@^84@8OPG@=5@u(G@KAHc5@AO.CG@%`6@}G@яc6@G@,i$7@3=OT H@)7@(G@ 5&f8@4+G@0U8@yY>gG@=.59@z #G@|3)9@lZG@`2:@f,>EH@0Zی:@K@H@`͖:@V!H@x;;@:VG@@$:;@>{ڳG@Ö <@6gG@@B(<@]/G@h <@(g2F@0);<@DF@w#H5@P< J@ x:@ѝ/L@4H~:@rlһK@ x:@rΝfK@H9@)-EilK@pt{N9@As&$K@B`s8@P< J@X;{7@:J@u>7@;K@OAH)6@ )K@hU6@YAJK@X`6@J:mK@mAP6@ϲMK@FD5@aK@#H5@%jOL@P 36@N=+L@ȾD7@Vީ#L@U8@ѝ/L@:=9@%WL@Ou9@҈ L@4H~:@rlһK@x#H5@rlһK@3<-<@.L@(t{I;@(WL@";@tCL@ ";@k71aL@3<-<@HA L@x`:;@9CK@4H~:@rlһK@Ou9@҈ L@:=9@%WL@U8@ѝ/L@ȾD7@Vީ#L@P 36@N=+L@#H5@%jOL@84&5@UdL@i35@~H-L@(:;6@nL@(`"Q7@ \[̀L@c"8@hIL@"P8@ROL@`"*9@.L@OQ9@\q{L@:v:@8ML@(t{I;@(WL@yY3V7@(WL@ !<@l 58M@ 4!+;@(ټM@s)*;@ؼM@ !<@^oM@@jMk;@Բ\M@VG@5H)@PjۻG@P!7=)@G@&`H(@ G@`R&@- G@@,;%@-G@ )$@ 귦G@q#@kCG@ig>0#@"o5G@pʓ !@XG@@M{Vu @8ȉG@@`:oG@.ʳ_@.^*H@`2 @rFH@:@~-ٙH@Dʾ@p]H@@V!|H@l,@(3cI@j@JTfI@Db@YI@@DȊ[@KJI@h._@`=J@5C^@ʝpJ@%gܞ@x}J@7f@`'-J@B>@)(J@:jEP> @J@@!@}K@$!@PU&2K@m !@`.;{K@Kh"@MYjK@t#@\[}K@U#@@Ax^LK@ au%@hwH.K@@\%@K@6'@(o&K@q )@`~5:@4OgE@D >;@{еF@iAX;@tE@[mގ<@fE@t" <@tE@ z;@{`IE@c);@nV!E@п";@x\H-E@t:@D@(q+:@\[D@B{29@ D@~8@0J7D@hN+7@;WD@/l)6@v0bCD@PW{6@@`D@\"ja6@A&xC(E@D克6@ ;E@=o6@ZGJE@(DԚ6@ީsE@W{k6@X9TE@~D 6@pYGE@@ i6@R:9F@xU:6@uF@|07qg&4@D)(uA@`u:@D@6 =J:@cfA@_AH=*:@A@Km8@D)(uA@G7q)8@1`A@xՃ7@x֣A@D17@Ҝc=A@7q%?8@t\[A@ |cjg9@.AfA@`9@ JvPA@ ׹9@|) A@ =J:@cfA@/l)6@v0bCD@hN+7@;WD@~8@0J7D@B{29@ D@(q+:@\[D@t:@D@`u:@X^D@ kK:@iwD@9"Ó:@ƌ{|iD@Ur9@@5 mD@f8@ dQ9yD@q7@WD@rh8@ -D@P%Kd7@3"C@ `W7@$C@S~a6@ 8@RB@3p7@P"B@0]h7@pzB@dd6@( B@PO{'7@g6B@t{r}6@0k0z4B@pDۆ5@@R(lB@0ЅK5@ঢ়B@p]5@ TH'C@ȏc4@@{bC@74@pxjόC@07qg&4@mC@=p4@pD@–̬4@(7D@0U4@X=JD@)\!5@p&zkD@`3ɕ5@4wD@@ :-6@`.D@n36@PͳD@[6@鷯D@/l)6@v0bCD@}:[ :@ (A@|uWteF@x\H-E@D6PbF@ǎЕB@\:ӏ%F@P1B@L8E@D̠B@`FccE@@\QPB@D/f,E@BNvlB@ORo%D@{`B@r\-VD@B@(eC@t[B@x1 YC@4A[B@(q.xC@ZsB@DB@tOB@xHo^B@hB@)ӺWB@A=!B@LGr5B@62B@ ƒm+B@ (A@,r\A@ 3#B@ ͕B@B.& GSB@0A@B"n`HB@*{v[A@3eB@@aKqA@t'B@d.,A@@Ʈt B@ >?@P?7wRB@*`">@FVB@8 d>@PY!B@,1=@NMzB@(<@0 iVB@`$;@ҖHTTB@G:| ;@-TB@'׸vQ:@B7C@3:@Xc-~C@Ö+:@ οWC@cG;@5D@:<@Wt:D@(p==@!)(D@)[%?@BB.7D@@|y,@@4/O=D@|)$A@@ݖHmE@0>5RwA@ܒ&E@tYtB@?D@G,C@V -EkyD@t.C@4T'D@ /D@D@P:D@rv0bD@k_MOE@.OgD@/jE@YˋD@p}.WE@-O^D@pME@ږt D@ +{33F@٣D@|uWteF@oCC@9~. F@fn+ͶC@h5F@y$C@F@{\B@LbF@6;NѕB@PbF@ǎЕB@t:@D@п";@x\H-E@c);@nV!E@W#<@.ﺺD@X Ö <@kY>dD@⸿r<@33D@ow;@5D@,?1;@mtdXD@t{[:@MqtD@[ :@یOD@9"Ó:@ƌ{|iD@ kK:@iwD@`u:@X^D@t:@D@~ _M3@mC@)\!5@~}XE@)\!5@p&zkD@0U4@X=JD@–̬4@(7D@=p4@pD@07qg&4@mC@0O3@5&C@=3@XC@\)g3@W D@i׭Q3@{]D@` Og3@xtlD@:?3@(D@>,_3@(zSD@;E2,_3@X SD@ _M3@\H-E@03@~}XE@ 63@xD@E@+e4@S:XKE@R"H4@@&xC(E@q 4@c*E@e4@Ӄ ~D@@٠t4@0w0}D@;jv4@8oD@`e34@Vv D@)\!5@p&zkD@p _P+@ \[p=E@@007c3@!z@G@+4AH0@!z@G@ H0@C&0G@pfL1@$F@Ht2@pdH6F@eo2@UF@@[)3@ {F@@007c3@ڿ$FF@?$Gpg3@BT)nF@07q2@pqF@cם1@ӅF@,j1@NeF@0@wF@ rQ0@:9F@02/@NeF@@pj/@8@&hF@%`Z=0@*xC,F@6qt0@H WGF@:0@"wE@0= L1@/!E@ӧǬ1@遼E@(\2@03333SE@8sN4s2@bp=E@mD4s2@ \[p=E@0[cj1@8lE@/0@:E@>0@oE@RY.@xd F@.@As(F@`^2-@ٿ^F@ Ԟ-@ WF@@Uz,@NeF@H+@8fF@ _P+@F@x[+@͘F@Sn+@R7 F@Eq,@nQF@@00-@8uYEF@vjE-@`F@ ħ.@F@1JHݥ.@V* F@P$xW/@tB&F@_/O]/@?TzG@4AH0@!z@G@:&@sF@fé$@XG@ig>0#@"o5G@=D#@> g2~G@@+"@{(G@ i#@ juG@fé$@:_rG@0U $@Εީ=G@~#@$N(G@`Ы]"@hzX8G@7!@$M.G@@jc @ȨG@ H @E<G@E"@gF@al@sF@`C_@[hF@@m$@XR6G@:&@@T"G@II&@pQ\G@@3)@&ӤG@@O`?@jyYYG@"@ ꑹG@@`:oG@@M{Vu @8ȉG@pʓ !@XG@ig>0#@"o5G@P@l:@vQH@@(3cI@l,@(3cI@@V!|H@Dʾ@p]H@(2N@vQH@@l:@CH@d 2!@ݏ I@l,@(3cI@֌-@CH@j@p׮ͼI@j@JTfI@l,@(3cI@d 2!@ݏ I@@l:@CH@'g2@& H@@~5$@X(H@@j @`҂0I@Sk@cI@ErD@FeI@֌-@pi@I@)] @6یBI@Hν$ @P|hBI@)] @ AI@l30@0~5I@@x]@p׮ͼI@r0m@`(g2ƄI@j@JTfI@)] @JTfI@5C^@aTJ@%gܞ@x}J@5C^@ʝpJ@h._@`=J@@DȊ[@KJI@Db@YI@j@JTfI@r0m@`(g2ƄI@@x]@p׮ͼI@l30@0~5I@)] @ AI@Hν$ @P|hBI@'On@nI@@@: J@@K@aTJ@%gܞ@x}J@ O #l0bLkB@mLHe#E@!G"QqD@ైF X!=E@C LHe#E@{Ҿ <;D@ј> bD@A#W]D@q즬@ND@m@5T߰D@vDgn7D@qtҭZ*D@^\ .~}D@0D xCC@>|ؖȕC@pcdmlكC@`Ĉ~o0b/C@0f C@_1DB@VK%>VB@`XnhMB@@ %ll0bLkB@y bK}B@(p6!Xȿ$5oB@@nD~!hKJ_B@ha'!VxC"C@Jr.""-C@O #bd^C@_",:9/C@(Rv"ZC@g!Ͽ$dD@-!x0\aD@,!;AD@ ,H!D@G"QqD@ ("(j0b2A@P@mE@3`XnhMB@VK%>VB@_1DB@0f C@`Ĉ~o0b/C@pcdmlكC@>|ؖȕC@0D xCC@^\ .~}D@qtҭZ*D@vDgn7D@m@5T߰D@q즬@ND@A#W]D@ј> bD@{Ҿ <;D@C LHe#E@ైF X!=E@G"QqD@(G! KE@ ("ohE@WmE@0|@AE@0Zť/E@@=0d:9E@;~# sZE@Rk|{`E@lY cZE@2߀?D.JE@2&ns?eE@L:?j+E@@[gnS@{>l%C@S&t8B@<FtjOB@W+T\[JVB@ S BVTB@*y"nVB@p&pEq )B@c~-6(j0b2A@Щ:w\B@0f_-& /B@pcXl0bxB@`XnhMB@@r D#6ީI@?!(3ؐK@ 4tV[^ J@?!0⚓J@'P&K!J@Пw5!6ީI@@r D#ީI@p"U")(nJ@I`#O}J@ ~UK@ЍdI(3ؐK@ڼvLRDLK@ЍdIAtK@0_ݮo K@4tV[^ J@d@9df64"d@62 4 Ld@4{5dZ!3d@t\n454"d@I%'(6l ]d@9df6uId@z4!6> h.d@5؍d@ɗZ&5,7`d@q4d@62 4& d@,~4K#d@u4Aed@ ?4Ld@4{5H:X 4c@0˜%Ld@0&e% ހ Cd@7!&$Ld@0˜%rf6d@9~أ% T;*d@ؑ/h$6[ 4[=d@f$ހ Cd@7!&$bvi5d@tN03##0d@ėX#@^9d@8>9!|BVd@',أ Tpd@',أ n(d@^T%r="bvi5d@tN03#tEd@˜# d@MX#Er.c@.#(Szc@wmG# ~c@c|"||BV d@$"|BVd@te6g8#tEd@˜#(Szc@D@ h?9c@T 9= 4a[[c@"!hGc@P^u: 8qc@gsᨼc@X>Gc@]̲GtEs=c@uq=(Szc@D@ v4zc@`OX(::c@*dQܪc@T{"oݜc@?:X 4c@`4pS~b^c@0&ev4zc@`OX(* yJd@RGPf@VzH9AB$fXf@bqDLAf@YjMDD~ef@K<3gD޷ue@=NDxD?9,e@DEs1e@/D9G8He@::Dlhe@QWC^e@""C2Ee@ ÒCY[de@{fCue@4CXJMe@:mȰBLXe@[B3[F5e@%MuDBqe@Ber1Że@ZAZ.(Ze@0#\ùCA9e@VzH9A$pe@pÚB:6'nqe@PRZCB!yfe@CfBEe@YBlf@B .f@10 BT@f@K7BPf@bB~јHf@ifJC?f@JCL&f@ƨCVAQf@5aC !f@(bCfXf@bqD証^5e@c)E6$Pe@|\E証de@'AE;re@Nf:D[~e@<3gDHu{e@/:azDZe@~X&:?D|馠e@/uDkWe@Ļ~DAe@'vDj:e@'DVTe@:D"T e@7E e@i7*|E^–e@e,EBHe@K\:EVd߉e@E#]~ne@ @ Ftyee@NOrFxtSe@l WFy:e@v-GUt*e@RG|n) e@*YZOGјpd@C0%%Ga d@TA&G yJd@n%FkN|d@kcR3FX2'n e@YFlae@oQE証^5e@c)E@hU\@"{:E92c@QlpV%ivb@ugD(* @b@Q*"VpDb@ 4E hλb@Y4E?}b@)Enrb@<0xEHE[b@"{:E29Ub@jYE0nOAb@J{^Eˆ]-b@o XEXZaq)b@\RMEpb@ΔDsBb@B@ZDڨ<,b@m:reDnKb@D]b@S~Divb@ugD_@PƤ@'E_@G]@${/_@jtz@hB_@വ@7^@[c@:C^@9A @rS^@ {oAI%S^@വ@E(#%^@"f@L2]@ᴵ@*!]@~2AAeFx]@j=>i;A9]]@Ɨ_AA#]@Ċ)IAV R]@:BA2(]@_`6AB\@.>Dxv1A;\@=Dx'A\@o+3g@L\@4,u̍Ͽ\@ps_> |\@NXZ v=s\@_GIk<gXt\@2<"R\@E:%^@%L15^@0D_J^@>p40Ăić_@S0-oM_@OsU"/UF;_@L)Qs&.aGJ_@M:\\-?sXj_@L'-t Uk_@7~v,}r_@<ݭֱ,($_@/%1,LK#I_@\vG+@*7_@(p ̢+7(_@5ƍ,Ȃ `@k-&`@M-;3`@z-,q-`@  b,<]o<`@ݭ<+ }SJ`@* +HE`@Pm 7*`vS`@4^)&g`@e6^(:݅w`@ ۚ(PUh`@<Qc:(Lԑ`@ 4'Ta@ e6C'-a@!&]>a@@$&r}a@QlpV%tFIa@PϐP& a@eCő'sBֵa@ܭ'a@U(٨^V"c@zs>͇c@0% ??x͗kfc@[ {fF@b@KJ@6#b@lts@zdxVb@t'Az:b@`3ANƀb@"AC#hb@K5ByFb@nBfb@TnB.qb@i7B#wb@\SRBx-(7lb@ NOC0]b@o עMC Vj,Jb@{wC:/b@ZLC0b@n5C4!b@zHB9յb@| CXSa@f(gCua@%DC a@^0Cia@ec}'C ب1Aq7a@.3gՉAJ}a@3YAllF+a@|ZAoB0a@/НA{`@^cqAL`@E=AL`@HNUV@;`@b@:?`@[9l@n`@O@$q`@/o@7`@?oLqj`@\~??9%1`@F@%?µ`@BE*?la_@4GR!$@_@PƤ@hPw(}S@@V/Y@.mrT@`"#@ .mrT@Л@ѥ!hT@pQ{V@MT@ q@U{KT@@V/Y@S@4\ @Pw(}S@(f @6u T@`"#@Y15T@8٨=o"@yST@p9 !@.mrT@Л@~=i9kR@d22@܉*`@سYJ@ zi^[@d22@*4_)[@|ur2@u}([@-^3@sG[@d*/3@bzՊ[@^4@G@>ໃJU@fqQG@b@hnU@;OG@+*qU@(`q V:H@"ZRU@cKFH@ U@ }H@¶U@c H@PV@"g2LH@[Ь6V@eH@q V@Rم7G@!V@f-ŮqG@+:}V@)F@dwV@\) F@ϪW@#jϺF@Z/W^W@1-E|F@l@hW@5-F@6DأW@$"F@ HW@еE@%\X@4\E@ \X@4_E@e'X@)CE@P"6Y@|;&TE@`PuY@,\[AE@V^Y@'D@96m!Z@bDD@u=Z@wD@E FZ@t1E@9ZZ@ZjO=E@*'O[@ͅ7}BE@_[@coE@y3¶L[@ #E@L[@8E@K 5[@S] F@6K[@As:F@ԼJ?[@$F@}r\@i}F@$]\@xjωgF@,v\@:F@ \@Zq F@w̍-]@<읰1G@qf'Z]@^U7VG@2]@wYgG@ s]@pXG@}u]@K&G@2u]@ީG@Ln]@(H@V R]@XvNG@l]@F&/^@gI@اSs.^@BJ@XkU ^@W`J@b2@^@0-J@zU^@8BJ@H)^@سYJ@8]D_@KJ@|_@mlzeJ@ت(_@&{bI@K#_@]DOgLI@Ԅe_@hoH^I@s_@PH@4r,`@ceH@%R`@{Vef]H@ȔH_`@X"G@f6`@*G@408`@0R{H@܉*`@ 6=H@Pp`@/L G@_`@m2G@@7r져`@avG@8f`@(׆qF@AE|`@UF@EB[``@{F@T;i`@fFFF@+Hd`@1O wE@4HT`@ sE@ TzT`@'2E@u ?`@)(!~E@d3`@e6E@H-8`@̅DD@(]{`@r+D@b_@FgD@Tj_@lWD@z_@h*0D@iE_@ HD@hVp_@C@XiF^@|C@h^@]ѕC@}C^@/TrC@e^@As0C@7X^@fDC@\Bʊ^@hw͘6D@]Gh^@ޓM#yD@>611^@п$KD@Bݕ=]@3C@'o]@~AsLC@d]@t%C@=b]@j^C@IѾ҃]@"nC@Js3]@8B@J?X]@xCgB@<¶]@`/&B@&4^@SH-jB@ܗBm^@~AsB@}r^@Gq,B@ G^@^wB@ݞF^@I^SB@(^@ȮtCB@T9.]@&% A@.d]@xCvtA@V~iď^@<\[.A@t '^@uAs8@@*N^@::@@dz^@ĺ&W2?@4W 5y^@@O>@ P^@nE>@9`^@o$>@61^@x =@AW 5|^@`=@y/k^@-19<@dz H^@@Hy"<@0)oO^@d ;@Mx]@G4ˣ9@89. ]@!8@Ct/R]@ǼSߟ7@4\@8daj6@Ό\@6@SÉ\@ &WH96@|GGs\@mg_6@mO\@qk& 6@vgi[@,+]'5@@6E[@;n8e5@9#k[@MW4@|^|x[@, OH4@j.%+h[@*5@ [Sw[@yx"e5@wqiu![@+]'5@"f'6[@`h5@ܩdZ@j5@ZF 5NZ@F76@TmZ@l`#U6@ʉQysZ@Ԟ6@miUZ@ Z7@LN؄Z@lҳ6@$IY@d)6@N\?Y@Ps6@DkY@}xv6@5iY@HyuQ6@[PfsY@L,5@ҾHQY@u35@o@ 5KY@'A?o5@U/#IY@P35@"e̍Y@\5@S|X@(15@,!jX@xI6@) X@+r56@RsX@F o$7@0ƕAX@+38@+fX@ RP7@99p_nX@4A?i9@dX@P-L09@}X@mE]X:@ym1X@#;A;@حJ?X@`҂I;@ ^zX@nV<@oTX@7LB<@!X@ ܼS9i<@FSX%X@i<@Nx X@qs=@A3GW@R=@ր/9W@0.G=@*ItZW@|lI<@.3 W@寙;@CV@ 2ߐ;@,CV@xOe <@xV@|Ҡ<@HV/WV@dK<@s^V@7A? <@ 4V@xL;@.V@`<<@LV@e;@bʦU@|Xn8i;@@FtU@ԮÍ4<@89@U@?qՌ<@$ ZU@(F<@T@U&WQ=@2@#ǦaT@0.7l>@,GT@ej.>@ӷ*S@?+>@2e JS@Dk?@ o/WS@ܜ O@@XES@Z3=@@T{^S@:RH@@I/S@2h@@rv(bS@55)A@&<˘uS@F;A@CW S@ j0A@MifR@$OYUB@B R@")(B@M\R@ {<µB@7|R@PیB@l#YR@Iq ~0C@Tq({R@"RMC@,[Pr{R@@C@~=i9kR@-2C@p}R@)({C@HtR@F:mC@q(R@~.D@$R@ GD@:ڎ!S@ݴ6D@9S@ I7D@`S@k"nD@I/WˢS@0mD@JT@>KJE@9T@,E@`=^@o0]w5@ض9.|^@G1K9@ iq^@d8@d=K^@Ԟu6@f'/^@o0]w5@_^@)#6@=^@C;g7@\Gu,^@y׉8@]ή_^@G1K9@ض9.|^@urb8@ iq^@d8@F@daY[OB@0 xz2@4ŎG@WBMfé$@:_rG@0S&@ R,`G@@SdT&@gHxG@raN(@4ŎG@pЫ(@ؙ{`?bG@MY+@X0AG@Ыne+@%G@:7+@iF@,H*@8KF@;(@ljްF@B(@ƦSqF@`,;݅(@͘LF@Pe|-)@{ F@) +@:t:E@quD,@BhaE@0mH.@RBD@PV5/@)^ D@%`~+0@D@ѿSX/@BC.BD@(p0@dQD@X>1@֙yD@yk!@)2sD@ q. @{ffK@0f|Ha)@f.qL@t#@\[}K@Kh"@MYjK@m !@`.;{K@"d= @آDK@ q. @EL@0Ы^ @ gL@ q=!@L@&T"@FxCL@P#@H=VL@ڂ(%@f.qL@>Y4%@$V!L@ &$@B qL@:o$@~{NL@&q %@Ȃ:L@ sgU%@,-j L@:o$@ )(RL@qL#@ (K@t#@\[}K@&(@͘BL@0f|Ha)@2b.K@0ΧO.(@{ffK@ħOK&@n\۳K@k%@(K@&(@͘BL@ЍdIHFH@Ռ?hGQM@84tV[^ J@0_ݮo K@ЍdIAtK@ڼvLRDLK@ЍdI(3ؐK@@NuF K@եeK@@ْہTK@^0!VK@@:!1 YK@xX ӧK@.zK3#L@䓔5g2{dL@@`%L@Lm= PFPM@@@FM@i? hGQM@KػRL@}6Ȥp\&RL@|֓6Y RL@$'\oL@0t@K@#f^@UzK@԰ [}K@F&OK@[ۿwHp;K@w?H EJ@P?:9 wJ@Ռ?x͘^J@C˵?k J@[Ǣ?"CI@=Z2?XU` I@gU?@bI@VW32>2cI@s҃]7@I@rH]3YI@@ O:I@@%0+xQ+I@@YHFH@G]4XxQpI@@Mm=zQI@p5Q HAsI@ta I@Y)gI@^5I@YUѮ8eҒ&J@@\d~HkJ@6LQ0H\J@FK*pǏ5ȳJ@X`:̀S8:XzO@6{.8+P@xs-HV-P@.jz-["sP@6{.8+'`HP@-'[RMP@[J`1@O@X6)2:XzO@'{s33VbO@ˮ!R6$O@HJ5MCP@}7/6E9P@XZ5/6[RpEP@=i8:6m:XP@X`:̀S8;ޠgP@!7!u͐P@dG"6g]REP@dT4nP@$+3ԳP@pr1JP@6/*0P@xs-HV-P@z|uWteF@Дﺛ"C@i G2I@b*D@,#؃m3G@b*D@?)WG@VD@ȯG@*0 D@gG@ aaD@aK_G@(v0D@ΒJH@b7D@)H@F?`!D@tr8H@cUID@$ȱ I@/YCD@i G2I@r3 D@BMH@$zD@ڗH@Dž7"C@coH@LC@ڢmH@\ahC@$NqH@(C@63QH@Дﺛ"C@_Z`H@FeC@-H@^O@C@}3H@'C@xO^G@C@؃m@G@` g2bC@aK=G@mliC@:MkG@ bC@~F@32C@ܠ*F@D@pF@\[GD@PyF@``gD@F@T ~D@@P:z|F@ZHŸD@4 ԛF@XotD@~6F@PۏD@t5@G@ز:?D@1vQG@T AD@OG@\KʄD@؃m3G@b*D@ u=bG@4%^C@ F@KoC@xI^yF@>VC@|uWteF@oCC@ AF@ZoC@'9M)F@DWbC@ :MF@C@e F@XC@ u=bG@4%^C@=C@ز:?D@1vQG@6E@=C@E@ D@6E@pYiR vD@`JE@{2E@W2E@@ GE@v^E@ڧ1E@XKGE@:DF@ Į"[E@pQ2F@N4[@E@N@aF@k E@؃m3G@b*D@OG@\KʄD@1vQG@T AD@t5@G@ز:?D@~6F@PۏD@4 ԛF@XotD@@P:z|F@ZHŸD@/jE@YˋD@k_MOE@.OgD@P:D@rv0bD@Tߔ~D@еAD@{N D@41OgRE@_pD@DE@kWt#)D@ ŮwE@=C@E@[P'K]@pR@tf'e_@$V2@n-6W`{̍^5^@ ?h)@9.^@G]*@&w8K^@>PK*@a^@#R#*@,P^@80?i(@{̍^5^@ ?h)@V{^@#K#@^@3a\$@_^@A%@f'^@%@ ^@*$@_@x'w&@\B^@XOI$@;^@OGr#@Lᕽ^@"@ە^@ N\ "@R^@C=m#@V{^@#K#@;D¶_@4cT @12_@P\@tf'e_@u@tG_헌_@s@ Xp5u_@`cX,@`MZIW_@f\%@@k_@0JA2@}r`Y_@pR@t>%_@`:@ԅ^@p`@ )_@'NCq@P)w ^@90U@Ȓz^@,@PUմ^@JpZ@:x^@QKG@վz^@2@X^@H* @J?P^@ @ jF6^@×,b!@Qy^@X1 { @{~&_@h?!@@h0_@H/ɺ!@iD+^_@W!@J#`Z_@PљJ#@2@_@Ux"@_@Pdk3!@;D¶_@4cT @y K]@"@[P'K]@( @=ˆj]@xר=?""@!1Ø]@v^#@(0]@dX$@FZ]@jZ"E&@#]@0%@+]@PyB$@y K]@"@oX^@x92@d'^@@n1@(Qy^@CV1@2N%^@PzÍ/C0@Ukj^@XōU/@odS`^@ߑ?.@u n^@! ,@;^@ k3o,@oX^@Z"O,@L`G^@crms+@<7^@pzZ"y*@$; _@S)@<-o_@@#R)@%^@*@ŭo^@N+@o{^@F'%_*@@oX7^@;+@ H^@ E+@ ;(^@88y+@C{+^@(*Š,@`y?^@ xG -@ݞ_,^@Uj-@2$^@8,@L^@X4-@Qp~]@?L .@ݕ]@ #]0@%[U^@d' o0@­^@lca]1@~i-^@$V2@H[PT^@dca 2@|^@К 72@ZM6^@uz2@oX^@x92@t^@ʆ&@ x^@`'@_C_^@9w*'@Hw^@1*'@@w t^@X|DT&@ LШ^@>Ɍ{%@Ii*^@$@ly/W}^@¶%@t^@ʆ&@)`_@(j LS(@b }Y@`(@+:Y@@@@J?DY@@wIY@@/ 7@p1tY@V̏D>@}A 5 Y@ ^̏@hY@90H@\Y@JA@eY@S@jk@yݞY@<@EW|AY@`BvF @ƒ{Y@`+ @F(Y@`JҌT@oY@ Y@N(Z@]'?NؤZ@@+S?!BY@ ?x(Y@My?w8YY@+%@QY@ ) @,Y@[@56#Y@@_yzՖ Y@f?@mhięY@ )@b }Y@`(@Bsx]@TDڌ@bF@]@p9@hWGd\@G9@,8\@` Z @E6\@e@|0Iħ\@mI?b s\@:Ra{?ț(7\@3j?0 V\@Uڏ?T [@л.?[P/[@ )O??\_[@~?o"u[@ i? @CGZ[@70?Ҟ?_|[@t;5?~@h[@B@[@CP@ f'?\@`n@໠m\@\$E%@&s\@pb~@6\@`@fӦ\@9/Yd@<\@=@g|$]@q(@^I6]@Д!@Bsx]@TDڌ@X&s\@`@ p\@py@ p\@py@Lp\@,'@<\@=@N#-sP@ O_=@u@SM P@J. >@F͘O@˖?@Z0boO@t#?@"{-O@?K 6>@AN@`<@pWs@N@"[<@ZĕPs@N@H\<@:s@N@(SA:@X/WD6N@(ϸ"8@V$VN@(~R6@UM@`Q~oJ6@hQz*2N@–~R5@.\N@[RY|5@t5@-LVWQ@0f2[6@~5Q@ |7@iA2 9Q@ xU8@cKV)Q@H>p9@Y1EQ@e{-:@oBͲtQ@Pi~w;@q#Q@x Ö=@CZ1qQ@P<@ON%DQ@ JF0@$!g2G@`6@_/>V)H@!`6@ThvH@mAG6@AsiH@h?m6@K 6H@J5@(H@,!4@AsOH@@'\;y4@HO`x HH@!=2=4@:)H@f3@H@KO3@o "H@,,3@bީAH@w2@؄0bw H@X=N2@G@ m1@$!g2G@XM }1@OV! G@06q0@bH@JF0@Ue)~2@d@H@#ɥ+2@F3H@ef2@hQH@p–2@nީ\H@ԧg2@ tH@D2@mɷH@/07R3@_/>V)H@y43@j5xʛH@ؠttj4@|9H@ Q4@5H@hO5@b9z,H@`6@ThvH@0X0z(@{Y>GH@ԧg2@vVI@#:.@N3I@@`.@!}rdI@ )j=0@PMOYI@ -0@36I@(Y/0@cI@ӧg0@+~2@d@H@D1@ѮH@=1@ѮsH@Y1@gfH@1@PohH@6q0@iLH@O)0@\[dH@PU0@>Og]H@:.@0bH@ۂ-@Rq{H@-&,@{Y>GH@#1+@.+GpH@GY *@NH@: )@. H@p(@At& H@X0z(@8\["I@,;)@t߅7=I@@^*@.]I@p,@#a@ީEC@#>a@|~}/B@@EIP1a@Jt,B@7Řa@$:eA@a@A@ 7_a@sUA@~+&a@ƚMA@v``@Xr~@@`@8]@@`@bc[LA@*`@v\Y0A@VD`@<\@@.8_`@U`@@@L`@uA2@@7j`@1k oJs?@ڳ#QU`@iz?@?9zF`@ Ԟk?@x ]SN`@$&(@@I :`@N@@*PZ!-`@)(@@oSK`@kjT@@6[1E\`@RA@&%aK|`@\ _A@xwÓ`@mlyA@$E3w`@tA@įc`@.U!yA@ʞ)a@: B@,a@iB@zzq[a@lRB@L)ama@htC@`a@2*C@"&ĤD|a@&HD@dȉa@p"D@VZΫa@^ttD@@'ĤAa@XFC@XyNa@@&C@|0b@5E@E3D*b@s">1F@YZaa1b@M7E@"b@~E@4a@xg2cD@ gUa@L7VE@75'a@?`D@:~a@5D@޸#Q)za@V@)HE@a@_HE@t-a@űE@4a@}bF@"hka@NF@'da@mlSAF@ a@4OHF@|0b@5E@L`@ X@@`3u`@ݷA@eƯ`@ >V@@o`@D.A@n`@A@ګL-`@5@@4b`@ g2@@ _`@@i@@`@x@@y`@xC/Z@@9`@~@@L`@ X@@ bWO,vnj;>%K ?W3!9vXLM0E<-4~>rLik46L(6F8NpL+;;H6#a%K-a8-١%K8H'6KS)9׬ PKԤ?9r;dKV-::.wK GIIc;X'=L>L,vnj; LE3Ye;4O OM m;g?Ll:;9 +{LX~)9dEMOgMXZ~8ڬ(N8Ve8r;\lN1%,av7bWO,a?6!]E%Oq7 5 "O4AvXN~u<3N ?W3TMNb1^[3 ,{M.ikO39vXLM0E<-4x@wlME@,)@1J@1n83@,4c5RJ@1n83@IMdJ@AY1@1J@7I0@mN1J@0mOa0@yJ@$Nz/@HmJ@XN1/@;vI@UY.@CěH@ Ftj-@4wVH@],@ CӕH@Xk+@|*U.G@(pI,@^G@?brm7/+@?)[G@_*@_ TF@,*@P F@*@ TF@1* *@C$AzF@hљj)@o~F@A0f)@GN?F@@˯w|q)@`~.jF@,)@yѽE@y-y F)@l; E@Ȏj q*@rWt/E@+@pVoAE@@ ,@{.5rE@j-@@wlME@(~G:m.@Uo gE@.@O+{YE@:p/@: niE@/@\?)cE@X7hY0@LE@L0@qWtҎE@Ť1@'޽E@\'z1@+{PE@VgQ1@1F@jF i1@ˢP:F@0n1@Xb,3F@PtrUU1@\|.G@~Í;1@o_G@c؈H1@?)G@]'30@G@b1@|wH@,qժ*2@<.H@JVݝ2@4c5RJ@1n83@xNFcPA@X7hY0@.UK@@@Llm_zA@*G[=@ B@tZ2=@'@B@@LX=@X&M^B@5=@,_uB@wHy>@}B@`IJV>@GB@ ->@xL FB@,Z'?@s.C@KN@@CC@@@Ȕ33D@1~?@$FD@7h0?@۽ZF@-'-=@kHG@TuY=@$pۺG@P=@x>MG@$ <@`B5H@I]'P<@TD_gH@u;@WH@dv;@VFH@ȟ(;@0I@`č:@QwAI@8@M I@!ߣ8@~J@K7@lހK@#)76@몚K@U6@.UK@K6@KfK@j3@4c5RJ@1n83@<.H@JVݝ2@|wH@,qժ*2@G@b1@?)G@]'30@o_G@c؈H1@\|.G@~Í;1@Xb,3F@PtrUU1@ˢP:F@0n1@1F@jF i1@+{PE@VgQ1@'޽E@\'z1@qWtҎE@Ť1@LE@L0@\?)cE@X7hY0@|3%SE@;N0@]e,E@Tgg1@oWt"E@]y1@yD@B1@ 7@ͽ C@$8@JoB@0.I8@ZѓB@Y8@`'MКB@y09@奏?wB@dÍ[9@D҈QB@U9@1B@L7h:@1wA@\c`;@XH A@q7<@NFcPA@P3<@,8dA@h}Z<@|3jA@l><@lm_zA@*G[=@fVf@^O#=KU]i\THZSy`H60S~TG 6uS-PϓF60SWE3#)7T2P4r=P%);TוxvwoP%Y%TוxvwoPJ,-#TpݏPw^TŜzRb LNR¶Q'R8QGQ?Q&X428Q>TɓQ"yQU)^`Q}QH8EBQE5QdqH=8Q$0U]nQPQYR_Q!gQJfEþCQفQDo.Qk}kTQOQe3,Qd]Q_NQu QQ12QݵU Rg}QicR\Q > Rp0R։RQ|7R9DR}xRz)R wR_ߜ RGR96uR_ RQŜzRb XWRvgeXT;R OXWR g#!$2YѪ RWQsY\NRCTu*Yc9Qʧ^.:jR>ʧ^a2jR3^TR@gM^5t:`Ru] +jRr\X]y~^R|ơ]͟wauR`^Rf-rg^2׃Rʧ^.:jR._>]Ro+1_㻛]Ru_F$c)ORcc_y~^R IZ _7pwR9B`'_͟wauR*az_6ɏoR._>]R vd6jS vdfSK$bcd+KS>?'d)4:TS)`ddS|oc1SӑpcRS&JO$d[ߖS9Nd&5:SeT`d4:qSĀ!bdLS vd6jSf@g-Uf@VfVfg-UFh(~f ,.U{0JafUy7(f%U|$f Uţf0BZUsOewU eeU\v=e%Y2"UKںeUDsemmU5kp>e TeUbef1U7 dj$UCQԅdV"<4U=d*)HU˽Hc=$WU`]&fc/0_FUĺ%bH'RUb@yfUcݢrS\/vЁRNdH<\P/bRt-$\ kRd.[HDRU;>[RF-["?R1UZ<-R,gZjm R8ZGRP-xYACRhZqYjm RH`P)YGATRlExY8Y\Rx40YkR >I,PY$f؋Ro]YPpԆRs@Y(%oR E Yic,WRw;Yp'R(|Y L0RE?fYt T4RtY L0R| 1X 㻛T:Rd#ᛇX r$MRPlX4!cRdlĊX\ {tzgR?W`f^RkW,w(RR]#Wh̬JRԅVJ"YRb]έVpTR(^NV#R"Vi@Rn,UɏKR UvHERhAuMLU`f^R^QT,5aR8TwWhRzp#^T^ɆvRH +T`f^RHFL=Th HR4;,S,5aRjBA{S0ZRS]N:SwWhRPE3S { ~RdQ]RwR\8vR5t:iRX5RJ"YRPx6Q{PR`Q8`IR ;Qi@R|J9P@8q2R@kPHd R` PX&R^PQܼ'P뱻QkPoPP^PGAP SP=jCPܢPh@pP<{WP{t^yP(H^$P/vfP4|-xL PcDJPIgO|6i9Pi5-O,%)PUO/X%P[N)0HPՄZNd̠P M0DnO1ϔM\"OxyuLMVOسL^OPWSL"AO@LV\Or;NMh~ P(ZąM((wP, Mh+t PSNN?"P NwK"P-O즈\$3Pp[AO3EPhJ SO=K _PLKOxԁvP'^O>K.P@8gOĹy;PLrO?P'آPXN{PTѨm8PP\Pd`P9P9鳐jP} Pԩ&TPNaQt?#a(2P8H8s+QnO@xU~:Q0qoAOqHNQX/dOgQ˭IOZxQx1l#O荘Q@N@߭Q@:RxC#QXzSd sQ_ESjBrQL(S6; R T+S ~RL(S@H R$-Sr9bOS$-S|S;Sw~FS%dR_ QS(gRlfmcS4uiRfmzS$qT`Eh1iT@C UbDZVT`K/VCpwUTr;""B),GT(su1A$jm9Tɷ'@)Ƭ71T=i>(f%Twֿ<\ȠT 6AA=lUo S\= UzS\=pXSp?RR)S H5@B1S썣AB1SZ.wA|Z]S#^ uA( =S@&C̩A4&S0o@pxS;.@ iS$H@>bIWS@V=%r2DS< T+S@4+;HemS ): S\y9 S t7S\oS5fu6aSp95X4R?4,}(R:2>'R!1jm R d$?0R@x)g/ R h.PpԆRDt9w0wRjb0(2׃r]R@w5.8`IR}"N,xaQfU4,:MQgdzQXCͿQCA?Ē3Q /?@5Qƚ\.@оynQ@,}d@@.@HCΙQ*/@ ɏQ1@XetzQ|U32@%wQ>fB3@F"3yQf0`4@̬QCs5@ف.}QK5@HCΙQ fđ6@Q7@/TQw3c8@60ӞQP09@60ӞQt;@p+:MQ`U<@fʔQ>v&=@)@rH{Q0 x>@ {tlpQD{?@,'jQ̈́`@@H8XQ@\Wt@@lc){5QHdi@@e* Q᥏ItA@p}1*QYmA@d@Q8GoB@+OQHyB@fJQ8B@fF_aQ@SC@XwWqQ륏}C@E"bQp +{D@lD GQ uD@4q;QD@ n&Q0f'xE@DcQ~F@_ 7QHՃmrF@DQQi&F@J\oEPZ҈m@G@LyP¸G@6'P,H@5mPp~H@$PM$A'H@X$Pq`I@GAP@SwyI@`GPPjQI@&:P]NJ@=gPxJ@{t^yP_KDK@ZtP'K@j~xPpq-L@D,b~PFcQkQ@qHNQ䊭cQ@hkQT)&Q@rH{QH_P@.׃Q@HP@Q^>DQ@H\Q[Pw;Q@rQԂsQ@GAFQ@HP@TBQO@h-Q@H R@vwQ@䤱DRlQ@RR Q@yQ>Q@(QHsR@T\Qxs5ER@@߭Q0ZUR@P,LQ\#ZwR@%wQtϪuR@XwWqQ)R@n~+oQ8(S@gQu(FiS@Xp]QD GS@u~DQ_mjS@ظB,Qk+X@}P쩭pX@}P@c̍X@X$PVXX@}PbY@,@PȰJ,9Y@=jCP_ eY@l/vPw8FY@w dPIGY@<ݽlPpQZ@D,b~PD 5$:Z@41PZ@d=ջP KZ@$MPTb5[@$MP\f''J[@XN{PM[@;ɬP[@Ĺy;Phb [@|kP8_m7\@*PHf\@j~xP"o֘\@ؓ Pd|m\@gڸP@h\@;ɬP0_,]@\GP X]@,@P8]@ RPݞN]@!`(P u7^@N#PB_i^@GAP[뀔^@@PX )^@h|"P{/W_@ƧPHo|AJ_@w Pؾl_@@Px__@@PnF_@\GPP?y`@#PPJ6`@=jCP5rY`@Ĺy;P y&y`@gڸP `@gڸPjh`@A@Hv]@@*^A@ *w @@DR3IA@p8A@yA(A@Hv]@@*^A@ A@8q@@yA(A@d\Wt!@@0^YӔA@[@@\yAsA@~y@@KJ A@菊I@@]YA@Y=s@@B'TIA@Δ~@@A@-w@@@ #}A@p8A@njO2}A@vj}@@DR3IA@S>@@t>YA@ *w @@6A@Hv]@@*^A@@@::1 ;k5@nY)8KJGA@?L%[xA@&XX>֒CA@:t&@@*s5%& n@@nY)eS@@_'jW!@@}(@ GpmqM>@`ڼl>@?sd>x|P=@~-< SPH=@i%Y!4t^<@_'T! ;@Ae! ;@ !y*;@`~y"G];@~x# .f:@(w5`$ .f:@h_?%y-:@D&`a(:@Ho':@G(:@".~)P&W&8@PH?+ѓ7@*8v;@'<) <@ X`'@ &<@TK@&8@^>@h8@ ܝ?@ 1*9@|г?@If~:@?@&u;@Qi>R?@CRs<@7ܘ?@5!<@ǘ>@n=@:/?@#S>@) 0y?@6{>@'IL?@%?@\Cm?@Yn?@h>@aۢ@@4iB?@kC8@@Жs)?@\m@@,0du>@!!A@@'8?@o!A@ '8?@.< `iA@1U0=@vA@)W=@|DL$RA@~Жs=@ 06A@&X<@sA@;@@@K4;@9@0G@@ ;@SZ@@,Cj<@Wf,6@@G`=@Q)@@\=@.9^@@x( <@*@@%̴+;@ea A@3k$:@Gŧ@#@XÚ1>@=j$@P[:>@#@F`?@~F%@4Hy?@x%@Ox @@SP&@78/@@9YB&@ @@@8J^S)@(vye@@`!**@{,}p@@9]+@Z![@@@!7}.@xi5$!@@.m/@JR`?@` 0@05Y.?@E|'f2@4Ry>@ <3@@"2D>@I3@0#>@=yX 4@qZ>@%3@u7Ou?@K"4@ lV}@@@ǝ4@h"lxZ@@a5@`Ck@@P6@KQ@@`<7@p@@P7@"@@@ ףp7@i>"@@ 8@Tt@%78@m, P >@9@scv==@9@Q9@9@6@?"z@@G`` @wg G@h:-@;wg G@K7 @{6>W{F@\@(t^cE@$@-[닄bE@/@"JvE@ Tƿ@["\sD@3~Y@ڊD@&KZ@y]bD@9@0@hnC@ٱ׵@DC@G`` @.=rC@pd @˄_UC@_ @ 7C@D @DL$zC@k @H#smB@ܟ@J\B@ܟ@ A@+ @ A@-؂YZ@d%A@/$@ϠZA@*5{`@xZ| A@HKM@u A@@ o_@xR @@ u@?"z@@],#@@n@@ @l@@@oB! @ %@@L~^!@9]@@@3m*#@-! A@(B%@HH@(WXݬ!@wg G@K7 @4cyG@%X@GE@f2]"@E@ Ih˹#@aۢvE@ <; $@(rGE@@4)%%@wocE@ӏ%@8YE@w&@>E@w/&@bFcUE@4ct%@/0F@;$@> NF@h$@ =IF@de%@xRG@@%@@`oHG@u $A&@H@|Db&@{0H@@jZ"?&@D^yH@@H8=&@^yH@b!m<&@DW=@@[E}RA@@Kά@@gfffff.Q?@~ p>@ǘ;tJk>@,P'@ O=@@[EDW=@` v |=@U=@$fGʿp 3=@Zc?X)>@,;?0w>@TRaDW?(ÖH>@- ?8 ,?@@:>@m@w>@y @J0Zw>@ @4)>?@TA@  ?@CVw @MW@@?ޫV@`@@Q@q= ףA@@(D!T=A@Ciq @8LLA@<n@}RA@ʉvR~? o_VA@ ?ףp= A@{Gz?Pw`@@`q?Kά@@gfffffHPYa=@@tWWib07>@,P'tJk>@,P'ib07>@?Ɇ.H >@IL K@`OiV4@Z8O?K8=@:vx>K֡=@@tWWHPYa=@ ̶ x=@p.rVe@,P'@pj/@03333SE@s߉3@NeF@(\2@03333SE@ӧǬ1@遼E@0= L1@/!E@:0@"wE@6qt0@H WGF@%`Z=0@*xC,F@@pj/@8@&hF@02/@NeF@ rQ0@:9F@0@wF@,j1@NeF@cם1@ӅF@07q2@pqF@?$Gpg3@BT)nF@P$#g3@BnF@P67^3@`9vnF@Ю_3@b('6F@s߉3@@F@`bX9t3@m4E@=73@+j0 E@063@D$]E@@G۴2@ X4E@(\2@03333SE@;jv4@p&zkD@/l)6@A&xC(E@\"ja6@A&xC(E@PW{6@@`D@/l)6@v0bCD@[6@鷯D@n36@PͳD@@ :-6@`.D@`3ɕ5@4wD@)\!5@p&zkD@`e34@Vv D@;jv4@8oD@@٠t4@0w0}D@e4@Ӄ ~D@ Ϡ4@nD@Q4@@5YE@LJZ5@h"lxE@-j5@VcE@?5@&E@\"ja6@A&xC(E@eo2@VcE@W{k6@evY>G@0eo2@UF@nDp2@kVF@%`3@evY>G@^84@8OPG@D4@h}F@U4@|KKF@%`{5@c0F@!5@<lbF@t{$%6@ौ<=F@t{u6@cYF@(Bm6@3IF@!Xy6@a4F@xU:6@uF@@ i6@R:9F@~D 6@pYGE@W{k6@X9TE@(DԚ6@ީsE@=o6@ZGJE@D克6@ ;E@\"ja6@A&xC(E@?5@&E@-j5@VcE@PC5@E(E@P 5@g\88E@Bi5@X}bWE@жE 5@ꭁVE@0Lp5@ hnE@D@`~R2@ $E@8sN4s2@bp=E@(\2@03333SE@@G۴2@ X4E@063@D$]E@=73@+j0 E@ 7{3@ZE@zG3@m^$]E@Pd3@E@`.!V4@fsE@LA4@&1hE@+e4@S:XKE@+e4@nD@Bi5@HpΈҢE@e4@Ӄ ~D@q 4@c*E@R"H4@@&xC(E@+e4@S:XKE@LA4@&1hE@"-4@=qE@WV4@[='E@?4@HpΈҢE@L4@TE@i㈵$5@\ʈE@ ףpNHz%@(\rN(\µ%@HzwNQ8$@(\N$@NGz.$@GzN|G$@أp= NQ%@07@ @d%A@ :l~(@?J0Zw>@ @$Ö=@e)ޱ@ xK=@\9g@xM\(=@0@ xY<@@r@m<@M &@O;@0g(7@XdAH_;@u(@6;@%-4@PEw:@Kp@Ö6:@@giƸ/@ؚK9@?@ 9@[R@XI07l9@2L@w?8@(~Xu @07@`Ђ6M=!@еl18@^;u!@ H 8@V{!@K@8@Fמ#@\L9@`aD*$@` f9@ro{$@ :Y9@`PE$@U.2z:@ix#@fy:@O "@,;@`GF#@ۤc;@@]5#@cS8;@ "@`„r<@ "@d==@@]5#@>=@L #@pˁs=@ir|z+$@@!#=@~m$@ |>@ z2j#@H=(UZ?@Fמ#@Հ?@ `%@P ȋ53@@'G)&@8(@@R\'@Gu @@`HX'@v1^V@@Ķ (@8Z(_@@ :l~(@x|@@`9[(@b)@@tD&@x|@@yp%@)i@@HU$@Z@@샽#@0rAg@@@>"@8́tP@@@(˶"@ %@@L~^!@l@@@oB! @@n@@ @?"z@@],#@xR @@ u@u A@@ o_@xZ| A@HKM@ϠZA@*5{`@d%A@/$@O_bOA@$tc@q= ףA@@`@@Q@MW@@?ޫV@  ?@CVw @4)>?@TA@J0Zw>@ @geopandas-1.2.0/geopandas/tests/data/naturalearth_lowres/naturalearth_lowres.shx000066400000000000000000000027541525650651600304100ustar00rootroot00000000000000' fVf@VIT@2""+.0n3;?BF@G@I4KMNOt(bchhcf8j`kVpk nxoThtup{}xp8h8`D04XH4l `,ph @PXx(xx PtXD04XHXLPlx0PprrpŠ`ÊĞƪ^*`Ȏ`˔z̶@Hbx|0pؤX8<ߔ(@`h0hxXt84@xx8$ :,p|XPP   @ H *>hP`R!*"#$z& &* ,X,-X/ 0*01^2Z$4 5x7"x9NpO OQxS0U WWXY|ZZ[]$]^X^geopandas-1.2.0/geopandas/tests/data/null_geom.geojson000066400000000000000000000007721525650651600230570ustar00rootroot00000000000000{ "type": "FeatureCollection", "crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } }, "features": [ { "type": "Feature", "properties": { "Name": "Null Geometry" }, "geometry": null }, { "type": "Feature", "properties": { "Name": "SF to NY" }, "geometry": { "type": "LineString", "coordinates": [ [ -122.4051293283311, 37.786780113640894 ], [ -73.859832357849271, 40.487594916296196 ] ] } } ] } geopandas-1.2.0/geopandas/tests/data/nybb_16a.zip000066400000000000000000024130501525650651600216340ustar00rootroot00000000000000PKVWH{Ynybb.dbfUT ,V2lXux }JC1?+qBp&$AԂ$`+WMVAҞEș3|9ΏRV4n-Ug|L.'u]I+sjziE߬W,tCh(X@tkka[GR-n1~0Aκ / '}=>NT0d!wh!_+{2 CBʼn0AjA(_|cz{rK#aVB΃d'`>{ J=ЧCd`}8qBV$\hȺ PKVWH= nybb.shpUT ,V2lXux l To?!)BTQTxOR TDdmD$K DFYR m*KHB,%JI%5ι?<ε=͂eJS ƍ*;t|g xd1Tgf.XCVw&EA'c63gm&,zԁϰl8k]󰨟>Nn_vE5dq vzLU/Ή )`~OlCmk|`)G-\UjHCuaiV{xp~2ۍH3&^R}VO`s~ 8 _T6쬳(=@7R"d/rAMl9LWlR)[߼k9,˶PE7oƙlg>$ e[K)_c?H'i ~f1xd爯pXf^16aS-=ԗ9~&BXDۜzB.,z( {'qQ: |ʿјֳM2Ҍ!wߏ'͝'E}>ki#)a<78yn]/Xt"D8Fy5^|* Ǭ_ W_@ ?x)wG+1 X,%O*`^e ˹ȕȟrd ^KW*!g*@8z5ō[9 mz)w:t;ʟg񭲅z oA^/ F-y4or2J*\b gb )=&V@ .hZsm1^4_s^_;:s|Stg94'C*ooA'[ ] Ҧki͵;Iݣ!Xw-oӧ`}Q&c>@~Yh|2ÇFGQF>~_Jkhˬ1WaHװDw V/UG}"uho-'}t!yjoOx빎?4_u:z^E~hg;gM@X+G̨Aaqs*2QV:_ZpQ:QEUM)ɀڛk4zxpSsbX٠ߺ+?]S p۴>~ vxM#Qw)N޿#(Iϛ.X^*0+Gz˕it_h㝰[J?6?yprNlC߹C{Hؑ_6MֆdRw&R>Ik|c<7E./ҮVt_8_z~qn=JR* 7ϟx^KHmx<_5|BI(lC[E:U^9WbYH8|3rZ~}-7s)O:^ WWEaC>Ò?ZUMٍ o4GB3X{;w.sw ̞ _1Q]|6FG`29sv֭bszgɓO]ʼhʣ̿l3֞ͬfޝ1̼iܧLY1`|=<ćuV(y@}>wp?4)[Z;3Bb8~ s)tl+FW>/צ5n='pFGt>^]XyF,~N|O^GzK U|̼}\wY樯8}8FEꛚT8ߠ!5ʬj0=W|'=H_Z<}C|E߸2]̺<@8lca5k8YfQK71w= ˲-lx}"aQݷ⩟> pա$症}(X78/,1ٔDhWtDZ;5u;^NyY߼B^h?XyH=ʷvl>b47zhù?|0~^.c}9`ϡ!%'dveBZW9k0oOM`qu|aߗ׮Py/aɭڿ)K`-zۂ?__S$Y2;?f{>Oyx`'7VS7ih/مcS^SA'#i.΁y(K'l\<] :Yc ;úTjNvccxMlU8$뼑ګa]8`%sP.,*9ժpd7޷y+Ͽ?޴S{X:._L엙E3:'t~NGpjO ҏZ~?,u}yz1T oaݷ%xQZ(_a} C.F#7z=c1>7'ogmԯmjNi=?^LGR>GR1?~(<'FO؊W -] uc`N[ M(=(nCokk!Eh݁B߷_9'<@ol\,/7w&+?WkB<SٮSXZG!`wi+3`,84܉a?aN簼jD~(7Y}?ū+ԧJ> k'`g񽇽b˶!(kLai^k,qTy~;EJ,*GX 3+DX'ΧYR{fknmMSPm/}jH[gtEU`>csWmdv<fѦyv<ܠ`N0v'NGQs 0O=|lc흪NNӝ*V 逡rޏG 2^U/E.G2x g6Ʒ d{) 1H?˱.yq,ȺYLz0oś(_~qk]04PwWe%އi}H|U[nBM(a/K џQ`Y}<[G[b1`_x Ycz㈯/ev l¡WaJ\~)E})#d( yCGg8%{;aa%Xk ЄTf˹BOߣ񨮀K%䠐#(!>8#fg.HOn0' AXO:}Kct>Dzyyhk.ykXTYt[yoXR 'r",wgNNW(OʼEH% 91YkXAp%/,R ݯr wVw72k,?]s%pbɬeڹQA} &ʯPp P rg^h{l2}~aɦ8_/>x#+B)ޗ/f_;ܩ~Wao28|<,ŗofI8 ovd݇'mi}篷6fXjt9Sp8|m56,=u ɣ҂\k?=`NCw~0׹#ް4:,6gâd,=z􅅙WO4z{0H:+sZK ;YS|BОM摣34,uw0zO9JGgw>t"{O:^}b,~~iax0Kؼ!_0O#Y H2E$<7,>@*37[8\]`#k6I.Ycy|{ʿ(%Kfu`(sNlGq:ΣI 1u$L/{ES׿+Cy8}B:syb ?N!ϞA>n0}9l3 \(a[eB/n"sB=}Qr;_KVl7-,u5OūOpF^]dM`$8(r NoNÏQzk|"Y䩛a#,-ur\(]Ʈ~I45t׷,p jO:vO;F['pziy9oL04LW!(=h̿C=WIyTy߷hN?1 ֽ 7Fm2vMZSsȎ"XbM>VV(Ŕ58wJʯrNK)&ϣ`*?~;?~AT~y`gEA?}y]aqok|j?Vs<<.t򇟀nݠEoBlcϷph _6~Qj?W`F8D+O;(_)q/:O6;ف_wu}ghXXiL`y""տt9r*=+` ?=W_P.xPr;\;gv~S4}^ NHAgf;gwia#jM\+]^> vyojGCy|\`e|U[^Syp*QF9$ "ur8?BQ,X2_7ݕpPzSzLZCW@s>Cc\i|̩0JI=Hy>I8>ǒs[lA1Y8w\Oᰝ<ʿyz^>݉&3)?i%FdJX#G\B=*"GO2^.a h HG '"8Hє7mto|&q0YӴwt5Eq(=JsSI^وV:_w {i< yj:_Qt^C{S}t>HQ~[yO^1 lsg'))V}G$Q{Oљpǔ2ڻSH>d9\ϑԟd6'~)Oe_|D\KI տx=JݷT_T5O~>(\߶kڟASŗBӰI~tD灌 fTbi|݄A#c%;;˟6D=?Fy6u7ԓEg<\G76B<'^_~ =yn'.bHOn̵rN+xnG~~`W6;g|xk,~¼Yp ['穣npv^|n=CUa|J;oP{unTF~h.s/ESޕ_t&wg\wu8Qc ITG,=ov4b o4>~ߏQCӰ{}]A) v g "sm]0_2eϦ2 ;yyo)C'>O/*-6~JKYJ?s=Kz\oB x-yQ":|/y% CLjq֡;Z0Z/߇q~F;~Uz8kxtt5UansPHOJNGϦox-!ݏO>XR~=sn $5ӻ9wA_2z&KsYf w27΅پN='iya[p|D{KHZ#?Pdԗag<>Y~dU[fYa;y,YQ}OnQ*9Xp(fK,0ޤgѿ0">yGªǒaIH9K/i!dp¾|sI|' |)s>`>̯ Gh}AJ`kŻCa3ZthsXmX0ůe,dzAH? "^L^"'ϟƟ lȄgfX;zKз6-ss0ۼiR|_69)'y=̟g,ݧ#h|cU4~K";eZ-,?J:CJW!w9c:_c?y*,c lZ`L.Ƨj]85Uĩj &g_#]0igXwWh߬߉30>aZEhJ|uudӑX/\`vXP^vl6sk!G/fa] K0wgye0wvb$gUC-Оؿ#Pt/?coZ,t=1S7h_`hZI$j˟I\o1N1z9J oK1|YA4`UrHOK:üeQ07>/:%o]wúϻ& .ػOoO8Hg=훿~>#O"2_dXlb9Oߤ?렍 :pvҠV'ƟAηJ2;넩}'w&,* K.USi}[d XZ_’2X70M2H;tUO:оɰ$+OPbugs@CcܡcSSgy'J;+ >e34c[4(BD4}X/k?z}LJFtO_=:u$;7': \^Ǜ11Y\bgnMbe~_<ƾ2Ktߚc_pYM֭rW WkXLb'̷j_W̷y = IFL`N JW`.fCpig~˼nwA;̖i =u1 /'x&sSǎ1zw:Y*u`}x`8}S`FxU1]ZDh,BWzp?NW\UAP8,vQO;P=sB{\/<=W ODҭоOyMyH_J|| `̚l<[(U4Z'5c>sUg KUBD\Y$Vy*kFu-W`moIklyL@7sị+1`~,)9aʏY|+og`n >5>y /45dަf3 N|%2z Μ=zNf{c6’o69Yzr{@딇vf;ӎ̱ҪZx*GƷx|RW(s"v|?=%ӹ}sȻ8jHz؄CokbNQ..2 ^W/>7&V d tҩ<Ώ0oֹWwyحYP*wO\4I>y[Bwv~Odkyd΋'6fz(szS@o{W \K|Ќ7aCk\蘳goۻGi. v>99 ;~~K?5VT:a@^n[k{2W:`bvYٛd-KxV'gN{4:#G<_kLem*Ϻ#9dQn_q ƌSC3z񎬥lw~^zZϞTk ?.b\  ?$+C3{_i|}Gv P5;i?Piy<¡}T~|7'-dJ (xw"UaW4?`;}hk11;o]==%/s(k;҂F*xnَT~2F,@$_nT>7a4p%+P|NE X8W>gO]i|!VO8]TXǕ5ڛACfO!ＺDZM?8w wh */-FM3А{3?"^RPnC/3җMxFzUWEGS?` Kc_7%}g/'tlq=y0(y{h|F~ i ?|P@o*sޢզP>t~Vɟꑞ~ʫ߂Eo =jUrw۽U gPu \!(qlޅG݋/ab*]+c߻d7e+WoḾ#=>L}Xֿ՗FGoImWh۪^FX?%y!D-߱G(y-(c!}h:OwuNu~E}hI6Oxgbsxm37* d ;3,YV `Iu~TGEv~wZ~Hf+wb6ҽKaI?x(]~<*[?S~}D)>ut[Xqϥ:ZaU1xy|ўtKXFuFh؏i9’=r"߼kz[*|)Gz}Z^>گ|"RWE#} qlܞMuwS~=A/~&~xzCA:o(&~'i|hqy˂iDP|f+?*s>t-T懯'GytD~>VZ߇DXcrqOe+;G*4?^(=dY Uf~Ud:% )~97r? y<3ҧKWKxuF‚KT,6y+S<Э^ :Hdz}heD{|8BG2?Xz0^Cna!4^/ՄJ;Q]vxR>p+KEw(yI s4ކEi͗P ]+_%~#2  ?lq?,3s$Pj,O6w9)ORG0i {yU􁟨.'wb/78 584WyOb~V=`əd`[f|zxժE̝V|~WUy!,eاm7poxO 2^(+~I ' ls<>?`>zWH= ?(]so9㷄g\B/W~y+F~<#jvKplC|jÜX=`OٜCy?pzt?9t_}pصB𫆿 #mYv5y)_s'~epi_z ^g`1+ Oʧ?^yoyy̳'H HڶEqףsef>i2=V/saS2M{'"xC~BpP|,r/g} = `cCx[ԯ%QVF%Uv0^^;a=N}>~T1Aef/&דGE1A)y_?z?񐞜(>g~ᕵFJ'XhLN *JpT JPRJ7nQ _L*@ܬ}pMsrT1h|=~;߲cz7@8/o\8o'(`^yR3q3,~.綍ļ0u`[o_TfQYݚN;+|Fh{[`}^mΗKXⵓ`ps[a<3wZGMVo~e+ӊvπu%Q}X_Ed̋}!&pL9 SH"st^ylޗPg!}/"i¹'}#aծd()w! s&g7%;Xst>쒇BHcc,h=+.?G>C}i7H_ў [uPs iGU6F:Jc?ҧS)]e-(wU )ú{PnHwqح֗k6 #K"Xt8EaZ -pzZ*ﾅWH8wQ>֯XYŜ[$? t텮lҤKKXh (?Jw JQZOK* Ru͇=Wq9O6z@[Oa=T^Q~\Ju*z{u+51Vh7bkXCgBo7F=M}<0 1wih,]OIsѿnE_=ʥ?Q)(__A%d'?u o>?B2i&sKMO{|@oo7T:Vu=Js*ɔ?ILˣ1 JķJ3ݡKr6O/&)>d~4 s~P7D5:zW"QQ_Z!i?/("ZR8\Zj.)?pP[8Ǝʻ_Η^ܮk) Np]G6ua h|N\eEmVxn}F.װT!  U>5dpoxw;X;Åp_okX yy쯂 _s+y늩n㋷D}&ce97*<#<0~]EY<֫lJ?ޙ΋1t^P7\]J/ӕilvZ_ܨr' c=k1 $/{#ڇ9\ ;t~4O>7(Q:տ6%+'t~RgԿͫ輴iaco ;﹕*+zH' ؕw[-8,g'3Oxz^[7OA>G)P}75h|c<ܴ. Vm&GݥyD륝}CUM~_5~6A)rXxf?R3O;Kd0CN_گVG<:yM`\09l3ݺEN12=h?ܑK烿il>+= ϭ \JIZ -)=5?EmQ}s_OU |5JOL#8/k,>m=WXU'fxyjk[+>S >(a5%Uj|#Zz8heۑ?R<YB}=#Dzql€g1a]: C.^?~pUsov,Y!O <'⳨?,*O6CDǔRa4_c+c4m+!m^4)i~;!wv9MG~zȫ?՜}sp}y~!_?]| 63S2Cr<_+?Nt1o_ l4ڟ'ǩZ=_AK'mt,OCA /ȿy9 X,ZgoK|Η;o'l: i|.iMz^S<(ޣX~'c=_zW~#@:K$,_:D~9J0ac d>W@ݎlW{j~mH󔟻fA7ײʇ w[n[vHwVyJOC?K)4^? !+U1O6/NPB]~Udne~.}%\/zIAߨ|Qg#,mtɀRJ6N:̧O=@>T;S%pnaOWS1Y{qF)8_$i>6n Vߏ>4;¼Tߙ`kxM>?ʰ~4RcaUڏ_nui=l#륐?Ok{4yXtA =IG$;;Vz}w yțwО_<'ȣ`ONӭANxm@C{ o9Χ2:JH=kK$:?0ʯ A<%:^택zʊ|Ssi\x\DITyP\j5+/ͥKs%W~}%U#ߗCa[-KڈJɿpϟ>͕,JCE㳔T?֛7kz"m+4BJ(=\RHyxXԴӍ7GCm/65z=Utݯ:HS(Me2w?rS=ᴾoԡ)(3Pp;E|/YͿ]:Rh>Љ |ZH~RQ0>0Pz1g8z7tXԷ?睊О,:{u/pCT_-8H%W F<#ߨ[`N]eMK{mY@m\<) g+)+'ӱríޗq|Qvzw_iQwOϤXKrY\x4uy1;L篳ksC}~1ͨOc*F~'9/1ҫ%:g]TzZ/1ɂz=wM`^Qd8kZΈOIt~eCn0=OHm1{^Ґn{֋z\?5Eg|Z>gȢHX6|T<%xPv#]O>m/jG36Cď;z,<[?հs/\L{IL=X0J/>,@5>S6PvjdwܥV>%++\s﷬ԟ(*NKԎVb]f|[Dōrh=bh 捶!6t/} 76R}ˌ`gKh\|=LNA{R믑rZߕ0g&/+5VGyUz-><~ d$<8t_C`0YQt‘~3귩Sͧy;4?o+T_ǵ:?0_Us p?vvzHn)CŜ֧H~P~^ >B9lD]x_n&ɴ~WކnNtKעKTDo>b'XG򫕢~"#oXdx؝,j"{qţf~w'lI7?,H4 ]֋Cvl3HHk󿪙t_wH /q{eԾ'G?1,/ZJ}7nxfm⡦&s)S_x3Ό_~f=yͪwaN L?Ysaq*ƯKbn1yƓn ϣH5`]T/)ԾWw*/B; ;+GQ ߥ:?~ d< _G>TPy}pU!&XD~6vVKhLzyXQk]5TT8}+݈-UHsF W,G2Z? -$I,Q ksfӿ%;p;.%!fpU os\x@t_ $K(]/.ٽw*L/\>qpCZھP|99Wy<~P ±ԟcF~}g} ~KyP*:V}wOyF,ߟa:ҠO|?*~)OLML8(,{ȝ +ǨϳpB2] S}=ԟ[(l y98_~JMr?]JweO}|]ʐwi~LvP%SMi5S>N1 =.?)T޵ :C>xMIp ?/(e~* Q|h]3hI¢՞TUr? ϣ㰟nk'¡EU ϣblϿ^@ZY_#]pOQo^D/a XU/Z~L%cՂzP?.R{??[8\8ߐBSzh,6ګ+ϣUT/4=w|(ޥp_jJ .InyБO:|8=g鷻땒͞Et>e`8SɳzP3a:4E TKX'oOk@O4u6`540OԁLϦxH"oFx.ve͔n{o}(lSH G 3t~G{,HJ)ݺA~_ ݨ~Yګk]ߗ伇pIoEpp?*#iΟ~rQ_o;Ou(5Q>R]{>K݅m&P{`hk~ɷlN҄9כZ;e_9Þꟼ GLp+2:liVs)nRR~ ~̒_8p|9S!_d$KGswZO! yRX>g]`|=/,>\ >blc-_&J$6JW K[vSIWQEW`AMa)YxD_O~ < x=>FZ~H~S}(ꯑ \̛mGӧOȢ4^(y}n[ꟴ'?9J遝W(}P lB<}rC7.ӻbqPBü\8x L˖}2,B^I7NBFG,~J^qէ:Y22ΝNO+~YL& ~{pT/iNKc :r'5Uwj?-~_'* wP5pv/Q9-IO?O(/7_ bϻ\*'LC0; yS{DHt)n37GdXէivp^`UT_YWj5ş~GL{R gwT߁NR =RdŇy/ n?Ow1n.1JP|ϖ]L|n:%?ua|c+EXՖߓO͔?o?'aN{ Ж>~Fu=?\t0kq[Wרּn!}4zXqY=N_tNl/,9GTV7CiI7v'yYJ ;:$zw%__}[xH{n/[aEO:ݞA>;5G݉Tb/xE F!Iz+̩8{RpbC9"#v;.^h%η/Q̬_+fNO:=8Yw̑w_?~yu3G ve0fZ3sr>{ |Aw 3?a2 }|WVy`p)?`q m̶9wn .輈YS:Y̲Ie*#;/=?`Ц߯.Ho?,F|xծ)!߻u f{\m*0O wO7#[ düe^ a>=0_jytTҿGv] _|/~}i/+n^)7 gWM6̥烔8wxWg?,tP+s{ ?݌#>5>ݏpY;XڰΞ'p>r< xs~0/ \3'c^ǂgx1spuJxZf~DZ6^B1 }s3-uй PtUOBFn` .m_`KӰ^öf!}n+/Ey5y,:O?,8s['N|x]ovN|׀)lx JϰO27**b\xL?b~$+ouBs$yԅ.MA= =yuᄇfv58uQmW&~S`r 5 Օ<-P`9.ݦc2OwѤ+;汕]1?9Us%[߽l$G_E䷗Q)dK)ڝe-%y3̘ [ɬ?x sўy.?Z@w<-*S^ e~اۑ?kc^,rӐ.a fg.UIy' <O1?|ڻuγef8"a̱pE)ӾNh Φ1hT,r aiu4\}y#<Τ<<3,귛gRݗ|iͬPd:_n[}pnGo{hB9!}_T|ŌS['R7ú9 @jʠ{-,qJ"G~d X#'ʿ>Lz tz{!2?^B o87o:ov,Ji3|ݵFWVC(iGNǿ#RT~xXC9<(;Gs\`~J֏=K|a0_2YE '-oodq(=q1{,?q[~~##2"玴 }my(sЗqbvz[ˬ {FfŨoa̺ 9>.Y#e2ۏ:&Ȝ>yʜp=˖YZ /,o>-?n#;)}xY.<,Cf(wNq7e7E.1i~0WU\y9'wW;gO4g-^F_}(S1dU[fuKi9Mq! C{laװ@pf{Cf!R̭̿-1߷j=p=7"b<񩬽ȩ̄V8d2rgOdڵqf/jMw5NW1ҩ MDAg C `N<[)1S3{bcWh,2;ڋ2pĝ9sv̳=yP=2F^D\ҷ&òh'#D7a=?G G:Oc7Ǟw"݈m8gS1s3qpnW.6J,މttr-+:|0OHoov/]a](=j=Ϋmph_#m+wP ,ty]G `ծ-[|:& `nTg'|~|eoFOE{?7F'eA |ܫ4h疧\Xvn/߆=9s4h{ mby~X=_g/l0;~'DSstµ6aoRvo٩9 _bwoU1{▗ Wn=ů3(/귳;zvcɬ/X˷aVw~Ps1˘;޿}4+Yi=͉#/i3R[=MzJ۸^{a隙XUycܴ6n~\뺴4QYA/۸⎗͘f6n nq VGMio,,43S ^C_}z5=ݓK$',ZE;nqZf\asZ99qڧM~|\iIbnww~ebvUU8}1M~Y-QzӺ@p[ekDt?)E!2oŤ6ndHf\CMn˙O\ml<πk` c_GOaZ>Nqn\Ù\b젬K/hʜ~6tEk [ Sqתf[_dv[3gd9IEڸ=+ 7o<ƍ6u5f8Y݅OwiW^hL:ݾ߅8>o$0?s&w#Ab~4QsKm&m4oT)fe̍uQu>_z<:̙Swf45Xkf;2̕o,?@`ok9sOȅ-`婖<.&k?־wsz&3w>G7']kY%VÓd)>xz\9}GϋOdXDScʿlt~!O/$/ȆG_g_tTPy1;U/%oy[2f]׍Y#eN"waѵk[01Ke>wߴcQduW2oK駙/=8 ΋43V2+y+|&3Η]0o9|OG?,=/XxgՋl, yRRy+QqH/%HIc~|X0җG\ 2sq cyk2];9v_4֏`u I`ޚ5h1v>͎Y<ظ67ACV,HfO2hl3kM'C7ܙOr{zin4(1?ðտTڙoޢo'ڸ8Z"=?ҽQ-şd3w>;|'s9;$̻Vc>`h/CynݭK1km1sʰ[O~&1?뷳E/2wS[}QV̞A˳\Y᯦,&}i?\UNs:3;p[O?<1cӝ cVd'/1^춱y>/KXTH1z~ck{̵W6q%ݿ8#3Ø#fo8ncMOwE7g?qӦq1G`a xC6n]y7M*ƽnxuuC,K6ɧ̣n3z=:yMʫ}:`_?U9Gt[I!)1saKDv?qxz?/? ejoZ ݴN,~!f~(IIiH抏ګw^\m'Oc1WT;ҲkPOSYzfMū.oϘ9K['ᷬJyƺs̢fa1dPB0shnj S?9R*[CaG {.307}>yw2724(]&7y3w}{}SZ2Tls9kTJ}OX˼e7&=)%k<5|4=?d48~Mj;V̯ 2c=0[*Zɜ0Yo0N2uw;Р ?ZAO/ū0?2cN}+7C擁˚ZK 'L|k[g9H[\'yWVgJf7Iխ\aEGBu-gGU+W<sJ]K=~[f$f:G3m;V#O>_sq${7 a^g8`.Rѽ̿ ՘ǝIb:koKօ1Us+`_6~ō/f3Ѿy^Vfl%&O3?Zp]NҘ?k[0/ՠ}9tlV<}*z0e+/t<\)f~mi,NO\֖m0G{Y f K+WۮN=IV^w==gqTVW2G_|-Qzs;0ON7sШo|=k/ {RV z߼SV ~[֍yyfyd1g>0\e!CoVn _+dꆡ/ C3o);)̉K7 XE!0nv0smH F_;5ٶ ҥG#f=c aG DYv͚ӇY=͆׭ʱb :d`:o]k [n}t{H2G|-8k?fuX</li#fx'î^0?s݌k湢Ql| lTsc ֵgx"`?k2kK5娧7sV2 za^vn9bWMv~ݸ:A;;WE"5pU),_:v`Xc+y?,M.iP)!᠕ r*?#w2yKfdv~w)%Ntҵ.’*?1_gC|/\ #;oT9GV/&~KPFW` ,Qt=o mGon5¢OpS ̫wu^2smJ 9G>54VқF6'֒M_VR{Yᠥȵ=e8A0-aqQT = *yܳ/6R`)e0w ,R{ї֋̡pT^οHgk{y{g }N>3҇~-FTw叞Z/|Xjї6{Qq؝_sgאN?$}ב{.%do=,FE 1K~m/QC¢)jŰ0/nNi!kK܊8{@$kV<T(~Ɵp XrN/R{>DJVLQ/>od3 փM#*Nɦt_jߖglw5ʧEnX-_셛T>:?%?"OtOmRjb(9mع|xRL~o'8U7NMyG;N>ȯ\)C%# ? S/R_S`gI0W77ѧ/~lP}鱽Wh=&u'_MU/*QyWS6i~P{r{8HߋIa`гViIzU6V̧ 6.[nnHondYJPV7CoQgr?/2:4mY&Λ-W ۢ=ggr w*`Y8~6Q /r Vu1tUkd[o>tAS`WC 2[\p[TSWZoz bЭh)oceԷf)B_63_]v"BX5JݓFf+X?}#w9Ց~)fٚގÙ/l9>]@`VnXA3f _Vn}ųeGb+cßL:o]:xZηr `V:{YZP`I!q3OZ4^Sjs_bϳ=W5a~i1fGEZϏ_~"D㴟 ɿYt2c;2.-1;= _bfݿXGL`NM5)(?8G޻GqU [w2iL83{Wƒli⯶yD3YnmD3%w2oOy BZ6r˂f3*NWX2ٜǜo|gqLP=ڏYk[;*0W\cɈ?E9z!C-u̳26T,{Io֟*)_+".|Afǯ\'-ژ>em`hcx.;FӦ/ȋ=!޺\_^z-yW9Y5Wп9FA0Yi,U_՛_eK0w\#Ue=lP޶%Um0Z3|V%_Kܔͬ? ynKRdn췩ʿ Gx+dn f4(61wjq{9kE̚a7P\q,ssځzÇwbT<)Cti Z$x|'[gm{̝fNϜWnկyŒ?un,N {ҙ}7#Q)aa1拎m#_~l|ڿ@N~̧rg+Jlf,ˎaiMzOeN<\?9% Z9W#i3?ucksjg>qj\dֈ|_歡\1sh0rj۷Ex_xغt\J5XWdܷ5e߽vJ?'<*>'q_O9~+0! +s;KEV^gagZw~D';SPw3rBlsyo_.$[{.6OP/ڡp.{2%a 44acM:a̫O5`ot]Q_0d3wR{a>I=Y/{qԗӥKZ9~צ3׎}zEiKi~`ʰdEw27Vspak mܘSs_ꯗƉ?ft.^t~;6 6AO9Ƥs`yuj/~)3KH;ΆEfQb$96TRۧ+~ymm㺗Lm&6.]z0&e&OBxv>7͐4iSDN̛|\g]`.U,NNd̛wEͳGl]Cy{{t߉ϧ, yo[h|߈j4>55Q9!*ߙ^Ʃ#ڟlU|Պ&y3'{0/^pB=[B/^x >'iHw 5BoQ}EaɉP_ sXo_U. \)AwÒ-*y?1巋xt*U5|YNOF`<alPJ_m}F _TJ;N',rYE/*/ɽKɧԿAnEv.>/Qgl- u`o,Wϥx ;i侍iy]|{'o(~b#wXEETߔ2o3뤰V6?l';yq0/j.V#q(󾰀\tU6ҙ̅~&O*dv;߿Չw g(ˮdZX8oh 7$% t}5dg>2 _n{B硼ۆ]ȒbZmXek7#^šK@{U^ . s^oQ{ўm.~xREOVI(-Qr~^i FoקzתfE!>zn亩ПM?,F "Ɨ[r<{v|ooވKBfC=E|ОQhX$v@{Apr5'<'.b_u7υԩ>>ҭu(#خow<>EQ˺wo̧됾6$ģFB? ?85<9xkE 놄ߡ%t\G{7;۷ SoL+5F=5sM'^ා߀^#s{;74P&M'_qpT^+5o`ipPdX6҇}0zJ{EN~eW?JE }b"}+8ibwn㾧a^UzpXul,>8zfHX69~&jW_#T }i5!j;~q_ixà `]0Ӿ#c|*>9߭1~K$=COl|6u_n[k/wawOh_a8Bfp=ʭx~ =:?uǰ{d|ΥщLpb8NgE'y,=4`y$-CrYl V>7<@}N8۲ۛ{ouNk{jI:1y}ftF=S ӫB=`]w{7{Bһw`}b.KUo8~)vi} ;#X8G~ú zaZ,u,j㪘\ꎙx>9D /̽]-6DZw㎯$+nc>>fW?yY>.Mo﹇Hz -v1}tG\ TB{ʗec~l3ߏW>xN`}=Cu;C7})w=[+X0ºzptd7'{4e]6Yzyxhaد?2t9Pc̅a%Ν ?Z/pR"k C&(v>yd;:!]yqEOE$5MmtS~lbԯH<-8?{Lnպ|W}Sa= saժbdlQS<]VvЭ r_ժ!IKՒ82Pp^X{vݸ$f΁_^'lMRÙ<5y;43vϞ+z:3.cϚ[9"'a}*˫gfҺfOQX^}gSloO޳ni'qI8m߇=UM/~tP5=۰k)7 gm;˗=2j,Q}`ZO79.{:9:uSlij6O9]dARoL-X> Qx;׫q k+_><Kwf|ϖloU '0U~w ayqg A~\v15[=3iYm^b}>]hQc+o3&YG\#6YѬV$z}}abysa%[9ެxǷ{GTV޼z[}EeVɫKf8)Y +Lof.77`ǐ,oX_ ~De^vJXV[ED3.qVagaƛw;L;)7z+blQ}ؾ.Kr_~?w̻b{.949~BGa;2gkUl68# V^/`}Mz"L?$M1b|/J@?IMXa>סpzٖ ù>^̓հ2N+ML 8 NL=&+I1X_|"!y>>g+q&-KX~dKȌWP'>.=[tutӯ1(4$$3C\_sI^Q/뿋iQ>g}b²B=aMaX5obq|x3^,%,A\/.zfWۏ^)x}L/crp~%EOt֋C/@\q?([zwH*ƽ|ωi%lRlva=} aZxǘ\jq|鮛F/ l~bW;˯`}7/Zx%}M3HR6ٗ{kOږaˉ}&r}0>#`"5׹^9,_g鏲I?YO~;*'Ob{n{jb}#ޡdg HzOzG1~Y❓MxLfy/&|WUp~ H:r<`>TeSxG;sQK>ӏ"W]?Od؟NxL.&v?U~Y[hdYC]%?keUSO%#?;՛zvz<%bg0[#?K49> O܁<#uyMK,obz˗r>Ry@I}XًgTŌeyMeS׉-שr=,g"Sy~aqӸӸ|>iHswx});_Q7LjOLQ2Xij+O4^:=Eq}ڈ?z"|*l56^.>dLr~gߜU.Ǚ^mbI-gq|؞X/Xn˗oKk}ә>7,o~e}E,onV(9+ղ\Nٿ`Ensy_N}pu1\XQu}9?|7 9L2nyElG~7u;U[޿|6sU+\r7H'e&gLaGqb{^3>e, G釕zQkw֗l#zQsx}!y :ۻt.Rq)ypϓ}_|fKϘn/rf> ˗ή~IlvH|^;7L,B[ρIslT|HׯMHƈ"q=џҞY37YS|#Ғy*+_˯ 1_`Kto/|c~],/>eWYߛL]m`>?Hoe<ݵX_`0}㻖vT_(q .>zYk&pyszˍ+~޷F̿x!fȩp?[%g2y,/cſ׋N_ӗ yq*t!cG&D7g}ɭk2[~9gTYߙAܟ_}r my~B{wxE<q_:,]d"`ڞ+(ݛCޭiύ {=xN,WGwz+YVXVMo0Z_<'y6eUp>>1&Z׷Ua/ًo ;K8_ o`}\u$KzK6`=\O g9p!׷f|e,NdKw>}Y@_7Ra=6~xž_r8yԅ<_&y5Cw#ֶZx~1,\<_j燭fy; fRX\O ~Mw\JAh߻Vo(g~łw6-{ ,f u#ۦa[Qygy61YpɾbWו"^(\-o F|*s}tI1T1XT9q{?ZׯZ^oZZ^/J-wٿpw~ [W>~8q8>L7Vgx81?,u尶$Ş 迤#-)?eb]׊.ӿ}5qQ2q'w <[EeY:Uae+'O gU5|E5:ر毾L y͘p|uŽE6h)BG#׎"M2B|_^p$A](aY$7)Wq5߿r||.)}k]j{MtV~{ʼQNtM_ICbdb7ey_2#/hȶ>llC?r<=>/#Go#ϫ 9m?za`d'g&}<_#}Jz{l?=T1>VE?ccX_X% :8` ~w2)a., NǭQ58^Cg'Y˰7Xav~c ۱ m5En羳~hPG?γaiaI->C7e Mtc<-άןqy?X?^YޞbdX?RE}cL? &Wf+}TGwc|8#py&mL4DmX kZ?Ĩnư}u +/Ϭ1ڿe ,ʤfU[qϟcZaɺLxQ5GAeuԗ[U% =0]M F0^sjhVa:t!\(GŽ2>Ʊv%ʄ=9pFJX237b##jEm? TX!AP^݋sXƴN:b~]mg}û ǫ:ma,X΍u }y'Z8*2 ۖ?`}Խ.~?P˼ºaIt|7Xwc@=>tnPڮ '[r9&T:j́%}Qݴܣ 0Վk38IeresKς\ulOSH1~>c9>̶__Xr%_qjt\pu'WbXO'8aU5=KX[gnN:_窱 ;lTc͉yn K|t[^_zl5'dvdyM`u|'kk`UH%3,cx}g8 g^dy_:%s/Eb<6)$Ž ono9;.-N4*^{ahuk$b,iof.Gե5ak =Z{`eD3w174?32JBak0~9a"̏28 +Z_saK̐}Xu2h Kj1>3t]>֕>ZetQj/x `WgXVhS=y 8`mڜ]^o(hn>M77;b|MW2{>$޵;7u?*34q +t(;`ot~Cp^[bVXbw9MMFf2@8={Q^ז|$+94D`M}?՗w7y0[0$أi%Y]aŽ9>WhEy2j Ey`}42Hg8|y~oE•Ǎm`{F#hC{~|3\r Ow\Vp/5><*97u ]ͷ|/iy n}eWZlvx3 UrZnZP^;Ξ3;9ovHU/z_=UO'9Dpıyw.yL6we#MW燂&Xg7u(+ 6N]c>5/;niC7 '5ڍ6k V[:|Xbd'CYpib5Gbne}q)X?Ϡo=hF~ήx؄PozNj!pS4^wSNh Koa<1V5j%{*ac}z_aaɾel_\X5WFOϴ;fò؋ y9u5w0_+XgzVm%6Ay)aEYPM8{w&}cKz>c(oXkU NͮIcFV,BOXZmӿӧw{پqpeLv_jY{Lf0/cI,n0n\ 'T5FQ_>ӷQ3 j`[Ò[G ,9yګz<o6a|uX*K=% X3 ~0~=9Ls, [_EN3o3n=ɨ>-^t,Xԓ;ɰb^OӪx_X{y3\oXoeUfyI[N%u O'Q}AT/73 ' 9ކߑf5Z?2/䏺\/ƴhTVӳ-`^]ӌ6ƕIJכF5,>z;>P;2O66;ywh̯D.}jXXQk; 9G_GQƍb=پE( fϳO,l-Sao!79_6TB'᯸>;֡\zÚE;ÒW`ًLo9%8yr(ڑlT5O·0}j l!4~ ukفy%9ד hp^Xd(l ϋ`2W;y18 ]F>;ˎÚ7AM* }z{ y+6eJ.o| }-fowlυ\}g{vs~KcOÊ߹fzYܯ1>ϣ97ԃ0~s+>^ڌKfzrkٟKBEty.C%^giy>ayʷd/nӏC Y<ߗO!ᨔ6+SN3 QSXX*:ֺ4*7tU}:9 gh}K4??^:}^gb|KX䝜ہ@kq8F|k2^V=g]df1Jl\^:[-30#)f k1xkؾ?&+R7nc^=j^/دOw p۰qC8gQϹ)[8=͐}_a7Ѩ}_ ktz|1g`E7ַx8mݝ׋ܟ6ݹ}} DW7%^ OrW/FvM~qb"o?d|k>}a$t,=;w~~7;.;_oY_C,؞׵,SXdb{61}b0~;yaAQw[j[ڊ#Nud{;A=3ޠ/ e>۳oio3}Dت]gݸ^>zX2~COz,1X ׽;=/Z/+iHKL~hTy a7L_=/K, Ks= 1X8W&'N/Mep؋K$@4.q[&<@ޅk/՘~A\rX86sf;`U;Zۭ`,Ow7Ue5T1),5Gxv5 {*Lo6Wq]kGDՓ39գLf1lf :aEڰ'oxOC7,ӯgy?S^La2Ɵ`_X[g6saG4,ZN0 Dw3kXv'V__XdzS1Jۘ) lDk !_Prbd:JWXyǷ ^̓` ,Y,/dΗoou+f3aiW^X6͆mz-jNGjJ1v/pA/^O3M15-f<=dۋ1ՅO;gO 8` _t&_M97C`X-=UWGYXư}cY杰$ΗYiyouGX߹}pTgt 2q);`= ,XY^Cyt|$Njxǎ_&s`N gHu;,?t\5yK0 ,;u{ljBWIq}8G .C&G,&3>'8%šw|AWGd%3V:FI ̿7Ƹk.޺Dg*_5L]uk؟YplOT8,*^O~`?`}a}C?8,jbe5We<׀5 B15pYF-O>OUW}i٨)pB%ڳX0_l>kJlopX%Z|/bOM Dw,h.5,33~p>Z2l`OEnLH_.pz[ƭ=9yOs'6~Y{lϊwu.;v9u  rF=W>i_$2#x]Q8`w}Xxȳot cJ2˟JMb3pi}f{36{pa7\shCZ?-[p7}u,soÙo1:zov=:G#"t%>7&™}s}O#9b{a9^~:p1Eأ]-\3 -׿*$V;3568-aILaSek?!GSSyt1멺h}pغLxhpBnSc[rGavc<#;1KYw6܂҅I]aEd7O\ {. ۅu?ϭ\O$uVpV1Y?+flR/R'm`t/lmcu3inÎo67[NπɑGC*8oq=Fxܬ %g.nWWIBX:CLֆ^cy`4ϼ<24?}pz)iPϧf{P^kvUw߲'I#l{~2b{Uxۋgghg_Xzx._AKk]a], hY~GRy% vo]vCQ9<_ c?9,-e~cNꆡFylsa{H-kQ%ì/7b{e{>6N) V_ ۓx_܏L?9C>}g:KayܯW>K?6%~IW<Jĸԏ}Uisk_~_3ޢϣoP|~lXbE77zt˛]t,/rZC>kIJ3epRow_ Gt I~RIJZUqb~T挧-yPSlV=!W0,=˺v#~\?}m`[σۆ?d1-q1TqJ0*p~WQMb8$:Gx|{#WϕvV= 5Hpxw] "Wc~G&-gR9bm?L#xz MU[bןtQ^|]Jy)UͪR;D<+7D/c^x=-be<JaCr]+y+~17'eϯ7&fz.dbG|zw+2\RZ̲X[k' [TXs*,'yK괬pv*r/; i38=uޭP~c]$zA;QO/4sO0Ѧ +ReoZ6h濲Qo~39X75cS-mgkw{;T8X11VNfXi}=m1G=v™Y^Y rFUرS-.3}}M"B @W:߹Ҙ,o._qϰ]%vaYnHoCk Kӳs>94нIwgVKXtm=7ςeW9ҹpGeK=kW?\`fL/a}]a}8>Uv%!^Ϸ4ݼٟJ_=QvΏ^ d}S`l_pP} i- yU\oƽ6~) D߼za}ojF܃)6 jAHρlo\,+-~ڃªv`EO֌lX: *xg|q?*+\0? v\uY48x8pQ/<[jX{p5t+JlT8K{,d{~LϜڧǫT>cg}pR=k\Xy?qG-6qpnq2d lx75Xϸ[q>{q΃%lv 7 l߂1;!,X5 gC0E_ ǷʕiC>1-m˻GE6dz޷  w#tgš2K>v.pnӦF3Uép~%,4bjXeݢ#UOk8wZMXR6<,OcSgcŌΝx܄uS8^=?۲?7a"?؍g*pXY* XESET8,fS8R^>MOu9~G2h33  VMx9bWrbKޱھӸߋ;fgg*a{"cX|׏?noay{rk|ΣvB?X _~)cye7`O/zD|cyXɍ#.I3ByaUړݓ`OX{+])V?+Q17^=\aI!8w29ǭ˰%zYV]g/L_{\۷l'Y߇sPZoKޒ8_n ڼUahsybGXs8֮ú@X9^*s=Gf4ЙڒUk!nXuwuIUczyٙᨱaE#d^+ƳB0Z0pԊl_pyi[X7Ҏf8q1^YMܹOK0loMKXL{;|-pZg'9U-b|\آMؿ+I"ٍrpd7;\ѝa5Ζ\3{gs?_0,῭msb=xG@ww0=Gv{r l>EW}"ccte+, ~+5?OpQ~^ˋKUa N36y{_ n}/ ilEyg$ ׃o؟3];|IeיT MaH#x' SܹF-hN M<$N\_c=U jZsw=XpWY`Exy'x̸Ղ[NhXwoK{ub<{AK?_o|~ȅMڗSQ©XFޔhk񴌭}oID1[ci_a 3Sr:گ+_^^ x|`jo~>/f~VvZXbV*Q.)^6~1۷X+-asXo-gsaD?@ڟM%m2p8M8K$G=ѾK?=Nz~6_l$iWGѷ/$t6r_jl)sULK Wutn+y 1 &-,GiK~h/lk[ WZu"Cl# 'Htqd䋂4^p nVZқLE&/Mpz.ƛT򪬂uV8_.znьѼ_^²w7Qam>n!+-K*׬pBcqKĭ:gGK:'jUXe7úJ1 31_hZ-U1}U#h)=2"Uk+:cjg#X ˷Wk~R}綞PWZ5xEo Ƣa=v|A|²V8\XڼN#aʽvXoV듧XW1ˏ~[,{Ƹh,)Xm v<VHc|LkC{/r+)PKXGݷeai֌zq6t]ҭ L t~J ֱ|-'k_f[%uXۯ2zz Ԅ%W NUX s`Em3`<]n5`Yv|%1`>V3.;f58q?a<^yV6% {]7\h8.W>}e?Ϋm5xȉ1POpi֧}wkE]Pߺ\ϳ]lʣl +hίDU륤r6glΰXox?b ƻ2ܟ&'Gys#p]Xz}߯ byo>N]7+b'’ VX9xqq#~~>繥ir`<~ً3<?l\V2݄qYVMQS]ok,;ۺ҇sL? kФvÚ`X;阘ߪN>x,ugZWYތu+4vq[X-_a]{KǔV /߁͂1D Q*ߛ O\`׷}h'.y\͑b_p-0-7ad⻉aWQ>0~š7)X}o\忝3Vc{wT:' e7aouN#"gl⿞%ֲ݇&AH?h 3 }Y}:=Sg0}me|'`Y'La3}t,;,_S9t㢹nœ6'5 W. &8m ? Mg63Upİޱ>`֪mXo#Xyxw{|eư׿Bzul&f2ߗf|gX?Cp!"v\5λ_>1~_3)lێ %>cZ3 EsU)b–v0G;|NOz8;˳7d$Tq=`l-4czxQʓi%8QHWFb6e8Eq<=pq}Iy^dFy~;oK6|;Qn4kx~6ю*prr(%,oɞu{ڳzV{#qb ^u]L$Qz%v샥0}/֋d+8tgSD'C~eIBOeׇٓ1? 7$$l6n5]" c{{ss4XlX):uy [TSz;7 >>g;~ z4}Gzy8Q\f^kga?zc&JCGgNb 7` F;pBT=׫ް bsb;ݱ1Tg-y?8vFOִb1\=6hjTXmvO*;0_ᤁ[d%vaY1h_ۇ_}x9׏'<7gg8p`Yv?_7k/V};N`%m-Cδx1P^Ӫ+G#\|jj\1Ϳc6A,>4p=Gnޯck^TTg߽ǽ]wJIp[ԵH9悫gQ[xXf`+:̩X3DQM N:wE{ÊR%oZm{Ef*¬?m9^1? \`o]^6nչYP['(y}2V<ṇG\OKaY}^e9益OU`Uؿҳ7lvf@aMAT 8a=QZU'sa|?i>2t?XXɣJ#xi#8x,w2S`e\_VwRi RTģzN$Xi=CT-S=kW[ZP1?WϚ#]V϶R~*su&983Gd$C][7FnWjrU- M;Q_\ONêOyތsTho3+ETF6x;A|7`Yj}V/?ʬyޣryTlSMjUp`LT^/8Vyra*U̶)m݂m#Y|msea~ϪSR ~רmzxJAqQ&G<_pQu~8`z 5WgnUW3&$୻vU6;;*qG8Np?)|L࠳\waVOzD<|pzA.eNq~V-17lTxZdǰ*֛ρ gU_])qLgsl~4귱8m2ΧuY\}9}.Š) :5O'{uF:̇3˗Y*aŃAcyML_e ӏp +ǰypPtǰ7,eoV3O3Wu<.pԍ˼ P^q75ot<}pB?4̕u7->3X= ePiˈ/( 7^OH;1=_A"Y^n?mikr+ /]fۏc1}ltT-۽k~\a3Mw &QނX a?wtw ֯|e+3Ѿ{'p|aeQuK]_^F ?=eZk`}'ߓ֪#[K7Xc?,ib\1yC7 fyq$_0< 㻨>.~DysWM f+a|yte{Iհ$U֓D` D ֥1Sl#_ ̇UoauJZ+R2^n6/loh=`}:Q_5h#=p^1~)CJ\ߧV,S^:2n'"NVUxa[d 㚍JSgi&\-YS +J8{-˿VY`d;f|sg ֿg5=WVo3n4\r 8 {wϣ`XoPP>ʏ{]_B$zf"GYih ?x {r#}o%ӝNc#pA;6N>vu6xviG-i&F`y[@5cCj̃%~;XڎI+_Ō7KGf$ysa'YL/a?8`Y~~ G ld1yN0g:?z-t|`Yo /ҧp `']E7/vorҖUšJ o7ر_,s<b -p8^ogUzEn3s{te|?+ V sY {knZa G wF_k(kGkchXwDrukVm148`Y7py&XV| [;ػT {lϽ8B^tƭ9]SXeG}NVnRqDXS{ =263N.<qQclT?Ol ZgtLS2){{X fe|8J q%ͪ!25 XaYv4X;=kʹ<~ve<./]ޓ+a}3eW`Ma:-V縞=.r=9OgaC|=ka8{aֳ>t$Y^Wђpd|t&osУ>&w}=[x׬My`^o+-a8d|}Xȕ^|d0XvV&L0ޞ<7U1Y\68,D;\m9o1JWXnG؂_s?,U+1 G> l ۳lQ;; ge1Xfִ;2}tVg0{_XDIC3ư#ۣycwyx~u+9 L$pxO1A'^߮݁/~p.:ZYMkFU4kri뒸u[|m'?RKco ߄#>j!ly}5aa%5sy?ނ.wצv;牝ǣ#,/l-?4Fgw$7Ӂ_%۸-d\V1KYނ> zYI{™#YŰ|fow0޶luaz +kƱ+-np7,t5|\Ϭޱ[²;IMz[|c~`Xm? 8s k'{U1_ey}]G$g8jal5ݝ｀}>{vܿ=q,-g  X_᤽zer<uw-|.fV s>r}fY*JXO%˹wYp7q=>iaل5Mܡ#&&;.se{'W k_a}`?5zRxN/Nj_K1S>O4̿&%2(Nya9]pI/uq1˻/C8~S|[lM8 fo5O=}x}?)b ۿN_p~E*hzH[Mqƭʿi3^=MQC͹~ x}6nފC;OM;ޟH\ߒtرz޷cW }d=x=}ڇ3_0?ҞC_/VE+WA,?Њ׻j+וa}8:Йa ,sZNJw9QO=К,ϱ*+}z_ʃ==Xrv< :Xa\^'9WMEmkܒ'$zTсIjUj f)e`;/.>[+f8^Nq?:g;ף":Ƿ1 *M||.4mpr}TeX_hXz] ;c}Fο`?LKso[vi67X뒸7K_KI9\/#X׋r29d18{ lҀQmσIN0ϟWa=|{E*=~No`,cyw+MCW|~gϗpTZ6'a,f?kGwaMb!Fx9k%̉@!|ylx/3mz:>X˚ٶ}Px,nlX +y',mJe\]cKeXx}fQMgw㒖c̑.]M_' 濡xZK܍u6yn-wgZcq|N?~5,i7Bl V| C|`qÚU_2 붃[?: .,g 0즸v2X GX!w -/ae#L ./}xocW9+h9k/,{QXM6cl+Xf%g#GXz$x4xqX@퇽C@zBgz鹏>V mD&O5>~ڒ:/+X77&+y>҃C^Q.aP>6 ÚsKa΄e'W-êSrgٚlQ9Hal2?GVr`Oaъ *36N ?&ra+7$x+3O{cRrDOTHv.͟u\n713N|ypwa~ EYw?C sfy7UÀ }IE:L{bc4I'?~g}c^PfU)^\mƾ_I1Wq|zT %E&`wc;U:@Q[x[?xaWR矉|DVu5r>_}W-D )Rm$]^א[ nc 7je#Pva6K^ᏭK_=aq}p&V9Ϲ~aYwx~`=::3}Űt$d +2vjC-t V!,*lrK멼:щzֺtgX_-ߤ%b[8􇥆: =U]k?lʼ*o&=Yu?{s|^獄cX^m9g='f>ߗ54矺#~ꁬ͒Vk&8, QyTsw?/ZGm,?w\!C2Y|6w9q|_`X . f|,?yQ9kጇ.Ɠ|lǧ#NfzLmlMѵkyʧҕo%Xߍh;9?*9/ZgƎS[yМjh8*R-$8;̉KXsP1?_/7 le>v)o2t5~-dΏ,Y郇>pԸl|>_;qw۴+O+m:l-x0> 'db+7HakqfkT8y]qº/5`~^W(U?llꛭ1Mqp~)[ *˗)W² [c{5g}ѰH[[$,Ў5x؁b ؞3܃Lqx*q>`t[Ol,3;rkq=#`km6i8_X{J!֟I_?"ޏ3 Ig*^c q}`v$(,[=d2Ux2oQK25G y]s}- ~DUپJ L:WOa>p(. ,yu =5 =`7Va`Ej38I1)YS^FK[Zeռ|K*(O-#{yn}xm;wۻ$uKg -p|JG}ry>n/ 5jO9{NfyCO_粽Qyl]fZKT--ﶣž<5D%wWlhX|A:ŽF ǧW13]؟J`lϦet߰a8Q_tcXaocuXvhVVM|I38hg0^`]8wPwaϳk?.MO_L ~7S:,iҎקGl?۟Wy67G?ai8-ZXŠWZ`C/ gL-ai%œY7 UZu[x},^`}*>vq{w3{egڏ`i͉/"6 xdΤzq?zBV \AwOF \(ϝ}VnR8>eP~'L߱->j?tqGg7ÒUx=(y>]aϻNpњ*Y۟a׮5x l_#`/^_cy^=`\:i[ґ7nUg#]ߔV/Ϸ6'~,j&E駽C? ?tx$smϼʌ?Ewz3}i^3Ϗk)"һw`|q8k0~mx?X (Dm_[^Oyl=X GưXbXX ԟpn;+؏Ǖu}^x)b8~CA%V~"ۓ3 G'xrg"''<'}ypz/5 >+z~K8_y }wt='߇xtypΗjn"ɒ)˟{ ÷j,/gu>0߿o㋌D/U;~?ܵ^ՆU4XKNyʤu,68uZw* a=9հθcK X;(mPYoؖgK{Ϲlo=,x\ }Ǹu'}GO7?M`{q~̿1YW os)ftٖxg7 t=8wipRX3C֯(_иL/ b<^`auxK.Кxv۟<_&_d)\n@Kj< ²ɰGeƇluG]Y,:WZ'N秎'os .xުs_\ ^|w/Zq} Z}#Y_Y\N^}nz0ۣ8#Ja𤋮 ߏz8WqC\ZÄW{ ' Ԏ!Uus(?9qM\ړVFr\ygI={9㥇aiikW8AXcay(ke}ٿW^.q=op0(/2%5wgaUp:,ePI{XW4?ՙD 1nIo ]aՇϛ%"yr#yvygu}LV7=w~a3֋~p_N?;g/9M/?m/̝դL)OcKo΃-Fn|;>f ۵Lבp;xKWFo@Nwg VlydW6I0.9؛o97jf# @Z 'Ŭ5zr?nuGa)Y9- kw_pkK;I N~5H̾τ5{~r6 a%Wf,kҦ 1?_&c,,;2y=߳X#Wޒdy+S}WX"wa{՗>Tq|,-b%C}k@Oǿǃo#]7*^_ >ha~ӡݫ؆VX1LPyw?O/{2}3p{~g)O5oߧmVڷb{Nyv  w cGQ(F$5Ovqeޣ1P{(߷'>?PӋ|ayq}W4O;\g ֗_׫Ec|{ntϤ)o3,d%9^C`a tP;v^ HeWޥ7Q6d- WMǸ(Wd{[rfI {$’~Kh{::?Mv{E60}rVu߷ gs,o>_1SXbh,z,#}h4">=mHN{o,7ٿ\/v\G`fT8+wUe}3${{(XzSͳ,}>Bرyɓ_,c[>SϽ` /ZX]Ӿsm\YҖm>hs`2͡-|^5%i|<+pRJlO<^NLpw?2lӥ4ga]QM/2o_K6|ւ~{ϑt;FY$X7`)aؾxobΟ/?[ >{QC`˳i yh%O 3^[Lc}%ٞ`<%8 ~9w֝UaYu@[}^=a֏:R[p}SÎ%{s͵=p6G}YlKNc379Q\7` 8 &퓴ں{^&_[3LMS?[I <Ô쯾`2 eu.p0Zaod}mC9ZF4i[ egpv/IUcXk5[I2 ||Mf f uU }T_G;1I$he,,U;6P2AtoNG.o)~QϼpR|jF;pP KY~nC?~3.[O_M3ֺleOiU@2mŠ< g[yَ]*O~' yۼ_:+*~Kﴚ6t1JVbF_iw]x}Ud+ºEy?X /^6%żT#w^M뱿x<ӗGauk#z\עϰlo Ϗ'sMdypٱ%td`kC<@wO]OE,7XX43fByfhfXay䘚{͹%vyץ87؞Tw<֧gO͎9i;h^g9~Kbcjce7q=vvӳψgOXR!wŸMlf2} C"/[VmW7p]e.R~d0B=Ϻ?{<yptx־byL<"\3=bXpT$WřAߓSLo F<X^d{FzX+ =)2;z\x rF4C[hx_ }>sp˛~P?6HvXRx[܏뒒`ŌEOCyp)%lў!}ajwM_zr;O߼뿡"1r'ʷuQXo>w 2u+h 5h 5jy gٍӆ\_ X8L.Qwo+~>_׹LVylm>K;S,gz~+s/&Qk瑭z;?-i/_]JE\=P̴7/h=}~ /kD.UYNB~>Fvȅgw s1zL8k/8Y Sf+`79gox1^O 9KO2pYwX ;ayċeۀΰ>=}, /fk}Ή [aHX>.Or%,|+_h,[t cp{&g0%kb>WƳiϺje ʳ_o {^kz>+G]qx~z VA&4<7OyUyr OS`XX7 goԜD~]d=XPdWPs̺_w7X^9ַl?+ԟa''g05Y`ٮ/&I֫{_qշkҀ,%ҟ){ӷQ+0}V1X Kҧa?ο"7ǀ4_@k|d@Q]Z[pRW.YΝ(xY;Z\3>z\6'{^dLpޯli*eQ%~QA,\~}~l_/LYh׻4'xtZc?%RZϿKoaϟWr|X{ X?wg?{єh:87[Mdpuy 6W kްK%aO{S$_ a,uk`}`]X֠Be6hy[ b//  M-Ӽ}{Y#Ԡ-_t<"~Ce`i5&Z~A&Q$Ғ ~#라uԿowIyi{43lhK'`U~e"xV>ϟYpfXgK`*>b|}/2}'\c=|8xj=>ʫ%2>Ͼ3nQkV{>Djca}C|s. Va7DX ]nmqpX 79G,M?Fz/>e ~~>J1_kElvi`m@[aF|'\/s+mY#hWyֻk(~bXy{',n/27)ϧ>b#,yh[<2I K6Vo +}a+r y򺰾6zy}Ejd8UwجI_1Y5|w/%^_a<{Lo{Ckz=x=z gzêO|2ѿ;مq<-BkŽq=dW֗GUb5Zz>^-\>/Q9O`tIaS94j?X܏20ncLGOwI5RX'o%iְ,]}Nu Y{b_Pߌz`ǃNhl< ˣA&eׁ/^8zr UV|5WqUy,|NK·aFX^ڨ߮=Xմ=U۳IH}Cϻ;7{0Ưp§,/vaaYΰF*?_e&sئ\Xוoz>j"kW˲|c ɵY_I\-5@|U|㕝3;[@|`?,Y V9 -ϰRa/?2~r2?/: e"b6߫߆c7f!?X؇{YKҨk|V' O,o;gNh.ސ4å|z\*YW/&xFu7~҄^yհ*?)lS&,rG|wtt   Oj*K^N=*~t?Sz"í L{}{=`iso>k \i%=/v\N)`}V+p ^If"ʕ , f狷5`x7Bo +7zYd~kD|9â={?;N~˷ʿ*!t `Oϋ`G|}\ǝqG)?}Ǡ#CrXkc~_{z>ǯ߇;Vᛒq5sOul|-N#rӮ߽j[7՝28EO >Reg-0?Mߖ&fS8`QMQ /Dc\TwJfhSO Եl"hE;jDk /⅄I¿uD+¿t[7at w{D zD}E)?ww_u.fRq jRm~]UiJ[Q[:\.{iš7̫FpUQ,2ae|"?W&]aI^Xi6+nf,7r}- T -_+~xWXfPW 年 "VKƿ$Mb5QE?LRX7§SS<^M☰j{} +6{aɰ_j~e~qUHGcֻ`UJU5=aor;\-ӦNh M ҋՖT2Zo ,z;'[7o=KwRV\ׅ#퓘A ϶6eCoh5,G/R' eyՖM cy|R;~#i?;\~k+82` q7R<,ͺV3w{X괇c??1ͺ =SNcpB8X4w70ٰbw__3`qpۿ>2j%'""}j'N-_w9.ጥ޻@;1?(]{֯dO6l5b»â\W8^T`e(_o:Q s*pt&_Vu>?OcUR_7_r׾b|?o1^W0T7/^\S֧}'Xd5K\nY,?,ۙS7kc ޺jp"* nb{w{SL1e2|g%N`:᫮Uߢ0%szWq΃Gʫe'{¹-i7MIm?p?ɉ};;Y姝z;Ǜ|B!iFk?s)ߌhyϲ|> ˏɏ^g~C: y_~Ђ?|_XzPKtWO ?"Ը?݊><;r9񆏪'qpLW¥7ңN_˒v`/ g}"YLz a\pa_kK^Uc l/+M^_s f/`Oae?\^J|$Ń'r{ͧz{2nt6]\=B澰xW?`m~-4'z׿cʗ;l pl''?zwooU[o=ah3 *Ώ+^p wג`z^̺ko;9>k7jʟY(oFkίXjAǡb'ql y~|6:g/8$5ê4/7=w\T=??Vw\fpnnɭL8Ȏ~eE,c\^>K Gy÷`e `Ť:N2<NXl˾¦k39\~Ϥa#Gmvd>G\dS1#KpQ󫯧 ԂMDs>Km˳z^;~3{,n gN fpFGsQX7'<%>a5Xs?;9F&'V6=;1Og<+7`J3by,V.l3`ɉUߝ5 &gŠF QσN[K`p؎ Xn +L|c! =1љA?,؂.Tf߸?TX`I(Xo:Z(؅ǘ0PX590c9k3~xҗዿ> Eqc7NŰsocW\65R[6}MCŰV^cИb8qJ6ꪯGU4,+ +~^Ϩ)3֎Gz~,U!_/y'<<<<fsZ\q_˖hs?.~zz.c+qԍpI!'!kM<ǿ1haI$ccJw <2Wv,3ݓ5{?Y`?3|QqiX0~+S0?C:'_JX2n?SV?){8K>`Fa9Nb +-dĪX^ 7;Z9$1ŰjF:6kGp|LYbyHwN՞Y#,gG2.X?l€ .kyXli i𲣖խ:%K0~e~[,?S^r}U _ellY7P5ukh om0c1w+-u}ؿ0|i @8>,sw [yX1h,3~6~} 1%;#dKo~ WEa鶝x~zE?aMKl5+%a>ưhׇlO,n"?K#1X<7 U{rᚁ}'8fE/òg/fx1iw'33ӟ$m_x 1oXW[[M ?81.A^_}?sybm3a-MsWv3 Bx>l}:S,2wHG=87#y>b~,$5YϿLnF.ϻWG1_} <C"AV'l)g|vՇ;KSU W3Xj`E|iLo+EI*Xv6mZ#ჺòmSy:6mg:񏳃Rm$zHX6 I6 =8.zGJ8QEi<Gٹ?7w+՛ +ϖ/J̯6)%Øݚ˜Ew{׸[~.:ZҶI(# ? Vm |D|t%#tfR$|u+{ʊؾ-pr,wf?k1,;Љħܖm^06 f~8>}OM;ª96b|#X1hCujNJci124_ Y޻0_OލpOƷlii%W֡֌Ѝ3Bb+љ[q|{VO/ ^8v*|/I6I~%k>7O?NsNZh׌p^ Kᰳ y²rgבsX:9[xVQ "^3,%7â) 0.:oȴM4O8aT?-aW1<.w{.,$3|`9(oaNq}xE f~{֢~׺rV}l'?33͘~uϋc <"yO~wa{.U_eDzCW"8CuC 9.NEcWUWM ?3fv>24n2e@0\;ek ~ם)p|Q}g>ۢ,؏Ys>j76{˥YÌ_l_R ˣ/T8'ڸ&lY`wχ|o|҆U_ {G3YXAfN=^uV6fy>ª-4u,`}]Ȓ>,3L6.V Uor?o ﮅMӗp> ];ˏ5 v2K`x>,zOW n OMPX9[Ztd{cE{mwF.O:As47~X3vjE΂ŭp94,bvXEjWxtoZo8}uk?/`5/s|r[,8l.9"|IY28[dØ>v",:5@o>}oJ+=I>?m>T}ctJLggaZ)i"Ͽьp~ieT՘xAi ߮ &ӯo۳C8̯9OGBmwzx?;^P46x'}p2l2x3}Ds5po~ARjWt}LY{,ՆWk=&ݙoࢵuylWɰUu~ ^} VG<%+*o|Z;]K<@f'1 8Cn[$a '.tAg.VJcyA]`ÅRοEY7,}+zoSE/F=.,2 .E.aC>`G>)́MT_S4LX+}Rg5KQhm/ݳWs_*7p5Ge|ĕ=`DWdp̯r ǫ?M}e? ҪקE?8~߃ 6{=.mi'wX_Ks_o?Kc}y_y8]2ϑdς/Woxo +[ ?Ǫ7W{!*'kVvzވw&*s?Hj⾱ %ח^?y\Y| mpc`Uq1\Y|GoGWkXp9 og^MwM}X_~=3 yY`Wq;I^'69C|]sID_Nfy;?o ÷37e;F?C9NH=ٕ'%F2?fxoy ²4/.'oMP=\j}!5KŠzltaljXN8;\.Vfֻ_BZ~ Vwe6_V8 . s-Nf-ܘs`sxLZ3~cg6بi),msUw:\wsیE_E`O&i}wUοgp-L_^%7/0yv|^{an]C=@k\ƚÒp?m0,E"'c>>c,,U-+U$i]$}rMO߲χv`|1K3| XZ|^LvY^Mi}tYFO0O8{|1.^.6k/';1~!hB3_^♑,<< b~wr< {{G]~;6͈ayE-G4}<3 KCzwMM`]k_q>OX׷ks|<`pɹih?Ovl׏y|=wb _uy,&}'ҍ2^,oiU[bOx Y?! v8v w+5[ s Tb|"ǝa`7o!nGBy_׎'"-MUKX~v̝>=X|7 y0kZ9$$2sïhKؤG(s΁3~=I2qq<`+`Q>ڱ\r>ck\%=D6I<$2Upn+ܯ0#pݓq~&0g\O_ }v)?ÂG^XhCJYn K=[#҃q|(#?Y[~Cfw ?dy4`~7aޤiOu5z~aˆl;}d5|d*ݝ)fa1W|hfXZ=?'hc}8>oMO^?s ZO}Rgna~!8_CeX~!l|!_q|<>xJ1;HߒLij>\o xϏv]#-0|AhgfP?W#>? ?yprKN t|g ";>O.3&0?񰶡%7 Ey&o.]4W?j>e]eVKb?-z'sbo'Wo[w U$lytmZ(Jw$Vc?[_CT~a~Ik? S_# 6<'?_=ffGW LoG'me~Miykd:-4~as(å?Miaq~:)9.=v(l|=/eGx2kp$X 뭱=ԗ7s_f~h_Ye^?^p@Xr=p!)Lr>}0_lK2o8ګT.-EGy}~&y%{o~\*=S}M0kБ^}z~CX1B勢 aU I d f~Xf׏N_ן} F1Qz&4)Ǽ{ߧ|?ޫµ3Utxh}aiۿEQSOo/|6)ÏzHyA[_^l4i&o͠жZ5wϧޚV|uk׀G֊;7_jƇg*2I?W{;s%F;Ò=|QۻYx퉾m`qH}>ݗ+b|:a+Y Y~E?g_<9+*X['߳a>+M^YV\N3|ҒFT~%Wa#FB*X#߿>ӝv8'lr$[?KT~'uFKzc)Maq竧}\t&2S 5$_ W{'G1;OqQ0[kז"#x+=%“?y7l߳a#a/$f73_KwZ]8=56Cě@'G^^~Wia4;XvTmnʞSEGgk > 7]xlgS=)x4V>FYpGճ;t/O }U;\:y߅߅E9ƊOLYh/xj}]s"R~5?۱i#8ZfNd~ّC6IsoE"~6|,߯i{z}MgMXhK+iw>9\σd-lso|'^>z]YާͰ*ހkrW]W;km>R[ؤ~͹-_]UnK!/8>bwf+MYD_E^S5RN>YgG2OŎk8#k9c>-by:t? lP;5ϻ}siy_%:bDYgYZ+[9 eH>GW/_}KzCyGva=my4[N|Hu)Na:4_P~i쿫_0_LaN݇{sQhO=wl/mz?U$ٞU3`\SGdnQ+>uWUo~~I,A WwQ;8G3ʩbhKeImj_Z»|?̈́3**XOҎaq~2M:1\"I3߷.a鬓~g&f6br1G7}g/2VN0u׋x= ؍n7251ԣg,Y@,ޔ\vW89+i9/JC~#͐} Kr>4VUmqv}[S!M&9 ׯY=?Ij{;Y=y]Q:GS(3\Gz[7he9ӹ_d‘_:iޟ?})AzSN?K/kg8;$GpZL2)sQ7 {+~Ɂ8X{AK睮y/f>[y__|..aQ]*pBfބo~E|5S `?t=,fz%ؿr?m0~zJI*OQz6+OOxi?kK|kOpL~Il,l㺍G J>gO|i.x1_a?__7kZ=﫵eǢ븿K/¹:rj3\z*lSvZ`q~_R4=v0`#rޟB~9ry{vk(O7~p%szF43wJspLo`#- ;|}uA,lQ88)pd?.-'y_K肁wYy6p؍8ٔο0v oTcpcL2uf>|T07_<ِ~mzx"_Ŵa{z|tmO<[|0] +vfx;Xv iO},l{ kOO=L>B#[F`oPcX{ ,{5 6) uzW<ߐQ7 Tz߮7d<2d __GWzߘa_ Gn]|5rXVuHv{Du/gkJUc5k#y{+Z|/TV|hyӊR>o;0'>y=ۚuVUg̡K<8?}gbY׷GOr7tGSz%OK}7޵væ %Csl}-X 8;ilcSc~_;e}uU_Y^!ϣ~|}d-X6˦ׇ7`qyX;oxؑ{,T;خi-^~7,bxR:q5ˌ?cy'FM[M? gmM Ṇ=yb!ˋEc{_7_\_|ЗсnBJ|f8{NdzCE|Ǻ-LKuoLk6ތ/3Nva~ҰFVX=? ~5p{`8^|`a/ V a~#an g}wփDC~`2Y)<| oWˁ#}4=v_`P3>VY?Cᢩ 4[=cQ,M~MyZ<ӌOXek=8rM$U;֔Vn40'Xo7ick =׸L8 ӫjvKwm7$p ?MZba'p3rw1ŪGo=B#GѬϐ|Zy-{ iYIBZ1c)ӧ#=zfXWe~u7h8C3`)\y#țՀ.ӛld;քvyQs;8|LS-5=x^o Kjdvj) }Z+Z2m8SXSZ4is6,o.d [OkHi=4dS _wW`NNÏ{%G5js8ۥfx]go¦ׂܙ#Vpa~aKU?ck’`7Mtn#XvprnmX[,sOIդ_>dIV/5On^s,zvX zZOX{9m:VZѾp}j˓߇]0!rYcaNu~N umA5ԧ7, Ī)0Yr~?+ z2Ǩ9, I{*4='g{3 [&b_;a# {#9oVxoME7Ơ}~'yv:_<:ڿg+A]:/ղQeePD'(=f `~~ ^1uk.K]]?2:'Fs ϶xGNQpR`ZcV=&kP?LNe#ʹ(z7m[M+у n\oeZi,Wi>.ZpX34z?Mf~"IY{N0:rW' /:V.zFߴu4H_ X00@h9,KClܡh"{uS6"J8d 1"?Ws˯ ;XifG2~wmcY{!/Z?gSp4p w1W0}z:8(W[I2V1Ko6ۓ=~ uY:Xc׃2Nc~g~ɬIWMp7,O;\並&pߗY7rH,?𸃰L?M3~5~VaL-{XJXz0x3z'`hfw.t,Nw󯱾]9ea{'`TW񙱈%7uzi*f}r"9 ?3.iWkv݃`dZ4kUfF{h|?;KXJ禰r8WHkX8wEM_sf]B G7b}eo`Iok׌/hWЍ<|2/*S*ҭ^.ccú_.+N2?C9]xk]dx>pZٟ+w)X1·5H`O^W4o{׶ &Sa̿+W+lz pX¢0k: :ґc~ufxݓ`*4?b}. g?ؿmp?f;T!D~.<}==Vܒ’?r%EOl sٞoRSoJo׌Y?h^n,?etH׍,ϋͣlS'O1:| |0p8>"?%s|L0˗_bx(2c.%y; 2}%p8΅6?4{% o2~-w ˛VdߎA8{:|_ ;y$2G 3\i \>Hs`n5i*\z[`I uWy'z]]/n+^iVCS =e&wuZ7^P'o{O,8KZQOU{ veBF=9P[H`eU\.}p6t tl悝ʒQ%ʗِ}k6 ~aL<6jH/ʬvxzOpxAV&\GaMV!m cװq߈)sE׸h^_k nUr,_^_!QpSي(ާ/o驰2ßZa6)a?|y֏p;\4|ӿ_3> xy$!³ ~F|m`kh#{yx ovFQRo/-Vu"Ey!/Y +)O;V\}/oVe~cw1}. [#^6o.1ϫtN=E1*T?X*~7s-po:߷s4~*G>< =^pXVpÏ^t`O9,շZB{\1AC=׭'xZ.~雺V/x͟ ;X6|v!>>&9QloJk^{o}B;炍kVX.EK~S"S' sV˳jrW?N 8\;u[zw#d?VrQyW5H7GK^u57 & ?^ؕ%m.`ۈKFM=پ_`}vi4gn&7`G9lWO7x1l"vY3}847Lb,_&#XqO (eԵG5=μ a~?+3WD_Eo/gvcXo0]ơx?0_[_d gK8Jӛo֢SjŶf:;{('Cn);.:5zi)eUO}odBၫ=.l |HTLp9G6kT#Ә9~jeWI:K?YQX=g~HUgW 66k(e!kuNfŠ^(:au' 4r<'o+J*lQxA?]p-} ,̇#B{gδkuqU??trςg%]얺RO6=&ޱ ^[bzs.|t6f獖a\%g> N߮˶,6}SU٫9@%=ۢ@m{)EZ>U~@i1;\՚pK" ӱhL"߂?Xpԧ{MQsv4$ݳ 3cZ`=/a6X)loIhkWwТpnax=xpo4,nf5G#0i 4>{?Geƴtt'O k3_hOIn!XbE<6)(8Uj+W܍u:Y .%y!Ӻr j޼#צ=X^y;;O#Ϸ\ n'_7m% ڶ<3AeSt]pѾmw9r2ˠ/3#ϫVy>M&l[O ĶTNs_yU'&ZV(x=S[;y!Aˊ=,GCھf{2 ݴSvI3٤1 6IEŎn/Κr-m~#>.'bFY;Lt(,EU @}n d/z{a2)kGQ|>Vpag~z}\XqVpbGxM:Wܫj iS6G$\E}ӓzZ]ɲeqP֖?~:j7 yvKmvE]?2G_[hO-Q$k=tYG .z|R.`{`}-/S>OufJ_G߬KwRJ]~ǏE3[!̭xU椺+S9^'{% zzEŵ$'w Zk?ySֲ=gpJ|5o_2 ]|o>p1ϷCm|)xh#I'M*x!W ~gnQpeC/:5X}g73P۵U[o.P~cਸ਼=M]~~ z?8[Fl0K&~qc4W iP[v^}C.ػic5hl ߺձN_\$X> ;eVK=[;y<0 N <+آт إ6+xS(}5n*̗L>Yz]^{_Ql3's7ֱ|gA;^rǼ{|Yi޺j˂o x=g8]z>-,k?1жKۤXkzVkڿ?U&xiL%wC]Ǭx|F;>Z9>WZ L8 l>q}am'ڨN]D_S_*hYMj}u{ɍqcou'%U#oބ[θ}`+^ ͮ.Rn xvQ뀯6ՙ}|SK]|= ;t^lNuzqzHoN E^KS>h%~nz=gOq*ؠM3r`O&Y :%e+/փV|SpբćW̓j 1:xOب 63e >X~x:T;ә>ۤg"QJp]q[p 6 6C{W;x`f9mҶY 6L|wAo[kY X-]'^FpJS?V8DbB?*ڸ`#,J[}iZ*2׽\ݿ .wo}\vr^%SǖɴRpt@U7'!-x9<#[Bf>v58k\|TXQ_}Jژ&Qf wC|4c\ϔxz&\Q1`vhxU.FNݷB<=IN?[_|rɂ{R߿OpR ^~Uz>?؜ʿdpy[xk:osS E!G- 8zfx:)xã:V! z*pGT :/ɱ-k/Q罙Ҷ\'}:=6/SVS=,?KNyKf#`> % ~^<5QpzlR桶nHZ.'[vN]l<+j o~rZ:K Rwr~ui4+ڣz6jL|\^NFxbn9A=]Cfa xh~=Ssa_oRT6WPF;x/cዂN~^?EVpUի1ӫ`͕zx}ͱ+x+}З?|7/n= Z9M[q/vưYgGtx_>3o]ZJG_n,a2+O{gyK"}X M}Z6|,V3í|o9mz&yc>?? FbDwWF}/W=D^Fa|<:]Wy}mEapN) kc8_Ma?w$bUpy=BC:<4hn?kJ]5ppkW~ba,RbQӣX_C_Xٕ-!XN|c7;+f/|nBk[]f/z ~zG|_p~wz|{Mm7' :p'W_Y {ֻѱGϳX9ѱToO8e%@,˜s9:1,?I?ڽ奆 ldOzM}پL;f1_lнl+eӹ_8ޢpXY,/m3_b~pi 5Dk: z2.Gk?b~GGI|~XOT>-4;r[ _}gr"ґ'%WcN1]9.1"0?6o (b}fzYgojHXk.6Vprdƿָb2}f& 3s8lk=dJ[Xr/,jſm=ϚYZY_ٰh?Mpt>8qgEѻ9?d5د W˰c 8~F9-++uϿ 2Xq%ޣ}4O-d{~]~;UM_)V ?sYEt'&S(ܽojfE|Pq`9]k}Q#i+ lac/9; ֊c|YxfcD7Re$ɴ ܋ey7)q6y-Բ>Ϋ/>̀Sc ܏Laٔxcm> +Qf6~?LaM`ѩH?I_o`?Sa#P}/V7/\݂Q7qw\ތߌ;s`ϲib$<Yj.aWE#4FhΫlOIwh3ز07ݡb7 +'܏vSvtל}=Y9l| G_쐿8[Vߟ9MZ!Yo} Kj^}%7_x4 :v499(0o}Ps’sY>p5Kb0c?m};:v쳙kj{6gsT8?NϦFߝy<0^O_,?n5Ϫ;'Ivrݺ%K-M{La* 3[+aR"*XONMU oM&=ݚ|"k8k.g ve ~hIBXt˃5~@&|l_\tΗ)|^~o1Gw3e 'usy_Xlpe~^pƎilF϶e'Rl3wNҝŽRy lW-b{ky㼾Ub٨X߇p/|~uPy.߿U'  op{,_'!d3OPY62y_;K>Wb|  \7;~c`34;ap/'|H_reޚؾQ_y>^2F||ߤJX9#y3/z/n?_(/nf~q|w[@G]/O{b: Gҩ-K~Q1Ya|/Mܓ׆}~yIY?OkCM氽h(X^5) ?BJ4aw/Xob;kh3[?eeŻCI/\װZ]CkO+G/mT9>:HϟTiy<,X: m F9o2|,GOC8_~a7}48v Ka;w[ؤ=X2ˆQWZi"qUa<=_~~8:V(xϞ LnJFar~,MDz̯׎,KG{ʪ|Y KNMǼivO JRw]BHU KIgk$^ٽX~Ocj0=DjS4\r,~e 2`ޡjϖK+ xTm(~TlXp|*VˮP;,i[߳`~Ֆ5D 3\vӰmw[_KhAUm홾_ KJQMOjmxЄ M8c>4/Fи"'z y"+X[~0,zeY=*<< KνUdx XYlߨ}6Ow/vN 3ڰ:X"y}pXVn yvy@[ɫL`(?A wG;ϒ6Òx0d[^Ez?[zݜ5 eʗhoX0n5ؔù=;1;ka̿7cpuWg6`UE#m9̳M\ `q M陖uw]oL`?gaZL4Ycb`Op0X1⺳}51|'>͔ Oxd {-=˧~vS҇#YC/ߠ#~ ?,XCΗ+(~_.ؠzp3|vj~_&m#>uB˶{1};L"XQٕߙh&·[g{^UUwY7~+W.QWz3}3M)o| NXMY7bHx'{Zl=\rs%* X3]; mÁ Lgk9]15v,OjׄpKtSO0ay8_7G^eGhFY(1K|M&¢}Qs\IUk` Lg8:hgk7m>)|6^Mm쇃4:ubVAikMFy_5~!=*Q?O=n2=t 1\$fv{ֽz4_h=2=^ 79 z(~ ZFyzkY.s,v=ſ1t1eUgX͍ᣭڬԼG6cqvWa÷Ja jόXznGIk ͩz/RG 󻺛(#2%ޗ_h\VEx ǞÿrV(^+k>1$bxo8}c`U~VwO7>\ߟOi~7eHg?|]JykOT4_Qy >ǿ_Ȏt8> {}jp}-@.!9"7hl7$~1'#`(X?6 ,@~◭D_ɽ^my_g}_m3gk=#lTX8[Ngy)q'?w`=0_c5c|х⇰dse+ᰬj 7o%ghOX35,z/T[8!hQɿM_yLo֧_"\2=`XX1"ΝfXɿ69-J|߳~{Wa=ο)9} ܂eY?zV9 ~axh8[ +>aY962=1,jjʚ_2ߞ f;wߐ_)7HX\S o>Xi v:Q VXaHXTe s.3#Ϭ5>/ 6{gOªA,_/ hW7߻V%{nGp S,gr>%#}҃Ok=yb K1-,z]WOâͰ),.̿9?Ȭ|@N\bNRdX{ZXT5\;y.grrq/Íٿ᢮MMXÏ?D~S_΅/f}%ف ~^V"'َ",ք,iw*lߏ,fq;Xtw>Vиe'{x>npƜa̢? 8 m-QxH-N;>Ym$0U!缔y)1WL!?w k/`l_i$l'w^zoR6.+$a09!_VwѾL ;ęқ7҂רOɏ;| VL%vҦ{I>㮰F_^`ӬSXu‰nv9 7t(,s¢c?/9? `I\Oi;6} ^; %n`~-9{Of`HX5E1Dymee`ժF4U*_d % 8_r1koxo~*v^Xl;,iXg\?70~?o} x;ru9Jk߲3k:G 8=kcay~{߾ ˲f0~}XvSU/+GO*cX%KҎ_/ewFf?<)w`t8l=^Kؿo` ?Qp;y&2|8R8,V淃E|oCƟ K`y¥WJ#~+Ow`{&_ۿ>iuU}u~Å񟾿 ``lg;\a68٬o?;IL~ڻacqJIe w[²h|p㛻쏶I\O`qo]88蔜GoMf[o,Z 83V&u+ťV,~[&q>g[{XϪ]W$b;?_,VLa}υe1 OghO^aτKNgC͜]âMaeˆ ~p 8^V{?vmN,cĒ3{z 5Ԥwx kr,X\V|☍woiM'= )l:lKU\S`.йpQNb;~RXr5V\ a7';|5Fef?1ՙs]?|:O{g:χ ܟV=p4X5ӓئsykSN=BXzm7KnâXP ^~Df#p tJ8CߓU4`Lnbŷa~h~ W (v+#ZG)`.?r#Gm5=ڝv ¹+5c~IȄ'|d˳M{I5Z,?1w[&Lb{dV[9b˯+~*#v`qt9,*C=8Khr>[ldڰY/D"[jffW{qa=oX_kXNwqo|Xuh<&,KZJlvϗYU9}փ%#K'0?Vdv+a.p;^ pb&.Xza2~Oa ,}KvfT̈́lb#3~) ` k/h l82wF[?~#j,XgK;ף/*^>π}\}ϼcпkK{5b]{?C{7jy=GŁ9 {y{_׺&.㙰\!ҹXzgi{=l1^䄌f}κ+˞V},iFv/98-T!v4\:U"ҷF=Z1|Օ](?k['%p,MLMÒП }`"1 [QIKR]"Q6;˾NxNb-sϲ#oK`4X0LK'cw֫VF[òaQߥ ?KMWg~ްS~s:6P?Xq0=qyJi8{e#qNk3|@0|0Dl1ME-nc>,ڇ:zƠ~uWm~Y24vz*`#w^%Źh~; .91g.P qZ(XTmr7A~?wѾ>`pQҷ]rG;? ]{#,i{YBBS~9H[ߕXe[9+޵۶|5m.laz?2 70.E {8ᰘQL?.JmRO?̇u`pg, ፮o7LF 7råa>l;q5Ktta r>CKX466 Ο컎SU(77,r<c==7;oz<}LJ<~Y06nlOZgssެm_X8ny^$@{t{./WNi~3sejijV>SV ]9ĉWVԘ%;׵~W ɓ72C0.OU63`e,3 6z0|_˒aqWYVNOeV&S`e{ (/k,l gl;~oX-g/{0KmMq^cX 6q`.`qM[[#v/[5R~y87[h;+G;x c^ۚ#VE'[s’V/tB_DI0.j1ax lg&,҇,`QZ'p>G |K7sG}N<4CyeT1Er籼s_ֆExpҗA{aISw`Ŀr6ʳκ=1:q<y@w ~OC-ߪS1>DXߛNA~!'M}$@DvjGCS9Yal.3ή Ea"8Ƈkc?j d=aj1V|] #m RbjbA_Ywe|#X?4.r˿5Ja `VXQ6ǹN"~yjM ,;Jm:/| ʟBqMF~ca(ӷj;x ``Ӫh-Xs$6d;X1[ ̛#؞_ GKvv-k_ߦTmR/XP7+|F}zêa `ia'K;n :Tۣf+G^aqhOk}+'lj%q#U2K-ўZ҅{ Ӆ,mJgAo`cX%s飺]A~8Z\N|dB,[²XҽO{֧2{6,:xV #aSk9~"8vzSA*c'`a!J+rG.ay/LFE[,kD?<ݏX~j רO.0 ObG'E)ϡ6yxGϧ>`/U;p~j/ۣNoym8_~iU9̗o9#thrXu9˳F|- W,3;{m%JXޔ=?z EYq'뗺 V>;bxX9٘w~QQ?ǰjnX$,{<|3Ua/mD-*Z}H?:]{|ӶtH,j^l~pИ/#0.Wa;@O-^yo%izbˣo~1cH]K*,ϘI.,:v#^&k, 7Y__YXl'8gaCl%Qq` / d}Z[a. 6zل:2DwlOk{ _Y&{b}n2cOX/քaQ␮ۿ-Q"oۖɳ;bqQXtw 7CѰx̣<ڶ~; 8c15ϣp9+’=]a3w`Yڰ7;ãٟҋ~b&8NgM^ւEU`I֜}_jHygZ K $~K8F<+n0cqvw7Cz'??{7U3|I[ }Ɇ3^ {/? &jGê֧Z*Š-Cse~o!ԧG_ӛCτd~ٚ²o_E' Q^<^bKXGj>9OsC/i)!,Uk?> KՆMc#m筰 #.UAa ?`!k&3ߦ;w-%mj¢: 7l '۽hgΆ%dX;:NQf<u`#aQ7PaEt8&F/{O|y&xdO~sn-۽if}-Fo|} ()oo'1@X}a2{9 ?i+3G7Kj9)sl4Ђ%dxRSX׉woBXv _ +f /p#pƜX!å`Q ޿H8Ҽ,3ENL@~j/>9`3gqî v;`6'X*_'߷ga,u=>)ܑ1 leb44`{b܄jhr}nid),q^ yJgX*ρU>cw; J`'X^0kv eީnFsc~7m}4x}JH+֧S{+`p{ωI a!&LlB/sf'aIm_G˱?d>UKv=a?;yv1qRpQp]XyvgFɑmH?gL#hσ҆Gr a֐?$>.*=%?k WS,b1lG?7oc[s2uE1g㣱;˫5卶*Kg8$\7VS^ 8^5BQhϙao3 hړ/a;1QXak/owfn2}X PM^K7@|cGXcn}"g >[E<ʆ5j35X(,݆ae"å^,JؿK,m3C$ov8L&^~yd/3#X _9akoy¹I5壜~ō/]Z5*m;aWn9:w΁tgOtG{z:8c ֯o5`~a3='+U#w`Qvؤ27m㽳3;>C5u~_ Kr.Kgy>pX-c~ﻔLJr,>+a_X2{'Xj)ρ0;X sX^Lo5HӞ` :G[=ILߌ[{_fks>+7s}~%8?E?`Ş/0}ηtZr34b> ;VyO_-y}T:k3K%ϧY+|:g<ۓ3~P׃?g2ۺvlwz{Z1,cײ=1Y~^,n؅YVJS?gXMwaիo y}UYY/_{Ű/2\ +[Oӗnbi[g^e4 +'~qtzx-b} qF8$,0E;g),Mf-z1=\:&2^YĆpybǏ_8TcI*; XOb|MzMX6 &J_ZoyI?5>zW?|g?{-}P:0VU.g7 Cpyt$ؤX5v3<7,?ǵ7[2|Wb=gbئOK܀ˏ27q [f'mR)_c6lwũY8;:KF0|ƇLKoCT`XX1!y~ Y/Τ!pdZ"oͬO2('ށ=^fؿ8% ypg+m鏫pQL`G?ŋ?z]沼2ߙ%>c31p8>0X^UO' \;unqkOD&6?p.].G.㛜xO`~at#,<^U5SS¹a:ỳ#5CY?\a[䳽,5lsr&1!~zD]WoŒ fʆ}X+vL_Gf>t"OM Ӭ:ܿ)a<-#,S+G}ue{ =qM~8,JU`X^&l:JoH]dw95fOs?e׷tg<6P(:G _'&:O =$%KX7DRY$e+ A(&iAڐeD,Y"[~y<u_u_v>ܧ4\Yq<&x)a`IJrp"U+DUo561tӟl0ds<Կ2Qrphd$8fvU\їf+~ߝF{_q)wi/'9A_OF{]hO~ךA`Ǫ5#A0~5&yDjϻwM8ͷey(7KKE{gNu=Llf}byWl;>ZOyOhl)R~;f>Xx#D5poU(x__ƓVlBUmFVMߚ `{u_d VQ?8{f)XEQ)G[cM'Q~[0B,Pa-pn='ʷ\Kuf|?# !ypyZwgPlL~ї+=X}k .5@g)jP@'`G`s\\;+kKeޓ9` `5RH|rΰ k\4zlV?/S. ._t=CtG2 }N`vnJRj)C}UǦ]_l0D;Mkak(W/' D.Ocgn 3_Ev$ѫNӟd]گAS '֧4_g%D{Bl/?rƣ Vdi%%-ȪݔS?:Xq\RWӾϥ~h$X›Xq2g}ۏ~єߦ~]Ё"SԷbew']]E}{JKۈwg G'K{^mS}MgQ0T KZ0^`ծ,_J%~#ܹ|'gM[%'w +KwgӞo:ɼ`_.RVXY#qEM;ppM7l1Z?8REdp%g1m=ǵ%oJC}k"XQ6+ya,O]YgzW[N=Xfv/|@cS7`{`O8:h?,۔F{dEhع1Q|Ak!&o^e3Xwly-X? _Nݜ)E`vb:%6`ẅƂ!}ҾTOMXϑZ'˻ړw\,"iB{/_>j0Dy&`"$Q~ҫ(tH{ρG&iB`<8U?9!^G4Fsi/jO^rRt~Ŕ4¿?sg9o O Fʿcsc`YʃA'zPwXeK{uQx1Rն}ƌ'}>C%oAu eiPޱr:%`\Go:*`M|' vA`H)kj(-Rlc9~Tr7cpssl?3B ^Oya=\̧;_x ?@pn@W.=fm|OKploe/uYo9V8RCP^wo5% ah =x<o,w,kmw^FQu|B߁ޖ`sX~Up7XO}+w(KVكh?d<}Xn.^ׁ715)>i.旔*oc.Oʗd}n'ȇo]``I%1,ࢇ,9 ,mqzS$+kM 'M~X1Q|pТ[ "Elrow}f3>5\l87\u?2@qhQGc9ZQ+l"W`EPܛw,9 *f캔f꿱=fe^9g}cv_! d`e)r:`qOJ.W`+_z$ְXp29EYg׃`]Y~>˾S`ىh7S$/CB%ƿN|i*i%LV'-|Y~3ki~?uXnn,LONdGS[7^"ώ,,-_ UovL# F{QKaopvR{z 6|X@yߕ`uc"\G_K`Yi_ 5r,w;YXy9_+eOv?+mp8z=/=">')YX/+)K/-f}/r#}/˛ںgz^0&ٕ_m8=l#Orٿh?~3{{2[ϸ:3f?V(P>!lo/U5A39>~}BV/+J`Uп( ,+o֧= my,{ha`eiWў1cX^[k 1N?F /8悝C~`<Vn*+%>Nnkn|E(\by+=EuFb%*h4.5z v;%}Wçl>懲~Mc}Ȫa+o-œ,.8xJȲag)G^c*3Y.zH 'gJq>p7`-X:$yr>UO׃?Z%Kv^0z27[9c Xc2T3Y}(?֖uA!]ߒl 3ޜ _r3p6 5!랰?) -T_QJI>I`MU`ii{Ļ@}8g>-X_9)fkr]~ ǧ8fGs K~{dm;ҒQHɪ/x8^Pl<P1jG<|rqNjprYWq~I{m:Q~c'5u!9뻯˛Cm0>p={h[a =O)_q~X"9^wˢ.>B{kwmG  Y`մ)C_'fUy|Rpy߰`wڷ9]ChCFq~ ڑ.`ÉVԏ0ͰXјiwпPC/?c Ճ7 Co5[KY X6 'M,HvY)kYT" x!η>b*͜Hy`o,za Eeڳ8/>+Zs1ޞ5~]c6l:}Ou˳_z1v)9Ok'Drf QnfчU=o6_1`٥^`Ê߽XYVxXNx gR.}.2oVʞx_+|m N` GjK9{S,?-X -5`SXdE{IkӾ [&sxV%/'[ho 8 aڟI`>4M,e翷iXCFXC y1 劏Gܑ鷁uK9XMlƁ_2+md,pP5 Ju4З늘/X:㭈`~]=''1)n8(,$/ۀD'"}'\Ʊ?̉#[fs[ .m_g_xe/B[ KT`K) 6Zf1`ClWiwO%ݦ}o7O |SE ֘8^">iˮӑ _V87۳n&?﷬ڛlYr۪_K>+wr=h ׇg vLI  9q-~ޟGmHGb\/X;;.k{vtpL/zus XehKMNy+S__?-h|?G6`̇Č{`o8?ǁ|yCԷUHVd~es}Q^Xog~Xfrp~ϥMޢdEY_LºPt@X;6[!s3nj">e'/_*C%i?|gspSfo)1Oέ9B.鸢G&Ko dW~㍧ G<V$ux?[ӥP}|D kġ#ȳ˘cvB4x^,9cxe'Fxͮ2~0x?/{Vcm>qŖ}2g篁69mjwyAlI[o'==nS?x[M.&:?h$?nt#R>=LD׻3oh#pN6u>˽F FnemI l^Kj,=R[Aj 'Υ~ c~Q1wos,r(8ߠQ{.{Oj$Jygo:!pfw}&KB{z?,3u|C:/i39X;NKf2#ϚA_sXv*wG9^!-2 \YXu/a8],AP^g_,ӈ/(oT` mYzHvfO3\Ohkp>G~p>|{BoE y5 gxj'WyL'M 2hto'>C5ןJ)٥Lnv%a7 ׭ܷG9lk 7+ghd|y|䲞?Y=6~|+\k;yۮ"yF}{{Ύ#]f};ܐQ|{{v񋓧t)Z \0TFV[n.k'6mܼA\hf0;PՎ\}ubA<㵃8ջyAW[kF{ף [`V}{a&RL|6ܒvqC.X#;ͶnHAjU]%I;Wxnkt(K"-̇'\gT_Z4c$O?mEɻ. htmK#MJ+.o+$;-$rαhcͫ/Nl!p^ݯH_N(n;[/KKڅ_-=$/2M;kR>kL {#N'VZ?班Y}DޭJdv2iάC0'< +ǜdg`M1`Q*Y,"q䣟zI:?-tsfUcɃ܏uHױqF&Go/ՐM䇇|DG͍5h)'g/Kѱm,~Ue31}|VOB]Zi^?vY`^Z~=@y _Jn,H~+|jj$|*{H_.U>+GCX/l+ZF} Q~g|jbXH6>}_>kH>raR':ƻ..ax3&nh#u:фÌjV"p0ɬn2trп_c3^h61l-] /s0}[Oa?C^ .Ϋ<ャ0j{yElXDv=D3a-6j$_79zYkZVO^ 7ϋ6Cusf$({no/x0?tU&ߖ'%_ K+S*5?Pjx8I+OqnLNatS :MɛS}0꼌0n'p OOW |fkLO6 'w/#Չ5 𛃖7 3zF8gדֶ.٦IRyoFsﵓ׌ϏlbϙiJ,؇6f;lS+pr:.~W6N]a$oںsvw=Lo[#p܊/Wɫ/skeL]e$kk0_թ^UfN'qPBK8^WפoU{:0}]l2}כu̢.uu oym2Wx~n?y^P`.4H/g|/XtGjo6N!Uqn"q yߞUydk<Yk;y_~O^~%7]q^X\ys`>AQh\&z{.uifٸk^ЃWi Q @}W{hSeI|LoN^3ͨ*oXPO3\ju=, 78dG* v[o:LcׄfoD)ɑ&!8x`}nES~:W^ӆV~RtrR9| ~%;llq iR\`Up/*~4/jߜߤ:ޅ_c{GW3+\0.} ~uhxG>X='o! lows Z# |؞_dvkJ7ֻ;cv\ y^S,*ip6/Z k(ظ}_51uwF(pVD֍WM?Flϲ z;y#}xe% {_QXc;eUUuTPL]; lx{@nex: gU|;ypW>rcq|oo>xνQl \,ISt5hT,}SWNl_շ \$!+jzf'_F`O=R-a xg]J"Gu!tk[,G cߣsrBxy-|X;H_%pO9SUx̓7^y|j/aw!Q?ZY=xWWܪ;y5lO3xԴS %t_hO{%|-_s RRZ|XyG+|¼>8b1t:_fy-[=Կ?p\l>cu^\|B|>]nyؔ x܏ϋGcÏ=Dy%`T`|߬l {b=ߏzf`.|N]5/L֓}Ĺjpbߗ[c6wmX~B 47T`Mw/_gmFm;_wwu| ~ij<"%%גEY@xEȷY_ ͥ|V`UXQ}4/wz }˻`#wW$A.Gx~< VUy;^)wɌkΟԷ;9GcɱiiAx~:re{%c}Ge><<9p]<y_5ZX,k #Z_侇^Vo+Xtp~OaS_g^V'NG5{q}9_Iq8~f~qGWR 8~o[mJ #{]0W X_+Zu?3\#p~qMN<+8e_h&^;Rskw~CVol/O.{(#hȺG.5A.Ҟuk~o:{'P?_j~$uwN}`PW~dPgdril/7;3sE{[6f-c}ΟGo\{(^S6R_^L{jr\Ugi!~/%B vמ}ީ/s^h#Iv{7<%-d|I}O&CުдjX= ] ? \/S?}0 m'Q^q K\es8O+t eĂҗ槒=5gIX9Հ]zEQ'ўf^COMSR0/%|F\~Jc{ ΃UM7׃uڿ)u%֏/5@<֔ڋ,׉Mz{])(ZS|@?X:l:&q}`߰@~Ol2o60W/G룢?SiU$ye 81;(BK{a~cC^$V߻vlO8ڑɌnO~yӢd~5&7Q0t4ڛ)CK0a,t \ Q[e<pdxֿ/`s쩠˗0~nj[,o%r`)&cNz^cz+˖caV*;c#&v~#rʮb:l ˫YcO7ɔ9:C *{^b~ C(?)yFIO۩?.(ɢqeߝK|35K`|W-pG_ҋKykƷ[>'i3_QY76_=b}/9?ؐGr>֧:П}T%YrO5Q9g}ZVi@O?@>_{,k}b|<-Z[WY'J>fr]#8x(.GdA\G{ë>:;qb?~߸G7XLo&l&'G:ԟ"?V4rA+#OcOi ,a<=?nS?eD \Yau(Y>12/nO'I]c _I=Һeg%V9/({W=Mf)}˙]K)W}'mtT ϲ|wОUMVz?22?u~ʩM{a\"k箢 Hgc'tOt6|*g)P1_7Lle|~]Jj[SHiOhj(?&d5G [#V_rߤS?/E?E_[ -Ǐ]"gX'~J]_\*to]a}gZ= !} 1~[`Y>X2أ7d>7C"&q>Pl` ,7#'oSǣm)Fֈ`Mo_1FZ7Y9\j_֡͠^rêƕ;onQWgV:ױ Ҿ6rrVɹj8 >CAJNxTF>bF8WrЇ}QыQlU?7յeQo Z6`߃h+pqXWAy9eC_F`{;udi/K|}Π9o3tu%K+*?;'.L-lŀsUyKx=qlu)y hXʝXȏSku7ւkSٸ#wc|eEރ)P.':y9׃{+|XX+8W^z,dž!#Ct`IA+gQ`f,yJK=;xX3{aB?ֽ$԰{zaX|lk6\`KZ">wP"kWE/ C}&riI`$_ v|t*S&Xj|v> MT2]3:?l#B{ד/EP>#[D¿Jp{4a/8%b$嶱0Q2*zK$uu/7J6uM~ތ/:SKF/=b%TrǗ~h6/ZK$R8ޒaE 7C~m 64V{re课Y> \*ߴC˟<#!߷!j,/ =dkF /L,ßܶ; (&J7'Tm)pF ?~zިkB~_lu~p6'&4'X3f#~6'39܎xKd}n@r~Vk\ώlKyz_'Xv']E{K֭>$]?E`OgQ6ۅ3lm]_݌Cw 9|>r,U`v^҅A `IJNhI{ߩO?}Ȧ{_O_-{ڝl=擳g%ï_ͣ~2g1XU <M-c6  ]2Ŵ?oWrQNxҷ c.rOOf4udWo},|iMo jg?'ewL4[cG]- Qj4y7pB[?oGzZi`վUߧ`s`i7UKp>N?M$*# ԋ`Ik`Ezg[>~s/JΕ $˫{~@s+p|!#WOm o?XZ̠/>s- k`{Qcyِ1^/% ,v%֯0_3}_-v|/gF,&.O˿ȗ4!|tBcy8Xt39TKyAwﱓ`j}9`I{oZ*\ZXs9⋮[B=6O?R?[`=X pevW_rl_|w`}b3K;B|zp8ַ:p Fk(_ d࢜l*ϜOϱ^.N= V G9v|c#O,o֒+,ӻ}>`]1t.y\*P`1M{.Oi ؐwa{D49/;+Tvg[gf͇3a`횆:py`_H&Xs9-굆 XD>~3j]x"`>X(/Kl֞-fgֽ|?2ܘeW?,AQ3 ]EO;q+/mۻȷ\Rnvuhv|xS/o!k^;}?sJG`i 4{@oUc?7Hq0Jp5_8:_|;I5#qL~e(b{.w;z|'ߡϥ hzbyBLm$XquuC.{R58dCN ʻ}MEȇcAy{ǵ3r%5ԗFhWlE-oK[b||b݉)i=]9\/d}bDwͦ~B/__w=ɵ`]# ؛cwPw_АNZc$~YI7c+GOJ? XMݲ2zl;_+O!K3QoϖO+\{ico_PpQ?aqo OMe%3R$(l߱5%miﮎsq_g{g ;\MY]i` .)8rj`դMqV[Y0xl>c<+eRnHbhpwtk> `٪?a;rr j`OzyVg.>Xҩ.8b &`eG9~`}Kg (c`ix $#ƫypg,6i_"Qʕ!`}owx:ʽzb}STZn};V=s)Ҽ?X9 J\/ws<Ks}*5gqG׹^Ja=x}y* ˭\7_>`>6ѿ8_4K"nc}Ȟ9-G'ڛAD_Z8,s/\ ֍F!Wg4X;G!,{vӔO~n.QO&w}T`?Yށȫr/X^RWXn6g,?^ϼQ.QvO }F? ?˯zSߔF`ߝ(dR$۳9 5Gz˚(FAn`Ij(Q꽋hnep.lG0qgQ,/0+|Kpy^)_S^>}BU='MKPE?ړNyȈc~,58@Yzۧ~4<ǃ#7/ax1]%XV"K]:e,0? _ٰ(X:Ud ";%UOQqTrMey'!Ҁlu"I{l%\rl[ǣ `A\Y>";zds.)˱Ʒ ?g9&ʏ7q)c8/Z` 6+SV]ql(\ 6-_{r[?e`Ռ\OJP.fM {tKa=1S~X'9s`$_t`pp2~Ԣ ȯ7_ vFIP4{"Pl`{u}[vlet 49xVf{֠|EY+?vX4ҩ9˯ڵ&K?~Ku7;gWU_oFk}T墽`ӔԷueM??O~@:t'Kx{=ɺglOc)Xl:a$Ye#[fG#m;ЩIpB;|2ٍqg6D{߃6۫3To؞Qߑ)x|3|(dO-\x*Jy |04=Y87Țy#qvVM?1ԷVwLOp~xc痖?:&rw />Z 8O|81u'|)>rW뫨iry pl>_8}c~Ɓ^`igs,x?l;?;3Uó/-"o]#/mjo/hD vXB^#ʇ }r>g}5$,S1Xivl*i?4uo=o;x;G$ۜsi?pQvufۗО7^ra[Ƚ| y/XҊ.kjpEX@NGG}"7-?eZu뷘r?l>pQ_KrΩߟTٳ6`ޔ 63fW=$`w05=W|[U})}T SʴƳ8 d<4!iߓ98׸ZB .Z@ySK/X}gYrp^QK؃:$7 [E/Pp,Oxj!y͏Mm'RRxj4H6]ɺ.2T; О[ӉbJL8^'1Yj]i%obY͵WAV)Z7Նuܯmlvs^rdڿd@nVYd1mW `x`!{pvlӄjC_5[F>rP!nq~*<Nc<`E\>e=,B6W}M3lz[./Aҁ,?XE:|TG(_I޺l3"}N6<KӸ>V75E{`m#;) .z}g_pn.q 6efq6}PSjp|xr g^9JD;fSrh6tcqsS_/v~e~o?\vI[_M~W,?.A߱>jy#^OxJl> rؙK;D{r=`R 4w/.]rW M`w^+=E;+kW%(vH}\^9} oFGAR3V-#XU`817.S^zpL^*VP3.lf ߺJ؟m u76]3џ:{dӇsxE6cptߐ~ n+)G{YRzϫ%`'iZpКR䵟y?0XRU?[&G8woq =%w ]~d_ZNXєCY#/6B{a2l岅lI_(T;[Ux_\/pBnxPb{xUZ[F/K֒ϴ;~ՄtH0, pJ?3ot$uK;]C/|YY'rC.d>u2es_Ç,1 ogxTy]|=+t8_8/pPZmX},fQy3RsAlk9; X?txCwԴ#Ittwzڑ/beQn!\z~A`Jr98P cbԞD^4%қ^|N?(?t\N8МCqI crj52SǑ;%Ȯ3y=JVt>ShJnJֵ,5"KZ9S~ ,ӌ \?E.ɲ,II^}!oYsB:ρ`En/ОL|):x1܏ VFze,߮&83GɖTj6n1\1$=H&eNGs%,^}\ogW&ÊmYW;V LfJ{x#n38O[_MFxggyP88hn p.y[ZL͖m`eռ?O|?n $g~@!ohw=I{smF?uH]Nx84wZܽI؆1z֢ޏW9\'޿y#jOL|Њt9YZ{"ئ?zաY;2,#g[iGpķ}jCyltS0qGyO vLE?t|Eϙ~v"_-`~巯q??s7G߯o!/`Yt/P{@nm vb'>ߞE?S3ߪb\' ~d_}t,/%>"wa ߯5-q|gZm*%q==i࢞2Olowy.n$4JzlXAV?G\9A|@&o{Ӿ1vqX/{+]wn/nK1`mik!ZѾZal<, ;&r|Λ]i8HKHB`LUO}gS^S)JU)u6R?7 ۿ:禤^pر5.oc˾pM>i3\ϊ`lK_Ew7!L{ֳ}ExV"~WM5;^G8: .+a~|eپ)'Xcڀ/15W,V0[V/\/e{ᆻu?\_Y9,%:g!'g^Bcl UY)laCWg1y?ǃwjv~*g$Oʾq,oUKY%eCX?Ƽ`'g~;<`)=%rϏ0]J~/̡mf&nDqa:<\ 6Ә}`XO㼘eY_~`E_\$X;0a?ɋs>yTw樂/7|%y'hOr;^+|)z7}'FxbeXCq= <]^l ղ.ǿ [/b<x x lTMOOQw&$$7\(2/瀵ibe`׬ϰ4_뚌WVhGplqf{b>gsKGgiOUrE~l!|w:K`)p/ IH7jdI otiFn5 Д~ +YR4uzy *٦xjνv_ojlu_&Z^ 6T[Bγ;o1MT2'[ N nOyszX/ )7+a+k8ѕ嶖#+6ux?ŦW^8k$B?3nVCT}=JRN\#s]og1bt`e럶 _UA"8e9wkGmkO8_\J=~ƌ_&be?֪ww)kw~HI߱y5iІ\ kJ{u#p?hlt?'"?'lK 勪s?m5>lԹƓ'g>H7l`ܯ| \d0pqOSb-߶~g 8 SD|'Qp&*g>+Knu~ܟvkԡ;o3>Vvo% G/>j߀6.?wM_{6kƀ-/Ҿo딫+_. {mj,b&5yG.MS\ً-Z0>v(xzג~xp5X:/% 3edi>l[3첄*v'Y8ȧO <&Oj&-xFm"e|- twU:=Vԗ<aq|2aym/s)M}o%ݭxKTQr!Vl_+v0{`a; xO0_Яt8)|s&׷3S Ҥ~*џ.̢53g,D|bm's^8\9ywn[ݬxy-j}ΜV K|8ݴ׃f5ꝑ]|L%#z |N&rU'/U>]Y7Q=1乁kYmqr"7`y˩.#^l0XmI_ىY.v\ɺcEHNUoߋi(櫟^@N,_h0K%>Fyˎ5lO]0v]/;ta֖֋Ic0;SE1b{.sQ*84OՅ|N䧔GIz3 .ygx{_3E~)XVԕ.jrW]Y]_v:^ygѕ3VEx%?QVn7X]F~n6U?R~_,o9,_z׊kJTXb;Ɏ_)h%DtxxvdFпkzvS?դ4ͨn柳嗀PL!||-9_, n/ la>Q+GnB1`ET.,,0Sq~dp2ɭ7owc{m?EfopƁ#όvx-LΏj^8eџƁ>$|X?c"umwô;OꜸ?P.E/؟tL۳XXN`+x.ڕ繃1-vx{N7chEw/^_uKR l~#֧O/g/$ +1d`_rq!bq|K˩=X ўoDYK\މ*';_~1=/3k]z2cqvfO {d>L-`wZ箞?7yGOmV䨞mE,C_<%~4>d=9#7'_y Bo=G?+Y~בq?8Ao]Q_!X-IXxǪ7U=ϯ7\֖,@Л#f|fF-xTH}؟aUGa{7/8JȶyqOX3ܖE}قyr4XfP_xpQrx"{87?ذ7m$Xr,}]lUek ޠY~s gӴ9ߥ~o].~(vF_3)>^`/kt淢=W}~sQ#,Iչ־8 <T! )Cfs\m ,vk>w=5S:!]`i?X$w9]_n6Yu  $`i=Xf?rG9yx3r8p?}khOX?{y2:V3p`#џ5Ut bTr/u=Fs_}Vri80 ,Y/K>}_}So<.p<(/[w=oџsA_gkDVޒSu:3Rw,;nVs8bF/߀ /o؀a r[l>=,[o֧lyw }VZH|P6EW&p|kTB=o8ӢU}^_Rm[ղ-~,܂c\:<ծtxK9HIvxǞ*˴%/֢vlކ$4kC}MVa xv6r! \!iX5x ,qB;C#} E{v;wL'ckoUԙmC |eO%23eVy+׷=Sߺ5uaul`9?G_zN{eŌg o?~鎖7(g]}}SX bS܆0?㛜"j`X3>XWQ}orf8/d9w]7w`~ ˿6/g}?ͥ{X}2'b}*>/; ;[`:jh{^Gtn>كS:s!㕟|7mS|7K/Dmb V Egb6p *TA.Iy.껋w,`^g7Zp l<|7}GK:պ<mqhX]3d E-pn; XѭX3m^+S3ifw] T>_>3wbվjVK~紧vqh=holp@ʻˆgz>nyh H :=)ZՏ-6|@b.a`My3颽P˾Odxo<; w^Iw]D,;wQ?; F h:|<#ܼF!`Z (7: ֖G`ǔ5r].Q蟫Zu#;e~rm=_OFu;wϷi4b,ݴwJ`Ukԏ\V{>| vYb=6z-gn ,oԵ?݆Q t9?sr!looS>,ki6+sFe<)oѾg 8ˏ~gG~_dO{=yXp`MsfvGh 6mךx,Y9, G ٯ+ߵ蹮zŀ Y`m)XX׾cؓ^F< "e.oWWSɪZe-%~})ܯ%X?,q'$ݨ Lu.3ZW8zipCzΜ3oPS%p56.pT3]#է߳0s'-RYX~\=i^r$XՃAsXėדCrMٿFɜ`|f*pQ,8ȟVݽF#VojҸ߹؏T*{],ࠝ'x.m :,!C{<h^ ~xy?+jE>'c![>J{RoXLߏk6 wc6|W<"?15Ғ`cSKہgOAs L8 yQ`~4Dd~|B~MOJ_ٖc͓_uS%OeGݓyy/+sd/-MK`_˧DyL0Niڙe.Y'ߙmS86wW50~2~Ǔ&M/-e.٬k`Q̷;,LVMb+˩1uOhO_g ?f7g'_dz?bx~cп3ŏdo>G8~]n1MȦñ9iMSyEHʂ _JGa;]x@ݧnq~zC_՚N NJe}y^FޙGL;&W7Kd^c7B+֋L8Lyh [f)y wQuƃ"QM{Syީber_bLbUΏ&՘p}0|*Ux_0o!bA'&ㅌ/緢؀맢IXMR.`<ׁ/0`z |=7_Ľ_XD}(2EĈ+kNc7VXp=YW3OZԦX2C1'~WYC_yYl5z?S~|W_W7+7xyV5;_rؚBiY`ɧTO8>enx&0~}=ɲy\+qyǣ.<}7+YI7OKԏe9<ѾƦƳz/YqylGY𼙀y䴉W\K2zW)#ȱiNGzO"za<^)Te{y1Q^#Ozy.Yv!σՋ??x"_3yB99ua#08h&8o|2Xq<1ƃA4+e8^6*%-ƟwL}vqE{7xlY pwǿ=o3P^~)z\ ḞS]GZX?9 5 .-`~~e,{Hڗln_~'.a~d̯_]M0׍m^0js/~ߊcv d +0G)}y?MU1wU3`S~T8;}oY.}t|lP8eZ1n`g4Vخ/˓t`i@x:W ?(+@a`]oŅ`SG+`V4/Qk.^ideԌ_Xg{Glosgcv,/9ΌO*OyswX|WwO}ǁ#>1_ _5`$j3 28].3P_ WE.|S2X(rǿ̥%5\4ؿ)/}F>gJ.zW)f|j؁ 'K7S|Nd`#w߲΄v_UηStdq6\ߑr~Ks~Q=G4*ڸ#N<E.7|X_3{W,G6\dIa${0;Xf^pB, Jɝq?-3?ogu75`H5˗\kzl>6 gv9|ҝ%>_>hr .\=`EsqlZ'_ǘ;T95 7ͤ-O!ph]ypGA|rrz ?=8^8q1/_o~KwL{h U!4oweK߷!8~N9I"GfC?&|>pcgܟM1:T:(mumzs~B{ǃ\\(/_ 𠽡'y0|]>T|t -yzFxPmh7l@|z)g=CYq\FG5og|~R_8sJ'>>ε(8}U7BTʛ2+G%:yoK'ptP>6T|'w{/Z癚}:|>S7n3XtO X@w債C$'طQߞSj3˽LTgLyϻW>/6|q)ϓ=Xy>`|~ow?OܕE|`BcKΟ)w'W}lՐ+l `5`iZMԏ'^%=G(WQv9تW۵}7`}˫|(+7TJ;{(o3XQQ3kQ:_)o8yRl>R7kn=}Yz<'yi|>uWy^h?։ϣ}[E{{V[}u=bi 7(xEnN|\m>l\,SFo燇łMmz`où M<_i'|T[Q(~寨?3'OWmڎd;6_|('=xQHpѮNO؞~n׾O .~_q_A W N|+';[A?eONp)/&j&s I+]4w {j|+`&a!-7}܃'Ң =S#g7ݢ}W ϣ Yp?"}ֱ7ζ:H٪xFJ#htbd,߿Qf->Px:yol0r卉wˆ㎼@b< Y`m{<:1ه-st6͇ܲ$C$c YO{Ր|w|s 8/n硥i)7$ZVہ33~.h5wY<`D%X奁7AsMO~R`ULٖ\#ґ?=XSd2`iD``{x#o]'\D&锿ߧ<, | Wq}p|}{G7;<K['O; 9t[~[/ $߯B;uͻڙYMF`IleC?k Fy1_Y΋w)`^d}niC5 7~IE{Gu{|p*/`t5:}~9;﷔@2 'wM@Ϣ ^KSl9swY)?9Xq:1w\E{^픿W}&a\O'Ȯ3x$ZSK :(?3\3;Ls~)a!Y\8 m|+vr)cVa X~upřR{6X[gQ,`HzN1!g4]9-3,#%\Ռ5RAYYQ vi9[1 MP^8^okR=g{K8^].M'g_z{M>s> ]}Cm`+W=KҾOKȷZi_l(L+#o~p8Mv/QV`z*g/Q5=Sq$H&p83!+1ם+\/&ϡJ;qVB,2˴?: ILW8oE|! _)뷚5yvQ=/y6y"Oz9fz9ر7E/'sOn%oZQK]"^zG޳zG/ #.{?1D"?E/WdYxER'7߫#?Vy {+Ok$KmZ`1`U&`٧EQPqt%o+pfʗl?G.f _9{6w<4h-xfW[!!_1}z俾+#Xh+֟adٴXsOۅe+`ŷLM߫(f۫kwfhK kcʛk^Π~Ԁ8码hA _.cX;q j`kx+;bW-QȤmH3Xg@>fқh۷wv~ǗYIB=mG=O(=׳?&${\9ޮ Ġw)SׂuOjs(O{THT5 wQ>fX|;+{O_a`XpW`?~;?^ya'_y}gGS^ lO'?]  ^Xﱟe( X7 f4Lݦ{F~aC7BGߨ7XꃢB#/oRj_z_Y?>uU~e φ cB=,'˂v]?2X; ?*驞@p̟_ pFB~9&N|{8ݘ!VY>v5{ڂGUd 9V6[ؿ#ہͪ)sbb~wEޚ`}/٧)0~<ػg@Pgx+/ 66=Mi#˷ ,Og},g'ڣ}ڃU_(QDfSOͤ6ΜU+G9ppK>B_:knG][$Wַg,l;?؞_8<-m N?Y R4X,@QR6*8%k>ϻ`uZ]֕=3O4yؼVg3 No65/3q2A H)HZ/kBR X?n=x0|ȳ|Q} N9TOuO&7׃3Y3kў3cY_T| k(Su?s)?)*8EG֟{,mo킾/؞OݟI `U l@Eګx5Ƌ`38􉓙in>~p2LXW3_5;w 9>$}wKN|GMe,+-_~&㯒? HV`wz8pRʋǁuJO}5%Fxu+ō2Ӌ?h`/J'eC-HX.JIpʔ VDQ^\Js˯`urd0߭i/e>~2[Lk- n2?_H鏢u5F0|4,ܛπ cz9vU%?,XOV[d]lǷ_=(<"FjXә_ )x4Y܊෾<@E>R%|=Ǥڨoz}''PS%? V]/ٔ,w?ԟi .S{8^Y`{>V5e,{ٞgݖa{\?M:x4'~xS[g8{ vs[o~dY 'y/)|gt6`U1;G]/?yONf|O *v$}I'vOyʗ/azy.;&ԥ>%Y_˯,Oq(y@=@cv;J{*maRnrW?֍g.R~ٌ?{Ay9\O\:c?SgQ yP'9Ӿ(fo\ O1kE&\_ zrӊ8+ݘGھÏ1eYկ`_CX܉X;O`~.ef:!ՍS/~y;=β7ݐU7Idͣ_A=yp)]B?-r7oB5g6XOj+v30?֦4';ǀ4gV R,U%)[P^ W:xW˵L^.;[GmQ12`0ƯQ$ᓆ`WGs5?m#ƬqlmS7v끝ߵe{zM2?%83\Q?Uf3Xߘ᷍z=ۃv8>wy)K wp~;[\6K7X_wOo^c5;PǫXOIU&0hpo]kF gz`@i7rt_Piܘ77` g`ޏ?Օmx?n0i<ov:D{mPU*87"ÿwܢ|gp+Q><_8eKuȏn_ÿu?IN}Tg_܈1ul?ZOp<}sE<u}?ǛZC׃s(U^.ܛ#x?cxOw!j﻾l ˻Ȃ*^{< m#ݷ[=WcK1J~xwr?w"}`&)ӑYL~SխˏWh8ֻȃu{M +f}u 'LOYSm>1,V%ޓi7̱Ou1U(O6#IZYӂ5m1r ym5(fӇ}H/ЪOтUuP NY#H?vj9|n6,oѴwsceǒ{ dWb^!m iuhrUSm67oDy#QEW*!|  P9]72V@p;|4g>џz=5e mߚ1YL3KR|N6wRL`oVR]U'aی1Tp˼e&7\pr򼒷DZTcV5i(?am/++` )wDoM`lqr\yA^o+X5`/?1}NBy> lw(Ys{p=j+Vt ~~_fק*o8+?3ع|K [.z6,& ΃ƀU3w ʧ?g`yiUg+f: ~Ӥϛ7$"|zlKI2tR\&Ϛ+&8hj`>-esu/Gi?屽tlw7M;#{g?SϢ# /h)n|KQE8Z3 ?Ř& m1F_b\u]FYm'doYc:_<+Sj_}^Wê >Xշz?>?#8~_~%SϾwWi!xҸمncם-xmTٮKqȎjk;7ʚ {vFOG <{?߰ۿ }?۵XbeDywVxjHs= e}9zOCgo?^a\YsRC55 p 2y}#誯 <+?jHLc0ݻ?D\D|;k2o,".,y Ź f$}w ?!-+O1_ςuq8ՀU?p<ݟ㟝䶭S>>8ZRXW08VBlMS7漯۾3 8VeyNY N?[AۃinGm>EgsjRm#_$G ڂ Ƃ#]!l>.X)ߣG7+TWVwvCXLc|c )繃~b|$ppK9wgZ`ٟo9 D5Xv4 ɂ=^Ac+};~kc []gp~٫Xm[ou1Wn<_y>| =Vﻅxv~n`T7pkk/s̒`.)Nyݫ;y?I`P4k>6b}{ǜ_m+>Mۓyt-exmǀ3rO[ w;O+11F)o q X{jo`&Tʿ+vvPC[Y|WsG'jd|lylp{\sr$pT ͲP-wm91CNzD[]p||8ÿ:EwhS vo~¹,7y%~`Ku$ s b_uAGw'6]a_/q OgGS&ܯ/EkḟA>A2 M jf,B,Gg\cJu_7e),F|EMΛxz yi'pMP{)"Bp#鹂=w<kxL*}/eGǓW-_VEb&Oy1Wn+i=DRZL%'}%%wb>l,_#l|2:L>Dp$/854 }@~ןU0=hOU dճ#\\&ȯgOzZh] xX擔N|F];j~$8wb[7W4PBgV1A}\9X,ڱ? g`l$GQRԣmӑ`4GeF>fecz73"B-cvq>!WZ{e!`y8;|^]jneڟ*xW6ο_5`]sG5dmo%?.g6)qaG֯99FP~ۼa]|=4?\|Ou'I6ݔ`!N[oٌ$7NY`WXr)9T};v#+VЇ|h7숤>E"3*\Q*㿳;??8GauHT_E!v5c{ +"Y^k#㧵|ͻ`Ў|k0k: J}[~2`ǴޏR뜿еv|ȎSvCפE KQϢ2vӟ31x@yXַ pﷄ`ݩE}+Q-[0\ Wxݷ|\p+{7O{4x=r1`'# .x]fO~q4{Χ=;p ,fb@7ˍ |{d_V;unJ_Wݤ~t/EbNOwx-:=m8ߝ9S| iܓxBdC0Ӌn768_]B頻M|TKV4UVZߨ:ey9c683W|aGM3c?387%\Ӟi6#v,hmqᙊAߎ c~65=8lW ' )DzD8ʫn}2Lў9~;s3os=NKW9I 2^=X0㿺6I/LJSA\5Kb9(V\,xA]Ca}O>bx_0,a?W i.Uŋ=g}0p{\ڿLbׂC~XiY흴u`= -Ei=k7wW[7;36E)%8שc~'>`iO[(CwcheV7_gy󮿱M OxaeGVX":.uGVX%سâO[ka஡~fp>&(36=+P L}Ubikj,8Έ:ϓgv+gcΦ]sMpۣ{C-s oSv.g/dm#_2Oӂ3}$n!dUV_7I/e77?ȺM|߲=b9޽KVj6,~di% [:g#/=O}U&b~?"|Ӣg.5=6e<i٣Y?x)O|<(op Ώm;cOg^-{Ȳ*V`$ZWVcW*ne˃cerKEy2? J_?r?)_SH3WCm.ο=FP[`{_}5Y7+\?9-տkst>,XS/kɪrKhDN} |OuTW "7,w4ld۫7tc2$-:Ν}}`U>;V#k7{ K.>@}X 3;Y^VCv3'5>ު>`1o:.jOgzd@}3^F?m ۗ_]';SnIqg߹R{}N,aWoуXdۏZK8c}&/4\pM|| Ƭ~}[cE,:`M-9X7Q_7Ğ. _R=O~50*v"ӻ_|o~T_;GVz]B/r~.̇ v53;=^`-^Կ,_P2r~0d7 WV $[ X%l}WN\NJr-t:([xy=%تݯך+} ܽIc~sP=5LCoHqIkc?k\^Gm7=0KiB2g&xIor9Fpwb7|?ApC;7Mw>q)c׭vr=2&X/sW>[>b^=fo=T~Ev +{>}/t`5{"ۀu͌a_T?_TRl{6Dk#;3h9 ;S[So#j1 /?W׿Vz+Ν' x߾ 7? NOI^V{|/`JE-{_ߟ l|7;wOt ӳU]o@f7 |T=^G"P_,%7wN̗kjWPؽtdv<`,Hp?r~d.8`N ݥzsG^?HYf%(E__V. p+``mxP1[pavp"/i_yܑtm%>՜w9 6N[gSTs o)dOޒq1ȲUFEY8 !Je2I. $hui3ؾ,lOp̯n*XIn?KT oԕWC_Tu7vd幂~ʼF]S9Yͩ[pAhkz x3.lߢwqg>D{x3?ݜ >e{儦_ZMwezۿn l|O?+ojll'^B?:.PfzWq?7UUZw˫Jb;}X+;{1=?}Jv o3?#Ofo^%aNjhT\, m_Ǵ`#adu[Vw%_@':VNlr]c'?Hl,C}tsƯ2 } ,&|Oʌtf z4ɉ3X6$q&/ ֎rخS~xdJy#6 Y3r!xrfS8^ x2~JXq[S.9Kkׂb|ؗ?xPO}C6o`mn}嵷p>|+YQ`nӖRn2=U`<ڷExr8[/pU'Xl^ GR[W2,Ԕ|=<],3*1='bD}6|ѥ'&ٿ8S&X7x^ȓF&r _X^X_+3 `oT1 y.wa}[.R^Y^8?vƅԳ6_9K0?)h|#߇o1>TC[iJ,4#OSH;Vew|?z;.ԍvRkB>3~X'Ds5'T`դ3T,`Z+|'+zK?b=M[TssSL3*-2/+w)}Ȳw\O %}a(fX1yܓe.F9GDYP.G_<7KeS-Jޮ1`e3bϦaG\Xm;3O`ݩO@ƏwHrT=O ?18q6Eg}ό?V1Xc!x.߃ 7{=*};/K|пoNhS=5F}c߯9#|ؖS30?έixcSw nk|pD G|a]`^c '6pL #87 ' 8#ydEr sk*p_[])p|x'q|?dXUGzUzH\%}durdIw;Lgx0}WsI`pqi˓v#?kU~L{y70??8>O|\ =YLoK'L}+&UuǢ'|o;3%[fq^?n?,k4}2˥g+-@[6[Xnk6|#~'< 2Va<5?mޖzxABJͮ_H`4ޛb̝ȠJ/G/l,xI߅6^K˟(8\{~e[%~8 NԵtc,R]Ä>_Eݷ٪|߂]ׅW#xF,级C" .haSWl=f<=7tטh͖U<3 3|j!]}*rճ |vV&=ޯs`3u~0fڐ%ai'6|~.g~0~;/5V}i<,q)7JpÏ ~s1Uo]ܰ^cXM y!8fbPK<`jri Rpӂaj=y$ rc5 ([?UXyǮ;^'xD [sJ2dyC, Voa\ū^t>U|m1|"lEP̭W #c bO5Ⱥ:+x؞g"M=o=/Nn"Ǧ YYe,=n 6V6.y=o`].aga=zZDpou]C^|2l|XBwX,ZΨ'XA} XBR8~!-kp0{)[Ճ>=Yz_l\Eo[{^pòz~k[6&q{٣[;;5z!8LH8O}"٫yt+Op&l|Wʛ].woQܪZQ^M2| Nzk/ar,)S~\Ng| jx`Nc l*t2G\3d T[s~x8֚2urm; .dln៷<.xaVTc'5}$/*xnqZ$8F)OTW 'L_-xQm]WRROO$嘂fh T(xN[g/ُ|] ^\7xya>QړgT/hGx1:]5^+ˍ\BH~Mڼ ^joS[6w_l; xFYTl7Ge$K IrYM< ~Qace3كaIs;>.xЀ' kw|nq?6DӖml ;nRpH s {e7}60[8_)xzt@pU?lbz9/.8,xFK[=W,8(C e+?~+e,? zCH,ͤk;^a'`{`Oltxi4_i hߗra`@'r3?pVTgtɉwµS( NMht[3B9c 6+RrMA(8F~"DK({3`UΧ{L$;I{FI=4$>:0YEN_w>B'K!HGܑ,oK? EO~| Hm?+J[y5F99kegYݏ<W&W)ϫ {{*yD++/E2qY4p*I<{܈5\`O5ѿu,T}H[(ݯ>(xSs?0wC[<8k l{ /Rի 㿪7*r kƬhIg݂O7 9`_ ^4c%ݿXa+=ڦ?e" .h?x`&łleyL߬6HKo^$?7pcֽG\ju9o3OS^<#kFiiJM \i8.m)88N뎤& J$%F_4YwyO.k\k4uTt\p vt̜uYiς,nhg|> s?07{#rԮ[ž%)tƴ]V]zl-b9-E96U 9w*Xoׁc^WBS_#pJjyepn%?/+EioY8JU|<ʫ> |G3MgʡVޢ_e}ve68~6 V?cm0|A(ػmo ++,L_@r14Aޗ}͛Qn+`}~ta;%GIWu9Eyd yԧ!}y45Lo6g-~$&O n'w@뫏[ζգ>\nV~sXrc?<,?e:-(Fk;X_7w +~8^ KxK(<[scޟ9xO;M+V~3փe `Hv1~Hl {޿ut K&H+:+ xz/<|8޿`ف:÷oDay_YpƼ/,p?ۧ)8>Ɂѻ {v<;ع'gV@ .i`ˉP|`_ RٹM>ߙGUM82?SmBq1U߲)Mb>"0_Lm VLPI#N2ϫ{pP^lqLser+<1<}//?Vf~.'XaXӝhH V<*͊^׳-g[öO\zr^_>O ypt|}ˉ`,#="0.  -x#"ns0o~X~n8r*˿!XfS1-6"d6[Sw*`GE{&0sXխi =>=^2Xۈ7X>bӹ]a=&cF#nos#o촮8췳52?sׇ8jdXk;tC_6z9m+fGTF[`QW7g{3}^r?c9Xq 3o/ǚj;՜`jQ%{``n)7}mj]w+b ^}{u$X?*yG ޫW5q`x^oB!`YY_OUwvg*O ӟ+*\en!8Y/ygobq 7E5%^vJfa(_l*g8e-7߫[[ڻ.g~'h2u p|oE~{lfO]韬?]oW, ܏;3q@iV^ vP=j%s О71Qۇ7!o jymF,kӞoSk/W5pOVXZ#ճ/k]b{fXu)rڔNE Ȟ ַ})Ocz%zpw~/R'_U7>u |̣|ga= 1w^g3] 诵;c0WΧD㏹͟ӞܛXHgB |/dG:㳶)}VڹIudHEn/t8~bn\)aOv񬬇[u794 Q.SA/ 9d[SZ,o > 1\6Z6e~'|y _|'ўHo=ӏZ݇'t7p Yþ0h6wj,{6/tibY>U5|h{l0:w47/In@5|n ?5FByxNenu;/paw#S$+wS-Q~#zW(okJ?+ja~xKg{U ؼ|jf]ΟxgOr4hF&q W\jְ!9\ؔRQ_W$#@u^#Q(v_A_%^ 5Z3~a#OǾK㳝sq(;}+hslȗy͟fzo\٬+f uzԑ>7#GTE`}gY^ϨoYp14gy~b}B}V@}?+ip?yoRy-!,ϑ~o[8S4);ዤl|l*}F!;{{/{YN̟9i\M$_k;:۟z&l~g؜`_~Tc+ˇ-C24tm;))էkYv?2]pqpz `kSm87ܑ,8L[u`.x'Yw0󳗬+L?:+4/<މ V˓w=w   *g VDv<8 o~.O'em:{(* Ҍ|mX,=B)n֔ 't{5<2ӞOv.osW~-9sTmX>{F`{=zc'hp}Eg5lggtǯ-f{K<%O@=~q{^em\<^[@Im~XAVԫv^BО yCKS'ߠpEe2[ONS~lx(OEWB汞R]szGv#k lK ďNmX Vo/f/4mj ̉5;ړvݼݤod߃i jfYt#_f6.?5[vkz}1ǷT_1ZAl_m?9už)7I?Ϳ-) !kX__ l7JGy7cilg=}kuOư?.;͉ʶmT=v "㺃|dy-cZRn6lV츕7o?^ c9<} }ǍQ>FoQަ[B ~(/Etϛ7$]cZ7빃>Ut31 87 2O}F70?b=vCRP>Wz`? 7sH9{|1ߕ>S"- X>=o*|\$^WIد2[II`/Už)<{1gjZؾ ?K3`ipߧG#S3!vAߚ̭HYoG^\\ Vo;[eO- ˠɟ6?6XW>UIrBx6دl527٘8 ͼb[껙^1QNS75Zvd|,lK nLLMI]xDy<>5}c,:c<7}`'?ΕO(Ǎ6`朄we5{1oAk~.h7]*WZ| X5sjʿG$w,B(85=o! ?+d\b:. _}WߗOQ~EcK_¿ZGy16_.ڶ_ $/$G]ֿ)⫗}.ݙ\-c~B9먿4̗۹QQa>(_yb%XW=2Dɞ4T:جMo{xr7WECl7"ɩLQ?؞h0߭ g~Ҭ5~w|ÿǎN$/֧*oӜV`c쿍e_DC>~mQ_ec[.xϹcq賱rz V.:RYp// Z)#MB>+ $xIlS@cf _)k nlc϶>u|p_MX<lk#巟|=:l[C5wM,pw+2Z +fP?W8֯t]9߫Mv`=n2X G ߻}ug/Z~+\z(ܞS~X3C씝KYM{^\2k~}RFxŲz<~Jlÿ3_1Ǟ7 Pv둫ʐ,z}ЧRyHY5kwa3@l`H“9k1y˿5Sk{2*]?ˆu[{- cc/fznE6JI +hؿK)f|\_~i {e6)دx~4X72ә(oKf(8j'wUD|Hu)?|֔; 6cLvlxuQߦ <M퉬E0V;MV{|N[ݸb&Ǻ=aBghߙ`-` $Sng6Jd6FMD[uֆTKh XU/a_OܶaOȩuqz`UǶ/x] HR2屡L v ~Y![?hdx_'ҶmK[fwZRXř;rQn(s~ :QX&!|V |%xx?+ ^>~gΉo ? y6w3Y&U>箻`+|'NfxPYpF1}{ w*g!``s_\G4~'J*1ǴG?G-fhX$k-Yz{Xٽ.u`ݯm?buO,lhOՍJ }6E}h7 ];?Ru%{_r2k=ҟ_mKw3#csc+ús/!v߲ {$Oy{cdzn;|m g yg4~=uYf-?`v,XV>k]=ξ֨/az/X9TkU˚?C뭝?ת3zx6ZXŠ|r5./S 'N+b#W*tCi_?ݦBm7Z ψy{}`F|/e-XރVoiDu՜Oh >r*yAB\3:$=|I7x*owu^ {|)?3ʭ+KbZ? :mFeo5KP[5WǺNm-x` wNv<^%?/.Ӱ^C >hlk'ceݭ{u6y ~ᆗ,5/W_4o}%X Q5ޯQ o+5 o}w_ESװMh@p}ł#&.-e7|~-$ 8(| T>yFd V'KL*'(b*%ߟ 8gT{K!wT(B;#dw37nQ{uhʂOZLn!?as}L~̾-&"lCeLh.v6}Q/9ݙz%wаpC2mqv/`9Nj1wr K厫Th; |ыPN=)19_^/2|qK?z ٖ-|#8uwDh`5~LTh?: 8Zm=fY|~ڮ++w(K#xghuC?4~$q%4.p|aobi)Q }|x믓Oz8OQhTn>[pCӨzF{w*޲[7 ]nO2[{IqX~Ȳ}=NYi-~xO=<4{p ǁWHO +W4YhV}F|_ >Yq᫘X5O996Q~2%ǼW9t2X+X=o8v{w3t\o_''>{J}-c;ԗ0ISm/=/+VN&s3w〃`8߃Z4ݯ}c3ugn=ƹ;9ˏg_̿݊4wK{1|H& 9˳=~}:.{|s0?o~e ʲ>[ɼ89X+w5'QulU=֨7tk{=om^4/=%O2[#`&%yQY;GgQ^5 t&vԛ%ؖɼ{c[^}?7r1_N.gMNjxƹ|//lØ6/>d{lDVovJ#UyMXvay}b8w/, +Fҿs*(1\Z7vgrw>\˂خU!~9/4|-Xatc[%㛓ZI>ʆ}4`C $-ثy<rȞO4y1[Z㽼N`|O_Rڍ{}:\g=^_$ۓ 7~|x~?~֭:B~Xvq7`63|Jb~-ysuy?{2N.l&HY Q~?n E|ۏ ] pfƬOIZ6o)YW{!;ʗ-fpm':ʧRN/mrס/;pkj2~$_ ˡ8 I{Ri,ަJz̥2v=.W՞ߙ_l,Q_8%YKL[-9KRn |eXO)nV{OƑ#wlB{%+fmg_;Yißӊ/('V|nhcXVϋe_5zut+q>dg^F>D ~*{ ~aVg/pX=e:ΣgS5^{2y߉wnv{}|P7|mw>WG܅a=3sq],٥xwKyx8ׁg޼9~=Nґg~!7CN"+b} XЌO?}&LßwdegFUC{^7zL?b}SfF*O{f[/xIV,}.<}IOM?]!/x~7MycsK3Cl7WcgG~/GǟX_- x}d'rvc5KpV G`?6;z༻9]y\l$speԡW-C}="s_Uߟ_e| #9ZI*> kL\V=CdEplbsb)"w ?.`?_7q|Z~Л4wfGG85r|2f {r~7<gͅ5pp|b:?^> a˲qgiwJ?!%)U~~RA5s 8#u͗?&q1'(o|Mدb7͈2 26w?FҬh>YնH߼bZ VOt[Uo18 g6ٿ||ewA>-nVlBz.]8߲8eo#|J[ȟW+ȵm)O8>P{rQR=@V~QJݶěg.M"7=XPoO~p}E}TrfdO}Տ;rFBk?ɽ±_>t~~7>ׁs]2.ܟe;Xm?T9cr<Pkx~Wnߣt>obGZ#p/AΟE\q~io~ {I}C׃5Kv|;+uJK p| rF@/$.U{W F}6OR6˾.>VʕeyGj>mւ"_Hos;|߿*r9X~\ڟbmF"U>weavLGFHvc)׼? 0:v9Cx?ĝ="OJ\D6\R뫂Gn?EO)_rOpu+](A}0i h GsY^Fe^MG`:!g{ 8K1Cy\ĀO_g:`*w(_P ](6 P4ֽބ mXf;~ Vꇟ>"ؼ)LJk:1C9~<R ;خ{spގ/{yB1ؿDr/r?XOi/;p}QSosț>~z?g ?_-ʗH$-/ql[|`g3 GG <{ќ.8n {{՗1{ƝR_NIV9HlsϪ Nq&78&@.@6)ܦl6qC%D{2+W<ַ`mZn@ƶ ֕cMԼ?ʧp>ʯ8'T({|Uʭ' Uo#y+e+8slmo(}CRi p]/` X|7?` yjmi`ًaͤ'ϼO6s6,)9I`rבn0wRֆ\JjJ}gLj3Ǻ僫ҾO1~U_7@ߜܷ=}KTOrIL};ٞ_>ˇMB}sԣ}π> .͚oٟ&W>˷n{)Uv)K&͈%/tc/o 姰70ɋ_+úg݃/w\ݧ (_69 z*D2vMs?;k^<+Nf=ay:Vo;Js'4{f_n:7OP,H{_4w/ٳt6,x_Xm/'կe`aeY7~Q?>䅙`m*# Hr2KG=Nޗ5h?]ڟ6t~O~{Gp;Xypi_~W^m_0?Cyߖ7tWHp?:]|rmq6C ;/\a}0?u4꣼ˮQZsYڋ.8GLr+}F6t/Յܿ>Zs>90ڷ~幥AH g|?qSWU}yㇻsJsd/{͐.w?U?LJ 42?ҴJNeNiiNih9,z|QO+TT<~Pi}J?}*﯏#ӗVHGr=޺`E@r`˥+X>U'fQ0oTp*ʂf*)sϼ*^ԍۜRvgy=9+&}9s;7,zKq|wCi͑Q=WY=yIY=eߋ8͍p_B|Iʧ{{U`ݞ~2W (v\nKv8p~XL/»>]%7gѾ{80t13|h Ohu"ʻ5ow#W? O~ ^{оrįgR¤rY~_<륁e7a8MjNyHrY))? %>xI>-*?˧sJeٿFޜ9W̠??Wl3EuOWmg{gpLRiw\Qo}xNp@X6t;/`՞B`3&Sk}{<B|ylf/=WcLb~>NΙ`^OsC>|TӭY"O6'ҟ<Gt?2WBAPi{?p>Q5{۷4_?pB|}ਿ/L֦ڢҬ^tV{]B_^q4o8C}UO'ͷ>W3oUŧM3>Α&(?;,/eֻ>Ǟ _t_R?WVN+^iW(`Y^/_XV4ͮ2+XN_p܎qr\OU|Xs^fi48>s/Iְ}m|l{z>Ïv56,E`ٓ3hO7HG71s"؟;EH?XƷy*Z0لեG1o#iו?+KI 7kp!3埞f^Q?Fm|g a}q~}ǩl=NWDѰ+ӥ~g(vNN2< =UfmJ~;p}Z7zƔi zs'P,D~/yf|BcRL w25~) Skژ˃T} /Og{:^fN32{QFn\Μ֕]j8ah3Oܟ癭S`l?t9ןϝ"oGZi~%õM7 ֭&JVV/ة2B^sUf' n3yY`*{ nڿM+}}G ~zќ6KEoѶWՂFU9X_N7o?i^\: z## 1/MG|rE:g׆7+~Mp5g+yx|xP>*'g-]B+m\g?}9saWX50X?U6Ή4՟*Q~ Z7U:ct<'+F t]/,85E*ro7V~ZpN}/yړ>5@X'7qҷSB&89,Oae}=&Q6ɓ(?`n08oN5>6EP.G&nu /0Y{|1X~h΀x[wvX[xM.lw8|Sڡ#kѵOWclf} ve7k}gNS*yV ʣ>mi4߃δ\D}\i`oK`k p8v5Z+s/X>qꮬ*#jo߻~=;7v{M7 :xq#{}=S,t9;`Sym9}ٹT=_Q} )qf _҅eG.54U׮w_3/k$xEo+'IɅ5m%mrA[a!5*Y Ҫ7en-!f>sŒJa֣ngS!_{* k=D] +>3/r+^~7Wn&ct`Uu[io~UR l;Jv`ߠ9Rਵzz!XᗧG}ik`m1L1ғ%ѵzg_OXX ^?lw:c;i^2+6Ec}dsޜOODJ{~ 57P-|pgվP瀋}Ik3LlyY;?Xv!8ę.tJgCnu2XηG3xFΟ f.3a _5X!ڞaF`٠yʓv|LUM ;+ҏf̷H||Sm<_[f5wv_RN18tn/|_ߓ`] _ P 6,'"[->'u'pl/w-e,a`mr?~pʔ rzM$# `G\3G]mDp ə5dF.`z^?\ȇK )e[KX֑:jR߲mW%^*JGyر*ߝ/I'?߸A9>.P`` )=ame~y:o7*Qr`: `3Q~;[1/rnݤ6'Prߗ`ELIy~̃Ө?Wk?ￅ/;Z Vڵe[E{Vۭemo;~ rfa[V' ĖWUG0?#Q>dWME:?6+5j v*-˃./Ug̴fVVWl{s=[E?G tbz;п*3pE/@}wz2`yh?6}8QJo  W5> qwZ+O,}ԀVnI|emS)hO\M:ϴ%1-Yԗq%ƭ{$}sR>އQY?*.Gzƕbz[jZF<4|U x] c;{Nz oej?I `I+Ù^nY =4 |@~\7'. 8ik'?vwρ什Kb;'Bd2or :E2ppZv΁*8egp2~!XX{s0Xqt pOhIz+|Ombß?k8\;)r p>-k1`MװASxϕ[<,nN0|GgX~s;Cm2oL~^&뇀U5^~6`gO' sNy&RvbzN)ּ`屦Xf#PS|Օ/lك᭮S>} _L*k G;4 X0iؘ~7Ft=X溅=wIlnuNbxU䈳q3iRy #ܑ|D}ہP\P2[+6"/_8QYP9Ԃ1ՀX,{q13{Vn[zPW}$kj?=?NNyx_h Noa^AiN`o9 vPNԌ%O/i=)_9.k=||IΣ\{3߿'s?^ cOC)HkS􋦼5X^V oFC/?{埻}}^~+'ٱo4ß6CyO9Ôw} OIOre A]~?=ً `ߗ6sJ{.D3Y`Asy8<{^%0jt.>@ⱏY7eS~m-XkGe3|;8שG4OUfW Șv>Vｸāߘ}={5-ؼFd` 6kK KS3)GS\ ?Sv~ԃ{Iԅ=2|‹l_^cy-/㇅͠|pʶ9p|]BڧW.6e\Fہold׬Kei&`>_dW^Lўu9鴷:7wId' 15X-(_pfWMПCE{bM>>T{:{p!+URf B+~6|/ +*Og^sNNsL/"W ~."ZMǦMm[h5v+..}vVll{\ ?EnEQBr[r7Z",B((TI(B*μ^}||3眙fX5aǡL߻ܒ>+Ңni,%(V-So;=%U 5ށs/p%t`8,+ ҾXϕ,`^F?uq>w '>fL݂߿rvW؜5 ٙp Ϯ#=I`IgWOCZa3,`OJeuLcWqY'{Nˣ(2~8x,.` ֧קGQk/ i]2I]Zt)9p/ݡF^O9س_m~?Q8J#~E$+;{3KAG{,} W דo;M:2)^Kps0k4粿8s|McE L_l;_ R2 W(_ 7;,8^5`X:6+9+ߌ12?+3ԞZI߷Ƀ531,ۅZ< 2HMl,/ :C&!MECjqX6dxC-~kcXݞQzW`ޏ UaWa~M~3|* ȃ~?)[7`Y~4=#Do9K7Ts9|8r3l]Yv]aMo'Q# 9n%6yů \7B^bp& Ϯمa}?؁3kԌQW~A9틅cOh:,3cc~mcy^J~-7ھl63b6zv;^pޣd?,o_l%gT  ngJU[SgÙMBX`&O{n`~!o5K81ۯnx V\YŪ -.`~ XۂS՛n`<^ vTω@X}z;v (ϺЈxV]aF\я=keC|?U盺1Ψ~ko܄&\oް[0?rCο^>(//ՇMx>l]eS`y,oƇ%*%,"Ayš.Crr WkY~ΧG#l8ڑd|?.?cy}$psƱ}z+Y.aSw8~d} a~LUa\ig>+ua/}.B`.460\"81bt.7`ᠦj`ΫKށ2|2!H1"wE3`>a+ [ncys+vq<ͺRng;o^ߩ9>i~wA?o9̿< x8;E3z/ᐘS Qs1~Bazos+1 :tQm]/+_,zU~L=۶~M/t%޲|>)_ułSrY-l37R 9ht<7r;-;x;rfmx!weQu27m ek-F6ۤ롆 /n-}a&GQ=í0R}Uzh7,f~9>0ApZחzNW멦;Y>z|muGXwu>2X^fz?-û-4||;_}.W|{xQO/xN_M#nAj FMw}L8t v]WKq?tTWI'%n?,_S0_E{38?Nuf~F5g! ΜN)SUt޼:3v~zsyLk#+X TuK>VP>^p2X[w07Eh {?[ÅÙ>wnv>)돨D)Ԑ[+շ daEwd/|/6R܇vg`K }z=ku}itm?*Y; eWXsp%,Sa+`9ߣe,oHb 60t3a]),oj"Cú~Y^pX5e3`hNp3L5C+ڐP ss*<s.1|f`M` SM~ M`-lfa}lOsX/ )pHy#/2<7j b~%C k'ͨ:Ͽ,[Lv>.O 81wj֦2~Lks1vI寄Տ.Ы|aYD*eᶔ zE9pIeMɓc ;.QDFV=p=XW{-XA[mpm1~1, ge(+zcpԏ._%(n 5SX~p8lx)aaT)c{mݿ,U6ضXWo~;i=lac;.#`.IN()7nӺu,G3_Mw\x|z)ˋ&/2sN?'x|1ui8G|w`fM`U 'Xq6 Sҍk1( g0hwܔ3fJ[r,/\uQӄ Xeu;L*oXӲCy>uKOne01a_ ? \`uQR6|ЮVpX6? Q;Xx5\,w ze~i$f}1.U> z|׬ c`j_gVuY+KK'E}ֲp)'/ڧgf[ />S>Kb;wKlO Ys_?f0lYs.65u#P9I" nWkf3_09TTӸ!'b6;8kS _axU=!W_ 6k>Or߇NJ?S%mW2LT튕,:wy8]3~ʖl Z%ut\Uw󏍇Aǘ_S>t)QUX;k,9T,Z:mNd̹u%01| 7?[ocf;vX`O,o73[mUXRS+8 V۬JdKf}MNHgurN 6\R]/aԞf&1cӺ =eT_Zw0A llp;-玱=td\ݤNk J|㘿}|&xe|^[J 5TS_ \;To-~R^Z|H0ϯ?d1`#Y : N,ut|0m ƿi8V'܃]NO'r<%> q,eUgw63@}.|809EgOJGu<'y3wLg}u0_F-FK̿FsMACץכ|֯KKج I^E;7a~g*8NxY3h863K=I'$8#+ ;U/:.Te|<0_jq}maE"O3_\w6|yK8p戎tD8Vw۟%g0P>*qdj]%gT#dۣ-էWqvxVq~MY^?w5ߤl0r'1 ]c"7g{/`ӴykZ\aՏ0k& iwUq]͗\V{U! -=r}?@ML`ַ]r#fX&4F~&9 598֌~ƇG.g²n9~zI~.7,wJ~~ϗR3gP{ayuP^f _sc4Mb~Лoe0[X?- `4u2)na~ 9_l0?˰C87.374Bj_{wgy]F0 k˿`|ƒO:BepP! ǫSaX'~1kj4W ~1BĆ)lװ_4wjxG?i5L&a!˟jL[ YYǩ5`ukF^L 7ۣtZ ӷdyN}7!} Êux8QN>t8'6>IXYL_>X:64SS,L?a Yc? e]a?X6mᨨJD 9ԃUkڝ nZ֜h/69M6o2k>vbxU X/CBWu0ox{>T!-`z1S`sgy=1/s$e~8T ؾ΋ne}k-af8Clcczt/އ݂Hz<؞g -ל=5F.gcGe"ˤUϲ2d3r Y!`˩-β|H?gXa aMUpdDWfb?ԟ^_]jX/kY6+|綟~d =Fuߊ:m\uؾd8,W*R}u*UoW4U:㡪R~X?~M?n9ւ_g`zSXWƯнpt|)i KF7{eY$se3+;D|=\iY&ް|UstzbIOQ3 `nۥyD0}SX8ۑ#?sg`xNߛrEʿ\:>޴^,Ԉǵ%l;Ua~;lip8,o>F <-5˫pKG'旴ὕ ,8WF؞CYɷY~COoxNӓws~Ci/տGm /w3_k+;0c>zTf~Cࠦ icLV9erLb}Њd^~x=9נ?->s>2GWi^~~`ǿX(S ɋxt(8TB?E2֯)^(gKp?[)U kTunonFX$2}]?-e<|5mx?u_ 7Nߐ^Sx}$rK_8G ׳SYtr$-fPs\$=?~p=q$ aӫϗ@ZՍQMH3-K,7燡_L"MyLo"-|ZS"sKe.>~ߧpykiY=n-?^;9v՝Kq k92&qOD<׋)vXI__Z5Rѕle*g,^&sAZS[?f;E#yѓ,Ob޿5yz6~4k8_g~co:}z&movU+x_db~Ə̒)/0B3t׬oIO%_SmSoxvzvi__$G]`\`ys᠑m/*ޙV[J9>6{1֬Ɍ^Τa{>E&o>#y>)W+`~2Q_Ӧ|^=y5Oa۰l i?wۙOؿcazZg;oC̼?1;tZ?X~hw޼rg}7KG?M;a&SJi'כo^>s>3y^/f5xZvi^UU[wK+ztjx_o}SSX׺_xI ozYݒ%vLv+'v+-0%9\)ϵϏ~syy|Lٌ_#gbJqg~ϧEo,>ٷ4=VL;!6~c1,s5S}d|8='~ GHK.~0MyCIv+z!lgBa}X[Xws=^? Wfr毳Lzl kͅ%bȎKSWay+5pačfy}OTWXd 홳l` LoZVm{((獈cU JeM.>53`u9د.H;guyRc|~2~P3v7>Kf}` &\[u`lkk2pP-hvv҆b`m ݕ !pq\_ƿf{7օ-a=J^4eñƳ6 *l*w]u s,}y[vZnk',տd~&`R 1)<wEzz&8Xm:Z `wG8$7 [*_ vPi+ W%<~aum8=VUK}cxD 8lߚ Fuanr(  kލ5#A,!,FC`EXoMzЫdڰ7ey~La27پpqZ?7lg83!ާg3 / 5?!pT\5OV=2>_^uIpнV kMپZ}_Ow;dcb(kR7!5Y?ɰ>%Ř_gk`_az: -S1,ؽgy_kiz1ݘ7Nի[~ T 8)aMRoNom`3mnje| aTfMO3/fnFejzPl&On7UgM8A_Ro` +VܕOc3~R9l'3g/>Tfఌj\*׌WV_-ws55r=pjt-)Xo{>e%rd#J WX?P~,X5e0[tv:4寊sDzI8J7uEezE%LVP>zò{[t`~v3Y-QR8o3MbxoC=q\=5-Ee}aw O; #~2E'`?wC 0gC |#[7UQ3G+Xc|[GXV8(`͗OCW l5Å#U 7g1!0eLoq-n=-E$ VΟU{`ck6z-^.avק{'>|&`91>ZS G|=Py_Lzڛ߿| v:TךOc\ꡇFF xi`y_33d^㛶LpK㳂/l?'Hl>f?k\ g g0d`;?9wt8~IS'}=~Yp&qUE\T˷d [Cc̵}cwXpڙp١&=Fpa^~3Rkߤ5{ebYl*?~Ղ,W[AWf ^6ik}[e1ף8 6Dpį{=K! =Zvw_੥vt_,|tQiG#o_`U8?d5R; 31 iM?L\Yv/8;oc1~M jU_/貔TG~o-xEB ۇ_##/լk"}k&{ӫj&OSiJ:}ï nǷn$ذ`W;1c5n#?# H9Gk kPX߻}:$g'^u`/;ԣ{L%G;[>Ua!s`k|w nl0oׯ)x}kF!kx,'LtzZeу(xp%ʇG8-U#K_g{*xLzx5-?o ~h|W/{ =Gi&x䁹D~ɬ<]/Fʛ}y; x| b4/,ʳ(2Fod/N9:; 6P^;V%x #c ȑJ}S _ Mh?So˾l /Q1< ޖvL7k|#M'ydzm\wk2!=m֘q?:™b%l;C^ N}*}O Ư ]e e%oi9LϹ|eYI2oh]\8q9h]s 9Q+ڥkr?"?i ]Β;zS\ar?5IeW{פ. ;9ba?t)oK"xVn#_^ +=C\Y?YRWԞʏaW"]:a$ vySGÏ`g?_c7->1KmQ OvyH$]RxPa?Skg{`3[i< {y6N[^q}[/?]^q|a#V2ԗh8d\n VXp o!;'yS3hg7W:>gKg#2}Q6R\|.s,X;қqK6x wbSپd #s, 6Y?9kT~3S_{mr/7օ-c1j3g ×[l% p8ZjIq<~~]~5)_ͮY?m~꺻hw=(:1 }1N:FpaV'86edfsJ/ /k}VKkI;4wtx ,$aj]?ϻM&|j"Rd5 W;ל7CYբ5~Ӳ aGיwuk#5C5{JV\6w1no*?^%7!sc),߈ye<'j_=g|L?]2|d?V;xBZ칐Ima{ei~vC)~Dإ͎{<+I}+c{.O|` z޷L6*-$oz|;gr|J[2Oe˾<,]_)}ۯH-2vpbF5|1}t<),W[ 72xgWսMrBxUjb\C[wYzmuGqsʄ:tH[[Eq}> ̏^\: 'o9#h= ^ e[]mV+:T{sr ;oMp~'=uʼn凲y=v֬EfͅYh.ÙGX8~mַX]D j3~=+ؽ{\9QҪ88![[mi:_%TUͮX]zx,槌ƴv':ϧ_-pc~Nz,Ui<͎lN&ׅvQpX@O:MA-9)aA,?*8pڃq}(q8v7^!I,V34=Nc>RLgxO_WͶcTXmu>;+%LIhL'v_K;m^SaϢ s%la{YnY/8w[X~ٷXPWC:fnM~AK] NrsN?,X5?-.1BX;3 ~?`zO'T+x4aIz E7 o.N0z 5Yp31l66/!?ORիs}sc1tt})Ìֵ,kؖ詭ah_-_`}7`~=X~=3vv*Їm f>ڏ^$'m9G.%kXގ8d+?v{`Uv$šǘ~axԿMÖ IloYkRM 3GM}aWl^'?*OJߌ)[K*a}X1Un|-A~openö0|c27 ]rTJ"ok_:X֝U3.wfX& 7vgMď3m`ΰ<'u(3ٍb/a Z#|X4-QK㎰gC`Ūb,4[ȕUO&lfkc̷`]uV²:XSs~5\[YgMV`|,6؏vyc(? y=}Ǯhp؇OQe`{^W~%S~*+oaM7>œȯI`c)[YC},R8t_`~oh =/3 r}t@y29OLcagsܯ(\_Η8n5 P, OQpBX .tG{]En =CV~c"l; ]cy˽N?{z蘃U`Yܒl k_"y?Y$o Wq#úB_s՗prq7oo4S_q=|r<?Ì;z6vQY ϳ"eͰl%׷-Ӵ)ں8= Sz;? VM 6:n#=g՗jk`}SaIBڷXAhQ_4;do X7#sKaR7{u;q|-Jdzi<{) gq:xxR75ݮ-o /~{^2X`#&_)xŴ1MML1+/s!oqw|ȈϭqVpgNnM ?m}~.vV#8jЁon:PpNu)o,:%Ggr|aCm- ]X1|ʂi/5t= n"p|ݾ7-ٻxmO|oŘznR*ӆ mga}apiy'Vp=/z)e߈[k~㥐gok{vL{;T? Go<3R^Zi"}yOj#xixnWl;`YܟMX~Ed:~ ?ի=e鸿o[k{4R~+h^!uw) MKg ߮GN_6:)ǽFJmZ5nypܿspE|Ќ /?7sMf[VO5iiFJs#< 0,#[~U#ey/4%GM[lߋ7q.)+ Ð)5㛵x/쏛])8Unl8Tpѭu?p-H9tɅwۤNߵ3 ;bk_B/% /ʕo<ދւ/ jw럦/[獔gnvs#TC5O߻3]YO+xINp/(~9˘Q܏HLzG;v;9qDi7*1W+`EHj<~zH;wSEݣ]q`P+kYk골Fʨ7.۹aϪlϓo_5k>#eG\5m'xF>QCqUwADb8 ^{0 gu滰?I].燾Ěw1?koÊ?`]r8dȆ<KC^~zq_uE6U&|*̜ڐcQo' 'Dº ɨozVz!W˯_F]}x. Xć[oA[c5ְv=8r{O|g-ڀ݅%e&g+ODp@O7p:NamC ;~`frb/^1/_GWl_ىrX3 >X/Î"d +:{z DxT)}]G|ٖǰt8}M>{<+.pKXU^V "²+hR}!\Y !M+(ў:Xv\ Q3wgۋ:=mQ?Tsdz40~n3}OME/ۦ`{}ˬ wQXO-^&-:x[XxzMё*M1u/`]1VsLc(Ƌ=JD|k~k-G}mvX-}x,њjZX]ha͋eD|b3S ؞E[=eG]ֵNuԯWh]tX7Uh ,o*›`n/V]?EmԊY~V[/Ix*Ard%(ڪws^t֢Fʉω.k/7;pU%#L8Qki"~^ ?x<<VAxEcWUWψ, Q~;q僌9zǢqg޲5lE*7#K6W1Nv?yӛͧQX^|/X>):Q{GfPb筆eSic5lJj/K^u/S{Ӻyp(Khmo_8ꈔtXL6g}G'3ܟT{+>XXۭ,KoP_fya˻2'c}pΰ!Rt=6\:NptM4߫d}07Ca77jRMpflO <^`˪پK[~j9n'=G`v"=^J5Q} c.x <)csXQ$Wuǔ!z_~P(0ѰXSׂ{K_σoՙ`ur#Xdn9ښޞh׾,)e>{` ͬ*l/Y=?ݘta}zW3C]8>[ja*` ozr滛8?؎ kRm`ݦSR|P䟞 mC1(wPk=`)^7[o j?2O=jkX,p5OVvL5p]`G _zMVkhyo_c^[8zimXgtu)ߒAaK9xp#aUhvd~ cm\ V-<-N-߳xnkGyba>sYS?Ч&^ˠzTfmk2_עڬOt'9 ^Y0 `,|i*e {c ϰ"7ګRYaߗadԷ,;? - on1w`y83ECUXQOr =NOX&uFq>UV=/IV5UxxgMጢ&qh#^ֈ lX^^P W}/u+mpC>ړ2z֛~ 6(B[`Ulo« d}>Go=Y^e8 }{|NW ߢ*XP*P$5s{1|A-ܤdž{.Ê V\P!eTٞ-O7lps(J^}X*_ ]l=͝7;v|3 lfh/lg'kN;9eL˃g>soVpvR/Mϳ"xĵLTU[o`ya ?ݹzEpL_;|pT$ |NeNy y7}l \uC3z [1E>lwq>a.Y,36^:>.,{Cekwhӈr<]=, :$'Xղ@ο}Y}k_Z:oN>V=kO5_6'3\PcNoG=y":{2\t|)7 d=_GX38#yG MOie{ߟHToysH/?߃-yvώgðY!8EF{;#.U]"65׫\:[t/櫩7z_7rڕ 'a廮FI|{hMg_?! _Okmm3pֹ'lτ){/C4/yzL8s~>]6vLHi,k};VHqb|zp^z\Ou|rXO6]7֓HZgSV~OQ*XuVEwKh׳[sբIhف)Y Z7|k/`nmzr ֬׫`EGW[֣?=ł黿SЪIWOQ[n86&Z G_nO-kWe}Beƃ^8ik*^  d~a3/3_6Y2plcZ~۳m_h:% Ys5[U(hK] ;۳?TE{J.+<kNЪ\-\wDmU۲i1,E^m]{kҿ^5c~70e?2aZ8X,t<4u<49;>w8W8KGJe,srb+s>2WZc_WrҼNua9\Ru<߿l^8//O\?ImaEQR-cOI/;֋8~qm4WXs*mcvnfRFN61a2b-9[;d}03C_Q-ڳ[a?ۗObح$?);cOd[ssx?zJG8P k[ RG]qh4PZGrLu,oCp^l+o:YMY-0~h9˗gk2G>}:M_*px\^9-w49RӶTC?cluS7V~ֽ ۮjhEC1\nnlV0H?zW҇>:4UooDOaX -lJ`dpI a~zG2z_u<^Yxߐ9ރG~Y3˓^".R46f0Óx>ū_w9ze#{~e_ rߞc8H?&GmۑO8~]quR!]ϻOoo M5BO#~~U]jesXm]|g~Zw3o2gnu6oc]~kZnR{okZY<{z~|xg1|X=OUrֆ'z_\M7Ƹ]y ?c~Yx9"?tWccuZPs |)#}ӿP~96E}BJnPˆ0ńoMԶ ӊ?r~ ?ZwMM8Oz x}7F{Syq# Ϗ8-C1tPL\[/Q 8v >_wϯCyWd'++Xl_&x~Mނ%#aGX<N/ri`7sL)E8p8mP`? };E~Ϸwpq|~:џY{y,n-s[NM?= ;0x>\洑U` pr>2[pQp zhb>: I_.-wgšs'`9sX^f"` =~6 Ke`5q\֨Up3p`x͂m{y)*wÑ~ݘ߲#99v;1Ҋퟏp=n:ߎιA.ahQ+N|i3[?)\~5\o@o86'3aQ=]c??7\v`'xg7a/ {%t)`ַ|+ݞּUnZUiC~qzpt08Q/o{oo`ϯ׺HXl)pGL~.L+hm^4=IA+l&! ^ cϦ2`gQv~{e[Gr5p5]^gu >0??S͏3!̯(۹NZu aݍ% >[v2BU!}K^hN$X 9 +RL\U~cZr :0~Tءp =xz8={v7̆\Y޵Xd(8dg/:~Pj̀Q-^'VnTTik =\<:mx }*].1<"~/G,*S_^7ٸЄU>ބ=2ǟ|AOL8*/`zhuUY{^B? 4|ia=W_v{򖮃uo `Ev,tppnNتO))Xӧv] w~K ;0~q6l5 ٜc`GgX>|O2=Uz_:ϯg|}iErk߇,O>ڿkOr<[_Ym^]8C7c62;5UYuhEP8;O=8Ҋa9MS!\U-b~KRڂTx> ү{<59E%sm `;CsB{n#/A5o(^QZx,l5ɈG /dgQf|[?j.?:‚'/ӫf| Kz~8%w [8pSU onUv^-a6tx1q,b,[s~^; {wVk|G.HðK\!~A~f2^E^ `]=p.Z6r?n;sL+*E3}ӻưy|%a=&tv:gDӊ%%LiT_S3킮,o E/3zH}Ow!sP ?w\>F*?/_H1?”G[{O}?)]Oa\5}|-Aǚ,okl0Ƨ.Qgn/r?. (]/s,+_󹿻:-ta*7x)~pDcf©[px??Of|Y~_l,?hQUX+U>>R{~8msv_YMrW.(Z/)VJ׻}8=Ì/>cSγo`uDwkX;ԥP^t-w*xKjďӀOX_Hoy/ J5gk~EkJE~wv ~Ͱ[OdkPS )ύ;?3_Me2}~x1~ߨogoVwm_n,xoAXڿOq)Uy?VTܡtPOت]^sN7cl)uߣS5l/T970G|yIW;tg9sig$UzAv7R?O Dq~Y;i?{Hتv?j--_ˢn3M]#徵-s]r֒UJ_mNOi=i-YGx}jYc۬;+x=+moSr8g7i>یO6U?vV>!| i~\:OzGxtrt . lUa<&_ ƃI4_5Y՚W9m=39Le!~f}%vQ?M<5[xjq2R.|^YC77R;%l`ť?u?xxMli #!7<@?cW_Tlg iw񇖼<٤Œl}xH>y]-*nFԳ{&+x*C >0bIb~%֮)-oilojksVUpЯ53i9~C ؟G)Ư,/,#AI߆On7|MQ;)ߐHZa%v`=;xWmg\ֿ<8pLv_QJ?},O mSM/-Qp`O:p~ԽvZC;z:h4 ~zbZwgWdJ>nQtj^ʽms>^  f}8!94GaǡlO]q~J!~+ {.j~F~I+Ve}Y~ʟpi FIEc]Ճx?C/#Qz>m7Ftvj>f<7?3D\~Nsw~k)?m~a/KCP68_"Wfo,%Yb>͙Vya~]א.~aX ]s<11k6?`? u_Y#kE{;a>AH_g om w>ި5?ٚo^n CCz68%aCysf)]bXn;(@yAQG\Zc>H_纫:Gup5?;~HXyXx~pA_/:zBX[.PtznX?YtaB(> S lQުEx;,n>1{6Cby 扶<nelo1m `(~oKEgC*zNjJo רUtZggg/:R=:ghEq6:77f*xc%`GѾ)96m y Y۟w M񛖰6В`w<\hû~Eo3ʉ9}-#{n~8+vscp>'2@Xےp~7zO9,$vIK`KamYUJX~~Cіi.E#ڲ/3E/Mg0X]fm^܅+W KۆmMњ/}x}CpUtjzk1ߪ̽يSg0-ٞG)1Xxp2ړ;Ve;%~[G0g@hͺD6r5s!=E:*:=$z9a^yr1~DF2|姆ޔT?{` :P=O~~N:1Dg{ވ2.+?rv'#њcpv گHVO%{hYD38^?6):og!:2I﷉Ύ:k[[$9lW<]1Mt{6~O˂֏36G8gzvN.!ڮ+i:HA2 ڬdT^v:f6G om) im&S;p>LUgd_ii>MCm>Qܟ#[5{j· D+6|¯Dl跾X.z`8_;of_j6shϵ0kkE&<{F E;B[-烦H!mbp=s91KtJ>zbh`hcjܚÏh߸X:DtV\?J]En[؋kF)[1˪Wj,Z1ɘU[t.9U"jh[}k_ˤn#6E;M{%:eW='n]%^X-s}aՖ_#oX V>?y mxr]6ҤO{8&ӿNEeK0}8lXcnL}*ࠇtlzuQxC/lh3`5R{KKP{M`OæR9;e},RoͮE9~Uym?ׯgɞHޝ^᱁Vg;|h!Xn2iXZGоZE#ܦ-v"l; [`kVxck{xT=uQݞ^; `x~~/`,_Fҧ|SLp+"xl- g8aG7#_e}{ex;EyV]^Sߎ/f?-՞geg)?3$]]؀1Rkɝ/۫fp<ƿlGde~ڀ5C8 :OhdJpogE:[mۈ4\[+xf鍟 `Qq][١c_^Yp%_ m2\ 9_/'G?U~i&صtcֆgwiobO&ݎ4G1Up+}E?1?4f@Y9k‰n%ӑ%W_ S;7|[# V FM=s+ķ:ptn WX_:G,_ڔ>.Z[#VYUzChya%-wyh&4状 SiCY1^]`xíj`7U9?XG:$|Ry'g9 H/ou[^ʎ`~| zq`PgDtwNaU|u[9p~53 #`_{;ϧŜZHvXޔ;Fe]yra >*5+6\P6_9=ԟŵ9gYT?bMK2*/E ƷICzV~ӊ֯^!}Ẫ6ԫ^=?Ū'/o9~O |.` ?nY]˻{wLV o/9piz!+xf aw5_3a]4?D‘s9$}q~ҪLtVQzR8" vGIq+or= wisw[v B8߶Y ; D6?>4;߹:GN. +`Йi21ɕ?]+o돋,Uc=]*8ﳟ]s .[i:c]Շs.gso<Sg1vpN'}A}=ucξ72?GA౑=n10g[mEpeWb>E\,?{4n/H5INjiڊ{B;uay ߑͤV6{2RRηuYߋaUY>_{T"!Eq[[?N Bg .xE w7E,'ry.E[Dyc#HWMf}b>Uloէ`dp `P)=~d٫ӶsRECFO*y.CWwRͲ@?K6=jgpmO?e]޿vߥj gM| VB|J w|w?fZGE{$}Cc.qxt/:9";,3_Imby}[8S*_I\zSo{XQ]M 蛡~#}_/?/%#Jrd8øG(Z`|_f}n9^pl3 O'sJσ'zH|y7{}zȟ"m!ߧq*C?g}]k?k'B]4 SX$/8Afg7ӸO$)1;RWI_7~\eO׼;rऱk~sk^`ӱ7 }M8N+p8KJRaOzE zɚp~._vr0lSRuˇz&J|fVJ:~!޲<*:Ezߪ֗)~EVja}tn"_atMzy|7Ϸɸ>pC\hLod;Wlz۞~?/;?0|o?wZv#lS ^gm|#c%/9?^|bj|&[oC7m5X?VϬ6ch9A|?{}в& 8NW~بOx/2odI=,wl[}xYu_3oKnad\:p;o`xlV-õ}/1?=yƟO(߇&8Fp w q> }imWN?JG.xY_d/xGE|-Y?Kw dž_Cx*o-|U1{AYWںv||9Vs^aXg8_CbvimU6-V~,{}[67:mwZarv%%z~}  c}ɭ 1T9XO |WB퉃k}cy.؞plJ4 M_G-Bl;ׯn2v'~1ڼ> bVhIl%~@Id#G/]boNY>*<0eUv'2jHւO&صӵkEgLy)ZVXO~ 8{F)zܴ{11ɂ_9E/z_.{ 6ojY!+Z0aէp6k!濪us|Ć;&)~`y s4pq_Pߧǔzl=f3Ge^hIY]\k/v$/lJz_}gFvߗ]{d>qg>d7G}[&xa kݶ|z3߿n>QpgKEiVE_9qų^ڻ kW*.שn/EO>O\Qݫ)0{)΂"{n7ʒ'mO|yАc~̓ތl\7~LpF=Ayp_Z b~=In5f`ӡi:ѕu[_̯[<](fgwrkd`Ae g3[Ŀ& T*xjBs+_WOmx|}zkL|ҹmoyZ7#}7Wwgha^ ?skpL,7Eɦ4 ۗCW Z(ñ[n\_i-R|_kOv᭴h9ڔ?x,3l@Dn8j#gDXK.t]Gm,qEٵ;{I|߬o-p ?_kce"~AgIؾ6ܡh}{b30lݫk?8vak>RvK>+Snhk'|J2m]љ^UInkm -"Į2<.7}xgu{SrF{"E %ɤ>^IϏ34VUZvZz ؛ѷ!g~"ʼؓbzz`~{;7R^9I ?4_쏲ǖ \7|3m8ϰp>Gm͡j^ǶܪRCoonc$..U[fz >XIt!}7`3O'w7W_=7}f4j!G:DN}7,te6a>kDH(8ǘ}pTNjW_QLV˦s V<2!5x} KrS7OԊA?P~m#s2B⿎bòt{||3q}x3yf.31[&=} 1M|FIm{-~/hCx~pѝy0ƊLk0w {v+}^ZJWI_ޯ18&qtrk{:J)VF >}L?"~o]9gZ!~oum+u9gv.n-^4n* )ZF .پ6\AK^b[oܚs<+_;%:b_]/Z0NhF_i|ezks7sj+w4$\bWx53+ W_? x?c{6‹~cm[/ N}OkZ!Ƥeþ7x}5䅃P_͛*~ kq3m5rjWVJxIpV:as'MAywWzhax%by;m;+m__9?o`+7 ^{xoLG.²~ek+x;j;u, >>i|o{ ~YծJ'}eV ٣_c7>1W6z!pWf|OjlOol%]v^#1|R7oӯ*j?VOxyOYe| xQG }&V w+NhQbt=}WQ0yI_iR囫>X_.q.B}$8ݯ-3 +-. o!_ãȳl./2myon ߶1χ|z@wz]?U Wg˚_n[퓶~}U^UڿRqwUO}"Pӕf+~ .;1j^|U7KWצÇ >ߦa }g],OoJ+H[jgdnᙀOL3:᧑1.-x5OEw2;gd nwI1q_^}/\(xP/[ '{n ~8`mϤ j޾C~eekLh_}WCƣ{dŞ8-bXߖYbtQU[ngF4ex-طx1G##0nZ'x湋q=ʧbacS(N[8)q=lӼ/l6Z}_< 6[F0\uYX^VfFXLoSpXAUM 悿N;޽_jJ]mpbSft\"gY j(y>^?7y ~[U[m807 %|w'O}sw>ǀ𽌯u{ jt? ~h3 J1_ N(ҥAz/I|Md*7VgXQmN޸ x[w ^9io/VWfxa_OYqTߛ]\b-~{QVZ _j{k5c V|a`ҿnmy|׬_[u\_XWOpδ~Nα=;C1R&+U?c"{?翲4&{ 2s^ 1KMh$[1w3r l9f6{|s}gVr].آ[9Y7c{"nL[.#fqXoz _~^69o H<>'=;uz e 7O-99[g럂\U_/5HGw_~Cᠷ~)7VG޸߃D{{ FyKoŰlpׯߘCK[v#6ۆ^{E.~/?;?hsQ77 qi8l'KO^[ȯvԜZo~{oFmy-4.qm_޵r_)ʕNYWԱ6yo&g.-fvLV%fLP^k~/|-;҆Q~Of`%T^FGd59?O&o {OMؿ}B.77Kez?kr6c\Of5~hhC[/*8ɠݳ|'vMztjf;]5WX,~?sa\{}ժ#[g %{^|?-eY?\kr *}j!/;Mu()\6x?;= g oQc[ÖG${m%^^ ;x?hʌf3{_9*޾~ 3oMdMU㮵O5'&}m$W:ޤDFvCar^=;JpvbV~k|B|9xDz`#O&*p@Hrmm_MlXixhU\M_YݰH 3yJzv+݈x|JׅGGdzz2C߇G`F:|]m_ogwg.'T#+uau|]Z;Z_ Ǫs~QGxV߯8#olO@hK# &͊־BYjrϫWѺ!*XQ٩FNn˳;7`}[ ,n >Y92~x*'lr%O~̀}F{ 2Z}F)ןiKO:_[c^"֝VlC˽~-ֆfEg {7nKN~?iwvt ޥIUzeַh8|tm ku'M6eN;mRŕet?eG ? _[pYy,/,o0_y7ǰ8z1y$8Oh[k=cpݥY5k'!pΖ K3gmh\C7&_3qK-bz^6q=~o?9~/r;lק_A?c?nWhya*3ocCA03l|9#?^h V>ɇqL̤'ns2'Su-ܯm/`?&0y}1?y-|_ݶ~y ت^]ӧle.0~Ù]w51ͻMգmUw0e}feY~BXSӊQؖ?uYlc^otGYK304Lm&](crX]v ׃˪-`uRn~[pF7XY  g:OXXi].z2|t7Y&S|9^ca;~N~0^m'dis2+i8)օ~{ kmNa~5EᱰORz:';6s)}Os*Y }5Oߋaz+XYay`߫8DKߛ{`}ai,V8lv; 7 ?/u wАw+Κ=>|^ӄW  }K> i>w\E:˕e_#Wt,#w?ެb_ 5 ޺ f΢~Ւ`]3̿`=$6`6QGY08Mwkۆfy3`M*/ΜfXxb)=3l~greiv Z؎kzL 춯7oԙ#`[au+3cz02诏YyƠ 3Ӈ8Qv@N70.s/dSúb>}%^f& @r8x8-gx}`Xș9v:>>=aE).?O)8I1؞a<~{ϫs M}SD#ι| 31;.Zue`YFaToaa|1AM]\Xu? #{#lu'Z΍Ytx5K?0G}Y. 6X0p` נ }>;Gb-dNN?`MIػ'XUo'~>>./`8/#oeG:܆+ZDߟGM=1pqCt;: XobgUU!X^-?Xk~2w  G>C~2)+ ֯umYp{9ӷf%˿~x ؾ;>ԤI6-TI)$Tƞ)!mh(҂"ҢRI }<>zι{sA䥨< ~xJж~:цIm-ܞƔ E24Щ5ɋtDhQg!l_Hmrh4J_JolEK_M"a1{)WSFhLm9|vF*DnL93oB?$PŸ@婮>Pf?M~ʝlOqzϦDg<E:c5*FsMAơ5"R3ؿ7!kt*3U/FP 0j*2;5ҟE袓d3]P:@VKSC5F JGwPyʆK=(C3YhQtZ$CJ;STCk\_HdjK7,:M1C|5Iۦ Ph9IꗃNs2ŪzTm#"kr9B؋,[.#s{d(}DsNnJbN.J B^3qz.#i?m(܃2!i?-C2ݟtP$?k>H፲hAT}1E<_'?G%Z'KmC|닺([_Y~)_%CTFK(j0|Oj?h_sx[ّjO>1вц(=h*'og9{zgT)&4LQݽ肼 * SmB%#Ξ TSM7(~#}iJ?K](߂')](| 7pi35MЭ=0?;7@є޽h̃TGhOoD E)2?!:9}ݟ~:]=J$EQz%d -'6gQ®[-dWj߾(btW&ߛV;DIb-CPG+zFQKI#vhAtEQ^Y/\]5h ԟĭ(5 W^L)BbRP|I/KȖmv~H{׌KW6y"+|tn9|$Y%K@;RybWɰTh{U;! 3=П hN@$|g*uf>: b6GڏG1])yUhuWݕC~#M͢C~`\[V"ŢbP}ډh=I'wt4Ş={E?mes\;x G[-SzQh΄%pgt*82Œhz 4Z+U#SvidxqT)nǨ|lJ=+ }~G-G794>E`.|fyF1BGW5z|( -NKCs[&L# aDh ]pqZɕ?[DeBbvW{0֟͏>_C} Մ2V Cm/͵P>?Kܤ.A?'oAG_贵ɔ߾hЋh֨X_DX)I['Sy&3QxC8\B)w G+ F L5+H~3Zl~f y}h "@Hvm@ #Z!*O~Cn3Z綛hUL;w7F.jxs(h.lhNetnP)Z(Mi]M #=<o-hsaom;^/xt0:c /\Bn:Y/Ac}Y۰<&P~J1=C]?&T)GhwyMAlzݰ(>:&sh{n4+찌@ ){7bYkV FwOnzъ_>9B] hD[[PIw-SZ)F妮(+<.}wn-/Zi;:m+ݯl+*_.cB.Cn xS*i Ñ̽p29xp2W9.z*Y6{$÷i+_lJEWcho&/ub`VNmK ﻯ:#c3x =.>Tsg&qMB;pۜa7͛W&p~ 5s/ά*hm4/>&}V36,t?0>6P.:{]`pRLy_<֔oʝ㖁;. [㡪G|9|q9xzL'r`=. J h\!{]^rQ8=)ay~1^YZqBV`s}>3W)|PGgW)پY.[Y\_׭w[:׀M.} >kIfm]6lPG| ]pv)XG䐤A!ߜ'i|'l_Wplx-!q~3HhɜNdkBadghu m@݆ _GUM:)K_f o4AO:PEhЅTLe W^@eNK_@g<խx6|-οz%嗖>lztxTj*:r9~%KZ(7+ClDTԯ!:f0*5 &/߯^ҔV^꟱K*=h~L_̿SO+|Fs4!4Ggdsh4Bs6[y|?-ےCD{3퓕Fckӡ'<\oRG-8~(| ڏugYkޠo891Nhٚ[hQ!zU4g s3~EQ~-1NC'A #ۭGfS9h)AhfXC]Q3^r4?ؗү+ռ]}(k"~]?c'7I[6ܹm(F;+1{WSqx>?WQvc6P~j(`% -މ'/ A,Ђ } )]^Z?+T~IB아G=RtXIuYrbWUɱ\Kr oN'=ᒛNR7Q>2ͺMdehb2ZSK^ht<͟8AFmHT(~W(acȋ?p|&JTцT}((u7^c;o֊+ڢtZI\ޡke}bh΃RtdL O@+6R~CNd}OS(=3\Oz*s`v}I"ϦƢcgRӇeRx>6u-*OC &ҿ -% #OL|dֆ+贒[qd;Ь(]B eF87 'BW?BAsS(>ʈ߫ ~M!wP ZvcY7)|Kxf/(K%χ;xVGX!)?mXحݜM9gH^C݄Z:2B|z1Q)!*o`$B}t1%>n&JƸvGkdѩ\/uyT3:KNeo6vb Ӽ ܃ӱ}EseR~#ƣ=*zys( w󄕋WBt.M'ɾѥNTSxO׋ZyKGtzA"ZO(?Iq|`x%72:@EQ:Rߪ; i-h8Ƛh GXF όASXΠEG)V=LOE}6S-VB jZa7ҿ`KM؟9Mð~X} m3=:okC]ԟnbx/QS'ɥ^1<݉ҷC?oOT+5h[3j1hx%5,5OɺNOQx+͝K~w ]#u/ƨ,o5r38k,_MonhhaFjXj=+Qs}¿YХ'=Gɏ'byq܅D D V3ɮ'4TRZtApad\O 1*a9/|_ͽz]P=-D{Ԭт6p>WّHgeye~&'@}"?G*I2hֆORha>իTaVp9N~~Ÿӎ'5ldp_~ڡ ֠-(}C]2_rԟFhȥp,_zw3V"FGk<:꿱or۩7;hpNG PDEz_!*Oh"d'wb|A/b4rU-)O=Hh*:"Ϣ gR} L~RKGoOZWdjpLIx\? SzЊ9΁i=Kѓ#~zwp?=c1sΗOC[Bwh~t~L /*>R_i~_+- }A_Ax'wyUXw\'oSdOI\,Σzp4 7!Ÿ&4@@ׯ.=2I֫ PMχeP{Rd2z|ߣИSe|T>%?gi4>}U3Y8ͷC;/T|t`8g`|ّ䌅>,hZ)r]N k*/x}bO$9f4?,؅TB0|زY+\^{o'z?]Iݑ癪cm 6* j?'L||*paf0N%-k_K!zZj-u+_ )~e=CeQ7H7K-oa`c#W+c87l5\oJT|s/ h|sUtczQ_%968^l"͒x7XENII5XH1=VG+~xXCǔht7ƥmh~b߫GƆbyY^gVRE^O[) Z?R/z7Cv˞k(>(xndgqL /0q MF c{RL? fWٽbܚ$Nduӯ;PB!ʹǂJQyiZ\ Zay򍋩Naтΰg4@><F>`()_}h, *mOi?q/˜s\; - sd6[/Pq[\3!Zxe/p ֧J*_t# -BF,/b&bv[ZEntJ\U|Wh )T?ᆎ)s_Zkq1~3rJ:!,B+ Vv<5Z"B(tc^w G%wX+Bo%ZO0g5_DcՕkDxOf47"/%CۘHW5qԾ`}_.F 'el-KyyUSiUVcEg,I/>T>&fa%Z&ZL-WOv'h %4?N{wck}#z@qmhїq|'kMqALy7wpEڣ\dtqM%8_DmzL1!_VNx2-VkB?GbyoBޡfHǑdxhVdA z]+ͽJBQWb*OU/~]9‹[5h#Bh'l>O[_EjhRvVTO;57OOQ?d=[]#1F׬39Uh|?IJÿ _4=j?7<_(a+Qtn!Nt&*_! 3)䳓ѭdz l1s\P|tx* xҰ^HvB@Qol@}o>ns֞DWYDSֺқ? Cnֻ u|)#(\cs̾b}tSy;s*Kkp+G ߧwŒ0 _U i'mg15-TH'EֿX h}|Wj> )Żuc18mi:s%xQpa>8·~-ZKŸX-%VpY0Xwd>va$b|qZZ,:xc"\/|([Z?5\9h\ڢ7.X{"Gj#˕eA[0_Q֏F_!'N[SLr~7k~,g2tn"_\߫ﻏ1}H n ^n nk3GA|fo<6vN߆%rhمVmL{:N9ҒK03Е@yb ݢ'渃?dPOSR:'2-_>ܱHI?zvGr1x}KEia/4Sz惿1Uo۹b'pӐI޳@ {]U&Nw/1/Лv  |I۠xuÚ8>zc~R+J Mo:WܜnV^t #~, K%wL^L) d5,|>*ܚCS1f6gH1+眛5\/tX{Blp s~ _uNfL >Yx{?Gs~6m+%/u+ͻ ǐrN/ YGJ|sɏ@ MS_QP.g~#cl{3yxŷ!i{-o;yy po݅ixt')3fK?n; ;Ѩ *wOiKJ޷P<.ղL:Vrz~P l՟˔iiGxovͨ? raCy˝ZJT+ߧ{T=nBr/f$~1 iW lc J#Nǻ WTD[_A7no`±TЀz^2x=ߗhΡQ'楏s6z[a 59N~sn]x|}&\k=d$a3W{\t*my|# 8;w/y)a V5T ~zѿm ?`k5[6)7wF ?4?}sBiU^n-#sS^| v ?|>|O}Bh587-HW%aU>ᶥCb10tC|b￵[N>z$ le~YܻT.xCϺٚ`=ZnRy%.3?2N?5S?GT;Ę-ZwL7r21=/UM6( 7Ry*w7SN:2֚۷e̿.Apű`py B:o\v,dX pcpQ۶ _na.4Sl~қg7_Y_tLsϺߵ\l۸$5ۺ&L7߳:pJtp>1dPߴ5WNK o3(~/܀\Zop#O\kj$ ?,(%;Χ5eGd}R<3^ox^޻㋶UOw΍ ߗ ~$#ԃ3Ebpd# oIſO_W nTҗ} n/<~2}=ʛoث]fxU5'ܰt+엮bߡ2*xHi OKMo87/ߧiN/:-h4OvHr6pK0Nk~M(iRHV}mP >U@̧(Yʿw] _* n2ιclj69twn O`W7OLy*Vc.nݿu!xWQo aBL|wKxp%˭ԧDL)t3TF8EwGz)s`s?0 l|fFG x!W`ܭ]7uJA]Z}GsQ޷EsX\i^FRMa9ypU,ŕ y=*-o^<l214&p(pQbu2%Xo1W?,<kgzdK_Oqu2lr٫~5E7S[+cE+Bߍڕ_ ^hp2ﯥ7y ~`Z ~Y:c)}/n.W>2>5t%رB3f|2gHfa~ǤLמ0]1]ƽ,a@7N?4]90He-_2e)N6Zzx6\Ewߗ$'>YRb7S65~O%ʺyXءc"%lF\,1}5W`|h=rVX6Y ?K7o3^/h~E߷9Η~&-HB:R.O'r*>WpwO9}mr5~o'k]^e!~*TGi׀9vş*-?S{i='=vAה~s _S7?+Mi#=_Q~_E mwMN!Gf(KuJo|oGcNL?*w?OTw=nF>_y-8mC"TpՈO&]`L}np}IX)IVvˀn .6J?; <z7Z.,{1B˦ykpԳEց CG_vn ^QtuAM>)m#&پ\{3(eAve/jR/I{`kV9NnCpipi\3aO{#W [L%|kן.*I48V2`|,nkJuhmOߛ+X㥰Ql+TlS_^nס贕WZp/\.? ߇=dM*yB0~Uw _-g\HrwK, QFXpZ㓩AHeL{]&0PUENk":oCt>ICg4;eF{!#;'}Cz~RkO5 eO-hkKq$>ѳ}]~ R,C[~т.MkARY)GLDs 3)~#ڴ^r2so^cz_(= =]=pZEߖ=)@Ol^ wRWO5~GQ M+z?l=ZHkߗGmVߣj>f\wɥ0~8>hVeȭMW 0oR}NLrHU8qIQڌc.;^9WkZp&Z`y.}LofK"|C, m4d1cѨ!TS簼Y0EFČC3;=wk[䀆Uno 5 [&o俻MyzdԅfG㫞惇_4썗`9.K`=kı =8lzP_ UϏ]R|^{McQ8gC\YmR6ߞ=p<Ǔ;ۛ^Ic<ܑ͛qVXb V6b.c ;i.q8ښ(pbz_΂cˮ.gZ,:5aWk2g/lQ}1MmUo9yӥxtx{h+wN"wҫ<Fˀײy].;_!'g~m2]?nA оv 7#ٍKaz;B;lާaN`7K[~yᘿlҋC1[ټWE~K-+oxy>gdƊifxymֶ:wyO?}bܿT:S|tmzjq7wh̊Nٮ3| ~8PhgxC^6Opj迿|c~.ͩ_wk!Lg¢8 \eۛ=vC`j[̷чؼg]/n9,,}q;ތEKCpil&`)A !p?k\t-*1l"za^aGa:4}^|2Ɛ=Ol^3#S>|吏񅾿#q{=L8yq[ cy2 ZcjꫣgZH4&3uxX㥗bؼ%%~**#(}3l^вJep p߫rWY6Opϧ:'ټG7vc.^}s;v#T p$;.;P88}y)]{4o78%C7灟V_Kfe7vm omsRܸe!5\=nc~RѢi>')]k(Ht^Yn0+laV\+5Xv6onOpէց;u WZެ6\bV \͉_i^ ݺ ;Cn`z-3),h萵߃KئjgE~}=$9No25//SW:\%s)ϴrv1_z XLD0wzcGzC,Xؿ[è>i~{)˗΃ h٭h|U=Ak Gڄ.xWY2o2P8ўNKY1) #Mh<7JKO\qvW Ҍ7Tُ揆6cOkNxa/B Ьhiv*k5Z؜Ettsr1{(Z<8wh~-|IVt@scqNΏ>n'i~{՞&/F|~hٕi~Rm.UM묢4z2C>F&őˎv{aIтt,m*\&O9yPK~w¿U{y/Ђ'4?ԑ )cu+O>.-6\3?(؍RnTGŹh"朻揯sj G&g;;gWxL ^w[{d/B[>4W|um1[nxjƚ_ yG-[Y\tyx`\#=\ ;.яxtMxZ.4׎r-3w}x98gg:xUV\"pO(7=SoZ̏s`/e [ NZ}rxJ |7f?āWF2ڍk>V3' ^&Oeww*Mtȩsz0?hzo#{Z8ȃ%]?<\l^|MG8_0G۸ӗVfחwa8ԨE|.p9͇Yh^sSo`?䋹&ON~ ̿v=6^xKi>` u70_Ny37;-8y+֋&_Λy4m+Sfœ *1l1eg:tnY>TrM4 wΚs#O:ЄPZ ֲ Fn}7ƧG mwnOyf^WYQe (>a}xUE>7z9 Ҷ\-K=Ƃ?r`j~ گ齁a/Ξjd8ObuyE-zo77ع^p9==>Gs/8mnh0`ZвxNfz6Culhuk jټc;tkq(\ٸj)?{u/˯z-NICkyMn/ʩMyϹ)J9 -På碋,$h5XϷΕއ;+ؼ3N^sw/[V~ӜNș^%w'Z&y׍T͇+.Xc;7ݚ/ֿ5s4aeT'w2w99jdL_g>85`=֏ =RFGv8XL~.(Ms28y8\lhy+8>`yŗ_Շ_?hNˋ`^VZ*8'ټ5 7Ozyƛlukgv.0 >616e(5'poC"ټw XR'EMx޻  pipK'en(QuxfƆstqLO Z )z=wπ(>1ÓOn{ S& x^3g? e6] 9ڥϥ&eSZ4m<0Q=7)Op2k4ǽw`6oh+wyFJug=郪>3Clޠ~rt)-l^o?apQ3xS:o`JNˀE}ڋu lޅ#F\>+g/M }&?ƭ+yk Ӿ`~?ޏ8O0gaS"s"{OVZeN:+ ~Ti?FzSW5׏˟}¸jl^7'y =l޾o9W^u0,wmjnOP}|z~\6:˯eO|fD=k7ymxvo!xò3A~7t>:j.akM?{byW?-exU/a^nma;kZv(O>R`ЅZ3ټ'[}OܽխgSu6RiXy^g/WxcWj-u"<~\i,sV4c1+v@~~S=!/k^>kgU B8`2V{`.6dQp<ͤ?U:mߤa[ˏkxWLz.OkX>|o/pI 3T8n7i>7g,5K|?غ۱!Aom?6zE無 LP&_M:`=9,7cvffjZQh$׿l#Y1<`_\dRIa13>u(ϲ_ٲci"[θwW>clǨ!`mLyUo䲍f*Ll0o0Li~8\:ܧf>tnw{]GP|fs?)F'o{L*w?鍾nKkO7bO.4WsUIt O^KܕuxҚN`uW|`mc?$3/[P2Kgx򝘯c; CgM3 jjo+ A1J ] ?V#rXxg`uC>3~,?y}ܘp?;>60?meq YZ`x@trPZyo9^8~ʤr!8Mj@.0hZ-Z~m:R^xRG(o;oTx^vz3Lhq+׽\Z&zg~p~ wq3>kkk>6X>'bx]5y'SYNzoq w6pWࢿqQ`Y3:>lh/(m8pÓ&_*/P_ŀ1+(4+ײrr6oZryrwo|g]@ <}_(?,7cb˾Ѧ[+3J# !%yK5xOU4.?Hc&XmOk ))61';9q`śc;ǁ+v+Ӈ FN} _\5CV)c!ʭA}j*Ev}˗ w?t0[?%P[5V eL0l8rg x -o_ɮV s|f?[FQv7a X%l c[9K,S?0 iwװgղAgރun>^iuj, f]3pJwRy s/;(x]W{1_xC:صJu=7O/}V L |OYkKfy`VOwP%/bdw`j}V;fMjy/8!eaxa{ݿ>1uߋmP sMߌUF>RChf>L7 kOpJB&h-x]^w8x~.ay@S~Trcw{ȆzyG%KC7O-T+:6Zh ܙNעYxO`cj{CmQy+% S9/kKv |"0)/AhAFG96o;߾2|*ν7/_J-O 5/)W'[ [^ฑ`ˇWg juT|F`𝢰=} HCY\f*P*~KGo< +ۏwDntϭ8Y)Ny)h~8sZ<=1-v{]Ǜ&yB%jSYNEl}hc y- ^`}?;{,(Q*~gƅu:"O dW*`}qtbv ӛv*;G[:1gJ%oޅv글Y A^e^(<>ǘӅ޶Ch~S3P}GQu!M_+~3rjŗˠ`eea;/YW} 3ǜDKƋnƃ#O7@m8̽Nk{KK)'T_n&R{D} ZȆӒO,eQ714?}P}ģI%؞3,,zW{ b1~Kp.P蠹x5E$?;H򋻋E~ɢpYm0F#/nDzi>i8.e]Q_`}VrMwʠ8>E]~o. `Nf va}^@sʲ}ۂCy粋gYvp&h,os+ VԛC#W L[3Dt}Bj"+)߻[Z,hiXH^7SЂEhI奄&-4s4?Xm>Nʡm=NxlxІڨxw5#7lȢ`RK_B mG>e7ىVinwY$h]x{z9Gs= ЧV?\OD̷^TAOd~Z@'UmAM/ MC?t@XE[hi|5$yr4_g i~;)a?KndoT> t^J/ j~x^<+[;ՓDrL_k8%gP!ݟ8~9Ew)6P%}_4\>7K;ZtOpis4k~u<~m/s4~wRC*^M5Zxe;ٛSjdMjO&!3;>_LТѐyΐsѥ*4^֢Jʔ~n$:(XKW4~Ѭ/i~/hi]lͧi~]݀.Ȓx1_UOoBё ͟BzwTh7n>$QMWHLKsKf̥xt>nă_.g>[|FSR(s!)h]qo0O4v>A.*b!CחCk}"rH=Kҿ]ڨ}U~5>Q^{^"?ho11 O)}׼&GͿa N'7$:tY _Ո;E*G$%[h? {;\mH B{ шSYJ t+wyJ)?xZhKWL%7Bƪ7R+7< .Jp~R#]oφuv/łck:W|6w7t\8s}j4:~I>oчyRm׌`ׄc^fG>bMiUKv!ֵUSy;w1=o|.݄OQK*_λ**:]A ٌ֘=07;1|Ԧ~SWS,sUӬ&5|DzO7zjWWrӤ?#۬'44jxĿeYKg'~oE+7MhA.?v;o<{n g|l+q;J):vޏ0^oN>,b *C~("۞͋;tnJz1 KSNK`|Ve~ ŝtǁK|XNMY~^3leUΙRټߗĀO~/_uۇn)Yl]k]ؼS\꿊ͻKXMQ|Zc`xӳ8?A%ј`Mkhsy q`o,˝dpe5d{v.Oןf^g@ppwo.9 6tkНͱ Y`Wã_^{+h4wN'tt3_kW;(-UzK-"= -x9==كꯃ[qq=Ynϵ'iYcDEfSWМ֔qtd-89bO^[F?hQL~jJr-G3]o/_y ]:8_Fkb~CPkq>_M'h T._4gj H'#Vmh|:r)ܗzw]xVPWjS1 <۱Afڀusn=Z58TC<# ςm# F RţczG9(]qAH/ߓm@-ޖͿqt}l*Z%QHZ8]s~/NߛlcK_Ϧqh\JŖ֨y|L59 't?=oO$tCh]7,V rvT?wgc(@Wj 0\O-(&LzVtAV;_>e5s(c4(-+ r)rό@; m@W=7_яv/EkGߣ)}7d9t{g<WmhRk5o<ח^./=̗ %3`aq*tzC)fk=۷{bAzIւ+Jk?f'Zb ;3SJ1rb2Hn޽u,>&!p[?f%ӛ<}sz4,Wds%3W *Κ x(9 UՙۼK&2 k[3z:ozLm ƃT2;<+Y^Y/v8_lot8~˧A/]塼^%[j3ro[;7ƶf_^x͟[:%+Va;?li%;k9i#I3|+ƀ.W[J3 >+l-޷GۃufWiʬ\ 1XH]7ܰc[FMnygOy};vpׂCc0|\Pc.X|s5y3U%G\x V ̡\/Hkw,gh9lC9pGEj\_D^a~`<ͯU%]7Mc8{Q>xrp͡u-`ގ5\wNm|vpuܒ1"ϾN8]e'}Xjox(|xdSF/x, {q>,`]Nw X^.pjId%3m #PpNwvo^r?c&OƋKs߀?^|ͷg) )6鑬x8Y7<~oޥ#Q7r  `s/]Mg+'tEQdvɚCa1WjnU?[6пI6o #XR V8 3/G4ԕ-}uF|qQ{Z҃g~:Q1 Y44g'{`/rs_Ɓ娖Z~*plӸbo[MJ>GW:fg?~ο΂f\(掫!c}G; BO]FjzgvCjkq .&~9;y SlS{O ;.Ȳ~:=zsֈ+Wf-x#ڏ6#%c&Ҳ"4-vӧC-Dsgc FoSncJm}㝳&7}2׌;- !B}:r>fu0.41 CE'{B =,}_|Hr'}o\`o)|tI$47KLݵQ=_Mf-t=-N9D/ tG{RoN7B%ׯ4ڍݢHOnӺvĀw+:AY-G)EP{,7PywRI_Ms>:]/VrB S~AhSfgN=:٬ʣ)2?/.~7Jk$gv:\f$['7QyLS{SO'S 1}t]ނbj=%6 پԿ#ЊPȓOR(tiCkhsV Er5,[uI~=&y]/߉_(_r?x~]?ys547O|%ǒ=Cߋ_{A=Gߋ Z$YB#q 4?ğAYF4g,J?Ƈ&js/I§͢c%;̢OEF|܏43+l[J@UK~>tH+ZXR_:OR?#)G%^=J)גJ>Syt_W|?@z@_hQ^XEkS}DQ_Jx ~f?e%Oʓr_\^RQz_WXTE/VSEsWbG%qZS}{+6C.HWSFP{z a>ʗtpMK'6o2W(^ ^:ڤzz A[:6puE*Gf tԿīI0Rl{vwl.ڷpʴǁmz|AZ!xܓϿׁRndf5g.}_2=<Ӿ|\/I&KP6.?GkA1ts%vǖ7G*cU^j/_$&`5ǵJJO/}o z~u =| g#<۔6oo3߻fr <2vBoB0,3\^mc j1~i|(f}_S'bOϟ.99r8bmM^' p{~/*ҙ9F }(h&\xy3^BES5G>Z@MdSSfkwcf-`3RO~dgvW.Wyu kN~AZU6P8>~a `^gvzl-ߌ!"O/{Lx__>Әߘ崁;lP60:i*։[D7Ur<ndzX]?o::B./*NwwcʳTg>py|Ɣu*916-ﹱz]ځfk_f63Ź}nݾ.nNbµݧ-ox>xj~yڿy1ᇗ X nYX >%xEEfBV/gs{KU2g{˭Z <3[-\ pO2\X};U.9[*+P^Â8KsR3ê o[y8^=mBcݩ^dM>Nt5KinDv.}{%|*ʌ/u*/~j[L0 aO e(#ml,XjdbpS __|^?7ZWMԝw'm!/O:lIQAG6ԝo𴐣Lv@ ]F0p뫑{o >8CyR: iʜ ߶;g~޴薨y(?7l7$˰?j+θ~2Y/.|]6'2|Fz:omX8n`*[nׄʕI/6f/㳟o0{2Ku}cmm I$SƩop_M8Y12׃vhM*֯?gao}k<8>SRƏϜWG_,^\Uas/+k^rW3e"QӜy#^|9>n#0%]^~>S2k2x8WD-{~rkxv{W|لV?-ns;)m\`nWC8or9Q ]}`FI?`|'u?aGD47뼛kQ.v-=;;0O+ϝICSTIr>֍˜Goe e2X/4l奂fS\7\dƒ_]^|< 7{ ~6fc$S^A3fcg| Zz^3Swͅ5?Ηr`~fh9;܏:FOdŸ}\ n9 n/J[+acG +!q7N2c+/]Ϝot=?OvJΣ>7?dSq9}쎡*7^H6ٯv/9v׏J!հ*R/آ[gA].KeG _UC`KM+[7O YĝO`%Y;ܑހ8AkpFtD&}־xJ5;h>'nfm|9q Y{TU_eVJeN^Eԃy8J~{yW'0 h1Ŝ\n\$.V*ҝls#s^ϻqM1_dN>f2ퟕoz3KBM6łwhԘ&+p?)fjQ(o6; &!G .OkU9zlpcdNA~"Њ}byo? A OKSt0|7:r~Wh.3~_z/"8| ip~߳eEh,}R_Ɣ)=q[| Ȝ? D/Teb?JcJ߃@IVk58=Y3KJB`zn~Xuj_-}O"Ϊߴ~/4-0؈ɻAחvcs7O2o{ /;-/C nz[O ޞk\}sSM}aM%4LXƱ~kXgbx}L_|=hm7` kc~c>^B 1z w:CЍ/5v*ͯz=_oM:IE3x?/?$ -=Kvs87UJqrZlĄGIX~Rst^$Z}mk/D ;0m 5߁|Wdi&p~X:ε:Xpw<sH7cl@O}` ǟKE\i|X`yGR{1=lpFo8NW`[գX YS 18s146N}/]Ƙ{/_}^Mrf`}: bW|푠8-!=)i_k:*YK|BO=_%WGL9GE.hX>hgvOmeМ/ppF`=/_ ϸe:Ɨ'rg.s9x}k_ZjP=~^񗱏>Q,YkYyQqȊ4clm#8c@5Z ]pU6wSNZ&0qÖ ua5"Jfq2H8HQHQFH )'IEH1""~>K'#xG&YϙX[]Puv?Pщ`MuC|UE{k=u@~MaųutU"?MIW#?X䯉s[IwqY /{X~3wR41:3V4vѹȱU&,̀e[N?*Hs;Xz' &+u68wygUx Atݭ7W,xFxak}>fbƷ?>Vu^X5f1_x{u 勺>|a6}ZT7c_ r-ӑm*umغ, Rp5\x6jf#[Zv`xuߖeM>&l~I1sʌ㹑iW1[ t4`u_UOj԰TUX}9 ,'@_Fma/àRXx_9}|3R8D_f=wD![MGgMpg>Ӈ\Y?ဈO\ :{>eUppBoTG>~d17"L@G>יQpLn|wJ>sL(VՍg}LYkI{RaD8#͞0‘JjfoeF_őpV*voU~-ÊaQ lҝg[a7 ͤ`7|_ w`|v4?ca'}珑 /-wAgJ-{L?ى+-W<.JV?Vm+ͧ /zS&c˗/'G^OlL|a#b{(w4Cx̥+#o}-g{+51kk4{=ii|ϥp39M|\%?{k_^8[&MH|y*s9Iϵr}v9.ai}X+>2*xmI8p{0(wh3i=to,oXh WJ]&\(JigCVs*i~2>__{h?gi +\6#j!~pڝ#W_K 'pX% _o {taݙ0E?_ՁsKf~V0/KZM՜-Y]`ocatUpnÆ9֟WLUG*3J] cgi {'2i)~XsO"*IQL+Hv8.c6t-P x<$Sө¹s&ẊT0ޟuCx?>oFz{TqXO%gMo1<rlseSoh<vv^ %g8,i~CO +fѰwYne wJ9 '"wֆl \_0Clzld#)E2|ٽ/GL?]GNpr+eGEI^ߑ+Y1s" l`% dGYlOKQ2~כ[yêL +ŽvRy!_fǐ =oa#WBXŨcl/[^p(7غ.G^Mp:8-˷Z6u[&\۬],`|Ɵ|KmxGk~CXlqDXV!׍u;QX=J?p4 /-TKUhZwءn5i=%]EF!7܏?YkXo~ˢ,N}C0E_ب$3ޏn3}=,XY7N_,So,-R#΄5aam $3g百fròQ1_G<8+b6!lK8 g1V,S‘vs5`S~m, ˽G0!ز@2{ߋ -#n>V,҂}cѮam`wCo%gأc/xlK9#\ ѯZ{q!8Xl$?'ac3:ԸӮ~#`ˌYg^dx#ppxW1l4VO8߾(ɍa=L6 V9stg5If,=,_J_F深YԿ(4aePfmV0e t_a񬟬Y<򛼓oVd9JXy*? E 5J@z)_0k\`|p$ s<M a!W`3~ 93`g>/4^;HDr2`_kwءz1Np܂gbW*wuLjV Q.KssgAȡՒ_@5˸>h6s=*l5_uƣOgԭ;se ܎^+ 7pZ"/,o[9^x%Ra'_uEXs̓aKޠ%g>LF [~)od ]I?Nٞ54ײV@?ÑW7r<̇n0z6) / I*7NlpzǒYA.ltOE_fR8GT rnsM[grVWJzp_JI&Ytuhm {wg.K`OٟfWJ, p g8O>~\;W8  L J GΧ5a1yo [ޖ+֣쏨W>oJ(uUKY>|RS`j i\#3}4ׁ^ytC*1̯{*{׻$%'{[G߫3ğ7^w0=9' OqbOqY 3~,; zw.2ѻGl |ϥEj u,'ߪ?}y"׮6+MN?n֧YSR^ECNs}_K15vЛMb Ow(c~&+>t'åm<_)Hia'_~ s=K.\7mGdFOCU9GZ3-׋?w?$"V2K/Z2: חu{}gK﹊eؤq]>{23OR2پ1gF#mE߾i\s={dn\=>zH_ǀ_%c{vI~S<|0zKWWƦswH:Ryf2}:w7`|<dldpZ_+WDXuoS͗λp'˯ Ob7v +; L? 6N~\dט9j1f}|u'X~#+F;.xɅ{A +&I[۲j %Cpc.دm8m5y0Y .lbU?m|XfL:L~<*^r1FzQ":=F/5=2!׵q̘>=T~n4x_.ɳ{t^XdJ'rfṛ;yEGXvdW.Co "=U]f$(g4 5 x0~`7z<[E07U&'gSI sƲ>>p$M0915'N}&=ߓ5 |K'D>/m)Y쿇Z>x;o{+|5H 頳|㯰c~5o`={|`wGa}|p'l#&ǚ 9 3^3 |Xnl7߿.ݻǜZNGK/R-YNJvPDz1:\;L=[C{=nxw!ղH\ϦAQv><>Bg>AAm=GN2%֣aڰ𮐇ڐl촇oޡ>OO /`B}B[%XIavU߽WWQ&#tX7\I3`supjwhXyXXk N?oS+F#F~~;\ZݏMAssS`X3*\T~#"~L>հP>_MCJt.r첋# 6 я5,_֛uq==,odL2VV8laRS>{#2֨^U?&/bKWc&:+.6Dx wY_>Mi/:VSD 5k|64GEZo >q6k{ضYß͚hKX㗰+6꿽٨β)?ۡ~waVoȡ Wsj3m\#?~ηst/ՕƝt6s(_uZ0b2[4Rʯv^wg|]u ff))Yiٕ c_n-ҿR..V0~>:4 uvm:ηJRx lvß*#iOXYc$ύ XZԷ vx`ܵoQOlOXfτ 1;<\ kmn6_[4cC }r'/Y [  g kv[">, _Jj,zshNtT)|_sXn+Uc}66Ո?4 `՛Ψr\؞GpBFez}XͅV| e[%[a^j{(JGy;] +9&`|}B}7w{'c|+\ 2s}55e7ݡM٦X[ 붊ꕰbxcLZ_m ;-Ua˘DשR?~ | T=a-Y*g.G`YGֳou3pr O)#mgɿ;_8$w=/kT5{ЁP5g~Q,֞8R>EOeOf92F2>p 7SR?0Fz GY޲[ _{ ˻q1\NHE^G5d~ۮ vSiX/NsB`uERXQ~׬\Ώ?7}9_ahf^ti?Vw`ut˽z 8+]y`*T񪳑U6ωpUuڇx<UN_)Q>͝<&|{0%r<={LyLy׋c2.ּxNQzJ8>0Kuq/#xzF3_)ƿjH2<~9sm=oڕa=˚o3) ;_q`Wgx^7竅o]puA<`~{xAnl1?AsoN86,^zr v,֧h O)ږp +p萇)d+o7=aSLot?k3;azON~݇W߇eח&gOBf< PS~.=ٴq mE> VʼI|7zίa͆ *z#Xղm._r*ڿֶlkeW Xm/њeofs΁K špbX4`k9|*í83>L?Vey1lYh&l4-VsofCaف~ ?nˆeD}Yw|; eOʯcXQÑ]1 ~ VlkN[;_&Ϸ7zY~Kj5^ Q:17Wk^jZ_-_3pR 2gk>]aŏJO`MrU`7&,K+}S>2egay/7'ixt¥o?9~t+/LZ1p ?$綖uTң̿`:ÓN30 tSwNac , {xp>gy]-x}zZ}(9ʂ|a{_k%5%>N9aRbجbCpO%ԝUszMƟmM<ޚ&0ܷ q闶C2lϑ7 ۼkÑ0!Sؾ !Nz Q[&Π3\#m1/kX[Úzߦj p=܅K/NcY+ay>qX#6Ga|+Cq?%Kq2*M0|}8f|o5;vA6F>>ڒ zl߽v?+7cxH3'k>M7rl/ɀժnϝ~yЍw+w3OX `bޙÏt@y9œ+yq{-[ {NE[􆭗yސu4aO?6¤)H.8E w51\v1n'3~~E8N1J[*YpKnPd?+`(l3\53h8/3moh~_Ta8?^ -wPzM7Mdxmg8 uqs;2߂! WX<N= I^uVُ՝R9?+<>'>&FRTئE*5=ǥབྷaepdv2o s4XmX{Ӈ 6I+L)pۧa⓬}e~v8΁U۷[a-,\t;7fr=byc5K&r~E@2\{#LJ_Lydߎ֌|х6HʯN KRd 7%h>f)_ˇ 2Zx 8ip~Mg}(o~`kl_b[3X?j4hz ʂYj_]8aIWG*ox?kw VN1}|X|-XVDp~'BT?1T\oLg`Bipצ<ڏ7dw}ƻ,u+4n%9C/xVTPy\Lc V䏛Ъ ?g^HA7^k_#`u:\ύpn;|6+瞸fTy#4 tz,gLz .mVۂIK͍b8<}yczm;Kۯ!~-Dx7_ܿ>z-u.q%na].6 j\xX7}lϏOe?@׈ay?˼y_G<ԫ 7??>@xX_U/!Cvx"CUN{N= v tr-?]^bk̑v?XUi+BWܾpTla˹< 9֠TϪ[g w85T?]ϯO/Vzc츊pW8%GUy8$m7_ ;Eh2h M `Ō:> M쿃}s4WϷ9206]x^2~eMV|=& gy*!i6_u㩛,+: tM5, ϋ<<9_2~pX?X/YƧ0FUb}@wOT!gIlBCzl_.+皰 _ ?*p3lσEsM8IȎ;:M]Gx_n8_D{lf+Fxwn6P~G8_<Omez'+XQ lږ2o׃ۥ:.փ"-`sδ3\_:1?f35iE %쁭m᧖Ϯ](l, /Tv>);; w(vES/<4DRrcm%ܥ8?_UqOjW*BᵿwϽ(o.{]j0%>{㼌zO=IϪ\z㥊RL6~pKUkR#KM먩jj#a62pdo!6y>f~29s<}ɽx?6|"m=Êgă?r-GF֛?|P6׽'{{&9a1_&,g}vN|b[Z}ʆGצEzooߏx]:i>7M~) ;՝IϷ_cϟLwAo^lMwb>+L|o4/ =;uap/>_sJ b_7?p<=`L'oio7VO)B}m[aQ9䧼/m|:_y=~`dz*c/Ewjg{U׵gOUmC\sz:{Gd}^yso\_u>s+ZXUUMO}xsT~SIm>Uޡ7d~_5n4x]ۢ}IWNϫ7'9r-vګ|"wr:/l{Q|/ >:{7N9߶$YϷDࢇ _nةsVקwMd4ۻ5;sL~?`rH]{ߣd]Bc~G/Υ1=$ s@|.U?{ pBX:;̤Yۄy Uq}?Wx/m7~m}N;y:}nC_e1ou5 ^lmng1VN;0ؗ:+7F ,;]{'P kr2!fzgo#XUח}߰P8ώenc'{k`Uֶ|Z濶K[Ƨ.Bj0_bE~!/7~R] u ֠ێt(8k xUjs$B e3{˪fnZgPWY+ksD^;`w>!aX~ON_sF ֭^7ޮ?Q}osd}\?ϖ:kY"ޡ}E.UaCfz9Ӥ-~_#[i^h^7D]>|*䰣ީaU߆_Ri9~Ҩy{W:~3V_p乑=VuM^&arі;{Uoa="/C!tV5Op=ٻwEowm= 5`%?3E9>} Ic>g6?á p}[x}KоScuV'Ӹ^ވ3?A_yXy1s6MܤsX>uvYiD?mUwSJcUJ9iq lE"=Ym<~:{nw'*i_xVktXis Dc(v2z-_m/k\4?*@aŢQI G0w|[x]5ʋޠGm홏w܆3Xr9E'\o;^e7^ ԄwtBxȾ L/pg8<'C H?u}tqS;vvkk`.`[\ko\X=a.2X}f'q~ ;?G|_sǟ]ؾ__!'a/m]v1eKF%ُ߇NV2 ;е2p^Z=eד>`0scO91g_G65{ZIj`U1"[_~S4>HZ^?CZ唘 3}a|h?~;U Ƌe~uΏK0^U8\V^k]&Z~$8~ wav{~Ҿp .?A8m݇uCo5SF j[=8Z/ ay_n~k'{>+\'d+/-֕c;)=jZ9rcX}= 즵v|ϋªahspGV\,<{R}uW\ַja/nO^j=6㮴~E_? ?hV$<3 F2.iXBM?}l]x,ESN]V6Dmn~v6~kٹ[1=, jd_Tc%Aw=[LAxXY_ :?jY0b'%ʿSpvw>>y3=Ovvޗ!=hOثFMp^IʠZsr7yґSO^1Kxoɿ +0%l𭾏g1} ~?#wZ˄g5.I\u] ǞJ_ lja&7[y g3LE W>;̴*JO T;HؾͪS½V0'Nq9.NVw1+f$ac]} OR|,hlr]vGI5j 88׷}Sj5*la#{lf~bI|gYlc.5wg N] 0X4]צvV qi\xΪX_,oVFC*p4 a;bx:iWe׻0>˿Nel'zϿr5_AacY8;&'_&o>w3ڬL'4YWf`8ؤ[V~2gy|FZJ6fYm[5cUߌZ?Edy­Uiyo-8aUl_F۶~bQ~쿨{kaN_T8䝙}.rGnS`/4v8Fk|1m(n}vc?Y߃j SJA%829a!f u0el:|>zv _ bS4bAwaxtS nOmi8X8Z<[t%0tzbYʻY/2*RYH.)l_2.w+`5-)baq>R<ܴ/?E f~; aEdp3̿j}yoӫzϾxjEҷ '0}aG89Ϸayϳp :܁"Vƿ$>NˌJopQF*{Leyzi9~<`tͯ1< [ϣ`}#}FXwXGw|)˫w/Kq@}W%} YEo}Tf2m2k>Ekm?7yu@/X1oТ7y*-;+ GJv`痴b`V {'3`l>sad6,:&Zw`=Cw,?w(˫FpQGG_7W8v< ߪz7OG93W 887C]_3LMm7եS)Ć 2Q^<4cgqiemU֟8z^k }8Od䢲^L{lRi* eC獜O`d\f<o#0<i+=gnca|,/0h';V*;_e|}6+\oUimysp|\b;3~kZqm-J4&YaLRYIe2ֿ[q*e]k~*^5{YkTOF+rL> mjӲzTW["ltTJÙ 0;X=k''΄#W>`}_c?X2Xc+Yҧuۛka } v##Ryx7BMe#1<=| >x-?ʟY~Y՗-YR'ǗB+x΀r\C}\o|\ϸg zk;T{ o"|rt?~f_il|~%ݿiNߩ NtejaLeL?7Hax2`+|&+FuXGٮf?+XY!wp]S^g3>}fdXZ7`~EFϿ#^ hk2}|<:ӛ6a~ u͙3}i}4KU'pn*[6ʫ$_UU+Jc硼 OI7_o+~yzwƿgu,ѯ1_K/.Ō)k'=>u>O~N.(oئR>w]}]8Xe' Yr?2cz>ό>}H^5د-]7y2/,Z3cG*}7|wo}ͮ'|_8CBsawp߷lߑM>s"񿿦O]a<Ə˖bxR"gھdy~s4)ײ?D3 qE{aI{߰]]#o>["el%y}2\^!=Xh}V*^8_k΃#48$}27VYƊQKZv . T2dG7] 22=slY re;Y]Xv mXcZՂcYO8fÿ5byNcؿ?~gG3|p/Wv6W٘8jlOPUkWa|P濽"'f}j#>{{o~>Zg߷5}q)(cM8xx/}k Yfzs$c!]~2l˧ހs$ª;LQSʒ?6q_8Spi3Zp ֬7^*԰ k/ӣ;ᄌOhE daz 7'aŭ?~,+Le|OMf9C 덽ܘ!}u;L_`l78g"w1~$8xy:Ve.,r\4=Jg)9W-H˓e}{:U7qed}O=ޢ5ϛ8 'h?wcㅏ=[fI# N~+ _&mw6rp_t=m5c.|ÁFy WkSi`7uәs&dIwƁYFt}s  smpgj*'ӕoy%Sn+y|꾷[U}oa}UhgƎ 'uX:o0s?)>Nj5Sž k=yp܎*?u^ZZœ>$\]:AgJ-}_v|UtߏyXSM}µmOvv,n0&!xpI t߃Uh."jj,|`yQnVy%B~ FׅH|vvX,iL7~2+~}ڳjDžS w뚣;y/9vɖ4֍_Dq/jkx7pyzeUH™GnJ -;ͯk7/+W|߷΍sչlt]~u.nӭ/wOO}L~ _n3Whf]v'Ztv٣R o.^4K?>Λt{<x]-`^qG]> +^Esσ%zՅ11pX/NIއ=6)} c&\q3g}vSھ•"Bӎ7M¯SO.V3Y"`$lq+O~3]Fw~uW  x<ɭ'zq}oFwڴENٵQm^Ÿt޸4#iiԅl /НjZ<0Yh%uK-uIǦ ٳ%Nw9^8'+{kϩ G6+;ׄU y?}%kn?j=pp{C.U~saޯxٗ7kl ?>y&1ǐ 5*nħ)w:/'X`ᆯ/f {j5Fض$*Q' ;Z8fQ^[T?9/Ig*l X}pxӱc }4eZ녧TnqZxAc}H(|p'f{ewo&|fiv'opHX;mpAi'M;=^$h{%[8,@ܓa#'\Ÿf<3w5}^z՜ ֓]u[)Z{I[uv?yGnA*p~mly}µ!|t_Vn 8X_̴] G51\*\hh{>8{a뉢~?Y>_; ?JxM˅ý 7lj>+ p ju͸.n} |Mon~n6]rUVPu]yt߿ 0 +|`:)Yoi~I|װplߺj?GôY:VS~|%GtσC.>p.4kplL~yٵu=y|~&k1¾:*|rӊ0_ߝ 7;/JSD7 FKnG;)kw۞ /8ڰX̫>K}/u}u/r辧qto_­vzH8gMӄ'ovΘBxstwҷWz~ud)v_o'Wн(L~SwDێN/?{HS_aQʕ:7ms6ʰսoota~utO<}yp%u /iD%oВVNoxpo{uݧwyY0n£Ed4P7j`w2ypAg&8pV7Y/<^OM=|p:!lvekȾ=@@އ;O8aqG Glj'QnβVSOMWX>cs]};zf 7[}G!nortд <^C;h}ӗ—'nIxPLx!)̮wַBfw)Jk|{2u;LxgDs]5^#šSMwlR>ڎs>|H+\t@a]> /X^}?G.x$AX|̳bUrK)dԪV🽻Yl.rtLa^# l+G4Rw"@i\yEOunmVSggНw~,wC߈QMlrJ4;<+\SS_%7$eV2kWc3_gtST˽ύ~"z7k]b=}h@[>5>W&f /55O8n1I%|M7>\WҞW_$ s${ûmZxYW 7?,Oػ5[ G}r!F=ov--' /}pQH6J.⚪nzx9[-.#g |gY/i'M-ۄ?ϧzM_J =n ƿ OmtG k-|jְڮ S }.0yraOl_%\30{p멓 ߯~݆ W^>wp752y9{ 1;])M8|p>_ao[bW&i`ʡn8僚|}+`N_>zOB>|VxLx +Z|<|!f+O!YYY8ٿm5)_UЁ2Quo}>7 (ZP_ܔڛO^]x5+>q< F 8p߫S3d_ ?}8ordp Iv^PvEyw.]C7gy W)wu }{y=4a}^(1}x{|k0`p#^epF61hX~$ukfn.q}{O*ut5' v떰C1wG4sԋ!zc˄7yU{ՕgqkS?Ns,|YxkeVPx޴S+ xwx<@_jF+m:!|jKx3Қo@˞:xwX;aEsvVg[lG1CaJo ogyԉ!9rh.kt5S v|Ez~k8?S{Ω8?FVZW_yόF͖"XY6l Vc`7w|E+7_pvی:}Åk'W'Ӻa#$~f{Z݌/^OIx4PMN' wQ빉pM_h奫 O{9WRou =. n=_ٵ. *ayɁw ?h4RWm |_HH—NCu~x!u+UB&֧Oϟ#ϣ |!ӿU TU*QY |b|Hk8Tj_ePGq~|,N.n_ :`.JFޭP}e>uQ!vke?uRc(Ax&_.׊T;wUaonA*G>? 6Ex҈.9m[ 7_HAD_ٗ.>[Wus\t} { wO~VgoU5u_NwD+\Q&c/u:'*7Uz-<(T9z#w{ybnyj+x&szz0pMciOS_TڭQGGD&.o?s=^-b&ko"tW]x/* W0]p{ &:7P6@ŕ;GGAWyᚏg %ʫkravguExpiqŸ5*Nx,op5u2ȡb}5wӆo{ۅ^^x/JN2SMgӒsށ3 Y"pӡǮvvZxwf2G7ju/i_&UD ,!"}a%q@p+;7dwsRx鹀;S=g /Hi(b#_,{Stni3Z{хvrW=mCUy[m=jK߳iZ6‹;Wxv;?Sol$i1Ѷ_~4i}/\o]…G$?A'7.24'f˪HމrW?Э7zߙ% z`pqĭM¯οb/ɡ ]:*8Axun]7۹=Vm#*woE 5wZτ*]Xy+pkM.3p5*ޕ6n _ݿ֨0G8*Cp]p we؟ڼ+Vg&kwTWG L\(\lR{n~+Z& G,q kYxj9{|S&O^}5C.T!\+}EᄃN Q^G* ``K / )lspo#5^5y '1}qg.C#< }7tEՍ:T_!K6<4hv͞Zĕ‰7jl9ZЫ-첄u0pp݄/w4u3S^`T&9N>Vx줵utM,u`.~->xσg /v\xW}w4ޙBw6Tmjd}Kvu?Gr; ;r;Qc\γ1[nl|Ͽobwyi){5[rѝj.0v{a \9\8Mj-w> G0XF%Uz/>ڻ~xcuݮOb?]Uc߫-c >ZMloQxN:ڌ#6z1ڢWrWZ~Fp߻լgV=я^.j̬S/}Soa۝5џ_7>/+#QOϲ:;(W9e)F|~rVb}j`qf#~Yױ}aОQ_6k8οԈywz$*ܤnA-'ʲqO{ID=g;]{E}RFS`VX_^|uxr8 CNp O m|xKKf}O@եtＯϯ>FӊƇp=P۹df)룗Օ q:4VwU/d}<6o0"Z,/pV%GF8߇nOo:X {N&sW? 9H5[D2~\P_+V7,=eJ>p= {Hưz=Vu{`gj,m?ۏU;`ŭ\Xv!>m+?®f=b >0oi9R{3KX#byM"5q7o+X^~8+SW<>o2|=eO߰?*B{K?ǜsg}`I#`ٰ{lO]E]7w [by:r|ާes1|9pʵx?V@0Gͣaϳn{aE(^ V>nk}*rX+Wbyt̿iW#OՃ=_ s!XY=2? VLzV96z)NؾL~_m`ռ,)-lGt[l龖1ַw'f=CU߆xDu[dx9~o.hODn9gV\mnp\v"?z{7V2lBc=-hΧ#8=kp = koC{c2x̯) jk~R Wꥅ`7t#p!wZpypT5?9{},\ g'>agg%=Fo^!́=ǣ1ڧ8AfkG¹~'_d|qFDzofs e{qQFru~*s,ҋ{!w~N-^5S~ <o6S=ύ|T(r8:ꟴ~/?/ _"6EmqND}#ʣ>_MP7+? ɿsias/3?f͹?-nK=^H;v]̗rPK`gZy%\y9gҪ|?ӷ¿YiM*hb5ӻK/ERk>+~;gNPgV>iyVK+R?{{xZa/oҲw?\ sRW8V~#:=reh)Cl zCo{iT)*7mkfGY>1> L7hG| ^ǴʅN/5\5R{އzd}IRX>ϐjz糬ɵ9?]u 9m~V;I%^``{=h$g-r{\9nr+d:zi|]Lm#U7_R]ί޴?6|?Bk-hy|-V6U*M6g̒< |Kw޿i^:D+ri ;>=sx-7Ŭf'gWpS=Ii|ꇉloJ82Iqs^KM.S#ۻO1뿞G//[o,mRד}ؿ6֧OwMʟ"V qa]8\~1gzNk'f03tgZkF{s<}z {FIMǺ )|a7[2ۅ_ q@6e:ҊmؿIk҇|+y%-/omiepu=$k7D-?GZq]_րT~_O;M??0(Q)!Rzm5W,7 R|5I'쵔73)V-GcAR;XoToRx38Or?˽:Vރs#a t͂-=w şzi 8T^@^zw"\^?Oi<˿Wޜf0Z{4~pZ)Rl_4{]{_#mvz)OWvkǗJ=nZl+PZs _ )=?EMl Y~*Vf.կWZ}ַloN WѭK左i_MkWIVNYU 8~}ۓeКl4߼X~~;~"ѿXgp>4{x0SZ^vwgK#Zl ש"?e'iVLcb+/u|]tϣo(XT#۳VܑR{p: c-`{K/@Zwt\r}˷^ Zv.ʗ[ƟC+ӥ>KIr5Zك k#yi}Ҟxj%J}}ZY҅1tߜh!-~Z])ܗNFz!h+7ɒ:Oe[zIt,Z{@77<~:`M>[ Ge2OLzˤ//iʔq)`kBۯi='ZeA R hLZo>Fde:9.BKMiY';9+ϡghR/mAGV5Ui?`Tv1rZHs]ֿ~MКt-[IiRiEiC\KM^g:3 c'hn HmE#x=m%?XTiM7ӢQB I+^Z1B+H?]泔>Z[η&tl_u4U[@+~ChM%* e½Z>VW5ϱs|᜴_CHK-Vt(ZIxi.$-wG%مvvUi6=<_lOlG+IeK63tnLoHUohyEO3g1Pr;i&fOKIᑴii^b~/$_5%tS|NIׇ1DZz/h~68(uie߼Ʒ!Z37g~%'%_iu8m.9VѪ903ZN~Rw֮Htpy)}>-{ps@rGh ?OZ;H6(gzQtHZ6)_E=.:-IwJiٴ.S~{Kq1fRM˞f I_i[it>uOktgZ݆Hi?$)7RIliM7ZA+L;#_q9~4^y^pDVK%gFiY} B,Uig'Zqn'姕?9cK9ׇ8|? ~T_~_xg{"+wolԚ(8֬o4׏h={WVM0SvDsI5ahφ 7>XVva~Oq=оvCXT{7{rJKآ~~5Fy!saM^7s܃U`fv|Igu}GKcay!?l3_V0~q{=XsS]Xt3(|Vg|xO+:7rՇ#Gٲ!K 5jFr.}% v, YRw Nf}*̈́/18Q'5+g8 [_>?P05l2lSSWVF|V4孿zY j+ӟ_pA1Z-F!ްWq&ː:VƖU0*#&93Yo\dEp@'79c!oz,\e(V?Er0c<ZV=~?'Ҙg,\8B7 ?z6tǎV\x\ַ7WG*?[S`YP'j# Q~Bݓc.;ףS=9Vi\nѾL_F wpX>|$_>r附E[mCU2#оJv" CM0j:Ɠ@?m_^zƧzJ̟;g?1wpkwa7|Ou [ps~;Kҫp=8.Gy{{ t_%Í \S"?7V6dy=x|Fyr2oň/wٹkia{Cw #K3O`$ ;p*6f}&_z%<%ʃ^/ ՚p8fRe2׻ kLԵm=6p Qp<u '0~9\O ٭AUE1A\GSO7yVG7 {Lty(oًCx{7ՃY_ǿ>~!J?W9 Lџ=8-;{`eG_6' gao`hEiEֻH<`//1>Ty/x]+MV^/aU`!c²'9[u-?%(%o8'ΔzWI?f6!{߂FY_YOѰ9i>g.aе\߬Sa˥9<0 /wkj+8`R5'>Ε_\m~zEw1<.;2~hئ?ǗbCK'&A=9.WsaU\Mz8z#~޲NyzeGe񈯔Xeg_v񽣾`3y8&Vyܧ#?vX9c{".c^1_McU0_>Nw5FvZ0$<Vmyd|Qq? +k~sfW8WͲbXr_osT|~y>qC|ƣ>E[Wytk"m&p݌w}am_)ޏq#+0l|op;30{wbq̂5j[c"? ]pVvL0S$}Y*K]<!,u {`缾UDkzFi/j=aͬ%p/o\oLarwf}8jc_^x_>6wb'}mH#,a~ PX=VayӖteOU\͔O&۵8VSFLQ8cK7{Th:;w} 7ifk 1S.OoWk_eP?*Q874UggG±Nw.9xM۹)|8T =tTWѾlOA6\nؠhS3>?gEۼΟ+<ᕷmާL*p;O5>rXqk*|w O;$vt֖1 qHۑ_''C[k*/YN4|皿p^vsŠӸueE=<[Y{^MQ/3}͆]"lfg. w]`pwݽfxyhj';{ 뺓ٿ"u`Y |ݔn #gGU~OcW3)lomX{"+)ϰ/k?=8_F?z?, Uő/4وyhO~KXYpzss/֌ `,f/jלSgR_1Z \[1][@kކSV(B~Ygê#WkCxøj KЍ0BB{ z| 9H\5Vm܉pͣnCx f}<#v5([E~cߐެ#; 3re, W}~Mŝ6iN7oBS#y2fSE}Z {_aC>#}L޿Xmsom,>qgry>HzWWa#_ v\9)Qxmjv^Nְ,m]w휾&GjX]ocnjlyP~aos.iZr%\˥}>nj;rKk dU{~d dig=ػi WF:=!!b( ,dI" 6-c{dKL0AKDAl^quιεsuݖ@sD}EA΋H|j(U߷mxU8o[З|ʝoh_3/%oڳ|E&_L[e -Inw`}{PZn +'4`Mo:([V``m}K ΙTjXZ S=`u 濩ll߶e)kL38gWh!D~urt'mO<7_|5vWbxNf9[ X;4mVV-I`(ܳG ش~7>B_i_0 /EE{wR.?\UlnC}O-~hb88獽X?+kym.3*N`ɖzotOpc}H*nc?M;`fWKz``Kf{ 8>hxE=kiv9ߟ^UA\_&7lU䁜y7u3AX䑸^h`{x l\Ay<ډ05wd9_?E`͝_ m19ؒvO O9i7/SLB[exJ^l|IٟCnڱoQ{9ٖ;!7UӞOz[EguX^uK1~No8_#ʺ>cR|8e1E7Qwwjd<NjsNe]lX_ |Je5_"IJesi(_R߳YS̆Pލ\N Gr@\_/yGzD"X|ϧM`eq^?/>i%!ŗy*>=sL|ݟ> }uQȇA|m _b{$Cgoz`CTJX-'(vΏ__S0t|?ɗ;p(_":F`2 ,+.? /%˧sW+٘UWO{AvX 6jٞ=#XGb=^ V~`I:r DW?%X_nG|"_.u`_ìo[V}ey96Xدi+`x P~5E?]gOB~@yC~?O+'"^^Qқ}_V6|+U:jDǿ%uJѾeش4c{ɦYyٿ~ӻr&ޞlûk{`3IE~2J[êҍOی56Ea?0(wT?_5{X߰e9ddK{ita;vz=^KUz~?2"4$D~D񨪼ZVUٕ}kG5*8 N'bWhͪb}1|_#׫ 5ɩB?S\\<3/fCWbsRL:,?69(ѿk`C} < NyiXUo39xzL&b~?E9K&Ӟ Fhޓez/F8~>_R둹Jｄ2X{~_v[WzHq} e_D~k i?zh47+U~{fb=v{I6ĂySRϝ8Џ**c2{;pp.q|y>Lf+Tsq/i/r 2*[)~4E5Xv(}̏BOEV )#֟ԟߗOьg Uz?d9j$(j/e eqhVA_=c{E%`oE{Iɗ`ror^pTkޯ2~Aq>jY;?p3˗[ V'8~EaSgqb%`kKOUje~j{S`W{ʝoפl!^?c}xG[/}*m*`œuc`YW7~ey!oFLaQ~` pHR iY ǧzx"2'۬y=d?;{t+_>kvyvpi4(S?"Q<?6<(O7a,_ y֖=Bƒ|ʟ|=[ԃ6_ebџYK|0o+ȭ0жߘ\0C@>֔Gh!ѝ$ڲ= 괚?n4 aW%{qy* Xyυmd9_Xu2Dʛ ;㱪g!&=gJ)>lX6oD6O<~(w>7c_ϡZsg h|(Wz1|_0A |G}֏.3&97_%q$$͗ O +sFxiRYVe`E dsk%N⒡\GО'٣sP_٬,ӞuGe95~ {o}-,xΣ~hG \d)|/\.~c_TR_ GY y= +uI ;>?5xʻkq}p8#|^g5x6ty]B?dxq# 9D7>:!O csR!^8]!n\ko 뙥Zr&:߇=%x^yG{* `~Kv\wm g]GnC%8'*nMC^ڿ?WWxib?|=;a*SRܯ(R4Ғ`SXXi<][6yK~{!_69د\/vF3 XS%| X'}u6իӾ8?T/c'%/_ZO"d /E?ead9+F~8?l8|%4a!#ܿ9P̪۳K[86˫2__v=S{pֽk8Osg"7k,r oYB~Vn><?uJ6i,K"~޶=> /><RϷ=} ewbB~yUC+#~pC}}@O&-?%g %ߏqh8OE~NX~%|N;&? DcSɭ;ց5dS:Q//X1a6/Sܦ`4iZY)>-w[_~I`:T?E|U d#5![v׬i>I pƜ݌8ɲ_WV:L3'u/`ڛF}w8-5ɴ}<`W/:׌'e8~Q=Vzuς[Uw7WnQ4|zAE-%^1[lGKQKJb:z[Iq{M:/K{|Om5ww2E#$Xo|7cz~i `Ōl,?;5)c1 d`X ]|i!c^|:x 1+CEkcW SH<%/?|p1^̀oDڝ h/?Y50eZe(>.o:}6i+74mWy߳ Qks|=9BOV[V70r7ˮv<$γ=4|[st]O_fX1M8c|G+ ;`}_l=݁γ!++|о4WKoGm'Z_Z`kF)m(o5\X=yZ_|Ý,ׁUOy~ڥč_]nfq\B' ./>7LpN=ǂU+o^`_۴j럂 `EsG^L U'΀-y=Ti ֓Vtx{BaO!hܺ]!\z' [9>y]7[޺zǴ9}!獕[+<Z T+rM}۫r :v(ۮGU+y_*ARkteB~.\졅 ԒrLܼon|Ӛ&m~ahB.-do{ϴf];1y1xÔkLBNڤ :}^h|~6Jڻ|kt!z{qB^zɐBN󶹗Yzu trғ>'ޘB-i |zwkB{KB֤sSȗ%/r’NBs\z`0FB<3یB~y2]!<~Q!cv0  ä]?n#b귓/+ڂĪeyɭj5>|X7`xpN|^;=z'v:8x6_yo.څp RחK dl3>NѨ _u _\=;<y"7JcXމ3L h3}Ȧa3[]lxWڰ>v_vI_yX3ʮ||mߑox@?\Jo>-|upƇ|$D ݹZЉqڐSU'VnE{?WcSvzw *aF}/9=Ūow0#?X|M}oTSD5z3Ur~/-V7}2`I̟ёz?U=uOr,oVn~/|AV|c_Zre\W li"\a^ !?YLBJlE{K#~M_W߆l_m`Ȣ]| ;p/C~ l8$xlK]gE91l6N|u"Xs{x 8ēXf7b>뵛*/98<8ӟjȿ`d7'񴯳{2A?9/ο5[)g)>E_zpcQ?Ȳ|pαW+-f&<1˲Sa'EAA|`۾ ̢G9;S~1װ~['4`mG~Mds7^ F3&/t I!$}pV>O!ym)_7lIuy)o'=Xە\'gsN~Oo悍s;8vgoT33ysiz_FgyŚcgW,c@*c{4֯Mچa9i+j3p+o?lYWƂ39߷5y1sx߯/q"}Z?Σ}澠~T>gj]@{ZM"?9osd`Uk`eWѹ!+#:ߤ58g@S'> +R#u`C'%_Dߜ?9 xݘ&;fk48JUO|K9ؼW>̯;Yw+\3VwX\te߾dGe_DGjgM*w r#GK,כNbNwm$zy7zXXΗI~lě\ k+.|}XwoTpFt[ 5ęZ 96Ѧge%?CѮ虇D}-fPiQ9yly~gq,I.Σm=<{7 ߋB{s Q^$XqϦlzxf 5Nn/8SX'O\,w#'Q`E_X3UfMYGd C. 9I;|K5}ƒz{ ɽz %{ vkN'7VW]!UdK+17o'WقhNhOͪOB>[7߅s%sy{&zg W5/Cݿ1XlHϓoEn4w.8"a' `GWx $Ϋ'8xj xRߓ څ߇h?v 6Ix NWq_x-7n?q|%ҫCu_w"_yYkړ;Z~M!s+8f?廯gX~IGgmc IJGK~ܨ,8X!\?>䥇ԁ\v_1t'ƓN+6l7m~lW_mx6_AئcoȂۖ]пU d< ,щm?裾iVsL3H![rr`prv-/ d}~v)o- r߼\P9׬@.[s7V`C7+`l'Pg'hO < @'Y/o(?Ɣ*|$D{%{s,\f?b YG ?aT]Hz ~x67_dQ3ܱ~ *Vށg_~GHeg l%` y'#Wy%ڼ_.Ϛ,`CSz`vq?- ]3-3,j֏WD<׵kGhϳ<'IRިTȽ>\`1%_ge|7x;폐Cw8K״3}2gk~͗Oe_@-_g?[nf> 9$UhThz,3cj/+wιCdr?k]pf7WP{Ҋo_$5E{H?; P[d#"K̟y:GW%M/mU`G"?qV?TģL>C~1bQb 4`^":MW>Nk{CYܢ 8yB;| bMXh Hv4X{ؐmxL}D#Z+x(u=UC^ګ_,덡VoYP ga>yx `y="{v+>QGXQO-y2 Vk9o #EƱ#o`V.9U$$ad!ߕ}$/y떱g.daq%V5SRrϮ`E/2=)ȿމ& <|"yZr0( t c\_1/hVo6-j؞ )o 3;U:p~?,yEc%ZdE(g`XmR-֞Y_y9vp% !< 9\I~KY{j/$$_j/.?ug{;Tv[֡:y#Q:σݜ vs=D{g&%'2%`oy<ܟﳅPnב_%+Y\W V-ɷJ#Zu޿8^Ms޷{d` ,OXp1Y;C ;3(^N|ck>m#kzJ_~j P?K]aLWr ,pjLy@/ 5/6;Q_#q1#k>Y^Va`i`}|X:d`c3M7ryHw[>0WKG-=;|߿FX~(чgi`Í\ڎ5U "9svp;l l[~,WC\װ=|+U!)؟  ˗xQ΂ kS߯Y~\%?/X[^_$#Wíb>}/:?z/oh,KN `w '_Fy|vXչ{EO/L>1_38"`}9jv-9]K9#{Qw`JWX'8)"kK{L`y`37.ϲ _F1|\sO(swXp< ?y:bg1eV#ߠFG]c~F~+`eZL!w03Dsy|~MrƷB4׃eޱo(W؇|fCN<8*]2ç\g-?$ř8X38~_[F׫'=]?0Xzz}{`]Oo}`mq_B"Mzv>q+=`V1j`eTW} s_D%ߞذX&oWy691 Y\-םH?E'8~^>k/C[p7-C~nO/`CxXr*גQulCEV]Մ{WFv1^ǔ`L' $B,LMy'oEug<*(NqTgRoΐ'C{fwCگ,`œ5`.o|O0n1u?i?:د1?[*~!-%o>|=ݺw߿ ^>f rlygbdsr r| 5:p]}mD{W>v|x;Y]?kRyQ%omXd)'k3~`ǗB~%b|;W/ưfq`E/·_gt?*Hf R e}%_3'^˥)ix7*8W]s'38hlHˑxZ}ʟ t?s>oD.S3&X]W񾴣c\ougoǧ\_7y}(U ֺϫJ鼾;y!Ǫ`&U +hPr4vIi/k,d}ROm6F}P?gQR?:l:Փ7읝Xl%;P_Ç^R&7Kf}wE~yr_Ǭ@VP_f.XWK0񛯥|gA'` {ϢdC{s?q!e?`{Kv?yӂe?`fr#.;yY˗ + ,D6ve#_g9`UOpF,^ʹPyJ1f,Am?a# =iCPyCg sOW h|3E^K7{x=d~nwǛ E!y)6,著\ϙ?F;o0Xɚ<72s& Tc%֑o,[$U!枧}\pƶs%`곴 幛)V;IC`Ӱ1zaCc/{w|8IYAKgR~,X2Ssw^h ,s,8vk'뷲;TWicVkIn@{,`nͲo6R"?2Bx}n :`à|ng#_*_ k|^nɇ^EOg( /˓sUW8L3 _ظ"/^g5X>K`N3>Og$ ?=K<M/Ǚ:1=+cb/c?C4\dS nZs'~5Ǔ8BQ8lܟ4|E4w='ӞX5_fb}qgK=e4zT,_Xo3Hw2X?S[/ϐWa;,J< R;8Z'f>Px"?9_'"^h8VחQU~.3X^N)3bS䃭X~R+Sq=8 bJpHqx?Y9F~ aEK)?]D[Ɂ{=,p\r 3fe/naJ325ݳX^8|k$D!gZDy-#9 /-/Q]Vx.T_|ƛo9rj鮔wF_wG N۪`Ih9:ΧV8_#*MG{~?4+K _/~S2`X{\;cG)63 ΗNbh@ְ>2ssIJ{}u>6&7IRfpifp`ƒ<KhqY(X>1l6f{;8`EȮkQ_W+ueklWz9ϙ}kK{ځc5 Eg2`]'v|P/m8eN_ U%*F|eh߷Ei7d %qxY?{3[ƕ|lG^9 ,]!e+P^74 lvl ƷpԣMS6d`HuwǗoJ5_w/FY9mu@?39%+AnO(O a|P r2eo`~[̢4+r,/\|hEn2B %KoZ)wYi ̇ y`P 7`{H ϯ/ ֭\I=s<ԏgOd}G]K 3Qf&[';$/5bJB}{9,6,2γ8~Oc]i&ԟo7kspȯ.?.8UH`|g _q>߻`sz$^'7j'f|`ˇoA"?iBKGSE<:d~(`KÚ!Xdlc'_LٴhlUɕ\RSFrs;a7yquΧJ(Vfr =xVe|fZޤOQ1BQS*1k_o3_+| /ؿrCE0m?2s+*5l<*X ymSA8X+V]Hٳ5wg>ws5>Y۔r_1,f<>O`yq}:ZW2&偧i_\/_6)nRD}s_=G򟓘{VsC? +~~ˀ5sn {qywIJtrvKS=9M]G{br^oOv_ň\_%Cx^_ږl9q}:V=J;Q5ў&Wx+}*qj5 N>CS!5})̬6Ki&s?A{.9`U{[(Pͦ}XKT`nOg&,OU[E?w1^o.WY?`ӓӌǣ8c9,Ug"=,q21VW$U9,_ȝ]˻nxX2H#pMum.ؒ֝c}ɭJ7ÍdCiog+5F X?kG|Jf|èqOM{dOaͪ.GwD~M>y\~Reؾ[XPwqk`ǹ\_rpWʓ8qGS& ?z;-91`MyVu_[ڿ|]sucNbKp|+𤽪`MK9Qfy`QxwDK{Y ]f-;}F= S})R!woBg;r6R,æ`99r_yq>M'G݆/e;{X\OY~2G}{pTLs-|ȃV1Ycg`$s=L)i=wpFT_ K52i?m2c1+jr=yLvNm;ޮx>7`'H-·*`ݲ`x9X6_ԩFdf~3{ZY+]pb{/ד;MjG;._ 6< 4f^@0`CB^61o`~& SQ^nD1U+ `ͱsp)nd=Ay; f?VNY۪.^A]&Al[-\Y,Ѓ\RGak5vRȮ  * *qpEO ׂ5C(\eB[2ګ`~:yBqe\0EWCwh)߸Mj.vP*[C3b?[YJ^Pȁfk `9xܼnƜ/A<)[C_Ȗ 8x`ƨ!r5O-)Ŭލ917vO!j}!Vx[ZPX/֭iYvһݪ.uinOjum!4TS"ߩY\yS傕pj ?6m]q)OAZdS%Rz_˓8׍r]:#ohiodvM-1 9B} %~oXǭw|o[n(,S(q}@Ip¿JK$K J$  VH;J$NɮBc jH$K 9K$ v*X@ߞjrL|Mo6>ZUrk8F=_97iօ~~Y̅3{49͵:/M`,=R7ZZׯ*ࡃ9ع(ڣTmɰWaCfǧ]Qkf_gd?QW?7kl&4|!yƿOb6a O!VSE_0f}FX>Kh^wl9Oy-Tgؐ}(8ڜ!OܜvRD_mENjJ;F2'ע vBbC_XwrOG97k^%ǜ'K$[2r`àD{3`չ`ujOs,Y>_ 6+M"[C)a?HOkK?[+!CzGV>`UR4 ey)z v X>f=ejr_prid*\=aGZ^%"~_|*Y3 m}x8|PIQ,ie%X^]8UeJ!8#O\?"ND{4/Cc[? I%J{=|,e|q'i_ _#E~`2YOОj7ڲ?5~)`j%CmۏK{m9 b< ެo7+޴ ǟ#{sPP*pNwo^%{3_Kџ:<->~_a7goeᯣ Bycdk!?d{)cBT 턽x`"nx"Ϻ.w*?iCڱYwoZg4Iqm8y?EN?Io6NrbJu9ؚJyr)/rsx֒I;][#; I-9$ߋd3| ݒQ8Xm+;҂է>';laZp;I.`94ؾQ}s~\ 1[ITrLsR95g}~\_fl?ʯnۏr]TWsT1>qOJeՌ,-o|őkO]׏g$Le6-1O_/6'[? O<9?6:)4e$';m8|gʽkN3_-[O?4<25z4[س6A>rf}7azՄ1Egj&uD1箃M[sȒt!/"-BE&D4/9$_9#gΰ8SkfQCnw Y{5X>l^)*%(oN{Q;&,;sScK dfw[cϱ9>jAr[|ފdwKn5B>%Zӈs{A`ˇU{lrڋs28 yhQ_8ʋ ;>H?R1Ma] {K{UlY7@|^Qp]PӀ!{wmg-C>&]N̢c119SiNE?[%xYLL"L)<[%e \`Ա#߫'+O{]MhU zMVer8W%K_K/MT)ПdSQwSEʟq}S87ҴG}ik}Wr?OI7_̧`dvS}޽=l9چKjrKm Xe(TZ}s}Οm?+?x#}Y5]9h8Aw;ϟ>cu`0%{L3r`/C(/ o"7M53^9#ȍ=n41'NFUլ~`iI'JѬ_D+pa|gͰEߡO!))U>XO>uZa%ؤN-7we}y =BD{?i/i4YN|w qNQ,=E+>uƼ~% XZcOhoN>68vMkRLeR; W?3oL mL;6%7KKyq`Ďk^?zਐ>`M-OW۽WkeAyl.I+7ׇMvSGi8kպ/[Ck}PD3' 㿻}ݑ&6 y":~{5i X}=yaoq|,uNu&p2`%58?T L\Yr,{2Cfuio 5N,YzU`{pH6`,:,OAVٞi3Xru,XխXf}r_WW!ؐ:T۠;L>K+X`֕/[ɟ2Z߹:젿F+{Xp!N[ yFy^`:%rr~&tؑf__b yh ?D8 ƛx?~ Xg!أgLrsQ-c- _6Xu ù.XlJTn@R kq|Ń4\`|)=pOadcb~ m}P~ OBڳ~ugJplc=dŶd:,nl8KM?5.Om%N7*xў*וھ[U_=[㹿_6y a窫p}Xxo? }pբ} b`YV O._ ,%w7J= # YٿX=_yo-_r!`~ٞMpN=CRnK?q߻ :΋S~/5K^ik"yFU Xߞ`7_ CnW\3Eϣ2O^qtͿLnR>|"X=![/~`نԏlX><A6E{^B (l$XXRx֔ShyU+ !W+_`~8~.prsu=;-cݾ۲}7aMTj+{kCcҡ`.7lדM.}E}JSlD/1~㧥UQoڎ!dY`ʱiJ埚|\X!Tƀxrȷ 'l?k V[1Mڂ_nVih ou?n2rkGo pF g^vq'glXtmz.; ;xܨ?ۀ["R}K5 E{|(m'Nyǵ8%K 3 %mڗ6Ê`Mntϋ'wh_l2X>홋5)+t*Y:>XA NXR[оXVij '1^C"dCpl2},NKS l[ ,`%}ǃCC=MV >"!티=e`G_G* PUҼOQff+Y*8"gl(1l^Lִ [|~7ٿoHOtJ lxƁD/7%8^'^~I:8>__R(ߜ|;W,'ْ /Sre8,b{iO lA̷.|~:Y';oXQI`E dC;`~$"7z#/)њɳo0X]`;/FG,t@PXu95O伯֏YpHy:pd+*m=$?i ˯\8\ޙϗ8#X<Pݜ67 kF3"Eub=ʊ';+)F_7b<:~iOk?*zbn,4j8mr`}7-)Y"`]$0җ iX*XM_^m(9woo_?m5i^_gġJ!`١a}Gv9QO{c'_ JA~$+^}C`!ba)mw,f79;ƿW_|s-?Xփ7E~̟j~. k.T]cpxK%XW'b='p9\oag4eމ*Zez\=8{^O$I Y~0),1-[zmnmAEƛD!ᖞK'+~`)_ئV-_+WĐ ,U[r(5Cﹻ#o֜!փ`X_<){:l.X1(1]҆NtW?A:+Wā3Qs:,IA,MgC|!1_f_5*+1^b>$= lC&n|_ Fy`0 My6l3'5u91S*J/yI}-| .4c<]x?_UX47R?74oh?f8z@SȲXpz,o ?҇o86/#/RYߒOeyduM|O"d롴Wc4Xk sFyb?v vܛ+w%P'o'R5J['4R|ު8lY Y_lx(y.>O>jY`ؗ}>S|>ܮ<Ɵ~C_QMǫU/kS.`c4>x?d$Xy$7|k]zVO1j`Y`E\^ ߧP~'y؆N,[7O{`|8D- :"ngKwߟqL)y^1\Uj=Zux?'ޟ &R.\O:?ӄ8>x.S Wfo^o]'SS-ڱպ~`U})`{|3ﾭ^[&>2?W ׋&9}d ?>ןijB_O}BQ91w}`ygvp}ݢ?~Fz9(?,m`}>< $$xxVKz]@䧎$79y3. zETm'c&ߗTZ¶شՕq`uhbm#&l" I/8*˟7pLIӧMP5h_31lDeI`i3?ҫ| WckY1_w|8'_uq./V.`ýW,:XƓfS< &K{a~8`;'Yarĵ/ŝ-S}ҁS?S{^*l,{y,glOlwc]xmlpSxauxT>~wܯ%_~cR_"yN5uYzXH6(9NBn_?}Ѽ_s?Q>z18V=쥽'Y(s*X ߚ;^|d[۱K73VVΗI^Idkwv$][Dy{~zm͊}L/2?,|&S{73&we*Y`_!Imi?2b<<}Dly?1ׂksԤ|m޿veE_~-ƃc/o楸NרϹ?D_>t-Mˌd3|"fy]#<_HN8Jy`G^#ue3o\Xb^y&S-=yM΀H;߭X;o yHXWt[9G})wͤ~"_ ߤ_%?m6Z; l󄴏U-}u<_ȹjDEك5&u뾴Qܳ-[UׂT7+x߭!rWi7,|\چs_풬ϴk'KoORAB+dϣ\XӲ`ߏ rF16_iKP/?re+G%R^=U_+rD!O-͟/,e>'=a|[|U{Z鷸>Y|~=mwd X1>Upp|\6i2Ț[p?ܟT͝~оƹ8#%詨oo*V^ `e6ڿwQw.dYƳ0G<> =fߢ75Z| gg3YʪkԲtʧ~k{ dz6|lr MO'GPrXL^NH#XqF)|Fgm7m(?OcRt>t72_( =Ze<K^6br\D-v[fb~bRb29M|_3jF5r` >)qh 7Ƚrp}_h\Y58]*ܛ|ojEړiCOx6Q;sjpƭ=;A_ ` `K(՝e|Uځ%CSXi2xƄ3?vB{ ?/qG5I`] KY9US{|Jy'ïۼxz3~9ws{`پ)_Ö*dՕ_N.gδR ,ݑkc}<[1>O'-oƙd?jYJcyCNdŶel?O+ڦMך)n=g`M? +{/+J֥"ش6{j2o.^r*ݸ?C%!h\^3l,C8-̞AP#>L{llJya'79G Es5뫨}_+C$̧4`XrC So~]ɲl| 8Ww,~y%W=8jCpʿK[6S>+Xc0w z>뚿?Φ6YBy7)]'ӗ߳X>~wcq\ѿ(g|L{D䌃C^!3ށ_r ӛ<Ӄi/qt|qJ{wc~>I'~w1=ۋHg"W"̇5ҋb߀U[Q_&[#<ɽ|O/ؿaSP$ӃN;C#G {6o3LiDuWj pcs诗lΪK{r~-)l3.:QlU3췿2KNXIVZNV diU9YGo8Wo`<2pTAurvfklWf]0_K5x!^J> Wߖv`Yۡۍ*q|HbPm΂"\T;Rè?WK/]%΋^%7ȧlO/ђyxK ''ﹳ=p>G|!/y[xq?,\`}R{W#w7z7ߗpQFPp++{/SV!>l`+m)؞5x~!_1*Ѹ-#6¨B|Oeg]D {y|ևL#wn(I|q?IyVmr?$,o\ Jy*~߾.3حJfv" V !CoXpl #/m{EGⴚREObد0\68ߩo58q?666-f},gѾu6YS>4X~w_|Ki? sz[_Þ!e^`D#ƀ3Z[o)`m (/Y VOwK,cZ:Q]b]`s``?/Je-/ןϋc~s{`MBVg|Y۽)i F{?mowȧo4+5b'Xwg|^`c. N,[?B<ƪ?/ӢZ~BZfo`} m_7TmN` SB,8ÃcaT_ ב;& /K278l-&fKZ3&XV&l&n1){ѝ_ )-ϒm#luS=._ '.Kn#\%]|K{*/z/)?uWqDd_Md?d˯klaʌKh+Ք-뵎W9zeCrJ`mbWՏ2ˀ?Sdg?v凕g{J|/OS0i*X5'?*xs*y\F뫌/'kJITKR׼b7yMxbE{_V &ӿ&|yq|}/덞[99&#״בS2{3NFY=]<پx=ۯL?>Vzps_ʝςeOԮ9>ϕcSg5`(ƀ1F|v]~E ou}Q~~ ߋ CN: 3`ޙFshV46Bu _eu?39&f|hye_yn}:|6=:9gËpxW0Ҝ?N<)eb^`힏6O\]hv o s|SmO}<ߔiGWCKmZ ssp?&Uo߼>+׺0OA3' 6D?Q)qk1_Ms-W}/X~}U|\n[9X_s[}}7/lL-9`ujn 3D[;0)Jߕ惥?Do/Qz,9({9XO}q+:Y-`ńuoΚ*f [)_8lp۫YzHy\ϝ$o=[OH?$ {Sr g|?P;[uݏMfX՛{Pxk`lJrW8/^߫o9'?=9+K+Pn,MPL IUj|_|8fӲǚ߹:8)~ZY ; ~#3zӾcWڲg}DY#홟_ v`{{6|1]N6^&\\Ooش y%>OXg| q֧R>o\F~D+z1Nx^pcg{`˿BiR?oW=;SUٴg#Oߟo\O4=n@{qcyq?x7X6_G_ލ\Fگ8x^u}?=2;O3/J%߿ڝ)^=^_LNVi\3 Չ lz]Yf֦ϗE"2ߠɟ<[\{lHT`e3E|Z?8fg#*nxUW|Z;XRQ,=#fS8*+Vx:޸rX~)%_?cȍ6\揺 V,=<;.u:y>GO be<8Uڿ+r`ms3X;lgnC2}'>/+ff}έyPw,?xA_.[Qe`VkZc,;㓈$ld|{wZ?~_C(_`Kp 2}dQ^ؽ,NIw] ߟQ>'Ҷ8?}gETц8<8~Bw|ڗۮ9flm_6ӟ7Sjp8K68Ʃ`OzO}泼 ^ʯ=`u#2|z|G~u# w9Av8zɷ%̳wia;U_iܯwnSUܿɝ },@yk}y|"l]lzVp7uBqLL?f68f* VYΰ=F X=g'__챍/$˯|5^fCMǔJ}-O; ]QE`ɡN Hsfe G'sє|>8F+'[ϳ>=} ؿewG0ZG>{8NWb=6{ '+{gQ~08Nwb|):%SPpB(+]@+YzKݒgq~1C}/a"X-X {tQ~XY(廇V`QC?UHuHcA-iϟ:2~G}M[v?S̿y!RTgy`)9nESz.6{N콌a>RWe 8ړ}I}뿒lE63)pPL߫=3T8XgK}oB6sMyzk, [r&_w+,3% xv;2ƻ`s-[ac7UYɃ AM}5ȖM п&OiߒI(ӛkN"ogeYB].Xyu߻'ƋǓ"aߖ;`)ǿVC}:/*3YGxr9^6_<ދD}Sn$[r>1O `թ,WE+G(MxC}{oѰpȲ7b'Wڋ>82 DsEdpfpX5 by>5S^ ?D(_MM-RB+z6B{Sv-g 3CƊcdUk{w?8Bv+D]]1̟ќNѿU5V kA״ _Q YT}#+LxL`˙VL5u*(JjhpHXC94$Eu?^Q4mǽGyZ2HvQ¡74<hݟ%G38g_z(2Cۅ :ރ8b<*}iOsR}Z>ʛ}Z, ΊwqYWz8ziksZ3~ٓKnRp!7FTQjn~ Hv\Uok el jR_U cbD=>Rߨ26=A}ݙlFoȥGs~y/Zv>|5ϵ0^]dӷNӞ=hϑdW)/wBxku;c)䆉2 enay60d,A{ OJ_\uwrWpT,7<8mRabK\ UKڲ- zSb(Z6~Ԍ[cٷ|dz+eF#G4}5 l(2+W1ŮW AygPP}oHo1?!ȭ.qŮ6I^)kr|Y hh|S 34꛿՞5M> 6_Sw_J@uo{r<Ⱦ98+_O+,׋s77O_[Mb2`8_/· n=F̡|tx2[[]@}h_oZ+,>@{[:s%3}ti,3 ׾g}`:k-{3ג)!PB}'[8v:kbeǁklg l՛<M)Q#mEߕYq/1`m`ŝ`ui-3=_:>Z+2Ɓ.4`_ l6wf'{3ޜ@N{]|ƀI9әi!)l&guL?87pӤfHk9}22M[ \W0aO(z;+&]q6k/|'Y i{zJg"e++GL[LQ ]:M[OTN2띾q_%6P~؞qҥ#]?Ӕ?ϋ#*~u=KvbE)?pC~sOp4{qB] ֜"K~6 `yIdM#.j`k$Za1>gzBKD?^-}#w,ب9i{1M.e̋P VF-ƻ=SD|+˱#(38s[WMd;3ȓr.pEis? ̿LyAZ S?GT)/˯{kpַ=&ouٿ K9wdy`f2N\@(O9I߱ YТ`R,sO9_(7ŃME{9byzt}/sў-`s~D{ԃ5 [P߀H1,٭,?|É]z'X񯻿TƊAhYģQolG&W4m|gBb~~ 6Ug(_^3EVP㧥s<ޞA!-ϰu58mQ+dM/ZRloi}ylG`[#y?61%ӗVϋSgixy9.QߚtpؽLv}Aਧ\_Ne| ֧-:`ct&+t09}L3NQrW?*GӞW'+td_%a^1*am;ߕ ˁuF3%'~{0X{)Sg}VRet5>WIjrUn8/ lZC6a{X|XL'OVѾ-dl\'G3;_UFA}/Ν<u;ֹ<#_?1~\Gm51M'uQLg:$e"l~]K}|,A[~<[+-~gMa5W {K#7Q$[Ud`_faSȯȰ'w[/5)/גzW##k l+$ aԗQJ) 6JS^]x^Zql |}7t3ۼڼ(gV eaԯ|p0gVI{>1LoB}!թ^[=끳oe V- -P~$'TVl/k[_g }zp +vjڳcs޸'YLq^Yk=$uGp߬lo{74x >zy:ZV>6끣`8L<ѝ+cKiOao~"'c05ms#~?8ٲ>m6~ž?^/ Xoou>kXc_qMg'PA*ʽ-gW+6}3NMh]o+~ف6lGʓvM*[}֕2TΙRD_ =l+{v5uQgxW}\Q,+:uMSlA$/< rK{\ɽۃ=la{ZCa+П]9O?[.ko_/Oπ|^ٵ +61+uTW/|1X|.Z19ۘUTE~s+LJoQ ۴*snV^Օkل̼F}kvY]MonvVk9Oa,rkǿQ3_4q${Y@;]O,kvij-ihpWYڟכ黿ۇ?_t\fX[(U',o`{ m<}BOum&~>.SCi{1.`my.;C֔];o8rarnpo|R? Q1!ub:KOYE!6LC{w,>}M-gK7`~i`u%grp#g+Xy&+u͖4X؁mA|f0d L4xj>꣍_Ak_zcd1ak6 (O4E+>Gͭ31v+'k6.Y 4;L|Wzii`0pH Gj)]| dpVU~oR5c-RLgsr<^1ɷP~M *kdyQ㩯n`"=xoZ6tM{L^qf-L6>zЧP<.[8+kc8=kpϝeP7 ʯzL wMe;Cq~V'ڮ\.1'{JՓ5>=<__F}$kYFVAޖ_ܔ>=ufMB%H{ y1T(g[q,)kOVf o \1me 1A_qKOw q?yMT|-;U >o& E[LH7]E羽McK݅}KR /}k v(E}y{PᯁB5=D6"yCZ كʋ:#2[7 9 Ǜ">FV!F1Iuf{9x\/U OEgǗ[΃+}L?]ki&G8f ,BnE[z]9M=)yG=!/uFַ ܙqV<,E &VػK_.D}YUMN锇yqb\ڷqW)n9~~!wI_5K`m*yLKAgSwH>e~Ti<~U1/M׽TￄJߠ{(U;Wf񬒱4{6c}*IAW<㧖㣡\i b|s[Gzg}&?_ȏM@uz& :QBx|Jw$7B_{gRr] ߢO )Z]g*{xK$ +h?o+Ǡ(o E@'^ nr _6b}YnQ#?R./gyE1LjP`]y< cO0~ ^iDŽQnQBQ/!Cמbf{()g#>Tc{!_=O?dzYfŒdCP^xޠX)򛨿+C[һi<0d=x?gx`;>$EB؟MӢ"A\DGybq8S*ӧp|ω},̿:}"/+rµxϮ;{nrV5q<'v݊|Zm޷t/E?-Ϸ_~:<ɤ=*'x~jK{SMv Bcez؆r9a~;?TfS A'&пՆP~ X-eMO)o4F29~h9߲VRxUp׵ DUV?}vZ-1Ɯߚx+].}#ջ-7>_cdlO-},2e]<)糽nU}f|[ WM/<]**j>*߭KgSިUq+WgHSNEqc'kTM V#wB̟EQПtxss n 8!U+ k0#\_~P8Rsմ'@.h#VgyüsZ{y&,ߍSZ='6Bv.&'v,8d5S*z}:/ߟmZܟ CV/Pr [9n4}/:K^l?Vs25ăӍI1Fܿ߾R̿)f ^=~ei|_!neiʆw(i6{gVAp߮tpG)_9 Vk,tN^$7cW1~?^o },S+~O#o[֙O ^x:e{3;~c(F{!Q^l0=E}О5 `;ɲv`c"=[u{#!.|Gg.N=?HY9⥌6z1}7i۔}"~_rOc`ڷk3]C rg]7 N;FGSO,qb<\'G1w i!w:-`%aZ?\1x,q<Xb <~4SAWߝ6`[OW37(z2 ޿ʐW~MyslW)ܧ`ˇWۼDnϿ{;vp|G1W)w[TߝnvioJ`ToYll0ҾhAdz~,fڿb>tm/X 6;qP3q,8lXl}œ+>A~S_6ǦՁ/;8mp;O2'ӷ^$PԟrgL: lܔH_ReH`ÂLʳʢa_ lvgnSA x؎;(OJ3JϧЯ(:Zߊ_P%(<X+x4IfZuooװoɍ cМAs^%_6#e/acʍ9}33|{BǨ_ւQ`q +Qv[ArG?&fܞ~i(i/T8'Uȟx>, xN:W{禂s*f?myXS;g 7?'gNyr.`w&Nͼc)/'Do<[;~+C܊2x%C[M/*/[{0uy2\[SU7GK^x9=gsjJΠ|?eveWU\x{Ge\<3Df`/fW)em~~`n+xͻ~cE)Gqgtٛ`~`Q(pLI+mD~sLo;vo :6 E2k0 l[ 99 wFG[F+2]~G ׫ޔkpy_lz߇Xо9/Mnv,bSpQ/}C_9<u&XYTK]Jă;d,q-r~ M 7pTģ:ZPKOO.wb'G=+j]aSm^ǣ#gGuɽvq|F+u!|狙joU2Qs8(4_Ռ[~_iz/r,QxN؛ߗ(9 {(Oac ~ R>}kW_}'e\ی)x5w+ʟԯ N{u~[g`U?rr`)I* ֖(e%M~o/^OowY{~L#jNr~5CuRmvk? R1>UY9׏ɛ/u [9ǽ#|[9,oW\/W1;qLykeW>ݱzŏO94{,!{ckƤS^#lU囫Bi}>q8\>nAj {GbrnLaR\O[?a?8-9YwZsw2V>Y~,/}M9"^tMўwc h3U=Dʦ gR yMp52Ι K9V"l&#Ʒ c5f1yRS诎C~c$;;SGC{ǎ`S ˋUS-fbmƖ c[oKӵLMΪٸHoUN؟^$s|y_`\G_!Om׸n<ٿW?X~`\̕ge[o0z=?K$_E?iV G/o*Y#+਻h-أRφ#}g0~홿)?Owsfjk  k}i5_άV)7:oZ<2|8>VA9siK-T}|L֟,=Vje3p%'W kWvz$-_ Q2W\<&A¿`CcRqzUl;wnݝ,[w=2Jqt3٣ |#OkԿ4});d\5]͑Wz1Ɓ=bJ3kCWd{gvUijM;-mVC ~ǥ?)|]|(iIx`92v51LJCMPoJ^Y,-ߒט-GĂLuH_HN+|fP&WiDԮP߱#)?U|Trt^ 2dџi% w?'Ǹ_;}fne]Ųu- a2yAU)![TwSNzsW3u}O |iJف;?-A{)"8Ef޺'?Y2ֽ57ش:ro+n[9_/Cڳ}N7O~ڜwc},_Ms`-ʛ8%yX/z}#/ެzx^m46plЫ-,x`@ůEHY v\=cL x omT77_ϟ!gK]1^9z훳ՋzQaʄ$0?zP+n{Tx_'6fs;.뙣_Dw$H+C̕Wo'o#w07/+Y-|Lc?tOޏ*>/Z2+u y 8a?;գ9a)u70>]`igH}v.2V#2#8ZG1p\CgpNX"9/{:EKY-6PvxJd Oe M,=ճy`|ϼN-7m>D_eۢé .֟=Gy8@^ПZb :Ynp>i ̟ qH>ۯ ۀl߬<ݝUt?u^.friZ5zI !>8䮋?^*/ϣ>^k O!T[Tm@{x!'c~p???֟nץ} /`7=_ +;q>| 6H/S?_5\^6CQ#F~D2]AJU~*IA~RjG)BOGJC7,8R/r~beGy=l9=G:E8oȼzRۂs^bJrD/M-Zs^?<R`B h]Wx@a lcdWz_͗?פ?+1 9ng$,O{C<' }`]qYVl4!t;?ǿ9<_\k=yƍ1LyQԷԧ-ͯ-_l0~`Wß[vl!6?˯POՇS~. >D<ZҾ\bz`͗FS?Ps|?՝2l/fuiL_l^?w8- yGNs3W9C}VO)t5خ!#}w~?y>i y"ﵙ'P_!dFQݘlw!Gm( ֎{"/y޷}9?gP:Xw8!Eֿ7,a_N`8=!.%Ggܥ/L_ yHq>kWYo\ΞtoZ!_D;hk>s3d鯰ZFԩdσoV2XOvS߳HrK|8Ň9(Xmbګ<S7ϧ?sR~A((wX<.W1OZ!7MSFyG{_Q=w|8Rw?( 8amH^ɍ{|oa/}XjY2ݿV|ezqCnyXqtG'??\'ch83h3`W[sZԟ`-x`ϡgizǓ7yu _d3qT wLN-ο8fyj9%|p8˺?Q*7xϿK=cxQkF{[>[G!՟_kYqSFo*=9IFr3opG{%9.C` N=lߩoXkgK+#~nO 7oͫ`:yÙ~Fη+0^^z=;1}"ޚR%טϏK-W*𷷕ו1_ǁMۋ@z4dh|ۖ;;svQ6{6y'ۉx{ф< ;M1/T1;ԝ3߱!A*bU`כ/e;TRe{WaX}'~s xaI}n^b=|_r|7K (Nd!KY~H{v[zܦ58FӾ~#׷R_d8⹝`ܵK|>嶔˖y7K+$'ʀ2})k:r}oyהF/'~?8?KQ݇ܧy8ц,_󸠘Oi?ph%<0Oul{}'ݙ~ Q^pbAC'>&k:PHp֝7>Ӎۻ>7]~NW?\ :lV}`~eJi}סXNSeq?O<-9`ox~lh@]XŹҸBC 8wYz Rq[%sU`\$}3Kоb \׍oS,/(E{|gaI`gV)_;Sh?ز UGMl)+&pPF=[.c ֧|O7lmK|a:Nk!Yu#`oC?~uԦb`}3>l7os?] gux;ehn`S1Łu7p(+q?=KBF:)y]UO J2h?nCS^/VKf;EolOE}Y[ |p+Ϸ\SYe6~k {D<%S]"8LC nj^\/(wŬe0Xl| o"g{.cc?y6b<a}2Y(oeDK5Gy ;yOi +ո>xbֈ>?dHzKף_8lmzdÿ`}ëWY9q۾ϣ\;CJ_+V1Y* V8zت7M~Sn`m=7+l81)r?mZc <$8l7Vq]ؓAC2wkWQ_>+xcEB<뱁|({wbm<4ˇPE_8?N|f~QߚWq@rW.ˈCYnkp}6|*N{Vz5f`8'Px60|jD Crz;k$m ڝٞqږicMrW.Ѿ_/uX_+[QI&W#7/o$K>2^~7/]o:%'%xq+l0zX 'Q03\w#HBeئi.la|m~6e&>}B]b-e}M`P3 >"<ϘbM9G}8>EyYky)17半MFy`]x)>x^m1)8<Χy_0ַ~<(/ l nNXY{gU-I k 9ݠ|s"KR.Vn\3J2֥y53OV2} :zCcYLr6 {ײDX.x[l<|qA8<w;C˿Xqԕ:~Q{PfG'tRfNYgyRR}AC>WF+r53ϋޓu~))zϛ6n+tfVo[ZGo!t<*{\x#ǿtiʛiϊv$wsdy-Ca;h~`uDCjUzxj-[s=nc} }_ڴ a̿i=߱]!muwG!7Xp7(O3X ؼ!w*_?m5ٿBsWH VǸskۑL>+ ԊYƐ3^6<kGvB֟NJ lGRt |ިeϋs9~mN[`~ԧq%;r>vtᑌ_ӟhO鎌eWGʺ/fԧ={HՂ^eǗОQ\s?{ؿΧks?9_*xU}(KS.S_/%ͼMS0}E+>5 /~$?zoد_+XihMb\]+Urec dVs<点uk;9>O{8zgXs$W`<(+_|;˿ rZ)`=izJv'1`aY^(?Uޣ<_jquO_)eXgJ>֜-c|_x۫,]>/??w7>L^菖?y^W~j7lp|9rkՐ/2Ɨi P~۟<߬t/mv-`z?~%sV,o q̔ʧ>29WE}Cxy0W1w7yCZZ( X'S;A(:`{T }<]d{^iGl/>?"{D9:u!Hp~9{:7p +l罝,MeTb-$ګry?H ֟,+m_7tbV="Up7<^1G>G.W; ɽ^yޔ2CВ+imd %}19بB8| R}E6tf:#wTOQ>0 }k]r[:kO|>ulcL_BTO|_-]cmvOi>Ϸe-~}6[~0p~?p*{~_?R?>`C=q:u[Q3Cm If;_'~Ze Gy^d(߸t?:>ptR}A|_'!Yߏ8qU^Bd`Sq_\3o?UX輸\_4n67mV{y^)S,V< !)Q,Q_cWpV̿sO ϟG֭Ӿ؁~nAm{vw6G r|9D!DKxvpP(tT:jVghbG{_y'_Qz gzcn +>Bn=Yڀ*y3>/wi ]+ƁIR*j{io1_ +0^>W6<h ZOF؂ÒO+SqDfu}D~oOl76e&7=6'I6JkesG1=`,cЉ { 7?"y_(';/CokӺbOָ)>gsW.'Lk$:Ek>H.f3g32{_9#mpP|)} 9 6M2w^L4^~Uٵ)/כkoE{{LӤ~=%ѭ%NCiRt=fS"^s){^sxY8ۋy`|b1omq(4 clx)D}W} o }/ }W)ߗNSפT ~U:/fa}l/{ qϛ̠G{fw'exێQSf{ꅙa |[c}Yoq|Ogrhq2x"ޏyNƺɟ,2*--ϊ`GrGlY4d.i,N1_}s'ƻQx_$%BYieZoӡM%X[[*WzXr>mqESfn`:϶%Yo|L<9B&_-;Ҷ!8$5=N`XM'P=9B^ x;0v`b}_q'k}Du6}~=#X͊s{N`{{ yOl?O$X7gÚuߎg?`D.uluo&^ҧhu^wؐ]ܮ8qlq7I?(/g["/K;6x;nVߗmr,? _~/O[$䍿2W {ƚ{?ԟQ|sB{ T_o PF~UWuˋ͡=K?o0a83?;wݓ<-ۯ#{ X_5S)z,qXRh,BJ`0}a_/]7]b^}PhO?!=z`㦶> Ʌ s.#k7bErwwc0k,;t揌fb Da  Jv-E?*Cz*>`|gKd~p/os,^'}n 0ߟԑ:ȑK97|MHqs}rrn gBISDMhB!?u/g}|s׆79>Na{!myw(_paV eW9#-`݁F)Y{J̯-K_kl1_/}|a]m@nU }rBHX_V7fO{s*R>Au 9\Yf7=r"Xs(5.nN4=qXB_l0fP!aIہ\d+} na_6?6,WE {9u>Ω`(ω?%+SFԿ_eƃX]VmA[&ױC}]U=R ,ڳTꌗڔ\Ɩ^&5Xb\O%G~\?G夊ypVmГL_Ĉ~[-C%ˬO\'fpH`;^li_N OcE}NZyG~#_M7I-oW mZ+~>ѳk= pzG1_sÍͪE2JՅ<Y[;łϞ}x֫rի!x=;Lup?J}u8> xBV؞YYz*H}ozo'yw2V%}gcSޕ|[[ 1 "={;%ת>!˶>#O=^}ٴ?axEYәM7R}k-[QWCl0V1]KE#!=G5``Q6*U#XlO,W]E+G '&k5܉Y#<1'HD)8!gn2}(Ƴnڝ+Gp%PH{ >'ab|5J؞Z1J!¿+A7p9Hسd]7=|_7n1>-XM}E}NO}}~N}>7[b~J{F%l 7🫈"ș9q* {բ~?0Ŷ-6h-{hg QD?G?̈́sBS8M#ϊ"DMחm"hSo#QbG1bw#,S(E{7k{FQ>GUDEU׋ϰJ";0}9_ 7pd]X6"}IQ̏GŅ<ǃb_a[7lTx!r|l'փdE"}&ƻ)?릊%^pg?TƵ=D~r^}7-"'ehX?:Cm"8!dhAZ}&{,ol`Ab}-QG'h /t$$؜ e2LS8ߘ1u1߆ {ɚa2y1_"e.-oZ$o{LGR{-g2}ѢNƖٲB /ceSYGGŋxYO_{5Jr b`-l-yV1?m¾gŸEy/D{)wŸD|}%yb*xH]/5}O"pv Q8:,/ ܖ$,O8B-b Klr)1?8ߝSbWP?&!yb9wKJ77w/3<yзw `wV\DQuǐ!c7Es)#+EUqLuT}cxZ*3AM \_4S9LG1ϭwEsQ'O'Z/D񆬮 cE}&G3^RWH?T$1H?B;Ky1__߆m?yO*$Ϭ(׾߿4@  (QGC9Cg_RD/ݰw`SY lhX>lso"Oaa5ػ++; p_M&85X(ϑgy҅VѝQ͗̿68+yApXJeloHD`7ׂ\Yi+e{Lk=QM/;Y?_q+[^ـЀ`I>&sx4͚e5fl|4Qw {|>oE{wYGٞ^~W0;}Dgf'C,/] N7~ΔS__yQ7/Djo\ՊX'?\MNW45c_^h@C)1G?."!?"կ\Uk2ޯҾW[9_TC?)MmwFR8$Otc|_7wKkL> lħ=uv  mrԷL"VӞ'"ޞ~Co[E?gb=1^Ge_>_Gu9k /Xi/l~}ioP`_O5zš9:Q>),{#&ӟXs+NѮ׆mxl2vOn\k;anS6+K_\Lz|[]wTsmVѤ.I_X#ǛL_5 gYO/=dmp֨]b?RY{!%/QpX}wa]'V[ ktY|XJ6-VG{ڣ>}2g,PNo&8a)x{TWafU[_hp"cXz8\]eYY˞-N}0ؼ+[΃+1LnE1k1vpox>EqM+p>e|1=:ŝKq?oS-t|hxlg+}b]snOW*Lc[wwuѾ/=CSzKZo{`ic(RΏzXz2~/S m^/Xߣzo ΜۚcV^k4=P {}vGM 6|7yX=#vlk"/{7N}  Ͷ}r3)_ݑ뗷6_U\, ۱g{~7˃U{{Hk[߬w3<[|>)5߷; l{\8 s(OYA|/pn~߫9?~ϥXуSfGʙrS|?T*'8sF{+~'ٍgud|I1~{{%9.Lc98Y  {!Wl}"D,cq|_냻8=1m!ǫІ`0WziXnPz,C>c>,^ 8Y{y;81;7mZqߖ7ܥހUcsx{OcBK~y VO>:G hO>M/'ojN\26=jNʗkioX[pV}byq#=9m'l'{[,eKcn]gKǹ_uо߱_O1u>J#dMrCh7)[~Mh_OV?|07 =Nx;yϖ/dwwχ8M)Y,#;uM}ސՖNH|[w?=tIO4${L6 ? ~N{02}`zϙ~Xr7#_bY`۴/;.tg|?$, P8/ f +R߸o\^1QR 韬7^g}j%nɗ5W>g풕X%O: ӅH{Gie|^w̞_ +&圹&ѻ=7u{˽(lHf}]XbdnR"˃Y|ޙ|>7RerbpZxͯgy(xmOʭ+|i&qfy͇3VTY߇eՎ鏼ߋ{oJWufW/Y^I ࠆ;dܣr|[򟌐|R{W1MۮXT' ~ X.^MGm:&xyS`Ņo|nGg;Pf XWz8WTҼ?LҀ|iysc }w }__vf;B-uXm{^%  xfx0_~Ύ^}Iǧ0}Z`j8ZR>m 7- .O{ln k6ɝy?)Ypa;[Gn|E>=<.Pދ1 )8c'GN/v֢R?E¦(-Nٓg[jIUσ/0V$M ?IL+GsvWu?aRW !/%-(?I,nO8SY_Edl-lkՑuuK]A4v}6~~K)^kGy.d|~4Xt$逌Mћx*~գ%`ü߼&c ^_ _Ӱ3'W#vqb26%c}cNO~oTn>SZ۞`sl8N{^8 x_ OΗiLn[[jӿտbT{F{,ϖ=i=9}΄c+dnßi˾|ss6gB o=KQM >s?c#T{|MawW_Uwxl`vk=\bx?߻,)f “_g߯y[;_VP›˔[@,ss ߱8gyOX^Bڻ/}K>ab,97_"'Z-.OqU2.gb3Oa ?A-2}>]Ƚ+jQϗp['9~6]~jU-mXX/RXh1vb} ֻ6~*K +Ogcrvo[Z~i*/< 9 .Ŭ|˂O-F<͓g&W.V{uIAOw_;9_`)a]>3slL+'=)x鏩fgm N瞒=Ú<%l3U?~'wynT,T柸6gv q^Qz[kYyI?128dp0__U*ٟUM~MR}#SG1j8KBx\O}WOnT a?*̣#oЅ<ϜO9RW{iW3Οi 8w"{k~z7ߗKI>_n~39ޙ?soR}-߱|>!/k>ysFa]r%ucqah P<=²olj+K\ѝ`f˛sd2ǯyjp)_<0k`^mLlmx^`7v75x٤8UW l -nL?;eؕŦ uF#)_\' SVJZ2i:X3i(&h_Pqpֽ7`dڻ>Nkx@]ib=;97+>9`[`,W a^r qPڛT7קW yXwJ>In457O+N "Ot{ +˩?`xh-ϵ0}Էw7{ 2"e)^;[O~ul|`>?7r]'G {eysr2'_}0q`6(uY^l_b狦W,?}7n/ǻ,',AuÖe?}|Ddyyih>߽oxS&;#^2 59 |>Q(X9SOa%-qO+/Q:M 68SCK=oSy̸2Fpsg͛KWdxKٛ-67=1)Wf}8y%?xu%ӰY|~tZjz7W}1`cਲQPܯI|5}I<'p¡Ҕgg3_voyIσz>`>* \[;|&ٵO7`KO+J7i]Y3鋻yO%ح#P޳X. (^W3NV{pHfLߣ\m wv腾'oC5Y]H dWޚ̿ Z~"/ fW3~G3ܔҟ68q$s3<~d uHzxwGޟB!kcЧԅ/5ܜw>Qn9uϫ`}bQVhy^? [;J >:w+12 }恾GQy^<9 3^'y("҇-;嫂s;3}~guW5ru5>wZ>}zdc>;9ׁf{N~wZּ9/9Jk?NJ, vX^v:ْlV;0;Scc=BKU#pgo0O~Rׯa}M=~aze_}>`ckf=>w48'`x?bY,ӛB?VhLM^iMMLrUvgCA][,!+=΀Vo$Gt'3ly:Y73wX+˛3n|֙G<[6fkoIVg 4+s,kў+M(iCSGlB4)DT&hYSMMnޮ[]"bߓU )a#Q]?[`GD 30YzPee,|g ߋ:ԗ\<{$ Ciéoc?5Dؿ{wa= w V}.X_u_`Zpg`=%ռ0X*։<b}]#X縅惭3Ѿ9KY}ؒlZ/+`w>_8fS8cQʳN~M-7o˃1&H; LlV-@ʍFM3{֧:|8o&omgʟ//_l j.gP?`~CFS_IabvମI8+kn 7{/pVWo%8`?Hؔ֓k_/MNcrUBa:ESw~<3r|ZڂCBm7aW* _?غkC7X={_A[8S}DU"´G]-$-+}xr  FJcsԷ0n#)cm_Sz MBn+=߱ +c87})Z/Vb{3ɦmYў.!%m1`+q$ {ٟ?O$ՔDPn:Q lg=# _g"iOxw6<s\8֍7͚E?a`lm/e]pbnajʷNڋx6*X2~_ey*}5l6ä)(E/'DV'ӧ ELl:7V P~,PpbL?xxo%p8'v]9LWr'i`KY~*[l6c{C"zu+P0Oփx``s`Uc;ia:*VzN%hpR1;U7$e_`Co[_JkeM[osp\hX}%!p)6 ksߣ /Wg 'ή8/ce>oԋb7A+iua2 {-8_L@Gy틔ox*GSǙXx8 W_弐w>se}i 6:um_}V!ՕƁ %geՉfXVp(4\ny>+Uw-0OXpN_g8x|YchG\.ǥT#[K7)[iZc?O+bo#m8ӧIM)'!C~~ěA[϶r}os]/{!a|t^\'8VUrR_gqk`T7pD~<ϺUzA)ScOs u6Z_#k5w}soUuSYp z& Oқg}a/m9kk@/;h]gT|2U=W?Gqކ=j$mX1񚭔;J-kTf|Q%=Ay+IY(IpT'T=OσtFkW>WyӨ_A`ݰcVԥrGq0weao pGnB- eCn'O?>WwEs ˬ?= 6ao.ƛY/p?9lLC+V~_9-}U9𧡔=+ٕ{pIYN6OVI\=c E"&>¾84e(Wq 7Զ 8CXܧW2Ny@Q[PMء|y\vQ*Oe$l kls#-]R$xԝ]icot[\GfOY>=?Is .lϲ?۫|4-JF)s9#?3 cbgo?Ue[! _J681^`yӉメ5ߘ'Q >˼W܏M>\\l3-cSy"al6li֐h[g &~mBu=އ2hYExeO΅qKm0T-XW}Bf{9[Q?>ϛ5`uݒ{Xш  v{Wlp T,ؘv5s_Gm.cS[>΁򛇶p옾`yώ`=v n ru0^% >S~ ۿ&:/)7×m'_=۝#+٣vϗ<U9F|i82淬+sbmʵߗvk{ޡ"E`JcS>KF}> n1a<>?╏4^7Z .IE=m?|Ozz_?lz󸚟HlERD+QTP"KdHBh!'l)G&5D,ߜ1}߮kk9mt$ˆqD]ጻ.8ДGƭQ?/xҵ+;2&0~tFiOW3t.Dχ 2vBՌ;>J2if89qb^)/:Oژ~=]jLc%cu)VFM=d>*^QM?C>C^<6ËqaY)ƖpOƳ $a:UgMZ6Aݮͧ31N}Ο,5]xUM{0o'Fxe|LM_r1W X/Jqƫ/tPOac0=2_nqL,?̋Q>x*d2ƱNz[/c>@eQOvլ)]K/8䱣yJ[O>)cS񬗭Է1V~a߳˨вK{Vzҙ*3War߼vNN&{D3t*Glޜ5])]:1K=|a"濲,{C.>sK3^8a|oF=Sxsxb+$lN4`Ōbƚϟ7e,J6a҇ϜxV-<kֿgXuA54I˗ӭ]ο`fn[}틧|ٺ7g?P{r_ &* ?_wq }n3hӟU67?eOw=1 2f㓩{o/QF2=nX%7ɏ58߫:26}cCe֬BƝ8֒Ѫ'Nʸ{fO웩O SD}|:HѾM_̀j3Rj|dql~>cء㚃3g]va\yzB?Ɲ޵153ƴʸSnOEztS0oaN1|/՞ܵig1㛫|aPu7?k2u q.1,DZ-/{u}VWfO`k͙jFkJraQ ɇ񔘓1,~w_ d#_$3O|XXN^V1n_fq΃|j5h!vsWơbmJ4Nj\Y:Xaji[/ dg>X?ۡKL_?xcQ Z:|FfORױleZ^ z5Qw_o?o+e[/8UYݹ_O{o쬧pɴFHkCxY/[NvԌa<ﮨugUU=AKN:ZKgT=yLS+0gl* ;0;Zڇ3}qԴ- ٌg9DjƑ>q'םX6'l#gyf|kNyy|_m3ޯbl3FﯭSc w)nT.d|HCcj)J}WIl"ɵ3'+P.t*NS}oԲGYj 2^wv:C׽k,q2~Xr宪x}73.m ge͒?2;̸:4ֻN*e}Mu]U+~gX䵮e!I;|yWxG`T9>x+ؓ i~&fZX6zdږK^Tx ?Ռ/d_g9c[o8.}W\z1qs6l}hd2wOnb<_r_&nr=T="ĸ'ʖ070qSkdy1Mޑ0~+ݜq'ǛwS$ WaMPb4{c`HtrN6~/=|cOroaɏmafi8V_NW> 3g[Ʒ|A6ek8PkCkl9$zƕn%߯#̦|˧O Myp-cVm1m/c%cc3[o9Vc7JHg t8yO>J_1{bܨ)Kswb#gX Q=>e".o]?7 Y/)^nE[[EVOBU\Q\Q͛c|3+;OU0{U{8lˮ.~xG>>R٣6qtB<4JHl"2]^Ǔ+&>+ik.ۏ ,֧Sqy9v1X~.C7N6?_$=eb+=c?F2vOy[sEֳ=pt15A0wjjԞi'/6dvX7b3+2+39AŌw﯈NJzCpg:# ơk+[^7K6_/޷f~#+}ƌb}qe6V|Z49LE؜l ]h^,?7z,㖚EVCp꾟l~]x|㠁޳$2xҾv'2.f8"4&zfvd֍c9ikO?e=72zƛgDNXX]{Ȯ-:%Yij$뇌W :Kg {θϘ ; i~!=rkq)o{Gv[1K$c〷AF 1glonjƣ|Q*c cͬs|e?U10R{W۳|MS`2kH|KwM>9ɍdv3L&;ŸJ+_Sw_GN:8c{/\Nx`GuƧ4{C{\>ް6k'3+s90>S< }aVw c39 YlyS3><#W\[SuY̮w? i6Ǫ~b5Xs&ci+YK ZǚXŚ wf|-PV|ڶ}}'|飷;&cCeLX~y8X|ns{ڎnKYrvֲ3s zhxSG=V_58d /Η0o:S{v+ۯJzxq-֝.=+fzMN[cʹi7*^ eTx[U^s|YEuM|;:z aB{߲?͞2̿G:, f`VQ*v9[Tvq~c]Aˌc?\G}nj{ݿrjƗ8|zn߬2~մt,}+L찜ݻ$i3R~{sکƞw%SnW'ݬ#]T=!wޟw!㢚uٲFUqÌ{/{/vc.6iĖ&fߴ$qk@iovd5YϞ^ߕmgTv1y_V*֚X`3As?X<^BMimu?= kTũ{Hg|7` Ǖ+߸d~댮w:LwJ{fKWq2 ʯ^xtC9:ޯAmݭak<.S4{3W?~agA0㒚cf32p<;_VmFEr6:\UEڌ;Цx1o:ZeM֝_+߷1Y6Dٿڈc/'zǜ ^ @E,t$^a-'6hM\hXVa K&6A,G,캂_Dlu Tbh]byJM]dm"ɤ5h<JNA,5D,LK+ 5 nR?M#q' %Vx"oJ,Cl5XAvҍX;z ZW+e'b<7ԭ/%WcON\ ?xabF"˯H^!- ׈`~'Q#|*X ,{| XXT|Xx qte59/:@g_u3?;V ?z^Wb?>?;x587qW m"ye 7's_M7}%Ϳk{2K$?q͙Ă('b]w# Mt#aRgoXKvmXx*|JӁ&He7=|E;b~dϋ1ԿW%;G,[<|Bh#鯰]H>̬΃+|D,ɘF=bEQM4.$4~)Ni|_=&V(&릾?-ۄ҅o_|;C$:DlO,8p3bxSbw+bmKby/F ol N 59G 7G,Hx%&N+7…nԞD} 3XܝX.zL|p4`47\7wulegNXj9x4=ϧY+ʼ:;[LDK0>wm)~eψ1ٷPoǑ}nao>G[e=IQ {DmW ')CuJbټ!$_u~E)(#qsT; ;QWo|$þO RC] sbbfq9X?M)X_2i<֜e=ba{Go)ʿ~teLjM;z[4AbŃb.$oA.׃mâ?77{*z?;j#?8f) /v ~ Fӊ~CRxiзt1s3)'A ,gmq=bGĂUfې:b oA 0S6 wV [xma>qaͶm 'OXIbphIaҧ ? ~yb]wEEa_Ga ߔЗ8 7ۛ7rFX~8o:q7./τ%YNgZ6 \=8&6_{?W9Y ~/ø }+x5ȝ(^_=5ۍ˫~|<6cn% Q`\`j5~vRUޞ3փPoqZ)bo^ī8ba6xNœi {&:7^"b(?hq½,{ž Űw7cLϵO*@*/դ|`6K|- .'~~B8u q;fSK<>Q 7<9XhNHCAJ\Z<)[]޹vvdϧ baݖFLD>޴ }eJ, .C,s%6 OޅdClg<7e0?D{o8#E0w"֙QmAZDrAZb2bلꑔ<{RyQ-#0QL99q /zQpV0C׌ƣ$Xf8XؾXR<%ݚLi<~Ix& } 4ZۃAb޲n4~߿AUQ5~qNd߰ifthJYh .}G ^} R%ƨ$ΈoIj[Jlt)&F!]Z^4O]{b]i_Y侃`|Q#rˎ<9Jk뷒E:o"%'Dl%:^HHKٴ:y?/m7K=ֿ"1rQn"{&^']%͞Mу8(\ڨooi Y[K,1a# 15bES|^uǰ~%6Ͷ:Ah2bb1O@xhI ՏI;Av'&.(n)>jx,g-bـc`⻒*^ψ{QyW`bAX bkoh9bӼ'%ɁԞw9fYCFoJ%OoQF>K,DLW=t)ŗ6PߎX6cXX^d[Q{^\ !7.#hC#u3ĦC B=wZzKE5 {mG^]r_CDpF^}ku _M<$oH,qBĦ;ɿ% Os+dx仕RN,߷8/">ףvb1^;kH1V;b>/|yBlqw-"_b< ?P{_?Y;C67أҊxj 'xAgb -KAwTq!$!M{O,L @%s׈#f +2Se4S{{Rⰾovݎ\B{_@G&ĚpbX+ vɍ}`I P^?a6(Pԣ-[~R*gi8:an` RKmnkBk>ZK,* E'&^B,"N˫@yc.qĢ=;0ˉ%\޹7v@~>ۊ,IlK qسľe;˵`oOob#XoϢ2g ⤱XKc6ẇ<6`b޷!S4I(I,r )ުCL#yg(~#qTˈ>7Q?u^'"I3'.3ZrP8~?=,nz5 +_NQ3fE0AOx%o 7B0}9ɍ|.B3Q{m݉}sg-lx%!5툥!=|*.5~=?KiiKm!*|>>}bMcoJ O=i37+naq+w"/-Tev+t=Ӡ_^F,m _iC\w@vi\>x!)?Vɰ_oƧþcdؿ &8 ]\+"ME} ?~G؍G~g|Q(?f$=|p=iOg= (VȈ ?8B-eG}C">F|&-(8E"?YHEn4 ͛cF!фL/A1E/'P5"Q&$cz؞\bub|>͇ /`v5"Xҥدd#|y}Kp=m%f7PrQX&@_s82̗CS}Cv~=0ثyX}{l&%Bn݂Xs+w}5/&6g&%%?,55 םLhnbxplbQG34q1!wX:3X 8֯mОb/;X7iWJ,<@HlQz"מ?¶7(w4ſŁ2C'=Eh}h녈~4دB{8L6<ؾ= fb|Ƕ|ڕϟQyOw"%:/'Y`> öp&6$Y`Q byv'0'k-:3wDgENKQ\zXbB!wN#.}L,}=&kK;$ӡorbqo.Z\4_*ⴕ7̈ުKɝp^{gIJX|q _~y"_ 6So1; {C_b'+kMߐKkK#Oby~ݥt ?3p?Y6kw*A<=cbUɡ![ދO^X?i/e/b<4-F`J&hcčSўHb[iL\ǫ%qLj/,I,[_q!}~m3_TIsJdjO}<*IA>lNKPb33CèUħ)˘XN m鼮<֜ ڸKq5r姚 T^ڙ' q֢V(.ߠxh_H, wbsn6~-Uco}T?.wȾ/V H~Ki8lF%J(98|3ڿZ2~ bLˠ_[b%|L"Y4~7SN9%gKWYnMK\4{VǔgSbQ>8BOem)Yx ǥ/ao~%'\O݋eC,i ٥KH.2mSţ^RP{Bu4^G>>9_K(xCu겘ZG. Zg#Qr{{E.?WJ4o g(t)01 [ȳTuTM+=mV ߔ,+zܴ'_}W_j?ƍCUlN,[ՏQ/%+Tl+Ѥf%K$vGg (/<3Io"ޭ&ZT~mA5e?F۩#]wdYTfu׫P+9-*XeZk/CVO8Ke?&m(%[o564^=wǾV u@ZHt<چg$)Mh˗$ua@aX_|?`5͇0k)}WnS$6Zg@rn5doo.jIbS'̷[fZ!' l9T1tWbu\ˀs߫ԓ'Ėoi|:Ń>rDţ^?|?zw`bKJ8+=}_Y:VJV\eB+ѝ:o)JY>V<N߷IYd+6ђ35-ϒӔҢnŸɞ}в#>ib|Ô5K3pKbMv"|nFO*sp_K|uꟆ iFb~Xs2~5Jv}Srڏ4>rQW%c|\+Wr哒ϵxN/f\M,*{x*a=˃q v2 ]=3>ƣIY~B/ ZPƻe:?'|ў<晒ߩ(^xJጏ3Xᯛ/ZQ0U(_-qJyFOS ='X8Vxg{4(lo1q73=~,PoB3!lK,?ħ=Wgxyȣo}wO[G|fay㬾K*]pwVcU73~qM'_x޾qFEt6`|clz36OLV7,vYbTK>g<1@㉉J{1C T[~w-/*nc}نUd:?1>|[w]_Qҡ>d?\x Fdmwja,9*/-|7His1>aQ۝qr3eU7صc\~*=uFgc?TWG9j%'93*LxeL7 Ǔ7ŽDx Y>wˬK4r#G}+'cσg+ -;0+h?hf+3V_C@ƻ& wbV:d7}UsQ9?57ďۿl5q]ߙ|mv!)aZGtSr35,Ww:$W槽 >-mX{L^EUg|vq|e۸sNC'=JW}c:yU9Y"{H,|X3mdoCwXl]xgIg} cԽEr~uԿ Re)b+c = P?~'V0^p{z}Ʀb|sN,f^1q?ox)͌-tߝ&u<~O_֟߹soۣqaW-8PL guqi|uLVluWZc@Ml|B'o_MNOyƹwsQŌY?G멍9řAX۷&VId)ec|q<acwG[ьcם]8M>sFb?:*X朗%!wodsH|_hq"bIS)oi?!8|Aɾ5i'hC~'~; FxbyQگ  J?optٷ+Xf%WCG#Y>Xq鋫U[XhY?}og`FH ~7aZq'je o]jh*Y^-X9ЎqςO[^&wlx)CmmҖp̘O/ukf6V8p(Geɟ 5[GbPfkS5[iq'waO46q:N-jk8v f>('Ì t{0˼΄'[Ck~Z$խ(>ܣYJ6j@;_;u)O݄,(YdǤ;~4!ǦkM鬆XR'ui AmGbh\M9 ?3A_U}O8ZPy UԾTu+ߚ{sXx’?ha{%ESvԟz}p[v/ aɧ[ 3oxbŞchvWKM~Lvr^<މ#]&L,yw&1MfmG#Vd gR{1 "n4t3𿪢 ]b񿪡;i:z.Z[K8GNo }'8hHLcJ,kLOv;G_.vqm 'Qj 6B,(AkF ]i|9ogU7䫨Y.*eןĒ3}9or ٟ }~ 9ߝbԿ~:RURBb[[b; :A*wjObAělQc\V7˻j";W'/Eny3װxqmhK5qf专/)-ffJ~]XM<׌M/*R㻷^#F{n&>(|%g`sx|^i7ƖZn&QJVpO}|JKoq颏xKɷ ̛՟֛{9ߨĢ':_IM; >jiQ:n@U^ܖ~ UW{C|C3K\W_?&wҠ?=Ś?:']8uj8NJשTA4Fߗ<;GK|_V?G_m;;*XSU,]l+V>c}G1N[{ ٣ۥof c|jO-=IZ#iAFU:3.m85_,5 Y4N16O9~n<=oG1ZU|y M/kldz΄J[=q'?+tQ7^{aWw @ܞKYxɾӪ%1}_OOK)5nƸǛ~9̞9]Ͽ`52~\'IDygρT}::$MjYk"z#BUױ. c|}*2f>0#IW k6^H[ k 3辎QeW65K1=s nxȥLzQX?,"T7MNsK#h>$n6؝7XATţ3tG7byBߙsi?ψF~ -Z| ϲSHTrٝ)^j6li|oau΋8ɸjj>wh)Ȅ'y'WFRĮ8p9ﲝa?Q]E[1ުwצ/~V3^[H(tel=m'f~߈e)=IJ$bœĒE~aby}b7Ϻ#"F7u_~QMbQVbŪ?+Q>+(/,WkҊ SM EC{)Yjf97:-?۠u*8T?r!FЯ/H\pۏto:z xXb)&xC܅7Jrb7GzU@m=q}G,nG-:xVnx߳&Xf6mO} oo; i<!6U?%4M7GbpLbG~7/K7?a{П%7B wr7)xr%[w8 6V{#ii߾?yڴ_@^~3毼b`ߕ,XG:q2"4$3䟢` w ['nxi"=">Yc#ѾK7GTj!.E|B>M`owK 5#Wj}~Oz xM׀\uY?,w<-kĦi4 bEMmm1OQ~X6엵 =qx_bq[#>!o X3?֛V`J z^EPFI\8{∀XFK7p^ OM,;Gltg;18{J1.FQ޿hb%w>8pMM,0@mp>j 5\G!č=(4]" 5tA@ē+J$N[RpvK&N6kCCo&߉EaO >oq9b9g2qZiהB{sۭNEyt>es-&sRD,Y~PJlnt̸@f4@|a>7N,]8(G̖{}-htwb"∦7x' <ﵴZ|M A_;$Ev}}`΂`~ ܾ1Ty:k؉X6J٭ϗRo> L_D!|8ĴzD u0ߣ0}ɰTC~V>?zUw#ɃkW\Xb:ܣDіdoYĶ_w凓\8mPgq{![KX[?j#Ǔ=['T?m>/= bXbiĒ b"!7kMXd}X(>H=Xxؾ+/ucb٣>t-\;7,"Tz-O6/,_uŔd鰿ЋqQ4y҅XrΆآl6?%nc yQ0qMNbijT^ؓ8aXxomJ;'9h\&hrC:7 Nۯ+Q y m/zY XyE,J/̧/§ o%hy7_9g 00FJ,ɳ'NۼX|Qз7XW:XX~{!{ n\> $vnV!ABմw=K 7h:wIy_#joo5$qNnwۊ!P\H,HClVyZ5ɯCDس%q͑8鲖[3X4XVeb~CbsQ_#ѝ5h?X\G7\N#|Ls:qbWRَ bm#ąf(@,g{v]OD#cA1ސDS;Nq3kqP߰XL|}ie _Ļ9ݔ2=f+ k- >@\-_$sr,b×Y18p/o'3ffky6~X7x~F} p}bi7'/@`'i4\P ⰱ{Xtc/ _':{vƿ3˰˧&Np+TACo׍u_љXCH^WV{s q}w @^or8Կ8GE[O1ZN,} m`m_[+m55dab/ ~2tbŞ g'B߼AW q~Ģrd7ӡO!܇_}_%ĺZDž75쇿vF~I:Hm#tN'sb# *i P_; @j Ot=p?ڏ "V?GvS ?tLqoJ}{c`N9j>|Qܾ \?=nz]s)&>7)K~˥Ea_H\|\D?/~bM\Ni?wv1r0?k9Q?8iYB zb_dv:>q(?]}q!NT.!?оqR'^a*-ߔ ~86q z7{LbyĢĦiM,mK|þ%w^VzF˽P_p_O&n|6xya0g?6͊*bQz~V6'y_Xn8>n&v SvpRܙ\'ͦb6G>ݰq_v'q;n E2;Г/{0 8'!o:X%Ԁ17I_sخ_݌u]iu(yF3o#N#ewDՐe 8=LsW= .A߽'`gq~&{UG{y35SJ,y։$Ո-&%ժH[w|ȺEFs䛧`ѠAĊ#OI;ۿy"s>Xg,ʧ0@_QUbiuAk|-+l)zޣ/=g[!߱A7?o!G^'{y8lO)[GStkw&_u&ރ?o :[_COBߕ6/!\̯`5t?#9E0 qZZ-~GBM8W=wg?⤒n(,5yEZpKQh}X" '#F_<]k^n/WQџX9XLbIf'd6o~Ă;˵'|JO‘/?`#;@>56a*|~=#V@~nNGW#_=>A@~ @ȶ𷐳U O;7PҘĺfm_4b&kވY%ٺ[0 L}ǣ$gb >~M&RtGL ;`OP;іW b}È]\ q1q|d XO_skhy&mE,.%v :>&]әd'ܯk*t}9׉"4;[bḞhz^*vߪĢ)~|}h_y+dX8~p%_6~K|=D7"s7OA,} AIJ4?U?5Rѿ7F_m|ʨ$˂ OH+oSK^Z=]Ԟ]s`b1k"^\f>p մF͈6-Y3r\SXM]`[$\v//.ibo Kt!#.K-X>< +<'7kĦSѾq𐗐kDF~ݎθ^qt~{qDX_U4J \oSbA s}}y>NߐG^sEWQ~VqrȫKwB^>XvjEwy5rbݐGɈkg4>ȃG\oZ'/=vi VL٧^wy:F%}`i p3ie2e.&P}b=11`Q-_D,l}{G%v_ {@l$iF o+e9qڦ'ߠĚ?31_-]B|&~i[M螎B__;, uFL qMK n?;E͇i,@RuK}/㽃8B/G(o4,k E{Lboc(8ߥ8A5?y%|4\ts򅢧!G=7?ot!1 q?4?\,/톥c8=ޢ|5w;ط- >"?C吧'W(_*x-zK+b+b_˶ o06q俚+ } }E.Ēdsb駞/O\ ]/B `aRlP [wr'N{=t$NҜlE>;0{ q?xBf~RQzP vxF_'`ĺN[wך}6&xI\>"B K\о g58ғ8 ' N+XxGJU|s9Ӗ!XՎtymD[ BxҴX~&OXvžqwPj-XbQSbE!XV?XنX#o#]Q=qeз,oR N۷wk?VBwW [~]FlQʹd ,* |q4(*GVp{ؖ8xcs,=O19œX%APK-} "ԉM ]|\ke,W.UhzY'^$6:!Nc<,C{-NI`iP< }ĚOB>N4!֓a`)ps>ps я[3yewpqx!XE'bQ1b໛Ew od͉% -<;z>]any~:MTG$~_WzKG݉}$۹Qޣxk$.P"QS!mĺk)D>oo1NQ^#=ouĊ*x_߃ipخG%oK\Xy3 _]Sam|Xoqmz]`PK7#VOM'>D#[E\߫7آZbaP/bIyhBW8ګA_/=Gx:8c=/޷WB~7?5e} >O+MmNH~<5OA0޿ nIlD*x}BܘǯU=^&GH!f3!2}Or_Du޷+Dhq~O$]$7ɗKS,ߟtaxfM #ԗ7%GV}VE<xk[ǩ"ϕ)^fUֿj2v9R.U6{Yzئ’q5=d8_;uG#*>HL;ȿ t|DžhMrpMUldnNw'_ХxGFwqTWJ+>D|R1SŁkX}_]*b3[?U4Ֆm%e5Նq7tfډ藺5,/=DХdUq/3]6mNA?~c_㶿ߙs6k{[(2C]:,)qkMl}3\1.a)0*O`|r(oE*N;n9L]nu_oun~Nk [$ݘxtdzb#[ut|ȷ?G}XҺ&QGM\t"~W$w|S|f1_{.{5oJO̿UTz7b?H_:'G47D_| K'm/Xz؝bq?2>_t/3sڶ=bb7GC4{ t%Q'c!HQc/ŽB? lm;GŔou%^~b'#mFlk?ط[y+2/@/˻\XK?yľGnJ>F,2Xy<`,xw6bk )Fyb˃[؅8d5j.i+ԈE;P<'x$\ڟhvg4%. ^D *cAQ{`VGڼ%}ziB,(!v3' F1ڝXT7; ?wF=~o(/o_#grk*oPƱ':B۳aGbEQF,ZO*&VwTfTxKo-doo]&ߜQ#_:8"2?ʛ}߷pIcG?n>݂KIT ~e%}Mh9d!'4^K[@ ͇_36tyڗڸ^~9C^{X; T/@,&gz h_'u7>}Ga٣6$NJ\4tI_zĊ?}`C#_՗O@m7I0X.VwXآ;KN%\w ?ϢZU}î1i,>~>oX"\eVrm ƯS:xޣW8qQ.RYM&9Q?#_$P8EhXnYn$1|؅B=/p~>q͑(E-A,$N)]$|7cDؒXS F%5fhRbߣOx؎7xg jrOg'k> 5Ums/'lK,>}Yaf<_UiI!7;KuU] ڛq8k DxkЧҡO4mΔ5 r2s$o_ЯQE4^P_J\M|%Q]]bD xH;#vmO"mp>Ec]5Ċ]nCe#_ݩ҈Euз*X{;! }G܈Kco~w'VivXrq>W9P{Sq'5~(X~ۂH6o y,$V?3Xg YC3bbAI(OB{<1ldG;@_^Sy`-\.XI^_n?d0:o_Йh}x$VJ%E˯s}vǹ>qF?ڀ8j壝~)yGICY^X6r/wv$NjcK"uɔq[[4񃭾^/?kx~Bl]JқXIR&Ě'#77䅋Qv̧KO0"v+_|aȷga>Ń%;tK!V7zlG[Gp.Q:+C\h?b,փ>ĢQp_?XlC,&F n^nB4ro/XQlZN-&5~N,~ k(s=:8! qwc gOпju(D*w0~ v\#q _܉X(aG/{R#R;+j}> <}ľUnП~r_gbٙ[wwx}{ke`!ByS"}?B7Mq% L?M ֞ # >@,3 ]4y{|Ē˸~^ž WbǮ/GbM㽨aE\>s uJ!*1%G>gW% _Q׽lF =zlBr۷@۟ 7A\M/ ;IЗ'Yu3|R!wdq$ˆ l+Ѿg'wHGg y< M'^2p#+|7qX"b_8}x 57yl#֕>DEZ"xjen@"#ǫ=<5-b!51<nF~EH2\{#^Vxb>,%!f nO'Z/elFՋp \B_#}/ϣ'3>Pbސ/^;qixE]V/hJ?3f_cRiݐ$^G0Cu]b>H TbB\hԁ ''w8׿:X_~ owD}A6oڈI|mķ-__v|V0>'؏Nz_6e~Elgģ. ]_~w?՝~l'_cPiarnjamj1т=hBnђibu1M;7M/X/^V_ۉ kQ?͈X;rwKQ޵o{O;n|,CWb=7E;shnC΀|=Ӓ5'+|Lѐ1ĢWCy*2q/,8e8H~wГ|nޟ/\_E?x$pоy++ ϗѿџp܉u\o&gVs`<'6ó BBì=%ՒM /,_BOrШ3툓s lDToU ȵs7Ox5 8KËy<%-o]4ڇB >~R?G^|=Bli1~g==߿ 6i`a]n7ľ5ޜxou!Kx\Kk(ߕyv5~~q s?!nṁ~=cNkuo_|C@Uō[i>W=_1Ӷ)2V Q`pUr9+ӊXƔj6+}&KJA_Z'jrQGOX<ѿ'۪oG?߯xDn}@Y`xb7Ļ"h7(_!W@L^(ڿ?jg||A.0kۊiQXo֠*\ukkCޱ5?;w ޏ |3;;^IJʽ޿4փ2mUx }_cʧgMlW$z_!jKC)?g%?0K0>Y?4jQa'qD_ Q⻮Sor%i?B^Wx_%!Ps_;aϋn(/XZ+|SY9o? o_ c 8ea8hJ\{M_7WqO#DZ?-?ͯ￨ow=fS K/gwxx>)}N㧁]ϱG ܵs>5pOϘ:+8z/7( ]3y%|,{s9?OO^x<}>Lؓf&=a qa_UkDm+Dz'_'G5>_!z.促P>Ck#!y|}CSy_론!O Ç_|XhbIx| Cns"?r,|ڏ]s/ pI !!X_K: b*.t#LF}ml̇)(߫! AY{B}@iQZ._C,) @\g?dShn4w<_y|w퇾]z㧰E}/0H yd4M[=<}ʏgg%B^Sx|g?yȗcP83U T[-ܸf/3ہ;2<}ӎ;uWQ>;?tQȧB΍c~;>@,w=ޕ˃/NOCXO}-# 'ȿo}ķw0qm&|>OaZ,$_|s:? s{_YYZ6_[M$]Z6#!凄|}A|};%4\z?+D{kr~݈]4~`{%`. :yscw)e8ǽy *|qy/ć%Bȓs}~hC2X/ҧp6>|Zv#ߜ~9G0o`(_̓ ~Gߙ<߲G&|]9oztO,_yS#>q~#_)v|￧b_/`Wڷ~PӸ_bMK;c=V0!?N>-^~~mE ϻ\9>qºT~Iyi5>oհJ9/rSF,8ʧ'KG壽w:?[U {R p=J5`qjVI8URεSҹ<+Hʸu։oX|? >αWcTX ܞYȧg:ga~rL' ?EJcos*07kM+m!^XWkSO_WCx~}z"ۖuݐ/6 a=+}O14^ $EK k\D39X5b}3qzn{{ľ!:bGw(g_?MD>z}i)xD/>>3K__8 ?o*(_$~%k'!xbc~xQNl]\Hl_P !E}v'n7š|QWY'󒱞Lɘ˿d̿|S2E ,/|8Sqy)ڳl등=/c= #.'^Ye)BI˷q1e<^?q~5)' 8w)$); _*č8_s |+?+W`o)p>/]'p|^Pߖ؞y?o}|8u(9xjΡ}?/x'n"^gY ,y8 LU)FYqk,'ùq+m0?:r9% #0,1n:1%?x:xʯǖA>3|i_v~ixA\c؝_?> }yzg_l='n\7[{-^΅$+;׏ˎ׀h_^5e{~ǯoZA;! y|bInX7sk6þcߘ<͉Vx}|X/Q>0ч G}O 1m!O?,yC`⚂|=Ol:?OV88A,"C_؃~g~ffa~cy-A=qqX֖N?$pt;q^F񔟯ׅeF8+ >cB,)ه#E\oOo~? =PW0_C򛳉/{fBaOǰ]|;No4iv>7'^nZ0{ g(;{{e5=1?MP>1;܏X Fy]Eܞ]X_P*f)ϣم |"Umva~类HC> !Ċ=d'=xN^KmH iށ_2$hy41?~sh%7ܸy+{iP0(7$X'؂5KxX@nvvo$yqZP}mw ^|FZ'G˷c<}ĂM>xbֳoӉ59{߷#oj*_m|@Ɍk Ga|7ff mASԷ2_{{jOBԷz~Z ַë } 1xߓp0G!]vB_Bԏz8߯7# 0 B?Ҵ# uo𽑒ϼ{&7ʻo{W6 KN_Bzy =w /?{g1q"81k¹g|"qEB.w~П[sG;`=lv<5=μ''-l2i}8~3;;(}XM~M] ~JGRd`u?nΡ胐~h|Lt;ohz(8rbi$^IN#_(ߗ9 M{B?DBqw~ v6/.Ht䳓p1]{Dt8 Kھ{D>L=8=`3Λ=FU/>X1z {"j6VbbK!6#nA {zt Gn{Uaj66 Xp|}L $8~b+wi|_fbqM57LR*ϱz g V #< +8hb?">;o8SiT/ZC_j;zqyo8/zB'bp Y}ƹOI\=7ζFz"_ rԧR&nZ=*AA<4>(Ǥ Ē8s|Y4%;`'Cq1VbU1Ip)?+]㶢xd?~F 3%EbZ ?ćjd_>e'w<8?_hXb/# `_z ?{6οMtJe!3CC>)鱈CI} Y$ eү&Cq}x _و O/ 칃B^Sy/ß Zw1ހ~ OauA5wKe{%>θiT:|SL/?DL1f=n?lPw0_އDw`EyXX %Vk1g /i~׈-Cb|y a[2UX_YPqL>zyGe#T~ N,mMB;ˏ7}}aR_[޴k6oq8@G,D {sN,_]$tRB>W :%f?݉oi ׈͏}l(} '+TbM,T#V}%{:/F2' ~G;7XznR0? |Λ}8X/wW)^o?mOVv/:Iom#}3:#=&уλmOg(ztD Nt *Dbޙtq2 S J>:V!Cb&sW_~G+*nakO"\#W#~ǿ@A酶U7cK ĮqDVa}&n;*S*ܗzS@vw˻S.ӳIޫxZDzv>/;gc}d 3X?DX{CrYv?fz `'Wb~^ ֳa"֗)}Z>=UyR,=O}/oOr زvVOԁ~Z4-س8N>[&N#78m4KyD`v_E[JrӸQAǰ~ T3<F/bx߲,{mS}Lh=gwv`k'xrk؟9 K/{*_/xXT 6yEomR~=U+s[@ <5=r<;jGve׳Waot_ik ?箷d`{gxIӇE/YA b~剗i&ƍGHoT%`W':Ǜ;|:y~_q%_< ap.6W@2-Nj`Rou + Ce[f? ҏlO\_bV+%gWM|ŃqzP}>By_[G;e-C^ӇRW GF:'>ي65Ejg[<R7Q{ɻ.$jOOO|n/4zslr!'7?ՙ\αOqj]k/su96T:5[Y7\濗-:pRX tGzʝȿ"b\2+#wz7x:0X!qlhpu2^/3 ;`~OG2ߘaץb']bo~iGZ-Oail=~WžLbׄ ;R뫤odRB,z@-< mj`?п))_x^݁_Jʅu"⦼[_<{.|cK}ҫS< ~X}J]?5}^VG;9Wk{}gu/%/WvC||@\IWƝ5yώ=-C;My !sp'RͰSUނ?5AMRa߰߳Z4_AOBqleil U6w{[`?FK^k ? `J/XO P-G%V>"ð__L&[ :O~#w#b$#ñ_$ `Ek<7(|->ӯ%Vߛ$}E\ v= cUQ^#(?=wMGB-%{>4O6 [k z5n2>E.Ч =kwGzb?A.ʳ=B1^)=v-YaUF?$xTGϻ0G&ıi~/)r`ވVQwꉥ6~ `U87W6^/~[Hﲑ8T?O?j4IboˈK!bKy{=b376?!v7xƧ x u"ػڝ.=dV([w(oo%hE콽җ\~Zl{ Q i~m+O[݃0ʆd_qz9kc?KuMrƛ)]a?=<4eT>Q,ާ'\f{8|1Ť/V9"١]ٓ-xI黵v|DQpeχIJ#[1s ʯBKbeP>ߺc>%N o Ix+_ -w8=)  i;2nEF$zMi1X8?k%y2b1&<#uQUb~<olwWBVD%r\vRF,9L,M,ITa.g<4`3աȿrڠLPZȳ|ˀ=*.k{uC'궝=oRVSΡ5b]ߟ2g;x)ZkvqCݿ):tUzh5ޗhD^@4҅X_]K!Q''w7?/_yǃc CtK^;bć"b#8eN=_A@3ˎC<۳4滥81p׶9H,wK일X|! }=QMx\q7pեb+O})v!%Fs1xuv^9Կ[v7yq,l篙]_e]P:M}u՟mh 8wt~KΈ;^~Χڌf"~+}x3/_E|i|۹ڮU2;br>2z*⑐RWpLܫ5OO|K{{;|FNOG()rJg޽?ȁ?{M \byטz(8_ 71>kRaVz/[˟P{ g}UП)/|[{lK_񄛳Yrxj\ZoN3ɡmc1b}'z>Q/ԅG_^g(7%r!v{o񘱟Zrl#_>7Fy8͖Μ7#y$W6'[#nڡ8Z;k<%So^>4?Oibu Q~*;̧P8mB-^l\w ' 5}y+s>.dOhϑSU,F/M ;08GWE704y+ SqzսQGu|.v_H>h= 6utbUhHϋdz!O:V*nGBm6eD& 7NG~%ۉ_Fy:yB#8l3je" +(bo۲9 oC,~FgGyr zA71;w$0?܅!^(X2:X?EzB_e=ɾҽBB _bU#$ȿ0uAy:-C ba!o>OxAr&+&u;rP{1 AN,>;uE>C}L4$B|gZ!> 1:mRCՙ [0_N%dC~-!,þZbNj_`O qr gx SM.ְ_'{K_k?Cbc1yC}(!w!=oUYy{? ,k+p~f'<_ȅ<x xBy3n׉u\̷i1}[^9Oq*ee)e6WqdbU!'^{GO^x4OZGt 6S Ũ/7O;'={$?$O-󑟢L\o:V!}}(qb½䟪*o]>l>_9t'P^l](Y}93˧~+0ObF~ӻU]-O|yWG6^&m^P0_'MD}7"_4į;[hy a0| C `&xp77bx{4gՕ{`_W8zY'm ]U Uﶽ ǣEyuN`>23_}UG[|T|?_{X*wJ7YJ+y8򣯱~Z !7CYX[BF, %ߒᷰ aߴqzo{ " ׶~K ߷G /+bso1[N&V8n?c:;u XHC@_; O߯K+ ?|ʈk]~[`Ϧ7솿fא^g>싙< }'-?ˇT1C~r_}hx 'ѿ:"Oq+CK N\_Ft%l$؊_3p}ݮhX՞@ɳSo[1 [#W&!F߬hQGQ߂EX_ VOIE2c4#>DH?$ }wao].OḂJյHq\OnLhwuO>oo?X5k=".'jϩ}ߢj/ˡ?-xc(亵f=/ߡ} d >/u*SS>Z}Wu#>^r7|g\D\[ ꟒=o?9B5Q߳3?h*x}G,+2B<@YK7kbebm}+z|1_”X.abpsaU} nBFGw9eBsxЄ VOVM%4&sg[_%+<=ܙbUϠAVЉ+}(=+ӈˎmN"yh+byǑ*Ov"v=1sƥ+a/n}ɰV /Xy;64[h~e߼ v'bg.ܳoRlxb{)G??Lly8=#?) NrVWO<_x<o}<`;?WY Z߫\DJ>qmu~Na.[xpF}O<Cȥ_ݣx>iWhȏMħ+X$dOc~Kr]SkbBb~,,^Q?GܳE?1*w mb||0 _t#ϿY޳ qG^\O9pJKmJo~F͡Zcxy*]=pX~`d~/dQots }O|p'C?a|זϧLc|}|W,Il.fM, $r?hGpޑƅ86!ME,{0͘Alv<3H]g9H.~Ak#}y:FOKon!V|N?q`_4VvM -Li>*XBɞ85cX086b~k'ۑ?9>ɋb;FsW&HqmS9^5l:ǃ߈wxqJ?Ov L8wbs9qrksW=ߧ¿ ju5d;?g_0bKy m@3+70 z7?Gon{O%N?Ÿ=yK1l~}(ް&8 e ]yom8}kΛOE}`QUЏx0x,s@ ]:/_M/9GpjGY&[96`Y>q??q5&Xx|Z,IW7/EC ײ*9.nv.}vnom0{#9zp<%ܿZJ~?'$ ?@u-pDu:G+P}St8`ÿ5,ޝU3y_WDωS.bs3q_Ũw`/*1ȇ%v5`j߸r* l ܽ 'WI\Lsl<)Da+qz OdW٪1>'oMyQ_O 9%4ׁ?~rlrMܟߢv&*/}g)ʶ)08M'οUm2Wr3,n'o/ qbIĮC#"+-X>?&V4biˎD3E[uX_N,vʁQ>yu 7 /c1MIxͽ}q?V*?rW}5b]tV]KxR֧5 yN_^͉`57/ 2O?}nX>ޅؼƢ=}ms3a|~Oɖ0&uvoGw4zZfo4NIJ2LA~Q'Wi{\{C~S~[~!^$^_"ޮ/++7Kon$6%Ciʂ:5_܇iHodE<::ß`wwx0 o<HoގزS45%n~wg/ԣb?X~Cd7g>5ǖ,D,]E,ۄe#.kw{pX M~9Ƴ/o>V5^Xݸ#ҮWiA/wj3/'ہmVQIk/*As]wX A7ɻ*ˈ {%@oHW_] {IJٛ%7."}'5_7EAK| osId;D-%A7yLNM{X `baX[ǂX5,ĵĐyM,;W˼?KwOCznO{ق8rItbĀ=\_GaOVtB+K}y/ߴ2"J:+i; 0X-U#lj#7DQENHm;q̏X#'-\K2`bUA957c)?;z#לo9U8 ∅pPS !պoԶh\ז wX9X3l)d D};)9wKlڅXz͒XX[j_S RU.K |3կ!/}L"q"o^qH?j{Xq 2~YX f2#WIJCN&>&Vv(`A_KFNq^K_b&V?7ؤsٟ6ͱGW8bm+9nww22]+6r\XE_m8 ̺8[x(bjف9!g/8^|SokeH}M^xW+Uws(گKdx|)Cs2k/&vqOmy([9|8f{_'Ǔ&ߊս5ZRn`-:_O6޹5LbYVs*2)gnrlڢV,2HSTcMQ#4ER?q岝h_ȱdlM_m8Κm^]n4EsjűgOO4EoK:/˩ș˰"]9:OG;(zI;EkJ i'<|㨧`O[2MS䣟*ptMZu{=tTMɸa&2|hZix6fWMQ&>.%=ώtY)7w<5|҉7쫎wq+}K4Ec7[qRgS.ǞE+'k۷XyU-ΘSp kqploUf)})#ǫӪ}N|yf()_.ڡn0'o"mAB$:s|3 98j1cop&je[Oy>->O|Ssv"y߃8~2w$˟rxr\G4:pavK{&Moz5㜬k>gP~2e/GO{rc[iin J94M=q͏8yc;_-84Qj2{>]{qtq5k>͆ Ϸs|m sm;cϯ񱍗5EٝFVs\&_mDZE!o8ΰJSȁoh?:>~9>u7=ydGoQgC~8W|dh?[OL-5Egw7KmKWdFhMS}V99גѭ0Ook˱-G"EUi'5 %8iV#3Mc{pbxh|o[[e дc]]2 }ϱ.Am")eg )۳t@OͷjZ&*ZRoLn>j}]SLE4^o)ģs84~hzrfb)17qkԂ<}mOMQ_V톊uj'n)=g.a] 4Ez|ځu,{\Jxc.w5 s_ntC[j;FľXWWf =.hq|aƽ5Ev| =A7fRNm"O\~voq|AYth}(hG۹ Zu;tN^CJĒӈvzh6z ]B1n~J"꒼߁gػU'b!bn%[=!{}Cg?ǮC/Ԡsq&SW=ihbκX?/'KN#{𿰲tw$%ֳXCd#Kg8}bLHw֐.cf!}Js#}{k ⇻s|S;U 1=6js*Honi(eY/Nѹ!zZyU5DK4/_xGyԞZv&$ipq{ J6;bO/ 5,>!YPUQXg]J6##;%G֭ X0 &Q{4m>Dk]ď>NNX+Kޤ8߽b4t r󥶑yyԞBh^jNC86t#g/oǑ:;wt8q;w[{8?4Oq9>p\efZ.FϸY޶aDZZ` ǖi Jni|&i5*)zɃ] .Oђc=|_z6SUo$ZpN]_ voBm MR;M8tB7? ;4㢦!w獗y&ܵzqˢ 8Vٷy}t9R73F: 歑zTci|2>;,q !oіckM -4I6<3Ss΄x9$4]ܾs|O&uKmwh=>3nh;9gڗUEplwh@O׃9VB91m-!6J%W nq- ?3mlf5rϗ.5Tq<&Oq}g2 ѱ-LWqܵݕ82#q뛂mb9~wW pyudžqnBz"Vz[k=s~$Mo/kp"Pv-%C;k}]=k&wlX^?{e>gL4[C_cv!f~˯dp¾D,[96lZB8VFZx:cDn6#_C,ީWbeO* x6ccs9s1c-'m+Sw\sD>6p }wk;c{u+mx7/ɫ ݎPO3Qkc#83xq9gzj&}aI$G/ǘ{bꖗ?5DZq8.5|3Ѧ㚛)Xذc>+^7w8xρ?xbf27^*zq7o9s+9j+8>2"i/9+:㋦XrsxH_{zR~o4.,kY7i+봚޿˾"ױ185_ 8ick۱[d0.Fu9"ǂ6S8y\q6gOZAXq +^q@XCPg,#=(߿ʣkӜ}!?h[IC8>yu3_DV8:o9sl4^pg;4b&s4Pi#vvp{d<Ul|}9~2Am&r~nIaǵIcemmgD펥Of""WA?,i[zo8;@bkڜEzw-jgs-xep׎cKfCô m70c߽[,k<28J9Ή|Ld8,-Zq6 w,l0_"pШ3 'y.> 4DsV;3zt HCcW'DC48_cuOgOʍW7s|KNio`ۆ׮1WsV9-HL8~`0 xϋ~-5D= k_xꋹ?,0!?a`#ؘզ[O̒i6|Z3M 7h lɱѵI-lӣ>r(7o\#hlMۡ!Jȸa7͗ԛvBq1E5;}x?q폕[pd]> D}=M?5-0bK-ď*,Xa /th-yļ_!C㇙=OG$MǼcU7biXAZ,_b?mizpLz}xڧQ~.oH;PK8GՈ` tw+;xf`Mqm3ׂ/Ph#|#5D,t/7C[}ڀK}*`?շUw{+| { %΂Wh-UI#~{b yqߟ5i=q!ZuX/lKkD5hE,xI,44wSNZ`jlu?E4׀ S\eF(1ʛ2X2/فX  T#r!9rڥxB=w8r`A:Vd}`_˿wo=ǣL^<&GCzss/ղ !Zp+o8og9w!&!:Vc.x>r[jl\^;gq\e{_cǦr5DZmfpm|y{hڳc[GxW ~0=6ocCh7qBQ3XvXގtM,9:XL4j$⦋ǏqĒ^߻%m+G!\gq6% ^k0eɋ;x33]vOʡ{,<֊Xe39#O#x9WH)p|oE8U3-Y{>9*bHuzKȾ|]OYe*ȿfߟgg c QKrYF_Y0?p;S\Jt"}ˣl /GOI,b+qYYm PHZQo>yz.q~ s Ok/b٪4߁Ađ#UT^עy|Bb޽ɐ$niXXd>">,о+Tg>YUs.B~y8È]Ԟp>k9jCIٕ|HD}XhJ\AC z+a^S>AY!Mՙ)c_;]I*؈ۏK}k0~[>AylWpbU> :# jc~L<LpO2@$/izKjK, ND_kGDmmS+Tk !> }ĵ+7@?WT_f?MĖ/ao%Gp%3 C*sco~cr$bW?Cgb`Ot"V3VKWfUAOgxY6GQ}ϯ_bi~v߫:sxL;by7MϚh}`O |z 빳z=A,;hy+#ӿ] ĵk>CT>[Z/E[&[ޕLTbHrqʵG˷?/ƞB~S.+?Xτ:syw"|3gvY@_7\:lbY[bmbak'w8qvw>]%kz WM3c3GpX:'Z; C9~=5կL8.xf]JvxBvu1o;n:[Gon/N蘲ς;s3/ukxN֐Ygbۻ/Oض?G6ם[Щ7:7+ڒMouwb&/;%/SJ%h^}O*1^&vO(y`#7xǞUU]k߹MHuSymt&ǟoKwns nϪr}CSKkxP|~'vڜzp\,6*ck38>pڨq\mtOxվgI?Y.]hK2O1{"8~, ת#CT{9YOt._wfqu'Fo㼅E|$>su0cäZ6O8~ѺIxH~?u_ñ"+ir3ѭ,&bI9#ofrۻ젰?s<Զ 8N90ЅcA#osӻׯ\47 3ql:EWT wiz ōV|K97p?:CWL?{dzl/]tݑxW[[!C856/e 8nFdzG!8ŏݻ8w ہG1_o02;c^1Cp1Ǫ5ߞr}P4~r%w|/㇏boW)o5gYaКIJ!4^AwB,)y TN 9A6t##y`|VCԙ w@X?#͟^KbdKKB۲KO|GGK6⼗_iat|n }}魈u6٣s8Ƨ;9'}{7ɒX9Mx9㐀TDϣ Or'9nF&9}xV#s"9^qc|jLHp<~xJ8Q^>@UޫW!¨0/{Ap];`x?1&cYt|οA(\gg-u>c=wb3 ZfXxM OWaLb8X4H^bQ78 +蛿|m*oAߜsH8n۩ПX%,gyDjd8ȇ}dA(_e+z㕘@Ϗ@<Ϫ;A}GmDՈ< oО>Y3ߖ y>e%[ZCl9;YzMJaļsG _D{]Xv,^mi}8$ݠ{;Vܤr0;##-'be:ǏX). {~Zbu Nq .&Y".z@}lUH+CB\xY܋g8tH9^ -e񐗍F/]A, {w~;^xub^O(On-Jޗ]Ŕv m*O8Ϻ y ˿oÈ;faV h'ib&%ÿտ7qhӐ?]E,(gE5ƗU&Alpݍa_@ͧ/dit *_F,Zma;] {qU ;ш_ bYg5bqG6Kj%KfX6 -Lu 98l Br\ؓ-"h2v_S_幱 'S7ߗ^T#8h˯8E{V>4>ڶa f ψgf٪ʫʖl#-Q{>O,_Z3*0ⵞ8e$X#1 joȕ-"\G730 V+@~%o:(_:b((8E wWl_aWUe&D+ae>/z#W7٣Īs#>E2[+~}ȟ',E{=M6eW?28[q8Eɞ a=^ '`(TBl,6˰^DBpp)1 _WN\ yQ"5MaSK\{);+.ً0" ?Ԕ86,Es7ʇXUː~Z_ ;NǞ`O%~Lx۱)tTSdiqt_$%Bb={Kx[B}E[a/=̖~}L/X쿋=I>Ci[ۡ qU7T'$Xf{#a_uq,(;K䶗:~5 ĂSyiI绯Ī#}WD/#ˉ~X\8['==F|_n0:5Pyl@0>TL 9S.>8Rp˥r E C~A%;ޚө[5@{i$YzB98XޙX|ypqQ4aĂb&w,ʉSzAߑׄ0`޸MdK,*a ;xGjFfEwcp$Q߲rb`}?(^fLOYόXjN 'oh,^Jk\XXj{;O!tdC,ӃXɅn+?(_$r~7C`!)G/S_2NF  zt/q!Q4` p5bm/_>`᥶)ߑ6E|Y5տX"VeF!d_%1UI̞}q>į#b]}/6&Yx ,f7?Y8~ښ[O,/ /_1l<;D$2o "=) cĒ(U(oL`ӂ·ɝ 5HDl~|2ZLlix}Idu?CxAΟf)1OU݇>m~G=bUnyz_f;mwn=?ޞ8ڮDVH[r&':s~K\>i'lؗ&V]@_>eK#-C?x8.{@,6ۇ7By79t@/aĪu[ _Nk ,'\9>~20ב7;?ܞ;,߃X,)@'W=쩾 qm:l@z ܫc6lE(G[Lb%8v-bw'sSKmP_JGіΗ ½&Ek}ÉVI]'{9?GXiwzq/]V8x_ClgXN,αCQĪ([@,TE:_š8HәKl>Ђvb$O3 V⩂X6;#P$O4C.Xۡoz;n.2 *葿OG]G~EoV+K RqņX{%ĊsrGF 7I%5b^>oZAzE8E{&ݓG:|5,`7[ EıWGZ"ÿˆtbUl,bm {~Xa)wz#?ezg#_hb5N ӻ@ q] ru@F}'- Կy<֍Xwүo}GN}!V3GG̀W.x cjnb@,_X94X1o#NJER5#V~b黐w@lnvj偷 O"N,Byj¿`5ПN= ,wF  .ۏUO{(Oq؛/!;'. p اYCJpDyo&nB}fGc.d^,sxV=?o7f?kdb!Vaq4[b/X>Ј'w?8=f/m$$V-fT⢍{P?}JhpS9Tg7yKe_A_1 }eKѾcuU?C~SPۉ'By>}A=q#wmlB+v=7b@S~$s D:X X-Xjz2<%wA,ڣ4% 9`ޞybkK9xx[kƁ7~$.ʰzОY[n/vl+bW3S8@;?Q~i*X|+۲xԛkoAa8rHI,qejw!~!V 91,m8:q[b*X&5!j!3~a>oq*p"bˤwo`Xw6#x-&/oPD,sB.})obyX~lj(*A~!}HwX߿A,3^<$[ψQ>7'XȆ|C~ &b7h|_8u+)X}ݎM'ְ&^^tđ/!ZAt%NIt[`=wX!;<4F)6 QAס&F'V  ^V!Q_ß/@ykWc: ݯo%=K {eQ:_~Ŀy?Ԃ8{>%UGܲXE>g" ω'vӕuAġ[Se8buQX0GbYbaA{ CyaҗI%_~|$eoAw 9m$'n(C~/!ߵ ڦKsS3* lOkeS[QáWo.?k쟉%NKb+/: 7ElسF~|͊@~Cn l,Bzc)&^7܃ !ר"TK F޾-ϐZ/>/y T<;`_'IWk ;RX]2XXkq_M,_>/%Hb9(qPg̐~i u%1LtmM}U+`?#y'Xj"ʗ;55!Vv͌8AJmsPSggjb,Ymʃʵ)ylCv ֬ކ|!qQX^"V?!7fxC(#{# AK`lڣ;X`GlUt!M,[G,alwmpF؋i;Fg|q6Ӈ\x'z+bFc5QM-'M kNdK ոg ߮cYTCXO?Ks!7Bmxi}:O8%:P``(pImo+ ~7ba%wo+v$f MĨϕKODpb~6 qwr?gJ$0B,@~QiEzX"R3cQ2Pa _!VmC-#ߴ{OЗ0{reLѾ;D)w"?ba+L'yrUы'A~#鬏:3ЗJ+m,пx"!.{7:NY`>yyC:5gyU}^0n" -qcb~%֍8: ĖJ%c6, MߔF̫7ݮB3bcWa|rꎢ<8X48s3q}#LI,T'B\9 wD\z472<qيXf?O8jrhX>85#/OrY~>%.sӦQ> qz6+gDĪ2Hp1*'WDT BR {R_bËP>EıqY}[b Wx V;!'bcĵ'&"~NC=)}B8-*1kWNR"^)Hw4?7r6ȯ) +5Xy:*q0qA P?b$@+ɟآ&!W/Q}x G%5Wrr=b8vsiyϊǡ|C{h QuyH߉R%U ~9t2cQ L?r;e1G~X\sJ҈+o`/ފ(_vr"%yh[E{0&칢~Do{7$AĖ}qm;"{oLܔŽN{;_foCK,;\)1~M.*{̿Jƥ0/ }rķ GОY9}06/L?[(N ^hbo 69͂?Poe$m⪄hzbWKs/GW75tUe}wd W sG=2GA<]wD>o;b~{VKbu:"^Js0sx[xuҿ%$A_3Wq_<&F ,rn.( Vby]_س80{߹`mX}|~XS6ڻ|x ̧%o>Og SP?QЯ4 +723-eg>MbA_qx>8o2vKb~GpmɰlW"K?(K_w)ז/ /~\1~W`S'WIw?e)*s`Ö8z$+@D'?OƓ=aN&Uf\7Yk˿x\ѿ{1Cp ă}nOY? < cByR`ɏ1 F<a-C﯃r %nBl|xGg;]/?뿝+Y7f!լ~V3?*8 sDZsY~u$oZ]tCoGP>)P n/K{t1`tӁՏ;Ƨߗzw7:{ĸK;?K.ǹÿB&7sG1{_yb)v)Fc?q fcl(һ32,"wi'ȁ;c%6>LEyP>ޡb3L|j*⹀Av2f`{=6K*<0~,@ow,$RBf!uc_HXĜXr(a^=߀oe'ʧ-!njs/"[EgkV}L[$Xru yqpreā(X}ih({"Sk<[X~ez;<1?I/y"~Yt61Яrˈ="V_O}+^X0{=1_ ?շ'KxX}_/P%ıq/ Cо cPOLx'y —Po> ПaO+/-/h߳o1 {kK0~cofox5L^쭐žnbIyaO/=X\ʏ<XW,7_ tį9+`:@%}YIļ5YaQ^ko鈏LǨ//FzهLl>6b{=Ez^_31< 'muXa g`+tPCx)|u/30a&)%iѹY/XprKHҟ`33^g^'x!՟;qʼnLFR9E}Ab .UG|OA Wz!_~1+Q̟V]3X@|:cȿ+jO!17E|9?*yQ׏e1T0j_%1~y`^s?܂ ̆|_ C/83/`/J!?aΣ>+"pAf?:o(E}٢*wB 9_k;BnFa_?{{(Wl ~/4V߷#>t&<ȿ͖g>A_go#Wݍ;߅Db}^{'6=N|ՒZGCsa3k u>>gU_>>O3gA w1(7\F`|KWfJدt|YT)6=tka}/[_!Z/S +ПP#i dԇq{ƱX~;X}n2DŽA+ `[!=O\VlKg?cKKG~Md';?>V<7g/X}>,7];ʧތkJ=ַO1f|6C*@7 }P_Q(wBKHoΞ?62L޽tO>bm{΅&ϋXƞ=Q_RK7 .(Oes-f<̗^ X-3s,Q||fOrPQ*7'1-q$E}{L) {nX=R+S:c|KfLu9>EjWE2Q[X{ $Jl4ӶAGZw9!71vBkY%Ne>NGr}d<$Ez=w:'.H_ ~qkNlYτ}%ϊXgӯu=\1}YaM?VHsV),0[1{kVƚbv _ւ7}5cd)qQ zx:䆬>Im|d4[ʾH7xd̏4=}]׽CŨoPgqtԻ46;Mb5J4 QfK!G$1lSi?D։cHHx4c"'cG+cI.7+oL@ʛ'oC&$O1\쇩 ?6>0!&`>W1cGaL~b<>cXʳjC.ѰC;)Km!ٟ}7'{ءC.C?|=~7 =bin5} ~_v?Y劳۾ڕ}aaƃ( >Ga^Lwؾ+]կq!s8(S߃kҏAy߮3/ g>dZ1~@iW߻x~2XO0rxWD/~ WO,N[|σWk:v/:ez1l`'b?a~T+jIE}횈R1x(&r J숛._oTc77 />gC.>[IM~ nF%Ă3Xd |ubq Tйž߭)|>+q/b|cilbً&bh?ȳ^|? އBte#ӈvu4o&6Q[u [M!~ c#WJ>Ok}yU{?"}b#v]Jߓ_/M"܃O Cl@bgv$}/~{1} L42XR-BÓ`#Hy7G_M,q-"(@^ O-a!Vj͆=zLWj#RyN%fĂS )".q<Ǒ8p.X泜2i#]`~(b`e┹u w4 Hqv4䲴KI/_ `BoòqrLX_?Q`_-iK?+/?Ir=ȷ0>ؕ8t,X6;)"NwA~̓aÐ׉X1ӌŃ'x`gD?oMULGxE*G|H?A9:-"X)݉GY} % ֞X ogF!#Vޅ;hM ֭ڀO[c%xXz QQrb8"-;p!WHY{HSXa a?lXXxʧu$=%VƼE\_[bM[XLv'v|G=cC,_ ؖY'\B=Xo6b3)[|0/gsQ+i0]^SJRa{L=/|a|UW%kz,Èy>J=gGkЇǞIJ& EzqkІ_{)paƏE+z1}H5arb7L_l@%׆=?GY-C#U %d :)\?{{(Ʒ8bu9O \G *EBPofڧ6ֱ~Kkl?WrSēvGQB\v/(ıGRx -R%G{e1>TSb_%Xgz+[ђcYafR_%v X^髲X}ѿEiRg [7 %'"^'Y#) {3g.`Kް5h1;L VYO@~#|Zd1=$6%AG_M%N;/h?SaO"f `׾%A~ׇ?%V̿u~ߏa̭;~L~Ա#o$aO'*&$Azӿ7QC'_m[g%6#Q[;ߌ52硾/ģci}]1uC~1yÜGUM] e:ڬ|5sP~>j[|<:?{ X{9eƃ z6x>İ-|S+Is yUg&׽feGw<ħx{mp&CWijUz_[3p/1= ,<|/ DE}oc<}X u7y`qSṡ"c#x]O,RC>З[LH=+u~C>Oxx\UbYl_}k^ -ÑZ> ^q_r3ұbG?ax}_y9{L{jR+oj`=kH\J Ӈ#ga,>(g_~?C8C;!8e=/&gq'NoUM{c`/x %װWq_w+׃=Iw7z"=_5c?N{^)!d#=wDe fϚ/q=x,`gfW)y/K 󏝧{`{ǛO)3⿀=`?fek׼|U?R/dߨ/̺~J_O4䅝wdϓc_؎=:Gy!ٟ{N8%x~6켖8>uR!ou7F쯔EHﷳo/Wۚ;`|:`uu 7o9}w"xߟ$Mq_<b)}~$*aj yY< ߪ7 }$gOJ}pO~+fuQ̟MAwfoaUe,}߻ +g;.!(=[ūE ^> Ǎc`bq=σ7l ԚʷJ|9LlI;q~u͇C,i_p{g2ny?h+OLJY ?:O_ ѿG\-3Aߤ Λ ?#o7;K~`l?'w~E8wK g% ~H/,<; ai+9Kse?:n{|YG]>۝)S??̄8G߽C4@~2.M 4OpCd/}Uد-0^% {m(@~t$v  2"?!bn;Ld?SR$IS<0UawBls{{⾘@l{+ogbA|Mmb2?tM{!n} 8RC[θpOs:%^[H%b܏dHc9tTR]""ٯĖ't1J\.@lqZ`W;i?JeC5]3_+YcKv áoل㵉ճVŸI6}{UN8$8o)~W->"8!,pߟF;y}- %q}6x)^ܷG;Ƌ#1>w!')(}U[{m}k-[{[VH;ɻ{m7]BYoe5 1o?Fs)q,Oly\=JRB(؋J$Jb&K)kdHJ1iQ(T֩PR#!%e}^wq|\g~uU6aw7z !6ˈ#~[0Lg!}"25>TB?5^É]+l^(*6ْ3 4%VlI y -/\Y=3Hc%\1E& u[z'yW.&hw#wf6^,3l{7#İgS>Fb9WwρM/@x>Y.|gc8w_ 䟾8lbb˲mD'b'ppu+Wɿy"9/rȯbI[^Wn7bB~o>e-͜֎[ Bj _'5$}?gbw{ E_`GTޖ Ձ}y$€XnGny+:}㡐wo:`> %qA|]/ 8gPϜ-nmLr(cb-;;A\fЎO6;F~͈Mr{J?[~ΨC_}&kCݦ@7Xv#!&2m*5.F~;WZ׬ BxT۸<Я>b(sbۣW2X bO' ߀>#KxyS}jZ^9ĊErgf2`䯚·r%?XA?~64JZ|zq? t} `|S6v/Յ} _c> wJ٪-K,jp{pc4zT?>bיϜz\lb)k"Sc$%&ž bٴgq\;75#.S9 a/O9r]c_ !}/p_)H#q_D^y2{?o:3@;w׺H?,g+[qZH/}J8E|QpK%682"VnՐ߯ąS=J$k7) b^ Ⲯ}35-aQ:8t0E %Nsg{݆~WEoKy&jކX ʻ.?>KZ2Bg`?k:4{ O:>wVj*b$ą ?ZXb%,&9D:$. ֏_1ғy~8~2Wb^^wbbX@\SyRǹ.ZMZт]7^@b>+lד<)9ďCzjC oD}fž$ޯr {Ûg"~뭝H<C&đ=!jO}z1 E {.7@z.ĮOC;*W!N{͂;b3b/u|%}@CB5] <sq{ֺ K.vh K_WXp g ?_-}>sG޶kbOZN/IʴĢ/e~qvOߎZk|RC}vj c(EYOk^Y⸖ L_L,ՏFz;c Nlt4oԢ:')]!`/^tqw'k>+Èm<2w[|{Cެ5Ӊ D o_ b(+qCH?ϪB1++EjDli ^;[Vtn4:H߲g " 43DxA! ;|ҠF_/v@~{+׎K.?qٞXP%3 +[vkN9W2rg]p-Hxw)8Xp#X%!"y%N>,{'WAyCSk 7Y lkBC2w_AH3uC4@Wr_[b Ⲡ?T# n76r/  Ot'=3K?EzLeA_; w(".{f ;?'.5OpIy))q[~ _˽뫌iYK,p3=] =%Cj_O?)_BbAOJ$k_7K]zW-ћAޑ,būJ|:udR÷v[F'NAf4qa9 &|w?3z |@l%Mޘf;> V\Fs+ӏ+Rz{ _o/T^eOG\VOVx9~L߈C%Z]O-@z?~q>q1lXX$GdݎX)CZ ~?}$۟/@F ~pb竐#ٚy}-7zC݄ܩ8-@EyEqܿ!bwܟNbҏ_yϞK\ #k?#A'pS{)x;y&:E,|ŕSG'>~LdLXy}q9[G$6Dpk>Mr{8'#Uve=qnEl2"-/WB<-b_H- DN!.t7<06$nX{i/bu?$'wBKp!*G5Q`Ak:@  }۳KˤE҈joGȓ;{G5w?.IUSxHÍĹw54ҫ&N9_O=asHғ<Ī*Wa!0K7?wt#o-#eO"ve 6? <.%/}U[yxOX }㸿n%!_H?XO,o p¿HQ;p(1~WRQ+'Lg+~Uj|NI%_k_>qaW?>X7qD y ic Z*џ@|qɷa? s#}e}oGKNzi )whbQ3/>8roL,Cbxy䂭(o}.q]Яm4hF~s'Aco/B~zqG ⁐^棷qثX9qɵoH L,SPxI>;J- [F,y\Db7a:‡!v=0X_8|!ic=rq>qvdb+xG]]!=8g?ҿ`Olt}/;s+!đ W&8n-䗒O4+Az#ɱHߛ=D~ąej![%ҟ4<;Xz#ʫ~~Um 60jK"e)FĢ/IJ=kw]FZ1\EloZI,?=Yu|b@_bI~eЧCzޅB HQ7,=#[q-~DJs-bKw~}^Vu6#-ezTwOז8.{fگ%0 xȗ'MH,)G\՝XHǶ !8RiL,IllJ4AXaG^+kb( 덈MEfS~k[ |T 0y@.oN(ϷMjxH]s(ZE,K>ˈEɐ&^>_XYF!on⸧HJߋXOFp}JQ<>P|;hω]n#= &`e2j}7E\%䓃S|7[]BT^G>;F;|Vᝋm=oą~/qƇDgOxKZAхwwi>\¿Ēۼm*F}P{t+ "hQSCz"s,@+u׀_@K;xz>G ܲߺ.B^TYL݂\dD'~MYs+_s6k?-E|/ʫ(I\U+_ {Au$_!6(h6?RpKޭ#rÙS>'4beb٨^ ޶%fo Bn`Gm/s磾ϮoU wԉ\rؿl?F"VxX^Q_#@bYh$6_oR~WAmˇ ,BxEB?U v1q\\ޞҝ(|bch/e͛cG\R|/ob/>A6CLbǻoW‰nFSy7G,z'[r.ԇX$Vz;!ù4 *\՗U,qN؏^_wg!|?BuCW_צͺ+>O\o #k߯KTYd]| b w[]."nL,1f\{d*t}|4Ͽ'J2IJ7G:^^<wqdϱ /ߧ_.<ω'qv!K;s}\D{A>cŷσ.3y h}FyAXϳJa|;>qgYj.[5vq5qg3H,:&=~IG8>r^M<ƀN?&6;|yʀF0ο1=,M=qJj/bV0(8bSDFvIW7N=5XӓXq'ۏȉe/P>㓿P/4əcZkƦ)XvĢܕ'GO@K䷭$cwKKBJ9L|2 Y{=UJ5?. [=M$kq?O;?"6IwJ촱+u%NL4:^}0b>!b<;1>i/9Mb [߇CQrYɳ'Ÿ:Bbk]zݐ3oxWrK?S4\}g&|_['[wq`t'Bl30YrX ͂%[2xx#KN\f<Bx?bk_?}rr [M|O>&﹞ % "P"Jh} Ά>N”X0WbJB;T}^٭C+1`4Ȼ T=~R`<51!E~oF\JO>۳>?ݑ~ qxOP|lI.: c Og¿bq袋xٖhcG52w4￈c(&|wOr ( Bͣc0_g=^M^6AXDZ]hp~g5x?5ġ)Zz|W/oiytL*kmԋ_Utز]xJf$#}吠t?߹Bn J,pIg?(9a}\In^ NA?υ=gb8y=EV_6 9Wv E'w} O)r3n$(Ʒf&<cD:ۣKg?P|eo'wgrŷ<q0q8nOeqC$nEFlrmKq3DsGN%y /8yJWID,hE|$^АB-;>ˮϞĂ'_WQi~b%bσ't#'l3$⒡Kr^+b׹_9̄#0bQ:3u+h ӧ{wag/?ԟQ}>|Q[TSnܿLbSߒĢ{̗78=}#ѕn1?"z+G%ы} I)'RtBeCpNĖ]_YNK}͟)&w)"(}37Lro!T~MPSKEO#?loB\vXP.BdY~iReӎ#x9 Cx[) V!XuweVYI3oRAd>? |$$^$W R|`AyY0q)<0ޔ*KaA-tRRO2 t_zt ~Ba/g#Wa8kf_va0_!;%Grϟ˺ZH?G,F_lAr8=zQGlp)‡$.3.[A\eBw^E|?U8 yOOK'W*j~=q].~#\V@S;,Aln$єl%N1͘wT`̋Uks!OFqQ_=?.ӰucCՑ}mI4~.Oڕ?)bH/4+>KFp{U{7%R o`G?}2s|^vTD?a;|Gק Y>K#Evy#.Qߝr{}RϞ-(Vkkbיϐ~'꟡dr!ї[TJ G͵xyot_r8=D1?ׅ8BO`1Vk>?'OZKp.OOf!|՗g>v{n9җ=] ԇlBq?Z\Y=\~{{ݚ?xy,I^jΗ>< qVϖIu!+}sYb۝a|bgG?=ٷv~}R LOMD,H)$1دWܢ=*`O`%qv*ğh1p{:?$"Wlm3n-6F8 ==~-G3>"70KF;j?Pq~N,迌_BK oÃئ=/_|*BV8E}gDw$H>+KTo3BKh6פpE_6;$qdY)#Ϝ}zFܰ30k%Ho+Cvn=_qɧoE}ܟE /-k~߆|ۘ?X7CRP=]OMVsĂhfLT M:Oy. <(e , AVQ[W63\#EN$W`Y|W'}$q4_vf(eo3_:9(ƍCJL$X+-iV<G|Q$'ȰGF{>~~a<6-B29δ߯:v6 CO, j#xHwo)|A9/v~ ݦ=-"V&GurݔGY`4Z>~!il|Hu ~h_C,G~.&ND bނC?`~@}XGl4 zbȯSx y{.q _C?T}UMez%>:WܒOۜ%,(?'|^}#_ m.ȵ޲MfKa}P #=|$qM,ب5ʣ8kLImFmKφ{Km\qsvȹ~fljoE3z) G/bFҋhaO0򷹁R5O|]_@zgE5{N~&"弽רAyGB@6@3_٢[_ˤQ!wExvd ~2B?}}]_lQ's_9qd3M,!lAys;}poI,ק: kI,>L4[lC4k>8e,xM qX~tW1&.t\vlF_U!ݱ@k~i7q A.ȯ dO$1*r#]9 oV_ÝB}s_e鳐o*%? QۉCGr:[5`X~9 ȣտa=݀,A{]Mr׉۠16CYKM6!|2ba_B~GNF 6=[<uġ*WQVAE#yW%q]tyǡ?\#.n{xHZ9}JC~"_ia<"zG'j&,X^ b^Fv^;fu)U$ @yop}_Hyp%g U^u_ɡ,$p CmCl"5_<`/ߝ(rwbuw7w-I\짨p‡ Nz^Kq>޴ȾKգ||6g|,!5e׀=ӈr u>MYmOy߽=P@\,4q[oa/%]4FKHUon ?kP|G3bl>^K#R}ʾE}|-%V˄5j9''_m2>cp 1Bd*Lȋ%h|??64JOοۑ\aZ{\ R{ }W>{/D,u@}Oߏ%qc玃Aue $ΛT F/%Y}G%{{ C|؛RG] C¿RU?aIgؑSUYd_+#sʱ`+ ki=3~Y67zR2w֘ ~[D5'٣"t0r]|y}3>-/T> ڳ#7]W_ouL u,h ~>7 1]9.H, a" @l9!)<Rx}PjosN82$W>pN]vg'襻&D ӈ #?'W[B+<ؗslSYvfi?^i,?ٱd0.1?vˢS`-}5HK5=>#9Z̉'x^װ_%ŸYF)!h_u=vvP]Wy&^#m{iog'n76#N_x7=|ԏ+&kʡxx"q2ԟl"{|)YJ,6kS!峈_Pa ڂƿg8?x@{!X_Zi|XCr%ąk̻]3#OVCyQa)ￛ=hI-Ϸxw}?Zfo͍-$W3vQׂ w8nG B]|֑}PhoKĂjb_*P'G@>;9K7 m^jF?ra æd¿ by#ox[>\>_& G͕?<;=#|SbKo# {[?{ QoӋXq\']GFJL,[|{($j!Ju?}*?r'a Ci'xL>->"&˜ oC)[=ۈhR&7?Ez^`_ws/]g?O(?k-V/r~%ڡ~I{ƻ¹+ø~ 2;П{,‚=8T^ߗqN?O}[ҋن:?F8_h|ucE q"?eVrA>]yzC`6bA1Ʒr7:O+N~;¤懢t~):-y[g|J'É:6|CђGP?ia..Ԅؿ 7=`m p(o&0@vKxyIj:ot+sb.JrI~p]so}0w$~KCPFXtH/l [lF5^_ZCjb\QW;kmcJl9EIHocS4H4jQj(7RzXVlV8⊀:PȈ]Ks%!ϮH>`iǂ_k7oυgo ܏cL//ArE|1{= gR|ʯf_±/X {k S1ϧGQ>,n9ݧZD~O:Px|b~; ^9#pJdAlm}?} {WKǁ+ht_jX}lk`?OR%%? E&[ )r1>Ģ/ZC:/鵲!6E`>}7ABM7IYq,e,q~ض!Z 5bQ~Vߣ[L6]%vR1oe+ߝ<_6wvġL=:XiToSF_Wjđ!~i#q-(t 6J~;ۂE9/6Qby_~'G\=]xGz>NYPD'DžE|m\a/b!_Vȯ>NXEC۴kuٟhS7wᐻdaoA_ԓ|b|譧!e轑_IJ^>LQ!|G(u;Gۂ8y /$kUd!!.[8sb$`?Y|zOokr^u&}b|Y}}Y /B1_A^3O"N2ߺtOc/>Ėџ7[W!?U>:y7۴hM Ǐ$}K,mQݦT>'jbbFms.qM)?.퓗 ˂5T&V|?↵S4q6' :O"ܪGXzg+4CZ$qhA7ń)w>F+2|D nVk Y Oܦ;"gkxU@eoEzXR :EĪHI3{IH P\?{A8 |H=h{ ]Q3&xG-L<6f;Q?}H/>;{7 W}FWOD"aOos'AXsr}ך񾫲 zཛྷtO>M}Dzka57Ax'o#Ex6g}i~? qc }7zܟG6n*P> þ?}4Ҏ зy_>xLmzH~4;bx/Y`2T0x2K@u:ˊ\$N{Z9xSE{YM)Nz;]SKÇ+ljﱅxO3V"9x'חW,Ey>g"⸂#?Րؽ>o̓_ +Q~^xHx'Aw_="߭ާ&g+4R_I'O<#Dlpr]d;yxMGb^_齌X𓈓U%e=oB_tbK|v |m+<X]mz=NZq̈́1xJ,}tvE\զeo{ޱh/ ɍe㹼/wzJyB%o8}>;Z"=U.ǎXWYY#ZFx:?-p8US#!sr^1F]:Ng7BJlRnu;υ} iP&$?oe'>Ds=}}>|D1?:Ԃ"6Яkg+0Y='y@d}VQ&o|EGjǯzV!~a|gG?A__{4֟?$G:>U֫G.yU5ϧ(Zf3O75-qQqo ~wE%|xlu%h_4!y' F}fG5 ȒlxXs܂+7G5q.o{*6H/bO3˟ZJ>>LtiJ??&qCs_2oSDt'3|_s#!_<|~Hnȳ0[$K 'wޏt t:q n>>ُ~˷j?/M$.Ehj l[l w~W|L\V@z~_&o89,WK a{5r#&\A&œG.=L>F d! #~Rb? v><@&X0^%mRqm}S &^ebcاM,o-/=7p\Oj{'y7$̗"N=-n~`+bj_O,6E%}bޭbobd{~2}ڼ="NzœC*Brb2ԇ/bǏӛG+)!gM9G d?{8.W7Z~G߿3/gBpv쟪m0y1)}]J[nu|7{K_@\X}'v X#|Q u#A+eąO2qǐw|QTbKZԀ~KoY#Ӆ}?kڵ=ڠ&' ?{[|5OT;ў8_ye "rFmA7s9ߖ#6lM,n"N+Gz/}柄9t7X2*X̾ }}OΥ?+mp~sʳ >6[`R8NWRڦ"+{x?~ig=h KP<Z8L&.As Zv-M>#=[W䧊F{Ė#9֢<#!gQgJ!oOZN$V.n|.O?ζب]1Bzw [|՝}DE\!wn MF~2?|-qNE,]V{*p8޷ \#Gf.?ԛ5me'dݹxxһ:#>~y{WaO; 췞^uъH5"kbY `E/[Ս?T >H?wSMd7¯~6G}=!2}fg|e_ó?sgK&6rk9`uxgf Wa$/O~:"!7lvC6đ++g?Uo[j'+a6ļ=q"/چ˞"KOs5Q_h)(?Ubf{g؟@1V`}> S,:=%/Ro4>HpiiC,o(A +qm}~Bkb\y5ZLSpx9Tg]\w!Gx+BߖKs1IgLe 7@qi;$d*y@ ) ?d,%61`/c7mF"Cʫ?(wQ% oTz]c|0mGz*H1屌̡W"6?MOJJKuy/sD$)E|R-Cj2JZJ,P;?ENoŠ=𧢶GzRⲞ[N+v@nxq4&k0!\r7Eљ_#mOl1}}HEc1\c~o}Qso:¯iz^p%w|xZ/Ʈu3 \ͧW0ٿ_}oy!>eNI^=kV4tMվqvxx.c&>bUw[k#K=mqŹ:"Ɓe7/^j_+>xՅL'nka=\'^Gc7y^.cߎ 7of|%dO_,Oۗfتݛ[6t͛A9{M禰n|X{>3nb?җqhtn W8M" =u}xHs}xnMUFDA 0ΌiY02x?{~?_bA5q';NK='wfS~x%]3lӓ+ϗ<>y]3A!`Wy}iླྀJ +y ?@>-ΓkÉܗ O-zºXGo$~-CHo^?k? "jDӖbT^6 &LB̑NĊb(&ȯUK6F\I4ͷⶐx֋V`hb68Ax]TX  hnsġN"p8c!k8yUoțu$V}JY$oV'Vj!2G.Q~NsأFc$ҏ;}Y qB<@l`UJ|"u ,FtyC5N;ƿZbwZ{±xC,Va~MlX ۧ0/*!Fe]Z@&b ۬&g/I_#@h ۅOKl"GH\8}mAnv AzqD]GH'\? &.i>Nj%ލ"͍o>w;!=M>/Byo' VWkXFz@ r:kQ,X*z{X e T[N&ԶD 7ȟXyzm%5BaB9'si!hrġĂe3{ (6?XO3ġݪ_ nsdb,hba׭xG-%F6{%/l~Pb.` NXnb^7`8n[췝n6??/2ڃH482|7?YG\ He -X? [>.A')LLf ]N7c}$_ ~*)"GEYĮwzA{ ?gBH_˒dK[t;hdjs=}o}(@ORګd)Į\|6G,s=ġ-vXze7o'#=k3g|}Rf(%V^\RwXvOB:o,m-ǿkHXg/m yx|  @.rbeg;Q>l&֟S5K|q_1ߐ C0@vbYC e:$OeB8xIW8oӖkak ݊m%]qu<یQ f׈9ٹ0>IEzHoğ忴 |d3qͥUHƇX$ M;u=_&%Jgb⼫&֧lWڟ|9guK~F?XJ,Bjd#|q ) k' q~q~p^*F~fC_CI+ p~ykt(L~jBz_aMk7gYs?)Fzr1?Yy55C8oM #V`'Ѽ|@%w/^]~mtCmpoGJpE_5~GX|;-𧚇MjwⲉݐDu'$qр;Xbg/$>*EvIʃpI2*Ο0B_uoUa_: U=C<߇"nkpw}U Gy{} }J=->q]O9mIJv_ {R~O(~<@,?Y+q 9ܵ#ci+pB'u`9tx Y]m>f|#̳9^ CxN:."pbSeO u{A3y+q8đVĪGc{G=!{A4 yz|>)>\#60MɈUnyy6b}:% @rOex~o6@~UUH,3w̷>gNN<`QNi|>B+}{0֗z~yz'e԰^V[ؤOXP0c?EXҍ+#zEc&q$(OL2s0ĖiѼ<\\=O|=q򗳉k&A#o !?/5A @|s?"_}?~L&r},25B޼}7qy[^qߍhO,_zgQqSG#/)ܴjC6`<~jG^ yON0hXA|ǟ_* .p.[Esy?|L2 v{u1ܞ)Ѵ\".t--6/]M6".`26L&֞ߧU|k& /]g c}xi-)F0g~0I'&֛x;_d+WggوsPʍ/A욍+O<?d$_qnF9yCyt`I}V}DyeY<|_΀eN/O\yN  6ٌ8?ۄ&<} ;iu_VS%4paOC3~85@O#."ּ|q>$Od1\yM?.5FkAҶT?~tfq{3֫8붛o}}rg[Pѐ_H/" #Pz[AUI8ۙ8*[0~><4? pX3y{[>|I)?9E8yWjߗs3^8'9oW˞sqQь8]Ԟ'.="ƃ_.ߗH߰f̿Wb;+]֝߇FFp?|xz0^Au?Ϗ۠GaZGz!_(O fr PV38Ey}xx]sfN^_!HA`)˥}ko#/!'@7_ ޟ܈Bv9/W/u'8Adi޿F{qF]+x.MYO%^Y~!SbT>>O\^$k~ 60yy!/d<܂X B0,U4^?7/gzo?u`>X.x!oZ'>_%pPx7)P_#x|-'η[!wΛ'O)~Cnb/(A潾pF_yږBOpSBԟs 6Mf_Cu;yb}J+_"/'|1G_{ +u~pr]5$OFD-yLVO`zbE:ba@? z'W|n__5pʳ ֡m:2~%\9qWFj}LWa(5 DbC2K fi3e)|=_DzXe!п+~+-On`=^O,Q>kk_AU!?uA{pd^߿V+ͱl[ ><oJ"dA{$Xf8]֢ QZ+X1$|?G|K|?'#>(}Nk-\'A(pN k0_[=s?z${?(O} ,ޡgp>_lyG8?'L!]kpl_V8ɂ/+Go׽yvU?oއX @ۅ91^z&OPƁ}E K<8=Qv@7fE,9]3l Dxߣ&k//|ioNzDK'Y]Dd ;RM^skkSǜ7ڽ8Ox3I[M.7V>ju¼w8u{ ^́7a={l!>;-[{8'{yS&+_e|{ӱޕ?5w">W>@P:N<|//)wc6GPwX)o7&1c>tV(gm1t:wrwأjl㧊Xi.exn({v`0vC1+vX Wr{5kpؗq]V~wb !QF͹=pv no'Hx`Gj@>R'i+^բ>?zP8AkbIrbarbXG Į=~S{gByV)ġk$'U nGDD9hْ_7i-wS t V%.O q ϳl"$WGk1Qm|Ϛ0˸٥YmVbllb*cw19*@˻ƽ{TWP6;o.c|9Tb6jx鸥Mۤ]?Ȭh^|g\ئzݖm+͖2|7=7 ]+-75ݧne Ѹmo㺊71^"n<5ivZ]ggwŗqҏn0N|tqo%d{{xCMj??"H{āϾaSҌ횸,NuOn-=g<7oqNQ#u3lk?6G1rFm$w,?UHX870ś ~fnl?hp9s63 a, 7T9VM+j86{?u/殨^VOBص 3_>|Vh|o_\|^vkʿ<C~Q~Ă_0fM,֟r-lbϲ ً9s Bi'{ \Kn\\ZPxg.߸̢" Wr!/9p7p^)"bo$w ^fl{خ͘w^8.Mc]3^¸Ӡ?Y 98U+/e-Sc5>޻;OjܧcV0mCѲ.uw?}[ѤXl4,B,1XZ4ke/3a 1 C"|GgW#Vy'[8 U~|VL@IP]\@e]B߽}).AyB#]JCw<G( HbP~+u`iHΉ?Yo ^c k~H6c^oįM'61i۳4Oq/l%?[̈́ ZOAH, PrUwC:os݃&ȭ:>*!jo&V}kt{ڶQ!bY~a`t@~q,~ACp4IP( j:%_};A(Hb}e1S/ʳl&kGMi@{F{Iʃ0_Z#2_S5[|ό BI,'VA~z#ճ###hK- .G|jPG/u??,X=S:F(ONl} X|ٻ1k'akK#ñz6z&yK,1Spᷠ{Gi?0߸N}8> .KO"O"Y(x}lBWc+~ߍvĢZĒ9(|]_ѶIJ wxK,h'K>M>a{F~ՋEҰXB,yORbewx-% L#[ \`J_|t?,9+0XZ$_~¿xږXG-TpY kyv}x:~# R[_oa_yz%q546`/l{"'`Y7 n%O^~_Z>m,U_{@וxw G\r?eqә`]|/9] Nif'4bX(yfa}Dc=᧠JF@a+ &`ywiDŽkHB-qMO{+W~c~r%xg}aYj։QXJADݜ8'}Ζ:Чx4ϯ{ལ_oS{曝 %}"xOL;An69X1NևH4ىr"j bt{bY>w݆KڧlG|//7ޫB0_~7;Qx_Xy@rF(CuV*^c O yf䗭o'Qߩ>*QM[O_f9_'w!߹Aau1G7MWq1~7*|}O·^n >wj9E} /c.{Qx0݈͆ɰ~ B^r%6P>8ޭN, G4 ME/PS{6›lyvA8|#k:ǃ1)- UاO|Un<%GKqv5VR؏X,?R 7iđ6H{brce]*P? }X?Rxa|kVġiQxIw݂X8,)@=QA sW (b@Ƿj),t1γ<ws#?J?ЧșXQFGB b6' YXJm;IڷX2U+'N柶>Ψ%nn1'P_8J캻 |1H\xv*BFe7qhg 6'KC'?@4b*ȧ'}{X_mc!8/}}8m'%=f+^j/2m'ũAT7OXB,җ14Wb@ ̩~}_zs5ZU);NCwy_%ϸyS! A_ce1M|O/2̌oͩ~dw8{~>dc˶r\kLG16QG&1@|ot^#},Nkwn-f|;k Ǯe,0;qNV1^4qٸ&s2jnyKeOQe8/*Aj綴H]Xy߆س4i{R0xuƽvgM0c/u9x1>!Uضb)㛳T>?bTZfYfl4}\ ?vceaPƮw-]Ue~PIP86F$f?}X9Fk6Rώxi͛g}w`Vp[sV>1ޥNåVޟyȂM_Yכ]~Nn_pdRۦ}:;ok9 U+a?>WD]QCey.j6b4>s{Ȱ_`lނ+[%vg׽WdC/]1mE-m~j7м cNX4~ok?dݬxcC;_fb5X_O1V_h1+ ? ;*o&/z=X֑Kw72nݢ^|sQM|wK7u:"l~;2uNĺyMxϒZ+Wl+ƙ)l*һq8|5qև3Ou*O9Orq8-IƟ#0Z68cϲU+}Yh|JϵPTD,=4>߶QO#XY:}Ë76ߙ(3WyA^=Oz-/Fl)KyB#;'1z鲇B][\:{`ƚ#u&~N>im`z#Hτ#0~mk1v y2rxmƵ.4-U3n vF6m.%II _J,nbR{fu"VNZE'_' i~g-#n2brrrI2X&wt0+?=Pȝ;Q~:cN_6ؙ8ݵ%6H7X oblbIE/g{) H~k?O| Œ8=}>[܅ʟy>_҂dDxޤuH,9 y5QyTÈ[PzSy>{ UxoXuf0t,/9» ՌX<]XH׎ԟ΢c2b3пz5`tʿ&pݴ`|oOvv4^n #Vt%.ݝX!̐w8bك6l څ'_@JzTFĢ}-3H_y+'W74i|vO&.EӛI:w#/;J_2j  )ˬЯGb1c>ǠN%4eF5_6=]-S&Vx]oA|9 qMjT^[ X2up9bHor;u*I% 'm(Xa57~-+Rү!Xvm*hmXe"wb8]֟Dm*#);S)?A#0?,&}uk,342?,K9I, '8H,<+-ۙR}8M'h~+k 1}~(?3biQ:ȺcQM3N^: K2Kv?MPmFY 1q 3u<\M\ϚG˺4.I%K{#}C-5׋6 M}=W4KO+#.<bU{JOUM,*E7R #}}__W2̿`+Ҹs5/ٌ\]-K78G~Yy[xa.+c|΂<)qvas%v?w\w7򦍅Ŗev~ Z8thR{G e\p_vƕZ|j!,`; a|o)l1XמR4k%C>%:rC*_kH>v._ٱĒћ_uYc~y&D,|_Ih}H/ڜsϜ/\?..+<`,?5-@ׇ5=gxpWB cM0 fsug\5.I_X/w<)CCGU;vwyn0kq63v3R7dX<8OkoPQ޳_yYc?0»|LqTѯcԩ&>Ģ{Lj pi5V.7&$DMD,1G’S'}WXbLlS%%5>r[Ԟ;9sE"=ub攟o _>܋X@o=[E8zt1@}LjPw0i1N׈E(~cb5B9Xvh+B^ ?ʯ*8K7kG>KIn$3LX7X3=(rIe!UM\?xo$VN,(!^5<]TIJF-7peךR6 0!wJmE4/H,r Ie4vIJb;zտ)z>QIJ%Ă#_E%򿯧*=?4Pv-JubIT3ZXPM,&XzC7ʿ !v])pȗ#Vkh5'>_GǕvAE?h~ cÿS?P ڷX=Sx#_'bsTg ;0_=bCbι1լ#ֿpG) .ÉEo) F^IJ|bY Wy`CU %ݿQnz+܈-u X0ע??bw+ڣ~ƵFg  )~wCbabdkgȟI!OmAFٖ1*:ɕ94~@:'tl+?GGl,+4$"ۨW(I /:8~#鮏:ɹG+[:, &.[ڇXXA9%l9HX9qa`.wb :O1uvĂNoT5,ցlj uE0Z!~Og0Z溛q0{;ub|ؗRm78ԶI1>1뢆 6R~ Z:j2{rDacGPBў[4I߶]%[hb? C]{beĒ?)Nϡ$*֒|G+$J,,pPs54Yz4~"֟NKm- #k1%&O3.9qmS'dWo ,cLv+0){f+75XV3fz>/xk]UY͎}//czyWc߫'+׵8yiI_o0ڗ~[0UՌuIƸ9g5v2g%^L_q9 g2wX)`eɁ]*_[SdS0W|qG]w ֌ i68Rȶ|"Z3Z[&=X'iikꩉm,szvi!njveܺd饗%lئPg+5q`L~FqomKe]ai&^qǃ>køYtdOySʯ o˜t^(O*~ob؊O.fN^byW1t~*B_M,{K HEŅOM,2RMo?_vM&~Su`UglbJO;W:} ˩: Ǫݽ1O`ubh<+_L~H#=~Dy: 4ce|w9i߰qd j_ʢlpvQ/[yՓRz; Q5?Ċ/)ZM4KM\'0siR jbֺk1^jx?4qvU# L%S&v G/WV[&^rk`־ycU-Q[[Mڧs maēč{?VD7w=11~&x#Ɨud[&Wl`.3i9!*iN_VMܧԺG^Oߧ3}!4nt ?e] ϰ?S\",}Zz|*))> NYEߧh`uXʣUvP‹&z-+f36}@^b+|lqjl?{e}sz0V=UW:85/X{;Wͧx,i^x6^;Ӥϐ;;0绌熿f<=׶/ eQcf{?3vz냷Hz+-hgƃG9݂+=G;ʸȰo5gk[3^Qbf7v,ZS3ċKa<.u Zbx̦s(47 _e8͠N{4|J37MgQolIfϋH:~NP='A#XdtޢVCN~ӡ{^HrSVKU!g.-2ox}@⚭^IJoc{`}c9qrc.Ly~[?9sƶa;jJ1v%"bCADzw׀:ơ/JyUvi|yֶƽ 1QdLc&`m}__|i~{l{#j38O>{q^xÇyҦDbN4gN=?ͪŋ%ssU[N0nyL%c-}O:xzgN5c_щo3v0ZA2np7w-&%S~!-ܸn=nWi su=2.u9cO3W0x"5m}HW=K$wvKz;6,;pC&yɟ :XNOQoqԨ%Q[ī8P~˞nnglC6S2;mW;ƪW3h&[3L;p2L{J>E@Smԟj%]1db3Ǝ>eh!޻"g`=QiUN\@1yc[O>S9tz ^9.GnɾC͌/;O«ŌO;:=>#OW_gѮ`ckٌwאq+0_B\j:M*Pjӗ{3;~)~fn-K5yEO>˟e}[ x]J cvF[5C<ύ7}ㆮ+3^6ٌ;xApGkƷ+DȸeuSz5>Ķz&O϶P<`GrubAIıXCEy0ϟ7wn |DԞ+ Z༒k^]zW-Xk͹m"\|Ge%eKmC{=_@4:&5e[v|S\GbK"iqO,q'N-@f/?%eu>; yvt*55v!}lOĂjz84S\{btJO0g K#Է<}aٺ^QMI%x=cX8`$&f![3)sKmH.7;EN(uqBCn>S>ǻM?4?M_\OM T{F?b}Aį^F(/2'G{]5x䭛YA,x^>N(Hr7 ԗ=qCDG~ZFN?yΣ C89_:˧5koGݩL`bGc}846p tm;9ji_Vb>*)'} >YjfP}Uw=W>UT?xW Ma|TDN?! =a&6h y'T[c>t+'(o[!@ kqz<ޟ'G},Gy~ą'/BQ~7}Dx? ͿoW6W`ȦOyh>%[ex"҆^342[gxG]N3~sf>elM`xӗ_p_s=ceDZL?Ϗg3a "%/ߏ8yDe| 4dZ񔬠cK?Mai4H~'oGy˸3"~,`Wm{̗uX.Se!>ωkۋ⮟/c\`}6׻-dWό"-iJƍILӢn.&v>u)v7]K#_kss6Ʃo30Դ1Ź匵6]gyA?,|`Bz;*5ݜ+_w} 䋚Y 1uW5q] &s>akSOQi1:*"Tߡ87'oo|ToC]0/ ~Wx3/^j/՗XCGȕԚXZt߯PsZR<4񀗟PX8:z[W]|*ƯYSmin~{Zx0N y,8L5 39Q+ڪ]ϝpeu߄v9NB%7;qEFMrt?'uR<>PǩT~\rߟ7/=1]&OP6soeW'}cŸspRSxIa|4d SM65E~O<;+Lk&}ǫM&&V}Z,bQֵ/\6mBo_E6}ixܜV_ᒹU˒4~޽DJuXb34V <OFbY]oh.vY()?l`ɟS367= S6h6dR.W8\s; ~[eZWFi O yNO$׌P&* xMVM8G䜊ˑg'ϢlSb*[ySөLobW0XG}O[u3PL;bw1~;t_sz-Vr\|UtΓ񰁵<͚N:v"cuDzO9Ϣ)}v)} ,>ւ?Z6I.>xӖڶm18&lj;[ ZUY7W  }>ai~n nԿj҈ 4cMW3d="&hDhVzՌt.\q5PD(;c%#qJȈd'N1""tJhY>$'.y8MO΍ԳDp0 (=YׇgoJ -rX=bZgmye3X?.z6.\X]f&?%=J%+&hdžE1~ ~x1,{0Ek5r=Z55'5#Gz 鰛yhJZ[}ZlZS^ KU|:.4׳8֯dWVҳ)IPqpՀ|=u&o [f~JokGfC_x^=vBCU`5!Ruƨl_pZphl&ۛ^" K՘Zs}xed}?w1&gaWV&OeBSp$m)dgӗp~^hZgH[A__#o/Z[mim`u3G%}AHol豟[ c<(ܻ[IH+c5m"zr. C;PSt«n=aoMb昭a } )ʏ<]uY?{"F?j;ξTln撆dg|7.nܿ\?dXVw;tk-kr2On{noxo{s/GzZ kPrᝫ=Wçp|lf`˳_} \lףvh,Oرl ̗ b'`=4՟+BGbL6;Ǐo>?aՕPͰZ?Vo#Qnϥ{;CpUH-=:vLO}G^.&W\C@֙p｠qu(o$®s}x?kVXg#'Ymظ 15^Q5#X=aZK O+8]\Ss|DFyNC_#~a-c+r*vy?/|n> ۳mo).܆/Q?\.]=6 ߁& 8~Yނ;V)ۭuhpbWwx3p~s:<2}/ZNtypX{8l|`49B+|]2h,9Q{BgS!1̿:8?*_fhO]0>6=`uX '0?뷮 )>~ZH..xjCa?ehW`( }wP~;_P.8^pa˰C/K.D{/&ث(OV|,5|Ċ}c]S`Tl]ۇzXףhX~jAxfAʟ8/u{yl2)y G?ktzK\Seֳw=oi'r~Vxi xo~7$Sdo,J'~dN99qCGaftdb1sيۼ*|zfL[?fhʫY!\SIk.b>V ,r7Kéh}'iqun.7xY(|v +ίX?Mug|i=S݆~yOURo+_>_^ ?0gh$\pbw|ɈקԬXY7lo*Z\6ɶy՟_0?L@q~P1/X16Qs'߈_R+e|4MK/P:ؾl_ZmhX yɚjZeUoȫի$M¿N ayۗwL+ik ^%{͖pvkZd!CGA:Z|.IVY9=`ՖQpI6r*_hvX}3GHoh0~7iy:mt, .4wjL2]>-ͨ\-^q|*Vxߠ7}B|6/}{ twi}3ϙ _К/T8Oη|iW8b_q0UD 1&n)|mPVPTџ6`wx.;yBV#:7?_3/֧KˠΟpvEX[aeçc͢%qI-{.MKS3z(c4l~9>VbHU`Us?m.-\[1\-kb>Ol}#wx>3a2h/b%j#<)# hhC2XVV;rrIWڅ6H]}eHتk'E`#˔Fbo̿*2}ӂ>^owg`Ur.KՉw qFuh9 "[d\ .ms_?ȝ1`]xǡ\5a?쾏-~#GÛ.*1U׿^ŞyY ԸV;<^}g%0U)q3'z}ʑaTXk&plzϿ% abpIE_*67>oK \Uc(En7j~ZpN_+_o?2П/lqWY? _iw/,]~.t}'[ҳx)ڄeh[ݳ|px/%qP'4Ϯv6ر;g=m;*~L׊u*"o-S>UJ-o܏֏E5yZ pUx~嫪fڳw'Gy;~qpv':}C'̹%˻`6ÕkFyWd'úv.S_i9&|^j^ϯpTG:/`~p:!6Z˱>FMlmўi_~?vKykyp 9*-3LycߒMd  uV\*A~,ot,:<Fc[ `PKh OmKuSԃQ9p0~rb[Lw3/=u~p_ \< v1R['c#3Mޘ}aEӸ޷Q>4\״w<Yl9/?~ユ9#sGma/\_r}] /Nu_%Wj߻!Z&s)&tVVy w ~~*ϷkB fù_?61+Z s/Gx|385&=0?`qu7J™̬G %mްYߣ:TZ8tۑ1KE.||!DfK-+ܛ0bpY ׅoԆ_"|: mƉsbw ~'V/Tg6b iw9M~_~Qb;9Gy{L}η%f8nW| oZ?Kp=L䑸ަq=R&߽9藍>:צ+:?a{yxQ-cup ߮<ٻr^O,qS#æk"[Vd?"9HU!\X IC?+|b?7RQO+nr+գeпϲVvtj.'x=2-F>hor\?nX1 .YVf@}<"c \v jof>,)|/묺ǿ+?,O9Baɘh柳N?[-N^x kD=~pf@F;5lsb VV=z >sQD8$g?qkT3w^R+_y7FZߠ|..٭sozx\_Yy5{e~XRؠwsJ4zK\+~mJǙfG},o>ڮY^N/U٦5* >])|fW5T2Y 7\h)6$,Oݠ?㹠 G]qMCWғbKU[ñ_`I&5wo34}.,)̄Z9L|M+wN#|cmb=_.65%|:aU]8vVk;3ʘ; 66ސ`} ~mޅy$ϰȗn1>QknrۗzVT^n3OZ4G񃃯QX$ϋ6N;"q,̀5Ӟ!i7B] `Y82soxj>?P1? X~7HgC{כ5w:^zAX:O'_r:yLWvbv;˓zcW6:GįR"'e S8ӠR-oPvF7 8օWή6tp㧞kdo}Xp׫…XKGũݜk>nfb>lpUw VZ%nª!aK3< DUyrgcrǯ#xŞ1~q(8DN#[?Kzl/lq(am\KV< [^!鏉­O%,{ I~-$Q޵ [vxhlerF.fx9f6u.o*O*1mzn %`zᓊv k^l⥫h=U|vv4=NW6i<%6Ivctf}"`[l} _lSz~{11-p-;5b}Z~Zgƿ`q篖`7?΄',89Ƨ<Ŀ,ytNw?bLϧ #>1?:Q7ט`Voto|9ܵn93;?#+:w~s7G':Eߞݿy]Byt7^8:-:l?ilvn~Z =A V8v..]G.z+uragd7CZdv$CaJzYsZ*|W7S̔)5n3~wЮ}eguN/Sf.Zh>M"2.g G}8?.njGhE"~&npI?7{NVE5x?TƖo:?~X?}-տoޑ_I/&;~a=957z??tb=_4;}xgJVSw^j~Ea?YT+Ͼ/ 9\p}E;|~w?)KmWoxR7Uwv6<'|oZ֨ ;ۅ {882Wncﵓ]] sa֥{wؠA$,M]էޤ1h)sx}{=EdwKvhÿmm#v~ڡv+yZ+X~;^Zԫ銡96ZoKC6튿)~{uwdA|`i֒mp)^at%?kd5Uܴ{c%SVm<KdaY5Oks_ VLh|{ĿGK]ē{bAZ[G8'nD\?ɿݤ,8B7S[ C_|/Xel܊׶m*}Y};\{hݜ 9i M& <>89eh8̇~Ϟ#WmT s&xTi)OF}R`oaxu%s~Myҫ^eZ #kkىv6ӛyNtMֽ-!ox%~?fH5h8?2Okb"K6dF#kV.ub?WOF?'eՉp8$쒮=hR)n Lo9/-\865%旬G=V>4^#}Gƚ48{X+6w& *5ɕaM  K^{-/>r(>TyGxe"w.hJ&bRR_;w5|l`1۳%mTXa\r=㫇wg-_`<\2ώϮ=RG/;WVMa%|^pia%6myPXIAc{g~i4Ǖ2cn;ΨDL,--XvY +$.! &ÜU+xvp UXy^^j9}f~pI->;ONB; {ugOc`r[D!#`rXKt)Lz|"Z 85~.<=+!H/>^c ˝s=ayO,_r1ySrX=?X:^'NS3No\'uKA}%ZKϵ|NρǶfGLVGgS vi/X_=pV >'z*ư>tێp/-êٯ''_ێ̆]Ld(-8^T NwWO,߁)s/'E`}o-,rY׍'6܌L~ӑ4պgߙ, knE2݌^ 'n^;x` n[&sS]_cc~skO8>R{7pNg+着M-8 aa `Z 2|py_+nG/_ NQcmhMBٛ?gi5|w sћ-w}o`ƙ0Wn>{P/kfAW?2>oo{{q.E`Y2.չ)yY9ѰK{o4eNÚ#_t;uEezye)句寔%1F|tG*=Ybf< WXxïy]__{}-Wkwf=68bR8秅cƭ^ 섣{\";dE KN?5 mGoԲ ΟM l7e= CaDB_m~r?+K/OCJ+7Ov} ,|H|?Y,^|5agToK;Z]`|)oA9K&9P7=|cO#eY(O߁{.뱖_|FyS>g}`j^rrG;5~?:s+$sNgup1~;3Oz(>첥CS[}7Q򡈯qiAt#֤6?G/1,~?j&{*o=i*WYXk 1Ou+if__n7`e?{>g*jШ:"ԧ4h,1_V#?u]q?ZxXH _zԽK_Ô߃9c/{!m/ml?ͪw Uvh]奧?%?~J$<> y˔Ӷ|V-yast!]va#P~\*+ o0:UB/CX?Ocx>•fw S]Mc/G}T]oMZe\o&[Zh]H k, پ :NZkT3~LksmOe=[txlqLQ(/%O=ӻ!E柦DxߛZyl_ކ=-5fP+dAw',`}a\6IJtUܹ*~h0G9ٞ3/aM Vk;? A=K7U &1kL3T#Ka>3?#{|,f|܎Z'6?8p||y`%^2G/pPgX{"Mjc<]%5z=1݃п O`W#>ÚڞtWp=ɉsY0jz5 ap8'6d%śb<[cy<''i\/]F.ۈ=`X.k4Y V+bCy ۟}KnC5*]PŃ1yG>C}wcKS"~O1H_ lr, l2ͺg, S,oڋ>*{:#g?`:nA.\[}\xOK6!xX2o3鏖C#[dygh5^,ܗ KrpM+b~-c~+V5&r~DDbL:";NQ_]1^H/4 ZLFk <@Xaҗ4Û-U]oLEAۦ<Àq , K"geL?f'\2m WpJ]x+wv_ӝw4/ިOmX=9+[nuyiA!ӑLXKnE{_Կ.|6g4krtLzǚš^?h?, l_ݓg6fXu'ړ:gxw 5s~q%w*^迉b^ ynCK4[E\94{zk7`ոKkCl/o{_ؿe^M7G}'(xZW#>\em+\G}zH+{_ZU{:r#zOxؽN+}V8އ;;Fꧢ|z>w:sE,cS 1&QV, ?#fv?gr{ S6Cy[}0Ӟ~}?no1Bz%nF,= ұmhyǿoVG ֚bXKogxFoDX-e)}-пS`h qR/vu8> K;xqwZ[᛽6Q&N nsN>sǽpk߅Lk@?89VjFK=6 zN;F/ 9Fٰ~Eۅ?rz?RbԬ+ƒz7n­e-lW1vXA^0c{RŠ1͗.uF6m{ypka J N_S~^A|ec(O³>w -Fʨkׇ}ޗҫf5759>Sjw|T)ix5 j%s?0-հV0>k/g{9>3owɃ^~ J@NW- Ӛ<`}WYM=_Wle&{>%Bcz s'U1N8'_B/dApغC-_sa9BPkan*pD:IO^Cxn'a?yo!. ؉}_ N=;|7oC{+cʦ0tbch0pE%*琿9{΂~u4;ӕSǰw-k~W `D_fٰCOD˯=Ћf1Up~>stgo*,Bki_̿Ncvƭ2d; 㗟x߼j,fqEG9lu<]?}ʏԁ e?v|4]J=.٩;U@} ks:Úa͈IpjƗ[eLl [vpxC|&W|3>pyN8D~ⷳ?󽤯c0}4NXBfmp_J na~}`s(˫ӅWa-~\xH˟*/ƗOD~O`],D pkؾ):%,AKÚgX>2)' xHGL KcWqqAW0< 6xV +rF~S ל^ꁰINڥ pY=q<2Ojr=н"m 0v/K5&9_GWCrkDž ?hszm4sL)> QjŊ{ZD_wI8?UT?6/xIp9_@}_s?pMGivCٟ_Yˬݸ~zBC:%^f<} V:F8{FyOb춿[`?xOkޛhx'L7=ˑ<_Fy~2 y^_<E綀ëWAyq?so>0?k| %ėnb~MY߸(_x۳?{Cj#m,w3Yvj &ڍ6UFhOt>'x|`MW12w;~1~?ۿ d~nUyi`Ow}G^:߫?Y]ϤïUKs ibMmToJpeܟ8uew𹸢Z|Ox/|OWNE-aUVOs+Z\_Q@בx>`kp}#kP< \4 +J+?wlox! Vj!t6w>kax%5X]1`MA[uwq]ݒE]8!C al2)SzÃߩUd[~'X)q[ xKcx|;w;oB]?ʌ2_p'֐OT<'<>հ9ߌ_Owm_;67S؁RLUP9Loؿ0\S\ooэ+[=K!WiΗv-eoSc˿/`*tA{b:sfDwEwv XU؜Xww^Dŗ`i'k\oQq+w \fBy2>s}\Iυ,K}>o~&c^yO~;RwYƧb+|Q,~]Tޟ{2*-?Fj8:}cMUJ/wE!b0ߧc}RW~v.R1̿zo'~1}/{о]M͘oNq\`O`dG?| zGx G#O4]J>;7>~V)<ڟ wxmo @5C+-ҟ=y紽g5.`Լt1s͇ߡW=8F!%OEc܀jKqρ[9pنL?|CWkq߼r^υ sw²Y|揧X_3荞_}?Ƿs>*6N?˹79O"+<Wr |i0\?ύ[^J6P W4{su K˟M} ԸOS2r5\2^d{ w_衸Se>O,gh_ W'I} ?-ń ׼} >yJQ+E?Mvn<;?ڪ>,gu_s4k_i>!Wy}dgVwo,},o]Vn.ߤ/B_{_3zь70΅rg4>7=GοӰU/mG}TL LW6UaMf,1^0㡸VkNcJ9yRX]n?``A67+$ZNw5Ja3ml"}*{Xd/M϶Ǧq ў3y_ZxfS 6xm VM^ZզկT%<}KF7j7_}I_a{Jx\^F{c ~kWf s dwUm YO킆8&N/k̺E8li^]EKZ-^#^{{Z5©І1t;"s\#\PJاnc#2J^z7~٧ sBxȊ/cAȭJDii}U$<فe[~zGTP2~?sN\!o:^Gᐟ]{F []k+>߄T~i6AUm4'.TNj:#Gߚ46㏇UVoHCEǼOeOYcY2+cT mAgX%-Nvjuʤ 駆z5)֓K *m6̜OP`>L~2XoҜNܟBy_i[ aH}>pG!,99=e~ʃp Sw؏<5ݗcZ;v{.6O@~_7ʻ'"~ X=ŕ]X^V}^IqS@6aՕϰ o=g>Vw~eW]olm\cy.ڛ I%/f汿~w., Ǘ<ϤSpxhXP{#|_Noo cP&\^l)7՘`EXKU?nUKDާO)FB}󇙰rh&8/=mq}ea5 r^t]`] /HB1^AJΗy(U/-o ZX٢rMo=3~} ,iW' oU!C 8QXA-ߧ= +6p>]ڏ{}) x;LZյ}uo$XOyxEv+`;򿰿_2k*KLEV1WfC/ŏ=r[P߸ :N-81tKE\Az` {]ZhK;Gh-U-571_8ia"=?]BD~2aZKU-jmSW˗MH6l5?ZNk4N%!:^ZW’|$(I$G^.m~ vWiZ=7qкUX,OXZKi:;euʽ_Ǡwnz; =V_i?Ƴovt''߷a2 Š aZۛ=A4XʩZNAc6E2Xhꘇ`AaXTkGI"|/pѣ4? NsTMgXx}1o5/XVUkQa1~^^@6 h-{V^|0~c|Ǘ״VlFW,mnW0_:@}4ޗ/" Ηs?#37N!~}'m1V|ʘu c=~'aɆ$o yaŲ@]_[(yN:xΗiq]5>D矸#ɰbv8e8FqzwXr /)<|T`h0σ]+npb:- -².t^U7Ok8o&ka kpoVҳ/NM&:J.%z#]N(Sk  d}k{c fhϴ /(܏F`ҺmxKx>9mq6g/[~×hO??]&KP:&oƕ@?X kvTUkl?~*(Îr`yLv#{lo<V4NF} )VƑ<.+ .`~5f`jXV%||F{ 7rO\ 8^=K_oep k&&F,P:'6x헍:uǯ(O*,_I~;g. 01{%̇[3aUW,?K{?u2n{E7;(ͺʧՁP?mlqplNk7g;`wt7NJUNDGq?iwJwgidl"~X6h G})v8>3 r.K1c?"kqkXzÞ~,VŤ?OÇ`~+M1>ṱ:c|S{mf1}}w'G\W _z.X$\Xඞ]+6eý}^",͘w 퀿lV rX '`Х^憳߯=f8x· R_כG%l[ \|wT_ mfpU + >LC kV?g|N|h'Dz?ީ%8?~n6I_kՁf7vCOJ0߭ەp>Az[!<| {r8$7kD9:L;N.Nplo%? i-5u&j-m{ oNkZq8qE/pE7:sd9vBsٟʻQnoQeX'ۿrˆ~feVG .x!:5st #6Ͱw_*ȗomn7~QY*4k(_Ehod]{6VXC&,[<7;Y_p]O9><^B_|3ꍛI<NEy%v{5{ڸuП,xo%8SP)5`MƵ(َ}#_M1<]9aB%v.~H%Nr.˗JO|kSuڗw-Y*'Ű08|dX:~,U|pWWnxNzCbƠc1_=GX}ê\ \3ʓ/Z ހ:I(t&Q}%s~k[~ e&r;/)pA>pKG2mI“M*X_בL"'wF6>9G 'Mw<%\{vȝqpY)ŸZ 5T.'j⌺fQ_[Ńs ;`ow* aǞSF 5L'OO{K>3jpSU݄/<; r,idnk³*:v!h;+7Lx9㟆ٌj~ -m<᜚Kz ;d|RX䞝֖b?x62\[fEY&}߬`x)B_ZŖf*ÉfšY"۱[Ցgh'k,{IOOqhbeA'0ʤy}+"kxoJ?a?OjLXSzc+Ua+Xk,?ߛOmxm#5LуC`WE fzaFoe8—V,{|kUL,wby~bY;)G"N>l&)2h)[;2ɰ-<\xun-ߝޔsG_^]9;&݅N}A:V m;vQLp/,b;zUҭ=,M&ՃU a7'xgdj<6%iN7՛ߠq{X܋4GXy1C2,L0VW BcH;GSja s gx,,36.΀K2|y)XLgxT« N%b~!),q8Eo ;}q<שpC'Xa\{&'waѕ[l=X'`yMOwj? LJYp*wnl>?e D#? (}SWp)OU|/e7Xrw7pa#lI?9|d_rlUVX|a9?\_ٿ*g95CY+o p\O"`ǝj`9˾h@ъ_w!WL_X^Z7eῸ_{w?!psN1 ]6Gpc Ñ_{t+?:˗7ZrÅ[.0[4:'^Ssk=9.o=69x7O> wfƇ]vV _ vpߒ- `]ƦkiW￬Y _X 6 `~^% ZtgOؾ|x}g~M1>lo \c +LֵOӝ~mJϑ)53~|HDۗÚkl %b(㟈Uk0;pV9+-| 8 ,+M_*_w&ϗ1O`uWג+8?3`MɰE:/e/3Vc_%Epʫ,/?+7XqVrG, /X_[}mI#dי#gٰGNfcVǷ]tOӶp"f ϡ| $9LjmYKѿcFD@ld"SQυ:o?']3:= \xW@=8<µޯ3 R+P V tU(]; #r|/^ZOJ{Cz]sgo|&\u5:fp]mW͈(Q/Wm:^8ŀNߌ]R_Uso/pm_^6.h}&[w$]k˯ֺܸ))>/}u]v.w"i4K7WxIl!]?\epam*e.lh,_hի;T~j 8X>qPU~;qEGQvmW]uj)OoK§ x|T{}߰;#}@yJ[~uG(xr]p4hEKVxj%u;y~k2ߟpatJ%_`1vx^!FZ^OI+rMTdX^oS+v1Ry~9Vq[Zsr oH+7c}iK&~u'+_73n۟n@ C xVe%-:י0ǰfװbjW\{VٗuR.leƿo efCt'`Uwbٟ&0=ߩbxKϱ̓.#r9[bx6Xcr]v,3iG x.f w]{~`XOvo 8{w08-/zd1}paL˒XtwX] mbu7m,L0OØ_р\qV;ΏO5g7dLX1z`̓)L82܇ ߋbC߬nWe3*X]S-_>3F⟣m'N0,9vْ|icpQ9,xXntӱ1w~F?{eϫHg.8`M|o/V@?1=`id3w Zl|<쯏ao cyzՉS7/ؿ߲p>e}zty7<- XU[*nyq9x_{\ONk1O>O6k)lr|5 =aM|OgL0}.t8h1G!iꡙz 0Y_W?huL_6~yY4Hdb>Ʈȅg|"zV].>=-D٠ߤ~ca},O?`CLB V4lÿMS)a!+,ϿMB{ Of{6xm+`U_o1o6kfN{x˿OX_K.^nub,Noՙ ?y5mww'y솽;1\r:-a!~KWJ)-SuD|Lgm2raUT_wFn=}MEG:M{o6tRy`sOָ:x?B{eI+`~c8%'&qٓS|D{3>wd$7cae߂3f+72KsպTqv"a.=N@gG ,5ph<=Ӈ##߹` g ?Tl]x<+e O y˙)gyGa.Gl3/Y>pxۧXunb [ Z2~Y$*{DVE'荣u*=9E .-zcںF~c}:9'ҙ8 [EE=a".<.aŭnL)؈{&*?CgsG^š`EobtFwu.YWrOjYleU[W%< C~2|Tؠo.-[O؏[;Vަ='g\r+9òi_>_};pLcLoyVOυ<V~g~ɶpٰ l\8wY40p{}̏--aǍ^{4׭Y>~sL7?n.9Ƿ~j0jNժ̯w3`{7zre{`*FȉICO[_vXp*Ipa^lskίpr \Rx-;^e~-vÉR>.ܲD1sVOao ~ዦq}wFƝaٸp֧ZyW+(HIXpMgxW뵁/MOz2.<>X_*pюLQgOZ' Z0jXi/yllAמ3-Fqs_2د%ӈߪ3W'̿?es΅c’ۣ{0~@vhϯܾO}=dZkz%η-W!͑_i 2]͠g\g)K4+i1 #4Y5jxA5sAWGgҮUږ>!-(|V9|PjX/oa˹|zP\Q[ʻ|?:8$Z0@}6'̫`>x0}׫_׏&?}GO19]wcaM]e#I'uV-OuŒ׫y~g.xI2 5LBI~a0^kW V_u`Ŋg2Xmْ~2W6m'²M7OVf2?<;z{?W7衾u_3Kː5pa$X5yͮ0I#p' ':>rE1dzDڻ#}r7نi-, 4ȉ㟹RD@)/?'[GQŜ_)~G a{Vg~ ; Oo:|ZxN<C˴7Z.Wԥ־GE\auܟtb'EU x㯯߻Wók,KOm}qxP7;}6'뙾\GSui ׉.3Ye^~.?+2>{=9g3^?b<#e>?'}{T$) Q|ɿJnԜY=w), vlTW ߇.w'vyV߄CgHX(h)lUt2pr=2o ؛>Ago>{/86zXV'j.ohXz~w0˒>~>2Ba-YBo8$o + qKoX̘Ϳ˿י|L݂s\:Ãv2<2>0 +f%ቸWZd|/1|V|`yeu%̱_ e28{;pso ſԛp7=Y1m`sVk}nc~-ײ~۞>wd|OE<_՟0\R{TWOffz^'at_ XךƖAC{W!aQau/{a&#^D/~cԿC7 %`7ُ-=VG~q|c#?}SjzM?F=R 1~ݞ~`Uߢg  =Zk]b/W %y?Z"\:+oMޗ{ՕWz~'8r8{pV8nc W?2U2y?ni]oG]KcY|]{&8_N?T V:/g~^mu}hߊ`Ӗ)S>W^G弿[^CVd|m)W*no<{.c{"g,ldxU @5迤i|^`lf#;…Njh17gT?ίQU|de?Wq)-u3@Թ|:];o+Uܘpb3](K?c=ϻVȄ˛aK>ya)\<Ëd|,>?55@`Z>ќCgm;,3s o\=ʊ6hG_xV[ gݽAt?#w'pC7EWԼ7Hp`">dV d=/9 דFR>=և4r+T6ϫc7-b?jˊ[bt w̱U\|Wt-w 3+4_"򋝯`"lU(y= V͝yley|}8˟W.>`=&v\?pe̿.8$}=1Vn Ս#r}6ԃne?a?dm^^o{mq]?НZ/ҵ|_pտ7`,7= Q gtI8]La(_$:08Jw~^a{7_9ߝE^K9_CVi4dJ7AqWח|t+2)pmxn}2McO8W6\[cp-U8,3r?nKgs}R6oJyۯ%>Že.>,3v V M7x! W1r l`W<>XtgyfG9 N~_p~| S 7aXޑV<ߘ=1~x`?,Lq{G#ٿݳʿNklq JtϿ} 퉽{>}'lϏa_^a#jk~u`?ufzmp|zѥ/'}ebb~0`o5_/G/~C'>#8xo&U3_Uofr}Cj,^'a[lU|4q \8i ϯ|~]]~N|]Zא,Ey3w Ups.^KnU}ƒCyK~("ioB>,YA-${)g. Cokex. HKZ2h_VK/ˊ;ɒܶ_+ROe mm 3jK2,iщYm^gen Lm:nJ(::l0D`_K_euޏ > /K:+xKƝ`yZ//Ϡ3rp(Fc˶}7Y]*W;uO<1r_.S<ؼg)pL[{/Ӱͳ`ɕ]ZU_eriUvljGo%"!3tC gn*G|ߚB8lT_0R9ףyy{\X~QG+.BJB7 &|~Yvt37OdڷxeaA߸IL_ږ^V%1~,c`ʬ,8lao>ŻH_R'25:}Ө28G~wX{?'$߬pI,op Χ㣙lxַJ%gĿޮ W2t,DiL5}_Ysm_1| -`~K2af/z|i 7H\>$mRg_}I៹#{ LK OLC/waռ󅯰V]j]gdV;L_TQM7ouޭ@w~QO1>[M_chW䩺iW]yz/7T]v׫n5\tn?8G+׿ ׿]ϮppLkƷe}6ׅÊMX枬_V`ҟN O>r.ߗ|# 8Nc]>LAawSu@K|T]l;߿ {-]Wrt~unj |Zw O'{䘣P V{U58.IJFۃ`}0 m^~?ԯZ.iXwOG'_OVG.7{iK8d \ϲ8և)HB/š#Y~ `>L?| ,'̘~acd}ٝcZ[UK[߇l΋5OL_vVN5?Y6@_:pU ~G53w8%f X1|Ka>+Z|]b>Z3|EY~S80Ǘ1|#_uNK_ׅ#ٞ<Xᶋ cPh D{3=]*g3?]To lA(]m/|q8;"ag 9~p(Xٚ+%]!peiz}{5Rj1~0G0q+V!u}<07pi`^_2'g%L"w&b$$"~Hbh+|G"96|ީN)E{z3+Ǔ7E?`Y;W5W7?J,ns~-gr<˙^N4U9ep<8r~J|fi6^g =>N='>˿ue~]2~=L7>ݶjI{b`},d;gؾF8^ٟ-^`z,NY~7Z} s̿:zj[O,BH'ߗ?w.7/IHVtݦ ?Pgq">7`=<Ǡp X7?@'pj1l_3ET;(_i]ا{a=tߣbX^Оg {c²W`߅(75㗾 =W7^".r/{0-1 _=ՒYo#3 9CkS _| V7?G3r? 77c 6ݐ)Zkz]=51ub Q~oJ]u,擏+8 1/Zzh$қ~a俰z3ݰ@ך4?ۭvϩgp~_sk*Ayސ$y7랰A :+o3Wt|o~»}Q`kHZ}lK_vil5C{VQE_b=͆י?'m߇60~d#3Kk͢>o1^鯶5w7G}jʳb|?Dn0ρ5GL^Z/4B|o5! - ˕27\8ܛ|>j]a2lMl]okgXUdhk#k>Uq _V Q%#,}ߗ}U,RCkRXleu[{ؿQ{+T~gu:6s\Ϳh44WfM}:>'Z/hHkNA;Y9wu5Zo-!VoRNV_>Z{ +B Gi[ a˗aȩW.Sի;_F~U |X6w%øԺLHVKMeYz:O K?MDX|+ ǜ/JNkhs4܆?mG*4fk&?9uJ# ;}:\x.,7pRkQ-< VLb>c}mFj} ;{Vb|;s:,E}3}@;)K { [>3Yv_KО1Xyp| gb1ְ3+Jv~VOئ{],I\X!s̯)X57.7"w>Au w7 Se!#흷vvg}ʮL/ΟrTgEriW.<Ƭ(h}s8ӏ><5c~K#aú=碿 /1%pahoMξ[N?eZZ/^>O ghXn|3]~CWBx˓Ǐ1rnL#]}͇C| Xf}{loR_kdV*,-bT+|_~[gOt-o̞pe0ؿcm?tEtɟW;'/2~4Ohfw9%<;֨Nk_+[M/1}úC0_;Y1_[f}]`$[ VxGDݹhO7oJ! QZ٩v8k蟈L_[F ?շ=3?C`>}dОu^7j7Z=+c(oM^s*1}]~1oH+M?Շ5D?y_3uAi ?{1<vayMZzFXkaQGت>-y*s(wcw }zxlM3_ݫ+|Xow;|owcDs~{m>g妭gOX%X~s0ozyGHW}Y:{Şm2HW?S8Hx$+G>Q#U/ǞwU{¹zؼ| 󷶥 c~n7b8PןΰĖwǔk\ao>XlI?(?yl`vҦBk85ww&Y~Es8:Wփ{~޼auL~781Êݿ^3app'Ou.{,gݪc k>@v W[ZtC{|e?wG˥9>ꗹ1Ϗ Q?˞Loy /~!w7x9${ӵ~r 2:q:IuG=ܟ|2ywߎ#U/}( OOއbXpk;Լ&~_x0~#AwHw4n]^+ӯh׶}{?!RJ)cQd Q(Y'RJʌ! (AJfLj(dczxO鄠13s 7s2 ޷5gǐ@s.y`Xz~{X~.2˰Wӛ,t+x?]=) gx6ް,dtWY -jk>2Z>'FCIX_CGbSG8p7SDc?}0?OxUJXx&=w{yo3'ϋnB뿌ϟ_ߎ&[/x~azׅJx%<`-sd4k}/y`>rޱ}Q-?oϋX!~bX2Fbr|W\_MFhXִpGyyRPgK0m5CuV׻ڦyhN,#So5 xctGXңs๎,?%/->㹼55W~ /j(q}6%.?W:QȹZmq~R vdk_Pޡo<3#|}K3O:,ҟߗ{[s=}Y+elo02ekۈyQ~T;m׿ .|sAR{ߢFy~=YN:%l+%r(^?A=-8xo,C Sn_RRj[tYؤ), z;O>b'ߓOr} >i#=xb ּ)m3GoS[V/6wLXIA}Cc#P*W쾥ίC;bsyXqی׊6 72}oxso,@7z<  wr]x=ށ\Qj}X%7 q19)6 shiWx:IgR?e!,,Hއdߓ!l8 ӥg9?Ǒ,ƟNl2 F{EԖ+v+O`w1J`Q֥DԿ}/Me!%hXu w/!&wg7%4,#-)-%ÂQb?N㘞4ڕy#=aSgoC{v`}~i8σCQDz5Xoqsvfvs3ϛ˰`ܿ`[)رV.f鰴,g<'m|7ρWf|=ΰ|V82 ˟Y { ,*Fxӟ3pJ{)[.mV.9Nbw$k,O= u =|ܳmppt-ﶒ&E?XX_\gaYئ,G'0Xm, ڶ au{v\;YvIapSX? 3isܯl._X}#ʏЁ=:'ld:cky}uEA{c`q(_‚n8b<4{{0SX;[[WƠ| @9ϩBQ_7Uz ާt:KO֏"cgWG qvez˝n-㪲[an=ӛr ij?0tʥVw}Q:X{uYѲST6{_>?`-;u@G}M,B{/' LJ (:&A'{wmBZAmCѾiLOmV"^jKׅz3ԧWT?Wij?YNAzÊ9ߞ3xMP^et_gi;8Y;Xˌr0yt,3o`}#R=V鮩'?y:R|We}F aabػ51 x CWzy*v-!>)H?tچ[:r!mk2ϳ=IzVXՁџC ;-p:`|y?;߫߇oJj=X>9u=})_|iGڳLdK`Ch?<=`~LaQ~{]~ ?ȱ&`WbxWg&_BOڊ,q/,K Geq<6òӰǭtYdc<;u'ӷ̹Gp^@? G9p^Od"p?S`;aٛX_~7zMe}6KGѝf˺S`kqz},+ Wp?:q>3s_X5V::sfb?ػ,i?q'~s[XX ,{37Hg'??{mC} >aw2{+r+1=:OY>EӊXZksc} vY#0|Ê-}rXlz㣛|tm_2w9XyWE'z2oQ~s>tBMT&˟KMFk?GǸ>i_8a<mOc~G8^'D{QnG*~D&l)g׎hcɁ#o~ \r(w?5ހK,9AC>Aorr?8wX`]⛰ W566tl3̂3ZCIߝ;y^WEOw`K~D2˙ը& [p271 A,ѰKazvo3ϗo4x.7҂tƻg:]^Xt;[8,=~23pQ}v x%iI G`ezW9 O_nsNWs_tXq·`EX=20a킸_`յj6 1/.BɰBgYg`žCx1LnL ev4J?/']`G']_l҇n3>8miYU7>"z eE0~3x>>7BO4ޯ;9d{za˽jG:CaI3cX.xO9q={63o'z|69噳GaYIOT9 ˂}pU-Ɨ .z)a|E3 ȋygm!,Qa‚x)90>u1La6zp^e{5_}僽0Y X*kXl8ܝ<= gF—gSe9Z  B^L?Mˌ&оZ/XڑuezC78MayLҹްͬߠ'-oF<G`?k}_XtE1~' ].?u,oƑj:4~8W4fzrXzOKg-SPbX6l7pG {?Ks' ֜pbb:m_9~K7]:,>[t6ۛ}`ّpFXo Nxb$72̞_xtls{5_%w~)l̙6u z?O jH՜oE{̷^/{w,M03.Xܻ]?WǪ7ܟf%(g|ٿ?-7d9O z՚8as;k bt}90#]2҅ 7cߟ!i_a"eObז{a>=~ _mնX__]ō3:*q+rhp7eF?:poX .,x 58-hSWX }Q ZL1о/\g5Hs=']hԓk'Z/oxp?،yb\`' YۿYĦX"Ob^oM \ǃgòtg/ý`I].a{XXQa*9 %l8>O-tQ8h3 'rj&NވXעoE6w=٨{!D<_rXcLG1zӆߍj yNK^HhM,gy>p^aݑ.xM׶@{/߰iu`E1_v3y09֯R뽩狯s6| ?مMl]c 3 ޓɼsݞHﰃ臨EC_19 dD1Gw[z8|G7|./N$cu.A?7z 4~ G`|2&7tgmޚ{Lb'~к{ 'Ez;?ݻ>|e+Ƿ"_C,_GmA'F!畟jbt5lgjKu`ovT+XgC,ve%;lwz />g3-BcfUϝ>ZG󱟟Nlj9Y 8_ X؞yqYXZyQ0\szvB,5+ehA*7_Bm#?DElNUJgSob}|8Q8B%oyOQX-:Qyѷ7>-3jy0,k']y(?r-ϗئF4]oi+/c^ٱ|K_nѿwӧ`ԧYWeZ=aђvxb~pX=z"Η7m"xGuvSf Xq13GczIۇ< ]?9ՔݗKa?L_ݟbJX?㎈wk`ߗa~vx},=l+lqNxj;i p~CkZ~-MmyN\2}VZob9ta}x>z ZaÑoc{8ۢ_6uagv&4fve|IO_SgwCw#֧K+g ۿFN5n%|$2۞v| wX֎.3#`C)8:ϐ翱ԃe)"=, -ѿ`њ#Zh{%ϫU,k\-"oPa!8ӗ-u̒0npZwI_sEdX'_oA;+sx=pu1%˛ogv#aYK 5 ۣMNO1E-hyƼ]>v7ţy>7M_ƛ֘3MUf3),_ ze 5n|cf}h wZX^]-Hhxڿ1F}`Q;XPu9{ߩIw #hB6MN6CzQX_l;F ?/-te#WuX {Ng!2!'2ٰIX?.+̪cp9 p>_:-,P_H*1ِAt;,7tvS8S}ZY- ,;~a>vâi88 1^` < J{aEa}XE}^:z:%`Eߥ7.X\]g~|e X0lb&b}+srT0= `FcXh9tހzh1]n9μ`/VHVt8+~&~:~Bu`qXR9?{,Fi_,.;oxe)miEr:T-ZCK}K/3aqJ8P0vt !8כcQ_ ? a']-xwa}3H7:hJũZh7,3'vfzR5l/S*.,R_>2h;|.ov8߾8޳Q^QF~ןknq~>FMψxe Ź5tV8o #:+lw Ǩ_kߛO\yVmpy X7SytwX/K$te8rM>+jb<Ka3,`OXt{l^_oEAysX0z3̏ XN\t僧^%U9ߖ0iԞL_/~:=f/tXiӐv.qG~EnQKίFX,Km}_|9iEΗ xs=[>y]KYRϟl@7s}/޿e9?ֿtnW,uJ9" XT'ˡ[Âu0%Ym+ܷ†g^aI5W_uR#=Uf²c_'X>| 3~}C#Z0}|?sX<9,ì-N͚W8K1b9,_x62Oy/C6`{™>9~ϧ凿N6^̟zM71{>r=f<̭Q3ַd{|'φE^w9,ץEC֯Lxa /ԟ<O-~.!Ų1%U 3wqX:$c!i/rqM~Yq'qXodj@P.,`7 T"}J'NaG^(9a 3X(KϏ#W>6<֓UzrA`~~{w ‚^p~pH0:;Oۣ_:xϋ:.ywWYwd/&]B^g\68o}]n<寞G{Gk-̓aE|8gϋ&Ov+aeʄeVa<U98oyÂ&VWQ߂ş?Ui 8:l:wf3=eS`O:A v3?dzetrW{P8pk3)ptX#,8;̏aXu)3Tsxfa!pJ z7[6$)ҎYqӷuOƻkp'(X2V<5x_|4eatXX 'a~\z@E?EF/}(?,@d¡X?>>58bkO3oosXr}e+`M! .ϻrN1=%^cCo32;H?lÑ:2ϨEw֗$;rhp(Xs 9•?o~A`X3Ӄ`2S緁^}*أOm Ux}">)`zDٹ[,M`A[(/l?#nÒ8)p-E3Gc? =W0u?zpU%{ư=e#'EZў gѿC4:[ d4,d™X2zJ86T]~ GXfH? D\* ?!~5;mC#ҝ vs\o~ӝأb0-:MX_،Om˜b鋴L9㛡\Ҳ>ne:s~2t5zΚÉ39r^9`az{oX07s銇pyGyoDb '&uNԆUa'Ob˝,_ܗ̫l`7]<ϻo4^?ʁwrߩ/\Ⳁ +Y~m}Xo#;KG_ܻw,0=v)ӗErguL?ZRupd,߁WqW\tM~SZ~",f^xֈ۸1#l8)#Hz1ӻzp=q|9sxx\ 'r֌?%0N>pl?]ָKg-fb_>eZ|=2׻UέX߼5O4{9ֻ 7m wK'pbro Ku1}}8]=!wݮCc|| xoB8 l.%`ϯ!W ռJ0zn>O=>-'.[Y~3},Cv4Gh!~1x;.?9^>\tcx?s}UuH]ǯyEl,hOReZ@򄟿n^|!nZR +ݫ`A ?4<p% >1Y|"O?g_`~+|"+p~>,9`;g{2LGZ1ݴ5>XuztezѰ̒y~.:%aEQҵr0 :!}NXZ?{W;0p"'wF`U?&{/. W8cz?_'`~;{=eygotC,Z}~+y<+|A|v+_ <'n -=Ϭьo|!TU8>kU fkһ%O4ځ߇7²_{,bG7.ՅYL`{hcg8rzx~߶Z|v7kqg w+9^wD˳kWfGz7xD־ϷО,s=mg~JzH%rZ&F{8 al}':Ω|aeǦPrR_S g[?q~fk†ySK,[UXg'We} wh78\Ix;/E3m_oZ&=tI: >)hˎ[pJvg|Wp]aLd,P tv gҌANJz e2cO~78x6+x~-C%dpJ_p =Vzz. &`_'ڡσUI̹g?dZ|69b::{,B' : U7Z8g?,,g%,/2)lϾ Vz[e?g%Vgz*cq㛽gsbzP kcp}f/`ZK+%ۓL{-8ACwXǕٛX[{Xes`~{׎\ORgaq7{?zԃҵՋ?tZӛyno*m*5d7/p?ݭj >.pt7AIXYRZqm¦.bV{69gtWxgF9:=A/o?|6y*u-Zɭ /׷ǪytS ;*ϯt8~!Sʯz8~=#p~eWʍb6]P9iG-Py\NeaU~6*N٘P0|RhmUynRvkWwjQK-jr:.S-|.#]L^| NoyQy;XUHOt^Y30] 1f<6\ۂd5YF]\t-^{nQy~eχw|;xRx+ij/Wqȯ1߇^ ~.<(?>#yIj{p_KY!1]i6N|݋Q0?#?r?=OxRWY[`~5YY}MW>3bEkv>T}_nby[ù{d.\ӆ {'Ͼ[.zd$?ֻ m6ocgkz jtyL/oG+-`iM~{?i?ͭ1ônߗPҦo{ܢu_Y6I~b-cf= >pCXf܅{.r?!\'KSk{׫XQZԁ7Unڕ >t=mrTo^{zk\y_es |$mL4ᔴ:\s o-mxk_dʣRZwrר*_lGK]fTȅv*Kӿ5iҫ9cg[cyשǾFwaiTvA*~kvueTڡʵ[%*R_WW~=*4WWp9?PE²ܿALOury9 h_=|O*>\VUT~Y4QӨ1op,5ɣHwzLY]VHT*3%N빞֘36hީlwўI]{4&gpcܿLvmgZz0 '^=S3B̿6яvy.M՜pɚ +L=7`|G=-c}u/C`揘no0Dy #`ђ4w0k>}ށƿb~ǼBjx=ayp+gV8lM7= GM#kdpj2wE%sCdN㱣q:3s p[L~,ϧmxUCWjv>9|`AX~=, 'N;EMXxKV)3aٮw_,1]_+Z(X9s)!׿#D}=%mX0rXȘ1 Yی%y>ǣopP,dpX Vdw(`,o2_V,7bM#2e%Y2NcP #.3|c{vpf^] $.*<2dh8ڋ8" ^_x=X_lĦ顛0>ay28lqx}.ofx&~]W2ZXbtl`5gb}\o~'pF_7xM9}W.&dc80JV8B|`WSzgN NklOˋO1oƣkV;:}GMa诲֍/ {¿m8z\aƚ[8oix:Ϲ,0`|1+j~ G=x.M3>D1Ӆ_z]WU<<VJX؋>㥻R'{k8nW=pf_?J{m+Unz.J;l~O_yi x>Dd^sDxe6.EKo-_nӉ;hS_8xYǗ, ȉ/y=ټ*T 갼u8Y=گGy-zXp3Lp=njþb y(S+~mּ>?+ %O%jhRKZ`)cw奝zÑ^Fyp_#柵\J] zZlSF O%~׭4, V$f~}71`SVv-C4ܿŝ˰b{@Z8x ~۸$q4^WQ5c߰>.>BpgWׯJ>OM _:;$3Yj^??~_^z bWyS++L*z4RNsY͂9^@vipɶl}7\!X$lϿ za \uo?骷O1= .y&;U}Ӝ&p !owe--/ғL/xAp Kw: 5<k6B{3HG}/8&>yVuPhcve !)\Ck>lшմ?\tEiwh"_ܯ6MoRn+4z<8M d{#>/͂񥶃]Yы`S/oݙ1'hf|Z[l裿-b<;"]Ԋnz=)\Ԛ益?5nRWk 6ծ?0S1~_W+OykV_t7UZ鼿%o U>=H9W|yYSy^NܯbXz՗e=ux\7f܏?nze-FAUn:*le{ W:zdpGWpg ]+?b~H_͓+]>H'uنjykѿ޼SBp snNǩ&y3x"0?P-*bcy_iv)t'<& 5M;lmiM١}k7Μ5I]̗W4˛M^gyzM^Ê^  g&KKaሥx<'~cO^[_QxvH&Niv>|o dg87j{OS+`G/ܲX,LyBռ? Or$,t t77-5o5ǰ˰_'y1>ϕwgil =rױX?5;yp>XUI7ӤWjx]!]wueųɰo\-1>)m0:2ئ%X 'v\?VG2^a27zx ˴0{km?]vCNa|M|/9SkDK`YXZ -3u.%9>g#zkM6] JX2,igvy+ڹ3ʆ4FpalS%ϻ,i}Xr8,n ;G4S;v Ϸipwi4/g,(nGV[uX%3-9ʢ]RXFGmD}wpfŲ>)ED),{3M6X7qpfTʋ·]~r?nSXW4ֺ͊ W|G]X+_^_sm c]G87,  `-pJ<c`Apy$K3y4f<կM`O9۳f}_[X6dKUN7qk=jƜGo}%ǧj%EX>$Y~S]Jҁ 7cC04Y4|],?=m0 %l 6# ~;4|y~;~>lx%:w۳?;8?龟Q^ϖc?.`ѥd8l--\m/H%h>_cj+eR>cc\>^7Qj>/PaQŎ\ςjGwK,lXSgoQ_`AX% Zmf,}956jkj'VA|m|>Yo>ϯ@y|mEjٖ;a#|~#;0Jm5|ɺz||XS%utE4y6N߇1] ym)jG1xGcj^GIfȟ}>_} }!m| 83 ͚GT[nϋ _6AXɥlOvv@ߐ=XT:JEpWw-4!1ߍF%1,};t۸(Ű0CXaE;N9wwRK7WҼ;{1~-X~>&~WwLWšݸ@}V-^$ϙAxHwAaܹ;BNWI7ba_a0fl`~d䷼+N8Ɋ`V'R{覣^p*{פ4oehuϠ|bXoY<~Lo/I`QYNn{1\_X`W[eŠ/cb<ux$l<~L<T9,4K2]w3(Xd%!XV38ţ?s37k~g!} *!k~0w6_H?v Z\I] p+[aVOpd}iq@}le!gf0q37,xVs᢯M5[o7W[*wܫ9eJ.21Q^rMNݥc~[e;xM*_U]~]Te6V9yo\c/^?W+MTopsXҡo"Kyeo^q̃ava^8%>󏝟 nsUیWdaɗ*1yWW\~#`oB[N ܦÒ-@qQ+̻3}6n=끩S{SSaQ^ .O`hy`1e~_["eŮ>`<]g,\O.RC0^- ,<19+s️?p\j*,]r@Ln]GmwX~Ax]Eg AjuQ{.,ڼʄHXDX,kr鱏.wh](` ?-CXća5,qPm eoޣ Glo<<˔%tf?kv~ijEM`~KrK]%|~6Cr_xJmoaY_.։`6?xўO*ڢJ6ƫ}Ֆ[}e$Xy?d}AaUP>Y"^wmhFc.ߊ''a, Iٕ뉖WKX>G>Xa*^G<ōo0\k􉛭y/k}Po'ң0m1>YEo]X>%=/1vR"`q-}8x\3^,O׺-4z6'Ca^#4Ej@9ˋBGgN`qj/#n:}?Z`6jx,g§pܿFACն[0kŖBNGb|fk vU{,_Mg1wO~ڳ̓n60/Nf<4N@{i )83.Vs~ Hbpɡ0o0 n?+Y]$њZ6ݓS0O>Wp;_"pli/Ә3[rlw]Zr:Nj'MgM5|% OGXbEs|e`IfcM@4o+5g͆f 55˹~$wgXs˘~e0^Ux%`ٸ_쏡~ ou a|7Э5vdۅ+Қg!gi1iXXW|1Êf*8wʓ?_u>ґYj_w߮}X*ukΆCUqzZnmuSp=Sn-Z|I,Uyd~}wƳa ,ܾ]+l!ۚ.pI v./g<K}-t r.`;_{:x@,MpصoaQlo3M)œN |9+aiG8:o`X'Pmar,S=-z@V,_iԫS7b9K:.@^4^aq)6Á~o,Ep~- ,?/["e?acZxq7Z 터hEHoM'вν`Z8zpX\y",{̈́AĄ`zHX`be(,,3`GX>-z c?B!m5 .'/2m ^N뼆ovAl(Z`QL}X^  d™7YXL݂.K>`’96,tI.l1@/g~`>ҦH䗕ᔵz|.^p};ϧ!|?,y`~ t<7[jx`M[Gi1LSYO|: &{=G?{7[ G6Y?Y-Xt _1ӣ=a[q$3G/E^ E}splϯ Er̂ޱ(V֮[AW#=8*a=_ew_>,( C|Om[ʶ?.9#W7:pQ=\,܎'va}7J7F/6rڃ}7 CI{0bv`s>ZeNN~\>ړH6>&V0ՑSh&WU»-N?N7#Wi/s/HX_7aK s;,<70`k%E'8,Nܔ;܄S[{/UXfep),s9ޥ }uyl -^asuǕr maAe@!ַ5Xzc׉DwX/f[c\ +:h=m`_;,K;2eh-/GL/3Y> \`6>q'㱹'&âގ.kۿB}L8 ش†MU]4l fV#c#wtGG1p|77z~u/c9㕛&J4g,h]\I 轭Qͅw?0>pxD ,=²{W[3ǧ+lNxjy'`ly[3E9aƱ -g_ی2w;񋬬!&\oQ^#,.9F~]N"Vo03u,(D ey 1T_4[,3j5goߺPvC-Μ2l; igyͣ4l,ak~~wσ|I8NnMzki8ox>w~+ezur Kdw=lǞlkreX߅yC}`AFw.C;oBkG&kt?l#}*,\05ۉԞ~w./&zmGwX~[1I=E,&$N`= G> , Is# hoo8p /3]#ShOd?z.#>zta(,_ص=;`%'b]̟Yjυc _~wE,AzZAt1|N8q hkqŵeΣ^{aFG~tM`lO`v,GWuRaX;881&c,GwG P.V1z7Vb?E_""B3џeY8/Ѥ?=ހeO讹31%.'ap 콟I7g`l kcd 3l#듋`X~oԐ™ N}/茣,VK nâBX*,({^fzb9l|ϣOlZ?N5: Jl/Hٛti]XugH:zp[{?gˆ2m'Xkזd[}pZNm7Èk*~k5-V`l~wS:Vҋ`VU^=c'Q e{O`d\~,ӋI0]><Vf}BO 7jx6:GV3 8;{V=tx<\?遫b؟hrM `9` %b}Y_Ͽ5ba27Br^vуi%\bFd2]ރ6 + A0\5Lkxaxm9K.3^{”ƥf2/`Er>ۻĿaۏlO˺B{G/Вvt/a֧X=,9\rf }Ep=8so;) 7s;uz5sdg(_` I[Wi)ͅ!Wy>jAW/zuapULω%mԽpV3Xo OKݫ\ࢗs^7mEuA0ס3MU`qSX~ vierX #C޿w{r^Ê~apssX=\5p k&p຿^vWXR  Q#`' 0+Q~Έ<~;G`;e,sS*a+aywoQ ;#/q?νip*#飷h`zꗠGE2_:ŠO- ˟|8kO ۗ.-ůvq^1ᶈX'7U1+o.9 p`k-]muAѐZ|6,kͶ^Fgz5t2QoF.-TڨuT@U@pU@oDC]`ꟋꟛxտIsT-U۪=2x9l/r^Sfov/LL| p3q k^s7ּBAuyo*P`] .D/ߣ~fY`yѿLyv˘"N13 3kE Xj63E-OaG n8G53M6~}7ֲ̺ð 9+7|&aqD"l 6o3ApQWeKG_MYX=g6zkaeZ7OoV57ŰWXYVt6BFj`og ,23d~#|'F];OoT'3}FHOSBy۰QZjEcb!,nFe4z#eY#`q9XIu:짢u+ 1~DX1թ~<1,85zWXöm]v߀M?SaZ Xf<0lv.MJ ,2y]3bS$a=?8N^xn?757/ˉ<e֔#dy=(, SꃰpKX8ͻ>G{qwXTE'X3]+7a78---b<½Nlу{n?9MGcƩb8؋ e?yx|8~Vs`IOwփ2;`dj5d7KVLk,/*}GnS֧R]zŴ3_avzkj',ތ 6t،ڛ퇶aYئP=/AUoӖmS^tz1YpULZЁye O4Ns{}`Owcl1r?S/=I"w pUSEߠ/Y /ܚ-'=vy3hϋO NۼW}N l'VV{òp~ Xf{ol//҂O`7wwoƜ<礉8Iw<'*0yt ~ ]}KG: Zû_E;`٦ΰGLXsEJk^>\+ v;AH \Ն4 ׫doBY`x˯o ߁y.L-?"81PsO/ba,3a~Ͱ$!Tf" ,_{Jt :y^=;g8IW;La}f1pG(K?ta{· #EET_ẑ=- 28k]NaF2ۭ)_t4;x󭽓thH^F?d!ȋBӍ$ۨT1}9(t[D:krJ^X 7n,8ׯz%֛l[>Wk9kmڸ.o-v`6n,a۰@(~Vh_σǫ㰼9ɪ[Ӽm PnQ+nVcન\=oM|G]& ?u`Xzɔ7T 'Uq?[Q*\U~UvTYly2BO9~Eu=,K l4{wG'/d>"Cu~]+\r=T~fWw)NR^ؓ8t@ea[:<ҥ*',z}9Tי|ͅF*ۡzi~hO7c_N/Pŗdܹe?@fyOazN{T^V^߶*wtxmsRT?H4-<ذNOMnsjyNFiV\hW{f*†yfXz~^o'-~%`K[̽6-}KX_B;}˻Z/2=/~?SOwcg(}2D!KګzZ3B~Cd%3]Lc Qtoho8ϗnT|%aoaa?̟ iXϔ>'qIl> C} `_!_ nn1mwQ|Sx?wgf/mC\X\܁ ` 792{XY+)ᨣR&wp8?oXE[n0p >˛Ɠ2 9݇_la6apUzSW$G{Uc o\~)~͆. G0(z󿴂%ʘzsibu6,Ɂ_~,Z9af tczM7WqNoOL~<wʜi_WmaK37:}.6 X~!g{M}li4azxG|-= ˷~K*>j20b(c;ժ`i;a:y{x>H&BQ/P_R*-7ծ=9_殨fq EuMPȫM{]{Tu={-x (wן)q[^g:箛-kBp2^'a$\vg8KyFj ?e l|xfm<ĩ%__Sh] w>&Xn61_}#cj+NL,j\S;7a=sby3?w/nAkauq>X05c]̭o1^KOv%'mo%oig[ Wְw؟kx181q ,{g73+YߵWѿmLNCaqw4wM%#}ۏWvY vGؿwؾO,]:1/m<֯ /cw?gc_NxMKd1L83M@IzCY (q8{ DO~~|R>'XԻXM`/֟ٺwNle= qޅ50t( q;;i2?6amU8]|V=zL)tO$"qtu1\~&>!gyַqf=y.?L 3IbA0/Dncx>}`w5 Xo8{7o*  sfE GTiS?<);5m[jU ?UƇ9BXcOJaÝ46KgǮ=T2[˙ާYb:hywOǫ/;1,}{4_O57{ dS=o${XXPVH,=A8Xqx,0LA.9๿ad,S`ӵL7z-:]m|?=ES]k"_V2)ף=yo9Cm74x& ,> 6-y}..zY ,tקoC'؞l#=<{IBVq߼ E1a_lX⸙:Lx] `~kyb}`x?A۸hS_X3hGo,Xq1ϻHVO{XXVy{ 5mDz\1\˝vk{8M6yYoXV Y~t?l3 =6 V*Y_'/gHaA\/~D{,dy=٣!;ͼY/=x3O]j8."o˚p~&1h9W~w/=~2󮰝qK}'qyY=pR?׈"h'̑w qU{HW˸ݿZr6=b}~X> ̖-׉m,y^'1 `SvդR/)EW {Wp=ǣȧ\[#<`$7E16K.FR7Kd03oz3؎6K_1\^pɁpNX >ocqni'w(OgâaY~L Xǧ~Co~gX~|Xy.,J7Q3>6p|x=x' Ӝ9g^\v ]n,Y(;3v_ð2<I)8Y,ulCj:ɥW*,8c%r _!@|UsoN@,EXI5G I~NևÊ[BGvZ1N%sX~l,uхe=7ٙGp[òM #5HbqHypǙᒖ8?(g,ϋXgY o|7~Ev{8Jom'=|7s%\b7`nCHחl/5,m尰ùC'G/H/E6s?pPXFr~&_7}g>dpE"]rU+/飢(xEJ'e4Ya8VGsgz@`^i[8R=$h9i:{gjWoxXAx^| HS^aѠ}Ib3/Q׆pzvOk䗺"S<#q׏|r>3dLΟVXY?o#8ߖ*'?z;)Rެ/Dt(+CQ+E~]9y>W`_Jy}.3Kg6`YО0} 垣`װsv^z=og`:bp)׻'g?aE ]9N͍9?i?ю!^br oτJ;gHopgz/a"_ϊNx|}#] w&$t`?sa'my.Vc8(_K.pn۽,Kсm[\"9S# ϯV? xh0w6zEexZd܏:\/~_8}r9]^OuJ~I[bVVו/3~Qps41τ3g 'ː'XZ#~ɞ\BGC9WvL]<-31ÐENtTlc| ;t9>]K^"=Bs>DhA7̳u>F3>erӔ7?C'hԊϽVs=7B>|ZEy߿4uzn=ӯ^3l \bןU/SW~~Mq3}xK3` i  Zu?Vl^W}P>q=uH |yw{5,[<G4wg`B|83*ߴ\c߼ _is?g,Z}zyы_1}$P X9۫C~ŀz|}{ʖH͟_{3gwf?_߉`Qʊm/-Op_Խ"לwkZ醰rovϠ4iHx+?+i;<&̀;ϨYo`yx 㪄̹5O5C|oxޏx4+xFg܍EޯӼp3;[9۽'7.I/F]gZi^3$j>ȁ휓8'ux^j>{k]tA;\NKa>-M̟p6z'5 _t/~W^c*ƚ%ZoM; ?Y%\~ 4JyaMw <B~Hoc6=/gp`C,oXo_HDoOX'D~5ϗ{P<._mF_ua"~J63bL=压>h ,=tg_3y'JRc)YұsْGl'kBd $["˱%,)Bou>>>\s5\3}f v[v S=N9t1Ea}Ƿ?gV7ˠ?O9TK- (u3J/%䥙`x6fyߖQ(ng҅3* l@o;Ҳ,m\_^>,WuK~3Z0&|SXaU67/c`JW})Οj8}nLn3^ə?md Mx[_Qw D&D2\_ h*}Ce>-.=oc3t;81ￔu7M/2xߠ(>`>:7 8> sDm96GjE\:%`?_=??޳͘3S^h[}6˜۔/{ߔo 7_S'Kf`pAsg|Z{+NӞ$Z V,'߀f}`Z%z/K _mDjor[=Spp7<^]sa? 8Wq>Q޷}m=j1'DzN4gͬ~Vk/\h ΂Jw~ tJڗ+gp\{ooE0|ݳ{֫d u`ϡ?WUzoYMS8px!O?wt- ylA 'f{^gC/< H:S'+xT>宽U~=[#[#>X<,OQu߸=iߵϰ_T+k%r=%ƛc]iVQk̲֙'?~`7Rܯ; Vnl_r/Lyӧ빟/ww[X䝐88C{ -'5Eߣ_ZVoi%k|3sSq;L?49t5͔?#?sO_m7ʠpݪ?Nzl7;#{h!UJ5ӑ?'y [ߏ;_:Ca_h?=`/gKW:MY>:qYR8DÒ>֫ؿWIBjkd|rc[ U_55|^̭Ձ=oo1bqtx>6~q^P<6û%Gfn?DU~N~] JqU+OY鑫#G}˱[oa2C]?] x^p/KS7V|y ?_N:uc~L߸ cܧ}ڨoGnN(/=גe 'O}\RB``Wֿo1+|=X&YM)oC+-w=ԯ{%vJ }5v2 ۤFK韨ƓjzO)ߣ,ޏ6{R>gOփ3]RxTr`o`;^"Y18nt'|r_j9Xּ԰3cj xfezYoѵGl~F@MHW灵[s?{oW%x=BfsQ~@y쟈CM]lX1|}k=z1~G6q=_ΧO 2oXm ֶm?YMkRޑ`7C[NPS$Wq{SfVذ|; hnv ZXkDF 36|>̋뙦pŭI/i a,ͪ&3=g&U!7 vlDOeH|`ݤ<jpPߢ XR68L7%_6F)z839ݢ `?2]/3=ut'p.Ɇ Q})#ZGi>}ў&[i[!?8_׬NO<h" ^O;^*oCwj-HZ?!;NG"C璫|G'{Kωx{4۹ޓ.u4=̜IZS9W=Ud}Y9~<ߤq@qɲ{ҀU{FdMmy6_كSWf.K`}KVmXK q|}K#i qW䗫2%+]/CsYyN_Է1ԟvIk?3j//=d7/ɢD zlow|v=r߻nϯu:/u_mA^Ωؚ}-E\ICfW3k{>9{iMNR|rcsP{?Q_rk%W3y,ksK-\֝`"=ߜO}*r=3Ϸv_}5֒ו[ zW|zНO=6wyҷ9f95F|?83t)ۛ[>w[_|Vހ58p}%3}G>'"nKwQvyO{IEu?W9 uGOxmb^\)Q쟯Y>W,+Ny!Fi7r["Cl4X3D{>?֝HpZ򝺁=CX̿gӇ{qvgMEyڋ _q.e7. 5S}.wWf6G^ny? wS֠/NyaFH7{,KNkxSl?sR/>/{iG8ѾWwJ-i6[>0ڵG~ͱU 1h_A%>Oo[Vcz<ȏvs*XO =c";xA`&`o~?Y[>h3[q||M0Tb{ZLCmoC~܃a`頥3XY5S70Ub%l6|-˦vp߄ tw≪ןIce.]ߡ>61ȯ8sfȗy5g<^pbpXe;8L=!oCwh0%Wp&PT0p~ 5̯> :RNq_FA8㵦-ܯf0{ 1VfXl K%_ qHf;7]N;a`.?_8LeNd.K.xGNs哿?^kl`eѬzޏR},YMڳ Ҳ9 LkqNqPks%')'wa e=|+l;݂uqcKBoLwkr/ ^=V/Ӟ>xNN e~~|X~e$6RqQ'-Uɿ3<#qַ? q1;~^/myGӚYahOS sSumoI cRָ?-kkJ ޯmѻ[P%=aϙA7ipߛw(+K[o fߌlk3^n-:#"rv/i%s?J9ݓIWo O~*ZN0^ sd@pL> s4fz`?ǫ^I}c8^{2ȯӌw5V\+ūg46eUp˩ ga0zq-/tŢMj7}*s|{ZMh ya>ZWZt 5ǣ-7ƴogD~u=?6CكD/Mc؞k/ou9Ssx.K2bX.B2 1˻E䆇Q,xƿ0 x,)-݊maw [&%mqٝګ3pd*_Wܯ7ۖ ,M^m*Z3}';>6 ݭ ǺhˋxdG3XӸ2W%P k~=P)9"^;|wn/-s%·3h{+)Q4hSKΗUS{g';]xO~ӇN'Ӡ_z$XғC3oz?_|?19{`߱ڊ7>% lɟ=ql:|4}sMʋ~t;oply{c8} g2FEzh'v^#^?/o>ޚtOϻ.ӽ=3VY#<_ F L{'d`m"ɐD#l{1}m_OYwD~͍`N`eE vU4nd}ȯ[+gZKf΋JϘKy sKn֗xѦQ/0Ԥ1἟6kM#|b98M?#? }.u}gX_ g2}t;f}8Z>͔'Wv:S5C09xl:r  uYav~𡸏;t48˷uN,>eLwC>qf{)EK Wmt/8z[͢ {@~ o֧KJ>Ij7k w?`>-xچ`ﲥ~e(ol13-W+iS^·9/w ,i"X=Gesvpg{:o'? W~|3#~;/2W\ϨQӅw1f|(p{e'Rߥ_VI~"c!ƃ5ҁ|@d8-?zN>7[ 9V5;,!`噣,?m<P{Xq3g6`B16z< ~u+QKY~s81f5EV#?c%dF%q~N+-*a!%8qUoO_wqAOy`{r }z`ܚ'KCyi/M8̬t9?ҊT!߯>7q:[| ʵSaVI%ץAg)_?&[y>MpAeic)l߅0kXڒ|)hBy`7<] zd| N6 }|W+FHO3K);6N-_ҎDR>}3s}G>&S/mo?xœyݣmr`/5;[`&\|x3=^]Y3w# ܚT^\< ;2xg}n R^2g9'i"w1=CDzY:/}McYߊ%`pb\gr}ʵ9?\NR[t'O)%XXw/Y~SO~cӻă / [s XcwLjqVF+˛q< R\H+zI,{82Xo9'T^ f#62=Íт}ݫcyD*?}!n? l4ر``WfkjO-99X7?ҩ֛#*r>1ӝ9t^t`۹^Нf˜ﺶz2wesaK";Yq|ۚ9_Э/g[ōq:+9Ady?m\5VNzc$wH+r\piykrw+z˻o F@gߧU^:%zطVf X_氝or?rߗJs#`{ksˀݞ-gs̅̿q| VcQ_iLN3xvogFV4 Mv݄H N5sv#9ۚ ^[lʈxP?_c&uqLm+狲` r`GX رbWw(-(EJQ"]]D\Yu~+8pTvs濽$WJNR983.X3\?H['m;xvI> ĴL_d/@8xfP/uloH毲h d}1}Kͣ9>OIl5`ͩYlͺ`s3>;K7i^/$X} x;XY8zʻ{iےn5*Q"6w/}2S,JUy6;4=\Wfgɲ?҃J\0_5@tI`i;پqKx4cq'.ޓ} Lo VoY`lO`*8'1E{~R_5_>3?_R"St_ؿyws[[Q*=x>˜F.}3lom%v|rrcGWG{uG38pI%8nLp%i~ `0kW:lTU lܑ#n G@7•_,Ww_X8R5=ϑ-ʋ㘿Y8/ev> 8m9Ӈ_O\c'2}5حw.M}bzg׼^G(8ӗ\cN/rm꽙R`%GYQu_yv3ǃ@*VyΔAg E?.oɺ+~y?KɿvggHf w}3m^zgL4PjB jt|$bqy 8Qdw70aO,uK<-OtwkgyUQ~)_M|2g΂5?h u9Z>YV/}JWw`ٰ5OKpa~3=EyNO i~JkGyc@Nmdg.3σuQ~H:ؿRii*X`Fvѱmj?{}ےhhk?/`&5~c8 iKy۷{\ʝEuXreI?_I!푵vb!8S8u_6~HO{NIxzr98yiϘYU{l}F~|r 8M[O5]d?ʈvM =qX\cK-L'[_Y؏xoSM5nM*~HqOsS%mM3 2Ə}Upپqg=tQs.`|dA1SֽYVnEy'gXkxկ'U/yiS} l,߷$k fߧ>+O]vߚ1 ў~fًEhod(o;sQcd w^ ]xL6~v 7-6~1W۵5?<-(}asqN^RUN5]Ze_W6V4/gx?:l&YL<xNe?u?D Nk/ͪ㜯ܟ2i˴0[5;+㹿+qX:#m~Y%T>oA[ßJ=4fj ,tm+΅07+\F!O KG}taS=#/6pκ.fb-8,隍bej86x2ޘ8iG4vwepkthe㻇XqNkb-]1z@>X}M N4 q8i|ke}ڂM.c< 6;9oF}; +״dz` }66; )=;3 4p^n=_#{`'wPY\pΞhOC3}_w`}`A`7|pQLp8xK׏`QpΕn ~N 3pbF`{pcO1C-. Ply*|i` ON9q-Xs=S{ ]Fml6&uQmD[F7T72o qW1'8W9czqgدq H0-}|2ڛkߙ[4%׃ R_\vl ;b8^p`߈f14_סLاqoz`8Jԗ\e?ㄒ7*YXh C&͆~{w=x3j0ѯch+pE 1OwF~펋`mAQH`ڋ#˓;}o~gn@#䛕.\5^#V^Z'{,x ^11,+HZ }燃{Cȡf.(ym8٩-8ayo_pqأVK 6M~l]޹0[}߈FކѕžFhrt|d?_v(]Q9oWOHx^֏kqJggscK90gjA/rl9xEpy7&/<3=mi 8X!=>>* /Bӥ ά`xw7su$Kߗ`o}-` S=o7m3e4zcm/~6/YְsֹHB 98;cC=V^A{D.{62["+Пۺ0JOk]}8~!cCUo!=Q*/dDuU]׳`^O'90lfm NX\7WBij!6io^-_Gm9+m̪#à+8b=8X}LR;Q_h{P^&^ST%+jE`5CaXo,߄El8:];E {֡?5c)_ф*]XwnI a``Y.g?;w.,vl UǠϻ(oq?Vtٽ=JC(?+՜x*O~ # zXn% B^X?3IN|{m(F}/zCO[N0lDs댎~&߻ =7 \F=aZ_}m0^Qub(w%Wq*tۖni>l,u#7﫿b+;?e+n(q¿>#H}%7O=ߓmF}mzĠ~ɼ?_* q'ѝpAO yB~x2Koi*8m=xĎLwO,2(x4^'=mxa'i&#'OW9MԿ~ X1A6~ uϑGwmPgjE~/ %[[x?&?wIlqG/<.?,`87ȫLxBeX1s"t> 븼7߿?? { P:ٙH AyGmg /mx" [y^n:왡 o>'>U]#|_&Ƌd{T;Bx|y8ˬ'_`XGmٜyC"R*}#F$nY 6ѯ,P`F[b_n#^{Ļ8.dqrK~2}o2]Yy֦ oֲQb<9dqy.4 ?72q.ꤱgkUi~\&nco9tު DAG=,ޞKJ/߿CP8~ޘ?'0em3zm!Uowj\ v/gUȍ0U Gv(8iڦ{ඕ 9 NB{Vt5Z/]mek ^p>pgt%ӻ6lE1S[ӑ '鈩jnK28gGOPCE&GspU 鋺;xT42"}TCp}SJMSS;j/i,b7s?7I~iȟkXo_|͢/-/ݧ6RG\/dNnZ*|.[t?TU`7Hڑ vT{WAqzSհnթuVФOuw#{鿩֚VLf)ړԨkLUK|~:Jִ Uo[V[ƭ6U Zp.yQ_ѽ.Is.1^^KvaKm {h=o8ܩ`{|'_4}B_^cMU_<%wٓeނwvLƣ};6ݙ4TUP{kSN5lT]G}VYp_VLx\Evi: 7UUW/_ ss;;TLX=J%{.ȯ{ZIvs7U=tQ_..2m=QҥE1anq.ȵG Sa[Wz+O4q"<)[4)k`~߾MvL}l%Zx}NAkTH?cÝJyUhSpo{U]s (셲oګ{f_bߨ붰,G5Iwlj3Z8$MǀUYKy ^Y#> [ѸI{Y;j!gkO5 i?ޥܺڭfG=si#5inߐ-'uW2S wi)o[_g~zs[$f ~SUR=C50\&82OpjUͶ1]x^|Zh?g7;oozwkҳ.Sf]2? oPg|?۝dCޔziL[:/OmM,"ᓍ۷0gMº ~аe|ޤTg[$|W>٬~XO7I/̬L.0ڋmY V^`+_v\~eؠߜ$T*xwՂhy`=KU.8$ u7^\T@{1).r}&xZ7 |e/,/o^c͠"Yt/QAƢ^0{zT|pՄbBg^N޺`kxpsw[qo|6Fm{1Lz$|Oo2#.KH,o)}a}$9 ]}[rsWG*p߆2g Cm2ց3rQ`NC_}\࿲6+~c=lh/XkEK[߻;WG8_A-߬C`Ɂ`۾1Lov.|i ɾ.qe7 lhMlb|sn'xx˹Ҭd$ȷLUSv-YS ޴gǯn7KeU{7wJrؑndmRL)Dw4㘽]'/|:>5lY'ہN h#)xѹX Fpf;GpXѭ$C|J??T͗>w>],] t;v@{>ol⌧-_)?s쎵]OH{Urjkzn͵ qTO]/>~jM76:0Zpk'ޯ=Rwk?ug+(˂nXIZ;b` [!]چw&;s?--عmEۜVJz~=xwyߗ>u W \k҄yȢł?pQ痯g5"xeDY騗Wj"^杛`.#߃C~|2}- ?st;X N{^%{j寧^){m)HC*s<_}^iѾ=LUY ~o׸т?lO*Y;bGww+g.JC LU GT2{ -[d0eNZyss0]ݝj2U}\𭍍?h/vo W_ 8JOhm{ }nKpD6Z9oVs-vK[) \Ox2L7IC:B+{']SpOmw.v ʪV}6IpdGeWk.ђMkZ_k| >cY[yw(]E싙}hJ'T|GG9 pͺLU 8V7Xpڝ)BwNk "צ& #>vN9^1Kx ӘGWxZP]NMklqvrl}56lX<}(Ky(ܩr4ާ{Fb9H7/.V.0Kد`띰\Yo.󼔁B_{. _yVZ!v1X{}87T 2esἔ2vڃ5kU?dt>UAy{3SJ1X;"!ȚG|fVW20?nGKa؟f؅;ر0l?,~$W(P0v0lkF|U+ir}+ԧ">!iU:0U2ꋑUy5Q&xuLU ]mWTaS5G>p@-oPj\Քrg5;*s'ktgo8K%">o)'xrH>^=9C`񑋊>\Imх>=I]g6pX7b6{OYkr=Gϕ``G0eקw*ʫ)ot`l;e?yā _ز+M؟aē=.{?.tl4[(~d׿3%;,/svq.C}cϘYWOxښ=ҭfoiޛ3l6y`f,?\LpݭG~AbG%s3Xg+]}praǯ_镯R3$5ˇ7>0~,mlXIw:l89`|PPXtv``A`O;YX|L֞e;\mf과ݣ)άJK_G_t3{)YY9ɞ d hd tέ&{B|O,38Ozn}d!c8?&6Auo)4 ,_x^{(8iQ=X"V$ ` TlW)phgW>EyNEMｻŮ~ ufE08{8}/gqCK{[Cw{6m(n|^т?4wx{wL1:rqn|H^2U7X~IsX68ԯ᩾\<6~ӝ3ʢ}~K|g}N+bOn+_*!2yj {dVO?ˡ<;rm=wz?q|XtX2Fk=E=TWE7?5oVewsXx[pi^'\G꯵ș3NO#q<yV|JNy Hc=y OBVw|-_;s<~VѾ8޺ :h:׮\ԷXٿJ\nkr=ll[4wt/3#;[<뫴f*ػa$g~kK ǫnfWSv<8v3cOܭv,ӀVx? {] {~ }S>8LٟAKK!쿘_̑L;߂wpذ|^`{wvfs+-3skpdϢlyc)Xcu5mޣ5R;6T2ݏnضz|>j eWy`Y/oX_p滟 L1’𗴣| .6`=}ƃL_ v3Gb/#g9SFCnl/p rmNnhӕOn>ej{GZxK`َwj(ꞌ7q=؟3>8wٶ,ӓH7}U`Y(k-iRl,F}gQ;9V}^qY`ll2tSXxpLWrio쟼e`>_O2ִos2c>5Ns0̚q)*CC:_7cวOl*0?X+Ȟ ʱK/xQ͆E6蟱O hieڳZC|,l992ӕ;SD.xp'&t 凉UtG'H^>û!{zibKGn>Y'X!kmF{֨eF[(82s˛E~%gXSSpP=7u|+اOultE3We{z"rȫD(x{S[rkxH \J{gN3l{~njc9._ح`'ۺ^k=h/F[Ƈ m)&9};ML|M}͠1`9%p|ƣJ` n#[mzKc%l;8m_ܧWL} t`\_=FG_~c߶uΰaHb~_lx kC(l8 ߘ 1o;1t.0uGΙ,/5&I:6n{c2X{8lnw;XH=OaY(098K jnM؟ ߥi8zlWYZzu Ug\` r^4W28jʟx=z+ӟ 6lOLAwN^0p+?V5ϰ7}Em~P7(~mC5.^c_OzU1ll(83(/O^w`2הl{Y}ʄإMs*'zu}"8)7sJѴWΗP菴q(H( ,oW~g9^yӾ ˂)-IY~ҿxP~q#XcW`}Ou]_-MUuڗ1W ^w|qf ^9~nW[.mcVsO-Nӣ$'xāI'3tgHWo'G*0;Wݥ1CzObl-_צ?`rBj$]jJ4ׂ9?XOys8v:vUs+Ɵov;{MOSO.7c}Y`W sWv|]v,3o{_¹3ߖRy[)wkcv`SR|IeN`Q =?d雴OEdbwU}koc{WE߮DV-?Di2{CY?`T+oڧUS?e/y'7A|ic\tc:c_};·U[R֭[ A$Xd3[pE?Z~n1 f`{^R(W._t###2Þty-ϥ\]/8yG|y9^:huiV?j\rGd&1~)$[Kzٴ/z;#s>YZ|&`q+'Kn 5O~lBsC)%H֒Z8zjJYsǃ7Qmx`3ē*g.ay1ReOH_J{+$y 1;OgsT3Ʀ4~^sߔ`e9lO )e(mb/[͆4?|W3(/i8ani#͟ؾ,k]Hj-ט%W!oL}OjoD}[6|d"#8J5ԟOs3;.Άm#\ʮeJ /Ff szO:K dϐeeJ9H}eCC%v~qd#Y>a&?âp}g4)sdM~|d3rk/$l~M[S kΕW.~e5Cɿ7e,Ǔa_Vp},8k5'rⲛ>x=rԝhД=Bɞ|9~*={ܟٴ;-̻2䟭tZkǡ 0~y˺x2ڥi_Ki{qCw~y/gWt~xA O_{ImfWb'6O axyU+s }ދ^hhsA?\1t%SqW97VOLy?Up ɝ~$MpT/Z ~8G#N;Dg%86}e؏AՊS(ϔBOFv ї_d؏v%;/ϖtV!{<-Uomp[F3\92+v͢~-3?Kpj&M=<*֎+ߺ9Q!Xp{ 7ׯ{`mֿ)]{s,?>>]*]+?0" ߟ|;E7/&R}&`GJVH7u.SZI(Y x5bŒ >nO|, 2?uHn]pcU[/ESįCkzY)xSS {_ps99]}`|{tX V([S9 嫎`|]az# /&ؤA~ 7U}8}ؖ׿:*kƾZTZRp#ӊꙪٴD*Mi뉂ot]*2Y[ -u_.~E~4𾯍搮V/vsaT'g]@9MU$|JO_,(_f,i^iE߲: 6g fU=&u7 ZUlrpc ^nE9mwX+gدZnTǃLU3Wqoֻ_kwn/e~y/cOK7aH}X7b!9$"O,3.}XDNXaQܭ][\8{48Y,(7]p N^7 _]3Yϒ Jb>_\v~xyuf] |YTpu>Un8{\n$#nC Od돛?},/%a7 _P0{՝M?]럂0j7[6>R0> dؿ4ukLis8QWƊY'SpMnkL^'X[LG>>,8CnAZ`+D՚^2=-'آtzQK}+׷g~ >3dWUGe`DOY3!WorLd^o)c mmqՠX}%oT"aUcJ ]&?N^)fY~),yv21j+ء|`Ov?M1?_Z*%CbË= G#λ'x~dkLRuMBow^ ,1/\TyL濿7j_ݩkwPn<'zk=n/شIu4db]2!>zZC~IO=/{ t/Z x݁] bX)c2u/*X/j ~V]p^$Kuߋ jH5 .4l!8u]^U:&'NwT& ;u,U>v銂v\U|$uaV_aYC}^C~_.L*`HW6.b)x*^{n=#x]?QIp.[ !k鬄hIpj}TK罽&xɹW7 )'FpA Oy0BںW"xA#;k tL; {/?İYtI<~}]4~;Lp̼ UԚ#<?:J%r΄΂j#գ[ ~}kMoDpN j N]x{SM,i'8۩ܶ-~CtA﯂^-.+Cpg~P<~1%0W,Gk[4uɂ6 i~Nj@c[Wˍ5w੗HliͧBq_ V/ 4FǦ ne]Ce">_mAi*]ߥ6oN1K\sr;VRϠߣxƶ-`-uBg_kpW7:b/~F歞>ǧE'tK#YŹ]l:'^M{m|E'w6ozܠβsӬ"wIgGG#^:n`"yxnb|r|~±Cܾ>Re[>*KsoP~m*A5\𱡝i>rhM+тgM1g_\!xu;ml%QW+խsy7`^p3dNH5&k]!kg .37*hOldt_ݚoo(gic پq5h,+ӈ-+ ! s{?f 06yU'Fx[1A3uA^@öq<&a|L*m:gEYT ?lxU&{I:ʋj:sy_&Wi{p=,U:}ݥ/.3f0^Y}UON7 U_? 0}SHyjfQeK~ WCu}K5"7:X8GYm+p?xxc:ykpr^5"o{P\ /|,p:/K:R=~[uγ* ϊ4PG9 \yC+*J灖ƗBpO~!ϱ-]W|~O{_*WNxC}>!O7-W(> T`cIgR,]ݡ%ʘ"e1| "*m5}־+Ր7 k|Q48mZ[iw$%~3| Y& `侕_cf}U9Jf {I_;S)K? C8/s/rt l{2m.W}z!њG,]< 5ӥރY%7o^-kAuzDQkϻ&ʻW!\y6l!R^`_EXcޥC&\CW`YuzaW+1>Cn/n?M{oU <"G-[T`Yt/9y}p;,߿C[?ؿit^6kLTmM?t^f8,ihJ`<+<_>3_:7xMeͤ/庖ram*ic.cXQ3,y?>F 5uW<,t fB=;Ac,?րZZofM._nf]5`˷PESis5C#ig)`eK~,#~sR<=v;G+G}yar|Aث˖d(\#/&`j'޿+/ %+ V`|ʊG_++ᷗG?YE(>ˢq1t=ӝ?Y;tiyQw߅7RgP~;aOR>{F(H[уe]FR^ǟϛ0 $!?[ߗLjy*v('~F\/z`]A`[eSV2}kgm5n}_eWHEء[sޟ&JE` އ&fmG?Z\6OA3+vv||Fާ_pU,벽>%g.G| }PZ' !OW{ʥJPGfҏer<Ym'ɿA<FHW}جUi?dXsY/'m g'~[L;yr]^]>!I)pu`?pDpϳj쎂1xWX<6FR~kKO^~d}`郲xMy ցuq)sקvP7~q Q"a/1f2rч< BcsbfKhv({>,!l87uy]]3z%6s>Y0 lsau34]Ui3J?![VepO\LwwG'Ң!~p@k0Y7maj_BłZ&8wVEoۅgRrk[ʄyWU_ 6;N)ھfmOYwq<(qWVp5ow/|)ϐ_ȹu_=BWw(X$Cu<2:G6n;udw,f2 =,J-X(\sp*ǔs75z:"j};9V ?[tm{MiMTmi68 {`5%_XGp*?i2{{%/e%x_j/2̿z ǂd%nv%7Nl^nąL6ߵ^ K<["=/u1/_JV~j"To-OdAXɒ\`+ypNc2lJjz` _ջ\?cGgwWh/Cy*/+ JvZzMCNgu]U{< jW!lڿ2>,^Tw\c뷛=h}˽|+ڳ﵃t!t6ܗvi-?THt c|/xĸf wYSKW8(Fpn~[VAQ8v-菟Vٝ ~G.=6U]\p=C ѮnT~OW{l7WUP|A&]2 -+~?i_M2&5wn=P6mjFE;~R/lDwGU.x}m'LտU&:6#?U㰾֛Dލ4_^pQUۖd,ʗ<SggLof8Ȳ;5sNv,w동ƘT/}ݰު90>_uY[/qX wtݑ&Fm-8ڬ=\o87Þz1#PLTm?$8ǂ[kGg͚38J?G~5gu=w2ժLA&*eۆ#B+m<ð~H?(xcd*O:ӯ4W[8;_ >yiׅ ]4<tTz][ 1n.MTT iu>+ą-EM{M}p>gւ,qi8ؿaa">ڻ-oꍂEO/8ifۿԈHnjA<_\˜7n4<`Ǫ5x}5)lY('GN }N2$߂ 6C[#yzm!&yҋf ^ye|ߴCϔ ^6QY]2+K̎" ]+bs> * n¼Z7]U{.k< nbʀՂ7;QVݗ[>y+3wqjĻ`{zW}n*w鳥ἔ{7%m|)AՁ_:IGv}k[ ߿K>\nu/,j"l7]tU:-yٽa9:^+xG-c kTpJ BN>S_$xQ|s'_k#k7<^V?)[U'fmpU%[~#=&VUz>=t}|)};XNA{'9y?eѴe8wj]NhS>7}PY$p}֏;<-YO~eBso)i>TWp Gr}_yIX}]=fo>lo+%ҵ84 D{\{ j:5ঢ় σ+Tl kz |[U ^u2p_VN=6QMpka~L>A /åҾe#`}T'o [uH׽LT'Sk8}^.#Vz{Ҿz>b"O,4&^<Az,b>a8!>$iDWYmߘ}{Teu͂G]&\~Lc*MTK,Wu2ė>tok޵o-oO? c՝MT5xjw+gpkvWm3y7y}`)f>*Ń#Jl"[rp@O??xvn8^F%gڗc8۾m^yur;L9iMKO9pFLd?10=VpZo6hnv3b5Se_7xki{T:$CszhпT|paõeB? *(쿣2wLd?,R,66<p?Oϟ1w̙i!˗lH;k|o6'\Mh4A?s!8~C,z{Z7`M zBܣ䀘E7Goh'G/=Bgukc뭘6'SU o1Fg>o6ʿk}ߗ`2c#t 1RIncqX/8LujVWx V_G ߵ`ǭ oE$Rm ;. 5ox˗vA-jA/7s6`pb_=_i <N{OCӞN1^?uU֨`>^/Ԓ[{pȇ^ 6k3LoCr^yu1?;FMׂ"rGUeX]|w V-!}g9f`=[9SܭD0W-5}pGPĠL#X6:Xh2EJ Vuve|D;E1^j|0ϻL_PsE}L~֗V"Ɨe,wZNsDhPo"M!zKꂍ) 6Okn|A|"riKKiKqg}#~b>r\uz -x߇Uzg3o›c>5\aփq__1@q*g$YCϸ]w+֯qʛi#1dS\1h_Uu\O;8О;O״_ىIX'?4gz=dinW]eY8nk.,wPÛ]?~>;"Q9p1w"3Ϥ1XA?'ϖtDTuORty{3,vӖ?vkV(ۛNޡ+C#=-jSz~.' h.}~keR${93c0T{ٴG%X̿%[syf}0ݷb3GY?9SˮY^3}z55[6B V\1X36:FrF+ědِഅiZ-6~UMTm욆8n=_˲ g8}w_;o1\~oU93bЦ*m1Q-R|abKD~^yRD5lLw;DDcf'U}?_eUg .VF=G+\[r + =žzJy)-̔`ko))v`2(1Xu*XX7iUq{$d vxW@_1/^}; C!P)= 0_M;kӿzGޛyf7+> =q9XV{u~&`͡`=K_G_Qog ֆ^9Sfk2gN`م;~Yd <;J|Ȳ=ci~+ckXr<+wk,)8䯽W;G/Gn VZ˾?].k΂Nw d}㟰wqgUi1<[^3>y{? I%q&yw {ۂlC^L72< Nv}|E9rrp< 'ؙCiuM*[wBm+ٕUN410ې_qE IS ^w^?wi)Uܿ_Ϛ]~~egtN~Io`eBOHD{orҸG3^OPYjkg>4YIз(ȿz|+uUgasGm)!G>HGGAt"[4Yje>} ~UGQ j)ZA*68Z(K{[ m_A?^YKV+>L_.r_i85|SpVNLW+-*e,q6Xgl<ϰo .xjC2Y_ }H7E섿6>5-ucߞ6F~I`eα /JX߈xp<5|t/|:S.'9x |`۵3`zv_hv ʉ}n˜ނsl" \?x/D3^cq6>3?``ސ2 5 BWfvkF Ẅ́2u`(O6eGگfdڒ3#n>;C8pm:ʻt`G/IO[+UoCk kר9~=9^} =,7Y 48t 8${S~S^O+";l*nSP?bhzX8v{㯜+{r?846c[`EoSLvEtSv Z连o lR OJ8h#gO~ W%1j>Xꃽr?n}8<,2G+ÞQ^v;шa*ma1>(/+&<Wp,ݐs|}/S8vXȖ=UVeTQpw\-gR~xU-nv<.%Fvf=tZͷh_OHpVs5 wqtkkbhJ5SBHB>Y[HYGRB""K(^Rߙk>y<s=s=ιTg|h"ر9Ι3<I9Fz&8jG^ s躓ߢ^{vXS9ڷ*,dx۸t=^v S6Y4qs]G;k6byomv&gwg-`wʒgp~Y8TO[e# /1C(_+3{ds6?O}-8W_kH!hX jJOX;lY>xsY'۞5e{/S~s{?=s=8'v*O_?:-bqr>z1m'r_Zgb-Jce/?Ԅ],Ϸ`k^G$Eې%Z6'#ٔ+6Lbq:gA] FOvJz.\%sn9oY  /QEr/aMztݮ?\6d?^uvWoQV]Kj u!N_1^Ϫ>q3챧-.ﺒߺ<gq~9ўv}/mE™WpnCgWڳl/Rݚ3UNt[#?ɾd8ꪚoS4I'BހLDeW|KR>Z gx+_e!pBu 2|^+^=pb>z^Gg~d|-cgdr8QbGC{=Gb/W)7;`]cXV!ώeb>ԫWpQ~wS^F4:" 7C+ko/%͒Pq Ka*|>ވ |*Zv' qm[kKu/<&xM޷ޱ1k=vzU ,?GB^>WyX57y/y/xt7֤c/Ou ڷyxPaU|'C{8]M;LΥ ~d]؟\xA]yQN|6$v4_Llaqo՝xR;g?=~ia8ӗ_.<{a>'_ c$'8{#>4<򺹙;MxK87k|7kLkWê:W,+֛o݃g]k"ܵ*jZwo^K{yi6~D~jRQӘޙ- _iJPIplU<乬WQޓMI/:Z嚃vÿ־JYů: =;͚σT'~M6O̸XشM`R{1?6Mk<;Zx:?LH#!4:u_k[]8׹vOv~>m>\o!-/;9Q7k, K5deV_ YU~V~Fn~3îEHwb΁a}8K%<ºӄdyo.] ;=5N|s`{E|4\;1#^|q'?cUx;o]]&|J9/W|6Lx~ }[~ w0הEU\6Ӿipzm1SjZ_9׈f:#|{ Z=g7Xd/dŅ>=Hxѕ|x{Z8!E*;ӬlᝅpUm7kurcT9ߍՅ渦?.y~'ozyKM^\p`E̜?jêc"=tla hka28nz{k3(EA1(AF!_h& µO4rG著&yp I=&?f^ᴘMF uknpw.EP)aa{#?wN;tpr9] ]_1@ȉ ʷh*\nj ||g ](|ARC#;vIl詅> '-Cwaߚ(g3^av(LW9~[9X ֳ? z%?yw/%bqTfԌwK0U,>v'C"3z>̶sEԻK!~}g~R岆5/-!Uy&eڐjVR6^ V ʍ>O`qpz,o:2&8rC:۟ ͇,QU"app%-ǻyK+_뗍CPcyk.w.Hts^E!~<^VEMn9dc{*Ld=wmK;x k&_/̈́+Gy1Ge{/gwt=5Os<ϫk؀ߎ1<4k7G;x#`CӁ-e$rӢ7.| ":ӋpVNA|?>UgS#VZrQ*PRYZq|Cc͚]>^%ߥ{zͽ/SADxK>1):VGFopwMI$M{j(aiyArw{SsZ]k9Tx`Cu|#߃ɽ5dxUCk> 5'ƛڷ6޺Q'Gwu+:1~4tl>lD8&+qt3OCpFc5)?ՄMq`-(p*[+n_A4kۻL4fpn Cmnl0>v9n[pr𬼼DO,h^|gIߩy/O}Co -m*O}Ϻюw:(4ڏz/ǫ^ӄb/x}#xڋOTY1G? .[ / ?&Ug^7T>_~d*|讏nRJ!R 8 ,tZ #%bѻqzlO賄ټCqɀ=7Q^s[.>G G,W},l7UO[OSƟ܃"Ry4Zav Oi9q[[.|K~݅K+mY0Dxҕk<(:"auە'64S_|hN «?u!ڃ: f8J)a v NS~(۾!i??Wg|MĄ{pfI /8߱c8y9wOTpvѳ&V/u*} })+읬Y>iꈌ,|z)'’9m+|gx k.@m^x>54`/;D} #+_{Wcܳc7;^ւ=RՂ 'F̏it%l_GѰ _qNFo=4Յ57`.ߏu[cfiH",o࿒ef`ɸ_]y)Οʌ5nmg"=Kfv\T pn5 U[Uΰo +ʽ[3|iW?ov"~ݟǥ WQ*Ώ"~ mqo>.Kv?́5U߽`]=رp{j[yp^_GYz<y` ۏ$}^(cMXj+ޱ0Q_8b9u-2Ay~FMY;Vonҗoq*ojY}/~@xWs|#ٕSְ"i}5㙰%6r }%?kRӲ?8:V7/p~x¶*dRlnb_,}`a<;xduue~7^G|ً㈯2BaEko Z +:2:rcS_88bl^=‹vUª[aԝd@U(Ư +ڴ;8W _ {Et7VÙֆX_ ÖX\v^Ϟ? X |GeOd>?B}4Ym8^H䊜Ks_}儈2׋M%(}Җ^/:'&(cG_ǶP^Dy:7<#HisgK +ASc:e0lk%JR;(G6.Fxp}W5c*gEfgIsǻUcey7c snf6EAyO*&\E{:Mjim5 6v͑c eD.uC/aY^lM=6N~qq#_/T=_Cy܅ٟ+ǁY :(};&\Xyb=ٍD8l}R}B՗jO񥇨 n#](UZ ?)J2qP&nr ;5u6K[6]T*sdAGV^)U.]NUtۥ1 4o3s@mRi٢>ԨUwVI5ӠQћ%o%n|sgmD{HDRp_acO#D~ǯPt22ŚvNJ~&~<6kpaЮ=cwLtbҳsf|&UTnkmTbK]^*3ly~TYwEz+T](ӈ[r^͗*SOpTbdzL/iϕ*OW?HMWC5Ä㎷q=<$Voޤnsn> {.{k>q,ω'kR POq_s"6 ~'~>ܕ:U@ra>G /sʱ3\%LhTX=YJ+ 8(* !퍿rr g(VD/%q>Aٯ ቷK6*."Udڱ׵HIIZ |kf뜩oM0 o,G|zThG_fa~:/3 >ZR8AY|T_׈Mx]xzdT-9V Z^I8_[SƢ>;yW"zT>m0㟪(21&w~ 3L8n?p MyI˄7;*~zhO!=uZC! [ht/^&W!/KkvBnP{K/l?f|ڸUQةזHI#K+ȳ(_ ʬŸ_*g{>c>zh3lvi&}QSb>lud#vOir~"çZnX[> U/3|T>o8,ϛNϫe7L^q<ϖtMB4LxMux^iy.֘ iѾ]amÌ^-폶7'xIx.+Ȉ)`I/',g\x>)DŠK8JGC~5VgZ[{p<5+0>(n[|o99aC3s5ط{aHo']!I9~ WϠoDմ/QtsyC{ڽ[بJGM{ClQr}« -^'c[ Upy.~ZS}lqI 7?kO OLwV s6 Tz#URX%Sqoʝw;_^HX3uFp~#M~.~ILRIw᳣%y}ה;AN+شE;YxeGd*\nZ JU-mTY6b+&}D=NGIVT;=WLo\dp#ۣ. kW9"\.}63a1tX134XoJׇn6io֧a2W:YY4B D5M:XV2~imgxwX+bj:{y<>_z\wy=)nW˲lVf^O_ ?^ޭUpn:KV™~mE f~C^b3mCx KnL-G/ ׻'I ` Vg+wzs5[?$ ~]LRU!XFwn_. >dIHQ̔W+Ml٧ 'X\q#3̴<oLif,ZO_G;=^$lhG+CLa{Mbp̗ wxoZZǗ  kkt̰ZynzGJ#WP) )cËGy¹7>y>zªW>i) FE{x"?UQ8~qP3ߑ}2'#l:(|&qܫw4}#,{ڭiwacy=m;c/z΄5Y'Jl3i|.7݂;+*[ ܟL|L̉=yonya o}׋<>vq'WZ@ymbrFyd _.GZm[}wwt_6=ۺ?X+FyT%mXOҞb{Ӛ<^ؾqhK w_|lԇ߿E4o߰x6kz!w:]S5cwgФ+}_wWhORwp. M+KH4AhLy!\3oJsG@O;V4,pl<gX8_} 8!{ɩos1s~ϰ罆hV5gVJ6zPI O~w,>:0?,^(?Ks%9Ҹ[]A}Z |u|U G6%xoGU%`i8X- ) k{Vbxi\a\k1|r>;A:ФqJ8i17g?n G\?g~Ogz6!_,aǻYz3O31\oJ\ˊ9X4c_\:F‰9.˭,KUsN]Nٗ5᩿ϏtIw #V&Ӳ'9_0ϣAK&S.XYUG{ hL.7wpUH<|6fϟ̶׉χdkKvs\m8ߞ04 ᗲ0^tL 2cjl05?km5c:[G-.|K6Ouej6φe i{= ȬqC@axm[ ߛHkk.~z3,)ewgҜ!>Iޏ:4B+1{.ce c 0U)WqV㵑1%30_j2P1xZ7m-6}Fcpfr|ίKA7 +z,hpho AÚYq\U77n??̒a;_οN+oW`} N.Gۚ f2|iQ^C_pN 8?Gl#V;j ]#>8ӘU6s#|3ќ9ѹ NiGԛrڠJ[y',|S]w~DO/va}l6bhJCǰ"9 kGu4kpy8[rV,׎uxS6+<_z$ϮX%NXu%ZOg7@k o#pw]*x$%pMsE2S'ܱA:ʘZ t(9Rߧ3R C҄~|)dW5i7J_6NxnM&XV׋G VlOyÑߐ|?Q0\ԫQa(gxz&VXgmu2p{=6]=˧ѷnb]ln5֝ȯ_4+_xoGzcHs" {[uEyeJ2"Kӓ*nP~V)=ȿӰ?U1< ǫHdHϤ r}{wwr҅9+u6bp}ay!܅kGXzm,ߏaIp*ޟʽ}ٶzfU5,X_L7:ְHy-X'1|;V/KOm_LG#<׋x+? +6V9gT0X}d*Wݙ޻pF+v G7=L^aY5JEf_vo*+ܹzԬMRe^|;u6{f='F^o_ʰmާs Åm\?fzQ7x/MXqn-a`޺Ǵ{X5N\0J`yqsg~`fCgʟ%ΰEtx ۬/z`}^wy>BxWƏ;>/a;[jQnO?|GVegυU2’v5Y߲~eco_zMd} Ƴ~]N~;r(3=gW c*HNoHc@ } Xcٿ KWo7$euGzitlKep)f~)rC~&Nxǿά!C~.FFc=9,XSq '8Tx\0֋V؆2Ne _}^y /l}&82u(`*13m4m3m'-NP˺.7_}hE/_X7~DWn&9x;j@ޯL}vtfxwF>L.U(y eyڬp{9Xѭx͗(fݧ쑙j#,Nug}ĭCq YIVf2dߖUVaE`?x܍D`~{bz?J}EcY j98;Kx^ _vnN1۷]Sײe\qy/tgF?muzU\q)27kYYϯUYf 8nS8jf^x^0~[X%pWeW9?Xu^/ Xv.+{/m ~=צ?g8^_>gw5 m+"m+e7w?pYav ?` ޿UN[4=+ògx~>~Qsr8^԰d뚰e`*7r^ѳ$Xrnԃ˞Sp#]\NW,r`ªYNcIzS/bMY7~Eo)Vw?CVQ|2`Xfv֨ w_s_3e2|:%pD4X[ڟgÒ*9jN3^|Vºo`fLO>69YXߎ3kBJgV WP^*^pp<%n+ =Ybz%axL1A7$NtZ׏wnx= {tS~>wp,V]Of^1]?2=_.{  `xZ k>׋i]_۫nq6e]K~j&љg-5֙5[yNBxXymN=W[q8x8ӉlaV]aYU`hg5Y_3NTzP9'9Wde9חe:Mܾ^%6Ʒ'{b_'\ea _kMDn7sۙ ,wF1 _㫂}T;l#ηǬ8G Z gYpe͜tv`rMCy?hZH?kd<gh۰+μǜ᪰[/w`h~#aE*q4ַЖpѬ1N_& ,+j0 8᝹ +Fyh;1+UJ’a2cLXu9G|ZY3(ԠUmL/;{=ZGb!K`MWX/W.]p)I 3\qTO'NrdW1uk}H/#d~!ݤBӓuaW;9Exa'Ǜ\ct_&\+;Nկl%ݔ٨<oq+v*_9k¬Ka7*[v׷}Zv | rc;Ǭ^zDP[ۦ?#U_mWNO3^.FgvfV JC Zoۿ$ P<յ%V <;Uـ ^zЮ਼޷I_]v}Cn?c0gNpZ)~r4 C{ gy>caoa3a|<M,Gl1m]P5;V|-LWoIwVּs!\ڨ+ǟ x;׫a%s՞ )ˮl)+gwhpv3u۷>W1ҳx9>[B'Lj1nyԟRC:|+w17ӻ tDMb~Xr=O3HOwK]jap++bvF~vG}MpqFWZ y 'ʲk%ub5xP-aCnѰvin<8޹Qbm O"{<{+{F^fKƸ~LmfY9~mJQe!Ǔ^[|=fwxr# ˝~0kk?nFx&۲껤l~dtY t S5Ԕ- 5<, WAN837 ]q *'ªFɕٟ/{a݁ưG,'M^?SډĀHnX% 5x2l5pfX1,y|ۛ M<˒QWF}˵ )a"k0=%Ջ?``ݾH?3u;\xQXV+_}~O?[к.nX.p1'hOڊh 8xrX`e>6.Kvu{T+BrJWE_3|U:,lBLy{q=(2֜.o "? V3'.s66TL/-G;4C|L*!~o++Ηc{\&2^V"`xش}` ز>-,?9 AzO~-ia;, `+0Ю kԏaݍ޳6פ1F^lqXe10D% <-l.USûR< o?n&} t?"]?|QbU֘_%g0= S8saܾZգ;*_ ˞b~\S&,k.Pn#cZtU5v:ak!?9?Y=Z^OZrq$X-;M{]6*R/m XLX;|ӛ1ְk.b5r}Jy_qu&e`;pbX2w+kc毭օj u^7X砪8^s aE.aJ&?cYsgְ+?AC0,kV4wakXQ>|iܬ[svZ8}좣=/f}Y"gb{&C2# $؟zG>ϚGGn7Pe'>MV`qpĭ_ ݙϣ-YHcX.+9[핰z+jX5,_:6:F}/p]Q|xlӏmafl,vh 'x> ^kl+a{LUO巢3}>:xm#[jm};XsSQԗ4;%k$e!*pjf]PVkl?iu.泃?8-M|`?i0c->?u-m? J8s},΃UKøΰv<ؤ8c`͕ڬo{{pn~Vf֔`mF%n ˪~{M8[;gto|>gaǾ_ Tap鸷QLO[߁_,r0c|iX%Ξ~֚0Xi ӥ` jyQ{ǫKd+ö:!]Cm?̏U!#GXvWX5AߖlZmo+>0'sac>+Nd|p*b8˞S 50=)ؾl͐_o1}?oU`+/>_[x(ӎZ+-ly_aY*l> _]_]zqeˡCUQf?yd85]vLN i/y=qX;X?,N_80~}~X/{S K|U)s^tu!:(aɧ/?S䏊)<[·- +/r5wΧ cu{7xJۯP}:g ׻썗 v0n2^/\OZ/ eync}..3hnk?Ð~Gwy_~IRKÖGOҟ.y^~\#`T3ž`}9a]*XSw og<{’}~@Sۧ63~TxN?+s'\WߤvA\O hTxk.?+aE;§7t7~W}3|m>ԩqvS,{n=<$峇I)[7mHO憻* J';:rQ}Χ ?VN^;vpEceؿ">'}m̿?| `|OX=35DKڛ1Ҫ2;k [ w,ilڽ5cMXw'_7*,۾'~sz'WTq`;q{_wX O{}1)amؿU*Ê'f}#vEX++z6q -u,b]rU-~z)loS݄-w{Ypc' ݷw'a ]n ǿ5/m/<|_c e$vؿՊim_|WVlMZ {W8k=H^ߒU6Mv-Z]xѥ+ W+\8Ꮄ"lU%wO#S zO:}Ռ;.Jxp[iʻ/kuyϮEE/~P۟ ]>3B<íGaxj)oy︿pAaUٌ NU[êL Kc|g֝< V!~㬇86T; .u?b>1ZX2ⰿõ/ѣ1r פNclUėMDO|uOk_8%^wXu3<VlKfÒam ޳"362 ^`MB%6ODXf}ё}n#&tgŽW2x^/1 ۶n/ԇ j1Y&ְ73wi`=EG˯]TW\E/|hY3X{,sքuKeְWKoȅe7é[a><5b7_71ܮ ="1^P>q%n_WwC~O|Ad=^*QwXgϸn|gWKalyd~<_PmPŠo|Cf^ )G?gkϟrw?u]o`z`EX]r>7󩬾^&!iwޏtA.2bxyG ?Γ{Xm:O[콚 ϻ=Bsex3{ٖ7gmx(~ . a2b6A?on kkͲC~֙/;[vѲ!4߯G YQY-QChIx `EEo$8:-woRwWg8Tg/Ez~v ۷l^Ob}ld<_hy$'V'a.M3Wsfh/OeFu_q )_{<>ɰkTX52ѰYy| VؗL?' + g`ON>,Ob~ n>M~H80&H/r/5|kWX\i}h;;7BlkM^pVѲpnoukge o{}IZO V@}u_, ] .'ӵ;&>a^j[ቃ}+ 3g ־4-5+xipQ×x,+ߙ`أy/N^VM'^a=Vj O{W;*DX31oyjR8tT&Upwú6?V弸5Ӱn]ؤle]gM?_+#,q T/l}V 3a ϩ:(ОlB{8Ɂ䚰DضMt^ qeegq~9>:ge*d k[;y=?-|;t]kz W'i6/ ;"0棾w?|W&R}`'8VY*&3/S'axY{d綯<]9>I|~Qxֿe/IA]0S5[|0K;]nc eb~ij!d88wVqǥ,캽>gKcXGUf{XNk |Vsrf䝰|<.~Z<9kz >lr`۸R?_0=[1}*X+V`IQ,׺R@ \oǙOɯw"*%|2wnnT9C@b=J'^q>kG9;n)x%^|6ez5qYq}l7?dc{8f7/` d9y3)֎p80]a'o;z;+'.H/{4Ohfp3&_+a= x,c8_v} z?#R61X/19hӳux7+Yތ>CFT[Oh( [0~דϮ|roƯ',/z&֯Y?zb=Έ轏]|?X;ˊ3=~aXlÎywGy`FL7̍k7j~G5[n?wGz!UuZ{enAzXcdXfkue[~#̶`}jםSx va}M&}~'\.E8zcfV/|O)]6.Ѹ pmޘDl}߃XXk`}l_k5֗-su$ClϢR;z9Ki=-H>{ џƜz>7eo`ΰv5{ۋ忐gz1_aD?8 6U}߬{u hc7l?;,pՈ_66Z/ϖvUKez0s-daeXumfnwqO^[55iW8s?h9| iu:"c0Wh }i:;z~#ۓs;#䤷љ _Rê1O8N69 ]߮(XgN\ k[)QjXuxjMwMzmv%Ek`v10۷ x aݚ s' .l[ot݌>?{\9[5S"|N((Og{m@83|Yzgf0F`02NAh1ijGzF ?N(~iz$Էpg?eK˧?U+MSsI ohqlq~ԯU'sWMLE}5| [N;qӄy"tMnCx2XQ0l ;;aXut%˅epp{noS=ҫ7MG>|;QrV}(S+qW֗a*~?8ߺrUT1YW/h  +Wj_hʰe+Kgrn=r/~Jo)g l]U9s®z(ik>?M+(}tYUihwmūE.澖3KFUFQ6~oVc +O1qUe9Z WiDNݗ}=IUe9c]eSm{6Q;c~H\%#)UsJ:[{.;{CoO~!z܅5҇9Š+T(aɌzr|[~fn:Ǜ;l`8M#8g'OZO(氤+ud,56c V˯U^nURՉ5ؿ'̀uuVU3׸+3~|d߰*wОn)ﻟ G VJ|ʃh*5pѽ?lQXb-<.]֍ax38jEC nۺL1 g?Ozjo:Tv50| sf}eɌ3at7p4=WM:W+ਃ8lg{{M㰮>p8eǣSé'`}Lj UIfcm72e*'3hP >;l'Nx4ܠu:w13_Z\vtX^:׋EmVbQ \]6V?n||xjkB-Fz-aחX_eiϙB8w_ըoyxnJX~Ζsap}prqQWIE8"eG4*X>}Uea-`{sGimPּ=$\Ƒ5/ȊO? *[Z`Fcϋdݫy%|/dk µv/E =6V8~jG<\{%JxY* jѥpoZ)9ΰG+/?V6ލd6 %+`E{pFJp]>OMX]4q+x8N%kQ~*sa {w K3(>Sj Ό[_pS]w3a9?ww)g@8P[);))#HIFh47xԶ B~5#xOa X}mz Vu[%:`#`*}3kk3Q}_[2v4[>FtB5X(PKVWHq{T2 nybb.shp.xmlUT ,V,Vux [{o@dNpowNZus%֮$jE*~~~ΐ,9Σ SopΎOx!F^:iФC=Ȩw LeZGQvySDz֡ejPv&'O;Y9fVjNl9Nus] vu4c)DHc\4/\1] NY.mC)ИYQ|VBn܇bQ`ŰcgQmj&"}elCv0&h-@;;:|)UttzN6±iݣq1Q ؜bEӰGG0+pU@=¶ZLJV=`0`A}\|;Dio;i|=VPXc]# )e+'5]\"ϋ~L;`X_%u 2o{%ߙͶyxlZFA$m3`S8tqI`<1KV`~4um<\TH(yJ釸nD] %RwAV䁇oQ'߄}-: ɷk!ҷq߼o7Y2Zi.Ʈ&Ll^&XI OA5}\"kXfxOslNg眉w_(ym'Br֟_]T(^謺EYAuGVizVjYbX]<4u̯Wy1d"U_!/@^;T#$9 U9o΋r֪a`:C4TG~3`ePv_AL*@A UXaC ,d+Y@D0 "PlML4 U]+%mmcKSeS 5%sҟNCR{!F #H@Yw,e;G-QꕲDV?2<zᤱdb Ul@) ZdF=/@=Z>l7̍ޱPlq!tPZΕL%t~f𷪧#'5fTAPt<XɪBD# 煑(4[EvKCMTjd7WX82?Ui-9)SkB"jRIL3);!x)4x9 ,"EݕFeNHT]LvdF]{)@e?⦐܆vD#qT-W2*"(P0/.V@PD;)XZoYFERIa@D;QBfO}dU[/oX><ʙSBMf3b i&>!gT8GBzKҌޅ,f6~+T":p0+D60l{4_.ɖ؃ ƆjOn⍪AMgίÓފw @fOs9yYXRY.HP7? ӔD0f eL " hSz~˗ +ڸa$ugqJ́'(Qr#YyH[/ F>*qoaBdY\BAȅ\۞\%ͫ,8PӢgc*!ʡ“c_cUA[4|Q׵mr4cSI%{/d 7 Q{Ʒy4ǜdߜ_Ǭ6Qg#3ݲ {.TSEQ?t̆z,Y*8$ |LWh8!_A Ig̘FN(-N:1fU Ō- ۖK}=GC.0Flz0tơaT$%(%&cw!_;L',!* BJfA^ybNM H1a{dB8(8~yKS߆2^!a瑗u㏊M.`9TUI)z'b#xUHu$XZ TwM,wY)ul 9eIpB ׾DkuN ^eu^lN{\V`7;u`FOGFjFv 8,Iŭgy.zV%AFS$g[Ġ ȸ-]tD<7*ypdKc@ E _Ή}CCRTPR.NC WM |hD&/^0v_tza7Ճ9iWQԞ0Ŝ__?E.&c(,e90*|U~E9)KCp-G^Oo:ifivj.RU&WA"vP ʙ*,gczt t%ΨmШ흧SOJ`X 5;=Z&`SY)^hp==^,$,>웤cN(/'0^yz#q~PKVWH9Wnybb.shxUT ,V2lXux c`Pb^030i;Q'9/FS#8##FI1 a`jӹ9 N 58PKVWH{Ynybb.dbfUT,Vux PKVWHŅVI2:nybb.prjUT,Vux PKVWH= nybb.shpUT,Vux PKVWHq{T2  nybb.shp.xmlUT,Vux PKVWH9W nybb.shxUT,Vux PK geopandas-1.2.0/geopandas/tests/data/overlay/000077500000000000000000000000001525650651600211635ustar00rootroot00000000000000geopandas-1.2.0/geopandas/tests/data/overlay/geom_type/000077500000000000000000000000001525650651600231535ustar00rootroot00000000000000geopandas-1.2.0/geopandas/tests/data/overlay/geom_type/df1.geojson000066400000000000000000000515431525650651600252230ustar00rootroot00000000000000{"type": "FeatureCollection", "features": [{"id": "9", "type": "Feature", "properties": {"acres": 4.96074455173779, "idx": 9, "value": 2}, "geometry": {"type": "Polygon", "coordinates": [[[-95.58937565036604, 60.755964062925905], [-95.58937565036604, 60.75598209180554], [-95.58936454178152, 60.75598209180555], [-95.58936454178152, 60.75599110624536], [-95.58935343319699, 60.75599110624537], [-95.58934232461245, 60.75599110624537], [-95.58934232461245, 60.75600012068519], [-95.58933121602794, 60.75600012068519], [-95.58933121602794, 60.756009135125026], [-95.5893201074434, 60.75600913512501], [-95.5893201074434, 60.75601814956483], [-95.58930899885887, 60.75601814956484], [-95.58929789027435, 60.75601814956484], [-95.58929789027435, 60.756027164004664], [-95.58928678168982, 60.756027164004664], [-95.58927567310528, 60.756027164004664], [-95.58927567310528, 60.75603617844449], [-95.58926456452075, 60.75603617844449], [-95.58926456452075, 60.756045192884315], [-95.5892534559362, 60.75604519288431], [-95.5892423473517, 60.7560451928843], [-95.5892423473517, 60.75605420732413], [-95.58923123876716, 60.75605420732413], [-95.58923123876716, 60.75607223620378], [-95.58922013018262, 60.75607223620377], [-95.58922013018262, 60.7560812506436], [-95.58920902159808, 60.7560812506436], [-95.58920902159808, 60.756099279523234], [-95.58919791301358, 60.756099279523234], [-95.58919791301358, 60.756117308402885], [-95.58918680442903, 60.756117308402885], [-95.58918680442903, 60.75612632284271], [-95.5891756958445, 60.75612632284271], [-95.5891756958445, 60.75614435172235], [-95.58916458725996, 60.756144351722334], [-95.58916458725996, 60.756162380602], [-95.58915347867546, 60.756162380602], [-95.58915347867546, 60.75617139504182], [-95.58914237009091, 60.75617139504182], [-95.58914237009091, 60.75618942392147], [-95.58913126150641, 60.75618942392147], [-95.58913126150641, 60.75621646724094], [-95.58912015292186, 60.75621646724094], [-95.58912015292186, 60.75625252500023], [-95.58910904433733, 60.75625252500023], [-95.58910904433733, 60.756279568319705], [-95.58909793575279, 60.756279568319705], [-95.58909793575279, 60.756288582759524], [-95.58908682716829, 60.75628858275951], [-95.58908682716829, 60.756306611639175], [-95.58907571858374, 60.75630661163917], [-95.58907571858374, 60.75632464051881], [-95.58906460999921, 60.75632464051881], [-95.58906460999921, 60.75633365495864], [-95.58905350141468, 60.75633365495864], [-95.58905350141468, 60.756351683838275], [-95.58904239283017, 60.756351683838275], [-95.58904239283017, 60.75636971271793], [-95.58903128424562, 60.75636971271793], [-95.58903128424562, 60.756387741597564], [-95.58902017566109, 60.75638774159757], [-95.58902017566109, 60.75639675603739], [-95.58900906707656, 60.7563967560374], [-95.58900906707656, 60.756414784917034], [-95.58899795849204, 60.756414784917034], [-95.58899795849204, 60.75643281379669], [-95.58898684990751, 60.7564328137967], [-95.58898684990751, 60.756441828236504], [-95.58897574132297, 60.75644182823651], [-95.58897574132297, 60.75645985711615], [-95.58896463273845, 60.75645985711616], [-95.58896463273845, 60.75648690043562], [-95.58895352415392, 60.75648690043561], [-95.58895352415392, 60.75679339138959], [-95.58894241556939, 60.756793391389586], [-95.58894241556939, 60.75682043470906], [-95.58893130698488, 60.756820434709056], [-95.58893130698488, 60.756829449148874], [-95.58892019840034, 60.756829449148874], [-95.58892019840034, 60.75684747802852], [-95.5889090898158, 60.756847478028526], [-95.5889090898158, 60.75686550690818], [-95.58889798123127, 60.75686550690817], [-95.58889798123127, 60.756874521348], [-95.58888687264673, 60.75687452134798], [-95.58888687264673, 60.75689255022763], [-95.58887576406222, 60.75689255022763], [-95.58887576406222, 60.75691057910728], [-95.58886465547768, 60.756910579107284], [-95.58886465547768, 60.75692860798693], [-95.58885354689315, 60.75692860798692], [-95.58885354689315, 60.756937622426754], [-95.5888424383086, 60.75693762242674], [-95.5888424383086, 60.7569556513064], [-95.5888313297241, 60.756955651306406], [-95.5888313297241, 60.75697368018604], [-95.58882022113956, 60.75697368018604], [-95.58882022113956, 60.756982694625854], [-95.58880911255503, 60.756982694625854], [-95.58880911255503, 60.75700072350551], [-95.58879800397048, 60.75700072350551], [-95.58879800397048, 60.75702776682498], [-95.58878689538598, 60.75702776682499], [-95.58878689538598, 60.757063824584264], [-95.58877578680143, 60.75706382458427], [-95.58877578680143, 60.75707283902409], [-95.58876467821693, 60.7570728390241], [-95.58876467821693, 60.7570818534639], [-95.58875356963239, 60.757081853463916], [-95.58875356963239, 60.75709086790375], [-95.58874246104786, 60.75709086790374], [-95.58873135246331, 60.757090867903734], [-95.58873135246331, 60.75709988234357], [-95.58870913529427, 60.75709988234356], [-95.58869802670974, 60.75709988234356], [-95.58869802670974, 60.75710889678338], [-95.58845363785008, 60.757108896783386], [-95.58844252926555, 60.75710889678338], [-95.58844252926555, 60.757117911223204], [-95.58843142068103, 60.75711791122322], [-95.5884203120965, 60.75711791122322], [-95.5884203120965, 60.75712692566304], [-95.58840920351196, 60.75712692566302], [-95.58840920351196, 60.757135940102856], [-95.58837587775838, 60.757135940102856], [-95.58836476917384, 60.75713594010286], [-95.58836476917384, 60.757144954542674], [-95.58828700908214, 60.75714495454267], [-95.5882759004976, 60.75714495454267], [-95.5882759004976, 60.7571539689825], [-95.58820924899044, 60.7571539689825], [-95.58820924899044, 60.757144954542674], [-95.58819814040592, 60.75714495454267], [-95.58819814040592, 60.757135940102856], [-95.58818703182139, 60.757135940102856], [-95.58818703182139, 60.75712692566304], [-95.58817592323685, 60.75712692566303], [-95.58817592323685, 60.757117911223204], [-95.58816481465232, 60.75711791122322], [-95.58816481465232, 60.75710889678338], [-95.58815370606777, 60.75710889678338], [-95.58815370606777, 60.75709086790374], [-95.58814259748327, 60.75709086790374], [-95.58814259748327, 60.757072839024104], [-95.58813148889872, 60.7570728390241], [-95.58813148889872, 60.75705481014444], [-95.5881203803142, 60.75705481014444], [-95.5881203803142, 60.75660408815332], [-95.58810927172965, 60.75660408815332], [-95.58810927172965, 60.75658605927365], [-95.58809816314515, 60.756586059273666], [-95.58809816314515, 60.75656803039402], [-95.5880870545606, 60.75656803039402], [-95.5880870545606, 60.7565590159542], [-95.58807594597607, 60.75655901595421], [-95.58807594597607, 60.75655000151438], [-95.58806483739156, 60.75655000151438], [-95.58806483739156, 60.756540987074565], [-95.58805372880703, 60.75654098707455], [-95.58805372880703, 60.75653197263474], [-95.58804262022248, 60.75653197263473], [-95.58804262022248, 60.75652295819491], [-95.58802040305343, 60.75652295819491], [-95.58802040305343, 60.75651394375508], [-95.58799818588436, 60.75651394375509], [-95.58799818588436, 60.75650492931526], [-95.58797596871531, 60.75650492931526], [-95.58797596871531, 60.756495914875444], [-95.58792042579266, 60.75649591487544], [-95.58792042579266, 60.75648690043562], [-95.58789820862361, 60.75648690043561], [-95.58789820862361, 60.75647788599579], [-95.58787599145457, 60.75647788599579], [-95.58787599145457, 60.75646887155597], [-95.58786488287002, 60.75646887155597], [-95.58786488287002, 60.756459857116155], [-95.58784266570096, 60.75645985711616], [-95.58784266570096, 60.75645084267633], [-95.58782044853191, 60.75645084267634], [-95.58782044853191, 60.75644182823651], [-95.58780933994737, 60.75644182823651], [-95.58780933994737, 60.75643281379668], [-95.5877871227783, 60.756432813796685], [-95.5877871227783, 60.75642379935685], [-95.58776490560925, 60.75642379935686], [-95.58776490560925, 60.75641478491704], [-95.58775379702472, 60.756414784917034], [-95.58775379702472, 60.756405770477215], [-95.58774268844017, 60.75640577047721], [-95.58774268844017, 60.75639675603739], [-95.58773157985567, 60.75639675603738], [-95.58773157985567, 60.75638774159756], [-95.58772047127113, 60.75638774159757], [-95.58772047127113, 60.756378727157745], [-95.58770936268662, 60.756378727157745], [-95.58770936268662, 60.756360698278094], [-95.58769825410208, 60.756360698278094], [-95.58769825410208, 60.75634266939846], [-95.58768714551755, 60.75634266939845], [-95.58768714551755, 60.7563246405188], [-95.587676036933, 60.756324640518805], [-95.587676036933, 60.75631562607899], [-95.5876649283485, 60.75631562607898], [-95.5876649283485, 60.756297597199335], [-95.58765381976396, 60.75629759719934], [-95.58765381976396, 60.7562795683197], [-95.58764271117943, 60.7562795683197], [-95.58764271117943, 60.756261539440054], [-95.58763160259488, 60.75626153944006], [-95.58763160259488, 60.75624351056042], [-95.58762049401038, 60.75624351056042], [-95.58762049401038, 60.7558108174489], [-95.58763160259488, 60.755810817448925], [-95.58763160259488, 60.75578377412945], [-95.58764271117943, 60.755783774129455], [-95.58764271117943, 60.755765745249796], [-95.58765381976396, 60.75576574524981], [-95.58765381976396, 60.75575673080998], [-95.5876649283485, 60.755756730809985], [-95.5876649283485, 60.75574771637016], [-95.587676036933, 60.75574771637016], [-95.587676036933, 60.755729687490515], [-95.58768714551755, 60.755729687490515], [-95.58769825410208, 60.755729687490515], [-95.58769825410208, 60.755720673050696], [-95.58770936268662, 60.75572067305069], [-95.58770936268662, 60.75571165861086], [-95.58772047127113, 60.755711658610856], [-95.58772047127113, 60.75570264417103], [-95.58773157985567, 60.755702644171045], [-95.58774268844017, 60.75570264417104], [-95.58774268844017, 60.75569362973122], [-95.58775379702472, 60.75569362973121], [-95.58777601419379, 60.75569362973122], [-95.58777601419379, 60.75568461529139], [-95.58780933994737, 60.75568461529141], [-95.58780933994737, 60.75569362973122], [-95.58783155711644, 60.75569362973123], [-95.58783155711644, 60.75570264417104], [-95.58785377428549, 60.755702644171045], [-95.58785377428549, 60.75571165861086], [-95.58787599145457, 60.75571165861086], [-95.58787599145457, 60.75572067305069], [-95.58789820862361, 60.75572067305069], [-95.58789820862361, 60.75572968749052], [-95.58790931720813, 60.75572968749053], [-95.58790931720813, 60.75573870193034], [-95.58793153437719, 60.75573870193034], [-95.58793153437719, 60.75574771637016], [-95.58795375154625, 60.755747716370166], [-95.58795375154625, 60.75575673080996], [-95.58797596871531, 60.75575673080998], [-95.58797596871531, 60.755765745249796], [-95.58798707729986, 60.7557657452498], [-95.58798707729986, 60.75577475968964], [-95.5880092944689, 60.755774759689636], [-95.5880092944689, 60.75578377412945], [-95.58803151163798, 60.75578377412945], [-95.58803151163798, 60.75579278856928], [-95.58804262022248, 60.75579278856928], [-95.58804262022248, 60.75580180300909], [-95.58806483739156, 60.755801803009085], [-95.58806483739156, 60.755810817448925], [-95.5880870545606, 60.75581081744891], [-95.5880870545606, 60.75581983188873], [-95.58832033483573, 60.75581983188873], [-95.58832033483573, 60.75581081744892], [-95.58833144342026, 60.75581081744891], [-95.58833144342026, 60.75580180300909], [-95.58834255200479, 60.75580180300909], [-95.58834255200479, 60.75579278856928], [-95.58835366058933, 60.75579278856927], [-95.58835366058933, 60.75576574524981], [-95.58836476917384, 60.7557657452498], [-95.58836476917384, 60.75573870193034], [-95.58837587775838, 60.75573870193033], [-95.58837587775838, 60.75568461529141], [-95.58836476917384, 60.75568461529141], [-95.58836476917384, 60.755666586411756], [-95.58835366058933, 60.75566658641174], [-95.58835366058933, 60.75565757197193], [-95.58834255200479, 60.75565757197194], [-95.58834255200479, 60.755648557532105], [-95.58833144342026, 60.7556485575321], [-95.58833144342026, 60.755639543092286], [-95.58832033483573, 60.755639543092286], [-95.58832033483573, 60.75563052865246], [-95.58830922625121, 60.75563052865246], [-95.58830922625121, 60.755621514212635], [-95.58828700908214, 60.75562151421264], [-95.58828700908214, 60.75561249977282], [-95.58826479191309, 60.75561249977282], [-95.58826479191309, 60.755603485333], [-95.58825368332856, 60.755603485333], [-95.58825368332856, 60.75559447089318], [-95.58824257474404, 60.75559447089316], [-95.58824257474404, 60.75558545645335], [-95.5882314661595, 60.755585456453346], [-95.5882314661595, 60.75557644201353], [-95.58822035757497, 60.75557644201353], [-95.58822035757497, 60.75556742757371], [-95.58820924899044, 60.75556742757369], [-95.58820924899044, 60.75554939869405], [-95.58819814040592, 60.75554939869406], [-95.58819814040592, 60.75553136981441], [-95.58818703182139, 60.75553136981442], [-95.58818703182139, 60.75551334093476], [-95.58817592323685, 60.755513340934776], [-95.58817592323685, 60.75550432649495], [-95.58816481465232, 60.75550432649495], [-95.58816481465232, 60.7554862976153], [-95.58815370606777, 60.75548629761529], [-95.58815370606777, 60.75546826873566], [-95.58814259748327, 60.75546826873565], [-95.58814259748327, 60.75545023985601], [-95.58813148889872, 60.755450239855996], [-95.58813148889872, 60.755432210976366], [-95.5881203803142, 60.755432210976366], [-95.5881203803142, 60.755260936619734], [-95.58813148889872, 60.75526093661973], [-95.58813148889872, 60.755233893300264], [-95.58814259748327, 60.75523389330026], [-95.58814259748327, 60.75521586442062], [-95.58815370606777, 60.75521586442063], [-95.58815370606777, 60.755206849980794], [-95.58816481465232, 60.755206849980794], [-95.58816481465232, 60.75518882110116], [-95.58817592323685, 60.75518882110116], [-95.58817592323685, 60.75517079222149], [-95.58818703182139, 60.7551707922215], [-95.58818703182139, 60.75516177778168], [-95.58819814040592, 60.75516177778167], [-95.58819814040592, 60.755143748902036], [-95.58820924899044, 60.755143748902036], [-95.58820924899044, 60.75492740234629], [-95.58822035757497, 60.7549274023463], [-95.58822035757497, 60.754909373466646], [-95.5882314661595, 60.75490937346664], [-95.5882314661595, 60.754891344586994], [-95.58824257474404, 60.754891344587], [-95.58824257474404, 60.75488233014718], [-95.58825368332856, 60.75488233014717], [-95.58825368332856, 60.754873315707364], [-95.58826479191309, 60.75487331570736], [-95.5882759004976, 60.75487331570735], [-95.5882759004976, 60.754864301267524], [-95.58830922625121, 60.75486430126753], [-95.58830922625121, 60.75487331570735], [-95.58833144342026, 60.754873315707364], [-95.58833144342026, 60.754882330147176], [-95.58835366058933, 60.75488233014717], [-95.58835366058933, 60.754891344586994], [-95.58837587775838, 60.754891344586994], [-95.58837587775838, 60.754900359026834], [-95.58839809492746, 60.75490035902682], [-95.58839809492746, 60.754909373466646], [-95.58840920351196, 60.75490937346664], [-95.58840920351196, 60.75491838790648], [-95.58843142068103, 60.75491838790648], [-95.58843142068103, 60.7549274023463], [-95.58845363785008, 60.75492740234629], [-95.58845363785008, 60.75493641678613], [-95.58847585501913, 60.754936416786116], [-95.58847585501913, 60.754945431225934], [-95.58848696360367, 60.75494543122592], [-95.58848696360367, 60.75495444566575], [-95.5884980721882, 60.754954445665746], [-95.5884980721882, 60.754963460105586], [-95.58850918077275, 60.754963460105586], [-95.58850918077275, 60.754972474545404], [-95.58852028935725, 60.754972474545404], [-95.58852028935725, 60.75498148898524], [-95.5885313979418, 60.75498148898522], [-95.5885313979418, 60.754999517864874], [-95.58854250652632, 60.75499951786488], [-95.58854250652632, 60.75501754674452], [-95.58855361511087, 60.75501754674452], [-95.58855361511087, 60.755026561184344], [-95.5885647236954, 60.75502656118434], [-95.5885647236954, 60.75503557562417], [-95.58857583227991, 60.75503557562416], [-95.58857583227991, 60.75504459006399], [-95.58858694086445, 60.75504459006399], [-95.58858694086445, 60.755053604503814], [-95.58859804944898, 60.755053604503814], [-95.58859804944898, 60.755062618943626], [-95.58862026661804, 60.755062618943626], [-95.58862026661804, 60.75507163338346], [-95.58864248378708, 60.75507163338346], [-95.58864248378708, 60.75508064782328], [-95.58865359237161, 60.75508064782327], [-95.58865359237161, 60.75508966226311], [-95.58867580954069, 60.75508966226311], [-95.58867580954069, 60.75509867670293], [-95.58869802670974, 60.75509867670293], [-95.58869802670974, 60.75510769114275], [-95.58870913529427, 60.75510769114275], [-95.58870913529427, 60.75511670558255], [-95.58873135246331, 60.755116705582566], [-95.58873135246331, 60.755125720022384], [-95.58875356963239, 60.75512572002238], [-95.58875356963239, 60.75513473446221], [-95.58880911255503, 60.75513473446222], [-95.58880911255503, 60.755143748902036], [-95.5888313297241, 60.755143748902036], [-95.5888313297241, 60.755152763341854], [-95.58885354689315, 60.75515276334186], [-95.58885354689315, 60.755170792221506], [-95.58886465547768, 60.75517079222149], [-95.58886465547768, 60.75524290774008], [-95.58885354689315, 60.75524290774008], [-95.58885354689315, 60.75526093661973], [-95.5888424383086, 60.75526093661973], [-95.5888424383086, 60.75526995105955], [-95.5888313297241, 60.75526995105955], [-95.58882022113956, 60.755269951059546], [-95.58882022113956, 60.75527896549938], [-95.58862026661804, 60.755278965499365], [-95.58860915803352, 60.75527896549937], [-95.58860915803352, 60.7552879799392], [-95.58859804944898, 60.7552879799392], [-95.58858694086445, 60.75528797993919], [-95.58858694086445, 60.75529699437902], [-95.58857583227991, 60.755296994379016], [-95.58857583227991, 60.75530600881885], [-95.5885647236954, 60.75530600881885], [-95.5885647236954, 60.755324037698486], [-95.58855361511087, 60.75532403769849], [-95.58855361511087, 60.755351081017956], [-95.58854250652632, 60.75535108101796], [-95.58854250652632, 60.755432210976366], [-95.58855361511087, 60.755432210976366], [-95.58855361511087, 60.75544122541619], [-95.5885647236954, 60.755441225416185], [-95.5885647236954, 60.755459254295836], [-95.58857583227991, 60.755459254295836], [-95.58857583227991, 60.75547728317547], [-95.58858694086445, 60.75547728317548], [-95.58858694086445, 60.7554862976153], [-95.58859804944898, 60.755486297615285], [-95.58859804944898, 60.75550432649495], [-95.58860915803352, 60.755504326494936], [-95.58860915803352, 60.75552235537458], [-95.58862026661804, 60.75552235537459], [-95.58862026661804, 60.75556742757369], [-95.58863137520257, 60.7555674275737], [-95.58863137520257, 60.755585456453346], [-95.58864248378708, 60.75558545645334], [-95.58864248378708, 60.755603485333], [-95.58865359237161, 60.755603485333], [-95.58865359237161, 60.75562151421265], [-95.58866470095614, 60.75562151421264], [-95.58866470095614, 60.75563052865246], [-95.58867580954069, 60.75563052865245], [-95.58867580954069, 60.75563954309229], [-95.5886869181252, 60.755639543092286], [-95.5886869181252, 60.755648557532105], [-95.58869802670974, 60.755648557532105], [-95.58869802670974, 60.75565757197193], [-95.58870913529427, 60.75565757197193], [-95.58870913529427, 60.75566658641174], [-95.58873135246331, 60.75566658641175], [-95.58873135246331, 60.75567560085157], [-95.58875356963239, 60.75567560085157], [-95.58875356963239, 60.75568461529141], [-95.58895352415392, 60.755684615291415], [-95.58895352415392, 60.75569362973122], [-95.58897574132297, 60.755693629731226], [-95.58897574132297, 60.75570264417104], [-95.58898684990751, 60.755702644171045], [-95.58898684990751, 60.75571165861086], [-95.58900906707656, 60.755711658610856], [-95.58900906707656, 60.75572067305069], [-95.58903128424562, 60.755720673050696], [-95.58903128424562, 60.755729687490515], [-95.58904239283017, 60.755729687490515], [-95.58904239283017, 60.75573870193034], [-95.58906460999921, 60.75573870193034], [-95.58906460999921, 60.75574771637015], [-95.58908682716829, 60.75574771637015], [-95.58908682716829, 60.755756730809985], [-95.58909793575279, 60.75575673080998], [-95.58909793575279, 60.7557657452498], [-95.58912015292186, 60.7557657452498], [-95.58912015292186, 60.755774759689636], [-95.58913126150641, 60.75577475968963], [-95.58913126150641, 60.75578377412944], [-95.58915347867546, 60.75578377412944], [-95.58915347867546, 60.755792788569266], [-95.58916458725996, 60.75579278856928], [-95.58916458725996, 60.755801803009085], [-95.58918680442903, 60.755801803009085], [-95.58918680442903, 60.75581081744891], [-95.58920902159808, 60.75581081744892], [-95.58920902159808, 60.75581983188873], [-95.58922013018262, 60.75581983188873], [-95.58922013018262, 60.75582884632856], [-95.5892423473517, 60.75582884632856], [-95.5892423473517, 60.75583786076839], [-95.5892534559362, 60.755837860768395], [-95.5892534559362, 60.7558468752082], [-95.58927567310528, 60.75584687520821], [-95.58927567310528, 60.755855889648025], [-95.58928678168982, 60.755855889648025], [-95.58928678168982, 60.75586490408786], [-95.58930899885887, 60.75586490408785], [-95.58930899885887, 60.755873918527676], [-95.5893201074434, 60.75587391852767], [-95.5893201074434, 60.75588293296748], [-95.58934232461245, 60.755882932967474], [-95.58934232461245, 60.75589194740732], [-95.58936454178152, 60.755891947407314], [-95.58936454178152, 60.755900961847146], [-95.58937565036604, 60.755900961847146], [-95.58937565036604, 60.755964062925905]]]}}]}geopandas-1.2.0/geopandas/tests/data/overlay/geom_type/df2.geojson000066400000000000000000000672421525650651600252270ustar00rootroot00000000000000{"type": "FeatureCollection", "features": [{"id": "77", "type": "Feature", "properties": {"acres": 3.859654188573176, "idx": 77, "value": 1}, "geometry": {"type": "Polygon", "coordinates": [[[-95.58719836779824, 60.755928005166616], [-95.5871872592137, 60.7559280051666], [-95.5871872592137, 60.75552235537458], [-95.58717615062918, 60.75552235537459], [-95.58717615062918, 60.75468401247108], [-95.58716504204466, 60.75468401247108], [-95.58716504204466, 60.75448569479499], [-95.5871761506293, 60.75448569479499], [-95.5871872592137, 60.75448569479498], [-95.5871872592137, 60.754494709234805], [-95.58719836779824, 60.754494709234805], [-95.58719836779824, 60.754503723674624], [-95.58720947638278, 60.754503723674624], [-95.58720947638278, 60.75451273811445], [-95.5872205849673, 60.75451273811445], [-95.5872205849673, 60.75452175255427], [-95.58723169355183, 60.754521752554275], [-95.58723169355183, 60.75453076699409], [-95.58724280213636, 60.75453076699409], [-95.58724280213636, 60.75453978143392], [-95.5872539107209, 60.75453978143391], [-95.5872539107209, 60.75454879587373], [-95.5872650193054, 60.75454879587374], [-95.5872650193054, 60.754557810313564], [-95.58727612788995, 60.75455781031357], [-95.58727612788995, 60.7545758391932], [-95.58728723647448, 60.7545758391932], [-95.58728723647448, 60.75461189695249], [-95.58729834505903, 60.7546118969525], [-95.58729834505903, 60.75462992583214], [-95.58730945364353, 60.75462992583214], [-95.58730945364353, 60.754647954711785], [-95.58732056222807, 60.754647954711785], [-95.58732056222807, 60.75466598359144], [-95.5873316708126, 60.75466598359144], [-95.5873316708126, 60.75468401247107], [-95.58734277939715, 60.754684012471074], [-95.58734277939715, 60.7546930269109], [-95.58735388798165, 60.75469302691089], [-95.58735388798165, 60.754711055790544], [-95.5873649965662, 60.754711055790544], [-95.5873649965662, 60.75472908467019], [-95.5873761051507, 60.754729084670196], [-95.5873761051507, 60.75474711354984], [-95.58738721373524, 60.75474711354984], [-95.58738721373524, 60.75475612798965], [-95.58739832231977, 60.75475612798965], [-95.58739832231977, 60.754765142429484], [-95.58740943090432, 60.75476514242948], [-95.58740943090432, 60.7547741568693], [-95.58742053948882, 60.7547741568693], [-95.58742053948882, 60.75478317130912], [-95.58743164807336, 60.75478317130912], [-95.58743164807336, 60.75479218574895], [-95.58745386524244, 60.75479218574894], [-95.58745386524244, 60.75480120018877], [-95.58747608241148, 60.75480120018877], [-95.58747608241148, 60.75481021462859], [-95.58748719099601, 60.7548102146286], [-95.58748719099601, 60.75481922906841], [-95.58749829958055, 60.75481922906842], [-95.58749829958055, 60.75482824350824], [-95.58750940816509, 60.754828243508236], [-95.58750940816509, 60.75483725794807], [-95.5875205167496, 60.75483725794807], [-95.5875205167496, 60.75484627238787], [-95.58753162533414, 60.75484627238789], [-95.58753162533414, 60.75486430126754], [-95.58754273391865, 60.75486430126753], [-95.58754273391865, 60.754882330147176], [-95.58755384250318, 60.75488233014718], [-95.58755384250318, 60.754891344586994], [-95.58756495108771, 60.754891344587], [-95.58756495108771, 60.754900359026834], [-95.58757605967226, 60.75490035902682], [-95.58757605967226, 60.75490937346665], [-95.58758716825677, 60.754909373466646], [-95.58758716825677, 60.75491838790648], [-95.5875982768413, 60.75491838790647], [-95.5875982768413, 60.75492740234629], [-95.58762049401038, 60.75492740234629], [-95.58762049401038, 60.754936416786116], [-95.58769825410208, 60.75493641678611], [-95.58769825410208, 60.7549274023463], [-95.5877093626868, 60.75492740234629], [-95.58773157985567, 60.7549274023463], [-95.58773157985567, 60.75491838790648], [-95.5877426884403, 60.75491838790648], [-95.58775379702472, 60.75491838790647], [-95.58775379702472, 60.75490937346665], [-95.58776490560925, 60.75490937346664], [-95.58776490560925, 60.75490035902683], [-95.58777601419379, 60.75490035902683], [-95.58777601419379, 60.754891344586994], [-95.5877871227783, 60.75489134458701], [-95.5877871227783, 60.75483725794807], [-95.58777601419379, 60.75483725794806], [-95.58777601419379, 60.75481922906842], [-95.58776490560925, 60.75481922906842], [-95.58776490560925, 60.7548102146286], [-95.58775379702472, 60.7548102146286], [-95.58775379702472, 60.75479218574894], [-95.58774268844017, 60.75479218574894], [-95.58774268844017, 60.7547741568693], [-95.58773157985567, 60.75477415686929], [-95.58773157985567, 60.754756127989644], [-95.58772047127113, 60.754756127989644], [-95.58772047127113, 60.75474711354983], [-95.58770936268662, 60.75474711354984], [-95.58770936268662, 60.75472908467019], [-95.58769825410208, 60.754729084670196], [-95.58769825410208, 60.754711055790544], [-95.58768714551755, 60.754711055790544], [-95.58768714551755, 60.7546930269109], [-95.587676036933, 60.7546930269109], [-95.587676036933, 60.75468401247108], [-95.5876649283485, 60.75468401247108], [-95.5876649283485, 60.75466598359144], [-95.58765381976396, 60.754665983591444], [-95.58765381976396, 60.75464795471179], [-95.58764271117943, 60.754647954711785], [-95.58764271117943, 60.75462992583215], [-95.58763160259488, 60.75462992583214], [-95.58763160259488, 60.7546118969525], [-95.58762049401038, 60.75461189695249], [-95.58762049401038, 60.75458485363303], [-95.58763160259488, 60.754584853633034], [-95.58763160259488, 60.75455781031358], [-95.58764271117943, 60.754557810313564], [-95.58764271117943, 60.75453978143393], [-95.58765381976396, 60.75453978143392], [-95.58765381976396, 60.75453076699409], [-95.5876649283485, 60.75453076699409], [-95.5876649283485, 60.75451273811445], [-95.587676036933, 60.754512738114435], [-95.587676036933, 60.7544947092348], [-95.58768714551755, 60.7544947092348], [-95.58768714551755, 60.75448569479498], [-95.58769825410208, 60.75448569479499], [-95.58769825410208, 60.75446766591533], [-95.58770936268662, 60.75446766591532], [-95.58770936268662, 60.75444963703569], [-95.58772047127113, 60.75444963703569], [-95.58772047127113, 60.75444062259586], [-95.58773157985567, 60.754440622595865], [-95.58773157985567, 60.754422593716214], [-95.58774268844017, 60.75442259371622], [-95.58774268844017, 60.754413579276395], [-95.58775379702485, 60.75441357927639], [-95.58776490560925, 60.75441357927639], [-95.58776490560925, 60.754404564836584], [-95.58777601419379, 60.75440456483658], [-95.58777601419379, 60.75439555039674], [-95.58778712277842, 60.754395550396744], [-95.58779823136284, 60.75439555039675], [-95.58779823136284, 60.754386535956925], [-95.5878093399475, 60.75438653595693], [-95.58782044853191, 60.754386535956925], [-95.58782044853191, 60.754377521517114], [-95.58783155711644, 60.754377521517114], [-95.58783155711644, 60.75436850707728], [-95.58784266570109, 60.75436850707729], [-95.58785377428549, 60.75436850707728], [-95.58785377428549, 60.75435949263747], [-95.58786488287015, 60.75435949263747], [-95.58787599145457, 60.75435949263746], [-95.58787599145457, 60.75435047819764], [-95.58788710003908, 60.75435047819763], [-95.58788710003908, 60.75434146375782], [-95.58789820862374, 60.75434146375782], [-95.58790931720813, 60.75434146375782], [-95.58790931720813, 60.754332449318], [-95.58792042579284, 60.754332449317985], [-95.58794264296174, 60.754332449317985], [-95.58794264296174, 60.75432343487817], [-95.58814259748327, 60.754323434878174], [-95.58814259748327, 60.75433244931799], [-95.58816481465232, 60.754332449317985], [-95.58816481465232, 60.75434146375782], [-95.58818703182139, 60.75434146375782], [-95.58818703182139, 60.75435047819764], [-95.58820924899044, 60.754350478197644], [-95.58820924899044, 60.75435949263746], [-95.5882314661595, 60.75435949263746], [-95.5882314661595, 60.75436850707728], [-95.58824257474404, 60.75436850707729], [-95.58824257474404, 60.754377521517114], [-95.58826479191309, 60.75437752151711], [-95.58826479191309, 60.75438653595694], [-95.58828700908214, 60.75438653595692], [-95.58828700908214, 60.754395550396744], [-95.58830922625121, 60.75439555039675], [-95.58830922625121, 60.75440456483658], [-95.58832033483573, 60.75440456483657], [-95.58832033483573, 60.754413579276395], [-95.58833144342026, 60.7544135792764], [-95.58833144342026, 60.754422593716214], [-95.58834255200479, 60.754422593716214], [-95.58834255200479, 60.75443160815605], [-95.58835366058933, 60.75443160815605], [-95.58835366058933, 60.754440622595865], [-95.58836476917384, 60.754440622595865], [-95.58836476917384, 60.75445865147551], [-95.58837587775838, 60.75445865147551], [-95.58837587775838, 60.754476680355154], [-95.58838698634291, 60.75447668035514], [-95.58838698634291, 60.75448569479499], [-95.58839809492746, 60.75448569479499], [-95.58839809492746, 60.7544947092348], [-95.58840920351196, 60.7544947092348], [-95.58840920351196, 60.75450372367462], [-95.5884203120965, 60.754503723674624], [-95.5884203120965, 60.75451273811444], [-95.58843142068103, 60.75451273811444], [-95.58843142068103, 60.75452175255426], [-95.58845363785008, 60.75452175255427], [-95.58845363785008, 60.75453076699409], [-95.58847585501913, 60.75453076699409], [-95.58847585501913, 60.754539781433905], [-95.58848696360367, 60.75453978143391], [-95.58848696360367, 60.75454879587374], [-95.5884980721882, 60.75454879587374], [-95.5884980721882, 60.75455781031357], [-95.58850918077275, 60.75455781031356], [-95.58850918077275, 60.75456682475338], [-95.58852028935725, 60.75456682475338], [-95.58852028935725, 60.7545758391932], [-95.5885313979418, 60.7545758391932], [-95.5885313979418, 60.754593868072845], [-95.58854250652632, 60.754593868072845], [-95.58854250652632, 60.75461189695248], [-95.58855361511087, 60.7546118969525], [-95.58855361511087, 60.75462091139233], [-95.5885647236954, 60.75462091139232], [-95.5885647236954, 60.754629925832134], [-95.58857583227991, 60.754629925832134], [-95.58857583227991, 60.754638940271974], [-95.58858694086445, 60.75463894027197], [-95.58858694086445, 60.754647954711785], [-95.58859804944898, 60.754647954711785], [-95.58859804944898, 60.75465696915161], [-95.58862026661804, 60.75465696915162], [-95.58862026661804, 60.754665983591444], [-95.58864248378708, 60.754665983591444], [-95.58864248378708, 60.754674998031255], [-95.58865359237161, 60.75467499803125], [-95.58865359237161, 60.75468401247108], [-95.58867580954069, 60.754684012471074], [-95.58867580954069, 60.75469302691089], [-95.58869802670974, 60.75469302691089], [-95.58869802670974, 60.75470204135073], [-95.58870913529427, 60.754702041350725], [-95.58870913529427, 60.754711055790544], [-95.58873135246331, 60.75471105579055], [-95.58873135246331, 60.75472007023037], [-95.58875356963239, 60.75472007023037], [-95.58875356963239, 60.754729084670196], [-95.58880911255503, 60.754729084670196], [-95.58880911255503, 60.75473809911001], [-95.5888313297241, 60.75473809911002], [-95.5888313297241, 60.75474711354983], [-95.58885354689315, 60.75474711354983], [-95.58885354689315, 60.75475612798965], [-95.58887576406222, 60.754756127989644], [-95.58887576406222, 60.75476514242948], [-95.58889798123127, 60.754765142429484], [-95.58889798123127, 60.7547741568693], [-95.5889090898158, 60.75477415686931], [-95.5889090898158, 60.75478317130913], [-95.58893130698488, 60.75478317130912], [-95.58893130698488, 60.75479218574894], [-95.58895352415392, 60.75479218574895], [-95.58895352415392, 60.754801200188766], [-95.58913126150641, 60.75480120018877], [-95.58913126150641, 60.754792185748954], [-95.58914237009104, 60.75479218574895], [-95.58915347867546, 60.754792185748954], [-95.58915347867546, 60.75478317130914], [-95.58916458725996, 60.75478317130913], [-95.58916458725996, 60.754774156869296], [-95.58917569584463, 60.75477415686931], [-95.58918680442903, 60.7547741568693], [-95.58918680442903, 60.75476514242948], [-95.5891979130137, 60.754765142429484], [-95.58920902159808, 60.754765142429484], [-95.58920902159808, 60.75475612798965], [-95.58922013018262, 60.754756127989666], [-95.58922013018262, 60.75474711354983], [-95.58923123876728, 60.75474711354983], [-95.5892423473517, 60.75474711354984], [-95.5892423473517, 60.754738099110014], [-95.58925345593639, 60.754738099110014], [-95.58927567310528, 60.754738099110014], [-95.58927567310528, 60.754729084670196], [-95.58944230187322, 60.75472908467019], [-95.58944230187322, 60.75473809911002], [-95.58943119328869, 60.754738099110014], [-95.58943119328869, 60.75475612798966], [-95.58942008470416, 60.75475612798965], [-95.58942008470416, 60.754774156869296], [-95.58940897611964, 60.754774156869296], [-95.58940897611964, 60.754792185748954], [-95.5893978675351, 60.754792185748954], [-95.5893978675351, 60.754801200188766], [-95.58938675895057, 60.754801200188766], [-95.58938675895057, 60.75482824350824], [-95.58937565036604, 60.75482824350824], [-95.58937565036604, 60.75483725794807], [-95.58936454178152, 60.75483725794807], [-95.58936454178152, 60.754855286827706], [-95.58935343319699, 60.754855286827706], [-95.58935343319699, 60.754873315707364], [-95.58934232461245, 60.75487331570736], [-95.58934232461245, 60.754891344587], [-95.58933121602794, 60.75489134458701], [-95.58933121602794, 60.754909373466646], [-95.5893201074434, 60.75490937346665], [-95.5893201074434, 60.75491838790648], [-95.58930899885887, 60.75491838790648], [-95.58930899885887, 60.75493641678611], [-95.58929789027435, 60.754936416786116], [-95.58929789027435, 60.75495444566575], [-95.58928678168982, 60.75495444566575], [-95.58928678168982, 60.7549724745454], [-95.58927567310528, 60.754972474545404], [-95.58927567310528, 60.754990503425056], [-95.58926456452075, 60.75499050342504], [-95.58926456452075, 60.755008532304686], [-95.5892534559362, 60.755008532304686], [-95.5892534559362, 60.75501754674452], [-95.5892423473517, 60.75501754674452], [-95.5892423473517, 60.75503557562416], [-95.58923123876716, 60.75503557562416], [-95.58923123876716, 60.75505360450382], [-95.58922013018262, 60.7550536045038], [-95.58922013018262, 60.75506261894363], [-95.58920902159808, 60.75506261894363], [-95.58920902159808, 60.75507163338345], [-95.58919791301358, 60.755071633383466], [-95.58919791301358, 60.75508064782327], [-95.58918680442903, 60.75508064782327], [-95.58918680442903, 60.7550896622631], [-95.5891756958445, 60.75508966226311], [-95.5891756958445, 60.75510769114275], [-95.58916458725996, 60.75510769114275], [-95.58916458725996, 60.755116705582566], [-95.58915347867546, 60.75511670558255], [-95.58915347867546, 60.755125720022384], [-95.58914237009091, 60.75512572002239], [-95.58914237009091, 60.755143748902036], [-95.58913126150641, 60.75514374890204], [-95.58913126150641, 60.755152763341854], [-95.58912015292186, 60.75515276334186], [-95.58912015292186, 60.75516177778168], [-95.58910904433733, 60.75516177778169], [-95.58910904433733, 60.7551707922215], [-95.58909793575279, 60.755170792221506], [-95.58909793575279, 60.75517980666133], [-95.58908682716829, 60.75517980666133], [-95.58908682716829, 60.75519783554097], [-95.58907571858374, 60.755197835540976], [-95.58907571858374, 60.7552068499808], [-95.58906460999921, 60.7552068499808], [-95.58906460999921, 60.75521586442063], [-95.58905350141468, 60.755215864420634], [-95.58905350141468, 60.75522487886044], [-95.58904239283017, 60.75522487886044], [-95.58904239283017, 60.75524290774008], [-95.58903128424562, 60.75524290774008], [-95.58903128424562, 60.75525192217991], [-95.58902017566109, 60.75525192217991], [-95.58902017566109, 60.75526093661975], [-95.58900906707656, 60.75526093661973], [-95.58900906707656, 60.75527896549938], [-95.58899795849204, 60.75527896549938], [-95.58899795849204, 60.755287979939204], [-95.58898684990751, 60.75528797993919], [-95.58898684990751, 60.75529699437903], [-95.58897574132297, 60.75529699437902], [-95.58897574132297, 60.75530600881885], [-95.58896463273845, 60.75530600881885], [-95.58896463273845, 60.7553240376985], [-95.58895352415392, 60.75532403769849], [-95.58895352415392, 60.75533305213831], [-95.58894241556939, 60.75533305213831], [-95.58894241556939, 60.75534206657814], [-95.58893130698488, 60.75534206657813], [-95.58893130698488, 60.75536009545778], [-95.58892019840034, 60.75536009545778], [-95.58892019840034, 60.75536910989761], [-95.5889090898158, 60.755369109897615], [-95.5889090898158, 60.755378124337426], [-95.58889798123127, 60.75537812433742], [-95.58889798123127, 60.755387138777245], [-95.58888687264673, 60.755387138777245], [-95.58888687264673, 60.75539615321708], [-95.58887576406222, 60.75539615321708], [-95.58887576406222, 60.755414182096715], [-95.58886465547768, 60.755414182096715], [-95.58886465547768, 60.75542319653654], [-95.58885354689315, 60.755423196536555], [-95.58885354689315, 60.755441225416185], [-95.5888424383086, 60.755441225416185], [-95.5888424383086, 60.75545925429584], [-95.5888313297241, 60.755459254295836], [-95.5888313297241, 60.755477283175466], [-95.58882022113956, 60.755477283175466], [-95.58882022113956, 60.75549531205512], [-95.58880911255503, 60.755495312055125], [-95.58880911255503, 60.75551334093477], [-95.58879800397048, 60.75551334093477], [-95.58879800397048, 60.75554939869406], [-95.58878689538598, 60.75554939869405], [-95.58878689538598, 60.755594470893165], [-95.58879800397048, 60.755594470893165], [-95.58879800397048, 60.755603485333], [-95.58880911255503, 60.755603485333], [-95.58880911255503, 60.75561249977282], [-95.58882022113956, 60.755612499772816], [-95.58882022113956, 60.75562151421263], [-95.5888313297241, 60.75562151421264], [-95.5888313297241, 60.755630528652446], [-95.58885354689315, 60.75563052865245], [-95.58885354689315, 60.755639543092286], [-95.58886465547768, 60.75563954309228], [-95.58886465547768, 60.755648557532105], [-95.58888687264673, 60.755648557532105], [-95.58888687264673, 60.75565757197192], [-95.5889090898158, 60.75565757197193], [-95.5889090898158, 60.755666586411756], [-95.58892019840034, 60.755666586411756], [-95.58892019840034, 60.75567560085157], [-95.58894241556939, 60.75567560085157], [-95.58894241556939, 60.75568461529141], [-95.58876467821693, 60.75568461529139], [-95.58876467821693, 60.755675600851575], [-95.58875356963239, 60.75567560085157], [-95.58875356963239, 60.755666586411756], [-95.58874246104786, 60.755666586411756], [-95.58874246104786, 60.75565757197192], [-95.58873135246331, 60.75565757197193], [-95.58873135246331, 60.755639543092286], [-95.58872024387881, 60.75563954309229], [-95.58872024387881, 60.75562151421264], [-95.58870913529427, 60.75562151421264], [-95.58870913529427, 60.75554038425424], [-95.58872024387881, 60.75554038425425], [-95.58872024387881, 60.755522355374595], [-95.58873135246331, 60.75552235537459], [-95.58873135246331, 60.755513340934776], [-95.58874246104786, 60.75551334093476], [-95.58874246104786, 60.75549531205512], [-95.58875356963239, 60.75549531205512], [-95.58875356963239, 60.75547728317548], [-95.58876467821693, 60.75547728317547], [-95.58876467821693, 60.755468268735655], [-95.58877578680143, 60.755468268735655], [-95.58877578680143, 60.755441225416185], [-95.58878689538598, 60.75544122541619], [-95.58878689538598, 60.755387138777245], [-95.58877578680143, 60.755387138777245], [-95.58877578680143, 60.75536910989761], [-95.58876467821693, 60.7553691098976], [-95.58876467821693, 60.755351081017956], [-95.58875356963239, 60.75535108101796], [-95.58875356963239, 60.75534206657814], [-95.58874246104786, 60.75534206657813], [-95.58874246104786, 60.75533305213831], [-95.58873135246331, 60.75533305213831], [-95.58873135246331, 60.75532403769849], [-95.58872024387881, 60.75532403769849], [-95.58872024387881, 60.75531502325866], [-95.58870913529427, 60.75531502325867], [-95.58870913529427, 60.75530600881885], [-95.5886869181252, 60.75530600881885], [-95.5886869181252, 60.75529699437902], [-95.58866470095614, 60.755296994379016], [-95.58866470095614, 60.755287979939204], [-95.58864248378708, 60.7552879799392], [-95.58864248378708, 60.75527896549937], [-95.58858694086445, 60.75527896549938], [-95.58858694086445, 60.75526995105955], [-95.5885647236954, 60.75526995105956], [-95.5885647236954, 60.75526093661974], [-95.58854250652632, 60.755260936619734], [-95.58854250652632, 60.75525192217991], [-95.5885313979418, 60.75525192217991], [-95.5885313979418, 60.755242907740076], [-95.58852028935725, 60.755242907740076], [-95.58852028935725, 60.755233893300264], [-95.5884980721882, 60.755233893300264], [-95.5884980721882, 60.75522487886043], [-95.58847585501913, 60.755224878860446], [-95.58847585501913, 60.755215864420634], [-95.58846474643462, 60.75521586442063], [-95.58846474643462, 60.7552068499808], [-95.58844252926555, 60.7552068499808], [-95.58844252926555, 60.755197835540976], [-95.58843142068103, 60.75519783554097], [-95.58843142068103, 60.75518882110114], [-95.58840920351196, 60.75518882110116], [-95.58840920351196, 60.755179806661324], [-95.58838698634291, 60.75517980666134], [-95.58838698634291, 60.75517079222151], [-95.58837587775838, 60.75517079222149], [-95.58837587775838, 60.75516177778168], [-95.58835366058933, 60.75516177778167], [-95.58835366058933, 60.75515276334184], [-95.58833144342026, 60.75515276334186], [-95.58833144342026, 60.75514374890204], [-95.58830922625121, 60.755143748902036], [-95.58830922625121, 60.755134734462224], [-95.58810927172965, 60.75513473446222], [-95.58810927172965, 60.75514374890204], [-95.58808705456079, 60.755143748902036], [-95.58807594597607, 60.755143748902036], [-95.58807594597607, 60.755152763341854], [-95.58806483739168, 60.75515276334186], [-95.58805372880703, 60.755152763341854], [-95.58805372880703, 60.75516177778169], [-95.58804262022248, 60.75516177778168], [-95.58804262022248, 60.755170792221506], [-95.58803151163798, 60.7551707922215], [-95.58803151163798, 60.75517980666134], [-95.58802040305356, 60.755179806661324], [-95.5880092944689, 60.75517980666133], [-95.5880092944689, 60.755197835540976], [-95.58799818588436, 60.75519783554097], [-95.58799818588436, 60.755206849980794], [-95.58798707729986, 60.75520684998081], [-95.58798707729986, 60.755215864420634], [-95.58797596871531, 60.75521586442063], [-95.58797596871531, 60.755233893300264], [-95.58796486013078, 60.755233893300264], [-95.58796486013078, 60.755260936619734], [-95.58795375154625, 60.75526093661973], [-95.58795375154625, 60.755441225416185], [-95.58794264296174, 60.755441225416185], [-95.58794264296174, 60.755468268735655], [-95.58793153437719, 60.755468268735655], [-95.58793153437719, 60.75547728317548], [-95.58792042579266, 60.75547728317547], [-95.58792042579266, 60.755495312055125], [-95.58790931720813, 60.75549531205512], [-95.58790931720813, 60.75550432649495], [-95.58789820862361, 60.75550432649495], [-95.58789820862361, 60.75551334093476], [-95.58788710003908, 60.75551334093477], [-95.58788710003908, 60.75552235537458], [-95.5878759914547, 60.75552235537459], [-95.58786488287002, 60.75552235537459], [-95.58786488287002, 60.75553136981442], [-95.58785377428549, 60.75553136981441], [-95.58785377428549, 60.75554038425424], [-95.58783155711663, 60.75554038425424], [-95.58782044853191, 60.75554038425424], [-95.58782044853191, 60.75554939869406], [-95.58775379702472, 60.75554939869405], [-95.58775379702472, 60.75554038425424], [-95.58773157985567, 60.75554038425424], [-95.58773157985567, 60.75553136981442], [-95.58770936268662, 60.75553136981441], [-95.58770936268662, 60.75552235537459], [-95.58769825410208, 60.75552235537459], [-95.58769825410208, 60.75551334093476], [-95.587676036933, 60.75551334093477], [-95.587676036933, 60.75550432649494], [-95.58765381976396, 60.755504326494936], [-95.58765381976396, 60.75549531205512], [-95.58764271117943, 60.755495312055125], [-95.58764271117943, 60.755486297615285], [-95.58762049401038, 60.7554862976153], [-95.58762049401038, 60.75547728317547], [-95.5875982768413, 60.755477283175466], [-95.5875982768413, 60.75546826873566], [-95.58758716825677, 60.755468268735655], [-95.58758716825677, 60.75545925429583], [-95.58757605967226, 60.755459254295836], [-95.58757605967226, 60.755450239855996], [-95.58756495108771, 60.75545023985601], [-95.58756495108771, 60.755441225416185], [-95.58755384250318, 60.755441225416185], [-95.58755384250318, 60.755432210976366], [-95.58754273391865, 60.755432210976366], [-95.58754273391865, 60.7554141820967], [-95.58753162533414, 60.755414182096715], [-95.58753162533414, 60.75539615321708], [-95.5875205167496, 60.75539615321708], [-95.5875205167496, 60.75537812433743], [-95.58750940816509, 60.75537812433742], [-95.58750940816509, 60.755369109897615], [-95.58749829958055, 60.75536910989761], [-95.58749829958055, 60.75535108101796], [-95.58748719099601, 60.75535108101795], [-95.58748719099601, 60.75533305213831], [-95.58747608241148, 60.75533305213832], [-95.58747608241148, 60.75531502325867], [-95.58746497382697, 60.75531502325866], [-95.58746497382697, 60.75529699437902], [-95.58745386524244, 60.75529699437901], [-95.58745386524244, 60.755116705582566], [-95.5874427566579, 60.75511670558255], [-95.5874427566579, 60.75510769114275], [-95.58743164807336, 60.75510769114275], [-95.58743164807336, 60.755098676702914], [-95.58740943090432, 60.755098676702914], [-95.58740943090432, 60.7550896622631], [-95.5873316708126, 60.755089662263096], [-95.5873316708126, 60.755098676702914], [-95.5873205622282, 60.75509867670292], [-95.58730945364353, 60.755098676702914], [-95.58730945364353, 60.75510769114275], [-95.58729834505903, 60.755107691142754], [-95.58729834505903, 60.7551257200224], [-95.58728723647448, 60.755125720022384], [-95.58728723647448, 60.7555674275737], [-95.58729834505903, 60.7555674275737], [-95.58729834505903, 60.755585456453346], [-95.58730945364353, 60.755585456453346], [-95.58730945364353, 60.75560348533298], [-95.58732056222807, 60.75560348533298], [-95.58732056222807, 60.75562151421264], [-95.5873316708126, 60.75562151421264], [-95.5873316708126, 60.75563052865246], [-95.58734277939715, 60.755630528652446], [-95.58734277939715, 60.75563954309227], [-95.58735388798165, 60.755639543092286], [-95.58735388798165, 60.755648557532105], [-95.5873649965662, 60.75564855753211], [-95.5873649965662, 60.75565757197193], [-95.5873761051507, 60.75565757197192], [-95.5873761051507, 60.755666586411756], [-95.58739832231977, 60.755666586411756], [-95.58739832231977, 60.75567560085157], [-95.58742053948882, 60.75567560085157], [-95.58742053948882, 60.75568461529141], [-95.58747608241148, 60.75568461529141], [-95.58747608241148, 60.75569362973121], [-95.58748719099601, 60.75569362973122], [-95.58748719099601, 60.75570264417104], [-95.58749829958055, 60.755702644171045], [-95.58749829958055, 60.755711658610856], [-95.58750940816509, 60.755711658610856], [-95.58750940816509, 60.75573870193034], [-95.5875205167496, 60.75573870193035], [-95.5875205167496, 60.7557657452498], [-95.58750940816509, 60.7557657452498], [-95.58750940816509, 60.755801803009085], [-95.58749829958055, 60.75580180300909], [-95.58749829958055, 60.75581081744892], [-95.58748719099601, 60.755810817448925], [-95.58748719099601, 60.755819831888736], [-95.58745386524268, 60.755819831888736], [-95.5874427566579, 60.75581983188873], [-95.5874427566579, 60.75582884632856], [-95.587420539489, 60.75582884632856], [-95.58740943090432, 60.75582884632856], [-95.58740943090432, 60.75583786076839], [-95.5873983223199, 60.755837860768395], [-95.58738721373524, 60.75583786076838], [-95.58738721373524, 60.7558468752082], [-95.5873761051507, 60.7558468752082], [-95.5873761051507, 60.755855889648025], [-95.58736499656632, 60.755855889648025], [-95.58735388798165, 60.75585588964804], [-95.58735388798165, 60.75586490408785], [-95.58734277939728, 60.75586490408786], [-95.5873316708126, 60.75586490408785], [-95.5873316708126, 60.75587391852767], [-95.58732056222807, 60.755873918527676], [-95.58732056222807, 60.75588293296749], [-95.58730945364366, 60.755882932967495], [-95.58729834505903, 60.75588293296748], [-95.58729834505903, 60.75589194740732], [-95.58727612789012, 60.755891947407314], [-95.5872650193054, 60.75589194740732], [-95.5872650193054, 60.75590096184715], [-95.58725391072103, 60.75590096184715], [-95.58724280213636, 60.75590096184715], [-95.58724280213636, 60.75590997628696], [-95.58722058496747, 60.755909976286965], [-95.58720947638278, 60.755909976286965], [-95.58720947638278, 60.755918990726784], [-95.58719836779824, 60.755918990726784], [-95.58719836779824, 60.755928005166616]]]}}]}geopandas-1.2.0/geopandas/tests/data/overlay/nybb_qgis/000077500000000000000000000000001525650651600231405ustar00rootroot00000000000000geopandas-1.2.0/geopandas/tests/data/overlay/nybb_qgis/polydf.cpg000066400000000000000000000000121525650651600251210ustar00rootroot00000000000000ISO-8859-1geopandas-1.2.0/geopandas/tests/data/overlay/nybb_qgis/polydf.dbf000066400000000000000000000015231525650651600251130ustar00rootroot00000000000000uBoroCodeN BoroNameCPShape_LengNShape_AreaN 5Staten Island 330470.0103319999761881623819823.8099999427795 4Queens 896344.0477630000095813045212795.1999998092651 3Brooklyn 741080.5231660000281411937478507.6099998950958 1Manhattan 359299.096470999997109636471539.77400004863739 2Bronx 464392.9918240000260991186924686.4900000095367geopandas-1.2.0/geopandas/tests/data/overlay/nybb_qgis/polydf.prj000066400000000000000000000010131525650651600251450ustar00rootroot00000000000000PROJCS["Lambert_Conformal_Conic",GEOGCS["GCS_North_American_1983",DATUM["D_North_American_1983",SPHEROID["GRS_1980",6378137,298.257222101]],PRIMEM["Greenwich",0],UNIT["Degree",0.017453292519943295]],PROJECTION["Lambert_Conformal_Conic"],PARAMETER["standard_parallel_1",40.66666666666666],PARAMETER["standard_parallel_2",41.03333333333333],PARAMETER["latitude_of_origin",40.16666666666666],PARAMETER["central_meridian",-74],PARAMETER["false_easting",984250],PARAMETER["false_northing",0],UNIT["Foot_US",0.30480060960121924]]geopandas-1.2.0/geopandas/tests/data/overlay/nybb_qgis/polydf.shp000066400000000000000000045124001525650651600251570ustar00rootroot00000000000000' J7.+A%S@(vI0A,1A7.+A%S@K-A@rA#/8rx қ-A`ZAn-AMAC-AMA(i-A+Ap-A Ag-ACAK-AIAWݜ-AȺAN-ACAL-A AKU-A uA8bS-AjA--A %A-AyA@-AA-AAP'-A AP-A Ax-A=A8#C-AAb-A`AX$n-A@gA@q-A@A@q-A@NAr-AA0]i-A`AVT-As A(8-A |vAx -A@WA2-A A|-A[Ajњ-AFAŚ-A=A@-A OTA0-AA-AA-AHA-A`ÍA0-AA-AA -AqxA-A AAF-A[6A8y-A lA-A`VA -A Ax қ-A`ZAT -AGA -AD!AApO-A`HA’-A@s^Aȩj-A^Am-A XaAL-A4hA-A `AT -AGAp0,AA,A@QA ,A ZA/9,A`6Aح,A@$ Aˇ,A ApH,A`-!A ,Ap!Ahܬ,AH"A跲,A@g-"A(,A`v"AXS,Aı"AS#,A#A,A#A,A%A@,Aj|&A4,A;'A,A@)A(,A,AY,Ap9.Aի,A@ /AU,A`95A),A7A0q,A":A ],A<Ae,A`?Ah,A!:Ag,AВ9Ad,A H8A87,A@?8AH,A7A~a,A`"7Az,A`6A9,A /5A@S,A@4AP8,A3Ayٳ,A@2A,A2AC,AF1A.,AA1AD,Am00A,Ȳ,Av.A`%,Ȃ-AK,A@,AH?,A ",Asu,A`+AJ,AY+A,A +AHֱ,A@ +A7,A*AHᣱ,A@`)Ast,A j(A b,A@C'Ah@F,A$A8 ,A q2"A(mH,A` A>,A Ap0,AA(8W-AlAoX-A`+mA tX-AnAx5Y-AmA#Y-ATMlAPgpZ-A ukAZ-A kA`1n\-AlA]-A@pjA@2^-A iA Tb-A`91_Ab-AB_Ab-A]A0c-A`\A;d-A`9YAسd-AWA`/f-A$PAPM h-AFAGh-A ~GAxi-ARHAeKi-A &GAi-AFAj-AGAi-AhIAFsi-A_IA؟ci-AIAi-A-JA`i-AJAyTi-AwJAgi-A@+KAitj-AOA8֧j-A@$NA0!j-AaLAGj-A4KAði-A IAH-j-A@,YGA`vj-AFAгmj-A@^FA0j-AдEAk-AGAH!k-A@GA`Pj-AzEA((k-A`GDAk-A CAxIj-A`LFA88Cj-A$ FAh/j-A0*EA oj-A`iDAj-ABAXk-A cDAPrk-A QWDAbk-A^EAHl-AaCA/k-A2BAh/k-A3AA0l-ACA !l-ABA@l-AjBA l-A@RBAHl-A`vBA 6l-A[BAl-AAAx-k-AfBA,l-A "BAl-A0,BAk-A5AA&l-A AA,l-A?$AA2l-A AAl-A@E@A l-Aw@A,l-A`B@Ap0k-A5AAk-A@AA^k-A@Ak-A4T@AXk-A`?Al-A?AAjm-A@W]FAp m-A 8xFA +n-A@FAKn-A`}FAX n-A@'FA +n-AvBFAîl-A@MAATl-AX@AHm-A}XAA Pm-Ai?Am-A >Aذ#m-Aݒ>ATl-A>A,l-A4=Ayl-A@;Afm-A<A8nm-A<Afm-A =A`1m-AR:Axm-Ao:Am-A l?:Ad|m-A`9Awm-A9Ax9m-A"9A8sYm-A`T;8Am-Ai8Am-A@y8A m-As 8ATn-A uK8A=n-AU6AHP(n-A 5A~9n-A4An-A@4Ajn-A&4A@tn-A@P74A:~n-A o3Ann-A`3Ayn-A2A@n-A`3An-A A@3Ao-A3APo-Ay3Ao-A~3AP*o-A E3Ah^o-Av#3A+o-Ay2An-AF2A5n-A`2An-A2j2AYn-Ag1A_n-AE1An-A1A05o-A@`02A;o-A@P1Amn-A0A*{n-A&-Ahn-A-An-A+A(m-A+Am-Az-APl-A ,APm-AN:)Ap-A?+A{p-At)Ahj-A&Aj-AI"A+&q-AL&A>Cq-A$ATj-AvA'jk-AAlm-ApA([bm-AAzm-AAm-A@Ah;q-A@yAq-AVA@m-A /A> &m-A4ܨAm:`m-AԘAm-A0A7 ?m-A`{AJKTm-A tnAͰCAm-A]JdAm-AKs[Am-A+KUA"sm-AmQA +m-A[nPAcm-AmLRA@m-AWA@m-AsAXk-AVAXk-A5Ak-AAȓj-AwAk-A$ARk-A*A@Tk-A@ qA"k-AFA#k-A@APZQk-A`T#AqVk-AͳAErk-AAIk-AAk-AA k-AͳAH)l-A <A-l-AAl-A^\AVl-AA~l-A@5Al-ADAl-AAl-A2Aol-A`BAj-A@A-j-A{Auj-A"XAp[j-AQA@€p-A@bAљp-A A(qp-A+ACj-AqA9[j-AߚA0.j-A@Aj-A A0j:l-AൈA=l-A`YAPyl-A@mAPyl-A@Am-AEA m-A Aql-A@,Aql-AAAAPk-AvA7ak-A[A7ak-A2-AHrj-AA+j-A}Ax {j-A QA@cj-ARA}k-AAk-A9A0qj-A@ƲA@k-A@3A8'/k-A`ڞAHk-A`]#Avu-AAu-A@ApxWk-AKAQNk-A ؚAh3l-A lA8l-AFA@Ql-A@AhVl-A AzSl-AAxrl-A\AȲl-AzAk-A[zApk-A yA8gk-AD\xA8"k-A@GwA&l-AZpAl-A]A{n-A LAXo-AgILA&o-A JAp8Vo-AEIA[~o-AOHA}o-AGA`Go-A3WGAPo-A@6GAo-AGAXp-A FAp]p-AMDAhp-ACASip-A[CA hp-A@oCAop-ACA([p-ABA.p-A XBA(p-Af@AA&p-AI@A`p-A@{@A8p-A?A`q-A>Aq-A`;>A@q-A=A(q-AО=AFq-Af=Arq-A;Avq-AІ;Avq-A;;AP q-A` :Ahēq-A`:A]q-A՚9Aq-A՚9Axq-A09Aq-A@9Abq-A9AUq-A@v9APq-AZ9A(zq-A(R9AXq-AaE9A@q-A 8Aq-A 8Aq-A@i{8Aq-A`D8A."r-A7Ar-A`7A0*r-A@77Ar-A5AhWs-A&3A#Qs-A3AJTt-A/Au-A,A +u-A,Aw-A@>#Ax-AAx-A@>A0{h0-AV-5A@5%-AAAQ-A A#-AA` -AR A`-AA0*V-A@PA00-AfA(>-A#A-AnA-AA88-AX=A--A` AB؅-A;A0{-AmA6-AhwApN-A4AP ˆ-A`AB-A`)|A#"-A`LA(K-ARAI-A qAB-A`m}A`C-A`A@$-ARAB-A@AxKz-A`AX#i-A#A`d-AdA#S-AAXM-AApD-AwA7-ApTAp4-ÄABL-A HApE-ApA-AEA-ArAP8O-AR>A`·-AQAxK;-A൪AB-AjAh-AMDAP<#-AbA,-A ZvAQ-AAݰ%b-AItAOCeq-AVyArR~-AlA#UN-A#4WaAW-A 1"Af-AE6\A}W-A8WGWAf-A)A:!-AnuA :-AfAl-AAȺ-A൐A-AAq-A9AX_-A ۫A4-AAh;5-A AA8VI-AAb-A`A9-A_Ah⚊-A'A(>-AQAP'-A ܯA 0-A`Aps-A7AP<-AAw-A`)A +ύ-AAa-ABԣAh.-AfAM-A ZA0wY-AwA8-A賛AX-A&1AU-ARAW-AߒA-AzAHn4-A@yAQ-A`AH܄-AA`ˑ-A`KA*-AIAh$-A TAج-A}A-A(2AP-A AQ-A tA-A`íA`đ-A`DA&-AbAؒ-AAƑ-A`K$Aϑ-A⹍Az-A@tA -AOA-AA -A@`xA1)-A$NA(-A$A@C-Ah}A(-A@`=yA Ȕ-A@%QwA`֔-AZvAf-AItA-AAsAf1-A|pA>-AľoA N-A@`oAG-A]A3E!-Ayя'\AWN7-Ag,[AsH-A~,ZA!MU-AA@(YA^-A#v!XAc-AWALKf-Ag1=}ZVA(pd-AWW &UA3.U^-AbiTAdAS-Ak{0&TAȔC-A \rSAxǕ-A (*QA㐕-AଧMA:Y-A~%MAڹk-AWRLA[-AsumLAPM-A`)KA-A@HA;t-A`{GA ×-A+DAW-AzCAh#-ACA$-ACA/-ABA-A@BA-AxBA@-A lhBAԗ-A sBA-A,BAJ-ABA R-AKCAp-A`CAHi-A/7DAo-A 1%DA8ک-ACA@Wk-A@>#CAB-A`BAH-As88A1-A7AU-A@E7A@D-A`)7AX1-Ai AG]-A4A ݇-AVAxч-A Ax -A@A-A A8Ƈ-A(AY-A@A-ASA]-A A-A A@S~-A ;AV|-AhcA W|-ATuA0g}-A& Aȓ}-A&Aw}-A cҿAHN}-AZAl }-A`{AV|-AکA_z-AzAw-A$Ar-A 8oAEr-ABA0q-A@Aq-A0cAuo-A`JA8gn-A}AhQOn-AzAAp-AOuAPp-AtA,]n-A%yAH m-A`KowACj-AiAxh9i-A=eAPg-A4VA(g-AXeVAg-A:UAP h-Aj%UA(h-A'UAh-A`UAf%h-AzTA/h-A`"TAH8h-A |TA`Bh-A TA Kh-A@`TAISh-AaTAj[h-A TAech-A@ TA5kh-ATArh-ANJTA:zh-A (zTAXdh-A@gTAxKh-A/TTAxh-A`=TAxh-A$TAhh-A TAh-A@ySAPh-ASAh-A&SAh-A@SA0h-A|SAh-A\SA`Ph-A`Bi-A<RAIi-ARA^Ui-AQAai-A)QAxhi-A@>QA`oi-AQA0vi-AQA?}i-A QAТi-AޗQAPi-AdQA0{i-AiQAi-A`PQAi-AX6QAi-A QA舤i-APAྪi-APA0Hi-ADPA8"i-A PAXJi-AyPA i-A]PAxi-A@APAwi-A&PAXi-A@ PA8i-AOAi-A OA6i-A@OA@i-AOAj-AOA j-A OAbj-AOAj-A`OAp[$j-A c'PA+j-A-TPA2j-APA0v5j-A]PAe8j-A JPA0;j-A PA>j-A`]PA/Bj-A`]PAXEj-A PAHj-APApJLj-A PAyOj-APAPxRj-APAPPAx^j-A`0PAH^j-A#PA`]j-APAXj-AOARj-A@OALj-A@.OAȶEj-ApOAUFj-ASOA_Gj-AK7OAHj-A@OAIj-AG OAx-Jj-ARNA0Jj-A`DNAIj-AKNA]Ij-A6NA0Hj-A@NA0Gj-A JNAEj-A NA8Cj-A@NAFAj-ANAl>j-A@rNAx>;j-A`eNA7j-AKZNA4j-A 8PNAH 0j-A GNA+j-A@NA@#j-A`;NA`Cj-Ad3NAj-A`)NA0j-A MAti-A`MAi-AMAi-A MAhi-A@MA8i-AMAȘi-AMAi-AുMAi-AFMAi-AMAi-A@MAHi-A`BMAi-AjMA8'i-A`MAi-A MAi-AMA^i-A`TMAh;i-A@MAi-AMA@i-A`MAHni-AMA؎i-A(NAi-A`TPNAi-A wNAPs,AC eAop,A0deA ],AH^ eANQn,A=%dA,}H,A GhdAپ,A3dA(q,Aa"eAf%|,AsveA))0,AeAuZo4,AqNLfAtOa,Ai3fA ,A$Y`gA`=,A^hAy,A3hALon*,A3iA6v,,AQzA,A`zA虙,A}zAn,A׋{A,A@rxAX ,A3wA,A wA),AJwA.,A`7wAHL[,AvA``,AևvA؎,A@uA,A`uA5,AFuAH,A vA8,AuA,A`uA,A`uA=,A@)uAؚ,AhtAX,AJtA ,A`\tA߱,AuÄ,A2uA8 ,A@wA,A`wA,A3wA,AGsxA',AuAQ#,A uAX0,A quA83,A=(tA),AtAW,AwA.,AzA ,A A4,A`KAf_,A@ងA,A3Au,ARAf,A \ُA,A A,A Au,A`)|A,AA`,A쥙A;,AHA)L,A Ak,AhA`a ,A3wAG,A`gAM ,AWA,A`fݗAL,A9AP,A s˕A~,A@2A8,A`)AHa,AMAp8,A:A,A@ᔓAx,A \A,AfA,A`ߖA,AAz,AA),A=A@d,AɶA:,AA>,A*ɑA,A4AP",AA',A3ӑA,A =A,AQA؞,AA80,A̓A 0,A|AV,AAX ,AA,A@ːA(:,A[/A /,AKA ,AA,ALaA0{,AAs,AA@F,A]!ATo,AԎAu,AAj,A^A8e,A`rAJ,A=A/,Ag~AT',A@CXA!,AV?AH,AA,A`)4AHK<,AA`Ǽ,AďAh,AhA8,A@WRA,A@ A|,ARAHnK,Aa^A,ApAB,A`΍AA,AA8,A"A' ,A`K A7,AoAh>,A WAi,A`)A,AApp,AˋAX ,A`AǼ,AA0ȷ,A`bmAx,AjAho,AފA0{,A uՊA=x,A`SAxk,A4A e,A.AG,AAff,A4uAu,A`A,A |:A,A NA`a#,AׯAg(,AĝAS,AvAY,AA,AMA,A 87AH,AqA0H,A A R,A |A,AAW,AA# ,ApaAȶ,AIA,A@pԇApJ,A㺇AX$,AoAf,A)*A,AAX,AGAf,ALJA,A`XA`Ы,AjA3,A sAH{,AF~A0 ,A@A)C,A=6AXp,A |Av,AxA,A܆Ap,A džAh,A0-A,A^A,A]}A ,A@%gA`#,A@A;,A9Ap_,A@W9AHm,A`A@F,AZAsF,A 8\A ,AA ,AϚA&T,,AkwiA_6,A3AK,AsDğA/I8,AJܵ߄A虉,APAT`|,A` A%),A"A|$,AJ?0MAd엒O,AfRA&}")v,ADūA@,A@,Ae8,ALA*,A\Az ,AQ)2A,AfųA6x,AdYA9,A`APf,AA,0,A_2_A{,A?Y<AfAR,A9Ab,AA<,AA,,A@A,A0Ap,A qA,A A8,AAp8,A`)AH0,AAhv,AzA RS,A@NAhs,AvAp8,A`)At,AHA8,A A,A,A8,AAm^礢,AH.A2V,AHQAO,A ;AI,A?$5tAIGY,A<0NAp,A`KAkS,A)A(.P,AY%A &-,A'w_A,A@gA,b,AmAl,,AKAc ,A̹A,ALAzw,ALANQ5,AF8?}wA,A@yHA:,AVuj,Ar0K,A=,Av,A~$AP,AyAYB,AA +,A'RAI%EO,A)DAkZ,AƌAޱ^V,A&^ >A[,AAC,A@-Ag%*;,A AD^+,A5Ax},AIA&,A{?A,A`"QA,A`VA@H,A`VA9v,A<Aԁ,A}'*Aso,AshA,Ŧ,A"oߘA[,A3FAC,AmAA`,AukUApC,AEfA7j,A+e*Ac,A:AZy,AAD,AAh,A6zAh3*,A[A@q ,AhAH,A;A`Ci,AOA ,A@]A@,A@*A(~,A`sAn,A]WA8,AAγ,AA R,A lA^,AyKA,AA,AApJ,A Ap,A@gAXt,A =A?,A`mAP,A@,A,A A(%,AVAxzA,A`mKAK,AA8Qf,A`BA0p,A@iAf,AAБ,A`A,A@,9A@},AA@,AA8,A@DAP,AA,Ak[AC2,AAB,ApA,AA+,AzA,AAHR,ADA7H,AA0,A#A`],A`2A(z6,A0CAI4,AMA,ANA ,A@A,A-A@B,AA!,A2A,AA ,A6Aȃ,A &A,A@#Ax,A@TA%,A@WA,A`A6,A:A>,AAL,AAR,AAnt,AAHv,A`AR,ASAT,A@>JAb,AApg,AvA`2,A6-A>,A@ A,AA,AA,A4Ah,A9A X,A@AxhB,AmA8,AALJ,A`mAX,A_AX ,A@υA,A}OA,A`A,Aj&Ac,A` Ac,A`Aj,AjA@,AA,AAp,,AVA,,AAX,A`A,AtA,A`ܪA,A൭AxzT,A`KTA؎.,AQA,AZ0AC-,A:AFa,ABsijXA`#u,A"A,BS,AOqAAc^6,ASA]z.,AYdA },A bBAP(,Ajb=Axm,A`AUN,A@/As3,AAk?,A{AG,AnyA,R,AOAg,AA s,AQA8,AyA",AIA(,AAZ,A A,ArAx-,A-?A,A*%AH),AIA@,A@A m,AB+A0v,AA0;,A sAp0,A`Ai,A`A ,A`!A ,A@A W,AoA(J,A A,A൭A,A@A@s,A Ahb`,A@5RA19,A`A&,A@#*AR,A}PA,AAh,A`A,A@Ap,AHAq,A@A{d,A5AE,A@ UA@9,A ɁA,AA(,AAx),A?Ah,A9A0,AB6A,A ZA,AA,A@Ak,AiwAc,AACA,A`A7,As5AX 8,AZA,,AA~,A`Al,AAxK,A஫A),A;A,A A,AA ,A`nA@(,AA`4,A`BAKQ,AaAo,A 0AHP,Ac"A,AA0,AA,ATA,A A p ,A@A2,A |A@,AqAg,ALYA 0u,A'A,A uA7,A 8XA,A<A\,AA ,A`A,AA@ ,AױA,A}A$,AA(-,A@AH,AA,A Ahճ,AA,A@AP*,AEAd,A`WA,AA0x,AFAj,AcA,AzA0,A@A`,A A8,A?A:,A$A0,AxA`,AoA,A`A ,A@A,AA8 ,A1A 6,AA;,A@AO,AZA]S,AAg,A AKA`1P,A@iAYS,A` A k,A@WAdl,Ad]AH,A<A0,A`ôA;p,A q'A(~e,AA &,AA`%,A`TA! ,A`Ax-,AQA0{,AH A,A+A0,AA,A|eAX,A YA,A BA[,A ZA,A8=A,,A|0Al,A`A@q,A@AhQ,A cA`,A /A,AAL,A[*AHM,AAH,A^AJ1,A6AP,,AqA@(,AׂA,ApKA6,A@.A2),AA(9/,AAذ_,AAu,A@Aث,A޽A@~,A Aؚ,AhYA0f,AոA},AVA,A`0A`,ALA,AteAH,AAH,A`vA,AmyA`,AӭA,AAq ,AK"AȤ,,AA/,A@iAH:,AfbAp0,A@G8A,A@A),AAg,A֞Ar,A lAvx,AtA,A@ AH,AേA ,AA@ ,AsA,A,A4:,AA<,AA`c,A lLAq,A A4,APA ,A _A,A}A0,AA,AA,AA,AvA/,AAH.,AA0,Ah+A,Ah+A@Ӭ,A`"Aى,A|A,A RA8Iq,AA` R,A@gA`E,A[A ;,A@0A.,ABAHP%,AOA@,A@ A ,AનA,AarAԢ,A5A8,A@dA,A<A,A`A,A =A,A`SAu,A+A8g,AA,AyAX,AtAE),A{A R;,A`AN\,A@QA\,A~A8b,,A;A83,A`TaA(m{,A LA,A@]A(,AA,AAȃ,AMA,AAN,A ZA,AA,A A ,A lA(,AّAi,ASA,AէA`,AzbA`,AA,AA#,A |Ap,A |XA@W,A GA,AA0Ԇ,AA@v,AA,AŶA09,AIA ,AAې,A`Av,AAk,ADAM,A ZAR<,A`A7%,AaFA,AɳA,AA,AAd,AtAa,A`vA,A`A8,A A0;,A9AY{,AAX9g,A@Ah,A@CA@,A)A0,A`]qAF,AAh,A AN,A:A!,AAV,A)AI,AA@,APA,AcA",AA,A&AP,A[<Ay+,AAAP|,AgAx(,A`Ao,AA,A@9A`,A Axz,A`*Aep,AA Z,APAX ,A@A+,AA[,A`NA,A`]A(,ATA8w,Ao'A,AA`,A Ax,A@EAh,A A,A`)A,A Ah,AA,A{A0G,A`TSA@,AA,A4AX,ARA%,A`d4A؎2,A`A(I,AAV,A qzAbp,AAu,A@Ab,A`A,AA,A@Aw,AAU,A5AT,A`A,AމA( ,A,zA(),ApWA8,AA:,AA _(,AjAE,A=A,A}A ,A̻Ap,AA8x,A`vA8,A@5gA{,A`Ag,A`ݾA0w,A@NA,AOMAג,A@dAr,A ޿Al,A@AK,ArAyF,AA8',A lA!!,AA,AӆAXh,AA,AjA,A cAY,AդA٦,AgAج,A`3AL,AOA8k,A@Ac,AA,AvA,AAp,A !AP^,A_A0,A bAXJ,AA <,AWRAh ,Ay6A@B2,A`A@59,AADb,AANh,A˿AO,A*AV,AAH,AuA,A u^Apk,AA,A@GA@q!,A 8A,',A`KAP,A QAV,A:Ao,A@ڻA,AAXۣ,A@A,AտAa,AAw,ANAȺh,AA,AA(z,ATAj,A@AK,A@AD,A Ap,AA,A Z A,Am+Ab,A`PA>,AA`1,AA^Z,A՚A%,A`eA,A@,A0*,AA,AiA,AվAhL,AA S,A`A,A Q:A,A"A#,AA#,A`HA.,AjA(,AwA@),A 8˺A@F5,A A(U,A`A]m,A ƹAxw,A JA,ADA3,AʇAB,ANAPk,A@G7A{,A nA0,A@ynA ,A?A,AڼAX,AAX2,AA0X,A$A7],A`!AxiS,A@5A&,A A*,AsCA,A@pA襪,AXAL,A dA,AA,A@,A,AɶA~,A4A,ACAȋ,A 8XA ,ARA(,AHAA,AմA(K,A`AE,AA,A@OAX ,AZA,AA8ɓ,A A]j,A`AV,A`An,AA8Q,A`2fAo,A`-A@,A@A6,A?Axif,AϽAЕ,A,A؛,A@YA@%,AלA`,ApA,AH-A o,A}Aa,A A,A`լAw,A`dA(q,A A,A۸A,AׯA ,A ߸A̚,A@赸A$,A@A,AR~A,AR8A,A |ηAxh,A`A,A 8<Ax ,A@i"A1,A`ܑAP9,A uAc,A׵AHi,AA,A`"AM,A?A,,A`]JA(,A1A,AxA,A4_A/9,A@²AH,A^ҲAs,A ۛA$,A_0A8I,AزA07,A AG,A hA(im,AֲAha,A`ٲA`J,A@vA3,AAJ),ApA",A SA0,ARAO,AA@=,A$NA`*,A`XAH,A6A8E,A?̲A,Av-AD,AkAX,AAN,AȵAG,AAXWA,A A ,A@ؽAP8,AAi,AʗAp,A@A2,A ҾA I,A[Ah3,A`f0AB,ARA0>,A`;Ap",AA,A)zA,A3WA`,AٶA,AյA,AQuAHP,A@y>Aȩ,A GAև,AmAHm,ADA,AٮAp_,A ®AH`,A첫AS,A |A,AAy,AA,AMA#p,A`Afd,A4Ax:,AzA(,Az8A,AkDiDAG,Am½IAG,A`THAK,AÁLAge j,AMB<ANO,A'*$A`6,AA,AsA,A?1A o,A AXW,A A,AA,AA_,A@ A`,A`)A0٪,A{A0,A;APڕ,AVAՏ,AtA,A@A[,AVA^,A`pA,A`AV,AOAW},A=AX{,A.A =y,AAPw,A Av,AAPu,A`Au,A cٴAHu,A`ǴAZv,A ALw,A AHx,A`KAUz,Az|A0|,A|iAXR,AWA|,AFA@,AH7A跋,A!A ,A- A0,A;Aȥ,A@%ڳA`,AA8,A`)A3,AܲAm,A ~IJA2,A@!AH,AAU,A`{A,AbA؛,AzJA,A2AR,AA(,,A`A(,A@pAx,A ɱAG,AAX,A` Ae,A^A,AqiA@,A A,A԰A,A`;A,A^AÌ,A3rA,ARAX ,A@>4An,A3APl,A`gA~j,AAA g,A@Aod,AAh`,A`ϮA0Y^,AAhb\,AࣰAZ,AA`Y,AAY,A}AX,ALkAX,A}YA Y,A GAأZ,A6Ah,A APi,ATdAh,ACAHh,A"Ag,AFA؁e,AA c,A"¬AP8a,AൢAu^,A׃A{Y,A^AT,A c:A@BN,A /Ap H,AA(mA,A`2ԫAp:,AA`5,AAȔ0,AA,,A A',AwA( $,AeA؎ ,A`mQA0v,A <A,A4'A(~,A A0,A`A@,AAL,A ʪA,AtA,A Ap,AA,AA,A@yAx),A[jA,A`[A4,AMAp,A`@A ,A 5A8$,AcA8(,A A+,AީAH-,ATA`a/,A4AP1,AnAE3,A"fA5,AiIA8,A@`-A`<,A@!A=@,AAhD,AdިAH!I,AƨAN,AA@T,AzAZ,AtAs_,A`"VA(~d,Ai6A`%i,AAfm,A=A@s,A=ŧAPz,A3Axi,A-jA,A=>Al,AA虗,AA`q,A@`ǦA,A띦A`,AV~Aª,A^A ,AM=Ah,AA ,AxA(ܰ,A@إA@1,A`A@,AՕA0],A`BuA8,AfUA؛,Aa6AX,ARA,A`APo,AѤA ,AAL,A_As,ALA,AwAHˎ,A}gA,AWAPk,A`IA),A@><At,AR"Ap,A q A,AA(,AۣA,A@ţA6,AoA,A@Ah,AィA,A eA(q,A-GAp,A3'A0,AA,A=Aذ,A`vA,AAX,A΢A,A`2A ,A@ؤAH ,A 䎢A ,AwxA ,AaAL,AJA,A7Apx ,A Z%Ao ,A A ,A (A(, ,AA,AHߡA8g,A ϡA,AAM,AA0j,AAP,A0A,AQAH,A`A,A`rA,A#bA>,AQAB,AAAh,A@A,A A,AƠA,A`KA ,A@xAH,ARA,A-A,A`TAp,A@GAS$,AA),A؟A83/,A4ǟA5,A@AH<,AॴA0B,A A0H,AAN,AAkP,AvAXQ,A@C_AR,AGAR,A/AR,A Ah@R,AA4Q,AAO,AОA`N,AAuM,A 䔞A@L,AvAL,AHXAM,A`9A8M,AAN,A@AkP,A`A`S,AaA!W,A A8[,A }A_,A^Ad,A AAP|k,AA0r,AAz,AҜA,AA.,AZA8I,A@rAx>,AA@,A (A,AAʒ,AAP,A"A8V,AuA,A iAXl,A0]Aߘ,AMPA,AGCA0,A5A,A (A(,AA8,AA(ʓ,AA(,AAB,A A,AAج,A A,A@ܛA],A2ٛA ,AכA |,A֛A]y,A כAv,AڛAr,A ߛA n,AdԛA@k,ARțAg,A`A@d,AVAb,AAP_,A@ A],A zA'\,AhAx[,A@UAVZ,ACAZ,A@/AGZ,A A=Z,AAZ,AޚAq[,AൿA\,AAXW^,A@A0j`,A@dAb,AGAe,A`)*Ai,A AXl,AۙAo,ApA|r,A 늙APt,AbA{v,A8A`w,A \AP7x,A`A x,A` Aw,AȘAhv,A`;A8Vu,A6A`s,AAXlq,AmA`n,A@NXAXk,ACAh,A0A e,A@pAa,A A8\,AxA@AX,A`A1,AA#,A@A #,A$A9,A}A$,A= AA,A@A _`,AJAxd{,A ԔA,A9nA0,A`.A2,AA,A拓Ap,A@(A@X,A iA8b ,AAK,A+ґA,A@ǑAV,A@ϜA },A 3Awq,A@5ӐAe,A]`A0,A`,Ax],AKsA },A@ A,A`ΊA,A=Ah,AsAZ,A@!A,A Ap c,A qAps,AoAP,AA 4,A@$A?,A`A,AA ,AjA(,A@5Ah,A@ApZ,A@>Axh,A |"|A ,A |"|A,AWg}A,,A0}A,A{A8,A3zAp,AzAx,A {A,A`K{A@,A@qA,AA ,A@AP@,AՁA ,A>zA,A2yA0j,A`{A,A`]|Ai,A|A+蒞,AJ|{AEׂ,A({AEf,AB{AJ.dK,Ao?4t{A"e2,Ae1+{A p,A zA|/,AIL$zAD,AQvfyA&,AxA[I4z,AKjýwARjZ,A@vALH~=,Aӹˆ vAP$,A|#uA"#,A q\tA,AsA0G,AerA0,AqAX,A`MmAг",AkAH,A@EkA,A kA8tl,AHlA,A HlA^u_,A!>kA,A#jA,A \iA+,AiAX([,A`hA,A`KvgAg,A`DfApx,A`ohA,A@DgAh1,A?fAC-,ARaA},A%^A@$,A`T]Ah,A YA(h0,A;UA/,Ap;UAP,AlQAh';:,A PAx,AZ)OA렁,Ab9OA +,A"NA,A@WMAp,A MAk,ALAI ,A 8JAZ!,A`)JA`,,A`JAȺ;,A`JAO,A A4JA,A`mJAX9,AJAh,AdJAТ,AvHA +0,AEFAy,AMHA_M,A6HAJD,AHA ,A A=HA,A@y>GAO,A EAځd>߿,ADAxgݿ,A]X@ADAlܿ,A%DA8Qܿ,AvDAԤܿ,A74_DA ݿ,AB JDA?vR߿,A=`ͫDAi,A ۟DAx,AfDA,A/DAh.,A<#DA!>,A&DAo,ADA ,A ECA3 ,A*CA5 ,AʝCAH ,A;CA8\+ ,AіҝCA{} ,AwCAf#,AYvCAX,AfCA Rٿ,AJEBA,AVBA%_,Aζ?BA1 ,A*4BAS^u,AI6BA-Rc,A DBAQ,AkD]BAA,ABAFe4,ACBA',A{BA ,A@lBAb_,AmVFBA,ABAKf [ݾ,AHAAjm¾,A8>AA,A`ՖAA`,A@֒AA .^,ANAADn,A,gAA|\,A /AAAHz,A [>A(,A&7A,A`D_6APޖ,A 5APZ,A4A,A Z*3AϷ,A}1AH,Aw1A+,A1A@,A0AH,A`0A!,A0Aߺ,A d0A(Һ,A@h0Ah",A6=1AF,AP1AȺ,Ah<1Ay,AV1AV,A{0A@3,A//A0+,A`/A6,A /A >,A[/A(@,A=6/Axֹ,A@6-A8,A;k-AW,A-Ax,Aྚ-AÛ,A@֍-A!,A V-Ap|,A@,A,A@*Aѹ,A`9s*AȘ,AP,AcKF&,A~g+A A:{,A*An>,A#&TŻ)A 1,AR(Aњη,A'A,AxGAA,A`v^Ax԰,A@G'AW,AOA,AHA(-,A| APE,A`m Aa,A Ap5,A6 Ap,A@ A(,A | A ,A \ A`ޭ,A Aխ,A@ A(ŭ,Aa A@ӫ,A@ A(薭,A A8,A > A;,A8v Aw,A` AXys,A@r Ahe,A AXa,A A؁K,A@,A7,A AŪ,A`_A&,AAh@,A`AD,AA_,A@AG,A@A",A`K?A,A[A9թ,AzAo,A@AxNJ,A@`0 AAؽe,A2-AI@,A@,A[@ȶd,A@>z@x,A3@,A@x\,A@w@k,A@4),Aw@,Aa@P,Aq-@(ß,A@@,A@/@8,A@@O,A)@<,A@p@,A@@k,A@N@Z,A[@t,AǼ@i,Ai @(9/,A&޺@],A@͹@p,AVD@,A@@ 7,AA@P ,A2@0,Aƴ@1,A@ʹ@xv,Ao7@@Z,Aӵ@&,AX@,Aj@,AQ׸@``,A1y@,A@hv,A\~@зE,A@4,A@ѝ@Poؕ,A@#@瀕,AT@``S,A@9@ѓ,Ab?@Z,A@@*,A@(~Đ,A@@@2,Az@ $,An@,ATIa@%,Ak7a@&,A@`@Ĺ,A{`@xi,A `@ʇ,A_@:,Am_@W,A_@X,A(_@!9!D,A{ ^@',A {^@L,Arj_@ 3 ,A#_@E&,Au _@m,A@w_@`l,AYa@F,Ab@:,A8n@> ,A6Uo@Q,A)o@,Abo@,AÅvp@0 ,A"Xmp@0u,Ap@۰ K,A"8q@A2,A!wq@w],Aq@QAƄ,APaq@#,Ahq@P7en,A&q@C,Ap@qA,AbTp@-@G>,Aaqp@6:,AnwWp@=56,AHsBp@}d81,AE4Y4p@",,A-,p@zc',Ar+p@M",A4'`1p@E,AP=p@Am,A Pp@F,Aip@?{6,AL%Gp@x,A*p@x ,A6p@3,A4t@x1,A -u@=,Arv@Ñ,Am;zov@`崃,Aw@沃,A@w@[,A2x@,A{@`,A2}@n,Al ~@(D,A@A~@0,A@A~@/,AS~@гg,A@y~@,AD@P/,A 2@7,A*@`,Am@|F,A}@ ~,A@ߑ{@ \~,A(z@M~,A@8z@@:~,A@z@`O ~,Az@0},Az@Jg},Ax@;|,Au@xR|,AIr@ {,Am@e{,Adm@ {,A=n@0z,Ak@0y,Ah@Lhy,A@g@)y,A.g@ w,AIa@?Aw,A@.\@v,A@IX@u,A@Q@2t,AE@q,A}0@+ވq,AUv,@a!q,As5)@~׶p,A%wm%@ir]p,A !@Ƹp,AAÙ@o,A:@ Ro,Aoq@أo,A@?@ޝn,A @%Ȉn,A5p @+Pn,A@p:o,A}@- ;o,A+1^t@Io,Aj8R@W\>o,AE@6L>o,AT;9@p=o,AG`,@(J`=o,A @h'@*0om,A@Ym,Am@83Gi,A)l@>1i,ARmd@j+i,AF@C[Ih,A]  ^@4'h,A@fh,A%I4d,AZ@\c,A+@c,A @]a,A@0;_,A9?@7!Ӗ_,A4O-@ha_,AUs=@;"R,_,A o@^,AFÊ@^|^,A};8@h6d^,Ag P͋@a^,A@=!^,As,@f9],AYs@MB],Ahg1@ q],A.@1j\,A& B@'h;p\,ANriy@P<[,A@[,A+Juh@%[,AnR@\][,A֠q@$[,A̢@AZ,Af@/E;Z,A~@3eY,A1Yj@-Y,A(@SY,A@@{'=Y,A>q@xX,AHT@Ms;X,AD֬@q 1lX,A2@@%X,A@i@@ FX,A6B@WfsnX,A)O2@}W,A6y8@9 W,AHPU@"mW,A7H@tBvW,A@ѫέ@ mW,A2{N(@q"SW,A\=q@sW,As @{V,AѦ@U#W,A@s@Po2W,Az@0QY,A@?Y,A@5h@HY,AX@X,A@RX,A@@X,A@ _X,Al~2@''5X,A]@ X,AP1^@-W,AFO5@JW,AD@wGW,A@8W,A@G2@XIcW,As@Β0q@U,Ar @U,Amհ@cU,ANb@/U,A-@#T,A@G2@b)Q,A%@hO,A4}@@yN,AO*@eM,A@AM,A@ߏ@L,A@dL,AF@(L,A@L,A?]@8L,A^10@_BpL,Aw)@`QL,A@=z<(L,A-K@2"K,A1*@pTK,AT@ޱK,A@rWK,A@W@mI,Aʾ@0yF,AXƷ@![F,A ~L@dߏE,A(v@j)E,A@oAD,A?a@pAD,ArB@NqUC,A^`ݬ٬@3,inC,A@ҥd C,ArH(@;mB,A'@רHB,AV@;A,A;@B,A@p@IB,A@C,A@j@B,A"@գB,A@@YB,Aa@A,Aȟ@0A,Aa@CA,A@@C&@,Ac2+@ è@,ALq@ M$a@,AHP7@PV@,AbU@Qݦq?,A$k@^n?,Ask4>@-Ci?,A@@6?,AF-@ِ?,A]vZ|@a>,A+x@X6>,AiHx@p>,Aw@^[ >,A &}w@@s>,A؞[w@~fD>,AU!v@pΔ>,A@v@ Bن>,ARF[v@%y>,Av@D sn>,AV8u@ڊd>,Alku@v\>,A@u@WS>,A?t@ȺI>,As@LZjA>,AMs@8>,A@r@=,AWħEp@$=,A0m@ZW.=,A{uj@P7[=,Ag@ؼX=,Ag@U=,AΗg@0GP=,A@N_g@hJ=,A@f@xhG=,A@ f@`H=,AXXf@J=,A f@XT=,A@>e@Z=,At#e@Z=,A e@V=,A]d@~O=,Aid@A=,A-ed@hs6=,Ad@ }/=,Ad@ )=,A\d@}#=,A֩d@ `6n=,AAd@ ==,AmV8d@8=,Admd@P<,A}Fb@Jh<,AGjva@Vמ<,A`h_@I~<,A}L,^@PQb<,ACl\@I<,A@Z@lM<,A "rZ@důP<,A+BZ@wR<,AU'Z@8S<,A(Y@EWR<,A+kY@5Q<,Av:WpY@*N<,A)?Y@$@J<,A$[QY@SCSF<,AyX@ZrI@<,AiX@ª ;<,A.fX@ZӪ4<,A(͡X@t.<,AޡX@_%W'<,A;[X@ <,AKX@<,ArX@&A<,AR0X@_# <,AÏX@<,A2T@wX@Ŭ1<,AWGWX@Ԋf;,A0X@- q;,AFSmX@ k;,AW@Z;,A>V@U Ԇ;,AYT@;,A@GTS@J;,A^zR@Rx;,A4 R@p;,A@rQ@f[];,Aϐ7"O@@:,A@L@p:,AiK@I:,AUJ@㪄:,AJ@h:,A@hI@Q[8:,AJgG@ :,AHPuFF@9,AjzD@&~̷9,A4T7B@9,AP A@wuĎ9,A,;@@39,A/kt@@/9,Arf@@v9,An)@@Às9,AUp?@i9,A@?@9,Ag6]>@!gh9,A[R8=@V`9,Aڊ=@09,A@4<@|9,AsJ*Y<@ tAbn9,Az<@)3a9,A<@KU9,AGL+-=@ȇK9,A@=@09,A@?@8,A2@@8,Ap4@@G8,AҾ?@@C;8,APFA@t}@S3(0,A8@&jI 0,A0Cs@ ۵M/,AYE;@/,AB@0E/,AnHE`@B,,A`l@W,,A:@M',,Au"ڮ@j+,A"A@+,ALc@-+,Aξ~@Oa+,AVU@]$/+,A%@*,A@@I*,AU@@R*,A:3u@Y)-O*,A]@*,A7m@4O),Aw1@),A7@3h),A^6J@1),A@e),Af3ϟ@y(,Aܙ;}@jl(,A8#9@^#(,A5@iSe(,A%xO@_@(,A@@sn(,Az4@P',A@U@wzf',A1(@);I',AWݐ@',AfKf@(v',A@ @',A@@7b!',A@<.)O',A>´@J.G&,A'@w9&,At@%)ʞ&,A@9Q&,Ak)p@Db&,A_ @& $MR&,Aς@K[@&,AG@,&,AQ@j&,A@Os&,A{@%,A@!%,Aruބ@xG%,A@^D@\A%,Ae@(%,Afw@%%,AXz@z%,Ap@(O%,AKtI@A$%,A;@$,A@ &$,A|`@^$,A~W,@ޥ$,A~Vp8~@3}$,AUP?@(e$,A@1q(Y$,Aj@SK$,AA1@);$,A@s)$,A{$@+W}$,Av@$,A g@12W#,Aɫt|@,t #,AaGx@x#,AR@,Aъ@,Av@#,Ax_@x,AD@Xym,A@N'@@Wb,A@;R,AF@hB,A@1,A0@P!,A_@,A@XW,AHs@5,A@Nq@,Ad@W,AT@(,A8@,A@N,A@`!,A@,A@0t,A>@0k,A4@8c,A@^Z,A@@Q,A@@AI,A9@ .,A6@,A@,Ag@8D,A6@D,A@ @,AI@(,A@@b,A"@X,A@sN,Aq@uD,A@ֽ@:,A@x0,A]u@',A@L@B,A @9,A@Y@(:,A2@X ,A@@,A@p,A@,A@@أ,A@@,An@,A8@,A@%@hL,As@=,A^@D,A?[@0,Am@,A@,A@b@ ,Ab@g,A=@@,A @2,A@p&@O,AT@,A@,AV@,A:@,A@3@y,A @H,A#@ ,A9@#,A@pL@P,Ab@,Au@(h,Aф@,A@@ɐ,A@Ϙ@,A@r@ ,Az@(v,A@ce,AX@T,A@XD,Am@~3,A@sW@",A>@,A-#@;,A@,A@0,Aq@,A@`,A4@8,AD@X ?,A@v@83+,A%@e,AA@Hi,A@,A@0 ,A@,A@+^,A64@ʶ,AL0#@,A`@ϟn,Ar85@u ,AQM@. ,A{B@zY ,ARmd@O ,Akm@ ,A@E@sk ,ARbT@a\E ,Ai@킝 ,A "@.,A৊ݱ@L,A@g^@ t,Aa@0",A @,Ai@*,A@Q@X[,A@"@|,A@sӭ@ yV,ALD6@sV&,AٓAw@Xn,A8̣ӗ@%Ю,A9v@nV,AbI~@|Zj,A-[H@أ],A@g@0p,A5 Ф@P,At&ݮ@PF,AZ:@h,AM&ѝ@#b3,A %5U@,Aǘ@أ ,A@⦗@8,A9%@X ,A@v$,à @,A@r |,Ab/R@ [,A8ͣ@iB<,A9?@#,Aκ@(_,Ak@6,Aڄ@2B,A];@W1,A|@,Agw@j,A6B{v@J>},A{B}u@],At@ވM,Am t@;,A1txt@2o),A+C7Xt@ <,A#KPt@,AN-_t@'|+AB)t@!;(+AMVt@83+A@u@4D+A㕩u@֏+AVu@m+Awlu@.K+Au@8)+A]u@|+A̕M[Z+Azr@vA+A3r@k)+A&&s@ڷ+A?Gs@F *+A;8بs@+AS$t@+At@w+AM6Zu@LT+Au@vz +A=u@Z[+A v@W(+AuGu@+A@u@߃3+AV(qHu@R+A&$t@O+AMt@(A?=+A9 t@'w+A>pt@p+Ast@MV+Aet@!Ԃ6+Aet@+A=~t@&+ɑ+At@5 R?R+AQ~s@BX+AНr@@v+ASq@+Ap@Ym6+AdҬn@.> +A:}m@D/l+AnXj@I +AEh@V+A]A^af@O+Ac@X!+A@b@&a+Af`@j+AKa2q]@:oC+AΎZ@.4+A&X@Uv+AN3V@8E+AT@B(+AѽS@8 `{+A%S@}+A_٦S@ը+AM S@pm@L+A_1T@dP>+Aj+T@&++A+\U@*F#+AdK-V@:+Az$W@]c+ACBX@s+AL@Y@d,S+A=YZ@S+A@y\@ +A^@22+Ad@ +Ag@0b+A@5l@+Ao@ш+A2.t@f!$+A\Q"w@c,i+A($<{@@1d+A~@`+AHa@+AW-|@+A@@!+AV@0+A8fZ4̌@j+A,y;@+Av=@ +A@?=+AYW@9+Ap@{>i+A!ɗ@}Z=+AH@;+Angޙ@p}+Aš@d+A@+@(+AΝ@8+A@ƨ@+Ah@@+A@_f'S]+A$~5%@+An@+A@6@9a+APХ@](+A*X@K̠+ATO@-9.+AQ`-3@$-+AeE,@$e+A9@Kx[>+A[Y@'C+A@=Re+A]|ɵ@ W+A@P+A@so@B+A1`9@P:+A@@p$+A2^@<*3 +A3k'@H#+AC@C=7+A+AH@|.a+AN!L@@H+A@:+A@I@0S+A@@XJ+A88@#[+A@@Q+A=0@@S]+A\@P+Aj@V+A$@k+Abi@M+A@>@h.@+Aj0@d+A@g@+AL@%t+Aō@$b+A@$@_+A@@a+A5@جj+A@ @+AE@+A@+A@P@ R$+A@6+A@@i+Aʓ@+A@+AfK@0+A@E+A@G+Axݨ@X+AV`@(+A@ +A@@+A@S@Ș+A@#ȯ@s+A8F@+AZ@M+Ah@n+A@^+A5@+A@%@ A+A@@ү+A@ @X+Aʵ@Ʋ+A@J@+A@`Ѷ@pw+At@+A@0@+A@b@ +A@p}+A~@H +Aȹ@|+A6V56e@+AY @\'@( ?+Ah/@`T+AE@_ʼnu +A|tS @~)$+Aok+AӝS@6p6q+Ae@iq x+AQ%@?)+AC݆>@#J|+AdZM@0.+AQ@+A51@M+A n@M+f+Ad@]Pϵ+AQ@+A@3+A4'@ C+A=F@+A_@}$+Ax@XI+A<@!M+A@+A@P< @+A` @+A@ @@+A@# @`%+A-0 @+A @/+A$@g+AX]@h+A_b @(+A2R @ho+A @h+Ak# @ +A @PI+A) @\+A_:@;+A@-+A@h~+A@+A@J@@dj+A@+A9 @+A@@+AF@+Az@؟+A@+A@@x+A$@H)l+A @0+A)@6+A@/z+Au@'h+AE@r+A@+AB@؝+AM,@ p+A @+A@@+Ae@`q+AM@+A@fU+A@0/+AT@4+A|@+A@p@x+A@N@B+A0@db+A`@X+AQ @B$+A @P+AT@H̋+A?@Ϡ+Az@.~+A_ @o+A@N@8E +AZ#@+A[3@+A+.@(+Av.@C+Af:@b+A2x9@+A@3@C+A@%#@(+Ae@3+A4@0+A8@+A@x +A@֏@+A@i@t+A2>@0G+Ax@+AB@+A@P@+A@@+A@@+A@ے+A@%@`2+A@ |n+A@@+A{ @H+A @`vu+A#@+AM%@p +A@@'@ R+Ad,@ Ε+A]s,@@$+A@,@Q+A7,@L+A1M3@%+A@ 3@`Ǜ+A-@+A@,@hD+A,@g+A@߫,@p +A@(@ +AZ(@`S++AD(@)E+A@//)@+A@g-@+A8.@ȓT+A)@!+A@-@1+A@Q-@x5+Aj5@+AA@h+AB@G+AE@`a+AF@(+A|G@ +AG@0. +AG@ +AG@5+A8G@w+AHF@p+A F@x(+AF@H+A@F@+A@ZG@n+AH@W+A[I@+AJ@u+AK@z$+A@gM@(\U+A@LO@+A]P@P8+A4{Q@+AQQ@xW+A"YQ@hr+A+O@(+AhEM@@3+AxL@+A@gL@0+A}L@h+A=K@+AD"M@!+AP@+A>V@g+A`@/+AzGd@+Ah@sA+ABg@8t~+A-c@+A)a@+A@`@ؚH+AQb@`1+A"'h@˼+A@N{k@x+A@!p@T,A@\s@(y,A(s@ۈ,AU/u@0S,Aau@IS,Av@(-,ASBz@(9m+A@,r|@+A8X}@+A@W@P+A @x+AzU@&+A'@@S +Afa@H+A@R@+A&@v+AĈ@ C+AH@P^&+A(@X+AĈ@H +A$z@+A5@X+A@8 +Ag@+A'@x+AP@+A@km+AH)@`aI+A@֭@(E+A@p=<+A@r@p++A@rY@(\3+A|@hR+A@g+A)@0.y+A)\@pz+A$@P~+Ah@B}+Aй@`af+A@uR+A@)I+AS@#?+A@ʼ@B9+A@@1+Ao@0G3+A<@}9+Ah@N7+A@92+A_@N-+A@s++A@++A@^@0+A%@8c/+Â@h3,+A@+A@p+A@.@P+A6@+A@(+A};@+A@P8+A @b+A;>@8+A8@H+A2@+A@+AU@P+A@d@ث+AΡ@+A "@+A@@Є+A@8}+Au@,l+A@@@I+A@@hb+AÔ@*+A@(+Av@`+A@@+Aa@+A@h+A@Y+A@Q@@5+A@ّ+AT @舑+A%@I+A@g@0L+AA@B+A2A1o+AcçAi+AlMABw0+A۝b AU+A_ A|+Aǻ Ay+Ay A+AKY A+A` A:\#+Az:d$% AH +A&N AP($+A,DAc$+AB._OA +A Ae_+AA07+AAI+ATDA+AA +AA +A^FAa+A}A#+A`pA8+A2A+AAx)+A AP+A`;BA( +A A+AA+AEAi+A AXh7+A0AX9+Az<A(KX+A$Am+AyAy+A@E3A+A [A8D+A qrA`6+AvAД+A@Al+AmA+A}uAH2+A A+AA+A@>>AP+A<A+AA+A[A+AA7+A=Ax +A{A+AA`+A ZA+AH.AP+AA+A A+A`BAu+A LA+AKpA +A@A+AAp,+AAy+A  AZ+Ak Af+A A0+A`!A0$+Aզ!A4+AT"AB+A (#AR+A=#Afk+A 8_$Ar+A$A_x+A@5%A|+A%Ah@+A`&Ao+A@>'A(τ+A'AX5+A(A0f+AM(A9x+A(Aq+AV(Apb+A &)A>]+A!)AY+A@EH)A`X+A@Ca)AOY+AVs)A}_+A)Au+A+,AP8+A@,AX+Aח-A+A>.AX,+AH /A9+Ap/AA+A/Ap8H+A50A)N+A^0A(\Y+AÝ0A8d+A 0Afo+AH!1Az+AV1Au+A`1A+A1A(+A`B2A(+A q2A`+A2A8+A 73Ak+A3A+A3A(+A(4Ap+AW4Ap=+A 4A`a+Ai5A`a+A |6A+A6Ap +A`fN7A+A7A+A;8A8+Aw8A+A48A0.+A`)`9A+A9Ah/+AR9Ap+A0<A^+AM=A+AYAAp|P+AMHAhD+AHA`L+A3 RAp=V+A@:RA#`+A_RAP8i+AxRAv+ARA3+A̴RA3+ARA`+A RA+ARAh+ARRA`+A`SA`+ApMSA+AறSAP+AٳSAp+A SA#+A"TA83+A@>`TA8+A` TAX +A TAX +A UA+A1UA+AXUA@+ARUAu+A`UAP8+AGVA+A@>hVA+A@VA`+AgVA+A@ WAH\+A3WAأ+A jWA@+ARWA+A XA+AXA+AXA +A3/YA+A xYA0.+A |YA+AH+ZA+AR~ZAأ+A=ZAX+AR[A+A[AL+A`\Ah3+Az|\Ah3+A@\A`a+Az\AB+A(]AP+AzV]Aѻ+Aஃ]A0+AR]A+A]A8+A]A(ܠ+A!^A0.+AZ^A#+A |^AG+A^A3+AHA_AP}+AR_Aw+A |_AP8m+A`A0.a+A E`A0.X+A@>^`A`aN+ARz`A#C+A `A8+A@>`A`a4+A`Apk,+A aA&+A@>aAX +A^aAp+ApaA +A3saA+AdaA+A`)^aA+A@gdaAL+A`kaA+AuaAu+A q}aAǻ+A͂aA+A aA@+AHaA)+A4aA+A`]#bA+AH?bA0+AhbA+A@gbAp+AGbAX +AzbA+AcA)+A)cA ҭ+AIcA +AbcA@+AGcAX +A͖cA+A cA̕+AcȀ+A3dA+A >dA(t+ARdAp+A |dAi+AdA0d+A dA`+AeA#\+A`)2eA`aU+A`^eAO+A`eAG+A`eAA+AeA8:+A@eAW5+AfA2+A@ 1fA(1+A _fAB.+A@fA*+AfAf$+AfA+AgA@+A6gA#+A@glgAP8 +AHgA`+A@gA+A]gA+A`hA+A@>*hA+AEhA+A@nhAP+A]hA+AhAQ+A hApk+AhAH+A@hA+ARiA+A#iA+A |FiA+A aiA+AHiA+AʹiA8+A3iAp=+AiA8+A qjAP+A9jAǍ+A jA`a+A`ÝjAW+AjA`ay+AjAr+A \kAe+A%kAX ]+Ag2kAU+AqA+AzqAǚ+AqA+A@gqA+A qAx+A qApn+AqA`d+AqA83\+AqA`e[+AqApU+AqA83Q+A rAHN+A ,rAHO+AWrA)S+A grAfZ+A@rA`x+A | sA`+A@>\sA0+AsAp+A@sA8+A7tAਡ+A`wtAW+A`tAp=+A`)uA3+AmuA+A uA+A q vAਘ+AfvA+ARvA0+A wA`a~+A)rwAps+AwA@Wj+A MxA#f+AxAQ_+AxAZ+AH9yA(\S+A |yAzO+A`)yAHE+A`yA<+AyA83(+AJzA+A^zA +AtzA+A`)vzA8+A3ozA+A@hzA+A@>VzAp+ADzA8+A`2zA+A \/zA+A9zAx+AOzA`d+AtzA8]+AgxzApQ+A@tzAG!+A`zA+A@zAp+AHyA+A |yAB+A3iyA(\+A |*yA8+A3xA8+AíxAL+A@xAX+A kxA+A]AxA#+A`"xA)+A)xA-+A`wA`+AணwAP8+A`|wAG+AjwAŽ+A fwAH\+AnwAt+ĀwA`ai+AwAhH+A wAأ;+AwAz/+ARxA+A $xA`+AGMxAC +ASxA8+A |nxA+ApxAf+AxA+AxA83+A yAp=+A`çyA+A`yA+A@ +zA`+A@>JzAp+AozA(\+A@zAѧ+A@zA(ܤ+A {AH.+AE{A8+Ad{A+A {A+A@{A+Aň|AH+AT|Ah+A@}Aأ+A}A+A (}A #+A=}A=+A-}Am+A}A`w+A}A +A~AX+A` ~AP*+A&F~A(+An~Aӫ+A~Ah+A^A8+A`As+AA+AA+A@A2Q+ARAS+A (cAKV+AsA(]+AʔA$y+AAH?+AgA與+A -A+AX;A@+ALAz+A첄A'+A`mAP+AA}+A`Aȇ+A@aAз+AɆAw+A/A¥+AIA`+AA+A@4AP<+AjhAő+A~A(O+A ۔Aȓm+A.AX`+AmcAhA+A`щAPx4+A@A5(+A`m?AH2$+A\A/ +AzA+A@Ax(+A@ߒA+A`vA@+AA +ÅA$+AA++AwfA++ArAt,+A~AP ,+A ۖA_(+AaA@$+AIA+AjDAH+A@uA, +AA/+AʌA+A@A0T+A GAؽ+A`vA8Q+A uA((+A ֎A+AZA(f+AA0+A`NA+AAAP +A@KA@+A`wAhѽ+A@aA,+AAD+A`;A`T+A@.BAث+A 8hA+A@ؚA+AwڗAx+AA+A@pJA(+A}A`+A2Aӛ+A`vԘAX+AA(+A@=A`+A,}A6+AٙAP͐+A@A+A,AH+AzAL+A]ךA+AzA+AA+A@>6A+AA{+AӟAk+AɡALw+ALAj+AA{S+A`A8++A qA%+A6Aأ"+A`SA`+ARpA8+A샨A+A짨A#+A@ƨA(\+AA+A3A`+A 1A̳+A OA̪+AgA@W+A A+A@A83M+A`fA/+A`A(\'+A@>^AG+AAk+AA+AחAB+A AW+A`A`+A qeAW+AzpA8+AA@h+AApG+A bA<+A A8-+A=ܶAf"+AHA +A A+A.Ag+A@[A+A;A+A_A`+A`AIl+A-Ahb+A@A9 +A@+A8+A@%A0̙+A}A+A@CgA@u+A@A+A`DAw+A`A+AOA[+A ?A+A@A8'+AA`4+AsxA+A Z"AS+A`0A8Ҕ+A&AN+AQAe+A%AS+A=A(^+AHAp+A|AP8+AmAPf+A pA@+A`)A`+AAH.:+A A#U+A qABt+AA@W+AA@׮+AA+AA+ApAh5+A APP+A3As+AeA”+A@A+A`A+A8A`a+A qA`a+A A`'+A4'A0.?+AOAP8T+A zAz+A`A#+A'AX +A@gAP+A A+A3)Ap+A tAW^+AAu+AA(ܘ+A3A`a+A`#A+A3uA+A@A-+A A,A@>AhUJ,AOAYg,A`;A#s,A QA~,A A,AeAx ,AAݴ,A`dA,AA`,A2A,A@A,AA.,AA@,A`A\,A4qA t,AŬAP,A Ahќ,A AP,AnA,A`DA ,A 1Ar,AA,A1Ap,A XA,A`LAP,AKAp=#,AjAv7,AŮAE,AH AVR,A`vA`vd,AO Ai~,A@A8 ,A`dA ,A` AX.,A SAK,A`d}A&q,A@%A,A@PA,AFbA,AA0,AMAm,AA`,A@NAp:,A4A8e,A(Ad,A`A0,A@r A,Ay A,A# A`!,As A #,A A><,AAY,A~AXs,A A.,AwA,AA8E,AZ8A(O,AaAp,A qA,A=A^4,AApS,A (A h,A`;jAw,AAx,A@߻A(ۛ,A A4,A"A,A@Af,A@5A ,AQA',A#oA*K,AVA,A%At,A%A,A6&A+_,Ay6&AKM,A\5D/'Amk5,A'A ,A-v>(A9,ACw(A!,Aґ3)Aq7,Ao)AQ,A)A&{ ,A51Ap ,A71A ,A41A ,A'#1A8# ,A0A ,A`]0A@ ,A0Au ,Aො0Aȩ ,A Q0Ax ,Afr0A` ,Akm0AHP ,A`j0A8 ,A`e0A2 ,A`e0A(6 ,Aph0ANC ,Ao0A0G ,Ao0A0I ,Ao0AfK ,Akm0ADM ,A`j0A"O ,A`e0AQ ,A`0AR ,A`[0Ar] ,A@./0A.a ,A"0A ,A@,/A( ,A@ c/A> ,A G/AQ ,A?1/A ,A /AT ,A`.A(( ,A`.A ,A,.A8 ,A`].A ,A@r.A^# ,A}.A;% ,Av.AX' ,Aj.A@) ,A].A/ ,A@.A1 ,A .A3 ,A.A0A ,A S-Ax~^ ,AA-Ahv ,A@>,A( ,A,Ah ,A4,As ,A +A ,A+A ,A+A8I ,A+A` ,A 8+A,A`T+AK,A`%+A_,A++Ao,A5 +AP ~,A+AX,A5 +AI,A@+A0٪,A +Ap,A`-+AH,A}>+Ao,A`T+Ae,AMk+APZ,A +Aq,A+A`2,A^,AHr,Ay.Ap,A`2.A`%,A`/A,A/A,A/Ab,A ~/Ay,An/Ax,,A@0A6,A@'0APf?,A>0Ah"C,A@H0AE,A@W0Aa,Ah0A,A1A@,A03A`%,A A;5A2,A5Ah3?,A@5AK,A`O6Ax:V,A (6A@`,A@*7Ack,A&7Au,A`28Ap ,A@N8A,A@9AP+,AK:A(m,AI;A,A<A,A<A ,A [=A`q,Ad,>AD,A+>AP.,A@?AHy,A%AA0],AoBA,A`)CA@S/,A&DAf|,AR@FA@W,A7GAG,ATHA,AIA<,AKAO,ALAذl,A@ߜMA~,AMAԗ,A`B,NA5,A`K_NAhU,A@iNA,A`D=NA:,A`MA",AuMAh6,A@MA`b,ALAP,A7LA,AVALAs ,A#LAvK,A@WMA,AMA*,A?MA,AMA6H,A`^MA@{,A FMA,AcMAP/,ABMA(,A |>NAh,A`ܼNA8V,A  OA,AkOAc$,AJuOAfʩ,AwOA\[+,AOA,A@OAD޲,A4hnOAB,A/OALߍ,AA3 PAC,A_6u0PAҊ,AVPA!ē,A6$ܻPAĠ,AЉQA #v,A(ATmQAd,AQA=7,A'M-QAuRO,AQAـ#,A,QA,A4QAXI&,AWQA{|,AAQA;y,A0bQA,A ¹QA(z,A`;6RAh,ATtRA8&,ARA$,A*SAP!,A`SA%,A)SA 2,A@dTAO,A /TAk,A`;UA,A\UA,AFVA,A`ʋVAxz,AଲVAPL,AVA8Io,A@VAy,AVA_,AVA+,A` WA@,AFWAXy,AyWA!,A WAx,AWAp[,AWA!,AMAz%,ALAp%,ALAp &,AH_LA@&,A@>DLAf`&,ALA x&,A LA&,A@MA &,A/MA&,A %MAh&,A 8LA',ALAP',AHLA8',A`õLAf<',A`LA=z',A@6LA',A LA(\',A_LA# (,A`)LA#)(,AYMAأ9(,A`]MA`A(,A@gTNA](,A NAe(,A)NAo(,A)OA~(,ANAW(,A[NA`(,ARNAp(,A`]MA(,AMA(,AMAxF(,A@yLA(,ApQLA`(,A KA(,AKA`a(,A@KA$),AKAj),ALAq5),ALA-9),ARLA ;),A@%MA>),Ay%NA8EA),ANA@F),A@#dOA@S),AOAx),ApOA),AjOA),AýOA@),A]PAxW),APAx),AQAĸ),AQAP),AJRA),AORA*,A &,RA'-*,AQA;V*,A |QA@O*,A QAxF*,A-QA*,AxQA&*,APAX*,APPA*,A`[PAX*,AOAx*,AޤOA0*,ANA*,A NA`C+,A |NA0.3+,A3fNAPg9+,At*NA=+,A@%MAC+,A@GMAC+,AjEMA^+,A`LAPkq+,A@PoLAȇt+,A`dKAp r+,ABKAr+,A`ʘJAxs+,A@IAx>}+,A&UIA҇+,A`THAh.+,AYHAu+,AEHAdž+,AGA8+,AFAV+,A]EAz+,ApDAP,,Az&DA!,,AHCAfD,,A`ECA8e,,A +CA,,A@gTCAp,,AHCA,,AH/DA,,A3_DAp8,,A3iDAB-,A@rDAN-,A@DAt-,A׫DA̠-,A@>DAPf-,AMEA-,AEA-,A FA-,AHGGA-,A qaHAр-,AHAps-,AXIÁ-,AJA8-,ALAP8-,AHLAw-,ALA(_-,A#MA(W-,AfMAQ-,A NAH-,A3qOAZ-,A`fPABe-,APAp-,ARPAW-,A qPA8-,A"PAf-,A)OA`a-,A OA-,ANA-,AzMAǥ-,A uMA0.-,AMA-,A`ÕMA-,AVMA-,ALA-,A`)KA-,A )JAX.,A qHA`.,AEAk2.,AEAJ.,A@>DAp8|.,ACAp.,AzCAh3/,A 8DAh3/,A@DAf.,A]EA(\.,AFA8/,A`FAQ.,A3HA(.,ApJA0..,ARLA8.,AOAu.,APA!i.,AQAR.,AQAr.,AoRA蕏.,A`TBSA8V.,A hSA .,ASA.,AQSAHPI/,AOAH/,AhOAPZ/,AaNA/,A [MAx$/,A LA(0,A`JA)^0,AFHA˷0,A@ƘGAMu1,AHA;1,A UIA'lvWj2,A$&JAA2,Al_bW KAV3,A /KA4,A`LA(o4,A`ձMA4,A`NApw5,A3PAPwD7,AUAȓ6,A cXA_6,AGZA6,ANw[Ab6,A[A†7,A@]Aۜ7,A`B]Aؒ7,A[A7,AZAp7,A`ZAp!8,A [AC8,A ZAnb8,A0YAN8,A`2YAu8,ARYA8,A`f ZA8,A`ZA8,A`VZA`8,A |RZA)9,A@gYAk,9,A aYAأ9,A YA9,AVXA)9,A|XA59,A cXA8;9,A3XAkF9,A WA T9,AgWA\9,AHaWAc9,A@ [WAs9,AWA9,AWAB9,AaXA9,A XA9,AXA9,AXA9,A XAp:,A)XAz0:,A`4YA#S:,AYA(s:,A YA:,A`{ZAW:,A3ZA:,A^[A:,A[AV:,A[A`:,A=\A :,A\A8:,A \Aϣ:,A\A:,A]AX:,A<]Ap:,A,e]AБ:,A@]A؟:,A u]A7:,A ]AW:,A ^AXW:,A@ $^A˵:,A@<^A:,A`U^A :,A@n^Aӹ:,A`^A( :,Ap^A:,A^A :,A`^A:,A^A:,A ^A :,A |_A8:,A_Ap:,A@,:_Ap:,AW\_A@N:,A`f}_A:,AE_A:,A _Aj:,A2_A;,A_A#;,A`A@N;,A+`A;,AtF`Aȃ;,A_`A0&;,A@ x`A.;,A@`AL7;,A `A@;,Aྷ`AH;,A`A`R;,A@`Ap `;,A `A n;,A`A|;,A@>`AO;,A@`Ap;,A`A@q;,A`A`G;,A `A;,A`AK;,A&`Ag;,A`AX4;,AaA0;,AaA;,AR$aA.;,A#.aA¬;,A`5aAt;,A ;aAx:;,A`?aAp ;,A |@aA;,AJaA+;,AVaAX;,A baA(:;,AdpaAp;,AaAh;,AaA ;,AޞaA c;,A@aA@;,AaA0;;,AaA`;,A@aA };,A@aA;,A QbA8';,AbA;,A@+bAD;,A;bAhD;,AKbAP;,A[bA=;,A`]ibA;,AbA;,A cApI;,Ax[cA3;,A`cA؟;,A`cA;,A dA;,A@>HdA;,AqdAG;,A`dA*;,A dA0;,A`)dAXl;,AeAʿ;,A@eA;,AieA;,A`)eA;,A eA{;,AeA[;,A@fAأ;,AFfAQ;,AwpfAa;,AfA;,A@fA;,A@gfAS<,AgAp[<,A4gA<,AXgAY <,A&|gAK)<,A4gAЄ2<,AgA<<,AgAj?<,AgAE<,AgAȵV<,A@`.hAPZg<,AzahAp<,A)hAp,<,AhAHP<,A OhA$<,A SiA襟<,A)iA@Ϥ<,ACiAH<,A@P_iA <,A{iA<,AiA@µ<,A iA0̸<,A iA<,AiAPٻ<,AviAD<,AjA@<,A6jAp<,AVjA<,AxjA`<,A /jAH<,AjjAH<,AkA(<,A;kA07<,A |nkA <,A@ϢkApA<,A@ kAX,=,A@!lA0 =,A ElA8=,A|lAp=,A lA=,A@,lAO'=,A#*mAx.=,AemAН5=,A lmA+<=,A mAhQB=,AnAH=,A\nA`M=,A0nAHR=,AnAV=,AoAZ=,A@[oAu^=,A oAa=,A@oAhod=,A pAf=,AapAh=,ApA#j=,ApA/t=,ARqA~=,A@CFrA N=,A rAa=,A+sA̟=,ATtA=,AuA=,AڰuA=,A@^vA_=,A@rvAX9=,AvAЦ=,A`vAH=,A 1vAB=,A lvAu=,A@vA E=,A vA@=,ATvAʷ=,AwA=,AwA=,A`$wA#=,A`1wA=,A68wA/=,A@y@wAߓ=,AJwA=,A`DVwA`=,AcwA{=,A rwA@Ot=,A@wAl=,A`wAe=,A@wAa=,A@wA@^=,A ~wAZ=,A[wA8xW=,A-wA@T=,AxAOR=,AFxAHKP=,AX,xAN=,A@AxA0nM=,A`WxAL=,AFnxA(-L=,A@ƄxA)L=,AVxAL=,A@رxA]M=,A,xAN=,A6xA0.P=,AxAhT=,A ZAyAZ=,A`fyA_=,A@yAȥe=,A`&zA#l=,ARrzAp=,A zAؒt=,AzAPx=,A@{A+{=,AN{A }=,A`{A=,A {A =,A`{A =,A|A@=,A*=|Ax=,Ae|AΕ=,A@|AR=,A|A3=,A|Axm=,A$}A=,A |$}A8V=,AhR}A@=,Aq}AH)=,A}Ah=,A}Ax=,A~A=,A)~A 0=,AQ~Ah =,A,x~Ap==,A@~A}=,A ~A`>,A~A >,AA`>,A|'A>,AMA >,A@sA%>,AAPI2>,A`A2>>,AHAI>,A֠AT>,A A8_>,A SAd>,AAi>,A;Ao>,A8߁Au>,Ap A2|>,A@8A>,A`]dAp8>,AA>,AA>,AGA>,ABA>,AĥA>,A A>,A oA>,A@A>,A@߲A>,AՄA>,AA?,ADA@?,AQCA?,A@hA ?,AڎA` ?,A0A ?,AۅA ?,AA8#?,A )A#?,AzPA ?,AAl?,A` AC?,A@WeA?,AAT ?,AA ?,AyAЄ ?,A9A ?,AA ?,AA?,AʈA?,A`܈Ab?,A@%A?,A]AX>,AA>,AgAB>,A,A>,A`"7AЄ>,ABAz>,A`)PA>,A^A3>,AnA@>,ATA&>,A A>,AA>,A A>,A*܉A>,A Av>,A@E Aa>,A` CA>,AafA>,AA>,AAh^>,A}ӊA8>,AA>,A@y Ae>,AqGAȺ>,AnA>,AוAXy>,AAԶ>,As;A>,A`TAHܶ>,A3Az>,A@A@>,A@QAH>,A͉A>,A@Ap=>,A@A(>,A'A >,AXUAp>,A`A>,AA?>,AiߎA88>,A@ A>,A`2:A>,A`fAм>,A`KA>,A@ AP'>,AFAt>,A#ϐAw>,AXAH.?,AA;?,A A`?,A9A?,AeA`?,AǑA?,AjA?,A@Au ?,AAu ?,A=>A[?,A@Ah?,AA?,A`ZA"?,AA&?,AA*?,A 8=AR.?,A3AU1?,AA3?,A4A5?,A` GA`7?,A?A8?,A͖APY9?,AA09?,A@WTAp=9?,A×AT9?,AA@9?,AAAXF8?,A`mA 7?,A@5A5?,A?A3?,AmAp81?,AA83?,A3A?,AӛA ?,AA ?,AUAP?,AHAh>,A`T֜A >,A A(>,A@TAX>,A<A&>,AwA`%>,A@A>,A AAHK>,AR*A8t>,AfA@>,A@A>,A AP>,AAHz>,A]A0>,AA>,A`ڡA>,AA̪>,AXA>,A`ӣAC>,AA2>,Ao2AP>,A`aA Ҭ>,AA>,A Aɫ>,A`AЦ>,A@Ap>,A@ OA0%>,A`}AǤ>,AApΣ>,A`KץAp>,A/A@>,AyA>,AA ޝ>,AGA(>,A ۮAHܜ>,AA0f>,A`ZAe>,A螨A0ٮ>,AmAп>,A;%A`>,AFgA`>,A@A>,A AH>,A &4Ap>,A2A>,A ̪A8V>,A}A8g>,AhA`?,AAhQ?,AA)?,AVA?,A ݬA8E,?,A6eAA:?,AMAG?,AvAU?,ARA a?,A8A ^n?,AAmz?,AA?,A@>*A`?,AHAP@?,AA,?,A`̳A8?,ATGA0?,ARڴA?,A`A0?,AXbA?,A AA?,A`)A;?,A1A?,A]wAh3?,A@ᚷAL?,AA0.?,A Af?,AӺA#?,ARA8@,A`)ֻA8 @,A][AX@,AઞA83@,A Af@,A4A&@,AA;@,A ⮾AD@,A@(AzZ@,AAf@,A)6Af@,A`fAǤ@,AAp@,A A`a@,AA@,ARA0@,A` AP@,A4A0.A,A`AH\A,A`AXA,A`JAp=,A,AA#aA,A UAXA,AGApkA,A@AA,A͞AQA,AHQAA,AAzB,A QAB,A=A؞&B,AHAW3B,A`ÁAPf@B,AA`IB,A^AP8UB,A@gAfB,A qIApB,A@>Af{B,A ÂB,A|A`aB,AA)B,A AأB,AAB,A`)DAB,A@ApB,AA)B,A`)@AP8B,A@AB,A@gAB,A@A̖B,A aAB,A AѣB,AA8B,A ApB,AߡAB,ARApB,A AfB,AA8B,AA)B,AA C,A:AC,A A`"C,A qA8'C,AeA8'C,AA&C,Az,AG:C,A4AWA`@C,A AIC,AqA`VC,A oAhC,AA`uC,A`fA0C,A \AAC,A AC,AAWC,A8ALC,AAH\C,AAfC,A@ALC,A`AC,A@AD,AAD,A`AD,A`A!D,AAH&D,A AB(D,A Ap8(D,ArAQ(D,AzA'D,AA("D,A`)DAuD,A]ApD,AA D,A |XAp D,AAp D,A-Ap8D,AA8 D,A`A D,A@>DAD,A`'AD,AzAH.D,AR*ApC,AzAC,A]A#D,AaA#D,A Ap8'D,ARAH.2D,A=HAH>D,ARtAMD,AHAHXD,A A8jD,AA(\xD,A AD,A A(ܓD,A 9AGD,A@>AD,A@AD,AzAfD,A]AtND,ATQAaҕD,AX'AD,AGɐAjqD,A"o%Ahh E,Ac]AlqE,Af AB.E,AAXEE,AAp=]E,A5ApE,A qoADŽE,A@>A`aE,A@><Ap E,AA؞E,AA`aE,A`)NA0.E,AAE,A3AE,A\ApE,A)AWE,A ABE,A3A`E,A`f4Ap E,A dAfE,A=AzE,AåA8E,AAѹE,A3A`E,AA(E,A 1MAp9^F,A`AXG,Ap AG,Aa AuG,A_E!AG,A@W!AG,A "AH,A@"AKH,A0[#AKH,A9#AG,AiX$AG,A*$AG,A3$AۿG,A $AG,A,%AG,AU%A`G,A%AG,Ak&A)G,AĂ&Am H,Ad'A# H,A(AȩG,A0(AWG,A@߾)AG,A~*AGG,At+AG,A4,AxG,A`b,Ap_G,AS-AG,A-A+G,A<.AG,At.AQG,A`/AAH,A 0A8D]H,A1AyH,A`2AH,AĽ3AH,A4A H,A |5AH,AN5AH,AR6AXJH,A6A0݉H,A@7AH,A)8AzH,Ao8AsH,A@i8A8\H,Ac39ADH,AO9A(~(H,A l9APH,Ao9A@G,A s9APH,A9A* H,A<:A@H,A:ApH,A ;AP,H,A5X;A?H,A ;AVH,A;AKXH,A@;AKXH,A@S<A]H,A<AHiH,Aӽ<A@uH,A4<ARH,Aw<AAH,AL<AبH,AL<AH,Az<AjH,AV<AfH,A<Ap I,AV=AI,A=Ap,I,A{>A\7I,A 7?ApwAqQL,A wAPL,A-xAXdSL,AךxAbL,A`yA(OlL,A (TyAnL,A&yA( ]L,A^wzAGPL,AzA)ML,A@{AhbLL,Ajm{AOL,A?{Ah[L,A6{Ap=`L,A#|AaL,A|A/bL,A|AHdL,A }AoL,A ;}AvL,A`]}APwL,A}AX~L,AsL~AL,A}~A؋L,A$A/L,A4cAjL,A;AL,A@ZA0L,AA8ҩL,AAHL,A?ACL,A!AEL,A JA L,AtAM,A xAx(M,AuAM,A QA(M,A 볂AM,AAM,A%AM,A]A()M,AaAM1M,AA8M,A6A4A, M,A cAeM,A`<A0"M,AwaA%M,AmA8g&M,A އA (M,A` A[/M,A` $A(m;M,A!A`eRM,A`A `M,A (@AXhM,AeAPkM,A예APkM,AЈAPkM,A}ArM,A4AXwM,A sJA |M,A`iAM,A3AM,AQdA0%M,A AM,AAhoM,A9AxFM,AzA0M,A AM,A@i{A(M,AE(AM,AApM,A&Ap+M,A AM,AA@M,AAM,A@NAؒM,AA@׶M,ATAM,A^6A`oM,A@KAgM,AŔAheM,A<APkgM,A@hAPvM,AAoM,A3VApM,A`KAPZM,AjAH\M,A AM,A@*AM,AAxM,A@9AM,AADM,A@A M,A@GAx)M,AAbM,A)AM,A"bA M,A6AM,AAM,AaBAqM,A`խA:M,A ApM,A8A9M,AcA M,A ŢAM,A`ANM,A s4A N,A`]MATN,A@lAxN,A͞AX9N,A AhbN,A@AN,Ag֣ApxN,A ٣AGN,A Z A@` N,AOAXN,A AN,A`դA` N,AH]AN,A}A}?N,AA3gN,A#A`GzN,ATSA-N,A`KAN,A0AyN,A`msA&iN,AgħAUN,AXAKN,A5A08N,A A/N,A6rA\.N,AǩAHn:N,AA8N,A@A8N,A`TlA0;N,AAhbCN,A`9֫A8INN,AAQN,A2AxhXN,A@NAxhXN,A@[A PN,A0BAFN,A`AEN,A 2Ah AN,AAh AN,A OAh AN,A ZAx>N,A@`سA:N,AvA(6N,A`9:A!(N,AApN,A`K AM,AALM,A A(M,A$ALM,A}AM,A?A(M,Ag&A`M,A@ nAM,AAM,A@ߟAHPM,A`dAۑM,AHAM,AsGA(JM,A srA@M,A`<A:M,AA M,A 6AHM,A AM,AHA7 M,AAƨxN,AEA ON,AAo}"N,AGAH l,N,A,BA 4N,A6HuA".f:N,AAA#F>N,A"Abo,?N,Ax"AS>N,A _Ar];N,AB"6A;N,AA4EN,AV@AE;ON,A"ݗ`AkY[N,Ar= zAgN,A[AuN,Aq.!)A8sN,AA(VB!N,A `AO70N,Al_AF¡N,AiA{N,AW65AmN,Aׄ4A(,\N,A OWA0N,A`A跍M,A0(AHDM,AA tM,AnA8M,A@AfM,A A7M,A eA}]N,A`ܪA`N,A AЮO,AAuO,AA N,A@AO,AhJA(Q,A?AS"oQ,A!.A9%Q,ARiAwQ,AY8A=Q,AMOdA=V*'R,AgA:)R,AJA [i?R,A -A!UR,AP AiR,A0D)A|R,AJ1VAR,AྎAo R,A_H2AuwR,A5AgtR,A3q!]AS,AAe#R,AV?AQ,A^A@P,A`AQ,A`A*R,AApNS,A'Ah^S,AMA4QR,Av&AtR,A@A\_&R,AXAiR,AǢYAOR,AcNAR,A"A=R,A#iSJASR,ANAX%)R,A㱭A,*R,ARmAR,AA61R,A&RAe8R,AJ7߈AS,AgA S,A#,A&{3S,Ayr+ AbJS,A1GA`C`S,AZAزzoS,A%A1l]zS,AAoAHS,AAnȇS,A"_BAaBS,AAVS,AqogA}S,AގPAUwS,Ai jףAXujS,A`;AqgSS,A)6Ak̗?S,AڊA.S,A/A&S,A`TAR,A`"lAwR,A@AƈR,AAxR,Aw Ah|R,A`]$AR,AAR,AAS,A A(S,A|HA`#S,A@,A)HS,A sAXhS,A`AȊS,AA'S,A uAS,AgAT,A~AִT,A iAU,ALAiU,AAe4V,AߜiAB"V,A Aa8BV,A׀nN.At`V,Avc|dALά{V,ACQkAfV,A@AfHZV,AI&aAnV,AAƓ锝V,AA_B V,AUA2V,A?xAuQ V,A{IlAV,A HAfEW,A;;4A+W,ABAfPiW,AkAЭ\ZW,A%JAW,A! AԇX,A(:A[)ZDX,AS[P]Ar=[rX,AMնA9X,A녨Ag6X,A=lAX,A@E?AX,A1AjX,A58AX[X,A=AvK6X,At0AnX,Ac|ABX,AaNAzX,AA 6X,Aι0AbUVX,Aw?AX,A@2ACaX,AfKjA-X,AHAݢX,AAX,AFAX,A -A/U?X,AeUA5!3_X,AAlX,Ay.AoX,A 5^A0 Y,A A(:Y,A\ AHY,A A(Y,A@` A@Y,A, AY,Aw A*Y,A@ AY,A`A!Z,A@ AHm/Z,A@r#A(K1Z,A`/A5EZ,A`ܮA YZ,A FA2fZ,AA'pZ,A`A9}Z,A?AZ,AAPx~Z,AtAZ,AHAKZ,AAZ,A/AZ,AApZ,A#gAxZ,A}AZ,A AZ,A$AoZ,ANAp[,AQAW2[,AʃA8P[,AAa[,A@|Ahs[,A AX[,AA[,A2A[,AHAX9[,A0VA#[,A@A[,A)JAp[,A6Ai[,AJAG[,A`A` [,A`FA \,AA \,A( Ax"\,A; A#\,A@yA A %\,AsF A;:\,A AM\,A7!A@_\,A}!At\,A!A\,A@GL"Ad\,AH"Aq\,A$"A\,A$"A\,A#AH\,Av#A^\,Av#A\,A#Az\,AHE)AP\,Ar)Ay\,At)AС],A`B)A 4 ],AT*Ap],A+AI],A+A`],A@K,A8t\,A,AT\,A`N-A8\,Aa-AH],A -Ap],Av-A 6],A w.A,;],A@5.A?C],A p/A2M],A 0A^],A]0Af],A 0AUb],AZ1AR],As1Aȶ;],A`bv2A$],A@N2AD],A5A[,AqGQA0&AZ,A@PZA(VY,AVbA@W,ApqA0̓T,A@Q|A@NR,AfA`6Q,AdA(Q,A4A(iQ,A qA R,AWAVR,AAnCV,A AV,A gA@5bV,A_c A *V,A A*V,A AU,A@ AhU,A`# AU,A AU,A) A|U,A A_ AU,A A(U,A ۬ A(U,A@5G AeU,A` AU,A@ϔAU,A#4AU,AA8ֻU,A@EzAzU,A`AU,AAcU,A(AU,AzA NU,A AjU,A`mAxuU,A OA6U,A@)AzU,A@yyAxuU,AAU,AE AxU,AŗAZU,AA0U,AAU,A`"AHiV,AWAH{V,AAx!V,A@!TA``)V,A A6Ah@GV,A$A@fV,A3ApkxV,A[A~V,A@jA$V,A@A8V,AMA(V,A@pAV,AKA9V,A#AQV,AAsV,A`UA0V,AAH-V,A`A =V,A$AV,A@`A`V,A,AȔV,A?AqV,AAPJV,A`\A#V,AAxU,AAU,A0AhUU,A (ArU,A;AU,Aw?AV,A`Ax!V,A /A``)V,AaBA *V,A(A-V,A` AU3V,Aj!AU3V,A@!A-V,A`9"Ae$V,A "Aȩ V,A (0#AV,A|#Az V,A`@$AH V,A n$AU,A%AXU,A`%A(U,A@NO&AU,A&AxuU,A`w'AجU,A`'Ax~U,A^(A"U,A)AaU,A`)AXU,A`?*A U,Ac*AP|zU,A`g+AأsU,A',AoU,A ZO,AtU,A,AȵU,A ,AOU,A,AX(U,A1-AXU,A`t-AgU,A-A舌U,Ax.AXJU,A.AU,A`2?/AjmU,A]/A(]U,A`u/A;]U,A Z/A@1gU,A9/ApU,A/A8ĮU,A /AU,A/AHV,A/A *V,Ays/AKV,A/A7mV,Aø.Ap}V,Aj.AxKV,Aݟ.AV,A}.AV,A 2/A8V,A/AV,A|/AV,A`0AV,Anl0A5V,A0Ap_V,A@0AV,A0AV,AxQ1APoV,AMy1AxV,A 1AhtV,At1AKV,Az1A"V,A@1A*V,A1AU,Ad1AU,A`K1A(U,A42A(U,A`d2AxuU,A2AU,A3AU,A q43AU,A&z3A0U,A3AgU,A@ 4A U,A%4A舌U,A`B4AqU,Ag5A=yU,A 5A_U,A@56AFSU,A`6APAU,Aq7Ah?U,A7A>U,A/8A`8U,A98AJ2U,A8Ap"U,A^9A0&U,A@:A0U,A:AHzU,A;N;AHzU,A;A;U,A``<A(T,A <APT,AJ=AT,A@`=AtT,Aq=AT,AF=A3T,A@,=AثT,A@=AߦT,AI >ATT,AD>A'T,A9>AT,Ay>Ah"T,A#?AhT,A ?APT,Ak@APxT,Av@A@T,AAAmT,A@y!AA(T,A |AA@T,A5BAU,ABA06 U,AtyCAU,AFDA@T,ADAT,AYEA84T,A EAhT,AFAT,Ak:GAT,A`GA(OT,AHA'T,AIA9T,A`fIA8T,AIAT,A JAh"T,A@iJApT,A IAT,AIAdT,AIAYT,A}IA\PT,ARJAP@MT,A JA8IT,A4KAKCT,A?LA9T,ALA0T,A }MAX+T,A@NA@)T,ANNA@)T,A hOA')T,A` PAe*T,A@PA!.T,AWQAx4T,AQAHrU,AXAFSU,A0YA(_U,A@YA;]U,ANZA@AXU,A3ZAYU,A}[APPU,A`T>\A}=U,A6\A`a:U,A@p]A;U,AX^A}=U,A^A@U,Ay]_AHU,A `APPU,A@`A ZU,ABaAdU,AaA+lU,AbAhsU,A*cA_wU,AcA{U,AqdA0wU,A`TeAqU,AeA\U,A fA͈U,A |VfAgU,A@GfA@U,A@gAH\U,AEhAx~U,AhA U,A iAwU,AaiAoU,A@5iA(jU,AjA(jU,AK)kAMjU,AkAhiU,AllA(jU,A?mA+lU,AmAkU,AaWnAbU,A3nA@AXU,AoAVDU,AzpAx-/U,AkpAT(U,ApAU,A pAU,Ag&pA@T,A9XpAHT,A`rAhU,AaAkU,AxAV,AEtAeV,A3_AYkV,AkA|V,A0٘AEV,A AP V,A (AYV,A@5ÙAV,AbAV,AA(V,A(AIV,ALAIV,A ڜA(V,A2AV,A@!A'V,A`žAh"V,A$gAX(V,AAP8/V,A?A #-V,AKAp,0+V,A>A8k(V,A.hAH^1%V,A/A Y"V,AGA䎦V,A^A9pMV,A˦vAAV,A9OA iV,AMSAfmV,Al0AM V,A֒rAV,A<A bhV,AE4A:w`V,A|GAV,A*AX(V,AaA V,Af&A V,A2942A>GV,A`;A4V,A=CASV,A r?IAWPV,A,LA4V,A}=MAWgV,AU>LAN"V,AZHAwS&V,A BAg(V,Aop:A +V,AEJ0Av-V,AĨ%AP8/V,AKA.V,AKAV,A dAV,A@TAV,A`A@$V,A9AV,A@9A`XW,A`[ۣARW,AॄAo W,Aq+AH W,A?ҥA W,AqAW,AA!W,A`A)W,A@ WA@1W,A`A9W,A0AsAW,A 7AIW,A֪AQW,A@.vAXYW,A AXYW,A ~A[W,Ag/A`aV,AнAV,A-ACMRV,AũvAb,A;A0jJb,A^Ac,A 8`Ac,A@,tAXc,AЩAxc,A@A]c,A"Ac,AWAc,A<A(9c,A@Ac,A`̭AX9c,A`KA"c,A JxAc,A=iAc,A6Apc,A Ax:c,A OAc,A@Ac,ApAc,AA҃c,A Atc,A@iAhc,A`[AZac,A QA@~\c,A@3A8VTc,A AMc,A;AGc,A A6c,AA}!c,A`bAHc,Ad%Ac,AA!c,A"A !c,AOA@*c,AEA4c,AAp8tc,A`mAc,A`ZAmc,AtsAc,AiAlc,AAA%c,A3*APod,AA{d,AApd,A`AId,A`Ad,A`ofAd,A=A:!d,A`;A1d,A`K]A`?d,A`fvAwKd,A@yA \d,A PANmd,A3,ACd,AAhd,A A0d,A'Afe,ATAX.e,AAxXKe,AA8Dhe,A HAye,A_Ae,AtAie,A Ap xf,ADA`Ts,A@0A u,A:5A u,A`2APu,AQ3A v,A5AP^:v,Aͣ6A(bv,AQ7A5v,A9Aw,A@9AHEw,A@:A8Ew,A 9Aw,A9APHw,A9AP&w,A@W9AhQx,A 9A>x,A 9A@*x,A`J:Ah!x,A@Ϡ:AXw,A`2;AxKw,A ҅;Aw,A;Aw,A (;AhQx,A;A2fx,A<Axx,A<A(Kx,A<AXx,A" =AUx,A=A>x,AU=Ax,A}=APx,A`<A@x,A`<A0x,AUs<A0x,AA<A(x,A$<Azx,A Z;Acx,A;Ax,A;Anx,Av;Ax,AW;AQy,AOK;A y,AM@;Axy,A`&;A(m#y,A:A@$*y,Ak:A_+y,A::AX/y,A:AP6y,A9A 1AN{,A@1A{,AtF2A7{,A@q2AL{,A2AP8{,A@3A`{,A H3A8{,A'3Ax{,A`3A{,A&4Ae{,A@`4A{,A=4A`G{,AB4A0&{,A 1Y5Aĩ{,Am5A8b{,A5A {,AD5A`C{,A "6AW{,AK6A ={,A*h6A{,A`}6A }{,AM6AP{,A6AP{,A@`6AHK{,A 7A{,A r7A^{,Aկ7Aأ{,A7A{,A`[8A{,AT8AH.{,AR8A@~{,A u9AP͜{,A@9AT{,A 8g9Ax{,A#9A`{,A`9A{,A@6:AHЄ{,As:Aj{,A@r:Ap{,A,:A'{,A@:A{,AM;Aoy{,A;Aj[{,A`s<AYN{,A=A@FA{,A=A.6{,AX\>A`-{,A@N>Ap=){,Aq.?A%{,A0?A${,A?Apx {,A|?A~{,A?A{,A ?A{,A @A0*z,A@Ahoz,AyAAxhz,A"ZAAU{,AƨAA {,A AA{,A@N=BA{,AfaBA,{,Aq}BAL7{,A JBA,?{,ABA>{,A@BAX4>{,A(BAX@{,A[CA(D{,A`m%CA*S{,A@CA`h{,AAMCAz{,ARCA{,A`QCA07{,A bCAX({,AmCAź{,A?CAhQ{,ACA{,ADCAP{,A`KCA`O{,ACAp|,A`KCA@p|,ACA,+|,A`"DA`5|,A 9DA>|,A]DAM|,A|DA_|,ADASx|,AvDAZ|,A@.+EA|,A`;EA#},A /EAH J},A EAs},A`]EAP},AvDAH},ADA},ADA#~,A`vDAxB~,A+DAc~,A;DA~,ADA@~,A DAh~,ACA@,A@iCA,A FACQۯ,A;4%hFA 7,Az#^FA@>,AFA跤,A` GA{,A%GA=,A?pOGA긢,A  H#GA*:RǢ,AOV,GAΆ3,A0;5GAŪ,A%{=GAx,AEGA_,AGAHCԄ,AcGA(ބ,AGA@,A^HA83,AGAЦ,AGA },A@GA(1 ,A`̛GA,AOGA,AFA$,A FA5,AAP&=,A6<AE,A;AG,Av;AfO,A`n:AT,A :A@X,A@ߴ9AnZ,AH9A8\,A8A\,A,u8AY,A"8A@S,AO7AI,A`K#6AHI,A)5A0D,A4AX8,A 4A0,A4A$,AP4A,A4AP ,AT4A*,A4A,A4A ,A@53APل,A3AЄ,AK3A`XDŽ,AXn3AX,Ab3A,A `3A ,AfV3A堄,AUH3A8Z,A*3Aˌ,A`2A,Ao2As,A`P2A)|,A$2A0z,AE1A{,A[1A,A1AXh,Ay1AH,Ad1A0,A@81Ad,A %1AH,A1Ay,A1Aq,A@0Aj,A@0Ak,Aʔ0A(r,A`0A(Jz,AH00A@$u,Aޒ/Ahx,AO/Aez,A/A`r{,A.Ak,Aה.Aτ,A@.A,A /c.A,A--Ab},A߻-A|,Aʞ-A{y,AH -A_u,A ,AGn,Ac@,ABh,A@ ,A_,A ɻ+A@W,A@ߍ+AU,A}}+A0LO,A>+A\I,AR*A9,A)AX 6,A_)Ap,4,A@)A@.,A X)A(.,A%)AX ,,A J(Aг(,A6(A&,A+(A,An'A_,Aj'A,A@5G'A ,A&A ,A-&Ap ,A`-&Az,A%A,A%A ,A $A ,A@p$A@_,A[g$A),A[$AJ,A`Y$AP%,A`bm$A',A$A _*,A6$A-,A%A3,A &AL9,A A]'AA,A'AN,A@K(ApJO,A`](AHU,A l.)Ah@[,AV)A/{,A+A,A=b+A0H,A@.+A`6,A`),Ac,A#-AX,A`]V-A,A -Aɬ,A-Ah@,A .A ,A Ҋ.Aų,A@]/A,A/AHÄ,A@W/A(Մ,AO0A,A`l0A,A0A,Aj0A,A0Ar,A0A*A؎O,A*APU,A@%)AZ,At*APW,A;*AhX,A@k*A[,A u*A `,A*A0a,A*Aъ,A3)AX,A`f&)A,Ap(A&,A-(A,A`)A,A5)Ah,A'[)AQ,A*As,A`+Af,AG+A_,A3+AO,A`<.AxK,AOM0A P,A1A:V,At2A0T,Aj3AuS,Aa3ATU,A`3Ab,A;4Af,A4Axk,A5A(lu,A)5Ax:,A`Dd5A,A@>|5A0X,A`]f5A,A@M5AHʊ,A@r<5A`׊,A5L5AH,At5AZ,As5A#,A$5Aq>,A 5AƤ,A`5A0,A5AE,A@5A01,A@y5AxX,AY5As,A`5A,A 4A0,A4A c,A@>4AG,A$j4Ap,A<4AE,A$4Ap[,A#4AS,A'4AX׺,A '4AŌ,A#X4A,A`4AHP,A4Ah,A`)15A ,A5A,A5A`,A`G6A@,Ak7A N,A`7A8,AzS8A,Ar8AF,At8A0]ތ,A`b8AHř,A 8A@qŌ,A#9A0jČ,A@x9ANЌ,A9AՌ,A9A,A09A0,A` :Ax\,Az:A{,A`:APw,AG;A,A;A,A<A,A Q<A0,A`<A,A =A],A<=A,A@d=A,AM=A,A=A@0,A q=A ,A =A@ڌ,A >A8Ό,A6>A,A5?AN,A@#BÁ,A=XCA8҆,A3zCA7,ACApJ,ACA`C,ADAė,A`m+DA,A@CAz,AhCA8,AbCAÌ,A:CAPˌ,ACA@Ԍ,ACA,ABA,A#CA ,AZCA,A`CAc*,AiDA7;,A@`DAH,A ɻEA([K,AFA ,AtHA0G,AHA[,AHAk,A HAh,ACHA 0,A (GAX,A uGAR,AәGA 4$,A iGAP/,A`mGA8,ApFAOR,A}FAȺu,AFA},A@>=EA,A`NDA,ACA(2,A tCAU,A`BAq,ABA0j,A /BAHK,AAAܑ,A @A ,AF?A%.,A lr?AxG,A`v)?Aq`,A>A0n,Az>A r,A>A7,AF>A(,A@>=API,A =A,Ag<A <,A<Ae,AMH<A,A` <A,A ;AE,Aq];AHy,A;A i,A@i:A_,A9AV,A`T=9A>P,A8AT,A&7A^,Ap:7An,A 6Aq|,Al6A[,A8,6A2,AX5AH,A5A,A5A,A`T5Ap,A 5Aג,A@#6A!,A@y*6A,AX96A,A?*6A =),A`5An,A5A#q,A`5A,AZ5A,A@&5A,A 4A/,A4Ahb,AR4A@,A`24An,A,4A,A63A },A=3A,ATx3A8ż,A`63A <,A@2A/,A2A`!,A2AB,A@#2A88p,A`T2Ahe,A@2A[,A2A84],A02Ah,A@2A%t,A y2Apy,A}O2AKi,A@2A@Y,A2A U,A (2AV,A1A'M,A@ O1A=7,A@i&1A(,A & 1A3 ,A0As,Aॐ0Aw,A`d0A,AR20AP<,Aj 0Af,A/A(> ,A0A+$,A@I0A]B,Af0AHrL,Ah0AXQ,A`2D0A S,Aq/A@$W,A /A^,A`O/Ap,A@.Ay,A@ּ.AXW,A .AX,AN.AC,Ai.Ap,A|z.Ad,A6.Aȓ,A6.A8Eԓ,A S.Arܓ,A.AX,A@.Aȱ,A`v.A8 ,A.AN7,A9.AcS,A`].Aȃl,A.Aw,A}/A@…,A&/AՑ,A/Ah,A /.A,A /AH̰,A@E/AX˔,A /AД,A!0A#ڔ,Ac0As,A 0Ag,A$51Ap,A`;1APG,A1Aw,A` 2A8,A2A,A2AH,A@1A,A`Ә1Aƕ,A/X1Aɕ,A`0A0{ĕ,A0AHл,A@y0A ,A0Aۨ,A{0A`O,A[0A0,A@NU0A ,A`]0Aŕ,A,>0AHKӕ,Ah/A,A[/AO,A.A,A@߰.AHK,A.A0ٕ,A{.A,A-A8,A -AM,A`,Ah@ ,AR+Ak,A@N+Ap,A``+Ahה,A C+Aذ֔,A+A8',At+A,A@'+A ,A`!+A,AF +Av,Aw*Ak,A*A#,A#'*Abޔ,A )Axܔ,A J})A,A2)AXu,A(Aj,Ah(Ac,A஧(A,A (AxX,A`(A",A@a(A,A (A,Az'A@,A^f'A8g ,A@<'A,A'A`$,Ay&A]1,A&A9,A cB&A >,A`%A:,A@>%AH)2,A $A(,A$A",Aw$A(,AWM$Ap00,A@,$ATB,A@,#AV,A`#Ac,A`v#Ak,A#Ap}q,A O#A0w{,A@`#Ah,A u#A⇕,AN#Ah,A4 #A@,A3"Axv,AӉ"A(,AV<"Aܦ,A@."AW,A !Aذ,A3!A8t,A[!A8ɥ,As0!A8D,A \ Ap,A9 Aث,A 8wA׶,AA,A`A(,AAa,AA,A+A,AN|A,A`7A,AA,ARAXJ,AՐAp,AdA,AB#AQ,A`A8,AA,A`TLA #,A@-A~,A*AU,A A={,A A(r,AfAx k,AAJi,AAd,AnAa,A3Awe,A`AXj,AAo,AAht,ApA ps,ANAp,AoJA3l,A`Aȥf,A`A@d,A`AHW,AtAH,A`A(m*,A AAxi,A@A8,AzA@~,A AL,AAB,AAP,A@EAX,z,AFA`h,AqA#U,A40A@,A@1AЦ,A6AkΓ,AA`,A;A`,A]A͟,AZ/A`X,AaA ,A`A6,A-A(܆,A@sAy|,AGAwk,AAW,A`A/,AAXV,AA@,A$A,ALAP̒,A@Aj,ARhA`ǐ,A *AD},AEAm,AAhb,A@Ax[,A]A Y,A /Ax[,A-A`,AApa,AAgf,A@AhDm,A Apq,AA{,A?A`2,A tA,AҶAX,AAy,A@5*Aʒ,ANA@,AlA_,ArA *,AWA@WQ,A=ATp,A/A,A ZA@ӓ,A4AAط,AAۓ,ÃAB,A$AI,A@Ad,A@Afy,ADA*,A ^A,A_kA,ArA,A}AsҔ,A@ƃAH,A AA`1,A6A,AAp+1,A@AE,AA1X,A^Axvd,A6Aim,AxAu,AXAh{,AoA~,A`LAY,A`APk,AAhw,A@i>Ahsx,AtA |,A@yA䂕,AA,AټAhQ,A AP,A`DA,A |A,ATA[,A`)1A,ATA#,A@WA𒾕,AAcÕ,A JAXϕ,AIA֕,AszA(ܕ,A A`rܕ,A$:Aӕ,A@sAHiڕ,A`]r"AP&,A H&A,A H&A^,A)AQ,A޽)A,A+A,A`m+A8,A@k&AT>,A@%A@=H,A ."AhM?,A"A,A &A,AX#AX,Az&A`SU,A@,%AHr,An$Ap,AE<$A7Ü,A6#A,Am#Ax+,A >#A ĝ,As!Ax,AQ#Aܟ,AkO A,A@y#Apa,A~AP,A@iAm,A`)AxS,A)Aؠ,A AmI,ABA`p,A@A0,A 1Ap,A`o:AT,A-A8,A>(A֣,A;l'A88J,A@Az,A l5AG,AA颤,A(kA,A *Axd,A`AH.,A tAY,Aw1Aޥ,AdAP+,A =A å,A Akߥ,A:%jNu A,AH AT1,A? A Bg,A AT,Aj AAFզ,Az A8 ,A A,A O%ApQ,A`AH]Y,AAv,AA,A AAPΨ,A?A,AAx$D,A4Ax,A@3Ap,A OA;,A,AH,A2-Ap9$,AjA,A ZAH.e,A;A𴶫,AOA@,A?A0ث,A`7A,A WA:,A8A,AA9,A)2A0 0,A@AD,A`DA2Ƭ,A@iAHi,A`2A,A@EVAP,A ~AHƭ,AzA@,A҂ Aȩծ,A ^ A,A`]AP,A&Ai ,A`mIA,,A@A@A,AYAJ,AzAQ,A Ah,AA_X z,AwBAخܤ,A -AVƟ,AAj0 ,A4KAÍŮ,AqXAxخ,A A,A`A?,A@!A*,A`KcAذ#,A@yAP>,A@`nAH;O,A-"AT,AXAT,AAbr,A TA0,ATAX,AྦA#,AA8ɯ,A@ZA,A@A(h4,AA(lW,AxA%Y,AA`Tb,A`)(Al,A|AEu,A`BAN},A$Apy,A@#^A7,A`ÉAX,A A,A 8wAb,A At,A Aȃ,A@A,A AIذ,AziA0w,A<AP},A<A0*,AWA),A A_,A`A7,AVA r,A4eAf[,AtAؒ,A`A[Ҵ,A A3,ApA,AoAPg,A@NKAHi,A$A8,AA`,A`A8ڷ,A Au,A@AXu,ADAX,N,A A(+,AA,AA/,AIAn,A`2A(-,A@5A1 ,AAٹ,A+Aq",A|A>,AA_X,A TA@,A^Ahs,A#(A,A`Ai1,ADApx,A OAN˻,A2A ,A\AؼV,AA,Aa Aoμ,Aa A(,A0AUh,AGA,A Ap ǽ,A cA,A Ad,A Aw,A`vA$,A pA\Z,AA(\,AVA8S,A@AX5M,AFAX5M,A ~MAX5M,A`AX5M,ADiA\,A s;Ap,A@yA`f,A|ApJ;,A@>mAP,A`A%ÿ,A`;Apƿ,A A,A8AXz,AfA1,A0AP,A sUA,A`AxK,AXA(B,Ax,A0]C,A`;$ARR,AA\,A" A,A9yA,A`vPAH{H,AHA,A Ah,AAH:F,A A,ǎN,ALFAjX,A~GA7#sb,A@A7Gpm,A.ݐABɴx,APy)A:m,A# Agǎ,A~A,A`A,A@A f,A 1A`ey,A@ϔAX,AtA;,A`1A~,AA~,A A~,A3}Ap,AA ,AfJA,AAh,A+HAV,AA(f,ApAm,AFAh,AM:Ae,A A,A`A,A`BAh,A@5Av,A`Ag,A`A ,A AP^,A`A},A^A,AA p,A@rAD,AAi,A,A@,AHA@,,AgoA8T,A5A(x,A@AΊ,AM"3JA7t/,AES\Aդ ,AjxvA#h/,A2|3A@_0,Axb)Aቾ,A)A,AZ A ,A`2AX,ANA,A`,A,A`7A,A`7AP,A A2AS',A $Ap|V,A?DApy,A ZAIi,A@A,A٣ A,A Z Ah,A`o A,AM A ;,A ' A8,AX A0,A ZY AP,Ah AX,AA,AAh3P,A A(Je,AA,A  A,AH A,A`, A",A@  A,AN As,A@WAH ,A/A`q,AA,AhA(9<,A@A( [,AAHs,AMA(,AAh,AvA,AyAH;R,A VA8,A`ܭA@,A AG,A~A ,A?A0jn,A Ap,A3CA8Q,A``Aح,A`BAˆ,A A`G*,ABAUE,Ah5.gAa_,A47YAi^v,A|1AA,AJVh.Af%,A*?A ,A ApΜ,AAH7,AK A 6,A AHn,A@'"A,A!A:(,AM#AC,AL!A`,A [!AH,AA0,AkAWg,AA,AdA ,A?A,A A(T,AA؟w,AVA,A`JAb,AA` ,A<A#,AAnX,AF AIb,A2!A,Ax A ,A A8,A Al,A@A8Q?,A vAC,A@tA0,AAP P,AAPoJ,AׇAPY,Af.Au,AyA,AA,A;A@,AfA,B,AzDA,A2vAPZ,A`2kAB,AWJA,A ZAE!,A RA),A@TA0*,A ˕A,A)TA@H,A`m{Ah,A`A8;,Aq A(=,A APV,Ax Ap,,A A,A A8g,AA,A5A,AA5,Az Ax,A@# AP,A A,A$ A0,A+ A,A ~ A,A 6AX5,AA ,A9zA$,ApAI,AjAf,AjA`,A (A@,AAx,A`A,ArA8,A@,#A,A0A,AA,A Auw,AA@ ,A |A ,AA,AA&E,A@>XALi,A@A@,AA,A{A,,AVbA`l,A`wAx,A@A@,A`"AH,A@nAh,AkAIC,A:AHr,A JAh,AA`,AA ,At]A(B,A@9A],AjA,A@EA@ ,AJA7,AA$,A"AP/b,A@#AV,AA,A|LA`,AA,A#EA,A@PA`,AbAk^,AxWA%,A`A>,A@qA~,AA0*,AwAM,A \AR,AjFA065,AA[,A A,AIGAp,A`Ax,AAP,A3A?,A A %,A`A%,AigA&,AiAh7,A`AH.>,AvAI,A`A_,A AA],A@#SAR,A MAxI,A(Ahe,AA@m,AZAi,AHXAm,A@iA-A"A-A Ad&-A`]A`H-AڪApF-A`WA:-A H`A -A DA-A` Al-A@AE-A\A`X7-AxA6-AAXA-A[AE-A KAP^L-A`DTA8b[-A@Av-A AHf-AaAo-AjA`j-AAHr-AA@u-A A(-A@ A-A A u-A |Ac-A A"-AAiD-AAH?f-A'Aq-A`AK-A` wApm-A&1Aޕ-AηAߘ-A`A/-A; A(-A AH-AA-A`A-A Ae-A@AP-A cgA-A̍A-A@A@-A@A -AǾA-AЛAhj-A EAV-AAP& -A@rYA-A#A^-A$&A -A'A%K-A@`^A8n-AKxA@w-AXA`-AA-A9AK;-AkgA8g-A@5r(AP-A(ANo-AVMA?F-A@ AL-A kA -A@ "A2-A 7"AP-Aj(A-A`(A -A $*A`2-A*Ax6-A@%$AX-Af[$A(-A@`ApN-A A0.F -A@A( -A l!A] -A+"Ax(F -A %A ҁ -A-&A -Ag"A` -A"AP -A6"AX/ -Am"Apj -A A -A@U Ah -AO4 A` -AW Ad -AAx' -AhAНh -A0 A( -Afu A&} -A AL -A["Aٮ -A*)Aȩ -A{)A{ -A7#A`Bb -A$AY -A c %A  -A`T(A( -A`+A$L -A0A(-v -AS0A -A-A@ -A+Ap -A@)A؟X -AՓ'AM -A%A} -A8%A -A`)%AL -Ah&A; -AHs'AI -Ag'AQ -AV1A -A1Aj -A`Q(A`ry -A]g(Aޛ -A''A? -A`"&AX -AOY'A-Am](A7v -A6?3Ah͡ -A 3A؁-A`ܟ(Aج[-A@`)A-A3J-AΉ-A`x/Ax-A Z@/A@-A`2A_-A6Am4-A`;;Ax>f-A@5<A-A(AA -A@ }@A@F-A^;A`-A՝;AX -A7AXׄ-A9A0]-Ah7A-A>A -A BAH?-A CAus-A`ʏGA0]-A`TFA6#-A CA:-A`BAx6U-AaBA-A"CAP-AcGA-A]KA0-A@`LAhon-A JMAm-ALAZ-ALAC-A cLA`!-A@yLA i&-ALA&&-AMAp'-A`NAH='-A NA'-A`NAX'-A MA''-AMA(-A"zMA)(-A9aMA U(-A?\MA_}(-AHRMAK(-A@5fMAP|(-AMA8(-AMA)-AMA+-ALLAp--AJA--AKA8s --A`KA&--A-KAj,-ADLAP,-A@xLA --A@*LA(\$--A[LA:--AB8LA--A KAi--A-AOA?-A;QA{?-A [QA>?-A`ӈRAxmdC-AYAx@D-A@ZAuF-A;_A cG-AA`A¯I-AfAHiI-AXgA0%J-A}}gA&J-AFgAJ-A uMiAaaK-A 8kACN-A WpAqO-AerAh.P-A@rAQ-A`rAQ-AzurA8ZQ-ARrAQ-A1rA0UQ-A1qAR-AVqA` 0UR-A1&;pAS2R-AaMkpARR-AoAK S-AސoA1LS-ApAp/AA(#q/A %A Edq/A %Ahq/AAiq/AjAx(q/AGAX5r/AGA`Bxr/A ZZA&r/A qA0r/A`Ar/A A;s/AAXl(s/AAhAs/AAKs/A XAQs/AA|s/A`Aݝs/A AOs/A ADs/AHDAs/A:A`vs/AAxps/AA`s/A`AUs/AAS@t/AhAXmt/AhAt/A@A=t/AmA t/A+Aht/A`Aەt/AOA`Xt/A`]At/AA0t/A`AhZt/A@zANt/AfAz9t/A@A3t/AA _t/A As/AAs/AoAs/A@SAjs/AAs/AAPs/A`kAs/A@ ADs/A \AvTs/AApds/AhAxNs/A`[cAx#s/AVA0&r/A` 9A0r/AA`Wr/A AP(r/AAH Tr/A}A,Nr/A6AMHr/AଂAHr/AAPoq/A,Aq/AOAhq/AAp_q/A`wAq/A`AXtq/A9A@`_q/AQA$q/AAСSq/AKA4gq/A`Axq/AvAxץq/AA@q/AHAq/AYA(pq/AOA$q/A=A0f0q/A#&AOq/A@A~q/A=ADq/A A8q/A`]A`'r/A 8A8r/AAɻr/AAr/A2 A@ s/AA0;1s/A`5AK\s/A)AP/s/AAjs/A uEAPs/A`As/AOAPIt/A ɥA8)t/A@A3t/AW0AKt/A@lA(t/A@lA0Ut/A0ZA(~t/A#&At/AAt/A@֖A t/AAt/A.At/A0A0t/A(At/AդAhbt/A |sA(mt/AྯAt/A-At/A At/A`-A6t/AyAAt/AA&t/AcABt/A`o.A^t/A`A#t/A;$As/A;$Axxs/Af.A@03s/A Ar/A 1ALr/A ShAr/A@ [A`Wr/A`mA0#r/A ShAxdq/AuAhq/A Q'A?eq/A`A$q/AvNAp/Ap}Ap/A`XAAp/A"AYo/AIAo/AmA83o/A6Ao/AAdo/A`Ao/A?A@o/A@WA3p/AAhxp/AZAbp/AzAXFp/AxAp/AA@q/A`Aq/AAhq/A`)A3q/A6Ar/A@%/A0r/A6Ax~mr/A=sA7r/A{Ar/A@cAs/A.ARs/A`TAPs/AAQ-s/A`dAHr/AAXr/ATAr/A qAr/AeAQr/A iAq/AoAq/A@sAq/A@EA oq/A@EA8Dq/A6A8p/AA^p/A cA^p/A]&Aho/A`BA`o/A9@APro/A`AZ9o/A_Ap5o/A'An/A"Aan/AAon/AHAh]%n/A_AXcm/A_A=m/Af8Aߩm/A`Adlm/A ALm/A OA&m/A A m/A A{l/AAh@l/A'Al/AqAll/AUAXMl/ALAr(l/AsA07l/A$=Ak/A`AHk/AA8k/A AHxk/A7AZk/A AYGk/AAk/A@A0j/AࣕA@j/A@ύA$j/A@A/j/A@5aA/j/AAj/A=sAªj/A+A/j/A Q'A8sj/A œAHqj/AAXl^j/A@yA%=j/A,A+j/AjAPi/AMA,i/A}Aci/AAh/A@Ajh/AnAh/AA`gh/ADA Qh/A\Ag/A@Axmg/ARA1g/AA]g/A]Ang/A SAlg/AAutg/A 5A]g/AiAxe/AsAle/AHAle/AA =3e/AAd/AAh d/A@ DAod/AoApd/APAe/A3AXye/AtAe/A@~Ae/AA@We/A=Aqe/AKAe/A ZA3d/A6AIyd/A`DA`d/AAKc/A1AOc/A`A`Sb/A)AXƗb/AaA8b/AAhѕb/AKAb/AAtxb/AaA0b/A@p Ab/A@=A[a/A`;Aa/A3A06pa/A`Aha/A`KAHòa/A-A`a/AAb/A Ak~a/AN\Asa/AHAd`/A sAHr`/A;A3`/A[A(w`/AFApi`/A`IAB`/A8AP!`/ARAO_/A`fKAȱ_/A@#A4_/AA@q_/A 8Ae_/A 8A;H_/A &PA_/A 8.A_/A cA^/A`JA^/AOAx^/AyA^^/AmAw^/A@mA07X^/A[A<^/AVA'^/AAd]/A0Axv]/A6A]/A?Ap]/A(qA]/AAhn]/AA9]/AA7]/AA8\/A A\/A@A\/A@>A\\/A J(A88<\/A6AP\/A@֘Ag\/A :AXs\/A@rAxq\/AA.}\/Aj~Ax6\/A`'AM\/A&Ap\/AsA\/AAȲ\/A=AȲ\/A5A\/A@?A\/Av9A(m"]/AAL]/A A@T]/AAj]/AW=A>]/AA0]/AA`]/A_A(F^/AAp^/AgOAXt^/Ai Aq^/A*C A`^/A@ A _/A`2 AKC_/A$ A>_/AD A0_/A AX_/Aӆ A_/A A`/A AHX`/A Ag`/A` AO`/A A a/Aq APkHa/AA0Ta/A[AUa/AAa/A AXb/ARAoTb/A`AxKb/A`*Ahb/AA0b/A APMLc/A*A kc/A/Ac/AArd/A2A@Fd/AXhAh:e/A QA8֦e/AXrA8Ve/AA7^f/A Apڲf/A%ANf/A%A 0dg/A q A(g/A@iAP/h/A`2AX[h/A2Axh/AHA-i/A`AHLi/A`)FAi/AOA)j/AAq]j/AAxj/AAA`j/AnAk/A sA`6k/A|Alk/AWAPk/A gA`ek/A Ak/A@NA0k/A A8'k/AN AP縥/AfHA姥/A@GA/A`)GAX/AGAX/AeGANm/A`FAo/A`dFA8t/AEA/ArEA/ALEA8q/A`DEA`/A/EA?/A EA -/A=EA#/ADA!/A%DA#/A?CA)+/A`xCA0{/A CA\/A CA/A@iaCA/A 8BA0 /A@%BBAp /A AAp /AAAp /A@, @Axv/A 8?AW/AX>A`/A>Axv/A=[=A@/A@<A@$ /A#;Axv/A /Y;AUҤ/A ;A7ä/A:AƤ/A:A/A?f9A8d/A 9Ax-/A{8A/A`78Aͣ/A 7Aض/AE/7AԔ/A6Apxg/A6Ax=/A@'7A/AF,7A1/AE/7AGТ/A@p6A/A]=6A`/A 5A@o/A һ4A(\/AB4ARW/A`3Ae/A`2AБ@/A22A/A3A0ɡ/A34AXlv/A`4AF4/A J5A@٠/A6Aנ/A@'7A:ߠ/Am7Aנ/A@7AT̠/A`V8A/A 8A³/A89AHw/A@߲:A +/A,<A88/AS=A/A`m>A5/A3 AAs /AVAA/A0BA8Q/A`DAf/A~FAX"/AHA8/A IAP۝/A[KAH/A4 LA`t/AMA)/AtNAX/AOA/AJQAꄜ/A`DiRA9V/A@>SA`ܛ/A"UA駛/A`VAo/A XAPJ/Ag1ZA-%/A/\Ah /Ai]A`aĚ/A՟^AXlؚ/A_A?Ԛ/AaAĚ/A@bcA/ABdA/ATeAΚ/A_fAXݚ/A|xgAXݚ/AqhA/A@ߏiA/A=jA``/AkA /AmA/A`mroAC/A`pA/AavrA"/A`tA(7/A`"uAl/A5vAÛ/A`vA/A&vAxK/A_wA/AaexA0T/AxyA0nC/A@xAݫ/ApxAxu/A lwAW/ATzuAٟ/AKsARr/Az]rA)/A]}qA//A qkpAҌ/A`܅nA\A/AUmA~ɣ/A@#kA(/A *kA/AiA/A`gAF/A-gAXƐ/AseAw/A@`dAt/AbAPz/A@w`A^إ/A ^A/A\Ayѥ/At[A姥/AޞZA/AfYA/A XAxƘ/AWApZ¥/AWAx$ץ/A VVA"ƥ/A@gUAȩ/ATA07/ASA/ASA`S/A,RAxƘ/AQA;/A@N2PAɥ/A@ OA0ϥ/AlNA@_/A\MAa/A`ӽLA /A xKA /AĻJAɥ/A3JA/AIA姥/AgIAU/A ZHAP縥/AfHA@5/A &AȺ/AۗA//AA/AA>/AAF/AbAxGV/A$Aȇl/A$:A0و/AAL/AAP/AfAp/AAPο/ArA.ƿ/A@%BA`!/A A/A@ÑA/AquAP/A)$A@F/AʷA@“/AhHA/A`ƏA+z/ATAk/A 1AxKU/A`rA&/A@`AH!%/A A/A@cA0/A A/A3A([վ/A@TA ľ/AAʾ/A 8Aʾ/A A;/A`TA;/A"A;/AAӾ/A ZaA/A߈A0/A@A8/A A"A/AAP /AIA/AȆA0 /A隆AH!%/A[A0/A1A/Aj A/A (AX/A[A@о/A`A/A@CAx霾/A@dA/A`̝AXe/A&A(hR/A^CA//AvA/AjA/AaFA ݽ/A`9A(mƽ/AAR/AЇA}/AЇAȩy/A@AW/ARA?/A0Ao7/AjA'/AfAPM/AA/ALAd/A A@/AwAPޑ/Aa݄Af/AjAX/AAЦR/A^A:/AA*/AA/AAO/A`A߻/A AȻ/AڃA޶/At΃Aƛ/AAgx/A7A@a/AA X/A ZԄA~J/A +A/9/A`A3/A@#AX0/A@aA*/AA/A 8A/AOA/A A*/AA 9/A lŃAL/AAQ`/AAPw/A_A8/A"qA/A_A/AJA8g/A@@AXtл/A YA8/ASAXW/A:A /AA?&/A 4A8ZD/A"AlY/A 4A0we/A'wA؁q/Aq˃AGx/AAт/A CAX5/A@+Apȼ/A CA@/AdA/A }A/A/APj1/A`mA88A/AA88A/AjAI/A (APT/Aj AhUb/AdAu/A`A}/AHAԩ/A@`"A5н/A@p4AH/AOAX/AyAj /AA0;/AAR:/A@dA0.Y/Aj.AHw/A(A@`/A旆A/AjAUϾ/AfA@p/A` AP/AjA(2/A`7A NX/A@:A#q/A4AP/A@Ax׫/A=A,ÿ/A`jA0{Կ/A`ApN/A ZAS/A$tA/A\Ax) /AA /AAp/A JA./AAf/ApA`/A@ AP^/AA/AjYA/AAH/A`̤AC޿/AkA8s˿/ABAܱ/A`~A/A~A_/A~APH/A`~Axh-/A~A/A+~A/Aj5A /AzGA/ABA/A ~Ae/A~A;/A~A/A~Axm/A~Aي/A~A |/A΃~A0u/AY~Af/A@,~AȤN/A`>~AX9/A h~A 2/AӉ~A,/A@~AX9/A=AX9/A@PAH2/AAE+/A`̤Ah@%/AٳAh@%/A#A)/A@`MA//AzA="/A@A/AAHm/A cPAX#/A sbA /A`A@/AA`޽/A sbAݽ/A@,A/A AȔ/AɡA /A zA(`/AZ#A/A (~Axzս/A@~Ag/A~AE/A@~A{/A~Aa/À~AK/Aw~A5>/A@S~A6/Aޕ~A//A (~A $/A@NAW/A`~AG /A~AH/Aޕ~AXѼ/AJ~A MǼ/A?}AOʼ/A}AH/A`}A2/A}A؋/AP}A?o/A&}AlY/A}AЦR/A l|AC/A \|AH4/A}A#/A`}At/A Z|A/A` |A/A q|AP޻/A@Y|A0%/A@Y|AL/A`V|A(:/A`/|AXdu/A{AP [/A{AhF/A{Ai2/A)|A'0/A{|A/A}A(O/A_}AH?/A}A/Aj~A/A V~Aa/AӉ~A /A~A$/A~A/A }~Aw/AD~AP~AϺ/A)~A/AM}An/A}A/A/}A@/AI|A/A 5|A/A@{A/A?{A/9/Ao{A@Wf/A&~{A4/A){A/A2{A7Ի/AD{A /AD{A?&/As{A0=/A{A*W/A#|Ay/A _|A@/A@ |A/A"|A˼/A4|A0j/A`D|A/Aq|A/A}A88A/A5}AhUb/A`S}A{/A_}AE/A@h}AZ/A }AĽ/A}A/A}A0/A@}}A/A M}A/A5}A/A@y|A,/Aa|A?/A O|A p[/AQ|A8 x/AQ|Ax霾/Ao|AH/A l|A/At|A/A}A`/AJ}A؟#/A`e}A3:/Aw}A@FO/Ab}Ac/A/}Aq/A}A/A}AH /A}Amſ/A#}A/AG}A/A}A/A@E}A`1/Ad ~A/AM~A8(/A` ~A(//Ag2A=/AɡAcK/AqA0R/AA6a/A[kADp/A%A`o/A݂AȺe/AhAxc/AA!I/A*A2/A|A`1/A @Aؚ/AAX/A;ԅA3̿/A_Aⷿ/A OAH /A`Aз/A A({/A`;AX[g/Az@A(S/AשA0 V/A&A8t/A`AC/A 万A/AA/ApXAVa/A෩A@>F/A`APx?/AhsA7/AA//A)OA./ANJA//AA;C/A@%mA S/A`mAX[g/A APky/ABA;/A@5A/A`mAp[/AA,ÿ/A iAܿ/A&A/A s A/A@TA/A A/A AA 4/A cA"/A{A7/AʓA@G/AɏA8b/AAV/A`ُA/A@AD/Az9AX/A&َAf/AlAx/AxA /A@,A/A~A+/ATA/AA@./A@A7$/A`BA/AǛA/A`vWAX4/A3Aw/AgYA #/AA2 /A@PAH /AR;A/A@A /Af?A/A%AH/A *A8#/A@VAx/AߥAb/A A _/AA@/AAX9~/A}App/A`]A0Ȟ/A@A/AfA8D/A=A6/A`vA`G/AGAP/A Ah/AA/ARvA(/Ah8A/A A-/AV~AC/A~AE/A~AأB/AQi~AP9/A S~AH)3/A}A8:/A}AiK/AH}A\/A`}A"j/A`o}A5s/Ag+~A|/A/~AHn/Ag+~A/A@~A0/AF}A c/A@}A@B/A }AP/Aҿ/A EwA𣧿/A`ywAk/AvA!U/A?vA F/AvAX9-/A vA(h /A`vA5/AvAھ/AvA¾/AvAS/A`vA\/A`vAn/AXvAV/A4wA((5/A3YwA, /AcwAȥ/A]wApJ½/A_2wA/A4wAXV/A@ wAѳ/A AvA0/A`vA[/A{vA0/AgIvA?/A]-vAxK/A /vAP/AMvA,u/A`;pvAm/AvAwk/A wAlO/A`ywA`P9/AwA !/A@WxAP/A /xA/A`2wAH߼/A wA$ɼ/Ap*xAh̯/A AzxA /AyA`/AXyA/AxA(O/AbxAgٸ/A`UxA@/AxAը/AuxAHC/AxA/A@wAՆ/AtlwA*p/A əwApO/A@wA+,/A wA/A_wAٷ/A8wAO/AvAܷ/AhivAO/At\vA/ABvA֘/AvA/AEwA/A@wAtx/AxAPQ/A@wA:/A_wAD$/A@wAX/AhvAѶ/A8wA88/AgywAP/A6wAs/A6wAs/A`[xA@uh/A`[xAj/A`B8yAx\/A /nyAN/A ZyAL/A`jyAXuF/AxA4/A`[xA/A[wAP/AwAֵ/A ;xADе/AxA8/A@xAӏ/ALAyA8Q/AլyAh]r/AAHzA Ej/A@NzA8U/AqzAK/AzA93/A zA/A4zA0vд/A`)bzAE/AozAu/A`|zA`vj/A@5zAh/A AX{AW/Ak{AhK/AzA>/A@rzA@/A syA9/A 1yA/AxA/A`oxAjͳ/A OxAj/A !xA`/AbxAR=/AixAxz%/A ¹xAk/AEyA/A yAp*/Av.zA:/AGzA8 E/AH{A&O/A|Apq/A|A{/A u|A摳/A|A,/AI|AU/A J|AXг/A}A/A J|A/A \|Ah/A T|A`!/Aa|Ah/AW}A/A`K}Aڳ/Aj~AO/A | Aɇ/AمAU_/AA&O/AHA`C/A OAP8/A BA0"/A" A/A A /A`Aa/A]ًA[/A ҳA[/A]AX#/A_AA/AH@AK/A@A>b/AcA~/AA8/A5AX2/AֆA.g/AϐA(/AzAP//A@5YA@/A IAu1/A0A f/AzA/A cA/AzA<(/A`vA^/AAk/A`A(/AAʷ/A\A`/ANwAp !/AIAO/A9ӔA@>/ATAĸ/A 'Ap/AЖA?>/A@zA8l/AkA\/A Aѹ/AA˹/A$A(/A A /A 'A /ATA@/AĪAQ/A@PAX/A ~9A'6/A}A'6/A}.AX%/AA'6/ALAd/ALaA9/AzqAѹ/AߐAh/A@FA./A A͹/A sA/A3AV/A`vAxt/AzAJB/A אA/A@Ay/AKA~/A`vA/A`KAX9/AnAؼ^/ARAn/A`A/A؜A@ /AzA3/A;]A$/A38Ah./A@ʉA/AAO0/AɅA/AAX/A@"Ap%/A{AZ/AA@d/A}A(/A A{h/A /A/AA/A@AXc/A@A(/A (UA{/A@A/A@#AP&D/A@.TA{/AIAXִ/AA0n/A (UAp /AA/AbA /AzA/A 8wA@q]/AsA&/A'ŽA/A`AE/A A/A=<A(/A޶AX4Q/A;A$/AԑAȶ/AA/AAR/A`KYAp/AA}/AڱA@/A~APr/A`2ApAZ/A@AM=/A`)\A*/AX”A!/A@(A/AA޿/AhA/AhA@$/AhAhr/AAxS/AAH;/AǰA /A`A /AGA/A Ao;/A?֕A@q/AA _/AwMAHa/A@yApt/A'}AM3/A'}AQ/A@yA 0ؽ/ArAX諽/AOeA`/A Aro/A`AP/A/!A /A_Aq/AvA/AgA( /A'A`/AAf۷/AAҷ/A@!A/AbA/AI3A/A:Aԙ/AzA/AA/A_Axhķ/AAx/A@A/A Aذ/AǰAj/ApbAY/A@yAT/A AA\/A9A_]/AڱAH/AUA( /AAx/A@#AG/AA]/A`Az/ADAEg/A:A(L/A:AX /A`KYAp/AtAp/AA&/A\Aw/A sAA8R/A+A@O1/AAh/A`;]A5/A`;]A(o/A`;]AH\:/AAA/AA/AA6γ/A`A⾳/A@W(A)/A ֎A(y/AߕA #m/AH,AX9J/AتAh /A Ae/AAԌA /AA`v/A3A=Ѳ/A@BAh/A@BAؽ/A`:AТ/A A/AA/AWAH/A jA/A@.TA0{/AA /AגAPF/AwAd/A@ A(z/AΕA|/A`AJ/A іAٳ/AabA/A` AE/AKA@/A@A`/Af;At/A@5nAho/A ApD/A<A:_/A`<A@/A ƟAx:̵/As_AZ/A_1AY/AqA؍/A$A/A`yAA/A "A /A`dAPƷ/AAP/A`bApr/AèA$/A6A@1 /A0AH޺/AzyA /A쩫Ax/A`TCA7 /A`A0X/A@pA|/AEAd@/A@,A/AA/A A/A'A/AoAy/A`[IAKhz/A AZw/A ഊAPEt/AA@}m/AA gp/A|ØA0p/AyuA6r/AA@ϕr/AOAPt/A`;״A'w/A8A@¿x/AAy/A`[IA@/A A/A@RA0/A@ϛA q~/A@\A@/A (CA@/A A0!0AtAC0A uAH0A HAl0AfAt0A`LAܨ0A&^A)0A@#,Au0A`[Al0ALAt +0A AP70A@EA\D?0A2AvH0A AK0AlADP0Ah#A@]0A@A3b0AAl0A~Ahr0A5ATA0AA0A@#Adl0A`mA0A@#A|0AAy0A mA0A`OAl0ACAK0A62AT0AA~0A A)0A஭AF0A A0AvA] 0A[At0A[A"0AA%0AhvADk(0A@LAL$00A@Au10A` bAL20A`A80A`ðAC0AAG0AxAH!P0Ah@A\0Ac-A̔_0A Ali0AAm0A`Aȶt0AAw0AAh0A|dA0AaA0A`ܑA0Y0Az1A<Ñ0ATAp0AA 0A`A0A zAHp0A`EA\0A`Aɏ0AA0AךA0A@A$0A@~A0AqA0AdAJ0AFOAh3|0A@!Azx0A Aw0A;ALv0AA<=u0A?AAdj0A@.+A|0A"A40AA 0AXAW0A@.wAd 0AAB0A@.A0AA'0AA!0AHAZ.0AyAp0<0AGAXnJ0A AsZ0A@A(~l0AA\0A@PA\0AA&0A6AT0A٭AL0A Ap10A`NA@B0A(uAt0AWA0A ޹A0A]A0A AY0Aj(AX[0A??AOp0ARA\}0A QAA0AaA(0A $A|V0A(A(0A JAM0A`]ATh0AJAt0ARA, 0A` <At 0AЅA`P 0AACxT 0AA|ыA\X 0A3AGy-_ 0A$=A7e 0A,rA&k 0A2A{<q 0A8`A‰cw 0A4'1Aݠ{ 0AܒpVAhU 0AX:JA  0AxA- 0AWA-Z 0A~ýA!T 0AhI:Ar 0AA~N 0AKA 0A`0AN+= 0AO?XAl 0AQoANw 0AskuA 0AiAr 0AodA 0AQA 0A?A4 0A$A\ 0A^APk( 0A` A2 0A A7; 0AAE 0A@>AAܦ 0AzDA[ 0ANA 0AT6A& 0AA/ 0A A9 0A AC 0AIAM 0AhAT 0A AY 0A@.A8e 0AIA k 0AA#t 0A lA5{ 0A A@ 0A;Aƌ 0A@A 0A@޼A\M 0AɼA| 0AA0{ 0AA 0A`wAt 0A 2A޼ 0AfA8 0A sA 0AaA 0A`)@AP 0AtSAP 0AgZA| 0ABAT 0AYAHE 0AwA 0Ah}A 0AA 0A<A 0AA 0A`9A% 0AA8' 0A`Ax|2 0An¼A: 0AԼAC 0AA0H 0A A$O 0A$ALS 0A ؼA[Y 0AA1^ 0AkAl` 0A@CA0b 0AAb 0A@ԼApc 0A@AdRf 0A``Ag 0AzAp 0A 8AQz 0A@ǽAL 0A@нA< 0A½A 0AdA 0A`ouA 0ADA$W 0A A( 0AܼA֫ 0AA 0A SATc 0AAL 0A(ٻAy 0AAp= 0ALA 0A`3A 0Ax5APm 0A`AA 0AuAN 0A൅AH6 0A`AL 0A9Am 0AzɻAs 0Af{Aɀ 0A@%A| 0A@ƖA` 0AAS 0AA$ 0A<}AXh 0Aq`Aǰ 0A&WA9 0AUA+ 0AYAˠ 0AFnA@ 0A`2ĽA 0AjAp 0AA" 0AaCrA`z0A'򺦻A1WF0AXwA 0A A.V'0A A0I/0A+R$A!70AGS%3AЉF@0Ax6A/aH0A-AQH Q0AUA[X0AaCA_0AѻAd0A֋ʻA6j0AMKȻAȐo0AwDͻAPat0AٻAky0AE*eA~0A`KAM0A Aŧ0A[nAf0A~A]0AzA $0A{ALf0A`ÑA0A`A]0AA0ALAt0A $A'0AsA0AgAĀ 0AAt0AEAz0A OWA@40A2A4&G0A wALlv0A`?A`0A`iAT0AHA-0AEA40AA0A=AHv[0AwAp0AA 0AٝAQ0ApAhl0A FAx)m0A5}A0AG xAo 0AgA 0A &[A 0A~6A4 0A@4AX 0A@.4APϡ 0A4At 0AjI2A- 0A@3-AH 0A ,Af 0AK,A 0Anߛ,Aze 0A-Aj/ 0A;*-A. 0A-A] 0AI-ASm 0A$D0-AƠ 0ADžq-A(Ց 0A,Ao:1 0AR"(,A- 0A,As 0A&#­,A"ak 0A8h,AЁ 0A;,Aַ" 0A1,A2g} 0AȈO+Any 0AG_+Au 0A+Ab 0A`f +A(|a 0A*ALA 0A=*A 9dEC 0AGYcE*A'C 0A)A"Q 0A Q)A\R 0A)Ad9V 0Až)ATxe 0A*A i 0A`*A( d 0A@p*A_ 0A O*AZ 0A;)A(U 0A c)A\@T 0AF)A0N 0A@ (AG 0A)N(A D 0AG(Ah> 0A'AP; 0Av'A{: 0ARa'A\3 0AF&A1 0AF&A+ 0A ?&A) 0A])&A$ 0A@<&ApQ 0A)Ad!N 0A u)A|O 0Ad)A(uC 0A0)AQC 0An'~)AA 0Aa8)AL 0A$&A 0Aօɠ&ATy` 0A۲&A1 0A2&AYA 0Ap7&Af£ 0AԹz&A 0ABv&A 0A0et&AvJ 0Ak u&A{ 0AO-x&A: 0AB}&A8`N 0A%sJw&A) 0Adz{9&Aa 0A蜰&AC 0A7&A(o 0A`T&A m0A4D'AL̷0A?'AĒ30AL:(AE10AL(A~0A:w(Abç0Ad{.)A 0AMZ)As%r0Ar)AIt[0A8w)A0D0Ag)AX-0A )A1Z0ALR (A0A'K](A20AD'ARd_0A˵V'AA0A-&A,A0A5&Aܮ0A;g%A,0A@-4$Auһ0AQ$A<0Aܽ$A$d0A˛#AA z0A;3)#AO0A"AH0AJ"AN:0A5L!AMc0ABc!A2[0A-!A0AOLW Aq0A A0A< Au0AJ%U Akh0Aor AYڛ0A[d Ak0A5ՖU A0AUyE A0A6 AL0A[Aȼ0AAͳ0AA,+0A`AT0AAs0A@A0A`AWq0AMrApZ0A`cAY0AAf0A^qAf0A`]A0j0AAx0AAx0AN Ab0A@#$AU\0AnKj$A(T0A~$AK0A/$A %8B0A@%AR70Ao%A-0A"%Ai +0AU=%Ah(0A%Al&0A%ATL$0A!%AU"0A$%AwY 0Adx,%A#0A)A7%AA0A@ C%ATx0AQ%AN10A|V%Aړ|0A=%A7K20A] I%ARƁ0A׊&A40A &M&AN"0AC+c&A0Aax&A8b0ANZ8&A$&e0A &A00A׶T&A$}0A`T&A@90A&Av0A!&A"0AQvw&A a0AE&AZfP0Aŗw9&A0AFT.&A>0A$&A40Aeh &A10AB&A<00A(&Aps0Au.&Add0Af枞&&Av,X0A{&Aֳ0AV%Ac0A K%AXe0Aɣ%A?'S0AD%ARad0AT %A0Aw2w%A@ 0As_%AiS0A%A 0A7!s%AN20A-%AЭ0A(%AzI0A͂%AVAH0Ap-%AL̯0A%A̖00AP%AjK0Ao%A e0A҅%A-0A1mw%A|W 0AO%Ah0A_2%A0AL%A'0A/MV%A;0AW%A-0Aa9}%A|Ǹ0Aൠ%Aw0A٨%Aq10AӋc%A 0AsӤ%AP0AM%Ang0A~5բ%AV0A+u%A׬c0A"<%AbE0Aу?%A6q0A$O%A\0Aɷj%A^0A,҉%A䃿0A@%AUs0A%_Q%A };0Af"%A0A$AXY0A$Ao0A3]$A@0AYߔB$Amx0Aʨ$AXW0A@$Am>}0A=5$AjKw0A$ARq0A$Ak0A<$AD+j0Al;$AEr`j0A4`j$A7;i0A=(q$A@h0Aնa$Ah0Aa=l~$ADg0Ay$A,e0A! kv$AD&,d0At$A1c0A:lNGt$Azb0AY0u$AřZa0AT'6;x$AR'2M`0ALBb|$A#Y_0A@ہ$A^0A`$A\0ADЁŘ$Ap,6aZ0AqK$A B>W0AB$AdAU0A$Ae.~R0AWb$AO0Aʺ$A$8hM0A;%>O$AZgK0A$A?#I0A$AݤvH0A_$AX$G0A2gN%AƎz(G0A+%Aa*G0Avp]B%A̝G0A`X%AfB0AQ9%A};0A@i%Aq;0ANl[%AUr;0A%Az9;0A^X%AA::0A4J%Az4k:0A!I%AH\90A%A!690A%AY 80A{/%A'70Az%A;60AQJk%AnXG360Aar%AAAt50AM %A40AV%A_ <;/0AP-%AjT)0Aֳ%AJsq"0A}:%A~H0A|%Al0A@W%A#0A!SH%AR10A1&.%A0Ab%A•V0AdI^œ%A0Ac4%Ao0A2_F%ARS0A,%ABO/0AR%A8n0AoL%Aj)0A/a6y%A(0AGʿs%A. 0A)sm%A #0A;g%Aز+ 0A$ %%AD0ALX$AXl0Aþ$Apv0A?犄$AЬP0Am_$AP2#0A\RUD$AF0Aն3$A 0A.$A0A{i@$Aq0AME#A_=0A/#A90Ark#A.0A|2#AH0A"Ap0A\KW"AHl0A"AL&0A`l"A=0AHtD"A`0Als!"AN0A "A*S0AY!AP0A !AR0A苃!A8~?0AB0!Ag0APg!A秳0A"!A%_0AoYM!AY00Az$!ASI@0Ay!AV0A!AL0A@!A 0A!AHԜ0A4d!AD"0A;N!At0A@yr!A0Aേ!A#UR0Azl!A$0A)X!A{ 0AcS!AXD0Abl!A-t0A,Rd!Ay"B0A/?!A0A`!Ay|0A3]t!Ag0A !AFh0Az!AjP~0AR~!AR0A|!AP*0A9!A׫z0AlV!A$8(0A}K!A 40A !A|-60A0+4!A}70Ac>!A480A0A%!Avq90A!AsO:0AY!A;0Aݥ!A\h;0A Ǚ!AU r;0A&*u!Ab6;0AmE!AAZ:0AQu!Az790A3Vj!A490A&a!A70AY!AP/0A˱ A=AR(0A_7v j A"0A>IAN^L0A fAP^0AA}60AiAM0AnaAِ/!0A~Ae/'0A[ǎAT/0AA,30A A590A A|;0AAL&>0A'A0B0AAHG0A`AL0AA8'T0A]Als0A=Av0AAX0AmAlx0AZAQ0A`vA0A9A40AtA;0A`"AH0An!A20A+$A0[[0ABW%AV0A`Kx&AT0A Qs&ApR0A&A\O;0A~&A390As&AP\0A%A/0A`K%Ah 0Ai%A 0A&W%A0Ao$Al0A$A0A$A 0Af|$Ap0AT$A0A"AP0A"A0A O!Am0A !A<&0A!A<](0A !A"0A!A 0A!A 0A@ !A 0A!A0A"A00As"A*0A@,"A,0A`"A0A"A$N0A;d#Al-0At#A0A`]#AC0A |#Ax0A@P $A0Awa%A$0A@&A0A '&A,)0A2&Ay+0A&A890AZ&A<0A@&A8%D0A&AAL0A"'AN0A.'ApR0A`5'A,]0A|>'Aԩa0A A'AtDd0A`A'Ak0A4A'Acm0AA'A4v0A`)2'A8ޘ0Aa&Aӟ0A&A 0A&A0A \&Ap0AoU&AT0A&A0A &A0A@%A0A`BR%A`0A J.%A0A@L%AN0A@%A<0AA%Aԫ0AF%A60A&A0A`o=&A\0AF&A 0A &AԚ0A@&Al0A׿&A`É0A@&A|0A &Arm0A ('A _0A, 'A(s`0A]x&Ao]0A_z&A b0Aྎ%A̬0A s%A0AO$A 0AG$A0A $A0Avh$A\M 0A`$A| 0A$A0E 0AU%AQ 0Aq%AȾL 0A `%A0b 0A%A n 0A@%&A x 0AQ{&AJ 0A*Ap 0A?*Ahf 0AK+A< 0A@`)Az 0AXcA,=!0A= A=F!0A< A\dY!0AX ALV!0AT AIV!0AV A\T!0A qb A(mQ!0A[ A0 O!0A AK!0A@ AF!0A 6 ADiD!0Ai? A=!0Ac A4!0A@ A*!0A@ߟ A!0A ATp!0A A` 0A At 0A@5 Axګ 0AЛ AŢ 0A A 0A = AՄ 0A< A} 0AH A|w 0Ak At 0Aw At 0AAq 0A@Al8m 0AAg 0AAd 0A ( Atc 0Apn At&` 0Ad AW 0A A, > 0A A0[8 0A@ An$ 0Ay A" 0A0 A` 0AƲ A̽ 0A K AD 0A@G A 0AA 0A`AA 0AS A/ 0A@N Al 0A+ A 0Aʝ A( 0A@ A:0A A|0A`A AГ0A ^ Al0A ^ AP 0A(T AW0A_? AL0A` A0A A0A A܄0AY AT0A@> A0A S Al0A J AQ0A A 0A+ A0A@5 A40A? A}0Ag Aey0A`T Aȉp0A Ag0Aͯ Ax:_0A AIU0A A(L0A` AA0A AA0A,{ APE0A@5{ AZE0AAl Al;0A@,l A;0Aw A>0Aw A$>0A A(;0AR Al20Af+ A,0AF2 AI#0A@g= A}0A (F A!0A@L A 0AB A0A] A0A A@0A@g A䲻0A@g A0A0f A0A' A80A`] A0A _ A(D0Ao` A@0Aq A£0A@, A:0A J, A0A AT20A A|0A@r AFm0AO Adc0A+ A[0A9 AmT0A@E At5<0A A4B0A| Ap0A@ Aw 0A` A 0Au AA0A J0 APz0A+ A̎0A4 A0AF AT0A ~ A0AV A(O0A A40AF A 0A`C A a0A A0Ai A40A J AD0A@m A0A+ A40AU AhB0A~ A0A A:0Ay A|Ǫ0A@= A0Aw A0A@> A0A A0A A\uj0AF A a0Ao AȖZ0A AcK0A@ A10A A('0A[ AX0A' AT0A A0A w A0AK A0A@ APk0Ai3 AD0A A8G0A A^0A A$0A`) A0A Al0A C A,+0A@ A0A A܄0A& Ab0AL A50A`ʍ A ٴ0A`K A0Ah A0AHA0ATADkt0A JAXY^0A`A T0AA\C0AA%0AaAX10A Ađ0A@ A0A` A 0A`| AD0A ۦ A^0A A0A@ A<0A AL0A A0A A˄0AQ Ad|0A` At0A` Ak0A`o~ Ab0A@ l A`Y0Ahu A4R0A`T AAJ0A@ A70AR AH.0A At&0A A0A4| A0A`e A0A`G A` 0Af Am0A` A 0A AT 0A A( 0A@go A 30A?E A50A`0 Ax0AU Ay0A A<;0A Ag0A` A,s0A. A}G0A@/ A60A` A`0A AXy0A / AaG0A A10A  Axk0A A,0A Aڅ0A  Af0AA$L0AAhq30A@ϘALH0A`KA(/0A@EAlG0A`A(0A A0AADs0ARA\0AAR0A cfAZF0A@A|40A@A#"0AfwA(0AuA 0AqA0AjA0A bA0A`ZA0J0A@AA\-0AZ+A Q0A /Ad0A*Ah"0Aa~Az0A J'A`4`0A ˴A@0A c/AA>0A (AP0A`Aڨ0A`)A(0AÃ0AWAQ0A ,Ah>0AAA,Z00A`MA\*0ARA0A;YAp0A WA]0A-CA0A5AY0A"ALW0AAv0AAN0AA<0AAt0A@#Ax0A&A$w0AAz0A`A.0A`A90A AYw0A \Ao0AAL0AftA\80A`nA*0AiA?0A SkA0AqA 0A͇A8 0A؀A 0A`{A^ 0A@yyAH 0A~A 0A`A䥞 0AA 0A@pA 0AA} 0A Ahk 0A`ôA2c 0A A|[ 0AAT 0A*A@L 0A@`HAC 0A\A: 0AvA|0 0AZA( 0A |A 0AA 0AiA 0A`A 0A@G A 0A <Ae 0A SA 0A@GfAx 0AzA 0A@,A`c 0A6A 0A@A4 0A A4¶ 0A6Ad 0A`TA7 0A`A0ً 0A Aq 0A@Ai 0A[AZ 0AՈA`Q 0A;AI 0AA; 0AAL2 0A]A* 0A (AD# 0A@>A 0A IA B 0A NA  0A`KhA 0AzA 0A?A4 0AhA 0A@,AL 0A@`Ah( 0AF"A$ 0A`TKAL 0A nAQ 0AfA 0A ZAn 0AHA|?c 0A@A|VQ 0AQ ApH 0A@>AA 0A!A9 0AQ5A$&& 0A` BA 0A@,EAx 0A@iCAz 0AbA4 0A A s~ 0AAi 0A>ANV 0AALLe0A`KAw;0AAPš0A %A0A@As0AA0AOAo0A-A0AA0AAT0A`KVA@QB0A2A0A`AP\0AJA|t0A@Ad[0A@2A0ArA}0AoA80AA0A@ At0A47A0AKAzX0A APz=0A tAs!0A@OA0A_ Ah0AAt0A[hAxL0A cJA0Ah-A20AAy0AAp0A9GAL]0AAt0AADX0AAqG0A@5A]/0A (lA 0A`TA//A`dAp/A c5A8D/A8A /A%A/A Axz[/AA /AoRA/AVAk/A`A/AANt/A`qA /AA /A A@/A`AC/A7Ae/A`KA/AA/AA#/A@,A /AכA/AA0e/A ɒAQ/A٫Aq/AA`/A@APM/AEAZ/AA(/AAPh/AA]./A@A( /A0A/A A&/AAA/ATA/AڹA`/AA'A/AxA4&/AlAO/AgAP8/A mAht/AZkAho\/A@dAHL/A\Ah;/AsPA@/AA/AA/AA`2/AZA/A@Ac/A@AJ/AA/A@A/AzAP/x/A&eAd/A`JA`BY/A_BAD/A=As./ADA/ATAأ/A`AO/A@A /A A /A AP/AAX/A@`A ~/A^uAke/A@FA0/AjAXW/A A0/AzA@/A@ mA/A]jAX]/A3MAt /ADAN4/A39A/AL)AH/A?AH\/AAp+/A\A/A@ BA/A/A p/A/A/A`8A83/AcRA8x/AAH/A Ah/AadAp/AA /A@A0/A A[/A A8/AA /AA-/AApM/AEAw/AA/A4Az/A;"As/A`A_/AAR/ARTA[=/A"A#/A|A0/AAXV/A SA8/A@`A@F/A-AV/A>AK/A`9HAG/A0KAo/AaGA`²/A<Axˎ/A0A U/AA"=/AAD/A@AP/A` VAJ/A AsV/AAXy/ABA/A@ A0T/ApA0 /ARSA*Q/A@A /AAC/AfAН/A@@A8R/A@A0 /A6A`/AʘA8/A@sAr/A\A`/A|YA(4/A@rA"/AkA/A` LAj/A2dAe>/A?A_/A A/AEA0f/A?A~/ARAU/AzAX#%/AqA/A@!APk/A`[vA_/ANAo/A]$APf/AFAl/AAZ/A[A./A`TiA/A (A8/A AH/A@rAxƕ/A`T=AȘu/A@AXS/A AP0/A A@/AAP/A஼A@/A`AUS/A`2|A8*/AňA/AzA0̭/A0AX/AA<A/AHzA|/A@[A#G/A1A/A@Az/A@yAr/A㓿A0/AfA /AIAiA0H/A9AV/AA@/A@ A /AAȱ/A}8Ap/AXRA旮/A ZmA@F/A AkAHM/A`OA/AdAX/AܖAX/A@AO/A0A$/A`MAK /A(Az/AA۬/A`A0/A@ҔA q/A֬Aޫ/A_AX U/A`)AX A/A`)A/A (AA8t/AZAPYB/A =Axw/A 1uAZ/AmA@W/A`Aث/AA /A#AB/AA3v/AAH-T/A@~A0{5/A4pAX/A[A8R/A;A@Bۧ/A@Ahݼ/AAXV/A AU/A@vAA/AjA8$/AA /A6APϦ/A`$A@/A An/AA`r/A@،A/A'AΞ/A~AW/ACAX/A ~Apٝ/A`A؎/AEtA/A@gˈAP/A ZyAG/A^\A/A@־Ax;/A jA/AۅAPd/Ä́A`2/A,A/AOAY/A`A/A΀A8/A@!AxF/AAI/A~Ap/A ~A F/A+3}A/A {Aȃ/A S{AH/AqyAȩU/A yAّ/A wAP/A׌wA]/A7wAא/AqvA`G/A5xtAf/A tA/AtAu)/A qA؋/ApA]m/A`OpA=/ATpA0/AoAxъ/A`=oAS/A@ nA /A`)kA/AMkA(z/AiA8Vs/AhAX/A0hAA/AgA/A fAh̅/ACfAsQ/A`dA}܄/AdA0߃/AbA/A`AH/Am_A +/A^A /A s\Ap/A@[A߿~/A;ZA}/A`[zYAz<}/AXA}/ARXA8P|/AWA{/A`VVA֍{/AHUAHS{/AH^UA6{/AOJUAx{/A J>UA0z/AF:UAјz/AOUASz/A`T9UAz/ARTAy/A{TA([Wy/ASAx/A@`RA0nx/A |RAȓx/A^QAw/A`2QA7w/A@5:QA@v/AgPAyv/AAXJm/A` W>A2_m/A>Al/A=Al/A<Ae|l/A`]<Ak/A@8Aj/A@gw8A`i/A`5A i/A4A i/A3Azh/A S;3A@Bh/A`(2Ag/Apl1A/ g/A@u0A7e/A`è-Ac/AD*A+a/A&A7`/A 87$Ap^/A Au\/AnAHZ/A`DA(FY/AApYW/A Ax4W/AdAHU/A`AS/A- ABR/A` A Q/ARAQ/A ÄQ/AA0aQ/A A@=/Q/AcAP/AxAP/AAТaP/AAP/A'AxFO/A@AНO/A |=AzO/AA(`O/A)AТzN/A`KA4-N/A`×AM/AA xM/AqZA)M/AA~L/A@AylL/ATA/A`vuAP 5>/A@AB%>/A`]A=/A`26Ax:=/A9AG:/A`fA07:/A@Ah9/A@ZA(9/AA>=9/A c%A8/A SCA6/AA4/AfAH4/AEAk4/AsA%4/A (~A)3/AŔA 3/AxAp/3/AAȂ3/A A`T3/AEA3/A2A3/A=A`S}3/AAdw3/AAxq3/A@AP3/AA(\2/AA2/A`fSAd2/A`KkAxK 2/A`A`1/AAv1/A@>Ap 1/AE A<1/A?}A 0/AaAHn0/A`kAe//AA Z//A8A//A JA./AڟA@B./A`̖AH./A@AI./A൅A0-/AtoAh-/A>APP-/AAh;-/AAHP-/AdAЕ,/AN Axi,/A A@,/An>A+/A@P_AoW+/AgAP$+/A A*/AYA%+/AApΩ+/A@5A*+/A #A E+/A A8c*/A@TAj*/A`DA3*/A`@AP*/A`DAq*/A&IA`Ge*/AAA9*/A"AXW*/ApA)/AּAp)/AqAo)/A`"A@Q)/AAk8)/AXAHz)/A}A(/A`+A(/A` AM(/A`A@(/A AK(/ASAX[;(/A@,3A(#(/AA(/AA7'/A`A0L'/AA֕'/AAxv'/A 8`A-D'/AAP /'/A ͽAXV('/AiŽA@B'/AAQ '/A ½A&/AǽA =&/AҽA&/AA,&/AA&/A`νAj#'/AɻAppy'/AwƸAn'/AdA'/AӶA&/AA2&/AnAp&/AAAz&/A`Aج%/AzIAj%/A A%/AAVC%/A CA $/A`Az$/AKAT$/AA#/A+A#/AzA@`"/AAH"/A`fA`"/A9AtI"/AཱུA "/A害A0Y#/A`KA@ #/A`yAy"/ADAh?"/AA0f<"/A`oAH)5"/A@A0"/AjõA!/A"|A̧!/A.A w!/AܴAhQ9!/AzWAV!/A,Av /AӳA /A_A /ACAp= /AֲA() /A 1A k/A Aђ/AA7/AIA2/A֭A` /A`;A/A4jA@/AAE/A)«AТz/ARAo/A A@/AUAk/A`A/A@AP/AجA/A`A( /A|AHP/AxsA`/A A3/A`̳A`/AA/A íA|/A߿A_/AAA/A@Ax)/AA/A@ A(`/A`;iA0/AVA/A`;-Az/A}Aog/AŬAh7/AjA(/ALA(/A *A0/A@rAP/ARիA(/AEAP/AwAP~/A`7Ad/A AI/A JԪA!/AAh/Ad?Au /A@%A#/A`A/AF9A/A@*A/A-Axư/A٩A,/AĩA/A`A_/A`T]A H/AR7A$/A@A/AרA/A qA@/AA/AzgA/A;A@/AdAe/A JܧAG/A@ߟA@/A;hA</A`2ަA|/AaA0 /A?APIy/AAXt\/AG,AG/A@AXu!/AA/AЦAz/AĦAX[/A@>Apy/AVA8/AාAP /AgAf/A;AK/AfA8V?/A`A-3/A`AP (/A@ ХAp=/AgA /A`BAu/A`Ah/AqAQ /A AqA/AgA0H/A ZdA؎/A AWA`a/A6A/A %A֮/A@A0/A@A[/AפAP/AAl/A6Ah]/AT{AxzN/A`_Ah=/A@A,/A!A/AHA /AߣAP8/AॽAL/ARA/A@yiA/As6Ah̬/A A`G/A`o A(/A2Ax-y/A#ǢAč/A@A9/A աAp/A` Af/A2AHV/A APB/AൃA,4/AtA8/A?WA/A@?A/AAp=/AApp/A`)ϡAQ/A 뼡A/AAX۰/A5A֥/AjA0G/A`WA p/A*7A@/AhAw/A`oAo/A@ՠAf/A A]/AAT/A AM/A5rAFF/AZA7?/A@EA8./AA&/AA/A`)A/AʟA[/A A/A=Ae /A@A/A`8A/Aग़Au/A@pA=/AAA/AnAhQ/A#XA/A@$A/AtAB/AA(z/A̞AТ/A APv/A+A(n/A A`X/A`nAO/A S]A R6/A'Ae-/A|A'%/AA/A A@/A@۝A /A ƝA/A A/A`AXF/A@PAe/AoAX /A@[A`/AqJA/A@;AH/Ap$Ap/A`A/AӜA/AǜAp/ABAf/AAxK/AAQ/A+}AH/A@5AX/A A /ANAPk/A@yݚAU/A|A(/A !A /AzיAP/AcAx/AAPy/A]A7/A?Ao/AuA/A_A``i/A`KrAh@/AA/A A/A (A`eJ/A+A/ARIAP/A` DA/A ;AH/A 0A@/A^"A`/AHAѻ/AA/AA()/AڒA/AɒAX[/A@yA~/AAkw/AjAp q/AjgAi/AIHA`\/A)'A#U/AAtM/A@yAE/A` A>/AKA6/A^ΑAȓ./A`A#&/A3A/AٚAB/AjA k/A`|AP/AVmA@ /ATA' /AHA /AT3AL /AqAX /AA /A@A /AːAo /A AAК /AAo /A}Ax) /A`;iAHLy /A@`PA,l /A5A_ /A A`S /AAL /AfA7K /AAI /AHԏAH /AAK /ARAO /A jAAT /A,?AU /AAGX /A ŎA X /A6ŽAp0U /A@ƗAL /AyAؽ> /A eA7 /ARgAH, /A JjA# /A A /A3ȎAs /A A /A#A /ATAk /AFA /A問A( /AAp /AAQ /A偏A /A`}A@ /AjyA /AqxA /A`wA /AsvA( /A^nA(| /A}gAhs /A@>aAj /A XA`a /AqPAV /A'GAxzL /A=AxzB /A,5A`B9 /A`-AA0 /A'AX& /AA /AUA` /A@Ap /A|AG /A`[A /A@#A /AAx /AA7 /A`Ap /A`A /A`ێAP /A͎Aho /AŎA /AॽA̎ /A츎Au /AA} /A?As /A`KA@g /AA^ /A*AV /AAXVL /A@,AjB /AEA8 /A@AP1 /AzA ) /AnA /A?[A /A|GAF /A2Ap /A Ao /A A /A6A /A@A /AٍAG /ApA /AAP /A6Az /AQoA /AMAV /A +Az /A  Ap n /A@Akb /A@ȌAU /AA RG /AA? /A@vA@7 /A (cA/ /AOOAV' /A@<A /A-AP /AA /A`AP /AʋA(- /AзAu /A 8Ax /A?AX /AzAH. /A@rhA0L /A VAt /A3DA`Ǿ /A1AV /A ZA# /A# A¨ /AA@ /AA[ /A qԊA /A |Ay /AЍAm /AsA,_ /A|UA(W /A`CAPO /Az1AG /A !ApA /A6A(4 /AA(( /A?߉AH2 /AʼnA /A 䧉AH/AFA/A vAX/AcA0/A@5XA /A`NAp/A@GAp/A*A{/A(A/A@&A/A`DŇA(/AAI/A A/A`tA/ADvA/A;xA/A`A8D/AMA/A S͇Ah/AՇAx/A2A͓/A?A[/A`ˆAǀ/A@ψAhy/AԈAWo/AԈA)f/AԈA[/A`҈A O/A҈AC/AgшA7/AψAB./AˈAa&/A@ ƈA/A@5ˆA( /AA0/AA/AؾA/A A(/A ˆA/A`]ˆAh/AfAh/AA(ܳ/A"Aأ/AĈA/ÄA /A ՈA7/A[؈Az/A?ۈAj/AhAH_/AAhS/AAHE/A@,A0{8/A`؈A2/A ψA0"/ALjA/A`2ƈAV/A`A/A`AH/A3AP/A`A/A@iA؞/AGA/A?wA/AI^A-/A@iCA/A+Af/A`2A[/A AQ/A A/A҇A/AٺA:}/A^Apx/A \Ap/A׊Af/A AqA ]/AVA8R/A;A"G/A A:/A`A./A A["/A@yÆA/A@`AG /AA`/ApbAV/AAA/A`""A/A@A/A(Ax)/A AG/AŅADz/A`Apۥ/A ˁAۘ/A`]A(ܐ/AKAˈ/A@8Aj/A&A`w/A`Ak/A`Ah_/A A0T/A@>̈́AH/A̷A;/AVA//A` A#/AxA8 /A*KAx-/A3A/A A(z/A JA/AA/A`׃A#/A ƃA /A A/A좃AX[/A@Aج/A A~/A?mA8֋/AYA-/AEA py/A4A((q/A]$APi/A`Ata/A@AS/A APG/A@>قAQ;/AÂA`//A@,A%/A@A3/AA8/AtA8/A[A/A`>Ae/A/A/A`] A/ARA`/A JAx/A@A/AցA0/AA(/A ۪AH{/A A/A uAp=/A`vAx\/AbA[/A PA/A}1A~/A|Aos/AdA88g/A AX/AT؀AXg/A`KPA 4w/A@Ax/A ˺AZx/A ۙAkr/AjdApf/A@AAY/A <A Q/A`OAL/A`9AȲD/AVAE7/ANjA؁3/AsA%/AA/AAH/A A/AAL/A@A(/AfAx/A |AP/AtA/A@kA6/AHcA/Aj[AP/ARA臟/AjHA9/A>A(:/AW9A0./AQ/As/A'Api/AQ$Ax`/AA(W/AtAN/A# A"C/AA8s5/A?A`'/A`AH/AAD/AA/A@AHK/AײA/AAH-/AXA/AtA0/A@AE/A yA@q/AkA®/A^A/A;RAȺ/AEA/A <AXh/A3A z/A$A(q/A`)Ah/A`mAh_/AAV/A`[~AvL/A,~AhA/A~AT6/A~AQ(/A~A /A~A/A~A! /A ~AP/A@o~A`/AyA@.AyAh..A yA@= .A=yAo.A_yA.AyA@p.A@yA.A@yA.AyA.AyA.A yAP.A@yA.A yAێ.A`yAP.AayAw.A=yAxzm.A`yA d.AHyAP8Z.AyA'R.A`oyA`M.A΋yAx?.A JyAp3.A`yA8*.AyAxu".A|yA8V.AwyA .A@>qyA.AgyA .As^yAH.A WyAP8.AJyA.A J8yA0{.A)yA.AyA୿.A@ yA-.AxA =.AxAV.A6xA.AxAXۗ.AxAТ.A xAأ.AxA`{.AfxAr.AQQxAأj.A;xAa.A$xAW.A xA)O.A`owAE.AwA;.AwA(4.A`wAX(-.AwAF .AiwA.AjwA .A`wA.AmwAf.A\wA.A4FwA R.A q2wA.A wAp.AwA.A-wA.AdvAL.AvAp .AvA@u.AvA؎.AvAe.AGvvA0.A@jvA.A[vAw.AKvA#g.A %vA^.A3vAaV.A`vA(O.AduAHG.AhuA@.AuA1.A6uA[%.A`uA(.AtuA .A ~]uAo.A@ BuAA.A3,uA.AuAX.A`uAPj.A^tA.AtA.AftA.A3tA@.A`]tA@`.A^tAH2.A3tA.A"tA@`.AytAxu.AitA(-.AjYtAج}.A`2HtAt.A^8tAhk.A&'tAb.AtA(zX.A@tAO.A |sAF.A` sA >.A (sA؞8.AsA10.AsAA%.A@}sAP.A`bsA(z.A}MsA = .A9sA8#.A@isA.A`2sAQ.AorAX.A`rA-.ArA.A rAȓ.A־rA[.AնrAK.A_rA.A@اrA7.ArA.ArA.A QmrA.AYrA7.A?rA|.A \*rAv.A<rAPo.AqAg.A`qAk`.A^qAW.AqAR.AqAaG.A`KqA@?.AzqAx5.A`;uqA,.A^qA0j&.AGqA8 .A .qAf.AqA.A@pAhs!.A@oAr.A`ToA_.A=oA@.A pA8.A JpA`.ApA؎.A`pA.ApA(z.A*pAv.A ApAHa.ApAP.A`fpA .ApAs.A`pAh.ApA0L^.A2qAT.A@qA K.AqAA.A_qAx6.A<qA-.AqA$.AqA.A@qA.A qA#.ApqA@.A@!qA[.AqA`.A)qAp.A A qAj.A qAТ.A`qA.AtqAF.A`[qAXW.A,'qA .A@-qA.AqA8Ҳ.AqA.AOpAp=.A@pAf.A pAё.ARpA.A`pAp=.A`fpAn.A pAe.A?pA`\.ArpApS.AfpAI.AXpAjA.AMpAB9.A3BpAo/.A`f1pA'.A $pA.A6pAx .A oAp.AoA.AHoAh.AoAV.A`ժoA=.AzoA.A`oAh.AtoAV.A`oAF.AJoAp[.A >oA R.A@#.oA[.AoAۍ.A oA ҄.AnA@|.A nAGv.A@nAl.A^nAd.A nAX.A nAHM.A ynAC.A|cnA(z:.A2PnA@1.A@>nA).A30nA.A qnA..AnA0 .AmAP.A`mAt.A mA.AηmA.AࣧmA`e.AmA.AmA0.ApmA8.A^mA.ALmA .A?9mA8.A#mAU.A@mA.A 8lA@`.A@GlAxu.A (lAф.A (lAP}.A lAu.AؚlAh3k.AlAQc.A ulA8Z.A clAQ.A+SlAE.A?lAL;.A.lA2.A"lA8*.A`lAp".A lA(z.AQkA.AQkAX.AkA`.A@`kA.A`)kA(.AfkA(.AkA.AQykA`e.AkkA.A@akA[.A QkA.A (?kA~.A ~#kAp.A kAH.A (jAV.AjAa.A` jAj.A@PjAX׆.A jAA.A sjA~v.A`~jAo.AfjA(-i.A`PjA a.AZ6jAj[.A |#jAWU.A jA؎M.A6iA`I.AViAz:.A siA[1.A`iA).A iA`2 .AiA.AiA .A?iAp[ .A_gA.A TgA.A HgA<.Ap2gAƦ.A JgAt.AfAਔ.AfA`.AfA&.A]fAX[.AffA.AФfA(.A_fA v.A`fAH.l.A_ffA`d.A,WfA`e[.AGfAQ.A@y5fAI.A`fAB.A`eAf<.AeAe5.A@eAx,.AźeA =$.AeA<.AْeA``.AUeA( .A{eA@u.AxeAu.AreAp.A=ceAP.A_NeA p.A=1eA.A@eA.A@eA.A?eA.AdA̺.ApdA.AdAX֩.AdA,.A dA.AdA7.AdAhQ.AodA8.A@ \dAz.AOCdAr.A0dAXi.AdAb.A dA~Y.A cA2Q.A cAjJ.A cA[A.AcAG;.A`cA)4.A@cA*.A+wcA#.A ccA.A@NcA.AQ=cA( .A%cA.A cA.AbA.AbA(.AbA.A |bA(z.A^bAP.A@֠bA7.AbA.A Z|bA.A fbA(.A`MbA(`.A=bAh.A 'bA跊.A"bA.A`]bAaw.AZaAPo.A aAFh.A`)aA(z_.A`aAV.AŶaA0O.A[aA3G.AaA?.AgaAp1.A`aaA8).A3NaA.A`5aAt.AgaAp.A aA-.A+`A.A`AP.A `AH.A `Ap.A`2`A.A`A.A+u`A.AZ``A0̽.A` J`Ap.A/`AK.A`A.A+_A.A<_Ap<.A@_AH).A`)_A;.A_AȘ.A_A.A _A.A_Ah~.A_A`~.A _A}.Aʹ_Ap}.A@G_A.A@r5^A].AB]Aۢ.A`]Aբ.AI]AC.A f]Ax.A?]A k~.A]Aux.A`^A@[.Av_AH[.A x_A.[.Az_A`Z.A{_A8L.A_AB.A@Ƙ_A:.Aପ_A2.A^_Ax).A_A!.A_A.A``A.A `AȔ.A@9`Ah.A``A`.A`A0L.Ad`A .Af`A(.Av`A .A`Au.A@`A.AaAXR.A%aA.A@`4aAX.AGaA@B.A NaA׽.A@PaAp=.A@`RaA( .ARaA.A`NaA蔖.A LaA.A LaA.AzIaA({.AoFaAp s.A-CaAn.A BaA_.Ag=aAR.A[:aAI.A6aAP@.A@1aA06.A 'aA$+.AqaA .A aA.AaA.A `A.A@``A(.Ag`AH..A `A.AZ`A(.A ~`A.A`A.A`A.A^`A@.A<`AP.A?`A`.Ah`A`B.A`|`A2.A``A7.AO`Ax(.A@5`A`ǃ.A`A|.A+`At.A@_A-n.A_Af.A _A@].AG_A((X.A_AeO.A 1|_A[@.ARi_A(7.Ag]_A,..ApP_A".A?_A.A,_At .A_Aho.A _Ap.A^AF.A ^AF.A^Ay.Ap^A .A`^A\.A^A.A Ak^A.A@^AX(.A ^A0fu.A@]Am.A]A kf.A]A7`.A`"]AV.At]AM.A]A2B.A@ ]A@p4.Ax]A'.ARa]A.AG]AX.A 9]AP .A *]A.A]AG.Az\A07.A\Ah.A}\A.Aú\AP.AĮ\AA.AƠ\A(۩.A\AM.A ۂ\A.Ao\Au.A S\A0.A@7\A~.A4&\Apx.A`[\A`o.A@[Ape.A` [Ac.A[AZ.A[A`L.As[AP8E.A,[A=.A[A..Az[A%.A@k[A0.A@\[A.A` @[A .A?%[A.Ay[AG.A`ZA[.AZAأ.AZA.A ZAx(.AsZA.ATZA.A(ZA.A_YAa.AQYA.A-YA(-.A4YAP.A gYA.A8FYA.A@YA().A@EYAh.A`XAo.AؔXA.AXA.AXA.AtXA`.A ɿXA`.AXAx.A@gXA.A YA0L.A?;YA0.A`YA M.A`YA<.A@YA.AYAxƵ.A@ZA.A 8$ZAXװ.A?IZAP8.AiZA.AZA3.AЧZAl.AZA".AZA'.AZA›.A@>ZAu.A[A.A@?[A =.Am[AX9y.A@ϐ[Av.A [A8o.A[Ai.A@>[A`d.A \AH2^.A\A_V.A B\AP P.A]\AH.A@u\A0f9.A֒\A1.A]\A*.A\AH-".A \A .A`\A`2.A J\Ap=.A@\Ak.A \AȔ.A@,]A.A]A``.A`"]A7.A &]A.A-]A8.AF6]A(.A<]Ax\.AsD]AL.AK]A .A@Q]A.AX]A88w.A]]An.A`"b]AQe.Ae]A\.Ai]APM.A` p]AxWA.Ax]A@=;.A@y]A`.A]A8.A`]A .A@]A.Aߧ]A.A]A07.A]A(z.A@`]AQ.A ]A.A]A.A]A.Aq]A`.A ]A.At]AX[.An]A.An]Atx.A n]Aho.Ag]AP`.AZ]A@X.A P]A0Q.A G]ApC.A36]A5.A#]A ).A`]AH'.A O ]A`.A\AH.A\A`.AQ\A@.Az\A(.A\A.A@ \Ax.A {\A(.Ap\Ak.A$p\AX.A@5k\A.Ah\A.At\\AX֦.AG\A.A`:\AH).A ,\AF.A@\A.A \A7x.A[APr.A[A2l.A[A_.A`[AȔQ.At[A2D.A[Ah.8.A[AL+.Azm[Ah.A-U[A.A'?[A.A-[A0.A [Ax.A[A.A[ZAHa.AZA.A@iZA.AZAh.A ZA.AZA3.A }ZA`.A@mZA .A_ZA.ARZA@.AHZAu.A"0ZAجm.A JZAww.Al_YA(.A@CXA¬8~.AeXAS |.A~!XAZ{jy.A3wXAef'Iv.ApmXA+;r.AkeXAh[n.A T'`XA/j.A}]XA*~f.Au\]XA۽jb.Ah`XA骫_.Al\GfXAz[.AjnXAX.AM,yXAU.AXA7JI.AbXAv@.A.YA].A HYAlO.Ad wNYAp*q.A[@YA^.A3YAuQ.A6KXA5b$.A~XAM.ABXAQ?.A( WA.AٓWA`v.A@;WA.AO 0WA .A<%WAz͝.AsWA a.A }WA6U .A62WAKR.AU WAdj.A WAm.A WAAJ.A\gWACV.A)R%WA(.AWA̒.AؚlWA妃.Aޅ[WA8.A!XA.AN1XA1ehY.AEN.XAUc%.AjyXA{w.A)WAڿ.AF{WAv&.AkRiWA8Z`.A@y WA}_.AVAʧ`.AbPVAhc.AfVA@^b.AfMaKVA_.A%2VAo;\.AtijVAҸW.A$VAFiR.AyUAqsL.A*|UA`F.A@CUA$uQBA.ACLUA<@<.Av5UAa8.A]UAF=h5.Aol VA;2.AwwVA%s0.A2Qd-VAt/.A[AVAHr..A |UVAmI&.AcHVA Y!.Ae7jWA.AWA@.AEXAB.AvXA.A|XAFg.A#7YA=z .AYA.AFYA(J.AYAތ.AYA]%n.AE:&YAk_ƙ.A0\ ZA .A@ZAh.A@5YAku.A|MYAż.A`WeYAmz.ACbYAH.AӋYAjn.AJXAݽs".A!WXA0;.A~WA;m.ALS]GeVAq.As$;UAt}.AgsuTA~03J.AZRA@6[}.AWYQAe.A`fOAQ.A%1NAӿ @.AtAMA&Ɖ.AiM|LAjΌ.ATv\1KAߍ#.ATxIA"s.Aeń_cHA.AOFA*.AqkMEAs.A`TCA=.A6AC.A #Ah .AA8.A )AE .AtAD.AA6.A`KAW.A@A%.AlA@.A |Aݛ.AfA.A@lAҗ.AwWAyP.AZuL1AǗU.AǐA/.Al6Apߔ.AyH*WA`)'.A]JvA Sq.ARE%rsAL+.A,Ax .A.MAY~].AG&YAcư.A)QA.AfA(.A1 AoK.A$A.A-AW5h.A0$.Av.A!XA&.AW2 Aw&M.AtS2޽A{EV$.A1:A u.AvᦼA+.A$Aږ.AA2)&.AR\kUA@.AA܅.A`DAH#.A5NAPoq.AAq.A@gA3.A}AMd'i.A AU) N.A- NAxX%8.AOA*J>'.A|AEn.Aʦ`AY.A.Mڵ A$.A%#A6*.AT|%AP.Aà"'As/-.A(7*A!9?.AZ,A"W.AL.Aͱt.A, >1ASP.Apl3AS!J.A.Xg5Ai.AW"8Ar:p.AA;]:AO.A{Fp<A쉌.AI>Am8Ȍ.AM^@AV?D} .ACHCAT.AŢ$EA8.A$0GA 0.A*g2IAFYpG.A;(KApߠ.A@pMA.A@EYARy5.AsZApG%].A 6c[AD œ.Av&\A80.A(\A h~.A:}w]AɷV .As^ANHy.A!^A@.AQ^An_^[.A;_A*@͖.AWX}_A0=T@.A+g_A}.A_A=T.AN\_Az.A [_A޾Yޘ.A_A5%A.A5M_AI.A-_AGl.A-`A& i.ApU>`Ab`ɚ.A`AZ(.AXaAja.A|RraA.AlEZybA|m=.Aeg6Q#cA6'.A ~cA.A ndAli>.A ceA(.AmfA/r.AiA$ .A}A^..A~A# .A]0AuΞ.A~#XA{N.ACἂAOav.A.AW..AK&A![.A;A;;§.A6eA'.A`bJAg.A@AXa.A0A$ƪ.A9A.ABAFH.A`mA`1.A At .Aq7AH.A`GA.A`.A(.A@4A .AXAs.AAxŴ.AApP.AA p.AAA.ACAx.AwA.AcA.A+A۶.AzA.A@A-̶.A;A|.A%A.A%A؎d.A=ŗA>.AAz.A^A.A9A00.A`ßA!.A /tA .A븢Aq.A sA7.AAp9|.AAP.ARAh.A AX R.AĮA.A Aj.AA`G.A~A@.A@KA.A| A.AjA&.A /PAU.AA.A@`Aà.A qA .AA.A`]Aِ.A`A.AA '.A JA.A`Ah".A QAx6 .A[A .A"A.A"Ab.AMϿA;.AA0.A A@.A;A(.A;A0.A^AH.A A.A`3AXhE.A`KAHz.AwAp.AAp.A3A.AA$.AѠA`%%.A~Ax\.A Appn.AƎA؎}.A[ʶAF.AA虗.A A\.A ȶAp.AA@$.AA0.AspAٺ.AA.AA.AXApP.A 퓺AF.AܢAFAޠ0.AgAXL.A} 79AnQ@l.A,jǤA W.A2eA}P.AO=hAeH.A*ߊأA؂3o.A~s A0.AtyAp[.A As!`N.AnRA9H.A&A{{/J.A@b*AL#.AwzvA1J\.A^џAͩ .AnC;A.u.A 8Af.A CAZ.AEQAq.At AN.ACQA,*.A(&A֦].At )A6.oo.A>AI2.AQfA9_w.AHyA;Nq.AALj.Ax>NAf{U.AdAM.AwCA5.Ap`ٟAb.AlȊA=.A9Aex.AvLAW_.ADܢAAE.AzA7ǣAAQTO.A;A.A˥A.AAx.A?A J.AØAL.AA.AMgA k.A|<A2 .A`:$ApA5.AZfKA3c.AG9A}g.AlAI.AZZAS.A٭AR¾-.AOaAc.AHÁǝAZp.A\ݾA}.A0eʝAF.AA<.Ap)"Ao.AfA5.AFhs㿞AT>.Ag ,Aj .AL_#A_].A%<A&j.A{A11:L.A^@8A;(.A^AF.A7AzE.A!A2.AA-.A /AX.AA.A A0m.AjApN.A &A.A A@.A ۓAx.AAr.A@iA.A`;?A{.AiAP.AAp.A`xApp.A AAP@ .A`AL.A Aa.A=Ak.AA`ԉ.ARA.AA.A 4A7C.AA.A?AHP.AA<.A`A.AAؽ.AA(?.AnAp_.A AX E.A@,A趶.A`mnAhb!.AoA`d.ABA`.A`AH.ARsAd.A AP/.A&AP<.AAhA.A`mA`.AA.ANWA(h4.Af(AhUs.A3Ae.AʀA˖.AA@>.Ag>A茑.AMA(>.AAї.AAH.AdA/.AAx.A$rAH?+.A AH.[.A A.AAA .A`"Am.A@Ao.AA$.A JAX.A@>A .A`2A`q.AqA.A A(.A'A.AA.A l:A@.AkA0;.AuA.AMrA(.AZA:.A .A@.A@A".A,AV.A`A$.A 1Adn.AA@.A]Ab.A@?A#.AMAh.AjAP^.A@0A.Ao1Ax.AAo@/A#A`S/AMAп /A |ZA$/AA\/AqA R&/A=ATR/AAo/A`SAľ/AAP/A3A/A$A`T/AAI/A`A(/A"A/AHiAx$O/A3A,/A$Aث/AA/A`bAH/A |A c/AXA(܀/A@GiA/A \A/A}A8E/AAǢ/A A(z/A`A#=/A sAh/AVA/Ai,AF/A(AP/AMAX/A`]A`/AA8b/Ai,A0/A@ A7/AAȃB/AA0>/AA?8/AA`v/A@PAX(/A`T2A`M/A=AD/AAJ/AA~/AEAP/A AA(/AA//A@rA?/AׯA( /AAl/A@.AX$/AzOAX/A`bAr/A ˊAp'/A@AXJ/A[]A]/A@A/AvbAn/AAp/A-As/A=wAe/ANA0/A;A`/A@4A(/A ;A((*/A=A@5/A@AA8 ?/A[@A`J/A4>AW/A:AЄc/A`d3A k/A*Am/A(Axw/A2$A@}/A !A\/A A/AAPo/AA(/A" A/AAU/A3A跷/AA/AA/A`AX/A*A/A@A0/A@A(/AA$/AjAP</A JA/A`AW/AA##/AA@+/AA؎5/AyAP?/A$AXWI/A[6AhQ/A@rFAZ/A@_Ad/AHzA[m/AŒA(t/AƤA{/A Aփ/A6Aň/A@5A(\/A AAۘ/A A /A AX/A:Ax/ASA,/AiA/A|A/A`KA(/A`oA/A@A/AAH./AHAP /AA /A_A /AtA /AF4A /ADA% /A ZTAX. /A hA7 /AtAQ@ /A6~AAX /AZA7 /AA /ARA /AڪAX /A AXW /A`2A0 /A@`A* /A A6 /A/A7 /A QAD /AAO /AA؎W /A QAV_ /AZAH.l /A@yA0Gt /AA=} /AqA@… /A@ A8֏ /A@`A0 /AwAU /ApA /A`"hA( /A`]`A[ /AXAu /A (KA0 /AdAA /A ;A /A7A[ /A}1A\ /A +Aj /A`"A, /A@App) /A@AV* /A`A4 /A`AB /AA`O /A AZ /AEA(c /AA pm /A?A0v /AA83 /AA8 /A`A /ARAPk /A`fA R /AľAP /A ˳AX /A@ A(z /A@!APo /AA /A@AP /AA0. /AEA /AA  /ARAP8 /A@5A /A`[Ap /A`Ah( /A`KAؼ3 /A<A7 /AA> /AנAHI /AA0jT /A At] /A`APh /A6Ar /AA| /A A@u /AAp /A,Ap /A`A /AA /A A /ApAV /AdA8# /A`A /AAp8 /A{Ap /AuA/AqA( /A`KnA=/AmA%/AjAH1/A AiA8V</AfA(D/AeAeM/AzcA`W/AcABa/A`fcAj/AcAp/A_cAs/A)cAxz~/AbA/A`fcA/AeA`/A gAѤ/AjA0{/AmA/AoA/A/AHrA)L/A@uA[/AzAg/A~A(\n/A}Ay/A|A2/AxA/AtA/A tAa/A@tA88/A vA/A@xA/A@yA"/AzA/AzA/AzA/AqzA /AdyAG/A@yyAX/A@ryA /A QyAX[/A@yAHz/AyA0(/A/A qUAX#@/A@]ZAnA/A^AlC/AacA0E/AhAmF/A෗lAVH/A-qAp}7J/AT~tA> L/AwA8N/A@-{AgxP/A` {A(Q/A`{ARR/A` {AT/A)yAH) W/A!wA\Y/AtA.Y/A JuA@[/AЇuA`]/AvAd_/ABwvAP7_/AvA0&a/AZwAc/A@wA`f/A=xAb1f/AQxADf/A.N}yAAzWZf/AmimzA,3tf/ANž{ABT큒f/A|#|Af/A A}At9yѼf/AW{d~Af/A@#~A6$tf/AX iGAf?f/AwA[Nx g/A1_'A&g/A*A-:g/A}kACPg/A@rAXg/AhAxg/AvԁAg/A_A0 g/A@Ag/A@`(Ahg/AAg/AAhg/A@%NA( Yh/AA@F`h/A |AXҖh/AOA`1h/A7A@ j/A;A0Cl/A$FADl/A`\A@Fm/A]A-w/A.A[w/A AЀw/AӒAHy/A`d5Aaqz/A`muAЕ{/AkpA(O|/A`Ah |/AʇA } |/A`;iA̍}/A AƘ}/A dA}/AAH}/AAG}/A`APf~/A3LAh~/A äA(~/AE?AXF4~/A`2AC~/AHA#L~/AfAXO~/AAy]d~/A3[<Ad~~/AFA~/Abc0AO/x~/AeEA~/Af#AX/A'Ah/A0A/A 1AH/AٲA/AzA/A@C³A8֯/A` ƳA 0/AA/AʴA(/AѳA/AѳA( /A`A(/AcA8/A@PA0/A AK/A@,AЄ`/A@5A{/AůA0/AA/AdA‰/A A(Ӊ/A {Aȋ/A9A8'/AAkM/AAw/A+AH/A@AZ/AAߌ/A xA/A<A`,/AEŶA!/AAH/AAs/AA8'/A@iQA(/AhAˑ/A@p7AXh/AAA/A|bAڦp/AfҿAے/AA/Aʅ~aAz_-/AmAxW/A AS/ALAxR9ē/AUA/AڼLAi(/A0A0W/AAh]/ANA/AA̢/AAHˆ/AhAhA/A0gA&/A.A./Ag@A7/AAWa/AYAPMr/A@E~Al/A`A@m/AA/AAἕ/AAP/A+A#/A A0,/A AA/A` Ah[/A6A/A"A`/A}A/AjAy/A}A/A/AXF4/A@AX/AADk/A A(r/AAU/AA([/AApʭ/AA /AA˗/A`A"ؗ/A`WA>/A@A/AsDA/AbA/A`QA8$/A9Aȥ,/A`;A`]/A@ϬA/AQAⲘ/AAƘ/AxAPԘ/AA{/A@A /AA/AQAXhI/A`Ah/U/A@;APh/AEAȲu/A@PA(/A&A8/A sAxd/AA!ƚ/AAܚ/A *A/A A/A`6A0 /A A4/A (4A5/Aq4ACj/AAn/AQA8/AAPכ/AAH]/A A@O/A ZAh/AA!/AA5/AgAwF/AtAHnT/A@Ad/A Ahx/A@ A(~/AnAq/AdA/AdA՜/ArAa/A|Ay/A`APH>/A9Ao/AĀA /AA=/AgA՝/A9lA/A JAȋ/A`vA@06/A^AP/A@>AVc/AvA@$p/ATA/AsAե/AoNA}/AaA0/A 8A/A`ÕAN.Ϛɞ/Ap8Afܞ/AVA /AA:/A`̲A$A A(u/AwuAh}/AxA@/A`bAx~/AA߅/A XA/A|Aʚ/AA@/A YAP*/A@!A oբ/AhPA+/A/sA8/A@yAH/A4A0/A`Apƣ/AẠ/A,A@_/AA/AA/A5A(/A<A/A८A/AݭAף/A,Aɣ/AvA)ţ/A@Ap/AHA/A@A8/A@AHR/A`)AQ/A`åA`rM/A`A{H/A A/A=`A̲/A`)<A#/AHoA=/A@rA@׮/A ~Az/AA#/A=AQ/A@ !A@׵/A/A_/A`)Af/AAm/ARAx/A@rAR/A`A P/A"A8,/A AH\(/A qA/AzAu /AzA(ܸ/AAf/A`)4A@/A-]A/A=cAG/A܄A/A`AJ/AAV/A |A~/A A7/AjA/AA0/A#Af/AqA؞/A:A/A&A/AfA/AA ֣/A@A@W/AA¢/A \wAp /AzA0./A@A'/AzAf&/AAp/A@:A/AAX /A3AH./A ^A8/AnA@W/AsAջ/A@;Aã/AA(ģ/ABAXΣ/A (AxFϣ/A A5أ/AtA٣/A@YAHK/AA/AA/AA/AcA/A/BA/AFAP/A}3AHn/AAXH/AA S/AyA@]/AA$ڳg/A@Ū>A9q/A A+4C/AzjAAu/A)Aȥ/A Auե/A`A0/AOAxם/AAĴ/AAP/A` AD/A A`//AA8I/AA~7/A`AX!/AA( /A QAhD*/ASA+/ApAB/Af$AU/A Q<A/AAƦ/AhA1Ȧ/A 5Apʹ/A`AX/A 1A/A,A\/A/A/A:A`/A9;A~/A@A=/A@A/AAA/A<A/A2A8/A/,A¦/AO$AĦ/AHAH2Ǧ/A@AϦ/A |Aզ/AA Rۦ/A`2tA/AA/A@A/A@WA8/AA;/A TA /A6A/AA/A@GA/A`TA /AxA0*#/A`Aw%/AA'/AAh-/A9A4/AAQ/AA8X/AAz_/A)Ap/A`[ A}/A!A7/AAY/ARA-/A@5A/AjA(/A/Ap_Ƨ/AA0jԧ/AIAx/A$2A,/A \A౨/A@*A(/A@{A/AAUX/A@CAa/A@g-Aq/A+Axw/A@Ap /ApA/AA|F/A AXh/AAq/A@Apyz/AAl/A`A7/A3A/AxAأ«/AcA /ͫ/AcCA/AAi/A@,A8/AhA([/AAe_/A`Ac/AױAsq/A@tA0v/AK_A|/AMA/AVFA/A AAx䆬/A@<A튬/A:Ah⓬/Ak:A/A$@A/AMJA/ASA0ݯ/AQAt/AnTA0w/AWA(ܽ/AXAqͬ/AZA1ܬ/A`T\Ah/AjZAk/A3QA`/AIA A/A@?Ah/AAho9/AAW/A&kAAd/A@SA怭/A3%A1/A`) Ah/A@,A8/A;A/A`A/A Ap/A`A0+/A Ap7/AhA@/AઢA J/AA0S/A@hA`/A@7AxWh/AAo/A@5Au/AA8u/ARAz/AAހ/A@GA؍/A`AĈ/A lAy/A`TA/A*AH/A`A(/A@WA0叮/A`Aю/AA /AA@d/A (0AP/A)A/AŒA/A AX/A?A/A@yA/A |A0/AAP/A@rAtƮ/AAؼծ/A^ARخ/A9Aڮ/A`mAڮ/AFAڮ/A ZALخ/AAp׮/A`2BAP֮/Aa#AYծ/A A0jӮ/A@,AҮ/A A Ӯ/AoAi֮/A \Acخ/AA<ۮ/AEA`ۮ/A@AA,ܮ/A<Aܮ/A`8AȺݮ/A(5Aޮ/A1A߮/A.AЦ/AX)Ah/A A%AGA/A`]AX(+/A A0/A`A 6/A AȶQ/AA[/AA/A JPAƬ/A A3/AلAK/AA1/A`AL/A@5AD/A6}A0/A {A8"/AAx/A J~A( /A?AR/A@A`/A OA/A`AP+/AA&"/AZA $/A`AȘ*/AA ./A1A";/A@%A>/A` AA/A`IAI/A@,?A8"Q/A ZAZ/A`oA]/A A0&_/A4aAȶ_/AA_/A`Ap_/AFJA(K_/AA,_/AA@_/A@WAF^/A AX/AAZW/AAT/ABA0R/AAC/A A8/A O-A/ALA /AA"ܱ/AAHб/AA@/A PAA/A΁AX^/A`]AT/AAX/A5A/AxAJ/A/JAp^/AAN/AAT=/A A*/A[A[ׯ/A@!*AxK˯/AAAd/AfA r/A AU/A^Ap4/AhA/A_AҮ/A_Aa/AKPA;/A3AG/A=iAXҭ/A൧A`a/A Aݡ/A lAxGJ/AHAH/AAu/AA #//A8A$ /ATA8b/A-hA{/AsAV/AuA /AZuAx/AtAp/AsA /ArAm/A`oA/AjAp/AaA@/AaBAY/A Z;AP/A5A>/A@>2A`/A /A/A .AhD/AT.Ap/A.A/A .AZƫ/A7Ah/A`dIA`Б/AZA/A`cA/AiA߄/A pAXw/A Af/AAXFI/A`;A00/A@iA7/A :AP /A ZAp ɪ/AAx/A^rA/AV~AD/AAp=}/A`ܰAs/AA@n/AA8j/AA8"g/A'Ap8d/AA P/A̓A85/A7AH//ANhA"/A AA@~/A<A/A@Aذ/AkA/A Ap/A`CA8/ANAؒ/A@yA;/A@>A=%/ANAH,/A@A 0/A0(A9/A@DAdA/AcAȃD/A tA#G/AAEX/A@AQ[/AhA h/A@πAjn/AsApo/AvAxp/AA q/A|A]q/AmAq/A`Ar/AA w/A@iAw/AnAw/A AНw/A@>)A`aw/A 0Av/A O8AYv/A ?Au/AFAgs/A@WTAsp/AaAi/AxA,V/A@A0jQ/A@ApO/AAM/ApAoK/A`KA)I/AA0@/ALA/A`AxA/A"AB/A@N$AXC/A@&AhC/A<)AD/A},A cE/A?0AF/A`4A(G/A>AG/AJAgH/AXAH/A |AؚG/AA.C/A A`A/A`A@/A AEA @/Aq`AhA/AlAA/AxAB/A A@C/AXAoD/A`A [/A`]A0L{/AlA/AA(J/AfAxi/AA /AAXۖ/At(A/A6Aꘪ/AHhAз/A^zA c/ARA(:/A?AA/A[A(z/AAΟ/A A/AũAh/A`;A A/A 8AZ/A[AV٪/AW Aܪ/A a A/A /k A/A AV/A A/A& AP/A2 AV/A A A /A AU/At A8/A5 A /A A/Af AB/A`̱ A t-/Ay+ A"=/A@y A\>/Aj AN?/A A Ahb@/A Ahb@/A AP?/AQ A(9/AT AX 7/A A }6/Aj A~6/A © A 7/A` AH 8/A`9 A'8/A` A:/A A?/A`) A=D/A AJ/A@0 AhR/AF Ae/A`;r Aj/A~ Am/Az Aew/A` A/AR A$/A#AV/A +A1/A(A1/ApzAe/ATAPë/A`;-A5ȫ/A`IAݫ/AA/AAh/A3A>/A@A%/AA/AdA/AwA8/A߇Ax/A`9Ax/AHA0/A A/A@iA(O/AA/A Ah/AٸA6/A`A$/AAH/AA~/A |A8/A"APE/A`@A@uW/A@sAdY/A@WyAp9[/A`A^/AA4g/AAAxhj/AAPm/A=Ao/AAq/AAP{/A@Ao/A@%AH̯/A?AUì/AdKAHn/A`)]A/AH`A< /AQqA+/A]AO/AA#u/A Az/AAXۆ/A JA3/A_AU/A,Axɭ/A9qA٭/A`KA/AwA /A;A)/A`AP6/AAܯ/A=A/AzA3/AAt&/A Ax*/A A M4/AA8/A@A@L/A@W|A6i/A`CA/A`A/AA/AwzAP۰/AAi/A`Ap+/AA;/A A/A.Ad+/A Ah@/A3 AQ/Al A^R/A-i AS/AQe AS/A`2a AS/A\ AT/AX A1T/A'S A T/A"H AzS/A<> AR/A2 A@M/A@ AL/A APkL/A@ A{K/A8 AHK/A@ AL/A l A[O/A`v AO/ARi A(P/Ae AQ/A`)a ATX/AjO A_/A@,> A{/A& Ax-/A@y A"/A A(욱/A l AP랱/A A'/AV A+/A A0f/A`) A,±/A Aȱ/A A0G/A A </A Ax /AVA,/A A:%/A@C5A2/A@AX>/A A2T/AIAm/AgAأv/A A}/A@Ap/AcAho/AA/A|BAơ/AoAp/AA/A@AЮ/A>Ac/A`)A̱/A?A8I/A+wAН/AA$/AA#/A`9A¨/A-oAx/AUA(/A`KCAۦ/A >A0٦/A A/A`)qA }/A VA(>/A9A/A@APޡ/AAh/AA/AA/A`A%/AA/A AՐ/AQA~/A \AXz/AXAv/AAu/A6A]t/AoAfs/AOAr/AA\r/AAhr/A`A0q/AA@q/AjA0q/AA`ؖ/AAxƚ/AЩA/A@ #AW/A@yA&Բ/A@#,AX/AA/A2A8'/AjAt/AA/A@NtA/A OhAt/Af[At//A A1/AjA3/A@A_:/AA( A@W/A` AW/A@l A/A@ { Ak/AG A/A A# /Ap3 AF/AM8 AB!/AR Aq/A- Ao/A` A /A A /A A0o/A` Am/A@AHr/A@ϔA /AiAm/A`Ah/A6+A|2/A4A@2/AcA//A"_AU/A`GA`/ADA/ABAPx/A(>A/A0A³/A@!A/AA/A`A$/A A}/A APHk/A`]Ad/A ɼAH{=/A@yHA|7/A6A(/A AX/A@Az/A dAX /AGAp=h/AAuv/A`f4A1/A@WAP1/A`AP)/A}A(9/AHA˳/AAf/AWgA/A^A0;/A]AX/Ap`AV/AlAUy/AAxm/AoAc/AAp/A~A/AA/A5A% /AWA/AA(iֱ/A@IA0;/AXAXRv/AAki/A"Ad:/A@PA 2/A A*/ADA$/AA/AAJ/AO4A/AYAd/AjA/AyAu/A3D A%/Aab AȓӰ/A-"A` Ұ/AU7"Aȃ/A /A $A/A$A`/A`$A/A$A`/A &$A/A9%A/A%A /A Sd'AP/Ah'AQ/AG0'Au/A/ 'A/A&A/A`ܵ&An!/Am&A0!/AT&A #/Af&A#/A[%Ao$/Az%A$/A %A@%/AZX%APw&/A$A&/A@$A<&/A@$A%/A#AHn%/AO#A`$/A`"e#AH!$/AJ#A(:"/Af"AP@,/A "A0n//Aѝ"A7+/A`m"A",/A|"A3/A`D{"A:/A7#APM/Aq/#AhM/A4#A/A #A(/A"A~/A`m"Ap/A "A /AH"A E/A"Aз/A@{"Ax˰/Ab"Ai/A/#A(\W/A6e&Ah"H/A&A>W/A1nH'Axg/A'A0{z/A'Ap,/A='A ]R/A_Q(A%/A ((Ab"{۰/A{W)A/A[G+A /A+A /A ^,Am/A! },A3/A,A/A/0,A0!/A,Aec%/A,A7*/Af-A0/A3-AwJ7/A@$,-A@=/A9-A>/A:-AEJ/ARvVJ-A]W/AdQ-A&d/AO-A0m/A@rL-Ax/A Q-Ah3/AT-Az/Ab-A{/A=n-A R/A=t-A/A`fz-A/A-AB/A@-A/A=-AHܼ/A]}-A/Az~-Ap=±/A-A#ű/A-AXƱ/A-Ap=ȱ/Aש-AQϱ/A-AH.ر/Az-A`aޱ/A`f-Ap=/A |-A8/A.A/A@$.A/A`*.AG/A..A/A=.A`/A@L.A/AW.AB /A \[.AX /A@ W.A8/AV.A8/A^.A8/A`fb.A`/Ak.A`'/A@z.A+/A`).A@./A.A//A.AH//A.A`a//A.A1/A@ .A7/A .A1Ap=/AGK1A8/A3]1A{/AGm1A-/A1A R/A1AL/A`f1A/A1A /A 1Aأ/A1AL/A̰1A/Az1A/A`)1AP/A31AP8#/A 1A(/A@1A//Aֳ1AVP/A@r1AR/A1AZ/A2A( [/A[2A]/Ap2A_/A(L2A_/Aw2Apk^/A`f2A`/A2ALb/Aõ2A@Wf/A2A@j/A`f2AXn/A(2A)q/A`~2Ar/A`f2As/A`)2A`t/Aק2Afu/A2Aw/A`2Ap|/A2A8/A2A𣅳/Az2A#/A`f2AP/A2A̘/A` 3A/A(3AP8/A@ M3A`a/A@ e3Aǘ/A3A/A`f3AQ/A33A/A3A/A3AP8/A 4A0./A4AP/A 3A/A@3A׸/A`3A`ẳ/A4AB/A4A؞/A@ +4Ak/AB4Adz/AnX4Aȳ/A q[4A؞ͳ/A qm4A޳/A=4AP8/A@ 4A/A4Ap/A \ 5A/A`f,5Az/A`:5Aأ/AA5Au/A@>B5A`/AR@5Ah3/A@>5A`/AJ5A R/AW5A0./A`)^5A/ARh5AP/AHw5A/A@g5A/A`ñ5A(\/Az5A؞/A`)5AP/A@>5A/A`5AP/A'6A R/A76A/A P6A/AGe6Ak/A@>|6Ah3/AR6AX /A36A/A6Ak/A6A0./A֑6A%/A֏6A{,/AR6AP85/A6A/AdC;AЄ>/A9;AWK/A0;Aj/A ;A/A:Ax~/Ag:A Ab/A 0>A@0m/AXa>Af/A`K?Ak/AH?Aj/Ax?Ab/A?Ak_/A@A@Fq/AՑ@At/A@,?A(\o/AK?Aq/A8?Ap=x/A?A0~/Aa>AXے/A,?>AѪ/AG>A/A=A"/A>A t /A`_>AH/A@in>A(@/A?AT/A;?AhDX/A@y??A8[/Az@?A0La/A/>AHj/A>AP /A?A(&/A?A`%./A?A<*/A3?A/A?A/A U@Ad/A`W@AXt/A@Ac7/A A@A0:/Aa@Ah?/Aa@APC/A>AiU\/AC>AIJt/A.+`>Ay6/Au;>AraS/Aڨ%>A@þ/A >AXl޾/A])>A_6c/A~pC>AYt-/A6n>Aa:0S/A谟>A,w/A0>A0t/AM?A/A_?A^/A@?A`B_/A`K?AH\/Ax@A8]/Ay@AHa/A`o@Ao/A`?AA/A=DAA/AvAA/A@!AA`/AAA 2/AAAH3/A3AAD/AqAAU/A,cAA;?D /AЮ&AAzo\R/AaZ@A/Ay@A%|/Af@A\!/A'Dt@A$de/A&AA]/A-gbAAWZ/A0>[ñAAb*/A`.BA h/A9(!BAFB?/A41CA, /AxgCAqk/A?!iPDA`G/A@EEA+X/A +EATl/AEA/A0EA8/AFA()/A FA/ABFA@/A@FAp/A`FAٸ/A`FA/A GAx/AGA@~/AGAp_/A;>GA/A`vFGA/A}CGA6/A>GA!/A}(GAȇ./A@1GA:/A\GAE/AxGA@R/Aw|GAt/A`vcGAH{/A0iGAY/A9|GAp/AGA؁/A dGAx/AB*GA/A GA_/AGA/A$GAЀ/AGA8/A AFA /AFAE/AGA:/AGA0/ARGA8//A=GAhMC/AFGAhU/A@NGAZ/AGA]/AGAHa/A#HA8Zj/A \>HA@qp/A;HA2/A'HAԊ/AV2HAR/A ZHA/A}GA@1/AVGAxƷ/AGA0/AךGA/A 9GAu/AWGAH/A"GA/A`mGAp/A GA(O!/A 8GAh$/A|GA0]//AGA@/AGA(I/A sGAH/A@NHAЕJ/A@9HA`\/AGHAg/A cHAhm/AdHAz/AqHA/A`HAv/A3IA`ϔ/AtIA[/AIA/A`]IA4/AJA/A&0JA/A`TJA0H/A AJAD/AJA$/AfJA}/A KA%/AKAp6/A@$KAA/Ad0KA(Q/ABKA`/AKKA(p/A`)WKA;/A{KAأ/A KAP7/AKAT/A|KA/Ah LA3/A SlLA/AMLA+ /A@LA0;/A sMA`X3/AdMAw/A6|NA0&/A@ NA&/A`22OAm/A߃OAp/A OA/A 'PAp+/A`fPAk/A PA/A@QA@ /A&bQA/A cQA/AQA0'/A +RAy5/A@NkRAHB/A 1RA@FT/ARAذd/ARAذq/A+SA{/A`oSA-/A SA /A`SA/AoTA@/A)TA@/A`TGTA/A9eTA }/A`TAP/A@TA0]/ATA/AݩTA+/ATA9/ARTA R/A@TAg*/ARTA8g9/ATA8F/A ~UAT/A@FUAxWd/A`|UAH r/A@UA|/AkUA/A@GMWA88 /AwWAw/ALXA!/AӈXA+/AoXAH)=/A;YA%]/A YA@}z/AXjZA/AZA(/A[A/A[A'/A ]AV/A^A 0{/A^A/A}Q_AP /A@N`A-/At7aAIK/AaAH q/A\bA/A`bA/ABbAd/A`}cAh /A}KdA/A`!eAU@/A eAP]/A`fA>v/A dfA /A`vfAp=/A/!gA/AgA]/AgAX/A &hA/AhA/ARhAȥ!/A}&iA12/AjiAXWG/AբiA}i/AjAx}/AhjA0/AjA/A@#+kAܿ/AQkA/AnkA8/A]tkA3/A@NakA/ATkA8/A8kA؞/A6kAX0/AqOkAVI/ApkAWd/A&kA/A`BkA/A lAV/A?lAo/A@dlA/A0lA`B/AğlAmQ/AjlA /AlAp,/A &lA' /A kA /AmkAP/A ‚kAP/ATkA/A@!A(Q/A@~>AQ/A@=A8R/A |=AHzT/A}M=A1W/A (#=AY/A@ <AX[Z/A`à<A(`X/AX<A[S/A;A8"K/Ad;A@u@/Ag:A`:/A`d:Aj7/A`(:A<5/AT9AX[2/AП9AH-./A@R9A(`+/Am9A'/A8A/A`2P8A8/A.2A/A@1A/A|1A/A0A07/A@iw0AHu/A/A0j/A`6/AX/Ay.A:R/At.AL/A3-A@/A,A6/A`;),AX#*/A +AU#/A]v+Ap/A,+A/A*A/A@*Ap[/A*A /A9*A`/A *A/A)A`/AO)AP/Adc)A/A(AX/A@ Z(Ax/A'A/A7'AA/A st&A`/A^&A`/A Q%APj/A+1%A`/AI$AP/A`K$A-/AT$Ax-/AT$Aj/AR#A/A#Ap/AQ#AЄ/A`f!#AH/A"Ax-/A@%"AG/A Z!A/Ab!A/A? A/ARA((/AAP/A JnA/A A/A^A~/AA/A AP/AA2/A` A/AA-/A GAX/AAX/A`̃Ah/AAP/AA(` /A`]zA /A Q3A /A |A=/AcA" /A!AP /AA8x/A@GA@/AflAH./A`A@5/AAЄ/A`;A/A}AX/A#A/A (A/A@OA/AAM/A JnAo/AAx/A3lA /AAh/A SA/A`KA/AAأ(/AdA@//A`$A5/A A8?/A٧AG/A{AxX/A`!A%f/A@ Ar/A A0/A@ A/Ak A0/Av1 As/A$ A`؝/A A/A A'/AJ A/A Ax/A9 APٽ/A H A|/AH A 4/A- A3/Aa A /A*A(/AA]/A xAH/A+Aǣ/A mAĢ/AA8R/AH\A8Q/AA8I/AA/A,Axm/A` nAH/A (:AЦ/A@_A跋/A`-Aw/AbAh/AA+/A@?AX/A@p Ar/A.Az/AsAy/A At/AA`t/A6 A@$o/A`ANn/AA{i/A?A0h/A`mA8c/A@EA (/AAu'/A@gAhL/A,A0/ADAY/A`dAh/AA@/AAw/AսA({/A?A/A RAT/AA/AAʾ/AAX/A`vA@/A@ A`P/A SR AU/A`O A@/A` A/Ak Aأ/A`2 AH-/AE AH2/A A@I/A AL/A` A_/A Ad/AX A b/A@G AX^/A` AdO/A` AV/A Arf/Aj Ai/A@G Ask/A@p Ah/A" A/A Ah/A q A0w/A Aȱ~/A`; Ax/A A/A A /Aܾ AXƝ/A_ A/A* A8 /A= AIWѸ/AsE_A A#A8 /All A|r/Aߙb AG+/A?& A/A A+/A Av/A` A״/A A_/A@A/Aa2AP|/AAC/A JAH/AaAp/A A/A&{AH/AAP/AA`/A#A(`/AjA0/AVA`T/AA@ƌ/A`TA?/A AO/AKA/AA/A`AP/AA@d|/AAxx/A@,A@s/A"A^/AAYY/A~A/AgA/AvAq/A A/A٥Afk/A|Aq/A@Aq/AnAt/A`KA0y/A#=AX(~/AcA@>/AA/A/MA/A'A _/A`3Ah"/AvA`/A@AhĒ/A@PA/A (AY/A`bA/A<Ap /AAЮ/AA/AA/AAHa/A଼A3/AAP/A?Adz/A`A-/A?A/AA/A]7A/A@W<A/A@ Ah]/A@>8 A@/A1 A@/A_AT/A@JAxZ/A .A@a/A7AFe/A ]AXa/A A [/A AT/A( AT/ALALk/AAon/AVAȱm/AApo/A A7|/A@AZ/A@ Aؒ/ARAH/AaA4/AA/A`A/AA/A A0/AX A/A,AHm/AsA/A A0/A@p0Ah/A*AhU/A=/A/A A /AU3A/AA`/AA /AIA/AAb/A$A/A`A/A %APM/ALeA0/A SA/A@NA`/A91A/A oA/AAh/A@A/Aq>AHi/AzA/AA/A+A /A1AL/A^A/A`A0/AAP/A@!AXy/AF`A(/AA/A?AXy/A<AhU/A@WA(/A A'/AA/A4A8t/AGfAp=/A`A;/A A/A&iAp/AA(/AA/A HAd/AAֵ/AA`/AA/AWAHP/A AxG/A^A"/A1 AA/A A o/A=!Aƾ/A@>!A0/A!Aѹ/A`!A8/A`d"A߼/AE"A0L/A"A7/Ap"A /A@5"A,/A#Aȶ/A` &#A/A]#AH-/A A#A6/A@#A/A@i $Aѯ/AHR$A/A`f$A/Am$Ap/A`7%A/A`x%AZ/A`%Ae/A &A/AzU&A1/A&Aq/A+&A/Ao&A/A@P)'A /Al'AU/A'A /A'A /A?(A/Ad(A/A J(A/A)AL/A 9)AhU/Al)A0/A`)A"/A|)A(/A@,)A/A)*Ap/A@,C*Ap</A^\*A/A *A/A@*A /A*A/A +A@F/A[+A A/A +A/Aza,A0f/A,A 5AТT/A@5A8Vb/A 6Aw/AD6AH~/A@!X6A@A/A6Aڨ/Aլ6A`/A6Ao/A7A@B/A E7A0/A@n7A/A`f7A/A7Ae/A-8A/Aod8A<*/A8Az2/Ao8A@=/A(9A8E/AT9APP/A9Ao[/AF9A(c/Af9Ajo/A`2:Aw/A!:AL/A`]:A,/A֚:A'/A@:A1/A:Ai/Ag ;A/ALg;Aj/A Z;A-/A S;A#/A"<A/AH <AX[/A=<Ah/A y<A/A@`<Ah$/A=A&/AJ=A+/A y=App6/A =AF/A=A`GF/A@ =AoL/Ad'>AW/Ah>A@d/AR>Apyg/A>A`Pr/A@ >AЄy/A>AF/A>Ah"/A@ 0?AhQ/AC?AH/AP?A/A e?A/A;?Axz/A?Ak/A(@A_/A@@A /AQ@A`%/A1A !/A`hAL,/AoA;/AAЄS/A A0\/A@A8t_/A ҆A;Y/A`dApQ/A AQ/AfUApZ/A~Aj/AߏAv/A`}AA/AtAph/A` AwV/AൊA6/A`At/A 펩A{/AhAБ/A iA/A 7A(`/A A/A A/A l A{/A(Ail/AE&A]/A`b7A8O/A`@AvC/A?A0L9/A`4A/AڧA>/AɧA/AħA/A@ߺA/AAX/ApA8/AhpA/AGA/A`'AQ*/A36A@0/AAb9/A@ڤA C/A笤A_/A`cA/Ah5A`/A`Ah"/A߶Ae/Af{A0/A;CA/AAAx-"/A9Ah +/A :A2/AּA;/AAg/A@.!iA0+/A`hAX/A@hA/AgA/AGgA/AsfAR/AfA0U/A`eAUt/AQKeAb/ARdA]/A@EdACS/AdA0%G/A@dA9/AdA0+/A,jdAX/A ZdAx/AQdAX/A#PdA6/AEOdA0/A TdA@q/A``dAP|/AjdAx/A`qdAN2/AWdA*/A,FdA/At@AdA/AcdA'+[/AZ૔dAz3!/A"dA/A@ydA`6/AudAȁv/A7-YSdAx)o/A0,dAH]/A`cA6F/AgfcA@z=S3/Ay3bAHP#/A@`bA@k"/AhbX(aA /A@# aA~/A1aA./A9d= aA u4/AX `A\c/A |`A\ i%/AC"`A7+/A`m`APA/A$PA/A@OAxW/A@OA }/AViOA~/ANA@/A@NA/A@MAH/AdtMA/ALAث/A`LA/ALA@/AEXLAU/A3.LAPk/AsLAЮ/ALA`Á/ALAl/A0 LApS/ARLAH/Ad*LA,=/A/AcNAT/A`NA/A`NA)/A@`NA{/A OAΣ/A (SOAx>l/A`OAx/AX6PAX/A NPAH/A |gPAx/A qvPAx/A{PA/ApwPA/A`RPA/A1PA@]/A SOA./A`POA/A?NA/A&MAA ji/Aז>A?n}4/A5>A?c/AW =A((/A9T=A!y/AH<Am/A@P:<AK[h/A5';Ax)e/A}n;AXWd/A :Ac/Ag:An/AE8Aۍ/A@&7Aܞ/At6AvT/AT6AB7_/A5Ah/AY5Ah/A5AU/A`o@4A8x/AΆ4AЮN/A2Ap}w/A 1Ai/A@M1A/A 0AX/A`1A ҭ/A@1Au/A1AB/A1AH/A`0A~/A`]0A/AE0AP'/A?.Ah/AN.A\/A Ɍ,A3/AI+A;l/Au)A+x/A'P)A]S,/A^4)Ażה/A#")A/A@>)A/A$O!)A&/A ")A]M/A,)AP7/A`)A#c/AUԡ(APg/Af(A@/Ag(A]/A@!-(A f/A (A@o/A('Aq/A@'A@s/Am'APMp/A k&A+k/A %Ad/A@$ANe/A@,$A f/AH$Ai/As$Am/A e$Ags/A _$Aً/A ^$Ah/AY$AH/AL$AΞ/A %$A/A+.$A/A@NJ$A``/A@ r$Af/A`}$A(;@/Aiu%A&ν/AT%A$~/AWٸ:&A\_/A&A@/Ae D'A/A 'A/AG'A~*/A(A`4/A`K(A8IM/A (A[/A@)A\/AH)AP_/A)AZt/A*A/Ap*A/A`̲*A0*/A I+A/A+A>/A`+Ax-/A,AE/AX -A/A,-A/AW-A/Ay-Ah/A Z-AP/A-A6Q/Aj&+.Azb/A.;.A A/A %\.A`/A{.A9tva/AŚ.AOU/A])܄.A/AJO.A/A.Ao~/Ayc.A󏚣/A;.A/A^A.Am Î/A].Ar/A".A`Pm/ALJ.A@/A.AHn/A@0AQZp/A0A|r/AWG0At/A`d0AjhGw/Al0A.j`y/A50A0|/As0A`T~/A0A*cX/Aʩ0AHЍ/A`0AXW/AH0APͷ/A0A/A0A/A`1Ah@/A1A"/A-1A1/Ak1Ax/A`1A/A"1A/AL1A0j/A2A/A`'2A/A@H2A/A@yg2AG'/A O2AS7/A2Ah"[/A$2AH2i/A2APkx/A@ 2A`/A /3A(/A`f,3Ax/ARI3Ax/At3A/A3A(/A?3A~/A@ 4A@/A"4A=/A4Ax/A5AA-/Asd?A/A?A/A|@A՝/AE@A%/A )CA/A 1^CA/A CA/ADAi/A`mwDA,/ADAN6/A EAG/AŎEAr]/AFA^/AFAd/AFFA0ݷ/AIAȓ/AMA83./A }NAH/A0LOAȲ/ARA(/A=RA/A6SA/A@SA0f/A`TAho/A@UTAP/A`fiTAh/A~TAH/A2TAp/ATA/ATA/A@%TA\/AUA A /A7UA (/A ZUA8/AVAxP/A}VA^/A@VAc/A*WAd/A1WAXa/A3jWA0_/A WA(`/A`WA~h/A WA!r/A@ XA}/A 7XA/A`eXAX[/A XA/A:YA/AYAP/A NZAF/A ZAp[/AZA%t/AZA/AhZAE/A ZA/A 8ZA8/AZZAQ/A+[Ah/A@C[A /A@V[Ah/A@ c[AxKV/A /[A/A`;[AH/A [A٨/Ayn[A*/A%f[Ac*/ApޥZA0g4/A7umZA{/A?)ZAߨ/AYA/AYA(D/Ar*YA/A]YAX/AYA ./A/XALH/A]P);XAÖ^/AaWA_/A,TWA/AdVAw/AȚxVAE/A`VA(/AxUA$ /AhUvUA~K9/A}0TA*/AtsTAx)5/ASAO6/ASAy6/ASA;6/ASAH5/A@SA +3/ASA%*/A`ASA$/ASAXj/ASAЯh/AaSAwq/AdSApr/A1SA8/AQHSAС/ARSA]/A`SAK/A@RA@=s/A)RA`Pt/A RA`j/ARAhLh/AmRA(!/AKRA/AQA]/A@`QA`]/AQAXd/A&QAU4/AmPA $/AnPAȰ 0/A,\PA,>/AxUOA"Q/AF%OA_/ANAUk/ẠNA(t/A 1%NA8xx/A"MAz/A'aMAz/AiMAx/A3MA~Ay/A=9T MA8{/A ۻLA2~/Ad]LAV/AsLA /AU6~LAlF}/A?hnLAי/A\LA/A$LAۑ;/AOLA /ALAZL/AݱyKAj/A^/KAt;c/A&KA!/A@rKA"{/AWKA/A`/KAYt/A^c^ KAh/AgJA`!/A JA /A&JA(/A`JA؎/A@JArn/AI{}JAU/A OcJA/ABh JAج//A`DIA1/A`IAJX6/AEO/IA:/AIA=/ANsIA?6?/AƱn[IA@/A9}AIACc@/Ab.(IA2?i?/A IAhn=/A`2HAf/Ay6HAi A/AhpeHAF/A%879HAM/AHA\/A?GAj/A`GAyt/AXb/AI&zDAx/A^DAcn/A0DAؒb/AɧCAbb/A#CAYb/AǠCAcb/A-CAyb/A`CAb/A/CAb/A̚CAb/AtCA5c/A`2CAc/A@֕CAd/A൓CAe/A ґCAxg/A@CAk/A ACA@z/A ZCA/A qCA/A lCA/A@}CA軄/A|zCA/AwCA/AuCAH-/AQtCAi/ArCA/A pCAhՆ/AlCA/A`jCA/AhCA8/A CAx/A`KeBApu/A`BA(u/AAAPu/APAAPYt/A"AAq/A`[@AXJo/A`@Ah/A @Af/A @A0fa/A@Nd@A]/A`@A`]/A@A(]/A@A]/A@AD^/A @AH^/A @A{_/A@AV`/A+@A(\a/A?Ac/A ?A`g/Aj?Ajv/Ax?Ay/A2?A}|/A?AH!/A?Ap,/A?A /A?Ax/A ?Ap=/A@`?A/A?A@‚/A߳?A@/A`?A`e/A@ N?Ap<|/A ?A ku/A`T>A]/A@y=ApR/A=A[L/AFF=A`H/A@=A(H/A@G<A(-H/A<ANH/A<AH/A <A`I/Az<AI/A<APMJ/A<A,K/A<AxdM/A<AO/A0<AS/A&<AVa/AÍ<Ap,g/A`<Ai/As<AP'l/A {<Axm/A3Au)/A3AmM/Au3A*/Aʮ#3Aq%3/AT)2A~:/A2AKQC/A¡}2AQdsM/A 2AZ X/A.ؘ2ACid/A{Yw2AeMp/Ak t2A|/Ay2A/Ag2A{/SZ/A$Bi2A$k{/A 6~2A/A 2AXp/A`]2Ax/AO2A!/A2A~/A 2A/AL2Ax~/Aa2A&/A2AH/A3Ax-/A^3A(,/A3AXJ/A`3AXT/A` 3A f/A^(4An/A |84Aw/AD4A8t/AL4A/A P4AX/A@P4Ah/AK4AŪ/A C4Ah/A@G64At/A4A~/A@3A@u/A@3AX,/As3A98/A43A8c/A`2Aώ/A2A/A&U2A_/A`2A!/Ay1A9/At81Ag/A |0A4/A=0A؎/A s0A`Ǽ/AL0A/A`K?0Ae/A@,0A/AF0AX0AM0A0A/A8"0A/A@00A`2j/AL$60A|`/A:0A\/AX>0A ]/ATr@0A_/AB0A@c/AC0ATi/AD0ABp/AF0Ax/AHG0As/AJ0At/AXL0A /A S0A@d0A`E[0A 81A(]0A`)u1A|2c0A=1Alc0A1AHd0A 1AlOe0A`b2Ak0A02A<=l0A92A%m0AC2A3s0A 2Av0A;2Av0A2A`w0A2Ajw0A2ADXw0A$2Ah(w0A3Av0A 3Ayv0A`3Av0A3At0A+*3As0A@C83A, q0AE3ADl0A[Z3A܆`0A@P3A5Y0A3AG0A@W3A?0AR4AL>0A4A$<0A$4AԼ90A <4A60AV\4At;/0A`4AW,0A=4A*0A4AQ)0A@r4AP"0A5Ar0AZ15A,+0A@>H5A 0A X5A/Aw5A/A`95AH/AÆ5Aȼ/A 5Aa/Ad|5Ap/A@}5Apۏ/A5A/A5Aj/Aα5Ac/A5A@]/Až5AD/A 5A0*/A5A@/A 5A/AD;Aj3/A`b;AQ'/A`5;AU/A`;;An /A:Ay/A":A0;/A}:A@/A`՛:A7/AR:A(/AF:A(i/A@:A(:/A:A7/A:Ap~/A:A8m/A`d;A=^/AN;A=Q/At;AF/A;A A/AV;A;/A@<A؁2/A`<AX*/AO<A $/A@G=A"/Aj=A"/A =A%/A@>A!&/A?>A'%/A%?A@"/A_Z?AI/A@?As/At?A0 /A@?A /AM@A@5/AVv@A(`/A`@AXy/A[@AS/AA4AAX/A@`AA07/A&{A e/A{{A+K\/Asa;M|AN/Axb|A\;/A4[}ALE0/Ao}Ak*/AsF~AHa)/Ap~AAa3-/ArH;A/V6/A&䝱A#?D/AV=AV/AAcHp/A#uAn/AUAQ/Aȓ7A/Af_Ah/A<Acfp/A~"@AR/A< IA^U'/AUAy/A#}xfAk, /ATzAh/A<A/AnCgA/AUłAr/AqA/A`AV/Ar^lSAWe%/AcKAJ2/A;Ap[B/A@58AN܆L/Aul][ARH8(U/AA;\/A%wA`/A^A!c/ARryAEd/AXPAeu-c/AskA!_/AxAHZ/A<CAPR/AFA(H/AufҰAYA/A MA?/A0AZ%oOB/A AH/AB$AS/AA+p/A{A qV/AmNAIcB/A#"Aͫ/A0uA ޶/A@lcڍAo`/AWyʻA'UJ/ABA1Ș/Ao򑚀A]O/AB!RaA$z/A>Ah//AAF/AG7e Ai?yr/Ai&Z,A=vif/A"&=A⋌]/APApZY/AkxeAPY/A`[{AB#9W/AA9Z/AcIA6*_/AЉAqXi/AnAc_fu/ApiAA/A@yAD*/AA`/A+jAu(/A܂fA$/AcV~ĝA}e/A!qEA7/Aɜ})AY/Ann\A87#/Ao"A8k/ATA/AVA/AG{ iA{5/ACp,AgO/A.!rA}Ʊ/A{#AF/AN }A񳱸/AOjA/Au!vAҦA4r/A^!AO/A:0AoW:/A߃oKA/A;WAG/Abh{AOO^/A֎mڨA/AccާA- /A֡A8kS/AVx\Ay/AV2䠨Ap"/ApAV5/A7s-A/A@PrA\а /AVyA%U,/A!A)W5/Agw9mAQM9/A\ͪA:/A/A/ALAm/AlAx /A61#Ay v/A'A~/AjiA/A`A/A<ԯA?/Am%AvJ|/AnF˃jAn/AL˷Ac\/AA%AZ/AMA/A`A(ё/AɼA)Ha/Ad`ӼAD/AxؼAM/AtdؼA/A[z'ӼA'/A]ȼAsnۮ/A`ӹA?>/A;^Ay/A`AJ-C/A2xlA|/A EMA4A1/A:{C3Ahه/A,A~/AIA$u/A=A-Jk/A6{&xAxza/A A!1eZ/A#! AiCS/AT ASJL/AXeAE/AA(?/AAw7/A-O]ۻA庄W//A,лAnֵ&/AFӁ˻A~/AٍEʻAN/A@GϻAx /AA A/A`A/A )A/A@mA ^/A6Ag/A@,8AV/A?WAxi/AOAX/AAܓ/A׽A|/A`KAk^/A:A@5J/A`;WAXt7/A`)hA./A kA2&/AkA"/AjAU/A@gA/AX]AI/A+A/AA(/AA(/AMA@/AAf/AhAP/A-A@/A`GA|/AUA`ǥ/A ZfAPg/A ҶA[h/AMTAX/AِA/A9aAH/A aAȇ/A Ap/ADA(i/A A AZ/ATA`6Z/A UAQ/A@AJ/AA C/A1A*A/A ObAX;/AkA@$8/A/A@3/A@N=Ap%/A Ah"/A^A /AA`/A |A`/A`A /AaA /A8A/AA/A@PMA/A`RAd/A iApN/AAP/AĨA/AA/A8A/AXAP/AA/A9 Alu/AAZh/A?Az/AvAs)-/ARѰiA7ׅo_0ADAp0A}A{0A`]A0A A{0AA0A A0AA0!0AtAPo/AAP|/AAg/A ZFA/AA/AMpA/AdA/A2A" /AAu+/AA0/AqgA/AHA()/ABA/A@5A|/AQPA?/AA0YM/A 2AK/A@d A(6/A A5/A A"/A` A6/Aq A/A A(~/A` A`/A A/A@>|Af]/AANj/A AP//A4A_/A`8A՛/AAt/AAX/A3.A/A A@/AbA*/A@>A*)/A,2AP&/A`2 A(X/A-A/A^mA/A@LAP/AIA/A >A;/A:A/AA0/A@A'/A0AAf/A`Aq/A (AP/A lA/AB6Ax/AaAr&/AAPx*/A@AU/A`;Aؒ/AYA8b;/A Ah?v/A|AF/AAh/AAS/A@A/AdA@1/AA/A A)/A3A//AdAN/A@cAX/A@WA/A1Ahn/AfA R/AgA/A@yA/AAD/AAA/A@WAh/4/A`AXd/A1AHLo/AsA/A A/AoA@/ATaA/APA/AKA@5#/AMAW*/A8A$/AqA/A c'A//ANAH:/A A8'U/A Ax/AAP/A@eA/A A/A 82A/A*\A /A A;/A`A(/APA /A@=A(-/AA0&/A@A/AA/AnAx&/ARCA((/A@`A06/A@Azc/Aa)Ax/AAl/A@9AӺ/A1AW/AAx/AA Z/AsAl!/AsA8/A?A /A`)>A/A ɋAl/A@hA /A`ʃA /AߴA -/AIA/As,A"^/A AF/AAh?v/AAx/AAP+/Am&A/AJA`/AtAh/A AGU/A)A`Q/A`TzANg/AAXhU/A6SAEN/AA/A֠A`/A֠A@F/A 8KAb/A AH/A&AЄ/A AAݪ/A Ab/A-eA pv/A`A[/A`DAT/A}AP/AHvA˵/A YAp/A sA!/A"AP^-/A@AP/A ܨA4/AA( /AA8/A;Ai/A ZAi/A@Ay/A6AA/AwA/A@AP/A3UA/AA#/A`̷At/A}A/s/AA/A ɵA/A&.AЄ/A@AKv/A`յA(/AgݶA/AA /AA#/A`,A;/AA\/AA@k/A-aA@ӆ/AhA/AA/A ĹA/AA@k/A@A5/Aa{A0/A@iA$/AzA /ATbA/A`]Ax~%/A`fA@`i/AXAN/AAxW}/A`vA0/AAHD/A<A p/AoA(d/AA/A 8A /Aw*A@`/A*AxG'/AA/AA/A:Ab/A3 Aq /A4HAh/A AP/A)Ah\/AAB/AA/A@EAHv/ADA$/AA/A`*A/Af~AxQ/A A /A2A蕼/Ay A1/A A'/A@w AxmW/AtA=/A@#A'/Ai[A/AY!Ap8M/A$A؞/A(APfg/A}*Axd,/A@*AI/A:,Aȋ/A !+Aؒ}/Af)Aa/A'Ar/A=c%Ai/A ~#A2O/A "A0w]/A`!A m/A&!Ad/A@HA+h/AyA8q/A \\APk/AHA?/At}A#/A0gA7>/A#ApZE/A$A8A/A@A=/AA0*./A cA0*./AtAPoKA`Xt/A KAY-t/A;JAHt/A@yJAht/AJA>s/A`)YIAs/AUHA Rs/AHAss/A*tGA@s/AFAms/AFApNds/A`PEAWs/AྞDAVs/Ac"DA2as/ACA;bs/A{BA}is/A AApNds/A /AAOs/AeAA7s/AAA(s/A`KAA!s/A`#AA8s/Ao@A7s/AG@A8Qs/AfX@A8Ecs/Ao@AȘls/Aw@Aps/A@d@As/A Q?ARs/AdW?Avs/At>A8ts/AN>Ashs/A@=ATs/AaU=AX6s/A/<Ah]s/A H<AFr/AX<Ar/At;Ar/A`B;A,|r/A |;A6*r/A lQ:AIr/Aa9HA1~r/A GA8r/A`mGAUs/A?3HA*s/AHAVs/A VIABts/A_ JAps/A`RKAQzs/A,cLA s/ALMAys/A[MApxs/AMAHVs/A@%OATs/A$OA7s/AtsPAX#s/AHPA0*.s/A'QA@5Ps/AQAP^s/A@RA@s/A 8RAks/A`ogSAxs/ASA舏s/A 3TA s/AQTAbs/A`TA"s/ATAps/A@$UAs/ASUAt/A$UAHCt/A;UACt/AUA@@t/A`/VADWA0{Gt/AV_WA(Ct/A&WA@Bt/AXWA3;t/AWAh;9t/A XAP/;t/A2XA,;t/A\XA`2t/A_XA)t/AgXA8 t/A`DXAHt/AmXA"t/A>XAX(#t/A XAt/A ZWAt/A`WA*t/A@ߩWA&t/A@|WAX(#t/A`2>WA#t/AWA Nt/A@VAnt/AVA, t/A[VA(t/A9VAs/AVA\s/A`VAs/A sVAas/A@,VA0*s/A'*VAOs/A@VAs/A5UA@s/AUAs/A`UAps/A@UAs/A sUAs/A@lUAs/AN;UAs/A@UA8s/ATAXs/A@ TAs/ATA s/AoTA~s/ATArs/ATAgs/A`vTA\`s/AR UAUs/ATAvDs/AR UAH9s/A@-UAhU3s/AkdUA.s/A'UA%s/A UA`+s/A`KUA*s/A sVAps/A2(VAs/A`7VAts/AUAs/A@UA0 s/AyUAs/AJUASs/AR UAns/ATAP'r/A TAr/AoTAr/A HTAr/AbTAr/A/TA Ns/A@TA"s/AhSAxs/ASAP'r/ASAx r/ASAv/A6]AaCv/A\AKBv/AO\A@1Ev/A+[A-v/A[[Av/A`B+[A0u/AZAh@u/A`)ZA޿/A`bAȩݿ/A^{A/A5|A@0/A@pAd/A@.YAx/A bAP@/AzkA0/A@ fA%X/ANAF/A`8Ag/A`A؟{/A*A(`&/AWAp,y/ADAf/AhA8/A/fAe:/A TAp!/A`<A0/AMApĮ/AuA/AFAhդ/A`b8Aj/A>A t/A@DA` /Aj&WA/A cA o/A``AM/A`÷nA/A 6sA/AzA c)/A؀A޿/A`bAО/A4dA؝/A 8 ~Ar/AFAx /A `Ap/AmLA/AHBA/A,7AС/A2Ap[Ρ/A ,ApE/A`(A,/Ac2Ap/A#A^/AsA 3/A A@/A`ZAJ/AjA~/A:Adś/A Z1FAp/A`TAD/A2`Ah7/A@gAŖ/A`*sA@//AhnA1/ApAؼ/A`?|Afؕ/AɍA@/A ΣA8祗/AF_A@z/A-Ab/A`AО/A4dAȃj/A@AI/AA/AdgA@ RA/Al*\Aʁ {/Ac9AU/A=3z|Au/AYA0/AANu6/A;As&Y/AkmVyA*x/AxAe/AM^A)/AA؈x/A>59A/A^ASVNj/A7h= Aab_ɋ/AXWvAHŋ/A`߳Alu/AA/A AA(:/A`okA@$P/A9A𴝏/A`AUΏ/A@y}A/A ZAa/AA/A^Ah8/A {AH!/AtAP͔/AnA0/A`/hAx>/A3R_A O/AL\AD/A`էWA/AVA>C/A 8wYAk-/A`dNA:/AHAx/A@Ax/Aa=Ah/A;A@/A+@A[/AAA!/A;Aq/A E3AH /AH<,A@/AO&Aз/A$A\/A "Aye/A@y{Aye/AF]Ah/AA]/AjA/A`;5 AXE/A@A@/AAF/AA/A=*A(h/A\A-DY/AocAV8N/AvN\A*G/AAu APF/AuCAI/AYyAQ/AO{uA<&^/A[uCA ^o/AK/AO/A'x"yAܞ/A?Ax /A6Ah@/A _AF/AAv/AjjAD6/A`~A`T/A ZA0/AA6/A`A/A<AК/A`A(U/A`DBAຝ/AA/A$A()p/AAѷ/A@A /A@5AX5/A@A(/ARA-/AgApj/A TAH/AAH/A`A@/AAj/A ZA /A`AP/A A /A A/ANAZP/A A/ADA`r/A nA0,/AnA@/A HA/A SAx/A@A6/A uA/A@A1"/AoA/A A@}/A Api/A@A,/A PA/AOAذϘ/A nAF/A YA9/ARAx/AvA‚/AAʫ/AAߘ/A AذϘ/AA4/A@A/ApRA&/A"A]/A[Au/A2A`r/AAߙ/AIpA,/A@A/A@,PA/AdA o/AAқ/A AP՛/A@rAȋM/AAhͲ/A㯱APY/AOAiA/A A K/A@AF/AA8"/AA@/AAÚ/ANAXؚ/A4Aj/AAh]R/A`AiF/A=xAh/Aj5Apo/A@wA)/A`ΐAt;/AA tA/A` A(\/A@JA¦/A=zAƙ/AcA렙/A _A/Ap{A/A 'AX/A`fA~/A~`A/AgVAe`/AHA=/A`93@Ad(/A@[3A/A+A&/AK*AB/A@y#AР/A[:"A05b^/AC*(!A @/AM" A2&/A^ A$</A5KA[/Aֆ'A /A-A&/A:ADƗ؎/A6GAԎ/AQ]!AL8+Ԏ/At/[UnA"u؎/A /A{QO/AY- Aq%/A_vbAQ/A̾A/AAUvNI/AfAI7/AUS AБ^/AfD̵AP!/Af9y;aAZ'/AUn\A/A ~Ax/A6 A /A@A0{/Ak1A/AFAU/AiAH{r/A= AB/AA/A8iA@B/A \ AHc/A A /Aྦྷ A@d_/AU AZ%/A6 AA/A`) A2t/A A/A`) Axil/A \ A/A@A/AZ A1/A6 APn/A@ AP/A`) AԪ~/A- A0}/A A(|/A Ax+|/A A0{/A  Az/A | A@y/A 8M A0[w/A Au/A٣ A Rs/A Ar/A \ A1p/ANAo/Aj!Ap/A 0ADp/AMAp/A`A p/AAj,q/A A؁q/AW"Axmr/A $Ar/Ack'As/A )A0Xt/A`b*Au/AF+Asu/A ~,A{u/A -Av/A`/APw/A .Aw/Ay-Ah4x/A-%,AFx/Aj)A@_x/A@߷)Aw4y/An*A/y/A`+AXoy/A%-Aby/A -A)+z/AK.A Θz/A .A`z/A<0A(J{/A@W$1Abf{/A1Az{/A1A*{/A/A${/A7.A=|/Aa-AX|/Aa-AH}/Aa-A0{a}/A Ғ,A}/A-%,Ap|~/A Ғ,A~/A@.A~/A`[/A~/A 60AHj/A`B1A~/AБ2AN/A`K3A@/A#5AH;/A#6Aj/A57A/A8Av/A>7Ag/AlyL7AVջ/A0s7Agˀ/A\&Z97ArdWڀ/Ahv87Al6U/AD7AD/A8Ab /AB8Aln /A^\q8A/A8Ah/Atk28AO/A=09Ap /AY9A:WFSL/A_<:A {/A?o;AԬ/AՖ;A7/A~zv<AL/APV=A@R/A =A,\/A@>A5ʑj/A!2?A>.|/A,?AeW/A?A /A X@Ai;/A ]@AUcƂ/Ag @AZ؂/Al@AIg/A\hy@A`Z/AgAA8/A@A/$/AEm@A6/AˡyQ@ATG/A@A\w7/Am @A+/Ap;p?AFt5$/Ar>A!/Atd*>A 4"/A=Au'/A<AT1/AlC<Ar6?/A/Aൂ2AV8`/AV2AT/AS]X3Ag/ARf3AXh/A&a4Ǎqdž/AK!4AB׆/AArR5AP/A@46A 8/A`K8A`a/A9D:Ax)/AHh:AH/A@#:Ah//A:A/A:AY/A:A(h(/A`v;A /A`:A=/A8AF/Ai8AE/Az\8An/A 7AX/At7Ap0/Aiq7Aڅ/A+7A0*υ/A6Aȅ/A 26A ą/A5A…/A@!5Aż/A5A`G/AS5A讅/A4APM/A34A/A@4Ahb/Aߺ4A`/A4A(/A ^5Aܬ/A9j5A/A5Aw/AOO6A( ą/A6ANŅ/AO6Aυ/A@.7AЅ/A`97Amх/A`E7A@1܅/A@y7Ax݅/A7A/Al8A//A9|8Av/A 8A{/A@9A/A 9A/Af9A`/A@:Ax/A`):A/A:A/A:A/A9;A/AG;A1/AFh<A2/A@y<Al;/A <A`#/A=A/&/A =AA/A ~=A M/A`m<Aq/A <AЄo/A2<A0 G/A<A@3/A;A _+/AD;A[/A`;A`/A;APގ/A;A@O͆/A$:As/A:A/A`:A/A ۡ:AX#/A :A_/A;Aц/A<A/A>A//A?A /A>A"/A@W>AxzB/A@>@Ap=/A@\EAˇ/AEGA /A3wGA7/A`FAH/AfGA7/ABGAH/A_GA?/A SGAe/A}HA@3/A@GA3/A@.GA*/A@#GAh.*/A@5GA"/A cGA(/A@5FAb/A AGFACr/AGA88^/A@CGA88^/A$GA/AFGA/AGAi/AfGACr/A9FA`/AEA@j/A@iFAx؊/A;1FA0/AEAiG/A`;EA(yQ/A SEAY/A3EA a/AEA"/ADA@ԋ/ADA( /A/EA;/A"!FA(L/AБFAS/A9FAxƌ/A`GAxƌ/A _GA Œ/AGA/A9GAX4/AGA:/AӄHAPߊ/A!IA/AIAQ/A@IAI/A#^IAHN/A[IA0L/A1IA"/A`IA`/AIAȈ/AT#JAˆ/AIAIV/AJA=f/AMAL/A?PAC/AOA@/APA/A 3QAM/A uRAq/ASA+/A`FUA/A$YVAp/A@VA"/A`WAVV/AXAxn/AYAu/A@[APQ/A` \A8,/A%]AX/A1^A:0/A `A/A4aAU/A /aAs/A@`bAh/ANzdA7/AvfAѨ/A`$hA/A`mhA@/A BiA /AsrlAOډ/ATtAH/A vA/A?zA0Չ/A:{A@/A`o|APÉ/A 8}AXhӉ/ASA0&/A)`Ar /AnA$/A@NAN/A( Ai/A!AQp/AAPc/A0 An/A`mAP//AkA7/AǥA06J/A͇AP/A`gAW/AA젉/A@yA0Y$/A`APwΊ/AA; /A,A/A5A 4W/A`ŐA'/A@WAKI/ADAз/AAǕA -/A?ԖAQ/A=A-0/A`pA~a/A`ApIV/A8A@q/A J(A0*/A AAh/AٞA/A`A} /A ]A pC/A èAb׏/A;A؟ُ/A` A0H/A&A/AiA/A [A/AA0H/AFAXyR/AWA7/AtoA@5/AjmA/A AĎ/At*AX/A (A/AaA`a͎/A@@A /AcAe/A`A>/A@A؁/AA`/AèA0Ӎ/AApÍ/A̴AP8/Af/AP/AXAݩ/A[AP/A`A/A`o;A@S/A_BA!g/ATA F/AwAP|)/A`At/A6A/ApAI/A'A/AA2/A@,AҊ/A`]Axm/A@޽A`َ/A@AÎ/A^A걎/A^kAч/A@VA)G/A AP-/A@؝A /A`m4Al/A GAȘO/A@ZA/A A /A dAoi/A`大A/A .Ax/A@,MAHnC/APA`/AA/A%A//AA8/A<AG/A@AX#/A(APZ/A@OA/A qvA@$/A^^A8_/AgAen/AAH;>/AA//A@7As/A`qA6/AgA/AA4/AA@=/AAK/A@ANz/AMA`*/A0AXӃ/AAf/AA(l/ABAs/A[Awp/AsAwp/A@.4AH/A&AK/AQAeH/A<A`q/AA8b_/A PAf%/A PA,/A AX/A IA0/A}ZAX5p/AcA;>/A-A{ /AtAI~/AtA./A3AX5p/A$A)/A`VAP,/A`!A/AAP</A Ax\~/AAH}/A A4Z|/A`iAH{/A9Az/AA`ey/A`[A(y/AA0x/A,A #v/A,A}v/A 85Ahu/AFAt/A@r`APs/A4Axq/A]Aɛq/AJA`q/A@CA r/A 5ASr/Ao6A0]r/A 6Atr/A+7Ar/A`%7A(r/A [7Aplr/AЕ7Ar/A`7Ar/A@8Afs/A?8A8s/Au8A؁Ms/A Җ8A`fs/A8Aȓs/A@8As/A;j9Ads/A@:As/As:At/A@N:Azt/AV;A%t/A cp;A"'t/A <A:t/A}<AYt/A <Akht/Aa=Ayt/AI=At/A =AxXt/AF>At/A`K>Axt/A>A;t/A>At/A`>A؎u/Ak>A,)u/Ags>AhCu/A@^>Aou/A>AXhu/A`>Au/A@-?Au/A>?Agu/A@-?A`u/A@؀?Av/A?A(v/A?AX>v/A?A(Vv/AG@Aeiv/A@Axv/A@Av/A @Av/A`@AA`6v/AFrAAȥv/AOAAv/A@.AA@v/AAAv/AAA(v/ABAIfv/AAAXEIv/AAA A,v/AߟAAbv/A@~AApyu/A_AAku/A |@Au/A@Agu/A`@A`u/Ay@A+u/AP@AhLu/AC@Amu/A.@A Zu/A Z@A Bu/Az?A@"u/A?A`u/A@@AP@t/A<7@ANt/Aف@At/A AT@AhѬt/AC@AHt/AC@AHt/A4?AAt/A Q?APmt/A ?AWt/A ?A&Dt/AC@A`t/A`#AA2t/A &AAp+(t/ABA t/A ^BAs/ABAs/Aa!CAs/A@NCAhs/ACA(s/A"TDAs/A3DA(ms/A'EAt/A@#UEAt/A'rEA2t/ALvEAP8t/AEAP8t/A=FA=t/A fFA=t/A`FAY-t/A_FA4)t/AF>GA3t/A@GAgKt/AOxGAP[t/A`TGA]t/A@GAPmt/A HAXtt/AHAyt/A^HAt/AE#IA t/A/IAEt/A=HA@Ot/AzHA8 t/A QyHAt/AqHAt/A ZHAxt/A AIAt/A@PIAxt/A2IAt/A;IA6t/A`IA(u/A@IA u/AJAp[.u/Ah JAJA:0x/A JA@$\x/AIA@x/AHA0Gx/AGAx/A GABy/A:FASy/A`EAty/ApEAy/ANEA(-Ny/A@EA9y/AFAXRy/A \xGA8ły/A@`GApy/A@`GAhy/A@%HA Ay/AIAhy/ANJAy/A`KARy/AELAxy/A)MA8Zy/A`MMAy/A>NAy/ANA,y/AOAעy/AjPA;y/A@ZQAy/AQAy/A`;QAz/A /QAz/A RA-z/A"SAO z/A@SA*z/A7TA};z/A TAhJz/A TANz/ASAAz/ARAIz/A RAVAy/AUAy/A`UAPHy/A`2pTA8y/A@TAy/A@.SAH]y/A`SAH]y/A"SA~y/A 8tRAy/A RAy/A&QAhy/AnQA`sy/A?PApy/A`]}PA0ry/A@OAcy/A'cOA'Wy/AOASJy/A@*OAzFy/A@#NAHGy/A@,NA1y/A9NAp.y/A`MA87y/AWMA87y/AMA'y/A@ؽLAhy/ABLA Ry/AkKA5y/AFGKAy/A` JAy/AJAp.y/AJASJy/ANJAHky/A@pJAly/A`zJAly/AgJA]y/AIAUy/AdIASJy/A SHA>y/A`՜HA%y/AHAHx/AHAEx/A IAx/A`BIA!x/AFJA6x/AjJA@ex/AИJA@}Hx/A JAx/A@ؽLAMAjv/A@NlMA4Tv/A@,gMAe>v/AMA7v/A`NAxu#v/AӹNA7v/A@ENA`Gu/AOAu/A@ NALu/A`NAu/AMA7v/AMApI0v/A`MMA`Cv/ALAUYv/ALA@Ftv/A${LAv/A cLAXqv/AKA /]v/A`KAk5v/AxKA v/A |aLAu/ALAu/A ACMAٽu/AoMA`u/AྤMAТu/A^ NAxu/A8NA0j{u/AHhOAvu/A @PAu/A GRA(iu/A RA v/A A@TAH$v/ATALv/A@NUAv/A@WVA v/A(XAXv/A`EYAw/AZA@1Mw/A ZAh^w/A\Aw/A l\Axzw/A0]AhQ x/A^Abx/A _Ap.x/A`A&x/AOaA"x/A`ܠaA7x/ACbA8w/A ŒbA0qw/A_bA6w/A`TcAv]w/AĭdAgw/A@%eA(>w/A4eA`w/AeAx/A HeA~Qx/AfAvx/AgAh3|x/AggAvx/A hAUx/A@hA@5x/A@hAXx/A0iAPkx/A@yiAXx/AMjA0x/AjAKEx/AgrkAex/A lkAgx/A@lAx/A0lA@$x/A`[mA(>x/A@rnAXx/A@E2oA`x/ApA@$x/A@&rApߤx/A'lsAUx/AftA<~x/AKuA<~x/AvvAtx/A`wAUx/AR0xA"x/A@xA`w/A8yA@ήw/AneyA*w/AmyA~_w/AyAp2w/A8zA w/A@EzAv/A5{Ah^v/A@,v{A0v/A {A(Fv/Aמ{AHv/A l {A u/A@zAԏu/AazA( eu/AyA8Z6u/AAzAhu/A5{A0u/AVr|A"u/AO}AFu/A@r}A\u/A2~A\u/AX~AXDu/A-A"u/AfA(>t/A@,>AXt/A AЀt/AV:Agt/A݀A t/A`At/AX[At/A6At/A@A`t/A`ՄAu/AɅAu/A`Ah&u/A 7ApNu/AAu/A @APMu/A@pކAFu/A@NֆAv/AABv/A`̩A~mv/AmAHyv/A@rAv/A@#A8xv/AI$A v/A Av/A(Aźv/AۋA8v/A Aov/AgzAv/AvA@1[v/AkAB_v/A`"jAvkv/A_Av/AAv/A?׊Auv/AzALv/A‰Av/A@#Av/AA|0v/A dAv/AAu/AfAPu/Ai AËu/A-Avyu/A c8A9ku/AA7.u/A@A(it/A_Apt/AƈAÙt/A@ΈAjt/A<A( st/Ai Aot/A`xATt/A`pA /:t/A$At/A4‡AFt/A'Aثs/A@ ADs/AxAvs/AńA}s/A@Axs/A@#څA9s/A潆ABs/A`xAds/A@GA0s/AAxs/AUAxs/A >As/AamAxWs/A@>As/A AxHt/A`>AUt/A`WACt/A$AH\Zu/A[mA]u/AAxv/AʜA 0 v/A'Au/AgAbv/A`֪A徯|v/A){lAvv/A愕A} v/A?#mA3:v/A^|>A1zw/AnArFw/Ak7˵AK}w/AAhdw/A)cAҭY7w/Aa5Ab}w/A"A}4w/AcɻAw/A _2ݼA$w/A3AKw/AIAC8uw/ANH* A 4`w/A@AKw/A-h_A^2w/AAwv/Aq?A(mv/A`AP/u/AܳA`u/AAu/AVAHu/AT(Ayu/A Apu/AHApΫt/A@ABt/ADnA0YZt/AAhs/A&WAUZs/A(Aīr/A`<APs/AAQs/A\A(s/A Al?u/AAh{/A A0|/AOA8~/A`zAp/AAhS/AAꄣ/AvwjA?/A\K1A? lG/AI AtY'/AB5A/A7Amƈ?/A8٭A2/AKA'L2/A8Jf5A~v2/A'A]&ƀ/A_ RAF:̈́/A*'dAR/AmdA|0Z_/AZ Ap/A.~AX/AHAK%/A Z`A ņ/AAх/AAc/A Aȃj/A@A~>/A Ap;/A A8/AAJ7/A`AW7/A bA`m7/A]A7/AxA 7/AhA6/A ANRT6/Ae ,Au6/A?Y=A7/A?A > /7/A$A>1A-:JG7/A=lIA"_7/A@A7/A nA(`F8/A@ ѷA?`8/A@YA c8/AJADg8/A@7Aly8/AٶAp,8/A RAb9/A@A8w9/A zAx :/AA7:/AA7:/A@譫A$:/A +A]:/AMAx~:/A@PϤA:/A A;/A%A`O;/A`A';/A/9A;/AAh/Ao|AX>/AލA@>/A@A ?/A‰A(~f?/A@5A(?/AzAey?/AE9A5e?/A6LA.N?/ApޅA-7?/A@DžAm1x#?/AEoAi8?/AO;m A7 ?/AIkAǥl?/AD0AY#0>/AI˻A>/AFA!?/A!??A?/Af.7A?/An.A7t?/A%AW?/ASp A.?/A͆A?/AE A7>/AD=AyE@>/Az-A+;x>/AA~>/A ACAy/A@5A*%y/AA؁y/AAy/A畻Ay/A`KA/y/A˼A0?y/A&WA0by/AAԌy/A`KAy/A`;A@~y/AkAXcz/A@,OA3z/AA0z/AAy/A ACAy/A@5Ad}/A` A/Av A(/A A/AsR A:/A6 Ap/A" A܎/A@U A3v/A`v Ad}/A` A]/A7 AT/A4 A1B/A`S4 AHN/A@i4 A`V/A{4 A_/A4 Aзh/A 4 A0p/A@4 AFx/A4 Aȓ/A 4 A@u/AE5 A`/Ax#5 AQ/AP5 A/Ae5 A(/A8w5 A@B/A@5 A/A`5 ApŎ/A 5 A[ώ/A5 A َ/AX5 A/A-5 A/A5 AX(/A` 6 A/A$6 A/A AF6 A k/A]6 A(/A6 A/A@,6 A/A`6 A/A6 A/A 7 A/A 1:7 A/A`;I7 Ap/AR7 A1/A8W7 A /AL7 Aj /AR7 A`/Aj7 A/A z7 Ap/A z7 AȘ"/A7 A(/A7 A0/AVz7 A 3/A5e7 AН6/AC7 A08/A !7 A #;/A`6 A@/A`6 A`F/A 6 AM/A6 A8VX/Amh6 Ac/AEJ6 A8n/A46 A #w/A*6 A/A8!6 A/A@6 A(/A6 A8/A@.6 A/A 6 AƲ/A6 A0L/A5 A@=Ï/A95 Aˏ/A5 Aԏ/A5 A@ޏ/A 5 A/A r5 A/A@25 AL/A4 A/A4 Ax /AM4 A/A 4 Ap/A4 A@$/A`4 Ah./AVf4 Ax7/ABF4 AB/A"4 A0fN/A 3 AT/A J3 A@Z/A3 AXya/A 3 Axzg/A`D3 Aجl/A߅3 A@Fv/A`3 Ah}/AxC3 AX/A@(3 A劐/A3 A(/A2 A@/A2 AF/A2 AXV/A@s2 A/AN2 A-Ő/A2 A`ϐ/A Z1 AՐ/A1 Adܐ/A1 A/A-1 A-/AVn1 AL/AjP1 A /A#-1 A0/A 1 Ax- /A;0 A/A`0 Ao/A@`0 A /A s0 A'/AP0 AU./A .0 Ap4/A0 A:/AF/ AX(@/A@/ AF/A/ AM/A / A8U/A@i/ A ]/Ai/ Ae/AS/ A0n/A@y=/ ALx/A%/ AQ/A/ Aѕ/A. AxF/A`. A/A. A/A h. A(z/A M. A3/A6. A/A@%. Aȑ/A- A8ϑ/A@- AUؑ/AЮ- A/Aa- AP/A`vk- A'/A S- A/AB- A/A- AP/A, A /A, A`G/A, A7#/A`x, A8./A L, A4/AV4, Axz:/A, A8@/A, AL/AO+ AR/A+ A@Y/A`+ Ah/A@l+ A(p/A`vO+ A~w/A`5+ A/Ax+ AXƇ/A+ Aڎ/A * Ae/A@* A6/AѾ* AhL/A* Ah/Ay* A趷/Ab* A/AiJ* Aƒ/A 81* Ah͒/A * AWՒ/Aa) A1ݒ/A ) Ai/AA) A@F/A) Ao/A) A8 /Ap) Ah/A0V) Aȃ /A`:) AX/A c) AV/A ~) Ax&/A( A(//A( A>/AՔ( AE/A}( AK/Af( AR/A@O( A(Y/A4( A`/AV( A@uf/A( A0fp/A`' Az/A`' A/A$' A/Ao' AL/AW' A'/A9' A/A@!' AXV/A' Aᮓ/AV& AH/A@& Aﻓ/A A& Au“/A & Atȓ/AT& AГ/Ao& Azؓ/A S& A`/A,2& A8V/A& A7/AL% Ap/AX% A0j/A@% AP7/A {% Aj/A`R% A'/A (% A[2/A`% Ax$ As/A`v!$ Aج/Ax$ Ah̍/A# A'/A# A A0f/A0 A<'/A`K! A2/A A0L=/A ApF/A@  ApAR/A A[/A`" ApAf/A A pn/A A,v/A A0ꃛ/A A0j/A`" Ah/Ai A(z/AS Ap=/A< A /A, A /A&  A,͛/A, A<כ/A  A3ݛ/A A`/Aţ A/A A/A { A/Ad A /AL A0/A`8 AF/A! A"/A`  AL,/A AТ3/A@% Ao:/A APD/A, AK/A ɣ AU/A  AKb/A O AȘj/A| A(zr/Ao Ax-{/A0Z A/AB A`/A* A`/At  A@W/A A1/A` AՖ/A  A #/Axy A/AW AТ/A1 A/A A/A@ A舓/A൹ A"/A A /A@^ AX /A: A/A` Aց/A@W A'/A J A@|/A A`w/A ld At/A`@ A'r/A`[ Ao/A Axl/A Aj/Aٷ Atf/A A_/A F A0\/A`& A@W/A AU/Aa A`V/A` APg/A`L Aȃp/A A" /A@ A!1/A A"/A@ A!"/A Aȱ/An A2'/A@, AHKZ/AY Ah/A@a A/A@ A@BF/A AN/A A8W/A2x AA`/A`j APj/Aj` Ahr/A2Z A|/AN Ax/A: A8/A + AX/A` A/A Ahѧ/A' A7/A A/A' Ap/A` Aʤ/AN A@Ӥ/AV Auݤ/A A-/A A0/A A/A` A/A A,/A` A/Aܡ Ah/AV A(/A` A01/Aj A:/A AC/A`~ APoM/A| AjV/A'y Aa/A@i Ak/A;e A0y/Aa A`~/A^ AF/A`S A`e/AH A/A@? AB/A@,6 A߳/A`+ Ax/A AΥ/AM AX[ۥ/A` A/A`9 A"/A A/A A/At A /A A`/A@ A/A A&/AF A p//A྄ A7/A 8q A B/AZ AM/AC AxQ/A6 AZ[/A! AFe/A@ A8k/A As/AL APt/A A}/A9 AXƈ/A@N APf/A A/A A-/A A/A A/AL Aպ/Al AĦ/AW A83ͦ/AH Aצ/A4 Ah/A$ Ae/A A0j/A AH/A AV/A A /A Ah"/AT AQ/A@ Aȶ!/A ( AU(/A A@B1/A@ A9/A  AA/A@ AJ/A AhS/A Aj^/A  A0f/A@N Ap/A; APw/A` Ak/A ۅ A(-/A Ap/A6 A/A AhѦ/A՘ AHЯ/A` A༧/A AƧ/A A AZͧ/A  Ap=ا/A Ap As9/A@> A!/A@ AX#&/A A@-/A@ AU5/A A@/A6 AG/A@ A@pQ/A- AX/A A`/A@y A"j/A  A/A=H AH/A=H AP{/A` A /AF, A /A"; AV/A N APo/A a A/At A /AA A`/A Z A'±/A  A`б/A? A1ͱ/A? A`ѱ/A9 A/A9 A@1߱/A? A@ٱ/A? A(α/A Aӱ/AA A[ܱ/A@ AH/A 1 A/A A/A  A"/A + A8Q /AA Ap</A@U AU /Ah A8V)/A0z AH2/A A/E/A; A?/A; AB/A 8 AP/W/A 8 AhQ/A; AhoM/A; AX(C/A AзL/AX AppR/A AxY/A` Ahb/AM Ak/A  Aoq/A`( A Ax/A`; AZ/A@#K A/Ab Ap/A } A/A AP暲/A` AꜲ/A & A /A`  A/AX2 A0/A9U A`/A$ A /A Axu/A`; A /A` A/A A8/A> AP/AUb APf/A` AP/A AH/A A/A  Ap=Ʋ/A Aβ/A  AfӲ/A A۲/A  A/A`" A /Af A(/A AI/A Ah/AT A/A  A%/A2 A./A= A`9/A) A8DC/A; A0J/A^ AS/A@ A@]/A= AP8h/A A s/A A͒/A@ Ak/A} AHm/A A/A A(/A A뺳/A Aó/A_y AXʳ/A`m Apӳ/A` AX޳/A R A/A`I A/A: A0j/A-. A/Aa A < /A@ A`2/A A /A& A#/A " A0f-/Ac A6/A  A@/A A@uK/A  AR/A AX]/A A0`/A` Ac/At AX[e/AD Ag/Af! AXl/A 8 Aq/A`m A``t/AE A7w/Aّ A@y/A g A/AS A,/A JO AX[/A`[J A/AC A t/A`; AA/A /2 A(/AX& A Ĵ/A A`ʹ/AD AТԴ/A  Aߴ/A@ ApA/A A_/A@W A /A / A/A Aj/A; A /AO Af'/A A_0/A`m A:/A  AFD/A@ A#P/A / A AY/A AVc/A An/A AQx/A A/A6 A7/A- A(/A` AL/A`  A/A` Aవ/A ~ AxK/A0 Ax(Ƶ/A`( Aѵ/A@0 Aݵ/A: A(/AA AP A /A6 A3/A@% A A"=/A@1 A 0/A@ B A/A A־/A Aʾ/A AӾ/A Aܾ/A Aз/Ad A/A A'/A A/A@ A/A A/Ads Ah/A-b A*/AP A4/A@C Ah>/A83 A0F/A@& A`Q/A" AHY/AO AZc/A` A@l/A As/A AF~/A@ A/A@ A;/A A(/A AP/A} A軮/At A/A; A/A A˿/At APؿ/A App/A` A/A A(/A Z A@/A` A/A- A /A A /A_ A/A׫ A`/A Ah"/Acg A /A=Z AP /AA A=/A A(/A A8 /A  A/Aࣸ A/A` A/A@ A(/A` A( /Axw Aج!/A@e A*/AX A؎2/AN A:/A 8A AC/A&3 A1L/A@$ A`T/A A([\/A A0ef/A` APo/A@ A`w/A; A/AM Az/A6 Aȶ/Am A_/A AU/A A /A[ A@~/AM A/A`D Aȶ/A Ah/Ay A /A`t A/Aj` A/A@,X Ap/A: Ah%/A A/A@x A;/A@P Aà/A# AX/A Aw/A Ax>/A@y A`/A AU/A A`/AQ Aȫ/A`\ A 6/A AhF/A A`eN/Av A8V/A@ A\/A Af/A6 Aq/A0v A[y/A@Cd Aj/A`BR A0/A`B Aя/A3 A/A! A`/A A`±/A A/A2 A0/A@ A/A; A'/A@` A/A`ܽ AP/A@Ƶ A /Av A/A` Ap/A྘ A/A A/Ahx A8"/A`j A=*/A^ Ax4/AO A@/A= AhK/A- A(U/A! AV`/A Ag/A Ar/A A( }/A`K A7/A' AAsow'/AN  A?w}/A:w AdH/A7o A)/A Ax AǟIG/AWNDŽ A[rc/A+}ʢ Ax~/A A4Ά/AT& A/Ahq AQV/A/5v A )/AOz AZ/A-| A /A{ Asb/AC/y A/A=u A]sV/A,pPo A^50/Ach A*/AT_ ADJ/A1V A/~/A}BK A?/A? A8Ž/A6 A/Al3u A8/A" AT|\j/A|R Ap2"/A% A>zA#/Axue AEuFE/AL?Mn A|Wg/Ak A/Ajx Am8t(/A*D A幗/AmOx A \h/A2C1 AF4/Af, A߾u/Av A0/A A7/A0 A8M /Ad AD/A{ Am,/AtrW A#/Ax A/A| AW/A~& A2 /AS A"/A2- A{#/Ab[ AX$/Ay ANAj$/ADq A2/AviK AX/A8=/A@ A@?/A | A'D/A W AH/A 2 AM/A AhQ/A`m AHT/A8ͳ AجW/A㪳 A6Z/A AP^/A O A(b/A  A@g/Ao A@i/A 1ʲ Ah"l/A Ar/A Zf Au/A@ A({/A A(/Aױ A/A A/A > AX(G/A AtO/A൫ A-/AM A~'/Ax A/A|߾ A8/AT A`/A A/A ( A5/Au A@/A A/A A4/A ACu/A AP/A AP/Au A0f/A AF/Aଠ Aִ/A APj /A0 Am/A A/AX& A/Aj Ax/A A֋/Ai AP/A` A/A`W A/A6L A8/AZ A;/A`9 Ah/Anj A~/A"a A(m/A A/Ax A/A6 A/A@5 A`/A@ A/A AD/A Aڤ/A A-/A@r A/A A /A A0o/A Aw;/A A&/A`Ө Ah/A A 3/AK A/A@ A/A@ A(/Ao A6~/A A8ge/A AaS/Aِ Ae A/A A  A;/Ao AZ/AB A=,/A9Y A./Ad A6/A~ APo@/A AI/A AP/A AhL[/A` A@=e/A@ Am/A Apv/A  AX/A A/A/ A/A@ AX/AK Ad/AU AP/A lb A/A@Pm A@/A | AX/Ao A/A@֌ Az/A@ A؎/AV A/A A`/A AX/A@ A /A A/A c Ap/A Aؒ/A& A 4U/A0 A/A | A1`A/A  A̕(p/A9„{ ADΜ/A_@ A/Aa A`c/A`2 AP;I/A9 m A)0/A]d AI82:/A1( A:@/Ag A"/A@ As/A~g AO/A~$:[ ASA/AZ AH /A AU*/A>5U A X4/A6 Aa$=/AL AhwD/A`(Co AQJ/AB#J AcN/A-/! Au AN%H/ASKM AP/B/A) ABF/Al' AlL/A& A_sa/A~[ܹ AϬz/AoQ AO /A; A=~/A A/A@9 Ab /AE A/Ah].W Ag/Af A`/A` A@#/AM A/8H/A 9 Ar0o/AGƣ A@/A@ A/A  A/A] A"W/A` A/A  A/A Ax(/AB Adp/A0F A;/AB Avpa/A>H A/AG Ag{Z/Ab A*/A;S A0L/A Q A/A + A"j|/A4gK A/UX/A3R A)/Ax A+/A] A*/A${ݎ A#/A@! AS0Oe//A^%' A%x>/A A`Q/A-Y* A8Dg/A \S AH.x/A_ż AOy/A: ; A`6/AN Aj/AuΊ A)ݑ/AB{ܺ Aمy]/A As+/AMZ1 A:/AR˹ A;PC/A1O A!hF/AZYи A:1cE/AVYP A>/A6ӷ AM/Aq Ai/A A/ADҳ A(1/A` AX /Ad A//A`ҩ A @/A@Z AV/A١ Aqn`X/AC{ A,~1/A` A /A ~ AY/Av=ߟ AJ/Aq7! A`G/Ak AXl/A`m AUs/A U1 Aj /A A3:P/A{ A_/A A0U/A?& Ahu/A A/A@Eg A/AP A@/A} AO/A  Aأ/At A(/A`F Ax/A( Ax/A`vc A/Aә A8/A@y Ak/A ~ Ah./A Aql/A@WF AQ/A A^T/A@Ϫ Af/A B Ak/AM A_/A A/A@E? Ah/A) Ap/A밗 A,/A A6H/Aa A h/Ap A^/A; AP/A AV//A  A:/AdÌ Adf/A`]$y Av/AXr A/A ˏk A@F/A`]&a AЦ~/A`CX A/A5gR A/AZO Au3/AN Apk*/A HH AF/A ~]E A0/A Z3E AP/AaE A`"/AD AF/AC AP/A ~2C A410AG A0A@5H A~0AH Al0A lZJ A0ALjJ A0AH A0A#iH Aȅ0A Z G AH! 0A HlG A 0A`G A@Q0AٲF A0AF A0AE A40AǝF AX/A B AB/A AA A/A@; A %0AH,6 AZ!0A&2 AH0A`v. AXΧ0A]& A0AӠ% A0A?# A|@0A l A0A`v= A0A Atļ0A_ Ay0Awi A_00A# A0A A P0A` A0A@= A0A0 A=0A A(0A ( AL5 0A`TW AP|y0A` A0A. A,0A| A0A` A.e0Aw AL0A V A: 0A9Z Ai 0AG A, 0A` A 0A 13 A,# 0A; A`ǯ 0A A$} 0Ay A 0A. A 0A l A 0A4ι Av 0A@ A 0A` Aj 0A% A` 0Ak A 0A 1 A.= 0A ALI 0A Aov 0A4: Ap 0A`ó A(3 0A y Ap 0A)α A M 0A`B AM 0A@!7 A M 0A`)+ AM 0A AM 0A A4L 0A' AHJ 0Aϯ AB 0Ag A? 0A@? A4 0A`Kͮ A2 0A`K A%1 0A` A0 0A@胮 Af0 0Aր AK0 0A`} A<0 0A`vy A80 0Au AB0 0Ar AW0 0Aao AXy0 0ABl A0 0A`Di A0 0Ahf A\&1 0A c Ax1 0Aa AdA2 0A$\ A;3 0AW Ag4 0AS A7 0A`M A}: 0AH A ; 0A G AbF 0A0@ ARO 0A: AS 0A`)8 A` 0A`9 Ag 0AA Ao 0A Q ALLx 0Aj A 0A`v A 0A A' 0A@ A 0A&ܮ AX 0A`f A 0A`] Aܸ 0A< Au 0A`) AT Q 0A AT 0A  A(0A$ Ax 0A` A?0A Ad0Az A0AM AԸ 0A J As0A@ AA0A A)0AЍ A0Ae A$ 0A`ܟ A//0An AKE0A Þ ASe0Aޘ A<=~0A c A A0A@` A蹪0A A0A` A`0Aߥ AhB0A`d AXJ'0A Apb0A  AU0A |A Ac0Aݔ A0A$ A0A |( AX0A쇒 Ad[0A Α Ap0A磐 AI 0Ax AH0A Z AI#0Ax A40A ATH0A`2V AĀS0A`f/ AȖ0A@% A$Ƞ0A ADX0AN Av0AHC~ At0A } A60A'| A@10A{ A!0A.{ Az0A z AG0A Nz AI40A|ky A,4H0A@E(y AdL^0A@ _y AHpx0AIz AǢ0A`Yy A70A/uw A\ 0A@؂v A[F0AFxu A<0A`mt A.0A@Ns AH%0AjCq A,40AZp A0ATp AJ0Ao A0AT o A .0Al Av0AFi A0Ai A0Ag Aࢷ0A f Ax|0Ae A0A@Ưd AA0A&d Al20Ac A*T0Ab AԚ0A&a A'0A_ At0A ^ A"0A] AH0A@[ AI0A,[ AI0A Z At$H0A@pY Ap>0A-X A:0AjW Ad?0A@V AhWE0A@yV A$60AU A&0A`BT A/0AT A)0A#"S A''0A2R A|'0AkR A, $0A@Q A '0A`P A20A@ O A~>0AOO AB0A6O AO0AO A`6U0AN AtV0AN A dW0AN ASX0A@N AX0AN ATX0A@N ATX0AN ATX0AN ATX0A`N ATX0AN ASX0A N AS0A`=N AnM0AaM A0L0AM AK0AM A AK0A3~M A AK0A@>tM AK0AHjM AL0A`XM AnM0AtBM AQ0A M ASX0A`L AL]0A_L A`0A DL A`0A =L AXYa0A8L AL&d0A`!L A4g0AL A\i0AK APk0AXK An0AaK ADZo0AaK A88q0AgK As0A K AG A80AG A̽0A@WQG Ax0AaGG Ax0A@gBG A|0Av3G Ap0A@G Adʧ0A@F AX0A`DF Av0A`F Ap0AzF APܴ0A`F A,0AF A0A={F A0A PF A0A@:F A,0A$F A 0A?F A0AvE ALf0AE Aq0AkE AO0AKE A>0A7E AP0A#E Al0AD Al0A D A 0AମD AJ0A?D AD0AƕD A90A`̐D AD0AыD A(0AֆD Aض0A_D A0AdzD A0ArD Ax)0AtkD As0AcD A40AaD A|0A\D A0AWD A]0APD ApL0AHD A,%0ALD A0A@C A 0ApC Aȇ0A cC A0A}B A\0A B AC0A`B A20AB A20A`B A20A`;B A20A`KB A0A[B A\0AhB Av0AeB A'0A`VB AX0AGB AX0AN;B Aȇ0Ac'B Aȇ0AB Aȇ0A@A Aȇ0AA Av0ATGA Ae0A<@ A0A`b@ A'0A@ AX0A@ A0A+@ A0A_@ A0A@!@ AX0A ? Ad0As? A`0A ? Ak0A`2? AZ0A0? A 0A? A!0A ? A\>0A /? A0A4? AD0A@? A*0A`2? A[,0Az? A0/0A-@ AL,0A@ A,0A`@ A}*0A5A AH.*0ATGA A(0A@gA A'0Ac'B A'0AtB ATr&0AB A<%0A,C A#0A@YC AH!0AjC A0A@C A0A C A!0AwC A 0AC AT0AD A'0A@D A0A/D A H0A`܁D Ax+0AƕD A0A9D Ac0AD Apn0A7E A0A-E A\0AvE A8E0AZF A8x0A`}F A0A`F A 0AF A0A F AI0A?F A0A@0G A|0AJG A\0AbG A\ 0AlG A|00AG A0AG A̬0A H A0A3H AE0A VH A80A0cH A8~0AhH Ar0A3H AJ A*N0A`)FJ A`L0A HJ A]K0APJ AJ0AUJ A 0I0A`\J A4Q90AJ A*0ApiK A8#0A@5K AL0A=L Aj0A =L A0A`TqL AT0AL A0A"M AA0A]M A0A@PM AЄ0A}'N A 0A 8hN A̔0AN A8V0AzN A0A uN A0AjN A0AfN A4"0AN A4f0AfN A0AN A<0A`N AH0AN A\0A SON A60A N A50A@M A`ڛ0A M A$n0AvM A0ADoM A0AHjM Ahs0ANeM A,0ANeM A0ANeM Ag0ANeM Ax0AgM A:0AHjM Ap 0A@>tM Aވ0A@.M A"0AM A }0A0M Aq0A 8hN AP|m0AN A_j0A@N Api0A?N Ah0AN Af0A 8WO Ae0AmO ALe0A wO Avd0AO Ac0A`O Ab0AO Aa0AO AXj`0AdO AL]0A ZO A\0A`TO ATZ0A ҵO A1Z0A ҵO AS0A4O AdyC0A`K2P AD0A IQ A\FF0A@sR AtH0A`KtS A\N0A@oT Ac0A@ߵU A}0A`KV A0A@?X Aл|0AY Ak0A l>[ A*Q0A c\ A(50AD^ A0A`` Aܵ0A ` A0Aa ABj0Adb Ae0Ad A0A O f A(0Af APK0A`9g A0Ah AxZ0A`h AL0A Ai A0A`̣k A A0lR0A`U AP0An AKD0AI At>0Ao Aخ70A A0/0Aj A'0A ̵ A`#0A A0A@p A0A / Ab 0ABͶ A\~0A AH0A AȾ0A`" A 0A A 0A AĀ 0A  A! 0AI A0 0A AhB 0A^ A 0AI A 0A A 0A`d۹ A 0AjA A8M 0AN AlI 0A A 0A A< 0Aa A 0Az A< 0Am AX 0A d AK 0A 8t AL 0Az A 0A@ A4O 0Aw A8 0A@,G A 0Aז A" 0A@ Aw 0A AC 0AD A 0AB Ap 0A AD 0A A\ 0A A 0A@5 A 0A (1 A}0A Ax0Ai Ax0A` Ax0AZ Ax0AX Ax0A` Al0A@ A0A J A}0A A0 0A` A 0A - AD 0A@h A 0Ajn Af 0Aஆ A" 0A`B A 0A@! A 0AT A8 0A@ Al 0A@Ơ A8 0A` A 0A@y Atf 0A A\ 0A A 0A^ A 0A A3 0AD A 0A A P 0A A0A`fA ATi0AE A4 0A@M A(s 0A`KZ A8 0Aw\ Ab 0A Ad A| 0A`p A~0Am AW0A A:0A`KI A '0Au A0A A90A A0A< A00A` A0A A; 0A Z) AlG$0A I A|&0A c[ A B)0Aj A-0A H| A+/0A A030A[ Ad50A A 60A A;0AV A>0Aե AH'B0Aե AD0AV AM0A A09O0A@ߛ AO0Aޛ AQ0A@ߛ A0Q0A@ߛ A4R0A@ߛ A3T0A[ AT0AV A.Y0AΪ AXY0A@ AZ0AK Ax^0A`; A=_0A6 A,c0A A$(i0Ak A̬i0A@y Apk0A`f Ahm0A` App0A@>1 A s0AjI A s0AO Au0Aj Aw0A@` Adx0A`ã A<y0A@ A A 0A E Ax 0AO Ax 0AQ Ax 0AW A(O 0A`\ A; 0Ay^ AQ 0A$j Ax 0A`̈ A !0A0 A`z!0AL A`z!0A[ A`z!0A A`z!0A` A 0A Ax 0A Ah0A8x A0A@ A0A AS0A̟ A A0A A00A A\0A A`6%0A { A'0A& A 1*0A A}*0Ah5 Ap*0AL A +0A`w A*0A@ Ap*0A 8 A,)0A`f AT(0A H A4$0A&< Aؖ$0A`M AlG$0A`K AlG$0A A%0AfZ A(0A 8 A B)0At: Ad(0A} A=(0A} AT(0A Ah'0A Au&0AHQ A$0Af A6#0A | A"0A A0A A\0Aҭ A\>0A Ҿ A0Ah Al0A@y Ahh0AI A0A A(@ 0A` A90AЋ A 0AN A 0A At 0AЩ AH0s 0A`V Ap_ 0A & AP` 0Ay AWA 0Aú A|2; 0A0 A` 0A AHa 0A@k Ađp 0A A^ 0A`f A/Z 0A{ AC 0A@ Ah3 0Av At0 0A` AC 0A. A4Y 0A- AD 0A A$ 0A| A 0A A 0A@ AȘ 0A A 0A` A\ 0A A7 0Af A< 0A A 0A A4 0At A0A I A@y0A`< A40AA Ad0A A0A A0A# A,t0A AL0A AH0A A4/0AF AvF0A O AȶY0A@P AVk0A# A|?z0A Aͅ0Ah A70A AHp0A@y AR0AF A @r0A A [0A`] AJ0A  A3E0A  A\:0A ? A%0A`/ Ae0AT6 AHn0A`D A0A A90A& AlV0A  A%0A A 0A8] Ad0A A,ɾ0A AI0A`]p A,T0AVG A|0A@h A+0A Q A0A A AHv0Aͳ A0A` A|(0Ajp A00A= A<0A`2 Ap0A(% Ap0A  AD@ 0A2 A  0A  A 0A`Î A 0A ALh 0As A` 0A@ A 0A`b A 0A A 0A@y A 0A@! AQ 0A@ ! Aг 0A{" Al 0A@" AK 0A!# AI 0Aƍ# AX 0A_?$ A8G 0AY$ Aȅ 0A% AI 0Aپ% Af 0Aw|% Ay 0A8٪Q<& A 0AdS7& A,w 0A5x[& A 0A' AWO 0As>' A&%cI 0A2d$l' A'\ 0Am' Az 0A' A^y 0Ac 'y' A 0A ' A5 0A -( A@cw 0Avwf( A,j 0Azݛ( ANWO 0A]( A,ק 0Aӆ( A 0A` ) A-Ff 0AozB:) A 0A-V) Ao? 0A2Sڣu) A7 0Al>) A`N 0A]x%̷) A  0A) A0L 0A ) A4B 0AD- Aԑ 0A4- A y 0A 1v- A 0A. A0Y 0A`. A̿T 0A. A!S 0A`Q. A\>9 0A`Q. A\>9 0A/ A5T 0A_/ AS 0A@N/ A̟ 0A. A(0A/ A\0AW/ A-0AXs/ A؟%0A?/ A ,0A-/ A60A0 A =0A lQ0 ALB0Aa0 AW0A 1 A:)0A2 Ab0A 4 Au0A Z6 A-0A@ϴ7 A00A Z7 A0A8 Az0A#q9 A 0A@i9 Al֓0A?J: Aė0A@: A0Ah:; AXH0A&> A20A> Ap0A$@ A(޷0A@B AG0AYE A 0A@(G AP<0AWG A\0A JL A0A`ӾS A50AX AZ40A@,[ A0A6-^ AL0A@j A0A@r8f A tZ0A>P Ad^0AiB A0A+: A|F0A@6 A^0A-) A10A#$ Axڞ0A! Ai0A`T  A0A= AH0A@R AK@0A  A`E 0A- A4f!0A= A$$0AI AH%0A`; A`ػ&0Az AX]'0A} ATC(0AI A.)0A/< AC*0A AVV+0A`2 A`,0A@> A$-0A8 AX/0A A d00A` AX10A Agx20A S{ A20A4 A$20A3 A20A> A20A* AX30A@% A]z30AÖ Am70A9k{ A:0Ai A`\:0A g AL:0A`"{f Ar:0A^e A<:0A&d AE;0A^ A$}=0A@R A\=0A (nP AT@0AM< A`TC0A?+ ARE0AK AqF0A@5 A G0AɌ Ax:G0A AG0A@ A>G0AS ADxG0A` A('H0Ay AuH0A AwH0A: AܙH0A`G AH0A" A%I0Af ArI0A} A(vI0A S3| AsI0A 6{ A(>!I0AQ%x A?H0A@r AG0A`]g A|AG0A@W[ A$G0A AW ApF0AzP AGE0A@>E A0E0AA A ;E0A : ADD0A A,/ AD0A V( AHvC0A@`V$ A86/C0A@5 AtB0A  A wA0Aӕ Al;A0A!Ar@@0AA<>0AUA )>0A,A\u>0AIA)0A (չA0a(0A`A_(0A 0A@r)0A@פA( )0AA)0A@AY*0AهA4*0A Ҽ|Aݩ*0A |Ax*0A@`zAlV*0AtyAm+0AIA+0A HA),0A@Ahc,0A ck6AIg,0AV5ALi,0A 5A{,0A-A[},0AV-AȼS,0AZ'Am,0A@eAh,0A lA8,0AxA8,0AAi,0AB*A@S,0A[A`e,0AvA1,0A gA ,0AM(Ap],0AAt,0AzA,0A IAB-0ARAHC-0APAD-0ALA0U-0A@iU>A$=]-0A6:A`-0AJ8A|ǐ-0A5&A|-0A? AJ-0ABA-0AAܵ-0A A-0AA-0A[A}-0A@LA-0AAض-0A@A -0A A-0A^A-0A+Ac-0A 9Ap-0AK AX`,0AZ/A$N,0A@ؔA +0A !A+0A`fA+0A`bAܨ+0A'vAD+0A 7oA +0A@@gAx+0A^_AI+0AWA~7+0A@COA=+0AGA02+0A ?AL0+0A|=A, @+0A@7AU+0A@3A]+0A%1A86+0A@-A8+0A &)A0H+0Ag(A+0A`"A3+0A!A}+0A`A8+0A@EAQ$+0A`oAWV+0A:Aht+0A AȜ+0A s4A@+0A A^,0A+A,0A@AP4-0AA|u-0A`A4-0AMAG-0A@ԄA.0A@A*.0A Ap/.0A@HA=.0A ~PA,.0A APӻ.0A`BuA .0A 9AD.0ALA̿--0AkAp)0A Aķ(0AAh$'0ASA8%0A0A>#0A03A"0A`wA0AHmA0A^A0AyXAx0A`]UA0ASA]0AOA0A@`KA(0AB'/AV0AWA)A$ ]0A !A@b 0AA 0A`vA;0A@%A>0AtA 0A-Ap2/0A6gA:0A/,AHC0A+AǛ0AAql0A`~Apd0AAP<0A?GA\0A4yA0A`KA30AHYA0A cA p0A Ah0ALA0A`TA$.0A͸A(^0A(A0A@,A8*0A=AP0A@A0A_A;0AvA0{0ARAG0A`A0A Ah0AճA`' 0A`DA 0A@iA(0A`vA\0A /A40A`vA0A`"A0A?Al0A0EA:0A,A0AQAlx0A cAlI0AIALr0A`Al70AQxAg0A@A0AAŽ70ABZ4A^%0AI9AB0AjA5Ҽ0A@ ABԵ0AA-0AAk7i!0A35@A0AA@\0AA10A/HYARi0Ao A0A$A0A AT;0AӓA%AK0A[6A#0ARNLA0AŘͭeA0AHA0AvA  0AA0AA0A$vAHV0AŐA70A@%A\0A`NA"0A (qA70A@,fA0A`]=A܆0A`;AA0AdAt0AľA-0AHA<0A@%A@ 0A*6AD`0AA}-0AڮA8A0A HA@sZ0AA\0A@A0AA 0A;Aź0AA`v0A"A 0A ɂA0 0AA"B0AUAS0ARAlV`0ABA0AAj0ArA`0A8AXJZ0A`AT0A<AP0AAPL0AI0AH0A_AC0AA<>0AA30A+A;00A@QA/0AUAl/,0A sA4'0A`KA#0A@5A:"0A@Aԫ 0AA8~0A`A|?0AA0AA/0AA(0AAf0AAw0A@ A|0AA0A@A0AA0A$A>0A$AH0A$AH 0AA0A@A 0A`A@0A@AXH0AaA0AgA0A?A8ػ0AA\0A@Ad*0A`[Al0AAg0A0APO0A`A70AgCA0A=kA0A`An0A@Al0A*AЧ0AzA0AADo0A@,iAv0AAR0AA=0AAd&0A`At0A-zA00A HaA 0A^^AQ0A@GAK0AA:90A@=A~0AaIA0A,A}L0AzAhN0A HA.O0AvAZAT!20A@>TA-0A YAh,0A@YA#*0AYA8<$0AdA 0A`YA0A`̞A0AA:0AR~A0AsAHa0A@gA0AAX0A=vA0A Al0AA0A 1|A0AKcAp0AVAྠ0AGA0A'AF0A A0A`A̓0AAL(z0AZAy0ASAX9y0A IAx0A &3AX9y0AAwz0A`Awz0AA0AA40A[]AX0AmA90AWAxi0An>A`0A A~0A Ag0A`TA[0A`AX~0A~A|x0A@ALlv0AXAHq0AA`'l0AǮA\e0A@aApc0A MAh`0AB<AdY[0A`AT0AAM0A`AA0A+A|70AA0.0AA&0A`AU#0A` A0AA!0A`mA0AA'0A`*A{0A>A 0ACA1 0AHAT0A`2KAg0A JA0AHA0A 8FA`<0A>A 0A`*A}0AAO0AuAN0AOAk0A@A0A2A80A#AC0A@AT0A AATT0A[Al0A AU0AEoAu0A ,ATc0AA+\0AsA>0A@A/0AUAP 0A AT0AWAL,0AMA 0ApAx0A@A0A@>A 0A@gtAT0Ax,Axk0AAQ0A`A0AZA0A A0A@5A$0AxAH0A یAё0AD A?ÿ0AR AMlɐ0A!tk`AQ0A:AIp0A1A_%9]0A?(AK0AA%Nn?F0Al]AAA0AC)A߼>0A%kA<0A\A|<0AZ2A!=0A;AQiG@0A&_~ApCD0AAd0A@RAu0A4=Aړ0A"<A(0A cA0A`;A>0A`;A0AA R0A@`A0AKA0A A/0A Ap0A AD"0AAdf0AeAX0A Axܥ0A$ A@0A AD 0A`iAD 0A (AG0AAl0A uApP0A5Az0A@NyA؎0A qA0A@A!0AޥA50Af?A;0ANAOl0AmB$A'_0AнA0AxAl0AA D0A ]AFu0AzWAi%0A mAs"^0A=uAEun(0AAmAor0ALA$0A@> A<0A`A0AANs0AA0AAu0A~Adf0AfZAT0A`;KA{0AA?U0A ƻAmc=0A1DcA20Ak"AѦZ0A/L=(Aŭ0AȽoAh0A J[AP0AlDAXٷ0A (~A80AjiAml0Aʸ/Aiҧ0A$AY0AAu@0AŃ\AĂ0AA}&0A2A:0AA _0Ak\*Ao0ARCA0AANLz0A_r Aˏp0AAȅd0A`At$]0A sA7N0A|/Ad@0A@A10ARLA!0A<AhAG0AgȂ5AM 0A\A0a0AA#$0A W1A.O0Aݨ_+A {3 0A4uA 0ANA>0A)A0A$(qA0AU!AM0Av +A}0A_QA0A\eyA0A A*~0A]HAβ0AኚAO@0A A&0A bA 0A@gA0A`sAT!0A@>Ax0AKA0A|AY0A̘AHa0AןA0A@>^A8E0A_Ad0AgA&0AWA$W0A@A80AA 0AHA\50A`AV0A At0A`A0A`]A(s0A;A0A$A,0A`cAe0A6@AU0A@&A&0AT~A40AA90A;AwC0A@!A=0AA^ 60A0`A|00AWA>+0AGfAK9ن(0AeAu&0A\01(AI&0Ae=sA#7/(0AjXA9+0AwYA0/0A-FAmK$70AOA@?0AeيA]I0AF'A;P0A7aWA W0A}{A_0AoAh0A Afqsn0ATe A%t֚r0AhGe_AjAu0A0p|Au0AA0A`DAX*0A ZoAEk90A=AYn0A]A{0AW\Au20AB.A؎0A&aA78+z0A*VAUXe0AnپAzB0A'A0A 8Af܅0AV Ar 0Aɚ`#Ao0AHi<Aǔ0A],]XA~0A:wduAۇͽ0A;A~0AňAǔ0AFAo0AsAr 0A6e Af܅0AQ A0A=+ A%:p0A2^ AXZ0AU An@Y0A= Am0A | A؃0A\`l A7=0AI A0A0L AOԊ0AUAOg0AwLA{0A.(~Ay0Al~,A0AJyA/?0A?Ax0A@AI0A®AƱ0A AAt 0A`AfL0AIEA(0AAr0AA,F30AA¯A£sx0A7\ASu0Ar A$0AAx0A \+A*A0Aǣ /AS%0AU(4A 0A74V9Aq|0A%>A80AOCA!$r0A0GA&¸0Ag$LACj'0AiOA/0A4SA"0AUARa0AċA[0AuW5A н0AA#0Ao~ A0A ALȲ0A# Aʮ0A~M AAJ0Aۜp-w A4W0A) A40A/ !Aڭ0AJr!A%nl0A\9kN!A !0A$"A0A@ "A~0Aa[#At0A$Ab0A&AX0A̜'A0A *A!Đ70AK*A/0Ae+A\'0AE+Ai/$0A&^P+An~"0A: ^+A( 0Ao+Ak7+0A&5憂+AC0AC +Az6p0A3'+Ak2;0A-!+Ay0A+A(+0AG,+A0L0AT,A0Ap_,A%s0AZ} ,A0A-A$}Y0AEG-A8)0AVEu-A㶠%0AY-A@qZ 0AsXؽ-AL0A-A0Aj-A0A-A}0A+-A)0A@ .A|P0A2.Al0A O0A$0A1Aƚ0A H3R2A90A7U3Am0A3AӬ0A!vT^4AXU' 0Any4A60A5A84$0As5A͍0Al5A\0A5A0Ag5A0A5AΤ0A.P5AV|0Abq5AT x0A`16A؞t0A=F!6AS!q0Ad2vC6A o0Ajh6Aa %n0A֎6Am0A|6A n0Ab 6Aɝn0A6Am0AZda7ASl0AF7A[Ak0A07A?Ai0ADsD7A, g0A^V7A=e0AV`{ V7AEY+4c0ArV]_X7A^4]a0Ab@H ]7Av_0ALd7An^0A#m7A \0AZQ#x7Aǜ[0AW7AVZ0Aۛ7AmD4Z0A?(Yd7AvY0Ax,n7AZ0A8*"7A`Z0AK7A T0A C8A K0AV8A B0AL9AP60A1k9A)0AH9A؄'0ABp9A&0Apƅ9A^$0A@Ͼ9A.!0A9Az0AA5:A?0A@K:A0AG :A@p 0Ab:A 0AA;A 0A$˟;A 0Af\;AQZ0A x@;AWa0AѴ'<A!0AZ:#SQ<A-F 0AEnq<Ae0A <ATpT0AЏ<AԳ0Avj<A` 0A`윲<A0A <AK0A3ek<Aj0A2l =A 0A@2=A 0A WNuu=AME0APuz=ASך0A=A/<0À2>Ax0Ayg>AC0A[䷕>A!Y0ANq>A'0A)>AH0A}/>A0A>AsШ0Ar ?AM)0A6a1 4?AZB0A'_S?A\0A u?A90AՏ?A+̪0A e2?At0A?Alw0A  @A zwp0Ak=@A\i0A@x@Ae0A @AT0A8AA%M0A&EAAg0ABA(0A}xCAXS0A~(CAU0A`lDAX0A.-j2EA<4 0AEA~$0A`"FAn(&0AYƪVA|F0A,VAl@0Aa:WA<60ATWAe00AEWA^%0A@ WAx1 0AXAL{ 0AXAHV0AwXA0j0AiYA0A@YAX_0AYA00AZA쾗0A[A0A [AhT0A \A?0A^A 0A)`Ao0AbAa0A`;bA3-0AocAX0A9{aAD>0Au`Ap70A :`A #0AFaA0A@bALU0A AaAO'0AcA`\0A!cAΔ0A9)3cAB|T0Ao2dA=0AldA0A`"dAۡ].0A[eA60A51eA-0AE!"GeAz0AZDVeA9#0AEEE^eA0A^eA[p0A`eAUC,0AӑseA\0AgeAI0AheADQ0A;hAt$0A iAh0A ɷiAd}0AAjAp0AhAfn0A@kgA,4a0AfA)0A`aA0A}E\A@0A(^A$0A`^A 0A,_Atd 0Ap]A00A %]A80A`]A`0A| \AO0A@iEA50A@ DA$9a0A BA0A AAA0Acg9A0A#V8Aqӭ~0ANQ/H7Aa0AmwX7A#0Ae6A#0A Q6A4+:0A?i4AnGR0Aΰܺ2A%l0A^81AQz0AXuq/A 0Aئ!-A0Aƾ|F,A0A@5*AĂ*0A'A F;0A0g&A-RN0AÜ$AhSb0A j#A@c0A`S#Ac0A`dO#AYf0Aei2"Al0Ao!Aٹt0Ax X!A*~0A"VR A$Ɗ0A- AA0AN9A10AtgW/A%x0A+ A?#z0AH~K7 A)]-{0AMx A&bx0AC A6>t0Abd3 A,#m0Ai AF}j0AuõW A'1h0AfV A*h0AEo Ahi0A Aam0AmK= ANqo0Ab?_A) o0A).(cA kl0A=#ZA1Rg0A}jA`0A@2ALU0AA R0AAV0AA;S0AjAI0AAF0A@kAXE0A&A <0A9Ap40AIA90AtA90AྙA,+0AAw*0AdA(0AF?A4q!0AIA0AAe0AaA0A@HA$0A@g2A0A %Ae0AA|2 0A?A4 0AOA4 0ATA4 0AA0A6A$0A&A70AA00A6A<_0AA 0A5A0AI0Ac0ARAL0AAX0A,AW0A =A0A A0AtAH0A;A0A IA0AoxAP0A@UAh0A<0Ah0A ZA0A@aAh0AR-At0A`AO0A Ap0AAp0A%Al80A 3AxZ0AA,0A QvAl0AagAt0A`A}0A@GYAY0A@ KA0A#A0A A0A`)Al0AA(0AA@|0AA w0A ATp0AA|pw0A"Arx0A`As0A`Ad{f0A Aԫ]0AwTATcV0A@A(R0AhA<<0AtA%10A OAؖ)0AA4%0A`CA]0AqA0A`iA80AѐA^0A A(D0AA p0A AA(0A@\A0A AD+0A ~Ax0A}AXb0A @AXM0An7A &0A A0A0Aw}A@0A.Al0AfALu0AAa0AaAҳ0A >A0A`9AL{0A^)AT0A@NA?0AAp"0A_OA0A@iA[0A ~A0AAA,0A&A<0A[A0A Aպ0AAPK0A@#A40A zA$q0AA$(`0A^At\0AAP0AuA @0A@WA (0AٗAH!0A <AW0A,A0A@bA0A@7AJ0Az Al0ARA=0A`'A0A@A0A`D^Ax0AA m0A`ADf0A0\A`6R0AH#AH>0AtA^/0AmAP"0A AH0AA0A@A0AxA`0A-A*0AlAe0A\A0AxHA 0A3A80A` A8m0A A0AwAX0AKA0AA0A@A[0A`Al0AlA8g0A (AU0AGAlS0AR;AI0A` AA0A A850AA/&0A`vAE0A?yA0A`MAl0A6:A0A Ax0A@>A 0AA0A` A0A9A@0AfA0AA B0AA\0A@VAl0AhA̝0A` A0A>A0AAp0AW޾AL0A}Aw0AAp0AகAlOc0A`;A^_0AuAL\0AmAIO0AEYAXG0A0IA>0A l-A4 90AA40A`A|.0AιAU*0AෙA$0AwEAh 0A(A0A@A0A,A 0A(AT 0A 1A0A_3AQ0A`@A( 0A`v_A 0A`ӒAS 0A@謽A 0A@NA; 0A圽Axi 0A@wAԫ 0A4WAY 0A` Au 0AA 0A}yA@± 0A`4A( 0A&A4 0AaA 0AƕA 0AuATɚ 0AzAH 0A_Al' 0AzHAt 0A<.A 0A cAy 0A!Ap.q 0A ADn 0A@"A$i 0A /A0c 0A`A[ 0A"ܺAeM 0A ںA`%F 0A`ºA< 0AAZ6 0A A1 0AAX) 0A`m`A" 0AvUA|! 0A OAz 0A ~BA2 0A`;Ae 0A@i>A] 0A`-A 0AzAl 0A2A* 0A׹A 0A絹A( 0A AX 0AyA1 0A ~qAX 0A@ƆAl 0AA 0A`啹AA|P 0A7Ah 0A1AT 0A OAp 0A8A 0AIA 0A`fAC 0A9Adݣ 0A@A 0AqA 0A@yZAi 0AtA 0A`)A! 0A3ATo 0A@wA^ 0AhAxN 0A#_A D 0AV\A40 0Ah>A@ 0A`dAp 0AA 0AA\> 0AfAXH 0A@/APM 0A$A+ 0A ͭALf 0AiAD 0A`TA 0A A 0A`kAh 0A`mGAH 0AA< 0AA 0A` A 0AA 0A |A0 0A@Ap 0A A q 0A@#As 0ATŪA8l 0A@ A c 0A {A[ 0A$9AtI 0AA ( 0A)A܌ 0A`] Ah 0AA% 0A (A@ 0A?=Ab 0A|CA 0A 8A0A[ A@0AA,C0A`Ax0A|zA0A OIAP0A@&A 0A@AӼ0A ~A8ֶ0A@Aë0A@#RAʥ0A=9AD@0A@NAؿ0A̦APz0A=A~0AaAw0A<(Aاr0AAf0A uA^0AwApLT0A iAHH0ApA50A`dAK00AMA*0A@A7!0AwA0AAt0AڐÃ0A|A 0AVPA 0A-A<0A0A0AФA880AĮAd0A`A0A&xAhH0A]ZAl0A*,A00A}AA0A`ޣAո0AAf0A`[A"0A`£Ad0A^ͣA$0AA20AڣA-0A@߷A|0A2ALp0A jAc0A@5qAȇS0A[NAQM0A1AhQE0A`KA8:0AA.0AA 0A+A)0A ҢA0AA(0A>A 0ATAq0AϡALu0A`2áA80AsȡAH0AnġA0A@EA"0A ~A0A Q2A0AAy0A@`A0A@A°0A@֠A۬0AgA0A A{0A@#Ax0AӠAP0A@yA.0A?AL0ATA`E{0AǹAXJv0AՑA,ci0A /^A\0AAO0A"A50A?A #0A@؝A0AK~A`0A@P"A|0A[A0A GA0AA;0A@A,)0A!A(0A &Aj0A (Ap0A3A,t0A יA0AՙA|0A+A:0A0A0$AV;0A3 AX80ARAR50A`mAJ20A`AE/0AAhH,0A A8^'0A@A r"0A A0A"A0AF#At0A"Ad0A`A 0A`A0A A 0AA\u0AA 0AAd0A Ap0AAD0A A٘A0AИAt0A]ȘA&0A=`A;nA|6]0A9mA\#0AjA8 0AiAd,0AiAxo 0A*iAt0A5hA0AhA/A`hAP|/A|hA/ATWhA /A& hAP/A~gAB/AyEgA/AfA /A@p`fA/A 3fA/AeAС/AFeAm/AQeA̮/AĬdA/A]dAd/AZ!dA]/A`cAy/AincAz^/AbAH/A aA@/A`Az=/A`Aȃ>/A_Ah"?/A_ApV?/A@_A?/Ax_ACm 7/A{_A*/AVQ^A12W:/Apy^A/A@]A/A%]Aɒ/A%mS]A/Arj]A>/AQ]A 9/Aړx4]Ab/AB]AD/AT'\AB^/A\A/Aw\A/A[A}/AE-[A? :/Ao9[AA9/A}8N[AH/AN[At:/A+n[A 9*/A !W[A/A!qA[Ax/A@/[A\R/A- [Ahz]M/A ZA/A]ZA$/AZA,F'/AZj_ZA/Ax4ZA8/A ZA/AbLYA[/A^lYAk\XA*A5n/A쯓LXA_^/A*TXA`"Q/A炨XA z /AXA`"Q/Ae}XA_^/A4XA*A5n/APlXA7/AXA/A@i\WAP-/A{b:WAl3>/Ar.ZeWA/As VAn/AB VA!m/A߰VAج/AVA&/A̺TAx6/A`[{SA4/AwRAaEG/AcnGRATrU/A^hʘQA`/AkPAP7g/A`2/PAPg/A O)PAXg/A^OAg/A@iOAg/AOAPf/A mOAd/A@?OA2a/A OA0j]/A@%NA4Y/AҋNA`T/AQNAO/ANA`J/A MA7D/AMA>/A/MA(9/AࣻLA/5/A@ GLA0/A`2KA/-/A"]KA+/AH+KA~(/AJA |%/AJA "/AJA(/A@gJA/A^8JA/A| JA/ANIAxh /A`IA`r/AOIA/A@UIA3/A`4IA/A)IAhD/A`2HA>/A (HA/AHA&/A$HAP/AMHA /A jHA/A ARHA( /A+;HA/Ap%HAho/A` HAx/A@EGA!2/A FAp/AEA؎ /AsEA/Ao9EAb/ADA8V/AФDA/AEXDAڴ/A8DA[/A lDA`2/ACA``/ACA/ACAΒ/A#CA/ANeCA/A>CAHÂ/A CA3~/A`BA }/A$BA|/ABAB{/ABA`y/ABA w/A 1BAAu/ABAxr/ABA`ep/ABA@m/A`;BAhk/A)BAei/A`BA8tg/A@BAXe/A JBA{a/ACA@`/A-CA3_/ACAj]/A9CA7[/ACAhX/ADAأU/ADA]S/A& DAfQ/A`DApO/ACAN/ACA`M/A CAfM/AECA{M/A CA`N/ACAN/A CAM/AzCAJ/A@CAxG/ACA`6D/A BAHK@/A BA;/A\BAh7/A$%BA1/AHAA1/A@AA R1/AնAAt0/A JAA"//A AA\-/A6eAA`%+/AJAA(/A@C1AAm%/A_AA!/A[AA/AR@A=/A+@A8 /A O@Ap/Ȁ@A/An@A`B/A`L@A2/A'@A( /Ac@Ax/A?Ax>/A?Av/A ?A#/A \?AG/A?A/A൉?AX/A@q?A`/AY?AP/A@>B?Ap /A+?A/A@r>A^/A@yr>Aأ/A>AY/A->Ah/A>AԵ/A=A/A=Aڳ/A@=Ay/AH=A/A`ʺ=A/Af=Aϴ/AH=A/A=Aͮ/AP=A]/A=A]/A&<A/A@<A]/A`u<A/A><A /A +Ac/A+AY/A@%,AF/A@N-A/A`u.A/A/A8/A@0A^/Al3A/AV6Ax:/AK;A|/A&>A@7/A>Apq/At@Ax/AUAAE/ACA@F/A$DA/ADA /AxEA[)/AoFAxzI/At0GAO/A GA@C/A]HAPgM/AIA5/AHJAsA/A`ӕJAV/A`1KAc/ALA3v/A`LA/AhMAH]r/A`MAU/A@CNA)J/A@yNADX/AOA(~r/AJPA(|/AA3QA=f/AQAGB/A`2'RA)/ALQAЦ /A@%QA/AޞQA`/A@QAPk/A_QA`/AkQAx/A -QA(`/A`PA6/A PA/A[PAp/A@PA/A5QA&u/Am4QAR/AZQA0/A*QA$/A c4QApN/A@LQAD/A RA/AൖRA8t/A qSA/A`KTA8c/ASAh/A+SAP//ARAZ/ASAoX/A JqSAR/A` QA5/A@rPA[//AEPA/APA/A"yNA/AMA/AAMA@>/AMA`r/A?MA0]/AFAg/A @AP /A;Ahod/A@8A\/A1Af/A 1+A`/A`@$A_1/A#A$/AzZ#A@/A` #A_1/Au#AQO/A`@$AH݌/A`%Ao/A`)&Au/A@[&AU/A &AH./A^(ApN/A@(Aȇ/AT)A0/A(AHY/A`I+A=/A@+AC/A+Ap_/A`v+A/A -As/AH<.AK/Ai.A0/A c#.A&%/A (0AP/Af0A /A1A_+/Az0A0؋/A Y3AXm/A3A1t/A 3Aذ/A3A(K/Ah6A/A@N6A8/A}6A#/A836Ah]W/A 8A>8/A`;8AV@/A29A5^/A8A`/A;AHм/A g;Ap/A S;Ap/A@;A /Ah;A"/A>A$/A`>A /A6>AH'/A M>AU/A@Auj/A AAp/AX?AA؎/A&@A/AtyCA/A`)CA/ACAH/ACAQ/A2FAP6/A`;xFAr=/A FAZ/AV_FAM/A,HA/A_"IA8/A`)TIA/AtIA(`/AƋKAp/AKA /AFLAP%/A`KA y/ANA /A OAZ$/AOA/A`NAP/ANA`/APAx/A-QA/A@QAPȎ/A RA/A@!QA@/A?(QA0XP/ASAQ/ASAHzN/ASAb/A`TA/AaTA =/A"TAT/AaSAp}/A TA( :/A VA߶/Aoo^Ap{/AV_Ap/A bA(O/A=n 2AK(/A671A@/A l0A/Apg2A/A@0A@/A+A&/A&(A(J/A@,.#Ay/A@C1A3/A_A0٢/A KAP*m/A@w A@}r/A@59 AHw/A2A/AA'/AnA(/A@~A/A*Ap/A`RA8E/A@`A1/A@`A/A@`AȘ/A@>A }|/A`DhAQ/A@Ap/A |A/A |Ai/A SA/A@yAPHn/A`#Ah]#/A$A /A@A9/AAh./AA /A@A/AՂA(/AyAz/Af Ap/A JnAP/A`|AQ/A`|A@o/A`|A =N/A`|A/A`|AP/AӊA//A-A/A$7A8R/AAX/AhAPȤ/A`!A?/A@A/A`2EA1p/A@ZA8/A6A Ծ/A*PA̾/AxSAuž/A@^A /A`A9/A`v[Ap/A A8 z/AA 9/A`KA /A@ϪA,/A"Az/A ?AТ\/A"Aذ*/AફApy,/AApy,/A`_A(3/ALA/AoA/AoA؎/Ag(A`/AApl/A`APx/AZRA /AaA/A Ap޻/AA/A`9CA`/A cA`/A4A(`>/A JA|/AzA/AzA0/A`A֝/A$Ao/A@PlAj/A@PlAXT/AAA4/A A`"/A`A؟/A$Ap /AAp /ABAp /A AX#/AِA/AxAh۹/A`9CAo¹/AA?/A#Aω/A[ZA0/AAPM/ARA#͹/A@DAH?/A |aAX#/AhA=/A/A]/AmA/AA`/A Ax׺/AAP|ں/A`AϺ/A`MADܺ/A@ AD/A@BAD/AA8g%/AA0,/A@5AJ/A`ܦA/AA(@/AgNAI/At$A/i/A@P%Ax܌/AeA@ξ/AzA޻/A@A/ABAx /ABAB/A A_A0e/AA`k/AA؎/AA/A`2AƼ/A@hAx /A=wA/AwAP̈́/AAri/AA8D/A@ A`C&/A@)A'/A^A/AqA/A H]ApJ /ApA6"/A`"#A(JO/A`oAxzf/A&AT/A@Aȱd/AAPx/AN[AT/A cAQ/A$A0R/A@,3AT/A@AV/A@Am/A SAeo/A 82Ad/AAX5X/A (lAeo/A@5BApx/A`vsAW/ACA/AA8/AʷA/Aa/Aȼ/A@Aqμ/AA&/A AX(/AOyAx-7/AkAI/A$AqR/AF#A7Xع[/AQ6Ayc/AGOAj/A`lA k/A"RAWh/A=Ac/A@5EAP]/A ~AI U/AwgAL/A Ae>M/Aj;!AEP/A1ZAv&QV/Aa;G[AWk]/A ZAVg/AAA{/AJ6h A=/AjDA/AWA3/ACAky/AZ;AW|/A:AO?Ľ/ArzAA(>"Ͻ/ACX %PAҬHٽ/ALJ_eAl/ATAN/Aēz8AC/A/A${/AzA/y/AިA/AKA0H/A@AP/A3pAm/AߝAK^/AA'23/AaP=K+A?{E/A0]h$AS/AA~]/A`\A-,b/As݄gA w7Bh/AE[oAn/Azk9sA s/A.ψ/Ak AǾ/A:wA~޾/AjBA:6/AׂAP</ApAs#/AtmA`S9/A AuAwA ^/A &2A`h/AhAۓo/AAUr/A|8 oA(q/AAp)%h/AJ~AQ^/A:AiR}S/AɰA/I/A>?APm @/A@A 8/A'Y$AUz0/A؏"A;%*/AW;AF~5%/AX_A17!/AbAc7/ /AA/AA=!/AibAd'/AHArB2/AMqAFA/A'A[6U/A7%bA@l/AA k/A5㢳AX^j/AK-4AX|k/A[>9A Um/A1f :A;=p/ARxTA_!t/A:,lAx/AAv~/A`ÒA@/A@AhaϿ/A!AR/AA;8/AeAi/A A`/AQ*A&S/A}?8A/A@3AAD\/A0:PA81/AZzA /Aͪ EAܤ,/AfAN3o'/Ab5<Ai//AۑA3/A@*A4/Aϑ7A71/AIARu+/A/A!/A,Av/A;}$Ac /A_"A}/Aa!'A|T/A#1AFN5/ATAA7/AOcWAYa/A٥iqA'/A,@LA/AXAM3/AA*5 /AX.A/A -A/A$AqW?/A2GHL A/A` A;l@/A+ AA/A A /A& A/Az+ AV]p/A|MI A{1F/Ap}h AK!_/Aٞ AO@/A:7 AN/A2 AF/A> A4/A v A/A A!M{/A A /A" Ahѭ/A%PZ As6=/AXK Axme/A+ AGn/AI49 A0{u/AFr Amje/AB A{R/A]R RAP/>/A@%A!h:/A >AP8/A@>XAn6/AXxA0i5/AzޅAWL6/A&rdA4'8/A'Av;/AA?/A`AhnD/AA$K/AZAR/A8mA#Z/A/{Aʥb/AAJxbk/AAs/AA /AeAP~A/A 7SZA Ǣ/AnI\AP/Am_rhA/AAC/A19Aoz/Ax AI/A#Aijh/A _AmU/Aӏ A /ABA?}l%/A(xrAG+/AaϰAH70/AÐվAU3/AfsA/6/Avz0hAW7/A<~"AO.7/A{AAW5/Ae`A 2/AQh9~A./AcAU)/AAvP%/AѱA}~\!/A;3vA $/AMo{Aۦ/A!ZA H@/ACAlI/Afd]A/AHwF.A^/A`DAҺn'/AmA2//A^A0-3/AYA3/AA,0/Anc>Apl+/A |MA@$/A*ώ<A/A A:΀/AIA/AabA/A! .A@/AqA/AAP/A$A0 /ABA0 /AZA/A AH/A A /AA /A A2/AAB/A@A'_/AдAy}/A`As/Aa<A /A`A/A0gA/AଵA/AA /A@EAh]#/AAh./A==A1/A6A0&%/AdA*/A`0AAE/AA:/AAH/A`jA%g/A@NAj/A`2AJ/A}kAJ/AaAPHn/AnA Ox/A&4: Aȉ/A2З AmW/A8 A"T+/Aml!AZ |/A (E!ABs/ADvAN"Aaf/AQ"Aq\/A"AAE/A#A0]e/Ai$A@/An8%A/A%A@/A`m@&A8V~/AB&Ac/A'A/A@,(A/A@5(A/A(A/A`(AP /A+)A/A@y-A+/A+H/A M/AVI1A$/A3A/A`!4ABA/AYF{4A/Am4A$/AĐ C5A/A 85A.*/Aŗ5A/A5A6/A5A/A6AL/AQ")6AxF/A86ApI/A_7\86A_e//A#H6A`H/AiPU_i6As+`/A|R6AhUv/AμQ*6AfF/A1 E#7Ax/A y7A/A`;,8A/Ar8A1/A#Q8AqJ/AJ$9A0/Aymf$9A/Aك9A</A4t:APM /A:AM3 /AN;Ag /A@>;Ati/A-N<AE/Ark<A)+/Aj<AN/Au;<A0/A`0=A{k/AKSg=AYH&*/A*'=A(/AѢ=A@Š/A`B*>A:/A<.>AiL/A u>AM/AaA?Aȥ0/Ay?A 0M/A 1l@AT/A8ɟ@Aq]/Aiƭ@Akhۯg/A[@Ar/AN@A> I}/AScӢ@A7[/Aw@AG&'/A0C@A'q/AI@A5t/A(Ep@A4p/Ax @A`/A@AK\/A=ra@A1/A+<@A[O/AZ@A/Aez?A(\l/Afz?ANa/AINv?AMR/A d?A /AT b?A/Aѹ?A N/A ¹?Aȶ6L/A+;?AӜ /A>`?AX'/AqL7?A׆0/AQ?Ap8/AV@A?/A>@A!E/Ae@AUH/Aw@AN/A)AA.U/A@&BAˣdO/Ao;zBAmG/AVRBAqJ=;/ACAIp'-/Ac?QCA(-/Ah@WCA< /A*{CAH/ACAkي/A@gCA=gr/AtDA~/A;V.DANzf/A{ 9_DA|p=/ADAk'/A0DAѵ/A`EAPȵ/A`;EA/AްsEAǻ/AEA"ŋ/AGEFASB/AXFA-/AyGAŷ/A8 HAϭ/A!HAJ[/A*IAв/A- IA5/AhIAROU/AԊqLJA x/A@JAVB/AJAQp/A 4JAvZ/A#KA6g1/A?:KAw/AIaKAG<_?/AΧJA/A߭JA"/A@{HAPoA/A@cGAx/ATFA`/Ah?FAa/AEA /A@NEA@/A EAhD/AGGAU/A HAذ;/A`]HA;/AtHAX/AHA/AHA$/A`HAaN/A@HAf/A.IAO/AJA]/A`GLA #/A |yMAK/AMAV/A` OA(#/APAz/AQAJ/ATAh/A@,OVA/AwA/AVA`%^/AA/At<AwU/A`A/AA` /A Ax%/A`3AX5#/A1Aq/AAm/A@A`X/A Ae/A<A(>/AAt/A@IAN~/A`6A{Yzd/AdAtyL/ATA7n7/A?Ac%/A fEA vz/A"A7! /ASGNA_/ACg~A;*/AܒA>/A5)jL`A/AA`/A`KA/AQdA7/Aj5A '/AZpASWN/Ar:A>/A+A1,/A~A/A$A/AA/A@A`L/A A8#h/A|A/A&A@S;/AoA/ANXA68/A`A-^/A@A=6/AxAl/A@AX/AAx9/AuAT/APAwx/AlA0H1/AA/AWA/AA/AgAk%/A ˇA/AA/A AP+/AmA/AgEAƔ/A A1/A>A$/A)pA/AAw[/A|A]/AKxA3/A0A/R/A`)A >/A`]AA/A A,/AA=1/A_UA_&/A KA^/AA E/A AP;/A@ A /AA(~/AEA/A4A/A |A(\9/AA/A /A/AqAx/A Ahr/A@VAiJ/AA=/AA/A`2A/A෍Ax/A`jA /A@FAB/A`ܔA=4/A`A/A@ypAif/A#A/AA /A/A$]/AA(M/AA@>/A A@5/AAҽ/AzAPĽ/AA{/AXA9/A`aA"¼/A@A8/A`)bAvú/ALJAp*/A`A !/AjA@/A@`$ AP룴/AAd/AA)Ю/A@!Ax-/A Al/A !AR/A!A8(/A m"A//Az"A`/A@y'A T/A.A})/A1AV/A`mq4Ap0#/A4A/Au6A 4/A8A1/A=<A/A?AxZ/A AAp?/A@5BAǝ/AoCA,/AEAfh/AMFA:/AJA@/AIOAYw/ArrOAzOOR/AsPA(.&*/AEKQAo/AI[a8QAwEҗ/A.yRAX㣗/A}RA n/AzRAm|8/AL5SAu/AU5)wSAI})xƖ/AmԨSA+OBV/AU_SAQ/A'SAV>&/A]ogvSAxܕ/A SA 19/AmySVSA@1\/A-TA/A`9TA>ޔ/Af3{TA/AF/AAFv/A]3A+JF/APAc/AcAYR/A a>Ah@҈/Af Ap/A@GAW/AA/AMuA0A/AA؎p/A AD/A A[Y/ACmAdg=q/A3A5*$/A0.A~/A±A~ϊ/A_CU1PA/A OA/AA#/A9A/AxA /A`vA@}/A=AO/A UAr/A`rAR/AA /AA74/A`]A8)/AsIAc6/AsAe/A5m6A$}/AOjIAˌ/AaAA/A{A*/A ~8A/A A؞/Am}A/A •Aج/A A2:/A/APP/Am}ARC/AeAY/A@A/AAV'˳/A*TA֢/ADob]AǧA/AB!A6o/AAG4k/Ak;A!?/A= A竐B/AA/AbYA%5/A`A˶/AA`Թ/AA`/AA`/A@Ag/A@Aj/AA؍/AAh/AVWA/A`6A/AAd/A࣐Ag/A \A/A CA؁$/AAFf]/Ak-fA/A`!A>6і/AB'%A]g /A'sAH/AA/AtrA*/A<AH/A@HA/A AA/A3A/AA@OQ/AA3I/A) A//A 8$A0*/A@#hAh^r/AA\>/A#8 AH!lw/A(cdA/AqAT#/A<2A@0)/AA/A@Ah/A)fA8"%/A@bA%&/AQA`/A AV/A /AH/A /AB/A@AXl/A0Adޜ/AdA`G"/A@WA(/A@yMA@/A`vA=/A@EA/A A/A (As/A0A)I/A`ʯA/A@عA/A`̺A,/A A/AA/A[A /A AS+/A@HA(3/AVAz/AAH/AIAv/A2An/AhkeAPoe/A@ƘAq!5!W/A3S@xAI/AA׌=/A1A] 3/A,U AN1F+/AA USA%/A@- Ap!/A`AE/A@A/ATAث/A |jAy/A Al&/AA`/A$A(/A^Am/AvA;W/A A8VP/AAJ/AVAxi/A pAJ=/A&AHЙ/AfDA1Ș/A`d#A`˗/A AX/A@߃APb/A 8 A@`Q/A5A/ANAX/A4A濕/AAؒ/A2A/A<A/AA/A AT/A@-Ap/AIvAxhД/A3Aۙ/AWA@/A]A9y/A`A[w/A Am/AIA/A2_A@/ADAcÓ/A'Aґ/A@AhG/AA/AA/AW AP//A` Axے/A` APՒ/A@p: Az/AF AX/A AxR/A@ϙ A`V/A# AfI2/A[ Av/A8bI A`/Ah` A(ӑ/Ag& Ađ/Am A /A`"AZ/Ap\i\As/A\4AÐ/A EA{0l/Az AG݄C/AۆAk.Ə/A$8E/A!TA6!N/AIAV/A AŴ/A 1<Ac/AAA}/A;AxS/AKDA(,/AA*6/ASDiA`喋/A`TAN3pg/AD፳A9/AgIAx/A^*DAp/ABhN7kAC/A@ A5d/AF4AU"ٍ/A(|@Aĺq/A2+AR/A8KGAp2/AAY/AA7K/A\/CA` Ɖ/Ao712jAp/A@8AJM/Ao&APm/A@A7/ALtnpA*~`/A$DA;ψ/A5kAJ/A_kQArXr/Ah2A1GG/A4a[AUp/AY A/AAކ/Aq5Aц/AA Ƙ!dž/A3HAv$/Ak5MAg~/A\PA /AA7/AʣA@6/A@TA/AmhA/A 8|AÆ/A`AĆ/ACA k/A^tA$/A bAZ/An2DA0f/A`\AK^1}/AA~p/AABg/AhvFAp b/AA/AA/A*A0݆/AaAhӆ/A?A` /AA0/A q9A`/A@A˚8U/A)ޟAM/AĝA#VlJ/A/\AJ/A@AXrJ/ATwAKH/A9LAD/A@#AK(9/AQ _A))/A%GA;D/AlyYAp=/A`<A/AbA /ބ/AŸAׄ/A#Aa/AA0/AحA^)惲/AӴ`vAF/AAF Xą/AsA0ԅ/AA /A+ϫAh/AA-/AAh*/AHdA!@H/AHko"A}/A'~GA}g/APF AP /AXA?V /A4cA+/ATetAqߪ/AcA$ x)/A|A:/A`)AhN/A!yAY^/AxA Ԣi/A"ԬcAsp/AaDA_s/Ap/Aڂr/A$yAl/A֡AfGDu/AmߝsA{3/A~A h/AkAS 7/AW@ZA84Æ/A@pAPۆ/A'eA"|a /AjAnf/AKKA͆ /AŹ/xZAo^/AuA /A?A(/A.AA/AnAbj%/AqAK3 A/AྜAH/AgYA5/Aa A1/A=ĊA m/A&UAxq/AFˣAi/A61wA/AW*( AKq/A CA/A JA oЇ/AeAA| /A0 A| /AA? /A$΁AϠ/AdA(/A`28A{y/AA'/AzlIA=oO /A l~AcR/A|1;e~A }AN/AE+}A ]F/AJ}A&-L@/A&|Ã;/A);.|A%E7/Af7|A(5/AЅ|A@>/A:$|AK/A{A2jY/A~a{Ad/AzA` j/A,zA1gm/Ap*)zA_o/A`~zA9:o/AgyhSzAb-;m/A-J)zA{1Bqi/AϗzAc/AyA^/ADyARUX/A`bܫyAq`x=R/A1\yA K/AFyA3T5/A xZyA)/Asɉ9yA=/AZMc/(yA/A3B%yAք/AQ3yA]8/A!wyAps/A3z;yA8;/AV.zAGn/AzAS\/Aj{AsP/A.D{A8G/A@"|AQ /AMAƃ/A@A/Ad|A&Ϣ/A|A*'Ӡ/A⬃{AÁ/A?v{AY/A[h-{A5/A'`zAc݁z/AqzA Cj/AXj)~zA&]ZX/AdWzAWE/A u=zAvy!2/A&E7Xق/AǕzAH)#̂/A:zAV/AKHzAL/Aa"?{A)/AG@ {AƮ/A{A7/A$|Aj‰/A!S|A["͑/A K&&|A9[/AB'|Av/AI}AI/A`}APp/A }AӚEo/A|.%A}?l/AπAFg/A.fAqr6a/AzAY/A4A$FyJ/AP7A*iz׸G/A[ѲzAD.B/A7kzA:/A }\zAc1/A <9zA='P'/AzA/AzA^#/A5zAL |/ATXyAV/A.3yA/A}jyAج܁/AyA=bˁ/A yA O/A~XPxAsAʔ/AU5stA\/AaA?tAȚ/A6XtAo/AgtApiR/AvtA.to/Adm}tAF_/At{ytA@O/A%GltA`?/AVtACc:=/ADtAՕZ:/AVV|4tAՋ6/A7XC&tAd2/AtH>tAKJC./A?tAv5}v)/A/ tA|~$/A tAl~/Ac tA/AG9tA(/A-tA/A̍[!tA /A.tAx~/A`Q~tAl~/AT1׿tA+S`6~/A0tA2~/A6uAO- y~/A%uAFdA]u/A`ҰAجu/A@,Ahu/A AHu/A@A54u/AA)u/AHAEu/A Au/AO1Au/A|AUu/A=A)~v/A (OA5v/A*Afv/AQAP'v/A'QA)v/AA@w/A`myA w/AAh{qw/AĔAgw/AjA8Qw/A@>APox/A A@5'x/A 8A8QUx/AQAЯx/ALAux/A Ax/AAy/AaA!y/AAP^"y/AʊA>3y/A`APWy/AAly/A6Asy/A`[Aby/AAy/ALA3y/A`HAy/AABy/A6A8y/A`Ay/AR'A z/ATA2z/AVAJz/AyAp,dz/A`Az/A`)AТz/AOAz/ApAHz/A \A%z/A`Avz/A@5ZA؎z/ANcAH){/A A-O{/AKAu{/AAxm{/ApA`{/AAH]{/A`KAӖ{/ATAh"{/AAߎ{/A@Au{/A A2{/AoKA0{/AAAw{/A`wA[s{/A %A{/A|A0n{/AAњ{/AA`{/ASA[{/A cAH2{/AA{/ABA8{/AhA8E{/A`vA{/A A%|/AA ||/A ZAe(|/A$ A0|/AsBA@4|/AA0&2|/A@Aa)|/AHA 0|/A`]AR{/AA{/AA {/A`]A0f{/A$GApʎ{/AAx)j{/A|A(F{/AA%6{/AA {/A}%A{/A[SAz/A u}A^z/A AUz/A A Az/A^ A"z/A`LA0z/A APwz/A`TAp{z/A`KAnz/A`v Afz/AeAfaz/AA( Uz/AsAx=z/AA`v z/A!A z/A@Az/A@rcA`z/AĐA z/A`̻Ay/AA-y/AAy/A%Ay/A``Apy/AdAy/AaAȺy/A"A(y/A iAxy/A 1A y/A A8 y/AyAiy/AAh@y/AAx z/AA([ z/AA03z/A@ACz/AΞAYz/AhApz/AAdžz/AAPz/AIA0*z/A@>A/z/AA@Nz/A`f!Az/AtAQz/Aq`A0;z/AYAH?z/A@A'z/AxAyz/AAXz/A`A Sz/A Av7z/AA$ z/AXrAz/AμAz/AnASz/A Az/A!A0z/A^A`z/AsAy/AWA(y/A`vbA(y/AFA ty/A \A8y/ADApy/AAH)y/A^A84y/ADAhy/AA[y/AAALy/A_A`!y/AAy/AAy/A@,A@y/A@NAy/A Apy/A`AUy/AtA@y/A`A(qy/AIFA0fy/AA0ٳy/Aज़A0Ly/A`Apy/AA y/A`vAy/A A0y/A JA0y/AAny/AAz/A`KAe*z/A Z;A03z/AARz/A@<Ahz/AaPAtz/AAvz/A AXz/AGAz/A |Aϔz/A/&AX5ez/AA9z/AAYz/A`ANy/AX A@By/AAy/AVA@`y/A-Ay/AAjy/A@,-Ajy/A`]EA*y/AAly/Ap<A"z/A4A`e/z/A sAȔRz/A`Aqz/AAz/A@sAFz/A`A p{/ABA{{/A A8O{/AA({/AVAX{/ApA{/A@gAQ{/A͹Aھ{/A@A{/AA\^{/AAx2{/A JA8 {/AA {/A JA{{/ANJA{{/A`bA{/A QA.z/A&A^z/A`bAoz/AsAP0z/AAA+z/A@gAgo/AnZNkA&jo/Ad.kAm&yn/AXlA:n/AX#lA(Jn/A`!mAƅ(n/AߎmA) n/A'EnA;m/AS:vnAj.m/A:UoAvA$m/AѴoAbƏm/A|PpA2m/AOpAЛm/A&qAhDm/AJrA{BRm/AS:rAks6m/AssA]0m/A4-ɍ,tAH1m/AJ/A8h3*qA79J/A^qApJ/A6rAPJ/ArAJ/A@>rAJ/A0{sA(J/AsAP^K/A !tAu K/A~tAK/AٹtA%K/A@tA0 2K/AH@uAJ3K/AjuAh0K/A`uAb;K/A ɷuA8DK/AVuA RJK/AquALK/AuAxPK/AauA@dWK/AuA<^K/AuAPYaK/AuAcK/A`uA@}{K/A[uAK/AQuAڛK/A@uAK/A cvA୧K/A@1vAXtK/AyvA +K/A@vA`vK/A@`vAHK/A\vANK/A@yvAHK/A@uAK/AquAK/AwuA(cK/AxXuAhL/A~BKuA]-L/A ?PuA3NJL/AΉ(guA.fL/AˇLuA! ̀L/A^;uAvL/A5VvAL/A@hvA pL/A@%vAаL/A`vAzL/AmwA$L/A xA0L/A@xAL/A 0yAXL/AfyA L/AzAjL/A zA`L/A zA`eL/AzAHL/A]LzA(L/AF`zAL/AzAL/A`zAL/A6^{AXyL/A@{AsL/Ap|AQL/A`B|A@L/Ay|A%L/Ay|AL/Ay|AL/At}AL/A 9}AnL/AZ}AL/A;}A``M/AĤ}A }M/Aɟ}A`P(M/AՕ}A#4M/A@g}A%PM/A@ߋ}AWM/Aஸ}AT`M/A@P~A]M/A`~A]M/Ax~AhUM/A~A[M/A+AhbM/AdApcM/A6A\M/AA(XM/A GA`RM/AځA@dQM/A`|ASM/AA\M/A`9A2bM/A@ރAnM/AAp[wM/AfA}M/A@APM/A<A`ÍM/A@ARM/A@>A(M/A4APfM/AAM/AAM/A@/A,M/AanAM/AAM/A@AM/A AM/AA`vM/AQA'M/A]vAM/AA@$M/AͅA`6M/A ZAȺ N/A JAN/A4A(N/A |GA4N/A A8c@N/A A@ABN/AAXKN/A`[Ap|dN/AxA N/AAx:N/AA`N/AAN/AAGN/AOMAHN/A@A0N/A AcN/A`)_AxO/A[A8O/A`AMO/A`AOO/A`m A@PO/A@iAryO/A_Ah;O/AqAO/AjAܫO/A@`AO/AZA}O/A牊A4O/A uAX$O/A`zAO/A@A$P/AdA=P/AA,PP/A@6A&UP/AN~A`!ZP/AČASP/A6A@uGP/AMmA@P/A`Ah//P/A`DA'P/A@YAP+P/A A"P/A&A P/A`A3P/A`A8 P/A>APZP/AAPP/AHŏA`O/A<ATO/AAqK$AgmO/AΐAO/A`A*GO/A$o,ANO/ADCAiO/A IAhN/AĒA-CN/AjA9N/AAyN/A*˾A^N/AA@N/A`בAM/AAttM/A<AHM/A}qA$LPM/A-A M/APaAJ'oM/A@ƑAsħUM/Aʺ>A>M/A Q?AS7M/A&A4/M/A8AX&M/A'sAM/AxGJA!OAM/AZ KASb` M/A6AήM/A^&Aw)nL/As!A:-L/A19Ap L/A`UAtEL/AAA8ڣrL/AQ&A%8L/AtAkK/A)zA|K/A"ߎACBK/A2$\7AeK/A`[AJ/AIA{9]J/Ahf&A0J/Ag@ A_ųJ/A` AqΚJ/A=͍AЦJ/A?A ~J/A-A:pJ/AnnpAȻcJ/AoA0XJ/A@.7A#t?J/AT%ACo(J/AZA9IJ/AV҈AżdJ/Agx֒ALv=I/AZ9GA-I/A@A pI/A ҸA zI/ASVA4Z[I/AHκ)A"I/AĀAعI/A`mvA %lI/A7OrIA OI/A tz-A41I/Af "A JI/Aoq(AɱH/A^I4@AH/AhAstH/APB†AkH/AѨ]o AAG/A.?AĵAG/AͼbALF/ArAx:F/ANlA5F/ANAF/A"%AGH9YF/A,AG.x1F/AᦻAT F/AG?4AohE/A5ZLYAHE/AAE/A$AUE/AAE/As- A)D/AޢoAbD/AM_ÉApD/A AP@D/A(CAI C/A4見AٹC/A# AyC/AA1W@C/A:G:A\Ż8 C/AVK،AB/A 냍A6DB/AhA'EB/AA| SB/A!TA^ܸB/AP'eAʭB/ASarA)湨B/A|yA[9 GB/AR#{A@B/A=wAx3hjB/Ao`KA|uB/A)AB1bB/AA)NB/Ar^ A;B/A= Aá'B/A+gAB/A1AҤ 7A/A4,A/AU]AiA/A#AbA/AVAD@:@/APA$-@/A@ A`]q@/A9+A?@/AŅjiA@/A`dA=@/A<A@/AJ֖A& ê@/AFpAe[@/A2A1,@/AeЂA|2Q@/A@Q%AN@/Aͩ&AԜ@/AmA@/AMA~@/Ak;LAx@/AAGl@/A ݜA\\@/AGAYH@/Al)YA/@/Av A@/AƘb2Az?/AVAKA5?/AHؠAأ?/AVA%WB?/Aڴ{A o?/A^8>A:RST?/AAo9?/AR.}ۀA^+?/AZ S_A!n?/AH-Aպ>/AA>/AAes>/Aj Aڶ^8>/A62tADT>/AqaA;>/A5NAO>/A ]=Av>/A .AW>/AA5L>/A# ATyM >/AEJ A^->/A>Ax6>/AA">/AACI>/AK A1u>/AaAtT>/AL][A.4>/A 鳵AzӒb>/AaW)xAFi=/A:+A(=/AўA(-=/A ҰAP6/A`)Aa6/AැA6/A=jA6/A`7Au6/A@(A6/A`BA6/ApA6/AzAp= 7/A oA7/A`fnA77/AA[7/A@gA`w7/A̴A7/A`)A07/ARlA7/AHYA7/ApWAf7/A \wA)7/A \A`a7/A 3A7/AXAk7/AA87/AAH\7/AzA7/A@>AB7/A3A 7/A@8A7/AR`A 7/A`fzA7/A HAX7/A]A#7/A A#7/A=<A7/AA*8/A@ AC8/AFAP8/A`A`e8/A`Ap o8/A`DA8y8/A qA8/A@A)8/A AL8/AcA88/AAp 8/A@Ah8/A`FAz8/A=Aأ8/A AQ8/AA8/A`)A8/AfA؞8/A \A8/AA8/A@ A08/A`!A8/A`A9/AAP 9/A@A.9/A A RB9/A=A8I9/A/AH.B9/A@hA8,9/A`ÛA)9/A@ Au9/Ap A8/AA 8/AAأ8/AAf8/A`Ah38/AA8/A \A`a8/A@A8/ASA8/A`)pAf8/AסA8/A3A9/AeA8 9/AAH\9/AA{9/A HA(-9/A@ϏAU9/ARAP*[9/AAH.`9/A]AfW9/A qAk9/AQA9/AzAh 9/A3A9/A@ A/9/AAZ9/A`fdAQ9/A@ A9/AzA(ܤ9/AAHP9/A A9/AGX1An9/A[A=9/AYv=A5/g9/AA9/A@CYA8l 9/A,QYA7{9/A6AA?9/A3˚Alm9/ALUA@19/A A@9/ALpA]9/AܾA!d9/A61ACcE9/A{>CVA`S%9/A A4P!8/A'=SnA?8/A YA08/A |A3n8/ASNUAـ8/AR dA {8/A"AEj8/A$p6AxX[8/A AZ;K8/AAI>8/A A.T8/AqAi8/AmDFA<{8/A]A#|8/A5A@ ~8/AgqA/Y~8/AGmA\~8/AA~`d~8/Aqq;A~}8/AA"|8/A"`,Aϔ:Gz8/A+Aix8/ANAOKu8/A^Azr8/AdAo8/A\? A֒l8/A;o Ai8/A` AfF8/Aޞ.À%8/A@A8/A$3A׽8/A*Ar477/AwAjh9M7/AAƕ$7/AȸAz:7/AAJ7/AJrAIG7/A,Ae7/AM30Aׁ7/A A7/A@iA ʤ7/AAApft 8/AvljA8/AAo8/AUAN)8/AA酽 8/A*ۋA6`8/A1]Ax8/A~A@7/A$qqA87/AOF+gA7/AD`AE'7/A|RcAg o7/A,SbAYv7/Aί/]A7/AMwTA(p7/AhծHAǪ7/AD9As!~7/Al'A]#-7/A\2;A7/A A+b7/A?An,7/A? Ak7/Akn(A77H7/A;Ad7/AAE57/ArqA7/AaTA0u7/AiAN­7/AHA@7/AՙAw~^7/AR2F}AӍn7/A2(*dA7/Ag QAzak7/Ac\vBAl7/A@r9AC7/ACDAhR7/A1o,RAf7/AmvaAʣ7/A$rAfY 7/A$@{AO7/AAA'v[7/AAf'K7/AybA* T=7/A)=?#A .>a27/A'PA؟*7/AA`r&7/A/G At$$7/AAf%7/ACA%7/APy{ Ansy"7/ALA=7/AA`ASg7/AԥlA=7/A% >A7/AzALU6/AUAp6/A 7A=b6/AAbBI6/A[ʊAsZ56/A TA$6/AFA2>wAg8d6/AppA&f6/AK HA 6/A Acp6/A=aAP^6/AAA`6/A{cwA6/AWA Y6/AElmA\P~6/A5RAʠ(6/ASq)Ap66/A@G]A6v6/A$AH6/AcAX?o6/A-IhAj6/AoԖAS6/A$;̂A6/AtA6/AsvA,&7/A~ AзW7/A͍Az{7/AVl*A./7/A9A&7/A`A37/Ae%XAm?7/A,Ag7/A鈉,A88/AAw8/A.A18/A+YA8/AdTxAFܬ8/ARƴA?8/A8AP8/AA%9/AH[AF9/A7ABAPL9/AS<A4|+R9/AE|=AW9/Aa:Ay[Қ]9/A|4Ab9/A;)AKDg9/ADAhk9/A  A, h9/A}Af9/AQA[f9/Aw=8Aaf9/AAɭg9/A2\jA+g9/AJKA3|f9/AiANKf9/A\˾Am߂f9/A|ڗA'f9/Ax͎A"Be9/A w/A=Jt8/AsAVq8/AMA`ap8/A&Ar8/ADŽA t8/A AK1t8/AwAu8/AAj.|We8/ACAyT8/AJA~^C8/AgpvAE18/Ad]NaA0{8/ARA8/A̧qAG8/AANA#8/AR(A#7/A@ Ay'B7/A AO67/A3qAu7/A AF7/A&A7/A"5A ~07/A/TANݮ7/AA.07/A5AEv 7/AyˆAeW7/A@ A-ѫ7/A[iA {7/ABA9Y7/AV"A|j7/AdAb7/AxA%7/Ax cAʬ7/AcAbt/7/A Anq7/ApAod"7/AGEH\A( 8/AA8 8/A@>VA\\8/A"GA6$8/AOJ) 7A88/A@A@8/A \3A<8/AA.8/AHCA8*8/AAX 8/AQAx{8/A6A[8/AXُA- 7/AˎAL17/As|A7/AmAs7/AA3\~A=7/AAA`a7/A 4A7/A GAHܙ7/AzA R7/AAL7/AkAYT|7/A\}Qy:AD{7/AUۨbA}7/A@iA\~7/A0A,~7/A0. A n7/AR5Ads~7/AiJA;~7/AkZaA{W³}7/AbGAVk|7/AgA {7/A. AHy7/A&z AQw7/Ab OAm@3u7/A7Ar7/A8Ap7/AzA@+f7/A!j߄A'< \7/Ay|AKDS7/AyEmA~J7/AXA'7/AA{GC7/Aۿ̙AZs7/AtڝA7/AmzA B7/AxAFi 7/A݊tpAŭ# 7/A8jA8 7/A^UmeA7/A3,cA?*V7/AFbA87/AdAp )6/ApA¯5/AzlA83m5/AbA'd5/A,Ay_5/AޔAu]5/AbQ)A`5/AM7Ae5/AvZAՊj5/AAОj5/ASbdAHnf5/ANv?A6G\5/AɌNAM5/Av}A/M5/Ai5AtK5/ApJ-TAH5/A|A E5/AB~Aڡ/@5/AӰA\Sv:5/AAE45/A^2&A-5/A0A3/ArA83/ArAЪ 2/A_Aج 5/AZ}AC6/A϶A!8/A@AX=:/A`A!/A@Ah=/A 1A@ /=/A`9A3/A AH{H@/A OA7OA/AA?B/A AMA/A`a0Apc%A/A74A]@/A@AAX@/AKA@/A`MA?/A@iSA0>/A_dA@>/A,yeAH>/A efA>/A8(gApű>/A:hA=/A`WvA;/A@tA(9/A -sA^8/AqA<,7/AbA,6/Az4A8]6/AAh&5/AyA(5/A=1A5/A cAo4/A5A3/A`A`3/AALf2/A *A1/AA1/AA1/AAh0/A-AL*0/A`f5AJ//ACAx./AAh*-/A`zA`T+/ABAp,-+/AA&+/A sA0+/A5A*/A'A*/A96A)/A IA=)/A"A)/Aho-Aڝ(/A+5AB(/A?S=Ap'/A`oDApŢ'/AmyLA0&O'/ATA"&/ADsAp%/A[tAB&/A)|Aw2%/A@ErA"/AHAqa/AivAH/A/TA/A SAc/A|SAh^ /AZ]EA0/A;AD/A .A(/A|!AXy.A_AQ.A9A+.AQ A.A ,AHu.A Aȃ3.A`APUU.A@AH .A`A .ATAt.ApA.A@ AI.A AاM.AVA0.A+ A..AAG.A Ar.A@DAPQ].A`Ay..AEAL.A A(S.A@`wA0.A`A.AA .A`wA.A[:A .A .A0r2.A2A2.AcOAHvo.AWA].A dXAg.A@?YA0.Al]A.A`0bAmn.ATgA ɹ.A_5iA .AkA,.A`[pAЈ.A uA8r.A@rfAЯ.A@#@A` .A늇Ap.A-A?.A`LAc.A ~A.A@r/A.A_{A.A AA.A`]-ApS.A)ۚA.AkќAڴ.AA.AAn.A\A@,.A@5A.A ~AO.A`YXAXT.A ZA@W.AQA(\.ANAX}.A`ՈA.A (AF.AஃA.A`;zA?.A |AHu.A#tAH.A Aھ.A@`rA".A Ax.A@A>.AkAHvq.AAZ.A`kAP.A A غ.AZ Aȭ.A@ri AX0=.AATk.AWA.A AXѶ.A@^A6.A XA0L.A G A.A /R#A(.AH0AxL.AT2A.A@33A[`.A6A(Ȳ.A|>AY.A JAd.A@PPA.AWA@.A XAH?.A`ܪXA0̘.A@!ZAQ.A&aA =ɭ.AdAX֭.A)eAp.AeA.AfAF.AVgAjs.A`"hA ͏.A`hAPfĮ.A5iA.AiA.AiAB .AiA.AiA.A iAh.A`DjA.A!jA0v.AD:lA.Ao`oA(R.AkAz.A 0nAB{.A &3nAQ{.AjnAz.A@nAȓx.AnAuu.A@nA=g.AnA.W.A@ pA t.A (pA.A pA.A qA@S.AOqA(.AࣳrAoݱ.A0rAı.A`tAT.A@uAX.AuAA.A`vAB.A$vA`z.AHwA`T.AWXyA@.AzAxG.A q{AH.A]t}A#.A)A.ADAP.A@nA`I.A@E`A`B3.A@GA84.AцA07A.AvAl@.A`Ao=.AO+A$8.AvMA((2.AoA((-.AטA).AA#.A·A.AkA.A A.A@W8Axz.A`]A83 .A@yA .AA.AƈA.A A3.A@ A0.A`-A.A0NAF.AzuA.A ˝AV.AHʉA.AA.A`,A .A /^A R.AAj.AĊA..A`fA.A9Apޮ.ApAzڮ.AA׮.A&A8Ԯ.ARAxЮ.A`A̮.A`3A0Hɮ.ASA2Ʈ.AqAB®.A`A.A &A.ARیA.AA .A`A.Aj8A.A`XAP<.A cAୢ.A@A8.AύA.AA`.AA.A)A8Q.ARAТ.A@uA|.AෑAu.A`)A@um.AAH)f.A&A[a.A0PAP \.AӀAKX.AWA kT.A q̏ABP.A/AtM.AAyH.A:ABE.A@pZAP@.AA0=.AHA@8.A ɻAأ4.A@CؐA`0.AvA@,.A"AH)*.A cSAp(.A}Ah'.A@A8&.A0ƑAp &.ANApk$.A&A#.A/Aj#.A@ ZAF!.A;A.AA\.A ӒA.AA`a.A34A.A`gAp.AA@B.A@WГAh.AAL.AH@A.A@XAH.AA.AA p.A(AHz.AA3.A`WAp.AՔA(.AЕAU.AA.A Q;A.AmA.A<A.A`mA7.A lږA .A \Ah.A/Ap.A[`A.A෋A0v.A鰗Ah.A3ؗA.AAН.A%AQ.AMA@.AVvAQ.A@Ax.AȘAh.AA.A(AH.AXA .A9AX!.AWAp!.A@G֙A#.A0Aг$.A(A`&.A5AW0.A@%OAН8.A q`AȓB.A3vA J.A猚AQ.A墚AZ.A=A#b.AAc.AA_.A cAZ.Av5ApU.A Z^AP.A&A`M.ḀAfL.A͛Ap=M.A`)AO.A@AQ.A/>AT.A ZbApW.A@A@Y.A`ծA[.A̜A`^.AA`.A AQb.ABA pc.AcA`ed.AOAe.AѸA c.A`[Apk`.AAz].A /<A`Z.AbABW.A@APS.A@A0fO.A AJ.AݞAE.AAp.A PA:.A@pA0f6.AAh2.AdAp=/.A@A,.AA).AE6Ap8%.A`^A0!.A ⃠A.A9AH.AA.A@A0v.A`A.A (=Ap.A@yeAH .A`܏Ax.AfA.A A.A ZA.A9A(.A`XAj.A{Ap=.A cAL.AAȓ.AA".A`2A`a.A3AK.A@N]A`v.A`]A0.A cA .AϣA0߭.AAfۭ.AA ׭.A@?AЭ.A`iAPMʭ.A;A =ĭ.A@NAA.A3ؤAA.A`A.AA .A@g5A`G.ATAp.AaoAx.A (A.A;Aё.A@gեA.AA(.A"AH.AaKA.AymA.A슦A0.AxA@.A̦A".AAh.AkAXV.A:A.AXA`.A AX.A`DAؽ.A`§A.A A@p.AAX֣.A@8AH{.AuAh.A@Af.ARA跫.ADA@.A'AH.ANAA.A /pA.AA@.A@.ʩA.AAG.A@.$AH.AXAۛ.A@yAF.A AX֔.A ΪA.A`;A0.AAH.A9?A8.AE`AX.A/A.A 8ALJ.A`ԫA.A$A=.A`!A.ANA|.AuA=s.A`mA`Gj.A 8ǬA8b.A@AhQ[.A AS.A`;AhK.ABA3G.A`m^A-@.AՌA0.8.A&Ak/.AŭAe&.AA(`.A`2A`.A@p"AP .AEA.A[rAF.A眮A`.A lȮA.AAݬ.A&AhL٬.A&CAӬ.A sApѬ.AA(Ь.A`AzЬ.A`DޯAxWЬ.A Ah̬.A3A=Ǭ.A@WXA .AsA.A A.A﫰Ak.A$A跧.AxAo.AAP8.A(A֋.A@FA}.A `AAq.A rAc.AHAzX.A`mA pO.A⥱A(-G.A(A>.AMűAxz8.AѱAQ/.AA(&.A`A.A`[ Af.A.A.AXAh3.A@A.AæA.A@ȲAX.A'Ap.A`fA.A6Ap.A@bAH.AA.AaAݫ.A4ʳA0{ث.AAӫ.A QAΫ.A`?A0ƫ.ARoAz«.A A k.A9AB.A ܴA8#.AaAН.Aa!A8.AdMA@B.AnA.AAh.A`vA(.A`vӵAh.AAy.A@.A匫.AA .AEA`.AqAቫ.A$A֌.AͶAh.AA2.A@ Ax\.AAa.A7A@.A@`A`.A`Ap .A cAꨫ.AAp쯫.AA`ಫ.A`;A8ѷ.A@nA,.AoA_.AAH2ū.AAxKʫ.AAЫ.A \UA֫.AFAxګ.AٳAh߫.A@ A".A3'A8.A IA.A+fAP .AכAZ.AϺAi.AA0L.A O*AH.ASA6.A{A .A3AЦ.AֻAН.AA`.A.A}.AfA8%.A@֥Ak'.A@NƼA(~+.A AP0.A0A t5.A;bAd:.AđA~A.AwȽA`6F.AAK.A[AN.AA V.A/QA2].AA`.A@NAA"Ǭ.AEkAyʬ.AॆAVϬ.A<A@Ԭ.AfAZڬ.A@ AXެ.A (A.A9A.A &XA.A 1yA.A`Ax.A@AG.A ZAXL.A AXY.A`AUg.AA0v.A@ A.A O AА.AAHK.A`AH.A` A.A A8#.ARAȭ.A OA֭.A A.AAO.AF!A}.A`]'AX.Aa*A.A`/Ax.A7AX#.Aq;AK..A`=AX9.A>A`D.A H=AOM.A (:AZV.A8Ata.A@2Ak.A.A!v.A//AP .A&2A.A ;A.Ag@A冷.A@5EAZ.A SJA0̻.ANAƮ.A RA(~Ю.AXAPٮ.A]A.A`bA.AgA.AkA.A`oA0.A@ qAH.AqA'$.A pA8-.A@nA(z5.A mA!>.AoAOG.AqA@Q.A tA\.A OxAj.A}A0ew.A A.AAH.A Ah.AؑAK.AA舺.AAȶï.A@%A ί.AFAtد.AA.A Ap.A`AK.A As.AଫA.A A@.A`̸An'.A`)A3.AHAG.AAV.AwAb.A[Al.AA@u.AA}.A AXۉ.A A7.AdA8.A&A.A ||A.A.A`ÙA K.A@~AxU.AomAP_.AI_Ac.AYAhj.ADA@x.A@.Aہ.AgAG.A A,.AxA.AAxF.A A[.AA³.AATγ.A`jAHٳ.A CA.AA .AA6.AAج .AA.AFA.A)A .AA($.AA@-.AA5.A@AsE.A A`M.AՕA].A A7e.A@zAQl.A QtAXx.AfApۄ.AxXA.A@IAP.A/A0.A'AТ.A`A.A3AX(ɴ.A Ap[״.AEA2.AA;.AA.A /A.A`bAp.A ˾A.AA .AAX%.AzAؒ,.A@Ax-2.A@5A7.A?Ai<.AcAX(A.A@AC.AׯAF.AAFH.AEAXVJ.Ac AL.A |BA8R.AH{A V.A@AAW.AA[.AEAd.AAm.A l!Av.AAAh{.AHA.AXIA.A?FA`6.A@y6A.A@8Aˢ.ALA.AgAx鴵.AtAP.A A8ĵ.A ‡A,յ.A@PAH.AAPZߵ.AAXֵ.A|Apε.AAxŵ.A OA張.A"An.A /A1.A`:A꡵.AGAU.A9VA8.AfAt.A`)xAx.AAk.A`A].A HAU.A A@FN.A Ap?.A OA1.AhA,$.A@A.AXA' .AA.AA.A OA.A@E AԴ.AA˴.AA2Ĵ.AAA.A Ao.A@GA.A`mA軚.A@ Aߏ.A)A<.A 8Ahu.AJAj.A XA_`.A@gAUW.A@rAO.A`A`C.AA:.AݰA0.AnAd$.A@AH;.AA .A#A.A|6Aج.ABA`.A`DMA0.AWAxٳ.A^cA@γ.A lAdų.A@ sAPZ.A&zAho.A=A[.A*Ae.A=Aj.AA .A@AX(.AA.AA`y.A`$AHq.A6AHi.A?APZg.A2EAX^.A@YAX.A;jAhUN.A@A7F.A At=.A A5.A A..A@A&.AAȘ.A` 'A.A9As .A`JAj.AWAX.AHcA.ApA.A|Aز.A@>A0Ͳ.AA².A;AHP.A`DA(`.A2A2.AEA؍.A@A.A JA.A@yA.A`A`.A޺Ay.AAs.A`AZj.AA_.A`fA(U.AxA`PJ.A A(?.ANA0j5.A`!A8V+.A*AA!.A5Ad.A$BA .A ZMA<.AQAP.ATVAZ.AZA`.AcA@_ڱ.AsA@б.A ADZ.A@AXt.A`A.A@A趪.AAx-.A@yA.A@A.AA #.A܄A,t.A$Ag.A@yA`[.A@AP.ATA(F.AA2>.A A 4.A`A`+.A`bAP#.AA.A AS.A lA8.A@A.A@Aj.A A_.A&A.A7A@F.A3EA`ݰ.AQAҰ.A[ApAɰ.AbA覾.Ac`AZ.A`_A.AcA".AfhA.AvAȘ~.A@{At.AAx-j.AAp<`.AՉAXW.AAR.AAh"F.AXAs+.A`A߯.AA.AA0꺯.ArA.A`pA;.A@PiA` 1.AiA.A`xA_ˮ.A@A.A6At.AA.A`bA.A A.AA0국.AAh.A A̜.A`Aȱi.AAhA.AA.AvoA0LҬ.A`]Ah.AA8։.A*A`.AVA.A A .AWA.AAh.AA~.AA>@.AAȔ5.A"A.AիA..AתAPM.A`]A2_.A<'A0C.A|A~3.A`Ah/}.A@#A.t.A A0 Z.A [A.A-$APjV.A`&A:.A A.A@pA.A`AA.A`Ahޭ.A@AH.AQA@׾.AA '.A QA(z1.A@AH. .Af+Ao.AA.A@AN.A -A _i.A`A07.ATA.Ad AXW.A&Aȶ%.A@,A .A O{A0}.A@A.AA q.AbA@J.ATA7.ARA .AA@.AfG A.A AQ6.A` AF.A@  A( 0.A  Al.A AX>.A`ܽ A .A` AL.Af; A`P.A@,T Ap-.ADZ AB.A@ A .A AӀ.A࣑ A`K.A`~ A@'.A` A`.A6 A».A" A@.A # A(.A`X$ A#.At% A@y.Ar& At.A& Aj.A;=' A.A' AL.A l^( A{.A`( A([[.A`dS* A`.AR- Ab.A@. Aԋ.Aa/ AK.A1 A.A3 A\K.A`2 AH=.A"23 A%.Az4 AP.A x5 A&.A5 AR.A`6 AP:.A (E7 AТ0.A$7 AP#.A8 AX7.A֜8 A%.AՊ= Ab.A 1>@ A.A Z/D A|.AྀG A#.A`ܽK AX؇.AO Aч.A@P AL؇.AP Aۇ.A@-P A0Hׇ.AFP AGu.AQ A`<.A`;R A.A`ʏR A E.A R AP.A R A ؆.A`S A ͆.AES A.AS A@આ.AS APځ.A}-T A@1l.A_T Aa.A`yT Ax$.A \T ALЅ.AU A`尅.AU A(\.AFV A`؞.AV A.AV A.A@V A|.A@V Aw.AE$V An.Ap4V Ac.AELV A#%.A AV A .A!W AƄ.ALW Ah.ARX A(~V.AX A@<.AX A.AN@Y Ao.AqY A`eۃ.ADY A@.AdZ A풃.A`K>Z A,.A2Z AȂ.A[ A؁.Ax] A0݀.A^ A.Aj a A 9.A@a A@u~.A`b AÙ~.A`2b Ah~.A c A8~.Ac A9~.A sc A~.A@N!c A@~.A&c A~.A+c A 4~~.A1c Ap}~.A7c A@u|~.A \Gc A`{~.A lXc A{~.A`mjc Ahz~.Apc A|~.A6c A;~.Adc A~.Ac A[~.A@c AP~.A`c Aߔ~.Aac Ac~.Ac Aؗ~.ARc AA~.AEc A~.Ac A~.A@%c AȬ~.Ac A୶~.Ayc Ah@~.A@c A ~.A c A8~.Ac Ah~.Aקc AP~.A@c A~.Ac A~.A6c A~.A.A6 AH*.A@ A' .A_ A.AFى ALj.A0 AP.A@~ AOLJ.A@~ A`.A q| A`.A&{ ArՆ.Az A(.A@ z A@3.A"y A .A y Ap+ .A`)x A.Ax Ay.Ax A݅.A x A`eɅ.A@x Aߺ.ALx Ahb.A@5x A.A`Px Aӝ.Aw Ah⌅.A4w A.A4ew A{.A@w AYr.Ajv AO.Aov Ai{.A;u A`.A u A\~.A`ʊu AHO.Au A(.A 8Ft A:.ASt A䴃.A=Ys Aas.Aas Aj.AL߮]s AzWw.A-t&Os A@5d.A@5s A;.As A tؘ.AI$her Ah.A@.}r AH܂.A@]r A5%íт.A.r AȂ.AMq A8 ‚.A^q AX.A2p Ai.A`^o A =.A`̈n A8.A2l AL.Ajl A.Axk A|.AD0k AxV~.A?4j Ao{.Axg A }t.AtEt Axt.A Zt AH)"t.Au Ar.A`w A0{r.Ax A``q.Avy A#p.AIz Ahp.A-y Atp.AMz AHnp.A`_z A( Kp.A@z A@1%p.Az Ao.Az A0o.A{ A(-o.A@W<{ A_o.A k{ AX(co.A{ A),o.A9{ Amn.A[| AOn.A Q| An.A| AHm.AD8} A@5m.A@n} Am.Az} Azm.A`} Atm.A} A0Xnm.A c} A0Xm.A@t} AePm.Aq} A@ m.A uq} Asl.A@yk} Ael.AQ} A\l.AEJ} A@Ll.AD} AXRl.AH} AMk.A`U} Ak.A 8i} APMok.Au} A`Wk.Au} A@Qk.A Zr} AIk.A"k} Aȥ/k.A@F} A.%k.A@;} A k.A@%*} A k.A 1"} Aj.A`K} Aj.A`2} A@$j.A} Aoj.A`; } AXaj.AT} AN?j.A| A/j.A | AVj.A@y| AH)i.A| A6i.AW| A`i.A| Ai.A@} A`2i.A } A@$i.A`v } Ax\i.A } A`vi.A`m} Ai.A0} Ai.A | A֮i.A^| Ai.AF| Ai.A| Aɡi.A4| Aȩi.Aw| AP*i.Aٳ| Aыi.Ac| Axdi.A@>| AMi.A || Agi.A@| Akbi.A<| A(]i.A#| AP8h.AFb| A(h.AC| Ap=th.A1| Ach.A '| AH?h.A | A/h.AX| A@`g.A{ AXۉg.Af{ APg.A}{ A vg.A`{ Ax\Vg.A 8{ ApJg.A`z{ Ag.A`)N{ Apf.A`0{ Af.A@.){ A f.A %{ A0]f.A`m&{ A( f.AN+{ A ޸f.A`v,{ A f.A |.{ Af.AU5{ APf.A T{ A8f.A\{ Af.A`Kb{ Axf.A`c{ Apf.A`d{ A`f.Ac{ A(if.AZ{ AxQf.A&I{ Au9f.A /{ AH:+f.A{ AP f.A z AXe.Az Ae.A`z Ae.A ɝz A((e.A@z A@e.A Xz Ake.A*z A0LKe.Ay AHe.A y AEe.Ay A #Be.A \y A\?e.A`y AP/:e.A@߫y AX,e.A Iy A)e.A`:y A&e.A.y A$e.A`$y AP!e.Ay A e.AUx AWd.Ax AH{d.Ax A(\d.A`fx A(d.A+x A0wd.Acw A0jd.A mw Ax)]d.AoPw A@Md.Af5w A&d.Aw AXd.Axv Ad.A v Ahc.A&?v A0{c.Aou A3_c.A A].A`D' A].AI AC:].Al,Vl A&,].A6`e A ].A'( A^? ].AMWz͔ A4&].A Af].A@W A(].A A\.A8 Aؚx].Ao Ax].AҞ A].A4 A].A AO^.A Ah^.A( AhԹ^.Aњu A 4V^.AAF A7J^.A{>U A1 _.Aǁ A51)_.A#Zӧ A(qhJ_.AK Al_.A2L A< _.AP A&_.A AQ`.A[ A4`.A A8x`.A ⼩ AǙ`.A= Ap`.A A^.A` A"^.A@iL A^.AKi A]^.A` AO^.A@ A@^.AE A_.A`i A _.A@i A~6`.AKb A0`.A A`.A?U Aa.AA AVma.A A9Va.A A2]a.AM AP6a.A@h A1a.AX A`.AF AP+`.A`, AH_.AF A_.A2! AS`.A Ap<_.A U Ay`.AӰ AP`.A@ Aa.Aؗ A`qa.AH Apia.A@ư A@Ϗa.A@Ű Aga.Aٛ AP8a.Atʰ Aݘa.A#QD AgVa.AN; A#b.A Ax8kKCb.A zp8 Ap9db.ArZ Axb.An Azb.A߲ AQb.A@ h A a.AD& Ahݺa.AXb AQa.Aγ A(Na.Aӳ APfJa.A ٳ APFa.A A8Ca.A@ A@a.A A=a.A A8x;a.A6 A`9a.A A7a.A ) AF6a.A@8 APM5a.A@pG A4a.A/W A4a.A`g A4a.A@v A 5a.A A5a.A 8 A06a.Af A`8a.A AO:a.A̴ Aia.Av̻ A8a.AmYٻ A(a.A| A [$a.Az A&Mͼa.Ab(5 A2a.Ac Ax-a.A| A€~a.AU9 A6xa.A Bf A2wa.A@ҽ ADza.A:> Aw÷Ӂa.AQrW Apca.AKC A`qa.A`` Ad!a.A.2Ͽ Aa.A$e3 A6a.A&. A6GRa.A, A`b.A AHRb.A> A@b.A A|$c.A|O AWc.A|O Ac.A` A"Œc.AQ0Cc An@Ѷc.A+.{ Aohoc.A\ A]Sc.A"HW AHc.AN AYc.A`Q A}8b.A) Ab.A Axb.A, AQ0c.A AQfc.A A`~c.A` Aȭc.Ay Ajc.A@ Ac.A`R A?c.A Af.AM0 A:.k.Ax Apk.AH A( k.A' A0]~k.AmY A/k.A I A(Vl.A A)/l.A@ A(k.A Appk.A`o Ahn.A Ao.A AHTk.A5 Ah.AF APg.Ag AXg.A`I A $h.A Agh.A@ AV7i.AA ApRh.AB AxCh.A AXBh.A` APoh.A Ah.Ai Ah.Ax Aܒi.A AYi.A` AHj.A A@ej.AQ AH.^j.A Ap8j.A ASlj.A` A`Էj.A@,: Ak.A`K A@1k.A@P AVl.A` A4'm.AI A m.A` Asm.A@ Aؽm.A@E Apm.Ah AX?o.A0 A*o.A Axo.A`f AHo.A Ap.A Q A _,q.A2 Aȋq.A A(r.A! APr.A $ A"ys.AX+ Ax-s.AiA, A s.A, As.A[- Apt.A- At.A]. Al=t.AR/ Aw.A /D A y.AaO A\z.A`W Apf~.A#r Av~.As A~.A u@t Ahs.A4փ AX=.A4 APs/.A Aڸ.A A A7އ.A Au.A AK.A@P At.A@y AHئ.AI ASN.Aw AH.A> A.A@C As..A` A.A l AH/.A4 Apw.A@.X AH2k.A@ A?.A`< A;.A AH.A? AǗ.Aි AV.A AdŖ.A` Aʕ.A&e AЄ.A u A0=.Az A=.A@z AP.A l A`!.A u A8.A A j.A Ac.A A A8g5.A` AB.A Av.A`Be APr.AM A;.A`J AȶT.A` A8|.A]. AŅ.AГ A.A` Axȕ.A# Ahԓ.A1 AHŐ.AA A E.A Ap4.A@ AL[.A A.A@P A봑.A6O A`.A`D$ Aq.A' Ak?.A T' Ak.A+ Ax.Ao/ AY.A .1 A^.A ]1 Aht.A@P1 A .Aװ/ A.Ap/ A~.A AA0 A.A A0 Ad..A`0 A..A`. Aa .Aa. Aͤ.A) A.A' AZ.Aɦ$ AP.A|K A-.A A.Ad AZ.A AT A Nc.A@P" ApŤ.A# AP8:.A,,- A.A3 AH.A 4 Ak.A4 AR.A`BG5 A}.A"5 A .A9 ACc.A < AxK.A I AH%.Ay/L Aڰ.A M A%.AP Ah"n.A:N[ A.Aa0e AF.Aam Ayֹ.A4q ARt.As Apm.A`u A~.A 1^v A.A@gv Am.A { Aib.A Q A@.A[Œ Aq.AC AX.A`܌ A.A Au.A cm A0.AK ALJ.A AU.Aï A`.A瓱 A .A@5L A .A A0P.A@` A@9.AJf AOs.A3 A8.Aj Aȭ.A At.A`K A1.Al A<.Aj8 APZ.A 8/ A(ۘ.A Q AS.A`bq A'c.A A.Av AHz.Av APg.A` A(.A@y! A.A" Ak.A & Axu|.A( A.ATA Ap=<.As3 Ahj.A`Ғ AXX.A`~ A.A4 A0ٕ.A` AX.A` A.At A.A Ҍ AX.AH A].A`^ Aj.A@ A.A`2D AXR.A AH:.A A[.Ap A)5.A`f AXd*.AzW A.A`ˆ A&.A@y> Alq.A3 Ahe.A. AQ.Ao AG.A ˸ Ax5.Ax Aot.Aṡ Ab..A APM.A@Gۂ A.A`y Ap .A\y A=.A@y A".A =y A`.A q!y A.Ay A.A`y A[.Ax Ax.Ax A.A ɽx A.Ax Aɪ.A`mx AP.Ay APf.Ay Av.Aw AZ.Av A3.A g AT.A8f A0.A`;_ A .A$t_ A.A`dG_ A.A@C _ Ap.A^ A5.A`^ A .A^ AT.A[^ A[.A^ A #_.A.] Au;.Aa\ Au.A l[ A .Aǝ[ AЮ.AO~[ ApZ.A|Y[ A.AX A@a.A`dS A ;.A`2P AP7.A`N AP.AnZK A<.AJ A.AayI AXt.A`vJ A#.AtoK AF*.A:P Ai.A` U A0.A\ A-.A\ Ay<.A 8] Ae.A@y^ A*.A#` A.AM` A@B.A^ A.AZg A( .A@ef A`/.Af A`>t1.A7f A6^(4.AVnf ADwz9.A2{g A.>.A>1#g A ]D.A~ 2g ALJK.Aq v.Ag Az.A,,g Ap;.Arg A@.Aig A~.AQg A$Q.A"NYg A.A g Axذ.A`Ch Ae* .AJj[h AM.A'1qh A2(.AEh A).Aspah Av.Ach AA.Aa|0Wh AXh.A h A=.Ah A޷^.AI{ i AS/.A&i A.Av:i A.A69Ki AdT .AVi A`.A@>]i A5-.Ai A;(..Aրi A/.AIi Aj'g42.AM.lmi AX15.Ai AJ8.A6i A .Ҷ<.Aa_j AGA A.Ap%j AE.A!j A*EJ.AIj A(N.A`fj APIj.A`cj Aal.Aayj AIo.Aßj ACms.Aaj Aŝw.A|j A?}.AiSYj AY.AG[j Ay.AH*j Aw.Aj A0.A`K'k ATPY.AM9k A.A Ik AH,.A>7 Yk A@µ.A@Efk AYgh.Aqk AG.AjByk A_.Aeu~k A 2Gx.AҠk A0 .A|k A.A3k AC!N.Abk A].Al ATC.Aա(l Ar*.AZy;l A|i .AHl A`.A`Ol At.A?em At.A;jm AFu.Aqm Au.Avm Av.A$~m A$w.A m Aw.Am Ay.A`m Ay.Am ApAz.Am A{.Am A|.A |m A^}.Awm A~.Aqm AX.A`mm A8.Agm AX.Aam A.A]m A8ք.Am A.Am A`S.Am A .ALm AЉ.A m A`.Am AH.AAm A,.Am A .A.A q A.AFq A[.A Aq A9.A;!q A8x.A #q A8V.A6&q A84.A 1+q A8.A-q AQ.A@,0q A/.A2q A .A&5q A.A@7q AȺ.A@7q A覼.A :q AЄ.A.A@ru AP'?.Au A?.Au Af@.A]u AA.Aհu ADB.Aеu AXB.A@Gu AȂC.A`Bu ApD.A`u AF.Au ApF.Au A G.Au AI.A"u Ap[J.Au A8K.A`u AL.Au AXN.A u AVO.Au A3Q.A`u ArR.Av AS.Av A`U.A`v AV.At v AX.Aov AY.Av A[.A@iv AЦ].Av A`^.A`dv A``.A v AHb.A^v Ahd.A^v AH]f.A` v Ag.A` v Azi.A Z#v AWk.A Z#v A5m.A Z#v Ao.A Z#v Ap.A Z#v A0r.A Z#v At.A Z#v Au.A Z#v Aw.A` v Ay.A` v A{.A^v A8|.A v A ~.A v A@.A`dv A _.Av A.A@iv A{.Aov Ax.A v A.At v AX׉.Ayv A@.Av A.Au Aю.A`u A.Au A(O.A`u A؍.A@u A̓.Au A( .Au AI.Au A.Au A.Avv A0H.A`v A.A@gv A .Aav A0j.Aؚv A.A Qv A+.Aɩv AH.AĮv A.A;v AM.A@v AM.A@,v AH.A@v AH.Av AH.Av A.A v A.Av A.Av AH.Asv AH.Aw AH.A cw AM.A`w AM.A@w A.A@Nw AH.A@#w AH.A@>+w A+.A2w A.A@.:w A .A(?w A.A@w A.AFw AH.ANw A.A@Sw AP&.AZw Ad.A`_w Ah.A`fw AB.Akw A.Apw Ah.Akxw A.Af}w A=.A@`w AP|.A`[w A.AUw A.A@Pw A.AΓw AX.A ɘw A8.A`Ýw A3.A@w A.A;w AO.Aw Ax-.A@5w A .A0w AXJ.Aw AX(.A*w A@.A`w A@.A`w A@.A&w A.Aw A.Aw A.A w Aؚ.A w Ax.A w AV.A w A4.A w A.A w A.A w A.A w Aج .Aw A .Aw AP .A&w A.Aw A.Aw Ac.A+w ApA.Aw A.A0w A^.A@5w A <.Aw Az.A`;w AX.A@w Ax.A`Ýw AXu .A ɘw A !.AΓw A #.A@Pw A$.AVw A`&.A`[w AN'.A@`w A(.Af}w Ax).Akxw A +.Apw AH,.Akw A`-.A`fw A&..A_w A`e/.AZw A0.A@Sw A`C1.ANw A1.A`Fw A p2.A@w A؁2.A(?w Ah3.A@.:w A_4.A2w AH4.A@>+w AH4.A@#w A5.A@Nw A=6.A Sw A=6.A`w A06.A cw A06.Aw A06.Asv A|7.Av A|7.Av A|7.A v A06.Av A06.Av A06.A@v A=6.A@,v A=6.A@v A5.A v AH4.A Av A_4.Aɩv Ah3.AQv AH!3.Aؚv A؁2.A@ߕv A1.A@gv AC1.A`v A0.A v A`e/.A|zv A&..Auv A,.A@ nv AH,.Aiv A +.Adv A().A\v A(.A`Wv AN'.A Rv A@&.AMv A`1$.AHv A".A Cv Aг!.A9Av A.A?v A88.A9Av Ap.A4Fv AȺ.A/Kv A.A)Pv AP.A$Uv A_.A Zv A .A`_v A.Aеu Aa.Aհu A؟.A]u A>.Aau Ahޠ.Au A0.Au AP.Axu A[.A}u A0.Au A0.A|u AP.Awu A9.Apu A9.Ahu A٥.A*au A٥.A@Yu Apx.ATu Apx.A?Mu Apx.AEu Apx.AO>u Apx.A6u A٥.A1u A٥.Af*u A٥.A "u A9.A uu A9.A u AP.Au A0.Au A[.A@u AP.At A0.A t A}.A(t Ahޠ.A-t A>.At Ah.At Aa.At Ah".AHt A.A@Nt AhD.ARt A.A ۼt Aǘ.At A.At AI.At A( .A@t A̓.Ast A؍.A@yt A.A}t A(q.At A`2.At AT.A`t A.A t A7.A`t A(.At AX.A t A =.At A.At A .A~t AB.A |t Axd}.A |t A%|.Ayt AGz.Ayt Aix.A &wt Av.A &wt At.A &wt APos.A &wt A8q.A &wt A8R.AM.An AL.A@n ApJ.An AI.Am AG.A@m AfF.A@m AP'E.Am AC.Am A B.A@!m A@.A@!m AP?.A@!m AN>.A@m Ap<.A@m A2;.A@m A9.A@m A.AWl Ah.A`]l AH.A cl AA.A l A.Al A.A`l Ac.A@l Ah.Al A$.A`l A.A l AP.Al A.Al A.Al A.A` l A*.A@%l A.A+l A(.A0l Ahn.Al A@.A}l A.A@xl AQ.A@vl A@.Asl A.Anl AЕ.A`Kll AW.Ail A`.A@Pgl Ax:.A`dl A.AVbl A@.A@_l A~.A[]l A.AZl Aa.AZl A8#.A@`Xl A8E.AUl Ax.AUl A(.AUl A.AfSl AP.AfSl APͪ.AfSl A.AfSl Ah"S.AYk Ay"X.A1qRk A}\.A߆Gk A_R`.AVx:k Awd.A*k A3Rg.A'ik Ay*ii.Ak Aj.AH8j ALk.AnBj Aj.A@j AKil.A?F7j An.ALHSj Ap.A7.j A[@0_r.A'=|j Agbs.AHj A+cu.Ajj AW|v.A2 0j AQw.A]Ϧj A˹w.Aj Axw.A@j A$}.ACwj A-.Aj AN.Aj Ar?.Aj A9u.Aoj A0.Ah&Ztj A,.A=bj AXQ4.AUNj ANu.A8j AҒͿ.AӬ>!j AƤ .Adsq j AT.A i A{.Ai AX.A i A=.ANVi AJ(ߩ.Ai A< k.AAsi A4[֬.A5i Ajb.A)i AG!.AtYi AԲ8.Ati AG~.A`:i AŰ.Ai A`ð.Ai A.A~yi AK[.A\Ci A!և.Aӂi A.A!g i Ad.ADi A\w5.A i AT .Ai A(6.AZi Az.Ayli AJ.A_Ui A1T0;.A >i AP.A Q'i A.Ah AFP.AMh Aj.AgLh AN^.AWQh AVڛ.A ?h Aj8.Ax%h ATٹ .A% kh AZ{.A>=Uh ApQ.AU8h A.A8h A,z.AVfh A؟.A`mMh AW*.Ad7Jh AF.AڈEh A_).A 6}?h Aܢ.A&68h A"[ .A/h A !.A&h A%.A@h Amv*.Aǝh A/0.Aǧh A׮[~5.Aoءh Aų:.Adh Al ?.A?͘Ph A&dD.Ah A.TG.A78g AK.AGEIg Aa+M.A?fg A\,O.Aog A9O.A[3j g ArO.Apȓg AN.A~g A㥠q.A000g Aiw.A<_(g AL}.Ag A0H.A g A(.A@!af A".A a A۷.A Z A@.AIZ A`P.A`][Z Ah3.A[ A0.A@[ A$q.A/Ca A)~.Aa A.A )[ A+.AZ A.A`vZ A1.A@PZ A`.AqZ AP8.Ap}Z A(.ACZ Ah.A 0Z AU.A`Y A.AKY A.A`vY A.AX AOb.A.agY A*R.A(]7Y A.AMɎQY A.A \gY A_:v!.A(8߆Y Ap%J&.A0٣Y Ač",.A?=#Y A^/2.AY A`X:.AY A:.AV鶐$Z Ai4wg=.A.bZ AuB.A$5}Z AjJ.A淾Z Ap/U.A=[ AXa.Am孬+[ Ay5o.A' J[ Ax}.A@_[ AJ.AO-/w[ AϞ.AQ[ A#WU.A_)[ Aoӊ.Ab[ A.AOW[ A7ۢ.A%[ Aȩ.A}[ As$.A|\ Aa.ApF\ Ae.AH4y\ A,T.A{z*\ A:.AQR]\ Aa.Aީ^\ A.A\ A0e#.A(]<] A0a[.AcOt+] A-.A0jA] A }.AoYoU] At9.AFSf] A}.AtZu] Aq.A] Aأ.A`f] A! .AW] Aj .AS"J] AQ.A\p\+^ A0Jr.Alt%X^ A=Z*.A}pY~^ Ao7.A'g^ AE.A^ A]F.A91i^ ArvH.AC^ A9K.A^ A$mN.A W _ A6 r.S.AZF_ Aq%X.A4`$d&_ A*1].A_/_ AWc.A5_ AmPf.A]n_ Ah4l.Ae_ A_b u.AJ_ A59.AX` AG.A&` Am.ArܡB` A+.AU` Avl.Av` A).AHquE` A].Arİ` Aj V*.As` Aџ.A,` Ap.A 1` A-.A,a A .AΤpOa AQ.Ad[|a AvCt.A*7a AS.A3pa A\ .AXaa A.AT6Ca A.ARb A.Ah "b A #f.A=3V.b AH.A VCb A .AǓVb A&.AW,gb AVx .Aub A<%.A{o݀b AxI{*.AVˎb Ax0.A`b AB3.Afg7b AcV9.AS1Ub A"O!A.AF@!c A)`J.AnIc A^U.AlQGjc A~*Tb.A"t[c ASqo.A@ޑc AmN}.A-Ӹc A.A>ٔc A}ډ.AӾ:c AN?.Ayrc Aذ.A|Tc A1.AAn}%/c Ac.A Jc A0.A\hc AQ3j.A%xc A)d.Ac A,.A}kb AI .A^b AsLjz.A]&b AA?.A'Ҋ'b AE.A ub AJ.Af?b At .AÚɉb Aj.A^Xb A[$.Azb AWc0.A薰b A6:.At3Xnb AoC.AHGb A tJ.A|Gb A?fP.Ala AS.Aa A`Z.A̶4a AY `.A*a Asf.Ay(a ALk.A:oO}a AF8o.AVea Ar.AKa A`.Ay5a A齒.AkFa A͠.A|_E` Azˬ.A#b` Av$`.A(#v` AE.AF4` Aj+.Au/0` AW̦.Ar(` A .Az$` Ab,.A` Ag-.A=K` Aoi.A@#_ AvyF.AlϞ_ Ab.A`_ A|7.A:U_ A?F.A@_ A.A |<_ A .AMX_ A.A-'_ A p<.At^ Ax .A^ A m$".A) ^ AZt&.A^ A+.A^ A;0.A2^ A^ZL4.A((_^ AzMp7.AR^ AX:.Aʀ)~^ Aڅ=.AZRl^ A}>.AeDY^ A0?.A,E^ A@?.A1^ AzI.A<8^3^ AYS.Ae1/^ AL].AH!$^ Ag9qg.AJ^ Aq/p.AlMk] A$ʗw.A] APUXz~.A] A8.A] Aca.AWv] A.A9M] A0~q.A9] A.A\ A`d.Ap\ AK.A\ AH.A z\ Ap=t.Aac Ay.ALd AZ.A`]h A).A4i A.A_d A.Ap` A.AZ_ A[.A@W(a A%.A!c Ah.Ab A.A l[ AH.A[ A=%.A[ AG.A|[ A,>.A$ tg[ Aw .Abx[ AQ .Acmb[ Ap.AI[ A3.Aɓ:[ A!-.A@o"[ AV^:.A[ A(F.AmZ AEO.AZ A\V.A=LuoZ A[.A@4Z AG^f.Ap"Z Ao.AC~ Z ANqx.A?Y AH.AY Agt㝌.AhgUY APۘ.AtFY AM .A~Y A%3ƭ.AI\WY A .A{++Y A$d].ACX A.A|X A+.AbX A.Aڂ X A\.AX Af].Ap}X Ao .A&bX Ah.ADX A3Vn.A}5X A}.A=X Are .Aן W A1.AW ADL.AW A&.A~k|W A+.AaGW AjF2.A>W A.8.Afyn1W AXTȇ=.A!W A*k1B.A6t(W AvF.A(V AI.A V AnTeZ.AQ%V Aށj.A…bV AZx.A/jvV AhD.A?V AXż.AR W AH.AV A.ATW A8.A`W A=.A@ U Aq\y.A%uU AdiC.ApxU Av.ARU A.A +U A6[).AAio*U A,.AhT"U A.A5U A0.ACnT A~d.Aeq T AC0.AuXhT Av:.A$6T AW.A tT A .AM&\IT A.AT A.A ST Aj.AT Az.AGcT A '.A޻T ADh;.A3T A̮.Aڹ T A̟ .A5S AY .A{M A0w.A`TM Ao.AL AAV.A<2L Ap.AkL Ay.A#LWL AXdz.A̬}QL A:.A4"L As.AG;K A{.AIFK A>ŕ.A5үK A|.A5T?K A O~.Au=K A&r.A HzK ALs.AekK A0ot.A5[K A֒t.AoVKK AqMt.Asd2;K ADl$s.A<##,K AycMWq.AӸK A\n.AvK AVc2 l.A͋K Ah.A;UJ Ave.AeeJ Aaa.AGRJ A`8].A}ݳJ AyY.Ad=/J A 4U.A`bJ Ae-;S.A!J AܰO.AύJ AH.A4P?]J A64@.AO2J A)?7.AE"q5 J A *,.A`I A y.AI A0.A I A`ω.A3I Ar%.ADI Aa.A%KΝI Aٴ .A:nI Aɠ.A[I A.A ILI A7l.A, @I A.ACS7I A.A]2I AFJ.Ax>H A9x\.AĦ-(H A{Z.AA%tH A v.AvaaH A0.A ;H Ab .AVfH A.A/H A٤.A/G AJwN.AJ!G A1.Ar!G Ag.AG Afj)6.AG AOj.A-嬜G AH.A |G A&ƌ.AzG A{.AI_G A5A.Ami+G A.AF A y.A'AF A6p.AvF Ad.A f6F AxX.AwF AJ.AcF A+K.A?ȏNWF AW_K.AHJF A J.Ah>F A}I.A2F AG.A}I F AGLC.AF f3F AǕ:@.AČGF A8<.AE AG9.AF E A$5.AWRUE ASU[}2.AzE Af/.Aħ8E Aȥ+.A E Ag*.AE Ah(.Ap,EE ABZ&.AfE A ڱ".AZRbJE A7.Ad0E A3Q.A1t!E Am.ACV{E A?y8 .A?D A.A_D AȺ.A@5D A.AD Ah3.A D A .A0C A.A B A8.A`VB A.AA Aܭ2#.ANB A4 /.Ap;1B A,%1<.AFUdIB AZQ J.AHQ?XB ARX.A1]B Af.A(!ZB A[t.AQ|MB A0݁.A(7B ACu .AB Ae.AwB AWhr .ATGB A$Jw .A_7"B A M-.A@B Ah.AB A.A ZA A2.At.= A@_.AD; Axv.A07 AH.Aށ- A.At) A .Ap) Axǖ.A' A.A5S& AP .A@E$ Axy.AO! A.Ad A.AO A'.A`V Av].A> Ag.Af AXo.ABl A(iD.A A#).A k A.Ao  A .A A.A A .A^ A/.Ax( API.A` A.A`b A.Al AЄ*.A  A8X.A A.Ay AS.A@P A8S.A@ A 4.Ag# A.A% A` .A`d& A/(.A ' Ai.A( A.A) A[?.A) Ab.A* Axx.AF+ At.AdP- AX.Ak. AQ.Ac. A =F.At=. Ai.AB ( A#.AB6 A5.A`] A.A@! AQo.AI AXk.AŘ A`.A A\.A AN.A AP.A Ax:.A M Aʸ.A Z A@T.A Ax-.AD" AH.A A3.A` Ahn.AdY A.A AS.A@ Aa.A (S Au.AD A=.A A.A`f Ab/A`t A/A` Ap/A & A/A A}/A?r A7/A< Aߕ/Acl A(/Aߍ AH\/A A@/A AH/A`T= A/A$; A/A A/A Ae/A` A/A@ Ah//A@" AP/A0 Ah/A 8 AH/A A/A4 A/A  AH2/AG A/AE A/A A/AU Al/A_O AM/A`A A/A qU A+/A43 Aח/A Aq/Aa A/A` A/A| A/AQ AG/Axi A(/A Ad/A@S Ap9/A` AC/A`R A/A  A/A0g A/A[ Aٚ/AD A/A` A/Ab AX/AQ A'/A@c AXVD/Ay A+x/A`D A /A A/A` Ax/A A0L/A Ar/A@8 A[/A  A/AC( AX/A?4+ Am/AL/ AL/A 8h2 Aج5/A*4 Ax(/A ^5 AH\/A*6 A /A=6 A@/A-7 Ax/A7 A&/AG8 A/AWe8 AP)/A9 A*/A@< A`/A&@ A=R/A JD AKj/AF A/A`KaG A/AG AX[v/AzJI Ax/AJ A /AJ A _/A`@K A*/AK A/AI=J A8/A J A#'/AJ A`/AaJ A]/A@ K APMk/A׾K A8/AL AZ/A`L A/AM A" /AQr A%/Aq API%/A@߉q A8D%/Aq A@%/A q A|%/Aq A*%/Aq A%/A@Nq A؁%/A}q A0%/Azq AP+%/A zq A0%/A xq A%/Axq A%/A`bwq AM%/A&uq Ap%/A@ tq A8%/A`qq A`%/Aqq Af%/Aoq A0%/Aqmq Axv%/A2jq AE%/Aiq A%/A;hq A`%/A hq AP%/A?gq A%/A sfq A~%/Adq A%/Abq A%/A `q AP%/A^q ApJ%/A@^q A%/A ]q A%/A@[q Av%/A`vZq A(m%/AUq A5%/ATq A%/A`mSq A %/A@Qq A%/AQq AR%/A@Pq A%/A@Nq A%/AMq Ax%/AHq A%/AHq A%/AFq A%/A 8Cq AH)%/A@?q A'%/A?q A%/A=q AHn%/A =q AN%/A%/A4p A%/AIp Aܱ%/Agp A%/A p AHm%/A@p A%/A`p Ar%/A 8p AH%/ARp A %/A lp Ax%/Ap A˯%/Ap A%/Ap Ax%/Ap A`%%/A@p A%/A0p A Ү%/A@Np Ax%/A lp A~%/Ap A,%/A p A%/A@p A٭%/Ap A%/A3p A%/AIp Aj%/A_p Ah3%/Ap A%/Ap A%/A`p Ad%/A`;p A;%/A_p A8%/Ap A%/A`p Aп%/Ap A%/A`p Axm%/Ap AhD%/A?p AH%/A p A(%/Ap Aw%/Ap A N%/A /p A0%%/AXp AP%/A@p A%/A`p AX%/A,q Aȵ%/Aq A %/A q AXd%/A 8q A(`%/A=q A9%/Ahq A@%/A q A§%/Aq Ap%/ARq AxF%/A q A( %/A`q A@Ϧ%/A q A˦%/Aq A%/A`Kq A~%/Azq AXV%/A@q A0.%/A q Aݥ%/A@Gq A%/A |q Ah%/A q A@=%/Aq Ah%/A Qq AH%/Aq A8Ť%/Aq AH%/A q AM%/A cq A%%/Aq A%/Aq A8֣%/A q Ah%/AFq A%/AKq A _%/A`q A%/A/q AX%/Ajq A%/Aq A%/A q Aq%/A q AJ%/A[ q A #%/A` q Aԡ%/A q A%/A q A7%/A  q A%/A O q Ax%/A` q AJ%/A q A%/A q A`%/A q A%/A` q Ad%/A &q A%/Aq A}%/Aq AW%/A q A@1%/ARq A %/Aq A%/A@q A%/A)q A`%/Aqq AHr%/Aq A0L%/A q A&%/A@Nq A%/A q A8%/A@,q Ah%/A q AΘ%/A@q A%/A@6q A@%/AOXq Ah%/A4q A z%/A`;q AXz%/A_q AH#/A"p A@!/Aa A/A_ A1/A@Q AX/A[P AX/AM Ah) /A G Ap O"/A/B Axc"/AA A(&/A^6 A8'/Aas5 APo*/A2, A؎+/A 8S, A+/A൮) A F-/AQ;% A/./AД" AŶ3/A  A:/A AL:/A@, A(~:/A A:/A` A:/A`9 AȰ:/A  AY;/A^ Ay;/A@`` A2/A@WE A0?/A` AKq?/A A,?/A Ax$0A/A  AB/A) A C/A AC/A AxC/A |? A8sC/A AC/AY ApC/A` AB/Aβ AB/A#\ AA/A@% A?'@/A A0@/AH A@/A$ AP?/A@ t A@?/A` ] AN?/AK AU?/A@? A̹?/A@p6 A"?/A}? A>?/AJ AH?/A QZ AFu?/Am A@h?/Aw Ae>/A AE>/AK A8>/AFa A0+>/Aq Ah>/A} AQ>/AO A` >/A@߂ A>/A`| A=/A@`Y Ae=/A`3 A=/Az AR=/A} AU=/A AP=/AD A=/A A5=/A ~ Ah=/AZ AX(=/A& A=/A ~ Ap=/A A=/Ak A=/AZ A=/A`Q A=/AN Ax=/AR A(1|=/A [ A@=/A Ah8=/A AH=/A AXl=/Az A8 =/A AXy/A: A>/A A>/A A'c>/AFJ AV;>/A / Atq=/A`md A86/A@} A(B6/A`˵ A/6/A@ AQ 6/A A5/A+ A5/A* A5/A" A@S5/A`ֲ Ak4/Aܲ A~4/AK A 4/Aϲ Au3/ATβ A3/A@Wʲ Ahћ3/A; A3/A A3/A` A'3/A A2/A@ A22/A$a Av2/A@C A2/A@ A)3/AҢ A 3/A 똪 Aۼ3/A A4/A`, AhL4/A`̣ A-5/A A5/AǗ Ah7/A, Ag8/AK A k8/A@p AP9/AP A8ֶ9/Ai A !:/Ah A[E:/A`Ԁ A`p:/Aހ A:/AO AP:/A} A:/AZ| A:/AaO| AT;/A`m~ A؛Q;/A ݀ Av`;/A`ǀ A/;/A J} A:/AVp| AH :/A| A;;/Aߔ} ARG;/A@Np} A:/A Z{ Ao;/A'0z A;/A@:{ A;/A@+{ A;/Ag{ A _;/A { A;/A@!| Aa;/A)| AK;/A | A;/A'} AG A`/Aw A@?/A@ Axd?/A@ Aߝ?/A@ѐ Ah^[?/A` Ah>?/AՐ A:?/A@ؽ Ap?/A@` A@?/A AF>/A F A>/AF1 A(l>/A  APx>/A@p{ Ab>/Aߍ A(>/Acʍ A>/A) Ap,>/A A݂>/Aqn APk>/AY Ahd>/A H A`>/A  AE>/A- AP'A>/A Ax'>/A`K A#>/A@g0 A0.>/A 8 Ap>/A@r Ah@=/AՈ AJ=/A$ A=/A AHr=/A J Ag=/A  A`=/A@5 AU=/A`& A=/A} Ac >/A` A>/A A>/A@P A>/A@Nj A>/Ag^ A>/A ΃ A>/AL A`/?/AC AP?/A`] A Q?/A`[ A0w?/A Ap}?/A,{ AU?/Aw/ A?/AF A@?/A. Ah?/Aﻀ AȂ_?/A Ԁ A0?/A A A9t?/Ao; A(mm?/A@L A0]P?/A Ah@J?/AN A-?/A c A&?/A`K A.>/A A >/A Ah;>/AH& A҇>/A A`Â>/A AhS>/A@" AM>/A 0 A>/A@Ϯ A0>/Ag A/=/A A5 A=/AB A؁=/Ax A`=/Au A@=/A㉆ Af=/A6 Ab=/A= AOF=/Ao AhoA=/A`ք A =/A` A@p=/A A/A`{ A?.>/Az As>/A$z AN>/A@yy Ax->/A@!y A>/A`x A@J?/A`v/x Al?/Aw A07?/A`Lw Ak?/A*3w A(?/Av Ax:?/Av A ^k?/Aw A(`d?/A`]w A(90?/A_x A'?/A`3x A:>/A@x A@>/AVx AȾ>/A (9y AXV>/A6Ny A(>/A0y A~>/A 8y AM>/A`2Sz A@E>/A Zgz AN>/Az A_ >/A@z A=/A@Pv{ A==/AO{ AXh=/A | AE=/A| Ac=/A| AH[=/A+| A8E*=/A@)} A@"=/A`<} A``Z/A/q A @/A`9un APuB/AOk AB/A?l AB/A`2l AxuB/Am AB/A`im AB/Aǎl A(B/Al AB/Ak A@B/A@ߗk A؞GC/A`̧h Ax-D/A`'f APD/A`f AD/A g A7E/ANg A F/Ag A(`F/Ae A@pvG/A@d A -O/A[d AXJO/A@nf AhO/A f ADO/A g AN]N/Ap AJ*J/AzFh A<J/A4g A>I/Ag AלI/Ah A` N/A}r A`O/Ag ApO/Ag ArP/A Hng AP/A`e AB'Q/AWf A88Q/AHi A R/AWj A6U/As AW/A| AgX/A AVuY/AU A`XY/A AsZ/A, A@Z/A@  A~[/A@ A}[/A" A[/As AX[/A . A[/A@ A[/A൬ A[/A d AH\/AN AI\/A`t A\/Akƌ A]/A@WH Ap8]/A AC]/A A6]/A@pܑ A]/A` AТ"]/A@ AkB]/Aվ AQ]/A9 Ah^/A \ Ap^/Af A"_/A1 AH_/A Apea/A@ A-b/A`á A,b/Aˠ Ap=Zc/Atݠ Ac/AW A0\d/A Ad/A; Ad/AIp Axe/A  A(Qe/A6$ A d/A@%V Axzd/Ao( A\d/A`;7 A_Fd/A' AhoHd/A`" A^d/A" APɩ A=a/AΩ A@ a/Ab A( `/AE An_/A4 Ax`/A A`/A A/`/AZ A `/A@< AF_/A`m& Au_/A` Aj_/A O A-u_/A AP_/AF AP_/A A!_/A! AS_/A@>ܧ A_/A 1 Ax:_/Aȧ A^/A AQ^/A API^/A A'^/AӦ A^/Aa A^/A`ަ Ap^/Ao Aw^/A Ap^/A AE^/A@2 A M/A A>PK/An A`LK/A A9K/A AȥJK/AH A p[K/A[ ASK/A/ AWBM/A ADQM/A`K AeM/A@ AeM/A AM/At AtM/A A88%N/A l AN/A# A(N/A A`RN/A@N AoM/A@ AhM/At AbN/A & AN/A AN/AMK AhO/A;} A?P/A AGP/A AO/A`b A0O/A@ A>+O/Av Aĭ4O/AI AQ膿O/AJ= A lO/AA21 Agz8O/A8 AYO/A! AO/AL  Ao! P/AyM AP/Av} A#(P/A@  A>Z?P/A5 AQP/A`s Ax-P/An Ap=O/AM AO/A` ApP/A% AYP/A ) AV#P/Ar* AP-P/A@+ AȘ9P/A- A;P/A`܅- A`

/A< 7A` Q>/A'A=/A@hAٚ=/AgAAWe=/At~AT)=/Aqk2$A+/A}AE>/A A`>/AC QA>JB`~>/A Aɛ>/AM}ȻAS7->/A@ Ak>/AA{?/A A#B/A@5Af`B/A[AaB/A oA52B/A#kIA-(C/Au@'A؇C/A 2 Am.%1C/A LuKA!q`DC/A%AHtUC/A%$YȼALAbC/AAlC/AnA7$rsC/Aw˽Au̩OuC/Am'*AsC/A AOJ~C/AXA-qC/A;"AsiC/A+6>A1C/ABLIA:B0C/A{BXAmC/ARfuAȊD/A$8Alޛ8D/ADžN9AaD/A AʡD/AhAHu sD/AS8AJJE/A¤ׁA3+E/A sAOE/A ,PAzKCF/A9}AaF/Aև8AkF/A.AB;DG/A8 BAvJG/A_9gA9G/AAFH/A7AwZH/AJxAnhDH/Ač$APAI/A@Aj`I/A6A{I/AbA]rI/A&B*AړI/AYAIeñI/AFA1I/A2AQI/AT5=AkI/A@vAJYI/AS*AI/A@W AKJu}I/Ae\B{4Aa=PI/AMATQI/AwSS&mA)H/AV-AH/AwA.vH/A\8A7H/A,A݋V/AkAWwV/AlKctA = dV/AGoA#5>PV/A9^AGP7>V/A!?:=@Ae,V/AI)An V/AkplA)MV/AAjʆV/A?>`A:U/A'uδAU/AwAU/AwA8\CV/Ar=rK:AV/A:6J6AS) U/Ah{풾AU/AKpAU/AQyIAaU/AhliA)j$U/A;ԖAU/A)A06~U/A`DA t$eU/AyFATMU/A#A-U/AzwbA U/A奣AS T/AB-A0{T/A@AisiT/Ai,ATE޴T/AC&AaT/A]ƄA#^yT/A9_AVT/AgAsm/T/AA8T/ATAvցGS/A. ;>AS/AKKfAOS/AYAS/A:ATyS/A`\AS/ASQA[S/As0)AmS/AAqS/A=A]aS/A$<:A]gwS/A"D]oA0S/AYAۏcPS/A5fAS/A=1AQS/AD[cA\ynS/A+H޻A9KS/AHFFA %%S/A8A2R/A2lu?AI~R/AmҹAwR/AEݕvA|OEqR/A.+A8?R/A3CA R/A`B˸ArQ/AARQ/A E|AQ.4Q/A.=A$X@Q/AhA ,8Q/AA)ۅQ/A:.6AP6 }rQ/A;ʶAךaiQ/A+YAhbdQ/A@A4Q/A<aA͏XQ/AA5P/AsAMMP/A֘fr7ANk`P/ANZAY'P/A,0ijADXO/A/d^AӴO/A~sAzO/AJfAWaVAO/AԬA7RO/A'ڳAh?N/A@Ak"\N/AoQA)N/AJ"AVN/Aϗ=A~$xN/AɨJAskN/A6VAL3N/Aj6A xN/A%RkltAN/A;׶A)N/A/\A?N/A|ALWN/A_"oA2N/AA~kN/AS+؆AN/A=AN/AkzpFAԗ0N/AqxAVN/ABx/)AֈWN/AAA hC O/A4iAWQ&O/AZA}CO/A"K:A6'eO/AOAO/AzZA⥋O/A"]A8O/Apae UA8P/AplpAA1D†GP/A>3%"AGP/A`Aa~P/AI0&AiP/AAec)qP/AM8.AQ/AAvX.0Q/A@aA2GQ/AΌ3AYQ/Aǿ~UAo4zhQ/ANm{AXqQ/A@EAV}gzQ/A|e*AcQ/AQ)tAXߚQ/AGgtAu%Q/AzA_-Q/Aث'A9lQ/A:7AWW[R/AFBAWj>R/AuPHAخmR/A*`GAP7R/A@y?APR/AeMtdAR/ALRAS/ASeAD!4S/A]ƠA#%cS/AuȠAܕS/A!d6AS/Aݡ5}AWMT/A*WAiOhBT/AUҲ$Am T/AVҴAl9bT/A>iQA#1 U/A/AI99PU/AtA];U/A= r:AU/AjABV/AopAo)A@W/A`A6W/AK@X AW/A"Aa\wW/Auņq AUHiW/AAW/AKA5x.A.A`S AS.AyS A_.A]S Ar.A >T A w.A_T A.AT AD.A@yT AТ.A-T A@.A?T A.AҨT A@Ӆ.AT A.A`;T Ad.A 1T A.AسT AxK.AеT AQ.A`T AP/.AoT AP擁.ART A(z.AT AĔ.AT A.AFT A.AmT A .A`T A8.A@T Aꔁ.AT A֔.A@T A.A T Al.AFT A.A |T Ao.A@T A N.AVT Aː.A 1T Ap.A`vT AD.A_T A.A T A .A &T A.A uT AH?.AT Ax.AT A.A`bT A.A ZU A0.A@rU A؟.A U A.A AU A8.A U A0.A l"U A⇁.A*U A@.A8U ApɌ.A?U A .A cDU AI.A`KU Aߔ.A MU A.A@IU AP.A`bEU A0ح.A@U Ay.A@,2U Aǁ.AU Ap߁.AT AP.AtT A6.AP APf.A3O Ah.A+mO AԢ.AO ABل.A`N AH.A4N Ab.AaM A4.AM A,.A@.K AÆ.AZI AH.͆.A@0I A(.A` H A兇.A }F AXҘ.A "F A{.AFE AB~.AE A.AWE A0.AD A0.A/$C A.A`2C A`.A? AТ.A/= A3.A8 A8܈.At8 A.AE7 Ae.A@}5 AR.A5 AY.A5 Ab`.Ap5 A.A2 A.A@3 Aʉ.A#?3 A .A1 A.A0 A!.A`)0 A.A@E 0 Ak.A@& A=.A Axŋ.A A(.At A .A@' A2|.A QO A3z.A= A#.A@ Ap:.A& AB.A A<.Aä A86.Aj A(Ғ.A`D A(ړ.A`9 A@uҔ.A՘ANޔ.AyAꚕ.A.A #Au.A`]Ax~.ABA.AAPk.A`)A8 .AA@.A A0..AXA2.A@ Aɥ.A`AQΥ.AA;.AA`Њ.ANAaD.A AE.A A.AA@.AA m.A`;ܛA6.A4A .A A.A:Ah.A@螗AP.A@A8.AvAH߬.AzAP׬.A ғAMɬ.A~A(.A~AG.Af{AЄ.A/A_.AAF.ÅAp+.A uEApp.AoA`.A`Ap.A'A^.A0aA.A@ 6AН.AFnAԈ.AaoA`.AsAWx.A`;Av.AhAh;u.AAs.AAHr.AqAWp.AoAj.A πA8h.A܀Axzg.AA =f.A &A8a.AA\.AAgW.AA\Q.AAH)E.A A#.A A8.A"AX.AAx.A8AP.AYApޣ.A AXͣ.AA`!ʣ.AvAjƣ.A@ŁAƷ.AUAx.A`Aa.A%AH).A l*A0.A/A.A@4A.A`8AXʙ.A8A`.A`"4A@p.A@*Au.A@A`!z.A@ AHs.A`A(H.A6AQA.AA\.A XAh.AD,Ap.A`A.AA(.AA0.AA.A3AX.A@AG.A`mmA`.A@!A@.A A .A~A@.A~A0.A"~Ac٢.A~ApТ.A~AǢ.Ah~A(z.A\|AH.A`|A>ա.Aj{AС.A`{Aš.Ai;{A!.A` zA`.AzA.AezA@}.AVKzAh.A/zAL.AzAU.A@yA.A`yA.A yA k.A෇yAP.A -uAH!.AtuA#.A^ uAM%.A@5tApA&.AtA&.A tA%.AxtA$.AastAXd$.AetA(!.A$tA(.AoAT.AWoA鈢.AnA.AanA}.A/nAFy.A mA(p.AKmApd.A@lAwZ.A@G]lApO.AclA=D.AkA7.A@ dkA2.A`HkA+.A@-kAL$.AZkAx.A HjA(.AjA(z .A ZjA.AjA.A3jA)h.A`hAҝ.AameAPǝ.A SweA\.A]}eA(ܺ.A`}eA`!.AeA8.A ~eAv.AMmeA\.A]eA8'D.AoDeA,.A"eA8.AXdAXd.A`dA7.A@0dA제.AcAp.AccA.AֈcAq.A`BWcAX[c.A?cA3L.A8bAPxG.A bAHC.A@bA<.AWQbA:.A 'bA@7.AaA7.A`aAF7.AaAp8.AOaAB:.AkNaAB.A`A؞L.Ac`AY.A_Ac.A#_AXd.A`_Af.A}_Ahk.A`k_An.A (e_AzB.A[`A.AL7\A(௝.A %\A.A@![A( ٝ.A`m1ZAo_.AsTA.AGQA°.APAx$.APAQ.A@N/PANʜ.A )OA[.A`MA虉.A\MAz.A OJMA8p.A@:MAIh.A 'MA`d.A@>MA`a.AMA\.A`]LA0{X.A@LAU.ALAT.ALAS.A LAfS.ALAR.A tLA #T.AHLAp.A:<AH.A`))8AQ.A@ B5A(.A\9Abv.AY3Ar˝.Ay/AȶS.AD)A֞.A"A .A஠ A(>Y.Ai A0Z.A@E A``].A@A].AA_.AfAHa.A6AP7c.A`Axe.A@iAg.AALi.AoAh.A AQi.A@AP+k.AsAl.AAn.A lpAQx.A%A.A"A(>.A`A`e.AGAI.A`BAԟ.A@iA #.AA(z.A \A.A@A0.A@ƱA .A`uA`.A8Au.AA`J.A@,A"C.A@A@A.A@A0{>.A@A_<.A.A(O<.A[EA0<.A`[A(\?.A3AlC.A-AH.AAO.AAr.A`rA(z~.A A".A`TA8⃠.AAF.A`A@A.A G!A .A;)A8.A J+1A.A͆5A".A@?Ay#.A`AA83.A0AAx@.A ZAAK.A JAAP P.A`DAAS.A@>BAr[.A:BA/^.A@NTBAh`.AWmBA8a.A BA}a.A BA(`.A`BAU`.A@BA_.A2BAZ.A`BAH2T.A |CAHM.A>CA!J.ALCAjF.A`bWCAA.A bCA0:.A@lCA(5.AjCAx1.A bCA p.A`mTAA.A@NYFA.AzFA #.AFAH.A; GA(.A@NSGA_.AGA@`.A@` HA.Af[HA.AHA0 .A@HA$.A@GHA-.A HAB9.AO&IABN.ANmIAk.A߇LAX ӟ.ALA.A LAo.A@LA.AMA.AMAP.AWMA.A,MAp,.A@QMA@ .AMAhL.A MA.A`;NAX .A)!NAF#.Ap0NAPI$.A8NA#%.ADANA0L%.A xNAZc.AVA.A@p[AX[Ǟ.A q^AN6.AaA<.A`)dAeD.A@|dA@.A0AfA4.AzfA-.AfAo.A@iAh.n.A`tA`1l.A?uA*l.AuAn.AࣾuAq.A;uAPx.A@,4vAh.A |qvA.A vA.AvA.A mwAx.AFwA"ţ.A 1wA8أ.A@"xA.AdxA0v.A@xA.AxA #.A@ yA0S.AyAgp.A yAu.AஎzAˤ.ADzA ֤.A$zA E.A`{A@.A"{A>.Aa3{A.A`f@{A@.AI{A.A \O{Au,.A@%Q{Azѩ.A@>A pש.A`Ah.AWA`e.A A0.AB Ax.AA.A A .A H,A`.A@9Al.A`?A,.AXNA";.A3^AC.AwgA0R.A`zA0{_.AAk.AXA"w.A构Ap.AA0Ȋ.AתA.AAe.A@Ax.A`vA.AࣻA ͼ.AAŬ.A`A8Ǭ.A 8AKЬ.A`Aج.A萄A.A9}A.ABfA.AVRA7.A`);Au.A`!A .A@pA.A`mAf.AW΃A(.A@PA3.AWA(>.A eAxH.A OEAR.A@$Ap].AAh.AނAr.AcÂAB{.AA@B.A`mA@.AwAf.AcAj.AZDA.A8'A7.A@A@u.AAH.A`؁Az.A ZAȭ.AAhҭ.AxAܭ.AzMAH.A %Ax.A ZAH.AЀAz.ABA .A#A.AcA,.A@@A.AfA$.AAH,.AAH1.AaAA.AxA0LF.A]rA0Q.AHAp].AA`h.A3~A`r.A~Av.A~A3}.A~AL.A~A.A`~A.A@CX~AQ.A`/~Aأ.A~A.A@}A.A}A7Ȯ.AM}A`aӮ.A}Aڮ.A`d}A3.AB}A.A@g}AB.A|A.A`|A[.A q|APk.Aݍ|A.A ld|Ak .A`5|AP.An|Az.A{A2.A@G{AH2#.AX{A`).A{A8/.A@^{A85.A8{A;.Ao{AVB.AzAH.AzA8N.A@zAR.ALJzAxU.A`czA@W.Ad?zAu\.AzA`.AyAȘd.A (yAh.A`ܫyA(k.A@yA([r.A~yA.AHwAX(.A /vvA(ٯ.A`sA#.A=sAp.AsA.AQsAB.AߣsA8 .A ZrAG.A`frA`v.AfWqA.A`mqA=}.AqAk.ApA`.A@ypA`V.ApAG.AvpA81.A*pA.AoA.A nA^.AlA0{ì.A/iAx>?.AoRfA".A$fA4.A\eAX.ABcAQd.A`bwcAg.A RcAef.A`%cAe.AbA0f.AbAx.AA[`.A6A.A@33AxL.AT2A(.AH0A.A /R#A0L.A G A6.A XAXѶ.A@^A.A ATk.AWAX0=.AAȭ.A@ri A غ.AZ AP.A AZ.A`kAHvq.AA>.AkAx.A@A".A Aھ.A@`rAH.A AHu.A#tA?.A |A.A`;zAF.AஃA.A (AX}.A`ՈA(\.ANA@W.AQAXT.A ZAO.A`YXA.A ~A@,.A@5An.A\A.AAڴ.AA.AkќApS.A)ۚAA.A`]-A.A A.A_{A.A@r/Ac.A ~A?.A`LAp.A-A` .A늇AЯ.A@#@A8r.A@rfAЈ.A uA,.A`[pA .AkA ɹ.A_5iAmn.ATgA.A`0bA0.Al]Ag.A@?YA].A dXAHvo.AWA2.AcOA0r2.A2A .A .A.A[:A .A`wA.AA0.A`A(S.A@`wAL.A Ay..AEAPQ].A`Ar.A@DAG.A A..AA0.A+ AاM.AVAI.A A.A@ At.ApA .ATAH .A`APUU.A@Aȃ3.A`AHu.A A.A ,A+.AQ AQ.A9AXy.A_A(/A|!AD/A .A0/A;Ah^ /AZ]EAc/A|SA/A SAH/A/TAqa/AivA"/AHAw2%/A@ErAB&/A)|Ap%/A[tA"&/ADsA0&O'/ATApŢ'/AmyLAp'/A`oDAB(/A?S=Aڝ(/A+5A)/Aho-A=)/A"A)/A IA*/A96A*/A'A0+/A5A&+/A sAp,-+/AA`T+/ABAh*-/A`zAx./AAJ//ACAL*0/A`f5Ah0/A-A1/AA1/AA1/AALf2/A *A`3/AA3/A`Ao4/A5A5/A cA(5/A=1Ah&5/AyA8]6/AA,6/Az4A<,7/AbA^8/AqA(9/A -sA;/A@tA=/A`WvApű>/A:hA>/A8(gAH>/A efA@>/A,yeA0>/A_dA?/A@iSA@/A`MAX@/AKA]@/A@AApc%A/A74AMA/A`a0A?B/A A7OA/AAH{H@/A OA`<>/A AL/A@A!AqW'2/Ac,Aj֎Y&2/Ah8A[m3$2/AwH A_q!2/A:AMA&2/Ao(Ah2/A1XeAQ2/A3A2/A fA81/AAՑ1/AAA!1/A!JAyH1/A 5A1/A;A?#V1/A]NAA= 1/A[tC&A>?1/AƞAjF1/AjRxA,55c1/AyxAEv1/AGxrA!R1/AnA1/AlAKW1/A`%-5AZ~1/A{A81/A ATr]1/A{GDAH1/AMHA92/AnAB2/A`wA:2/AAH\V2/A {AH\k2/A@>vA0.[2/AAi!IW2/ACqA;9S2/A؀ANQ2/A<`AqbO2/A8WA=N2/A{NyApM2/A$eAegN2/A8QAxO2/A,i>=AOQ2/Ai+AEtT2/AtNA%X2/A" A8\2/A \AH%Se2/AGAHѥl2/ANj2AHq2/A@FA[Wo2/A5-A@m2/AjIJAZCi2/AkAwtd2/AךALB_2/A#%A[Y2/A.Ai‹S2/A(PA?L2/Az^A8F2/A HA'2/AtA0#$2/A1gSAln2/AǀUAj]2/AXA2/A6GAO2/A,ЏA 2/A A2/ApъAb 2/A$>qAeY1/AVknVA4@1/Aۤ9AK1/A9A&1/AfAA1/AiAh2/A(Ah32/A=AM(2/A~{oAةuP2/AoB9A3n/|2/A*Fv A ѫ2/A Aʴ:2/A9zAD3/AzA43/A@ =A8,5/A A95/AEcA#~5/AI̻A"K&6/APpAuvx6/AGR[A6/A A 7/AhA7/AxA`m7/A]AW7/A bAJ7/A`A8/AAp;/A A~>/A A>/A&AC>/AAX,->/AxAп=/AЀqA`=/ATslAXJ}/Ac6AP>/A?5AeÕ=/A:5ADHAcREjA/AYUAO/AYEVAu/A AfKc/AN#Atc/AN-A@/A mAi[/A`*&AgI/ABKAu/A4xA9/A_=2A?/A+A ZtJ/AUFAg1/AfڃARˇ/A+Atk/A2pA2Z/A*%A*/A AA@>/A`3A /A~A /AA = /A lA /A A /A@AX /A@ϜA /A`AhL /A`AX /AA /A@.2A /A@ =A@5 /AQA(> /A A /AHAP /AXKA( /AOxAx˭ /A0A4 /AxAi /A A@m /A`2 Axi /A 0AP8f /APAP` /AA?] /AAT\ /AAC\ /A@,AG /AAG /AAbF /A 1AD /AXAxzF /A`A`aD /A@GA C /AA]C /A `A;E /AAzA /ABA)8 /A@ A8 /AM A 6 /Ax A0 /A}A/ /A`A0+ /A@ A%' /AAD /A@@Ap /A&NAHa" /A"\A /AHA /ADAB /A A( /AA0 /AưAϝ /A`dAp /AAPړ /AA5 /A (A՘ /Ax_Aߎ /A A@~ /AqSA"o /AAPb /A &AHU /A AnE /A@߷A0 * /A`1A3# /A ɺAH /A 8AA /AuAk /AךA /AuA0.( /A`AnE /AVAcO /A /\AP7[ /A Z4At /AyAr} /AN>A+ /A Z4A /A`Ak /AMA` /A@A@ /A@A /AA /AxAJ /AA8 /A{A)8 /A@|AXh9 /A A V /A AXk /AuA"z /A  A /A A0 /A 3!Ax /AR!A /A ~S!AĖ /A`!AĖ /AD"AĖ /Av"A /A#AH /A@i#AQ /A^#AH\ /AJ$A8 /AR$A| /Ar$Ah^ /A $AD /A%%A)8 /AM%A8 /A?N%A$ /A%AP| /A%Ap_ /A%AP /A`%A /A`%Al /AЦ%A` /A`%Ar /A` %A`T /A  %A /A%A84 /A-%Apʢ /A>%Aj /AA%A# /A C%A{ /A%Ax /A`%At /A%A@j /A |%%ANc /A/%A`Q /A}%A= /A ۤ%A0/ /A`v%A( /A %Ar /A&%A( /A%ApN /Al&A" /A[l&A* /AK&AU /A&A0 /A&A /A஻&A؁ /A?&A0{ /AE&Ax{ /A&ACp /AV&Aa /A&'AP T /A lR'AA /AΌ'Ax>, /A'A, /A'A% /Ah'A /A(AP /A(Aѿ /A ('(A /A`d5(Aw /A`o5(AP /AX(A /A(A( /A@(Aȇo /A)A( g /A:)Ahf /A`;)AHiP /A)A: /A)A38 /A *A#8 /A *A([5 /AL.*A-. /A*A+ /A@*A+ /A*A& /ABP+A(" /A`ܮ+A /A@,A  /A`2,A`e /A`T,A` /A (,A1 /A |,AP /As-A /A ɉ-A /A-A/ /A@-Aؚ /A@.A` /A.AO /A.A /A.A /A Z.AHr /AT/Axz /A lY/Aӓ /A /A /A%0Ax /AT0A l /A0A(zR /A \1AD /AO1As; /A21A" /A_2A /Aj2Aп /Aӱ2AR /A3G3APk/A`;3Ab/A3A/A`e4A/Aj4A7/A65A/AQ5A1/Av5A/A}C6A0L/Apv6A(K/A 6A/AR7At/AH7App/A@7Ap/A7A0&/A<8Aؚ/A;A/AA|/A@A4/A`AAX/A d@A/A$?A9/A`v>Ax\/A@yR?AA/Atn>A>/A`9K<A/A@9;A3/A`2:A؁/AД;A M,/ANAPM.A`NAp.A` OA%.AൂOA@B.A AOAp.AOAP.AGPA$.A$QA .A-tQA@.A6QAx.A`RAx.Af SAo.A`JSA"n.A&SAhG.ASA8.A@WUA(.AZWA .A`)XAR.A VYA.A[ZA0.Aל]A#.A@^A .Am_AX4.AJ`AhG.AaA^P.AaA/1.AْbA.AD&cA .A_cA.A`mXdA.A /eA .A@ bfAH:.A3gA.A@fgAd.A@W?gAX.A`DgA8V.ADgAxV.A \EgAP7.A܈gA.A܈gA07.AKgA.Az(gA0.A"gAR.A[hA@N.A@yhA@.AhAh.AO"iA4.AliAh.AsiA`Ǽ.AtiA0 .AqiA |.ADjAxd.A`RjA8.A |^jA .A0jA!.A@jA.A`jA`6.A jA .A`.A`mAPo>.AmAW6.AfmAW+.AnAHm.AoAh.A5[oA.AoA Ҝ.ApA@.AqA.AO+qA.A>qA؞.A`PqA|u.AmnqA(j.AqA^.A qAR.AqAF.AqAm:.ADqAX ..A \qAR$.ArA.A`%rA.A;rA(~.ASrA.AkrA4.A ZrAx.A)rA.A \rA .ArAA.ArAH\.AsA`.AZsA.A 1sA|.AsA.AtA.A`TVtA.AtA".AxtAx .AtA0y.A`tA(s.A tAj.AuA8a.A#uAY.A6uA0 S.ANuAgD.AuAoA.A`ܝuAxG<.AuAp7.AuA3.A`uA&/.ALvAx+.A7vAu(.A YvAx!.A{vA.AvA.A 8vA .A#vA3.AvA.AwA.A`6wAHK.AaVwA.AtwA.AwA؁.A wA$.A(wA.A`wA0գ.A`wA.A xA ҋ.A` xA.A3xAh3s.ADxAl.A FxAwf.A`ExA`.A@#CxAY.A >xAS.A9xAhM.A@i1xAG.A (xA0A.AxA8b=.AxA0]<.AjxA7.ADxAP2.AwA<.AEuA+.A^tA-.A YvA .A@y\vAP.A`vAP@.AdvA.A ivA`Ü.ApvA .A|tvAĘ.A#yvA.AvA8.A|vA8E.AʟvA E.AӰvAl.AvA#c.A wAZ.A@9wAТQ.AgwAH.A qwAD.AwA@.AwA0<.A 8wA 8.AqwA3.A wA0*..AmxA(.A` xA@q#.A!xA .AT,xA.A5xAh.A =xA .A`DxAh?.AWIxA0.AaLxAho.AMxA.A@ MxAX.A`oIxA.ACxA.AgV.A@uA8T.A@>uA RR.AuA8P.AwuAO.A ~luAsO.AKVuARO.A@uA O.Ah.uAO.A )uAQP.AuAoQ.A@tAR.A`KtA(T.A ZtAH.W.A tA=Y.A ~tA[.A ?tA(^.AvsA b.A;sAe.AdsAj.A`BsAn.AsAs.A`rAHPy.ArA9.ANrA.A 8rAF.AoqAf.A`fqA&.A`qA@.A@pAȤ.A pAX.AVvpA.AoA@.A`oA.A XoAp.ApnAH.A nAG.A@>nA0Y'.A"6nAp7.A`"mAȇ8.A@mAsH.AFmA(zY.A`XmAH2`.AGA4o.AB9GA0j.AmGA^.A@GAkY.AGATY.A'GAYL.AGAdB.A@,.AEA>.A2EA>.A,DAȘG.AT]DAK.AXDAO.AhCAD^.AБCA4i.AaCAz.ABA@S.A$7BAH.A@ϗAAŔ.AhAA4.A7AA.AAAw.A ~~@A.A@B@A޸.A)@A8.A`f@Aj.A.@Aw.A_/@A`X.AO@A`e.Ae@A.A @Aj.A@A.A`@A .A`AAXW.A AAx.Af AA0U.A S@A~.A l@AC!.A0?An!.A`?AP$.A?A2.A?Ax):.At?A >.A@?A>.A?A'F.A ?AX.A>AHaZ.A>A)[.A]>A+l.Aq>Apj.A">AP ].At>Ao.A=Am.A@M=A8.A@K=A.A@;=A@.A&=AѺ.A =At.A<A*.A`e<A.A H<AX.A`;Aп.A+;A.A,;A`.A;A0/A:A /A Z9Ap_/AI9A /A9AȘA/A8A(Q/A@N8AU/A஭8A(~Z/A}8Af/A<8A@Au/A7Aau/A7A0e/A lA7A/A?@7Aٷ/A>7ApJ/A>7A N/A>7Ax~/A>7AW/A47A|/A@ &7Ah/A6A /A`6A/A@ߜ6A/A@|6A@4/AR]6A8C/AxH6AVJ/A66A`PU/A+6Aa/Aj5A)o/A5A+x/A०5A/A;5AȂ/Ae5A`ӫ/A;5Ap}/A 5AH]/Aa4A8/A4A/A`4A </A4As/A@4A`/A`e4AP8</A.4A_/A@ 3AH/A3AJ/A a3A/A03A /A`2AX/AE2AH/A |2A/A^O2AHr/AK2A /A&@2Aذ/A?2A/A"1A &/A61API8/A@1AR/A`q1A]/A?_1A8 t/A 1A`x/Aa1APf/A0APj/Aו0AP/A.AO/A@5~)Ap/A@"Ap9-/AAP /A`[A /AaA؟ /A0 A /A@Ah"r /A@.A//AA /AZAw/ARA8 /A'A/AA5/A`;TAϳ/A`AXF/AA /AzAHPR/AQANv/A A/A A:A/A&[A0/AMAS/A`Av/A"Aг/A A&W/AeAp/AgA /A#0A\ /AA(/A@yBA0/AKAE/A9AB/A 5A.AK>iA&':.AhLA&5.AmrA.AsAhb.ALqA /.A\A(# I.AJM[A{?#?.AȅYAv".A0kcXAH6).Ar WAtQ.Aa^.A`fvA@Z.AovAU.Af{vAA.AwAx.AgzAhj.A`8{A@.A`~Ax .AqSAj.AAd.A`A0]Y.A`Ap[.A@WA8.AlAp_.A JAR.A yAЄL.AA~.AஅA.AʡA0j.A2Aa.AġA׀F.AzVAx.A\|UA[~D.A{QAw0.A;;Am.AϻA V޲.AlUAbG솽.A ,ݰA ].A_VAg8.AEA.AA=i.AD'A4LӼ.Av*A儚W.AEAdN.AޭAH.AYr.LjA$p.AAZУa.AbA9V.AyӫAzeQ.AW@ASP.A}AX.A`A`.AUAX6.As{Ah.A{A]e.A$vA6A.ARdA0.Aӵ`A.AaAP.AbA.A@5@bA.AdA\u.A hAyy.AqikA.A`՝kA.AkkA.AyplA.A]lA8t.ASkA.A jAh.AjA( .A AQjAx .A`>jA6.A iAN@.A@#iAm.A`8iAt.AK$iA8.A`hAPީ.AΗhA.Ag!hA.A@ hAt .AgAz.A`fxgAD.A fA`6J.AfAPw.A wfAp.A afAƬ.AeA.A`eA.A^eAp.A`vCeA.AdAq .AdAL.A |CdA|U.A A,dAh́.A`cA5.AcA舸.A&&cA8־.A@ycA7.AbAX.A0bAh.A`TbAo%.A@bA@L.A@aAhS.A SaA]y.A 'aA(>.AdaAȦ.A`A .Aՙ`A.A5`Aa.A@!`A.A@p`Ah .A_A/.AD_AX6.A`22_A;].A@^AX .A`_A(.A_A].A`^A`|.A5^AS.A`B_AH.A1_A>.A E^APx.A@]Ah.A^]Ax.A@`+]A0.A`\A.A@i\AX[A.A&k\AXI.A V\Aq.A [Apx.A[A8Q.A6r[Axu.AW\A-.AjJ\A.A5[A0؇.A^Ap.A ۍ]Ao`.A ]A>;.A\A.Aj[A 0.AZA{.AYAx..A WA(iH.A@>WA}Y.A |XAm.AWA.A&\AD].A[AW.A[Ao.A`fE\A(p.A@5WA8I.A WA¢.AVAm.AnWA@5_.ATWA).A@PVA.A _VA:w.A VAq.A uVAذ-.AWA0-.AWA*.AQWA#(.A q%WAx&.A *WAhD.A|VA.AſVA`G.AVA .A`VA.AٷVAb.AVA[.AUAt.AVA([.A`2VA{b.AhCVA`.ACUAJ!.Aٳ>KUA}.AFdTA.A%{ITAR.ASA.ASAX.A0SA9.A\SA.A9SA#.A@SA.ARA.ARA.A@yRA8.AhRAx .A5RA .ARA-.AjQA".A`QA +.AUrQAy3.ACQA@=.A`,QAG.A`QAhsR.AQA0*].APA0 h.A/PAs.A?PA@~.APA.A`PAh.AîPA5.APAXh.APA8.APA.A`PAh.A`PA*.A`;PAX.AaQA(:.AQA.A"-QA.A`BQAЀ .AYQA`1.ArQA =.ArQAG.AUqQAE#.A cnQAh/(.AiQA,.A`fcQA1.Aq[QA6.A QQA N:.A`FQAH>.At:QAHA.A ,QAh^E.A@QAkH.A@ QAK.A PAPoM.APA[O.APApP.APAQ.APAR.AܜPA0LV.ANPA^Z.AOAX^.AOAc.A@cOAh.AOAn.A`NAt.AT}NA({.Ay1NA.A,MA;.ATMAٚ.AcAMAէ.A`dLA0.A ZLA.A@NQLA.AFLAk.AHKA3.A^jKA`T.A@KA .AJA.AJAh".AJAȱ-.AJA8.A@ JAD.A JAh@O.AJAZ.AJAe.AƤJAx q.A&JAI|.A3JA~.AJA0.A OJA.A` JAh.A`]JA0.AjJA.A@NJA.A 0JA0.A4JA7.AXJA(:.AIA`%.A IAC.AaIA.AIAx.AIA(.A`IAs7.AIAXF.AqIAPU.AwTIAxzd.A`5IAHs.AIAh.AHAŏ.AHA0ǝ.AHA@.AHA.A aHAe.A@8HAp.ABHA9.A@pGA(.A^GA@.A qGA .AGA.A\GA .A39GA.A,GA.A$FA '.A?FA +.A@ϷFA0.A9FA4.AFA8.AyFA^<.AacFA?.A KFAB.A3FA"E.AFAPG.A FAI.AEAJ.AEAK.AEAL.AaEA8RL.A@}EAL.A`bEAK.A HEAJ.A-EAI.AQEA(mH.A@DA0&H.AxDAEH.ADAH.ADAI.AH{DA K.A?]DAL.A?DA(N.A;"DA]Q.AgDA;T.A &CAPxW.ACA[.ACA.a.AÚCAf.ACAol.AMgCAq.A@KCA0Hv.Ag/CAz.ACAp~.ABA,.A`BAR.AʹBAP .AdBAPZ.AtsBA7.ARBA.A;?BAX.A[0BA@.A cBAR.A@AAPH.A@AA8|.AAAw.A@,AAp s.A`mAAq.A`mAAm.A@AAh.A@xAAЄd.A^AA`.ACAAr].AA(AAhZ.A AA0W.A`@AX.A@A@Z.A@A \.A`p@A'^.AXG@A`.A0@Ac.A u?APf.A@5?AXj.A?An.Af~?AFs.A W?AHx.A;2?AHP}.AF ?A0݂.A&>Aˆ.A>A.A>A.AF>Ac.A`K~>Axi.Aa>A.AB>A.A>Ah.A@=A(>.AF=A@.A`f=A1.Ao=Ap.AD=A ^.A=AF.A<AȺ.A <A(.AА<AA.A`c<A.AQ<A".A A<Ak.A@1<Ax.A?$<A8.A <A(l.A_ <A.A ~ <APf.AD<A~.A;A.A`;A.A@;A A .A;A.A;A.A;Ax:.A<A3.A[<A(.A<As.An+<A.A`6<AxW.A` A<AV.AJ<AȤ.A`R<A7.A`vY<A .A@^<A".ANb<A&.A3d<A).AVd<A8#,.A`b<A" r4.Az:9A<AE9=.Aa$<Ad3OG.A#ò <AR.A;A/orq.A] *7;A.AE R<AE.A0<Av9v.A&tхZ<A~pf.A<A.A;<A0e.AE<AX!.A`=A(z..A=A;.A9/=AFJ.AL=AX.A`fh=Ag.A=A@v.A@5=A.A-=A@u.A@=A .A@,=A.A`)=A`P.A`v=A(.A` >A.A`>A(.A $>A0.A`->AU.AF5>A8$.A@>;>Aؽ4.A`v?>A D.AA>AL.A->AcT.Af>Af\.A A >Ad.A`=Al.Aa=A{u.A=A"~.A=A.A =Ahď.A=Aȶ.A=A.A=Aƪ.A=Aڳ.A@y=A.A=A.A@y=A@.A=A@.A=A .A@=A .A@>A`T.A B>A(.A`)>A.A8v>A.Ak>A+.A@`c>Ap.A\>A`´.A@pW>A.AT>AЕ.AR>A.AR>AHr.AT>A0T.A`X>A`%.A^>A.A`be>Ap|.Acn>A.Ay>A`C.A"}>A .A>A.A9>A`T.AD>A$.A@|>A,.A Zx>AH25.Ajr>AX=.Aj>A cE.A a>ANM.AV>AU.A@J>AV.A6>>AVX.A2>AxZ.A(>A8\.A>A_.AK>Ab.AV>Ae.A@ >Ah.A>A8l.AV>AНo.Az>As.A`;>A\v.A>Ay.A@ >AP|.A@>A).A`v0>A̩.A`O>A .Al>A@.A 1>A.A@֑>A.A>AH.Ac>Ah/.A`K>AE.A>AH?j.A>An.A@N>A.A`>Ap.A<>A.A >A.A lc>AF.AD>Ah@u.A=AW}.A =A@d.A=A(.A=A.AA=A誘.A=Az.A`(=A.A= =A@W.A<Ak.A<Apl.A`<A.A@<AH.A<A.A<A%..A1<A0E.A;AHna.A:A(-.A:Aª.AHe:AH..A@f:A.A@2:A.A q:Ak.A 9A.A9Ak .A9A..A`;%8A6.Ap7A\.A 5A([.A@N5A(\X.AR5AT.A4A8R.A@4AhT.A#3AY.A2A[.A]k2Ab.A#2AX h.AR2Api.A!2A`l.Apq2Ahn.Ap2AXw.AR43A8.A[3A`a.A=R3A).A =3AL.A2AH\.A H1A=.A0A.Ap0AX.Ą0A R.A0Au.A30AW.A/AG.A`/A.A3O/A.AN/A.A@p]/A.A I/A.A=/AX .A.AQ#.A@.AH.7.Al.A(G.A3Q.A(T.A \+.Ai.A]-AH.À,A.A*,Af.A`),A8.A+Az.Az+A.A2+Ap.Az*A`a.A*AG.A@*A}.A Q*AH..A`)AG.A@)A“.A`)B)Aњ.A#)A.A")A`.A1)Af.A2)A`a.A`)A).AR(A.A`f(Ah3.A(Ap.A`f(A`.AY(A8.A?(A.A5(A.A (A).A'A0.A&A .A@\&AHܭ.A1%A.A`$A@ן.A@h$AL.A`@$Ap=.A`$AǬ.AR#A.A]#A.A=#A.A#AW.A@"Au.A^"Ap.A3"A.A"A.A"A0..A"AL.A!Af.A`)!Ak.A`[!Ar.Az Ad.A`f` AV.A3 AN.A \A8I.ADAu;.AࣼA#<.A@ZAp@.A A:.A@Ak/.A AP!.A@gjA.AA .A[Ap.A KAH.AA.ARA{.AVA.AAP.ARAX .A`fA.AzA.AA8.A xA.A@(A R.AA.A`)XA#.AzA.A`)XAQ.A A`v.AדA83\.AAPS.A`AP.A@GAp=I.A@AG.A A@.A]=A2.A A&.A`A.A'A.AAz .AA .A3A.A@XAH".A HAp=.AzAX.AA@%.A`)<Ap(.A \A%.AAL.A`)rAk .ASAp.AH/AH.A@Ap8.AAf.A͜A8.AVA .AAp=.A`A.AAH.A@ A.AtA.A`DAp.A@AG.AzA.A(A.A= Aǵ.A`)Z A.A[% A.ARV A0.A׍ Ah3.Ap A).AQ A.Ac A{.A`) Ap=.AAH\.A wAp.AA.A"AB.A |A.AR~Ak .AzAH .A HA .A=HA.ARAX .AbAG.AAp.A=Ap8.A@.A.A@A.AjAu.ARA.A@AX .A`fA.A`hA.A@XAH.A@6A.AAX .A]Ap8 .AzA.AQ AH.ApA .A*A.A`)A8.A̰Az .A`A#.A@<A@1.AzAk?.A@A`O.AHAQ`.A`Ae.AHAul.AzAQo.A:Ax.A?Aƒ.AAL.A`)Au.A A`a.Az:Az.A A(\.AAk.A \]A.A`(Az.AA~.AQAG.A`A0..ApeA`a.AHA=.A]7Au.AA.ApA`.A AL.AA.AHA.A+AL.A qA.A Ak.A@AH.A@A.A@Ap.AIAX .A$A8.Az Ah3.AHA.AAf.A}A8.APA.A`A .AAp .A/A{ .A HA{ .A@8Ap.A HA(!.A HAu0.AMAk=.A \AHE.A=A#`.AAr.AGA(y.A \Az.A`)A`{.AaAQ.A6Ap=.A]Az.A`fA( .AiAG.Ap9A.A AX .AA`~.ARAp8t.A`éApkg.AzA^.A[AZ.AAP.A[AC.A"AL8.A=@AL$.A VA).A=\A.A=JAX .AR(AX.AA.A HAL.AcA#.Ap!Ap=.AA+.A \kA5.AA:.A`AD.AIAzR.A@AT.A3AuU.A`XAf^.A/Azn.A3 A`.AA0.AAL.A A`.AA.A oAP8.A GA#.AR&A8.ApAB.AA0..AzAk.A@tA .AA` .AA.A`)A .AAh3.A@gA=.A`)A.A @A).APA=6.A HOAK.Az*Ap.AA.AUA.AAk.ARAPN.A<Ay.AAأ.A`LA.A3A.AA`.A`)PAz:.AAe.AA(܁.A@Az.ARHAL.A A.AA.AA+.A̼ALU.AzA@Wz.A`A8.A@AQ.A`A8.AA.A@gA.AA.AA&.ARA@.AA)N.AAB_.A@gA~.AAp.A.Aǻ.A)A`.AA(\.ARA`.AAh.A3+A7.A9A0.d.AA.AAk.AA.A`÷A.ARAL..ApAkA.A@jA8b.A^A0.AzBAHܠ.ARA.AA .A QA(\O.AA`a.A`VA.A@AP.AQA.AeAB(.A`)AJ.AA(\t.A@ A8.A Ap.A qA.A@|A).A`@A$.AAأ2.A@AH\G.A`jAa.A`) Aj.A`Asm.A@Ap.AA8z.A+A8.AzA#.AR0A̱.A`A.A#A.AA.A9Aأ.A`|A.A=A@W.ApAX .A`vA.A Ap=.AzAp8.AA.A5A.ARA( .AAf.A5AQ.AA.A`)Aֆ.A Ao.AzA`.A 9AT.A`fAH\K.A A6.A=dAH#.AA.A }A#.A`hA.A 'AP.AA(\.A[A.AAp=.AAB.A A{.A=dAr.A SAp=i.A`)lA@Wk.A@ AQm.AAf.A A)S.A6AF.Az2A=.AAV2.AA8(.A AG .AxA.AAأ.A A8.AA׎.ATAp=.A@DAXw.A \EALp.A@ZAkw.AA{.A \Ap.A%Ȧ.AkA.A@gAt.ApAb.A̎A`U.AmA@G.A@g@A8.AAu%.A=AQ.AR8A83.AAp.ApCAX~.A AU.A?AE.AA0/.AA#.ARA@.A3AHa.AA.A@A83.AA.AAy.A(Aa.AAL.AA9.AAG.A@AP.AMA.A HAp.ARA`.ApA.A \mAd.A q APC.AA &.A q[A# .A /A0.A=.AQ.AMA.A@lA`.AkA8u.A GA@W`.A ApN.A A#J.A0Ap=K.AAJ.AAXP.AzDAh.A3A8s.A A(t.A3sA`x.A=,Aft.AA`u.A=A`.A`AG.ApKA8.A H;A`.A HMAp.A׋AL.AA.AA`.AzA`.ApA&.A=pAfC.A AAJ.A3A8N.A=A#J.AAO.ARfAp=O.A` AK.A \A(B.AAp;.ARA8<.A`)A@.A@ AXL.A@ }AP8\.ASA)m.A |fA83.A@GA.AAp.AAu.A@"A`.ASAp=.AדAQ.AzA .AzA84.ARAC.A'AL.A=PA[.AׅAu.AAǐ.AA.AA(\.A Ap8.AAB.AA.AR`A`a.A3A8 .A@A.A]EA؞7.A HoA`I.AAX].AAx.AA.AA.AA.A=FA%.A@AX.A A|.ApA.A \A«.A=A Ҹ.AA.AzAu.A@ sAk.A@\AX .AkA.A AX.AA R.AA8 .A=4A.A A*.ARA89.AA?.A^AGB.A@AH?.AA9.AAH.9.A3'AK.AyA)d.AQA`a.AWA0..AAP8.AñA.A`A.A]AX.AAG.AA.AA!.AA/.AAI.AAT.AAd.A GAy.ALA.AwA83.A@A.A@ A.A`)LA`.ApSAz.A@ }A.A HA).AA8.A Ap8.A HeAp=.A֕AB.AA83.AAX .A)A.A=tA(\.A`̲A.AzAu.A HAp8.A`̲A.AsA$.A@A=A.A@Al.AA.AA¨.A@gnA.A?A.AAp.AA#.AzbAA.A`) A(T.AAXY.AAأf.ARRA8.ARAB.AqAH\.AA.AA88.A( A6.AýAT.AAus.AzJAG.Az*A#.A3AW.AzA׬.A \-A.A \AL.A=2Ak.A A#.AA.AAP8.A~A.A HA`.ARbA8.A=A.ARA.A5A—.A@ A.A@VA.A@A|.ARAb.AAP.AR|A82.AA.AAQ.A`pA`a.AA.AmA.AA.A=VA).A]}A`h.A A`%.A@ oA.A`)Af.A=θA.Ap7Aƒ.AݷA([.A|Az2.A3A .A A.A`)&A.A޵Ap.A`Ap.AҴAR.A@GAu.A |0Ak.A VA`.A`)Ab.A@JAp.A`Au.A$A`.A`)AW.A Ap.A@Aǿ.AGAu.A]Ap.AAP.A1A0.A@tA.AAh.AAX.A$A.A3eAk.A׫A.A3A.A`<A@.A \sAk.AA=.A`A R.A@g԰A(.A@A8.AA.A@APf.AAL.A`)A83.AA.A3A!.AA`,.A@ A8:.Az^Ap8K.AAF.AA`aC.AzA=.ApA88.A`fбA0.ADZA,.A`fAp=+.AGA0.A]A(\:.A2Ap>.AeA9.AzAk0.AQA{.AWA.A?A.A Aأ.AA R.A3WAǸ.A3Ap .A A0.A@ᆰAp.A 1AB.A`A.AHAP8~.A AAg.AAP.AۮAB..AA%.ApAH*.A`XA5.A`)TAu6.A`fAG8.AGAu&.A=ȬA.A A.A3A8.A AH.ARFA.A \A.AҩA.A`vA.A=A.A= AH.AAL%.AóA( ;.A sA(=.AiA>.AzPA4.A?AG$.AGAp8.A`fTAX.Az\A.A33Ak.A`)A.A`fAp.AGAB.AjA.A3WA.AAp8.ARA8.A[A.AA#.A@ ٦A'.A@A/.APA(=.ARAuJ.AAX.A@>A8Z.ArA83\.A`ApS.A[ABK.A@AfH.A \A(@.A q7A3.AԥAp1.AQA'.AAp$.A \ŤAp'.AtA".AA.AA.A`̾A.AzA.AzzA`a.A qAp.A(^A ҷ.AGA̮.A A@.A kAk.A[Aמ.AA0.A8AHܜ.A8AG.A@ጞA0.ApEAB.A@A`}.A ĝAu{.AAy.A@:Akt.AzA؞r.A`)6Ah3o.AΛAQ_.A`f@ABU.A=ʚA(\O.A@vAPK.A`f6ApH.ApA؞C.ApAB8.A`VAP/.A`fA&.A^A(.A A.ApAf.AjA.AHAX.AA.A@ A#.A`ŖAut.AAe.A qAR.A q_AG.A AG@.A`f̕A6.A [A؞$.AA.A 3A0 .AӓAf.AAp.AJA`a.AzA.A[ےA83.AޒA0..A Ap.A ϒAp=.A A.AuABz.A>AXi.A@6AZ.A3AV.A AU.AAP8>.A ˑA8,.A@>A.A`ÑA  .A`NAH.Az*A0..A`A.A A.A \Ap .A@Aأ.AzA¹.AHAש.A ݐA.A@ A`.A`)A@W.AzpAh.AIA`aQ.A 1AA.A`f$Ak#.AA.A |ȏA.A3A.AGoA.A`TA8.AHA.A=Aq.AGCA\.A_A0.?.A=HA(5.A]A$.A3A(.AAH.AA`.A3uAP8.ABA`.A=Ap=.AQA .A`)Av.AQ܎A)b.A@ᘎAP.A@ZAh3?.A 1A..A3AG.AzAp.AAz.A HAP8.AA.A@ḌAp.AA.AaAX.A!Ay.ARAWU.A`fAk?.A@GъA'.A`̀A=.AAp.AӉAu.ApAu.ApWA83u.AALS.AA?.A A".AqA) .A ZAH.AMA8.A \A5.ARaAPf.AeAأ.AfA.AVAu.A&A.AA(u.A`)ԇAp=d.AAQ.A=A@.A@ᖇA)*.ARA .A@ qAP.ARDA.A`A.A qӆA.AzA.A``A}.A`)8Ai.A`),ApF.A qA@W".ARA(.A`fA83.A A8.ARAG.A ݅A8.A3A#.AA8.AcAfe.A^AX O.A`PA=:.AGA).A3SA(\.AࣂA.A3A.A`A0.A=nA@W.A=@Au.AAk.AԄA)I.AAX 9.ÅA(.AhAP.A /A#.A`fAW.AA.A |Au.A`f AB.A%Az.A$A(\}.A A8i.A`fAK.A3AG7.A dA1.ApGA*.AA'.A A.AAf .A@A{.A`iAȢ.A`UAۅ.AjNA6R.AjXAhB.A_A,/.A'AԺ.AA .A3AB.A*A0..A :APy.A (VAn.A{A0.c.AAH.AR҄AP84.AAk!.A`A.A=ބA.A`AH.AoAB.AAA.AQ6Ap.A@XA-o.A`AHc.AA`^.AĄAY.A=ބAD.A3AH.A A).AA.A`A.A@A.A`fA.AGAp .A@A@W.AτAB.AބA.AA.A@A.A4A).A3IAz.AAp8.A@GA.AӅA8.A@څAB.AA.AA(.A`),Ap.AXAt.A뉆Ak.A`)A;d.A ؆A].AzAXM.A}A:.A A8 $.AyA.AوA.A^ωA.AñA`.AAΑ.AOAHY.A)AB.A`T{Ax.AA.A ÏA .A`sA.AYAH;.A`A;~.A@ȔAPz.A`yA~.AsA.A wA@`.AA.AA8.AA&.A AAR).ADA(.AAH&.AA0{8.AVA`<.A^Ah@?.ABAI.A@vAN.AkAK.A 8zAD.A@PIA D.AA>T.AA`.A OƦA k.A^A8f.A`DAZ.ABAQ.A q#AW.A`A0e.A`ܠA0;g.Af4A`q].A`A0R.A4FAP.AG4AuU.AT\AV.A AD[.A&AVj.AXA@r.A{A4{.A`bA.A/A1.A'A.A`tA.AMA.AƱAh.AH^A.A삳AXl.A`9AX.A`mA!.A Ad.A`[A7.A⻸A.A`K AH.A8A .A`BCA.A`.A.A/A.A5A .AAz.A<Ak.A`AQd.AvA8Vc.AX?Ao.A 휻Aؒ{.AV"AXv.A͉Ape.A`A5.A@lA.A A-.AZA.A@վAx.A ޿Ahg.AA9`.A`A8X.AA^@.AAs".A8A .A&A 4.A l A؞.A6A.A ZA".A@A6.AiA).A`A.AlA.AxA.AA .AA p.AApxp.A@>AwY.A`A(F.A A>.AAB.A AC.AAG.AA(S.A^A,V.A Ab.A Am.AHpAz.AA.AA.A@A@5.A@WAH\y.AiAV.A AA(0.AA` .AMA`.A ەA .A?AC.A0AH.AfAׯ.A AX4.A-A^.AA E.A Al .A` A.AApA.ANA .A}A؍.A uAe.A,A\.ADAH2W.A`mA?N.Aj,AS.AHOAc.A A.A`]A@.AAA``.A gAp.AjyAS.A EA.A+Ah.A@A9.A߽A`.AMAs.ANA0.A@oAD|.A2TA0s.AA਋.AԽA Ά.AA`B.A@`HA@O.AuAP.A} A8.AաA@p.AA8.A AX.AA@.Ah9A.A J)A@q.A`qAp.AzA>.A`cA8$.A[A@F.A$Ax.A$A&.AA`.A Aw.A֠AHr.A uA.AtiAxi.AAa.A@GA\.AA {.AEA r.AnAa.A A0Y.A"AG.A5AЄ?.A [A/2.AFA.AlA`.AැA .AA" .AA;.AA@ .A HAoG.A4A`~.A@A`r.AA .AA.A@Aث.A#A.AsA.A 8zA{.AlA$M.A,@AJ.ALAE.AahA̞.AtA.Aj,A.AA0.AQgAh.APA.A+Ax.AA.A@A .AA6.A[NA@.A`]A8xX.A Ax~R.A@AD.A3A0_.AAm.AdA.AAi.AA0.A;YAp .A@EOA*.AӝAm.A@qAk.A{A؁>.A?A?.A@ihAT.AA 0.Aa8AHn2.AAP/.A{AhL.A4A.AnAh.AA.A MA .AA}.A A>F.AAXF5.A APZ.A@iiA.A 1/A8.AB APkc.A(A.A A =.A;AZ.A6^A.AbA.AANl.AA".AB8A>.AEA.AXAu.ABAȔ.AॺA.A`A0&.AhOA.A A.AsA.AοA{.AAxf.A sA{.AVA3.AAP .A@JAx.A Apxx.A@#u Ař.A` A1w.A A0.A= AL(.A A.A@`" A@.A A`G.A[r A`O.AB A(:.A9? Ad.A`d APk.A1 A?..A Ah".A`fM A0:.A}$A/.A AJF.A@%A].AJA].A}$Aȩ.A AP .A@A.A`TA84.AGA.A`AF.AA(`.AAH.A!Ax.A-A٢.A"A0&r.A4A@dv.ABrA(:.A ;A.A;aA@u.A@yAC.AA@>.AAn.AעAr.A@Ap.A@rAf.AA.AA.A A0H.A9A@=.AA@F.AAA.A`AP.A`AJ.A@A.AྋAgs.AXTAxK.A AX.A@,"Aؾ.A`A8'.AMAh.AA Q.AqAЄO.A`]jALU.A`ZAPM.AҟA<.AA *.A`AC.A(A.AѺA .A;^A!.A2AV.A A؎.A[A <.AAu.AA.AՔA.A^XAPo.A`շAV.AvHA0y.A`Ab.AiA#.A@NA.AA.AQAh.A#Ao/.ArAP>.A#Ao .A"A .AA.A3A.A<,A.AA@ .AOA.AA0]'.A Ai.A ~qAi.A`KAh.AhA..A UAPHT.A OAsx.A Ax .A@AB.AA8o.AAՖ.AaA+.A@ϵAX.A &A.A A.A!A.A0A.AcA.ARA[.AA .A JA .A@|A.A` bA.A}YA.ABA('.AzA8.AfAM.ATA2:.A \A1;.AզA H.AA1O.A A =.A螙AC.AXApS.AXXAX.ACA0L.A?Aɲ.AԌA.A`mbAƹ.AdA(˫.AhA.A_ڈA`a.A⭈A`B.AA.A`;A.A@rAs.A@yA ^.ANAR.AUAXP.A AE.A`;:A .A TAXy.AˁA,.AnAAx.A}A.Ax}Ah.A_%~A`.A8xA.AjAM.A AP8.AA.A (UA.AxAh.AAl.A`AP.AwKAK.A*XA.A_|Aݗ.Ai+A% HX.A;~AF'.AZtA~қ.AJAzf.A7jA.AVMAK.A[A.AGA.A-~APkh.A@#ՎA7.A@ŽA!.A@N@A@.A` DA.AjzA.AVAȶ.A?đAP.A Am.A`A(h.A@+AH:.ARA@.AQ^APD.A@-A8R.AࣂAp8F.ArA@WZ.AA`l.AЍA~.A@API.A`Ak.A \A.AAأ.AAA.ASA.A<>A.A߉A(.Aj`AP.A@yA #.AAB.AG׈AW.ARA.A һAؽ.A'AL .A AL.A@ᾆAK.A͆A: .AA *.A`eAP@-.AMA0..A7Ak'.A@GمA..AAX0.AօA*3.AA=.A@VAP@.A?A3.A)AX =.A=΄A>.AA8D.A@nA(\N.A`A0X].AvAPkl.A@Aht.A 뒃A`v.A`zAn.AmAx.AA(y.A`m Ay.AAp8.A3A`.AR2AȺ.AA(.A*A  .ARAc.AVAZ.A=APP.AൢA|C.A@!4A8.A`vA 5.AɃA`v/.A A,.A/A$.A·A".AA R0.AA '.A(Ax.A@A.ABAp.ArAx.ATA).AAl.A AH.A`ӘAX.AmA.A0AP͹.A RAǮ.A]ωA`ӑ.AӊA.A4ՊA@.AAp=.AA(.A|A`P.A`jA|.ANAxr.A@A0o.A JAQy.A A`p.AHsA@n.AA@Be.AA88Y.A@%AH.Q.AQۍAXN.A|A@W].AG AW.ATAA.A@A?.A@1A;.A QA3.A`fA%.A3A.AvAf.A AX#.A`̼A83".AяAk,.AߏAL.AGYA.A`ËAa.A`ËA8.A෾A(s.AۏA0Y.A_A(D.A`cAx) .AA.A%AH.A'A.AAZ.A:A-.A`6Ah.A |VAv.A`lA0.A2A.AaAW.A jAHn,.AAv.A l{Al.A/{A.A ~ZyA .A`tyAPx.A@xA@q .A=xA].AtA`T.A |OtAH.A |rA.A@3sAA.ARrA.ArAxX.AUmApU.A`pAJ .A@`qAXW.A rA .A ZrA.A@:sA.AsA(>.AuA\.A8uA.A / A.AAr.AwAF.AقA8E.AB|A0d.AXAHٿ.A4AXǿ.A PA.As1AH.A`vkA0.A OrA(.A ɰA.A`~AXW,.A۔A.AmAo\.AAH#.AkApV.AKA(8.A$2A<.A`AX5G.A2A.A`A-.A@iA.A`A8m.A&AO.ApMA`ؽ.AQA8.AAp8.A[A뼽.AzAϽ.AAŽ.A`̠A߽.ABAP8.A@ AHb.A5AzN.AIAo.A`) Aأ.A׋Ap=.A MAk.A෤Ak.A=A#.AֵAW0.A AkF.A뉲A 9.AAp.ApPAӾ.A`oA0H.A A1O+a'.ARڹA}.wA.AAx^.A`vA .A`hA.A͞A5N.AAww.AA.A@CKA`.A J&AH.A`oAu.A AT9.ATA.A`KA.A@Ac.AAh.A`OAa.AtA8V.A`HA3.A^AhbV.AAE.A&A#.AA@5.AA.A A.A`o2A.A`nAJ.A@,A.AcAi.A_qA .AJA .AN$AX.A@kA.AA(W.A{VƿA?.AN*A Da.A#hA@.AA'7 .AbA$P.A#WtA2qa.AaiA.AAk.AsA0&.AN$A.AA胥.A)A.A@AmY.AAppM.A AH.A@A`=.A CA .AMA^.A@A˽.AZAǾ.A(A؟V.A0Aο.AAֿ.A@Aο.A`m5A@.A AP^(.AWAS2.AgA.A@A.ALA.AAp<*.AvA4.A?BA0Ժ.A`A(~.A A&.A+gAЦ.AAо.A@AH2.A@`A˾.A ~A@䫿.AAH!.AaA0.AAJ.A,GAhb.AsAx.A`ôA֎.A86A .AvtAr.AA(J.A@A=.A`A .AQ!A@).A@`AX4.AʺAXb.A "ApyP.AA?.A J*A.AgA3.AQ*A@).Aj AȔ.AA.A}Aܽ.A*qA5.A`]ֺA.AA^.AA.AවAq.A*q^A}.Ad:A0t.A?r(A.A@`A0.A c Ak.AA.AA.A޳Ah.A A\.A |A8.A@ȯA(.AݯA(iK.A@N̪AA H.At>A$#C.A5ޟxA9$i>.AyADb18.A-8dAh?1.ARAU.A`A.A@AQ.AGA.A6Ak.A١A.A`{A0l.A|YA.g.A#GA#a.A9:d8Au[.A5-AU.AV'Axp^O.A%AxI.A&A3%.A@Axʺ.A?A.AA`S.AAPڋ.A@A4q.A`Ap.A~APx.AM٣Ad.AiAL.A`AH..A=PAɺ.A(A>.AZAu6.AׁA(\v.A@NAl[.AKA>.AAp!.AٳA.AAHP.A`A.ANA(ܣ.A@Ag .A@WA8En.AԖA@Bٹ.A`(A.A1A.A Aq.AAx.A!A<.AQA.ArAX.AorA.ArAZ.Ay"lA6.AHRkA.A |fA.A@%eA.A`{eA .AgeAؒ@.A`dAS.A`)LeAY.ADeA=t.AfAi.AࣦfAi.AfAp.AfA8.AVfA.A`6fAG|.A`ffAQa.A@BeAj.Af5eA tg.AQeALV.AÃdA`e.A=DdA}`.A7dA`.A6dA.A@scAhb.A cA(.A4bAН{.A 8[eAȇ!.AfAذ+.A gA茌.A`eA.A-_bA.A`HbA`e.A`aApI.AaA k.A@iaA.AR`Axu\.ATaA`.ADZaA8u.AQbAn.A3bA@ƚ.A2bAHmZ.AcA.A@eA.AykeAl.A@cAH\k.A@ cA֞.A@cA.A`cAH'.A @eA$1.A|eAߋ.AtbA.ANbA[.A`aA}.A caAXg.A aAH\k.A`DaAx).Aע`APQ.A2`Ad.A`_A.A@i_Aʋ.A؂_A0.Ax_A.Ac_AP.A+_Ah.A=8bAe.AAbAf.AdA8.AGdA.A@ qbAp.AaA.A`fzaAP8p.A`.bA`Z.AࣜaAp\.A \aA.A_A8V.A_A6.A ^AbAE.A bA;,.AbA^;.AbAH)J.AbA].A bAk.AbA}.A`bA.A@bA ߜ.A]}bA8.AywbA@.A4fbAȥ.A3`bAR.AFTbA4.AKbA(.A<.ABbA(\J.A`;IbA_.ATbAi.A \YbAؼ.AdbAȩ.A`ebA().ApbA0.AqbA(z.AࣁbAw.AΈbA0w.AbAo.A (bA.A`bA8.A@ bA.AॳbA&.A ¹bA>.AbAD.A}bA Q.AbAa.A`bA8Ip.AbAج{.A`bA{.AbA.A bA0;.AbA8.AcA`2.A@W cAj.A`)cA5.AFcA.A#cA@}.A@+cAh.A2cA@$.A;cA0L4.A3DcAX>.A /IcAM.A2QcA].A`TYcAl.A4acApx.A3gcA .ApcA.A@ucA.A΀cA .AઆcAp=.AcA0..A@ idAG.AhdA.AzbA.A bA .A`f.cA.A \GcA.ABcA0.A bA8.AbA@$.A ۥbA{.AơbAHp.AbA`.A`bA@L.A@`bAH.AabAH9.A܀bAP&+.A@pybA* .A sbA.A kbA".AebA7.A6^bAxK.A/WbA0L.AQbAȤ.A@>KbA =.ACbA@.A@p?bAp.A`6bA( .A`0bAE.A?*bA~.Ad"bAu.A bA`i.AbAȶZ.A 8bA0{O.A bAc?.A`mbAl5.AdaAx((.AaA@.AaA0T .A$aA.AaA.A aAk(.AuaAu".A}^AV&.A@^^A8+.A`)T^AE.A Z^A(V.A _^A[.A`^AYl.Apd^A p.A e^AK.A`i^Af.A`;k^AX.A|o^A'.Ap^A .A`t^AT.Av^Ad.A {^Ax.A|^A].A Ҁ^Ah.Ac^A.A^A].A^A.Aj^A.AΎ^A(.A^AH,.A ɔ^A8>.AK^AF.A`9^A_U.A^A _^.A6^A(l.A@^APy.A@!^A`.AO^Ah.A ۮ^Ar.A ^A.A@^A`ص.A-^A.Ax^A .A ~^A@.A^A .A^AH!.AQ^Axd.A^Apx.A^AH.A@^A".A^A0-.AT^A<.A^AȃH.A^AU.A]^A^.A`^At.A-^Ax.A`^Ap=.A^A.A@^Ae.A^A.A^A.A`_AB.A_A.A@_Ap=.A@_A.A^AX'.A@^A.A_;^A^.A` ]A#.A \]A.A4\A8Q<.Aʬ\AWj.A#\A".AmT\A t.A)\A.A#\AP|.A`1\A#M.A`k\A.A@ϐ\A.A#\A@.AM]AX.AH4]A.A;]A$.Ad.]A?:.A ]AO.A\A_.A\Ax.AMv\A#.A@^\Apʿ.A 3\Ar.A[A+.A8;[AxE.AZAu.AUZAТ.AӧYAu.AxYAH.AXXA.AILXAP.A9XA .AILXA+.AʵXA7.A1YA`1[.AoYAd.A`YA.AYA8.AsZA +.A [A.A[AV#.A`v3\AV.A\Ap.Ah\Ad.AZ]]A.Ai]A8.Ai]A.A ]A.A /I^Aȥ,.A`^AxK.A`^Ahd.A6h^Aw.A /I^A([.Aܙ^AH?.A@5_AXl.A_A .A`A.An`APM1.AF`ATP.A^`Aw.A _AJ.A`_Aչ.A_AG.AHL`A`.A `AP2.A Z]aAV.A@%WaA .A aAչ.AxaA .A+aAX.A `AV#.A@,`AxE.AxaA`m.AqaAp .AaA=.A.AcA .A9dAp=.AcAL1.A OcAZ.A 8;cA>.A OcAp,.A`GcAx.A bAr.A 8;cAx\o.A^cA.AhdA.ABeA.AdA)<.A QkdA f.AndAH\o.AcA.AlcAH.A 8;cA.A bA`2..A@>bA0,.ALaA9.A#.A@kA1.A@dkAJ.A kAQ_.A[lAPt.A'lA.AlA.AmA0G.A&mAP.A/mA.A@ImA .A QgmA'.AmA.A`mAp.AmA9:.AmAP.AʠmA@~p.A tmAHL.A mA .AlA7 .AlA^C.A`lA0X\.AwlAn.AulA.A`{lA,.A=lA.A$}lAhs.AFlA.A lA.AlA.AclA7<.AplA0R.AlAHg.AElA.AmA8ڍ.A@W:mA ߡ.AcmA^.AimA*.A`zmA.AmA .A mA.AnAH{-.AnA=.A`$nA #O.A SnAp].A nAp_f.AwnAXn.A-mA(:.AmAp9.A nA.AG@nAX.A PnA .AdnA).A$nA0$.AnA0e%.A nA4.A nAI.A #oAU.A;oAhoe.AKJoAXo.A-oAXu.A`ToAp|.AzoAP.A0 oA8.A>oA1.A2oA͒.A`vpA@.A dpAĠ.A@pA:.ApA0.A`m+qA.A`dwqA.A4qAj.A}%rA.A@PArA.AΞrA(.ArA89.A=rAH.A A8sAWX.AnsAo.AHsAx.AsA.A&sAh.A@rtA(.AtCtA0G.A itA.A tA.A tAHz1.A'uAPV.A@]uA@Fn.A"uAz.A3uA@.A uA 0.AuAg.A3uA.A`uAp8.AuA[ .AIuA#.AvAe>.A@TvAW.A ZvAXh.A"vAHy.A0vA`e.A@g wA8 .A7wA軷.A&PwAx.AcwAP&.AywA 0.AxwA.AwA%0.A@!wAJ.A KxAY.A `xAq.ATxAb.AmxA`×.AĴxAa.AxA..AxA.AvqyA.A yAP.AyAhM.A`TyAH.A yA".AyAHa,.A@yA 3.A_yA(<.AyAUH.AyAM.A`yAZQ.AXyApU.AzAV.A3zABU.A`YzA0*R.A zA0TP.AzAP.A{APU.A`F{A0\.A{A8h.AU{A v.A`|Aȩ.A JC|A.Awx|A@ӝ.A@i|A.A0|A:.A }A|.AI}A.A@b}A>.Aj}AC.A`}Apr.AA~A.Av~A0.A ۚ~APk.A ~A.A@~AX.A ~A虹.AAP.AA.AL*A`.A@>;A8 .AJA[.A+jA`e.AA.AA0.A !AH;.APA0L.AAh].A@ Axs.A,A #.A ̀A.A@AX.AƁAt.A-A0.A 89AP@^.A jA].A@A.AHA!.A`[A>.A$Aa.AAP.AgA?.A@A.AqA`.AhFAX.A)APM.A)Ap8.A@Awj.A`TAƨ.AAp.A=A .A{A-/A`^Ap9W/ALA{/A`?A_/A#AH/AA /A@rAH{I/AـA|/A/ˀAr/ADƀAk/AƀA0f/A@A`6/AAN/AA@/A_A /A4ـAG.7/ATIA=yP/At yA Ei/AAu/A܆̀A} //Aj.AIU/Ak+AyJbg/A;8TpA/Am]Ay_/AX%A /A{A.R/AsA./Ag4Aϧ/AC(݂Ax</AvWYA:/A%rATJ/A.AXŽ/AFIɜBA"/AQA,/A\ACM/A^zA@/AuAXd@/ApACb/AŎmAl/A shAֈ=|/AAfAE2/AvIA i /A|HA9O/AoHA/A=a;zGA˶/AyZFA6( /AàHFA¢ /AJcEA > /A+@EA{\M /AoPDA욣X /AڈwDA:߄` /Anv DAOd /A>CAe /A+CA qSe /AN\ίBAeh /AJU?ALW( /Az)?A /A,ĄO?AKѭ /AA(u /AF/٫>A /A9L>A ' /Ab=A /A 8=A瘍 /AP ,=A\rV /Aڢ<Ao5 /A@B<A3å- /AGUJ;AW /A/m;A(  /A];A< /Ah5:A4 /AT:A]% /AeV9A7@% /A"˧9A\ /AG8A ͝ /A2|8Ap /A`[7A~ /A5AM\ /A74AE /AK Z2ANyȘ /A# 0A(lM' /A.Am:"= /AN;-A~t X /A;g+A1w /A]/l)A.xڜ /APmh (AHL /Askg&Aq' /A@$A8b( /Aqv]9#A} 2` /AS2!Aw /A2 AQH7 /AFDGqVAYT /AbrAZ /AYņAIS /AA1 /AKhA뜞 /AԖA|V /AzG0A" /A~8A`} /A59A|(} /AZvA /A@A /A!A /A0Ax /Ah_A /AʏAAb /A Ae& /Aj_ AxK( /A` A/AAW/AA /A`UAH? /A`bA"r/A@POA@W9/AXA /A@%AX/A A8*/A`A]/A3A@$x/A@tAP/~/A lA/AA/A=A/A7A([/ACA(z/A`RAg/AA8/A@A`/A@A.N{/Aq^4AP//A9Awr/AչA$v0/A|\ A2R/ABAh(x/AA˱/A@AyS_/AdtAY/Ax<ALIX@/AZZ Aa}/A/A8)$PAw /ABȕ*PA@}/A-,PAHXx/AzU}*PAs/A&PAp/A`PA/4/Ah>چOA^==_/A .OA?/AM\NA)w/AvNAW9(5/A(HNAK.Ay(gNADA.A쓛 NAEk.ANA"`.A@NA5.AûNAg .AHn-MA\N.At0MA͝<.ApMAГ.A DMAp.AQ?MA\.ALA %.ANLAL.A3LA.Af.A7lrLA)3~.AeqLAF.AZLA..AcLAH.AjPLA.AWLA@ƞ.AhLA3td.AZKLAL;).ALA.AZG='MA|bů.A, EMA9Ur.Aɮ2 QMA#4.A-MKMA.AP4MAo].Ab MAh.ApVLAPwD.ÄLA,.ASKA +l.A2wKAI.A?JAW .A@nJA?.A!K5JAo˅.A>IA4].AbHIA04.AHvIAYA.A9^yQIA6R5.A UAXO.Ab>Aovo.A5% >A)N.AIz=AŅ@.AJ=A3mC.A?'Kp=AM.Ag- _=A8+}.AQ_=AEWu.A]q=A1.A@`=A7^\.A Wa?A .AZ9bBAf".AssDAߞ,.AB!GAqu.A@,yIAטB.ASKAI .AYNA3.ANi`PAZ!C.AlRA P.A4ATAQ .Ax WArs+.AQ/~WA ~.AXA}.A~ fYADäo[.AiGZAv9.Ap3[A .AdPO'\A}.A@P$]Ak;.A.m^A uk.Aȹ_A,t.AWx aAIRS.A.abA=.AUjcAY |.A$ eA=.p.AIcfA.A9gA .A iA4ڞ.AcZjAcqܩ.AwkA[.A(/lA4.Aw0&*nA,W.AØboA~.A3pA6e.A&AH}.A@WGAe.A`fAx)F.AA.A`ܶA .A`MAX.A uķA.A"A@.A/A j.A`KA 8.A׶A0H5.A@,A.AJA.A A.AkAx.A$AH/.A`A.A@A@>.A`)AJ.A@ʹAh.AzA.AضAs.AAd.A(2AP.AA $.A`6A`e .A@JAhM.AADF.AA.A@i*A55U.A'*%kAҠw.AUAp [.AAAV<.AVqA36.AWеA(;f].AUA`.AَAx.A@'AH.AR AM.A`,A.AFA.A?A`!.A`"A.AkA.A 4A.A)As.A|APo.AՀAIl.ARAH{M.A`)sA .AWA.A@/A>;.A=IA.A`A .AǶA(K.AɈAxG.A@[A8s.A_A'.A cAX.AAX`.AUθA%s.AA).A@AY.AެAh`.A A.A`KAг.A` ANJ.AHϽA.AݽAE.A As*.AYAO.AVAp[.A lA.A|Ag.A@ևAЮ.A 끾A@Az.AlAxT.A`iA02.AdA.ARlA.A`iA.AQAc-_*.A>"!AE &.A*Aj".A ֣ A.AA.A AX.A`9ϲA(j.A ZQA&B.A@AP.AA+.A(Ae.AVAۯ.AQDAϩ.Ac7A.A˷AhΠ.AQAPg.A/A.A%A.A2=AH16.A;[ARՓ.A9_Aa.A =cAwEo.A`ٸA.Ao A0.A`?Aэ.Az(A8.A%A.A`TA99l.AuxA+.A0A(.A@lAJ.AfZ핽A.A|A4 t.A ݮA.L.Aґg{A-%.A%+{A[.A[13EA.AA "ų.A YAGR$.AhzA6~ m.ACS jAbN.AK+5˺Aٺ"4.Avl:"A*9.AnpAZ>i .Aɜ|:A.A@A.AcőA7.AOb4A_ (.AA$>.A(r-ӞAF‰W.AHjA r.Ab5HAg.A z8Ab.A`o;A .AeYA`.A`mA{T.A=/xA'P.A@=8vA0Y.A gAٔV.A ]bnbA).A9ZQAG:;.A(5AK.AA(Z.A)I޵Atg.AtŤARq.Aג@eAy.A@ AxG.AA0ٲ.ArA୕.AA.A$A.A`޲A0;.AӲAʼn.A`庲AXh.A@Ay.ATAe.A .A@.A`A.A0ޱA.AaqA`.AAxi>.A@ΰA .AfA`.A |EAxv..A`A.AʜAxo.A Al.A`{Aٯ.AZЭAP|$.AAp.AF8A@.A@˫A<.A@rAP.A0Ah.A'A.A AH?.A͔A@J.A S A.A@PAp.A@yhAz.AAp<.AA( .A@P=AH.A`TAS.AǻAT!p.AAm>.A[V_^'Ayz.AX^Arf1.AߐA[.A<)#A;5.AućiAD`.AC2AAf;.AݿfAZ.AAo.A`m|A.AA`T.A l}A .A`aAXyw.ArA@z.A sA`.AOAx5.ACA t.AݰA0.AvA.AA.AIA`.A 8iA.A ZA.A2}A.AiA .A<A`%.AfA( .A`vA.AӱAd.A`ñA.AA.ABıAhs.ABڱA@.AEA8.A^A$.A A*3.A9IAp,5.A &A`C6.A,A8;.A0A`aJ.ASAhO.A`A P.A若A S.AiAHLQ.A`.A8I.A8_AE.A A`?.A/AH!+.AA.AQAp.A<ԷA .AͼA.A`A`.A޹A.AZAw.A>A.A42Av.A@PA8b.ABA .AR2AG.A@A.AkA`T.A`A؛.A3A0.A 8=AY.A,eAH.AuAx.A1A舲.A LAp8.A@]A.A`)jAV.A |}Ag.AͮAu.AA .AA.AA`й.A@-A؟.A Z_Ap.A=qAx-.A`|A.A@APo.A|A7.A quA@W.A-A`.AvA".AA.AAx .Ah A.A@eAp|.AAv.AδA@.AAȓ .A`TA8.A`KA.AA?.AQ,A(m.AEA.A A]AP .A \sA.ABAn.A&Apl .A sA`a .AA@.AsAK.A`A.A.Ap,.A`9BA.A TAp|.A hAp+.AkA( .A`)A..AA.AA.AAH.AhA.AA.AA,.A)Axh.A$=A.AhA.A@zA(.ADA@.AA#.A A0.A$A.A@A@.AIAo.A` )A.A`JAo.A`wA.AA8 .A`A.A A@.A@WA.A@AX9 .A Ap .A)A.AIA.A`zA".A AH +.A@ A9.A ZhA=.AA*D.A ZAH.A@AXN.AGA9S.A`KAT.A@iqAP.A`A8L.A` A2G.AAyC.AATC.AE^AF.A APO.A4AN.A@nA@1L.A[AbH.A3'Ax:H.AMA;O.AфAS.AձA0&T.A sAP X.AA0X.A+A(S.A>A8S.AqNAXS.A wA`X.AAh[.AAhb].AAs[.A|-AY.A@iXAxZ.AwA(a.AAKf.A`2Ah.A@Ai.Aa(A0Yk.A`TA(){.AA.A@A<.A A#{.A?Aux.A Az.AAz.A4A y.AuAu.A`[AH].A4APT.AAUH.A`],AGB.AIA@.AfAB.AOA0HF.A3AX4H.AjA`H.A`B APC.A:AL).A 7A$.A]A.A ҠAhs.A3A .A4;A> .AAx.AAX[.A Z;A.AA0v.AIA.AA.A CAp=.AwA.ARAP .A Ah".AA5.A@A.AyAhb.A'eAx.A@5A.AvA@.A'A~ .AJA.A XA(.A~A.AA.AAXh.A4A8t.A׳A`.A2A(\.AA.AAx.A@Ah.A5A.AzQA.A@plA.A@A.A ғA@$.A@ߟA0*.AAP.A Ac.A AH.AAN.AAѻ.AAhD.AA8.AAhs.A`AXJ.A@,A N.A ~Aȓ.A QA(-.AhA).AzA趨.AA.A@A(.A A.Ap)Az.A`AA.A6ZAܰ.A 8sAU.AqAQ.A AѰ.AA.AHA.A A88.A'AX.A q(A.A`@A.AOWA`.AnA.A AAX.A`bA.AAHa.A``AX.AUAX.AjA#.AA8.A`AH{.A ZAR.A:AP.A\AhQ.A~Au.A@A(.AAp.ARAʝ.A2A0.AhIA4.AtAB.A@AH.A@gAX .A3AX.A@Ahğ.AA.A&/A.A3LA@.AhA{.A`fA-.A`Ap}.A SA@Wz.A`A x.AA4v.A.A t.A=DAs.ASAȶr.A bAq.A qAn.Ag~Ael.AൊA@i.A •Aze.AAHa.AnAP^.A@ AP8\.AAH Z.A AMX.A Z AhV.A6A#V.A MAHU.AcAU.A`zA(U.A?AdV.AAW.A`KAHaY.A@g,Ah"].AL>A``.AOQAHc.AReAx)f.A4zA^h.A׏A$j.AA |k.A`ռAHal.AA0&l.A Axzk.AAp_j.AAAh.Ag.Af.A@DA8d.AXABS.A=AP.AA@N.AA`XM.Aq APfL.Ag=A(K.A`ZAL.AwAN.AATQ.AABS.A`AR.AACR.A -A_R.A @AR.A$SAS.A eAPIV.A@{A@dX.A@AZ.A`A(m[.A`AW\.AA\.A@ A\.A=A%\.ALoA)[.AAJ\.A A\.A@5Ah].AGA`\.AAأ[.AGAY.A`AW.AA3T.A AP.A >Ah3M.A_AH<.AA(-;.Ao&A&:.A'`A9.A`Ap9.AA9.AjAp:.A )AH;.AXA`<.AAkD.A@ AE.A+A{G.ALA0I.AmA88L.AAh3O.ARAR.AAoU.A`pAY.AAxZ.A`AZ.A`A[.AA[.A@AZ.A@9AY.A`SAX.ApmA0.W.AA(W.A"A8tW.AA@5X.AAX[Y.A-A`Z.A AXl].A@NA_.AA`ea.A`(Ab.A?6A8c.ADAe.A`fAxig.AAh.A`A>i.ALAri.A`"A(-i.AAmh.A6A4g.A-YAe.A \{AH\c.AAh3X.A A'U.A NApR.AAh@P.AA`eN.A@NAL.A:A K.A`\AH.A@.~Ah.F.A&AC.A`A?.A`2A;.AAL7.A`)AH1.A2A*.A (GA$.A`YA.A@gjA,.A rAH.A|Ap.A@χA.AAP.A?A(.AAأ.A33 Ap.A`\ Ax).A@ A`.Aɲ Ag.A A/.A Aj.A7 A.A@Ge A.A} A(:.A A.A#3 A6.Ayx AH.A  A.A A.A% A.A@CG AB.A^h A.A Ao.A@ A.A` AP.AM A.A Q AP^.A" Az.A> A0.A a An.A@ A.A AB.A AH..A Ah.A JAH.Ao1A~.A sRA).A=tAH.A`9AZ}.A`Ap{.A`Az.A AXhz.A@iAHz.A`'Az.A 5A8{.AAA(}.A`TNA~.A`ZA8 .ALhA.AmwA].A=Al.AAH.A<AF.AA.AA.AQAp=.A0A.AT A8g~.A@AG{.A2AXv.A c\AhQp.AAH.j.AͬAHd.AA~^.A;AW.Az4AR.ANWAuL.A@C{A0]G.A@CAHB.A6A>.AA@:.AA7.A <Ah5.A@ dA0*3.ÅAF0.AA,.A`AD).AA)%.Az A .ADA.AgAJ.A Az.A 8AB .A A`.A{A0.A AP7.A`A.A@A.ApA.A@N=Ar.A `A.A@5AQ.A A.A@!A|.A`)A.AA.AAXy.AApJ{.A<Afs.A`)^AXp.AlAm.Av|A_k.A &Ai.A@Ag.A/A Re.A`Ab.A`vA `.A߼A].A@A0X.A QADT.AATP.AAL.A@AI.A@A <.A?A 0.AxzAH$.A@>A~".A@5AТ .AfA$.AA .AA@`.A`A`%.AA(\.AA.AkA8.AJA.ARbA.A`yA.AA0{.AɦA@.A`KA.AA@ .AAP' .AdA.AAh.AAF.AH&A .A6A.AHA .A[Ah.AwAj.A$A.A`As.AiAp.A@%A{.A@ A R.A!A.A .A.A`9A.ADA0..A@LAg.AWA.AWcA.AoA{.Ac|A_.ANAh.A`)AЕ.AA.AA .A Ax:.A A.A&Az.A |AAX .A \[Aݷ.ApAM.A@߄A`.A`A.A෨Aͥ.AEAݢ.AA`S.A`;A3.A`dA.A`)Au.AA@=.AA(~.A`A9.A`Ap.AAH\.A^A.AOA.AzAɒ.A&Am.AǫAȂ.A@#A .A@Ap .A A.AAȇ.A u Ap+.A3AC.A+A`|.AK:Aw.AGAr.A]SAf.A`fAXF_.AҺAPX.A(AR.AXAHM.ATQA(H.A@ AF.AїAE.A`A E.AAD.A/AE.A`AE.ANA@F.Az AG.A@) AhH.AG A@H.Ae AhG.A@P AF.A̠ AC.A@ AH?.A@ Aȱ;.A8!Ap 7.A@ 9!A84.AN!A1.A/c!APZ..Av!A*.A!AX&.A&!Ak".Ap!AX.A!A .A+"A.Apm"A0.AB{"A^.A"AHz.A`"AF.A"A@.A-"A.A "A( .A "AX.A"A0 .Ap"A).A@#A.A!#AH.Ap;#A.Al#A8.A`[#A.Ap#Ad.A#A0.A#Ak.A$A.AQ$A<.A`K&$A.A9$A.AL$A.A e$A!.A~$A.Aྗ$Aa.A@$Ap=.Az$A.A$A.A$Aho.A`$A.A@%$A.A-$A.A$AX.A`"$A).A$Ax.A$A.A`) %Aa.A%A.A )%AЦ{.A <%A}l.A@.P%Az].Af%ApY.Ati%AU.Atn%A:R.Au%AN.A}%AK.A@%A@H.A@N%AF.A<%AC.A5%A`A.A%Aȃ@.A+%Aؼ>.A@,%A<.A %A*:.A%A@7.A`2%A4.A%A1.A%Ah*.A %A`a".A%AH.Aq%A`X.A%Ap .A` &Ap.Ax3&AxK.A \&Ah.A&A`.A`ó&Ap.A &AD.A 'AY.A 9'Aĵ.A h'Ax.AH'A.A'AH.A'A.A@(A0.A9(A.A`(A`.A(A8.A (A M.AB(AhL.A=(A.A@(Ah.A(A~.A)A~.Af)Ap~.A`).)A=.A@G)A.A|`)APZ.Ax)A.A)A _.A)A].A`)A.A@W)A.A@)Aш.A)A <.A A)A_.A-)A@O.A)A.A=)Amv.A`)APk.A)A(`.A`f)A"\.A`)A}W.A)AR.A)APN.A`v)AJ.A)APF.A \*AP.An,*Aw:.AA*A:7.A@yX*At4.Ap*A()2.A[*AH\0.A \*AB/.A *A..Aq*AH\..A@ *A`..A*A(>/.A*AU0.A*A1.A*A03.A*A7.A}+A:.A+AH<.A +A?.A|.+Ax@.A@>+A cB.AO+A[C.A \a+AHC.A3s+AHPC.A`+A(>B.A}+A@.A+AБ>.A+A<.A+A _7.A$+A.3.A ,Axu/.A ",Ap9,.A Z;,A).A U,A'.Ah,A&.Ak|,A0$.A,A =$.A@,A$.Aù,A%.A ,Ah@'.A,A7(.A,Aȱ(.A,A(.A,A"(.A,A'.A-AP".A-Ahn.AL.-AH.A(B-A0.A]L-A.AX-Aj .A&e-A` .A@ys-A).A@-A0.AL-A.A-AB.A3-A`.A@-A.A.A.A,.A8E.AD.A`.A`;\.A.A`s.AP.A.A`a.A=.A.A`.A".A྽.A.A.Ak.A`f.A.A`.A.A .Ah3.A`K.A.A .Ap.A`).AX.A/Ah.A/A.A&/A.A@ 9/A.A L/A.A`/A0.A@ t/A.A /A0..A \/A.A`]/AX.A@y/A@.A 0Ap.AK0A8.Au0Aq.A0A.A@0A.Ap0Aج.A@i0A.A` 1A.Aj 1A.A`)1A.A@>1A0 |.A 1AXyx.A$1At.A1AQq.A1Ak.A1AHf.A` 1A{a.A'1Ar\.A/1AW.AX91AR.A`D1A~N.A Q1AQJ.A^1A@H.Avn1AE.A`|1A |B.A1Ah?.A 1Aq;.Aෟ1A؁7.A 1A(\3.A1A1.A̭1A@..A`ܮ1A,.A1A*.A 81A(.A 81A'.Ax1A8&.A1A&.A1A*'.A2AQ(.A /2A?).A@A2A).AMT2A).A@,g2Ah).A`y2A(.A2Ax'.A2A@&.A2A$.A 2AP!.A=2A.A 2A.AV 3AP.A@$3A .A=3AX.A``3A.A3AL.A3A.A-3Axi.A#4AH\.Ab4A#.A4A.A5A`e.A 55A0.AQ5A.A m5A8.A`95AP.A 5AH.A5A.A`5A.A 5A.A5A`.A@>6ApN.AI6A(.A6AG.Az6A@.Ao6A.AK6A茚.A7A0.A?!7Ax.A;7A.AGU7AL.A@7Aӗ.A7A.A7AHr.A7A0.A7A 4.A7Ar.Ah7AQ.A8A(.A8A.A8A[.A&8A.A-8AR|.A68A0w.A@A8Ahs.AMN8Ap.Ap\8Al.Al8Ai.A@|8Ae.AǕ8A(a.Aǭ8AG].A8AL.A@ 8A8I.A8AYG.A#8AQE.A 9AC.A@E 9A-B.A9A8#A.A 9Az@.A(9A>.AE9Ap<.Aa9AH9.A \}9AxG6.A ˆ9A(2.A9A8..A`"9A*.A9A@B$.A9AH.A@ְ9A.A9A.A 9A.A9A.A`9Ax .Aa9AH. .A@ 9Ap=.A`:Au.A:A.A:AP8.Ap;AǢ.A ;A.AT<A|.A3<A)g.A<AF.A@<A).A3=Au .A`X=A\.ALk=A.A=A@.A`>A`a.A,>A.A%>ApͿ.A`f >A.A>A.A >ALr.A?A U.AH?A=.A3?A`*.A@?A.A?A`a.AH?AQ .A?A0.Az?A.A \?AG.Ap@A.A@A8.A"@Aܾ.Az(@AӾ.A`)6@A`Ǿ.AzT@AǾ.An@A.A`)|@AG.A`)@A.AR@AHᗾ.A̮@Ap.A̸@AX.A@Auy.A @AQt.A@An.A@A#[.AIAAT.AAAuP.A(AA`aG.A AAB=.AAA86.AGAA-.A]AAX'.AAA83 .A AAX.AQAA@.A BA .A]?BA).A_BAG.A@pBA .ABA#ҽ.A=BA(\ʽ.ABAʽ.A`)CA̽.ACAн.A 3CAp Խ.A`fHCALԽ.A=jCA`ѽ.A`)CA=ͽ.ACAĽ.A CA#.ADAP.ADAp .ADA .A=DA`.A@CAB.ACA`ွ.A CA`w.A`)CA^.ADA`S.A`DAF.A3)DA8.AGDA-.AlDAp).A@DAp8*.A@DA,.ADAu..A \DAk,.A \DA(.A@g EAH.%.A@ EAp.ADA( .A DA.A3DAP.A=DAf.ADA.A@>DAH\.A qDA޼.A3DA׼.A3DA0Լ.ADAӼ.ADAѼ.Az EAB̼.A@ !EAh3ļ.A3EA0.A@>|EAfm.AEA3X.A EAp K.A FAأ?.A?FA3.ArFA().A]FA`.AFA.A@FA.AGA.A`f GA@W.AzDGAp .AGAB .A3)HA .AMHA(.A wHA8.AHAp.A@IAXӻ.A=|IA˻.AHIAk.A3SJAp.A@tJA毻.ApJA.AJAf.ApJA̎.AzJA0.ApJAׂ.A mJA0~.A3EJA@x.A@>2JAzp.A7JAX h.AOJAL_.AxJAU.AGJA8F.ApJA؞7.A`)JAP80.AJA'.A@>JA8.AJA.AJAp.AzJA؞.A@JA8.A1KA0..A `KAp.ARKA0. .A֧KAf.AzKA.A`fKAL.A KAQ޺.ApKA`Ժ.A KA8Ϻ.A= LAƺ.A@\LA83.ApLAp=.AMAf.A{MAuw.ApMAf.A=MA?.Az.NA3.AHQNA0.+.A`frNA$.A3NAG.ANA.ANA`a.A3NA.A NAz.A`)NAk.ANA8.ANAع.AzNA).ANA).A OA.AOAf.AOA𣈹.A`%OA@ׂ.A`9OAzq.AחOA#i.A[OAu_.AOAQ.AOAC.A׽OA9.A̤OA8.A OA1.A3OA+.A@OA{.AOA.A`DOAp.AROAX.A@>OA.A3PAB.A%PAL.A@0PAkӸ.A TPA(̸.A gPAL˸.A sPAP˸.APAuӸ.A`fPAP߸.AzPA.AzPA0..APA R.AzPA.APA.A3PAƸ.A`fPA).APA؞.Az QA8.A`PAˆ.A*PAਅ.A3PA#|.A`)PAp=N.APAp=F.AzPAB@.A 0PAp7.A PAp4.APA@W4.APA833.APAX-.ApPA!.ApPAL.AGQA.AQ$QA(.A4QABӷ.A3QAP.A3+QA .A( QAP.A(QA.A(PA.AzQAP.A]!QA ~.A?QA{.A }QAp|.A`)QAp .AQA).AQAp.AQAp.AQA`a.AQA(܂.AQAy.AQAo.A@RAd.AQAuT.AQA0.F.A@QAأ;.A@QA..ApQA`(.A`fQAQ%.A`QA@W".A` RAG.A5RA.A`)VRAL.ARA.A@ RA.A@RA(.A`fRAڶ.A |jRA̶.A3?RAQǶ.A |.RA.A@"RADz.A)RAʩ.A A4RA8.A8RA.ABRA롶.ARPRA⡶.A YRA(ܡ.A3aRA(\.AlRA®.A@RAֺ.ARAp=ƶ.A`)LSAQ̶.ASAH.̶.AHSAƶ.ASAH.A`)TA.AR.TAB.AoTAL.A@gTA`av.ATA#j.ATAc.ARTA0_.A TApZ.A \kTA`aT.ApQTA#H.A=TA:.A0TA/.A1TA8&.A GTAB.A`)lTA.A@TAP .A TA .A@gTAz.A@>TA.A@gTA؞.ATA.A3TA0.ܵ.A 'UAе.A QUA`.ApkUA.A@gxUA`᥵.A@UA8.A`)UAf.A]UAL.A(VA.A(YA.A[YA.A@YA .A[YA.A YA`a~.A@|YAXx.ARvYAzs.A`zYA`\.AYAP8U.A YAkL.A YAp E.AYAQ>.AGYA8+.A@ YAp.A`TYA.A CYA`.AEYA .ARVYAp .AkYA(\.A}YA.AYA8.A@g~YA`a.A wYAu.A3cYAݱ.AR6YA(ı.AzXA`ḱ.A \XA.A@XAp=.A@XAB.A`ÇXA.AnXAH..AQXA8ʱ.A 1XAϱ.A=XA)ұ.A XAuұ.AWAб.A`WAp=̱.A@WAƱ.A@WAf.A@ WA`a.AWA.AWAV.A@WA.A \WAB.A׻WAz.A@GWA(\x.A=WAp=w.A`fWA{u.AWAp=r.ARWAd.AXA_.A`f XAp=Y.A \XAQ.ARXA K.A= XAE.A@ XALB.AXAH;.AXA5.A3WAX +.A`WAh3$.A \WA`.AWA0..A`fWAp .AVAB.A`VA(\۰.AzfVA(\Ȱ.AQ"VA(\.ApUAG.A]UA(\.A (UA.AUA`.A TA.A`)|TAz.ATAp=.A`)SAx.A@ SAj.ASAVd.A@fSAV].AWSAW.A USAHT.AVSAp8Q.A@RSAN.AHSApI.A3/SAuE.A@SA8A.A qSA;.A \RA6.A qRAB3.A@SA{-.ARSAP'.A RA!.ARA .ARA .ARAp=.A@rRAX .AXRAB .ADRA.A1RA#.AR,RA.A`.RA@ .Ap1RA.A@4RAأ.A@G3RAH.A |0RAأ.A \RAH\.A3QAG.A@QA@.ApeQA#.A`fHQAu.Ap/QA.AQAk.AQQA.A PAf.APAh3ٯ.A`fPAЯ.A PA`aʯ.APAp.A RPA².AROAh.AOA².AOA.AOAѸ.AGOA`.A OAů.AשOAL̯.A̮OA(Я.A[OALӯ.A̞OAԯ.AOAկ.Az\OA Rӯ.A@.OAWЯ.A3OA R˯.A qOABï.A@ NAѺ.A`NA.A`)NAW.ANA.A`xNA`.ANAQ.A3MA8.A`)MAp .A3MA8.AMAB.AMA`.A MAP8.AuMA.A`)DMA(܆.A*MA؞.A=MA0.ALA8.AGLA8.ALAk.AoLAP8}.A``LA`as.AZLAe.A``LA[.AgLAp=V.ApLA`R.A~LA)N.A`̄LAJ.A]LA`D.A@tLA>.A`f^LAp8.A;LA#+.A@KA'.A@KA$.A=KA`%.A`)KAf0.A&KAp7.AJA;.A@JA<.AJAp=<.A mJAL9.AzNJA3.A`),JAz..AIA*.AIA'.AIAB".A3IA.A`fIA .A3IA.AqIAk.A \]IA`.A \?IA.AIAX .AHA`.A |tHA#.A@ UHA.A@G3HA.AHA.AGApݮ.A`)GA֮.A \GApϮ.A`fVGA`î.A]FA@.A 1FAר.AëEAp.AEAz.A`mEAh3.A[[EAP8.AIEA.AG7EA.A EAأ.A@gDAǍ.ARDA8.A׫DAP抮.A@DAp .A|DAB~.A@dDA|.A=ZDAz.A q1DA#~.ACAP8~.ARCA=z.ACAGt.A`fCA(k.A wCAe.A`)hCA)`.ASCA8\.A`.CApW.A3BA(\T.A \BA3O.A`fBA0.G.A@BA<.ApBAP1.A}BA`).AgBAP%.ASBAu!.A,BA`.A@BA`.A@AA`a.AGAAh.AoAA@.AfAA.AAA8.AAAͭ.AAAH.AUAA𣴭.AzAA8.A`fAAQ.A`BAX .ABAѩ.A0BAB.Ap=BA.AzFBAQ.A@VBAV.A@fBAk.A |BA̫.A BAB.A \BA̖.A@ BAp.A`)BALJ.ARBA.A |BA|.A \BAP8u.A}BAo.AkBA)i.AbBA^.A=fBAS.A`zBAأJ.A`fBA4.A@ BAG#.ARBAB.A3BA.ABA.ABAP8.ABAh.A`CA .A6CAz .AKCA`.A@dCAp.A`rCA.A |rCA(\.A _CA߬.A [CA`aج.AeCApϬ.AtCA83Ȭ.AxCAp.A@dCAѵ.A \]CA̫.A=jCAz.ACAǛ.AדCA.A@CA{.A׻CAu.ACAHm.ACAd.A`fCA8Y.A` DA`aA.A>DAP!.AׅDAk.A`)DAh3.ADAPf.Ap3EA.AEAث.A[EAPʫ.A FA؞.ABFA.A`fXFA沫.A ]FA.A gFA.AFAp.AFAfv.AFAHV.A`ftGA(\>.ARGA!.A@ 7HA.AHA`Ъ.AIA R.AhIA.A@IA`%n.AkJAn.A&lJA]j.A`JAa.A (JAK].A JAU\.A@rKA(m[.AKAX.AvKAJV.AKAU.AjKA@R.A@`$KAL.A`v1KAI.A5KAI.A 6KA,.A@%[KA&.AxeKA".AlKA@.AKA ߩ.A KAcϩ.A_LA.A (HLA囹.A vLAX.A`fLAΞ.ALAh.A]LAs.ALAn.AwLAHj.ALAf.ALA/d.A@LAc.A LA\.AoLADW.AMAC.A~MA3A.AMAB;.AMA0.AMA&.A@NNAs.Aj\NA.A@ NAxm.AuOAX.A OA蠨.AVOA{.A`$PAl.A`;PA`e.APA3[.Ag)QARN.AaQA?.AQA..AQA0%-.AQA%.A`[QA.A<@RA' .A QRA.A@RA ç.A RA.A@SA0{.A@gSAг.A߃SAȋ.AOSAx${.A`2SA o.ASAl.ASAH?b.ASA].A TAW.A@%TAhM7.A@TA.AVRUA4.A`mUUAh.AXUA> .A6UA.AUA.A@UAY.A@5vVA.A VA.A`2VAXR٦.A ZBWA0Ԧ.A@]WAPϦ.A$tWA¦.AqWA虴.A WAh.A@XAu.A@9XA.A@>JXAXz.A@gcXAHM.AYA .AYA.A&_Aɤ.A @_A.A@E_A>.A$\_A<.AZ_A;.A Y_A6.A*W_A,.AK_A,.A@J_AH,.AJ_A(`+.AG_AH\#.A6_Aذ.A_AU .A _A .A^AH!.A@^A.A^A.A^A0.A^A .A^A +.A^A.A^A).A^A@.A^A&.A@N^A@1.A^A .A^A@S.A ^A@.A@^A@u.A_A.A__AxX.At_A;.A"_As.A#1_A@A.AJ_A`e.AfV_A$.A@ u_A أ.A_Aף.A@_AhУ.A`)F`A.A^1aA`ǿ.A?aAH-.A҄aA@乣.AaA .AaAX.A`baAa.A bA.AbA0H.A =bA@`.AGbAH.A_bA`e.ATzbA.AbAXh.A$bA؁.A@EbA84x.AbAhm.AbA[.A@bAQ.AbA=.A AbAx6.AbA3.AbAp}.AbAU.A+cA .A6cApk .A 1 cA .A cAN.A@,cAPТ.A@.cA.AcAs.A`%dAz.AROdApa.AdAV.A`)dA@NT.A`dA83N.A eAp A.A`\eA(@.A]eA6.ApeA8..A eA8 .A`ZfA#.A \fA.A fAG.AJgA-.A׏gA`a.A hAW.A`ÑhAp.AhA8.A-iAH.A}iAp.AiA%.AjAo..A`mbjA0.AtjAQ?.AjA^.A`*kApp.ARpkAp.AkA`.AkA`a.ARkAQ.AkA@ל.A@,lA8.A`f^lA(\.A͒lAz.AlAW.A lAp=Ţ.A3mAQ΢.AmAP8Ԣ.A -mABޢ.A`)ZmAk.A@>mAh3.A@nAN.A oAe.A=foAH.p.AQoABt.AoA`au.A3oAp=s.AGpAq.Az"pAm.ADpAf.AGepApd.A |pA83d.A pA`a].A`pAأH.A@qA0.<.A \1qA2.AR:qA'.ABqA#.A`)VqA.A_xqA.AqAx.A`vqA8.A`fqA8.AqAu.AzrAu.A`fNrA .A@trAP.AzrAP8.A@rAk.A sA.A`;sA`aӢ.AJsA(.ARsA`.AXsAp.ALsA𣅢.A1sAXx.ArApi.ArAc.AFxrApR.AR&rA=.A`fqA&.A͢qA.A \qA#.A`~qA.A@dqA).A@G'qA̡.ApA=.ApAأ.ARrpAє.A`f^pAG.A apAy.A`fxpA@W^.AppA4.A=qA@W-.A!qAz$.A@ kqAp".A`fqA .A@&rA.ArA@W.AsAp8.A@gZsA .A@gsAX .A3sA.AןsA(.AzsAWؠ.A:sAX ͠.A@>rAH\.A@ YrA.A`)rAP8.ARqAX .A=^qȀ.A`)qAPf~.A`pA{v.AطpAr.AGpAX.AlpA0?.AzJpA(*.A0JAh=.A@ )JA K.A3QJAL.A{JAGI.A`çJAأD.A JA0C.A JAPfC.AqKA>.AG5LAP@.A ]LA`C.AiLA`aN.ApLAT.A`tLAU.A`LALN.ApLAp M.AࣰLAM.A@ELA)N.A`LAX Y.ARLA_.ALA@f.ALAȃh.ALA)l.AzLAs.ALAp=.AqLAL.ASLA.ALAp8ɤ.A=KA83Ф.AKAVӤ.ApmKA)Ӥ.A8KAp=Ф.A[JAp8Ƥ.A@>JA).AzJA.AJAP8.A kJAp.AMJA.A %JAVƤ.A IA֤.AIA(.AכIA.AIA( .A̜IA.A`ÿIAQ'.AIAP8;.A JAfJ.A JAuV.A@JAe.A`IAy.AIA`a.AIA0ҥ.A`fIA.A gIA.A_IA(.A1IA .A`HA.A@HA8.A \HA.A=fHA .A=FHA.A0HAp8#.AHA#,.AGA2.AGGA?.AoGAHG.AeGAQ.A@nGA`Z.A GA8e.AGApv.A GA(.A࣌GA‘.AiGAf.AUGA.A@2GAP8.AQ(GA.A`),GA̼.A(FA0.Ϧ.AFAH.ARlFA .A EAP".AEA4.ANEA8?.A  EA?.A DA@.ADAPD.A`DAK.A`)DA8V.AHDAj^.ADA(\c.A`DAi.A DA8j.ADAh.AnjDA8h.AgDA_.A \EDALU.A0DAHP.A`)DAQ.A3DA(\U.ACApa.ACA#t.ACAz.A=CAp8}.A׳CA8.A*CA0n.A`CAk.A`fBAȧ.A`)>BAҧ.AAAܧ.AAA.A CAA.A3#AAf.A@A.Ap@A .A@>@AP.A @A.AÓ@A(.Aps@A4.AL@A ?.A@AfV.A?AWd.AR?A8m.Aׯ?A-~.A`a?AQ.A`:?A.A3?Au.A=>A@W֨.Aͦ>Ah{ب.A>Az.A>A.Ae>As.AB>A8.A >A`a.A@>A.A=A .A`=AP8.Al=A).A-=AW2.A`f<A5.A<A`:.A <A<.At<A(=.AU<A=.AM<AQ@.A &<AF.A <AWP.Ap;A\.AG;Ab.A`);ALj.Ap;At.A ;Az|.AG;A.A׋;AG.Ap;A̅.AJ;A.A@>$;A`ᤩ.A:Af.A`):Aɩ.AK:A8.AH:A.A9A(>.A@9A0.A9Ap<.AnO9AH.A?9A*".A 59A$.A4)9A$.A &9A%.A`9Ac(.AN9A3.AŢ8A6.A߉8A 9.ABv8A89.A u8A(z;.An8AM.AZ8A0Z.A`7Ahc.A 87Ahk.A`d7An.A ۟7A.A@%Z7A0Y.A T7A2.A47A`ý.Aw6AHKϪ.A`06A@ݪ.A$5A( .A5A@.A@Ϛ5A0.A2r5A .A35AF.A@P4Ab.A`4AX{.A ]4A.A`)4A.A4AH!.A 4A3.AV3Ah.A3ASث.A 13A.A2A0.A 2A.AD2A('.A 1AI.A@/1AثV.A0A0].Aǿ0AHb.Aa0A8x.A 0A|.Av/A`.Ag/A(.A`/AH.Ao/A>.AH.A`.A .AXV.A`.Ak.Az.A@έ.A b.Aӯ.AXV.A`嵬.A`0.AX(.A.A(.A.A.A@-ARǬ.AT-A\ͬ.A-A ج.A-A /.A-A6.Ahi,AN.A,A`W.A`+A@ ^.A|+A `(A'.A@PB(AUG.A'Adu.A6&A0ju.A@&AY.Af_&A̋.A2&A0.A@C&Ab.A %A۹.A%A0î.A@,%A.A qf%A(\.AU%A.AA%A/.A3%A .A %A.A`$A` .A$AHC).AՒ$A..A r$A5.AJ$A#G.A`#A(Y.A A@#AP`.A "APf.A͠"Azl.A6ABʰ.A@A.A eA .A AXc.A@iApy.A@_A8.A`[?A.AA؁$.A`Ah2.AAA.A ATI.AQA`P.AfAh].A-Ac.AAAi.ADA0m.AyAt.AA|.A tA!~.AhAX.AAA0؆.AAHP.AA0ꓱ.A oAp.A`9Ag.A͜A8.A"EAs.A(Ax.A@A8'.AAd.A JA .AUAHi.A SAP8.ApA0.AzJA .A$A.A A@.AHA .A A`.A(A.A@ihA#.A`7A+.ARA3..A A0j0.AA22.AA3.AvAk4.AA\4.A)A4.AiA#6.AKAPo7.A (=A#9.A (/A;;.A"AЮ=.AAv@.A  A8C.A`vA(F.A[A=K.A@AXhO.A+AQS.A@AhV.AA9Z.AA%].AtA0_.A3A@h.AiAXr.A0Aw|.AAP.A`)A{.A"Aᙲ.A hA碲.A@`:A.A AH˳.AAأ.A=A{Ų.AIAв.AtA8Ӳ.AA@޲.AA.AGA.A@Av.A@yAX.ALA0L.A sA.A}AH?.AwA(.AtpAh.AjA(\.AdAHK.A]Ag.A WA.ARAj.AOAA.A PAx.A RA.A SA@~.ATA.AUA.A UAH.AHWA.AWA.A-UAج.AQAk .A@yLA .AEA.A@P7A.A'A`.A@`A.AEA8.A@ A".A ~ APY$.A]m AЮ&.A\ Ax).AdM AH,.A[? A0.A A8 A3.A2 AU7.A. A;.A), A>.A`]+ AB.A6, AxF.A. A.AdAxiB.AAuF.AAF.A`vA`N.A AؒR.A`fAo.A@At.A@PAxK.A$AO.A Aj.A 8A.AAㅷ.AŐA|.A c~AH.A}ANj.A@`zA.A@_AН.A0$A߯.A`A.AQAt.A@AA( .AݕAhMH.A` A_\.AA\.A`AP].AFA^.A͵A`.AVAa.A?ApZa.AަAP8c.A`vAd.A`2vAXf.AJAg.AwCA3h.A@Aqi.A6A`%j.A 2Apj.A^/Aj.A/Apl.A'As.A AP.AoApո.AA R.A`A(~.A`A.A@XAs.A/LA,.ADA .As9AX.A.A|.A@A.A^A\/.A AC.A !AY.AAs.A@(A|.A Ab.AA㎹.AW|APo.APAXۛ.AA{.A3A窹.AA.AAs.AA.A/A0ǹ.AtA /ѹ.A[APӹ.A NA cܹ.A s"A`6.A+A+.AA.A`AP.AzA.A@]ApN%.A`9A2.A AZ.AAb.AAx(l.AAp<.AGAP.AIlAB.ARA@.AtDAz.A4Aﾺ.AAϺ.AQABϺ.AA(Ϻ.AA޺.AA.AA( .A/A.ADžA.Aj_Aw.A_UA`.A#A.A Ah".A+A(.AA0-.ArA(E.AA 0F.AAYJ.AAO.AFA^.AwAxk.A`OA.r.A99Ax)w.A "Apx{.A  A.A A.AMA`1.A`fA.AZ,AHr.AA̯.AnA׻.AKA`ܻ.A$A.A@EA.AA.A A.AAX#1.AApA1.A A_1.A2AJ.A`\APc.AA`h.A@iAh.A/A n.AǨA-.A_A@.A@[AP<.A@>;A.AA .A Aљ.AA .AA.A<A㸼.AyAK.A<Aﾼ.Ax*A8ü.A-Aļ.A AƼ.AHAɼ.A cA\ʼ.A`AXʼ.AA޼.AÝA8.A@Ak.A`xA8.A^Aa.AT?A.A@2A.AA`.A@A.AÉA-.AuA( .A dAp/.A (ApP.A AXa.AAH.d.AALr.AA`w.AA`ᅽ.A@> A.AA(.AA(.A q;A#.A qA(\.AA0..ARAp.A qAx.A4AB.A`)A(ܯ.A|A.ASAG.AR(Az.A`)A(.A Au.A \AP.AAp Ľ.A=A`ǽ.A@GAhʽ.A͈Aҽ.A 1Ah3ܽ.A`AB.A`AG.AGA.A@ZA#.A 7A0..AAP8.A Ap.A@gA .ARA`.A@pAX".A3[A8*.AYA1.ASAX4.A[QA@.A[%AkU.AA_.A{ALg.AHMAq.A@As.A AVs.AMApo.A Aq.A |Av.AͲA).AA8.A OAX .A A}.AA}.A AX.A]sA.AaA.ApAAp.A(NAH..AeA.A Af.AA`a.AQA뾾.AAþ.A3gA(Ⱦ.AQHAо.AAzھ.A AW.AA0.A A.ABA.A A.AA.AA %.A०A c(.A AH(.AanA8).AUAp/.A:A:.A@APA.AA{D.A`ATM.ApAPX.AA@d.AHyAm.A`KAh y.AZApJ.AhAh.AAl.A@A@W.Av^AJ.A |EA线.A:A[п.A A1A+ۿ.A c$Ap .AIA.AdAu.AAп.A AJ.A`[A` .AOAh{.A;A0H/.A =A:.A;AM.A@`%AV.A AP^.A+Ae.A Axf.A@EAe.A AA|h.A`]AHn.AAP+t.AAxu.A`WAhbx.A@=Aā.AjA .AA#.AA0.A A.AA].A`AH?.A A.AA.AA0f.ArAl.AfA5.AeA@.A O]A@S.A<Ap.A@ AH .A`A8.A`A.AFA .AA.AA.A@A7".AtAx+.AlA2.AdA=.AoAJ.A@kAпW.ASA@1s.A`A.AA+.A4A.A@AЦ.A A.A`Ax.A?A.A An.A A.A@A.A-ApN.A`"AxK.A@AP.A@ A8D.A`AA.A`TXA0.A9A.A&A.AF A .A6Ax.A`A!.A Ay#.AॆA@ .AtA.A@#aAK.AtTAHL.A;AX.A5A`C.AA(i.A@,A@u .AA.A c[A@ 2.A ZAH.A`rA`h.AAx׈.AAXW.A |qA`v.AflA@.A rAp.AA .AAX.A<A.AzA@.AdA.Ad>AXW.A@AH].AA@.A?AP .AaA( .A0A4.AZAe.AR}AP.A At.A`A.A@?AHP.AoA R.A`9AK.A9Ax.AAv.A@WAx.AБAL.A;A`.A@A0{.A A`.A |kA؁.AyA(.A A.AA k.A@g|A&.A@hA.AjA8c.A tA8.AtAp.AdA&.A`>AQ.AwA.A`A8ɸ.A@5Aྵ.A@vA .A ]Aߺ.A\A0ٿ.AdkA R.A-Ap.AA.A4Ao.AaA2.A cA".A@>A.AѽA.AA\ .A3A.AAj.AwAp/.A`ZA`%>.A`YAoH.A@aAX U.AtACk.A$Au.AA,y.A[A|.A`uAX9.A}A8#.A|xA.A`\A.A@"A .AA .A@`Axؙ.AxfA/.A[3AhQ.A2A`.A1A.A Ab.A A}.AA=u.AA8.A/A~.AGA#p.A AH.AA..A MAk.A`)&AH.A \A=.ARA)Ͻ.A wAأ.A 9A.A`A憽.A |ҸA`a.ApAA.A=A˛.AawJA>.K¼.AwtAk.A.ʫA\JLO.A!KAWy.AdbA(%.AkoyAd.AVA(Q.AsA}1ݔn.AM PA5M.A~J%A/.AA.A <AD.A mA`!5.A aA#.AA.A+Ac.A ;A.A`AH.A`"ݪAHߺ.A@AѺ.A@EA(.AANJ.Aj{A@o.A6 AU.AA`10.A`ީAP8!.AsA .A c0Aٹ.AAȩ.A[A𴡹.ACAx.A`)"A(`u.A AY.A﫧A I.A(AP&8.ATAH.AAθ.AAp.A ҌA8".AŝAX.A൝AP^X.AaAF.AӼAk.A A.A4Ax.A^A0.AAh.AA*H.A|A.AA.A AXh.A`A).AAw.AAw.AծA.A A.A`UAV.AA.A PAP.A` Ap.AA(:.AAp.AA(q.AAH .A9AhQ.ABAȇ.AAA.AAB.A`Ar.AżA r.A@*Ah.A`.A(\D.A@5A+.A9vA(.AӦAڵ.A ҌAl.AIdA.A;AfD.AfcA@}D.AApk*.AAug.ANAhc.A JEAPx.AA`.A9!APژ.A`;ܣAor.A8]Ad.A6Aor.AףA(\.A 4AZ.A*A.A@ UA.A<A.AAA3.A AA.A?ΣA.AsA`%.A`2A.A AơA@.A`mpAXڴ.A)Aڴ.A A̴.A oA8.AXAk.A }A.AAPD.A@ϔA۳.A@Aó.A AXh.AƣA 9.A&A..AAM<.A`ģAT/.AeAH.A Ax.AկAP<.AA.A`KAҲ.A@W%A4 .AA.A Ah.AନA0.A=dAt.A DA\.A ɤAА .AWA( .ANAs.AoA`.AAق.AA0}.A`AX.AXyAh'.AbAK.A^ܥAް.AAXE.A7A.A@AT@.AA̯.A\A`.An)A^.AӿA 㭮.A 8ͤA1.A$A.A$Aan.AAXhU.A1A"G.AEAP;.A`6A09.A sAC.AA@Y.A=0AX.AAR.A]գA.A`B̢Al.A8A@#.A`aA.ATHAg.A@9A.AvA'.AˢA耬.Az$AxU.AA"7.A "A`.A A@«.A2AD.ADA.AAX.A`)>Ah0.AsAJ.AAP.AA.A ~GA˪.A@5Axm.AA.A ¸A{.A=A+.A4A.AAX5.AO$A1ݩ.A "A.AA0.ASA8g.AA[j.A6|As^.AfA9.APApx.AXAȨ.A`]VAh.A`]߲A-ߨ.A`'AX.A KAI.A A l.A`KA@3.A@AQV.AA 8.AA8R.AapAh.AAڧ.A`)|A.A9Ae.A A𴦧.A 'AB.AA.A`A.A͵Aps.A$A0|.A0HAጧ.A`) A͠.A0HA .A_A.A׀A(.A@θAx.A@MAXg.A`A K.AιA8.A0AS.AV@A .AA.A6ȸAxm.AAH)֦.A`\AlĦ.A#A.AA.AA.AŤAh.AOA7r.AjAh.A ܵAp`.A AP^i.A-AY.A`A@.A&APY .A|EAH.A-fAe.AvA&.A 䖹A4.AgǹAH.A.A@5.AQ.A`mYAXT.AjA@).AsAG.A}ӻAP?.A@A.A`hAQ.A$AXE٦.AAPȦ.AlA`¦.A`XAJ.A Aߘ.A`ӻA0t.A#]An.A@>LA[H.A@PкA C.A/A`.A-MA(ͥ.ADA.A`wAH.A$RAPMޥ.A Aă.A`ùA`Ǟ.AzA8gȥ.A[A.A /A.A@RA@c.AD^Ahc.A_Ah.A|ݻA.A AX[֦.A@ټA$Ӧ.A@A .AKAz.A9׾A,.A0A,.AA.AAPf.A`vA¦.A<A(y.AKA-.A ͽAe.A$A@S.AླA0.AA0U.A@Ax.AA 0ڥ.A-A.A A.AA(#.A A`6'.AM Ah/.A=(AX.AA`.AAv.A/AU.A?Ağ.A?ÿA+.A[ֿA䪦.A=Apή.AϿAڶ.AAr.AA`.Ay̿AHл.A ٿA .AAFæ.A59AXhԦ.ArA@Aަ.ABA̦.ApA3.A[A`.A OA.A Aгz.A`daA3.A`A6.AxA}.A[Ah.AAA`v.AA.A 9A<.AQLA.AoA8.AAX:.AA.AAq.A,AHi,.A u]A:ҭ.AAt.AAТu.A`)A3.A@!+Ax.A@{A0ʭ.AAPx.A.A٘.AA`r.A@NAk.A?Aؒb.A lnAз;.AA6.A Av .ABA.A@`!A.AA.AUA.AAb.A lAs\.A Aw?.AЄAF.A@W]Ap.A@,dA =.A quA^f.A@ƛA@BC.ANAxJ.A Ax.A`A.AAr?.A`]A.A-AB.A`[AxI.A A+.AA1.A@#kAV.A`"As.A .A.Ay%AHƤ.A`Aɤ.A)AtҤ.AAX.A൚A0Ӥ.A_Aܤ.A=A@.A3A .ALFA6.AA1.A6A=.A |AH.AA.A$gA #.AXA(횤.A`TAK.A ZAH..AdlA&x.AJAZ.AoA( 9.A-aAQ7.A ~EAU.A ZAP.AAY.AA.AA`C .AA.A@A,.AjAG.A AVR.A 8Aгb.A |;A Mv.ABA.AA8g.A;A`6.AA.A4wA&֤.A@A(Ӥ.A&A@_.ARAʸ.A`A.AAPYڤ.A+HA@ݤ.A`RA.AAxKV.AAC.AZA`6E.AA.AAl.A@A.A@xAx .A 8DA8֣.AA.A ۝A.A3uAΣ.A@Aeţ.AAP.AA؁.A Ap.AAP.AA<.A`*AP..A@NA8.AX5Aʣ.A 80AX8.A'A@.A^Ad.AAH9.A@WZAdģ.A#QAף.AFA(zӣ.AAD.AAHۣ.A=A .A`YA".A@, APM .A6FA@ƣ.AAeأ.A`,Ap£.AgA@.A &A3`.A SAЄT.A`Ap .A A.AA(.AkAxKڡ.A6Aɍ.AAȵa.A@fAPO.AA`.A஌A 0.A A[ޡ.AoA.AM,A/.A A0.A@aA.A@%A`D.AAp١.AAXD.A_A@N.A ZAX.A@yA|.AA+l.A ZjAc.A`[[A8.A`A&.A`AH~.A`SA3.AAؠ.A c[A@.AaAhn.AA;.AlA.A:AX誠.AGAPچ.A:A@ౠ.A NA(l.AwAxb.A`v#AZ.A`UA~.A"AP$.A@ jA@.A]AP.AA@.AA.A@,dAPS.A̚A`-.AEAf1.AziA8t.AbA}.A`b?A`.A 7App.AFAj.AFA}.A`KA 3.A AK.AAhP.AVAp Q.AOAO.A>AM.AacAA.AacAA.AR~Ax$4.AR~AH.3.A@WA@.AF]A(.A AU.A@A8.A A5.A AH.3.AΤAD.AA8C.A@#A0Q.AA 0W.A`NA(՟.AIJAӟ.A)A(KY.A A@\.A`o0A\.A2Aß.A@Aß.A@OA=.A[A=.A ZAp_.A A8 .AײAhi.A AT/.A utAH.3.AA*.A/A 4.ANxAp.AhA.AAP .A qAxG.AA72.A`]Ax$4.A AT.A@ggA-A.AMAN.A@AO.AzA()u.A@ AU.AA 4.A MA.A@#tA`=.A@sA@S.A}AV.A΁AY.A47A[.AWwA8j.A0{A`h.AAe.AMAIc.A}Ax.AA(.A@Ap.AVAX .AAH:.A.A".A`A|.AhA–.A`A.AA@.A`A0.AsAh@~.AqA~.A଩A&.AA.AApI.A`VApڄ.AA.AA7.A`A].A ۇA.AAxˊ.A Ap}.A $A .A`A Ҍ.A:A8Ů.A9/A-.AAö.A An.A`A.A@MA ܠ.A}A.A?Axz.A AX .A@PA@.AaGAp.AAQ.A@GA(q.ARA.AshAP'.A6A .AͺA83ɠ.A ۠A.A`)A8g.A@!AC .A@ A Ԡ.AG;AXѠ.A0AIݠ.AA|ߠ.A` A@.A 1AM.A@#A.A@8AI.AtAF.AtzA.F.A{A]E.A}AKE.A}AHa.A`A!.A JBA*#.AH>A(K$.AEA%.AWA(.A@AAߠ.AfAm.A@A.AsA".A.AA؟F.A ABJ.ACA`a.AALc.A Am.AAy.AA`b.A3 A3.AzAأš.AA`!.A ~A֡.A 0A}С.A"Ad.AwAН.A@!OA.A<A`.A`&A(?.A AXA.Ay?A|.AmXAġ.A΅Aϡ.A A.A6 AҤ.AA.A lA5.AA.AQA*.A AMt.AaAj.A@߃AK.AA8ݠ.A#A`.A`AX.A`A.AAQ.A $AP'.A@ A.A`A.AA.A^zA+.A`K[A #.AAP).A9A+.AAd9.A`A'.AAz .Az(A@.A`IAh.A@!A8.AyA.A`bAج֠.AAW.A``A#.A@GqAx5.A VAV.AUA1.A-A9.AAB.A`f,A^.A` A&'.Ao@AX$.A@NA4%.A`A1.AxA3.AjAGF.AAI.A |A0_.A`KAc.ADkA{k.A sCA.AA𴒠.AftAX .AbA.ANAǕ.A`)@As.A A̮.AA >.AʤAZ.A`GA.A&+A.AFAd.AA..AoA.AA.AAP8.AAx.AAN.AA`ʟ.A34AΟ.A@ A.AA=.AA0.A^A.A APo.AA.A Aן.A6Aҟ.A AHz.A9Aؽ.A1A.A A4.AA0L.A'Aȓ.AVAp.AA|.A 8AH̟.A`KA.A A c.A҇Aؒ.AxA.A@XAWM.AA?.AxxA,.A 0Ap.A@AG.AAk.AZA̿.A3A(\.A@8AB.A`)A.ANA8.AApl.ATAQ}.A`vAxt.A@AVy.A A(܏.AG]Ap룞.A]A`a.AAu.AlA?.A6}A.A A.A3!A`e*.AjA`8.AA8B.A A$.A`A0.AzApʞ.A3A̞.AR,Aeɞ.A.AАÞ.A`2A.AJA.AxMAlt.AgA8n.A@`kA@].AwA @.A$_A@S:.A`mZAxF .A 5A.AHApӝ.AA`a.A@ A&.AA،.AmA#.AA .AAL.A=Auʝ.AAȵΝ.ARAԝ.A +Ah.A`ZA ).A@A\.ApAɞ.A\Ax:.AQ0A!0.A@AP~.AA8Ţ.A AW.A`QA跍.A xAc|.A_ALi.AfAȩ.AhwAxuܞ.AA.AkA A:.A`A A.A e AN.AX AA.AG Ap2.A`= A".A Q A4.A S A˜.A`z A.A A.AB Ap.A@ Azq.A` AXhR.A` A<.A@ A>!.A`" AXy.A A0f.A AЄ .A`D A"/.A A`$.A~ Axu.A@N Aě.A A𣼛.A@` A0 .A Ap.A` A ț.A A.A A.A A8B.A AȘ2.A֔ A6.A= A`T=.A@ A@.A`AȔL.A@A@r.A;A <.AA@.AAٛ.A`dA.A0AP;.AA@q.AuA3.ApSA2.A@AȤ_.A@rAX.AKAX.A`*Aϳ.A@cA{.A-AX.AyAǒ.A@GAB.A`)A.A+$A.A A2.AAX.A@AΝ.A@>AP.AA7.AAŽ.A@>Ax).A|APx.A@2Ah.A6A=ԝ.A_A.AOA.A!A(.AA'.AjA'.A5A..A3bA:.A@r<A 8.A`A`~.AA.A(Ax诞.A)Aў.AA`6ݞ.AA;.AA <.ALFAz.A@A`.AA(L.ArA0H%.A0AS(.AܾA`*.AܾA-.ARA0 .A}AhD .AA֞.A`mAP՞.A@A О.AA@f.A JAh.A,Afd.AAP.AArH.AA>.A)A07.AjA+.A6A컞.A`A.AAh.AtQAP.AA.AZA.A3A.A@A!.A Aު.AOA`.A cA0.AeAt.A[cAX.AAF@.AMA2.AmAo@.A} A<.AA .A?3A .A^AL.A` FA .AoMA.AAh'.AjA:.A Ai-.A A@#.AA8s".A`-Ay.A!APk.A}A-.AAȶ.A.A0=.AVAeO.AfAkI.A=A@uD.A@Am.A`9AXz.A`AA.A߻Aݵ.AAJ.A <A.AFAxi%.AA@T.AgoA(j.A`AX.A`hAlП.A Ax)۟.A~A1.A&A@.A@`A0.A@,Ap0%.AeA1.A9AHC..A A).A 8A.A`;ApA/.AVA.AA?.A;A(.AJA >.A6A0Te.AAp=z.AoAB.AaAV.A@pAhL.ApA .A Z!A`.A-A΋.A@y>A'.A`*Aȇ.AGAo.A>A#.AA.APA/.A A.A`A8Ơ.A@A.A@A.A`!AJ.A@NǷA0Hߠ.AV.Ax.AaAsk.A {Apb.AūA[j.A@)AY}.A ێAC.A5IA.A@Ap.A ASɞ.A`BtA.A uAc .AհA.AQA(y.A,AV#.AAH.AA .A=A0'.A`A@.A UAό.AAX^.A+A^.AwAE.AhA0*G.AXAxW$.A`qA@&.AѯA3.A A8 .A J?AБ..A@ᆱAK.A)AH.AHcA.A@A%S.AײA.A;A.A-˲A8..A ۱AP@q.AvAu.A@W"Ad.AzűA7.A`KOAQ .A԰A`%.AzZAI.AǯAp`.AHAp.A WA.ALSAx.A۬AD.A٫Ar.A`pAy.A qA`u.A A8n.A!A~.AdA⚥.A AR.AAz.A@ ʩA4s.A=Ar].A`hAc.A (A pݥ.A A<ڥ.A sA U.A쭧AhV.AA0P.AnYALD.AWAp.A$AY.A֤A.AAp.AqAb?.A@WADx.A A.AA`.AŐAX.ALAH.A=Ap.AMvA!.A@ئA".AVApN.A@iGAh.AA7.A A@Ͽ.AvA|.AAX5@.A?A(1.AΟA)*.A^AL.A@fA.AQAT .A3A0.A|A*.A0*A =3.A`vܤAe.A@A.A@Ap.A'AXh.A 3AH).A|}A8"`.A@yסAV.A;A8痨.A$A8痨.A cRApʨ.AşA4.AZ`AV^.A@N AL.ASA.AAͨ.AZA8tר.A׎Aϩ.A`vAP.A ӛA!.A<<A9.A ĚA@FȪ.A|A2o.A`]AJH.AI'ANk.AĝA@ׇ.AVgAǡ.ATAX.AƜAP.AF›A`d.A@zAf.AAת.AgxA .AA ..A APH.AA`a.A뵗AXh.ARTA`v.ApA~.AA.ARA8.AGAf.Ap A`ᔫ.A`)4A83.AGOA.AjA#ƫ.AVA0.ӫ.AjAX.AA؞.A[Ah3 .AAP8%.AALE.A[Apc.A Ar.A |AV.A`)ZA淬.A %A@.AږAW .A`˖AX ".AץA0.,.A@gAz2.A@ AQ5.AԖAW7.ARAأJ.A$A#X.A"AP8p.AGAp.A A.A&AP.A`)0A.A@ ;Akڭ.A35Ap.A0A.A`)6A` .A<A`.A 0A`(.A*Aأ4.A =AP8F.A wA8O.AA\.AzA`al.A@ЗAz.A]їA̔.AAB.A=A@.AA{ή.A]A.AAX.ApA.A&AP.AQ>Af.AUA.ArA.AAp.AÇA.AA@+.AA(;.AA`K.AGAf^.ApA@p.AӘA(\.A`fAP8.A A̾.AA).AzԘAأ.AGɘA.A̶A0.A@ᢘA.AQA8.A`fA1.A cAkL.A TALZ.Ap_Af.ApAp=x.AcA8.A )Aΰ.ApAp.AǗA.A뗗AQ.A{AW.A]AL(.ANA4.AQAE.A `Apc.AzTAfp.ATA.A3EAX.A3/AQڱ.A2A.A=,A).AKA`.AJA.A@ GA*.AZA?.AGcA[.AgAw.A`)VA .A=RA{Ȳ.Az\AV.A jA.AUAQ.A;A4.A@ =AY.A`+Au.AA`.AA.A &A Գ.AA.A[AH.AA.AAQ3.A[AHP.AA`.A`A`an.A@Apy.A=A@׎.AAp=.A`Aਲ਼.A3ӖAp.A`Aƴ.AzZA=۴.A6A.A`fDAX .A`)AB,.AږAG.A`)A_.A 2Azw.A@gPA(܂.A3OA.A =A@W.A=Aŵ.A@ Az۵.A9A.A@ 3Ap.AAQ.A`A0 .A['AP88.A DAH\J.A>AfZ.A@8An.A@2AHa.AA듶.A=A8.A3A֦.A= A{.A:A溶.AZAʶ.A\Aڶ.AoAL.AYAp= .A[=A(.ASA+.AAX<.A`)ƗAK.A]AfN.A>AV.AOAa.AKAP8j.ABAp.ATAWs.AAkz.A׭AL.AؘA0..A@A.ARAHɷ.A AHѷ.A`f AH\Է.A4Aҷ.AGQAӷ.A]Aݷ.AqA.ApAs.AA.A`9 A? .A AX .AߙA` .A ՙA.AA%.AfA(.A8A,.AAF7.AjA@0N.AAV.ABAPn.A`92A(y.A-A.AAPk.A4A{.ADA .AFA>ʸ.AIkAHθ.AAX[ָ.At}A/.A A.AA(y.AA` .A-A(q.A cA8x.AA&.A`KAȩ..A@A12.A{A8.A@uA֐.AΎAX(.AÛAbp.A 8Ax.AQA.A3A .A@rÜA@䈹.AʜA&.A ɏA0.AAW.ARA8穹.AΑA2.A A8ʹ.AܝAxhع.A`BA.Ah%Ao.AKA`.A@EfA\(.AǀA=.A sAQ.AmAqg.A A y.A;NA.AXAǗ.A}A̺.A@ ПAۺ.A@A9ߺ.A@g֟A.A;A.AמA޺.AxAP'ں.A]5Aк.AKAXFͺ.A؞AȲϺ.AqAպ.AA޺.A sA0X.AAg.AAp .A3Ah .ARA.A |A(.AğAf .AA.AAV.A#A8,.A92A0K.A}CAȩX.A.AYm.A`:A(O.A`fA挻.A uAF.AݠAhb.A A'.A,A`C.Ah5A(\˻.Ap'Apֻ.A@N+A 0ܻ.Ao8A8.AmdA(.A 8APk.AA.A@Ap.AңA/.AA( .AޡA.AVA.A@%A7.Az5ABS.A OA j.A@yHAP|q.A0A(x.A A؟.A A.A@ϡA0.AA`Ƽ.A 룡AѼ.A6AP׼.A áAxܼ.A ߡAi޼.AAּ.A`9A̼.A@pAPȼ.A@ AGǼ.A@rAh@̼.ACAӼ.AUAX޼.AQ٢A.AA{.A;Apl.AޢA84.AfɢAp.ANA .A@5Aȶ .ATƢAx.AԢA!.ARݢA:.AA(D.A`AN.AA@X.AA(c.A@A i.A A@k.At5Ak.AKAg.AcAHf.A`pAh.A@xA y.A A|.AA.A@rڣA.A@CA`6y.A,Ay.A6ZAO.A0A8ւ.AA8.AͶAl.AҤA@1.AAD.A"A軜.A3A3.A8AĶ.A@N;AŽ.A;A{˽.A lMA8Eҽ.A``AW.A`ՀA6.AA.AאA .A|A.A`{A.A)}A%.A`mA;.A@EAU.A@yߥAu.AKA .Ah,Aƴ.AeAa׾.A쎦AP.A;AQ.AϦA<.AA-.AA<9.AAA.A`AXG.AAQ.AAn.A OA\|.A[qA,.AA0.AAP.AĽA` .A@AHPʿ.AAlٿ.AN#A.A@A.A`A.AA).A@gЧA2.A֧A@=.AA4B.A`AD.A9AS.ABAP\.AըA0d.A@NAv.A@A.AA.AwAS.AAz.AA8.A` 5A.Ah_A.A[AY!.ASAпK.ATAh[.A eA`d.A}uA k.A@5A@p.A AH)z.AAP.AA0.A;A8.A`A3.AAHa.A`A*.AA .A)xA`2(.AkA()3.AsA9.AࣂA8?.A坩AH.A` A(W.AAPjb.A~A`vl.AfA`Px.A l]A8}.A_]A@‡.ASA^.A?A 0.A@-Ad.AZA.AhƨA).AA N.A@A .A@`רAP^.A`A.A@pA.AD&A .A.A(.A 0AH-.A`Ai.AAPx.ApA#.A`hAHn.A;oAd.A(tAe.A`oA0j.AMA@.A@pA8#.AA=.AA@.A@ϓA`.A/A .AzA.AhAR.AvA/.AjZAB.A$AP.W.A zjAA{n.Ak*A1….A5٦AM.AoAh˳.A A@0.A@A-m.A.&)AǑ!.ApXu6AdyA.ASEAq.A TAf7٨.A;̙DcA8z.Aǵ"rA.AQAK.AdnAH.AA~ .AR;!A.AMlAtn.AktoAs;.A2\i<AX_M.Ao3ǧA.AӍ.˧Anb.ALZDͧA _[.AĶA.AʧAzӀ0.A1JAicپ'.AndA]3H.AxQu_Aog.AA $.A.ӦAMa_.AseA.A#^#APK.A0'AUt:.AR!>A^.A|A~.A)A.A`Ah.A'A.A 6ޢA R.A@rA 5.AA@.A6A>i5.Aq5>A2/.AoA2..Ad,5<Afe3.A++Avכ[<.AlӺAfMfJ.AGUDLA\.AAjb.AmlA9c.AڍHAa?Ja.ADjA [.A AS.A&.Ap|R.A ~AP.AɗA U.AAs`.A NAs.A$Ax.A SA.AA .AԖA.ADA.Ac!A.A A.A|A .AlA.A`tAH\.AwALO.A?AH.AmA (.A  A.Ac`AA.A /A.AēAp.A6KAA .AkA`.A`ǑA@ .A첑A.AvAP.A@ A .AcAq%.A@A$.A |A .AA.A@A.A l[A' .A*A.AA ".A[ǎA8?.AǎAHH.AAHR.A?AR.A@pAHM.AQAdA.A QA0..ApAf.AÈAI.AAx.A)uAX.A@OA(.A \A7.A`΍A.A QAh.A`QA.A Ah.AwAX.A 8rA.A c?Ax.A:AA .AFA(`.A@EMA:.AhA8.A@,A(&.AˋA+1.A`DЋA<.AA0{F.A&}A^.A͊Ao.A A@.A6A@.A lAH.AXAhވ.A/A.A ~҈Aw.AA8#k.A ZA`W.AM1A@-.A5A.AA(.A` pA.A %AP.AAx.A Ax).AA9.AgA(J.A&AQ.AA%.AMnA.AzoA,.ÀAP.AAȲ.AʄAx.A ZApp .A;A"..ALA!8.AхA0=.A@A9.A`RA00.ArA,!.A|xA.APAh.A` A@ .A ܅AY.AhA.AA0.A4AX.AaWAXI.A AA`#.A`f΄Ap=.AରAp.A qA.A&A.A 䨄Ar.A /A .Af=Ak.A`AhQR.A@)A8.A7A.A8A .A(.A.AAL.A`KA(.AA.A@iAX(.A؃A.A)A.A@wA.AGANt.A*7APj.A$AHe.AAb.A AW.AAR.AAJ.A`A@.AʂA #1.AЂA'.A AP$.A@WA,.A@dA c..A`0Aw'.A`m A0.A`BA.A"Ah.A@(A.A A.A`́A.A=AP.AA A.A@pA.AApq.A@ɁA$_.AdAQ.A#A L.A-@A&J.ApAL.A`AO.A@AI.A4ςA 08.AڂAj*.AقA!.AA:.A@WAp.A"Ap(.ASA }6.AA;.AA7.A, Ah+.AfdA>+.A@A/.A߄A)5.A@,AH/.A gAE!.A A&.A A0.A;Ap.A؅A .AAЦ.A@A.A@ AP.A s A@.AЅA .AA.A`A8E.A@A.AA֭.AA\.A`]AH.A`Aq.A@hA@pY.A@QAK.A0A#.A`A8 .AՄA.AτA.A`ƄAP.A=AP.AࣔAP.AjA8.A=6A`a.A Ah3.AAh}.AЃAWd.AAR.AAG.A AB.AGAkA.A YA7.AGA`..ABA`.A GAuտ.A]CAz.A (A֣.A[Aأ.AAy.A3A#n.AAp=f.A Ah3a.AAP8b.A@Apc.A@>nAa.A֓AQ.A߃Az/.A Ah3.A˄A.AA.AAp ޾.A-A(.A`A`.A@AH̃.A A8m.A`)!AM.AMA`.A܅A.AAIڽ.AnAЮѽ.A pAQƽ.AA.A+A`C.A`KA8.AtA.A A𸌽.A`Aw.AA".A`0Ab.A$A.AAJ.A A؛.AʆA^.AB{A0b.A`Aս.ApA0;.A@A.A@ւAH.AA.A@W=A p.AA R.A ՁAx.AA.A pAx0.AJA \.A3Au.Ad1Aޅ.ABA0H.A3݁AIξ.A@*Aؾ.A@,)A.A (#APo.A#A.A"A.A@gBAP.AGAA#.A@2A.AA؞.A@ AP8.A@A.A qAp $.A@A,.AA3.A@ A8H.AA c.A@A`am.A |A@v.A`)Aأ~.AA׆.AA-.ARAu.ABAB.Ap3AV.A@G+AP8.AR*AL.A`f&Ap.AAB.AA8.AA).ARAQ.A@HA.A@2A.Az(A֥.AAf.A~Aּ.A ~A.A ~A8.Apo~Ap=ÿ.A:~Aſ.A3~AH.̿.Az}A8ҿ.Ap}A8ܿ.A }AH.A@>}A.A}A.A@g}A`.A}A8.A }AH.A@g}A.A}A .A}A(.A@}A.A@ }A*.A }Ap=7.Ap}Ap?.A d}AK.A[Y}AQS.A`)f}AzY.A }AP8_.A}A)v.A}A0.A@g}A.A@}A.A}}A8.A@\}AL.AR>}A.Az6}AP.Ap5}A .ARd}AX .A}A.A@>}A.A}A#.A@>j}A.AN}A.Az,}Az.A@>}A8.A}A.AR}AX ..A`)}AC.A`+}ALX.A%}A(܁.A`}A).A@>}Ak.A)}A.AGI}AH.A[y}A".Aj}AH).A6~AB.A l~A%.AR~A9.A֑~ABQ.A }~A@Wh.Ap[~Ak.AG}A.A@>}AP.A@>}A.A3}AP*.A L}A=.A O}AH\N.Av}ALX.A}A-^.A~AZ.Ak~AkV.Ap~AGJ.AQ~AG9.A~A/.A~AL$.AAk.AyA.AAW.A`fA.ARA.A@G AW4.AA`9.A%AA.A@:APE.A=4AH.A`fA RI.AAP.A A83U.A \A R].AAg.AA(n.A`A(v.A࣬AG.AçA.A׿AQ.AAHa.A@ A.A qA.A'A.AzAH.AAp.AA.A@A.A`/AX.A`) A.A`)ځAL.AA(\.AzA#.A@`A0.A3SA`.A qUA`:.A`8A`V.A,A=g.Az.A z.A=:Ap.A \5A.A/Af.A@@A.A[Ap=.AA.AAQ.A3Ak.AA`a.A@<A8.AGAM.A A`x.AуAk.AȃAL.AûA.AA`.ApA .A ⤃AX.ARA.ARAH.AA.A`)ƒA.AzʃAk.AσA8,.A׃A<.A A@K.A`fApP.A3 A`].A=ڃA`g.A=Af.A |A#^.AGGA@WV.A@gA#O.A@A`J.A@lAp=B.AA<.A`)A;.AǁA$.Az|A!.A \UA(.Az,A5.A qA)C.AAI.A@ AT.AA`].ACAH.b.A A(^.A ՁAY.A`)A`W.A`)FAX.A@dA8].AyA`ae.AGA(q.A@ኂAp={.AA`}.AGقA}.ApA~.A A}.AA8z.A(Af|.AQA….AmAp.Az|A.A`fxAf.A]mAp.A3CAW.A`fAf.AGAh.AA0..A3A.A(Ah3.Ap AB.A=,AW'.AGA#D.AAN.A?AT.AR@AY.A \!AS.ApAVQ.A3AH.Q.AτA`V.ApÄAuc.AʄAX q.AAL.A`AG.A`fAH..A`ԄA.A`nA#.A@>A`a.AzA.AGA.Az܃Ap.A܃A).A݃A.AƒAB.AApn.AÃAp8^.A qALV.AzBAU.A*A`Z.A Ap=^.A At.AA̋.A3!A8.AA.Az$AG.A 8A.A@>4A`.A'A.A#A.A 2A`.ApUA RS.AaAm.A3eA Ru.A@rAWx.AA8y.ARAf{.A`fڃAu.A`fAǃ.AA.A Aœ.AAª.Ap˃A8.A A(\.AAp=.AuAH.A]A8.AzJA.AA.A3A4.AEA(a.A׿Au.A`nA.A`f*A.A@ĀAأ.A`A8.A(~Ap=.A@J}A8.A|Aj.A@Y|A.Az{A.A~{A.A |zA.A`yA.ARxAG.ARwAp.A q1wA.AwA.AvAp x.AHvAj.AvAGZ.AzvAP8G.A`)rvA.A \CvAp.A =vAX{.A?vA( .A`fXvA.AjvA\.A`VvA R.AKvAxz.A6vA.A1vA)J.AvAf.AuA.A@ {uA`.AuA`.AtA(\.A(tA.A+tA.A=tAy.AtAz-.AR,uAp.A`huA`.A`uAk|.A uA>.AuA8 .A@GuA.A@uA.AR#uA0.AsA.A@sA .A sAX.A $tA.AktA`.A`ftA}.AtAB..A  uA.AG}uAX p.A |uAI.AuA.AuAzѾ.A0vA.A6vAp8.ArvAV.A@ vA0.A@6wAP.A wAP8.ARwAQq.A@.xAG3.A`ÁxA.A xA.A3YyAf.A]yAud.AQyA%.AzA.AQ.zA.AQ4zAz.A&zA`].A 3zAp?.APzA.A zA.AzALa.A@{A=,.A@|A.A`@|Au.A=R|A3.A|AH..A!}A(.A}Au.A -~Ak.A~AY.Az Ak.AA83Ʒ.A`A.A3]AL.AA.A AQ.A`.Am.AzbA88.AA3.A ́AL̵.A@g A.ARfAkH.AAh.ARVA(ܪ.A=܃Ap0.A`A.A`A`.A[Aw.A |A.A`$Aϲ.A~Ap=.AĆA`aA.A!A8.AGA𣔱.ApɇA O.AAp.A iA.ARĈAp8X.A3A.A(NAG.AGiA(\.ApA›.AzA(\f.A‰AX-.AA.A \A`Į.A`*A̍.A`f<A`].A`JA1.A@GOA83.A@JAQ.AtA.A@GA@n.AÛA)?.AA.A@ AŬ.A|Aч.A`)`A83I.Ap=A R.AA8E.AZAh.A@yA.AA.A|Az.A`mA ܪ.AFiA\Ԫ.A~APZҪ.AAΪ.AωAxK.A2Aot.A`fAt`.AވA.A@ԊAG2.A6A .A@A@.AuA`.AxzA׃.A A_'.A@Aج.ASA\s j.A݅$AuK-;.A\φA5.A,+sAZ.AA&A(0.Af A8.A`ACy.A(ἋA"%y.A%% A Rn.A^A l.AiA8l.AkIkJAm.A[*A9o.A8 A9,ls.AA#Ax.A~@\ׂA F}.A0vA9.A+HAX[.AXAh@.AA|.AsyxAJ也.AzA.AA^ſ.AjAAW14Ÿ.A廄~A3ğ.A,½sA ş.A1wAgAIgƟ.AZ㗭ZAv&A ǟ.A`]AMAPƟ.A#+O?AƟ.A@2A$5B.AEOAк.A@EفAx.AvAH.A eA.A@`eAd.AeA.A"vA_.AA \.A`A`CF.AAH.A Ap<.A AB.A A΂Ah.A`̂A< ߂.AXA&u.A6Aȶh.A@1AfUc.AROA6`.ApAM1].AhިғA׳].A0Aګ!].AyԥۃA7)_.AO]Ab.AT; AM8g.A6?Aom.A`ZAQp.AMG_At.AsfA٧v.AoA}Z]y.ASyA{R{.A* A@(|.A]3A}.AKA}.A Ax-P.A$A83$.AsA.AFuA`,.AAh3.ADA5.A`A.A`K[AX .A`mAX.AvAL.AanAr0>.ADGGdA%Y;1.A3' QA! $.A305A\[2.A>Ac.AAi.A፨ޭA_+:r.A,_Ѡ~A(Л.AYA0Ʒ.AV4FA5.A@AA:;.ATyJA#09k.AcaA@S.A`ՅA%L.At]Al:/vE.AuPAW=.AxpA̋5.A#ÙAe*..A̞A&.A(A.AA.Ar@{Aݨaš.ANA7u.AsA.A=̓AxF0.A[AXu.A狄AǙ.A@ۄAPș.AKɃARL.A |ЃAp8.AkA9.A`BAPߗ.A;pA8Iڗ.A@>ąA.A@`A0&v.AAȔt.A`ÚA u.AiA0.A@,HAXJ.A`fAx>.AA.AxA.A@NA.AApJ.AA.AAP .A[A .A`eA@Ʒ.A@.jAN.A`A@۔.A ZA@ה.AUA>ה.AUA.AzA9ѥ .AW_Aw.AVPA2.Atz8Aa.AK߸A.AthfAKOړ.A/EnAJrӓ.ApDLA͓.Az%Aɓ.AfA0ȓ.A@DAн.A`"<A .A|؄Av.AzۄA~.AA( `.A3Af.A@Aq.A@>Axh.A-A𢿒.A A.AAБ.A A0.AA.AVڃA@ .A@yA p .A3}AB .A!A(ː.AQBA`ː.AyAh .AYAM .A@ A.AA.A^{A.AZA'.A lYA'.AyA@(.AA@".A`DA`X".A҆A3.A؆AP.A AK.A A@F.A#Axx.A A'v.AAr\.AA=.A`A0Ǽ.AT Aʏ.Av}Ay.Av}Apo.AWA)G.A Aˎ.AaA.A@.AP.A ZA8֌.AAp.A=ƅA0.AąA`ᗎ.A`)^Au.A`fTA~.AAX#J.AAHPL.AX<Ah.A,A.A` A.A`DZApA.A+A`S.A@pA0{.AA..AAH+.AQAx.A \A@.A`]Aq.A@$A@.AA.ABA`!g.AAVh.A[Ahh.A"DA@i.A@A"j.AA`k.AAk.AׅAxl.AsAm.A#Az.A&Az.AH6A.A@6Ax.A #A83k.A@4Azk.A`Ah/b.A@A 1.AAx*.A Ax.AA .A`TAފ.A3A֊.A@A@d.A@Ap.A(A0p.A@yAWh.AAXl:.A@A20.A`A8/.AA0b.AAb.A uA8a.AÅAa.AA0a.A^A`.AVAJ.Av{AUF.AAH".A`߇A$.A}AѪ.AA .A A.A $AH .A@.A .A SA5.ALAډ.A AAȤщ.AaAx.A`A(:.A SA?z.A@An.A A((n.A@A@.A&&APf.AEAW.A@ A8.AA@1%.AqA".A`)FA(\0.AA3.A A?.A ¿AwA.AAE.A8A+.AQHA(.ARA( .A+AG .A`DA&.AQnAȇ$.A/Ap.A`$A8#.ABA4.A Ah.AAPψ.A 8A(.AIAm.A(AM.A AV=.AzA@}.AAE.AdA.AYAʅ.AA .A滉A.AhA0҅.AA@丄.A`A.A}A.AA(.A`A.A9A0.A`sA'Ԃ.AA'ă.AȅA^|.ANƅAȶw.AൖA@>.AƉA㘂.AHA@G.AaA J.A Am.A"Am.A@5A`$.AA .AA$A08.AQA,:.A`]2Al.A0A(~.A@PhAh~.A |AH:.A`TA~.A9SA ^}.A (A8|.A@`Ap8}.AxA }.A@rAH|.A@SAh {.A-A!|.AIAo|.A?@Aпr{.AAty.AFA ov.A 6APiu.A ]Acs.Ag[A p.AAX$`n.A`oA.n.A^A[n.AA(n.A@yxAo.A)yAx(p.A OyA`1q.A`zA^s.A{Au.A`{Av.A X|AxiPw.AŤ{AIz.AjxA |.AsvA~.AZtA̾.A@F.ApAG.A`oAM.AoAޘ.A pA`! .A pA < .A`oA #.AoAP.ApAl1.AspA1.AoAi.A@yoA[i.A@oA^.A`;oA.AKoAi.ADoAi.A oA 1.A@oA4.AnAך.AoA'ؚ.A`TpA.AڦpA$.A@PoA .A@%oA .AEpA _ .A`pAP".A oApE.A`"oAȱT.A`ܮoAe.ApAՁ.A}pA``.AFpA`Û.AxppARқ.A(pA.AVoA.AfoAh. .A &mAX.A\A.A`2\A`rm.A0VApf.ASAx-.AOMAΝ.A`8MAF.A7A.A`]6A`>.AF1A@.A;0Ahަ.A`/AP.A`Ì/A.A '/AӖ.A/Ap–.A.AH.A.A.A c+A.A@+A.A`.Ah"Z.A.Aѕ.A/A.A_1Aד.A2AMT.A2A".ALj2A .A1A.AP1Axz.A`v-0A'.A`D-A5ޒ.AH-AHp+6ؒ.A*-A*ђ.Aȩ--Afʒ.AF-A&’.A6B-A ?".A"-Aҵ.A-Aе.A-Aj.A@5/AS\9.AP)C/A1磀.AmO/AMAK.A@Z/AH.AEGcd/Ah..Aol/A ,g.Aq/Aƍ.AbFu/AJf.A#rv/AlPو.A0,Iu/A)[Ke.A^q/A .A&l/AБؐ.A"/A.Ah.A9.A`KN-A<4.AaףHg-AŒ/.AM-A$L,.A@9N-A{/*.A@$-A9%c).A\'N-A).A.Ae;+.A5x%.AVI..AcC.Ad2.A2U_.Ae7.APxx.Aq=.A2|.A>D.AVTF.AL.A@.A\bH.Ak.Am,B.AQe/A/;.Ac*/Ay/A.A@Ǝ/AQ-.AfN/Ae q.Ak\1/A?.Ag/Ae.AU{9/As~.A4g.Ah~.A@.A}>~.A@EU.Aw~.AHd<-Aw ,a~.AfX#y-A?xN~.AN,A sA~.A}Uo,Ax8~.A9e+A54~.AL+ApN~.A O1+AS~.A@r+A~.AL+A~.A@*A ~.A#Y*A<}.A`T@)A}.ABB)A[|.A :)A(>|.A@4)Am|.At)AK|.A_(Aجz.A'Ax~x.A&A7x.A3~&A8'Lw.A@{%Axmv.A %Aw%v.A#Au.A$A,u.A@g#ALu.AZ#AxXt.Ac#A't.A`T#AA5t.A@9#A@s.Ak"As.A s"Apr.A s"A`r.A s"A$r.A Zl"Auq.A@'"A@q.Ai!App.Af A Asp.A c* Azo.A` AHo.Aov Ax 4o.A` AZn.A`o An.A A@Pn.A AXm.Av A0Ym.A@y Am.AE A nm.A@_ A0&Mm.A` ACm.A@iP Ap<8m.AUAm.AOqAhm.AA`6m.A Al.A g!All.A A!Aj.A A!A&j.A A!AAi.AR!Ah.A@ A3h.A&A%h.A`KANh.AHA Fh.A@RA3h.A4KAPh.A AAg.A/Asg.A AWf.A QA`d.A=Ac.A əA1c.A,AXb.A`}Aзb.A@AH}b.A*Ab.A`fAOwb.AA`ab.A`YAGb.A`Ax:b.AR ATa.AÕA`.AqA_.A5A4_.A`)FAk^.AX/A^.AA`vK^.A Apl[.A?PA@uZ.AAxY.AlA W.AAP/lV.A`"A c;U.A`fAu2S.A AP.A#AX:P.AAD!P.AA1IP.AҭA8P.ATAh&P.AxAP.A`[CAO.A`WAO.AnAx[O.A PAXN.A`1AN.A`9AhM.AAM.AAL.AaiA ML.A`AKL.AUAwK.A@GAK.Aj'AFL.A S A3L.A> AL.Av6 AK.A AABK.A`kAp0J.AAfJ.A{ANH.ATA`4H.A A`G.A A NG.Ah AxG.A@ؖ AH.AYAH.AOAH.A=RAvG.AAG.A@YAP^gG.A; A`CEG.A@ye A0G.A  AVF.A  Ah%E.AT A 2D.Aa, A-C.Ax} A8#NC.A m AgDC.Ad APC.AAC.AA$C.AՄA`TlC.A uAhQYC.ARAYOC.AA`LC.AAЀBC.A` A15C.AA0H C.Ap AC.A A`wB.A@ A@uA.A@ Aأ@.AXx AI @.A@iAP?.ATA>.AiAd>.AgAd>.A ,A(>.A ,A>.A Ap >.A@YA[>.AxHAg>.A;)AHL=>.A`CAxi=.A@A=.AA;.A 8,Apʰ;.A@5&A@%{b;.AkVA0+;.AL~A:.A4A}~:.AA}B5:.A9 @AX9.AjA#:.A`.A0:.A[A9.AQAp9.A॥Aȱ 9.AjA9.A =A`9.A@A9.AŪA9.AsA 9.ADAb9.ADb!nAͫ9.AtpAi9.A{AHݎ9.A@5AS u9.A~9nA`CZ9.A@A";49.AqAp 9.A`;Awΐ8.A ;A;"8.AY@YkA78.Aǧ*Ag7.A4@<AR7.ABsAbN^7.A;8gAh?7.AD05AH6.A Aa 6.ApAB5.AAXF_5.A' A15.A`DA *5.AAuH5.A Av5.AOAh5.A})A6.A`Ah] 6.A{>AyaN6.AA\6.A1pzPAl_6.Ak As!26.AҔA!6.Ad^+AOiݲ%6.A lAS7 )6.ACA9f+6.AQA)y-6.AUANk.6.AWnA6/6.AiZA6.A|A 5.AAxd5.A#6AX4.AIaAȓ4.AEApJ4.A(RA4.A@ϥAz'4.A;At3.A(A:2.AA(1.AOARO1.A@>\Ah@0.AAn0.AdA8p0.AYApJ|0.A0A0.A ^A@0.AxAX-1.A A".AfAb".ARAF".A/EA0*!.A$@A!.AAP7!.A`Ah{!.A A@5!.A̓AU .AmAp.AAPK.A֘A0.A<APk.AXA.A wAz.AA@.A A.ATAho.AgA\.AAsH.AOAxK.AA.A@As .A A^.AA`,.A@Aԫ.ALAN<.A@A.A $A.AA.A 4Aܝ.A qcAA.A`!A.AZAN.A@%JA.AQA0I.A@,pA^..AvA(n.AgA.AhA.A@!A .A`Ap.A@A8.A`AН.A@Ap.AA*.A@6Ab.AA0Ǽ.AAxd.A OA0.A`AM.A@,pA".A@,pA?.A:A.AT3AJ.AM$A.AAA.AAؚ.AAx)F.AעA?1.A`!A?1.A5A).AAx .A<A.A@`8A.A@rA=.A:A`!.AA .A@A`Bc .A`5Ak& .A [A\ .A \A@k .AFAz .AA0L! .AA`vJ .AEA@; .ADAH1 .AQA .A]Ag .AAK .A@yA .A[A0 .AછAeP .A;Azx .AˢFpAiH.Ap@AZ8.AA@~.A A gu.ARAF$.AA.AԒ\A.A@A`.AAvAȔ`.AA;.AWA`.A@ Ah%.A ^A`6.A?Ah.A`lA.A/A0[.A#A.A`fA)yU.AebAmޥ.A 'A(<.AaMAI.A@ Al.A-ˬA;޽N.ACmSA*-.A߁0A6V.A&dtfAzl.A,An[.An9: Aw .AKR A1W.AGO A}s'.AM AX.A AG.A ` A<.And A8.A6 A,D.Ak A.A&D A~yVq.A٘ A|B Y.ABr AB.Aŋs9 ATT/.Ac Asr.A]> A. .A|: / Ap.Ad Ar.A A.AXL A0,.AO A^.AZX߲ AVڻ.A6JQAcrP.AkA`C/.A8&7aAz}=-AvA-A0A4t-A˦|A]8-Aj-A(-A`A-AMA ^-AA,-A A3A-A@,A3-A@EAP@=-A`hA;-Aҁ&A6`-A@i'A -AS0A_y-A}Ц2A:-Aq4A x-A#‹U7Adn-A\X9Ai-AB <A-A@-A{ocA5jZ4-AF eAʍ-A@#fADv-ABZgAf e9-A/ܠgA0v-AhA,,-ApMiAN)6H-Aٻ(tiAy*-AVZviAT-AcjAxAO-AOcjAeĢ-AP bkA#-A8oOSlA(B-A`;mA6b-AmAx-A[znA*j-A?9oA@*-A@pA6-At*pAW^-AjqAREJ-ArA@S'-A!rAx|-ABKsA-A ItA6 .-A( UtA6p-A4vuA&p5-AoԼuAX(G-AYvA)d-A;wAppT-A fxA;-AvA0.A iuAH.A rA .A]qAxj.AqAp.A rA@.AyqAnHWX.A`pA.A pA Z.A WpAj.AI,,pAx*?.ApAB{X( .AY pA\Ctm .Av93pAj{R .An)pAV= .A'BRpAU: .AippAh| .A pA( .A7qAid6 .AaPqA' .A rA(A .AjVrA6 .ACrA!; .A0erAFr .AmKrA%Ϫ .AܵrA;6̧ .AJrALy6 .Aw'rA,Q .AۙrA(9 .A .A`6iA4DE .A@@ևhAq.A'hA[m5.Ang.hAM.AhAXz.AFZAu/.A >YAi:].ACYA Ȯ.Arq YA\.AYA.AqYA&|.Ap|+YAoK.AVYAKHz.AviYA~h.A>'YAj.A 5ZA.AdZA~^.AD[A.AXAH? .AXA]Z .AWAMX!.ATAhݐ".A VTA/#.A TAh"#.AUAbX$.AVApS$.A*WAH#.A}zVA#.AVAHr$.A@,WA$.A@NWA $.A@WAxƩ$.AכWAs^$.A 1WAd$.AVAX$.A@*WAY$.AmWA$.A XAS&.A@`_A v4&.A5bKG`A]]e&.A%`A@ %.A-|FaA/N%.AHSaAd%.A bA(#&.A+eAy'.A@lA7(.A`nA (.AVoA'.A/ApA0'.A-juA _&.AoA`%.AnA|%.A GfmAc%.AsulAx*7%.A4ޠAkA6A%.AMFjAP$.A^FhA15p$.AsgAг$.A`!fAak$.A9.bAf $.At9`AWy#.A^A`=#.A,^AH!.AtaAa!.AaDdA & .AfAf.A@NfA@.AaZiA@.A mAP.A nAR_.A u{oA.AqAe.ArAPk1.A&rA .A NtAPk1.AuAhy.A`DvA@k.A OؑA4ىod.A͹(&A1a.A2b.ACc.A[@ړAZi.AZCr҄As.Ax+AW.A"͕APk.AhA(.A`;XAxY.A (Ac|+<.A3 j^A#q".A1SA , .A꓉A`.AAV .AxI͈AM.AX/DA*+.AH^WA๕.A1AS.AV*Ah~.A;"A<t.A"ׇAؽm.A>A4Mg.A@{A0c.A'HAES.A<AA.A!:A[D-.AlņA.A챆A$R.A|A3t.ACjAq.A+ Ab.AZ~ABJTk.AUdAVo.AUWA|.A0HAN.AN A1B1.A"CAjd.A0ݙAG.An1Aӑ.ALacAX.AA c.A5U#~A9Gm.A67n~AdN.A~-~AB0J.Ai/}Ahi.AHG}AaP.A@}A2.AG9}Anĕ.AFn}Aw.A)Ui}A):.A'}AϨ.A\|A.A;u|Afk.AIn#|Ahm٢.Ay9{A;g7.Ar骔{A;.Ay[{A^_Vq.An-{Al/\.A:H {A'G.AtJIzA(j1.A zA$.A 9 zA.A`{Ax[.A_zA t.A zA(.AzA.A x{A.A z}A2.A ۇAn .AτA`r.AdA.A@/AQ.A`A`G.AjzA.A`[A@< .A@CA} .A@DA .AA؎ .A`A@@ .AjAH\x .A XAH .AA@ .A'AJ .AA .A`A~ .A@>{Ax| .A \A@ .A@WAh8 .AtJA .AA .AA(+ .A=A8V .A]A@ .A`Aɚ .A&A8V .A&Aw .ASA}{ .A u`A /? .A}Ae .A"Ahb .AwA06 .A uAp .A@5Ash .A A-/ .A SAXQ .A SA(Kz .A-A.A@NA@.A@AAa.A`vA.A JAX.A@+AM.AA/.A?Aj[.AA[.A`Ao.ADA /A.A$A9=.AHA.A A.A@5 AH.Aa_Ae.A@iA0-.A`IAp,.AAj+.A+XAPI(.A`}AF.A@iAP8 .A[A .AoA .AA.A@ϯA.A_A@.A A.AAใ.A  As.A Ae4 .AAX: .A$ڬA7 .AA: .A@߫A8.A{Aȶ.A ~IAi.AAX.AzSAH.A`]A3o.AܰAKd.AA`Ÿ.AA( .AqAp .A@5qA .A SA)U .A@A .A Az .A`A .A@ AP .AAxz .A@gA .A A0e .AA`T .A@p6Ax .A@,A .A4Ao .A@A P .A$A . .AaA( .AOAH .A QA8 .A>Ax3 .AzAxL .A _A_ .A A܀ .A@.BAܢ .A Aȩ .A@r"A .A sfA .AAv .AzAfP.AAH\.A@7A 7.APAʺ.A?6A3.A0+#A%.A,Al.AW91_AN.A#UAXw.AՀA+ .A`oAHr .A`VAR.AveA\Y.A@0A.AA .A+AH\B .AAa .AAx .A@`As .AyƺAc{ .AFAa+\ .A姟`AsB@ .AEAs-( .AMxlCAh1 .A5 AS| .A`5SA2.ADA+5.A/@A.A`A0S .A0A* .A /Aj .AxAH .AA] .AAA7 .A@AHכ .A{QveAIɷg .AouA2X .ACA=K .AsWAj .A#DA`) .A/<- AK .Aj>AR .AQXAL7)~ .A`mAu .Ahc_|AȔk .A@,A .A@pA:JK .AG%AZ .AxTEA .AP6E} A r .AOAA .AĝAP.A lAH(.AA.A^KA6B.A$As.A@A(.AApʲ.A`DA:t.A dAyֽ2.A/e} AeA(t-A@,CA(-A`KEA'?-A*GA-As HAXXb-A7IAz-A YJA%V-ARLAoD-A`lMA,]3-A OA`-AwPAׅ/W)-AX)Q~QA@-A}RA+H-AhSA-A`"TAH@-A`BGWA4-A@ZWAHC5-A@ilZA8a-A;]AGZ'A-A :z^A-AǨ_AF5]-AJ`A\BͲ-A'8`A U-Aݨ\RaAá>-AJ daAJ-AbAk-AaA-AeaA-AfaAX-AoHcAy-AŰcA-AƉeAHN-A:fAH-AhA-AjA&-A{AbkA t5-A 0alA--AݙlA~j-ADAm2-A7ĩA`2-A {AA(߼-A]=&ƪA|6ļ-A~A~-Ad=A+~-ABz-AIi$N+AP@a-A:1La,ALFM-A8wy-Ay@ =-AtS.A1-Aٶ/A/iu.-AWV0A--A@.!2AI 0-A 4AT9-Ac 5A/F-AF7A<;-Ay:AH-AAAh-AHEA0-A@6IA-A@NAp-ANA -AtV WPA}5mͮ-AѨQAΐ~-Aؗ-0SA0뫮-All9XTAw-AVJVA3C-AmWAl-AFXA4/-A AZA蛮-A7z[A-A$\AN-A@pbAP7-A ™fAE%-AmAO-AvA,-Ai1A(>-AA8-A -A-A4AX-AAQ-A AO-Ah(A,S-A8E8A[-AɑzGAyh-ATSAߩy-AZA*䑐-A,8[AANث-AZUACo˰-A'FA^-A`MW-A@-AU AH-A?FAhQ-AܱA`-AA-A A?-AeAX[-AAh]-A-<Aj-A A0=-AAP-A`dNAxǥ-A@mA@h-A PA8-A) A8ӹ-A7A-A0〽A]&[-AV;Aw-A<4ұ APw-A A9X6-AK.Aix-AAݽ-Ar&A@p-A GAH?q-A_A |-A`"Aι-AAHĹ-A.A;Ჽ-AK A#-A%`$OAn-A|A-A >=A;-AJAX-AI`WAeVɹ-ANXAԹ-A#߅A`-A`A.¾-A@8AB-A`]9Ay-AA>Ax(?-A5A؟-AU7A-A@E7Apf-A`ܬ5AXu-AFu6A_-A8Ad-AR8Af-A:A~-A];AxK-A:Ap-AM9A`6L-AA9A-A <AI-AIAdg-A?A-A?A>-A!AA-A;^DApl-ADAXa-A XAAOd-A`%AA4-A,@A0-A@`<A)-A \=A>-A ~?AU-A;9?AЀ-AH<AhM-A6:AsH-AD@Aw-A`$HAO-A ?TAȘe-AAlA(-AЕmAP-A |4qA*-AMoAx-AGXAp0m-A`[AHi-AYA`-A@>\AX-A@]A0-A _^Ajq-A$iA8-AvjA-AjA-A@jAH-ASaA~-AhcA-A`2aA8-A@cAP-A@kAS-AnA-A@T|A@-A͢A-A A[-AڅAO-A!APY^-A`DA>-AoA-A A c-A@ՍA-AAps-AgA-A[TA-A`f8A-A`At-A@rA RJ-A`{A]-ACAm-A AhD-A@xA-A@!\A8t-AzAذ+-AA[-A`ӔA@ -A=`AX-A98A,-AAN-A@ȍA8tv-AA`-A 8A-A 8CA2-A@A8gu-A@`˝A`a-AIA1-AApi-AqA"-ArA^.-AAPQ-A?Ad-ANAT-A,AT-A|AX(-AщA0&-A'A`f-AWAچ-A`bA-AAТ-A`MA^.-A&A-AFA'7-A']A[-AAR-AA`%-A"A;-AxALN-AlAȘo-A0A-A@MAa-A Axh-AA(-A AZ-AA8-AA -AA@c-ADZA-A`6Ahbm-AǁAp[-A"Ah&-A pAHu-AHA-AA#-AAo-Ap2Aʐ-AwMA?P-A A8i-A@ԭA+-A A$-AIгAv-AYA(-A FAH2-AWA-AdA-AŦA-AVήAʒ-AޮA~ -A`8A@-A`8A\-A UA3-AA~-A HAx\-A3A -A@Aȓ(-A֪A\V-A֪AUo-A`AH˔-A^A-A$A-AZA8-A Ax,-A`A0L-ABAȓ-AA-A A E-A(AJ-AA-AoA@-AA-A ɹA(-A"A-A`8A\-AXAA-A ȾAj-A`fAPv-A Ax-AgA_-A`A -A5MA-AA(-A`BA'-A JcA@1-AAŒ-A`A0;-A A-A@Axi1-AghA-AbA -AFA`-AA(-AAp8m-AAx-A BAұ-A@A0{`-AJAP/-A@AF-A J3A-A J3AD-AA-A.A-A@uAh-AA@OI-AA-A A1-A`AX-AAȲ-A Ax)-AॣA!-A@ At-A A+.A@ Aa.A$ARw.AHiA>.AAs;.A 'A+7.A`5A%7.A@5A-.AvSA-A@,"A0-A@A)C-Ao0Ar7-ASA'-AA!-A A-AA0f-A AY-Af A@`W-A AIW-A@ AV-A A^O-A@ Ah -Az AcE-A  Ah"-A A(-AA-A A`-A AP<-A A0]-A!A-A2A-A@r_A-A_AP-AeAA-A`A(-A AH-AA--AOA覟-A5Az-AV A_K.Aj A .A`m%A.A` A&.A`A2.AZAX.A$ A.A`d A(C.A@A-A2A9-AA-A`A8-AjAPo-AAhb-A`mA-A Ahё-A@PA(ː-A@A`z-A $A+-A A8gx-A HA֚-A`]%A(1-A# Ax\-A`mA-AHLAG-ASAw{-A |Aȱ-At{Aʨ-A{A0-AhA-AAw-A ɇA(m-A|Ap2-A&A2-AA0-AA83/-A@yA`.-AA.-A`KAZ-A`A83-A"A8-AAH6-A`A-AiwAТ/-A lA0t-AA-AAv-A`AYC-A ZAqD-AA(\E-A@WADI-AAȩ-A (A6-AAPo-A A -AA$-A AP8-AaA-AA8X-A@Y$A8-A%AǷ-AH[&A~-Ay&A-A.'AX-A<'A-A@i$A,-A~%A\-AV%A-A&A"-A`{&A|-A$(AȺO-A |g$A-AN"A8t-A@"A`a-A@ "A@-AHP A I-A !A(-A@!AHy-A#A@-A`Ab-A@WA0-A AKA(-A`A AY-A ZAPv-A Aq-AvAF-AA A-A@,A6-A`DKAx -A@A-A@5OA-AqA _-AyA]-A W A-A!AN-A J"A6-A "AH-A"AHi-A`#A4-Am#A -AW#A-A1$A9-A |9$A4-A Aq5-A}A88-A`Ad9-A}A:e-ActA!.AUAx$.A@,Ap4.A@A.6.A`9A0.A@A`.AAp-At(A].A@A-A A%AW-AK*A0.AAh.AA w.AvA;.A$A .AAXa.A6]AI.ARAȘ.AAP8c .A@W#AhI .AhA(< .AHA .A_A .A` Aw .A"A(K.A4:A.A`=A@ .A=Am.A=Ap.Aப=A.A @ATG.ABBAPj.A@FAD~ .A=tEA7p .A JAd .A`PA .A@,iUA9 .A VA5 .A0WAՂ .A@4VA@ .A 'VA`ӆ .AUA }{ .A`TUAl .Az TA}= .AZOA; .AOA5 .AOAe .A43OAI .AVSAr .AWAp .AXA`] .A@\Aa .A@]A .A}{^A .A `_AH .A B`Aذ .AQaA@) .AWhAH> .A`VjA .A AqA= .AsAx .A tA8 .AଗrA A .AGkA F .A ccAHPB .A QcAxz .A@\A.ASA ).AzVAb.AOAx.A MRA-.AKDAx.ASCAr.A[2EAЕ.A$FA8.A (GAW-A2IAe-AnKAe-A@oKAx-A(KA@Oy-A`JAH-AKAU-A`JAr-A cwJAQ-Aa.DA0-A&DA]-AF7A-AR7Ahё-A4i6AM-Aa6AR-A&;Q6A$-A&=6A,@-A8,%6AvQ-A~?N 6AA-AB5A[Y-A Ǯ5A-AѠO5A -Ar5AS -A p5Ay=-A~Ej5A=#l-A(oU5A|Atʖ-AϒwkD5AP-A3&75AP-AS.5AT=-A5*5A&C{-A *5A1t-AIk05A4m-A8:5AgŌg-AI5A8Cyb-Aw[5Ag[]-AfAVq5A[Z-Ax5A]W-A 5AV-Ah5AzV-A5AkW-A*5A Y-A 6Av~X<]-A!-6AT!a-AZC6A*?f-AiV6A&.l-ABe6A/^ws-A7p6Aez-A'v6A~-Afx6AТ-Aax6A-As6Ax\-A}6A]-A]7AX-A|7A-A ;A-A`EA-AGAY-A"RLA-A@i]AЄ&-A`jAF-AlAPMc-A`Ar-ANRAH-ArRA-A`f@A06-AM&BA-ArRA -A`QAp-A්*AQ-A$A{-A@!'Ah-A@,AP^-AsA3A(1-AfAp -AnA-A`uA؇-AtAh3-AFnAPH-A.A-A@i*AR-A(A--A(Aa-A.A(-A;1A@d-AxRAH-Ax SA -A@`WA:-A`EYA -A`EYAD-AT0_A(o-A`A$D-AcA-A@gA(9B-A`iA-A@$_AHP-A`Kg_Av-AO`A-A@iAU-A9~jAx-A-jA<-Ac84skA5-A\-kAU-ABlAR-A lA:g-AlAnF-A/mAGM$-A87dGmA-A*fR^mAP-Ave&cmA,gԺ-AUmA/m-AiK|6mA濓s-A:ݳmA]oS-A`:lAw{5-A<1slA-A@lAY-A`pAY-A5dvpA=5-A;D/pA-A@oA>h_-A~ oA6-AEoA !G_-A[oAv#@-AuoA!-ApA-A`v2rAH-AMjA-A_kA(O-AsA-AuAwc-A`wAЮ-A`vzA( -A`fmA9u-ARnA@}-A|AX-A |{A`2U-AaA~-A`aAX#8-A SA(-A@y {AXR-AA-AॿAHn-A` A -A$A>-AۆAC-A`xA(`-A`[ |A()-A@`AD-A l΋AS-AVA8 -A8A -AbA`X-A`A-A@ƋA@-A ˴Al-A@P Aȃ-A`KA{-AA-ABA^-AײA-Ap A0 2-A AA-A` A-A`CA@Ww-A@rAp-AXAHL-AA<-A AA)-A &A -A/A-AzA(\-A+A -A9A2-AA-AA(-A@AB-A_A3]-AsA@~-AcA-A |A@-AHA-AFA@>-AA-A/A@B-AA,-Aޘ AXFA-A!A-A*A-A1A-.A:A.A`KAAH.A KA3.A`GAX9p.A;JA^.AଧGA@F.A'MAĔ.AVA/.A,[A.A|_A.A/aAp.AqeA.A0kAM`.AAtAh.AvAx1.A`[%xA.Aڹ~A\ .A`vA8 .AWAѵ .A`]sA0 .AA0!| .A A .A`vAX( .AA9 .A@,AȠ .AA .A@6AЈ .AA`%! .ABA0 .AA@ .A A{ .AA` .AsZA.A9A0'.AA.AAp .AA*.A@?A l.AAxO.AA|s.A`f"Axm.A@ Ax.AzA$.AOWA5/.A4A<.A A].AAȘ.AA.A Ax1K.AA0݉.A+A.AQ AP.A@AM.Az%Apλ.A\Apλ.AA.AOA.A`uA.A@5bA.A ?At.A`KdAX07.AOA.AMA<.AAG.A`A8'.A`óAh/.AA3 .AAX.AaAIB.A@\A`.AA .AAH.A`A4.A`}Ah3.A qA.ApA.A@A.A A.AA .AA.A`A.AA`.A@`SAr.Ag&A1.A`sA`6.A8AH.A`]TA06 .AA8" .A}A .A@A !.ADAxM!.A@rA!.A`mAxX".A@#AX".A@EAm".AA3#.AA(1%.AA%.A A; &.A'9A@&.AkrAo2&.A>0AUWF&.A A W&.A\.A%7ld&.A 5>%Am&.A`ӻA&.AHAQ&.A@Aj&.AMA*.A`;)AR,.A@AS-.APAbZ-.A@ƍA\e-.AJAz-.A ?;A c-.A ?A8..A@jA..A|A..A@OAP ..A@#A..A FA,..AF A`rr/.AAr/.A(A8/.A+A/.ADAZ2.A 0Ad2.ARAH+3.A`A@>L4.A@pAB4.AA5.AtA5.AAx:6.AFAU6.A@eAx-Y6.AvnA$z6.AOAXp6.AA6.A A6.AAh7.AƘA7.A`KA6.A`AXi6.ACAxƭ6.A cA 7.A;Aȇ;7.A@WAXU7.A^A7.AţAd7.AbA7.AKCA 7.A rA7.A \\Aޘ7.A|/A؟7.Ak)A@7.A`KA+8.AsdA +8.A@ƷA8.A ZAV"8.A`B&APg18.A6AC8.A@qAPg18.AOA(m68.AAH8.A,AA8.A A <8.A`Ax=8.A`).AX@7.A@ A:B7.A 8AA8.A@?A:.A`8A̴:.A@PA:.A \ AhL*:.A ~vA88~:.AA؁:.A `A":.AIA';.AVBA`!;.A<A0<.A@#qAP/=.AAXn;.A A <.A`~Aׯ<.A >A=.AgAr>.A@A>.A5A=.A@GAx5=.AAx{=.AAm=.AF:AȪ=.A AA%=.AA0*=.AAR>.AʸAN>.A@rAxH>.ANA>.A@%A>.AA @.A0.APH>.A@Ax=.A@A=.A3n޴AF=.A0{Af=.A0[AuPW=.AkGA,g=.A7>mAz>5 >.A *$AD5>.A7% AbH5>.A,e滲AY|M>.A:[A|f>.AJAw~>.AAC)Ȗ>.AWAD>.At3ӲAWY>.A֌A>.A`KA86?.A@AA.AA8xA.AAp B.A`KnA0B.A jAJJB.A8aAp0B.AH<APB.AfAQC.A/AD.A@nAD.A`fAJBE.A"AP8D.A@ibAvC.A AA\C.A A9C.A AhB.AA0B.A0AMB.A A7C.A A%1D.Ax5AbFD.A@iA G.AQA@G.A%APQH.A`sA /H.AT׷ADH.A1AxD.A`AXE.A@AأG.A`AmE.A`A@1E.A?HAAHG.A`A H.A)yAjH.A OZA0{:H.AzAC.AwAȲY@.A ;A`@.A]A@B.AA0D.AAxIC.A (UAg@.AaAAA.ANoAhC.AzQ AX[E.A%A@F.A+A0*G.AAUH.A`A@5BH.A6AH %H.A`+AuD.AA0gE.A }AH.A A?CI.A la AhI.A`Aq`I.A`AJ.A@!Ab J.A@5A*O.A7AO.A:AO.AAR N.A"ApK.A`KAL.A`UA"K.A&AA@K.A`AJ.A34ApNJ.A"A GK.Az+AhLK.A65-Ay K.A--AwJ.A@#v.AHG=J.AQ/AlJ.A@ 0A(BJ.A /AHJ.A /A9!K.A`/A8x>K.A`1AK.A@1AJ.A@k2AX}J.A3A(K.Aյ3AxqK.AŽ4AXpK.A`5Ax>J.A7A=J.A7AxK.A]7A@9-K.A7AuhK.AH7A0K.Av6A(-K.A i7AL.A'8A`-K.A}8A L.AxT:APxIL.A`:AhbdL.A`B;A0*mL.A z<A.L.A@!BAL.A`CAM.ACAL.ACAL.A`@EAL.A EAL.A0$FARL.A{FAL.A}FAhL.A}MGA L.A@EGAOL.A HAL.A HA@W M.AIA8(M.AZHA;.M.AHA0M.A*IAIM.A@!bJA0ݒM.A JAx M.AIAM.A@JA NM.AinJAxM.A9KAM.A@DKALJM.A`KA(M.AWEMAxqL.ANA?M.AwZAWN.A$?dAHPO.AlA>"P.A3sAIP.A 8!tAdP.AtAYP.A@,uAP.AzxA֗P.AxAKWP.A{yAlQ.A AhQ.AAX0Q.A6A0Q.AtAXQ.AkAQ.AfA ZQ.A ATRR.A`A0*R.A`vAHÏR.AAͪR.A 8qAR.A厉A$R.A`tډAR.A@AR.AAXS.AơA@dS.A@֍A@S.A sA9S.A }AS.A`AȩT.AKAHT.AM~AHT.AAHT.AAHXdT.AzږAhT.AAAhQjT.AoIAJpT.AߜA(T.A cAf8U.AA#tU.AA2V.A &6AH iV.AAuV.A@eAhȤV.AAPgV.A^A`V.AuAV.Aw0AV.A@>iApc9W.AiAkW.A*Ax]X.AiWAiX.A6A茡X.A@AxX.A_ҭAY.AAZ.Aa!Ad1[.A@A;[.AwA{[.AtA0[.ADA[.AzA V[.A@A8 z\.A`(A}[.A`nAq[.A`KeAB\.A"ZAh/\.AA].A (A].AAv"_.A@. AF_.AJAb.AA c.AAd.A AxWQd.AA>A8d.AAd.A`]-A(c.A Aǣc.AA~zc.A@RAH_.AdAs_.A /AZ].A^A8|\.A=A؅}\.A@itA"\.AA\.A ZA8+\.A A0L].A (A(:].AB7AI].AAȏ].AAH].AſAH2].AX A].A ApAz].A Aأm].AQR A$].A` AD].A` A8H].A` A?].A6 Aw\.A A X\.A`T A\.A A[.A`y Aw[.A( A([.A . Aj[.A<1 AZ.A aB4 A[.A}5 AZ.A 7 A`[.A@): A[.A; AS[.A > A[[.A@r@ A[.A zB A p[.ARC A.>\.A@]E Ahѥ\.A@yH A{].AFwM A^.AU A^.AU A? _.AV A$_.A@y W AHzj_.A fX A _.AX Ap_.A=Y Ax_.AoVY A A_.AY AȘ_.A$Z A0v_.AZ Ay_.A`TZ A@u_.A}[ Ap `.Af[ Af<`.A\ AXH`.A`#] A0``.Af] A l`.A^ A4t`.A5^ A}}`.AW^ A3`.Aw^ A`.A@C^ A #`.A^ A(`.A@W_ A0`.AV_ A`r`.A`` A!`.A F` A`.AZp` A3a.A@#1a AYa.Aa Aka.Aa A̎a.A (b A Ra.A c Axa.A+c AHKa.A d Ab.A0d A (b.A`d AЄ>b.AOd A vb.A3e Ab.A cSf AEb.Abf A b.Aqf A0b.AUf A#b.A@f Ab.A3f Ab.A?g A@ub.A@>g APZc.AA@ -AEAx>-A@.AA -A`bVEA Z-A@ FAk-A@DAۣ-A@rFA0-A"KA b-AKAH_-AMAP^:-A@NA03-A 1NAŜ)-ARA`k-ARA`2y-AkSA)˕-A%-L SA= -AkRAͥ-ARAȢ-Ao6tRAX=-A-QRAϨ-A LAc-A@kHAĔ.AVA@F.A'MA-A ?ZAPo-A` bA/.A,[AĔ.AVA+-ADlA-A@iA0-A@jA-A@kA-A@5kA@Ɠ-AMGkAȶ-AjA@qg-A6jA=I-AVhAd-AhA8-AM`jA-A0jAN-A!kAH)-A OjA?-A@iA@}-A`iA-A6iA-A`bhA-A VfA@-A^A&m'D-A8x^A]H-Atg^AAK4M-AY^A5cR-AS5O^A:mK;X-AX4`H^AH ^-AZlYE^A_d-Af4F^Ai-AJ^A]-A`ml`A{R-A`A0-AbAx(-A"{bAHP-AbAC{ܥ-AUkO cADT-A 5cA`-A`ׯWcA؞-A`ocAp2-AcA{3-A`]cAh;-AcAJ\-A`cA&-AbA R-A@bA-A@ylbA-A6bA@q-A` aA-A`AP-A`A-Ah`Ap-A@_API-A^A$-A`^Ah-A@^A-A8O^A07-An]AH-A`]]A8-A`T]A(-Am]A M(-A Jm]A0%-A`i]AP!-Af]AX-A cz]A-AR^A-A`1^A)-AO^^Aܑ-A~^AXd-A` ^Aȓt-A Oc_AI-A5_A8s>-A *_A-A^A`-AZ]A-A[]A`-Aޕ]A-A ]A@-A*t]AP8-Aj]Ap-A@`]AQ-AT]A-AN]Aѓ-A&G]As-A=]AW-A2]A@-A@#]AЏ-A`]A-A@]Ag-A\A--A\AQ-A@\As-A\APg-A@i;\AH.-A@#\A0-A \A <-Af\AZ-A3}\Arz-A SrYAȺq-AGYA-ArXA-A&XA-A!XA -AWAe-AWAۯ-AFWA-A@WA} -A J!VA(-ARAP-ABm8Aأ-AM7A-Aa01A-A&0AC-A|0A0;a-A0A[C-A/Ah6-A;&A-A 'A-A 'AE-AZh%A-A`)"A-A"A(<-AAȇb-A`fA˟-A`TA@-AA tq-AmMAS-A"A -A$AP8x-A iAE-A|A-A@A-A`A.-A3A0{[-AC"Ah-A`KgA-AaAH-A@ d AH-Aa AHJ-A Ak-A`FA0 -AFA-A AH}-A$ A(-A` Ax-A6A@-A_ A`"-A AH?C-A A -AaSAg-A@AЄ-AfA,-A@AA~-A@Ah1-A/Aj-A`meA#-A,$A0-A`"ApS-A3QA-AZAc-A^A-ADOAȘq-A`DA-AAЄ-A`SAP-AxEAփ-A bA -AƩA[-A A}L-A ZA-A`AD-AzAh-AA0]-Ay2A<-AAK-A`A 0-A@E A d-A A)-A A-A Az-A} Ah-AA -A@KA8g-A-Ah-A`mA-A AB -A@ PA=2-A@AM-A@PAp-A AH-AfAu-AAx-AӸAl-A!Aг-AR $A8r-A%A-A(A-Ay(AQ-A E*A`_-Aq*A-A-AW;-A /A v-A*#0Ah-A`U1A@-A`1A0-A`2A1-Aྣ3Ã-A@I5AH-AP5A-A`7A(-A@yw8Aku-A;9A{-A /d;ApT-A1>AN-AfBAH2-A:DA8-AREA>-AךGA-ABKA0H-AOAf-AOA؎~-ARA-A@ JUA3-A5XA$-A@>?XA -AoZA@-A`]]AQo-A`KgA)-A[hA-A@!hAp|-AGiA-AĩiA7 -A3?iA(9-A^HjADį-AWʂjA-AjAb&-AͷjAYX-A(jAZot-AᨚijA?-A>KjA(-A jA"tcC-AԍjAЮ-AQjA-A` jAɺ-A` jAp -ACkA0ي-A`TlkAg-A@kA+-ADlAp.AqeA.A/aA.AHjA؟V.Ap\oA.AQtA.A0kAp.AqeAh.AvAM`.AAtAXt.A#|AP.A <AB.A7A.Aڹ~Ax1.A`[%xAh.AvA.A{AU .A@,hAw .A@,{AX .AହAhk .A,CAX .A@,A0 .AŒA .A`vA0!| .A A d.A A.A{A@ .A A0 .AA:..A`9Ae.A A@ .A A` .AsZA{ .AA(i .A`oAX .AA.A9A` .AsZAU-A BAB-A c0Ax$-AA0·-A`դA0-A`-Aע-A@AR-AʎA8:-AAx\-AkAj?-A AhD-AڲA -A@mAPM-A)Aq3-A cAת-A AH-A` A8-A_A-AyAp`-AAptl-AټAx-A.AXp -A?AX-A Af-AؿAX-A@ AXFñ-A AwA -ABFA`K-A#A--A)sAU-A BA|s.A`f"AxO.AAhnV.ARA.AXA!N.A@A7.ATA | .A@yUAH.A@ ~Ax.AzAxm.A@ A|s.A`f"A<.A A5/.A4A0Q.A@NUAp.A@ρAR.A@A].AA<.A A.AQ A0݉.A+AQz.AFA0o.AAl8.AfA .A ҮA`.AmA.AQ A8/.A+Ar/.A(A/.AWA{2.Ay8A#2.AA@/.A_A/.ADA8/.A+AȗP.A OAX0Q.A6AhQ.AA ҅P.A`,AP.A AAP.A@CA@kP.AvAQP.A`ӇAeP.A A P.A`AȗP.A OATKR.AgA@dS.A@֍AXS.AơAxWR.AޡA88HR.A 8MArQ.AA _Q.AAdR.ADATKR.AgAV.A@>iAV.Aw0AȇU.A*APiAkW.A*Apc9W.AiA1$U.A/CA0 zU.AAkW.A*AZ.AfA0[.ADA{[.AtA0TZ.AxAZ.A YAhZ.AIApAZ.A6AZ.AfAj[.A<1 A([.A . A'Z.A4. AY.Ah. APX.A`- AX.A`. Ap+mX.A`;- AgX.A7. A[YX.A4. AXTX.A?. APX.A . A83NX.A`. AXHX.A. A}IX.Ac / AX.A/ A>X.A &- AvQW.A`3- AUGW.A>- A>W.A-r- A(2W.A / A7W.AO4/ AGBW.A4W/ A((W.A@/ Aȥ%X.A / AH$X.A / A:OX.A`b/ A8PX.A@%/ AdX.A/ AceX.A/ AyZ.A0 A0Z.A1 AH]Z.A1 Aj[.A<1 A@Y].A-Z A!Q].A`- AV\.A@` A2\.A A\.A Ax-@].At AxY].A|] A@Y].A-Z AV.A AV.A@ Ax\V.A) A`V.A`w A8kV.Aീ A V.A AV.A A0&4V.A@ AZV.A= AȺV.A Ax$V.A c2 A(,V.Akb APfV.A<\ A` W.A} AW.Au A8 W.A` A V.Ak AV.A Ae.AHU A Ҽ.A`D+ A.A A]T.A. A .A& A0(.A53 ADӀ.A A}.A  A}.A`) A[|.A@iH A|.A} A{.AX܈ A9?z.AB;~ A%%z.AǙ} AxJy.APx A0Fy.A S7x A w.ANo A v.Ai A(iJv.A`]g AX[t.A ca Aur.AU A.tr.AT A=q.A@Q Aq.AQ A$q.AhmL Ahp.A@J AHp.AG Aȃ,p.A4B An.A; A`an.AՖ: A]n.AI: AHn.A99 Ann.A@#F9 AMn.A 8 Al.A1 Ax)j.A& ( Anj.A' Ah"i.A@N AKf.AV Af.A`P AHd.Ak A c.A Ab.A@O A!b.AF- A@$b.A) A`.A  A8@`.A Q A0_.AM A4F^.A^6 A*m<^.A Ak\X4^.A Ұ A.^.A`B AYz?"^.Ac A)^.A U& A ^.A_ AS].A*N A<].AΒ A~̈].Ay |$| A ].A#o AQ|].A4(l A a].A Fs AHƝ].A֮M Ai].ANVĞ A].A AH(].A= AU\.A`: A ].A Av\.Aj= A+\.A^q} A}\.A" h A\.A O Aa\.Aofrz@ AͦJ\.AW&s AH\.A39 A; M\.AhҦ A].Ax AZ ].A&n AG].A؀^E A].At A3].A`d8 AX^.A  A`.A@= A(E`.AD AL`.A`R A`va.A  AX)b.A R A^[b.A A^c.A[ AxX e.A L( Af.AW2 ATg.A`7 Ah.A`m= Ani.A /B AX[i.A C Api.A D Ai.A`yD Axii.A`D Adi.AfE Ai.AE AGj.A@ G AHj.AK A/6m.A c+Z AP/Zm.A`mZ A(9|m.A`[ Am.A\ APm.AtX] Am.A@ؙ] Ayn.A``` AP'Tn.A` An.ABc AX59o.A 8b Ap.A@}j A`&q.Am Ah՚q.Apuq AX&r.A@dv AXs.Aྮ A(s.A`D A0&t.A At.Ak A +u.AĽ A }bw.A Aw.A֓ A:x.Aɗ A>z.Aݧ A0}.A1 A~.A A%~.A`26 Aƥ~.A^ A$~.A ~ A~.A A@/.A An.A A(.A H A A.A`d Ap.A A¥.A6 Aأ.A2 AD".A AG.A`6 A$.A# Al.A A"..A Q Ak.A A* .A; Ap[.Aل A`C[.Ay Aw1.A= A؞.Aa AX.A+Y AR.A6z Ap,^.A3 AP .A' A*.A? A(.A A.A@ A0B.Aa AD.Aoe AY.AEe A8.A A0Hۊ.A`2 Aˊ.A Ad.A@W AP.A@ A/.A0 A߈.Aa A`.Aޛ AP8.AQ Ae.AHU Am.AgP AϏ.AƙO A@ .A`O A.AR A\.A`fvR AX.A P Auw.AcL A 4 .A #K AH.AcJ AX.A J AfF.AJ A8E.AD A.A C A@J.AmA AЄ,.A@:B AC.A D A(~^.A+E A៌.AF Aƌ.ApF AP.A@%5G AH .A$L A@ȍ.AM A1΍.AO Ah.A R A&.A/V AXVk.A5W Aڪ.AU Aؚ}.AS As.AQ Am.AgP A1.A^4 A` .AL0 AB.A`- A̭.Av+ AH>.A' AL.A`R$ A@е.A`v# A&%.A) A0X.A 1 A.AE A.A AP.A A_ڸ.A` A޺.Aax A.A`dE Aʽ.A` A;J.A@ ApNU.A AX(.A u A.AAv A0.A`B A6.A@߲ A0f.A A`.A9 A54.A@a AH..Ah A`̾.A AL.Ao0 AFּ.AC AXRǼ.Af A 0.Ap AW.A`̃ A`.A}Ҷ A .A A@B.A API.A@ Ao.AR Aȶ6.A@ Aʱ.A_ AC.A`} Ax].Ao A.A@G9m Aܭ.Ase Aڭ.A 1je A.A d Ai.A c Aذ_.A`Kc AjY.A`Íc APk6.Ac A|ج.A@a Am.A̵` A.Axt` AX.Aa` A%k.A` Ac.A"` A02.A a A.A a Ah+.A_ A.AT A+.AS Ag8.AS AHC.AR A.AR A.A@fR A.A ҚQ A+.AR Ax.A-W A;.A/^ A텟.A"` A@ϟ.Aidb A .Aje Ah.AOi A4.A`m AR.A Pq A`.As AÓ.A`w A0j͓.A`rz A\.AM| A.A l} A\.A@ A?m.A Ax.A Ap.A B A@.AЗ AX.A@Cĵ A! .A cz AHU.A A0;.A@i A$.A ? AfN.A`D AY.A > Az.A( A0.Ag A.A AH.A| A .A[ A8".A7 Ap.A Aʧ.A A<.AZ A .A@, A.A AP.A0S Ag.Ak A`Ce.A@ AY.Al A"X.A@m Ap.A`e An.A3f A\Y.A Zh A@K.A`  A4.A@P A0.A@yo AH0.A} A.AK A_.Ap AY.A`D AN.A% Al.AՖ A0.A@a AHi8.A A.AY A .A$ AXR.Aા Am.A Abq.A ARb.A@ A(-+.AI A0囨.AD AH,.A`& A.A AXF.AV A@.A- A(&.A_b A H.A`i Axhp.AR- A.A ALҨ.A Aʨ.A 1 A@.A) A@A{.Aѡ Ah/P.A`$ Ac.A A@`.A  A蕾.A ? Ap~.A^ APT.A^ A .Aa A@.A`V AMڦ.A`V AH̰.A t A.A`V AHl.A@ ABE.A@ A(.At A.A \ A舭.A ( Ah~.A A( q.A@ AP/l.Az Atg.A`; Ap.A g A.A@U A1.Aߜ A𴭦.AH AƦ.A ALަ.Aa A.A- AO.A A4.A)F Ax).A} Ay.A A* A).A`mx? A.AN A A.A*A A.AxA Aʰ.A&@ Aӱ.Ae= AD[.A@g; A1.A^4 A;.A (wL A-.A@!L A}.A@%L A.AҠL A .AN A.A@uO Ap.A (P At.A@Q A.A=Q AX.A (Q A ʡ.A@RQ As.A qQP A.A3O AH@.A (?M A;.A (wL A.AoR A8{.ALR An_.A ZSR A@3.A ZR Aؚ.AT AP^.A@U AX.A@V Af£.AoW A>ѣ.AX A.A W AK.A@yW Ap6.AIV AH)M.AU A𒎤.A=wS A.AoR A(s.A A7.A=GA0.AAh̨.A͝AG.A^mXAƯ.AA_.A_MLAxa߰.AQA{H.Aq>LAiɬ.AkPA +.AHAMo.A|bA8.At@AA.A&A`Xz.AA{sk.AHDAu`.AfbQAY.AᰴA6W.A$yAp a.AJ7AqzTo.A_A弁.AoP67ABoʗ.AYrZAY1.A󐜘Ą.A|gAM9.A@d|DAE;5.At&5AH&.A@8Ab.A*j)AJHc.As8.A(#lة.AHGA̴.A ltAZm;K.A,.NAS.A0u AY׸.A_,EpA.A5qAR.ArAX$ .AtĚAb&.AAb 1.A& pA?.AE ƛA P.AAT.AAn.AqǛAa>.AG@A_p".A9Aݫ.A ]A' .A!xA Σ;.Ac}ANJ k.A}SNnAS .A PJAH.Ǭ.AAɣ.A |*A9.AAf\Q .AKg갈Akn".AWAŵ9.AJքA>1Q.A Aqg.A|A|.A QAt.A@AP,.A ZAh.A`Ahċ.AߴA.A uAj.Ag-A]Я.ATA ѯ.A@A0.A`<A8.A@AU.A=ܗAU.AA-.A`堗Ap8.A A@<.AAA.AAm:.AsAp7.ARA`A.AIA L.A <AXJY.A Am.A`ȖAԅ.A@>A.AA۠.A ɖAxW.AAp,.AൖAb.A@`A _.AA`.A~AX.AaAPɹ.A_Ah@.A ,A汰.A@A.A@Aϰ.A@A.AוAN.A०A.AMAi.A= A%.A A".A AޔA8(.A єA(.A@AiW).A oA56&.A~WAּ~.Al+Ao.A7`wAг.A։A.A5@A.A"1]SA[qW.AA2>-.AgA0v.AAPɰ.A1A汰.A^טhzAK.Ah1Aa~H.AdwApň.A䕀AV터.A/EA$&.AQ(}A%%.Adx|Ai..AU{A0 .A-N zAB&뻰.A 8xA]S}հ.A;wAC.A{HvAѼ.A8 VuA5l=.A/issA)&i.A?HrAFW.AÉqARk'ͱ.A pA zj.AF}oA9;@.A nA~.AemA1.A^ mA .AؖIlAk)9M.AD$WkAlY.AF| jAȵ.A`ijA֙.A2?%rjA:!.A2?%rjA0.A`ijAI[3.A.A@6jA.AgjAP.A jAdb.AVjAw.AFjA0U.AjA .A`kA.AT>kA .A(kAᴵ.A`>kA0.A@kkAѵ.A qkA;.AjkA@.AkA@F.A`lA.A wA.A@ wA@ .A (zwA0.AwAh..AxAM.AspwA(`.A`twA.AwAxX.AwAmߴ.AxAuڴ.A xAѴ.A@yA˴.A cyAؒô.AyA ɴ.AyA.A zAٻ.AtRzAX챴.A6zA.A@!zAPM.AzA.A@P<{A.Aw{Ai.A@%{AX.A 8{A.A@{AXF.A A{Ap|.A@,&|A.A`B|A.A`]h|A.Ai~|A.A||Az.A@g|APk|.A|A6z.A/|AH?u.A|A`%l.A c}Amd.AA}A7b.A@p}A`v_.A@}A@W.A |}A8P.A }A\N.A@~A`R.A@L~AH:R.A~AM.A`~AXK.A@~AE.A~A@.A@A2.A`T~A*.A`AA*.ABtA0X#.AAЄ.A A.AA.AYAc.A@A@0.AEA0ճ.AZAͳ.AAƳ.AUAX³.AnAX³.A#A.A_AhMų.ApAhMų.A3A0ó.A ZAų.Af͂AhMų.AxA˳.AAʳ.A@Aأӳ.AAҳ.A@'A(ٳ.A 0ABڳ.A SA8ݳ.AyA.A A.A`oAp.A Z؃A .A`A0&.AQ A(.A`]#A.AQA(.A pA6.A/A.A`A.AEA(.A0A3/.A\Ax)4.AA;.AAxI.AȅA:J.A<AJP.AAS.A4AX.AA].AVAPg.A@5A#.AA{ܴ.A`KA.A4}A).A wA@uB.A#A@uB.A@Ax.A@A-%sB.AXcCA)ok.A$A"߷.A/AA(+.A |Ui~A*Ys.A ]}A.AH|ACej.A |A-*6¸.A&=|AfMŸ.A0|AplǸ.A |A0 ɸ.A7܄|Aɸ.An|AH75ɸ.Ak Y|A*Ǹ.AwC|AŸ.Ag/|A¸.A?|A=-.A} |AẸ.A{A}=8ڔ.AQ6l^{AO|r.ALzA6OR.A&PUyA37.A/)=yAH.AuxAKm .AvPxwAX.AUOvA.A ~uA}/S.AWtAc3/.AmzDDsA@.ABqA~5.A)qA+.AԆyqAD-p߷.A)䤭qAqݷ.AqAX=ڷ.Ay[qAz9b׷.AO}qA(~ӷ.A`qqAڷ.A@qAmj߷.AN8LqA.A \VqAme.A^qAX.ADcqA_W.AhPWqA^M.ADqA.A*qA~.A1LqA◄.Ag5qAԕ".A.hpAVaC%.AHRpAk^c'.A7pAc_(.AzpA0(.AgpA(.AlDpA`+.AepA[T0.AoA4"c7.AoAc@.AgoA}g@.A^doA;@.AqaoA@.A'_oA3A.AK\oAA.A ]ZoAECB.AR}XoA'LB.A@ WoA;՜C.AGKVoAwXD.ATiUoA5E.A,tUoAE.A) i&VoAuۃF.An'D0WoA'G.ASMXoAG.A@ZoAjI.A ӈ^oAO>K.A}XaoA(M.AboApsO.A2ĿfcoAQ.AhboAeR.A `oAT.A;g]oAcV.A &YoA>V.A!NUoACKoA! W.AjHoA렒W.A7=PFoA2X.A~ DoA X.At'oAs.AoAx v.A膆, oA` z.AmPknAF~.A6}nA:(.AEnAҖ.AWnA2,ˌ.A|zV_nAP'.A nAd֕.AIBnAmR`.AnAh.Agd߯nA?L.AnA: P.At]vnA.AnAy ۤ.A`nAI.Aڱ4snAϣ.A%I^nA? .A0InAܙ.Ag]>6nAG.A%nAig.AFnA.A`K nABⒸ.Aq9k9nAzב.AlnAc䐸.AnnA .AunA^.AN=mAO׎.A7mAի~.ARcmAU.AڊfmA].AdmArq.AdohmA .A&,mA딏.A4>FmA R.AmA7a.AÈdmA`8.ADmAǘ.ACW[mA.A۶mA֜.A>)mA@.AmAD.AmA虯.A SmAX.A SmA0Ƚ.AmA.A4mAĸ.AmAʸ.AmAи.A=mAո.AmApٸ.AmmA߸.AnAp}.AWnA,.AznAx.AamA .A@mAb.A@`mAA.A mA0.A&mA.A@`mA.A`ʋmAM.AmAM.AwmA).AbmA0.AYmA.AFmA.AR8mA.A`$mAH.A`mmAXF.AlAp<.A`dlA.Ag.AlAPf.AXlApf.AslAP8o.AǔlA z.AWzlAX|.AzlAP@.A{lAX솹.A3glA3.A@ OlA`.A@zA ^$.A@#MzA.A/0yA.AھwA@.A wAPZE.A@vAy.A@vA8D).A`muAY.AXuA.AatA.A@WtAP.A@ytA.A9-tA-.A@sA,8.AsApV.A HsA x.A sAp.A@sA.A 'sAjD.A;rA&.AJrA0].ACqA&k.A6pA^.A@WmA( .A3mAxǢ.A lAX(.A@N.lA[.AkAx.A(kAp(.AjAhC.AmjAZV.A`viAZ.AiAXc_.AviA3\.A@iA@c.A@iAk.AMwiA.ADhA׈.A`dhAh/.AkhAY.A;#hApJ.AgA.A`ӃgAR.A?gA@5.AMfA].AǕfA.A>fA..AfAp_.A@>eA.AeA0j.ACeAP.A /dAHaC.A cAX,h.A@EbAw.A@ZbA.AbAX.A \aAȺ.A`AU.Af`Ah.Ae_Ax-.A_A.A4^Av.A^ApN.A ]A.A l\AX.A`\Ax.A&[AXt.AD[Ax-.AZA(`.A`YA.A`YA N.AXAG.A=XA.AqXAw.AXA0.APXA`.AWA`q.AWA(`.AHWA.A WA7.A`)iWA.A`!WA06.AaVAE.A@>VA0%.A`VA.AdVA8.ASVA.AK!VA.A cUA8.A`)]UAG.ACUA襝.A TAV.ATAH.ATA.A=SA|.A4SAJq.A@SAxj.A?SAe.ALSAY^.ASA`TZ.ARA_.AvRAp|g.AARA s.AQA`s.AQAȔl.A`fQAk.A+QA^.APA\.AjPAHX.APA0R.A9 PAVN.A`9OAppY.A cUOA@L.ABNA8bJ.AaNA7H.ANAJ.A@PNAE.A`NAHD.AMA(L.A0MA8H.A*MAH?.A@CLA1G.A \LA(D.A-LA&B.A`KKA)D.A`m,KA$M.A`BJAX[A.AJA ?.A[OJA0;.A`IAE;.AIAZ..AIA/.A@WHAP/.A@.OHAu+.AW/HA..AGA`).A cGA (.A ~EGA@+.A`8GAx".A FAT.A@pFA`G.AFA..A&OFA.A` FA.AEAa.A` EA.A3EAP.A \EAxz.A@DA@q.A DA(.ADA".A `DA.ADAp.AqCA.A CA.ACA.A@\CA.A ,CA?.A CA.ABAX.ABA@.AiBA@1.A$BA.A*AA.A QAA.A@,AAA0.Ap0AA@ƽ.AYAAXƬ.A_AA8.A9AADy.AAAY.AXAA`4.ABAP.A9aBA.ABAK.ABAhQ.A3BA.A`AA.AAA臽.AEAA5.A@A.A@A(1.A0@A88.A@A.A@5}@A.Ae@A.A#@A~.Aj?A.AM>A8.A>Apڠ.A y>AԱ.AW>A0j.A=A8'.Av=AH.A E=A .An=AHr.A<A@.Ak<A 0.A`T<Ax.A B<AR.A <Au.A?;AH{.A@E;A`t.AH`;A`p.A@#5;APg.AN+;AhT.A;A&8.A`:A&.A:AX.A`:Apy .A R:Ap .A6 :Aa.A 9A .A`]9AH .A09Ax.A\9Ax .A@69Ax.A@58Ap .A8A`C.A 8A*.A`KI8AЄ.A 8A.A7Al.A7A؁.A|7AH.A@6A.A"6A.A`5A.As5A.A95Aѫ.A 4A.Ayb4A^.A34A3.A14A0Y.A@ 3AȂ.A 3Aȥv.ABM3Ai.A +3A)Z.A02AR.A` 2A`9.A@R2A(0.Aa2A..A1A/.A`T1A0-.A81A&2.A81A&2.Ai1A2.A81AB,.A 1A ,.AV0A0!.A0A?#.Ap0Ap=.A$10A.A`/A .A`/Ag.A@ /A_ .Ap/A .AB/Ah".A@/A.A O.A.A ۡ.A.A`p.A.AN.A=.A.A.A -AP.A ~s-A.AT+A d.Az *A.As>(A`.A%AE.Ay"A.AJ!A j.Ak Al.AC Al.A@W0A0j0.A&AS.A A@1.A lG Asw.A` Ah@n.A`] A5.A@> App.A`m4 A8ɾ.A@ A.A AX5s.A AXyK.A`8 AH.A A.A#OAhD.A(Aig.A@Ah .A A~.AA.AIAW.A3AV.AAp2.A^AU4.A ?AP^1.AAp#.AA&.A@ hApÿ.A A-.AA.A@pAh◿.AA}.A@A].A`fjA`E.AVPA+0.A /A##.A A.A)\A0e.A@Ax\!.AA0.AtDA?.AAI.A AU.AAl.AAXJ.A`ܳA.AnA@.AdAͿ.A vA޿.A@hA7E.A0.]A.A$\Ah..AAp_A.A /A M.AAR.AăA)ZZ.A|Anb.AŤpA< i.A_A p.A;JA@u.Aw1A\$x.AXA|.AŽAV.AkˁA.AA(-Aҍ.AA.AIAb.AUA.A0A0Թ.AqAP|.A<A;.AhA0.AA`.A`]0A.A@CA8I.A`A.A IA.AqA.AAT.A AJ.A`mBA}.A A@w.A A(yc.AOA@Y.AsA/S.A=AP;.A-RA5.AA.A` YAt.AV)A.AA.AiA`#.A%A ).AA`&.A@,A]8.AnA?.AvrA(yc.AhA?.AVMA0{L.A.A).A`KAx.A@%A(.AV2A.A@poA.A`CA.A/A(.A- Apܿ.A$Aֿ.AAٿ.A@5A`Կ.AA̿.AHA0̮.A A@>.AA.AAʔ.A(A0 s.AWA].AbAR.A(A 4H.A AoD.AtAA.AcA<.A@7A +.ARA.AHAr.AIcAx.A`A([.A4A}.AqA`.AA!.A@yAe.A JAP.AAج.AAX׽.AAX9ҽ.AA .AA@.A~A8㬽.A`2qApp.AMAј.AgA(.A@AӋ.A;Ah.AoKAd.Ad8A8\.A[AX.AAT.A AI.A0A=G.A ªA@A.AྑA6#.A@hA.A KA`.A@ A.A &A@uټ.A&Ax˼.AaA Eȼ.A 1At.AA .AoAQ.A?;A ^|.AAcQ.A8Ai;.A,XA`-.A +Ad.AvA.AVAO.AAS.AAһ.ARAJǻ.AA@_.A`ӑA.A jAd.A ZAAW.A A&.A A .A`"A(q.A-A}.A gA/f.A`;AT.AyA=.A`Ap.A~AxF.Aq'A.AA .A@gAP7.A 8eA; .A5A .A@>A7.A SA.ALA.AXAQ.AzA.AAp ݺ.A`mA}޺.AAhӺ.A#qAQӺ.AOA.A -Aк.AAhú.A`A.A@PAT.AA@q.AA 4.AEAع.A9Apҹ.A A̹.A"A(`Ϲ.A`AXŹ.A"Aﻹ.A Ao.A (A3.Ak>AX9.A A@.AA˘.A`ܴA`P.A yA.AWAks.AtAc.A`A@W.AzAZ.A*AW.AQAQ.AAE.A@dA[8.AA`1.A+A((.A Ag).AA +$.A5A%-.ARAȥ.A`Ab.AA.A`mA.AhA@.AA.A`iAX4~.A 7A4.A@GAP.A-Aŷ.A;<Af.A AX֍.A Axdv.A Aj.ADA^.A.A؟R.AEA8ER.A`"ZA.AA(.AA8.A@NAxõ.AXAx-.A (EAz.A;zAPr.ANA(f.AAa.A@CAW.AXA.A@i"A.A-Ahѵ.A AH.AӗAXd.A`~A!.A`mAX0.A=A+o.AgYAxz.A /IA𴉶.Ad!Ax.AAAն.A.A A`%/.A@A.A AA.AkA.A`#A).A%AP.AAPβ.A`Aǿ.A`Aг.A1A}.A?WA*.A`Ag.A@A.AHA.AHAP.A@A .AA.A?Ap.A@A׫.A җA.A Aս.Aj_A(m.AX>Ag.A%A Dz.A (!AƲ.AA0.A@A\.AoA2.A@rA9.ATA.Av:AH̉.A`8A@.A$Ap.A$Ap.A@AȲi.A@A /j.AAu`.A}A_.AA ].A8AYd.ARjAsr.A`fCA`{.A AIA`{.A@Ax.A Apw.AA.|.A?A.A`)A.A ONA.A?PAњ.A AP.AAp .AA}.A'Asr.A@Ax)k.A AY.AAhc.AAv.AAo.A _A_.AzOA0\.Af AS.A&AMT.AǵAO.A?^A0K.AAwN.A` A(1X.AzAm.Ag<AHac.AsAx6i.AApu.AzA跌.A`KA8.A`Ab.AA@.AyA(m.A@ydAȱ².A@}A`ʲ.A`bhA-ղ.A@gCA.AvAA?.A`A.A`A.A`AX4.A`A".A8A01.AgAxF<.A:A oH.A@AL.AA"S.AAPX.A@A0e.A@AIq.A@!AH{.AA@.A@5|A.ACA\.A-6APn.A^SA.A0AP.A`mQAPg.A@EAh.A@y,A.AAp .A@`A .A;A0.A-A.A A .AA.A_A.A \A .AڗAp/.A@AXE5.A@,A(N.A |A^U.A'A].AA_P.A@pA`v.A@/AQ.AoA.A Axd.AXqA.A`vAK.A`mA9.A`yAݍ.A!A8R.A`yAL.A5AHKl.AA0i.AA@F.A "A>.A`vA>.A9AQ.A@AhN.A׉A8.AzA#.AA5.A@ѿA01.A|A@B".AпAhb.A@aA*.A qBA.AؾA01.AA"/.A`A.A뾾A .AaAM.A<A.AA.A_Ahb.AѽAJ.A@,AJ.A )A.AAȲ.A@>&Ax5Ǵ.AA}.AfVAY.AA(̴.AAlʴ.AȼASѴ.A A0ߴ.A`o3A8.A ,AN״.AdA0.AA.Aq̼A(9ش.A@A״.Aj|Ah.A`oA.A UA3޴.A%ASѴ.AػAD̴.AwApJƴ.AA.A ջAx.AAδ.A xAfд.A@A8ʴ.AA ɴ.A@A@.A鯼Ah.AA@|.AA.AkA.A/AȒ.A Ap,}.A DApa.A@ixAH\.A++A8E.A HAP^G.A`^Ap).A`vA&.A iA0-.A-A08.A/APo5.A@AD.AҼAJ.A`Ax.AmAI.AAH:C.AA>.A@gA6.AA84.AXfA@;.A=AH:C.AoNAV.AI"AT.A@CAP8Z.A`A\.AA2`.A A].ATAPkc.AwA0d.ATAPj.A Aei.AAXs.AػAu.AAy.AjAv.A |ĻA@g.AdܻAc.A |ĻA_.A (ȻA b.A;޻A\.AAxX.A ջA=T.A;޻ApW.A@NAgR.A0AE.AAk.A0lA(\^.AAXj.AٺAtn.A@yA(.A ƺAB.APA8.ACA@.A@pAȲ.A@ Aˣ.A_A؎.A ZAɴ.AAHaִ.AA1.AR5A.A@iAˣ.A DA( .AL*A0e.A ~Aei.A@ A[.AºAU.AA8.AXպA0-.ADAL.A`IA0I.A|3A7).ArA|&.AdA).A ]A%.ABBA>!.A HA`!.A@.A!f.A}A `.A PAQ.A;iAsM.A7A00.A`A.A@iݸAi'.AA.A4A.AYA4.A8A#Գ.A@,ոAh.A AH.A@Ap.A*A0ȳ.A@!ԹAW.AιAO.AXAe.AiAP.A@KA0.Af As.AԷA`.A A.A@`(AA.Az۶A.A@Ah?.ADA.A٘A.A޴A.AAFٲ.A Q{AHɲ.AZAH:.AdCAX.AsAp_.A&As.A@5سA(`.AųAQ.A<óAD.ADAX(=.A@NA A3.AUApp&.AwA.A ײAx-.A1Axܱ.AXA0.AD1Ah̨.A6A".AAeɱ.A ZA୩.AA.AHƯA}.A;uAd.A`Ah N.AcAЦ5.AaAX8.AAG.A JYA`%7.A@EA6.AX8A#H.A@٤A(yI.A`AL.A@A(\P.AAP.A A cQ.AA`a.A@p A£.AKAxi.A9TAl.A]AҠ.AgA.AqAҠ.A |A(m.A APj.AaAã.AAp.AǟA0f.AVA.A`A.AXA`v.AA.A3A.A3A.A3A۱.A`f$A .A +AL.AsApD.AABk.AzALa.A A/.A`Ap[.AAP.AX AxX.A@`QA.A A>.A'A滲.A@GA.A`AH\.AA)h.A7Ah3.A@Ay.A`mA06.A lA].A@ AH!.A@ A}.AxAp.A@5nA.AcA.AZA޾.APAxG.AHA8b.AjAA:.A;Ap߶.A/7Ap_.A^4A˱.A03A83.A3AhL.A A.A#Af.AܥAJw.AWAH.p.AA>k.A`"AFg.A lƤA@g.A4ƤAb.A ɣAȘl.A Ae).A \AД.A`) A8A.A@A0.AƛAȥ.AfAa.A@,BA[.A/mAH{.AEAH?¬.A`]zA`qC.A笀A襛.AM{A&.A@&{Az.AdzAd.A ?zAPP.A`yA=<.A yA7.AqxA%..A`ܧxA.A@wA3.AvA<.A mvA0 ͪ.AVuAâ.A StA~.AF A5.Aբ A |.A¢ A.As AL.AR A`e.A" A .A J Ap.A(k Ah.A@f AЦ.A \2 A.A Au.Aՠ A.A A(.AHC Aٻ.AI A Е.A u AX9ƕ.Aw Aʕ.A`] AЄՕ.A= A.A@a A.A A.A@6 Ap6.A AHY.A@r,~ A(k.A}} A8j.A} AXp.A} Ahn.A} Apu.A f} A{.A`+} A8.A | AU.AWt| A@.Ad9| Aȶ.Az{ A?.A@{ A͍.A{ A(.A@={ A[.Adz A\.A |z A.A y A`ӥ.A*y As.AIy AXҟ.Ax A`.Ax AH.AӉx A@.A=w A.A@v At.A`v APH.A@Eyv A3.A}v Ab.AjKv A4.A@y@v A.A`v Aج.A u Aٷ.AZiu AL.A H!u A5.A`Tt A.A@Qt Ap.A`fs A( Ɩ.As A<і.A {q AӖ.A`mpq Aؖ.A@Zq AqՖ.Aq AԖ.A* q AՖ.A q AHٖ.A` p Aۖ.Alp ANٖ.ATp AӖ.A p AUՖ.AKo AԖ.A@Oo AȲӖ.Asn Aؖ.A@Gun Am֖.A&n AH?ږ.A m AՖ.Aom A ۖ.Aւm AH?ږ.ARm Aޖ.A@%m A@.A։l Aܖ.A ql A@.ALl Ax)ߖ.A +l A ۖ.ANk AP.A2k A@.A^k A.Apk A .A j A.A A"j A.A4i A.A`B:i AP .A /h A8.A|h A.Ag A.A@f A c .A@f A<.A@i^ A.Ag^ A8t&.Afg\ AP|:.A`T$X A\.A`KP A`b.A`P At.AJ A,.A@oC A".AXA A.A@ A A}.A= Ap<.A9 A ͗.A1 Abؗ.A0 Ahؗ.A/0 APߗ.Ad. A.A, A(.A@>+ A`T.A' Ax.AF& A.A`Dv& A.A:% A7.A# A.A " A(q.A A! A.AB AP.A A.A`j A.A Y A.A A((.A@ A*.A`[ Ah4.A  A2.A$ AP:.Am Ad.AP A8.A A.A? A؊.A ANj.A@- A.A AΞ.A1 A.A  AP.A` AD.A A> AsK.AD AsK.A@$ AM.A A@I.A AI.A AM.A AM.A qt A2H.A$= AhbG.A` AQ.AO AsK.Aෝ AO.AO A@I.A Z% AsK.A AE.A AH;J.AЊ AH;J.A}F AH;J.A ApG.AT A(F.A;B A(L.Aa AH;J.A` A K.A@W A`B.A, A?.A A`B.Az] A@>.A` A5.A` A3.Ad Ah7.Ac A 7.A`% A;.A Ah7.A@ A<.A( A 3.A< A@_:.Aj A3.A`ܘ A0.A`a A8.AF7 Ah7.A6 AM6.AL] A/;.A A@.A A 3.A<5 A &.A Ax}.A` Az.A` Ay.A@#c A@y.A@ Av.A@X A@ u.A AجU.A A59.A@5 Ax*.A@WY A.ADM A.A  A.A2% A(×.A A@.A A.A! Am.A@.Q At.A`қ Aplu.Aթ A@º.A@ Ayė.A; Aq.A (C A.A/Z A$.AU A쥙.A!~ AXh̙.A3} A.A`F| A.A'| A-.A@Wz A9.A8y A(.Aiu A0.A`Kr AX.ADh A.A g A8.A@yg A.A9g Ap.A g A8#.A-f Ax͝.A@f Aם.Adf APx.A@,=e Ax:.Ad A0X.Ay]c AEj.Ab Ah^q.A` b AXWz.Acb A .A/b Aԝ.Aga AX,.ADa A8.AO` AL.Ac` AǞ.A.` A՞.A`_ A.Ah_ A(.A *^ A .A Z] APM.Ah\ A@FQ.A`=\ AhQY.A\ Ah^.Ap[ Ax.AZ([ Ah|.AZ Apۈ.A`Z A.AZ A.AY APY.ARY AX.AX A0Y.A=uW AX#.AV A".A`MV A/.AU A0@.AVU AJ.A5U Ap.AS Au.AS Aʹ.A}Q Ah/Ԡ.As@P A.A27O A.A %O A .A/N AX(֠.A@yoM A.AL A3 .A oK A`.A? K AQ.AI A녡.A`F Ahơ.A`KC A(m.A 4B A| .AΊ@ A.A`@ AxA.A= AppN.A= A^.A}; A0m.A+v: A0ݒ.A`7 AĢ.A 7 A0.A@"7 Ar.A 5 Ax.A 5 A07.A}I4 A(.A c3 Al.A`2 A.AE1 Ab .A Z. A>).AQ, Am=.A`m.) A3.A /$ Ah{&.Ao A.A q A8.A0 An.A A@^.A` A.A Af.AE A`l.AE A@F%.AK A;7.A0 AB2.A`m AwI.AZ A@<.A@P Ah].A A.ADJ AXW.A@P A.Az A`! .A AH.A| Aȃ.A Z AUs.AC A@.A Aw.Aෙ A ʝ.A@ AhJ.A@= A.A^f ApkҜ.A A.A Axa.A`K Aʛ.A` A8gs.A A(-c.AW A.A@ Aħ.Aa AТ(.A Ahۙ.AQ A(͙.A@ APxW.AӇ A8ј.Ax APx.AJ A.A A.A0 AY~.A A8D .AL A.A A.A$ AXՕ.A` A`.A@5 A|.A`mT ApA~.AM AX.A@Ϯ Ap0.AQ A.A AhE.A d AxWړ.A@#^ A6.Aƽ A8Eh.A A(\.A> AP&.A) A0.A Am.A(y A`.A1 Aȥ.A3 A .A}׬ AS.A APo.AE A j.A@6 Ab.A 87 A8k.A`s A?.A"~ A`.A: A&ؐ.A@ A .A` A_.A@E8 AB.Aoء A>.A A`$.A{ A.A 뭟 A8.A@G A.A1 A.A` A@.AM AV.A$ AU.Aܘ AK.AX AJ*.A@ݗ A .A9 A_k.A`v Aٌ.A q Ah.A Apɞ.Afނ A83.A ւ AH.A A^.Aa A瀋.A At.Ac Aj.A F A@1\.A`]" A0H.A A;.AÁ A5.A2 Ap'.A A.Aw AX.A@W( Aq.A` A܊.A A2ӊ.A A8Ɗ.A(} Au.A@e A`.A9 Apz.A ALH.Ai A.A~ A3.At~ Ac.A`N~ A8E.A (:~ Ap.Aq~ A6Ή.A`o} A8.A*y} A0峉.A*y} A.A`)S} AN.A5} Ah.A@} AP8w.A-| A .A&{ A.A{ A;܈.A b{ A0͈.A9{ A@่.Ad{ A(".A~y A.A Jy AP'.A`o'x A@.A`w A .A<%v Anj.A )u AX4B.A\t AX[.A@s A(m.A@s A.A@es A-.A@r A _.A`Qr Az.Aq Al?.A`"p A.A|;p A@F.A@o A3.A o Ax.A`dDn Am.A;m AT.Aem A8A.A Zl A0.A@wl AH.A Qk A.A /Bk A.A@nj AX҃.A`վi AG.A`h A(K.Ag A_.AEe A@߂.A?xX A,.AOU A(;.A 2N Ag{.A`H AQ.A B? A8ń.AXB AA.A iA Ae.AA At.A S? Ag.A> AɅ.A6< A.A`@7 A0懅.A`1 A(.A`) Ac.Aw( AP+=.AT' A3+.A-& ArK.AN߆& Am j.Ae:& AIF.A4+% A(.An*s% A.Ak$ A ۿ.AC)z$ A^ʆ.A`# AV.A AXƆ.Az A.Aab A.A AX#.A Jk A8.A`) A[.A A㶂.A ^ A\.A` A@ƺ.A-l A5#.A I Agn}.A@ AW|.A Ap{.Av Aey.Ac Azx.A$ A8bw.A ۊ Aw.A  Av.A% Au.A`D A8t.A A8s.A Ap=q.A Ap.A@ A8an.AH A@Nl.A!| Ai.Ap A07h.A Am ACh.A` i Ag.A h A f.A-b Ae.A,^ A49e.A[ A#e.A@[[ A;Xd.A`W A8c.A AU Ac.AS A؟c.AS Anc.AQZS A@_c.AVS AJc.A JR A.c.AR A5b.AO Av b.A K Aha.AVJ A a.A J AX`.A D A`.A |~C A_.Af= AH=_.A)8 A(^.A4 A:N^.Axy3 A'N^.A y3 Af].A?C1 AT\.A`) , Ah[.Ar% AH[.A$ A83[.A@$ A`7Z.A A%Y.A? AY.A AE1Y.A9 AW.A| AV.A A@1U.A ~ AX9T.A L AN.AB A\wN.Aw AXN.AK AH6N.A  AM.AE# A 8N.Ae A:N.A AH.N.A@m AN.AhP A2 N.AR AN.Ah AM.A A>M.A@ A M.A^ A]M.A AЀM.AF A%M.A AM.A A"M.Ad AM.A4 Ax׋M.A A&sM.A`Î A0qM.AUq AlM.A@M A`gM.A : AdcM.A A(OWM.A` AIM.Au A8EM.AU A9M.A A@3M.A A/M.A_ AeM.A@E AHL.Ap AݡL.A A@L.A AЄL.Aג AL.A AL.A`)g ASL.A@K A(L.A@1 A0gL.AE AFL.A A[L.A@# AТ L.ARX AXL.A . AHL.A3 AXK.A A`aK.A AK.Ak AXWK.A9 AK.A` AdK.A`2 AH)K.A u A8K.A 8 AzK.A`. AP'oK.A R Au^K.A`o A8DK.A W A5K.A@ AY1K.A` AK.A Ap} K.A[ ApJ.A} APJ.AǤ AJ.A AJ.A AHJ.A A0tJ.A& A`%KJ.A] A2J.Al AJ.A@# API.A AI.A AI.Aظ AI.A AI.A AdpI.A A_fI.A A_I.A A0wMI.A&k Az*I.A?_ A@ I.A`T A ߰H.A况 AHˊH.A AH.A@ AȺxH.A" AitH.AE7 A@mH.A; AliH.A`dP AX$cH.A`dP A`v\H.Ag AlEH.A쭱 A3H.A  A lE.A& AlfE.A`2 AZE.A A(E.A A7D.A`v AjD.A"v AX D.A@,t AD.A?Nt A0D.As A9D.AZr AD.A@q AD.A S&q AD.Aap AȘD.AyMp AD.A{o AD.AFn AD.A`fm A(D.A@Gm AD.A8m AD.A`[ m APD.A@5l A୸D.A ~k A( D.A$^k A8ֳD.AEk A0ȹD.Ad1k AD.A@9i A&D.Ai AD.A@h AD.Ag AvD.Ag AD.A f AXD.Alf AD.A d A5D.Ab AD.A b AD.A@,`a A!D.AV` AD.A P` A'D.A_ A |D.At_ A8D.A |_ AdD.A`9_ A8D.A_ AD.A^ AD.A3r^ AD.A@`] A3D.A*[ ApD.A൶Z AD.AmZ AD.A`Y AYD.AOY AD.A4W AD.A W AE.A"O AD.A O A1D.AaWO AW@.A V A@.A@%KU A@.AU A8@.A`1U AP@.A9.U APޯA.AaS A(ܤA.AS A@.A T A@.AT A5@.AU A@.AgR A@.A R Ax@.ARR Ag@.A`ÎR ApA.A$qQ AjA.Ah1Q AH@.A SR A@W@.A@W`R Aȶ@.A rR Ap@.A`bmR A@.AP AT@.A QO AH@.AЇP A@.AtP AP@.A P AXd4A.A@,O AxX6A.AjqO AH/A.A`IO A!@.A O A@.A O A@.A`܇O AH@.A` N AH@.AD'N A|@.A4)N AH@.A6N A,A.A4N A%,A.AAN AGTA.A NN AUA.AN AgA.A N AmA.AF5N A0jA.A`BM A0A.A@M A&C.A J A B.ArJ AMC.AI AxC.AକH A _TD.AL Ah; D.A@%cM AD.A{M A@lD.APL AC.AH A>C.A`K}H AD.AH A(D.A@ϝH A4D.AFH AYCD.A H AD.A |NG AwC.AuG AD.A@rG A`C.AfH AnC.AZH AC.A sH AC.AfH ACC.A@RI Ap6C.AH A9C.A H ACC.A H A0NC.AM>H AIRC.AG AOC.A#wG A0EC.AaG A2C.AF AC.A pF A؁C.A& AlaA.A = A@ukA.A = AA.A J; AA.Az9 ADA.AY7 AA.A6 AA.A4 AA.A`ma4 A=A.A 3 ApA.A3 A`A.A`3 AA.A O3 A~ B.A`U3 A` B.AO3 AB.A83 AxiB.A`2 A( B.Aޘ2 AB.AA2 AxKB.AK2 AB.Ap2 A B.A_2 A(B.Af2 AF7B.A@a2 A؞,B.A42 AF7B.Ah2 ApAB.A 72 AOB.A`,2 AhVB.AK2 A0LaB.A92 ATkB.A@H2 AXrB.A`"2 AhB.A`2 AjB.A(1 AX\B.A1 ACWB.A@1 APB.Ak1 AEB.Aa1 Ai:B.A41 AH\+B.A*1 AX#B.A 0 AB.A`v0 A\B.A_0 AB.A0 AP>B.A>, AТrB.A@N' AB.Ag& Ax5B.A J% AŘB.A$ AHB.A`K! A0 B.A" AxC.A< A@C.A ASC.A AV-C.A+ A^:C.A A(1MC.AN A;gC.A` AD{C.A=6 ARC.AǓ AȓC.A<  AC.A AC.A`b AD.A`- AOGD.A@e A wD.A5 AH\D.A l AXFD.A A@ E.A3 AE.A  A5\E.A A0E.AC A$E.A5 A@FE.A A E.Av Ap F.A`' AX)F.A A0JF.A AdF.AQ9 A0 sF.A O A\F.A/ A-F.A`m A8F.A AhoF.A A;F.A AG.AA A%G.A A/5G.A AAG.A@r/ ApxKG.A`Y AOG.A  AUG.A`5 AZG.A A]G.AA4^G.A AVG.A`XARG.AgADG.A2AVBG.AA 9G.A@Ap$G.A A G.A}AhG.A`dbAG.A`AF.A`A8F.AjA#F.AAjF.AA(F.A=AF.A6APG.A AG.AAG.AAh#G.ARA.G.A A/G.A@xANF.AUAAȩF.AA84F.AiAqF.A}A F.AAF.A 3AF.AAUF.AAn{F.A@,AhcF.A`YAl[F.A`2qAؚUF.A@ƋAGF.AA@:F.AAC7F.A_A5+F.A@+APF.A@AF.AApE.AAHE.AFoA E.A`;AuE.A@ƧAE.A`&AE.A CA(E.A@AE.A AE.ANALE.AA bE.A8ANE.A~A88E.AߢAȥD.A@}A)D.A,AЦ D.AAjC.A7/6ʠA\qC.AW4#A*[C.AkGAG]C.A^ݡAIB.A`ՍA0B.A;AO+B.A]1-lA6DA.A}ߙ}A!IA.A%)PwA>B@.A| qAwQU@.AR&zjA ?.AuX&dA`6N?.Ac^A A?.A_]A`8?.A ]A0?.A2]A@u"?.A\Ah?.A I\A?.A \[A?.A5[A=.A`'NA0]=.ANAl=.A`IAWS=.AGAxi*=.A`KEAXW=.AOzEAw<.AO BAc<.A J?AV<.Af>A"><.A`=AG;.A@.h;A.;.A q:AhI;.A:8A`e%;.A`m7A:.A6A :.A „3Ap9.A2A}9.AD1A$R9.A 0A:9.A@gN0A58.A/A 8.Aw/AX8.AR/AuL8.AZ~/A7.AO/A`l7.A51/A56.A=.A }6.A.A@O6.A`m.AO4.A 9.AX(4.A.A0 4.A.Ax 4.A`T.-AP|3.A -A`61.Ae,A`2.A@ 'A$..A%A,.AB%Ao+.AR$AН&.A"Aa#.A4M Ap#.A32 Ap9".A/A0;z".A`2 Ag".A cJ A^".A A".A`o(A ".A(A!.A`(A@!.A /)A!.A@L)A1!.A@p)A!.A_)A!.Ab)Ax .A(AXG .Aӣ(Ax .Azw(A.A-(A .AX'Aذ.A &A@g.A4]&A.A%A/.A`v%Al.A$AHH.A$AJ.A~$A.Ae$A(:.AL$APju.A0$A.A#AXh.A&"A.A QP!Ax\n.A`7!AH$.A *!Ah/.AW A1.A@ A.AA@.A@WApl.A}Ahĭ.AA V.A`TtAج .A 8Ab* .AA@q .A#AhՑ .A`ʶAP8 .AA& .A൫A .A*gAl .A OAN .AA .A -AP< .A@~AS .AAأ2 .AtA .A<%A .A"A .A@RAX .AA> .A2AX[| .A@kA .A`fCAr .AA@s .AgAl .A@Aؚa .A@AL .A-A+A`V-A@NAO-AOA=-A` Ap/-AA-A` Avi-A)AP&-AI^A8-AA-A /5A[*-AVAb-A`]A-AौA|-ARA /-AA-A \AX({-AA8-A gA-A@A@-A`RAZ-AcA-A&0A1-A`A-A2AXK-AAQ-AAM-A AuU-A`GAPR-A`]kA@W-A {A W-A@ơAXZ-AAX-AEA^v-A@)AXj-A`Ax(-AvA(z}-A`Ai-A@A-A|VAb-A&A0wD-AbA?-A UA-As]A}-AA:M-A&9A -A qAj*-AgA(%-A@yA-A^AA-AbA`B-A`IA( -A *A"-A@A0-A A2-AAKP-A *AC-A@NA0-A4AȱI-ALA-AA3-AVA4-A Q*A`C-A6A]-AA-AUA8-A`IA26-AbA-AĺAs-AAȋC-AAj-AnA]-A 8~AY-AnA4-A@GAx+-AApX-A,Aqt-AAz-AvAc-AOAPk-AAhl-A_A(>t-AcA-A@IA~-A/A-AA[-A[|Ah-A Z_A0̔-A(A.-A%A-AA%-A`ԿA@-AjA-AѶA-A@eAM-A`DA-A`AD-AA-A 8A%-AE`A0-A +Ah-A A-AA`-AMApI-A/A-A@ᱺAx-A#hAC-AjTA'-A@`úAx)-A@>ƼAڝ-A@A(-AҿAhv-AfA8f-AGAU-A AL-AFA+-AA-A`lA-A`ݾA -A=A-A ℾA-Az}Ap0-AqA-A`žAx-AhAp-A`;A -A SAy -AAȥ-AxCAB%-A JA.-A`NAA-A0_AzM-AZAJK-A`mAA-ACA'-A`vNA0-AdAp9 -A@iAP<-AA-A Ax-A`AW-AQܿAy-A`[Ak-A-vApy-A`ABE-A`TοA #F-AaAN-AA_-A@CA07-A`[A0-A졺A-ADA茛-AšA[-AyAx^-AA;D-AA.-AA-AtA-A AX-A A -A3A0-AЛAq-AAX(-A@EA+-A ~A--AAA-A 1%ApO-A AH)W-A&A0W-A SùAH-AA -A HHA -A-\A8-A|Ah-AhA@-AwwA }-ALAؔ-AA?-A`A-A#A R}-AAI-AA&-A ӽA -AlA -A@'A-AޘAx6-A9Ax<-A ZAhD-A#A?-A App2-Aq|A -AzA-AϵA\-AHA( -A@DA>-A@A-A9AxX-AǺA-A/Ah"-A@5ٻAh-A#As=-A`~AP+@-A A>-A A3-AVA`6#-A HHAY-AAB-AA-A{A--AaAS-A@An-A ׺APZ-AձAs-A6iA#-A@ oA$-A@ƗAs-AܔA-A Z9A`%-AMA$-AxfA-A@`AX[-A_ A^(-AA(-AJAP-A@A-A@ýA,-A$ȽAз -A$aA/-AtA5-A lA";-AUA0-A`TA-A@޽Ah{-AֽA-AA-AdA`-AAM-AA˥-A;RA@Wt-AnA@Wt-A`)A@-At7A-Ad0AH-A`A-AbAx-A@?AX{-A&7AZq-ABAo-AA6T-A@EA'-AH%A-AOAx:-AAP+-AzA9-A`HA'-A`LA-ArA--A$ǺA`-A@кAЦ-AA-AA@-AiA@-AAX-A:AF-AvMAp-A+PA=-AETAPD-AwABE-A@A؟l-AŔA@-A[A-A`]/Ajt-AǛA]-A`ApC-A`m`AE-AnA@F-A AA-A 1AX-A`ۻA-A$ؼA-A`]ԼA-A`T AHn-A@A-A`ZA-AgA-AA~-AнA7 -A A]#-A`d A-AFA-AA@-A ZAp4-A@WA8tY-AA*-AoAX-A &A-AA^-AWA`T-AX=AH-A[ZA-A@WA}-AZA=-AcAx-A APڊ-A`2A3~-AAPC-AA-A9AX-A`A-A=A`-AdA&-A;A-A#AT-AAaP-AAX5-AWA0-A _A-AVAO-AIA-A?nA ߬-AgA-AA?u-A@Aa-A`̶A(V-A`SAJ-A@,AE-AAp9-A&Ax2-AyA.-A A,-AAp,&-AFOAX+-A2QA()-ARA+-A`vAp/-A sAp+4-AAk<-AyAy6-A ~A6-AoAX;-A AhsA-AXAE-A@Ad-A AXt-A EAn-AA-A 89A)-A3 A-A ZAi-Az,A-A@rA"-A+8A`C-A`)KAh-ARA-AZA8g~-ArA7-A`[Ap -AAX-A`v5App-A1A8Ez-A@<A@q-A ~'A k-A@-Ad-A |$Ab-A`3Af-A>A`vU-A`cA\A-A`dSAj*-A (YA R$-A EA-A QVA -A@=Ax-AjIAX-A@AH-A`K+A0-A <A-A1A-A"As-AR+APZ-A`A-AA0G-A@,A-A"A-AA覇-AA8E-A@!AP|-AZAhw-A`A"r-A@Ap,i-A@A^-AAhX-A@,A}R-AA7-AA-A*Ax-AAXEu-A@ iA-A ,AX-A@NpAP-A@AH\-AR{Ah-AA-AA'-AAx9-A 8A*f-AQAj-AA0&o-A`m%A#u-ABA~-A?}AHm-A ˻Ae-A`]A-AF AS-A@56 Ah-A@yS A=-Atc Ap-A` Ax~ -A ^ AL-AX A(4-A=* AG-A A;:-A`AE&-AAN-A^A-AAP-A`ӯA-AvA-A`ӯA8-A`As-AA-A@RAH-ABAp|-A` A.-A9Aߥ-AAP-AfA7-A'kA,-A/_A(-AྍA-AAm-ABA-AAx-AA(-A4AP-AA-AAG#-AA'-AA(-A`vxA(.-A6KA5-AH/A9-A@A:;-AA=-A ۰AF-A /A`1N-A@yAXP-A1Aȓ-A Aȓ-A 4Ax-A/OAx -AAА -A@FAa-A5A#-A AXũ-A!Ax-A *Aם-A |dAH-A߮A-AA-A0A_s-A,=%A-A+A8-A@i.A0;'-A_4A::-A`B5A^-A79A-A@>=Ap΢-A AA-ADDAA-A.JA-AVJAh -AYJA-A uLJA9-A@ߛIAk-AQDAHr-AkxBA@-A EAJ-AFAu-AHAu-AIIAĄ-A`}JA-A@AKA-AKA#-A`ofLA-A`3MAXV-APAà-AQAH-AFQA8t-AmSA@`g-A ZRAba-A cQA:-AiNA07-A lMA.-ALA,;zP--AE}XLAo;+-A$xLA5>)-AhoLA~'-A лLA:%-AU@tLAG8"-A(GLA+-AkLA1-AjLA `-ALAm'-A-2LA$_-A LAs-A~HLA -ADŽLAf-AH*LAW-A`LA c-AgMAK-A SAx-AߏXAp-A `A@-AE4bA@-AeA*-AiA؎_-ARtrA(q-A {A-A琂AC-A AL-A``A8_-AA=p-AA`-AA-A@A-AMAX-AAy-AAp-AwA-A_A0-A@ÊA -A`AQ-A@AK-A`K-A-A`f݊Av-A@A'-AH|A$-A(A;-A`2ޑAE-A ҒAc-AÕAb-AA<|-A`ooAk-A@}A(j-AMAR>-A#rAxX?-A@衘AU-AFA+u-A>A5-AA-AiAb -A`;ɞA5-A?Aj-AA-A`գA-A СA@-AyPAx-A=A0-AA-A 8A(l-A %A5 -AAx7-A A@O5-AwA+l-AeA p-A_A-A`DA-A@N4A-A@GA(l-A@A-AcAX-AA@3-A`bAR7-A@#UA_-AA-A?Ah@-A`ݥA-AAP<-A@A-A)A -A 1[A@!-A`bZA4-AXA0G-A AxL-A`ެAf-AAx-A`oA-AXA)C-A$Aw-Aa AU-A`iA7-AA 8-A@`A-AA-AsAH-AAs-ADGA ^-A /A-AN1AL-A`b5A 0-AA~-A@C)Ak-A`bA8y-A!Af-A#A-AOA0u-A(A-ArAJ-AA(qd-A@A-A/ AH-A AЄ-A s) A0-A A-Ab AH-A`) Ar-AH A`-A( Ad-A`/ Ah"-A0 ApA$-A3 A"-A &3 AP/-A 3 A0D-A7P43 AZ-A/3 AH-AU3 AoT-A ^ 4 A2t-A)04 A)-A c]4 AM*1-A4M4 A軋/6-AwwHk4 ALr8-A 5 A\㍝8-Ay=G5 Ako5-A)#5 Ay0-A 5 AGQB)-A`Z5 AЕ-A s#6 Ay-AgB6 A -A#\6 A|m;-Ap6 A`-A”}6 A1-A6 Ah-A :; AHn-A@>; Aw-A? A`G -A=`> A-A@.dA A8D-A_J A8 i-AKyR AV-AgR AZ-A`vS AQP-AA7S A!5*G-Af8cS A?-A0CS Ai:-AN#S A+8-AJNT A4&7-AF(u>T Ay9-Ap%wT AM>-A_ îT AЕD-AKT AL-AWKU AV-A[8*7U AKb-AWU Ab-A U ACv-A U A-A\ A؞-ALd Ae-Aji A s-Ai AЮ}-A'l A"$r-A/2BNl A6$7h-A h{l AC"`-A+@l AIZ-AM{l AX5V-A!m APLc-AP\'m Ab}p-A.'m AȘ}-A!m AA-Am Ap-A 84m A8-Al A-Ap A`-A@Cv A@N-A@ v A/-A2v A/-A#v A(`-A@v Aٖ-A w Aa-Aw Ak]-A0+w A^-ARy AQD-Ay A(mG-A{z Aؽo-A Ao-A r AT8-A`{ A&-Afm A-A A ^-A` ٔ A-A Axh-AQ As-Ax Aؼr-A@y Ax-A A(-A Ap,-A An7-A A--A@ A'-A AG-A ֫ AbS-A AXn-AoQ A -A&E At-A`9P Ac-A O A~-A A-A` A-A@r? APx/-A@ Ae-A A8'-A@Ŷ A-A` A@-A8Z A -AMN A-A\ A-Aʬ AP^-A A-A`; A0-Aû Ap-A i A-A@ A@ -A p A-A ڹ A-Ax A`v-A A -Au A(-A Ag-A`T As-A`o A-Ao A q-A` A(r-A@ AT-Aݷ A˸-A Ax -A`) A8Z-A A`-A ~ A`-A@u AhL-Az Axu-Az A-ABػ A-A` A+-Af8 A"-A|ʼ AЄ-A H A8-A`dn Aº-AB A6-Aj A-A AЀ-Af AP/-A` A-A A-AF Ah]-Ak A-AzP Aȶ-A AP&-A@q A(-Am A+~-AW Ap}-AҞ AZ-A? A-A@H A-A (E A-A  A^-AV A٧-A ¼ A-A'ڼ Ahj-A,| A`c-AZ AX^-A l A[-AK AW-A | AYS-A/ AYM-Ajg AJ-Ap APD-A |G AN-At+ AP7-A@  AH;-A A-A8 A5-Ac AE$-A`m@ Ap/-A;U A81-A AP;-Aǖ A?-A A`G-AfƼ AN-A lx A`G-A ( A -AN A8-A_& A+-A A21-Aֹ A8E0-A@ Ai-A ҹ AX-A O] A` -A@ AJ-A, A 4-A@< A-A@ A(-AHU AX+-A`T A0eD-A4 A}-A0N Aȩ-AЪ A$-A% A$-A9 A-A A-AH API-AZ A-A` A(-Ag A8-A Ap, -A AP/-AH AX-A@߭ A-A@ A-A c AP"-A' AЕ%-A y A`'-AǾ A.-A@ A?|-A`< AXhi-A_ App\-AX Ap]-A A|-A Az-A AP -A \ AH-A Ahq-At Ah^-A`@ A-AM A-Ac A-A@ϒ A-A`a Axz-A` A-A@W A[-A@u Ac-A`B A-A0 A-Ad& Ay-A s A(h-AU Ax-A@V A-AF A_-A A-A޵ A-A_ A-A  A@u-AQ; A-A= A-AM A8-A&5 A-A J Aȇ-An A-Aj A(-A AH-A" A0G-A@r AH]8-A A(,-A |C A(,-A`]z AQ-A 9 AZ-A Zg Ag-A^ A8y-A A+-At' AU-A AXFh-A` Axme-A A0H-A Axz-A AhM.-Af A-A A AR+-A AEM-Aj Ah-A A@O~-A`2 Acx-A@W A@-AS A-A  AP<-A < A%-AI+ ABl-A% Aɶ-A A@1-A Z Ap-A` A-A A,-A Ah-A  A-A` A0}-A`} AϪ-A1 A -A,j A`Å-A A"-Ae A؎-A A -A? A p-A`f A@B-A; A-Adn AP<-A A -A@ A-A@5 A8R-A< A؟-A@ AB -A`" A(-AV Aw-A` A -ABg Ax -A@> A-A  A;-A`! A#C-A$V A-A sK A6-A c A@]-A A(,f-A A0Q-A9+ AXK-A A@5-A AX#-A` Ax( -A< AЯ-AH A@-A@` A@-A0x A-A  A-Aǂ AC-A AH -AgZ Ap=(-A" AH,-At; A0W-A@i AS-A AP Al-A  A Υ-A3 AX_-A A-A Zc AY-A c A߳-AU A-A" A-A&" A-A$ A G-A H$ A`-A<' AU-A`T:' Ah-A' Ax-A* A-A=x) A8c-AK, AX-Aݲ. A0-A L0 A R7-A`9I2 A`-A 8#8 Aȱ=-AO9 Aɉ-A`K; A-A`D< A-A@#> A(~-AD A,,-A?C A<-Aj:J At-A K A -AL Aj-A@EuP ALa-A (Y AP-A Y AHG-A`mZ A`K-A@ykZ A/R-A@,a A^-Aa Ao-Aa A@-Aa A-A@`a A-AX A oY.A}] A@.A=\ A6.Aҗ] A(y.A^ AB.A;_ A؎[.A q` Acy.AKa AD.AQa A;.A |a A(.A@%b A6.A;b AW.A Nb A`P.A@,b AĂ.A@a A.Aa A..A_ A虸.Aja AV.A 2c Ab,.Ad A0.A`mNh A .A?vk A .Apo A`k.A lm AX#f.A4t AÝ-A A0-A Ap-AU A-AA A5-A AZ-A A.Av A<.A |I} A".AE ANR.A` A.A`* At.Aǜ A`X.A@ A.A~ A .AR AH.A`H A].A A4.A Az.A# A.A A뿤.Az Az$֌.Ap# AOа.A*, A ]_.AP1 Aa(> .A2 A8.A . Aȵ&.An AX.A` A@` .A A`C< .AT̋ AE .A6 A9 .A?\ A .Ax A .A AHaJ .A@ A.A` Ae.A { A8Q.A[ A(`.A`B A;/ .A`2 A@b .A Q? A .A/ A` .A A{ .A ANh .A ArW .A G A@K .A| A@F .A@i AH{J .Av A&V .A@ɞ Ah .AU A8 .A< A .AAz AX5 .A AN .A  A0 .A٠ AX& .A A1 .A` A, .A 덢 A(.A 1; AZ .A㙲 A.A)Ҳ Aȃo.A@z A0&.A@, A.AI A+.A` A*q .AD Au .A f A .A`K A0eb.A@Ns Ap .A ٰ Ah.A A .AR A- .A A(w.A@ A.A A;.A@ A.A@S A@W.A@ A8.A,_ Aȓ .A A@N .A@e Ajj .A. AXd .A^ AX4 .AU A7.A Ag.A@M A.A A.A Ax--.Aif Aw.A Jw A .A`J A! .Ai A`n.A- Ap[^.A A.AD2 AR.A` Apʜ.AU A؎Z.A@i AǢ.A; AN.A`f A.A`̞ A R.A@R A)L.A \ AC.A A.A \ A RS.A AH\W.AR~ AXl.A`) A.A3 A{.A=4 Az.A \ AH.Ao A.A \ AH.A \= AЄ.A AP+.A AKK.Ak Ax].A Ai.Aл Ap.A@ A(n.AjG A@@.AQ A`Q.A@ AR.A`m Ax.AE A$.AE A.A` A=y.A A AI.A u A.A@x A#.A+ AV.A5 A(.An An.A A@.A= AP.A A.A@ A@W.A A(`.A AL.AV AM.Ae A.AЫ AP.A6 At.Az A h.AR A,.A`mO A0$.A AH.A c A.A A!.A A06.A A.A$ A8.A, A(`.A A<.A A>.A3@ A).Aྎ A{.A}* A@N.A AX.Aw A.Az A.A@E Ar.A॒ A8.A | A(.A@p AP .A A9.A A_.A`)i AX.Af Ad.AX A0.A@y A.A@8 A`.Af> AА.A A8D.A AD.AR A@.A`_ A@u.A A .A^> A( .A> A!.A!@ A!.A aC APڇ!.A`9C A .A`oC AH .A cC AN^ .AC A, .AC A }.A pC AW.A 4C A.A@!"C A^n.A =C A5.AkC A.A?C A.A,uC AC.AiBC A\.ApC AH{(.AC A.A C ApZ.A/C AKz.A@!"C A3.ALB AR.AB AA.A B A.AC Af.A@!"C Aث_.A@!B ApI.A (B Ap:.AƮB A%.A(B Aݻ.AB A.AD A!.AE A8Ib!.A`F Ai!.A`bG Aq!.A}G Aq!.A;EH A؎m!.A`sI A".A`; R A".A@T A0".A T A#.AFmV A>#.AV A$#.AV A(S#.AaX A 3#.A Y A>@#.A@Y A`#.A{Y A(K#.A [ Ap#.A[ A#.A k\ A0&.A xs AP2'.A@$w Ap '.A`mky Ap(.Ayl A(.A_ A 0^'.A Aa&.A E A8l%.A`9 A+R%.A |3 A(&.A` Ab'.A Av'.A Al'.A A'.A̍ AÇ'.A3ۋ A(.A Aĺ).A`TN A@y).A`TЊ AB).AX A@o).A AxiZ).Aq AAa).A` A@q]).A`]Ɗ AhK).Ay Ab).AQ Al(.A  AEZ(.A`2 A?(.A m Asm(.A A`C(.A Z A 0t(.A A(.A`ܱ Av).A Ar).A A0).A@ o AǞ).A AL).A`b[ A" *.A A*.AH AK(.A`9 A`v'.A Aq'.AsY A'.AR A%(.A 7 Ap).A` Ae*.A ޑ A,.A AH/.Ak Ao0.A` AM1.A AX1.AVg At2.A A3.AX AK3.AH Apn4.A A̝4.A@1 AA6.A@O Aj8.A@N[ Axd|9.AM A&;.A`o A<.A` AXۿ<.A A=.A2F A=.A! A.=.A?! A0X=.A /" A0Z>.A`=% A>.A) AX5W?.A, A?.A@. A$?.A0 A`B@@.A3 A@.AЦ6 AaA.A<; ACA.A@5? AA.Az!@ Ax>-B.A3A A5KB.A@KC AأB.AD AHB.A qE AHC.A`mH AuhC.AOJ AՒC.A`{L Ah޲C.AgM AC.A]M AD.AO A#D.AlP A D.A@ P AТD.AU AE.A@ [ A*E.A2^ AlF.A q7_ AhgF.A`T` A+rF.Aa AF.A@b A cF.Aad A؎F.Axd A8G.AZg A ͋G.A[i A(G.A |l ApWI.Av AXI.Ay A8J.Ay~ AJ.A@M A(IK.Aa AAL.A@yV A ML.A@؛ AxL.A@ A4L.A@ F A:M.A AItM.A A {M.A A`CM.A- AM.A ҕ A*$N.A줘 ASN.Aљ AhN.A-I A_N.A鶚 AsN.AП AxXN.AǞ AN.A`C A,O.A" AȵBO.A m AX9LO.A@y Ax`O.A` AzO.A?% AzO.A㪣 AO.A JJ AO.A AP.AH A)P.A˭ AP.A AP.A J AQQ.Aɯ AЕ\Q.A- AQ.A` AQ.A A8c"R.A AaR.A APR.A@ AER.A@5 A0.X.A@ Aw0Y.Aq AGY.A/E A#Z.Ae AҎ[.A A].A AP|].A A(^.A` A_.A A Y`.A ACa.A` Apga.A 8% Aa.A4 A0fa.At Axa.A AxWa.Ap Ap+ta.AR A/a.AQ AEa.A A v.A | Av.A# Aw.At Alw.A A&4w.Aá) Agw.A  Atw.A*%x AEw.A  Aosw.A>jgJ* A@Qw.Ad5 AMKw.A`m@ Afww.A9I ACKw.A/FQ A+ w.A`W Av.Ax A8"v.A`) AԿv.A ~ Aȩjv.AO A0ԩv.AR AcTv.A Aɂv.ATH Ahv.A@ Aqv.A;uF A$v.An AXv.A`T A7Pw.A A_w.A A_w.A@4 A*w.A Ah{ x.A@S Ap9x.A Apx.A- Ax.A`K- Ax.A@X Ax.A` Aȶ#y.A  A`Dy.A A][y.Ax Ay.A`)q Ay.A`Ӕ A)z.A V A8_z.A@ A`|z.AAz.A@./A8z.AqA@z.A`vA1{.A`;A0Xm{.A@ A{.As Ap0{.Az A{.A AU{.A?) A0{.A`g A|.A A(|.AAp=M|.AA(Kz|.A1ApJ|.AAN|.A@5A|.A@ƆA|.AA60}.A`1AЕa}.A@zA]}.A*AH }.A A?}.AA}.AjA}.A8vA}.A@`Ap}.AP A}.A] Aȩ}.A`!A}.A@G?"A}.A6#A,}.A#A}.A9%AH}.A`v]%A~.A }&A}.Av)AW}.A3)Ah3}.A3*A}.A@b*Ap}.A3i+A}.A,Ah3}.A3S-A=}.A µ-Aױ}.A .Az}.A.A`}.A A A|.A`?Afw|.A JAAd|.A ^CARl|.A@yEAP{|.A`EAxz|.AvFA8|.A@GAȂ|.A`!JA|.A`KAk|.ALA"|.A@NA|.A ~OAh|.A`9ePA83|.AQA|.ATA |.A`VA|.A WA`a|.AXAk|.A!YAh|.A]YA|.A?[A|.A[Aأ|.A`f\A#j|.A\A`%[|.A\AQ|.A@z]A0.R|.Az^A(B|.A`\_A(;|.A_A8|.AH`Ap=|.AaAB|.A@gFbA|.A`)dbA|.ARbAP|.A ~cA|.AcA(|.A`cA` |.AcAp8{.AzxdA{.AdAH{.A3dAp|.A@eA(\|.AeAz|.ADeA` |.AXeAp{.ApeAX{.A |eAp{.A37fA{.A fAH.{.A@ fAp={.AzgA{.ApgAU{.A hA|.A-ehA |.AhA |.AKiAh3*|.AjAHI|.ADjAqh|.AbkA(n|.A`kA }.AoAS}.A2} oA}}.A]+oAH%}.A`7oAi-}.A==oA6}.A?oApJ>}.A=oAKÆtF}.A1b6oA0xTN}.AV+oARװU}.A+oAx\}.A+ oAo}.AnA0 y}.A`nA t}.A |KnA(g}.A@*nAذg}.A&mAho}.AkmAxu}.AnmA{}.AmA%}.A@{mA8g}.AĒmAP}.A`nAh}.AkOnA}.A`nA@}.AoAd}.A&oAH}.AoA?}.A SoA}.AAoA&}.AॽoA}.A@5xoAh}.A3oA>}.AhoA@}.AhoA0}.AR*oA}.AzoA`}.AoA/}.A_oA}.A`spA8}.Ap%qAp}.AYqA~.A&qAP=~.A`WqAQ~.A qAhd~.A`ӄqA@p~.A@PqA2~.APqAP~.AqAH~.AqAp~.A |qA}~.AhqA`.ArA8 .A@ sA`/.A@ sA'4.A sAYI.A6sA'[.AAtAi.Av>tAx.ADtA/.A`>tA|.A ɊtAb.AitA8.AtAʸ.ANuA.A@`tA.A@,MuAH.A3uA#.AuA(.A "vAؒ.A=vAh/.AvA?;.A@wAP P.AawAЦ_.A wAx.A#wAW.A]wAH΀.AHuxA Հ.A@.xApl.A xA.A@pxA(>.A@`xApp.A`f6yA .A`ayA.AyA .AyAH.A syA87.A`yAP'=.A@ zAe.AQJzAto.A8yAJf.A2yA1e.AyAn.ARyA`}.A`;yAث.AyA8.AƱyA0堁.AyA.A yAc.A`mxAȁ.A`xA!́.A #yAہ.ApyA.A yA.A`yA.AzA.A`zA(2.A@N{A9U.AzAQu.AzAs.A@A{AhĒ.A"|A(.A|A].A`d }AsY.A j}A`X.A@y}A.A`vA.A@A.A@zAp<}.AAȅ.A? Ah.A)AH.AA-.AhsAc.A̒A.A ۄA·.A@߆A}.ABA.AZA{.A \AxI.A2Ac.A6QAxu.A@A.A mAG.A@ A .ANA.AIAF.AE Av.A'A.A@ A8֟.AAȉ.AwAБ.AAH{?.A@A@].A@7A&}.AA͊.A ֱA@.A A A.A)A;.A}A .AThA͋.AHAH.A ?Ai.AOAW~.A*AА.A`A@$͌.AAЯ .AӖA,1.A@lAw.A A.A{AТ.A (A9[.A@EA~.A`)YA0X.AgApZŎ.AA.A@>AH.AX%A&e.AAt.A$As.A -A0g.AOA T.A=A@X.A7AP8Ր.A+AN.A(Ae.A`pAH.AAY.AAr.A@`;A.AxA@>.AAx.AnAh.AA ӓ.A?As.AӬAU<.AHAq.AA.AAg".A3 AM.A App.A A0*.A z AF.AAh@ .ARBAh@ .A`vA -.A|A"^.A"A߆.A`oAuޘ.A@AP .AOA.AoA*.AǟAI.AHAI.A A\.AA 4.AAެ.A`mA .A`2A@O.A AP.A`m%Ax.A`dU(A(.Aɤ(A.A (App.A@%(A@.A`\*AW.AM)A@.AHc)A0.A.)A/4.AX*A`vD.A (O*Ax.A,APw.Aa-A.A2w/A!.A`0A().A1APޗ.Ar1A.Aા1AV˛.At6AIǛ.A7AΛ.Al7A?.A7A.A7Ak.A 8AR.A08AQ.AI8A@5=.A`8A9.A#7A`%(.A`G7Ap0".A`7A.A k6A .AE6A .A66AXE.A:6A`.AP6A5.AO8A`G2.AT8A@(.A@Cf8A%A.A@>9A{M.A`'9AhJ.A@9AhP.Ad9Aꟛ.A c8AP.A /8Aƽ.Ad8AU؛.AM8A֛.A8A@ɛ.A9A0Û.A:A.A`;AJ.Az;Ae.A9;Ag9.A <A'.A+<A8g(.A c<A0XK.A=A0.A<>A@.A`>A.AF@Ap^.A`AA`.AEAX$ϛ.A)DA舛.A@,/BA8.A0%AA@~%.A`AA؟7.A@BAA.A CAȱD.A?CAF.A`dCA=.A|_DA?-.AEAȱ.AEAI.A/FA.ApHA.ALAzG.AnNA8#P.A`jPA.ATA`؜.AVA=.AYA().AmD\A୹.A_AP.AcbA g.AH*fAp0ߞ.AhA@8.A \kAX.A 6yA.AzA).A (Apv.AxA Ң.A |'A.A9A kL.A(A0Ա.AA.AĒA }.A |+A.A:A8c.A4AH.A OA..A-AI.A@pA(1n.A`zA(s.A A<.A`9,ATy.A=Aou.A@yAln.A/9AW.AXXA^.AqŬAW.A`ˬAg`.AQApe.AQA Rr.A JAPZ.A ȭAr|.At2A<.A`9,A`K#.A@>~A.AA2.A@P?yAb.A`pA.AchA;.AeA0Y.AoZaAh38.A@`A.AEMaAX# .AR bA.AbA0..ATcAq.A.AmA.A }nA.AoA.AjoAҾ.A9pAp,.A.AB}A.AB}Ah.A}A0.A}AX#.A}A&.A}Ah.A`թ}A.Ay}A0].A }A(.A l}AL.A}A.A`}A&}.Af}A0v.A}Ar.A`}A(p.A@N~Apd.A ~A b.A`~A,N.A`0~A@5?.Ap]~AX1.Ai~A .A`x~Ab.Ay~A.Ax~A.A@~Aw.A@~AHi.A@~A.A ~A0H.A`~A@.A~A.A~Ah.A~Ap0.A~A(:.A* A.Ap"AX.A A/.A8AG.AtAu.A)A@.Aa@Ax.Aa@A.Af;Ar.A`m'AhW.AaA].AZFAW.A@AxR.ABAR.A ~^A0D.A`fA0=.A`xA1.A`tA84*.AXA#'.A`ÜAp|.A`A.AA8.A`A.AA*.A@NA.AAv.A@FA.AFA.AUA.AAH.A;AgW.A;AE.AA2;.A A:..A@ćA.AAc.A A`G .A |Ah.A+Ap.A}2Ag.AJAx)ۿ.A WA(ֿ.A`dAJĿ.A菈A .AA8.A6Ax:.A^A~.AA8g.AAl~.A OAȔ.A@Axm.AkԓAx}.AA8r.AvdAP.A`A,.A)ޮA).A`fAh.A@GA(r.A"A.A A(L.AA }.A`DAt.A@ݓA.AAh&.AA`K#.A@>~A D.A A,iO0A,1A9! %+J > C H N V \ l r w .Ab Ac.A j A.A S_ A؟).A? AG.AdU AX.A# A.AB A.A4 ApW.AC A.A AP7.AO A.Ab AHm.Av AHj.A@y Ar.Av Aп@.AL Ab@.A AU+.AV AH.A A`.A 8 A 6.A 1 Ax).AV AxKn.AT A.A A8Q.Av A.Aq AТ".A@ AB.A/! A`.A AȤy.As A.A A.A`e A.AX0 AX.A6 A.A൥ AfA.AE A[.A` Aav.AK A{.AB Aз.A, Ap<.A A.A Aa.Aڏ A .A A-.A A\ .A AxK.A` AXW1.A@ ApZ.A-  A0{l.A Z A@W.A Ax.A`v A0{/A$A AaU/A`[< A/A A/A AGM/A@` A@Sn/A`; A`/A` A8Ѡ/AJ A`/AO A/Ao AL3/A AЄ}/A  A/A_A A(\/AT A/Ad A3!/A* AH/A@ A_/A( Ap/A@j A/A A/A^k AZ/A@#B AH/At A /AI A 4/A`m: AN/A`K A)[/AH Ak/A A{/A A@/A^ A/A At/A A/A5c A@/A , A(/A`; A؎/A< A8'$/A`v AHa*/A Ap*/A@ A+/A4 Ah-/Ap AXt./Av AX//A AH0/Af Aȃ0/A A0/AO A1/A A`C1/A`[ AR1/A$ AZ1/A` AP^1/A` AP^1/Az AxW1/AD AK1/A A;-/A Q1 Az'/Ai A[/AZ A/ApL A@` /A@,l Ap/A A/AC A_/A] A(/Aw A/A A`/A@ A/AQ A/A A`/A O A/A@ A/A. A/A> A[ /AK A /A R A/A+W Ak/A`"Y A(/A`"Y A/AX A|/AoU AL#/A lP A0+/A@ A//A5 A3/A) A=/A`2 AJ/A AZ/A`{ Ai/A`$ Az/Aʵ A/A  AS/Ah| A虴/A?6 A(/A A/A A(\/A# AhQ/A A/A A/A A"/A Or A)/Aa Ah//A)Y A1/AV A6;/AN A@S/A@? A0/A`9 APڨ/A q A/A' A/A A5/A A/A-x A=/A` A()N/Ay AhQ{/AM AA/A@, A/Ai A/A^1 A/A A`/A APN/Avy Ah/A( Ac/A AN/A A/Aa ApA/A@` A7% /Au AhoV /As= Ap /A@& A` /A@ A8 /A= Az /A A@ /A A` /A@ A3 /A` AO> /A`m AجG /A Aq /A`2 AV /A ɋ A> /A{ A{ /A Oe A" /A 8A AI7 /AR/ ANd /A@! A@ux /AV Aݙ /A A = /A AL /A A6 /A`| A[ /Apl AB /A &c A /A 8[ A /A$Q A7 /A@F A7 /AhF A Y /A6 At /A# AH /A@ A0Ǽ /A` A /A$ A3/A N AKq/A AU/A@ A,/A H A36/A  A@=y/AM A/AF Ae/A 8 A8/AE AV/AU Aw/A ApA/Ax AA/A@C Aj/Av3 AP/A A/A` Ah/Ac AXh/AR A-/A AXE/A` ApV/AC Atj/A c A(-/A@ A[/Av AG/A` AK./A@ A[/AF A2/AW A``/A A/A Ax//A Aзh/AW AxƝ/A A/Au A[/A` Ax/A A/AA AhQ /A@W A2=/A A2q/A Ax/A`Ù A/AV AHL/A A/Aa A/A ` Aj/AC A/A A0/A` A/A9 A0)/A@E A(?/A A`a/A L Am/A`v3 A|/A A`/A@ Ax/A@W Ax/A-^ A/Ao: AJ/A A1/A Ax/AH A@u/Ar AD/ABZ A/AV< A/AF A'/A l Axv7/A A0</A@a AI/A# AHY/A Ax(/AG A/A AXu/A6 Aڵ/ATg AP/A" A/A@yq A(/Aa Az/A' A'/AO A02/A`l A'`/A$ A0./A A /A A@B/AaE A@ /A`v A8-/A = A E/A4 A |/A`n Aho/At+ A8ɧ/A \ A/AC Ah"/A AA#/AMs Ac/A A/A ~0 A ^/A A/A@ AE/A A Au/A`d AU/A A/A AX./A APF/A ۜ AJO/A͒ A/A A8/A( A=/Aʭ Ab/A B A(i/At3 Aq/A`b& A /A AU/A@P A/A A/A} Ap/AB A8/A A?/A AX/AT Ap/Az AXև/A@P AX[/A AP/A B A/A A\[/A` A୕/AG AB/A- A /A _ A/AQV A'/A A,/A' APi/A' A/Av A(/A A8t/AӤ A@u /A@PO Aؼ4/A R A]/A` A/A@.: A`/AVV A8I/A  A/Aջ Af/A A /A S A /A Ah /A` ù A  /A 8 AH\ /A0 Ah30 /A} AH.5 /A<" AH; /A AP7M /At A[^ /A@ߗ Ai /Ad Av /A` AP| /Aડ A} /A`2o AG~ /A`? A~ /AB A  /A@iɰ A # /AM A8x /A@` Aք /A@ A`B /Ad AX /A@,X A /A@Ю A /A@ߢ A /AĬ AD /A8Q9 A(ǐ /Aǿ APy /A' AYp /Ax( AXp /A@ A /A` A] /Acp A=E /A?7 Ae /A1Z AmE /ALj A /AU A_ /Aa A /Au A/Ax A{/A A,/A  A8/A A>%/AUT At/A`ߐ A@/AА AZ/Ao A/A` AX/Aތ Ad}/A` A3v/A`v AP@/AmNJ A`/A`S A/A2 Aa/A?׉ AFr/A` A`I/A@rC A/A A>X/Af/ AJvHX/AMP\K A/Aδxv A(/A` A8G/AW̉ AC/Ah A߸..=/AS A\J/A ~ A`!1/Ah Ag֪/Aࣟ AXGh$/A[Ж Agze/Ay| Ak/A R A /A- A/Acɉ A'/Aof A'L//A( A1/Ae A+B1V/AOLJ AV`S/AƉ^ AՆ/AT AR/Abʈ~ A/A A8/AM A(Kp/Azs A /A@ϋ A*/Ad A.A A f.A* A(m.A89 A.A) A.A 8 AxQ.A Agg.A鼕 Ax.A`A A興.A Az.A`Dk A..A@. Au.A/: A8D.A`dդ A.Ay A%.A׿ A.Ap A~%.A`D. A.A A/.A㊺ A(-.A@ Axm.A@ Ax.AFJ Ada.A A.A`" Aߖ.A ջ A%.A@ A.A^ Ap.Aɽ AM.A] A.A A c.Aoq A0.:.A Ae.A͍ AP.Aj A=.AXe Ae.A@ Ap.A`̱ Axu.A AXJ.A AA.A A'.A}u A M.A A.A OE AU.A& A.A  A.A( AX.A  A- .A A.A{ A.A}u A>.A`m{ Ahѫ.A A.A A.A A.A As.A AU".A  A5.AG AHrJ.AM A/b.A , Ap.Ac AH!;.A@, A.A: A0*.A& A^;.A@A Aj.A A.AT A-.A`÷ AX9.Aྭ A¸.A?d AE.A A07 .A Ax˚.A 1 A=.A6 A0.A9r Ax.Azh A`.Aw AH .A% Ac.A<< Ab.Ay A` .A` + Ahՙ.A At.A AHm.Av At[.ȀF Ad.A4 A$.A& A蕲.A A0.A! A EM.A઱" A7.A`}% A`].A+4 A0.Ak5 A.A@V5 Ah.A@WA At[.ȀF A /A~ A/A+ Ar/A g A k/A^Ͼ A /A~ A@u/A`KA@/A@y;AXFW/A@ A Ū/A}Ax/A.AG/A nA ձ/A` A@u/A`KA0/AjApA/A` ʞAЄ/A'A`/A9A0{B/A`̇A0/AjA/AAb/A )Am/AEAb/A CA|/A`mA/AA/AAP/A@A/A`AQ/A?A@B/A@P+A//A`9As/A-AТ/AAp/A@>A/{/AAG/AD>Ar~/AAX}/AAX}/AA/AJA`1/AkA/AūA|/ArAƗ/Ad A/A`A/A]ApJ/A3AHr/A gA"/A$A/AZAP//AH%A@/AT޿AX/AxA/A AH/A``Ax/A`ܽA/AHA|/AA(-/AEA>/AFкAȔ/AËA /AA0H /AtrA;/AA48/A׻A/A%AЄ/AAHA/A TAh>/A |A(/AZAp/AFAg/A-RA/AiAg/A߸A/A@=Ap/A`vA/A?A0/AKAl/A ਵAg/A<Af/AĴA0/AA`/A`oA@_/A?tA`/AAc/AqA@_/AA8Z/AྫAս/AiAR/A?AѮ/AA@S/AaaAW/A 1A]/AAL/AA@5/A,YAf/AA k /AحA/A׭Aq"/AҭA5/AҭA |F/AAHP/A@y0AhU/AXAH]/A:Av/A0.A/AAH/A 7AȔ/AaWAЙ/A A/A`ЧAP/A`֧A/A@y A/AAh/A!Az/A[Ao/AA(qx/Az&A/AA`/A@Aؚ/A@jAH!/A |AP&/AAH/AXA*/A lAX,/A 8,A/A`Ah/A`;A0/AA0/AÜAX,/AēA8'/A]A` /AA(/AߛA/AfIAh/A 8Ah/A@#A(/AA/A 힙A/A A@/A઴A@/AxA/A*AX/A`TA/A6A/AA/AF[A/A ˺A/AܖAc/A`~Ah"f/A@gAp/A@Ax/AA>/A`gA@ /AA1/A列Aਙ/A@,Ag/AvOAp[j/A0;A./AA /AƔAБ /AxA35/A@A83F/AʖA}/A`9A{N/A@%ĕA8`/AʕAhq/A߯Ax/A߯A`/A AxF/A` A\9/A AP/A@ ՕAQ_/A[Al/ATA |/A]ÔA*/A#Axm/ApAp/A^A0/AAx)w/AgA8gY/AA*/A`˒A`/A`ܯA8/A YA0s/A@A /A@PAhn/AѐAp/AСA0/A`PA /A|xAH]X/AwA+/AwA@ߙ/AwAx\/ASwAЄb/AwA8b/A twA6/A xAH>/ApwAI/AwAl/AwA@5o/A QwAG/A&xAh/Ap/AVRAN/A%RAN/A QAh]0/A |QA/A?(SA(/A`TpSAp/A}TA/AYSA|/ASA/A`?TAC/A_TA8/A UAPg/A_UA/A_UApl/AT7UApl/AH UAI/A@TAi'/A`HTAf/A-TAf/AxSA/A)TAX/A 1TA/A 1TA{/A@TA/AqTAv/A6TA(/AzSA8b/ASAX(%/AQgSAi'/A#SA!~/A 8 SAd/A#SA/ASA/ARAȇ/A&RAhD/A&RA@y/A(RA@y/ARAP+/ARA`a /ARA /A@E:SA&=/ARA/A =RAXE/AV>PA)/AnNA/AaOA|/ANAy/A KA@/A @IAw/AGA/A@EAT/A SDA`/A@gCAHs/AoCA 1/ADvBAA/Ao@AM/A?Ax/A@,BA/A@>aBA~/A JBA/A`9OBA@B/Ay3CA/ADAh]/A 8EAf/AKCA/A`AAq~/AI{;Aki/AN;Apx/A.AAhU/A?AȘD/AO=A7/A [=A0F/A&@A/AvAA/AAA`Q/Aྒ?ApY/A ;?A0/A`>Ai/Aj=A /ARM=AP/A@q8A(i/A`7AN/A5A(/A`4AL/A@&3AX5L/A@>3Al0/A`0A3D/A@0AxW/A 3A l/A4A8'j/A>4AS/AER4Al~/Ak4A/A_4AH /A4AK /A A.6Aȋ/A5A@/A4A/A`Ӫ4Ah/A=^4A` /A4A( e/A S2A`%T/A` 1A`/AH0AcM/A`D-/A`/A:.A/A R1A((/AN2A/Aǿ2A@/A1AX/Ag-Ap7/ANQ/A@E/A`0A-/Aa0A/A8-AxGL/A'-Ap/A൞)A`ظ/A &A/A r&A /A%A`/A%A/Aֹ%Ad/A%A/A%A-/A &AK/A9&AXa/A&Ai/A ˑ%AB}/A#A\/A"AxO/AW A@q/A@Ajx/A`;2A/AAh./A AX/A)$!A/AC'AP/AC'A/A'AH!/A'A(/A A /AK A@ /A'A(/A`'AH3/AO!A$/AF!A`/A&APM/Ap%A/AW A0e/A E AI/Aj%A/A@[$A( /AsAx/A`mA|/A C$A(/A"#AY/A 'AT /AA4/A#Au/Ag!AȘ6/AA$/A vAp/A^!Ap9/A@ Aq/AA[^/AAh?/A@A@Q/A ZGA1/A@A-/A`A;/AdkA\/A |(AS/AA>/A A38/A Ah5/A2#AؼW/A`;GAY/A@,A@}/A |(A1/AgAho(/A  A.&/A AX/A A/Ac A/A@G A"/A3 A/A A/A) A/A8; A0/AQ A`e/AF A/AK AX,/A, Aȋ/A` Am/AM A/A Ah /A;Axm/A5 AB/AoN A /A@X A/A`A AhU/A`P A;/A`A Al/A`ÕA0X,/AA^/A A8E/AɮA/AA/A,A0/A@ wAv/AƈA./AA#:/A AHaU/AAT/AAĥ/A A/AA/ApAPf/AƈA/A@A/AA/AɮA4/AA(/Aृ A@B/A A/A@ A//A@* AP/A` A@ϐ/A9 Al/Aޜ A8E{/A@ Ah./A`: Ah/Ad A/A A@/Aa2 A /Aમ A 0//A` Ay/A Aa/A@ A(@/A A-/A A*\/AQ2 A=t/ABS A /A@g A9/A AHC/A Ag/Ao AԻ/A@W~ A/A A/A* A/A e AP7/A A(/A= A2/A@ AȺ6/Ab A8B/A e A /AA_]/ATA/A@ AТK/A AD/A ? AK2/Aq7 A2/A@  AI/AoN A(W/A@~AH./A .A /AA /AA /Aj=A`/A;A0/A Aп5/A2AN/A AA`/AADn/A 'A2/A@ Ahb/A@A(/Ao A@K/AyAPt/A`"Au/A A8/AJAT/A O)A/Ao AP/A ' AXu /AW AȘ /A@ Axm/A Ahޡ/A@, Aݯ/A@yJ A(J/A^' AR/A S AX[/A@y Aa/A A 0/A Ap/A ZE A/As A /A \ AH/AE AP/Al AP/A=3 AH/A A$/A A/A@Z A/AQ A/A A/A A/A ~ A0{/A cj A/A+ A/A@ APx/A E A(J/A A0X/Ap AQ/AaE AS/A $ A/A AX/A@ Z A/A A؟/AI A Ex/AV Az/A Zx A`/A@ A!/A`]l AJ/A@ A/A J A"/AR A%+/A L Aؼ!/AH AD/A` Ah;/A / A@Q/AV AX/A A/Az AX4/A A@S&/A@ Ae/A AXW/A & Aؽ/A`9 A`/A` A@/A uD A4/A`ob AA/A K A/A` APx/A2 Ad/At A\!/A`B AxG//A@ A/AY A/A`B A`Z/Azi Al/AT A}/A AH/A`= APM/A`m AgF/AR A>]/A0 A`P/A$ A /A 8| Ap0/Av3 AS/Ah Aq/A A/A A/A A/A`| A/A+ A/A/ A'/A- Aj /A A/A A3/A [ A/A@r A8Ң/A- A6/A AP/A AX/A`; Ax/AK AE/A AP8/A A>/A AHr//A / Ay/A AP /A A`+/AR AX/A= A/A@ Apk/A| AP/A A/A A(:/A@ A /A A/A3H A8R/A`v A/AR AL/Ah A/A A8sW/A@a A@Y/A& A@/AW A /A@i AxV/AO Ac`/AM APm/A Ax)/A A`c/A A=F/A AhR/A  AHH/AR6 A6/A 2 A`-/A A@~/AO Ah/A' Ap6/A྅ A`1/A| Ap /A O> A0/A A0/A a Ae/A Aܓ/AO:Axu/AA('/AAP/A@A`v/A@A/AVAO/AAv/AA0t/AAL/A A(5/A@yWA-/AmA`/A@yWA/A[A)/A@A8c)/A A8c)/AEA,/AA7/AOAx H/A@pAc`/AAv/AA/A@LAխ/A`2A/AI As/A`2A/AKAXʙ/A@yy A7/A7 A/A`TA0/A &Aȩ/A[nA/AA/A&A /A 1A/AA+/A sA /A`{Ap/A1A(\/A`AP,A8U/AJ,A /A=/AL/A Z2A/A@5A/A@ 5A^AN/Ap>A/A>Aj~/A>AxO/AƲ>AxE/A?A8Ҁ/Ad?Aa/A`o@AXK/Ar@AB*/A`[AA8#}/A@@A` w/A^2AAhD/A|NBAh/A lBAuc/AzCA(_/A`)HCA8&/A4`CAo?/ATGAհ/A3FA =/A@FA@/A`pFA46/A`pFA46/A FAb/AFAP7/A@FAP7/A`;5GAp[E/AGAXK/A`OIA/A IA0e/A &IA/ABJA/A@JA3/A5KA/AfKAg{/AOA( /AOAH/AOAPw/A 7PAh/AQA`/A;SA[/ATA/AͅUA@/A~WA؞/AdpXAP/AXA(/AWA@&/A6{XA \/AYA(/AuZA:/AVZAPn/A3VYA0n/A\A8/A`A/AndA8/AeA@/A%eAv/AQdA/A@dA/A dA@/A>dA/AdA W/A`cA_/A@ #cAPK/A cA5/AdA /AdAx/AyxdApc/A;dAa/AQdA،/A` eAP@/A@dA//A%eA/APeAH /A(|eAs/A UeAP͡/AeAM/A$iA@/AZiA7/AiA(/AGiAP/AT4iA`/AMhA軻/AhAX/A@GgA/A0gA4w/A!iAH/AiAv/A$jAe/ANiA(/A 8jA88/AjA9/AKcjAxK/A 8jA/A`iAI/AiAA/A c+kAx/AlA,$/AjA /AjA}/A )mAD/A#nA./A@gpA84/A^PpA/AkkpA@0-/AкpA0H&/A@5=qAp/A@pA/ApA#/A-[qA/A̓sAb/A`)tAP'G/AvA/AvA/A"vA8'/AvA /AxfvAg/AvA#/A@3vAH/A@3vAHm/A@GuA/AtuA/A}euA@/AZuAN/AuA@/A@ uA/AvAY5/A"vAK/A_tA:/AYtA#/A4 vAHm/A@.uA/A sA)/A sA}/A}uAU/AtA/A (sAd/A sA{/A tAd3/A 8gtA\1/A AtAXWv/A tAh/AuAy/A4 vA/A JtAH/A uAq/AvA~/AwAh/AuA`!/A=VvA /A6wAp,/AfLwA8/A@kvA`/A`)wA/AMOyA;/A@TxA/A 8xAxvg/A AwA Y/A xA\/A^yA/AzA,/AzA/AKzA X/A yAP'T/A $zAH/AIzA~/A`zA/A zA/AzA(z/AzA趪/A{AD/Aߦ{AŮ/A q|A/A`|A(%/A1}A/A6A84/A@lA0/A@ B}A A/A &}A0L/A`VAX-/A~Ap=Z/AB|A;/A|A /AX~A/At}ApJ/A{A;/A|A /A (|A8/AB5{A\/A {APx/A{A8/A@r{AP{/A`}A,/A`b}A 2/A/}A`/A|A/A`2H{A/A`h{A0%/A 2{AH?/A zAX/A zAPx/A%{A/AK|A!/AK|AX*/A&|A+P/AUOzA\u/AzAz/ADzA7 /ApyAX /A`yAB?/A`9zA/A|A;/AJ{A/AzAk/A RxA`q/A`)xAX/ATwA8/AwA%a/AJxA]/A`xxA蕂/A1zAo/A 2{A /AozAO/AZzA_/AAX/AA/Acj}A/AAhq/A`~Am/A Q~A/A@`:A:/A&A0fb/AYA`/A[mAժ/AAȤ/A A@s/AzA8/A xAX/A wA8^/AzA ҃/A`zAL/A{AT/A{A0;/A/A@SA/AAY/AaA%/A A`./AA($/A@W~AM/A`oA-/A=HAQ/AKA7/A$A/A@56AϤ/A&AP|/A6A/AA{/ATA/A5AP;/AA(./A@ۄA/A`߆A@F/A9A/A=A@S/A@y'AHt/Ai A/A SA`/AAp/AkA0{/AlA4/A ~AY/A`A=/A`aA/A QAxW0/Aä́Axd/AAP/A A؁/ArAp,/AAA/A@QA/AMAZ/AA0{/AApl/AApIt/A`zA }/AMA`/A`̄A /Ax>A8z/AFA0{/AjA /AMA`/A@yA M/A ^A//Ap"A0/AA0/A_5A8/AݬAP@/AEwA8/A`AY/A@Ao/ATAh"/AjlA4/AAY/A |A/A %AB/A`A@/A (tA,/A`EA*0/AqA/A`KAhD/A@A@כ/AA୰/A /A(/AŌA}/A<ٲAU4/A@ȰAsG/A AP/AձAX/A/A(/AxwA /A ɍA؎[/A`mAFl/A`Aq /A AP$/A`,Axp/A@5AXh/A@AF/A`]UA0:/AA@S/A`oAU/A ˳AX/A,A\/AA@/A`mAH/A`YA~z/AAP/AjA)/A`A o /A޴A`1e/Az2A]q/AA ~/AHkA`/A(A./AgAx-/AӷAF/A`A/AAH/A dA%/A@ A7/A \AHk/A@rA F/A AA@I/AٵAB~/A HvA"/A'uAp+/A@r;A/A{Ar/AA2/A-A{/AӽAg/A ʼA/A"ڼAƼ/A@A/A NA^!/AT=AH(/AA/AA0Ԇ/A@%.Ai~/ApAȱ>/A SAP*/A|AHO/A`A/A`ùA/AAT/A`ApA/A6A//AA@ߙ/AaA2r/AջAw/A SAh/A A~/AZAȃn/A΅ApN/A`A./AA=/AAp|/A`AX~/A@A/A=A/AؽA~/AA0F/A-AԔ/A`AA/Ad۾AhU/A/(A /A`A'/A XA/AAv/AAF/AGAh/A-hAs/A 1A/AA89/A`A7]/A ŒAv/A`^Axz/A`AO/A`[A0/ApyoAu/AKAB/A` FAh/Ad4AX'/A0A/A`f.A/A _8A/A`CA0/A`IA7/ALOAh/A`cA /A[xA(8/AyA9/Az{Ax/A3{Ah/Aq{A%:/AI{AAcw/A@A}#/AOA@q%/AA1/A`rA=}/AaAxƷ/A@vAxƷ/A}A/AӒAؽ/A`AС/AgAո/AaZAx-/A=$Ae/A`AU/A A(/A ҁAH/A lEAX/A Ap=/AA`/A^5Ah/A`8A@u/AA(/AjAh/A@>bAh/AV;A8/A)[Ah/A`fAh/A Ap=/AWAM/A;AM/A >AX/A@Ax-/A QcAp=/AA/A`)A i/AAX#/A@A/AsAX/AA /AЪApA/AA,iO0AVA89/A`A/A@!oA-/AqAX5//A4A3/AgVAP6/AAk9/A^A=/AAP/A7Apm/AVAv/A`A07u/A#A07u/AIALr/A@ƞAh/AA`/AXAS/A`AJ,/A Ah/AA t/AazA /A_4A /AA /A@-A^/AA(~/A@A@/AfA/AA /ATA/A A0/A@>A/ASA`/A`A /A 8A/A YAX/AAA(/AAs/A AxZ/A FAK/A/A@/AAY4/A+A`%/A`AP/AzA/A QA/A 4A@/AOA /AzAxu/A@A*/AA*/AVA@/AsApʩ/AA/AosA|/A@EyAP^r/AAso/A Axi/AbA>/AxA8/A@PAɆ/A`tA8 ~/A`tAP^r/A}]A(m/A A]/AzA(V/AzAȇI/AKAHR/A`ApS/A`AHR/A@,A R?/A@AAp0/A@AAP'/AA(/A`AF /AA/A`A /A+A0/AA/A/A(/A^A$/AGA/A@jA/A@jAX/A YA@N/A@ 6A8/A@ 6Am/A^A0/A A"/AA,/A`Aaj/A6A;O/A{A0./AྟA /A@A/AA\/AA/A AAn/AAK/A A8#%/A Aج/AAA/AྟA`v/A@qA@u/A6A _/A8A~z/A40AX/A[Ax0/A@,A&/AӗA /A (iA/ANA(/A uEAο/A@A/AAB/AŊA׉/AaA6k/AgA5H/AosA3/AA+/A`]A /A Ai /A`9As/AVA/A@!tAp/A@yA/A}]Ah^/AA /A@AA?/A`A /A_*AhU2/A;A``B/A|Ax[/A A\/A AXWx/A@GA8/AA8/AzA@d/AA8/AWAp/A AP/A(A/AKAhb/A@QA/A@yA/A cA7/AfA8/A@A /A@{AXc/A@iAp/AAC/AgLA/A;/Aho/A@FA/A?Axz/AnAA`e/AA%/A`K_A%/A@A@/A SwA1/A`A(hZ/A@WA8t/A@.OAX/A AW /A`[AXP/AAXۆ/AA/A@[A/A6gA/AaaAU7/A6gAn/A 8A`X/A Ay/A`;AX[ /AbA=/A@>Ai/AhyAH/A A/AB.A@/A@AHa/A J#Ab/AKiAТ/AKiA2/A:AXyA/A A(P/A HA:m/AA/A LA(/A`"A`2/AA/A@yAh/AAI /AA/A@!oA6/ABA/A}AH/A _A\/A`oA6/ABA R/A@A?/AӎAp/A;AHЫ/A|Aئ/AAb/AैAq/AzA/AbA/A@\A/A Q%A/AApw/AA t/A_A1s/ARAr/AAXn/AwAPj/A ANb/AбAQ/A$A@E/A`A9/AͳA(./AAx#/AwA0/A`DA/AAb /A:APk/A ^An/AXAn/A`A/AAn/AaAn/ASAn/A`Al/A`vA` /A@5A /AA0/Aj?A0/A`~A/A஬A ="/AA(./AKA1/AA@/A/A~F/A cmAL/AAS/A`AhV/AvA ^Y/A$&APa/A [Aذs/A`A%z/A`AhU/A l!AF/A`bgApl/AA^/AKA(J/AA/AA/A@gRA/A3pA`/A0rA/AA/A=A/A6A/AzpA N/A$A`X/A@A`X/AA/A@PA/AlA/AAE/A@#A2/A`A`/AA#/AHKA+/A`uA7/AɔAD/AAp8N/AA0^/AAg/AA{q/AA{/AA R/A@A#/A$A%/A\A1/A\AX$:/A^AA/A PAp0N/A`@AX/AgA8\/AHA\/A@.A@c/A*ADp/A&APHv/A@5AJz/ALAP/A@WAӉ/A?AW/AYA֎/A`A0Ջ/A@Aْ/AAH]/A lkA@`/A9A_/AA\/AAH]/AhAۖ/AAݙ/AA^/A cAݙ/ABzAp[/A&JAۖ/AAޛ/A`A/AA/AྜྷA/AoAh^/A@7APޚ/A`f A _/A AAPo/A}kA@/A@lA8E/A^{As/A@rA

/A@ cA=/AJOfAK>/A`l+iAx/>/Ax}iA>/AMiA=/A 0iAx=/AjAp[V=/AjA=/Ap*mAO*>/A&2mA蝴>/A YlAP>/A%lAr?/A%kAЮ?/A0flA(Bi@/AImAIh@/AInAX#?/A1nA(K?/AKnA@X?/AnAxKo>/A9qA>/Ap,rA9?/AhrA@/A{rAx@/ArA@/Ap,rA #sA/AБgsAXIB/A }-vA;D/A0!wALE/A0Tn{A^E/A}|A8>D/A|Ahf4C/AҪ|AhC/A }A\C/A 9}Ap=B/A@o~AX_vC/A@ AD/AA7E/Ab[AP/E/AGɅA@ F/A0EAxF/A@ AnbG/AWAxG/ẢAyG/AIAWH/A@<=AtH/A;4A7I/AA$J/A4Af=J/AA` K/A(A?H/AwAMH/AxA(F/A |A E/A@^yA G/APxAuH/A0kwAZNJ/AuAteK/A`etAL/A@<6xAM/AyAN/AwA@RP/AuAS/A1rA U/A:pAxm]W/AZoA&X/AnABXX/AiAXmX/AgAP/vY/AifAƨ[/AfAp]/A`CfAO_/AxfA̳`/A }aA`/A 96_A)`/A]Am`/A 9\Aa/AКZAjLa/AEYAԉa/A WAxqa/A0kVA%b/Ap UA;b/A@MSAb/AxRA&d/A ]LA.Rd/AhSKAOd/ALwiKAqܸd/A5KAld/ALJA(d/A0JARd/AIA d/A" UNIARqd/A-eHANVd/AboHAd/AHAP c/A**CApc/Ad@AEe/A`6APf/Ao-AȩEg/AK-A`h/A,Ak/AF*AJm/A)AHp/A@L&A0r/Ap_$%A0t/A_#AEv/Ap!A)|x/A4 A{/A`nAH{/A@&A }/ApApV~/APҎA/AZADυ/A BA0X/AA@|/A`?A/AA8"7/AVA؞/APA/A HA/AAG//A<A_/A%AT/AEAW/APAȶp/A (Aɕ/Ap>A`/ApAx/AdA\/ABA/A!A/Ap,Ah/APAا/AG A#/A  A0/A0" A/Apg A𒥦/A"3A(/Ap=Ag/AăA' /AA/A $AV/A HA5/AVApV/A@gAP/AAi/A ]A( G/A'A/A2SAٗ/A`AKZ/A`]A/A@EAў/A>A$/A`bA_/AQ0A/ATAP/AA '/A@iZAx(H/A㟸AH/A@sA%Ŀ/AIAH?/A@xA/AzAD/A{A5/Acu~Al/A@%xA׹/A7qA/ATiA /A ˠ[A⟶/A&ZA%/ABOAx)/Ap\A/A`bAHA/AeA`B/A /VA/AzmTA@}۱/A"HAP|/AZJA*/A?A|/A >A8'*/A@yAA쟭/A`=A%/A >A(0/AEA@h/A@rVAy/A OA0M/AMAP|/AyFA /AEAL/A@g;AXʬ/A6A/A@T5Aꔯ/A F#AJ/AQ4)A /A8'Ah/A`vAX/A`T~A/A3A(`/A`0WAsT/A@EcA5/AtxgA/AnA /A१}Avr/A\A 0d/AM0A`P/AA8 /AAT/A@ߨA)/A`]AX/AұA/A&)A؁/A3EA oi/AAH!/AԸAP^/AOA/A@.LAHib/AkA؎//AAx/AdA/A 俾A@`/AmAD/AgA3/A ҪAf/A \A/A@iA؍j/A`A/A$ZA@>/AA੹/AVA`/A3AX /AʇA/A@A,/A3As/AOϮAN/AAp/A3AzZ/A` AP /A?SA0f0/A uA/AA8I/AӻAû/A@ ǡAҗ/AX>A /A JA{/AOAV/A /zA( z/A@rAR/AEmA@A/A`hAP/A@ZfA؟K/AdA@/AgAi/AviAk/A&mAd/Ax0rA((/ARrAHr/A3\mAЦ/A`eA8c/AXbAn/A ¶]ALM/A2iA,/A`sA,/AjtA, /A@%tA/A cqA`/AnAX/A` jA͐/A JfAe/A`adA`+/A hbA2/A`]]AȤ/AEZAxi/A`-XAxi/A`mUA/A@zTA/AɄQA c>/A`OA`c/ANAY/A NA@S/AwLAl/AIAxF/AHA$9/A)GA`/A@PsDA/A@AD/A 0<AO/A66AHL/Aaz9A/A =A/A`a@A(-/ABA{/A0EA/A 8IGAX[/AEHA/AHA]/A)GAX/A EA/AVzBA`Gc/ABI>A@/A ;A 7/AX4AI/AN AН/AWA@/A@yA`4/A%A@'/A@KA֪DE/AhԷA;7ئ_/A֖7Ac%/A)EAWPi/ASd9qA\,A*/A +A@.c/APA X/AAx/AAXd/AAr/A2gAAq/A (AHah/AQAHÔ/AAU/A`A8/AhA0/A+;AHah/A`A/A Z}A/AJA88/AAek/AjApA_/A`A@Z(/AYƄ[Alō1/A iAwu/A"AB^}/AcAP|/AzAf/AοAC}/A%A_ /A@&AQqG/A\Aq/A$Ac/AA /A,{Ax`)/AJB;AQ/A$4AɣY/A6nA/A?@AlID /AҳAj\/AZ_AdH/AgzF~A/ALUDrA_2/A9AH/A@A /Ap,A/AzаA8/AAAB/A$AVj/A9fAIn/AQ(TFA) R/A4 L8Aآ6/Ae<A|/ALRAT4/AUzA/AAȺ/A`夰Aa8/AAz|/A(7A\+/Axi>o̫AH޿/AUTAo/AW}ШA،wP/A@A/AW}?A3Ҿ/A}A_' /A[KRA-g/A<6A`p7/AQ͖ڞA@F /AA4{/A(xA%޼/A؞AGu/AΝA»/A SAdKϻ/A-AW`/AuؑAۭ/A}ϐA/AWId̏A/0/A9>ЎAhDS/AEŬݍAY]y/AB;/AY /A JAѼ/AAAl/A#2|Au]/A؛A/Aى-cA/A2?AX /AAHu/A|A(/AlA2/AdA8Z;/Ab$cA ׺/AbAFt/AaA֑/Aj8`A*h/Ay_AfK/A7s^A R2/Ak]AGY]/A `90\AS푗 /Af[A/AG TAW~/A/YTAT/ALUAm*/A VA^Xϐ/A]MkWA%n/AԑXAOFyP/A`YSYApw6/A [A=b"/A ,\A"/AI]S]AVM/Ak2~^A h)/A2*_A1/A\`A /A bAk/AMyCqcAtnP/A/)dA/AJN7eA3׻ڵ/AjAfAŵ/A HgA5Tw/A+z%jAs%/Ao9;lAKδ/AgWoA^Tt/A+*rA*r>/A6 NuA%9/A>ExADbK/A\LzAo6/A M}A%4q/AlA */ApA/AA׮/A ƋA /AAGg/AW{XAO?/AJAXD/A- A/AKb2Awo/A'~A֏4/A6,lFAd'BP/Az"AXNpo/A0)AږE/AA*#/AَϠAhqݰ/AiA/A /A07(/At{-AJ/AYSd=A`k/AqQ`A5n,/AdAX /AóڡA`Ŷ±/A/A0a}Nڱ/Al5A>ۆM/AAS/A {>yA#S /AOeAFT/A~Ap/AAlAa4i/A˲tAI+v/A=( A3/AltTAxh/A ɠA:@/AigeAx\~/A[;0Ar$/Aj(A8 /AuߣA>P/AA¯Ad/A@WA{/A`A,/Aa{A3C/A@A/A`)A̦/A`Aqbաb/AؔAA /A.LۂAJߠ/A AIֻ/Aۢc^Ahז/A5?ۚA4,p/A͂AWT|I/AAX!/A`AxK/AAn=/AA/A'5A} /A\AI/AfAcB/Ak[Aq0t/A:AEAXy/A@hk3A ojt/A5RAp/AsAFܝk/A;UA ad/A3XA]/A.vAV/A`DmA=:2O/AMqA(G/ANA)B/A]A- =/AMAkJ8/AEAoX3/A\A+P./Aa\AX)/A`At%/AA A)"/AWAre/ATALi/A0MA /A517A7|/A0A#/A<,A_x/AC>A8g/A OAmGm/A?2FAf/AAA_%\/A/oGA` c/AA"d/ /A'R#.AQP/A֚PA/A;lA~۞/A\uA@ӿ/A<A=/Aɏ$Ar;F/AwLŋA`/A˸ @AÝ/AA/결/A2(AX&/AJ73AOGw/A1uDrIAߓ-p/A0QAY/AALA'FC/AUV8A`1//A sA7(/AyAOz!/AūkA /A0Au$/A8Ad /AA/A[A8ٸ/ABA3X/AU]A*/A/Ah /AHAx/AtdAP!*Ŝ/A oyAHn#/A7AgAqx/A ~RAX$N/A |Ad;/AFA+/A%EAf)/A pAn4/A1T=A/AIoAPo/AyA%]; /Al/A.RA<S/A'AWT/AJ-;AYhԛ/AAh;_ƛ/A=A-/A ZA&t/AcGAY/A.A=L/A^APYS/A`Ap=k/AA&}/A cAgy/A !AX]/A`AB/AA~/A@-AH/AfAa/A@A/A6EAЄ/A JAx/AvAxW/A ¯A`v/A sA/A@EeAÙ/AZA᜙/A Ay/AvAH`/AApQ/A`6A+A/AVA N0/AA/A`5A0"/A+A//AA^/A &AH:Y/AZAn-/ADA8E/A$A/AAA/AHA̛/A`A_(/A@A(/A@5AM=/AtAx/AZ AwJ/A`A /A 7AV]/AA`AN8/ANd/AH/AvA/Aqc^AhDʔ/A@,A@/A1jA /Ao^9Aɝ/Ay?*A. /Ai 4A=/Aମ~AȰ/A u}AH/A\}AȰ/A|A/A||A Ρ/A-{AQ/A[s{AhՍ/AzAX/A`QzA`T/AyAȰ/A;xA@½/A}xA?̔/AXxA/Aj|xA /ATxA !/AQxAP(/A cxA"/A@yTxA)/A`wAȱ/AwApӔ/A@wAɔ/A wAɔ/A@#uvA|ɔ/A@qvAÿ/A@NuAǵ/A`muAǵ/A`tAǵ/A sAɔ/A?VsAڔ/A`)rA0/A@qA( /A1qAP(/A-pAI/A JipAs/A QUpAs/AM_pAԕ/A[7pAP /A`oA:@/A=oAo/AoA!/AeoA/AoA8/A |oA –/AoA̖/AroAH/A nAr /AbnA9/A DnA`Z/AzunAhy/AnA/A@ nA0—/A@!nA(O˗/A@nA ʗ/AUSnAPꛗ/A൷mAx\q/AjmAd/A@SmAh/A@>clA/A`kAG/A-jA0>/AjAHK8/ARiAXu/A`iAx\/A |iAԎ/A iA0/AgA𴛖/A@fAPF/A bA/AJaAO/A@F`A/ܗ/AzV_Ax/Af!^AD0/A\A\/A S]\A//AW\Aȶ/A\A,ܘ/A`f\A0ܘ/A@ \A/A`\A/A[AP@˘/A43ZA0/A`;YAr/A@{XAP/A cWA`/AWA٠/AVA|/A2VA7/AXUAW/ATA&3/A SA0T,/A+RA8/APA@͗/ANPA嫗/A`PA /A*PA/A~PAcr/A OA^/ANAa/AsMAa/ALAu/A`3LA;/A"KA+җ/A3KA@/A;lKA/AL:KA/A lJA/A ~JA+җ/AsJA0/A lJA A/AZKAk/AZKA@/AJAb7/AsJAr /A JA/A3IA/A GAؖ/A@Ap뙘/A?A=/AAAw/A`BBA7/A5CA!/A`TEA#/AMEA`T /A EA //A@%EA(a/A ۚDA(~U/A@nDA\>/A@,DA,/ADAh"/A BA4g/At"BAG/A?@A83/A[?Ad/A@r>A(m=/A`o=AM/A=A _!/A`<A/A<Aڗ/A~;API֗/A`m;AXhϗ/AN;A/A QE;A/AT;A/A+V;A /Al;A8/A`m;A/A@5=A/A;=AY,/A@g0>A(>/A;>A@U/A+%>ATg/A=A 4/A@E=A/A@=A( /A>A7/A3?Ah]D/A?Aa/A 8"@Ah]t/A[@A( /Aࣦ@Aذ/A)@A/A (AA Ś/ADQAA/AAA/AAA /A`.BAج#/ApBAu8/A3BA(F/A 8BAb/A@`1CAq/A ^CAP /ACAhޤ/A CAЀ/A`8DAVǛ/AbDA/A?DA/ADA"/Am@EAP8/A HkEA83:/A`EA:/A`fEAX/AmFA*/AFA(\S/A`fEAn/Ap#EApc/A(EAJ/A`fEAp=A/A=EA$/A]=EA&/A@yEAF/A@DAs/ADA`1ϛ/A}9DAPٿ/A DA/ACA땛/ACAiy/A5CA=j/ACAqN/AzBA>/A`BAhM9/A`osBA{/A@`fAAX͚/A`-AA/A u{@A@䎚/A 8o@A(;/A r?A)/A=A/AkW=A/AdY:Aؒz/A :Aٓ/A&:A@/Az7A0%0/AX6A0{/A &6Aޙ/A5A4/A@`z5A_/A 5AP8&/Ac3A`aG/A3O2A /A"8A/A 6A/A~6A?/A`9:AАW/A9A/A5A{/A?4AP u/A3AM/A4Ah{/A J7Ah/At7AxW/A?3Ar/A3APom/A&X3A@/A2A,/A %4AHn4/A`4A螛/Av2AH/A2AΛ/Akd1AJ/A)2A0*/A@1A֛/A /1A((/A 0AJ/A`/A0GR/A 0AT/A <0A`W/AX0AeY/An0A(]/Aߝ0A@‰/A@%2A8/AM2AP/A (N3A(/A4A0/A`4A/A`[4A@ǜ/A`65Aɜ/A lO5Af/AF6Ap/A_6AP/A@6AU/A 6A /A`y7A@ 2/A@y(7A(./AX 7A /AfR7Aأ/AN6AJ/A_6Ax /A cO6A/A-46A`v/A=5Aݜ/A_5A(/A`T 4Aຨ/A@~3Ax/A D3Ap/A2AX/A͘2A/A{2A酜/A`1A/A1Aw/AHO1Au/A 71Aui/A`0A8g/A@0Af/A0AXb/AMi0AXa/A ^0A5[/AB 0AY/A`90AXX/A/A(R/A@#/Ap/A@ /A,/A,P/A$/A`)/A'/A*0AH27/A0AaB/A1AbD/A@Ϝ1AQ/A ZA2A+a/A03Axn/A@3AXp/A`;3A0/A@>4AP냝/AE4AM/A l5Ah^/Aֺ5AQ/A`C7A&/Aq7Ap/A7A͝/A&<8AЯϝ/A@rY8ANߝ/A9A0/A %9A /A9A&/A9A /A:A( /A:A(/A@f;A`/Az;A0/A`;A?/A`N;A(s.A*/Aߗ_.A(X/A,x.Aܪ/Aw.AdQ̝/AeLX.A0;/A}0.A/A.A֫/A H/A(V/Ah.AR/A/A/A6/A#/A@ .Af/A /Az2/A`)1A8R/A 3AZq/A A05Aƀ/A6AU/Aྐ6A /A`f6A/A=8Awž/AX9Ap̞/A-:AΞ/A F:Aٞ/A:A/A`x=A-/AR@>A/A Hk>A/A׋>A`6/A =>Az4/A>A/A7>A/A$<Ao/A3;A/A`;AB/AQ:AO۞/A y:A( מ/A<:A0՞/A:AnϞ/A9A8Ǟ/A`9A@/A8A/A@8Aѱ/A@&8A0/AH 8Aã/AY7A/A@N@7Ap/A6A|/A6AP/A`v5AP/A5A|/AR"5A)z/AO5Adk/A@H4Ah/A 1)4ApZ/Aj{3AСX/A"^3A(L/A$2AJ/A q2AP >/A@2A o/A-Aȝs/A x,A6v/AQGx,Av/A,Ap/An,A(ޞ/A@ ]/Ap/A[/A(\/A/AQ֞/A@g/Ah/AR7A(p/Ap7AQ/Ay=A(/A=A0/A=AWğ/A>A/A@ >A/A@T>A /A@g>A/A >A(/A?Aأޞ/Ar@A/Az@A(/A*@Ap/Az?A'/A 1@AGv/A3?APP/A/1AL/A]0Af"/A`.Ap!/A.A#/Az.A/AI.A/A`)*+A/A]+A83۞/A *A@/An(A/A ](Ap/A (A/A=%A/A %A{/AR$Ap=m/Az$Ae/A`#A0.(/A#A&"/AH#AV"/A`#AV"/A@#AH/A=&Ax\I/Aޕ&APfG/A&A( C/A H&A8C/A H&ALA/A̜&Aȥ@/AU&A(\&/A $Ap%/A|$Apk%/A@t$A#%/An$Ap$/Ag$A#/A=.$Az /A=:$A/AP%AW./A]&A=*/A&A&/A&A/A&A/A&A@/A 7'A]/AZ'A8/A |'A8//A 'Ap=./A H&A-/A&A7/A&AG/A&AWI/A@&AWN/A&APN/A &A(\Q/A&A7l/A(A/Ad)AG۝/A)Aߝ/A \)Aڝ/A (AѾ/A (A(\/A(ABk/A )Akv/A@ )AHᐝ/Ao)Aĕ/A)Aт/Af)A/A` *Auh/A`;*Au/A*AWg/A)AXZ/A!)A( [/A q!)AXp/A`; )A /Ak,As/A@5T,A,Cp/As@R2,Adk/A8WY,AbMe/A*h?+AYl^/AĘ*+AcV/A>ۈ>+AwݶM/A+AD/A +AX~/A+Aq/Ai+A՟f/A+A0;/Av,A̘/A,b,A}b/Ax3,Az/A0U,A@Nz/A-AbPb/AMP-1-A K/AN-A`i>/A'P-AI1/A~EJ-A %/A<-A9L/A"Wd&-AUܡ/A{! -A!@/Ao,AX/AҰ,AX /AQ",A /A`[b,A؛/As>A,A/A0,A,;/A,A G/A뷊}+A.Qj/Aq+A )T/AnXN+A\@/A*AuL0/A{*A#/A3JL#*AhQ/A)AhD@/A)A(̛/A*Aɛ/A *A /A`f +A(K/A[*APf&/Ac*A*/A*A/A ;*A'/A *A |/A`)A@/A *Aik/A@y)A@/A@y)Ar/Az夆)A׊!/Aen(Ax-/AϵRr(Ax?1>/Aת'ACӼR/AoG'A7 j/A,xE&AG/A$k&Ab/A9v&A;›/A*n&Aޛ/A`9~&A /A6&A R/A@f&AȘA/A@:&Asd/A %Aw/A %A/Ao!%A/A %A{/A x$AH/A`$APM/Ad#AP/A`B"A Ĝ/A`!A`Ϝ/A lY!A !Cڜ/A AS/Aa./ A߫,/AaݖApޜ/AׅlA&5 ֜/As#iA<Ȝ/AŦNA=/A,RA䠜/AA0~/A`tASZa/A35A1B/A]3Ai謄!/AVu AS</A A#ޛ/An1A8/AA:oU/ADA ëy/ApA-XpRY/A1~AO:/A UA/A5A4/AJ]:Aq &/A:ALHޚ/AZ,AК/A 4FA-;#ǚ/AlA0Gš/A8Ah9ǚ/A! A4Eʚ/ABAb%T˚/A:Ayp]ʚ/ALkA hhǚ/A(_SAۋš/AED(A#뻚/ALAз/A AO/A`A[|/A`AM]/A`fAd?/AG$AT</A tzA\/A`Aٙ/AZB2A7︙/AzTyE#AV/AmAL}/AЋUAU d/A*AM/Ab̛,Ao1;/AyA@~,/AAҿ/A{_AI/A `f AZ/APVmA֖/A֨A /AA7/A˒A /A,5sA3/AbN AO<ܘ/Au % Aj3Ș/Aa!Aj,/A-d!A))/Aƛb_"A\xw/AA|#ACU/Ar#A؁1/A`#A'/A~m#A.#M/A9xm.$Ax]/Aa$Abn/An/$AoV/AyA[$A`/A %AOY/A"$?%A%/A|oi%A. /A8%At ח/A%%A/HƗ/A8>%A./A@ϵ%A=Y/Aa!%Aa1q/A(%A }N/A@%A22tH/An$%A$+[B/Aa%AU`$/A_M.~%A _!#/Aof%A5#/AKCM%AU$/A 5%AsY_2./A18%A5/A`$Asf;/AD$Al>/Apt<$A?/A+#Aj["A/A`"A\l/Ap\}"A@$/At"A<}/A&b"AFz/A_=S"AA[/A\H"A}/A[.=@"A-ݕ/AH<"A7mѕ/AqCRk"AÕ/AZ"Ac/A#{"A/Ai"Abt/A{A"AQNσ/Aƞ"As/Aق "Ac/A"AN$Y/Awz"Az>N/A[X"A]A/Ae>"Az3/AS^."AmĔ[%/A)Q3'"A/A׋$)"A=/A`5"A;/A5J"A{I/ARK9h"A-b/Adʤ"AN0۔/Abȹ"AHӔ/A֮k"AΔ/A,!#A%Д/A'T#A<_LԔ/AIb2#Aٔ/A#A%۔/APF#Akܔ/A-,#A+mݔ/A=c#AA/}ݔ/Aj#m#A#?ܔ/A$Ad|w۔/A$AN׉ٔ/AnH$Aה/Agk*$A8?Ԕ/Aӻ4$AД/APe=$A$!͔/A(%C$AHɔ/AnF$AVŔ/A@G$A:/AeP_$AM~/A|*m$Aȩ/Ao$Aۅ>T/A+.$Adu!/Ar$AH/A^%A8/Af%ATX/A`"7%AesV/A.z%A%V/A/t$ARLmW/At3 $AJ7AZ/AWAHnb/AVAUu/AA(Kz/AklAcʗ/AvA0/A[Ax/AAx՗/A`v]A(/A`AXB/A|=AXe/A@G!Au/A@A`z/A4{Ay/A0*Aj/A AW/A@k AV/A4h A(iC/A q AH9/Am A,/A@D Ap#/A@# Ap#/A`] A( &/A3 A/A S% A/AZ A/AA /AEAH9/A@yqAЦV/A 1Ao/A HrAR/AA/A`̫A`Դ/A@A纘/A AA뻘/A@GA0/A@lA˘/A@WA ܘ/A@AR̙/AsA8Iə/AgAߙ/AAh/A`[AȤ/A `Al/AA0f/AAOۘ/AAA8Ԙ/A & AOۘ/A`9. A0f/A@ A`/A` A0/A ( A /A / Ax /A A /!/A`$ A %/A A AO+/A Al4/A u1 Ay8/A A 8/A i A?/A A5i/A` A8sh/A`K A%/A$ Al/AT A8/AT AP/A A/A A /Aa A8m/A Ah?l/A 1 AfB/A AxZ/A Am/A A8ə/A 8w Av/A+ A(/A Aϙ/AoG Aә/A  Aؙ/AN Aߙ/A A?/A@ A/A@ A/A2 A/AN A/AN A/A ( Ah//A A/A+ Ah/Ao AF/A`D A2/A ( A2/A A/AH A3/A Av/ATp A(/A$a AX/A A/A@ AАٚ/A4 A]ݚ/A 8g AX/A K A/A 8g A(/AH A/A6 A/A` Ah//Aq Ahؙ/A - A@0/A AԂ/A2 Amt/A` Af/A AVX/A  A؟W/A` AR/A`] A؞?/A` A71/A`] Ai/AWL A/A` AǗ/A A/A AX/Ao Au/A AJ/ADl A/Ar A/A` A͖/A A'/A A/Az A0Y|/A A{/A JA A&/A A'/A A'/A% A&/A& A߅/A@ A%u/A@#o A(U/A A =/A}E A8//A u A8//A AV@/A@ie A#E/A AWL/A` AN/AqX AS/Af8 A$]/A Aa/A AXp/A A0Y|/A AZ/A`b A[/A]{ A͖/AA A*/A` A]ܖ/A x A˖/AM A)ɖ/A1 Ax)Ֆ/A A*/A`) A/A{ A0{/A`x AE/A` Ah/e/A@ Acx/AD A/A  Ah/A; Aڗ/Ama A؛/A`t A /A׷ Ah*/A? AXB/A? AN/A@ AN/AD APk8/A A`/A A/A A֗/A A`2/Acq Apn/A7 Ax>/A@ Ae/A  A_/A A,/A` A,/AK1 A/Ax A0"/A Ax&/A@ A7/An AE/A w Ah/e/A w A/An A/An A֗/An A/A@ A5/A@ A6/A@ AН'/Aֺ AP7%/A; Ai/A* Ag/A* A—/A6! A/A AhQ/A` A-/A@W A%/AO7 A/A ھ Ap /A A^/A| A /A s' Ap /Aq A_$/A@g Aq-/Aк A` A D/A|ْ AP/A_ A^/AG Ah/Ah3 A-q/A ( AP&t/A  AGy/A AXz/AΑ A}/A A(h/A Aq/AQ A{/A@n A(/A3c A/A` AR/A`P Ah/A@- AP+/A׎ AÙ/A  A84/Aq# AH /Aӊ A`/Aെ Ax--/AE AtC/A AU/Aj Ah/Ag. APov/A[ A\/A < AX/AWY AGś/A` AdΛ/A`0 AP c/Ao Ah_/A$ Ap{/AՈ Ax:/A AHn/A`K A(/Aه A/A$| A š/A AP7К/A A[ۚ/A AP/A A/A@W A/AzT A/Av Ax/AΆ A /A  A؁/A A A؎/A A@/AO A/Aj? A/AXՅ A/A Ah/A` A @/A> A\/AVك Aa}/A`k AH헛/A`< A@/A A /A Ah^̛/Ao A@כ/A 8@ AXW/A AL/Af Aq/A| Aq/As- Ax /Ao A/A@5; A;/A AO/A` A-/A@ R A)/A` A ֛/A@5 Aԛ/A@,R AX/AV]~ A0/A@} A/A~} A7/AH} A@?/A`| AЕ}/Ad} A0/A0} A4D/A 8| AF/A z| A(O/A@>| ABV/A~{ Ab/A`{ Avo/Az AhD/Aaz APx/Aez Ay/A@z Ap/A`bz AGœ/A`z A0Hݜ/A`2z A/Az AP|/A@z Ah/A@,mz A8/Az AzҜ/A'z AĜ/Ay A@F/A`iy Aߪ/Ax AXy/ALx Ay/ABx Aǜ/A ەw A /A ҽv AX/AJv A@!/Acu A:/Au AT/A-u Ar/Au A苝/Aau A趨/A2v AЄŝ/AJv A@5˝/AJv AXܝ/AJv A@S/A3wr AP1/A q A",/AOq A(/Ap AX%/Ap A/Ap A!/A@%p AH!/Ao A0 /A Po A/A`n A8V3/An Ax:K/AWm AF/A{f*n ACL/A}Vn AWP{/AO.rn A!$7/AƟv~n A,c/Au0zn A{5/A0Een A]h/A dDAn A`a#/A@ n A;M-/AHpn A(%?7/Ae2 n Af@/A3;n AEJ/AXŚXm AHuR/Aױm AY/Am As_/AFRm AHbd/Ay8b:m A :g/Ax[m A\&xi/A4m Aei/A; m AhNe/A|Xl A^/A0k AjU/AXj AxC0U/A]Gj AʨR/Asaj A*L/Aj AIaC/A0i A3À8/ApHIi A&{S+/A2gi A8/AA;i A /A]i AF/Aebi AFԣ/A'Li ARD/Am j A./AGLŸj A /A` k Aȶ/Aj A/A Ri A=/A ~Bi A/A@h A,/Ah A+/Ah A/A Sh Ad/A@Ei AN/A@E,i ApM/A6ei A/A Ui AN/AWj AU)/A/j A^{-x/ABj AT>/A2j A/A k A/kϠ/Azk A ]5/A 6!k A"f/A\k A2/AOj A5/A j AEA͟/Atj AL/Aj AH^k/Ahi At7/A@&i Aư>/Agi AtI͞/AMQi Api AN;/A#&i A,t/A򅸃i Apk/A8Õ/h A>d/Aڑh Aī^/Ah A"F[/AŽQh AEZ/Ah A ?[/A,6@"g A8x_/A`g A b/AÊDg A6i/A2Qf An}u/Atrf Ax/A@Gf A<{4/AlMFe A/A>Old A@Ә/A@c AI/A@Ɖb A+/A 6b A!t/Aa A@/Aka Am*ޜ/AG` Aɜ/AgS` A8/A q_ AĬ/AQx_ A%F/A{E _ AΈ|/A/At[^ AȤ/A ] A4/ /A(] A2#/AruU] A\/AXNv] Ar/Ahzb1:] A.ܛ/A\ A@+֛/AG\ A nԛ/Aܹf\ A:(՛/AAXR\ A؛/A~~[ APߛ/A[ ANF/A9SO[ A'V/A 5[ A@/AZ AA/A>Z A'/AbmcyZ AXW6/A2Z AO=qA/Ax" Y A/N4I/AC`Y AckM/Aq6Y A6sM/AX A#%J/AQSX Ax(D/A;-X A$#Z>/AMW A6/AzhW Ap})/AzX A(/A X A/A@gX A/A@QX A./AoW Axd,/A W A3/A 8JW A%/AVáV Ax/ArV AHW/Ax&tl+V AT/A@!U APԛ/A\נU A/A$S-U AdԹ/AH\U AJ|K@/A"wU AÒ/AmtT AGMG/A}T A+jO/A*(T A-䇛/AKS A/AHxS A:k君/AIR A/AR;PR AS̔/AQ A/AL AXڝ/ALL Ax>/A L Axu/ApO AV/A@O A /AFI Al Ɏ/AS eI A=qe^/Ap I A1/Aʨ8H A/AOH A/AVIG Ak)v/A'8Y.{G AJރ_/AɪpG AsF/AgF A^/AF AfS_/Aw8EeE A(r/AaD A&\T/A6nC A /A@. C A&E/AR[B A-(/AlA A]/A]ĐA Aq/ArW@ A Ahw~/A!֚> AP/A= ApZ2/At,= AP^/A* ? AC/AR\? ARj/A>? A~Uǝ/AI` @ A|ߝ/A'T@ AR7R/A .0@ A`S/A@ A&&/A/4A A \2/APZ7lA Am7;/Al)@A Aj?/Ao7B Aq/Х@/AQ͠B AM!G=/AyW C A(5/A`nC ApkH./AN; D As*/A(4D A`9+/A{]LE A//AXE ArY3/Az F A S8/A&F Am=/A=F A(D/ATQF A.b'L/A? o_F Auq&T/AHZiF A^m\/A*mF AHd/A@PkF Aәlo|/A=F A|ߧ/ADH}E AfH A+/A`T!H A+/A G AB6/AF A-I/AyF A8U/A`]xE Ak/AD A|/AD A/A AD A堫/A AD AHг/AVD A/A;D A`v/A`[C A8/A9C A_/AC A0J/AfC A(>/A`fB A#=/AB A0J/A+A Ad/A@ A/Ax@ AU/AyO@ A/AA@ AX/A`oA@ Ax˫/AQ@ Aͫ/A`)? A@ǫ/A$? A/A?? Ai/At? AW/A,6? AxU/AO? ApAC/A`n> AP77/Af= ApAC/A6< A@M/Aҷ; APe/As; AT/A`; AҠ/A͖; A`«/A`; A|/A@N; Ap/A`De; Ap/A Z'; AHӫ/Ao: Ap+/AF: Ap/A: Ah}/A: A@n/Ag: A_g/A@S9 Ais/A8 As/A7 A١/A` 6 AHn/A`5 AHӫ/A5 AP*/A`5 AP0/A@W6 A[/A.6 A`k/A@N5 AF/A5 A/A 65 A>/A4 AHӫ/A?4 A֫/A@3 A0 ޫ/A`B\3 Ax/A 1B2 Ax>/A}1 AhH/Aan0 Aq/A,0 AF/A?!1 AӬ/AJ1 A/AJ1 AO /A0 A/A,0 A@/Aan0 A/A@W70 A/A/ As/A 1[/ As/A*x. Az/AH- Aլ/Ap * APլ/Aa * AWշ /A@e AX/A Ap=g/A`D Aզ/ANI Ap=/A5 A(/A$ A@Bk/A A[/A 8 A/A`" A7'/A@W( APC/A| A`S/A@WJ AU/A Ap@Ka/Aք=: A1}S./AMؘS Ab !/AF A`ʲ/Aw A隲/A A/A@A A0{/Ao Aw/AM{ Ag`/AM{ A pX/AY AL/A)& AH/A` A@;/A0 AK(/A@ A /A9 A/A`s A /AX A/A A/A`TW AK/A߾ AТ[/A@ Ax#/A Aa/A $ APẔ/A@T A8'/A A/A`[ AU/A+ Aϱ/A@ AXı/A AHr/A A/A A(n/A 8 A`[/A AT/AE+ AP/A0- AͰ/A; Aİ/A A'/A F A胊/Ah A0ݞ/A$ Ac/Ax AЀ/A` Ah/A AѰ/A+ A/AI Al/A| Avp/A@ Ah/Am AP'/A^) Ax$/A A/A`] A E̱/A]# AH]/A A//A Z Ah./A# AE/A A`1'/A- A8_/A¿ A}/A AH?/A AԲ/A`T A/A8 A/A A0/A@,% AJ/A AH{/A`K? A/A A!/A`H AN/A b A0j/AF A/A@ A(1ز/A` APk/A A1/A A~/Af A8Z/A 7 A [/A AW/A Z A(D/A@ A77/A` A8/A d A/A*x Ar/A' A/AM7 Axmb/AV~ A7/A+3 AX2/AZ A (/A;| A/AQ A/A 7 AP/AN Axi/Ax A/A A/A ɷ A0 ۰/Aj AŰ/A P AH/A A_/A4 AFT/A1 A./A A0)/A+ A_/A~ AXF/A Z A`̰/Ay A߰/A l Ah/A A/ /A`TT AX/A`) Ah?/AIgm Ac&/A`] AЉ/A$@f ACk/A3 A< /A1Q< AY]2/ADt8m A/A? A[R/A= AJac/APHa!z AЮ/A[c A*/Ap ' A_͟/A)/ԯh A(˃/AT AN/A@. A(/A A/A A[/AQ A/A`]r Apʫ/A AȻ/A  A8/Awn A0/AV] A 0ʫ/A A̫/A& AM/AE Aw/Ae AE/A3q AȤ/AI A0Ȭ/A A8/ABQ AX,/A`] A/A6 A/A_ A=!/A A(z7/A JG Ax?/A") AXU/A A@]/A ABi/A Aq/Ay Au/A A`/A A/A A /A` ABЭ/A & Ah;߭/A A/A Ax)/Aƞ Al/A] A>)/A@y Ap3/A AA/A A0jR/A6 A/Aq A`/A} ApI/AS A( /A`K A //AC A(?/A A5G/A A5b/At! A`Tj/A \ Ax:/A| Ap/A (d AН/A` A-/A@ A/A A#/A- A,/A A@ԯ/AM_ A/A? A/A A p/A Aү/A6 Aʯ/A@c A/A AhQ/A A0ꕯ/As AX/A A*/Az A1/A`) A/A@ AP /A`\ A@/A`܅ A0.ˮ/A@# AP7Į/A3K AS/A AX/A A/A A/A` A=_/A@7 AuQ/AEp AhG/A8 AP2/A@v A_ā A;XL/AJw AV/AZ?> A/a/AF Al/Az@  AM4$x/A5;>! Ah̃/A  A~/A񝅏 Ar;] /AN ABq/Au A/AR A>/AɾK AT#/Ab @~ AaW p/A,/} Ay /A;7t| A=/Ad{ Aw/A/gy AfJn/AHx AP /A@rw AX/At A/AHq A`/A@wn Ax/A`k A/AWj AX(R/Af Am /A՘c Al/A (` A(:/A-_ Axmi/A@E] Ah1/AQ[ A(/A#[ A"/Aiw[ A1/AxiE[ A&/Ap[ A#Z/A][ A(>2/Ag[ A?;D/A~-Z Ax/A`K6Z AI{/Aw=Z AOu@M/A'[ A1 /Ai[ AЀ/AA[ AtU~/A $\ AZ64/AD5Z\ A灘i/A08~\ A5/AFqm\ AhR/As\ A/A3] A/A v^ A8/A@y^ A}/A`E_ AH/A{a A@N/Ac A`/Ae AD/Asi A/Afk Au̹/A iՄl A7o/Am Au!/A 0o A0_T/ASsp A1X|/Aݧq Ah%/A r A0/A@Gr A /Ay p AaB/Ap A06/Ap A/A r Ah/A@t A`Ì/ACt Axp/At AhD+/Aiu A/Ay A/A=K~ ADո/A& A0'/A@,{ Aܶ/A Aܶ/A| A*S~ǵ/A6aq Aqn/Ab}֒ A25a/Ac.. ASc4/AYw A*ʑ?/AC, A׵/Aߓ A੕/A@B A|3/AW A ȴ/Aн AXa/A@p A;/A qV AXy/A sғ A@=P/Am˒ Aq/A AŨ/A`)) Ak/AĐ A9/A0 Ax+/A`D Am/A A/A 8x AWy /A/; A9/AU A $E/A`Ѝ AX/AXzǥ Ah ߱/AP AO|б/Aek A6?/AjH^ AB2!-/A|k[ A?/Akc AEiJ/A {v AH/A Ғ AЅ9i/A+( A*U/AOȎ AjE/A3zp A:/A+ AX /A A2(/A4- AЦ:/A6 A}/A A/A> p A(%/A` A/A A!/A A8 /A A@$/A@q A/A A /Asї AP/A A84/A@NF A` e/A?|L~: A;/AsL.& AD/A@> Ah2W/Ab0p A?T /AqP' AY/A9Ԟ AX$/A֡ A5/A Υ A0LR/A0z- AG/A@D A{d8ԩ/A۹T Am/Aٶ A$@/AwXBx AЀ/A`T. AZ\K/AJ3h AF/ q/AW0u A`./Adz Aѧ/A9gW A$o/AAٶ A=7 /AijL AA S/Ax AcM8/A A.Jʥ/Alv2PN AxmY/A A/AFE% AG/A Asa/A0ſ APM4/A A/A; Aã/A\ A`/A,i0F Al/Aܓ1$ AfB/A A /A A/A` A/A@W AD7//An$ A_+֝/A<+J? A /AI y AdDU/Ax A-/ALپ A\7r/A k Afw7c/AG ARS/AҐ) AB/A/ AoJT4/Aݛ Aԉ:$/A^a A /A A@/A`" A /A/g* A/|gRߡ/Au_ AXxС/AP؞ Agġ/A A/A Ax/A@.F APu/A AC/A@ AP$/AV A/A A(/A Apy/A` AX/A@ AX[/A A8#/A As/AMj APj/A`m Aa/A൵ AW/A; AaI/A@Ͽ A?/AD As3/Ay A`C%/A@` AN/A Aݟ/A A8/A Au/A3 AF/ATM A` /A`a A/A AMӞ/A`) AW/A A1/A  A\/A A887/A`; A/A A/Ae A( Ý/A A/A AD/A AV֜/AA APȜ/Af AF/A  AБ/A Ax/A@t AБ/A ± AЦ/A 9 A/A5\F AZWs/A AU/AZ] Ap4/AoJ A /Am- A`T/A  A0 ϛ/A&_N Aﮛ/A A!uڄ/Aх}_ A`Y/A0: Ap_I/A,0 A]h/AD A߅D/A A`Sš/A@1 Aٲ/A ޿ Ah/AԿ A'/A AH/A A /AS A /A 9 AP뜚/A ? A/A@Ph AȺ/Ac A![/A A![/AN AX5/A@% A/A@1 A0/A3 Ah*/A#3 AD֙/Aq AÙ/Aq AC}/Aq AϘ/AM8 A@j/A@ A /A@W A^/A5 A/A A/A A 0S/A A(/A 8] Axؾ/AP A跀/A` AP/A2) Abo/Az Ae/A A8l/A$ Asc/A$ AP<\/Aj A8;/A A8/A`y A@ /AMz AP|/A@> A0B/A@t A6/An A00/A` A,,/AI A(*/A 1G A00/A|> AY./A ۞ A/A ¡ A/AJ ApJ!/AJ A"/A Ac/A` Aw!/A`bc Ap/A SK A6ޒ/A A6ޒ/A Ahے/A A`ɒ/Aj A8/A` AX#/A@, Ap/Avt Aޑ/A X An/A Ac/A AX/A` Ao/A O Aޏ/A Aѩ/A A0h/A AP7/A8> A }/A c A導/A= A@B/A AH/A APx|/A A Aߍ/Ah A6Ӎ/A Qܿ A /A`ve Axm/Aa A/AR A(-/A@k A0e؍/Ak A%/Ai A ō/A Zټ A]ύ/A A8/A A()/A Af1/Ao A`Xk/A APb/A@] A/A`;ι A]ύ/A  A(:G/A Ap[C/A` AÍ/AH A%/Aɼ Aӭ/A/U A(/A@@ A0/At A`/A 3 Ar/A`f A/A' AϢ/A`m Am/A` AE/Aſ A0*/Ad A /Ayc A /A A/A A/ARݾ A/A; A5/A A5/A 폾 AV/A v A/A v A/A@p A/A J{ A׋/A 폾 AË/A`] A`/A`; A,/A`T\ AX/A A A/A Zټ A/A@ļ A0p/A m A 7/A_= A/A AP /AXZ Ap!/A (r A /A[X A/AW AEt/A P A*(/A AP|2/A@ A"/Aܻ A/A Awȉ/AK A8D/A`] A/A?̸ A()ω/A Azى/A A:/A@NB AZ/A6Q A؟ӈ/A҂ A/AV Ao/A`m5 A&C/A A`/Aع AՇ/A ( A/A\ A0/A | Aja/A?̸ AtB/A_o A`@/AE AK=/A` AP:/A$ AP:/A`mN A.լ Atz/AjLG A,~&z/A@9d' A,Yn#z/Az͐ A{y/A~@S* Agy/AЕP Aŧu y/ALYe AVx/A:i Aax/A>\ Ahx/A @ Aaŵw/AZߣ A~B^w/A:$; r Aѡ w/AF A @ĸv/Aq Ay(kv/Au: A]!v/A1rMC A 9Du/A A# vu/A A\u/A%;ϔ0 A"u/Amm Ayt/A6 AHjt/Ap A~b`t/Ax醪 AyZt/A,:9q A8Yt/A^7 Av]t/Ae7 ATqft/AzƂ Ahtt/A"/ A?Ft/A֩` AO 1t/AX&4 At/AAq~ AYt/A| AEt/APmv{ A.T%u/A z AQu/A1 zy Au;u/A*x APu/Aw Au/A;r Ap v/Af Acu/A Fe Ahu/Ad4d AX[|u/A^d A o\t/A@4i?d AӈVt/A䦥}4d Azs/A9O=d AKqG0s/AG[d ACOr/A^r_d A%0!] r/A\%sd A {q/A>/e Avjp/Afڝe Afi[p/A2 f AƨNo/AJES f Ad@o/Azy(ag A ln/Abh A+n/ARah Arm/Ai Aecl/A J^l AG9k/A n Ax6j/Aѥ%n As.j/A@,fo Axi/Ao A|i/AdH Aȩb/A? Ab/A@j A`2b/A qe APMb/Ad A c/A`f A0'c/A Aic/A  A@™c/A`ܳ A誴c/A| A((c/A` Ac/A A?d/A^ Ax1d/A Z AtCd/A}[ A8d/A@g- Ag3d/A^ A7d/A A`Xd/A Add/A? A83xd/A A Hd/A A(Nd/A  A0sd/A APHTd/A@` Am@d/A j_ A2Z.d/ALr A _d/A@El A;:.d/A A0vnd/Asy AB3d/A߉'G ARc/A: Asd/A Ad/A Asd/Ai.' ALc/A! A=Ic/Ap( ADDc/AO0 AN[c/A- Ac/AdJn AQc/A&m Ac/Aq Aw`c/A9,_ A>c/A[F AEȪ!c/A^! Ac/A A0Ic/AQ A?c/A\%X A v:c/A&3 Ah9c/Al AY&=c/A%~ A(CFc/AAU2 A|fSc/Ah AKdc/A: Azc/A$r. AAc/A AX c/AdS AHc/Ax A0$e/A  A:e/A`2 A f/A4 AmGf/A AZf/A`m Ate/A@Q AHmye/A`b A%e/A`q A0e/A. Aږe/A  Ae/Av Ae/A  A}e/A< Ae/A% APe/A@N& A(e/A&' A(e/A( Ae/A) A8me/A7) Are/A) A e/Aa-* A8Ve/A* Are/A* Aȥve/A, Aȥve/A - Axe/A@y.. A@Pe/A q/ A(e/A O0 AXe/Aye0 AKd/A&0 AUd/Ak1 Asod/A`&2 Aqd/A2 A@d/A@3 AUd/A@W54 Ad/Ay4 AQd/A@i6 Au4d/A`9 A Alc/ACu> A0c/AhB> Ac/A&? A&%c/A/? A:c/AH? Ahc/Ap? ApjQc/A? A//=c/AJ*? Axm,c/Av=EQ@ A8c/A0@ A>@c/AA A!A!c/AGPXA A]c/A-A A@b/A`TE Aa/A&M A~a/A`Q Axa/AOU AHP`/AW A0`/A\ A <:`/A`^ A _/Aɀc AF_/A`uh A 0_/A Li A _/A[j A_^/A 8l A^/A~n A,^/A@n AXy^/A`2o A w^/Ax~q ACr^/Aq Aзm^/Aőr A1e^/A0s AAa^/A)u AW^/A`)v Ah^/A 1r A9_/Aw APK_/A A Ra/AM Aa/A Z Ab/A  Ad/Ab A!e/A[ Ăe/A Ae/Ajm Ae/Apr A e/AϬ Ahe/A"~ A AWEc/A`H AOc/A` APc/A A /c/A Apc/A@g Aq c/A { A@$nc/A@r AYTc/A@\ A {c/A;b Ac/As A0c/A6w Ac/A i Aӭc/A i A`c/A@ A>c/A< AXc/A- AXc/Aj A4rc/A A`c/A AHc/A Amc/A r Ac/A@Wr Ac/A Ac/A 8 A8tc/Ak: Ac/AN APc/AN Ajc/A9@ Ac/A`F Ad/A A\d/A A\d/Ak: Ad/A`7 Ald/A} A*d/A} A d/Aʚ AEc/A@ X Asc/A@ A8c/AH Afc/A Asc/A A@c/A@iA A$c/AR A$c/A2 AͿc/A Ac/A&y AGc/A, Ac/AXs Ac/A@J Ac/Aޓ Ad/A Ad/A(r A d/A(r A1d/Ajg A6d/A A]-d/A A1(d/A O APd/Ay Ac/A@Q Ac/Ah AHd/A# A%d/A A8#c/A@C Adc/A@% Ad/A1 A8 d/A@% A`c/A A c/A^ Ac/Ac AX d/A@/ Aw.d/A`BK Aw.d/A8 Ad/A` Ad/Ao# Ah7d/A > A8d/A  A:d/AF Ad/A ApCd/A ApCd/Aqw Ad/Ab A`d/Aﯾ AhDHd/A6پ A%Od/A, Am"d/A`x Ah%d/A/ ApNTd/A AVd/A`ڽ Aw.d/A@̽ AY5d/A  A/[d/A@PC A\d/A ( A,d/A A3d/AgY Acd/At A9gd/AR AY5d/A^" A?d/A Ahd/A@ A0jd/A`B} A:d/A`Z ADd/A@ A@od/A`DѺ A@od/A A/d/A@ A/d/Aj And/Aj And/A9 AId/Af A%Od/A Alxd/A썹 Alxd/Ak ARd/A I ApNTd/AjƸ A0{d/A ۸ Avd/A@# A,d/A`f A,d/A@ A0{d/A`C A}d/A`m A 00d/A@η Ah7d/AOu AMd/A@y Axd/A 1| AY5d/A (E A:d/A Axd/A) AȎd/A^T ACd/A?[ Ahd/Aʵ A8vd/A`B Alxd/A` Ax( APg'b/A;y8 AXpb/A 85 Ab/A 4 Azb/A`2 A8 hb/A-2 Ab/A c1 AX$c/A@+ A,c/A Z) A|ac/A`}( A\c/Aq' A΁c/AR& Arc/A@o% AH)c/A@G\# Ac/Ar! Ahc/AM A`c/A@ AIc/A Axc/AT AБuc/AX AHpc/A A/lZ/AM A`Y/A! APY/A1' ATmY/A6 A-Y/A>g AHX/AO AQOSX/AGW ACX/As Al_X/A{ݖ A5X/AI A X/A@ܱ AW/Ay ApAW/A AEW/A APxV/A & AV/A S AqV/AB A( V/A N AהT/A, A,S/A l2 ASS/A@% AhS/A, AR/A A@R/A ' Ax-Q/A`) A;P/A` A+O/Ah AM/A`D A eM/A S A`!L/A}X AL/A % A L/AH A K/AK/A AT$tvK/A v A RK/A& A7L/AM' AZY(XL/Av[* Ah`L/A; AL/Am AiM/A'%k Ap9M/Ar AW_M/A8 AنM/A?b APM/A6 A.;M/Aq Aނh"N/A_, A^N/A`뎽 A/9;N/AC AN/Aį+& A6O/Aof\ A v5O/A]} A0bO/AuX AO/Ajb A{fO/A(A A'O/Aq}` AO/Aa AO/A A(O/A, AQO/A@ A(O/A` h A2O/A APO/A@% AMO/AU A8O/A R A(}O/AΜ ARhO/AR4 A~EO/A@v A4O/A۬ AF.O/A@5 A3O/A@ A^O/A@Ū AyO/AU AO/A@Ɵ AxO/A A8VO/A A(O/AN[ AyO/A?o A[O/A& A =O/A AO/A > AxO/AR APN/AgZ A@dN/A- AN/Ax AN/A (- AN/AR A\yN/A - AgZN/AX AJN/A Z A8'DN/A@ A/CN/A@ A؛6N/AB AJ"N/Ak A N/Aw AV\N/A`;ș Ax5`N/A AQN/As AxNN/A앙 AN/A~ A8N/A#T AP N/AK AtM/A^{ A3M/A q AĴM/A9ʖ ARRM/A AM/Aq A8/N/A3 A0N/A A(KM/A6֔ A8sM/A`Ü A{M/A`K A8sM/A AxM/A@ AM/A9 A M/AN A0GM/Aas AH.M/A` A0M/A AHrM/A \ AgN/AR. A@rN/A AM/A@9 AM/A` AP^N/A B A8EN/A3 ALM/A` A8M/Ay A0M/A`;, AM/A׍ AM/A@C[ AM/A`׌ A(N/Az A`N/A2 A3N/A`p A&N/A- AXdQN/AF A0LbN/A@W؉ A eN/A`m AjN/AU AqN/A AN/AtΈ A୍N/A AN/A]Z APMN/A1 AO/AY A,O/A`x A@P/A` AP/A4 A(\O/AN A@uO/Asu AHN/A`Ti AH O/A} A0O/A`v4 Ah;0O/Ac A _nO/Aބ AO/A[) A P/A'z AӂP/A AP/A A/Q/A9 AXKQ/A A$fQ/A ~} A {Q/A { AQ/A y AqQ/A@w A8UQ/A`du A@`Q/A !t AQ/A (r A(yQ/Aq A:R/A5q A =R/A@p A@R/A #p A.4S/A3o A?S/A$l A\OS/Adi Ah9S/Ahh A8'R/Akf AGjR/A d A t R/A |c Ah@Q/A}c AUQ/Ac ANQ/A@>Jb AP/A@a A(>P/A@G7a APȰP/Aa A0P/AHb AѮP/Ac A6P/Atc AyP/A|c A0&fP/Ab A0&fP/Aib A0&fP/A`Rb A@RP/Ad b AP>P/Aa A)P/A@,a AP/A"b A O/A`Rb AO/Ab AO/Awc AO/A`2c A@~O/A`v`d AHO/A`)d AQO/A-6e AHK[O/A<` A(O/A`` A8O/ANe AH)N/A`[e A@N/A൚e AN/AFe A`%DN/AFe AxM/A &Le AXM/Ac A0ٲM/Ab A`M/A`K` A_M/A@,q` AOM/AQ_ AXtM/A`2P_ A@sM/A} _ Ax'M/A`_ AM/A@E_ A2zL/A ^ AK/A ^ AhQK/A^ Ah@&K/A`@_ AK/Aw_ Ax˰J/A`2` AH2 J/A`;_ ApsI/A`2` A8H/AT_ A PH/Aw_ A>E/Ao^ AxE/A@a^ A`D/Ay^ A$D/A@ ^ AxD/A _ A`%C/Af_ AC/A}f_ A8B/A`_ AsB/AX^ AB/A^ A!:B/A@Eq^ A ߿A/A@N^ A;8A/A@_ AD@/AOj]_ A`t:_@/AӴ` A-:?/A>(a A8?/A-b A?/Ab AH:?/A?c A?/Ac A @/Ac AR@/A 2c AH?@/Ab A`@/A]a AXyA/Agfa A8QA/A` A֒A/A |` A4A/A)a AA/Ab ApA/A=2c AHA/A`8d AA/A>e AhA/Ae A%A/A0uf App@/AZg AI@/A g AH@@/Aർg A @/Axg A@?/A`DEg Ax?/A |Ag A3?/A@g AC?/Ag A8?/A@g AP|>/Ag Am>/Ag Apy>/A`9Gh AL>/A9h AXh5>/A h A|C>/A`m;g ARv>/Af AX>/A=f A>/A`Te Ax?/A@Pe AF?/Ad Ap?/Axd Asq?/A@rc AhT?/AIc AI?/A|c AK;/AXw Ah;/A@Ew A:/Aw A`:/A`Tw APo:/Aw A 0:/A@w Al:/A*w At]:/AXv A8EL:/AKv A@D:/A`u AxW9:/AOWu AXR5:/A`mt A'::/Aଇt AH:/A t AbN:/A0s APg:/A]5s A`v:/Ar A:/A q A:/A`Xq AP:/A@p AH:/ADIp AH::/A`o A:/A o A`:/A@Wo Ax؎:/A`o A3x:/A }o ACd:/A o A%":/Aq AM9/Ar A9/At A=9/Av A8b9/A`w A9/ATx Aྜ9/AЈz AXے9/Ah| AS9/A3~ A0 9/Aag A9/AH A9/A A跹9/A`Á AP9/A@io Az9/A+ A9/A@X A|9/A@#~ A9/A`| A`X9/A{ A@9/A z A:/AKy A@N::/A'qy Ar:/A` _y A0:/A sy AN!;/AT\y AT;/AR~ Ah;/A` A ;/AЙ AD8;/A@J A:/A@ AX~:/A끠 Ac:/A@ A8Ia:/Aӛ A Q:/A AH9:/A@## A(:/Aڜ Ap,9/A徜 A0%_9/A@iT A849/A`V AH8/Az4 A8/A^ A 7/A A87/AA Ar8/Aa AЄ'8/A?ޗ A)8/A@p A0668/A9 ATQ8/A Axzc8/A Axe8/A֘ A@8/Af A88/AI A8/A@/ A8/Aھ Av8/A A08/AnG A8/A AP 9/A9 A99/A@y? A{V^9/A?& A)1H9/A6H/ A9/A ' A<Ǭ9/AQ A|n`9/Aw A Xn9/Aݤ{* Aܰ9/A5 ͑ AV9/Am A~߮9/Ab~ Ap9/A? AWɟ9/AI) A+u9/A.>AА AQ9/A| AMN9/A7 Ai̠9/AƂ A4ū9/A? A*69/A*6 A9/A At |9/AAj A]9/A'7ш AbV9/Aݼ A39/A^Wݪ A~9/A𘧛 Ao9/AU AH9/A` APSd9/A(#T̓ Ax9/Aā AM٭w9/A< A9/Ay A_΁9/A2 AjH|9/A@VY AEY9v9/Af3 Acr9/A7L An9/A҈ Aj_9/AB A$WJ9/A+΃3 AEM09/AOލ A'9/A胏 A[8/As4 A`L8/AtYy AE*8/AZ0h A,8/AO# O AA/v8/Axg+ A26L8/A_} Ad8/A"rT AK67/Afy AwU7/Aze# Ap.7/A7Ԟ A^G7/AfhLK A2| 7/AF,Ș A[ϩ6/Aw~3 A!6/AȆ A)M6/Aי AH3/A l A2/A^ AL./A@,| A8'/A8 Am%/Ae A#/A @ As /A`KX Aho/A A/A@iJ Ay/A / A:/A6 Ar/AX AS/A2 A@/A  A/-/A| A-h/A} A/A? A`P/AH A/Aj A8 /AF Ag./A`m4 AL/Ax Al/A A _/A` A/AL A /A`D AG]!/A AP!/Aݭ A0Lf!/A6k ADz&/AOx A(-%/Aco A#/A9bZ AHY/A@ A&/A`0A A</Aj? A&+/A@V? A/A = A8E}/A: AL/Aa^4 AЕ/A4 AH./A S4 A ͑/A |r- A0%/A_- Aȥ7/A@i8. A0;3/A-;. AXj/Aj4 Al/A S4 AU/A e4 AX/A35 A@ /A?S6 A/A@6 A@ /A 8v7 A/A 7 Ap9/A@Ps8 A/A S8 A@=/AB6 A/A5 A/A Ɂ6 Ah/A@9 A?/A cD; A/A; A /A`K7 A/A`U: A*/A/J> Ap\/A-> Af/A@#> AB/A B A 1/A`;LD A/A.E AXF/A@FF A/A`]H A8g/A rI Ad/AJ A/A ~J AGT/AJ AD/Aq K APl/AJ A!/A /A ~I A /ABI AXU /AH AXl /A iH A  /A&H As /A|H A`& /AG A /AG A`! /A lG A0N /A"G AP /AVF A/AF Ai/AdF Aq/A F A0/AF A$C/AFF A/A F A(/A(F AC/A@rF A޵/A?F A/A E Ap/AdD A,/A@E A5d/AxE A/A&E A/AQE AHn/ACE A8\/AD Av/AD A-Y/A D A3/A`mC A/A hC A/A l Aa.A@l AH2<.A@l Ah.A@=l A.A`~l A.Atl A.Afl Ah/k.A@"m AH2@.Am A.A`m A@.AQn A~.A@yAn AY,.A`Vn A.Azn A.A=n A.An A 4?.A@!k A@$.A ~-k A.Ajs A0 .A`s AH.A`s A.A`s AJ.Ak Apr.Al Ao.A g APq.A6f Ap|s.AJf A.A@>e A .A`ve A.A_(e A.A`9e A`.Ae A.Ad Aҋ.A+d A.A`bGd A0.A`5d AÖ.A@5c AH.A`[c A.Ac A2.A d Aȇ.Ad Aؽ.A ge A0n.Ad A]T.A |e Ah.Ae AÜ.ARg Ac.A@k AN.Ak AX5P.A (wk AZ-.AHYk A(,.A3mk A.A@%k Aj.A@Us A.Az.s Ah.A s AȈ.A}s Aw6.A(_s A.Ar Ax.A@,r A .Ar Aن.Ar AhQ.A@Wr A.A :r A&.A@ Br Aȓ.A^Pr A`{.A`r Axz;.A[{r A.A3r AY.A r A W.A@!r A.Aq AB".A`~l Ah.AKl A8I.Ar A)U.A`Dy A`.Aty Ai.A qw Aȥ.A`Lv A`s.A`5v A軤.A[{r AH.ABrq A7.AOn A.Am Ap.A@l A.Al A.Am A0.An A݉.Aq Ap.A`gs AȘD.A@ v A.A Ju A0.A9t A8.A׻r A.A /r Au.At Aȶ.Avu Aun.ARu A@.As A@$.A@r Ab.A/p A.Am A,.A?m A(~.Ao A8'[.A[{r AU&.A,u A .A@,t AH.A(t A .A`]s A .A Z+r A.A0q Aw.Asq AX.A0q A.Av;q AXy.App A.Ap Ar.A@Gzp AX.A+o A`.Amo A誋.A`Wp A b.A@gq A%.AVq Ax1.A;go AS.A@"m A',.Al A .AXo A~.Aٳq A[.A@Wq AG.Avp AХ.A  o Ah~.An AHX.A@ϰp A*U.Asq A.A`Mq AH .A`Tpp Ax.A@n AW.Aj A-3.Ai Ap.A`vln A.A$p A .AKp Ak.Ap AOs.Ao A8x.A |vn A}.A)n Aπ.AMm A`x.A`dm A8gw.Am A(-.A`dmm A8g.A5Am A.A@5l A.A@k A[.A^Mk A;.AA͇bk AǴ.Ak AKWBY.Avk Axa$.Ab8k ARx.AHk ANG9.AV(.Aa Ay:.A4F^da AңIF7.ADa A4.A_a A3.A0 b A@>3.AHb A1.A b A&.AZ'b A0*.A@.Hb Ax.Ab Ak.A .A^( AX.A Z#' A.A & Aj.A& AGx.A& A(h.A& AJ.AB"& A d.A,& Ar.A% A0Ǻ.A`T% AP.A;% A0X.A$ Ax0.Af$ AH#.A`f $ AP.A$ Ah.A@.$ A.AC% ACv.A &x% AW.Ak% A8:.A@% A0.A$ Ap*.A$ Axh.A" A"e.AR" A0.AG# A83.A@ # A.A`9$ A.A`m$ AEl.A@G# A0B.A# A7e.A" Aୢ.A`;! A .A! AP/.A A.A ~ A<.A&B A>.At A .A@ % AP.Aa A .A A.A^ A0j.AQ Aǰ.A6 Ah.Aྶ A0{v.A`2 AR.Af A >.A@R A44.A A7 .A`] AH.A`D Abm.A A.A AH.A A@p.A Ask.AN A3.A@ A,.A A0.A  AH.Ax AP'.Ap A`.A@, A$d.A Z Ah.A@ A@.A@d A .A ApI{.A` Ax [.A AC.A ( A1.A ( A.A ( A`.A@% A࠻.AO A.A@ A=}.A # AZy.A J+ APJ.AD AX.AÖ Ah\.A Az.A` AUC.A c4 A߬.A Q A .A@d A(1 .Ao A Ⱦ.A: Am.A& A<.Aߏ AH.A) A7.A@s A4d.A`" A].A~& AH.A ( A0ʹ.Am. A^l.Aܴ1 A.A@9 Ahֲ.A< A.A@@ Axm߰.A+C A (.A@E A.A@߂G AXX.AG A4.ATHF AV.AjqG AP.A@I A?.Ad~Ah&.AA.AAt.A@ݓA }.A`DA(L.AA.A A(r.A"Ah.A@GA).A`fA,.A)ޮAP.A`A8r.AvdAx}.AAxm.AkԓAȔ.A@Al~.A OA8g.AAC7.AT A.AAq.A ʉAH.AA0J.A lA$.A`bA0.AXA.A:Aʺ.A Ax.AyApco.AAp.A@#As.ApAX_.A Aȭ.A@{A.AZAF.A@`AHö.A4AǶ.A`ݑAy.AAv.ABgAj.A_lA8\.AVrAa;.AA9%.A sUA#,.A A-.AA#.AA0.AA .AA($.AoA].A`A.AApѲ.AA0?ò.A0Ay.AХAwZ.A Aұ.A#xA(.A`)A0x.A`LA7.A A7.A}A/ .AA8.A A`.A@mApϰ.A@%A.AȫAR.AѬA_.AAAhb>.A`A.AtƭA.A@ѭAp.AA.A@%AA[.A_sAP3t.AAg.A@%AG.AAL?.A nA :.A5]A&.A GAPZ.A ȭA Rr.A JA&0.A $A9.A S@A9.AAۭ.AxApŭ.A $AI.APA.A A}A吭.A@iAх.A@ߍA.AgAxA.AaAP.ATA>.A lۮA.A`AhQ}.A@A{.A@ߍA(dd.A\AQ.ATA:.A A}A,.A'At.AA.A;A.A Ab.AhAPYR.AAE.AA.A@A.A`Aj.AhAH˹.AA.A}Ax.A`Ai.AK$A M7.A0*AA.A J,A@.AX.A.Am/A\w uں.AB 0Aú.An0A0Z.A1A` ټ.A;A .A%D=AY.A?A] R.A%?AO.A1bu"@AY4N.A_`H@AWQ.A@n@A0l.APQ@A).AWA….AgXAW.A[YA .A@I`Ah.Ai2fA8.A@$ fAo.AzPfA8'w.AjA.AӦyA:ǯ̚.A+DyAa.AyAd.A* #zAUF}.A2"s4zAo.A@OzAPL yU.A-WszA2J>.A ?"zAn*.Ae=~zAM.A {A+<.Aw{Av.AE-{A.f.AtA{A曚.A!U{A{Aa.Axjii{A.A]s{{AȎR".AZn{A=).A%{AV.AQ{Avq(U.A)V|A .Arf(|Ak.AD|A#.A³|A#.ASAµ.A łAH..AA.A0"AP.AMA.Aa~A؞.A9A0..A`)юA`b.A`\ٓAߋ.A A.AhAfz.A{˔A\.A6_A.AėA.ABAx.A0nAX r.A`\*A8.A0w%A.A!Au볯.A*ZA P.A蘝A='.AdܝAl0.A6T$A.A#pA0.AAdn1.A'؞AN1.Ar-Ac/.AU: A+.A AT&.Aw5A.ApIA$Y1.Ak AӂH.As#AG[d.AЊA\E'.AnqZAߚ.A=iJA.A^Ai .A2HAЕ.AG̡Aɯ.A {A)D-.A*KAx=.Aw;AP+X.A AX.A,1AH.A@nAZ.A.A.A@qA|.A@<Ah.}.AOAؖ{.A`:A|R.Apw.Ac.Ap5ԏAx.AvAGg.A$N~AȤ.A8~AH.Ar~ANw/Av}A:/A 8 |As/A{A/AzA /AxA!f /AP&vA#/APZqAh/ApAx/AypA/A pA`6/ApA5/Ap5YpAp I/A`]toAq;/A`mA/A^lAP/ABkA0Y7/A-jA/A?hAgeopandas-1.2.0/geopandas/tests/data/overlay/nybb_qgis/polydf.shx000066400000000000000000000002141525650651600251560ustar00rootroot00000000000000' F7.+A%S@(vI0A,1A2LRΜyB(@n geopandas-1.2.0/geopandas/tests/data/overlay/nybb_qgis/polydf2.cpg000066400000000000000000000000121525650651600252030ustar00rootroot00000000000000ISO-8859-1geopandas-1.2.0/geopandas/tests/data/overlay/nybb_qgis/polydf2.dbf000066400000000000000000000004621525650651600251760ustar00rootroot00000000000000u avalue1N value2N 1033296 793054 1063988 793202 1094680 793350 1125372 793498 1156064 793646 1186756 793794 1217448 793942 1248140 794090 1278832 794238 1309524 794386 1340216 794534geopandas-1.2.0/geopandas/tests/data/overlay/nybb_qgis/polydf2.prj000066400000000000000000000010131525650651600252270ustar00rootroot00000000000000PROJCS["Lambert_Conformal_Conic",GEOGCS["GCS_North_American_1983",DATUM["D_North_American_1983",SPHEROID["GRS_1980",6378137,298.257222101]],PRIMEM["Greenwich",0],UNIT["Degree",0.017453292519943295]],PROJECTION["Lambert_Conformal_Conic"],PARAMETER["standard_parallel_1",40.66666666666666],PARAMETER["standard_parallel_2",41.03333333333333],PARAMETER["latitude_of_origin",40.16666666666666],PARAMETER["central_meridian",-74],PARAMETER["false_easting",984250],PARAMETER["false_northing",0],UNIT["Foot_US",0.30480060960121924]]geopandas-1.2.0/geopandas/tests/data/overlay/nybb_qgis/polydf2.shp000066400000000000000000000300541525650651600252350ustar00rootroot00000000000000' +A@p0AdCA(+A@N,,A@BN,,AS@̱+,A/AM@麢*,A#i@ل(,Aۡs"@k4[&,AR>bd@Ol#,Aܨ,@ꞙ@Bkݽ,ASnp@җg ,A;K@J,A%\,@6x,+A,YF#@++AQ1ًy@|rk+A(Z@:,W+A#sq@.+A@=+A"sq@ 1+A(Z@ꞙ@zɡ+A@GÕ8+AiAT@d G+Aܨ,@%h+AQ>bd@*&{1k+Aڡs"@E]K+A"i@d3Nn+A.AM@+AS@d3Nn+AaҐ@E]K+As~@*&{1k+A$^a@%h+AcYB@d G+A#Wz/z@GÕ8+An<ˮ@zɡ+AZ@_G+A @b"+As+6@2-hƲ+AQ#;[@Z+Axfz@ʇTH+AWӦ@;sN+A&t@ 1+AJ@=+A݌g@.+A@:,W+Aތg@{rk+AK@++A&t@5x,+AXӦ@J,Azfz@җg ,AQ#;[@Bkݽ,As+6@<"l,A  @}n5,AZ@T@Bk5,AS*@җg ,A;d@Jy{,A%@6x,t,A,YF@+Sm,AQ1ً@|re,A(Z@:,WN^,A#sq@V,A@=N,A"sq@ 1hG,A(Z@+,A:d@b"%,AS*@_Gg,A>T@zA,A@GÕ,AiAԲ@d ,Aܨp@%hx,A(NSqA*&{1 ,AP9Q,AE]K ,A4JAd3N,AOfhA,AAd3N,A_AE]K ,A}9A*&{1 ,A0ƾA%hx,A౬Ad ,A+WAGÕ,A7K_4AzA,AuvGMA_Gg,AdAb"%,A֘xA2-h>+,A݊A1,A-AȜAB>-AHdA̱-AOEA麢-A4P'Aل-AP9 Ak4K-A)NSAOl-AnBA-AHdA>-AHdA(v,A A-AzAB-AAA̱U-AO"A麢5-A4ƐAلX-AP9Ak4Ï-A)NSAOl|-AnB8AO-A98 AG-A A=?-A98 A 1X8-A[u- AA%hh,A౬Ad -A+AGÕ-A7K_%Az1 -AuvA_GW-AAb"-A3A2-h.-A]EA"-AbA;sN0-AXg:lA 1X8-AtA=?-Ao3džxAG-AzA:,W>O-Ao3džxA{rV-AtA+C^-AXg:lA5x,{e-Ai\>bAJil-A=m~!UAҗgr-A]EABk%y-A3A<"~-AA}n5-A uvAA麢5-A}9~A̱U-A_)`A-AAA-AAA(q-AHA..AHWAB..AA̱ .AO&A麢 .A4Aل .AP9Ak4;.A)NS1AOl.AnBxAc.AO Ab.AH Ad3N>c.A_ AE]K^d.A}9? A*&{1;f.A03 A%hh.A౬P Ad l.A+l AGÕp.A7K_ Azʙt.Auv A_Gݿy.A@ Ab"n.A A2-h.A A*.A AOl\n/AnB8# A A*&{1.AP9[ AE]K.A4Ɛy Ad3N.AO AV.A Ad3N.A_) AE]K.A}9 A*&{1.A0> A%hH.A౬. Ad .A+I AGÕ.A7K_%d Az.Auv| A_G7.A Ab".A A2-h.A] A/A A;sN/AXg: A 18/A A=!/Ao3dž Av)/A A:,W1/Ao3dž A{rγ8/A A+#@/AXg: A5x,[G/Ai\> AJIN/A=m~! AҗgT/A] ABk[/A A<"`/A A}n5e/A uv| A A麢v/A}9 A̱5w/A_) Aw/A Aw/A A (S/AH[ A/AHAB/Aȓ A̱/AO&u A麢/A4V Aلΰ/AP99 Ak4/A)NS1 AOl/AnBx AA+0A98 :A3 0A8A^ 0A98 :AB0A[u->A9'0AEAʇT/AT,QPA/AÒLn]A2-h/A Wn2mAb"/AF)wA_G'/A| uAz/ARȪAGÕp/AȴajAd /AnBA%h8/A(NSqA*&{1/AP9QAE]K/A44Ad3N/AOfRAF/AqAd3N/A_AE]K/A}9A*&{1/A0ƾA%h8/A౬Ad /Aȕ+AGÕp/A/RAz/A:A_G'/ABypE'Ab"/A kL1A2-h/Aqyn:A/A6PBAʇT/Aִi.HA9'0A NAB0AuEiQA^ 0A7cTA3 0ATA+0A7cTAo90AuEiQA؉0A NA<%0Aִi.HA@r0A6PBAg˳"0Aryn:A%0A kL1AP\(0ACypE'A?e+0A:A\5-0A/RAo6/0Aɕ+A5mI10A౬AlBg20A0ƾAu]Qڂ30A}9Ax40A_AC40AqAC40AqA (_"0A Ap0AdCABp0A$AxNp0A{PӗAu]Qھo0A AcAlBgn0AwyA5m˅m0AXkAo6k0A7j!|]A\5i0AdZ0UPA?g0A|)DDAQ\e0A8A6b0A#[.Ag˳"_0A+79%A@r[0Ab@&WA0ATLbAi*|:0A*KAZ70Ab@&WA4L30A+79%A_J100A#[.A-0A8AH=<+0A|)DDAM(0AdZ0UPAX2&0A7j!|]A˒e4X%0AXkA $0AwyA%#0A AcA2'"0A{PӗA_"0A$A2'"0AtXtA%#0A土A $0AWcA˒e4X%0AkXVAX2&0AȕAM(0A/AH=<+0A:C A-0ABypEA_J100A kA4L30Aqy)AZ70A60Ai*|:0Aִi.7A9'>0A <ABA0AuEic@AԚE0A7cBAoI0AdCA+CM0A7cBAo9 Q0AuEic@AT0A <A,A Ap0,AA;|-A}8A 0-A`Aps-A7AP<-AAw-A`)A +ύ-AAa-ABԣAh.-AfAM-A ZA0wY-AwA8-A賛AX-A&1AU-ARAW-AߒA-AzAHn4-A@yAQ-A`AH܄-AA`ˑ-A`KA*-AIAh$-A TAج-A}A-A(2AP-A AQ-A tA-A`íA`đ-A`DA&-AbAؒ-AAƑ-A`K$Aϑ-A⹍Az-A@tA -AOA-AA -A@`xA1)-A$NA(-A$A@C-Ah}A(-A@`=yA Ȕ-A@%QwA`֔-AZvAf-AItA-AAsAf1-A|pA>-AľoA N-A@`oAG-A]A3E!-Ayя'\AWN7-Ag,[AsH-A~,ZA!MU-AA@(YA^-A#v!XAc-AWALKf-Ag1=}ZVA(pd-AWW &UA3.U^-AbiTAdAS-Ak{0&TAȔC-A \rSAxǕ-A (*QA㐕-AଧMA:Y-A~%MAڹk-AWRLA[-AsumLAPM-A`)KA-A@HA;t-A`{GA ×-A+DAW-AzCAh#-ACA$-ACA/-ABA-A@BA-AxBA@-A lhBAԗ-A sBA-A,BAJ-ABA R-AKCAp-A`CAHi-A/7DAo-A 1%DA8ک-ACA@Wk-A@>#CAB-A`BAH-As88A1-A7AU-A@E7A@D-A`)7AX1-Ai AG]-A4A ݇-AVAxч-A Ax -A@A-A A8Ƈ-A(AY-A@A-ASA]-A A-A A@S~-A ;AV|-AhcA W|-ATuA0g}-A& Aȓ}-A&Aw}-A cҿAHN}-AZAl }-A`{AV|-AکA_z-AzAw-A$Ar-A 8oAEr-ABA0q-A@Aq-A0cAuo-A`JA8gn-A}AhQOn-AzAAp-AOuAPp-AtA,]n-A%yAH m-A`KowACj-AiAxh9i-A=eAPg-A4VA(g-AXeVAg-A:UAP h-Aj%UA(h-A'UAh-A`UAf%h-AzTA/h-A`"TAH8h-A |TA`Bh-A TA Kh-A@`TAISh-AaTAj[h-A TAech-A@ TA5kh-ATArh-ANJTA:zh-A (zTAXdh-A@gTAxKh-A/TTAxh-A`=TAxh-A$TAhh-A TAh-A@ySAPh-ASAh-A&SAh-A@SA0h-A|SAh-A\SA`Ph-A`Bi-A<RAIi-ARA^Ui-AQAai-A)QAxhi-A@>QA`oi-AQA0vi-AQA?}i-A QAТi-AޗQAPi-AdQA0{i-AiQAi-A`PQAi-AX6QAi-A QA舤i-APAྪi-APA0Hi-ADPA8"i-A PAXJi-AyPA i-A]PAxi-A@APAwi-A&PAXi-A@ PA8i-AOAi-A OA6i-A@OA@i-AOAj-AOA j-A OAbj-AOAj-A`OAp[$j-A c'PA+j-A-TPA2j-APA0v5j-A]PAe8j-A JPA0;j-A PA>j-A`]PA/Bj-A`]PAXEj-A PAHj-APApJLj-A PAyOj-APAPxRj-APAPPAx^j-A`0PAH^j-A#PA`]j-APAXj-AOARj-A@OALj-A@.OAȶEj-ApOAUFj-ASOA_Gj-AK7OAHj-A@OAIj-AG OAx-Jj-ARNA0Jj-A`DNAIj-AKNA]Ij-A6NA0Hj-A@NA0Gj-A JNAEj-A NA8Cj-A@NAFAj-ANAl>j-A@rNAx>;j-A`eNA7j-AKZNA4j-A 8PNAH 0j-A GNA+j-A@NA@#j-A`;NA`Cj-Ad3NAj-A`)NA0j-A MAti-A`MAi-AMAi-A MAhi-A@MA8i-AMAȘi-AMAi-AുMAi-AFMAi-AMAi-A@MAHi-A`BMAi-AjMA8'i-A`MAi-A MAi-AMA^i-A`TMAh;i-A@MAi-AMA@i-A`MAHni-AMA؎i-A(NAi-A`TPNAi-A wNAPs,AC eAop,A0deA ],AH^ eANQn,A=%dA,}H,A GhdAپ,A3dA(q,Aa"eAf%|,AsveA))0,AeAuZo4,AqNLfAtOa,Ai3fA ,A$Y`gA`=,A^hAy,A3hALon*,A3iA6v,,AQzA,A`zA虙,A}zAn,A׋{A,A@rxAX ,A3wA,A wA),AJwA.,A`7wAHL[,AvA``,AևvA؎,A@uA,A`uA5,AFuAH,A vA8,AuA,A`uA,A`uA=,A@)uAؚ,AhtAX,AJtA ,A`\tA߱,AuÄ,A2uA8 ,A@wA,A`wA,A3wA,AGsxA',AuAQ#,A uAX0,A quA83,A=(tA),AtAW,AwA.,AzA ,A A4,A`KAf_,A@ងA,A3Au,ARAf,A \ُA,A A,A Au,A`)|A,AA`,A쥙A;,AHA)L,A Ak,AhA`a ,A3wAG,A`gAM ,AWA,A`fݗAL,A9AP,A s˕A~,A@2A8,A`)AHa,AMAp8,A:A,A@ᔓAx,A \A,AfA,A`ߖA,AAz,AA),A=A@d,AɶA:,AA>,A*ɑA,A4AP",AA',A3ӑA,A =A,AQA؞,AA80,A̓A 0,A|AV,AAX ,AA,A@ːA(:,A[/A /,AKA ,AA,ALaA0{,AAs,AA@F,A]!ATo,AԎAu,AAj,A^A8e,A`rAJ,A=A/,Ag~AT',A@CXA!,AV?AH,AA,A`)4AHK<,AA`Ǽ,AďAh,AhA8,A@WRA,A@ A|,ARAHnK,Aa^A,ApAB,A`΍AA,AA8,A"A' ,A`K A7,AoAh>,A WAi,A`)A,AApp,AˋAX ,A`AǼ,AA0ȷ,A`bmAx,AjAho,AފA0{,A uՊA=x,A`SAxk,A4A e,A.AG,AAff,A4uAu,A`A,A |:A,A NA`a#,AׯAg(,AĝAS,AvAY,AA,AMA,A 87AH,AqA0H,A A R,A |A,AAW,AA# ,ApaAȶ,AIA,A@pԇApJ,A㺇AX$,AoAf,A)*A,AAX,AGAf,ALJA,A`XA`Ы,AjA3,A sAH{,AF~A0 ,A@A)C,A=6AXp,A |Av,AxA,A܆Ap,A džAh,A0-A,A^A,A]}A ,A@%gA`#,A@A;,A9Ap_,A@W9AHm,A`A@F,AZAsF,A 8\A ,AA ,AϚA&T,,AkwiA_6,A3AK,AsDğA/I8,AJܵ߄A虉,APAT`|,A` A%),A"A|$,AJ?0MAd엒O,AfRA&}")v,ADūA@,A@,Ae8,ALA*,A\Az ,AQ)2A,AfųA6x,AdYA9,A`APf,AA,0,A_2_A{,A?Y<AfAR,A9Ab,AA<,AA,,A@A,A0Ap,A qA,A A8,AAp8,A`)AH0,AAhv,AzA RS,A@NAhs,AvAp8,A`)At,AHA8,A A,A,A8,AAm^礢,AH.A2V,AHQAO,A ;AI,A?$5tAIGY,A<0NAp,A`KAkS,A)A(.P,AY%A &-,A'w_A,A@gA,b,AmAl,,AKAc ,A̹A,ALAzw,ALANQ5,AF8?}wA,A@yHA:,AVuj,Ar0K,A=,Av,A~$AP,AyAYB,AA +,A'RAI%EO,A)DAkZ,AƌAޱ^V,A&^ >A[,AAC,A@-Ag%*;,A AD^+,A5Ax},AIA&,A{?A,A`"QA,A`VA@H,A`VA9v,A<Aԁ,A}'*Aso,AshA,Ŧ,A"oߘA[,A3FAC,AmAA`,AukUApC,AEfA7j,A+e*Ac,A:AZy,AAD,AAh,A6zAh3*,A[A@q ,AhAH,A;A`Ci,AOA ,A@]A@,A@*A(~,A`sAn,A]WA8,AAγ,AA R,A lA^,AyKA,AA,AApJ,A Ap,A@gAXt,A =A?,A`mAP,A@,A,A A(%,AVAxzA,A`mKAK,AA8Qf,A`BA0p,A@iAf,AAБ,A`A,A@,9A@},AA@,AA8,A@DAP,AA,Ak[AC2,AAB,ApA,AA+,AzA,AAHR,ADA7H,AA0,A#A`],A`2A(z6,A0CAI4,AMA,ANA ,A@A,A-A@B,AA!,A2A,AA ,A6Aȃ,A &A,A@#Ax,A@TA%,A@WA,A`A6,A:A>,AAL,AAR,AAnt,AAHv,A`AR,ASAT,A@>JAb,AApg,AvA`2,A6-A>,A@ A,AA,AA,A4Ah,A9A X,A@AxhB,AmA8,AALJ,A`mAX,A_AX ,A@υA,A}OA,A`A,Aj&Ac,A` Ac,A`Aj,AjA@,AA,AAp,,AVA,,AAX,A`A,AtA,A`ܪA,A൭AxzT,A`KTA؎.,AQA,AZ0AC-,A:AFa,ABsijXA`#u,A"A,BS,AOqAAc^6,ASA]z.,AYdA },A bBAP(,Ajb=Axm,A`AUN,A@/As3,AAk?,A{AG,AnyA,R,AOAg,AA s,AQA8,AyA",AIA(,AAZ,A A,ArAx-,A-?A,A*%AH),AIA@,A@A m,AB+A0v,AA0;,A sAp0,A`Ai,A`A ,A`!A ,A@A W,AoA(J,A A,A൭A,A@A@s,A Ahb`,A@5RA19,A`A&,A@#*AR,A}PA,AAh,A`A,A@Ap,AHAq,A@A{d,A5AE,A@ UA@9,A ɁA,AA(,AAx),A?Ah,A9A0,AB6A,A ZA,AA,A@Ak,AiwAc,AACA,A`A7,As5AX 8,AZA,,AA~,A`Al,AAxK,A஫A),A;A,A A,AA ,A`nA@(,AA`4,A`BAKQ,AaAo,A 0AHP,Ac"A,AA0,AA,ATA,A A p ,A@A2,A |A@,AqAg,ALYA 0u,A'A,A uA7,A 8XA,A<A\,AA ,A`A,AA@ ,AױA,A}A$,AA(-,A@AH,AA,A Ahճ,AA,A@AP*,AEAd,A`WA,AA0x,AFAj,AcA,AzA0,A@A`,A A8,A?A:,A$A0,AxA`,AoA,A`A ,A@A,AA8 ,A1A 6,AA;,A@AO,AZA]S,AAg,A AKA`1P,A@iAYS,A` A k,A@WAdl,Ad]AH,A<A0,A`ôA;p,A q'A(~e,AA &,AA`%,A`TA! ,A`Ax-,AQA0{,AH A,A+A0,AA,A|eAX,A YA,A BA[,A ZA,A8=A,,A|0Al,A`A@q,A@AhQ,A cA`,A /A,AAL,A[*AHM,AAH,A^AJ1,A6AP,,AqA@(,AׂA,ApKA6,A@.A2),AA(9/,AAذ_,AAu,A@Aث,A޽A@~,A Aؚ,AhYA0f,AոA},AVA,A`0A`,ALA,AteAH,AAH,A`vA,AmyA`,AӭA,AAq ,AK"AȤ,,AA/,A@iAH:,AfbAp0,A@G8A,A@A),AAg,A֞Ar,A lAvx,AtA,A@ AH,AേA ,AA@ ,AsA,A,A4:,AA<,AA`c,A lLAq,A A4,APA ,A _A,A}A0,AA,AA,AA,AvA/,AAH.,AA0,Ah+A,Ah+A@Ӭ,A`"Aى,A|A,A RA8Iq,AA` R,A@gA`E,A[A ;,A@0A.,ABAHP%,AOA@,A@ A ,AનA,AarAԢ,A5A8,A@dA,A<A,A`A,A =A,A`SAu,A+A8g,AA,AyAX,AtAE),A{A R;,A`AN\,A@QA\,A~A8b,,A;A83,A`TaA(m{,A LA,A@]A(,AA,AAȃ,AMA,AAN,A ZA,AA,A A ,A lA(,AّAi,ASA,AէA`,AzbA`,AA,AA#,A |Ap,A |XA@W,A GA,AA0Ԇ,AA@v,AA,AŶA09,AIA ,AAې,A`Av,AAk,ADAM,A ZAR<,A`A7%,AaFA,AɳA,AA,AAd,AtAa,A`vA,A`A8,A A0;,A9AY{,AAX9g,A@Ah,A@CA@,A)A0,A`]qAF,AAh,A AN,A:A!,AAV,A)AI,AA@,APA,AcA",AA,A&AP,A[<Ay+,AAAP|,AgAx(,A`Ao,AA,A@9A`,A Axz,A`*Aep,AA Z,APAX ,A@A+,AA[,A`NA,A`]A(,ATA8w,Ao'A,AA`,A Ax,A@EAh,A A,A`)A,A Ah,AA,A{A0G,A`TSA@,AA,A4AX,ARA%,A`d4A؎2,A`A(I,AAV,A qzAbp,AAu,A@Ab,A`A,AA,A@Aw,AAU,A5AT,A`A,AމA( ,A,zA(),ApWA8,AA:,AA _(,AjAE,A=A,A}A ,A̻Ap,AA8x,A`vA8,A@5gA{,A`Ag,A`ݾA0w,A@NA,AOMAג,A@dAr,A ޿Al,A@AK,ArAyF,AA8',A lA!!,AA,AӆAXh,AA,AjA,A cAY,AդA٦,AgAج,A`3AL,AOA8k,A@Ac,AA,AvA,AAp,A !AP^,A_A0,A bAXJ,AA <,AWRAh ,Ay6A@B2,A`A@59,AADb,AANh,A˿AO,A*AV,AAH,AuA,A u^Apk,AA,A@GA@q!,A 8A,',A`KAP,A QAV,A:Ao,A@ڻA,AAXۣ,A@A,AտAa,AAw,ANAȺh,AA,AA(z,ATAj,A@AK,A@AD,A Ap,AA,A Z A,Am+Ab,A`PA>,AA`1,AA^Z,A՚A%,A`eA,A@,A0*,AA,AiA,AվAhL,AA S,A`A,A Q:A,A"A#,AA#,A`HA.,AjA(,AwA@),A 8˺A@F5,A A(U,A`A]m,A ƹAxw,A JA,ADA3,AʇAB,ANAPk,A@G7A{,A nA0,A@ynA ,A?A,AڼAX,AAX2,AA0X,A$A7],A`!AxiS,A@5A&,A A*,AsCA,A@pA襪,AXAL,A dA,AA,A@,A,AɶA~,A4A,ACAȋ,A 8XA ,ARA(,AHAA,AմA(K,A`AE,AA,A@OAX ,AZA,AA8ɓ,A A]j,A`AV,A`An,AA8Q,A`2fAo,A`-A@,A@A6,A?Axif,AϽAЕ,A,A؛,A@YA@%,AלA`,ApA,AH-A o,A}Aa,A A,A`լAw,A`dA(q,A A,A۸A,AׯA ,A ߸A̚,A@赸A$,A@A,AR~A,AR8A,A |ηAxh,A`A,A 8<Ax ,A@i"A1,A`ܑAP9,A uAc,A׵AHi,AA,A`"AM,A?A,,A`]JA(,A1A,AxA,A4_A/9,A@²AH,A^ҲAs,A ۛA$,A_0A8I,AزA07,A AG,A hA(im,AֲAha,A`ٲA`J,A@vA3,AAJ),ApA",A SA0,ARAO,AA@=,A$NA`*,A`XAH,A6A8E,A?̲A,Av-AD,AkAX,AAN,AȵAG,AAXWA,A A ,A@ؽAP8,AAi,AʗAp,A@A2,A ҾA I,A[Ah3,A`f0AB,ARA0>,A`;Ap",AA,A)zA,A3WA`,AٶA,AյA,AQuAHP,A@y>Aȩ,A GAև,AmAHm,ADA,AٮAp_,A ®AH`,A첫AS,A |A,AAy,AA,AMA#p,A`Afd,A4Ax:,AzA(,Az8A,AkDiDAG,Am½IAG,A`THAK,AÁLAge j,AMB<ANO,A'*$A`6,AA,AsA,A?1A o,A AXW,A A,AA,AA_,A@ A`,A`)A0٪,A{A0,A;APڕ,AVAՏ,AtA,A@A[,AVA^,A`pA,A`AV,AOAW},A=AX{,A.A =y,AAPw,A Av,AAPu,A`Au,A cٴAHu,A`ǴAZv,A ALw,A AHx,A`KAUz,Az|A0|,A|iAXR,AWA|,AFA@,AH7A跋,A!A ,A- A0,A;Aȥ,A@%ڳA`,AA8,A`)A3,AܲAm,A ~IJA2,A@!AH,AAU,A`{A,AbA؛,AzJA,A2AR,AA(,,A`A(,A@pAx,A ɱAG,AAX,A` Ae,A^A,AqiA@,A A,A԰A,A`;A,A^AÌ,A3rA,ARAX ,A@>4An,A3APl,A`gA~j,AAA g,A@Aod,AAh`,A`ϮA0Y^,AAhb\,AࣰAZ,AA`Y,AAY,A}AX,ALkAX,A}YA Y,A GAأZ,A6Ah,A APi,ATdAh,ACAHh,A"Ag,AFA؁e,AA c,A"¬AP8a,AൢAu^,A׃A{Y,A^AT,A c:A@BN,A /Ap H,AA(mA,A`2ԫAp:,AA`5,AAȔ0,AA,,A A',AwA( $,AeA؎ ,A`mQA0v,A <A,A4'A(~,A A0,A`A@,AAL,A ʪA,AtA,A Ap,AA,AA,A@yAx),A[jA,A`[A4,AMAp,A`@A ,A 5A8$,AcA8(,A A+,AީAH-,ATA`a/,A4AP1,AnAE3,A"fA5,AiIA8,A@`-A`<,A@!A=@,AAhD,AdިAH!I,AƨAN,AA@T,AzAZ,AtAs_,A`"VA(~d,Ai6A`%i,AAfm,A=A@s,A=ŧAPz,A3Axi,A-jA,A=>Al,AA虗,AA`q,A@`ǦA,A띦A`,AV~Aª,A^A ,AM=Ah,AA ,AxA(ܰ,A@إA@1,A`A@,AՕA0],A`BuA8,AfUA؛,Aa6AX,ARA,A`APo,AѤA ,AAL,A_As,ALA,AwAHˎ,A}gA,AWAPk,A`IA),A@><At,AR"Ap,A q A,AA(,AۣA,A@ţA6,AoA,A@Ah,AィA,A eA(q,A-GAp,A3'A0,AA,A=Aذ,A`vA,AAX,A΢A,A`2A ,A@ؤAH ,A 䎢A ,AwxA ,AaAL,AJA,A7Apx ,A Z%Ao ,A A ,A (A(, ,AA,AHߡA8g,A ϡA,AAM,AA0j,AAP,A0A,AQAH,A`A,A`rA,A#bA>,AQAB,AAAh,A@A,A A,AƠA,A`KA ,A@xAH,ARA,A-A,A`TAp,A@GAS$,AA),A؟A83/,A4ǟA5,A@AH<,AॴA0B,A A0H,AAN,AAkP,AvAXQ,A@C_AR,AGAR,A/AR,A Ah@R,AA4Q,AAO,AОA`N,AAuM,A 䔞A@L,AvAL,AHXAM,A`9A8M,AAN,A@AkP,A`A`S,AaA!W,A A8[,A }A_,A^Ad,A AAP|k,AA0r,AAz,AҜA,AA.,AZA8I,A@rAx>,AA@,A (A,AAʒ,AAP,A"A8V,AuA,A iAXl,A0]Aߘ,AMPA,AGCA0,A5A,A (A(,AA8,AA(ʓ,AA(,AAB,A A,AAج,A A,A@ܛA],A2ٛA ,AכA |,A֛A]y,A כAv,AڛAr,A ߛA n,AdԛA@k,ARțAg,A`A@d,AVAb,AAP_,A@ A],A zA'\,AhAx[,A@UAVZ,ACAZ,A@/AGZ,A A=Z,AAZ,AޚAq[,AൿA\,AAXW^,A@A0j`,A@dAb,AGAe,A`)*Ai,A AXl,AۙAo,ApA|r,A 늙APt,AbA{v,A8A`w,A \AP7x,A`A x,A` Aw,AȘAhv,A`;A8Vu,A6A`s,AAXlq,AmA`n,A@NXAXk,ACAh,A0A e,A@pAa,A A8\,AxA@AX,A`A1,AA#,A@A #,A$A9,A}A$,A= AA,A@A _`,AJAxd{,A ԔA,A9nA0,A`.A2,AA,A拓Ap,A@(A@X,A iA8b ,AAK,A+ґA,A@ǑAV,A@ϜA },A 3Awq,A@5ӐAe,A]`A0,A`,Ax],AKsA },A@ A,A`ΊA,A=Ah,AsAZ,A@!A,A Ap c,A qAps,AoAP,AA 4,A@$A?,A`A,AA ,AjA(,A@5Ah,A@ApZ,A@>Axh,A |"|A ,A |"|A,AWg}A,,A0}A,A{A8,A3zAp,AzAx,A {A,A`K{A@,A@qA,AA ,A@AP@,AՁA ,A>zA,A2yA0j,A`{A,A`]|Ai,A|A+蒞,AJ|{AEׂ,A({AEf,AB{AJ.dK,Ao?4t{A"e2,Ae1+{A p,A zA|/,AIL$zAD,AQvfyA&,AxA[I4z,AKjýwARjZ,A@vALH~=,Aӹˆ vAP$,A|#uA"#,A q\tA,AsA0G,AerA0,AqAX,A`MmAг",AkAH,A@EkA,A kA8tl,AHlA,A HlA^u_,A!>kA,A#jA,A \iA+,AiAX([,A`hA,A`KvgAg,A`DfApx,A`ohA,A@DgAh1,A?fAC-,ARaA},A%^A@$,A`T]Ah,A YA(h0,A;UA/,Ap;UAP,AlQAh';:,A PAx,AZ)OA렁,Ab9OA +,A"NA,A@WMAp,A MAk,ALAI ,A 8JAZ!,A`)JA`,,A`JAȺ;,A`JAO,A A4JA,A`mJAX9,AJAh,AdJAТ,AvHA +0,AEFAy,AMHA_M,A6HAJD,AHA ,A A=HA,A@y>GAO,A EAځd>߿,ADAxgݿ,A]X@ADAlܿ,A%DA8Qܿ,AvDAԤܿ,A74_DA ݿ,AB JDA?vR߿,A=`ͫDAi,A ۟DAx,AfDA,A/DAh.,A<#DA!>,A&DAo,ADA ,A ECA3 ,A*CA5 ,AʝCAH ,A;CA8\+ ,AіҝCA{} ,AwCAf#,AYvCAX,AfCA Rٿ,AJEBA,AVBA%_,Aζ?BA1 ,A*4BAS^u,AI6BA-Rc,A DBAQ,AkD]BAA,ABAFe4,ACBA',A{BA ,A@lBAb_,AmVFBA,ABAKf [ݾ,AHAAjm¾,A8>AA,A`ՖAA`,A@֒AA .^,ANAADn,A,gAA|\,A /AAAHz,A [>A(,A&7A,A`D_6APޖ,A 5APZ,A4A,A Z*3AϷ,A}1AH,Aw1A+,A1A@,A0AH,A`0A!,A0Aߺ,A d0A(Һ,A@h0Ah",A6=1AF,AP1AȺ,Ah<1Ay,AV1AV,A{0A@3,A//A0+,A`/A6,A /A >,A[/A(@,A=6/Axֹ,A@6-A8,A;k-AW,A-Ax,Aྚ-AÛ,A@֍-A!,A V-Ap|,A@,A,A@*Aѹ,A`9s*AȘ,AP,AcKF&,A~g+A A:{,A*An>,A#&TŻ)A 1,AR(Aњη,A'A,AxGAA,A`v^Ax԰,A@G'AW,AOA,AHA(-,A| APE,A`m Aa,A Ap5,A6 Ap,A@ A(,A | A ,A \ A`ޭ,A Aխ,A@ A(ŭ,Aa A@ӫ,A@ A(薭,A A8,A > A;,A8v Aw,A` AXys,A@r Ahe,A AXa,A A؁K,A@,A7,A AŪ,A`_A&,AAh@,A`AD,AA_,A@AG,A@A",A`K?A,A[A9թ,AzAo,A@AxNJ,A@`0 AAؽe,A2-AI@,A@,A[@ȶd,A@>z@x,A3@,A@x\,A@w@k,A@4),Aw@,Aa@P,Aq-@(ß,A@@,A@/@8,A@@O,A)@<,A@p@,A@@k,A@N@Z,A[@t,AǼ@i,Ai @(9/,A&޺@],A@͹@p,AVD@,A@@ 7,AA@P ,A2@0,Aƴ@ ",A;̴@Ol,Aܨp@k4Ӟ,A)NSqAلh,AP9Q,A麢E,A4JA̱e,AOfhAƤ,AAƤ,AA̱e,A_A麢E,A}9Aلh,A0ƾAk4Ӟ,A౬AOl,A+WAAf{B,A ÂB,A|A`aB,AA)B,A AأB,AAB,A`)DAB,A@ApB,AA)B,A`)@AP8B,A@AB,A@gAB,A@A̖B,A aAB,A AѣB,AA8B,A ApB,AߡAB,ARApB,A AfB,AA8B,AA)B,AA C,A:AC,A A`"C,A qA8'C,AeA8'C,AA&C,Az,AG:C,A4AWA`@C,A AIC,AqA`VC,A oAhC,AA`uC,A`fA0C,A \AAC,A AC,AAWC,A8ALC,AAH\C,AAfC,A@ALC,A`AC,A@AD,AAD,A`AD,A`A!D,AAH&D,A AB(D,A Ap8(D,ArAQ(D,AzA'D,AA("D,A`)DAuD,A]ApD,AA D,A |XAp D,AAp D,A-Ap8D,AA8 D,A`A D,A@>DAD,A`'AD,AzAH.D,AR*ApC,AzAC,A]A#D,AaA#D,A Ap8'D,ARAH.2D,A=HAH>D,ARtAMD,AHAHXD,A A8jD,AA(\xD,A AD,A A(ܓD,A 9AGD,A@>AD,A@AD,AzAfD,A]AtND,ATQAaҕD,AX'AD,AGɐAjqD,A"o%Ahh E,Ac]AlqE,Af AB.E,AAXEE,AAp=]E,A5ApE,A qoADŽE,A@>A`aE,A@><Ap E,AA؞E,AA`aE,A`)NA0.E,AAE,A3AE,A\ApE,A)AWE,A ABE,A3A`E,A`f4Ap E,A dAfE,A=AzE,AåA8E,AAѹE,A3A`E,AA(E,A 1MAp9^F,A`AXG,Ap AG,Aa AuG,A_E!AG,A@W!AG,A "AH,A@"AKH,A0[#AKH,A9#AG,AiX$AG,A*$AG,A3$AۿG,A $AG,A,%AG,AU%A`G,A%AG,Ak&A)G,AĂ&Am H,Ad'A# H,A(AȩG,A0(AWG,A@߾)AG,A~*AGG,At+AG,A4,AxG,A`b,Ap_G,AS-AG,A-A+G,A<.AG,At.AQG,A`/AAH,A 0A8D]H,A1AyH,A`2AH,AĽ3AH,A4A H,A |5AH,AN5AH,AR6AXJH,A6A0݉H,A@7AH,A)8AzH,Ao8AsH,A@i8A8\H,Ac39ADH,AO9A(~(H,A l9APH,Ao9A@G,A s9APH,A9A* H,A<:A@H,A:ApH,A ;AP,H,A5X;A?H,A ;AVH,A;AKXH,A@;AKXH,A@S<A]H,A<AHiH,Aӽ<A@uH,A4<ARH,Aw<AAH,AL<AبH,AL<AH,Az<AjH,AV<AfH,A<Ap I,AV=AI,A=Ap,I,A{>A\7I,A 7?ApwAqQL,A wAPL,A-xAXdSL,AךxAbL,A`yA(OlL,A (TyAnL,A&yA( ]L,A^wzAGPL,AzA)ML,A@{AhbLL,Ajm{AOL,A?{Ah[L,A6{Ap=`L,A#|AaL,A|A/bL,A|AHdL,A }AoL,A ;}AvL,A`]}APwL,A}AX~L,AsL~AL,A}~A؋L,A$A/L,A4cAjL,A;AL,A@ZA0L,AA8ҩL,AAHL,A?ACL,A!AEL,A JA L,AtAM,A xAx(M,AuAM,A QA(M,A 볂AM,AAM,A%AM,A]A()M,AaAM1M,AA8M,A6A4A, M,A cAeM,A`<A0"M,AwaA%M,AmA8g&M,A އA (M,A` A[/M,A` $A(m;M,A!A`eRM,A`A `M,A (@AXhM,AeAPkM,A예APkM,AЈAPkM,A}ArM,A4AXwM,A sJA |M,A`iAM,A3AM,AQdA0%M,A AM,AAhoM,A9AxFM,AzA0M,A AM,A@i{A(M,AE(AM,AApM,A&Ap+M,A AM,AA@M,AAM,A@NAؒM,AA@׶M,ATAM,A^6A`oM,A@KAgM,AŔAheM,A<APkgM,A@hAPvM,AAoM,A3VApM,A`KAPZM,AjAH\M,A AM,A@*AM,AAxM,A@9AM,AADM,A@A M,A@GAx)M,AAbM,A)AM,A"bA M,A6AM,AAM,AaBAqM,A`խA:M,A ApM,A8A9M,AcA M,A ŢAM,A`ANM,A s4A N,A`]MATN,A@lAxN,A͞AX9N,A AhbN,A@AN,Ag֣ApxN,A ٣AGN,A Z A@` N,AOAXN,A AN,A`դA` N,AH]AN,A}A}?N,AA3gN,A#A`GzN,ATSA-N,A`KAN,A0AyN,A`msA&iN,AgħAUN,AXAKN,A5A08N,A A/N,A6rA\.N,AǩAHn:N,AA8N,A@A8N,A`TlA0;N,AAhbCN,A`9֫A8INN,AAQN,A2AxhXN,A@NAxhXN,A@[A PN,A0BAFN,A`AEN,A 2Ah AN,AAh AN,A OAh AN,A ZAx>N,A@`سA:N,AvA(6N,A`9:A!(N,AApN,A`K AM,AALM,A A(M,A$ALM,A}AM,A?A(M,Ag&A`M,A@ nAM,AAM,A@ߟAHPM,A`dAۑM,AHAM,AsGA(JM,A srA@M,A`<A:M,AA M,A 6AHM,A AM,AHA7 M,AAƨxN,AEA ON,AAo}"N,AGAH l,N,A,BA 4N,A6HuA".f:N,AAA#F>N,A"Abo,?N,Ax"AS>N,A _Ar];N,AB"6A;N,AA4EN,AV@AE;ON,A"ݗ`AkY[N,Ar= zAgN,A[AuN,Aq.!)A8sN,AA(VB!N,A `AO70N,Al_AF¡N,AiA{N,AW65AmN,Aׄ4A(,\N,A OWA0N,A`A跍M,A0(AHDM,AA tM,AnA8M,A@AfM,A A7M,A eA}]N,A`ܪA`N,A AЮO,AAuO,AA N,A@AO,AhJA(Q,A?AS"oQ,A!.A9%Q,ARiAwQ,AY8A=Q,AMOdA=V*'R,AgA:)R,AJA [i?R,A -A!UR,AP AiR,A0D)A|R,AJ1VAR,AྎAo R,A_H2AuwR,A5AgtR,A3q!]AS,AAe#R,AV?AQ,A^A@P,A`AQ,A`A*R,AApNS,A'Ah^S,AMA4QR,Av&AtR,A@A\_&R,AXAiR,AǢYAOR,AcNAR,A"A=R,A#iSJASR,ANAX%)R,A㱭A,*R,ARmAR,AA61R,A&RAe8R,AJ7߈AS,AgA S,A#,A&{3S,Ayr+ AbJS,A1GA`C`S,AZAزzoS,A%A1l]zS,AAoAHS,AAnȇS,A"_BAaBS,AAVS,AqogA}S,AގPAUwS,Ai jףAXujS,A`;AqgSS,A)6Ak̗?S,AڊA.S,A/A&S,A`TAR,A`"lAwR,A@AƈR,AAxR,Aw Ah|R,A`]$AR,AAR,AAS,A A(S,A|HA`#S,A@,A)HS,A sAXhS,A`AȊS,AA'S,A uAS,AgAT,A~AִT,A iAU,ALAiU,AAe4V,AߜiAB"V,A Aa8BV,A׀nN.At`V,Avc|dALά{V,ACQkAfV,A@AfHZV,AI&aAnV,AAƓ锝V,AA_B V,AUA2V,A?xAuQ V,A{IlAV,A HAfEW,A;;4A+W,ABAfPiW,AkAЭ\ZW,A%JAW,A! AԇX,A(:A[)ZDX,AS[P]Ar=[rX,AMնA9X,A녨Ag6X,A=lAX,A@E?AX,A1AjX,A58AX[X,A=AvK6X,At0AnX,Ac|ABX,AaNAzX,AA 6X,Aι0AbUVX,Aw?AX,A@2ACaX,AfKjA-X,AHAݢX,AAX,AFAX,A -A/U?X,AeUA5!3_X,AAlX,Ay.AoX,A 5^A0 Y,A A(:Y,A\ AHY,A A(Y,A@` A@Y,A, AY,Aw A*Y,A@ AY,A`A!Z,A@ AHm/Z,A@r#A(K1Z,A`/A5EZ,A`ܮA YZ,A FA2fZ,AA'pZ,A`A9}Z,A?AZ,AAPx~Z,AtAZ,AHAKZ,AAZ,A/AZ,AApZ,A#gAxZ,A}AZ,A AZ,A$AoZ,ANAp[,AQAW2[,AʃA8P[,AAa[,A@|Ahs[,A AX[,AA[,A2A[,AHAX9[,A0VA#[,A@A[,A)JAp[,A6Ai[,AJAG[,A`A` [,A`FA \,AA \,A( Ax"\,A; A#\,A@yA A %\,AsF A;:\,A AM\,A7!A@_\,A}!At\,A!A\,A@GL"Ad\,AH"Aq\,A$"A\,A$"A\,A#AH\,Av#A^\,Av#A\,A#Az\,AHE)AP\,Ar)Ay\,At)AС],A`B)A 4 ],AT*Ap],A+AI],A+A`],A@K,A8t\,A,AT\,A`N-A8\,Aa-AH],A -Ap],Av-A 6],A w.A,;],A@5.A?C],A p/A2M],A 0A^],A]0Af],A 0AUb],AZ1AR],As1Aȶ;],A`bv2A$],A@N2AD],A5A[,AqGQA0&AZ,A@PZA(VY,AVbA@W,ApqA0̓T,A@Q|A@NR,AfA`6Q,AdA(Q,A4A(iQ,A qA R,AWAVR,AAnCV,A AV,A gA@5bV,A_c A *V,A A*V,A AU,A@ AhU,A`# AU,A AU,A) A|U,A A_ AU,A A(U,A ۬ A(U,A@5G AeU,A` AU,A@ϔAU,A#4AU,AA8ֻU,A@EzAzU,A`AU,AAcU,A(AU,AzA NU,A AjU,A`mAxuU,A OA6U,A@)AzU,A@yyAxuU,AAU,AE AxU,AŗAZU,AA0U,AAU,A`"AHiV,AWAH{V,AAx!V,A@!TA``)V,A A6Ah@GV,A$A@fV,A3ApkxV,A[A~V,A@jA$V,A@A8V,AMA(V,A@pAV,AKA9V,A#AQV,AAsV,A`UA0V,AAH-V,A`A =V,A$AV,A@`A`V,A,AȔV,A?AqV,AAPJV,A`\A#V,AAxU,AAU,A0AhUU,A (ArU,A;AU,Aw?AV,A`Ax!V,A /A``)V,AaBA *V,A(A-V,A` AU3V,Aj!AU3V,A@!A-V,A`9"Ae$V,A "Aȩ V,A (0#AV,A|#Az V,A`@$AH V,A n$AU,A%AXU,A`%A(U,A@NO&AU,A&AxuU,A`w'AجU,A`'Ax~U,A^(A"U,A)AaU,A`)AXU,A`?*A U,Ac*AP|zU,A`g+AأsU,A',AoU,A ZO,AtU,A,AȵU,A ,AOU,A,AX(U,A1-AXU,A`t-AgU,A-A舌U,Ax.AXJU,A.AU,A`2?/AjmU,A]/A(]U,A`u/A;]U,A Z/A@1gU,A9/ApU,A/A8ĮU,A /AU,A/AHV,A/A *V,Ays/AKV,A/A7mV,Aø.Ap}V,Aj.AxKV,Aݟ.AV,A}.AV,A 2/A8V,A/AV,A|/AV,A`0AV,Anl0A5V,A0Ap_V,A@0AV,A0AV,AxQ1APoV,AMy1AxV,A 1AhtV,At1AKV,Az1A"V,A@1A*V,A1AU,Ad1AU,A`K1A(U,A42A(U,A`d2AxuU,A2AU,A3AU,A q43AU,A&z3A0U,A3AgU,A@ 4A U,A%4A舌U,A`B4AqU,Ag5A=yU,A 5A_U,A@56AFSU,A`6APAU,Aq7Ah?U,A7A>U,A/8A`8U,A98AJ2U,A8Ap"U,A^9A0&U,A@:A0U,A:AHzU,A;N;AHzU,A;A;U,A``<A(T,A <APT,AJ=AT,A@`=AtT,Aq=AT,AF=A3T,A@,=AثT,A@=AߦT,AI >ATT,AD>A'T,A9>AT,Ay>Ah"T,A#?AhT,A ?APT,Ak@APxT,Av@A@T,AAAmT,A@y!AA(T,A |AA@T,A5BAU,ABA06 U,AtyCAU,AFDA@T,ADAT,AYEA84T,A EAhT,AFAT,Ak:GAT,A`GA(OT,AHA'T,AIA9T,A`fIA8T,AIAT,A JAh"T,A@iJApT,A IAT,AIAdT,AIAYT,A}IA\PT,ARJAP@MT,A JA8IT,A4KAKCT,A?LA9T,ALA0T,A }MAX+T,A@NA@)T,ANNA@)T,A hOA')T,A` PAe*T,A@PA!.T,AWQAx4T,AQAHrU,AXAFSU,A0YA(_U,A@YA;]U,ANZA@AXU,A3ZAYU,A}[APPU,A`T>\A}=U,A6\A`a:U,A@p]A;U,AX^A}=U,A^A@U,Ay]_AHU,A `APPU,A@`A ZU,ABaAdU,AaA+lU,AbAhsU,A*cA_wU,AcA{U,AqdA0wU,A`TeAqU,AeA\U,A fA͈U,A |VfAgU,A@GfA@U,A@gAH\U,AEhAx~U,AhA U,A iAwU,AaiAoU,A@5iA(jU,AjA(jU,AK)kAMjU,AkAhiU,AllA(jU,A?mA+lU,AmAkU,AaWnAbU,A3nA@AXU,AoAVDU,AzpAx-/U,AkpAT(U,ApAU,A pAU,Ag&pA@T,A9XpAHT,A`rAhU,AaAkU,AxAV,AEtAeV,A3_AYkV,AkA|V,A0٘AEV,A AP V,A (AYV,A@5ÙAV,AbAV,AA(V,A(AIV,ALAIV,A ڜA(V,A2AV,A@!A'V,A`žAh"V,A$gAX(V,AAP8/V,A?A #-V,AKAp,0+V,A>A8k(V,A.hAH^1%V,A/A Y"V,AGA䎦V,A^A9pMV,A˦vAAV,A9OA iV,AMSAfmV,Al0AM V,A֒rAV,A<A bhV,AE4A:w`V,A|GAV,A*AX(V,AaA V,Af&A V,A2942A>GV,A`;A4V,A=CASV,A r?IAWPV,A,LA4V,A}=MAWgV,AU>LAN"V,AZHAwS&V,A BAg(V,Aop:A +V,AEJ0Av-V,AĨ%AP8/V,AKA.V,AKAV,A dAV,A@TAV,A`A@$V,A9AV,A@9A`XW,A`[ۣARW,AॄAo W,Aq+AH W,A?ҥA W,AqAW,AA!W,A`A)W,A@ WA@1W,A`A9W,A0AsAW,A 7AIW,A֪AQW,A@.vAXYW,A AXYW,A ~A[W,Ag/A`aV,AнAV,A-ACMRV,AũvAb,A;A0jJb,A^Ac,A 8`Ac,A@,tAXc,AЩAxc,A@A]c,A"Ac,AWAc,A<A(9c,A@Ac,A`̭AX9c,A`KA"c,A JxAc,A=iAc,A6Apc,A Ax:c,A OAc,A@Ac,ApAc,AA҃c,A Atc,A@iAhc,A`[AZac,A QA@~\c,A@3A8VTc,A AMc,A;AGc,A A6c,AA}!c,A`bAHc,Ad%Ac,AA!c,A"A !c,AOA@*c,AEA4c,AAp8tc,A`mAc,A`ZAmc,AtsAc,AiAlc,AAA%c,A3*APod,AA{d,AApd,A`AId,A`Ad,A`ofAd,A=A:!d,A`;A1d,A`K]A`?d,A`fvAwKd,A@yA \d,A PANmd,A3,ACd,AAhd,A A0d,A'Afe,ATAX.e,AAxXKe,AA8Dhe,A HAye,A_Ae,AtAie,A Ap xf,ADA`Ts,A@0A u,A:5A u,A`2APu,AQ3A v,A5AP^:v,Aͣ6A(bv,AQ7A5v,A9Aw,A@9AHEw,A@:A8Ew,A 9Aw,A9APHw,A9AP&w,A@W9AhQx,A 9A>x,A 9A@*x,A`J:Ah!x,A@Ϡ:AXw,A`2;AxKw,A ҅;Aw,A;Aw,A (;AhQx,A;A2fx,A<Axx,A<A(Kx,A<AXx,A" =AUx,A=A>x,AU=Ax,A}=APx,A`<A@x,A`<A0x,AUs<A0x,AA<A(x,A$<Azx,A Z;Acx,A;Ax,A;Anx,Av;Ax,AW;AQy,AOK;A y,AM@;Axy,A`&;A(m#y,A:A@$*y,Ak:A_+y,A::AX/y,A:AP6y,A9A 1AN{,A@1A{,AtF2A7{,A@q2AL{,A2AP8{,A@3A`{,A H3A8{,A'3Ax{,A`3A{,A&4Ae{,A@`4A{,A=4A`G{,AB4A0&{,A 1Y5Aĩ{,Am5A8b{,A5A {,AD5A`C{,A "6AW{,AK6A ={,A*h6A{,A`}6A }{,AM6AP{,A6AP{,A@`6AHK{,A 7A{,A r7A^{,Aկ7Aأ{,A7A{,A`[8A{,AT8AH.{,AR8A@~{,A u9AP͜{,A@9AT{,A 8g9Ax{,A#9A`{,A`9A{,A@6:AHЄ{,As:Aj{,A@r:Ap{,A,:A'{,A@:A{,AM;Aoy{,A;Aj[{,A`s<AYN{,A=A@FA{,A=A.6{,AX\>A`-{,A@N>Ap=){,Aq.?A%{,A0?A${,A?Apx {,A|?A~{,A?A{,A ?A{,A @A0*z,A@Ahoz,AyAAxhz,A"ZAAU{,AƨAA {,A AA{,A@N=BA{,AfaBA,{,Aq}BAL7{,A JBA,?{,ABA>{,A@BAX4>{,A(BAX@{,A[CA(D{,A`m%CA*S{,A@CA`h{,AAMCAz{,ARCA{,A`QCA07{,A bCAX({,AmCAź{,A?CAhQ{,ACA{,ADCAP{,A`KCA`O{,ACAp|,A`KCA@p|,ACA,+|,A`"DA`5|,A 9DA>|,A]DAM|,A|DA_|,ADASx|,AvDAZ|,A@.+EA|,A`;EA#},A /EAH J},A EAs},A`]EAP},AvDAH},ADA},ADA#~,A`vDAxB~,A+DAc~,A;DA~,ADA@~,A DAh~,ACA@,A@iCA,A FACQۯ,A;4%hFA 7,Az#^FA@>,AFA跤,A` GA{,A%GA=,A?pOGA긢,A  H#GA*:RǢ,AOV,GAΆ3,A0;5GAŪ,A%{=GAx,AEGA_,AGAHCԄ,AcGA(ބ,AGA@,A^HA83,AGAЦ,AGA },A@GA(1 ,A`̛GA,AOGA,AFA$,A FA5,AAP&=,A6<AE,A;AG,Av;AfO,A`n:AT,A :A@X,A@ߴ9AnZ,AH9A8\,A8A\,A,u8AY,A"8A@S,AO7AI,A`K#6AHI,A)5A0D,A4AX8,A 4A0,A4A$,AP4A,A4AP ,AT4A*,A4A,A4A ,A@53APل,A3AЄ,AK3A`XDŽ,AXn3AX,Ab3A,A `3A ,AfV3A堄,AUH3A8Z,A*3Aˌ,A`2A,Ao2As,A`P2A)|,A$2A0z,AE1A{,A[1A,A1AXh,Ay1AH,Ad1A0,A@81Ad,A %1AH,A1Ay,A1Aq,A@0Aj,A@0Ak,Aʔ0A(r,A`0A(Jz,AH00A@$u,Aޒ/Ahx,AO/Aez,A/A`r{,A.Ak,Aה.Aτ,A@.A,A /c.A,A--Ab},A߻-A|,Aʞ-A{y,AH -A_u,A ,AGn,Ac@,ABh,A@ ,A_,A ɻ+A@W,A@ߍ+AU,A}}+A0LO,A>+A\I,AR*A9,A)AX 6,A_)Ap,4,A@)A@.,A X)A(.,A%)AX ,,A J(Aг(,A6(A&,A+(A,An'A_,Aj'A,A@5G'A ,A&A ,A-&Ap ,A`-&Az,A%A,A%A ,A $A ,A@p$A@_,A[g$A),A[$AJ,A`Y$AP%,A`bm$A',A$A _*,A6$A-,A%A3,A &AL9,A A]'AA,A'AN,A@K(ApJO,A`](AHU,A l.)Ah@[,AV)A/{,A+A,A=b+A0H,A@.+A`6,A`),Ac,A#-AX,A`]V-A,A -Aɬ,A-Ah@,A .A ,A Ҋ.Aų,A@]/A,A/AHÄ,A@W/A(Մ,AO0A,A`l0A,A0A,Aj0A,A0Ar,A0A*A؎O,A*APU,A@%)AZ,At*APW,A;*AhX,A@k*A[,A u*A `,A*A0a,A*Aъ,A3)AX,A`f&)A,Ap(A&,A-(A,A`)A,A5)Ah,A'[)AQ,A*As,A`+Af,AG+A_,A3+AO,A`<.AxK,AOM0A P,A1A:V,At2A0T,Aj3AuS,Aa3ATU,A`3Ab,A;4Af,A4Axk,A5A(lu,A)5Ax:,A`Dd5A,A@>|5A0X,A`]f5A,A@M5AHʊ,A@r<5A`׊,A5L5AH,At5AZ,As5A#,A$5Aq>,A 5AƤ,A`5A0,A5AE,A@5A01,A@y5AxX,AY5As,A`5A,A 4A0,A4A c,A@>4AG,A$j4Ap,A<4AE,A$4Ap[,A#4AS,A'4AX׺,A '4AŌ,A#X4A,A`4AHP,A4Ah,A`)15A ,A5A,A5A`,A`G6A@,Ak7A N,A`7A8,AzS8A,Ar8AF,At8A0]ތ,A`b8AHř,A 8A@qŌ,A#9A0jČ,A@x9ANЌ,A9AՌ,A9A,A09A0,A` :Ax\,Az:A{,A`:APw,AG;A,A;A,A<A,A Q<A0,A`<A,A =A],A<=A,A@d=A,AM=A,A=A@0,A q=A ,A =A@ڌ,A >A8Ό,A6>A,A5?AN,A@#BÁ,A=XCA8҆,A3zCA7,ACApJ,ACA`C,ADAė,A`m+DA,A@CAz,AhCA8,AbCAÌ,A:CAPˌ,ACA@Ԍ,ACA,ABA,A#CA ,AZCA,A`CAc*,AiDA7;,A@`DAH,A ɻEA([K,AFA ,AtHA0G,AHA[,AHAk,A HAh,ACHA 0,A (GAX,A uGAR,AәGA 4$,A iGAP/,A`mGA8,ApFAOR,A}FAȺu,AFA},A@>=EA,A`NDA,ACA(2,A tCAU,A`BAq,ABA0j,A /BAHK,AAAܑ,A @A ,AF?A%.,A lr?AxG,A`v)?Aq`,A>A0n,Az>A r,A>A7,AF>A(,A@>=API,A =A,Ag<A <,A<Ae,AMH<A,A` <A,A ;AE,Aq];AHy,A;A i,A@i:A_,A9AV,A`T=9A>P,A8AT,A&7A^,Ap:7An,A 6Aq|,Al6A[,A8,6A2,AX5AH,A5A,A5A,A`T5Ap,A 5Aג,A@#6A!,A@y*6A,AX96A,A?*6A =),A`5An,A5A#q,A`5A,AZ5A,A@&5A,A 4A/,A4Ahb,AR4A@,A`24An,A,4A,A63A },A=3A,ATx3A8ż,A`63A <,A@2A/,A2A`!,A2AB,A@#2A88p,A`T2Ahe,A@2A[,A2A84],A02Ah,A@2A%t,A y2Apy,A}O2AKi,A@2A@Y,A2A U,A (2AV,A1A'M,A@ O1A=7,A@i&1A(,A & 1A3 ,A0As,Aॐ0Aw,A`d0A,AR20AP<,Aj 0Af,A/A(> ,A0A+$,A@I0A]B,Af0AHrL,Ah0AXQ,A`2D0A S,Aq/A@$W,A /A^,A`O/Ap,A@.Ay,A@ּ.AXW,A .AX,AN.AC,Ai.Ap,A|z.Ad,A6.Aȓ,A6.A8Eԓ,A S.Arܓ,A.AX,A@.Aȱ,A`v.A8 ,A.AN7,A9.AcS,A`].Aȃl,A.Aw,A}/A@…,A&/AՑ,A/Ah,A /.A,A /AH̰,A@E/AX˔,A /AД,A!0A#ڔ,Ac0As,A 0Ag,A$51Ap,A`;1APG,A1Aw,A` 2A8,A2A,A2AH,A@1A,A`Ә1Aƕ,A/X1Aɕ,A`0A0{ĕ,A0AHл,A@y0A ,A0Aۨ,A{0A`O,A[0A0,A@NU0A ,A`]0Aŕ,A,>0AHKӕ,Ah/A,A[/AO,A.A,A@߰.AHK,A.A0ٕ,A{.A,A-A8,A -AM,A`,Ah@ ,AR+Ak,A@N+Ap,A``+Ahה,A C+Aذ֔,A+A8',At+A,A@'+A ,A`!+A,AF +Av,Aw*Ak,A*A#,A#'*Abޔ,A )Axܔ,A J})A,A2)AXu,A(Aj,Ah(Ac,A஧(A,A (AxX,A`(A",A@a(A,A (A,Az'A@,A^f'A8g ,A@<'A,A'A`$,Ay&A]1,A&A9,A cB&A >,A`%A:,A@>%AH)2,A $A(,A$A",Aw$A(,AWM$Ap00,A@,$ATB,A@,#AV,A`#Ac,A`v#Ak,A#Ap}q,A O#A0w{,A@`#Ah,A u#A⇕,AN#Ah,A4 #A@,A3"Axv,AӉ"A(,AV<"Aܦ,A@."AW,A !Aذ,A3!A8t,A[!A8ɥ,As0!A8D,A \ Ap,A9 Aث,A 8wA׶,AA,A`A(,AAa,AA,A+A,AN|A,A`7A,AA,ARAXJ,AՐAp,AdA,AB#AQ,A`A8,AA,A`TLA #,A@-A~,A*AU,A A={,A A(r,AfAx k,AAJi,AAd,AnAa,A3Awe,A`AXj,AAo,AAht,ApA ps,ANAp,AoJA3l,A`Aȥf,A`A@d,A`AHW,AtAH,A`A(m*,A AAxi,A@A8,AzA@~,A AL,AAB,AAP,A@EAX,z,AFA`h,AqA#U,A40A@,A@1AЦ,A6AkΓ,AA`,A;A`,A]A͟,AZ/A`X,AaA ,A`A6,A-A(܆,A@sAy|,AGAwk,AAW,A`A/,AAXV,AA@,A$A,ALAP̒,A@Aj,ARhA`ǐ,A *AD},AEAm,AAhb,A@Ax[,A]A Y,A /Ax[,A-A`,AApa,AAgf,A@AhDm,A Apq,AA{,A?A`2,A tA,AҶAX,AAy,A@5*Aʒ,ANA@,AlA_,ArA *,AWA@WQ,A=ATp,A/A,A ZA@ӓ,A4AAط,AAۓ,ÃAB,A$AI,A@Ad,A@Afy,ADA*,A ^A,A_kA,ArA,A}AsҔ,A@ƃAH,A AA`1,A6A,AAp+1,A@AE,AA1X,A^Axvd,A6Aim,AxAu,AXAh{,AoA~,A`LAY,A`APk,AAhw,A@i>Ahsx,AtA |,A@yA䂕,AA,AټAhQ,A AP,A`DA,A |A,ATA[,A`)1A,ATA#,A@WA𒾕,AAcÕ,A JAXϕ,AIA֕,AszA(ܕ,A A`rܕ,A$:Aӕ,A@sAHiڕ,A`]r"AP&,A H&A,A H&A^,A)AQ,A޽)A,A+A,A`m+A8,A@k&AT>,A@%A@=H,A ."AhM?,A"A,A &A,AX#AX,Az&A`SU,A@,%AHr,An$Ap,AE<$A7Ü,A6#A,Am#Ax+,A >#A ĝ,As!Ax,AQ#Aܟ,AkO A,A@y#Apa,A~AP,A@iAm,A`)AxS,A)Aؠ,A AmI,ABA`p,A@A0,A 1Ap,A`o:AT,A-A8,A>(A֣,A;l'A88J,A@Az,A l5AG,AA颤,A(kA,A *Axd,A`AH.,A tAY,Aw1Aޥ,AdAP+,A =A å,A Akߥ,A:%jNu A,AH AT1,A? A Bg,A AT,Aj AAFզ,Az A8 ,A A,A O%ApQ,A`AH]Y,AAv,AA,A AAPΨ,A?A,AAx$D,A4Ax,A@3Ap,A OA;,A,AH,A2-Ap9$,AjA,A ZAH.e,A;A𴶫,AOA@,A?A0ث,A`7A,A WA:,A8A,AA9,A)2A0 0,A@AD,A`DA2Ƭ,A@iAHi,A`2A,A@EVAP,A ~AHƭ,AzA@,A҂ Aȩծ,A ^ A,A`]AP,A&Ai ,A`mIA,,A@A@A,AYAJ,AzAQ,A Ah,AA_X z,AwBAخܤ,A -AVƟ,AAj0 ,A4KAÍŮ,AqXAxخ,A A,A`A?,A@!A*,A`KcAذ#,A@yAP>,A@`nAH;O,A-"AT,AXAT,AAbr,A TA0,ATAX,AྦA#,AA8ɯ,A@ZA,A@A(h4,AA(lW,AxA%Y,AA`Tb,A`)(Al,A|AEu,A`BAN},A$Apy,A@#^A7,A`ÉAX,A A,A 8wAb,A At,A Aȃ,A@A,A AIذ,AziA0w,A<AP},A<A0*,AWA),A A_,A`A7,AVA r,A4eAf[,AtAؒ,A`A[Ҵ,A A3,ApA,AoAPg,A@NKAHi,A$A8,AA`,A`A8ڷ,A Au,A@AXu,ADAX,N,A A(+,AA,AA/,AIAn,A`2A(-,A@5A1 ,AAٹ,A+Aq",A|A>,AA_X,A TA@,A^Ahs,A#(A,A`Ai1,ADApx,A OAN˻,A2A ,A\AؼV,AA,Aa Aoμ,Aa A(,A0AUh,AGA,A Ap ǽ,A cA,A Ad,A Aw,A`vA$,A pA\Z,AA(\,AVA8S,A@AX5M,AFAX5M,A ~MAX5M,A`AX5M,ADiA\,A s;Ap,A@yA`f,A|ApJ;,A@>mAP,A`A%ÿ,A`;Apƿ,A A,A8AXz,AfA1,A0AP,A sUA,A`AxK,AXA(B,Ax,A0]C,A`;$ARR,AA\,A" A,A9yA,A`vPAH{H,AHA,A Ah,AAH:F,A A,ǎN,ALFAjX,A~GA7#sb,A@A7Gpm,A.ݐABɴx,APy)A:m,A# Agǎ,A~A,A`A,A@A f,A 1A`ey,A@ϔAX,AtA;,A`1A~,AA~,A A~,A3}Ap,AA ,AfJA,AAh,A+HAV,AA(f,ApAm,AFAh,AM:Ae,A A,A`A,A`BAh,A@5Av,A`Ag,A`A ,A AP^,A`A},A^A,AA p,A@rAD,AAi,A,A@,AHA@,,AgoA8T,A5A(x,A@AΊ,AM"3JA7t/,AES\Aդ ,AjxvA#h/,A2|3A@_0,Axb)Aቾ,A)A,AZ A ,A`2AX,ANA,A`,A,A`7A,A`7AP,A A2AS',A $Ap|V,A?DApy,A ZAIi,A@A,A٣ A,A Z Ah,A`o A,AM A ;,A ' A8,AX A0,A ZY AP,Ah AX,AA,AAh3P,A A(Je,AA,A  A,AH A,A`, A",A@  A,AN As,A@WAH ,A/A`q,AA,AhA(9<,A@A( [,AAHs,AMA(,AAh,AvA,AyAH;R,A VA8,A`ܭA@,A AG,A~A ,A?A0jn,A Ap,A3CA8Q,A``Aح,A`BAˆ,A A`G*,ABAUE,Ah5.gAa_,A47YAi^v,A|1AA,AJVh.Af%,A*?A ,A ApΜ,AAH7,AK A 6,A AHn,A@'"A,A!A:(,AM#AC,AL!A`,A [!AH,AA0,AkAWg,AA,AdA ,A?A,A A(T,AA؟w,AVA,A`JAb,AA` ,A<A#,AAnX,AF AIb,A2!A,Ax A ,A A8,A Al,A@A8Q?,A vAC,A@tA0,AAP P,AAPoJ,AׇAPY,Af.Au,AyA,AA,A;A@,AfA,B,AzDA,A2vAPZ,A`2kAB,AWJA,A ZAE!,A RA),A@TA0*,A ˕A,A)TA@H,A`m{Ah,A`A8;,Aq A(=,A APV,Ax Ap,,A A,A A8g,AA,A5A,AA5,Az Ax,A@# AP,A A,A$ A0,A+ A,A ~ A,A 6AX5,AA ,A9zA$,ApAI,AjAf,AjA`,A (A@,AAx,A`A,ArA8,A@,#A,A0A,AA,A Auw,AA@ ,A |A ,AA,AA&E,A@>XALi,A@A@,AA,A{A,,AVbA`l,A`wAx,A@A@,A`"AH,A@nAh,AkAIC,A:AHr,A JAh,AA`,AA ,At]A(B,A@9A],AjA,A@EA@ ,AJA7,AA$,A"AP/b,A@#AV,AA,A|LA`,AA,A#EA,A@PA`,AbAk^,AxWA%,A`A>,A@qA~,AA0*,AwAM,A \AR,AjFA065,AA[,A A,AIGAp,A`Ax,AAP,A3A?,A A %,A`A%,AigA&,AiAh7,A`AH.>,AvAI,A`A_,A AA],A@#SAR,A MAxI,A(Ahe,AA@m,AZAi,AHXAm,A@iA-A"A-A Ad&-A`]A`H-AڪApF-A`WA:-A H`A -A DA-A` Al-A@AE-A\A`X7-AxA6-AAXA-A[AE-A KAP^L-A`DTA8b[-A@Av-A AHf-AaAo-AjA`j-AAHr-AA@u-A A(-A@ A-A A u-A |Ac-A A"-AAiD-AAH?f-A'Aq-A`AK-A` wApm-A&1Aޕ-AηAߘ-A`A/-A; A(-A AH-AA-A`A-A Ae-A@AP-A cgA-A̍A-A@A@-A@A -AǾA-AЛAhj-A EAV-AAP& -A@rYA-A#A^-A$&A -A'A%K-A@`^A8n-AKxA@w-AXA`-AA-A9AK;-AkgA8g-A@5r(AP-A(ANo-AVMA?F-A@ AL-A kA -A@ "A2-A 7"AP-Aj(A-A`(A -A $*A`2-A*Ax6-A@%$AX-Af[$A(-A@`ApN-A A0.F -A@A( -A l!A] -A+"Ax(F -A %A ҁ -A-&A -Ag"A` -A"AP -A6"AX/ -Am"Apj -A A -A@U Ah -AO4 A` -AW Ad -AAx' -AhAНh -A0 A( -Afu A&} -A AL -A["Aٮ -A*)Aȩ -A{)A{ -A7#A`Bb -A$AY -A c %A  -A`T(A( -A`+A$L -A0A(-v -AS0A -A-A@ -A+Ap -A@)A؟X -AՓ'AM -A%A} -A8%A -A`)%AL -Ah&A; -AHs'AI -Ag'AQ -AV1A -A1Aj -A`Q(A`ry -A]g(Aޛ -A''A? -A`"&AX -AOY'A-Am](A7v -A6?3Ah͡ -A 3A؁-A`ܟ(Aج[-A@`)A-A3J-AΉ-A`x/Ax-A Z@/A@-A`2A_-A6Am4-A`;;Ax>f-A@5<A-A(AA -A@ }@A@F-A^;A`-A՝;AX -A7AXׄ-A9A0]-Ah7A-A>A -A BAH?-A CAus-A`ʏGA0]-A`TFA6#-A CA:-A`BAx6U-AaBA-A"CAP-AcGA-A]KA0-A@`LAhon-A JMAm-ALAZ-ALA J K-AO LA2-h.-A]EAb"-A3A_GW-AAz1 -AuvAGÕ-A7K_%Ad -A+A%hh,A౬A*&{1,A0>AE]K,A}9~Ad3N,A_)`Av,AAAd3N,AO"AE]K,A4ƐA*&{1,AP9A%hh,A(NSAd -AnB8AGÕ-AȴaAz1 -ARH{A_GW-A| udAb"-AF)OA2-h.-A Wn=A"-AÒL-AʇT)-AT, AO-A98 A|rV-A[u- A+C^-A|A6x,{e-AT, AJil-AĒL-Aҗgr-A Wn=ABk%y-A F)OA<"~-A| udA~n5-ARH{A-AHdA>-AHdA̱-A_A麢-A}9?Aل-A0Ak4K-A౬AOl-A+A-AHdA#eҞwd,A47.b@~od,Aev@>4d,AZ@\c,A+@c,A @]a,A@0;_,A9?@7!Ӗ_,A4O-@ha_,AUs=@;"R,_,A o@^,AFÊ@^|^,A};8@h6d^,Ag P͋@a^,A@=!^,As,@f9],AYs@MB],Ahg1@ q],A.@1j\,A& B@'h;p\,ANriy@P<[,A@[,A+Juh@ q[,Ai1@ @:,WN^,A#sq@#eҞwd,A47.b@(POD,A?F@pAD,ArB@NqUC,A^`ݬ٬@3,inC,A@ҥd C,ArH(@;mB,A'@רHB,AV@;A,A;@B,A@p@IB,A@C,A@j@B,A"@գB,A@@YB,Aa@A,Aȟ@0A,Aa@CA,A@@C&@,Ac2+@ è@,ALq@ M$a@,AHP7@PV@,AbU@Qݦq?,A$k@^n?,Ask4>@-Ci?,A@@6?,AF-@ِ?,A]vZ|@a>,A+x@X6>,AiHx@p>,Aw@^[ >,A &}w@@s>,A؞[w@~fD>,AU!v@pΔ>,A@v@ Bن>,ARF[v@%y>,Av@D sn>,AV8u@ڊd>,Alku@v\>,A@u@WS>,A?t@ȺI>,As@LZjA>,AMs@8>,A@r@=,AWħEp@$=,A0m@ZW.=,A{uj@P7[=,Ag@ؼX=,Ag@U=,AΗg@0GP=,A@N_g@hJ=,A@f@xhG=,A@ f@`H=,AXXf@J=,A f@XT=,A@>e@Z=,At#e@Z=,A e@V=,A]d@~O=,Aid@A=,A-ed@hs6=,Ad@ }/=,Ad@ )=,A\d@}#=,A֩d@ `6n=,AAd@ ==,AmV8d@8=,Admd@P<,A}Fb@Jh<,AGjva@Vמ<,A`h_@I~<,A}L,^@PQb<,ACl\@I<,A@Z@lM<,A "rZ@důP<,A+BZ@wR<,AU'Z@8S<,A(Y@EWR<,A+kY@5Q<,Av:WpY@*N<,A)?Y@$@J<,A$[QY@SCSF<,AyX@ZrI@<,AiX@ª ;<,A.fX@ZӪ4<,A(͡X@t.<,AޡX@_%W'<,A;[X@ <,AKX@<,ArX@&A<,AR0X@_# <,AÏX@<,A2T@wX@Ŭ1<,AWGWX@Ԋf;,A0X@- q;,AFSmX@ k;,AW@Z;,A>V@U Ԇ;,AYT@;,A@GTS@J;,A^zR@Rx;,A4 R@p;,A@rQ@f[];,Aϐ7"O@@:,A@L@p:,AiK@I:,AUJ@㪄:,AJ@h:,A@hI@Q[8:,AJgG@ :,AHPuFF@9,AjzD@&~̷9,A4T7B@9,AP A@wuĎ9,A,;@@39,A/kt@@/9,Arf@@v9,An)@@Às9,AUp?@i9,A@?@9,Ag6]>@!gh9,A[R8=@V`9,Aڊ=@09,A@4<@|9,AsJ*Y<@ tAbn9,Az<@)3a9,A<@KU9,AGL+-=@ȇK9,A@=@09,A@?@8,A2@@8,Ap4@@G8,AҾ?@@C;8,APFA@t}@S3(0,A8@&jI 0,A0Cs@ ۵M/,AYE;@/,AB@0E/,AnHE`@B,,A`l@W,,A:@M',,Au"ڮ@j+,A"A@+,ALc@-+,Aξ~@Oa+,AVU@]$/+,A%@*,A@@I*,AU@@R*,A:3u@Y)-O*,A]@*,A7m@ ),AG@ل(,A*^a@k4[&,AcYB@Ol#,A$Wz/z@@8+A8@H+A2@+A@+AU@P+A@d@ث+AΡ@+A "@+A@@Є+A@8}+Au@,l+A@@@I+A@@hb+AÔ@*+A@(+Av@`+A@@+Aa@+A@h+A@Y+A@Q@@5+A@ّ+AT @舑+A%@I+A@g@0L+AA@B+A2A1o+AcçAi+AlMABw0+A۝b AU+A_ A|+Aǻ Ay+Ay A+AKY A+A` A:\#+Az:d$% AH +A&N AP($+A,DAc$+AB._OA +A Ae_+AA07+AAI+ATDA+AA +AA +A^FAa+A}A#+A`pA8+A2A+AAx)+A AP+A`;BA( +A A+AA+AEAi+A AXh7+A0AX9+Az<A(KX+A$Am+AyAy+A@E3A+A [A8D+A qrA`6+AvAД+A@Al+AmA+A}uAH2+A A+AA+A@>>AP+A<A+AA+A[A+AA7+A=Ax +A{A+AA`+A ZA+AH.AP+AA+A A+A`BAu+A LA+AKpA +A@A+AAp,+AAy+A  AZ+Ak Af+A A0+A`!A0$+Aզ!A4+AT"AB+A (#AR+A=#Afk+A 8_$Ar+A$A_x+A@5%A|+A%Ah@+A`&Ao+A@>'A(τ+A'AX5+A(A0f+AM(A9x+A(Aq+AV(Apb+A &)A>]+A!)AY+A@EH)A`X+A@Ca)AOY+AVs)A}_+A)Au+A+,AP8+A@,AX+Aח-A+A>.AX,+AH /A9+Ap/AA+A/Ap8H+A50A)N+A^0A(\Y+AÝ0A8d+A 0Afo+AH!1Az+AV1Au+A`1A+A1A(+A`B2A(+A q2A`+A2A8+A 73Ak+A3A+A3A(+A(4Ap+AW4Ap=+A 4A`a+Ai5A`a+A |6A+A6Ap +A`fN7A+A7A+A;8A8+Aw8A+A48A0.+A`)`9A+A9Ah/+AR9Ap+A0<A^+AM=A+AYAAp|P+AMHAhD+AHA`L+A3 RAp=V+A@:RA#`+A_RAP8i+AxRAv+ARA3+A̴RA3+ARA`+A RA+ARAh+ARRA`+A`SA`+ApMSA+AறSAP+AٳSAp+A SA#+A"TA83+A@>`TA8+A` TAX +A TAX +A UA+A1UA+AXUA@+ARUAu+A`UAP8+AGVA+A@>hVA+A@VA`+AgVA+A@ WAH\+A3WAأ+A jWA@+ARWA+A XA+AXA+AXA +A3/YA+A xYA0.+A |YA+AH+ZA+AR~ZAأ+A=ZAX+AR[A+A[AL+A`\Ah3+Az|\Ah3+A@\A`a+Az\AB+A(]AP+AzV]Aѻ+Aஃ]A0+AR]A+A]A8+A]A(ܠ+A!^A0.+AZ^A#+A |^AG+A^A3+AHA_AP}+AR_Aw+A |_AP8m+A`A0.a+A E`A0.X+A@>^`A`aN+ARz`A#C+A `A8+A@>`A`a4+A`Apk,+A aA&+A@>aAX +A^aAp+ApaA +A3saA+AdaA+A`)^aA+A@gdaAL+A`kaA+AuaAu+A q}aAǻ+A͂aA+A aA@+AHaA)+A4aA+A`]#bA+AH?bA0+AhbA+A@gbAp+AGbAX +AzbA+AcA)+A)cA ҭ+AIcA +AbcA@+AGcAX +A͖cA+A cA̕+AcȀ+A3dA+A >dA(t+ARdAp+A |dAi+AdA0d+A dA`+AeA#\+A`)2eA`aU+A`^eAO+A`eAG+A`eAA+AeA8:+A@eAW5+AfA2+A@ 1fA(1+A _fAB.+A@fA*+AfAf$+AfA+AgA@+A6gA#+A@glgAP8 +AHgA`+A@gA+A]gA+A`hA+A@>*hA+AEhA+A@nhAP+A]hA+AhAQ+A hApk+AhAH+A@hA+ARiA+A#iA+A |FiA+A aiA+AHiA+AʹiA8+A3iAp=+AiA8+A qjAP+A9jAǍ+A jA`a+A`ÝjAW+AjA`ay+AjAr+A \kAe+A%kAX ]+Ag2kAU+AqA+AzqAǚ+AqA+A@gqA+A qAx+A qApn+AqA`d+AqA83\+AqA`e[+AqApU+AqA83Q+A rAHN+A ,rAHO+AWrA)S+A grAfZ+A@rA`x+A | sA`+A@>\sA0+AsAp+A@sA8+A7tAਡ+A`wtAW+A`tAp=+A`)uA3+AmuA+A uA+A q vAਘ+AfvA+ARvA0+A wA`a~+A)rwAps+AwA@Wj+A MxA#f+AxAQ_+AxAZ+AH9yA(\S+A |yAzO+A`)yAHE+A`yA<+AyA83(+AJzA+A^zA +AtzA+A`)vzA8+A3ozA+A@hzA+A@>VzAp+ADzA8+A`2zA+A \/zA+A9zAx+AOzA`d+AtzA8]+AgxzApQ+A@tzAG!+A`zA+A@zAp+AHyA+A |yAB+A3iyA(\+A |*yA8+A3xA8+AíxAL+A@xAX+A kxA+A]AxA#+A`"xA)+A)xA-+A`wA`+AணwAP8+A`|wAG+AjwAŽ+A fwAH\+AnwAt+ĀwA`ai+AwAhH+A wAأ;+AwAz/+ARxA+A $xA`+AGMxAC +ASxA8+A |nxA+ApxAf+AxA+AxA83+A yAp=+A`çyA+A`yA+A@ +zA`+A@>JzAp+AozA(\+A@zAѧ+A@zA(ܤ+A {AH.+AE{A8+Ad{A+A {A+A@{A+Aň|AH+AT|Ah+A@}Aأ+A}A+A (}A #+A=}A=+A-}Am+A}A`w+A}A +A~AX+A` ~AP*+A&F~A(+An~Aӫ+A~Ah+A^A8+A`As+AA+AA+A@A2Q+ARAS+A (cAKV+AsA(]+AʔA$y+AAH?+AgA與+A -A+AX;A@+ALAz+A첄A'+A`mAP+AA}+A`Aȇ+A@aAз+AɆAw+A/A¥+AIA`+AA+A@4AP<+AjhAő+A~A(O+A ۔Aȓm+A.AX`+AmcAhA+A`щAPx4+A@A5(+A`m?AH2$+A\A/ +AzA+A@Ax(+A@ߒA+A`vA@+AA +ÅA$+AA++AwfA++ArAt,+A~AP ,+A ۖA_(+AaA@$+AIA+AjDAH+A@uA, +AA/+AʌA+A@A0T+A GAؽ+A`vA8Q+A uA((+A ֎A+AZA(f+AA0+A`NA+AAAP +A@KA@+A`wAhѽ+A@aA,+AAD+A`;A`T+A@.BAث+A 8hA+A@ؚA+AwڗAx+AA+A@pJA(+A}A`+A2Aӛ+A`vԘAX+AA(+A@=A`+A,}A6+AٙAP͐+A@A+A,AH+AzAL+A]ךA+AzA+AA+A@>6A+AA{+AӟAk+AɡALw+ALAj+AA{S+A`A8++A qA%+A6Aأ"+A`SA`+ARpA8+A샨A+A짨A#+A@ƨA(\+AA+A3A`+A 1A̳+A OA̪+AgA@W+A A+A@A83M+A`fA/+A`A(\'+A@>^AG+AAk+AA+AחAB+A AW+A`A`+A qeAW+AzpA8+AA@h+AApG+A bA<+A A8-+A=ܶAf"+AHA +A A+A.Ag+A@[A+A;A+A_A`+A`AIl+A-Ahb+A@A9 +A@+A8+A@%A0̙+A}A+A@CgA@u+A@A+A`DAw+A`A+AOA[+A ?A+A@A8'+AA`4+AsxA+A Z"AS+A`0A8Ҕ+A&AN+AQAe+A%AS+A=A(^+AHAp+A|AP8+AmAPf+A pA@+A`)A`+AAH.:+A A#U+A qABt+AA@W+AA@׮+AA+AA+ApAh5+A APP+A3As+AeA”+A@A+A`A+A8A`a+A qA`a+A A`'+A4'A0.?+AOAP8T+A zAz+A`A#+A'AX +A@gAP+A A+A3)Ap+A tAW^+AAu+AA(ܘ+A3A`a+A`#A+A3uA+A@A-+A A,A@>AhUJ,AOAYg,A`;A#s,A QA~,A A,AeAx ,AAݴ,A`dA,AA`,A2A,A@A,AA.,AA@,A`A\,A4qA t,AŬAP,A Ahќ,A AP,AnA,A`DA ,A 1Ar,AA,A1Ap,A XA,A`LAP,AKAp=#,AjAv7,AŮAE,AH AVR,A`vA`vd,AO Ai~,A@A8 ,A`dA ,A` AX.,A SAK,A`d}A&q,A@%A,A@PA,AFbA,AA0,AMAm,AA`,A@NAp:,A4A8e,A(Ad,A`A0,A@r A,Ay A,A# A`!,As A #,A A><,AAY,A~AXs,A A.,AwA,AA8E,AZ8A(O,AaAp,A qA,A=A^4,AApS,A (A h,A`;jAw,AAx,A@߻A(ۛ,A A4,A"A,A@Af,A@5A ,AQA',A#oA*K,AVA,A%At,A%A,A6&A+_,Ay6&AKM,A\5D/'Amk5,A'A ,A-v>(A9,ACw(A!,Aґ3)Aq7,Ao)AQ,A)A&{ ,A51Ap ,A71A ,A41A ,A'#1A8# ,A0A ,A`]0A@ ,A0Au ,Aො0Aȩ ,A Q0Ax ,Afr0A` ,Akm0AHP ,A`j0A8 ,A`e0A2 ,A`e0A(6 ,Aph0ANC ,Ao0A0G ,Ao0A0I ,Ao0AfK ,Akm0ADM ,A`j0A"O ,A`e0AQ ,A`0AR ,A`[0Ar] ,A@./0A.a ,A"0A ,A@,/A( ,A@ c/A> ,A G/AQ ,A?1/A ,A /AT ,A`.A(( ,A`.A ,A,.A8 ,A`].A ,A@r.A^# ,A}.A;% ,Av.AX' ,Aj.A@) ,A].A/ ,A@.A1 ,A .A3 ,A.A0A ,A S-Ax~^ ,AA-Ahv ,A@>,A( ,A,Ah ,A4,As ,A +A ,A+A ,A+A8I ,A+A` ,A 8+A,A`T+AK,A`%+A_,A++Ao,A5 +AP ~,A+AX,A5 +AI,A@+A0٪,A +Ap,A`-+AH,A}>+Ao,A`T+Ae,AMk+APZ,A +Aq,A+A`2,A^,AHr,Ay.Ap,A`2.A`%,A`/A,A/A,A/Ab,A ~/Ay,An/Ax,,A@0A6,A@'0APf?,A>0Ah"C,A@H0AE,A@W0Aa,Ah0A,A1A@,A03A`%,A A;5A2,A5Ah3?,A@5AK,A`O6Ax:V,A (6A@`,A@*7Ack,A&7Au,A`28Ap ,A@N8A,A@9AP+,AK:A(m,AI;A,A<A,A<A ,A [=A`q,Ad,>AD,A+>AP.,A@?AHy,A%AA0],AoBA,A`)CA@S/,A&DAf|,AR@FA@W,A7GAG,ATHA,AIA<,AKAO,ALAذl,A@ߜMA~,AMAԗ,A`B,NA5,A`K_NAhU,A@iNA,A`D=NA:,A`MA",AuMAh6,A@MA`b,ALAP,A7LA,AVALAs ,A#LAvK,A@WMA,AMA*,A?MA,AMA6H,A`^MA@{,A FMA,AcMAP/,ABMA(,A |>NAh,A`ܼNA8V,A  OA,AkOAc$,AJuOAfʩ,AwOA\[+,AOA,A@OAD޲,A4hnOAB,A/OALߍ,AA3 PAC,A_6u0PAҊ,AVPA!ē,A6$ܻPAĠ,AЉQA #v,A(ATmQAd,AQA=7,A'M-QAuRO,AQAـ#,A,QA,A4QAXI&,AWQA{|,AAQA;y,A0bQA,A ¹QA(z,A`;6RAh,ATtRA8&,ARA$,A*SAP!,A`SA%,A)SA 2,A@dTAO,A /TAk,A`;UA,A\UA,AFVA,A`ʋVAxz,AଲVAPL,AVA8Io,A@VAy,AVA_,AVA+,A` WA@,AFWAXy,AyWA!,A WAx,AWAp[,AWA!,AT@b"%,AS*@2-h>+,A:d@1,A%@ʇT8,A,YF@Ap/AA(#q/A %A Edq/A %Ahq/AAiq/AjAx(q/AGAX5r/AGA`Bxr/A ZZA&r/A qA0r/A`Ar/A A;s/AAXl(s/AAhAs/AAKs/A XAQs/AA|s/A`Aݝs/A AOs/A ADs/AHDAs/A:A`vs/AAxps/AA`s/A`AUs/AAS@t/AhAXmt/AhAt/A@A=t/AmA t/A+Aht/A`Aەt/AOA`Xt/A`]At/AA0t/A`AhZt/A@zANt/AfAz9t/A@A3t/AA _t/A As/AAs/AoAs/A@SAjs/AAs/AAPs/A`kAs/A@ ADs/A \AvTs/AApds/AhAxNs/A`[cAx#s/AVA0&r/A` 9A0r/AA`Wr/A AP(r/AAH Tr/A}A,Nr/A6AMHr/AଂAHr/AAPoq/A,Aq/AOAhq/AAp_q/A`wAq/A`AXtq/A9A@`_q/AQA$q/AAСSq/AKA4gq/A`Axq/AvAxץq/AA@q/AHAq/AYA(pq/AOA$q/A=A0f0q/A#&AOq/A@A~q/A=ADq/A A8q/A`]A`'r/A 8A8r/AAɻr/AAr/A2 A@ s/AA0;1s/A`5AK\s/A)AP/s/AAjs/A uEAPs/A`As/AOAPIt/A ɥA8)t/A@A3t/AW0AKt/A@lA(t/A@lA0Ut/A0ZA(~t/A#&At/AAt/A@֖A t/AAt/A.At/A0A0t/A(At/AդAhbt/A |sA(mt/AྯAt/A-At/A At/A`-A6t/AyAAt/AA&t/AcABt/A`o.A^t/A`A#t/A;$As/A;$Axxs/Af.A@03s/A Ar/A 1ALr/A ShAr/A@ [A`Wr/A`mA0#r/A ShAxdq/AuAhq/A Q'A?eq/A`A$q/AvNAp/Ap}Ap/A`XAAp/A"AYo/AIAo/AmA83o/A6Ao/AAdo/A`Ao/A?A@o/A@WA3p/AAhxp/AZAbp/AzAXFp/AxAp/AA@q/A`Aq/AAhq/A`)A3q/A6Ar/A@%/A0r/A6Ax~mr/A=sA7r/A{Ar/A@cAs/A.ARs/A`TAPs/AAQ-s/A`dAHr/AAXr/ATAr/A qAr/AeAQr/A iAq/AoAq/A@sAq/A@EA oq/A@EA8Dq/A6A8p/AA^p/A cA^p/A]&Aho/A`BA`o/A9@APro/A`AZ9o/A_Ap5o/A'An/A"Aan/AAon/AHAh]%n/A_AXcm/A_A=m/Af8Aߩm/A`Adlm/A ALm/A OA&m/A A m/A A{l/AAh@l/A'Al/AqAll/AUAXMl/ALAr(l/AsA07l/A$=Ak/A`AHk/AA8k/A AHxk/A7AZk/A AYGk/AAk/A@A0j/AࣕA@j/A@ύA$j/A@A/j/A@5aA/j/AAj/A=sAªj/A+A/j/A Q'A8sj/A œAHqj/AAXl^j/A@yA%=j/A,A+j/AjAPi/AMA,i/A}Aci/AAh/A@Ajh/AnAh/AA`gh/ADA Qh/A\Ag/A@Axmg/ARA1g/AA]g/A]Ang/A SAlg/AAutg/A 5A]g/AiAxe/AsAle/AHAle/AA =3e/AAd/AAh d/A@ DAod/AoApd/APAe/A3AXye/AtAe/A@~Ae/AA@We/A=Aqe/AKAe/A ZA3d/A6AIyd/A`DA`d/AAKc/A1AOc/A`A`Sb/A)AXƗb/AaA8b/AAhѕb/AKAb/AAtxb/AaA0b/A@p Ab/A@=A[a/A`;Aa/A3A06pa/A`Aha/A`KAHòa/A-A`a/AAb/A Ak~a/AN\Asa/AHAd`/A sAHr`/A;A3`/A[A(w`/AFApi`/A`IAB`/A8AP!`/ARAO_/A`fKAȱ_/A@#A4_/AA@q_/A 8Ae_/A 8A;H_/A &PA_/A 8.A_/A cA^/A`JA^/AOAx^/AyA^^/AmAw^/A@mA07X^/A[A<^/AVA'^/AAd]/A0Axv]/A6A]/A?Ap]/A(qA]/AAhn]/AA9]/AA7]/AA8\/A A\/A@A\/A@>A\\/A J(A88<\/A6AP\/A@֘Ag\/A :AXs\/A@rAxq\/AA.}\/Aj~Ax6\/A`'AM\/A&Ap\/AsA\/AAȲ\/A=AȲ\/A5A\/A@?A\/Av9A(m"]/AAL]/A A@T]/AAj]/AW=A>]/AA0]/AA`]/A_A(F^/AAp^/AgOAXt^/Ai Aq^/A*C A`^/A@ A _/A`2 AKC_/A$ A>_/AD A0_/A AX_/Aӆ A_/A A`/A AHX`/A Ag`/A` AO`/A A a/Aq APkHa/AA0Ta/A[AUa/AAa/A AXb/ARAoTb/A`AxKb/A`*Ahb/AA0b/A APMLc/A*A kc/A/Ac/AArd/A2A@Fd/AXhAh:e/A QA8֦e/AXrA8Ve/AA7^f/A Apڲf/A%ANf/A%A 0dg/A q A(g/A@iAP/h/A`2AX[h/A2Axh/AHA-i/A`AHLi/A`)FAi/AOA)j/AAq]j/AAxj/AAA`j/AnAk/A sA`6k/A|Alk/AWAPk/A gA`ek/A Ak/A@NA0k/A A8'k/AN AP縥/AfHA姥/A@GA/A`)GAX/AGAX/AeGANm/A`FAo/A`dFA8t/AEA/ArEA/ALEA8q/A`DEA`/A/EA?/A EA -/A=EA#/ADA!/A%DA#/A?CA)+/A`xCA0{/A CA\/A CA/A@iaCA/A 8BA0 /A@%BBAp /A AAp /AAAp /A@, @Axv/A 8?AW/AX>A`/A>Axv/A=[=A@/A@<A@$ /A#;Axv/A /Y;AUҤ/A ;A7ä/A:AƤ/A:A/A?f9A8d/A 9Ax-/A{8A/A`78Aͣ/A 7Aض/AE/7AԔ/A6Apxg/A6Ax=/A@'7A/AF,7A1/AE/7AGТ/A@p6A/A]=6A`/A 5A@o/A һ4A(\/AB4ARW/A`3Ae/A`2AБ@/A22A/A3A0ɡ/A34AXlv/A`4AF4/A J5A@٠/A6Aנ/A@'7A:ߠ/Am7Aנ/A@7AT̠/A`V8A/A 8A³/A89AHw/A@߲:A +/A,<A88/AS=A/A`m>A5/A3 AAs /AVAA/A0BA8Q/A`DAf/A~FAX"/AHA8/A IAP۝/A[KAH/A4 LA`t/AMA)/AtNAX/AOA/AJQAꄜ/A`DiRA9V/A@>SA`ܛ/A"UA駛/A`VAo/A XAPJ/Ag1ZA-%/A/\Ah /Ai]A`aĚ/A՟^AXlؚ/A_A?Ԛ/AaAĚ/A@bcA/ABdA/ATeAΚ/A_fAXݚ/A|xgAXݚ/AqhA/A@ߏiA/A=jA``/AkA /AmA/A`mroAC/A`pA/AavrA"/A`tA(7/A`"uAl/A5vAÛ/A`vA/A&vAxK/A_wA/AaexA0T/AxyA0nC/A@xAݫ/ApxAxu/A lwAW/ATzuAٟ/AKsARr/Az]rA)/A]}qA//A qkpAҌ/A`܅nA\A/AUmA~ɣ/A@#kA(/A *kA/AiA/A`gAF/A-gAXƐ/AseAw/A@`dAt/AbAPz/A@w`A^إ/A ^A/A\Ayѥ/At[A姥/AޞZA/AfYA/A XAxƘ/AWApZ¥/AWAx$ץ/A VVA"ƥ/A@gUAȩ/ATA07/ASA/ASA`S/A,RAxƘ/AQA;/A@N2PAɥ/A@ OA0ϥ/AlNA@_/A\MAa/A`ӽLA /A xKA /AĻJAɥ/A3JA/AIA姥/AgIAU/A ZHAP縥/AfHA@5/A &AȺ/AۗA//AA/AA>/AAF/AbAxGV/A$Aȇl/A$:A0و/AAL/AAP/AfAp/AAPο/ArA.ƿ/A@%BA`!/A A/A@ÑA/AquAP/A)$A@F/AʷA@“/AhHA/A`ƏA+z/ATAk/A 1AxKU/A`rA&/A@`AH!%/A A/A@cA0/A A/A3A([վ/A@TA ľ/AAʾ/A 8Aʾ/A A;/A`TA;/A"A;/AAӾ/A ZaA/A߈A0/A@A8/A A"A/AAP /AIA/AȆA0 /A隆AH!%/A[A0/A1A/Aj A/A (AX/A[A@о/A`A/A@CAx霾/A@dA/A`̝AXe/A&A(hR/A^CA//AvA/AjA/AaFA ݽ/A`9A(mƽ/AAR/AЇA}/AЇAȩy/A@AW/ARA?/A0Ao7/AjA'/AfAPM/AA/ALAd/A A@/AwAPޑ/Aa݄Af/AjAX/AAЦR/A^A:/AA*/AA/AAO/A`A߻/A AȻ/AڃA޶/At΃Aƛ/AAgx/A7A@a/AA X/A ZԄA~J/A +A/9/A`A3/A@#AX0/A@aA*/AA/A 8A/AOA/A A*/AA 9/A lŃAL/AAQ`/AAPw/A_A8/A"qA/A_A/AJA8g/A@@AXtл/A YA8/ASAXW/A:A /AA?&/A 4A8ZD/A"AlY/A 4A0we/A'wA؁q/Aq˃AGx/AAт/A CAX5/A@+Apȼ/A CA@/AdA/A }A/A/APj1/A`mA88A/AA88A/AjAI/A (APT/Aj AhUb/AdAu/A`A}/AHAԩ/A@`"A5н/A@p4AH/AOAX/AyAj /AA0;/AAR:/A@dA0.Y/Aj.AHw/A(A@`/A旆A/AjAUϾ/AfA@p/A` AP/AjA(2/A`7A NX/A@:A#q/A4AP/A@Ax׫/A=A,ÿ/A`jA0{Կ/A`ApN/A ZAS/A$tA/A\Ax) /AA /AAp/A JA./AAf/ApA`/A@ AP^/AA/AjYA/AAH/A`̤AC޿/AkA8s˿/ABAܱ/A`~A/A~A_/A~APH/A`~Axh-/A~A/A+~A/Aj5A /AzGA/ABA/A ~Ae/A~A;/A~A/A~Axm/A~Aي/A~A |/A΃~A0u/AY~Af/A@,~AȤN/A`>~AX9/A h~A 2/AӉ~A,/A@~AX9/A=AX9/A@PAH2/AAE+/A`̤Ah@%/AٳAh@%/A#A)/A@`MA//AzA="/A@A/AAHm/A cPAX#/A sbA /A`A@/AA`޽/A sbAݽ/A@,A/A AȔ/AɡA /A zA(`/AZ#A/A (~Axzս/A@~Ag/A~AE/A@~A{/A~Aa/À~AK/Aw~A5>/A@S~A6/Aޕ~A//A (~A $/A@NAW/A`~AG /A~AH/Aޕ~AXѼ/AJ~A MǼ/A?}AOʼ/A}AH/A`}A2/A}A؋/AP}A?o/A&}AlY/A}AЦR/A l|AC/A \|AH4/A}A#/A`}At/A Z|A/A` |A/A q|AP޻/A@Y|A0%/A@Y|AL/A`V|A(:/A`/|AXdu/A{AP [/A{AhF/A{Ai2/A)|A'0/A{|A/A}A(O/A_}AH?/A}A/Aj~A/A V~Aa/AӉ~A /A~A$/A~A/A }~Aw/AD~AP~AϺ/A)~A/AM}An/A}A/A/}A@/AI|A/A 5|A/A@{A/A?{A/9/Ao{A@Wf/A&~{A4/A){A/A2{A7Ի/AD{A /AD{A?&/As{A0=/A{A*W/A#|Ay/A _|A@/A@ |A/A"|A˼/A4|A0j/A`D|A/Aq|A/A}A88A/A5}AhUb/A`S}A{/A_}AE/A@h}AZ/A }AĽ/A}A/A}A0/A@}}A/A M}A/A5}A/A@y|A,/Aa|A?/A O|A p[/AQ|A8 x/AQ|Ax霾/Ao|AH/A l|A/At|A/A}A`/AJ}A؟#/A`e}A3:/Aw}A@FO/Ab}Ac/A/}Aq/A}A/A}AH /A}Amſ/A#}A/AG}A/A}A/A@E}A`1/Ad ~A/AM~A8(/A` ~A(//Ag2A=/AɡAcK/AqA0R/AA6a/A[kADp/A%A`o/A݂AȺe/AhAxc/AA!I/A*A2/A|A`1/A @Aؚ/AAX/A;ԅA3̿/A_Aⷿ/A OAH /A`Aз/A A({/A`;AX[g/Az@A(S/AשA0 V/A&A8t/A`AC/A 万A/AA/ApXAVa/A෩A@>F/A`APx?/AhsA7/AA//A)OA./ANJA//AA;C/A@%mA S/A`mAX[g/A APky/ABA;/A@5A/A`mAp[/AA,ÿ/A iAܿ/A&A/A s A/A@TA/A A/A AA 4/A cA"/A{A7/AʓA@G/AɏA8b/AAV/A`ُA/A@AD/Az9AX/A&َAf/AlAx/AxA /A@,A/A~A+/ATA/AA@./A@A7$/A`BA/AǛA/A`vWAX4/A3Aw/AgYA #/AA2 /A@PAH /AR;A/A@A /Af?A/A%AH/A *A8#/A@VAx/AߥAb/A A _/AA@/AAX9~/A}App/A`]A0Ȟ/A@A/AfA8D/A=A6/A`vA`G/AGAP/A Ah/AA/ARvA(/Ah8A/A A-/AV~AC/A~AE/A~AأB/AQi~AP9/A S~AH)3/A}A8:/A}AiK/AH}A\/A`}A"j/A`o}A5s/Ag+~A|/A/~AHn/Ag+~A/A@~A0/AF}A c/A@}A@B/A }AP/Aҿ/A EwA𣧿/A`ywAk/AvA!U/A?vA F/AvAX9-/A vA(h /A`vA5/AvAھ/AvA¾/AvAS/A`vA\/A`vAn/AXvAV/A4wA((5/A3YwA, /AcwAȥ/A]wApJ½/A_2wA/A4wAXV/A@ wAѳ/A AvA0/A`vA[/A{vA0/AgIvA?/A]-vAxK/A /vAP/AMvA,u/A`;pvAm/AvAwk/A wAlO/A`ywA`P9/AwA !/A@WxAP/A /xA/A`2wAH߼/A wA$ɼ/Ap*xAh̯/A AzxA /AyA`/AXyA/AxA(O/AbxAgٸ/A`UxA@/AxAը/AuxAHC/AxA/A@wAՆ/AtlwA*p/A əwApO/A@wA+,/A wA/A_wAٷ/A8wAO/AvAܷ/AhivAO/At\vA/ABvA֘/AvA/AEwA/A@wAtx/AxAPQ/A@wA:/A_wAD$/A@wAX/AhvAѶ/A8wA88/AgywAP/A6wAs/A6wAs/A`[xA@uh/A`[xAj/A`B8yAx\/A /nyAN/A ZyAL/A`jyAXuF/AxA4/A`[xA/A[wAP/AwAֵ/A ;xADе/AxA8/A@xAӏ/ALAyA8Q/AլyAh]r/AAHzA Ej/A@NzA8U/AqzAK/AzA93/A zA/A4zA0vд/A`)bzAE/AozAu/A`|zA`vj/A@5zAh/A AX{AW/Ak{AhK/AzA>/A@rzA@/A syA9/A 1yA/AxA/A`oxAjͳ/A OxAj/A !xA`/AbxAR=/AixAxz%/A ¹xAk/AEyA/A yAp*/Av.zA:/AGzA8 E/AH{A&O/A|Apq/A|A{/A u|A摳/A|A,/AI|AU/A J|AXг/A}A/A J|A/A \|Ah/A T|A`!/Aa|Ah/AW}A/A`K}Aڳ/Aj~AO/A | Aɇ/AمAU_/AA&O/AHA`C/A OAP8/A BA0"/A" A/A A /A`Aa/A]ًA[/A ҳA[/A]AX#/A_AA/AH@AK/A@A>b/AcA~/AA8/A5AX2/AֆA.g/AϐA(/AzAP//A@5YA@/A IAu1/A0A f/AzA/A cA/AzA<(/A`vA^/AAk/A`A(/AAʷ/A\A`/ANwAp !/AIAO/A9ӔA@>/ATAĸ/A 'Ap/AЖA?>/A@zA8l/AkA\/A Aѹ/AA˹/A$A(/A A /A 'A /ATA@/AĪAQ/A@PAX/A ~9A'6/A}A'6/A}.AX%/AA'6/ALAd/ALaA9/AzqAѹ/AߐAh/A@FA./A A͹/A sA/A3AV/A`vAxt/AzAJB/A אA/A@Ay/AKA~/A`vA/A`KAX9/AnAؼ^/ARAn/A`A/A؜A@ /AzA3/A;]A$/A38Ah./A@ʉA/AAO0/AɅA/AAX/A@"Ap%/A{AZ/AA@d/A}A(/A A{h/A /A/AA/A@AXc/A@A(/A (UA{/A@A/A@#AP&D/A@.TA{/AIAXִ/AA0n/A (UAp /AA/AbA /AzA/A 8wA@q]/AsA&/A'ŽA/A`AE/A A/A=<A(/A޶AX4Q/A;A$/AԑAȶ/AA/AAR/A`KYAp/AA}/AڱA@/A~APr/A`2ApAZ/A@AM=/A`)\A*/AX”A!/A@(A/AA޿/AhA/AhA@$/AhAhr/AAxS/AAH;/AǰA /A`A /AGA/A Ao;/A?֕A@q/AA _/AwMAHa/A@yApt/A'}AM3/A'}AQ/A@yA 0ؽ/ArAX諽/AOeA`/A Aro/A`AP/A/!A /A_Aq/AvA/AgA( /A'A`/AAf۷/AAҷ/A@!A/AbA/AI3A/A:Aԙ/AzA/AA/A_Axhķ/AAx/A@A/A Aذ/AǰAj/ApbAY/A@yAT/A AA\/A9A_]/AڱAH/AUA( /AAx/A@#AG/AA]/A`Az/ADAEg/A:A(L/A:AX /A`KYAp/AtAp/AA&/A\Aw/A sAA8R/A+A@O1/AAh/A`;]A5/A`;]A(o/A`;]AH\:/AAA/AA/AA6γ/A`A⾳/A@W(A)/A ֎A(y/AߕA #m/AH,AX9J/AتAh /A Ae/AAԌA /AA`v/A3A=Ѳ/A@BAh/A@BAؽ/A`:AТ/A A/AA/AWAH/A jA/A@.TA0{/AA /AגAPF/AwAd/A@ A(z/AΕA|/A`AJ/A іAٳ/AabA/A` AE/AKA@/A@A`/Af;At/A@5nAho/A ApD/A<A:_/A`<A@/A ƟAx:̵/As_AZ/A_1AY/AqA؍/A$A/A`yAA/A "A /A`dAPƷ/AAP/A`bApr/AèA$/A6A@1 /A0AH޺/AzyA /A쩫Ax/A`TCA7 /A`A0X/A@pA|/AEAd@/A@,A/AA/A A/A'A/AoAy/A`[IAKhz/A AZw/A ഊAPEt/AA@}m/AA gp/A|ØA0p/AyuA6r/AA@ϕr/AOAPt/A`;״A'w/A8A@¿x/AAy/A`[IA@/A A/A@RA0/A@ϛA q~/A@\A@/A (CA@/A A0!0AtAC0A uAH0A HAl0AfAt0A`LAܨ0A&^A)0A@#,Au0A`[Al0ALAt +0A AP70A@EA\D?0A2AvH0A AK0AlADP0Ah#A@]0A@A3b0AAl0A~Ahr0A5ATA0AA0A@#Adl0A`mA0A@#A|0AAy0A mA0A`OAl0ACAK0A62AT0AA~0A A)0A஭AF0A A0AvA] 0A[At0A[A"0AA%0AhvADk(0A@LAL$00A@Au10A` bAL20A`A80A`ðAC0AAG0AxAH!P0Ah@A\0Ac-A̔_0A Ali0AAm0A`Aȶt0AAw0AAh0A|dA0AaA0A`ܑA0Y0Az1A<Ñ0ATAp0AA 0A`A0A zAHp0A`EA\0A`Aɏ0AA0AךA0A@A$0A@~A0AqA0AdAJ0AFOAh3|0A@!Azx0A Aw0A;ALv0AA<=u0A?AAdj0A@.+A|0A"A40AA 0AXAW0A@.wAd 0AAB0A@.A0AA'0AA!0AHAZ.0AyAp0<0AGAXnJ0A AsZ0A@A(~l0AA\0A@PA\0AA&0A6AT0A٭AL0A Ap10A`NA@B0A(uAt0AWA0A ޹A0A]A0A AY0Aj(AX[0A??AOp0ARA\}0A QAA0AaA(0A $A|V0A(A(0A JAM0A`]ATh0AJAt0ARA, 0A` <At 0AЅA`P 0AACxT 0AA|ыA\X 0A3AGy-_ 0A$=A7e 0A,rA&k 0A2A{<q 0A8`A‰cw 0A4'1Aݠ{ 0AܒpVAhU 0AX:JA  0AxA- 0AWA-Z 0A~ýA!T 0AhI:Ar 0AA~N 0AKA 0A`0AN+= 0AO?XAl 0AQoANw 0AskuA 0AiAr 0AodA 0AQA 0A?A4 0A$A\ 0A^APk( 0A` A2 0A A7; 0AAE 0A@>AAܦ 0AzDA[ 0ANA 0AT6A& 0AA/ 0A A9 0A AC 0AIAM 0AhAT 0A AY 0A@.A8e 0AIA k 0AA#t 0A lA5{ 0A A@ 0A;Aƌ 0A@A 0A@޼A\M 0AɼA| 0AA0{ 0AA 0A`wAt 0A 2A޼ 0AfA8 0A sA 0AaA 0A`)@AP 0AtSAP 0AgZA| 0ABAT 0AYAHE 0AwA 0Ah}A 0AA 0A<A 0AA 0A`9A% 0AA8' 0A`Ax|2 0An¼A: 0AԼAC 0AA0H 0A A$O 0A$ALS 0A ؼA[Y 0AA1^ 0AkAl` 0A@CA0b 0AAb 0A@ԼApc 0A@AdRf 0A``Ag 0AzAp 0A 8AQz 0A@ǽAL 0A@нA< 0A½A 0AdA 0A`ouA 0ADA$W 0A A( 0AܼA֫ 0AA 0A SATc 0AAL 0A(ٻAy 0AAp= 0ALA 0A`3A 0Ax5APm 0A`AA 0AuAN 0A൅AH6 0A`AL 0A9Am 0AzɻAs 0Af{Aɀ 0A@%A| 0A@ƖA` 0AAS 0AA$ 0A<}AXh 0Aq`Aǰ 0A&WA9 0AUA+ 0AYAˠ 0AFnA@ 0A`2ĽA 0AjAp 0AA" 0AaCrA`z0A'򺦻A1WF0AXwA 0A A.V'0A A0I/0A+R$A!70AGS%3AЉF@0Ax6A/aH0A-AQH Q0AUA[X0AaCA_0AѻAd0A֋ʻA6j0AMKȻAȐo0AwDͻAPat0AٻAky0AE*eA~0A`KAM0A Aŧ0A[nAf0A~A]0AzA $0A{ALf0A`ÑA0A`A]0AA0ALAt0A $A'0AsA0AgAĀ 0AAt0AEAz0A OWA@40A2A4&G0A wALlv0A`?A`0A`iAT0AHA-0AEA40AA0A=AHv[0AwAp0AA 0AٝAQ0ApAhl0A FAx)m0A5}A0AG xAo 0AgA 0A &[A 0A~6A4 0A@4AX 0A@.4APϡ 0A4At 0AjI2A- 0A@3-AH 0A ,Af 0AK,A 0Anߛ,Aze 0A-Aj/ 0A;*-A. 0A-A] 0AI-ASm 0A$D0-AƠ 0ADžq-A(Ց 0A,Ao:1 0AR"(,A- 0A,As 0A&#­,A"ak 0A8h,AЁ 0A;,Aַ" 0A1,A2g} 0AȈO+Any 0AG_+Au 0A+Ab 0A`f +A(|a 0A*ALA 0A=*A 9dEC 0AGYcE*A'C 0A)A"Q 0A Q)A\R 0A)Ad9V 0Až)ATxe 0A*A i 0A`*A( d 0A@p*A_ 0A O*AZ 0A;)A(U 0A c)A\@T 0AF)A0N 0A@ (AG 0A)N(A D 0AG(Ah> 0A'AP; 0Av'A{: 0ARa'A\3 0AF&A1 0AF&A+ 0A ?&A) 0A])&A$ 0A@<&ApQ 0A)Ad!N 0A u)A|O 0Ad)A(uC 0A0)AQC 0An'~)AA 0Aa8)AL 0A$&A 0Aօɠ&ATy` 0A۲&A1 0A2&AYA 0Ap7&Af£ 0AԹz&A 0ABv&A 0A0et&AvJ 0Ak u&A{ 0AO-x&A: 0AB}&A8`N 0A%sJw&A) 0Adz{9&Aa 0A蜰&AC 0A7&A(o 0A`T&A m0A4D'AL̷0A?'AĒ30AL:(AE10AL(A~0A:w(Abç0Ad{.)A 0AMZ)As%r0Ar)AIt[0A8w)A0D0Ag)AX-0A )A1Z0ALR (A0A'K](A20AD'ARd_0A˵V'AA0A-&A,A0A5&Aܮ0A;g%A,0A@-4$Auһ0AQ$A<0Aܽ$A$d0A˛#AA z0A;3)#AO0A"AH0AJ"AN:0A5L!AMc0ABc!A2[0A-!A0AOLW Aq0A A0A< Au0AJ%U Akh0Aor AYڛ0A[d Ak0A5ՖU A0AUyE A0A6 AL0A[Aȼ0AAͳ0AA,+0A`AT0AAs0A@A0A`AWq0AMrApZ0A`cAY0AAf0A^qAf0A`]A0j0AAx0AAx0AN Ab0A@#$AU\0AnKj$A(T0A~$AK0A/$A %8B0A@%AR70Ao%A-0A"%Ai +0AU=%Ah(0A%Al&0A%ATL$0A!%AU"0A$%AwY 0Adx,%A#0A)A7%AA0A@ C%ATx0AQ%AN10A|V%Aړ|0A=%A7K20A] I%ARƁ0A׊&A40A &M&AN"0AC+c&A0Aax&A8b0ANZ8&A$&e0A &A00A׶T&A$}0A`T&A@90A&Av0A!&A"0AQvw&A a0AE&AZfP0Aŗw9&A0AFT.&A>0A$&A40Aeh &A10AB&A<00A(&Aps0Au.&Add0Af枞&&Av,X0A{&Aֳ0AV%Ac0A K%AXe0Aɣ%A?'S0AD%ARad0AT %A0Aw2w%A@ 0As_%AiS0A%A 0A7!s%AN20A-%AЭ0A(%AzI0A͂%AVAH0Ap-%AL̯0A%A̖00AP%AjK0Ao%A e0A҅%A-0A1mw%A|W 0AO%Ah0A_2%A0AL%A'0A/MV%A;0AW%A-0Aa9}%A|Ǹ0Aൠ%Aw0A٨%Aq10AӋc%A 0AsӤ%AP0AM%Ang0A~5բ%AV0A+u%A׬c0A"<%AbE0Aу?%A6q0A$O%A\0Aɷj%A^0A,҉%A䃿0A@%AUs0A%_Q%A };0Af"%A0A$AXY0A$Ao0A3]$A@0AYߔB$Amx0Aʨ$AXW0A@$Am>}0A=5$AjKw0A$ARq0A$Ak0A<$AD+j0Al;$AEr`j0A4`j$A7;i0A=(q$A@h0Aնa$Ah0Aa=l~$ADg0Ay$A,e0A! kv$AD&,d0At$A1c0A:lNGt$Azb0AY0u$AřZa0AT'6;x$AR'2M`0ALBb|$A#Y_0A@ہ$A^0A`$A\0ADЁŘ$Ap,6aZ0AqK$A B>W0AB$AdAU0A$Ae.~R0AWb$AO0Aʺ$A$8hM0A;%>O$AZgK0A$A?#I0A$AݤvH0A_$AX$G0A2gN%AƎz(G0A+%Aa*G0Avp]B%A̝G0A`X%AfB0AQ9%A};0A@i%Aq;0ANl[%AUr;0A%Az9;0A^X%AA::0A4J%Az4k:0A!I%AH\90A%A!690A%AY 80A{/%A'70Az%A;60AQJk%AnXG360Aar%AAAt50AM %A40AV%A_ <;/0AP-%AjT)0Aֳ%AJsq"0A}:%A~H0A|%Al0A@W%A#0A!SH%AR10A1&.%A0Ab%A•V0AdI^œ%A0Ac4%Ao0A2_F%ARS0A,%ABO/0AR%A8n0AoL%Aj)0A/a6y%A(0AGʿs%A. 0A)sm%A #0A;g%Aز+ 0A$ %%AD0ALX$AXl0Aþ$Apv0A?犄$AЬP0Am_$AP2#0A\RUD$AF0Aն3$A 0A.$A0A{i@$Aq0AME#A_=0A/#A90Ark#A.0A|2#AH0A"Ap0A\KW"AHl0A"AL&0A`l"A=0AHtD"A`0Als!"AN0A "A*S0AY!AP0A !AR0A苃!A8~?0AB0!Ag0APg!A秳0A"!A%_0AoYM!AY00Az$!ASI@0Ay!AV0A!AL0A@!A 0A!AHԜ0A4d!AD"0A;N!At0A@yr!A0Aേ!A#UR0Azl!A$0A)X!A{ 0AcS!AXD0Abl!A-t0A,Rd!Ay"B0A/?!A0A`!Ay|0A3]t!Ag0A !AFh0Az!AjP~0AR~!AR0A|!AP*0A9!A׫z0AlV!A$8(0A}K!A 40A !A|-60A0+4!A}70Ac>!A480A0A%!Avq90A!AsO:0AY!A;0Aݥ!A\h;0A Ǚ!AU r;0A&*u!Ab6;0AmE!AAZ:0AQu!Az790A3Vj!A490A&a!A70AY!AP/0A˱ A=AR(0A_7v j A"0A>IAN^L0A fAP^0AA}60AiAM0AnaAِ/!0A~Ae/'0A[ǎAT/0AA,30A A590A A|;0AAL&>0A'A0B0AAHG0A`AL0AA8'T0A]Als0A=Av0AAX0AmAlx0AZAQ0A`vA0A9A40AtA;0A`"AH0An!A20A+$A0[[0ABW%AV0A`Kx&AT0A Qs&ApR0A&A\O;0A~&A390As&AP\0A%A/0A`K%Ah 0Ai%A 0A&W%A0Ao$Al0A$A0A$A 0Af|$Ap0AT$A0A"AP0A"A0A O!Am0A !A<&0A!A<](0A !A"0A!A 0A!A 0A@ !A 0A!A0A"A00As"A*0A@,"A,0A`"A0A"A$N0A;d#Al-0At#A0A`]#AC0A |#Ax0A@P $A0Awa%A$0A@&A0A '&A,)0A2&Ay+0A&A890AZ&A<0A@&A8%D0A&AAL0A"'AN0A.'ApR0A`5'A,]0A|>'Aԩa0A A'AtDd0A`A'Ak0A4A'Acm0AA'A4v0A`)2'A8ޘ0Aa&Aӟ0A&A 0A&A0A \&Ap0AoU&AT0A&A0A &A0A@%A0A`BR%A`0A J.%A0A@L%AN0A@%A<0AA%Aԫ0AF%A60A&A0A`o=&A\0AF&A 0A &AԚ0A@&Al0A׿&A`É0A@&A|0A &Arm0A ('A _0A, 'A(s`0A]x&Ao]0A_z&A b0Aྎ%A̬0A s%A0AO$A 0AG$A0A $A0Avh$A\M 0A`$A| 0A$A0E 0AU%AQ 0Aq%AȾL 0A `%A0b 0A%A n 0A@%&A x 0AQ{&AJ 0A*Ap 0A?*Ahf 0AK+A< 0A@`)Az 0AXcA,=!0A= A=F!0A< A\dY!0AX ALV!0AT AIV!0AV A\T!0A qb A(mQ!0A[ A0 O!0A AK!0A@ AF!0A 6 ADiD!0Ai? A=!0Ac A4!0A@ A*!0A@ߟ A!0A ATp!0A A` 0A At 0A@5 Axګ 0AЛ AŢ 0A A 0A = AՄ 0A< A} 0AH A|w 0Ak At 0Aw At 0AAq 0A@Al8m 0AAg 0AAd 0A ( Atc 0Apn At&` 0Ad AW 0A A, > 0A A0[8 0A@ An$ 0Ay A" 0A0 A` 0AƲ A̽ 0A K AD 0A@G A 0AA 0A`AA 0AS A/ 0A@N Al 0A+ A 0Aʝ A( 0A@ A:0A A|0A`A AГ0A ^ Al0A ^ AP 0A(T AW0A_? AL0A` A0A A0A A܄0AY AT0A@> A0A S Al0A J AQ0A A 0A+ A0A@5 A40A? A}0Ag Aey0A`T Aȉp0A Ag0Aͯ Ax:_0A AIU0A A(L0A` AA0A AA0A,{ APE0A@5{ AZE0AAl Al;0A@,l A;0Aw A>0Aw A$>0A A(;0AR Al20Af+ A,0AF2 AI#0A@g= A}0A (F A!0A@L A 0AB A0A] A0A A@0A@g A䲻0A@g A0A0f A0A' A80A`] A0A _ A(D0Ao` A@0Aq A£0A@, A:0A J, A0A AT20A A|0A@r AFm0AO Adc0A+ A[0A9 AmT0A@E At5<0A A4B0A| Ap0A@ Aw 0A` A 0Au AA0A J0 APz0A+ A̎0A4 A0AF AT0A ~ A0AV A(O0A A40AF A 0A`C A a0A A0Ai A40A J AD0A@m A0A+ A40AU AhB0A~ A0A A:0Ay A|Ǫ0A@= A0Aw A0A@> A0A A0A A\uj0AF A a0Ao AȖZ0A AcK0A@ A10A A('0A[ AX0A' AT0A A0A w A0AK A0A@ APk0Ai3 AD0A A8G0A A^0A A$0A`) A0A Al0A C A,+0A@ A0A A܄0A& Ab0AL A50A`ʍ A ٴ0A`K A0Ah A0AHA0ATADkt0A JAXY^0A`A T0AA\C0AA%0AaAX10A Ađ0A@ A0A` A 0A`| AD0A ۦ A^0A A0A@ A<0A AL0A A0A A˄0AQ Ad|0A` At0A` Ak0A`o~ Ab0A@ l A`Y0Ahu A4R0A`T AAJ0A@ A70AR AH.0A At&0A A0A4| A0A`e A0A`G A` 0Af Am0A` A 0A AT 0A A( 0A@go A 30A?E A50A`0 Ax0AU Ay0A A<;0A Ag0A` A,s0A. A}G0A@/ A60A` A`0A AXy0A / AaG0A A10A  Axk0A A,0A Aڅ0A  Af0AA$L0AAhq30A@ϘALH0A`KA(/0A@EAlG0A`A(0A A0AADs0ARA\0AAR0A cfAZF0A@A|40A@A#"0AfwA(0AuA 0AqA0AjA0A bA0A`ZA0J0A@AA\-0AZ+A Q0A /Ad0A*Ah"0Aa~Az0A J'A`4`0A ˴A@0A c/AA>0A (AP0A`Aڨ0A`)A(0AÃ0AWAQ0A ,Ah>0AAA,Z00A`MA\*0ARA0A;YAp0A WA]0A-CA0A5AY0A"ALW0AAv0AAN0AA<0AAt0A@#Ax0A&A$w0AAz0A`A.0A`A90A AYw0A \Ao0AAL0AftA\80A`nA*0AiA?0A SkA0AqA 0A͇A8 0A؀A 0A`{A^ 0A@yyAH 0A~A 0A`A䥞 0AA 0A@pA 0AA} 0A Ahk 0A`ôA2c 0A A|[ 0AAT 0A*A@L 0A@`HAC 0A\A: 0AvA|0 0AZA( 0A |A 0AA 0AiA 0A`A 0A@G A 0A <Ae 0A SA 0A@GfAx 0AzA 0A@,A`c 0A6A 0A@A4 0A A4¶ 0A6Ad 0A`TA7 0A`A0ً 0A Aq 0A@Ai 0A[AZ 0AՈA`Q 0A;AI 0AA; 0AAL2 0A]A* 0A (AD# 0A@>A 0A IA B 0A NA  0A`KhA 0AzA 0A?A4 0AhA 0A@,AL 0A@`Ah( 0AF"A$ 0A`TKAL 0A nAQ 0AfA 0A ZAn 0AHA|?c 0A@A|VQ 0AQ ApH 0A@>AA 0A!A9 0AQ5A$&& 0A` BA 0A@,EAx 0A@iCAz 0AbA4 0A A s~ 0AAi 0A>ANV 0AALLe0A`KAw;0AAPš0A %A0A@As0AA0AOAo0A-A0AA0AAT0A`KVA@QB0A2A0A`AP\0AJA|t0A@Ad[0A@2A0ArA}0AoA80AA0A@ At0A47A0AKAzX0A APz=0A tAs!0A@OA0A_ Ah0AAt0A[hAxL0A cJA0Ah-A20AAy0AAp0A9GAL]0AAt0AADX0AAqG0A@5A]/0A (lA 0A`TA//A`dAp/A c5A8D/A8A /A%A/A Axz[/AA /AoRA/AVAk/A`A/AANt/A`qA /AA /A A@/A`AC/A7Ae/A`KA/AA/AA#/A@,A /AכA/AA0e/A ɒAQ/A٫Aq/AA`/A@APM/AEAZ/AA(/AAPh/AA]./A@A( /A0A/A A&/AAA/ATA/AڹA`/AA'A/AxA4&/AlAO/AgAP8/A mAht/AZkAho\/A@dAHL/A\Ah;/AsPA@/AA/AA/AA`2/AZA/A@Ac/A@AJ/AA/A@A/AzAP/x/A&eAd/A`JA`BY/A_BAD/A=As./ADA/ATAأ/A`AO/A@A /A A /A AP/AAX/A@`A ~/A^uAke/A@FA0/AjAXW/A A0/AzA@/A@ mA/A]jAX]/A3MAt /ADAN4/A39A/AL)AH/A?AH\/AAp+/A\A/A@ BA/A/A p/A/A/A`8A83/AcRA8x/AAH/A Ah/AadAp/AA /A@A0/A A[/A A8/AA /AA-/AApM/AEAw/AA/A4Az/A;"As/A`A_/AAR/ARTA[=/A"A#/A|A0/AAXV/A SA8/A@`A@F/A-AV/A>AK/A`9HAG/A0KAo/AaGA`²/A<Axˎ/A0A U/AA"=/AAD/A@AP/A` VAJ/A AsV/AAXy/ABA/A@ A0T/ApA0 /ARSA*Q/A@A /AAC/AfAН/A@@A8R/A@A0 /A6A`/AʘA8/A@sAr/A\A`/A|YA(4/A@rA"/AkA/A` LAj/A2dAe>/A?A_/A A/AEA0f/A?A~/ARAU/AzAX#%/AqA/A@!APk/A`[vA_/ANAo/A]$APf/AFAl/AAZ/A[A./A`TiA/A (A8/A AH/A@rAxƕ/A`T=AȘu/A@AXS/A AP0/A A@/AAP/A஼A@/A`AUS/A`2|A8*/AňA/AzA0̭/A0AX/AA<A/AHzA|/A@[A#G/A1A/A@Az/A@yAr/A㓿A0/AfA /AIAiA0H/A9AV/AA@/A@ A /AAȱ/A}8Ap/AXRA旮/A ZmA@F/A AkAHM/A`OA/AdAX/AܖAX/A@AO/A0A$/A`MAK /A(Az/AA۬/A`A0/A@ҔA q/A֬Aޫ/A_AX U/A`)AX A/A`)A/A (AA8t/AZAPYB/A =Axw/A 1uAZ/AmA@W/A`Aث/AA /A#AB/AA3v/AAH-T/A@~A0{5/A4pAX/A[A8R/A;A@Bۧ/A@Ahݼ/AAXV/A AU/A@vAA/AjA8$/AA /A6APϦ/A`$A@/A An/AA`r/A@،A/A'AΞ/A~AW/ACAX/A ~Apٝ/A`A؎/AEtA/A@gˈAP/A ZyAG/A^\A/A@־Ax;/A jA/AۅAPd/Ä́A`2/A,A/AOAY/A`A/A΀A8/A@!AxF/AAI/A~Ap/A ~A F/A+3}A/A {Aȃ/A S{AH/AqyAȩU/A yAّ/A wAP/A׌wA]/A7wAא/AqvA`G/A5xtAf/A tA/AtAu)/A qA؋/ApA]m/A`OpA=/ATpA0/AoAxъ/A`=oAS/A@ nA /A`)kA/AMkA(z/AiA8Vs/AhAX/A0hAA/AgA/A fAh̅/ACfAsQ/A`dA}܄/AdA0߃/AbA/A`AH/Am_A +/A^A /A s\Ap/A@[A߿~/A;ZA}/A`[zYAz<}/AXA}/ARXA8P|/AWA{/A`VVA֍{/AHUAHS{/AH^UA6{/AOJUAx{/A J>UA0z/AF:UAјz/AOUASz/A`T9UAz/ARTAy/A{TA([Wy/ASAx/A@`RA0nx/A |RAȓx/A^QAw/A`2QA7w/A@5:QA@v/AgPAyv/AAXJm/A` W>A2_m/A>Al/A=Al/A<Ae|l/A`]<Ak/A@8Aj/A@gw8A`i/A`5A i/A4A i/A3Azh/A S;3A@Bh/A`(2Ag/Apl1A/ g/A@u0A7e/A`è-Ac/AD*A+a/A&A7`/A 87$Ap^/A Au\/AnAHZ/A`DA(FY/AApYW/A Ax4W/AdAHU/A`AS/A- ABR/A` A Q/ARAQ/A ÄQ/AA0aQ/A A@=/Q/AcAP/AxAP/AAТaP/AAP/A'AxFO/A@AНO/A |=AzO/AA(`O/A)AТzN/A`KA4-N/A`×AM/AA xM/AqZA)M/AA~L/A@AylL/ATA/A`vuAP 5>/A@AB%>/A`]A=/A`26Ax:=/A9AG:/A`fA07:/A@Ah9/A@ZA(9/AA>=9/A c%A8/A SCA6/AA4/AfAH4/AEAk4/AsA%4/A (~A)3/AŔA 3/AxAp/3/AAȂ3/A A`T3/AEA3/A2A3/A=A`S}3/AAdw3/AAxq3/A@AP3/AA(\2/AA2/A`fSAd2/A`KkAxK 2/A`A`1/AAv1/A@>Ap 1/AE A<1/A?}A 0/AaAHn0/A`kAe//AA Z//A8A//A JA./AڟA@B./A`̖AH./A@AI./A൅A0-/AtoAh-/A>APP-/AAh;-/AAHP-/AdAЕ,/AN Axi,/A A@,/An>A+/A@P_AoW+/AgAP$+/A A*/AYA%+/AApΩ+/A@5A*+/A #A E+/A A8c*/A@TAj*/A`DA3*/A`@AP*/A`DAq*/A&IA`Ge*/AAA9*/A"AXW*/ApA)/AּAp)/AqAo)/A`"A@Q)/AAk8)/AXAHz)/A}A(/A`+A(/A` AM(/A`A@(/A AK(/ASAX[;(/A@,3A(#(/AA(/AA7'/A`A0L'/AA֕'/AAxv'/A 8`A-D'/AAP /'/A ͽAXV('/AiŽA@B'/AAQ '/A ½A&/AǽA =&/AҽA&/AA,&/AA&/A`νAj#'/AɻAppy'/AwƸAn'/AdA'/AӶA&/AA2&/AnAp&/AAAz&/A`Aج%/AzIAj%/A A%/AAVC%/A CA $/A`Az$/AKAT$/AA#/A+A#/AzA@`"/AAH"/A`fA`"/A9AtI"/AཱུA "/A害A0Y#/A`KA@ #/A`yAy"/ADAh?"/AA0f<"/A`oAH)5"/A@A0"/AjõA!/A"|A̧!/A.A w!/AܴAhQ9!/AzWAV!/A,Av /AӳA /A_A /ACAp= /AֲA() /A 1A k/A Aђ/AA7/AIA2/A֭A` /A`;A/A4jA@/AAE/A)«AТz/ARAo/A A@/AUAk/A`A/A@AP/AجA/A`A( /A|AHP/AxsA`/A A3/A`̳A`/AA/A íA|/A߿A_/AAA/A@Ax)/AA/A@ A(`/A`;iA0/AVA/A`;-Az/A}Aog/AŬAh7/AjA(/ALA(/A *A0/A@rAP/ARիA(/AEAP/AwAP~/A`7Ad/A AI/A JԪA!/AAh/Ad?Au /A@%A#/A`A/AF9A/A@*A/A-Axư/A٩A,/AĩA/A`A_/A`T]A H/AR7A$/A@A/AרA/A qA@/AA/AzgA/A;A@/AdAe/A JܧAG/A@ߟA@/A;hA</A`2ަA|/AaA0 /A?APIy/AAXt\/AG,AG/A@AXu!/AA/AЦAz/AĦAX[/A@>Apy/AVA8/AාAP /AgAf/A;AK/AfA8V?/A`A-3/A`AP (/A@ ХAp=/AgA /A`BAu/A`Ah/AqAQ /A AqA/AgA0H/A ZdA؎/A AWA`a/A6A/A %A֮/A@A0/A@A[/AפAP/AAl/A6Ah]/AT{AxzN/A`_Ah=/A@A,/A!A/AHA /AߣAP8/AॽAL/ARA/A@yiA/As6Ah̬/A A`G/A`o A(/A2Ax-y/A#ǢAč/A@A9/A աAp/A` Af/A2AHV/A APB/AൃA,4/AtA8/A?WA/A@?A/AAp=/AApp/A`)ϡAQ/A 뼡A/AAX۰/A5A֥/AjA0G/A`WA p/A*7A@/AhAw/A`oAo/A@ՠAf/A A]/AAT/A AM/A5rAFF/AZA7?/A@EA8./AA&/AA/A`)A/AʟA[/A A/A=Ae /A@A/A`8A/Aग़Au/A@pA=/AAA/AnAhQ/A#XA/A@$A/AtAB/AA(z/A̞AТ/A APv/A+A(n/A A`X/A`nAO/A S]A R6/A'Ae-/A|A'%/AA/A A@/A@۝A /A ƝA/A A/A`AXF/A@PAe/AoAX /A@[A`/AqJA/A@;AH/Ap$Ap/A`A/AӜA/AǜAp/ABAf/AAxK/AAQ/A+}AH/A@5AX/A A /ANAPk/A@yݚAU/A|A(/A !A /AzיAP/AcAx/AAPy/A]A7/A?Ao/AuA/A_A``i/A`KrAh@/AA/A A/A (A`eJ/A+A/ARIAP/A` DA/A ;AH/A 0A@/A^"A`/AHAѻ/AA/AA()/AڒA/AɒAX[/A@yA~/AAkw/AjAp q/AjgAi/AIHA`\/A)'A#U/AAtM/A@yAE/A` A>/AKA6/A^ΑAȓ./A`A#&/A3A/AٚAB/AjA k/A`|AP/AVmA@ /ATA' /AHA /AT3AL /AqAX /AA /A@A /AːAo /A AAК /AAo /A}Ax) /A`;iAHLy /A@`PA,l /A5A_ /A A`S /AAL /AfA7K /AAI /AHԏAH /AAK /ARAO /A jAAT /A,?AU /AAGX /A ŎA X /A6ŽAp0U /A@ƗAL /AyAؽ> /A eA7 /ARgAH, /A JjA# /A A /A3ȎAs /A A /A#A /ATAk /AFA /A問A( /AAp /AAQ /A偏A /A`}A@ /AjyA /AqxA /A`wA /AsvA( /A^nA(| /A}gAhs /A@>aAj /A XA`a /AqPAV /A'GAxzL /A=AxzB /A,5A`B9 /A`-AA0 /A'AX& /AA /AUA` /A@Ap /A|AG /A`[A /A@#A /AAx /AA7 /A`Ap /A`A /A`ێAP /A͎Aho /AŎA /AॽA̎ /A츎Au /AA} /A?As /A`KA@g /AA^ /A*AV /AAXVL /A@,AjB /AEA8 /A@AP1 /AzA ) /AnA /A?[A /A|GAF /A2Ap /A Ao /A A /A6A /A@A /AٍAG /ApA /AAP /A6Az /AQoA /AMAV /A +Az /A  Ap n /A@Akb /A@ȌAU /AA RG /AA? /A@vA@7 /A (cA/ /AOOAV' /A@<A /A-AP /AA /A`AP /AʋA(- /AзAu /A 8Ax /A?AX /AzAH. /A@rhA0L /A VAt /A3DA`Ǿ /A1AV /A ZA# /A# A¨ /AA@ /AA[ /A qԊA /A |Ay /AЍAm /AsA,_ /A|UA(W /A`CAPO /Az1AG /A !ApA /A6A(4 /AA(( /A?߉AH2 /AʼnA /A 䧉AH/AFA/A vAX/AcA0/A@5XA /A`NAp/A@GAp/A*A{/A(A/A@&A/A`DŇA(/AAI/A A/A`tA/ADvA/A;xA/A`A8D/AMA/A S͇Ah/AՇAx/A2A͓/A?A[/A`ˆAǀ/A@ψAhy/AԈAWo/AԈA)f/AԈA[/A`҈A O/A҈AC/AgшA7/AψAB./AˈAa&/A@ ƈA/A@5ˆA( /AA0/AA/AؾA/A A(/A ˆA/A`]ˆAh/AfAh/AA(ܳ/A"Aأ/AĈA/ÄA /A ՈA7/A[؈Az/A?ۈAj/AhAH_/AAhS/AAHE/A@,A0{8/A`؈A2/A ψA0"/ALjA/A`2ƈAV/A`A/A`AH/A3AP/A`A/A@iA؞/AGA/A?wA/AI^A-/A@iCA/A+Af/A`2A[/A AQ/A A/A҇A/AٺA:}/A^Apx/A \Ap/A׊Af/A AqA ]/AVA8R/A;A"G/A A:/A`A./A A["/A@yÆA/A@`AG /AA`/ApbAV/AAA/A`""A/A@A/A(Ax)/A AG/AŅADz/A`Apۥ/A ˁAۘ/A`]A(ܐ/AKAˈ/A@8Aj/A&A`w/A`Ak/A`Ah_/A A0T/A@>̈́AH/A̷A;/AVA//A` A#/AxA8 /A*KAx-/A3A/A A(z/A JA/AA/A`׃A#/A ƃA /A A/A좃AX[/A@Aج/A A~/A?mA8֋/AYA-/AEA py/A4A((q/A]$APi/A`Ata/A@AS/A APG/A@>قAQ;/AÂA`//A@,A%/A@A3/AA8/AtA8/A[A/A`>Ae/A/A/A`] A/ARA`/A JAx/A@A/AցA0/AA(/A ۪AH{/A A/A uAp=/A`vAx\/AbA[/A PA/A}1A~/A|Aos/AdA88g/A AX/AT؀AXg/A`KPA 4w/A@Ax/A ˺AZx/A ۙAkr/AjdApf/A@AAY/A <A Q/A`OAL/A`9AȲD/AVAE7/ANjA؁3/AsA%/AA/AAH/A A/AAL/A@A(/AfAx/A |AP/AtA/A@kA6/AHcA/Aj[AP/ARA臟/AjHA9/A>A(:/AW9A0./AQ/As/A'Api/AQ$Ax`/AA(W/AtAN/A# A"C/AA8s5/A?A`'/A`AH/AAD/AA/A@AHK/AײA/AAH-/AXA/AtA0/A@AE/A yA@q/AkA®/A^A/A;RAȺ/AEA/A <AXh/A3A z/A$A(q/A`)Ah/A`mAh_/AAV/A`[~AvL/A,~AhA/A~AT6/A~AQ(/A~A /A~A/A~A! /A ~AP/A@o~A`/AyA@.AyAh..A yA@= .A=yAo.A_yA.AyA@p.A@yA.A@yA.AyA.AyA.A yAP.A@yA.A yAێ.A`yAP.AayAw.A=yAxzm.A`yA d.AHyAP8Z.AyA'R.A`oyA`M.A΋yAx?.A JyAp3.A`yA8*.AyAxu".A|yA8V.AwyA .A@>qyA.AgyA .As^yAH.A WyAP8.AJyA.A J8yA0{.A)yA.AyA୿.A@ yA-.AxA =.AxAV.A6xA.AxAXۗ.AxAТ.A xAأ.AxA`{.AfxAr.AQQxAأj.A;xAa.A$xAW.A xA)O.A`owAE.AwA;.AwA(4.A`wAX(-.AwAF .AiwA.AjwA .A`wA.AmwAf.A\wA.A4FwA R.A q2wA.A wAp.AwA.A-wA.AdvAL.AvAp .AvA@u.AvA؎.AvAe.AGvvA0.A@jvA.A[vAw.AKvA#g.A %vA^.A3vAaV.A`vA(O.AduAHG.AhuA@.AuA1.A6uA[%.A`uA(.AtuA .A ~]uAo.A@ BuAA.A3,uA.AuAX.A`uAPj.A^tA.AtA.AftA.A3tA@.A`]tA@`.A^tAH2.A3tA.A"tA@`.AytAxu.AitA(-.AjYtAج}.A`2HtAt.A^8tAhk.A&'tAb.AtA(zX.A@tAO.A |sAF.A` sA >.A (sA؞8.AsA10.AsAA%.A@}sAP.A`bsA(z.A}MsA = .A9sA8#.A@isA.A`2sAQ.AorAX.A`rA-.ArA.A rAȓ.A־rA[.AնrAK.A_rA.A@اrA7.ArA.ArA.A QmrA.AYrA7.A?rA|.A \*rAv.A<rAPo.AqAg.A`qAk`.A^qAW.AqAR.AqAaG.A`KqA@?.AzqAx5.A`;uqA,.A^qA0j&.AGqA8 .A .qAf.AqA.A@pAhs!.A@oAr.A`ToA_.A=oA@.A pA8.A JpA`.ApA؎.A`pA.ApA(z.A*pAv.A ApAHa.ApAP.A`fpA .ApAs.A`pAh.ApA0L^.A2qAT.A@qA K.AqAA.A_qAx6.A<qA-.AqA$.AqA.A@qA.A qA#.ApqA@.A@!qA[.AqA`.A)qAp.A A qAj.A qAТ.A`qA.AtqAF.A`[qAXW.A,'qA .A@-qA.AqA8Ҳ.AqA.AOpAp=.A@pAf.A pAё.ARpA.A`pAp=.A`fpAn.A pAe.A?pA`\.ArpApS.AfpAI.AXpAjA.AMpAB9.A3BpAo/.A`f1pA'.A $pA.A6pAx .A oAp.AoA.AHoAh.AoAV.A`ժoA=.AzoA.A`oAh.AtoAV.A`oAF.AJoAp[.A >oA R.A@#.oA[.AoAۍ.A oA ҄.AnA@|.A nAGv.A@nAl.A^nAd.A nAX.A nAHM.A ynAC.A|cnA(z:.A2PnA@1.A@>nA).A30nA.A qnA..AnA0 .AmAP.A`mAt.A mA.AηmA.AࣧmA`e.AmA.AmA0.ApmA8.A^mA.ALmA .A?9mA8.A#mAU.A@mA.A 8lA@`.A@GlAxu.A (lAф.A (lAP}.A lAu.AؚlAh3k.AlAQc.A ulA8Z.A clAQ.A+SlAE.A?lAL;.A.lA2.A"lA8*.A`lAp".A lA(z.AQkA.AQkAX.AkA`.A@`kA.A`)kA(.AfkA(.AkA.AQykA`e.AkkA.A@akA[.A QkA.A (?kA~.A ~#kAp.A kAH.A (jAV.AjAa.A` jAj.A@PjAX׆.A jAA.A sjA~v.A`~jAo.AfjA(-i.A`PjA a.AZ6jAj[.A |#jAWU.A jA؎M.A6iA`I.AViAz:.A siA[1.A`iA).A iA`2 .AiA.AiA .A?iAp[ .A_gA.A TgA.A HgA<.Ap2gAƦ.A JgAt.AfAਔ.AfA`.AfA&.A]fAX[.AffA.AФfA(.A_fA v.A`fAH.l.A_ffA`d.A,WfA`e[.AGfAQ.A@y5fAI.A`fAB.A`eAf<.AeAe5.A@eAx,.AźeA =$.AeA<.AْeA``.AUeA( .A{eA@u.AxeAu.AreAp.A=ceAP.A_NeA p.A=1eA.A@eA.A@eA.A?eA.AdA̺.ApdA.AdAX֩.AdA,.A dA.AdA7.AdAhQ.AodA8.A@ \dAz.AOCdAr.A0dAXi.AdAb.A dA~Y.A cA2Q.A cAjJ.A cA[A.AcAG;.A`cA)4.A@cA*.A+wcA#.A ccA.A@NcA.AQ=cA( .A%cA.A cA.AbA.AbA(.AbA.A |bA(z.A^bAP.A@֠bA7.AbA.A Z|bA.A fbA(.A`MbA(`.A=bAh.A 'bA跊.A"bA.A`]bAaw.AZaAPo.A aAFh.A`)aA(z_.A`aAV.AŶaA0O.A[aA3G.AaA?.AgaAp1.A`aaA8).A3NaA.A`5aAt.AgaAp.A aA-.A+`A.A`AP.A `AH.A `Ap.A`2`A.A`A.A+u`A.AZ``A0̽.A` J`Ap.A/`AK.A`A.A+_A.A<_Ap<.A@_AH).A`)_A;.A_AȘ.A_A.A _A.A_Ah~.A_A`~.A _A}.Aʹ_Ap}.A@G_A.A@r5^A].AB]Aۢ.A`]Aբ.AI]AC.A f]Ax.A?]A k~.A]Aux.A`^A@[.Av_AH[.A x_A.[.Az_A`Z.A{_A8L.A_AB.A@Ƙ_A:.Aପ_A2.A^_Ax).A_A!.A_A.A``A.A `AȔ.A@9`Ah.A``A`.A`A0L.Ad`A .Af`A(.Av`A .A`Au.A@`A.AaAXR.A%aA.A@`4aAX.AGaA@B.A NaA׽.A@PaAp=.A@`RaA( .ARaA.A`NaA蔖.A LaA.A LaA.AzIaA({.AoFaAp s.A-CaAn.A BaA_.Ag=aAR.A[:aAI.A6aAP@.A@1aA06.A 'aA$+.AqaA .A aA.AaA.A `A.A@``A(.Ag`AH..A `A.AZ`A(.A ~`A.A`A.A`A.A^`A@.A<`AP.A?`A`.Ah`A`B.A`|`A2.A``A7.AO`Ax(.A@5`A`ǃ.A`A|.A+`At.A@_A-n.A_Af.A _A@].AG_A((X.A_AeO.A 1|_A[@.ARi_A(7.Ag]_A,..ApP_A".A?_A.A,_At .A_Aho.A _Ap.A^AF.A ^AF.A^Ay.Ap^A .A`^A\.A^A.A Ak^A.A@^AX(.A ^A0fu.A@]Am.A]A kf.A]A7`.A`"]AV.At]AM.A]A2B.A@ ]A@p4.Ax]A'.ARa]A.AG]AX.A 9]AP .A *]A.A]AG.Az\A07.A\Ah.A}\A.Aú\AP.AĮ\AA.AƠ\A(۩.A\AM.A ۂ\A.Ao\Au.A S\A0.A@7\A~.A4&\Apx.A`[\A`o.A@[Ape.A` [Ac.A[AZ.A[A`L.As[AP8E.A,[A=.A[A..Az[A%.A@k[A0.A@\[A.A` @[A .A?%[A.Ay[AG.A`ZA[.AZAأ.AZA.A ZAx(.AsZA.ATZA.A(ZA.A_YAa.AQYA.A-YA(-.A4YAP.A gYA.A8FYA.A@YA().A@EYAh.A`XAo.AؔXA.AXA.AXA.AtXA`.A ɿXA`.AXAx.A@gXA.A YA0L.A?;YA0.A`YA M.A`YA<.A@YA.AYAxƵ.A@ZA.A 8$ZAXװ.A?IZAP8.AiZA.AZA3.AЧZAl.AZA".AZA'.AZA›.A@>ZAu.A[A.A@?[A =.Am[AX9y.A@ϐ[Av.A [A8o.A[Ai.A@>[A`d.A \AH2^.A\A_V.A B\AP P.A]\AH.A@u\A0f9.A֒\A1.A]\A*.A\AH-".A \A .A`\A`2.A J\Ap=.A@\Ak.A \AȔ.A@,]A.A]A``.A`"]A7.A &]A.A-]A8.AF6]A(.A<]Ax\.AsD]AL.AK]A .A@Q]A.AX]A88w.A]]An.A`"b]AQe.Ae]A\.Ai]APM.A` p]AxWA.Ax]A@=;.A@y]A`.A]A8.A`]A .A@]A.Aߧ]A.A]A07.A]A(z.A@`]AQ.A ]A.A]A.A]A.Aq]A`.A ]A.At]AX[.An]A.An]Atx.A n]Aho.Ag]AP`.AZ]A@X.A P]A0Q.A G]ApC.A36]A5.A#]A ).A`]AH'.A O ]A`.A\AH.A\A`.AQ\A@.Az\A(.A\A.A@ \Ax.A {\A(.Ap\Ak.A$p\AX.A@5k\A.Ah\A.At\\AX֦.AG\A.A`:\AH).A ,\AF.A@\A.A \A7x.A[APr.A[A2l.A[A_.A`[AȔQ.At[A2D.A[Ah.8.A[AL+.Azm[Ah.A-U[A.A'?[A.A-[A0.A [Ax.A[A.A[ZAHa.AZA.A@iZA.AZAh.A ZA.AZA3.A }ZA`.A@mZA .A_ZA.ARZA@.AHZAu.A"0ZAجm.A JZAww.Al_YA(.A@CXA¬8~.AeXAS |.A~!XAZ{jy.A3wXAef'Iv.ApmXA+;r.AkeXAh[n.A T'`XA/j.A}]XA*~f.Au\]XA۽jb.Ah`XA骫_.Al\GfXAz[.AjnXAX.AM,yXAU.AXA7JI.AbXAv@.A.YA].A HYAlO.Ad wNYAp*q.A[@YA^.A3YAuQ.A6KXA5b$.A~XAM.ABXAQ?.A( WA.AٓWA`v.A@;WA.AO 0WA .A<%WAz͝.AsWA a.A }WA6U .A62WAKR.AU WAdj.A WAm.A WAAJ.A\gWACV.A)R%WA(.AWA̒.AؚlWA妃.Aޅ[WA8.A!XA.AN1XA1ehY.AEN.XAUc%.AjyXA{w.A)WAڿ.AF{WAv&.AkRiWA8Z`.A@y WA}_.AVAʧ`.AbPVAhc.AfVA@^b.AfMaKVA_.A%2VAo;\.AtijVAҸW.A$VAFiR.AyUAqsL.A*|UA`F.A@CUA$uQBA.ACLUA<@<.Av5UAa8.A]UAF=h5.Aol VA;2.AwwVA%s0.A2Qd-VAt/.A[AVAHr..A |UVAmI&.AcHVA Y!.Ae7jWA.AWA@.AEXAB.AvXA.A|XAFg.A#7YA=z .AYA.AFYA(J.AYAތ.AYA]%n.AE:&YAk_ƙ.A0\ ZA .A@ZAh.A@5YAku.A|MYAż.A`WeYAmz.ACbYAH.AӋYAjn.AJXAݽs".A!WXA0;.A~WA;m.ALS]GeVAq.As$;UAt}.AgsuTA~03J.AZRA@6[}.AWYQAe.A`fOAQ.A%1NAӿ @.AtAMA&Ɖ.AiM|LAjΌ.ATv\1KAߍ#.ATxIA"s.Aeń_cHA.AOFA*.AqkMEAs.A`TCA=.A6AC.A #Ah .AA8.A )AE .AtAD.AA6.A`KAW.A@A%.AlA@.A |Aݛ.AfA.A@lAҗ.AwWAyP.AZuL1AǗU.AǐA/.Al6Apߔ.AyH*WA`)'.A]JvA Sq.ARE%rsAL+.A,Ax .A.MAY~].AG&YAcư.A)QA.AfA(.A1 AoK.A$A.A-AW5h.A0$.Av.A!XA&.AW2 Aw&M.AtS2޽A{EV$.A1:A u.AvᦼA+.A$Aږ.AA2)&.AR\kUA@.AA܅.A`DAH#.A5NAPoq.AAq.A@gA3.A}AMd'i.A AU) N.A- NAxX%8.AOA*J>'.A|AEn.Aʦ`AY.A.Mڵ A$.A%#A6*.AT|%AP.Aà"'As/-.A(7*A!9?.AZ,A"W.AL.Aͱt.A, >1ASP.Apl3AS!J.A.Xg5Ai.AW"8Ar:p.AA;]:AO.A{Fp<A쉌.AI>Am8Ȍ.AM^@AV?D} .ACHCAT.AŢ$EA8.A$0GA 0.A*g2IAFYpG.A;(KApߠ.A@pMA.A@EYARy5.AsZApG%].A 6c[AD œ.Av&\A80.A(\A h~.A:}w]AɷV .As^ANHy.A!^A@.AQ^An_^[.A;_A*@͖.AWX}_A0=T@.A+g_A}.A_A=T.AN\_Az.A [_A޾Yޘ.A_A5%A.A5M_AI.A-_AGl.A-`A& i.ApU>`Ab`ɚ.A`AZ(.AXaAja.A|RraA.AlEZybA|m=.Aeg6Q#cA6'.A ~cA.A ndAli>.A ceA(.AmfA/r.AiA$ .A}A^..A~A# .A]0AuΞ.A~#XA{N.ACἂAOav.A.AW..AK&A![.A;A;;§.A6eA'.A`bJAg.A@AXa.A0A$ƪ.A9A.ABAFH.A`mA`1.A At .Aq7AH.A`GA.A`.A(.A@4A .AXAs.AAxŴ.AApP.AA p.AAA.ACAx.AwA.AcA.A+A۶.AzA.A@A-̶.A;A|.A%A.A%A؎d.A=ŗA>.AAz.A^A.A9A00.A`ßA!.A /tA .A븢Aq.A sA7.AAp9|.AAP.ARAh.A AX R.AĮA.A Aj.AA`G.A~A@.A@KA.A| A.AjA&.A /PAU.AA.A@`Aà.A qA .AA.A`]Aِ.A`A.AA '.A JA.A`Ah".A QAx6 .A[A .A"A.A"Ab.AMϿA;.AA0.A A@.A;A(.A;A0.A^AH.A A.A`3AXhE.A`KAHz.AwAp.AAp.A3A.AA$.AѠA`%%.A~Ax\.A Appn.AƎA؎}.A[ʶAF.AA虗.A A\.A ȶAp.AA@$.AA0.AspAٺ.AA.AA.AXApP.A 퓺AF.AܢAFAޠ0.AgAXL.A} 79AnQ@l.A,jǤA W.A2eA}P.AO=hAeH.A*ߊأA؂3o.A~s A0.AtyAp[.A As!`N.AnRA9H.A&A{{/J.A@b*AL#.AwzvA1J\.A^џAͩ .AnC;A.u.A 8Af.A CAZ.AEQAq.At AN.ACQA,*.A(&A֦].At )A6.oo.A>AI2.AQfA9_w.AHyA;Nq.AALj.Ax>NAf{U.AdAM.AwCA5.Ap`ٟAb.AlȊA=.A9Aex.AvLAW_.ADܢAAE.AzA7ǣAAQTO.A;A.A˥A.AAx.A?A J.AØAL.AA.AMgA k.A|<A2 .A`:$ApA5.AZfKA3c.AG9A}g.AlAI.AZZAS.A٭AR¾-.AOaAc.AHÁǝAZp.A\ݾA}.A0eʝAF.AA<.Ap)"Ao.AfA5.AFhs㿞AT>.Ag ,Aj .AL_#A_].A%<A&j.A{A11:L.A^@8A;(.A^AF.A7AzE.A!A2.AA-.A /AX.AA.A A0m.AjApN.A &A.A A@.A ۓAx.AAr.A@iA.A`;?A{.AiAP.AAp.A`xApp.A AAP@ .A`AL.A Aa.A=Ak.AA`ԉ.ARA.AA.A 4A7C.AA.A?AHP.AA<.A`A.AAؽ.AA(?.AnAp_.A AX E.A@,A趶.A`mnAhb!.AoA`d.ABA`.A`AH.ARsAd.A AP/.A&AP<.AAhA.A`mA`.AA.ANWA(h4.Af(AhUs.A3Ae.AʀA˖.AA@>.Ag>A茑.AMA(>.AAї.AAH.AdA/.AAx.A$rAH?+.A AH.[.A A.AAA .A`"Am.A@Ao.AA$.A JAX.A@>A .A`2A`q.AqA.A A(.A'A.AA.A l:A@.AkA0;.AuA.AMrA(.AZA:.A .A@.A@A".A,AV.A`A$.A 1Adn.AA@.A]Ab.A@?A#.AMAh.AjAP^.A@0A.Ao1Ax.AAo@/A#A`S/AMAп /A |ZA$/AA\/AqA R&/A=ATR/AAo/A`SAľ/AAP/A3A/A$A`T/AAI/A`A(/A"A/AHiAx$O/A3A,/A$Aث/AA/A`bAH/A |A c/AXA(܀/A@GiA/A \A/A}A8E/AAǢ/A A(z/A`A#=/A sAh/AVA/Ai,AF/A(AP/AMAX/A`]A`/AA8b/Ai,A0/A@ A7/AAȃB/AA0>/AA?8/AA`v/A@PAX(/A`T2A`M/A=AD/AAJ/AA~/AEAP/A AA(/AA//A@rA?/AׯA( /AAl/A@.AX$/AzOAX/A`bAr/A ˊAp'/A@AXJ/A[]A]/A@A/AvbAn/AAp/A-As/A=wAe/ANA0/A;A`/A@4A(/A ;A((*/A=A@5/A@AA8 ?/A[@A`J/A4>AW/A:AЄc/A`d3A k/A*Am/A(Axw/A2$A@}/A !A\/A A/AAPo/AA(/A" A/AAU/A3A跷/AA/AA/A`AX/A*A/A@A0/A@A(/AA$/AjAP</A JA/A`AW/AA##/AA@+/AA؎5/AyAP?/A$AXWI/A[6AhQ/A@rFAZ/A@_Ad/AHzA[m/AŒA(t/AƤA{/A Aփ/A6Aň/A@5A(\/A AAۘ/A A /A AX/A:Ax/ASA,/AiA/A|A/A`KA(/A`oA/A@A/AAH./AHAP /AA /A_A /AtA /AF4A /ADA% /A ZTAX. /A hA7 /AtAQ@ /A6~AAX /AZA7 /AA /ARA /AڪAX /A AXW /A`2A0 /A@`A* /A A6 /A/A7 /A QAD /AAO /AA؎W /A QAV_ /AZAH.l /A@yA0Gt /AA=} /AqA@… /A@ A8֏ /A@`A0 /AwAU /ApA /A`"hA( /A`]`A[ /AXAu /A (KA0 /AdAA /A ;A /A7A[ /A}1A\ /A +Aj /A`"A, /A@App) /A@AV* /A`A4 /A`AB /AA`O /A AZ /AEA(c /AA pm /A?A0v /AA83 /AA8 /A`A /ARAPk /A`fA R /AľAP /A ˳AX /A@ A(z /A@!APo /AA /A@AP /AA0. /AEA /AA  /ARAP8 /A@5A /A`[Ap /A`Ah( /A`KAؼ3 /A<A7 /AA> /AנAHI /AA0jT /A At] /A`APh /A6Ar /AA| /A A@u /AAp /A,Ap /A`A /AA /A A /ApAV /AdA8# /A`A /AAp8 /A{Ap /AuA/AqA( /A`KnA=/AmA%/AjAH1/A AiA8V</AfA(D/AeAeM/AzcA`W/AcABa/A`fcAj/AcAp/A_cAs/A)cAxz~/AbA/A`fcA/AeA`/A gAѤ/AjA0{/AmA/AoA/A/AHrA)L/A@uA[/AzAg/A~A(\n/A}Ay/A|A2/AxA/AtA/A tAa/A@tA88/A vA/A@xA/A@yA"/AzA/AzA/AzA/AqzA /AdyAG/A@yyAX/A@ryA /A QyAX[/A@yAHz/AyA0(/A/A qUAX#@/A@]ZAnA/A^AlC/AacA0E/AhAmF/A෗lAVH/A-qAp}7J/AT~tA> L/AwA8N/A@-{AgxP/A` {A(Q/A`{ARR/A` {AT/A)yAH) W/A!wA\Y/AtA.Y/A JuA@[/AЇuA`]/AvAd_/ABwvAP7_/AvA0&a/AZwAc/A@wA`f/A=xAb1f/AQxADf/A.N}yAAzWZf/AmimzA,3tf/ANž{ABT큒f/A|#|Af/A A}At9yѼf/AW{d~Af/A@#~A6$tf/AX iGAf?f/AwA[Nx g/A1_'A&g/A*A-:g/A}kACPg/A@rAXg/AhAxg/AvԁAg/A_A0 g/A@Ag/A@`(Ahg/AAg/AAhg/A@%NA( Yh/AA@F`h/A |AXҖh/AOA`1h/A7A@ j/A;A0Cl/A$FADl/A`\A@Fm/A]A-w/A.A[w/A AЀw/AӒAHy/A`d5Aaqz/A`muAЕ{/AkpA(O|/A`Ah |/AʇA } |/A`;iA̍}/A AƘ}/A dA}/AAH}/AAG}/A`APf~/A3LAh~/A äA(~/AE?AXF4~/A`2AC~/AHA#L~/AfAXO~/AAy]d~/A3[<Ad~~/AFA~/Abc0AO/x~/AeEA~/Af#AX/A'Ah/A0A/A 1AH/AٲA/AzA/A@C³A8֯/A` ƳA 0/AA/AʴA(/AѳA/AѳA( /A`A(/AcA8/A@PA0/A AK/A@,AЄ`/A@5A{/AůA0/AA/AdA‰/A A(Ӊ/A {Aȋ/A9A8'/AAkM/AAw/A+AH/A@AZ/AAߌ/A xA/A<A`,/AEŶA!/AAH/AAs/AA8'/A@iQA(/AhAˑ/A@p7AXh/AAA/A|bAڦp/AfҿAے/AA/Aʅ~aAz_-/AmAxW/A AS/ALAxR9ē/AUA/AڼLAi(/A0A0W/AAh]/ANA/AA̢/AAHˆ/AhAhA/A0gA&/A.A./Ag@A7/AAWa/AYAPMr/A@E~Al/A`A@m/AA/AAἕ/AAP/A+A#/A A0,/A AA/A` Ah[/A6A/A"A`/A}A/AjAy/A}A/A/AXF4/A@AX/AADk/A A(r/AAU/AA([/AApʭ/AA /AA˗/A`A"ؗ/A`WA>/A@A/AsDA/AbA/A`QA8$/A9Aȥ,/A`;A`]/A@ϬA/AQAⲘ/AAƘ/AxAPԘ/AA{/A@A /AA/AQAXhI/A`Ah/U/A@;APh/AEAȲu/A@PA(/A&A8/A sAxd/AA!ƚ/AAܚ/A *A/A A/A`6A0 /A A4/A (4A5/Aq4ACj/AAn/AQA8/AAPכ/AAH]/A A@O/A ZAh/AA!/AA5/AgAwF/AtAHnT/A@Ad/A Ahx/A@ A(~/AnAq/AdA/AdA՜/ArAa/A|Ay/A`APH>/A9Ao/AĀA /AA=/AgA՝/A9lA/A JAȋ/A`vA@06/A^AP/A@>AVc/AvA@$p/ATA/AsAե/AoNA}/AaA0/A 8A/A`ÕAN.Ϛɞ/Ap8Afܞ/AVA /AA:/A`̲A$A A(u/AwuAh}/AxA@/A`bAx~/AA߅/A XA/A|Aʚ/AA@/A YAP*/A@!A oբ/AhPA+/A/sA8/A@yAH/A4A0/A`Apƣ/AẠ/A,A@_/AA/AA/A5A(/A<A/A८A/AݭAף/A,Aɣ/AvA)ţ/A@Ap/AHA/A@A8/A@AHR/A`)AQ/A`åA`rM/A`A{H/A A/A=`A̲/A`)<A#/AHoA=/A@rA@׮/A ~Az/AA#/A=AQ/A@ !A@׵/A/A_/A`)Af/AAm/ARAx/A@rAR/A`A P/A"A8,/A AH\(/A qA/AzAu /AzA(ܸ/AAf/A`)4A@/A-]A/A=cAG/A܄A/A`AJ/AAV/A |A~/A A7/AjA/AA0/A#Af/AqA؞/A:A/A&A/AfA/AA ֣/A@A@W/AA¢/A \wAp /AzA0./A@A'/AzAf&/AAp/A@:A/AAX /A3AH./A ^A8/AnA@W/AsAջ/A@;Aã/AA(ģ/ABAXΣ/A (AxFϣ/A A5أ/AtA٣/A@YAHK/AA/AA/AA/AcA/A/BA/AFAP/A}3AHn/AAXH/AA S/AyA@]/AA$ڳg/A@Ū>A9q/A A+4C/AzjAAu/A)Aȥ/A Auե/A`A0/AOAxם/AAĴ/AAP/A` AD/A A`//AA8I/AA~7/A`AX!/AA( /A QAhD*/ASA+/ApAB/Af$AU/A Q<A/AAƦ/AhA1Ȧ/A 5Apʹ/A`AX/A 1A/A,A\/A/A/A:A`/A9;A~/A@A=/A@A/AAA/A<A/A2A8/A/,A¦/AO$AĦ/AHAH2Ǧ/A@AϦ/A |Aզ/AA Rۦ/A`2tA/AA/A@A/A@WA8/AA;/A TA /A6A/AA/A@GA/A`TA /AxA0*#/A`Aw%/AA'/AAh-/A9A4/AAQ/AA8X/AAz_/A)Ap/A`[ A}/A!A7/AAY/ARA-/A@5A/AjA(/A/Ap_Ƨ/AA0jԧ/AIAx/A$2A,/A \A౨/A@*A(/A@{A/AAUX/A@CAa/A@g-Aq/A+Axw/A@Ap /ApA/AA|F/A AXh/AAq/A@Apyz/AAl/A`A7/A3A/AxAأ«/AcA /ͫ/AcCA/AAi/A@,A8/AhA([/AAe_/A`Ac/AױAsq/A@tA0v/AK_A|/AMA/AVFA/A AAx䆬/A@<A튬/A:Ah⓬/Ak:A/A$@A/AMJA/ASA0ݯ/AQAt/AnTA0w/AWA(ܽ/AXAqͬ/AZA1ܬ/A`T\Ah/AjZAk/A3QA`/AIA A/A@?Ah/AAho9/AAW/A&kAAd/A@SA怭/A3%A1/A`) Ah/A@,A8/A;A/A`A/A Ap/A`A0+/A Ap7/AhA@/AઢA J/AA0S/A@hA`/A@7AxWh/AAo/A@5Au/AA8u/ARAz/AAހ/A@GA؍/A`AĈ/A lAy/A`TA/A*AH/A`A(/A@WA0叮/A`Aю/AA /AA@d/A (0AP/A)A/AŒA/A AX/A?A/A@yA/A |A0/AAP/A@rAtƮ/AAؼծ/A^ARخ/A9Aڮ/A`mAڮ/AFAڮ/A ZALخ/AAp׮/A`2BAP֮/Aa#AYծ/A A0jӮ/A@,AҮ/A A Ӯ/AoAi֮/A \Acخ/AA<ۮ/AEA`ۮ/A@AA,ܮ/A<Aܮ/A`8AȺݮ/A(5Aޮ/A1A߮/A.AЦ/AX)Ah/A A%AGA/A`]AX(+/A A0/A`A 6/A AȶQ/AA[/AA/A JPAƬ/A A3/AلAK/AA1/A`AL/A@5AD/A6}A0/A {A8"/AAx/A J~A( /A?AR/A@A`/A OA/A`AP+/AA&"/AZA $/A`AȘ*/AA ./A1A";/A@%A>/A` AA/A`IAI/A@,?A8"Q/A ZAZ/A`oA]/A A0&_/A4aAȶ_/AA_/A`Ap_/AFJA(K_/AA,_/AA@_/A@WAF^/A AX/AAZW/AAT/ABA0R/AAC/A A8/A O-A/ALA /AA"ܱ/AAHб/AA@/A PAA/A΁AX^/A`]AT/AAX/A5A/AxAJ/A/JAp^/AAN/AAT=/A A*/A[A[ׯ/A@!*AxK˯/AAAd/AfA r/A AU/A^Ap4/AhA/A_AҮ/A_Aa/AKPA;/A3AG/A=iAXҭ/A൧A`a/A Aݡ/A lAxGJ/AHAH/AAu/AA #//A8A$ /ATA8b/A-hA{/AsAV/AuA /AZuAx/AtAp/AsA /ArAm/A`oA/AjAp/AaA@/AaBAY/A Z;AP/A5A>/A@>2A`/A /A/A .AhD/AT.Ap/A.A/A .AZƫ/A7Ah/A`dIA`Б/AZA/A`cA/AiA߄/A pAXw/A Af/AAXFI/A`;A00/A@iA7/A :AP /A ZAp ɪ/AAx/A^rA/AV~AD/AAp=}/A`ܰAs/AA@n/AA8j/AA8"g/A'Ap8d/AA P/A̓A85/A7AH//ANhA"/A AA@~/A<A/A@Aذ/AkA/A Ap/A`CA8/ANAؒ/A@yA;/A@>A=%/ANAH,/A@A 0/A0(A9/A@DAdA/AcAȃD/A tA#G/AAEX/A@AQ[/AhA h/A@πAjn/AsApo/AvAxp/AA q/A|A]q/AmAq/A`Ar/AA w/A@iAw/AnAw/A AНw/A@>)A`aw/A 0Av/A O8AYv/A ?Au/AFAgs/A@WTAsp/AaAi/AxA,V/A@A0jQ/A@ApO/AAM/ApAoK/A`KA)I/AA0@/ALA/A`AxA/A"AB/A@N$AXC/A@&AhC/A<)AD/A},A cE/A?0AF/A`4A(G/A>AG/AJAgH/AXAH/A |AؚG/AA.C/A A`A/A`A@/A AEA @/Aq`AhA/AlAA/AxAB/A A@C/AXAoD/A`A [/A`]A0L{/AlA/AA(J/AfAxi/AA /AAXۖ/At(A/A6Aꘪ/AHhAз/A^zA c/ARA(:/A?AA/A[A(z/AAΟ/A A/AũAh/A`;A A/A 8AZ/A[AV٪/AW Aܪ/A a A/A /k A/A AV/A A/A& AP/A2 AV/A A A /A AU/At A8/A5 A /A A/Af AB/A`̱ A t-/Ay+ A"=/A@y A\>/Aj AN?/A A Ahb@/A Ahb@/A AP?/AQ A(9/AT AX 7/A A }6/Aj A~6/A © A 7/A` AH 8/A`9 A'8/A` A:/A A?/A`) A=D/A AJ/A@0 AhR/AF Ae/A`;r Aj/A~ Am/Az Aew/A` A/AR A$/A#AV/A +A1/A(A1/ApzAe/ATAPë/A`;-A5ȫ/A`IAݫ/AA/AAh/A3A>/A@A%/AA/AdA/AwA8/A߇Ax/A`9Ax/AHA0/A A/A@iA(O/AA/A Ah/AٸA6/A`A$/AAH/AA~/A |A8/A"APE/A`@A@uW/A@sAdY/A@WyAp9[/A`A^/AA4g/AAAxhj/AAPm/A=Ao/AAq/AAP{/A@Ao/A@%AH̯/A?AUì/AdKAHn/A`)]A/AH`A< /AQqA+/A]AO/AA#u/A Az/AAXۆ/A JA3/A_AU/A,Axɭ/A9qA٭/A`KA/AwA /A;A)/A`AP6/AAܯ/A=A/AzA3/AAt&/A Ax*/A A M4/AA8/A@A@L/A@W|A6i/A`CA/A`A/AA/AwzAP۰/AAi/A`Ap+/AA;/A A/A.Ad+/A Ah@/A3 AQ/Al A^R/A-i AS/AQe AS/A`2a AS/A\ AT/AX A1T/A'S A T/A"H AzS/A<> AR/A2 A@M/A@ AL/A APkL/A@ A{K/A8 AHK/A@ AL/A l A[O/A`v AO/ARi A(P/Ae AQ/A`)a ATX/AjO A_/A@,> A{/A& Ax-/A@y A"/A A(욱/A l AP랱/A A'/AV A+/A A0f/A`) A,±/A Aȱ/A A0G/A A </A Ax /AVA,/A A:%/A@C5A2/A@AX>/A A2T/AIAm/AgAأv/A A}/A@Ap/AcAho/AA/A|BAơ/AoAp/AA/A@AЮ/A>Ac/A`)A̱/A?A8I/A+wAН/AA$/AA#/A`9A¨/A-oAx/AUA(/A`KCAۦ/A >A0٦/A A/A`)qA }/A VA(>/A9A/A@APޡ/AAh/AA/AA/A`A%/AA/A AՐ/AQA~/A \AXz/AXAv/AAu/A6A]t/AoAfs/AOAr/AA\r/AAhr/A`A0q/AA@q/AjA0q/AA`ؖ/AAxƚ/AЩA/A@ #AW/A@yA&Բ/A@#,AX/AA/A2A8'/AjAt/AA/A@NtA/A OhAt/Af[At//A A1/AjA3/A@A_:/AA( A@W/A` AW/A@l A/A@ { Ak/AG A/A A# /Ap3 AF/AM8 AB!/AR Aq/A- Ao/A` A /A A /A A0o/A` Am/A@AHr/A@ϔA /AiAm/A`Ah/A6+A|2/A4A@2/AcA//A"_AU/A`GA`/ADA/ABAPx/A(>A/A0A³/A@!A/AA/A`A$/A A}/A APHk/A`]Ad/A ɼAH{=/A@yHA|7/A6A(/A AX/A@Az/A dAX /AGAp=h/AAuv/A`f4A1/A@WAP1/A`AP)/A}A(9/AHA˳/AAf/AWgA/A^A0;/A]AX/Ap`AV/AlAUy/AAxm/AoAc/AAp/A~A/AA/A5A% /AWA/AA(iֱ/A@IA0;/AXAXRv/AAki/A"Ad:/A@PA 2/A A*/ADA$/AA/AAJ/AO4A/AYAd/AjA/AyAu/A3D A%/Aab AȓӰ/A-"A` Ұ/AU7"Aȃ/A /A $A/A$A`/A`$A/A$A`/A &$A/A9%A/A%A /A Sd'AP/Ah'AQ/AG0'Au/A/ 'A/A&A/A`ܵ&An!/Am&A0!/AT&A #/Af&A#/A[%Ao$/Az%A$/A %A@%/AZX%APw&/A$A&/A@$A<&/A@$A%/A#AHn%/AO#A`$/A`"e#AH!$/AJ#A(:"/Af"AP@,/A "A0n//Aѝ"A7+/A`m"A",/A|"A3/A`D{"A:/A7#APM/Aq/#AhM/A4#A/A #A(/A"A~/A`m"Ap/A "A /AH"A E/A"Aз/A@{"Ax˰/Ab"Ai/A/#A(\W/A6e&Ah"H/A&A>W/A1nH'Axg/A'A0{z/A'Ap,/A='A ]R/A_Q(A%/A ((Ab"{۰/A{W)A/A[G+A /A+A /A ^,Am/A! },A3/A,A/A/0,A0!/A,Aec%/A,A7*/Af-A0/A3-AwJ7/A@$,-A@=/A9-A>/A:-AEJ/ARvVJ-A]W/AdQ-A&d/AO-A0m/A@rL-Ax/A Q-Ah3/AT-Az/Ab-A{/A=n-A R/A=t-A/A`fz-A/A-AB/A@-A/A=-AHܼ/A]}-A/Az~-Ap=±/A-A#ű/A-AXƱ/A-Ap=ȱ/Aש-AQϱ/A-AH.ر/Az-A`aޱ/A`f-Ap=/A |-A8/A.A/A@$.A/A`*.AG/A..A/A=.A`/A@L.A/AW.AB /A \[.AX /A@ W.A8/AV.A8/A^.A8/A`fb.A`/Ak.A`'/A@z.A+/A`).A@./A.A//A.AH//A.A`a//A.A1/A@ .A7/A .A1Ap=/AGK1A8/A3]1A{/AGm1A-/A1A R/A1AL/A`f1A/A1A /A 1Aأ/A1AL/A̰1A/Az1A/A`)1AP/A31AP8#/A 1A(/A@1A//Aֳ1AVP/A@r1AR/A1AZ/A2A( [/A[2A]/Ap2A_/A(L2A_/Aw2Apk^/A`f2A`/A2ALb/Aõ2A@Wf/A2A@j/A`f2AXn/A(2A)q/A`~2Ar/A`f2As/A`)2A`t/Aק2Afu/A2Aw/A`2Ap|/A2A8/A2A𣅳/Az2A#/A`f2AP/A2A̘/A` 3A/A(3AP8/A@ M3A`a/A@ e3Aǘ/A3A/A`f3AQ/A33A/A3A/A3AP8/A 4A0./A4AP/A 3A/A@3A׸/A`3A`ẳ/A4AB/A4A؞/A@ +4Ak/AB4Adz/AnX4Aȳ/A q[4A؞ͳ/A qm4A޳/A=4AP8/A@ 4A/A4Ap/A \ 5A/A`f,5Az/A`:5Aأ/AA5Au/A@>B5A`/AR@5Ah3/A@>5A`/AJ5A R/AW5A0./A`)^5A/ARh5AP/AHw5A/A@g5A/A`ñ5A(\/Az5A؞/A`)5AP/A@>5A/A`5AP/A'6A R/A76A/A P6A/AGe6Ak/A@>|6Ah3/AR6AX /A36A/A6Ak/A6A0./A֑6A%/A֏6A{,/AR6AP85/A6A/AdC;AЄ>/A9;AWK/A0;Aj/A ;A/A:Ax~/Ag:A Ab/A 0>A@0m/AXa>Af/A`K?Ak/AH?Aj/Ax?Ab/A?Ak_/A@A@Fq/AՑ@At/A@,?A(\o/AK?Aq/A8?Ap=x/A?A0~/Aa>AXے/A,?>AѪ/AG>A/A=A"/A>A t /A`_>AH/A@in>A(@/A?AT/A;?AhDX/A@y??A8[/Az@?A0La/A/>AHj/A>AP /A?A(&/A?A`%./A?A<*/A3?A/A?A/A U@Ad/A`W@AXt/A@Ac7/A A@A0:/Aa@Ah?/Aa@APC/A>AiU\/AC>AIJt/A.+`>Ay6/Au;>AraS/Aڨ%>A@þ/A >AXl޾/A])>A_6c/A~pC>AYt-/A6n>Aa:0S/A谟>A,w/A0>A0t/AM?A/A_?A^/A@?A`B_/A`K?AH\/Ax@A8]/Ay@AHa/A`o@Ao/A`?AA/A=DAA/AvAA/A@!AA`/AAA 2/AAAH3/A3AAD/AqAAU/A,cAA;?D /AЮ&AAzo\R/AaZ@A/Ay@A%|/Af@A\!/A'Dt@A$de/A&AA]/A-gbAAWZ/A0>[ñAAb*/A`.BA h/A9(!BAFB?/A41CA, /AxgCAqk/A?!iPDA`G/A@EEA+X/A +EATl/AEA/A0EA8/AFA()/A FA/ABFA@/A@FAp/A`FAٸ/A`FA/A GAx/AGA@~/AGAp_/A;>GA/A`vFGA/A}CGA6/A>GA!/A}(GAȇ./A@1GA:/A\GAE/AxGA@R/Aw|GAt/A`vcGAH{/A0iGAY/A9|GAp/AGA؁/A dGAx/AB*GA/A GA_/AGA/A$GAЀ/AGA8/A AFA /AFAE/AGA:/AGA0/ARGA8//A=GAhMC/AFGAhU/A@NGAZ/AGA]/AGAHa/A#HA8Zj/A \>HA@qp/A;HA2/A'HAԊ/AV2HAR/A ZHA/A}GA@1/AVGAxƷ/AGA0/AךGA/A 9GAu/AWGAH/A"GA/A`mGAp/A GA(O!/A 8GAh$/A|GA0]//AGA@/AGA(I/A sGAH/A@NHAЕJ/A@9HA`\/AGHAg/A cHAhm/AdHAz/AqHA/A`HAv/A3IA`ϔ/AtIA[/AIA/A`]IA4/AJA/A&0JA/A`TJA0H/A AJAD/AJA$/AfJA}/A KA%/AKAp6/A@$KAA/Ad0KA(Q/ABKA`/AKKA(p/A`)WKA;/A{KAأ/A KAP7/AKAT/A|KA/Ah LA3/A SlLA/AMLA+ /A@LA0;/A sMA`X3/AdMAw/A6|NA0&/A@ NA&/A`22OAm/A߃OAp/A OA/A 'PAp+/A`fPAk/A PA/A@QA@ /A&bQA/A cQA/AQA0'/A +RAy5/A@NkRAHB/A 1RA@FT/ARAذd/ARAذq/A+SA{/A`oSA-/A SA /A`SA/AoTA@/A)TA@/A`TGTA/A9eTA }/A`TAP/A@TA0]/ATA/AݩTA+/ATA9/ARTA R/A@TAg*/ARTA8g9/ATA8F/A ~UAT/A@FUAxWd/A`|UAH r/A@UA|/AkUA/A@GMWA88 /AwWAw/ALXA!/AӈXA+/AoXAH)=/A;YA%]/A YA@}z/AXjZA/AZA(/A[A/A[A'/A ]AV/A^A 0{/A^A/A}Q_AP /A@N`A-/At7aAIK/AaAH q/A\bA/A`bA/ABbAd/A`}cAh /A}KdA/A`!eAU@/A eAP]/A`fA>v/A dfA /A`vfAp=/A/!gA/AgA]/AgAX/A &hA/AhA/ARhAȥ!/A}&iA12/AjiAXWG/AբiA}i/AjAx}/AhjA0/AjA/A@#+kAܿ/AQkA/AnkA8/A]tkA3/A@NakA/ATkA8/A8kA؞/A6kAX0/AqOkAVI/ApkAWd/A&kA/A`BkA/A lAV/A?lAo/A@dlA/A0lA`B/AğlAmQ/AjlA /AlAp,/A &lA' /A kA /AmkAP/A ‚kAP/ATkA/A@!A(Q/A@~>AQ/A@=A8R/A |=AHzT/A}M=A1W/A (#=AY/A@ <AX[Z/A`à<A(`X/AX<A[S/A;A8"K/Ad;A@u@/Ag:A`:/A`d:Aj7/A`(:A<5/AT9AX[2/AП9AH-./A@R9A(`+/Am9A'/A8A/A`2P8A8/A.2A/A@1A/A|1A/A0A07/A@iw0AHu/A/A0j/A`6/AX/Ay.A:R/At.AL/A3-A@/A,A6/A`;),AX#*/A +AU#/A]v+Ap/A,+A/A*A/A@*Ap[/A*A /A9*A`/A *A/A)A`/AO)AP/Adc)A/A(AX/A@ Z(Ax/A'A/A7'AA/A st&A`/A^&A`/A Q%APj/A+1%A`/AI$AP/A`K$A-/AT$Ax-/AT$Aj/AR#A/A#Ap/AQ#AЄ/A`f!#AH/A"Ax-/A@%"AG/A Z!A/Ab!A/A? A/ARA((/AAP/A JnA/A A/A^A~/AA/A AP/AA2/A` A/AA-/A GAX/AAX/A`̃Ah/AAP/AA(` /A`]zA /A Q3A /A |A=/AcA" /A!AP /AA8x/A@GA@/AflAH./A`A@5/AAЄ/A`;A/A}AX/A#A/A (A/A@OA/AAM/A JnAo/AAx/A3lA /AAh/A SA/A`KA/AAأ(/AdA@//A`$A5/A A8?/A٧AG/A{AxX/A`!A%f/A@ Ar/A A0/A@ A/Ak A0/Av1 As/A$ A`؝/A A/A A'/AJ A/A Ax/A9 APٽ/A H A|/AH A 4/A- A3/Aa A /A*A(/AA]/A xAH/A+Aǣ/A mAĢ/AA8R/AH\A8Q/AA8I/AA/A,Axm/A` nAH/A (:AЦ/A@_A跋/A`-Aw/AbAh/AA+/A@?AX/A@p Ar/A.Az/AsAy/A At/AA`t/A6 A@$o/A`ANn/AA{i/A?A0h/A`mA8c/A@EA (/AAu'/A@gAhL/A,A0/ADAY/A`dAh/AA@/AAw/AսA({/A?A/A RAT/AA/AAʾ/AAX/A`vA@/A@ A`P/A SR AU/A`O A@/A` A/Ak Aأ/A`2 AH-/AE AH2/A A@I/A AL/A` A_/A Ad/AX A b/A@G AX^/A` AdO/A` AV/A Arf/Aj Ai/A@G Ask/A@p Ah/A" A/A Ah/A q A0w/A Aȱ~/A`; Ax/A A/A A /Aܾ AXƝ/A_ A/A* A8 /A= AIWѸ/AsE_A A#A8 /All A|r/Aߙb AG+/A?& A/A A+/A Av/A` A״/A A_/A@A/Aa2AP|/AAC/A JAH/AaAp/A A/A&{AH/AAP/AA`/A#A(`/AjA0/AVA`T/AA@ƌ/A`TA?/A AO/AKA/AA/A`AP/AA@d|/AAxx/A@,A@s/A"A^/AAYY/A~A/AgA/AvAq/A A/A٥Afk/A|Aq/A@Aq/AnAt/A`KA0y/A#=AX(~/AcA@>/AA/A/MA/A'A _/A`3Ah"/AvA`/A@AhĒ/A@PA/A (AY/A`bA/A<Ap /AAЮ/AA/AA/AAHa/A଼A3/AAP/A?Adz/A`A-/A?A/AA/A]7A/A@W<A/A@ Ah]/A@>8 A@/A1 A@/A_AT/A@JAxZ/A .A@a/A7AFe/A ]AXa/A A [/A AT/A( AT/ALALk/AAon/AVAȱm/AApo/A A7|/A@AZ/A@ Aؒ/ARAH/AaA4/AA/A`A/AA/A A0/AX A/A,AHm/AsA/A A0/A@p0Ah/A*AhU/A=/A/A A /AU3A/AA`/AA /AIA/AAb/A$A/A`A/A %APM/ALeA0/A SA/A@NA`/A91A/A oA/AAh/A@A/Aq>AHi/AzA/AA/A+A /A1AL/A^A/A`A0/AAP/A@!AXy/AF`A(/AA/A?AXy/A<AhU/A@WA(/A A'/AA/A4A8t/AGfAp=/A`A;/A A/A&iAp/AA(/AA/A HAd/AAֵ/AA`/AA/AWAHP/A AxG/A^A"/A1 AA/A A o/A=!Aƾ/A@>!A0/A!Aѹ/A`!A8/A`d"A߼/AE"A0L/A"A7/Ap"A /A@5"A,/A#Aȶ/A` &#A/A]#AH-/A A#A6/A@#A/A@i $Aѯ/AHR$A/A`f$A/Am$Ap/A`7%A/A`x%AZ/A`%Ae/A &A/AzU&A1/A&Aq/A+&A/Ao&A/A@P)'A /Al'AU/A'A /A'A /A?(A/Ad(A/A J(A/A)AL/A 9)AhU/Al)A0/A`)A"/A|)A(/A@,)A/A)*Ap/A@,C*Ap</A^\*A/A *A/A@*A /A*A/A +A@F/A[+A A/A +A/Aza,A0f/A,A 5AТT/A@5A8Vb/A 6Aw/AD6AH~/A@!X6A@A/A6Aڨ/Aլ6A`/A6Ao/A7A@B/A E7A0/A@n7A/A`f7A/A7Ae/A-8A/Aod8A<*/A8Az2/Ao8A@=/A(9A8E/AT9APP/A9Ao[/AF9A(c/Af9Ajo/A`2:Aw/A!:AL/A`]:A,/A֚:A'/A@:A1/A:Ai/Ag ;A/ALg;Aj/A Z;A-/A S;A#/A"<A/AH <AX[/A=<Ah/A y<A/A@`<Ah$/A=A&/AJ=A+/A y=App6/A =AF/A=A`GF/A@ =AoL/Ad'>AW/Ah>A@d/AR>Apyg/A>A`Pr/A@ >AЄy/A>AF/A>Ah"/A@ 0?AhQ/AC?AH/AP?A/A e?A/A;?Axz/A?Ak/A(@A_/A@@A /AQ@A`%/A1A !/A`hAL,/AoA;/AAЄS/A A0\/A@A8t_/A ҆A;Y/A`dApQ/A AQ/AfUApZ/A~Aj/AߏAv/A`}AA/AtAph/A` AwV/AൊA6/A`At/A 펩A{/AhAБ/A iA/A 7A(`/A A/A A/A l A{/A(Ail/AE&A]/A`b7A8O/A`@AvC/A?A0L9/A`4A/AڧA>/AɧA/AħA/A@ߺA/AAX/ApA8/AhpA/AGA/A`'AQ*/A36A@0/AAb9/A@ڤA C/A笤A_/A`cA/Ah5A`/A`Ah"/A߶Ae/Af{A0/A;CA/AAAx-"/A9Ah +/A :A2/AּA;/AAg/A@.!iA0+/A`hAX/A@hA/AgA/AGgA/AsfAR/AfA0U/A`eAUt/AQKeAb/ARdA]/A@EdACS/AdA0%G/A@dA9/AdA0+/A,jdAX/A ZdAx/AQdAX/A#PdA6/AEOdA0/A TdA@q/A``dAP|/AjdAx/A`qdAN2/AWdA*/A,FdA/At@AdA/AcdA'+[/AZ૔dAz3!/A"dA/A@ydA`6/AudAȁv/A7-YSdAx)o/A0,dAH]/A`cA6F/AgfcA@z=S3/Ay3bAHP#/A@`bA@k"/AhbX(aA /A@# aA~/A1aA./A9d= aA u4/AX `A\c/A |`A\ i%/AC"`A7+/A`m`APA/A$PA/A@OAxW/A@OA }/AViOA~/ANA@/A@NA/A@MAH/AdtMA/ALAث/A`LA/ALA@/AEXLAU/A3.LAPk/AsLAЮ/ALA`Á/ALAl/A0 LApS/ARLAH/Ad*LA,=/A/AcNAT/A`NA/A`NA)/A@`NA{/A OAΣ/A (SOAx>l/A`OAx/AX6PAX/A NPAH/A |gPAx/A qvPAx/A{PA/ApwPA/A`RPA/A1PA@]/A SOA./A`POA/A?NA/A&MAA ji/Aז>A?n}4/A5>A?c/AW =A((/A9T=A!y/AH<Am/A@P:<AK[h/A5';Ax)e/A}n;AXWd/A :Ac/Ag:An/AE8Aۍ/A@&7Aܞ/At6AvT/AT6AB7_/A5Ah/AY5Ah/A5AU/A`o@4A8x/AΆ4AЮN/A2Ap}w/A 1Ai/A@M1A/A 0AX/A`1A ҭ/A@1Au/A1AB/A1AH/A`0A~/A`]0A/AE0AP'/A?.Ah/AN.A\/A Ɍ,A3/AI+A;l/Au)A+x/A'P)A]S,/A^4)Ażה/A#")A/A@>)A/A$O!)A&/A ")A]M/A,)AP7/A`)A#c/AUԡ(APg/Af(A@/Ag(A]/A@!-(A f/A (A@o/A('Aq/A@'A@s/Am'APMp/A k&A+k/A %Ad/A@$ANe/A@,$A f/AH$Ai/As$Am/A e$Ags/A _$Aً/A ^$Ah/AY$AH/AL$AΞ/A %$A/A+.$A/A@NJ$A``/A@ r$Af/A`}$A(;@/Aiu%A&ν/AT%A$~/AWٸ:&A\_/A&A@/Ae D'A/A 'A/AG'A~*/A(A`4/A`K(A8IM/A (A[/A@)A\/AH)AP_/A)AZt/A*A/Ap*A/A`̲*A0*/A I+A/A+A>/A`+Ax-/A,AE/AX -A/A,-A/AW-A/Ay-Ah/A Z-AP/A-A6Q/Aj&+.Azb/A.;.A A/A %\.A`/A{.A9tva/AŚ.AOU/A])܄.A/AJO.A/A.Ao~/Ayc.A󏚣/A;.A/A^A.Am Î/A].Ar/A".A`Pm/ALJ.A@/A.AHn/A@0AQZp/A0A|r/AWG0At/A`d0AjhGw/Al0A.j`y/A50A0|/As0A`T~/A0A*cX/Aʩ0AHЍ/A`0AXW/AH0APͷ/A0A/A0A/A`1Ah@/A1A"/A-1A1/Ak1Ax/A`1A/A"1A/AL1A0j/A2A/A`'2A/A@H2A/A@yg2AG'/A O2AS7/A2Ah"[/A$2AH2i/A2APkx/A@ 2A`/A /3A(/A`f,3Ax/ARI3Ax/At3A/A3A(/A?3A~/A@ 4A@/A"4A=/A4Ax/A5AA-/Asd?A/A?A/A|@A՝/AE@A%/A )CA/A 1^CA/A CA/ADAi/A`mwDA,/ADAN6/A EAG/AŎEAr]/AFA^/AFAd/AFFA0ݷ/AIAȓ/AMA83./A }NAH/A0LOAȲ/ARA(/A=RA/A6SA/A@SA0f/A`TAho/A@UTAP/A`fiTAh/A~TAH/A2TAp/ATA/ATA/A@%TA\/AUA A /A7UA (/A ZUA8/AVAxP/A}VA^/A@VAc/A*WAd/A1WAXa/A3jWA0_/A WA(`/A`WA~h/A WA!r/A@ XA}/A 7XA/A`eXAX[/A XA/A:YA/AYAP/A NZAF/A ZAp[/AZA%t/AZA/AhZAE/A ZA/A 8ZA8/AZZAQ/A+[Ah/A@C[A /A@V[Ah/A@ c[AxKV/A /[A/A`;[AH/A [A٨/Ayn[A*/A%f[Ac*/ApޥZA0g4/A7umZA{/A?)ZAߨ/AYA/AYA(D/Ar*YA/A]YAX/AYA ./A/XALH/A]P);XAÖ^/AaWA_/A,TWA/AdVAw/AȚxVAE/A`VA(/AxUA$ /AhUvUA~K9/A}0TA*/AtsTAx)5/ASAO6/ASAy6/ASA;6/ASAH5/A@SA +3/ASA%*/A`ASA$/ASAXj/ASAЯh/AaSAwq/AdSApr/A1SA8/AQHSAС/ARSA]/A`SAK/A@RA@=s/A)RA`Pt/A RA`j/ARAhLh/AmRA(!/AKRA/AQA]/A@`QA`]/AQAXd/A&QAU4/AmPA $/AnPAȰ 0/A,\PA,>/AxUOA"Q/AF%OA_/ANAUk/ẠNA(t/A 1%NA8xx/A"MAz/A'aMAz/AiMAx/A3MA~Ay/A=9T MA8{/A ۻLA2~/Ad]LAV/AsLA /AU6~LAlF}/A?hnLAי/A\LA/A$LAۑ;/AOLA /ALAZL/AݱyKAj/A^/KAt;c/A&KA!/A@rKA"{/AWKA/A`/KAYt/A^c^ KAh/AgJA`!/A JA /A&JA(/A`JA؎/A@JArn/AI{}JAU/A OcJA/ABh JAج//A`DIA1/A`IAJX6/AEO/IA:/AIA=/ANsIA?6?/AƱn[IA@/A9}AIACc@/Ab.(IA2?i?/A IAhn=/A`2HAf/Ay6HAi A/AhpeHAF/A%879HAM/AHA\/A?GAj/A`GAyt/AXb/AI&zDAx/A^DAcn/A0DAؒb/AɧCAbb/A#CAYb/AǠCAcb/A-CAyb/A`CAb/A/CAb/A̚CAb/AtCA5c/A`2CAc/A@֕CAd/A൓CAe/A ґCAxg/A@CAk/A ACA@z/A ZCA/A qCA/A lCA/A@}CA軄/A|zCA/AwCA/AuCAH-/AQtCAi/ArCA/A pCAhՆ/AlCA/A`jCA/AhCA8/A CAx/A`KeBApu/A`BA(u/AAAPu/APAAPYt/A"AAq/A`[@AXJo/A`@Ah/A @Af/A @A0fa/A@Nd@A]/A`@A`]/A@A(]/A@A]/A@AD^/A @AH^/A @A{_/A@AV`/A+@A(\a/A?Ac/A ?A`g/Aj?Ajv/Ax?Ay/A2?A}|/A?AH!/A?Ap,/A?A /A?Ax/A ?Ap=/A@`?A/A?A@‚/A߳?A@/A`?A`e/A@ N?Ap<|/A ?A ku/A`T>A]/A@y=ApR/A=A[L/AFF=A`H/A@=A(H/A@G<A(-H/A<ANH/A<AH/A <A`I/Az<AI/A<APMJ/A<A,K/A<AxdM/A<AO/A0<AS/A&<AVa/AÍ<Ap,g/A`<Ai/As<AP'l/A {<Axm/A3Au)/A3AmM/Au3A*/Aʮ#3Aq%3/AT)2A~:/A2AKQC/A¡}2AQdsM/A 2AZ X/A.ؘ2ACid/A{Yw2AeMp/Ak t2A|/Ay2A/Ag2A{/SZ/A$Bi2A$k{/A 6~2A/A 2AXp/A`]2Ax/AO2A!/A2A~/A 2A/AL2Ax~/Aa2A&/A2AH/A3Ax-/A^3A(,/A3AXJ/A`3AXT/A` 3A f/A^(4An/A |84Aw/AD4A8t/AL4A/A P4AX/A@P4Ah/AK4AŪ/A C4Ah/A@G64At/A4A~/A@3A@u/A@3AX,/As3A98/A43A8c/A`2Aώ/A2A/A&U2A_/A`2A!/Ay1A9/At81Ag/A |0A4/A=0A؎/A s0A`Ǽ/AL0A/A`K?0Ae/A@,0A/AF0AX0AM0A0A/A8"0A/A@00A`2j/AL$60A|`/A:0A\/AX>0A ]/ATr@0A_/AB0A@c/AC0ATi/AD0ABp/AF0Ax/AHG0As/AJ0At/AXL0A /A S0A@d0A`E[0A 81A(]0A`)u1A|2c0A=1Alc0A1AHd0A 1AlOe0A`b2Ak0A02A<=l0A92A%m0AC2A3s0A 2Av0A;2Av0A2A`w0A2Ajw0A2ADXw0A$2Ah(w0A3Av0A 3Ayv0A`3Av0A3At0A+*3As0A@C83A, q0AE3ADl0A[Z3A܆`0A@P3A5Y0A3AG0A@W3A?0AR4AL>0A4A$<0A$4AԼ90A <4A60AV\4At;/0A`4AW,0A=4A*0A4AQ)0A@r4AP"0A5Ar0AZ15A,+0A@>H5A 0A X5A/Aw5A/A`95AH/AÆ5Aȼ/A 5Aa/Ad|5Ap/A@}5Apۏ/A5A/A5Aj/Aα5Ac/A5A@]/Až5AD/A 5A0*/A5A@/A 5A/AD;Aj3/A`b;AQ'/A`5;AU/A`;;An /A:Ay/A":A0;/A}:A@/A`՛:A7/AR:A(/AF:A(i/A@:A(:/A:A7/A:Ap~/A:A8m/A`d;A=^/AN;A=Q/At;AF/A;A A/AV;A;/A@<A؁2/A`<AX*/AO<A $/A@G=A"/Aj=A"/A =A%/A@>A!&/A?>A'%/A%?A@"/A_Z?AI/A@?As/At?A0 /A@?A /AM@A@5/AVv@A(`/A`@AXy/A[@AS/AA4AAX/A@`AA07/A&{A e/A{{A+K\/Asa;M|AN/Axb|A\;/A4[}ALE0/Ao}Ak*/AsF~AHa)/Ap~AAa3-/ArH;A/V6/A&䝱A#?D/AV=AV/AAcHp/A#uAn/AUAQ/Aȓ7A/Af_Ah/A<Acfp/A~"@AR/A< IA^U'/AUAy/A#}xfAk, /ATzAh/A<A/AnCgA/AUłAr/AqA/A`AV/Ar^lSAWe%/AcKAJ2/A;Ap[B/A@58AN܆L/Aul][ARH8(U/AA;\/A%wA`/A^A!c/ARryAEd/AXPAeu-c/AskA!_/AxAHZ/A<CAPR/AFA(H/AufҰAYA/A MA?/A0AZ%oOB/A AH/AB$AS/AA+p/A{A qV/AmNAIcB/A#"Aͫ/A0uA ޶/A@lcڍAo`/AWyʻA'UJ/ABA1Ș/Ao򑚀A]O/AB!RaA$z/A>Ah//AAF/AG7e Ai?yr/Ai&Z,A=vif/A"&=A⋌]/APApZY/AkxeAPY/A`[{AB#9W/AA9Z/AcIA6*_/AЉAqXi/AnAc_fu/ApiAA/A@yAD*/AA`/A+jAu(/A܂fA$/AcV~ĝA}e/A!qEA7/Aɜ})AY/Ann\A87#/Ao"A8k/ATA/AVA/AG{ iA{5/ACp,AgO/A.!rA}Ʊ/A{#AF/AN }A񳱸/AOjA/Au!vAҦA4r/A^!AO/A:0AoW:/A߃oKA/A;WAG/Abh{AOO^/A֎mڨA/AccާA- /A֡A8kS/AVx\Ay/AV2䠨Ap"/ApAV5/A7s-A/A@PrA\а /AVyA%U,/A!A)W5/Agw9mAQM9/A\ͪA:/A/A/ALAm/AlAx /A61#Ay v/A'A~/AjiA/A`A/A<ԯA?/Am%AvJ|/AnF˃jAn/AL˷Ac\/AA%AZ/AMA/A`A(ё/AɼA)Ha/Ad`ӼAD/AxؼAM/AtdؼA/A[z'ӼA'/A]ȼAsnۮ/A`ӹA?>/A;^Ay/A`AJ-C/A2xlA|/A EMA4A1/A:{C3Ahه/A,A~/AIA$u/A=A-Jk/A6{&xAxza/A A!1eZ/A#! AiCS/AT ASJL/AXeAE/AA(?/AAw7/A-O]ۻA庄W//A,лAnֵ&/AFӁ˻A~/AٍEʻAN/A@GϻAx /AA A/A`A/A )A/A@mA ^/A6Ag/A@,8AV/A?WAxi/AOAX/AAܓ/A׽A|/A`KAk^/A:A@5J/A`;WAXt7/A`)hA./A kA2&/AkA"/AjAU/A@gA/AX]AI/A+A/AA(/AA(/AMA@/AAf/AhAP/A-A@/A`GA|/AUA`ǥ/A ZfAPg/A ҶA[h/AMTAX/AِA/A9aAH/A aAȇ/A Ap/ADA(i/A A AZ/ATA`6Z/A UAQ/A@AJ/AA C/A1A*A/A ObAX;/AkA@$8/A/A@3/A@N=Ap%/A Ah"/A^A /AA`/A |A`/A`A /AaA /A8A/AA/A@PMA/A`RAd/A iApN/AAP/AĨA/AA/A8A/AXAP/AA/A9 Alu/AAZh/A?Az/AvAs)-/ARѰiA7ׅo_0ADAp0A}A{0A`]A0A A{0AA0A A0AA0!0AtAPo/AAP|/AAg/A ZFA/AA/AMpA/AdA/A2A" /AAu+/AA0/AqgA/AHA()/ABA/A@5A|/AQPA?/AA0YM/A 2AK/A@d A(6/A A5/A A"/A` A6/Aq A/A A(~/A` A`/A A/A@>|Af]/AANj/A AP//A4A_/A`8A՛/AAt/AAX/A3.A/A A@/AbA*/A@>A*)/A,2AP&/A`2 A(X/A-A/A^mA/A@LAP/AIA/A >A;/A:A/AA0/A@A'/A0AAf/A`Aq/A (AP/A lA/AB6Ax/AaAr&/AAPx*/A@AU/A`;Aؒ/AYA8b;/A Ah?v/A|AF/AAh/AAS/A@A/AdA@1/AA/A A)/A3A//AdAN/A@cAX/A@WA/A1Ahn/AfA R/AgA/A@yA/AAD/AAA/A@WAh/4/A`AXd/A1AHLo/AsA/A A/AoA@/ATaA/APA/AKA@5#/AMAW*/A8A$/AqA/A c'A//ANAH:/A A8'U/A Ax/AAP/A@eA/A A/A 82A/A*\A /A A;/A`A(/APA /A@=A(-/AA0&/A@A/AA/AnAx&/ARCA((/A@`A06/A@Azc/Aa)Ax/AAl/A@9AӺ/A1AW/AAx/AA Z/AsAl!/AsA8/A?A /A`)>A/A ɋAl/A@hA /A`ʃA /AߴA -/AIA/As,A"^/A AF/AAh?v/AAx/AAP+/Am&A/AJA`/AtAh/A AGU/A)A`Q/A`TzANg/AAXhU/A6SAEN/AA/A֠A`/A֠A@F/A 8KAb/A AH/A&AЄ/A AAݪ/A Ab/A-eA pv/A`A[/A`DAT/A}AP/AHvA˵/A YAp/A sA!/A"AP^-/A@AP/A ܨA4/AA( /AA8/A;Ai/A ZAi/A@Ay/A6AA/AwA/A@AP/A3UA/AA#/A`̷At/A}A/s/AA/A ɵA/A&.AЄ/A@AKv/A`յA(/AgݶA/AA /AA#/A`,A;/AA\/AA@k/A-aA@ӆ/AhA/AA/A ĹA/AA@k/A@A5/Aa{A0/A@iA$/AzA /ATbA/A`]Ax~%/A`fA@`i/AXAN/AAxW}/A`vA0/AAHD/A<A p/AoA(d/AA/A 8A /Aw*A@`/A*AxG'/AA/AA/A:Ab/A3 Aq /A4HAh/A AP/A)Ah\/AAB/AA/A@EAHv/ADA$/AA/A`*A/Af~AxQ/A A /A2A蕼/Ay A1/A A'/A@w AxmW/AtA=/A@#A'/Ai[A/AY!Ap8M/A$A؞/A(APfg/A}*Axd,/A@*AI/A:,Aȋ/A !+Aؒ}/Af)Aa/A'Ar/A=c%Ai/A ~#A2O/A "A0w]/A`!A m/A&!Ad/A@HA+h/AyA8q/A \\APk/AHA?/At}A#/A0gA7>/A#ApZE/A$A8A/A@A=/AA0*./A cA0*./AtAPoKA`Xt/A KAY-t/A;JAHt/A@yJAht/AJA>s/A`)YIAs/AUHA Rs/AHAss/A*tGA@s/AFAms/AFApNds/A`PEAWs/AྞDAVs/Ac"DA2as/ACA;bs/A{BA}is/A AApNds/A /AAOs/AeAA7s/AAA(s/A`KAA!s/A`#AA8s/Ao@A7s/AG@A8Qs/AfX@A8Ecs/Ao@AȘls/Aw@Aps/A@d@As/A Q?ARs/AdW?Avs/At>A8ts/AN>Ashs/A@=ATs/AaU=AX6s/A/<Ah]s/A H<AFr/AX<Ar/At;Ar/A`B;A,|r/A |;A6*r/A lQ:AIr/Aa9HA1~r/A GA8r/A`mGAUs/A?3HA*s/AHAVs/A VIABts/A_ JAps/A`RKAQzs/A,cLA s/ALMAys/A[MApxs/AMAHVs/A@%OATs/A$OA7s/AtsPAX#s/AHPA0*.s/A'QA@5Ps/AQAP^s/A@RA@s/A 8RAks/A`ogSAxs/ASA舏s/A 3TA s/AQTAbs/A`TA"s/ATAps/A@$UAs/ASUAt/A$UAHCt/A;UACt/AUA@@t/A`/VADWA0{Gt/AV_WA(Ct/A&WA@Bt/AXWA3;t/AWAh;9t/A XAP/;t/A2XA,;t/A\XA`2t/A_XA)t/AgXA8 t/A`DXAHt/AmXA"t/A>XAX(#t/A XAt/A ZWAt/A`WA*t/A@ߩWA&t/A@|WAX(#t/A`2>WA#t/AWA Nt/A@VAnt/AVA, t/A[VA(t/A9VAs/AVA\s/A`VAs/A sVAas/A@,VA0*s/A'*VAOs/A@VAs/A5UA@s/AUAs/A`UAps/A@UAs/A sUAs/A@lUAs/AN;UAs/A@UA8s/ATAXs/A@ TAs/ATA s/AoTA~s/ATArs/ATAgs/A`vTA\`s/AR UAUs/ATAvDs/AR UAH9s/A@-UAhU3s/AkdUA.s/A'UA%s/A UA`+s/A`KUA*s/A sVAps/A2(VAs/A`7VAts/AUAs/A@UA0 s/AyUAs/AJUASs/AR UAns/ATAP'r/A TAr/AoTAr/A HTAr/AbTAr/A/TA Ns/A@TA"s/AhSAxs/ASAP'r/ASAx r/ASAv/A6]AaCv/A\AKBv/AO\A@1Ev/A+[A-v/A[[Av/A`B+[A0u/AZAh@u/A`)ZA޿/A`bAȩݿ/A^{A/A5|A@0/A@pAd/A@.YAx/A bAP@/AzkA0/A@ fA%X/ANAF/A`8Ag/A`A؟{/A*A(`&/AWAp,y/ADAf/AhA8/A/fAe:/A TAp!/A`<A0/AMApĮ/AuA/AFAhդ/A`b8Aj/A>A t/A@DA` /Aj&WA/A cA o/A``AM/A`÷nA/A 6sA/AzA c)/A؀A޿/A`bAО/A4dA؝/A 8 ~Ar/AFAx /A `Ap/AmLA/AHBA/A,7AС/A2Ap[Ρ/A ,ApE/A`(A,/Ac2Ap/A#A^/AsA 3/A A@/A`ZAJ/AjA~/A:Adś/A Z1FAp/A`TAD/A2`Ah7/A@gAŖ/A`*sA@//AhnA1/ApAؼ/A`?|Afؕ/AɍA@/A ΣA8祗/AF_A@z/A-Ab/A`AО/A4dAȃj/A@AI/AA/AdgA@ RA/Al*\Aʁ {/Ac9AU/A=3z|Au/AYA0/AANu6/A;As&Y/AkmVyA*x/AxAe/AM^A)/AA؈x/A>59A/A^ASVNj/A7h= Aab_ɋ/AXWvAHŋ/A`߳Alu/AA/A AA(:/A`okA@$P/A9A𴝏/A`AUΏ/A@y}A/A ZAa/AA/A^Ah8/A {AH!/AtAP͔/AnA0/A`/hAx>/A3R_A O/AL\AD/A`էWA/AVA>C/A 8wYAk-/A`dNA:/AHAx/A@Ax/Aa=Ah/A;A@/A+@A[/AAA!/A;Aq/A E3AH /AH<,A@/AO&Aз/A$A\/A "Aye/A@y{Aye/AF]Ah/AA]/AjA/A`;5 AXE/A@A@/AAF/AA/A=*A(h/A\A-DY/AocAV8N/AvN\A*G/AAu APF/AuCAI/AYyAQ/AO{uA<&^/A[uCA ^o/AK/AO/A'x"yAܞ/A?Ax /A6Ah@/A _AF/AAv/AjjAD6/A`~A`T/A ZA0/AA6/A`A/A<AК/A`A(U/A`DBAຝ/AA/A$A()p/AAѷ/A@A /A@5AX5/A@A(/ARA-/AgApj/A TAH/AAH/A`A@/AAj/A ZA /A`AP/A A /A A/ANAZP/A A/ADA`r/A nA0,/AnA@/A HA/A SAx/A@A6/A uA/A@A1"/AoA/A A@}/A Api/A@A,/A PA/AOAذϘ/A nAF/A YA9/ARAx/AvA‚/AAʫ/AAߘ/A AذϘ/AA4/A@A/ApRA&/A"A]/A[Au/A2A`r/AAߙ/AIpA,/A@A/A@,PA/AdA o/AAқ/A AP՛/A@rAȋM/AAhͲ/A㯱APY/AOAiA/A A K/A@AF/AA8"/AA@/AAÚ/ANAXؚ/A4Aj/AAh]R/A`AiF/A=xAh/Aj5Apo/A@wA)/A`ΐAt;/AA tA/A` A(\/A@JA¦/A=zAƙ/AcA렙/A _A/Ap{A/A 'AX/A`fA~/A~`A/AgVAe`/AHA=/A`93@Ad(/A@[3A/A+A&/AK*AB/A@y#AР/A[:"A05b^/AC*(!A @/AM" A2&/A^ A$</A5KA[/Aֆ'A /A-A&/A:ADƗ؎/A6GAԎ/AQ]!AL8+Ԏ/At/[UnA"u؎/A /A{QO/AY- Aq%/A_vbAQ/A̾A/AAUvNI/AfAI7/AUS AБ^/AfD̵AP!/Af9y;aAZ'/AUn\A/A ~Ax/A6 A /A@A0{/Ak1A/AFAU/AiAH{r/A= AB/AA/A8iA@B/A \ AHc/A A /Aྦྷ A@d_/AU AZ%/A6 AA/A`) A2t/A A/A`) Axil/A \ A/A@A/AZ A1/A6 APn/A@ AP/A`) AԪ~/A- A0}/A A(|/A Ax+|/A A0{/A  Az/A | A@y/A 8M A0[w/A Au/A٣ A Rs/A Ar/A \ A1p/ANAo/Aj!Ap/A 0ADp/AMAp/A`A p/AAj,q/A A؁q/AW"Axmr/A $Ar/Ack'As/A )A0Xt/A`b*Au/AF+Asu/A ~,A{u/A -Av/A`/APw/A .Aw/Ay-Ah4x/A-%,AFx/Aj)A@_x/A@߷)Aw4y/An*A/y/A`+AXoy/A%-Aby/A -A)+z/AK.A Θz/A .A`z/A<0A(J{/A@W$1Abf{/A1Az{/A1A*{/A/A${/A7.A=|/Aa-AX|/Aa-AH}/Aa-A0{a}/A Ғ,A}/A-%,Ap|~/A Ғ,A~/A@.A~/A`[/A~/A 60AHj/A`B1A~/AБ2AN/A`K3A@/A#5AH;/A#6Aj/A57A/A8Av/A>7Ag/AlyL7AVջ/A0s7Agˀ/A\&Z97ArdWڀ/Ahv87Al6U/AD7AD/A8Ab /AB8Aln /A^\q8A/A8Ah/Atk28AO/A=09Ap /AY9A:WFSL/A_<:A {/A?o;AԬ/AՖ;A7/A~zv<AL/APV=A@R/A =A,\/A@>A5ʑj/A!2?A>.|/A,?AeW/A?A /A X@Ai;/A ]@AUcƂ/Ag @AZ؂/Al@AIg/A\hy@A`Z/AgAA8/A@A/$/AEm@A6/AˡyQ@ATG/A@A\w7/Am @A+/Ap;p?AFt5$/Ar>A!/Atd*>A 4"/A=Au'/A<AT1/AlC<Ar6?/A/Aൂ2AV8`/AV2AT/AS]X3Ag/ARf3AXh/A&a4Ǎqdž/AK!4AB׆/AArR5AP/A@46A 8/A`K8A`a/A9D:Ax)/AHh:AH/A@#:Ah//A:A/A:AY/A:A(h(/A`v;A /A`:A=/A8AF/Ai8AE/Az\8An/A 7AX/At7Ap0/Aiq7Aڅ/A+7A0*υ/A6Aȅ/A 26A ą/A5A…/A@!5Aż/A5A`G/AS5A讅/A4APM/A34A/A@4Ahb/Aߺ4A`/A4A(/A ^5Aܬ/A9j5A/A5Aw/AOO6A( ą/A6ANŅ/AO6Aυ/A@.7AЅ/A`97Amх/A`E7A@1܅/A@y7Ax݅/A7A/Al8A//A9|8Av/A 8A{/A@9A/A 9A/Af9A`/A@:Ax/A`):A/A:A/A:A/A9;A/AG;A1/AFh<A2/A@y<Al;/A <A`#/A=A/&/A =AA/A ~=A M/A`m<Aq/A <AЄo/A2<A0 G/A<A@3/A;A _+/AD;A[/A`;A`/A;APގ/A;A@O͆/A$:As/A:A/A`:A/A ۡ:AX#/A :A_/A;Aц/A<A/A>A//A?A /A>A"/A@W>AxzB/A@>@Ap=/A@\EAˇ/AEGA /A3wGA7/A`FAH/AfGA7/ABGAH/A_GA?/A SGAe/A}HA@3/A@GA3/A@.GA*/A@#GAh.*/A@5GA"/A cGA(/A@5FAb/A AGFACr/AGA88^/A@CGA88^/A$GA/AFGA/AGAi/AfGACr/A9FA`/AEA@j/A@iFAx؊/A;1FA0/AEAiG/A`;EA(yQ/A SEAY/A3EA a/AEA"/ADA@ԋ/ADA( /A/EA;/A"!FA(L/AБFAS/A9FAxƌ/A`GAxƌ/A _GA Œ/AGA/A9GAX4/AGA:/AӄHAPߊ/A!IA/AIAQ/A@IAI/A#^IAHN/A[IA0L/A1IA"/A`IA`/AIAȈ/AT#JAˆ/AIAIV/AJA=f/AMAL/A?PAC/AOA@/APA/A 3QAM/A uRAq/ASA+/A`FUA/A$YVAp/A@VA"/A`WAVV/AXAxn/AYAu/A@[APQ/A` \A8,/A%]AX/A1^A:0/A `A/A4aAU/A /aAs/A@`bAh/ANzdA7/AvfAѨ/A`$hA/A`mhA@/A BiA /AsrlAOډ/ATtAH/A vA/A?zA0Չ/A:{A@/A`o|APÉ/A 8}AXhӉ/ASA0&/A)`Ar /AnA$/A@NAN/A( Ai/A!AQp/AAPc/A0 An/A`mAP//AkA7/AǥA06J/A͇AP/A`gAW/AA젉/A@yA0Y$/A`APwΊ/AA; /A,A/A5A 4W/A`ŐA'/A@WAKI/ADAз/AAǕA -/A?ԖAQ/A=A-0/A`pA~a/A`ApIV/A8A@q/A J(A0*/A AAh/AٞA/A`A} /A ]A pC/A èAb׏/A;A؟ُ/A` A0H/A&A/AiA/A [A/AA0H/AFAXyR/AWA7/AtoA@5/AjmA/A AĎ/At*AX/A (A/AaA`a͎/A@@A /AcAe/A`A>/A@A؁/AA`/AèA0Ӎ/AApÍ/A̴AP8/Af/AP/AXAݩ/A[AP/A`A/A`o;A@S/A_BA!g/ATA F/AwAP|)/A`At/A6A/ApAI/A'A/AA2/A@,AҊ/A`]Axm/A@޽A`َ/A@AÎ/A^A걎/A^kAч/A@VA)G/A AP-/A@؝A /A`m4Al/A GAȘO/A@ZA/A A /A dAoi/A`大A/A .Ax/A@,MAHnC/APA`/AA/A%A//AA8/A<AG/A@AX#/A(APZ/A@OA/A qvA@$/A^^A8_/AgAen/AAH;>/AA//A@7As/A`qA6/AgA/AA4/AA@=/AAK/A@ANz/AMA`*/A0AXӃ/AAf/AA(l/ABAs/A[Awp/AsAwp/A@.4AH/A&AK/AQAeH/A<A`q/AA8b_/A PAf%/A PA,/A AX/A IA0/A}ZAX5p/AcA;>/A-A{ /AtAI~/AtA./A3AX5p/A$A)/A`VAP,/A`!A/AAP</A Ax\~/AAH}/A A4Z|/A`iAH{/A9Az/AA`ey/A`[A(y/AA0x/A,A #v/A,A}v/A 85Ahu/AFAt/A@r`APs/A4Axq/A]Aɛq/AJA`q/A@CA r/A 5ASr/Ao6A0]r/A 6Atr/A+7Ar/A`%7A(r/A [7Aplr/AЕ7Ar/A`7Ar/A@8Afs/A?8A8s/Au8A؁Ms/A Җ8A`fs/A8Aȓs/A@8As/A;j9Ads/A@:As/As:At/A@N:Azt/AV;A%t/A cp;A"'t/A <A:t/A}<AYt/A <Akht/Aa=Ayt/AI=At/A =AxXt/AF>At/A`K>Axt/A>A;t/A>At/A`>A؎u/Ak>A,)u/Ags>AhCu/A@^>Aou/A>AXhu/A`>Au/A@-?Au/A>?Agu/A@-?A`u/A@؀?Av/A?A(v/A?AX>v/A?A(Vv/AG@Aeiv/A@Axv/A@Av/A @Av/A`@AA`6v/AFrAAȥv/AOAAv/A@.AA@v/AAAv/AAA(v/ABAIfv/AAAXEIv/AAA A,v/AߟAAbv/A@~AApyu/A_AAku/A |@Au/A@Agu/A`@A`u/Ay@A+u/AP@AhLu/AC@Amu/A.@A Zu/A Z@A Bu/Az?A@"u/A?A`u/A@@AP@t/A<7@ANt/Aف@At/A AT@AhѬt/AC@AHt/AC@AHt/A4?AAt/A Q?APmt/A ?AWt/A ?A&Dt/AC@A`t/A`#AA2t/A &AAp+(t/ABA t/A ^BAs/ABAs/Aa!CAs/A@NCAhs/ACA(s/A"TDAs/A3DA(ms/A'EAt/A@#UEAt/A'rEA2t/ALvEAP8t/AEAP8t/A=FA=t/A fFA=t/A`FAY-t/A_FA4)t/AF>GA3t/A@GAgKt/AOxGAP[t/A`TGA]t/A@GAPmt/A HAXtt/AHAyt/A^HAt/AE#IA t/A/IAEt/A=HA@Ot/AzHA8 t/A QyHAt/AqHAt/A ZHAxt/A AIAt/A@PIAxt/A2IAt/A;IA6t/A`IA(u/A@IA u/AJAp[.u/Ah JAJA:0x/A JA@$\x/AIA@x/AHA0Gx/AGAx/A GABy/A:FASy/A`EAty/ApEAy/ANEA(-Ny/A@EA9y/AFAXRy/A \xGA8ły/A@`GApy/A@`GAhy/A@%HA Ay/AIAhy/ANJAy/A`KARy/AELAxy/A)MA8Zy/A`MMAy/A>NAy/ANA,y/AOAעy/AjPA;y/A@ZQAy/AQAy/A`;QAz/A /QAz/A RA-z/A"SAO z/A@SA*z/A7TA};z/A TAhJz/A TANz/ASAAz/ARAIz/A RAVAy/AUAy/A`UAPHy/A`2pTA8y/A@TAy/A@.SAH]y/A`SAH]y/A"SA~y/A 8tRAy/A RAy/A&QAhy/AnQA`sy/A?PApy/A`]}PA0ry/A@OAcy/A'cOA'Wy/AOASJy/A@*OAzFy/A@#NAHGy/A@,NA1y/A9NAp.y/A`MA87y/AWMA87y/AMA'y/A@ؽLAhy/ABLA Ry/AkKA5y/AFGKAy/A` JAy/AJAp.y/AJASJy/ANJAHky/A@pJAly/A`zJAly/AgJA]y/AIAUy/AdIASJy/A SHA>y/A`՜HA%y/AHAHx/AHAEx/A IAx/A`BIA!x/AFJA6x/AjJA@ex/AИJA@}Hx/A JAx/A@ؽLAMAjv/A@NlMA4Tv/A@,gMAe>v/AMA7v/A`NAxu#v/AӹNA7v/A@ENA`Gu/AOAu/A@ NALu/A`NAu/AMA7v/AMApI0v/A`MMA`Cv/ALAUYv/ALA@Ftv/A${LAv/A cLAXqv/AKA /]v/A`KAk5v/AxKA v/A |aLAu/ALAu/A ACMAٽu/AoMA`u/AྤMAТu/A^ NAxu/A8NA0j{u/AHhOAvu/A @PAu/A GRA(iu/A RA v/A A@TAH$v/ATALv/A@NUAv/A@WVA v/A(XAXv/A`EYAw/AZA@1Mw/A ZAh^w/A\Aw/A l\Axzw/A0]AhQ x/A^Abx/A _Ap.x/A`A&x/AOaA"x/A`ܠaA7x/ACbA8w/A ŒbA0qw/A_bA6w/A`TcAv]w/AĭdAgw/A@%eA(>w/A4eA`w/AeAx/A HeA~Qx/AfAvx/AgAh3|x/AggAvx/A hAUx/A@hA@5x/A@hAXx/A0iAPkx/A@yiAXx/AMjA0x/AjAKEx/AgrkAex/A lkAgx/A@lAx/A0lA@$x/A`[mA(>x/A@rnAXx/A@E2oA`x/ApA@$x/A@&rApߤx/A'lsAUx/AftA<~x/AKuA<~x/AvvAtx/A`wAUx/AR0xA"x/A@xA`w/A8yA@ήw/AneyA*w/AmyA~_w/AyAp2w/A8zA w/A@EzAv/A5{Ah^v/A@,v{A0v/A {A(Fv/Aמ{AHv/A l {A u/A@zAԏu/AazA( eu/AyA8Z6u/AAzAhu/A5{A0u/AVr|A"u/AO}AFu/A@r}A\u/A2~A\u/AX~AXDu/A-A"u/AfA(>t/A@,>AXt/A AЀt/AV:Agt/A݀A t/A`At/AX[At/A6At/A@A`t/A`ՄAu/AɅAu/A`Ah&u/A 7ApNu/AAu/A @APMu/A@pކAFu/A@NֆAv/AABv/A`̩A~mv/AmAHyv/A@rAv/A@#A8xv/AI$A v/A Av/A(Aźv/AۋA8v/A Aov/AgzAv/AvA@1[v/AkAB_v/A`"jAvkv/A_Av/AAv/A?׊Auv/AzALv/A‰Av/A@#Av/AA|0v/A dAv/AAu/AfAPu/Ai AËu/A-Avyu/A c8A9ku/AA7.u/A@A(it/A_Apt/AƈAÙt/A@ΈAjt/A<A( st/Ai Aot/A`xATt/A`pA /:t/A$At/A4‡AFt/A'Aثs/A@ ADs/AxAvs/AńA}s/A@Axs/A@#څA9s/A潆ABs/A`xAds/A@GA0s/AAxs/AUAxs/A >As/AamAxWs/A@>As/A AxHt/A`>AUt/A`WACt/A$AH\Zu/A[mA]u/AAxv/AʜA 0 v/A'Au/AgAbv/A`֪A徯|v/A){lAvv/A愕A} v/A?#mA3:v/A^|>A1zw/AnArFw/Ak7˵AK}w/AAhdw/A)cAҭY7w/Aa5Ab}w/A"A}4w/AcɻAw/A _2ݼA$w/A3AKw/AIAC8uw/ANH* A 4`w/A@AKw/A-h_A^2w/AAwv/Aq?A(mv/A`AP/u/AܳA`u/AAu/AVAHu/AT(Ayu/A Apu/AHApΫt/A@ABt/ADnA0YZt/AAhs/A&WAUZs/A(Aīr/A`<APs/AAQs/A\A(s/A Al?u/AAh{/A A0|/AOA8~/A`zAp/AAhS/AAꄣ/AvwjA?/A\K1A? lG/AI AtY'/AB5A/A7Amƈ?/A8٭A2/AKA'L2/A8Jf5A~v2/A'A]&ƀ/A_ RAF:̈́/A*'dAR/AmdA|0Z_/AZ Ap/A.~AX/AHAK%/A Z`A ņ/AAх/AAc/A Aȃj/A@A~>/A Ap;/A A8/AAJ7/A`AW7/A bA`m7/A]A7/AxA 7/AhA6/A ANRT6/Ae ,Au6/A?Y=A7/A?A > /7/A$A>1A-:JG7/A=lIA"_7/A@A7/A nA(`F8/A@ ѷA?`8/A@YA c8/AJADg8/A@7Aly8/AٶAp,8/A RAb9/A@A8w9/A zAx :/AA7:/AA7:/A@譫A$:/A +A]:/AMAx~:/A@PϤA:/A A;/A%A`O;/A`A';/A/9A;/AAh/Ao|AX>/AލA@>/A@A ?/A‰A(~f?/A@5A(?/AzAey?/AE9A5e?/A6LA.N?/ApޅA-7?/A@DžAm1x#?/AEoAi8?/AO;m A7 ?/AIkAǥl?/AD0AY#0>/AI˻A>/AFA!?/A!??A?/Af.7A?/An.A7t?/A%AW?/ASp A.?/A͆A?/AE A7>/AD=AyE@>/Az-A+;x>/AA~>/A ACAy/A@5A*%y/AA؁y/AAy/A畻Ay/A`KA/y/A˼A0?y/A&WA0by/AAԌy/A`KAy/A`;A@~y/AkAXcz/A@,OA3z/AA0z/AAy/A ACAy/A@5A]/A7 AT/A4 A1B/A`S4 AHN/A@i4 A`V/A{4 A_/A4 Aзh/A 4 A0p/A@4 AFx/A4 Aȓ/A 4 A@u/AE5 A`/Ax#5 AQ/AP5 A/Ae5 A(/A8w5 A@B/A@5 A/A`5 ApŎ/A 5 A[ώ/A5 A َ/AX5 A/A-5 A/A5 AX(/A` 6 A/A$6 A/A AF6 A k/A]6 A(/A6 A/A@,6 A/A`6 A/A6 A/A 7 A/A 1:7 A/A`;I7 Ap/AR7 A1/A8W7 A /AL7 Aj /AR7 A`/Aj7 A/A z7 Ap/A z7 AȘ"/A7 A(/A7 A0/AVz7 A 3/A5e7 AН6/AC7 A08/A !7 A #;/A`6 A@/A`6 A`F/A 6 AM/A6 A8VX/Amh6 Ac/AEJ6 A8n/A46 A #w/A*6 A/A8!6 A/A@6 A(/A6 A8/A@.6 A/A 6 AƲ/A6 A0L/A5 A@=Ï/A95 Aˏ/A5 Aԏ/A5 A@ޏ/A 5 A/A r5 A/A@25 AL/A4 A/A4 Ax /AM4 A/A 4 Ap/A4 A@$/A`4 Ah./AVf4 Ax7/ABF4 AB/A"4 A0fN/A 3 AT/A J3 A@Z/A3 AXya/A 3 Axzg/A`D3 Aجl/A߅3 A@Fv/A`3 Ah}/AxC3 AX/A@(3 A劐/A3 A(/A2 A@/A2 AF/A2 AXV/A@s2 A/AN2 A-Ő/A2 A`ϐ/A Z1 AՐ/A1 Adܐ/A1 A/A-1 A-/AVn1 AL/AjP1 A /A#-1 A0/A 1 Ax- /A;0 A/A`0 Ao/A@`0 A /A s0 A'/AP0 AU./A .0 Ap4/A0 A:/AF/ AX(@/A@/ AF/A/ AM/A / A8U/A@i/ A ]/Ai/ Ae/AS/ A0n/A@y=/ ALx/A%/ AQ/A/ Aѕ/A. AxF/A`. A/A. A/A h. A(z/A M. A3/A6. A/A@%. Aȑ/A- A8ϑ/A@- AUؑ/AЮ- A/Aa- AP/A`vk- A'/A S- A/AB- A/A- AP/A, A /A, A`G/A, A7#/A`x, A8./A L, A4/AV4, Axz:/A, A8@/A, AL/AO+ AR/A+ A@Y/A`+ Ah/A@l+ A(p/A`vO+ A~w/A`5+ A/Ax+ AXƇ/A+ Aڎ/A * Ae/A@* A6/AѾ* AhL/A* Ah/Ay* A趷/Ab* A/AiJ* Aƒ/A 81* Ah͒/A * AWՒ/Aa) A1ݒ/A ) Ai/AA) A@F/A) Ao/A) A8 /Ap) Ah/A0V) Aȃ /A`:) AX/A c) AV/A ~) Ax&/A( A(//A( A>/AՔ( AE/A}( AK/Af( AR/A@O( A(Y/A4( A`/AV( A@uf/A( A0fp/A`' Az/A`' A/A$' A/Ao' AL/AW' A'/A9' A/A@!' AXV/A' Aᮓ/AV& AH/A@& Aﻓ/A A& Au“/A & Atȓ/AT& AГ/Ao& Azؓ/A S& A`/A,2& A8V/A& A7/AL% Ap/AX% A0j/A@% AP7/A {% Aj/A`R% A'/A (% A[2/A`% Ax$ As/A`v!$ Aج/Ax$ Ah̍/A# A'/A# A A0f/A0 A<'/A`K! A2/A A0L=/A ApF/A@  ApAR/A A[/A`" ApAf/A A pn/A A,v/A A0ꃛ/A A0j/A`" Ah/Ai A(z/AS Ap=/A< A /A, A /A&  A,͛/A, A<כ/A  A3ݛ/A A`/Aţ A/A A/A { A/Ad A /AL A0/A`8 AF/A! A"/A`  AL,/A AТ3/A@% Ao:/A APD/A, AK/A ɣ AU/A  AKb/A O AȘj/A| A(zr/Ao Ax-{/A0Z A/AB A`/A* A`/At  A@W/A A1/A` AՖ/A  A #/Axy A/AW AТ/A1 A/A A/A@ A舓/A൹ A"/A A /A@^ AX /A: A/A` Aց/A@W A'/A J A@|/A A`w/A ld At/A`@ A'r/A`[ Ao/A Axl/A Aj/Aٷ Atf/A A_/A F A0\/A`& A@W/A AU/Aa A`V/A` APg/A`L Aȃp/A A" /A@ A!1/A A"/A@ A!"/A Aȱ/An A2'/A@, AHKZ/AY Ah/A@a A/A@ A@BF/A AN/A A8W/A2x AA`/A`j APj/Aj` Ahr/A2Z A|/AN Ax/A: A8/A + AX/A` A/A Ahѧ/A' A7/A A/A' Ap/A` Aʤ/AN A@Ӥ/AV Auݤ/A A-/A A0/A A/A` A/A A,/A` A/Aܡ Ah/AV A(/A` A01/Aj A:/A AC/A`~ APoM/A| AjV/A'y Aa/A@i Ak/A;e A0y/Aa A`~/A^ AF/A`S A`e/AH A/A@? AB/A@,6 A߳/A`+ Ax/A AΥ/AM AX[ۥ/A` A/A`9 A"/A A/A A/At A /A A`/A@ A/A A&/AF A p//A྄ A7/A 8q A B/AZ AM/AC AxQ/A6 AZ[/A! AFe/A@ A8k/A As/AL APt/A A}/A9 AXƈ/A@N APf/A A/A A-/A A/A A/AL Aպ/Al AĦ/AW A83ͦ/AH Aצ/A4 Ah/A$ Ae/A A0j/A AH/A AV/A A /A Ah"/AT AQ/A@ Aȶ!/A ( AU(/A A@B1/A@ A9/A  AA/A@ AJ/A AhS/A Aj^/A  A0f/A@N Ap/A; APw/A` Ak/A ۅ A(-/A Ap/A6 A/A AhѦ/A՘ AHЯ/A` A༧/A AƧ/A A AZͧ/A  Ap=ا/A Ap As9/A@> A!/A@ AX#&/A A@-/A@ AU5/A A@/A6 AG/A@ A@pQ/A- AX/A A`/A@y A"j/A  A/A=H AH/A=H AP{/A` A /AF, A /A"; AV/A N APo/A a A/At A /AA A`/A Z A'±/A  A`б/A? A1ͱ/A? A`ѱ/A9 A/A9 A@1߱/A? A@ٱ/A? A(α/A Aӱ/AA A[ܱ/A@ AH/A 1 A/A A/A  A"/A + A8Q /AA Ap</A@U AU /Ah A8V)/A0z AH2/A A/E/A; A?/A; AB/A 8 AP/W/A 8 AhQ/A; AhoM/A; AX(C/A AзL/AX AppR/A AxY/A` Ahb/AM Ak/A  Aoq/A`( A Ax/A`; AZ/A@#K A/Ab Ap/A } A/A AP暲/A` AꜲ/A & A /A`  A/AX2 A0/A9U A`/A$ A /A Axu/A`; A /A` A/A A8/A> AP/AUb APf/A` AP/A AH/A A/A  Ap=Ʋ/A Aβ/A  AfӲ/A A۲/A  A/A`" A /Af A(/A AI/A Ah/AT A/A  A%/A2 A./A= A`9/A) A8DC/A; A0J/A^ AS/A@ A@]/A= AP8h/A A s/A A͒/A@ Ak/A} AHm/A A/A A(/A A뺳/A Aó/A_y AXʳ/A`m Apӳ/A` AX޳/A R A/A`I A/A: A0j/A-. A/Aa A < /A@ A`2/A A /A& A#/A " A0f-/Ac A6/A  A@/A A@uK/A  AR/A AX]/A A0`/A` Ac/At AX[e/AD Ag/Af! AXl/A 8 Aq/A`m A``t/AE A7w/Aّ A@y/A g A/AS A,/A JO AX[/A`[J A/AC A t/A`; AA/A /2 A(/AX& A Ĵ/A A`ʹ/AD AТԴ/A  Aߴ/A@ ApA/A A_/A@W A /A / A/A Aj/A; A /AO Af'/A A_0/A`m A:/A  AFD/A@ A#P/A / A AY/A AVc/A An/A AQx/A A/A6 A7/A- A(/A` AL/A`  A/A` Aవ/A ~ AxK/A0 Ax(Ƶ/A`( Aѵ/A@0 Aݵ/A: A(/AA AP A /A6 A3/A@% A A"=/A@1 A 0/A@ B A/A A־/A Aʾ/A AӾ/A Aܾ/A Aз/Ad A/A A'/A A/A@ A/A A/Ads Ah/A-b A*/AP A4/A@C Ah>/A83 A0F/A@& A`Q/A" AHY/AO AZc/A` A@l/A As/A AF~/A@ A/A@ A;/A A(/A AP/A} A軮/At A/A; A/A A˿/At APؿ/A App/A` A/A A(/A Z A@/A` A/A- A /A A /A_ A/A׫ A`/A Ah"/Acg A /A=Z AP /AA A=/A A(/A A8 /A  A/Aࣸ A/A` A/A@ A(/A` A( /Axw Aج!/A@e A*/AX A؎2/AN A:/A 8A AC/A&3 A1L/A@$ A`T/A A([\/A A0ef/A` APo/A@ A`w/A; A/AM Az/A6 Aȶ/Am A_/A AU/A A /A[ A@~/AM A/A`D Aȶ/A Ah/Ay A /A`t A/Aj` A/A@,X Ap/A: Ah%/A A/A@x A;/A@P Aà/A# AX/A Aw/A Ax>/A@y A`/A AU/A A`/AQ Aȫ/A`\ A 6/A AhF/A A`eN/Av A8V/A@ A\/A Af/A6 Aq/A0v A[y/A@Cd Aj/A`BR A0/A`B Aя/A3 A/A! A`/A A`±/A A/A2 A0/A@ A/A; A'/A@` A/A`ܽ AP/A@Ƶ A /Av A/A` Ap/A྘ A/A A/Ahx A8"/A`j A=*/A^ Ax4/AO A@/A= AhK/A- A(U/A! AV`/A Ag/A Ar/A A( }/A`K A7/A' AAsow'/AN  A?w}/A:w AdH/A7o A)/A Ax AǟIG/AWNDŽ A[rc/A+}ʢ Ax~/A A4Ά/AT& A/Ahq AQV/A/5v A )/AOz AZ/A-| A /A{ Asb/AC/y A/A=u A]sV/A,pPo A^50/Ach A*/AT_ ADJ/A1V A/~/A}BK A?/A? A8Ž/A6 A/Al3u A8/A" AT|\j/A|R Ap2"/A% A>zA#/Axue AEuFE/AL?Mn A|Wg/Ak A/Ajx Am8t(/A*D A幗/AmOx A \h/A2C1 AF4/Af, A߾u/Av A0/A A7/A0 A8M /Ad AD/A{ Am,/AtrW A#/Ax A/A| AW/A~& A2 /AS A"/A2- A{#/Ab[ AX$/Ay ANAj$/ADq A2/AviK AX/A8=/A@ A@?/A | A'D/A W AH/A 2 AM/A AhQ/A`m AHT/A8ͳ AجW/A㪳 A6Z/A AP^/A O A(b/A  A@g/Ao A@i/A 1ʲ Ah"l/A Ar/A Zf Au/A@ A({/A A(/Aױ A/A A/A > AX(G/A AtO/A൫ A-/AM A~'/Ax A/A|߾ A8/AT A`/A A/A ( A5/Au A@/A A/A A4/A ACu/A AP/A AP/Au A0f/A AF/Aଠ Aִ/A APj /A0 Am/A A/AX& A/Aj Ax/A A֋/Ai AP/A` A/A`W A/A6L A8/AZ A;/A`9 Ah/Anj A~/A"a A(m/A A/Ax A/A6 A/A@5 A`/A@ A/A AD/A Aڤ/A A-/A@r A/A A /A A0o/A Aw;/A A&/A`Ө Ah/A A 3/AK A/A@ A/A@ A(/Ao A6~/A A8ge/A AaS/Aِ Ae A/A A  A;/Ao AZ/AB A=,/A9Y A./Ad A6/A~ APo@/A AI/A AP/A AhL[/A` A@=e/A@ Am/A Apv/A  AX/A A/A/ A/A@ AX/AK Ad/AU AP/A lb A/A@Pm A@/A | AX/Ao A/A@֌ Az/A@ A؎/AV A/A A`/A AX/A@ A /A A/A c Ap/A Aؒ/A& A 4U/A0 A/A | A1`A/A  A̕(p/A9„{ ADΜ/A_@ A/Aa A`c/A`2 AP;I/A9 m A)0/A]d AI82:/A1( A:@/Ag A"/A@ As/A~g AO/A~$:[ ASA/AZ AH /A AU*/A>5U A X4/A6 Aa$=/AL AhwD/A`(Co AQJ/AB#J AcN/A-/! Au AN%H/ASKM AP/B/A) ABF/Al' AlL/A& A_sa/A~[ܹ AϬz/AoQ AO /A; A=~/A A/A@9 Ab /AE A/Ah].W Ag/Af A`/A` A@#/AM A/8H/A 9 Ar0o/AGƣ A@/A@ A/A  A/A] A"W/A` A/A  A/A Ax(/AB Adp/A0F A;/AB Avpa/A>H A/AG Ag{Z/Ab A*/A;S A0L/A Q A/A + A"j|/A4gK A/UX/A3R A)/Ax A+/A] A*/A${ݎ A#/A@! AS0Oe//A^%' A%x>/A A`Q/A-Y* A8Dg/A \S AH.x/A_ż AOy/A: ; A`6/AN Aj/AuΊ A)ݑ/AB{ܺ Aمy]/A As+/AMZ1 A:/AR˹ A;PC/A1O A!hF/AZYи A:1cE/AVYP A>/A6ӷ AM/Aq Ai/A A/ADҳ A(1/A` AX /Ad A//A`ҩ A @/A@Z AV/A١ Aqn`X/AC{ A,~1/A` A /A ~ AY/Av=ߟ AJ/Aq7! A`G/Ak AXl/A`m AUs/A U1 Aj /A A3:P/A{ A_/A A0U/A?& Ahu/A A/A@Eg A/AP A@/A} AO/A  Aأ/At A(/A`F Ax/A( Ax/A`vc A/Aә A8/A@y Ak/A ~ Ah./A Aql/A@WF AQ/A A^T/A@Ϫ Af/A B Ak/AM A_/A A/A@E? Ah/A) Ap/A밗 A,/A A6H/Aa A h/Ap A^/A; AP/A AV//A  A:/AdÌ Adf/A`]$y Av/AXr A/A ˏk A@F/A`]&a AЦ~/A`CX A/A5gR A/AZO Au3/AN Apk*/A HH AF/A ~]E A0/A Z3E AP/AaE A`"/AD AF/AC AP/A ~2C A410AG A0A@5H A~0AH Al0A lZJ A0ALjJ A0AH A0A#iH Aȅ0A Z G AH! 0A HlG A 0A`G A@Q0AٲF A0AF A0AE A40AǝF AX/A B AB/A AA A/A@; A %0AH,6 AZ!0A&2 AH0A`v. AXΧ0A]& A0AӠ% A0A?# A|@0A l A0A`v= A0A Atļ0A_ Ay0Awi A_00A# A0A A P0A` A0A@= A0A0 A=0A A(0A ( AL5 0A`TW AP|y0A` A0A. A,0A| A0A` A.e0Aw AL0A V A: 0A9Z Ai 0AG A, 0A` A 0A 13 A,# 0A; A`ǯ 0A A$} 0Ay A 0A. A 0A l A 0A4ι Av 0A@ A 0A` Aj 0A% A` 0Ak A 0A 1 A.= 0A ALI 0A Aov 0A4: Ap 0A`ó A(3 0A y Ap 0A)α A M 0A`B AM 0A@!7 A M 0A`)+ AM 0A AM 0A A4L 0A' AHJ 0Aϯ AB 0Ag A? 0A@? A4 0A`Kͮ A2 0A`K A%1 0A` A0 0A@胮 Af0 0Aր AK0 0A`} A<0 0A`vy A80 0Au AB0 0Ar AW0 0Aao AXy0 0ABl A0 0A`Di A0 0Ahf A\&1 0A c Ax1 0Aa AdA2 0A$\ A;3 0AW Ag4 0AS A7 0A`M A}: 0AH A ; 0A G AbF 0A0@ ARO 0A: AS 0A`)8 A` 0A`9 Ag 0AA Ao 0A Q ALLx 0Aj A 0A`v A 0A A' 0A@ A 0A&ܮ AX 0A`f A 0A`] Aܸ 0A< Au 0A`) AT Q 0A AT 0A  A(0A$ Ax 0A` A?0A Ad0Az A0AM AԸ 0A J As0A@ AA0A A)0AЍ A0Ae A$ 0A`ܟ A//0An AKE0A Þ ASe0Aޘ A<=~0A c A A0A@` A蹪0A A0A` A`0Aߥ AhB0A`d AXJ'0A Apb0A  AU0A |A Ac0Aݔ A0A$ A0A |( AX0A쇒 Ad[0A Α Ap0A磐 AI 0Ax AH0A Z AI#0Ax A40A ATH0A`2V AĀS0A`f/ AȖ0A@% A$Ƞ0A ADX0AN Av0AHC~ At0A } A60A'| A@10A{ A!0A.{ Az0A z AG0A Nz AI40A|ky A,4H0A@E(y AdL^0A@ _y AHpx0AIz AǢ0A`Yy A70A/uw A\ 0A@؂v A[F0AFxu A<0A`mt A.0A@Ns AH%0AjCq A,40AZp A0ATp AJ0Ao A0AT o A .0Al Av0AFi A0Ai A0Ag Aࢷ0A f Ax|0Ae A0A@Ưd AA0A&d Al20Ac A*T0Ab AԚ0A&a A'0A_ At0A ^ A"0A] AH0A@[ AI0A,[ AI0A Z At$H0A@pY Ap>0A-X A:0AjW Ad?0A@V AhWE0A@yV A$60AU A&0A`BT A/0AT A)0A#"S A''0A2R A|'0AkR A, $0A@Q A '0A`P A20A@ O A~>0AOO AB0A6O AO0AO A`6U0AN AtV0AN A dW0AN ASX0A@N AX0AN ATX0A@N ATX0AN ATX0AN ATX0A`N ATX0AN ASX0A N AS0A`=N AnM0AaM A0L0AM AK0AM A AK0A3~M A AK0A@>tM AK0AHjM AL0A`XM AnM0AtBM AQ0A M ASX0A`L AL]0A_L A`0A DL A`0A =L AXYa0A8L AL&d0A`!L A4g0AL A\i0AK APk0AXK An0AaK ADZo0AaK A88q0AgK As0A K AG A80AG A̽0A@WQG Ax0AaGG Ax0A@gBG A|0Av3G Ap0A@G Adʧ0A@F AX0A`DF Av0A`F Ap0AzF APܴ0A`F A,0AF A0A={F A0A PF A0A@:F A,0A$F A 0A?F A0AvE ALf0AE Aq0AkE AO0AKE A>0A7E AP0A#E Al0AD Al0A D A 0AମD AJ0A?D AD0AƕD A90A`̐D AD0AыD A(0AֆD Aض0A_D A0AdzD A0ArD Ax)0AtkD As0AcD A40AaD A|0A\D A0AWD A]0APD ApL0AHD A,%0ALD A0A@C A 0ApC Aȇ0A cC A0A}B A\0A B AC0A`B A20AB A20A`B A20A`;B A20A`KB A0A[B A\0AhB Av0AeB A'0A`VB AX0AGB AX0AN;B Aȇ0Ac'B Aȇ0AB Aȇ0A@A Aȇ0AA Av0ATGA Ae0A<@ A0A`b@ A'0A@ AX0A@ A0A+@ A0A_@ A0A@!@ AX0A ? Ad0As? A`0A ? Ak0A`2? AZ0A0? A 0A? A!0A ? A\>0A /? A0A4? AD0A@? A*0A`2? A[,0Az? A0/0A-@ AL,0A@ A,0A`@ A}*0A5A AH.*0ATGA A(0A@gA A'0Ac'B A'0AtB ATr&0AB A<%0A,C A#0A@YC AH!0AjC A0A@C A0A C A!0AwC A 0AC AT0AD A'0A@D A0A/D A H0A`܁D Ax+0AƕD A0A9D Ac0AD Apn0A7E A0A-E A\0AvE A8E0AZF A8x0A`}F A0A`F A 0AF A0A F AI0A?F A0A@0G A|0AJG A\0AbG A\ 0AlG A|00AG A0AG A̬0A H A0A3H AE0A VH A80A0cH A8~0AhH Ar0A3H AJ A*N0A`)FJ A`L0A HJ A]K0APJ AJ0AUJ A 0I0A`\J A4Q90AJ A*0ApiK A8#0A@5K AL0A=L Aj0A =L A0A`TqL AT0AL A0A"M AA0A]M A0A@PM AЄ0A}'N A 0A 8hN A̔0AN A8V0AzN A0A uN A0AjN A0AfN A4"0AN A4f0AfN A0AN A<0A`N AH0AN A\0A SON A60A N A50A@M A`ڛ0A M A$n0AvM A0ADoM A0AHjM Ahs0ANeM A,0ANeM A0ANeM Ag0ANeM Ax0AgM A:0AHjM Ap 0A@>tM Aވ0A@.M A"0AM A }0A0M Aq0A 8hN AP|m0AN A_j0A@N Api0A?N Ah0AN Af0A 8WO Ae0AmO ALe0A wO Avd0AO Ac0A`O Ab0AO Aa0AO AXj`0AdO AL]0A ZO A\0A`TO ATZ0A ҵO A1Z0A ҵO AS0A4O AdyC0A`K2P AD0A IQ A\FF0A@sR AtH0A`KtS A\N0A@oT Ac0A@ߵU A}0A`KV A0A@?X Aл|0AY Ak0A l>[ A*Q0A c\ A(50AD^ A0A`` Aܵ0A ` A0Aa ABj0Adb Ae0Ad A0A O f A(0Af APK0A`9g A0Ah AxZ0A`h AL0A Ai A0A`̣k A A0lR0A`U AP0An AKD0AI At>0Ao Aخ70A A0/0Aj A'0A ̵ A`#0A A0A@p A0A / Ab 0ABͶ A\~0A AH0A AȾ0A`" A 0A A 0A AĀ 0A  A! 0AI A0 0A AhB 0A^ A 0AI A 0A A 0A`d۹ A 0AjA A8M 0AN AlI 0A A 0A A< 0Aa A 0Az A< 0Am AX 0A d AK 0A 8t AL 0Az A 0A@ A4O 0Aw A8 0A@,G A 0Aז A" 0A@ Aw 0A AC 0AD A 0AB Ap 0A AD 0A A\ 0A A 0A@5 A 0A (1 A}0A Ax0Ai Ax0A` Ax0AZ Ax0AX Ax0A` Al0A@ A0A J A}0A A0 0A` A 0A - AD 0A@h A 0Ajn Af 0Aஆ A" 0A`B A 0A@! A 0AT A8 0A@ Al 0A@Ơ A8 0A` A 0A@y Atf 0A A\ 0A A 0A^ A 0A A3 0AD A 0A A P 0A A0A`fA ATi0AE A4 0A@M A(s 0A`KZ A8 0Aw\ Ab 0A Ad A| 0A`p A~0Am AW0A A:0A`KI A '0Au A0A A90A A0A< A00A` A0A A; 0A Z) AlG$0A I A|&0A c[ A B)0Aj A-0A H| A+/0A A030A[ Ad50A A 60A A;0AV A>0Aե AH'B0Aե AD0AV AM0A A09O0A@ߛ AO0Aޛ AQ0A@ߛ A0Q0A@ߛ A4R0A@ߛ A3T0A[ AT0AV A.Y0AΪ AXY0A@ AZ0AK Ax^0A`; A=_0A6 A,c0A A$(i0Ak A̬i0A@y Apk0A`f Ahm0A` App0A@>1 A s0AjI A s0AO Au0Aj Aw0A@` Adx0A`ã A<y0A@ A A 0A E Ax 0AO Ax 0AQ Ax 0AW A(O 0A`\ A; 0Ay^ AQ 0A$j Ax 0A`̈ A !0A0 A`z!0AL A`z!0A[ A`z!0A A`z!0A` A 0A Ax 0A Ah0A8x A0A@ A0A AS0A̟ A A0A A00A A\0A A`6%0A { A'0A& A 1*0A A}*0Ah5 Ap*0AL A +0A`w A*0A@ Ap*0A 8 A,)0A`f AT(0A H A4$0A&< Aؖ$0A`M AlG$0A`K AlG$0A A%0AfZ A(0A 8 A B)0At: Ad(0A} A=(0A} AT(0A Ah'0A Au&0AHQ A$0Af A6#0A | A"0A A0A A\0Aҭ A\>0A Ҿ A0Ah Al0A@y Ahh0AI A0A A(@ 0A` A90AЋ A 0AN A 0A At 0AЩ AH0s 0A`V Ap_ 0A & AP` 0Ay AWA 0Aú A|2; 0A0 A` 0A AHa 0A@k Ađp 0A A^ 0A`f A/Z 0A{ AC 0A@ Ah3 0Av At0 0A` AC 0A. A4Y 0A- AD 0A A$ 0A| A 0A A 0A@ AȘ 0A A 0A` A\ 0A A7 0Af A< 0A A 0A A4 0At A0A I A@y0A`< A40AA Ad0A A0A A0A# A,t0A AL0A AH0A A4/0AF AvF0A O AȶY0A@P AVk0A# A|?z0A Aͅ0Ah A70A AHp0A@y AR0AF A @r0A A [0A`] AJ0A  A3E0A  A\:0A ? A%0A`/ Ae0AT6 AHn0A`D A0A A90A& AlV0A  A%0A A 0A8] Ad0A A,ɾ0A AI0A`]p A,T0AVG A|0A@h A+0A Q A0A A AHv0Aͳ A0A` A|(0Ajp A00A= A<0A`2 Ap0A(% Ap0A  AD@ 0A2 A  0A  A 0A`Î A 0A ALh 0As A` 0A@ A 0A`b A 0A A 0A@y A 0A@! AQ 0A@ ! Aг 0A{" Al 0A@" AK 0A!# AI 0Aƍ# AX 0A_?$ A8G 0AY$ Aȅ 0A% AI 0Aپ% Af 0Aw|% Ay 0A8٪Q<& A 0AdS7& A,w 0A5x[& A 0A' AWO 0As>' A&%cI 0A2d$l' A'\ 0Am' Az 0A' A^y 0Ac 'y' A 0A ' A5 0A -( A@cw 0Avwf( A,j 0Azݛ( ANWO 0A]( A,ק 0Aӆ( A 0A` ) A-Ff 0AozB:) A 0A-V) Ao? 0A2Sڣu) A7 0Al>) A`N 0A]x%̷) A  0A) A0L 0A ) A4B 0AD- Aԑ 0A4- A y 0A 1v- A 0A. A0Y 0A`. A̿T 0A. A!S 0A`Q. A\>9 0A`Q. A\>9 0A/ A5T 0A_/ AS 0A@N/ A̟ 0A. A(0A/ A\0AW/ A-0AXs/ A؟%0A?/ A ,0A-/ A60A0 A =0A lQ0 ALB0Aa0 AW0A 1 A:)0A2 Ab0A 4 Au0A Z6 A-0A@ϴ7 A00A Z7 A0A8 Az0A#q9 A 0A@i9 Al֓0A?J: Aė0A@: A0Ah:; AXH0A&> A20A> Ap0A$@ A(޷0A@B AG0AYE A 0A@(G AP<0AWG A\0A JL A0A`ӾS A50AX AZ40A@,[ A0A6-^ AL0A@j A0A@r8f A tZ0A>P Ad^0AiB A0A+: A|F0A@6 A^0A-) A10A#$ Axڞ0A! Ai0A`T  A0A= AH0A@R AK@0A  A`E 0A- A4f!0A= A$$0AI AH%0A`; A`ػ&0Az AX]'0A} ATC(0AI A.)0A/< AC*0A AVV+0A`2 A`,0A@> A$-0A8 AX/0A A d00A` AX10A Agx20A S{ A20A4 A$20A3 A20A> A20A* AX30A@% A]z30AÖ Am70A9k{ A:0Ai A`\:0A g AL:0A`"{f Ar:0A^e A<:0A&d AE;0A^ A$}=0A@R A\=0A (nP AT@0AM< A`TC0A?+ ARE0AK AqF0A@5 A G0AɌ Ax:G0A AG0A@ A>G0AS ADxG0A` A('H0Ay AuH0A AwH0A: AܙH0A`G AH0A" A%I0Af ArI0A} A(vI0A S3| AsI0A 6{ A(>!I0AQ%x A?H0A@r AG0A`]g A|AG0A@W[ A$G0A AW ApF0AzP AGE0A@>E A0E0AA A ;E0A : ADD0A A,/ AD0A V( AHvC0A@`V$ A86/C0A@5 AtB0A  A wA0Aӕ Al;A0A!Ar@@0AA<>0AUA )>0A,A\u>0AIA)0A (չA0a(0A`A_(0A 0A@r)0A@פA( )0AA)0A@AY*0AهA4*0A Ҽ|Aݩ*0A |Ax*0A@`zAlV*0AtyAm+0AIA+0A HA),0A@Ahc,0A ck6AIg,0AV5ALi,0A 5A{,0A-A[},0AV-AȼS,0AZ'Am,0A@eAh,0A lA8,0AxA8,0AAi,0AB*A@S,0A[A`e,0AvA1,0A gA ,0AM(Ap],0AAt,0AzA,0A IAB-0ARAHC-0APAD-0ALA0U-0A@iU>A$=]-0A6:A`-0AJ8A|ǐ-0A5&A|-0A? AJ-0ABA-0AAܵ-0A A-0AA-0A[A}-0A@LA-0AAض-0A@A -0A A-0A^A-0A+Ac-0A 9Ap-0AK AX`,0AZ/A$N,0A@ؔA +0A !A+0A`fA+0A`bAܨ+0A'vAD+0A 7oA +0A@@gAx+0A^_AI+0AWA~7+0A@COA=+0AGA02+0A ?AL0+0A|=A, @+0A@7AU+0A@3A]+0A%1A86+0A@-A8+0A &)A0H+0Ag(A+0A`"A3+0A!A}+0A`A8+0A@EAQ$+0A`oAWV+0A:Aht+0A AȜ+0A s4A@+0A A^,0A+A,0A@AP4-0AA|u-0A`A4-0AMAG-0A@ԄA.0A@A*.0A Ap/.0A@HA=.0A ~PA,.0A APӻ.0A`BuA .0A 9AD.0ALA̿--0AkAp)0A Aķ(0AAh$'0ASA8%0A0A>#0A03A"0A`wA0AHmA0A^A0AyXAx0A`]UA0ASA]0AOA0A@`KA(0AB'/AV0AWA)A$ ]0A !A@b 0AA 0A`vA;0A@%A>0AtA 0A-Ap2/0A6gA:0A/,AHC0A+AǛ0AAql0A`~Apd0AAP<0A?GA\0A4yA0A`KA30AHYA0A cA p0A Ah0ALA0A`TA$.0A͸A(^0A(A0A@,A8*0A=AP0A@A0A_A;0AvA0{0ARAG0A`A0A Ah0AճA`' 0A`DA 0A@iA(0A`vA\0A /A40A`vA0A`"A0A?Al0A0EA:0A,A0AQAlx0A cAlI0AIALr0A`Al70AQxAg0A@A0AAŽ70ABZ4A^%0AI9AB0AjA5Ҽ0A@ ABԵ0AA-0AAk7i!0A35@A0AA@\0AA10A/HYARi0Ao A0A$A0A AT;0AӓA%AK0A[6A#0ARNLA0AŘͭeA0AHA0AvA  0AA0AA0A$vAHV0AŐA70A@%A\0A`NA"0A (qA70A@,fA0A`]=A܆0A`;AA0AdAt0AľA-0AHA<0A@%A@ 0A*6AD`0AA}-0AڮA8A0A HA@sZ0AA\0A@A0AA 0A;Aź0AA`v0A"A 0A ɂA0 0AA"B0AUAS0ARAlV`0ABA0AAj0ArA`0A8AXJZ0A`AT0A<AP0AAPL0AI0AH0A_AC0AA<>0AA30A+A;00A@QA/0AUAl/,0A sA4'0A`KA#0A@5A:"0A@Aԫ 0AA8~0A`A|?0AA0AA/0AA(0AAf0AAw0A@ A|0AA0A@A0AA0A$A>0A$AH0A$AH 0AA0A@A 0A`A@0A@AXH0AaA0AgA0A?A8ػ0AA\0A@Ad*0A`[Al0AAg0A0APO0A`A70AgCA0A=kA0A`An0A@Al0A*AЧ0AzA0AADo0A@,iAv0AAR0AA=0AAd&0A`At0A-zA00A HaA 0A^^AQ0A@GAK0AA:90A@=A~0AaIA0A,A}L0AzAhN0A HA.O0AvAZAT!20A@>TA-0A YAh,0A@YA#*0AYA8<$0AdA 0A`YA0A`̞A0AA:0AR~A0AsAHa0A@gA0AAX0A=vA0A Al0AA0A 1|A0AKcAp0AVAྠ0AGA0A'AF0A A0A`A̓0AAL(z0AZAy0ASAX9y0A IAx0A &3AX9y0AAwz0A`Awz0AA0AA40A[]AX0AmA90AWAxi0An>A`0A A~0A Ag0A`TA[0A`AX~0A~A|x0A@ALlv0AXAHq0AA`'l0AǮA\e0A@aApc0A MAh`0AB<AdY[0A`AT0AAM0A`AA0A+A|70AA0.0AA&0A`AU#0A` A0AA!0A`mA0AA'0A`*A{0A>A 0ACA1 0AHAT0A`2KAg0A JA0AHA0A 8FA`<0A>A 0A`*A}0AAO0AuAN0AOAk0A@A0A2A80A#AC0A@AT0A AATT0A[Al0A AU0AEoAu0A ,ATc0AA+\0AsA>0A@A/0AUAP 0A AT0AWAL,0AMA 0ApAx0A@A0A@>A 0A@gtAT0Ax,Axk0AAQ0A`A0AZA0A A0A@5A$0AxAH0A یAё0AD A?ÿ0AR AMlɐ0A!tk`AQ0A:AIp0A1A_%9]0A?(AK0AA%Nn?F0Al]AAA0AC)A߼>0A%kA<0A\A|<0AZ2A!=0A;AQiG@0A&_~ApCD0AAd0A@RAu0A4=Aړ0A"<A(0A cA0A`;A>0A`;A0AA R0A@`A0AKA0A A/0A Ap0A AD"0AAdf0AeAX0A Axܥ0A$ A@0A AD 0A`iAD 0A (AG0AAl0A uApP0A5Az0A@NyA؎0A qA0A@A!0AޥA50Af?A;0ANAOl0AmB$A'_0AнA0AxAl0AA D0A ]AFu0AzWAi%0A mAs"^0A=uAEun(0AAmAor0ALA$0A@> A<0A`A0AANs0AA0AAu0A~Adf0AfZAT0A`;KA{0AA?U0A ƻAmc=0A1DcA20Ak"AѦZ0A/L=(Aŭ0AȽoAh0A J[AP0AlDAXٷ0A (~A80AjiAml0Aʸ/Aiҧ0A$AY0AAu@0AŃ\AĂ0AA}&0A2A:0AA _0Ak\*Ao0ARCA0AANLz0A_r Aˏp0AAȅd0A`At$]0A sA7N0A|/Ad@0A@A10ARLA!0A<AhAG0AgȂ5AM 0A\A0a0AA#$0A W1A.O0Aݨ_+A {3 0A4uA 0ANA>0A)A0A$(qA0AU!AM0Av +A}0A_QA0A\eyA0A A*~0A]HAβ0AኚAO@0A A&0A bA 0A@gA0A`sAT!0A@>Ax0AKA0A|AY0A̘AHa0AןA0A@>^A8E0A_Ad0AgA&0AWA$W0A@A80AA 0AHA\50A`AV0A At0A`A0A`]A(s0A;A0A$A,0A`cAe0A6@AU0A@&A&0AT~A40AA90A;AwC0A@!A=0AA^ 60A0`A|00AWA>+0AGfAK9ن(0AeAu&0A\01(AI&0Ae=sA#7/(0AjXA9+0AwYA0/0A-FAmK$70AOA@?0AeيA]I0AF'A;P0A7aWA W0A}{A_0AoAh0A Afqsn0ATe A%t֚r0AhGe_AjAu0A0p|Au0AA0A`DAX*0A ZoAEk90A=AYn0A]A{0AW\Au20AB.A؎0A&aA78+z0A*VAUXe0AnپAzB0A'A0A 8Af܅0AV Ar 0Aɚ`#Ao0AHi<Aǔ0A],]XA~0A:wduAۇͽ0A;A~0AňAǔ0AFAo0AsAr 0A6e Af܅0AQ A0A=+ A%:p0A2^ AXZ0AU An@Y0A= Am0A | A؃0A\`l A7=0AI A0A0L AOԊ0AUAOg0AwLA{0A.(~Ay0Al~,A0AJyA/?0A?Ax0A@AI0A®AƱ0A AAt 0A`AfL0AIEA(0AAr0AA,F30AA¯A£sx0A7\ASu0Ar A$0AAx0A \+A*A0Aǣ /AS%0AU(4A 0A74V9Aq|0A%>A80AOCA!$r0A0GA&¸0Ag$LACj'0AiOA/0A4SA"0AUARa0AċA[0AuW5A н0AA#0Ao~ A0A ALȲ0A# Aʮ0A~M AAJ0Aۜp-w A4W0A) A40A/ !Aڭ0AJr!A%nl0A\9kN!A !0A$"A0A@ "A~0Aa[#At0A$Ab0A&AX0A̜'A0A *A!Đ70AK*A/0Ae+A\'0AE+Ai/$0A&^P+An~"0A: ^+A( 0Ao+Ak7+0A&5憂+AC0AC +Az6p0A3'+Ak2;0A-!+Ay0A+A(+0AG,+A0L0AT,A0Ap_,A%s0AZ} ,A0A-A$}Y0AEG-A8)0AVEu-A㶠%0AY-A@qZ 0AsXؽ-AL0A-A0Aj-A0A-A}0A+-A)0A@ .A|P0A2.Al0A O0A$0A1Aƚ0A H3R2A90A7U3Am0A3AӬ0A!vT^4AXU' 0Any4A60A5A84$0As5A͍0Al5A\0A5A0Ag5A0A5AΤ0A.P5AV|0Abq5AT x0A`16A؞t0A=F!6AS!q0Ad2vC6A o0Ajh6Aa %n0A֎6Am0A|6A n0Ab 6Aɝn0A6Am0AZda7ASl0AF7A[Ak0A07A?Ai0ADsD7A, g0A^V7A=e0AV`{ V7AEY+4c0ArV]_X7A^4]a0Ab@H ]7Av_0ALd7An^0A#m7A \0AZQ#x7Aǜ[0AW7AVZ0Aۛ7AmD4Z0A?(Yd7AvY0Ax,n7AZ0A8*"7A`Z0AK7A T0A C8A K0AV8A B0AL9AP60A1k9A)0AH9A؄'0ABp9A&0Apƅ9A^$0A@Ͼ9A.!0A9Az0AA5:A?0A@K:A0AG :A@p 0Ab:A 0AA;A 0A$˟;A 0Af\;AQZ0A x@;AWa0AѴ'<A!0AZ:#SQ<A-F 0AEnq<Ae0A <ATpT0AЏ<AԳ0Avj<A` 0A`윲<A0A <AK0A3ek<Aj0A2l =A 0A@2=A 0A WNuu=AME0APuz=ASך0A=A/<0À2>Ax0Ayg>AC0A[䷕>A!Y0ANq>A'0A)>AH0A}/>A0A>AsШ0Ar ?AM)0A6a1 4?AZB0A'_S?A\0A u?A90AՏ?A+̪0A e2?At0A?Alw0A  @A zwp0Ak=@A\i0A@x@Ae0A @AT0A8AA%M0A&EAAg0ABA(0A}xCAXS0A~(CAU0A`lDAX0A.-j2EA<4 0AEA~$0A`"FAn(&0AYƪVA|F0A,VAl@0Aa:WA<60ATWAe00AEWA^%0A@ WAx1 0AXAL{ 0AXAHV0AwXA0j0AiYA0A@YAX_0AYA00AZA쾗0A[A0A [AhT0A \A?0A^A 0A)`Ao0AbAa0A`;bA3-0AocAX0A9{aAD>0Au`Ap70A :`A #0AFaA0A@bALU0A AaAO'0AcA`\0A!cAΔ0A9)3cAB|T0Ao2dA=0AldA0A`"dAۡ].0A[eA60A51eA-0AE!"GeAz0AZDVeA9#0AEEE^eA0A^eA[p0A`eAUC,0AӑseA\0AgeAI0AheADQ0A;hAt$0A iAh0A ɷiAd}0AAjAp0AhAfn0A@kgA,4a0AfA)0A`aA0A}E\A@0A(^A$0A`^A 0A,_Atd 0Ap]A00A %]A80A`]A`0A| \AO0A@iEA50A@ DA$9a0A BA0A AAA0Acg9A0A#V8Aqӭ~0ANQ/H7Aa0AmwX7A#0Ae6A#0A Q6A4+:0A?i4AnGR0Aΰܺ2A%l0A^81AQz0AXuq/A 0Aئ!-A0Aƾ|F,A0A@5*AĂ*0A'A F;0A0g&A-RN0AÜ$AhSb0A j#A@c0A`S#Ac0A`dO#AYf0Aei2"Al0Ao!Aٹt0Ax X!A*~0A"VR A$Ɗ0A- AA0AN9A10AtgW/A%x0A+ A?#z0AH~K7 A)]-{0AMx A&bx0AC A6>t0Abd3 A,#m0Ai AF}j0AuõW A'1h0AfV A*h0AEo Ahi0A Aam0AmK= ANqo0Ab?_A) o0A).(cA kl0A=#ZA1Rg0A}jA`0A@2ALU0AA R0AAV0AA;S0AjAI0AAF0A@kAXE0A&A <0A9Ap40AIA90AtA90AྙA,+0AAw*0AdA(0AF?A4q!0AIA0AAe0AaA0A@HA$0A@g2A0A %Ae0AA|2 0A?A4 0AOA4 0ATA4 0AA0A6A$0A&A70AA00A6A<_0AA 0A5A0AI0Ac0ARAL0AAX0A,AW0A =A0A A0AtAH0A;A0A IA0AoxAP0A@UAh0A<0Ah0A ZA0A@aAh0AR-At0A`AO0A Ap0AAp0A%Al80A 3AxZ0AA,0A QvAl0AagAt0A`A}0A@GYAY0A@ KA0A#A0A A0A`)Al0AA(0AA@|0AA w0A ATp0AA|pw0A"Arx0A`As0A`Ad{f0A Aԫ]0AwTATcV0A@A(R0AhA<<0AtA%10A OAؖ)0AA4%0A`CA]0AqA0A`iA80AѐA^0A A(D0AA p0A AA(0A@\A0A AD+0A ~Ax0A}AXb0A @AXM0An7A &0A A0A0Aw}A@0A.Al0AfALu0AAa0AaAҳ0A >A0A`9AL{0A^)AT0A@NA?0AAp"0A_OA0A@iA[0A ~A0AAA,0A&A<0A[A0A Aպ0AAPK0A@#A40A zA$q0AA$(`0A^At\0AAP0AuA @0A@WA (0AٗAH!0A <AW0A,A0A@bA0A@7AJ0Az Al0ARA=0A`'A0A@A0A`D^Ax0AA m0A`ADf0A0\A`6R0AH#AH>0AtA^/0AmAP"0A AH0AA0A@A0AxA`0A-A*0AlAe0A\A0AxHA 0A3A80A` A8m0A A0AwAX0AKA0AA0A@A[0A`Al0AlA8g0A (AU0AGAlS0AR;AI0A` AA0A A850AA/&0A`vAE0A?yA0A`MAl0A6:A0A Ax0A@>A 0AA0A` A0A9A@0AfA0AA B0AA\0A@VAl0AhA̝0A` A0A>A0AAp0AW޾AL0A}Aw0AAp0AகAlOc0A`;A^_0AuAL\0AmAIO0AEYAXG0A0IA>0A l-A4 90AA40A`A|.0AιAU*0AෙA$0AwEAh 0A(A0A@A0A,A 0A(AT 0A 1A0A_3AQ0A`@A( 0A`v_A 0A`ӒAS 0A@謽A 0A@NA; 0A圽Axi 0A@wAԫ 0A4WAY 0A` Au 0AA 0A}yA@± 0A`4A( 0A&A4 0AaA 0AƕA 0AuATɚ 0AzAH 0A_Al' 0AzHAt 0A<.A 0A cAy 0A!Ap.q 0A ADn 0A@"A$i 0A /A0c 0A`A[ 0A"ܺAeM 0A ںA`%F 0A`ºA< 0AAZ6 0A A1 0AAX) 0A`m`A" 0AvUA|! 0A OAz 0A ~BA2 0A`;Ae 0A@i>A] 0A`-A 0AzAl 0A2A* 0A׹A 0A絹A( 0A AX 0AyA1 0A ~qAX 0A@ƆAl 0AA 0A`啹AA|P 0A7Ah 0A1AT 0A OAp 0A8A 0AIA 0A`fAC 0A9Adݣ 0A@A 0AqA 0A@yZAi 0AtA 0A`)A! 0A3ATo 0A@wA^ 0AhAxN 0A#_A D 0AV\A40 0Ah>A@ 0A`dAp 0AA 0AA\> 0AfAXH 0A@/APM 0A$A+ 0A ͭALf 0AiAD 0A`TA 0A A 0A`kAh 0A`mGAH 0AA< 0AA 0A` A 0AA 0A |A0 0A@Ap 0A A q 0A@#As 0ATŪA8l 0A@ A c 0A {A[ 0A$9AtI 0AA ( 0A)A܌ 0A`] Ah 0AA% 0A (A@ 0A?=Ab 0A|CA 0A 8A0A[ A@0AA,C0A`Ax0A|zA0A OIAP0A@&A 0A@AӼ0A ~A8ֶ0A@Aë0A@#RAʥ0A=9AD@0A@NAؿ0A̦APz0A=A~0AaAw0A<(Aاr0AAf0A uA^0AwApLT0A iAHH0ApA50A`dAK00AMA*0A@A7!0AwA0AAt0AڐÃ0A|A 0AVPA 0A-A<0A0A0AФA880AĮAd0A`A0A&xAhH0A]ZAl0A*,A00A}AA0A`ޣAո0AAf0A`[A"0A`£Ad0A^ͣA$0AA20AڣA-0A@߷A|0A2ALp0A jAc0A@5qAȇS0A[NAQM0A1AhQE0A`KA8:0AA.0AA 0A+A)0A ҢA0AA(0A>A 0ATAq0AϡALu0A`2áA80AsȡAH0AnġA0A@EA"0A ~A0A Q2A0AAy0A@`A0A@A°0A@֠A۬0AgA0A A{0A@#Ax0AӠAP0A@yA.0A?AL0ATA`E{0AǹAXJv0AՑA,ci0A /^A\0AAO0A"A50A?A #0A@؝A0AK~A`0A@P"A|0A[A0A GA0AA;0A@A,)0A!A(0A &Aj0A (Ap0A3A,t0A יA0AՙA|0A+A:0A0A0$AV;0A3 AX80ARAR50A`mAJ20A`AE/0AAhH,0A A8^'0A@A r"0A A0A"A0AF#At0A"Ad0A`A 0A`A0A A 0AA\u0AA 0AAd0A Ap0AAD0A A٘A0AИAt0A]ȘA&0A=`A;nA|6]0A9mA\#0AjA8 0AiAd,0AiAxo 0A*iAt0A5hA0AhA/A`hAP|/A|hA/ATWhA /A& hAP/A~gAB/AyEgA/AfA /A@p`fA/A 3fA/AeAС/AFeAm/AQeA̮/AĬdA/A]dAd/AZ!dA]/A`cAy/AincAz^/AbAH/A aA@/A`Az=/A`Aȃ>/A_Ah"?/A_ApV?/A@_A?/Ax_ACm 7/A{_A*/AVQ^A12W:/Apy^A/A@]A/A%]Aɒ/A%mS]A/Arj]A>/AQ]A 9/Aړx4]Ab/AB]AD/AT'\AB^/A\A/Aw\A/A[A}/AE-[A? :/Ao9[AA9/A}8N[AH/AN[At:/A+n[A 9*/A !W[A/A!qA[Ax/A@/[A\R/A- [Ahz]M/A ZA/A]ZA$/AZA,F'/AZj_ZA/Ax4ZA8/A ZA/AbLYA[/A^lYAk\XA*A5n/A쯓LXA_^/A*TXA`"Q/A炨XA z /AXA`"Q/Ae}XA_^/A4XA*A5n/APlXA7/AXA/A@i\WAP-/A{b:WAl3>/Ar.ZeWA/As VAn/AB VA!m/A߰VAج/AVA&/A̺TAx6/A`[{SA4/AwRAaEG/AcnGRATrU/A^hʘQA`/AkPAP7g/A`2/PAPg/A O)PAXg/A^OAg/A@iOAg/AOAPf/A mOAd/A@?OA2a/A OA0j]/A@%NA4Y/AҋNA`T/AQNAO/ANA`J/A MA7D/AMA>/A/MA(9/AࣻLA/5/A@ GLA0/A`2KA/-/A"]KA+/AH+KA~(/AJA |%/AJA "/AJA(/A@gJA/A^8JA/A| JA/ANIAxh /A`IA`r/AOIA/A@UIA3/A`4IA/A)IAhD/A`2HA>/A (HA/AHA&/A$HAP/AMHA /A jHA/A ARHA( /A+;HA/Ap%HAho/A` HAx/A@EGA!2/A FAp/AEA؎ /AsEA/Ao9EAb/ADA8V/AФDA/AEXDAڴ/A8DA[/A lDA`2/ACA``/ACA/ACAΒ/A#CA/ANeCA/A>CAHÂ/A CA3~/A`BA }/A$BA|/ABAB{/ABA`y/ABA w/A 1BAAu/ABAxr/ABA`ep/ABA@m/A`;BAhk/A)BAei/A`BA8tg/A@BAXe/A JBA{a/ACA@`/A-CA3_/ACAj]/A9CA7[/ACAhX/ADAأU/ADA]S/A& DAfQ/A`DApO/ACAN/ACA`M/A CAfM/AECA{M/A CA`N/ACAN/A CAM/AzCAJ/A@CAxG/ACA`6D/A BAHK@/A BA;/A\BAh7/A$%BA1/AHAA1/A@AA R1/AնAAt0/A JAA"//A AA\-/A6eAA`%+/AJAA(/A@C1AAm%/A_AA!/A[AA/AR@A=/A+@A8 /A O@Ap/Ȁ@A/An@A`B/A`L@A2/A'@A( /Ac@Ax/A?Ax>/A?Av/A ?A#/A \?AG/A?A/A൉?AX/A@q?A`/AY?AP/A@>B?Ap /A+?A/A@r>A^/A@yr>Aأ/A>AY/A->Ah/A>AԵ/A=A/A=Aڳ/A@=Ay/AH=A/A`ʺ=A/Af=Aϴ/AH=A/A=Aͮ/AP=A]/A=A]/A&<A/A@<A]/A`u<A/A><A /A +Ac/A+AY/A@%,AF/A@N-A/A`u.A/A/A8/A@0A^/Al3A/AV6Ax:/AK;A|/A&>A@7/A>Apq/At@Ax/AUAAE/ACA@F/A$DA/ADA /AxEA[)/AoFAxzI/At0GAO/A GA@C/A]HAPgM/AIA5/AHJAsA/A`ӕJAV/A`1KAc/ALA3v/A`LA/AhMAH]r/A`MAU/A@CNA)J/A@yNADX/AOA(~r/AJPA(|/AA3QA=f/AQAGB/A`2'RA)/ALQAЦ /A@%QA/AޞQA`/A@QAPk/A_QA`/AkQAx/A -QA(`/A`PA6/A PA/A[PAp/A@PA/A5QA&u/Am4QAR/AZQA0/A*QA$/A c4QApN/A@LQAD/A RA/AൖRA8t/A qSA/A`KTA8c/ASAh/A+SAP//ARAZ/ASAoX/A JqSAR/A` QA5/A@rPA[//AEPA/APA/A"yNA/AMA/AAMA@>/AMA`r/A?MA0]/AFAg/A @AP /A;Ahod/A@8A\/A1Af/A 1+A`/A`@$A_1/A#A$/AzZ#A@/A` #A_1/Au#AQO/A`@$AH݌/A`%Ao/A`)&Au/A@[&AU/A &AH./A^(ApN/A@(Aȇ/AT)A0/A(AHY/A`I+A=/A@+AC/A+Ap_/A`v+A/A -As/AH<.AK/Ai.A0/A c#.A&%/A (0AP/Af0A /A1A_+/Az0A0؋/A Y3AXm/A3A1t/A 3Aذ/A3A(K/Ah6A/A@N6A8/A}6A#/A836Ah]W/A 8A>8/A`;8AV@/A29A5^/A8A`/A;AHм/A g;Ap/A S;Ap/A@;A /Ah;A"/A>A$/A`>A /A6>AH'/A M>AU/A@Auj/A AAp/AX?AA؎/A&@A/AtyCA/A`)CA/ACAH/ACAQ/A2FAP6/A`;xFAr=/A FAZ/AV_FAM/A,HA/A_"IA8/A`)TIA/AtIA(`/AƋKAp/AKA /AFLAP%/A`KA y/ANA /A OAZ$/AOA/A`NAP/ANA`/APAx/A-QA/A@QAPȎ/A RA/A@!QA@/A?(QA0XP/ASAQ/ASAHzN/ASAb/A`TA/AaTA =/A"TAT/AaSAp}/A TA( :/A VA߶/Aoo^Ap{/AV_Ap/A bA(O/A=n 2AK(/A671A@/A l0A/Apg2A/A@0A@/A+A&/A&(A(J/A@,.#Ay/A@C1A3/A_A0٢/A KAP*m/A@w A@}r/A@59 AHw/A2A/AA'/AnA(/A@~A/A*Ap/A`RA8E/A@`A1/A@`A/A@`AȘ/A@>A }|/A`DhAQ/A@Ap/A |A/A |Ai/A SA/A@yAPHn/A`#Ah]#/A$A /A@A9/AAh./AA /A@A/AՂA(/AyAz/Af Ap/A JnAP/A`|AQ/A`|A@o/A`|A =N/A`|A/A`|AP/AӊA//A-A/A$7A8R/AAX/AhAPȤ/A`!A?/A@A/A`2EA1p/A@ZA8/A6A Ծ/A*PA̾/AxSAuž/A@^A /A`A9/A`v[Ap/A A8 z/AA 9/A`KA /A@ϪA,/A"Az/A ?AТ\/A"Aذ*/AફApy,/AApy,/A`_A(3/ALA/AoA/AoA؎/Ag(A`/AApl/A`APx/AZRA /AaA/A Ap޻/AA/A`9CA`/A cA`/A4A(`>/A JA|/AzA/AzA0/A`A֝/A$Ao/A@PlAj/A@PlAXT/AAA4/A A`"/A`A؟/A$Ap /AAp /ABAp /A AX#/AِA/AxAh۹/A`9CAo¹/AA?/A#Aω/A[ZA0/AAPM/ARA#͹/A@DAH?/A |aAX#/AhA=/A/A]/AmA/AA`/A Ax׺/AAP|ں/A`AϺ/A`MADܺ/A@ AD/A@BAD/AA8g%/AA0,/A@5AJ/A`ܦA/AA(@/AgNAI/At$A/i/A@P%Ax܌/AeA@ξ/AzA޻/A@A/ABAx /ABAB/A A_A0e/AA`k/AA؎/AA/A`2AƼ/A@hAx /A=wA/AwAP̈́/AAri/AA8D/A@ A`C&/A@)A'/A^A/AqA/A H]ApJ /ApA6"/A`"#A(JO/A`oAxzf/A&AT/A@Aȱd/AAPx/AN[AT/A cAQ/A$A0R/A@,3AT/A@AV/A@Am/A SAeo/A 82Ad/AAX5X/A (lAeo/A@5BApx/A`vsAW/ACA/AA8/AʷA/Aa/Aȼ/A@Aqμ/AA&/A AX(/AOyAx-7/AkAI/A$AqR/AF#A7Xع[/AQ6Ayc/AGOAj/A`lA k/A"RAWh/A=Ac/A@5EAP]/A ~AI U/AwgAL/A Ae>M/Aj;!AEP/A1ZAv&QV/Aa;G[AWk]/A ZAVg/AAA{/AJ6h A=/AjDA/AWA3/ACAky/AZ;AW|/A:AO?Ľ/ArzAA(>"Ͻ/ACX %PAҬHٽ/ALJ_eAl/ATAN/Aēz8AC/A/A${/AzA/y/AިA/AKA0H/A@AP/A3pAm/AߝAK^/AA'23/AaP=K+A?{E/A0]h$AS/AA~]/A`\A-,b/As݄gA w7Bh/AE[oAn/Azk9sA s/A.ψ/Ak AǾ/A:wA~޾/AjBA:6/AׂAP</ApAs#/AtmA`S9/A AuAwA ^/A &2A`h/AhAۓo/AAUr/A|8 oA(q/AAp)%h/AJ~AQ^/A:AiR}S/AɰA/I/A>?APm @/A@A 8/A'Y$AUz0/A؏"A;%*/AW;AF~5%/AX_A17!/AbAc7/ /AA/AA=!/AibAd'/AHArB2/AMqAFA/A'A[6U/A7%bA@l/AA k/A5㢳AX^j/AK-4AX|k/A[>9A Um/A1f :A;=p/ARxTA_!t/A:,lAx/AAv~/A`ÒA@/A@AhaϿ/A!AR/AA;8/AeAi/A A`/AQ*A&S/A}?8A/A@3AAD\/A0:PA81/AZzA /Aͪ EAܤ,/AfAN3o'/Ab5<Ai//AۑA3/A@*A4/Aϑ7A71/AIARu+/A/A!/A,Av/A;}$Ac /A_"A}/Aa!'A|T/A#1AFN5/ATAA7/AOcWAYa/A٥iqA'/A,@LA/AXAM3/AA*5 /AX.A/A -A/A$AqW?/A2GHL A/A` A;l@/A+ AA/A A /A& A/Az+ AV]p/A|MI A{1F/Ap}h AK!_/Aٞ AO@/A:7 AN/A2 AF/A> A4/A v A/A A!M{/A A /A" Ahѭ/A%PZ As6=/AXK Axme/A+ AGn/AI49 A0{u/AFr Amje/AB A{R/A]R RAP/>/A@%A!h:/A >AP8/A@>XAn6/AXxA0i5/AzޅAWL6/A&rdA4'8/A'Av;/AA?/A`AhnD/AA$K/AZAR/A8mA#Z/A/{Aʥb/AAJxbk/AAs/AA /AeAP~A/A 7SZA Ǣ/AnI\AP/Am_rhA/AAC/A19Aoz/Ax AI/A#Aijh/A _AmU/Aӏ A /ABA?}l%/A(xrAG+/AaϰAH70/AÐվAU3/AfsA/6/Avz0hAW7/A<~"AO.7/A{AAW5/Ae`A 2/AQh9~A./AcAU)/AAvP%/AѱA}~\!/A;3vA $/AMo{Aۦ/A!ZA H@/ACAlI/Afd]A/AHwF.A^/A`DAҺn'/AmA2//A^A0-3/AYA3/AA,0/Anc>Apl+/A |MA@$/A*ώ<A/A A:΀/AIA/AabA/A! .A@/AqA/AAP/A$A0 /ABA0 /AZA/A AH/A A /AA /A A2/AAB/A@A'_/AдAy}/A`As/Aa<A /A`A/A0gA/AଵA/AA /A@EAh]#/AAh./A==A1/A6A0&%/AdA*/A`0AAE/AA:/AAH/A`jA%g/A@NAj/A`2AJ/A}kAJ/AaAPHn/AnA Ox/A&4: Aȉ/A2З AmW/A8 A"T+/Aml!AZ |/A (E!ABs/ADvAN"Aaf/AQ"Aq\/A"AAE/A#A0]e/Ai$A@/An8%A/A%A@/A`m@&A8V~/AB&Ac/A'A/A@,(A/A@5(A/A(A/A`(AP /A+)A/A@y-A+/A+H/A M/AVI1A$/A3A/A`!4ABA/AYF{4A/Am4A$/AĐ C5A/A 85A.*/Aŗ5A/A5A6/A5A/A6AL/AQ")6AxF/A86ApI/A_7\86A_e//A#H6A`H/AiPU_i6As+`/A|R6AhUv/AμQ*6AfF/A1 E#7Ax/A y7A/A`;,8A/Ar8A1/A#Q8AqJ/AJ$9A0/Aymf$9A/Aك9A</A4t:APM /A:AM3 /AN;Ag /A@>;Ati/A-N<AE/Ark<A)+/Aj<AN/Au;<A0/A`0=A{k/AKSg=AYH&*/A*'=A(/AѢ=A@Š/A`B*>A:/A<.>AiL/A u>AM/AaA?Aȥ0/Ay?A 0M/A 1l@AT/A8ɟ@Aq]/Aiƭ@Akhۯg/A[@Ar/AN@A> I}/AScӢ@A7[/Aw@AG&'/A0C@A'q/AI@A5t/A(Ep@A4p/Ax @A`/A@AK\/A=ra@A1/A+<@A[O/AZ@A/Aez?A(\l/Afz?ANa/AINv?AMR/A d?A /AT b?A/Aѹ?A N/A ¹?Aȶ6L/A+;?AӜ /A>`?AX'/AqL7?A׆0/AQ?Ap8/AV@A?/A>@A!E/Ae@AUH/Aw@AN/A)AA.U/A@&BAˣdO/Ao;zBAmG/AVRBAqJ=;/ACAIp'-/Ac?QCA(-/Ah@WCA< /A*{CAH/ACAkي/A@gCA=gr/AtDA~/A;V.DANzf/A{ 9_DA|p=/ADAk'/A0DAѵ/A`EAPȵ/A`;EA/AްsEAǻ/AEA"ŋ/AGEFASB/AXFA-/AyGAŷ/A8 HAϭ/A!HAJ[/A*IAв/A- IA5/AhIAROU/AԊqLJA x/A@JAVB/AJAQp/A 4JAvZ/A#KA6g1/A?:KAw/AIaKAG<_?/AΧJA/A߭JA"/A@{HAPoA/A@cGAx/ATFA`/Ah?FAa/AEA /A@NEA@/A EAhD/AGGAU/A HAذ;/A`]HA;/AtHAX/AHA/AHA$/A`HAaN/A@HAf/A.IAO/AJA]/A`GLA #/A |yMAK/AMAV/A` OA(#/APAz/AQAJ/ATAh/A@,OVA/AwA/AVA`%^/AA/At<AwU/A`A/AA` /A Ax%/A`3AX5#/A1Aq/AAm/A@A`X/A Ae/A<A(>/AAt/A@IAN~/A`6A{Yzd/AdAtyL/ATA7n7/A?Ac%/A fEA vz/A"A7! /ASGNA_/ACg~A;*/AܒA>/A5)jL`A/AA`/A`KA/AQdA7/Aj5A '/AZpASWN/Ar:A>/A+A1,/A~A/A$A/AA/A@A`L/A A8#h/A|A/A&A@S;/AoA/ANXA68/A`A-^/A@A=6/AxAl/A@AX/AAx9/AuAT/APAwx/AlA0H1/AA/AWA/AA/AgAk%/A ˇA/AA/A AP+/AmA/AgEAƔ/A A1/A>A$/A)pA/AAw[/A|A]/AKxA3/A0A/R/A`)A >/A`]AA/A A,/AA=1/A_UA_&/A KA^/AA E/A AP;/A@ A /AA(~/AEA/A4A/A |A(\9/AA/A /A/AqAx/A Ahr/A@VAiJ/AA=/AA/A`2A/A෍Ax/A`jA /A@FAB/A`ܔA=4/A`A/A@ypAif/A#A/AA /A/A$]/AA(M/AA@>/A A@5/AAҽ/AzAPĽ/AA{/AXA9/A`aA"¼/A@A8/A`)bAvú/ALJAp*/A`A !/AjA@/A@`$ AP룴/AAd/AA)Ю/A@!Ax-/A Al/A !AR/A!A8(/A m"A//Az"A`/A@y'A T/A.A})/A1AV/A`mq4Ap0#/A4A/Au6A 4/A8A1/A=<A/A?AxZ/A AAp?/A@5BAǝ/AoCA,/AEAfh/AMFA:/AJA@/AIOAYw/ArrOAzOOR/AsPA(.&*/AEKQAo/AI[a8QAwEҗ/A.yRAX㣗/A}RA n/AzRAm|8/AL5SAu/AU5)wSAI})xƖ/AmԨSA+OBV/AU_SAQ/A'SAV>&/A]ogvSAxܕ/A SA 19/AmySVSA@1\/A-TA/A`9TA>ޔ/Af3{TA/AF/AAFv/A]3A+JF/APAc/AcAYR/A a>Ah@҈/Af Ap/A@GAW/AA/AMuA0A/AA؎p/A AD/A A[Y/ACmAdg=q/A3A5*$/A0.A~/A±A~ϊ/A_CU1PA/A OA/AA#/A9A/AxA /A`vA@}/A=AO/A UAr/A`rAR/AA /AA74/A`]A8)/AsIAc6/AsAe/A5m6A$}/AOjIAˌ/AaAA/A{A*/A ~8A/A A؞/Am}A/A •Aج/A A2:/A/APP/Am}ARC/AeAY/A@A/AAV'˳/A*TA֢/ADob]AǧA/AB!A6o/AAG4k/Ak;A!?/A= A竐B/AA/AbYA%5/A`A˶/AA`Թ/AA`/AA`/A@Ag/A@Aj/AA؍/AAh/AVWA/A`6A/AAd/A࣐Ag/A \A/A CA؁$/AAFf]/Ak-fA/A`!A>6і/AB'%A]g /A'sAH/AA/AtrA*/A<AH/A@HA/A AA/A3A/AA@OQ/AA3I/A) A//A 8$A0*/A@#hAh^r/AA\>/A#8 AH!lw/A(cdA/AqAT#/A<2A@0)/AA/A@Ah/A)fA8"%/A@bA%&/AQA`/A AV/A /AH/A /AB/A@AXl/A0Adޜ/AdA`G"/A@WA(/A@yMA@/A`vA=/A@EA/A A/A (As/A0A)I/A`ʯA/A@عA/A`̺A,/A A/AA/A[A /A AS+/A@HA(3/AVAz/AAH/AIAv/A2An/AhkeAPoe/A@ƘAq!5!W/A3S@xAI/AA׌=/A1A] 3/A,U AN1F+/AA USA%/A@- Ap!/A`AE/A@A/ATAث/A |jAy/A Al&/AA`/A$A(/A^Am/AvA;W/A A8VP/AAJ/AVAxi/A pAJ=/A&AHЙ/AfDA1Ș/A`d#A`˗/A AX/A@߃APb/A 8 A@`Q/A5A/ANAX/A4A濕/AAؒ/A2A/A<A/AA/A AT/A@-Ap/AIvAxhД/A3Aۙ/AWA@/A]A9y/A`A[w/A Am/AIA/A2_A@/ADAcÓ/A'Aґ/A@AhG/AA/AA/AW AP//A` Axے/A` APՒ/A@p: Az/AF AX/A AxR/A@ϙ A`V/A# AfI2/A[ Av/A8bI A`/Ah` A(ӑ/Ag& Ađ/Am A /A`"AZ/Ap\i\As/A\4AÐ/A EA{0l/Az AG݄C/AۆAk.Ə/A$8E/A!TA6!N/AIAV/A AŴ/A 1<Ac/AAA}/A;AxS/AKDA(,/AA*6/ASDiA`喋/A`TAN3pg/AD፳A9/AgIAx/A^*DAp/ABhN7kAC/A@ A5d/AF4AU"ٍ/A(|@Aĺq/A2+AR/A8KGAp2/AAY/AA7K/A\/CA` Ɖ/Ao712jAp/A@8AJM/Ao&APm/A@A7/ALtnpA*~`/A$DA;ψ/A5kAJ/A_kQArXr/Ah2A1GG/A4a[AUp/AY A/AAކ/Aq5Aц/AA Ƙ!dž/A3HAv$/Ak5MAg~/A\PA /AA7/AʣA@6/A@TA/AmhA/A 8|AÆ/A`AĆ/ACA k/A^tA$/A bAZ/An2DA0f/A`\AK^1}/AA~p/AABg/AhvFAp b/AA/AA/A*A0݆/AaAhӆ/A?A` /AA0/A q9A`/A@A˚8U/A)ޟAM/AĝA#VlJ/A/\AJ/A@AXrJ/ATwAKH/A9LAD/A@#AK(9/AQ _A))/A%GA;D/AlyYAp=/A`<A/AbA /ބ/AŸAׄ/A#Aa/AA0/AحA^)惲/AӴ`vAF/AAF Xą/AsA0ԅ/AA /A+ϫAh/AA-/AAh*/AHdA!@H/AHko"A}/A'~GA}g/APF AP /AXA?V /A4cA+/ATetAqߪ/AcA$ x)/A|A:/A`)AhN/A!yAY^/AxA Ԣi/A"ԬcAsp/AaDA_s/Ap/Aڂr/A$yAl/A֡AfGDu/AmߝsA{3/A~A h/AkAS 7/AW@ZA84Æ/A@pAPۆ/A'eA"|a /AjAnf/AKKA͆ /AŹ/xZAo^/AuA /A?A(/A.AA/AnAbj%/AqAK3 A/AྜAH/AgYA5/Aa A1/A=ĊA m/A&UAxq/AFˣAi/A61wA/AW*( AKq/A CA/A JA oЇ/AeAA| /A0 A| /AA? /A$΁AϠ/AdA(/A`28A{y/AA'/AzlIA=oO /A l~AcR/A|1;e~A }AN/AE+}A ]F/AJ}A&-L@/A&|Ã;/A);.|A%E7/Af7|A(5/AЅ|A@>/A:$|AK/A{A2jY/A~a{Ad/AzA` j/A,zA1gm/Ap*)zA_o/A`~zA9:o/AgyhSzAb-;m/A-J)zA{1Bqi/AϗzAc/AyA^/ADyARUX/A`bܫyAq`x=R/A1\yA K/AFyA3T5/A xZyA)/Asɉ9yA=/AZMc/(yA/A3B%yAք/AQ3yA]8/A!wyAps/A3z;yA8;/AV.zAGn/AzAS\/Aj{AsP/A.D{A8G/A@"|AQ /AMAƃ/A@A/Ad|A&Ϣ/A|A*'Ӡ/A⬃{AÁ/A?v{AY/A[h-{A5/A'`zAc݁z/AqzA Cj/AXj)~zA&]ZX/AdWzAWE/A u=zAvy!2/A&E7Xق/AǕzAH)#̂/A:zAV/AKHzAL/Aa"?{A)/AG@ {AƮ/A{A7/A$|Aj‰/A!S|A["͑/A K&&|A9[/AB'|Av/AI}AI/A`}APp/A }AӚEo/A|.%A}?l/AπAFg/A.fAqr6a/AzAY/A4A$FyJ/AP7A*iz׸G/A[ѲzAD.B/A7kzA:/A }\zAc1/A <9zA='P'/AzA/AzA^#/A5zAL |/ATXyAV/A.3yA/A}jyAج܁/AyA=bˁ/A yA O/A~XPxAsAʔ/AU5stA\/AaA?tAȚ/A6XtAo/AgtApiR/AvtA.to/Adm}tAF_/At{ytA@O/A%GltA`?/AVtACc:=/ADtAՕZ:/AVV|4tAՋ6/A7XC&tAd2/AtH>tAKJC./A?tAv5}v)/A/ tA|~$/A tAl~/Ac tA/AG9tA(/A-tA/A̍[!tA /A.tAx~/A`Q~tAl~/AT1׿tA+S`6~/A0tA2~/A6uAO- y~/A%uAFdA]u/A`ҰAجu/A@,Ahu/A AHu/A@A54u/AA)u/AHAEu/A Au/AO1Au/A|AUu/A=A)~v/A (OA5v/A*Afv/AQAP'v/A'QA)v/AA@w/A`myA w/AAh{qw/AĔAgw/AjA8Qw/A@>APox/A A@5'x/A 8A8QUx/AQAЯx/ALAux/A Ax/AAy/AaA!y/AAP^"y/AʊA>3y/A`APWy/AAly/A6Asy/A`[Aby/AAy/ALA3y/A`HAy/AABy/A6A8y/A`Ay/AR'A z/ATA2z/AVAJz/AyAp,dz/A`Az/A`)AТz/AOAz/ApAHz/A \A%z/A`Avz/A@5ZA؎z/ANcAH){/A A-O{/AKAu{/AAxm{/ApA`{/AAH]{/A`KAӖ{/ATAh"{/AAߎ{/A@Au{/A A2{/AoKA0{/AAAw{/A`wA[s{/A %A{/A|A0n{/AAњ{/AA`{/ASA[{/A cAH2{/AA{/ABA8{/AhA8E{/A`vA{/A A%|/AA ||/A ZAe(|/A$ A0|/AsBA@4|/AA0&2|/A@Aa)|/AHA 0|/A`]AR{/AA{/AA {/A`]A0f{/A$GApʎ{/AAx)j{/A|A(F{/AA%6{/AA {/A}%A{/A[SAz/A u}A^z/A AUz/A A Az/A^ A"z/A`LA0z/A APwz/A`TAp{z/A`KAnz/A`v Afz/AeAfaz/AA( Uz/AsAx=z/AA`v z/A!A z/A@Az/A@rcA`z/AĐA z/A`̻Ay/AA-y/AAy/A%Ay/A``Apy/AdAy/AaAȺy/A"A(y/A iAxy/A 1A y/A A8 y/AyAiy/AAh@y/AAx z/AA([ z/AA03z/A@ACz/AΞAYz/AhApz/AAdžz/AAPz/AIA0*z/A@>A/z/AA@Nz/A`f!Az/AtAQz/Aq`A0;z/AYAH?z/A@A'z/AxAyz/AAXz/A`A Sz/A Av7z/AA$ z/AXrAz/AμAz/AnASz/A Az/A!A0z/A^A`z/AsAy/AWA(y/A`vbA(y/AFA ty/A \A8y/ADApy/AAH)y/A^A84y/ADAhy/AA[y/AAALy/A_A`!y/AAy/AAy/A@,A@y/A@NAy/A Apy/A`AUy/AtA@y/A`A(qy/AIFA0fy/AA0ٳy/Aज़A0Ly/A`Apy/AA y/A`vAy/A A0y/A JA0y/AAny/AAz/A`KAe*z/A Z;A03z/AARz/A@<Ahz/AaPAtz/AAvz/A AXz/AGAz/A |Aϔz/A/&AX5ez/AA9z/AAYz/A`ANy/AX A@By/AAy/AVA@`y/A-Ay/AAjy/A@,-Ajy/A`]EA*y/AAly/Ap<A"z/A4A`e/z/A sAȔRz/A`Aqz/AAz/A@sAFz/A`A p{/ABA{{/A A8O{/AA({/AVAX{/ApA{/A@gAQ{/A͹Aھ{/A@A{/AA\^{/AAx2{/A JA8 {/AA {/A JA{{/ANJA{{/A`bA{/A QA.z/A&A^z/A`bAoz/AsAP0z/AAA+z/A@gAgo/AnZNkA&jo/Ad.kAm&yn/AXlA:n/AX#lA(Jn/A`!mAƅ(n/AߎmA) n/A'EnA;m/AS:vnAj.m/A:UoAvA$m/AѴoAbƏm/A|PpA2m/AOpAЛm/A&qAhDm/AJrA{BRm/AS:rAks6m/AssA]0m/A4-ɍ,tAH1m/AJ/A8h3*qA79J/A^qApJ/A6rAPJ/ArAJ/A@>rAJ/A0{sA(J/AsAP^K/A !tAu K/A~tAK/AٹtA%K/A@tA0 2K/AH@uAJ3K/AjuAh0K/A`uAb;K/A ɷuA8DK/AVuA RJK/AquALK/AuAxPK/AauA@dWK/AuA<^K/AuAPYaK/AuAcK/A`uA@}{K/A[uAK/AQuAڛK/A@uAK/A cvA୧K/A@1vAXtK/AyvA +K/A@vA`vK/A@`vAHK/A\vANK/A@yvAHK/A@uAK/AquAK/AwuA(cK/AxXuAhL/A~BKuA]-L/A ?PuA3NJL/AΉ(guA.fL/AˇLuA! ̀L/A^;uAvL/A5VvAL/A@hvA pL/A@%vAаL/A`vAzL/AmwA$L/A xA0L/A@xAL/A 0yAXL/AfyA L/AzAjL/A zA`L/A zA`eL/AzAHL/A]LzA(L/AF`zAL/AzAL/A`zAL/A6^{AXyL/A@{AsL/Ap|AQL/A`B|A@L/Ay|A%L/Ay|AL/Ay|AL/At}AL/A 9}AnL/AZ}AL/A;}A``M/AĤ}A }M/Aɟ}A`P(M/AՕ}A#4M/A@g}A%PM/A@ߋ}AWM/Aஸ}AT`M/A@P~A]M/A`~A]M/Ax~AhUM/A~A[M/A+AhbM/AdApcM/A6A\M/AA(XM/A GA`RM/AځA@dQM/A`|ASM/AA\M/A`9A2bM/A@ރAnM/AAp[wM/AfA}M/A@APM/A<A`ÍM/A@ARM/A@>A(M/A4APfM/AAM/AAM/A@/A,M/AanAM/AAM/A@AM/A AM/AA`vM/AQA'M/A]vAM/AA@$M/AͅA`6M/A ZAȺ N/A JAN/A4A(N/A |GA4N/A A8c@N/A A@ABN/AAXKN/A`[Ap|dN/AxA N/AAx:N/AA`N/AAN/AAGN/AOMAHN/A@A0N/A AcN/A`)_AxO/A[A8O/A`AMO/A`AOO/A`m A@PO/A@iAryO/A_Ah;O/AqAO/AjAܫO/A@`AO/AZA}O/A牊A4O/A uAX$O/A`zAO/A@A$P/AdA=P/AA,PP/A@6A&UP/AN~A`!ZP/AČASP/A6A@uGP/AMmA@P/A`Ah//P/A`DA'P/A@YAP+P/A A"P/A&A P/A`A3P/A`A8 P/A>APZP/AAPP/AHŏA`O/A<ATO/AAqK$AgmO/AΐAO/A`A*GO/A$o,ANO/ADCAiO/A IAhN/AĒA-CN/AjA9N/AAyN/A*˾A^N/AA@N/A`בAM/AAttM/A<AHM/A}qA$LPM/A-A M/APaAJ'oM/A@ƑAsħUM/Aʺ>A>M/A Q?AS7M/A&A4/M/A8AX&M/A'sAM/AxGJA!OAM/AZ KASb` M/A6AήM/A^&Aw)nL/As!A:-L/A19Ap L/A`UAtEL/AAA8ڣrL/AQ&A%8L/AtAkK/A)zA|K/A"ߎACBK/A2$\7AeK/A`[AJ/AIA{9]J/Ahf&A0J/Ag@ A_ųJ/A` AqΚJ/A=͍AЦJ/A?A ~J/A-A:pJ/AnnpAȻcJ/AoA0XJ/A@.7A#t?J/AT%ACo(J/AZA9IJ/AV҈AżdJ/Agx֒ALv=I/AZ9GA-I/A@A pI/A ҸA zI/ASVA4Z[I/AHκ)A"I/AĀAعI/A`mvA %lI/A7OrIA OI/A tz-A41I/Af "A JI/Aoq(AɱH/A^I4@AH/AhAstH/APB†AkH/AѨ]o AAG/A.?AĵAG/AͼbALF/ArAx:F/ANlA5F/ANAF/A"%AGH9YF/A,AG.x1F/AᦻAT F/AG?4AohE/A5ZLYAHE/AAE/A$AUE/AAE/As- A)D/AޢoAbD/AM_ÉApD/A AP@D/A(CAI C/A4見AٹC/A# AyC/AA1W@C/A:G:A\Ż8 C/AVK،AB/A 냍A6DB/AhA'EB/AA| SB/A!TA^ܸB/AP'eAʭB/ASarA)湨B/A|yA[9 GB/AR#{A@B/A=wAx3hjB/Ao`KA|uB/A)AB1bB/AA)NB/Ar^ A;B/A= Aá'B/A+gAB/A1AҤ 7A/A4,A/AU]AiA/A#AbA/AVAD@:@/APA$-@/A@ A`]q@/A9+A?@/AŅjiA@/A`dA=@/A<A@/AJ֖A& ê@/AFpAe[@/A2A1,@/AeЂA|2Q@/A@Q%AN@/Aͩ&AԜ@/AmA@/AMA~@/Ak;LAx@/AAGl@/A ݜA\\@/AGAYH@/Al)YA/@/Av A@/AƘb2Az?/AVAKA5?/AHؠAأ?/AVA%WB?/Aڴ{A o?/A^8>A:RST?/AAo9?/AR.}ۀA^+?/AZ S_A!n?/AH-Aպ>/AA>/AAes>/Aj Aڶ^8>/A62tADT>/AqaA;>/A5NAO>/A ]=Av>/A .AW>/AA5L>/A# ATyM >/AEJ A^->/A>Ax6>/AA">/AACI>/AK A1u>/AaAtT>/AL][A.4>/A 鳵AzӒb>/AaW)xAFi=/A:+A(=/AўA(-=/A ҰAP6/A`)Aa6/AැA6/A=jA6/A`7Au6/A@(A6/A`BA6/ApA6/AzAp= 7/A oA7/A`fnA77/AA[7/A@gA`w7/A̴A7/A`)A07/ARlA7/AHYA7/ApWAf7/A \wA)7/A \A`a7/A 3A7/AXAk7/AA87/AAH\7/AzA7/A@>AB7/A3A 7/A@8A7/AR`A 7/A`fzA7/A HAX7/A]A#7/A A#7/A=<A7/AA*8/A@ AC8/AFAP8/A`A`e8/A`Ap o8/A`DA8y8/A qA8/A@A)8/A AL8/AcA88/AAp 8/A@Ah8/A`FAz8/A=Aأ8/A AQ8/AA8/A`)A8/AfA؞8/A \A8/AA8/A@ A08/A`!A8/A`A9/AAP 9/A@A.9/A A RB9/A=A8I9/A/AH.B9/A@hA8,9/A`ÛA)9/A@ Au9/Ap A8/AA 8/AAأ8/AAf8/A`Ah38/AA8/A \A`a8/A@A8/ASA8/A`)pAf8/AסA8/A3A9/AeA8 9/AAH\9/AA{9/A HA(-9/A@ϏAU9/ARAP*[9/AAH.`9/A]AfW9/A qAk9/AQA9/AzAh 9/A3A9/A@ A/9/AAZ9/A`fdAQ9/A@ A9/AzA(ܤ9/AAHP9/A A9/AGX1An9/A[A=9/AYv=A5/g9/AA9/A@CYA8l 9/A,QYA7{9/A6AA?9/A3˚Alm9/ALUA@19/A A@9/ALpA]9/AܾA!d9/A61ACcE9/A{>CVA`S%9/A A4P!8/A'=SnA?8/A YA08/A |A3n8/ASNUAـ8/AR dA {8/A"AEj8/A$p6AxX[8/A AZ;K8/AAI>8/A A.T8/AqAi8/AmDFA<{8/A]A#|8/A5A@ ~8/AgqA/Y~8/AGmA\~8/AA~`d~8/Aqq;A~}8/AA"|8/A"`,Aϔ:Gz8/A+Aix8/ANAOKu8/A^Azr8/AdAo8/A\? A֒l8/A;o Ai8/A` AfF8/Aޞ.À%8/A@A8/A$3A׽8/A*Ar477/AwAjh9M7/AAƕ$7/AȸAz:7/AAJ7/AJrAIG7/A,Ae7/AM30Aׁ7/A A7/A@iA ʤ7/AAApft 8/AvljA8/AAo8/AUAN)8/AA酽 8/A*ۋA6`8/A1]Ax8/A~A@7/A$qqA87/AOF+gA7/AD`AE'7/A|RcAg o7/A,SbAYv7/Aί/]A7/AMwTA(p7/AhծHAǪ7/AD9As!~7/Al'A]#-7/A\2;A7/A A+b7/A?An,7/A? Ak7/Akn(A77H7/A;Ad7/AAE57/ArqA7/AaTA0u7/AiAN­7/AHA@7/AՙAw~^7/AR2F}AӍn7/A2(*dA7/Ag QAzak7/Ac\vBAl7/A@r9AC7/ACDAhR7/A1o,RAf7/AmvaAʣ7/A$rAfY 7/A$@{AO7/AAA'v[7/AAf'K7/AybA* T=7/A)=?#A .>a27/A'PA؟*7/AA`r&7/A/G At$$7/AAf%7/ACA%7/APy{ Ansy"7/ALA=7/AA`ASg7/AԥlA=7/A% >A7/AzALU6/AUAp6/A 7A=b6/AAbBI6/A[ʊAsZ56/A TA$6/AFA2>wAg8d6/AppA&f6/AK HA 6/A Acp6/A=aAP^6/AAA`6/A{cwA6/AWA Y6/AElmA\P~6/A5RAʠ(6/ASq)Ap66/A@G]A6v6/A$AH6/AcAX?o6/A-IhAj6/AoԖAS6/A$;̂A6/AtA6/AsvA,&7/A~ AзW7/A͍Az{7/AVl*A./7/A9A&7/A`A37/Ae%XAm?7/A,Ag7/A鈉,A88/AAw8/A.A18/A+YA8/AdTxAFܬ8/ARƴA?8/A8AP8/AA%9/AH[AF9/A7ABAPL9/AS<A4|+R9/AE|=AW9/Aa:Ay[Қ]9/A|4Ab9/A;)AKDg9/ADAhk9/A  A, h9/A}Af9/AQA[f9/Aw=8Aaf9/AAɭg9/A2\jA+g9/AJKA3|f9/AiANKf9/A\˾Am߂f9/A|ڗA'f9/Ax͎A"Be9/A w/A=Jt8/AsAVq8/AMA`ap8/A&Ar8/ADŽA t8/A AK1t8/AwAu8/AAj.|We8/ACAyT8/AJA~^C8/AgpvAE18/Ad]NaA0{8/ARA8/A̧qAG8/AANA#8/AR(A#7/A@ Ay'B7/A AO67/A3qAu7/A AF7/A&A7/A"5A ~07/A/TANݮ7/AA.07/A5AEv 7/AyˆAeW7/A@ A-ѫ7/A[iA {7/ABA9Y7/AV"A|j7/AdAb7/AxA%7/Ax cAʬ7/AcAbt/7/A Anq7/ApAod"7/AGEH\A( 8/AA8 8/A@>VA\\8/A"GA6$8/AOJ) 7A88/A@A@8/A \3A<8/AA.8/AHCA8*8/AAX 8/AQAx{8/A6A[8/AXُA- 7/AˎAL17/As|A7/AmAs7/AA3\~A=7/AAA`a7/A 4A7/A GAHܙ7/AzA R7/AAL7/AkAYT|7/A\}Qy:AD{7/AUۨbA}7/A@iA\~7/A0A,~7/A0. A n7/AR5Ads~7/AiJA;~7/AkZaA{W³}7/AbGAVk|7/AgA {7/A. AHy7/A&z AQw7/Ab OAm@3u7/A7Ar7/A8Ap7/AzA@+f7/A!j߄A'< \7/Ay|AKDS7/AyEmA~J7/AXA'7/AA{GC7/Aۿ̙AZs7/AtڝA7/AmzA B7/AxAFi 7/A݊tpAŭ# 7/A8jA8 7/A^UmeA7/A3,cA?*V7/AFbA87/AdAp )6/ApA¯5/AzlA83m5/AbA'd5/A,Ay_5/AޔAu]5/AbQ)A`5/AM7Ae5/AvZAՊj5/AAОj5/ASbdAHnf5/ANv?A6G\5/AɌNAM5/Av}A/M5/Ai5AtK5/ApJ-TAH5/A|A E5/AB~Aڡ/@5/AӰA\Sv:5/AAE45/A^2&A-5/A0A3/ArA83/ArAЪ 2/A_Aج 5/AZ}AC6/A϶A!8/A@AX=:/A`A!/A@Ah=/A 1A@ /=/A`9A3/A AH{H@/A OA7OA/AA?B/A AMA/A`a0Apc%A/A74A]@/A@AAX@/AKA@/A`MA?/A@iSA0>/A_dA@>/A,yeAH>/A efA>/A8(gApű>/A:hA=/A`WvA;/A@tA(9/A -sA^8/AqA<,7/AbA,6/Az4A8]6/AAh&5/AyA(5/A=1A5/A cAo4/A5A3/A`A`3/AALf2/A *A1/AA1/AA1/AAh0/A-AL*0/A`f5AJ//ACAx./AAh*-/A`zA`T+/ABAp,-+/AA&+/A sA0+/A5A*/A'A*/A96A)/A IA=)/A"A)/Aho-Aڝ(/A+5AB(/A?S=Ap'/A`oDApŢ'/AmyLA0&O'/ATA"&/ADsAp%/A[tAB&/A)|Aw2%/A@ErA"/AHAqa/AivAH/A/TA/A SAc/A|SAh^ /AZ]EA0/A;AD/A .A(/A|!AXy.A_AQ.A9A+.AQ A.A ,AHu.A Aȃ3.A`APUU.A@AH .A`A .ATAt.ApA.A@ AI.A AاM.AVA0.A+ A..AAG.A Ar.A@DAPQ].A`Ay..AEAL.A A(S.A@`wA0.A`A.AA .A`wA.A[:A .A .A0r2.A2A2.AcOAHvo.AWA].A dXAg.A@?YA0.Al]A.A`0bAmn.ATgA ɹ.A_5iA .AkA,.A`[pAЈ.A uA8r.A@rfAЯ.A@#@A` .A늇Ap.A-A?.A`LAc.A ~A.A@r/A.A_{A.A AA.A`]-ApS.A)ۚA.AkќAڴ.AA.AAn.A\A@,.A@5A.A ~AO.A`YXAXT.A ZA@W.AQA(\.ANAX}.A`ՈA.A (AF.AஃA.A`;zA?.A |AHu.A#tAH.A Aھ.A@`rA".A Ax.A@A>.AkAHvq.AAZ.A`kAP.A A غ.AZ Aȭ.A@ri AX0=.AATk.AWA.A AXѶ.A@^A6.A XA0L.A G A.A /R#A(.AH0AxL.AT2A.A@33A[`.A6A(Ȳ.A|>AY.A JAd.A@PPA.AWA@.A XAH?.A`ܪXA0̘.A@!ZAQ.A&aA =ɭ.AdAX֭.A)eAp.AeA.AfAF.AVgAjs.A`"hA ͏.A`hAPfĮ.A5iA.AiA.AiAB .AiA.AiA.A iAh.A`DjA.A!jA0v.AD:lA.Ao`oA(R.AkAz.A 0nAB{.A &3nAQ{.AjnAz.A@nAȓx.AnAuu.A@nA=g.AnA.W.A@ pA t.A (pA.A pA.A qA@S.AOqA(.AࣳrAoݱ.A0rAı.A`tAT.A@uAX.AuAA.A`vAB.A$vA`z.AHwA`T.AWXyA@.AzAxG.A q{AH.A]t}A#.A)A.ADAP.A@nA`I.A@E`A`B3.A@GA84.AцA07A.AvAl@.A`Ao=.AO+A$8.AvMA((2.AoA((-.AטA).AA#.A·A.AkA.A A.A@W8Axz.A`]A83 .A@yA .AA.AƈA.A A3.A@ A0.A`-A.A0NAF.AzuA.A ˝AV.AHʉA.AA.A`,A .A /^A R.AAj.AĊA..A`fA.A9Apޮ.ApAzڮ.AA׮.A&A8Ԯ.ARAxЮ.A`A̮.A`3A0Hɮ.ASA2Ʈ.AqAB®.A`A.A &A.ARیA.AA .A`A.Aj8A.A`XAP<.A cAୢ.A@A8.AύA.AA`.AA.A)A8Q.ARAТ.A@uA|.AෑAu.A`)A@um.AAH)f.A&A[a.A0PAP \.AӀAKX.AWA kT.A q̏ABP.A/AtM.AAyH.A:ABE.A@pZAP@.AA0=.AHA@8.A ɻAأ4.A@CؐA`0.AvA@,.A"AH)*.A cSAp(.A}Ah'.A@A8&.A0ƑAp &.ANApk$.A&A#.A/Aj#.A@ ZAF!.A;A.AA\.A ӒA.AA`a.A34A.A`gAp.AA@B.A@WГAh.AAL.AH@A.A@XAH.AA.AA p.A(AHz.AA3.A`WAp.AՔA(.AЕAU.AA.A Q;A.AmA.A<A.A`mA7.A lږA .A \Ah.A/Ap.A[`A.A෋A0v.A鰗Ah.A3ؗA.AAН.A%AQ.AMA@.AVvAQ.A@Ax.AȘAh.AA.A(AH.AXA .A9AX!.AWAp!.A@G֙A#.A0Aг$.A(A`&.A5AW0.A@%OAН8.A q`AȓB.A3vA J.A猚AQ.A墚AZ.A=A#b.AAc.AA_.A cAZ.Av5ApU.A Z^AP.A&A`M.ḀAfL.A͛Ap=M.A`)AO.A@AQ.A/>AT.A ZbApW.A@A@Y.A`ծA[.A̜A`^.AA`.A AQb.ABA pc.AcA`ed.AOAe.AѸA c.A`[Apk`.AAz].A /<A`Z.AbABW.A@APS.A@A0fO.A AJ.AݞAE.AAp.A PA:.A@pA0f6.AAh2.AdAp=/.A@A,.AA).AE6Ap8%.A`^A0!.A ⃠A.A9AH.AA.A@A0v.A`A.A (=AQp.A}[A.AȠA:,W.A98IA|r;.A[u-ɦA+.A<A6x,.AT,AJ.AĒLAҗge.A WnrABkݍ.A F)A<"<.A| uOA~n5b.AR A Ad .AnB8# AGÕ.Aȴa Az.ARH A_G7.A| u Ab".AF) A2-h.A Wn A/AÒL AʇT /AT,ѕ A A`/A` A>/A A>/A@P A>/A@Nj A>/Ag^ A>/A ΃ A>/AL A`/?/AC AP?/A`] A Q?/A`[ A0w?/A Ap}?/A,{ AU?/Aw/ A?/AF A@?/A. Ah?/Aﻀ AȂ_?/A Ԁ A0?/A A A9t?/Ao; A(mm?/A@L A0]P?/A Ah@J?/AN A-?/A c A&?/A`K A.>/A A >/A Ah;>/AH& A҇>/A A`Â>/A AhS>/A@" AM>/A 0 A>/A@Ϯ A0>/Ag A/=/A A5 A=/AB A؁=/Ax A`=/Au A@=/A㉆ Af=/A6 Ab=/A= AOF=/Ao AhoA=/A`ք A =/A` A@p=/A A/A`{ A?.>/Az As>/A$z AN>/A@yy Ax->/A@!y A>/A`x A@J?/A`v/x Al?/Aw A07?/A`Lw Ak?/A*3w A(?/Av Ax:?/Av A ^k?/Aw A(`d?/A`]w A(90?/A_x A'?/A`3x A:>/A@x A@>/AVx AȾ>/A (9y AXV>/A6Ny A(>/A0y A~>/A 8y AM>/A`2Sz A@E>/A Zgz AN>/Az A_ >/A@z A=/A@Pv{ A==/AO{ AXh=/A | AE=/A| Ac=/A| AH[=/A+| A8E*=/A@)} A@"=/A`<} A``Z/A/q A @/A`9un APuB/AOk AB/A?l AB/A`2l AxuB/Am AB/A`im AB/Aǎl A(B/Al AB/Ak A@B/A@ߗk A؞GC/A`̧h Ax-D/A`'f APD/A`f AD/A g A7E/ANg A F/Ag A(`F/Ae A@pvG/A@d A -O/A[d AXJO/A@nf AhO/A f ADO/A g AN]N/Ap AJ*J/AzFh A<J/A4g A>I/Ag AלI/Ah A` N/A}r A`O/Ag ApO/Ag ArP/A Hng AP/A`e AB'Q/AWf A88Q/AHi A R/AWj A6U/As AW/A| AgX/A AVuY/AU A`XY/A AsZ/A, A@Z/A@  A~[/A@ A}[/A" A[/As AX[/A . A[/A@ A[/A൬ A[/A d AH\/AN AI\/A`t A\/Akƌ A]/A@WH Ap8]/A AC]/A A6]/A@pܑ A]/A` AТ"]/A@ AkB]/Aվ AQ]/A9 Ah^/A \ Ap^/Af A"_/A1 AH_/A Apea/A@ A-b/A`á A,b/Aˠ Ap=Zc/Atݠ Ac/AW A0\d/A Ad/A; Ad/AIp Axe/A  A(Qe/A6$ A d/A@%V Axzd/Ao( A\d/A`;7 A_Fd/A' AhoHd/A`" A^d/A" APɩ A=a/AΩ A@ a/Ab A( `/AE An_/A4 Ax`/A A`/A A/`/AZ A `/A@< AF_/A`m& Au_/A` Aj_/A O A-u_/A AP_/AF AP_/A A!_/A! AS_/A@>ܧ A_/A 1 Ax:_/Aȧ A^/A AQ^/A API^/A A'^/AӦ A^/Aa A^/A`ަ Ap^/Ao Aw^/A Ap^/A AE^/A@2 A Aل8t/AP9[ A麢v/A4Ɛy A̱5w/AO Aw/A Aw/A AvQw/A {/A87 A`t{/A AH{/A 1 Aj{/AM AP{/A1 A)|/A` Ah"S|/A/ Ahs_|/AF Abi|/A` A(r|/AH A0||/A  A |/A`K% Ae|/A`KM AXW|/A t A8|/A AНz/A s Az/AS Az/A`9 A8z/A Ax>z/A AAz/A$} A0|/A@> A|/A A k|/A@, Ax|/A Ah|/A@N AH|/Aq A|/A AQ}/A A(}/Ap A8R}/A, A8#}/A`d= Ap&}/A X AX6}/A`{ AZ}/A` A8j}/A Apq}/A`; A-x}/A"+ A(}/As A`a}/A A p}/A`b A0̮}/A` A}/A`v Aأ}/A ۋ Ah}/A`X A83}/A/ A}/A@ߗ A}/AD A}/A@ AТ}/A$s A}/A` AH.}/A;# A}/A A}/A` Ax}/A AH}/Aa A(}/A  A}/A Q AH}/A A}/A A`}/A A}/A A(-}/A@& A0}/A`o_ Ao}/AW A(}/A cu Am}/A` A}/Aaa Ak}/AĖ A}/A A-}/A` A8'}/A` Ay}/A` A<}/A@WL A`}/Aj A(}/A`o AH-}/A] A}/A 8 Ae}/A@ A%}/A A0̐}/A  AH2}/A`r Ad}/A Ap}/ANe A,}/A Af}/AD A}/A< A}/A Ah@}/A Ad}/A \ A@}/A5 A}/A8 Ah}/A A}/A A A4}/A A}/A@ A}/A A舁}/Aj Aj}/A Ae}/A | Ab}/A Ah`}/Ac A)`}/A` A_}/A`T A5_}/A 8 A^}/Af A^}/A@y Aq^}/A A ^}/A A]}/A` A^]}/A AP]}/A|( A]}/AU0 A]}/A7 A<_}/AE AOa}/AfT A(i}/A| Aأm}/As A@Ox}/A@ A@{}/A@. A0}}/Aq A}/A A #}/A A}/A Ah}/A A؞}/A@ A,}/A` Ah}}/AX Av}/AƏ Ag}/AT A"V}/Ad ApP}/A~ ApAI}/A@N A8RD}/A A18}/A@N A0w5}/A`b A,3}/Aw A+}/Aj A#}/A> A#}/A@` A9}/A`B A }/A A`}/Ap A3|/A@i A|/A$ A|/A= A|/AO! A|/A` A@|/A A/|/A}E Ap|/A` A&|/A & A0|/A^ A'|/Ah| A |/AB At}|/A`ܫ Apu|/A@ Ad|/Az AXW^|/A` AȃX|/A@y AؼN|/A APF|/A AmD|/A A A|/A* A87|/AO A4|/A@ Ao2|/A AH1|/A AX#1|/A A/|/A` Au.|/A  A-|/A A8+|/A A|*|/Ac ApJ)|/A Aq(|/A A;(|/A A A%|/A A#|/A@ Aa"|/A! Aؒ!|/A@.6 A8g!|/Ag Ap #|/Aʡ AG$|/A@ A)|/A AT+|/AXK A+|/A`mi A)|/A A(|/Af A}%|/Aγ A3|/A A|/AJ Ap[|/A } AP|/A AHa|/A A8|/A? A{/A A({/A A(-{/A`B Ah{/A`] A{/A AX{/A@5 AF{/A A{/A A{/A A{/Ad A`%{/AR AG{/A} A{/A |j AA{/A c] A`{/A\ A({/Ac\ AM{/Ac\ A{/A\ A{/AV] A +{/AN^ A{/A+a A({/A@We Ap{/A j AP{/Ap A0.{/A`"w AP{/A< A{/A A{/A Ap{/A_ A{/A A={/A Ae{/A@. Au{/AT A5{/A@ A{/A, A`{/AH A0{/A`BZ A{/A m Ad{/AǗ A({/AO A{/AЄ AȤ{/Az AX{/A` Ah{/A A8ڼ{/A A`%{/A A_{/AO Ap|{/Aj A@{/A A{/AH AhD{/A A{/A` A(z{/A A{/A` Ah{/AN A8z{/A A@o{/AE A@d{/A`T AW{/A A`L{/A[ A"B{/A@C A>{/AN AH7{/Ak` A\){/AX AQ{/A  A( {/A  A8{/A A{/A < A AF/A@o AjC/A  Ap=@/A  A#>/A@% A,=/Aa  Ax=/A 9 Aؼ>/Ah A@/A ɏ AjC/A_ A0E/A  AtJ/A A2P/A AW/A> Apc/At A3k/AW A =u/A@ֲ A /A AX(/A`" A/A`" AV/A`"  A؎/A A/A`mN A!/Ao Ap/A` A/AЌ A?/Ai AxG/A A0/A A(/Az A/A@y A0/Aa A-/AX AP8&/A@y AV0/A@ A#;/A  AG/A@, A~S/A A#`/A Aj/A Au/A` A~/A A/A@N A趧/AjH A/A À/A$ Axր/Aao A/A A/AF A2/A] A(z /A O A /A A@/A A'/A` Ak&/Av A-/A@_ A,6/A`vI AX?/A5 AhI/A Z$ AЄS/A A[^/A Aeh/A@ Ap/AD Au/A & A-x/A A0e/A@ A/A AP/A A AH2/Ab AP/A / A(/A Az /A Ay/A / AQ/A A/A = AB/A`Ka Ax/A` A/A`; APk/AM Ap[)/A A//A & Ap[8/A A8gB/A AC/A  A]/Afs A#/A@, AL/Ai AT/A9 AZ/A0  A2^/A} AX^/A Ac/A@ Ag/Aྦ Aȃj/A  ALm/A@e AUp/AD As/A@%" A0t/A A`{/A# A=/A5 A~/A E Aі/AU AX(/Aj A =/A A7/A A/A൩ A[dž/A ˻ A׆/A@ A8݆/A Ap/A A>/A A">/A  A9/A@, AxG4/A`* AH3/AV A78/AX A:/A@#Y Ae?/AoL AD/A`+ AJ/A  AUQ/Aa AxW/A  A`/A, Apx׆/A \ A8x/A[@ A0n/A 8 Al/A`H APm/A 8a A7q/Ao Ax/A{ A/Aq A/A`v_ Ax/A@,V A̗/AZ AQ/AZj Aǰ/A@ AX۶/A`K AÇ/A Ahʇ/A A@·/A Ahև/A`m  A`ڇ/A, A<ۇ/A,D A8 އ/Af A/A A/A@N A/A@ A/A; A/Ad A /A` A@/A6 A/AU A/Au A /A@ƙ A /A A/A A/A; A8V/AN A/AL1 Aj/A8 A/A2 A@/Aj" A@u/ATP At/A St APf2/A Apy;/A AuD/A AN/AN AXyY/A APf/Av Ahl/A Axzx/A@ A(/ANA A([/A`i A /A Z A̫/AT AP/A  A[/A A/A@W AÈ/A: APʈ/A`oZ AtЈ/Av APk׈/A/ AUވ/A& A/A A0./AE A[/A@ Ap/Ax A/AV AX /An8 A/A] A"'/A Z A-/A  A'4/A޽ A`9/A AG>/A  A G/A` A N/AvC Apʈ/ADd$ Ap݈/A@C$ AY/AV APb/A AQf/A Ao/A! AQz/ANG! A8/A`m`! A /Aix! A /A ! A攉/A! Ao /A?d% APo0/A?% A/A " A~Ƥ/AOaI" Av؉/A`6# A)܉/AM# A/A@0$ A.D/A&@0*$ A`P+/A@h% AE/A6& AxQ/A`& AD]/A& A 8/A 8( AJ/A ) A軺/AO) APˊ/A) A|#/A{^* A~l/A̜K+ AoK/A u, A/AV, A|R/A- A /AQE'. A./Aw/ A /P/Ai0 A(zp/Aд0 AK|/A=.1 AȄ/AD\91 AE͌/AoÍ1 ABZR/A,K2 A? K/A9&p22 A9>/A2 A0Lg/A@2 A6p/A 82 A8{/AN2 AXu/A2 A`/A@2 Aأ/A;3 Ap[/A3 AX(/A`3 A0./A`D,3 AXƍ/A@W83 AXuэ/ANE3 Aڍ/A2R3 A^/A7 A]/A7 Af.A;d{ A`f.Ac{ A(if.AZ{ AxQf.A&I{ Au9f.A /{ AH:+f.A{ AP f.A z AXe.Az Ae.A`z Ae.A ɝz A((e.A@z A@e.A Xz Ake.A*z A0LKe.Ay AHe.A y AEe.Ay A #Be.A \y A\?e.A`y AP/:e.A@߫y AX,e.A Iy A)e.A`:y A&e.A.y A$e.A`$y AP!e.Ay A e.AUx AWd.Ax AH{d.Ax A(\d.A`fx A(d.A+x A0wd.Acw A0jd.A mw Ax)]d.AoPw A@Md.Af5w A&d.Aw AXd.Axv Ad.A v Ahc.A&?v A0{c.Aou A3_c.A A].A`D' A].AI AC:].Al,Vl A&,].A6`e A ].A'( A^? ].AMWz͔ A4&].A Af].A@W A(].A A\.A8 Aؚx].Ao Ax].AҞ A].A4 A].A AO^.A Ah^.A( AhԹ^.Aњu A 4V^.AAF A7J^.A{>U A1 _.Aǁ A51)_.A#Zӧ A(qhJ_.AK Al_.A2L A< _.AP A&_.A AQ`.A[ A4`.A A8x`.A ⼩ AǙ`.A= Ap`.A A^.A` A"^.A@iL A^.AKi A]^.A` AO^.A@ A@^.AE A_.A`i A _.A@i A~6`.AKb A0`.A A`.A?U Aa.AA AVma.A A9Va.A A2]a.AM AP6a.A@h A1a.AX A`.AF AP+`.A`, AH_.AF A_.A2! AS`.A Ap<_.A U Ay`.AӰ AP`.A@ Aa.Aؗ A`qa.AH Apia.A@ư A@Ϗa.A@Ű Aga.Aٛ AP8a.Atʰ Aݘa.A#QD AgVa.AN; A#b.A Ax8kKCb.A zp8 Ap9db.ArZ Axb.An Azb.A߲ AQb.A@ h A a.AD& Ahݺa.AXb AQa.Aγ A(Na.Aӳ APfJa.A ٳ APFa.A A8Ca.A@ A@a.A A=a.A A8x;a.A6 A`9a.A A7a.A ) AF6a.A@8 APM5a.A@pG A4a.A/W A4a.A`g A4a.A@v A 5a.A A5a.A 8 A06a.Af A`8a.A AO:a.A̴ Aia.Av̻ A8a.AmYٻ A(a.A| A [$a.Az A&Mͼa.Ab(5 A2a.Ac Ax-a.A| A€~a.AU9 A6xa.A Bf A2wa.A@ҽ ADza.A:> Aw÷Ӂa.AQrW Apca.AKC A`qa.A`` Ad!a.A.2Ͽ Aa.A$e3 A6a.A&. A6GRa.A, A`b.A AHRb.A> A@b.A A|$c.A|O Aт,c.A|O Ad3N>c.AO AKTEc.Ak5 A^!c.A< A}8b.A) Ab.A Axb.A, A?2Lc.A& A^!c.A< ADl-A AA8jr/ARf,:s AS/3     )4]@)/A@`vzA((/AyAݕ'/A@QyA%/A uAh"-%/A AgxA>$/A@.|A(#/AA;"/A@AP v"/AྱAhp"/AAp7"/A5A` !/A^\Aa!/A cA^!/A@Ah#/A_^A&/A΋AH(/AnAx>*/APAq+/AA./AǏA//A`A+1/AqA2/A_AiG3/A@yAQ3/ArA864/A@` Aȱ 5/A=A}5/AA6/A`նAH{A6/A嶶AHa5/AIAذ5/A`JA5/A渨A5/AKAh4/AǕAh3/A A(\3/AAw]2/AoA&2/A+A0/A@OA`r//AAT./AOA['-/A`+A&,/A+A@+/AA+/A |A@)/A@`vzAH.$/A7AP7$/A AV #/A #AH.$/A7A:.AIAA83.A@A .Az@A .A@A( .A[@Af.A@A33.A`k@ApN.Af@Ag.Ap@A#.A@t@A.A^@A.Aa@Af̷.A[@A.A @AV.AAAf!.Az8AAp-.ApSAA`<.A_AAQ.AkAA`h.A`fjAAp=w.A ^AA.AFAAz.ABAA.ADAA.A]YAA`͸.AaAA`׸.A dAA`ݸ.A [AA8.ApIAA.A1AAأ.A`)AA`a.AzAA`.A@A.AAAp.A AA.A+AA.A /AA83.A]#AA%.AAA#1.A@A=.A@AD.A@Ap8K.A@@AO.A@g@AXS.A @ABW.A@AY.Ap@ApY.A @AU.A@@A(P.AG}@AM.ARb@AN.A[C@AVR.A`@AY.A?Aa.A ?AHak.A`)?Au.A q?Ap~.A@?A慹.A෶?A8.A@?A(\.Az?A`.A?A`.A@Aœ.A`@A.A?A@W.A?A.A@ @Aɹ.A@A0.ٹ.A`?A0.A@?Ah.A?A R .A ?A`.AR|?A8.A`fd?A.Az2?AH.A?A`a.A3>AX#.AH>AH\*.A=>AP87.Aj>A;.AU>AuD.Ap>A[.A`̔=A].A3=A#[.A@p=AP8X.A m=AS.Apo=Ap=@.A@G=AL=.A \=A<.A@=A R=.A=Ap?.AGg=AA.A=R=AP8D.A \1=AC.A=ALE.A<ABI.AQ<A{c.A o<Ah.A][<Ap=o.AGM<A8|.A <<A`a.A 3<Ap.A 2<A8.A ?<Ap=.AE<A֑.AK<Ap.A@N<Au.AI<A#.Az*<A83ƺ.A[;A83Ժ.A@g;Akܺ.A;A`.A3y;AQ.ARl;A.A3W;A.AF;A8.Ap+;A.A` ;AB.A:A .A:Ap0.A:AzA.A\:A`^.AR:A؁p.A9A@.A ¯9A8.A9AG.Aj9Au.AC9AHλ.A=9Aٻ.A8A`.A8A.A8A8.Ap8A.Aw8A@.A o8A` .A=Z8A.AJ8AQ.A 58A&.AR8Ak0.A7A8.A7A`?.A7AuF.A=7AN.As7AV.AL7A`a.A3!7A0i.A=7Ap .A 6A0.A 6A.AGm6A R.A@F6A R.Ap#6AX ü.A3 6A)м.AR5AU޼.A5A.Az5A .Azz5A(\.A@H5A+.A3+5Ap=.A@4A@H.A4AU.A@ 4AQ`.A`)4A@g.ARl4A`at.A O4AP8.A=84Ap .A 4AǙ.Ap 4A0..A`3A0..A3A.A3AL.A@3A .A@3A@$.AG 3A-.A 2Af7.Az2A?.A]2AG.A=2AQ.A@`2A[.AD2Af.A )2Aus.A 2AX.A=1Ap둾.A1A`a.A`f1AH.Az1A0.A=1Aھ.A]1Az.A1Aأ.A1Az.A 1A؞.A 1Az.A 1A.AGw1A.AZ1A.A 51A.A`)1A.A0A+.A@L0A@1+.A:0A8g+.A(0Ap+,.A0Az-.AH0Ap9/.A@/Aؒ0.A`B/A81.A;/A`2.A/A72.A4m/A2.A@r4/A3.A.A5.A=.AH6.A@.A8.AM.AX9.At.A,<.A -A>.A -AGB.A [-A@F.Am-A0J.A`;,AP^O.A},AT.A@>_,AX Z.A !,A_.A+A@e.A+Al.A[+Ar.A+A`z.A*AX.A*AH݈.A`*AH.A &*A𒘿.A@y)A8.A)A誩.A+)Ap.ARJ)Aп.A=(AԿ.A$(Anٿ.A#(AN޿.AMn(Av.A\(A.AiL(A.A=(Ak.A`0(A.A((Ap+.A`#(Ap.A` (A\.A (A .A(AXy .A#(A".A%(A.A`&(A .A%(A@.A"(AH.A@(A@.A@y(A.A3(A.A (A8.A(AP.A(A.A(Ap.A(AQ.A(A{ .AR(AP .A'A.A 'A.A'A.A 'Ah.A'A.A'A.A'AM.AX'A.A A'A.A'Ay.A p'AX[ .A@Z'A(.A@`E'AD.A1'AB.A'A>.AB&Ap A.A#0&A@dD.A&AH.A@5&Ax)L.A0%AP.A%A)U.A%A_X.A@%AS[.A%AP].A@%A>`.A@%Ab.A%Aȃc.A-%A0Ye.A%APg.Az%ApAj.Aq%AGm.A@b%A`p.AU%A Rt.AJ%Any.As9%A~.A`m*%A|.A%A_.A@C%As.A`B%Ah.A@ $APڛ.A|$AƠ.A`m$Ap.A$Aҩ.A$AP.A`$A0.A$A .A`b$A.A}$A.Ag$A.AR$A8.Ap#A@.A@M#A .A`v#A.A"Ai.A@"A.Az"AP.AjR"A".A@>+"A`a.A "At.A?!A( .A*`!A#!.A !A'.Am Aj..A Aذ5.A A _=.A@l A`rE.A cH AM.A3% AxhS.Aa A }X.A?Ap].AAJa.A uAd.A@ Am.A@^Aw.A@+AH.A`AP.A A.A^A.A`A.A֍A0.A`uAxv.AK_Ah.AJAh.A`f8A.A .A }.A<#Ap.AA0.A A8.AGA.ARAxd .AଌA>.Ah{AxX.AkA.A?]A4$.AvPA*.AGEAF.AA@J.A APMM.AAhP.A 8A}R.AA8T.A@Aa.AVAd.A9AHg.A Ak.A`KAfp.AA8s.A@WAw.A@.AH{.A@AG.AA.AgA.A A@.AA.AA.A`BA+.A AC.A-A3.A@Ah.AwA.A 8dA.AOAp.A8A.A#A`.AA`.AA.A=A .A)AP.A`KAP.A@A.A@A`.AA0..A`|AW.AAx6.AAH.A6A!.A`A0$.AQA&.AA).A`ܶA+.AA,.A Aw..AoA0.ANAo3.Ad.A@6.AA:.A@A`=.AGAh^@.AoA(hB.AǨA(C.A |AE.A }AE.A@gAE.ApQAE.A5BAD.A@2AD.A/#AE.AAHF.A-AhH.A3AJ.A@>A`Q.AA8W.A3A0].A`pA`ab.AEAPn.A AQ.A'A.AQAz.A@WA(ܳ.A`fA}.A@A(z.A@GA.AHuA c.A9AXW-.A+A0@.A=AD.A]AH H.Ao2AqL.A@ALQ.AA\T.A@Ap W.A(AMY.AତA&[.AA`\.AyAHg.A A8Zj.A A(Jm.A A(p.Aq Ast.A5X AȤx.Ah@ AP8}.A`) A.A6 A.AV Aأ.A3y A`.A@NP A0.A( Aȫ.A? A.AH A.A As.A`K A.A@. A.A`br Apl.AZ Ap8.A@D A`.A > A.A`: A(.A7 A.A u6 Aȇ.A6 Av.A8 AR.A`< A.A A A.A`H A0 .A@PM A .A P A.A@P A`.A0O Ah.AK A.AF A@u.A: Ap.A, A%#.A A%.A | A=(.A`K A*.A= A_1.A AX9.A ABA.A3i A ^E.AT AI.A \A AN.A`/ A#T.A A:X.Ai A[.A AQ_.A AGb.A3 A`e.A| A0h.A& Ak.AF[ Am.A4 A`o.A= Af.AzAQ.AAp.A`mApJ.AwA`.AcA88.A 1>AP.AA0.A@jAh.AHAP.A9&A.A`TA.AAH.A A.A@ A`.A=tA.A-WA.A 8;A.A| A.AA.A3A.A@A.AEApl.A`A.Aq~A`q.A;oA.A_A .AMAP .A@ <A..A@C)A.AA8c.AA.AA{.A AL.A@ gAr.AKA6.A0A .A@A .AA@.AA.A@gA .AA[.A`sAN.AOA.A@ ,Au.AAp=.A`)~A .ATnAh].A^A.AOA@p.A`AAȥ.A`]4AB.A`f(AD.A 8AP.AXAp.A A.A4AX.A@EA.ARA.A@AX.AA.A?gA8.AKA.A`/A.A@A.AhAؼ.ARA.A`mlA.A`4A.AtA.AzA.A A.AMA.A^A.A|AH.AAW.AA.AA(.A{A.A:A.A=.A.AA.AAT.A As.AǞAP'.AAv.ADAg.A|A8.AsA.AMA@$.Ao%Aȵ.AA.AA.AQA8.A@`~AX.ARA t.A Z<A@.A%A(.A A.AvA.A`dA.A@Ah.AzAxv.A@A.AߞAh.A JA.AA.A̜AR.AA0.A@A@`.A tA).AgAu.AYA .AA.AAX.AyA-.A`"AHr.A OA .A6A83.A@ Ah.A@NA.AmA8.AWAu.AdDAh.A_1A.AA`.A A.A`9Ap.AAxd.AA.A0AБ.A`Ah.AjvA .A WA.A?8AP.AAk.A`A_.A@AǼ.AAȤ.AA.AV`A.A9A.A[A@.AA(.AA.A`̠A( .ApA.A`DAAu.A@AT.AsA.AzAhb.AA@.A VAذ.A%Af.AA.AAH.A A8.A^A.A +AP.A oAX9.ARA8.A@C6A.ALAp.AAp=.AA.AA(K.AnA.A3A9.A`Ah.A9A .A}AP8.A?A.AAE.A`6Aǭ.A A0.A<AX.A?gAD.A`@A8.AA`.AAV.A@>A.A`A`.A`9Am.AjAذ.ALA(z.A.AǬ.AA.A`"AHܶ.AoA Ҽ.AaAP.A`LAБ.A8A.A%A.AA.A3AD.A@EA.AA.AA.AhAP.A@A.A[A(.AA.AA .AA`2.A AL1.AfApkD.A`A pW.A`)AZ.A \AX\.AAM^.AVA_.A@NA0`.A;A8Va.AfA؞a.A &Aha.AӸA`.A ɱA`.A \AT.ABnAB.A2A`4.AA.A AX.AA =.AphA7.A@4Aj.AAu.AAM.A0A]8.A ҞA3.A A81.A&AJ/.AokAs+.A SA(.A@iIA +.A@&A.A@AO.AAȥ.A`dxAí.A@^A.A@>A .AA ^.A`fAho.AiA4.A//A(- .AzA@F.A@iAH.AfaA(.A3A.A Am.AF~AL.A@^AP&@.ALA2.Am=A[%.A`m2A.A^.Ae.A &Ax:.A@'A.A1AP.A4AI.A`9A8.A>APxg.A4AU.AE;AȩC.AOA0j9.AzbA8V3.A spA,.A (Ah%.A A.A A.A Ab .ARAf.A<Ah.AA@.A@y A .A@%mA(w.AaxAr.A 8{A@o.AxAlm.AuA(j.A@gAjf.A`)Ac.AA kZ.AAp}V.A4A =X.AAZ.A@`AX.AAfS.A`A)=.A Aq+.A Ah.A@YA.ALyA .A A.AgA.AĿA`6.AfA0.AzA(>.A?A.A%A.A@,A 0.A0A.A`2A.AGAh.AdAXһ.A jA.A cAB.A ¸A/.AA(.A@pAHn.AеA`.A@A@W.A/AFP.A`=APxH.A]A;.AA-.A@>AO.A sA.A`Ax.AfAF.AjASֿ.AAp8ο.AAʿ.AAɿ.AAǿ.AApɿ.Ap1A83Ϳ.A@NAp8п.A=fApͿ.A qAp8ʿ.A \A.A qA.AHA8.A qAѡ.AAš.AA.A`)6Ak.AUAX.A wAז.AA.AA𣔿.ARAX .A`A#.A:A~.A ZAX}.A Ap=.AAp.AA(܏.A AX.A&A.A=AP.A`)RA(ܮ.AQdA뱿.AA8.A=A̭.A3A.A`A板.AAX.A AP.AR"Aa.AAh3Y.AGAS.AAQ.AAM.A`)AWG.AA?.A"A 5.A`f6A-.A@ KAأ&.A`lA0".A A!.AzA#.AͪAh'.AA`*.A A),.A@A؞+.A@2AG*.A@A#.A A]A0w#.A]A( .A@yjA .AuA.A A(\.AAأ .A׮AX4.AA.AQAP8.A'AЮ.Ap8A.A`IA.A&\A@.AnA@.AAf.A A0e.AAx.AA.A@A.AFA.A^AD.A ZwA.A ۏAT.AA.A`A.ApA޾.AQAھ.A@A?׾.A#A5Ծ.AN?AѾ.A[A(\Ͼ.AxAž.A@A#ž.AAHiƾ.AA?Ǿ.AAǾ.A -A`Ǿ.A@AǾ.A$TA*ƾ.A gAľ.AyAþ.AgA.A`A@>.AլAL.AA.A@A84.AA8.A`TA0v.A AЦ.A!A =.AX9Ap=.AQA8".A A.AXAΊ.A A.A!Au.AAg.AA(yZ.A>AXM.AGAvJ.AA`F.A ApB.A JAh>.AyA9.A0A4.A`JAQ/.AcA^*.A0~A%.A AX!.AA1.A lA.A@ A .AN1A' .A@nA.A A.A A.A@ !A.ATAٽ.AyA؎н.A<Aǽ.A@AP޾.A-A.A@>fA0f.ApAxˡ.A`AŖ.A KA@W.AgA.AA@z.AAYu.A@(Ao.A`KKAi.A`]mAc.A?A\.AݭAT.A&ABM.A@ AC@.A)A2.AiA@%.AA.AA.A@ A`6.AnA@.AA ռ.A A Rż.A^Ax鵼.A+A.A6Aݕ.A`gA<.A`A6t.AAb.A`)dAYU.AyADH.AA;.A9FAp=/.AA.AAT.A` A.A2A0*һ.AAH .A% AB.A`̠ A .Ak A0e.A A`S.A AH܊.A! Aˇ.A2 AS.A 8B A0{.A^P AM|.A\ Aw.Ag A s.Ap Ah3n.Aw AK.A AC.A A;.Aj A4.A. A+.AL A 4#.Ag A`a.A`f A/.A@ؙ A.A ¯ AH.A` AD.A@ A=.A` A( .A \ A.A \w AXʙ.A3 A/.AɎ AՎ.A A@Ɖ.A A0.AG A.A A(|.A Ax.AR A`h.A AP8T.A*AQ.A`f@AA``.AQAf.AfA8.AA.A \AR.AAX$.AA`.A@A.A  A.A&A).A |,Ax.A@,9A𒟹.A@CGA@Ƣ.AVA0.A#gA8.A@xA(\.A`A`X.AɑA.AAX[.AA`r.AίAN.A;A.A3A.AAiy.AK Ak.A`6A;i.A c<Af.A`@A c.A`CAp,a.A DA`^.A lDA[.A`fBAX.A>AZV.A ,A0HS.AAO.A A`K.AAG.A)ABC.ARA@.A Ah>.AAX<.A@A9.AA87.AAp06.A`A4.AA(3.A`T A`3.AAh2.AcA2.A(A83.A1A5.AXAP6.AzAH7.A@gA6.A/A>5.AjAU3.AA@0.AVA@..AA%+.A AP'.A@ Aa.A c$A.A;AP .A@PA.AB^A.A`dmAX.A}A.AAG.AjA@S.A Aݸ.AA.AGA9.A@qA .A`A`.AAko.ApYAj.AnAe.A@PAR`.A4APZ.AAT.AAN.AANH.A@WAA.A3AP<.A A8.A ZA3.AAXE/.A cAG+.AA83.ApiA: .A`̏A.AA).AA.A`A`.ANA(ڷ.A)A`Ϸ.A qAA.A@5|Aʯ.AݸAk.A \A.AAh.A`%A.A :AH.A`NAP8.AGaA~.A@ynA3w.A/}Ahp.AaAj.A`Ac.AA].AUA)X.AAТS.AA(N.AAI.A ZA=D.A$A>.A@53A08.A`f@A5.AyFAC2.A@ KA0..A NAf+.A lOAX'.A-OAy$.ANMA!.AIA(.A@DA.A@N>A.A`f6A.A@51A`.Ay-Ap|.A`K+A .A*A+ .A+A8.A@W.AH!.A |2Ap.A8A .A>A.AFAh.AOA؎.AYA(.AdA.AxnAP.A3xA.AA.AUA.AAA$.A@Au.AA.A@A.AAX.A@PAy.A|A@5.A sAL.AA8t߶.AA߶.AA:߶.A@A߶.A A.AA.A`AP.AA.A`f A.A`A.AQ A8.A*A.A@E5AX.A?AȘ.A4HAH.ARPAͶ.AÓA<̶.AA,ʶ.AAXǶ.A AHŶ.AA¶.A@A0.A`A(ۼ.AA.AAH.AA.A A0j.A Ah.AA.A@AP.A/A.A@yA`a.A`fA(l.A A .AA`%.A/Aߞ.AAA4.A6RA-.A \aAH\.A ‹Aނ.AܖAX.AРA}.A}Ay.A̰A@`v.AࣶApr.A`A#o.AAPwl.A}Ai.A`vAf.A`AFd.AսAPa.ATAhs_.A`2Ap].AA@dW.A@ AQ.AĐAkJ.A@AhQG.A@օA5D.A A'A.AA06>.AA p;.AA8.AęA6.A?A4.AA`3.A`A1.AA=%.AAx~.A+LA].AHyA .AA8.AA`.AA`O.A AP.A@.Aݵ.A)A:ֵ.A`o=Aε.A sOA8ǵ.A`A.A fA^.AkA㶵.AoAx\.AMpAӫ.AoAU.A`lAP꠵.ANhA.AaAhѓ.AWA).AOLAP.A ?A^}.AE0AXFv.AAio.A@ Ah.AAf.A`mA d.AAFa.AvAX^.A`AR[.AAFX.AA0HU.A`;AiR.AUAO.A@,AQM.AAPL.A@AL.A` AxJ.A@AGI.AqAiG.AA RE.A@gAi?.AA839.A3Ah7.A ( A06.A?(A6.A 0Ax6.A`9A R7.ARBA7.A@A36.A@>A4.AA02.A%A0.AWA-.A Af*.AA@&.A=A@$.A 8A؎!.AA.A@PA_.A 8A.A8,A.A 8CA0..A YAu .A ZlAj.A~A.AAo.A?A(.AAxi.AൺA.A JA.A`]A.A@A޴.A@Aٴ.AtA8մ.AAȘд.A3Ab̴.A  A}ȴ.A`f AĴ.A#& A.A 7 A̭.A@ Ap.At)!AӇ.A!ABt.A !A0q.AE"Ahn.A@P"Ak.A "Ah.A@$"Aad.A-"Ao`.A4"A@S\.A:"A8X.A >"AhS.A6A"AiO.A@A"AK.A@@"AH.A;"A E.A8"AA.A@>7"AH>.A7"A;.A9"AБ8.AH="A`5.A`B"A3.A`I"A0.AQ"A9-.Af"A 0*.AR{"A'.AV"A@%.A "A@_#.AN"A!.A "A.A"A.A #A(.A@%#A.A@>#Az.A@V#A.An#A .A_#A.A#APw.A#Aj.A@$A.A9M$A@ӳ.A$A`̳.A $A0YƳ.A $A}.A$AU.A$AP氳.A$A}.A$AX۩.A@$A.A%AH .A` %A.A%Ah.A`]%A.A@%AB.AR%AX,.A A%A2.A %A0f.A%Aم.A&%A(.A /%A.A9%A=.AD%Ah3.ARP%A#|.A%APYz.A-%Ax.A&A0*u.A "&Aq.A;&Am.AR&A(>l.Ah&Aj.Ax|&Ag.A 1&AȲd.A`&Appa.A`&A].A&AY.A`o&A U.A&AP8Q.A&A'Ax.AM'AG.A]'A.A (d'A`.A k'A0.At'AQ.A~'A%.A'A^ .A;'A .A'A .AB'AXu .A'AL.A'A((.Am'AV.A''A.A(A.Az(A@ #.A@ (AS&.A,(A /).A:(AP+.A H(Ap-.AW(A/.Ag(A`e1.Av(A02.Af(Ag3.A`D(A3.A_(A 3.A(A+3.A@(Ap82.AR(A$0.A`)A-.A q[)Ap|*.A`)A&.A@!)A@".AH*AЮ.A@[*Ah.A Q*A.As*A.A$+A(.A@f+A.A+A8E.A+A`a.A!,AЮ.A B,Ax.A`Kb,A.A4,A.A෡,AЄ.A,A.A3,A5.A 0-A\.A`;-Aܲ.A3-Aղ.A 1-Alϲ.A A$.A@ɲ.ANO.A8IJ.A@G{.Ad.AV.AԵ.AT.A׭.A3 /A .A//A@ࣲ.AQ/AW.At/AϚ.A/A×.AX/A8.A/A83.Az/AB~.AR0AH|.A0A؁z.A 0A x.A ~0Anu.A0Ap}r.A c0AUo.A0Al.Aa0AXh.Ap0AHe.A0Apa.Az0Aȱ].A`0AhY.A`0ARV.A0AR.A@50AO.A00AL.A@0AJ.A`շ0A@H.Aٸ0AF.Aॻ0AhC.A 0A0B.A0A @.A c0A?.A0Au<.A`0A`9.A (1Av7.A s1A`5.A O-1A3.A@D1AP+2.A^1A0.Aqy1AP/.A]1Ac/.Ax1A(\/.A@1A..A`1AX-.A@&2AQ,.AT2AY(.AO2A$.A@2A(!.A2A.A S3A.A+3A.ARV3AH.As3A3.A@3A9.A@3A.A Q3A .A`)3A.A@3A.A@ 4A.A(4Ax.AF4A .Axd4A(!.AH4AJ1.A5ABA.Aઍ5AQ.AR6AW.A?6A(>^.Al6Ae.A6A(Kl.A6As.A@i6A{.A7A'.A:7Aό.A`7A̕.Ap7A(.A`7A˵.A 8A0Ų.AP8AH.Բ.Ap8A(-޲.A8AP.A`8AK.A@'9Af.AT9A8&.AR9A0<.AO:AP8y.A;A׸.A ;A).A(~<A87.A3=A#z.A`=A.A M>A0..A`>Aأ.AY?A R!.A`)?A R%.A ?Ap%.A?A".A \?Af.A"@AH\.AGI@A.A[[@AG .A@gz@AG,.Ap@AH5.A @AG.AÍ@AT.Az|@A8`.A p@Api.Ape@Ao.ApQ@AX x.A`)6@A`a.A$@A.A`)@A.A(@A@Wѵ.A[@A.A`g@A.A@>l@A.A i@Az6.Az`@A RX.Ab@Ac.Ap@Am.A̒@Aft.A@Au.Az@A@w.AR AA|.AG+AAp.AR.AA.A'AA`a.A=AA’.A3!AA.A*AA`᝶.A6AAB.A>AAL.A ?AAX.A .AA.AAAf.A`)AAL¶.A@A(\ж.A@A@.A]AA83.A +AA`.AG]AA0.#.A(AA,.AAAP83.ApAAp:.A~AAQ=.AnAA=.A=ZAA:.AIAA#/A9:A@%!/A@9Ag"/A@>dAY"/A@,~YA#/A9:AɜI/A<(A`GI/Aa&A`GH/A`o7'AF/A"Ah9F/A AjE/Af AD/A@A`O5D/A ZtAJA/A 'AXI@/AVAU\?/AR|A>/ARAp<8/APA8/A@ %A`6/A,AmX5/A@3A93/At;A 2/AGDA1/A}JA2/A RA5/A`_Ap5/AaA؞6/A bAȋ6/A`dcA8y7/A,dA8/A aA.7;/A ]AlfD/A[AXD/A@VYAE/AVAF/A`[VAGH/A eQA@1HI/A`MAppJ/AsHAK/A)CAP+8M/A|h9Ah{K/A4AJJ/A@,AɜI/A<(A`)/A@WqA-(/AXiAh]%'/AjA'/AzsA5)/A^uA`)/A@WqA`OU/A}AU/A3}AV/AХ}AuV/A`}AV/Aqu}ApV/AwI}AdV/A/}ARV/A`}AV/A}A("V/A@`}AV/A|AHLV/A ~|AV/A 8|AV/A|A#V/A|A(V/An|A/V/A` c|A$6V/A@h|A؟=V/A`ok|A >V/A,O|A@FDV/A7|A/GV/A Q|APGV/A {A0nHV/A{AHIV/A@5{APV/A{AXV/A{A0aV/A{ApZfV/A{AhnhV/A`mk{AhV/A0E{A kmV/A9{AW/AyA4W/A@yA-W/AyA)W/A$OyA#W/AR yA W/AxAW/AxAAW/A`TxAW/A|xAV/AhxAV/Ao\xAV/A@>xA(V/AxAHV/A ZwApV/A`mwAV/AڷwApV/A@wA٨V/A@EwAV/AwA̜V/A`bkwAV/A(wAMV/ATvAxV/A qvA( V/A`;vA`rV/A@i\vAV/A`չvA0LV/A vAV/A`7wAV/A@ swA8s W/A@ swA.W/AqwAX+W/AwA(>W/AwAH]TW/AwA(bW/AwA` dW/AxA NXW/A@y_xAUVW/A8xA/`W/A sxAPrW/AyAtW/A@_yAtW/AyAyW/AyAhW/AHyAP^W/A LzA(OW/AzARW/AjzAeW/A T{A88W/A`ܢ{AW/A@{A W/A@(|A}W/A /T|Ax W/A;|AW/A|AW/A}AW/AAX}AX/A@N}A X/A`]}AhoX/Aw}ATX/As}A`2&X/A`m}A3X/Ao}A7X/AD}APCX/A@r }ATX/A|A;fX/A`|A(uX/A@,|AȃX/A;|A|X/A`k|ACX/A`\|AX/A`\|AX/A{|AOX/Aߪ|A(X/A`)|AY/A%}ApY/AAX}Aг Y/A@N}A0Y/A }APoY/A}A7Y/A,}ADY/A }AOY/A`]}A8VY/Ah}AUhY/A@#`}AxwY/AAX}A߆Y/A QT}AY/A@}A`GY/A}AX Y/A@G|AY/A |AhY/A c }AY/A|AHnY/A@,|ALY/Ag|A _Y/A<|A؟Y/A<|AhY/A@`|AY/A Z|AtY/A |AXY/A 8|A(JkY/A |AJY/A|A5Y/A}AH\$Y/A@G|A(`#Y/A;|AvY/A]H|Ak Y/A_|APoY/AD{AvY/At{{AY/Ag{AY/A {AX/A@{AjX/Ac{A@X/A@{AX/A@{AX/Ao{AX/A;H{A`X/A{AX/AzA0X/A@zA(X/A`zAiX/A@ؚzA`ЁX/AzAHvX/AozA|X/A,zAHX/A zANX/AzAX/AT8zA؟X/A@(zA@X/AEpAKY/A VpAxY/A`pAY/AQpApY/A@!7qA(~Y/A@qA$uY/AtqAUhY/A CrA(QY/A@rA oA؎PX/AoAppXX/A QoAQ`X/A`oAppXX/A`moAXNX/ApAOX/A6pAT/A yAl5T/AzA'T/A`d4zAT/A@czA` T/A3zA` T/A^zA/T/A`zAUS/A {AhsS/AP{AS/At{{AS/A{AS/A S{A"S/AF{A0*S/A[{A5S/A@({AjS/AID{AS/A{A@ƙS/A{ApS/A@{A S/A |A0~S/A4|A0~S/A@`|A!S/A J|A!S/A@W|A(-S/A}AS/A(}AS/AAX}AS/A/}A`%S/A}A S/A}AšS/AW~AS/A@P~AQS/AH~AXtS/A@,m~AGS/A X~AS/A@g8~A`6S/A`m ~AS/As}A@qS/A@%}AS/A`}AS/Al}A`S/AÒ}A0;S/A }ATS/A}AXS/A J~A@T/A8~AXyT/A~AH;%T/A ~A5T/A~AAT/A@P}AQT/A0}Ah;`T/A }ApT/A}A(`T/A}AȲT/A@P}AT/A*~AxT/A ~AT/A8~AT/A,~A'T/AD>~Ap=U/A`E~A U/A`<~Ap%U/A~A8'U/A@ }Ab0U/A }A2U/A*~A>6U/Ax5~A`=U/A@`d~AFU/A`)~A]U/A 8~AlU/A~AzwU/A 1~A(,U/A`~AU/AAU/A A@OU/A A߶U/A~A@WU/A`b~AX,U/A`"~AU/A~AU/A ~APjU/A@,m~A`OU/A`I~A`OU/A~A`OU/A}A~R/A OAR/A`yA^R/A`zAR/AAA`R/A㍄A0R/A֐A0R/A֐A>R/Ai~AR/A?]A(R/A`KLAR/A@r5ADR/AA8R/AA(R/A@A`R/A^փAR/AAAR/A AhR/A`A(R/AEA(R/A A S/A@.AS/A 1AsS/A q'A%S/AA>)S/A@G A*S/A A04S/A كA:S/AăACAS/A@AX9KS/A |AlSS/AzAx]S/A;@A8eS/AA3mS/AׂAXtwS/A`AXzS/A^A8bzS/AAPxS/AAӀS/A/ցAȃS/AA֌S/A_AqS/AZ:AȃS/A$ AS/AՀAxS/AA@S/A@莀AS/A vA pS/A3zAȔcS/A@yA^]S/A&AX(WS/AAIS/A@i0Ah"9S/A#Ah"9S/AA4;S/A@yA-S/A`TAX S/AAx(R/AYAh"R/A |AR/AĀAR/AA0ԺR/A# Ax(R/A4AR/ArAOR/AAFR/A@́AR/A@́AR/A сA`R/AA`R/AAR/A@ Ah R/AN/AR/AIA(R/AWAXlR/ApA!R/A@,~AXdR/Ao}AxR/A`9{A8VR/A@EjA R/AyAHR/AA0UR/AXAxR/A@ւAR/A A~R/AAyR/AA(oR/A]AgR/A@$AHngR/A2AiR/A @AmR/AZA`2qR/AuAPrR/AAHmR/A A$mR/AjŃA nR/A׃AX9sR/AڃAxR/A`ՃA|R/A6߃A(:R/A 8A,R/AAR/A@pA}yR/A 1AsR/A3ApR/AXIAZoR/A]APhR/A wAhR/AAnnR/A^AuR/A@yAR/A AH܇R/A@A'R/A`A~R/A OA?PR/A AJR/A 8 ApCR/AZA?R/A@rىApCR/AƉAnIR/A ЉANR/AANOR/A ۣA(\VR/A ۣAP<\R/AQAbR/A AhR/AωAspR/A9͉AsR/AMA5tR/A@A{R/A@,AX|R/AyAX|R/AiAX|R/A`SA`R/AMNA@R/A@WAxdžR/A@XAR/AGAR/A6A=R/A'1AxR/A2AHR/A`#AhљR/AAx:R/A4AXhR/AA(,R/A=AR/AƈAx R/A`dAR/A A88R/ARAxGR/AAxuR/AuA(R/A`hAHR/AgApR/AgAR/A ~Ah^R/A܈A{R/A A5tR/AAlR/A əA(9hR/A㧈A_R/AqAPjYR/A@AWR/AA(\VR/A QЈAMSR/AA"NR/A4APGR/AA``FR/A)ALR/A`(A RR/A60A"NR/A QA``FR/A UA':R/AYAPI,R/A`rA'R/A`rAY'R/A bAi&R/AIA!R/A;Ax>R/A 3A 0R/A@yA"R/AA|R/A-A@R/AAR/AxAR/A痈A"R/AAhnR/AA R/AAXR/AaAHQ/A AhQ/A-tAQ/A ~AhQ/A५A(>Q/A`ՈA-Q/AqAHQ/A@yA@Q/AU.AsQ/AHAR/AW]AdQ/A}zA Q/A 䃉AHQ/AyAXQ/A clAQ/A^|A8xQ/AfA8xQ/AkAHKQ/AщA(Q/A@yA/Q/A@ AQ/AAQ/A0AЀQ/A@AQ/AԉAIQ/AA8Q/AAQ/A@yA:Q/AwAQ/AtA.Q/AtA֩Q/AA0ءQ/AVAPQ/AAխQ/A`AgQ/A@AqQ/A sGAQ/AmArR/AA@R/AA(R/A$A5=R/A`uAMR/A@EJA@SR/A/AMSR/AA?PR/A A )/Ag@Ay/&/A`wAN&/A3AXyG(/AA )/Ag@A gp/A|ØA@}m/AAl/A ⬚A0p/AyuA gp/A|ØAh0$/AѐA!/A{A@+!/AA@ /AA(/A`dЮAh/A#~A/A@``A1!/A A!/A;uAh0$/AѐAM/A@$AM/A-A(\M/AAM/AghA@dN/A`#A88&N/A@A7N/AAHN/A ZAx6TN/AA gN/AAxwN/AmAN/A;{AΜN/A AN/A;{A9N/ANApN/A`9)AN/AApN/A@A@1N/A`AN/A AX9N/AXA O/A`AO/A`A.O/AA:O/AA^FO/AAX[KO/ABAHWO/A@A lO/A &AXV~O/A`AOO/AAO/A ]AtO/At!AfeO/AA-:O/A״A@S,O/AA`$O/AbA0]O/A AO/AAx'O/A` Ax'O/AlA+O/A*3A-:O/A0AEO/A%AXOO/AAQZO/A@AX9bO/A AhhO/A3EAZrO/AAHwO/A@Ax{O/AAHO/AwAdO/AHAO/AྻA@O/ARA[O/A`AO/AqAO/A)_AoO/A GAO/A 6A`6O/A`'AO/A@W!A(O/AAP@O/A@ A O/A@yA O/A AZO/A?AHO/A cuAvO/AVAFlO/AH=AbO/A@A(ZO/A@GAUO/A |ApXO/AėAdO/ABAgO/A[A`%nO/A2;AsO/A ATvO/A@A {O/A lAdO/A`AO/AtAO/AAO/AAO/A`AO/A@A@O/AAO/AAp0O/AŢAHO/AFpAO/A@JA[O/A`AHO/A}A|O/A`A@O/A`DAXO/AWA`O/A`*A"O/A`dAO/AAO/A`mAh3O/AAO/A)kA@O/A&@AO/AFApJO/AIA=O/AZAO/AfA |O/AtAx~O/A`AtO/A cAXWkO/A@kAeO/A`PA2fO/A7AأjO/A`DA@sO/A@GAyO/A@rA@}O/AAȁO/AAO/A@؅AO/AnAWO/A'VAWO/A4*AO/AAO/A`ADzO/A AHgO/AHA$UO/AAhCO/A@A 5O/A@A+O/A@AA(O/AZAг'O/A`.A"O/AqA &O/AAp,O/A AV3O/AIiA7O/A SA4O/A@CA8O/AAȩO/A@AO/AnA0O/ARjAPO/A0sAx O/A`oAeO/AவA8'O/A&ټAHO/A`;AO/A`;AhQO/A$ܼAhO/AҼAO/AѬA=O/A@AqO/AguAO/A=_AO/A`=AO/A AO/A$AO/AAO/A A(mO/Ay.APO/A`mBAO/A *AXhO/A$AO/AջA O/AA(yO/AઌApNkO/AzAPdO/A@ SAMO/A PA o9O/AV`AA(O/AvAPjO/A&}A O/ArAN/ArAN/AfAH)N/A?AN/AA(N/A=ACN/AA(~N/A@#TA`N/A`AXtN/AA@N/AzA EN/A`bA7N/ADtA@N/AݎAhN/AAeN/A$ϹAN/A`AN/AR7AN/AMjA7N/AAȇN/A`A8bN/A?~A@N/A@#TAwO/A_#Ap O/AAO/A qܹAСO/A@AȂO/AaA O/A rA(O/A&JAO/A/AA(O/A8AV3O/AA>O/A ɸAxGO/A飸ARO/A`hAfO/AIAGuO/AaBAȉO/A Z#ALO/AA8'O/A'APH/AAH/AAСH/A cAH/AA8#H/A@AH/A AAH/ADKAH/AtAH/AA'H/A-AkoH/AO?AUH/A+A _0H/A A H/A VApH/AA88G/AnA(G/A=A`G/AA߿G/A`A>G/AAG/AFAG/A;ɡAXG/A ⁡A`ӍG/A/AG/A ͠A>G/AHzAPG/ANAG/A AG/A`)7A G/A@yAG/AAG/AzAYG/A@NA`G/A`;AG/A`f'AxG/A ˑAxG/A`mAG/A@RAhoeG/AUAPZG/A'ŠAQG/AAHEG/A`vnA9G/A ApJ G/AOAHKF/A@bAmF/A`[3A(F/A ZAF/A`mAdF/A AF/A AF/A ApIfF/A AMF/AGA0PF/A0AXF/AAhs[F/A^AȤNF/AFA`3F/ABAp_F/ABAF/A@bAF/A@pA'E/A@WȟA8E/A`mApE/AA̟AE/A@pAXtE/AQA8gE/Aw՟AȥE/A`#AȥE/A9~A +E/AXvA@)E/Aj/A0-E/A A0E/A@pA0E/A&zA9E/A A@BE/A lATZE/A@5+AnE/A AE/AfܝAhE/A@AE/AJAE/A AxE/AAfE/A@qAXE/AmAF/AXRA0F/A`FA?F/A`6ASF/AA8'oF/ABAhF/A`6A/F/AAF/A`mțAF/AaApF/A A F/AŨAmF/A`YAF/A̙AG/A;ApF/A`JAiF/A`YApF/A@,Ah F/AmAFF/A&A`F/AȘA@F/A (}AEgF/AFuA=F/AFuAxF/A`̔AE/AUqA|E/A 8yA E/AAFE/A]AyE/AAoSE/A;6A AE/AX.A +E/A@QADE/AA`vE/A_A0D/AAPAyvD/A QrAjD/A@nA pED/A tAh+D/A@AAD/AMĪAp[C/AcATC/AXA|C/AsAYC/AA؎/C/AA C/AAB/A ѬA%B/A-DA5B/A@AB/A3A@B/A AC/AIAa8C/AA"LC/A` (A {C/AhAH|C/A8AHvC/A`MA(hrC/A)5AVC/AOɳA4C/A0ӳA C/A lpAбB/AAPkB/A`A`LB/A`A!B/A@ApB/AAA/Ah!AA/A AKA/A#'AwA/AǓArA/AAhA/A`EAa B/ARA RB/AA@B/A|A@BB/A?A B/A_A0A/A`A(,A/AFA0A/A_A^A/A@r.AHrA/AL@AA/AvA((A/A?AA/AUηA0A/A*AȂA/A?AݫA/Aj4AxA/A nAA/A*A0A/A?A A/AθA((A/A?AHzA/AA0qA/A A8iA/AUAeA/A?*A0X`A/A?*A0X`A/A SAp=jA/AjAmA/A uAؒrA/AAؒrA/AjA(hnA/AӹAp=jA/AApcA/A*"Ap]A/A 0APA/A@5uAQA/AjAQA/AͺA((OA/AAȲCA/A&A"BA/A3Ai7A/A\AR:A/A*A?A/A A0?A/A»A];A/AۻAr=A/AA88DA/AAp}>A/AA;A/A =A((6A/AiAxm0A/AA0X.A/A ӼA(/A/AA-A/A ;AM-A/A iA3A/AA.A/AAؒ'A/AjŽAR!A/AjA0H A/AA(h#A/A*[AA/A`5AMA/A?+A A/A@AA/A?]A A/A|AA/A?A8x@/AjA8x@/A ѽAxA/AA A/A`A8 A/A AA/A3AA/AQAxmA/AUrA A/AA@/A`zA@/AUA@/A*A@/A`ARA/A*A0A/A?AMA/A HAA/A?AȂA/AA(A/A?οAr A/A AA/AAB@/AA]@/AA@/Aj6A(@/AEA8@/A8A(@/AQAM@/AjA@/AAx@/A`A8@/AA@/A*A@/AUA @/AA»@/AAȂ@/AAݱ@/A #AТ@/A?,A@/A?EAؒ@/A?^A8@/AdA@/AAb@/A*Aؒ@/AA8@/A`A@/A'A8x@/AAp=@/AjA@/AA@/A?&Ax-@/A,AȂ@/AGA0@/A`A@/A*A(h@/A AB@/A`A0H@/AA(@/A*A0@/AA@/AUAp@/A?A0HA/AUA0A/AA0A/A A"A/AAp}%A/A(Ap}%A/A ^AxA/A?ABA/AA(A/AAA/A9A.A/AUgA88DA/A qARA/AjiAТgA/A?LA0sA/A`$AƒA/A?A0A/AjoAA/AU"A]A/AA(A/AAA/AUA A/AA8xA/A`AA/ATA0A/A*VArA/A*VAA/A*=Ap}B/A`ArB/A`AB/AA" B/AUGA8xB/AAxB/A Ab)B/A`Ar7B/A*ACB/A?Ap}QB/AA`B/A?A(hhB/A A(tB/A?dA]B/A %A0B/A?οAȲB/A AB/A_AТB/A`Ap=B/AھAp}B/AA0ȣB/AUrAp=B/A*UAp=B/A*#AxB/AAȂB/AɽAp}B/AjA0B/AjAB/A iAC/AYA0C/AYA((*C/ARA3C/A A3C/A A81C/AjA((*C/AUeA"C/A?A C/A7A8B/A AB/A*AxB/AAB/A uA cB/A^AB/A?PAB/A,A0B/AٺA B/A*Ax-B/AdA(hB/A2ARB/A? A0HC/A 뷹Ap=EC/A 뷹A0eC/AU¹A8zC/AKڹAx-C/AA0C/A AC/A*TAbC/AkA]C/AA0ȶC/AAC/A?AC/AiA(C/AU?AxC/Ac AC/A AC/A?ٹAxC/AjݹAMC/A ̹A8C/A߹A0C/AAC/A &"AȲC/A7A D/ACA]D/ARAD/AUqA(hD/AAxmD/AA-D/A`A;D/AA0H@D/A {ABLD/AwAxVD/A`yARZD/AՖAr]D/A*ABeD/AͺA(huD/AjAD/AկA0D/A ApD/AUAHܭD/A@A0D/AcAhD/A0AsD/AAND/A@ADD/A@A"D/AAE/AN,A@E/A$A }E/A`A#E/AmѹA *E/AmѹA0E/A$A26E/A`2A 9E/A4[A+@E/A/AtRE/AAkaE/AtuAlE/ALAXsE/A`2AˆE/AAH E/A 1A0E/AhA`qE/AhAE/AApyE/A[AxE/AAPME/AA@~E/A AE/A,(AhE/A@yBAXE/A DA 0F/A!A83 F/AAxF/A`)A]#F/AعAPZ(F/A@AȺ'F/A A&F/AA@AH;,F/A'hA1F/AwAJ/ASA`J/A HAxJ/A`1A0J/A:A誖J/AxA pJ/A"AJJ/A qVA8J/A ݼAXJ/ABJAJ/AAWJ/AAPJ/AA(J/A˾AkJ/A$A@RJ/ACA7J/A@>A5J/A@ ArAXJ/A@yAJ/A A(J/AVAiJ/AApJ/AAJ/AARJ/AU+AȥJ/AfiAJ/A`vAHaJ/AA J/A@,A1J/A@yA0J/A@yA؁K/A3AK/A 8]AbK/AؖA0K/AAK/AAK/A@-A K/AV^AK/A@A(K/AAK/A`AwK/A9 A$ K/A'RA`K/A zA K/A AH-K/AMA:K/A?AGK/AAZK/AAgK/A`bA mK/AHA`wK/AAPK/AA8K/AAH.K/A`mAK/A :AK/ALAjK/AxAK/AA@K/A`DAK/AA _K/AcA`CK/AAwK/A֪A RK/A AK/A`nA&K/A`wA@K/A;>AIL/AAL/A? Ap,0L/AAML/ALAML/A cAML/AANL/ApiAPPL/A=4AQL/A@ASL/A 8A]L/AVAbL/A$ABdL/AA$hL/A^}AxL/A84AL/A84A%L/AjiA@AL/A sAȶL/AAPL/A`AL/A A@`L/A A]L/A`AYL/A`vA7L/AKA0L/AAkL/AAXšL/A@wAL/A AL/A8AL/AA}L/AAL/AVA'L/A@ AЀL/A 8A}L/A AL/A+AL/A uAL/AɾAL/A APL/AAL/AAL/A`] AL/A-AL/ALA,L/AൊA8L/AmA8L/A@AHL/AAL/A`APWA[/AkKA[/A BA[/A9A@5\/Ag Ax\/A ~A8\/A A1'\/AA1'\/A-A0-\/A*As7\/A[ AA\/AAJ\/AA0{V\/AA@d\\/AAȔb\/A`KAXt\/A@A;\/A-A\/AA\/AvA\/AaAPZ\/AaA\/A@8A\/AA\/A@ءA\/A`A\/A@A\/A^AV\/A ZwA\/A_A\/A@EApp\/A A\/A@EA\/A A范\/A[A\/AkA–\/A}A0&\/AQ%A0&\/A@Ai\/AfYA\\/A9Ah\/AIA`C\/A@A`\/AAL]/AxjAf^/A A`%_/A4A`/A`m<AD`/A`$Ag`/AtIA@1`/A@6Ah`/A`A`/A 8AȘ`/AmA a/A ALa/AAha/AxA`O a/A@tATA5g/A`AXyg/A AAHg/AxAf/AhZAsf/A@NA g/A@֊A|g/A`}AXg/AAg/A Z+Aأg/A /AHuh/A`Ahh/A AX2i/A Axi/AUA/j/AA6j/A yAWk/A3Aql/A,Afl/A8Al/A@Al/A0A@>l/AAhl/A |As l/A@MAk/A'Azk/AACk/AUA5j/AAԕj/AAAj/A`uA!j/AA`i/A | Ax\i/A@ tAi/A@wA _i/AZAi/AAi/AA0 i/A@NAXcj/A@yvA`Xi/A@A0i/ApA0Ei/A`K>Ahi/A A0i/A@ݵA)h/A` HAh/AAh/AòAxwh/A|A Nh/A=A+h/AZANg/ANAEg/A@iA g/A-A؍yg/A@yڬA3qg/A`Amg/AKCAdg/AVĨAOg/A@jAJg/AAJg/A`LA:g/A AP*1g/AzA#g/AաA@uf/AРAf/AVAff/AƠA f/AA0jf/AkA0jf/A pAhf/A#ZA`f/A;A{f/AZrAIf/A'AHf/A`)vA;e/AA(ܡe/A A%ee/A̙AXFe/AौA4e/AjAm!e/AܤA8e/AAd/A AWd/A@.A0d/A4Axod/A׈Aad/A_A`Zd/AAh{d/A  Axod/AAad/A QA>3d/AA)d/A6өAEVd/AAHdd/A[Aad/AW2APQd/A"ѬA7d/AǬAAAd/ALA'd/A@rAc/A AXc/AGAxvc/A Asc/A.A@ec/ANAac/A)-Azc/A`{Aʖc/A?A@c/A Ah d/AA5d/AzcAh@:d/AdAȘJd/AAad/AkAd/AOAd/A)AHad/AաAxi e/AnAH;e/AGApe/A`巟Aޯe/A`巟A e/AAe/AtAe/AAe/A ~Ae/A APɮe/A^MA،e/AXATe/A Ae/A`A`d/A`Ad/A ANd/AA8Vd/A`KAvd/A`>A5d/AA8d/AۘAc/A!Aͤc/AoA"c/A@#Alc/A`TAx\c/A Ab/AjsAb/AzǖAVb/A`)ږAa/ANAXa/AtAa/AA a/A cAPa/A}AHa{a/AAxia/A AӎAa/AA%a/AVA0b/AA4Kb/A@2A8pb/A@Ab/Ak`AHib/A@Ab/AAb/ALA#c/AڍA7c/A`f1APgNc/AʍAxzIc/A\AX4c/AތAX4c/AXA6c/A֋AGc/A4AGc/A ⃌AHVc/AAmc/A2xAHc/AA0c/A |?A#c/A֌A#c/A@5AP/c/AAP/c/A9dA`c/A`Adc/A`mҊAxcc/AkAHvc/AWAoc/AŲA#c/A&A#c/Ax{AhQc/A4ƇAMc/AAsc/A'<A&_c/ACArKc/A콄A?c/AKA,c/ASAP c/AA!c/A<A<c/A ݅Ab/AAb/AA7b/AAdb/A lA%b/A…AUa/A㳅AQa/A3;APa/A]%AXa/AvA(>a/AҨAH8b/AH,APAb/A`KA(b/AdxAa/A`APa/ANAa/AAga/ARAa/AGA@ b/A`~Ap b/A}A}b/A@,|A`b/A*|AXa/A*|A֜a/A`Ӻ{A8Ea/AzA0Ra/AzyAP/a/A5yA`/A@yzA`/A`p{A`/A\|A`/AU}AR`/A #}A$`/AC|AO`/A`v{AX _/Ac{A8_/A 8zAТr_/A` LyAX_/A?yA^/A?yAY^/AyA0Ts^/AhxxAJ^/A`YwA`"^/A`vAs]/AvAJ]/AwA"w]/A OyAQE]/A kxAS\/A`wA\/A/vA@¬\/A vAX<\/AoTvA[/ABtuA{[/AuA4[/A "vAxZ/A vA'Z/A@WwAUZ/A|ZxA\Z/AxA [/AxA*[/A AxAW[/AxA(z[/A` 5xA0[/AxA> \/AxA0LS\/A|ZxA@W\/AxA \/AxAp\/A`ryAh]/A6zA]/A`E{Ah"1]/A |AH-i]/A%|AА]/A`{AD]/AhX{A*^/AhX{A\W^/A}{Af^/Ak{A@­^/A s{A8^/A 8|Ap^/A|A^/A@yc}Ahs^/A ~A^/A^~A^/A-IA@­^/A AX^/AAv^/A A(z^/A@>A_/A~A- _/AH~A4C_/A=}Ax:__/ATV~AH_/Ai~A_/At~A_/AA%`/A^~AR`/Aൗ~AY`/Ao~AD`/A@>Ac1`/A`2AP`/A XA`T_/AƢAM_/A.AJ_/ÁAF_/ApPA =m_/A /AE_/A;A_/AA^/A`A^/AA q^/AuAbz^/A Abz^/AA9^/A`)A0^/A@tAЦ!^/AAN^/AA`^/A RAas^/A {~Abz^/A`}Af^/A|A(`l^/A|ApR^/A@N|AU4^/A |AO^/A}A(J]/A>}Ap]/A`"Z}A]/A}A ]/A`}A6]/Ai~A84]/AA2]/A@Ahމ]/AdaA݂]/A`)A؁d]/A?TAP|H]/AOAX]/A.AH-i]/A uAH-i]/A<A()T]/A@A5]/A`9A ]/AIAr]/Aj}AP]/AAAp]/AA\/ÁA\/AA`\/Aj4AX[\/A)A@W\/AA@W\/AaA0\/A`HA0\/AwAHPh\/A㜄AU\/AdA@\/A@5AU\/AA@s\/AEA\/A lA\/AAX[\/ASA(\/A@WA\/AlA(\/A6Ap_\/A@qAX\/AňA(\/AQA \/A=ˉAX[\/A1Ap\/AcASv\/A@$AHPh\/A AMZ\/AASv\/AOA\/AیASv\/A/Aq\/AॖAxe\/AAHPh\/A#dAMZ\/A@AN\/AAx9\/A`VA\/A@sA?\/AǏA`\/A`1Ap\/AʎAؒ\/AגA\/A>A?\/AAB"\/A A> \/ẠA`$\/AAH&\/AxAؒ\/AxA;[/AڊAp\/AcA+\/AAB\/A S;ApIE\/A=ˉAx9\/A ~dAU\/A,wAx\/A ~dA0\/AAx\/AனAj\/ABA@G\/A`A(2\/A~AH;\/A AXE0\/AAP\/AA[/A AyA[/AAh/[/A}Ah/[/A鸄A06[/A`v#A[/A ƃA =\/AU{A\/A@P_AB"\/A? A\/A`vlAH&\/A@!AI\/AZAc\/AA\/A.A(\/A ڀAг\/A@NA\/ANAX[\/AA\/AzAг\/A`d~Ap_\/A@N:~Ap_\/A}A \/An+}A \/A|A \/A A|A0\/A@y{A(\/AO{AY\/AAzAq\/A`IzA`P\/A@yAp\/A@,$zA[/AzA-[/AzAx([/A c<{A%[/A|AX$[/A@N|Atn[/A|AM[/Al}Af([/A }A [/A_~AZ/AAXRZ/A (-ALZ/AAHZ/A`T AZ/AA0Z/A A`Z/AAZ/A_AXRZ/A`vlAЦZ/A܂A@NZ/A0AXZ/A଄A0tZ/AkAAkZ/AwAZ/A`AHZ/A`AZ/A0%AhZ/ALAXZ/AFALZ/A AXfZ/A^A@}Z/A@#A(JZ/AA؟Z/A `AZ/A@$AHZ/A@AvZ/A^*A?]Z/A`dFA?]Z/A@#A0tZ/A AZ/AzAOZ/AibAZ/A̋AZ/AAZ/ADAZ/A@A(`[/A@A&[/A2A&[/AAT[/AhAvu[/AhA}[/A@yHA@~[/A=ˉAҚ[/AAҚ[/A@$A}[/AËAX$[/A =A!z[/ATA@F[/A@A8[/A3Ai6[/AیAf([/A@>9Ah#[/A`TAe![/AzmA [/A:A^Z/A`A0YZ/A蝐AxZ/AAЦZ/AAxKZ/A`A0tZ/A~AhZ/AA_Z/A;A_Z/ApA`CrZ/A A`GZ/A J(AZ/AʎA?]Z/A@A;HZ/A:AH.Z/A`D A0Z/A /AxY/AA`Y/A ZōA8xY/AIqAY/ANAH ZY/A`΍A>Y/A@p5A`TY/A vA@ Y/A@A@ Y/AॖAY/A`AQY/AOAHPY/AHAQY/A`7A@ Y/AAp"Y/A(AY2Y/AAгIY/A5A aY/A,wA@}Y/AA–Y/A`mAkY/A`AjY/A@qAiY/A6ApzY/AɇAWY/A`A>Y/A`B0A@W$Y/ARAHPY/ARA0HX/AMhA0HX/A &A0HX/AնA`X/AbA@ Y/AAH-Y/A A^GY/ALAccY/AQ͆AY/AQ͆AXY/A`A–Y/AA–Y/A lAY/A`ëAccY/AA\@Y/AqAY/AajAQY/A`KAQY/A@5AQY/A A0Y/A@ AгIY/A`KCAཱྀY/AAY/A`AY/A =A'Y/A JA'Y/AAP|Y/A@tALY/A]AY/AzAjY/A]A0wY/A4A bY/AwAFRY/AA>Y/AހAX+Y/A$AEY/A?TA8X/A \A&X/A \ApX/A?TA`X/A(Ax)X/AAKX/A좀A(-X/A cAKX/A@> AxuX/A`AfX/A_A@ZX/AAH?KX/AAXRFX/AwAp>X/A@r€A@,X/A@AX/A3AYX/A |DAW/AsAW/A \ApW/A6AW/A@AiW/A /AW/A[ApAoW/A RAlW/A}A0oW/AAثwW/AAoW/A`6ApgW/Aj AiW/A~AmW/A~A~wW/A~A|W/A[~AiW/A`B~AX]W/AI}AXW/A }AQW/A W}AWW/A}AbW/A |A`SW/A@߰|A0>W/A |A9W/A}AzW/A@,}AH W/A/}AV/A(}ARV/A@E }AȔV/A|AV/A|AAV/A}|APkV/AT|AV/A@E||AV/A|AV/A|AHV/AE}A V/Aq}AjV/A4}A2V/AH}ḀV/A@`}A٦V/A~AH?V/A^~AV/A@N~AБnV/A~AX^V/A/A0fIV/AFA,V/AAx V/A`fCA(U/A@WAU/A`̀A(U/AŀAh"U/AŀAU/A ҷApAU/AIAHyU/A AfU/AxAZU/A ҷAX[RU/A |ـAFU/AA$U/A@ApU/A AU/A`vAU/A QAU/A΀A`CU/AqĀAH;T/A6AT/A@̀AxT/A AUT/A Ah@T/A AAXT/A`AީT/A@ᅀAT/A삀AT/A zAhzT/A &tA cT/A_wAYT/AA`PT/AhǀAH!OT/A`ART/ABAx\XT/A@fA`\T/A-A(VT/A AP UT/AhLA`PT/AAOT/A AH?RT/A ALT/A`,AKT/A@yXAIT/A-˃AcKT/A=AKT/A昄A1NT/AلATT/A@A`SdT/A_A`SdT/A)A^T/A}A^T/AхAdT/AslALnT/A`AA$zT/AXA規T/AA|AT/AApT/AA@T/AeA0T/A l;AT/ALA8T/AeA@T/AvAp0T/A`LJAU/ACAT/A,_AT/A,_AXhT/AvAZT/A-A`TT/AHA@T/A qAT/A@WCAXU/A uvA8U/A@֥A_]U/A@ȉAȘU/AAU/A@.DAH{U/A`2AXU/AAhU/AhA U/A`HA)U/A`mjAV/AlA"V/A @A 0 V/AApJV/A QҊA@V/AjA U/AiAU/A`;0AX9U/AR AU/A͉ApU/A A"U/A@AhU/A |lA@U/A@PMAU/A@5ABU/A \وASU/A`vA{U/A`DAPMU/A&ɈA@uU/A8AU/A JAU/A`TA |U/A0߉AV/A)A6V/AЯA(V/A@bAyBV/AwAJV/A"A>V/A@eA4V/AAT1V/A@݉A@V/A AP,V/AGAXl>V/AlA[?V/AɊA=V/A0A0CV/A=A:V/AuA,V/A`A!V/AEA'V/AA2V/A A`XDV/A DA(i[V/A`5A( iV/A`AȔzV/A0AV/AXMAV/AxAH.V/AȌAYV/AAV/A?5AV/A`KAH{V/A^hA W/AA@W/AύAPMW/AwAW/AMAW/A`pA`a*W/A ujA)7W/A`TAAW/A3ΎA0JW/A֎A[W/A0ӎAqW/AA{W/A^AW/AMFAW/A;AW/AMAXW/A TAW/A@CUADW/AotApAW/A@A8W/A@ǎA0W/A@юAƄW/A A0yW/A@AqW/ArAiW/A ZWAiW/A1APaW/A?ACWW/A A[LW/At/AHrAW/A JAn.W/A JAW/A]APZW/A jAV/AAqV/ADڏA]V/AA W/A`QASW/AEA W/AݐAW/A`)'AhW/A TAg W/AsyA(W/A]A(W/AőA03W/AaAZ4W/A@C$A(h8W/A`)LA?W/A@SAxHW/ATAIW/AvAaBW/AA;W/A`mAM0W/AA@*W/AܒAd%W/Aj A)W/A/A(0W/A |TA(W/A`[A6#W/A AAW/A6A(9W/AA@V/AēA0V/A@yAV/A)ѓA+V/A@AV/A` A(V/A-,A8V/A HAV/AMAЄV/A3A\V/AA(~V/A6AOV/A`KA(V/AA-V/A@A8'V/A` A8R~V/A@E APjV/A@E AeV/A@AXV/AAFV/A`Ao9V/A~A,V/A Ah%V/A@NAP$V/A-̓A 'V/A@E A,V/A=A2V/AzA7V/AߣADV/AAQV/A9ݔA7^V/A@ AkV/A (AUyV/A`mRAV/AqAwV/AzAV/A`A V/A#AV/A`KIAV/A`tA tV/A \A޴V/AHAYV/AϖAXVV/AAfV/A` AHV/ACAH.V/AoAzV/A6A0ԓV/A 䨗AЀV/A@ җApqV/A@A@bV/A@A7^V/A |HA0^V/AAptV/A̐A؞V/A;AP V/A@AHzV/A]:AƏV/AtA V/AAEV/A@NA tV/A@ƙAxV/A9љAhV/A`ApV/A AЀV/AF]AHV/A AoV/A ØADbV/A ØATV/AAhPV/A֘AQV/AWAHPV/A |HA[V/AwAlV/A`mA(OV/AjAV/A`AzV/A4ARV/ATAǧV/AʖApV/AA`V/A}A`V/A[0As|V/A`TڕApYV/A ZpA3KV/AvA@6V/A ~A#V/AEA"V/A@YAXV/A&AV/AoA0 V/A锔AV/A`2ACV/Aj2AV/A@#ۓA V/AA'V/A`6A/V/AAJ(V/AA)V/A u9AP(V/AAl&V/A`BđAV/A`БAo V/AAU/ArA=U/A` A8"U/A@.AU/AAPkU/ARВA(U/AA(U/A@,GAnU/A u9A8U/A [AU/AÒA0U/A"AU/A@JAxرU/AFA+U/A Z&AIU/A@ ޒA`U/A@ABU/A5AؒU/A (A@U/AAYU/A*A U/AMAU/A/AU/A`D_AU/A`vtAPMU/A 8sAU/AuAV/A`vtA6V/Ak#AJ(V/A OA/V/A`fA9V/AA@7V/A!ADV/AXAOV/A`"VAplVV/AAaV/AXƒA(\oV/A`BA`|V/A@A(ʊV/AAAV/A;ARV/A@ >A1V/A <AV/A`*AW W/A,AW/A ~AW/A@hAW/A@C$AW/A |/ASV/A!AV/AёA؟V/AcAV/AqAcV/ABA.V/A` AHV/A@A`V/A^A V/AAXʢV/A@AƏV/AAlnV/A@lA|=V/A@rJAN#V/A AU/A@C0A`%U/ATAXU/A AȩU/AAnU/AxAx\JU/AєAAU/AAp_-U/A ՋA`U/A<AXT/A SAbT/A SA@T/ALA@T/AՏAIT/AApnT/A}QA`PT/A;UA@$2T/A-iAT/AzVA*T/A ,AKT/A֊A0 T/A汊AAT/AI|A0HS/AjJAUS/AAS/A J6ANS/AYAXS/A쐊APS/AA0S/A&ĊA'S/AۊAcS/AAA|S/AA(iS/A l6A=TS/AUA _KS/AA>S/A`9ȋAȶ4S/A sA0S/AA#S/AA88S/AA@qS/A`6AȵR/AeAR/A AR/A@AP R/A@AUR/AAR/AgA@ӨR/A"ApR/ABAgR/A`qArR/AaAP[R/A$Ap[CR/A qA`3R/A qAxR/A`A4 R/AA|Q/A AQ/AذAQ/ARAQ/A@謍AUQ/A_ЍAh]Q/AA8tQ/A 2AQ/AXVAQ/A jApQ/A`mA8'Q/A`qAT~Q/AfAhsvQ/A`AmQ/AͼAȤiQ/A &AhQ/AoAZQ/A2'AYQ/AAHaQ/A@ A[Q/A`mAQQ/AJAj6Q/A`+A()Q/AA(Q/AA Q/A@.AZP/A`6AhP/AhRAxP/A}AXP/A AP/AȎA P/A}A((P/A`6A`P/A@.A˘P/A[A˘P/AɴAϗP/AyAޓP/AdJA#P/A@.A`6}P/AA0P/AǨA0P/AAP/A^AP/A`BJAP/AAEyP/AA0wlP/A`9ȋA@]P/A`vAQP/A)̋AJP/A`AJP/A*A@DP/AQFA@8P/AaAn,P/AqAH P/A^AP/AAP/A`mARO/AaAh^O/AAO/AЋAO/AЋA`O/AA O/A@yuA0O/A EAO/A;AؒO/AۋA`O/A"ABO/AeA O/AAPO/A-ԌA O/A A/O/A@>AX$O/A@CAX$O/A@A O/A`mA O/A(AhO/A S܎AO/A A#AO/A्A O/AЏA RO/Av܏ApO/AA8xO/A@VA@NO/A A(O/AAO/AAPO/A*;AO/A`^AO/AAxO/ȂAPO/A`A`O/AKAeO/A+AO/AtAO/AAЦO/ADA@OO/A SAP/AAP/AqAn,P/A SAX9:P/A3ACP/AjAXSP/A`ZA@]P/A3AXhP/A4AiP/AؒAfP/AAcP/AI~A@]P/A`OA_P/A'AdP/A^A@dqP/AApIxP/AfAP/A`v'AP/A@AP/A97ApJP/AH3A,P/AAP/A@ԐA8P/A@ԐA P/A A@uP/A CAxGP/A CAHQ/A CAQ/A@y*Ap%Q/A@Af7Q/A AX9CQ/A APRQ/AA fQ/A-AX}Q/AKAX#Q/AܐAQ/AA@Q/ANAQ/AA/Q/ABAQ/A@ԑApQ/AAQ/A`OAQ/A`AТQ/A`ܒA@uQ/AARR/A`ZARR/AARR/A 7AHKR/AnA R/A QAȶ+R/A QAHr=R/A׭AUR/A |ŔAHjR/A@NєAPuR/AAQR/AZAۦR/A@yAR/A`]͔AR/A QAR/AjA'R/AnApR/A'AۦR/A'AxR/A?AaR/A@rCA/R/A/A` R/A8A07R/A`KɓA07R/Ak~A R/Ak~A`R/Ak~A0R/AjAN S/A AGACS/A]A S/A൭AS/A@,ѓA#S/AAX'S/A#AX'S/A`[A%S/A橔A0"S/AA)S/A)_A@qS/AHWAR/A gAR/AvA07R/A 뱕AQR/AAYR/A@!(AR/AGAR/A-gA˪R/AIA@ӨR/A (AϩR/AD(AR/AcAHR/A@YAQR/A`UAR/A@IAR/A@AؒS/A9AX$"S/A*A3S/A A DS/A@זAHS/A@ƐAiRS/A:A@]S/AAXiS/AA0wS/A AS/ATA}S/AA S/ATAS/AA0S/AAS/AA8xT/A@AX$+T/A@p"A3'T/A|aA3T/A-ABT/A@A|VT/A"A,kT/A"A}T/A'>APT/ADyAqT/A`AxKcT/A@חAxKcT/AACeT/A NAP8hT/A 8A zT/AܜA@†T/AeAxT/AYA[T/A@ APT/A@5ȘAX9T/AAAT/A` RAT/A ZAT/AङAzT/A ܙApIT/AA(:T/AbAT/A@ĚAXU/A-ܚAU/A`v AxU/AsNAXJ*U/A@}A89U/A`A89U/AmԛA8LU/A@Ah]U/A`AidU/A@6A^U/A`yATU/AĜA8LU/AAEU/A@#3AFU/A@>nAHRU/A` vA`qbU/AčAHKlU/A`;A {U/A@WAɍU/A@E3AU/A@^AU/A ɕAU/AAU/A`A͌U/A*CA }U/AZAY/A1AGY/AAY/AEʢAY/A` AAY/A6-AY/A AeAQY/A`nAw'Y/A@AX/AپA`X/A.AX/A`ͦAX/A ~PAX/A 8ANUX/AlA(-X/A@AW/AJAW/A AXҎW/A tA]W/A2Aj1W/A`Ax V/A A0V/A]AaW/A@,A%W/AகA`AW/AApxwW/A&GA*W/A@A3W/A LAW/AtϨA:W/A`[AW/AaA՜W/A.A0ٱW/A@p!AW/A4Ah;W/AqA:W/A@5A@X/AA@X/AvACX/A`KAKX/A l*APzX/AA0XX/AAsX/A A PX/A]AXF+X/A]ApX/AAX/A 1dAX/A`AX/A&AX/A@A;X/Ah3AgX/Ah3Ax\X/AcAaX/A`̵A X/AcA`X/A`"~AUxX/A 8AXWX/AKA4X/AoAh6X/A緸AXWX/A`VAX/AA`X/AƹAX/AA`X/AA[X/A@ATqX/A)ANUX/AptAG2X/A;A(X/A[ApX/A`lApX/AܽAؚ&X/AADX/AܽA8eX/A AA0XX/AAUxX/A@ ARjX/A?AKGX/AﯿApX/A`˿AX/AĆAh X/A JA/X/AAO\X/AAX/AAX/A ۭAPMNX/A'6AXF+X/A`TA~!X/AOA?X/AAh X/AApX/AkA@A%X/A A8I9X/AA PX/AUAUxX/AA X/AFAX/AA0Y/A A~JY/A`djAP{Y/A,Ap=Y/ArAY/ArAY/A@HA Y/AsAY/A*A0*OY/AUAY/AމA`v Y/A AHr Y/Aa*ApY/AzAw'Y/AA{>ABl/AzBAl/A Apl/AAl/A3AX l/A@A`al/A`)xAzl/A6Al/AAl/AAp8l/A Al/ARA#l/A 1ABl/A31Al/AA Rzl/AA ll/A A`aml/A(ALl/A3qA`l/A Al/A@ Al/AAl/AzDAXxl/ARApWl/AAAl/A]A=&l/A Azl/A Ak/A Ak/AA`k/A `Ak/AAp k/AAk/A aA8k/A@>FApk/A^AX k/A Aztk/AATk/A uAP8Lk/AA=k/A A,k/ApA) k/A Aj/A HA8j/A AQj/AAj/A:Apj/A3qA(wj/AA@Wlj/A`A@Wnj/A}A(ej/AYAB[j/AtAHaGj/A A3j/A1A(j/ApOAj/A \YAXi/AUAX i/AAki/A@ Ai/A3 A0i/A0Ah3xi/A@RAfdi/A eA#Vi/A AKi/AHAfHi/AzAzJi/AmAW\i/AA0.mi/A{Ahti/AzAki/AABi/AAWi/A]7Aj/A GALYj/AkAtj/AAp=j/A HA`aj/ApKAp j/A A`j/A@vAk/AHAP81k/A A@k/A Abk/AֻALk/AzApk/ARJAP8k/AHAl/AbA8(l/A`)A5l/A`)A8nl/A=Akl/A@GApl/AzAQJm/A8A83m/A0Ap n/A A 'n/A 8Adn/A`f A0.n/A 9 An/A \k An/A- Ao/AAp=.o/A ABo/A |A]o/A Apho/A`LA@lo/AAso/AA@no/AAGio/Az Axfo/AhUAXeo/A@^A_o/A`ftAKo/AzZA6o/A HMA%o/Ap]AXo/AxAH\o/A׻A0n/AA Rn/A7APn/A`fAo/AåA o/AýAXo/A AB$o/ApA#5o/A`)AfAo/A AHo/A@VApPo/A=APo/A =AXo/AzA(\do/AR^Afao/A#Ago/A#A83ho/A`)A_o/A׉A?o/A -Ap=!o/AAko/ARAn/A2ABn/A`).An/AAn/A3An/AALn/A qAzn/AAp=n/AABn/A An/ARXAPn/A Apn/A A3n/A9Azn/A]Akn/A`)A(\`n/AQA(Qn/AQVA8@n/AR>A:n/A m AQ?n/A@g~!A>n/AL"A>n/A "A8:n/A H"A0n/A}#An/A` $A|n/A`@$A)m/A$A`m/AR$AH\m/A=J%Apm/Ap%AGm/A`ù%A)m/A`"&A`m/A&Am/A!'Am/Ay'AP8m/A'Ap=m/A H%(Am/AY(AX n/A(An/A`(AH n/AR)Af&n/A3)Apk(n/A)A4n/A*Ap=>n/A`*A{n/A@G)An/A)A8n/AR*An/A *An/A)An/A@G)Ap=n/A3m)Ap=o/A@R)A"o/A \U)A85o/A M)AXIo/A;)AX Uo/A)A`go/A (Aho/A(AH.io/A(~(AHaio/A`t(Aio/Ai(Aio/A^(AX jo/ApS(AGjo/AH(A8jo/A@<(Apjo/Ap1(Ajo/A@&(A jo/A%(Ajo/A(Ajo/A](Afjo/A (A]jo/A`v(Aio/A 'A83io/A'A#io/Az'Aho/A'ALho/A'AH.ho/A H'Afo/A`f'Afo/A'Aeo/A@>'A ceo/A@>'Ado/A'Abo/A`)'A)ao/AR'Aao/A`d'A`o/A 'A^o/AÓ'Ap^o/A@'Ap\o/A['A8\o/A@>'AuZo/A z'AGZo/Ay'Ap8Xo/A@gn'AX Xo/Apm'AWo/A@gl'AWo/A[k'AUo/A&a'AUo/A`)`'AkUo/A3_'AH.So/A@>T'ASo/A[S'APo/A@gH'APo/A G'AVNo/Az<'AH.No/A;'AKo/Az0'AVKo/A-'A83Ko/A -'AHo/A"'A8Ho/A 'ApFo/A`'A@o/A`)&A>o/A3&A=o/A&AL;o/A &A9o/AR&A7o/A[&AT7o/A&A(5o/A&A4o/AR&AȘ3o/A`ܷ&A1o/A&A/o/A&A-o/A&A,o/A@>&A*o/A`Õ&A&o/Ap&Af&o/A`&AB#o/A H}&A o/A q&A o/Af&A{o/AZ&Ap=o/A`fN&Ao/ARJ&A`ao/A@B&Apo/A6&AHo/A`m1&Afo/A +&Ap o/A &A@$ o/A&AP o/A&Ap o/A@&A#o/A`%AHo/A@%Ao/A@%AXo/A`%Ao/A%Ao/A%AP8o/A%APn/A`Û%APn/A%AP8n/A`Ï%APn/A ƒ%AP8n/A w%A n/A8t%ABn/A`)l%A8n/A``%A`n/A@X%An/AT%An/AH%A n/A@?%A)o/A=<%Ao/A5%Ap=o/A3/%A o/A`)%Ao/Ap!%AxKo/A@%Ap=o/A%Afo/AR %A(ho/A%ALo/A`%A@$ o/A A$A8 o/A $A@o/A`K$Ao/A@$Ao/AV$A;o/A$Ao/A`$Ao/A[$Ao/A@$A6o/A@$Ao/A@$Ao/A@$Ao/A@ $A`a#o/A=$Aj#o/A Q$A&o/A$A)o/A$A*o/A$AX +o/A3$AG,o/A`f$A`a-o/A $AW.o/A$A-/o/A $A1o/A 1%A2o/A3%AH.3o/AH %Ap3o/A3 %Ap=4o/A@ %AP4o/A@%A)5o/A`)%A@6o/A%A(->o/A@y8%AFo/A`ff%A(\Go/ARh%ApGo/A=j%AHo/Al%A8Ho/A \o%A8Io/Ap%AQIo/A`r%AIo/At%AIo/Au%AJo/A@x%AJo/A=z%A}Lo/A(%ALo/A%AuNo/A`%AuRo/Aן%ARo/A%ATo/Aק%ApVo/A%A`Xo/A q%AZo/A %A]o/A%Ap_o/AH%Axh`o/A`%A`ao/AH%Apkdo/A@ %Ago/A%Aio/A%Ap8lo/A@%A(no/A%AQqo/A`)&Auro/A` &Aгso/A  &A#to/A@ &AБuo/A&A`vo/A`&Axo/A@ &AHxo/A`&AQo/A`&Aho/A&Adžo/A&Aho/A`&Ao/A@ &A8o/A@ &A‡o/A@ &Ao/A@ &AP|o/A`&AH.o/A&APZo/A&Ao/A&Ao/A &Ao/A&AP8o/A &Avo/A&Avo/A&A8o/A@&Ap=o/A`&AǏo/A"&Ao/A@"&Ao/A@ #&Ao/A #&Ao/AH#&Ao/A/&A2o/A`m1&A`o/Az<&A`o/A`<&Apo/AXE&Ao/AI&Ao/A`I&Apo/AV&Auo/AV&A-o/A=^&Ap=o/A q_&A@Wo/A qa&Ap=o/A \s&Ap=o/As&AXlo/A`"w&A̘o/A`)&A(Jo/A&A#o/Ap&A#o/A@>&Ao/A@ &Ao/A&Aљo/A &A̙o/A&Ao/A &Ao/A@g&Ao/A&AQo/A@>&AGo/A`&Ax o/A'A`o/A@g 'A8o/A H'ALo/A%'A)o/A('Auo/Az4'AQo/A6'Ao/A C'Auo/AE'AOo/AAH'A)o/A[Q'Ao/A@>T'Ao/A _'A@הo/Ab'Aǔo/Ae'Apo/A Hi'Apo/A@k'AHo/A l'AHo/Azz'AHo/Az~'Apo/A'A`o/A H'Ao/A['AOo/A@i'Afo/Ap'A@o/A֫'A̗o/AN'Ax o/A@ƺ'Axǜo/A3'A@¡o/A`'AP8o/A'A#o/A H'Ao/A'AQo/A 'A`o/A`f'Aǧo/A'Ąo/A`f'A̩o/A 'A̪o/A'A«o/A='Ao/A`)'ALo/A(A0o/Ap(Ao/A H (Aأo/A H (A0no/Ak(Ao/A(Azo/A(AFo/A%(Ao/A@,(A(ܾo/A(4(Ao/A@7(A`o/A(<(A^o/A@,K(Ao/A{(A8o/Am(A8o/A`(Ap=o/A((A`So/A`՚(Ao/Aק(A o/A`(A؞o/A(A@1o/A (A@1o/A(A(\o/A')A`So/A@()A`o/A4)Ao/A_)A0.o/A@ i)A`o/A`fr)AXo/Au)AWo/A)A*A"o/A`?*AP8o/ARD*Ao/AI*Aњo/A3O*Ax o/A |S*A`o/A`*A`o/Af*Ao/A m*Afo/An*Ao/An*Ap=o/AHu*Aho/A Q{*A8o/A q*Ao/A@ *AQo/A*A|o/AR*Ahzo/A`*Ayo/A *Ap=wo/A \*Ano/A*Ano/A@!*Ako/A*Afko/A`*AHaho/A3*Abo/A*Af_o/A \*A\o/A`*A`Xo/A*ApUo/A@*ARo/A*Ap/A.Ap/A (.Ahp/At.AQo/Aa.Ao/A .A0o/A -AHo/A t-Aho/At-Ao/A ,A! p/A2,Ap/A@E+A7p/A@E+ABp/A`;n+AXp/A +Arp/A (*Amp/A}}*AHp/A7*Awp/A)Ap߰p/A`ܗ)AQp/A@E;)Ap/A(Ap/A (A4p/A(Ap/A(Ap/A (A q/A2(A"q/A (h(A2q/A`F(A:q/A@N4(AQq/AW'Ameq/Aa'AZq/A2a'A8q/A 'AP+q/A2&Aq/Atp&A4q/A@&Aq/A`&Ahq/A &AXq/A%'Aq/Ah'A0q/A@E'Aq/A`'AP+q/A ((Aq/AC(Apq/A `(ATq/A (A=q/A@E(A(>Bq/A`;)A8Eq/Ad)ApFq/A})AJq/A`*ARq/Aj*ASq/A}*ApFq/A`*A8q/A2*A46q/Aj3*A46q/A)A0q/AW)A4q/AX)AQq/A}Q)App/Ad)Awp/Aj)Ap/A`:*AQp/A@n*AP+p/A`Ӟ*A!p/A+Ap/A`>+AŒp/AL+A8}p/Aɥ+Atp/A@E+Ahp/AWA,A4p/A@E,Ap/A@E#-Ap߰p/A-Ap/A`2-A!p/A`Z-A8p/A`-A4q/A -A q/A <.A q/A.Aq/AK/AQp/A/AHp/A0A8p/A o0AXp/At0A!p/A1AP+p/A1Adp/A2Q2AQq/Aa2AQAq/A`;2Akq/A`3AP+q/A@N$3Aq/Aj3AXq/A23A4q/A_4A8 r/A4A r/A 5A0o/A` wHAo/A\HA83 p/AiHA\p/A c.HA0]'p/A`BGA(i>p/AGAxQp/AGA tfp/Aq/A k9Aq/A`9AQq/A@F9AQq/A8AGq/A@~8A¸q/Aa8Aq/A`7Aq/At<7Adq/AW6A0vq/A`;^6AWq/A (6AIq/A@E6A=q/At6A46q/A`5Aq/A`5Amq/A25Amp/Aa5Ap/AW5A!p/A5Adp/A5Aip/At6AHKp/A@E6AXp/A"6Ao/At6Apo/Aj5Ao/A 5Avo/A5A)Vo/A 6Au\o/A@g-6Axto/AK6AAOo/A>A o/A&J?ASo/A`o?Ap/A@8@ADp/A@AH2p/AXOAA@p/AAAho/AIBAh3o/ABA=o/AVCAzo/AfCA@o/A`"CAo/ADAPMo/A`'DA!o/A?jDAЄo/ADAo/A0EAHܝo/A`]OEAo/A EAXo/A3/FA΅o/A FA0\o/AdGA0]o/AGA _o/AiHAXo/AfHAcLo/A[HAAo/A HAxBo/A6IAVIo/A3IAh Qo/Ak;IA0o/A` wHACN/A $PAAN/A`tOACN/A NARN/A@vNABiN/A NA@N/ALMA`N/AMAXN/A?MALO/A \qMA؁;O/A QMA AO/A`THLAxKO/A0uKAsUO/AJAsfO/A@yZJAȶlO/A yIA`}O/A HA/O/A>HAqO/A5GAO/ACGAxP/A;FASP/AW>FA0{P/A FA(zP/AFAyP/A= FAX5Q/A^FAA;Q/A FA Q/A`:FAQ/A`FAQ/ALEA"R/A EAR/A EAR/A`JEAxR/A@DA0Q/A@,BDALQ/A CAtQ/A NCAQ/A ABA Q/A (MBAثQ/A`AAv}Q/A|AAgQ/A}KAA0XQ/A@r@A&MQ/A j@A6Q/A`?A#Q/A`+?A4"Q/A`>A'Q/A qq>AP^P/A@">AؼP/AX=ADP/A`=AhDP/A=AQP/A`D=A@_P/A<AlP/A l<AhP/A(<AP/Aj=A^Q/A=APQ/A@<AЮQ/A"<A(P/Aݬ;AyP/AX:;A P/A2:A*P/A@:ARP/A:A@SwP/A`:ApaP/A-:AxJP/A 9Ah-P/A Q:AxP/A :ApO/A@9A-O/Ae9A@O/A`̞9AO/A 9AO/A9A"iO/A9A8(O/A!9Ag O/A/8APN/A 8AN/A@7AiN/A*7A֎N/A`n6AȆN/Aq.6A8xxN/AX5AfN/A5A]WN/AX5A 8N/AŴ5AX/N/A i5AC%N/A`B4A8Q-N/A׽4A5?N/Aʤ4ATN/A@o4A k_N/A54A]N/A;3AAN/A3A8Q-N/A 3AxN/A`o3AM/An94A`M/A@ m4A`M/A`)4A8M/A`m}4A |M/AN4AȤM/A+4AM/A@,4AM/Aܭ3AXM/A@3AHM/AB3AM/A`3A*M/A2ApM/A 2AnM/A A2A,M/Ak2A0G{M/A a2A,kM/A.2AbiM/A1AȥoM/A@E1ATrM/A|1ATrM/A`)1ApM/A0ApM/A0AnM/A A0A`gM/A0AUaM/A@i/A\M/A'/AHKM/A/A9M/A/A*M/A/A"M/Au/A M/A l?/AH&M/A)/A;M/A@`.A@M/AT.AM/A@.AP*AP&C/A!*AiC/Ax*APC/A *AؒC/A)AC/A)A8'C/A`c)AC/A@9)AC/A} )A@C/A)AxC/AF.)Ak}C/A)Ax|C/A` (A rC/Aީ(A(fC/AА(A XC/A(AxIC/A(A=C/A<(AТ7C/A@.(A#C/A@.(AC/A~(AC/A 10(ARC/A`(AB/AZ'AnB/A(AB/A`fP(AFB/A`"[(A۾B/A@e(AhB/A@e(AB/A>(ApΔB/A(A(܋B/A'AH-xB/A'AhB/A(AHUB/A`(AH.EB/A(AH!,B/A(A!B/A(AB/A?(A/A/A(AHA/A?(AA/A`(A0A/A 'AA/A 'AXůA/A`'AA/Ah'A>A/AZ'A`ӕA/A!(AӄA/A@S(AuA/A@S(APaA/A)(AP@_A/A`'A0%`A/A'AkA/A'AkA/A@5d'APA/A U'A BA/A U'A6A/A@'A%A/A&A& A/A`m&A4A/A5'AO@/A@yY'AP@/AG'Ax@/A'AH@/A@'AX(@/A &A@/An&AP@/Ab&A`@/A&AP @/A=%A@/A@#%A@ߪ@/A;e%AH@/A@P%A@/A@R%AH}@/A ҋ%A@/A४%Ax)@/A"%A@/A7&Aȇ@/A|&A @/A'A6@/A@yY'AQ@/A`'AQ@/A>(Al@/Ad(Al@/A@(A؅@/Ao5)A(mx@/A̠)AЕn@/A`)ARW@/A*A@SF@/AO*A;@/A@L*A;@/A *A:@/A#*A)@/AG+AP@/A@+A(9@/A`,A@F-@/A a,AH/@/A,A'@/Ai-A'@/A?-A)@/A6.AxF@/A 8.A@/A/A9?/A(J/A?/A(J/AP?/A(J/AX?/A l?/Ah?/AR /Au?/A1/A`e?/AD8/AXJ?/AD8/As$?/A/A?/A@.AY?/A`h.A8>/A-A>/A@_-A0%>/At2-A >/A@y?-AH>/A k-A >/A-AH>/A@(.A5>/A0.A脻>/A`.AG>/Aa5/Ax>/A/A>/A/AH>/A`b-0Af>/A`}0A>/Ao0AP>/A0A>/A@>.1A >/A1A0>/A1A>/A1A>/A1A`?/A :1A?/A1Ax)(?/A`$1Ah4?/A211A`@?/A1A@BW?/A c1A@f?/A`o2A?/Ap2A06?/Aſ2A?/A53Af?/A@3A?/A'4AXu?/A q4Asf?/A4A[?/A (R5A(h?/Aǧ5AH.w?/A 5A~?/Ax5Ax5?/A`]5A?/A5A?/A 6A6?/A``6Aȇ?/A c6A ?/A`d6AXR?/A@6A0{?/AW7AH?/A@6Ag @/A6A@/A7A8@/AgH7A @/A37Ab'@/A7A=@/AgH7A K@/A7AHa@/At+7As@/A@@7Ah]t@/A7Ajp@/AR8Ax]@/Aq8AJ@/A08Ao<@/A<8Ab'@/A8A$@/A9A.@/A`279Ax:L@/A9Ahg@/A8APIz@/A`f8A}@/A[G9Au@/A9Ac@/Az9Ae@/A:Aw@/AH:Ah]t@/A@:Aw@/A:AHr@/A;As@/Ah;Ah@/A :AHz@/A`:Aȩ@/A@ߗ:A@@/A H:A@/A`3:A@/AV[:AA/A`]:AA/A":AXA/A`;Aa A/A'e;Ap,A/A ;Ah$A/A;Ap*A/A <Ahb#A/Aa<A,A/A ғ<AЦAA/A /_<AwOA/A`T<A\A/A`o,<APzA/A@p;AA/A,;AA/A<AA/A@Eu<AA/A5<AA/A e=A tA/A@">A"A/A@p>AB/A>AV;B/A@>AUnB/A |?AB/A |?APoB/A>ASB/A=AأC/Ao=A6C/Az=ApGC/A=Al9C/A >Ax-C/A@,>A)3C/As?ApGC/A?AX^C/A7@APkC/A`f@A&C/Aҍ@A C/A Zx@A&C/A@N@A@NC/A@?A C/A S ?AC/A;>AC/A=AC/Ah=A%C/A=A3D/A <A#TD/A`<APwD/A`B<AȶD/A`<A޳D/A@j<AXD/A;AxD/A cA;AXJD/A@:AsD/A4g:AD/A*9AѼD/Aa^9AP/D/A/8AؚD/AU8AXD/A 8AD/A ɩ7AID/AQP7AXD/Am6A`D/AF6AD/A@.96AXJD/A@5A 3A UE/A3AE/A 2AE/A`[2A(E/AR2A7E/A ( 2A`6 F/Ak2A4F/A2AcF/A 81AՠF/A1AF/A`0AF/A (0AF/A/AG/A@/A)G/A'/Ap=XG/A@/AݧG/A/AxG/A/A 6H/A &/AcH/A2/0AXRH/A-0A`H/AFq1APH/A@,?1AI/A0A@RI/A/AӔI/A (0A@1I/A A0AhsJ/A@#Z0A0J/Aя0AWKJ/A0ArJ/A0ATJ/A@W0AJ/A1AHJ/Az1AJ/A`v2ASJ/A`[2A K/As)3A+K/A`3A)CK/A3A`gK/A@t3A ^K/A73AK/A@t3AK/A`#4AK/A@4A0]K/A 5A8xK/A6AּK/Al7AּK/A@8A8'K/A8AYK/A P9AؒK/A l9AXK/A O;AK/AM<A K/A=A AK/Am>AK/A9?AxK/A@AּK/A@@AK/AAAK/AٶAA K/A _BAoK/AL CAUK/AdCAUAP4/A`UA3/A7VA(K3/AVAX3/ALWAT3/AVAh3/A 8VA3/A |BVAs3/AUA؟w3/AUAlk3/A`TA\3/AjTA NP3/ASAbE3/A`KSAA93/A`5SAȔ3/AzRAHr3/A \RAX( 3/A` QA~3/AQAz23/A`v'QA3/A`QAHr3/AjPA3/AoPA2/A}tPA82/AIPAx2/A$#PA|2/AOAXWM2/ATOAX(.2/A8dPA2/AaPA 1/A PA`1/A@PAXc1/APAyx1/A PAHq1/ABQAHq1/AQA0}1/AQAj1/A@RAX#d1/A SA~e1/A@.qSAPb1/ASA(ma1/AŅTA\1/A3UA`OF1/A઄UA,1/A"UA(1/A |BVA0/AAVAx0/A WAЄ1/A@WWAj$1/A WA8D1/A_VAf1/A-XVAc1/A-XVA&1/A@VAض1/A\WAض1/A@WAਗ1/AXA0}1/A JZYAR1/AYAx˟1/AiZA}1/AYA1/A 9YA1/AXA 2/AwXA`/2/A0rWAxm>2/A WAN2/A +WA(Y2/AWAcd2/A`2WA0&2/A cXA0 2/A@EWA)2/A QWA2/A`;WAe2/A WA3/A WAh.3/A\WAp_T3/A`6WAVz3/A`WA0*3/AEXA^3/AHXAG3/AYA{3/A@ZA3/A3NZA3/A-ZA4/AZA@94/A`<[Aؽo4/AX[Aj4/A][AHK4/AZA64/ADYA84/A qXA@4/A`2WAw4/A'WAW/A cAMW/A bA X/A;bApp@X/A@ aAhzX/A aAX/AaA Y/A`XaAG.Y/AOPaA@~fY/AqaA0Y/A(bA(Y/A@yBbAZ/AqaA(&Z/A@yaAh@Z/Aar`Ah Z/A`_A[Y/A,q^A@Y/A O]AY/Aٌ\AЮY/A`U[AZ/A`ZAP+RZ/A3ZAx~Z/A3ZA`Z/AYAX [/A@YAX9([/A AYA8b[/A]YAX[/AYA[/AH}ZAP[/A ZA~[/Af[Ap \/AL[A`2%\/AZAP\/A@ RZAw[/AYA$[/Ac YA[/A XA[/AXWA$[/A HVAP\/AUAx-\/A3UAP@[\/ATAъ\/AZUAؼ\/A@@UAP\/ANTAb ]/A@TA N1]/A`hTAS]/ATA~v]/A c TA8]/A ASA]/A@,1SA2]/ARA$]/AjRA]/A9QA ^/A QA+^/A@*QAU^/AߟPAU^/A cIPA]/A cIPA]/AkPA]/A ҹPA0|]/APAH5]/APAh@\/AZPAx\/ANOAhb\/A`NA0b\/AXNA\/AMAxǔ[/ALA0L*[/ALAUZ/A]mLA|Z/A@pLA+Z/A@yLAY/A@pLAxY/ALA -Y/A@ZLAHrX/A JMAP&X/A NAX/AT6OA8Y/A?OAkY/A`PAdY/A aQAX*Y/A [RAxGX/A`SAhX/A@yUAqX/A JWAX9oX/A`XApZX/A VYAX/A`XA&Y/A`XA?Y/A`f>ZAh(Y/A@[A`X/A J]Ax@X/A]AcW/A^AW/A@p?`A0̀W/A bA@nW/A`bcAW/AAcAP7W/AcAW/A cA5ac/A /A*qc/AA8c/A@>Ac/A`Ac/AA8c/A |Ac/AAxKc/A.Ah3c/A@~Ac/A@ذ~A=d/A~A$d/A@~ASOd/A`d~Add/AM~Ad/A~A`d/A஺~Aݓd/AK~Aid/A~A^d/A"~A8td/A}`~Ad/A8~A(9d/A}Ae/A9}A e/A~Ae/A1~AP8 e/A|z~A8d/A@5~Ahsd/A4~A( d/A $A"d/A Ad/AAd/A`Ad/AAe/AFAp e/AA,(e/AaCA@Ce/A@5~A0ce/A|z~AXwe/A@~Ae/A}Aze/A@,}AFe/A}Ake/A@?|Abe/A{A Ae/A {AP< e/A@5!{Ane/A@EzAp=0e/A4yAd/AayAd/A=yA@1d/A` $yAtd/A@IxA@d/A`-xA0d/A`dwAxd/A+wA@ d/AtmvAc/A uAxc/A>uAc/A@rdtAc/AmsA|c/AqrA Mc/A@YqAho+c/A`oRpA0; c/A[oA0c/AnAxc/A_nA؞c/A]mAb/A`?mAb/AfmAob/AmAh b/AV$nAb/AnAb/AoAb/Av[pA@Slb/AapABb/A LpAp|b/AypA; b/A 1qA@a/AෘqAXa/A"qA8a/AqA&Ra/A`mqA(1a/AqAX a/A AnrAȃ a/A c+sA0U`/A}1sAPZ`/AIsAX`/A sA `/A=/A}^A8#@/A ёAPoA/A ɆAH%F/A`AȵI/AQA`0M/A pAlP/A4AR/Ah&AnT/AAp}Q/A`A(Q/A痢A@$ R/AOA/R/A`]A0R/A,AP$R/AxA(R/AhAH)R/A`@Ac%R/A Ac%R/AIAH"R/A ұA!R/A@qAJR/AKRA@$ R/A CAQ/A9APQ/A`\AQ/A`2Ax)Q/A ೡAjQ/AٲAxǯQ/A@NAX9Q/A@#rATQ/AoAxzQ/A qA?Q/A-AÒQ/AAQ/AHAQ/A`lA#Q/AA Q/AfТAIQ/A ZA Q/A`.AhbQ/A %A0fQ/A!AjQ/A`"AXQ/AA(Q/AAQ/A /AQ/A`բA(-Q/AڻA0Q/A,Ap}Q/A`A7H/A,AVU.H/A6xAPn'H/A8Ae#H/A.gA4"H/AՏF AwZ$H/Ad1ٱA(H/A#Aiy+0H/APlRAC9H/A@aA2EH/A0OASH/A0"⵰A@cH/A AT+rH/AjfrAͨH/AkHAFH/A@ApH/A9ޯAxJH/A7AfL<,H/A:]AQH/As(ZA}NH/AT+ԮAXRH/AAy&UH/AȭAZH/Ak AkH/A[eKAlR2H/AeFAQ9I/As A;)I/A&[PAqMI/AIs罩AS#sI/A`J8AI/A:?.¨AJI/A\ZA)b&I/AATK=(J/AAoYJ/AA`%J/AOfAʩJ/A9eAV갾J/A\!-SAŇJ/Aq/Ar J/A"AJ/AMfA9K/AlkAq!{K/A_A(bf%K/AA()K/A`NAK/A w%A5K/AE夤ףAvJ/Ay)ABJ/A;7A}Wk^J/AS3̎A^DJ/A,lAWJ/AG!Af +J/ArАAI/A@>AUNI/A[-cARxI/A=AcƠI/AAUI/AA.2aI/A ACI/A 4A .&I/AlkAX^ I/Ḁ<ȱA@SH/AAďH/A5||.A i)H/A~NARG/A SdAfo{G/Aa7A4G/A@`ĥAqgC!G/A7 A4 G/A2Q#A -F/A"AOF/A%A@"aF/AjAUF/AAvBF/A,vޥA?xF/AҬAAu{F/AvIpAmF/A )AGF/A}hp?A!F/AA9E/A-ѢA &}E/A) kAܟ 'E/AqDBA҆\D/A43ȟAgpD/A|yAvD/A[(A*=C/A Ф^AULC/A3ļAȓB/A| AB/A%~៛AB/AK3ASA/AطښAk~EBA/A 閚A@/A*LgAH>j@/AmMAP\?/A% HAs?/AVXAN&?/A !}A

/A< 7A` Q>/A'A=/A@hAٚ=/AgAAWe=/At~AT)=/Aqk2$A+/A}AE>/A A`>/AC QA>JB`~>/A Aɛ>/AM}ȻAS7->/A@ Ak>/AA{?/A A#B/A@5Af`B/A[AaB/A oA52B/A#kIA-(C/Au@'A؇C/A 2 Am.%1C/A LuKA!q`DC/A%AHtUC/A%$YȼALAbC/AAlC/AnA7$rsC/Aw˽Au̩OuC/Am'*AsC/A AOJ~C/AXA-qC/A;"AsiC/A+6>A1C/ABLIA:B0C/A{BXAmC/ARfuAȊD/A$8Alޛ8D/ADžN9AaD/A AʡD/AhAHu sD/AS8AJJE/A¤ׁA3+E/A sAOE/A ,PAzKCF/A9}AaF/Aև8AkF/A.AB;DG/A8 BAvJG/A_9gA9G/AAFH/A7AwZH/AJxAnhDH/Ač$APAI/A@Aj`I/A6A{I/AbA]rI/A&B*AړI/AYAIeñI/AFA1I/A2AQI/AT5=AkI/A@vAJYI/AS*AI/A@W AKJu}I/Ae\B{4Aa=PI/AMATQI/AwSS&mA)H/AV-AH/AwA.vH/A\8A7H/A,A݋V/AkAWwV/AlKctA = dV/AGoA#5>PV/A9^AGP7>V/A!?:=@Ae,V/AI)An V/AkplA)MV/AAjʆV/A?>`A:U/A'uδAU/AwAU/AwA8\CV/Ar=rK:AV/A:6J6AS) U/Ah{풾AU/AKpAU/AQyIAaU/AhliA)j$U/A;ԖAU/A)A06~U/A`DA t$eU/AyFATMU/A#A-U/AzwbA U/A奣AS T/AB-A0{T/A@AisiT/Ai,ATE޴T/AC&AaT/A]ƄA#^yT/A9_AVT/AgAsm/T/AA8T/ATAvցGS/A. ;>AS/AKKfAOS/AYAS/A:ATyS/A`\AS/ASQA[S/As0)AmS/AAqS/A=A]aS/A$<:A]gwS/A"D]oA0S/AYAۏcPS/A5fAS/A=1AQS/AD[cA\ynS/A+H޻A9KS/AHFFA %%S/A8A2R/A2lu?AI~R/AmҹAwR/AEݕvA|OEqR/A.+A8?R/A3CA R/A`B˸ArQ/AARQ/A E|AQ.4Q/A.=A$X@Q/AhA ,8Q/AA)ۅQ/A:.6AP6 }rQ/A;ʶAךaiQ/A+YAhbdQ/A@A4Q/A<aA͏XQ/AA5P/AsAMMP/A֘fr7ANk`P/ANZAY'P/A,0ijADXO/A/d^AӴO/A~sAzO/AJfAWaVAO/AԬA7RO/A'ڳAh?N/A@Ak"\N/AoQA)N/AJ"AVN/Aϗ=A~$xN/AɨJAskN/A6VAL3N/Aj6A xN/A%RkltAN/A;׶A)N/A/\A?N/A|ALWN/A_"oA2N/AA~kN/AS+؆AN/A=AN/AkzpFAԗ0N/AqxAVN/ABx/)AֈWN/AAA hC O/A4iAWQ&O/AZA}CO/A"K:A6'eO/AOAO/AzZA⥋O/A"]A8O/Apae UA8P/AplpAA1D†GP/A>3%"AGP/A`Aa~P/AI0&AiP/AAec)qP/AM8.AQ/AAvX.0Q/A@aA2GQ/AΌ3AYQ/Aǿ~UAo4zhQ/ANm{AXqQ/A@EAV}gzQ/A|e*AcQ/AQ)tAXߚQ/AGgtAu%Q/AzA_-Q/Aث'A9lQ/A:7AWW[R/AFBAWj>R/AuPHAخmR/A*`GAP7R/A@y?APR/AeMtdAR/ALRAS/ASeAD!4S/A]ƠA#%cS/AuȠAܕS/A!d6AS/Aݡ5}AWMT/A*WAiOhBT/AUҲ$Am T/AVҴAl9bT/A>iQA#1 U/A/AI99PU/AtA];U/A= r:AU/AjABV/AopAo)A@W/A`A6W/AK@X AW/A"Aa\wW/Auņq AUHiW/AAW/AKAա.Aj{AС.A`{Aš.Ai;{A!.A` zA`.AzA.AezA@}.AVKzAh.A/zAL.AzAU.A@yA.A`yA.A yA k.A෇yAP.A -uAH!.AtuA#.A^ uAM%.A@5tApA&.AtA&.A tA%.AxtA$.AastAXd$.AetA(!.A$tA(.AoAT.AWoA鈢.AnA.AanA}.A/nAFy.A mA(p.AKmApd.A@lAwZ.A@G]lApO.AclA=D.AkA7.A@ dkA2.A`HkA+.A@-kAL$.AZkAx.A HjA(.AjA(z .A ZjA.AjA.A3jA)h.A`hAҝ.AameAPǝ.A SweA\.A]}eA(ܺ.A`}eA`!.AeA8.A ~eAv.AMmeA\.A]eA8'D.AoDeA,.A"eA8.AXdAXd.A`dA7.A@0dA제.AcAp.AccA.AֈcAq.A`BWcAX[c.A?cA3L.A8bAPxG.A bAHC.A@bA<.AWQbA:.A 'bA@7.AaA7.A`aAF7.AaAp8.AOaAB:.AkNaAB.A`A؞L.Ac`AY.A_Ac.A#_AXd.A`_Af.A}_Ahk.A`k_An.A (e_AzB.A[`A.AL7\A(௝.A %\A.A@![A( ٝ.A`m1ZAo_.AsTA.AGQA°.APAx$.APAQ.A@N/PANʜ.A )OA[.A`MA虉.A\MAz.A OJMA8p.A@:MAIh.A 'MA`d.A@>MA`a.AMA\.A`]LA0{X.A@LAU.ALAT.ALAS.A LAfS.ALAR.A tLA #T.AHLAp.A:<AH.A`))8AQ.A@ B5A(.A\9Abv.AY3Ar˝.Ay/AȶS.AD)A֞.A"A .A஠ A(>Y.Ai A0Z.A@E A``].A@A].AA_.AfAHa.A6AP7c.A`Axe.A@iAg.AALi.AoAh.A AQi.A@AP+k.AsAl.AAn.A lpAQx.A%A.A"A(>.A`A`e.AGAI.A`BAԟ.A@iA #.AA(z.A \A.A@A0.A@ƱA .A`uA`.A8Au.AA`J.A@,A"C.A@A@A.A@A0{>.A@A_<.A.A(O<.A[EA0<.A`[A(\?.A3AlC.A-AH.AAO.AAr.A`rA(z~.A A".A`TA8⃠.AAF.A`A@A.A G!A .A;)A8.A J+1A.A͆5A".A@?Ay#.A`AA83.A0AAx@.A ZAAK.A JAAP P.A`DAAS.A@>BAr[.A:BA/^.A@NTBAh`.AWmBA8a.A BA}a.A BA(`.A`BAU`.A@BA_.A2BAZ.A`BAH2T.A |CAHM.A>CA!J.ALCAjF.A`bWCAA.A bCA0:.A@lCA(5.AjCAx1.A bCA p.A`mTAA.A@NYFA.AzFA #.AFAH.A; GA(.A@NSGA_.AGA@`.A@` HA.Af[HA.AHA0 .A@HA$.A@GHA-.A HAB9.AO&IABN.ANmIAk.A߇LAX ӟ.ALA.A LAo.A@LA.AMA.AMAP.AWMA.A,MAp,.A@QMA@ .AMAhL.A MA.A`;NAX .A)!NAF#.Ap0NAPI$.A8NA#%.ADANA0L%.A xNAZc.AVA.A@p[AX[Ǟ.A q^AN6.AaA<.A`)dAeD.A@|dA@.A0AfA4.AzfA-.AfAo.A@iAh.n.A`tA`1l.A?uA*l.AuAn.AࣾuAq.A;uAPx.A@,4vAh.A |qvA.A vA.AvA.A mwAx.AFwA"ţ.A 1wA8أ.A@"xA.AdxA0v.A@xA.AxA #.A@ yA0S.AyAgp.A yAu.AஎzAˤ.ADzA ֤.A$zA E.A`{A@.A"{A>.Aa3{A.A`f@{A@.AI{A.A \O{Au,.A@%Q{Azѩ.A@>A pש.A`Ah.AWA`e.A A0.AB Ax.AA.A A .A H,A`.A@9Al.A`?A,.AXNA";.A3^AC.AwgA0R.A`zA0{_.AAk.AXA"w.A构Ap.AA0Ȋ.AתA.AAe.A@Ax.A`vA.AࣻA ͼ.AAŬ.A`A8Ǭ.A 8AKЬ.A`Aج.A萄A.A9}A.ABfA.AVRA7.A`);Au.A`!A .A@pA.A`mAf.AW΃A(.A@PA3.AWA(>.A eAxH.A OEAR.A@$Ap].AAh.AނAr.AcÂAB{.AA@B.A`mA@.AwAf.AcAj.AZDA.A8'A7.A@A@u.AAH.A`؁Az.A ZAȭ.AAhҭ.AxAܭ.AzMAH.A %Ax.A ZAH.AЀAz.ABA .A#A.AcA,.A@@A.AfA$.AAH,.AAH1.AaAA.AxA0LF.A]rA0Q.AHAp].AA`h.A3~A`r.A~Av.A~A3}.A~AL.A~A.A`~A.A@CX~AQ.A`/~Aأ.A~A.A@}A.A}A7Ȯ.AM}A`aӮ.A}Aڮ.A`d}A3.AB}A.A@g}AB.A|A.A`|A[.A q|APk.Aݍ|A.A ld|Ak .A`5|AP.An|Az.A{A2.A@G{AH2#.AX{A`).A{A8/.A@^{A85.A8{A;.Ao{AVB.AzAH.AzA8N.A@zAR.ALJzAxU.A`czA@W.Ad?zAu\.AzA`.AyAȘd.A (yAh.A`ܫyA(k.A@yA([r.A~yA.AHwAX(.A /vvA(ٯ.A`sA#.A=sAp.AsA.AQsAB.AߣsA8 .A ZrAG.A`frA`v.AfWqA.A`mqA=}.AqAk.ApA`.A@ypA`V.ApAG.AvpA81.A*pA.AoA.A nA^.AlA0{ì.A/iAx>?.AoRfA".A$fA4.A\eAX.ABcAQd.A`bwcAg.A RcAef.A`%cAe.AbA0f.AbAx.AA[`.A6A.A@33AxL.AT2A(.AH0A.A /R#A0L.A G A6.A XAXѶ.A@^A.A ATk.AWAX0=.AAȭ.A@ri A غ.AZ AP.A AZ.A`kAHvq.AA>.AkAx.A@A".A Aھ.A@`rAH.A AHu.A#tA?.A |A.A`;zAF.AஃA.A (AX}.A`ՈA(\.ANA@W.AQAXT.A ZAO.A`YXA.A ~A@,.A@5An.A\A.AAڴ.AA.AkќApS.A)ۚAA.A`]-A.A A.A_{A.A@r/Ac.A ~A?.A`LAp.A-A` .A늇AЯ.A@#@A8r.A@rfAЈ.A uA,.A`[pA .AkA ɹ.A_5iAmn.ATgA.A`0bA0.Al]Ag.A@?YA].A dXAHvo.AWA2.AcOA0r2.A2A .A .A.A[:A .A`wA.AA0.A`A(S.A@`wAL.A Ay..AEAPQ].A`Ar.A@DAG.A A..AA0.A+ AاM.AVAI.A A.A@ At.ApA .ATAH .A`APUU.A@Aȃ3.A`AHu.A A.A ,A+.AQ AQ.A9AXy.A_A(/A|!AD/A .A0/A;Ah^ /AZ]EAc/A|SA/A SAH/A/TAqa/AivA"/AHAw2%/A@ErAB&/A)|Ap%/A[tA"&/ADsA0&O'/ATApŢ'/AmyLAp'/A`oDAB(/A?S=Aڝ(/A+5A)/Aho-A=)/A"A)/A IA*/A96A*/A'A0+/A5A&+/A sAp,-+/AA`T+/ABAh*-/A`zAx./AAJ//ACAL*0/A`f5Ah0/A-A1/AA1/AA1/AALf2/A *A`3/AA3/A`Ao4/A5A5/A cA(5/A=1Ah&5/AyA8]6/AA,6/Az4A<,7/AbA^8/AqA(9/A -sA;/A@tA=/A`WvApű>/A:hA>/A8(gAH>/A efA@>/A,yeA0>/A_dA?/A@iSA@/A`MAX@/AKA]@/A@AApc%A/A74AMA/A`a0A?B/A A7OA/AAH{H@/A OA`<>/A AL/A@A!AqW'2/Ac,Aj֎Y&2/Ah8A[m3$2/AwH A_q!2/A:AMA&2/Ao(Ah2/A1XeAQ2/A3A2/A fA81/AAՑ1/AAA!1/A!JAyH1/A 5A1/A;A?#V1/A]NAA= 1/A[tC&A>?1/AƞAjF1/AjRxA,55c1/AyxAEv1/AGxrA!R1/AnA1/AlAKW1/A`%-5AZ~1/A{A81/A ATr]1/A{GDAH1/AMHA92/AnAB2/A`wA:2/AAH\V2/A {AH\k2/A@>vA0.[2/AAi!IW2/ACqA;9S2/A؀ANQ2/A<`AqbO2/A8WA=N2/A{NyApM2/A$eAegN2/A8QAxO2/A,i>=AOQ2/Ai+AEtT2/AtNA%X2/A" A8\2/A \AH%Se2/AGAHѥl2/ANj2AHq2/A@FA[Wo2/A5-A@m2/AjIJAZCi2/AkAwtd2/AךALB_2/A#%A[Y2/A.Ai‹S2/A(PA?L2/Az^A8F2/A HA'2/AtA0#$2/A1gSAln2/AǀUAj]2/AXA2/A6GAO2/A,ЏA 2/A A2/ApъAb 2/A$>qAeY1/AVknVA4@1/Aۤ9AK1/A9A&1/AfAA1/AiAh2/A(Ah32/A=AM(2/A~{oAةuP2/AoB9A3n/|2/A*Fv A ѫ2/A Aʴ:2/A9zAD3/AzA43/A@ =A8,5/A A95/AEcA#~5/AI̻A"K&6/APpAuvx6/AGR[A6/A A 7/AhA7/AxA`m7/A]AW7/A bAJ7/A`A8/AAp;/A A~>/A A>/A&AC>/AAX,->/AxAп=/AЀqA`=/ATslAXJ}/Ac6AP>/A?5AeÕ=/A:5ADHAcREjA/AYUAO/AYEVAu/A AfKc/AN#Atc/AN-A@/A mAi[/A`*&AgI/ABKAu/A4xA9/A_=2A?/A+A ZtJ/AUFAg1/AfڃARˇ/A+Atk/A2pA2Z/A*%A*/A AA@>/A`3A /A~A /AA = /A lA /A A /A@AX /A@ϜA /A`AhL /A`AX /AA /A@.2A /A@ =A@5 /AQA(> /A A /AHAP /AXKA( /AOxAx˭ /A0A4 /AxAi /A A@m /A`2 Axi /A 0AP8f /APAP` /AA?] /AAT\ /AAC\ /A@,AG /AAG /AAbF /A 1AD /AXAxzF /A`A`aD /A@GA C /AA]C /A `A;E /AAzA /ABA)8 /A@ A8 /AM A 6 /Ax A0 /A}A/ /A`A0+ /A@ A%' /AAD /A@@Ap /A&NAHa" /A"\A /AHA /ADAB /A A( /AA0 /AưAϝ /A`dAp /AAPړ /AA5 /A (A՘ /Ax_Aߎ /A A@~ /AqSA"o /AAPb /A &AHU /A AnE /A@߷A0 * /A`1A3# /A ɺAH /A 8AA /AuAk /AךA /AuA0.( /A`AnE /AVAcO /A /\AP7[ /A Z4At /AyAr} /AN>A+ /A Z4A /A`Ak /AMA` /A@A@ /A@A /AA /AxAJ /AA8 /A{A)8 /A@|AXh9 /A A V /A AXk /AuA"z /A  A /A A0 /A 3!Ax /AR!A /A ~S!AĖ /A`!AĖ /AD"AĖ /Av"A /A#AH /A@i#AQ /A^#AH\ /AJ$A8 /AR$A| /Ar$Ah^ /A $AD /A%%A)8 /AM%A8 /A?N%A$ /A%AP| /A%Ap_ /A%AP /A`%A /A`%Al /AЦ%A` /A`%Ar /A` %A`T /A  %A /A%A84 /A-%Apʢ /A>%Aj /AA%A# /A C%A{ /A%Ax /A`%At /A%A@j /A |%%ANc /A/%A`Q /A}%A= /A ۤ%A0/ /A`v%A( /A %Ar /A&%A( /A%ApN /Al&A" /A[l&A* /AK&AU /A&A0 /A&A /A஻&A؁ /A?&A0{ /AE&Ax{ /A&ACp /AV&Aa /A&'AP T /A lR'AA /AΌ'Ax>, /A'A, /A'A% /Ah'A /A(AP /A(Aѿ /A ('(A /A`d5(Aw /A`o5(AP /AX(A /A(A( /A@(Aȇo /A)A( g /A:)Ahf /A`;)AHiP /A)A: /A)A38 /A *A#8 /A *A([5 /AL.*A-. /A*A+ /A@*A+ /A*A& /ABP+A(" /A`ܮ+A /A@,A  /A`2,A`e /A`T,A` /A (,A1 /A |,AP /As-A /A ɉ-A /A-A/ /A@-Aؚ /A@.A` /A.AO /A.A /A.A /A Z.AHr /AT/Axz /A lY/Aӓ /A /A /A%0Ax /AT0A l /A0A(zR /A \1AD /AO1As; /A21A" /A_2A /Aj2Aп /Aӱ2AR /A3G3APk/A`;3Ab/A3A/A`e4A/Aj4A7/A65A/AQ5A1/Av5A/A}C6A0L/Apv6A(K/A 6A/AR7At/AH7App/A@7Ap/A7A0&/A<8Aؚ/A;A/AA|/A@A4/A`AAX/A d@A/A$?A9/A`v>Ax\/A@yR?AA/Atn>A>/A`9K<A/A@9;A3/A`2:A؁/AД;A M,/ANAPM.A`NAp.A` OA%.AൂOA@B.A AOAp.AOAP.AGPA$.A$QA .A-tQA@.A6QAx.A`RAx.Af SAo.A`JSA"n.A&SAhG.ASA8.A@WUA(.AZWA .A`)XAR.A VYA.A[ZA0.Aל]A#.A@^A .Am_AX4.AJ`AhG.AaA^P.AaA/1.AْbA.AD&cA .A_cA.A`mXdA.A /eA .A@ bfAH:.A3gA.A@fgAd.A@W?gAX.A`DgA8V.ADgAxV.A \EgAP7.A܈gA.A܈gA07.AKgA.Az(gA0.A"gAR.A[hA@N.A@yhA@.AhAh.AO"iA4.AliAh.AsiA`Ǽ.AtiA0 .AqiA |.ADjAxd.A`RjA8.A |^jA .A0jA!.A@jA.A`jA`6.A jA .A`.A`mAPo>.AmAW6.AfmAW+.AnAHm.AoAh.A5[oA.AoA Ҝ.ApA@.AqA.AO+qA.A>qA؞.A`PqA|u.AmnqA(j.AqA^.A qAR.AqAF.AqAm:.ADqAX ..A \qAR$.ArA.A`%rA.A;rA(~.ASrA.AkrA4.A ZrAx.A)rA.A \rA .ArAA.ArAH\.AsA`.AZsA.A 1sA|.AsA.AtA.A`TVtA.AtA".AxtAx .AtA0y.A`tA(s.A tAj.AuA8a.A#uAY.A6uA0 S.ANuAgD.AuAoA.A`ܝuAxG<.AuAp7.AuA3.A`uA&/.ALvAx+.A7vAu(.A YvAx!.A{vA.AvA.A 8vA .A#vA3.AvA.AwA.A`6wAHK.AaVwA.AtwA.AwA؁.A wA$.A(wA.A`wA0գ.A`wA.A xA ҋ.A` xA.A3xAh3s.ADxAl.A FxAwf.A`ExA`.A@#CxAY.A >xAS.A9xAhM.A@i1xAG.A (xA0A.AxA8b=.AxA0]<.AjxA7.ADxAP2.AwA<.AEuA+.A^tA-.A YvA .A@y\vAP.A`vAP@.AdvA.A ivA`Ü.ApvA .A|tvAĘ.A#yvA.AvA8.A|vA8E.AʟvA E.AӰvAl.AvA#c.A wAZ.A@9wAТQ.AgwAH.A qwAD.AwA@.AwA0<.A 8wA 8.AqwA3.A wA0*..AmxA(.A` xA@q#.A!xA .AT,xA.A5xAh.A =xA .A`DxAh?.AWIxA0.AaLxAho.AMxA.A@ MxAX.A`oIxA.ACxA.AgV.A@uA8T.A@>uA RR.AuA8P.AwuAO.A ~luAsO.AKVuARO.A@uA O.Ah.uAO.A )uAQP.AuAoQ.A@tAR.A`KtA(T.A ZtAH.W.A tA=Y.A ~tA[.A ?tA(^.AvsA b.A;sAe.AdsAj.A`BsAn.AsAs.A`rAHPy.ArA9.ANrA.A 8rAF.AoqAf.A`fqA&.A`qA@.A@pAȤ.A pAX.AVvpA.AoA@.A`oA.A XoAp.ApnAH.A nAG.A@>nA0Y'.A"6nAp7.A`"mAȇ8.A@mAsH.AFmA(zY.A`XmAH2`.AGA4o.AB9GA0j.AmGA^.A@GAkY.AGATY.A'GAYL.AGAdB.A@,.AEA>.A2EA>.A,DAȘG.AT]DAK.AXDAO.AhCAD^.AБCA4i.AaCAz.ABA@S.A$7BAH.A@ϗAAŔ.AhAA4.A7AA.AAAw.A ~~@A.A@B@A޸.A)@A8.A`f@Aj.A.@Aw.A_/@A`X.AO@A`e.Ae@A.A @Aj.A@A.A`@A .A`AAXW.A AAx.Af AA0U.A S@A~.A l@AC!.A0?An!.A`?AP$.A?A2.A?Ax):.At?A >.A@?A>.A?A'F.A ?AX.A>AHaZ.A>A)[.A]>A+l.Aq>Apj.A">AP ].At>Ao.A=Am.A@M=A8.A@K=A.A@;=A@.A&=AѺ.A =At.A<A*.A`e<A.A H<AX.A`;Aп.A+;A.A,;A`.A;A0/A:A /A Z9Ap_/AI9A /A9AȘA/A8A(Q/A@N8AU/A஭8A(~Z/A}8Af/A<8A@Au/A7Aau/A7A0e/A lA7A/A?@7Aٷ/A>7ApJ/A>7A N/A>7Ax~/A>7AW/A47A|/A@ &7Ah/A6A /A`6A/A@ߜ6A/A@|6A@4/AR]6A8C/AxH6AVJ/A66A`PU/A+6Aa/Aj5A)o/A5A+x/A०5A/A;5AȂ/Ae5A`ӫ/A;5Ap}/A 5AH]/Aa4A8/A4A/A`4A </A4As/A@4A`/A`e4AP8</A.4A_/A@ 3AH/A3AJ/A a3A/A03A /A`2AX/AE2AH/A |2A/A^O2AHr/AK2A /A&@2Aذ/A?2A/A"1A &/A61API8/A@1AR/A`q1A]/A?_1A8 t/A 1A`x/Aa1APf/A0APj/Aו0AP/A.AO/A@5~)Ap/A@"Ap9-/AAP /A`[A /AaA؟ /A0 A /A@Ah"r /A@.A//AA /AZAw/ARA8 /A'A/AA5/A`;TAϳ/A`AXF/AA /AzAHPR/AQANv/A A/A A:A/A&[A0/AMAS/A`Av/A"Aг/A A&W/AeAp/AgA /A#0A\ /AA(/A@yBA0/AKAE/A9AB/A 5A.AK>iA&':.AhLA&5.AmrA.AsAhb.ALqA /.A\A(# I.AJM[A{?#?.AȅYAv".A0kcXAH6).Ar WAtQ.Aa^.A`fvA@Z.AovAU.Af{vAA.AwAx.AgzAhj.A`8{A@.A`~Ax .AqSAj.AAd.A`A0]Y.A`Ap[.A@WA8.AlAp_.A JAR.A yAЄL.AA~.AஅA.AʡA0j.A2Aa.AġA׀F.AzVAx.A\|UA[~D.A{QAw0.A;;Am.AϻA V޲.AlUAbG솽.A ,ݰA ].A_VAg8.AEA.AA=i.AD'A4LӼ.Av*A儚W.AEAdN.AޭAH.AYr.LjA$p.AAZУa.AbA9V.AyӫAzeQ.AW@ASP.A}AX.A`A`.AUAX6.As{Ah.A{A]e.A$vA6A.ARdA0.Aӵ`A.AaAP.AbA.A@5@bA.AdA\u.A hAyy.AqikA.A`՝kA.AkkA.AyplA.A]lA8t.ASkA.A jAh.AjA( .A AQjAx .A`>jA6.A iAN@.A@#iAm.A`8iAt.AK$iA8.A`hAPީ.AΗhA.Ag!hA.A@ hAt .AgAz.A`fxgAD.A fA`6J.AfAPw.A wfAp.A afAƬ.AeA.A`eA.A^eAp.A`vCeA.AdAq .AdAL.A |CdA|U.A A,dAh́.A`cA5.AcA舸.A&&cA8־.A@ycA7.AbAX.A0bAh.A`TbAo%.A@bA@L.A@aAhS.A SaA]y.A 'aA(>.AdaAȦ.A`A .Aՙ`A.A5`Aa.A@!`A.A@p`Ah .A_A/.AD_AX6.A`22_A;].A@^AX .A`_A(.A_A].A`^A`|.A5^AS.A`B_AH.A1_A>.A E^APx.A@]Ah.A^]Ax.A@`+]A0.A`\A.A@i\AX[A.A&k\AXI.A V\Aq.A [Apx.A[A8Q.A6r[Axu.AW\A-.AjJ\A.A5[A0؇.A^Ap.A ۍ]Ao`.A ]A>;.A\A.Aj[A 0.AZA{.AYAx..A WA(iH.A@>WA}Y.A |XAm.AWA.A&\AD].A[AW.A[Ao.A`fE\A(p.A@5WA8I.A WA¢.AVAm.AnWA@5_.ATWA).A@PVA.A _VA:w.A VAq.A uVAذ-.AWA0-.AWA*.AQWA#(.A q%WAx&.A *WAhD.A|VA.AſVA`G.AVA .A`VA.AٷVAb.AVA[.AUAt.AVA([.A`2VA{b.AhCVA`.ACUAJ!.Aٳ>KUA}.AFdTA.A%{ITAR.ASA.ASAX.A0SA9.A\SA.A9SA#.A@SA.ARA.ARA.A@yRA8.AhRAx .A5RA .ARA-.AjQA".A`QA +.AUrQAy3.ACQA@=.A`,QAG.A`QAhsR.AQA0*].APA0 h.A/PAs.A?PA@~.APA.A`PAh.AîPA5.APAXh.APA8.APA.A`PAh.A`PA*.A`;PAX.AaQA(:.AQA.A"-QA.A`BQAЀ .AYQA`1.ArQA =.ArQAG.AUqQAE#.A cnQAh/(.AiQA,.A`fcQA1.Aq[QA6.A QQA N:.A`FQAH>.At:QAHA.A ,QAh^E.A@QAkH.A@ QAK.A PAPoM.APA[O.APApP.APAQ.APAR.AܜPA0LV.ANPA^Z.AOAX^.AOAc.A@cOAh.AOAn.A`NAt.AT}NA({.Ay1NA.A,MA;.ATMAٚ.AcAMAէ.A`dLA0.A ZLA.A@NQLA.AFLAk.AHKA3.A^jKA`T.A@KA .AJA.AJAh".AJAȱ-.AJA8.A@ JAD.A JAh@O.AJAZ.AJAe.AƤJAx q.A&JAI|.A3JA~.AJA0.A OJA.A` JAh.A`]JA0.AjJA.A@NJA.A 0JA0.A4JA7.AXJA(:.AIA`%.A IAC.AaIA.AIAx.AIA(.A`IAs7.AIAXF.AqIAPU.AwTIAxzd.A`5IAHs.AIAh.AHAŏ.AHA0ǝ.AHA@.AHA.A aHAe.A@8HAp.ABHA9.A@pGA(.A^GA@.A qGA .AGA.A\GA .A39GA.A,GA.A$FA '.A?FA +.A@ϷFA0.A9FA4.AFA8.AyFA^<.AacFA?.A KFAB.A3FA"E.AFAPG.A FAI.AEAJ.AEAK.AEAL.AaEA8RL.A@}EAL.A`bEAK.A HEAJ.A-EAI.AQEA(mH.A@DA0&H.AxDAEH.ADAH.ADAI.AH{DA K.A?]DAL.A?DA(N.A;"DA]Q.AgDA;T.A &CAPxW.ACA[.ACA.a.AÚCAf.ACAol.AMgCAq.A@KCA0Hv.Ag/CAz.ACAp~.ABA,.A`BAR.AʹBAP .AdBAPZ.AtsBA7.ARBA.A;?BAX.A[0BA@.A cBAR.A@AAPH.A@AA8|.AAAw.A@,AAp s.A`mAAq.A`mAAm.A@AAh.A@xAAЄd.A^AA`.ACAAr].AA(AAhZ.A AA0W.A`@AX.A@A@Z.A@A \.A`p@A'^.AXG@A`.A0@Ac.A u?APf.A@5?AXj.A?An.Af~?AFs.A W?AHx.A;2?AHP}.AF ?A0݂.A&>Aˆ.A>A.A>A.AF>Ac.A`K~>Axi.Aa>A.AB>A.A>Ah.A@=A(>.AF=A@.A`f=A1.Ao=Ap.AD=A ^.A=AF.A<AȺ.A <A(.AА<AA.A`c<A.AQ<A".A A<Ak.A@1<Ax.A?$<A8.A <A(l.A_ <A.A ~ <APf.AD<A~.A;A.A`;A.A@;A A .A;A.A;A.A;Ax:.A<A3.A[<A(.A<As.An+<A.A`6<AxW.A` A<AV.AJ<AȤ.A`R<A7.A`vY<A .A@^<A".ANb<A&.A3d<A).AVd<A8#,.A`b<A" r4.Az:9A<AE9=.Aa$<Ad3OG.A#ò <AR.A;A/orq.A] *7;A.AE R<AE.A0<Av9v.A&tхZ<A~pf.A<A.A;<A0e.AE<AX!.A`=A(z..A=A;.A9/=AFJ.AL=AX.A`fh=Ag.A=A@v.A@5=A.A-=A@u.A@=A .A@,=A.A`)=A`P.A`v=A(.A` >A.A`>A(.A $>A0.A`->AU.AF5>A8$.A@>;>Aؽ4.A`v?>A D.AA>AL.A->AcT.Af>Af\.A A >Ad.A`=Al.Aa=A{u.A=A"~.A=A.A =Ahď.A=Aȶ.A=A.A=Aƪ.A=Aڳ.A@y=A.A=A.A@y=A@.A=A@.A=A .A@=A .A@>A`T.A B>A(.A`)>A.A8v>A.Ak>A+.A@`c>Ap.A\>A`´.A@pW>A.AT>AЕ.AR>A.AR>AHr.AT>A0T.A`X>A`%.A^>A.A`be>Ap|.Acn>A.Ay>A`C.A"}>A .A>A.A9>A`T.AD>A$.A@|>A,.A Zx>AH25.Ajr>AX=.Aj>A cE.A a>ANM.AV>AU.A@J>AV.A6>>AVX.A2>AxZ.A(>A8\.A>A_.AK>Ab.AV>Ae.A@ >Ah.A>A8l.AV>AНo.Az>As.A`;>A\v.A>Ay.A@ >AP|.A@>A).A`v0>A̩.A`O>A .Al>A@.A 1>A.A@֑>A.A>AH.Ac>Ah/.A`K>AE.A>AH?j.A>An.A@N>A.A`>Ap.A<>A.A >A.A lc>AF.AD>Ah@u.A=AW}.A =A@d.A=A(.A=A.AA=A誘.A=Az.A`(=A.A= =A@W.A<Ak.A<Apl.A`<A.A@<AH.A<A.A<A%..A1<A0E.A;AHna.A:A(-.A:Aª.AHe:AH..A@f:A.A@2:A.A q:Ak.A 9A.A9Ak .A9A..A`;%8A6.Ap7A\.A 5A([.A@N5A(\X.AR5AT.A4A8R.A@4AhT.A#3AY.A2A[.A]k2Ab.A#2AX h.AR2Api.A!2A`l.Apq2Ahn.Ap2AXw.AR43A8.A[3A`a.A=R3A).A =3AL.A2AH\.A H1A=.A0A.Ap0AX.Ą0A R.A0Au.A30AW.A/AG.A`/A.A3O/A.AN/A.A@p]/A.A I/A.A=/AX .A.AQ#.A@.AH.7.Al.A(G.A3Q.A(T.A \+.Ai.A]-AH.À,A.A*,Af.A`),A8.A+Az.Az+A.A2+Ap.Az*A`a.A*AG.A@*A}.A Q*AH..A`)AG.A@)A“.A`)B)Aњ.A#)A.A")A`.A1)Af.A2)A`a.A`)A).AR(A.A`f(Ah3.A(Ap.A`f(A`.AY(A8.A?(A.A5(A.A (A).A'A0.A&A .A@\&AHܭ.A1%A.A`$A@ן.A@h$AL.A`@$Ap=.A`$AǬ.AR#A.A]#A.A=#A.A#AW.A@"Au.A^"Ap.A3"A.A"A.A"A0..A"AL.A!Af.A`)!Ak.A`[!Ar.Az Ad.A`f` AV.A3 AN.A \A8I.ADAu;.AࣼA#<.A@ZAp@.A A:.A@Ak/.A AP!.A@gjA.AA .A[Ap.A KAH.AA.ARA{.AVA.AAP.ARAX .A`fA.AzA.AA8.A xA.A@(A R.AA.A`)XA#.AzA.A`)XAQ.A A`v.AדA83\.AAPS.A`AP.A@GAp=I.A@AG.A A@.A]=A2.A A&.A`A.A'A.AAz .AA .A3A.A@XAH".A HAp=.AzAX.AA@%.A`)<Ap(.A \A%.AAL.A`)rAk .ASAp.AH/AH.A@Ap8.AAf.A͜A8.AVA .AAp=.A`A.AAH.A@ A.AtA.A`DAp.A@AG.AzA.A(A.A= Aǵ.A`)Z A.A[% A.ARV A0.A׍ Ah3.Ap A).AQ A.Ac A{.A`) Ap=.AAH\.A wAp.AA.A"AB.A |A.AR~Ak .AzAH .A HA .A=HA.ARAX .AbAG.AAp.A=Ap8.A@.A.A@A.AjAu.ARA.A@AX .A`fA.A`hA.A@XAH.A@6A.AAX .A]Ap8 .AzA.AQ AH.ApA .A*A.A`)A8.A̰Az .A`A#.A@<A@1.AzAk?.A@A`O.AHAQ`.A`Ae.AHAul.AzAQo.A:Ax.A?Aƒ.AAL.A`)Au.A A`a.Az:Az.A A(\.AAk.A \]A.A`(Az.AA~.AQAG.A`A0..ApeA`a.AHA=.A]7Au.AA.ApA`.A AL.AA.AHA.A+AL.A qA.A Ak.A@AH.A@A.A@Ap.AIAX .A$A8.Az Ah3.AHA.AAf.A}A8.APA.A`A .AAp .A/A{ .A HA{ .A@8Ap.A HA(!.A HAu0.AMAk=.A \AHE.A=A#`.AAr.AGA(y.A \Az.A`)A`{.AaAQ.A6Ap=.A]Az.A`fA( .AiAG.Ap9A.A AX .AA`~.ARAp8t.A`éApkg.AzA^.A[AZ.AAP.A[AC.A"AL8.A=@AL$.A VA).A=\A.A=JAX .AR(AX.AA.A HAL.AcA#.Ap!Ap=.AA+.A \kA5.AA:.A`AD.AIAzR.A@AT.A3AuU.A`XAf^.A/Azn.A3 A`.AA0.AAL.A A`.AA.A oAP8.A GA#.AR&A8.ApAB.AA0..AzAk.A@tA .AA` .AA.A`)A .AAh3.A@gA=.A`)A.A @A).APA=6.A HOAK.Az*Ap.AA.AUA.AAk.ARAPN.A<Ay.AAأ.A`LA.A3A.AA`.A`)PAz:.AAe.AA(܁.A@Az.ARHAL.A A.AA.AA+.A̼ALU.AzA@Wz.A`A8.A@AQ.A`A8.AA.A@gA.AA.AA&.ARA@.AA)N.AAB_.A@gA~.AAp.A.Aǻ.A)A`.AA(\.ARA`.AAh.A3+A7.A9A0.d.AA.AAk.AA.A`÷A.ARAL..ApAkA.A@jA8b.A^A0.AzBAHܠ.ARA.AA .A QA(\O.AA`a.A`VA.A@AP.AQA.AeAB(.A`)AJ.AA(\t.A@ A8.A Ap.A qA.A@|A).A`@A$.AAأ2.A@AH\G.A`jAa.A`) Aj.A`Asm.A@Ap.AA8z.A+A8.AzA#.AR0A̱.A`A.A#A.AA.A9Aأ.A`|A.A=A@W.ApAX .A`vA.A Ap=.AzAp8.AA.A5A.ARA( .AAf.A5AQ.AA.A`)Aֆ.A Ao.AzA`.A 9AT.A`fAH\K.A A6.A=dAH#.AA.A }A#.A`hA.A 'AP.AA(\.A[A.AAp=.AAB.A A{.A=dAr.A SAp=i.A`)lA@Wk.A@ AQm.AAf.A A)S.A6AF.Az2A=.AAV2.AA8(.A AG .AxA.AAأ.A A8.AA׎.ATAp=.A@DAXw.A \EALp.A@ZAkw.AA{.A \Ap.A%Ȧ.AkA.A@gAt.ApAb.A̎A`U.AmA@G.A@g@A8.AAu%.A=AQ.AR8A83.AAp.ApCAX~.A AU.A?AE.AA0/.AA#.ARA@.A3AHa.AA.A@A83.AA.AAy.A(Aa.AAL.AA9.AAG.A@AP.AMA.A HAp.ARA`.ApA.A \mAd.A q APC.AA &.A q[A# .A /A0.A=.AQ.AMA.A@lA`.AkA8u.A GA@W`.A ApN.A A#J.A0Ap=K.AAJ.AAXP.AzDAh.A3A8s.A A(t.A3sA`x.A=,Aft.AA`u.A=A`.A`AG.ApKA8.A H;A`.A HMAp.A׋AL.AA.AA`.AzA`.ApA&.A=pAfC.A AAJ.A3A8N.A=A#J.AAO.ARfAp=O.A` AK.A \A(B.AAp;.ARA8<.A`)A@.A@ AXL.A@ }AP8\.ASA)m.A |fA83.A@GA.AAp.AAu.A@"A`.ASAp=.AדAQ.AzA .AzA84.ARAC.A'AL.A=PA[.AׅAu.AAǐ.AA.AA(\.A Ap8.AAB.AA.AR`A`a.A3A8 .A@A.A]EA؞7.A HoA`I.AAX].AAx.AA.AA.AA.A=FA%.A@AX.A A|.ApA.A \A«.A=A Ҹ.AA.AzAu.A@ sAk.A@\AX .AkA.A AX.AA R.AA8 .A=4A.A A*.ARA89.AA?.A^AGB.A@AH?.AA9.AAH.9.A3'AK.AyA)d.AQA`a.AWA0..AAP8.AñA.A`A.A]AX.AAG.AA.AA!.AA/.AAI.AAT.AAd.A GAy.ALA.AwA83.A@A.A@ A.A`)LA`.ApSAz.A@ }A.A HA).AA8.A Ap8.A HeAp=.A֕AB.AA83.AAX .A)A.A=tA(\.A`̲A.AzAu.A HAp8.A`̲A.AsA$.A@A=A.A@Al.AA.AA¨.A@gnA.A?A.AAp.AA#.AzbAA.A`) A(T.AAXY.AAأf.ARRA8.ARAB.AqAH\.AA.AA88.A( A6.AýAT.AAus.AzJAG.Az*A#.A3AW.AzA׬.A \-A.A \AL.A=2Ak.A A#.AA.AAP8.A~A.A HA`.ARbA8.A=A.ARA.A5A—.A@ A.A@VA.A@A|.ARAb.AAP.AR|A82.AA.AAQ.A`pA`a.AA.AmA.AA.A=VA).A]}A`h.A A`%.A@ oA.A`)Af.A=θA.Ap7Aƒ.AݷA([.A|Az2.A3A .A A.A`)&A.A޵Ap.A`Ap.AҴAR.A@GAu.A |0Ak.A VA`.A`)Ab.A@JAp.A`Au.A$A`.A`)AW.A Ap.A@Aǿ.AGAu.A]Ap.AAP.A1A0.A@tA.AAh.AAX.A$A.A3eAk.A׫A.A3A.A`<A@.A \sAk.AA=.A`A R.A@g԰A(.A@A8.AA.A@APf.AAL.A`)A83.AA.A3A!.AA`,.A@ A8:.Az^Ap8K.AAF.AA`aC.AzA=.ApA88.A`fбA0.ADZA,.A`fAp=+.AGA0.A]A(\:.A2Ap>.AeA9.AzAk0.AQA{.AWA.A?A.A Aأ.AA R.A3WAǸ.A3Ap .A A0.A@ᆰAp.A 1AB.A`A.AHAP8~.A AAg.AAP.AۮAB..AA%.ApAH*.A`XA5.A`)TAu6.A`fAG8.AGAu&.A=ȬA.A A.A3A8.A AH.ARFA.A \A.AҩA.A`vA.A=A.A= AH.AAL%.AóA( ;.A sA(=.AiA>.AzPA4.A?AG$.AGAp8.A`fTAX.Az\A.A33Ak.A`)A.A`fAp.AGAB.AjA.A3WA.AAp8.ARA8.A[A.AA#.A@ ٦A'.A@A/.APA(=.ARAuJ.AAX.A@>A8Z.ArA83\.A`ApS.A[ABK.A@AfH.A \A(@.A q7A3.AԥAp1.AQA'.AAp$.A \ŤAp'.AtA".AA.AA.A`̾A.AzA.AzzA`a.A qAp.A(^A ҷ.AGA̮.A A@.A kAk.A[Aמ.AA0.A8AHܜ.A8AG.A@ጞA0.ApEAB.A@A`}.A ĝAu{.AAy.A@:Akt.AzA؞r.A`)6Ah3o.AΛAQ_.A`f@ABU.A=ʚA(\O.A@vAPK.A`f6ApH.ApA؞C.ApAB8.A`VAP/.A`fA&.A^A(.A A.ApAf.AjA.AHAX.AA.A@ A#.A`ŖAut.AAe.A qAR.A q_AG.A AG@.A`f̕A6.A [A؞$.AA.A 3A0 .AӓAf.AAp.AJA`a.AzA.A[ےA83.AޒA0..A Ap.A ϒAp=.A A.AuABz.A>AXi.A@6AZ.A3AV.A AU.AAP8>.A ˑA8,.A@>A.A`ÑA  .A`NAH.Az*A0..A`A.A A.A \Ap .A@Aأ.AzA¹.AHAש.A ݐA.A@ A`.A`)A@W.AzpAh.AIA`aQ.A 1AA.A`f$Ak#.AA.A |ȏA.A3A.AGoA.A`TA8.AHA.A=Aq.AGCA\.A_A0.?.A=HA(5.A]A$.A3A(.AAH.AA`.A3uAP8.ABA`.A=Ap=.AQA .A`)Av.AQ܎A)b.A@ᘎAP.A@ZAh3?.A 1A..A3AG.AzAp.AAz.A HAP8.AA.A@ḌAp.AA.AaAX.A!Ay.ARAWU.A`fAk?.A@GъA'.A`̀A=.AAp.AӉAu.ApAu.ApWA83u.AALS.AA?.A A".AqA) .A ZAH.AMA8.A \A5.ARaAPf.AeAأ.AfA.AVAu.A&A.AA(u.A`)ԇAp=d.AAQ.A=A@.A@ᖇA)*.ARA .A@ qAP.ARDA.A`A.A qӆA.AzA.A``A}.A`)8Ai.A`),ApF.A qA@W".ARA(.A`fA83.A A8.ARAG.A ݅A8.A3A#.AA8.AcAfe.A^AX O.A`PA=:.AGA).A3SA(\.AࣂA.A3A.A`A0.A=nA@W.A=@Au.AAk.AԄA)I.AAX 9.ÅA(.AhAP.A /A#.A`fAW.AA.A |Au.A`f AB.A%Az.A$A(\}.A A8i.A`fAK.A3AG7.A dA1.ApGA*.AA'.A A.AAf .A@A{.A`iAȢ.A`UAۅ.AjNA6R.AjXAhB.A_A,/.A'AԺ.AA .A3AB.A*A0..A :APy.A (VAn.A{A0.c.AAH.AR҄AP84.AAk!.A`A.A=ބA.A`AH.AoAB.AAA.AQ6Ap.A@XA-o.A`AHc.AA`^.AĄAY.A=ބAD.A3AH.A A).AA.A`A.A@A.A`fA.AGAp .A@A@W.AτAB.AބA.AA.A@A.A4A).A3IAz.AAp8.A@GA.AӅA8.A@څAB.AA.AA(.A`),Ap.AXAt.A뉆Ak.A`)A;d.A ؆A].AzAXM.A}A:.A A8 $.AyA.AوA.A^ωA.AñA`.AAΑ.AOAHY.A)AB.A`T{Ax.AA.A ÏA .A`sA.AYAH;.A`A;~.A@ȔAPz.A`yA~.AsA.A wA@`.AA.AA8.AA&.A AAR).ADA(.AAH&.AA0{8.AVA`<.A^Ah@?.ABAI.A@vAN.AkAK.A 8zAD.A@PIA D.AA>T.AA`.A OƦA k.A^A8f.A`DAZ.ABAQ.A q#AW.A`A0e.A`ܠA0;g.Af4A`q].A`A0R.A4FAP.AG4AuU.AT\AV.A AD[.A&AVj.AXA@r.A{A4{.A`bA.A/A1.A'A.A`tA.AMA.AƱAh.AH^A.A삳AXl.A`9AX.A`mA!.A Ad.A`[A7.A⻸A.A`K AH.A8A .A`BCA.A`.A.A/A.A5A .AAz.A<Ak.A`AQd.AvA8Vc.AX?Ao.A 휻Aؒ{.AV"AXv.A͉Ape.A`A5.A@lA.A A-.AZA.A@վAx.A ޿Ahg.AA9`.A`A8X.AA^@.AAs".A8A .A&A 4.A l A؞.A6A.A ZA".A@A6.AiA).A`A.AlA.AxA.AA .AA p.AApxp.A@>AwY.A`A(F.A A>.AAB.A AC.AAG.AA(S.A^A,V.A Ab.A Am.AHpAz.AA.AA.A@A@5.A@WAH\y.AiAV.A AA(0.AA` .AMA`.A ەA .A?AC.A0AH.AfAׯ.A AX4.A-A^.AA E.A Al .A` A.AApA.ANA .A}A؍.A uAe.A,A\.ADAH2W.A`mA?N.Aj,AS.AHOAc.A A.A`]A@.AAA``.A gAp.AjyAS.A EA.A+Ah.A@A9.A߽A`.AMAs.ANA0.A@oAD|.A2TA0s.AA਋.AԽA Ά.AA`B.A@`HA@O.AuAP.A} A8.AաA@p.AA8.A AX.AA@.Ah9A.A J)A@q.A`qAp.AzA>.A`cA8$.A[A@F.A$Ax.A$A&.AA`.A Aw.A֠AHr.A uA.AtiAxi.AAa.A@GA\.AA {.AEA r.AnAa.A A0Y.A"AG.A5AЄ?.A [A/2.AFA.AlA`.AැA .AA" .AA;.AA@ .A HAoG.A4A`~.A@A`r.AA .AA.A@Aث.A#A.AsA.A 8zA{.AlA$M.A,@AJ.ALAE.AahA̞.AtA.Aj,A.AA0.AQgAh.APA.A+Ax.AA.A@A .AA6.A[NA@.A`]A8xX.A Ax~R.A@AD.A3A0_.AAm.AdA.AAi.AA0.A;YAp .A@EOA*.AӝAm.A@qAk.A{A؁>.A?A?.A@ihAT.AA 0.Aa8AHn2.AAP/.A{AhL.A4A.AnAh.AA.A MA .AA}.A A>F.AAXF5.A APZ.A@iiA.A 1/A8.AB APkc.A(A.A A =.A;AZ.A6^A.AbA.AANl.AA".AB8A>.AEA.AXAu.ABAȔ.AॺA.A`A0&.AhOA.A A.AsA.AοA{.AAxf.A sA{.AVA3.AAP .A@JAx.A Apxx.A@#u Ař.A` A1w.A A0.A= AL(.A A.A@`" A@.A A`G.A[r A`O.AB A(:.A9? Ad.A`d APk.A1 A?..A Ah".A`fM A0:.A}$A/.A AJF.A@%A].AJA].A}$Aȩ.A AP .A@A.A`TA84.AGA.A`AF.AA(`.AAH.A!Ax.A-A٢.A"A0&r.A4A@dv.ABrA(:.A ;A.A;aA@u.A@yAC.AA@>.AAn.AעAr.A@Ap.A@rAf.AA.AA.A A0H.A9A@=.AA@F.AAA.A`AP.A`AJ.A@A.AྋAgs.AXTAxK.A AX.A@,"Aؾ.A`A8'.AMAh.AA Q.AqAЄO.A`]jALU.A`ZAPM.AҟA<.AA *.A`AC.A(A.AѺA .A;^A!.A2AV.A A؎.A[A <.AAu.AA.AՔA.A^XAPo.A`շAV.AvHA0y.A`Ab.AiA#.A@NA.AA.AQAh.A#Ao/.ArAP>.A#Ao .A"A .AA.A3A.A<,A.AA@ .AOA.AA0]'.A Ai.A ~qAi.A`KAh.AhA..A UAPHT.A OAsx.A Ax .A@AB.AA8o.AAՖ.AaA+.A@ϵAX.A &A.A A.A!A.A0A.AcA.ARA[.AA .A JA .A@|A.A` bA.A}YA.ABA('.AzA8.AfAM.ATA2:.A \A1;.AզA H.AA1O.A A =.A螙AC.AXApS.AXXAX.ACA0L.A?Aɲ.AԌA.A`mbAƹ.AdA(˫.AhA.A_ڈA`a.A⭈A`B.AA.A`;A.A@rAs.A@yA ^.ANAR.AUAXP.A AE.A`;:A .A TAXy.AˁA,.AnAAx.A}A.Ax}Ah.A_%~A`.A8xA.AjAM.A AP8.AA.A (UA.AxAh.AAl.A`AP.AwKAK.A*XA.A_|Aݗ.Ai+A% HX.A;~AF'.AZtA~қ.AJAzf.A7jA.AVMAK.A[A.AGA.A-~APkh.A@#ՎA7.A@ŽA!.A@N@A@.A` DA.AjzA.AVAȶ.A?đAP.A Am.A`A(h.A@+AH:.ARA@.AQ^APD.A@-A8R.AࣂAp8F.ArA@WZ.AA`l.AЍA~.A@API.A`Ak.A \A.AAأ.AAA.ASA.A<>A.A߉A(.Aj`AP.A@yA #.AAB.AG׈AW.ARA.A һAؽ.A'AL .A AL.A@ᾆAK.A͆A: .AA *.A`eAP@-.AMA0..A7Ak'.A@GمA..AAX0.AօA*3.AA=.A@VAP@.A?A3.A)AX =.A=΄A>.AA8D.A@nA(\N.A`A0X].AvAPkl.A@Aht.A 뒃A`v.A`zAn.AmAx.AA(y.A`m Ay.AAp8.A3A`.AR2AȺ.AA(.A*A  .ARAc.AVAZ.A=APP.AൢA|C.A@!4A8.A`vA 5.AɃA`v/.A A,.A/A$.A·A".AA R0.AA '.A(Ax.A@A.ABAp.ArAx.ATA).AAl.A AH.A`ӘAX.AmA.A0AP͹.A RAǮ.A]ωA`ӑ.AӊA.A4ՊA@.AAp=.AA(.A|A`P.A`jA|.ANAxr.A@A0o.A JAQy.A A`p.AHsA@n.AA@Be.AA88Y.A@%AH.Q.AQۍAXN.A|A@W].AG AW.ATAA.A@A?.A@1A;.A QA3.A`fA%.A3A.AvAf.A AX#.A`̼A83".AяAk,.AߏAL.AGYA.A`ËAa.A`ËA8.A෾A(s.AۏA0Y.A_A(D.A`cAx) .AA.A%AH.A'A.AAZ.A:A-.A`6Ah.A |VAv.A`lA0.A2A.AaAW.A jAHn,.AAv.A l{Al.A/{A.A ~ZyA .A`tyAPx.A@xA@q .A=xA].AtA`T.A |OtAH.A |rA.A@3sAA.ARrA.ArAxX.AUmApU.A`pAJ .A@`qAXW.A rA .A ZrA.A@:sA.AsA(>.AuA\.A8uA.A / A.AAr.AwAF.AقA8E.AB|A0d.AXAHٿ.A4AXǿ.A PA.As1AH.A`vkA0.A OrA(.A ɰA.A`~AXW,.A۔A.AmAo\.AAH#.AkApV.AKA(8.A$2A<.A`AX5G.A2A.A`A-.A@iA.A`A8m.A&AO.ApMA`ؽ.AQA8.AAp8.A[A뼽.AzAϽ.AAŽ.A`̠A߽.ABAP8.A@ AHb.A5AzN.AIAo.A`) Aأ.A׋Ap=.A MAk.A෤Ak.A=A#.AֵAW0.A AkF.A뉲A 9.AAp.ApPAӾ.A`oA0H.A A1O+a'.ARڹA}.wA.AAx^.A`vA .A`hA.A͞A5N.AAww.AA.A@CKA`.A J&AH.A`oAu.A AT9.ATA.A`KA.A@Ac.AAh.A`OAa.AtA8V.A`HA3.A^AhbV.AAE.A&A#.AA@5.AA.A A.A`o2A.A`nAJ.A@,A.AcAi.A_qA .AJA .AN$AX.A@kA.AA(W.A{VƿA?.AN*A Da.A#hA@.AA'7 .AbA$P.A#WtA2qa.AaiA.AAk.AsA0&.AN$A.AA胥.A)A.A@AmY.AAppM.A AH.A@A`=.A CA .AMA^.A@A˽.AZAǾ.A(A؟V.A0Aο.AAֿ.A@Aο.A`m5A@.A AP^(.AWAS2.AgA.A@A.ALA.AAp<*.AvA4.A?BA0Ժ.A`A(~.A A&.A+gAЦ.AAо.A@AH2.A@`A˾.A ~A@䫿.AAH!.AaA0.AAJ.A,GAhb.AsAx.A`ôA֎.A86A .AvtAr.AA(J.A@A=.A`A .AQ!A@).A@`AX4.AʺAXb.A "ApyP.AA?.A J*A.AgA3.AQ*A@).Aj AȔ.AA.A}Aܽ.A*qA5.A`]ֺA.AA^.AA.AවAq.A*q^A}.Ad:A0t.A?r(A.A@`A0.A c Ak.AA.AA.A޳Ah.A A\.A |A8.A@ȯA(.AݯA(iK.A@N̪AA H.At>A$#C.A5ޟxA9$i>.AyADb18.A-8dAh?1.ARAU.A`A.A@AQ.AGA.A6Ak.A١A.A`{A0l.A|YA.g.A#GA#a.A9:d8Au[.A5-AU.AV'Axp^O.A%AxI.A&A3%.A@Axʺ.A?A.AA`S.AAPڋ.A@A4q.A`Ap.A~APx.AM٣Ad.AiAL.A`AH..A=PAɺ.A(A>.AZAu6.AׁA(\v.A@NAl[.AKA>.AAp!.AٳA.AAHP.A`A.ANA(ܣ.A@Ag .A@WA8En.AԖA@Bٹ.A`(A.A1A.A Aq.AAx.A!A<.AQA.ArAX.AorA.ArAZ.Ay"lA6.AHRkA.A |fA.A@%eA.A`{eA .AgeAؒ@.A`dAS.A`)LeAY.ADeA=t.AfAi.AࣦfAi.AfAp.AfA8.AVfA.A`6fAG|.A`ffAQa.A@BeAj.Af5eA tg.AQeALV.AÃdA`e.A=DdA}`.A7dA`.A6dA.A@scAhb.A cA(.A4bAН{.A 8[eAȇ!.AfAذ+.A gA茌.A`eA.A-_bA.A`HbA`e.A`aApI.AaA k.A@iaA.AR`Axu\.ATaA`.ADZaA8u.AQbAn.A3bA@ƚ.A2bAHmZ.AcA.A@eA.AykeAl.A@cAH\k.A@ cA֞.A@cA.A`cAH'.A @eA$1.A|eAߋ.AtbA.ANbA[.A`aA}.A caAXg.A aAH\k.A`DaAx).Aע`APQ.A2`Ad.A`_A.A@i_Aʋ.A؂_A0.Ax_A.Ac_AP.A+_Ah.A=8bAe.AAbAf.AdA8.AGdA.A@ qbAp.AaA.A`fzaAP8p.A`.bA`Z.AࣜaAp\.A \aA.A_A8V.A_A6.A ^AbAE.A bA;,.AbA^;.AbAH)J.AbA].A bAk.AbA}.A`bA.A@bA ߜ.A]}bA8.AywbA@.A4fbAȥ.A3`bAR.AFTbA4.AKbA(.A<.ABbA(\J.A`;IbA_.ATbAi.A \YbAؼ.AdbAȩ.A`ebA().ApbA0.AqbA(z.AࣁbAw.AΈbA0w.AbAo.A (bA.A`bA8.A@ bA.AॳbA&.A ¹bA>.AbAD.A}bA Q.AbAa.A`bA8Ip.AbAج{.A`bA{.AbA.A bA0;.AbA8.AcA`2.A@W cAj.A`)cA5.AFcA.A#cA@}.A@+cAh.A2cA@$.A;cA0L4.A3DcAX>.A /IcAM.A2QcA].A`TYcAl.A4acApx.A3gcA .ApcA.A@ucA.A΀cA .AઆcAp=.AcA0..A@ idAG.AhdA.AzbA.A bA .A`f.cA.A \GcA.ABcA0.A bA8.AbA@$.A ۥbA{.AơbAHp.AbA`.A`bA@L.A@`bAH.AabAH9.A܀bAP&+.A@pybA* .A sbA.A kbA".AebA7.A6^bAxK.A/WbA0L.AQbAȤ.A@>KbA =.ACbA@.A@p?bAp.A`6bA( .A`0bAE.A?*bA~.Ad"bAu.A bA`i.AbAȶZ.A 8bA0{O.A bAc?.A`mbAl5.AdaAx((.AaA@.AaA0T .A$aA.AaA.A aAk(.AuaAu".A}^AV&.A@^^A8+.A`)T^AE.A Z^A(V.A _^A[.A`^AYl.Apd^A p.A e^AK.A`i^Af.A`;k^AX.A|o^A'.Ap^A .A`t^AT.Av^Ad.A {^Ax.A|^A].A Ҁ^Ah.Ac^A.A^A].A^A.Aj^A.AΎ^A(.A^AH,.A ɔ^A8>.AK^AF.A`9^A_U.A^A _^.A6^A(l.A@^APy.A@!^A`.AO^Ah.A ۮ^Ar.A ^A.A@^A`ص.A-^A.Ax^A .A ~^A@.A^A .A^AH!.AQ^Axd.A^Apx.A^AH.A@^A".A^A0-.AT^A<.A^AȃH.A^AU.A]^A^.A`^At.A-^Ax.A`^Ap=.A^A.A@^Ae.A^A.A^A.A`_AB.A_A.A@_Ap=.A@_A.A^AX'.A@^A.A_;^A^.A` ]A#.A \]A.A4\A8Q<.Aʬ\AWj.A#\A".AmT\A t.A)\A.A#\AP|.A`1\A#M.A`k\A.A@ϐ\A.A#\A@.AM]AX.AH4]A.A;]A$.Ad.]A?:.A ]AO.A\A_.A\Ax.AMv\A#.A@^\Apʿ.A 3\Ar.A[A+.A8;[AxE.AZAu.AUZAТ.AӧYAu.AxYAH.AXXA.AILXAP.A9XA .AILXA+.AʵXA7.A1YA`1[.AoYAd.A`YA.AYA8.AsZA +.A [A.A[AV#.A`v3\AV.A\Ap.Ah\Ad.AZ]]A.Ai]A8.Ai]A.A ]A.A /I^Aȥ,.A`^AxK.A`^Ahd.A6h^Aw.A /I^A([.Aܙ^AH?.A@5_AXl.A_A .A`A.An`APM1.AF`ATP.A^`Aw.A _AJ.A`_Aչ.A_AG.AHL`A`.A `AP2.A Z]aAV.A@%WaA .A aAչ.AxaA .A+aAX.A `AV#.A@,`AxE.AxaA`m.AqaAp .AaA=.A.AcA .A9dAp=.AcAL1.A OcAZ.A 8;cA>.A OcAp,.A`GcAx.A bAr.A 8;cAx\o.A^cA.AhdA.ABeA.AdA)<.A QkdA f.AndAH\o.AcA.AlcAH.A 8;cA.A bA`2..A@>bA0,.ALaA9.A#.A@kA1.A@dkAJ.A kAQ_.A[lAPt.A'lA.AlA.AmA0G.A&mAP.A/mA.A@ImA .A QgmA'.AmA.A`mAp.AmA9:.AmAP.AʠmA@~p.A tmAHL.A mA .AlA7 .AlA^C.A`lA0X\.AwlAn.AulA.A`{lA,.A=lA.A$}lAhs.AFlA.A lA.AlA.AclA7<.AplA0R.AlAHg.AElA.AmA8ڍ.A@W:mA ߡ.AcmA^.AimA*.A`zmA.AmA .A mA.AnAH{-.AnA=.A`$nA #O.A SnAp].A nAp_f.AwnAXn.A-mA(:.AmAp9.A nA.AG@nAX.A PnA .AdnA).A$nA0$.AnA0e%.A nA4.A nAI.A #oAU.A;oAhoe.AKJoAXo.A-oAXu.A`ToAp|.AzoAP.A0 oA8.A>oA1.A2oA͒.A`vpA@.A dpAĠ.A@pA:.ApA0.A`m+qA.A`dwqA.A4qAj.A}%rA.A@PArA.AΞrA(.ArA89.A=rAH.A A8sAWX.AnsAo.AHsAx.AsA.A&sAh.A@rtA(.AtCtA0G.A itA.A tA.A tAHz1.A'uAPV.A@]uA@Fn.A"uAz.A3uA@.A uA 0.AuAg.A3uA.A`uAp8.AuA[ .AIuA#.AvAe>.A@TvAW.A ZvAXh.A"vAHy.A0vA`e.A@g wA8 .A7wA軷.A&PwAx.AcwAP&.AywA 0.AxwA.AwA%0.A@!wAJ.A KxAY.A `xAq.ATxAb.AmxA`×.AĴxAa.AxA..AxA.AvqyA.A yAP.AyAhM.A`TyAH.A yA".AyAHa,.A@yA 3.A_yA(<.AyAUH.AyAM.A`yAZQ.AXyApU.AzAV.A3zABU.A`YzA0*R.A zA0TP.AzAP.A{APU.A`F{A0\.A{A8h.AU{A v.A`|Aȩ.A JC|A.Awx|A@ӝ.A@i|A.A0|A:.A }A|.AI}A.A@b}A>.Aj}AC.A`}Apr.AA~A.Av~A0.A ۚ~APk.A ~A.A@~AX.A ~A虹.AAP.AA.AL*A`.A@>;A8 .AJA[.A+jA`e.AA.AA0.A !AH;.APA0L.AAh].A@ Axs.A,A #.A ̀A.A@AX.AƁAt.A-A0.A 89AP@^.A jA].A@A.AHA!.A`[A>.A$Aa.AAP.AgA?.A@A.AqA`.AhFAX.A)APM.A)Ap8.A@Awj.A`TAƨ.AAp.A=A .A{A-/A`^Ap9W/ALA{/A`?A_/A#AH/AA /A@rAH{I/AـA|/A/ˀAr/ADƀAk/AƀA0f/A@A`6/AAN/AA@/A_A /A4ـAG.7/ATIA=yP/At yA Ei/AAu/A܆̀A} //Aj.AIU/Ak+AyJbg/A;8TpA/Am]Ay_/AX%A /A{A.R/AsA./Ag4Aϧ/AC(݂Ax</AvWYA:/A%rATJ/A.AXŽ/AFIɜBA"/AQA,/A\ACM/A^zA@/AuAXd@/ApACb/AŎmAl/A shAֈ=|/AAfAE2/AvIA i /A|HA9O/AoHA/A=a;zGA˶/AyZFA6( /AàHFA¢ /AJcEA > /A+@EA{\M /AoPDA욣X /AڈwDA:߄` /Anv DAOd /A>CAe /A+CA qSe /AN\ίBAeh /AJU?ALW( /Az)?A /A,ĄO?AKѭ /AA(u /AF/٫>A /A9L>A ' /Ab=A /A 8=A瘍 /AP ,=A\rV /Aڢ<Ao5 /A@B<A3å- /AGUJ;AW /A/m;A(  /A];A< /Ah5:A4 /AT:A]% /AeV9A7@% /A"˧9A\ /AG8A ͝ /A2|8Ap /A`[7A~ /A5AM\ /A74AE /AK Z2ANyȘ /A# 0A(lM' /A.Am:"= /AN;-A~t X /A;g+A1w /A]/l)A.xڜ /APmh (AHL /Askg&Aq' /A@$A8b( /Aqv]9#A} 2` /AS2!Aw /A2 AQH7 /AFDGqVAYT /AbrAZ /AYņAIS /AA1 /AKhA뜞 /AԖA|V /AzG0A" /A~8A`} /A59A|(} /AZvA /A@A /A!A /A0Ax /Ah_A /AʏAAb /A Ae& /Aj_ AxK( /A` A/AAW/AA /A`UAH? /A`bA"r/A@POA@W9/AXA /A@%AX/A A8*/A`A]/A3A@$x/A@tAP/~/A lA/AA/A=A/A7A([/ACA(z/A`RAg/AA8/A@A`/A@A.N{/Aq^4AP//A9Awr/AչA$v0/A|\ A2R/ABAh(x/AA˱/A@AyS_/AdtAY/Ax<ALIX@/AZZ Aa}/A/A8)$PAw /ABȕ*PA@}/A-,PAHXx/AzU}*PAs/A&PAp/A`PA/4/Ah>چOA^==_/A .OA?/AM\NA)w/AvNAW9(5/A(HNAK.Ay(gNADA.A쓛 NAEk.ANA"`.A@NA5.AûNAg .AHn-MA\N.At0MA͝<.ApMAГ.A DMAp.AQ?MA\.ALA %.ANLAL.A3LA.Af.A7lrLA)3~.AeqLAF.AZLA..AcLAH.AjPLA.AWLA@ƞ.AhLA3td.AZKLAL;).ALA.AZG='MA|bů.A, EMA9Ur.Aɮ2 QMA#4.A-MKMA.AP4MAo].Ab MAh.ApVLAPwD.ÄLA,.ASKA +l.A2wKAI.A?JAW .A@nJA?.A!K5JAo˅.A>IA4].AbHIA04.AHvIAYA.A9^yQIA6R5.A UAXO.Ab>Aovo.A5% >A)N.AIz=AŅ@.AJ=A3mC.A?'Kp=AM.Ag- _=A8+}.AQ_=AEWu.A]q=A1.A@`=A7^\.A Wa?A .AZ9bBAf".AssDAߞ,.AB!GAqu.A@,yIAטB.ASKAI .AYNA3.ANi`PAZ!C.AlRA P.A4ATAQ .Ax WArs+.AQ/~WA ~.AXA}.A~ fYADäo[.AiGZAv9.Ap3[A .AdPO'\A}.A@P$]Ak;.A.m^A uk.Aȹ_A,t.AWx aAIRS.A.abA=.AUjcAY |.A$ eA=.p.AIcfA.A9gA .A iA4ڞ.AcZjAcqܩ.AwkA[.A(/lA4.Aw0&*nA,W.AØboA~.A3pA6e.A&AH}.A@WGAe.A`fAx)F.AA.A`ܶA .A`MAX.A uķA.A"A@.A/A j.A`KA 8.A׶A0H5.A@,A.AJA.A A.AkAx.A$AH/.A`A.A@A@>.A`)AJ.A@ʹAh.AzA.AضAs.AAd.A(2AP.AA $.A`6A`e .A@JAhM.AADF.AA.A@i*A55U.A'*%kAҠw.AUAp [.AAAV<.AVqA36.AWеA(;f].AUA`.AَAx.A@'AH.AR AM.A`,A.AFA.A?A`!.A`"A.AkA.A 4A.A)As.A|APo.AՀAIl.ARAH{M.A`)sA .AWA.A@/A>;.A=IA.A`A .AǶA(K.AɈAxG.A@[A8s.A_A'.A cAX.AAX`.AUθA%s.AA).A@AY.AެAh`.A A.A`KAг.A` ANJ.AHϽA.AݽAE.A As*.AYAO.AVAp[.A lA.A|Ag.A@ևAЮ.A 끾A@Az.AlAxT.A`iA02.AdA.ARlA.A`iA.AQAc-_*.A>"!AE &.A*Aj".A ֣ A.AA.A AX.A`9ϲA(j.A ZQA&B.A@AP.AA+.A(Ae.AVAۯ.AQDAϩ.Ac7A.A˷AhΠ.AQAPg.A/A.A%A.A2=AH16.A;[ARՓ.A9_Aa.A =cAwEo.A`ٸA.Ao A0.A`?Aэ.Az(A8.A%A.A`TA99l.AuxA+.A0A(.A@lAJ.AfZ핽A.A|A4 t.A ݮA.L.Aґg{A-%.A%+{A[.A[13EA.AA "ų.A YAGR$.AhzA6~ m.ACS jAbN.AK+5˺Aٺ"4.Avl:"A*9.AnpAZ>i .Aɜ|:A.A@A.AcőA7.AOb4A_ (.AA$>.A(r-ӞAF‰W.AHjA r.Ab5HAg.A z8Ab.A`o;A .AeYA`.A`mA{T.A=/xA'P.A@=8vA0Y.A gAٔV.A ]bnbA).A9ZQAG:;.A(5AK.AA(Z.A)I޵Atg.AtŤARq.Aג@eAy.A@ AxG.AA0ٲ.ArA୕.AA.A$A.A`޲A0;.AӲAʼn.A`庲AXh.A@Ay.ATAe.A .A@.A`A.A0ޱA.AaqA`.AAxi>.A@ΰA .AfA`.A |EAxv..A`A.AʜAxo.A Al.A`{Aٯ.AZЭAP|$.AAp.AF8A@.A@˫A<.A@rAP.A0Ah.A'A.A AH?.A͔A@J.A S A.A@PAp.A@yhAz.AAp<.AA( .A@P=AH.A`TAS.AǻAT!p.AAm>.A[V_^'Ayz.AX^Arf1.AߐA[.A<)#A;5.AućiAD`.AC2AAf;.AݿfAZ.AAo.A`m|A.AA`T.A l}A .A`aAXyw.ArA@z.A sA`.AOAx5.ACA t.AݰA0.AvA.AA.AIA`.A 8iA.A ZA.A2}A.AiA .A<A`%.AfA( .A`vA.AӱAd.A`ñA.AA.ABıAhs.ABڱA@.AEA8.A^A$.A A*3.A9IAp,5.A &A`C6.A,A8;.A0A`aJ.ASAhO.A`A P.A若A S.AiAHLQ.A`.A8I.A8_AE.A A`?.A/AH!+.AA.AQAp.A<ԷA .AͼA.A`A`.A޹A.AZAw.A>A.A42Av.A@PA8b.ABA .AR2AG.A@A.AkA`T.A`A؛.A3A0.A 8=AY.A,eAH.AuAx.A1A舲.A LAp8.A@]A.A`)jAV.A |}Ag.AͮAu.AA .AA.AA`й.A@-A؟.A Z_Ap.A=qAx-.A`|A.A@APo.A|A7.A quA@W.A-A`.AvA".AA.AAx .Ah A.A@eAp|.AAv.AδA@.AAȓ .A`TA8.A`KA.AA?.AQ,A(m.AEA.A A]AP .A \sA.ABAn.A&Apl .A sA`a .AA@.AsAK.A`A.A.Ap,.A`9BA.A TAp|.A hAp+.AkA( .A`)A..AA.AA.AAH.AhA.AA.AA,.A)Axh.A$=A.AhA.A@zA(.ADA@.AA#.A A0.A$A.A@A@.AIAo.A` )A.A`JAo.A`wA.AA8 .A`A.A A@.A@WA.A@AX9 .A Ap .A)A.AIA.A`zA".A AH +.A@ A9.A ZhA=.AA*D.A ZAH.A@AXN.AGA9S.A`KAT.A@iqAP.A`A8L.A` A2G.AAyC.AATC.AE^AF.A APO.A4AN.A@nA@1L.A[AbH.A3'Ax:H.AMA;O.AфAS.AձA0&T.A sAP X.AA0X.A+A(S.A>A8S.AqNAXS.A wA`X.AAh[.AAhb].AAs[.A|-AY.A@iXAxZ.AwA(a.AAKf.A`2Ah.A@Ai.Aa(A0Yk.A`TA(){.AA.A@A<.A A#{.A?Aux.A Az.AAz.A4A y.AuAu.A`[AH].A4APT.AAUH.A`],AGB.AIA@.AfAB.AOA0HF.A3AX4H.AjA`H.A`B APC.A:AL).A 7A$.A]A.A ҠAhs.A3A .A4;A> .AAx.AAX[.A Z;A.AA0v.AIA.AA.A CAp=.AwA.ARAP .A Ah".AA5.A@A.AyAhb.A'eAx.A@5A.AvA@.A'A~ .AJA.A XA(.A~A.AA.AAXh.A4A8t.A׳A`.A2A(\.AA.AAx.A@Ah.A5A.AzQA.A@plA.A@A.A ғA@$.A@ߟA0*.AAP.A Ac.A AH.AAN.AAѻ.AAhD.AA8.AAhs.A`AXJ.A@,A N.A ~Aȓ.A QA(-.AhA).AzA趨.AA.A@A(.A A.Ap)Az.A`AA.A6ZAܰ.A 8sAU.AqAQ.A AѰ.AA.AHA.A A88.A'AX.A q(A.A`@A.AOWA`.AnA.A AAX.A`bA.AAHa.A``AX.AUAX.AjA#.AA8.A`AH{.A ZAR.A:AP.A\AhQ.A~Au.A@A(.AAp.ARAʝ.A2A0.AhIA4.AtAB.A@AH.A@gAX .A3AX.A@Ahğ.AA.A&/A.A3LA@.AhA{.A`fA-.A`Ap}.A SA@Wz.A`A x.AA4v.A.A t.A=DAs.ASAȶr.A bAq.A qAn.Ag~Ael.AൊA@i.A •Aze.AAHa.AnAP^.A@ AP8\.AAH Z.A AMX.A Z AhV.A6A#V.A MAHU.AcAU.A`zA(U.A?AdV.AAW.A`KAHaY.A@g,Ah"].AL>A``.AOQAHc.AReAx)f.A4zA^h.A׏A$j.AA |k.A`ռAHal.AA0&l.A Axzk.AAp_j.AAAh.Ag.Af.A@DA8d.AXABS.A=AP.AA@N.AA`XM.Aq APfL.Ag=A(K.A`ZAL.AwAN.AATQ.AABS.A`AR.AACR.A -A_R.A @AR.A$SAS.A eAPIV.A@{A@dX.A@AZ.A`A(m[.A`AW\.AA\.A@ A\.A=A%\.ALoA)[.AAJ\.A A\.A@5Ah].AGA`\.AAأ[.AGAY.A`AW.AA3T.A AP.A >Ah3M.A_AH<.AA(-;.Ao&A&:.A'`A9.A`Ap9.AA9.AjAp:.A )AH;.AXA`<.AAkD.A@ AE.A+A{G.ALA0I.AmA88L.AAh3O.ARAR.AAoU.A`pAY.AAxZ.A`AZ.A`A[.AA[.A@AZ.A@9AY.A`SAX.ApmA0.W.AA(W.A"A8tW.AA@5X.AAX[Y.A-A`Z.A AXl].A@NA_.AA`ea.A`(Ab.A?6A8c.ADAe.A`fAxig.AAh.A`A>i.ALAri.A`"A(-i.AAmh.A6A4g.A-YAe.A \{AH\c.AAh3X.A A'U.A NApR.AAh@P.AA`eN.A@NAL.A:A K.A`\AH.A@.~Ah.F.A&AC.A`A?.A`2A;.AAL7.A`)AH1.A2A*.A (GA$.A`YA.A@gjA,.A rAH.A|Ap.A@χA.AAP.A?A(.AAأ.A33 Ap.A`\ Ax).A@ A`.Aɲ Ag.A A/.A Aj.A7 A.A@Ge A.A} A(:.A A.A#3 A6.Ayx AH.A  A.A A.A% A.A@CG AB.A^h A.A Ao.A@ A.A` AP.AM A.A Q AP^.A" Az.A> A0.A a An.A@ A.A AB.A AH..A Ah.A JAH.Ao1A~.A sRA).A=tAH.A`9AZ}.A`Ap{.A`Az.A AXhz.A@iAHz.A`'Az.A 5A8{.AAA(}.A`TNA~.A`ZA8 .ALhA.AmwA].A=Al.AAH.A<AF.AA.AA.AQAp=.A0A.AT A8g~.A@AG{.A2AXv.A c\AhQp.AAH.j.AͬAHd.AA~^.A;AW.Az4AR.ANWAuL.A@C{A0]G.A@CAHB.A6A>.AA@:.AA7.A <Ah5.A@ dA0*3.ÅAF0.AA,.A`AD).AA)%.Az A .ADA.AgAJ.A Az.A 8AB .A A`.A{A0.A AP7.A`A.A@A.ApA.A@N=Ar.A `A.A@5AQ.A A.A@!A|.A`)A.AA.AAXy.AApJ{.A<Afs.A`)^AXp.AlAm.Av|A_k.A &Ai.A@Ag.A/A Re.A`Ab.A`vA `.A߼A].A@A0X.A QADT.AATP.AAL.A@AI.A@A <.A?A 0.AxzAH$.A@>A~".A@5AТ .AfA$.AA .AA@`.A`A`%.AA(\.AA.AkA8.AJA.ARbA.A`yA.AA0{.AɦA@.A`KA.AA@ .AAP' .AdA.AAh.AAF.AH&A .A6A.AHA .A[Ah.AwAj.A$A.A`As.AiAp.A@%A{.A@ A R.A!A.A .A.A`9A.ADA0..A@LAg.AWA.AWcA.AoA{.Ac|A_.ANAh.A`)AЕ.AA.AA .A Ax:.A A.A&Az.A |AAX .A \[Aݷ.ApAM.A@߄A`.A`A.A෨Aͥ.AEAݢ.AA`S.A`;A3.A`dA.A`)Au.AA@=.AA(~.A`A9.A`Ap.AAH\.A^A.AOA.AzAɒ.A&Am.AǫAȂ.A@#A .A@Ap .A A.AAȇ.A u Ap+.A3AC.A+A`|.AK:Aw.AGAr.A]SAf.A`fAXF_.AҺAPX.A(AR.AXAHM.ATQA(H.A@ AF.AїAE.A`A E.AAD.A/AE.A`AE.ANA@F.Az AG.A@) AhH.AG A@H.Ae AhG.A@P AF.A̠ AC.A@ AH?.A@ Aȱ;.A8!Ap 7.A@ 9!A84.AN!A1.A/c!APZ..Av!A*.A!AX&.A&!Ak".Ap!AX.A!A .A+"A.Apm"A0.AB{"A^.A"AHz.A`"AF.A"A@.A-"A.A "A( .A "AX.A"A0 .Ap"A).A@#A.A!#AH.Ap;#A.Al#A8.A`[#A.Ap#Ad.A#A0.A#Ak.A$A.AQ$A<.A`K&$A.A9$A.AL$A.A e$A!.A~$A.Aྗ$Aa.A@$Ap=.Az$A.A$A.A$Aho.A`$A.A@%$A.A-$A.A$AX.A`"$A).A$Ax.A$A.A`) %Aa.A%A.A )%AЦ{.A <%A}l.A@.P%Az].Af%ApY.Ati%AU.Atn%A:R.Au%AN.A}%AK.A@%A@H.A@N%AF.A<%AC.A5%A`A.A%Aȃ@.A+%Aؼ>.A@,%A<.A %A*:.A%A@7.A`2%A4.A%A1.A%Ah*.A %A`a".A%AH.Aq%A`X.A%Ap .A` &Ap.Ax3&AxK.A \&Ah.A&A`.A`ó&Ap.A &AD.A 'AY.A 9'Aĵ.A h'Ax.AH'A.A'AH.A'A.A@(A0.A9(A.A`(A`.A(A8.A (A M.AB(AhL.A=(A.A@(Ah.A(A~.A)A~.Af)Ap~.A`).)A=.A@G)A.A|`)APZ.Ax)A.A)A _.A)A].A`)A.A@W)A.A@)Aш.A)A <.A A)A_.A-)A@O.A)A.A=)Amv.A`)APk.A)A(`.A`f)A"\.A`)A}W.A)AR.A)APN.A`v)AJ.A)APF.A \*AP.An,*Aw:.AA*A:7.A@yX*At4.Ap*A()2.A[*AH\0.A \*AB/.A *A..Aq*AH\..A@ *A`..A*A(>/.A*AU0.A*A1.A*A03.A*A7.A}+A:.A+AH<.A +A?.A|.+Ax@.A@>+A cB.AO+A[C.A \a+AHC.A3s+AHPC.A`+A(>B.A}+A@.A+AБ>.A+A<.A+A _7.A$+A.3.A ,Axu/.A ",Ap9,.A Z;,A).A U,A'.Ah,A&.Ak|,A0$.A,A =$.A@,A$.Aù,A%.A ,Ah@'.A,A7(.A,Aȱ(.A,A(.A,A"(.A,A'.A-AP".A-Ahn.AL.-AH.A(B-A0.A]L-A.AX-Aj .A&e-A` .A@ys-A).A@-A0.AL-A.A-AB.A3-A`.A@-A.A.A.A,.A8E.AD.A`.A`;\.A.A`s.AP.A.A`a.A=.A.A`.A".A྽.A.A.Ak.A`f.A.A`.A.A .Ah3.A`K.A.A .Ap.A`).AX.A/Ah.A/A.A&/A.A@ 9/A.A L/A.A`/A0.A@ t/A.A /A0..A \/A.A`]/AX.A@y/A@.A 0Ap.AK0A8.Au0Aq.A0A.A@0A.Ap0Aج.A@i0A.A` 1A.Aj 1A.A`)1A.A@>1A0 |.A 1AXyx.A$1At.A1AQq.A1Ak.A1AHf.A` 1A{a.A'1Ar\.A/1AW.AX91AR.A`D1A~N.A Q1AQJ.A^1A@H.Avn1AE.A`|1A |B.A1Ah?.A 1Aq;.Aෟ1A؁7.A 1A(\3.A1A1.A̭1A@..A`ܮ1A,.A1A*.A 81A(.A 81A'.Ax1A8&.A1A&.A1A*'.A2AQ(.A /2A?).A@A2A).AMT2A).A@,g2Ah).A`y2A(.A2Ax'.A2A@&.A2A$.A 2AP!.A=2A.A 2A.AV 3AP.A@$3A .A=3AX.A``3A.A3AL.A3A.A-3Axi.A#4AH\.Ab4A#.A4A.A5A`e.A 55A0.AQ5A.A m5A8.A`95AP.A 5AH.A5A.A`5A.A 5A.A5A`.A@>6ApN.AI6A(.A6AG.Az6A@.Ao6A.AK6A茚.A7A0.A?!7Ax.A;7A.AGU7AL.A@7Aӗ.A7A.A7AHr.A7A0.A7A 4.A7Ar.Ah7AQ.A8A(.A8A.A8A[.A&8A.A-8AR|.A68A0w.A@A8Ahs.AMN8Ap.Ap\8Al.Al8Ai.A@|8Ae.AǕ8A(a.Aǭ8AG].A8AL.A@ 8A8I.A8AYG.A#8AQE.A 9AC.A@E 9A-B.A9A8#A.A 9Az@.A(9A>.AE9Ap<.Aa9AH9.A \}9AxG6.A ˆ9A(2.A9A8..A`"9A*.A9A@B$.A9AH.A@ְ9A.A9A.A 9A.A9A.A`9Ax .Aa9AH. .A@ 9Ap=.A`:Au.A:A.A:AP8.Ap;AǢ.A ;A.AT<A|.A3<A)g.A<AF.A@<A).A3=Au .A`X=A\.ALk=A.A=A@.A`>A`a.A,>A.A%>ApͿ.A`f >A.A>A.A >ALr.A?A U.AH?A=.A3?A`*.A@?A.A?A`a.AH?AQ .A?A0.Az?A.A \?AG.Ap@A.A@A8.A"@Aܾ.Az(@AӾ.A`)6@A`Ǿ.AzT@AǾ.An@A.A`)|@AG.A`)@A.AR@AHᗾ.A̮@Ap.A̸@AX.A@Auy.A @AQt.A@An.A@A#[.AIAAT.AAAuP.A(AA`aG.A AAB=.AAA86.AGAA-.A]AAX'.AAA83 .A AAX.AQAA@.A BA .A]?BA).A_BAG.A@pBA .ABA#ҽ.A=BA(\ʽ.ABAʽ.A`)CA̽.ACAн.A 3CAp Խ.A`fHCALԽ.A=jCA`ѽ.A`)CA=ͽ.ACAĽ.A CA#.ADAP.ADAp .ADA .A=DA`.A@CAB.ACA`ွ.A CA`w.A`)CA^.ADA`S.A`DAF.A3)DA8.AGDA-.AlDAp).A@DAp8*.A@DA,.ADAu..A \DAk,.A \DA(.A@g EAH.%.A@ EAp.ADA( .A DA.A3DAP.A=DAf.ADA.A@>DAH\.A qDA޼.A3DA׼.A3DA0Լ.ADAӼ.ADAѼ.Az EAB̼.A@ !EAh3ļ.A3EA0.A@>|EAfm.AEA3X.A EAp K.A FAأ?.A?FA3.ArFA().A]FA`.AFA.A@FA.AGA.A`f GA@W.AzDGAp .AGAB .A3)HA .AMHA(.A wHA8.AHAp.A@IAXӻ.A=|IA˻.AHIAk.A3SJAp.A@tJA毻.ApJA.AJAf.ApJA̎.AzJA0.ApJAׂ.A mJA0~.A3EJA@x.A@>2JAzp.A7JAX h.AOJAL_.AxJAU.AGJA8F.ApJA؞7.A`)JAP80.AJA'.A@>JA8.AJA.AJAp.AzJA؞.A@JA8.A1KA0..A `KAp.ARKA0. .A֧KAf.AzKA.A`fKAL.A KAQ޺.ApKA`Ժ.A KA8Ϻ.A= LAƺ.A@\LA83.ApLAp=.AMAf.A{MAuw.ApMAf.A=MA?.Az.NA3.AHQNA0.+.A`frNA$.A3NAG.ANA.ANA`a.A3NA.A NAz.A`)NAk.ANA8.ANAع.AzNA).ANA).A OA.AOAf.AOA𣈹.A`%OA@ׂ.A`9OAzq.AחOA#i.A[OAu_.AOAQ.AOAC.A׽OA9.A̤OA8.A OA1.A3OA+.A@OA{.AOA.A`DOAp.AROAX.A@>OA.A3PAB.A%PAL.A@0PAkӸ.A TPA(̸.A gPAL˸.A sPAP˸.APAuӸ.A`fPAP߸.AzPA.AzPA0..APA R.AzPA.APA.A3PAƸ.A`fPA).APA؞.Az QA8.A`PAˆ.A*PAਅ.A3PA#|.A`)PAp=N.APAp=F.AzPAB@.A 0PAp7.A PAp4.APA@W4.APA833.APAX-.ApPA!.ApPAL.AGQA.AQ$QA(.A4QABӷ.A3QAP.A3+QA .A( QAP.A(QA.A(PA.AzQAP.A]!QA ~.A?QA{.A }QAp|.A`)QAp .AQA).AQAp.AQAp.AQA`a.AQA(܂.AQAy.AQAo.A@RAd.AQAuT.AQA0.F.A@QAأ;.A@QA..ApQA`(.A`fQAQ%.A`QA@W".A` RAG.A5RA.A`)VRAL.ARA.A@ RA.A@RA(.A`fRAڶ.A |jRA̶.A3?RAQǶ.A |.RA.A@"RADz.A)RAʩ.A A4RA8.A8RA.ABRA롶.ARPRA⡶.A YRA(ܡ.A3aRA(\.AlRA®.A@RAֺ.ARAp=ƶ.A`)LSAQ̶.ASAH.̶.AHSAƶ.ASAH.A`)TA.AR.TAB.AoTAL.A@gTA`av.ATA#j.ATAc.ARTA0_.A TApZ.A \kTA`aT.ApQTA#H.A=TA:.A0TA/.A1TA8&.A GTAB.A`)lTA.A@TAP .A TA .A@gTAz.A@>TA.A@gTA؞.ATA.A3TA0.ܵ.A 'UAе.A QUA`.ApkUA.A@gxUA`᥵.A@UA8.A`)UAf.A]UAL.A(VA.A(YA.A[YA.A@YA .A[YA.A YA`a~.A@|YAXx.ARvYAzs.A`zYA`\.AYAP8U.A YAkL.A YAp E.AYAQ>.AGYA8+.A@ YAp.A`TYA.A CYA`.AEYA .ARVYAp .AkYA(\.A}YA.AYA8.A@g~YA`a.A wYAu.A3cYAݱ.AR6YA(ı.AzXA`ḱ.A \XA.A@XAp=.A@XAB.A`ÇXA.AnXAH..AQXA8ʱ.A 1XAϱ.A=XA)ұ.A XAuұ.AWAб.A`WAp=̱.A@WAƱ.A@WAf.A@ WA`a.AWA.AWAV.A@WA.A \WAB.A׻WAz.A@GWA(\x.A=WAp=w.A`fWA{u.AWAp=r.ARWAd.AXA_.A`f XAp=Y.A \XAQ.ARXA K.A= XAE.A@ XALB.AXAH;.AXA5.A3WAX +.A`WAh3$.A \WA`.AWA0..A`fWAp .AVAB.A`VA(\۰.AzfVA(\Ȱ.AQ"VA(\.ApUAG.A]UA(\.A (UA.AUA`.A TA.A`)|TAz.ATAp=.A`)SAx.A@ SAj.ASAVd.A@fSAV].AWSAW.A USAHT.AVSAp8Q.A@RSAN.AHSApI.A3/SAuE.A@SA8A.A qSA;.A \RA6.A qRAB3.A@SA{-.ARSAP'.A RA!.ARA .ARA .ARAp=.A@rRAX .AXRAB .ADRA.A1RA#.AR,RA.A`.RA@ .Ap1RA.A@4RAأ.A@G3RAH.A |0RAأ.A \RAH\.A3QAG.A@QA@.ApeQA#.A`fHQAu.Ap/QA.AQAk.AQQA.A PAf.APAh3ٯ.A`fPAЯ.A PA`aʯ.APAp.A RPA².AROAh.AOA².AOA.AOAѸ.AGOA`.A OAů.AשOAL̯.A̮OA(Я.A[OALӯ.A̞OAԯ.AOAկ.Az\OA Rӯ.A@.OAWЯ.A3OA R˯.A qOABï.A@ NAѺ.A`NA.A`)NAW.ANA.A`xNA`.ANAQ.A3MA8.A`)MAp .A3MA8.AMAB.AMA`.A MAP8.AuMA.A`)DMA(܆.A*MA؞.A=MA0.ALA8.AGLA8.ALAk.AoLAP8}.A``LA`as.AZLAe.A``LA[.AgLAp=V.ApLA`R.A~LA)N.A`̄LAJ.A]LA`D.A@tLA>.A`f^LAp8.A;LA#+.A@KA'.A@KA$.A=KA`%.A`)KAf0.A&KAp7.AJA;.A@JA<.AJAp=<.A mJAL9.AzNJA3.A`),JAz..AIA*.AIA'.AIAB".A3IA.A`fIA .A3IA.AqIAk.A \]IA`.A \?IA.AIAX .AHA`.A |tHA#.A@ UHA.A@G3HA.AHA.AGApݮ.A`)GA֮.A \GApϮ.A`fVGA`î.A]FA@.A 1FAר.AëEAp.AEAz.A`mEAh3.A[[EAP8.AIEA.AG7EA.A EAأ.A@gDAǍ.ARDA8.A׫DAP抮.A@DAp .A|DAB~.A@dDA|.A=ZDAz.A q1DA#~.ACAP8~.ARCA=z.ACAGt.A`fCA(k.A wCAe.A`)hCA)`.ASCA8\.A`.CApW.A3BA(\T.A \BA3O.A`fBA0.G.A@BA<.ApBAP1.A}BA`).AgBAP%.ASBAu!.A,BA`.A@BA`.A@AA`a.AGAAh.AoAA@.AfAA.AAA8.AAAͭ.AAAH.AUAA𣴭.AzAA8.A`fAAQ.A`BAX .ABAѩ.A0BAB.Ap=BA.AzFBAQ.A@VBAV.A@fBAk.A |BA̫.A BAB.A \BA̖.A@ BAp.A`)BALJ.ARBA.A |BA|.A \BAP8u.A}BAo.AkBA)i.AbBA^.A=fBAS.A`zBAأJ.A`fBA4.A@ BAG#.ARBAB.A3BA.ABA.ABAP8.ABAh.A`CA .A6CAz .AKCA`.A@dCAp.A`rCA.A |rCA(\.A _CA߬.A [CA`aج.AeCApϬ.AtCA83Ȭ.AxCAp.A@dCAѵ.A \]CA̫.A=jCAz.ACAǛ.AדCA.A@CA{.A׻CAu.ACAHm.ACAd.A`fCA8Y.A` DA`aA.A>DAP!.AׅDAk.A`)DAh3.ADAPf.Ap3EA.AEAث.A[EAPʫ.A FA؞.ABFA.A`fXFA沫.A ]FA.A gFA.AFAp.AFAfv.AFAHV.A`ftGA(\>.ARGA!.A@ 7HA.AHA`Ъ.AIA R.AhIA.A@IA`%n.AkJAn.A&lJA]j.A`JAa.A (JAK].A JAU\.A@rKA(m[.AKAX.AvKAJV.AKAU.AjKA@R.A@`$KAL.A`v1KAI.A5KAI.A 6KA,.A@%[KA&.AxeKA".AlKA@.AKA ߩ.A KAcϩ.A_LA.A (HLA囹.A vLAX.A`fLAΞ.ALAh.A]LAs.ALAn.AwLAHj.ALAf.ALA/d.A@LAc.A LA\.AoLADW.AMAC.A~MA3A.AMAB;.AMA0.AMA&.A@NNAs.Aj\NA.A@ NAxm.AuOAX.A OA蠨.AVOA{.A`$PAl.A`;PA`e.APA3[.Ag)QARN.AaQA?.AQA..AQA0%-.AQA%.A`[QA.A<@RA' .A QRA.A@RA ç.A RA.A@SA0{.A@gSAг.A߃SAȋ.AOSAx${.A`2SA o.ASAl.ASAH?b.ASA].A TAW.A@%TAhM7.A@TA.AVRUA4.A`mUUAh.AXUA> .A6UA.AUA.A@UAY.A@5vVA.A VA.A`2VAXR٦.A ZBWA0Ԧ.A@]WAPϦ.A$tWA¦.AqWA虴.A WAh.A@XAu.A@9XA.A@>JXAXz.A@gcXAHM.AYA .AYA.A&_Aɤ.A @_A.A@E_A>.A$\_A<.AZ_A;.A Y_A6.A*W_A,.AK_A,.A@J_AH,.AJ_A(`+.AG_AH\#.A6_Aذ.A_AU .A _A .A^AH!.A@^A.A^A.A^A0.A^A .A^A +.A^A.A^A).A^A@.A^A&.A@N^A@1.A^A .A^A@S.A ^A@.A@^A@u.A_A.A__AxX.At_A;.A"_As.A#1_A@A.AJ_A`e.AfV_A$.A@ u_A أ.A_Aף.A@_AhУ.A`)F`A.A^1aA`ǿ.A?aAH-.A҄aA@乣.AaA .AaAX.A`baAa.A bA.AbA0H.A =bA@`.AGbAH.A_bA`e.ATzbA.AbAXh.A$bA؁.A@EbA84x.AbAhm.AbA[.A@bAQ.AbA=.A AbAx6.AbA3.AbAp}.AbAU.A+cA .A6cApk .A 1 cA .A cAN.A@,cAPТ.A@.cA.AcAs.A`%dAz.AROdApa.AdAV.A`)dA@NT.A`dA83N.A eAp A.A`\eA(@.A]eA6.ApeA8..A eA8 .A`ZfA#.A \fA.A fAG.AJgA-.A׏gA`a.A hAW.A`ÑhAp.AhA8.A-iAH.A}iAp.AiA%.AjAo..A`mbjA0.AtjAQ?.AjA^.A`*kApp.ARpkAp.AkA`.AkA`a.ARkAQ.AkA@ל.A@,lA8.A`f^lA(\.A͒lAz.AlAW.A lAp=Ţ.A3mAQ΢.AmAP8Ԣ.A -mABޢ.A`)ZmAk.A@>mAh3.A@nAN.A oAe.A=foAH.p.AQoABt.AoA`au.A3oAp=s.AGpAq.Az"pAm.ADpAf.AGepApd.A |pA83d.A pA`a].A`pAأH.A@qA0.<.A \1qA2.AR:qA'.ABqA#.A`)VqA.A_xqA.AqAx.A`vqA8.A`fqA8.AqAu.AzrAu.A`fNrA .A@trAP.AzrAP8.A@rAk.A sA.A`;sA`aӢ.AJsA(.ARsA`.AXsAp.ALsA𣅢.A1sAXx.ArApi.ArAc.AFxrApR.AR&rA=.A`fqA&.A͢qA.A \qA#.A`~qA.A@dqA).A@G'qA̡.ApA=.ApAأ.ARrpAє.A`f^pAG.A apAy.A`fxpA@W^.AppA4.A=qA@W-.A!qAz$.A@ kqAp".A`fqA .A@&rA.ArA@W.AsAp8.A@gZsA .A@gsAX .A3sA.AןsA(.AzsAWؠ.A:sAX ͠.A@>rAH\.A@ YrA.A`)rAP8.ARqAX .A=^qȀ.A`)qAPf~.A`pA{v.AطpAr.AGpAX.AlpA0?.AzJpA(*.A0JAh=.A@ )JA K.A3QJAL.A{JAGI.A`çJAأD.A JA0C.A JAPfC.AqKA>.AG5LAP@.A ]LA`C.AiLA`aN.ApLAT.A`tLAU.A`LALN.ApLAp M.AࣰLAM.A@ELA)N.A`LAX Y.ARLA_.ALA@f.ALAȃh.ALA)l.AzLAs.ALAp=.AqLAL.ASLA.ALAp8ɤ.A=KA83Ф.AKAVӤ.ApmKA)Ӥ.A8KAp=Ф.A[JAp8Ƥ.A@>JA).AzJA.AJAP8.A kJAp.AMJA.A %JAVƤ.A IA֤.AIA(.AכIA.AIA( .A̜IA.A`ÿIAQ'.AIAP8;.A JAfJ.A JAuV.A@JAe.A`IAy.AIA`a.AIA0ҥ.A`fIA.A gIA.A_IA(.A1IA .A`HA.A@HA8.A \HA.A=fHA .A=FHA.A0HAp8#.AHA#,.AGA2.AGGA?.AoGAHG.AeGAQ.A@nGA`Z.A GA8e.AGApv.A GA(.A࣌GA‘.AiGAf.AUGA.A@2GAP8.AQ(GA.A`),GA̼.A(FA0.Ϧ.AFAH.ARlFA .A EAP".AEA4.ANEA8?.A  EA?.A DA@.ADAPD.A`DAK.A`)DA8V.AHDAj^.ADA(\c.A`DAi.A DA8j.ADAh.AnjDA8h.AgDA_.A \EDALU.A0DAHP.A`)DAQ.A3DA(\U.ACApa.ACA#t.ACAz.A=CAp8}.A׳CA8.A*CA0n.A`CAk.A`fBAȧ.A`)>BAҧ.AAAܧ.AAA.A CAA.A3#AAf.A@A.Ap@A .A@>@AP.A @A.AÓ@A(.Aps@A4.AL@A ?.A@AfV.A?AWd.AR?A8m.Aׯ?A-~.A`a?AQ.A`:?A.A3?Au.A=>A@W֨.Aͦ>Ah{ب.A>Az.A>A.Ae>As.AB>A8.A >A`a.A@>A.A=A .A`=AP8.Al=A).A-=AW2.A`f<A5.A<A`:.A <A<.At<A(=.AU<A=.AM<AQ@.A &<AF.A <AWP.Ap;A\.AG;Ab.A`);ALj.Ap;At.A ;Az|.AG;A.A׋;AG.Ap;A̅.AJ;A.A@>$;A`ᤩ.A:Af.A`):Aɩ.AK:A8.AH:A.A9A(>.A@9A0.A9Ap<.AnO9AH.A?9A*".A 59A$.A4)9A$.A &9A%.A`9Ac(.AN9A3.AŢ8A6.A߉8A 9.ABv8A89.A u8A(z;.An8AM.AZ8A0Z.A`7Ahc.A 87Ahk.A`d7An.A ۟7A.A@%Z7A0Y.A T7A2.A47A`ý.Aw6AHKϪ.A`06A@ݪ.A$5A( .A5A@.A@Ϛ5A0.A2r5A .A35AF.A@P4Ab.A`4AX{.A ]4A.A`)4A.A4AH!.A 4A3.AV3Ah.A3ASث.A 13A.A2A0.A 2A.AD2A('.A 1AI.A@/1AثV.A0A0].Aǿ0AHb.Aa0A8x.A 0A|.Av/A`.Ag/A(.A`/AH.Ao/A>.AH.A`.A .AXV.A`.Ak.Az.A@έ.A b.Aӯ.AXV.A`嵬.A`0.AX(.A.A(.A.A.A@-ARǬ.AT-A\ͬ.A-A ج.A-A /.A-A6.Ahi,AN.A,A`W.A`+A@ ^.A|+A `(A'.A@PB(AUG.A'Adu.A6&A0ju.A@&AY.Af_&A̋.A2&A0.A@C&Ab.A %A۹.A%A0î.A@,%A.A qf%A(\.AU%A.AA%A/.A3%A .A %A.A`$A` .A$AHC).AՒ$A..A r$A5.AJ$A#G.A`#A(Y.A A@#AP`.A "APf.A͠"Azl.A6ABʰ.A@A.A eA .A AXc.A@iApy.A@_A8.A`[?A.AA؁$.A`Ah2.AAA.A ATI.AQA`P.AfAh].A-Ac.AAAi.ADA0m.AyAt.AA|.A tA!~.AhAX.AAA0؆.AAHP.AA0ꓱ.A oAp.A`9Ag.A͜A8.A"EAs.A(Ax.A@A8'.AAd.A JA .AUAHi.A SAP8.ApA0.AzJA .A$A.A A@.AHA .A A`.A(A.A@ihA#.A`7A+.ARA3..A A0j0.AA22.AA3.AvAk4.AA\4.A)A4.AiA#6.AKAPo7.A (=A#9.A (/A;;.A"AЮ=.AAv@.A  A8C.A`vA(F.A[A=K.A@AXhO.A+AQS.A@AhV.AA9Z.AA%].AtA0_.A3A@h.AiAXr.A0Aw|.AAP.A`)A{.A"Aᙲ.A hA碲.A@`:A.A AH˳.AAأ.A=A{Ų.AIAв.AtA8Ӳ.AA@޲.AA.AGA.A@Av.A@yAX.ALA0L.A sA.A}AH?.AwA(.AtpAh.AjA(\.AdAHK.A]Ag.A WA.ARAj.AOAA.A PAx.A RA.A SA@~.ATA.AUA.A UAH.AHWA.AWA.A-UAج.AQAk .A@yLA .AEA.A@P7A.A'A`.A@`A.AEA8.A@ A".A ~ APY$.A]m AЮ&.A\ Ax).AdM AH,.A[? A0.A A8 A3.A2 AU7.A. A;.A), A>.A`]+ AB.A6, AxF.A. A.AdAxiB.AAuF.AAF.A`vA`N.A AؒR.A`fAo.A@At.A@PAxK.A$AO.A Aj.A 8A.AAㅷ.AŐA|.A c~AH.A}ANj.A@`zA.A@_AН.A0$A߯.A`A.AQAt.A@AA( .AݕAhMH.A` A_\.AA\.A`AP].AFA^.A͵A`.AVAa.A?ApZa.AަAP8c.A`vAd.A`2vAXf.AJAg.AwCA3h.A@Aqi.A6A`%j.A 2Apj.A^/Aj.A/Apl.A'As.A AP.AoApո.AA R.A`A(~.A`A.A@XAs.A/LA,.ADA .As9AX.A.A|.A@A.A^A\/.A AC.A !AY.AAs.A@(A|.A Ab.AA㎹.AW|APo.APAXۛ.AA{.A3A窹.AA.AAs.AA.A/A0ǹ.AtA /ѹ.A[APӹ.A NA cܹ.A s"A`6.A+A+.AA.A`AP.AzA.A@]ApN%.A`9A2.A AZ.AAb.AAx(l.AAp<.AGAP.AIlAB.ARA@.AtDAz.A4Aﾺ.AAϺ.AQABϺ.AA(Ϻ.AA޺.AA.AA( .A/A.ADžA.Aj_Aw.A_UA`.A#A.A Ah".A+A(.AA0-.ArA(E.AA 0F.AAYJ.AAO.AFA^.AwAxk.A`OA.r.A99Ax)w.A "Apx{.A  A.A A.AMA`1.A`fA.AZ,AHr.AA̯.AnA׻.AKA`ܻ.A$A.A@EA.AA.A A.AAX#1.AApA1.A A_1.A2AJ.A`\APc.AA`h.A@iAh.A/A n.AǨA-.A_A@.A@[AP<.A@>;A.AA .A Aљ.AA .AA.A<A㸼.AyAK.A<Aﾼ.Ax*A8ü.A-Aļ.A AƼ.AHAɼ.A cA\ʼ.A`AXʼ.AA޼.AÝA8.A@Ak.A`xA8.A^Aa.AT?A.A@2A.AA`.A@A.AÉA-.AuA( .A dAp/.A (ApP.A AXa.AAH.d.AALr.AA`w.AA`ᅽ.A@> A.AA(.AA(.A q;A#.A qA(\.AA0..ARAp.A qAx.A4AB.A`)A(ܯ.A|A.ASAG.AR(Az.A`)A(.A Au.A \AP.AAp Ľ.A=A`ǽ.A@GAhʽ.A͈Aҽ.A 1Ah3ܽ.A`AB.A`AG.AGA.A@ZA#.A 7A0..AAP8.A Ap.A@gA .ARA`.A@pAX".A3[A8*.AYA1.ASAX4.A[QA@.A[%AkU.AA_.A{ALg.AHMAq.A@As.A AVs.AMApo.A Aq.A |Av.AͲA).AA8.A OAX .A A}.AA}.A AX.A]sA.AaA.ApAAp.A(NAH..AeA.A Af.AA`a.AQA뾾.AAþ.A3gA(Ⱦ.AQHAо.AAzھ.A AW.AA0.A A.ABA.A A.AA.AA %.A०A c(.A AH(.AanA8).AUAp/.A:A:.A@APA.AA{D.A`ATM.ApAPX.AA@d.AHyAm.A`KAh y.AZApJ.AhAh.AAl.A@A@W.Av^AJ.A |EA线.A:A[п.A A1A+ۿ.A c$Ap .AIA.AdAu.AAп.A AJ.A`[A` .AOAh{.A;A0H/.A =A:.A;AM.A@`%AV.A AP^.A+Ae.A Axf.A@EAe.A AA|h.A`]AHn.AAP+t.AAxu.A`WAhbx.A@=Aā.AjA .AA#.AA0.A A.AA].A`AH?.A A.AA.AA0f.ArAl.AfA5.AeA@.A O]A@S.A<Ap.A@ AH .A`A8.A`A.AFA .AA.AA.A@A7".AtAx+.AlA2.AdA=.AoAJ.A@kAпW.ASA@1s.A`A.AA+.A4A.A@AЦ.A A.A`Ax.A?A.A An.A A.A@A.A-ApN.A`"AxK.A@AP.A@ A8D.A`AA.A`TXA0.A9A.A&A.AF A .A6Ax.A`A!.A Ay#.AॆA@ .AtA.A@#aAK.AtTAHL.A;AX.A5A`C.AA(i.A@,A@u .AA.A c[A@ 2.A ZAH.A`rA`h.AAx׈.AAXW.A |qA`v.AflA@.A rAp.AA .AAX.A<A.AzA@.AdA.Ad>AXW.A@AH].AA@.A?AP .AaA( .A0A4.AZAe.AR}AP.A At.A`A.A@?AHP.AoA R.A`9AK.A9Ax.AAv.A@WAx.AБAL.A;A`.A@A0{.A A`.A |kA؁.AyA(.A A.AA k.A@g|A&.A@hA.AjA8c.A tA8.AtAp.AdA&.A`>AQ.AwA.A`A8ɸ.A@5Aྵ.A@vA .A ]Aߺ.A\A0ٿ.AdkA R.A-Ap.AA.A4Ao.AaA2.A cA".A@>A.AѽA.AA\ .A3A.AAj.AwAp/.A`ZA`%>.A`YAoH.A@aAX U.AtACk.A$Au.AA,y.A[A|.A`uAX9.A}A8#.A|xA.A`\A.A@"A .AA .A@`Axؙ.AxfA/.A[3AhQ.A2A`.A1A.A Ab.A A}.AA=u.AA8.A/A~.AGA#p.A AH.AA..A MAk.A`)&AH.A \A=.ARA)Ͻ.A wAأ.A 9A.A`A憽.A |ҸA`a.ApAA.A=A˛.AawJA>.K¼.AwtAk.A.ʫA\JLO.A!KAWy.AdbA(%.AkoyAd.AVA(Q.AsA}1ݔn.AM PA5M.A~J%A/.AA.A <AD.A mA`!5.A aA#.AA.A+Ac.A ;A.A`AH.A`"ݪAHߺ.A@AѺ.A@EA(.AANJ.Aj{A@o.A6 AU.AA`10.A`ީAP8!.AsA .A c0Aٹ.AAȩ.A[A𴡹.ACAx.A`)"A(`u.A AY.A﫧A I.A(AP&8.ATAH.AAθ.AAp.A ҌA8".AŝAX.A൝AP^X.AaAF.AӼAk.A A.A4Ax.A^A0.AAh.AA*H.A|A.AA.A AXh.A`A).AAw.AAw.AծA.A A.A`UAV.AA.A PAP.A` Ap.AA(:.AAp.AA(q.AAH .A9AhQ.ABAȇ.AAA.AAB.A`Ar.AżA r.A@*Ah.A`.A(\D.A@5A+.A9vA(.AӦAڵ.A ҌAl.AIdA.A;AfD.AfcA@}D.AApk*.AAug.ANAhc.A JEAPx.AA`.A9!APژ.A`;ܣAor.A8]Ad.A6Aor.AףA(\.A 4AZ.A*A.A@ UA.A<A.AAA3.A AA.A?ΣA.AsA`%.A`2A.A AơA@.A`mpAXڴ.A)Aڴ.A A̴.A oA8.AXAk.A }A.AAPD.A@ϔA۳.A@Aó.A AXh.AƣA 9.A&A..AAM<.A`ģAT/.AeAH.A Ax.AկAP<.AA.A`KAҲ.A@W%A4 .AA.A Ah.AନA0.A=dAt.A DA\.A ɤAА .AWA( .ANAs.AoA`.AAق.AA0}.A`AX.AXyAh'.AbAK.A^ܥAް.AAXE.A7A.A@AT@.AA̯.A\A`.An)A^.AӿA 㭮.A 8ͤA1.A$A.A$Aan.AAXhU.A1A"G.AEAP;.A`6A09.A sAC.AA@Y.A=0AX.AAR.A]գA.A`B̢Al.A8A@#.A`aA.ATHAg.A@9A.AvA'.AˢA耬.Az$AxU.AA"7.A "A`.A A@«.A2AD.ADA.AAX.A`)>Ah0.AsAJ.AAP.AA.A ~GA˪.A@5Axm.AA.A ¸A{.A=A+.A4A.AAX5.AO$A1ݩ.A "A.AA0.ASA8g.AA[j.A6|As^.AfA9.APApx.AXAȨ.A`]VAh.A`]߲A-ߨ.A`'AX.A KAI.A A l.A`KA@3.A@AQV.AA 8.AA8R.AapAh.AAڧ.A`)|A.A9Ae.A A𴦧.A 'AB.AA.A`A.A͵Aps.A$A0|.A0HAጧ.A`) A͠.A0HA .A_A.A׀A(.A@θAx.A@MAXg.A`A K.AιA8.A0AS.AV@A .AA.A6ȸAxm.AAH)֦.A`\AlĦ.A#A.AA.AA.AŤAh.AOA7r.AjAh.A ܵAp`.A AP^i.A-AY.A`A@.A&APY .A|EAH.A-fAe.AvA&.A 䖹A4.AgǹAH.A.A@5.AQ.A`mYAXT.AjA@).AsAG.A}ӻAP?.A@A.A`hAQ.A$AXE٦.AAPȦ.AlA`¦.A`XAJ.A Aߘ.A`ӻA0t.A#]An.A@>LA[H.A@PкA C.A/A`.A-MA(ͥ.ADA.A`wAH.A$RAPMޥ.A Aă.A`ùA`Ǟ.AzA8gȥ.A[A.A /A.A@RA@c.AD^Ahc.A_Ah.A|ݻA.A AX[֦.A@ټA$Ӧ.A@A .AKAz.A9׾A,.A0A,.AA.AAPf.A`vA¦.A<A(y.AKA-.A ͽAe.A$A@S.AླA0.AA0U.A@Ax.AA 0ڥ.A-A.A A.AA(#.A A`6'.AM Ah/.A=(AX.AA`.AAv.A/AU.A?Ağ.A?ÿA+.A[ֿA䪦.A=Apή.AϿAڶ.AAr.AA`.Ay̿AHл.A ٿA .AAFæ.A59AXhԦ.ArA@Aަ.ABA̦.ApA3.A[A`.A OA.A Aгz.A`daA3.A`A6.AxA}.A[Ah.AAA`v.AA.A 9A<.AQLA.AoA8.AAX:.AA.AAq.A,AHi,.A u]A:ҭ.AAt.AAТu.A`)A3.A@!+Ax.A@{A0ʭ.AAPx.A.A٘.AA`r.A@NAk.A?Aؒb.A lnAз;.AA6.A Av .ABA.A@`!A.AA.AUA.AAb.A lAs\.A Aw?.AЄAF.A@W]Ap.A@,dA =.A quA^f.A@ƛA@BC.ANAxJ.A Ax.A`A.AAr?.A`]A.A-AB.A`[AxI.A A+.AA1.A@#kAV.A`"As.A .A.Ay%AHƤ.A`Aɤ.A)AtҤ.AAX.A൚A0Ӥ.A_Aܤ.A=A@.A3A .ALFA6.AA1.A6A=.A |AH.AA.A$gA #.AXA(횤.A`TAK.A ZAH..AdlA&x.AJAZ.AoA( 9.A-aAQ7.A ~EAU.A ZAP.AAY.AA.AA`C .AA.A@A,.AjAG.A AVR.A 8Aгb.A |;A Mv.ABA.AA8g.A;A`6.AA.A4wA&֤.A@A(Ӥ.A&A@_.ARAʸ.A`A.AAPYڤ.A+HA@ݤ.A`RA.AAxKV.AAC.AZA`6E.AA.AAl.A@A.A@xAx .A 8DA8֣.AA.A ۝A.A3uAΣ.A@Aeţ.AAP.AA؁.A Ap.AAP.AA<.A`*AP..A@NA8.AX5Aʣ.A 80AX8.A'A@.A^Ad.AAH9.A@WZAdģ.A#QAף.AFA(zӣ.AAD.AAHۣ.A=A .A`YA".A@, APM .A6FA@ƣ.AAeأ.A`,Ap£.AgA@.A &A3`.A SAЄT.A`Ap .A A.AA(.AkAxKڡ.A6Aɍ.AAȵa.A@fAPO.AA`.A஌A 0.A A[ޡ.AoA.AM,A/.A A0.A@aA.A@%A`D.AAp١.AAXD.A_A@N.A ZAX.A@yA|.AA+l.A ZjAc.A`[[A8.A`A&.A`AH~.A`SA3.AAؠ.A c[A@.AaAhn.AA;.AlA.A:AX誠.AGAPچ.A:A@ౠ.A NA(l.AwAxb.A`v#AZ.A`UA~.A"AP$.A@ jA@.A]AP.AA@.AA.A@,dAPS.A̚A`-.AEAf1.AziA8t.AbA}.A`b?A`.A 7App.AFAj.AFA}.A`KA 3.A AK.AAhP.AVAp Q.AOAO.A>AM.AacAA.AacAA.AR~Ax$4.AR~AH.3.A@WA@.AF]A(.A AU.A@A8.A A5.A AH.3.AΤAD.AA8C.A@#A0Q.AA 0W.A`NA(՟.AIJAӟ.A)A(KY.A A@\.A`o0A\.A2Aß.A@Aß.A@OA=.A[A=.A ZAp_.A A8 .AײAhi.A AT/.A utAH.3.AA*.A/A 4.ANxAp.AhA.AAP .A qAxG.AA72.A`]Ax$4.A AT.A@ggA-A.AMAN.A@AO.AzA()u.A@ AU.AA 4.A MA.A@#tA`=.A@sA@S.A}AV.A΁AY.A47A[.AWwA8j.A0{A`h.AAe.AMAIc.A}Ax.AA(.A@Ap.AVAX .AAH:.A.A".A`A|.AhA–.A`A.AA@.A`A0.AsAh@~.AqA~.A଩A&.AA.AApI.A`VApڄ.AA.AA7.A`A].A ۇA.AAxˊ.A Ap}.A $A .A`A Ҍ.A:A8Ů.A9/A-.AAö.A An.A`A.A@MA ܠ.A}A.A?Axz.A AX .A@PA@.AaGAp.AAQ.A@GA(q.ARA.AshAP'.A6A .AͺA83ɠ.A ۠A.A`)A8g.A@!AC .A@ A Ԡ.AG;AXѠ.A0AIݠ.AA|ߠ.A` A@.A 1AM.A@#A.A@8AI.AtAF.AtzA.F.A{A]E.A}AKE.A}AHa.A`A!.A JBA*#.AH>A(K$.AEA%.AWA(.A@AAߠ.AfAm.A@A.AsA".A.AA؟F.A ABJ.ACA`a.AALc.A Am.AAy.AA`b.A3 A3.AzAأš.AA`!.A ~A֡.A 0A}С.A"Ad.AwAН.A@!OA.A<A`.A`&A(?.A AXA.Ay?A|.AmXAġ.A΅Aϡ.A A.A6 AҤ.AA.A lA5.AA.AQA*.A AMt.AaAj.A@߃AK.AA8ݠ.A#A`.A`AX.A`A.AAQ.A $AP'.A@ A.A`A.AA.A^zA+.A`K[A #.AAP).A9A+.AAd9.A`A'.AAz .Az(A@.A`IAh.A@!A8.AyA.A`bAج֠.AAW.A``A#.A@GqAx5.A VAV.AUA1.A-A9.AAB.A`f,A^.A` A&'.Ao@AX$.A@NA4%.A`A1.AxA3.AjAGF.AAI.A |A0_.A`KAc.ADkA{k.A sCA.AA𴒠.AftAX .AbA.ANAǕ.A`)@As.A A̮.AA >.AʤAZ.A`GA.A&+A.AFAd.AA..AoA.AA.AAP8.AAx.AAN.AA`ʟ.A34AΟ.A@ A.AA=.AA0.A^A.A APo.AA.A Aן.A6Aҟ.A AHz.A9Aؽ.A1A.A A4.AA0L.A'Aȓ.AVAp.AA|.A 8AH̟.A`KA.A A c.A҇Aؒ.AxA.A@XAWM.AA?.AxxA,.A 0Ap.A@AG.AAk.AZA̿.A3A(\.A@8AB.A`)A.ANA8.AApl.ATAQ}.A`vAxt.A@AVy.A A(܏.AG]Ap룞.A]A`a.AAu.AlA?.A6}A.A A.A3!A`e*.AjA`8.AA8B.A A$.A`A0.AzApʞ.A3A̞.AR,Aeɞ.A.AАÞ.A`2A.AJA.AxMAlt.AgA8n.A@`kA@].AwA @.A$_A@S:.A`mZAxF .A 5A.AHApӝ.AA`a.A@ A&.AA،.AmA#.AA .AAL.A=Auʝ.AAȵΝ.ARAԝ.A +Ah.A`ZA ).A@A\.ApAɞ.A\Ax:.AQ0A!0.A@AP~.AA8Ţ.A AW.A`QA跍.A xAc|.A_ALi.AfAȩ.AhwAxuܞ.AA.AkA A:.A`A A.A e AN.AX AA.AG Ap2.A`= A".A Q A4.A S A˜.A`z A.A A.AB Ap.A@ Azq.A` AXhR.A` A<.A@ A>!.A`" AXy.A A0f.A AЄ .A`D A"/.A A`$.A~ Axu.A@N Aě.A A𣼛.A@` A0 .A Ap.A` A ț.A A.A A.A A8B.A AȘ2.A֔ A6.A= A`T=.A@ A@.A`AȔL.A@A@r.A;A <.AA@.AAٛ.A`dA.A0AP;.AA@q.AuA3.ApSA2.A@AȤ_.A@rAX.AKAX.A`*Aϳ.A@cA{.A-AX.AyAǒ.A@GAB.A`)A.A+$A.A A2.AAX.A@AΝ.A@>AP.AA7.AAŽ.A@>Ax).A|APx.A@2Ah.A6A=ԝ.A_A.AOA.A!A(.AA'.AjA'.A5A..A3bA:.A@r<A 8.A`A`~.AA.A(Ax诞.A)Aў.AA`6ݞ.AA;.AA <.ALFAz.A@A`.AA(L.ArA0H%.A0AS(.AܾA`*.AܾA-.ARA0 .A}AhD .AA֞.A`mAP՞.A@A О.AA@f.A JAh.A,Afd.AAP.AArH.AA>.A)A07.AjA+.A6A컞.A`A.AAh.AtQAP.AA.AZA.A3A.A@A!.A Aު.AOA`.A cA0.AeAt.A[cAX.AAF@.AMA2.AmAo@.A} A<.AA .A?3A .A^AL.A` FA .AoMA.AAh'.AjA:.A Ai-.A A@#.AA8s".A`-Ay.A!APk.A}A-.AAȶ.A.A0=.AVAeO.AfAkI.A=A@uD.A@Am.A`9AXz.A`AA.A߻Aݵ.AAJ.A <A.AFAxi%.AA@T.AgoA(j.A`AX.A`hAlП.A Ax)۟.A~A1.A&A@.A@`A0.A@,Ap0%.AeA1.A9AHC..A A).A 8A.A`;ApA/.AVA.AA?.A;A(.AJA >.A6A0Te.AAp=z.AoAB.AaAV.A@pAhL.ApA .A Z!A`.A-A΋.A@y>A'.A`*Aȇ.AGAo.A>A#.AA.APA/.A A.A`A8Ơ.A@A.A@A.A`!AJ.A@NǷA0Hߠ.AV.Ax.AaAsk.A {Apb.AūA[j.A@)AY}.A ێAC.A5IA.A@Ap.A ASɞ.A`BtA.A uAc .AհA.AQA(y.A,AV#.AAH.AA .A=A0'.A`A@.A UAό.AAX^.A+A^.AwAE.AhA0*G.AXAxW$.A`qA@&.AѯA3.A A8 .A J?AБ..A@ᆱAK.A)AH.AHcA.A@A%S.AײA.A;A.A-˲A8..A ۱AP@q.AvAu.A@W"Ad.AzűA7.A`KOAQ .A԰A`%.AzZAI.AǯAp`.AHAp.A WA.ALSAx.A۬AD.A٫Ar.A`pAy.A qA`u.A A8n.A!A~.AdA⚥.A AR.AAz.A@ ʩA4s.A=Ar].A`hAc.A (A pݥ.A A<ڥ.A sA U.A쭧AhV.AA0P.AnYALD.AWAp.A$AY.A֤A.AAp.AqAb?.A@WADx.A A.AA`.AŐAX.ALAH.A=Ap.AMvA!.A@ئA".AVApN.A@iGAh.AA7.A A@Ͽ.AvA|.AAX5@.A?A(1.AΟA)*.A^AL.A@fA.AQAT .A3A0.A|A*.A0*A =3.A`vܤAe.A@A.A@Ap.A'AXh.A 3AH).A|}A8"`.A@yסAV.A;A8痨.A$A8痨.A cRApʨ.AşA4.AZ`AV^.A@N AL.ASA.AAͨ.AZA8tר.A׎Aϩ.A`vAP.A ӛA!.A<<A9.A ĚA@FȪ.A|A2o.A`]AJH.AI'ANk.AĝA@ׇ.AVgAǡ.ATAX.AƜAP.AF›A`d.A@zAf.AAת.AgxA .AA ..A APH.AA`a.A뵗AXh.ARTA`v.ApA~.AA.ARA8.AGAf.Ap A`ᔫ.A`)4A83.AGOA.AjA#ƫ.AVA0.ӫ.AjAX.AA؞.A[Ah3 .AAP8%.AALE.A[Apc.A Ar.A |AV.A`)ZA淬.A %A@.AږAW .A`˖AX ".AץA0.,.A@gAz2.A@ AQ5.AԖAW7.ARAأJ.A$A#X.A"AP8p.AGAp.A A.A&AP.A`)0A.A@ ;Akڭ.A35Ap.A0A.A`)6A` .A<A`.A 0A`(.A*Aأ4.A =AP8F.A wA8O.AA\.AzA`al.A@ЗAz.A]їA̔.AAB.A=A@.AA{ή.A]A.AAX.ApA.A&AP.AQ>Af.AUA.ArA.AAp.AÇA.AA@+.AA(;.AA`K.AGAf^.ApA@p.AӘA(\.A`fAP8.A A̾.AA).AzԘAأ.AGɘA.A̶A0.A@ᢘA.AQA8.A`fA1.A cAkL.A TALZ.Ap_Af.ApAp=x.AcA8.A )Aΰ.ApAp.AǗA.A뗗AQ.A{AW.A]AL(.ANA4.AQAE.A `Apc.AzTAfp.ATA.A3EAX.A3/AQڱ.A2A.A=,A).AKA`.AJA.A@ GA*.AZA?.AGcA[.AgAw.A`)VA .A=RA{Ȳ.Az\AV.A jA.AUAQ.A;A4.A@ =AY.A`+Au.AA`.AA.A &A Գ.AA.A[AH.AA.AAQ3.A[AHP.AA`.A`A`an.A@Apy.A=A@׎.AAp=.A`Aਲ਼.A3ӖAp.A`Aƴ.AzZA=۴.A6A.A`fDAX .A`)AB,.AږAG.A`)A_.A 2Azw.A@gPA(܂.A3OA.A =A@W.A=Aŵ.A@ Az۵.A9A.A@ 3Ap.AAQ.A`A0 .A['AP88.A DAH\J.A>AfZ.A@8An.A@2AHa.AA듶.A=A8.A3A֦.A= A{.A:A溶.AZAʶ.A\Aڶ.AoAL.AYAp= .A[=A(.ASA+.AAX<.A`)ƗAK.A]AfN.A>AV.AOAa.AKAP8j.ABAp.ATAWs.AAkz.A׭AL.AؘA0..A@A.ARAHɷ.A AHѷ.A`f AH\Է.A4Aҷ.AGQAӷ.A]Aݷ.AqA.ApAs.AA.A`9 A? .A AX .AߙA` .A ՙA.AA%.AfA(.A8A,.AAF7.AjA@0N.AAV.ABAPn.A`92A(y.A-A.AAPk.A4A{.ADA .AFA>ʸ.AIkAHθ.AAX[ָ.At}A/.A A.AA(y.AA` .A-A(q.A cA8x.AA&.A`KAȩ..A@A12.A{A8.A@uA֐.AΎAX(.AÛAbp.A 8Ax.AQA.A3A .A@rÜA@䈹.AʜA&.A ɏA0.AAW.ARA8穹.AΑA2.A A8ʹ.AܝAxhع.A`BA.Ah%Ao.AKA`.A@EfA\(.AǀA=.A sAQ.AmAqg.A A y.A;NA.AXAǗ.A}A̺.A@ ПAۺ.A@A9ߺ.A@g֟A.A;A.AמA޺.AxAP'ں.A]5Aк.AKAXFͺ.A؞AȲϺ.AqAպ.AA޺.A sA0X.AAg.AAp .A3Ah .ARA.A |A(.AğAf .AA.AAV.A#A8,.A92A0K.A}CAȩX.A.AYm.A`:A(O.A`fA挻.A uAF.AݠAhb.A A'.A,A`C.Ah5A(\˻.Ap'Apֻ.A@N+A 0ܻ.Ao8A8.AmdA(.A 8APk.AA.A@Ap.AңA/.AA( .AޡA.AVA.A@%A7.Az5ABS.A OA j.A@yHAP|q.A0A(x.A A؟.A A.A@ϡA0.AA`Ƽ.A 룡AѼ.A6AP׼.A áAxܼ.A ߡAi޼.AAּ.A`9A̼.A@pAPȼ.A@ AGǼ.A@rAh@̼.ACAӼ.AUAX޼.AQ٢A.AA{.A;Apl.AޢA84.AfɢAp.ANA .A@5Aȶ .ATƢAx.AԢA!.ARݢA:.AA(D.A`AN.AA@X.AA(c.A@A i.A A@k.At5Ak.AKAg.AcAHf.A`pAh.A@xA y.A A|.AA.A@rڣA.A@CA`6y.A,Ay.A6ZAO.A0A8ւ.AA8.AͶAl.AҤA@1.AAD.A"A軜.A3A3.A8AĶ.A@N;AŽ.A;A{˽.A lMA8Eҽ.A``AW.A`ՀA6.AA.AאA .A|A.A`{A.A)}A%.A`mA;.A@EAU.A@yߥAu.AKA .Ah,Aƴ.AeAa׾.A쎦AP.A;AQ.AϦA<.AA-.AA<9.AAA.A`AXG.AAQ.AAn.A OA\|.A[qA,.AA0.AAP.AĽA` .A@AHPʿ.AAlٿ.AN#A.A@A.A`A.AA).A@gЧA2.A֧A@=.AA4B.A`AD.A9AS.ABAP\.AըA0d.A@NAv.A@A.AA.AwAS.AAz.AA8.A` 5A.Ah_A.A[AY!.ASAпK.ATAh[.A eA`d.A}uA k.A@5A@p.A AH)z.AAP.AA0.A;A8.A`A3.AAHa.A`A*.AA .A)xA`2(.AkA()3.AsA9.AࣂA8?.A坩AH.A` A(W.AAPjb.A~A`vl.AfA`Px.A l]A8}.A_]A@‡.ASA^.A?A 0.A@-Ad.AZA.AhƨA).AA N.A@A .A@`רAP^.A`A.A@pA.AD&A .A.A(.A 0AH-.A`Ai.AAPx.ApA#.A`hAHn.A;oAd.A(tAe.A`oA0j.AMA@.A@pA8#.AA=.AA@.A@ϓA`.A/A .AzA.AhAR.AvA/.AjZAB.A$AP.W.A zjAA{n.Ak*A1….A5٦AM.AoAh˳.A A@0.A@A-m.A.&)AǑ!.ApXu6AdyA.ASEAq.A TAf7٨.A;̙DcA8z.Aǵ"rA.AQAK.AdnAH.AA~ .AR;!A.AMlAtn.AktoAs;.A2\i<AX_M.Ao3ǧA.AӍ.˧Anb.ALZDͧA _[.AĶA.AʧAzӀ0.A1JAicپ'.AndA]3H.AxQu_Aog.AA $.A.ӦAMa_.AseA.A#^#APK.A0'AUt:.AR!>A^.A|A~.A)A.A`Ah.A'A.A 6ޢA R.A@rA 5.AA@.A6A>i5.Aq5>A2/.AoA2..Ad,5<Afe3.A++Avכ[<.AlӺAfMfJ.AGUDLA\.AAjb.AmlA9c.AڍHAa?Ja.ADjA [.A AS.A&.Ap|R.A ~AP.AɗA U.AAs`.A NAs.A$Ax.A SA.AA .AԖA.ADA.Ac!A.A A.A|A .AlA.A`tAH\.AwALO.A?AH.AmA (.A  A.Ac`AA.A /A.AēAp.A6KAA .AkA`.A`ǑA@ .A첑A.AvAP.A@ A .AcAq%.A@A$.A |A .AA.A@A.A l[A' .A*A.AA ".A[ǎA8?.AǎAHH.AAHR.A?AR.A@pAHM.AQAdA.A QA0..ApAf.AÈAI.AAx.A)uAX.A@OA(.A \A7.A`΍A.A QAh.A`QA.A Ah.AwAX.A 8rA.A c?Ax.A:AA .AFA(`.A@EMA:.AhA8.A@,A(&.AˋA+1.A`DЋA<.AA0{F.A&}A^.A͊Ao.A A@.A6A@.A lAH.AXAhވ.A/A.A ~҈Aw.AA8#k.A ZA`W.AM1A@-.A5A.AA(.A` pA.A %AP.AAx.A Ax).AA9.AgA(J.A&AQ.AA%.AMnA.AzoA,.ÀAP.AAȲ.AʄAx.A ZApp .A;A"..ALA!8.AхA0=.A@A9.A`RA00.ArA,!.A|xA.APAh.A` A@ .A ܅AY.AhA.AA0.A4AX.AaWAXI.A AA`#.A`f΄Ap=.AରAp.A qA.A&A.A 䨄Ar.A /A .Af=Ak.A`AhQR.A@)A8.A7A.A8A .A(.A.AAL.A`KA(.AA.A@iAX(.A؃A.A)A.A@wA.AGANt.A*7APj.A$AHe.AAb.A AW.AAR.AAJ.A`A@.AʂA #1.AЂA'.A AP$.A@WA,.A@dA c..A`0Aw'.A`m A0.A`BA.A"Ah.A@(A.A A.A`́A.A=AP.AA A.A@pA.AApq.A@ɁA$_.AdAQ.A#A L.A-@A&J.ApAL.A`AO.A@AI.A4ςA 08.AڂAj*.AقA!.AA:.A@WAp.A"Ap(.ASA }6.AA;.AA7.A, Ah+.AfdA>+.A@A/.A߄A)5.A@,AH/.A gAE!.A A&.A A0.A;Ap.A؅A .AAЦ.A@A.A@ AP.A s A@.AЅA .AA.A`A8E.A@A.AA֭.AA\.A`]AH.A`Aq.A@hA@pY.A@QAK.A0A#.A`A8 .AՄA.AτA.A`ƄAP.A=AP.AࣔAP.AjA8.A=6A`a.A Ah3.AAh}.AЃAWd.AAR.AAG.A AB.AGAkA.A YA7.AGA`..ABA`.A GAuտ.A]CAz.A (A֣.A[Aأ.AAy.A3A#n.AAp=f.A Ah3a.AAP8b.A@Apc.A@>nAa.A֓AQ.A߃Az/.A Ah3.A˄A.AA.AAp ޾.A-A(.A`A`.A@AH̃.A A8m.A`)!AM.AMA`.A܅A.AAIڽ.AnAЮѽ.A pAQƽ.AA.A+A`C.A`KA8.AtA.A A𸌽.A`Aw.AA".A`0Ab.A$A.AAJ.A A؛.AʆA^.AB{A0b.A`Aս.ApA0;.A@A.A@ւAH.AA.A@W=A p.AA R.A ՁAx.AA.A pAx0.AJA \.A3Au.Ad1Aޅ.ABA0H.A3݁AIξ.A@*Aؾ.A@,)A.A (#APo.A#A.A"A.A@gBAP.AGAA#.A@2A.AA؞.A@ AP8.A@A.A qAp $.A@A,.AA3.A@ A8H.AA c.A@A`am.A |A@v.A`)Aأ~.AA׆.AA-.ARAu.ABAB.Ap3AV.A@G+AP8.AR*AL.A`f&Ap.AAB.AA8.AA).ARAQ.A@HA.A@2A.Az(A֥.AAf.A~Aּ.A ~A.A ~A8.Apo~Ap=ÿ.A:~Aſ.A3~AH.̿.Az}A8ҿ.Ap}A8ܿ.A }AH.A@>}A.A}A.A@g}A`.A}A8.A }AH.A@g}A.A}A .A}A(.A@}A.A@ }A*.A }Ap=7.Ap}Ap?.A d}AK.A[Y}AQS.A`)f}AzY.A }AP8_.A}A)v.A}A0.A@g}A.A@}A.A}}A8.A@\}AL.AR>}A.Az6}AP.Ap5}A .ARd}AX .A}A.A@>}A.A}A#.A@>j}A.AN}A.Az,}Az.A@>}A8.A}A.AR}AX ..A`)}AC.A`+}ALX.A%}A(܁.A`}A).A@>}Ak.A)}A.AGI}AH.A[y}A".Aj}AH).A6~AB.A l~A%.AR~A9.A֑~ABQ.A }~A@Wh.Ap[~Ak.AG}A.A@>}AP.A@>}A.A3}AP*.A L}A=.A O}AH\N.Av}ALX.A}A-^.A~AZ.Ak~AkV.Ap~AGJ.AQ~AG9.A~A/.A~AL$.AAk.AyA.AAW.A`fA.ARA.A@G AW4.AA`9.A%AA.A@:APE.A=4AH.A`fA RI.AAP.A A83U.A \A R].AAg.AA(n.A`A(v.A࣬AG.AçA.A׿AQ.AAHa.A@ A.A qA.A'A.AzAH.AAp.AA.A@A.A`/AX.A`) A.A`)ځAL.AA(\.AzA#.A@`A0.A3SA`.A qUA`:.A`8A`V.A,A=g.Az.A z.A=:Ap.A \5A.A/Af.A@@A.A[Ap=.AA.AAQ.A3Ak.AA`a.A@<A8.AGAM.A A`x.AуAk.AȃAL.AûA.AA`.ApA .A ⤃AX.ARA.ARAH.AA.A`)ƒA.AzʃAk.AσA8,.A׃A<.A A@K.A`fApP.A3 A`].A=ڃA`g.A=Af.A |A#^.AGGA@WV.A@gA#O.A@A`J.A@lAp=B.AA<.A`)A;.AǁA$.Az|A!.A \UA(.Az,A5.A qA)C.AAI.A@ AT.AA`].ACAH.b.A A(^.A ՁAY.A`)A`W.A`)FAX.A@dA8].AyA`ae.AGA(q.A@ኂAp={.AA`}.AGقA}.ApA~.A A}.AA8z.A(Af|.AQA….AmAp.Az|A.A`fxAf.A]mAp.A3CAW.A`fAf.AGAh.AA0..A3A.A(Ah3.Ap AB.A=,AW'.AGA#D.AAN.A?AT.AR@AY.A \!AS.ApAVQ.A3AH.Q.AτA`V.ApÄAuc.AʄAX q.AAL.A`AG.A`fAH..A`ԄA.A`nA#.A@>A`a.AzA.AGA.Az܃Ap.A܃A).A݃A.AƒAB.AApn.AÃAp8^.A qALV.AzBAU.A*A`Z.A Ap=^.A At.AA̋.A3!A8.AA.Az$AG.A 8A.A@>4A`.A'A.A#A.A 2A`.ApUA RS.AaAm.A3eA Ru.A@rAWx.AA8y.ARAf{.A`fڃAu.A`fAǃ.AA.A Aœ.AAª.Ap˃A8.A A(\.AAp=.AuAH.A]A8.AzJA.AA.A3A4.AEA(a.A׿Au.A`nA.A`f*A.A@ĀAأ.A`A8.A(~Ap=.A@J}A8.A|Aj.A@Y|A.Az{A.A~{A.A |zA.A`yA.ARxAG.ARwAp.A q1wA.AwA.AvAp x.AHvAj.AvAGZ.AzvAP8G.A`)rvA.A \CvAp.A =vAX{.A?vA( .A`fXvA.AjvA\.A`VvA R.AKvAxz.A6vA.A1vA)J.AvAf.AuA.A@ {uA`.AuA`.AtA(\.A(tA.A+tA.A=tAy.AtAz-.AR,uAp.A`huA`.A`uAk|.A uA>.AuA8 .A@GuA.A@uA.AR#uA0.AsA.A@sA .A sAX.A $tA.AktA`.A`ftA}.AtAB..A  uA.AG}uAX p.A |uAI.AuA.AuAzѾ.A0vA.A6vAp8.ArvAV.A@ vA0.A@6wAP.A wAP8.ARwAQq.A@.xAG3.A`ÁxA.A xA.A3YyAf.A]yAud.AQyA%.AzA.AQ.zA.AQ4zAz.A&zA`].A 3zAp?.APzA.A zA.AzALa.A@{A=,.A@|A.A`@|Au.A=R|A3.A|AH..A!}A(.A}Au.A -~Ak.A~AY.Az Ak.AA83Ʒ.A`A.A3]AL.AA.A AQ.A`.Am.AzbA88.AA3.A ́AL̵.A@g A.ARfAkH.AAh.ARVA(ܪ.A=܃Ap0.A`A.A`A`.A[Aw.A |A.A`$Aϲ.A~Ap=.AĆA`aA.A!A8.AGA𣔱.ApɇA O.AAp.A iA.ARĈAp8X.A3A.A(NAG.AGiA(\.ApA›.AzA(\f.A‰AX-.AA.A \A`Į.A`*A̍.A`f<A`].A`JA1.A@GOA83.A@JAQ.AtA.A@GA@n.AÛA)?.AA.A@ AŬ.A|Aч.A`)`A83I.Ap=A R.AA8E.AZAh.A@yA.AA.A|Az.A`mA ܪ.AFiA\Ԫ.A~APZҪ.AAΪ.AωAxK.A2Aot.A`fAt`.AވA.A@ԊAG2.A6A .A@A@.AuA`.AxzA׃.A A_'.A@Aج.ASA\s j.A݅$AuK-;.A\φA5.A,+sAZ.AA&A(0.Af A8.A`ACy.A(ἋA"%y.A%% A Rn.A^A l.AiA8l.AkIkJAm.A[*A9o.A8 A9,ls.AA#Ax.A~@\ׂA F}.A0vA9.A+HAX[.AXAh@.AA|.AsyxAJ也.AzA.AA^ſ.AjAAW14Ÿ.A廄~A3ğ.A,½sA ş.A1wAgAIgƟ.AZ㗭ZAv&A ǟ.A`]AMAPƟ.A#+O?AƟ.A@2A$5B.AEOAк.A@EفAx.AvAH.A eA.A@`eAd.AeA.A"vA_.AA \.A`A`CF.AAH.A Ap<.A AB.A A΂Ah.A`̂A< ߂.AXA&u.A6Aȶh.A@1AfUc.AROA6`.ApAM1].AhިғA׳].A0Aګ!].AyԥۃA7)_.AO]Ab.AT; AM8g.A6?Aom.A`ZAQp.AMG_At.AsfA٧v.AoA}Z]y.ASyA{R{.A* A@(|.A]3A}.AKA}.A Ax-P.A$A83$.AsA.AFuA`,.AAh3.ADA5.A`A.A`K[AX .A`mAX.AvAL.AanAr0>.ADGGdA%Y;1.A3' QA! $.A305A\[2.A>Ac.AAi.A፨ޭA_+:r.A,_Ѡ~A(Л.AYA0Ʒ.AV4FA5.A@AA:;.ATyJA#09k.AcaA@S.A`ՅA%L.At]Al:/vE.AuPAW=.AxpA̋5.A#ÙAe*..A̞A&.A(A.AA.Ar@{Aݨaš.ANA7u.AsA.A=̓AxF0.A[AXu.A狄AǙ.A@ۄAPș.AKɃARL.A |ЃAp8.AkA9.A`BAPߗ.A;pA8Iڗ.A@>ąA.A@`A0&v.AAȔt.A`ÚA u.AiA0.A@,HAXJ.A`fAx>.AA.AxA.A@NA.AApJ.AA.AAP .A[A .A`eA@Ʒ.A@.jAN.A`A@۔.A ZA@ה.AUA>ה.AUA.AzA9ѥ .AW_Aw.AVPA2.Atz8Aa.AK߸A.AthfAKOړ.A/EnAJrӓ.ApDLA͓.Az%Aɓ.AfA0ȓ.A@DAн.A`"<A .A|؄Av.AzۄA~.AA( `.A3Af.A@Aq.A@>Axh.A-A𢿒.A A.AAБ.A A0.AA.AVڃA@ .A@yA p .A3}AB .A!A(ː.AQBA`ː.AyAh .AYAM .A@ A.AA.A^{A.AZA'.A lYA'.AyA@(.AA@".A`DA`X".A҆A3.A؆AP.A AK.A A@F.A#Axx.A A'v.AAr\.AA=.A`A0Ǽ.AT Aʏ.Av}Ay.Av}Apo.AWA)G.A Aˎ.AaA.A@.AP.A ZA8֌.AAp.A=ƅA0.AąA`ᗎ.A`)^Au.A`fTA~.AAX#J.AAHPL.AX<Ah.A,A.A` A.A`DZApA.A+A`S.A@pA0{.AA..AAH+.AQAx.A \A@.A`]Aq.A@$A@.AA.ABA`!g.AAVh.A[Ahh.A"DA@i.A@A"j.AA`k.AAk.AׅAxl.AsAm.A#Az.A&Az.AH6A.A@6Ax.A #A83k.A@4Azk.A`Ah/b.A@A 1.AAx*.A Ax.AA .A`TAފ.A3A֊.A@A@d.A@Ap.A(A0p.A@yAWh.AAXl:.A@A20.A`A8/.AA0b.AAb.A uA8a.AÅAa.AA0a.A^A`.AVAJ.Av{AUF.AAH".A`߇A$.A}AѪ.AA .A A.A $AH .A@.A .A SA5.ALAډ.A AAȤщ.AaAx.A`A(:.A SA?z.A@An.A A((n.A@A@.A&&APf.AEAW.A@ A8.AA@1%.AqA".A`)FA(\0.AA3.A A?.A ¿AwA.AAE.A8A+.AQHA(.ARA( .A+AG .A`DA&.AQnAȇ$.A/Ap.A`$A8#.ABA4.A Ah.AAPψ.A 8A(.AIAm.A(AM.A AV=.AzA@}.AAE.AdA.AYAʅ.AA .A滉A.AhA0҅.AA@丄.A`A.A}A.AA(.A`A.A9A0.A`sA'Ԃ.AA'ă.AȅA^|.ANƅAȶw.AൖA@>.AƉA㘂.AHA@G.AaA J.A Am.A"Am.A@5A`$.AA .AA$A08.AQA,:.A`]2Al.A0A(~.A@PhAh~.A |AH:.A`TA~.A9SA ^}.A (A8|.A@`Ap8}.AxA }.A@rAH|.A@SAh {.A-A!|.AIAo|.A?@Aпr{.AAty.AFA ov.A 6APiu.A ]Acs.Ag[A p.AAX$`n.A`oA.n.A^A[n.AA(n.A@yxAo.A)yAx(p.A OyA`1q.A`zA^s.A{Au.A`{Av.A X|AxiPw.AŤ{AIz.AjxA |.AsvA~.AZtA̾.A@F.ApAG.A`oAM.AoAޘ.A pA`! .A pA < .A`oA #.AoAP.ApAl1.AspA1.AoAi.A@yoA[i.A@oA^.A`;oA.AKoAi.ADoAi.A oA 1.A@oA4.AnAך.AoA'ؚ.A`TpA.AڦpA$.A@PoA .A@%oA .AEpA _ .A`pAP".A oApE.A`"oAȱT.A`ܮoAe.ApAՁ.A}pA``.AFpA`Û.AxppARқ.A(pA.AVoA.AfoAh. .A &mAX.A\A.A`2\A`rm.A0VApf.ASAx-.AOMAΝ.A`8MAF.A7A.A`]6A`>.AF1A@.A;0Ahަ.A`/AP.A`Ì/A.A '/AӖ.A/Ap–.A.AH.A.A.A c+A.A@+A.A`.Ah"Z.A.Aѕ.A/A.A_1Aד.A2AMT.A2A".ALj2A .A1A.AP1Axz.A`v-0A'.A`D-A5ޒ.AH-AHp+6ؒ.A*-A*ђ.Aȩ--Afʒ.AF-A&’.A6B-A ?".A"-Aҵ.A-Aе.A-Aj.A@5/AS\9.AP)C/A1磀.AmO/AMAK.A@Z/AH.AEGcd/Ah..Aol/A ,g.Aq/Aƍ.AbFu/AJf.A#rv/AlPو.A0,Iu/A)[Ke.A^q/A .A&l/AБؐ.A"/A.Ah.A9.A`KN-A<4.AaףHg-AŒ/.AM-A$L,.A@9N-A{/*.A@$-A9%c).A\'N-A).A.Ae;+.A5x%.AVI..AcC.Ad2.A2U_.Ae7.APxx.Aq=.A2|.A>D.AVTF.AL.A@.A\bH.Ak.Am,B.AQe/A/;.Ac*/Ay/A.A@Ǝ/AQ-.AfN/Ae q.Ak\1/A?.Ag/Ae.AU{9/As~.A4g.Ah~.A@.A}>~.A@EU.Aw~.AHd<-Aw ,a~.AfX#y-A?xN~.AN,A sA~.A}Uo,Ax8~.A9e+A54~.AL+ApN~.A O1+AS~.A@r+A~.AL+A~.A@*A ~.A#Y*A<}.A`T@)A}.ABB)A[|.A :)A(>|.A@4)Am|.At)AK|.A_(Aجz.A'Ax~x.A&A7x.A3~&A8'Lw.A@{%Axmv.A %Aw%v.A#Au.A$A,u.A@g#ALu.AZ#AxXt.Ac#A't.A`T#AA5t.A@9#A@s.Ak"As.A s"Apr.A s"A`r.A s"A$r.A Zl"Auq.A@'"A@q.Ai!App.Af A Asp.A c* Azo.A` AHo.Aov Ax 4o.A` AZn.A`o An.A A@Pn.A AXm.Av A0Ym.A@y Am.AE A nm.A@_ A0&Mm.A` ACm.A@iP Ap<8m.AUAm.AOqAhm.AA`6m.A Al.A g!All.A A!Aj.A A!A&j.A A!AAi.AR!Ah.A@ A3h.A&A%h.A`KANh.AHA Fh.A@RA3h.A4KAPh.A AAg.A/Asg.A AWf.A QA`d.A=Ac.A əA1c.A,AXb.A`}Aзb.A@AH}b.A*Ab.A`fAOwb.AA`ab.A`YAGb.A`Ax:b.AR ATa.AÕA`.AqA_.A5A4_.A`)FAk^.AX/A^.AA`vK^.A Apl[.A?PA@uZ.AAxY.AlA W.AAP/lV.A`"A c;U.A`fAu2S.A AP.A#AX:P.AAD!P.AA1IP.AҭA8P.ATAh&P.AxAP.A`[CAO.A`WAO.AnAx[O.A PAXN.A`1AN.A`9AhM.AAM.AAL.AaiA ML.A`AKL.AUAwK.A@GAK.Aj'AFL.A S A3L.A> AL.Av6 AK.A AABK.A`kAp0J.AAfJ.A{ANH.ATA`4H.A A`G.A A NG.Ah AxG.A@ؖ AH.AYAH.AOAH.A=RAvG.AAG.A@YAP^gG.A; A`CEG.A@ye A0G.A  AVF.A  Ah%E.AT A 2D.Aa, A-C.Ax} A8#NC.A m AgDC.Ad APC.AAC.AA$C.AՄA`TlC.A uAhQYC.ARAYOC.AA`LC.AAЀBC.A` A15C.AA0H C.Ap AC.A A`wB.A@ A@uA.A@ Aأ@.AXx AI @.A@iAP?.ATA>.AiAd>.AgAd>.A ,A(>.A ,A>.A Ap >.A@YA[>.AxHAg>.A;)AHL=>.A`CAxi=.A@A=.AA;.A 8,Apʰ;.A@5&A@%{b;.AkVA0+;.AL~A:.A4A}~:.AA}B5:.A9 @AX9.AjA#:.A`.A0:.A[A9.AQAp9.A॥Aȱ 9.AjA9.A =A`9.A@A9.AŪA9.AsA 9.ADAb9.ADb!nAͫ9.AtpAi9.A{AHݎ9.A@5AS u9.A~9nA`CZ9.A@A";49.AqAp 9.A`;Awΐ8.A ;A;"8.AY@YkA78.Aǧ*Ag7.A4@<AR7.ABsAbN^7.A;8gAh?7.AD05AH6.A Aa 6.ApAB5.AAXF_5.A' A15.A`DA *5.AAuH5.A Av5.AOAh5.A})A6.A`Ah] 6.A{>AyaN6.AA\6.A1pzPAl_6.Ak As!26.AҔA!6.Ad^+AOiݲ%6.A lAS7 )6.ACA9f+6.AQA)y-6.AUANk.6.AWnA6/6.AiZA6.A|A 5.AAxd5.A#6AX4.AIaAȓ4.AEApJ4.A(RA4.A@ϥAz'4.A;At3.A(A:2.AA(1.AOARO1.A@>\Ah@0.AAn0.AdA8p0.AYApJ|0.A0A0.A ^A@0.AxAX-1.A A".AfAb".ARAF".A/EA0*!.A$@A!.AAP7!.A`Ah{!.A A@5!.A̓AU .AmAp.AAPK.A֘A0.A<APk.AXA.A wAz.AA@.A A.ATAho.AgA\.AAsH.AOAxK.AA.A@As .A A^.AA`,.A@Aԫ.ALAN<.A@A.A $A.AA.A 4Aܝ.A qcAA.A`!A.AZAN.A@%JA.AQA0I.A@,pA^..AvA(n.AgA.AhA.A@!A .A`Ap.A@A8.A`AН.A@Ap.AA*.A@6Ab.AA0Ǽ.AAxd.A OA0.A`AM.A@,pA".A@,pA?.A:A.AT3AJ.AM$A.AAA.AAؚ.AAx)F.AעA?1.A`!A?1.A5A).AAx .A<A.A@`8A.A@rA=.A:A`!.AA .A@A`Bc .A`5Ak& .A [A\ .A \A@k .AFAz .AA0L! .AA`vJ .AEA@; .ADAH1 .AQA .A]Ag .AAK .A@yA .A[A0 .AછAeP .A;Azx .AˢFpAiH.Ap@AZ8.AA@~.A A gu.ARAF$.AA.AԒ\A.A@A`.AAvAȔ`.AA;.AWA`.A@ Ah%.A ^A`6.A?Ah.A`lA.A/A0[.A#A.A`fA)yU.AebAmޥ.A 'A(<.AaMAI.A@ Al.A-ˬA;޽N.ACmSA*-.A߁0A6V.A&dtfAzl.A,An[.An9: Aw .AKR A1W.AGO A}s'.AM AX.A AG.A ` A<.And A8.A6 A,D.Ak A.A&D A~yVq.A٘ A|B Y.ABr AB.Aŋs9 ATT/.Ac Asr.A]> A. .A|: / Ap.Ad Ar.A A.AXL A0,.AO A^.AZX߲ AVڻ.A6JQAcrP.AkA`C/.A8&7aAz}=-AvA-A0A4t-A˦|A]8-Aj-A(-A`A-AMA ^-AA,-A A3A-A@,A3-A@EAP@=-A`hA;-Aҁ&A6`-A@i'A -AS0A_y-A}Ц2A:-Aq4A x-A#‹U7Adn-A\X9Ai-AB <A-A@-A{ocA5jZ4-AF eAʍ-A@#fADv-ABZgAf e9-A/ܠgA0v-AhA,,-ApMiAN)6H-Aٻ(tiAy*-AVZviAT-AcjAxAO-AOcjAeĢ-AP bkA#-A8oOSlA(B-A`;mA6b-AmAx-A[znA*j-A?9oA@*-A@pA6-At*pAW^-AjqAREJ-ArA@S'-A!rAx|-ABKsA-A ItA6 .-A( UtA6p-A4vuA&p5-AoԼuAX(G-AYvA)d-A;wAppT-A fxA;-AvA0.A iuAH.A rA .A]qAxj.AqAp.A rA@.AyqAnHWX.A`pA.A pA Z.A WpAj.AI,,pAx*?.ApAB{X( .AY pA\Ctm .Av93pAj{R .An)pAV= .A'BRpAU: .AippAh| .A pA( .A7qAid6 .AaPqA' .A rA(A .AjVrA6 .ACrA!; .A0erAFr .AmKrA%Ϫ .AܵrA;6̧ .AJrALy6 .Aw'rA,Q .AۙrA(9 .A .A`6iA4DE .A@@ևhAq.A'hA[m5.Ang.hAM.AhAXz.AFZAu/.A >YAi:].ACYA Ȯ.Arq YA\.AYA.AqYA&|.Ap|+YAoK.AVYAKHz.AviYA~h.A>'YAj.A 5ZA.AdZA~^.AD[A.AXAH? .AXA]Z .AWAMX!.ATAhݐ".A VTA/#.A TAh"#.AUAbX$.AVApS$.A*WAH#.A}zVA#.AVAHr$.A@,WA$.A@NWA $.A@WAxƩ$.AכWAs^$.A 1WAd$.AVAX$.A@*WAY$.AmWA$.A XAS&.A@`_A v4&.A5bKG`A]]e&.A%`A@ %.A-|FaA/N%.AHSaAd%.A bA(#&.A+eAy'.A@lA7(.A`nA (.AVoA'.A/ApA0'.A-juA _&.AoA`%.AnA|%.A GfmAc%.AsulAx*7%.A4ޠAkA6A%.AMFjAP$.A^FhA15p$.AsgAг$.A`!fAak$.A9.bAf $.At9`AWy#.A^A`=#.A,^AH!.AtaAa!.AaDdA & .AfAf.A@NfA@.AaZiA@.A mAP.A nAR_.A u{oA.AqAe.ArAPk1.A&rA .A NtAPk1.AuAhy.A`DvA@k.A OؑA4ىod.A͹(&A1a.A2b.ACc.A[@ړAZi.AZCr҄As.Ax+AW.A"͕APk.AhA(.A`;XAxY.A (Ac|+<.A3 j^A#q".A1SA , .A꓉A`.AAV .AxI͈AM.AX/DA*+.AH^WA๕.A1AS.AV*Ah~.A;"A<t.A"ׇAؽm.A>A4Mg.A@{A0c.A'HAES.A<AA.A!:A[D-.AlņA.A챆A$R.A|A3t.ACjAq.A+ Ab.AZ~ABJTk.AUdAVo.AUWA|.A0HAN.AN A1B1.A"CAjd.A0ݙAG.An1Aӑ.ALacAX.AA c.A5U#~A9Gm.A67n~AdN.A~-~AB0J.Ai/}Ahi.AHG}AaP.A@}A2.AG9}Anĕ.AFn}Aw.A)Ui}A):.A'}AϨ.A\|A.A;u|Afk.AIn#|Ahm٢.Ay9{A;g7.Ar骔{A;.Ay[{A^_Vq.An-{Al/\.A:H {A'G.AtJIzA(j1.A zA$.A 9 zA.A`{Ax[.A_zA t.A zA(.AzA.A x{A.A z}A2.A ۇAn .AτA`r.AdA.A@/AQ.A`A`G.AjzA.A`[A@< .A@CA} .A@DA .AA؎ .A`A@@ .AjAH\x .A XAH .AA@ .A'AJ .AA .A`A~ .A@>{Ax| .A \A@ .A@WAh8 .AtJA .AA .AA(+ .A=A8V .A]A@ .A`Aɚ .A&A8V .A&Aw .ASA}{ .A u`A /? .A}Ae .A"Ahb .AwA06 .A uAp .A@5Ash .A A-/ .A SAXQ .A SA(Kz .A-A.A@NA@.A@AAa.A`vA.A JAX.A@+AM.AA/.A?Aj[.AA[.A`Ao.ADA /A.A$A9=.AHA.A A.A@5 AH.Aa_Ae.A@iA0-.A`IAp,.AAj+.A+XAPI(.A`}AF.A@iAP8 .A[A .AoA .AA.A@ϯA.A_A@.A A.AAใ.A  As.A Ae4 .AAX: .A$ڬA7 .AA: .A@߫A8.A{Aȶ.A ~IAi.AAX.AzSAH.A`]A3o.AܰAKd.AA`Ÿ.AA( .AqAp .A@5qA .A SA)U .A@A .A Az .A`A .A@ AP .AAxz .A@gA .A A0e .AA`T .A@p6Ax .A@,A .A4Ao .A@A P .A$A . .AaA( .AOAH .A QA8 .A>Ax3 .AzAxL .A _A_ .A A܀ .A@.BAܢ .A Aȩ .A@r"A .A sfA .AAv .AzAfP.AAH\.A@7A 7.APAʺ.A?6A3.A0+#A%.A,Al.AW91_AN.A#UAXw.AՀA+ .A`oAHr .A`VAR.AveA\Y.A@0A.AA .A+AH\B .AAa .AAx .A@`As .AyƺAc{ .AFAa+\ .A姟`AsB@ .AEAs-( .AMxlCAh1 .A5 AS| .A`5SA2.ADA+5.A/@A.A`A0S .A0A* .A /Aj .AxAH .AA] .AAA7 .A@AHכ .A{QveAIɷg .AouA2X .ACA=K .AsWAj .A#DA`) .A/<- AK .Aj>AR .AQXAL7)~ .A`mAu .Ahc_|AȔk .A@,A .A@pA:JK .AG%AZ .AxTEA .AP6E} A r .AOAA .AĝAP.A lAH(.AA.A^KA6B.A$As.A@A(.AApʲ.A`DA:t.A dAyֽ2.A/e} AeA(t-A@,CA(-A`KEA'?-A*GA-As HAXXb-A7IAz-A YJA%V-ARLAoD-A`lMA,]3-A OA`-AwPAׅ/W)-AX)Q~QA@-A}RA+H-AhSA-A`"TAH@-A`BGWA4-A@ZWAHC5-A@ilZA8a-A;]AGZ'A-A :z^A-AǨ_AF5]-AJ`A\BͲ-A'8`A U-Aݨ\RaAá>-AJ daAJ-AbAk-AaA-AeaA-AfaAX-AoHcAy-AŰcA-AƉeAHN-A:fAH-AhA-AjA&-A{AbkA t5-A 0alA--AݙlA~j-ADAm2-A7ĩA`2-A {AA(߼-A]=&ƪA|6ļ-A~A~-Ad=A+~-ABz-AIi$N+AP@a-A:1La,ALFM-A8wy-Ay@ =-AtS.A1-Aٶ/A/iu.-AWV0A--A@.!2AI 0-A 4AT9-Ac 5A/F-AF7A<;-Ay:AH-AAAh-AHEA0-A@6IA-A@NAp-ANA -AtV WPA}5mͮ-AѨQAΐ~-Aؗ-0SA0뫮-All9XTAw-AVJVA3C-AmWAl-AFXA4/-A AZA蛮-A7z[A-A$\AN-A@pbAP7-A ™fAE%-AmAO-AvA,-Ai1A(>-AA8-A -A-A4AX-AAQ-A AO-Ah(A,S-A8E8A[-AɑzGAyh-ATSAߩy-AZA*䑐-A,8[AANث-AZUACo˰-A'FA^-A`MW-A@-AU AH-A?FAhQ-AܱA`-AA-A A?-AeAX[-AAh]-A-<AH-A:rA=e-A98A-AHA:,W-A98A|rK-A[u-IA+-AżA6x,-AT,AJ-AĒL. Aҗgu-A WnABkݝ-A F)7-A<"L-A| uAA~n5r-ARXA .A`VjA .A AqA= .AsAx .A tA8 .AଗrAo .A?8“oAل .A0~yAk4;.A౬^AOl.A+A-AA-A/A@B-AA,-Aޘ AXFA-A!Ay-AL*A_GG.A| uAb".AF)w A2-h .A Wn2A.AÒLnAʇT.AT,QA A[[.A@r@ A[.A zB A p[.ARC A.>\.A@]E Ahѥ\.A@yH A{].AFwM A^.AU A^.AU A? _.AV A$_.A@y W AHzj_.A fX A _.AX Ap_.A=Y Ax_.AoVY A A_.AY AȘ_.A$Z A0v_.AZ Ay_.A`TZ A@u_.A}[ Ap `.Af[ Af<`.A\ AXH`.A`#] A0``.Af] A l`.A^ A4t`.A5^ A}}`.AW^ A3`.Aw^ A`.A@C^ A #`.A^ A(`.A@W_ A0`.AV_ A`r`.A`` A!`.A F` A`.AZp` A3a.A@#1a AYa.Aa Aka.Aa A̎a.A (b A Ra.A c Axa.A+c AHKa.A d Ab.A0d A (b.A`d AЄ>b.AOd A vb.A3e Ab.A cSf AEb.Abf A b.Aqf A0b.AUf A#b.A@f Ab.A3f Ab.A?g A@ub.A@>g APZc.Ag.ARf,:s A%hh.A(NSa Ad l.AnBE AGÕp.Aȴa+ Azʙt.AR A_Gݿy.A| uOAb"n.AF)A2-h.A WnrA*.AÒLAʇT.AT,A-A@.AA -A`bVEA Z-A@ FAk-A@DAۣ-A@rFA0-A"KA b-AKAH_-AMAP^:-A@NA03-A 1NAŜ)-ARA`k-ARA`2y-AkSA)˕-A%-L SA= -AkRAͥ-ARAȢ-Ao6tRAX=-A-QRAϨ-A LA -Af2o13JA;sN`-AXg:IA؃-AM BA+-ADlA-A@iA0-A@jA-A@kA-A@5kA@Ɠ-AMGkAȶ-AjA@qg-A6jA=I-AVhAd-AhA8-AM`jA-A0jAN-A!kAH)-A OjA?-A@iA@}-A`iA-A6iA-A`bhA-A VfA@-A^A&m'D-A8x^A]H-Atg^AAK4M-AY^A5cR-AS5O^A:mK;X-AX4`H^AH ^-AZlYE^A_d-Af4F^Ai-AJ^A]-A`ml`A{R-A`A0-AbAx(-A"{bAHP-AbAC{ܥ-AUkO cADT-A 5cA`-A`ׯWcA؞-A`ocAp2-AcA{3-A`]cAh;-AcAJ\-A`cA&-AbA R-A@bA-A@ylbA-A6bA@q-A` aA-A`AP-A`A-Ah`Ap-A@_API-A^A$-A`^Ah-A@^A-A8O^A07-An]AH-A`]]A8-A`T]A(-Am]A M(-A Jm]A0%-A`i]AP!-Af]AX-A cz]A-AR^A-A`1^A)-AO^^Aܑ-A~^AXd-A` ^Aȓt-A Oc_AI-A5_A8s>-A *_A-A^A`-AZ]A-A[]A`-Aޕ]A-A ]A@-A*t]AP8-Aj]Ap-A@`]AQ-AT]A-AN]Aѓ-A&G]As-A=]AW-A2]A@-A@#]AЏ-A`]A-A@]Ag-A\A--A\AQ-A@\As-A\APg-A@i;\AH.-A@#\A0-A \A <-Af\AZ-A3}\Arz-A SrYAȺq-AGYA-ArXA-A&XA-A!XA -AWAe-AWAۯ-AFWA-A@WA} -A J!VA(-ARAP-ABm8Aأ-AM7A-Aa01A-A&0AC-A|0A0;a-A0A[C-A/AY%j-A9K'AJ-A=m~a2A5x,-Ai\~?A #-AW~ByFA>-AךGA-ABKA0H-AOAf-AOA؎~-ARA-A@ JUA3-A5XA$-A@>?XA -AoZA@-A`]]AQo-A`KgA)-A[hA-A@!hAp|-AGiA-AĩiA7 -A3?iA(9-A^HjADį-AWʂjA-AjAb&-AͷjAYX-A(jAZot-AᨚijA?-A>KjA(-A jA"tcC-AԍjAЮ-AQjA-A` jAɺ-A` jAp -ACkA0ي-A`TlkAg-A@kA+-ADlAU-A BAB-A c0Ax$-AA0·-A`դA0-A`-Aע-A@AR-AʎA8:-AAx\-AkAj?-A AhD-AڲA -A@mAPM-A)Aq3-A cAת-A AH-A` A8-A_A-AyAp`-AAptl-AټAx-A.AXp -A?AX-A Af-AؿAX-A@ AXFñ-A AwA -ABFA`K-A#A--A)sAU-A BAj[.A<1 A([.A . A'Z.A4. AY.Ah. APX.A`- AX.A`. Ap+mX.A`;- AgX.A7. A[YX.A4. AXTX.A?. APX.A . A83NX.A`. AXHX.A. A}IX.Ac / AX.A/ A>X.A &- AvQW.A`3- AUGW.A>- A>W.A-r- A(2W.A / A7W.AO4/ AGBW.A4W/ A((W.A@/ Aȥ%X.A / AH$X.A / A:OX.A`b/ A8PX.A@%/ AdX.A/ AceX.A/ AyZ.A0 A0Z.A1 AH]Z.A1 Aj[.A<1 A@Y].A-Z A!Q].A`- AV\.A@` A2\.A A\.A Ax-@].At AxY].A|] A@Y].A-Z AV.A AV.A@ Ax\V.A) A`V.A`w A8kV.Aീ A V.A AV.A A0&4V.A@ AZV.A= AȺV.A Ax$V.A c2 A(,V.Akb APfV.A<\ A` W.A} AW.Au A8 W.A` A V.Ak AV.A Ap#c.AbK( A c.A Ab.A@O A!b.AF- A@$b.A) A`.A  A8@`.A Q A0_.AM A4F^.A^6 A*m<^.A Ak\X4^.A Ұ A.^.A`B AYz?"^.Ac A)^.A U& A ^.A_ AS].A*N A<].AΒ A~̈].Ay |$| A ].A#o AQ|].A4(l A a].A Fs AHƝ].A֮M Ai].ANVĞ A].A AH(].A= AU\.A`: A ].A Av\.Aj= A+\.A^q} A}\.A" h A\.A O Aa\.Aofrz@ AͦJ\.AW&s AH\.A39 A; M\.AhҦ A].Ax AZ ].A&n AG].A؀^E A].At A3].A`d8 AX^.A  A`.A@= A(E`.AD AL`.A`R A`va.A  AX)b.A R A^[b.A A^c.A[ AxX e.A L( AF@4e.AK|, AE]K^d.A}9? Ap#c.AbK( A 0.Au A$.A# Al.A A"..A Q Ak.A A* .A; Ap[.Aل A`C[.Ay Aw1.A= A؞.Aa AX.A+Y AR.A6z Ap,^.A3 AP .A' A*.A? A(.A A.A@ A0B.Aa AD.Aoe AY.AEe A#ӯ.AV A2-h.A A 0.Au Am.AgP AϏ.AƙO A@ .A`O A.AR A\.A`fvR AX.A P Auw.AcL A 4 .A #K AH.AcJ AX.A J AfF.AJ A8E.AD A.A C A@J.AmA AЄ,.A@:B AC.A D A(~^.A+E A៌.AF Aƌ.ApF AP.A@%5G AH .A$L A@ȍ.AM A1΍.AO Ah.A R A&.A/V AXVk.A5W Aڪ.AU Aؚ}.AS As.AQ Am.AgP A1.A^4 A` .AL0 AB.A`- A̭.Av+ AH>.A' AL.A`R$ A@е.A`v# A&%.A) A0X.A 1 A.AE A.A AP.A A_ڸ.A` A޺.Aax A.A`dE Aʽ.A` A;J.A@ ApNU.A AX(.A u A.AAv A0.A`B A6.A@߲ A0f.A A`.A9 A54.A@a AH..Ah A`̾.A AL.Ao0 AFּ.AC AXRǼ.Af A 0.Ap AW.A`̃ A`.A}Ҷ A .A A@B.A API.A@ Ao.AR Aȶ6.A@ Aʱ.A_ AC.A`} Ax].Ao A.A@G9m Aܭ.Ase Aڭ.A 1je A.A d Ai.A c Aذ_.A`Kc AjY.A`Íc APk6.Ac A|ج.A@a Am.A̵` A.Axt` AX.Aa` A%k.A` Ac.A"` A02.A a A.A a Ah+.A_ A.AT A+.AS Ag8.AS AHC.AR A.AR A.A@fR A.A ҚQ A+.AR Ax.A-W A;.A/^ A텟.A"` A@ϟ.Aidb A .Aje Ah.AOi A4.A`m AR.A Pq A`.As AÓ.A`w A0j͓.A`rz A\.AM| A.A l} A\.A@ A?m.A Ax.A Ap.A B A@.AЗ AX.A@Cĵ A! .A cz AHU.A A0;.A@i A$.A ? AfN.A`D AY.A > Az.A( A0.Ag A.A AH.A| A .A[ A8".A7 Ap.A Aʧ.A A<.AZ A .A@, A.A AP.A0S Ag.Ak A`Ce.A@ AY.Al A"X.A@m Ap.A`e An.A3f A\Y.A Zh A@K.A`  A4.A@P A0.A@yo AH0.A} A.AK A_.Ap AY.A`D AN.A% Al.AՖ A0.A@a AHi8.A A.AY A .A$ AXR.Aા Am.A Abq.A ARb.A@ A(-+.AI A0囨.AD AH,.A`& A.A AXF.AV A@.A- A(&.A_b A H.A`i Axhp.AR- A.A ALҨ.A Aʨ.A 1 A@.A) A@A{.Aѡ Ah/P.A`$ Ac.A A@`.A  A蕾.A ? Ap~.A^ APT.A^ A .Aa A@.A`V AMڦ.A`V AH̰.A t A.A`V AHl.A@ ABE.A@ A(.At A.A \ A舭.A ( Ah~.A A( q.A@ AP/l.Az Atg.A`; Ap.A g A.A@U A1.Aߜ A𴭦.AH AƦ.A ALަ.Aa A.A- AO.A A4.A)F Ax).A} Ay.A A* A).A`mx? A.AN A A.A*A A.AxA Aʰ.A&@ Aӱ.Ae= AD[.A@g; A1.A^4 A;.A (wL A-.A@!L A}.A@%L A.AҠL A .AN A.A@uO Ap.A (P At.A@Q A.A=Q AX.A (Q A ʡ.A@RQ As.A qQP A.A3O AH@.A (?M A;.A (wL A.AoR A8{.ALR An_.A ZSR A@3.A ZR Aؚ.AT AP^.A@U AX.A@V Af£.AoW A>ѣ.AX A.A W AK.A@yW Ap6.AIV AH)M.AU A𒎤.A=wS A.AoR A(s.A A7.A=GA0.AAh̨.A͝AG.A^mXAƯ.AA_.A_MLAxa߰.AQA{H.Aq>LAiɬ.AkPA +.AHAMo.A|bA8.At@AA.A&A`Xz.AA{sk.AHDAu`.AfbQAY.AᰴA6W.A$yAp a.AJ7AqzTo.A_A弁.AoP67ABoʗ.AYrZAY1.A󐜘Ą.A|gAM9.A@d|DAE;5.At&5AH&.A@8Ab.A*j)AJHc.As8.A(#lة.AHGA̴.A ltAZm;K.A,.NAS.A0u AY׸.A_,EpA.A5qAR.ArAX$ .AtĚAb&.AAb 1.A& pA?.AE ƛA P.AAT.AAn.AqǛAa>.AG@A_p".A9Aݫ.A ]A' .A!xA Σ;.Ac}ANJ k.A}SNnAS .A PJAH.Ǭ.AAɣ.A |*A9.AAf\Q .AKg갈Akn".AWAŵ9.AJքA>1Q.A Aqg.A|A|.A QAt.A@AP,.A ZAh.A`Ahċ.AߴA.A uAj.Ag-A]Я.ATA ѯ.A@A0.A`<A8.A@AU.A=ܗAU.AA-.A`堗Ap8.A A@<.AAA.AAm:.AsAp7.ARA`A.AIA L.A <AXJY.A Am.A`ȖAԅ.A@>A.AA۠.A ɖAxW.AAp,.AൖAb.A@`A _.AA`.A~AX.AaAPɹ.A_Ah@.A ,A汰.A@A.A@Aϰ.A@A.AוAN.A०A.AMAi.A= A%.A A".A AޔA8(.A єA(.A@AiW).A oA56&.A~WAּ~.Al+Ao.A7`wAг.A։A.A5@A.A"1]SA[qW.AA2>-.AgA0v.AAPɰ.A1A汰.A^טhzAK.Ah1Aa~H.AdwApň.A䕀AV터.A/EA$&.AQ(}A%%.Adx|Ai..AU{A0 .A-N zAB&뻰.A 8xA]S}հ.A;wAC.A{HvAѼ.A8 VuA5l=.A/issA)&i.A?HrAFW.AÉqARk'ͱ.A pA zj.AF}oA9;@.A nA~.AemA1.A^ mA .AؖIlAk)9M.AD$WkAlY.AF| jAȵ.A`ijA֙.A2?%rjA:!.A2?%rjA0.A`ijAI[3.A.A@6jA.AgjAP.A jAdb.AVjAw.AFjA0U.AjA .A`kA.AT>kA .A(kAᴵ.A`>kA0.A@kkAѵ.A qkA;.AjkA@.AkA@F.A`lA.A wA.A@ wA@ .A (zwA0.AwAh..AxAM.AspwA(`.A`twA.AwAxX.AwAmߴ.AxAuڴ.A xAѴ.A@yA˴.A cyAؒô.AyA ɴ.AyA.A zAٻ.AtRzAX챴.A6zA.A@!zAPM.AzA.A@P<{A.Aw{Ai.A@%{AX.A 8{A.A@{AXF.A A{Ap|.A@,&|A.A`B|A.A`]h|A.Ai~|A.A||Az.A@g|APk|.A|A6z.A/|AH?u.A|A`%l.A c}Amd.AA}A7b.A@p}A`v_.A@}A@W.A |}A8P.A }A\N.A@~A`R.A@L~AH:R.A~AM.A`~AXK.A@~AE.A~A@.A@A2.A`T~A*.A`AA*.ABtA0X#.AAЄ.A A.AA.AYAc.A@A@0.AEA0ճ.AZAͳ.AAƳ.AUAX³.AnAX³.A#A.A_AhMų.ApAhMų.A3A0ó.A ZAų.Af͂AhMų.AxA˳.AAʳ.A@Aأӳ.AAҳ.A@'A(ٳ.A 0ABڳ.A SA8ݳ.AyA.A A.A`oAp.A Z؃A .A`A0&.AQ A(.A`]#A.AQA(.A pA6.A/A.A`A.AEA(.A0A3/.A\Ax)4.AA;.AAxI.AȅA:J.A<AJP.AAS.A4AX.AA].AVAPg.A@5A#.AA{ܴ.A`KA.A4}A).A wA@uB.A#A@uB.A@Ax.A@A-%sB.AXcCA)ok.A$A"߷.A/AA(+.A |Ui~A*Ys.A ]}A.AH|ACej.A |A-*6¸.A&=|AfMŸ.A0|AplǸ.A |A0 ɸ.A7܄|Aɸ.An|AH75ɸ.Ak Y|A*Ǹ.AwC|AŸ.Ag/|A¸.A?|A=-.A} |AẸ.A{A}=8ڔ.AQ6l^{AO|r.ALzA6OR.A&PUyA37.A/)=yAH.AuxAKm .AvPxwAX.AUOvA.A ~uA}/S.AWtAc3/.AmzDDsA@.ABqA~5.A)qA+.AԆyqAD-p߷.A)䤭qAqݷ.AqAX=ڷ.Ay[qAz9b׷.AO}qA(~ӷ.A`qqAڷ.A@qAmj߷.AN8LqA.A \VqAme.A^qAX.ADcqA_W.AhPWqA^M.ADqA.A*qA~.A1LqA◄.Ag5qAԕ".A.hpAVaC%.AHRpAk^c'.A7pAc_(.AzpA0(.AgpA(.AlDpA`+.AepA[T0.AoA4"c7.AoAc@.AgoA}g@.A^doA;@.AqaoA@.A'_oA3A.AK\oAA.A ]ZoAECB.AR}XoA'LB.A@ WoA;՜C.AGKVoAwXD.ATiUoA5E.A,tUoAE.A) i&VoAuۃF.An'D0WoA'G.ASMXoAG.A@ZoAjI.A ӈ^oAO>K.A}XaoA(M.AboApsO.A2ĿfcoAQ.AhboAeR.A `oAT.A;g]oAcV.A &YoA>V.A!NUoACKoA! W.AjHoA렒W.A7=PFoA2X.A~ DoA X.At'oAs.AoAx v.A膆, oA` z.AmPknAF~.A6}nA:(.AEnAҖ.AWnA2,ˌ.A|zV_nAP'.A nAd֕.AIBnAmR`.AnAh.Agd߯nA?L.AnA: P.At]vnA.AnAy ۤ.A`nAI.Aڱ4snAϣ.A%I^nA? .A0InAܙ.Ag]>6nAG.A%nAig.AFnA.A`K nABⒸ.Aq9k9nAzב.AlnAc䐸.AnnA .AunA^.AN=mAO׎.A7mAի~.ARcmAU.AڊfmA].AdmArq.AdohmA .A&,mA딏.A4>FmA R.AmA7a.AÈdmA`8.ADmAǘ.ACW[mA.A۶mA֜.A>)mA@.AmAD.AmA虯.A SmAX.A SmA0Ƚ.AmA.A4mAĸ.AmAʸ.AmAи.A=mAո.AmApٸ.AmmA߸.AnAp}.AWnA,.AznAx.AamA .A@mAb.A@`mAA.A mA0.A&mA.A@`mA.A`ʋmAM.AmAM.AwmA).AbmA0.AYmA.AFmA.AR8mA.A`$mAH.A`mmAXF.AlAp<.A`dlA.Ag.AlAPf.AXlApf.AslAP8o.AǔlA z.AWzlAX|.AzlAP@.A{lAX솹.A3glA3.A@ OlA`.A@zA ^$.A@#MzA.A/0yA.AھwA@.A wAPZE.A@vAy.A@vA8D).A`muAY.AXuA.AatA.A@WtAP.A@ytA.A9-tA-.A@sA,8.AsApV.A HsA x.A sAp.A@sA.A 'sAjD.A;rA&.AJrA0].ACqA&k.A6pA^.A@WmA( .A3mAxǢ.A lAX(.A@N.lA[.AkAx.A(kAp(.AjAhC.AmjAZV.A`viAZ.AiAXc_.AviA3\.A@iA@c.A@iAk.AMwiA.ADhA׈.A`dhAh/.AkhAY.A;#hApJ.AgA.A`ӃgAR.A?gA@5.AMfA].AǕfA.A>fA..AfAp_.A@>eA.AeA0j.ACeAP.A /dAHaC.A cAX,h.A@EbAw.A@ZbA.AbAX.A \aAȺ.A`AU.Af`Ah.Ae_Ax-.A_A.A4^Av.A^ApN.A ]A.A l\AX.A`\Ax.A&[AXt.AD[Ax-.AZA(`.A`YA.A`YA N.AXAG.A=XA.AqXAw.AXA0.APXA`.AWA`q.AWA(`.AHWA.A WA7.A`)iWA.A`!WA06.AaVAE.A@>VA0%.A`VA.AdVA8.ASVA.AK!VA.A cUA8.A`)]UAG.ACUA襝.A TAV.ATAH.ATA.A=SA|.A4SAJq.A@SAxj.A?SAe.ALSAY^.ASA`TZ.ARA_.AvRAp|g.AARA s.AQA`s.AQAȔl.A`fQAk.A+QA^.APA\.AjPAHX.APA0R.A9 PAVN.A`9OAppY.A cUOA@L.ABNA8bJ.AaNA7H.ANAJ.A@PNAE.A`NAHD.AMA(L.A0MA8H.A*MAH?.A@CLA1G.A \LA(D.A-LA&B.A`KKA)D.A`m,KA$M.A`BJAX[A.AJA ?.A[OJA0;.A`IAE;.AIAZ..AIA/.A@WHAP/.A@.OHAu+.AW/HA..AGA`).A cGA (.A ~EGA@+.A`8GAx".A FAT.A@pFA`G.AFA..A&OFA.A` FA.AEAa.A` EA.A3EAP.A \EAxz.A@DA@q.A DA(.ADA".A `DA.ADAp.AqCA.A CA.ACA.A@\CA.A ,CA?.A CA.ABAX.ABA@.AiBA@1.A$BA.A*AA.A QAA.A@,AAA0.Ap0AA@ƽ.AYAAXƬ.A_AA8.A9AADy.AAAY.AXAA`4.ABAP.A9aBA.ABAK.ABAhQ.A3BA.A`AA.AAA臽.AEAA5.A@A.A@A(1.A0@A88.A@A.A@5}@A.Ae@A.A#@A~.Aj?A.AM>A8.A>Apڠ.A y>AԱ.AW>A0j.A=A8'.Av=AH.A E=A .An=AHr.A<A@.Ak<A 0.A`T<Ax.A B<AR.A <Au.A?;AH{.A@E;A`t.AH`;A`p.A@#5;APg.AN+;AhT.A;A&8.A`:A&.A:AX.A`:Apy .A R:Ap .A6 :Aa.A 9A .A`]9AH .A09Ax.A\9Ax .A@69Ax.A@58Ap .A8A`C.A 8A*.A`KI8AЄ.A 8A.A7Al.A7A؁.A|7AH.A@6A.A"6A.A`5A.As5A.A95Aѫ.A 4A.Ayb4A^.A34A3.A14A0Y.A@ 3AȂ.A 3Aȥv.ABM3Ai.A +3A)Z.A02AR.A` 2A`9.A@R2A(0.Aa2A..A1A/.A`T1A0-.A81A&2.A81A&2.Ai1A2.A81AB,.A 1A ,.AV0A0!.A0A?#.Ap0Ap=.A$10A.A`/A .A`/Ag.A@ /A_ .Ap/A .AB/Ah".A@/A.A O.A.A ۡ.A.A`p.A.AN.A=.A.A.A -AP.A ~s-A.AT+A d.Az *A.As>(A`.A%AE.Ay"A.AJ!A j.Ak Al.AC Al.A@W0A0j0.A&AS.A A@1.A lG Asw.A` Ah@n.A`] A5.A@> App.A`m4 A8ɾ.A@ A.A AX5s.A AXyK.A`8 AH.A A.A#OAhD.A(Aig.A@Ah .A A~.AA.AIAW.A3AV.AAp2.A^AU4.A ?AP^1.AAp#.AA&.A@ hApÿ.A A-.AA.A@pAh◿.AA}.A@A].A`fjA`E.AVPA+0.A /A##.A A.A)\A0e.A@Ax\!.AA0.AtDA?.AAI.A AU.AAl.AAXJ.A`ܳA.AnA@.AdAͿ.A vA޿.A@hA7E.A0.]A.A$\Ah..AAp_A.A /A M.AAR.AăA)ZZ.A|Anb.AŤpA< i.A_A p.A;JA@u.Aw1A\$x.AXA|.AŽAV.AkˁA.AA(-Aҍ.AA.AIAb.AUA.A0A0Թ.AqAP|.A<A;.AhA0.AA`.A`]0A.A@CA8I.A`A.A IA.AqA.AAT.A AJ.A`mBA}.A A@w.A A(yc.AOA@Y.AsA/S.A=AP;.A-RA5.AA.A` YAt.AV)A.AA.AiA`#.A%A ).AA`&.A@,A]8.AnA?.AvrA(yc.AhA?.AVMA0{L.A.A).A`KAx.A@%A(.AV2A.A@poA.A`CA.A/A(.A- Apܿ.A$Aֿ.AAٿ.A@5A`Կ.AA̿.AHA0̮.A A@>.AA.AAʔ.A(A0 s.AWA].AbAR.A(A 4H.A AoD.AtAA.AcA<.A@7A +.ARA.AHAr.AIcAx.A`A([.A4A}.AqA`.AA!.A@yAe.A JAP.AAج.AAX׽.AAX9ҽ.AA .AA@.A~A8㬽.A`2qApp.AMAј.AgA(.A@AӋ.A;Ah.AoKAd.Ad8A8\.A[AX.AAT.A AI.A0A=G.A ªA@A.AྑA6#.A@hA.A KA`.A@ A.A &A@uټ.A&Ax˼.AaA Eȼ.A 1At.AA .AoAQ.A?;A ^|.AAcQ.A8Ai;.A,XA`-.A +Ad.AvA.AVAO.AAS.AAһ.ARAJǻ.AA@_.A`ӑA.A jAd.A ZAAW.A A&.A A .A`"A(q.A-A}.A gA/f.A`;AT.AyA=.A`Ap.A~AxF.Aq'A.AA .A@gAP7.A 8eA; .A5A .A@>A7.A SA.ALA.AXAQ.AzA.AAp ݺ.A`mA}޺.AAhӺ.A#qAQӺ.AOA.A -Aк.AAhú.A`A.A@PAT.AA@q.AA 4.AEAع.A9Apҹ.A A̹.A"A(`Ϲ.A`AXŹ.A"Aﻹ.A Ao.A (A3.Ak>AX9.A A@.AA˘.A`ܴA`P.A yA.AWAks.AtAc.A`A@W.AzAZ.A*AW.AQAQ.AAE.A@dA[8.AA`1.A+A((.A Ag).AA +$.A5A%-.ARAȥ.A`Ab.AA.A`mA.AhA@.AA.A`iAX4~.A 7A4.A@GAP.A-Aŷ.A;<Af.A AX֍.A Axdv.A Aj.ADA^.A.A؟R.AEA8ER.A`"ZA.AA(.AA8.A@NAxõ.AXAx-.A (EAz.A;zAPr.ANA(f.AAa.A@CAW.AXA.A@i"A.A-Ahѵ.A AH.AӗAXd.A`~A!.A`mAX0.A=A+o.AgYAxz.A /IA𴉶.Ad!Ax.AAAն.A.A A`%/.A@A.A AA.AkA.A`#A).A%AP.AAPβ.A`Aǿ.A`Aг.A1A}.A?WA*.A`Ag.A@A.AHA.AHAP.A@A .AA.A?Ap.A@A׫.A җA.A Aս.Aj_A(m.AX>Ag.A%A Dz.A (!AƲ.AA0.A@A\.AoA2.A@rA9.ATA.Av:AH̉.A`8A@.A$Ap.A$Ap.A@AȲi.A@A /j.AAu`.A}A_.AA ].A8AYd.ARjAsr.A`fCA`{.A AIA`{.A@Ax.A Apw.AA.|.A?A.A`)A.A ONA.A?PAњ.A AP.AAp .AA}.A'Asr.A@Ax)k.A AY.AAhc.AAv.AAo.A _A_.AzOA0\.Af AS.A&AMT.AǵAO.A?^A0K.AAwN.A` A(1X.AzAm.Ag<AHac.AsAx6i.AApu.AzA跌.A`KA8.A`Ab.AA@.AyA(m.A@ydAȱ².A@}A`ʲ.A`bhA-ղ.A@gCA.AvAA?.A`A.A`A.A`AX4.A`A".A8A01.AgAxF<.A:A oH.A@AL.AA"S.AAPX.A@A0e.A@AIq.A@!AH{.AA@.A@5|A.ACA\.A-6APn.A^SA.A0AP.A`mQAPg.A@EAh.A@y,A.AAp .A@`A .A;A0.A-A.A A .AA.A_A.A \A .AڗAp/.A@AXE5.A@,A(N.A |A^U.A'A].AA_P.A@pA`v.A@/AQ.AoA.A Axd.AXqA.A`vAK.A`mA9.A`yAݍ.A!A8R.A`yAL.A5AHKl.AA0i.AA@F.A "A>.A`vA>.A9AQ.A@AhN.A׉A8.AzA#.AA5.A@ѿA01.A|A@B".AпAhb.A@aA*.A qBA.AؾA01.AA"/.A`A.A뾾A .AaAM.A<A.AA.A_Ahb.AѽAJ.A@,AJ.A )A.AAȲ.A@>&Ax5Ǵ.AA}.AfVAY.AA(̴.AAlʴ.AȼASѴ.A A0ߴ.A`o3A8.A ,AN״.AdA0.AA.Aq̼A(9ش.A@A״.Aj|Ah.A`oA.A UA3޴.A%ASѴ.AػAD̴.AwApJƴ.AA.A ջAx.AAδ.A xAfд.A@A8ʴ.AA ɴ.A@A@.A鯼Ah.AA@|.AA.AkA.A/AȒ.A Ap,}.A DApa.A@ixAH\.A++A8E.A HAP^G.A`^Ap).A`vA&.A iA0-.A-A08.A/APo5.A@AD.AҼAJ.A`Ax.AmAI.AAH:C.AA>.A@gA6.AA84.AXfA@;.A=AH:C.AoNAV.AI"AT.A@CAP8Z.A`A\.AA2`.A A].ATAPkc.AwA0d.ATAPj.A Aei.AAXs.AػAu.AAy.AjAv.A |ĻA@g.AdܻAc.A |ĻA_.A (ȻA b.A;޻A\.AAxX.A ջA=T.A;޻ApW.A@NAgR.A0AE.AAk.A0lA(\^.AAXj.AٺAtn.A@yA(.A ƺAB.APA8.ACA@.A@pAȲ.A@ Aˣ.A_A؎.A ZAɴ.AAHaִ.AA1.AR5A.A@iAˣ.A DA( .AL*A0e.A ~Aei.A@ A[.AºAU.AA8.AXպA0-.ADAL.A`IA0I.A|3A7).ArA|&.AdA).A ]A%.ABBA>!.A HA`!.A@.A!f.A}A `.A PAQ.A;iAsM.A7A00.A`A.A@iݸAi'.AA.A4A.AYA4.A8A#Գ.A@,ոAh.A AH.A@Ap.A*A0ȳ.A@!ԹAW.AιAO.AXAe.AiAP.A@KA0.Af As.AԷA`.A A.A@`(AA.Az۶A.A@Ah?.ADA.A٘A.A޴A.AAFٲ.A Q{AHɲ.AZAH:.AdCAX.AsAp_.A&As.A@5سA(`.AųAQ.A<óAD.ADAX(=.A@NA A3.AUApp&.AwA.A ײAx-.A1Axܱ.AXA0.AD1Ah̨.A6A".AAeɱ.A ZA୩.AA.AHƯA}.A;uAd.A`Ah N.AcAЦ5.AaAX8.AAG.A JYA`%7.A@EA6.AX8A#H.A@٤A(yI.A`AL.A@A(\P.AAP.A A cQ.AA`a.A@p A£.AKAxi.A9TAl.A]AҠ.AgA.AqAҠ.A |A(m.A APj.AaAã.AAp.AǟA0f.AVA.A`A.AXA`v.AA.A3A.A3A.A3A۱.A`f$A .A +AL.AsApD.AABk.AzALa.A A/.A`Ap[.AAP.AX AxX.A@`QA.A A>.A'A滲.A@GA.A`AH\.AA)h.A7Ah3.A@Ay.A`mA06.A lA].A@ AH!.A@ A}.AxAp.A@5nA.AcA.AZA޾.APAxG.AHA8b.AjAA:.A;Ap߶.A/7Ap_.A^4A˱.A03A83.A3AhL.A A.A#Af.AܥAJw.AWAH.p.AA>k.A`"AFg.A lƤA@g.A4ƤAb.A ɣAȘl.A Ae).A \AД.A`) A8A.A@A0.AƛAȥ.AfAa.A@,BA[.A/mAH{.AEAH?¬.A`]zA`qC.A笀A襛.AM{A&.A@&{Az.AdzAd.A ?zAPP.A`yA=<.A yA7.AqxA%..A`ܧxA.A@wA3.AvA<.A mvA0 ͪ.AVuAâ.A StA~.AF A5.Aբ A |.A¢ A.As AL.AR A`e.A" A .A J Ap.A(k Ah.A@f AЦ.A \2 A.A Au.Aՠ A.A A(.AHC Aٻ.AI A Е.A u AX9ƕ.Aw Aʕ.A`] AЄՕ.A= A.A@a A.A A.A@6 Ap6.A AHY.A@r,~ A(k.A}} A8j.A} AXp.A} Ahn.A} Apu.A f} A{.A`+} A8.A | AU.AWt| A@.Ad9| Aȶ.Az{ A?.A@{ A͍.A{ A(.A@={ A[.Adz A\.A |z A.A y A`ӥ.A*y As.AIy AXҟ.Ax A`.Ax AH.AӉx A@.A=w A.A@v At.A`v APH.A@Eyv A3.A}v Ab.AjKv A4.A@y@v A.A`v Aج.A u Aٷ.AZiu AL.A H!u A5.A`Tt A.A@Qt Ap.A`fs A( Ɩ.As A<і.A {q AӖ.A`mpq Aؖ.A@Zq AqՖ.Aq AԖ.A* q AՖ.A q AHٖ.A` p Aۖ.Alp ANٖ.ATp AӖ.A p AUՖ.AKo AԖ.A@Oo AȲӖ.Asn Aؖ.A@Gun Am֖.A&n AH?ږ.A m AՖ.Aom A ۖ.Aւm AH?ږ.ARm Aޖ.A@%m A@.A։l Aܖ.A ql A@.ALl Ax)ߖ.A +l A ۖ.ANk AP.A2k A@.A^k A.Apk A .A j A.A A"j A.A4i A.A`B:i AP .A /h A8.A|h A.Ag A.A@f A c .A@f A<.A@i^ A.Ag^ A8t&.Afg\ AP|:.A`T$X A\.A`KP A`b.A`P At.AJ A,.A@oC A".AXA A.A@ A A}.A= Ap<.A9 A ͗.A1 Abؗ.A0 Ahؗ.A/0 APߗ.Ad. A.A, A(.A@>+ A`T.A' Ax.AF& A.A`Dv& A.A:% A7.A# A.A " A(q.A A! A.AB AP.A A.A`j A.A Y A.A A((.A@ A*.A`[ Ah4.A  A2.A$ AP:.Am Ad.AP A8.A A.A? A؊.A ANj.A@- A.A AΞ.A1 A.A  AP.A` AD.A A> AsK.AD AsK.A@$ AM.A A@I.A AI.A AM.A AM.A qt A2H.A$= AhbG.A` AQ.AO AsK.Aෝ AO.AO A@I.A Z% AsK.A AE.A AH;J.AЊ AH;J.A}F AH;J.A ApG.AT A(F.A;B A(L.Aa AH;J.A` A K.A@W A`B.A, A?.A A`B.Az] A@>.A` A5.A` A3.Ad Ah7.Ac A 7.A`% A;.A Ah7.A@ A<.A( A 3.A< A@_:.Aj A3.A`ܘ A0.A`a A8.AF7 Ah7.A6 AM6.AL] A/;.A A@.A A 3.A<5 A &.A Ax}.A` Az.A` Ay.A@#c A@y.A@ Av.A@X A@ u.A AجU.A A59.A@5 Ax*.A@WY A.ADM A.A  A.A2% A(×.A A@.A A.A! Am.A@.Q At.A`қ Aplu.Aթ A@º.A@ Ayė.A; Aq.A (C A.A/Z A$.AU A쥙.A!~ AXh̙.A3} A.A`F| A.A'| A-.A@Wz A9.A8y A(.Aiu A0.A`Kr AX.ADh A.A g A8.A@yg A.A9g Ap.A g A8#.A-f Ax͝.A@f Aם.Adf APx.A@,=e Ax:.Ad A0X.Ay]c AEj.Ab Ah^q.A` b AXWz.Acb A .A/b Aԝ.Aga AX,.ADa A8.AO` AL.Ac` AǞ.A.` A՞.A`_ A.Ah_ A(.A *^ A .A Z] APM.Ah\ A@FQ.A`=\ AhQY.A\ Ah^.Ap[ Ax.AZ([ Ah|.AZ Apۈ.A`Z A.AZ A.AY APY.ARY AX.AX A0Y.A=uW AX#.AV A".A`MV A/.AU A0@.AVU AJ.A5U Ap.AS Au.AS Aʹ.A}Q Ah/Ԡ.As@P A.A27O A.A %O A .A/N AX(֠.A@yoM A.AL A3 .A oK A`.A? K AQ.AI A녡.A`F Ahơ.A`KC A(m.A 4B A| .AΊ@ A.A`@ AxA.A= AppN.A= A^.A}; A0m.A+v: A0ݒ.A`7 AĢ.A 7 A0.A@"7 Ar.A 5 Ax.A 5 A07.A}I4 A(.A c3 Al.A`2 A.AE1 Ab .A Z. A>).AQ, Am=.A`m.) A3.A /$ Ah{&.Ao A.A q A8.A0 An.A A@^.A` A.A Af.AE A`l.AE A@F%.AK A;7.A0 AB2.A`m AwI.AZ A@<.A@P Ah].A A.ADJ AXW.A@P A.Az A`! .A AH.A| Aȃ.A Z AUs.AC A@.A Aw.Aෙ A ʝ.A@ AhJ.A@= A.A^f ApkҜ.A A.A Axa.A`K Aʛ.A` A8gs.A A(-c.AW A.A@ Aħ.Aa AТ(.A Ahۙ.AQ A(͙.A@ APxW.AӇ A8ј.Ax APx.AJ A.A A.A0 AY~.A A8D .AL A.A A.A$ AXՕ.A` A`.A@5 A|.A`mT ApA~.AM AX.A@Ϯ Ap0.AQ A.A AhE.A d AxWړ.A@#^ A6.Aƽ A8Eh.A A(\.A> AP&.A) A0.A Am.A(y A`.A1 Aȥ.A3 A .A}׬ AS.A APo.AE A j.A@6 Ab.A 87 A8k.A`s A?.A"~ A`.A: A&ؐ.A@ A .A` A_.A@E8 AB.Aoء A>.A A`$.A{ A.A 뭟 A8.A@G A.A1 A.A` A@.AM AV.A$ AU.Aܘ AK.AX AJ*.A@ݗ A .A9 A_k.A`v Aٌ.A q Ah.A Apɞ.Afނ A83.A ւ AH.A A^.Aa A瀋.A At.Ac Aj.A F A@1\.A`]" A0H.A A;.AÁ A5.A2 Ap'.A A.Aw AX.A@W( Aq.A` A܊.A A2ӊ.A A8Ɗ.A(} Au.A@e A`.A9 Apz.A ALH.Ai A.A~ A3.At~ Ac.A`N~ A8E.A (:~ Ap.Aq~ A6Ή.A`o} A8.A*y} A0峉.A*y} A.A`)S} AN.A5} Ah.A@} AP8w.A-| A .A&{ A.A{ A;܈.A b{ A0͈.A9{ A@่.Ad{ A(".A~y A.A Jy AP'.A`o'x A@.A`w A .A<%v Anj.A )u AX4B.A\t AX[.A@s A(m.A@s A.A@es A-.A@r A _.A`Qr Az.Aq Al?.A`"p A.A|;p A@F.A@o A3.A o Ax.A`dDn Am.A;m AT.Aem A8A.A Zl A0.A@wl AH.A Qk A.A /Bk A.A@nj AX҃.A`վi AG.A`h A(K.Ag A_.AEe A@߂.A?xX A,.AOU A(;.A 2N Ag{.A`H AQ.A B? A8ń.AXB AA.A iA Ae.AA At.A S? Ag.A> AɅ.A6< A.A`@7 A0懅.A`1 A(.A`) Ac.Aw( AP+=.AT' A3+.A-& ArK.AN߆& Am j.Ae:& AIF.A4+% A(.An*s% A.Ak$ A ۿ.AC)z$ A^ʆ.A`# AV.A AXƆ.Az A.Aab A.A AX#.A Jk A8.A`) A[.A A㶂.A ^ A\.A` A@ƺ.A-l A5#.A I Agn}.A@ AW|.A Ap{.Av Aey.Ac Azx.A$ A8bw.A ۊ Aw.A  Av.A% Au.A`D A8t.A A8s.A Ap=q.A Ap.A@ A8an.AH A@Nl.A!| Ai.Ap A07h.A Am ACh.A` i Ag.A h A f.A-b Ae.A,^ A49e.A[ A#e.A@[[ A;Xd.A`W A8c.A AU Ac.AS A؟c.AS Anc.AQZS A@_c.AVS AJc.A JR A.c.AR A5b.AO Av b.A K Aha.AVJ A a.A J AX`.A D A`.A |~C A_.Af= AH=_.A)8 A(^.A4 A:N^.Axy3 A'N^.A y3 Af].A?C1 AT\.A`) , Ah[.Ar% AH[.A$ A83[.A@$ A`7Z.A A%Y.A? AY.A AE1Y.A9 AW.A| AV.A A@1U.A ~ AX9T.A L AN.AB A\wN.Aw AXN.AK AH6N.A  AM.AE# A 8N.Ae A:N.A AH.N.A@m AN.AhP A2 N.AR AN.Ah AM.A A>M.A@ A M.A^ A]M.A AЀM.AF A%M.A AM.A A"M.Ad AM.A4 Ax׋M.A A&sM.A`Î A0qM.AUq AlM.A@M A`gM.A : AdcM.A A(OWM.A` AIM.Au A8EM.AU A9M.A A@3M.A A/M.A_ AeM.A@E AHL.Ap AݡL.A A@L.A AЄL.Aג AL.A AL.A`)g ASL.A@K A(L.A@1 A0gL.AE AFL.A A[L.A@# AТ L.ARX AXL.A . AHL.A3 AXK.A A`aK.A AK.Ak AXWK.A9 AK.A` AdK.A`2 AH)K.A u A8K.A 8 AzK.A`. AP'oK.A R Au^K.A`o A8DK.A W A5K.A@ AY1K.A` AK.A Ap} K.A[ ApJ.A} APJ.AǤ AJ.A AJ.A AHJ.A A0tJ.A& A`%KJ.A] A2J.Al AJ.A@# API.A AI.A AI.Aظ AI.A AI.A AdpI.A A_fI.A A_I.A A0wMI.A&k Az*I.A?_ A@ I.A`T A ߰H.A况 AHˊH.A AH.A@ AȺxH.A" AitH.AE7 A@mH.A; AliH.A`dP AX$cH.A`dP A`v\H.Ag AlEH.A쭱 A3H.A  A lE.A& AlfE.A`2 AZE.A A(E.A A7D.A`v AjD.A"v AX D.A@,t AD.A?Nt A0D.As A9D.AZr AD.A@q AD.A S&q AD.Aap AȘD.AyMp AD.A{o AD.AFn AD.A`fm A(D.A@Gm AD.A8m AD.A`[ m APD.A@5l A୸D.A ~k A( D.A$^k A8ֳD.AEk A0ȹD.Ad1k AD.A@9i A&D.Ai AD.A@h AD.Ag AvD.Ag AD.A f AXD.Alf AD.A d A5D.Ab AD.A b AD.A@,`a A!D.AV` AD.A P` A'D.A_ A |D.At_ A8D.A |_ AdD.A`9_ A8D.A_ AD.A^ AD.A3r^ AD.A@`] A3D.A*[ ApD.A൶Z AD.AmZ AD.A`Y AYD.AOY AD.A4W AD.A W AE.A"O AD.A O A1D.AaWO AW@.A V A@.A@%KU A@.AU A8@.A`1U AP@.A9.U APޯA.AaS A(ܤA.AS A@.A T A@.AT A5@.AU A@.AgR A@.A R Ax@.ARR Ag@.A`ÎR ApA.A$qQ AjA.Ah1Q AH@.A SR A@W@.A@W`R Aȶ@.A rR Ap@.A`bmR A@.AP AT@.A QO AH@.AЇP A@.AtP AP@.A P AXd4A.A@,O AxX6A.AjqO AH/A.A`IO A!@.A O A@.A O A@.A`܇O AH@.A` N AH@.AD'N A|@.A4)N AH@.A6N A,A.A4N A%,A.AAN AGTA.A NN AUA.AN AgA.A N AmA.AF5N A0jA.A`BM A0A.A@M A&C.A J A B.ArJ AMC.AI AxC.AକH A _TD.AL Ah; D.A@%cM AD.A{M A@lD.APL AC.AH A>C.A`K}H AD.AH A(D.A@ϝH A4D.AFH AYCD.A H AD.A |NG AwC.AuG AD.A@rG A`C.AfH AnC.AZH AC.A sH AC.AfH ACC.A@RI Ap6C.AH A9C.A H ACC.A H A0NC.AM>H AIRC.AG AOC.A#wG A0EC.AaG A2C.AF AC.A pF A؁C.A& AlaA.A = A@ukA.A = AA.A J; AA.Az9 ADA.AY7 AA.A6 AA.A4 AA.A`ma4 A=A.A 3 ApA.A3 A`A.A`3 AA.A O3 A~ B.A`U3 A` B.AO3 AB.A83 AxiB.A`2 A( B.Aޘ2 AB.AA2 AxKB.AK2 AB.Ap2 A B.A_2 A(B.Af2 AF7B.A@a2 A؞,B.A42 AF7B.Ah2 ApAB.A 72 AOB.A`,2 AhVB.AK2 A0LaB.A92 ATkB.A@H2 AXrB.A`"2 AhB.A`2 AjB.A(1 A- ^B.A 1 A:,W.@.Ao33 A8.A4 A=0.Ao33 A 1H).A҆. A;sN!.AXg:' AʇT.Ai\ A.AA`vU-A`cA\A-A`dSAj*-A (YA R$-A EA-A QVA -A@=Ax-AjIAX-A@AH-A`K+A0-A <A-A1A-A"As-AR+APZ-A`A-AA0G-A@,A-A"A-AA覇-AA8E-A@!AP|-AZAhw-A`A"r-A@Ap,i-A@A^-AAhX-A@,A}R-AA7-AA-A*Ax-AAXEu-A@ iA-A ,AX-A@NpAP-A@AH\-AR{Ah-AA-AA'-AAx9-A 8A*f-AQAj-AA0&o-A`m%A#u-ABA~-A?}AHm-A ˻Ae-A`]A-AF AS-A@56 Ah-A@yS A=-Atc Ap-A` Ax~ -A ^ AL-AX A(4-A=* AG-A A;:-A`AE&-AAN-A^A-AAP-A`ӯA-AvA-A`ӯA8-A`As-AA-A@RAH-ABAp|-A` A.-A9Aߥ-AAP-AfA7-A'kA,-A/_A(-AྍA-AAm-ABA-AAx-AA(-A4AP-AA-AAG#-AA'-AA(-A`vxA(.-A6KA5-AH/A9-A@A:;-AA=-A ۰AF-A /A`1N-A@yAXP-A1Aȓ-A Aȓ-A 4Ax-A/OAx -AAА -A@FAa-A5A#-A AXũ-A!Ax-A *Aם-A |dAH-A߮A-AA-A0A_s-A,=%A-A+A8-A@i.A0;'-A_4A::-A`B5A^-A79A-A@>=Ap΢-A AA-ADDAA-A.JA-AVJAh -AYJA-A uLJA9-A@ߛIAk-AQDAHr-AkxBA@-A EAJ-AFAu-AHAu-AIIAĄ-A`}JA-A@AKA-AKA#-A`ofLA-A`3MAXV-APAà-AQAH-AFQA8t-AmSA@`g-A ZRAba-A cQA:-AiNA07-A lMA.-ALA,;zP--AE}XLAo;+-A$xLA5>)-AhoLA~'-A лLA:%-AU@tLAG8"-A(GLA+-AkLA1-AjLA `-ALAm'-A-2LA$_-A LAs-A~HLA -ADŽLAf-AH*LAW-A`LA c-AgMAK-A SAx-AߏXAp-A `A@-AE4bA@-AeA*-AiA؎_-ARtrA(q-A {A-A琂AC-A AL-A``A8_-AA=p-AA`-AA-A@A-AMAX-AAy-AAp-AwA-A_A0-A@ÊA -A`AQ-A@AK-A`K-A-A`f݊Av-A@A'-AH|A$-A(A;-A`2ޑAE-A ҒAc-AÕAb-AA<|-A`ooAk-A@}A(j-AMAR>-A#rAxX?-A@衘AU-AFA+u-A>A5-AA-AiAb -A`;ɞA5-A?Aj-AA-A`գA-A СA@-AyPAx-A=A0-AA-A 8A(l-A %A5 -AAx7-A A@O5-AwA+l-AeA p-A_A-A`DA-A@N4A-A@GA(l-A@A-AcAX-AA@3-A`bAR7-A@#UA_-AA-A?Ah@-A`ݥA-AAP<-A@A-A)A -A 1[A@!-A`bZA4-AXA0G-A AxL-A`ެAf-AAx-A`oA-AXA)C-A$Aw-Aa AU-A`iA7-AA 8-A@`A-AA-AsAH-AAs-ADGA ^-A /A-AN1AL-A`b5A 0-AA~-A@C)Ak-A`bA8y-A!Af-A#A-AOA0u-A(A-ArAJ-AA(qd-A@A-A/ AH-A AЄ-A s) A0-A A-Ab AH-A`) Ar-AH A`-A( Ad-A`/ Ah"-A0 ApA$-A3 A"-A &3 AP/-A 3 A0D-A7P43 AZ-A/3 AH-AU3 AoT-A ^ 4 A2t-A)04 A)-A c]4 AM*1-A4M4 A軋/6-AwwHk4 ALr8-A 5 A\㍝8-Ay=G5 Ako5-A)#5 Ay0-A 5 AGQB)-A`Z5 AЕ-A s#6 Ay-AgB6 A -A#\6 A|m;-Ap6 A`-A”}6 A1-A6 Ah-A :; AHn-A@>; Aw-A? A`G -A=`> A-A@.dA A8D-A_J A8 i-AKyR AV-AgR AZ-A`vS AQP-AA7S A!5*G-Af8cS A?-A0CS Ai:-AN#S A+8-AJNT A4&7-AF(u>T Ay9-Ap%wT AM>-A_ îT AЕD-AKT AL-AWKU AV-A[8*7U AKb-AWU Ab-A U ACv-A U A-A\ A؞-ALd Ae-Aji A s-Ai AЮ}-A'l A"$r-A/2BNl A6$7h-A h{l AC"`-A+@l AIZ-AM{l AX5V-A!m APLc-AP\'m Ab}p-A.'m AȘ}-A!m AA-Am Ap-A 84m A8-Al A-Ap A`-A@Cv A@N-A@ v A/-A2v A/-A#v A(`-A@v Aٖ-A w Aa-Aw Ak]-A0+w A^-ARy AQD-Ay A(mG-A{z Aؽo-A Ao-A r AT8-A`{ A&-Afm A-A A ^-A` ٔ A-A Axh-AQ As-Ax Aؼr-A@y Ax-A A(-A Ap,-A An7-A A--A@ A'-A AG-A ֫ AbS-A AXn-AoQ A -A&E At-A`9P Ac-A O A~-A A-A` A-A@r? APx/-A@ Ae-A A8'-A@Ŷ A-A` A@-A8Z A -AMN A-A\ A-Aʬ AP^-A A-A`; A0-Aû Ap-A i A-A@ A@ -A p A-A ڹ A-Ax A`v-A A -Au A(-A Ag-A`T As-A`o A-Ao A q-A` A(r-A@ AT-Aݷ A˸-A Ax -A`) A8Z-A A`-A ~ A`-A@u AhL-Az Axu-Az A-ABػ A-A` A+-Af8 A"-A|ʼ AЄ-A H A8-A`dn Aº-AB A6-Aj A-A AЀ-Af AP/-A` A-A A-AF Ah]-Ak A-AzP Aȶ-A AP&-A@q A(-Am A+~-AW Ap}-AҞ AZ-A? A-A@H A-A (E A-A  A^-AV A٧-A ¼ A-A'ڼ Ahj-A,| A`c-AZ AX^-A l A[-AK AW-A | AYS-A/ AYM-Ajg AJ-Ap APD-A |G AN-At+ AP7-A@  AH;-A A-A8 A5-Ac AE$-A`m@ Ap/-A;U A81-A AP;-Aǖ A?-A A`G-AfƼ AN-A lx A`G-A ( A -AN A8-A_& A+-A A21-Aֹ A8E0-A@ Ai-A ҹ AX-A O] A` -A@ AJ-A, A 4-A@< A-A@ A(-AHU AX+-A`T A0eD-A4 A}-A0N Aȩ-AЪ A$-A% A$-A9 A-A A-AH API-AZ A-A` A(-Ag A8-A Ap, -A AP/-AH AX-A@߭ A-A@ A-A c AP"-A' AЕ%-A y A`'-AǾ A.-A@ A?|-A`< AXhi-A_ App\-AX Ap]-A A|-A Az-A AP -A \ AH-A Ahq-At Ah^-A`@ A-AM A-Ac A-A@ϒ A-A`a Axz-A` A-A@W A[-A@u Ac-A`B A-A0 A-Ad& Ay-A s A(h-AU Ax-A@V A-AF A_-A A-A޵ A-A_ A-A  A@u-AQ; A-A= A-AM A8-A&5 A-A J Aȇ-An A-Aj A(-A AH-A" A0G-A@r AH]8-A A(,-A |C A(,-A`]z AQ-A 9 AZ-A Zg Ag-A^ A8y-A A+-At' AU-A AXFh-A` Axme-A A0H-A Axz-A AhM.-Af A-A A AR+-A AEM-Aj Ah-A A@O~-A`2 Acx-A@W A@-AS A-A  AP<-A < A%-AI+ ABl-A% Aɶ-A A@1-A Z Ap-A` A-A A,-A Ah-A  A-A` A0}-A`} AϪ-A1 A -A,j A`Å-A A"-Ae A؎-A A -A? A p-A`f A@B-A; A-Adn AP<-A A -A@ A-A@5 A8R-A< A؟-A@ AB -A`" A(-AV Aw-A` A -ABg Ax -A@> A-A  A;-A`! A#C-A$V A-A sK A6-A c A@]-A A(,f-A A0Q-A9+ AXK-A A@5-A AX#-A` Ax( -A< AЯ-AH A@-A@` A@-A0x A-A  A-Aǂ AC-A AH -AgZ Ap=(-A" AH,-At; A0W-A@i AS-A AP Al-A  A Υ-A3 AX_-A A-A Zc AY-A c A߳-AU A-A" A-A&" A-A$ A G-A H$ A`-A<' AU-A`T:' Ah-A' Ax-A* A-A=x) A8c-AK, AX-Aݲ. A0-A L0 A R7-A`9I2 A`-A 8#8 Aȱ=-AO9 Aɉ-A`K; A-A`D< A-A@#> A(~-AD A,,-A?C A<-Aj:J At-A K A -AL Aj-A@EuP ALa-A (Y AP-A Y AHG-A`mZ A`K-A@ykZ A/R-A@,a A^-Aa Ao-Aa A@-Aa A-A@`a A-AX A oY.A}] A@.A=\ A6.Aҗ] A(y.A^ AB.A;_ A؎[.A q` Acy.AKa AD.AQa A;.A |a A(.A@%b A6.A;b AW.A Nb A`P.A@,b AĂ.A@a A.Aa A..A_ A虸.Aja AV.A 2c Ab,.Ad A0.A`mNh A .A?vk A .Apo A`k.A lm AX#f.A4t AÝ-A A0-A Ap-AU A-AA A5-A AZ-A A.Av A<.A |I} A".AE ANR.A` A.A`* At.Aǜ A`X.A@ A.A~ A .AR AH.A`H A].A A4.A Az.A# A.A A뿤.Az Az$֌.Ap# AOа.A*, A ]_.AP1 Aa(> .A2 A8.A . Aȵ&.An AX.A` A@` .A A`C< .AT̋ AE .A6 A9 .A?\ A .Ax A .A AHaJ .A@ A.A` Ae.A { A8Q.A[ A(`.A`B A;/ .A`2 A@b .A Q? A .A/ A` .A A{ .A ANh .A ArW .A G A@K .A| A@F .A@i AH{J .Av A&V .A@ɞ Ah .AU A8 .A< A .AAz AX5 .A AN .A  A0 .A٠ AX& .A A1 .A` A, .A 덢 A(.A 1; AZ .A㙲 A.A)Ҳ Aȃo.A@z A0&.A@, A.AI A+.A` A*q .AD Au .A f A .A`K A0eb.A@Ns Ap .A ٰ Ah.A A .AR A- .A A(w.A@ A.A A;.A@ A.A@S A@W.A@ A8.A,_ Aȓ .A A@N .A@e Ajj .A. AXd .A^ AX4 .AU A7.A Ag.A@M A.A A.A Ax--.Aif Aw.A Jw A .A`J A! .Ai A`n.A- Ap[^.A A.AD2 AR.A` Apʜ.AU A؎Z.A@i AǢ.A; AN.A`f A.A`̞ A R.A@R A)L.A \ AC.A A.A \ A RS.A AH\W.AR~ AXl.A`) A.A3 A{.A=4 Az.A \ AH.Ao A.A \ AH.A \= AЄ.A AP+.A AKK.Ak Ax].A Ai.Aл Ap.A@ A(n.AjG A@@.AQ A`Q.A@ AR.A`m Ax.AE A$.AE A.A` A=y.A A AI.A u A.A@x A#.A+ AV.A5 A(.An An.A A@.A= AP.A A.A@ A@W.A A(`.A AL.AV AM.Ae A.AЫ AP.A6 At.Az A h.AR A,.A`mO A0$.A AH.A c A.A A!.A A06.A A.A$ A8.A, A(`.A A<.A A>.A3@ A).Aྎ A{.A}* A@N.A AX.Aw A.Az A.A@E Ar.A॒ A8.A | A(.A@p AP .A A9.A A_.A`)i AX.Af Ad.AX A0.A@y A.A@8 A`.Af> AА.A A8D.A AD.AR A@.A`_ A@u.A A .A^> A( .A> A!.A!@ A!.A aC APڇ!.A`9C A .A`oC AH .A cC AN^ .AC A, .AC A }.A pC AW.A 4C A.A@!"C A^n.A =C A5.AkC A.A?C A.A,uC AC.AiBC A\.ApC AH{(.AC A.A C ApZ.A/C AKz.A@!"C A3.ALB AR.AB AA.A B A.AC Af.A@!"C Aث_.A@!B ApI.A (B Ap:.AƮB A%.A(B Aݻ.AB A.AD A!.AE A8Ib!.A`F Ai!.A`bG Aq!.A}G Aq!.A;EH A؎m!.A`sI A".A`; R A".A@T A0".A T A#.AFmV A>#.AV A$#.AV A(S#.AaX A 3#.A Y A>@#.A@Y A`#.A{Y A(K#.A [ Ap#.A[ A#.A k\ A0&.A xs AP2'.A@$w Ap '.A`mky Ap(.Ayl A(.A_ A 0^'.A Aa&.A E A8l%.A`9 A+R%.A |3 A(&.A` Ab'.A Av'.A Al'.A A'.A̍ AÇ'.A3ۋ A(.A Aĺ).A`TN A@y).A`TЊ AB).AX A@o).A AxiZ).Aq AAa).A` A@q]).A`]Ɗ AhK).Ay Ab).AQ Al(.A  AEZ(.A`2 A?(.A m Asm(.A A`C(.A Z A 0t(.A A(.A`ܱ Av).A Ar).A A0).A@ o AǞ).A AL).A`b[ A" *.A A*.AH AK(.A`9 A`v'.A Aq'.AsY A'.AR A%(.A 7 Ap).A` Ae*.A ޑ A,.A AH/.Ak Ao0.A` AM1.A AX1.AVg At2.A A3.AX AK3.AH Apn4.A A̝4.A@1 AA6.A@O Aj8.A@N[ Axd|9.AM A&;.A`o A<.A` AXۿ<.A A=.A2F A=.A! A.=.A?! A0X=.A /" A0Z>.A`=% A>.A) AX5W?.A, A?.A@. A$?.A0 A`B@@.A3 A@.AЦ6 AaA.A<; ACA.A@5? AA.Az!@ Ax>-B.A3A A5KB.A@KC AأB.AD AHB.A qE AHC.A`mH AuhC.AOJ AՒC.A`{L Ah޲C.AgM AC.A]M AD.AO A#D.AlP A D.A@ P AТD.AU AE.A@ [ A*E.A2^ AlF.A q7_ AhgF.A`T` A+rF.Aa AF.A@b A cF.Aad A؎F.Axd A8G.AZg A ͋G.A[i A(G.A |l ApWI.Av AXI.Ay A8J.Ay~ AJ.A@M A(IK.Aa AAL.A@yV A ML.A@؛ AxL.A@ A4L.A@ F A:M.A AItM.A A {M.A A`CM.A- AM.A ҕ A*$N.A줘 ASN.Aљ AhN.A-I A_N.A鶚 AsN.AП AxXN.AǞ AN.A`C A,O.A" AȵBO.A m AX9LO.A@y Ax`O.A` AzO.A?% AzO.A㪣 AO.A JJ AO.A AP.AH A)P.A˭ AP.A AP.A J AQQ.Aɯ AЕ\Q.A- AQ.A` AQ.A A8c"R.A AaR.A APR.A@ AER.A@5 A0.X.A@ Aw0Y.Aq AGY.A/E A#Z.Ae AҎ[.A A].A AP|].A A(^.A` A_.A A Y`.A ACa.A` Apga.A 8% Aa.A4 A0fa.At Axa.A AxWa.Ap Ap+ta.AR A/a.AQ AEa.A A v.A | Av.A# Aw.At Alw.A A&4w.Aá) Agw.A  Atw.A*%x AEw.A  Aosw.A>jgJ* A@Qw.Ad5 AMKw.A`m@ Afww.A9I ACKw.A/FQ A+ w.A`W Av.Ax A8"v.A`) AԿv.A ~ Aȩjv.AO A0ԩv.AR AcTv.A Aɂv.ATH Ahv.A@ Aqv.A;uF A$v.An AXv.A`T A7Pw.A A_w.A A_w.A@4 A*w.A Ah{ x.A@S Ap9x.A Apx.A- Ax.A`K- Ax.A@X Ax.A` Aȶ#y.A  A`Dy.A A][y.Ax Ay.A`)q Ay.A`Ӕ A)z.A V A8_z.A@ A`|z.AAz.A@./A8z.AqA@z.A`vA1{.A`;A0Xm{.A@ A{.As Ap0{.Az A{.A AU{.A?) A0{.A`g A|.A A(|.AAp=M|.AA(Kz|.A1ApJ|.AAN|.A@5A|.A@ƆA|.AA60}.A`1AЕa}.A@zA]}.A*AH }.A A?}.AA}.AjA}.A8vA}.A@`Ap}.AP A}.A] Aȩ}.A`!A}.A@G?"A}.A6#A,}.A#A}.A9%AH}.A`v]%A~.A }&A}.Av)AW}.A3)Ah3}.A3*A}.A@b*Ap}.A3i+A}.A,Ah3}.A3S-A=}.A µ-Aױ}.A .Az}.A.A`}.A A A|.A`?Afw|.A JAAd|.A ^CARl|.A@yEAP{|.A`EAxz|.AvFA8|.A@GAȂ|.A`!JA|.A`KAk|.ALA"|.A@NA|.A ~OAh|.A`9ePA83|.AQA|.ATA |.A`VA|.A WA`a|.AXAk|.A!YAh|.A]YA|.A?[A|.A[Aأ|.A`f\A#j|.A\A`%[|.A\AQ|.A@z]A0.R|.Az^A(B|.A`\_A(;|.A_A8|.AH`Ap=|.AaAB|.A@gFbA|.A`)dbA|.ARbAP|.A ~cA|.AcA(|.A`cA` |.AcAp8{.AzxdA{.AdAH{.A3dAp|.A@eA(\|.AeAz|.ADeA` |.AXeAp{.ApeAX{.A |eAp{.A37fA{.A fAH.{.A@ fAp={.AzgA{.ApgAU{.A hA|.A-ehA |.AhA |.AKiAh3*|.AjAHI|.ADjAqh|.AbkA(n|.A`kA }.AoAS}.A2} oA}}.A]+oAH%}.A`7oAi-}.A==oA6}.A?oApJ>}.A=oAKÆtF}.A1b6oA0xTN}.AV+oARװU}.A+oAx\}.A+ oAo}.AnA0 y}.A`nA t}.A |KnA(g}.A@*nAذg}.A&mAho}.AkmAxu}.AnmA{}.AmA%}.A@{mA8g}.AĒmAP}.A`nAh}.AkOnA}.A`nA@}.AoAd}.A&oAH}.AoA?}.A SoA}.AAoA&}.AॽoA}.A@5xoAh}.A3oA>}.AhoA@}.AhoA0}.AR*oA}.AzoA`}.AoA/}.A_oA}.A`spA8}.Ap%qAp}.AYqA~.A&qAP=~.A`WqAQ~.A qAhd~.A`ӄqA@p~.A@PqA2~.APqAP~.AqAH~.AqAp~.A |qA}~.AhqA`.ArA8 .A@ sA`/.A@ sA'4.A sAYI.A6sA'[.AAtAi.Av>tAx.ADtA/.A`>tA|.A ɊtAb.AitA8.AtAʸ.ANuA.A@`tA.A@,MuAH.A3uA#.AuA(.A "vAؒ.A=vAh/.AvA?;.A@wAP P.AawAЦ_.A wAx.A#wAW.A]wAH΀.AHuxA Հ.A@.xApl.A xA.A@pxA(>.A@`xApp.A`f6yA .A`ayA.AyA .AyAH.A syA87.A`yAP'=.A@ zAe.AQJzAto.A8yAJf.A2yA1e.AyAn.ARyA`}.A`;yAث.AyA8.AƱyA0堁.AyA.A yAc.A`mxAȁ.A`xA!́.A #yAہ.ApyA.A yA.A`yA.AzA.A`zA(2.A@N{A9U.AzAQu.AzAs.A@A{AhĒ.A"|A(.A|A].A`d }AsY.A j}A`X.A@y}A.A`vA.A@A.A@zAp<}.AAȅ.A? Ah.A)AH.AA-.AhsAc.A̒A.A ۄA·.A@߆A}.ABA.AZA{.A \AxI.A2Ac.A6QAxu.A@A.A mAG.A@ A .ANA.AIAF.AE Av.A'A.A@ A8֟.AAȉ.AwAБ.AAH{?.A@A@].A@7A&}.AA͊.A ֱA@.A A A.A)A;.A}A .AThA͋.AHAH.A ?Ai.AOAW~.A*AА.A`A@$͌.AAЯ .AӖA,1.A@lAw.A A.A{AТ.A (A9[.A@EA~.A`)YA0X.AgApZŎ.AA.A@>AH.AX%A&e.AAt.A$As.A -A0g.AOA T.A=A@X.A7AP8Ր.A+AN.A(Ae.A`pAH.AAY.AAr.A@`;A.AxA@>.AAx.AnAh.AA ӓ.A?As.AӬAU<.AHAq.AA.AAg".A3 AM.A App.A A0*.A z AF.AAh@ .ARBAh@ .A`vA -.A|A"^.A"A߆.A`oAuޘ.A@AP .AOA.AoA*.AǟAI.AHAI.A A\.AA 4.AAެ.A`mA .A`2A@O.A AP.A`m%Ax.A`dU(A(.Aɤ(A.A (App.A@%(A@.A`\*AW.AM)A@.AHc)A0.A.)A/4.AX*A`vD.A (O*Ax.A,APw.Aa-A.A2w/A!.A`0A().A1APޗ.Ar1A.Aા1AV˛.At6AIǛ.A7AΛ.Al7A?.A7A.A7Ak.A 8AR.A08AQ.AI8A@5=.A`8A9.A#7A`%(.A`G7Ap0".A`7A.A k6A .AE6A .A66AXE.A:6A`.AP6A5.AO8A`G2.AT8A@(.A@Cf8A%A.A@>9A{M.A`'9AhJ.A@9AhP.Ad9Aꟛ.A c8AP.A /8Aƽ.Ad8AU؛.AM8A֛.A8A@ɛ.A9A0Û.A:A.A`;AJ.Az;Ae.A9;Ag9.A <A'.A+<A8g(.A c<A0XK.A=A0.A<>A@.A`>A.AF@Ap^.A`AA`.AEAX$ϛ.A)DA舛.A@,/BA8.A0%AA@~%.A`AA؟7.A@BAA.A CAȱD.A?CAF.A`dCA=.A|_DA?-.AEAȱ.AEAI.A/FA.ApHA.ALAzG.AnNA8#P.A`jPA.ATA`؜.AVA=.AYA().AmD\A୹.A_AP.AcbA g.AH*fAp0ߞ.AhA@8.A \kAX.A 6yA.AzA).A (Apv.AxA Ң.A |'A.A9A kL.A(A0Ա.AA.AĒA }.A |+A.A:A8c.A4AH.A OA..A-AI.A@pA(1n.A`zA(s.A A4-ANW+A3-AVA`6#-A HHAY-AAB-AA-A{A--AaAS-A@An-A ׺APZ-AձAs-A6iA -AhlnA4-ANW+A-A0)As-AܔA -A37A-A0)A<.A`9,ATy.A=Aou.A@yAln.A/9AW.AXXA^.AqŬAW.A`ˬAg`.AQApe.AQA Rr.A JAPZ.A ȭAr|.At2A<.A`9,A`K#.A@>~A.AA2.A@P?yAb.A`pA.AchA;.AeA0Y.AoZaAh38.A@`A.AEMaAX# .AR bA.AbA0..ATcAq.A.AmA.A }nA.AoA.AjoAҾ.A9pAp,.A.AB}A.AB}Ah.A}A0.A}AX#.A}A&.A}Ah.A`թ}A.Ay}A0].A }A(.A l}AL.A}A.A`}A&}.Af}A0v.A}Ar.A`}A(p.A@N~Apd.A ~A b.A`~A,N.A`0~A@5?.Ap]~AX1.Ai~A .A`x~Ab.Ay~A.Ax~A.A@~Aw.A@~AHi.A@~A.A ~A0H.A`~A@.A~A.A~Ah.A~Ap0.A~A(:.A* A.Ap"AX.A A/.A8AG.AtAu.A)A@.Aa@Ax.Aa@A.Af;Ar.A`m'AhW.AaA].AZFAW.A@AxR.ABAR.A ~^A0D.A`fA0=.A`xA1.A`tA84*.AXA#'.A`ÜAp|.A`A.AA8.A`A.AA*.A@NA.AAv.A@FA.AFA.AUA.AAH.A;AgW.A;AE.AA2;.A A:..A@ćA.AAc.A A`G .A |Ah.A+Ap.A}2Ag.AJAx)ۿ.A WA(ֿ.A`dAJĿ.A菈A .AA8.A6Ax:.A^A~.AA8g.AAl~.A OAȔ.A@Axm.AkԓAx}.AA8r.AvdAP.A`A,.A)ޮA).A`fAh.A@GA(r.A"A.A A(L.AA }.A`DAt.A@ݓA.AAh&.AA`K#.A@>~AJxD.A` AX'/A,1A I &-4@`?.AFA4 A0.Ak5 Ag$.Ax>c5 A?.AFA4 Aߑ.A{֊< Ah.A@WA A$].Ai~HB Aߑ.A{֊< A~O1/AAb/A )Am/AEAb/A CAh/AAz/ARȪA~O1/AA/AAP/A@A/A`AQ/A?A@B/A@P+A//A`9As/A-AТ/AAp/A@>A/{/AAG/AD>Ar~/AAX}/AAX}/AA/AJA`1/AkA/AūA|/ArAƗ/Ad A/A`A/A]ApJ/A3AHr/A gA"/A$A/AZAP//AH%A@/AT޿AX/AxA/A AH/A``Ax/A`ܽA/AHA|/AA(-/AEA>/AFкAȔ/AËA /AA0H /AtrA;/AA48/A׻A/A%AЄ/AAHA/A TAh>/A |A(/AZAp/AFAg/A-RA/AiAg/A߸A/A@=Ap/A`vA/A?A0/AKAl/A ਵAg/A<Af/AĴA0/AA`/A`oA@_/A?tA`/AAc/AqA@_/AA8Z/AྫAս/AiAR/A?AѮ/AA@S/AaaAW/A 1A]/AAL/AA@5/A,YAf/AA k /AحA/A׭Aq"/AҭA5/AҭA |F/AAHP/A@y0AhU/AXAH]/A:Av/A0.A/AAH/A 7AȔ/AaWAЙ/A A/A`ЧAP/A`֧A/A@y A/AAh/A!Az/A[Ao/AA(qx/Az&A/AA`/A@Aؚ/A@jAH!/A |AP&/AAH/AXA*/A lAX,/A 8,A/A`Ah/A`;A0/AA0/AÜAX,/AēA8'/A]A` /AA(/AߛA/AfIAh/A 8Ah/A@#A(/AA/A 힙A/A A@/A઴A@/AxA/A*AX/A`TA/A6A/AA/AF[A/A ˺A/AܖAc/A`~Ah"f/A@gAp/A@Ax/AA>/A`gA@ /AA1/A列Aਙ/A@,Ag/AvOAp[j/A0;A./AA /AƔAБ /AxA35/A@A83F/AʖA}/A`9A{N/A@%ĕA8`/AʕAhq/A߯Ax/A߯A`/A AxF/A` A\9/A AP/A@ ՕAQ_/A[Al/ATA |/A]ÔA*/A#Axm/ApAp/A^A0/AAx)w/AgA8gY/AA*/A`˒A`/A`ܯA8/A YA0s/A@A /A@PAhn/AѐAp/AСA0/A`PA /A|xAH]X/AwA+/AwA@ߙ/AwAx\/ASwAЄb/AwA8b/A twA6/A xAH>/ApwAI/AwAl/AwA@5o/A QwAG/A&xAh/AAP/A A؁/ArAp,/AAA/A@QA/AMAZ/AA0{/AApl/AApIt/A`zA }/AMA`/A`̄A /Ax>A8z/AFA0{/AjA /AMA`/A@yA M/A ^A//Ap"A0/AA0/A_5A8/AݬAP@/AEwA8/A`AY/A@Ao/ATAh"/AjlA4/AAY/A |A/A %AB/A`A@/A (tA,/A`EA*0/AqA/A`KAhD/A@A@כ/AA୰/A /A(/AŌA}/A<ٲAU4/A@ȰAsG/A AP/AձAX/A/A(/AxwA /A ɍA؎[/A`mAFl/A`Aq /A AP$/A`,Axp/A@5AXh/A@AF/A`]UA0:/AA@S/A`oAU/A ˳AX/A,A\/AA@/A`mAH/A`YA~z/AAP/AjA)/A`A o /A޴A`1e/Az2A]q/AA ~/AHkA`/A(A./AgAx-/AӷAF/A`A/AAH/A dA%/A@ A7/A \AHk/A@rA F/A AA@I/AٵAB~/A HvA"/A'uAp+/A@r;A/A{Ar/AA2/A-A{/AӽAg/A ʼA/A"ڼAƼ/A@A/A NA^!/AT=AH(/AA/AA0Ԇ/A@%.Ai~/ApAȱ>/A SAP*/A|AHO/A`A/A`ùA/AAT/A`ApA/A6A//AA@ߙ/AaA2r/AջAw/A SAh/A A~/AZAȃn/A΅ApN/A`A./AA=/AAp|/A`AX~/A@A/A=A/AؽA~/AA0F/A-AԔ/A`AA/Ad۾AhU/A/(A /A`A'/A XA/AAv/AAF/AGAh/A-hAs/A 1A/AAx/AGp\A/AX]A8/Ad:]A/AkR]A/AkR]A/AkR]A)/A c4]AX9/A`K\Ag/A[AĪ/A^A((+/Aŵ\AXo/A]A|/A@d]A(8/Ao\AgL/A"\Ah"/A]Ap/A\Ap/AE]A/Ak]A0/A`u^A/A W^A؟1/AV^A*/Aྲ^A(/A J_Ao/A qb_A#?/A`bM^Aa8/Ak^AC#/A@.^A}'/A S^A /A ]Ax/Ao']AΝ/A\AU/Ah[Aأ/A@ZAQ/A0ZA /A@8ZAH/A`ZZAL/A |YAx/A_YA}/A݇YA!R/AXA(Kz/A};XA0T/A=4YA/AYA/AXA/A`oWAa/AʵWA/A@XA~/A`XA/A QWA/A ɕWA/A VA/A 1=UA( /AjTA!/A`XSA3/A`KSA ߂/ATA/AoUAx6/AUA~/ATAHz/A =TAo/A@SAXy4/AiSA /A_|RA}n5R/A uvZAx/AGp\A{V /AD TQA/A?(SA(/A`TpSAp/A}TA/AYSA|/ASA/A`?TAC/A_TA8/A UAPg/A_UA/A_UApl/AT7UApl/AH UAI/A@TAi'/A`HTAf/A-TAf/AxSA/A)TAX/A 1TA/A 1TA{/A@TA/AqTAv/A6TA(/AzSA8b/ASAX(%/AQgSAi'/A#SA!~/A 8 SAd/A#SA/ASA/ARAȇ/A&RAhD/A&RA@y/A(RA@y/ARAP+/ARA`a /ARA /A@E:SA&=/ARA/A =RAd/An'QA{V /AD TQA6]/A\ݲOA/AaOA|/ANAy/A KA@/A @IAe/A?GA6]/A\ݲOA Z/A<ŮTBA@B/Ay3CA/ADAh]/A 8EAf/AKCA"_/A&)AA Z/A<ŮTBA Z:/ArQAu/AKAB/A` FAh/Ad4AX'/A0A/A`f.A/A _8A/A`CA0/A`IA7/ALOAo%/A'XA Z:/ArQAy 9/AmFAn/AAK/A A8#%/A Aج/AAA/AྟA`v/A@qA@u/A6A _/A8A~z/A40AX/A[Ax0/A@,A&/AӗA /A (iA/ANA(/A uEAο/A@A/AAB/AŊA׉/AaA6k/AgA5H/AosA3/AA+/A`]A /A Ai /A`9As/AVA/A@!tAp/A@yA/A}]Ah^/AA /A@AA?/A`A /A_*AhU2/A;A``B/A|Ax[/A A\/A AXWx/A@GA8/AA8/AzA@d/AA8/AWAp/A AP/A(A/AKAhb/A@QA/A@yA/A cA7/AfA8/A@A /A@{AXc/A@iAp/AAC/AgLA/A;/Aho/A@FA/A?Axz/AnAA`e/AA%/A`K_A%/A@A@/A SwA1/A`Au/A@ cA=/AJOfAK>/A`l+iAx/>/Ax}iA>/AMiA=/A 0iAx=/AjAp[V=/AjA=/Ap*mAO*>/A&2mA蝴>/A YlAP>/A%lAr?/A%kAЮ?/A0flA(Bi@/AImAIh@/AInAX#?/A1nA(K?/AKnA@X?/AnAxKo>/A9qA>/Ap,rA9?/AhrA@/A{rAx@/ArA@/Ap,rA #sA/AБgsAXIB/A }-vA;D/A0!wALE/A0Tn{A^E/A}|A8>D/A|Ahf4C/AҪ|AhC/A }A\C/A 9}Ap=B/A@o~AX_vC/A@ AD/AA7E/Ab[AP/E/AGɅA@ F/A0EAxF/A@ AnbG/AWAxG/ẢAyG/AIAWH/A@<=AtH/A;4A7I/AA$J/A4Af=J/AA` K/A(A?H/AwAMH/AxA(F/A |A E/A@^yA G/APxAuH/A0kwAZNJ/AuAteK/A`etAL/A@<6xAM/AyAN/AwA@RP/AuAS/A1rA U/A:pAxm]W/AZoA&X/AnABXX/AiAXmX/AgAP/vY/AifAƨ[/AfAp]/A`CfAO_/AxfA̳`/A }aA`/A 96_A)`/A]Am`/A 9\Aa/AКZAjLa/AEYAԉa/A WAxqa/A0kVA%b/Ap UA;b/A@MSAb/AxRA&d/A ]LA.Rd/AhSKAOd/ALwiKAqܸd/A5KAld/ALJA(d/A0JARd/AIA d/A" UNIARqd/A-eHANVd/AboHAd/AHAP c/A**CApc/Ad@AEe/A`6APf/Ao-AȩEg/AK-A`h/A,Ak/AF*AJm/A)AHp/A@L&A0r/Ap_$%A0t/A_#AEv/Ap!A)|x/A4 A{/A`nAH{/A@&A }/ApApV~/APҎA/AZADυ/A BA0X/AA@|/A`?A/AA8"7/AVA؞/APA/A HA/AAG//A<A_/A%AT/AEAW/APAȶp/A (Aɕ/Ap>A`/ApAx/AdA\/ABA/A!A/Ap,Ah/APAا/AG A#/A  A0/A0" A/Apg A𒥦/A"3A(/Ap=Ag/AăA' /AA/A $AV/A HA5/AVApV/A@gAP/AAi/A ]A( G/A'A/A2SAٗ/A`AKZ/A`]A/A@EAў/A>A$/A`bA_/AQ0A/ATAP/AA '/A@iZAx(H/A㟸AH/A@sA%Ŀ/AIAH?/A@xA/AzAD/A{A5/Acu~Al/A@%xA׹/A7qA/ATiA /A ˠ[A⟶/A&ZA%/ABOAx)/Ap\A/A`bAHA/AeA`B/A /VA/AzmTA@}۱/A"HAP|/AZJA*/A?A|/A >A8'*/A@yAA쟭/A`=A%/A >A(0/AEA@h/A@rVAy/A OA0M/AMAP|/AyFA /AEAL/A@g;AXʬ/A6A/A@T5Aꔯ/A F#AJ/AQ4)A /A8'Ah/A`vAX/A`T~A/A3A(`/A`0WAsT/A@EcA5/AtxgA/AnA /A१}Avr/A\A 0d/AM0A`P/AA8 /AAT/A@ߨA)/A`]AX/AұA/A&)A؁/A3EA oi/AAH!/AԸAP^/AOA/A@.LAHib/AkA؎//AAx/AdA/A 俾A@`/AmAD/AgA3/A ҪAf/A \A/A@iA؍j/A`A+=w/Al AE]K/A}9Ad3N/A_AF/AqAd3N/AOfRA; /A):A 7/AX4AI/AN AН/AWA@/A@yA`4/A%A@'/A@KA֪DE/AhԷA;7ئ_/A֖7Ac%/A)EAWPi/ASd9qA\,A*/A +A@.c/APA X/AAx/AAXd/AAr/A2gAAq/A (AHah/AQAHÔ/AAU/A`A8/AhA0/A+;AHah/A`A/A Z}A/AJA88/AAek/AjApA_/A`A@Z(/AYƄ[Alō1/A iAwu/A"AB^}/AcAP|/AzAf/AοAC}/A%A_ /A@&AQqG/A\Aq/A$Ac/AA /A,{Ax`)/AJB;AQ/A$4AɣY/A6nA/A?@AlID /AҳAj\/AZ_AdH/AgzF~A/ALUDrA_2/A9AH/A@A /Ap,A/AzаA8/AAAB/A$AVj/A9fAIn/AQ(TFA) R/A4 L8Aآ6/Ae<A|/ALRAT4/AUzA/AAȺ/A`夰Aa8/AAR/A_\A5x,ӿ/Ai\~A+/AXg:ӾA{r+/AFAlŧ/APAH/AAl/AoA /A`0A@/A`TA䌰/AqA t]/A@r<A*/AA p/A3Ab̯/A@A}/A;A}/AA/AIDA(/A@AD/A@AP맯/A`QA(K/A@#tA/A?Am/ApA?O/A/#AYR-/AZ.vA/A7\NA՜/A=kAx/Ad$KA@C/AF'A\X/AqAAj'/AbдMA Y/AAi/AHAt/A"QAQ& OX/A}/A%BT$/ANϖA /AnAٴy̬/A1 A`lΩ/A-+vAIzԬ/AJ+URA|-o/AAo6W/ARJAM=D/Ar;BACO6/A/wAfn,/AiwAHn(/A A/ARAj/A6A3/A$|AT-Ӈe/A8нA`M/AI$ A8/Agf: cAǺ&/A5OA`!/A-A/AT(A/AdAڪ/A9A뭪/A#AaR,/Ap:A}|/AχAGV/A79 A./A܇}A@/AAة/AzW A]/ANfpA'~/AA/PQ/A촙_A_%/A OLAM/A'"A,Ҩ/AC0A/ABA?6n/A)#^A~i/A ANL`M/A QAC5/AH%3иAS!/A{cA#W/A4>lAa4i/A˲tAI+v/A=( A3/AltTAxh/A ɠA:@/AigeAx\~/A[;0Ar$/Aj(A8 /AuߣA*/A ATxqa/AzA̦/A`Aqbաb/AؔAA /A.LۂAJߠ/A AIֻ/Aۢc^Ahז/A5?ۚA4,p/A͂AWT|I/AAX!/A`AxK/AAn=/AA/A'5A} /A\AI/AfAcB/Ak[Aq0t/A:AEAXy/A@hk3A ojt/A5RAp/AsAFܝk/A;UA ad/A3XA]/A.vAV/A`DmA=:2O/AMqA(G/ANA)B/A]A- =/AMAkJ8/AEAoX3/A\A+P./Aa\AX)/A`At%/AA A)"/AWAre/ATALi/A0MA /A517A7|/A0A#/A<,A_x/AC>A8g/A OAmGm/A?2FAf/AAA_%\/A/oGA` c/AA"d/ /A'R#.AQP/A֚PA/A;lA~۞/A\uA@ӿ/A<A=/Aɏ$Ar;F/AwLŋA`/A˸ @AÝ/AA/결/A2(AX&/AJ73AOGw/A1uDrIAߓ-p/A0QAY/AALA'FC/AUV8A`1//A sA7(/AyAOz!/AūkA /A0Au$/A8Ad /AA/A[A8ٸ/ABA3X/AU]A*/A/Ah /AHAx/AtdAP!*Ŝ/A oyAHn#/A7AgAqx/A ~RAX$N/A |Ad;/AFA+/A%EAf)/A pAn4/A1T=A/AIoAPo/AyA%]; /Al/A.RA<S/A'AWT/AJ-;AYhԛ/AAh;_ƛ/A=A-/A ZA&t/AcGAY/A.A=L/A^APYS/A`Ap=k/AA&}/A cAgy/A !AX]/A`AB/AA~/A@-AH/AfAa/A@A/A6EAЄ/A JAx/AvAxW/A ¯A`v/A sA/A@EeAÙ/AZA᜙/A Ay/AvAH`/AApQ/A`6A+A/AVA N0/AA/A`5A0"/A+A//AA^/A &AH:Y/AZAn-/ADA8E/A$A/AAA/AHA̛/A`AT% /AîA=E/Ao3A 1/AFA;sN@/AXg:ӾAʇT/Ai\~A}/A A/lZ/AM A`Y/A! APY/A1' ATmY/A6 A-Y/A>g AHX/AO AQOSX/AGW ACX/As Al_X/A{ݖ A5X/AI A X/A@ܱ AW/Ay Ak*W/A闕 AҗgT/A] AJIN/A=m~! A5x,[G/Ai\> A+#@/AXg: A{rγ8/A A:,W1/Ao3dž Av)/A A=!/Ao3dž A 18/A A;sN/AXg: AʇT /Ai\> A/A.Aa Ay:.A4F^da AңIF7.ADa A4.A_a A3.A0 b A@>3.AHb A1.A b A&.AZ'b A0*.A@.Hb Ax.Ab Ak.A .A^( AX.A Z#' A.A & Aj.A& AGx.A& A(h.A& AJ.AB"& A d.A,& Ar.A% A0Ǻ.A`T% AP.A;% A0X.A$ Ax0.Af$ AH#.A`f $ AP.A$ Ah.A@.$ A.AC% ACv.A &x% AW.Ak% A8:.A@% A0.A$ Ap*.A$ Axh.A" A"e.AR" A0.AG# A83.A@ # A.A`9$ A.A`m$ AEl.A@G# A0B.A# A7e.A" Aୢ.A`;! A .A! AP/.A A.A ~ A<.A&B A>.At A .A@ % AP.Aa A .A A.A^ A0j.AQ Aǰ.A6 Ah.Aྶ A0{v.A`2 AR.Af A >.A@R A44.A A7 .A`] AH.A`D Abm.A A.A AH.A A@p.A Ask.AN A3.A@ A,.A A0.A  AH.Ax AP'.Ap A`.A@, A$d.A Z Ah.A@ A@.A@d A .A ApI{.A` Ax [.A AC.A ( A1.A ( A.A ( A`.A@% A࠻.AO A.A@ A=}.A # AZy.A J+ APJ.AD AX.AÖ Ah\.A Az.A` AUC.A c4 A߬.A Q A .A@d A(1 .Ao A Ⱦ.A: Am.A& A<.Aߏ AH.A) A7.A@s A4d.A`" A].A~& AH.A ( A0ʹ.Am. A^l.Aܴ1 A.A@9 Ahֲ.A< A.A@@ Axm߰.A+C A (.A@E A.A@߂G AXX.AG A4.ATHF AV.AjqG AP.A@I A?.Ad~Ah&.AA.AAt.A@ݓA }.A`DA(L.AA.A A(r.A"Ah.A@GA).A`fA,.A)ޮAP.A`A8r.AvdAx}.AAxm.AkԓAȔ.A@Al~.A OA8g.AAC7.AT A.AAq.A ʉAH.AA0J.A lA$.A`bA0.AXA.A:Aʺ.A Ax.AyApco.AAp.A@#As.ApAX_.A Aȭ.A@{A.AZAF.A@`AHö.A4AǶ.A`ݑAy.AAv.ABgAj.A_lA8\.AVrAa;.AA9%.A sUA#,.A A-.AA#.AA0.AA .AA($.AoA].A`A.AApѲ.AA0?ò.A0Ay.AХAwZ.A Aұ.A#xA(.A`)A0x.A`LA7.A A7.A}A/ .AA8.A A`.A@mApϰ.A@%A.AȫAR.AѬA_.AAAhb>.A`A.AtƭA.A@ѭAp.AA.A@%AA[.A_sAP3t.AAg.A@%AG.AAL?.A nA :.A5]A&.A GAPZ.A ȭA Rr.A JA&0.A $A9.A S@A9.AAۭ.AxApŭ.A $AI.APA.A A}A吭.A@iAх.A@ߍA.AgAxA.AaAP.ATA>.A lۮA.A`AhQ}.A@A{.A@ߍA(dd.A\AQ.ATA:.A A}A,.A'At.AA.A;A.A Ab.AhAPYR.AAE.AA.A@A.A`Aj.AhAH˹.AA.A}Ax.A`Ai.AK$A M7.A0*AA.A J,A@.AX.A.Am/A\w uں.AB 0Aú.An0A0Z.A1A` ټ.A;A .A%D=AY.A?A] R.A%?AO.A1bu"@AY4N.A_`H@AWQ.A@n@A0l.APQ@A).AWA….AgXAW.A[YA .A@I`Ah.Ai2fA8.A@$ fAo.AzPfA8'w.AjA.AӦyA:ǯ̚.A+DyAa.AyAd.A* #zAUF}.A2"s4zAo.A@OzAPL yU.A-WszA2J>.A ?"zAn*.Ae=~zAM.A {A+<.Aw{Av.AE-{A.f.AtA{A曚.A!U{A{Aa.Axjii{A.A]s{{AȎR".AZn{A=).A%{AV.AQ{Avq(U.A)V|A .Arf(|Ak.AD|A#.A³|A#.ASAµ.A łAH..AA.A0"AP.AMA.Aa~A؞.A9A0..A`)юA`b.A`\ٓAߋ.A A.AhAfz.A{˔A\.A6_A.AėA.ABAx.A0nAX r.A`\*A8.A0w%A.A!Au볯.A*ZA P.A蘝A='.AdܝAl0.A6T$A.A#pA0.AAdn1.A'؞AN1.Ar-Ac/.AU: A+.A AT&.Aw5A.ApIA$Y1.Ak AӂH.As#AG[d.AЊA\E'.AnqZAߚ.A=iJA.A^Ai .A2HAЕ.AG̡Aɯ.A {A)D-.A*KAx=.Aw;AP+X.A AX.A,1AH.A@nAZ.A.A.A@qA|.A@<Ah.}.AOAؖ{.A`:A|R.Apw.Ac.Ap5ԏAx.AvAGg.A$N~AȤ.A8~AH.Ar~ANw/Av}A:/A 8 |As/A{A/AzA /AxA!f /AP&vA#/APZqAh/ApAx/AypA/A pA`6/ApA5/Ap5YpAp I/A`]toAq;/A`mA/A^lAP/ABkA0Y7/A-jA/A?hA,,g/A2u Am~/A A/A`4 A/Az؀ Aʯ/Az A/Af{ A /AҐ { AW+/A#7{ AyK/A&Rk{ AXi/A{ A׃/A 1Fx| Au/A,g| Ab/A"| A»/A[ } AZ*/Ay-I} A+/A9} Aһ/A;Q} AGZ/ACy~ A/A d~ Aۊ/AqW~ AE`S/A~ Aw/A.n~! A}Eѳ/Ao,*V A/A A9s/An Az/AD AU~l/AGS A,xU/A^ A;\zB/AT A01/A@  A7O3/A]Y,J Av7/A]Qpv A; =/A A0D/AA>_ā A;XL/AJw AV/AZ?> A/a/AF Al/Az@  AM4$x/A5;>! Ah̃/A  A~/A񝅏 Ar;] /AN ABq/Au A/AR A>/AɾK AT#/Ab @~ AaW p/A,/} Ay /A;7t| A=/Ad{ Aw/A/gy AfJn/AHx AP /A@rw AX/At A/AHq A`/A@wn Ax/A`k A/AWj AX(R/Af Am /A՘c Al/A (` A(:/A-_ Axmi/A@E] Ah1/AQ[ A(/A#[ A"/Aiw[ A1/AxiE[ A&/Ap[ A#Z/A][ A(>2/Ag[ A?;D/A~-Z Ax/A`K6Z AI{/Aw=Z AOu@M/A'[ A1 /Ai[ AЀ/AA[ AtU~/A $\ AZ64/AD5Z\ A灘i/A08~\ A5/AFqm\ AhR/As\ A/A3] A/A v^ A8/A@y^ A}/A`E_ AH/A{a A@N/Ac A`/Ae AD/Asi A/Afk Au̹/A iՄl A7o/Am Au!/A 0o A0_T/ASsp A1X|/Aݧq Ah%/A r A0/A@Gr A /Ay p AaB/Ap A06/Ap A/A r Ah/A@t A`Ì/ACt A#ں /A@t AJ/AĒL. A,,g/A2u Axhu/AD Ayt/A6 AHjt/Ap A~b`t/Ax醪 AyZt/A,:9q A8Yt/A^7 Av]t/Ae7 ATqft/AzƂ Ahtt/A"/ A?Ft/A֩` AO 1t/AX&4 At/AAq~ AYt/A| AEt/APmv{ A.T%u/A z AQu/A1 zy Au;u/A*x APu/Aw Au/A;r Ap v/Af Acu/A Fe Ahu/Ad4d AX[|u/A^d A o\t/A@4i?d AӈVt/A䦥}4d Azs/A9O=d AKqG0s/AG[d ACOr/A^r_d A%0!] r/A\%sd A {q/A>/e Avjp/Afڝe Afi[p/A2 f AƨNo/AJES f Ad@o/Azy(ag A ln/Abh A+n/ARah Arm/Ai Aecl/A J^l AG9k/A n Ax6j/Aѥ%n As.j/A@,fo Axi/Ao A|i/AdH Aȩb/A? Ab/A@j A`2b/A qe APMb/Ad A c/A`f A0'c/A Aic/A  A@™c/A`ܳ A誴c/A| A((c/A` Ac/A A?d/A^ Ax1d/A Z AtCd/A}[ A8d/A@g- Ag3d/A^ A7d/A A`Xd/A Add/A? AѧZkd/At f Azʉe/AR A_Gݯj/A| u϶ Ab"^p/AF)7 Axhu/AD Ageopandas-1.2.0/geopandas/tests/data/overlay/nybb_qgis/qgis-difference.shx000066400000000000000000000002141525650651600267140ustar00rootroot00000000000000' F8+AVU@(vI0A,1A2*Db Jxgeopandas-1.2.0/geopandas/tests/data/overlay/nybb_qgis/qgis-intersection.dbf000066400000000000000000000050051525650651600272640ustar00rootroot00000000000000uBoroCodeN BoroNameCPShape_LengNShape_AreaNvalue1N value2N 5Staten Island 330470.0103319999761881623819823.8099999427795 1033296 793054 5Staten Island 330470.0103319999761881623819823.8099999427795 1063988 793202 5Staten Island 330470.0103319999761881623819823.8099999427795 1094680 793350 5Staten Island 330470.0103319999761881623819823.8099999427795 1125372 793498 4Queens 896344.0477630000095813045212795.1999998092651 1217448 793942 4Queens 896344.0477630000095813045212795.1999998092651 1248140 794090 3Brooklyn 741080.5231660000281411937478507.6099998950958 1156064 793646 3Brooklyn 741080.5231660000281411937478507.6099998950958 1186756 793794 3Brooklyn 741080.5231660000281411937478507.6099998950958 1217448 793942 1Manhattan 359299.096470999997109636471539.77400004863739 1156064 793646 1Manhattan 359299.096470999997109636471539.77400004863739 1186756 793794 1Manhattan 359299.096470999997109636471539.77400004863739 1217448 793942 2Bronx 464392.9918240000260991186924686.4900000095367 1248140 794090 2Bronx 464392.9918240000260991186924686.4900000095367 1278832 794238 2Bronx 464392.9918240000260991186924686.4900000095367 1309524 794386geopandas-1.2.0/geopandas/tests/data/overlay/nybb_qgis/qgis-intersection.prj000066400000000000000000000007751525650651600273350ustar00rootroot00000000000000PROJCS["Lambert_Conformal_Conic",GEOGCS["GCS_GRS 1980(IUGG, 1980)",DATUM["D_unknown",SPHEROID["GRS80",6378137,298.257222101]],PRIMEM["Greenwich",0],UNIT["Degree",0.017453292519943295]],PROJECTION["Lambert_Conformal_Conic"],PARAMETER["standard_parallel_1",40.66666666666666],PARAMETER["standard_parallel_2",41.03333333333333],PARAMETER["latitude_of_origin",40.16666666666666],PARAMETER["central_meridian",-74],PARAMETER["false_easting",984250],PARAMETER["false_northing",0],UNIT["Foot_US",0.30480060960121924]]geopandas-1.2.0/geopandas/tests/data/overlay/nybb_qgis/qgis-intersection.qpj000066400000000000000000000010001525650651600273120ustar00rootroot00000000000000PROJCS["unnamed",GEOGCS["GRS 1980(IUGG, 1980)",DATUM["unknown",SPHEROID["GRS80",6378137,298.257222101],TOWGS84[0,0,0,0,0,0,0]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433]],PROJECTION["Lambert_Conformal_Conic_2SP"],PARAMETER["standard_parallel_1",40.66666666666666],PARAMETER["standard_parallel_2",41.03333333333333],PARAMETER["latitude_of_origin",40.16666666666666],PARAMETER["central_meridian",-74],PARAMETER["false_easting",984250],PARAMETER["false_northing",0],UNIT["Foot_US",0.3048006096012192]] geopandas-1.2.0/geopandas/tests/data/overlay/nybb_qgis/qgis-intersection.shp000066400000000000000000010236201525650651600273270ustar00rootroot00000000000000' 7.+A%S@,iO0AA7.+A%S@ ),A%cc@ ),AG@4O),Aw1@),A7@3h),A^6J@1),A@e),Af3ϟ@y(,Aܙ;}@jl(,A8#9@^#(,A5@iSe(,A%xO@_@(,A@@sn(,Az4@P',A@U@wzf',A1(@);I',AWݐ@',AfKf@(v',A@ @',A@@7b!',A@<.)O',A>´@J.G&,A'@w9&,At@%)ʞ&,A@9Q&,Ak)p@Db&,A_ @& $MR&,Aς@K[@&,AG@,&,AQ@j&,A@Os&,A{@%,A@!%,Aruބ@xG%,A@^D@\A%,Ae@(%,Afw@%%,AXz@z%,Ap@(O%,AKtI@A$%,A;@$,A@ &$,A|`@^$,A~W,@ޥ$,A~Vp8~@3}$,AUP?@(e$,A@1q(Y$,Aj@SK$,AA1@);$,A@s)$,A{$@+W}$,Av@$,A g@12W#,Aɫt|@,t #,AaGx@x#,AR@,Aъ@,Av@#,Ax_@x,AD@Xym,A@N'@@Wb,A@;R,AF@hB,A@1,A0@P!,A_@,A@XW,AHs@5,A@Nq@,Ad@W,AT@(,A8@,A@N,A@`!,A@,A@0t,A>@0k,A4@8c,A@^Z,A@@Q,A@@AI,A9@ .,A6@,A@,Ag@8D,A6@D,A@ @,AI@(,A@@b,A"@X,A@sN,Aq@uD,A@ֽ@:,A@x0,A]u@',A@L@B,A @9,A@Y@(:,A2@X ,A@@,A@p,A@,A@@أ,A@@,An@,A8@,A@%@hL,As@=,A^@D,A?[@0,Am@,A@,A@b@ ,Ab@g,A=@@,A @2,A@p&@O,AT@,A@,AV@,A:@,A@3@y,A @H,A#@ ,A9@#,A@pL@P,Ab@,Au@(h,Aф@,A@@ɐ,A@Ϙ@,A@r@ ,Az@(v,A@ce,AX@T,A@XD,Am@~3,A@sW@",A>@,A-#@;,A@,A@0,Aq@,A@`,A4@8,AD@X ?,A@v@83+,A%@e,AA@Hi,A@,A@0 ,A@,A@+^,A64@ʶ,AL0#@,A`@ϟn,Ar85@u ,AQM@. ,A{B@zY ,ARmd@O ,Akm@ ,A@E@sk ,ARbT@a\E ,Ai@킝 ,A "@.,A৊ݱ@L,A@g^@ t,Aa@0",A @,Ai@*,A@Q@X[,A@"@|,A@sӭ@ yV,ALD6@sV&,AٓAw@Xn,A8̣ӗ@%Ю,A9v@nV,AbI~@|Zj,A-[H@أ],A@g@0p,A5 Ф@P,At&ݮ@PF,AZ:@h,AM&ѝ@#b3,A %5U@,Aǘ@أ ,A@⦗@8,A9%@X ,A@v$,à @,A@r |,Ab/R@ [,A8ͣ@iB<,A9?@#,Aκ@(_,Ak@6,Aڄ@2B,A];@W1,A|@,Agw@j,A6B{v@J>},A{B}u@],At@ވM,Am t@;,A1txt@2o),A+C7Xt@ <,A#KPt@,AN-_t@'|+AB)t@!;(+AMVt@83+A@u@4D+A㕩u@֏+AVu@m+Awlu@.K+Au@8)+A]u@|+A̕M[Z+Azr@vA+A3r@k)+A&&s@ڷ+A?Gs@F *+A;8بs@+AS$t@+At@w+AM6Zu@LT+Au@vz +A=u@Z[+A v@W(+AuGu@+A@u@߃3+AV(qHu@R+A&$t@O+AMt@(A?=+A9 t@'w+A>pt@p+Ast@MV+Aet@!Ԃ6+Aet@+A=~t@&+ɑ+At@5 R?R+AQ~s@BX+AНr@@v+ASq@+Ap@Ym6+AdҬn@.> +A:}m@D/l+AnXj@I +AEh@V+A]A^af@O+Ac@X!+A@b@&a+Af`@j+AKa2q]@:oC+AΎZ@.4+A&X@Uv+AN3V@8E+AT@B(+AѽS@8 `{+A%S@}+A_٦S@ը+AM S@pm@L+A_1T@dP>+Aj+T@&++A+\U@*F#+AdK-V@:+Az$W@]c+ACBX@s+AL@Y@d,S+A=YZ@S+A@y\@ +A^@22+Ad@ +Ag@0b+A@5l@+Ao@ш+A2.t@f!$+A\Q"w@c,i+A($<{@@1d+A~@`+AHa@+AW-|@+A@@!+AV@0+A8fZ4̌@j+A,y;@+Av=@ +A@?=+AYW@9+Ap@{>i+A!ɗ@}Z=+AH@;+Angޙ@p}+Aš@d+A@+@(+AΝ@8+A@ƨ@+Ah@@+A@_f'S]+A$~5%@+An@+A@6@9a+APХ@](+A*X@K̠+ATO@-9.+AQ`-3@$-+AeE,@$e+A9@Kx[>+A[Y@'C+A@=Re+A]|ɵ@ W+A@P+A@so@B+A1`9@P:+A@@p$+A2^@<*3 +A3k'@H#+AC@C=7+A+AH@|.a+AN!L@@H+A@:+A@I@0S+A@@XJ+A88@#[+A@@Q+A=0@@S]+A\@P+Aj@V+A$@k+Abi@M+A@>@h.@+Aj0@d+A@g@+AL@%t+Aō@$b+A@$@_+A@@a+A5@جj+A@ @+AE@+A@+A@P@ R$+A@6+A@@i+Aʓ@+A@+AfK@0+A@E+A@G+Axݨ@X+AV`@(+A@ +A@@+A@S@Ș+A@#ȯ@s+A8F@+AZ@M+Ah@n+A@^+A5@+A@%@ A+A@@ү+A@ @X+Aʵ@Ʋ+A@J@+A@`Ѷ@pw+At@+A@0@+A@b@ +A@p}+A~@H +Aȹ@|+A6V56e@+AY @\'@( ?+Ah/@`T+AE@_ʼnu +A|tS @~)$+Aok+AӝS@6p6q+Ae@iq x+AQ%@?)+AC݆>@#J|+AdZM@0.+AQ@+A51@M+A n@M+f+Ad@]Pϵ+AQ@+A@3+A4'@ C+A=F@+A_@}$+Ax@XI+A<@!M+A@+A@P< @+A` @+A@ @@+A@# @`%+A-0 @+A @/+A$@g+AX]@h+A_b @(+A2R @ho+A @h+Ak# @ +A @PI+A) @\+A_:@;+A@-+A@h~+A@+A@J@@dj+A@+A9 @+A@@+AF@+Az@؟+A@+A@@x+A$@H)l+A @0+A)@6+A@/z+Au@'h+AE@r+A@+AB@؝+AM,@ p+A @+A@@+Ae@`q+AM@+A@fU+A@0/+AT@4+A|@+A@p@x+A@N@B+A0@db+A`@X+AQ @B$+A @P+AT@H̋+A?@Ϡ+Az@.~+A_ @o+A@N@8E +AZ#@+A[3@+A+.@(+Av.@C+Af:@b+A2x9@+A@3@C+A@%#@(+Ae@3+A4@0+A8@+A@x +A@֏@+A@i@t+A2>@0G+Ax@+AB@+A@P@+A@@+A@@+A@ے+A@%@`2+A@ |n+A@@+A{ @H+A @`vu+A#@+AM%@p +A@@'@ R+Ad,@ Ε+A]s,@@$+A@,@Q+A7,@L+A1M3@%+A@ 3@`Ǜ+A-@+A@,@hD+A,@g+A@߫,@p +A@(@ +AZ(@`S++AD(@)E+A@//)@+A@g-@+A8.@ȓT+A)@!+A@-@1+A@Q-@x5+Aj5@+AA@h+AB@G+AE@`a+AF@(+A|G@ +AG@0. +AG@ +AG@5+A8G@w+AHF@p+A F@x(+AF@H+A@F@+A@ZG@n+AH@W+A[I@+AJ@u+AK@z$+A@gM@(\U+A@LO@+A]P@P8+A4{Q@+AQQ@xW+A"YQ@hr+A+O@(+AhEM@@3+AxL@+A@gL@0+A}L@h+A=K@+AD"M@!+AP@+A>V@g+A`@/+AzGd@+Ah@sA+ABg@8t~+A-c@+A)a@+A@`@ؚH+AQb@`1+A"'h@˼+A@N{k@x+A@!p@T,A@\s@(y,A(s@ۈ,AU/u@0S,Aau@IS,Av@(-,ASBz@(9m+A@,r|@+A8X}@+A@W@P+A @x+AzU@&+A'@@S +Afa@H+A@R@+A&@v+AĈ@ C+AH@P^&+A(@X+AĈ@H +A$z@+A5@X+A@8 +Ag@{&+A%cc@5x,+AXӦ@J,Azfz@җg ,AQ#;[@Bkݽ,As+6@<"l,A  @}n5,AZ@ ,A6Uo@Q,A)o@,Abo@,AÅvp@0 ,A"Xmp@0u,Ap@۰ K,A"8q@A2,A!wq@w],Aq@QAƄ,APaq@#,Ahq@P7en,A&q@C,Ap@qA,AbTp@-@G>,Aaqp@6:,AnwWp@=56,AHsBp@}d81,AE4Y4p@",,A-,p@zc',Ar+p@M",A4'`1p@E,AP=p@Am,A Pp@F,Aip@?{6,AL%Gp@x,A*p@x ,A6p@3,A4t@x1,A -u@=,Arv@Ñ,Am;zov@`崃,Aw@沃,A@w@[,A2x@,A{@`,A2}@n,Al ~@(D,A@A~@0,A@A~@/,AS~@гg,A@y~@,AD@P/,A 2@7,A*@`,Am@|F,A}@ ~,A@ߑ{@ \~,A(z@M~,A@8z@@:~,A@z@`O ~,Az@0},Az@Jg},Ax@;|,Au@xR|,AIr@ {,Am@e{,Adm@ {,A=n@0z,Ak@0y,Ah@Lhy,A@g@)y,A.g@ w,AIa@?Aw,A@.\@v,A@IX@u,A@Q@2t,AE@q,A}0@+ވq,AUv,@a!q,As5)@~׶p,A%wm%@ir]p,A !@Ƹp,AAÙ@o,A:@ Ro,Aoq@أo,A@?@ޝn,A @%Ȉn,A5p @+Pn,A@p:o,A}@- ;o,A+1^t@Io,Aj8R@W\>o,AE@6L>o,AT;9@p=o,AG`,@(J`=o,A @h'@*0om,A@Ym,Am@83Gi,A)l@>1i,ARmd@j+i,AF@C[Ih,A]  ^@4'h,A@fh,A%I;Z,A~@3eY,A1Yj@-Y,A(@SY,A@@{'=Y,A>q@xX,AHT@Ms;X,AD֬@q 1lX,A2@@%X,A@i@@ FX,A6B@WfsnX,A)O2@}W,A6y8@9 W,AHPU@"mW,A7H@tBvW,A@ѫέ@ mW,A2{N(@q"SW,A\=q@sW,As @{V,AѦ@U#W,A@s@Po2W,Az@0QY,A@?Y,A@5h@HY,AX@X,A@RX,A@@X,A@ _X,Al~2@''5X,A]@ X,AP1^@-W,AFO5@JW,AD@wGW,A@8W,A@G2@XIcW,As@Β0q@U,Ar @U,Amհ@cU,ANb@/U,A-@#T,A@G2@b)Q,A%@hO,A4}@@yN,AO*@eM,A@AM,A@ߏ@L,A@dL,AF@(L,A@L,A?]@8L,A^10@_BpL,Aw)@`QL,A@=z<(L,A-K@2"K,A1*@pTK,AT@ޱK,A@rWK,A@W@mI,Aʾ@0yF,AXƷ@![F,A ~L@dߏE,A(v@j)E,A@oAD,A?a@(POD,A?F@+,A:d@b"%,AS*@_Gg,A>T@zA,A@GÕ,AiAԲ@d ,Aܨp@%hx,A(NSqA*&{1 ,AP9Q,AE]K ,A4JAd3N,AOfhA,AAd3N,A_AE]K ,A}9A*&{1 ,A0ƾA%hx,A౬Ad ,A+WAGÕ,A7K_4AzA,AuvGMA팛,AWSA ,A`SA ,A@SAx,A`;SA؟,A`FTA<,A@XTA(,AIbTA,A ZWTA(y,A TTA ',AIbTA8,A`TA\,A`oTA,AKTAƐ,ATAPk,A`VUAP|,A@ϽUA0,A`VAH,AVA8,A UAa,AUAx k,A`)UAP,A UAͬ,ARUA_#,AhQAXu#,A*TA_$,ASApJO$,A4nRA8#,A JRA̰#,A JRAU#,ARA #,A ɰQAh.$,APA$,A ҝPA) %,AdPA%,A PAأ%,A)nOAW%,ANA8=%,A qoNAT%,Ai4NApd%,A`bMALd%,AaMA•%,A@>MAz%,ALAp%,ALAp &,AH_LA@&,A@>DLAf`&,ALA x&,A LA&,A@MA &,A/MA&,A %MAh&,A 8LA',ALAP',AHLA8',A`õLAf<',A`LA=z',A@6LA',A LA(\',A_LA# (,A`)LA#)(,AYMAأ9(,A`]MA`A(,A@gTNA](,A NAe(,A)NAo(,A)OA~(,ANAW(,A[NA`(,ARNAp(,A`]MA(,AMA(,AMAxF(,A@yLA(,ApQLA`(,A KA(,AKA`a(,A@KA$),AKAj),ALAq5),ALA-9),ARLA ;),A@%MA>),Ay%NA8EA),ANA@F),A@#dOA@S),AOAx),ApOA),AjOA),AýOA@),A]PAxW),APAx),AQAĸ),AQAP),AJRA),AORA*,A &,RA'-*,AQA;V*,A |QA@O*,A QAxF*,A-QA*,AxQA&*,APAX*,APPA*,A`[PAX*,AOAx*,AޤOA0*,ANA*,A NA`C+,A |NA0.3+,A3fNAPg9+,At*NA=+,A@%MAC+,A@GMAC+,AjEMA^+,A`LAPkq+,A@PoLAȇt+,A`dKAp r+,ABKAr+,A`ʘJAxs+,A@IAx>}+,A&UIA҇+,A`THAh.+,AYHAu+,AEHAdž+,AGA8+,AFAV+,A]EAz+,ApDAP,,Az&DA!,,AHCAfD,,A`ECA8e,,A +CA,,A@gTCAp,,AHCA,,AH/DA,,A3_DAp8,,A3iDAB-,A@rDAN-,A@DAt-,A׫DA̠-,A@>DAPf-,AMEA-,AEA-,A FA-,AHGGA-,A qaHAр-,AHAps-,AXIÁ-,AJA8-,ALAP8-,AHLAw-,ALA(_-,A#MA(W-,AfMAQ-,A NAH-,A3qOAZ-,A`fPABe-,APAp-,ARPAW-,A qPA8-,A"PAf-,A)OA`a-,A OA-,ANA-,AzMAǥ-,A uMA0.-,AMA-,A`ÕMA-,AVMA-,ALA-,A`)KA-,A )JAX.,A qHA`.,AEAk2.,AEAJ.,A@>DAp8|.,ACAp.,AzCAh3/,A 8DAh3/,A@DAf.,A]EA(\.,AFA8/,A`FAQ.,A3HA(.,ApJA0..,ARLA8.,AOAu.,APA!i.,AQAR.,AQAr.,AoRA蕏.,A`TBSA8V.,A hSA .,ASA.,AQSAHPI/,AOAH/,AhOAPZ/,AaNA/,A [MAx$/,A LA(0,A`JA)^0,AFHA˷0,A@ƘGAMu1,AHA;1,A UIA'lvWj2,A$&JAA2,Al_bW KAV3,A /KA4,A`LA(o4,A`ձMA4,A`NApw5,A3PAPwD7,AUAȓ6,A cXA_6,AGZA6,ANw[Ab6,A[A†7,A@]Aۜ7,A`B]Aؒ7,A[A7,AZAp7,A`ZAp!8,A [AC8,A ZAnb8,A0YAN8,A`2YAu8,ARYA8,A`f ZA8,A`ZA8,A`VZA`8,A |RZA)9,A@gYAk,9,A aYAأ9,A YA9,AVXA)9,A|XA59,A cXA8;9,A3XAkF9,A WA T9,AgWA\9,AHaWAc9,A@ [WAs9,AWA9,AWAB9,AaXA9,A XA9,AXA9,AXA9,A XAp:,A)XAz0:,A`4YA#S:,AYA(s:,A YA:,A`{ZAW:,A3ZA:,A^[A:,A[AV:,A[A`:,A=\A :,A\A8:,A \Aϣ:,A\A:,A]AX:,A<]Ap:,A,e]AБ:,A@]A؟:,A u]A7:,A ]AW:,A ^AXW:,A@ $^A˵:,A@<^A:,A`U^A :,A@n^Aӹ:,A`^A( :,Ap^A:,A^A :,A`^A:,A^A:,A ^A :,A |_A8:,A_Ap:,A@,:_Ap:,AW\_A@N:,A`f}_A:,AE_A:,A _Aj:,A2_A;,A_A#;,A`A@N;,A+`A;,AtF`Aȃ;,A_`A0&;,A@ x`A.;,A@`AL7;,A `A@;,Aྷ`AH;,A`A`R;,A@`Ap `;,A `A n;,A`A|;,A@>`AO;,A@`Ap;,A`A@q;,A`A`G;,A `A;,A`AK;,A&`Ag;,A`AX4;,AaA0;,AaA;,AR$aA.;,A#.aA¬;,A`5aAt;,A ;aAx:;,A`?aAp ;,A |@aA;,AJaA+;,AVaAX;,A baA(:;,AdpaAp;,AaAh;,AaA ;,AޞaA c;,A@aA@;,AaA0;;,AaA`;,A@aA };,A@aA;,A QbA8';,AbA;,A@+bAD;,A;bAhD;,AKbAP;,A[bA=;,A`]ibA;,AbA;,A cApI;,Ax[cA3;,A`cA؟;,A`cA;,A dA;,A@>HdA;,AqdAG;,A`dA*;,A dA0;,A`)dAXl;,AeAʿ;,A@eA;,AieA;,A`)eA;,A eA{;,AeA[;,A@fAأ;,AFfAQ;,AwpfAa;,AfA;,A@fA;,A@gfAS<,AgAp[<,A4gA<,AXgAY <,A&|gAK)<,A4gAЄ2<,AgA<<,AgAj?<,AgAE<,AgAȵV<,A@`.hAPZg<,AzahAp<,A)hAp,<,AhAHP<,A OhA$<,A SiA襟<,A)iA@Ϥ<,ACiAH<,A@P_iA <,A{iA<,AiA@µ<,A iA0̸<,A iA<,AiAPٻ<,AviAD<,AjA@<,A6jAp<,AVjA<,AxjA`<,A /jAH<,AjjAH<,AkA(<,A;kA07<,A |nkA <,A@ϢkApA<,A@ kAX,=,A@!lA0 =,A ElA8=,A|lAp=,A lA=,A@,lAO'=,A#*mAx.=,AemAН5=,A lmA+<=,A mAhQB=,AnAH=,A\nA`M=,A0nAHR=,AnAV=,AoAZ=,A@[oAu^=,A oAa=,A@oAhod=,A pAf=,AapAh=,ApA#j=,ApA/t=,ARqA~=,A@CFrA N=,A rAa=,A+sA̟=,ATtA=,AuA=,AڰuA=,A@^vA_=,A@rvAX9=,AvAЦ=,A`vAH=,A 1vAB=,A lvAu=,A@vA E=,A vA@=,ATvAʷ=,AwA=,AwA=,A`$wA#=,A`1wA=,A68wA/=,A@y@wAߓ=,AJwA=,A`DVwA`=,AcwA{=,A rwA@Ot=,A@wAl=,A`wAe=,A@wAa=,A@wA@^=,A ~wAZ=,A[wA8xW=,A-wA@T=,AxAOR=,AFxAHKP=,AX,xAN=,A@AxA0nM=,A`WxAL=,AFnxA(-L=,A@ƄxA)L=,AVxAL=,A@رxA]M=,A,xAN=,A6xA0.P=,AxAhT=,A ZAyAZ=,A`fyA_=,A@yAȥe=,A`&zA#l=,ARrzAp=,A zAؒt=,AzAPx=,A@{A+{=,AN{A }=,A`{A=,A {A =,A`{A =,A|A@=,A*=|Ax=,Ae|AΕ=,A@|AR=,A|A3=,A|Axm=,A$}A=,A |$}A8V=,AhR}A@=,Aq}AH)=,A}Ah=,A}Ax=,A~A=,A)~A 0=,AQ~Ah =,A,x~Ap==,A@~A}=,A ~A`>,A~A >,AA`>,A|'A>,AMA >,A@sA%>,AAPI2>,A`A2>>,AHAI>,A֠AT>,A A8_>,A SAd>,AAi>,A;Ao>,A8߁Au>,Ap A2|>,A@8A>,A`]dAp8>,AA>,AA>,AGA>,ABA>,AĥA>,A A>,A oA>,A@A>,A@߲A>,AՄA>,AA?,ADA@?,AQCA?,A@hA ?,AڎA` ?,A0A ?,AۅA ?,AA8#?,A )A#?,AzPA ?,AAl?,A` AC?,A@WeA?,AAT ?,AA ?,AyAЄ ?,A9A ?,AA ?,AA?,AʈA?,A`܈Ab?,A@%A?,A]AX>,AA>,AgAB>,A,A>,A`"7AЄ>,ABAz>,A`)PA>,A^A3>,AnA@>,ATA&>,A A>,AA>,A A>,A*܉A>,A Av>,A@E Aa>,A` CA>,AafA>,AA>,AAh^>,A}ӊA8>,AA>,A@y Ae>,AqGAȺ>,AnA>,AוAXy>,AAԶ>,As;A>,A`TAHܶ>,A3Az>,A@A@>,A@QAH>,A͉A>,A@Ap=>,A@A(>,A'A >,AXUAp>,A`A>,AA?>,AiߎA88>,A@ A>,A`2:A>,A`fAм>,A`KA>,A@ AP'>,AFAt>,A#ϐAw>,AXAH.?,AA;?,A A`?,A9A?,AeA`?,AǑA?,AjA?,A@Au ?,AAu ?,A=>A[?,A@Ah?,AA?,A`ZA"?,AA&?,AA*?,A 8=AR.?,A3AU1?,AA3?,A4A5?,A` GA`7?,A?A8?,A͖APY9?,AA09?,A@WTAp=9?,A×AT9?,AA@9?,AAAXF8?,A`mA 7?,A@5A5?,A?A3?,AmAp81?,AA83?,A3A?,AӛA ?,AA ?,AUAP?,AHAh>,A`T֜A >,A A(>,A@TAX>,A<A&>,AwA`%>,A@A>,A AAHK>,AR*A8t>,AfA@>,A@A>,A AP>,AAHz>,A]A0>,AA>,A`ڡA>,AA̪>,AXA>,A`ӣAC>,AA2>,Ao2AP>,A`aA Ҭ>,AA>,A Aɫ>,A`AЦ>,A@Ap>,A@ OA0%>,A`}AǤ>,AApΣ>,A`KץAp>,A/A@>,AyA>,AA ޝ>,AGA(>,A ۮAHܜ>,AA0f>,A`ZAe>,A螨A0ٮ>,AmAп>,A;%A`>,AFgA`>,A@A>,A AH>,A &4Ap>,A2A>,A ̪A8V>,A}A8g>,AhA`?,AAhQ?,AA)?,AVA?,A ݬA8E,?,A6eAA:?,AMAG?,AvAU?,ARA a?,A8A ^n?,AAmz?,AA?,A@>*Aγk?,A-D!tA;sN?,AXg:A 1hG,A҆A=N,Ao3AV,AA:,WN^,Ao3A{re,A҆A+Sm,AXg:A5x,t,Ai\AJy{,A=m~Aҗg ,A݊ABk5,A֘xA<",AdA}n5 ,A uvGMA-AȜAB>-AHdA̱-AOEA麢-A4P'Aل-AP9 Ak4K-A)NSAOl-AnBA-AHdA>-AHdA8v,A A;|-A@rA(8W-AlAoX-A`+mA tX-AnAx5Y-AmA#Y-ATMlAPgpZ-A ukAZ-A kA`1n\-AlA]-A@pjA@2^-A iA Tb-A`91_Ab-AB_Ab-A]A0c-A`\A;d-A`9YAسd-AWA`/f-A$PAPM h-AFAGh-A ~GAxi-ARHAeKi-A &GAi-AFAj-AGAi-AhIAFsi-A_IA؟ci-AIAi-A-JA`i-AJAyTi-AwJAgi-A@+KAitj-AOA8֧j-A@$NA0!j-AaLAGj-A4KAði-A IAH-j-A@,YGA`vj-AFAгmj-A@^FA0j-AдEAk-AGAH!k-A@GA`Pj-AzEA((k-A`GDAk-A CAxIj-A`LFA88Cj-A$ FAh/j-A0*EA oj-A`iDAj-ABAXk-A cDAPrk-A QWDAbk-A^EAHl-AaCA/k-A2BAh/k-A3AA0l-ACA !l-ABA@l-AjBA l-A@RBAHl-A`vBA 6l-A[BAl-AAAx-k-AfBA,l-A "BAl-A0,BAk-A5AA&l-A AA,l-A?$AA2l-A AAl-A@E@A l-Aw@A,l-A`B@Ap0k-A5AAk-A@AA^k-A@Ak-A4T@AXk-A`?Al-A?AAjm-A@W]FAp m-A 8xFA +n-A@FAKn-A`}FAX n-A@'FA +n-AvBFAîl-A@MAATl-AX@AHm-A}XAA Pm-Ai?Am-A >Aذ#m-Aݒ>ATl-A>A,l-A4=Ayl-A@;Afm-A<A8nm-A<Afm-A =A`1m-AR:Axm-Ao:Am-A l?:Ad|m-A`9Awm-A9Ax9m-A"9A8sYm-A`T;8Am-Ai8Am-A@y8A m-As 8ATn-A uK8A=n-AU6AHP(n-A 5A~9n-A4An-A@4Ajn-A&4A@tn-A@P74A:~n-A o3Ann-A`3Ayn-A2A@n-A`3An-A A@3Ao-A3APo-Ay3Ao-A~3AP*o-A E3Ah^o-Av#3A+o-Ay2An-AF2A5n-A`2An-A2j2AYn-Ag1A_n-AE1An-A1A05o-A@`02A;o-A@P1Amn-A0A*{n-A&-Ahn-A-An-A+A(m-A+Am-Az-APl-A ,APm-AN:)Ap-A?+A{p-At)Ahj-A&Aj-AI"A+&q-AL&A>Cq-A$ATj-AvA'jk-AAlm-ApA([bm-AAzm-AAm-A@Ah;q-A@yAq-AVA@m-A /A> &m-A4ܨAm:`m-AԘAm-A0A7 ?m-A`{AJKTm-A tnAͰCAm-A]JdAm-AKs[Am-A+KUA"sm-AmQA +m-A[nPAcm-AmLRA@m-AWA@m-AsAXk-AVAXk-A5Ak-AAȓj-AwAk-A$ARk-A*A@Tk-A@ qA"k-AFA#k-A@APZQk-A`T#AqVk-AͳAErk-AAIk-AAk-AA k-AͳAH)l-A <A-l-AAl-A^\AVl-AA~l-A@5Al-ADAl-AAl-A2Aol-A`BAj-A@A-j-A{Auj-A"XAp[j-AQA@€p-A@bAљp-A A(qp-A+ACj-AqA9[j-AߚA0.j-A@Aj-A A0j:l-AൈA=l-A`YAPyl-A@mAPyl-A@Am-AEA m-A Aql-A@,Aql-AAAAPk-AvA7ak-A[A7ak-A2-AHrj-AA+j-A}Ax {j-A QA@cj-ARA}k-AAk-A9A0qj-A@ƲA@k-A@3A8'/k-A`ڞAHk-A`]#Avu-AAu-A@ApxWk-AKAQNk-A ؚAh3l-A lA8l-AFA@Ql-A@AhVl-A AzSl-AAxrl-A\AȲl-AzAk-A[zApk-A yA8gk-AD\xA8"k-A@GwA&l-AZpAl-A]A{n-A LAXo-AgILA&o-A JAp8Vo-AEIA[~o-AOHA}o-AGA`Go-A3WGAPo-A@6GAo-AGAXp-A FAp]p-AMDAhp-ACASip-A[CA hp-A@oCAop-ACA([p-ABA.p-A XBA(p-Af@AA&p-AI@A`p-A@{@A8p-A?A`q-A>Aq-A`;>A@q-A=A(q-AО=AFq-Af=Arq-A;Avq-AІ;Avq-A;;AP q-A` :Ahēq-A`:A]q-A՚9Aq-A՚9Axq-A09Aq-A@9Abq-A9AUq-A@v9APq-AZ9A(zq-A(R9AXq-AaE9A@q-A 8Aq-A 8Aq-A@i{8Aq-A`D8A."r-A7Ar-A`7A0*r-A@77Ar-A5AhWs-A&3A#Qs-A3AJTt-A/Au-A,A +u-A,Aw-A@>#Ax-AAx-A@>A0{h0-AV-5A@5%-AAAQ-A A#-AA` -AR A`-AA0*V-A@PA00-AfA(>-A#A-AnA-AA88-AX=A--A` AB؅-A;A0{-AmA6-AhwApN-A4AP ˆ-A`AB-A`)|A#"-A`LA(K-ARAI-A qAB-A`m}A`C-A`A@$-ARAB-A@AxKz-A`AX#i-A#A`d-AdA#S-AAXM-AApD-AwA7-ApTAp4-ÄABL-A HApE-ApA-AEA-ArAP8O-AR>A`·-AQAxK;-A൪AB-AjAh-AMDAP<#-AbA,-A ZvAQ-AAݰ%b-AItAOCeq-AVyArR~-AlA#UN-A#4WaAW-A 1"Af-AE6\A}W-A8WGWAf-A)A:!-AnuA :-AfAl-AAȺ-A൐A-AAq-A9AX_-A ۫A4-AAh;5-A AA8VI-AAb-A`A9-A_Ah⚊-A'A(>-AQAP'-A ܯA;|-A}8AOl|-AnB8AO-A98 AG-A A=?-A98 A 1X8-A[u- AA%hh,A౬Ad -A+AGÕ-A7K_%Az1 -AuvA_GW-AAb"-A3A2-h.-A]EA J K-AO LAC-A cLA`!-A@yLA i&-ALA&&-AMAp'-A`NAH='-A NA'-A`NAX'-A MA''-AMA(-A"zMA)(-A9aMA U(-A?\MA_}(-AHRMAK(-A@5fMAP|(-AMA8(-AMA)-AMA+-ALLAp--AJA--AKA8s --A`KA&--A-KAj,-ADLAP,-A@xLA --A@*LA(\$--A[LA:--AB8LA--A KAi--A-AOA?-A;QA{?-A [QA>?-A`ӈRAxmdC-AYAx@D-A@ZAuF-A;_A cG-AA`A¯I-AfAHiI-AXgA0%J-A}}gA&J-AFgAJ-A uMiAaaK-A 8kACN-A WpAqO-AerAh.P-A@rAQ-A`rAQ-AzurA8ZQ-ARrAQ-A1rA0UQ-A1qAR-AVqA` 0UR-A1&;pAS2R-AaMkpARR-AoAK S-AސoA1LS-Ap.AMűAxz8.AѱAQ/.AA(&.A`A.A`[ Af.A.A.AXAh3.A@A.AæA.A@ȲAX.A'Ap.A`fA.A6Ap.A@bAH.AA.AaAݫ.A4ʳA0{ث.AAӫ.A QAΫ.A`?A0ƫ.ARoAz«.A A k.A9AB.A ܴA8#.AaAН.Aa!A8.AdMA@B.AnA.AAh.A`vA(.A`vӵAh.AAy.A@.A匫.AA .AEA`.AqAቫ.A$A֌.AͶAh.AA2.A@ Ax\.AAa.A7A@.A@`A`.A`Ap .A cAꨫ.AAp쯫.AA`ಫ.A`;A8ѷ.A@nA,.AoA_.AAH2ū.AAxKʫ.AAЫ.A \UA֫.AFAxګ.AٳAh߫.A@ A".A3'A8.A IA.A+fAP .AכAZ.AϺAi.AA0L.A O*AH.ASA6.A{A .A3AЦ.AֻAН.AA`.A.A}.AfA8%.A@֥Ak'.A@NƼA(~+.A AP0.A0A t5.A;bAd:.AđA~A.AwȽA`6F.AAK.A[AN.AA V.A/QA2].AA`.A@NAA"Ǭ.AEkAyʬ.AॆAVϬ.A<A@Ԭ.AfAZڬ.A@ AXެ.A (A.A9A.A &XA.A 1yA.A`Ax.A@AG.A ZAXL.A AXY.A`AUg.AA0v.A@ A.A O AА.AAHK.A`AH.A` A.A A8#.ARAȭ.A OA֭.A A.AAO.AF!A}.A`]'AX.Aa*A.A`/Ax.A7AX#.Aq;AK..A`=AX9.A>A`D.A H=AOM.A (:AZV.A8Ata.A@2Ak.A.A!v.A//AP .A&2A.A ;A.Ag@A冷.A@5EAZ.A SJA0̻.ANAƮ.A RA(~Ю.AXAPٮ.A]A.A`bA.AgA.AkA.A`oA0.A@ qAH.AqA'$.A pA8-.A@nA(z5.A mA!>.AoAOG.AqA@Q.A tA\.A OxAj.A}A0ew.A A.AAH.A Ah.AؑAK.AA舺.AAȶï.A@%A ί.AFAtد.AA.A Ap.A`AK.A As.AଫA.A A@.A`̸An'.A`)A3.AHAG.AAV.AwAb.A[Al.AA@u.AA}.A AXۉ.A A7.AdA8.A&A.A ||A.A.A`ÙA K.A@~AxU.AomAP_.AI_Ac.AYAhj.ADA@x.A@.Aہ.AgAG.A A,.AxA.AAxF.A A[.AA³.AATγ.A`jAHٳ.A CA.AA .AA6.AAج .AA.AFA.A)A .AA($.AA@-.AA5.A@AsE.A A`M.AՕA].A A7e.A@zAQl.A QtAXx.AfApۄ.AxXA.A@IAP.A/A0.A'AТ.A`A.A3AX(ɴ.A Ap[״.AEA2.AA;.AA.A /A.A`bAp.A ˾A.AA .AAX%.AzAؒ,.A@Ax-2.A@5A7.A?Ai<.AcAX(A.A@AC.AׯAF.AAFH.AEAXVJ.Ac AL.A |BA8R.AH{A V.A@AAW.AA[.AEAd.AAm.A l!Av.AAAh{.AHA.AXIA.A?FA`6.A@y6A.A@8Aˢ.ALA.AgAx鴵.AtAP.A A8ĵ.A ‡A,յ.A@PAH.AAPZߵ.AAXֵ.A|Apε.AAxŵ.A OA張.A"An.A /A1.A`:A꡵.AGAU.A9VA8.AfAt.A`)xAx.AAk.A`A].A HAU.A A@FN.A Ap?.A OA1.AhA,$.A@A.AXA' .AA.AA.A OA.A@E AԴ.AA˴.AA2Ĵ.AAA.A Ao.A@GA.A`mA軚.A@ Aߏ.A)A<.A 8Ahu.AJAj.A XA_`.A@gAUW.A@rAO.A`A`C.AA:.AݰA0.AnAd$.A@AH;.AA .A#A.A|6Aج.ABA`.A`DMA0.AWAxٳ.A^cA@γ.A lAdų.A@ sAPZ.A&zAho.A=A[.A*Ae.A=Aj.AA .A@AX(.AA.AA`y.A`$AHq.A6AHi.A?APZg.A2EAX^.A@YAX.A;jAhUN.A@A7F.A At=.A A5.A A..A@A&.AAȘ.A` 'A.A9As .A`JAj.AWAX.AHcA.ApA.A|Aز.A@>A0Ͳ.AA².A;AHP.A`DA(`.A2A2.AEA؍.A@A.A JA.A@yA.A`A`.A޺Ay.AAs.A`AZj.AA_.A`fA(U.AxA`PJ.A A(?.ANA0j5.A`!A8V+.A*AA!.A5Ad.A$BA .A ZMA<.AQAP.ATVAZ.AZA`.AcA@_ڱ.AsA@б.A ADZ.A@AXt.A`A.A@A趪.AAx-.A@yA.A@A.AA #.A܄A,t.A$Ag.A@yA`[.A@AP.ATA(F.AA2>.A A 4.A`A`+.A`bAP#.AA.A AS.A lA8.A@A.A@Aj.A A_.A&A.A7A@F.A3EA`ݰ.AQAҰ.A[ApAɰ.AbA覾.Ac`AZ.A`_A.AcA".AfhA.AvAȘ~.A@{At.AAx-j.AAp<`.AՉAXW.AAR.AAh"F.AXAs+.A`A߯.AA.AA0꺯.ArA.A`pA;.A@PiA` 1.AiA.A`xA_ˮ.A@A.A6At.AA.A`bA.A A.AA0국.AAh.A A̜.A`Aȱi.AAhA.AA.AvoA0LҬ.A`]Ah.AA8։.A*A`.AVA.A A .AWA.AAh.AA~.AA>@.AAȔ5.A"A.AիA..AתAPM.A`]A2_.A<'A0C.A|A~3.A`Ah/}.A@#A.t.A A0 Z.A [A.A-$APjV.A`&A:.A A.A@pA.A`AA.A`Ahޭ.A@AH.AQA@׾.AA '.A QA(z1.A@AH. .Af+Ao.AA.A@AN.A -A _i.A`A07.ATA.Ad AXW.A&Aȶ%.A@,A .A O{A0}.A@A.AA q.AbA@J.ATA7.ARA .AA@.AfG A.A AQ6.A` AF.A@  A( 0.A  Al.A AX>.A`ܽ A .A` AL.Af; A`P.A@,T Ap-.ADZ AB.A@ A .A AӀ.A࣑ A`K.A`~ A@'.A` A`.A6 A».A" A@.A # A(.A`X$ A#.At% A@y.Ar& At.A& Aj.A;=' A.A' AL.A l^( A{.A`( A([[.A`dS* A`.AR- Ab.A@. Aԋ.Aa/ AK.A1 A.A3 A\K.A`2 AH=.A"23 A%.Az4 AP.A x5 A&.A5 AR.A`6 AP:.A (E7 AТ0.A$7 AP#.A8 AX7.A֜8 A%.AՊ= Ab.A 1>@ A.A Z/D A|.AྀG A#.A`ܽK AX؇.AO Aч.A@P AL؇.AP Aۇ.A@-P A0Hׇ.AFP AGu.AQ A`<.A`;R A.A`ʏR A E.A R AP.A R A ؆.A`S A ͆.AES A.AS A@આ.AS APځ.A}-T A@1l.A_T Aa.A`yT Ax$.A \T ALЅ.AU A`尅.AU A(\.AFV A`؞.AV A.AV A.A@V A|.A@V Aw.AE$V An.Ap4V Ac.AELV A#%.A AV A .A!W AƄ.ALW Ah.ARX A(~V.AX A@<.AX A.AN@Y Ao.AqY A`eۃ.ADY A@.AdZ A풃.A`K>Z A,.A2Z AȂ.A[ A؁.Ax] A0݀.A^ A.Aj a A 9.A@a A@u~.A`b AÙ~.A`2b Ah~.A c A8~.Ac A9~.A sc A~.A@N!c A@~.A&c A~.A+c A 4~~.A1c Ap}~.A7c A@u|~.A \Gc A`{~.A lXc A{~.A`mjc Ahz~.Apc A|~.A6c A;~.Adc A~.Ac A[~.A@c AP~.A`c Aߔ~.Aac Ac~.Ac Aؗ~.ARc AA~.AEc A~.Ac A~.A@%c AȬ~.Ac A୶~.Ayc Ah@~.A@c A ~.A c A8~.Ac Ah~.Aקc AP~.A@c A~.Ac A~.A6c A~.A.A6 AH*.A@ A' .A_ A.AFى ALj.A0 AP.A@~ AOLJ.A@~ A`.A q| A`.A&{ ArՆ.Az A(.A@ z A@3.A"y A .A y Ap+ .A`)x A.Ax Ay.Ax A݅.A x A`eɅ.A@x Aߺ.ALx Ahb.A@5x A.A`Px Aӝ.Aw Ah⌅.A4w A.A4ew A{.A@w AYr.Ajv AO.Aov Ai{.A;u A`.A u A\~.A`ʊu AHO.Au A(.A 8Ft A:.ASt A䴃.A=Ys Aas.Aas Aj.AL߮]s AzWw.A-t&Os A@5d.A@5s A;.As A tؘ.AI$her Ah.A@.}r AH܂.A@]r A5%íт.A.r AȂ.AMq A8 ‚.A^q AX.A2p Ai.A`^o A =.A`̈n A8.A2l AL.Ajl A.Axk A|.AD0k AxV~.A?4j Ao{.Axg A }t.AtEt Axt.A Zt AH)"t.Au Ar.A`w A0{r.Ax A``q.Avy A#p.AIz Ahp.A-y Atp.AMz AHnp.A`_z A( Kp.A@z A@1%p.Az Ao.Az A0o.A{ A(-o.A@W<{ A_o.A k{ AX(co.A{ A),o.A9{ Amn.A[| AOn.A Q| An.A| AHm.AD8} A@5m.A@n} Am.Az} Azm.A`} Atm.A} A0Xnm.A c} A0Xm.A@t} AePm.Aq} A@ m.A uq} Asl.A@yk} Ael.AQ} A\l.AEJ} A@Ll.AD} AXRl.AH} AMk.A`U} Ak.A 8i} APMok.Au} A`Wk.Au} A@Qk.A Zr} AIk.A"k} Aȥ/k.A@F} A.%k.A@;} A k.A@%*} A k.A 1"} Aj.A`K} Aj.A`2} A@$j.A} Aoj.A`; } AXaj.AT} AN?j.A| A/j.A | AVj.A@y| AH)i.A| A6i.AW| A`i.A| Ai.A@} A`2i.A } A@$i.A`v } Ax\i.A } A`vi.A`m} Ai.A0} Ai.A | A֮i.A^| Ai.AF| Ai.A| Aɡi.A4| Aȩi.Aw| AP*i.Aٳ| Aыi.Ac| Axdi.A@>| AMi.A || Agi.A@| Akbi.A<| A(]i.A#| AP8h.AFb| A(h.AC| Ap=th.A1| Ach.A '| AH?h.A | A/h.AX| A@`g.A{ AXۉg.Af{ APg.A}{ A vg.A`{ Ax\Vg.A 8{ ApJg.A`z{ Ag.A`)N{ Apf.A`0{ Af.A@.){ A f.A %{ A0]f.A`m&{ A( f.AN+{ A ޸f.A`v,{ A f.A |.{ Af.AU5{ APf.A T{ A8f.A\{ Af.A`Kb{ Axf.A`c{ Apf.A`d{ Af.A;d{ A*&{1;f.AP9~ AE]K^d.A4P A\:Qc.AQ A0Vc.A AZc.A6' A( ec.Aҹ AKTEc.Ak5 Ad3N>c.AO Aт,c.A|O AWc.A|O Ac.A` A"Œc.AQ0Cc An@Ѷc.A+.{ Aohoc.A\ A]Sc.A"HW AHc.AN AYc.A`Q A^!c.A< A?2Lc.A& AQ0c.A AQfc.A A`~c.A` Aȭc.Ay Ajc.A@ Ac.A`R A?c.A Af.AM0 A:.k.Ax Apk.AH A( k.A' A0]~k.AmY A/k.A I A(Vl.A A)/l.A@ A(k.A Appk.A`o Ahn.A Ao.A AHTk.A5 Ah.AF APg.Ag AXg.A`I A $h.A Agh.A@ AV7i.AA ApRh.AB AxCh.A AXBh.A` APoh.A Ah.Ai Ah.Ax Aܒi.A AYi.A` AHj.A A@ej.AQ AH.^j.A Ap8j.A ASlj.A` A`Էj.A@,: Ak.A`K A@1k.A@P AVl.A` A4'm.AI A m.A` Asm.A@ Aؽm.A@E Apm.Ah AX?o.A0 A*o.A Axo.A`f AHo.A Ap.A Q A _,q.A2 Aȋq.A A(r.A! APr.A $ A"ys.AX+ Ax-s.AiA, A s.A, As.A[- Apt.A- At.A]. Al=t.AR/ Aw.A /D A y.AaO A\z.A`W Apf~.A#r Av~.As A~.A u@t Ahs.A4փ AX=.A4 APs/.A Aڸ.A A A7އ.A Au.A AK.A@P At.A@y AHئ.AI ASN.Aw AH.A> A.A@C As..A` A.A l AH/.A4 Apw.A@.X AH2k.A@ A?.A`< A;.A AH.A? AǗ.Aි AV.A AdŖ.A` Aʕ.A&e AЄ.A u A0=.Az A=.A@z AP.A l A`!.A u A8.A A j.A Ac.A A A8g5.A` AB.A Av.A`Be APr.AM A;.A`J AȶT.A` A8|.A]. AŅ.AГ A.A` Axȕ.A# Ahԓ.A1 AHŐ.AA AG}ݐ.Ar5 AʇT.Ai\ A;sNP.AXg:S A 1.A A=U.Ao3F A.A A:,W.Ao3F A{r;.A A+.AXg:S A5x,.Ai\ AJ.A=m~ Aҗge.A ABkݍ.A A<"<.A@ A}n5b.A uv A Alq.A3 Ahe.A. AQ.Ao AG.A ˸ Ax5.Ax Aot.Aṡ Ab..A APM.A@Gۂ A.A`y Ap .A\y A=.A@y A".A =y A`.A q!y A.Ay A.A`y A[.Ax Ax.Ax A.A ɽx A.Ax Aɪ.A`mx AP.Ay APf.Ay Av.Aw AZ.Av A3.A g AT.A8f A;W^.A=$-a AE]K.A4Ɛy Ad3N.AO A].A}w A-.AF;B[ A0.A\ A-.A\ Ay<.A 8] Ae.A@y^ A*.A#` A.AM` A@B.A^ A.AZg A( .A@ef A`/.Af A`>t1.A7f A6^(4.AVnf ADwz9.A2{g A.>.A>1#g A ]D.A~ 2g ALJK.Aq v.Ag Az.A,,g Ap;.Arg A@.Aig A~.AQg A$Q.A"NYg A.A g Axذ.A`Ch Ae* .AJj[h AM.A'1qh A2(.AEh A).Aspah Av.Ach AA.Aa|0Wh AXh.A h A=.Ah A޷^.AI{ i AS/.A&i A.Av:i A.A69Ki AdT .AVi A`.A@>]i A5-.Ai A;(..Aրi A/.AIi Aj'g42.AM.lmi AX15.Ai AJ8.A6i A .Ҷ<.Aa_j AGA A.Ap%j AE.A!j A*EJ.AIj A(N.A`fj APIj.A`cj Aal.Aayj AIo.Aßj ACms.Aaj Aŝw.A|j A?}.AiSYj AY.AG[j Ay.AH*j Aw.Aj A0.A`K'k ATPY.AM9k A.A Ik AH,.A>7 Yk A@µ.A@Efk AYgh.Aqk AG.AjByk A_.Aeu~k A 2Gx.AҠk A0 .A|k A.A3k AC!N.Abk A].Al ATC.Aա(l Ar*.AZy;l A|i .AHl A`.A`Ol At.A?em At.A;jm AFu.Aqm Au.Avm Av.A$~m A$w.A m Aw.Am Ay.A`m Ay.Am ApAz.Am A{.Am A|.A |m A^}.Awm A~.Aqm AX.A`mm A8.Agm AX.Aam A.A]m A8ք.Am A.Am A`S.Am A .ALm AЉ.A m A`.Am AH.AAm A,.Am A .A.A q A.AFq A[.A Aq A9.A;!q A8x.A #q A8V.A6&q A84.A 1+q A8.A-q AQ.A@,0q A/.A2q A .A&5q A.A@7q AȺ.A@7q A覼.A :q AЄ.A.A@ru AP'?.Au A?.Au Af@.A]u AA.Aհu ADB.Aеu AXB.A@Gu AȂC.A`Bu ApD.A`u AF.Au ApF.Au A G.Au AI.A"u Ap[J.Au A8K.A`u AL.Au AXN.A u AVO.Au A3Q.A`u ArR.Av AS.Av A`U.A`v AV.At v AX.Aov AY.Av A[.A@iv AЦ].Av A`^.A`dv A``.A v AHb.A^v Ahd.A^v AH]f.A` v Ag.A` v Azi.A Z#v AWk.A Z#v A5m.A Z#v Ao.A Z#v Ap.A Z#v A0r.A Z#v At.A Z#v Au.A Z#v Aw.A` v Ay.A` v A{.A^v A8|.A v A ~.A v A@.A`dv A _.Av A.A@iv A{.Aov Ax.A v A.At v AX׉.Ayv A@.Av A.Au Aю.A`u A.Au A(O.A`u A؍.A@u A̓.Au A( .Au AI.Au A.Au A.Avv A0H.A`v A.A@gv A .Aav A0j.Aؚv A.A Qv A+.Aɩv AH.AĮv A.A;v AM.A@v AM.A@,v AH.A@v AH.Av AH.Av A.A v A.Av A.Av AH.Asv AH.Aw AH.A cw AM.A`w AM.A@w A.A@Nw AH.A@#w AH.A@>+w A+.A2w A.A@.:w A .A(?w A.A@w A.AFw AH.ANw A.A@Sw AP&.AZw Ad.A`_w Ah.A`fw AB.Akw A.Apw Ah.Akxw A.Af}w A=.A@`w AP|.A`[w A.AUw A.A@Pw A.AΓw AX.A ɘw A8.A`Ýw A3.A@w A.A;w AO.Aw Ax-.A@5w A .A0w AXJ.Aw AX(.A*w A@.A`w A@.A`w A@.A&w A.Aw A.Aw A.A w Aؚ.A w Ax.A w AV.A w A4.A w A.A w A.A w A.A w Aج .Aw A .Aw AP .A&w A.Aw A.Aw Ac.A+w ApA.Aw A.A0w A^.A@5w A <.Aw Az.A`;w AX.A@w Ax.A`Ýw AXu .A ɘw A !.AΓw A #.A@Pw A$.AVw A`&.A`[w AN'.A@`w A(.Af}w Ax).Akxw A +.Apw AH,.Akw A`-.A`fw A&..A_w A`e/.AZw A0.A@Sw A`C1.ANw A1.A`Fw A p2.A@w A؁2.A(?w Ah3.A@.:w A_4.A2w AH4.A@>+w AH4.A@#w A5.A@Nw A=6.A Sw A=6.A`w A06.A cw A06.Aw A06.Asv A|7.Av A|7.Av A|7.A v A06.Av A06.Av A06.A@v A=6.A@,v A=6.A@v A5.A v AH4.A Av A_4.Aɩv Ah3.AQv AH!3.Aؚv A؁2.A@ߕv A1.A@gv AC1.A`v A0.A v A`e/.A|zv A&..Auv A,.A@ nv AH,.Aiv A +.Adv A().A\v A(.A`Wv AN'.A Rv A@&.AMv A`1$.AHv A".A Cv Aг!.A9Av A.A?v A88.A9Av Ap.A4Fv AȺ.A/Kv A.A)Pv AP.A$Uv A_.A Zv A .A`_v A.Aеu Aa.Aհu A؟.A]u A>.Aau Ahޠ.Au A0.Au AP.Axu A[.A}u A0.Au A0.A|u AP.Awu A9.Apu A9.Ahu A٥.A*au A٥.A@Yu Apx.ATu Apx.A?Mu Apx.AEu Apx.AO>u Apx.A6u A٥.A1u A٥.Af*u A٥.A "u A9.A uu A9.A u AP.Au A0.Au A[.A@u AP.At A0.A t A}.A(t Ahޠ.A-t A>.At Ah.At Aa.At Ah".AHt A.A@Nt AhD.ARt A.A ۼt Aǘ.At A.At AI.At A( .A@t A̓.Ast A؍.A@yt A.A}t A(q.At A`2.At AT.A`t A.A t A7.A`t A(.At AX.A t A =.At A.At A .A~t AB.A |t Axd}.A |t A%|.Ayt AGz.Ayt Aix.A &wt Av.A &wt At.A &wt APos.A &wt A8q.A &wt A8R.AM.An AL.A@n ApJ.An AI.Am AG.A@m AfF.A@m AP'E.Am AC.Am A B.A@!m A@.A@!m AP?.A@!m AN>.A@m Ap<.A@m A2;.A@m A9.A@m A.AWl Ah.A`]l AH.A cl AA.A l A.Al A.A`l Ac.A@l Ah.Al A$.A`l A.A l AP.Al A.Al A.Al A.A` l A*.A@%l A.A+l A(.A0l Ahn.Al A@.A}l A.A@xl AQ.A@vl A@.Asl A.Anl AЕ.A`Kll AW.Ail A`.A@Pgl Ax:.A`dl A.AVbl A@.A@_l A~.A[]l A.AZl Aa.AZl A8#.A@`Xl A8E.AUl Ax.AUl A(.AUl A.AfSl AP.AfSl APͪ.AfSl A.AfSl Ah"S.AYk Ay"X.A1qRk A}\.A߆Gk A_R`.AVx:k Awd.A*k A3Rg.A'ik Ay*ii.Ak Aj.AH8j ALk.AnBj Aj.A@j AKil.A?F7j An.ALHSj Ap.A7.j A[@0_r.A'=|j Agbs.AHj A+cu.Ajj AW|v.A2 0j AQw.A]Ϧj A˹w.Aj Axw.A@j A$}.ACwj A-.Aj AN.Aj Ar?.Aj A9u.Aoj A0.Ah&Ztj A,.A=bj AXQ4.AUNj ANu.A8j AҒͿ.AӬ>!j AƤ .Adsq j AT.A i A{.Ai AX.A i A=.ANVi AJ(ߩ.Ai A< k.AAsi A4[֬.A5i Ajb.A)i AG!.AtYi AԲ8.Ati AG~.A`:i AŰ.Ai A`ð.Ai A.A~yi AK[.A\Ci A!և.Aӂi A.A!g i Ad.ADi A\w5.A i AT .Ai A(6.AZi Az.Ayli AJ.A_Ui A1T0;.A >i AP.A Q'i A.Ah AFP.AMh Aj.AgLh AN^.AWQh AVڛ.A ?h Aj8.Ax%h ATٹ .A% kh AZ{.A>=Uh ApQ.AU8h A.A8h A,z.AVfh A؟.A`mMh AW*.Ad7Jh AF.AڈEh A_).A 6}?h Aܢ.A&68h A"[ .A/h A !.A&h A%.A@h Amv*.Aǝh A/0.Aǧh A׮[~5.Aoءh Aų:.Adh Al ?.A?͘Ph A&dD.Ah A.TG.A78g AK.AGEIg Aa+M.A?fg A\,O.Aog A9O.A[3j g ArO.Apȓg AN.A~g A㥠q.A000g Aiw.A<_(g AL}.Ag A0H.A g A(.A@!af A".A a A۷.A Z A@.AIZ A`P.A`][Z Ah3.A[ A0.A@[ A$q.A/Ca A)~.Aa A.A )[ A+.AZ A.A`vZ A1.A@PZ A`.AqZ AP8.Ap}Z A(.ACZ Ah.A 0Z AU.A`Y A.AKY A.A`vY A.AX AOb.A.agY A*R.A(]7Y A.AMɎQY A.A \gY A_:v!.A(8߆Y Ap%J&.A0٣Y Ač",.A?=#Y A^/2.AY A`X:.AY A:.AV鶐$Z Ai4wg=.A.bZ AuB.A$5}Z AjJ.A淾Z Ap/U.A=[ AXa.Am孬+[ Ay5o.A' J[ Ax}.A@_[ AJ.AO-/w[ AϞ.AQ[ A#WU.A_)[ Aoӊ.Ab[ A.AOW[ A7ۢ.A%[ Aȩ.A}[ As$.A|\ Aa.ApF\ Ae.AH4y\ A,T.A{z*\ A:.AQR]\ Aa.Aީ^\ A.A\ A0e#.A(]<] A0a[.AcOt+] A-.A0jA] A }.AoYoU] At9.AFSf] A}.AtZu] Aq.A] Aأ.A`f] A! .AW] Aj .AS"J] AQ.A\p\+^ A0Jr.Alt%X^ A=Z*.A}pY~^ Ao7.A'g^ AE.A^ A]F.A91i^ ArvH.AC^ A9K.A^ A$mN.A W _ A6 r.S.AZF_ Aq%X.A4`$d&_ A*1].A_/_ AWc.A5_ AmPf.A]n_ Ah4l.Ae_ A_b u.AJ_ A59.AX` AG.A&` Am.ArܡB` A+.AU` Avl.Av` A).AHquE` A].Arİ` Aj V*.As` Aџ.A,` Ap.A 1` A-.A,a A .AΤpOa AQ.Ad[|a AvCt.A*7a AS.A3pa A\ .AXaa A.AT6Ca A.ARb A.Ah "b A #f.A=3V.b AH.A VCb A .AǓVb A&.AW,gb AVx .Aub A<%.A{o݀b AxI{*.AVˎb Ax0.A`b AB3.Afg7b AcV9.AS1Ub A"O!A.AF@!c A)`J.AnIc A^U.AlQGjc A~*Tb.A"t[c ASqo.A@ޑc AmN}.A-Ӹc A.A>ٔc A}ډ.AӾ:c AN?.Ayrc Aذ.A|Tc A1.AAn}%/c Ac.A Jc A0.A\hc AQ3j.A%xc A)d.Ac A,.A}kb AI .A^b AsLjz.A]&b AA?.A'Ҋ'b AE.A ub AJ.Af?b At .AÚɉb Aj.A^Xb A[$.Azb AWc0.A薰b A6:.At3Xnb AoC.AHGb A tJ.A|Gb A?fP.Ala AS.Aa A`Z.A̶4a AY `.A*a Asf.Ay(a ALk.A:oO}a AF8o.AVea Ar.AKa A`.Ay5a A齒.AkFa A͠.A|_E` Azˬ.A#b` Av$`.A(#v` AE.AF4` Aj+.Au/0` AW̦.Ar(` A .Az$` Ab,.A` Ag-.A=K` Aoi.A@#_ AvyF.AlϞ_ Ab.A`_ A|7.A:U_ A?F.A@_ A.A |<_ A .AMX_ A.A-'_ A p<.At^ Ax .A^ A m$".A) ^ AZt&.A^ A+.A^ A;0.A2^ A^ZL4.A((_^ AzMp7.AR^ AX:.Aʀ)~^ Aڅ=.AZRl^ A}>.AeDY^ A0?.A,E^ A@?.A1^ AzI.A<8^3^ AYS.Ae1/^ AL].AH!$^ Ag9qg.AJ^ Aq/p.AlMk] A$ʗw.A] APUXz~.A] A8.A] Aca.AWv] A.A9M] A0~q.A9] A.A\ A`d.Ap\ AK.A\ AH.A z\ Ap=t.Aac Ay.ALd AZ.A`]h A).A4i A.A_d A.Ap` A.AZ_ A[.A@W(a A%.A!c Ah.Ab A.A l[ AH.A[ A=%.A[ AG.A|[ A,>.A$ tg[ Aw .Abx[ AQ .Acmb[ Ap.AI[ A3.Aɓ:[ A!-.A@o"[ AV^:.A[ A(F.AmZ AEO.AZ A\V.A=LuoZ A[.A@4Z AG^f.Ap"Z Ao.AC~ Z ANqx.A?Y AH.AY Agt㝌.AhgUY APۘ.AtFY AM .A~Y A%3ƭ.AI\WY A .A{++Y A$d].ACX A.A|X A+.AbX A.Aڂ X A\.AX Af].Ap}X Ao .A&bX Ah.ADX A3Vn.A}5X A}.A=X Are .Aן W A1.AW ADL.AW A&.A~k|W A+.AaGW AjF2.A>W A.8.Afyn1W AXTȇ=.A!W A*k1B.A6t(W AvF.A(V AI.A V AnTeZ.AQ%V Aށj.A…bV AZx.A/jvV AhD.A?V AXż.AR W AH.AV A.ATW A8.A`W A=.A@ U Aq\y.A%uU AdiC.ApxU Av.ARU A.A +U A6[).AAio*U A,.AhT"U A.A5U A0.ACnT A~d.Aeq T AC0.AuXhT Av:.A$6T AW.A tT A .AM&\IT A.AT A.A ST Aj.AT Az.AGcT A '.A޻T ADh;.A3T A̮.Aڹ T A̟ .A5S AY .A{M A0w.A`TM Ao.AL AAV.A<2L Ap.AkL Ay.A#LWL AXdz.A̬}QL A:.A4"L As.AG;K A{.AIFK A>ŕ.A5үK A|.A5T?K A O~.Au=K A&r.A HzK ALs.AekK A0ot.A5[K A֒t.AoVKK AqMt.Asd2;K ADl$s.A<##,K AycMWq.AӸK A\n.AvK AVc2 l.A͋K Ah.A;UJ Ave.AeeJ Aaa.AGRJ A`8].A}ݳJ AyY.Ad=/J A 4U.A`bJ Ae-;S.A!J AܰO.AύJ AH.A4P?]J A64@.AO2J A)?7.AE"q5 J A *,.A`I A y.AI A0.A I A`ω.A3I Ar%.ADI Aa.A%KΝI Aٴ .A:nI Aɠ.A[I A.A ILI A7l.A, @I A.ACS7I A.A]2I AFJ.Ax>H A9x\.AĦ-(H A{Z.AA%tH A v.AvaaH A0.A ;H Ab .AVfH A.A/H A٤.A/G AJwN.AJ!G A1.Ar!G Ag.AG Afj)6.AG AOj.A-嬜G AH.A |G A&ƌ.AzG A{.AI_G A5A.Ami+G A.AF A y.A'AF A6p.AvF Ad.A f6F AxX.AwF AJ.AcF A+K.A?ȏNWF AW_K.AHJF A J.Ah>F A}I.A2F AG.A}I F AGLC.AF f3F AǕ:@.AČGF A8<.AE AG9.AF E A$5.AWRUE ASU[}2.AzE Af/.Aħ8E Aȥ+.A E Ag*.AE Ah(.Ap,EE ABZ&.AfE A ڱ".AZRbJE A7.Ad0E A3Q.A1t!E Am.ACV{E A?y8 .A?D A.A_D AȺ.A@5D A.AD Ah3.A D A .A0C A.A B A8.A`VB A.AA Aܭ2#.ANB A4 /.Ap;1B A,%1<.AFUdIB AZQ J.AHQ?XB ARX.A1]B Af.A(!ZB A[t.AQ|MB A0݁.A(7B ACu .AB Ae.AwB AWhr .ATGB A$Jw .A_7"B A M-.A@B Ah.AB A.A ZA A2.At.= A@_.AD; Axv.A07 AH.Aށ- A.At) A .Ap) Axǖ.A' A.A5S& AP .A@E$ Axy.AO! A.Ad A.AO A'.A`V Av].A> Ag.Af AXo.ABl A(iD.A A#).A k A.Ao  A .A A.A A .A^ A/.Ax( API.A` A.A`b A.Al AЄ*.A  A8X.A A.Ay AS.A@P A8S.A@ A 4.Ag# A.A% A` .A`d& A/(.A ' Ai.A( A.A) A[?.A) Ab.A* Axx.AF+ At.AdP- AX.Ak. AQ.Ac. A =F.At=. Ai.AB ( A#.AB6 A5.A`] A.A@! AQo.AI AXk.AŘ A`.A A\.A AN.A AP.A Ax:.A M Aʸ.A Z A@T.A Ax-.AD" AH.A A3.A` Ahn.AdY A.A AS.A@ Aa.A (S Au.AD A=.A A.A`f Ab/A`t A/A` Ap/A & A/A A}/A?r A7/A< Aߕ/Acl A(/Aߍ AH\/A A@/A AH/A`T= A/A$; A/A A/A Ae/A` A/A@ Ah//A@" AP/A0 Ah/A 8 AH/A A/A4 A/A  AH2/AG A/AE A/A A/AU Al/A_O AM/A`A A/A qU A+/A43 Aח/A Aq/Aa A/A` A/A| A/AQ AG/Axi A(/A Ad/A@S Ap9/A` AC/A`R A/A  A/A0g A/A[ Aٚ/AD A/A` A/Ab AX/AQ A'/A@c AXVD/Ay A+x/A`D A /A A/A` Ax/A A0L/A Ar/A@8 A[/A  A/AC( AX/A?4+ Am/AL/ AL/A 8h2 Aج5/A*4 Ax(/A ^5 AH\/A*6 A /A=6 A@/A-7 Ax/A7 A&/AG8 A/AWe8 AP)/A9 A*/A@< A`/A&@ A=R/A JD AKj/AF A/A`KaG A/AG AX[v/AzJI Ax/AJ A /AJ A _/A`@K A*/AK A/AI=J A8/A J A#'/AJ A`/AaJ A]/A@ K APMk/A׾K A8/AL AZ/A`L A/AM A" /AQr A%/Aq API%/A@߉q A8D%/Aq A@%/A q A|%/Aq A*%/Aq A%/A@Nq A؁%/A}q A0%/Azq AP+%/A zq A0%/A xq A%/Axq A%/A`bwq AM%/A&uq Ap%/A@ tq A8%/A`qq A`%/Aqq Af%/Aoq A0%/Aqmq Axv%/A2jq AE%/Aiq A%/A;hq A`%/A hq AP%/A?gq A%/A sfq A~%/Adq A%/Abq A%/A `q AP%/A^q ApJ%/A@^q A%/A ]q A%/A@[q Av%/A`vZq A(m%/AUq A5%/ATq A%/A`mSq A %/A@Qq A%/AQq AR%/A@Pq A%/A@Nq A%/AMq Ax%/AHq A%/AHq A%/AFq A%/A 8Cq AH)%/A@?q A'%/A?q A%/A=q AHn%/A =q AN%/A%/A4p A%/AIp Aܱ%/Agp A%/A p AHm%/A@p A%/A`p Ar%/A 8p AH%/ARp A %/A lp Ax%/Ap A˯%/Ap A%/Ap Ax%/Ap A`%%/A@p A%/A0p A Ү%/A@Np Ax%/A lp A~%/Ap A,%/A p A%/A@p A٭%/Ap A%/A3p A%/AIp Aj%/A_p Ah3%/Ap A%/Ap A%/A`p Ad%/A`;p A;%/A_p A8%/Ap A%/A`p Aп%/Ap A%/A`p Axm%/Ap AhD%/A?p AH%/A p A(%/Ap Aw%/Ap A N%/A /p A0%%/AXp AP%/A@p A%/A`p AX%/A,q Aȵ%/Aq A %/A q AXd%/A 8q A(`%/A=q A9%/Ahq A@%/A q A§%/Aq Ap%/ARq AxF%/A q A( %/A`q A@Ϧ%/A q A˦%/Aq A%/A`Kq A~%/Azq AXV%/A@q A0.%/A q Aݥ%/A@Gq A%/A |q Ah%/A q A@=%/Aq Ah%/A Qq AH%/Aq A8Ť%/Aq AH%/A q AM%/A cq A%%/Aq A%/Aq A8֣%/A q Ah%/AFq A%/AKq A _%/A`q A%/A/q AX%/Ajq A%/Aq A%/A q Aq%/A q AJ%/A[ q A #%/A` q Aԡ%/A q A%/A q A7%/A  q A%/A O q Ax%/A` q AJ%/A q A%/A q A`%/A q A%/A` q Ad%/A &q A%/Aq A}%/Aq AW%/A q A@1%/ARq A %/Aq A%/A@q A%/A)q A`%/Aqq AHr%/Aq A0L%/A q A&%/A@Nq A%/A q A8%/A@,q Ah%/A q AΘ%/A@q A%/A@6q A@%/AOXq Ah%/A4q A z%/A`;q AXz%/A_q AH#/A"p A@!/Aa A/A_ A1/A@Q AX/A[P AX/AM Ah) /A G Ap O"/A/B Axc"/AA A(&/A^6 A8'/Aas5 APo*/A2, A؎+/A 8S, A+/A൮) A F-/AQ;% A/./AД" AŶ3/A  A:/A AL:/A@, A(~:/A A:/A` A:/A`9 AȰ:/A  AY;/A^ Ay;/A@`` A2/A@WE A0?/A` AKq?/A A,?/A Ax$0A/A  AB/A) A C/A AC/A AxC/A |? A8sC/A AC/AY ApC/A` AB/Aβ AB/A#\ AA/A@% A?'@/A A0@/AH A@/A$ AP?/A@ t A@?/A` ] AN?/AK AU?/A@? A̹?/A@p6 A"?/A}? A>?/AJ AH?/A QZ AFu?/Am A@h?/Aw Ae>/A AE>/AK A8>/AFa A0+>/Aq Ah>/A} AQ>/AO A` >/A@߂ A>/A`| A=/A@`Y Ae=/A`3 A=/Az AR=/A} AU=/A AP=/AD A=/A A5=/A ~ Ah=/AZ AX(=/A& A=/A ~ Ap=/A A=/Ak A=/AZ A=/A`Q A=/AN Ax=/AR A(1|=/A [ A@=/A Ah8=/A AH=/A AXl=/Az A8 =/A AXy/A: A>/A A>/A A'c>/AFJ AV;>/A / Atq=/A`md A86/A@} A(B6/A`˵ A/6/A@ AQ 6/A A5/A+ A5/A* A5/A" A@S5/A`ֲ Ak4/Aܲ A~4/AK A 4/Aϲ Au3/ATβ A3/A@Wʲ Ahћ3/A; A3/A A3/A` A'3/A A2/A@ A22/A$a Av2/A@C A2/A@ A)3/AҢ A 3/A 똪 Aۼ3/A A4/A`, AhL4/A`̣ A-5/A A5/AǗ Ah7/A, Ag8/AK A k8/A@p A;9/A Le A|rγ8/A[u- A:,W1/A98 Av)/A~ A=!/A98 A 18/A[u- A A-.AF;B[ A+==/AW6g A R =/A Am A=/A} A/Aw A@?/A@ Axd?/A@ Aߝ?/A@ѐ Ah^[?/A` Ah>?/AՐ A:?/A@ؽ Ap?/A@` A@?/A AF>/A F A>/AF1 A(l>/A  APx>/A@p{ Ab>/Aߍ A(>/Acʍ A>/A) Ap,>/A A݂>/Aqn APk>/AY Ahd>/A H A`>/A  AE>/A- AP'A>/A Ax'>/A`K A#>/A@g0 A0.>/A 8 Ap>/A@r A=/AV A=/Aq)}; A`S/A?/ A8S/A AR/A@k AoR/AL A؞R/AK% ApiR/AV ALR/A`v AH/R/A`2P AQ/Ad9 AQQ/A| AAQ/A AؒzQ/A] AQQ/A  ALQ/A@pK AkDQ/A | A #)Q/A j A@Q/A | A'$Q/A A^Q/AG| A$۞Q/A AR/A@fj AuR/A", AQ/AQZ:b A9Q/AY AႬQ/A8L AtjQ/AV A{Q/A Ad`KQ/At^gc A hQ/A7cp A wQ/A A*Q/A@ A$Q/AkW AnM Q/Ao= AXKSQ/A]p= A~?Q/A ؀P AFEcQ/A)M AyM Q/A A3*HQ/A-J A+(Q/AtPS AP/A AP/A AP/Al AXP/A=4 A|P/A@z A89P/A]r A@$P/A // APDQ/A= A PQ/A$MC{ AkXQ/A;п A7z^Q/A:p AaQ/A V A0aQ/A S A%Q/A@ AQ/AAb A Q/A`; AmQ/A AhsP/AmV AP/A AP/A` AP/A AXjP/AT ApO/A@ A`zO/A A#O/AOC A0N/A| A(-N/A`[ AN/A AΛN/Az AWN/A`) AvN/A/ AXlN/A I A/N/A Ar+N/A@ A[*N/AA AM/A/ APM/AF ApbM/A0 A@S^M/A A?mM/A AiM/A४ Au\M/A` AfOM/A A9M/A; Ax>M/A A>PK/An A`LK/A A9K/A AȥJK/AH A p[K/A[ ASK/A/ AWBM/A ADQM/A`K AeM/A@ AeM/A AM/At AtM/A A88%N/A l AN/A# A(N/A A`RN/A@N AoM/A@ AhM/At AbN/A & AN/A AN/AMK AhO/A;} A?P/A AGP/A AO/A`b A0O/A@ A>+O/Av Aĭ4O/AI AQ膿O/AJ= A lO/AA21 Agz8O/A8 AYO/A! AO/AL  Ao! P/AyM AP/Av} A#(P/A@  A>Z?P/A5 AQP/A`s Ax-P/An Ap=O/AM AO/A` ApP/A% AYP/A ) AV#P/Ar* AP-P/A@+ AȘ9P/A- A;P/A`܅- A`

P/Az1 Ax>;P/A+1 A6P/A1 A0P/A1 A%0P/A@1 A)P/A 2 A`C"P/A*2 AP/A=D2 A$P/AX2 A'P/Ag2 AwO/A2 A O/A&2 ApO/A`22 AO/A@2 AIO/A2 AO/A2 AsO/A 2 AOmO/A 2 AgO/A 3 A`O/A H$3 A [O/AW>3 AGQO/A t3 AV;O/A@4 Axh#O/A@%4 AHO/A`m4 Ap8O/A 5 A O/A I5 Ag O/A5 AXO/A 5 A0O/AE5 ApO/AN6 APN/A@6 A؛N/A*5 AFN/A5 AُN/A@6 AO/Am6 A=O/Aଧ6 AO/A`6 A`O/A=6 A}O/A=6 Ah O/A6 A O/A`27 AO/A7 AX!O/A=7 A#O/A`F7 A%O/A@yN7 A'O/A"W7 A-(O/A0`7 A$.O/A`7 A72O/A u7 A4O/A8 A 06O/A/8 A6O/AV8 A4O/ADy8 A A3O/A8 A01O/A஗8 A/O/AT8 A2-O/A`8 A'(O/A 8 AO"O/A 18 A#O/A l8 AO/A#9 A2N/A`m,9 A(1N/A`)69 AoN/A K9 A0ݪN/AN9 AuN/A c9 AN/Amp9 A0N/A9 AxN/A9 AHvN/A^9 A0sN/A@9 ArN/A൰9 ApN/AH9 AoN/Av9 AoN/A`m9 Ap&N/A 89 A6(N/A J: A/pN/A@%?: AzrN/AEu= APtN/A= AhuN/A4= APvN/A?= AȥxN/A= AzN/A= ApN/A@= AՆN/A > AHiN/A q8> AN/A@߅> AʾN/A> AN/A > A؍N/A@i> AN/A@r> AON/A@> AN/A > APN/A Q> AhN/A,> AkN/Az> AN/A;> AXN/Az> A`N/A A> A0N/A> A"N/A` A A`2N/AFFA A)N/AlA AN/A@ؐA AX$N/A@iA AgN/AA AxN/AA AXN/A B AxzN/AB AN/AB AN/A (B A\N/A@"B AN/A0%B APMN/A %B AxN/AB APN/A`] B AN/A B A=N/A`KB A`vN/A@#B AXFN/A@B AN/A+A AcM/A@ AȶM/A`@ AM/A@ AXM/A@ AM/A |@ AhL/A> A L/A^> AK/A= AK/A^= A!pK/Aվ< AHlK/A< AJ/A+< AJ/A~< A`fK/AB%= AScK/A r= A)K/A= Ax(K/A> AZK/A0> AYK/A? AZK/A |@ AK/A`v? AK/A? A_K/AK@ ApeL/A @ ApiL/AE@ AM/AA ApM/A A AM/A`%A AM/A xA AM/AA AȥsN/ADDB AfN/AC APDN/A+C A0EN/AC AdN/A@~C AM/AXxC A(M/AC A8H/A@ B AH/A}> AXH/A}> AH/AzB Ap|H/AB A/H/A7B AN/A#C A@cN/AD AHWN/A@mE AHWN/A@roE AXN/ApE AYN/AjqE APYN/A rE A8V[N/A[sE A]N/AsE AgN/A sE A1lN/A@tE AKnN/A@uE ApN/A@wE AqN/A`zE Ax(rN/A|E A0rN/A~E AhN/A cE APN/AE AHN/A`F ApN/A,0F AୣN/A`fHF APN/AE[F AXtN/A@hF AN/A`omF A=N/AoF AN/AmF AwN/AmF AxN/AnF AN/AqF AN/A-uF AxdN/A zF AHN/A@F AN/AF A"N/A F AsN/A ɜF A N/A@F A(`N/AF AXVN/A`F A@N/A F AN/AoG AN/A0G AN/A'2G A8N/A͏G AP&N/ADG A)N/AG AP+N/AG AN/ARG A^N/A G A@N/A@rrH ARM/AVH ARM/AnsH AX 1M/A``H A@/M/AH AM/A@H AM/A``H A"M/A[H AأM/A4F AL/A4F A؁L/A`fG AH}J/A DG A`vJ/AwF AOXJ/A sF A(bJ/A[H AH}J/A RH APf|J/An|G AWL/AG A)L/AГH AmM/A H AM/AQH A04M/AH AH5M/A ɡH AjN/A@iH AN/AiH A0O/A ZH AO/A0H A+O/A(H AO/AH APO/AI A0O/A@I A(-O/Aa)I A O/ACI AQO/A lI ApO/AI AN/A-J AN/A PJ A(N/A[rJ ApN/A`J AN/ApJ ApN/A@WJ A(N/AK AN/Av)T A=N/Aa@T AUM/Aic A`;M/A/^c A8V9M/A Uc A8s6M/A Lc A/M/A>c Ah'M/A@g4c ApM/A-c A؎M/A b AXL/Ab A(L/Ab AXL/A`mb A(L/A@b AL/A ub A(L/A`"b A@uL/A 1b AP@L/Ab A0%L/A b AK/Atb A@K/A@gb A{K/Ab AEwK/Ab A`rK/A@>b AnK/Ab A'jK/A;b AaK/Ac A83^K/A&c AZK/AM)c AVK/A/A`mn AP8>/A@Yn A`e>/AM?n AЏ>/A3n AНt>/A@+n A8V<>/A/$n A1>/A]'n A&>/A ~1n A@>/ABn A8# >/A@]n AH2=/A{n At=/An A=/A`n A=/Adn A=/A &o A8t=/Aaio A@=/A@o A=/Aܬo A6=(=/A $7Sp Ax=/Ap A"1.X=/AsFq A%A+=/AAq A8I A:/AA,@ AvIځ:/AK AKr:/AG-)xa AY^xd:/A C AݛW:/A[糎 A3 L:/AM `և AxyqC:/A*ؑ A坁<:/ApE AA7:/Ay& A@5:/AÈ A=6:/A؈ A`7:/A AȲ9:/A A@<:/A- AЯ@:/A6 AE:/AL AU:/A A`X:/AӇ Aج[:/A} APg:/Aw Ah:/Aa Am:/A@ᮉ AX$r:/A AHs:/A A~u:/A ȉ A7x:/A@ډ Ay:/A As;:/A@Eu ABG:/Aʊ ApN|:/AD A=|:/AR AXuy:/A@ Aw:/A AHq:/A8 Ao:/A`[ Axn:/A ( A8n:/A: A(n:/Ab APp:/A@ At:/A Qŋ A8w:/A A@{:/A  AO:/A`m A&:/A@8 AH?:/A OS A:/A`)m Aୱ:/A@y A!:/Aی AB:/A A;/A`7 A ;/A \ AnA;/A} AБA;/A@~ AH;/Ak AN;/A- AX(W;/A㦍 Ap^;/A Aza;/A`͍ Ad;/Aۍ A7f;/A J A`g;/A9 APj;/A Al;/A B AxFp;/A Ap;/A` A0p;/As) A pn;/A@P Axii;/A AEb;/A AX;/AA AL;/A AHzF;/AȐ A7;/A | Ap1;/A`o@ A(,;/A o A(;/A@ AP';/A A';/Aɑ A(;/Aؑ A*;/A` AT/;/A7 AH.5;/A`;i Ap<;/A- AW;/AE A-o;/A`˓ A{;/A A;/AӦ A@;/Aʔ A`;/A% AxK;/AoP Au;/A@X A(;/A@y` A1;/A A%;/A@y A`;/A" A(;/A`2" A=/A A@ J=/A@ A W=/Av A@$=/A A=/A A=/AӖ A=/Aǖ A=/A AX=/A H A=/AE A(=/A$ A>/AX• A >/A֦ A`>/A A_0>/A A`4>/AF ApAL>/A ApO>/At A_R>/A`2k AYZ>/A`Z A؟b>/AO Au>/A; A0~>/A 10 AȎ>/A} A>/A3 APZ>/A@ A>/Am AI>/A Ah>/A A>/A् A@1>/A A>/A u A>/A6 A^>/A A>/A A(>/Aɓ A>/A`f͓ A>/A Г As>/A &ӓ A>/Aԓ A?/A@pӓ A"?/A˓ Ap*?/A_͓ Aث;?/Aړ AFW?/A8 A@Bt?/A`" Ax?/A |D A?/AW Ax6?/Atl AP?/Adz A?/A A(ܢ?/A`v A8?/AOԔ A(?/A@ AP?/A_ A?/Ah A+?/AK A?/A x AH?/A Ao?/A A(?/A@. A`?/A A?/A` A؍?/A A ?/A A(?/A A?/A@ A?/A` Ax?/A A@/A H AP|@/A  AP@@/A A @/A A@/Aa AQ'@/A / A2@/A`mG A07@/A`U A;@/Ase AtC@/AƉ A@J@/A" A^@/AX Apg@/AV A,p@/A AÇ@/A, A @/AL A@/ALk A`@/Aྰ Ap@/A A@/AL A,@/A՞ A0̝@/AX A؛@/A`; A@/A sݜ Ay@/A`" A˛@/A@, AH\@/A 4 A,@/AF= A<@/A HE Ap@/AL A@/ADT A@/A` b A p@/A`vm Aٮ@/A x A@/A鞝 A8@/A`] A`e@/A A(@/Aȝ Aho@/A@ A@/Ah Aܙ@/Acd A|@/AC Aא@/A/%: A#dA/A\ AhY A/AM}r A/ A/A) AY.:KA/AZ՞ Ax A/A A&W1A/A:E AMNA/Ak$ A^A/A`4 AA/A?EC AxA/ABO AbaUA/A&X A*U#A/A^ Ah(A/Aeb A5,A/A%b A:ν1A/Af5^ A) ^6A/AhX A:A/A JO A~2iA/A.۳6 A A=A/A@,_ AogB/AWC/A AC/Aj A(C/A ArD/Aa A[ D/A- ABD/A A(-D/A A&D/A` Aq)D/A@ . A)D/AF6 A*D/AV A,D/A< A@-D/A ~r A/D/Aj AK3D/A h A85D/Aٻ Ah9D/Aa' A>D/Ar ACD/A`ʣ A0FD/A`] ADJD/A@ɼ AMD/A`ؼ AHQD/A ATD/A AЄ[D/A` AaD/A`T AD/A`y A%E/Ag AE/As AyE/A AP F/A ACF/A} A+mF/A AXƒF/A`K A`F/A`4 AF/ABk AF/A A~G/A`B A3.G/A & AЄAG/AT A0rG/A`D AG/A@ AhG/Ax AHG/A AaH/AM A@uH/A _ A H/A@} AH/A A!H/A` A(&H/A A0G+H/A` A"/H/A 1: AY2H/Ae A4H/A u A78H/A AF8H/AW A8'5H/A߶ A.H/Av AP%H/A&y AH/A AHH/A`p Ax:G/A AG/A@S AG/AI AG/AA AkG/A AG/AF AG/A & AxRG/A 8 A#G/An AmG/A@ AG/A`d AF/A8 ApF/A AFtF/A% A-8F/A3 A-F/A@% AE/A AE/A AHrE/A@i AHE/A * AxE/A 4 A@E/A'` A E/A{ AGE/A, AE/A A[E/A@PY AE/A / AE/A" AXE/A . AFE/A AE/A AE/AqT AE/A A,F/A AF/A e Ah F/A AF/A Q7 A[F/A A`F/A AF/AL Ah3 F/A@ AzE/A AdE/A@Ʒ AHE/A" AE/A  AtE/A AE/AZ AE/A@ A-F/A ɍ AȘJF/Aj ARF/A AHaF/Ai A #F/A APZG/A`U AQG/A AG/A@a AG/A AeH/A`9 A9H/A A@@H/A S A`AH/A Ae.H/AM Ag-H/A A0 A,P/Av A0̏P/A A5P/AZ AhP/A`m AhP/A` AVlP/AƳ A^P/A A[P/AN AlP/Ac A`6~P/A AȃP/Am A( Q/A l AbQ/Ad7 A0Q/A A R/A ApjR/A _ A`R/A A@S/A3 A( lS/A AS/A, A#T/At A2T/A AxFT/A AT/A 7 A BU/Aં AyuU/A AxU/A@G\ A@U/A` A`U/A` A V/A@ AYSV/A@J AxV/A` A tV/A@q AV/A Sk A=W/A+ AONW/A9 AYlW/A AP}W/A4t Ap{W/A AȩhW/A AnW/A A8cW/A AW/A AxW/AF AW/A' A#X/A AGQX/Am AyX/AE Ax(X/A A?X/A@W. Ap,X/A AxY/A` A-Y/A AwY/A@[ AY/A AY/A A AjZ/Ad_ AlZ/Ads A`ǼZ/AZn A[/AD^ AP^I[/A;p AW[/A&v Aج|[/A@ Ao[/A A`"\/A@F A<\/A`K A`W\/A Ay^\/A AL\/An AG\/A` AN\/A> Axg\/A Ah\/A`J A\/A Ao\/AE Az\/A Ah\/AP A076]/A- AK]/A A8 _]/A " Ae]/A@`M AM]/AX Ar\/A< A"\/A r Al{]/A AP^/A් AH;]/A AXWY]/A@ AJ^/A Ap= _/A8# A`(_/A@.( A8F_/A$- APo_/Ax A_/A A̳_/A Ah_/A' AP_/Af A`/Afu A:`/AO AV`/A; AKx`/A`z Aכ`/AD AP`/A`Q A``/A` AR`/A A"V`/A@q A n`/A6 A(`/A^ Ap `/A` AXV`/A Z0 A8`/A Ax-`/A A@b/Az AP?c/A uO AXWb/A A b/A A(b/Ag A3e/A AXVAe/AAP A&f/A Af/A+` A@h/A` A&h/A# A0ng/A A0Le/A Ac/A A(b/Aoc Ac/A@> A e/AM A[f/A H Ag/A O A`Ei/A@ APi/A@ Ayi/AR Ab}i/Aݶ Axzi/A A@i/A] A8i/A0 Asi/Aﴵ Ai/A A |i/A@N Aȏi/A Api/Av AHi/A`H A\i/AE< Ai/A/ Awi/A # Ai/A= A0i/A`ͳ AJi/A A(i/Af Ai/A? Ai/A A`i/A Ƀ Avi/A' Aгi/A#˲ A #i/A`fq Axzi/A' A8i/A^ AX i/A A~i/A+y AIi/A@ԯ A}i/Ax_ Apyyi/Aܮ ACyi/Aˮ A8Qyi/A`Ʈ A@`yi/AĮ Awyi/A® A8yi/A &® Ayi/A A`yi/A Ayi/A A0yi/A} APyi/Ai Ayi/A c Ayi/A \ Ayi/A c Ayi/Ap Ayi/A@ Axyi/A A(yi/A AXzi/A9® AIzi/Avî A@zi/A 8Ů A`{i/AVʮ A ti/A J A@`i/Aϯ AHܮi/A@q Ai/A`[ Ai/A Ai/A4 Ai/A_ A@i/A@T Ai/A APji/A A[i/A@H Ai/A@` Ai/A` Ai/A@ô AǷi/Aд AHi/Azݴ Ai/A` AЀi/A Ai/A A0i/Aq Avi/A! ASi/A + Aii/A 0 Ai/A/ Aأi/A@- Aj/A A(j/A  A-j/A"ߴ APIYj/A A8Enj/A6f A=j/A N A j/A AB Axj/A 6 A@$k/A" A(k/A Q A7k/Aq A_k/A A؞lk/A A"xk/A@i Ajk/A AJk/A A(k/A& A(k/Ao Ak/A A؁7l/A ִ AHvl/A AUl/AZ* A8l/A`)8 Al/A< AXl/A/8 A8l/A1 AZm/AV& Am/A| ACm/A  A8Z&m/A A .m/AѴ A@4m/A@# A6m/A饴 A08m/A A @m/A?N AGm/A? AHMm/Aʧ AHSm/A`KC AȺTm/A& ABUm/A AUm/A@Nx AP'Vm/A(U ApXm/A6 AN\m/A 8 Agbm/A`K A0]jm/A@5 A3tm/A@Ɲ Am/AH| Aom/A[ Ap AOl\n/AnB8# A=A;-AJAX-AI`WAeVɹ-ANXAԹ-A#߅A`-A`A.¾-A@8AB-A`]9Ay-AA>Ax(?-A5A؟-AU7A-A@E7Apf-A`ܬ5AXu-AFu6A_-A8Ad-AR8Af-A:A~-A];AxK-A:Ap-AM9A`6L-AA9A-A <AI-AIAdg-A?A-A?A>-A!AA-A;^DApl-ADAXa-A XAAOd-A`%AA4-A,@A0-A@`<A)-A \=A>-A ~?AU-A;9?AЀ-AH<AhM-A6:AsH-AD@Aw-A`$HAO-A ?TAȘe-AAlA(-AЕmAP-A |4qA*-AMoAx-AGXAp0m-A`[AHi-AYA`-A@>\AX-A@]A0-A _^Ajq-A$iA8-AvjA-AjA-A@jAH-ASaA~-AhcA-A`2aA8-A@cAP-A@kAS-AnA-A@T|A@-A͢A-A A[-AڅAO-A!APY^-A`DA>-AoA-A A c-A@ՍA-AAps-AgA-A[TA-A`f8A-A`At-A@rA RJ-A`{A]-ACAm-A AhD-A@xA-A@!\A8t-AzAذ+-AA[-A`ӔA@ -A=`AX-A98A,-AAN-A@ȍA8tv-AA`-A 8A-A 8CA2-A@A8gu-A@`˝A`a-AIA1-AApi-AqA"-ArA^.-AAPQ-A?Ad-ANAT-A,AT-A|AX(-AщA0&-A'A`f-AWAچ-A`bA-AAТ-A`MA^.-A&A-AFA'7-A']A[-AAR-AA`%-A"A;-AxALN-AlAȘo-A0A-A@MAa-A Axh-AA(-A AZ-AA8-AA -AA@c-ADZA-A`6Ahbm-AǁAp[-A"Ah&-A pAHu-AHA-AA#-AAo-Ap2Aʐ-AwMA?P-A A8i-A@ԭA+-A A$-AIгAv-AYA(-A FAH2-AWA-AdA-AŦA-AVήAʒ-AޮA~ -A`8A@-A`8A\-A UA3-AA~-A HAx\-A3A -A@Aȓ(-A֪A\V-A֪AUo-A`AH˔-A^A-A$A-AZA8-A Ax,-A`A0L-ABAȓ-AA-A A E-A(AJ-AA-AoA@-AA-A ɹA(-A"A-A`8A\-AXAA-A ȾAj-A`fAPv-A Ax-AgA_-A`A -A5MA-AA(-A`BA'-A JcA@1-AAŒ-A`A0;-A A-A@Axi1-AghA-AbA -AFA`-AA(-AAp8m-AAx-A BAұ-A@A0{`-AJAP/-A@AF-A J3A-A J3AD-AA-A.A-A@uAh-AA@OI-AA-A A1-A`AX-AAȲ-A Ax)-AॣA!-A@ At-A A+.A@ Aa.A$ARw.AHiA>.AAs;.A 'A+7.A`5A%7.A@5A-.AvSA-A@,"A0-A@A)C-Ao0Ar7-ASA'-AA!-A A-AA0f-A AY-Af A@`W-A AIW-A@ AV-A A^O-A@ Ah -Az AcE-A  Ah"-A A(-AA-A A`-A AP<-A A0]-A!A-A2A-A@r_A-A_AP-AeAA-A`A(-A AH-AA--AOA覟-A5Az-AV A_K.Aj A .A`m%A.A` A&.A`A2.AZAX.A$ A.A`d A(C.A@A-A2A9-AA-A`A8-AjAPo-AAhb-A`mA-A Ahё-A@PA(ː-A@A`z-A $A+-A A8gx-A HA֚-A`]%A(1-A# Ax\-A`mA-AHLAG-ASAw{-A |Aȱ-At{Aʨ-A{A0-AhA-AAw-A ɇA(m-A|Ap2-A&A2-AA0-AA83/-A@yA`.-AA.-A`KAZ-A`A83-A"A8-AAH6-A`A-AiwAТ/-A lA0t-AA-AAv-A`AYC-A ZAqD-AA(\E-A@WADI-AAȩ-A (A6-AAPo-A A -AA$-A AP8-AaA-AA8X-A@Y$A8-A%AǷ-AH[&A~-Ay&A-A.'AX-A<'A-A@i$A,-A~%A\-AV%A-A&A"-A`{&A|-A$(AȺO-A |g$A-AN"A8t-A@"A`a-A@ "A@-AHP A I-A !A(-A@!AHy-A#A@-A`Ab-A@WA0-A AKA(-A`A AY-A ZAPv-A Aq-AvAF-AA A-A@,A6-A`DKAx -A@A-A@5OA-AqA _-AyA]-A W A-A!AN-A J"A6-A "AH-A"AHi-A`#A4-Am#A -AW#A-A1$A9-A |9$A4-A Aq5-A}A88-A`Ad9-A}A:e-ActA!.AUAx$.A@,Ap4.A@A.6.A`9A0.A@A`.AAp-At(A].A@A-A A%AW-AK*A0.AAh.AA w.AvA;.A$A .AAXa.A6]AI.ARAȘ.AAP8c .A@W#AhI .AhA(< .AHA .A_A .A` Aw .A"A(K.A4:A.A`=A@ .A=Am.A=Ap.Aப=A.A @ATG.ABBAPj.A@FAD~ .A=tEA7p .A JAd .A`PA .A@,iUA9 .A VA5 .A0WAՂ .A@4VA@ .A 'VA`ӆ .AUA }{ .A`TUAl .Az TA}= .AZOA; .AOA5 .AOAe .A43OAI .AVSAr .AWAp .AXA`] .A@\Aa .A@]A .A}{^A .A `_AH .A B`Aذ .AQaA .AHCeA麢 .A}9[A̱ .A_i=A..AA..AA̱ .AO&A麢 .A4Aل .AP9Ak4;.A)NS1AOl.AnBxAh_-A~ oA6-AEoA !G_-A[oAv#@-AuoA!-ApA-A`v2rAH-AMjA-A_kA(O-AsA-AuAwc-A`wAЮ-A`vzA( -A`fmA9u-ARnA@}-A|AX-A |{A`2U-AaA~-A`aAX#8-A SA(-A@y {AXR-AA-AॿAHn-A` A -A$A>-AۆAC-A`xA(`-A`[ |A()-A@`AD-A l΋AS-AVA8 -A8A -AbA`X-A`A-A@ƋA@-A ˴Al-A@P Aȃ-A`KA{-AA-ABA^-AײA-Ap A0 2-A AA-A` A-A`CA@Ww-A@rAp-AXAHL-AA<-A AA)-A &A -A/A-AzAV-Az^ A<"L-AA}n5r-A uvA0AUWF&.A A W&.A\.A%7ld&.A 5>%Am&.A`ӻA&.AHAQ&.A@Aj&.AMA*.A`;)AR,.A@AS-.APAbZ-.A@ƍA\e-.AJAz-.A ?;A c-.A ?A8..A@jA..A|A..A@OAP ..A@#A..A FA,..AF A`rr/.AAr/.A(A8/.A+A/.ADAZ2.A 0Ad2.ARAH+3.A`A@>L4.A@pAB4.AA5.AtA5.AAx:6.AFAU6.A@eAx-Y6.AvnA$z6.AOAXp6.AA6.A A6.AAh7.AƘA7.A`KA6.A`AXi6.ACAxƭ6.A cA 7.A;Aȇ;7.A@WAXU7.A^A7.AţAd7.AbA7.AKCA 7.A rA7.A \\Aޘ7.A|/A؟7.Ak)A@7.A`KA+8.AsdA +8.A@ƷA8.A ZAV"8.A`B&APg18.A6AC8.A@qAPg18.AOA(m68.AAH8.A,AA8.A A <8.A`Ax=8.A`).AX@7.A@ A:B7.A 8AA8.A@?A:.A`8A̴:.A@PA:.A \ AhL*:.A ~vA88~:.AA؁:.A `A":.AIA';.AVBA`!;.A<A0<.A@#qAP/=.AAXn;.A A <.A`~Aׯ<.A >A=.AgAr>.A@A>.A5A=.A@GAx5=.AAx{=.AAm=.AF:AȪ=.A AA%=.AA0*=.AAR>.AʸAN>.A@rAxH>.ANA>.A@%A>.AA @.A0.APH>.A@Ax=.A@A=.A3n޴AF=.A0{Af=.A0[AuPW=.AkGA,g=.A7>mAz>5 >.A *$AD5>.A7% AbH5>.A,e滲AY|M>.A:[A|f>.AJAw~>.AAC)Ȗ>.AWAD>.At3ӲAWY>.A֌A>.A`KA86?.A@AA.AA8xA.AAp B.A`KnA0B.A jAJJB.A8aAp0B.AH<APB.AfAQC.A/AD.A@nAD.A`fAJBE.A"AP8D.A@ibAvC.A AA\C.A A9C.A AhB.AA0B.A0AMB.A A7C.A A%1D.Ax5AbFD.A@iA G.AQA@G.A%APQH.A`sA /H.AT׷ADH.A1AxD.A`AXE.A@AأG.A`AmE.A`A@1E.A?HAAHG.A`A H.A)yAjH.A OZA0{:H.AzAC.AwAȲY@.A ;A`@.A]A@B.AA0D.AAxIC.A (UAg@.AaAAA.ANoAhC.AzQ AX[E.A%A@F.A+A0*G.AAUH.A`A@5BH.A6AH %H.A`+AuD.AA0gE.A }AH.A A?CI.A la AhI.A`Aq`I.A`AJ.A@!Ab J.A@5A*O.A7AO.A:AO.AAR N.A"ApK.A`KAL.A`UA"K.A&AA@K.A`AJ.A34ApNJ.A"A GK.Az+AhLK.A65-Ay K.A--AwJ.A@#v.AHG=J.AQ/AlJ.A@ 0A(BJ.A /AHJ.A /A9!K.A`/A8x>K.A`1AK.A@1AJ.A@k2AX}J.A3A(K.Aյ3AxqK.AŽ4AXpK.A`5Ax>J.A7A=J.A7AxK.A]7A@9-K.A7AuhK.AH7A0K.Av6A(-K.A i7AL.A'8A`-K.A}8A L.AxT:APxIL.A`:AhbdL.A`B;A0*mL.A z<A.L.A@!BAL.A`CAM.ACAL.ACAL.A`@EAL.A EAL.A0$FARL.A{FAL.A}FAhL.A}MGA L.A@EGAOL.A HAL.A HA@W M.AIA8(M.AZHA;.M.AHA0M.A*IAIM.A@!bJA0ݒM.A JAx M.AIAM.A@JA NM.AinJAxM.A9KAM.A@DKALJM.A`KA(M.AWEMAxqL.ANA?M.AwZAWN.A$?dAHPO.AlA>"P.A3sAIP.A 8!tAdP.AtAYP.A@,uAP.AzxA֗P.AxAKWP.A{yAlQ.A AhQ.AAX0Q.A6A0Q.AtAXQ.AkAQ.AfA ZQ.A ATRR.A`A0*R.A`vAHÏR.AAͪR.A 8qAR.A厉A$R.A`tډAR.A@AR.AAXS.AơA@dS.A@֍A@S.A sA9S.A }AS.A`AȩT.AKAHT.AM~AHT.AAHT.AAHXdT.AzږAhT.AAAhQjT.AoIAJpT.AߜA(T.A cAf8U.AA#tU.AA2V.A &6AH iV.AAuV.A@eAhȤV.AAPgV.A^A`V.AuAV.Aw0AV.A@>iApc9W.AiAkW.A*Ax]X.AiWAiX.A6A茡X.A@AxX.A_ҭAY.AAZ.Aa!Ad1[.A@A;[.AwA{[.AtA0[.ADA[.AzA V[.A@A8 z\.A`(A}[.A`nAq[.A`KeAB\.A"ZAh/\.AA].A (A].AAv"_.A@. AF_.AJAb.AA c.AAd.A AxWQd.AA>A8d.AAd.A`]-A(c.A Aǣc.AA~zc.A@RAH_.AdAs_.A /AZ].A^A8|\.A=A؅}\.A@itA"\.AA\.A ZA8+\.A A0L].A (A(:].AB7AI].AAȏ].AAH].AſAH2].AX A].A ApAz].A Aأm].AQR A$].A` AD].A` A8H].A` A?ZE9F].A AJY].A=m~ Aҗgc.AABkj.A֘A<"o.AA}n5t.A uvGAg.AVߝq#A0C.Aՠu Au8/l.A zu Axml.A@Geu Apl.A@Geu Al.A-du ApAl.Adu ATl.A`9Ru A{l.Au A-m.A"u A#qm.At A2m.At Ah/m.A`Dt An.A{t A(~in.A Lt A n.A;t A_o.ANps A|o.A@`Is A2o.A s Ao.A r AHo.A6r A +p.A`r A(\p.A#r Ap.Aq A2p.A q Aq.A`q A %q.Ahq APLq.A|kq A0Uq.ATaq A\q.AZq AP+fq.AtOq Ahlq.Aq Anq.Aq A0xq.Ap A{q.Ap A~q.A`p Ap q.Ahp Aأq.AŸp Aq.Atp Aq.A@ p AXq.AŮp Aq.Ap A8ҡq.Ap Aq.Ap A0&q.Aap ALq.Aӷp Anq.AMp Apq.A p Aq.Azp AHݢq.A@ߺp A@q.A@yp Apq.AVp A-q.Ap Aq.Ap Aɡq.Ap Aq.A-p A؟q.Ap AHq.A`p Aáq.Ap A8ҡq.ARp A q.A p Aq.A`p Ax q.AQp A$q.Ap Ah?q.Ap A\q.Ap A{q.Ap A¢q.Ap Aq.A p ADq.A`p A8Rq.A p ApIq.A_p A^]r.Ao ANt.A`]l At.A@zl At.ATl Aft.A@ l A:u.AFTk AVu.A@yj A0u.A9j A`wv.A`h A( v.Ah Aw.A0g AOw.A g A@>5x.A.A`S AS.AyS A_.A]S Ar.A >T A w.A_T A.AT AD.A@yT AТ.A-T A@.A?T A.AҨT A@Ӆ.AT A.A`;T Ad.A 1T A.AسT AxK.AеT AQ.A`T AP/.AoT AP擁.ART A(z.AT AĔ.AT A.AFT A.AmT A .A`T A8.A@T Aꔁ.AT A֔.A@T A.A T Al.AFT A.A |T Ao.A@T A N.AVT Aː.A 1T Ap.A`vT AD.A_T A.A T A .A &T A.A uT AH?.AT Ax.AT A.A`bT A.A ZU A0.A@rU A؟.A U A.A AU A8.A U A0.A l"U A⇁.A*U A@.A8U ApɌ.A?U A .A cDU AI.A`KU Aߔ.A MU A.A@IU AP.A`bEU A0ح.A@U Ay.A@,2U Aǁ.AU Ap߁.AT AP.AtT A6.AP APf.A3O Ah.A+mO AԢ.AO ABل.A`N AH.A4N Ab.AaM A4.AM A,.A@.K AÆ.AZI AH.͆.A@0I A(.A` H A兇.A }F AXҘ.A "F A{.AFE AB~.AE A.AWE A0.AD A0.A/$C A.A`2C A`.A? AТ.A/= A3.A8 A8܈.At8 A.AE7 Ae.A@}5 AR.A5 AY.A5 Ab`.Ap5 A.A2 A.A@3 Aʉ.A#?3 A .A1 A.A0 A!.A`)0 A.A@E 0 Ak.A@& A=.A Axŋ.A A(.At A .A@' A2|.A QO A3z.A= A#.A@ Ap:.A& AB.A A<.Aä A86.Aj A(Ғ.A`D A(ړ.A`9 A@uҔ.A՘ANޔ.AyAꚕ.A.A #Au.A`]Ax~.ABA.AAPk.A`)A8 .AA@.A A0..AXA2.A@ Aɥ.A`AQΥ.AA;.AA0C.AVߝq#A=U.A98IA 1.A[u-ɦAg.ARf,:s A}i.A`t Ai.At APwi.At A i.AUt AVi.A@t Axi.A`2t Aجsj.A!u Ak k.AXu A`GKk.Au Ack.Aדu Af~k.Au AIk.Aՠu A\k.A`u Ak.Au A@Ol.Au A8/l.A zu A 4؃-A@AV-Af2o13JA-AzA -A/A-A`AA+d-A4 AV-Az^ A-AzAc-A@kHA1-ABAhݬ-AFA8-A`"GA`X-A@ HAPA-A@EAO-AvEA-Aq?FAF-A<&BA+-A}=Aj-A q=8A4--A@%6AkI-Ad5ADl-AL6A V1-A`)>A؃-AM BA;sN`-AXg:IA -Af2o13JAc-A@kHAY%j-A9K'Ah6-A;&A-A 'A-A 'AE-AZh%A-A`)"A-A"A(<-AAȇb-A`fA˟-A`TA@-AA tq-AmMAS-A"A -A$AP8x-A iAE-A|A-A@A-A`A.-A3A0{[-AC"Ah-A`KgA-AaAH-A@ d AH-Aa AHJ-A Ak-A`FA0 -AFA-A AH}-A$ A(-A` Ax-A6A@-A_ A`"-A AH?C-A A -AaSAg-A@AЄ-AfA,-A@AA~-A@Ah1-A/Aj-A`meA#-A,$A0-A`"ApS-A3QA-AZAc-A^A-ADOAȘq-A`DA-AAЄ-A`SAP-AxEAփ-A bA -AƩA[-A A}L-A ZA-A`AD-AzAh-AA0]-Ay2A<-AAK-A`A 0-A@E A d-A A)-A A-A Az-A} Ah-AA -A@KA8g-A-Ah-A`mA-A AB -A@ PA=2-A@AM-A@PAp-A AH-AfAu-AAx-AӸAl-A!Aг-AR $A8r-A%A-A(A-Ay(AQ-A E*A`_-Aq*A-A-AW;-A /A v-A*#0Ah-A`U1A@-A`1A0-A`2A1-Aྣ3Ã-A@I5AH-AP5A-A`7A(-A@yw8Aku-A;9A{-A /d;ApT-A1>AN-AfBAH2-A:DA8-AREA #-AW~ByFA5x,-Ai\~?AJ-A=m~a2AY%j-A9K'A Lf-AL*A0[.A4 A $/4:ELT\gpuz-A*Ay-AL*Aڔ-Af*A-A*A-.A:A-A1A^-A27A˃\-AID1ɻ;A-AnBA-.A:AH.A KA.A`KAA-A`HA-AVRAH.A KAĔ.AVA@F.A'MA-A ?ZAPo-A` bA/.A,[AĔ.AVAp.AqeA.A/aA.AHjA؟V.Ap\oA.AQtA.A0kAp.AqeAh.AvAM`.AAtAXt.A#|AP.A <AB.A7A.Aڹ~Ax1.A`[%xAh.AvA.A{AU .A@,hAw .A@,{AX .AହAhk .A,CAX .A@,A0 .AŒA .A`vA0!| .A A d.A A.A{A@ .A A0 .AA:..A`9Ae.A A@ .A A` .AsZA{ .AA(i .A`oAX .AA.A9A` .AsZA|s.A`f"AxO.AAhnV.ARA.AXA!N.A@A7.ATA | .A@yUAH.A@ ~Ax.AzAxm.A@ A|s.A`f"A<.A A5/.A4A0Q.A@NUAp.A@ρAR.A@A].AA<.A A.AQ A0݉.A+AQz.AFA0o.AAl8.AfA .A ҮA`.AmA.AQ A8/.A+Ar/.A(A/.AWA{2.Ay8A#2.AA@/.A_A/.ADA8/.A+AȗP.A OAX0Q.A6AhQ.AA ҅P.A`,AP.A AAP.A@CA@kP.AvAQP.A`ӇAeP.A A P.A`AȗP.A OATKR.AgA@dS.A@֍AXS.AơAxWR.AޡA88HR.A 8MArQ.AA _Q.AAdR.ADATKR.AgAV.A@>iAV.Aw0AȇU.A*APiAkW.A*Apc9W.AiA1$U.A/CA0 zU.AAkW.A*AZ.AfA0[.ADA{[.AtA0TZ.AxAZ.A YAhZ.AIApAZ.A6AZ.AfA- ^B.A 1 AX\B.A1 ACWB.A@1 APB.Ak1 AEB.Aa1 Ai:B.A41 AH\+B.A*1 AX#B.A 0 AB.A`v0 A\B.A_0 AB.A0 AP>B.A>, AТrB.A@N' AB.Ag& Ax5B.A J% AŘB.A$ AHB.A`K! A0 B.A" AxC.A< A@C.A ASC.A AV-C.A+ A^:C.A A(1MC.AN A;gC.A` AD{C.A=6 ARC.AǓ AȓC.A<  AC.A AC.A`b AD.A`- AOGD.A@e A wD.A5 AH\D.A l AXFD.A A@ E.A3 AE.A  A5\E.A A0E.AC A$E.A5 A@FE.A A E.Av Ap F.A`' AX)F.A A0JF.A AdF.AQ9 A0 sF.A O A\F.A/ A-F.A`m A8F.A AhoF.A A;F.A AG.AA A%G.A A/5G.A AAG.A@r/ ApxKG.A`Y AOG.A  AUG.A`5 AZG.A A]G.AA4^G.A AVG.A`XARG.AgADG.A2AVBG.AA 9G.A@Ap$G.A A G.A}AhG.A`dbAG.A`AF.A`A8F.AjA#F.AAjF.AA(F.A=AF.A6APG.A AG.AAG.AAh#G.ARA.G.A A/G.A@xANF.AUAAȩF.AA84F.AiAqF.A}A F.AAF.A 3AF.AAUF.AAn{F.A@,AhcF.A`YAl[F.A`2qAؚUF.A@ƋAGF.AA@:F.AAC7F.A_A5+F.A@+APF.A@AF.AApE.AAHE.AFoA E.A`;AuE.A@ƧAE.A`&AE.A CA(E.A@AE.A AE.ANALE.AA bE.A8ANE.A~A88E.AߢAȥD.A@}A)D.A,AЦ D.AAjC.A7/6ʠA\qC.AW4#A*[C.AkGAG]C.A^ݡAIB.A`ՍA0B.A;AO+B.A]1-lA6DA.A}ߙ}A!IA.A%)PwA>B@.A| qAwQU@.AR&zjA ?.AuX&dA`6N?.Ac^A A?.A_]A`8?.A ]A0?.A2]A@u"?.A\Ah?.A I\A?.A \[A?.A5[A=.A`'NA0]=.ANAl=.A`IAWS=.AGAxi*=.A`KEAXW=.AOzEAw<.AO BAc<.A J?AV<.Af>A"><.A`=AG;.A@.h;A.;.A q:AhI;.A:8A`e%;.A`m7A:.A6A :.A „3Ap9.A2A}9.AD1A$R9.A 0A:9.A@gN0A58.A/A 8.Aw/AX8.AR/AuL8.AZ~/A7.AO/A`l7.A51/A56.A=.A }6.A.A@O6.A`m.AO4.A 9.AX(4.A.A0 4.A.Ax 4.A`T.-AP|3.A -A`61.Ae,A`2.A@ 'A$..A%A,.AB%Ao+.AR$AН&.A"Aa#.A4M Ap#.A32 Ap9".A/A0;z".A`2 Ag".A cJ A^".A A".A`o(A ".A(A!.A`(A@!.A /)A!.A@L)A1!.A@p)A!.A_)A!.Ab)Ax .A(AXG .Aӣ(Ax .Azw(A.A-(A .AX'Aذ.A &A@g.A4]&A.A%A/.A`v%Al.A$AHH.A$AJ.A~$A.Ae$A(:.AL$APju.A0$A.A#AXh.A&"A.A QP!Ax\n.A`7!AH$.A *!Ah/.AW A1.A@ A.AA@.A@WApl.A}Ahĭ.AA V.A`TtAج .A 8Ab* .AA@q .A#AhՑ .A`ʶAP8 .AA& .A൫A .A*gAl .A OAN .AA .A -AP< .A@~AS .AAأ2 .AtA .A<%A .A"A .A@RAX .AA> .A2AX[| .A@kA .A`fCAr .AA@s .AgAl .A@Aؚa .A@AL .A-A+A`V-A@NAO-AOA=-A` Ap/-AA-A` Avi-A)AP&-AI^A8-AA-A /5A[*-AVAb-A`]A-AौA|-ARA /-AA-A \AX({-AA8-A gA-A@A@-A`RAZ-AcA-A&0A1-A`A-A2AXK-AAQ-AAM-A AuU-A`GAPR-A`]kA@W-A {A W-A@ơAXZ-AAX-AEA^v-A@)AXj-A`Ax(-AvA(z}-A`Ai-A@A-A|VAb-A&A0wD-AbA?-A UA-As]A}-AA:M-A&9A -A qAj*-AgA(%-A@yA-A^AA-AbA`B-A`IA( -A *A"-A@A0-A A2-AAKP-A *AC-A@NA0-A4AȱI-ALA-AA3-AVA4-A Q*A`C-A6A]-AA-AUA8-A`IA26-AbA-AĺAs-AAȋC-AAj-AnA]-A 8~AY-AnA4-A@GAx+-AApX-A,Aqt-AAz-AvAc-AOAPk-AAhl-A_A(>t-AcA-A@IA~-A/A-AA[-A[|Ah-A Z_A0̔-A(A.-A%A-AA%-A`ԿA@-AjA-AѶA-A@eAM-A`DA-A`AD-AA-A 8A%-AE`A0-A +Ah-A A-AA`-AMApI-A/A-A@ᱺAx-A#hAC-AjTA'-A@`úAx)-A@>ƼAڝ-A@A(-AҿAhv-AfA8f-AGAU-A AL-AFA+-AA-A`lA-A`ݾA -A=A-A ℾA-Az}Ap0-AqA-A`žAx-AhAp-A`;A -A SAy -AAȥ-AxCAB%-A JA.-A`NAA-A0_AzM-AZAJK-A`mAA-ACA'-A`vNA0-AdAp9 -A@iAP<-AA-A Ax-A`AW-AQܿAy-A`[Ak-A-vApy-A`ABE-A`TοA #F-AaAN-AA_-A@CA07-A`[A0-A졺A-ADA茛-AšA[-AyAx^-AA;D-AA.-AA-AtA-A AX-A A -A3A0-AЛAq-AAX(-A@EA+-A ~A--AAA-A 1%ApO-A AH)W-A&A0W-A SùAH-AA -A HHA -A-\A8-A|Ah-AhA@-AwwA }-ALAؔ-AA?-A`A-A#A R}-AAI-AA&-A ӽA -AlA -A@'A-AޘAx6-A9Ax<-A ZAhD-A#A?-A App2-Aq|A -AzA-AϵA\-AHA( -A@DA>-A@A-A9AxX-AǺA-A/Ah"-A@5ٻAh-A#As=-A`~AP+@-A A>-A A4-ANW+A -AhlnA#-A@ oA$-A@ƗA-A0)A -A37A-A Z9A`%-AMA$-AxfA-A@`AX[-A_ A^(-AA(-AJAP-A@A-A@ýA,-A$ȽAз -A$aA/-AtA5-A lA0 R+-AG8qAd3N-AOfAf-AAd3N-A_AE]K-A}98A*&{1-A0ƾVA%hX-A౬sAd -A+WAGÕ-A7K_Az!-AuvGA_GG.AAb".A֘A2-h .AA.Az.Aݧ A0}.A1 A~.A A%~.A`26 Aƥ~.A^ A$~.A ~ A~.A A@/.A An.A A(.A H A A.A`d Ap.A A¥.A6 Aأ.A2 AD".A AG.A`6 A 0.Au A2-h.A A#ӯ.AV A8.A A0Hۊ.A`2 Aˊ.A Ad.A@W AP.A@ A/.A0 A߈.Aa A`.Aޛ AP8.AQ Ae.AHU A *b EM.A Ak*W/A AH 6C.Ab Ac.A j A.A S_ A؟).A? AG.AdU AX.A# A.AB A.A4 ApW.AC A.A AP7.AO A.Ab AHm.Av AHj.A@y Ar.Av Aп@.AL Ab@.A AU+.AV AH.A A`.A 8 A 6.A 1 Ax).AV AxKn.AT A.A A8Q.Av A.Aq AТ".A@ AB.A/! A`.A AȤy.As A.A A.A`e A.AX0 AX.A6 A.A൥ AfA.AE A[.A` Aav.AK A{.AB Aз.A, Ap<.A A.A Aa.Aڏ A .A A-.A A\ .A AxK.A` AXW1.A@ ApZ.A-  A0{l.A Z A@W.A Ax.A`v A0{/A$A AaU/A`[< A/A A/A AGM/A@` A@Sn/A`; A`/A` A8Ѡ/AJ A`/AO A/Ao AL3/A AЄ}/A  A/A_A A(\/AT A/Ad A3!/A* AH/A@ A_/A( Ap/A@j A/A A/A^k AZ/A@#B AH/At A /AI A 4/A`m: AN/A`K A)[/AH Ak/A A{/A A@/A^ A/A At/A A/A5c A@/A , A(/A`; A؎/A< A8'$/A`v AHa*/A Ap*/A@ A+/A4 Ah-/Ap AXt./Av AX//A AH0/Af Aȃ0/A A0/AO A1/A A`C1/A`[ AR1/A$ AZ1/A` AP^1/A` AP^1/Az AxW1/AD AK1/A A;-/A Q1 Az'/Ai A[/AZ A/ApL A@` /A@,l Ap/A A/AC A_/A] A(/Aw A/A A`/A@ A/AQ A/A A`/A O A/A@ A/A. A/A> A[ /AK A /A R A/A+W Ak/A`"Y A(/A`"Y A/AX A|/AoU AL#/A lP A0+/A@ A//A5 A3/A) A=/A`2 AJ/A AZ/A`{ Ai/A`$ Az/Aʵ A/A  AS/Ah| A虴/A?6 A(/A A/A A(\/A# AhQ/A A/A A/A A"/A Or A)/Aa Ah//A)Y A1/AV A6;/AN A@S/A@? A0/A`9 APڨ/A q A/A' A/A A5/A A/A-x A=/A` A()N/Ay AhQ{/AM AA/A@, A/Ai A/A^1 A/A A`/A APN/Avy Ah/A( Ac/A AN/A A/Aa ApA/A@` A7% /Au AhoV /As= Ap /A@& A` /A@ A8 /A= Az /A A@ /A A` /A@ A3 /A` AO> /A`m AجG /A Aq /A`2 AV /A ɋ A> /A{ A{ /A Oe A" /A 8A AI7 /AR/ ANd /A@! A@ux /AV Aݙ /A A = /A AL /A A6 /A`| A[ /Apl AB /A &c A /A 8[ A /A$Q A7 /A@F A7 /AhF A Y /A6 At /A# AH /A@ A0Ǽ /A` A /A$ A3/A N AKq/A AU/A@ A,/A H A36/A  A@=y/AM A/AF Ae/A 8 A8/AE AV/AU Aw/A ApA/Ax AA/A@C Aj/Av3 AP/A A/A` Ah/Ac AXh/AR A-/A AXE/A` ApV/AC Atj/A c A(-/A@ A[/Av AG/A` AK./A@ A[/AF A2/AW A``/A A/A Ax//A Aзh/AW AxƝ/A A/Au A[/A` Ax/A A/AA AhQ /A@W A2=/A A2q/A Ax/A`Ù A/AV AHL/A A/Aa A/A ` Aj/AC A/A A0/A` A/A9 A0)/A@E A(?/A A`a/A L Am/A`v3 A|/A A`/A@ Ax/A@W Ax/A-^ A/Ao: AJ/A A1/A Ax/AH A@u/Ar AD/ABZ A/AV< A/AF A'/A l Axv7/A A0</A@a AI/A# AHY/A Ax(/AG A/A AXu/A6 Aڵ/ATg AP/A" A/A@yq A(/Aa Az/A' A'/AO A02/A`l A'`/A$ A0./A A /A A@B/AaE A@ /A`v A8-/A = A E/A4 A |/A`n Aho/At+ A8ɧ/A \ A/AC Ah"/A AA#/AMs Ac/A A/A ~0 A ^/A A/A@ AE/A A Au/A`d AU/A A/A AX./A APF/A ۜ AJO/A͒ A/A A8/A( A=/Aʭ Ab/A B A(i/At3 Aq/A`b& A /A AU/A@P A/A A/A} Ap/AB A8/A A?/A AX/AT Ap/Az AXև/A@P AX[/A AP/A B A/A A\[/A` A୕/AG AB/A- A /A _ A/AQV A'/A A,/A' APi/A' A/Av A(/A A8t/AӤ A@u /A@PO Aؼ4/A R A]/A` A/A@.: A`/AVV A8I/A  A/Aջ Af/A A /A S A /A Ah /A` ù A  /A 8 AH\ /A0 Ah30 /A} AH.5 /A<" AH; /A AP7M /At A[^ /A@ߗ Ai /Ad Av /A` AP| /Aડ A} /A`2o AG~ /A`? A~ /AB A  /A@iɰ A # /AM A8x /A@` Aք /A@ A`B /Ad AX /A@,X A /A@Ю A /A@ߢ A /AĬ AD /A8Q9 A(ǐ /Aǿ APy /A' AYp /Ax( AXp /A@ A /A` A] /Acp A=E /A?7 Ae /A1Z AmE /ALj A /AU A_ /Aa A /Au A/Ax A{/A A,/A  A8/A A>%/AUT At/A`ߐ A@/AА AZ/Ao A/A` AX/Aތ Ad}/A` A3v/A`v AP@/AmNJ A`/A`S A/A2 Aa/A?׉ AFr/A` A`I/A@rC A/A A>X/Af/ AJvHX/AMP\K A/Aδxv A(/A` A8G/AW̉ AC/Ah A߸..=/AS A\J/A ~ A`!1/Ah Ag֪/Aࣟ AXGh$/A[Ж Agze/Ay| Ak/A R A /A- A/Acɉ A'/Aof A'L//A( A1/Ae A+B1V/AOLJ AV`S/AƉ^ AՆ/AT AR/Abʈ~ A/A A8/AM A(Kp/Azs A /A@ϋ A*/Ad A.A A f.A* A(m.A89 A.A) A.A 8 AxQ.A Agg.A鼕 Ax.A`A A興.A Az.A`Dk A..A@. Au.A/: A8D.A`dդ A.Ay A%.A׿ A.Ap A~%.A`D. A.A A/.A㊺ A(-.A@ Axm.A@ Ax.AFJ Ada.A A.A`" Aߖ.A ջ A%.A@ A.A^ Ap.Aɽ AM.A] A.A A c.Aoq A0.:.A Ae.A͍ AP.Aj A=.AXe Ae.A@ Ap.A`̱ Axu.A AXJ.A AA.A A'.A}u A M.A A.A OE AU.A& A.A  A.A( AX.A  A- .A A.A{ A.A}u A>.A`m{ Ahѫ.A A.A A.A A.A As.A AU".A  A5.AG AHrJ.AM A/b.A , Ap.Ac AH!;.A@, A.A: A0*.A& A^;.A@A Aj.A A.AT A-.A`÷ AX9.Aྭ A¸.A?d AE.A A07 .A Ax˚.A 1 A=.A6 A0.A9r Ax.Azh A`.Aw AH .A% Ac.A<< Ab.Ay A` .A` + Ahՙ.A At.A AHm.Av At[.ȀF Ad.A4 A$.A& A蕲.A A0.A! A EM.A઱" A7.A`}% A`].A+4 A?.AFA4 Ag$.Ax>c5 A.A@V5 Aߑ.A{֊< A$].Ai~HB At[.ȀF Ak*W/A闕 ApAW/A AEW/A APxV/A & AV/A S AqV/AB A( V/A N AהT/A, A,S/A l2 ASS/A@% AhS/A, AR/A A@R/A ' Ax-Q/A`) A;P/A` A+O/Ah AM/A`D A eM/A S A`!L/A}X AL/A % A L/AH A K/AK/A AT$tvK/A v A RK/A& A7L/AM' AZY(XL/Av[* Ah`L/A; AL/Am AiM/A'%k Ap9M/Ar AW_M/A8 AنM/A?b APM/A6 A.;M/Aq Aނh"N/A_, A^N/A`뎽 A/9;N/AC AN/Aį+& A6O/Aof\ A v5O/A]} A0bO/AuX AO/Ajb A{fO/A(A A'O/Aq}` AO/Aa AO/A A(O/A, AQO/A@ A(O/A` h A2O/A APO/A@% AMO/AU A8O/A R A(}O/AΜ ARhO/AR4 A~EO/A@v A4O/A۬ AF.O/A@5 A3O/A@ A^O/A@Ū AyO/AU AO/A@Ɵ AxO/A A8VO/A A(O/AN[ AyO/A?o A[O/A& A =O/A AO/A > AxO/AR APN/AgZ A@dN/A- AN/Ax AN/A (- AN/AR A\yN/A - AgZN/AX AJN/A Z A8'DN/A@ A/CN/A@ A؛6N/AB AJ"N/Ak A N/Aw AV\N/A`;ș Ax5`N/A AQN/As AxNN/A앙 AN/A~ A8N/A#T AP N/AK AtM/A^{ A3M/A q AĴM/A9ʖ ARRM/A AM/Aq A8/N/A3 A0N/A A(KM/A6֔ A8sM/A`Ü A{M/A`K A8sM/A AxM/A@ AM/A9 A M/AN A0GM/Aas AH.M/A` A0M/A AHrM/A \ AgN/AR. A@rN/A AM/A@9 AM/A` AP^N/A B A8EN/A3 ALM/A` A8M/Ay A0M/A`;, AM/A׍ AM/A@C[ AM/A`׌ A(N/Az A`N/A2 A3N/A`p A&N/A- AXdQN/AF A0LbN/A@W؉ A eN/A`m AjN/AU AqN/A AN/AtΈ A୍N/A AN/A]Z APMN/A1 AO/AY A,O/A`x A@P/A` AP/A4 A(\O/AN A@uO/Asu AHN/A`Ti AH O/A} A0O/A`v4 Ah;0O/Ac A _nO/Aބ AO/A[) A P/A'z AӂP/A AP/A A/Q/A9 AXKQ/A A$fQ/A ~} A {Q/A { AQ/A y AqQ/A@w A8UQ/A`du A@`Q/A !t AQ/A (r A(yQ/Aq A:R/A5q A =R/A@p A@R/A #p A.4S/A3o A?S/A$l A\OS/Adi Ah9S/Ahh A8'R/Akf AGjR/A d A t R/A |c Ah@Q/A}c AUQ/Ac ANQ/A@>Jb AP/A@a A(>P/A@G7a APȰP/Aa A0P/AHb AѮP/Ac A6P/Atc AyP/A|c A0&fP/Ab A0&fP/Aib A0&fP/A`Rb A@RP/Ad b AP>P/Aa A)P/A@,a AP/A"b A O/A`Rb AO/Ab AO/Awc AO/A`2c A@~O/A`v`d AHO/A`)d AQO/A-6e AHK[O/A<` A(O/A`` A8O/ANe AH)N/A`[e A@N/A൚e AN/AFe A`%DN/AFe AxM/A &Le AXM/Ac A0ٲM/Ab A`M/A`K` A_M/A@,q` AOM/AQ_ AXtM/A`2P_ A@sM/A} _ Ax'M/A`_ AM/A@E_ A2zL/A ^ AK/A ^ AhQK/A^ Ah@&K/A`@_ AK/Aw_ Ax˰J/A`2` AH2 J/A`;_ ApsI/A`2` A8H/AT_ A PH/Aw_ A>E/Ao^ AxE/A@a^ A`D/Ay^ A$D/A@ ^ AxD/A _ A`%C/Af_ AC/A}f_ A8B/A`_ AsB/AX^ AB/A^ A!:B/A@Eq^ A ߿A/A@N^ A;8A/A@_ AD@/AOj]_ A`t:_@/AӴ` A-:?/A>(a A8?/A-b A?/Ab AH:?/A?c A?/Ac A @/Ac AR@/A 2c AH?@/Ab A`@/A]a AXyA/Agfa A8QA/A` A֒A/A |` A4A/A)a AA/Ab ApA/A=2c AHA/A`8d AA/A>e AhA/Ae A%A/A0uf App@/AZg AI@/A g AH@@/Aർg A @/Axg A@?/A`DEg Ax?/A |Ag A3?/A@g AC?/Ag A8?/A@g AP|>/Ag Am>/Ag Apy>/A`9Gh AL>/A9h AXh5>/A h A|C>/A`m;g ARv>/Af AX>/A=f A>/A`Te Ax?/A@Pe AF?/Ad Ap?/Axd Asq?/A@rc AhT?/AIc AI?/A|c AK;/AXw Ah;/A@Ew A:/Aw A`:/A`Tw APo:/Aw A 0:/A@w Al:/A*w At]:/AXv A8EL:/AKv A@D:/A`u AxW9:/AOWu AXR5:/A`mt A'::/Aଇt AH:/A t AbN:/A0s APg:/A]5s A`v:/Ar A:/A q A:/A`Xq AP:/A@p AH:/ADIp AH::/A`o A:/A o A`:/A@Wo Ax؎:/A`o A3x:/A }o ACd:/A o A%":/Aq AM9/Ar A9/At A=9/Av A8b9/A`w A9/ATx Aྜ9/AЈz AXے9/Ah| AS9/A3~ A0 9/Aag A9/AH A9/A A跹9/A`Á AP9/A@io Az9/A+ A9/A@X A|9/A@#~ A9/A`| A`X9/A{ A@9/A z A:/AKy A@N::/A'qy Ar:/A` _y A0:/A sy AN!;/AT\y AT;/AR~ Ah;/A` A ;/AЙ AD8;/A@J A:/A@ AX~:/A끠 Ac:/A@ A8Ia:/Aӛ A Q:/A AH9:/A@## A(:/Aڜ Ap,9/A徜 A0%_9/A@iT A849/A`V AH8/Az4 A8/A^ A 7/A A87/AA Ar8/Aa AЄ'8/A?ޗ A)8/A@p A0668/A9 ATQ8/A Axzc8/A Axe8/A֘ A@8/Af A88/AI A8/A@/ A8/Aھ Av8/A A08/AnG A8/A AP 9/A9 A99/A@y? A{V^9/A?& A)1H9/A6H/ A9/A ' A<Ǭ9/AQ A|n`9/Aw A Xn9/Aݤ{* Aܰ9/A5 ͑ AV9/Am A~߮9/Ab~ Ap9/A? AWɟ9/AI) A+u9/A.>AА AQ9/A| AMN9/A7 Ai̠9/AƂ A4ū9/A? A*69/A*6 A9/A At |9/AAj A]9/A'7ш AbV9/Aݼ A39/A^Wݪ A~9/A𘧛 Ao9/AU AH9/A` APSd9/A(#T̓ Ax9/Aā AM٭w9/A< A9/Ay A_΁9/A2 AjH|9/A@VY AEY9v9/Af3 Acr9/A7L An9/A҈ Aj_9/AB A$WJ9/A+΃3 AEM09/AOލ A'9/A胏 A[8/As4 A`L8/AtYy AE*8/AZ0h A,8/AO# O AA/v8/Axg+ A26L8/A_} Ad8/A"rT AK67/Afy AwU7/Aze# Ap.7/A7Ԟ A^G7/AfhLK A2| 7/AF,Ș A[ϩ6/Aw~3 A!6/AȆ A)M6/Aי AH3/A l A2/A^ AL./A@,| A8'/A8 Am%/Ae A#/A @ As /A`KX Aho/A A/A@iJ Ay/A / A:/A6 Ar/AX AS/A2 A@/A  A/-/A| A-h/A} A/A? A`P/AH A/Aj A8 /AF Ag./A`m4 AL/Ax Al/A A _/A` A/AL A /A`D AG]!/A AP!/Aݭ A0Lf!/A6k ADz&/AOx A(-%/Aco A#/A9bZ AHY/A@ A&/A`0A A</Aj? A&+/A@V? A/A = A8E}/A: AL/Aa^4 AЕ/A4 AH./A S4 A ͑/A |r- A0%/A_- Aȥ7/A@i8. A0;3/A-;. AXj/Aj4 Al/A S4 AU/A e4 AX/A35 A@ /A?S6 A/A@6 A@ /A 8v7 A/A 7 Ap9/A@Ps8 A/A S8 A@=/AB6 A/A5 A/A Ɂ6 Ah/A@9 A?/A cD; A/A; A /A`K7 A/A`U: A*/A/J> Ap\/A-> Af/A@#> AB/A B A 1/A`;LD A/A.E AXF/A@FF A/A`]H A8g/A rI Ad/AJ A/A ~J AGT/AJ AD/Aq K APl/AJ A!/A /A ~I A /ABI AXU /AH AXl /A iH A  /A&H As /A|H A`& /AG A /AG A`! /A lG A0N /A"G AP /AVF A/AF Ai/AdF Aq/A F A0/AF A$C/AFF A/A F A(/A(F AC/A@rF A޵/A?F A/A E Ap/AdD A,/A@E A5d/AxE A/A&E A/AQE AHn/ACE A8\/AD Av/AD A-Y/A D A3/A`mC A/A hC A/A l Aa.A@l AH2<.A@l Ah.A@=l A.A`~l A.Atl A.Afl Ah/k.A@"m AH2@.Am A.A`m A@.AQn A~.A@yAn AY,.A`Vn A.Azn A.A=n A.An A 4?.A@!k A@$.A ~-k A.Ajs A0 .A`s AH.A`s A.A`s AJ.Ak Apr.Al Ao.A g APq.A6f Ap|s.AJf A.A@>e A .A`ve A.A_(e A.A`9e A`.Ae A.Ad Aҋ.A+d A.A`bGd A0.A`5d AÖ.A@5c AH.A`[c A.Ac A2.A d Aȇ.Ad Aؽ.A ge A0n.Ad A]T.A |e Ah.Ae AÜ.ARg Ac.A@k AN.Ak AX5P.A (wk AZ-.AHYk A(,.A3mk A.A@%k Aj.A@Us A.Az.s Ah.A s AȈ.A}s Aw6.A(_s A.Ar Ax.A@,r A .Ar Aن.Ar AhQ.A@Wr A.A :r A&.A@ Br Aȓ.A^Pr A`{.A`r Axz;.A[{r A.A3r AY.A r A W.A@!r A.Aq AB".A`~l Ah.AKl A8I.Ar A Y.AZx Az.Auv| A_G7.A Ab".A A2-h.A] A/A A;sN/AXg: A 18/A A=!/Ao3dž Av)/A A:,W1/Ao3dž A{rγ8/A A+#@/AXg: A5x,[G/Ai\> AJIN/A=m~! AҗgT/A] Ak*W/A闕 Aω.Ap]t A軤.A[{r AH.ABrq A7.AOn A.Am Ap.A@l A.Al A.Am A0.An A݉.Aq Ap.A`gs A&䐄i.AС2s Aω.Ap]t At.AQo A.Am A,.A?m A.A o At.AQo AS/A@t A(/AîAo   /A~ A/A+ Ar/A g A k/A^Ͼ A /A~ A@u/A`KA@/A@y;AXFW/A@ A Ū/A}Ax/A.AG/A nA ձ/A` A@u/A`KAm+:/AHؤ`A\:/A`]AbR/AiL]AXe/AU]Ao/A`K\Ax/AO\Ax/AGp\A}n5R/A uvZA /A_|RA8/A sRA(>p/AVRAN/A%RAN/A QAh]0/A |QA{V /AD TQAd/An'QAXE/AV>PA)/AnNA6]/A\ݲOAe/A?GAw/AGA/A@EAT/A SDA`/A@gCAHs/AoCA 1/ADvBAA/Ao@AM/A?Ax/A@,BA/A@>aBA~/A JBA/A`9OBA Z/A<ŮTBA"_/A&)AA/A`AAq~/AI{;Aki/AN;Apx/A.AAhU/A?AȘD/AO=A7/A [=A0F/A&@A/AvAA/AAA`Q/Aྒ?ApY/A ;?A0/A`>Ai/Aj=A /ARM=AP/A@q8A(i/A`7AN/A5A(/A`4AL/A@&3AX5L/A@>3Al0/A`0A3D/A@0AxW/A 3A l/A4A8'j/A>4AS/AER4Al~/Ak4A/A_4AH /A4AK /A A.6Aȋ/A5A@/A4A/A`Ӫ4Ah/A=^4A` /A4A( e/A S2A`%T/A` 1A`/AH0AcM/A`D-/A`/A:.A/A R1A((/AN2A/Aǿ2A@/A1AX/Ag-Ap7/ANQ/A@E/A`0A-/Aa0A/A8-AxGL/A'-Ap/A൞)A`ظ/A &A/A r&A /A%A`/A%A/Aֹ%Ad/A%A/A%A-/A &AK/A9&AXa/A&Ai/A ˑ%AB}/A#A\/A"AxO/AW A@q/A@Ajx/A`;2A/AAh./A AX/A)$!A/AC'AP/AC'A/A'AH!/A'A(/A A /AK A@ /A'A(/A`'AH3/AO!A$/AF!A`/A&APM/Ap%A/AW A0e/A E AI/Aj%A/A@[$A( /AsAx/A`mA|/A C$A(/A"#AY/A 'AT /AA4/A#Au/Ag!AȘ6/AA$/A vAp/A^!Ap9/A@ Aq/AA[^/AAh?/A@A@Q/A ZGA1/A@A-/A`A;/AdkA\/A |(AS/AA>/A A38/A Ah5/A2#AؼW/A`;GAY/A@,A@}/A |(A1/AgAho(/A  A.&/A AX/A A/Ac A/A@G A"/A3 A/A A/A) A/A8; A0/AQ A`e/AF A/AK AX,/A, Aȋ/A` Am/AM A/A Ah /A;Axm/A5 AB/AoN A /A@X A/A`A AhU/A`P A;/A`A Al/A`ÕA0X,/AA^/A A8E/AɮA/AA/A,A0/A@ wAv/AƈA./AA#:/A AHaU/AAT/AAĥ/A A/AA/ApAPf/AƈA/A@A/AA/AɮA4/AA(/Aृ A@B/A A/A@ A//A@* AP/A` A@ϐ/A9 Al/Aޜ A8E{/A@ Ah./A`: Ah/Ad A/A A@/Aa2 A /Aમ A 0//A` Ay/A Aa/A@ A(@/A A-/A A*\/AQ2 A=t/ABS A /A@g A9/A AHC/A Ag/Ao AԻ/A@W~ A/A A/A* A/A e AP7/A A(/A= A2/A@ AȺ6/Ab A8B/A e A /AA_]/ATA/A@ AТK/A AD/A ? AK2/Aq7 A2/A@  AI/AoN A(W/A@~AH./A .A /AA /AA /Aj=A`/A;A0/A Aп5/A2AN/A AA`/AADn/A 'A2/A@ Ahb/A@A(/Ao A@K/AyAPt/A`"Au/A A8/AJAT/A O)A/Ao AP/A ' AXu /AW AȘ /A@ Axm/A Ahޡ/A@, Aݯ/A@yJ A(J/A^' AR/A S AX[/A@y Aa/A A 0/A Ap/A ZE A/As A /A \ AH/AE AP/Al AP/A=3 AH/A A$/A A/A@Z A/AQ A/A A/A A/A ~ A0{/A cj A/A+ A/A@ APx/A E A(J/A A0X/Ap AQ/AaE AS/A $ A/A AX/A@ Z A/A A؟/AI A Ex/AV Az/A Zx A`/A@ A!/A`]l AJ/A@ A/A J A"/AR A%+/A L Aؼ!/AH AD/A` Ah;/A / A@Q/AV AX/A A/Az AX4/A A@S&/A@ Ae/A AXW/A & Aؽ/A`9 A`/A` A@/A uD A4/A`ob AA/A K A/A` APx/A2 Ad/At A\!/A`B AxG//A@ A/AY A/A`B A`Z/Azi Al/AT A}/A AH/A`= APM/A`m AgF/AR A>]/A0 A`P/A$ A /A 8| Ap0/Av3 AS/Ah Aq/A A/A A/A A/A`| A/A+ A/A/ A'/A- Aj /A A/A A3/A [ A/A@r A8Ң/A- A6/A AP/A AX/A`; Ax/AK AE/A AP8/A A>/A AHr//A / Ay/A AP /A A`+/AR AX/A= A/A@ Apk/A| AP/A A/A A(:/A@ A /A A/A3H A8R/A`v A/AR AL/Ah A/A A8sW/A@a A@Y/A& A@/AW A /A@i AxV/AO Ac`/AM APm/A Ax)/A A`c/A A=F/A AhR/A  AHH/AR6 A6/A 2 A`-/A A@~/AO Ah/A' Ap6/A྅ A`1/A| Ap /A O> A0/A A0/A a Ae/A Aܓ/AO:Axu/AA('/AAP/A@A`v/A@A/AVAO/AAv/AA0t/AAL/A A(5/A@yWA-/AmA`/A@yWA/A[A)/A@A8c)/A A8c)/AEA,/AA7/AOAx H/A@pAc`/AAv/AA/A@LAխ/A`2A/AI As/A`2A/AKAXʙ/A@yy A7/A7 A/A`TA0/A &Aȩ/A[nA/AA/A&A /A 1A/AA+/A sA /A`{Ap/A1A(\/A`AP,A8U/AJ,A /A=/AL/A Z2A/A@5A/A@ 5A^AN/Ap>A/A>Aj~/A>AxO/AƲ>AxE/A?A8Ҁ/Ad?Aa/A`o@AXK/Ar@AB*/A`[AA8#}/A@@A` w/A^2AAhD/A|NBAh/A lBAuc/AzCA(_/A`)HCA8&/A4`CAo?/ATGAհ/A3FA =/A@FA@/A`pFA46/A`pFA46/A FAb/AFAP7/A@FAP7/A`;5GAp[E/AGAXK/A`OIA/A IA0e/A &IA/ABJA/A@JA3/A5KA/AfKAg{/AOA( /AOAH/AOAPw/A 7PAh/AQA`/A;SA[/ATA/AͅUA@/A~WA؞/AdpXAP/AXA(/AWA@&/A6{XA \/AYA(/AuZA:/AVZAPn/A3VYA0n/A\A8/A`A/AndA8/AeA@/A%eAv/AQdA/A@dA/A dA@/A>dA/AdA W/A`cA_/A@ #cAPK/A cA5/AdA /AdAx/AyxdApc/A;dAa/AQdA،/A` eAP@/A@dA//A%eA/APeAH /A(|eAs/A UeAP͡/AeAM/A$iA@/AZiA7/AiA(/AGiAP/AT4iA`/AMhA軻/AhAX/A@GgA/A0gA4w/A!iAH/AiAv/A$jAe/ANiA(/A 8jA88/AjA9/AKcjAxK/A 8jA/A`iAI/AiAA/A c+kAx/AlA,$/AjA /AjA}/A )mAD/A#nA./A@gpA84/A^PpA/AkkpA@0-/AкpA0H&/A@5=qAp/A@pA/ApA#/A-[qA/A̓sAb/A`)tAP'G/AvA/AvA/A"vA8'/AvA /AxfvAg/AvA#/A@3vAH/A@3vAHm/A@GuA/AtuA/A}euA@/AZuAN/AuA@/A@ uA/AvAY5/A"vAK/A_tA:/AYtA#/A4 vAHm/A@.uA/A sA)/A sA}/A}uAU/AtA/A (sAd/A sA{/A tAd3/A 8gtA\1/A AtAXWv/A tAh/AuAy/A4 vA/A JtAH/A uAq/AvA~/AwAh/AuA`!/A=VvA /A6wAp,/AfLwA8/A@kvA`/A`)wA/AMOyA;/A@TxA/A 8xAxvg/A AwA Y/A xA\/A^yA/AzA,/AzA/AKzA X/A yAP'T/A $zAH/AIzA~/A`zA/A zA/AzA(z/AzA趪/A{AD/Aߦ{AŮ/A q|A/A`|A(%/A1}A/A6A84/A@lA0/A@ B}A A/A &}A0L/A`VAX-/A~Ap=Z/AB|A;/A|A /AX~A/At}ApJ/A{A;/A|A /A (|A8/AB5{A\/A {APx/A{A8/A@r{AP{/A`}A,/A`b}A 2/A/}A`/A|A/A`2H{A/A`h{A0%/A 2{AH?/A zAX/A zAPx/A%{A/AK|A!/AK|AX*/A&|A+P/AUOzA\u/AzAz/ADzA7 /ApyAX /A`yAB?/A`9zA/A|A;/AJ{A/AzAk/A RxA`q/A`)xAX/ATwA8/AwA%a/AJxA]/A`xxA蕂/A1zAo/A 2{A /AozAO/AZzA_/AAX/AA/Acj}A/AAhq/A`~Am/A Q~A/A@`:A:/A&A0fb/AYA`/A[mAժ/AAȤ/A A@s/AzA8/A xAX/A wA8^/AzA ҃/A`zAL/A{AT/A{A0;/A/A@SA/AAY/AaA%/A A`./AA($/A@W~AM/A`oA-/A=HAQ/AKA7/A$A/A@56AϤ/A&AP|/A6A/AA{/ATA/A5AP;/AA(./A@ۄA/A`߆A@F/A9A/A=A@S/A@y'AHt/Ai A/A SA`/AAp/AkA0{/AlA4/A ~AY/A`A=/A`aA/A QAxW0/Aä́Axd/Ao̫AH޿/AUTAo/AW}ШA،wP/A@A/AW}?A3Ҿ/A}A_' /A[KRA-g/A<6A`p7/AQ͖ڞA@F /AA4{/A(xA%޼/A؞AGu/AΝA»/A SAdKϻ/A-AW`/AuؑAۭ/A}ϐA/AWId̏A/0/A9>ЎAhDS/AEŬݍAY]y/AB;/AY /A JAѼ/AAAl/A#2|Au]/A؛A/Aى-cA/A2?AX /AAHu/A|A(/AlA2/AdA8Z;/Ab$cA ׺/AbAFt/AaA֑/Aj8`A*h/Ay_AfK/A7s^A R2/Ak]AGY]/A `90\AS푗 /Af[A/AG TAW~/A/YTAT/ALUAm*/A VA^Xϐ/A]MkWA%n/AԑXAOFyP/A`YSYApw6/A [A=b"/A ,\A"/AI]S]AVM/Ak2~^A h)/A2*_A1/A\`A /A bAk/AMyCqcAtnP/A/)dA/AJN7eA3׻ڵ/AjAfAŵ/A HgA5Tw/A+z%jAs%/Ao9;lAKδ/AgWoA^Tt/A+*rA*r>/A6 NuA%9/A>ExADbK/A\LzAo6/A M}A%4q/AlA */ApA/AA׮/A ƋA /AAGg/AW{XAO?/AJAXD/A- A/AKb2Awo/A'~A֏4/A6,lFAd'BP/Az"AXNpo/A0)AږE/AA*#/AَϠAhqݰ/AiA/A /A07(/At{-AJ/AYSd=A`k/AqQ`A5n,/AdAX /AóڡA`Ŷ±/A/A0a}Nڱ/Al5A>ۆM/AAS/A {>yA#S /AOeAFT/A~Ap/AAP/AA¯Ad/A@WA{/A`A,/Aa{A3C/A@A/A`)ATxqa/AzA*/A AT% /AîA_(/A@A(/A@5AM=/AtAx/AZ AwJ/A`A /A 7AV]/AA`AN8/ANd/AH/AvA/Aqc^AhDʔ/A@,A@/A1jA /Ao^9Aɝ/Ay?*A. /Ai 4A=/Aମ~AȰ/A u}AH/A\}AȰ/A|A/A||A Ρ/A-{AQ/A[s{AhՍ/AzAX/A`QzA`T/AyAȰ/A;xA@½/A}xA?̔/AXxA/Aj|xA /ATxA !/AQxAP(/A cxA"/A@yTxA)/A`wAȱ/AwApӔ/A@wAɔ/A wAɔ/A@#uvA|ɔ/A@qvAÿ/A@NuAǵ/A`muAǵ/A`tAǵ/A sAɔ/A?VsAڔ/A`)rA0/A@qA( /A1qAP(/A-pAI/A JipAs/A QUpAs/AM_pAԕ/A[7pAP /A`oA:@/A=oAo/AoA!/AeoA/AoA8/A |oA –/AoA̖/AroAH/A nAr /AbnA9/A DnA`Z/AzunAhy/AnA/A@ nA0—/A@!nA(O˗/A@nA ʗ/AUSnAPꛗ/A൷mAx\q/AjmAd/A@SmAh/A@>clA/A`kAG/A-jA0>/AjAHK8/ARiAXu/A`iAx\/A |iAԎ/A iA0/AgA𴛖/A@fAPF/A bA/AJaAO/A@F`A/ܗ/AzV_Ax/Af!^AD0/A\A\/A S]\A//AW\Aȶ/A\A,ܘ/A`f\A0ܘ/A@ \A/A`\A/A[AP@˘/A43ZA0/A`;YAr/A@{XAP/A cWA`/AWA٠/AVA|/A2VA7/AXUAW/ATA&3/A SA0T,/A+RA8/APA@͗/ANPA嫗/A`PA /A*PA/A~PAcr/A OA^/ANAa/AsMAa/ALAu/A`3LA;/A"KA+җ/A3KA@/A;lKA/AL:KA/A lJA/A ~JA+җ/AsJA0/A lJA A/AZKAk/AZKA@/AJAb7/AsJAr /A JA/A3IA/A GAؖ/A@Ap뙘/A?A=/AAAw/A`BBA7/A5CA!/A`TEA#/AMEA`T /A EA //A@%EA(a/A ۚDA(~U/A@nDA\>/A@,DA,/ADAh"/A BA4g/At"BAG/A?@A83/A[?Ad/A@r>A(m=/A`o=AM/A=A _!/A`<A/A<Aڗ/A~;API֗/A`m;AXhϗ/AN;A/A QE;A/AT;A/A+V;A /Al;A8/A`m;A/A@5=A/A;=AY,/A@g0>A(>/A;>A@U/A+%>ATg/A=A 4/A@E=A/A@=A( /A>A7/A3?Ah]D/A?Aa/A 8"@Ah]t/A[@A( /Aࣦ@Aذ/A)@A/A (AA Ś/ADQAA/AAA/AAA /A`.BAج#/ApBAu8/A3BA(F/A 8BAb/A@`1CAq/A ^CAP /ACAhޤ/A CAЀ/A`8DAVǛ/AbDA/A?DA/ADA"/Am@EAP8/A HkEA83:/A`EA:/A`fEAX/AmFA*/AFA(\S/A`fEAn/Ap#EApc/A(EAJ/A`fEAp=A/A=EA$/A]=EA&/A@yEAF/A@DAs/ADA`1ϛ/A}9DAPٿ/A DA/ACA땛/ACAiy/A5CA=j/ACAqN/AzBA>/A`BAhM9/A`osBA{/A@`fAAX͚/A`-AA/A u{@A@䎚/A 8o@A(;/A r?A)/A=A/AkW=A/AdY:Aؒz/A :Aٓ/A&:A@/Az7A0%0/AX6A0{/A &6Aޙ/A5A4/A@`z5A_/A 5AP8&/Ac3A`aG/A3O2A /A"8A/A 6A/A~6A?/A`9:AАW/A9A/A5A{/A?4AP u/A3AM/A4Ah{/A J7Ah/At7AxW/A?3Ar/A3APom/A&X3A@/A2A,/A %4AHn4/A`4A螛/Av2AH/A2AΛ/Akd1AJ/A)2A0*/A@1A֛/A /1A((/A 0AJ/A`/A0GR/A 0AT/A <0A`W/AX0AeY/An0A(]/Aߝ0A@‰/A@%2A8/AM2AP/A (N3A(/A4A0/A`4A/A`[4A@ǜ/A`65Aɜ/A lO5Af/AF6Ap/A_6AP/A@6AU/A 6A /A`y7A@ 2/A@y(7A(./AX 7A /AfR7Aأ/AN6AJ/A_6Ax /A cO6A/A-46A`v/A=5Aݜ/A_5A(/A`T 4Aຨ/A@~3Ax/A D3Ap/A2AX/A͘2A/A{2A酜/A`1A/A1Aw/AHO1Au/A 71Aui/A`0A8g/A@0Af/A0AXb/AMi0AXa/A ^0A5[/AB 0AY/A`90AXX/A/A(R/A@#/Ap/A@ /A,/A,P/A$/A`)/A'/A*0AH27/A0AaB/A1AbD/A@Ϝ1AQ/A ZA2A+a/A03Axn/A@3AXp/A`;3A0/A@>4AP냝/AE4AM/A l5Ah^/Aֺ5AQ/A`C7A&/Aq7Ap/A7A͝/A&<8AЯϝ/A@rY8ANߝ/A9A0/A %9A /A9A&/A9A /A:A( /A:A(/A@f;A`/Az;A0/A`;A?/A`N;A(s.A*/Aߗ_.A(X/A,x.Aܪ/Aw.AdQ̝/AeLX.A0;/A}0.A/A.A֫/A H/A(V/Ah.AR/A/A/A6/A#/A@ .Af/A /Az2/A`)1A8R/A 3AZq/A A05Aƀ/A6AU/Aྐ6A /A`f6A/A=8Awž/AX9Ap̞/A-:AΞ/A F:Aٞ/A:A/A`x=A-/AR@>A/A Hk>A/A׋>A`6/A =>Az4/A>A/A7>A/A$<Ao/A3;A/A`;AB/AQ:AO۞/A y:A( מ/A<:A0՞/A:AnϞ/A9A8Ǟ/A`9A@/A8A/A@8Aѱ/A@&8A0/AH 8Aã/AY7A/A@N@7Ap/A6A|/A6AP/A`v5AP/A5A|/AR"5A)z/AO5Adk/A@H4Ah/A 1)4ApZ/Aj{3AСX/A"^3A(L/A$2AJ/A q2AP >/A@2A o/A-Aȝs/A x,A6v/AQGx,Av/A,Ap/An,A(ޞ/A@ ]/Ap/A[/A(\/A/AQ֞/A@g/Ah/AR7A(p/Ap7AQ/Ay=A(/A=A0/A=AWğ/A>A/A@ >A/A@T>A /A@g>A/A >A(/A?Aأޞ/Ar@A/Az@A(/A*@Ap/Az?A'/A 1@AGv/A3?APP/A/1AL/A]0Af"/A`.Ap!/A.A#/Az.A/AI.A/A`)*+A/A]+A83۞/A *A@/An(A/A ](Ap/A (A/A=%A/A %A{/AR$Ap=m/Az$Ae/A`#A0.(/A#A&"/AH#AV"/A`#AV"/A@#AH/A=&Ax\I/Aޕ&APfG/A&A( C/A H&A8C/A H&ALA/A̜&Aȥ@/AU&A(\&/A $Ap%/A|$Apk%/A@t$A#%/An$Ap$/Ag$A#/A=.$Az /A=:$A/AP%AW./A]&A=*/A&A&/A&A/A&A/A&A@/A 7'A]/AZ'A8/A |'A8//A 'Ap=./A H&A-/A&A7/A&AG/A&AWI/A@&AWN/A&APN/A &A(\Q/A&A7l/A(A/Ad)AG۝/A)Aߝ/A \)Aڝ/A (AѾ/A (A(\/A(ABk/A )Akv/A@ )AHᐝ/Ao)Aĕ/A)Aт/Af)A/A` *Auh/A`;*Au/A*AWg/A)AXZ/A!)A( [/A q!)AXp/A`; )A /Ak,As/A@5T,A,Cp/As@R2,Adk/A8WY,AbMe/A*h?+AYl^/AĘ*+AcV/A>ۈ>+AwݶM/A+AD/A +AX~/A+Aq/Ai+A՟f/A+A0;/Av,A̘/A,b,A}b/Ax3,Az/A0U,A@Nz/A-AbPb/AMP-1-A K/AN-A`i>/A'P-AI1/A~EJ-A %/A<-A9L/A"Wd&-AUܡ/A{! -A!@/Ao,AX/AҰ,AX /AQ",A /A`[b,A؛/As>A,A/A0,A,;/A,A G/A뷊}+A.Qj/Aq+A )T/AnXN+A\@/A*AuL0/A{*A#/A3JL#*AhQ/A)AhD@/A)A(̛/A*Aɛ/A *A /A`f +A(K/A[*APf&/Ac*A*/A*A/A ;*A'/A *A |/A`)A@/A *Aik/A@y)A@/A@y)Ar/Az夆)A׊!/Aen(Ax-/AϵRr(Ax?1>/Aת'ACӼR/AoG'A7 j/A,xE&AG/A$k&Ab/A9v&A;›/A*n&Aޛ/A`9~&A /A6&A R/A@f&AȘA/A@:&Asd/A %Aw/A %A/Ao!%A/A %A{/A x$AH/A`$APM/Ad#AP/A`B"A Ĝ/A`!A`Ϝ/A lY!A !Cڜ/A AS/Aa./ A߫,/AaݖApޜ/AׅlA&5 ֜/As#iA<Ȝ/AŦNA=/A,RA䠜/AA0~/A`tASZa/A35A1B/A]3Ai謄!/AVu AS</A A#ޛ/An1A8/AA:oU/ADA ëy/ApA-XpRY/A1~AO:/A UA/A5A4/AJ]:Aq &/A:ALHޚ/AZ,AК/A 4FA-;#ǚ/AlA0Gš/A8Ah9ǚ/A! A4Eʚ/ABAb%T˚/A:Ayp]ʚ/ALkA hhǚ/A(_SAۋš/AED(A#뻚/ALAз/A AO/A`A[|/A`AM]/A`fAd?/AG$AT</A tzA\/A`Aٙ/AZB2A7︙/AzTyE#AV/AmAL}/AЋUAU d/A*AM/Ab̛,Ao1;/AyA@~,/AAҿ/A{_AI/A `f AZ/APVmA֖/A֨A /AA7/A˒A /A,5sA3/AbN AO<ܘ/Au % Aj3Ș/Aa!Aj,/A-d!A))/Aƛb_"A\xw/AA|#ACU/Ar#A؁1/A`#A'/A~m#A.#M/A9xm.$Ax]/Aa$Abn/An/$AoV/AyA[$A`/A %AOY/A"$?%A%/A|oi%A. /A8%At ח/A%%A/HƗ/A8>%A./A@ϵ%A=Y/Aa!%Aa1q/A(%A }N/A@%A22tH/An$%A$+[B/Aa%AU`$/A_M.~%A _!#/Aof%A5#/AKCM%AU$/A 5%AsY_2./A18%A5/A`$Asf;/AD$Al>/Apt<$A?/A+#Aj["A/A`"A\l/Ap\}"A@$/At"A<}/A&b"AFz/A_=S"AA[/A\H"A}/A[.=@"A-ݕ/AH<"A7mѕ/AqCRk"AÕ/AZ"Ac/A#{"A/Ai"Abt/A{A"AQNσ/Aƞ"As/Aق "Ac/A"AN$Y/Awz"Az>N/A[X"A]A/Ae>"Az3/AS^."AmĔ[%/A)Q3'"A/A׋$)"A=/A`5"A;/A5J"A{I/ARK9h"A-b/Adʤ"AN0۔/Abȹ"AHӔ/A֮k"AΔ/A,!#A%Д/A'T#A<_LԔ/AIb2#Aٔ/A#A%۔/APF#Akܔ/A-,#A+mݔ/A=c#AA/}ݔ/Aj#m#A#?ܔ/A$Ad|w۔/A$AN׉ٔ/AnH$Aה/Agk*$A8?Ԕ/Aӻ4$AД/APe=$A$!͔/A(%C$AHɔ/AnF$AVŔ/A@G$A:/AeP_$AM~/A|*m$Aȩ/Ao$Aۅ>T/A+.$Adu!/Ar$AH/A^%A8/Af%ATX/A`"7%AesV/A.z%A%V/A/t$ARLmW/At3 $AJ7AZ/AWAHnb/AVAUu/AA(Kz/AklAcʗ/AvA0/A[Ax/AAx՗/A`v]A(/A`AXB/A|=AXe/A@G!Au/A@A`z/A4{Ay/A0*Aj/A AW/A@k AV/A4h A(iC/A q AH9/Am A,/A@D Ap#/A@# Ap#/A`] A( &/A3 A/A S% A/AZ A/AA /AEAH9/A@yqAЦV/A 1Ao/A HrAR/AA/A`̫A`Դ/A@A纘/A AA뻘/A@GA0/A@lA˘/A@WA ܘ/A@AR̙/AsA8Iə/AgAߙ/AAh/A`[AȤ/A `Al/AA0f/AAOۘ/AAA8Ԙ/A & AOۘ/A`9. A0f/A@ A`/A` A0/A ( A /A / Ax /A A /!/A`$ A %/A A AO+/A Al4/A u1 Ay8/A A 8/A i A?/A A5i/A` A8sh/A`K A%/A$ Al/AT A8/AT AP/A A/A A /Aa A8m/A Ah?l/A 1 AfB/A AxZ/A Am/A A8ə/A 8w Av/A+ A(/A Aϙ/AoG Aә/A  Aؙ/AN Aߙ/A A?/A@ A/A@ A/A2 A/AN A/AN A/A ( Ah//A A/A+ Ah/Ao AF/A`D A2/A ( A2/A A/AH A3/A Av/ATp A(/A$a AX/A A/A@ AАٚ/A4 A]ݚ/A 8g AX/A K A/A 8g A(/AH A/A6 A/A` Ah//Aq Ahؙ/A - A@0/A AԂ/A2 Amt/A` Af/A AVX/A  A؟W/A` AR/A`] A؞?/A` A71/A`] Ai/AWL A/A` AǗ/A A/A AX/Ao Au/A AJ/ADl A/Ar A/A` A͖/A A'/A A/Az A0Y|/A A{/A JA A&/A A'/A A'/A% A&/A& A߅/A@ A%u/A@#o A(U/A A =/A}E A8//A u A8//A AV@/A@ie A#E/A AWL/A` AN/AqX AS/Af8 A$]/A Aa/A AXp/A A0Y|/A AZ/A`b A[/A]{ A͖/AA A*/A` A]ܖ/A x A˖/AM A)ɖ/A1 Ax)Ֆ/A A*/A`) A/A{ A0{/A`x AE/A` Ah/e/A@ Acx/AD A/A  Ah/A; Aڗ/Ama A؛/A`t A /A׷ Ah*/A? AXB/A? AN/A@ AN/AD APk8/A A`/A A/A A֗/A A`2/Acq Apn/A7 Ax>/A@ Ae/A  A_/A A,/A` A,/AK1 A/Ax A0"/A Ax&/A@ A7/An AE/A w Ah/e/A w A/An A/An A֗/An A/A@ A5/A@ A6/A@ AН'/Aֺ AP7%/A; Ai/A* Ag/A* A—/A6! A/A AhQ/A` A-/A@W A%/AO7 A/A ھ Ap /A A^/A| A /A s' Ap /Aq A_$/A@g Aq-/Aк A` A D/A|ْ AP/A_ A^/AG Ah/Ah3 A-q/A ( AP&t/A  AGy/A AXz/AΑ A}/A A(h/A Aq/AQ A{/A@n A(/A3c A/A` AR/A`P Ah/A@- AP+/A׎ AÙ/A  A84/Aq# AH /Aӊ A`/Aെ Ax--/AE AtC/A AU/Aj Ah/Ag. APov/A[ A\/A < AX/AWY AGś/A` AdΛ/A`0 AP c/Ao Ah_/A$ Ap{/AՈ Ax:/A AHn/A`K A(/Aه A/A$| A š/A AP7К/A A[ۚ/A AP/A A/A@W A/AzT A/Av Ax/AΆ A /A  A؁/A A A؎/A A@/AO A/Aj? A/AXՅ A/A Ah/A` A @/A> A\/AVك Aa}/A`k AH헛/A`< A@/A A /A Ah^̛/Ao A@כ/A 8@ AXW/A AL/Af Aq/A| Aq/As- Ax /Ao A/A@5; A;/A AO/A` A-/A@ R A)/A` A ֛/A@5 Aԛ/A@,R AX/AV]~ A0/A@} A/A~} A7/AH} A@?/A`| AЕ}/Ad} A0/A0} A4D/A 8| AF/A z| A(O/A@>| ABV/A~{ Ab/A`{ Avo/Az AhD/Aaz APx/Aez Ay/A@z Ap/A`bz AGœ/A`z A0Hݜ/A`2z A/Az AP|/A@z Ah/A@,mz A8/Az AzҜ/A'z AĜ/Ay A@F/A`iy Aߪ/Ax AXy/ALx Ay/ABx Aǜ/A ەw A /A ҽv AX/AJv A@!/Acu A:/Au AT/A-u Ar/Au A苝/Aau A趨/A2v AЄŝ/AJv A@5˝/AJv AXܝ/AJv A@S/A3wr AP1/A q A",/AOq A(/Ap AX%/Ap A/Ap A!/A@%p AH!/Ao A0 /A Po A/A`n A8V3/An Ax:K/AWm AF/A{f*n ACL/A}Vn AWP{/AO.rn A!$7/AƟv~n A,c/Au0zn A{5/A0Een A]h/A dDAn A`a#/A@ n A;M-/AHpn A(%?7/Ae2 n Af@/A3;n AEJ/AXŚXm AHuR/Aױm AY/Am As_/AFRm AHbd/Ay8b:m A :g/Ax[m A\&xi/A4m Aei/A; m AhNe/A|Xl A^/A0k AjU/AXj AxC0U/A]Gj AʨR/Asaj A*L/Aj AIaC/A0i A3À8/ApHIi A&{S+/A2gi A8/AA;i A /A]i AF/Aebi AFԣ/A'Li ARD/Am j A./AGLŸj A /A` k Aȶ/Aj A/A Ri A=/A ~Bi A/A@h A,/Ah A+/Ah A/A Sh Ad/A@Ei AN/A@E,i ApM/A6ei A/A Ui AN/AWj AU)/A/j A^{-x/ABj AT>/A2j A/A k A/kϠ/Azk A ]5/A 6!k A"f/A\k A2/AOj A5/A j AEA͟/Atj AL/Aj AH^k/Ahi At7/A@&i Aư>/Agi AtI͞/AMQi Api AN;/A#&i A,t/A򅸃i Apk/A8Õ/h A>d/Aڑh Aī^/Ah A"F[/AŽQh AEZ/Ah A ?[/A,6@"g A8x_/A`g A b/AÊDg A6i/A2Qf An}u/Atrf Ax/A@Gf A<{4/AlMFe A/A>Old A@Ә/A@c AI/A@Ɖb A+/A 6b A!t/Aa A@/Aka Am*ޜ/AG` Aɜ/AgS` A8/A q_ AĬ/AQx_ A%F/A{E _ AΈ|/A/At[^ AȤ/A ] A4/ /A(] A2#/AruU] A\/AXNv] Ar/Ahzb1:] A.ܛ/A\ A@+֛/AG\ A nԛ/Aܹf\ A:(՛/AAXR\ A؛/A~~[ APߛ/A[ ANF/A9SO[ A'V/A 5[ A@/AZ AA/A>Z A'/AbmcyZ AXW6/A2Z AO=qA/Ax" Y A/N4I/AC`Y AckM/Aq6Y A6sM/AX A#%J/AQSX Ax(D/A;-X A$#Z>/AMW A6/AzhW Ap})/AzX A(/A X A/A@gX A/A@QX A./AoW Axd,/A W A3/A 8JW A%/AVáV Ax/ArV AHW/Ax&tl+V AT/A@!U APԛ/A\נU A/A$S-U AdԹ/AH\U AJ|K@/A"wU AÒ/AmtT AGMG/A}T A+jO/A*(T A-䇛/AKS A/AHxS A:k君/AIR A/AR;PR AS̔/AQ A/AL AXڝ/ALL Ax>/A L Axu/ApO AV/A@O A /AFI Al Ɏ/AS eI A=qe^/Ap I A1/Aʨ8H A/AOH A/AVIG Ak)v/A'8Y.{G AJރ_/AɪpG AsF/AgF A^/AF AfS_/Aw8EeE A(r/AaD A&\T/A6nC A /A@. C A&E/AR[B A-(/AlA A]/A]ĐA Aq/ArW@ A Ahw~/A!֚> AP/A= ApZ2/At,= AP^/A* ? AC/AR\? ARj/A>? A~Uǝ/AI` @ A|ߝ/A'T@ AR7R/A .0@ A`S/A@ A&&/A/4A A \2/APZ7lA Am7;/Al)@A Aj?/Ao7B Aq/Х@/AQ͠B AM!G=/AyW C A(5/A`nC ApkH./AN; D As*/A(4D A`9+/A{]LE A//AXE ArY3/Az F A S8/A&F Am=/A=F A(D/ATQF A.b'L/A? o_F Auq&T/AHZiF A^m\/A*mF AHd/A@PkF Aәlo|/A=F A|ߧ/ADH}E AfH A+/A`T!H A+/A G AB6/AF A-I/AyF A8U/A`]xE Ak/AD A|/AD A/A AD A堫/A AD AHг/AVD A/A;D A`v/A`[C A8/A9C A_/AC A0J/AfC A(>/A`fB A#=/AB A0J/A+A Ad/A@ A/Ax@ AU/AyO@ A/AA@ AX/A`oA@ Ax˫/AQ@ Aͫ/A`)? A@ǫ/A$? A/A?? Ai/At? AW/A,6? AxU/AO? ApAC/A`n> AP77/Af= ApAC/A6< A@M/Aҷ; APe/As; AT/A`; AҠ/A͖; A`«/A`; A|/A@N; Ap/A`De; Ap/A Z'; AHӫ/Ao: Ap+/AF: Ap/A: Ah}/A: A@n/Ag: A_g/A@S9 Ais/A8 As/A7 A١/A` 6 AHn/A`5 AHӫ/A5 AP*/A`5 AP0/A@W6 A[/A.6 A`k/A@N5 AF/A5 A/A 65 A>/A4 AHӫ/A?4 A֫/A@3 A0 ޫ/A`B\3 Ax/A 1B2 Ax>/A}1 AhH/Aan0 Aq/A,0 AF/A?!1 AӬ/AJ1 A/AJ1 AO /A0 A/A,0 A@/Aan0 A/A@W70 A/A/ As/A 1[/ As/A*x. Az/AH- Aլ/Ap * APլ/Aa * AWշ /A@e AX/A Ap=g/A`D Aզ/ANI Ap=/A5 A(/A$ A@Bk/A A[/A 8 A/A`" A7'/A@W( APC/A| A`S/A@WJ AU/A Ap@Ka/Aք=: A1}S./AMؘS Ab !/AF A`ʲ/Aw A隲/A A/A@A A0{/Ao Aw/AM{ Ag`/AM{ A pX/AY AL/A)& AH/A` A@;/A0 AK(/A@ A /A9 A/A`s A /AX A/A A/A`TW AK/A߾ AТ[/A@ Ax#/A Aa/A $ APẔ/A@T A8'/A A/A`[ AU/A+ Aϱ/A@ AXı/A AHr/A A/A A(n/A 8 A`[/A AT/AE+ AP/A0- AͰ/A; Aİ/A A'/A F A胊/Ah A0ݞ/A$ Ac/Ax AЀ/A` Ah/A AѰ/A+ A/AI Al/A| Avp/A@ Ah/Am AP'/A^) Ax$/A A/A`] A E̱/A]# AH]/A A//A Z Ah./A# AE/A A`1'/A- A8_/A¿ A}/A AH?/A AԲ/A`T A/A8 A/A A0/A@,% AJ/A AH{/A`K? A/A A!/A`H AN/A b A0j/AF A/A@ A(1ز/A` APk/A A1/A A~/Af A8Z/A 7 A [/A AW/A Z A(D/A@ A77/A` A8/A d A/A*x Ar/A' A/AM7 Axmb/AV~ A7/A+3 AX2/AZ A (/A;| A/AQ A/A 7 AP/AN Axi/Ax A/A A/A ɷ A0 ۰/Aj AŰ/A P AH/A A_/A4 AFT/A1 A./A A0)/A+ A_/A~ AXF/A Z A`̰/Ay A߰/A l Ah/A A/ /A`TT AX/A`) Ah?/AIgm Ac&/A`] AЉ/A$@f ACk/A3 A< /A1Q< AY]2/ADt8m A/A? A[R/A= AJac/APHa!z AЮ/A[c A*/Ap ' A_͟/A)/ԯh A(˃/AT AN/A@. A(/A A/A A[/AQ A/A`]r Apʫ/A AȻ/A  A8/Awn A0/AV] A 0ʫ/A A̫/A& AM/AE Aw/Ae AE/A3q AȤ/AI A0Ȭ/A A8/ABQ AX,/A`] A/A6 A/A_ A=!/A A(z7/A JG Ax?/A") AXU/A A@]/A ABi/A Aq/Ay Au/A A`/A A/A A /A` ABЭ/A & Ah;߭/A A/A Ax)/Aƞ Al/A] A>)/A@y Ap3/A AA/A A0jR/A6 A/Aq A`/A} ApI/AS A( /A`K A //AC A(?/A A5G/A A5b/At! A`Tj/A \ Ax:/A| Ap/A (d AН/A` A-/A@ A/A A#/A- A,/A A@ԯ/AM_ A/A? A/A A p/A Aү/A6 Aʯ/A@c A/A AhQ/A A0ꕯ/As AX/A A*/Az A1/A`) A/A@ AP /A`\ A@/A`܅ A0.ˮ/A@# AP7Į/A3K AS/A AX/A A/A A/A` A=_/A@7 AuQ/AEp AhG/A8 AP2/A@v A p A(%/A` A/A A!/A A8 /A A@$/A@q A/A A /Asї AP/A A84/A@NF A` e/A?|L~: A;/AsL.& AD/A@> Ah2W/Ab0p A?T /AqP' AY/A9Ԟ AX$/A֡ A5/A Υ A0LR/A0z- AG/A@D A{d8ԩ/A۹T Am/Aٶ A$@/AwXBx AЀ/A`T. AZ\K/AJ3h AF/ q/AW0u A`./Adz Aѧ/A9gW A$o/AAٶ A=7 /AijL AA S/Ax AcM8/A A.Jʥ/Alv2PN AxmY/A A/AFE% AG/A Asa/A0ſ APM4/A A/A; Aã/A\ A`/A,i0F Al/Aܓ1$ AfB/A A /A A/A` A/A@W AD7//An$ A_+֝/A<+J? A /AI y AdDU/Ax A-/ALپ A\7r/A k Afw7c/AG ARS/AҐ) AB/A/ AoJT4/Aݛ Aԉ:$/A^a A /A A@/A`" A /A/g* A/|gRߡ/Au_ AXxС/AP؞ Agġ/A A/A Ax/A@.F APu/A AC/A@ AP$/AV A/A A(/A Apy/A` AX/A@ AX[/A A8#/A As/AMj APj/A`m Aa/A൵ AW/A; AaI/A@Ͽ A?/AD As3/Ay A`C%/A@` AN/A Aݟ/A A8/A Au/A3 AF/ATM A` /A`a A/A AMӞ/A`) AW/A A1/A  A\/A A887/A`; A/A A/Ae A( Ý/A A/A AD/A AV֜/AA APȜ/Af AF/A  AБ/A Ax/A@t AБ/A ± AЦ/A 9 A/A5\F AZWs/A AU/AZ] Ap4/AoJ A /Am- A`T/A  A0 ϛ/A&_N Aﮛ/A A!uڄ/Aх}_ A`Y/A0: Ap_I/A,0 A]h/AD A߅D/A A`Sš/A@1 Aٲ/A ޿ Ah/AԿ A'/A AH/A A /AS A /A 9 AP뜚/A ? A/A@Ph AȺ/Ac A![/A A![/AN AX5/A@% A/A@1 A0/A3 Ah*/A#3 AD֙/Aq AÙ/Aq AC}/Aq AϘ/AM8 A@j/A@ A /A@W A^/A5 A/A A/A A 0S/A A(/A 8] Axؾ/AP A跀/A` AP/A2) Abo/Az Ae/A A8l/A$ Asc/A$ AP<\/Aj A8;/A A8/A`y A@ /AMz AP|/A@> A0B/A@t A6/An A00/A` A,,/AI A(*/A 1G A00/A|> AY./A ۞ A/A ¡ A/AJ ApJ!/AJ A"/A Ac/A` Aw!/A`bc Ap/A SK A6ޒ/A A6ޒ/A Ahے/A A`ɒ/Aj A8/A` AX#/A@, Ap/Avt Aޑ/A X An/A Ac/A AX/A` Ao/A O Aޏ/A Aѩ/A A0h/A AP7/A8> A }/A c A導/A= A@B/A AH/A APx|/A A Aߍ/Ah A6Ӎ/A Qܿ A /A`ve Axm/Aa A/AR A(-/A@k A0e؍/Ak A%/Ai A ō/A Zټ A]ύ/A A8/A A()/A Af1/Ao A`Xk/A APb/A@] A/A`;ι A]ύ/A  A(:G/A Ap[C/A` AÍ/AH A%/Aɼ Aӭ/A/U A(/A@@ A0/At A`/A 3 Ar/A`f A/A' AϢ/A`m Am/A` AE/Aſ A0*/Ad A /Ayc A /A A/A A/ARݾ A/A; A5/A A5/A 폾 AV/A v A/A v A/A@p A/A J{ A׋/A 폾 AË/A`] A`/A`; A,/A`T\ AX/A A A/A Zټ A/A@ļ A0p/A m A 7/A_= A/A AP /AXZ Ap!/A (r A /A[X A/AW AEt/A P A*(/A AP|2/A@ A"/Aܻ A/A Awȉ/AK A8D/A`] A/A?̸ A()ω/A Azى/A A:/A@NB AZ/A6Q A؟ӈ/A҂ A/AV Ao/A`m5 A&C/A A`/Aع AՇ/A ( A/A\ A0/A | Aja/A?̸ AtB/A_o A`@/AE AK=/A` AP:/A$ AP:/A`mN A.լ Atz/AjLG A,~&z/A@9d' A,Yn#z/Az͐ A{y/A~@S* Agy/AЕP Aŧu y/ALYe AVx/A:i Aax/A>\ Ahx/A @ Aaŵw/AZߣ A~B^w/A:$; r Aѡ w/AF A @ĸv/Aq Ay(kv/Au: A]!v/A1rMC A 9Du/A A# vu/A A\u/A%;ϔ0 A"u/Amm Axhu/AD Ab"^p/AF)7 A_Gݯj/A| u϶ Azʉe/AR AѧZkd/At f A83xd/A A Hd/A A(Nd/A  A0sd/A APHTd/A@` Am@d/A j_ A2Z.d/ALr A _d/A@El A;:.d/A A0vnd/Asy AB3d/A߉'G ARc/A: Asd/A Ad/A Asd/Ai.' ALc/A! A=Ic/Ap( ADDc/AO0 AN[c/A- Ac/AdJn AQc/A&m Ac/Aq Aw`c/A9,_ A>c/A[F AEȪ!c/A^! Ac/A A0Ic/AQ A?c/A\%X A v:c/A&3 Ah9c/Al AY&=c/A%~ A(CFc/AAU2 A|fSc/Ah AKdc/A: Azc/A$r. AAc/A AX c/AdS AHc/Ax A0$e/A  A:e/A`2 A f/A4 AmGf/A AZf/A`m Ate/A@Q AHmye/A`b A%e/A`q A0e/A. Aږe/A  Ae/Av Ae/A  A}e/A< Ae/A% APe/A@N& A(e/A&' A(e/A( Ae/A) A8me/A7) Are/A) A e/Aa-* A8Ve/A* Are/A* Aȥve/A, Aȥve/A - Axe/A@y.. A@Pe/A q/ A(e/A O0 AXe/Aye0 AKd/A&0 AUd/Ak1 Asod/A`&2 Aqd/A2 A@d/A@3 AUd/A@W54 Ad/Ay4 AQd/A@i6 Au4d/A`9 A Alc/ACu> A0c/AhB> Ac/A&? A&%c/A/? A:c/AH? Ahc/Ap? ApjQc/A? A//=c/AJ*? Axm,c/Av=EQ@ A8c/A0@ A>@c/AA A!A!c/AGPXA A]c/A-A A@b/A`TE Aa/A&M A~a/A`Q Axa/AOU AHP`/AW A0`/A\ A <:`/A`^ A _/Aɀc AF_/A`uh A 0_/A Li A _/A[j A_^/A 8l A^/A~n A,^/A@n AXy^/A`2o A w^/Ax~q ACr^/Aq Aзm^/Aőr A1e^/A0s AAa^/A)u AW^/A`)v Ah^/A 1r A9_/Aw APK_/A A Ra/AM Aa/A Z Ab/A  Ad/Ab A!e/A[ Ăe/A Ae/Ajm Ae/Apr A e/AϬ Ahe/A"~ A AWEc/A`H AOc/A` APc/A A /c/A Apc/A@g Aq c/A { A@$nc/A@r AYTc/A@\ A {c/A;b Ac/As A0c/A6w Ac/A i Aӭc/A i A`c/A@ A>c/A< AXc/A- AXc/Aj A4rc/A A`c/A AHc/A Amc/A r Ac/A@Wr Ac/A Ac/A 8 A8tc/Ak: Ac/AN APc/AN Ajc/A9@ Ac/A`F Ad/A A\d/A A\d/Ak: Ad/A`7 Ald/A} A*d/A} A d/Aʚ AEc/A@ X Asc/A@ A8c/AH Afc/A Asc/A A@c/A@iA A$c/AR A$c/A2 AͿc/A Ac/A&y AGc/A, Ac/AXs Ac/A@J Ac/Aޓ Ad/A Ad/A(r A d/A(r A1d/Ajg A6d/A A]-d/A A1(d/A O APd/Ay Ac/A@Q Ac/Ah AHd/A# A%d/A A8#c/A@C Adc/A@% Ad/A1 A8 d/A@% A`c/A A c/A^ Ac/Ac AX d/A@/ Aw.d/A`BK Aw.d/A8 Ad/A` Ad/Ao# Ah7d/A > A8d/A  A:d/AF Ad/A ApCd/A ApCd/Aqw Ad/Ab A`d/Aﯾ AhDHd/A6پ A%Od/A, Am"d/A`x Ah%d/A/ ApNTd/A AVd/A`ڽ Aw.d/A@̽ AY5d/A  A/[d/A@PC A\d/A ( A,d/A A3d/AgY Acd/At A9gd/AR AY5d/A^" A?d/A Ahd/A@ A0jd/A`B} A:d/A`Z ADd/A@ A@od/A`DѺ A@od/A A/d/A@ A/d/Aj And/Aj And/A9 AId/Af A%Od/A Alxd/A썹 Alxd/Ak ARd/A I ApNTd/AjƸ A0{d/A ۸ Avd/A@# A,d/A`f A,d/A@ A0{d/A`C A}d/A`m A 00d/A@η Ah7d/AOu AMd/A@y Axd/A 1| AY5d/A (E A:d/A Axd/A) AȎd/A^T ACd/A?[ Ahd/Aʵ A8vd/A`B Alxd/A` Ax( APg'b/A;y8 AXpb/A 85 Ab/A 4 Azb/A`2 A8 hb/A-2 Ab/A c1 AX$c/A@+ A,c/A Z) A|ac/A`}( A\c/Aq' A΁c/AR& Arc/A@o% AH)c/A@G\# Ac/Ar! Ahc/AM A`c/A@ AIc/A Axc/AT AБuc/AX AHpc/AAcw/A@A}#/AOA@q%/AA1/A`rA=}/AaAxƷ/A@vAxƷ/A}A/AӒAؽ/A`AС/AgAո/AaZAx-/A=$Ae/A`AU/A A(/A ҁAH/A lEAX/A Ap=/AA`/A^5Ah/A`8A@u/AA(/AjAh/A@>bAh/AV;A8/A)[Ah/A`fAh/A Ap=/AWAM/A;AM/A >AX/A@Ax-/A QcAp=/AA/A`)A i/AAX#/A@A/AsAX/AA /AЪApA/AA,iO0AVA89/A`A/A@!oA-/AqAX5//A4A3/AgVAP6/AAk9/A^A=/AAP/A7Apm/AVAv/A`A07u/A#A07u/AIALr/A@ƞAh/AA`/AXAS/A`AJ,/A Ah/AA t/AazA /A_4A /AA /A@-A^/AA(~/A@A@/AfA/AA /ATA/A A0/A@>A/ASA`/A`A /A 8A/A YAX/AAA(/AAs/A AxZ/A FAK/A/A@/AAY4/A+A`%/A`AP/AzA/A QA/A 4A@/AOA /AzAxu/A@A*/AA*/AVA@/AsApʩ/AA/AosA|/A@EyAP^r/AAso/A Axi/AbA>/AxA8/A@PAɆ/A`tA8 ~/A`tAP^r/A}]A(m/A A]/AzA(V/AzAȇI/AKAHR/A`ApS/A`AHR/A@,A R?/A@AAp0/A@AAP'/AA(/A`AF /AA/A`A /A+A0/AA/A/A(/A^A$/AGA/A@jA/A@jAX/A YA@N/A@ 6A8/A@ 6Am/A^A0/A A"/AA,/A`Aaj/A6A;O/A{A0./AྟA /A@A/AA\/AA/A AAy 9/AmFAuAi/AhyAH/A A/AB.A@/A@AHa/A J#Ab/AKiAТ/AKiA2/A:AXyA/A A(P/A HA:m/AA/A LA(/A`"A`2/AA/A@yAh/AAI /AA/A@!oA6/ABA/A}AH/A _A\/A`oA6/ABA R/A@A?/AӎAp/A;AHЫ/A|Aئ/AAb/AैAq/AzA/AbA/A@\A/A Q%A/AApw/AA t/A_A1s/ARAr/AAXn/AwAPj/A ANb/AбAQ/A$A@E/A`A9/AͳA(./AAx#/AwA0/A`DA/AAb /A:APk/A ^An/AXAn/A`A/AAn/AaAn/ASAn/A`Al/A`vA` /A@5A /AA0/Aj?A0/A`~A/A஬A ="/AA(./AKA1/AA@/A/A~F/A cmAL/AAS/A`AhV/AvA ^Y/A$&APa/A [Aذs/A`A%z/A`AhU/A l!AF/A`bgApl/AA^/AKA(J/AA/AA/A@gRA/A3pA`/A0rA/AA/A=A/A6A/AzpA N/A$A`X/A@A`X/AA/A@PA/AlA/AAE/A@#A2/A`A`/AA#/AHKA+/A`uA7/AɔAD/AAp8N/AA0^/AAg/AA{q/AA{/AA R/A@A#/A$A%/A\A1/A\AX$:/A^AA/A PAp0N/A`@AX/AgA8\/AHA\/A@.A@c/A*ADp/A&APHv/A@5AJz/ALAP/A@WAӉ/A?AW/AYA֎/A`A0Ջ/A@Aْ/AAH]/A lkA@`/A9A_/AA\/AAH]/AhAۖ/AAݙ/AA^/A cAݙ/ABzAp[/A&JAۖ/AAޛ/A`A/AA/AྜྷA/AoAh^/A@7APޚ/A`f A _/A AAPo/A}kA@/A@lA8E/A^{As/A@rA

/AA੹/AVA`/A3AX /AʇA/A@A,/A3As/AOϮAN/AAp/A3AzZ/A` AP /A?SA0f0/A uA/AA8I/AӻAû/A@ ǡAҗ/AX>A /A JA{/AOAV/A /zA( z/A@rAR/AEmA@A/A`hAP/A@ZfA؟K/AdA@/AgAi/AviAk/A&mAd/Ax0rA((/ARrAHr/A3\mAЦ/A`eA8c/AXbAn/A ¶]ALM/A2iA,/A`sA,/AjtA, /A@%tA/A cqA`/AnAX/A` jA͐/A JfAe/A`adA`+/A hbA2/A`]]AȤ/AEZAxi/A`-XAxi/A`mUA/A@zTA/AɄQA c>/A`OA`c/ANAY/A NA@S/AwLAl/AIAxF/AHA$9/A)GA`/A@PsDA/A@AD/A 0<AO/A66AHL/Aaz9A/A =A/A`a@A(-/ABA{/A0EA/A 8IGAX[/AEHA/AHA]/A)GAX/A EA/AVzBA`Gc/ABI>A@/A ;A; /A):Ad3N/AOfRAF/AqAd3N/A_AE]K/A}9A+=w/Al Ageopandas-1.2.0/geopandas/tests/data/overlay/nybb_qgis/qgis-intersection.shx000066400000000000000000000003341525650651600273330ustar00rootroot00000000000000' n7.+A%S@,iO0AA2h7J(9v8I%oF"lbP" .z42LJpMv*bwzJgeopandas-1.2.0/geopandas/tests/data/overlay/nybb_qgis/qgis-symmetric_difference.dbf000066400000000000000000000052411525650651600307460ustar00rootroot00000000000000uBoroCodeN BoroNameCPShape_LengNShape_AreaNvalue1N value2N 5Staten Island 330470.0103319999761881623819823.8099999427795****************** 4Queens 896344.0477630000095813045212795.1999998092651****************** 3Brooklyn 741080.5231660000281411937478507.6099998950958****************** 1Manhattan 359299.096470999997109636471539.77400004863739****************** 2Bronx 464392.9918240000260991186924686.4900000095367****************** ********* ************************************************ 1033296 793054 ********* ************************************************ 1063988 793202 ********* ************************************************ 1094680 793350 ********* ************************************************ 1125372 793498 ********* ************************************************ 1156064 793646 ********* ************************************************ 1186756 793794 ********* ************************************************ 1217448 793942 ********* ************************************************ 1248140 794090 ********* ************************************************ 1278832 794238 ********* ************************************************ 1309524 794386 ********* ************************************************ 1340216 794534geopandas-1.2.0/geopandas/tests/data/overlay/nybb_qgis/qgis-symmetric_difference.prj000066400000000000000000000007751525650651600310150ustar00rootroot00000000000000PROJCS["Lambert_Conformal_Conic",GEOGCS["GCS_GRS 1980(IUGG, 1980)",DATUM["D_unknown",SPHEROID["GRS80",6378137,298.257222101]],PRIMEM["Greenwich",0],UNIT["Degree",0.017453292519943295]],PROJECTION["Lambert_Conformal_Conic"],PARAMETER["standard_parallel_1",40.66666666666666],PARAMETER["standard_parallel_2",41.03333333333333],PARAMETER["latitude_of_origin",40.16666666666666],PARAMETER["central_meridian",-74],PARAMETER["false_easting",984250],PARAMETER["false_northing",0],UNIT["Foot_US",0.30480060960121924]]geopandas-1.2.0/geopandas/tests/data/overlay/nybb_qgis/qgis-symmetric_difference.qpj000066400000000000000000000010001525650651600307720ustar00rootroot00000000000000PROJCS["unnamed",GEOGCS["GRS 1980(IUGG, 1980)",DATUM["unknown",SPHEROID["GRS80",6378137,298.257222101],TOWGS84[0,0,0,0,0,0,0]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433]],PROJECTION["Lambert_Conformal_Conic_2SP"],PARAMETER["standard_parallel_1",40.66666666666666],PARAMETER["standard_parallel_2",41.03333333333333],PARAMETER["latitude_of_origin",40.16666666666666],PARAMETER["central_meridian",-74],PARAMETER["false_easting",984250],PARAMETER["false_northing",0],UNIT["Foot_US",0.3048006096012192]] geopandas-1.2.0/geopandas/tests/data/overlay/nybb_qgis/qgis-symmetric_difference.shp000066400000000000000000045466301525650651600310250ustar00rootroot00000000000000' f+A@p0AdCA8+AVU@K-A JMAZ/8r@x қ-A`ZAn-AMAC-AMA(i-A+Ap-A Ag-ACAK-AIAWݜ-AȺAN-ACAL-A AKU-A uA8bS-AjA--A %A-AyA@-AA-AAP'-A AP-A Ax-A=A8#C-AAb-A`AX$n-A@gA@q-A@A@q-A@NAr-AA0]i-A`AVT-As A(8-A |vAx -A@WA2-A A|-A[Ajњ-AFAŚ-A=A@-A OTA0-AA-AA-AHA-A`ÍA0-AA-AA -AqxA-A AAF-A[6A8y-A lA-A`VA -A Ax қ-A`ZAT -AGA -AD!AApO-A`HA’-A@s^Aȩj-A^Am-A XaAL-A4hA-A `AT -AGAp0,AA,A@QA ,A ZA/9,A`6Aح,A@$ Aˇ,A ApH,A`-!A ,Ap!Ahܬ,AH"A跲,A@g-"A(,A`v"AXS,Aı"AS#,A#A,A#A,A%A@,Aj|&A4,A;'A,A@)A(,A,AY,Ap9.Aի,A@ /AU,A`95A),A7A0q,A":A ],A<Ae,A`?Ah,A!:Ag,AВ9Ad,A H8A87,A@?8AH,A7A~a,A`"7Az,A`6A9,A /5A@S,A@4AP8,A3Ayٳ,A@2A,A2AC,AF1A.,AA1AD,Am00A,Ȳ,Av.A`%,Ȃ-AK,A@,AH?,A ",Asu,A`+AJ,AY+A,A +AHֱ,A@ +A7,A*AHᣱ,A@`)Ast,A j(A b,A@C'Ah@F,A$A8 ,A q2"A(mH,A` A>,A Ap0,AA;|-A}8A 0-A`Aps-A7AP<-AAw-A`)A +ύ-AAa-ABԣAh.-AfAM-A ZA0wY-AwA8-A賛AX-A&1AU-ARAW-AߒA-AzAHn4-A@yAQ-A`AH܄-AA`ˑ-A`KA*-AIAh$-A TAج-A}A-A(2AP-A AQ-A tA-A`íA`đ-A`DA&-AbAؒ-AAƑ-A`K$Aϑ-A⹍Az-A@tA -AOA-AA -A@`xA1)-A$NA(-A$A@C-Ah}A(-A@`=yA Ȕ-A@%QwA`֔-AZvAf-AItA-AAsAf1-A|pA>-AľoA N-A@`oAG-A]A3E!-Ayя'\AWN7-Ag,[AsH-A~,ZA!MU-AA@(YA^-A#v!XAc-AWALKf-Ag1=}ZVA(pd-AWW &UA3.U^-AbiTAdAS-Ak{0&TAȔC-A \rSAxǕ-A (*QA㐕-AଧMA:Y-A~%MAڹk-AWRLA[-AsumLAPM-A`)KA-A@HA;t-A`{GA ×-A+DAW-AzCAh#-ACA$-ACA/-ABA-A@BA-AxBA@-A lhBAԗ-A sBA-A,BAJ-ABA R-AKCAp-A`CAHi-A/7DAo-A 1%DA8ک-ACA@Wk-A@>#CAB-A`BAH-As88A1-A7AU-A@E7A@D-A`)7AX1-Ai AG]-A4A ݇-AVAxч-A Ax -A@A-A A8Ƈ-A(AY-A@A-ASA]-A A-A A@S~-A ;AV|-AhcA W|-ATuA0g}-A& Aȓ}-A&Aw}-A cҿAHN}-AZAl }-A`{AV|-AکA_z-AzAw-A$Ar-A 8oAEr-ABA0q-A@Aq-A0cAuo-A`JA8gn-A}AhQOn-AzAAp-AOuAPp-AtA,]n-A%yAH m-A`KowACj-AiAxh9i-A=eAPg-A4VA(g-AXeVAg-A:UAP h-Aj%UA(h-A'UAh-A`UAf%h-AzTA/h-A`"TAH8h-A |TA`Bh-A TA Kh-A@`TAISh-AaTAj[h-A TAech-A@ TA5kh-ATArh-ANJTA:zh-A (zTAXdh-A@gTAxKh-A/TTAxh-A`=TAxh-A$TAhh-A TAh-A@ySAPh-ASAh-A&SAh-A@SA0h-A|SAh-A\SA`Ph-A`Bi-A<RAIi-ARA^Ui-AQAai-A)QAxhi-A@>QA`oi-AQA0vi-AQA?}i-A QAТi-AޗQAPi-AdQA0{i-AiQAi-A`PQAi-AX6QAi-A QA舤i-APAྪi-APA0Hi-ADPA8"i-A PAXJi-AyPA i-A]PAxi-A@APAwi-A&PAXi-A@ PA8i-AOAi-A OA6i-A@OA@i-AOAj-AOA j-A OAbj-AOAj-A`OAp[$j-A c'PA+j-A-TPA2j-APA0v5j-A]PAe8j-A JPA0;j-A PA>j-A`]PA/Bj-A`]PAXEj-A PAHj-APApJLj-A PAyOj-APAPxRj-APAPPAx^j-A`0PAH^j-A#PA`]j-APAXj-AOARj-A@OALj-A@.OAȶEj-ApOAUFj-ASOA_Gj-AK7OAHj-A@OAIj-AG OAx-Jj-ARNA0Jj-A`DNAIj-AKNA]Ij-A6NA0Hj-A@NA0Gj-A JNAEj-A NA8Cj-A@NAFAj-ANAl>j-A@rNAx>;j-A`eNA7j-AKZNA4j-A 8PNAH 0j-A GNA+j-A@NA@#j-A`;NA`Cj-Ad3NAj-A`)NA0j-A MAti-A`MAi-AMAi-A MAhi-A@MA8i-AMAȘi-AMAi-AുMAi-AFMAi-AMAi-A@MAHi-A`BMAi-AjMA8'i-A`MAi-A MAi-AMA^i-A`TMAh;i-A@MAi-AMA@i-A`MAHni-AMA؎i-A(NAi-A`TPNAi-A wNAPs,AC eAop,A0deA ],AH^ eANQn,A=%dA,}H,A GhdAپ,A3dA(q,Aa"eAf%|,AsveA))0,AeAuZo4,AqNLfAtOa,Ai3fA ,A$Y`gA`=,A^hAy,A3hALon*,A3iA6v,,AQzA,A`zA虙,A}zAn,A׋{A,A@rxAX ,A3wA,A wA),AJwA.,A`7wAHL[,AvA``,AևvA؎,A@uA,A`uA5,AFuAH,A vA8,AuA,A`uA,A`uA=,A@)uAؚ,AhtAX,AJtA ,A`\tA߱,AuÄ,A2uA8 ,A@wA,A`wA,A3wA,AGsxA',AuAQ#,A uAX0,A quA83,A=(tA),AtAW,AwA.,AzA ,A A4,A`KAf_,A@ងA,A3Au,ARAf,A \ُA,A A,A Au,A`)|A,AA`,A쥙A;,AHA)L,A Ak,AhA`a ,A3wAG,A`gAM ,AWA,A`fݗAL,A9AP,A s˕A~,A@2A8,A`)AHa,AMAp8,A:A,A@ᔓAx,A \A,AfA,A`ߖA,AAz,AA),A=A@d,AɶA:,AA>,A*ɑA,A4AP",AA',A3ӑA,A =A,AQA؞,AA80,A̓A 0,A|AV,AAX ,AA,A@ːA(:,A[/A /,AKA ,AA,ALaA0{,AAs,AA@F,A]!ATo,AԎAu,AAj,A^A8e,A`rAJ,A=A/,Ag~AT',A@CXA!,AV?AH,AA,A`)4AHK<,AA`Ǽ,AďAh,AhA8,A@WRA,A@ A|,ARAHnK,Aa^A,ApAB,A`΍AA,AA8,A"A' ,A`K A7,AoAh>,A WAi,A`)A,AApp,AˋAX ,A`AǼ,AA0ȷ,A`bmAx,AjAho,AފA0{,A uՊA=x,A`SAxk,A4A e,A.AG,AAff,A4uAu,A`A,A |:A,A NA`a#,AׯAg(,AĝAS,AvAY,AA,AMA,A 87AH,AqA0H,A A R,A |A,AAW,AA# ,ApaAȶ,AIA,A@pԇApJ,A㺇AX$,AoAf,A)*A,AAX,AGAf,ALJA,A`XA`Ы,AjA3,A sAH{,AF~A0 ,A@A)C,A=6AXp,A |Av,AxA,A܆Ap,A džAh,A0-A,A^A,A]}A ,A@%gA`#,A@A;,A9Ap_,A@W9AHm,A`A@F,AZAsF,A 8\A ,AA ,AϚA&T,,AkwiA_6,A3AK,AsDğA/I8,AJܵ߄A虉,APAT`|,A` A%),A"A|$,AJ?0MAd엒O,AfRA&}")v,ADūA@,A@,Ae8,ALA*,A\Az ,AQ)2A,AfųA6x,AdYA9,A`APf,AA,0,A_2_A{,A?Y<AfAR,A9Ab,AA<,AA,,A@A,A0Ap,A qA,A A8,AAp8,A`)AH0,AAhv,AzA RS,A@NAhs,AvAp8,A`)At,AHA8,A A,A,A8,AAm^礢,AH.A2V,AHQAO,A ;AI,A?$5tAIGY,A<0NAp,A`KAkS,A)A(.P,AY%A &-,A'w_A,A@gA,b,AmAl,,AKAc ,A̹A,ALAzw,ALANQ5,AF8?}wA,A@yHA:,AVuj,Ar0K,A=,Av,A~$AP,AyAYB,AA +,A'RAI%EO,A)DAkZ,AƌAޱ^V,A&^ >A[,AAC,A@-Ag%*;,A AD^+,A5Ax},AIA&,A{?A,A`"QA,A`VA@H,A`VA9v,A<Aԁ,A}'*Aso,AshA,Ŧ,A"oߘA[,A3FAC,AmAA`,AukUApC,AEfA7j,A+e*Ac,A:AZy,AAD,AAh,A6zAh3*,A[A@q ,AhAH,A;A`Ci,AOA ,A@]A@,A@*A(~,A`sAn,A]WA8,AAγ,AA R,A lA^,AyKA,AA,AApJ,A Ap,A@gAXt,A =A?,A`mAP,A@,A,A A(%,AVAxzA,A`mKAK,AA8Qf,A`BA0p,A@iAf,AAБ,A`A,A@,9A@},AA@,AA8,A@DAP,AA,Ak[AC2,AAB,ApA,AA+,AzA,AAHR,ADA7H,AA0,A#A`],A`2A(z6,A0CAI4,AMA,ANA ,A@A,A-A@B,AA!,A2A,AA ,A6Aȃ,A &A,A@#Ax,A@TA%,A@WA,A`A6,A:A>,AAL,AAR,AAnt,AAHv,A`AR,ASAT,A@>JAb,AApg,AvA`2,A6-A>,A@ A,AA,AA,A4Ah,A9A X,A@AxhB,AmA8,AALJ,A`mAX,A_AX ,A@υA,A}OA,A`A,Aj&Ac,A` Ac,A`Aj,AjA@,AA,AAp,,AVA,,AAX,A`A,AtA,A`ܪA,A൭AxzT,A`KTA؎.,AQA,AZ0AC-,A:AFa,ABsijXA`#u,A"A,BS,AOqAAc^6,ASA]z.,AYdA },A bBAP(,Ajb=Axm,A`AUN,A@/As3,AAk?,A{AG,AnyA,R,AOAg,AA s,AQA8,AyA",AIA(,AAZ,A A,ArAx-,A-?A,A*%AH),AIA@,A@A m,AB+A0v,AA0;,A sAp0,A`Ai,A`A ,A`!A ,A@A W,AoA(J,A A,A൭A,A@A@s,A Ahb`,A@5RA19,A`A&,A@#*AR,A}PA,AAh,A`A,A@Ap,AHAq,A@A{d,A5AE,A@ UA@9,A ɁA,AA(,AAx),A?Ah,A9A0,AB6A,A ZA,AA,A@Ak,AiwAc,AACA,A`A7,As5AX 8,AZA,,AA~,A`Al,AAxK,A஫A),A;A,A A,AA ,A`nA@(,AA`4,A`BAKQ,AaAo,A 0AHP,Ac"A,AA0,AA,ATA,A A p ,A@A2,A |A@,AqAg,ALYA 0u,A'A,A uA7,A 8XA,A<A\,AA ,A`A,AA@ ,AױA,A}A$,AA(-,A@AH,AA,A Ahճ,AA,A@AP*,AEAd,A`WA,AA0x,AFAj,AcA,AzA0,A@A`,A A8,A?A:,A$A0,AxA`,AoA,A`A ,A@A,AA8 ,A1A 6,AA;,A@AO,AZA]S,AAg,A AKA`1P,A@iAYS,A` A k,A@WAdl,Ad]AH,A<A0,A`ôA;p,A q'A(~e,AA &,AA`%,A`TA! ,A`Ax-,AQA0{,AH A,A+A0,AA,A|eAX,A YA,A BA[,A ZA,A8=A,,A|0Al,A`A@q,A@AhQ,A cA`,A /A,AAL,A[*AHM,AAH,A^AJ1,A6AP,,AqA@(,AׂA,ApKA6,A@.A2),AA(9/,AAذ_,AAu,A@Aث,A޽A@~,A Aؚ,AhYA0f,AոA},AVA,A`0A`,ALA,AteAH,AAH,A`vA,AmyA`,AӭA,AAq ,AK"AȤ,,AA/,A@iAH:,AfbAp0,A@G8A,A@A),AAg,A֞Ar,A lAvx,AtA,A@ AH,AേA ,AA@ ,AsA,A,A4:,AA<,AA`c,A lLAq,A A4,APA ,A _A,A}A0,AA,AA,AA,AvA/,AAH.,AA0,Ah+A,Ah+A@Ӭ,A`"Aى,A|A,A RA8Iq,AA` R,A@gA`E,A[A ;,A@0A.,ABAHP%,AOA@,A@ A ,AનA,AarAԢ,A5A8,A@dA,A<A,A`A,A =A,A`SAu,A+A8g,AA,AyAX,AtAE),A{A R;,A`AN\,A@QA\,A~A8b,,A;A83,A`TaA(m{,A LA,A@]A(,AA,AAȃ,AMA,AAN,A ZA,AA,A A ,A lA(,AّAi,ASA,AէA`,AzbA`,AA,AA#,A |Ap,A |XA@W,A GA,AA0Ԇ,AA@v,AA,AŶA09,AIA ,AAې,A`Av,AAk,ADAM,A ZAR<,A`A7%,AaFA,AɳA,AA,AAd,AtAa,A`vA,A`A8,A A0;,A9AY{,AAX9g,A@Ah,A@CA@,A)A0,A`]qAF,AAh,A AN,A:A!,AAV,A)AI,AA@,APA,AcA",AA,A&AP,A[<Ay+,AAAP|,AgAx(,A`Ao,AA,A@9A`,A Axz,A`*Aep,AA Z,APAX ,A@A+,AA[,A`NA,A`]A(,ATA8w,Ao'A,AA`,A Ax,A@EAh,A A,A`)A,A Ah,AA,A{A0G,A`TSA@,AA,A4AX,ARA%,A`d4A؎2,A`A(I,AAV,A qzAbp,AAu,A@Ab,A`A,AA,A@Aw,AAU,A5AT,A`A,AމA( ,A,zA(),ApWA8,AA:,AA _(,AjAE,A=A,A}A ,A̻Ap,AA8x,A`vA8,A@5gA{,A`Ag,A`ݾA0w,A@NA,AOMAג,A@dAr,A ޿Al,A@AK,ArAyF,AA8',A lA!!,AA,AӆAXh,AA,AjA,A cAY,AդA٦,AgAج,A`3AL,AOA8k,A@Ac,AA,AvA,AAp,A !AP^,A_A0,A bAXJ,AA <,AWRAh ,Ay6A@B2,A`A@59,AADb,AANh,A˿AO,A*AV,AAH,AuA,A u^Apk,AA,A@GA@q!,A 8A,',A`KAP,A QAV,A:Ao,A@ڻA,AAXۣ,A@A,AտAa,AAw,ANAȺh,AA,AA(z,ATAj,A@AK,A@AD,A Ap,AA,A Z A,Am+Ab,A`PA>,AA`1,AA^Z,A՚A%,A`eA,A@,A0*,AA,AiA,AվAhL,AA S,A`A,A Q:A,A"A#,AA#,A`HA.,AjA(,AwA@),A 8˺A@F5,A A(U,A`A]m,A ƹAxw,A JA,ADA3,AʇAB,ANAPk,A@G7A{,A nA0,A@ynA ,A?A,AڼAX,AAX2,AA0X,A$A7],A`!AxiS,A@5A&,A A*,AsCA,A@pA襪,AXAL,A dA,AA,A@,A,AɶA~,A4A,ACAȋ,A 8XA ,ARA(,AHAA,AմA(K,A`AE,AA,A@OAX ,AZA,AA8ɓ,A A]j,A`AV,A`An,AA8Q,A`2fAo,A`-A@,A@A6,A?Axif,AϽAЕ,A,A؛,A@YA@%,AלA`,ApA,AH-A o,A}Aa,A A,A`լAw,A`dA(q,A A,A۸A,AׯA ,A ߸A̚,A@赸A$,A@A,AR~A,AR8A,A |ηAxh,A`A,A 8<Ax ,A@i"A1,A`ܑAP9,A uAc,A׵AHi,AA,A`"AM,A?A,,A`]JA(,A1A,AxA,A4_A/9,A@²AH,A^ҲAs,A ۛA$,A_0A8I,AزA07,A AG,A hA(im,AֲAha,A`ٲA`J,A@vA3,AAJ),ApA",A SA0,ARAO,AA@=,A$NA`*,A`XAH,A6A8E,A?̲A,Av-AD,AkAX,AAN,AȵAG,AAXWA,A A ,A@ؽAP8,AAi,AʗAp,A@A2,A ҾA I,A[Ah3,A`f0AB,ARA0>,A`;Ap",AA,A)zA,A3WA`,AٶA,AյA,AQuAHP,A@y>Aȩ,A GAև,AmAHm,ADA,AٮAp_,A ®AH`,A첫AS,A |A,AAy,AA,AMA#p,A`Afd,A4Ax:,AzA(,Az8A,AkDiDAG,Am½IAG,A`THAK,AÁLAge j,AMB<ANO,A'*$A`6,AA,AsA,A?1A o,A AXW,A A,AA,AA_,A@ A`,A`)A0٪,A{A0,A;APڕ,AVAՏ,AtA,A@A[,AVA^,A`pA,A`AV,AOAW},A=AX{,A.A =y,AAPw,A Av,AAPu,A`Au,A cٴAHu,A`ǴAZv,A ALw,A AHx,A`KAUz,Az|A0|,A|iAXR,AWA|,AFA@,AH7A跋,A!A ,A- A0,A;Aȥ,A@%ڳA`,AA8,A`)A3,AܲAm,A ~IJA2,A@!AH,AAU,A`{A,AbA؛,AzJA,A2AR,AA(,,A`A(,A@pAx,A ɱAG,AAX,A` Ae,A^A,AqiA@,A A,A԰A,A`;A,A^AÌ,A3rA,ARAX ,A@>4An,A3APl,A`gA~j,AAA g,A@Aod,AAh`,A`ϮA0Y^,AAhb\,AࣰAZ,AA`Y,AAY,A}AX,ALkAX,A}YA Y,A GAأZ,A6Ah,A APi,ATdAh,ACAHh,A"Ag,AFA؁e,AA c,A"¬AP8a,AൢAu^,A׃A{Y,A^AT,A c:A@BN,A /Ap H,AA(mA,A`2ԫAp:,AA`5,AAȔ0,AA,,A A',AwA( $,AeA؎ ,A`mQA0v,A <A,A4'A(~,A A0,A`A@,AAL,A ʪA,AtA,A Ap,AA,AA,A@yAx),A[jA,A`[A4,AMAp,A`@A ,A 5A8$,AcA8(,A A+,AީAH-,ATA`a/,A4AP1,AnAE3,A"fA5,AiIA8,A@`-A`<,A@!A=@,AAhD,AdިAH!I,AƨAN,AA@T,AzAZ,AtAs_,A`"VA(~d,Ai6A`%i,AAfm,A=A@s,A=ŧAPz,A3Axi,A-jA,A=>Al,AA虗,AA`q,A@`ǦA,A띦A`,AV~Aª,A^A ,AM=Ah,AA ,AxA(ܰ,A@إA@1,A`A@,AՕA0],A`BuA8,AfUA؛,Aa6AX,ARA,A`APo,AѤA ,AAL,A_As,ALA,AwAHˎ,A}gA,AWAPk,A`IA),A@><At,AR"Ap,A q A,AA(,AۣA,A@ţA6,AoA,A@Ah,AィA,A eA(q,A-GAp,A3'A0,AA,A=Aذ,A`vA,AAX,A΢A,A`2A ,A@ؤAH ,A 䎢A ,AwxA ,AaAL,AJA,A7Apx ,A Z%Ao ,A A ,A (A(, ,AA,AHߡA8g,A ϡA,AAM,AA0j,AAP,A0A,AQAH,A`A,A`rA,A#bA>,AQAB,AAAh,A@A,A A,AƠA,A`KA ,A@xAH,ARA,A-A,A`TAp,A@GAS$,AA),A؟A83/,A4ǟA5,A@AH<,AॴA0B,A A0H,AAN,AAkP,AvAXQ,A@C_AR,AGAR,A/AR,A Ah@R,AA4Q,AAO,AОA`N,AAuM,A 䔞A@L,AvAL,AHXAM,A`9A8M,AAN,A@AkP,A`A`S,AaA!W,A A8[,A }A_,A^Ad,A AAP|k,AA0r,AAz,AҜA,AA.,AZA8I,A@rAx>,AA@,A (A,AAʒ,AAP,A"A8V,AuA,A iAXl,A0]Aߘ,AMPA,AGCA0,A5A,A (A(,AA8,AA(ʓ,AA(,AAB,A A,AAج,A A,A@ܛA],A2ٛA ,AכA |,A֛A]y,A כAv,AڛAr,A ߛA n,AdԛA@k,ARțAg,A`A@d,AVAb,AAP_,A@ A],A zA'\,AhAx[,A@UAVZ,ACAZ,A@/AGZ,A A=Z,AAZ,AޚAq[,AൿA\,AAXW^,A@A0j`,A@dAb,AGAe,A`)*Ai,A AXl,AۙAo,ApA|r,A 늙APt,AbA{v,A8A`w,A \AP7x,A`A x,A` Aw,AȘAhv,A`;A8Vu,A6A`s,AAXlq,AmA`n,A@NXAXk,ACAh,A0A e,A@pAa,A A8\,AxA@AX,A`A1,AA#,A@A #,A$A9,A}A$,A= AA,A@A _`,AJAxd{,A ԔA,A9nA0,A`.A2,AA,A拓Ap,A@(A@X,A iA8b ,AAK,A+ґA,A@ǑAV,A@ϜA },A 3Awq,A@5ӐAe,A]`A0,A`,Ax],AKsA },A@ A,A`ΊA,A=Ah,AsAZ,A@!A,A Ap c,A qAps,AoAP,AA 4,A@$A?,A`A,AA ,AjA(,A@5Ah,A@ApZ,A@>Axh,A |"|A ,A |"|A,AWg}A,,A0}A,A{A8,A3zAp,AzAx,A {A,A`K{A@,A@qA,AA ,A@AP@,AՁA ,A>zA,A2yA0j,A`{A,A`]|Ai,A|A+蒞,AJ|{AEׂ,A({AEf,AB{AJ.dK,Ao?4t{A"e2,Ae1+{A p,A zA|/,AIL$zAD,AQvfyA&,AxA[I4z,AKjýwARjZ,A@vALH~=,Aӹˆ vAP$,A|#uA"#,A q\tA,AsA0G,AerA0,AqAX,A`MmAг",AkAH,A@EkA,A kA8tl,AHlA,A HlA^u_,A!>kA,A#jA,A \iA+,AiAX([,A`hA,A`KvgAg,A`DfApx,A`ohA,A@DgAh1,A?fAC-,ARaA},A%^A@$,A`T]Ah,A YA(h0,A;UA/,Ap;UAP,AlQAh';:,A PAx,AZ)OA렁,Ab9OA +,A"NA,A@WMAp,A MAk,ALAI ,A 8JAZ!,A`)JA`,,A`JAȺ;,A`JAO,A A4JA,A`mJAX9,AJAh,AdJAТ,AvHA +0,AEFAy,AMHA_M,A6HAJD,AHA ,A A=HA,A@y>GAO,A EAځd>߿,ADAxgݿ,A]X@ADAlܿ,A%DA8Qܿ,AvDAԤܿ,A74_DA ݿ,AB JDA?vR߿,A=`ͫDAi,A ۟DAx,AfDA,A/DAh.,A<#DA!>,A&DAo,ADA ,A ECA3 ,A*CA5 ,AʝCAH ,A;CA8\+ ,AіҝCA{} ,AwCAf#,AYvCAX,AfCA Rٿ,AJEBA,AVBA%_,Aζ?BA1 ,A*4BAS^u,AI6BA-Rc,A DBAQ,AkD]BAA,ABAFe4,ACBA',A{BA ,A@lBAb_,AmVFBA,ABAKf [ݾ,AHAAjm¾,A8>AA,A`ՖAA`,A@֒AA .^,ANAADn,A,gAA|\,A /AAAHz,A [>A(,A&7A,A`D_6APޖ,A 5APZ,A4A,A Z*3AϷ,A}1AH,Aw1A+,A1A@,A0AH,A`0A!,A0Aߺ,A d0A(Һ,A@h0Ah",A6=1AF,AP1AȺ,Ah<1Ay,AV1AV,A{0A@3,A//A0+,A`/A6,A /A >,A[/A(@,A=6/Axֹ,A@6-A8,A;k-AW,A-Ax,Aྚ-AÛ,A@֍-A!,A V-Ap|,A@,A,A@*Aѹ,A`9s*AȘ,AP,AcKF&,A~g+A A:{,A*An>,A#&TŻ)A 1,AR(Aњη,A'A,AxGAA,A`v^Ax԰,A@G'AW,AOA,AHA(-,A| APE,A`m Aa,A Ap5,A6 Ap,A@ A(,A | A ,A \ A`ޭ,A Aխ,A@ A(ŭ,Aa A@ӫ,A@ A(薭,A A8,A > A;,A8v Aw,A` AXys,A@r Ahe,A AXa,A A؁K,A@,A7,A AŪ,A`_A&,AAh@,A`AD,AA_,A@AG,A@A",A`K?A,A[A9թ,AzAo,A@AxNJ,A@`0 AAؽe,A2-AI@,A@,A[@ȶd,A@>z@x,A3@,A@x\,A@w@k,A@4),Aw@,Aa@P,Aq-@(ß,A@@,A@/@8,A@@O,A)@<,A@p@,A@@k,A@N@Z,A[@t,AǼ@i,Ai @(9/,A&޺@],A@͹@p,AVD@,A@@ 7,AA@P ,A2@0,Aƴ@ ",A;̴@Ol,Aܨp@k4Ӟ,A)NSqAلh,AP9Q,A麢E,A4JA̱e,AOfhAƤ,AAƤ,AA̱e,A_A麢E,A}9Aلh,A0ƾAk4Ӟ,A౬AOl,A+WAAf{B,A ÂB,A|A`aB,AA)B,A AأB,AAB,A`)DAB,A@ApB,AA)B,A`)@AP8B,A@AB,A@gAB,A@A̖B,A aAB,A AѣB,AA8B,A ApB,AߡAB,ARApB,A AfB,AA8B,AA)B,AA C,A:AC,A A`"C,A qA8'C,AeA8'C,AA&C,Az,AG:C,A4AWA`@C,A AIC,AqA`VC,A oAhC,AA`uC,A`fA0C,A \AAC,A AC,AAWC,A8ALC,AAH\C,AAfC,A@ALC,A`AC,A@AD,AAD,A`AD,A`A!D,AAH&D,A AB(D,A Ap8(D,ArAQ(D,AzA'D,AA("D,A`)DAuD,A]ApD,AA D,A |XAp D,AAp D,A-Ap8D,AA8 D,A`A D,A@>DAD,A`'AD,AzAH.D,AR*ApC,AzAC,A]A#D,AaA#D,A Ap8'D,ARAH.2D,A=HAH>D,ARtAMD,AHAHXD,A A8jD,AA(\xD,A AD,A A(ܓD,A 9AGD,A@>AD,A@AD,AzAfD,A]AtND,ATQAaҕD,AX'AD,AGɐAjqD,A"o%Ahh E,Ac]AlqE,Af AB.E,AAXEE,AAp=]E,A5ApE,A qoADŽE,A@>A`aE,A@><Ap E,AA؞E,AA`aE,A`)NA0.E,AAE,A3AE,A\ApE,A)AWE,A ABE,A3A`E,A`f4Ap E,A dAfE,A=AzE,AåA8E,AAѹE,A3A`E,AA(E,A 1MAp9^F,A`AXG,Ap AG,Aa AuG,A_E!AG,A@W!AG,A "AH,A@"AKH,A0[#AKH,A9#AG,AiX$AG,A*$AG,A3$AۿG,A $AG,A,%AG,AU%A`G,A%AG,Ak&A)G,AĂ&Am H,Ad'A# H,A(AȩG,A0(AWG,A@߾)AG,A~*AGG,At+AG,A4,AxG,A`b,Ap_G,AS-AG,A-A+G,A<.AG,At.AQG,A`/AAH,A 0A8D]H,A1AyH,A`2AH,AĽ3AH,A4A H,A |5AH,AN5AH,AR6AXJH,A6A0݉H,A@7AH,A)8AzH,Ao8AsH,A@i8A8\H,Ac39ADH,AO9A(~(H,A l9APH,Ao9A@G,A s9APH,A9A* H,A<:A@H,A:ApH,A ;AP,H,A5X;A?H,A ;AVH,A;AKXH,A@;AKXH,A@S<A]H,A<AHiH,Aӽ<A@uH,A4<ARH,Aw<AAH,AL<AبH,AL<AH,Az<AjH,AV<AfH,A<Ap I,AV=AI,A=Ap,I,A{>A\7I,A 7?ApwAqQL,A wAPL,A-xAXdSL,AךxAbL,A`yA(OlL,A (TyAnL,A&yA( ]L,A^wzAGPL,AzA)ML,A@{AhbLL,Ajm{AOL,A?{Ah[L,A6{Ap=`L,A#|AaL,A|A/bL,A|AHdL,A }AoL,A ;}AvL,A`]}APwL,A}AX~L,AsL~AL,A}~A؋L,A$A/L,A4cAjL,A;AL,A@ZA0L,AA8ҩL,AAHL,A?ACL,A!AEL,A JA L,AtAM,A xAx(M,AuAM,A QA(M,A 볂AM,AAM,A%AM,A]A()M,AaAM1M,AA8M,A6A4A, M,A cAeM,A`<A0"M,AwaA%M,AmA8g&M,A އA (M,A` A[/M,A` $A(m;M,A!A`eRM,A`A `M,A (@AXhM,AeAPkM,A예APkM,AЈAPkM,A}ArM,A4AXwM,A sJA |M,A`iAM,A3AM,AQdA0%M,A AM,AAhoM,A9AxFM,AzA0M,A AM,A@i{A(M,AE(AM,AApM,A&Ap+M,A AM,AA@M,AAM,A@NAؒM,AA@׶M,ATAM,A^6A`oM,A@KAgM,AŔAheM,A<APkgM,A@hAPvM,AAoM,A3VApM,A`KAPZM,AjAH\M,A AM,A@*AM,AAxM,A@9AM,AADM,A@A M,A@GAx)M,AAbM,A)AM,A"bA M,A6AM,AAM,AaBAqM,A`խA:M,A ApM,A8A9M,AcA M,A ŢAM,A`ANM,A s4A N,A`]MATN,A@lAxN,A͞AX9N,A AhbN,A@AN,Ag֣ApxN,A ٣AGN,A Z A@` N,AOAXN,A AN,A`դA` N,AH]AN,A}A}?N,AA3gN,A#A`GzN,ATSA-N,A`KAN,A0AyN,A`msA&iN,AgħAUN,AXAKN,A5A08N,A A/N,A6rA\.N,AǩAHn:N,AA8N,A@A8N,A`TlA0;N,AAhbCN,A`9֫A8INN,AAQN,A2AxhXN,A@NAxhXN,A@[A PN,A0BAFN,A`AEN,A 2Ah AN,AAh AN,A OAh AN,A ZAx>N,A@`سA:N,AvA(6N,A`9:A!(N,AApN,A`K AM,AALM,A A(M,A$ALM,A}AM,A?A(M,Ag&A`M,A@ nAM,AAM,A@ߟAHPM,A`dAۑM,AHAM,AsGA(JM,A srA@M,A`<A:M,AA M,A 6AHM,A AM,AHA7 M,AAƨxN,AEA ON,AAo}"N,AGAH l,N,A,BA 4N,A6HuA".f:N,AAA#F>N,A"Abo,?N,Ax"AS>N,A _Ar];N,AB"6A;N,AA4EN,AV@AE;ON,A"ݗ`AkY[N,Ar= zAgN,A[AuN,Aq.!)A8sN,AA(VB!N,A `AO70N,Al_AF¡N,AiA{N,AW65AmN,Aׄ4A(,\N,A OWA0N,A`A跍M,A0(AHDM,AA tM,AnA8M,A@AfM,A A7M,A eA}]N,A`ܪA`N,A AЮO,AAuO,AA N,A@AO,AhJA(Q,A?AS"oQ,A!.A9%Q,ARiAwQ,AY8A=Q,AMOdA=V*'R,AgA:)R,AJA [i?R,A -A!UR,AP AiR,A0D)A|R,AJ1VAR,AྎAo R,A_H2AuwR,A5AgtR,A3q!]AS,AAe#R,AV?AQ,A^A@P,A`AQ,A`A*R,AApNS,A'Ah^S,AMA4QR,Av&AtR,A@A\_&R,AXAiR,AǢYAOR,AcNAR,A"A=R,A#iSJASR,ANAX%)R,A㱭A,*R,ARmAR,AA61R,A&RAe8R,AJ7߈AS,AgA S,A#,A&{3S,Ayr+ AbJS,A1GA`C`S,AZAزzoS,A%A1l]zS,AAoAHS,AAnȇS,A"_BAaBS,AAVS,AqogA}S,AގPAUwS,Ai jףAXujS,A`;AqgSS,A)6Ak̗?S,AڊA.S,A/A&S,A`TAR,A`"lAwR,A@AƈR,AAxR,Aw Ah|R,A`]$AR,AAR,AAS,A A(S,A|HA`#S,A@,A)HS,A sAXhS,A`AȊS,AA'S,A uAS,AgAT,A~AִT,A iAU,ALAiU,AAe4V,AߜiAB"V,A Aa8BV,A׀nN.At`V,Avc|dALά{V,ACQkAfV,A@AfHZV,AI&aAnV,AAƓ锝V,AA_B V,AUA2V,A?xAuQ V,A{IlAV,A HAfEW,A;;4A+W,ABAfPiW,AkAЭ\ZW,A%JAW,A! AԇX,A(:A[)ZDX,AS[P]Ar=[rX,AMնA9X,A녨Ag6X,A=lAX,A@E?AX,A1AjX,A58AX[X,A=AvK6X,At0AnX,Ac|ABX,AaNAzX,AA 6X,Aι0AbUVX,Aw?AX,A@2ACaX,AfKjA-X,AHAݢX,AAX,AFAX,A -A/U?X,AeUA5!3_X,AAlX,Ay.AoX,A 5^A0 Y,A A(:Y,A\ AHY,A A(Y,A@` A@Y,A, AY,Aw A*Y,A@ AY,A`A!Z,A@ AHm/Z,A@r#A(K1Z,A`/A5EZ,A`ܮA YZ,A FA2fZ,AA'pZ,A`A9}Z,A?AZ,AAPx~Z,AtAZ,AHAKZ,AAZ,A/AZ,AApZ,A#gAxZ,A}AZ,A AZ,A$AoZ,ANAp[,AQAW2[,AʃA8P[,AAa[,A@|Ahs[,A AX[,AA[,A2A[,AHAX9[,A0VA#[,A@A[,A)JAp[,A6Ai[,AJAG[,A`A` [,A`FA \,AA \,A( Ax"\,A; A#\,A@yA A %\,AsF A;:\,A AM\,A7!A@_\,A}!At\,A!A\,A@GL"Ad\,AH"Aq\,A$"A\,A$"A\,A#AH\,Av#A^\,Av#A\,A#Az\,AHE)AP\,Ar)Ay\,At)AС],A`B)A 4 ],AT*Ap],A+AI],A+A`],A@K,A8t\,A,AT\,A`N-A8\,Aa-AH],A -Ap],Av-A 6],A w.A,;],A@5.A?C],A p/A2M],A 0A^],A]0Af],A 0AUb],AZ1AR],As1Aȶ;],A`bv2A$],A@N2AD],A5A[,AqGQA0&AZ,A@PZA(VY,AVbA@W,ApqA0̓T,A@Q|A@NR,AfA`6Q,AdA(Q,A4A(iQ,A qA R,AWAVR,AAnCV,A AV,A gA@5bV,A_c A *V,A A*V,A AU,A@ AhU,A`# AU,A AU,A) A|U,A A_ AU,A A(U,A ۬ A(U,A@5G AeU,A` AU,A@ϔAU,A#4AU,AA8ֻU,A@EzAzU,A`AU,AAcU,A(AU,AzA NU,A AjU,A`mAxuU,A OA6U,A@)AzU,A@yyAxuU,AAU,AE AxU,AŗAZU,AA0U,AAU,A`"AHiV,AWAH{V,AAx!V,A@!TA``)V,A A6Ah@GV,A$A@fV,A3ApkxV,A[A~V,A@jA$V,A@A8V,AMA(V,A@pAV,AKA9V,A#AQV,AAsV,A`UA0V,AAH-V,A`A =V,A$AV,A@`A`V,A,AȔV,A?AqV,AAPJV,A`\A#V,AAxU,AAU,A0AhUU,A (ArU,A;AU,Aw?AV,A`Ax!V,A /A``)V,AaBA *V,A(A-V,A` AU3V,Aj!AU3V,A@!A-V,A`9"Ae$V,A "Aȩ V,A (0#AV,A|#Az V,A`@$AH V,A n$AU,A%AXU,A`%A(U,A@NO&AU,A&AxuU,A`w'AجU,A`'Ax~U,A^(A"U,A)AaU,A`)AXU,A`?*A U,Ac*AP|zU,A`g+AأsU,A',AoU,A ZO,AtU,A,AȵU,A ,AOU,A,AX(U,A1-AXU,A`t-AgU,A-A舌U,Ax.AXJU,A.AU,A`2?/AjmU,A]/A(]U,A`u/A;]U,A Z/A@1gU,A9/ApU,A/A8ĮU,A /AU,A/AHV,A/A *V,Ays/AKV,A/A7mV,Aø.Ap}V,Aj.AxKV,Aݟ.AV,A}.AV,A 2/A8V,A/AV,A|/AV,A`0AV,Anl0A5V,A0Ap_V,A@0AV,A0AV,AxQ1APoV,AMy1AxV,A 1AhtV,At1AKV,Az1A"V,A@1A*V,A1AU,Ad1AU,A`K1A(U,A42A(U,A`d2AxuU,A2AU,A3AU,A q43AU,A&z3A0U,A3AgU,A@ 4A U,A%4A舌U,A`B4AqU,Ag5A=yU,A 5A_U,A@56AFSU,A`6APAU,Aq7Ah?U,A7A>U,A/8A`8U,A98AJ2U,A8Ap"U,A^9A0&U,A@:A0U,A:AHzU,A;N;AHzU,A;A;U,A``<A(T,A <APT,AJ=AT,A@`=AtT,Aq=AT,AF=A3T,A@,=AثT,A@=AߦT,AI >ATT,AD>A'T,A9>AT,Ay>Ah"T,A#?AhT,A ?APT,Ak@APxT,Av@A@T,AAAmT,A@y!AA(T,A |AA@T,A5BAU,ABA06 U,AtyCAU,AFDA@T,ADAT,AYEA84T,A EAhT,AFAT,Ak:GAT,A`GA(OT,AHA'T,AIA9T,A`fIA8T,AIAT,A JAh"T,A@iJApT,A IAT,AIAdT,AIAYT,A}IA\PT,ARJAP@MT,A JA8IT,A4KAKCT,A?LA9T,ALA0T,A }MAX+T,A@NA@)T,ANNA@)T,A hOA')T,A` PAe*T,A@PA!.T,AWQAx4T,AQAHrU,AXAFSU,A0YA(_U,A@YA;]U,ANZA@AXU,A3ZAYU,A}[APPU,A`T>\A}=U,A6\A`a:U,A@p]A;U,AX^A}=U,A^A@U,Ay]_AHU,A `APPU,A@`A ZU,ABaAdU,AaA+lU,AbAhsU,A*cA_wU,AcA{U,AqdA0wU,A`TeAqU,AeA\U,A fA͈U,A |VfAgU,A@GfA@U,A@gAH\U,AEhAx~U,AhA U,A iAwU,AaiAoU,A@5iA(jU,AjA(jU,AK)kAMjU,AkAhiU,AllA(jU,A?mA+lU,AmAkU,AaWnAbU,A3nA@AXU,AoAVDU,AzpAx-/U,AkpAT(U,ApAU,A pAU,Ag&pA@T,A9XpAHT,A`rAhU,AaAkU,AxAV,AEtAeV,A3_AYkV,AkA|V,A0٘AEV,A AP V,A (AYV,A@5ÙAV,AbAV,AA(V,A(AIV,ALAIV,A ڜA(V,A2AV,A@!A'V,A`žAh"V,A$gAX(V,AAP8/V,A?A #-V,AKAp,0+V,A>A8k(V,A.hAH^1%V,A/A Y"V,AGA䎦V,A^A9pMV,A˦vAAV,A9OA iV,AMSAfmV,Al0AM V,A֒rAV,A<A bhV,AE4A:w`V,A|GAV,A*AX(V,AaA V,Af&A V,A2942A>GV,A`;A4V,A=CASV,A r?IAWPV,A,LA4V,A}=MAWgV,AU>LAN"V,AZHAwS&V,A BAg(V,Aop:A +V,AEJ0Av-V,AĨ%AP8/V,AKA.V,AKAV,A dAV,A@TAV,A`A@$V,A9AV,A@9A`XW,A`[ۣARW,AॄAo W,Aq+AH W,A?ҥA W,AqAW,AA!W,A`A)W,A@ WA@1W,A`A9W,A0AsAW,A 7AIW,A֪AQW,A@.vAXYW,A AXYW,A ~A[W,Ag/A`aV,AнAV,A-ACMRV,AũvAb,A;A0jJb,A^Ac,A 8`Ac,A@,tAXc,AЩAxc,A@A]c,A"Ac,AWAc,A<A(9c,A@Ac,A`̭AX9c,A`KA"c,A JxAc,A=iAc,A6Apc,A Ax:c,A OAc,A@Ac,ApAc,AA҃c,A Atc,A@iAhc,A`[AZac,A QA@~\c,A@3A8VTc,A AMc,A;AGc,A A6c,AA}!c,A`bAHc,Ad%Ac,AA!c,A"A !c,AOA@*c,AEA4c,AAp8tc,A`mAc,A`ZAmc,AtsAc,AiAlc,AAA%c,A3*APod,AA{d,AApd,A`AId,A`Ad,A`ofAd,A=A:!d,A`;A1d,A`K]A`?d,A`fvAwKd,A@yA \d,A PANmd,A3,ACd,AAhd,A A0d,A'Afe,ATAX.e,AAxXKe,AA8Dhe,A HAye,A_Ae,AtAie,A Ap xf,ADA`Ts,A@0A u,A:5A u,A`2APu,AQ3A v,A5AP^:v,Aͣ6A(bv,AQ7A5v,A9Aw,A@9AHEw,A@:A8Ew,A 9Aw,A9APHw,A9AP&w,A@W9AhQx,A 9A>x,A 9A@*x,A`J:Ah!x,A@Ϡ:AXw,A`2;AxKw,A ҅;Aw,A;Aw,A (;AhQx,A;A2fx,A<Axx,A<A(Kx,A<AXx,A" =AUx,A=A>x,AU=Ax,A}=APx,A`<A@x,A`<A0x,AUs<A0x,AA<A(x,A$<Azx,A Z;Acx,A;Ax,A;Anx,Av;Ax,AW;AQy,AOK;A y,AM@;Axy,A`&;A(m#y,A:A@$*y,Ak:A_+y,A::AX/y,A:AP6y,A9A 1AN{,A@1A{,AtF2A7{,A@q2AL{,A2AP8{,A@3A`{,A H3A8{,A'3Ax{,A`3A{,A&4Ae{,A@`4A{,A=4A`G{,AB4A0&{,A 1Y5Aĩ{,Am5A8b{,A5A {,AD5A`C{,A "6AW{,AK6A ={,A*h6A{,A`}6A }{,AM6AP{,A6AP{,A@`6AHK{,A 7A{,A r7A^{,Aկ7Aأ{,A7A{,A`[8A{,AT8AH.{,AR8A@~{,A u9AP͜{,A@9AT{,A 8g9Ax{,A#9A`{,A`9A{,A@6:AHЄ{,As:Aj{,A@r:Ap{,A,:A'{,A@:A{,AM;Aoy{,A;Aj[{,A`s<AYN{,A=A@FA{,A=A.6{,AX\>A`-{,A@N>Ap=){,Aq.?A%{,A0?A${,A?Apx {,A|?A~{,A?A{,A ?A{,A @A0*z,A@Ahoz,AyAAxhz,A"ZAAU{,AƨAA {,A AA{,A@N=BA{,AfaBA,{,Aq}BAL7{,A JBA,?{,ABA>{,A@BAX4>{,A(BAX@{,A[CA(D{,A`m%CA*S{,A@CA`h{,AAMCAz{,ARCA{,A`QCA07{,A bCAX({,AmCAź{,A?CAhQ{,ACA{,ADCAP{,A`KCA`O{,ACAp|,A`KCA@p|,ACA,+|,A`"DA`5|,A 9DA>|,A]DAM|,A|DA_|,ADASx|,AvDAZ|,A@.+EA|,A`;EA#},A /EAH J},A EAs},A`]EAP},AvDAH},ADA},ADA#~,A`vDAxB~,A+DAc~,A;DA~,ADA@~,A DAh~,ACA@,A@iCA,A FACQۯ,A;4%hFA 7,Az#^FA@>,AFA跤,A` GA{,A%GA=,A?pOGA긢,A  H#GA*:RǢ,AOV,GAΆ3,A0;5GAŪ,A%{=GAx,AEGA_,AGAHCԄ,AcGA(ބ,AGA@,A^HA83,AGAЦ,AGA },A@GA(1 ,A`̛GA,AOGA,AFA$,A FA5,AAP&=,A6<AE,A;AG,Av;AfO,A`n:AT,A :A@X,A@ߴ9AnZ,AH9A8\,A8A\,A,u8AY,A"8A@S,AO7AI,A`K#6AHI,A)5A0D,A4AX8,A 4A0,A4A$,AP4A,A4AP ,AT4A*,A4A,A4A ,A@53APل,A3AЄ,AK3A`XDŽ,AXn3AX,Ab3A,A `3A ,AfV3A堄,AUH3A8Z,A*3Aˌ,A`2A,Ao2As,A`P2A)|,A$2A0z,AE1A{,A[1A,A1AXh,Ay1AH,Ad1A0,A@81Ad,A %1AH,A1Ay,A1Aq,A@0Aj,A@0Ak,Aʔ0A(r,A`0A(Jz,AH00A@$u,Aޒ/Ahx,AO/Aez,A/A`r{,A.Ak,Aה.Aτ,A@.A,A /c.A,A--Ab},A߻-A|,Aʞ-A{y,AH -A_u,A ,AGn,Ac@,ABh,A@ ,A_,A ɻ+A@W,A@ߍ+AU,A}}+A0LO,A>+A\I,AR*A9,A)AX 6,A_)Ap,4,A@)A@.,A X)A(.,A%)AX ,,A J(Aг(,A6(A&,A+(A,An'A_,Aj'A,A@5G'A ,A&A ,A-&Ap ,A`-&Az,A%A,A%A ,A $A ,A@p$A@_,A[g$A),A[$AJ,A`Y$AP%,A`bm$A',A$A _*,A6$A-,A%A3,A &AL9,A A]'AA,A'AN,A@K(ApJO,A`](AHU,A l.)Ah@[,AV)A/{,A+A,A=b+A0H,A@.+A`6,A`),Ac,A#-AX,A`]V-A,A -Aɬ,A-Ah@,A .A ,A Ҋ.Aų,A@]/A,A/AHÄ,A@W/A(Մ,AO0A,A`l0A,A0A,Aj0A,A0Ar,A0A*A؎O,A*APU,A@%)AZ,At*APW,A;*AhX,A@k*A[,A u*A `,A*A0a,A*Aъ,A3)AX,A`f&)A,Ap(A&,A-(A,A`)A,A5)Ah,A'[)AQ,A*As,A`+Af,AG+A_,A3+AO,A`<.AxK,AOM0A P,A1A:V,At2A0T,Aj3AuS,Aa3ATU,A`3Ab,A;4Af,A4Axk,A5A(lu,A)5Ax:,A`Dd5A,A@>|5A0X,A`]f5A,A@M5AHʊ,A@r<5A`׊,A5L5AH,At5AZ,As5A#,A$5Aq>,A 5AƤ,A`5A0,A5AE,A@5A01,A@y5AxX,AY5As,A`5A,A 4A0,A4A c,A@>4AG,A$j4Ap,A<4AE,A$4Ap[,A#4AS,A'4AX׺,A '4AŌ,A#X4A,A`4AHP,A4Ah,A`)15A ,A5A,A5A`,A`G6A@,Ak7A N,A`7A8,AzS8A,Ar8AF,At8A0]ތ,A`b8AHř,A 8A@qŌ,A#9A0jČ,A@x9ANЌ,A9AՌ,A9A,A09A0,A` :Ax\,Az:A{,A`:APw,AG;A,A;A,A<A,A Q<A0,A`<A,A =A],A<=A,A@d=A,AM=A,A=A@0,A q=A ,A =A@ڌ,A >A8Ό,A6>A,A5?AN,A@#BÁ,A=XCA8҆,A3zCA7,ACApJ,ACA`C,ADAė,A`m+DA,A@CAz,AhCA8,AbCAÌ,A:CAPˌ,ACA@Ԍ,ACA,ABA,A#CA ,AZCA,A`CAc*,AiDA7;,A@`DAH,A ɻEA([K,AFA ,AtHA0G,AHA[,AHAk,A HAh,ACHA 0,A (GAX,A uGAR,AәGA 4$,A iGAP/,A`mGA8,ApFAOR,A}FAȺu,AFA},A@>=EA,A`NDA,ACA(2,A tCAU,A`BAq,ABA0j,A /BAHK,AAAܑ,A @A ,AF?A%.,A lr?AxG,A`v)?Aq`,A>A0n,Az>A r,A>A7,AF>A(,A@>=API,A =A,Ag<A <,A<Ae,AMH<A,A` <A,A ;AE,Aq];AHy,A;A i,A@i:A_,A9AV,A`T=9A>P,A8AT,A&7A^,Ap:7An,A 6Aq|,Al6A[,A8,6A2,AX5AH,A5A,A5A,A`T5Ap,A 5Aג,A@#6A!,A@y*6A,AX96A,A?*6A =),A`5An,A5A#q,A`5A,AZ5A,A@&5A,A 4A/,A4Ahb,AR4A@,A`24An,A,4A,A63A },A=3A,ATx3A8ż,A`63A <,A@2A/,A2A`!,A2AB,A@#2A88p,A`T2Ahe,A@2A[,A2A84],A02Ah,A@2A%t,A y2Apy,A}O2AKi,A@2A@Y,A2A U,A (2AV,A1A'M,A@ O1A=7,A@i&1A(,A & 1A3 ,A0As,Aॐ0Aw,A`d0A,AR20AP<,Aj 0Af,A/A(> ,A0A+$,A@I0A]B,Af0AHrL,Ah0AXQ,A`2D0A S,Aq/A@$W,A /A^,A`O/Ap,A@.Ay,A@ּ.AXW,A .AX,AN.AC,Ai.Ap,A|z.Ad,A6.Aȓ,A6.A8Eԓ,A S.Arܓ,A.AX,A@.Aȱ,A`v.A8 ,A.AN7,A9.AcS,A`].Aȃl,A.Aw,A}/A@…,A&/AՑ,A/Ah,A /.A,A /AH̰,A@E/AX˔,A /AД,A!0A#ڔ,Ac0As,A 0Ag,A$51Ap,A`;1APG,A1Aw,A` 2A8,A2A,A2AH,A@1A,A`Ә1Aƕ,A/X1Aɕ,A`0A0{ĕ,A0AHл,A@y0A ,A0Aۨ,A{0A`O,A[0A0,A@NU0A ,A`]0Aŕ,A,>0AHKӕ,Ah/A,A[/AO,A.A,A@߰.AHK,A.A0ٕ,A{.A,A-A8,A -AM,A`,Ah@ ,AR+Ak,A@N+Ap,A``+Ahה,A C+Aذ֔,A+A8',At+A,A@'+A ,A`!+A,AF +Av,Aw*Ak,A*A#,A#'*Abޔ,A )Axܔ,A J})A,A2)AXu,A(Aj,Ah(Ac,A஧(A,A (AxX,A`(A",A@a(A,A (A,Az'A@,A^f'A8g ,A@<'A,A'A`$,Ay&A]1,A&A9,A cB&A >,A`%A:,A@>%AH)2,A $A(,A$A",Aw$A(,AWM$Ap00,A@,$ATB,A@,#AV,A`#Ac,A`v#Ak,A#Ap}q,A O#A0w{,A@`#Ah,A u#A⇕,AN#Ah,A4 #A@,A3"Axv,AӉ"A(,AV<"Aܦ,A@."AW,A !Aذ,A3!A8t,A[!A8ɥ,As0!A8D,A \ Ap,A9 Aث,A 8wA׶,AA,A`A(,AAa,AA,A+A,AN|A,A`7A,AA,ARAXJ,AՐAp,AdA,AB#AQ,A`A8,AA,A`TLA #,A@-A~,A*AU,A A={,A A(r,AfAx k,AAJi,AAd,AnAa,A3Awe,A`AXj,AAo,AAht,ApA ps,ANAp,AoJA3l,A`Aȥf,A`A@d,A`AHW,AtAH,A`A(m*,A AAxi,A@A8,AzA@~,A AL,AAB,AAP,A@EAX,z,AFA`h,AqA#U,A40A@,A@1AЦ,A6AkΓ,AA`,A;A`,A]A͟,AZ/A`X,AaA ,A`A6,A-A(܆,A@sAy|,AGAwk,AAW,A`A/,AAXV,AA@,A$A,ALAP̒,A@Aj,ARhA`ǐ,A *AD},AEAm,AAhb,A@Ax[,A]A Y,A /Ax[,A-A`,AApa,AAgf,A@AhDm,A Apq,AA{,A?A`2,A tA,AҶAX,AAy,A@5*Aʒ,ANA@,AlA_,ArA *,AWA@WQ,A=ATp,A/A,A ZA@ӓ,A4AAط,AAۓ,ÃAB,A$AI,A@Ad,A@Afy,ADA*,A ^A,A_kA,ArA,A}AsҔ,A@ƃAH,A AA`1,A6A,AAp+1,A@AE,AA1X,A^Axvd,A6Aim,AxAu,AXAh{,AoA~,A`LAY,A`APk,AAhw,A@i>Ahsx,AtA |,A@yA䂕,AA,AټAhQ,A AP,A`DA,A |A,ATA[,A`)1A,ATA#,A@WA𒾕,AAcÕ,A JAXϕ,AIA֕,AszA(ܕ,A A`rܕ,A$:Aӕ,A@sAHiڕ,A`]r"AP&,A H&A,A H&A^,A)AQ,A޽)A,A+A,A`m+A8,A@k&AT>,A@%A@=H,A ."AhM?,A"A,A &A,AX#AX,Az&A`SU,A@,%AHr,An$Ap,AE<$A7Ü,A6#A,Am#Ax+,A >#A ĝ,As!Ax,AQ#Aܟ,AkO A,A@y#Apa,A~AP,A@iAm,A`)AxS,A)Aؠ,A AmI,ABA`p,A@A0,A 1Ap,A`o:AT,A-A8,A>(A֣,A;l'A88J,A@Az,A l5AG,AA颤,A(kA,A *Axd,A`AH.,A tAY,Aw1Aޥ,AdAP+,A =A å,A Akߥ,A:%jNu A,AH AT1,A? A Bg,A AT,Aj AAFզ,Az A8 ,A A,A O%ApQ,A`AH]Y,AAv,AA,A AAPΨ,A?A,AAx$D,A4Ax,A@3Ap,A OA;,A,AH,A2-Ap9$,AjA,A ZAH.e,A;A𴶫,AOA@,A?A0ث,A`7A,A WA:,A8A,AA9,A)2A0 0,A@AD,A`DA2Ƭ,A@iAHi,A`2A,A@EVAP,A ~AHƭ,AzA@,A҂ Aȩծ,A ^ A,A`]AP,A&Ai ,A`mIA,,A@A@A,AYAJ,AzAQ,A Ah,AA_X z,AwBAخܤ,A -AVƟ,AAj0 ,A4KAÍŮ,AqXAxخ,A A,A`A?,A@!A*,A`KcAذ#,A@yAP>,A@`nAH;O,A-"AT,AXAT,AAbr,A TA0,ATAX,AྦA#,AA8ɯ,A@ZA,A@A(h4,AA(lW,AxA%Y,AA`Tb,A`)(Al,A|AEu,A`BAN},A$Apy,A@#^A7,A`ÉAX,A A,A 8wAb,A At,A Aȃ,A@A,A AIذ,AziA0w,A<AP},A<A0*,AWA),A A_,A`A7,AVA r,A4eAf[,AtAؒ,A`A[Ҵ,A A3,ApA,AoAPg,A@NKAHi,A$A8,AA`,A`A8ڷ,A Au,A@AXu,ADAX,N,A A(+,AA,AA/,AIAn,A`2A(-,A@5A1 ,AAٹ,A+Aq",A|A>,AA_X,A TA@,A^Ahs,A#(A,A`Ai1,ADApx,A OAN˻,A2A ,A\AؼV,AA,Aa Aoμ,Aa A(,A0AUh,AGA,A Ap ǽ,A cA,A Ad,A Aw,A`vA$,A pA\Z,AA(\,AVA8S,A@AX5M,AFAX5M,A ~MAX5M,A`AX5M,ADiA\,A s;Ap,A@yA`f,A|ApJ;,A@>mAP,A`A%ÿ,A`;Apƿ,A A,A8AXz,AfA1,A0AP,A sUA,A`AxK,AXA(B,Ax,A0]C,A`;$ARR,AA\,A" A,A9yA,A`vPAH{H,AHA,A Ah,AAH:F,A A,ǎN,ALFAjX,A~GA7#sb,A@A7Gpm,A.ݐABɴx,APy)A:m,A# Agǎ,A~A,A`A,A@A f,A 1A`ey,A@ϔAX,AtA;,A`1A~,AA~,A A~,A3}Ap,AA ,AfJA,AAh,A+HAV,AA(f,ApAm,AFAh,AM:Ae,A A,A`A,A`BAh,A@5Av,A`Ag,A`A ,A AP^,A`A},A^A,AA p,A@rAD,AAi,A,A@,AHA@,,AgoA8T,A5A(x,A@AΊ,AM"3JA7t/,AES\Aդ ,AjxvA#h/,A2|3A@_0,Axb)Aቾ,A)A,AZ A ,A`2AX,ANA,A`,A,A`7A,A`7AP,A A2AS',A $Ap|V,A?DApy,A ZAIi,A@A,A٣ A,A Z Ah,A`o A,AM A ;,A ' A8,AX A0,A ZY AP,Ah AX,AA,AAh3P,A A(Je,AA,A  A,AH A,A`, A",A@  A,AN As,A@WAH ,A/A`q,AA,AhA(9<,A@A( [,AAHs,AMA(,AAh,AvA,AyAH;R,A VA8,A`ܭA@,A AG,A~A ,A?A0jn,A Ap,A3CA8Q,A``Aح,A`BAˆ,A A`G*,ABAUE,Ah5.gAa_,A47YAi^v,A|1AA,AJVh.Af%,A*?A ,A ApΜ,AAH7,AK A 6,A AHn,A@'"A,A!A:(,AM#AC,AL!A`,A [!AH,AA0,AkAWg,AA,AdA ,A?A,A A(T,AA؟w,AVA,A`JAb,AA` ,A<A#,AAnX,AF AIb,A2!A,Ax A ,A A8,A Al,A@A8Q?,A vAC,A@tA0,AAP P,AAPoJ,AׇAPY,Af.Au,AyA,AA,A;A@,AfA,B,AzDA,A2vAPZ,A`2kAB,AWJA,A ZAE!,A RA),A@TA0*,A ˕A,A)TA@H,A`m{Ah,A`A8;,Aq A(=,A APV,Ax Ap,,A A,A A8g,AA,A5A,AA5,Az Ax,A@# AP,A A,A$ A0,A+ A,A ~ A,A 6AX5,AA ,A9zA$,ApAI,AjAf,AjA`,A (A@,AAx,A`A,ArA8,A@,#A,A0A,AA,A Auw,AA@ ,A |A ,AA,AA&E,A@>XALi,A@A@,AA,A{A,,AVbA`l,A`wAx,A@A@,A`"AH,A@nAh,AkAIC,A:AHr,A JAh,AA`,AA ,At]A(B,A@9A],AjA,A@EA@ ,AJA7,AA$,A"AP/b,A@#AV,AA,A|LA`,AA,A#EA,A@PA`,AbAk^,AxWA%,A`A>,A@qA~,AA0*,AwAM,A \AR,AjFA065,AA[,A A,AIGAp,A`Ax,AAP,A3A?,A A %,A`A%,AigA&,AiAh7,A`AH.>,AvAI,A`A_,A AA],A@#SAR,A MAxI,A(Ahe,AA@m,AZAi,AHXAm,A@iA-A"A-A Ad&-A`]A`H-AڪApF-A`WA:-A H`A -A DA-A` Al-A@AE-A\A`X7-AxA6-AAXA-A[AE-A KAP^L-A`DTA8b[-A@Av-A AHf-AaAo-AjA`j-AAHr-AA@u-A A(-A@ A-A A u-A |Ac-A A"-AAiD-AAH?f-A'Aq-A`AK-A` wApm-A&1Aޕ-AηAߘ-A`A/-A; A(-A AH-AA-A`A-A Ae-A@AP-A cgA-A̍A-A@A@-A@A -AǾA-AЛAhj-A EAV-AAP& -A@rYA-A#A^-A$&A -A'A%K-A@`^A8n-AKxA@w-AXA`-AA-A9AK;-AkgA8g-A@5r(AP-A(ANo-AVMA?F-A@ AL-A kA -A@ "A2-A 7"AP-Aj(A-A`(A -A $*A`2-A*Ax6-A@%$AX-Af[$A(-A@`ApN-A A0.F -A@A( -A l!A] -A+"Ax(F -A %A ҁ -A-&A -Ag"A` -A"AP -A6"AX/ -Am"Apj -A A -A@U Ah -AO4 A` -AW Ad -AAx' -AhAНh -A0 A( -Afu A&} -A AL -A["Aٮ -A*)Aȩ -A{)A{ -A7#A`Bb -A$AY -A c %A  -A`T(A( -A`+A$L -A0A(-v -AS0A -A-A@ -A+Ap -A@)A؟X -AՓ'AM -A%A} -A8%A -A`)%AL -Ah&A; -AHs'AI -Ag'AQ -AV1A -A1Aj -A`Q(A`ry -A]g(Aޛ -A''A? -A`"&AX -AOY'A-Am](A7v -A6?3Ah͡ -A 3A؁-A`ܟ(Aج[-A@`)A-A3J-AΉ-A`x/Ax-A Z@/A@-A`2A_-A6Am4-A`;;Ax>f-A@5<A-A(AA -A@ }@A@F-A^;A`-A՝;AX -A7AXׄ-A9A0]-Ah7A-A>A -A BAH?-A CAus-A`ʏGA0]-A`TFA6#-A CA:-A`BAx6U-AaBA-A"CAP-AcGA-A]KA0-A@`LAhon-A JMAm-ALAZ-ALA J K-AO LA2-h.-A]EAb"-A3A_GW-AAz1 -AuvAGÕ-A7K_%Ad -A+A%hh,A౬A*&{1,A0>AE]K,A}9~Ad3N,A_)`Av,AAAd3N,AO"AE]K,A4ƐA*&{1,AP9A%hh,A(NSAd -AnB8AGÕ-AȴaAz1 -ARH{A_GW-A| udAb"-AF)OA2-h.-A Wn=A"-AÒL-AʇT)-AT, AO-A98 A|rV-A[u- A+C^-A|A6x,{e-AT, AJil-AĒL-Aҗgr-A Wn=ABk%y-A F)OA<"~-A| udA~n5-ARH{A-AHdA>-AHdA̱-A_A麢-A}9?Aل-A0Ak4K-A౬AOl-A+A-AHdA#eҞwd,A47.b@~od,Aev@>4d,AZ@\c,A+@c,A @]a,A@0;_,A9?@7!Ӗ_,A4O-@ha_,AUs=@;"R,_,A o@^,AFÊ@^|^,A};8@h6d^,Ag P͋@a^,A@=!^,As,@f9],AYs@MB],Ahg1@ q],A.@1j\,A& B@'h;p\,ANriy@P<[,A@[,A+Juh@ q[,Ai1@ @:,WN^,A#sq@#eҞwd,A47.b@(POD,A?F@pAD,ArB@NqUC,A^`ݬ٬@3,inC,A@ҥd C,ArH(@;mB,A'@רHB,AV@;A,A;@B,A@p@IB,A@C,A@j@B,A"@գB,A@@YB,Aa@A,Aȟ@0A,Aa@CA,A@@C&@,Ac2+@ è@,ALq@ M$a@,AHP7@PV@,AbU@Qݦq?,A$k@^n?,Ask4>@-Ci?,A@@6?,AF-@ِ?,A]vZ|@a>,A+x@X6>,AiHx@p>,Aw@^[ >,A &}w@@s>,A؞[w@~fD>,AU!v@pΔ>,A@v@ Bن>,ARF[v@%y>,Av@D sn>,AV8u@ڊd>,Alku@v\>,A@u@WS>,A?t@ȺI>,As@LZjA>,AMs@8>,A@r@=,AWħEp@$=,A0m@ZW.=,A{uj@P7[=,Ag@ؼX=,Ag@U=,AΗg@0GP=,A@N_g@hJ=,A@f@xhG=,A@ f@`H=,AXXf@J=,A f@XT=,A@>e@Z=,At#e@Z=,A e@V=,A]d@~O=,Aid@A=,A-ed@hs6=,Ad@ }/=,Ad@ )=,A\d@}#=,A֩d@ `6n=,AAd@ ==,AmV8d@8=,Admd@P<,A}Fb@Jh<,AGjva@Vמ<,A`h_@I~<,A}L,^@PQb<,ACl\@I<,A@Z@lM<,A "rZ@důP<,A+BZ@wR<,AU'Z@8S<,A(Y@EWR<,A+kY@5Q<,Av:WpY@*N<,A)?Y@$@J<,A$[QY@SCSF<,AyX@ZrI@<,AiX@ª ;<,A.fX@ZӪ4<,A(͡X@t.<,AޡX@_%W'<,A;[X@ <,AKX@<,ArX@&A<,AR0X@_# <,AÏX@<,A2T@wX@Ŭ1<,AWGWX@Ԋf;,A0X@- q;,AFSmX@ k;,AW@Z;,A>V@U Ԇ;,AYT@;,A@GTS@J;,A^zR@Rx;,A4 R@p;,A@rQ@f[];,Aϐ7"O@@:,A@L@p:,AiK@I:,AUJ@㪄:,AJ@h:,A@hI@Q[8:,AJgG@ :,AHPuFF@9,AjzD@&~̷9,A4T7B@9,AP A@wuĎ9,A,;@@39,A/kt@@/9,Arf@@v9,An)@@Às9,AUp?@i9,A@?@9,Ag6]>@!gh9,A[R8=@V`9,Aڊ=@09,A@4<@|9,AsJ*Y<@ tAbn9,Az<@)3a9,A<@KU9,AGL+-=@ȇK9,A@=@09,A@?@8,A2@@8,Ap4@@G8,AҾ?@@C;8,APFA@t}@S3(0,A8@&jI 0,A0Cs@ ۵M/,AYE;@/,AB@0E/,AnHE`@B,,A`l@W,,A:@M',,Au"ڮ@j+,A"A@+,ALc@-+,Aξ~@Oa+,AVU@]$/+,A%@*,A@@I*,AU@@R*,A:3u@Y)-O*,A]@*,A7m@ ),AG@ل(,A*^a@k4[&,AcYB@Ol#,A$Wz/z@@8+A8@H+A2@+A@+AU@P+A@d@ث+AΡ@+A "@+A@@Є+A@8}+Au@,l+A@@@I+A@@hb+AÔ@*+A@(+Av@`+A@@+Aa@+A@h+A@Y+A@Q@@5+A@ّ+AT @舑+A%@I+A@g@0L+AA@B+A2A1o+AcçAi+AlMABw0+A۝b AU+A_ A|+Aǻ Ay+Ay A+AKY A+A` A:\#+Az:d$% AH +A&N AP($+A,DAc$+AB._OA +A Ae_+AA07+AAI+ATDA+AA +AA +A^FAa+A}A#+A`pA8+A2A+AAx)+A AP+A`;BA( +A A+AA+AEAi+A AXh7+A0AX9+Az<A(KX+A$Am+AyAy+A@E3A+A [A8D+A qrA`6+AvAД+A@Al+AmA+A}uAH2+A A+AA+A@>>AP+A<A+AA+A[A+AA7+A=Ax +A{A+AA`+A ZA+AH.AP+AA+A A+A`BAu+A LA+AKpA +A@A+AAp,+AAy+A  AZ+Ak Af+A A0+A`!A0$+Aզ!A4+AT"AB+A (#AR+A=#Afk+A 8_$Ar+A$A_x+A@5%A|+A%Ah@+A`&Ao+A@>'A(τ+A'AX5+A(A0f+AM(A9x+A(Aq+AV(Apb+A &)A>]+A!)AY+A@EH)A`X+A@Ca)AOY+AVs)A}_+A)Au+A+,AP8+A@,AX+Aח-A+A>.AX,+AH /A9+Ap/AA+A/Ap8H+A50A)N+A^0A(\Y+AÝ0A8d+A 0Afo+AH!1Az+AV1Au+A`1A+A1A(+A`B2A(+A q2A`+A2A8+A 73Ak+A3A+A3A(+A(4Ap+AW4Ap=+A 4A`a+Ai5A`a+A |6A+A6Ap +A`fN7A+A7A+A;8A8+Aw8A+A48A0.+A`)`9A+A9Ah/+AR9Ap+A0<A^+AM=A+AYAAp|P+AMHAhD+AHA`L+A3 RAp=V+A@:RA#`+A_RAP8i+AxRAv+ARA3+A̴RA3+ARA`+A RA+ARAh+ARRA`+A`SA`+ApMSA+AறSAP+AٳSAp+A SA#+A"TA83+A@>`TA8+A` TAX +A TAX +A UA+A1UA+AXUA@+ARUAu+A`UAP8+AGVA+A@>hVA+A@VA`+AgVA+A@ WAH\+A3WAأ+A jWA@+ARWA+A XA+AXA+AXA +A3/YA+A xYA0.+A |YA+AH+ZA+AR~ZAأ+A=ZAX+AR[A+A[AL+A`\Ah3+Az|\Ah3+A@\A`a+Az\AB+A(]AP+AzV]Aѻ+Aஃ]A0+AR]A+A]A8+A]A(ܠ+A!^A0.+AZ^A#+A |^AG+A^A3+AHA_AP}+AR_Aw+A |_AP8m+A`A0.a+A E`A0.X+A@>^`A`aN+ARz`A#C+A `A8+A@>`A`a4+A`Apk,+A aA&+A@>aAX +A^aAp+ApaA +A3saA+AdaA+A`)^aA+A@gdaAL+A`kaA+AuaAu+A q}aAǻ+A͂aA+A aA@+AHaA)+A4aA+A`]#bA+AH?bA0+AhbA+A@gbAp+AGbAX +AzbA+AcA)+A)cA ҭ+AIcA +AbcA@+AGcAX +A͖cA+A cA̕+AcȀ+A3dA+A >dA(t+ARdAp+A |dAi+AdA0d+A dA`+AeA#\+A`)2eA`aU+A`^eAO+A`eAG+A`eAA+AeA8:+A@eAW5+AfA2+A@ 1fA(1+A _fAB.+A@fA*+AfAf$+AfA+AgA@+A6gA#+A@glgAP8 +AHgA`+A@gA+A]gA+A`hA+A@>*hA+AEhA+A@nhAP+A]hA+AhAQ+A hApk+AhAH+A@hA+ARiA+A#iA+A |FiA+A aiA+AHiA+AʹiA8+A3iAp=+AiA8+A qjAP+A9jAǍ+A jA`a+A`ÝjAW+AjA`ay+AjAr+A \kAe+A%kAX ]+Ag2kAU+AqA+AzqAǚ+AqA+A@gqA+A qAx+A qApn+AqA`d+AqA83\+AqA`e[+AqApU+AqA83Q+A rAHN+A ,rAHO+AWrA)S+A grAfZ+A@rA`x+A | sA`+A@>\sA0+AsAp+A@sA8+A7tAਡ+A`wtAW+A`tAp=+A`)uA3+AmuA+A uA+A q vAਘ+AfvA+ARvA0+A wA`a~+A)rwAps+AwA@Wj+A MxA#f+AxAQ_+AxAZ+AH9yA(\S+A |yAzO+A`)yAHE+A`yA<+AyA83(+AJzA+A^zA +AtzA+A`)vzA8+A3ozA+A@hzA+A@>VzAp+ADzA8+A`2zA+A \/zA+A9zAx+AOzA`d+AtzA8]+AgxzApQ+A@tzAG!+A`zA+A@zAp+AHyA+A |yAB+A3iyA(\+A |*yA8+A3xA8+AíxAL+A@xAX+A kxA+A]AxA#+A`"xA)+A)xA-+A`wA`+AணwAP8+A`|wAG+AjwAŽ+A fwAH\+AnwAt+ĀwA`ai+AwAhH+A wAأ;+AwAz/+ARxA+A $xA`+AGMxAC +ASxA8+A |nxA+ApxAf+AxA+AxA83+A yAp=+A`çyA+A`yA+A@ +zA`+A@>JzAp+AozA(\+A@zAѧ+A@zA(ܤ+A {AH.+AE{A8+Ad{A+A {A+A@{A+Aň|AH+AT|Ah+A@}Aأ+A}A+A (}A #+A=}A=+A-}Am+A}A`w+A}A +A~AX+A` ~AP*+A&F~A(+An~Aӫ+A~Ah+A^A8+A`As+AA+AA+A@A2Q+ARAS+A (cAKV+AsA(]+AʔA$y+AAH?+AgA與+A -A+AX;A@+ALAz+A첄A'+A`mAP+AA}+A`Aȇ+A@aAз+AɆAw+A/A¥+AIA`+AA+A@4AP<+AjhAő+A~A(O+A ۔Aȓm+A.AX`+AmcAhA+A`щAPx4+A@A5(+A`m?AH2$+A\A/ +AzA+A@Ax(+A@ߒA+A`vA@+AA +ÅA$+AA++AwfA++ArAt,+A~AP ,+A ۖA_(+AaA@$+AIA+AjDAH+A@uA, +AA/+AʌA+A@A0T+A GAؽ+A`vA8Q+A uA((+A ֎A+AZA(f+AA0+A`NA+AAAP +A@KA@+A`wAhѽ+A@aA,+AAD+A`;A`T+A@.BAث+A 8hA+A@ؚA+AwڗAx+AA+A@pJA(+A}A`+A2Aӛ+A`vԘAX+AA(+A@=A`+A,}A6+AٙAP͐+A@A+A,AH+AzAL+A]ךA+AzA+AA+A@>6A+AA{+AӟAk+AɡALw+ALAj+AA{S+A`A8++A qA%+A6Aأ"+A`SA`+ARpA8+A샨A+A짨A#+A@ƨA(\+AA+A3A`+A 1A̳+A OA̪+AgA@W+A A+A@A83M+A`fA/+A`A(\'+A@>^AG+AAk+AA+AחAB+A AW+A`A`+A qeAW+AzpA8+AA@h+AApG+A bA<+A A8-+A=ܶAf"+AHA +A A+A.Ag+A@[A+A;A+A_A`+A`AIl+A-Ahb+A@A9 +A@+A8+A@%A0̙+A}A+A@CgA@u+A@A+A`DAw+A`A+AOA[+A ?A+A@A8'+AA`4+AsxA+A Z"AS+A`0A8Ҕ+A&AN+AQAe+A%AS+A=A(^+AHAp+A|AP8+AmAPf+A pA@+A`)A`+AAH.:+A A#U+A qABt+AA@W+AA@׮+AA+AA+ApAh5+A APP+A3As+AeA”+A@A+A`A+A8A`a+A qA`a+A A`'+A4'A0.?+AOAP8T+A zAz+A`A#+A'AX +A@gAP+A A+A3)Ap+A tAW^+AAu+AA(ܘ+A3A`a+A`#A+A3uA+A@A-+A A,A@>AhUJ,AOAYg,A`;A#s,A QA~,A A,AeAx ,AAݴ,A`dA,AA`,A2A,A@A,AA.,AA@,A`A\,A4qA t,AŬAP,A Ahќ,A AP,AnA,A`DA ,A 1Ar,AA,A1Ap,A XA,A`LAP,AKAp=#,AjAv7,AŮAE,AH AVR,A`vA`vd,AO Ai~,A@A8 ,A`dA ,A` AX.,A SAK,A`d}A&q,A@%A,A@PA,AFbA,AA0,AMAm,AA`,A@NAp:,A4A8e,A(Ad,A`A0,A@r A,Ay A,A# A`!,As A #,A A><,AAY,A~AXs,A A.,AwA,AA8E,AZ8A(O,AaAp,A qA,A=A^4,AApS,A (A h,A`;jAw,AAx,A@߻A(ۛ,A A4,A"A,A@Af,A@5A ,AQA',A#oA*K,AVA,A%At,A%A,A6&A+_,Ay6&AKM,A\5D/'Amk5,A'A ,A-v>(A9,ACw(A!,Aґ3)Aq7,Ao)AQ,A)A&{ ,A51Ap ,A71A ,A41A ,A'#1A8# ,A0A ,A`]0A@ ,A0Au ,Aො0Aȩ ,A Q0Ax ,Afr0A` ,Akm0AHP ,A`j0A8 ,A`e0A2 ,A`e0A(6 ,Aph0ANC ,Ao0A0G ,Ao0A0I ,Ao0AfK ,Akm0ADM ,A`j0A"O ,A`e0AQ ,A`0AR ,A`[0Ar] ,A@./0A.a ,A"0A ,A@,/A( ,A@ c/A> ,A G/AQ ,A?1/A ,A /AT ,A`.A(( ,A`.A ,A,.A8 ,A`].A ,A@r.A^# ,A}.A;% ,Av.AX' ,Aj.A@) ,A].A/ ,A@.A1 ,A .A3 ,A.A0A ,A S-Ax~^ ,AA-Ahv ,A@>,A( ,A,Ah ,A4,As ,A +A ,A+A ,A+A8I ,A+A` ,A 8+A,A`T+AK,A`%+A_,A++Ao,A5 +AP ~,A+AX,A5 +AI,A@+A0٪,A +Ap,A`-+AH,A}>+Ao,A`T+Ae,AMk+APZ,A +Aq,A+A`2,A^,AHr,Ay.Ap,A`2.A`%,A`/A,A/A,A/Ab,A ~/Ay,An/Ax,,A@0A6,A@'0APf?,A>0Ah"C,A@H0AE,A@W0Aa,Ah0A,A1A@,A03A`%,A A;5A2,A5Ah3?,A@5AK,A`O6Ax:V,A (6A@`,A@*7Ack,A&7Au,A`28Ap ,A@N8A,A@9AP+,AK:A(m,AI;A,A<A,A<A ,A [=A`q,Ad,>AD,A+>AP.,A@?AHy,A%AA0],AoBA,A`)CA@S/,A&DAf|,AR@FA@W,A7GAG,ATHA,AIA<,AKAO,ALAذl,A@ߜMA~,AMAԗ,A`B,NA5,A`K_NAhU,A@iNA,A`D=NA:,A`MA",AuMAh6,A@MA`b,ALAP,A7LA,AVALAs ,A#LAvK,A@WMA,AMA*,A?MA,AMA6H,A`^MA@{,A FMA,AcMAP/,ABMA(,A |>NAh,A`ܼNA8V,A  OA,AkOAc$,AJuOAfʩ,AwOA\[+,AOA,A@OAD޲,A4hnOAB,A/OALߍ,AA3 PAC,A_6u0PAҊ,AVPA!ē,A6$ܻPAĠ,AЉQA #v,A(ATmQAd,AQA=7,A'M-QAuRO,AQAـ#,A,QA,A4QAXI&,AWQA{|,AAQA;y,A0bQA,A ¹QA(z,A`;6RAh,ATtRA8&,ARA$,A*SAP!,A`SA%,A)SA 2,A@dTAO,A /TAk,A`;UA,A\UA,AFVA,A`ʋVAxz,AଲVAPL,AVA8Io,A@VAy,AVA_,AVA+,A` WA@,AFWAXy,AyWA!,A WAx,AWAp[,AWA!,AT@b"%,AS*@2-h>+,A:d@1,A%@ʇT8,A,YF@Ap/AA(#q/A %A Edq/A %Ahq/AAiq/AjAx(q/AGAX5r/AGA`Bxr/A ZZA&r/A qA0r/A`Ar/A A;s/AAXl(s/AAhAs/AAKs/A XAQs/AA|s/A`Aݝs/A AOs/A ADs/AHDAs/A:A`vs/AAxps/AA`s/A`AUs/AAS@t/AhAXmt/AhAt/A@A=t/AmA t/A+Aht/A`Aەt/AOA`Xt/A`]At/AA0t/A`AhZt/A@zANt/AfAz9t/A@A3t/AA _t/A As/AAs/AoAs/A@SAjs/AAs/AAPs/A`kAs/A@ ADs/A \AvTs/AApds/AhAxNs/A`[cAx#s/AVA0&r/A` 9A0r/AA`Wr/A AP(r/AAH Tr/A}A,Nr/A6AMHr/AଂAHr/AAPoq/A,Aq/AOAhq/AAp_q/A`wAq/A`AXtq/A9A@`_q/AQA$q/AAСSq/AKA4gq/A`Axq/AvAxץq/AA@q/AHAq/AYA(pq/AOA$q/A=A0f0q/A#&AOq/A@A~q/A=ADq/A A8q/A`]A`'r/A 8A8r/AAɻr/AAr/A2 A@ s/AA0;1s/A`5AK\s/A)AP/s/AAjs/A uEAPs/A`As/AOAPIt/A ɥA8)t/A@A3t/AW0AKt/A@lA(t/A@lA0Ut/A0ZA(~t/A#&At/AAt/A@֖A t/AAt/A.At/A0A0t/A(At/AդAhbt/A |sA(mt/AྯAt/A-At/A At/A`-A6t/AyAAt/AA&t/AcABt/A`o.A^t/A`A#t/A;$As/A;$Axxs/Af.A@03s/A Ar/A 1ALr/A ShAr/A@ [A`Wr/A`mA0#r/A ShAxdq/AuAhq/A Q'A?eq/A`A$q/AvNAp/Ap}Ap/A`XAAp/A"AYo/AIAo/AmA83o/A6Ao/AAdo/A`Ao/A?A@o/A@WA3p/AAhxp/AZAbp/AzAXFp/AxAp/AA@q/A`Aq/AAhq/A`)A3q/A6Ar/A@%/A0r/A6Ax~mr/A=sA7r/A{Ar/A@cAs/A.ARs/A`TAPs/AAQ-s/A`dAHr/AAXr/ATAr/A qAr/AeAQr/A iAq/AoAq/A@sAq/A@EA oq/A@EA8Dq/A6A8p/AA^p/A cA^p/A]&Aho/A`BA`o/A9@APro/A`AZ9o/A_Ap5o/A'An/A"Aan/AAon/AHAh]%n/A_AXcm/A_A=m/Af8Aߩm/A`Adlm/A ALm/A OA&m/A A m/A A{l/AAh@l/A'Al/AqAll/AUAXMl/ALAr(l/AsA07l/A$=Ak/A`AHk/AA8k/A AHxk/A7AZk/A AYGk/AAk/A@A0j/AࣕA@j/A@ύA$j/A@A/j/A@5aA/j/AAj/A=sAªj/A+A/j/A Q'A8sj/A œAHqj/AAXl^j/A@yA%=j/A,A+j/AjAPi/AMA,i/A}Aci/AAh/A@Ajh/AnAh/AA`gh/ADA Qh/A\Ag/A@Axmg/ARA1g/AA]g/A]Ang/A SAlg/AAutg/A 5A]g/AiAxe/AsAle/AHAle/AA =3e/AAd/AAh d/A@ DAod/AoApd/APAe/A3AXye/AtAe/A@~Ae/AA@We/A=Aqe/AKAe/A ZA3d/A6AIyd/A`DA`d/AAKc/A1AOc/A`A`Sb/A)AXƗb/AaA8b/AAhѕb/AKAb/AAtxb/AaA0b/A@p Ab/A@=A[a/A`;Aa/A3A06pa/A`Aha/A`KAHòa/A-A`a/AAb/A Ak~a/AN\Asa/AHAd`/A sAHr`/A;A3`/A[A(w`/AFApi`/A`IAB`/A8AP!`/ARAO_/A`fKAȱ_/A@#A4_/AA@q_/A 8Ae_/A 8A;H_/A &PA_/A 8.A_/A cA^/A`JA^/AOAx^/AyA^^/AmAw^/A@mA07X^/A[A<^/AVA'^/AAd]/A0Axv]/A6A]/A?Ap]/A(qA]/AAhn]/AA9]/AA7]/AA8\/A A\/A@A\/A@>A\\/A J(A88<\/A6AP\/A@֘Ag\/A :AXs\/A@rAxq\/AA.}\/Aj~Ax6\/A`'AM\/A&Ap\/AsA\/AAȲ\/A=AȲ\/A5A\/A@?A\/Av9A(m"]/AAL]/A A@T]/AAj]/AW=A>]/AA0]/AA`]/A_A(F^/AAp^/AgOAXt^/Ai Aq^/A*C A`^/A@ A _/A`2 AKC_/A$ A>_/AD A0_/A AX_/Aӆ A_/A A`/A AHX`/A Ag`/A` AO`/A A a/Aq APkHa/AA0Ta/A[AUa/AAa/A AXb/ARAoTb/A`AxKb/A`*Ahb/AA0b/A APMLc/A*A kc/A/Ac/AArd/A2A@Fd/AXhAh:e/A QA8֦e/AXrA8Ve/AA7^f/A Apڲf/A%ANf/A%A 0dg/A q A(g/A@iAP/h/A`2AX[h/A2Axh/AHA-i/A`AHLi/A`)FAi/AOA)j/AAq]j/AAxj/AAA`j/AnAk/A sA`6k/A|Alk/AWAPk/A gA`ek/A Ak/A@NA0k/A A8'k/AN AP縥/AfHA姥/A@GA/A`)GAX/AGAX/AeGANm/A`FAo/A`dFA8t/AEA/ArEA/ALEA8q/A`DEA`/A/EA?/A EA -/A=EA#/ADA!/A%DA#/A?CA)+/A`xCA0{/A CA\/A CA/A@iaCA/A 8BA0 /A@%BBAp /A AAp /AAAp /A@, @Axv/A 8?AW/AX>A`/A>Axv/A=[=A@/A@<A@$ /A#;Axv/A /Y;AUҤ/A ;A7ä/A:AƤ/A:A/A?f9A8d/A 9Ax-/A{8A/A`78Aͣ/A 7Aض/AE/7AԔ/A6Apxg/A6Ax=/A@'7A/AF,7A1/AE/7AGТ/A@p6A/A]=6A`/A 5A@o/A һ4A(\/AB4ARW/A`3Ae/A`2AБ@/A22A/A3A0ɡ/A34AXlv/A`4AF4/A J5A@٠/A6Aנ/A@'7A:ߠ/Am7Aנ/A@7AT̠/A`V8A/A 8A³/A89AHw/A@߲:A +/A,<A88/AS=A/A`m>A5/A3 AAs /AVAA/A0BA8Q/A`DAf/A~FAX"/AHA8/A IAP۝/A[KAH/A4 LA`t/AMA)/AtNAX/AOA/AJQAꄜ/A`DiRA9V/A@>SA`ܛ/A"UA駛/A`VAo/A XAPJ/Ag1ZA-%/A/\Ah /Ai]A`aĚ/A՟^AXlؚ/A_A?Ԛ/AaAĚ/A@bcA/ABdA/ATeAΚ/A_fAXݚ/A|xgAXݚ/AqhA/A@ߏiA/A=jA``/AkA /AmA/A`mroAC/A`pA/AavrA"/A`tA(7/A`"uAl/A5vAÛ/A`vA/A&vAxK/A_wA/AaexA0T/AxyA0nC/A@xAݫ/ApxAxu/A lwAW/ATzuAٟ/AKsARr/Az]rA)/A]}qA//A qkpAҌ/A`܅nA\A/AUmA~ɣ/A@#kA(/A *kA/AiA/A`gAF/A-gAXƐ/AseAw/A@`dAt/AbAPz/A@w`A^إ/A ^A/A\Ayѥ/At[A姥/AޞZA/AfYA/A XAxƘ/AWApZ¥/AWAx$ץ/A VVA"ƥ/A@gUAȩ/ATA07/ASA/ASA`S/A,RAxƘ/AQA;/A@N2PAɥ/A@ OA0ϥ/AlNA@_/A\MAa/A`ӽLA /A xKA /AĻJAɥ/A3JA/AIA姥/AgIAU/A ZHAP縥/AfHA@5/A &AȺ/AۗA//AA/AA>/AAF/AbAxGV/A$Aȇl/A$:A0و/AAL/AAP/AfAp/AAPο/ArA.ƿ/A@%BA`!/A A/A@ÑA/AquAP/A)$A@F/AʷA@“/AhHA/A`ƏA+z/ATAk/A 1AxKU/A`rA&/A@`AH!%/A A/A@cA0/A A/A3A([վ/A@TA ľ/AAʾ/A 8Aʾ/A A;/A`TA;/A"A;/AAӾ/A ZaA/A߈A0/A@A8/A A"A/AAP /AIA/AȆA0 /A隆AH!%/A[A0/A1A/Aj A/A (AX/A[A@о/A`A/A@CAx霾/A@dA/A`̝AXe/A&A(hR/A^CA//AvA/AjA/AaFA ݽ/A`9A(mƽ/AAR/AЇA}/AЇAȩy/A@AW/ARA?/A0Ao7/AjA'/AfAPM/AA/ALAd/A A@/AwAPޑ/Aa݄Af/AjAX/AAЦR/A^A:/AA*/AA/AAO/A`A߻/A AȻ/AڃA޶/At΃Aƛ/AAgx/A7A@a/AA X/A ZԄA~J/A +A/9/A`A3/A@#AX0/A@aA*/AA/A 8A/AOA/A A*/AA 9/A lŃAL/AAQ`/AAPw/A_A8/A"qA/A_A/AJA8g/A@@AXtл/A YA8/ASAXW/A:A /AA?&/A 4A8ZD/A"AlY/A 4A0we/A'wA؁q/Aq˃AGx/AAт/A CAX5/A@+Apȼ/A CA@/AdA/A }A/A/APj1/A`mA88A/AA88A/AjAI/A (APT/Aj AhUb/AdAu/A`A}/AHAԩ/A@`"A5н/A@p4AH/AOAX/AyAj /AA0;/AAR:/A@dA0.Y/Aj.AHw/A(A@`/A旆A/AjAUϾ/AfA@p/A` AP/AjA(2/A`7A NX/A@:A#q/A4AP/A@Ax׫/A=A,ÿ/A`jA0{Կ/A`ApN/A ZAS/A$tA/A\Ax) /AA /AAp/A JA./AAf/ApA`/A@ AP^/AA/AjYA/AAH/A`̤AC޿/AkA8s˿/ABAܱ/A`~A/A~A_/A~APH/A`~Axh-/A~A/A+~A/Aj5A /AzGA/ABA/A ~Ae/A~A;/A~A/A~Axm/A~Aي/A~A |/A΃~A0u/AY~Af/A@,~AȤN/A`>~AX9/A h~A 2/AӉ~A,/A@~AX9/A=AX9/A@PAH2/AAE+/A`̤Ah@%/AٳAh@%/A#A)/A@`MA//AzA="/A@A/AAHm/A cPAX#/A sbA /A`A@/AA`޽/A sbAݽ/A@,A/A AȔ/AɡA /A zA(`/AZ#A/A (~Axzս/A@~Ag/A~AE/A@~A{/A~Aa/À~AK/Aw~A5>/A@S~A6/Aޕ~A//A (~A $/A@NAW/A`~AG /A~AH/Aޕ~AXѼ/AJ~A MǼ/A?}AOʼ/A}AH/A`}A2/A}A؋/AP}A?o/A&}AlY/A}AЦR/A l|AC/A \|AH4/A}A#/A`}At/A Z|A/A` |A/A q|AP޻/A@Y|A0%/A@Y|AL/A`V|A(:/A`/|AXdu/A{AP [/A{AhF/A{Ai2/A)|A'0/A{|A/A}A(O/A_}AH?/A}A/Aj~A/A V~Aa/AӉ~A /A~A$/A~A/A }~Aw/AD~AP~AϺ/A)~A/AM}An/A}A/A/}A@/AI|A/A 5|A/A@{A/A?{A/9/Ao{A@Wf/A&~{A4/A){A/A2{A7Ի/AD{A /AD{A?&/As{A0=/A{A*W/A#|Ay/A _|A@/A@ |A/A"|A˼/A4|A0j/A`D|A/Aq|A/A}A88A/A5}AhUb/A`S}A{/A_}AE/A@h}AZ/A }AĽ/A}A/A}A0/A@}}A/A M}A/A5}A/A@y|A,/Aa|A?/A O|A p[/AQ|A8 x/AQ|Ax霾/Ao|AH/A l|A/At|A/A}A`/AJ}A؟#/A`e}A3:/Aw}A@FO/Ab}Ac/A/}Aq/A}A/A}AH /A}Amſ/A#}A/AG}A/A}A/A@E}A`1/Ad ~A/AM~A8(/A` ~A(//Ag2A=/AɡAcK/AqA0R/AA6a/A[kADp/A%A`o/A݂AȺe/AhAxc/AA!I/A*A2/A|A`1/A @Aؚ/AAX/A;ԅA3̿/A_Aⷿ/A OAH /A`Aз/A A({/A`;AX[g/Az@A(S/AשA0 V/A&A8t/A`AC/A 万A/AA/ApXAVa/A෩A@>F/A`APx?/AhsA7/AA//A)OA./ANJA//AA;C/A@%mA S/A`mAX[g/A APky/ABA;/A@5A/A`mAp[/AA,ÿ/A iAܿ/A&A/A s A/A@TA/A A/A AA 4/A cA"/A{A7/AʓA@G/AɏA8b/AAV/A`ُA/A@AD/Az9AX/A&َAf/AlAx/AxA /A@,A/A~A+/ATA/AA@./A@A7$/A`BA/AǛA/A`vWAX4/A3Aw/AgYA #/AA2 /A@PAH /AR;A/A@A /Af?A/A%AH/A *A8#/A@VAx/AߥAb/A A _/AA@/AAX9~/A}App/A`]A0Ȟ/A@A/AfA8D/A=A6/A`vA`G/AGAP/A Ah/AA/ARvA(/Ah8A/A A-/AV~AC/A~AE/A~AأB/AQi~AP9/A S~AH)3/A}A8:/A}AiK/AH}A\/A`}A"j/A`o}A5s/Ag+~A|/A/~AHn/Ag+~A/A@~A0/AF}A c/A@}A@B/A }AP/Aҿ/A EwA𣧿/A`ywAk/AvA!U/A?vA F/AvAX9-/A vA(h /A`vA5/AvAھ/AvA¾/AvAS/A`vA\/A`vAn/AXvAV/A4wA((5/A3YwA, /AcwAȥ/A]wApJ½/A_2wA/A4wAXV/A@ wAѳ/A AvA0/A`vA[/A{vA0/AgIvA?/A]-vAxK/A /vAP/AMvA,u/A`;pvAm/AvAwk/A wAlO/A`ywA`P9/AwA !/A@WxAP/A /xA/A`2wAH߼/A wA$ɼ/Ap*xAh̯/A AzxA /AyA`/AXyA/AxA(O/AbxAgٸ/A`UxA@/AxAը/AuxAHC/AxA/A@wAՆ/AtlwA*p/A əwApO/A@wA+,/A wA/A_wAٷ/A8wAO/AvAܷ/AhivAO/At\vA/ABvA֘/AvA/AEwA/A@wAtx/AxAPQ/A@wA:/A_wAD$/A@wAX/AhvAѶ/A8wA88/AgywAP/A6wAs/A6wAs/A`[xA@uh/A`[xAj/A`B8yAx\/A /nyAN/A ZyAL/A`jyAXuF/AxA4/A`[xA/A[wAP/AwAֵ/A ;xADе/AxA8/A@xAӏ/ALAyA8Q/AլyAh]r/AAHzA Ej/A@NzA8U/AqzAK/AzA93/A zA/A4zA0vд/A`)bzAE/AozAu/A`|zA`vj/A@5zAh/A AX{AW/Ak{AhK/AzA>/A@rzA@/A syA9/A 1yA/AxA/A`oxAjͳ/A OxAj/A !xA`/AbxAR=/AixAxz%/A ¹xAk/AEyA/A yAp*/Av.zA:/AGzA8 E/AH{A&O/A|Apq/A|A{/A u|A摳/A|A,/AI|AU/A J|AXг/A}A/A J|A/A \|Ah/A T|A`!/Aa|Ah/AW}A/A`K}Aڳ/Aj~AO/A | Aɇ/AمAU_/AA&O/AHA`C/A OAP8/A BA0"/A" A/A A /A`Aa/A]ًA[/A ҳA[/A]AX#/A_AA/AH@AK/A@A>b/AcA~/AA8/A5AX2/AֆA.g/AϐA(/AzAP//A@5YA@/A IAu1/A0A f/AzA/A cA/AzA<(/A`vA^/AAk/A`A(/AAʷ/A\A`/ANwAp !/AIAO/A9ӔA@>/ATAĸ/A 'Ap/AЖA?>/A@zA8l/AkA\/A Aѹ/AA˹/A$A(/A A /A 'A /ATA@/AĪAQ/A@PAX/A ~9A'6/A}A'6/A}.AX%/AA'6/ALAd/ALaA9/AzqAѹ/AߐAh/A@FA./A A͹/A sA/A3AV/A`vAxt/AzAJB/A אA/A@Ay/AKA~/A`vA/A`KAX9/AnAؼ^/ARAn/A`A/A؜A@ /AzA3/A;]A$/A38Ah./A@ʉA/AAO0/AɅA/AAX/A@"Ap%/A{AZ/AA@d/A}A(/A A{h/A /A/AA/A@AXc/A@A(/A (UA{/A@A/A@#AP&D/A@.TA{/AIAXִ/AA0n/A (UAp /AA/AbA /AzA/A 8wA@q]/AsA&/A'ŽA/A`AE/A A/A=<A(/A޶AX4Q/A;A$/AԑAȶ/AA/AAR/A`KYAp/AA}/AڱA@/A~APr/A`2ApAZ/A@AM=/A`)\A*/AX”A!/A@(A/AA޿/AhA/AhA@$/AhAhr/AAxS/AAH;/AǰA /A`A /AGA/A Ao;/A?֕A@q/AA _/AwMAHa/A@yApt/A'}AM3/A'}AQ/A@yA 0ؽ/ArAX諽/AOeA`/A Aro/A`AP/A/!A /A_Aq/AvA/AgA( /A'A`/AAf۷/AAҷ/A@!A/AbA/AI3A/A:Aԙ/AzA/AA/A_Axhķ/AAx/A@A/A Aذ/AǰAj/ApbAY/A@yAT/A AA\/A9A_]/AڱAH/AUA( /AAx/A@#AG/AA]/A`Az/ADAEg/A:A(L/A:AX /A`KYAp/AtAp/AA&/A\Aw/A sAA8R/A+A@O1/AAh/A`;]A5/A`;]A(o/A`;]AH\:/AAA/AA/AA6γ/A`A⾳/A@W(A)/A ֎A(y/AߕA #m/AH,AX9J/AتAh /A Ae/AAԌA /AA`v/A3A=Ѳ/A@BAh/A@BAؽ/A`:AТ/A A/AA/AWAH/A jA/A@.TA0{/AA /AגAPF/AwAd/A@ A(z/AΕA|/A`AJ/A іAٳ/AabA/A` AE/AKA@/A@A`/Af;At/A@5nAho/A ApD/A<A:_/A`<A@/A ƟAx:̵/As_AZ/A_1AY/AqA؍/A$A/A`yAA/A "A /A`dAPƷ/AAP/A`bApr/AèA$/A6A@1 /A0AH޺/AzyA /A쩫Ax/A`TCA7 /A`A0X/A@pA|/AEAd@/A@,A/AA/A A/A'A/AoAy/A`[IAKhz/A AZw/A ഊAPEt/AA@}m/AA gp/A|ØA0p/AyuA6r/AA@ϕr/AOAPt/A`;״A'w/A8A@¿x/AAy/A`[IA@/A A/A@RA0/A@ϛA q~/A@\A@/A (CA@/A A0!0AtAC0A uAH0A HAl0AfAt0A`LAܨ0A&^A)0A@#,Au0A`[Al0ALAt +0A AP70A@EA\D?0A2AvH0A AK0AlADP0Ah#A@]0A@A3b0AAl0A~Ahr0A5ATA0AA0A@#Adl0A`mA0A@#A|0AAy0A mA0A`OAl0ACAK0A62AT0AA~0A A)0A஭AF0A A0AvA] 0A[At0A[A"0AA%0AhvADk(0A@LAL$00A@Au10A` bAL20A`A80A`ðAC0AAG0AxAH!P0Ah@A\0Ac-A̔_0A Ali0AAm0A`Aȶt0AAw0AAh0A|dA0AaA0A`ܑA0Y0Az1A<Ñ0ATAp0AA 0A`A0A zAHp0A`EA\0A`Aɏ0AA0AךA0A@A$0A@~A0AqA0AdAJ0AFOAh3|0A@!Azx0A Aw0A;ALv0AA<=u0A?AAdj0A@.+A|0A"A40AA 0AXAW0A@.wAd 0AAB0A@.A0AA'0AA!0AHAZ.0AyAp0<0AGAXnJ0A AsZ0A@A(~l0AA\0A@PA\0AA&0A6AT0A٭AL0A Ap10A`NA@B0A(uAt0AWA0A ޹A0A]A0A AY0Aj(AX[0A??AOp0ARA\}0A QAA0AaA(0A $A|V0A(A(0A JAM0A`]ATh0AJAt0ARA, 0A` <At 0AЅA`P 0AACxT 0AA|ыA\X 0A3AGy-_ 0A$=A7e 0A,rA&k 0A2A{<q 0A8`A‰cw 0A4'1Aݠ{ 0AܒpVAhU 0AX:JA  0AxA- 0AWA-Z 0A~ýA!T 0AhI:Ar 0AA~N 0AKA 0A`0AN+= 0AO?XAl 0AQoANw 0AskuA 0AiAr 0AodA 0AQA 0A?A4 0A$A\ 0A^APk( 0A` A2 0A A7; 0AAE 0A@>AAܦ 0AzDA[ 0ANA 0AT6A& 0AA/ 0A A9 0A AC 0AIAM 0AhAT 0A AY 0A@.A8e 0AIA k 0AA#t 0A lA5{ 0A A@ 0A;Aƌ 0A@A 0A@޼A\M 0AɼA| 0AA0{ 0AA 0A`wAt 0A 2A޼ 0AfA8 0A sA 0AaA 0A`)@AP 0AtSAP 0AgZA| 0ABAT 0AYAHE 0AwA 0Ah}A 0AA 0A<A 0AA 0A`9A% 0AA8' 0A`Ax|2 0An¼A: 0AԼAC 0AA0H 0A A$O 0A$ALS 0A ؼA[Y 0AA1^ 0AkAl` 0A@CA0b 0AAb 0A@ԼApc 0A@AdRf 0A``Ag 0AzAp 0A 8AQz 0A@ǽAL 0A@нA< 0A½A 0AdA 0A`ouA 0ADA$W 0A A( 0AܼA֫ 0AA 0A SATc 0AAL 0A(ٻAy 0AAp= 0ALA 0A`3A 0Ax5APm 0A`AA 0AuAN 0A൅AH6 0A`AL 0A9Am 0AzɻAs 0Af{Aɀ 0A@%A| 0A@ƖA` 0AAS 0AA$ 0A<}AXh 0Aq`Aǰ 0A&WA9 0AUA+ 0AYAˠ 0AFnA@ 0A`2ĽA 0AjAp 0AA" 0AaCrA`z0A'򺦻A1WF0AXwA 0A A.V'0A A0I/0A+R$A!70AGS%3AЉF@0Ax6A/aH0A-AQH Q0AUA[X0AaCA_0AѻAd0A֋ʻA6j0AMKȻAȐo0AwDͻAPat0AٻAky0AE*eA~0A`KAM0A Aŧ0A[nAf0A~A]0AzA $0A{ALf0A`ÑA0A`A]0AA0ALAt0A $A'0AsA0AgAĀ 0AAt0AEAz0A OWA@40A2A4&G0A wALlv0A`?A`0A`iAT0AHA-0AEA40AA0A=AHv[0AwAp0AA 0AٝAQ0ApAhl0A FAx)m0A5}A0AG xAo 0AgA 0A &[A 0A~6A4 0A@4AX 0A@.4APϡ 0A4At 0AjI2A- 0A@3-AH 0A ,Af 0AK,A 0Anߛ,Aze 0A-Aj/ 0A;*-A. 0A-A] 0AI-ASm 0A$D0-AƠ 0ADžq-A(Ց 0A,Ao:1 0AR"(,A- 0A,As 0A&#­,A"ak 0A8h,AЁ 0A;,Aַ" 0A1,A2g} 0AȈO+Any 0AG_+Au 0A+Ab 0A`f +A(|a 0A*ALA 0A=*A 9dEC 0AGYcE*A'C 0A)A"Q 0A Q)A\R 0A)Ad9V 0Až)ATxe 0A*A i 0A`*A( d 0A@p*A_ 0A O*AZ 0A;)A(U 0A c)A\@T 0AF)A0N 0A@ (AG 0A)N(A D 0AG(Ah> 0A'AP; 0Av'A{: 0ARa'A\3 0AF&A1 0AF&A+ 0A ?&A) 0A])&A$ 0A@<&ApQ 0A)Ad!N 0A u)A|O 0Ad)A(uC 0A0)AQC 0An'~)AA 0Aa8)AL 0A$&A 0Aօɠ&ATy` 0A۲&A1 0A2&AYA 0Ap7&Af£ 0AԹz&A 0ABv&A 0A0et&AvJ 0Ak u&A{ 0AO-x&A: 0AB}&A8`N 0A%sJw&A) 0Adz{9&Aa 0A蜰&AC 0A7&A(o 0A`T&A m0A4D'AL̷0A?'AĒ30AL:(AE10AL(A~0A:w(Abç0Ad{.)A 0AMZ)As%r0Ar)AIt[0A8w)A0D0Ag)AX-0A )A1Z0ALR (A0A'K](A20AD'ARd_0A˵V'AA0A-&A,A0A5&Aܮ0A;g%A,0A@-4$Auһ0AQ$A<0Aܽ$A$d0A˛#AA z0A;3)#AO0A"AH0AJ"AN:0A5L!AMc0ABc!A2[0A-!A0AOLW Aq0A A0A< Au0AJ%U Akh0Aor AYڛ0A[d Ak0A5ՖU A0AUyE A0A6 AL0A[Aȼ0AAͳ0AA,+0A`AT0AAs0A@A0A`AWq0AMrApZ0A`cAY0AAf0A^qAf0A`]A0j0AAx0AAx0AN Ab0A@#$AU\0AnKj$A(T0A~$AK0A/$A %8B0A@%AR70Ao%A-0A"%Ai +0AU=%Ah(0A%Al&0A%ATL$0A!%AU"0A$%AwY 0Adx,%A#0A)A7%AA0A@ C%ATx0AQ%AN10A|V%Aړ|0A=%A7K20A] I%ARƁ0A׊&A40A &M&AN"0AC+c&A0Aax&A8b0ANZ8&A$&e0A &A00A׶T&A$}0A`T&A@90A&Av0A!&A"0AQvw&A a0AE&AZfP0Aŗw9&A0AFT.&A>0A$&A40Aeh &A10AB&A<00A(&Aps0Au.&Add0Af枞&&Av,X0A{&Aֳ0AV%Ac0A K%AXe0Aɣ%A?'S0AD%ARad0AT %A0Aw2w%A@ 0As_%AiS0A%A 0A7!s%AN20A-%AЭ0A(%AzI0A͂%AVAH0Ap-%AL̯0A%A̖00AP%AjK0Ao%A e0A҅%A-0A1mw%A|W 0AO%Ah0A_2%A0AL%A'0A/MV%A;0AW%A-0Aa9}%A|Ǹ0Aൠ%Aw0A٨%Aq10AӋc%A 0AsӤ%AP0AM%Ang0A~5բ%AV0A+u%A׬c0A"<%AbE0Aу?%A6q0A$O%A\0Aɷj%A^0A,҉%A䃿0A@%AUs0A%_Q%A };0Af"%A0A$AXY0A$Ao0A3]$A@0AYߔB$Amx0Aʨ$AXW0A@$Am>}0A=5$AjKw0A$ARq0A$Ak0A<$AD+j0Al;$AEr`j0A4`j$A7;i0A=(q$A@h0Aնa$Ah0Aa=l~$ADg0Ay$A,e0A! kv$AD&,d0At$A1c0A:lNGt$Azb0AY0u$AřZa0AT'6;x$AR'2M`0ALBb|$A#Y_0A@ہ$A^0A`$A\0ADЁŘ$Ap,6aZ0AqK$A B>W0AB$AdAU0A$Ae.~R0AWb$AO0Aʺ$A$8hM0A;%>O$AZgK0A$A?#I0A$AݤvH0A_$AX$G0A2gN%AƎz(G0A+%Aa*G0Avp]B%A̝G0A`X%AfB0AQ9%A};0A@i%Aq;0ANl[%AUr;0A%Az9;0A^X%AA::0A4J%Az4k:0A!I%AH\90A%A!690A%AY 80A{/%A'70Az%A;60AQJk%AnXG360Aar%AAAt50AM %A40AV%A_ <;/0AP-%AjT)0Aֳ%AJsq"0A}:%A~H0A|%Al0A@W%A#0A!SH%AR10A1&.%A0Ab%A•V0AdI^œ%A0Ac4%Ao0A2_F%ARS0A,%ABO/0AR%A8n0AoL%Aj)0A/a6y%A(0AGʿs%A. 0A)sm%A #0A;g%Aز+ 0A$ %%AD0ALX$AXl0Aþ$Apv0A?犄$AЬP0Am_$AP2#0A\RUD$AF0Aն3$A 0A.$A0A{i@$Aq0AME#A_=0A/#A90Ark#A.0A|2#AH0A"Ap0A\KW"AHl0A"AL&0A`l"A=0AHtD"A`0Als!"AN0A "A*S0AY!AP0A !AR0A苃!A8~?0AB0!Ag0APg!A秳0A"!A%_0AoYM!AY00Az$!ASI@0Ay!AV0A!AL0A@!A 0A!AHԜ0A4d!AD"0A;N!At0A@yr!A0Aേ!A#UR0Azl!A$0A)X!A{ 0AcS!AXD0Abl!A-t0A,Rd!Ay"B0A/?!A0A`!Ay|0A3]t!Ag0A !AFh0Az!AjP~0AR~!AR0A|!AP*0A9!A׫z0AlV!A$8(0A}K!A 40A !A|-60A0+4!A}70Ac>!A480A0A%!Avq90A!AsO:0AY!A;0Aݥ!A\h;0A Ǚ!AU r;0A&*u!Ab6;0AmE!AAZ:0AQu!Az790A3Vj!A490A&a!A70AY!AP/0A˱ A=AR(0A_7v j A"0A>IAN^L0A fAP^0AA}60AiAM0AnaAِ/!0A~Ae/'0A[ǎAT/0AA,30A A590A A|;0AAL&>0A'A0B0AAHG0A`AL0AA8'T0A]Als0A=Av0AAX0AmAlx0AZAQ0A`vA0A9A40AtA;0A`"AH0An!A20A+$A0[[0ABW%AV0A`Kx&AT0A Qs&ApR0A&A\O;0A~&A390As&AP\0A%A/0A`K%Ah 0Ai%A 0A&W%A0Ao$Al0A$A0A$A 0Af|$Ap0AT$A0A"AP0A"A0A O!Am0A !A<&0A!A<](0A !A"0A!A 0A!A 0A@ !A 0A!A0A"A00As"A*0A@,"A,0A`"A0A"A$N0A;d#Al-0At#A0A`]#AC0A |#Ax0A@P $A0Awa%A$0A@&A0A '&A,)0A2&Ay+0A&A890AZ&A<0A@&A8%D0A&AAL0A"'AN0A.'ApR0A`5'A,]0A|>'Aԩa0A A'AtDd0A`A'Ak0A4A'Acm0AA'A4v0A`)2'A8ޘ0Aa&Aӟ0A&A 0A&A0A \&Ap0AoU&AT0A&A0A &A0A@%A0A`BR%A`0A J.%A0A@L%AN0A@%A<0AA%Aԫ0AF%A60A&A0A`o=&A\0AF&A 0A &AԚ0A@&Al0A׿&A`É0A@&A|0A &Arm0A ('A _0A, 'A(s`0A]x&Ao]0A_z&A b0Aྎ%A̬0A s%A0AO$A 0AG$A0A $A0Avh$A\M 0A`$A| 0A$A0E 0AU%AQ 0Aq%AȾL 0A `%A0b 0A%A n 0A@%&A x 0AQ{&AJ 0A*Ap 0A?*Ahf 0AK+A< 0A@`)Az 0AXcA,=!0A= A=F!0A< A\dY!0AX ALV!0AT AIV!0AV A\T!0A qb A(mQ!0A[ A0 O!0A AK!0A@ AF!0A 6 ADiD!0Ai? A=!0Ac A4!0A@ A*!0A@ߟ A!0A ATp!0A A` 0A At 0A@5 Axګ 0AЛ AŢ 0A A 0A = AՄ 0A< A} 0AH A|w 0Ak At 0Aw At 0AAq 0A@Al8m 0AAg 0AAd 0A ( Atc 0Apn At&` 0Ad AW 0A A, > 0A A0[8 0A@ An$ 0Ay A" 0A0 A` 0AƲ A̽ 0A K AD 0A@G A 0AA 0A`AA 0AS A/ 0A@N Al 0A+ A 0Aʝ A( 0A@ A:0A A|0A`A AГ0A ^ Al0A ^ AP 0A(T AW0A_? AL0A` A0A A0A A܄0AY AT0A@> A0A S Al0A J AQ0A A 0A+ A0A@5 A40A? A}0Ag Aey0A`T Aȉp0A Ag0Aͯ Ax:_0A AIU0A A(L0A` AA0A AA0A,{ APE0A@5{ AZE0AAl Al;0A@,l A;0Aw A>0Aw A$>0A A(;0AR Al20Af+ A,0AF2 AI#0A@g= A}0A (F A!0A@L A 0AB A0A] A0A A@0A@g A䲻0A@g A0A0f A0A' A80A`] A0A _ A(D0Ao` A@0Aq A£0A@, A:0A J, A0A AT20A A|0A@r AFm0AO Adc0A+ A[0A9 AmT0A@E At5<0A A4B0A| Ap0A@ Aw 0A` A 0Au AA0A J0 APz0A+ A̎0A4 A0AF AT0A ~ A0AV A(O0A A40AF A 0A`C A a0A A0Ai A40A J AD0A@m A0A+ A40AU AhB0A~ A0A A:0Ay A|Ǫ0A@= A0Aw A0A@> A0A A0A A\uj0AF A a0Ao AȖZ0A AcK0A@ A10A A('0A[ AX0A' AT0A A0A w A0AK A0A@ APk0Ai3 AD0A A8G0A A^0A A$0A`) A0A Al0A C A,+0A@ A0A A܄0A& Ab0AL A50A`ʍ A ٴ0A`K A0Ah A0AHA0ATADkt0A JAXY^0A`A T0AA\C0AA%0AaAX10A Ađ0A@ A0A` A 0A`| AD0A ۦ A^0A A0A@ A<0A AL0A A0A A˄0AQ Ad|0A` At0A` Ak0A`o~ Ab0A@ l A`Y0Ahu A4R0A`T AAJ0A@ A70AR AH.0A At&0A A0A4| A0A`e A0A`G A` 0Af Am0A` A 0A AT 0A A( 0A@go A 30A?E A50A`0 Ax0AU Ay0A A<;0A Ag0A` A,s0A. A}G0A@/ A60A` A`0A AXy0A / AaG0A A10A  Axk0A A,0A Aڅ0A  Af0AA$L0AAhq30A@ϘALH0A`KA(/0A@EAlG0A`A(0A A0AADs0ARA\0AAR0A cfAZF0A@A|40A@A#"0AfwA(0AuA 0AqA0AjA0A bA0A`ZA0J0A@AA\-0AZ+A Q0A /Ad0A*Ah"0Aa~Az0A J'A`4`0A ˴A@0A c/AA>0A (AP0A`Aڨ0A`)A(0AÃ0AWAQ0A ,Ah>0AAA,Z00A`MA\*0ARA0A;YAp0A WA]0A-CA0A5AY0A"ALW0AAv0AAN0AA<0AAt0A@#Ax0A&A$w0AAz0A`A.0A`A90A AYw0A \Ao0AAL0AftA\80A`nA*0AiA?0A SkA0AqA 0A͇A8 0A؀A 0A`{A^ 0A@yyAH 0A~A 0A`A䥞 0AA 0A@pA 0AA} 0A Ahk 0A`ôA2c 0A A|[ 0AAT 0A*A@L 0A@`HAC 0A\A: 0AvA|0 0AZA( 0A |A 0AA 0AiA 0A`A 0A@G A 0A <Ae 0A SA 0A@GfAx 0AzA 0A@,A`c 0A6A 0A@A4 0A A4¶ 0A6Ad 0A`TA7 0A`A0ً 0A Aq 0A@Ai 0A[AZ 0AՈA`Q 0A;AI 0AA; 0AAL2 0A]A* 0A (AD# 0A@>A 0A IA B 0A NA  0A`KhA 0AzA 0A?A4 0AhA 0A@,AL 0A@`Ah( 0AF"A$ 0A`TKAL 0A nAQ 0AfA 0A ZAn 0AHA|?c 0A@A|VQ 0AQ ApH 0A@>AA 0A!A9 0AQ5A$&& 0A` BA 0A@,EAx 0A@iCAz 0AbA4 0A A s~ 0AAi 0A>ANV 0AALLe0A`KAw;0AAPš0A %A0A@As0AA0AOAo0A-A0AA0AAT0A`KVA@QB0A2A0A`AP\0AJA|t0A@Ad[0A@2A0ArA}0AoA80AA0A@ At0A47A0AKAzX0A APz=0A tAs!0A@OA0A_ Ah0AAt0A[hAxL0A cJA0Ah-A20AAy0AAp0A9GAL]0AAt0AADX0AAqG0A@5A]/0A (lA 0A`TA//A`dAp/A c5A8D/A8A /A%A/A Axz[/AA /AoRA/AVAk/A`A/AANt/A`qA /AA /A A@/A`AC/A7Ae/A`KA/AA/AA#/A@,A /AכA/AA0e/A ɒAQ/A٫Aq/AA`/A@APM/AEAZ/AA(/AAPh/AA]./A@A( /A0A/A A&/AAA/ATA/AڹA`/AA'A/AxA4&/AlAO/AgAP8/A mAht/AZkAho\/A@dAHL/A\Ah;/AsPA@/AA/AA/AA`2/AZA/A@Ac/A@AJ/AA/A@A/AzAP/x/A&eAd/A`JA`BY/A_BAD/A=As./ADA/ATAأ/A`AO/A@A /A A /A AP/AAX/A@`A ~/A^uAke/A@FA0/AjAXW/A A0/AzA@/A@ mA/A]jAX]/A3MAt /ADAN4/A39A/AL)AH/A?AH\/AAp+/A\A/A@ BA/A/A p/A/A/A`8A83/AcRA8x/AAH/A Ah/AadAp/AA /A@A0/A A[/A A8/AA /AA-/AApM/AEAw/AA/A4Az/A;"As/A`A_/AAR/ARTA[=/A"A#/A|A0/AAXV/A SA8/A@`A@F/A-AV/A>AK/A`9HAG/A0KAo/AaGA`²/A<Axˎ/A0A U/AA"=/AAD/A@AP/A` VAJ/A AsV/AAXy/ABA/A@ A0T/ApA0 /ARSA*Q/A@A /AAC/AfAН/A@@A8R/A@A0 /A6A`/AʘA8/A@sAr/A\A`/A|YA(4/A@rA"/AkA/A` LAj/A2dAe>/A?A_/A A/AEA0f/A?A~/ARAU/AzAX#%/AqA/A@!APk/A`[vA_/ANAo/A]$APf/AFAl/AAZ/A[A./A`TiA/A (A8/A AH/A@rAxƕ/A`T=AȘu/A@AXS/A AP0/A A@/AAP/A஼A@/A`AUS/A`2|A8*/AňA/AzA0̭/A0AX/AA<A/AHzA|/A@[A#G/A1A/A@Az/A@yAr/A㓿A0/AfA /AIAiA0H/A9AV/AA@/A@ A /AAȱ/A}8Ap/AXRA旮/A ZmA@F/A AkAHM/A`OA/AdAX/AܖAX/A@AO/A0A$/A`MAK /A(Az/AA۬/A`A0/A@ҔA q/A֬Aޫ/A_AX U/A`)AX A/A`)A/A (AA8t/AZAPYB/A =Axw/A 1uAZ/AmA@W/A`Aث/AA /A#AB/AA3v/AAH-T/A@~A0{5/A4pAX/A[A8R/A;A@Bۧ/A@Ahݼ/AAXV/A AU/A@vAA/AjA8$/AA /A6APϦ/A`$A@/A An/AA`r/A@،A/A'AΞ/A~AW/ACAX/A ~Apٝ/A`A؎/AEtA/A@gˈAP/A ZyAG/A^\A/A@־Ax;/A jA/AۅAPd/Ä́A`2/A,A/AOAY/A`A/A΀A8/A@!AxF/AAI/A~Ap/A ~A F/A+3}A/A {Aȃ/A S{AH/AqyAȩU/A yAّ/A wAP/A׌wA]/A7wAא/AqvA`G/A5xtAf/A tA/AtAu)/A qA؋/ApA]m/A`OpA=/ATpA0/AoAxъ/A`=oAS/A@ nA /A`)kA/AMkA(z/AiA8Vs/AhAX/A0hAA/AgA/A fAh̅/ACfAsQ/A`dA}܄/AdA0߃/AbA/A`AH/Am_A +/A^A /A s\Ap/A@[A߿~/A;ZA}/A`[zYAz<}/AXA}/ARXA8P|/AWA{/A`VVA֍{/AHUAHS{/AH^UA6{/AOJUAx{/A J>UA0z/AF:UAјz/AOUASz/A`T9UAz/ARTAy/A{TA([Wy/ASAx/A@`RA0nx/A |RAȓx/A^QAw/A`2QA7w/A@5:QA@v/AgPAyv/AAXJm/A` W>A2_m/A>Al/A=Al/A<Ae|l/A`]<Ak/A@8Aj/A@gw8A`i/A`5A i/A4A i/A3Azh/A S;3A@Bh/A`(2Ag/Apl1A/ g/A@u0A7e/A`è-Ac/AD*A+a/A&A7`/A 87$Ap^/A Au\/AnAHZ/A`DA(FY/AApYW/A Ax4W/AdAHU/A`AS/A- ABR/A` A Q/ARAQ/A ÄQ/AA0aQ/A A@=/Q/AcAP/AxAP/AAТaP/AAP/A'AxFO/A@AНO/A |=AzO/AA(`O/A)AТzN/A`KA4-N/A`×AM/AA xM/AqZA)M/AA~L/A@AylL/ATA/A`vuAP 5>/A@AB%>/A`]A=/A`26Ax:=/A9AG:/A`fA07:/A@Ah9/A@ZA(9/AA>=9/A c%A8/A SCA6/AA4/AfAH4/AEAk4/AsA%4/A (~A)3/AŔA 3/AxAp/3/AAȂ3/A A`T3/AEA3/A2A3/A=A`S}3/AAdw3/AAxq3/A@AP3/AA(\2/AA2/A`fSAd2/A`KkAxK 2/A`A`1/AAv1/A@>Ap 1/AE A<1/A?}A 0/AaAHn0/A`kAe//AA Z//A8A//A JA./AڟA@B./A`̖AH./A@AI./A൅A0-/AtoAh-/A>APP-/AAh;-/AAHP-/AdAЕ,/AN Axi,/A A@,/An>A+/A@P_AoW+/AgAP$+/A A*/AYA%+/AApΩ+/A@5A*+/A #A E+/A A8c*/A@TAj*/A`DA3*/A`@AP*/A`DAq*/A&IA`Ge*/AAA9*/A"AXW*/ApA)/AּAp)/AqAo)/A`"A@Q)/AAk8)/AXAHz)/A}A(/A`+A(/A` AM(/A`A@(/A AK(/ASAX[;(/A@,3A(#(/AA(/AA7'/A`A0L'/AA֕'/AAxv'/A 8`A-D'/AAP /'/A ͽAXV('/AiŽA@B'/AAQ '/A ½A&/AǽA =&/AҽA&/AA,&/AA&/A`νAj#'/AɻAppy'/AwƸAn'/AdA'/AӶA&/AA2&/AnAp&/AAAz&/A`Aج%/AzIAj%/A A%/AAVC%/A CA $/A`Az$/AKAT$/AA#/A+A#/AzA@`"/AAH"/A`fA`"/A9AtI"/AཱུA "/A害A0Y#/A`KA@ #/A`yAy"/ADAh?"/AA0f<"/A`oAH)5"/A@A0"/AjõA!/A"|A̧!/A.A w!/AܴAhQ9!/AzWAV!/A,Av /AӳA /A_A /ACAp= /AֲA() /A 1A k/A Aђ/AA7/AIA2/A֭A` /A`;A/A4jA@/AAE/A)«AТz/ARAo/A A@/AUAk/A`A/A@AP/AجA/A`A( /A|AHP/AxsA`/A A3/A`̳A`/AA/A íA|/A߿A_/AAA/A@Ax)/AA/A@ A(`/A`;iA0/AVA/A`;-Az/A}Aog/AŬAh7/AjA(/ALA(/A *A0/A@rAP/ARիA(/AEAP/AwAP~/A`7Ad/A AI/A JԪA!/AAh/Ad?Au /A@%A#/A`A/AF9A/A@*A/A-Axư/A٩A,/AĩA/A`A_/A`T]A H/AR7A$/A@A/AרA/A qA@/AA/AzgA/A;A@/AdAe/A JܧAG/A@ߟA@/A;hA</A`2ަA|/AaA0 /A?APIy/AAXt\/AG,AG/A@AXu!/AA/AЦAz/AĦAX[/A@>Apy/AVA8/AාAP /AgAf/A;AK/AfA8V?/A`A-3/A`AP (/A@ ХAp=/AgA /A`BAu/A`Ah/AqAQ /A AqA/AgA0H/A ZdA؎/A AWA`a/A6A/A %A֮/A@A0/A@A[/AפAP/AAl/A6Ah]/AT{AxzN/A`_Ah=/A@A,/A!A/AHA /AߣAP8/AॽAL/ARA/A@yiA/As6Ah̬/A A`G/A`o A(/A2Ax-y/A#ǢAč/A@A9/A աAp/A` Af/A2AHV/A APB/AൃA,4/AtA8/A?WA/A@?A/AAp=/AApp/A`)ϡAQ/A 뼡A/AAX۰/A5A֥/AjA0G/A`WA p/A*7A@/AhAw/A`oAo/A@ՠAf/A A]/AAT/A AM/A5rAFF/AZA7?/A@EA8./AA&/AA/A`)A/AʟA[/A A/A=Ae /A@A/A`8A/Aग़Au/A@pA=/AAA/AnAhQ/A#XA/A@$A/AtAB/AA(z/A̞AТ/A APv/A+A(n/A A`X/A`nAO/A S]A R6/A'Ae-/A|A'%/AA/A A@/A@۝A /A ƝA/A A/A`AXF/A@PAe/AoAX /A@[A`/AqJA/A@;AH/Ap$Ap/A`A/AӜA/AǜAp/ABAf/AAxK/AAQ/A+}AH/A@5AX/A A /ANAPk/A@yݚAU/A|A(/A !A /AzיAP/AcAx/AAPy/A]A7/A?Ao/AuA/A_A``i/A`KrAh@/AA/A A/A (A`eJ/A+A/ARIAP/A` DA/A ;AH/A 0A@/A^"A`/AHAѻ/AA/AA()/AڒA/AɒAX[/A@yA~/AAkw/AjAp q/AjgAi/AIHA`\/A)'A#U/AAtM/A@yAE/A` A>/AKA6/A^ΑAȓ./A`A#&/A3A/AٚAB/AjA k/A`|AP/AVmA@ /ATA' /AHA /AT3AL /AqAX /AA /A@A /AːAo /A AAК /AAo /A}Ax) /A`;iAHLy /A@`PA,l /A5A_ /A A`S /AAL /AfA7K /AAI /AHԏAH /AAK /ARAO /A jAAT /A,?AU /AAGX /A ŎA X /A6ŽAp0U /A@ƗAL /AyAؽ> /A eA7 /ARgAH, /A JjA# /A A /A3ȎAs /A A /A#A /ATAk /AFA /A問A( /AAp /AAQ /A偏A /A`}A@ /AjyA /AqxA /A`wA /AsvA( /A^nA(| /A}gAhs /A@>aAj /A XA`a /AqPAV /A'GAxzL /A=AxzB /A,5A`B9 /A`-AA0 /A'AX& /AA /AUA` /A@Ap /A|AG /A`[A /A@#A /AAx /AA7 /A`Ap /A`A /A`ێAP /A͎Aho /AŎA /AॽA̎ /A츎Au /AA} /A?As /A`KA@g /AA^ /A*AV /AAXVL /A@,AjB /AEA8 /A@AP1 /AzA ) /AnA /A?[A /A|GAF /A2Ap /A Ao /A A /A6A /A@A /AٍAG /ApA /AAP /A6Az /AQoA /AMAV /A +Az /A  Ap n /A@Akb /A@ȌAU /AA RG /AA? /A@vA@7 /A (cA/ /AOOAV' /A@<A /A-AP /AA /A`AP /AʋA(- /AзAu /A 8Ax /A?AX /AzAH. /A@rhA0L /A VAt /A3DA`Ǿ /A1AV /A ZA# /A# A¨ /AA@ /AA[ /A qԊA /A |Ay /AЍAm /AsA,_ /A|UA(W /A`CAPO /Az1AG /A !ApA /A6A(4 /AA(( /A?߉AH2 /AʼnA /A 䧉AH/AFA/A vAX/AcA0/A@5XA /A`NAp/A@GAp/A*A{/A(A/A@&A/A`DŇA(/AAI/A A/A`tA/ADvA/A;xA/A`A8D/AMA/A S͇Ah/AՇAx/A2A͓/A?A[/A`ˆAǀ/A@ψAhy/AԈAWo/AԈA)f/AԈA[/A`҈A O/A҈AC/AgшA7/AψAB./AˈAa&/A@ ƈA/A@5ˆA( /AA0/AA/AؾA/A A(/A ˆA/A`]ˆAh/AfAh/AA(ܳ/A"Aأ/AĈA/ÄA /A ՈA7/A[؈Az/A?ۈAj/AhAH_/AAhS/AAHE/A@,A0{8/A`؈A2/A ψA0"/ALjA/A`2ƈAV/A`A/A`AH/A3AP/A`A/A@iA؞/AGA/A?wA/AI^A-/A@iCA/A+Af/A`2A[/A AQ/A A/A҇A/AٺA:}/A^Apx/A \Ap/A׊Af/A AqA ]/AVA8R/A;A"G/A A:/A`A./A A["/A@yÆA/A@`AG /AA`/ApbAV/AAA/A`""A/A@A/A(Ax)/A AG/AŅADz/A`Apۥ/A ˁAۘ/A`]A(ܐ/AKAˈ/A@8Aj/A&A`w/A`Ak/A`Ah_/A A0T/A@>̈́AH/A̷A;/AVA//A` A#/AxA8 /A*KAx-/A3A/A A(z/A JA/AA/A`׃A#/A ƃA /A A/A좃AX[/A@Aج/A A~/A?mA8֋/AYA-/AEA py/A4A((q/A]$APi/A`Ata/A@AS/A APG/A@>قAQ;/AÂA`//A@,A%/A@A3/AA8/AtA8/A[A/A`>Ae/A/A/A`] A/ARA`/A JAx/A@A/AցA0/AA(/A ۪AH{/A A/A uAp=/A`vAx\/AbA[/A PA/A}1A~/A|Aos/AdA88g/A AX/AT؀AXg/A`KPA 4w/A@Ax/A ˺AZx/A ۙAkr/AjdApf/A@AAY/A <A Q/A`OAL/A`9AȲD/AVAE7/ANjA؁3/AsA%/AA/AAH/A A/AAL/A@A(/AfAx/A |AP/AtA/A@kA6/AHcA/Aj[AP/ARA臟/AjHA9/A>A(:/AW9A0./AQ/As/A'Api/AQ$Ax`/AA(W/AtAN/A# A"C/AA8s5/A?A`'/A`AH/AAD/AA/A@AHK/AײA/AAH-/AXA/AtA0/A@AE/A yA@q/AkA®/A^A/A;RAȺ/AEA/A <AXh/A3A z/A$A(q/A`)Ah/A`mAh_/AAV/A`[~AvL/A,~AhA/A~AT6/A~AQ(/A~A /A~A/A~A! /A ~AP/A@o~A`/AyA@.AyAh..A yA@= .A=yAo.A_yA.AyA@p.A@yA.A@yA.AyA.AyA.A yAP.A@yA.A yAێ.A`yAP.AayAw.A=yAxzm.A`yA d.AHyAP8Z.AyA'R.A`oyA`M.A΋yAx?.A JyAp3.A`yA8*.AyAxu".A|yA8V.AwyA .A@>qyA.AgyA .As^yAH.A WyAP8.AJyA.A J8yA0{.A)yA.AyA୿.A@ yA-.AxA =.AxAV.A6xA.AxAXۗ.AxAТ.A xAأ.AxA`{.AfxAr.AQQxAأj.A;xAa.A$xAW.A xA)O.A`owAE.AwA;.AwA(4.A`wAX(-.AwAF .AiwA.AjwA .A`wA.AmwAf.A\wA.A4FwA R.A q2wA.A wAp.AwA.A-wA.AdvAL.AvAp .AvA@u.AvA؎.AvAe.AGvvA0.A@jvA.A[vAw.AKvA#g.A %vA^.A3vAaV.A`vA(O.AduAHG.AhuA@.AuA1.A6uA[%.A`uA(.AtuA .A ~]uAo.A@ BuAA.A3,uA.AuAX.A`uAPj.A^tA.AtA.AftA.A3tA@.A`]tA@`.A^tAH2.A3tA.A"tA@`.AytAxu.AitA(-.AjYtAج}.A`2HtAt.A^8tAhk.A&'tAb.AtA(zX.A@tAO.A |sAF.A` sA >.A (sA؞8.AsA10.AsAA%.A@}sAP.A`bsA(z.A}MsA = .A9sA8#.A@isA.A`2sAQ.AorAX.A`rA-.ArA.A rAȓ.A־rA[.AնrAK.A_rA.A@اrA7.ArA.ArA.A QmrA.AYrA7.A?rA|.A \*rAv.A<rAPo.AqAg.A`qAk`.A^qAW.AqAR.AqAaG.A`KqA@?.AzqAx5.A`;uqA,.A^qA0j&.AGqA8 .A .qAf.AqA.A@pAhs!.A@oAr.A`ToA_.A=oA@.A pA8.A JpA`.ApA؎.A`pA.ApA(z.A*pAv.A ApAHa.ApAP.A`fpA .ApAs.A`pAh.ApA0L^.A2qAT.A@qA K.AqAA.A_qAx6.A<qA-.AqA$.AqA.A@qA.A qA#.ApqA@.A@!qA[.AqA`.A)qAp.A A qAj.A qAТ.A`qA.AtqAF.A`[qAXW.A,'qA .A@-qA.AqA8Ҳ.AqA.AOpAp=.A@pAf.A pAё.ARpA.A`pAp=.A`fpAn.A pAe.A?pA`\.ArpApS.AfpAI.AXpAjA.AMpAB9.A3BpAo/.A`f1pA'.A $pA.A6pAx .A oAp.AoA.AHoAh.AoAV.A`ժoA=.AzoA.A`oAh.AtoAV.A`oAF.AJoAp[.A >oA R.A@#.oA[.AoAۍ.A oA ҄.AnA@|.A nAGv.A@nAl.A^nAd.A nAX.A nAHM.A ynAC.A|cnA(z:.A2PnA@1.A@>nA).A30nA.A qnA..AnA0 .AmAP.A`mAt.A mA.AηmA.AࣧmA`e.AmA.AmA0.ApmA8.A^mA.ALmA .A?9mA8.A#mAU.A@mA.A 8lA@`.A@GlAxu.A (lAф.A (lAP}.A lAu.AؚlAh3k.AlAQc.A ulA8Z.A clAQ.A+SlAE.A?lAL;.A.lA2.A"lA8*.A`lAp".A lA(z.AQkA.AQkAX.AkA`.A@`kA.A`)kA(.AfkA(.AkA.AQykA`e.AkkA.A@akA[.A QkA.A (?kA~.A ~#kAp.A kAH.A (jAV.AjAa.A` jAj.A@PjAX׆.A jAA.A sjA~v.A`~jAo.AfjA(-i.A`PjA a.AZ6jAj[.A |#jAWU.A jA؎M.A6iA`I.AViAz:.A siA[1.A`iA).A iA`2 .AiA.AiA .A?iAp[ .A_gA.A TgA.A HgA<.Ap2gAƦ.A JgAt.AfAਔ.AfA`.AfA&.A]fAX[.AffA.AФfA(.A_fA v.A`fAH.l.A_ffA`d.A,WfA`e[.AGfAQ.A@y5fAI.A`fAB.A`eAf<.AeAe5.A@eAx,.AźeA =$.AeA<.AْeA``.AUeA( .A{eA@u.AxeAu.AreAp.A=ceAP.A_NeA p.A=1eA.A@eA.A@eA.A?eA.AdA̺.ApdA.AdAX֩.AdA,.A dA.AdA7.AdAhQ.AodA8.A@ \dAz.AOCdAr.A0dAXi.AdAb.A dA~Y.A cA2Q.A cAjJ.A cA[A.AcAG;.A`cA)4.A@cA*.A+wcA#.A ccA.A@NcA.AQ=cA( .A%cA.A cA.AbA.AbA(.AbA.A |bA(z.A^bAP.A@֠bA7.AbA.A Z|bA.A fbA(.A`MbA(`.A=bAh.A 'bA跊.A"bA.A`]bAaw.AZaAPo.A aAFh.A`)aA(z_.A`aAV.AŶaA0O.A[aA3G.AaA?.AgaAp1.A`aaA8).A3NaA.A`5aAt.AgaAp.A aA-.A+`A.A`AP.A `AH.A `Ap.A`2`A.A`A.A+u`A.AZ``A0̽.A` J`Ap.A/`AK.A`A.A+_A.A<_Ap<.A@_AH).A`)_A;.A_AȘ.A_A.A _A.A_Ah~.A_A`~.A _A}.Aʹ_Ap}.A@G_A.A@r5^A].AB]Aۢ.A`]Aբ.AI]AC.A f]Ax.A?]A k~.A]Aux.A`^A@[.Av_AH[.A x_A.[.Az_A`Z.A{_A8L.A_AB.A@Ƙ_A:.Aପ_A2.A^_Ax).A_A!.A_A.A``A.A `AȔ.A@9`Ah.A``A`.A`A0L.Ad`A .Af`A(.Av`A .A`Au.A@`A.AaAXR.A%aA.A@`4aAX.AGaA@B.A NaA׽.A@PaAp=.A@`RaA( .ARaA.A`NaA蔖.A LaA.A LaA.AzIaA({.AoFaAp s.A-CaAn.A BaA_.Ag=aAR.A[:aAI.A6aAP@.A@1aA06.A 'aA$+.AqaA .A aA.AaA.A `A.A@``A(.Ag`AH..A `A.AZ`A(.A ~`A.A`A.A`A.A^`A@.A<`AP.A?`A`.Ah`A`B.A`|`A2.A``A7.AO`Ax(.A@5`A`ǃ.A`A|.A+`At.A@_A-n.A_Af.A _A@].AG_A((X.A_AeO.A 1|_A[@.ARi_A(7.Ag]_A,..ApP_A".A?_A.A,_At .A_Aho.A _Ap.A^AF.A ^AF.A^Ay.Ap^A .A`^A\.A^A.A Ak^A.A@^AX(.A ^A0fu.A@]Am.A]A kf.A]A7`.A`"]AV.At]AM.A]A2B.A@ ]A@p4.Ax]A'.ARa]A.AG]AX.A 9]AP .A *]A.A]AG.Az\A07.A\Ah.A}\A.Aú\AP.AĮ\AA.AƠ\A(۩.A\AM.A ۂ\A.Ao\Au.A S\A0.A@7\A~.A4&\Apx.A`[\A`o.A@[Ape.A` [Ac.A[AZ.A[A`L.As[AP8E.A,[A=.A[A..Az[A%.A@k[A0.A@\[A.A` @[A .A?%[A.Ay[AG.A`ZA[.AZAأ.AZA.A ZAx(.AsZA.ATZA.A(ZA.A_YAa.AQYA.A-YA(-.A4YAP.A gYA.A8FYA.A@YA().A@EYAh.A`XAo.AؔXA.AXA.AXA.AtXA`.A ɿXA`.AXAx.A@gXA.A YA0L.A?;YA0.A`YA M.A`YA<.A@YA.AYAxƵ.A@ZA.A 8$ZAXװ.A?IZAP8.AiZA.AZA3.AЧZAl.AZA".AZA'.AZA›.A@>ZAu.A[A.A@?[A =.Am[AX9y.A@ϐ[Av.A [A8o.A[Ai.A@>[A`d.A \AH2^.A\A_V.A B\AP P.A]\AH.A@u\A0f9.A֒\A1.A]\A*.A\AH-".A \A .A`\A`2.A J\Ap=.A@\Ak.A \AȔ.A@,]A.A]A``.A`"]A7.A &]A.A-]A8.AF6]A(.A<]Ax\.AsD]AL.AK]A .A@Q]A.AX]A88w.A]]An.A`"b]AQe.Ae]A\.Ai]APM.A` p]AxWA.Ax]A@=;.A@y]A`.A]A8.A`]A .A@]A.Aߧ]A.A]A07.A]A(z.A@`]AQ.A ]A.A]A.A]A.Aq]A`.A ]A.At]AX[.An]A.An]Atx.A n]Aho.Ag]AP`.AZ]A@X.A P]A0Q.A G]ApC.A36]A5.A#]A ).A`]AH'.A O ]A`.A\AH.A\A`.AQ\A@.Az\A(.A\A.A@ \Ax.A {\A(.Ap\Ak.A$p\AX.A@5k\A.Ah\A.At\\AX֦.AG\A.A`:\AH).A ,\AF.A@\A.A \A7x.A[APr.A[A2l.A[A_.A`[AȔQ.At[A2D.A[Ah.8.A[AL+.Azm[Ah.A-U[A.A'?[A.A-[A0.A [Ax.A[A.A[ZAHa.AZA.A@iZA.AZAh.A ZA.AZA3.A }ZA`.A@mZA .A_ZA.ARZA@.AHZAu.A"0ZAجm.A JZAww.Al_YA(.A@CXA¬8~.AeXAS |.A~!XAZ{jy.A3wXAef'Iv.ApmXA+;r.AkeXAh[n.A T'`XA/j.A}]XA*~f.Au\]XA۽jb.Ah`XA骫_.Al\GfXAz[.AjnXAX.AM,yXAU.AXA7JI.AbXAv@.A.YA].A HYAlO.Ad wNYAp*q.A[@YA^.A3YAuQ.A6KXA5b$.A~XAM.ABXAQ?.A( WA.AٓWA`v.A@;WA.AO 0WA .A<%WAz͝.AsWA a.A }WA6U .A62WAKR.AU WAdj.A WAm.A WAAJ.A\gWACV.A)R%WA(.AWA̒.AؚlWA妃.Aޅ[WA8.A!XA.AN1XA1ehY.AEN.XAUc%.AjyXA{w.A)WAڿ.AF{WAv&.AkRiWA8Z`.A@y WA}_.AVAʧ`.AbPVAhc.AfVA@^b.AfMaKVA_.A%2VAo;\.AtijVAҸW.A$VAFiR.AyUAqsL.A*|UA`F.A@CUA$uQBA.ACLUA<@<.Av5UAa8.A]UAF=h5.Aol VA;2.AwwVA%s0.A2Qd-VAt/.A[AVAHr..A |UVAmI&.AcHVA Y!.Ae7jWA.AWA@.AEXAB.AvXA.A|XAFg.A#7YA=z .AYA.AFYA(J.AYAތ.AYA]%n.AE:&YAk_ƙ.A0\ ZA .A@ZAh.A@5YAku.A|MYAż.A`WeYAmz.ACbYAH.AӋYAjn.AJXAݽs".A!WXA0;.A~WA;m.ALS]GeVAq.As$;UAt}.AgsuTA~03J.AZRA@6[}.AWYQAe.A`fOAQ.A%1NAӿ @.AtAMA&Ɖ.AiM|LAjΌ.ATv\1KAߍ#.ATxIA"s.Aeń_cHA.AOFA*.AqkMEAs.A`TCA=.A6AC.A #Ah .AA8.A )AE .AtAD.AA6.A`KAW.A@A%.AlA@.A |Aݛ.AfA.A@lAҗ.AwWAyP.AZuL1AǗU.AǐA/.Al6Apߔ.AyH*WA`)'.A]JvA Sq.ARE%rsAL+.A,Ax .A.MAY~].AG&YAcư.A)QA.AfA(.A1 AoK.A$A.A-AW5h.A0$.Av.A!XA&.AW2 Aw&M.AtS2޽A{EV$.A1:A u.AvᦼA+.A$Aږ.AA2)&.AR\kUA@.AA܅.A`DAH#.A5NAPoq.AAq.A@gA3.A}AMd'i.A AU) N.A- NAxX%8.AOA*J>'.A|AEn.Aʦ`AY.A.Mڵ A$.A%#A6*.AT|%AP.Aà"'As/-.A(7*A!9?.AZ,A"W.AL.Aͱt.A, >1ASP.Apl3AS!J.A.Xg5Ai.AW"8Ar:p.AA;]:AO.A{Fp<A쉌.AI>Am8Ȍ.AM^@AV?D} .ACHCAT.AŢ$EA8.A$0GA 0.A*g2IAFYpG.A;(KApߠ.A@pMA.A@EYARy5.AsZApG%].A 6c[AD œ.Av&\A80.A(\A h~.A:}w]AɷV .As^ANHy.A!^A@.AQ^An_^[.A;_A*@͖.AWX}_A0=T@.A+g_A}.A_A=T.AN\_Az.A [_A޾Yޘ.A_A5%A.A5M_AI.A-_AGl.A-`A& i.ApU>`Ab`ɚ.A`AZ(.AXaAja.A|RraA.AlEZybA|m=.Aeg6Q#cA6'.A ~cA.A ndAli>.A ceA(.AmfA/r.AiA$ .A}A^..A~A# .A]0AuΞ.A~#XA{N.ACἂAOav.A.AW..AK&A![.A;A;;§.A6eA'.A`bJAg.A@AXa.A0A$ƪ.A9A.ABAFH.A`mA`1.A At .Aq7AH.A`GA.A`.A(.A@4A .AXAs.AAxŴ.AApP.AA p.AAA.ACAx.AwA.AcA.A+A۶.AzA.A@A-̶.A;A|.A%A.A%A؎d.A=ŗA>.AAz.A^A.A9A00.A`ßA!.A /tA .A븢Aq.A sA7.AAp9|.AAP.ARAh.A AX R.AĮA.A Aj.AA`G.A~A@.A@KA.A| A.AjA&.A /PAU.AA.A@`Aà.A qA .AA.A`]Aِ.A`A.AA '.A JA.A`Ah".A QAx6 .A[A .A"A.A"Ab.AMϿA;.AA0.A A@.A;A(.A;A0.A^AH.A A.A`3AXhE.A`KAHz.AwAp.AAp.A3A.AA$.AѠA`%%.A~Ax\.A Appn.AƎA؎}.A[ʶAF.AA虗.A A\.A ȶAp.AA@$.AA0.AspAٺ.AA.AA.AXApP.A 퓺AF.AܢAFAޠ0.AgAXL.A} 79AnQ@l.A,jǤA W.A2eA}P.AO=hAeH.A*ߊأA؂3o.A~s A0.AtyAp[.A As!`N.AnRA9H.A&A{{/J.A@b*AL#.AwzvA1J\.A^џAͩ .AnC;A.u.A 8Af.A CAZ.AEQAq.At AN.ACQA,*.A(&A֦].At )A6.oo.A>AI2.AQfA9_w.AHyA;Nq.AALj.Ax>NAf{U.AdAM.AwCA5.Ap`ٟAb.AlȊA=.A9Aex.AvLAW_.ADܢAAE.AzA7ǣAAQTO.A;A.A˥A.AAx.A?A J.AØAL.AA.AMgA k.A|<A2 .A`:$ApA5.AZfKA3c.AG9A}g.AlAI.AZZAS.A٭AR¾-.AOaAc.AHÁǝAZp.A\ݾA}.A0eʝAF.AA<.Ap)"Ao.AfA5.AFhs㿞AT>.Ag ,Aj .AL_#A_].A%<A&j.A{A11:L.A^@8A;(.A^AF.A7AzE.A!A2.AA-.A /AX.AA.A A0m.AjApN.A &A.A A@.A ۓAx.AAr.A@iA.A`;?A{.AiAP.AAp.A`xApp.A AAP@ .A`AL.A Aa.A=Ak.AA`ԉ.ARA.AA.A 4A7C.AA.A?AHP.AA<.A`A.AAؽ.AA(?.AnAp_.A AX E.A@,A趶.A`mnAhb!.AoA`d.ABA`.A`AH.ARsAd.A AP/.A&AP<.AAhA.A`mA`.AA.ANWA(h4.Af(AhUs.A3Ae.AʀA˖.AA@>.Ag>A茑.AMA(>.AAї.AAH.AdA/.AAx.A$rAH?+.A AH.[.A A.AAA .A`"Am.A@Ao.AA$.A JAX.A@>A .A`2A`q.AqA.A A(.A'A.AA.A l:A@.AkA0;.AuA.AMrA(.AZA:.A .A@.A@A".A,AV.A`A$.A 1Adn.AA@.A]Ab.A@?A#.AMAh.AjAP^.A@0A.Ao1Ax.AAo@/A#A`S/AMAп /A |ZA$/AA\/AqA R&/A=ATR/AAo/A`SAľ/AAP/A3A/A$A`T/AAI/A`A(/A"A/AHiAx$O/A3A,/A$Aث/AA/A`bAH/A |A c/AXA(܀/A@GiA/A \A/A}A8E/AAǢ/A A(z/A`A#=/A sAh/AVA/Ai,AF/A(AP/AMAX/A`]A`/AA8b/Ai,A0/A@ A7/AAȃB/AA0>/AA?8/AA`v/A@PAX(/A`T2A`M/A=AD/AAJ/AA~/AEAP/A AA(/AA//A@rA?/AׯA( /AAl/A@.AX$/AzOAX/A`bAr/A ˊAp'/A@AXJ/A[]A]/A@A/AvbAn/AAp/A-As/A=wAe/ANA0/A;A`/A@4A(/A ;A((*/A=A@5/A@AA8 ?/A[@A`J/A4>AW/A:AЄc/A`d3A k/A*Am/A(Axw/A2$A@}/A !A\/A A/AAPo/AA(/A" A/AAU/A3A跷/AA/AA/A`AX/A*A/A@A0/A@A(/AA$/AjAP</A JA/A`AW/AA##/AA@+/AA؎5/AyAP?/A$AXWI/A[6AhQ/A@rFAZ/A@_Ad/AHzA[m/AŒA(t/AƤA{/A Aփ/A6Aň/A@5A(\/A AAۘ/A A /A AX/A:Ax/ASA,/AiA/A|A/A`KA(/A`oA/A@A/AAH./AHAP /AA /A_A /AtA /AF4A /ADA% /A ZTAX. /A hA7 /AtAQ@ /A6~AAX /AZA7 /AA /ARA /AڪAX /A AXW /A`2A0 /A@`A* /A A6 /A/A7 /A QAD /AAO /AA؎W /A QAV_ /AZAH.l /A@yA0Gt /AA=} /AqA@… /A@ A8֏ /A@`A0 /AwAU /ApA /A`"hA( /A`]`A[ /AXAu /A (KA0 /AdAA /A ;A /A7A[ /A}1A\ /A +Aj /A`"A, /A@App) /A@AV* /A`A4 /A`AB /AA`O /A AZ /AEA(c /AA pm /A?A0v /AA83 /AA8 /A`A /ARAPk /A`fA R /AľAP /A ˳AX /A@ A(z /A@!APo /AA /A@AP /AA0. /AEA /AA  /ARAP8 /A@5A /A`[Ap /A`Ah( /A`KAؼ3 /A<A7 /AA> /AנAHI /AA0jT /A At] /A`APh /A6Ar /AA| /A A@u /AAp /A,Ap /A`A /AA /A A /ApAV /AdA8# /A`A /AAp8 /A{Ap /AuA/AqA( /A`KnA=/AmA%/AjAH1/A AiA8V</AfA(D/AeAeM/AzcA`W/AcABa/A`fcAj/AcAp/A_cAs/A)cAxz~/AbA/A`fcA/AeA`/A gAѤ/AjA0{/AmA/AoA/A/AHrA)L/A@uA[/AzAg/A~A(\n/A}Ay/A|A2/AxA/AtA/A tAa/A@tA88/A vA/A@xA/A@yA"/AzA/AzA/AzA/AqzA /AdyAG/A@yyAX/A@ryA /A QyAX[/A@yAHz/AyA0(/A/A qUAX#@/A@]ZAnA/A^AlC/AacA0E/AhAmF/A෗lAVH/A-qAp}7J/AT~tA> L/AwA8N/A@-{AgxP/A` {A(Q/A`{ARR/A` {AT/A)yAH) W/A!wA\Y/AtA.Y/A JuA@[/AЇuA`]/AvAd_/ABwvAP7_/AvA0&a/AZwAc/A@wA`f/A=xAb1f/AQxADf/A.N}yAAzWZf/AmimzA,3tf/ANž{ABT큒f/A|#|Af/A A}At9yѼf/AW{d~Af/A@#~A6$tf/AX iGAf?f/AwA[Nx g/A1_'A&g/A*A-:g/A}kACPg/A@rAXg/AhAxg/AvԁAg/A_A0 g/A@Ag/A@`(Ahg/AAg/AAhg/A@%NA( Yh/AA@F`h/A |AXҖh/AOA`1h/A7A@ j/A;A0Cl/A$FADl/A`\A@Fm/A]A-w/A.A[w/A AЀw/AӒAHy/A`d5Aaqz/A`muAЕ{/AkpA(O|/A`Ah |/AʇA } |/A`;iA̍}/A AƘ}/A dA}/AAH}/AAG}/A`APf~/A3LAh~/A äA(~/AE?AXF4~/A`2AC~/AHA#L~/AfAXO~/AAy]d~/A3[<Ad~~/AFA~/Abc0AO/x~/AeEA~/Af#AX/A'Ah/A0A/A 1AH/AٲA/AzA/A@C³A8֯/A` ƳA 0/AA/AʴA(/AѳA/AѳA( /A`A(/AcA8/A@PA0/A AK/A@,AЄ`/A@5A{/AůA0/AA/AdA‰/A A(Ӊ/A {Aȋ/A9A8'/AAkM/AAw/A+AH/A@AZ/AAߌ/A xA/A<A`,/AEŶA!/AAH/AAs/AA8'/A@iQA(/AhAˑ/A@p7AXh/AAA/A|bAڦp/AfҿAے/AA/Aʅ~aAz_-/AmAxW/A AS/ALAxR9ē/AUA/AڼLAi(/A0A0W/AAh]/ANA/AA̢/AAHˆ/AhAhA/A0gA&/A.A./Ag@A7/AAWa/AYAPMr/A@E~Al/A`A@m/AA/AAἕ/AAP/A+A#/A A0,/A AA/A` Ah[/A6A/A"A`/A}A/AjAy/A}A/A/AXF4/A@AX/AADk/A A(r/AAU/AA([/AApʭ/AA /AA˗/A`A"ؗ/A`WA>/A@A/AsDA/AbA/A`QA8$/A9Aȥ,/A`;A`]/A@ϬA/AQAⲘ/AAƘ/AxAPԘ/AA{/A@A /AA/AQAXhI/A`Ah/U/A@;APh/AEAȲu/A@PA(/A&A8/A sAxd/AA!ƚ/AAܚ/A *A/A A/A`6A0 /A A4/A (4A5/Aq4ACj/AAn/AQA8/AAPכ/AAH]/A A@O/A ZAh/AA!/AA5/AgAwF/AtAHnT/A@Ad/A Ahx/A@ A(~/AnAq/AdA/AdA՜/ArAa/A|Ay/A`APH>/A9Ao/AĀA /AA=/AgA՝/A9lA/A JAȋ/A`vA@06/A^AP/A@>AVc/AvA@$p/ATA/AsAե/AoNA}/AaA0/A 8A/A`ÕAN.Ϛɞ/Ap8Afܞ/AVA /AA:/A`̲A$A A(u/AwuAh}/AxA@/A`bAx~/AA߅/A XA/A|Aʚ/AA@/A YAP*/A@!A oբ/AhPA+/A/sA8/A@yAH/A4A0/A`Apƣ/AẠ/A,A@_/AA/AA/A5A(/A<A/A८A/AݭAף/A,Aɣ/AvA)ţ/A@Ap/AHA/A@A8/A@AHR/A`)AQ/A`åA`rM/A`A{H/A A/A=`A̲/A`)<A#/AHoA=/A@rA@׮/A ~Az/AA#/A=AQ/A@ !A@׵/A/A_/A`)Af/AAm/ARAx/A@rAR/A`A P/A"A8,/A AH\(/A qA/AzAu /AzA(ܸ/AAf/A`)4A@/A-]A/A=cAG/A܄A/A`AJ/AAV/A |A~/A A7/AjA/AA0/A#Af/AqA؞/A:A/A&A/AfA/AA ֣/A@A@W/AA¢/A \wAp /AzA0./A@A'/AzAf&/AAp/A@:A/AAX /A3AH./A ^A8/AnA@W/AsAջ/A@;Aã/AA(ģ/ABAXΣ/A (AxFϣ/A A5أ/AtA٣/A@YAHK/AA/AA/AA/AcA/A/BA/AFAP/A}3AHn/AAXH/AA S/AyA@]/AA$ڳg/A@Ū>A9q/A A+4C/AzjAAu/A)Aȥ/A Auե/A`A0/AOAxם/AAĴ/AAP/A` AD/A A`//AA8I/AA~7/A`AX!/AA( /A QAhD*/ASA+/ApAB/Af$AU/A Q<A/AAƦ/AhA1Ȧ/A 5Apʹ/A`AX/A 1A/A,A\/A/A/A:A`/A9;A~/A@A=/A@A/AAA/A<A/A2A8/A/,A¦/AO$AĦ/AHAH2Ǧ/A@AϦ/A |Aզ/AA Rۦ/A`2tA/AA/A@A/A@WA8/AA;/A TA /A6A/AA/A@GA/A`TA /AxA0*#/A`Aw%/AA'/AAh-/A9A4/AAQ/AA8X/AAz_/A)Ap/A`[ A}/A!A7/AAY/ARA-/A@5A/AjA(/A/Ap_Ƨ/AA0jԧ/AIAx/A$2A,/A \A౨/A@*A(/A@{A/AAUX/A@CAa/A@g-Aq/A+Axw/A@Ap /ApA/AA|F/A AXh/AAq/A@Apyz/AAl/A`A7/A3A/AxAأ«/AcA /ͫ/AcCA/AAi/A@,A8/AhA([/AAe_/A`Ac/AױAsq/A@tA0v/AK_A|/AMA/AVFA/A AAx䆬/A@<A튬/A:Ah⓬/Ak:A/A$@A/AMJA/ASA0ݯ/AQAt/AnTA0w/AWA(ܽ/AXAqͬ/AZA1ܬ/A`T\Ah/AjZAk/A3QA`/AIA A/A@?Ah/AAho9/AAW/A&kAAd/A@SA怭/A3%A1/A`) Ah/A@,A8/A;A/A`A/A Ap/A`A0+/A Ap7/AhA@/AઢA J/AA0S/A@hA`/A@7AxWh/AAo/A@5Au/AA8u/ARAz/AAހ/A@GA؍/A`AĈ/A lAy/A`TA/A*AH/A`A(/A@WA0叮/A`Aю/AA /AA@d/A (0AP/A)A/AŒA/A AX/A?A/A@yA/A |A0/AAP/A@rAtƮ/AAؼծ/A^ARخ/A9Aڮ/A`mAڮ/AFAڮ/A ZALخ/AAp׮/A`2BAP֮/Aa#AYծ/A A0jӮ/A@,AҮ/A A Ӯ/AoAi֮/A \Acخ/AA<ۮ/AEA`ۮ/A@AA,ܮ/A<Aܮ/A`8AȺݮ/A(5Aޮ/A1A߮/A.AЦ/AX)Ah/A A%AGA/A`]AX(+/A A0/A`A 6/A AȶQ/AA[/AA/A JPAƬ/A A3/AلAK/AA1/A`AL/A@5AD/A6}A0/A {A8"/AAx/A J~A( /A?AR/A@A`/A OA/A`AP+/AA&"/AZA $/A`AȘ*/AA ./A1A";/A@%A>/A` AA/A`IAI/A@,?A8"Q/A ZAZ/A`oA]/A A0&_/A4aAȶ_/AA_/A`Ap_/AFJA(K_/AA,_/AA@_/A@WAF^/A AX/AAZW/AAT/ABA0R/AAC/A A8/A O-A/ALA /AA"ܱ/AAHб/AA@/A PAA/A΁AX^/A`]AT/AAX/A5A/AxAJ/A/JAp^/AAN/AAT=/A A*/A[A[ׯ/A@!*AxK˯/AAAd/AfA r/A AU/A^Ap4/AhA/A_AҮ/A_Aa/AKPA;/A3AG/A=iAXҭ/A൧A`a/A Aݡ/A lAxGJ/AHAH/AAu/AA #//A8A$ /ATA8b/A-hA{/AsAV/AuA /AZuAx/AtAp/AsA /ArAm/A`oA/AjAp/AaA@/AaBAY/A Z;AP/A5A>/A@>2A`/A /A/A .AhD/AT.Ap/A.A/A .AZƫ/A7Ah/A`dIA`Б/AZA/A`cA/AiA߄/A pAXw/A Af/AAXFI/A`;A00/A@iA7/A :AP /A ZAp ɪ/AAx/A^rA/AV~AD/AAp=}/A`ܰAs/AA@n/AA8j/AA8"g/A'Ap8d/AA P/A̓A85/A7AH//ANhA"/A AA@~/A<A/A@Aذ/AkA/A Ap/A`CA8/ANAؒ/A@yA;/A@>A=%/ANAH,/A@A 0/A0(A9/A@DAdA/AcAȃD/A tA#G/AAEX/A@AQ[/AhA h/A@πAjn/AsApo/AvAxp/AA q/A|A]q/AmAq/A`Ar/AA w/A@iAw/AnAw/A AНw/A@>)A`aw/A 0Av/A O8AYv/A ?Au/AFAgs/A@WTAsp/AaAi/AxA,V/A@A0jQ/A@ApO/AAM/ApAoK/A`KA)I/AA0@/ALA/A`AxA/A"AB/A@N$AXC/A@&AhC/A<)AD/A},A cE/A?0AF/A`4A(G/A>AG/AJAgH/AXAH/A |AؚG/AA.C/A A`A/A`A@/A AEA @/Aq`AhA/AlAA/AxAB/A A@C/AXAoD/A`A [/A`]A0L{/AlA/AA(J/AfAxi/AA /AAXۖ/At(A/A6Aꘪ/AHhAз/A^zA c/ARA(:/A?AA/A[A(z/AAΟ/A A/AũAh/A`;A A/A 8AZ/A[AV٪/AW Aܪ/A a A/A /k A/A AV/A A/A& AP/A2 AV/A A A /A AU/At A8/A5 A /A A/Af AB/A`̱ A t-/Ay+ A"=/A@y A\>/Aj AN?/A A Ahb@/A Ahb@/A AP?/AQ A(9/AT AX 7/A A }6/Aj A~6/A © A 7/A` AH 8/A`9 A'8/A` A:/A A?/A`) A=D/A AJ/A@0 AhR/AF Ae/A`;r Aj/A~ Am/Az Aew/A` A/AR A$/A#AV/A +A1/A(A1/ApzAe/ATAPë/A`;-A5ȫ/A`IAݫ/AA/AAh/A3A>/A@A%/AA/AdA/AwA8/A߇Ax/A`9Ax/AHA0/A A/A@iA(O/AA/A Ah/AٸA6/A`A$/AAH/AA~/A |A8/A"APE/A`@A@uW/A@sAdY/A@WyAp9[/A`A^/AA4g/AAAxhj/AAPm/A=Ao/AAq/AAP{/A@Ao/A@%AH̯/A?AUì/AdKAHn/A`)]A/AH`A< /AQqA+/A]AO/AA#u/A Az/AAXۆ/A JA3/A_AU/A,Axɭ/A9qA٭/A`KA/AwA /A;A)/A`AP6/AAܯ/A=A/AzA3/AAt&/A Ax*/A A M4/AA8/A@A@L/A@W|A6i/A`CA/A`A/AA/AwzAP۰/AAi/A`Ap+/AA;/A A/A.Ad+/A Ah@/A3 AQ/Al A^R/A-i AS/AQe AS/A`2a AS/A\ AT/AX A1T/A'S A T/A"H AzS/A<> AR/A2 A@M/A@ AL/A APkL/A@ A{K/A8 AHK/A@ AL/A l A[O/A`v AO/ARi A(P/Ae AQ/A`)a ATX/AjO A_/A@,> A{/A& Ax-/A@y A"/A A(욱/A l AP랱/A A'/AV A+/A A0f/A`) A,±/A Aȱ/A A0G/A A </A Ax /AVA,/A A:%/A@C5A2/A@AX>/A A2T/AIAm/AgAأv/A A}/A@Ap/AcAho/AA/A|BAơ/AoAp/AA/A@AЮ/A>Ac/A`)A̱/A?A8I/A+wAН/AA$/AA#/A`9A¨/A-oAx/AUA(/A`KCAۦ/A >A0٦/A A/A`)qA }/A VA(>/A9A/A@APޡ/AAh/AA/AA/A`A%/AA/A AՐ/AQA~/A \AXz/AXAv/AAu/A6A]t/AoAfs/AOAr/AA\r/AAhr/A`A0q/AA@q/AjA0q/AA`ؖ/AAxƚ/AЩA/A@ #AW/A@yA&Բ/A@#,AX/AA/A2A8'/AjAt/AA/A@NtA/A OhAt/Af[At//A A1/AjA3/A@A_:/AA( A@W/A` AW/A@l A/A@ { Ak/AG A/A A# /Ap3 AF/AM8 AB!/AR Aq/A- Ao/A` A /A A /A A0o/A` Am/A@AHr/A@ϔA /AiAm/A`Ah/A6+A|2/A4A@2/AcA//A"_AU/A`GA`/ADA/ABAPx/A(>A/A0A³/A@!A/AA/A`A$/A A}/A APHk/A`]Ad/A ɼAH{=/A@yHA|7/A6A(/A AX/A@Az/A dAX /AGAp=h/AAuv/A`f4A1/A@WAP1/A`AP)/A}A(9/AHA˳/AAf/AWgA/A^A0;/A]AX/Ap`AV/AlAUy/AAxm/AoAc/AAp/A~A/AA/A5A% /AWA/AA(iֱ/A@IA0;/AXAXRv/AAki/A"Ad:/A@PA 2/A A*/ADA$/AA/AAJ/AO4A/AYAd/AjA/AyAu/A3D A%/Aab AȓӰ/A-"A` Ұ/AU7"Aȃ/A /A $A/A$A`/A`$A/A$A`/A &$A/A9%A/A%A /A Sd'AP/Ah'AQ/AG0'Au/A/ 'A/A&A/A`ܵ&An!/Am&A0!/AT&A #/Af&A#/A[%Ao$/Az%A$/A %A@%/AZX%APw&/A$A&/A@$A<&/A@$A%/A#AHn%/AO#A`$/A`"e#AH!$/AJ#A(:"/Af"AP@,/A "A0n//Aѝ"A7+/A`m"A",/A|"A3/A`D{"A:/A7#APM/Aq/#AhM/A4#A/A #A(/A"A~/A`m"Ap/A "A /AH"A E/A"Aз/A@{"Ax˰/Ab"Ai/A/#A(\W/A6e&Ah"H/A&A>W/A1nH'Axg/A'A0{z/A'Ap,/A='A ]R/A_Q(A%/A ((Ab"{۰/A{W)A/A[G+A /A+A /A ^,Am/A! },A3/A,A/A/0,A0!/A,Aec%/A,A7*/Af-A0/A3-AwJ7/A@$,-A@=/A9-A>/A:-AEJ/ARvVJ-A]W/AdQ-A&d/AO-A0m/A@rL-Ax/A Q-Ah3/AT-Az/Ab-A{/A=n-A R/A=t-A/A`fz-A/A-AB/A@-A/A=-AHܼ/A]}-A/Az~-Ap=±/A-A#ű/A-AXƱ/A-Ap=ȱ/Aש-AQϱ/A-AH.ر/Az-A`aޱ/A`f-Ap=/A |-A8/A.A/A@$.A/A`*.AG/A..A/A=.A`/A@L.A/AW.AB /A \[.AX /A@ W.A8/AV.A8/A^.A8/A`fb.A`/Ak.A`'/A@z.A+/A`).A@./A.A//A.AH//A.A`a//A.A1/A@ .A7/A .A1Ap=/AGK1A8/A3]1A{/AGm1A-/A1A R/A1AL/A`f1A/A1A /A 1Aأ/A1AL/A̰1A/Az1A/A`)1AP/A31AP8#/A 1A(/A@1A//Aֳ1AVP/A@r1AR/A1AZ/A2A( [/A[2A]/Ap2A_/A(L2A_/Aw2Apk^/A`f2A`/A2ALb/Aõ2A@Wf/A2A@j/A`f2AXn/A(2A)q/A`~2Ar/A`f2As/A`)2A`t/Aק2Afu/A2Aw/A`2Ap|/A2A8/A2A𣅳/Az2A#/A`f2AP/A2A̘/A` 3A/A(3AP8/A@ M3A`a/A@ e3Aǘ/A3A/A`f3AQ/A33A/A3A/A3AP8/A 4A0./A4AP/A 3A/A@3A׸/A`3A`ẳ/A4AB/A4A؞/A@ +4Ak/AB4Adz/AnX4Aȳ/A q[4A؞ͳ/A qm4A޳/A=4AP8/A@ 4A/A4Ap/A \ 5A/A`f,5Az/A`:5Aأ/AA5Au/A@>B5A`/AR@5Ah3/A@>5A`/AJ5A R/AW5A0./A`)^5A/ARh5AP/AHw5A/A@g5A/A`ñ5A(\/Az5A؞/A`)5AP/A@>5A/A`5AP/A'6A R/A76A/A P6A/AGe6Ak/A@>|6Ah3/AR6AX /A36A/A6Ak/A6A0./A֑6A%/A֏6A{,/AR6AP85/A6A/AdC;AЄ>/A9;AWK/A0;Aj/A ;A/A:Ax~/Ag:A Ab/A 0>A@0m/AXa>Af/A`K?Ak/AH?Aj/Ax?Ab/A?Ak_/A@A@Fq/AՑ@At/A@,?A(\o/AK?Aq/A8?Ap=x/A?A0~/Aa>AXے/A,?>AѪ/AG>A/A=A"/A>A t /A`_>AH/A@in>A(@/A?AT/A;?AhDX/A@y??A8[/Az@?A0La/A/>AHj/A>AP /A?A(&/A?A`%./A?A<*/A3?A/A?A/A U@Ad/A`W@AXt/A@Ac7/A A@A0:/Aa@Ah?/Aa@APC/A>AiU\/AC>AIJt/A.+`>Ay6/Au;>AraS/Aڨ%>A@þ/A >AXl޾/A])>A_6c/A~pC>AYt-/A6n>Aa:0S/A谟>A,w/A0>A0t/AM?A/A_?A^/A@?A`B_/A`K?AH\/Ax@A8]/Ay@AHa/A`o@Ao/A`?AA/A=DAA/AvAA/A@!AA`/AAA 2/AAAH3/A3AAD/AqAAU/A,cAA;?D /AЮ&AAzo\R/AaZ@A/Ay@A%|/Af@A\!/A'Dt@A$de/A&AA]/A-gbAAWZ/A0>[ñAAb*/A`.BA h/A9(!BAFB?/A41CA, /AxgCAqk/A?!iPDA`G/A@EEA+X/A +EATl/AEA/A0EA8/AFA()/A FA/ABFA@/A@FAp/A`FAٸ/A`FA/A GAx/AGA@~/AGAp_/A;>GA/A`vFGA/A}CGA6/A>GA!/A}(GAȇ./A@1GA:/A\GAE/AxGA@R/Aw|GAt/A`vcGAH{/A0iGAY/A9|GAp/AGA؁/A dGAx/AB*GA/A GA_/AGA/A$GAЀ/AGA8/A AFA /AFAE/AGA:/AGA0/ARGA8//A=GAhMC/AFGAhU/A@NGAZ/AGA]/AGAHa/A#HA8Zj/A \>HA@qp/A;HA2/A'HAԊ/AV2HAR/A ZHA/A}GA@1/AVGAxƷ/AGA0/AךGA/A 9GAu/AWGAH/A"GA/A`mGAp/A GA(O!/A 8GAh$/A|GA0]//AGA@/AGA(I/A sGAH/A@NHAЕJ/A@9HA`\/AGHAg/A cHAhm/AdHAz/AqHA/A`HAv/A3IA`ϔ/AtIA[/AIA/A`]IA4/AJA/A&0JA/A`TJA0H/A AJAD/AJA$/AfJA}/A KA%/AKAp6/A@$KAA/Ad0KA(Q/ABKA`/AKKA(p/A`)WKA;/A{KAأ/A KAP7/AKAT/A|KA/Ah LA3/A SlLA/AMLA+ /A@LA0;/A sMA`X3/AdMAw/A6|NA0&/A@ NA&/A`22OAm/A߃OAp/A OA/A 'PAp+/A`fPAk/A PA/A@QA@ /A&bQA/A cQA/AQA0'/A +RAy5/A@NkRAHB/A 1RA@FT/ARAذd/ARAذq/A+SA{/A`oSA-/A SA /A`SA/AoTA@/A)TA@/A`TGTA/A9eTA }/A`TAP/A@TA0]/ATA/AݩTA+/ATA9/ARTA R/A@TAg*/ARTA8g9/ATA8F/A ~UAT/A@FUAxWd/A`|UAH r/A@UA|/AkUA/A@GMWA88 /AwWAw/ALXA!/AӈXA+/AoXAH)=/A;YA%]/A YA@}z/AXjZA/AZA(/A[A/A[A'/A ]AV/A^A 0{/A^A/A}Q_AP /A@N`A-/At7aAIK/AaAH q/A\bA/A`bA/ABbAd/A`}cAh /A}KdA/A`!eAU@/A eAP]/A`fA>v/A dfA /A`vfAp=/A/!gA/AgA]/AgAX/A &hA/AhA/ARhAȥ!/A}&iA12/AjiAXWG/AբiA}i/AjAx}/AhjA0/AjA/A@#+kAܿ/AQkA/AnkA8/A]tkA3/A@NakA/ATkA8/A8kA؞/A6kAX0/AqOkAVI/ApkAWd/A&kA/A`BkA/A lAV/A?lAo/A@dlA/A0lA`B/AğlAmQ/AjlA /AlAp,/A &lA' /A kA /AmkAP/A ‚kAP/ATkA/A@!A(Q/A@~>AQ/A@=A8R/A |=AHzT/A}M=A1W/A (#=AY/A@ <AX[Z/A`à<A(`X/AX<A[S/A;A8"K/Ad;A@u@/Ag:A`:/A`d:Aj7/A`(:A<5/AT9AX[2/AП9AH-./A@R9A(`+/Am9A'/A8A/A`2P8A8/A.2A/A@1A/A|1A/A0A07/A@iw0AHu/A/A0j/A`6/AX/Ay.A:R/At.AL/A3-A@/A,A6/A`;),AX#*/A +AU#/A]v+Ap/A,+A/A*A/A@*Ap[/A*A /A9*A`/A *A/A)A`/AO)AP/Adc)A/A(AX/A@ Z(Ax/A'A/A7'AA/A st&A`/A^&A`/A Q%APj/A+1%A`/AI$AP/A`K$A-/AT$Ax-/AT$Aj/AR#A/A#Ap/AQ#AЄ/A`f!#AH/A"Ax-/A@%"AG/A Z!A/Ab!A/A? A/ARA((/AAP/A JnA/A A/A^A~/AA/A AP/AA2/A` A/AA-/A GAX/AAX/A`̃Ah/AAP/AA(` /A`]zA /A Q3A /A |A=/AcA" /A!AP /AA8x/A@GA@/AflAH./A`A@5/AAЄ/A`;A/A}AX/A#A/A (A/A@OA/AAM/A JnAo/AAx/A3lA /AAh/A SA/A`KA/AAأ(/AdA@//A`$A5/A A8?/A٧AG/A{AxX/A`!A%f/A@ Ar/A A0/A@ A/Ak A0/Av1 As/A$ A`؝/A A/A A'/AJ A/A Ax/A9 APٽ/A H A|/AH A 4/A- A3/Aa A /A*A(/AA]/A xAH/A+Aǣ/A mAĢ/AA8R/AH\A8Q/AA8I/AA/A,Axm/A` nAH/A (:AЦ/A@_A跋/A`-Aw/AbAh/AA+/A@?AX/A@p Ar/A.Az/AsAy/A At/AA`t/A6 A@$o/A`ANn/AA{i/A?A0h/A`mA8c/A@EA (/AAu'/A@gAhL/A,A0/ADAY/A`dAh/AA@/AAw/AսA({/A?A/A RAT/AA/AAʾ/AAX/A`vA@/A@ A`P/A SR AU/A`O A@/A` A/Ak Aأ/A`2 AH-/AE AH2/A A@I/A AL/A` A_/A Ad/AX A b/A@G AX^/A` AdO/A` AV/A Arf/Aj Ai/A@G Ask/A@p Ah/A" A/A Ah/A q A0w/A Aȱ~/A`; Ax/A A/A A /Aܾ AXƝ/A_ A/A* A8 /A= AIWѸ/AsE_A A#A8 /All A|r/Aߙb AG+/A?& A/A A+/A Av/A` A״/A A_/A@A/Aa2AP|/AAC/A JAH/AaAp/A A/A&{AH/AAP/AA`/A#A(`/AjA0/AVA`T/AA@ƌ/A`TA?/A AO/AKA/AA/A`AP/AA@d|/AAxx/A@,A@s/A"A^/AAYY/A~A/AgA/AvAq/A A/A٥Afk/A|Aq/A@Aq/AnAt/A`KA0y/A#=AX(~/AcA@>/AA/A/MA/A'A _/A`3Ah"/AvA`/A@AhĒ/A@PA/A (AY/A`bA/A<Ap /AAЮ/AA/AA/AAHa/A଼A3/AAP/A?Adz/A`A-/A?A/AA/A]7A/A@W<A/A@ Ah]/A@>8 A@/A1 A@/A_AT/A@JAxZ/A .A@a/A7AFe/A ]AXa/A A [/A AT/A( AT/ALALk/AAon/AVAȱm/AApo/A A7|/A@AZ/A@ Aؒ/ARAH/AaA4/AA/A`A/AA/A A0/AX A/A,AHm/AsA/A A0/A@p0Ah/A*AhU/A=/A/A A /AU3A/AA`/AA /AIA/AAb/A$A/A`A/A %APM/ALeA0/A SA/A@NA`/A91A/A oA/AAh/A@A/Aq>AHi/AzA/AA/A+A /A1AL/A^A/A`A0/AAP/A@!AXy/AF`A(/AA/A?AXy/A<AhU/A@WA(/A A'/AA/A4A8t/AGfAp=/A`A;/A A/A&iAp/AA(/AA/A HAd/AAֵ/AA`/AA/AWAHP/A AxG/A^A"/A1 AA/A A o/A=!Aƾ/A@>!A0/A!Aѹ/A`!A8/A`d"A߼/AE"A0L/A"A7/Ap"A /A@5"A,/A#Aȶ/A` &#A/A]#AH-/A A#A6/A@#A/A@i $Aѯ/AHR$A/A`f$A/Am$Ap/A`7%A/A`x%AZ/A`%Ae/A &A/AzU&A1/A&Aq/A+&A/Ao&A/A@P)'A /Al'AU/A'A /A'A /A?(A/Ad(A/A J(A/A)AL/A 9)AhU/Al)A0/A`)A"/A|)A(/A@,)A/A)*Ap/A@,C*Ap</A^\*A/A *A/A@*A /A*A/A +A@F/A[+A A/A +A/Aza,A0f/A,A 5AТT/A@5A8Vb/A 6Aw/AD6AH~/A@!X6A@A/A6Aڨ/Aլ6A`/A6Ao/A7A@B/A E7A0/A@n7A/A`f7A/A7Ae/A-8A/Aod8A<*/A8Az2/Ao8A@=/A(9A8E/AT9APP/A9Ao[/AF9A(c/Af9Ajo/A`2:Aw/A!:AL/A`]:A,/A֚:A'/A@:A1/A:Ai/Ag ;A/ALg;Aj/A Z;A-/A S;A#/A"<A/AH <AX[/A=<Ah/A y<A/A@`<Ah$/A=A&/AJ=A+/A y=App6/A =AF/A=A`GF/A@ =AoL/Ad'>AW/Ah>A@d/AR>Apyg/A>A`Pr/A@ >AЄy/A>AF/A>Ah"/A@ 0?AhQ/AC?AH/AP?A/A e?A/A;?Axz/A?Ak/A(@A_/A@@A /AQ@A`%/A1A !/A`hAL,/AoA;/AAЄS/A A0\/A@A8t_/A ҆A;Y/A`dApQ/A AQ/AfUApZ/A~Aj/AߏAv/A`}AA/AtAph/A` AwV/AൊA6/A`At/A 펩A{/AhAБ/A iA/A 7A(`/A A/A A/A l A{/A(Ail/AE&A]/A`b7A8O/A`@AvC/A?A0L9/A`4A/AڧA>/AɧA/AħA/A@ߺA/AAX/ApA8/AhpA/AGA/A`'AQ*/A36A@0/AAb9/A@ڤA C/A笤A_/A`cA/Ah5A`/A`Ah"/A߶Ae/Af{A0/A;CA/AAAx-"/A9Ah +/A :A2/AּA;/AAg/A@.!iA0+/A`hAX/A@hA/AgA/AGgA/AsfAR/AfA0U/A`eAUt/AQKeAb/ARdA]/A@EdACS/AdA0%G/A@dA9/AdA0+/A,jdAX/A ZdAx/AQdAX/A#PdA6/AEOdA0/A TdA@q/A``dAP|/AjdAx/A`qdAN2/AWdA*/A,FdA/At@AdA/AcdA'+[/AZ૔dAz3!/A"dA/A@ydA`6/AudAȁv/A7-YSdAx)o/A0,dAH]/A`cA6F/AgfcA@z=S3/Ay3bAHP#/A@`bA@k"/AhbX(aA /A@# aA~/A1aA./A9d= aA u4/AX `A\c/A |`A\ i%/AC"`A7+/A`m`APA/A$PA/A@OAxW/A@OA }/AViOA~/ANA@/A@NA/A@MAH/AdtMA/ALAث/A`LA/ALA@/AEXLAU/A3.LAPk/AsLAЮ/ALA`Á/ALAl/A0 LApS/ARLAH/Ad*LA,=/A/AcNAT/A`NA/A`NA)/A@`NA{/A OAΣ/A (SOAx>l/A`OAx/AX6PAX/A NPAH/A |gPAx/A qvPAx/A{PA/ApwPA/A`RPA/A1PA@]/A SOA./A`POA/A?NA/A&MAA ji/Aז>A?n}4/A5>A?c/AW =A((/A9T=A!y/AH<Am/A@P:<AK[h/A5';Ax)e/A}n;AXWd/A :Ac/Ag:An/AE8Aۍ/A@&7Aܞ/At6AvT/AT6AB7_/A5Ah/AY5Ah/A5AU/A`o@4A8x/AΆ4AЮN/A2Ap}w/A 1Ai/A@M1A/A 0AX/A`1A ҭ/A@1Au/A1AB/A1AH/A`0A~/A`]0A/AE0AP'/A?.Ah/AN.A\/A Ɍ,A3/AI+A;l/Au)A+x/A'P)A]S,/A^4)Ażה/A#")A/A@>)A/A$O!)A&/A ")A]M/A,)AP7/A`)A#c/AUԡ(APg/Af(A@/Ag(A]/A@!-(A f/A (A@o/A('Aq/A@'A@s/Am'APMp/A k&A+k/A %Ad/A@$ANe/A@,$A f/AH$Ai/As$Am/A e$Ags/A _$Aً/A ^$Ah/AY$AH/AL$AΞ/A %$A/A+.$A/A@NJ$A``/A@ r$Af/A`}$A(;@/Aiu%A&ν/AT%A$~/AWٸ:&A\_/A&A@/Ae D'A/A 'A/AG'A~*/A(A`4/A`K(A8IM/A (A[/A@)A\/AH)AP_/A)AZt/A*A/Ap*A/A`̲*A0*/A I+A/A+A>/A`+Ax-/A,AE/AX -A/A,-A/AW-A/Ay-Ah/A Z-AP/A-A6Q/Aj&+.Azb/A.;.A A/A %\.A`/A{.A9tva/AŚ.AOU/A])܄.A/AJO.A/A.Ao~/Ayc.A󏚣/A;.A/A^A.Am Î/A].Ar/A".A`Pm/ALJ.A@/A.AHn/A@0AQZp/A0A|r/AWG0At/A`d0AjhGw/Al0A.j`y/A50A0|/As0A`T~/A0A*cX/Aʩ0AHЍ/A`0AXW/AH0APͷ/A0A/A0A/A`1Ah@/A1A"/A-1A1/Ak1Ax/A`1A/A"1A/AL1A0j/A2A/A`'2A/A@H2A/A@yg2AG'/A O2AS7/A2Ah"[/A$2AH2i/A2APkx/A@ 2A`/A /3A(/A`f,3Ax/ARI3Ax/At3A/A3A(/A?3A~/A@ 4A@/A"4A=/A4Ax/A5AA-/Asd?A/A?A/A|@A՝/AE@A%/A )CA/A 1^CA/A CA/ADAi/A`mwDA,/ADAN6/A EAG/AŎEAr]/AFA^/AFAd/AFFA0ݷ/AIAȓ/AMA83./A }NAH/A0LOAȲ/ARA(/A=RA/A6SA/A@SA0f/A`TAho/A@UTAP/A`fiTAh/A~TAH/A2TAp/ATA/ATA/A@%TA\/AUA A /A7UA (/A ZUA8/AVAxP/A}VA^/A@VAc/A*WAd/A1WAXa/A3jWA0_/A WA(`/A`WA~h/A WA!r/A@ XA}/A 7XA/A`eXAX[/A XA/A:YA/AYAP/A NZAF/A ZAp[/AZA%t/AZA/AhZAE/A ZA/A 8ZA8/AZZAQ/A+[Ah/A@C[A /A@V[Ah/A@ c[AxKV/A /[A/A`;[AH/A [A٨/Ayn[A*/A%f[Ac*/ApޥZA0g4/A7umZA{/A?)ZAߨ/AYA/AYA(D/Ar*YA/A]YAX/AYA ./A/XALH/A]P);XAÖ^/AaWA_/A,TWA/AdVAw/AȚxVAE/A`VA(/AxUA$ /AhUvUA~K9/A}0TA*/AtsTAx)5/ASAO6/ASAy6/ASA;6/ASAH5/A@SA +3/ASA%*/A`ASA$/ASAXj/ASAЯh/AaSAwq/AdSApr/A1SA8/AQHSAС/ARSA]/A`SAK/A@RA@=s/A)RA`Pt/A RA`j/ARAhLh/AmRA(!/AKRA/AQA]/A@`QA`]/AQAXd/A&QAU4/AmPA $/AnPAȰ 0/A,\PA,>/AxUOA"Q/AF%OA_/ANAUk/ẠNA(t/A 1%NA8xx/A"MAz/A'aMAz/AiMAx/A3MA~Ay/A=9T MA8{/A ۻLA2~/Ad]LAV/AsLA /AU6~LAlF}/A?hnLAי/A\LA/A$LAۑ;/AOLA /ALAZL/AݱyKAj/A^/KAt;c/A&KA!/A@rKA"{/AWKA/A`/KAYt/A^c^ KAh/AgJA`!/A JA /A&JA(/A`JA؎/A@JArn/AI{}JAU/A OcJA/ABh JAج//A`DIA1/A`IAJX6/AEO/IA:/AIA=/ANsIA?6?/AƱn[IA@/A9}AIACc@/Ab.(IA2?i?/A IAhn=/A`2HAf/Ay6HAi A/AhpeHAF/A%879HAM/AHA\/A?GAj/A`GAyt/AXb/AI&zDAx/A^DAcn/A0DAؒb/AɧCAbb/A#CAYb/AǠCAcb/A-CAyb/A`CAb/A/CAb/A̚CAb/AtCA5c/A`2CAc/A@֕CAd/A൓CAe/A ґCAxg/A@CAk/A ACA@z/A ZCA/A qCA/A lCA/A@}CA軄/A|zCA/AwCA/AuCAH-/AQtCAi/ArCA/A pCAhՆ/AlCA/A`jCA/AhCA8/A CAx/A`KeBApu/A`BA(u/AAAPu/APAAPYt/A"AAq/A`[@AXJo/A`@Ah/A @Af/A @A0fa/A@Nd@A]/A`@A`]/A@A(]/A@A]/A@AD^/A @AH^/A @A{_/A@AV`/A+@A(\a/A?Ac/A ?A`g/Aj?Ajv/Ax?Ay/A2?A}|/A?AH!/A?Ap,/A?A /A?Ax/A ?Ap=/A@`?A/A?A@‚/A߳?A@/A`?A`e/A@ N?Ap<|/A ?A ku/A`T>A]/A@y=ApR/A=A[L/AFF=A`H/A@=A(H/A@G<A(-H/A<ANH/A<AH/A <A`I/Az<AI/A<APMJ/A<A,K/A<AxdM/A<AO/A0<AS/A&<AVa/AÍ<Ap,g/A`<Ai/As<AP'l/A {<Axm/A3Au)/A3AmM/Au3A*/Aʮ#3Aq%3/AT)2A~:/A2AKQC/A¡}2AQdsM/A 2AZ X/A.ؘ2ACid/A{Yw2AeMp/Ak t2A|/Ay2A/Ag2A{/SZ/A$Bi2A$k{/A 6~2A/A 2AXp/A`]2Ax/AO2A!/A2A~/A 2A/AL2Ax~/Aa2A&/A2AH/A3Ax-/A^3A(,/A3AXJ/A`3AXT/A` 3A f/A^(4An/A |84Aw/AD4A8t/AL4A/A P4AX/A@P4Ah/AK4AŪ/A C4Ah/A@G64At/A4A~/A@3A@u/A@3AX,/As3A98/A43A8c/A`2Aώ/A2A/A&U2A_/A`2A!/Ay1A9/At81Ag/A |0A4/A=0A؎/A s0A`Ǽ/AL0A/A`K?0Ae/A@,0A/AF0AX0AM0A0A/A8"0A/A@00A`2j/AL$60A|`/A:0A\/AX>0A ]/ATr@0A_/AB0A@c/AC0ATi/AD0ABp/AF0Ax/AHG0As/AJ0At/AXL0A /A S0A@d0A`E[0A 81A(]0A`)u1A|2c0A=1Alc0A1AHd0A 1AlOe0A`b2Ak0A02A<=l0A92A%m0AC2A3s0A 2Av0A;2Av0A2A`w0A2Ajw0A2ADXw0A$2Ah(w0A3Av0A 3Ayv0A`3Av0A3At0A+*3As0A@C83A, q0AE3ADl0A[Z3A܆`0A@P3A5Y0A3AG0A@W3A?0AR4AL>0A4A$<0A$4AԼ90A <4A60AV\4At;/0A`4AW,0A=4A*0A4AQ)0A@r4AP"0A5Ar0AZ15A,+0A@>H5A 0A X5A/Aw5A/A`95AH/AÆ5Aȼ/A 5Aa/Ad|5Ap/A@}5Apۏ/A5A/A5Aj/Aα5Ac/A5A@]/Až5AD/A 5A0*/A5A@/A 5A/AD;Aj3/A`b;AQ'/A`5;AU/A`;;An /A:Ay/A":A0;/A}:A@/A`՛:A7/AR:A(/AF:A(i/A@:A(:/A:A7/A:Ap~/A:A8m/A`d;A=^/AN;A=Q/At;AF/A;A A/AV;A;/A@<A؁2/A`<AX*/AO<A $/A@G=A"/Aj=A"/A =A%/A@>A!&/A?>A'%/A%?A@"/A_Z?AI/A@?As/At?A0 /A@?A /AM@A@5/AVv@A(`/A`@AXy/A[@AS/AA4AAX/A@`AA07/A&{A e/A{{A+K\/Asa;M|AN/Axb|A\;/A4[}ALE0/Ao}Ak*/AsF~AHa)/Ap~AAa3-/ArH;A/V6/A&䝱A#?D/AV=AV/AAcHp/A#uAn/AUAQ/Aȓ7A/Af_Ah/A<Acfp/A~"@AR/A< IA^U'/AUAy/A#}xfAk, /ATzAh/A<A/AnCgA/AUłAr/AqA/A`AV/Ar^lSAWe%/AcKAJ2/A;Ap[B/A@58AN܆L/Aul][ARH8(U/AA;\/A%wA`/A^A!c/ARryAEd/AXPAeu-c/AskA!_/AxAHZ/A<CAPR/AFA(H/AufҰAYA/A MA?/A0AZ%oOB/A AH/AB$AS/AA+p/A{A qV/AmNAIcB/A#"Aͫ/A0uA ޶/A@lcڍAo`/AWyʻA'UJ/ABA1Ș/Ao򑚀A]O/AB!RaA$z/A>Ah//AAF/AG7e Ai?yr/Ai&Z,A=vif/A"&=A⋌]/APApZY/AkxeAPY/A`[{AB#9W/AA9Z/AcIA6*_/AЉAqXi/AnAc_fu/ApiAA/A@yAD*/AA`/A+jAu(/A܂fA$/AcV~ĝA}e/A!qEA7/Aɜ})AY/Ann\A87#/Ao"A8k/ATA/AVA/AG{ iA{5/ACp,AgO/A.!rA}Ʊ/A{#AF/AN }A񳱸/AOjA/Au!vAҦA4r/A^!AO/A:0AoW:/A߃oKA/A;WAG/Abh{AOO^/A֎mڨA/AccާA- /A֡A8kS/AVx\Ay/AV2䠨Ap"/ApAV5/A7s-A/A@PrA\а /AVyA%U,/A!A)W5/Agw9mAQM9/A\ͪA:/A/A/ALAm/AlAx /A61#Ay v/A'A~/AjiA/A`A/A<ԯA?/Am%AvJ|/AnF˃jAn/AL˷Ac\/AA%AZ/AMA/A`A(ё/AɼA)Ha/Ad`ӼAD/AxؼAM/AtdؼA/A[z'ӼA'/A]ȼAsnۮ/A`ӹA?>/A;^Ay/A`AJ-C/A2xlA|/A EMA4A1/A:{C3Ahه/A,A~/AIA$u/A=A-Jk/A6{&xAxza/A A!1eZ/A#! AiCS/AT ASJL/AXeAE/AA(?/AAw7/A-O]ۻA庄W//A,лAnֵ&/AFӁ˻A~/AٍEʻAN/A@GϻAx /AA A/A`A/A )A/A@mA ^/A6Ag/A@,8AV/A?WAxi/AOAX/AAܓ/A׽A|/A`KAk^/A:A@5J/A`;WAXt7/A`)hA./A kA2&/AkA"/AjAU/A@gA/AX]AI/A+A/AA(/AA(/AMA@/AAf/AhAP/A-A@/A`GA|/AUA`ǥ/A ZfAPg/A ҶA[h/AMTAX/AِA/A9aAH/A aAȇ/A Ap/ADA(i/A A AZ/ATA`6Z/A UAQ/A@AJ/AA C/A1A*A/A ObAX;/AkA@$8/A/A@3/A@N=Ap%/A Ah"/A^A /AA`/A |A`/A`A /AaA /A8A/AA/A@PMA/A`RAd/A iApN/AAP/AĨA/AA/A8A/AXAP/AA/A9 Alu/AAZh/A?Az/AvAs)-/ARѰiA7ׅo_0ADAp0A}A{0A`]A0A A{0AA0A A0AA0!0AtAPo/AAP|/AAg/A ZFA/AA/AMpA/AdA/A2A" /AAu+/AA0/AqgA/AHA()/ABA/A@5A|/AQPA?/AA0YM/A 2AK/A@d A(6/A A5/A A"/A` A6/Aq A/A A(~/A` A`/A A/A@>|Af]/AANj/A AP//A4A_/A`8A՛/AAt/AAX/A3.A/A A@/AbA*/A@>A*)/A,2AP&/A`2 A(X/A-A/A^mA/A@LAP/AIA/A >A;/A:A/AA0/A@A'/A0AAf/A`Aq/A (AP/A lA/AB6Ax/AaAr&/AAPx*/A@AU/A`;Aؒ/AYA8b;/A Ah?v/A|AF/AAh/AAS/A@A/AdA@1/AA/A A)/A3A//AdAN/A@cAX/A@WA/A1Ahn/AfA R/AgA/A@yA/AAD/AAA/A@WAh/4/A`AXd/A1AHLo/AsA/A A/AoA@/ATaA/APA/AKA@5#/AMAW*/A8A$/AqA/A c'A//ANAH:/A A8'U/A Ax/AAP/A@eA/A A/A 82A/A*\A /A A;/A`A(/APA /A@=A(-/AA0&/A@A/AA/AnAx&/ARCA((/A@`A06/A@Azc/Aa)Ax/AAl/A@9AӺ/A1AW/AAx/AA Z/AsAl!/AsA8/A?A /A`)>A/A ɋAl/A@hA /A`ʃA /AߴA -/AIA/As,A"^/A AF/AAh?v/AAx/AAP+/Am&A/AJA`/AtAh/A AGU/A)A`Q/A`TzANg/AAXhU/A6SAEN/AA/A֠A`/A֠A@F/A 8KAb/A AH/A&AЄ/A AAݪ/A Ab/A-eA pv/A`A[/A`DAT/A}AP/AHvA˵/A YAp/A sA!/A"AP^-/A@AP/A ܨA4/AA( /AA8/A;Ai/A ZAi/A@Ay/A6AA/AwA/A@AP/A3UA/AA#/A`̷At/A}A/s/AA/A ɵA/A&.AЄ/A@AKv/A`յA(/AgݶA/AA /AA#/A`,A;/AA\/AA@k/A-aA@ӆ/AhA/AA/A ĹA/AA@k/A@A5/Aa{A0/A@iA$/AzA /ATbA/A`]Ax~%/A`fA@`i/AXAN/AAxW}/A`vA0/AAHD/A<A p/AoA(d/AA/A 8A /Aw*A@`/A*AxG'/AA/AA/A:Ab/A3 Aq /A4HAh/A AP/A)Ah\/AAB/AA/A@EAHv/ADA$/AA/A`*A/Af~AxQ/A A /A2A蕼/Ay A1/A A'/A@w AxmW/AtA=/A@#A'/Ai[A/AY!Ap8M/A$A؞/A(APfg/A}*Axd,/A@*AI/A:,Aȋ/A !+Aؒ}/Af)Aa/A'Ar/A=c%Ai/A ~#A2O/A "A0w]/A`!A m/A&!Ad/A@HA+h/AyA8q/A \\APk/AHA?/At}A#/A0gA7>/A#ApZE/A$A8A/A@A=/AA0*./A cA0*./AtAPoKA`Xt/A KAY-t/A;JAHt/A@yJAht/AJA>s/A`)YIAs/AUHA Rs/AHAss/A*tGA@s/AFAms/AFApNds/A`PEAWs/AྞDAVs/Ac"DA2as/ACA;bs/A{BA}is/A AApNds/A /AAOs/AeAA7s/AAA(s/A`KAA!s/A`#AA8s/Ao@A7s/AG@A8Qs/AfX@A8Ecs/Ao@AȘls/Aw@Aps/A@d@As/A Q?ARs/AdW?Avs/At>A8ts/AN>Ashs/A@=ATs/AaU=AX6s/A/<Ah]s/A H<AFr/AX<Ar/At;Ar/A`B;A,|r/A |;A6*r/A lQ:AIr/Aa9HA1~r/A GA8r/A`mGAUs/A?3HA*s/AHAVs/A VIABts/A_ JAps/A`RKAQzs/A,cLA s/ALMAys/A[MApxs/AMAHVs/A@%OATs/A$OA7s/AtsPAX#s/AHPA0*.s/A'QA@5Ps/AQAP^s/A@RA@s/A 8RAks/A`ogSAxs/ASA舏s/A 3TA s/AQTAbs/A`TA"s/ATAps/A@$UAs/ASUAt/A$UAHCt/A;UACt/AUA@@t/A`/VADWA0{Gt/AV_WA(Ct/A&WA@Bt/AXWA3;t/AWAh;9t/A XAP/;t/A2XA,;t/A\XA`2t/A_XA)t/AgXA8 t/A`DXAHt/AmXA"t/A>XAX(#t/A XAt/A ZWAt/A`WA*t/A@ߩWA&t/A@|WAX(#t/A`2>WA#t/AWA Nt/A@VAnt/AVA, t/A[VA(t/A9VAs/AVA\s/A`VAs/A sVAas/A@,VA0*s/A'*VAOs/A@VAs/A5UA@s/AUAs/A`UAps/A@UAs/A sUAs/A@lUAs/AN;UAs/A@UA8s/ATAXs/A@ TAs/ATA s/AoTA~s/ATArs/ATAgs/A`vTA\`s/AR UAUs/ATAvDs/AR UAH9s/A@-UAhU3s/AkdUA.s/A'UA%s/A UA`+s/A`KUA*s/A sVAps/A2(VAs/A`7VAts/AUAs/A@UA0 s/AyUAs/AJUASs/AR UAns/ATAP'r/A TAr/AoTAr/A HTAr/AbTAr/A/TA Ns/A@TA"s/AhSAxs/ASAP'r/ASAx r/ASAv/A6]AaCv/A\AKBv/AO\A@1Ev/A+[A-v/A[[Av/A`B+[A0u/AZAh@u/A`)ZA޿/A`bAȩݿ/A^{A/A5|A@0/A@pAd/A@.YAx/A bAP@/AzkA0/A@ fA%X/ANAF/A`8Ag/A`A؟{/A*A(`&/AWAp,y/ADAf/AhA8/A/fAe:/A TAp!/A`<A0/AMApĮ/AuA/AFAhդ/A`b8Aj/A>A t/A@DA` /Aj&WA/A cA o/A``AM/A`÷nA/A 6sA/AzA c)/A؀A޿/A`bAО/A4dA؝/A 8 ~Ar/AFAx /A `Ap/AmLA/AHBA/A,7AС/A2Ap[Ρ/A ,ApE/A`(A,/Ac2Ap/A#A^/AsA 3/A A@/A`ZAJ/AjA~/A:Adś/A Z1FAp/A`TAD/A2`Ah7/A@gAŖ/A`*sA@//AhnA1/ApAؼ/A`?|Afؕ/AɍA@/A ΣA8祗/AF_A@z/A-Ab/A`AО/A4dAȃj/A@AI/AA/AdgA@ RA/Al*\Aʁ {/Ac9AU/A=3z|Au/AYA0/AANu6/A;As&Y/AkmVyA*x/AxAe/AM^A)/AA؈x/A>59A/A^ASVNj/A7h= Aab_ɋ/AXWvAHŋ/A`߳Alu/AA/A AA(:/A`okA@$P/A9A𴝏/A`AUΏ/A@y}A/A ZAa/AA/A^Ah8/A {AH!/AtAP͔/AnA0/A`/hAx>/A3R_A O/AL\AD/A`էWA/AVA>C/A 8wYAk-/A`dNA:/AHAx/A@Ax/Aa=Ah/A;A@/A+@A[/AAA!/A;Aq/A E3AH /AH<,A@/AO&Aз/A$A\/A "Aye/A@y{Aye/AF]Ah/AA]/AjA/A`;5 AXE/A@A@/AAF/AA/A=*A(h/A\A-DY/AocAV8N/AvN\A*G/AAu APF/AuCAI/AYyAQ/AO{uA<&^/A[uCA ^o/AK/AO/A'x"yAܞ/A?Ax /A6Ah@/A _AF/AAv/AjjAD6/A`~A`T/A ZA0/AA6/A`A/A<AК/A`A(U/A`DBAຝ/AA/A$A()p/AAѷ/A@A /A@5AX5/A@A(/ARA-/AgApj/A TAH/AAH/A`A@/AAj/A ZA /A`AP/A A /A A/ANAZP/A A/ADA`r/A nA0,/AnA@/A HA/A SAx/A@A6/A uA/A@A1"/AoA/A A@}/A Api/A@A,/A PA/AOAذϘ/A nAF/A YA9/ARAx/AvA‚/AAʫ/AAߘ/A AذϘ/AA4/A@A/ApRA&/A"A]/A[Au/A2A`r/AAߙ/AIpA,/A@A/A@,PA/AdA o/AAқ/A AP՛/A@rAȋM/AAhͲ/A㯱APY/AOAiA/A A K/A@AF/AA8"/AA@/AAÚ/ANAXؚ/A4Aj/AAh]R/A`AiF/A=xAh/Aj5Apo/A@wA)/A`ΐAt;/AA tA/A` A(\/A@JA¦/A=zAƙ/AcA렙/A _A/Ap{A/A 'AX/A`fA~/A~`A/AgVAe`/AHA=/A`93@Ad(/A@[3A/A+A&/AK*AB/A@y#AР/A[:"A05b^/AC*(!A @/AM" A2&/A^ A$</A5KA[/Aֆ'A /A-A&/A:ADƗ؎/A6GAԎ/AQ]!AL8+Ԏ/At/[UnA"u؎/A /A{QO/AY- Aq%/A_vbAQ/A̾A/AAUvNI/AfAI7/AUS AБ^/AfD̵AP!/Af9y;aAZ'/AUn\A/A ~Ax/A6 A /A@A0{/Ak1A/AFAU/AiAH{r/A= AB/AA/A8iA@B/A \ AHc/A A /Aྦྷ A@d_/AU AZ%/A6 AA/A`) A2t/A A/A`) Axil/A \ A/A@A/AZ A1/A6 APn/A@ AP/A`) AԪ~/A- A0}/A A(|/A Ax+|/A A0{/A  Az/A | A@y/A 8M A0[w/A Au/A٣ A Rs/A Ar/A \ A1p/ANAo/Aj!Ap/A 0ADp/AMAp/A`A p/AAj,q/A A؁q/AW"Axmr/A $Ar/Ack'As/A )A0Xt/A`b*Au/AF+Asu/A ~,A{u/A -Av/A`/APw/A .Aw/Ay-Ah4x/A-%,AFx/Aj)A@_x/A@߷)Aw4y/An*A/y/A`+AXoy/A%-Aby/A -A)+z/AK.A Θz/A .A`z/A<0A(J{/A@W$1Abf{/A1Az{/A1A*{/A/A${/A7.A=|/Aa-AX|/Aa-AH}/Aa-A0{a}/A Ғ,A}/A-%,Ap|~/A Ғ,A~/A@.A~/A`[/A~/A 60AHj/A`B1A~/AБ2AN/A`K3A@/A#5AH;/A#6Aj/A57A/A8Av/A>7Ag/AlyL7AVջ/A0s7Agˀ/A\&Z97ArdWڀ/Ahv87Al6U/AD7AD/A8Ab /AB8Aln /A^\q8A/A8Ah/Atk28AO/A=09Ap /AY9A:WFSL/A_<:A {/A?o;AԬ/AՖ;A7/A~zv<AL/APV=A@R/A =A,\/A@>A5ʑj/A!2?A>.|/A,?AeW/A?A /A X@Ai;/A ]@AUcƂ/Ag @AZ؂/Al@AIg/A\hy@A`Z/AgAA8/A@A/$/AEm@A6/AˡyQ@ATG/A@A\w7/Am @A+/Ap;p?AFt5$/Ar>A!/Atd*>A 4"/A=Au'/A<AT1/AlC<Ar6?/A/Aൂ2AV8`/AV2AT/AS]X3Ag/ARf3AXh/A&a4Ǎqdž/AK!4AB׆/AArR5AP/A@46A 8/A`K8A`a/A9D:Ax)/AHh:AH/A@#:Ah//A:A/A:AY/A:A(h(/A`v;A /A`:A=/A8AF/Ai8AE/Az\8An/A 7AX/At7Ap0/Aiq7Aڅ/A+7A0*υ/A6Aȅ/A 26A ą/A5A…/A@!5Aż/A5A`G/AS5A讅/A4APM/A34A/A@4Ahb/Aߺ4A`/A4A(/A ^5Aܬ/A9j5A/A5Aw/AOO6A( ą/A6ANŅ/AO6Aυ/A@.7AЅ/A`97Amх/A`E7A@1܅/A@y7Ax݅/A7A/Al8A//A9|8Av/A 8A{/A@9A/A 9A/Af9A`/A@:Ax/A`):A/A:A/A:A/A9;A/AG;A1/AFh<A2/A@y<Al;/A <A`#/A=A/&/A =AA/A ~=A M/A`m<Aq/A <AЄo/A2<A0 G/A<A@3/A;A _+/AD;A[/A`;A`/A;APގ/A;A@O͆/A$:As/A:A/A`:A/A ۡ:AX#/A :A_/A;Aц/A<A/A>A//A?A /A>A"/A@W>AxzB/A@>@Ap=/A@\EAˇ/AEGA /A3wGA7/A`FAH/AfGA7/ABGAH/A_GA?/A SGAe/A}HA@3/A@GA3/A@.GA*/A@#GAh.*/A@5GA"/A cGA(/A@5FAb/A AGFACr/AGA88^/A@CGA88^/A$GA/AFGA/AGAi/AfGACr/A9FA`/AEA@j/A@iFAx؊/A;1FA0/AEAiG/A`;EA(yQ/A SEAY/A3EA a/AEA"/ADA@ԋ/ADA( /A/EA;/A"!FA(L/AБFAS/A9FAxƌ/A`GAxƌ/A _GA Œ/AGA/A9GAX4/AGA:/AӄHAPߊ/A!IA/AIAQ/A@IAI/A#^IAHN/A[IA0L/A1IA"/A`IA`/AIAȈ/AT#JAˆ/AIAIV/AJA=f/AMAL/A?PAC/AOA@/APA/A 3QAM/A uRAq/ASA+/A`FUA/A$YVAp/A@VA"/A`WAVV/AXAxn/AYAu/A@[APQ/A` \A8,/A%]AX/A1^A:0/A `A/A4aAU/A /aAs/A@`bAh/ANzdA7/AvfAѨ/A`$hA/A`mhA@/A BiA /AsrlAOډ/ATtAH/A vA/A?zA0Չ/A:{A@/A`o|APÉ/A 8}AXhӉ/ASA0&/A)`Ar /AnA$/A@NAN/A( Ai/A!AQp/AAPc/A0 An/A`mAP//AkA7/AǥA06J/A͇AP/A`gAW/AA젉/A@yA0Y$/A`APwΊ/AA; /A,A/A5A 4W/A`ŐA'/A@WAKI/ADAз/AAǕA -/A?ԖAQ/A=A-0/A`pA~a/A`ApIV/A8A@q/A J(A0*/A AAh/AٞA/A`A} /A ]A pC/A èAb׏/A;A؟ُ/A` A0H/A&A/AiA/A [A/AA0H/AFAXyR/AWA7/AtoA@5/AjmA/A AĎ/At*AX/A (A/AaA`a͎/A@@A /AcAe/A`A>/A@A؁/AA`/AèA0Ӎ/AApÍ/A̴AP8/Af/AP/AXAݩ/A[AP/A`A/A`o;A@S/A_BA!g/ATA F/AwAP|)/A`At/A6A/ApAI/A'A/AA2/A@,AҊ/A`]Axm/A@޽A`َ/A@AÎ/A^A걎/A^kAч/A@VA)G/A AP-/A@؝A /A`m4Al/A GAȘO/A@ZA/A A /A dAoi/A`大A/A .Ax/A@,MAHnC/APA`/AA/A%A//AA8/A<AG/A@AX#/A(APZ/A@OA/A qvA@$/A^^A8_/AgAen/AAH;>/AA//A@7As/A`qA6/AgA/AA4/AA@=/AAK/A@ANz/AMA`*/A0AXӃ/AAf/AA(l/ABAs/A[Awp/AsAwp/A@.4AH/A&AK/AQAeH/A<A`q/AA8b_/A PAf%/A PA,/A AX/A IA0/A}ZAX5p/AcA;>/A-A{ /AtAI~/AtA./A3AX5p/A$A)/A`VAP,/A`!A/AAP</A Ax\~/AAH}/A A4Z|/A`iAH{/A9Az/AA`ey/A`[A(y/AA0x/A,A #v/A,A}v/A 85Ahu/AFAt/A@r`APs/A4Axq/A]Aɛq/AJA`q/A@CA r/A 5ASr/Ao6A0]r/A 6Atr/A+7Ar/A`%7A(r/A [7Aplr/AЕ7Ar/A`7Ar/A@8Afs/A?8A8s/Au8A؁Ms/A Җ8A`fs/A8Aȓs/A@8As/A;j9Ads/A@:As/As:At/A@N:Azt/AV;A%t/A cp;A"'t/A <A:t/A}<AYt/A <Akht/Aa=Ayt/AI=At/A =AxXt/AF>At/A`K>Axt/A>A;t/A>At/A`>A؎u/Ak>A,)u/Ags>AhCu/A@^>Aou/A>AXhu/A`>Au/A@-?Au/A>?Agu/A@-?A`u/A@؀?Av/A?A(v/A?AX>v/A?A(Vv/AG@Aeiv/A@Axv/A@Av/A @Av/A`@AA`6v/AFrAAȥv/AOAAv/A@.AA@v/AAAv/AAA(v/ABAIfv/AAAXEIv/AAA A,v/AߟAAbv/A@~AApyu/A_AAku/A |@Au/A@Agu/A`@A`u/Ay@A+u/AP@AhLu/AC@Amu/A.@A Zu/A Z@A Bu/Az?A@"u/A?A`u/A@@AP@t/A<7@ANt/Aف@At/A AT@AhѬt/AC@AHt/AC@AHt/A4?AAt/A Q?APmt/A ?AWt/A ?A&Dt/AC@A`t/A`#AA2t/A &AAp+(t/ABA t/A ^BAs/ABAs/Aa!CAs/A@NCAhs/ACA(s/A"TDAs/A3DA(ms/A'EAt/A@#UEAt/A'rEA2t/ALvEAP8t/AEAP8t/A=FA=t/A fFA=t/A`FAY-t/A_FA4)t/AF>GA3t/A@GAgKt/AOxGAP[t/A`TGA]t/A@GAPmt/A HAXtt/AHAyt/A^HAt/AE#IA t/A/IAEt/A=HA@Ot/AzHA8 t/A QyHAt/AqHAt/A ZHAxt/A AIAt/A@PIAxt/A2IAt/A;IA6t/A`IA(u/A@IA u/AJAp[.u/Ah JAJA:0x/A JA@$\x/AIA@x/AHA0Gx/AGAx/A GABy/A:FASy/A`EAty/ApEAy/ANEA(-Ny/A@EA9y/AFAXRy/A \xGA8ły/A@`GApy/A@`GAhy/A@%HA Ay/AIAhy/ANJAy/A`KARy/AELAxy/A)MA8Zy/A`MMAy/A>NAy/ANA,y/AOAעy/AjPA;y/A@ZQAy/AQAy/A`;QAz/A /QAz/A RA-z/A"SAO z/A@SA*z/A7TA};z/A TAhJz/A TANz/ASAAz/ARAIz/A RAVAy/AUAy/A`UAPHy/A`2pTA8y/A@TAy/A@.SAH]y/A`SAH]y/A"SA~y/A 8tRAy/A RAy/A&QAhy/AnQA`sy/A?PApy/A`]}PA0ry/A@OAcy/A'cOA'Wy/AOASJy/A@*OAzFy/A@#NAHGy/A@,NA1y/A9NAp.y/A`MA87y/AWMA87y/AMA'y/A@ؽLAhy/ABLA Ry/AkKA5y/AFGKAy/A` JAy/AJAp.y/AJASJy/ANJAHky/A@pJAly/A`zJAly/AgJA]y/AIAUy/AdIASJy/A SHA>y/A`՜HA%y/AHAHx/AHAEx/A IAx/A`BIA!x/AFJA6x/AjJA@ex/AИJA@}Hx/A JAx/A@ؽLAMAjv/A@NlMA4Tv/A@,gMAe>v/AMA7v/A`NAxu#v/AӹNA7v/A@ENA`Gu/AOAu/A@ NALu/A`NAu/AMA7v/AMApI0v/A`MMA`Cv/ALAUYv/ALA@Ftv/A${LAv/A cLAXqv/AKA /]v/A`KAk5v/AxKA v/A |aLAu/ALAu/A ACMAٽu/AoMA`u/AྤMAТu/A^ NAxu/A8NA0j{u/AHhOAvu/A @PAu/A GRA(iu/A RA v/A A@TAH$v/ATALv/A@NUAv/A@WVA v/A(XAXv/A`EYAw/AZA@1Mw/A ZAh^w/A\Aw/A l\Axzw/A0]AhQ x/A^Abx/A _Ap.x/A`A&x/AOaA"x/A`ܠaA7x/ACbA8w/A ŒbA0qw/A_bA6w/A`TcAv]w/AĭdAgw/A@%eA(>w/A4eA`w/AeAx/A HeA~Qx/AfAvx/AgAh3|x/AggAvx/A hAUx/A@hA@5x/A@hAXx/A0iAPkx/A@yiAXx/AMjA0x/AjAKEx/AgrkAex/A lkAgx/A@lAx/A0lA@$x/A`[mA(>x/A@rnAXx/A@E2oA`x/ApA@$x/A@&rApߤx/A'lsAUx/AftA<~x/AKuA<~x/AvvAtx/A`wAUx/AR0xA"x/A@xA`w/A8yA@ήw/AneyA*w/AmyA~_w/AyAp2w/A8zA w/A@EzAv/A5{Ah^v/A@,v{A0v/A {A(Fv/Aמ{AHv/A l {A u/A@zAԏu/AazA( eu/AyA8Z6u/AAzAhu/A5{A0u/AVr|A"u/AO}AFu/A@r}A\u/A2~A\u/AX~AXDu/A-A"u/AfA(>t/A@,>AXt/A AЀt/AV:Agt/A݀A t/A`At/AX[At/A6At/A@A`t/A`ՄAu/AɅAu/A`Ah&u/A 7ApNu/AAu/A @APMu/A@pކAFu/A@NֆAv/AABv/A`̩A~mv/AmAHyv/A@rAv/A@#A8xv/AI$A v/A Av/A(Aźv/AۋA8v/A Aov/AgzAv/AvA@1[v/AkAB_v/A`"jAvkv/A_Av/AAv/A?׊Auv/AzALv/A‰Av/A@#Av/AA|0v/A dAv/AAu/AfAPu/Ai AËu/A-Avyu/A c8A9ku/AA7.u/A@A(it/A_Apt/AƈAÙt/A@ΈAjt/A<A( st/Ai Aot/A`xATt/A`pA /:t/A$At/A4‡AFt/A'Aثs/A@ ADs/AxAvs/AńA}s/A@Axs/A@#څA9s/A潆ABs/A`xAds/A@GA0s/AAxs/AUAxs/A >As/AamAxWs/A@>As/A AxHt/A`>AUt/A`WACt/A$AH\Zu/A[mA]u/AAxv/AʜA 0 v/A'Au/AgAbv/A`֪A徯|v/A){lAvv/A愕A} v/A?#mA3:v/A^|>A1zw/AnArFw/Ak7˵AK}w/AAhdw/A)cAҭY7w/Aa5Ab}w/A"A}4w/AcɻAw/A _2ݼA$w/A3AKw/AIAC8uw/ANH* A 4`w/A@AKw/A-h_A^2w/AAwv/Aq?A(mv/A`AP/u/AܳA`u/AAu/AVAHu/AT(Ayu/A Apu/AHApΫt/A@ABt/ADnA0YZt/AAhs/A&WAUZs/A(Aīr/A`<APs/AAQs/A\A(s/A Al?u/AAh{/A A0|/AOA8~/A`zAp/AAhS/AAꄣ/AvwjA?/A\K1A? lG/AI AtY'/AB5A/A7Amƈ?/A8٭A2/AKA'L2/A8Jf5A~v2/A'A]&ƀ/A_ RAF:̈́/A*'dAR/AmdA|0Z_/AZ Ap/A.~AX/AHAK%/A Z`A ņ/AAх/AAc/A Aȃj/A@A~>/A Ap;/A A8/AAJ7/A`AW7/A bA`m7/A]A7/AxA 7/AhA6/A ANRT6/Ae ,Au6/A?Y=A7/A?A > /7/A$A>1A-:JG7/A=lIA"_7/A@A7/A nA(`F8/A@ ѷA?`8/A@YA c8/AJADg8/A@7Aly8/AٶAp,8/A RAb9/A@A8w9/A zAx :/AA7:/AA7:/A@譫A$:/A +A]:/AMAx~:/A@PϤA:/A A;/A%A`O;/A`A';/A/9A;/AAh/Ao|AX>/AލA@>/A@A ?/A‰A(~f?/A@5A(?/AzAey?/AE9A5e?/A6LA.N?/ApޅA-7?/A@DžAm1x#?/AEoAi8?/AO;m A7 ?/AIkAǥl?/AD0AY#0>/AI˻A>/AFA!?/A!??A?/Af.7A?/An.A7t?/A%AW?/ASp A.?/A͆A?/AE A7>/AD=AyE@>/Az-A+;x>/AA~>/A ACAy/A@5A*%y/AA؁y/AAy/A畻Ay/A`KA/y/A˼A0?y/A&WA0by/AAԌy/A`KAy/A`;A@~y/AkAXcz/A@,OA3z/AA0z/AAy/A ACAy/A@5A]/A7 AT/A4 A1B/A`S4 AHN/A@i4 A`V/A{4 A_/A4 Aзh/A 4 A0p/A@4 AFx/A4 Aȓ/A 4 A@u/AE5 A`/Ax#5 AQ/AP5 A/Ae5 A(/A8w5 A@B/A@5 A/A`5 ApŎ/A 5 A[ώ/A5 A َ/AX5 A/A-5 A/A5 AX(/A` 6 A/A$6 A/A AF6 A k/A]6 A(/A6 A/A@,6 A/A`6 A/A6 A/A 7 A/A 1:7 A/A`;I7 Ap/AR7 A1/A8W7 A /AL7 Aj /AR7 A`/Aj7 A/A z7 Ap/A z7 AȘ"/A7 A(/A7 A0/AVz7 A 3/A5e7 AН6/AC7 A08/A !7 A #;/A`6 A@/A`6 A`F/A 6 AM/A6 A8VX/Amh6 Ac/AEJ6 A8n/A46 A #w/A*6 A/A8!6 A/A@6 A(/A6 A8/A@.6 A/A 6 AƲ/A6 A0L/A5 A@=Ï/A95 Aˏ/A5 Aԏ/A5 A@ޏ/A 5 A/A r5 A/A@25 AL/A4 A/A4 Ax /AM4 A/A 4 Ap/A4 A@$/A`4 Ah./AVf4 Ax7/ABF4 AB/A"4 A0fN/A 3 AT/A J3 A@Z/A3 AXya/A 3 Axzg/A`D3 Aجl/A߅3 A@Fv/A`3 Ah}/AxC3 AX/A@(3 A劐/A3 A(/A2 A@/A2 AF/A2 AXV/A@s2 A/AN2 A-Ő/A2 A`ϐ/A Z1 AՐ/A1 Adܐ/A1 A/A-1 A-/AVn1 AL/AjP1 A /A#-1 A0/A 1 Ax- /A;0 A/A`0 Ao/A@`0 A /A s0 A'/AP0 AU./A .0 Ap4/A0 A:/AF/ AX(@/A@/ AF/A/ AM/A / A8U/A@i/ A ]/Ai/ Ae/AS/ A0n/A@y=/ ALx/A%/ AQ/A/ Aѕ/A. AxF/A`. A/A. A/A h. A(z/A M. A3/A6. A/A@%. Aȑ/A- A8ϑ/A@- AUؑ/AЮ- A/Aa- AP/A`vk- A'/A S- A/AB- A/A- AP/A, A /A, A`G/A, A7#/A`x, A8./A L, A4/AV4, Axz:/A, A8@/A, AL/AO+ AR/A+ A@Y/A`+ Ah/A@l+ A(p/A`vO+ A~w/A`5+ A/Ax+ AXƇ/A+ Aڎ/A * Ae/A@* A6/AѾ* AhL/A* Ah/Ay* A趷/Ab* A/AiJ* Aƒ/A 81* Ah͒/A * AWՒ/Aa) A1ݒ/A ) Ai/AA) A@F/A) Ao/A) A8 /Ap) Ah/A0V) Aȃ /A`:) AX/A c) AV/A ~) Ax&/A( A(//A( A>/AՔ( AE/A}( AK/Af( AR/A@O( A(Y/A4( A`/AV( A@uf/A( A0fp/A`' Az/A`' A/A$' A/Ao' AL/AW' A'/A9' A/A@!' AXV/A' Aᮓ/AV& AH/A@& Aﻓ/A A& Au“/A & Atȓ/AT& AГ/Ao& Azؓ/A S& A`/A,2& A8V/A& A7/AL% Ap/AX% A0j/A@% AP7/A {% Aj/A`R% A'/A (% A[2/A`% Ax$ As/A`v!$ Aج/Ax$ Ah̍/A# A'/A# A A0f/A0 A<'/A`K! A2/A A0L=/A ApF/A@  ApAR/A A[/A`" ApAf/A A pn/A A,v/A A0ꃛ/A A0j/A`" Ah/Ai A(z/AS Ap=/A< A /A, A /A&  A,͛/A, A<כ/A  A3ݛ/A A`/Aţ A/A A/A { A/Ad A /AL A0/A`8 AF/A! A"/A`  AL,/A AТ3/A@% Ao:/A APD/A, AK/A ɣ AU/A  AKb/A O AȘj/A| A(zr/Ao Ax-{/A0Z A/AB A`/A* A`/At  A@W/A A1/A` AՖ/A  A #/Axy A/AW AТ/A1 A/A A/A@ A舓/A൹ A"/A A /A@^ AX /A: A/A` Aց/A@W A'/A J A@|/A A`w/A ld At/A`@ A'r/A`[ Ao/A Axl/A Aj/Aٷ Atf/A A_/A F A0\/A`& A@W/A AU/Aa A`V/A` APg/A`L Aȃp/A A" /A@ A!1/A A"/A@ A!"/A Aȱ/An A2'/A@, AHKZ/AY Ah/A@a A/A@ A@BF/A AN/A A8W/A2x AA`/A`j APj/Aj` Ahr/A2Z A|/AN Ax/A: A8/A + AX/A` A/A Ahѧ/A' A7/A A/A' Ap/A` Aʤ/AN A@Ӥ/AV Auݤ/A A-/A A0/A A/A` A/A A,/A` A/Aܡ Ah/AV A(/A` A01/Aj A:/A AC/A`~ APoM/A| AjV/A'y Aa/A@i Ak/A;e A0y/Aa A`~/A^ AF/A`S A`e/AH A/A@? AB/A@,6 A߳/A`+ Ax/A AΥ/AM AX[ۥ/A` A/A`9 A"/A A/A A/At A /A A`/A@ A/A A&/AF A p//A྄ A7/A 8q A B/AZ AM/AC AxQ/A6 AZ[/A! AFe/A@ A8k/A As/AL APt/A A}/A9 AXƈ/A@N APf/A A/A A-/A A/A A/AL Aպ/Al AĦ/AW A83ͦ/AH Aצ/A4 Ah/A$ Ae/A A0j/A AH/A AV/A A /A Ah"/AT AQ/A@ Aȶ!/A ( AU(/A A@B1/A@ A9/A  AA/A@ AJ/A AhS/A Aj^/A  A0f/A@N Ap/A; APw/A` Ak/A ۅ A(-/A Ap/A6 A/A AhѦ/A՘ AHЯ/A` A༧/A AƧ/A A AZͧ/A  Ap=ا/A Ap As9/A@> A!/A@ AX#&/A A@-/A@ AU5/A A@/A6 AG/A@ A@pQ/A- AX/A A`/A@y A"j/A  A/A=H AH/A=H AP{/A` A /AF, A /A"; AV/A N APo/A a A/At A /AA A`/A Z A'±/A  A`б/A? A1ͱ/A? A`ѱ/A9 A/A9 A@1߱/A? A@ٱ/A? A(α/A Aӱ/AA A[ܱ/A@ AH/A 1 A/A A/A  A"/A + A8Q /AA Ap</A@U AU /Ah A8V)/A0z AH2/A A/E/A; A?/A; AB/A 8 AP/W/A 8 AhQ/A; AhoM/A; AX(C/A AзL/AX AppR/A AxY/A` Ahb/AM Ak/A  Aoq/A`( A Ax/A`; AZ/A@#K A/Ab Ap/A } A/A AP暲/A` AꜲ/A & A /A`  A/AX2 A0/A9U A`/A$ A /A Axu/A`; A /A` A/A A8/A> AP/AUb APf/A` AP/A AH/A A/A  Ap=Ʋ/A Aβ/A  AfӲ/A A۲/A  A/A`" A /Af A(/A AI/A Ah/AT A/A  A%/A2 A./A= A`9/A) A8DC/A; A0J/A^ AS/A@ A@]/A= AP8h/A A s/A A͒/A@ Ak/A} AHm/A A/A A(/A A뺳/A Aó/A_y AXʳ/A`m Apӳ/A` AX޳/A R A/A`I A/A: A0j/A-. A/Aa A < /A@ A`2/A A /A& A#/A " A0f-/Ac A6/A  A@/A A@uK/A  AR/A AX]/A A0`/A` Ac/At AX[e/AD Ag/Af! AXl/A 8 Aq/A`m A``t/AE A7w/Aّ A@y/A g A/AS A,/A JO AX[/A`[J A/AC A t/A`; AA/A /2 A(/AX& A Ĵ/A A`ʹ/AD AТԴ/A  Aߴ/A@ ApA/A A_/A@W A /A / A/A Aj/A; A /AO Af'/A A_0/A`m A:/A  AFD/A@ A#P/A / A AY/A AVc/A An/A AQx/A A/A6 A7/A- A(/A` AL/A`  A/A` Aవ/A ~ AxK/A0 Ax(Ƶ/A`( Aѵ/A@0 Aݵ/A: A(/AA AP A /A6 A3/A@% A A"=/A@1 A 0/A@ B A/A A־/A Aʾ/A AӾ/A Aܾ/A Aз/Ad A/A A'/A A/A@ A/A A/Ads Ah/A-b A*/AP A4/A@C Ah>/A83 A0F/A@& A`Q/A" AHY/AO AZc/A` A@l/A As/A AF~/A@ A/A@ A;/A A(/A AP/A} A軮/At A/A; A/A A˿/At APؿ/A App/A` A/A A(/A Z A@/A` A/A- A /A A /A_ A/A׫ A`/A Ah"/Acg A /A=Z AP /AA A=/A A(/A A8 /A  A/Aࣸ A/A` A/A@ A(/A` A( /Axw Aج!/A@e A*/AX A؎2/AN A:/A 8A AC/A&3 A1L/A@$ A`T/A A([\/A A0ef/A` APo/A@ A`w/A; A/AM Az/A6 Aȶ/Am A_/A AU/A A /A[ A@~/AM A/A`D Aȶ/A Ah/Ay A /A`t A/Aj` A/A@,X Ap/A: Ah%/A A/A@x A;/A@P Aà/A# AX/A Aw/A Ax>/A@y A`/A AU/A A`/AQ Aȫ/A`\ A 6/A AhF/A A`eN/Av A8V/A@ A\/A Af/A6 Aq/A0v A[y/A@Cd Aj/A`BR A0/A`B Aя/A3 A/A! A`/A A`±/A A/A2 A0/A@ A/A; A'/A@` A/A`ܽ AP/A@Ƶ A /Av A/A` Ap/A྘ A/A A/Ahx A8"/A`j A=*/A^ Ax4/AO A@/A= AhK/A- A(U/A! AV`/A Ag/A Ar/A A( }/A`K A7/A' AAsow'/AN  A?w}/A:w AdH/A7o A)/A Ax AǟIG/AWNDŽ A[rc/A+}ʢ Ax~/A A4Ά/AT& A/Ahq AQV/A/5v A )/AOz AZ/A-| A /A{ Asb/AC/y A/A=u A]sV/A,pPo A^50/Ach A*/AT_ ADJ/A1V A/~/A}BK A?/A? A8Ž/A6 A/Al3u A8/A" AT|\j/A|R Ap2"/A% A>zA#/Axue AEuFE/AL?Mn A|Wg/Ak A/Ajx Am8t(/A*D A幗/AmOx A \h/A2C1 AF4/Af, A߾u/Av A0/A A7/A0 A8M /Ad AD/A{ Am,/AtrW A#/Ax A/A| AW/A~& A2 /AS A"/A2- A{#/Ab[ AX$/Ay ANAj$/ADq A2/AviK AX/A8=/A@ A@?/A | A'D/A W AH/A 2 AM/A AhQ/A`m AHT/A8ͳ AجW/A㪳 A6Z/A AP^/A O A(b/A  A@g/Ao A@i/A 1ʲ Ah"l/A Ar/A Zf Au/A@ A({/A A(/Aױ A/A A/A > AX(G/A AtO/A൫ A-/AM A~'/Ax A/A|߾ A8/AT A`/A A/A ( A5/Au A@/A A/A A4/A ACu/A AP/A AP/Au A0f/A AF/Aଠ Aִ/A APj /A0 Am/A A/AX& A/Aj Ax/A A֋/Ai AP/A` A/A`W A/A6L A8/AZ A;/A`9 Ah/Anj A~/A"a A(m/A A/Ax A/A6 A/A@5 A`/A@ A/A AD/A Aڤ/A A-/A@r A/A A /A A0o/A Aw;/A A&/A`Ө Ah/A A 3/AK A/A@ A/A@ A(/Ao A6~/A A8ge/A AaS/Aِ Ae A/A A  A;/Ao AZ/AB A=,/A9Y A./Ad A6/A~ APo@/A AI/A AP/A AhL[/A` A@=e/A@ Am/A Apv/A  AX/A A/A/ A/A@ AX/AK Ad/AU AP/A lb A/A@Pm A@/A | AX/Ao A/A@֌ Az/A@ A؎/AV A/A A`/A AX/A@ A /A A/A c Ap/A Aؒ/A& A 4U/A0 A/A | A1`A/A  A̕(p/A9„{ ADΜ/A_@ A/Aa A`c/A`2 AP;I/A9 m A)0/A]d AI82:/A1( A:@/Ag A"/A@ As/A~g AO/A~$:[ ASA/AZ AH /A AU*/A>5U A X4/A6 Aa$=/AL AhwD/A`(Co AQJ/AB#J AcN/A-/! Au AN%H/ASKM AP/B/A) ABF/Al' AlL/A& A_sa/A~[ܹ AϬz/AoQ AO /A; A=~/A A/A@9 Ab /AE A/Ah].W Ag/Af A`/A` A@#/AM A/8H/A 9 Ar0o/AGƣ A@/A@ A/A  A/A] A"W/A` A/A  A/A Ax(/AB Adp/A0F A;/AB Avpa/A>H A/AG Ag{Z/Ab A*/A;S A0L/A Q A/A + A"j|/A4gK A/UX/A3R A)/Ax A+/A] A*/A${ݎ A#/A@! AS0Oe//A^%' A%x>/A A`Q/A-Y* A8Dg/A \S AH.x/A_ż AOy/A: ; A`6/AN Aj/AuΊ A)ݑ/AB{ܺ Aمy]/A As+/AMZ1 A:/AR˹ A;PC/A1O A!hF/AZYи A:1cE/AVYP A>/A6ӷ AM/Aq Ai/A A/ADҳ A(1/A` AX /Ad A//A`ҩ A @/A@Z AV/A١ Aqn`X/AC{ A,~1/A` A /A ~ AY/Av=ߟ AJ/Aq7! A`G/Ak AXl/A`m AUs/A U1 Aj /A A3:P/A{ A_/A A0U/A?& Ahu/A A/A@Eg A/AP A@/A} AO/A  Aأ/At A(/A`F Ax/A( Ax/A`vc A/Aә A8/A@y Ak/A ~ Ah./A Aql/A@WF AQ/A A^T/A@Ϫ Af/A B Ak/AM A_/A A/A@E? Ah/A) Ap/A밗 A,/A A6H/Aa A h/Ap A^/A; AP/A AV//A  A:/AdÌ Adf/A`]$y Av/AXr A/A ˏk A@F/A`]&a AЦ~/A`CX A/A5gR A/AZO Au3/AN Apk*/A HH AF/A ~]E A0/A Z3E AP/AaE A`"/AD AF/AC AP/A ~2C A410AG A0A@5H A~0AH Al0A lZJ A0ALjJ A0AH A0A#iH Aȅ0A Z G AH! 0A HlG A 0A`G A@Q0AٲF A0AF A0AE A40AǝF AX/A B AB/A AA A/A@; A %0AH,6 AZ!0A&2 AH0A`v. AXΧ0A]& A0AӠ% A0A?# A|@0A l A0A`v= A0A Atļ0A_ Ay0Awi A_00A# A0A A P0A` A0A@= A0A0 A=0A A(0A ( AL5 0A`TW AP|y0A` A0A. A,0A| A0A` A.e0Aw AL0A V A: 0A9Z Ai 0AG A, 0A` A 0A 13 A,# 0A; A`ǯ 0A A$} 0Ay A 0A. A 0A l A 0A4ι Av 0A@ A 0A` Aj 0A% A` 0Ak A 0A 1 A.= 0A ALI 0A Aov 0A4: Ap 0A`ó A(3 0A y Ap 0A)α A M 0A`B AM 0A@!7 A M 0A`)+ AM 0A AM 0A A4L 0A' AHJ 0Aϯ AB 0Ag A? 0A@? A4 0A`Kͮ A2 0A`K A%1 0A` A0 0A@胮 Af0 0Aր AK0 0A`} A<0 0A`vy A80 0Au AB0 0Ar AW0 0Aao AXy0 0ABl A0 0A`Di A0 0Ahf A\&1 0A c Ax1 0Aa AdA2 0A$\ A;3 0AW Ag4 0AS A7 0A`M A}: 0AH A ; 0A G AbF 0A0@ ARO 0A: AS 0A`)8 A` 0A`9 Ag 0AA Ao 0A Q ALLx 0Aj A 0A`v A 0A A' 0A@ A 0A&ܮ AX 0A`f A 0A`] Aܸ 0A< Au 0A`) AT Q 0A AT 0A  A(0A$ Ax 0A` A?0A Ad0Az A0AM AԸ 0A J As0A@ AA0A A)0AЍ A0Ae A$ 0A`ܟ A//0An AKE0A Þ ASe0Aޘ A<=~0A c A A0A@` A蹪0A A0A` A`0Aߥ AhB0A`d AXJ'0A Apb0A  AU0A |A Ac0Aݔ A0A$ A0A |( AX0A쇒 Ad[0A Α Ap0A磐 AI 0Ax AH0A Z AI#0Ax A40A ATH0A`2V AĀS0A`f/ AȖ0A@% A$Ƞ0A ADX0AN Av0AHC~ At0A } A60A'| A@10A{ A!0A.{ Az0A z AG0A Nz AI40A|ky A,4H0A@E(y AdL^0A@ _y AHpx0AIz AǢ0A`Yy A70A/uw A\ 0A@؂v A[F0AFxu A<0A`mt A.0A@Ns AH%0AjCq A,40AZp A0ATp AJ0Ao A0AT o A .0Al Av0AFi A0Ai A0Ag Aࢷ0A f Ax|0Ae A0A@Ưd AA0A&d Al20Ac A*T0Ab AԚ0A&a A'0A_ At0A ^ A"0A] AH0A@[ AI0A,[ AI0A Z At$H0A@pY Ap>0A-X A:0AjW Ad?0A@V AhWE0A@yV A$60AU A&0A`BT A/0AT A)0A#"S A''0A2R A|'0AkR A, $0A@Q A '0A`P A20A@ O A~>0AOO AB0A6O AO0AO A`6U0AN AtV0AN A dW0AN ASX0A@N AX0AN ATX0A@N ATX0AN ATX0AN ATX0A`N ATX0AN ASX0A N AS0A`=N AnM0AaM A0L0AM AK0AM A AK0A3~M A AK0A@>tM AK0AHjM AL0A`XM AnM0AtBM AQ0A M ASX0A`L AL]0A_L A`0A DL A`0A =L AXYa0A8L AL&d0A`!L A4g0AL A\i0AK APk0AXK An0AaK ADZo0AaK A88q0AgK As0A K AG A80AG A̽0A@WQG Ax0AaGG Ax0A@gBG A|0Av3G Ap0A@G Adʧ0A@F AX0A`DF Av0A`F Ap0AzF APܴ0A`F A,0AF A0A={F A0A PF A0A@:F A,0A$F A 0A?F A0AvE ALf0AE Aq0AkE AO0AKE A>0A7E AP0A#E Al0AD Al0A D A 0AମD AJ0A?D AD0AƕD A90A`̐D AD0AыD A(0AֆD Aض0A_D A0AdzD A0ArD Ax)0AtkD As0AcD A40AaD A|0A\D A0AWD A]0APD ApL0AHD A,%0ALD A0A@C A 0ApC Aȇ0A cC A0A}B A\0A B AC0A`B A20AB A20A`B A20A`;B A20A`KB A0A[B A\0AhB Av0AeB A'0A`VB AX0AGB AX0AN;B Aȇ0Ac'B Aȇ0AB Aȇ0A@A Aȇ0AA Av0ATGA Ae0A<@ A0A`b@ A'0A@ AX0A@ A0A+@ A0A_@ A0A@!@ AX0A ? Ad0As? A`0A ? Ak0A`2? AZ0A0? A 0A? A!0A ? A\>0A /? A0A4? AD0A@? A*0A`2? A[,0Az? A0/0A-@ AL,0A@ A,0A`@ A}*0A5A AH.*0ATGA A(0A@gA A'0Ac'B A'0AtB ATr&0AB A<%0A,C A#0A@YC AH!0AjC A0A@C A0A C A!0AwC A 0AC AT0AD A'0A@D A0A/D A H0A`܁D Ax+0AƕD A0A9D Ac0AD Apn0A7E A0A-E A\0AvE A8E0AZF A8x0A`}F A0A`F A 0AF A0A F AI0A?F A0A@0G A|0AJG A\0AbG A\ 0AlG A|00AG A0AG A̬0A H A0A3H AE0A VH A80A0cH A8~0AhH Ar0A3H AJ A*N0A`)FJ A`L0A HJ A]K0APJ AJ0AUJ A 0I0A`\J A4Q90AJ A*0ApiK A8#0A@5K AL0A=L Aj0A =L A0A`TqL AT0AL A0A"M AA0A]M A0A@PM AЄ0A}'N A 0A 8hN A̔0AN A8V0AzN A0A uN A0AjN A0AfN A4"0AN A4f0AfN A0AN A<0A`N AH0AN A\0A SON A60A N A50A@M A`ڛ0A M A$n0AvM A0ADoM A0AHjM Ahs0ANeM A,0ANeM A0ANeM Ag0ANeM Ax0AgM A:0AHjM Ap 0A@>tM Aވ0A@.M A"0AM A }0A0M Aq0A 8hN AP|m0AN A_j0A@N Api0A?N Ah0AN Af0A 8WO Ae0AmO ALe0A wO Avd0AO Ac0A`O Ab0AO Aa0AO AXj`0AdO AL]0A ZO A\0A`TO ATZ0A ҵO A1Z0A ҵO AS0A4O AdyC0A`K2P AD0A IQ A\FF0A@sR AtH0A`KtS A\N0A@oT Ac0A@ߵU A}0A`KV A0A@?X Aл|0AY Ak0A l>[ A*Q0A c\ A(50AD^ A0A`` Aܵ0A ` A0Aa ABj0Adb Ae0Ad A0A O f A(0Af APK0A`9g A0Ah AxZ0A`h AL0A Ai A0A`̣k A A0lR0A`U AP0An AKD0AI At>0Ao Aخ70A A0/0Aj A'0A ̵ A`#0A A0A@p A0A / Ab 0ABͶ A\~0A AH0A AȾ0A`" A 0A A 0A AĀ 0A  A! 0AI A0 0A AhB 0A^ A 0AI A 0A A 0A`d۹ A 0AjA A8M 0AN AlI 0A A 0A A< 0Aa A 0Az A< 0Am AX 0A d AK 0A 8t AL 0Az A 0A@ A4O 0Aw A8 0A@,G A 0Aז A" 0A@ Aw 0A AC 0AD A 0AB Ap 0A AD 0A A\ 0A A 0A@5 A 0A (1 A}0A Ax0Ai Ax0A` Ax0AZ Ax0AX Ax0A` Al0A@ A0A J A}0A A0 0A` A 0A - AD 0A@h A 0Ajn Af 0Aஆ A" 0A`B A 0A@! A 0AT A8 0A@ Al 0A@Ơ A8 0A` A 0A@y Atf 0A A\ 0A A 0A^ A 0A A3 0AD A 0A A P 0A A0A`fA ATi0AE A4 0A@M A(s 0A`KZ A8 0Aw\ Ab 0A Ad A| 0A`p A~0Am AW0A A:0A`KI A '0Au A0A A90A A0A< A00A` A0A A; 0A Z) AlG$0A I A|&0A c[ A B)0Aj A-0A H| A+/0A A030A[ Ad50A A 60A A;0AV A>0Aե AH'B0Aե AD0AV AM0A A09O0A@ߛ AO0Aޛ AQ0A@ߛ A0Q0A@ߛ A4R0A@ߛ A3T0A[ AT0AV A.Y0AΪ AXY0A@ AZ0AK Ax^0A`; A=_0A6 A,c0A A$(i0Ak A̬i0A@y Apk0A`f Ahm0A` App0A@>1 A s0AjI A s0AO Au0Aj Aw0A@` Adx0A`ã A<y0A@ A A 0A E Ax 0AO Ax 0AQ Ax 0AW A(O 0A`\ A; 0Ay^ AQ 0A$j Ax 0A`̈ A !0A0 A`z!0AL A`z!0A[ A`z!0A A`z!0A` A 0A Ax 0A Ah0A8x A0A@ A0A AS0A̟ A A0A A00A A\0A A`6%0A { A'0A& A 1*0A A}*0Ah5 Ap*0AL A +0A`w A*0A@ Ap*0A 8 A,)0A`f AT(0A H A4$0A&< Aؖ$0A`M AlG$0A`K AlG$0A A%0AfZ A(0A 8 A B)0At: Ad(0A} A=(0A} AT(0A Ah'0A Au&0AHQ A$0Af A6#0A | A"0A A0A A\0Aҭ A\>0A Ҿ A0Ah Al0A@y Ahh0AI A0A A(@ 0A` A90AЋ A 0AN A 0A At 0AЩ AH0s 0A`V Ap_ 0A & AP` 0Ay AWA 0Aú A|2; 0A0 A` 0A AHa 0A@k Ađp 0A A^ 0A`f A/Z 0A{ AC 0A@ Ah3 0Av At0 0A` AC 0A. A4Y 0A- AD 0A A$ 0A| A 0A A 0A@ AȘ 0A A 0A` A\ 0A A7 0Af A< 0A A 0A A4 0At A0A I A@y0A`< A40AA Ad0A A0A A0A# A,t0A AL0A AH0A A4/0AF AvF0A O AȶY0A@P AVk0A# A|?z0A Aͅ0Ah A70A AHp0A@y AR0AF A @r0A A [0A`] AJ0A  A3E0A  A\:0A ? A%0A`/ Ae0AT6 AHn0A`D A0A A90A& AlV0A  A%0A A 0A8] Ad0A A,ɾ0A AI0A`]p A,T0AVG A|0A@h A+0A Q A0A A AHv0Aͳ A0A` A|(0Ajp A00A= A<0A`2 Ap0A(% Ap0A  AD@ 0A2 A  0A  A 0A`Î A 0A ALh 0As A` 0A@ A 0A`b A 0A A 0A@y A 0A@! AQ 0A@ ! Aг 0A{" Al 0A@" AK 0A!# AI 0Aƍ# AX 0A_?$ A8G 0AY$ Aȅ 0A% AI 0Aپ% Af 0Aw|% Ay 0A8٪Q<& A 0AdS7& A,w 0A5x[& A 0A' AWO 0As>' A&%cI 0A2d$l' A'\ 0Am' Az 0A' A^y 0Ac 'y' A 0A ' A5 0A -( A@cw 0Avwf( A,j 0Azݛ( ANWO 0A]( A,ק 0Aӆ( A 0A` ) A-Ff 0AozB:) A 0A-V) Ao? 0A2Sڣu) A7 0Al>) A`N 0A]x%̷) A  0A) A0L 0A ) A4B 0AD- Aԑ 0A4- A y 0A 1v- A 0A. A0Y 0A`. A̿T 0A. A!S 0A`Q. A\>9 0A`Q. A\>9 0A/ A5T 0A_/ AS 0A@N/ A̟ 0A. A(0A/ A\0AW/ A-0AXs/ A؟%0A?/ A ,0A-/ A60A0 A =0A lQ0 ALB0Aa0 AW0A 1 A:)0A2 Ab0A 4 Au0A Z6 A-0A@ϴ7 A00A Z7 A0A8 Az0A#q9 A 0A@i9 Al֓0A?J: Aė0A@: A0Ah:; AXH0A&> A20A> Ap0A$@ A(޷0A@B AG0AYE A 0A@(G AP<0AWG A\0A JL A0A`ӾS A50AX AZ40A@,[ A0A6-^ AL0A@j A0A@r8f A tZ0A>P Ad^0AiB A0A+: A|F0A@6 A^0A-) A10A#$ Axڞ0A! Ai0A`T  A0A= AH0A@R AK@0A  A`E 0A- A4f!0A= A$$0AI AH%0A`; A`ػ&0Az AX]'0A} ATC(0AI A.)0A/< AC*0A AVV+0A`2 A`,0A@> A$-0A8 AX/0A A d00A` AX10A Agx20A S{ A20A4 A$20A3 A20A> A20A* AX30A@% A]z30AÖ Am70A9k{ A:0Ai A`\:0A g AL:0A`"{f Ar:0A^e A<:0A&d AE;0A^ A$}=0A@R A\=0A (nP AT@0AM< A`TC0A?+ ARE0AK AqF0A@5 A G0AɌ Ax:G0A AG0A@ A>G0AS ADxG0A` A('H0Ay AuH0A AwH0A: AܙH0A`G AH0A" A%I0Af ArI0A} A(vI0A S3| AsI0A 6{ A(>!I0AQ%x A?H0A@r AG0A`]g A|AG0A@W[ A$G0A AW ApF0AzP AGE0A@>E A0E0AA A ;E0A : ADD0A A,/ AD0A V( AHvC0A@`V$ A86/C0A@5 AtB0A  A wA0Aӕ Al;A0A!Ar@@0AA<>0AUA )>0A,A\u>0AIA)0A (չA0a(0A`A_(0A 0A@r)0A@פA( )0AA)0A@AY*0AهA4*0A Ҽ|Aݩ*0A |Ax*0A@`zAlV*0AtyAm+0AIA+0A HA),0A@Ahc,0A ck6AIg,0AV5ALi,0A 5A{,0A-A[},0AV-AȼS,0AZ'Am,0A@eAh,0A lA8,0AxA8,0AAi,0AB*A@S,0A[A`e,0AvA1,0A gA ,0AM(Ap],0AAt,0AzA,0A IAB-0ARAHC-0APAD-0ALA0U-0A@iU>A$=]-0A6:A`-0AJ8A|ǐ-0A5&A|-0A? AJ-0ABA-0AAܵ-0A A-0AA-0A[A}-0A@LA-0AAض-0A@A -0A A-0A^A-0A+Ac-0A 9Ap-0AK AX`,0AZ/A$N,0A@ؔA +0A !A+0A`fA+0A`bAܨ+0A'vAD+0A 7oA +0A@@gAx+0A^_AI+0AWA~7+0A@COA=+0AGA02+0A ?AL0+0A|=A, @+0A@7AU+0A@3A]+0A%1A86+0A@-A8+0A &)A0H+0Ag(A+0A`"A3+0A!A}+0A`A8+0A@EAQ$+0A`oAWV+0A:Aht+0A AȜ+0A s4A@+0A A^,0A+A,0A@AP4-0AA|u-0A`A4-0AMAG-0A@ԄA.0A@A*.0A Ap/.0A@HA=.0A ~PA,.0A APӻ.0A`BuA .0A 9AD.0ALA̿--0AkAp)0A Aķ(0AAh$'0ASA8%0A0A>#0A03A"0A`wA0AHmA0A^A0AyXAx0A`]UA0ASA]0AOA0A@`KA(0AB'/AV0AWA)A$ ]0A !A@b 0AA 0A`vA;0A@%A>0AtA 0A-Ap2/0A6gA:0A/,AHC0A+AǛ0AAql0A`~Apd0AAP<0A?GA\0A4yA0A`KA30AHYA0A cA p0A Ah0ALA0A`TA$.0A͸A(^0A(A0A@,A8*0A=AP0A@A0A_A;0AvA0{0ARAG0A`A0A Ah0AճA`' 0A`DA 0A@iA(0A`vA\0A /A40A`vA0A`"A0A?Al0A0EA:0A,A0AQAlx0A cAlI0AIALr0A`Al70AQxAg0A@A0AAŽ70ABZ4A^%0AI9AB0AjA5Ҽ0A@ ABԵ0AA-0AAk7i!0A35@A0AA@\0AA10A/HYARi0Ao A0A$A0A AT;0AӓA%AK0A[6A#0ARNLA0AŘͭeA0AHA0AvA  0AA0AA0A$vAHV0AŐA70A@%A\0A`NA"0A (qA70A@,fA0A`]=A܆0A`;AA0AdAt0AľA-0AHA<0A@%A@ 0A*6AD`0AA}-0AڮA8A0A HA@sZ0AA\0A@A0AA 0A;Aź0AA`v0A"A 0A ɂA0 0AA"B0AUAS0ARAlV`0ABA0AAj0ArA`0A8AXJZ0A`AT0A<AP0AAPL0AI0AH0A_AC0AA<>0AA30A+A;00A@QA/0AUAl/,0A sA4'0A`KA#0A@5A:"0A@Aԫ 0AA8~0A`A|?0AA0AA/0AA(0AAf0AAw0A@ A|0AA0A@A0AA0A$A>0A$AH0A$AH 0AA0A@A 0A`A@0A@AXH0AaA0AgA0A?A8ػ0AA\0A@Ad*0A`[Al0AAg0A0APO0A`A70AgCA0A=kA0A`An0A@Al0A*AЧ0AzA0AADo0A@,iAv0AAR0AA=0AAd&0A`At0A-zA00A HaA 0A^^AQ0A@GAK0AA:90A@=A~0AaIA0A,A}L0AzAhN0A HA.O0AvAZAT!20A@>TA-0A YAh,0A@YA#*0AYA8<$0AdA 0A`YA0A`̞A0AA:0AR~A0AsAHa0A@gA0AAX0A=vA0A Al0AA0A 1|A0AKcAp0AVAྠ0AGA0A'AF0A A0A`A̓0AAL(z0AZAy0ASAX9y0A IAx0A &3AX9y0AAwz0A`Awz0AA0AA40A[]AX0AmA90AWAxi0An>A`0A A~0A Ag0A`TA[0A`AX~0A~A|x0A@ALlv0AXAHq0AA`'l0AǮA\e0A@aApc0A MAh`0AB<AdY[0A`AT0AAM0A`AA0A+A|70AA0.0AA&0A`AU#0A` A0AA!0A`mA0AA'0A`*A{0A>A 0ACA1 0AHAT0A`2KAg0A JA0AHA0A 8FA`<0A>A 0A`*A}0AAO0AuAN0AOAk0A@A0A2A80A#AC0A@AT0A AATT0A[Al0A AU0AEoAu0A ,ATc0AA+\0AsA>0A@A/0AUAP 0A AT0AWAL,0AMA 0ApAx0A@A0A@>A 0A@gtAT0Ax,Axk0AAQ0A`A0AZA0A A0A@5A$0AxAH0A یAё0AD A?ÿ0AR AMlɐ0A!tk`AQ0A:AIp0A1A_%9]0A?(AK0AA%Nn?F0Al]AAA0AC)A߼>0A%kA<0A\A|<0AZ2A!=0A;AQiG@0A&_~ApCD0AAd0A@RAu0A4=Aړ0A"<A(0A cA0A`;A>0A`;A0AA R0A@`A0AKA0A A/0A Ap0A AD"0AAdf0AeAX0A Axܥ0A$ A@0A AD 0A`iAD 0A (AG0AAl0A uApP0A5Az0A@NyA؎0A qA0A@A!0AޥA50Af?A;0ANAOl0AmB$A'_0AнA0AxAl0AA D0A ]AFu0AzWAi%0A mAs"^0A=uAEun(0AAmAor0ALA$0A@> A<0A`A0AANs0AA0AAu0A~Adf0AfZAT0A`;KA{0AA?U0A ƻAmc=0A1DcA20Ak"AѦZ0A/L=(Aŭ0AȽoAh0A J[AP0AlDAXٷ0A (~A80AjiAml0Aʸ/Aiҧ0A$AY0AAu@0AŃ\AĂ0AA}&0A2A:0AA _0Ak\*Ao0ARCA0AANLz0A_r Aˏp0AAȅd0A`At$]0A sA7N0A|/Ad@0A@A10ARLA!0A<AhAG0AgȂ5AM 0A\A0a0AA#$0A W1A.O0Aݨ_+A {3 0A4uA 0ANA>0A)A0A$(qA0AU!AM0Av +A}0A_QA0A\eyA0A A*~0A]HAβ0AኚAO@0A A&0A bA 0A@gA0A`sAT!0A@>Ax0AKA0A|AY0A̘AHa0AןA0A@>^A8E0A_Ad0AgA&0AWA$W0A@A80AA 0AHA\50A`AV0A At0A`A0A`]A(s0A;A0A$A,0A`cAe0A6@AU0A@&A&0AT~A40AA90A;AwC0A@!A=0AA^ 60A0`A|00AWA>+0AGfAK9ن(0AeAu&0A\01(AI&0Ae=sA#7/(0AjXA9+0AwYA0/0A-FAmK$70AOA@?0AeيA]I0AF'A;P0A7aWA W0A}{A_0AoAh0A Afqsn0ATe A%t֚r0AhGe_AjAu0A0p|Au0AA0A`DAX*0A ZoAEk90A=AYn0A]A{0AW\Au20AB.A؎0A&aA78+z0A*VAUXe0AnپAzB0A'A0A 8Af܅0AV Ar 0Aɚ`#Ao0AHi<Aǔ0A],]XA~0A:wduAۇͽ0A;A~0AňAǔ0AFAo0AsAr 0A6e Af܅0AQ A0A=+ A%:p0A2^ AXZ0AU An@Y0A= Am0A | A؃0A\`l A7=0AI A0A0L AOԊ0AUAOg0AwLA{0A.(~Ay0Al~,A0AJyA/?0A?Ax0A@AI0A®AƱ0A AAt 0A`AfL0AIEA(0AAr0AA,F30AA¯A£sx0A7\ASu0Ar A$0AAx0A \+A*A0Aǣ /AS%0AU(4A 0A74V9Aq|0A%>A80AOCA!$r0A0GA&¸0Ag$LACj'0AiOA/0A4SA"0AUARa0AċA[0AuW5A н0AA#0Ao~ A0A ALȲ0A# Aʮ0A~M AAJ0Aۜp-w A4W0A) A40A/ !Aڭ0AJr!A%nl0A\9kN!A !0A$"A0A@ "A~0Aa[#At0A$Ab0A&AX0A̜'A0A *A!Đ70AK*A/0Ae+A\'0AE+Ai/$0A&^P+An~"0A: ^+A( 0Ao+Ak7+0A&5憂+AC0AC +Az6p0A3'+Ak2;0A-!+Ay0A+A(+0AG,+A0L0AT,A0Ap_,A%s0AZ} ,A0A-A$}Y0AEG-A8)0AVEu-A㶠%0AY-A@qZ 0AsXؽ-AL0A-A0Aj-A0A-A}0A+-A)0A@ .A|P0A2.Al0A O0A$0A1Aƚ0A H3R2A90A7U3Am0A3AӬ0A!vT^4AXU' 0Any4A60A5A84$0As5A͍0Al5A\0A5A0Ag5A0A5AΤ0A.P5AV|0Abq5AT x0A`16A؞t0A=F!6AS!q0Ad2vC6A o0Ajh6Aa %n0A֎6Am0A|6A n0Ab 6Aɝn0A6Am0AZda7ASl0AF7A[Ak0A07A?Ai0ADsD7A, g0A^V7A=e0AV`{ V7AEY+4c0ArV]_X7A^4]a0Ab@H ]7Av_0ALd7An^0A#m7A \0AZQ#x7Aǜ[0AW7AVZ0Aۛ7AmD4Z0A?(Yd7AvY0Ax,n7AZ0A8*"7A`Z0AK7A T0A C8A K0AV8A B0AL9AP60A1k9A)0AH9A؄'0ABp9A&0Apƅ9A^$0A@Ͼ9A.!0A9Az0AA5:A?0A@K:A0AG :A@p 0Ab:A 0AA;A 0A$˟;A 0Af\;AQZ0A x@;AWa0AѴ'<A!0AZ:#SQ<A-F 0AEnq<Ae0A <ATpT0AЏ<AԳ0Avj<A` 0A`윲<A0A <AK0A3ek<Aj0A2l =A 0A@2=A 0A WNuu=AME0APuz=ASך0A=A/<0À2>Ax0Ayg>AC0A[䷕>A!Y0ANq>A'0A)>AH0A}/>A0A>AsШ0Ar ?AM)0A6a1 4?AZB0A'_S?A\0A u?A90AՏ?A+̪0A e2?At0A?Alw0A  @A zwp0Ak=@A\i0A@x@Ae0A @AT0A8AA%M0A&EAAg0ABA(0A}xCAXS0A~(CAU0A`lDAX0A.-j2EA<4 0AEA~$0A`"FAn(&0AYƪVA|F0A,VAl@0Aa:WA<60ATWAe00AEWA^%0A@ WAx1 0AXAL{ 0AXAHV0AwXA0j0AiYA0A@YAX_0AYA00AZA쾗0A[A0A [AhT0A \A?0A^A 0A)`Ao0AbAa0A`;bA3-0AocAX0A9{aAD>0Au`Ap70A :`A #0AFaA0A@bALU0A AaAO'0AcA`\0A!cAΔ0A9)3cAB|T0Ao2dA=0AldA0A`"dAۡ].0A[eA60A51eA-0AE!"GeAz0AZDVeA9#0AEEE^eA0A^eA[p0A`eAUC,0AӑseA\0AgeAI0AheADQ0A;hAt$0A iAh0A ɷiAd}0AAjAp0AhAfn0A@kgA,4a0AfA)0A`aA0A}E\A@0A(^A$0A`^A 0A,_Atd 0Ap]A00A %]A80A`]A`0A| \AO0A@iEA50A@ DA$9a0A BA0A AAA0Acg9A0A#V8Aqӭ~0ANQ/H7Aa0AmwX7A#0Ae6A#0A Q6A4+:0A?i4AnGR0Aΰܺ2A%l0A^81AQz0AXuq/A 0Aئ!-A0Aƾ|F,A0A@5*AĂ*0A'A F;0A0g&A-RN0AÜ$AhSb0A j#A@c0A`S#Ac0A`dO#AYf0Aei2"Al0Ao!Aٹt0Ax X!A*~0A"VR A$Ɗ0A- AA0AN9A10AtgW/A%x0A+ A?#z0AH~K7 A)]-{0AMx A&bx0AC A6>t0Abd3 A,#m0Ai AF}j0AuõW A'1h0AfV A*h0AEo Ahi0A Aam0AmK= ANqo0Ab?_A) o0A).(cA kl0A=#ZA1Rg0A}jA`0A@2ALU0AA R0AAV0AA;S0AjAI0AAF0A@kAXE0A&A <0A9Ap40AIA90AtA90AྙA,+0AAw*0AdA(0AF?A4q!0AIA0AAe0AaA0A@HA$0A@g2A0A %Ae0AA|2 0A?A4 0AOA4 0ATA4 0AA0A6A$0A&A70AA00A6A<_0AA 0A5A0AI0Ac0ARAL0AAX0A,AW0A =A0A A0AtAH0A;A0A IA0AoxAP0A@UAh0A<0Ah0A ZA0A@aAh0AR-At0A`AO0A Ap0AAp0A%Al80A 3AxZ0AA,0A QvAl0AagAt0A`A}0A@GYAY0A@ KA0A#A0A A0A`)Al0AA(0AA@|0AA w0A ATp0AA|pw0A"Arx0A`As0A`Ad{f0A Aԫ]0AwTATcV0A@A(R0AhA<<0AtA%10A OAؖ)0AA4%0A`CA]0AqA0A`iA80AѐA^0A A(D0AA p0A AA(0A@\A0A AD+0A ~Ax0A}AXb0A @AXM0An7A &0A A0A0Aw}A@0A.Al0AfALu0AAa0AaAҳ0A >A0A`9AL{0A^)AT0A@NA?0AAp"0A_OA0A@iA[0A ~A0AAA,0A&A<0A[A0A Aպ0AAPK0A@#A40A zA$q0AA$(`0A^At\0AAP0AuA @0A@WA (0AٗAH!0A <AW0A,A0A@bA0A@7AJ0Az Al0ARA=0A`'A0A@A0A`D^Ax0AA m0A`ADf0A0\A`6R0AH#AH>0AtA^/0AmAP"0A AH0AA0A@A0AxA`0A-A*0AlAe0A\A0AxHA 0A3A80A` A8m0A A0AwAX0AKA0AA0A@A[0A`Al0AlA8g0A (AU0AGAlS0AR;AI0A` AA0A A850AA/&0A`vAE0A?yA0A`MAl0A6:A0A Ax0A@>A 0AA0A` A0A9A@0AfA0AA B0AA\0A@VAl0AhA̝0A` A0A>A0AAp0AW޾AL0A}Aw0AAp0AகAlOc0A`;A^_0AuAL\0AmAIO0AEYAXG0A0IA>0A l-A4 90AA40A`A|.0AιAU*0AෙA$0AwEAh 0A(A0A@A0A,A 0A(AT 0A 1A0A_3AQ0A`@A( 0A`v_A 0A`ӒAS 0A@謽A 0A@NA; 0A圽Axi 0A@wAԫ 0A4WAY 0A` Au 0AA 0A}yA@± 0A`4A( 0A&A4 0AaA 0AƕA 0AuATɚ 0AzAH 0A_Al' 0AzHAt 0A<.A 0A cAy 0A!Ap.q 0A ADn 0A@"A$i 0A /A0c 0A`A[ 0A"ܺAeM 0A ںA`%F 0A`ºA< 0AAZ6 0A A1 0AAX) 0A`m`A" 0AvUA|! 0A OAz 0A ~BA2 0A`;Ae 0A@i>A] 0A`-A 0AzAl 0A2A* 0A׹A 0A絹A( 0A AX 0AyA1 0A ~qAX 0A@ƆAl 0AA 0A`啹AA|P 0A7Ah 0A1AT 0A OAp 0A8A 0AIA 0A`fAC 0A9Adݣ 0A@A 0AqA 0A@yZAi 0AtA 0A`)A! 0A3ATo 0A@wA^ 0AhAxN 0A#_A D 0AV\A40 0Ah>A@ 0A`dAp 0AA 0AA\> 0AfAXH 0A@/APM 0A$A+ 0A ͭALf 0AiAD 0A`TA 0A A 0A`kAh 0A`mGAH 0AA< 0AA 0A` A 0AA 0A |A0 0A@Ap 0A A q 0A@#As 0ATŪA8l 0A@ A c 0A {A[ 0A$9AtI 0AA ( 0A)A܌ 0A`] Ah 0AA% 0A (A@ 0A?=Ab 0A|CA 0A 8A0A[ A@0AA,C0A`Ax0A|zA0A OIAP0A@&A 0A@AӼ0A ~A8ֶ0A@Aë0A@#RAʥ0A=9AD@0A@NAؿ0A̦APz0A=A~0AaAw0A<(Aاr0AAf0A uA^0AwApLT0A iAHH0ApA50A`dAK00AMA*0A@A7!0AwA0AAt0AڐÃ0A|A 0AVPA 0A-A<0A0A0AФA880AĮAd0A`A0A&xAhH0A]ZAl0A*,A00A}AA0A`ޣAո0AAf0A`[A"0A`£Ad0A^ͣA$0AA20AڣA-0A@߷A|0A2ALp0A jAc0A@5qAȇS0A[NAQM0A1AhQE0A`KA8:0AA.0AA 0A+A)0A ҢA0AA(0A>A 0ATAq0AϡALu0A`2áA80AsȡAH0AnġA0A@EA"0A ~A0A Q2A0AAy0A@`A0A@A°0A@֠A۬0AgA0A A{0A@#Ax0AӠAP0A@yA.0A?AL0ATA`E{0AǹAXJv0AՑA,ci0A /^A\0AAO0A"A50A?A #0A@؝A0AK~A`0A@P"A|0A[A0A GA0AA;0A@A,)0A!A(0A &Aj0A (Ap0A3A,t0A יA0AՙA|0A+A:0A0A0$AV;0A3 AX80ARAR50A`mAJ20A`AE/0AAhH,0A A8^'0A@A r"0A A0A"A0AF#At0A"Ad0A`A 0A`A0A A 0AA\u0AA 0AAd0A Ap0AAD0A A٘A0AИAt0A]ȘA&0A=`A;nA|6]0A9mA\#0AjA8 0AiAd,0AiAxo 0A*iAt0A5hA0AhA/A`hAP|/A|hA/ATWhA /A& hAP/A~gAB/AyEgA/AfA /A@p`fA/A 3fA/AeAС/AFeAm/AQeA̮/AĬdA/A]dAd/AZ!dA]/A`cAy/AincAz^/AbAH/A aA@/A`Az=/A`Aȃ>/A_Ah"?/A_ApV?/A@_A?/Ax_ACm 7/A{_A*/AVQ^A12W:/Apy^A/A@]A/A%]Aɒ/A%mS]A/Arj]A>/AQ]A 9/Aړx4]Ab/AB]AD/AT'\AB^/A\A/Aw\A/A[A}/AE-[A? :/Ao9[AA9/A}8N[AH/AN[At:/A+n[A 9*/A !W[A/A!qA[Ax/A@/[A\R/A- [Ahz]M/A ZA/A]ZA$/AZA,F'/AZj_ZA/Ax4ZA8/A ZA/AbLYA[/A^lYAk\XA*A5n/A쯓LXA_^/A*TXA`"Q/A炨XA z /AXA`"Q/Ae}XA_^/A4XA*A5n/APlXA7/AXA/A@i\WAP-/A{b:WAl3>/Ar.ZeWA/As VAn/AB VA!m/A߰VAج/AVA&/A̺TAx6/A`[{SA4/AwRAaEG/AcnGRATrU/A^hʘQA`/AkPAP7g/A`2/PAPg/A O)PAXg/A^OAg/A@iOAg/AOAPf/A mOAd/A@?OA2a/A OA0j]/A@%NA4Y/AҋNA`T/AQNAO/ANA`J/A MA7D/AMA>/A/MA(9/AࣻLA/5/A@ GLA0/A`2KA/-/A"]KA+/AH+KA~(/AJA |%/AJA "/AJA(/A@gJA/A^8JA/A| JA/ANIAxh /A`IA`r/AOIA/A@UIA3/A`4IA/A)IAhD/A`2HA>/A (HA/AHA&/A$HAP/AMHA /A jHA/A ARHA( /A+;HA/Ap%HAho/A` HAx/A@EGA!2/A FAp/AEA؎ /AsEA/Ao9EAb/ADA8V/AФDA/AEXDAڴ/A8DA[/A lDA`2/ACA``/ACA/ACAΒ/A#CA/ANeCA/A>CAHÂ/A CA3~/A`BA }/A$BA|/ABAB{/ABA`y/ABA w/A 1BAAu/ABAxr/ABA`ep/ABA@m/A`;BAhk/A)BAei/A`BA8tg/A@BAXe/A JBA{a/ACA@`/A-CA3_/ACAj]/A9CA7[/ACAhX/ADAأU/ADA]S/A& DAfQ/A`DApO/ACAN/ACA`M/A CAfM/AECA{M/A CA`N/ACAN/A CAM/AzCAJ/A@CAxG/ACA`6D/A BAHK@/A BA;/A\BAh7/A$%BA1/AHAA1/A@AA R1/AնAAt0/A JAA"//A AA\-/A6eAA`%+/AJAA(/A@C1AAm%/A_AA!/A[AA/AR@A=/A+@A8 /A O@Ap/Ȁ@A/An@A`B/A`L@A2/A'@A( /Ac@Ax/A?Ax>/A?Av/A ?A#/A \?AG/A?A/A൉?AX/A@q?A`/AY?AP/A@>B?Ap /A+?A/A@r>A^/A@yr>Aأ/A>AY/A->Ah/A>AԵ/A=A/A=Aڳ/A@=Ay/AH=A/A`ʺ=A/Af=Aϴ/AH=A/A=Aͮ/AP=A]/A=A]/A&<A/A@<A]/A`u<A/A><A /A +Ac/A+AY/A@%,AF/A@N-A/A`u.A/A/A8/A@0A^/Al3A/AV6Ax:/AK;A|/A&>A@7/A>Apq/At@Ax/AUAAE/ACA@F/A$DA/ADA /AxEA[)/AoFAxzI/At0GAO/A GA@C/A]HAPgM/AIA5/AHJAsA/A`ӕJAV/A`1KAc/ALA3v/A`LA/AhMAH]r/A`MAU/A@CNA)J/A@yNADX/AOA(~r/AJPA(|/AA3QA=f/AQAGB/A`2'RA)/ALQAЦ /A@%QA/AޞQA`/A@QAPk/A_QA`/AkQAx/A -QA(`/A`PA6/A PA/A[PAp/A@PA/A5QA&u/Am4QAR/AZQA0/A*QA$/A c4QApN/A@LQAD/A RA/AൖRA8t/A qSA/A`KTA8c/ASAh/A+SAP//ARAZ/ASAoX/A JqSAR/A` QA5/A@rPA[//AEPA/APA/A"yNA/AMA/AAMA@>/AMA`r/A?MA0]/AFAg/A @AP /A;Ahod/A@8A\/A1Af/A 1+A`/A`@$A_1/A#A$/AzZ#A@/A` #A_1/Au#AQO/A`@$AH݌/A`%Ao/A`)&Au/A@[&AU/A &AH./A^(ApN/A@(Aȇ/AT)A0/A(AHY/A`I+A=/A@+AC/A+Ap_/A`v+A/A -As/AH<.AK/Ai.A0/A c#.A&%/A (0AP/Af0A /A1A_+/Az0A0؋/A Y3AXm/A3A1t/A 3Aذ/A3A(K/Ah6A/A@N6A8/A}6A#/A836Ah]W/A 8A>8/A`;8AV@/A29A5^/A8A`/A;AHм/A g;Ap/A S;Ap/A@;A /Ah;A"/A>A$/A`>A /A6>AH'/A M>AU/A@Auj/A AAp/AX?AA؎/A&@A/AtyCA/A`)CA/ACAH/ACAQ/A2FAP6/A`;xFAr=/A FAZ/AV_FAM/A,HA/A_"IA8/A`)TIA/AtIA(`/AƋKAp/AKA /AFLAP%/A`KA y/ANA /A OAZ$/AOA/A`NAP/ANA`/APAx/A-QA/A@QAPȎ/A RA/A@!QA@/A?(QA0XP/ASAQ/ASAHzN/ASAb/A`TA/AaTA =/A"TAT/AaSAp}/A TA( :/A VA߶/Aoo^Ap{/AV_Ap/A bA(O/A=n 2AK(/A671A@/A l0A/Apg2A/A@0A@/A+A&/A&(A(J/A@,.#Ay/A@C1A3/A_A0٢/A KAP*m/A@w A@}r/A@59 AHw/A2A/AA'/AnA(/A@~A/A*Ap/A`RA8E/A@`A1/A@`A/A@`AȘ/A@>A }|/A`DhAQ/A@Ap/A |A/A |Ai/A SA/A@yAPHn/A`#Ah]#/A$A /A@A9/AAh./AA /A@A/AՂA(/AyAz/Af Ap/A JnAP/A`|AQ/A`|A@o/A`|A =N/A`|A/A`|AP/AӊA//A-A/A$7A8R/AAX/AhAPȤ/A`!A?/A@A/A`2EA1p/A@ZA8/A6A Ծ/A*PA̾/AxSAuž/A@^A /A`A9/A`v[Ap/A A8 z/AA 9/A`KA /A@ϪA,/A"Az/A ?AТ\/A"Aذ*/AફApy,/AApy,/A`_A(3/ALA/AoA/AoA؎/Ag(A`/AApl/A`APx/AZRA /AaA/A Ap޻/AA/A`9CA`/A cA`/A4A(`>/A JA|/AzA/AzA0/A`A֝/A$Ao/A@PlAj/A@PlAXT/AAA4/A A`"/A`A؟/A$Ap /AAp /ABAp /A AX#/AِA/AxAh۹/A`9CAo¹/AA?/A#Aω/A[ZA0/AAPM/ARA#͹/A@DAH?/A |aAX#/AhA=/A/A]/AmA/AA`/A Ax׺/AAP|ں/A`AϺ/A`MADܺ/A@ AD/A@BAD/AA8g%/AA0,/A@5AJ/A`ܦA/AA(@/AgNAI/At$A/i/A@P%Ax܌/AeA@ξ/AzA޻/A@A/ABAx /ABAB/A A_A0e/AA`k/AA؎/AA/A`2AƼ/A@hAx /A=wA/AwAP̈́/AAri/AA8D/A@ A`C&/A@)A'/A^A/AqA/A H]ApJ /ApA6"/A`"#A(JO/A`oAxzf/A&AT/A@Aȱd/AAPx/AN[AT/A cAQ/A$A0R/A@,3AT/A@AV/A@Am/A SAeo/A 82Ad/AAX5X/A (lAeo/A@5BApx/A`vsAW/ACA/AA8/AʷA/Aa/Aȼ/A@Aqμ/AA&/A AX(/AOyAx-7/AkAI/A$AqR/AF#A7Xع[/AQ6Ayc/AGOAj/A`lA k/A"RAWh/A=Ac/A@5EAP]/A ~AI U/AwgAL/A Ae>M/Aj;!AEP/A1ZAv&QV/Aa;G[AWk]/A ZAVg/AAA{/AJ6h A=/AjDA/AWA3/ACAky/AZ;AW|/A:AO?Ľ/ArzAA(>"Ͻ/ACX %PAҬHٽ/ALJ_eAl/ATAN/Aēz8AC/A/A${/AzA/y/AިA/AKA0H/A@AP/A3pAm/AߝAK^/AA'23/AaP=K+A?{E/A0]h$AS/AA~]/A`\A-,b/As݄gA w7Bh/AE[oAn/Azk9sA s/A.ψ/Ak AǾ/A:wA~޾/AjBA:6/AׂAP</ApAs#/AtmA`S9/A AuAwA ^/A &2A`h/AhAۓo/AAUr/A|8 oA(q/AAp)%h/AJ~AQ^/A:AiR}S/AɰA/I/A>?APm @/A@A 8/A'Y$AUz0/A؏"A;%*/AW;AF~5%/AX_A17!/AbAc7/ /AA/AA=!/AibAd'/AHArB2/AMqAFA/A'A[6U/A7%bA@l/AA k/A5㢳AX^j/AK-4AX|k/A[>9A Um/A1f :A;=p/ARxTA_!t/A:,lAx/AAv~/A`ÒA@/A@AhaϿ/A!AR/AA;8/AeAi/A A`/AQ*A&S/A}?8A/A@3AAD\/A0:PA81/AZzA /Aͪ EAܤ,/AfAN3o'/Ab5<Ai//AۑA3/A@*A4/Aϑ7A71/AIARu+/A/A!/A,Av/A;}$Ac /A_"A}/Aa!'A|T/A#1AFN5/ATAA7/AOcWAYa/A٥iqA'/A,@LA/AXAM3/AA*5 /AX.A/A -A/A$AqW?/A2GHL A/A` A;l@/A+ AA/A A /A& A/Az+ AV]p/A|MI A{1F/Ap}h AK!_/Aٞ AO@/A:7 AN/A2 AF/A> A4/A v A/A A!M{/A A /A" Ahѭ/A%PZ As6=/AXK Axme/A+ AGn/AI49 A0{u/AFr Amje/AB A{R/A]R RAP/>/A@%A!h:/A >AP8/A@>XAn6/AXxA0i5/AzޅAWL6/A&rdA4'8/A'Av;/AA?/A`AhnD/AA$K/AZAR/A8mA#Z/A/{Aʥb/AAJxbk/AAs/AA /AeAP~A/A 7SZA Ǣ/AnI\AP/Am_rhA/AAC/A19Aoz/Ax AI/A#Aijh/A _AmU/Aӏ A /ABA?}l%/A(xrAG+/AaϰAH70/AÐվAU3/AfsA/6/Avz0hAW7/A<~"AO.7/A{AAW5/Ae`A 2/AQh9~A./AcAU)/AAvP%/AѱA}~\!/A;3vA $/AMo{Aۦ/A!ZA H@/ACAlI/Afd]A/AHwF.A^/A`DAҺn'/AmA2//A^A0-3/AYA3/AA,0/Anc>Apl+/A |MA@$/A*ώ<A/A A:΀/AIA/AabA/A! .A@/AqA/AAP/A$A0 /ABA0 /AZA/A AH/A A /AA /A A2/AAB/A@A'_/AдAy}/A`As/Aa<A /A`A/A0gA/AଵA/AA /A@EAh]#/AAh./A==A1/A6A0&%/AdA*/A`0AAE/AA:/AAH/A`jA%g/A@NAj/A`2AJ/A}kAJ/AaAPHn/AnA Ox/A&4: Aȉ/A2З AmW/A8 A"T+/Aml!AZ |/A (E!ABs/ADvAN"Aaf/AQ"Aq\/A"AAE/A#A0]e/Ai$A@/An8%A/A%A@/A`m@&A8V~/AB&Ac/A'A/A@,(A/A@5(A/A(A/A`(AP /A+)A/A@y-A+/A+H/A M/AVI1A$/A3A/A`!4ABA/AYF{4A/Am4A$/AĐ C5A/A 85A.*/Aŗ5A/A5A6/A5A/A6AL/AQ")6AxF/A86ApI/A_7\86A_e//A#H6A`H/AiPU_i6As+`/A|R6AhUv/AμQ*6AfF/A1 E#7Ax/A y7A/A`;,8A/Ar8A1/A#Q8AqJ/AJ$9A0/Aymf$9A/Aك9A</A4t:APM /A:AM3 /AN;Ag /A@>;Ati/A-N<AE/Ark<A)+/Aj<AN/Au;<A0/A`0=A{k/AKSg=AYH&*/A*'=A(/AѢ=A@Š/A`B*>A:/A<.>AiL/A u>AM/AaA?Aȥ0/Ay?A 0M/A 1l@AT/A8ɟ@Aq]/Aiƭ@Akhۯg/A[@Ar/AN@A> I}/AScӢ@A7[/Aw@AG&'/A0C@A'q/AI@A5t/A(Ep@A4p/Ax @A`/A@AK\/A=ra@A1/A+<@A[O/AZ@A/Aez?A(\l/Afz?ANa/AINv?AMR/A d?A /AT b?A/Aѹ?A N/A ¹?Aȶ6L/A+;?AӜ /A>`?AX'/AqL7?A׆0/AQ?Ap8/AV@A?/A>@A!E/Ae@AUH/Aw@AN/A)AA.U/A@&BAˣdO/Ao;zBAmG/AVRBAqJ=;/ACAIp'-/Ac?QCA(-/Ah@WCA< /A*{CAH/ACAkي/A@gCA=gr/AtDA~/A;V.DANzf/A{ 9_DA|p=/ADAk'/A0DAѵ/A`EAPȵ/A`;EA/AްsEAǻ/AEA"ŋ/AGEFASB/AXFA-/AyGAŷ/A8 HAϭ/A!HAJ[/A*IAв/A- IA5/AhIAROU/AԊqLJA x/A@JAVB/AJAQp/A 4JAvZ/A#KA6g1/A?:KAw/AIaKAG<_?/AΧJA/A߭JA"/A@{HAPoA/A@cGAx/ATFA`/Ah?FAa/AEA /A@NEA@/A EAhD/AGGAU/A HAذ;/A`]HA;/AtHAX/AHA/AHA$/A`HAaN/A@HAf/A.IAO/AJA]/A`GLA #/A |yMAK/AMAV/A` OA(#/APAz/AQAJ/ATAh/A@,OVA/AwA/AVA`%^/AA/At<AwU/A`A/AA` /A Ax%/A`3AX5#/A1Aq/AAm/A@A`X/A Ae/A<A(>/AAt/A@IAN~/A`6A{Yzd/AdAtyL/ATA7n7/A?Ac%/A fEA vz/A"A7! /ASGNA_/ACg~A;*/AܒA>/A5)jL`A/AA`/A`KA/AQdA7/Aj5A '/AZpASWN/Ar:A>/A+A1,/A~A/A$A/AA/A@A`L/A A8#h/A|A/A&A@S;/AoA/ANXA68/A`A-^/A@A=6/AxAl/A@AX/AAx9/AuAT/APAwx/AlA0H1/AA/AWA/AA/AgAk%/A ˇA/AA/A AP+/AmA/AgEAƔ/A A1/A>A$/A)pA/AAw[/A|A]/AKxA3/A0A/R/A`)A >/A`]AA/A A,/AA=1/A_UA_&/A KA^/AA E/A AP;/A@ A /AA(~/AEA/A4A/A |A(\9/AA/A /A/AqAx/A Ahr/A@VAiJ/AA=/AA/A`2A/A෍Ax/A`jA /A@FAB/A`ܔA=4/A`A/A@ypAif/A#A/AA /A/A$]/AA(M/AA@>/A A@5/AAҽ/AzAPĽ/AA{/AXA9/A`aA"¼/A@A8/A`)bAvú/ALJAp*/A`A !/AjA@/A@`$ AP룴/AAd/AA)Ю/A@!Ax-/A Al/A !AR/A!A8(/A m"A//Az"A`/A@y'A T/A.A})/A1AV/A`mq4Ap0#/A4A/Au6A 4/A8A1/A=<A/A?AxZ/A AAp?/A@5BAǝ/AoCA,/AEAfh/AMFA:/AJA@/AIOAYw/ArrOAzOOR/AsPA(.&*/AEKQAo/AI[a8QAwEҗ/A.yRAX㣗/A}RA n/AzRAm|8/AL5SAu/AU5)wSAI})xƖ/AmԨSA+OBV/AU_SAQ/A'SAV>&/A]ogvSAxܕ/A SA 19/AmySVSA@1\/A-TA/A`9TA>ޔ/Af3{TA/AF/AAFv/A]3A+JF/APAc/AcAYR/A a>Ah@҈/Af Ap/A@GAW/AA/AMuA0A/AA؎p/A AD/A A[Y/ACmAdg=q/A3A5*$/A0.A~/A±A~ϊ/A_CU1PA/A OA/AA#/A9A/AxA /A`vA@}/A=AO/A UAr/A`rAR/AA /AA74/A`]A8)/AsIAc6/AsAe/A5m6A$}/AOjIAˌ/AaAA/A{A*/A ~8A/A A؞/Am}A/A •Aج/A A2:/A/APP/Am}ARC/AeAY/A@A/AAV'˳/A*TA֢/ADob]AǧA/AB!A6o/AAG4k/Ak;A!?/A= A竐B/AA/AbYA%5/A`A˶/AA`Թ/AA`/AA`/A@Ag/A@Aj/AA؍/AAh/AVWA/A`6A/AAd/A࣐Ag/A \A/A CA؁$/AAFf]/Ak-fA/A`!A>6і/AB'%A]g /A'sAH/AA/AtrA*/A<AH/A@HA/A AA/A3A/AA@OQ/AA3I/A) A//A 8$A0*/A@#hAh^r/AA\>/A#8 AH!lw/A(cdA/AqAT#/A<2A@0)/AA/A@Ah/A)fA8"%/A@bA%&/AQA`/A AV/A /AH/A /AB/A@AXl/A0Adޜ/AdA`G"/A@WA(/A@yMA@/A`vA=/A@EA/A A/A (As/A0A)I/A`ʯA/A@عA/A`̺A,/A A/AA/A[A /A AS+/A@HA(3/AVAz/AAH/AIAv/A2An/AhkeAPoe/A@ƘAq!5!W/A3S@xAI/AA׌=/A1A] 3/A,U AN1F+/AA USA%/A@- Ap!/A`AE/A@A/ATAث/A |jAy/A Al&/AA`/A$A(/A^Am/AvA;W/A A8VP/AAJ/AVAxi/A pAJ=/A&AHЙ/AfDA1Ș/A`d#A`˗/A AX/A@߃APb/A 8 A@`Q/A5A/ANAX/A4A濕/AAؒ/A2A/A<A/AA/A AT/A@-Ap/AIvAxhД/A3Aۙ/AWA@/A]A9y/A`A[w/A Am/AIA/A2_A@/ADAcÓ/A'Aґ/A@AhG/AA/AA/AW AP//A` Axے/A` APՒ/A@p: Az/AF AX/A AxR/A@ϙ A`V/A# AfI2/A[ Av/A8bI A`/Ah` A(ӑ/Ag& Ađ/Am A /A`"AZ/Ap\i\As/A\4AÐ/A EA{0l/Az AG݄C/AۆAk.Ə/A$8E/A!TA6!N/AIAV/A AŴ/A 1<Ac/AAA}/A;AxS/AKDA(,/AA*6/ASDiA`喋/A`TAN3pg/AD፳A9/AgIAx/A^*DAp/ABhN7kAC/A@ A5d/AF4AU"ٍ/A(|@Aĺq/A2+AR/A8KGAp2/AAY/AA7K/A\/CA` Ɖ/Ao712jAp/A@8AJM/Ao&APm/A@A7/ALtnpA*~`/A$DA;ψ/A5kAJ/A_kQArXr/Ah2A1GG/A4a[AUp/AY A/AAކ/Aq5Aц/AA Ƙ!dž/A3HAv$/Ak5MAg~/A\PA /AA7/AʣA@6/A@TA/AmhA/A 8|AÆ/A`AĆ/ACA k/A^tA$/A bAZ/An2DA0f/A`\AK^1}/AA~p/AABg/AhvFAp b/AA/AA/A*A0݆/AaAhӆ/A?A` /AA0/A q9A`/A@A˚8U/A)ޟAM/AĝA#VlJ/A/\AJ/A@AXrJ/ATwAKH/A9LAD/A@#AK(9/AQ _A))/A%GA;D/AlyYAp=/A`<A/AbA /ބ/AŸAׄ/A#Aa/AA0/AحA^)惲/AӴ`vAF/AAF Xą/AsA0ԅ/AA /A+ϫAh/AA-/AAh*/AHdA!@H/AHko"A}/A'~GA}g/APF AP /AXA?V /A4cA+/ATetAqߪ/AcA$ x)/A|A:/A`)AhN/A!yAY^/AxA Ԣi/A"ԬcAsp/AaDA_s/Ap/Aڂr/A$yAl/A֡AfGDu/AmߝsA{3/A~A h/AkAS 7/AW@ZA84Æ/A@pAPۆ/A'eA"|a /AjAnf/AKKA͆ /AŹ/xZAo^/AuA /A?A(/A.AA/AnAbj%/AqAK3 A/AྜAH/AgYA5/Aa A1/A=ĊA m/A&UAxq/AFˣAi/A61wA/AW*( AKq/A CA/A JA oЇ/AeAA| /A0 A| /AA? /A$΁AϠ/AdA(/A`28A{y/AA'/AzlIA=oO /A l~AcR/A|1;e~A }AN/AE+}A ]F/AJ}A&-L@/A&|Ã;/A);.|A%E7/Af7|A(5/AЅ|A@>/A:$|AK/A{A2jY/A~a{Ad/AzA` j/A,zA1gm/Ap*)zA_o/A`~zA9:o/AgyhSzAb-;m/A-J)zA{1Bqi/AϗzAc/AyA^/ADyARUX/A`bܫyAq`x=R/A1\yA K/AFyA3T5/A xZyA)/Asɉ9yA=/AZMc/(yA/A3B%yAք/AQ3yA]8/A!wyAps/A3z;yA8;/AV.zAGn/AzAS\/Aj{AsP/A.D{A8G/A@"|AQ /AMAƃ/A@A/Ad|A&Ϣ/A|A*'Ӡ/A⬃{AÁ/A?v{AY/A[h-{A5/A'`zAc݁z/AqzA Cj/AXj)~zA&]ZX/AdWzAWE/A u=zAvy!2/A&E7Xق/AǕzAH)#̂/A:zAV/AKHzAL/Aa"?{A)/AG@ {AƮ/A{A7/A$|Aj‰/A!S|A["͑/A K&&|A9[/AB'|Av/AI}AI/A`}APp/A }AӚEo/A|.%A}?l/AπAFg/A.fAqr6a/AzAY/A4A$FyJ/AP7A*iz׸G/A[ѲzAD.B/A7kzA:/A }\zAc1/A <9zA='P'/AzA/AzA^#/A5zAL |/ATXyAV/A.3yA/A}jyAج܁/AyA=bˁ/A yA O/A~XPxAsAʔ/AU5stA\/AaA?tAȚ/A6XtAo/AgtApiR/AvtA.to/Adm}tAF_/At{ytA@O/A%GltA`?/AVtACc:=/ADtAՕZ:/AVV|4tAՋ6/A7XC&tAd2/AtH>tAKJC./A?tAv5}v)/A/ tA|~$/A tAl~/Ac tA/AG9tA(/A-tA/A̍[!tA /A.tAx~/A`Q~tAl~/AT1׿tA+S`6~/A0tA2~/A6uAO- y~/A%uAFdA]u/A`ҰAجu/A@,Ahu/A AHu/A@A54u/AA)u/AHAEu/A Au/AO1Au/A|AUu/A=A)~v/A (OA5v/A*Afv/AQAP'v/A'QA)v/AA@w/A`myA w/AAh{qw/AĔAgw/AjA8Qw/A@>APox/A A@5'x/A 8A8QUx/AQAЯx/ALAux/A Ax/AAy/AaA!y/AAP^"y/AʊA>3y/A`APWy/AAly/A6Asy/A`[Aby/AAy/ALA3y/A`HAy/AABy/A6A8y/A`Ay/AR'A z/ATA2z/AVAJz/AyAp,dz/A`Az/A`)AТz/AOAz/ApAHz/A \A%z/A`Avz/A@5ZA؎z/ANcAH){/A A-O{/AKAu{/AAxm{/ApA`{/AAH]{/A`KAӖ{/ATAh"{/AAߎ{/A@Au{/A A2{/AoKA0{/AAAw{/A`wA[s{/A %A{/A|A0n{/AAњ{/AA`{/ASA[{/A cAH2{/AA{/ABA8{/AhA8E{/A`vA{/A A%|/AA ||/A ZAe(|/A$ A0|/AsBA@4|/AA0&2|/A@Aa)|/AHA 0|/A`]AR{/AA{/AA {/A`]A0f{/A$GApʎ{/AAx)j{/A|A(F{/AA%6{/AA {/A}%A{/A[SAz/A u}A^z/A AUz/A A Az/A^ A"z/A`LA0z/A APwz/A`TAp{z/A`KAnz/A`v Afz/AeAfaz/AA( Uz/AsAx=z/AA`v z/A!A z/A@Az/A@rcA`z/AĐA z/A`̻Ay/AA-y/AAy/A%Ay/A``Apy/AdAy/AaAȺy/A"A(y/A iAxy/A 1A y/A A8 y/AyAiy/AAh@y/AAx z/AA([ z/AA03z/A@ACz/AΞAYz/AhApz/AAdžz/AAPz/AIA0*z/A@>A/z/AA@Nz/A`f!Az/AtAQz/Aq`A0;z/AYAH?z/A@A'z/AxAyz/AAXz/A`A Sz/A Av7z/AA$ z/AXrAz/AμAz/AnASz/A Az/A!A0z/A^A`z/AsAy/AWA(y/A`vbA(y/AFA ty/A \A8y/ADApy/AAH)y/A^A84y/ADAhy/AA[y/AAALy/A_A`!y/AAy/AAy/A@,A@y/A@NAy/A Apy/A`AUy/AtA@y/A`A(qy/AIFA0fy/AA0ٳy/Aज़A0Ly/A`Apy/AA y/A`vAy/A A0y/A JA0y/AAny/AAz/A`KAe*z/A Z;A03z/AARz/A@<Ahz/AaPAtz/AAvz/A AXz/AGAz/A |Aϔz/A/&AX5ez/AA9z/AAYz/A`ANy/AX A@By/AAy/AVA@`y/A-Ay/AAjy/A@,-Ajy/A`]EA*y/AAly/Ap<A"z/A4A`e/z/A sAȔRz/A`Aqz/AAz/A@sAFz/A`A p{/ABA{{/A A8O{/AA({/AVAX{/ApA{/A@gAQ{/A͹Aھ{/A@A{/AA\^{/AAx2{/A JA8 {/AA {/A JA{{/ANJA{{/A`bA{/A QA.z/A&A^z/A`bAoz/AsAP0z/AAA+z/A@gAgo/AnZNkA&jo/Ad.kAm&yn/AXlA:n/AX#lA(Jn/A`!mAƅ(n/AߎmA) n/A'EnA;m/AS:vnAj.m/A:UoAvA$m/AѴoAbƏm/A|PpA2m/AOpAЛm/A&qAhDm/AJrA{BRm/AS:rAks6m/AssA]0m/A4-ɍ,tAH1m/AJ/A8h3*qA79J/A^qApJ/A6rAPJ/ArAJ/A@>rAJ/A0{sA(J/AsAP^K/A !tAu K/A~tAK/AٹtA%K/A@tA0 2K/AH@uAJ3K/AjuAh0K/A`uAb;K/A ɷuA8DK/AVuA RJK/AquALK/AuAxPK/AauA@dWK/AuA<^K/AuAPYaK/AuAcK/A`uA@}{K/A[uAK/AQuAڛK/A@uAK/A cvA୧K/A@1vAXtK/AyvA +K/A@vA`vK/A@`vAHK/A\vANK/A@yvAHK/A@uAK/AquAK/AwuA(cK/AxXuAhL/A~BKuA]-L/A ?PuA3NJL/AΉ(guA.fL/AˇLuA! ̀L/A^;uAvL/A5VvAL/A@hvA pL/A@%vAаL/A`vAzL/AmwA$L/A xA0L/A@xAL/A 0yAXL/AfyA L/AzAjL/A zA`L/A zA`eL/AzAHL/A]LzA(L/AF`zAL/AzAL/A`zAL/A6^{AXyL/A@{AsL/Ap|AQL/A`B|A@L/Ay|A%L/Ay|AL/Ay|AL/At}AL/A 9}AnL/AZ}AL/A;}A``M/AĤ}A }M/Aɟ}A`P(M/AՕ}A#4M/A@g}A%PM/A@ߋ}AWM/Aஸ}AT`M/A@P~A]M/A`~A]M/Ax~AhUM/A~A[M/A+AhbM/AdApcM/A6A\M/AA(XM/A GA`RM/AځA@dQM/A`|ASM/AA\M/A`9A2bM/A@ރAnM/AAp[wM/AfA}M/A@APM/A<A`ÍM/A@ARM/A@>A(M/A4APfM/AAM/AAM/A@/A,M/AanAM/AAM/A@AM/A AM/AA`vM/AQA'M/A]vAM/AA@$M/AͅA`6M/A ZAȺ N/A JAN/A4A(N/A |GA4N/A A8c@N/A A@ABN/AAXKN/A`[Ap|dN/AxA N/AAx:N/AA`N/AAN/AAGN/AOMAHN/A@A0N/A AcN/A`)_AxO/A[A8O/A`AMO/A`AOO/A`m A@PO/A@iAryO/A_Ah;O/AqAO/AjAܫO/A@`AO/AZA}O/A牊A4O/A uAX$O/A`zAO/A@A$P/AdA=P/AA,PP/A@6A&UP/AN~A`!ZP/AČASP/A6A@uGP/AMmA@P/A`Ah//P/A`DA'P/A@YAP+P/A A"P/A&A P/A`A3P/A`A8 P/A>APZP/AAPP/AHŏA`O/A<ATO/AAqK$AgmO/AΐAO/A`A*GO/A$o,ANO/ADCAiO/A IAhN/AĒA-CN/AjA9N/AAyN/A*˾A^N/AA@N/A`בAM/AAttM/A<AHM/A}qA$LPM/A-A M/APaAJ'oM/A@ƑAsħUM/Aʺ>A>M/A Q?AS7M/A&A4/M/A8AX&M/A'sAM/AxGJA!OAM/AZ KASb` M/A6AήM/A^&Aw)nL/As!A:-L/A19Ap L/A`UAtEL/AAA8ڣrL/AQ&A%8L/AtAkK/A)zA|K/A"ߎACBK/A2$\7AeK/A`[AJ/AIA{9]J/Ahf&A0J/Ag@ A_ųJ/A` AqΚJ/A=͍AЦJ/A?A ~J/A-A:pJ/AnnpAȻcJ/AoA0XJ/A@.7A#t?J/AT%ACo(J/AZA9IJ/AV҈AżdJ/Agx֒ALv=I/AZ9GA-I/A@A pI/A ҸA zI/ASVA4Z[I/AHκ)A"I/AĀAعI/A`mvA %lI/A7OrIA OI/A tz-A41I/Af "A JI/Aoq(AɱH/A^I4@AH/AhAstH/APB†AkH/AѨ]o AAG/A.?AĵAG/AͼbALF/ArAx:F/ANlA5F/ANAF/A"%AGH9YF/A,AG.x1F/AᦻAT F/AG?4AohE/A5ZLYAHE/AAE/A$AUE/AAE/As- A)D/AޢoAbD/AM_ÉApD/A AP@D/A(CAI C/A4見AٹC/A# AyC/AA1W@C/A:G:A\Ż8 C/AVK،AB/A 냍A6DB/AhA'EB/AA| SB/A!TA^ܸB/AP'eAʭB/ASarA)湨B/A|yA[9 GB/AR#{A@B/A=wAx3hjB/Ao`KA|uB/A)AB1bB/AA)NB/Ar^ A;B/A= Aá'B/A+gAB/A1AҤ 7A/A4,A/AU]AiA/A#AbA/AVAD@:@/APA$-@/A@ A`]q@/A9+A?@/AŅjiA@/A`dA=@/A<A@/AJ֖A& ê@/AFpAe[@/A2A1,@/AeЂA|2Q@/A@Q%AN@/Aͩ&AԜ@/AmA@/AMA~@/Ak;LAx@/AAGl@/A ݜA\\@/AGAYH@/Al)YA/@/Av A@/AƘb2Az?/AVAKA5?/AHؠAأ?/AVA%WB?/Aڴ{A o?/A^8>A:RST?/AAo9?/AR.}ۀA^+?/AZ S_A!n?/AH-Aպ>/AA>/AAes>/Aj Aڶ^8>/A62tADT>/AqaA;>/A5NAO>/A ]=Av>/A .AW>/AA5L>/A# ATyM >/AEJ A^->/A>Ax6>/AA">/AACI>/AK A1u>/AaAtT>/AL][A.4>/A 鳵AzӒb>/AaW)xAFi=/A:+A(=/AўA(-=/A ҰAP6/A`)Aa6/AැA6/A=jA6/A`7Au6/A@(A6/A`BA6/ApA6/AzAp= 7/A oA7/A`fnA77/AA[7/A@gA`w7/A̴A7/A`)A07/ARlA7/AHYA7/ApWAf7/A \wA)7/A \A`a7/A 3A7/AXAk7/AA87/AAH\7/AzA7/A@>AB7/A3A 7/A@8A7/AR`A 7/A`fzA7/A HAX7/A]A#7/A A#7/A=<A7/AA*8/A@ AC8/AFAP8/A`A`e8/A`Ap o8/A`DA8y8/A qA8/A@A)8/A AL8/AcA88/AAp 8/A@Ah8/A`FAz8/A=Aأ8/A AQ8/AA8/A`)A8/AfA؞8/A \A8/AA8/A@ A08/A`!A8/A`A9/AAP 9/A@A.9/A A RB9/A=A8I9/A/AH.B9/A@hA8,9/A`ÛA)9/A@ Au9/Ap A8/AA 8/AAأ8/AAf8/A`Ah38/AA8/A \A`a8/A@A8/ASA8/A`)pAf8/AסA8/A3A9/AeA8 9/AAH\9/AA{9/A HA(-9/A@ϏAU9/ARAP*[9/AAH.`9/A]AfW9/A qAk9/AQA9/AzAh 9/A3A9/A@ A/9/AAZ9/A`fdAQ9/A@ A9/AzA(ܤ9/AAHP9/A A9/AGX1An9/A[A=9/AYv=A5/g9/AA9/A@CYA8l 9/A,QYA7{9/A6AA?9/A3˚Alm9/ALUA@19/A A@9/ALpA]9/AܾA!d9/A61ACcE9/A{>CVA`S%9/A A4P!8/A'=SnA?8/A YA08/A |A3n8/ASNUAـ8/AR dA {8/A"AEj8/A$p6AxX[8/A AZ;K8/AAI>8/A A.T8/AqAi8/AmDFA<{8/A]A#|8/A5A@ ~8/AgqA/Y~8/AGmA\~8/AA~`d~8/Aqq;A~}8/AA"|8/A"`,Aϔ:Gz8/A+Aix8/ANAOKu8/A^Azr8/AdAo8/A\? A֒l8/A;o Ai8/A` AfF8/Aޞ.À%8/A@A8/A$3A׽8/A*Ar477/AwAjh9M7/AAƕ$7/AȸAz:7/AAJ7/AJrAIG7/A,Ae7/AM30Aׁ7/A A7/A@iA ʤ7/AAApft 8/AvljA8/AAo8/AUAN)8/AA酽 8/A*ۋA6`8/A1]Ax8/A~A@7/A$qqA87/AOF+gA7/AD`AE'7/A|RcAg o7/A,SbAYv7/Aί/]A7/AMwTA(p7/AhծHAǪ7/AD9As!~7/Al'A]#-7/A\2;A7/A A+b7/A?An,7/A? Ak7/Akn(A77H7/A;Ad7/AAE57/ArqA7/AaTA0u7/AiAN­7/AHA@7/AՙAw~^7/AR2F}AӍn7/A2(*dA7/Ag QAzak7/Ac\vBAl7/A@r9AC7/ACDAhR7/A1o,RAf7/AmvaAʣ7/A$rAfY 7/A$@{AO7/AAA'v[7/AAf'K7/AybA* T=7/A)=?#A .>a27/A'PA؟*7/AA`r&7/A/G At$$7/AAf%7/ACA%7/APy{ Ansy"7/ALA=7/AA`ASg7/AԥlA=7/A% >A7/AzALU6/AUAp6/A 7A=b6/AAbBI6/A[ʊAsZ56/A TA$6/AFA2>wAg8d6/AppA&f6/AK HA 6/A Acp6/A=aAP^6/AAA`6/A{cwA6/AWA Y6/AElmA\P~6/A5RAʠ(6/ASq)Ap66/A@G]A6v6/A$AH6/AcAX?o6/A-IhAj6/AoԖAS6/A$;̂A6/AtA6/AsvA,&7/A~ AзW7/A͍Az{7/AVl*A./7/A9A&7/A`A37/Ae%XAm?7/A,Ag7/A鈉,A88/AAw8/A.A18/A+YA8/AdTxAFܬ8/ARƴA?8/A8AP8/AA%9/AH[AF9/A7ABAPL9/AS<A4|+R9/AE|=AW9/Aa:Ay[Қ]9/A|4Ab9/A;)AKDg9/ADAhk9/A  A, h9/A}Af9/AQA[f9/Aw=8Aaf9/AAɭg9/A2\jA+g9/AJKA3|f9/AiANKf9/A\˾Am߂f9/A|ڗA'f9/Ax͎A"Be9/A w/A=Jt8/AsAVq8/AMA`ap8/A&Ar8/ADŽA t8/A AK1t8/AwAu8/AAj.|We8/ACAyT8/AJA~^C8/AgpvAE18/Ad]NaA0{8/ARA8/A̧qAG8/AANA#8/AR(A#7/A@ Ay'B7/A AO67/A3qAu7/A AF7/A&A7/A"5A ~07/A/TANݮ7/AA.07/A5AEv 7/AyˆAeW7/A@ A-ѫ7/A[iA {7/ABA9Y7/AV"A|j7/AdAb7/AxA%7/Ax cAʬ7/AcAbt/7/A Anq7/ApAod"7/AGEH\A( 8/AA8 8/A@>VA\\8/A"GA6$8/AOJ) 7A88/A@A@8/A \3A<8/AA.8/AHCA8*8/AAX 8/AQAx{8/A6A[8/AXُA- 7/AˎAL17/As|A7/AmAs7/AA3\~A=7/AAA`a7/A 4A7/A GAHܙ7/AzA R7/AAL7/AkAYT|7/A\}Qy:AD{7/AUۨbA}7/A@iA\~7/A0A,~7/A0. A n7/AR5Ads~7/AiJA;~7/AkZaA{W³}7/AbGAVk|7/AgA {7/A. AHy7/A&z AQw7/Ab OAm@3u7/A7Ar7/A8Ap7/AzA@+f7/A!j߄A'< \7/Ay|AKDS7/AyEmA~J7/AXA'7/AA{GC7/Aۿ̙AZs7/AtڝA7/AmzA B7/AxAFi 7/A݊tpAŭ# 7/A8jA8 7/A^UmeA7/A3,cA?*V7/AFbA87/AdAp )6/ApA¯5/AzlA83m5/AbA'd5/A,Ay_5/AޔAu]5/AbQ)A`5/AM7Ae5/AvZAՊj5/AAОj5/ASbdAHnf5/ANv?A6G\5/AɌNAM5/Av}A/M5/Ai5AtK5/ApJ-TAH5/A|A E5/AB~Aڡ/@5/AӰA\Sv:5/AAE45/A^2&A-5/A0A3/ArA83/ArAЪ 2/A_Aج 5/AZ}AC6/A϶A!8/A@AX=:/A`A!/A@Ah=/A 1A@ /=/A`9A3/A AH{H@/A OA7OA/AA?B/A AMA/A`a0Apc%A/A74A]@/A@AAX@/AKA@/A`MA?/A@iSA0>/A_dA@>/A,yeAH>/A efA>/A8(gApű>/A:hA=/A`WvA;/A@tA(9/A -sA^8/AqA<,7/AbA,6/Az4A8]6/AAh&5/AyA(5/A=1A5/A cAo4/A5A3/A`A`3/AALf2/A *A1/AA1/AA1/AAh0/A-AL*0/A`f5AJ//ACAx./AAh*-/A`zA`T+/ABAp,-+/AA&+/A sA0+/A5A*/A'A*/A96A)/A IA=)/A"A)/Aho-Aڝ(/A+5AB(/A?S=Ap'/A`oDApŢ'/AmyLA0&O'/ATA"&/ADsAp%/A[tAB&/A)|Aw2%/A@ErA"/AHAqa/AivAH/A/TA/A SAc/A|SAh^ /AZ]EA0/A;AD/A .A(/A|!AXy.A_AQ.A9A+.AQ A.A ,AHu.A Aȃ3.A`APUU.A@AH .A`A .ATAt.ApA.A@ AI.A AاM.AVA0.A+ A..AAG.A Ar.A@DAPQ].A`Ay..AEAL.A A(S.A@`wA0.A`A.AA .A`wA.A[:A .A .A0r2.A2A2.AcOAHvo.AWA].A dXAg.A@?YA0.Al]A.A`0bAmn.ATgA ɹ.A_5iA .AkA,.A`[pAЈ.A uA8r.A@rfAЯ.A@#@A` .A늇Ap.A-A?.A`LAc.A ~A.A@r/A.A_{A.A AA.A`]-ApS.A)ۚA.AkќAڴ.AA.AAn.A\A@,.A@5A.A ~AO.A`YXAXT.A ZA@W.AQA(\.ANAX}.A`ՈA.A (AF.AஃA.A`;zA?.A |AHu.A#tAH.A Aھ.A@`rA".A Ax.A@A>.AkAHvq.AAZ.A`kAP.A A غ.AZ Aȭ.A@ri AX0=.AATk.AWA.A AXѶ.A@^A6.A XA0L.A G A.A /R#A(.AH0AxL.AT2A.A@33A[`.A6A(Ȳ.A|>AY.A JAd.A@PPA.AWA@.A XAH?.A`ܪXA0̘.A@!ZAQ.A&aA =ɭ.AdAX֭.A)eAp.AeA.AfAF.AVgAjs.A`"hA ͏.A`hAPfĮ.A5iA.AiA.AiAB .AiA.AiA.A iAh.A`DjA.A!jA0v.AD:lA.Ao`oA(R.AkAz.A 0nAB{.A &3nAQ{.AjnAz.A@nAȓx.AnAuu.A@nA=g.AnA.W.A@ pA t.A (pA.A pA.A qA@S.AOqA(.AࣳrAoݱ.A0rAı.A`tAT.A@uAX.AuAA.A`vAB.A$vA`z.AHwA`T.AWXyA@.AzAxG.A q{AH.A]t}A#.A)A.ADAP.A@nA`I.A@E`A`B3.A@GA84.AцA07A.AvAl@.A`Ao=.AO+A$8.AvMA((2.AoA((-.AטA).AA#.A·A.AkA.A A.A@W8Axz.A`]A83 .A@yA .AA.AƈA.A A3.A@ A0.A`-A.A0NAF.AzuA.A ˝AV.AHʉA.AA.A`,A .A /^A R.AAj.AĊA..A`fA.A9Apޮ.ApAzڮ.AA׮.A&A8Ԯ.ARAxЮ.A`A̮.A`3A0Hɮ.ASA2Ʈ.AqAB®.A`A.A &A.ARیA.AA .A`A.Aj8A.A`XAP<.A cAୢ.A@A8.AύA.AA`.AA.A)A8Q.ARAТ.A@uA|.AෑAu.A`)A@um.AAH)f.A&A[a.A0PAP \.AӀAKX.AWA kT.A q̏ABP.A/AtM.AAyH.A:ABE.A@pZAP@.AA0=.AHA@8.A ɻAأ4.A@CؐA`0.AvA@,.A"AH)*.A cSAp(.A}Ah'.A@A8&.A0ƑAp &.ANApk$.A&A#.A/Aj#.A@ ZAF!.A;A.AA\.A ӒA.AA`a.A34A.A`gAp.AA@B.A@WГAh.AAL.AH@A.A@XAH.AA.AA p.A(AHz.AA3.A`WAp.AՔA(.AЕAU.AA.A Q;A.AmA.A<A.A`mA7.A lږA .A \Ah.A/Ap.A[`A.A෋A0v.A鰗Ah.A3ؗA.AAН.A%AQ.AMA@.AVvAQ.A@Ax.AȘAh.AA.A(AH.AXA .A9AX!.AWAp!.A@G֙A#.A0Aг$.A(A`&.A5AW0.A@%OAН8.A q`AȓB.A3vA J.A猚AQ.A墚AZ.A=A#b.AAc.AA_.A cAZ.Av5ApU.A Z^AP.A&A`M.ḀAfL.A͛Ap=M.A`)AO.A@AQ.A/>AT.A ZbApW.A@A@Y.A`ծA[.A̜A`^.AA`.A AQb.ABA pc.AcA`ed.AOAe.AѸA c.A`[Apk`.AAz].A /<A`Z.AbABW.A@APS.A@A0fO.A AJ.AݞAE.AAp.A PA:.A@pA0f6.AAh2.AdAp=/.A@A,.AA).AE6Ap8%.A`^A0!.A ⃠A.A9AH.AA.A@A0v.A`A.A (=AQp.A}[A.AȠA:,W.A98IA|r;.A[u-ɦA+.A<A6x,.AT,AJ.AĒLAҗge.A WnrABkݍ.A F)A<"<.A| uOA~n5b.AR A Ad .AnB8# AGÕ.Aȴa Az.ARH A_G7.A| u Ab".AF) A2-h.A Wn A/AÒL AʇT /AT,ѕ A A`/A` A>/A A>/A@P A>/A@Nj A>/Ag^ A>/A ΃ A>/AL A`/?/AC AP?/A`] A Q?/A`[ A0w?/A Ap}?/A,{ AU?/Aw/ A?/AF A@?/A. Ah?/Aﻀ AȂ_?/A Ԁ A0?/A A A9t?/Ao; A(mm?/A@L A0]P?/A Ah@J?/AN A-?/A c A&?/A`K A.>/A A >/A Ah;>/AH& A҇>/A A`Â>/A AhS>/A@" AM>/A 0 A>/A@Ϯ A0>/Ag A/=/A A5 A=/AB A؁=/Ax A`=/Au A@=/A㉆ Af=/A6 Ab=/A= AOF=/Ao AhoA=/A`ք A =/A` A@p=/A A/A`{ A?.>/Az As>/A$z AN>/A@yy Ax->/A@!y A>/A`x A@J?/A`v/x Al?/Aw A07?/A`Lw Ak?/A*3w A(?/Av Ax:?/Av A ^k?/Aw A(`d?/A`]w A(90?/A_x A'?/A`3x A:>/A@x A@>/AVx AȾ>/A (9y AXV>/A6Ny A(>/A0y A~>/A 8y AM>/A`2Sz A@E>/A Zgz AN>/Az A_ >/A@z A=/A@Pv{ A==/AO{ AXh=/A | AE=/A| Ac=/A| AH[=/A+| A8E*=/A@)} A@"=/A`<} A``Z/A/q A @/A`9un APuB/AOk AB/A?l AB/A`2l AxuB/Am AB/A`im AB/Aǎl A(B/Al AB/Ak A@B/A@ߗk A؞GC/A`̧h Ax-D/A`'f APD/A`f AD/A g A7E/ANg A F/Ag A(`F/Ae A@pvG/A@d A -O/A[d AXJO/A@nf AhO/A f ADO/A g AN]N/Ap AJ*J/AzFh A<J/A4g A>I/Ag AלI/Ah A` N/A}r A`O/Ag ApO/Ag ArP/A Hng AP/A`e AB'Q/AWf A88Q/AHi A R/AWj A6U/As AW/A| AgX/A AVuY/AU A`XY/A AsZ/A, A@Z/A@  A~[/A@ A}[/A" A[/As AX[/A . A[/A@ A[/A൬ A[/A d AH\/AN AI\/A`t A\/Akƌ A]/A@WH Ap8]/A AC]/A A6]/A@pܑ A]/A` AТ"]/A@ AkB]/Aվ AQ]/A9 Ah^/A \ Ap^/Af A"_/A1 AH_/A Apea/A@ A-b/A`á A,b/Aˠ Ap=Zc/Atݠ Ac/AW A0\d/A Ad/A; Ad/AIp Axe/A  A(Qe/A6$ A d/A@%V Axzd/Ao( A\d/A`;7 A_Fd/A' AhoHd/A`" A^d/A" APɩ A=a/AΩ A@ a/Ab A( `/AE An_/A4 Ax`/A A`/A A/`/AZ A `/A@< AF_/A`m& Au_/A` Aj_/A O A-u_/A AP_/AF AP_/A A!_/A! AS_/A@>ܧ A_/A 1 Ax:_/Aȧ A^/A AQ^/A API^/A A'^/AӦ A^/Aa A^/A`ަ Ap^/Ao Aw^/A Ap^/A AE^/A@2 A Aل8t/AP9[ A麢v/A4Ɛy A̱5w/AO Aw/A Aw/A AvQw/A {/A87 A`t{/A AH{/A 1 Aj{/AM AP{/A1 A)|/A` Ah"S|/A/ Ahs_|/AF Abi|/A` A(r|/AH A0||/A  A |/A`K% Ae|/A`KM AXW|/A t A8|/A AНz/A s Az/AS Az/A`9 A8z/A Ax>z/A AAz/A$} A0|/A@> A|/A A k|/A@, Ax|/A Ah|/A@N AH|/Aq A|/A AQ}/A A(}/Ap A8R}/A, A8#}/A`d= Ap&}/A X AX6}/A`{ AZ}/A` A8j}/A Apq}/A`; A-x}/A"+ A(}/As A`a}/A A p}/A`b A0̮}/A` A}/A`v Aأ}/A ۋ Ah}/A`X A83}/A/ A}/A@ߗ A}/AD A}/A@ AТ}/A$s A}/A` AH.}/A;# A}/A A}/A` Ax}/A AH}/Aa A(}/A  A}/A Q AH}/A A}/A A`}/A A}/A A(-}/A@& A0}/A`o_ Ao}/AW A(}/A cu Am}/A` A}/Aaa Ak}/AĖ A}/A A-}/A` A8'}/A` Ay}/A` A<}/A@WL A`}/Aj A(}/A`o AH-}/A] A}/A 8 Ae}/A@ A%}/A A0̐}/A  AH2}/A`r Ad}/A Ap}/ANe A,}/A Af}/AD A}/A< A}/A Ah@}/A Ad}/A \ A@}/A5 A}/A8 Ah}/A A}/A A A4}/A A}/A@ A}/A A舁}/Aj Aj}/A Ae}/A | Ab}/A Ah`}/Ac A)`}/A` A_}/A`T A5_}/A 8 A^}/Af A^}/A@y Aq^}/A A ^}/A A]}/A` A^]}/A AP]}/A|( A]}/AU0 A]}/A7 A<_}/AE AOa}/AfT A(i}/A| Aأm}/As A@Ox}/A@ A@{}/A@. A0}}/Aq A}/A A #}/A A}/A Ah}/A A؞}/A@ A,}/A` Ah}}/AX Av}/AƏ Ag}/AT A"V}/Ad ApP}/A~ ApAI}/A@N A8RD}/A A18}/A@N A0w5}/A`b A,3}/Aw A+}/Aj A#}/A> A#}/A@` A9}/A`B A }/A A`}/Ap A3|/A@i A|/A$ A|/A= A|/AO! A|/A` A@|/A A/|/A}E Ap|/A` A&|/A & A0|/A^ A'|/Ah| A |/AB At}|/A`ܫ Apu|/A@ Ad|/Az AXW^|/A` AȃX|/A@y AؼN|/A APF|/A AmD|/A A A|/A* A87|/AO A4|/A@ Ao2|/A AH1|/A AX#1|/A A/|/A` Au.|/A  A-|/A A8+|/A A|*|/Ac ApJ)|/A Aq(|/A A;(|/A A A%|/A A#|/A@ Aa"|/A! Aؒ!|/A@.6 A8g!|/Ag Ap #|/Aʡ AG$|/A@ A)|/A AT+|/AXK A+|/A`mi A)|/A A(|/Af A}%|/Aγ A3|/A A|/AJ Ap[|/A } AP|/A AHa|/A A8|/A? A{/A A({/A A(-{/A`B Ah{/A`] A{/A AX{/A@5 AF{/A A{/A A{/A A{/Ad A`%{/AR AG{/A} A{/A |j AA{/A c] A`{/A\ A({/Ac\ AM{/Ac\ A{/A\ A{/AV] A +{/AN^ A{/A+a A({/A@We Ap{/A j AP{/Ap A0.{/A`"w AP{/A< A{/A A{/A Ap{/A_ A{/A A={/A Ae{/A@. Au{/AT A5{/A@ A{/A, A`{/AH A0{/A`BZ A{/A m Ad{/AǗ A({/AO A{/AЄ AȤ{/Az AX{/A` Ah{/A A8ڼ{/A A`%{/A A_{/AO Ap|{/Aj A@{/A A{/AH AhD{/A A{/A` A(z{/A A{/A` Ah{/AN A8z{/A A@o{/AE A@d{/A`T AW{/A A`L{/A[ A"B{/A@C A>{/AN AH7{/Ak` A\){/AX AQ{/A  A( {/A  A8{/A A{/A < A AF/A@o AjC/A  Ap=@/A  A#>/A@% A,=/Aa  Ax=/A 9 Aؼ>/Ah A@/A ɏ AjC/A_ A0E/A  AtJ/A A2P/A AW/A> Apc/At A3k/AW A =u/A@ֲ A /A AX(/A`" A/A`" AV/A`"  A؎/A A/A`mN A!/Ao Ap/A` A/AЌ A?/Ai AxG/A A0/A A(/Az A/A@y A0/Aa A-/AX AP8&/A@y AV0/A@ A#;/A  AG/A@, A~S/A A#`/A Aj/A Au/A` A~/A A/A@N A趧/AjH A/A À/A$ Axր/Aao A/A A/AF A2/A] A(z /A O A /A A@/A A'/A` Ak&/Av A-/A@_ A,6/A`vI AX?/A5 AhI/A Z$ AЄS/A A[^/A Aeh/A@ Ap/AD Au/A & A-x/A A0e/A@ A/A AP/A A AH2/Ab AP/A / A(/A Az /A Ay/A / AQ/A A/A = AB/A`Ka Ax/A` A/A`; APk/AM Ap[)/A A//A & Ap[8/A A8gB/A AC/A  A]/Afs A#/A@, AL/Ai AT/A9 AZ/A0  A2^/A} AX^/A Ac/A@ Ag/Aྦ Aȃj/A  ALm/A@e AUp/AD As/A@%" A0t/A A`{/A# A=/A5 A~/A E Aі/AU AX(/Aj A =/A A7/A A/A൩ A[dž/A ˻ A׆/A@ A8݆/A Ap/A A>/A A">/A  A9/A@, AxG4/A`* AH3/AV A78/AX A:/A@#Y Ae?/AoL AD/A`+ AJ/A  AUQ/Aa AxW/A  A`/A, Apx׆/A \ A8x/A[@ A0n/A 8 Al/A`H APm/A 8a A7q/Ao Ax/A{ A/Aq A/A`v_ Ax/A@,V A̗/AZ AQ/AZj Aǰ/A@ AX۶/A`K AÇ/A Ahʇ/A A@·/A Ahև/A`m  A`ڇ/A, A<ۇ/A,D A8 އ/Af A/A A/A@N A/A@ A/A; A/Ad A /A` A@/A6 A/AU A/Au A /A@ƙ A /A A/A A/A; A8V/AN A/AL1 Aj/A8 A/A2 A@/Aj" A@u/ATP At/A St APf2/A Apy;/A AuD/A AN/AN AXyY/A APf/Av Ahl/A Axzx/A@ A(/ANA A([/A`i A /A Z A̫/AT AP/A  A[/A A/A@W AÈ/A: APʈ/A`oZ AtЈ/Av APk׈/A/ AUވ/A& A/A A0./AE A[/A@ Ap/Ax A/AV AX /An8 A/A] A"'/A Z A-/A  A'4/A޽ A`9/A AG>/A  A G/A` A N/AvC Apʈ/ADd$ Ap݈/A@C$ AY/AV APb/A AQf/A Ao/A! AQz/ANG! A8/A`m`! A /Aix! A /A ! A攉/A! Ao /A?d% APo0/A?% A/A " A~Ƥ/AOaI" Av؉/A`6# A)܉/AM# A/A@0$ A.D/A&@0*$ A`P+/A@h% AE/A6& AxQ/A`& AD]/A& A 8/A 8( AJ/A ) A軺/AO) APˊ/A) A|#/A{^* A~l/A̜K+ AoK/A u, A/AV, A|R/A- A /AQE'. A./Aw/ A /P/Ai0 A(zp/Aд0 AK|/A=.1 AȄ/AD\91 AE͌/AoÍ1 ABZR/A,K2 A? K/A9&p22 A9>/A2 A0Lg/A@2 A6p/A 82 A8{/AN2 AXu/A2 A`/A@2 Aأ/A;3 Ap[/A3 AX(/A`3 A0./A`D,3 AXƍ/A@W83 AXuэ/ANE3 Aڍ/A2R3 A^/A7 A]/A7 Af.A;d{ A`f.Ac{ A(if.AZ{ AxQf.A&I{ Au9f.A /{ AH:+f.A{ AP f.A z AXe.Az Ae.A`z Ae.A ɝz A((e.A@z A@e.A Xz Ake.A*z A0LKe.Ay AHe.A y AEe.Ay A #Be.A \y A\?e.A`y AP/:e.A@߫y AX,e.A Iy A)e.A`:y A&e.A.y A$e.A`$y AP!e.Ay A e.AUx AWd.Ax AH{d.Ax A(\d.A`fx A(d.A+x A0wd.Acw A0jd.A mw Ax)]d.AoPw A@Md.Af5w A&d.Aw AXd.Axv Ad.A v Ahc.A&?v A0{c.Aou A3_c.A A].A`D' A].AI AC:].Al,Vl A&,].A6`e A ].A'( A^? ].AMWz͔ A4&].A Af].A@W A(].A A\.A8 Aؚx].Ao Ax].AҞ A].A4 A].A AO^.A Ah^.A( AhԹ^.Aњu A 4V^.AAF A7J^.A{>U A1 _.Aǁ A51)_.A#Zӧ A(qhJ_.AK Al_.A2L A< _.AP A&_.A AQ`.A[ A4`.A A8x`.A ⼩ AǙ`.A= Ap`.A A^.A` A"^.A@iL A^.AKi A]^.A` AO^.A@ A@^.AE A_.A`i A _.A@i A~6`.AKb A0`.A A`.A?U Aa.AA AVma.A A9Va.A A2]a.AM AP6a.A@h A1a.AX A`.AF AP+`.A`, AH_.AF A_.A2! AS`.A Ap<_.A U Ay`.AӰ AP`.A@ Aa.Aؗ A`qa.AH Apia.A@ư A@Ϗa.A@Ű Aga.Aٛ AP8a.Atʰ Aݘa.A#QD AgVa.AN; A#b.A Ax8kKCb.A zp8 Ap9db.ArZ Axb.An Azb.A߲ AQb.A@ h A a.AD& Ahݺa.AXb AQa.Aγ A(Na.Aӳ APfJa.A ٳ APFa.A A8Ca.A@ A@a.A A=a.A A8x;a.A6 A`9a.A A7a.A ) AF6a.A@8 APM5a.A@pG A4a.A/W A4a.A`g A4a.A@v A 5a.A A5a.A 8 A06a.Af A`8a.A AO:a.A̴ Aia.Av̻ A8a.AmYٻ A(a.A| A [$a.Az A&Mͼa.Ab(5 A2a.Ac Ax-a.A| A€~a.AU9 A6xa.A Bf A2wa.A@ҽ ADza.A:> Aw÷Ӂa.AQrW Apca.AKC A`qa.A`` Ad!a.A.2Ͽ Aa.A$e3 A6a.A&. A6GRa.A, A`b.A AHRb.A> A@b.A A|$c.A|O Aт,c.A|O Ad3N>c.AO AKTEc.Ak5 A^!c.A< A}8b.A) Ab.A Axb.A, A?2Lc.A& A^!c.A< ADl-A AA8jr/ARf,:s AS/3     )4]@)/A@`vzA((/AyAݕ'/A@QyA%/A uAh"-%/A AgxA>$/A@.|A(#/AA;"/A@AP v"/AྱAhp"/AAp7"/A5A` !/A^\Aa!/A cA^!/A@Ah#/A_^A&/A΋AH(/AnAx>*/APAq+/AA./AǏA//A`A+1/AqA2/A_AiG3/A@yAQ3/ArA864/A@` Aȱ 5/A=A}5/AA6/A`նAH{A6/A嶶AHa5/AIAذ5/A`JA5/A渨A5/AKAh4/AǕAh3/A A(\3/AAw]2/AoA&2/A+A0/A@OA`r//AAT./AOA['-/A`+A&,/A+A@+/AA+/A |A@)/A@`vzAH.$/A7AP7$/A AV #/A #AH.$/A7A:.AIAA83.A@A .Az@A .A@A( .A[@Af.A@A33.A`k@ApN.Af@Ag.Ap@A#.A@t@A.A^@A.Aa@Af̷.A[@A.A @AV.AAAf!.Az8AAp-.ApSAA`<.A_AAQ.AkAA`h.A`fjAAp=w.A ^AA.AFAAz.ABAA.ADAA.A]YAA`͸.AaAA`׸.A dAA`ݸ.A [AA8.ApIAA.A1AAأ.A`)AA`a.AzAA`.A@A.AAAp.A AA.A+AA.A /AA83.A]#AA%.AAA#1.A@A=.A@AD.A@Ap8K.A@@AO.A@g@AXS.A @ABW.A@AY.Ap@ApY.A @AU.A@@A(P.AG}@AM.ARb@AN.A[C@AVR.A`@AY.A?Aa.A ?AHak.A`)?Au.A q?Ap~.A@?A慹.A෶?A8.A@?A(\.Az?A`.A?A`.A@Aœ.A`@A.A?A@W.A?A.A@ @Aɹ.A@A0.ٹ.A`?A0.A@?Ah.A?A R .A ?A`.AR|?A8.A`fd?A.Az2?AH.A?A`a.A3>AX#.AH>AH\*.A=>AP87.Aj>A;.AU>AuD.Ap>A[.A`̔=A].A3=A#[.A@p=AP8X.A m=AS.Apo=Ap=@.A@G=AL=.A \=A<.A@=A R=.A=Ap?.AGg=AA.A=R=AP8D.A \1=AC.A=ALE.A<ABI.AQ<A{c.A o<Ah.A][<Ap=o.AGM<A8|.A <<A`a.A 3<Ap.A 2<A8.A ?<Ap=.AE<A֑.AK<Ap.A@N<Au.AI<A#.Az*<A83ƺ.A[;A83Ժ.A@g;Akܺ.A;A`.A3y;AQ.ARl;A.A3W;A.AF;A8.Ap+;A.A` ;AB.A:A .A:Ap0.A:AzA.A\:A`^.AR:A؁p.A9A@.A ¯9A8.A9AG.Aj9Au.AC9AHλ.A=9Aٻ.A8A`.A8A.A8A8.Ap8A.Aw8A@.A o8A` .A=Z8A.AJ8AQ.A 58A&.AR8Ak0.A7A8.A7A`?.A7AuF.A=7AN.As7AV.AL7A`a.A3!7A0i.A=7Ap .A 6A0.A 6A.AGm6A R.A@F6A R.Ap#6AX ü.A3 6A)м.AR5AU޼.A5A.Az5A .Azz5A(\.A@H5A+.A3+5Ap=.A@4A@H.A4AU.A@ 4AQ`.A`)4A@g.ARl4A`at.A O4AP8.A=84Ap .A 4AǙ.Ap 4A0..A`3A0..A3A.A3AL.A@3A .A@3A@$.AG 3A-.A 2Af7.Az2A?.A]2AG.A=2AQ.A@`2A[.AD2Af.A )2Aus.A 2AX.A=1Ap둾.A1A`a.A`f1AH.Az1A0.A=1Aھ.A]1Az.A1Aأ.A1Az.A 1A؞.A 1Az.A 1A.AGw1A.AZ1A.A 51A.A`)1A.A0A+.A@L0A@1+.A:0A8g+.A(0Ap+,.A0Az-.AH0Ap9/.A@/Aؒ0.A`B/A81.A;/A`2.A/A72.A4m/A2.A@r4/A3.A.A5.A=.AH6.A@.A8.AM.AX9.At.A,<.A -A>.A -AGB.A [-A@F.Am-A0J.A`;,AP^O.A},AT.A@>_,AX Z.A !,A_.A+A@e.A+Al.A[+Ar.A+A`z.A*AX.A*AH݈.A`*AH.A &*A𒘿.A@y)A8.A)A誩.A+)Ap.ARJ)Aп.A=(AԿ.A$(Anٿ.A#(AN޿.AMn(Av.A\(A.AiL(A.A=(Ak.A`0(A.A((Ap+.A`#(Ap.A` (A\.A (A .A(AXy .A#(A".A%(A.A`&(A .A%(A@.A"(AH.A@(A@.A@y(A.A3(A.A (A8.A(AP.A(A.A(Ap.A(AQ.A(A{ .AR(AP .A'A.A 'A.A'A.A 'Ah.A'A.A'A.A'AM.AX'A.A A'A.A'Ay.A p'AX[ .A@Z'A(.A@`E'AD.A1'AB.A'A>.AB&Ap A.A#0&A@dD.A&AH.A@5&Ax)L.A0%AP.A%A)U.A%A_X.A@%AS[.A%AP].A@%A>`.A@%Ab.A%Aȃc.A-%A0Ye.A%APg.Az%ApAj.Aq%AGm.A@b%A`p.AU%A Rt.AJ%Any.As9%A~.A`m*%A|.A%A_.A@C%As.A`B%Ah.A@ $APڛ.A|$AƠ.A`m$Ap.A$Aҩ.A$AP.A`$A0.A$A .A`b$A.A}$A.Ag$A.AR$A8.Ap#A@.A@M#A .A`v#A.A"Ai.A@"A.Az"AP.AjR"A".A@>+"A`a.A "At.A?!A( .A*`!A#!.A !A'.Am Aj..A Aذ5.A A _=.A@l A`rE.A cH AM.A3% AxhS.Aa A }X.A?Ap].AAJa.A uAd.A@ Am.A@^Aw.A@+AH.A`AP.A A.A^A.A`A.A֍A0.A`uAxv.AK_Ah.AJAh.A`f8A.A .A }.A<#Ap.AA0.A A8.AGA.ARAxd .AଌA>.Ah{AxX.AkA.A?]A4$.AvPA*.AGEAF.AA@J.A APMM.AAhP.A 8A}R.AA8T.A@Aa.AVAd.A9AHg.A Ak.A`KAfp.AA8s.A@WAw.A@.AH{.A@AG.AA.AgA.A A@.AA.AA.A`BA+.A AC.A-A3.A@Ah.AwA.A 8dA.AOAp.A8A.A#A`.AA`.AA.A=A .A)AP.A`KAP.A@A.A@A`.AA0..A`|AW.AAx6.AAH.A6A!.A`A0$.AQA&.AA).A`ܶA+.AA,.A Aw..AoA0.ANAo3.Ad.A@6.AA:.A@A`=.AGAh^@.AoA(hB.AǨA(C.A |AE.A }AE.A@gAE.ApQAE.A5BAD.A@2AD.A/#AE.AAHF.A-AhH.A3AJ.A@>A`Q.AA8W.A3A0].A`pA`ab.AEAPn.A AQ.A'A.AQAz.A@WA(ܳ.A`fA}.A@A(z.A@GA.AHuA c.A9AXW-.A+A0@.A=AD.A]AH H.Ao2AqL.A@ALQ.AA\T.A@Ap W.A(AMY.AତA&[.AA`\.AyAHg.A A8Zj.A A(Jm.A A(p.Aq Ast.A5X AȤx.Ah@ AP8}.A`) A.A6 A.AV Aأ.A3y A`.A@NP A0.A( Aȫ.A? A.AH A.A As.A`K A.A@. A.A`br Apl.AZ Ap8.A@D A`.A > A.A`: A(.A7 A.A u6 Aȇ.A6 Av.A8 AR.A`< A.A A A.A`H A0 .A@PM A .A P A.A@P A`.A0O Ah.AK A.AF A@u.A: Ap.A, A%#.A A%.A | A=(.A`K A*.A= A_1.A AX9.A ABA.A3i A ^E.AT AI.A \A AN.A`/ A#T.A A:X.Ai A[.A AQ_.A AGb.A3 A`e.A| A0h.A& Ak.AF[ Am.A4 A`o.A= Af.AzAQ.AAp.A`mApJ.AwA`.AcA88.A 1>AP.AA0.A@jAh.AHAP.A9&A.A`TA.AAH.A A.A@ A`.A=tA.A-WA.A 8;A.A| A.AA.A3A.A@A.AEApl.A`A.Aq~A`q.A;oA.A_A .AMAP .A@ <A..A@C)A.AA8c.AA.AA{.A AL.A@ gAr.AKA6.A0A .A@A .AA@.AA.A@gA .AA[.A`sAN.AOA.A@ ,Au.AAp=.A`)~A .ATnAh].A^A.AOA@p.A`AAȥ.A`]4AB.A`f(AD.A 8AP.AXAp.A A.A4AX.A@EA.ARA.A@AX.AA.A?gA8.AKA.A`/A.A@A.AhAؼ.ARA.A`mlA.A`4A.AtA.AzA.A A.AMA.A^A.A|AH.AAW.AA.AA(.A{A.A:A.A=.A.AA.AAT.A As.AǞAP'.AAv.ADAg.A|A8.AsA.AMA@$.Ao%Aȵ.AA.AA.AQA8.A@`~AX.ARA t.A Z<A@.A%A(.A A.AvA.A`dA.A@Ah.AzAxv.A@A.AߞAh.A JA.AA.A̜AR.AA0.A@A@`.A tA).AgAu.AYA .AA.AAX.AyA-.A`"AHr.A OA .A6A83.A@ Ah.A@NA.AmA8.AWAu.AdDAh.A_1A.AA`.A A.A`9Ap.AAxd.AA.A0AБ.A`Ah.AjvA .A WA.A?8AP.AAk.A`A_.A@AǼ.AAȤ.AA.AV`A.A9A.A[A@.AA(.AA.A`̠A( .ApA.A`DAAu.A@AT.AsA.AzAhb.AA@.A VAذ.A%Af.AA.AAH.A A8.A^A.A +AP.A oAX9.ARA8.A@C6A.ALAp.AAp=.AA.AA(K.AnA.A3A9.A`Ah.A9A .A}AP8.A?A.AAE.A`6Aǭ.A A0.A<AX.A?gAD.A`@A8.AA`.AAV.A@>A.A`A`.A`9Am.AjAذ.ALA(z.A.AǬ.AA.A`"AHܶ.AoA Ҽ.AaAP.A`LAБ.A8A.A%A.AA.A3AD.A@EA.AA.AA.AhAP.A@A.A[A(.AA.AA .AA`2.A AL1.AfApkD.A`A pW.A`)AZ.A \AX\.AAM^.AVA_.A@NA0`.A;A8Va.AfA؞a.A &Aha.AӸA`.A ɱA`.A \AT.ABnAB.A2A`4.AA.A AX.AA =.AphA7.A@4Aj.AAu.AAM.A0A]8.A ҞA3.A A81.A&AJ/.AokAs+.A SA(.A@iIA +.A@&A.A@AO.AAȥ.A`dxAí.A@^A.A@>A .AA ^.A`fAho.AiA4.A//A(- .AzA@F.A@iAH.AfaA(.A3A.A Am.AF~AL.A@^AP&@.ALA2.Am=A[%.A`m2A.A^.Ae.A &Ax:.A@'A.A1AP.A4AI.A`9A8.A>APxg.A4AU.AE;AȩC.AOA0j9.AzbA8V3.A spA,.A (Ah%.A A.A A.A Ab .ARAf.A<Ah.AA@.A@y A .A@%mA(w.AaxAr.A 8{A@o.AxAlm.AuA(j.A@gAjf.A`)Ac.AA kZ.AAp}V.A4A =X.AAZ.A@`AX.AAfS.A`A)=.A Aq+.A Ah.A@YA.ALyA .A A.AgA.AĿA`6.AfA0.AzA(>.A?A.A%A.A@,A 0.A0A.A`2A.AGAh.AdAXһ.A jA.A cAB.A ¸A/.AA(.A@pAHn.AеA`.A@A@W.A/AFP.A`=APxH.A]A;.AA-.A@>AO.A sA.A`Ax.AfAF.AjASֿ.AAp8ο.AAʿ.AAɿ.AAǿ.AApɿ.Ap1A83Ϳ.A@NAp8п.A=fApͿ.A qAp8ʿ.A \A.A qA.AHA8.A qAѡ.AAš.AA.A`)6Ak.AUAX.A wAז.AA.AA𣔿.ARAX .A`A#.A:A~.A ZAX}.A Ap=.AAp.AA(܏.A AX.A&A.A=AP.A`)RA(ܮ.AQdA뱿.AA8.A=A̭.A3A.A`A板.AAX.A AP.AR"Aa.AAh3Y.AGAS.AAQ.AAM.A`)AWG.AA?.A"A 5.A`f6A-.A@ KAأ&.A`lA0".A A!.AzA#.AͪAh'.AA`*.A A),.A@A؞+.A@2AG*.A@A#.A A]A0w#.A]A( .A@yjA .AuA.A A(\.AAأ .A׮AX4.AA.AQAP8.A'AЮ.Ap8A.A`IA.A&\A@.AnA@.AAf.A A0e.AAx.AA.A@A.AFA.A^AD.A ZwA.A ۏAT.AA.A`A.ApA޾.AQAھ.A@A?׾.A#A5Ծ.AN?AѾ.A[A(\Ͼ.AxAž.A@A#ž.AAHiƾ.AA?Ǿ.AAǾ.A -A`Ǿ.A@AǾ.A$TA*ƾ.A gAľ.AyAþ.AgA.A`A@>.AլAL.AA.A@A84.AA8.A`TA0v.A AЦ.A!A =.AX9Ap=.AQA8".A A.AXAΊ.A A.A!Au.AAg.AA(yZ.A>AXM.AGAvJ.AA`F.A ApB.A JAh>.AyA9.A0A4.A`JAQ/.AcA^*.A0~A%.A AX!.AA1.A lA.A@ A .AN1A' .A@nA.A A.A A.A@ !A.ATAٽ.AyA؎н.A<Aǽ.A@AP޾.A-A.A@>fA0f.ApAxˡ.A`AŖ.A KA@W.AgA.AA@z.AAYu.A@(Ao.A`KKAi.A`]mAc.A?A\.AݭAT.A&ABM.A@ AC@.A)A2.AiA@%.AA.AA.A@ A`6.AnA@.AA ռ.A A Rż.A^Ax鵼.A+A.A6Aݕ.A`gA<.A`A6t.AAb.A`)dAYU.AyADH.AA;.A9FAp=/.AA.AAT.A` A.A2A0*һ.AAH .A% AB.A`̠ A .Ak A0e.A A`S.A AH܊.A! Aˇ.A2 AS.A 8B A0{.A^P AM|.A\ Aw.Ag A s.Ap Ah3n.Aw AK.A AC.A A;.Aj A4.A. A+.AL A 4#.Ag A`a.A`f A/.A@ؙ A.A ¯ AH.A` AD.A@ A=.A` A( .A \ A.A \w AXʙ.A3 A/.AɎ AՎ.A A@Ɖ.A A0.AG A.A A(|.A Ax.AR A`h.A AP8T.A*AQ.A`f@AA``.AQAf.AfA8.AA.A \AR.AAX$.AA`.A@A.A  A.A&A).A |,Ax.A@,9A𒟹.A@CGA@Ƣ.AVA0.A#gA8.A@xA(\.A`A`X.AɑA.AAX[.AA`r.AίAN.A;A.A3A.AAiy.AK Ak.A`6A;i.A c<Af.A`@A c.A`CAp,a.A DA`^.A lDA[.A`fBAX.A>AZV.A ,A0HS.AAO.A A`K.AAG.A)ABC.ARA@.A Ah>.AAX<.A@A9.AA87.AAp06.A`A4.AA(3.A`T A`3.AAh2.AcA2.A(A83.A1A5.AXAP6.AzAH7.A@gA6.A/A>5.AjAU3.AA@0.AVA@..AA%+.A AP'.A@ Aa.A c$A.A;AP .A@PA.AB^A.A`dmAX.A}A.AAG.AjA@S.A Aݸ.AA.AGA9.A@qA .A`A`.AAko.ApYAj.AnAe.A@PAR`.A4APZ.AAT.AAN.AANH.A@WAA.A3AP<.A A8.A ZA3.AAXE/.A cAG+.AA83.ApiA: .A`̏A.AA).AA.A`A`.ANA(ڷ.A)A`Ϸ.A qAA.A@5|Aʯ.AݸAk.A \A.AAh.A`%A.A :AH.A`NAP8.AGaA~.A@ynA3w.A/}Ahp.AaAj.A`Ac.AA].AUA)X.AAТS.AA(N.AAI.A ZA=D.A$A>.A@53A08.A`f@A5.AyFAC2.A@ KA0..A NAf+.A lOAX'.A-OAy$.ANMA!.AIA(.A@DA.A@N>A.A`f6A.A@51A`.Ay-Ap|.A`K+A .A*A+ .A+A8.A@W.AH!.A |2Ap.A8A .A>A.AFAh.AOA؎.AYA(.AdA.AxnAP.A3xA.AA.AUA.AAA$.A@Au.AA.A@A.AAX.A@PAy.A|A@5.A sAL.AA8t߶.AA߶.AA:߶.A@A߶.A A.AA.A`AP.AA.A`f A.A`A.AQ A8.A*A.A@E5AX.A?AȘ.A4HAH.ARPAͶ.AÓA<̶.AA,ʶ.AAXǶ.A AHŶ.AA¶.A@A0.A`A(ۼ.AA.AAH.AA.A A0j.A Ah.AA.A@AP.A/A.A@yA`a.A`fA(l.A A .AA`%.A/Aߞ.AAA4.A6RA-.A \aAH\.A ‹Aނ.AܖAX.AРA}.A}Ay.A̰A@`v.AࣶApr.A`A#o.AAPwl.A}Ai.A`vAf.A`AFd.AսAPa.ATAhs_.A`2Ap].AA@dW.A@ AQ.AĐAkJ.A@AhQG.A@օA5D.A A'A.AA06>.AA p;.AA8.AęA6.A?A4.AA`3.A`A1.AA=%.AAx~.A+LA].AHyA .AA8.AA`.AA`O.A AP.A@.Aݵ.A)A:ֵ.A`o=Aε.A sOA8ǵ.A`A.A fA^.AkA㶵.AoAx\.AMpAӫ.AoAU.A`lAP꠵.ANhA.AaAhѓ.AWA).AOLAP.A ?A^}.AE0AXFv.AAio.A@ Ah.AAf.A`mA d.AAFa.AvAX^.A`AR[.AAFX.AA0HU.A`;AiR.AUAO.A@,AQM.AAPL.A@AL.A` AxJ.A@AGI.AqAiG.AA RE.A@gAi?.AA839.A3Ah7.A ( A06.A?(A6.A 0Ax6.A`9A R7.ARBA7.A@A36.A@>A4.AA02.A%A0.AWA-.A Af*.AA@&.A=A@$.A 8A؎!.AA.A@PA_.A 8A.A8,A.A 8CA0..A YAu .A ZlAj.A~A.AAo.A?A(.AAxi.AൺA.A JA.A`]A.A@A޴.A@Aٴ.AtA8մ.AAȘд.A3Ab̴.A  A}ȴ.A`f AĴ.A#& A.A 7 A̭.A@ Ap.At)!AӇ.A!ABt.A !A0q.AE"Ahn.A@P"Ak.A "Ah.A@$"Aad.A-"Ao`.A4"A@S\.A:"A8X.A >"AhS.A6A"AiO.A@A"AK.A@@"AH.A;"A E.A8"AA.A@>7"AH>.A7"A;.A9"AБ8.AH="A`5.A`B"A3.A`I"A0.AQ"A9-.Af"A 0*.AR{"A'.AV"A@%.A "A@_#.AN"A!.A "A.A"A.A #A(.A@%#A.A@>#Az.A@V#A.An#A .A_#A.A#APw.A#Aj.A@$A.A9M$A@ӳ.A$A`̳.A $A0YƳ.A $A}.A$AU.A$AP氳.A$A}.A$AX۩.A@$A.A%AH .A` %A.A%Ah.A`]%A.A@%AB.AR%AX,.A A%A2.A %A0f.A%Aم.A&%A(.A /%A.A9%A=.AD%Ah3.ARP%A#|.A%APYz.A-%Ax.A&A0*u.A "&Aq.A;&Am.AR&A(>l.Ah&Aj.Ax|&Ag.A 1&AȲd.A`&Appa.A`&A].A&AY.A`o&A U.A&AP8Q.A&A'Ax.AM'AG.A]'A.A (d'A`.A k'A0.At'AQ.A~'A%.A'A^ .A;'A .A'A .AB'AXu .A'AL.A'A((.Am'AV.A''A.A(A.Az(A@ #.A@ (AS&.A,(A /).A:(AP+.A H(Ap-.AW(A/.Ag(A`e1.Av(A02.Af(Ag3.A`D(A3.A_(A 3.A(A+3.A@(Ap82.AR(A$0.A`)A-.A q[)Ap|*.A`)A&.A@!)A@".AH*AЮ.A@[*Ah.A Q*A.As*A.A$+A(.A@f+A.A+A8E.A+A`a.A!,AЮ.A B,Ax.A`Kb,A.A4,A.A෡,AЄ.A,A.A3,A5.A 0-A\.A`;-Aܲ.A3-Aղ.A 1-Alϲ.A A$.A@ɲ.ANO.A8IJ.A@G{.Ad.AV.AԵ.AT.A׭.A3 /A .A//A@ࣲ.AQ/AW.At/AϚ.A/A×.AX/A8.A/A83.Az/AB~.AR0AH|.A0A؁z.A 0A x.A ~0Anu.A0Ap}r.A c0AUo.A0Al.Aa0AXh.Ap0AHe.A0Apa.Az0Aȱ].A`0AhY.A`0ARV.A0AR.A@50AO.A00AL.A@0AJ.A`շ0A@H.Aٸ0AF.Aॻ0AhC.A 0A0B.A0A @.A c0A?.A0Au<.A`0A`9.A (1Av7.A s1A`5.A O-1A3.A@D1AP+2.A^1A0.Aqy1AP/.A]1Ac/.Ax1A(\/.A@1A..A`1AX-.A@&2AQ,.AT2AY(.AO2A$.A@2A(!.A2A.A S3A.A+3A.ARV3AH.As3A3.A@3A9.A@3A.A Q3A .A`)3A.A@3A.A@ 4A.A(4Ax.AF4A .Axd4A(!.AH4AJ1.A5ABA.Aઍ5AQ.AR6AW.A?6A(>^.Al6Ae.A6A(Kl.A6As.A@i6A{.A7A'.A:7Aό.A`7A̕.Ap7A(.A`7A˵.A 8A0Ų.AP8AH.Բ.Ap8A(-޲.A8AP.A`8AK.A@'9Af.AT9A8&.AR9A0<.AO:AP8y.A;A׸.A ;A).A(~<A87.A3=A#z.A`=A.A M>A0..A`>Aأ.AY?A R!.A`)?A R%.A ?Ap%.A?A".A \?Af.A"@AH\.AGI@A.A[[@AG .A@gz@AG,.Ap@AH5.A @AG.AÍ@AT.Az|@A8`.A p@Api.Ape@Ao.ApQ@AX x.A`)6@A`a.A$@A.A`)@A.A(@A@Wѵ.A[@A.A`g@A.A@>l@A.A i@Az6.Az`@A RX.Ab@Ac.Ap@Am.A̒@Aft.A@Au.Az@A@w.AR AA|.AG+AAp.AR.AA.A'AA`a.A=AA’.A3!AA.A*AA`᝶.A6AAB.A>AAL.A ?AAX.A .AA.AAAf.A`)AAL¶.A@A(\ж.A@A@.A]AA83.A +AA`.AG]AA0.#.A(AA,.AAAP83.ApAAp:.A~AAQ=.AnAA=.A=ZAA:.AIAA#/A9:A@%!/A@9Ag"/A@>dAY"/A@,~YA#/A9:AɜI/A<(A`GI/Aa&A`GH/A`o7'AF/A"Ah9F/A AjE/Af AD/A@A`O5D/A ZtAJA/A 'AXI@/AVAU\?/AR|A>/ARAp<8/APA8/A@ %A`6/A,AmX5/A@3A93/At;A 2/AGDA1/A}JA2/A RA5/A`_Ap5/AaA؞6/A bAȋ6/A`dcA8y7/A,dA8/A aA.7;/A ]AlfD/A[AXD/A@VYAE/AVAF/A`[VAGH/A eQA@1HI/A`MAppJ/AsHAK/A)CAP+8M/A|h9Ah{K/A4AJJ/A@,AɜI/A<(A`)/A@WqA-(/AXiAh]%'/AjA'/AzsA5)/A^uA`)/A@WqA`OU/A}AU/A3}AV/AХ}AuV/A`}AV/Aqu}ApV/AwI}AdV/A/}ARV/A`}AV/A}A("V/A@`}AV/A|AHLV/A ~|AV/A 8|AV/A|A#V/A|A(V/An|A/V/A` c|A$6V/A@h|A؟=V/A`ok|A >V/A,O|A@FDV/A7|A/GV/A Q|APGV/A {A0nHV/A{AHIV/A@5{APV/A{AXV/A{A0aV/A{ApZfV/A{AhnhV/A`mk{AhV/A0E{A kmV/A9{AW/AyA4W/A@yA-W/AyA)W/A$OyA#W/AR yA W/AxAW/AxAAW/A`TxAW/A|xAV/AhxAV/Ao\xAV/A@>xA(V/AxAHV/A ZwApV/A`mwAV/AڷwApV/A@wA٨V/A@EwAV/AwA̜V/A`bkwAV/A(wAMV/ATvAxV/A qvA( V/A`;vA`rV/A@i\vAV/A`չvA0LV/A vAV/A`7wAV/A@ swA8s W/A@ swA.W/AqwAX+W/AwA(>W/AwAH]TW/AwA(bW/AwA` dW/AxA NXW/A@y_xAUVW/A8xA/`W/A sxAPrW/AyAtW/A@_yAtW/AyAyW/AyAhW/AHyAP^W/A LzA(OW/AzARW/AjzAeW/A T{A88W/A`ܢ{AW/A@{A W/A@(|A}W/A /T|Ax W/A;|AW/A|AW/A}AW/AAX}AX/A@N}A X/A`]}AhoX/Aw}ATX/As}A`2&X/A`m}A3X/Ao}A7X/AD}APCX/A@r }ATX/A|A;fX/A`|A(uX/A@,|AȃX/A;|A|X/A`k|ACX/A`\|AX/A`\|AX/A{|AOX/Aߪ|A(X/A`)|AY/A%}ApY/AAX}Aг Y/A@N}A0Y/A }APoY/A}A7Y/A,}ADY/A }AOY/A`]}A8VY/Ah}AUhY/A@#`}AxwY/AAX}A߆Y/A QT}AY/A@}A`GY/A}AX Y/A@G|AY/A |AhY/A c }AY/A|AHnY/A@,|ALY/Ag|A _Y/A<|A؟Y/A<|AhY/A@`|AY/A Z|AtY/A |AXY/A 8|A(JkY/A |AJY/A|A5Y/A}AH\$Y/A@G|A(`#Y/A;|AvY/A]H|Ak Y/A_|APoY/AD{AvY/At{{AY/Ag{AY/A {AX/A@{AjX/Ac{A@X/A@{AX/A@{AX/Ao{AX/A;H{A`X/A{AX/AzA0X/A@zA(X/A`zAiX/A@ؚzA`ЁX/AzAHvX/AozA|X/A,zAHX/A zANX/AzAX/AT8zA؟X/A@(zA@X/AEpAKY/A VpAxY/A`pAY/AQpApY/A@!7qA(~Y/A@qA$uY/AtqAUhY/A CrA(QY/A@rA oA؎PX/AoAppXX/A QoAQ`X/A`oAppXX/A`moAXNX/ApAOX/A6pAT/A yAl5T/AzA'T/A`d4zAT/A@czA` T/A3zA` T/A^zA/T/A`zAUS/A {AhsS/AP{AS/At{{AS/A{AS/A S{A"S/AF{A0*S/A[{A5S/A@({AjS/AID{AS/A{A@ƙS/A{ApS/A@{A S/A |A0~S/A4|A0~S/A@`|A!S/A J|A!S/A@W|A(-S/A}AS/A(}AS/AAX}AS/A/}A`%S/A}A S/A}AšS/AW~AS/A@P~AQS/AH~AXtS/A@,m~AGS/A X~AS/A@g8~A`6S/A`m ~AS/As}A@qS/A@%}AS/A`}AS/Al}A`S/AÒ}A0;S/A }ATS/A}AXS/A J~A@T/A8~AXyT/A~AH;%T/A ~A5T/A~AAT/A@P}AQT/A0}Ah;`T/A }ApT/A}A(`T/A}AȲT/A@P}AT/A*~AxT/A ~AT/A8~AT/A,~A'T/AD>~Ap=U/A`E~A U/A`<~Ap%U/A~A8'U/A@ }Ab0U/A }A2U/A*~A>6U/Ax5~A`=U/A@`d~AFU/A`)~A]U/A 8~AlU/A~AzwU/A 1~A(,U/A`~AU/AAU/A A@OU/A A߶U/A~A@WU/A`b~AX,U/A`"~AU/A~AU/A ~APjU/A@,m~A`OU/A`I~A`OU/A~A`OU/A}A~R/A OAR/A`yA^R/A`zAR/AAA`R/A㍄A0R/A֐A0R/A֐A>R/Ai~AR/A?]A(R/A`KLAR/A@r5ADR/AA8R/AA(R/A@A`R/A^փAR/AAAR/A AhR/A`A(R/AEA(R/A A S/A@.AS/A 1AsS/A q'A%S/AA>)S/A@G A*S/A A04S/A كA:S/AăACAS/A@AX9KS/A |AlSS/AzAx]S/A;@A8eS/AA3mS/AׂAXtwS/A`AXzS/A^A8bzS/AAPxS/AAӀS/A/ցAȃS/AA֌S/A_AqS/AZ:AȃS/A$ AS/AՀAxS/AA@S/A@莀AS/A vA pS/A3zAȔcS/A@yA^]S/A&AX(WS/AAIS/A@i0Ah"9S/A#Ah"9S/AA4;S/A@yA-S/A`TAX S/AAx(R/AYAh"R/A |AR/AĀAR/AA0ԺR/A# Ax(R/A4AR/ArAOR/AAFR/A@́AR/A@́AR/A сA`R/AA`R/AAR/A@ Ah R/AN/AR/AIA(R/AWAXlR/ApA!R/A@,~AXdR/Ao}AxR/A`9{A8VR/A@EjA R/AyAHR/AA0UR/AXAxR/A@ւAR/A A~R/AAyR/AA(oR/A]AgR/A@$AHngR/A2AiR/A @AmR/AZA`2qR/AuAPrR/AAHmR/A A$mR/AjŃA nR/A׃AX9sR/AڃAxR/A`ՃA|R/A6߃A(:R/A 8A,R/AAR/A@pA}yR/A 1AsR/A3ApR/AXIAZoR/A]APhR/A wAhR/AAnnR/A^AuR/A@yAR/A AH܇R/A@A'R/A`A~R/A OA?PR/A AJR/A 8 ApCR/AZA?R/A@rىApCR/AƉAnIR/A ЉANR/AANOR/A ۣA(\VR/A ۣAP<\R/AQAbR/A AhR/AωAspR/A9͉AsR/AMA5tR/A@A{R/A@,AX|R/AyAX|R/AiAX|R/A`SA`R/AMNA@R/A@WAxdžR/A@XAR/AGAR/A6A=R/A'1AxR/A2AHR/A`#AhљR/AAx:R/A4AXhR/AA(,R/A=AR/AƈAx R/A`dAR/A A88R/ARAxGR/AAxuR/AuA(R/A`hAHR/AgApR/AgAR/A ~Ah^R/A܈A{R/A A5tR/AAlR/A əA(9hR/A㧈A_R/AqAPjYR/A@AWR/AA(\VR/A QЈAMSR/AA"NR/A4APGR/AA``FR/A)ALR/A`(A RR/A60A"NR/A QA``FR/A UA':R/AYAPI,R/A`rA'R/A`rAY'R/A bAi&R/AIA!R/A;Ax>R/A 3A 0R/A@yA"R/AA|R/A-A@R/AAR/AxAR/A痈A"R/AAhnR/AA R/AAXR/AaAHQ/A AhQ/A-tAQ/A ~AhQ/A५A(>Q/A`ՈA-Q/AqAHQ/A@yA@Q/AU.AsQ/AHAR/AW]AdQ/A}zA Q/A 䃉AHQ/AyAXQ/A clAQ/A^|A8xQ/AfA8xQ/AkAHKQ/AщA(Q/A@yA/Q/A@ AQ/AAQ/A0AЀQ/A@AQ/AԉAIQ/AA8Q/AAQ/A@yA:Q/AwAQ/AtA.Q/AtA֩Q/AA0ءQ/AVAPQ/AAխQ/A`AgQ/A@AqQ/A sGAQ/AmArR/AA@R/AA(R/A$A5=R/A`uAMR/A@EJA@SR/A/AMSR/AA?PR/A A )/Ag@Ay/&/A`wAN&/A3AXyG(/AA )/Ag@A gp/A|ØA@}m/AAl/A ⬚A0p/AyuA gp/A|ØAh0$/AѐA!/A{A@+!/AA@ /AA(/A`dЮAh/A#~A/A@``A1!/A A!/A;uAh0$/AѐAM/A@$AM/A-A(\M/AAM/AghA@dN/A`#A88&N/A@A7N/AAHN/A ZAx6TN/AA gN/AAxwN/AmAN/A;{AΜN/A AN/A;{A9N/ANApN/A`9)AN/AApN/A@A@1N/A`AN/A AX9N/AXA O/A`AO/A`A.O/AA:O/AA^FO/AAX[KO/ABAHWO/A@A lO/A &AXV~O/A`AOO/AAO/A ]AtO/At!AfeO/AA-:O/A״A@S,O/AA`$O/AbA0]O/A AO/AAx'O/A` Ax'O/AlA+O/A*3A-:O/A0AEO/A%AXOO/AAQZO/A@AX9bO/A AhhO/A3EAZrO/AAHwO/A@Ax{O/AAHO/AwAdO/AHAO/AྻA@O/ARA[O/A`AO/AqAO/A)_AoO/A GAO/A 6A`6O/A`'AO/A@W!A(O/AAP@O/A@ A O/A@yA O/A AZO/A?AHO/A cuAvO/AVAFlO/AH=AbO/A@A(ZO/A@GAUO/A |ApXO/AėAdO/ABAgO/A[A`%nO/A2;AsO/A ATvO/A@A {O/A lAdO/A`AO/AtAO/AAO/AAO/A`AO/A@A@O/AAO/AAp0O/AŢAHO/AFpAO/A@JA[O/A`AHO/A}A|O/A`A@O/A`DAXO/AWA`O/A`*A"O/A`dAO/AAO/A`mAh3O/AAO/A)kA@O/A&@AO/AFApJO/AIA=O/AZAO/AfA |O/AtAx~O/A`AtO/A cAXWkO/A@kAeO/A`PA2fO/A7AأjO/A`DA@sO/A@GAyO/A@rA@}O/AAȁO/AAO/A@؅AO/AnAWO/A'VAWO/A4*AO/AAO/A`ADzO/A AHgO/AHA$UO/AAhCO/A@A 5O/A@A+O/A@AA(O/AZAг'O/A`.A"O/AqA &O/AAp,O/A AV3O/AIiA7O/A SA4O/A@CA8O/AAȩO/A@AO/AnA0O/ARjAPO/A0sAx O/A`oAeO/AவA8'O/A&ټAHO/A`;AO/A`;AhQO/A$ܼAhO/AҼAO/AѬA=O/A@AqO/AguAO/A=_AO/A`=AO/A AO/A$AO/AAO/A A(mO/Ay.APO/A`mBAO/A *AXhO/A$AO/AջA O/AA(yO/AઌApNkO/AzAPdO/A@ SAMO/A PA o9O/AV`AA(O/AvAPjO/A&}A O/ArAN/ArAN/AfAH)N/A?AN/AA(N/A=ACN/AA(~N/A@#TA`N/A`AXtN/AA@N/AzA EN/A`bA7N/ADtA@N/AݎAhN/AAeN/A$ϹAN/A`AN/AR7AN/AMjA7N/AAȇN/A`A8bN/A?~A@N/A@#TAwO/A_#Ap O/AAO/A qܹAСO/A@AȂO/AaA O/A rA(O/A&JAO/A/AA(O/A8AV3O/AA>O/A ɸAxGO/A飸ARO/A`hAfO/AIAGuO/AaBAȉO/A Z#ALO/AA8'O/A'APH/AAH/AAСH/A cAH/AA8#H/A@AH/A AAH/ADKAH/AtAH/AA'H/A-AkoH/AO?AUH/A+A _0H/A A H/A VApH/AA88G/AnA(G/A=A`G/AA߿G/A`A>G/AAG/AFAG/A;ɡAXG/A ⁡A`ӍG/A/AG/A ͠A>G/AHzAPG/ANAG/A AG/A`)7A G/A@yAG/AAG/AzAYG/A@NA`G/A`;AG/A`f'AxG/A ˑAxG/A`mAG/A@RAhoeG/AUAPZG/A'ŠAQG/AAHEG/A`vnA9G/A ApJ G/AOAHKF/A@bAmF/A`[3A(F/A ZAF/A`mAdF/A AF/A AF/A ApIfF/A AMF/AGA0PF/A0AXF/AAhs[F/A^AȤNF/AFA`3F/ABAp_F/ABAF/A@bAF/A@pA'E/A@WȟA8E/A`mApE/AA̟AE/A@pAXtE/AQA8gE/Aw՟AȥE/A`#AȥE/A9~A +E/AXvA@)E/Aj/A0-E/A A0E/A@pA0E/A&zA9E/A A@BE/A lATZE/A@5+AnE/A AE/AfܝAhE/A@AE/AJAE/A AxE/AAfE/A@qAXE/AmAF/AXRA0F/A`FA?F/A`6ASF/AA8'oF/ABAhF/A`6A/F/AAF/A`mțAF/AaApF/A A F/AŨAmF/A`YAF/A̙AG/A;ApF/A`JAiF/A`YApF/A@,Ah F/AmAFF/A&A`F/AȘA@F/A (}AEgF/AFuA=F/AFuAxF/A`̔AE/AUqA|E/A 8yA E/AAFE/A]AyE/AAoSE/A;6A AE/AX.A +E/A@QADE/AA`vE/A_A0D/AAPAyvD/A QrAjD/A@nA pED/A tAh+D/A@AAD/AMĪAp[C/AcATC/AXA|C/AsAYC/AA؎/C/AA C/AAB/A ѬA%B/A-DA5B/A@AB/A3A@B/A AC/AIAa8C/AA"LC/A` (A {C/AhAH|C/A8AHvC/A`MA(hrC/A)5AVC/AOɳA4C/A0ӳA C/A lpAбB/AAPkB/A`A`LB/A`A!B/A@ApB/AAA/Ah!AA/A AKA/A#'AwA/AǓArA/AAhA/A`EAa B/ARA RB/AA@B/A|A@BB/A?A B/A_A0A/A`A(,A/AFA0A/A_A^A/A@r.AHrA/AL@AA/AvA((A/A?AA/AUηA0A/A*AȂA/A?AݫA/Aj4AxA/A nAA/A*A0A/A?A A/AθA((A/A?AHzA/AA0qA/A A8iA/AUAeA/A?*A0X`A/A?*A0X`A/A SAp=jA/AjAmA/A uAؒrA/AAؒrA/AjA(hnA/AӹAp=jA/AApcA/A*"Ap]A/A 0APA/A@5uAQA/AjAQA/AͺA((OA/AAȲCA/A&A"BA/A3Ai7A/A\AR:A/A*A?A/A A0?A/A»A];A/AۻAr=A/AA88DA/AAp}>A/AA;A/A =A((6A/AiAxm0A/AA0X.A/A ӼA(/A/AA-A/A ;AM-A/A iA3A/AA.A/AAؒ'A/AjŽAR!A/AjA0H A/AA(h#A/A*[AA/A`5AMA/A?+A A/A@AA/A?]A A/A|AA/A?A8x@/AjA8x@/A ѽAxA/AA A/A`A8 A/A AA/A3AA/AQAxmA/AUrA A/AA@/A`zA@/AUA@/A*A@/A`ARA/A*A0A/A?AMA/A HAA/A?AȂA/AA(A/A?οAr A/A AA/AAB@/AA]@/AA@/Aj6A(@/AEA8@/A8A(@/AQAM@/AjA@/AAx@/A`A8@/AA@/A*A@/AUA @/AA»@/AAȂ@/AAݱ@/A #AТ@/A?,A@/A?EAؒ@/A?^A8@/AdA@/AAb@/A*Aؒ@/AA8@/A`A@/A'A8x@/AAp=@/AjA@/AA@/A?&Ax-@/A,AȂ@/AGA0@/A`A@/A*A(h@/A AB@/A`A0H@/AA(@/A*A0@/AA@/AUAp@/A?A0HA/AUA0A/AA0A/A A"A/AAp}%A/A(Ap}%A/A ^AxA/A?ABA/AA(A/AAA/A9A.A/AUgA88DA/A qARA/AjiAТgA/A?LA0sA/A`$AƒA/A?A0A/AjoAA/AU"A]A/AA(A/AAA/AUA A/AA8xA/A`AA/ATA0A/A*VArA/A*VAA/A*=Ap}B/A`ArB/A`AB/AA" B/AUGA8xB/AAxB/A Ab)B/A`Ar7B/A*ACB/A?Ap}QB/AA`B/A?A(hhB/A A(tB/A?dA]B/A %A0B/A?οAȲB/A AB/A_AТB/A`Ap=B/AھAp}B/AA0ȣB/AUrAp=B/A*UAp=B/A*#AxB/AAȂB/AɽAp}B/AjA0B/AjAB/A iAC/AYA0C/AYA((*C/ARA3C/A A3C/A A81C/AjA((*C/AUeA"C/A?A C/A7A8B/A AB/A*AxB/AAB/A uA cB/A^AB/A?PAB/A,A0B/AٺA B/A*Ax-B/AdA(hB/A2ARB/A? A0HC/A 뷹Ap=EC/A 뷹A0eC/AU¹A8zC/AKڹAx-C/AA0C/A AC/A*TAbC/AkA]C/AA0ȶC/AAC/A?AC/AiA(C/AU?AxC/Ac AC/A AC/A?ٹAxC/AjݹAMC/A ̹A8C/A߹A0C/AAC/A &"AȲC/A7A D/ACA]D/ARAD/AUqA(hD/AAxmD/AA-D/A`A;D/AA0H@D/A {ABLD/AwAxVD/A`yARZD/AՖAr]D/A*ABeD/AͺA(huD/AjAD/AկA0D/A ApD/AUAHܭD/A@A0D/AcAhD/A0AsD/AAND/A@ADD/A@A"D/AAE/AN,A@E/A$A }E/A`A#E/AmѹA *E/AmѹA0E/A$A26E/A`2A 9E/A4[A+@E/A/AtRE/AAkaE/AtuAlE/ALAXsE/A`2AˆE/AAH E/A 1A0E/AhA`qE/AhAE/AApyE/A[AxE/AAPME/AA@~E/A AE/A,(AhE/A@yBAXE/A DA 0F/A!A83 F/AAxF/A`)A]#F/AعAPZ(F/A@AȺ'F/A A&F/AA@AH;,F/A'hA1F/AwAJ/ASA`J/A HAxJ/A`1A0J/A:A誖J/AxA pJ/A"AJJ/A qVA8J/A ݼAXJ/ABJAJ/AAWJ/AAPJ/AA(J/A˾AkJ/A$A@RJ/ACA7J/A@>A5J/A@ ArAXJ/A@yAJ/A A(J/AVAiJ/AApJ/AAJ/AARJ/AU+AȥJ/AfiAJ/A`vAHaJ/AA J/A@,A1J/A@yA0J/A@yA؁K/A3AK/A 8]AbK/AؖA0K/AAK/AAK/A@-A K/AV^AK/A@A(K/AAK/A`AwK/A9 A$ K/A'RA`K/A zA K/A AH-K/AMA:K/A?AGK/AAZK/AAgK/A`bA mK/AHA`wK/AAPK/AA8K/AAH.K/A`mAK/A :AK/ALAjK/AxAK/AA@K/A`DAK/AA _K/AcA`CK/AAwK/A֪A RK/A AK/A`nA&K/A`wA@K/A;>AIL/AAL/A? Ap,0L/AAML/ALAML/A cAML/AANL/ApiAPPL/A=4AQL/A@ASL/A 8A]L/AVAbL/A$ABdL/AA$hL/A^}AxL/A84AL/A84A%L/AjiA@AL/A sAȶL/AAPL/A`AL/A A@`L/A A]L/A`AYL/A`vA7L/AKA0L/AAkL/AAXšL/A@wAL/A AL/A8AL/AA}L/AAL/AVA'L/A@ AЀL/A 8A}L/A AL/A+AL/A uAL/AɾAL/A APL/AAL/AAL/A`] AL/A-AL/ALA,L/AൊA8L/AmA8L/A@AHL/AAL/A`APWA[/AkKA[/A BA[/A9A@5\/Ag Ax\/A ~A8\/A A1'\/AA1'\/A-A0-\/A*As7\/A[ AA\/AAJ\/AA0{V\/AA@d\\/AAȔb\/A`KAXt\/A@A;\/A-A\/AA\/AvA\/AaAPZ\/AaA\/A@8A\/AA\/A@ءA\/A`A\/A@A\/A^AV\/A ZwA\/A_A\/A@EApp\/A A\/A@EA\/A A范\/A[A\/AkA–\/A}A0&\/AQ%A0&\/A@Ai\/AfYA\\/A9Ah\/AIA`C\/A@A`\/AAL]/AxjAf^/A A`%_/A4A`/A`m<AD`/A`$Ag`/AtIA@1`/A@6Ah`/A`A`/A 8AȘ`/AmA a/A ALa/AAha/AxA`O a/A@tATA5g/A`AXyg/A AAHg/AxAf/AhZAsf/A@NA g/A@֊A|g/A`}AXg/AAg/A Z+Aأg/A /AHuh/A`Ahh/A AX2i/A Axi/AUA/j/AA6j/A yAWk/A3Aql/A,Afl/A8Al/A@Al/A0A@>l/AAhl/A |As l/A@MAk/A'Azk/AACk/AUA5j/AAԕj/AAAj/A`uA!j/AA`i/A | Ax\i/A@ tAi/A@wA _i/AZAi/AAi/AA0 i/A@NAXcj/A@yvA`Xi/A@A0i/ApA0Ei/A`K>Ahi/A A0i/A@ݵA)h/A` HAh/AAh/AòAxwh/A|A Nh/A=A+h/AZANg/ANAEg/A@iA g/A-A؍yg/A@yڬA3qg/A`Amg/AKCAdg/AVĨAOg/A@jAJg/AAJg/A`LA:g/A AP*1g/AzA#g/AաA@uf/AРAf/AVAff/AƠA f/AA0jf/AkA0jf/A pAhf/A#ZA`f/A;A{f/AZrAIf/A'AHf/A`)vA;e/AA(ܡe/A A%ee/A̙AXFe/AौA4e/AjAm!e/AܤA8e/AAd/A AWd/A@.A0d/A4Axod/A׈Aad/A_A`Zd/AAh{d/A  Axod/AAad/A QA>3d/AA)d/A6өAEVd/AAHdd/A[Aad/AW2APQd/A"ѬA7d/AǬAAAd/ALA'd/A@rAc/A AXc/AGAxvc/A Asc/A.A@ec/ANAac/A)-Azc/A`{Aʖc/A?A@c/A Ah d/AA5d/AzcAh@:d/AdAȘJd/AAad/AkAd/AOAd/A)AHad/AաAxi e/AnAH;e/AGApe/A`巟Aޯe/A`巟A e/AAe/AtAe/AAe/A ~Ae/A APɮe/A^MA،e/AXATe/A Ae/A`A`d/A`Ad/A ANd/AA8Vd/A`KAvd/A`>A5d/AA8d/AۘAc/A!Aͤc/AoA"c/A@#Alc/A`TAx\c/A Ab/AjsAb/AzǖAVb/A`)ږAa/ANAXa/AtAa/AA a/A cAPa/A}AHa{a/AAxia/A AӎAa/AA%a/AVA0b/AA4Kb/A@2A8pb/A@Ab/Ak`AHib/A@Ab/AAb/ALA#c/AڍA7c/A`f1APgNc/AʍAxzIc/A\AX4c/AތAX4c/AXA6c/A֋AGc/A4AGc/A ⃌AHVc/AAmc/A2xAHc/AA0c/A |?A#c/A֌A#c/A@5AP/c/AAP/c/A9dA`c/A`Adc/A`mҊAxcc/AkAHvc/AWAoc/AŲA#c/A&A#c/Ax{AhQc/A4ƇAMc/AAsc/A'<A&_c/ACArKc/A콄A?c/AKA,c/ASAP c/AA!c/A<A<c/A ݅Ab/AAb/AA7b/AAdb/A lA%b/A…AUa/A㳅AQa/A3;APa/A]%AXa/AvA(>a/AҨAH8b/AH,APAb/A`KA(b/AdxAa/A`APa/ANAa/AAga/ARAa/AGA@ b/A`~Ap b/A}A}b/A@,|A`b/A*|AXa/A*|A֜a/A`Ӻ{A8Ea/AzA0Ra/AzyAP/a/A5yA`/A@yzA`/A`p{A`/A\|A`/AU}AR`/A #}A$`/AC|AO`/A`v{AX _/Ac{A8_/A 8zAТr_/A` LyAX_/A?yA^/A?yAY^/AyA0Ts^/AhxxAJ^/A`YwA`"^/A`vAs]/AvAJ]/AwA"w]/A OyAQE]/A kxAS\/A`wA\/A/vA@¬\/A vAX<\/AoTvA[/ABtuA{[/AuA4[/A "vAxZ/A vA'Z/A@WwAUZ/A|ZxA\Z/AxA [/AxA*[/A AxAW[/AxA(z[/A` 5xA0[/AxA> \/AxA0LS\/A|ZxA@W\/AxA \/AxAp\/A`ryAh]/A6zA]/A`E{Ah"1]/A |AH-i]/A%|AА]/A`{AD]/AhX{A*^/AhX{A\W^/A}{Af^/Ak{A@­^/A s{A8^/A 8|Ap^/A|A^/A@yc}Ahs^/A ~A^/A^~A^/A-IA@­^/A AX^/AAv^/A A(z^/A@>A_/A~A- _/AH~A4C_/A=}Ax:__/ATV~AH_/Ai~A_/At~A_/AA%`/A^~AR`/Aൗ~AY`/Ao~AD`/A@>Ac1`/A`2AP`/A XA`T_/AƢAM_/A.AJ_/ÁAF_/ApPA =m_/A /AE_/A;A_/AA^/A`A^/AA q^/AuAbz^/A Abz^/AA9^/A`)A0^/A@tAЦ!^/AAN^/AA`^/A RAas^/A {~Abz^/A`}Af^/A|A(`l^/A|ApR^/A@N|AU4^/A |AO^/A}A(J]/A>}Ap]/A`"Z}A]/A}A ]/A`}A6]/Ai~A84]/AA2]/A@Ahމ]/AdaA݂]/A`)A؁d]/A?TAP|H]/AOAX]/A.AH-i]/A uAH-i]/A<A()T]/A@A5]/A`9A ]/AIAr]/Aj}AP]/AAAp]/AA\/ÁA\/AA`\/Aj4AX[\/A)A@W\/AA@W\/AaA0\/A`HA0\/AwAHPh\/A㜄AU\/AdA@\/A@5AU\/AA@s\/AEA\/A lA\/AAX[\/ASA(\/A@WA\/AlA(\/A6Ap_\/A@qAX\/AňA(\/AQA \/A=ˉAX[\/A1Ap\/AcASv\/A@$AHPh\/A AMZ\/AASv\/AOA\/AیASv\/A/Aq\/AॖAxe\/AAHPh\/A#dAMZ\/A@AN\/AAx9\/A`VA\/A@sA?\/AǏA`\/A`1Ap\/AʎAؒ\/AגA\/A>A?\/AAB"\/A A> \/ẠA`$\/AAH&\/AxAؒ\/AxA;[/AڊAp\/AcA+\/AAB\/A S;ApIE\/A=ˉAx9\/A ~dAU\/A,wAx\/A ~dA0\/AAx\/AனAj\/ABA@G\/A`A(2\/A~AH;\/A AXE0\/AAP\/AA[/A AyA[/AAh/[/A}Ah/[/A鸄A06[/A`v#A[/A ƃA =\/AU{A\/A@P_AB"\/A? A\/A`vlAH&\/A@!AI\/AZAc\/AA\/A.A(\/A ڀAг\/A@NA\/ANAX[\/AA\/AzAг\/A`d~Ap_\/A@N:~Ap_\/A}A \/An+}A \/A|A \/A A|A0\/A@y{A(\/AO{AY\/AAzAq\/A`IzA`P\/A@yAp\/A@,$zA[/AzA-[/AzAx([/A c<{A%[/A|AX$[/A@N|Atn[/A|AM[/Al}Af([/A }A [/A_~AZ/AAXRZ/A (-ALZ/AAHZ/A`T AZ/AA0Z/A A`Z/AAZ/A_AXRZ/A`vlAЦZ/A܂A@NZ/A0AXZ/A଄A0tZ/AkAAkZ/AwAZ/A`AHZ/A`AZ/A0%AhZ/ALAXZ/AFALZ/A AXfZ/A^A@}Z/A@#A(JZ/AA؟Z/A `AZ/A@$AHZ/A@AvZ/A^*A?]Z/A`dFA?]Z/A@#A0tZ/A AZ/AzAOZ/AibAZ/A̋AZ/AAZ/ADAZ/A@A(`[/A@A&[/A2A&[/AAT[/AhAvu[/AhA}[/A@yHA@~[/A=ˉAҚ[/AAҚ[/A@$A}[/AËAX$[/A =A!z[/ATA@F[/A@A8[/A3Ai6[/AیAf([/A@>9Ah#[/A`TAe![/AzmA [/A:A^Z/A`A0YZ/A蝐AxZ/AAЦZ/AAxKZ/A`A0tZ/A~AhZ/AA_Z/A;A_Z/ApA`CrZ/A A`GZ/A J(AZ/AʎA?]Z/A@A;HZ/A:AH.Z/A`D A0Z/A /AxY/AA`Y/A ZōA8xY/AIqAY/ANAH ZY/A`΍A>Y/A@p5A`TY/A vA@ Y/A@A@ Y/AॖAY/A`AQY/AOAHPY/AHAQY/A`7A@ Y/AAp"Y/A(AY2Y/AAгIY/A5A aY/A,wA@}Y/AA–Y/A`mAkY/A`AjY/A@qAiY/A6ApzY/AɇAWY/A`A>Y/A`B0A@W$Y/ARAHPY/ARA0HX/AMhA0HX/A &A0HX/AնA`X/AbA@ Y/AAH-Y/A A^GY/ALAccY/AQ͆AY/AQ͆AXY/A`A–Y/AA–Y/A lAY/A`ëAccY/AA\@Y/AqAY/AajAQY/A`KAQY/A@5AQY/A A0Y/A@ AгIY/A`KCAཱྀY/AAY/A`AY/A =A'Y/A JA'Y/AAP|Y/A@tALY/A]AY/AzAjY/A]A0wY/A4A bY/AwAFRY/AA>Y/AހAX+Y/A$AEY/A?TA8X/A \A&X/A \ApX/A?TA`X/A(Ax)X/AAKX/A좀A(-X/A cAKX/A@> AxuX/A`AfX/A_A@ZX/AAH?KX/AAXRFX/AwAp>X/A@r€A@,X/A@AX/A3AYX/A |DAW/AsAW/A \ApW/A6AW/A@AiW/A /AW/A[ApAoW/A RAlW/A}A0oW/AAثwW/AAoW/A`6ApgW/Aj AiW/A~AmW/A~A~wW/A~A|W/A[~AiW/A`B~AX]W/AI}AXW/A }AQW/A W}AWW/A}AbW/A |A`SW/A@߰|A0>W/A |A9W/A}AzW/A@,}AH W/A/}AV/A(}ARV/A@E }AȔV/A|AV/A|AAV/A}|APkV/AT|AV/A@E||AV/A|AV/A|AHV/AE}A V/Aq}AjV/A4}A2V/AH}ḀV/A@`}A٦V/A~AH?V/A^~AV/A@N~AБnV/A~AX^V/A/A0fIV/AFA,V/AAx V/A`fCA(U/A@WAU/A`̀A(U/AŀAh"U/AŀAU/A ҷApAU/AIAHyU/A AfU/AxAZU/A ҷAX[RU/A |ـAFU/AA$U/A@ApU/A AU/A`vAU/A QAU/A΀A`CU/AqĀAH;T/A6AT/A@̀AxT/A AUT/A Ah@T/A AAXT/A`AީT/A@ᅀAT/A삀AT/A zAhzT/A &tA cT/A_wAYT/AA`PT/AhǀAH!OT/A`ART/ABAx\XT/A@fA`\T/A-A(VT/A AP UT/AhLA`PT/AAOT/A AH?RT/A ALT/A`,AKT/A@yXAIT/A-˃AcKT/A=AKT/A昄A1NT/AلATT/A@A`SdT/A_A`SdT/A)A^T/A}A^T/AхAdT/AslALnT/A`AA$zT/AXA規T/AA|AT/AApT/AA@T/AeA0T/A l;AT/ALA8T/AeA@T/AvAp0T/A`LJAU/ACAT/A,_AT/A,_AXhT/AvAZT/A-A`TT/AHA@T/A qAT/A@WCAXU/A uvA8U/A@֥A_]U/A@ȉAȘU/AAU/A@.DAH{U/A`2AXU/AAhU/AhA U/A`HA)U/A`mjAV/AlA"V/A @A 0 V/AApJV/A QҊA@V/AjA U/AiAU/A`;0AX9U/AR AU/A͉ApU/A A"U/A@AhU/A |lA@U/A@PMAU/A@5ABU/A \وASU/A`vA{U/A`DAPMU/A&ɈA@uU/A8AU/A JAU/A`TA |U/A0߉AV/A)A6V/AЯA(V/A@bAyBV/AwAJV/A"A>V/A@eA4V/AAT1V/A@݉A@V/A AP,V/AGAXl>V/AlA[?V/AɊA=V/A0A0CV/A=A:V/AuA,V/A`A!V/AEA'V/AA2V/A A`XDV/A DA(i[V/A`5A( iV/A`AȔzV/A0AV/AXMAV/AxAH.V/AȌAYV/AAV/A?5AV/A`KAH{V/A^hA W/AA@W/AύAPMW/AwAW/AMAW/A`pA`a*W/A ujA)7W/A`TAAW/A3ΎA0JW/A֎A[W/A0ӎAqW/AA{W/A^AW/AMFAW/A;AW/AMAXW/A TAW/A@CUADW/AotApAW/A@A8W/A@ǎA0W/A@юAƄW/A A0yW/A@AqW/ArAiW/A ZWAiW/A1APaW/A?ACWW/A A[LW/At/AHrAW/A JAn.W/A JAW/A]APZW/A jAV/AAqV/ADڏA]V/AA W/A`QASW/AEA W/AݐAW/A`)'AhW/A TAg W/AsyA(W/A]A(W/AőA03W/AaAZ4W/A@C$A(h8W/A`)LA?W/A@SAxHW/ATAIW/AvAaBW/AA;W/A`mAM0W/AA@*W/AܒAd%W/Aj A)W/A/A(0W/A |TA(W/A`[A6#W/A AAW/A6A(9W/AA@V/AēA0V/A@yAV/A)ѓA+V/A@AV/A` A(V/A-,A8V/A HAV/AMAЄV/A3A\V/AA(~V/A6AOV/A`KA(V/AA-V/A@A8'V/A` A8R~V/A@E APjV/A@E AeV/A@AXV/AAFV/A`Ao9V/A~A,V/A Ah%V/A@NAP$V/A-̓A 'V/A@E A,V/A=A2V/AzA7V/AߣADV/AAQV/A9ݔA7^V/A@ AkV/A (AUyV/A`mRAV/AqAwV/AzAV/A`A V/A#AV/A`KIAV/A`tA tV/A \A޴V/AHAYV/AϖAXVV/AAfV/A` AHV/ACAH.V/AoAzV/A6A0ԓV/A 䨗AЀV/A@ җApqV/A@A@bV/A@A7^V/A |HA0^V/AAptV/A̐A؞V/A;AP V/A@AHzV/A]:AƏV/AtA V/AAEV/A@NA tV/A@ƙAxV/A9љAhV/A`ApV/A AЀV/AF]AHV/A AoV/A ØADbV/A ØATV/AAhPV/A֘AQV/AWAHPV/A |HA[V/AwAlV/A`mA(OV/AjAV/A`AzV/A4ARV/ATAǧV/AʖApV/AA`V/A}A`V/A[0As|V/A`TڕApYV/A ZpA3KV/AvA@6V/A ~A#V/AEA"V/A@YAXV/A&AV/AoA0 V/A锔AV/A`2ACV/Aj2AV/A@#ۓA V/AA'V/A`6A/V/AAJ(V/AA)V/A u9AP(V/AAl&V/A`BđAV/A`БAo V/AAU/ArA=U/A` A8"U/A@.AU/AAPkU/ARВA(U/AA(U/A@,GAnU/A u9A8U/A [AU/AÒA0U/A"AU/A@JAxرU/AFA+U/A Z&AIU/A@ ޒA`U/A@ABU/A5AؒU/A (A@U/AAYU/A*A U/AMAU/A/AU/A`D_AU/A`vtAPMU/A 8sAU/AuAV/A`vtA6V/Ak#AJ(V/A OA/V/A`fA9V/AA@7V/A!ADV/AXAOV/A`"VAplVV/AAaV/AXƒA(\oV/A`BA`|V/A@A(ʊV/AAAV/A;ARV/A@ >A1V/A <AV/A`*AW W/A,AW/A ~AW/A@hAW/A@C$AW/A |/ASV/A!AV/AёA؟V/AcAV/AqAcV/ABA.V/A` AHV/A@A`V/A^A V/AAXʢV/A@AƏV/AAlnV/A@lA|=V/A@rJAN#V/A AU/A@C0A`%U/ATAXU/A AȩU/AAnU/AxAx\JU/AєAAU/AAp_-U/A ՋA`U/A<AXT/A SAbT/A SA@T/ALA@T/AՏAIT/AApnT/A}QA`PT/A;UA@$2T/A-iAT/AzVA*T/A ,AKT/A֊A0 T/A汊AAT/AI|A0HS/AjJAUS/AAS/A J6ANS/AYAXS/A쐊APS/AA0S/A&ĊA'S/AۊAcS/AAA|S/AA(iS/A l6A=TS/AUA _KS/AA>S/A`9ȋAȶ4S/A sA0S/AA#S/AA88S/AA@qS/A`6AȵR/AeAR/A AR/A@AP R/A@AUR/AAR/AgA@ӨR/A"ApR/ABAgR/A`qArR/AaAP[R/A$Ap[CR/A qA`3R/A qAxR/A`A4 R/AA|Q/A AQ/AذAQ/ARAQ/A@謍AUQ/A_ЍAh]Q/AA8tQ/A 2AQ/AXVAQ/A jApQ/A`mA8'Q/A`qAT~Q/AfAhsvQ/A`AmQ/AͼAȤiQ/A &AhQ/AoAZQ/A2'AYQ/AAHaQ/A@ A[Q/A`mAQQ/AJAj6Q/A`+A()Q/AA(Q/AA Q/A@.AZP/A`6AhP/AhRAxP/A}AXP/A AP/AȎA P/A}A((P/A`6A`P/A@.A˘P/A[A˘P/AɴAϗP/AyAޓP/AdJA#P/A@.A`6}P/AA0P/AǨA0P/AAP/A^AP/A`BJAP/AAEyP/AA0wlP/A`9ȋA@]P/A`vAQP/A)̋AJP/A`AJP/A*A@DP/AQFA@8P/AaAn,P/AqAH P/A^AP/AAP/A`mARO/AaAh^O/AAO/AЋAO/AЋA`O/AA O/A@yuA0O/A EAO/A;AؒO/AۋA`O/A"ABO/AeA O/AAPO/A-ԌA O/A A/O/A@>AX$O/A@CAX$O/A@A O/A`mA O/A(AhO/A S܎AO/A A#AO/A्A O/AЏA RO/Av܏ApO/AA8xO/A@VA@NO/A A(O/AAO/AAPO/A*;AO/A`^AO/AAxO/ȂAPO/A`A`O/AKAeO/A+AO/AtAO/AAЦO/ADA@OO/A SAP/AAP/AqAn,P/A SAX9:P/A3ACP/AjAXSP/A`ZA@]P/A3AXhP/A4AiP/AؒAfP/AAcP/AI~A@]P/A`OA_P/A'AdP/A^A@dqP/AApIxP/AfAP/A`v'AP/A@AP/A97ApJP/AH3A,P/AAP/A@ԐA8P/A@ԐA P/A A@uP/A CAxGP/A CAHQ/A CAQ/A@y*Ap%Q/A@Af7Q/A AX9CQ/A APRQ/AA fQ/A-AX}Q/AKAX#Q/AܐAQ/AA@Q/ANAQ/AA/Q/ABAQ/A@ԑApQ/AAQ/A`OAQ/A`AТQ/A`ܒA@uQ/AARR/A`ZARR/AARR/A 7AHKR/AnA R/A QAȶ+R/A QAHr=R/A׭AUR/A |ŔAHjR/A@NєAPuR/AAQR/AZAۦR/A@yAR/A`]͔AR/A QAR/AjA'R/AnApR/A'AۦR/A'AxR/A?AaR/A@rCA/R/A/A` R/A8A07R/A`KɓA07R/Ak~A R/Ak~A`R/Ak~A0R/AjAN S/A AGACS/A]A S/A൭AS/A@,ѓA#S/AAX'S/A#AX'S/A`[A%S/A橔A0"S/AA)S/A)_A@qS/AHWAR/A gAR/AvA07R/A 뱕AQR/AAYR/A@!(AR/AGAR/A-gA˪R/AIA@ӨR/A (AϩR/AD(AR/AcAHR/A@YAQR/A`UAR/A@IAR/A@AؒS/A9AX$"S/A*A3S/A A DS/A@זAHS/A@ƐAiRS/A:A@]S/AAXiS/AA0wS/A AS/ATA}S/AA S/ATAS/AA0S/AAS/AA8xT/A@AX$+T/A@p"A3'T/A|aA3T/A-ABT/A@A|VT/A"A,kT/A"A}T/A'>APT/ADyAqT/A`AxKcT/A@חAxKcT/AACeT/A NAP8hT/A 8A zT/AܜA@†T/AeAxT/AYA[T/A@ APT/A@5ȘAX9T/AAAT/A` RAT/A ZAT/AङAzT/A ܙApIT/AA(:T/AbAT/A@ĚAXU/A-ܚAU/A`v AxU/AsNAXJ*U/A@}A89U/A`A89U/AmԛA8LU/A@Ah]U/A`AidU/A@6A^U/A`yATU/AĜA8LU/AAEU/A@#3AFU/A@>nAHRU/A` vA`qbU/AčAHKlU/A`;A {U/A@WAɍU/A@E3AU/A@^AU/A ɕAU/AAU/A`A͌U/A*CA }U/AZAY/A1AGY/AAY/AEʢAY/A` AAY/A6-AY/A AeAQY/A`nAw'Y/A@AX/AپA`X/A.AX/A`ͦAX/A ~PAX/A 8ANUX/AlA(-X/A@AW/AJAW/A AXҎW/A tA]W/A2Aj1W/A`Ax V/A A0V/A]AaW/A@,A%W/AகA`AW/AApxwW/A&GA*W/A@A3W/A LAW/AtϨA:W/A`[AW/AaA՜W/A.A0ٱW/A@p!AW/A4Ah;W/AqA:W/A@5A@X/AA@X/AvACX/A`KAKX/A l*APzX/AA0XX/AAsX/A A PX/A]AXF+X/A]ApX/AAX/A 1dAX/A`AX/A&AX/A@A;X/Ah3AgX/Ah3Ax\X/AcAaX/A`̵A X/AcA`X/A`"~AUxX/A 8AXWX/AKA4X/AoAh6X/A緸AXWX/A`VAX/AA`X/AƹAX/AA`X/AA[X/A@ATqX/A)ANUX/AptAG2X/A;A(X/A[ApX/A`lApX/AܽAؚ&X/AADX/AܽA8eX/A AA0XX/AAUxX/A@ ARjX/A?AKGX/AﯿApX/A`˿AX/AĆAh X/A JA/X/AAO\X/AAX/AAX/A ۭAPMNX/A'6AXF+X/A`TA~!X/AOA?X/AAh X/AApX/AkA@A%X/A A8I9X/AA PX/AUAUxX/AA X/AFAX/AA0Y/A A~JY/A`djAP{Y/A,Ap=Y/ArAY/ArAY/A@HA Y/AsAY/A*A0*OY/AUAY/AމA`v Y/A AHr Y/Aa*ApY/AzAw'Y/AA{>ABl/AzBAl/A Apl/AAl/A3AX l/A@A`al/A`)xAzl/A6Al/AAl/AAp8l/A Al/ARA#l/A 1ABl/A31Al/AA Rzl/AA ll/A A`aml/A(ALl/A3qA`l/A Al/A@ Al/AAl/AzDAXxl/ARApWl/AAAl/A]A=&l/A Azl/A Ak/A Ak/AA`k/A `Ak/AAp k/AAk/A aA8k/A@>FApk/A^AX k/A Aztk/AATk/A uAP8Lk/AA=k/A A,k/ApA) k/A Aj/A HA8j/A AQj/AAj/A:Apj/A3qA(wj/AA@Wlj/A`A@Wnj/A}A(ej/AYAB[j/AtAHaGj/A A3j/A1A(j/ApOAj/A \YAXi/AUAX i/AAki/A@ Ai/A3 A0i/A0Ah3xi/A@RAfdi/A eA#Vi/A AKi/AHAfHi/AzAzJi/AmAW\i/AA0.mi/A{Ahti/AzAki/AABi/AAWi/A]7Aj/A GALYj/AkAtj/AAp=j/A HA`aj/ApKAp j/A A`j/A@vAk/AHAP81k/A A@k/A Abk/AֻALk/AzApk/ARJAP8k/AHAl/AbA8(l/A`)A5l/A`)A8nl/A=Akl/A@GApl/AzAQJm/A8A83m/A0Ap n/A A 'n/A 8Adn/A`f A0.n/A 9 An/A \k An/A- Ao/AAp=.o/A ABo/A |A]o/A Apho/A`LA@lo/AAso/AA@no/AAGio/Az Axfo/AhUAXeo/A@^A_o/A`ftAKo/AzZA6o/A HMA%o/Ap]AXo/AxAH\o/A׻A0n/AA Rn/A7APn/A`fAo/AåA o/AýAXo/A AB$o/ApA#5o/A`)AfAo/A AHo/A@VApPo/A=APo/A =AXo/AzA(\do/AR^Afao/A#Ago/A#A83ho/A`)A_o/A׉A?o/A -Ap=!o/AAko/ARAn/A2ABn/A`).An/AAn/A3An/AALn/A qAzn/AAp=n/AABn/A An/ARXAPn/A Apn/A A3n/A9Azn/A]Akn/A`)A(\`n/AQA(Qn/AQVA8@n/AR>A:n/A m AQ?n/A@g~!A>n/AL"A>n/A "A8:n/A H"A0n/A}#An/A` $A|n/A`@$A)m/A$A`m/AR$AH\m/A=J%Apm/Ap%AGm/A`ù%A)m/A`"&A`m/A&Am/A!'Am/Ay'AP8m/A'Ap=m/A H%(Am/AY(AX n/A(An/A`(AH n/AR)Af&n/A3)Apk(n/A)A4n/A*Ap=>n/A`*A{n/A@G)An/A)A8n/AR*An/A *An/A)An/A@G)Ap=n/A3m)Ap=o/A@R)A"o/A \U)A85o/A M)AXIo/A;)AX Uo/A)A`go/A (Aho/A(AH.io/A(~(AHaio/A`t(Aio/Ai(Aio/A^(AX jo/ApS(AGjo/AH(A8jo/A@<(Apjo/Ap1(Ajo/A@&(A jo/A%(Ajo/A(Ajo/A](Afjo/A (A]jo/A`v(Aio/A 'A83io/A'A#io/Az'Aho/A'ALho/A'AH.ho/A H'Afo/A`f'Afo/A'Aeo/A@>'A ceo/A@>'Ado/A'Abo/A`)'A)ao/AR'Aao/A`d'A`o/A 'A^o/AÓ'Ap^o/A@'Ap\o/A['A8\o/A@>'AuZo/A z'AGZo/Ay'Ap8Xo/A@gn'AX Xo/Apm'AWo/A@gl'AWo/A[k'AUo/A&a'AUo/A`)`'AkUo/A3_'AH.So/A@>T'ASo/A[S'APo/A@gH'APo/A G'AVNo/Az<'AH.No/A;'AKo/Az0'AVKo/A-'A83Ko/A -'AHo/A"'A8Ho/A 'ApFo/A`'A@o/A`)&A>o/A3&A=o/A&AL;o/A &A9o/AR&A7o/A[&AT7o/A&A(5o/A&A4o/AR&AȘ3o/A`ܷ&A1o/A&A/o/A&A-o/A&A,o/A@>&A*o/A`Õ&A&o/Ap&Af&o/A`&AB#o/A H}&A o/A q&A o/Af&A{o/AZ&Ap=o/A`fN&Ao/ARJ&A`ao/A@B&Apo/A6&AHo/A`m1&Afo/A +&Ap o/A &A@$ o/A&AP o/A&Ap o/A@&A#o/A`%AHo/A@%Ao/A@%AXo/A`%Ao/A%Ao/A%AP8o/A%APn/A`Û%APn/A%AP8n/A`Ï%APn/A ƒ%AP8n/A w%A n/A8t%ABn/A`)l%A8n/A``%A`n/A@X%An/AT%An/AH%A n/A@?%A)o/A=<%Ao/A5%Ap=o/A3/%A o/A`)%Ao/Ap!%AxKo/A@%Ap=o/A%Afo/AR %A(ho/A%ALo/A`%A@$ o/A A$A8 o/A $A@o/A`K$Ao/A@$Ao/AV$A;o/A$Ao/A`$Ao/A[$Ao/A@$A6o/A@$Ao/A@$Ao/A@$Ao/A@ $A`a#o/A=$Aj#o/A Q$A&o/A$A)o/A$A*o/A$AX +o/A3$AG,o/A`f$A`a-o/A $AW.o/A$A-/o/A $A1o/A 1%A2o/A3%AH.3o/AH %Ap3o/A3 %Ap=4o/A@ %AP4o/A@%A)5o/A`)%A@6o/A%A(->o/A@y8%AFo/A`ff%A(\Go/ARh%ApGo/A=j%AHo/Al%A8Ho/A \o%A8Io/Ap%AQIo/A`r%AIo/At%AIo/Au%AJo/A@x%AJo/A=z%A}Lo/A(%ALo/A%AuNo/A`%AuRo/Aן%ARo/A%ATo/Aק%ApVo/A%A`Xo/A q%AZo/A %A]o/A%Ap_o/AH%Axh`o/A`%A`ao/AH%Apkdo/A@ %Ago/A%Aio/A%Ap8lo/A@%A(no/A%AQqo/A`)&Auro/A` &Aгso/A  &A#to/A@ &AБuo/A&A`vo/A`&Axo/A@ &AHxo/A`&AQo/A`&Aho/A&Adžo/A&Aho/A`&Ao/A@ &A8o/A@ &A‡o/A@ &Ao/A@ &AP|o/A`&AH.o/A&APZo/A&Ao/A&Ao/A &Ao/A&AP8o/A &Avo/A&Avo/A&A8o/A@&Ap=o/A`&AǏo/A"&Ao/A@"&Ao/A@ #&Ao/A #&Ao/AH#&Ao/A/&A2o/A`m1&A`o/Az<&A`o/A`<&Apo/AXE&Ao/AI&Ao/A`I&Apo/AV&Auo/AV&A-o/A=^&Ap=o/A q_&A@Wo/A qa&Ap=o/A \s&Ap=o/As&AXlo/A`"w&A̘o/A`)&A(Jo/A&A#o/Ap&A#o/A@>&Ao/A@ &Ao/A&Aљo/A &A̙o/A&Ao/A &Ao/A@g&Ao/A&AQo/A@>&AGo/A`&Ax o/A'A`o/A@g 'A8o/A H'ALo/A%'A)o/A('Auo/Az4'AQo/A6'Ao/A C'Auo/AE'AOo/AAH'A)o/A[Q'Ao/A@>T'Ao/A _'A@הo/Ab'Aǔo/Ae'Apo/A Hi'Apo/A@k'AHo/A l'AHo/Azz'AHo/Az~'Apo/A'A`o/A H'Ao/A['AOo/A@i'Afo/Ap'A@o/A֫'A̗o/AN'Ax o/A@ƺ'Axǜo/A3'A@¡o/A`'AP8o/A'A#o/A H'Ao/A'AQo/A 'A`o/A`f'Aǧo/A'Ąo/A`f'A̩o/A 'A̪o/A'A«o/A='Ao/A`)'ALo/A(A0o/Ap(Ao/A H (Aأo/A H (A0no/Ak(Ao/A(Azo/A(AFo/A%(Ao/A@,(A(ܾo/A(4(Ao/A@7(A`o/A(<(A^o/A@,K(Ao/A{(A8o/Am(A8o/A`(Ap=o/A((A`So/A`՚(Ao/Aק(A o/A`(A؞o/A(A@1o/A (A@1o/A(A(\o/A')A`So/A@()A`o/A4)Ao/A_)A0.o/A@ i)A`o/A`fr)AXo/Au)AWo/A)A*A"o/A`?*AP8o/ARD*Ao/AI*Aњo/A3O*Ax o/A |S*A`o/A`*A`o/Af*Ao/A m*Afo/An*Ao/An*Ap=o/AHu*Aho/A Q{*A8o/A q*Ao/A@ *AQo/A*A|o/AR*Ahzo/A`*Ayo/A *Ap=wo/A \*Ano/A*Ano/A@!*Ako/A*Afko/A`*AHaho/A3*Abo/A*Af_o/A \*A\o/A`*A`Xo/A*ApUo/A@*ARo/A*Ap/A.Ap/A (.Ahp/At.AQo/Aa.Ao/A .A0o/A -AHo/A t-Aho/At-Ao/A ,A! p/A2,Ap/A@E+A7p/A@E+ABp/A`;n+AXp/A +Arp/A (*Amp/A}}*AHp/A7*Awp/A)Ap߰p/A`ܗ)AQp/A@E;)Ap/A(Ap/A (A4p/A(Ap/A(Ap/A (A q/A2(A"q/A (h(A2q/A`F(A:q/A@N4(AQq/AW'Ameq/Aa'AZq/A2a'A8q/A 'AP+q/A2&Aq/Atp&A4q/A@&Aq/A`&Ahq/A &AXq/A%'Aq/Ah'A0q/A@E'Aq/A`'AP+q/A ((Aq/AC(Apq/A `(ATq/A (A=q/A@E(A(>Bq/A`;)A8Eq/Ad)ApFq/A})AJq/A`*ARq/Aj*ASq/A}*ApFq/A`*A8q/A2*A46q/Aj3*A46q/A)A0q/AW)A4q/AX)AQq/A}Q)App/Ad)Awp/Aj)Ap/A`:*AQp/A@n*AP+p/A`Ӟ*A!p/A+Ap/A`>+AŒp/AL+A8}p/Aɥ+Atp/A@E+Ahp/AWA,A4p/A@E,Ap/A@E#-Ap߰p/A-Ap/A`2-A!p/A`Z-A8p/A`-A4q/A -A q/A <.A q/A.Aq/AK/AQp/A/AHp/A0A8p/A o0AXp/At0A!p/A1AP+p/A1Adp/A2Q2AQq/Aa2AQAq/A`;2Akq/A`3AP+q/A@N$3Aq/Aj3AXq/A23A4q/A_4A8 r/A4A r/A 5A0o/A` wHAo/A\HA83 p/AiHA\p/A c.HA0]'p/A`BGA(i>p/AGAxQp/AGA tfp/Aq/A k9Aq/A`9AQq/A@F9AQq/A8AGq/A@~8A¸q/Aa8Aq/A`7Aq/At<7Adq/AW6A0vq/A`;^6AWq/A (6AIq/A@E6A=q/At6A46q/A`5Aq/A`5Amq/A25Amp/Aa5Ap/AW5A!p/A5Adp/A5Aip/At6AHKp/A@E6AXp/A"6Ao/At6Apo/Aj5Ao/A 5Avo/A5A)Vo/A 6Au\o/A@g-6Axto/AK6AAOo/A>A o/A&J?ASo/A`o?Ap/A@8@ADp/A@AH2p/AXOAA@p/AAAho/AIBAh3o/ABA=o/AVCAzo/AfCA@o/A`"CAo/ADAPMo/A`'DA!o/A?jDAЄo/ADAo/A0EAHܝo/A`]OEAo/A EAXo/A3/FA΅o/A FA0\o/AdGA0]o/AGA _o/AiHAXo/AfHAcLo/A[HAAo/A HAxBo/A6IAVIo/A3IAh Qo/Ak;IA0o/A` wHACN/A $PAAN/A`tOACN/A NARN/A@vNABiN/A NA@N/ALMA`N/AMAXN/A?MALO/A \qMA؁;O/A QMA AO/A`THLAxKO/A0uKAsUO/AJAsfO/A@yZJAȶlO/A yIA`}O/A HA/O/A>HAqO/A5GAO/ACGAxP/A;FASP/AW>FA0{P/A FA(zP/AFAyP/A= FAX5Q/A^FAA;Q/A FA Q/A`:FAQ/A`FAQ/ALEA"R/A EAR/A EAR/A`JEAxR/A@DA0Q/A@,BDALQ/A CAtQ/A NCAQ/A ABA Q/A (MBAثQ/A`AAv}Q/A|AAgQ/A}KAA0XQ/A@r@A&MQ/A j@A6Q/A`?A#Q/A`+?A4"Q/A`>A'Q/A qq>AP^P/A@">AؼP/AX=ADP/A`=AhDP/A=AQP/A`D=A@_P/A<AlP/A l<AhP/A(<AP/Aj=A^Q/A=APQ/A@<AЮQ/A"<A(P/Aݬ;AyP/AX:;A P/A2:A*P/A@:ARP/A:A@SwP/A`:ApaP/A-:AxJP/A 9Ah-P/A Q:AxP/A :ApO/A@9A-O/Ae9A@O/A`̞9AO/A 9AO/A9A"iO/A9A8(O/A!9Ag O/A/8APN/A 8AN/A@7AiN/A*7A֎N/A`n6AȆN/Aq.6A8xxN/AX5AfN/A5A]WN/AX5A 8N/AŴ5AX/N/A i5AC%N/A`B4A8Q-N/A׽4A5?N/Aʤ4ATN/A@o4A k_N/A54A]N/A;3AAN/A3A8Q-N/A 3AxN/A`o3AM/An94A`M/A@ m4A`M/A`)4A8M/A`m}4A |M/AN4AȤM/A+4AM/A@,4AM/Aܭ3AXM/A@3AHM/AB3AM/A`3A*M/A2ApM/A 2AnM/A A2A,M/Ak2A0G{M/A a2A,kM/A.2AbiM/A1AȥoM/A@E1ATrM/A|1ATrM/A`)1ApM/A0ApM/A0AnM/A A0A`gM/A0AUaM/A@i/A\M/A'/AHKM/A/A9M/A/A*M/A/A"M/Au/A M/A l?/AH&M/A)/A;M/A@`.A@M/AT.AM/A@.AP*AP&C/A!*AiC/Ax*APC/A *AؒC/A)AC/A)A8'C/A`c)AC/A@9)AC/A} )A@C/A)AxC/AF.)Ak}C/A)Ax|C/A` (A rC/Aީ(A(fC/AА(A XC/A(AxIC/A(A=C/A<(AТ7C/A@.(A#C/A@.(AC/A~(AC/A 10(ARC/A`(AB/AZ'AnB/A(AB/A`fP(AFB/A`"[(A۾B/A@e(AhB/A@e(AB/A>(ApΔB/A(A(܋B/A'AH-xB/A'AhB/A(AHUB/A`(AH.EB/A(AH!,B/A(A!B/A(AB/A?(A/A/A(AHA/A?(AA/A`(A0A/A 'AA/A 'AXůA/A`'AA/Ah'A>A/AZ'A`ӕA/A!(AӄA/A@S(AuA/A@S(APaA/A)(AP@_A/A`'A0%`A/A'AkA/A'AkA/A@5d'APA/A U'A BA/A U'A6A/A@'A%A/A&A& A/A`m&A4A/A5'AO@/A@yY'AP@/AG'Ax@/A'AH@/A@'AX(@/A &A@/An&AP@/Ab&A`@/A&AP @/A=%A@/A@#%A@ߪ@/A;e%AH@/A@P%A@/A@R%AH}@/A ҋ%A@/A४%Ax)@/A"%A@/A7&Aȇ@/A|&A @/A'A6@/A@yY'AQ@/A`'AQ@/A>(Al@/Ad(Al@/A@(A؅@/Ao5)A(mx@/A̠)AЕn@/A`)ARW@/A*A@SF@/AO*A;@/A@L*A;@/A *A:@/A#*A)@/AG+AP@/A@+A(9@/A`,A@F-@/A a,AH/@/A,A'@/Ai-A'@/A?-A)@/A6.AxF@/A 8.A@/A/A9?/A(J/A?/A(J/AP?/A(J/AX?/A l?/Ah?/AR /Au?/A1/A`e?/AD8/AXJ?/AD8/As$?/A/A?/A@.AY?/A`h.A8>/A-A>/A@_-A0%>/At2-A >/A@y?-AH>/A k-A >/A-AH>/A@(.A5>/A0.A脻>/A`.AG>/Aa5/Ax>/A/A>/A/AH>/A`b-0Af>/A`}0A>/Ao0AP>/A0A>/A@>.1A >/A1A0>/A1A>/A1A>/A1A`?/A :1A?/A1Ax)(?/A`$1Ah4?/A211A`@?/A1A@BW?/A c1A@f?/A`o2A?/Ap2A06?/Aſ2A?/A53Af?/A@3A?/A'4AXu?/A q4Asf?/A4A[?/A (R5A(h?/Aǧ5AH.w?/A 5A~?/Ax5Ax5?/A`]5A?/A5A?/A 6A6?/A``6Aȇ?/A c6A ?/A`d6AXR?/A@6A0{?/AW7AH?/A@6Ag @/A6A@/A7A8@/AgH7A @/A37Ab'@/A7A=@/AgH7A K@/A7AHa@/At+7As@/A@@7Ah]t@/A7Ajp@/AR8Ax]@/Aq8AJ@/A08Ao<@/A<8Ab'@/A8A$@/A9A.@/A`279Ax:L@/A9Ahg@/A8APIz@/A`f8A}@/A[G9Au@/A9Ac@/Az9Ae@/A:Aw@/AH:Ah]t@/A@:Aw@/A:AHr@/A;As@/Ah;Ah@/A :AHz@/A`:Aȩ@/A@ߗ:A@@/A H:A@/A`3:A@/AV[:AA/A`]:AA/A":AXA/A`;Aa A/A'e;Ap,A/A ;Ah$A/A;Ap*A/A <Ahb#A/Aa<A,A/A ғ<AЦAA/A /_<AwOA/A`T<A\A/A`o,<APzA/A@p;AA/A,;AA/A<AA/A@Eu<AA/A5<AA/A e=A tA/A@">A"A/A@p>AB/A>AV;B/A@>AUnB/A |?AB/A |?APoB/A>ASB/A=AأC/Ao=A6C/Az=ApGC/A=Al9C/A >Ax-C/A@,>A)3C/As?ApGC/A?AX^C/A7@APkC/A`f@A&C/Aҍ@A C/A Zx@A&C/A@N@A@NC/A@?A C/A S ?AC/A;>AC/A=AC/Ah=A%C/A=A3D/A <A#TD/A`<APwD/A`B<AȶD/A`<A޳D/A@j<AXD/A;AxD/A cA;AXJD/A@:AsD/A4g:AD/A*9AѼD/Aa^9AP/D/A/8AؚD/AU8AXD/A 8AD/A ɩ7AID/AQP7AXD/Am6A`D/AF6AD/A@.96AXJD/A@5A 3A UE/A3AE/A 2AE/A`[2A(E/AR2A7E/A ( 2A`6 F/Ak2A4F/A2AcF/A 81AՠF/A1AF/A`0AF/A (0AF/A/AG/A@/A)G/A'/Ap=XG/A@/AݧG/A/AxG/A/A 6H/A &/AcH/A2/0AXRH/A-0A`H/AFq1APH/A@,?1AI/A0A@RI/A/AӔI/A (0A@1I/A A0AhsJ/A@#Z0A0J/Aя0AWKJ/A0ArJ/A0ATJ/A@W0AJ/A1AHJ/Az1AJ/A`v2ASJ/A`[2A K/As)3A+K/A`3A)CK/A3A`gK/A@t3A ^K/A73AK/A@t3AK/A`#4AK/A@4A0]K/A 5A8xK/A6AּK/Al7AּK/A@8A8'K/A8AYK/A P9AؒK/A l9AXK/A O;AK/AM<A K/A=A AK/Am>AK/A9?AxK/A@AּK/A@@AK/AAAK/AٶAA K/A _BAoK/AL CAUK/AdCAUAP4/A`UA3/A7VA(K3/AVAX3/ALWAT3/AVAh3/A 8VA3/A |BVAs3/AUA؟w3/AUAlk3/A`TA\3/AjTA NP3/ASAbE3/A`KSAA93/A`5SAȔ3/AzRAHr3/A \RAX( 3/A` QA~3/AQAz23/A`v'QA3/A`QAHr3/AjPA3/AoPA2/A}tPA82/AIPAx2/A$#PA|2/AOAXWM2/ATOAX(.2/A8dPA2/AaPA 1/A PA`1/A@PAXc1/APAyx1/A PAHq1/ABQAHq1/AQA0}1/AQAj1/A@RAX#d1/A SA~e1/A@.qSAPb1/ASA(ma1/AŅTA\1/A3UA`OF1/A઄UA,1/A"UA(1/A |BVA0/AAVAx0/A WAЄ1/A@WWAj$1/A WA8D1/A_VAf1/A-XVAc1/A-XVA&1/A@VAض1/A\WAض1/A@WAਗ1/AXA0}1/A JZYAR1/AYAx˟1/AiZA}1/AYA1/A 9YA1/AXA 2/AwXA`/2/A0rWAxm>2/A WAN2/A +WA(Y2/AWAcd2/A`2WA0&2/A cXA0 2/A@EWA)2/A QWA2/A`;WAe2/A WA3/A WAh.3/A\WAp_T3/A`6WAVz3/A`WA0*3/AEXA^3/AHXAG3/AYA{3/A@ZA3/A3NZA3/A-ZA4/AZA@94/A`<[Aؽo4/AX[Aj4/A][AHK4/AZA64/ADYA84/A qXA@4/A`2WAw4/A'WAW/A cAMW/A bA X/A;bApp@X/A@ aAhzX/A aAX/AaA Y/A`XaAG.Y/AOPaA@~fY/AqaA0Y/A(bA(Y/A@yBbAZ/AqaA(&Z/A@yaAh@Z/Aar`Ah Z/A`_A[Y/A,q^A@Y/A O]AY/Aٌ\AЮY/A`U[AZ/A`ZAP+RZ/A3ZAx~Z/A3ZA`Z/AYAX [/A@YAX9([/A AYA8b[/A]YAX[/AYA[/AH}ZAP[/A ZA~[/Af[Ap \/AL[A`2%\/AZAP\/A@ RZAw[/AYA$[/Ac YA[/A XA[/AXWA$[/A HVAP\/AUAx-\/A3UAP@[\/ATAъ\/AZUAؼ\/A@@UAP\/ANTAb ]/A@TA N1]/A`hTAS]/ATA~v]/A c TA8]/A ASA]/A@,1SA2]/ARA$]/AjRA]/A9QA ^/A QA+^/A@*QAU^/AߟPAU^/A cIPA]/A cIPA]/AkPA]/A ҹPA0|]/APAH5]/APAh@\/AZPAx\/ANOAhb\/A`NA0b\/AXNA\/AMAxǔ[/ALA0L*[/ALAUZ/A]mLA|Z/A@pLA+Z/A@yLAY/A@pLAxY/ALA -Y/A@ZLAHrX/A JMAP&X/A NAX/AT6OA8Y/A?OAkY/A`PAdY/A aQAX*Y/A [RAxGX/A`SAhX/A@yUAqX/A JWAX9oX/A`XApZX/A VYAX/A`XA&Y/A`XA?Y/A`f>ZAh(Y/A@[A`X/A J]Ax@X/A]AcW/A^AW/A@p?`A0̀W/A bA@nW/A`bcAW/AAcAP7W/AcAW/A cA5ac/A /A*qc/AA8c/A@>Ac/A`Ac/AA8c/A |Ac/AAxKc/A.Ah3c/A@~Ac/A@ذ~A=d/A~A$d/A@~ASOd/A`d~Add/AM~Ad/A~A`d/A஺~Aݓd/AK~Aid/A~A^d/A"~A8td/A}`~Ad/A8~A(9d/A}Ae/A9}A e/A~Ae/A1~AP8 e/A|z~A8d/A@5~Ahsd/A4~A( d/A $A"d/A Ad/AAd/A`Ad/AAe/AFAp e/AA,(e/AaCA@Ce/A@5~A0ce/A|z~AXwe/A@~Ae/A}Aze/A@,}AFe/A}Ake/A@?|Abe/A{A Ae/A {AP< e/A@5!{Ane/A@EzAp=0e/A4yAd/AayAd/A=yA@1d/A` $yAtd/A@IxA@d/A`-xA0d/A`dwAxd/A+wA@ d/AtmvAc/A uAxc/A>uAc/A@rdtAc/AmsA|c/AqrA Mc/A@YqAho+c/A`oRpA0; c/A[oA0c/AnAxc/A_nA؞c/A]mAb/A`?mAb/AfmAob/AmAh b/AV$nAb/AnAb/AoAb/Av[pA@Slb/AapABb/A LpAp|b/AypA; b/A 1qA@a/AෘqAXa/A"qA8a/AqA&Ra/A`mqA(1a/AqAX a/A AnrAȃ a/A c+sA0U`/A}1sAPZ`/AIsAX`/A sA `/A=/A}^A8#@/A ёAPoA/A ɆAH%F/A`AȵI/AQA`0M/A pAlP/A4AR/Ah&AnT/AAp}Q/A`A(Q/A痢A@$ R/AOA/R/A`]A0R/A,AP$R/AxA(R/AhAH)R/A`@Ac%R/A Ac%R/AIAH"R/A ұA!R/A@qAJR/AKRA@$ R/A CAQ/A9APQ/A`\AQ/A`2Ax)Q/A ೡAjQ/AٲAxǯQ/A@NAX9Q/A@#rATQ/AoAxzQ/A qA?Q/A-AÒQ/AAQ/AHAQ/A`lA#Q/AA Q/AfТAIQ/A ZA Q/A`.AhbQ/A %A0fQ/A!AjQ/A`"AXQ/AA(Q/AAQ/A /AQ/A`բA(-Q/AڻA0Q/A,Ap}Q/A`A7H/A,AVU.H/A6xAPn'H/A8Ae#H/A.gA4"H/AՏF AwZ$H/Ad1ٱA(H/A#Aiy+0H/APlRAC9H/A@aA2EH/A0OASH/A0"⵰A@cH/A AT+rH/AjfrAͨH/AkHAFH/A@ApH/A9ޯAxJH/A7AfL<,H/A:]AQH/As(ZA}NH/AT+ԮAXRH/AAy&UH/AȭAZH/Ak AkH/A[eKAlR2H/AeFAQ9I/As A;)I/A&[PAqMI/AIs罩AS#sI/A`J8AI/A:?.¨AJI/A\ZA)b&I/AATK=(J/AAoYJ/AA`%J/AOfAʩJ/A9eAV갾J/A\!-SAŇJ/Aq/Ar J/A"AJ/AMfA9K/AlkAq!{K/A_A(bf%K/AA()K/A`NAK/A w%A5K/AE夤ףAvJ/Ay)ABJ/A;7A}Wk^J/AS3̎A^DJ/A,lAWJ/AG!Af +J/ArАAI/A@>AUNI/A[-cARxI/A=AcƠI/AAUI/AA.2aI/A ACI/A 4A .&I/AlkAX^ I/Ḁ<ȱA@SH/AAďH/A5||.A i)H/A~NARG/A SdAfo{G/Aa7A4G/A@`ĥAqgC!G/A7 A4 G/A2Q#A -F/A"AOF/A%A@"aF/AjAUF/AAvBF/A,vޥA?xF/AҬAAu{F/AvIpAmF/A )AGF/A}hp?A!F/AA9E/A-ѢA &}E/A) kAܟ 'E/AqDBA҆\D/A43ȟAgpD/A|yAvD/A[(A*=C/A Ф^AULC/A3ļAȓB/A| AB/A%~៛AB/AK3ASA/AطښAk~EBA/A 閚A@/A*LgAH>j@/AmMAP\?/A% HAs?/AVXAN&?/A !}A

/A< 7A` Q>/A'A=/A@hAٚ=/AgAAWe=/At~AT)=/Aqk2$A+/A}AE>/A A`>/AC QA>JB`~>/A Aɛ>/AM}ȻAS7->/A@ Ak>/AA{?/A A#B/A@5Af`B/A[AaB/A oA52B/A#kIA-(C/Au@'A؇C/A 2 Am.%1C/A LuKA!q`DC/A%AHtUC/A%$YȼALAbC/AAlC/AnA7$rsC/Aw˽Au̩OuC/Am'*AsC/A AOJ~C/AXA-qC/A;"AsiC/A+6>A1C/ABLIA:B0C/A{BXAmC/ARfuAȊD/A$8Alޛ8D/ADžN9AaD/A AʡD/AhAHu sD/AS8AJJE/A¤ׁA3+E/A sAOE/A ,PAzKCF/A9}AaF/Aև8AkF/A.AB;DG/A8 BAvJG/A_9gA9G/AAFH/A7AwZH/AJxAnhDH/Ač$APAI/A@Aj`I/A6A{I/AbA]rI/A&B*AړI/AYAIeñI/AFA1I/A2AQI/AT5=AkI/A@vAJYI/AS*AI/A@W AKJu}I/Ae\B{4Aa=PI/AMATQI/AwSS&mA)H/AV-AH/AwA.vH/A\8A7H/A,A݋V/AkAWwV/AlKctA = dV/AGoA#5>PV/A9^AGP7>V/A!?:=@Ae,V/AI)An V/AkplA)MV/AAjʆV/A?>`A:U/A'uδAU/AwAU/AwA8\CV/Ar=rK:AV/A:6J6AS) U/Ah{풾AU/AKpAU/AQyIAaU/AhliA)j$U/A;ԖAU/A)A06~U/A`DA t$eU/AyFATMU/A#A-U/AzwbA U/A奣AS T/AB-A0{T/A@AisiT/Ai,ATE޴T/AC&AaT/A]ƄA#^yT/A9_AVT/AgAsm/T/AA8T/ATAvցGS/A. ;>AS/AKKfAOS/AYAS/A:ATyS/A`\AS/ASQA[S/As0)AmS/AAqS/A=A]aS/A$<:A]gwS/A"D]oA0S/AYAۏcPS/A5fAS/A=1AQS/AD[cA\ynS/A+H޻A9KS/AHFFA %%S/A8A2R/A2lu?AI~R/AmҹAwR/AEݕvA|OEqR/A.+A8?R/A3CA R/A`B˸ArQ/AARQ/A E|AQ.4Q/A.=A$X@Q/AhA ,8Q/AA)ۅQ/A:.6AP6 }rQ/A;ʶAךaiQ/A+YAhbdQ/A@A4Q/A<aA͏XQ/AA5P/AsAMMP/A֘fr7ANk`P/ANZAY'P/A,0ijADXO/A/d^AӴO/A~sAzO/AJfAWaVAO/AԬA7RO/A'ڳAh?N/A@Ak"\N/AoQA)N/AJ"AVN/Aϗ=A~$xN/AɨJAskN/A6VAL3N/Aj6A xN/A%RkltAN/A;׶A)N/A/\A?N/A|ALWN/A_"oA2N/AA~kN/AS+؆AN/A=AN/AkzpFAԗ0N/AqxAVN/ABx/)AֈWN/AAA hC O/A4iAWQ&O/AZA}CO/A"K:A6'eO/AOAO/AzZA⥋O/A"]A8O/Apae UA8P/AplpAA1D†GP/A>3%"AGP/A`Aa~P/AI0&AiP/AAec)qP/AM8.AQ/AAvX.0Q/A@aA2GQ/AΌ3AYQ/Aǿ~UAo4zhQ/ANm{AXqQ/A@EAV}gzQ/A|e*AcQ/AQ)tAXߚQ/AGgtAu%Q/AzA_-Q/Aث'A9lQ/A:7AWW[R/AFBAWj>R/AuPHAخmR/A*`GAP7R/A@y?APR/AeMtdAR/ALRAS/ASeAD!4S/A]ƠA#%cS/AuȠAܕS/A!d6AS/Aݡ5}AWMT/A*WAiOhBT/AUҲ$Am T/AVҴAl9bT/A>iQA#1 U/A/AI99PU/AtA];U/A= r:AU/AjABV/AopAo)A@W/A`A6W/AK@X AW/A"Aa\wW/Auņq AUHiW/AAW/AKAա.Aj{AС.A`{Aš.Ai;{A!.A` zA`.AzA.AezA@}.AVKzAh.A/zAL.AzAU.A@yA.A`yA.A yA k.A෇yAP.A -uAH!.AtuA#.A^ uAM%.A@5tApA&.AtA&.A tA%.AxtA$.AastAXd$.AetA(!.A$tA(.AoAT.AWoA鈢.AnA.AanA}.A/nAFy.A mA(p.AKmApd.A@lAwZ.A@G]lApO.AclA=D.AkA7.A@ dkA2.A`HkA+.A@-kAL$.AZkAx.A HjA(.AjA(z .A ZjA.AjA.A3jA)h.A`hAҝ.AameAPǝ.A SweA\.A]}eA(ܺ.A`}eA`!.AeA8.A ~eAv.AMmeA\.A]eA8'D.AoDeA,.A"eA8.AXdAXd.A`dA7.A@0dA제.AcAp.AccA.AֈcAq.A`BWcAX[c.A?cA3L.A8bAPxG.A bAHC.A@bA<.AWQbA:.A 'bA@7.AaA7.A`aAF7.AaAp8.AOaAB:.AkNaAB.A`A؞L.Ac`AY.A_Ac.A#_AXd.A`_Af.A}_Ahk.A`k_An.A (e_AzB.A[`A.AL7\A(௝.A %\A.A@![A( ٝ.A`m1ZAo_.AsTA.AGQA°.APAx$.APAQ.A@N/PANʜ.A )OA[.A`MA虉.A\MAz.A OJMA8p.A@:MAIh.A 'MA`d.A@>MA`a.AMA\.A`]LA0{X.A@LAU.ALAT.ALAS.A LAfS.ALAR.A tLA #T.AHLAp.A:<AH.A`))8AQ.A@ B5A(.A\9Abv.AY3Ar˝.Ay/AȶS.AD)A֞.A"A .A஠ A(>Y.Ai A0Z.A@E A``].A@A].AA_.AfAHa.A6AP7c.A`Axe.A@iAg.AALi.AoAh.A AQi.A@AP+k.AsAl.AAn.A lpAQx.A%A.A"A(>.A`A`e.AGAI.A`BAԟ.A@iA #.AA(z.A \A.A@A0.A@ƱA .A`uA`.A8Au.AA`J.A@,A"C.A@A@A.A@A0{>.A@A_<.A.A(O<.A[EA0<.A`[A(\?.A3AlC.A-AH.AAO.AAr.A`rA(z~.A A".A`TA8⃠.AAF.A`A@A.A G!A .A;)A8.A J+1A.A͆5A".A@?Ay#.A`AA83.A0AAx@.A ZAAK.A JAAP P.A`DAAS.A@>BAr[.A:BA/^.A@NTBAh`.AWmBA8a.A BA}a.A BA(`.A`BAU`.A@BA_.A2BAZ.A`BAH2T.A |CAHM.A>CA!J.ALCAjF.A`bWCAA.A bCA0:.A@lCA(5.AjCAx1.A bCA p.A`mTAA.A@NYFA.AzFA #.AFAH.A; GA(.A@NSGA_.AGA@`.A@` HA.Af[HA.AHA0 .A@HA$.A@GHA-.A HAB9.AO&IABN.ANmIAk.A߇LAX ӟ.ALA.A LAo.A@LA.AMA.AMAP.AWMA.A,MAp,.A@QMA@ .AMAhL.A MA.A`;NAX .A)!NAF#.Ap0NAPI$.A8NA#%.ADANA0L%.A xNAZc.AVA.A@p[AX[Ǟ.A q^AN6.AaA<.A`)dAeD.A@|dA@.A0AfA4.AzfA-.AfAo.A@iAh.n.A`tA`1l.A?uA*l.AuAn.AࣾuAq.A;uAPx.A@,4vAh.A |qvA.A vA.AvA.A mwAx.AFwA"ţ.A 1wA8أ.A@"xA.AdxA0v.A@xA.AxA #.A@ yA0S.AyAgp.A yAu.AஎzAˤ.ADzA ֤.A$zA E.A`{A@.A"{A>.Aa3{A.A`f@{A@.AI{A.A \O{Au,.A@%Q{Azѩ.A@>A pש.A`Ah.AWA`e.A A0.AB Ax.AA.A A .A H,A`.A@9Al.A`?A,.AXNA";.A3^AC.AwgA0R.A`zA0{_.AAk.AXA"w.A构Ap.AA0Ȋ.AתA.AAe.A@Ax.A`vA.AࣻA ͼ.AAŬ.A`A8Ǭ.A 8AKЬ.A`Aج.A萄A.A9}A.ABfA.AVRA7.A`);Au.A`!A .A@pA.A`mAf.AW΃A(.A@PA3.AWA(>.A eAxH.A OEAR.A@$Ap].AAh.AނAr.AcÂAB{.AA@B.A`mA@.AwAf.AcAj.AZDA.A8'A7.A@A@u.AAH.A`؁Az.A ZAȭ.AAhҭ.AxAܭ.AzMAH.A %Ax.A ZAH.AЀAz.ABA .A#A.AcA,.A@@A.AfA$.AAH,.AAH1.AaAA.AxA0LF.A]rA0Q.AHAp].AA`h.A3~A`r.A~Av.A~A3}.A~AL.A~A.A`~A.A@CX~AQ.A`/~Aأ.A~A.A@}A.A}A7Ȯ.AM}A`aӮ.A}Aڮ.A`d}A3.AB}A.A@g}AB.A|A.A`|A[.A q|APk.Aݍ|A.A ld|Ak .A`5|AP.An|Az.A{A2.A@G{AH2#.AX{A`).A{A8/.A@^{A85.A8{A;.Ao{AVB.AzAH.AzA8N.A@zAR.ALJzAxU.A`czA@W.Ad?zAu\.AzA`.AyAȘd.A (yAh.A`ܫyA(k.A@yA([r.A~yA.AHwAX(.A /vvA(ٯ.A`sA#.A=sAp.AsA.AQsAB.AߣsA8 .A ZrAG.A`frA`v.AfWqA.A`mqA=}.AqAk.ApA`.A@ypA`V.ApAG.AvpA81.A*pA.AoA.A nA^.AlA0{ì.A/iAx>?.AoRfA".A$fA4.A\eAX.ABcAQd.A`bwcAg.A RcAef.A`%cAe.AbA0f.AbAx.AA[`.A6A.A@33AxL.AT2A(.AH0A.A /R#A0L.A G A6.A XAXѶ.A@^A.A ATk.AWAX0=.AAȭ.A@ri A غ.AZ AP.A AZ.A`kAHvq.AA>.AkAx.A@A".A Aھ.A@`rAH.A AHu.A#tA?.A |A.A`;zAF.AஃA.A (AX}.A`ՈA(\.ANA@W.AQAXT.A ZAO.A`YXA.A ~A@,.A@5An.A\A.AAڴ.AA.AkќApS.A)ۚAA.A`]-A.A A.A_{A.A@r/Ac.A ~A?.A`LAp.A-A` .A늇AЯ.A@#@A8r.A@rfAЈ.A uA,.A`[pA .AkA ɹ.A_5iAmn.ATgA.A`0bA0.Al]Ag.A@?YA].A dXAHvo.AWA2.AcOA0r2.A2A .A .A.A[:A .A`wA.AA0.A`A(S.A@`wAL.A Ay..AEAPQ].A`Ar.A@DAG.A A..AA0.A+ AاM.AVAI.A A.A@ At.ApA .ATAH .A`APUU.A@Aȃ3.A`AHu.A A.A ,A+.AQ AQ.A9AXy.A_A(/A|!AD/A .A0/A;Ah^ /AZ]EAc/A|SA/A SAH/A/TAqa/AivA"/AHAw2%/A@ErAB&/A)|Ap%/A[tA"&/ADsA0&O'/ATApŢ'/AmyLAp'/A`oDAB(/A?S=Aڝ(/A+5A)/Aho-A=)/A"A)/A IA*/A96A*/A'A0+/A5A&+/A sAp,-+/AA`T+/ABAh*-/A`zAx./AAJ//ACAL*0/A`f5Ah0/A-A1/AA1/AA1/AALf2/A *A`3/AA3/A`Ao4/A5A5/A cA(5/A=1Ah&5/AyA8]6/AA,6/Az4A<,7/AbA^8/AqA(9/A -sA;/A@tA=/A`WvApű>/A:hA>/A8(gAH>/A efA@>/A,yeA0>/A_dA?/A@iSA@/A`MAX@/AKA]@/A@AApc%A/A74AMA/A`a0A?B/A A7OA/AAH{H@/A OA`<>/A AL/A@A!AqW'2/Ac,Aj֎Y&2/Ah8A[m3$2/AwH A_q!2/A:AMA&2/Ao(Ah2/A1XeAQ2/A3A2/A fA81/AAՑ1/AAA!1/A!JAyH1/A 5A1/A;A?#V1/A]NAA= 1/A[tC&A>?1/AƞAjF1/AjRxA,55c1/AyxAEv1/AGxrA!R1/AnA1/AlAKW1/A`%-5AZ~1/A{A81/A ATr]1/A{GDAH1/AMHA92/AnAB2/A`wA:2/AAH\V2/A {AH\k2/A@>vA0.[2/AAi!IW2/ACqA;9S2/A؀ANQ2/A<`AqbO2/A8WA=N2/A{NyApM2/A$eAegN2/A8QAxO2/A,i>=AOQ2/Ai+AEtT2/AtNA%X2/A" A8\2/A \AH%Se2/AGAHѥl2/ANj2AHq2/A@FA[Wo2/A5-A@m2/AjIJAZCi2/AkAwtd2/AךALB_2/A#%A[Y2/A.Ai‹S2/A(PA?L2/Az^A8F2/A HA'2/AtA0#$2/A1gSAln2/AǀUAj]2/AXA2/A6GAO2/A,ЏA 2/A A2/ApъAb 2/A$>qAeY1/AVknVA4@1/Aۤ9AK1/A9A&1/AfAA1/AiAh2/A(Ah32/A=AM(2/A~{oAةuP2/AoB9A3n/|2/A*Fv A ѫ2/A Aʴ:2/A9zAD3/AzA43/A@ =A8,5/A A95/AEcA#~5/AI̻A"K&6/APpAuvx6/AGR[A6/A A 7/AhA7/AxA`m7/A]AW7/A bAJ7/A`A8/AAp;/A A~>/A A>/A&AC>/AAX,->/AxAп=/AЀqA`=/ATslAXJ}/Ac6AP>/A?5AeÕ=/A:5ADHAcREjA/AYUAO/AYEVAu/A AfKc/AN#Atc/AN-A@/A mAi[/A`*&AgI/ABKAu/A4xA9/A_=2A?/A+A ZtJ/AUFAg1/AfڃARˇ/A+Atk/A2pA2Z/A*%A*/A AA@>/A`3A /A~A /AA = /A lA /A A /A@AX /A@ϜA /A`AhL /A`AX /AA /A@.2A /A@ =A@5 /AQA(> /A A /AHAP /AXKA( /AOxAx˭ /A0A4 /AxAi /A A@m /A`2 Axi /A 0AP8f /APAP` /AA?] /AAT\ /AAC\ /A@,AG /AAG /AAbF /A 1AD /AXAxzF /A`A`aD /A@GA C /AA]C /A `A;E /AAzA /ABA)8 /A@ A8 /AM A 6 /Ax A0 /A}A/ /A`A0+ /A@ A%' /AAD /A@@Ap /A&NAHa" /A"\A /AHA /ADAB /A A( /AA0 /AưAϝ /A`dAp /AAPړ /AA5 /A (A՘ /Ax_Aߎ /A A@~ /AqSA"o /AAPb /A &AHU /A AnE /A@߷A0 * /A`1A3# /A ɺAH /A 8AA /AuAk /AךA /AuA0.( /A`AnE /AVAcO /A /\AP7[ /A Z4At /AyAr} /AN>A+ /A Z4A /A`Ak /AMA` /A@A@ /A@A /AA /AxAJ /AA8 /A{A)8 /A@|AXh9 /A A V /A AXk /AuA"z /A  A /A A0 /A 3!Ax /AR!A /A ~S!AĖ /A`!AĖ /AD"AĖ /Av"A /A#AH /A@i#AQ /A^#AH\ /AJ$A8 /AR$A| /Ar$Ah^ /A $AD /A%%A)8 /AM%A8 /A?N%A$ /A%AP| /A%Ap_ /A%AP /A`%A /A`%Al /AЦ%A` /A`%Ar /A` %A`T /A  %A /A%A84 /A-%Apʢ /A>%Aj /AA%A# /A C%A{ /A%Ax /A`%At /A%A@j /A |%%ANc /A/%A`Q /A}%A= /A ۤ%A0/ /A`v%A( /A %Ar /A&%A( /A%ApN /Al&A" /A[l&A* /AK&AU /A&A0 /A&A /A஻&A؁ /A?&A0{ /AE&Ax{ /A&ACp /AV&Aa /A&'AP T /A lR'AA /AΌ'Ax>, /A'A, /A'A% /Ah'A /A(AP /A(Aѿ /A ('(A /A`d5(Aw /A`o5(AP /AX(A /A(A( /A@(Aȇo /A)A( g /A:)Ahf /A`;)AHiP /A)A: /A)A38 /A *A#8 /A *A([5 /AL.*A-. /A*A+ /A@*A+ /A*A& /ABP+A(" /A`ܮ+A /A@,A  /A`2,A`e /A`T,A` /A (,A1 /A |,AP /As-A /A ɉ-A /A-A/ /A@-Aؚ /A@.A` /A.AO /A.A /A.A /A Z.AHr /AT/Axz /A lY/Aӓ /A /A /A%0Ax /AT0A l /A0A(zR /A \1AD /AO1As; /A21A" /A_2A /Aj2Aп /Aӱ2AR /A3G3APk/A`;3Ab/A3A/A`e4A/Aj4A7/A65A/AQ5A1/Av5A/A}C6A0L/Apv6A(K/A 6A/AR7At/AH7App/A@7Ap/A7A0&/A<8Aؚ/A;A/AA|/A@A4/A`AAX/A d@A/A$?A9/A`v>Ax\/A@yR?AA/Atn>A>/A`9K<A/A@9;A3/A`2:A؁/AД;A M,/ANAPM.A`NAp.A` OA%.AൂOA@B.A AOAp.AOAP.AGPA$.A$QA .A-tQA@.A6QAx.A`RAx.Af SAo.A`JSA"n.A&SAhG.ASA8.A@WUA(.AZWA .A`)XAR.A VYA.A[ZA0.Aל]A#.A@^A .Am_AX4.AJ`AhG.AaA^P.AaA/1.AْbA.AD&cA .A_cA.A`mXdA.A /eA .A@ bfAH:.A3gA.A@fgAd.A@W?gAX.A`DgA8V.ADgAxV.A \EgAP7.A܈gA.A܈gA07.AKgA.Az(gA0.A"gAR.A[hA@N.A@yhA@.AhAh.AO"iA4.AliAh.AsiA`Ǽ.AtiA0 .AqiA |.ADjAxd.A`RjA8.A |^jA .A0jA!.A@jA.A`jA`6.A jA .A`.A`mAPo>.AmAW6.AfmAW+.AnAHm.AoAh.A5[oA.AoA Ҝ.ApA@.AqA.AO+qA.A>qA؞.A`PqA|u.AmnqA(j.AqA^.A qAR.AqAF.AqAm:.ADqAX ..A \qAR$.ArA.A`%rA.A;rA(~.ASrA.AkrA4.A ZrAx.A)rA.A \rA .ArAA.ArAH\.AsA`.AZsA.A 1sA|.AsA.AtA.A`TVtA.AtA".AxtAx .AtA0y.A`tA(s.A tAj.AuA8a.A#uAY.A6uA0 S.ANuAgD.AuAoA.A`ܝuAxG<.AuAp7.AuA3.A`uA&/.ALvAx+.A7vAu(.A YvAx!.A{vA.AvA.A 8vA .A#vA3.AvA.AwA.A`6wAHK.AaVwA.AtwA.AwA؁.A wA$.A(wA.A`wA0գ.A`wA.A xA ҋ.A` xA.A3xAh3s.ADxAl.A FxAwf.A`ExA`.A@#CxAY.A >xAS.A9xAhM.A@i1xAG.A (xA0A.AxA8b=.AxA0]<.AjxA7.ADxAP2.AwA<.AEuA+.A^tA-.A YvA .A@y\vAP.A`vAP@.AdvA.A ivA`Ü.ApvA .A|tvAĘ.A#yvA.AvA8.A|vA8E.AʟvA E.AӰvAl.AvA#c.A wAZ.A@9wAТQ.AgwAH.A qwAD.AwA@.AwA0<.A 8wA 8.AqwA3.A wA0*..AmxA(.A` xA@q#.A!xA .AT,xA.A5xAh.A =xA .A`DxAh?.AWIxA0.AaLxAho.AMxA.A@ MxAX.A`oIxA.ACxA.AgV.A@uA8T.A@>uA RR.AuA8P.AwuAO.A ~luAsO.AKVuARO.A@uA O.Ah.uAO.A )uAQP.AuAoQ.A@tAR.A`KtA(T.A ZtAH.W.A tA=Y.A ~tA[.A ?tA(^.AvsA b.A;sAe.AdsAj.A`BsAn.AsAs.A`rAHPy.ArA9.ANrA.A 8rAF.AoqAf.A`fqA&.A`qA@.A@pAȤ.A pAX.AVvpA.AoA@.A`oA.A XoAp.ApnAH.A nAG.A@>nA0Y'.A"6nAp7.A`"mAȇ8.A@mAsH.AFmA(zY.A`XmAH2`.AGA4o.AB9GA0j.AmGA^.A@GAkY.AGATY.A'GAYL.AGAdB.A@,.AEA>.A2EA>.A,DAȘG.AT]DAK.AXDAO.AhCAD^.AБCA4i.AaCAz.ABA@S.A$7BAH.A@ϗAAŔ.AhAA4.A7AA.AAAw.A ~~@A.A@B@A޸.A)@A8.A`f@Aj.A.@Aw.A_/@A`X.AO@A`e.Ae@A.A @Aj.A@A.A`@A .A`AAXW.A AAx.Af AA0U.A S@A~.A l@AC!.A0?An!.A`?AP$.A?A2.A?Ax):.At?A >.A@?A>.A?A'F.A ?AX.A>AHaZ.A>A)[.A]>A+l.Aq>Apj.A">AP ].At>Ao.A=Am.A@M=A8.A@K=A.A@;=A@.A&=AѺ.A =At.A<A*.A`e<A.A H<AX.A`;Aп.A+;A.A,;A`.A;A0/A:A /A Z9Ap_/AI9A /A9AȘA/A8A(Q/A@N8AU/A஭8A(~Z/A}8Af/A<8A@Au/A7Aau/A7A0e/A lA7A/A?@7Aٷ/A>7ApJ/A>7A N/A>7Ax~/A>7AW/A47A|/A@ &7Ah/A6A /A`6A/A@ߜ6A/A@|6A@4/AR]6A8C/AxH6AVJ/A66A`PU/A+6Aa/Aj5A)o/A5A+x/A०5A/A;5AȂ/Ae5A`ӫ/A;5Ap}/A 5AH]/Aa4A8/A4A/A`4A </A4As/A@4A`/A`e4AP8</A.4A_/A@ 3AH/A3AJ/A a3A/A03A /A`2AX/AE2AH/A |2A/A^O2AHr/AK2A /A&@2Aذ/A?2A/A"1A &/A61API8/A@1AR/A`q1A]/A?_1A8 t/A 1A`x/Aa1APf/A0APj/Aו0AP/A.AO/A@5~)Ap/A@"Ap9-/AAP /A`[A /AaA؟ /A0 A /A@Ah"r /A@.A//AA /AZAw/ARA8 /A'A/AA5/A`;TAϳ/A`AXF/AA /AzAHPR/AQANv/A A/A A:A/A&[A0/AMAS/A`Av/A"Aг/A A&W/AeAp/AgA /A#0A\ /AA(/A@yBA0/AKAE/A9AB/A 5A.AK>iA&':.AhLA&5.AmrA.AsAhb.ALqA /.A\A(# I.AJM[A{?#?.AȅYAv".A0kcXAH6).Ar WAtQ.Aa^.A`fvA@Z.AovAU.Af{vAA.AwAx.AgzAhj.A`8{A@.A`~Ax .AqSAj.AAd.A`A0]Y.A`Ap[.A@WA8.AlAp_.A JAR.A yAЄL.AA~.AஅA.AʡA0j.A2Aa.AġA׀F.AzVAx.A\|UA[~D.A{QAw0.A;;Am.AϻA V޲.AlUAbG솽.A ,ݰA ].A_VAg8.AEA.AA=i.AD'A4LӼ.Av*A儚W.AEAdN.AޭAH.AYr.LjA$p.AAZУa.AbA9V.AyӫAzeQ.AW@ASP.A}AX.A`A`.AUAX6.As{Ah.A{A]e.A$vA6A.ARdA0.Aӵ`A.AaAP.AbA.A@5@bA.AdA\u.A hAyy.AqikA.A`՝kA.AkkA.AyplA.A]lA8t.ASkA.A jAh.AjA( .A AQjAx .A`>jA6.A iAN@.A@#iAm.A`8iAt.AK$iA8.A`hAPީ.AΗhA.Ag!hA.A@ hAt .AgAz.A`fxgAD.A fA`6J.AfAPw.A wfAp.A afAƬ.AeA.A`eA.A^eAp.A`vCeA.AdAq .AdAL.A |CdA|U.A A,dAh́.A`cA5.AcA舸.A&&cA8־.A@ycA7.AbAX.A0bAh.A`TbAo%.A@bA@L.A@aAhS.A SaA]y.A 'aA(>.AdaAȦ.A`A .Aՙ`A.A5`Aa.A@!`A.A@p`Ah .A_A/.AD_AX6.A`22_A;].A@^AX .A`_A(.A_A].A`^A`|.A5^AS.A`B_AH.A1_A>.A E^APx.A@]Ah.A^]Ax.A@`+]A0.A`\A.A@i\AX[A.A&k\AXI.A V\Aq.A [Apx.A[A8Q.A6r[Axu.AW\A-.AjJ\A.A5[A0؇.A^Ap.A ۍ]Ao`.A ]A>;.A\A.Aj[A 0.AZA{.AYAx..A WA(iH.A@>WA}Y.A |XAm.AWA.A&\AD].A[AW.A[Ao.A`fE\A(p.A@5WA8I.A WA¢.AVAm.AnWA@5_.ATWA).A@PVA.A _VA:w.A VAq.A uVAذ-.AWA0-.AWA*.AQWA#(.A q%WAx&.A *WAhD.A|VA.AſVA`G.AVA .A`VA.AٷVAb.AVA[.AUAt.AVA([.A`2VA{b.AhCVA`.ACUAJ!.Aٳ>KUA}.AFdTA.A%{ITAR.ASA.ASAX.A0SA9.A\SA.A9SA#.A@SA.ARA.ARA.A@yRA8.AhRAx .A5RA .ARA-.AjQA".A`QA +.AUrQAy3.ACQA@=.A`,QAG.A`QAhsR.AQA0*].APA0 h.A/PAs.A?PA@~.APA.A`PAh.AîPA5.APAXh.APA8.APA.A`PAh.A`PA*.A`;PAX.AaQA(:.AQA.A"-QA.A`BQAЀ .AYQA`1.ArQA =.ArQAG.AUqQAE#.A cnQAh/(.AiQA,.A`fcQA1.Aq[QA6.A QQA N:.A`FQAH>.At:QAHA.A ,QAh^E.A@QAkH.A@ QAK.A PAPoM.APA[O.APApP.APAQ.APAR.AܜPA0LV.ANPA^Z.AOAX^.AOAc.A@cOAh.AOAn.A`NAt.AT}NA({.Ay1NA.A,MA;.ATMAٚ.AcAMAէ.A`dLA0.A ZLA.A@NQLA.AFLAk.AHKA3.A^jKA`T.A@KA .AJA.AJAh".AJAȱ-.AJA8.A@ JAD.A JAh@O.AJAZ.AJAe.AƤJAx q.A&JAI|.A3JA~.AJA0.A OJA.A` JAh.A`]JA0.AjJA.A@NJA.A 0JA0.A4JA7.AXJA(:.AIA`%.A IAC.AaIA.AIAx.AIA(.A`IAs7.AIAXF.AqIAPU.AwTIAxzd.A`5IAHs.AIAh.AHAŏ.AHA0ǝ.AHA@.AHA.A aHAe.A@8HAp.ABHA9.A@pGA(.A^GA@.A qGA .AGA.A\GA .A39GA.A,GA.A$FA '.A?FA +.A@ϷFA0.A9FA4.AFA8.AyFA^<.AacFA?.A KFAB.A3FA"E.AFAPG.A FAI.AEAJ.AEAK.AEAL.AaEA8RL.A@}EAL.A`bEAK.A HEAJ.A-EAI.AQEA(mH.A@DA0&H.AxDAEH.ADAH.ADAI.AH{DA K.A?]DAL.A?DA(N.A;"DA]Q.AgDA;T.A &CAPxW.ACA[.ACA.a.AÚCAf.ACAol.AMgCAq.A@KCA0Hv.Ag/CAz.ACAp~.ABA,.A`BAR.AʹBAP .AdBAPZ.AtsBA7.ARBA.A;?BAX.A[0BA@.A cBAR.A@AAPH.A@AA8|.AAAw.A@,AAp s.A`mAAq.A`mAAm.A@AAh.A@xAAЄd.A^AA`.ACAAr].AA(AAhZ.A AA0W.A`@AX.A@A@Z.A@A \.A`p@A'^.AXG@A`.A0@Ac.A u?APf.A@5?AXj.A?An.Af~?AFs.A W?AHx.A;2?AHP}.AF ?A0݂.A&>Aˆ.A>A.A>A.AF>Ac.A`K~>Axi.Aa>A.AB>A.A>Ah.A@=A(>.AF=A@.A`f=A1.Ao=Ap.AD=A ^.A=AF.A<AȺ.A <A(.AА<AA.A`c<A.AQ<A".A A<Ak.A@1<Ax.A?$<A8.A <A(l.A_ <A.A ~ <APf.AD<A~.A;A.A`;A.A@;A A .A;A.A;A.A;Ax:.A<A3.A[<A(.A<As.An+<A.A`6<AxW.A` A<AV.AJ<AȤ.A`R<A7.A`vY<A .A@^<A".ANb<A&.A3d<A).AVd<A8#,.A`b<A" r4.Az:9A<AE9=.Aa$<Ad3OG.A#ò <AR.A;A/orq.A] *7;A.AE R<AE.A0<Av9v.A&tхZ<A~pf.A<A.A;<A0e.AE<AX!.A`=A(z..A=A;.A9/=AFJ.AL=AX.A`fh=Ag.A=A@v.A@5=A.A-=A@u.A@=A .A@,=A.A`)=A`P.A`v=A(.A` >A.A`>A(.A $>A0.A`->AU.AF5>A8$.A@>;>Aؽ4.A`v?>A D.AA>AL.A->AcT.Af>Af\.A A >Ad.A`=Al.Aa=A{u.A=A"~.A=A.A =Ahď.A=Aȶ.A=A.A=Aƪ.A=Aڳ.A@y=A.A=A.A@y=A@.A=A@.A=A .A@=A .A@>A`T.A B>A(.A`)>A.A8v>A.Ak>A+.A@`c>Ap.A\>A`´.A@pW>A.AT>AЕ.AR>A.AR>AHr.AT>A0T.A`X>A`%.A^>A.A`be>Ap|.Acn>A.Ay>A`C.A"}>A .A>A.A9>A`T.AD>A$.A@|>A,.A Zx>AH25.Ajr>AX=.Aj>A cE.A a>ANM.AV>AU.A@J>AV.A6>>AVX.A2>AxZ.A(>A8\.A>A_.AK>Ab.AV>Ae.A@ >Ah.A>A8l.AV>AНo.Az>As.A`;>A\v.A>Ay.A@ >AP|.A@>A).A`v0>A̩.A`O>A .Al>A@.A 1>A.A@֑>A.A>AH.Ac>Ah/.A`K>AE.A>AH?j.A>An.A@N>A.A`>Ap.A<>A.A >A.A lc>AF.AD>Ah@u.A=AW}.A =A@d.A=A(.A=A.AA=A誘.A=Az.A`(=A.A= =A@W.A<Ak.A<Apl.A`<A.A@<AH.A<A.A<A%..A1<A0E.A;AHna.A:A(-.A:Aª.AHe:AH..A@f:A.A@2:A.A q:Ak.A 9A.A9Ak .A9A..A`;%8A6.Ap7A\.A 5A([.A@N5A(\X.AR5AT.A4A8R.A@4AhT.A#3AY.A2A[.A]k2Ab.A#2AX h.AR2Api.A!2A`l.Apq2Ahn.Ap2AXw.AR43A8.A[3A`a.A=R3A).A =3AL.A2AH\.A H1A=.A0A.Ap0AX.Ą0A R.A0Au.A30AW.A/AG.A`/A.A3O/A.AN/A.A@p]/A.A I/A.A=/AX .A.AQ#.A@.AH.7.Al.A(G.A3Q.A(T.A \+.Ai.A]-AH.À,A.A*,Af.A`),A8.A+Az.Az+A.A2+Ap.Az*A`a.A*AG.A@*A}.A Q*AH..A`)AG.A@)A“.A`)B)Aњ.A#)A.A")A`.A1)Af.A2)A`a.A`)A).AR(A.A`f(Ah3.A(Ap.A`f(A`.AY(A8.A?(A.A5(A.A (A).A'A0.A&A .A@\&AHܭ.A1%A.A`$A@ן.A@h$AL.A`@$Ap=.A`$AǬ.AR#A.A]#A.A=#A.A#AW.A@"Au.A^"Ap.A3"A.A"A.A"A0..A"AL.A!Af.A`)!Ak.A`[!Ar.Az Ad.A`f` AV.A3 AN.A \A8I.ADAu;.AࣼA#<.A@ZAp@.A A:.A@Ak/.A AP!.A@gjA.AA .A[Ap.A KAH.AA.ARA{.AVA.AAP.ARAX .A`fA.AzA.AA8.A xA.A@(A R.AA.A`)XA#.AzA.A`)XAQ.A A`v.AדA83\.AAPS.A`AP.A@GAp=I.A@AG.A A@.A]=A2.A A&.A`A.A'A.AAz .AA .A3A.A@XAH".A HAp=.AzAX.AA@%.A`)<Ap(.A \A%.AAL.A`)rAk .ASAp.AH/AH.A@Ap8.AAf.A͜A8.AVA .AAp=.A`A.AAH.A@ A.AtA.A`DAp.A@AG.AzA.A(A.A= Aǵ.A`)Z A.A[% A.ARV A0.A׍ Ah3.Ap A).AQ A.Ac A{.A`) Ap=.AAH\.A wAp.AA.A"AB.A |A.AR~Ak .AzAH .A HA .A=HA.ARAX .AbAG.AAp.A=Ap8.A@.A.A@A.AjAu.ARA.A@AX .A`fA.A`hA.A@XAH.A@6A.AAX .A]Ap8 .AzA.AQ AH.ApA .A*A.A`)A8.A̰Az .A`A#.A@<A@1.AzAk?.A@A`O.AHAQ`.A`Ae.AHAul.AzAQo.A:Ax.A?Aƒ.AAL.A`)Au.A A`a.Az:Az.A A(\.AAk.A \]A.A`(Az.AA~.AQAG.A`A0..ApeA`a.AHA=.A]7Au.AA.ApA`.A AL.AA.AHA.A+AL.A qA.A Ak.A@AH.A@A.A@Ap.AIAX .A$A8.Az Ah3.AHA.AAf.A}A8.APA.A`A .AAp .A/A{ .A HA{ .A@8Ap.A HA(!.A HAu0.AMAk=.A \AHE.A=A#`.AAr.AGA(y.A \Az.A`)A`{.AaAQ.A6Ap=.A]Az.A`fA( .AiAG.Ap9A.A AX .AA`~.ARAp8t.A`éApkg.AzA^.A[AZ.AAP.A[AC.A"AL8.A=@AL$.A VA).A=\A.A=JAX .AR(AX.AA.A HAL.AcA#.Ap!Ap=.AA+.A \kA5.AA:.A`AD.AIAzR.A@AT.A3AuU.A`XAf^.A/Azn.A3 A`.AA0.AAL.A A`.AA.A oAP8.A GA#.AR&A8.ApAB.AA0..AzAk.A@tA .AA` .AA.A`)A .AAh3.A@gA=.A`)A.A @A).APA=6.A HOAK.Az*Ap.AA.AUA.AAk.ARAPN.A<Ay.AAأ.A`LA.A3A.AA`.A`)PAz:.AAe.AA(܁.A@Az.ARHAL.A A.AA.AA+.A̼ALU.AzA@Wz.A`A8.A@AQ.A`A8.AA.A@gA.AA.AA&.ARA@.AA)N.AAB_.A@gA~.AAp.A.Aǻ.A)A`.AA(\.ARA`.AAh.A3+A7.A9A0.d.AA.AAk.AA.A`÷A.ARAL..ApAkA.A@jA8b.A^A0.AzBAHܠ.ARA.AA .A QA(\O.AA`a.A`VA.A@AP.AQA.AeAB(.A`)AJ.AA(\t.A@ A8.A Ap.A qA.A@|A).A`@A$.AAأ2.A@AH\G.A`jAa.A`) Aj.A`Asm.A@Ap.AA8z.A+A8.AzA#.AR0A̱.A`A.A#A.AA.A9Aأ.A`|A.A=A@W.ApAX .A`vA.A Ap=.AzAp8.AA.A5A.ARA( .AAf.A5AQ.AA.A`)Aֆ.A Ao.AzA`.A 9AT.A`fAH\K.A A6.A=dAH#.AA.A }A#.A`hA.A 'AP.AA(\.A[A.AAp=.AAB.A A{.A=dAr.A SAp=i.A`)lA@Wk.A@ AQm.AAf.A A)S.A6AF.Az2A=.AAV2.AA8(.A AG .AxA.AAأ.A A8.AA׎.ATAp=.A@DAXw.A \EALp.A@ZAkw.AA{.A \Ap.A%Ȧ.AkA.A@gAt.ApAb.A̎A`U.AmA@G.A@g@A8.AAu%.A=AQ.AR8A83.AAp.ApCAX~.A AU.A?AE.AA0/.AA#.ARA@.A3AHa.AA.A@A83.AA.AAy.A(Aa.AAL.AA9.AAG.A@AP.AMA.A HAp.ARA`.ApA.A \mAd.A q APC.AA &.A q[A# .A /A0.A=.AQ.AMA.A@lA`.AkA8u.A GA@W`.A ApN.A A#J.A0Ap=K.AAJ.AAXP.AzDAh.A3A8s.A A(t.A3sA`x.A=,Aft.AA`u.A=A`.A`AG.ApKA8.A H;A`.A HMAp.A׋AL.AA.AA`.AzA`.ApA&.A=pAfC.A AAJ.A3A8N.A=A#J.AAO.ARfAp=O.A` AK.A \A(B.AAp;.ARA8<.A`)A@.A@ AXL.A@ }AP8\.ASA)m.A |fA83.A@GA.AAp.AAu.A@"A`.ASAp=.AדAQ.AzA .AzA84.ARAC.A'AL.A=PA[.AׅAu.AAǐ.AA.AA(\.A Ap8.AAB.AA.AR`A`a.A3A8 .A@A.A]EA؞7.A HoA`I.AAX].AAx.AA.AA.AA.A=FA%.A@AX.A A|.ApA.A \A«.A=A Ҹ.AA.AzAu.A@ sAk.A@\AX .AkA.A AX.AA R.AA8 .A=4A.A A*.ARA89.AA?.A^AGB.A@AH?.AA9.AAH.9.A3'AK.AyA)d.AQA`a.AWA0..AAP8.AñA.A`A.A]AX.AAG.AA.AA!.AA/.AAI.AAT.AAd.A GAy.ALA.AwA83.A@A.A@ A.A`)LA`.ApSAz.A@ }A.A HA).AA8.A Ap8.A HeAp=.A֕AB.AA83.AAX .A)A.A=tA(\.A`̲A.AzAu.A HAp8.A`̲A.AsA$.A@A=A.A@Al.AA.AA¨.A@gnA.A?A.AAp.AA#.AzbAA.A`) A(T.AAXY.AAأf.ARRA8.ARAB.AqAH\.AA.AA88.A( A6.AýAT.AAus.AzJAG.Az*A#.A3AW.AzA׬.A \-A.A \AL.A=2Ak.A A#.AA.AAP8.A~A.A HA`.ARbA8.A=A.ARA.A5A—.A@ A.A@VA.A@A|.ARAb.AAP.AR|A82.AA.AAQ.A`pA`a.AA.AmA.AA.A=VA).A]}A`h.A A`%.A@ oA.A`)Af.A=θA.Ap7Aƒ.AݷA([.A|Az2.A3A .A A.A`)&A.A޵Ap.A`Ap.AҴAR.A@GAu.A |0Ak.A VA`.A`)Ab.A@JAp.A`Au.A$A`.A`)AW.A Ap.A@Aǿ.AGAu.A]Ap.AAP.A1A0.A@tA.AAh.AAX.A$A.A3eAk.A׫A.A3A.A`<A@.A \sAk.AA=.A`A R.A@g԰A(.A@A8.AA.A@APf.AAL.A`)A83.AA.A3A!.AA`,.A@ A8:.Az^Ap8K.AAF.AA`aC.AzA=.ApA88.A`fбA0.ADZA,.A`fAp=+.AGA0.A]A(\:.A2Ap>.AeA9.AzAk0.AQA{.AWA.A?A.A Aأ.AA R.A3WAǸ.A3Ap .A A0.A@ᆰAp.A 1AB.A`A.AHAP8~.A AAg.AAP.AۮAB..AA%.ApAH*.A`XA5.A`)TAu6.A`fAG8.AGAu&.A=ȬA.A A.A3A8.A AH.ARFA.A \A.AҩA.A`vA.A=A.A= AH.AAL%.AóA( ;.A sA(=.AiA>.AzPA4.A?AG$.AGAp8.A`fTAX.Az\A.A33Ak.A`)A.A`fAp.AGAB.AjA.A3WA.AAp8.ARA8.A[A.AA#.A@ ٦A'.A@A/.APA(=.ARAuJ.AAX.A@>A8Z.ArA83\.A`ApS.A[ABK.A@AfH.A \A(@.A q7A3.AԥAp1.AQA'.AAp$.A \ŤAp'.AtA".AA.AA.A`̾A.AzA.AzzA`a.A qAp.A(^A ҷ.AGA̮.A A@.A kAk.A[Aמ.AA0.A8AHܜ.A8AG.A@ጞA0.ApEAB.A@A`}.A ĝAu{.AAy.A@:Akt.AzA؞r.A`)6Ah3o.AΛAQ_.A`f@ABU.A=ʚA(\O.A@vAPK.A`f6ApH.ApA؞C.ApAB8.A`VAP/.A`fA&.A^A(.A A.ApAf.AjA.AHAX.AA.A@ A#.A`ŖAut.AAe.A qAR.A q_AG.A AG@.A`f̕A6.A [A؞$.AA.A 3A0 .AӓAf.AAp.AJA`a.AzA.A[ےA83.AޒA0..A Ap.A ϒAp=.A A.AuABz.A>AXi.A@6AZ.A3AV.A AU.AAP8>.A ˑA8,.A@>A.A`ÑA  .A`NAH.Az*A0..A`A.A A.A \Ap .A@Aأ.AzA¹.AHAש.A ݐA.A@ A`.A`)A@W.AzpAh.AIA`aQ.A 1AA.A`f$Ak#.AA.A |ȏA.A3A.AGoA.A`TA8.AHA.A=Aq.AGCA\.A_A0.?.A=HA(5.A]A$.A3A(.AAH.AA`.A3uAP8.ABA`.A=Ap=.AQA .A`)Av.AQ܎A)b.A@ᘎAP.A@ZAh3?.A 1A..A3AG.AzAp.AAz.A HAP8.AA.A@ḌAp.AA.AaAX.A!Ay.ARAWU.A`fAk?.A@GъA'.A`̀A=.AAp.AӉAu.ApAu.ApWA83u.AALS.AA?.A A".AqA) .A ZAH.AMA8.A \A5.ARaAPf.AeAأ.AfA.AVAu.A&A.AA(u.A`)ԇAp=d.AAQ.A=A@.A@ᖇA)*.ARA .A@ qAP.ARDA.A`A.A qӆA.AzA.A``A}.A`)8Ai.A`),ApF.A qA@W".ARA(.A`fA83.A A8.ARAG.A ݅A8.A3A#.AA8.AcAfe.A^AX O.A`PA=:.AGA).A3SA(\.AࣂA.A3A.A`A0.A=nA@W.A=@Au.AAk.AԄA)I.AAX 9.ÅA(.AhAP.A /A#.A`fAW.AA.A |Au.A`f AB.A%Az.A$A(\}.A A8i.A`fAK.A3AG7.A dA1.ApGA*.AA'.A A.AAf .A@A{.A`iAȢ.A`UAۅ.AjNA6R.AjXAhB.A_A,/.A'AԺ.AA .A3AB.A*A0..A :APy.A (VAn.A{A0.c.AAH.AR҄AP84.AAk!.A`A.A=ބA.A`AH.AoAB.AAA.AQ6Ap.A@XA-o.A`AHc.AA`^.AĄAY.A=ބAD.A3AH.A A).AA.A`A.A@A.A`fA.AGAp .A@A@W.AτAB.AބA.AA.A@A.A4A).A3IAz.AAp8.A@GA.AӅA8.A@څAB.AA.AA(.A`),Ap.AXAt.A뉆Ak.A`)A;d.A ؆A].AzAXM.A}A:.A A8 $.AyA.AوA.A^ωA.AñA`.AAΑ.AOAHY.A)AB.A`T{Ax.AA.A ÏA .A`sA.AYAH;.A`A;~.A@ȔAPz.A`yA~.AsA.A wA@`.AA.AA8.AA&.A AAR).ADA(.AAH&.AA0{8.AVA`<.A^Ah@?.ABAI.A@vAN.AkAK.A 8zAD.A@PIA D.AA>T.AA`.A OƦA k.A^A8f.A`DAZ.ABAQ.A q#AW.A`A0e.A`ܠA0;g.Af4A`q].A`A0R.A4FAP.AG4AuU.AT\AV.A AD[.A&AVj.AXA@r.A{A4{.A`bA.A/A1.A'A.A`tA.AMA.AƱAh.AH^A.A삳AXl.A`9AX.A`mA!.A Ad.A`[A7.A⻸A.A`K AH.A8A .A`BCA.A`.A.A/A.A5A .AAz.A<Ak.A`AQd.AvA8Vc.AX?Ao.A 휻Aؒ{.AV"AXv.A͉Ape.A`A5.A@lA.A A-.AZA.A@վAx.A ޿Ahg.AA9`.A`A8X.AA^@.AAs".A8A .A&A 4.A l A؞.A6A.A ZA".A@A6.AiA).A`A.AlA.AxA.AA .AA p.AApxp.A@>AwY.A`A(F.A A>.AAB.A AC.AAG.AA(S.A^A,V.A Ab.A Am.AHpAz.AA.AA.A@A@5.A@WAH\y.AiAV.A AA(0.AA` .AMA`.A ەA .A?AC.A0AH.AfAׯ.A AX4.A-A^.AA E.A Al .A` A.AApA.ANA .A}A؍.A uAe.A,A\.ADAH2W.A`mA?N.Aj,AS.AHOAc.A A.A`]A@.AAA``.A gAp.AjyAS.A EA.A+Ah.A@A9.A߽A`.AMAs.ANA0.A@oAD|.A2TA0s.AA਋.AԽA Ά.AA`B.A@`HA@O.AuAP.A} A8.AաA@p.AA8.A AX.AA@.Ah9A.A J)A@q.A`qAp.AzA>.A`cA8$.A[A@F.A$Ax.A$A&.AA`.A Aw.A֠AHr.A uA.AtiAxi.AAa.A@GA\.AA {.AEA r.AnAa.A A0Y.A"AG.A5AЄ?.A [A/2.AFA.AlA`.AැA .AA" .AA;.AA@ .A HAoG.A4A`~.A@A`r.AA .AA.A@Aث.A#A.AsA.A 8zA{.AlA$M.A,@AJ.ALAE.AahA̞.AtA.Aj,A.AA0.AQgAh.APA.A+Ax.AA.A@A .AA6.A[NA@.A`]A8xX.A Ax~R.A@AD.A3A0_.AAm.AdA.AAi.AA0.A;YAp .A@EOA*.AӝAm.A@qAk.A{A؁>.A?A?.A@ihAT.AA 0.Aa8AHn2.AAP/.A{AhL.A4A.AnAh.AA.A MA .AA}.A A>F.AAXF5.A APZ.A@iiA.A 1/A8.AB APkc.A(A.A A =.A;AZ.A6^A.AbA.AANl.AA".AB8A>.AEA.AXAu.ABAȔ.AॺA.A`A0&.AhOA.A A.AsA.AοA{.AAxf.A sA{.AVA3.AAP .A@JAx.A Apxx.A@#u Ař.A` A1w.A A0.A= AL(.A A.A@`" A@.A A`G.A[r A`O.AB A(:.A9? Ad.A`d APk.A1 A?..A Ah".A`fM A0:.A}$A/.A AJF.A@%A].AJA].A}$Aȩ.A AP .A@A.A`TA84.AGA.A`AF.AA(`.AAH.A!Ax.A-A٢.A"A0&r.A4A@dv.ABrA(:.A ;A.A;aA@u.A@yAC.AA@>.AAn.AעAr.A@Ap.A@rAf.AA.AA.A A0H.A9A@=.AA@F.AAA.A`AP.A`AJ.A@A.AྋAgs.AXTAxK.A AX.A@,"Aؾ.A`A8'.AMAh.AA Q.AqAЄO.A`]jALU.A`ZAPM.AҟA<.AA *.A`AC.A(A.AѺA .A;^A!.A2AV.A A؎.A[A <.AAu.AA.AՔA.A^XAPo.A`շAV.AvHA0y.A`Ab.AiA#.A@NA.AA.AQAh.A#Ao/.ArAP>.A#Ao .A"A .AA.A3A.A<,A.AA@ .AOA.AA0]'.A Ai.A ~qAi.A`KAh.AhA..A UAPHT.A OAsx.A Ax .A@AB.AA8o.AAՖ.AaA+.A@ϵAX.A &A.A A.A!A.A0A.AcA.ARA[.AA .A JA .A@|A.A` bA.A}YA.ABA('.AzA8.AfAM.ATA2:.A \A1;.AզA H.AA1O.A A =.A螙AC.AXApS.AXXAX.ACA0L.A?Aɲ.AԌA.A`mbAƹ.AdA(˫.AhA.A_ڈA`a.A⭈A`B.AA.A`;A.A@rAs.A@yA ^.ANAR.AUAXP.A AE.A`;:A .A TAXy.AˁA,.AnAAx.A}A.Ax}Ah.A_%~A`.A8xA.AjAM.A AP8.AA.A (UA.AxAh.AAl.A`AP.AwKAK.A*XA.A_|Aݗ.Ai+A% HX.A;~AF'.AZtA~қ.AJAzf.A7jA.AVMAK.A[A.AGA.A-~APkh.A@#ՎA7.A@ŽA!.A@N@A@.A` DA.AjzA.AVAȶ.A?đAP.A Am.A`A(h.A@+AH:.ARA@.AQ^APD.A@-A8R.AࣂAp8F.ArA@WZ.AA`l.AЍA~.A@API.A`Ak.A \A.AAأ.AAA.ASA.A<>A.A߉A(.Aj`AP.A@yA #.AAB.AG׈AW.ARA.A һAؽ.A'AL .A AL.A@ᾆAK.A͆A: .AA *.A`eAP@-.AMA0..A7Ak'.A@GمA..AAX0.AօA*3.AA=.A@VAP@.A?A3.A)AX =.A=΄A>.AA8D.A@nA(\N.A`A0X].AvAPkl.A@Aht.A 뒃A`v.A`zAn.AmAx.AA(y.A`m Ay.AAp8.A3A`.AR2AȺ.AA(.A*A  .ARAc.AVAZ.A=APP.AൢA|C.A@!4A8.A`vA 5.AɃA`v/.A A,.A/A$.A·A".AA R0.AA '.A(Ax.A@A.ABAp.ArAx.ATA).AAl.A AH.A`ӘAX.AmA.A0AP͹.A RAǮ.A]ωA`ӑ.AӊA.A4ՊA@.AAp=.AA(.A|A`P.A`jA|.ANAxr.A@A0o.A JAQy.A A`p.AHsA@n.AA@Be.AA88Y.A@%AH.Q.AQۍAXN.A|A@W].AG AW.ATAA.A@A?.A@1A;.A QA3.A`fA%.A3A.AvAf.A AX#.A`̼A83".AяAk,.AߏAL.AGYA.A`ËAa.A`ËA8.A෾A(s.AۏA0Y.A_A(D.A`cAx) .AA.A%AH.A'A.AAZ.A:A-.A`6Ah.A |VAv.A`lA0.A2A.AaAW.A jAHn,.AAv.A l{Al.A/{A.A ~ZyA .A`tyAPx.A@xA@q .A=xA].AtA`T.A |OtAH.A |rA.A@3sAA.ARrA.ArAxX.AUmApU.A`pAJ .A@`qAXW.A rA .A ZrA.A@:sA.AsA(>.AuA\.A8uA.A / A.AAr.AwAF.AقA8E.AB|A0d.AXAHٿ.A4AXǿ.A PA.As1AH.A`vkA0.A OrA(.A ɰA.A`~AXW,.A۔A.AmAo\.AAH#.AkApV.AKA(8.A$2A<.A`AX5G.A2A.A`A-.A@iA.A`A8m.A&AO.ApMA`ؽ.AQA8.AAp8.A[A뼽.AzAϽ.AAŽ.A`̠A߽.ABAP8.A@ AHb.A5AzN.AIAo.A`) Aأ.A׋Ap=.A MAk.A෤Ak.A=A#.AֵAW0.A AkF.A뉲A 9.AAp.ApPAӾ.A`oA0H.A A1O+a'.ARڹA}.wA.AAx^.A`vA .A`hA.A͞A5N.AAww.AA.A@CKA`.A J&AH.A`oAu.A AT9.ATA.A`KA.A@Ac.AAh.A`OAa.AtA8V.A`HA3.A^AhbV.AAE.A&A#.AA@5.AA.A A.A`o2A.A`nAJ.A@,A.AcAi.A_qA .AJA .AN$AX.A@kA.AA(W.A{VƿA?.AN*A Da.A#hA@.AA'7 .AbA$P.A#WtA2qa.AaiA.AAk.AsA0&.AN$A.AA胥.A)A.A@AmY.AAppM.A AH.A@A`=.A CA .AMA^.A@A˽.AZAǾ.A(A؟V.A0Aο.AAֿ.A@Aο.A`m5A@.A AP^(.AWAS2.AgA.A@A.ALA.AAp<*.AvA4.A?BA0Ժ.A`A(~.A A&.A+gAЦ.AAо.A@AH2.A@`A˾.A ~A@䫿.AAH!.AaA0.AAJ.A,GAhb.AsAx.A`ôA֎.A86A .AvtAr.AA(J.A@A=.A`A .AQ!A@).A@`AX4.AʺAXb.A "ApyP.AA?.A J*A.AgA3.AQ*A@).Aj AȔ.AA.A}Aܽ.A*qA5.A`]ֺA.AA^.AA.AවAq.A*q^A}.Ad:A0t.A?r(A.A@`A0.A c Ak.AA.AA.A޳Ah.A A\.A |A8.A@ȯA(.AݯA(iK.A@N̪AA H.At>A$#C.A5ޟxA9$i>.AyADb18.A-8dAh?1.ARAU.A`A.A@AQ.AGA.A6Ak.A١A.A`{A0l.A|YA.g.A#GA#a.A9:d8Au[.A5-AU.AV'Axp^O.A%AxI.A&A3%.A@Axʺ.A?A.AA`S.AAPڋ.A@A4q.A`Ap.A~APx.AM٣Ad.AiAL.A`AH..A=PAɺ.A(A>.AZAu6.AׁA(\v.A@NAl[.AKA>.AAp!.AٳA.AAHP.A`A.ANA(ܣ.A@Ag .A@WA8En.AԖA@Bٹ.A`(A.A1A.A Aq.AAx.A!A<.AQA.ArAX.AorA.ArAZ.Ay"lA6.AHRkA.A |fA.A@%eA.A`{eA .AgeAؒ@.A`dAS.A`)LeAY.ADeA=t.AfAi.AࣦfAi.AfAp.AfA8.AVfA.A`6fAG|.A`ffAQa.A@BeAj.Af5eA tg.AQeALV.AÃdA`e.A=DdA}`.A7dA`.A6dA.A@scAhb.A cA(.A4bAН{.A 8[eAȇ!.AfAذ+.A gA茌.A`eA.A-_bA.A`HbA`e.A`aApI.AaA k.A@iaA.AR`Axu\.ATaA`.ADZaA8u.AQbAn.A3bA@ƚ.A2bAHmZ.AcA.A@eA.AykeAl.A@cAH\k.A@ cA֞.A@cA.A`cAH'.A @eA$1.A|eAߋ.AtbA.ANbA[.A`aA}.A caAXg.A aAH\k.A`DaAx).Aע`APQ.A2`Ad.A`_A.A@i_Aʋ.A؂_A0.Ax_A.Ac_AP.A+_Ah.A=8bAe.AAbAf.AdA8.AGdA.A@ qbAp.AaA.A`fzaAP8p.A`.bA`Z.AࣜaAp\.A \aA.A_A8V.A_A6.A ^AbAE.A bA;,.AbA^;.AbAH)J.AbA].A bAk.AbA}.A`bA.A@bA ߜ.A]}bA8.AywbA@.A4fbAȥ.A3`bAR.AFTbA4.AKbA(.A<.ABbA(\J.A`;IbA_.ATbAi.A \YbAؼ.AdbAȩ.A`ebA().ApbA0.AqbA(z.AࣁbAw.AΈbA0w.AbAo.A (bA.A`bA8.A@ bA.AॳbA&.A ¹bA>.AbAD.A}bA Q.AbAa.A`bA8Ip.AbAج{.A`bA{.AbA.A bA0;.AbA8.AcA`2.A@W cAj.A`)cA5.AFcA.A#cA@}.A@+cAh.A2cA@$.A;cA0L4.A3DcAX>.A /IcAM.A2QcA].A`TYcAl.A4acApx.A3gcA .ApcA.A@ucA.A΀cA .AઆcAp=.AcA0..A@ idAG.AhdA.AzbA.A bA .A`f.cA.A \GcA.ABcA0.A bA8.AbA@$.A ۥbA{.AơbAHp.AbA`.A`bA@L.A@`bAH.AabAH9.A܀bAP&+.A@pybA* .A sbA.A kbA".AebA7.A6^bAxK.A/WbA0L.AQbAȤ.A@>KbA =.ACbA@.A@p?bAp.A`6bA( .A`0bAE.A?*bA~.Ad"bAu.A bA`i.AbAȶZ.A 8bA0{O.A bAc?.A`mbAl5.AdaAx((.AaA@.AaA0T .A$aA.AaA.A aAk(.AuaAu".A}^AV&.A@^^A8+.A`)T^AE.A Z^A(V.A _^A[.A`^AYl.Apd^A p.A e^AK.A`i^Af.A`;k^AX.A|o^A'.Ap^A .A`t^AT.Av^Ad.A {^Ax.A|^A].A Ҁ^Ah.Ac^A.A^A].A^A.Aj^A.AΎ^A(.A^AH,.A ɔ^A8>.AK^AF.A`9^A_U.A^A _^.A6^A(l.A@^APy.A@!^A`.AO^Ah.A ۮ^Ar.A ^A.A@^A`ص.A-^A.Ax^A .A ~^A@.A^A .A^AH!.AQ^Axd.A^Apx.A^AH.A@^A".A^A0-.AT^A<.A^AȃH.A^AU.A]^A^.A`^At.A-^Ax.A`^Ap=.A^A.A@^Ae.A^A.A^A.A`_AB.A_A.A@_Ap=.A@_A.A^AX'.A@^A.A_;^A^.A` ]A#.A \]A.A4\A8Q<.Aʬ\AWj.A#\A".AmT\A t.A)\A.A#\AP|.A`1\A#M.A`k\A.A@ϐ\A.A#\A@.AM]AX.AH4]A.A;]A$.Ad.]A?:.A ]AO.A\A_.A\Ax.AMv\A#.A@^\Apʿ.A 3\Ar.A[A+.A8;[AxE.AZAu.AUZAТ.AӧYAu.AxYAH.AXXA.AILXAP.A9XA .AILXA+.AʵXA7.A1YA`1[.AoYAd.A`YA.AYA8.AsZA +.A [A.A[AV#.A`v3\AV.A\Ap.Ah\Ad.AZ]]A.Ai]A8.Ai]A.A ]A.A /I^Aȥ,.A`^AxK.A`^Ahd.A6h^Aw.A /I^A([.Aܙ^AH?.A@5_AXl.A_A .A`A.An`APM1.AF`ATP.A^`Aw.A _AJ.A`_Aչ.A_AG.AHL`A`.A `AP2.A Z]aAV.A@%WaA .A aAչ.AxaA .A+aAX.A `AV#.A@,`AxE.AxaA`m.AqaAp .AaA=.A.AcA .A9dAp=.AcAL1.A OcAZ.A 8;cA>.A OcAp,.A`GcAx.A bAr.A 8;cAx\o.A^cA.AhdA.ABeA.AdA)<.A QkdA f.AndAH\o.AcA.AlcAH.A 8;cA.A bA`2..A@>bA0,.ALaA9.A#.A@kA1.A@dkAJ.A kAQ_.A[lAPt.A'lA.AlA.AmA0G.A&mAP.A/mA.A@ImA .A QgmA'.AmA.A`mAp.AmA9:.AmAP.AʠmA@~p.A tmAHL.A mA .AlA7 .AlA^C.A`lA0X\.AwlAn.AulA.A`{lA,.A=lA.A$}lAhs.AFlA.A lA.AlA.AclA7<.AplA0R.AlAHg.AElA.AmA8ڍ.A@W:mA ߡ.AcmA^.AimA*.A`zmA.AmA .A mA.AnAH{-.AnA=.A`$nA #O.A SnAp].A nAp_f.AwnAXn.A-mA(:.AmAp9.A nA.AG@nAX.A PnA .AdnA).A$nA0$.AnA0e%.A nA4.A nAI.A #oAU.A;oAhoe.AKJoAXo.A-oAXu.A`ToAp|.AzoAP.A0 oA8.A>oA1.A2oA͒.A`vpA@.A dpAĠ.A@pA:.ApA0.A`m+qA.A`dwqA.A4qAj.A}%rA.A@PArA.AΞrA(.ArA89.A=rAH.A A8sAWX.AnsAo.AHsAx.AsA.A&sAh.A@rtA(.AtCtA0G.A itA.A tA.A tAHz1.A'uAPV.A@]uA@Fn.A"uAz.A3uA@.A uA 0.AuAg.A3uA.A`uAp8.AuA[ .AIuA#.AvAe>.A@TvAW.A ZvAXh.A"vAHy.A0vA`e.A@g wA8 .A7wA軷.A&PwAx.AcwAP&.AywA 0.AxwA.AwA%0.A@!wAJ.A KxAY.A `xAq.ATxAb.AmxA`×.AĴxAa.AxA..AxA.AvqyA.A yAP.AyAhM.A`TyAH.A yA".AyAHa,.A@yA 3.A_yA(<.AyAUH.AyAM.A`yAZQ.AXyApU.AzAV.A3zABU.A`YzA0*R.A zA0TP.AzAP.A{APU.A`F{A0\.A{A8h.AU{A v.A`|Aȩ.A JC|A.Awx|A@ӝ.A@i|A.A0|A:.A }A|.AI}A.A@b}A>.Aj}AC.A`}Apr.AA~A.Av~A0.A ۚ~APk.A ~A.A@~AX.A ~A虹.AAP.AA.AL*A`.A@>;A8 .AJA[.A+jA`e.AA.AA0.A !AH;.APA0L.AAh].A@ Axs.A,A #.A ̀A.A@AX.AƁAt.A-A0.A 89AP@^.A jA].A@A.AHA!.A`[A>.A$Aa.AAP.AgA?.A@A.AqA`.AhFAX.A)APM.A)Ap8.A@Awj.A`TAƨ.AAp.A=A .A{A-/A`^Ap9W/ALA{/A`?A_/A#AH/AA /A@rAH{I/AـA|/A/ˀAr/ADƀAk/AƀA0f/A@A`6/AAN/AA@/A_A /A4ـAG.7/ATIA=yP/At yA Ei/AAu/A܆̀A} //Aj.AIU/Ak+AyJbg/A;8TpA/Am]Ay_/AX%A /A{A.R/AsA./Ag4Aϧ/AC(݂Ax</AvWYA:/A%rATJ/A.AXŽ/AFIɜBA"/AQA,/A\ACM/A^zA@/AuAXd@/ApACb/AŎmAl/A shAֈ=|/AAfAE2/AvIA i /A|HA9O/AoHA/A=a;zGA˶/AyZFA6( /AàHFA¢ /AJcEA > /A+@EA{\M /AoPDA욣X /AڈwDA:߄` /Anv DAOd /A>CAe /A+CA qSe /AN\ίBAeh /AJU?ALW( /Az)?A /A,ĄO?AKѭ /AA(u /AF/٫>A /A9L>A ' /Ab=A /A 8=A瘍 /AP ,=A\rV /Aڢ<Ao5 /A@B<A3å- /AGUJ;AW /A/m;A(  /A];A< /Ah5:A4 /AT:A]% /AeV9A7@% /A"˧9A\ /AG8A ͝ /A2|8Ap /A`[7A~ /A5AM\ /A74AE /AK Z2ANyȘ /A# 0A(lM' /A.Am:"= /AN;-A~t X /A;g+A1w /A]/l)A.xڜ /APmh (AHL /Askg&Aq' /A@$A8b( /Aqv]9#A} 2` /AS2!Aw /A2 AQH7 /AFDGqVAYT /AbrAZ /AYņAIS /AA1 /AKhA뜞 /AԖA|V /AzG0A" /A~8A`} /A59A|(} /AZvA /A@A /A!A /A0Ax /Ah_A /AʏAAb /A Ae& /Aj_ AxK( /A` A/AAW/AA /A`UAH? /A`bA"r/A@POA@W9/AXA /A@%AX/A A8*/A`A]/A3A@$x/A@tAP/~/A lA/AA/A=A/A7A([/ACA(z/A`RAg/AA8/A@A`/A@A.N{/Aq^4AP//A9Awr/AչA$v0/A|\ A2R/ABAh(x/AA˱/A@AyS_/AdtAY/Ax<ALIX@/AZZ Aa}/A/A8)$PAw /ABȕ*PA@}/A-,PAHXx/AzU}*PAs/A&PAp/A`PA/4/Ah>چOA^==_/A .OA?/AM\NA)w/AvNAW9(5/A(HNAK.Ay(gNADA.A쓛 NAEk.ANA"`.A@NA5.AûNAg .AHn-MA\N.At0MA͝<.ApMAГ.A DMAp.AQ?MA\.ALA %.ANLAL.A3LA.Af.A7lrLA)3~.AeqLAF.AZLA..AcLAH.AjPLA.AWLA@ƞ.AhLA3td.AZKLAL;).ALA.AZG='MA|bů.A, EMA9Ur.Aɮ2 QMA#4.A-MKMA.AP4MAo].Ab MAh.ApVLAPwD.ÄLA,.ASKA +l.A2wKAI.A?JAW .A@nJA?.A!K5JAo˅.A>IA4].AbHIA04.AHvIAYA.A9^yQIA6R5.A UAXO.Ab>Aovo.A5% >A)N.AIz=AŅ@.AJ=A3mC.A?'Kp=AM.Ag- _=A8+}.AQ_=AEWu.A]q=A1.A@`=A7^\.A Wa?A .AZ9bBAf".AssDAߞ,.AB!GAqu.A@,yIAטB.ASKAI .AYNA3.ANi`PAZ!C.AlRA P.A4ATAQ .Ax WArs+.AQ/~WA ~.AXA}.A~ fYADäo[.AiGZAv9.Ap3[A .AdPO'\A}.A@P$]Ak;.A.m^A uk.Aȹ_A,t.AWx aAIRS.A.abA=.AUjcAY |.A$ eA=.p.AIcfA.A9gA .A iA4ڞ.AcZjAcqܩ.AwkA[.A(/lA4.Aw0&*nA,W.AØboA~.A3pA6e.A&AH}.A@WGAe.A`fAx)F.AA.A`ܶA .A`MAX.A uķA.A"A@.A/A j.A`KA 8.A׶A0H5.A@,A.AJA.A A.AkAx.A$AH/.A`A.A@A@>.A`)AJ.A@ʹAh.AzA.AضAs.AAd.A(2AP.AA $.A`6A`e .A@JAhM.AADF.AA.A@i*A55U.A'*%kAҠw.AUAp [.AAAV<.AVqA36.AWеA(;f].AUA`.AَAx.A@'AH.AR AM.A`,A.AFA.A?A`!.A`"A.AkA.A 4A.A)As.A|APo.AՀAIl.ARAH{M.A`)sA .AWA.A@/A>;.A=IA.A`A .AǶA(K.AɈAxG.A@[A8s.A_A'.A cAX.AAX`.AUθA%s.AA).A@AY.AެAh`.A A.A`KAг.A` ANJ.AHϽA.AݽAE.A As*.AYAO.AVAp[.A lA.A|Ag.A@ևAЮ.A 끾A@Az.AlAxT.A`iA02.AdA.ARlA.A`iA.AQAc-_*.A>"!AE &.A*Aj".A ֣ A.AA.A AX.A`9ϲA(j.A ZQA&B.A@AP.AA+.A(Ae.AVAۯ.AQDAϩ.Ac7A.A˷AhΠ.AQAPg.A/A.A%A.A2=AH16.A;[ARՓ.A9_Aa.A =cAwEo.A`ٸA.Ao A0.A`?Aэ.Az(A8.A%A.A`TA99l.AuxA+.A0A(.A@lAJ.AfZ핽A.A|A4 t.A ݮA.L.Aґg{A-%.A%+{A[.A[13EA.AA "ų.A YAGR$.AhzA6~ m.ACS jAbN.AK+5˺Aٺ"4.Avl:"A*9.AnpAZ>i .Aɜ|:A.A@A.AcőA7.AOb4A_ (.AA$>.A(r-ӞAF‰W.AHjA r.Ab5HAg.A z8Ab.A`o;A .AeYA`.A`mA{T.A=/xA'P.A@=8vA0Y.A gAٔV.A ]bnbA).A9ZQAG:;.A(5AK.AA(Z.A)I޵Atg.AtŤARq.Aג@eAy.A@ AxG.AA0ٲ.ArA୕.AA.A$A.A`޲A0;.AӲAʼn.A`庲AXh.A@Ay.ATAe.A .A@.A`A.A0ޱA.AaqA`.AAxi>.A@ΰA .AfA`.A |EAxv..A`A.AʜAxo.A Al.A`{Aٯ.AZЭAP|$.AAp.AF8A@.A@˫A<.A@rAP.A0Ah.A'A.A AH?.A͔A@J.A S A.A@PAp.A@yhAz.AAp<.AA( .A@P=AH.A`TAS.AǻAT!p.AAm>.A[V_^'Ayz.AX^Arf1.AߐA[.A<)#A;5.AućiAD`.AC2AAf;.AݿfAZ.AAo.A`m|A.AA`T.A l}A .A`aAXyw.ArA@z.A sA`.AOAx5.ACA t.AݰA0.AvA.AA.AIA`.A 8iA.A ZA.A2}A.AiA .A<A`%.AfA( .A`vA.AӱAd.A`ñA.AA.ABıAhs.ABڱA@.AEA8.A^A$.A A*3.A9IAp,5.A &A`C6.A,A8;.A0A`aJ.ASAhO.A`A P.A若A S.AiAHLQ.A`.A8I.A8_AE.A A`?.A/AH!+.AA.AQAp.A<ԷA .AͼA.A`A`.A޹A.AZAw.A>A.A42Av.A@PA8b.ABA .AR2AG.A@A.AkA`T.A`A؛.A3A0.A 8=AY.A,eAH.AuAx.A1A舲.A LAp8.A@]A.A`)jAV.A |}Ag.AͮAu.AA .AA.AA`й.A@-A؟.A Z_Ap.A=qAx-.A`|A.A@APo.A|A7.A quA@W.A-A`.AvA".AA.AAx .Ah A.A@eAp|.AAv.AδA@.AAȓ .A`TA8.A`KA.AA?.AQ,A(m.AEA.A A]AP .A \sA.ABAn.A&Apl .A sA`a .AA@.AsAK.A`A.A.Ap,.A`9BA.A TAp|.A hAp+.AkA( .A`)A..AA.AA.AAH.AhA.AA.AA,.A)Axh.A$=A.AhA.A@zA(.ADA@.AA#.A A0.A$A.A@A@.AIAo.A` )A.A`JAo.A`wA.AA8 .A`A.A A@.A@WA.A@AX9 .A Ap .A)A.AIA.A`zA".A AH +.A@ A9.A ZhA=.AA*D.A ZAH.A@AXN.AGA9S.A`KAT.A@iqAP.A`A8L.A` A2G.AAyC.AATC.AE^AF.A APO.A4AN.A@nA@1L.A[AbH.A3'Ax:H.AMA;O.AфAS.AձA0&T.A sAP X.AA0X.A+A(S.A>A8S.AqNAXS.A wA`X.AAh[.AAhb].AAs[.A|-AY.A@iXAxZ.AwA(a.AAKf.A`2Ah.A@Ai.Aa(A0Yk.A`TA(){.AA.A@A<.A A#{.A?Aux.A Az.AAz.A4A y.AuAu.A`[AH].A4APT.AAUH.A`],AGB.AIA@.AfAB.AOA0HF.A3AX4H.AjA`H.A`B APC.A:AL).A 7A$.A]A.A ҠAhs.A3A .A4;A> .AAx.AAX[.A Z;A.AA0v.AIA.AA.A CAp=.AwA.ARAP .A Ah".AA5.A@A.AyAhb.A'eAx.A@5A.AvA@.A'A~ .AJA.A XA(.A~A.AA.AAXh.A4A8t.A׳A`.A2A(\.AA.AAx.A@Ah.A5A.AzQA.A@plA.A@A.A ғA@$.A@ߟA0*.AAP.A Ac.A AH.AAN.AAѻ.AAhD.AA8.AAhs.A`AXJ.A@,A N.A ~Aȓ.A QA(-.AhA).AzA趨.AA.A@A(.A A.Ap)Az.A`AA.A6ZAܰ.A 8sAU.AqAQ.A AѰ.AA.AHA.A A88.A'AX.A q(A.A`@A.AOWA`.AnA.A AAX.A`bA.AAHa.A``AX.AUAX.AjA#.AA8.A`AH{.A ZAR.A:AP.A\AhQ.A~Au.A@A(.AAp.ARAʝ.A2A0.AhIA4.AtAB.A@AH.A@gAX .A3AX.A@Ahğ.AA.A&/A.A3LA@.AhA{.A`fA-.A`Ap}.A SA@Wz.A`A x.AA4v.A.A t.A=DAs.ASAȶr.A bAq.A qAn.Ag~Ael.AൊA@i.A •Aze.AAHa.AnAP^.A@ AP8\.AAH Z.A AMX.A Z AhV.A6A#V.A MAHU.AcAU.A`zA(U.A?AdV.AAW.A`KAHaY.A@g,Ah"].AL>A``.AOQAHc.AReAx)f.A4zA^h.A׏A$j.AA |k.A`ռAHal.AA0&l.A Axzk.AAp_j.AAAh.Ag.Af.A@DA8d.AXABS.A=AP.AA@N.AA`XM.Aq APfL.Ag=A(K.A`ZAL.AwAN.AATQ.AABS.A`AR.AACR.A -A_R.A @AR.A$SAS.A eAPIV.A@{A@dX.A@AZ.A`A(m[.A`AW\.AA\.A@ A\.A=A%\.ALoA)[.AAJ\.A A\.A@5Ah].AGA`\.AAأ[.AGAY.A`AW.AA3T.A AP.A >Ah3M.A_AH<.AA(-;.Ao&A&:.A'`A9.A`Ap9.AA9.AjAp:.A )AH;.AXA`<.AAkD.A@ AE.A+A{G.ALA0I.AmA88L.AAh3O.ARAR.AAoU.A`pAY.AAxZ.A`AZ.A`A[.AA[.A@AZ.A@9AY.A`SAX.ApmA0.W.AA(W.A"A8tW.AA@5X.AAX[Y.A-A`Z.A AXl].A@NA_.AA`ea.A`(Ab.A?6A8c.ADAe.A`fAxig.AAh.A`A>i.ALAri.A`"A(-i.AAmh.A6A4g.A-YAe.A \{AH\c.AAh3X.A A'U.A NApR.AAh@P.AA`eN.A@NAL.A:A K.A`\AH.A@.~Ah.F.A&AC.A`A?.A`2A;.AAL7.A`)AH1.A2A*.A (GA$.A`YA.A@gjA,.A rAH.A|Ap.A@χA.AAP.A?A(.AAأ.A33 Ap.A`\ Ax).A@ A`.Aɲ Ag.A A/.A Aj.A7 A.A@Ge A.A} A(:.A A.A#3 A6.Ayx AH.A  A.A A.A% A.A@CG AB.A^h A.A Ao.A@ A.A` AP.AM A.A Q AP^.A" Az.A> A0.A a An.A@ A.A AB.A AH..A Ah.A JAH.Ao1A~.A sRA).A=tAH.A`9AZ}.A`Ap{.A`Az.A AXhz.A@iAHz.A`'Az.A 5A8{.AAA(}.A`TNA~.A`ZA8 .ALhA.AmwA].A=Al.AAH.A<AF.AA.AA.AQAp=.A0A.AT A8g~.A@AG{.A2AXv.A c\AhQp.AAH.j.AͬAHd.AA~^.A;AW.Az4AR.ANWAuL.A@C{A0]G.A@CAHB.A6A>.AA@:.AA7.A <Ah5.A@ dA0*3.ÅAF0.AA,.A`AD).AA)%.Az A .ADA.AgAJ.A Az.A 8AB .A A`.A{A0.A AP7.A`A.A@A.ApA.A@N=Ar.A `A.A@5AQ.A A.A@!A|.A`)A.AA.AAXy.AApJ{.A<Afs.A`)^AXp.AlAm.Av|A_k.A &Ai.A@Ag.A/A Re.A`Ab.A`vA `.A߼A].A@A0X.A QADT.AATP.AAL.A@AI.A@A <.A?A 0.AxzAH$.A@>A~".A@5AТ .AfA$.AA .AA@`.A`A`%.AA(\.AA.AkA8.AJA.ARbA.A`yA.AA0{.AɦA@.A`KA.AA@ .AAP' .AdA.AAh.AAF.AH&A .A6A.AHA .A[Ah.AwAj.A$A.A`As.AiAp.A@%A{.A@ A R.A!A.A .A.A`9A.ADA0..A@LAg.AWA.AWcA.AoA{.Ac|A_.ANAh.A`)AЕ.AA.AA .A Ax:.A A.A&Az.A |AAX .A \[Aݷ.ApAM.A@߄A`.A`A.A෨Aͥ.AEAݢ.AA`S.A`;A3.A`dA.A`)Au.AA@=.AA(~.A`A9.A`Ap.AAH\.A^A.AOA.AzAɒ.A&Am.AǫAȂ.A@#A .A@Ap .A A.AAȇ.A u Ap+.A3AC.A+A`|.AK:Aw.AGAr.A]SAf.A`fAXF_.AҺAPX.A(AR.AXAHM.ATQA(H.A@ AF.AїAE.A`A E.AAD.A/AE.A`AE.ANA@F.Az AG.A@) AhH.AG A@H.Ae AhG.A@P AF.A̠ AC.A@ AH?.A@ Aȱ;.A8!Ap 7.A@ 9!A84.AN!A1.A/c!APZ..Av!A*.A!AX&.A&!Ak".Ap!AX.A!A .A+"A.Apm"A0.AB{"A^.A"AHz.A`"AF.A"A@.A-"A.A "A( .A "AX.A"A0 .Ap"A).A@#A.A!#AH.Ap;#A.Al#A8.A`[#A.Ap#Ad.A#A0.A#Ak.A$A.AQ$A<.A`K&$A.A9$A.AL$A.A e$A!.A~$A.Aྗ$Aa.A@$Ap=.Az$A.A$A.A$Aho.A`$A.A@%$A.A-$A.A$AX.A`"$A).A$Ax.A$A.A`) %Aa.A%A.A )%AЦ{.A <%A}l.A@.P%Az].Af%ApY.Ati%AU.Atn%A:R.Au%AN.A}%AK.A@%A@H.A@N%AF.A<%AC.A5%A`A.A%Aȃ@.A+%Aؼ>.A@,%A<.A %A*:.A%A@7.A`2%A4.A%A1.A%Ah*.A %A`a".A%AH.Aq%A`X.A%Ap .A` &Ap.Ax3&AxK.A \&Ah.A&A`.A`ó&Ap.A &AD.A 'AY.A 9'Aĵ.A h'Ax.AH'A.A'AH.A'A.A@(A0.A9(A.A`(A`.A(A8.A (A M.AB(AhL.A=(A.A@(Ah.A(A~.A)A~.Af)Ap~.A`).)A=.A@G)A.A|`)APZ.Ax)A.A)A _.A)A].A`)A.A@W)A.A@)Aш.A)A <.A A)A_.A-)A@O.A)A.A=)Amv.A`)APk.A)A(`.A`f)A"\.A`)A}W.A)AR.A)APN.A`v)AJ.A)APF.A \*AP.An,*Aw:.AA*A:7.A@yX*At4.Ap*A()2.A[*AH\0.A \*AB/.A *A..Aq*AH\..A@ *A`..A*A(>/.A*AU0.A*A1.A*A03.A*A7.A}+A:.A+AH<.A +A?.A|.+Ax@.A@>+A cB.AO+A[C.A \a+AHC.A3s+AHPC.A`+A(>B.A}+A@.A+AБ>.A+A<.A+A _7.A$+A.3.A ,Axu/.A ",Ap9,.A Z;,A).A U,A'.Ah,A&.Ak|,A0$.A,A =$.A@,A$.Aù,A%.A ,Ah@'.A,A7(.A,Aȱ(.A,A(.A,A"(.A,A'.A-AP".A-Ahn.AL.-AH.A(B-A0.A]L-A.AX-Aj .A&e-A` .A@ys-A).A@-A0.AL-A.A-AB.A3-A`.A@-A.A.A.A,.A8E.AD.A`.A`;\.A.A`s.AP.A.A`a.A=.A.A`.A".A྽.A.A.Ak.A`f.A.A`.A.A .Ah3.A`K.A.A .Ap.A`).AX.A/Ah.A/A.A&/A.A@ 9/A.A L/A.A`/A0.A@ t/A.A /A0..A \/A.A`]/AX.A@y/A@.A 0Ap.AK0A8.Au0Aq.A0A.A@0A.Ap0Aج.A@i0A.A` 1A.Aj 1A.A`)1A.A@>1A0 |.A 1AXyx.A$1At.A1AQq.A1Ak.A1AHf.A` 1A{a.A'1Ar\.A/1AW.AX91AR.A`D1A~N.A Q1AQJ.A^1A@H.Avn1AE.A`|1A |B.A1Ah?.A 1Aq;.Aෟ1A؁7.A 1A(\3.A1A1.A̭1A@..A`ܮ1A,.A1A*.A 81A(.A 81A'.Ax1A8&.A1A&.A1A*'.A2AQ(.A /2A?).A@A2A).AMT2A).A@,g2Ah).A`y2A(.A2Ax'.A2A@&.A2A$.A 2AP!.A=2A.A 2A.AV 3AP.A@$3A .A=3AX.A``3A.A3AL.A3A.A-3Axi.A#4AH\.Ab4A#.A4A.A5A`e.A 55A0.AQ5A.A m5A8.A`95AP.A 5AH.A5A.A`5A.A 5A.A5A`.A@>6ApN.AI6A(.A6AG.Az6A@.Ao6A.AK6A茚.A7A0.A?!7Ax.A;7A.AGU7AL.A@7Aӗ.A7A.A7AHr.A7A0.A7A 4.A7Ar.Ah7AQ.A8A(.A8A.A8A[.A&8A.A-8AR|.A68A0w.A@A8Ahs.AMN8Ap.Ap\8Al.Al8Ai.A@|8Ae.AǕ8A(a.Aǭ8AG].A8AL.A@ 8A8I.A8AYG.A#8AQE.A 9AC.A@E 9A-B.A9A8#A.A 9Az@.A(9A>.AE9Ap<.Aa9AH9.A \}9AxG6.A ˆ9A(2.A9A8..A`"9A*.A9A@B$.A9AH.A@ְ9A.A9A.A 9A.A9A.A`9Ax .Aa9AH. .A@ 9Ap=.A`:Au.A:A.A:AP8.Ap;AǢ.A ;A.AT<A|.A3<A)g.A<AF.A@<A).A3=Au .A`X=A\.ALk=A.A=A@.A`>A`a.A,>A.A%>ApͿ.A`f >A.A>A.A >ALr.A?A U.AH?A=.A3?A`*.A@?A.A?A`a.AH?AQ .A?A0.Az?A.A \?AG.Ap@A.A@A8.A"@Aܾ.Az(@AӾ.A`)6@A`Ǿ.AzT@AǾ.An@A.A`)|@AG.A`)@A.AR@AHᗾ.A̮@Ap.A̸@AX.A@Auy.A @AQt.A@An.A@A#[.AIAAT.AAAuP.A(AA`aG.A AAB=.AAA86.AGAA-.A]AAX'.AAA83 .A AAX.AQAA@.A BA .A]?BA).A_BAG.A@pBA .ABA#ҽ.A=BA(\ʽ.ABAʽ.A`)CA̽.ACAн.A 3CAp Խ.A`fHCALԽ.A=jCA`ѽ.A`)CA=ͽ.ACAĽ.A CA#.ADAP.ADAp .ADA .A=DA`.A@CAB.ACA`ွ.A CA`w.A`)CA^.ADA`S.A`DAF.A3)DA8.AGDA-.AlDAp).A@DAp8*.A@DA,.ADAu..A \DAk,.A \DA(.A@g EAH.%.A@ EAp.ADA( .A DA.A3DAP.A=DAf.ADA.A@>DAH\.A qDA޼.A3DA׼.A3DA0Լ.ADAӼ.ADAѼ.Az EAB̼.A@ !EAh3ļ.A3EA0.A@>|EAfm.AEA3X.A EAp K.A FAأ?.A?FA3.ArFA().A]FA`.AFA.A@FA.AGA.A`f GA@W.AzDGAp .AGAB .A3)HA .AMHA(.A wHA8.AHAp.A@IAXӻ.A=|IA˻.AHIAk.A3SJAp.A@tJA毻.ApJA.AJAf.ApJA̎.AzJA0.ApJAׂ.A mJA0~.A3EJA@x.A@>2JAzp.A7JAX h.AOJAL_.AxJAU.AGJA8F.ApJA؞7.A`)JAP80.AJA'.A@>JA8.AJA.AJAp.AzJA؞.A@JA8.A1KA0..A `KAp.ARKA0. .A֧KAf.AzKA.A`fKAL.A KAQ޺.ApKA`Ժ.A KA8Ϻ.A= LAƺ.A@\LA83.ApLAp=.AMAf.A{MAuw.ApMAf.A=MA?.Az.NA3.AHQNA0.+.A`frNA$.A3NAG.ANA.ANA`a.A3NA.A NAz.A`)NAk.ANA8.ANAع.AzNA).ANA).A OA.AOAf.AOA𣈹.A`%OA@ׂ.A`9OAzq.AחOA#i.A[OAu_.AOAQ.AOAC.A׽OA9.A̤OA8.A OA1.A3OA+.A@OA{.AOA.A`DOAp.AROAX.A@>OA.A3PAB.A%PAL.A@0PAkӸ.A TPA(̸.A gPAL˸.A sPAP˸.APAuӸ.A`fPAP߸.AzPA.AzPA0..APA R.AzPA.APA.A3PAƸ.A`fPA).APA؞.Az QA8.A`PAˆ.A*PAਅ.A3PA#|.A`)PAp=N.APAp=F.AzPAB@.A 0PAp7.A PAp4.APA@W4.APA833.APAX-.ApPA!.ApPAL.AGQA.AQ$QA(.A4QABӷ.A3QAP.A3+QA .A( QAP.A(QA.A(PA.AzQAP.A]!QA ~.A?QA{.A }QAp|.A`)QAp .AQA).AQAp.AQAp.AQA`a.AQA(܂.AQAy.AQAo.A@RAd.AQAuT.AQA0.F.A@QAأ;.A@QA..ApQA`(.A`fQAQ%.A`QA@W".A` RAG.A5RA.A`)VRAL.ARA.A@ RA.A@RA(.A`fRAڶ.A |jRA̶.A3?RAQǶ.A |.RA.A@"RADz.A)RAʩ.A A4RA8.A8RA.ABRA롶.ARPRA⡶.A YRA(ܡ.A3aRA(\.AlRA®.A@RAֺ.ARAp=ƶ.A`)LSAQ̶.ASAH.̶.AHSAƶ.ASAH.A`)TA.AR.TAB.AoTAL.A@gTA`av.ATA#j.ATAc.ARTA0_.A TApZ.A \kTA`aT.ApQTA#H.A=TA:.A0TA/.A1TA8&.A GTAB.A`)lTA.A@TAP .A TA .A@gTAz.A@>TA.A@gTA؞.ATA.A3TA0.ܵ.A 'UAе.A QUA`.ApkUA.A@gxUA`᥵.A@UA8.A`)UAf.A]UAL.A(VA.A(YA.A[YA.A@YA .A[YA.A YA`a~.A@|YAXx.ARvYAzs.A`zYA`\.AYAP8U.A YAkL.A YAp E.AYAQ>.AGYA8+.A@ YAp.A`TYA.A CYA`.AEYA .ARVYAp .AkYA(\.A}YA.AYA8.A@g~YA`a.A wYAu.A3cYAݱ.AR6YA(ı.AzXA`ḱ.A \XA.A@XAp=.A@XAB.A`ÇXA.AnXAH..AQXA8ʱ.A 1XAϱ.A=XA)ұ.A XAuұ.AWAб.A`WAp=̱.A@WAƱ.A@WAf.A@ WA`a.AWA.AWAV.A@WA.A \WAB.A׻WAz.A@GWA(\x.A=WAp=w.A`fWA{u.AWAp=r.ARWAd.AXA_.A`f XAp=Y.A \XAQ.ARXA K.A= XAE.A@ XALB.AXAH;.AXA5.A3WAX +.A`WAh3$.A \WA`.AWA0..A`fWAp .AVAB.A`VA(\۰.AzfVA(\Ȱ.AQ"VA(\.ApUAG.A]UA(\.A (UA.AUA`.A TA.A`)|TAz.ATAp=.A`)SAx.A@ SAj.ASAVd.A@fSAV].AWSAW.A USAHT.AVSAp8Q.A@RSAN.AHSApI.A3/SAuE.A@SA8A.A qSA;.A \RA6.A qRAB3.A@SA{-.ARSAP'.A RA!.ARA .ARA .ARAp=.A@rRAX .AXRAB .ADRA.A1RA#.AR,RA.A`.RA@ .Ap1RA.A@4RAأ.A@G3RAH.A |0RAأ.A \RAH\.A3QAG.A@QA@.ApeQA#.A`fHQAu.Ap/QA.AQAk.AQQA.A PAf.APAh3ٯ.A`fPAЯ.A PA`aʯ.APAp.A RPA².AROAh.AOA².AOA.AOAѸ.AGOA`.A OAů.AשOAL̯.A̮OA(Я.A[OALӯ.A̞OAԯ.AOAկ.Az\OA Rӯ.A@.OAWЯ.A3OA R˯.A qOABï.A@ NAѺ.A`NA.A`)NAW.ANA.A`xNA`.ANAQ.A3MA8.A`)MAp .A3MA8.AMAB.AMA`.A MAP8.AuMA.A`)DMA(܆.A*MA؞.A=MA0.ALA8.AGLA8.ALAk.AoLAP8}.A``LA`as.AZLAe.A``LA[.AgLAp=V.ApLA`R.A~LA)N.A`̄LAJ.A]LA`D.A@tLA>.A`f^LAp8.A;LA#+.A@KA'.A@KA$.A=KA`%.A`)KAf0.A&KAp7.AJA;.A@JA<.AJAp=<.A mJAL9.AzNJA3.A`),JAz..AIA*.AIA'.AIAB".A3IA.A`fIA .A3IA.AqIAk.A \]IA`.A \?IA.AIAX .AHA`.A |tHA#.A@ UHA.A@G3HA.AHA.AGApݮ.A`)GA֮.A \GApϮ.A`fVGA`î.A]FA@.A 1FAר.AëEAp.AEAz.A`mEAh3.A[[EAP8.AIEA.AG7EA.A EAأ.A@gDAǍ.ARDA8.A׫DAP抮.A@DAp .A|DAB~.A@dDA|.A=ZDAz.A q1DA#~.ACAP8~.ARCA=z.ACAGt.A`fCA(k.A wCAe.A`)hCA)`.ASCA8\.A`.CApW.A3BA(\T.A \BA3O.A`fBA0.G.A@BA<.ApBAP1.A}BA`).AgBAP%.ASBAu!.A,BA`.A@BA`.A@AA`a.AGAAh.AoAA@.AfAA.AAA8.AAAͭ.AAAH.AUAA𣴭.AzAA8.A`fAAQ.A`BAX .ABAѩ.A0BAB.Ap=BA.AzFBAQ.A@VBAV.A@fBAk.A |BA̫.A BAB.A \BA̖.A@ BAp.A`)BALJ.ARBA.A |BA|.A \BAP8u.A}BAo.AkBA)i.AbBA^.A=fBAS.A`zBAأJ.A`fBA4.A@ BAG#.ARBAB.A3BA.ABA.ABAP8.ABAh.A`CA .A6CAz .AKCA`.A@dCAp.A`rCA.A |rCA(\.A _CA߬.A [CA`aج.AeCApϬ.AtCA83Ȭ.AxCAp.A@dCAѵ.A \]CA̫.A=jCAz.ACAǛ.AדCA.A@CA{.A׻CAu.ACAHm.ACAd.A`fCA8Y.A` DA`aA.A>DAP!.AׅDAk.A`)DAh3.ADAPf.Ap3EA.AEAث.A[EAPʫ.A FA؞.ABFA.A`fXFA沫.A ]FA.A gFA.AFAp.AFAfv.AFAHV.A`ftGA(\>.ARGA!.A@ 7HA.AHA`Ъ.AIA R.AhIA.A@IA`%n.AkJAn.A&lJA]j.A`JAa.A (JAK].A JAU\.A@rKA(m[.AKAX.AvKAJV.AKAU.AjKA@R.A@`$KAL.A`v1KAI.A5KAI.A 6KA,.A@%[KA&.AxeKA".AlKA@.AKA ߩ.A KAcϩ.A_LA.A (HLA囹.A vLAX.A`fLAΞ.ALAh.A]LAs.ALAn.AwLAHj.ALAf.ALA/d.A@LAc.A LA\.AoLADW.AMAC.A~MA3A.AMAB;.AMA0.AMA&.A@NNAs.Aj\NA.A@ NAxm.AuOAX.A OA蠨.AVOA{.A`$PAl.A`;PA`e.APA3[.Ag)QARN.AaQA?.AQA..AQA0%-.AQA%.A`[QA.A<@RA' .A QRA.A@RA ç.A RA.A@SA0{.A@gSAг.A߃SAȋ.AOSAx${.A`2SA o.ASAl.ASAH?b.ASA].A TAW.A@%TAhM7.A@TA.AVRUA4.A`mUUAh.AXUA> .A6UA.AUA.A@UAY.A@5vVA.A VA.A`2VAXR٦.A ZBWA0Ԧ.A@]WAPϦ.A$tWA¦.AqWA虴.A WAh.A@XAu.A@9XA.A@>JXAXz.A@gcXAHM.AYA .AYA.A&_Aɤ.A @_A.A@E_A>.A$\_A<.AZ_A;.A Y_A6.A*W_A,.AK_A,.A@J_AH,.AJ_A(`+.AG_AH\#.A6_Aذ.A_AU .A _A .A^AH!.A@^A.A^A.A^A0.A^A .A^A +.A^A.A^A).A^A@.A^A&.A@N^A@1.A^A .A^A@S.A ^A@.A@^A@u.A_A.A__AxX.At_A;.A"_As.A#1_A@A.AJ_A`e.AfV_A$.A@ u_A أ.A_Aף.A@_AhУ.A`)F`A.A^1aA`ǿ.A?aAH-.A҄aA@乣.AaA .AaAX.A`baAa.A bA.AbA0H.A =bA@`.AGbAH.A_bA`e.ATzbA.AbAXh.A$bA؁.A@EbA84x.AbAhm.AbA[.A@bAQ.AbA=.A AbAx6.AbA3.AbAp}.AbAU.A+cA .A6cApk .A 1 cA .A cAN.A@,cAPТ.A@.cA.AcAs.A`%dAz.AROdApa.AdAV.A`)dA@NT.A`dA83N.A eAp A.A`\eA(@.A]eA6.ApeA8..A eA8 .A`ZfA#.A \fA.A fAG.AJgA-.A׏gA`a.A hAW.A`ÑhAp.AhA8.A-iAH.A}iAp.AiA%.AjAo..A`mbjA0.AtjAQ?.AjA^.A`*kApp.ARpkAp.AkA`.AkA`a.ARkAQ.AkA@ל.A@,lA8.A`f^lA(\.A͒lAz.AlAW.A lAp=Ţ.A3mAQ΢.AmAP8Ԣ.A -mABޢ.A`)ZmAk.A@>mAh3.A@nAN.A oAe.A=foAH.p.AQoABt.AoA`au.A3oAp=s.AGpAq.Az"pAm.ADpAf.AGepApd.A |pA83d.A pA`a].A`pAأH.A@qA0.<.A \1qA2.AR:qA'.ABqA#.A`)VqA.A_xqA.AqAx.A`vqA8.A`fqA8.AqAu.AzrAu.A`fNrA .A@trAP.AzrAP8.A@rAk.A sA.A`;sA`aӢ.AJsA(.ARsA`.AXsAp.ALsA𣅢.A1sAXx.ArApi.ArAc.AFxrApR.AR&rA=.A`fqA&.A͢qA.A \qA#.A`~qA.A@dqA).A@G'qA̡.ApA=.ApAأ.ARrpAє.A`f^pAG.A apAy.A`fxpA@W^.AppA4.A=qA@W-.A!qAz$.A@ kqAp".A`fqA .A@&rA.ArA@W.AsAp8.A@gZsA .A@gsAX .A3sA.AןsA(.AzsAWؠ.A:sAX ͠.A@>rAH\.A@ YrA.A`)rAP8.ARqAX .A=^qȀ.A`)qAPf~.A`pA{v.AطpAr.AGpAX.AlpA0?.AzJpA(*.A0JAh=.A@ )JA K.A3QJAL.A{JAGI.A`çJAأD.A JA0C.A JAPfC.AqKA>.AG5LAP@.A ]LA`C.AiLA`aN.ApLAT.A`tLAU.A`LALN.ApLAp M.AࣰLAM.A@ELA)N.A`LAX Y.ARLA_.ALA@f.ALAȃh.ALA)l.AzLAs.ALAp=.AqLAL.ASLA.ALAp8ɤ.A=KA83Ф.AKAVӤ.ApmKA)Ӥ.A8KAp=Ф.A[JAp8Ƥ.A@>JA).AzJA.AJAP8.A kJAp.AMJA.A %JAVƤ.A IA֤.AIA(.AכIA.AIA( .A̜IA.A`ÿIAQ'.AIAP8;.A JAfJ.A JAuV.A@JAe.A`IAy.AIA`a.AIA0ҥ.A`fIA.A gIA.A_IA(.A1IA .A`HA.A@HA8.A \HA.A=fHA .A=FHA.A0HAp8#.AHA#,.AGA2.AGGA?.AoGAHG.AeGAQ.A@nGA`Z.A GA8e.AGApv.A GA(.A࣌GA‘.AiGAf.AUGA.A@2GAP8.AQ(GA.A`),GA̼.A(FA0.Ϧ.AFAH.ARlFA .A EAP".AEA4.ANEA8?.A  EA?.A DA@.ADAPD.A`DAK.A`)DA8V.AHDAj^.ADA(\c.A`DAi.A DA8j.ADAh.AnjDA8h.AgDA_.A \EDALU.A0DAHP.A`)DAQ.A3DA(\U.ACApa.ACA#t.ACAz.A=CAp8}.A׳CA8.A*CA0n.A`CAk.A`fBAȧ.A`)>BAҧ.AAAܧ.AAA.A CAA.A3#AAf.A@A.Ap@A .A@>@AP.A @A.AÓ@A(.Aps@A4.AL@A ?.A@AfV.A?AWd.AR?A8m.Aׯ?A-~.A`a?AQ.A`:?A.A3?Au.A=>A@W֨.Aͦ>Ah{ب.A>Az.A>A.Ae>As.AB>A8.A >A`a.A@>A.A=A .A`=AP8.Al=A).A-=AW2.A`f<A5.A<A`:.A <A<.At<A(=.AU<A=.AM<AQ@.A &<AF.A <AWP.Ap;A\.AG;Ab.A`);ALj.Ap;At.A ;Az|.AG;A.A׋;AG.Ap;A̅.AJ;A.A@>$;A`ᤩ.A:Af.A`):Aɩ.AK:A8.AH:A.A9A(>.A@9A0.A9Ap<.AnO9AH.A?9A*".A 59A$.A4)9A$.A &9A%.A`9Ac(.AN9A3.AŢ8A6.A߉8A 9.ABv8A89.A u8A(z;.An8AM.AZ8A0Z.A`7Ahc.A 87Ahk.A`d7An.A ۟7A.A@%Z7A0Y.A T7A2.A47A`ý.Aw6AHKϪ.A`06A@ݪ.A$5A( .A5A@.A@Ϛ5A0.A2r5A .A35AF.A@P4Ab.A`4AX{.A ]4A.A`)4A.A4AH!.A 4A3.AV3Ah.A3ASث.A 13A.A2A0.A 2A.AD2A('.A 1AI.A@/1AثV.A0A0].Aǿ0AHb.Aa0A8x.A 0A|.Av/A`.Ag/A(.A`/AH.Ao/A>.AH.A`.A .AXV.A`.Ak.Az.A@έ.A b.Aӯ.AXV.A`嵬.A`0.AX(.A.A(.A.A.A@-ARǬ.AT-A\ͬ.A-A ج.A-A /.A-A6.Ahi,AN.A,A`W.A`+A@ ^.A|+A `(A'.A@PB(AUG.A'Adu.A6&A0ju.A@&AY.Af_&A̋.A2&A0.A@C&Ab.A %A۹.A%A0î.A@,%A.A qf%A(\.AU%A.AA%A/.A3%A .A %A.A`$A` .A$AHC).AՒ$A..A r$A5.AJ$A#G.A`#A(Y.A A@#AP`.A "APf.A͠"Azl.A6ABʰ.A@A.A eA .A AXc.A@iApy.A@_A8.A`[?A.AA؁$.A`Ah2.AAA.A ATI.AQA`P.AfAh].A-Ac.AAAi.ADA0m.AyAt.AA|.A tA!~.AhAX.AAA0؆.AAHP.AA0ꓱ.A oAp.A`9Ag.A͜A8.A"EAs.A(Ax.A@A8'.AAd.A JA .AUAHi.A SAP8.ApA0.AzJA .A$A.A A@.AHA .A A`.A(A.A@ihA#.A`7A+.ARA3..A A0j0.AA22.AA3.AvAk4.AA\4.A)A4.AiA#6.AKAPo7.A (=A#9.A (/A;;.A"AЮ=.AAv@.A  A8C.A`vA(F.A[A=K.A@AXhO.A+AQS.A@AhV.AA9Z.AA%].AtA0_.A3A@h.AiAXr.A0Aw|.AAP.A`)A{.A"Aᙲ.A hA碲.A@`:A.A AH˳.AAأ.A=A{Ų.AIAв.AtA8Ӳ.AA@޲.AA.AGA.A@Av.A@yAX.ALA0L.A sA.A}AH?.AwA(.AtpAh.AjA(\.AdAHK.A]Ag.A WA.ARAj.AOAA.A PAx.A RA.A SA@~.ATA.AUA.A UAH.AHWA.AWA.A-UAج.AQAk .A@yLA .AEA.A@P7A.A'A`.A@`A.AEA8.A@ A".A ~ APY$.A]m AЮ&.A\ Ax).AdM AH,.A[? A0.A A8 A3.A2 AU7.A. A;.A), A>.A`]+ AB.A6, AxF.A. A.AdAxiB.AAuF.AAF.A`vA`N.A AؒR.A`fAo.A@At.A@PAxK.A$AO.A Aj.A 8A.AAㅷ.AŐA|.A c~AH.A}ANj.A@`zA.A@_AН.A0$A߯.A`A.AQAt.A@AA( .AݕAhMH.A` A_\.AA\.A`AP].AFA^.A͵A`.AVAa.A?ApZa.AަAP8c.A`vAd.A`2vAXf.AJAg.AwCA3h.A@Aqi.A6A`%j.A 2Apj.A^/Aj.A/Apl.A'As.A AP.AoApո.AA R.A`A(~.A`A.A@XAs.A/LA,.ADA .As9AX.A.A|.A@A.A^A\/.A AC.A !AY.AAs.A@(A|.A Ab.AA㎹.AW|APo.APAXۛ.AA{.A3A窹.AA.AAs.AA.A/A0ǹ.AtA /ѹ.A[APӹ.A NA cܹ.A s"A`6.A+A+.AA.A`AP.AzA.A@]ApN%.A`9A2.A AZ.AAb.AAx(l.AAp<.AGAP.AIlAB.ARA@.AtDAz.A4Aﾺ.AAϺ.AQABϺ.AA(Ϻ.AA޺.AA.AA( .A/A.ADžA.Aj_Aw.A_UA`.A#A.A Ah".A+A(.AA0-.ArA(E.AA 0F.AAYJ.AAO.AFA^.AwAxk.A`OA.r.A99Ax)w.A "Apx{.A  A.A A.AMA`1.A`fA.AZ,AHr.AA̯.AnA׻.AKA`ܻ.A$A.A@EA.AA.A A.AAX#1.AApA1.A A_1.A2AJ.A`\APc.AA`h.A@iAh.A/A n.AǨA-.A_A@.A@[AP<.A@>;A.AA .A Aљ.AA .AA.A<A㸼.AyAK.A<Aﾼ.Ax*A8ü.A-Aļ.A AƼ.AHAɼ.A cA\ʼ.A`AXʼ.AA޼.AÝA8.A@Ak.A`xA8.A^Aa.AT?A.A@2A.AA`.A@A.AÉA-.AuA( .A dAp/.A (ApP.A AXa.AAH.d.AALr.AA`w.AA`ᅽ.A@> A.AA(.AA(.A q;A#.A qA(\.AA0..ARAp.A qAx.A4AB.A`)A(ܯ.A|A.ASAG.AR(Az.A`)A(.A Au.A \AP.AAp Ľ.A=A`ǽ.A@GAhʽ.A͈Aҽ.A 1Ah3ܽ.A`AB.A`AG.AGA.A@ZA#.A 7A0..AAP8.A Ap.A@gA .ARA`.A@pAX".A3[A8*.AYA1.ASAX4.A[QA@.A[%AkU.AA_.A{ALg.AHMAq.A@As.A AVs.AMApo.A Aq.A |Av.AͲA).AA8.A OAX .A A}.AA}.A AX.A]sA.AaA.ApAAp.A(NAH..AeA.A Af.AA`a.AQA뾾.AAþ.A3gA(Ⱦ.AQHAо.AAzھ.A AW.AA0.A A.ABA.A A.AA.AA %.A०A c(.A AH(.AanA8).AUAp/.A:A:.A@APA.AA{D.A`ATM.ApAPX.AA@d.AHyAm.A`KAh y.AZApJ.AhAh.AAl.A@A@W.Av^AJ.A |EA线.A:A[п.A A1A+ۿ.A c$Ap .AIA.AdAu.AAп.A AJ.A`[A` .AOAh{.A;A0H/.A =A:.A;AM.A@`%AV.A AP^.A+Ae.A Axf.A@EAe.A AA|h.A`]AHn.AAP+t.AAxu.A`WAhbx.A@=Aā.AjA .AA#.AA0.A A.AA].A`AH?.A A.AA.AA0f.ArAl.AfA5.AeA@.A O]A@S.A<Ap.A@ AH .A`A8.A`A.AFA .AA.AA.A@A7".AtAx+.AlA2.AdA=.AoAJ.A@kAпW.ASA@1s.A`A.AA+.A4A.A@AЦ.A A.A`Ax.A?A.A An.A A.A@A.A-ApN.A`"AxK.A@AP.A@ A8D.A`AA.A`TXA0.A9A.A&A.AF A .A6Ax.A`A!.A Ay#.AॆA@ .AtA.A@#aAK.AtTAHL.A;AX.A5A`C.AA(i.A@,A@u .AA.A c[A@ 2.A ZAH.A`rA`h.AAx׈.AAXW.A |qA`v.AflA@.A rAp.AA .AAX.A<A.AzA@.AdA.Ad>AXW.A@AH].AA@.A?AP .AaA( .A0A4.AZAe.AR}AP.A At.A`A.A@?AHP.AoA R.A`9AK.A9Ax.AAv.A@WAx.AБAL.A;A`.A@A0{.A A`.A |kA؁.AyA(.A A.AA k.A@g|A&.A@hA.AjA8c.A tA8.AtAp.AdA&.A`>AQ.AwA.A`A8ɸ.A@5Aྵ.A@vA .A ]Aߺ.A\A0ٿ.AdkA R.A-Ap.AA.A4Ao.AaA2.A cA".A@>A.AѽA.AA\ .A3A.AAj.AwAp/.A`ZA`%>.A`YAoH.A@aAX U.AtACk.A$Au.AA,y.A[A|.A`uAX9.A}A8#.A|xA.A`\A.A@"A .AA .A@`Axؙ.AxfA/.A[3AhQ.A2A`.A1A.A Ab.A A}.AA=u.AA8.A/A~.AGA#p.A AH.AA..A MAk.A`)&AH.A \A=.ARA)Ͻ.A wAأ.A 9A.A`A憽.A |ҸA`a.ApAA.A=A˛.AawJA>.K¼.AwtAk.A.ʫA\JLO.A!KAWy.AdbA(%.AkoyAd.AVA(Q.AsA}1ݔn.AM PA5M.A~J%A/.AA.A <AD.A mA`!5.A aA#.AA.A+Ac.A ;A.A`AH.A`"ݪAHߺ.A@AѺ.A@EA(.AANJ.Aj{A@o.A6 AU.AA`10.A`ީAP8!.AsA .A c0Aٹ.AAȩ.A[A𴡹.ACAx.A`)"A(`u.A AY.A﫧A I.A(AP&8.ATAH.AAθ.AAp.A ҌA8".AŝAX.A൝AP^X.AaAF.AӼAk.A A.A4Ax.A^A0.AAh.AA*H.A|A.AA.A AXh.A`A).AAw.AAw.AծA.A A.A`UAV.AA.A PAP.A` Ap.AA(:.AAp.AA(q.AAH .A9AhQ.ABAȇ.AAA.AAB.A`Ar.AżA r.A@*Ah.A`.A(\D.A@5A+.A9vA(.AӦAڵ.A ҌAl.AIdA.A;AfD.AfcA@}D.AApk*.AAug.ANAhc.A JEAPx.AA`.A9!APژ.A`;ܣAor.A8]Ad.A6Aor.AףA(\.A 4AZ.A*A.A@ UA.A<A.AAA3.A AA.A?ΣA.AsA`%.A`2A.A AơA@.A`mpAXڴ.A)Aڴ.A A̴.A oA8.AXAk.A }A.AAPD.A@ϔA۳.A@Aó.A AXh.AƣA 9.A&A..AAM<.A`ģAT/.AeAH.A Ax.AկAP<.AA.A`KAҲ.A@W%A4 .AA.A Ah.AନA0.A=dAt.A DA\.A ɤAА .AWA( .ANAs.AoA`.AAق.AA0}.A`AX.AXyAh'.AbAK.A^ܥAް.AAXE.A7A.A@AT@.AA̯.A\A`.An)A^.AӿA 㭮.A 8ͤA1.A$A.A$Aan.AAXhU.A1A"G.AEAP;.A`6A09.A sAC.AA@Y.A=0AX.AAR.A]գA.A`B̢Al.A8A@#.A`aA.ATHAg.A@9A.AvA'.AˢA耬.Az$AxU.AA"7.A "A`.A A@«.A2AD.ADA.AAX.A`)>Ah0.AsAJ.AAP.AA.A ~GA˪.A@5Axm.AA.A ¸A{.A=A+.A4A.AAX5.AO$A1ݩ.A "A.AA0.ASA8g.AA[j.A6|As^.AfA9.APApx.AXAȨ.A`]VAh.A`]߲A-ߨ.A`'AX.A KAI.A A l.A`KA@3.A@AQV.AA 8.AA8R.AapAh.AAڧ.A`)|A.A9Ae.A A𴦧.A 'AB.AA.A`A.A͵Aps.A$A0|.A0HAጧ.A`) A͠.A0HA .A_A.A׀A(.A@θAx.A@MAXg.A`A K.AιA8.A0AS.AV@A .AA.A6ȸAxm.AAH)֦.A`\AlĦ.A#A.AA.AA.AŤAh.AOA7r.AjAh.A ܵAp`.A AP^i.A-AY.A`A@.A&APY .A|EAH.A-fAe.AvA&.A 䖹A4.AgǹAH.A.A@5.AQ.A`mYAXT.AjA@).AsAG.A}ӻAP?.A@A.A`hAQ.A$AXE٦.AAPȦ.AlA`¦.A`XAJ.A Aߘ.A`ӻA0t.A#]An.A@>LA[H.A@PкA C.A/A`.A-MA(ͥ.ADA.A`wAH.A$RAPMޥ.A Aă.A`ùA`Ǟ.AzA8gȥ.A[A.A /A.A@RA@c.AD^Ahc.A_Ah.A|ݻA.A AX[֦.A@ټA$Ӧ.A@A .AKAz.A9׾A,.A0A,.AA.AAPf.A`vA¦.A<A(y.AKA-.A ͽAe.A$A@S.AླA0.AA0U.A@Ax.AA 0ڥ.A-A.A A.AA(#.A A`6'.AM Ah/.A=(AX.AA`.AAv.A/AU.A?Ağ.A?ÿA+.A[ֿA䪦.A=Apή.AϿAڶ.AAr.AA`.Ay̿AHл.A ٿA .AAFæ.A59AXhԦ.ArA@Aަ.ABA̦.ApA3.A[A`.A OA.A Aгz.A`daA3.A`A6.AxA}.A[Ah.AAA`v.AA.A 9A<.AQLA.AoA8.AAX:.AA.AAq.A,AHi,.A u]A:ҭ.AAt.AAТu.A`)A3.A@!+Ax.A@{A0ʭ.AAPx.A.A٘.AA`r.A@NAk.A?Aؒb.A lnAз;.AA6.A Av .ABA.A@`!A.AA.AUA.AAb.A lAs\.A Aw?.AЄAF.A@W]Ap.A@,dA =.A quA^f.A@ƛA@BC.ANAxJ.A Ax.A`A.AAr?.A`]A.A-AB.A`[AxI.A A+.AA1.A@#kAV.A`"As.A .A.Ay%AHƤ.A`Aɤ.A)AtҤ.AAX.A൚A0Ӥ.A_Aܤ.A=A@.A3A .ALFA6.AA1.A6A=.A |AH.AA.A$gA #.AXA(횤.A`TAK.A ZAH..AdlA&x.AJAZ.AoA( 9.A-aAQ7.A ~EAU.A ZAP.AAY.AA.AA`C .AA.A@A,.AjAG.A AVR.A 8Aгb.A |;A Mv.ABA.AA8g.A;A`6.AA.A4wA&֤.A@A(Ӥ.A&A@_.ARAʸ.A`A.AAPYڤ.A+HA@ݤ.A`RA.AAxKV.AAC.AZA`6E.AA.AAl.A@A.A@xAx .A 8DA8֣.AA.A ۝A.A3uAΣ.A@Aeţ.AAP.AA؁.A Ap.AAP.AA<.A`*AP..A@NA8.AX5Aʣ.A 80AX8.A'A@.A^Ad.AAH9.A@WZAdģ.A#QAף.AFA(zӣ.AAD.AAHۣ.A=A .A`YA".A@, APM .A6FA@ƣ.AAeأ.A`,Ap£.AgA@.A &A3`.A SAЄT.A`Ap .A A.AA(.AkAxKڡ.A6Aɍ.AAȵa.A@fAPO.AA`.A஌A 0.A A[ޡ.AoA.AM,A/.A A0.A@aA.A@%A`D.AAp١.AAXD.A_A@N.A ZAX.A@yA|.AA+l.A ZjAc.A`[[A8.A`A&.A`AH~.A`SA3.AAؠ.A c[A@.AaAhn.AA;.AlA.A:AX誠.AGAPچ.A:A@ౠ.A NA(l.AwAxb.A`v#AZ.A`UA~.A"AP$.A@ jA@.A]AP.AA@.AA.A@,dAPS.A̚A`-.AEAf1.AziA8t.AbA}.A`b?A`.A 7App.AFAj.AFA}.A`KA 3.A AK.AAhP.AVAp Q.AOAO.A>AM.AacAA.AacAA.AR~Ax$4.AR~AH.3.A@WA@.AF]A(.A AU.A@A8.A A5.A AH.3.AΤAD.AA8C.A@#A0Q.AA 0W.A`NA(՟.AIJAӟ.A)A(KY.A A@\.A`o0A\.A2Aß.A@Aß.A@OA=.A[A=.A ZAp_.A A8 .AײAhi.A AT/.A utAH.3.AA*.A/A 4.ANxAp.AhA.AAP .A qAxG.AA72.A`]Ax$4.A AT.A@ggA-A.AMAN.A@AO.AzA()u.A@ AU.AA 4.A MA.A@#tA`=.A@sA@S.A}AV.A΁AY.A47A[.AWwA8j.A0{A`h.AAe.AMAIc.A}Ax.AA(.A@Ap.AVAX .AAH:.A.A".A`A|.AhA–.A`A.AA@.A`A0.AsAh@~.AqA~.A଩A&.AA.AApI.A`VApڄ.AA.AA7.A`A].A ۇA.AAxˊ.A Ap}.A $A .A`A Ҍ.A:A8Ů.A9/A-.AAö.A An.A`A.A@MA ܠ.A}A.A?Axz.A AX .A@PA@.AaGAp.AAQ.A@GA(q.ARA.AshAP'.A6A .AͺA83ɠ.A ۠A.A`)A8g.A@!AC .A@ A Ԡ.AG;AXѠ.A0AIݠ.AA|ߠ.A` A@.A 1AM.A@#A.A@8AI.AtAF.AtzA.F.A{A]E.A}AKE.A}AHa.A`A!.A JBA*#.AH>A(K$.AEA%.AWA(.A@AAߠ.AfAm.A@A.AsA".A.AA؟F.A ABJ.ACA`a.AALc.A Am.AAy.AA`b.A3 A3.AzAأš.AA`!.A ~A֡.A 0A}С.A"Ad.AwAН.A@!OA.A<A`.A`&A(?.A AXA.Ay?A|.AmXAġ.A΅Aϡ.A A.A6 AҤ.AA.A lA5.AA.AQA*.A AMt.AaAj.A@߃AK.AA8ݠ.A#A`.A`AX.A`A.AAQ.A $AP'.A@ A.A`A.AA.A^zA+.A`K[A #.AAP).A9A+.AAd9.A`A'.AAz .Az(A@.A`IAh.A@!A8.AyA.A`bAج֠.AAW.A``A#.A@GqAx5.A VAV.AUA1.A-A9.AAB.A`f,A^.A` A&'.Ao@AX$.A@NA4%.A`A1.AxA3.AjAGF.AAI.A |A0_.A`KAc.ADkA{k.A sCA.AA𴒠.AftAX .AbA.ANAǕ.A`)@As.A A̮.AA >.AʤAZ.A`GA.A&+A.AFAd.AA..AoA.AA.AAP8.AAx.AAN.AA`ʟ.A34AΟ.A@ A.AA=.AA0.A^A.A APo.AA.A Aן.A6Aҟ.A AHz.A9Aؽ.A1A.A A4.AA0L.A'Aȓ.AVAp.AA|.A 8AH̟.A`KA.A A c.A҇Aؒ.AxA.A@XAWM.AA?.AxxA,.A 0Ap.A@AG.AAk.AZA̿.A3A(\.A@8AB.A`)A.ANA8.AApl.ATAQ}.A`vAxt.A@AVy.A A(܏.AG]Ap룞.A]A`a.AAu.AlA?.A6}A.A A.A3!A`e*.AjA`8.AA8B.A A$.A`A0.AzApʞ.A3A̞.AR,Aeɞ.A.AАÞ.A`2A.AJA.AxMAlt.AgA8n.A@`kA@].AwA @.A$_A@S:.A`mZAxF .A 5A.AHApӝ.AA`a.A@ A&.AA،.AmA#.AA .AAL.A=Auʝ.AAȵΝ.ARAԝ.A +Ah.A`ZA ).A@A\.ApAɞ.A\Ax:.AQ0A!0.A@AP~.AA8Ţ.A AW.A`QA跍.A xAc|.A_ALi.AfAȩ.AhwAxuܞ.AA.AkA A:.A`A A.A e AN.AX AA.AG Ap2.A`= A".A Q A4.A S A˜.A`z A.A A.AB Ap.A@ Azq.A` AXhR.A` A<.A@ A>!.A`" AXy.A A0f.A AЄ .A`D A"/.A A`$.A~ Axu.A@N Aě.A A𣼛.A@` A0 .A Ap.A` A ț.A A.A A.A A8B.A AȘ2.A֔ A6.A= A`T=.A@ A@.A`AȔL.A@A@r.A;A <.AA@.AAٛ.A`dA.A0AP;.AA@q.AuA3.ApSA2.A@AȤ_.A@rAX.AKAX.A`*Aϳ.A@cA{.A-AX.AyAǒ.A@GAB.A`)A.A+$A.A A2.AAX.A@AΝ.A@>AP.AA7.AAŽ.A@>Ax).A|APx.A@2Ah.A6A=ԝ.A_A.AOA.A!A(.AA'.AjA'.A5A..A3bA:.A@r<A 8.A`A`~.AA.A(Ax诞.A)Aў.AA`6ݞ.AA;.AA <.ALFAz.A@A`.AA(L.ArA0H%.A0AS(.AܾA`*.AܾA-.ARA0 .A}AhD .AA֞.A`mAP՞.A@A О.AA@f.A JAh.A,Afd.AAP.AArH.AA>.A)A07.AjA+.A6A컞.A`A.AAh.AtQAP.AA.AZA.A3A.A@A!.A Aު.AOA`.A cA0.AeAt.A[cAX.AAF@.AMA2.AmAo@.A} A<.AA .A?3A .A^AL.A` FA .AoMA.AAh'.AjA:.A Ai-.A A@#.AA8s".A`-Ay.A!APk.A}A-.AAȶ.A.A0=.AVAeO.AfAkI.A=A@uD.A@Am.A`9AXz.A`AA.A߻Aݵ.AAJ.A <A.AFAxi%.AA@T.AgoA(j.A`AX.A`hAlП.A Ax)۟.A~A1.A&A@.A@`A0.A@,Ap0%.AeA1.A9AHC..A A).A 8A.A`;ApA/.AVA.AA?.A;A(.AJA >.A6A0Te.AAp=z.AoAB.AaAV.A@pAhL.ApA .A Z!A`.A-A΋.A@y>A'.A`*Aȇ.AGAo.A>A#.AA.APA/.A A.A`A8Ơ.A@A.A@A.A`!AJ.A@NǷA0Hߠ.AV.Ax.AaAsk.A {Apb.AūA[j.A@)AY}.A ێAC.A5IA.A@Ap.A ASɞ.A`BtA.A uAc .AհA.AQA(y.A,AV#.AAH.AA .A=A0'.A`A@.A UAό.AAX^.A+A^.AwAE.AhA0*G.AXAxW$.A`qA@&.AѯA3.A A8 .A J?AБ..A@ᆱAK.A)AH.AHcA.A@A%S.AײA.A;A.A-˲A8..A ۱AP@q.AvAu.A@W"Ad.AzűA7.A`KOAQ .A԰A`%.AzZAI.AǯAp`.AHAp.A WA.ALSAx.A۬AD.A٫Ar.A`pAy.A qA`u.A A8n.A!A~.AdA⚥.A AR.AAz.A@ ʩA4s.A=Ar].A`hAc.A (A pݥ.A A<ڥ.A sA U.A쭧AhV.AA0P.AnYALD.AWAp.A$AY.A֤A.AAp.AqAb?.A@WADx.A A.AA`.AŐAX.ALAH.A=Ap.AMvA!.A@ئA".AVApN.A@iGAh.AA7.A A@Ͽ.AvA|.AAX5@.A?A(1.AΟA)*.A^AL.A@fA.AQAT .A3A0.A|A*.A0*A =3.A`vܤAe.A@A.A@Ap.A'AXh.A 3AH).A|}A8"`.A@yסAV.A;A8痨.A$A8痨.A cRApʨ.AşA4.AZ`AV^.A@N AL.ASA.AAͨ.AZA8tר.A׎Aϩ.A`vAP.A ӛA!.A<<A9.A ĚA@FȪ.A|A2o.A`]AJH.AI'ANk.AĝA@ׇ.AVgAǡ.ATAX.AƜAP.AF›A`d.A@zAf.AAת.AgxA .AA ..A APH.AA`a.A뵗AXh.ARTA`v.ApA~.AA.ARA8.AGAf.Ap A`ᔫ.A`)4A83.AGOA.AjA#ƫ.AVA0.ӫ.AjAX.AA؞.A[Ah3 .AAP8%.AALE.A[Apc.A Ar.A |AV.A`)ZA淬.A %A@.AږAW .A`˖AX ".AץA0.,.A@gAz2.A@ AQ5.AԖAW7.ARAأJ.A$A#X.A"AP8p.AGAp.A A.A&AP.A`)0A.A@ ;Akڭ.A35Ap.A0A.A`)6A` .A<A`.A 0A`(.A*Aأ4.A =AP8F.A wA8O.AA\.AzA`al.A@ЗAz.A]їA̔.AAB.A=A@.AA{ή.A]A.AAX.ApA.A&AP.AQ>Af.AUA.ArA.AAp.AÇA.AA@+.AA(;.AA`K.AGAf^.ApA@p.AӘA(\.A`fAP8.A A̾.AA).AzԘAأ.AGɘA.A̶A0.A@ᢘA.AQA8.A`fA1.A cAkL.A TALZ.Ap_Af.ApAp=x.AcA8.A )Aΰ.ApAp.AǗA.A뗗AQ.A{AW.A]AL(.ANA4.AQAE.A `Apc.AzTAfp.ATA.A3EAX.A3/AQڱ.A2A.A=,A).AKA`.AJA.A@ GA*.AZA?.AGcA[.AgAw.A`)VA .A=RA{Ȳ.Az\AV.A jA.AUAQ.A;A4.A@ =AY.A`+Au.AA`.AA.A &A Գ.AA.A[AH.AA.AAQ3.A[AHP.AA`.A`A`an.A@Apy.A=A@׎.AAp=.A`Aਲ਼.A3ӖAp.A`Aƴ.AzZA=۴.A6A.A`fDAX .A`)AB,.AږAG.A`)A_.A 2Azw.A@gPA(܂.A3OA.A =A@W.A=Aŵ.A@ Az۵.A9A.A@ 3Ap.AAQ.A`A0 .A['AP88.A DAH\J.A>AfZ.A@8An.A@2AHa.AA듶.A=A8.A3A֦.A= A{.A:A溶.AZAʶ.A\Aڶ.AoAL.AYAp= .A[=A(.ASA+.AAX<.A`)ƗAK.A]AfN.A>AV.AOAa.AKAP8j.ABAp.ATAWs.AAkz.A׭AL.AؘA0..A@A.ARAHɷ.A AHѷ.A`f AH\Է.A4Aҷ.AGQAӷ.A]Aݷ.AqA.ApAs.AA.A`9 A? .A AX .AߙA` .A ՙA.AA%.AfA(.A8A,.AAF7.AjA@0N.AAV.ABAPn.A`92A(y.A-A.AAPk.A4A{.ADA .AFA>ʸ.AIkAHθ.AAX[ָ.At}A/.A A.AA(y.AA` .A-A(q.A cA8x.AA&.A`KAȩ..A@A12.A{A8.A@uA֐.AΎAX(.AÛAbp.A 8Ax.AQA.A3A .A@rÜA@䈹.AʜA&.A ɏA0.AAW.ARA8穹.AΑA2.A A8ʹ.AܝAxhع.A`BA.Ah%Ao.AKA`.A@EfA\(.AǀA=.A sAQ.AmAqg.A A y.A;NA.AXAǗ.A}A̺.A@ ПAۺ.A@A9ߺ.A@g֟A.A;A.AמA޺.AxAP'ں.A]5Aк.AKAXFͺ.A؞AȲϺ.AqAպ.AA޺.A sA0X.AAg.AAp .A3Ah .ARA.A |A(.AğAf .AA.AAV.A#A8,.A92A0K.A}CAȩX.A.AYm.A`:A(O.A`fA挻.A uAF.AݠAhb.A A'.A,A`C.Ah5A(\˻.Ap'Apֻ.A@N+A 0ܻ.Ao8A8.AmdA(.A 8APk.AA.A@Ap.AңA/.AA( .AޡA.AVA.A@%A7.Az5ABS.A OA j.A@yHAP|q.A0A(x.A A؟.A A.A@ϡA0.AA`Ƽ.A 룡AѼ.A6AP׼.A áAxܼ.A ߡAi޼.AAּ.A`9A̼.A@pAPȼ.A@ AGǼ.A@rAh@̼.ACAӼ.AUAX޼.AQ٢A.AA{.A;Apl.AޢA84.AfɢAp.ANA .A@5Aȶ .ATƢAx.AԢA!.ARݢA:.AA(D.A`AN.AA@X.AA(c.A@A i.A A@k.At5Ak.AKAg.AcAHf.A`pAh.A@xA y.A A|.AA.A@rڣA.A@CA`6y.A,Ay.A6ZAO.A0A8ւ.AA8.AͶAl.AҤA@1.AAD.A"A軜.A3A3.A8AĶ.A@N;AŽ.A;A{˽.A lMA8Eҽ.A``AW.A`ՀA6.AA.AאA .A|A.A`{A.A)}A%.A`mA;.A@EAU.A@yߥAu.AKA .Ah,Aƴ.AeAa׾.A쎦AP.A;AQ.AϦA<.AA-.AA<9.AAA.A`AXG.AAQ.AAn.A OA\|.A[qA,.AA0.AAP.AĽA` .A@AHPʿ.AAlٿ.AN#A.A@A.A`A.AA).A@gЧA2.A֧A@=.AA4B.A`AD.A9AS.ABAP\.AըA0d.A@NAv.A@A.AA.AwAS.AAz.AA8.A` 5A.Ah_A.A[AY!.ASAпK.ATAh[.A eA`d.A}uA k.A@5A@p.A AH)z.AAP.AA0.A;A8.A`A3.AAHa.A`A*.AA .A)xA`2(.AkA()3.AsA9.AࣂA8?.A坩AH.A` A(W.AAPjb.A~A`vl.AfA`Px.A l]A8}.A_]A@‡.ASA^.A?A 0.A@-Ad.AZA.AhƨA).AA N.A@A .A@`רAP^.A`A.A@pA.AD&A .A.A(.A 0AH-.A`Ai.AAPx.ApA#.A`hAHn.A;oAd.A(tAe.A`oA0j.AMA@.A@pA8#.AA=.AA@.A@ϓA`.A/A .AzA.AhAR.AvA/.AjZAB.A$AP.W.A zjAA{n.Ak*A1….A5٦AM.AoAh˳.A A@0.A@A-m.A.&)AǑ!.ApXu6AdyA.ASEAq.A TAf7٨.A;̙DcA8z.Aǵ"rA.AQAK.AdnAH.AA~ .AR;!A.AMlAtn.AktoAs;.A2\i<AX_M.Ao3ǧA.AӍ.˧Anb.ALZDͧA _[.AĶA.AʧAzӀ0.A1JAicپ'.AndA]3H.AxQu_Aog.AA $.A.ӦAMa_.AseA.A#^#APK.A0'AUt:.AR!>A^.A|A~.A)A.A`Ah.A'A.A 6ޢA R.A@rA 5.AA@.A6A>i5.Aq5>A2/.AoA2..Ad,5<Afe3.A++Avכ[<.AlӺAfMfJ.AGUDLA\.AAjb.AmlA9c.AڍHAa?Ja.ADjA [.A AS.A&.Ap|R.A ~AP.AɗA U.AAs`.A NAs.A$Ax.A SA.AA .AԖA.ADA.Ac!A.A A.A|A .AlA.A`tAH\.AwALO.A?AH.AmA (.A  A.Ac`AA.A /A.AēAp.A6KAA .AkA`.A`ǑA@ .A첑A.AvAP.A@ A .AcAq%.A@A$.A |A .AA.A@A.A l[A' .A*A.AA ".A[ǎA8?.AǎAHH.AAHR.A?AR.A@pAHM.AQAdA.A QA0..ApAf.AÈAI.AAx.A)uAX.A@OA(.A \A7.A`΍A.A QAh.A`QA.A Ah.AwAX.A 8rA.A c?Ax.A:AA .AFA(`.A@EMA:.AhA8.A@,A(&.AˋA+1.A`DЋA<.AA0{F.A&}A^.A͊Ao.A A@.A6A@.A lAH.AXAhވ.A/A.A ~҈Aw.AA8#k.A ZA`W.AM1A@-.A5A.AA(.A` pA.A %AP.AAx.A Ax).AA9.AgA(J.A&AQ.AA%.AMnA.AzoA,.ÀAP.AAȲ.AʄAx.A ZApp .A;A"..ALA!8.AхA0=.A@A9.A`RA00.ArA,!.A|xA.APAh.A` A@ .A ܅AY.AhA.AA0.A4AX.AaWAXI.A AA`#.A`f΄Ap=.AରAp.A qA.A&A.A 䨄Ar.A /A .Af=Ak.A`AhQR.A@)A8.A7A.A8A .A(.A.AAL.A`KA(.AA.A@iAX(.A؃A.A)A.A@wA.AGANt.A*7APj.A$AHe.AAb.A AW.AAR.AAJ.A`A@.AʂA #1.AЂA'.A AP$.A@WA,.A@dA c..A`0Aw'.A`m A0.A`BA.A"Ah.A@(A.A A.A`́A.A=AP.AA A.A@pA.AApq.A@ɁA$_.AdAQ.A#A L.A-@A&J.ApAL.A`AO.A@AI.A4ςA 08.AڂAj*.AقA!.AA:.A@WAp.A"Ap(.ASA }6.AA;.AA7.A, Ah+.AfdA>+.A@A/.A߄A)5.A@,AH/.A gAE!.A A&.A A0.A;Ap.A؅A .AAЦ.A@A.A@ AP.A s A@.AЅA .AA.A`A8E.A@A.AA֭.AA\.A`]AH.A`Aq.A@hA@pY.A@QAK.A0A#.A`A8 .AՄA.AτA.A`ƄAP.A=AP.AࣔAP.AjA8.A=6A`a.A Ah3.AAh}.AЃAWd.AAR.AAG.A AB.AGAkA.A YA7.AGA`..ABA`.A GAuտ.A]CAz.A (A֣.A[Aأ.AAy.A3A#n.AAp=f.A Ah3a.AAP8b.A@Apc.A@>nAa.A֓AQ.A߃Az/.A Ah3.A˄A.AA.AAp ޾.A-A(.A`A`.A@AH̃.A A8m.A`)!AM.AMA`.A܅A.AAIڽ.AnAЮѽ.A pAQƽ.AA.A+A`C.A`KA8.AtA.A A𸌽.A`Aw.AA".A`0Ab.A$A.AAJ.A A؛.AʆA^.AB{A0b.A`Aս.ApA0;.A@A.A@ւAH.AA.A@W=A p.AA R.A ՁAx.AA.A pAx0.AJA \.A3Au.Ad1Aޅ.ABA0H.A3݁AIξ.A@*Aؾ.A@,)A.A (#APo.A#A.A"A.A@gBAP.AGAA#.A@2A.AA؞.A@ AP8.A@A.A qAp $.A@A,.AA3.A@ A8H.AA c.A@A`am.A |A@v.A`)Aأ~.AA׆.AA-.ARAu.ABAB.Ap3AV.A@G+AP8.AR*AL.A`f&Ap.AAB.AA8.AA).ARAQ.A@HA.A@2A.Az(A֥.AAf.A~Aּ.A ~A.A ~A8.Apo~Ap=ÿ.A:~Aſ.A3~AH.̿.Az}A8ҿ.Ap}A8ܿ.A }AH.A@>}A.A}A.A@g}A`.A}A8.A }AH.A@g}A.A}A .A}A(.A@}A.A@ }A*.A }Ap=7.Ap}Ap?.A d}AK.A[Y}AQS.A`)f}AzY.A }AP8_.A}A)v.A}A0.A@g}A.A@}A.A}}A8.A@\}AL.AR>}A.Az6}AP.Ap5}A .ARd}AX .A}A.A@>}A.A}A#.A@>j}A.AN}A.Az,}Az.A@>}A8.A}A.AR}AX ..A`)}AC.A`+}ALX.A%}A(܁.A`}A).A@>}Ak.A)}A.AGI}AH.A[y}A".Aj}AH).A6~AB.A l~A%.AR~A9.A֑~ABQ.A }~A@Wh.Ap[~Ak.AG}A.A@>}AP.A@>}A.A3}AP*.A L}A=.A O}AH\N.Av}ALX.A}A-^.A~AZ.Ak~AkV.Ap~AGJ.AQ~AG9.A~A/.A~AL$.AAk.AyA.AAW.A`fA.ARA.A@G AW4.AA`9.A%AA.A@:APE.A=4AH.A`fA RI.AAP.A A83U.A \A R].AAg.AA(n.A`A(v.A࣬AG.AçA.A׿AQ.AAHa.A@ A.A qA.A'A.AzAH.AAp.AA.A@A.A`/AX.A`) A.A`)ځAL.AA(\.AzA#.A@`A0.A3SA`.A qUA`:.A`8A`V.A,A=g.Az.A z.A=:Ap.A \5A.A/Af.A@@A.A[Ap=.AA.AAQ.A3Ak.AA`a.A@<A8.AGAM.A A`x.AуAk.AȃAL.AûA.AA`.ApA .A ⤃AX.ARA.ARAH.AA.A`)ƒA.AzʃAk.AσA8,.A׃A<.A A@K.A`fApP.A3 A`].A=ڃA`g.A=Af.A |A#^.AGGA@WV.A@gA#O.A@A`J.A@lAp=B.AA<.A`)A;.AǁA$.Az|A!.A \UA(.Az,A5.A qA)C.AAI.A@ AT.AA`].ACAH.b.A A(^.A ՁAY.A`)A`W.A`)FAX.A@dA8].AyA`ae.AGA(q.A@ኂAp={.AA`}.AGقA}.ApA~.A A}.AA8z.A(Af|.AQA….AmAp.Az|A.A`fxAf.A]mAp.A3CAW.A`fAf.AGAh.AA0..A3A.A(Ah3.Ap AB.A=,AW'.AGA#D.AAN.A?AT.AR@AY.A \!AS.ApAVQ.A3AH.Q.AτA`V.ApÄAuc.AʄAX q.AAL.A`AG.A`fAH..A`ԄA.A`nA#.A@>A`a.AzA.AGA.Az܃Ap.A܃A).A݃A.AƒAB.AApn.AÃAp8^.A qALV.AzBAU.A*A`Z.A Ap=^.A At.AA̋.A3!A8.AA.Az$AG.A 8A.A@>4A`.A'A.A#A.A 2A`.ApUA RS.AaAm.A3eA Ru.A@rAWx.AA8y.ARAf{.A`fڃAu.A`fAǃ.AA.A Aœ.AAª.Ap˃A8.A A(\.AAp=.AuAH.A]A8.AzJA.AA.A3A4.AEA(a.A׿Au.A`nA.A`f*A.A@ĀAأ.A`A8.A(~Ap=.A@J}A8.A|Aj.A@Y|A.Az{A.A~{A.A |zA.A`yA.ARxAG.ARwAp.A q1wA.AwA.AvAp x.AHvAj.AvAGZ.AzvAP8G.A`)rvA.A \CvAp.A =vAX{.A?vA( .A`fXvA.AjvA\.A`VvA R.AKvAxz.A6vA.A1vA)J.AvAf.AuA.A@ {uA`.AuA`.AtA(\.A(tA.A+tA.A=tAy.AtAz-.AR,uAp.A`huA`.A`uAk|.A uA>.AuA8 .A@GuA.A@uA.AR#uA0.AsA.A@sA .A sAX.A $tA.AktA`.A`ftA}.AtAB..A  uA.AG}uAX p.A |uAI.AuA.AuAzѾ.A0vA.A6vAp8.ArvAV.A@ vA0.A@6wAP.A wAP8.ARwAQq.A@.xAG3.A`ÁxA.A xA.A3YyAf.A]yAud.AQyA%.AzA.AQ.zA.AQ4zAz.A&zA`].A 3zAp?.APzA.A zA.AzALa.A@{A=,.A@|A.A`@|Au.A=R|A3.A|AH..A!}A(.A}Au.A -~Ak.A~AY.Az Ak.AA83Ʒ.A`A.A3]AL.AA.A AQ.A`.Am.AzbA88.AA3.A ́AL̵.A@g A.ARfAkH.AAh.ARVA(ܪ.A=܃Ap0.A`A.A`A`.A[Aw.A |A.A`$Aϲ.A~Ap=.AĆA`aA.A!A8.AGA𣔱.ApɇA O.AAp.A iA.ARĈAp8X.A3A.A(NAG.AGiA(\.ApA›.AzA(\f.A‰AX-.AA.A \A`Į.A`*A̍.A`f<A`].A`JA1.A@GOA83.A@JAQ.AtA.A@GA@n.AÛA)?.AA.A@ AŬ.A|Aч.A`)`A83I.Ap=A R.AA8E.AZAh.A@yA.AA.A|Az.A`mA ܪ.AFiA\Ԫ.A~APZҪ.AAΪ.AωAxK.A2Aot.A`fAt`.AވA.A@ԊAG2.A6A .A@A@.AuA`.AxzA׃.A A_'.A@Aج.ASA\s j.A݅$AuK-;.A\φA5.A,+sAZ.AA&A(0.Af A8.A`ACy.A(ἋA"%y.A%% A Rn.A^A l.AiA8l.AkIkJAm.A[*A9o.A8 A9,ls.AA#Ax.A~@\ׂA F}.A0vA9.A+HAX[.AXAh@.AA|.AsyxAJ也.AzA.AA^ſ.AjAAW14Ÿ.A廄~A3ğ.A,½sA ş.A1wAgAIgƟ.AZ㗭ZAv&A ǟ.A`]AMAPƟ.A#+O?AƟ.A@2A$5B.AEOAк.A@EفAx.AvAH.A eA.A@`eAd.AeA.A"vA_.AA \.A`A`CF.AAH.A Ap<.A AB.A A΂Ah.A`̂A< ߂.AXA&u.A6Aȶh.A@1AfUc.AROA6`.ApAM1].AhިғA׳].A0Aګ!].AyԥۃA7)_.AO]Ab.AT; AM8g.A6?Aom.A`ZAQp.AMG_At.AsfA٧v.AoA}Z]y.ASyA{R{.A* A@(|.A]3A}.AKA}.A Ax-P.A$A83$.AsA.AFuA`,.AAh3.ADA5.A`A.A`K[AX .A`mAX.AvAL.AanAr0>.ADGGdA%Y;1.A3' QA! $.A305A\[2.A>Ac.AAi.A፨ޭA_+:r.A,_Ѡ~A(Л.AYA0Ʒ.AV4FA5.A@AA:;.ATyJA#09k.AcaA@S.A`ՅA%L.At]Al:/vE.AuPAW=.AxpA̋5.A#ÙAe*..A̞A&.A(A.AA.Ar@{Aݨaš.ANA7u.AsA.A=̓AxF0.A[AXu.A狄AǙ.A@ۄAPș.AKɃARL.A |ЃAp8.AkA9.A`BAPߗ.A;pA8Iڗ.A@>ąA.A@`A0&v.AAȔt.A`ÚA u.AiA0.A@,HAXJ.A`fAx>.AA.AxA.A@NA.AApJ.AA.AAP .A[A .A`eA@Ʒ.A@.jAN.A`A@۔.A ZA@ה.AUA>ה.AUA.AzA9ѥ .AW_Aw.AVPA2.Atz8Aa.AK߸A.AthfAKOړ.A/EnAJrӓ.ApDLA͓.Az%Aɓ.AfA0ȓ.A@DAн.A`"<A .A|؄Av.AzۄA~.AA( `.A3Af.A@Aq.A@>Axh.A-A𢿒.A A.AAБ.A A0.AA.AVڃA@ .A@yA p .A3}AB .A!A(ː.AQBA`ː.AyAh .AYAM .A@ A.AA.A^{A.AZA'.A lYA'.AyA@(.AA@".A`DA`X".A҆A3.A؆AP.A AK.A A@F.A#Axx.A A'v.AAr\.AA=.A`A0Ǽ.AT Aʏ.Av}Ay.Av}Apo.AWA)G.A Aˎ.AaA.A@.AP.A ZA8֌.AAp.A=ƅA0.AąA`ᗎ.A`)^Au.A`fTA~.AAX#J.AAHPL.AX<Ah.A,A.A` A.A`DZApA.A+A`S.A@pA0{.AA..AAH+.AQAx.A \A@.A`]Aq.A@$A@.AA.ABA`!g.AAVh.A[Ahh.A"DA@i.A@A"j.AA`k.AAk.AׅAxl.AsAm.A#Az.A&Az.AH6A.A@6Ax.A #A83k.A@4Azk.A`Ah/b.A@A 1.AAx*.A Ax.AA .A`TAފ.A3A֊.A@A@d.A@Ap.A(A0p.A@yAWh.AAXl:.A@A20.A`A8/.AA0b.AAb.A uA8a.AÅAa.AA0a.A^A`.AVAJ.Av{AUF.AAH".A`߇A$.A}AѪ.AA .A A.A $AH .A@.A .A SA5.ALAډ.A AAȤщ.AaAx.A`A(:.A SA?z.A@An.A A((n.A@A@.A&&APf.AEAW.A@ A8.AA@1%.AqA".A`)FA(\0.AA3.A A?.A ¿AwA.AAE.A8A+.AQHA(.ARA( .A+AG .A`DA&.AQnAȇ$.A/Ap.A`$A8#.ABA4.A Ah.AAPψ.A 8A(.AIAm.A(AM.A AV=.AzA@}.AAE.AdA.AYAʅ.AA .A滉A.AhA0҅.AA@丄.A`A.A}A.AA(.A`A.A9A0.A`sA'Ԃ.AA'ă.AȅA^|.ANƅAȶw.AൖA@>.AƉA㘂.AHA@G.AaA J.A Am.A"Am.A@5A`$.AA .AA$A08.AQA,:.A`]2Al.A0A(~.A@PhAh~.A |AH:.A`TA~.A9SA ^}.A (A8|.A@`Ap8}.AxA }.A@rAH|.A@SAh {.A-A!|.AIAo|.A?@Aпr{.AAty.AFA ov.A 6APiu.A ]Acs.Ag[A p.AAX$`n.A`oA.n.A^A[n.AA(n.A@yxAo.A)yAx(p.A OyA`1q.A`zA^s.A{Au.A`{Av.A X|AxiPw.AŤ{AIz.AjxA |.AsvA~.AZtA̾.A@F.ApAG.A`oAM.AoAޘ.A pA`! .A pA < .A`oA #.AoAP.ApAl1.AspA1.AoAi.A@yoA[i.A@oA^.A`;oA.AKoAi.ADoAi.A oA 1.A@oA4.AnAך.AoA'ؚ.A`TpA.AڦpA$.A@PoA .A@%oA .AEpA _ .A`pAP".A oApE.A`"oAȱT.A`ܮoAe.ApAՁ.A}pA``.AFpA`Û.AxppARқ.A(pA.AVoA.AfoAh. .A &mAX.A\A.A`2\A`rm.A0VApf.ASAx-.AOMAΝ.A`8MAF.A7A.A`]6A`>.AF1A@.A;0Ahަ.A`/AP.A`Ì/A.A '/AӖ.A/Ap–.A.AH.A.A.A c+A.A@+A.A`.Ah"Z.A.Aѕ.A/A.A_1Aד.A2AMT.A2A".ALj2A .A1A.AP1Axz.A`v-0A'.A`D-A5ޒ.AH-AHp+6ؒ.A*-A*ђ.Aȩ--Afʒ.AF-A&’.A6B-A ?".A"-Aҵ.A-Aе.A-Aj.A@5/AS\9.AP)C/A1磀.AmO/AMAK.A@Z/AH.AEGcd/Ah..Aol/A ,g.Aq/Aƍ.AbFu/AJf.A#rv/AlPو.A0,Iu/A)[Ke.A^q/A .A&l/AБؐ.A"/A.Ah.A9.A`KN-A<4.AaףHg-AŒ/.AM-A$L,.A@9N-A{/*.A@$-A9%c).A\'N-A).A.Ae;+.A5x%.AVI..AcC.Ad2.A2U_.Ae7.APxx.Aq=.A2|.A>D.AVTF.AL.A@.A\bH.Ak.Am,B.AQe/A/;.Ac*/Ay/A.A@Ǝ/AQ-.AfN/Ae q.Ak\1/A?.Ag/Ae.AU{9/As~.A4g.Ah~.A@.A}>~.A@EU.Aw~.AHd<-Aw ,a~.AfX#y-A?xN~.AN,A sA~.A}Uo,Ax8~.A9e+A54~.AL+ApN~.A O1+AS~.A@r+A~.AL+A~.A@*A ~.A#Y*A<}.A`T@)A}.ABB)A[|.A :)A(>|.A@4)Am|.At)AK|.A_(Aجz.A'Ax~x.A&A7x.A3~&A8'Lw.A@{%Axmv.A %Aw%v.A#Au.A$A,u.A@g#ALu.AZ#AxXt.Ac#A't.A`T#AA5t.A@9#A@s.Ak"As.A s"Apr.A s"A`r.A s"A$r.A Zl"Auq.A@'"A@q.Ai!App.Af A Asp.A c* Azo.A` AHo.Aov Ax 4o.A` AZn.A`o An.A A@Pn.A AXm.Av A0Ym.A@y Am.AE A nm.A@_ A0&Mm.A` ACm.A@iP Ap<8m.AUAm.AOqAhm.AA`6m.A Al.A g!All.A A!Aj.A A!A&j.A A!AAi.AR!Ah.A@ A3h.A&A%h.A`KANh.AHA Fh.A@RA3h.A4KAPh.A AAg.A/Asg.A AWf.A QA`d.A=Ac.A əA1c.A,AXb.A`}Aзb.A@AH}b.A*Ab.A`fAOwb.AA`ab.A`YAGb.A`Ax:b.AR ATa.AÕA`.AqA_.A5A4_.A`)FAk^.AX/A^.AA`vK^.A Apl[.A?PA@uZ.AAxY.AlA W.AAP/lV.A`"A c;U.A`fAu2S.A AP.A#AX:P.AAD!P.AA1IP.AҭA8P.ATAh&P.AxAP.A`[CAO.A`WAO.AnAx[O.A PAXN.A`1AN.A`9AhM.AAM.AAL.AaiA ML.A`AKL.AUAwK.A@GAK.Aj'AFL.A S A3L.A> AL.Av6 AK.A AABK.A`kAp0J.AAfJ.A{ANH.ATA`4H.A A`G.A A NG.Ah AxG.A@ؖ AH.AYAH.AOAH.A=RAvG.AAG.A@YAP^gG.A; A`CEG.A@ye A0G.A  AVF.A  Ah%E.AT A 2D.Aa, A-C.Ax} A8#NC.A m AgDC.Ad APC.AAC.AA$C.AՄA`TlC.A uAhQYC.ARAYOC.AA`LC.AAЀBC.A` A15C.AA0H C.Ap AC.A A`wB.A@ A@uA.A@ Aأ@.AXx AI @.A@iAP?.ATA>.AiAd>.AgAd>.A ,A(>.A ,A>.A Ap >.A@YA[>.AxHAg>.A;)AHL=>.A`CAxi=.A@A=.AA;.A 8,Apʰ;.A@5&A@%{b;.AkVA0+;.AL~A:.A4A}~:.AA}B5:.A9 @AX9.AjA#:.A`.A0:.A[A9.AQAp9.A॥Aȱ 9.AjA9.A =A`9.A@A9.AŪA9.AsA 9.ADAb9.ADb!nAͫ9.AtpAi9.A{AHݎ9.A@5AS u9.A~9nA`CZ9.A@A";49.AqAp 9.A`;Awΐ8.A ;A;"8.AY@YkA78.Aǧ*Ag7.A4@<AR7.ABsAbN^7.A;8gAh?7.AD05AH6.A Aa 6.ApAB5.AAXF_5.A' A15.A`DA *5.AAuH5.A Av5.AOAh5.A})A6.A`Ah] 6.A{>AyaN6.AA\6.A1pzPAl_6.Ak As!26.AҔA!6.Ad^+AOiݲ%6.A lAS7 )6.ACA9f+6.AQA)y-6.AUANk.6.AWnA6/6.AiZA6.A|A 5.AAxd5.A#6AX4.AIaAȓ4.AEApJ4.A(RA4.A@ϥAz'4.A;At3.A(A:2.AA(1.AOARO1.A@>\Ah@0.AAn0.AdA8p0.AYApJ|0.A0A0.A ^A@0.AxAX-1.A A".AfAb".ARAF".A/EA0*!.A$@A!.AAP7!.A`Ah{!.A A@5!.A̓AU .AmAp.AAPK.A֘A0.A<APk.AXA.A wAz.AA@.A A.ATAho.AgA\.AAsH.AOAxK.AA.A@As .A A^.AA`,.A@Aԫ.ALAN<.A@A.A $A.AA.A 4Aܝ.A qcAA.A`!A.AZAN.A@%JA.AQA0I.A@,pA^..AvA(n.AgA.AhA.A@!A .A`Ap.A@A8.A`AН.A@Ap.AA*.A@6Ab.AA0Ǽ.AAxd.A OA0.A`AM.A@,pA".A@,pA?.A:A.AT3AJ.AM$A.AAA.AAؚ.AAx)F.AעA?1.A`!A?1.A5A).AAx .A<A.A@`8A.A@rA=.A:A`!.AA .A@A`Bc .A`5Ak& .A [A\ .A \A@k .AFAz .AA0L! .AA`vJ .AEA@; .ADAH1 .AQA .A]Ag .AAK .A@yA .A[A0 .AછAeP .A;Azx .AˢFpAiH.Ap@AZ8.AA@~.A A gu.ARAF$.AA.AԒ\A.A@A`.AAvAȔ`.AA;.AWA`.A@ Ah%.A ^A`6.A?Ah.A`lA.A/A0[.A#A.A`fA)yU.AebAmޥ.A 'A(<.AaMAI.A@ Al.A-ˬA;޽N.ACmSA*-.A߁0A6V.A&dtfAzl.A,An[.An9: Aw .AKR A1W.AGO A}s'.AM AX.A AG.A ` A<.And A8.A6 A,D.Ak A.A&D A~yVq.A٘ A|B Y.ABr AB.Aŋs9 ATT/.Ac Asr.A]> A. .A|: / Ap.Ad Ar.A A.AXL A0,.AO A^.AZX߲ AVڻ.A6JQAcrP.AkA`C/.A8&7aAz}=-AvA-A0A4t-A˦|A]8-Aj-A(-A`A-AMA ^-AA,-A A3A-A@,A3-A@EAP@=-A`hA;-Aҁ&A6`-A@i'A -AS0A_y-A}Ц2A:-Aq4A x-A#‹U7Adn-A\X9Ai-AB <A-A@-A{ocA5jZ4-AF eAʍ-A@#fADv-ABZgAf e9-A/ܠgA0v-AhA,,-ApMiAN)6H-Aٻ(tiAy*-AVZviAT-AcjAxAO-AOcjAeĢ-AP bkA#-A8oOSlA(B-A`;mA6b-AmAx-A[znA*j-A?9oA@*-A@pA6-At*pAW^-AjqAREJ-ArA@S'-A!rAx|-ABKsA-A ItA6 .-A( UtA6p-A4vuA&p5-AoԼuAX(G-AYvA)d-A;wAppT-A fxA;-AvA0.A iuAH.A rA .A]qAxj.AqAp.A rA@.AyqAnHWX.A`pA.A pA Z.A WpAj.AI,,pAx*?.ApAB{X( .AY pA\Ctm .Av93pAj{R .An)pAV= .A'BRpAU: .AippAh| .A pA( .A7qAid6 .AaPqA' .A rA(A .AjVrA6 .ACrA!; .A0erAFr .AmKrA%Ϫ .AܵrA;6̧ .AJrALy6 .Aw'rA,Q .AۙrA(9 .A .A`6iA4DE .A@@ևhAq.A'hA[m5.Ang.hAM.AhAXz.AFZAu/.A >YAi:].ACYA Ȯ.Arq YA\.AYA.AqYA&|.Ap|+YAoK.AVYAKHz.AviYA~h.A>'YAj.A 5ZA.AdZA~^.AD[A.AXAH? .AXA]Z .AWAMX!.ATAhݐ".A VTA/#.A TAh"#.AUAbX$.AVApS$.A*WAH#.A}zVA#.AVAHr$.A@,WA$.A@NWA $.A@WAxƩ$.AכWAs^$.A 1WAd$.AVAX$.A@*WAY$.AmWA$.A XAS&.A@`_A v4&.A5bKG`A]]e&.A%`A@ %.A-|FaA/N%.AHSaAd%.A bA(#&.A+eAy'.A@lA7(.A`nA (.AVoA'.A/ApA0'.A-juA _&.AoA`%.AnA|%.A GfmAc%.AsulAx*7%.A4ޠAkA6A%.AMFjAP$.A^FhA15p$.AsgAг$.A`!fAak$.A9.bAf $.At9`AWy#.A^A`=#.A,^AH!.AtaAa!.AaDdA & .AfAf.A@NfA@.AaZiA@.A mAP.A nAR_.A u{oA.AqAe.ArAPk1.A&rA .A NtAPk1.AuAhy.A`DvA@k.A OؑA4ىod.A͹(&A1a.A2b.ACc.A[@ړAZi.AZCr҄As.Ax+AW.A"͕APk.AhA(.A`;XAxY.A (Ac|+<.A3 j^A#q".A1SA , .A꓉A`.AAV .AxI͈AM.AX/DA*+.AH^WA๕.A1AS.AV*Ah~.A;"A<t.A"ׇAؽm.A>A4Mg.A@{A0c.A'HAES.A<AA.A!:A[D-.AlņA.A챆A$R.A|A3t.ACjAq.A+ Ab.AZ~ABJTk.AUdAVo.AUWA|.A0HAN.AN A1B1.A"CAjd.A0ݙAG.An1Aӑ.ALacAX.AA c.A5U#~A9Gm.A67n~AdN.A~-~AB0J.Ai/}Ahi.AHG}AaP.A@}A2.AG9}Anĕ.AFn}Aw.A)Ui}A):.A'}AϨ.A\|A.A;u|Afk.AIn#|Ahm٢.Ay9{A;g7.Ar骔{A;.Ay[{A^_Vq.An-{Al/\.A:H {A'G.AtJIzA(j1.A zA$.A 9 zA.A`{Ax[.A_zA t.A zA(.AzA.A x{A.A z}A2.A ۇAn .AτA`r.AdA.A@/AQ.A`A`G.AjzA.A`[A@< .A@CA} .A@DA .AA؎ .A`A@@ .AjAH\x .A XAH .AA@ .A'AJ .AA .A`A~ .A@>{Ax| .A \A@ .A@WAh8 .AtJA .AA .AA(+ .A=A8V .A]A@ .A`Aɚ .A&A8V .A&Aw .ASA}{ .A u`A /? .A}Ae .A"Ahb .AwA06 .A uAp .A@5Ash .A A-/ .A SAXQ .A SA(Kz .A-A.A@NA@.A@AAa.A`vA.A JAX.A@+AM.AA/.A?Aj[.AA[.A`Ao.ADA /A.A$A9=.AHA.A A.A@5 AH.Aa_Ae.A@iA0-.A`IAp,.AAj+.A+XAPI(.A`}AF.A@iAP8 .A[A .AoA .AA.A@ϯA.A_A@.A A.AAใ.A  As.A Ae4 .AAX: .A$ڬA7 .AA: .A@߫A8.A{Aȶ.A ~IAi.AAX.AzSAH.A`]A3o.AܰAKd.AA`Ÿ.AA( .AqAp .A@5qA .A SA)U .A@A .A Az .A`A .A@ AP .AAxz .A@gA .A A0e .AA`T .A@p6Ax .A@,A .A4Ao .A@A P .A$A . .AaA( .AOAH .A QA8 .A>Ax3 .AzAxL .A _A_ .A A܀ .A@.BAܢ .A Aȩ .A@r"A .A sfA .AAv .AzAfP.AAH\.A@7A 7.APAʺ.A?6A3.A0+#A%.A,Al.AW91_AN.A#UAXw.AՀA+ .A`oAHr .A`VAR.AveA\Y.A@0A.AA .A+AH\B .AAa .AAx .A@`As .AyƺAc{ .AFAa+\ .A姟`AsB@ .AEAs-( .AMxlCAh1 .A5 AS| .A`5SA2.ADA+5.A/@A.A`A0S .A0A* .A /Aj .AxAH .AA] .AAA7 .A@AHכ .A{QveAIɷg .AouA2X .ACA=K .AsWAj .A#DA`) .A/<- AK .Aj>AR .AQXAL7)~ .A`mAu .Ahc_|AȔk .A@,A .A@pA:JK .AG%AZ .AxTEA .AP6E} A r .AOAA .AĝAP.A lAH(.AA.A^KA6B.A$As.A@A(.AApʲ.A`DA:t.A dAyֽ2.A/e} AeA(t-A@,CA(-A`KEA'?-A*GA-As HAXXb-A7IAz-A YJA%V-ARLAoD-A`lMA,]3-A OA`-AwPAׅ/W)-AX)Q~QA@-A}RA+H-AhSA-A`"TAH@-A`BGWA4-A@ZWAHC5-A@ilZA8a-A;]AGZ'A-A :z^A-AǨ_AF5]-AJ`A\BͲ-A'8`A U-Aݨ\RaAá>-AJ daAJ-AbAk-AaA-AeaA-AfaAX-AoHcAy-AŰcA-AƉeAHN-A:fAH-AhA-AjA&-A{AbkA t5-A 0alA--AݙlA~j-ADAm2-A7ĩA`2-A {AA(߼-A]=&ƪA|6ļ-A~A~-Ad=A+~-ABz-AIi$N+AP@a-A:1La,ALFM-A8wy-Ay@ =-AtS.A1-Aٶ/A/iu.-AWV0A--A@.!2AI 0-A 4AT9-Ac 5A/F-AF7A<;-Ay:AH-AAAh-AHEA0-A@6IA-A@NAp-ANA -AtV WPA}5mͮ-AѨQAΐ~-Aؗ-0SA0뫮-All9XTAw-AVJVA3C-AmWAl-AFXA4/-A AZA蛮-A7z[A-A$\AN-A@pbAP7-A ™fAE%-AmAO-AvA,-Ai1A(>-AA8-A -A-A4AX-AAQ-A AO-Ah(A,S-A8E8A[-AɑzGAyh-ATSAߩy-AZA*䑐-A,8[AANث-AZUACo˰-A'FA^-A`MW-A@-AU AH-A?FAhQ-AܱA`-AA-A A?-AeAX[-AAh]-A-<AH-A:rA=e-A98A-AHA:,W-A98A|rK-A[u-IA+-AżA6x,-AT,AJ-AĒL. Aҗgu-A WnABkݝ-A F)7-A<"L-A| uAA~n5r-ARXA .A`VjA .A AqA= .AsAx .A tA8 .AଗrAo .A?8“oAل .A0~yAk4;.A౬^AOl.A+A-AA-A/A@B-AA,-Aޘ AXFA-A!Ay-AL*A_GG.A| uAb".AF)w A2-h .A Wn2A.AÒLnAʇT.AT,QA A[[.A@r@ A[.A zB A p[.ARC A.>\.A@]E Ahѥ\.A@yH A{].AFwM A^.AU A^.AU A? _.AV A$_.A@y W AHzj_.A fX A _.AX Ap_.A=Y Ax_.AoVY A A_.AY AȘ_.A$Z A0v_.AZ Ay_.A`TZ A@u_.A}[ Ap `.Af[ Af<`.A\ AXH`.A`#] A0``.Af] A l`.A^ A4t`.A5^ A}}`.AW^ A3`.Aw^ A`.A@C^ A #`.A^ A(`.A@W_ A0`.AV_ A`r`.A`` A!`.A F` A`.AZp` A3a.A@#1a AYa.Aa Aka.Aa A̎a.A (b A Ra.A c Axa.A+c AHKa.A d Ab.A0d A (b.A`d AЄ>b.AOd A vb.A3e Ab.A cSf AEb.Abf A b.Aqf A0b.AUf A#b.A@f Ab.A3f Ab.A?g A@ub.A@>g APZc.Ag.ARf,:s A%hh.A(NSa Ad l.AnBE AGÕp.Aȴa+ Azʙt.AR A_Gݿy.A| uOAb"n.AF)A2-h.A WnrA*.AÒLAʇT.AT,A-A@.AA -A`bVEA Z-A@ FAk-A@DAۣ-A@rFA0-A"KA b-AKAH_-AMAP^:-A@NA03-A 1NAŜ)-ARA`k-ARA`2y-AkSA)˕-A%-L SA= -AkRAͥ-ARAȢ-Ao6tRAX=-A-QRAϨ-A LA -Af2o13JA;sN`-AXg:IA؃-AM BA+-ADlA-A@iA0-A@jA-A@kA-A@5kA@Ɠ-AMGkAȶ-AjA@qg-A6jA=I-AVhAd-AhA8-AM`jA-A0jAN-A!kAH)-A OjA?-A@iA@}-A`iA-A6iA-A`bhA-A VfA@-A^A&m'D-A8x^A]H-Atg^AAK4M-AY^A5cR-AS5O^A:mK;X-AX4`H^AH ^-AZlYE^A_d-Af4F^Ai-AJ^A]-A`ml`A{R-A`A0-AbAx(-A"{bAHP-AbAC{ܥ-AUkO cADT-A 5cA`-A`ׯWcA؞-A`ocAp2-AcA{3-A`]cAh;-AcAJ\-A`cA&-AbA R-A@bA-A@ylbA-A6bA@q-A` aA-A`AP-A`A-Ah`Ap-A@_API-A^A$-A`^Ah-A@^A-A8O^A07-An]AH-A`]]A8-A`T]A(-Am]A M(-A Jm]A0%-A`i]AP!-Af]AX-A cz]A-AR^A-A`1^A)-AO^^Aܑ-A~^AXd-A` ^Aȓt-A Oc_AI-A5_A8s>-A *_A-A^A`-AZ]A-A[]A`-Aޕ]A-A ]A@-A*t]AP8-Aj]Ap-A@`]AQ-AT]A-AN]Aѓ-A&G]As-A=]AW-A2]A@-A@#]AЏ-A`]A-A@]Ag-A\A--A\AQ-A@\As-A\APg-A@i;\AH.-A@#\A0-A \A <-Af\AZ-A3}\Arz-A SrYAȺq-AGYA-ArXA-A&XA-A!XA -AWAe-AWAۯ-AFWA-A@WA} -A J!VA(-ARAP-ABm8Aأ-AM7A-Aa01A-A&0AC-A|0A0;a-A0A[C-A/AY%j-A9K'AJ-A=m~a2A5x,-Ai\~?A #-AW~ByFA>-AךGA-ABKA0H-AOAf-AOA؎~-ARA-A@ JUA3-A5XA$-A@>?XA -AoZA@-A`]]AQo-A`KgA)-A[hA-A@!hAp|-AGiA-AĩiA7 -A3?iA(9-A^HjADį-AWʂjA-AjAb&-AͷjAYX-A(jAZot-AᨚijA?-A>KjA(-A jA"tcC-AԍjAЮ-AQjA-A` jAɺ-A` jAp -ACkA0ي-A`TlkAg-A@kA+-ADlAU-A BAB-A c0Ax$-AA0·-A`դA0-A`-Aע-A@AR-AʎA8:-AAx\-AkAj?-A AhD-AڲA -A@mAPM-A)Aq3-A cAת-A AH-A` A8-A_A-AyAp`-AAptl-AټAx-A.AXp -A?AX-A Af-AؿAX-A@ AXFñ-A AwA -ABFA`K-A#A--A)sAU-A BAj[.A<1 A([.A . A'Z.A4. AY.Ah. APX.A`- AX.A`. Ap+mX.A`;- AgX.A7. A[YX.A4. AXTX.A?. APX.A . A83NX.A`. AXHX.A. A}IX.Ac / AX.A/ A>X.A &- AvQW.A`3- AUGW.A>- A>W.A-r- A(2W.A / A7W.AO4/ AGBW.A4W/ A((W.A@/ Aȥ%X.A / AH$X.A / A:OX.A`b/ A8PX.A@%/ AdX.A/ AceX.A/ AyZ.A0 A0Z.A1 AH]Z.A1 Aj[.A<1 A@Y].A-Z A!Q].A`- AV\.A@` A2\.A A\.A Ax-@].At AxY].A|] A@Y].A-Z AV.A AV.A@ Ax\V.A) A`V.A`w A8kV.Aീ A V.A AV.A A0&4V.A@ AZV.A= AȺV.A Ax$V.A c2 A(,V.Akb APfV.A<\ A` W.A} AW.Au A8 W.A` A V.Ak AV.A Ap#c.AbK( A c.A Ab.A@O A!b.AF- A@$b.A) A`.A  A8@`.A Q A0_.AM A4F^.A^6 A*m<^.A Ak\X4^.A Ұ A.^.A`B AYz?"^.Ac A)^.A U& A ^.A_ AS].A*N A<].AΒ A~̈].Ay |$| A ].A#o AQ|].A4(l A a].A Fs AHƝ].A֮M Ai].ANVĞ A].A AH(].A= AU\.A`: A ].A Av\.Aj= A+\.A^q} A}\.A" h A\.A O Aa\.Aofrz@ AͦJ\.AW&s AH\.A39 A; M\.AhҦ A].Ax AZ ].A&n AG].A؀^E A].At A3].A`d8 AX^.A  A`.A@= A(E`.AD AL`.A`R A`va.A  AX)b.A R A^[b.A A^c.A[ AxX e.A L( AF@4e.AK|, AE]K^d.A}9? Ap#c.AbK( A 0.Au A$.A# Al.A A"..A Q Ak.A A* .A; Ap[.Aل A`C[.Ay Aw1.A= A؞.Aa AX.A+Y AR.A6z Ap,^.A3 AP .A' A*.A? A(.A A.A@ A0B.Aa AD.Aoe AY.AEe A#ӯ.AV A2-h.A A 0.Au Am.AgP AϏ.AƙO A@ .A`O A.AR A\.A`fvR AX.A P Auw.AcL A 4 .A #K AH.AcJ AX.A J AfF.AJ A8E.AD A.A C A@J.AmA AЄ,.A@:B AC.A D A(~^.A+E A៌.AF Aƌ.ApF AP.A@%5G AH .A$L A@ȍ.AM A1΍.AO Ah.A R A&.A/V AXVk.A5W Aڪ.AU Aؚ}.AS As.AQ Am.AgP A1.A^4 A` .AL0 AB.A`- A̭.Av+ AH>.A' AL.A`R$ A@е.A`v# A&%.A) A0X.A 1 A.AE A.A AP.A A_ڸ.A` A޺.Aax A.A`dE Aʽ.A` A;J.A@ ApNU.A AX(.A u A.AAv A0.A`B A6.A@߲ A0f.A A`.A9 A54.A@a AH..Ah A`̾.A AL.Ao0 AFּ.AC AXRǼ.Af A 0.Ap AW.A`̃ A`.A}Ҷ A .A A@B.A API.A@ Ao.AR Aȶ6.A@ Aʱ.A_ AC.A`} Ax].Ao A.A@G9m Aܭ.Ase Aڭ.A 1je A.A d Ai.A c Aذ_.A`Kc AjY.A`Íc APk6.Ac A|ج.A@a Am.A̵` A.Axt` AX.Aa` A%k.A` Ac.A"` A02.A a A.A a Ah+.A_ A.AT A+.AS Ag8.AS AHC.AR A.AR A.A@fR A.A ҚQ A+.AR Ax.A-W A;.A/^ A텟.A"` A@ϟ.Aidb A .Aje Ah.AOi A4.A`m AR.A Pq A`.As AÓ.A`w A0j͓.A`rz A\.AM| A.A l} A\.A@ A?m.A Ax.A Ap.A B A@.AЗ AX.A@Cĵ A! .A cz AHU.A A0;.A@i A$.A ? AfN.A`D AY.A > Az.A( A0.Ag A.A AH.A| A .A[ A8".A7 Ap.A Aʧ.A A<.AZ A .A@, A.A AP.A0S Ag.Ak A`Ce.A@ AY.Al A"X.A@m Ap.A`e An.A3f A\Y.A Zh A@K.A`  A4.A@P A0.A@yo AH0.A} A.AK A_.Ap AY.A`D AN.A% Al.AՖ A0.A@a AHi8.A A.AY A .A$ AXR.Aા Am.A Abq.A ARb.A@ A(-+.AI A0囨.AD AH,.A`& A.A AXF.AV A@.A- A(&.A_b A H.A`i Axhp.AR- A.A ALҨ.A Aʨ.A 1 A@.A) A@A{.Aѡ Ah/P.A`$ Ac.A A@`.A  A蕾.A ? Ap~.A^ APT.A^ A .Aa A@.A`V AMڦ.A`V AH̰.A t A.A`V AHl.A@ ABE.A@ A(.At A.A \ A舭.A ( Ah~.A A( q.A@ AP/l.Az Atg.A`; Ap.A g A.A@U A1.Aߜ A𴭦.AH AƦ.A ALަ.Aa A.A- AO.A A4.A)F Ax).A} Ay.A A* A).A`mx? A.AN A A.A*A A.AxA Aʰ.A&@ Aӱ.Ae= AD[.A@g; A1.A^4 A;.A (wL A-.A@!L A}.A@%L A.AҠL A .AN A.A@uO Ap.A (P At.A@Q A.A=Q AX.A (Q A ʡ.A@RQ As.A qQP A.A3O AH@.A (?M A;.A (wL A.AoR A8{.ALR An_.A ZSR A@3.A ZR Aؚ.AT AP^.A@U AX.A@V Af£.AoW A>ѣ.AX A.A W AK.A@yW Ap6.AIV AH)M.AU A𒎤.A=wS A.AoR A(s.A A7.A=GA0.AAh̨.A͝AG.A^mXAƯ.AA_.A_MLAxa߰.AQA{H.Aq>LAiɬ.AkPA +.AHAMo.A|bA8.At@AA.A&A`Xz.AA{sk.AHDAu`.AfbQAY.AᰴA6W.A$yAp a.AJ7AqzTo.A_A弁.AoP67ABoʗ.AYrZAY1.A󐜘Ą.A|gAM9.A@d|DAE;5.At&5AH&.A@8Ab.A*j)AJHc.As8.A(#lة.AHGA̴.A ltAZm;K.A,.NAS.A0u AY׸.A_,EpA.A5qAR.ArAX$ .AtĚAb&.AAb 1.A& pA?.AE ƛA P.AAT.AAn.AqǛAa>.AG@A_p".A9Aݫ.A ]A' .A!xA Σ;.Ac}ANJ k.A}SNnAS .A PJAH.Ǭ.AAɣ.A |*A9.AAf\Q .AKg갈Akn".AWAŵ9.AJքA>1Q.A Aqg.A|A|.A QAt.A@AP,.A ZAh.A`Ahċ.AߴA.A uAj.Ag-A]Я.ATA ѯ.A@A0.A`<A8.A@AU.A=ܗAU.AA-.A`堗Ap8.A A@<.AAA.AAm:.AsAp7.ARA`A.AIA L.A <AXJY.A Am.A`ȖAԅ.A@>A.AA۠.A ɖAxW.AAp,.AൖAb.A@`A _.AA`.A~AX.AaAPɹ.A_Ah@.A ,A汰.A@A.A@Aϰ.A@A.AוAN.A०A.AMAi.A= A%.A A".A AޔA8(.A єA(.A@AiW).A oA56&.A~WAּ~.Al+Ao.A7`wAг.A։A.A5@A.A"1]SA[qW.AA2>-.AgA0v.AAPɰ.A1A汰.A^טhzAK.Ah1Aa~H.AdwApň.A䕀AV터.A/EA$&.AQ(}A%%.Adx|Ai..AU{A0 .A-N zAB&뻰.A 8xA]S}հ.A;wAC.A{HvAѼ.A8 VuA5l=.A/issA)&i.A?HrAFW.AÉqARk'ͱ.A pA zj.AF}oA9;@.A nA~.AemA1.A^ mA .AؖIlAk)9M.AD$WkAlY.AF| jAȵ.A`ijA֙.A2?%rjA:!.A2?%rjA0.A`ijAI[3.A.A@6jA.AgjAP.A jAdb.AVjAw.AFjA0U.AjA .A`kA.AT>kA .A(kAᴵ.A`>kA0.A@kkAѵ.A qkA;.AjkA@.AkA@F.A`lA.A wA.A@ wA@ .A (zwA0.AwAh..AxAM.AspwA(`.A`twA.AwAxX.AwAmߴ.AxAuڴ.A xAѴ.A@yA˴.A cyAؒô.AyA ɴ.AyA.A zAٻ.AtRzAX챴.A6zA.A@!zAPM.AzA.A@P<{A.Aw{Ai.A@%{AX.A 8{A.A@{AXF.A A{Ap|.A@,&|A.A`B|A.A`]h|A.Ai~|A.A||Az.A@g|APk|.A|A6z.A/|AH?u.A|A`%l.A c}Amd.AA}A7b.A@p}A`v_.A@}A@W.A |}A8P.A }A\N.A@~A`R.A@L~AH:R.A~AM.A`~AXK.A@~AE.A~A@.A@A2.A`T~A*.A`AA*.ABtA0X#.AAЄ.A A.AA.AYAc.A@A@0.AEA0ճ.AZAͳ.AAƳ.AUAX³.AnAX³.A#A.A_AhMų.ApAhMų.A3A0ó.A ZAų.Af͂AhMų.AxA˳.AAʳ.A@Aأӳ.AAҳ.A@'A(ٳ.A 0ABڳ.A SA8ݳ.AyA.A A.A`oAp.A Z؃A .A`A0&.AQ A(.A`]#A.AQA(.A pA6.A/A.A`A.AEA(.A0A3/.A\Ax)4.AA;.AAxI.AȅA:J.A<AJP.AAS.A4AX.AA].AVAPg.A@5A#.AA{ܴ.A`KA.A4}A).A wA@uB.A#A@uB.A@Ax.A@A-%sB.AXcCA)ok.A$A"߷.A/AA(+.A |Ui~A*Ys.A ]}A.AH|ACej.A |A-*6¸.A&=|AfMŸ.A0|AplǸ.A |A0 ɸ.A7܄|Aɸ.An|AH75ɸ.Ak Y|A*Ǹ.AwC|AŸ.Ag/|A¸.A?|A=-.A} |AẸ.A{A}=8ڔ.AQ6l^{AO|r.ALzA6OR.A&PUyA37.A/)=yAH.AuxAKm .AvPxwAX.AUOvA.A ~uA}/S.AWtAc3/.AmzDDsA@.ABqA~5.A)qA+.AԆyqAD-p߷.A)䤭qAqݷ.AqAX=ڷ.Ay[qAz9b׷.AO}qA(~ӷ.A`qqAڷ.A@qAmj߷.AN8LqA.A \VqAme.A^qAX.ADcqA_W.AhPWqA^M.ADqA.A*qA~.A1LqA◄.Ag5qAԕ".A.hpAVaC%.AHRpAk^c'.A7pAc_(.AzpA0(.AgpA(.AlDpA`+.AepA[T0.AoA4"c7.AoAc@.AgoA}g@.A^doA;@.AqaoA@.A'_oA3A.AK\oAA.A ]ZoAECB.AR}XoA'LB.A@ WoA;՜C.AGKVoAwXD.ATiUoA5E.A,tUoAE.A) i&VoAuۃF.An'D0WoA'G.ASMXoAG.A@ZoAjI.A ӈ^oAO>K.A}XaoA(M.AboApsO.A2ĿfcoAQ.AhboAeR.A `oAT.A;g]oAcV.A &YoA>V.A!NUoACKoA! W.AjHoA렒W.A7=PFoA2X.A~ DoA X.At'oAs.AoAx v.A膆, oA` z.AmPknAF~.A6}nA:(.AEnAҖ.AWnA2,ˌ.A|zV_nAP'.A nAd֕.AIBnAmR`.AnAh.Agd߯nA?L.AnA: P.At]vnA.AnAy ۤ.A`nAI.Aڱ4snAϣ.A%I^nA? .A0InAܙ.Ag]>6nAG.A%nAig.AFnA.A`K nABⒸ.Aq9k9nAzב.AlnAc䐸.AnnA .AunA^.AN=mAO׎.A7mAի~.ARcmAU.AڊfmA].AdmArq.AdohmA .A&,mA딏.A4>FmA R.AmA7a.AÈdmA`8.ADmAǘ.ACW[mA.A۶mA֜.A>)mA@.AmAD.AmA虯.A SmAX.A SmA0Ƚ.AmA.A4mAĸ.AmAʸ.AmAи.A=mAո.AmApٸ.AmmA߸.AnAp}.AWnA,.AznAx.AamA .A@mAb.A@`mAA.A mA0.A&mA.A@`mA.A`ʋmAM.AmAM.AwmA).AbmA0.AYmA.AFmA.AR8mA.A`$mAH.A`mmAXF.AlAp<.A`dlA.Ag.AlAPf.AXlApf.AslAP8o.AǔlA z.AWzlAX|.AzlAP@.A{lAX솹.A3glA3.A@ OlA`.A@zA ^$.A@#MzA.A/0yA.AھwA@.A wAPZE.A@vAy.A@vA8D).A`muAY.AXuA.AatA.A@WtAP.A@ytA.A9-tA-.A@sA,8.AsApV.A HsA x.A sAp.A@sA.A 'sAjD.A;rA&.AJrA0].ACqA&k.A6pA^.A@WmA( .A3mAxǢ.A lAX(.A@N.lA[.AkAx.A(kAp(.AjAhC.AmjAZV.A`viAZ.AiAXc_.AviA3\.A@iA@c.A@iAk.AMwiA.ADhA׈.A`dhAh/.AkhAY.A;#hApJ.AgA.A`ӃgAR.A?gA@5.AMfA].AǕfA.A>fA..AfAp_.A@>eA.AeA0j.ACeAP.A /dAHaC.A cAX,h.A@EbAw.A@ZbA.AbAX.A \aAȺ.A`AU.Af`Ah.Ae_Ax-.A_A.A4^Av.A^ApN.A ]A.A l\AX.A`\Ax.A&[AXt.AD[Ax-.AZA(`.A`YA.A`YA N.AXAG.A=XA.AqXAw.AXA0.APXA`.AWA`q.AWA(`.AHWA.A WA7.A`)iWA.A`!WA06.AaVAE.A@>VA0%.A`VA.AdVA8.ASVA.AK!VA.A cUA8.A`)]UAG.ACUA襝.A TAV.ATAH.ATA.A=SA|.A4SAJq.A@SAxj.A?SAe.ALSAY^.ASA`TZ.ARA_.AvRAp|g.AARA s.AQA`s.AQAȔl.A`fQAk.A+QA^.APA\.AjPAHX.APA0R.A9 PAVN.A`9OAppY.A cUOA@L.ABNA8bJ.AaNA7H.ANAJ.A@PNAE.A`NAHD.AMA(L.A0MA8H.A*MAH?.A@CLA1G.A \LA(D.A-LA&B.A`KKA)D.A`m,KA$M.A`BJAX[A.AJA ?.A[OJA0;.A`IAE;.AIAZ..AIA/.A@WHAP/.A@.OHAu+.AW/HA..AGA`).A cGA (.A ~EGA@+.A`8GAx".A FAT.A@pFA`G.AFA..A&OFA.A` FA.AEAa.A` EA.A3EAP.A \EAxz.A@DA@q.A DA(.ADA".A `DA.ADAp.AqCA.A CA.ACA.A@\CA.A ,CA?.A CA.ABAX.ABA@.AiBA@1.A$BA.A*AA.A QAA.A@,AAA0.Ap0AA@ƽ.AYAAXƬ.A_AA8.A9AADy.AAAY.AXAA`4.ABAP.A9aBA.ABAK.ABAhQ.A3BA.A`AA.AAA臽.AEAA5.A@A.A@A(1.A0@A88.A@A.A@5}@A.Ae@A.A#@A~.Aj?A.AM>A8.A>Apڠ.A y>AԱ.AW>A0j.A=A8'.Av=AH.A E=A .An=AHr.A<A@.Ak<A 0.A`T<Ax.A B<AR.A <Au.A?;AH{.A@E;A`t.AH`;A`p.A@#5;APg.AN+;AhT.A;A&8.A`:A&.A:AX.A`:Apy .A R:Ap .A6 :Aa.A 9A .A`]9AH .A09Ax.A\9Ax .A@69Ax.A@58Ap .A8A`C.A 8A*.A`KI8AЄ.A 8A.A7Al.A7A؁.A|7AH.A@6A.A"6A.A`5A.As5A.A95Aѫ.A 4A.Ayb4A^.A34A3.A14A0Y.A@ 3AȂ.A 3Aȥv.ABM3Ai.A +3A)Z.A02AR.A` 2A`9.A@R2A(0.Aa2A..A1A/.A`T1A0-.A81A&2.A81A&2.Ai1A2.A81AB,.A 1A ,.AV0A0!.A0A?#.Ap0Ap=.A$10A.A`/A .A`/Ag.A@ /A_ .Ap/A .AB/Ah".A@/A.A O.A.A ۡ.A.A`p.A.AN.A=.A.A.A -AP.A ~s-A.AT+A d.Az *A.As>(A`.A%AE.Ay"A.AJ!A j.Ak Al.AC Al.A@W0A0j0.A&AS.A A@1.A lG Asw.A` Ah@n.A`] A5.A@> App.A`m4 A8ɾ.A@ A.A AX5s.A AXyK.A`8 AH.A A.A#OAhD.A(Aig.A@Ah .A A~.AA.AIAW.A3AV.AAp2.A^AU4.A ?AP^1.AAp#.AA&.A@ hApÿ.A A-.AA.A@pAh◿.AA}.A@A].A`fjA`E.AVPA+0.A /A##.A A.A)\A0e.A@Ax\!.AA0.AtDA?.AAI.A AU.AAl.AAXJ.A`ܳA.AnA@.AdAͿ.A vA޿.A@hA7E.A0.]A.A$\Ah..AAp_A.A /A M.AAR.AăA)ZZ.A|Anb.AŤpA< i.A_A p.A;JA@u.Aw1A\$x.AXA|.AŽAV.AkˁA.AA(-Aҍ.AA.AIAb.AUA.A0A0Թ.AqAP|.A<A;.AhA0.AA`.A`]0A.A@CA8I.A`A.A IA.AqA.AAT.A AJ.A`mBA}.A A@w.A A(yc.AOA@Y.AsA/S.A=AP;.A-RA5.AA.A` YAt.AV)A.AA.AiA`#.A%A ).AA`&.A@,A]8.AnA?.AvrA(yc.AhA?.AVMA0{L.A.A).A`KAx.A@%A(.AV2A.A@poA.A`CA.A/A(.A- Apܿ.A$Aֿ.AAٿ.A@5A`Կ.AA̿.AHA0̮.A A@>.AA.AAʔ.A(A0 s.AWA].AbAR.A(A 4H.A AoD.AtAA.AcA<.A@7A +.ARA.AHAr.AIcAx.A`A([.A4A}.AqA`.AA!.A@yAe.A JAP.AAج.AAX׽.AAX9ҽ.AA .AA@.A~A8㬽.A`2qApp.AMAј.AgA(.A@AӋ.A;Ah.AoKAd.Ad8A8\.A[AX.AAT.A AI.A0A=G.A ªA@A.AྑA6#.A@hA.A KA`.A@ A.A &A@uټ.A&Ax˼.AaA Eȼ.A 1At.AA .AoAQ.A?;A ^|.AAcQ.A8Ai;.A,XA`-.A +Ad.AvA.AVAO.AAS.AAһ.ARAJǻ.AA@_.A`ӑA.A jAd.A ZAAW.A A&.A A .A`"A(q.A-A}.A gA/f.A`;AT.AyA=.A`Ap.A~AxF.Aq'A.AA .A@gAP7.A 8eA; .A5A .A@>A7.A SA.ALA.AXAQ.AzA.AAp ݺ.A`mA}޺.AAhӺ.A#qAQӺ.AOA.A -Aк.AAhú.A`A.A@PAT.AA@q.AA 4.AEAع.A9Apҹ.A A̹.A"A(`Ϲ.A`AXŹ.A"Aﻹ.A Ao.A (A3.Ak>AX9.A A@.AA˘.A`ܴA`P.A yA.AWAks.AtAc.A`A@W.AzAZ.A*AW.AQAQ.AAE.A@dA[8.AA`1.A+A((.A Ag).AA +$.A5A%-.ARAȥ.A`Ab.AA.A`mA.AhA@.AA.A`iAX4~.A 7A4.A@GAP.A-Aŷ.A;<Af.A AX֍.A Axdv.A Aj.ADA^.A.A؟R.AEA8ER.A`"ZA.AA(.AA8.A@NAxõ.AXAx-.A (EAz.A;zAPr.ANA(f.AAa.A@CAW.AXA.A@i"A.A-Ahѵ.A AH.AӗAXd.A`~A!.A`mAX0.A=A+o.AgYAxz.A /IA𴉶.Ad!Ax.AAAն.A.A A`%/.A@A.A AA.AkA.A`#A).A%AP.AAPβ.A`Aǿ.A`Aг.A1A}.A?WA*.A`Ag.A@A.AHA.AHAP.A@A .AA.A?Ap.A@A׫.A җA.A Aս.Aj_A(m.AX>Ag.A%A Dz.A (!AƲ.AA0.A@A\.AoA2.A@rA9.ATA.Av:AH̉.A`8A@.A$Ap.A$Ap.A@AȲi.A@A /j.AAu`.A}A_.AA ].A8AYd.ARjAsr.A`fCA`{.A AIA`{.A@Ax.A Apw.AA.|.A?A.A`)A.A ONA.A?PAњ.A AP.AAp .AA}.A'Asr.A@Ax)k.A AY.AAhc.AAv.AAo.A _A_.AzOA0\.Af AS.A&AMT.AǵAO.A?^A0K.AAwN.A` A(1X.AzAm.Ag<AHac.AsAx6i.AApu.AzA跌.A`KA8.A`Ab.AA@.AyA(m.A@ydAȱ².A@}A`ʲ.A`bhA-ղ.A@gCA.AvAA?.A`A.A`A.A`AX4.A`A".A8A01.AgAxF<.A:A oH.A@AL.AA"S.AAPX.A@A0e.A@AIq.A@!AH{.AA@.A@5|A.ACA\.A-6APn.A^SA.A0AP.A`mQAPg.A@EAh.A@y,A.AAp .A@`A .A;A0.A-A.A A .AA.A_A.A \A .AڗAp/.A@AXE5.A@,A(N.A |A^U.A'A].AA_P.A@pA`v.A@/AQ.AoA.A Axd.AXqA.A`vAK.A`mA9.A`yAݍ.A!A8R.A`yAL.A5AHKl.AA0i.AA@F.A "A>.A`vA>.A9AQ.A@AhN.A׉A8.AzA#.AA5.A@ѿA01.A|A@B".AпAhb.A@aA*.A qBA.AؾA01.AA"/.A`A.A뾾A .AaAM.A<A.AA.A_Ahb.AѽAJ.A@,AJ.A )A.AAȲ.A@>&Ax5Ǵ.AA}.AfVAY.AA(̴.AAlʴ.AȼASѴ.A A0ߴ.A`o3A8.A ,AN״.AdA0.AA.Aq̼A(9ش.A@A״.Aj|Ah.A`oA.A UA3޴.A%ASѴ.AػAD̴.AwApJƴ.AA.A ջAx.AAδ.A xAfд.A@A8ʴ.AA ɴ.A@A@.A鯼Ah.AA@|.AA.AkA.A/AȒ.A Ap,}.A DApa.A@ixAH\.A++A8E.A HAP^G.A`^Ap).A`vA&.A iA0-.A-A08.A/APo5.A@AD.AҼAJ.A`Ax.AmAI.AAH:C.AA>.A@gA6.AA84.AXfA@;.A=AH:C.AoNAV.AI"AT.A@CAP8Z.A`A\.AA2`.A A].ATAPkc.AwA0d.ATAPj.A Aei.AAXs.AػAu.AAy.AjAv.A |ĻA@g.AdܻAc.A |ĻA_.A (ȻA b.A;޻A\.AAxX.A ջA=T.A;޻ApW.A@NAgR.A0AE.AAk.A0lA(\^.AAXj.AٺAtn.A@yA(.A ƺAB.APA8.ACA@.A@pAȲ.A@ Aˣ.A_A؎.A ZAɴ.AAHaִ.AA1.AR5A.A@iAˣ.A DA( .AL*A0e.A ~Aei.A@ A[.AºAU.AA8.AXպA0-.ADAL.A`IA0I.A|3A7).ArA|&.AdA).A ]A%.ABBA>!.A HA`!.A@.A!f.A}A `.A PAQ.A;iAsM.A7A00.A`A.A@iݸAi'.AA.A4A.AYA4.A8A#Գ.A@,ոAh.A AH.A@Ap.A*A0ȳ.A@!ԹAW.AιAO.AXAe.AiAP.A@KA0.Af As.AԷA`.A A.A@`(AA.Az۶A.A@Ah?.ADA.A٘A.A޴A.AAFٲ.A Q{AHɲ.AZAH:.AdCAX.AsAp_.A&As.A@5سA(`.AųAQ.A<óAD.ADAX(=.A@NA A3.AUApp&.AwA.A ײAx-.A1Axܱ.AXA0.AD1Ah̨.A6A".AAeɱ.A ZA୩.AA.AHƯA}.A;uAd.A`Ah N.AcAЦ5.AaAX8.AAG.A JYA`%7.A@EA6.AX8A#H.A@٤A(yI.A`AL.A@A(\P.AAP.A A cQ.AA`a.A@p A£.AKAxi.A9TAl.A]AҠ.AgA.AqAҠ.A |A(m.A APj.AaAã.AAp.AǟA0f.AVA.A`A.AXA`v.AA.A3A.A3A.A3A۱.A`f$A .A +AL.AsApD.AABk.AzALa.A A/.A`Ap[.AAP.AX AxX.A@`QA.A A>.A'A滲.A@GA.A`AH\.AA)h.A7Ah3.A@Ay.A`mA06.A lA].A@ AH!.A@ A}.AxAp.A@5nA.AcA.AZA޾.APAxG.AHA8b.AjAA:.A;Ap߶.A/7Ap_.A^4A˱.A03A83.A3AhL.A A.A#Af.AܥAJw.AWAH.p.AA>k.A`"AFg.A lƤA@g.A4ƤAb.A ɣAȘl.A Ae).A \AД.A`) A8A.A@A0.AƛAȥ.AfAa.A@,BA[.A/mAH{.AEAH?¬.A`]zA`qC.A笀A襛.AM{A&.A@&{Az.AdzAd.A ?zAPP.A`yA=<.A yA7.AqxA%..A`ܧxA.A@wA3.AvA<.A mvA0 ͪ.AVuAâ.A StA~.AF A5.Aբ A |.A¢ A.As AL.AR A`e.A" A .A J Ap.A(k Ah.A@f AЦ.A \2 A.A Au.Aՠ A.A A(.AHC Aٻ.AI A Е.A u AX9ƕ.Aw Aʕ.A`] AЄՕ.A= A.A@a A.A A.A@6 Ap6.A AHY.A@r,~ A(k.A}} A8j.A} AXp.A} Ahn.A} Apu.A f} A{.A`+} A8.A | AU.AWt| A@.Ad9| Aȶ.Az{ A?.A@{ A͍.A{ A(.A@={ A[.Adz A\.A |z A.A y A`ӥ.A*y As.AIy AXҟ.Ax A`.Ax AH.AӉx A@.A=w A.A@v At.A`v APH.A@Eyv A3.A}v Ab.AjKv A4.A@y@v A.A`v Aج.A u Aٷ.AZiu AL.A H!u A5.A`Tt A.A@Qt Ap.A`fs A( Ɩ.As A<і.A {q AӖ.A`mpq Aؖ.A@Zq AqՖ.Aq AԖ.A* q AՖ.A q AHٖ.A` p Aۖ.Alp ANٖ.ATp AӖ.A p AUՖ.AKo AԖ.A@Oo AȲӖ.Asn Aؖ.A@Gun Am֖.A&n AH?ږ.A m AՖ.Aom A ۖ.Aւm AH?ږ.ARm Aޖ.A@%m A@.A։l Aܖ.A ql A@.ALl Ax)ߖ.A +l A ۖ.ANk AP.A2k A@.A^k A.Apk A .A j A.A A"j A.A4i A.A`B:i AP .A /h A8.A|h A.Ag A.A@f A c .A@f A<.A@i^ A.Ag^ A8t&.Afg\ AP|:.A`T$X A\.A`KP A`b.A`P At.AJ A,.A@oC A".AXA A.A@ A A}.A= Ap<.A9 A ͗.A1 Abؗ.A0 Ahؗ.A/0 APߗ.Ad. A.A, A(.A@>+ A`T.A' Ax.AF& A.A`Dv& A.A:% A7.A# A.A " A(q.A A! A.AB AP.A A.A`j A.A Y A.A A((.A@ A*.A`[ Ah4.A  A2.A$ AP:.Am Ad.AP A8.A A.A? A؊.A ANj.A@- A.A AΞ.A1 A.A  AP.A` AD.A A> AsK.AD AsK.A@$ AM.A A@I.A AI.A AM.A AM.A qt A2H.A$= AhbG.A` AQ.AO AsK.Aෝ AO.AO A@I.A Z% AsK.A AE.A AH;J.AЊ AH;J.A}F AH;J.A ApG.AT A(F.A;B A(L.Aa AH;J.A` A K.A@W A`B.A, A?.A A`B.Az] A@>.A` A5.A` A3.Ad Ah7.Ac A 7.A`% A;.A Ah7.A@ A<.A( A 3.A< A@_:.Aj A3.A`ܘ A0.A`a A8.AF7 Ah7.A6 AM6.AL] A/;.A A@.A A 3.A<5 A &.A Ax}.A` Az.A` Ay.A@#c A@y.A@ Av.A@X A@ u.A AجU.A A59.A@5 Ax*.A@WY A.ADM A.A  A.A2% A(×.A A@.A A.A! Am.A@.Q At.A`қ Aplu.Aթ A@º.A@ Ayė.A; Aq.A (C A.A/Z A$.AU A쥙.A!~ AXh̙.A3} A.A`F| A.A'| A-.A@Wz A9.A8y A(.Aiu A0.A`Kr AX.ADh A.A g A8.A@yg A.A9g Ap.A g A8#.A-f Ax͝.A@f Aם.Adf APx.A@,=e Ax:.Ad A0X.Ay]c AEj.Ab Ah^q.A` b AXWz.Acb A .A/b Aԝ.Aga AX,.ADa A8.AO` AL.Ac` AǞ.A.` A՞.A`_ A.Ah_ A(.A *^ A .A Z] APM.Ah\ A@FQ.A`=\ AhQY.A\ Ah^.Ap[ Ax.AZ([ Ah|.AZ Apۈ.A`Z A.AZ A.AY APY.ARY AX.AX A0Y.A=uW AX#.AV A".A`MV A/.AU A0@.AVU AJ.A5U Ap.AS Au.AS Aʹ.A}Q Ah/Ԡ.As@P A.A27O A.A %O A .A/N AX(֠.A@yoM A.AL A3 .A oK A`.A? K AQ.AI A녡.A`F Ahơ.A`KC A(m.A 4B A| .AΊ@ A.A`@ AxA.A= AppN.A= A^.A}; A0m.A+v: A0ݒ.A`7 AĢ.A 7 A0.A@"7 Ar.A 5 Ax.A 5 A07.A}I4 A(.A c3 Al.A`2 A.AE1 Ab .A Z. A>).AQ, Am=.A`m.) A3.A /$ Ah{&.Ao A.A q A8.A0 An.A A@^.A` A.A Af.AE A`l.AE A@F%.AK A;7.A0 AB2.A`m AwI.AZ A@<.A@P Ah].A A.ADJ AXW.A@P A.Az A`! .A AH.A| Aȃ.A Z AUs.AC A@.A Aw.Aෙ A ʝ.A@ AhJ.A@= A.A^f ApkҜ.A A.A Axa.A`K Aʛ.A` A8gs.A A(-c.AW A.A@ Aħ.Aa AТ(.A Ahۙ.AQ A(͙.A@ APxW.AӇ A8ј.Ax APx.AJ A.A A.A0 AY~.A A8D .AL A.A A.A$ AXՕ.A` A`.A@5 A|.A`mT ApA~.AM AX.A@Ϯ Ap0.AQ A.A AhE.A d AxWړ.A@#^ A6.Aƽ A8Eh.A A(\.A> AP&.A) A0.A Am.A(y A`.A1 Aȥ.A3 A .A}׬ AS.A APo.AE A j.A@6 Ab.A 87 A8k.A`s A?.A"~ A`.A: A&ؐ.A@ A .A` A_.A@E8 AB.Aoء A>.A A`$.A{ A.A 뭟 A8.A@G A.A1 A.A` A@.AM AV.A$ AU.Aܘ AK.AX AJ*.A@ݗ A .A9 A_k.A`v Aٌ.A q Ah.A Apɞ.Afނ A83.A ւ AH.A A^.Aa A瀋.A At.Ac Aj.A F A@1\.A`]" A0H.A A;.AÁ A5.A2 Ap'.A A.Aw AX.A@W( Aq.A` A܊.A A2ӊ.A A8Ɗ.A(} Au.A@e A`.A9 Apz.A ALH.Ai A.A~ A3.At~ Ac.A`N~ A8E.A (:~ Ap.Aq~ A6Ή.A`o} A8.A*y} A0峉.A*y} A.A`)S} AN.A5} Ah.A@} AP8w.A-| A .A&{ A.A{ A;܈.A b{ A0͈.A9{ A@่.Ad{ A(".A~y A.A Jy AP'.A`o'x A@.A`w A .A<%v Anj.A )u AX4B.A\t AX[.A@s A(m.A@s A.A@es A-.A@r A _.A`Qr Az.Aq Al?.A`"p A.A|;p A@F.A@o A3.A o Ax.A`dDn Am.A;m AT.Aem A8A.A Zl A0.A@wl AH.A Qk A.A /Bk A.A@nj AX҃.A`վi AG.A`h A(K.Ag A_.AEe A@߂.A?xX A,.AOU A(;.A 2N Ag{.A`H AQ.A B? A8ń.AXB AA.A iA Ae.AA At.A S? Ag.A> AɅ.A6< A.A`@7 A0懅.A`1 A(.A`) Ac.Aw( AP+=.AT' A3+.A-& ArK.AN߆& Am j.Ae:& AIF.A4+% A(.An*s% A.Ak$ A ۿ.AC)z$ A^ʆ.A`# AV.A AXƆ.Az A.Aab A.A AX#.A Jk A8.A`) A[.A A㶂.A ^ A\.A` A@ƺ.A-l A5#.A I Agn}.A@ AW|.A Ap{.Av Aey.Ac Azx.A$ A8bw.A ۊ Aw.A  Av.A% Au.A`D A8t.A A8s.A Ap=q.A Ap.A@ A8an.AH A@Nl.A!| Ai.Ap A07h.A Am ACh.A` i Ag.A h A f.A-b Ae.A,^ A49e.A[ A#e.A@[[ A;Xd.A`W A8c.A AU Ac.AS A؟c.AS Anc.AQZS A@_c.AVS AJc.A JR A.c.AR A5b.AO Av b.A K Aha.AVJ A a.A J AX`.A D A`.A |~C A_.Af= AH=_.A)8 A(^.A4 A:N^.Axy3 A'N^.A y3 Af].A?C1 AT\.A`) , Ah[.Ar% AH[.A$ A83[.A@$ A`7Z.A A%Y.A? AY.A AE1Y.A9 AW.A| AV.A A@1U.A ~ AX9T.A L AN.AB A\wN.Aw AXN.AK AH6N.A  AM.AE# A 8N.Ae A:N.A AH.N.A@m AN.AhP A2 N.AR AN.Ah AM.A A>M.A@ A M.A^ A]M.A AЀM.AF A%M.A AM.A A"M.Ad AM.A4 Ax׋M.A A&sM.A`Î A0qM.AUq AlM.A@M A`gM.A : AdcM.A A(OWM.A` AIM.Au A8EM.AU A9M.A A@3M.A A/M.A_ AeM.A@E AHL.Ap AݡL.A A@L.A AЄL.Aג AL.A AL.A`)g ASL.A@K A(L.A@1 A0gL.AE AFL.A A[L.A@# AТ L.ARX AXL.A . AHL.A3 AXK.A A`aK.A AK.Ak AXWK.A9 AK.A` AdK.A`2 AH)K.A u A8K.A 8 AzK.A`. AP'oK.A R Au^K.A`o A8DK.A W A5K.A@ AY1K.A` AK.A Ap} K.A[ ApJ.A} APJ.AǤ AJ.A AJ.A AHJ.A A0tJ.A& A`%KJ.A] A2J.Al AJ.A@# API.A AI.A AI.Aظ AI.A AI.A AdpI.A A_fI.A A_I.A A0wMI.A&k Az*I.A?_ A@ I.A`T A ߰H.A况 AHˊH.A AH.A@ AȺxH.A" AitH.AE7 A@mH.A; AliH.A`dP AX$cH.A`dP A`v\H.Ag AlEH.A쭱 A3H.A  A lE.A& AlfE.A`2 AZE.A A(E.A A7D.A`v AjD.A"v AX D.A@,t AD.A?Nt A0D.As A9D.AZr AD.A@q AD.A S&q AD.Aap AȘD.AyMp AD.A{o AD.AFn AD.A`fm A(D.A@Gm AD.A8m AD.A`[ m APD.A@5l A୸D.A ~k A( D.A$^k A8ֳD.AEk A0ȹD.Ad1k AD.A@9i A&D.Ai AD.A@h AD.Ag AvD.Ag AD.A f AXD.Alf AD.A d A5D.Ab AD.A b AD.A@,`a A!D.AV` AD.A P` A'D.A_ A |D.At_ A8D.A |_ AdD.A`9_ A8D.A_ AD.A^ AD.A3r^ AD.A@`] A3D.A*[ ApD.A൶Z AD.AmZ AD.A`Y AYD.AOY AD.A4W AD.A W AE.A"O AD.A O A1D.AaWO AW@.A V A@.A@%KU A@.AU A8@.A`1U AP@.A9.U APޯA.AaS A(ܤA.AS A@.A T A@.AT A5@.AU A@.AgR A@.A R Ax@.ARR Ag@.A`ÎR ApA.A$qQ AjA.Ah1Q AH@.A SR A@W@.A@W`R Aȶ@.A rR Ap@.A`bmR A@.AP AT@.A QO AH@.AЇP A@.AtP AP@.A P AXd4A.A@,O AxX6A.AjqO AH/A.A`IO A!@.A O A@.A O A@.A`܇O AH@.A` N AH@.AD'N A|@.A4)N AH@.A6N A,A.A4N A%,A.AAN AGTA.A NN AUA.AN AgA.A N AmA.AF5N A0jA.A`BM A0A.A@M A&C.A J A B.ArJ AMC.AI AxC.AକH A _TD.AL Ah; D.A@%cM AD.A{M A@lD.APL AC.AH A>C.A`K}H AD.AH A(D.A@ϝH A4D.AFH AYCD.A H AD.A |NG AwC.AuG AD.A@rG A`C.AfH AnC.AZH AC.A sH AC.AfH ACC.A@RI Ap6C.AH A9C.A H ACC.A H A0NC.AM>H AIRC.AG AOC.A#wG A0EC.AaG A2C.AF AC.A pF A؁C.A& AlaA.A = A@ukA.A = AA.A J; AA.Az9 ADA.AY7 AA.A6 AA.A4 AA.A`ma4 A=A.A 3 ApA.A3 A`A.A`3 AA.A O3 A~ B.A`U3 A` B.AO3 AB.A83 AxiB.A`2 A( B.Aޘ2 AB.AA2 AxKB.AK2 AB.Ap2 A B.A_2 A(B.Af2 AF7B.A@a2 A؞,B.A42 AF7B.Ah2 ApAB.A 72 AOB.A`,2 AhVB.AK2 A0LaB.A92 ATkB.A@H2 AXrB.A`"2 AhB.A`2 AjB.A(1 A- ^B.A 1 A:,W.@.Ao33 A8.A4 A=0.Ao33 A 1H).A҆. A;sN!.AXg:' AʇT.Ai\ A.AA`vU-A`cA\A-A`dSAj*-A (YA R$-A EA-A QVA -A@=Ax-AjIAX-A@AH-A`K+A0-A <A-A1A-A"As-AR+APZ-A`A-AA0G-A@,A-A"A-AA覇-AA8E-A@!AP|-AZAhw-A`A"r-A@Ap,i-A@A^-AAhX-A@,A}R-AA7-AA-A*Ax-AAXEu-A@ iA-A ,AX-A@NpAP-A@AH\-AR{Ah-AA-AA'-AAx9-A 8A*f-AQAj-AA0&o-A`m%A#u-ABA~-A?}AHm-A ˻Ae-A`]A-AF AS-A@56 Ah-A@yS A=-Atc Ap-A` Ax~ -A ^ AL-AX A(4-A=* AG-A A;:-A`AE&-AAN-A^A-AAP-A`ӯA-AvA-A`ӯA8-A`As-AA-A@RAH-ABAp|-A` A.-A9Aߥ-AAP-AfA7-A'kA,-A/_A(-AྍA-AAm-ABA-AAx-AA(-A4AP-AA-AAG#-AA'-AA(-A`vxA(.-A6KA5-AH/A9-A@A:;-AA=-A ۰AF-A /A`1N-A@yAXP-A1Aȓ-A Aȓ-A 4Ax-A/OAx -AAА -A@FAa-A5A#-A AXũ-A!Ax-A *Aם-A |dAH-A߮A-AA-A0A_s-A,=%A-A+A8-A@i.A0;'-A_4A::-A`B5A^-A79A-A@>=Ap΢-A AA-ADDAA-A.JA-AVJAh -AYJA-A uLJA9-A@ߛIAk-AQDAHr-AkxBA@-A EAJ-AFAu-AHAu-AIIAĄ-A`}JA-A@AKA-AKA#-A`ofLA-A`3MAXV-APAà-AQAH-AFQA8t-AmSA@`g-A ZRAba-A cQA:-AiNA07-A lMA.-ALA,;zP--AE}XLAo;+-A$xLA5>)-AhoLA~'-A лLA:%-AU@tLAG8"-A(GLA+-AkLA1-AjLA `-ALAm'-A-2LA$_-A LAs-A~HLA -ADŽLAf-AH*LAW-A`LA c-AgMAK-A SAx-AߏXAp-A `A@-AE4bA@-AeA*-AiA؎_-ARtrA(q-A {A-A琂AC-A AL-A``A8_-AA=p-AA`-AA-A@A-AMAX-AAy-AAp-AwA-A_A0-A@ÊA -A`AQ-A@AK-A`K-A-A`f݊Av-A@A'-AH|A$-A(A;-A`2ޑAE-A ҒAc-AÕAb-AA<|-A`ooAk-A@}A(j-AMAR>-A#rAxX?-A@衘AU-AFA+u-A>A5-AA-AiAb -A`;ɞA5-A?Aj-AA-A`գA-A СA@-AyPAx-A=A0-AA-A 8A(l-A %A5 -AAx7-A A@O5-AwA+l-AeA p-A_A-A`DA-A@N4A-A@GA(l-A@A-AcAX-AA@3-A`bAR7-A@#UA_-AA-A?Ah@-A`ݥA-AAP<-A@A-A)A -A 1[A@!-A`bZA4-AXA0G-A AxL-A`ެAf-AAx-A`oA-AXA)C-A$Aw-Aa AU-A`iA7-AA 8-A@`A-AA-AsAH-AAs-ADGA ^-A /A-AN1AL-A`b5A 0-AA~-A@C)Ak-A`bA8y-A!Af-A#A-AOA0u-A(A-ArAJ-AA(qd-A@A-A/ AH-A AЄ-A s) A0-A A-Ab AH-A`) Ar-AH A`-A( Ad-A`/ Ah"-A0 ApA$-A3 A"-A &3 AP/-A 3 A0D-A7P43 AZ-A/3 AH-AU3 AoT-A ^ 4 A2t-A)04 A)-A c]4 AM*1-A4M4 A軋/6-AwwHk4 ALr8-A 5 A\㍝8-Ay=G5 Ako5-A)#5 Ay0-A 5 AGQB)-A`Z5 AЕ-A s#6 Ay-AgB6 A -A#\6 A|m;-Ap6 A`-A”}6 A1-A6 Ah-A :; AHn-A@>; Aw-A? A`G -A=`> A-A@.dA A8D-A_J A8 i-AKyR AV-AgR AZ-A`vS AQP-AA7S A!5*G-Af8cS A?-A0CS Ai:-AN#S A+8-AJNT A4&7-AF(u>T Ay9-Ap%wT AM>-A_ îT AЕD-AKT AL-AWKU AV-A[8*7U AKb-AWU Ab-A U ACv-A U A-A\ A؞-ALd Ae-Aji A s-Ai AЮ}-A'l A"$r-A/2BNl A6$7h-A h{l AC"`-A+@l AIZ-AM{l AX5V-A!m APLc-AP\'m Ab}p-A.'m AȘ}-A!m AA-Am Ap-A 84m A8-Al A-Ap A`-A@Cv A@N-A@ v A/-A2v A/-A#v A(`-A@v Aٖ-A w Aa-Aw Ak]-A0+w A^-ARy AQD-Ay A(mG-A{z Aؽo-A Ao-A r AT8-A`{ A&-Afm A-A A ^-A` ٔ A-A Axh-AQ As-Ax Aؼr-A@y Ax-A A(-A Ap,-A An7-A A--A@ A'-A AG-A ֫ AbS-A AXn-AoQ A -A&E At-A`9P Ac-A O A~-A A-A` A-A@r? APx/-A@ Ae-A A8'-A@Ŷ A-A` A@-A8Z A -AMN A-A\ A-Aʬ AP^-A A-A`; A0-Aû Ap-A i A-A@ A@ -A p A-A ڹ A-Ax A`v-A A -Au A(-A Ag-A`T As-A`o A-Ao A q-A` A(r-A@ AT-Aݷ A˸-A Ax -A`) A8Z-A A`-A ~ A`-A@u AhL-Az Axu-Az A-ABػ A-A` A+-Af8 A"-A|ʼ AЄ-A H A8-A`dn Aº-AB A6-Aj A-A AЀ-Af AP/-A` A-A A-AF Ah]-Ak A-AzP Aȶ-A AP&-A@q A(-Am A+~-AW Ap}-AҞ AZ-A? A-A@H A-A (E A-A  A^-AV A٧-A ¼ A-A'ڼ Ahj-A,| A`c-AZ AX^-A l A[-AK AW-A | AYS-A/ AYM-Ajg AJ-Ap APD-A |G AN-At+ AP7-A@  AH;-A A-A8 A5-Ac AE$-A`m@ Ap/-A;U A81-A AP;-Aǖ A?-A A`G-AfƼ AN-A lx A`G-A ( A -AN A8-A_& A+-A A21-Aֹ A8E0-A@ Ai-A ҹ AX-A O] A` -A@ AJ-A, A 4-A@< A-A@ A(-AHU AX+-A`T A0eD-A4 A}-A0N Aȩ-AЪ A$-A% A$-A9 A-A A-AH API-AZ A-A` A(-Ag A8-A Ap, -A AP/-AH AX-A@߭ A-A@ A-A c AP"-A' AЕ%-A y A`'-AǾ A.-A@ A?|-A`< AXhi-A_ App\-AX Ap]-A A|-A Az-A AP -A \ AH-A Ahq-At Ah^-A`@ A-AM A-Ac A-A@ϒ A-A`a Axz-A` A-A@W A[-A@u Ac-A`B A-A0 A-Ad& Ay-A s A(h-AU Ax-A@V A-AF A_-A A-A޵ A-A_ A-A  A@u-AQ; A-A= A-AM A8-A&5 A-A J Aȇ-An A-Aj A(-A AH-A" A0G-A@r AH]8-A A(,-A |C A(,-A`]z AQ-A 9 AZ-A Zg Ag-A^ A8y-A A+-At' AU-A AXFh-A` Axme-A A0H-A Axz-A AhM.-Af A-A A AR+-A AEM-Aj Ah-A A@O~-A`2 Acx-A@W A@-AS A-A  AP<-A < A%-AI+ ABl-A% Aɶ-A A@1-A Z Ap-A` A-A A,-A Ah-A  A-A` A0}-A`} AϪ-A1 A -A,j A`Å-A A"-Ae A؎-A A -A? A p-A`f A@B-A; A-Adn AP<-A A -A@ A-A@5 A8R-A< A؟-A@ AB -A`" A(-AV Aw-A` A -ABg Ax -A@> A-A  A;-A`! A#C-A$V A-A sK A6-A c A@]-A A(,f-A A0Q-A9+ AXK-A A@5-A AX#-A` Ax( -A< AЯ-AH A@-A@` A@-A0x A-A  A-Aǂ AC-A AH -AgZ Ap=(-A" AH,-At; A0W-A@i AS-A AP Al-A  A Υ-A3 AX_-A A-A Zc AY-A c A߳-AU A-A" A-A&" A-A$ A G-A H$ A`-A<' AU-A`T:' Ah-A' Ax-A* A-A=x) A8c-AK, AX-Aݲ. A0-A L0 A R7-A`9I2 A`-A 8#8 Aȱ=-AO9 Aɉ-A`K; A-A`D< A-A@#> A(~-AD A,,-A?C A<-Aj:J At-A K A -AL Aj-A@EuP ALa-A (Y AP-A Y AHG-A`mZ A`K-A@ykZ A/R-A@,a A^-Aa Ao-Aa A@-Aa A-A@`a A-AX A oY.A}] A@.A=\ A6.Aҗ] A(y.A^ AB.A;_ A؎[.A q` Acy.AKa AD.AQa A;.A |a A(.A@%b A6.A;b AW.A Nb A`P.A@,b AĂ.A@a A.Aa A..A_ A虸.Aja AV.A 2c Ab,.Ad A0.A`mNh A .A?vk A .Apo A`k.A lm AX#f.A4t AÝ-A A0-A Ap-AU A-AA A5-A AZ-A A.Av A<.A |I} A".AE ANR.A` A.A`* At.Aǜ A`X.A@ A.A~ A .AR AH.A`H A].A A4.A Az.A# A.A A뿤.Az Az$֌.Ap# AOа.A*, A ]_.AP1 Aa(> .A2 A8.A . Aȵ&.An AX.A` A@` .A A`C< .AT̋ AE .A6 A9 .A?\ A .Ax A .A AHaJ .A@ A.A` Ae.A { A8Q.A[ A(`.A`B A;/ .A`2 A@b .A Q? A .A/ A` .A A{ .A ANh .A ArW .A G A@K .A| A@F .A@i AH{J .Av A&V .A@ɞ Ah .AU A8 .A< A .AAz AX5 .A AN .A  A0 .A٠ AX& .A A1 .A` A, .A 덢 A(.A 1; AZ .A㙲 A.A)Ҳ Aȃo.A@z A0&.A@, A.AI A+.A` A*q .AD Au .A f A .A`K A0eb.A@Ns Ap .A ٰ Ah.A A .AR A- .A A(w.A@ A.A A;.A@ A.A@S A@W.A@ A8.A,_ Aȓ .A A@N .A@e Ajj .A. AXd .A^ AX4 .AU A7.A Ag.A@M A.A A.A Ax--.Aif Aw.A Jw A .A`J A! .Ai A`n.A- Ap[^.A A.AD2 AR.A` Apʜ.AU A؎Z.A@i AǢ.A; AN.A`f A.A`̞ A R.A@R A)L.A \ AC.A A.A \ A RS.A AH\W.AR~ AXl.A`) A.A3 A{.A=4 Az.A \ AH.Ao A.A \ AH.A \= AЄ.A AP+.A AKK.Ak Ax].A Ai.Aл Ap.A@ A(n.AjG A@@.AQ A`Q.A@ AR.A`m Ax.AE A$.AE A.A` A=y.A A AI.A u A.A@x A#.A+ AV.A5 A(.An An.A A@.A= AP.A A.A@ A@W.A A(`.A AL.AV AM.Ae A.AЫ AP.A6 At.Az A h.AR A,.A`mO A0$.A AH.A c A.A A!.A A06.A A.A$ A8.A, A(`.A A<.A A>.A3@ A).Aྎ A{.A}* A@N.A AX.Aw A.Az A.A@E Ar.A॒ A8.A | A(.A@p AP .A A9.A A_.A`)i AX.Af Ad.AX A0.A@y A.A@8 A`.Af> AА.A A8D.A AD.AR A@.A`_ A@u.A A .A^> A( .A> A!.A!@ A!.A aC APڇ!.A`9C A .A`oC AH .A cC AN^ .AC A, .AC A }.A pC AW.A 4C A.A@!"C A^n.A =C A5.AkC A.A?C A.A,uC AC.AiBC A\.ApC AH{(.AC A.A C ApZ.A/C AKz.A@!"C A3.ALB AR.AB AA.A B A.AC Af.A@!"C Aث_.A@!B ApI.A (B Ap:.AƮB A%.A(B Aݻ.AB A.AD A!.AE A8Ib!.A`F Ai!.A`bG Aq!.A}G Aq!.A;EH A؎m!.A`sI A".A`; R A".A@T A0".A T A#.AFmV A>#.AV A$#.AV A(S#.AaX A 3#.A Y A>@#.A@Y A`#.A{Y A(K#.A [ Ap#.A[ A#.A k\ A0&.A xs AP2'.A@$w Ap '.A`mky Ap(.Ayl A(.A_ A 0^'.A Aa&.A E A8l%.A`9 A+R%.A |3 A(&.A` Ab'.A Av'.A Al'.A A'.A̍ AÇ'.A3ۋ A(.A Aĺ).A`TN A@y).A`TЊ AB).AX A@o).A AxiZ).Aq AAa).A` A@q]).A`]Ɗ AhK).Ay Ab).AQ Al(.A  AEZ(.A`2 A?(.A m Asm(.A A`C(.A Z A 0t(.A A(.A`ܱ Av).A Ar).A A0).A@ o AǞ).A AL).A`b[ A" *.A A*.AH AK(.A`9 A`v'.A Aq'.AsY A'.AR A%(.A 7 Ap).A` Ae*.A ޑ A,.A AH/.Ak Ao0.A` AM1.A AX1.AVg At2.A A3.AX AK3.AH Apn4.A A̝4.A@1 AA6.A@O Aj8.A@N[ Axd|9.AM A&;.A`o A<.A` AXۿ<.A A=.A2F A=.A! A.=.A?! A0X=.A /" A0Z>.A`=% A>.A) AX5W?.A, A?.A@. A$?.A0 A`B@@.A3 A@.AЦ6 AaA.A<; ACA.A@5? AA.Az!@ Ax>-B.A3A A5KB.A@KC AأB.AD AHB.A qE AHC.A`mH AuhC.AOJ AՒC.A`{L Ah޲C.AgM AC.A]M AD.AO A#D.AlP A D.A@ P AТD.AU AE.A@ [ A*E.A2^ AlF.A q7_ AhgF.A`T` A+rF.Aa AF.A@b A cF.Aad A؎F.Axd A8G.AZg A ͋G.A[i A(G.A |l ApWI.Av AXI.Ay A8J.Ay~ AJ.A@M A(IK.Aa AAL.A@yV A ML.A@؛ AxL.A@ A4L.A@ F A:M.A AItM.A A {M.A A`CM.A- AM.A ҕ A*$N.A줘 ASN.Aљ AhN.A-I A_N.A鶚 AsN.AП AxXN.AǞ AN.A`C A,O.A" AȵBO.A m AX9LO.A@y Ax`O.A` AzO.A?% AzO.A㪣 AO.A JJ AO.A AP.AH A)P.A˭ AP.A AP.A J AQQ.Aɯ AЕ\Q.A- AQ.A` AQ.A A8c"R.A AaR.A APR.A@ AER.A@5 A0.X.A@ Aw0Y.Aq AGY.A/E A#Z.Ae AҎ[.A A].A AP|].A A(^.A` A_.A A Y`.A ACa.A` Apga.A 8% Aa.A4 A0fa.At Axa.A AxWa.Ap Ap+ta.AR A/a.AQ AEa.A A v.A | Av.A# Aw.At Alw.A A&4w.Aá) Agw.A  Atw.A*%x AEw.A  Aosw.A>jgJ* A@Qw.Ad5 AMKw.A`m@ Afww.A9I ACKw.A/FQ A+ w.A`W Av.Ax A8"v.A`) AԿv.A ~ Aȩjv.AO A0ԩv.AR AcTv.A Aɂv.ATH Ahv.A@ Aqv.A;uF A$v.An AXv.A`T A7Pw.A A_w.A A_w.A@4 A*w.A Ah{ x.A@S Ap9x.A Apx.A- Ax.A`K- Ax.A@X Ax.A` Aȶ#y.A  A`Dy.A A][y.Ax Ay.A`)q Ay.A`Ӕ A)z.A V A8_z.A@ A`|z.AAz.A@./A8z.AqA@z.A`vA1{.A`;A0Xm{.A@ A{.As Ap0{.Az A{.A AU{.A?) A0{.A`g A|.A A(|.AAp=M|.AA(Kz|.A1ApJ|.AAN|.A@5A|.A@ƆA|.AA60}.A`1AЕa}.A@zA]}.A*AH }.A A?}.AA}.AjA}.A8vA}.A@`Ap}.AP A}.A] Aȩ}.A`!A}.A@G?"A}.A6#A,}.A#A}.A9%AH}.A`v]%A~.A }&A}.Av)AW}.A3)Ah3}.A3*A}.A@b*Ap}.A3i+A}.A,Ah3}.A3S-A=}.A µ-Aױ}.A .Az}.A.A`}.A A A|.A`?Afw|.A JAAd|.A ^CARl|.A@yEAP{|.A`EAxz|.AvFA8|.A@GAȂ|.A`!JA|.A`KAk|.ALA"|.A@NA|.A ~OAh|.A`9ePA83|.AQA|.ATA |.A`VA|.A WA`a|.AXAk|.A!YAh|.A]YA|.A?[A|.A[Aأ|.A`f\A#j|.A\A`%[|.A\AQ|.A@z]A0.R|.Az^A(B|.A`\_A(;|.A_A8|.AH`Ap=|.AaAB|.A@gFbA|.A`)dbA|.ARbAP|.A ~cA|.AcA(|.A`cA` |.AcAp8{.AzxdA{.AdAH{.A3dAp|.A@eA(\|.AeAz|.ADeA` |.AXeAp{.ApeAX{.A |eAp{.A37fA{.A fAH.{.A@ fAp={.AzgA{.ApgAU{.A hA|.A-ehA |.AhA |.AKiAh3*|.AjAHI|.ADjAqh|.AbkA(n|.A`kA }.AoAS}.A2} oA}}.A]+oAH%}.A`7oAi-}.A==oA6}.A?oApJ>}.A=oAKÆtF}.A1b6oA0xTN}.AV+oARװU}.A+oAx\}.A+ oAo}.AnA0 y}.A`nA t}.A |KnA(g}.A@*nAذg}.A&mAho}.AkmAxu}.AnmA{}.AmA%}.A@{mA8g}.AĒmAP}.A`nAh}.AkOnA}.A`nA@}.AoAd}.A&oAH}.AoA?}.A SoA}.AAoA&}.AॽoA}.A@5xoAh}.A3oA>}.AhoA@}.AhoA0}.AR*oA}.AzoA`}.AoA/}.A_oA}.A`spA8}.Ap%qAp}.AYqA~.A&qAP=~.A`WqAQ~.A qAhd~.A`ӄqA@p~.A@PqA2~.APqAP~.AqAH~.AqAp~.A |qA}~.AhqA`.ArA8 .A@ sA`/.A@ sA'4.A sAYI.A6sA'[.AAtAi.Av>tAx.ADtA/.A`>tA|.A ɊtAb.AitA8.AtAʸ.ANuA.A@`tA.A@,MuAH.A3uA#.AuA(.A "vAؒ.A=vAh/.AvA?;.A@wAP P.AawAЦ_.A wAx.A#wAW.A]wAH΀.AHuxA Հ.A@.xApl.A xA.A@pxA(>.A@`xApp.A`f6yA .A`ayA.AyA .AyAH.A syA87.A`yAP'=.A@ zAe.AQJzAto.A8yAJf.A2yA1e.AyAn.ARyA`}.A`;yAث.AyA8.AƱyA0堁.AyA.A yAc.A`mxAȁ.A`xA!́.A #yAہ.ApyA.A yA.A`yA.AzA.A`zA(2.A@N{A9U.AzAQu.AzAs.A@A{AhĒ.A"|A(.A|A].A`d }AsY.A j}A`X.A@y}A.A`vA.A@A.A@zAp<}.AAȅ.A? Ah.A)AH.AA-.AhsAc.A̒A.A ۄA·.A@߆A}.ABA.AZA{.A \AxI.A2Ac.A6QAxu.A@A.A mAG.A@ A .ANA.AIAF.AE Av.A'A.A@ A8֟.AAȉ.AwAБ.AAH{?.A@A@].A@7A&}.AA͊.A ֱA@.A A A.A)A;.A}A .AThA͋.AHAH.A ?Ai.AOAW~.A*AА.A`A@$͌.AAЯ .AӖA,1.A@lAw.A A.A{AТ.A (A9[.A@EA~.A`)YA0X.AgApZŎ.AA.A@>AH.AX%A&e.AAt.A$As.A -A0g.AOA T.A=A@X.A7AP8Ր.A+AN.A(Ae.A`pAH.AAY.AAr.A@`;A.AxA@>.AAx.AnAh.AA ӓ.A?As.AӬAU<.AHAq.AA.AAg".A3 AM.A App.A A0*.A z AF.AAh@ .ARBAh@ .A`vA -.A|A"^.A"A߆.A`oAuޘ.A@AP .AOA.AoA*.AǟAI.AHAI.A A\.AA 4.AAެ.A`mA .A`2A@O.A AP.A`m%Ax.A`dU(A(.Aɤ(A.A (App.A@%(A@.A`\*AW.AM)A@.AHc)A0.A.)A/4.AX*A`vD.A (O*Ax.A,APw.Aa-A.A2w/A!.A`0A().A1APޗ.Ar1A.Aા1AV˛.At6AIǛ.A7AΛ.Al7A?.A7A.A7Ak.A 8AR.A08AQ.AI8A@5=.A`8A9.A#7A`%(.A`G7Ap0".A`7A.A k6A .AE6A .A66AXE.A:6A`.AP6A5.AO8A`G2.AT8A@(.A@Cf8A%A.A@>9A{M.A`'9AhJ.A@9AhP.Ad9Aꟛ.A c8AP.A /8Aƽ.Ad8AU؛.AM8A֛.A8A@ɛ.A9A0Û.A:A.A`;AJ.Az;Ae.A9;Ag9.A <A'.A+<A8g(.A c<A0XK.A=A0.A<>A@.A`>A.AF@Ap^.A`AA`.AEAX$ϛ.A)DA舛.A@,/BA8.A0%AA@~%.A`AA؟7.A@BAA.A CAȱD.A?CAF.A`dCA=.A|_DA?-.AEAȱ.AEAI.A/FA.ApHA.ALAzG.AnNA8#P.A`jPA.ATA`؜.AVA=.AYA().AmD\A୹.A_AP.AcbA g.AH*fAp0ߞ.AhA@8.A \kAX.A 6yA.AzA).A (Apv.AxA Ң.A |'A.A9A kL.A(A0Ա.AA.AĒA }.A |+A.A:A8c.A4AH.A OA..A-AI.A@pA(1n.A`zA(s.A A4-ANW+A3-AVA`6#-A HHAY-AAB-AA-A{A--AaAS-A@An-A ׺APZ-AձAs-A6iA -AhlnA4-ANW+A-A0)As-AܔA -A37A-A0)A<.A`9,ATy.A=Aou.A@yAln.A/9AW.AXXA^.AqŬAW.A`ˬAg`.AQApe.AQA Rr.A JAPZ.A ȭAr|.At2A<.A`9,A`K#.A@>~A.AA2.A@P?yAb.A`pA.AchA;.AeA0Y.AoZaAh38.A@`A.AEMaAX# .AR bA.AbA0..ATcAq.A.AmA.A }nA.AoA.AjoAҾ.A9pAp,.A.AB}A.AB}Ah.A}A0.A}AX#.A}A&.A}Ah.A`թ}A.Ay}A0].A }A(.A l}AL.A}A.A`}A&}.Af}A0v.A}Ar.A`}A(p.A@N~Apd.A ~A b.A`~A,N.A`0~A@5?.Ap]~AX1.Ai~A .A`x~Ab.Ay~A.Ax~A.A@~Aw.A@~AHi.A@~A.A ~A0H.A`~A@.A~A.A~Ah.A~Ap0.A~A(:.A* A.Ap"AX.A A/.A8AG.AtAu.A)A@.Aa@Ax.Aa@A.Af;Ar.A`m'AhW.AaA].AZFAW.A@AxR.ABAR.A ~^A0D.A`fA0=.A`xA1.A`tA84*.AXA#'.A`ÜAp|.A`A.AA8.A`A.AA*.A@NA.AAv.A@FA.AFA.AUA.AAH.A;AgW.A;AE.AA2;.A A:..A@ćA.AAc.A A`G .A |Ah.A+Ap.A}2Ag.AJAx)ۿ.A WA(ֿ.A`dAJĿ.A菈A .AA8.A6Ax:.A^A~.AA8g.AAl~.A OAȔ.A@Axm.AkԓAx}.AA8r.AvdAP.A`A,.A)ޮA).A`fAh.A@GA(r.A"A.A A(L.AA }.A`DAt.A@ݓA.AAh&.AA`K#.A@>~AJxD.A` AX'/A,1A I &-4@`?.AFA4 A0.Ak5 Ag$.Ax>c5 A?.AFA4 Aߑ.A{֊< Ah.A@WA A$].Ai~HB Aߑ.A{֊< A~O1/AAb/A )Am/AEAb/A CAh/AAz/ARȪA~O1/AA/AAP/A@A/A`AQ/A?A@B/A@P+A//A`9As/A-AТ/AAp/A@>A/{/AAG/AD>Ar~/AAX}/AAX}/AA/AJA`1/AkA/AūA|/ArAƗ/Ad A/A`A/A]ApJ/A3AHr/A gA"/A$A/AZAP//AH%A@/AT޿AX/AxA/A AH/A``Ax/A`ܽA/AHA|/AA(-/AEA>/AFкAȔ/AËA /AA0H /AtrA;/AA48/A׻A/A%AЄ/AAHA/A TAh>/A |A(/AZAp/AFAg/A-RA/AiAg/A߸A/A@=Ap/A`vA/A?A0/AKAl/A ਵAg/A<Af/AĴA0/AA`/A`oA@_/A?tA`/AAc/AqA@_/AA8Z/AྫAս/AiAR/A?AѮ/AA@S/AaaAW/A 1A]/AAL/AA@5/A,YAf/AA k /AحA/A׭Aq"/AҭA5/AҭA |F/AAHP/A@y0AhU/AXAH]/A:Av/A0.A/AAH/A 7AȔ/AaWAЙ/A A/A`ЧAP/A`֧A/A@y A/AAh/A!Az/A[Ao/AA(qx/Az&A/AA`/A@Aؚ/A@jAH!/A |AP&/AAH/AXA*/A lAX,/A 8,A/A`Ah/A`;A0/AA0/AÜAX,/AēA8'/A]A` /AA(/AߛA/AfIAh/A 8Ah/A@#A(/AA/A 힙A/A A@/A઴A@/AxA/A*AX/A`TA/A6A/AA/AF[A/A ˺A/AܖAc/A`~Ah"f/A@gAp/A@Ax/AA>/A`gA@ /AA1/A列Aਙ/A@,Ag/AvOAp[j/A0;A./AA /AƔAБ /AxA35/A@A83F/AʖA}/A`9A{N/A@%ĕA8`/AʕAhq/A߯Ax/A߯A`/A AxF/A` A\9/A AP/A@ ՕAQ_/A[Al/ATA |/A]ÔA*/A#Axm/ApAp/A^A0/AAx)w/AgA8gY/AA*/A`˒A`/A`ܯA8/A YA0s/A@A /A@PAhn/AѐAp/AСA0/A`PA /A|xAH]X/AwA+/AwA@ߙ/AwAx\/ASwAЄb/AwA8b/A twA6/A xAH>/ApwAI/AwAl/AwA@5o/A QwAG/A&xAh/AAP/A A؁/ArAp,/AAA/A@QA/AMAZ/AA0{/AApl/AApIt/A`zA }/AMA`/A`̄A /Ax>A8z/AFA0{/AjA /AMA`/A@yA M/A ^A//Ap"A0/AA0/A_5A8/AݬAP@/AEwA8/A`AY/A@Ao/ATAh"/AjlA4/AAY/A |A/A %AB/A`A@/A (tA,/A`EA*0/AqA/A`KAhD/A@A@כ/AA୰/A /A(/AŌA}/A<ٲAU4/A@ȰAsG/A AP/AձAX/A/A(/AxwA /A ɍA؎[/A`mAFl/A`Aq /A AP$/A`,Axp/A@5AXh/A@AF/A`]UA0:/AA@S/A`oAU/A ˳AX/A,A\/AA@/A`mAH/A`YA~z/AAP/AjA)/A`A o /A޴A`1e/Az2A]q/AA ~/AHkA`/A(A./AgAx-/AӷAF/A`A/AAH/A dA%/A@ A7/A \AHk/A@rA F/A AA@I/AٵAB~/A HvA"/A'uAp+/A@r;A/A{Ar/AA2/A-A{/AӽAg/A ʼA/A"ڼAƼ/A@A/A NA^!/AT=AH(/AA/AA0Ԇ/A@%.Ai~/ApAȱ>/A SAP*/A|AHO/A`A/A`ùA/AAT/A`ApA/A6A//AA@ߙ/AaA2r/AջAw/A SAh/A A~/AZAȃn/A΅ApN/A`A./AA=/AAp|/A`AX~/A@A/A=A/AؽA~/AA0F/A-AԔ/A`AA/Ad۾AhU/A/(A /A`A'/A XA/AAv/AAF/AGAh/A-hAs/A 1A/AAx/AGp\A/AX]A8/Ad:]A/AkR]A/AkR]A/AkR]A)/A c4]AX9/A`K\Ag/A[AĪ/A^A((+/Aŵ\AXo/A]A|/A@d]A(8/Ao\AgL/A"\Ah"/A]Ap/A\Ap/AE]A/Ak]A0/A`u^A/A W^A؟1/AV^A*/Aྲ^A(/A J_Ao/A qb_A#?/A`bM^Aa8/Ak^AC#/A@.^A}'/A S^A /A ]Ax/Ao']AΝ/A\AU/Ah[Aأ/A@ZAQ/A0ZA /A@8ZAH/A`ZZAL/A |YAx/A_YA}/A݇YA!R/AXA(Kz/A};XA0T/A=4YA/AYA/AXA/A`oWAa/AʵWA/A@XA~/A`XA/A QWA/A ɕWA/A VA/A 1=UA( /AjTA!/A`XSA3/A`KSA ߂/ATA/AoUAx6/AUA~/ATAHz/A =TAo/A@SAXy4/AiSA /A_|RA}n5R/A uvZAx/AGp\A{V /AD TQA/A?(SA(/A`TpSAp/A}TA/AYSA|/ASA/A`?TAC/A_TA8/A UAPg/A_UA/A_UApl/AT7UApl/AH UAI/A@TAi'/A`HTAf/A-TAf/AxSA/A)TAX/A 1TA/A 1TA{/A@TA/AqTAv/A6TA(/AzSA8b/ASAX(%/AQgSAi'/A#SA!~/A 8 SAd/A#SA/ASA/ARAȇ/A&RAhD/A&RA@y/A(RA@y/ARAP+/ARA`a /ARA /A@E:SA&=/ARA/A =RAd/An'QA{V /AD TQA6]/A\ݲOA/AaOA|/ANAy/A KA@/A @IAe/A?GA6]/A\ݲOA Z/A<ŮTBA@B/Ay3CA/ADAh]/A 8EAf/AKCA"_/A&)AA Z/A<ŮTBA Z:/ArQAu/AKAB/A` FAh/Ad4AX'/A0A/A`f.A/A _8A/A`CA0/A`IA7/ALOAo%/A'XA Z:/ArQAy 9/AmFAn/AAK/A A8#%/A Aج/AAA/AྟA`v/A@qA@u/A6A _/A8A~z/A40AX/A[Ax0/A@,A&/AӗA /A (iA/ANA(/A uEAο/A@A/AAB/AŊA׉/AaA6k/AgA5H/AosA3/AA+/A`]A /A Ai /A`9As/AVA/A@!tAp/A@yA/A}]Ah^/AA /A@AA?/A`A /A_*AhU2/A;A``B/A|Ax[/A A\/A AXWx/A@GA8/AA8/AzA@d/AA8/AWAp/A AP/A(A/AKAhb/A@QA/A@yA/A cA7/AfA8/A@A /A@{AXc/A@iAp/AAC/AgLA/A;/Aho/A@FA/A?Axz/AnAA`e/AA%/A`K_A%/A@A@/A SwA1/A`Au/A@ cA=/AJOfAK>/A`l+iAx/>/Ax}iA>/AMiA=/A 0iAx=/AjAp[V=/AjA=/Ap*mAO*>/A&2mA蝴>/A YlAP>/A%lAr?/A%kAЮ?/A0flA(Bi@/AImAIh@/AInAX#?/A1nA(K?/AKnA@X?/AnAxKo>/A9qA>/Ap,rA9?/AhrA@/A{rAx@/ArA@/Ap,rA #sA/AБgsAXIB/A }-vA;D/A0!wALE/A0Tn{A^E/A}|A8>D/A|Ahf4C/AҪ|AhC/A }A\C/A 9}Ap=B/A@o~AX_vC/A@ AD/AA7E/Ab[AP/E/AGɅA@ F/A0EAxF/A@ AnbG/AWAxG/ẢAyG/AIAWH/A@<=AtH/A;4A7I/AA$J/A4Af=J/AA` K/A(A?H/AwAMH/AxA(F/A |A E/A@^yA G/APxAuH/A0kwAZNJ/AuAteK/A`etAL/A@<6xAM/AyAN/AwA@RP/AuAS/A1rA U/A:pAxm]W/AZoA&X/AnABXX/AiAXmX/AgAP/vY/AifAƨ[/AfAp]/A`CfAO_/AxfA̳`/A }aA`/A 96_A)`/A]Am`/A 9\Aa/AКZAjLa/AEYAԉa/A WAxqa/A0kVA%b/Ap UA;b/A@MSAb/AxRA&d/A ]LA.Rd/AhSKAOd/ALwiKAqܸd/A5KAld/ALJA(d/A0JARd/AIA d/A" UNIARqd/A-eHANVd/AboHAd/AHAP c/A**CApc/Ad@AEe/A`6APf/Ao-AȩEg/AK-A`h/A,Ak/AF*AJm/A)AHp/A@L&A0r/Ap_$%A0t/A_#AEv/Ap!A)|x/A4 A{/A`nAH{/A@&A }/ApApV~/APҎA/AZADυ/A BA0X/AA@|/A`?A/AA8"7/AVA؞/APA/A HA/AAG//A<A_/A%AT/AEAW/APAȶp/A (Aɕ/Ap>A`/ApAx/AdA\/ABA/A!A/Ap,Ah/APAا/AG A#/A  A0/A0" A/Apg A𒥦/A"3A(/Ap=Ag/AăA' /AA/A $AV/A HA5/AVApV/A@gAP/AAi/A ]A( G/A'A/A2SAٗ/A`AKZ/A`]A/A@EAў/A>A$/A`bA_/AQ0A/ATAP/AA '/A@iZAx(H/A㟸AH/A@sA%Ŀ/AIAH?/A@xA/AzAD/A{A5/Acu~Al/A@%xA׹/A7qA/ATiA /A ˠ[A⟶/A&ZA%/ABOAx)/Ap\A/A`bAHA/AeA`B/A /VA/AzmTA@}۱/A"HAP|/AZJA*/A?A|/A >A8'*/A@yAA쟭/A`=A%/A >A(0/AEA@h/A@rVAy/A OA0M/AMAP|/AyFA /AEAL/A@g;AXʬ/A6A/A@T5Aꔯ/A F#AJ/AQ4)A /A8'Ah/A`vAX/A`T~A/A3A(`/A`0WAsT/A@EcA5/AtxgA/AnA /A१}Avr/A\A 0d/AM0A`P/AA8 /AAT/A@ߨA)/A`]AX/AұA/A&)A؁/A3EA oi/AAH!/AԸAP^/AOA/A@.LAHib/AkA؎//AAx/AdA/A 俾A@`/AmAD/AgA3/A ҪAf/A \A/A@iA؍j/A`A+=w/Al AE]K/A}9Ad3N/A_AF/AqAd3N/AOfRA; /A):A 7/AX4AI/AN AН/AWA@/A@yA`4/A%A@'/A@KA֪DE/AhԷA;7ئ_/A֖7Ac%/A)EAWPi/ASd9qA\,A*/A +A@.c/APA X/AAx/AAXd/AAr/A2gAAq/A (AHah/AQAHÔ/AAU/A`A8/AhA0/A+;AHah/A`A/A Z}A/AJA88/AAek/AjApA_/A`A@Z(/AYƄ[Alō1/A iAwu/A"AB^}/AcAP|/AzAf/AοAC}/A%A_ /A@&AQqG/A\Aq/A$Ac/AA /A,{Ax`)/AJB;AQ/A$4AɣY/A6nA/A?@AlID /AҳAj\/AZ_AdH/AgzF~A/ALUDrA_2/A9AH/A@A /Ap,A/AzаA8/AAAB/A$AVj/A9fAIn/AQ(TFA) R/A4 L8Aآ6/Ae<A|/ALRAT4/AUzA/AAȺ/A`夰Aa8/AAR/A_\A5x,ӿ/Ai\~A+/AXg:ӾA{r+/AFAlŧ/APAH/AAl/AoA /A`0A@/A`TA䌰/AqA t]/A@r<A*/AA p/A3Ab̯/A@A}/A;A}/AA/AIDA(/A@AD/A@AP맯/A`QA(K/A@#tA/A?Am/ApA?O/A/#AYR-/AZ.vA/A7\NA՜/A=kAx/Ad$KA@C/AF'A\X/AqAAj'/AbдMA Y/AAi/AHAt/A"QAQ& OX/A}/A%BT$/ANϖA /AnAٴy̬/A1 A`lΩ/A-+vAIzԬ/AJ+URA|-o/AAo6W/ARJAM=D/Ar;BACO6/A/wAfn,/AiwAHn(/A A/ARAj/A6A3/A$|AT-Ӈe/A8нA`M/AI$ A8/Agf: cAǺ&/A5OA`!/A-A/AT(A/AdAڪ/A9A뭪/A#AaR,/Ap:A}|/AχAGV/A79 A./A܇}A@/AAة/AzW A]/ANfpA'~/AA/PQ/A촙_A_%/A OLAM/A'"A,Ҩ/AC0A/ABA?6n/A)#^A~i/A ANL`M/A QAC5/AH%3иAS!/A{cA#W/A4>lAa4i/A˲tAI+v/A=( A3/AltTAxh/A ɠA:@/AigeAx\~/A[;0Ar$/Aj(A8 /AuߣA*/A ATxqa/AzA̦/A`Aqbաb/AؔAA /A.LۂAJߠ/A AIֻ/Aۢc^Ahז/A5?ۚA4,p/A͂AWT|I/AAX!/A`AxK/AAn=/AA/A'5A} /A\AI/AfAcB/Ak[Aq0t/A:AEAXy/A@hk3A ojt/A5RAp/AsAFܝk/A;UA ad/A3XA]/A.vAV/A`DmA=:2O/AMqA(G/ANA)B/A]A- =/AMAkJ8/AEAoX3/A\A+P./Aa\AX)/A`At%/AA A)"/AWAre/ATALi/A0MA /A517A7|/A0A#/A<,A_x/AC>A8g/A OAmGm/A?2FAf/AAA_%\/A/oGA` c/AA"d/ /A'R#.AQP/A֚PA/A;lA~۞/A\uA@ӿ/A<A=/Aɏ$Ar;F/AwLŋA`/A˸ @AÝ/AA/결/A2(AX&/AJ73AOGw/A1uDrIAߓ-p/A0QAY/AALA'FC/AUV8A`1//A sA7(/AyAOz!/AūkA /A0Au$/A8Ad /AA/A[A8ٸ/ABA3X/AU]A*/A/Ah /AHAx/AtdAP!*Ŝ/A oyAHn#/A7AgAqx/A ~RAX$N/A |Ad;/AFA+/A%EAf)/A pAn4/A1T=A/AIoAPo/AyA%]; /Al/A.RA<S/A'AWT/AJ-;AYhԛ/AAh;_ƛ/A=A-/A ZA&t/AcGAY/A.A=L/A^APYS/A`Ap=k/AA&}/A cAgy/A !AX]/A`AB/AA~/A@-AH/AfAa/A@A/A6EAЄ/A JAx/AvAxW/A ¯A`v/A sA/A@EeAÙ/AZA᜙/A Ay/AvAH`/AApQ/A`6A+A/AVA N0/AA/A`5A0"/A+A//AA^/A &AH:Y/AZAn-/ADA8E/A$A/AAA/AHA̛/A`AT% /AîA=E/Ao3A 1/AFA;sN@/AXg:ӾAʇT/Ai\~A}/A A/lZ/AM A`Y/A! APY/A1' ATmY/A6 A-Y/A>g AHX/AO AQOSX/AGW ACX/As Al_X/A{ݖ A5X/AI A X/A@ܱ AW/Ay Ak*W/A闕 AҗgT/A] AJIN/A=m~! A5x,[G/Ai\> A+#@/AXg: A{rγ8/A A:,W1/Ao3dž Av)/A A=!/Ao3dž A 18/A A;sN/AXg: AʇT /Ai\> A/A.Aa Ay:.A4F^da AңIF7.ADa A4.A_a A3.A0 b A@>3.AHb A1.A b A&.AZ'b A0*.A@.Hb Ax.Ab Ak.A .A^( AX.A Z#' A.A & Aj.A& AGx.A& A(h.A& AJ.AB"& A d.A,& Ar.A% A0Ǻ.A`T% AP.A;% A0X.A$ Ax0.Af$ AH#.A`f $ AP.A$ Ah.A@.$ A.AC% ACv.A &x% AW.Ak% A8:.A@% A0.A$ Ap*.A$ Axh.A" A"e.AR" A0.AG# A83.A@ # A.A`9$ A.A`m$ AEl.A@G# A0B.A# A7e.A" Aୢ.A`;! A .A! AP/.A A.A ~ A<.A&B A>.At A .A@ % AP.Aa A .A A.A^ A0j.AQ Aǰ.A6 Ah.Aྶ A0{v.A`2 AR.Af A >.A@R A44.A A7 .A`] AH.A`D Abm.A A.A AH.A A@p.A Ask.AN A3.A@ A,.A A0.A  AH.Ax AP'.Ap A`.A@, A$d.A Z Ah.A@ A@.A@d A .A ApI{.A` Ax [.A AC.A ( A1.A ( A.A ( A`.A@% A࠻.AO A.A@ A=}.A # AZy.A J+ APJ.AD AX.AÖ Ah\.A Az.A` AUC.A c4 A߬.A Q A .A@d A(1 .Ao A Ⱦ.A: Am.A& A<.Aߏ AH.A) A7.A@s A4d.A`" A].A~& AH.A ( A0ʹ.Am. A^l.Aܴ1 A.A@9 Ahֲ.A< A.A@@ Axm߰.A+C A (.A@E A.A@߂G AXX.AG A4.ATHF AV.AjqG AP.A@I A?.Ad~Ah&.AA.AAt.A@ݓA }.A`DA(L.AA.A A(r.A"Ah.A@GA).A`fA,.A)ޮAP.A`A8r.AvdAx}.AAxm.AkԓAȔ.A@Al~.A OA8g.AAC7.AT A.AAq.A ʉAH.AA0J.A lA$.A`bA0.AXA.A:Aʺ.A Ax.AyApco.AAp.A@#As.ApAX_.A Aȭ.A@{A.AZAF.A@`AHö.A4AǶ.A`ݑAy.AAv.ABgAj.A_lA8\.AVrAa;.AA9%.A sUA#,.A A-.AA#.AA0.AA .AA($.AoA].A`A.AApѲ.AA0?ò.A0Ay.AХAwZ.A Aұ.A#xA(.A`)A0x.A`LA7.A A7.A}A/ .AA8.A A`.A@mApϰ.A@%A.AȫAR.AѬA_.AAAhb>.A`A.AtƭA.A@ѭAp.AA.A@%AA[.A_sAP3t.AAg.A@%AG.AAL?.A nA :.A5]A&.A GAPZ.A ȭA Rr.A JA&0.A $A9.A S@A9.AAۭ.AxApŭ.A $AI.APA.A A}A吭.A@iAх.A@ߍA.AgAxA.AaAP.ATA>.A lۮA.A`AhQ}.A@A{.A@ߍA(dd.A\AQ.ATA:.A A}A,.A'At.AA.A;A.A Ab.AhAPYR.AAE.AA.A@A.A`Aj.AhAH˹.AA.A}Ax.A`Ai.AK$A M7.A0*AA.A J,A@.AX.A.Am/A\w uں.AB 0Aú.An0A0Z.A1A` ټ.A;A .A%D=AY.A?A] R.A%?AO.A1bu"@AY4N.A_`H@AWQ.A@n@A0l.APQ@A).AWA….AgXAW.A[YA .A@I`Ah.Ai2fA8.A@$ fAo.AzPfA8'w.AjA.AӦyA:ǯ̚.A+DyAa.AyAd.A* #zAUF}.A2"s4zAo.A@OzAPL yU.A-WszA2J>.A ?"zAn*.Ae=~zAM.A {A+<.Aw{Av.AE-{A.f.AtA{A曚.A!U{A{Aa.Axjii{A.A]s{{AȎR".AZn{A=).A%{AV.AQ{Avq(U.A)V|A .Arf(|Ak.AD|A#.A³|A#.ASAµ.A łAH..AA.A0"AP.AMA.Aa~A؞.A9A0..A`)юA`b.A`\ٓAߋ.A A.AhAfz.A{˔A\.A6_A.AėA.ABAx.A0nAX r.A`\*A8.A0w%A.A!Au볯.A*ZA P.A蘝A='.AdܝAl0.A6T$A.A#pA0.AAdn1.A'؞AN1.Ar-Ac/.AU: A+.A AT&.Aw5A.ApIA$Y1.Ak AӂH.As#AG[d.AЊA\E'.AnqZAߚ.A=iJA.A^Ai .A2HAЕ.AG̡Aɯ.A {A)D-.A*KAx=.Aw;AP+X.A AX.A,1AH.A@nAZ.A.A.A@qA|.A@<Ah.}.AOAؖ{.A`:A|R.Apw.Ac.Ap5ԏAx.AvAGg.A$N~AȤ.A8~AH.Ar~ANw/Av}A:/A 8 |As/A{A/AzA /AxA!f /AP&vA#/APZqAh/ApAx/AypA/A pA`6/ApA5/Ap5YpAp I/A`]toAq;/A`mA/A^lAP/ABkA0Y7/A-jA/A?hA,,g/A2u Am~/A A/A`4 A/Az؀ Aʯ/Az A/Af{ A /AҐ { AW+/A#7{ AyK/A&Rk{ AXi/A{ A׃/A 1Fx| Au/A,g| Ab/A"| A»/A[ } AZ*/Ay-I} A+/A9} Aһ/A;Q} AGZ/ACy~ A/A d~ Aۊ/AqW~ AE`S/A~ Aw/A.n~! A}Eѳ/Ao,*V A/A A9s/An Az/AD AU~l/AGS A,xU/A^ A;\zB/AT A01/A@  A7O3/A]Y,J Av7/A]Qpv A; =/A A0D/AA>_ā A;XL/AJw AV/AZ?> A/a/AF Al/Az@  AM4$x/A5;>! Ah̃/A  A~/A񝅏 Ar;] /AN ABq/Au A/AR A>/AɾK AT#/Ab @~ AaW p/A,/} Ay /A;7t| A=/Ad{ Aw/A/gy AfJn/AHx AP /A@rw AX/At A/AHq A`/A@wn Ax/A`k A/AWj AX(R/Af Am /A՘c Al/A (` A(:/A-_ Axmi/A@E] Ah1/AQ[ A(/A#[ A"/Aiw[ A1/AxiE[ A&/Ap[ A#Z/A][ A(>2/Ag[ A?;D/A~-Z Ax/A`K6Z AI{/Aw=Z AOu@M/A'[ A1 /Ai[ AЀ/AA[ AtU~/A $\ AZ64/AD5Z\ A灘i/A08~\ A5/AFqm\ AhR/As\ A/A3] A/A v^ A8/A@y^ A}/A`E_ AH/A{a A@N/Ac A`/Ae AD/Asi A/Afk Au̹/A iՄl A7o/Am Au!/A 0o A0_T/ASsp A1X|/Aݧq Ah%/A r A0/A@Gr A /Ay p AaB/Ap A06/Ap A/A r Ah/A@t A`Ì/ACt A#ں /A@t AJ/AĒL. A,,g/A2u Axhu/AD Ayt/A6 AHjt/Ap A~b`t/Ax醪 AyZt/A,:9q A8Yt/A^7 Av]t/Ae7 ATqft/AzƂ Ahtt/A"/ A?Ft/A֩` AO 1t/AX&4 At/AAq~ AYt/A| AEt/APmv{ A.T%u/A z AQu/A1 zy Au;u/A*x APu/Aw Au/A;r Ap v/Af Acu/A Fe Ahu/Ad4d AX[|u/A^d A o\t/A@4i?d AӈVt/A䦥}4d Azs/A9O=d AKqG0s/AG[d ACOr/A^r_d A%0!] r/A\%sd A {q/A>/e Avjp/Afڝe Afi[p/A2 f AƨNo/AJES f Ad@o/Azy(ag A ln/Abh A+n/ARah Arm/Ai Aecl/A J^l AG9k/A n Ax6j/Aѥ%n As.j/A@,fo Axi/Ao A|i/AdH Aȩb/A? Ab/A@j A`2b/A qe APMb/Ad A c/A`f A0'c/A Aic/A  A@™c/A`ܳ A誴c/A| A((c/A` Ac/A A?d/A^ Ax1d/A Z AtCd/A}[ A8d/A@g- Ag3d/A^ A7d/A A`Xd/A Add/A? AѧZkd/At f Azʉe/AR A_Gݯj/A| u϶ Ab"^p/AF)7 Axhu/AD A+A@N,,A@?N,,AS@̱+,A/AM@麢*,A#i@ل(,Aۡs"@k4[&,AR>bd@Ol#,Aܨ,@ꞙ@Bkݽ,ASnp@җg ,A;K@J,A%\,@6x,+A,YF#@++AQ1ًy@|rk+A(Z@:,W+A#sq@.+A@=+A"sq@ 1+A(Z@ꞙ@zɡ+A@GÕ8+AiAT@d G+Aܨ,@%h+AQ>bd@*&{1k+Aڡs"@E]K+A"i@d3Nn+A.AM@+AS@d3Nn+AaҐ@E]K+As~@*&{1k+A$^a@%h+AcYB@d G+A#Wz/z@GÕ8+An<ˮ@zɡ+AZ@_G+A @b"+As+6@2-hƲ+AQ#;[@Z+Axfz@ʇTH+AWӦ@;sN+A&t@ 1+AJ@=+A݌g@.+A@:,W+Aތg@{rk+AK@++A&t@{&+A%cc@8 +Ag@X+A@+A5@H +A$z@X+AĈ@P^&+A(@ C+AH@v+AĈ@+A&@H+A@R@@S +Afa@&+A'@x+AzU@P+A @+A@W@+A8X}@(9m+A@,r|@(-,ASBz@IS,Av@0S,Aau@ۈ,AU/u@(y,A(s@T,A@\s@x+A@!p@˼+A@N{k@`1+A"'h@ؚH+AQb@+A@`@+A)a@8t~+A-c@sA+ABg@+Ah@/+AzGd@g+A`@+A>V@!+AP@+AD"M@h+A=K@0+A}L@+A@gL@@3+AxL@(+AhEM@hr+A+O@xW+A"YQ@+AQQ@P8+A4{Q@+A]P@(\U+A@LO@z$+A@gM@u+AK@+AJ@W+A[I@n+AH@+A@ZG@H+A@F@x(+AF@p+A F@w+AHF@5+A8G@ +AG@0. +AG@ +AG@(+A|G@`a+AF@G+AE@h+AB@+AA@x5+Aj5@1+A@Q-@!+A@-@ȓT+A)@+A8.@+A@g-@)E+A@//)@`S++AD(@ +AZ(@p +A@(@g+A@߫,@hD+A,@+A@,@`Ǜ+A-@%+A@ 3@L+A1M3@Q+A7,@@$+A@,@ Ε+A]s,@ R+Ad,@p +A@@'@+AM%@`vu+A#@H+A @+A{ @ |n+A@@`2+A@ے+A@%@+A@+A@@+A@@+A@P@+AB@0G+Ax@t+A2>@+A@i@x +A@֏@+A@0+A8@3+A4@(+Ae@C+A@%#@+A@3@b+A2x9@C+Af:@(+Av.@+A+.@+A[3@8E +AZ#@o+A@N@.~+A_ @Ϡ+Az@H̋+A?@P+AT@B$+A @X+AQ @db+A`@B+A0@x+A@N@+A@p@4+A|@0/+AT@fU+A@+A@`q+AM@+Ae@+A@@ p+A @؝+AM,@+AB@r+A@'h+AE@/z+Au@6+A@0+A)@H)l+A @x+A$@+A@@؟+A@+Az@+AF@+A@@+A9 @@dj+A@+A@J@h~+A@-+A@;+A@\+A_:@PI+A) @ +A @h+Ak# @ho+A @(+A2R @h+A_b @g+AX]@/+A$@+A @`%+A-0 @@+A@# @+A@ @+A` @+A@P< @!M+A@XI+A<@}$+Ax@+A_@ C+A=F@3+A4'@+A@]Pϵ+AQ@M+f+Ad@M+A n@dt+A>51@WBh=+A 9D@A\+A3"E@x+AV@ov+A#^3A@yeKV+A1$@@4$d3+AW@Z+Asj6@q+AaJ3@{+A8@@x+A@sQ+Aq! @` (+A@--@|uZ+Aoph@Wm+A|@+A7@+A@iq x+AQ%@6p6q+Ae@a>k+AӝS@Qf+AT@&ݏb+AmUg@(`+Aǜ)@_+Aa@ƵN+A{C@`;+A@~)$+Ao'@H+A@0+A@X+AO@8|+A1@"+A@%@ݷ+A@0+AD@[ +A;@k.+A@^!+A@@`+A@b@.+At@+A@@+Acb@dm+A ==@`+Ado@X2k+Ag8*@ZY+A .t@:+A!0@+AV@'+A5@z+Aϸ@s+A95/@\@k+Abi@V+A$@P+Aj@@S]+A\@Q+A=0@#[+A@@XJ+A88@0S+A@@:+A@I@H+A@ +A>@+AE@  +AJf@h+A@Nǂ@+A3@+A&ڀ@X+A@@\+A@@p}h+A&}@H+A@@y@tC̭+Av@tM'y@rudL+Aװx@ٞm+ADjWx@ 0+Az-x@3ʐ+Ay.x@܂+A<Gx@ DGn+AiKx@:c+A%y@o+At@0+At@8VM+A6-r@0(+Aqs@+A^s@1+A[Nt@+APt@H+At@ +A@.t@0+AGs@+At@+Ams@8+A@r@G+Adq@(+A@~l@`+Ak@5+A,h@|+A@ydc@(Q+A_@O+A}_@hI+A^@7.+A\@zL/+ASJFZ@n5+A< Y@m@+AZuW@+Q+AV@^4Oo+AMV{R@+AH@qD+A3 E@+A@PPA@ֻ+AN4@R+A@y4"@@S+A;(@ Ϥ+A<@3+A&{a@ ϴ+A!; @1E+AL) @#YY+AX@+A@@+Ab@+Aޢ@г+A&$@hG+Av)@jv+A{@K?+A֍v@C=7+A+A[Y@$e+A9@$-+AeE,@-9.+AQ`-3@K̠+ATO@](+A*X@9a+APХ@+A@6@+An@_f'S]+A$~5%@@+A@+Ah@8+A@ƨ@(+AΝ@d+A@+@p}+Aš@;+Angޙ@}Z=+AH@{>i+A!ɗ@9+Ap@?=+AYW@ +A@+Av=@j+A,y;@0+A8fZ4̌@!+AV@+A@@d>+AW-|@u+AQgV@r# +A\T'W@*+AoXTʃ@d)8+A F@+Aj+T@pm@L+A_1T@ը+AM S@}+A_٦S@8 `{+A%S@B(+AѽS@8E+AT@Uv+AN3V@.4+A&X@:oC+AΎZ@j+AKa2q]@&a+Af`@X!+A@b@O+Ac@V+A]A^af@I +AEh@D/l+AnXj@.> +A:}m@Ym6+AdҬn@+Ap@@v+ASq@BX+AНr@5 R?R+AQ~s@&+ɑ+At@+A=~t@!Ԃ6+Aet@MV+Aet@p+Ast@'w+A>pt@(A?=+A9 t@O+AMt@R+A&$t@߃3+AV(qHu@+A@u@W(+AuGu@Z[+A v@vz +A=u@LT+Au@w+AM6Zu@+At@+AS$t@F *+A;8بs@ڷ+A?Gs@k)+A&&s@vA+A3r@1L>[Z+Azr@9r+A 2r@6,+AM.s@8+As@g9+AY.Ht@P*+ACxrt@ƅq+AG ^Vu@+_IJ+ARӠu@+A ru@'"VJ+As1u@w+Au@If Ŭ+A,cu@ a+AUv@+A:v@5@+A$v@0+AEu@A6+Au@ f+Avlu@y+Ah̚Ru@=)l+Af9[u@|+A̕M},A{B}u@j,A6B{v@,Agw@W1,A|@2B,A];@6,Aڄ@(_,Ak@#,Aκ@iB<,A9?@ [,A8ͣ@r |,Ab/R@,A@v$,à @X ,A@8,A9%@أ ,A@⦗@,Aǘ@#b3,A %5U@h,AM&ѝ@PF,AZ:@P,At&ݮ@0p,A5 Ф@أ],A@g@|Zj,A-[H@nV,AbI~@%Ю,A9v@Xn,A8̣ӗ@sV&,AٓAw@ yV,ALD6@|,A@sӭ@X[,A@"@*,A@Q@,Ai@0",A @ t,Aa@L,A@g^@.,A৊ݱ@킝 ,A "@a\E ,Ai@sk ,ARbT@ ,A@E@O ,Akm@zY ,ARmd@. ,A{B@}I ,AY>QM@Z{ ,AQJι@w ,A@/@j ,A !_@ ,A4@" ,A@0; ,A 4@,AZ@Є,A@@h,A@|@0,A@@u ,A@~3,A@sW@XD,Am@T,A@ce,AX@(v,A@ ,Az@,A@r@ɐ,A@Ϙ@,A@@(h,Aф@,Au@P,Ab@#,A@pL@ ,A9@H,A#@y,A @,A@3@,A:@,AV@,A@O,AT@2,A@p&@@,A @g,A=@ ,Ab@,A@b@,A@0,Am@D,A?[@=,A^@hL,As@,A@%@,A8@,An@أ,A@@,A@@p,A@@,A@X ,A@(:,A2@9,A@Y@B,A @',A@L@x0,A]u@:,A@uD,A@ֽ@sN,Aq@X,A@b,A"@(,A@@,AI@D,A@ @8D,A6@,Ag@,A@ .,A6@AI,A9@@Q,A@@^Z,A@8c,A@0k,A4@0t,A>@,A@`!,A@N,A@,A@(,A8@W,AT@,Ad@5,A@Nq@XW,AHs@,A@P!,A_@1,A0@hB,A@;R,AF@@Wb,A@Xym,A@N'@x,AD@#,Ax_@,Av@>,Aъ@,A@,A"@Px,A]@N,AI@,A@c,Az@,AzC@6,A[@`vM,Ao@fd,Az@(`{,AɊ@T,Al@Pk,A@`@Z,AP@!,AՒ@),Aϗ@2,A@r@i;,A@@)D,A@@L,A_@@U,A~@^,A@o@f,Ad@o,A[@(w,AW@@,A@U@ֈ,A@5X@`e,A@^@X,AQg@i,Ap@9^,AE@A1,A_A@.ˋx,A@ c@# ,As%@,A@@P(Q,Ao][|Z@;뼝,AL5s@ps\,Ad@$6,A꺥.@1\u,A~|r@G[L,ACF'M@[qG,A0'@ _,A|.6@PaB,AQ$@d,AFqQ@+,A@c5x,AGp@}LN,AA o@)+,AA @ ,A@1l% ,A2p@|vQK ,A?@ ~q ,A7@lw ,AX!X@x ,Aߟ@͞2 ,AT`-@'!,A:ŀ@v !,AU@$!,Au@Aq",Avg @ӊO",Aُx@ -",AF_K@",AZ@{p",A^*@"#,AHX@´@7b!',A@ @',A@@(v',A@',AfKf@);I',AWݐ@wzf',A1(@P',A@U@sn(,Az4@_@(,A@@iSe(,A%xO@^#(,A5@jl(,A8#9@y(,Aܙ;}@e),Af3ϟ@1),A@3h),A^6J@),A7@4O),Aw1@ ),AG@麢*,As~@̱+,AaҐ@N,,AS@N,,AS@팛,A@ ",A-D!tA ",A;̴@T@Bk5,AS*@җg ,A;d@Jy{,A%@6x,t,A,YF@+Sm,AQ1ً@|re,A(Z@#eҞwd,A47.b@d,AW7c@#4Wd,AvTʭ@J8e,A|Ej@"Me,AX@0Ue,A|@@f,AZ@8sf,A@83g,A@;9h,A/@=FPh,A/2D@l]h,A % @fh,A%I1i,ARmd@83Gi,A)l@Ym,Am@*0om,A@ Ɔm,A>'@ļm,A+hLT@m,A$_@'um,Ag97H@2m,Ab}x@n,A{@(4o,A&@l5o,A/Pg@3T7o,Ab@ a8o,AKD@:f<:o,Ap@{;o,Apn @ho,AT;9@W\>o,AE@.%>o,Aj8R@_=o,AS^@I,Aaqp@qA,AbTp@C,Ap@P7en,A&q@#,Ahq@QAƄ,APaq@w],Aq@A2,A!wq@۰ K,A"8q@0u,Ap@0 ,A"Xmp@,AÅvp@,Abo@Q,A)o@> ,A6Uo@:,A8n@F,Ab@`l,AYa@m,A@w_@E&,Au _@ 3 ,A#_@L,Arj_@',A {^@!9!D,A{ ^@X,A(_@W,A_@:,Am_@ʇ,A_@xi,A `@Ĺ,A{`@&,A@`@%,Ak7a@,ATIa@ $,An@@2,Az@(~Đ,A@@*,A@Z,A@@ѓ,Ab?@``S,A@9@瀕,AT@Poؕ,A@#@4,A@ѝ@зE,A@hv,A\~@,A@``,A1y@,AQ׸@,Aj@&,AX@@Z,Aӵ@xv,Ao7@1,A@ʹ@ ",A;̴@ q[,Ai1@ @V,A@=N,A"sq@ 1hG,A(Z@(POD,A?F@oAD,A?a@j)E,A@dߏE,A(v@![F,A ~L@0yF,AXƷ@mI,Aʾ@rWK,A@W@ޱK,A@pTK,AT@2"K,A1*@=z<(L,A-K@`QL,A@_BpL,Aw)@8L,A^10@L,A?]@(L,A@dL,AF@L,A@AM,A@ߏ@eM,A@@yN,AO*@hO,A4}@b)Q,A%@#T,A@G2@/U,A-@cU,ANb@U,Amհ@U,Ar @3U,A>0q@"2V,AS\@dV,AˉF/@xV,A T@V,A@@MŅV,Aq@lW,APf@Βq@SY,A@@-Y,A(@3eY,A1Yj@ 9>;Z,A~@uZ,A@i]@/E+,A݊A1,A*Amz?,AA ^n?,AA a?,A8AU?,ARAG?,AvAA:?,AMA8E,?,A6eA?,A ݬA)?,AVAhQ?,AA`?,AA8g>,AhA8V>,A}A>,A ̪Ap>,A2AH>,A &4A>,A A`>,A@A`>,AFgAп>,A;%A0ٮ>,AmAe>,A螨A0f>,A`ZAHܜ>,AA(>,A ۮA ޝ>,AGA>,AA@>,AyAp>,A/ApΣ>,A`KץAǤ>,AA0%>,A`}Ap>,A@ OAЦ>,A@Aɫ>,A`A>,A A Ҭ>,AAP>,A`aA2>,Ao2AC>,AA>,A`ӣA̪>,AXA>,AA>,A`ڡA0>,AAHz>,A]AP>,AA>,A A@>,A@A8t>,AfAHK>,AR*A>,A AA`%>,A@A&>,AwAX>,A<A(>,A@TA >,A Ah>,A`T֜AP?,AHA ?,AUA ?,AA?,AӛA83?,A3Ap81?,AA3?,AmA5?,A?A 7?,A@5AXF8?,A`mA@9?,AAAT9?,AAp=9?,A×A09?,A@WTAPY9?,AA8?,A͖A`7?,A?A5?,A` GA3?,A4AU1?,AAR.?,A3A*?,A 8=A&?,AA"?,AA?,A`ZAh?,AA[?,A@Au ?,A=>Au ?,AA?,A@A?,AjA`?,AǑA?,AeA`?,A9A;?,A AH.?,AAw>,AXAt>,A#ϐAP'>,AFA>,A@ Aм>,A`KA>,A`fA>,A`2:A88>,A@ A?>,AiߎA>,AAp>,A`A >,AXUA(>,A'Ap=>,A@A>,A@AH>,A͉A@>,A@QAz>,A@AHܶ>,A3A>,A`TAԶ>,As;AXy>,AA>,AוAȺ>,AnAe>,AqGA>,A@y A8>,AAh^>,A}ӊA>,AA>,AA>,AafAa>,A` CAv>,A@E A>,A A>,A*܉A>,A A>,AA&>,A A@>,ATA3>,AnA>,A^Az>,A`)PAЄ>,ABA>,A`"7AB>,A,A>,AgAX>,AA?,A]Ab?,A@%A?,A`܈A?,AʈA ?,AA ?,AAЄ ?,A9A ?,AyAT ?,AA?,AAC?,A@WeAl?,A` A ?,AA#?,AzPA8#?,A )A ?,AA ?,AۅA` ?,A0A ?,AڎA?,A@hA@?,AQCA?,ADA>,AA>,AՄA>,A@߲A>,A@A>,A oA>,A A>,AĥA>,ABA>,AGA>,AAp8>,AA>,A`]dA2|>,A@8Au>,Ap Ao>,A8߁Ai>,A;Ad>,AA8_>,A SAT>,A AI>,A֠A2>>,AHAPI2>,A`A%>,AA >,A@sA>,AMA`>,A|'A >,AA`>,A~A}=,A ~Ap==,A@~Ah =,A,x~A 0=,AQ~A=,A)~Ax=,A~Ah=,A}AH)=,A}A@=,Aq}A8V=,AhR}A=,A |$}Axm=,A$}A3=,A|AR=,A|AΕ=,A@|Ax=,Ae|A@=,A*=|A =,A|A =,A`{A=,A {A }=,A`{A+{=,AN{APx=,A@{Aؒt=,AzAp=,A zA#l=,ARrzAȥe=,A`&zA_=,A@yAZ=,A`fyAhT=,A ZAyA0.P=,AxAN=,A6xA]M=,A,xAL=,A@رxA)L=,AVxA(-L=,A@ƄxAL=,AFnxA0nM=,A`WxAN=,A@AxAHKP=,AX,xAOR=,AFxA@T=,AxA8xW=,A-wAZ=,A[wA@^=,A ~wAa=,A@wAe=,A@wAl=,A`wA@Ot=,A@wA{=,A rwA`=,AcwA=,A`DVwAߓ=,AJwA/=,A@y@wA=,A68wA#=,A`1wA=,A`$wA=,AwAʷ=,AwA@=,ATvA E=,A vAu=,A@vAB=,A lvAH=,A 1vAЦ=,A`vAX9=,AvA_=,A@rvA=,A@^vA=,AڰuA=,AuA̟=,ATtAa=,A+sA N=,A rA~=,A@CFrA/t=,ARqA#j=,ApAh=,ApAf=,AapAhod=,A pAa=,A@oAu^=,A oAZ=,A@[oAV=,AoAHR=,AnA`M=,A0nAH=,A\nAhQB=,AnA+<=,A mAН5=,A lmAx.=,AemAO'=,A#*mA=,A@,lAp=,A lA8=,A|lA0 =,A ElAX,=,A@!lApA<,A@ kA <,A@ϢkA07<,A |nkA(<,A;kAH<,AkAH<,AjjA`<,A /jA<,AxjAp<,AVjA@<,A6jAD<,AjAPٻ<,AviA<,AiA0̸<,A iA@µ<,A iA<,AiA <,A{iAH<,A@P_iA@Ϥ<,ACiA襟<,A)iA$<,A SiAHP<,A OhAp,<,AhAp<,A)hAPZg<,AzahAȵV<,A@`.hAE<,AgAj?<,AgA<<,AgAЄ2<,AgAK)<,A4gAY <,A&|gA<,AXgAp[<,A4gAS<,AgA;,A@gfA;,A@fAa;,AfAQ;,AwpfAأ;,AFfA[;,A@fA{;,AeA;,A eA;,A`)eA;,AieAʿ;,A@eAXl;,AeA0;,A`)dA*;,A dAG;,A`dA;,AqdA;,A@>HdA;,A dA؟;,A`cA3;,A`cApI;,Ax[cA;,A cA;,AbA=;,A`]ibAP;,A[bAhD;,AKbAD;,A;bA;,A@+bA8';,AbA;,A QbA };,A@aA`;,A@aA0;;,AaA@;,AaA c;,A@aA ;,AޞaAh;,AaAp;,AaA(:;,AdpaAX;,A baA+;,AVaA;,AJaAp ;,A |@aAx:;,A`?aAt;,A ;aA¬;,A`5aA.;,A#.aA;,AR$aA0;,AaAX4;,AaAg;,A`AK;,A&`A;,A`A`G;,A `A@q;,A`Ap;,A`AO;,A@`A|;,A@>`A n;,A`Ap `;,A `A`R;,A@`AH;,A`A@;,Aྷ`AL7;,A `A.;,A@`A0&;,A@ x`Aȃ;,A_`A;,AtF`A@N;,A+`A#;,A`A;,A_Aj:,A2_A:,A _A:,AE_A@N:,A`f}_Ap:,AW\_Ap:,A@,:_A8:,A_A :,A |_A:,A ^A:,A^A :,A`^A:,A^A( :,Ap^Aӹ:,A`^A :,A@n^A:,A`U^A˵:,A@<^AXW:,A@ $^AW:,A ^A7:,A ]A؟:,A u]AБ:,A@]Ap:,A,e]AX:,A<]A:,A]Aϣ:,A\A8:,A \A :,A\A`:,A=\AV:,A[A:,A[A:,A^[AW:,A3ZA:,A`{ZA(s:,A YA#S:,AYAz0:,A`4YAp:,A)XA9,A XA9,AXA9,AXA9,A XAB9,AaXA9,AWAs9,AWAc9,A@ [WA\9,AHaWA T9,AgWAkF9,A WA8;9,A3XA59,A cXA)9,A|XA9,AVXAأ9,A YAk,9,A aYA)9,A@gYA`8,A |RZA8,A`VZA8,A`ZA8,A`f ZAu8,ARYAN8,A`2YAnb8,A0YAC8,A ZAp!8,A [Ap7,A`ZA7,AZAؒ7,A[Aۜ7,A`B]A†7,A@]Ab6,A[A6,ANw[A_6,AGZAȓ6,A cXAPwD7,AUApw5,A3PA4,A`NA(o4,A`ձMA4,A`LAV3,A /KAA2,Al_bW KA'lvWj2,A$&JA;1,A UIAMu1,AHA˷0,A@ƘGA)^0,AFHA(0,A`JAx$/,A LA/,A [MAPZ/,AaNAH/,AhOAHPI/,AOA.,AQSA .,ASA8V.,A hSA蕏.,A`TBSAr.,AoRAR.,AQA!i.,AQAu.,APA8.,AOA0..,ARLA(.,ApJAQ.,A3HA8/,A`FA(\.,AFAf.,A]EAh3/,A@DAh3/,A 8DAp.,AzCAp8|.,ACAJ.,A@>DAk2.,AEA`.,AEAX.,A qHA-,A )JA-,A`)KA-,ALA-,AVMA-,A`ÕMA0.-,AMAǥ-,A uMA-,AzMA-,ANA`a-,A OAf-,A)OA8-,A"PAW-,A qPAp-,ARPABe-,APAZ-,A`fPAH-,A3qOAQ-,A NA(W-,AfMA(_-,A#MAw-,ALAP8-,AHLA8-,ALÁ-,AJAps-,AXIAр-,AHA-,A qaHA-,AHGGA-,A FA-,AEAPf-,AMEA̠-,A@>DAt-,A׫DAN-,A@DAB-,A@rDAp8,,A3iDA,,A3_DA,,AH/DAp,,AHCA,,A@gTCA8e,,A +CAfD,,A`ECA!,,AHCAP,,Az&DAz+,ApDAV+,A]EA8+,AFAdž+,AGAu+,AEHAh.+,AYHA҇+,A`THAx>}+,A&UIAxs+,A@IAr+,A`ʘJAp r+,ABKAȇt+,A`dKAPkq+,A@PoLA^+,A`LAC+,AjEMAC+,A@GMA=+,A@%MAPg9+,At*NA0.3+,A3fNA`C+,A |NA*,A NA0*,ANAx*,AޤOAX*,AOA*,A`[PAX*,APPA&*,APA*,AxQAxF*,A-QA@O*,A QA;V*,A |QA'-*,AQA*,A &,RA),AORAP),AJRAĸ),AQAx),AQAxW),APA@),A]PA),AýOA),AjOAx),ApOA@S),AOA@F),A@#dOA8EA),ANA>),Ay%NA ;),A@%MA-9),ARLAq5),ALAj),ALA$),AKA`a(,A@KA(,AKA`(,A KA(,ApQLAxF(,A@yLA(,AMA(,AMAp(,A`]MA`(,ARNAW(,A[NA~(,ANAo(,A)OAe(,A)NA](,A NA`A(,A@gTNAأ9(,A`]MA#)(,AYMA# (,A`)LA(\',A_LA',A LA=z',A@6LAf<',A`LA8',A`õLAP',AHLA',ALAh&,A 8LA&,A %MA &,A/MA&,A@MA x&,A LAf`&,ALA@&,A@>DLAp &,AH_LAp%,ALAz%,ALA•%,A@>MALd%,AaMApd%,A`bMAT%,Ai4NA8=%,A qoNAW%,ANAأ%,A)nOA%,A PA) %,AdPA$,A ҝPAh.$,APA #,A ɰQAU#,ARA̰#,A JRA8#,A JRApJO$,A4nRA_$,ASAXu#,A*TA_#,AhQAͬ,ARUAP,A UAx k,A`)UAa,AUA8,A UAH,AVA0,A`VAP|,A@ϽUAPk,A`VUAƐ,ATA,AKTA\,A`oTA8,A`TA ',AIbTA(y,A TTA,A ZWTA(,AIbTA<,A@XTA؟,A`FTAx,A`;SA ,A@SA ,A`SA팛,AWSA  J K-A}8A-AzA-AAA̱U-AO"A麢5-A4ƐAلX-AP9Ak4Ï-A)NSA;|-A}8AP'-A ܯA(>-AQAh⚊-A'A9-A_Ab-A`A8VI-AAh;5-A AA4-AAX_-A ۫Aq-A9A-AAȺ-A൐Al-AA :-AfA:!-AnuAf-A)A}W-A8WGWAf-AE6\AW-A 1"A#UN-A#4WaArR~-AlAOCeq-AVyAݰ%b-AItAQ-AA,-A ZvAP<#-AbAh-AMDAB-AjAxK;-A൪A`·-AQAP8O-AR>A-ArA-AEApE-ApABL-A HAp4-ÄA7-ApTApD-AwAXM-AA#S-AA`d-AdAX#i-A#AxKz-A`AB-A@A@$-ARA`C-A`AB-A`m}AI-A qA(K-ARA#"-A`LAB-A`)|AP ˆ-A`ApN-A4A6-AhwA0{-AmAB؅-A;A--A` A88-AX=A-AA-AnA(>-A#A00-AfA0*V-A@PA`-AA` -AR A#-AAQ-A A@5%-AAA>h0-AV-5Ap7-AYѯA5Q_}9-A*B,A6-A_Am{xM(-A(uvAke-A@AT -A=yA~-AA@p~-A`Ap5~-Az$A }-A #AН~-A$ AX[~-A? APx}-A AA|-A` A(~-A SAH}-A tA1|-AA@0|-A]{A,|-A`jAv{-A@AP7z-AD+AN2z-AmAgy-A@7A;x-ApA."z-AA@{-A[ Ap;{-A,!Ay-AAjy-AA(|y-A`vAXz-A!A@dz-A#"A 4hy-A Aey-A@#Acy-A (A0{Ax-AAw-A@>#A +u-A,Au-A,AJTt-A/A#Qs-A3AhWs-A&3Ar-A5A0*r-A@77Ar-A`7A."r-A7Aq-A`D8Aq-A@i{8Aq-A 8A@q-A 8AXq-AaE9A(zq-A(R9APq-AZ9AUq-A@v9Abq-A9Aq-A@9Axq-A09Aq-A՚9A]q-A՚9Ahēq-A`:AP q-A` :Avq-A;;Avq-AІ;Arq-A;AFq-Af=A(q-AО=A@q-A=Aq-A`;>A`q-A>A8p-A?A`p-A@{@A&p-AI@A(p-Af@AA.p-A XBA([p-ABAop-ACA hp-A@oCASip-A[CAhp-ACAp]p-AMDAXp-A FAo-AGAPo-A@6GA`Go-A3WGA}o-AGA[~o-AOHAp8Vo-AEIA&o-A JAXo-AgILA{n-A LAl-A]A&l-AZpA8"k-A@GwA8gk-AD\xApk-A yAk-A[zAȲl-AzAxrl-A\AzSl-AAhVl-A A@Ql-A@A8l-AFAh3l-A lAQNk-A ؚApxWk-AKAu-A@Avu-AAHk-A`]#A8'/k-A`ڞA@k-A@3A0qj-A@ƲAk-A9A}k-AA@cj-ARAx {j-A QA+j-A}AHrj-AA7ak-A2-A7ak-A[APk-AvA(k-A@>AA=l-A A &m-A4ܨA@m-A /Aq-AVAh;q-A@yAm-A@Azm-AA([bm-AAlm-ApA'jk-AATj-AvA>Cq-A$A+&q-AL&Aj-AI"Ahj-A&A{p-At)Ap-A?+APm-AN:)APl-A ,Am-Az-A(m-A+An-A+Ahn-A-A*{n-A&-Amn-A0A;o-A@P1A05o-A@`02An-A1A_n-AE1AYn-Ag1An-A2j2A5n-A`2An-AF2A+o-Ay2Ah^o-Av#3AP*o-A E3Ao-A~3APo-Ay3Ao-A3An-A A@3A@n-A`3Ayn-A2Ann-A`3A:~n-A o3A@tn-A@P74Ajn-A&4An-A@4A~9n-A4AHP(n-A 5A=n-AU6ATn-A uK8A m-As 8Am-A@y8Am-Ai8A8sYm-A`T;8Ax9m-A"9Awm-A9Ad|m-A`9Am-A l?:Axm-Ao:A`1m-AR:Afm-A =A8nm-A<Afm-A<Ayl-A@;A,l-A4=ATl-A>Aذ#m-Aݒ>Am-A >A Pm-Ai?AHm-A}XAATl-AX@Aîl-A@MAA +n-AvBFAX n-A@'FAKn-A`}FA +n-A@FAp m-A 8xFAjm-A@W]FAl-A?AAXk-A`?Ak-A4T@A^k-A@Ak-A@AAp0k-A5AA,l-A`B@A l-Aw@Al-A@E@A2l-A AA,l-A?$AA&l-A AAk-A5AAl-A0,BA,l-A "BAx-k-AfBAl-AAA 6l-A[BAHl-A`vBA l-A@RBA@l-AjBA !l-ABA0l-ACAh/k-A3AA/k-A2BAHl-AaCAbk-A^EAPrk-A QWDAXk-A cDAj-ABA oj-A`iDAh/j-A0*EA88Cj-A$ FAxIj-A`LFAk-A CA((k-A`GDA`Pj-AzEAH!k-A@GAk-AGA0j-AдEAгmj-A@^FA`vj-AFAH-j-A@,YGAði-A IAGj-A4KA0!j-AaLA8֧j-A@$NAitj-AOAgi-A@+KAyTi-AwJA`i-AJAi-A-JA؟ci-AIAFsi-A_IAi-AhIAj-AGAi-AFAeKi-A &GAxi-ARHAGh-A ~GAPM h-AFA`/f-A$PAسd-AWA;d-A`9YA0c-A`\Ab-A]Ab-AB_A Tb-A`91_A@2^-A iA]-A@pjA`1n\-AlAZ-A kAPgpZ-A ukA#Y-ATMlAx5Y-AmA tX-AnAoX-A`+mAW-A@>lAW-ANkA8iW-AkA(8W-A?-A`ӈRA{?-A [QA?-A;QAx>-AOA t,<-AzLA/99-AzJA3-AJA8?3-A FAxF1-A`FA\0-A@GA/V/-A GA--AJAi--AbA;sN0-AXg:lA 1X8-AtA=?-Ao3džxAG-AzA:,W>O-Ao3džxA{rV-AtA+C^-AXg:lA5x,{e-Ai\>bAJil-A=m~!UAҗgr-A]EABk%y-A3A<"~-AA}n5-A uvAA麢5-A}9~A̱U-A_)`A-AAA-AAA q-A:rA`] .AHWAH-A:rA 1а-A[u-IAADl-AL6AkI-Ad5A4--A@%6Aj-A q=8A+-A}=AF-A<&BA-Aq?FAO-AvEAPA-A@EA`X-A@ HA8-A`"GAhݬ-AFA1-ABAc-A@kHA -Af2o13JA 1а-AFQA=e-Ao3UA-AHWA:,W-Ao3UA{rK-AFQA+-AXg:IA #-AW~ByFA8-AREAH2-A:DAN-AfBApT-A1>A{-A /d;Aku-A;9A(-A@yw8A-A`7AH-AP5Ã-A@I5A1-Aྣ3A0-A`2A@-A`1Ah-A`U1A v-A*#0AW;-A /A-A-A`_-Aq*AQ-A E*A-Ay(A-A(A8r-A%Aг-AR $Al-A!Ax-AӸAu-AAH-AfAp-A AM-A@PA=2-A@AB -A@ PA-A Ah-A`mA8g-A-A -A@KAh-AAz-A} A-A A)-A A d-A A 0-A@E AK-A`A<-AA0]-Ay2Ah-AAD-AzA-A`A}L-A ZA[-A A -AƩAփ-A bAP-AxEAЄ-A`SA-AAȘq-A`DA-ADOAc-A^A-AZApS-A3QA0-A`"A#-A,$Aj-A`meAh1-A/A~-A@A,-A@AAЄ-AfAg-A@A -AaSAH?C-A A`"-A A@-A_ Ax-A6A(-A` AH}-A$ A-A A0 -AFAk-A`FAHJ-A AH-Aa AH-A@ d A-AaAh-A`KgA0{[-AC"A.-A3A-A`A-A@AE-A|AP8x-A iA -A$AS-A"A tq-AmMA@-AA˟-A`TAȇb-A`fA(<-AA-A"A-A`)"AE-AZh%A-A 'A-A 'Ah6-A;&AY%j-A9K'Aҗgu-A򨑝"ABkݝ-AXAA+d-A4 A-A`A -A/A)-A &A<-A AAHL-AAp-AXA@Ww-A@rA-A`CAf>-A` A-A-Ac-A@Ah -A`AA|-AA8(-AmAh/-A@A R-A ApŬ-AA<-A {AX-A@AA-A`A-A;A0.-A?A-AྒAb-AYA?a-AVA6-AB4A\ -A@5AM-AEAp-AŔA9"-AMA-A2YAu-A`MA;-AH )A0-Ak At,-AxAb,-A%TA*a-AĸAݸ!-A~A (-AedAW-E^0-AӑOA ?8-A@@AKA-Au6AJ-AGvP3A)T-A 5A-AA-AA1-AsA*-A A-AgA-AAH-A@rAߊ-AA-AAjW-A A]9-A`A(-A@A%-A@A$-A@A-AUAf-AA؟-A[A-A u{A-A{Ap-A ZA-A ZAP^-A֥AHz-AnA(-A`2gA O-A]AMM-A]AJ-A=]A9-AA37-AA5-AtA8g$-A`PAX-AGA(\-A c:Aq-AAX-AAq-A`;Ah-A`7A-A`m0A(m}-AH)A[-A8$A Z-A@#A8V(-A A-A@A -A` A-A`Ah"-AAA-A A <-AA0{M-A A7Q-A@Aea-A@5A7-AA -A@AhD-AZAxX-AZA-AZA(-A@A-A7A<-A@¼Aу-A`܉Ap 5-A.A 1-A`-A#-AA-A3A@5-A`Ac-A A^-AR Ax-A`)A-AA-A@rnAX-AɳA7-Aj_A-A˭A(-Ao֭A -AA}-A@yRA-A`;A3-AaAb<-ADԫA"-AM@AA_-As6Aδ-AT fA`*-A ϭA9-Ak=ͧA˓-A4i!A -A ˧FAXJh-AA-A@WqASh-AlA{-A* i^A$3o-A -AۆA -A$AHn-A` A-AॿAXR-AA(-A@y {AX#8-A SA~-A`aA`2U-AaAX-A |{A@}-A|A9u-ARnA( -A`fmAЮ-A`vzAwc-A`wA-AuA(O-AsA-A_kAH-AMjA-A`v2rA!-ApAv#@-AuoA !G_-A[oA6-AEoA>h_-A~ oA-A@oA=5-A;D/pAY-A5dvpAY-A`pA-A@lAw{5-A<1slA]oS-A`:lA濓s-A:ݳmA/m-AiK|6mA,gԺ-AUmAP-Ave&cmA-A*fR^mAGM$-A87dGmAnF-A/mA:g-AlAR-A lAU-ABlA5-A\-kA<-Ac84skAx-A-jAU-A9~jA-A@iAv-AO`AHP-A`Kg_A-A@$_A(9B-A`iA-A@gA$D-AcA(o-A`AD-AT0_A -A`EYA:-A`EYA -A@`WAH-Ax SA@d-AxRA(-A;1Aa-A.A--A(AR-A(A-A@i*APH-A.Ah3-AFnA؇-AtA-A`uAp -AnA(1-AfAP^-AsA3Ah-A@,A{-A@!'AQ-A$Ap-A්*A -A`QA-ArRA06-AM&BA-A`f@AH-ArRAr-ANRAPMc-A`AF-AlAЄ&-A`jA-A@i]AY-A"RLA-AGA-A`EA-A ;AX-A|7A]-A]7Ax\-A}6A-As6AТ-Aax6A~-Afx6Aez-A'v6A/^ws-A7p6A&.l-ABe6A*?f-AiV6AT!a-AZC6Av~X<]-A!-6A Y-A 6AkW-A*5AzV-A5AV-Ah5A]W-A 5A[Z-Ax5Ag[]-AfAVq5A8Cyb-Aw[5AgŌg-AI5A4m-A8:5A1t-AIk05A&C{-A *5AT=-A5*5AP-AS.5AP-A3&75A|Atʖ-AϒwkD5A=#l-A(oU5Ay=-A~Ej5AS -A p5A -Ar5A-AѠO5A[Y-A Ǯ5AA-AB5AvQ-A~?N 6A,@-A8,%6A$-A&=6AR-A&;Q6AM-Aa6Ahё-A4i6A-AR7A]-AF7A0-A&DAQ-Aa.DAr-A cwJAU-A`JAH-AKA@Oy-A`JAx-A(KAe-A@oKAe-AnKAW-A2IA8.A (GAЕ.A$FAr.A[2EAx.ASCA-.AKDAx.A MRAb.AOA ).AzVA.ASAxz .A@\AHPB .A QcA F .A ccA A .AGkAo .A?8“oA .AHCeAذ .AQaAH .A B`A .A `_A .A}{^Aa .A@]A`] .A@\Ap .AXAr .AWAI .AVSAe .A43OA5 .AOA; .AOA}= .AZOAl .Az TA }{ .A`TUA`ӆ .AUA@ .A 'VAՂ .A@4VA5 .A0WA9 .A VA .A@,iUAd .A`PA7p .A JAD~ .A=tEAPj.A@FATG.ABBA.A @Ap.Aப=Am.A=A@ .A=A.A`=A(K.A4:Aw .A"A .A` A .A_A(< .AHAhI .AhAP8c .A@W#AȘ.AAI.ARAXa.A6]A .AA;.A$A w.AvAh.AA0.AAW-AK*A-A A%A].A@Ap-At(A`.AA0.A@A.6.A`9Ap4.A@Ax$.A@,A!.AUA:e-ActAd9-A}A88-A`Aq5-A}A4-A A9-A |9$A-A1$A -AW#A4-Am#AHi-A`#AH-A"A6-A "AN-A J"A-A!A]-A W A _-AyA-AqA-A@5OAx -A@A6-A`DKA A-A@,AF-AAq-AvAPv-A A AY-A ZA(-A`A0-A AKAb-A@WA@-A`AHy-A#A(-A@!A I-A !A@-AHP A`a-A@ "A8t-A@"A-AN"AȺO-A |g$A|-A$(A"-A`{&A-A&A\-AV%A,-A~%A-A@i$AX-A<'A-A.'A~-Ay&AǷ-AH[&A8-A%A8X-A@Y$A-AAP8-AaA$-A A -AAPo-A A6-AAȩ-A (ADI-AA(\E-A@WAqD-AAYC-A ZAv-A`A-AA0t-AAТ/-A lA-AiwAH6-A`A8-AA83-A"AZ-A`A.-A`KA`.-AA83/-A@yA0-AA2-AAp2-A&A(m-A|Aw-A ɇA-AA0-AhAʨ-A{Aȱ-At{Aw{-A |AG-ASA-AHLAx\-A`mA(1-A# A֚-A`]%A8gx-A HA+-A A`z-A $A(ː-A@Ahё-A@PA-A Ahb-A`mAPo-AA8-AjA-A`A9-AA-A2A(C.A@A.A`d AX.A$ A2.AZA&.A`A.A` A .A`m%A_K.Aj Az-AV A覟-A5A--AOAH-AA(-A AA-A`AP-AeA-A_A-A@r_A-A2A0]-A!AP<-A A`-A A-A A(-AAh"-A AcE-A  Ah -Az A^O-A@ AV-A AIW-A@ A@`W-A AY-Af A0f-A A-AA!-A A'-AAr7-ASA)C-Ao0A0-A@A-A@,"A-.AvSA%7.A@5A+7.A`5As;.A 'A>.AARw.AHiAa.A$A+.A@ At-A A!-A@ Ax)-AॣAȲ-A AX-AA1-A`A-A A@OI-AAh-AA-A@uA-A.AD-AA-A J3AF-A J3AP/-A@A0{`-AJAұ-A@Ax-A BAp8m-AA(-AA`-AA -AFA-AbAxi1-AghA-A@A0;-A AŒ-A`A@1-AA'-A JcA(-A`BA-AA -A5MA_-A`Ax-AgAPv-A Aj-A`fAA-A ȾA\-AXA-A`8A(-A"A-A ɹA@-AA-AoAJ-AA E-A(A-A Aȓ-AA0L-ABAx,-A`A8-A A-AZA-A$AH˔-A^AUo-A`A\V-A֪Aȓ(-A֪A -A@Ax\-A3A~-A HA3-AA\-A UA@-A`8A~ -A`8Aʒ-AޮA-AVήA-AŦA-AdAH2-AWA(-A FAv-AYA$-AIгA+-A A8i-A@ԭA?P-A Aʐ-AwMAo-Ap2A#-AA-AAHu-AHAh&-A pAp[-A"Ahbm-AǁA-A`6A@c-ADZA -AA8-AAZ-AA(-A Axh-AAa-A A-A@MAȘo-A0ALN-AlA;-AxA`%-A"AR-AA[-AA'7-A']A-AFA^.-A&AТ-A`MA-AAچ-A`bA`f-AWA0&-A'AX(-AщAT-A|AT-A,Ad-ANAPQ-A?A^.-AA"-ArApi-AqA1-AA`a-AIA8gu-A@`˝A2-A@A-A 8CA`-A 8A8tv-AAN-A@ȍA,-AAX-A98A@ -A=`A[-A`ӔAذ+-AA8t-AzA-A@!\AhD-A@xAm-A A]-ACA RJ-A`{At-A@rA-A`A-A`f8A-A[TAps-AgA-AA c-A@ՍA-A A>-AoAPY^-A`DAO-A!A[-AڅA-A A@-A͢A-A@T|AS-AnAP-A@kA8-A@cA-A`2aA~-AhcAH-ASaA-A@jA-AjA8-AvjAjq-A$iA0-A _^AX-A@]A`-A@>\AHi-AYAp0m-A`[Ax-AGXA*-AMoAP-A |4qA(-AЕmAȘe-AAlAO-A ?TAw-A`$HAsH-AD@AhM-A6:AЀ-AH<AU-A;9?A>-A ~?A)-A \=A0-A@`<A4-A,@AOd-A`%AAXa-A XAApl-ADA-A;^DA>-A!AA-A?Adg-A?AI-AIA-A <A`6L-AA9Ap-AM9AxK-A:A~-A];Af-A:Ad-AR8A_-A8AXu-AFu6Apf-A`ܬ5A-A@E7A؟-AU7Ax(?-A5Ay-AA>AB-A`]9A.¾-A@8A`-A`AԹ-A#߅AeVɹ-ANXAX-AI`WA;-AJA-A >=An-A|A#-A%`$OA;Ჽ-AK AHĹ-A.Aι-AA |-A`"AH?q-A_A@p-A GAݽ-Ar&Aix-AA9X6-AK.APw-A Aw-A<4ұ A]&[-AV;A-A0〽A8ӹ-A7A8-A) A@h-A PAxǥ-A@mAP-A`dNA0=-AAj-A AH-A:rA #0 R+-A*A8d.A 1 As]b-A*Aڔ-Af*Az!-AR5A^-A27A-A1A-A*A-.A:A-AnBA˃\-AID1ɻ;AGÕ-AȴajNAd -AnBhA%hX-A(NSqA*&{1-AP9QA4-ANW+A>-A AP+@-A As=-A`~Ah-A#Ah"-A@5ٻA-A/AxX-AǺA-A9A>-A@A( -A@DA\-AHA-AϵA -AzApp2-Aq|A?-A AhD-A#Ax<-A ZAx6-A9A-AޘA -A@'A -AlA&-A ӽAI-AA R}-AA-A#A?-A`Aؔ-AA }-ALA@-AwwAh-AhA8-A|A -A-\A -A HHAH-AA0W-A SùAH)W-A&ApO-A AA-A 1%A--AA+-A ~AX(-A@EAq-AA0-AЛA -A3AX-A A-A A-AtA.-AA;D-AAx^-AA[-AyA茛-AšA-ADA0-A졺A07-A`[A_-A@CAN-AA #F-AaABE-A`TοApy-A`Ak-A-vAy-A`[AW-AQܿAx-A`A-A AP<-AAp9 -A@iA0-AdA'-A`vNAA-ACAJK-A`mAzM-AZAA-A0_A.-A`NAB%-A JAȥ-AxCAy -AA -A SAp-A`;Ax-AhA-A`žAp0-AqA-Az}A-A ℾA -A=A-A`ݾA-A`lA+-AAL-AFAU-A A8f-AGAhv-AfA(-AҿAڝ-A@Ax)-A@>ƼA'-A@`úAC-AjTAx-A#hA-A@ᱺApI-A/A`-AMA-AAh-A A0-A +A%-AE`A-A 8AD-AA-A`AM-A`DA-A@eA-AѶA@-AjA%-A`ԿA-AA.-A%A0̔-A(Ah-A Z_A[-A[|A-AA~-A/A-A@IA(>t-AcAhl-A_APk-AAc-AOAz-AvAqt-AApX-A,Ax+-AA4-A@GAY-AnA]-A 8~Aj-AnAȋC-AAs-AA-AĺA26-AbA8-A`IA-AUA]-AA`C-A6A4-A Q*A3-AVA-AAȱI-ALA0-A4AC-A@NAKP-A *A2-AA0-A A"-A@A( -A *A`B-A`IAA-AbA-A^A(%-A@yAj*-AgA -A qA:M-A&9A}-AA-As]A?-A UA0wD-AbAb-A&A-A|VAi-A@A(z}-A`Ax(-AvAXj-A`A^v-A@)AX-AEAXZ-AA W-A@ơA@W-A {APR-A`]kAuU-A`GAM-A AQ-AAXK-AA-A2A1-A`A-A&0AZ-AcA@-A`RA-A@A8-A gAX({-AA-A \A /-AA|-ARA-AौAb-A`]A[*-AVA-A /5A8-AAP&-AI^Avi-A)A-A` Ap/-AA=-A` AO-AOA`V-A@NAx>-A+A-AA@-AA-A@WAXy-A)A*,-A@ߌA4f-AA-AA˥-AtA-A A -AA8*-A`AR-A)A ^X-A)Ah"a-AA0n-A}A)o-A`dA3-A ~6A+-A A-AA`-A@N5AA-A`A0 .A`Ah.AAP$.A 1eA2.A `A(.AyAV.A wA0o.AAr.A@ARe.A AQc.A@A"-A`fA-AAP*-A 3AxR-A@dAPE-A`:A.-AhwA-A AXup-AcAa-A |A7-A Q_A-A#PA-A JAP -A`A8.A<A07e.AaA.A+Ah/.AEA(-AAք-AAh5.A AK-ABBA.A`TAa&.AkA.A"A؍.AA/.AEAг.A (gA.A`mOA07.Az AH.AAJ.A@A8.AA`.A8ANA.A, AЄH.A@iA@B.A`BA8g.A`A .AA0.A MA5.A@ETAe.A Ah f.A@AX.A`~A.AV}A@/.AaAh3/.AAXJ.A$Am8.A pAU.ANA .AdA.A A@.AA.AHA!.A଴A.AaA^.A`APgz.A@bApI.A݇AЄM.A`AP^W.A@`A.AsAЀ+.A$A0.AAvh.A)cA g.A leAh.AgA0 S.A` A.AA.AA@.AxAU.A lAp.AAH.A@rA.AaAw.AAh.AsA.A$A.AAќ.A@:A.A̦A0A.AI A .A@ϳ AZ.A A' .A@Y Au .A)V Af .A ( A .Ap A .A@APw .AU_A .A/yAx .A@,AL .A .A2AX .AA .A@RA .A"A .A<%Aأ2 .AtAS .AAP< .A@~A .A -AN .AAl .A OA .A*gA& .A൫AP8 .AAhՑ .A`ʶA@q .A#Ab* .AAج .A 8A V.A`TtAhĭ.AApl.A}A@.A@WA.AA1.A@ Ah/.AW AH$.A *!Ax\n.A`7!A.A QP!AXh.A&"A.A#APju.A0$A(:.AL$A.Ae$AJ.A~$AHH.A$Al.A$A/.A`v%A.A%A@g.A4]&Aذ.A &A .AX'A.A-(Ax .Azw(AXG .Aӣ(Ax .A(A!.Ab)A!.A_)A1!.A@p)A!.A@L)A@!.A /)A!.A`(A ".A(A".A`o(A^".A Ag".A cJ A0;z".A`2 Ap9".A/Ap#.A32 Aa#.A4M AН&.A"Ao+.AR$A,.AB%A$..A%A`2.A@ 'A`61.Ae,AP|3.A -Ax 4.A`T.-A0 4.A.AX(4.A.AO4.A 9.A@O6.A`m.A }6.A.A56.A=.A`l7.A51/A7.AO/AuL8.AZ~/AX8.AR/A 8.Aw/A58.A/A:9.A@gN0A$R9.A 0A}9.AD1Ap9.A2A :.A „3A:.A6A`e%;.A`m7AhI;.A:8A.;.A q:AG;.A@.h;A"><.A`=AV<.Af>Ac<.A J?Aw<.AO BAXW=.AOzEAxi*=.A`KEAWS=.AGAl=.A`IA0]=.ANA=.A`'NA?.A5[A?.A \[Ah?.A I\A@u"?.A\A0?.A2]A`8?.A ]A A?.A_]A`6N?.Ac^A ?.AuX&dAwQU@.AR&zjA>B@.A| qA!IA.A%)PwA6DA.A}ߙ}AO+B.A]1-lA0B.A;AIB.A`ՍAG]C.A^ݡA*[C.AkGA\qC.AW4#AjC.A7/6ʠAЦ D.AA)D.A,AȥD.A@}A88E.AߢANE.A~A bE.A8ALE.AAE.ANAE.A A(E.A@AE.A CAE.A`&AuE.A@ƧA E.A`;AHE.AFoApE.AAF.AAPF.A@A5+F.A@+AC7F.A_A@:F.AAGF.AAؚUF.A@ƋAl[F.A`2qAhcF.A`YAn{F.A@,AUF.AAF.AAF.A 3A F.AAqF.A}A84F.AiAȩF.AANF.AUAA/G.A@xA.G.A Ah#G.ARAG.AAG.AAPG.A AF.A6A(F.A=AjF.AA#F.AA8F.AjAF.A`AG.A`AhG.A`dbA G.A}Ap$G.A A 9G.A@AVBG.AADG.A2ARG.AgAVG.A`XA4^G.A A]G.AAZG.A AUG.A`5 AOG.A  ApxKG.A`Y AAG.A@r/ A/5G.A A%G.A AG.AA A;F.A AhoF.A A8F.A A-F.A`m A\F.A/ A0 sF.A O AdF.AQ9 A0JF.A AX)F.A Ap F.A`' A E.Av A@FE.A A$E.A5 A0E.AC A5\E.A AE.A  A@ E.A3 AXFD.A AH\D.A l A wD.A5 AOGD.A@e AD.A`- AC.A`b AC.A AȓC.A<  ARC.AǓ AD{C.A=6 A;gC.A` A(1MC.AN A^:C.A AV-C.A+ ASC.A A@C.A AxC.A< A0 B.A" AHB.A`K! AŘB.A$ Ax5B.A J% AB.Ag& AТrB.A@N' AP>B.A>, AB.A0 A\B.A_0 AB.A`v0 AX#B.A 0 AH\+B.A*1 Ai:B.A41 AEB.Aa1 APB.Ak1 ACWB.A@1 AX\B.A1 A- ^B.A 1 A{rG.A҆. A+3O.AXg:' A5x,kV.Ai\ A?ZE9F].A A8H].A` AD].A` A$].A` Aأm].AQR ApAz].A A].A AH2].AX AH].AſAȏ].AAI].AA(:].AB7A0L].A (A8+\.A A\.A ZA"\.AA؅}\.A@itA8|\.A=AZ].A^As_.A /AH_.AdA~zc.A@RAǣc.AA(c.A Ad.A`]-A8d.AAxWQd.AA>Ad.A A c.AAb.AAF_.AJAv"_.A@. A].AA].A (Ah/\.AAB\.A"ZAq[.A`KeA}[.A`nA8 z\.A`(A V[.A@A[.AzA0[.ADAZ.AfApAZ.A6AhZ.AIAZ.A YA0TZ.AxA{[.AtA;[.AwAd1[.A@AZ.Aa!AY.AAxX.A_ҭA茡X.A@AiX.A6Ax]X.AiWAkW.A*A0 zU.AA1$U.A/CApc9W.AiAV.A@>iAP"P.A3sAHPO.AlAWN.A$?dA?M.AwZAxqL.ANA(M.AWEMALJM.A`KAM.A@DKAxM.A9KA NM.AinJAM.A@JAx M.AIA0ݒM.A JAIM.A@!bJA0M.A*IA;.M.AHA8(M.AZHA@W M.AIAL.A HAOL.A HA L.A@EGAhL.A}MGAL.A}FARL.A{FAL.A0$FAL.A EAL.A`@EAL.ACAM.ACAL.A`CA.L.A@!BA0*mL.A z<AhbdL.A`B;APxIL.A`:A L.AxT:A`-K.A}8AL.A'8A(-K.A i7A0K.Av6AuhK.AH7A@9-K.A7AxK.A]7A=J.A7Ax>J.A7AXpK.A`5AxqK.AŽ4A(K.Aյ3AX}J.A3AJ.A@k2AK.A@1A8x>K.A`1A9!K.A`/AHJ.A /A(BJ.A /AlJ.A@ 0AHG=J.AQ/AwJ.A@#v.Ay K.A--AhLK.A65-A GK.Az+ApNJ.A"AJ.A34AA@K.A`A"K.A&AL.A`UApK.A`KAR N.A"AO.AAO.A:A*O.A7Ab J.A@5AJ.A@!Aq`I.A`AhI.A`A?CI.A la AH.A A0gE.A }AuD.AAH %H.A`+A@5BH.A6AUH.A`A0*G.AA@F.A+AX[E.A%AhC.AzQ AAA.ANoAg@.AaAxIC.A (UA0D.AA@B.AA`@.A]AȲY@.A ;AC.AwA0{:H.AzAjH.A OZA H.A)yAAHG.A`A@1E.A?HAmE.A`Ap,>G.A`A.L.AA-L.A|AL.AjA\xF.AAD.A`mAID.A-AtK.A@`AJ.AAKD.AX%Aأ.A`KAWY>.A֌AD>.At3ӲAC)Ȗ>.AWAw~>.AA|f>.AJAY|M>.A:[AbH5>.A,e滲AD5>.A7% Az>5 >.A *$A,g=.A7>mAuPW=.AkGAf=.A0[AF=.A0{A=.A3n޴Ax=.A@APH>.A@A @.A0.A>.AA>.A@%AxH>.ANAN>.A@rAR>.AʸA0*=.AA%=.AAȪ=.A AAm=.AF:Ax{=.AAx5=.AA=.A@GA>.A5Ar>.A@A=.AgAׯ<.A >A <.A`~AXn;.A AP/=.AA0<.A@#qA`!;.A<A';.AVBA":.AIA؁:.A `A88~:.AAhL*:.A ~vA:.A \ A̴:.A@PAx>:.A`8APހ:.A`ӢAp9.AqAs8.AAX׻8.A Z#AH8.AzA~8.A1Ak&8.AAw7.AٕA7.AIAP.8.ATA(8.A@_A48.A=ApZB8.A2A}E8.A@A@1:8.AA K8.A@qAZ8.AbAp]8.A A R8.A@A K8.A@yASX8.A@AX\8.Aa`AL4.A@pAH+3.A`Ad2.ARAZ2.A 0A/.ADA@/.A_A#2.AA{2.Ay8A/.AWAr/.A(A`rr/.AA,..AF A..A FAP ..A@#A..A@OA..A|A8..A@jA c-.A ?Az-.A ?;A\e-.AJAbZ-.A@ƍAS-.APAR,.A@A*.A`;)Aj&.AMAQ&.A@A&.AHAm&.A`ӻA%7ld&.A 5>%A W&.A\.AUWF&.A Ao2&.A>0A@&.AkrA; &.A'9A%.A A(1%.AA3#.AAm".AAX".A@EAxX".A@#A!.A`mAxM!.A@rA !.ADA .A@A8" .A}A06 .AAH.A`]TA`6.A8A1.A`sAr.Ag&A`.A@`SA.AA.A`A .AA.AA.A A.A@A.ApAh3.A qA4.A`}AH.A`A .AA`.AAIB.A@\AX.AaA3 .AAh/.AA8'.A`óAG.A`A<.AA.AMAX07.AOAt.A`KdA.A ?A.A@5bA.A`uA.AOApλ.AApλ.A\AM.Az%AP.A@A.AQ A`.AmA .A ҮAl8.AfA0o.AAQz.AFA0݉.A+Ax1K.AA.A AȘ.AA].AAR.A@Ap.A@ρA0Q.A@NUA5/.A4A$.AOWAx.AzAH.A@ ~A | .A@yUA7.ATA!N.A@A.AXAhnV.ARAxO.AA l.AA*.A@?Ap .AA.AA0'.AA.A9AX .AA(i .A`oA{ .AA@ .A Ae.A A:..A`9A0 .AA`%! .ABAЈ .AA .A@6AȠ .AA9 .A@,AX( .AA .A`vA0 .AŒAX .A@,Ahk .A,CAX .AହAw .A@,{AU .A@,hA.A{A d.A A0!| .A A0 .AAѵ .A`]sA8 .AWA\ .A`vA.Aڹ~AB.A7AP.A <AXt.A#|AM`.AAtA.A0kA.AQtA؟V.Ap\oA.AHjA.A/aA.A|_A/.A,[APo-A` bA-A ?ZA@F.A'MA^.AଧGAX9p.A;JA3.A`GAH.A KA-AVRA-A`HA.A`KAA-.A:A -AhlnA-A0)A$-A@ƗA#-A@ oA -AhlnA -A37AE]K-A4A0 R+-AG8qA5-A lA/-AtAз -A$aA,-A$ȽA-A@ýAP-A@A(-AJA^(-AAX[-A_ A-A@`A$-AxfA`%-AMA-A Z9A -A37A 3@b.A}[AH.Ar5 Adkq|(Qp.A}[A0C.AVߝq#A;.AAQΥ.AAɥ.A`A2.A@ A0..AXA@.A A8 .AAPk.A`)A.AAx~.ABAu.A`]A>.A #AG.A HA.AA.AAHJ.A ~OA?z.AǹA.AAͪ.AM9AGa.A$(A0*F.A`A0.AvA .A`Ahޡ.A@EAա.AAס.A/DAӡ.AVAơ.A A ¡.AAb.A*AP4.A`fA7R.AAؒv.A?BAH.AXAi.AeA(.A@NEAX j.AfAa.A@ClA0=.AFA.A u%Aǜ.Av}A .AAg.A4At.AD"A2.A Aۛ.A A(Л.AܷAV.AA #.AA~.A rAB.A`AG|.AaAAz.AfAr.A&Aac.AAޘ.A`A.AcAޗ.A`~Aۗ.A 1Aݗ.AAϗ.A8AA.A9AHZ.A;Ap,.AAꚕ.AT A_.A]S AS.AyS A>.A`S A3.A@R A8.A;WR A .AR Ae.AQ A8Q.A`)Q A.AP AԀ.A/O A Ȁ.AjgO A..A@ N A.A`9M AhՍ.A+M A.AL A|.AL A:y.AK Aev.A@ƎK A".A`!L Ap,.A^P AX.A@V AX.A W Axu.A,W Apxg.AW A8\.AV A06.AW Ax.AW A`6.A@ W AX4.A RX As.A /X A.A@ W AY.A /X Ag.AX A`.AX AK.AY Aq~.A7S Ap}.A@]T Ax}.AS Axܳ}.AR Ap8}.AP Ao}.A^.P A`h}.AO A}.A4P A`|.AN A[|.A;J A`|.A (I A8|.A`J AP}.A A A\}.A@Q@ Ac|.A? A|.A@5A A`{.AG A{.A8KI Aj{.A@ƵJ A73|.A`]N A8Ei|.AQ A(|.AU A`6|.A7V Ah}.AzZ A;}.A@\ A}.A@\ A`}.Aྔ\ A2|.A c\ A|.A`o2] A@_|.AA] AY|.A0^] AB|.A`m] A|.A؅] Ae|.A@] AE|.A@y ^ A 9|.A"^ A`C0|.A@=^ Ah |.A6w^ AH\|.A{^ A|.A`f^ Axm{.A^ A{.A`_ A{.Az3_ A{.AE_ Ah{.AT_ A{.AX` Ax(y.A@b A0y.A`^b Ay.A3a Ab(y.AGc A88x.Ad Apx.A`5d A@>5x.Ag.ARf,:s Af.A;d{ Apf.A`d{ Axf.A`c{ Af.A`Kb{ A8f.A\{ APf.A T{ Af.AU5{ A f.A |.{ A ޸f.A`v,{ A( f.AN+{ A0]f.A`m&{ A f.A %{ Af.A@.){ Apf.A`0{ Ag.A`)N{ ApJg.A`z{ Ax\Vg.A 8{ A vg.A`{ APg.A}{ AXۉg.Af{ A@`g.A{ A/h.AX| AH?h.A | Ach.A '| Ap=th.A1| A(h.AC| AP8h.AFb| A(]i.A#| Akbi.A<| Agi.A@| AMi.A || Axdi.A@>| Aыi.Ac| AP*i.Aٳ| Aȩi.Aw| Aɡi.A4| Ai.A| Ai.AF| A֮i.A^| Ai.A | Ai.A0} A`vi.A`m} Ax\i.A } A@$i.A`v } A`2i.A } Ai.A@} A`i.A| A6i.AW| AH)i.A| AVj.A@y| A/j.A | AN?j.A| AXaj.AT} Aoj.A`; } A@$j.A} Aj.A`2} Aj.A`K} A k.A 1"} A k.A@%*} A.%k.A@;} Aȥ/k.A@F} AIk.A"k} A@Qk.A Zr} A`Wk.Au} APMok.Au} Ak.A 8i} AMk.A`U} AXRl.AH} A@Ll.AD} A\l.AEJ} Ael.AQ} Asl.A@yk} A@ m.A uq} AePm.Aq} A0Xm.A@t} A0Xnm.A c} Atm.A} Azm.A`} Am.Az} A@5m.A@n} AHm.AD8} An.A| AOn.A Q| Amn.A[| A),o.A9{ AX(co.A{ A_o.A k{ A(-o.A@W<{ A0o.A{ Ao.Az A@1%p.Az A( Kp.A@z AHnp.A`_z Atp.AMz Ahp.A-y A#p.AIz A``q.Avy A0{r.Ax Ar.A`w AH)"t.Au Axt.A Zt A }t.AtEt Ao{.Axg AxV~.A?4j A|.AD0k A.Axk AL.Ajl A8.A2l A =.A`̈n Ai.A`^o AX.A2p A8 ‚.A^q AȂ.AMq A5%íт.A.r AH܂.A@]r Ah.A@.}r A tؘ.AI$her A;.As A@5d.A@5s AzWw.A-t&Os Aj.AL߮]s Aas.Aas A䴃.A=Ys A:.ASt A(.A 8Ft AHO.Au A\~.A`ʊu A`.A u Ai{.A;u AO.Aov AYr.Ajv A{.A@w A.A4ew Ah⌅.A4w Aӝ.Aw A.A`Px Ahb.A@5x Aߺ.ALx A`eɅ.A@x A݅.A x Ay.Ax A.Ax Ap+ .A`)x A .A y A@3.A"y A(.A@ z ArՆ.Az A`.A&{ A`.A q| AOLJ.A@~ AP.A@~ ALj.A0 A.AFى A' .A_ AH*.A@ A`>.A6 A.Aʺ A.As ApyM.A`) A`ߋ.A /n A0.A`] A،.A Af.A A(9.A~ AP^F.A4o Ao.ADǓ Ah;.A A(༊.A`K AZ".A@d AH).Ar AТ?.A l AX.A~ A0%.A@5{ Am.Ap[x A=M.Axw A/.Aw A.A%u AXх.Atxt AI.Ar AMH.A@r AXŅ.A^#r Aѥ.A Sl A0T.A0j A=.A`[j A0.Aj A.Axj A@.A@j A0H.A@N j A`.Aj AX9.Aj A0{w.A &j Aje.AZ A@.AdZ A`eۃ.ADY Ao.AqY A.AN@Y A@<.AX A(~V.AX Ah.ARX AƄ.ALW A .A!W A#%.A AV Ac.AELV An.Ap4V Aw.AE$V A|.A@V A.A@V A.AV A`؞.AV A(\.AFV A`尅.AU ALЅ.AU Ax$.A \T Aa.A`yT A@1l.A_T APځ.A}-T A@આ.AS A.AS A ͆.AES A ؆.A`S AP.A R A E.A R A.A`ʏR A`<.A`;R AGu.AQ A0Hׇ.AFP Aۇ.A@-P AL؇.AP Aч.A@P AX؇.AO A#.A`ܽK A|.AྀG A.A Z/D Ab.A 1>@ A%.AՊ= AX7.A֜8 AP#.A8 AТ0.A$7 AP:.A (E7 AR.A`6 A&.A5 AP.A x5 A%.Az4 AH=.A"23 A\K.A`2 A.A3 AK.A1 Aԋ.Aa/ Ab.A@. A`.AR- A([[.A`dS* A{.A`( AL.A l^( A.A' Aj.A;=' At.A& A@y.Ar& A#.At% A(.A`X$ A@.A # A».A" A`.A6 A@'.A` A`K.A`~ AӀ.A࣑ A .A AB.A@ Ap-.ADZ A`P.A@,T AL.Af; A .A` AX>.A`ܽ Al.A A( 0.A  AF.A@  AQ6.A` A.A A@.AfG A .AA7.ARA@J.ATA q.AbA.AA0}.A@A .A O{Aȶ%.A@,AXW.A&A.Ad A07.ATA _i.A`AN.A -A.A@Ao.AAH. .Af+A(z1.A@A '.A QA@׾.AAH.AQAhޭ.A@AA.A`A.A`A.A@pA:.A APjV.A`&A.A-$A0 Z.A [A.t.A Ah/}.A@#A~3.A`A0C.A|A2_.A<'APM.A`]A..AתA.AիAȔ5.A"A>@.AA~.AAh.AA.AA .AWA.A A`.AVA8։.A*Ah.AA0LҬ.A`]A.AvoAhA.AAȱi.AA̜.A`Ah.A A0국.AA.AA.A A.A`bAt.AA.A6A_ˮ.A@A.A`xA` 1.AiA;.A@PiA.A`pA0꺯.ArA.AA߯.AAs+.A`Ah"F.AXAR.AAXW.AAp<`.AՉAx-j.AAt.AAȘ~.A@{A.AvA".AfhA.AcAZ.A`_A覾.Ac`ApAɰ.AbAҰ.A[A`ݰ.AQA@F.A3EA.A7A_.A&Aj.A A.A@A8.A@AS.A lA.A AP#.AA`+.A`bA 4.A`A2>.A A(F.AAP.ATA`[.A@Ag.A@yA,t.A$A #.A܄A.AA.A@Ax-.A@yA趪.AA.A@AXt.A`ADZ.A@A@б.A A@_ڱ.AsA`.AcAZ.AZAP.ATVA<.AQA .A ZMAd.A$BAA!.A5A8V+.A*A0j5.A`!A(?.ANA`PJ.A A(U.AxA_.A`fAZj.AAs.A`Ay.AA`.A޺A.A`A.A@yA.A JA؍.A@A2.AEA(`.A2AHP.A`DA².A;A0Ͳ.AAز.A@>A.A|A.ApAX.AHcAj.AWAs .A`JA.A9AȘ.A` 'A&.AA..A@A5.A At=.A A7F.A AhUN.A@AX.A;jAX^.A@YAPZg.A2EAHi.A?AHq.A6A`y.A`$A.AAX(.AA .A@Aj.AAe.A=A[.A*Aho.A=APZ.A&zAdų.A@ sA@γ.A lAxٳ.A^cA0.AWA`.A`DMAج.ABA.A|6A .A#AH;.AAd$.A@A0.AnA:.AݰA`C.AAO.A`AUW.A@rA_`.A@gAj.A XAhu.AJA<.A 8Aߏ.A)A軚.A@ A.A`mAo.A@GAA.A A2Ĵ.AA˴.AAԴ.AA.A@E A.A OA.AA' .AA.AXA,$.A@A1.AhAp?.A OA@FN.A AU.A A].A HAk.A`Ax.AAt.A`)xA8.AfAU.A9VA꡵.AGA1.A`:An.A /A張.A"Axŵ.A OApε.AAXֵ.A|APZߵ.AAH.AA,յ.A@PA8ĵ.A ‡AP.A Ax鴵.AtA.AgAˢ.ALA.A@8A`6.A@y6A.A?FA.AXIAh{.AHAv.AAAm.A l!Ad.AA[.AEAAW.AA V.A@A8R.AH{AL.A |BAXVJ.Ac AFH.AEAF.AAC.AׯAX(A.A@Ai<.AcA7.A?Ax-2.A@5Aؒ,.A@AX%.AzA .AA.AAp.A ˾A.A`bA.A /A;.AA2.AAp[״.AEAX(ɴ.A A.A3AТ.A`A0.A'AP.A/A.A@IApۄ.AxXAXx.AfAQl.A QtA7e.A@zA].A A`M.AՕAsE.A A5.A@A@-.AA($.AA .AA.A)A.AFAج .AA6.AA .AA.AAHٳ.A CATγ.A`jA³.AA[.AAxF.A A.AA,.AxAG.A Aہ.AgA@x.A@.Ahj.ADAc.AYAP_.AI_AxU.AomA K.A@~AA>.A`ÙAHP0.AA".AAA.AA .A/A.A3A.A AJ.A@+A۲.A`@AhԲ.A ZKA`1Ͳ.ARVAOŲ.A_A廲.AlAo.AzA@.AʈA0.AA7.AaAX.AAȃx.A[Ahh.AA_`.AAW.A<AN.A AdE.A`A;<.A[A2.A@NA_).A@ %A!.A H7A .A|BA _.A`)FA .A|BAi.A 7A.A H#Aڱ.AAѱ.AdA'ɱ.A@NA±.A`̤AA.A{AP.AOAh".AdDAA.A:A0.A5A߉.A l/AP7}.A@%*Ao.A&A@e.A`%A[[.A"A@1O.A A C.AxA7.AAX-.AA`#.A$AH.A<0AP.A&<A.AIA8.AWA .AbA`ְ.AfjAoʰ.A}pA.A.AoA(z5.A mA8-.A@nA'$.A pAH.AqA0.A@ qA.A`oA.AkA.AgA.A`bAPٮ.A]A(~Ю.AXAƮ.A RA0̻.ANAZ.A SJA冷.A@5EA.Ag@A.A ;AP .A&2A!v.A//Ak.A.Ata.A@2AZV.A8AOM.A (:A`D.A H=AX9.A>AK..A`=AX#.Aq;Ax.A7A.A`/AX.Aa*A}.A`]'AO.AF!A.AA֭.A Aȭ.A OA8#.ARA.A AH.A` AHK.A`AА.AA.A O A0v.A@ AUg.AAXY.A`AXL.A AG.A ZAx.A@A.A`A.A 1yA.A &XA.A9AXެ.A (AZڬ.A@ A@Ԭ.AfAVϬ.A<Ayʬ.AॆA"Ǭ.AEkAP¬.A>A0̻.A@ Ap,.A`]AK.A@A;.AwAȘ.AHOAH.A!A˦.AHA@d.AfAȘ.A`Ax˞.AA.AVAo.AA.AA0.A` A@d.A JAƌ.A ZsAXۉ.ANA.A0/AV.AA.A` A`.A`ʿA|.AA(,x.AEAt.A`A"r.A@?AZo.A`Ah.A@A.AMűA(-G.A(A pO.A⥱AzX.A`mAc.AHAAq.A rA}.A `A֋.A@FAP8.A(Ao.AA跧.AxAk.A$A.A﫰A.A A .AsA=Ǭ.A@WXAh̬.A3AxWЬ.A AzЬ.A`DޯA(Ь.A`ApѬ.AAӬ.A sAhL٬.A&CAݬ.A&A.AA`.A lȮAF.A眮A.A[rAP .AEA`.A@p"A(`.A`2Ae&.AAk/.AŭA0.8.A&A-@.AՌA3G.A`m^AhK.ABAS.A`;AhQ[.A A8b.A@A`Gj.A 8ǬA=s.A`mA|.AuA.ANA=.A`!A.A$ALJ.A`ԫA.A 8AX.A/A8.AE`AH.A9?A0.AA.A`;AX֔.A ΪAF.A Aۛ.A@yAH.AXAG.A@.$A.AA@.A@.ʩA.AAA.A /pAH.ANA@.A'A跫.ADAf.ARAh.A@AH{.AuAX֣.A@8A@p.AA.A Aؽ.A`§AX.A`DA`.A A.AXAXV.A:Ah.AkA".AA@.A̦A0.AxA.A슦A.AymAH.AaKA(.A"A.AAё.A@gեA.A;Ax.A (Ap.AaoA`G.ATA .A@g5A.AAA.A`AA.A3ؤA =ĭ.A@NAPMʭ.A;AЭ.A`iA ׭.A@?Afۭ.AA0߭.AA .AϣA0.A cA`v.A`]AK.A@N]A`a.A3A".A`2Aȓ.AAL.AAp=.A cAj.A{A(.A`XA.A9A.A ZA.A Ax.AfAH .A`܏Ap.A@yeAQp.A}[A\:Qc.AQ AKTEc.Ak5 A( ec.Aҹ AZc.A6' A0Vc.A A\:Qc.AQ Aт,c.A|O A^!c.A< AYc.A`Q AHc.AN A]Sc.A"HW Aohoc.A\ An@Ѷc.A+.{ A"Œc.AQ0Cc Ac.A` AWc.A|O Aт,c.A|O A?2Lc.A& Ab.AH Ad3N>c.A_ Ap#c.AbK( AHd.Ak Af.A`P AKf.AV Ah"i.A@N Anj.A' Ax)j.A& ( Al.A1 AMn.A 8 Ann.A@#F9 AHn.A99 A]n.AI: A`an.AՖ: An.A; Aȃ,p.A4B AHp.AG Ahp.A@J A$q.AhmL Aq.AQ A=q.A@Q A.tr.AT Aur.AU AX[t.A ca A(iJv.A`]g A v.Ai A w.ANo A0Fy.A S7x AxJy.APx A%%z.AǙ} A9?z.AB;~ A{.AX܈ A|.A} A[|.A@iH A}.A`) A}.A  ADӀ.A A0(.A53 A .A& A]T.A. A.A A Ҽ.A`D+ Ae.AHU AP8.AQ A`.Aޛ A߈.Aa A/.A0 AP.A@ Ad.A@W Aˊ.A A0Hۊ.A`2 A8.A A#ӯ.AV A*.A ASN.Aw AHئ.AI At.A@y AK.A@P Au.A A7އ.A Aڸ.A A APs/.A AX=.A4 Ahs.A4փ A~.A u@t Av~.As Apf~.A#r A\z.A`W A y.AaO Aw.A /D Al=t.AR/ At.A]. Apt.A- As.A[- A s.A, Ax-s.AiA, A"ys.AX+ APr.A $ A(r.A! Aȋq.A A _,q.A2 Ap.A Q AHo.A Axo.A`f A*o.A AX?o.A0 Apm.Ah Aؽm.A@E Asm.A@ A m.A` A4'm.AI AVl.A` A@1k.A@P Ak.A`K A`Էj.A@,: ASlj.A` Ap8j.A AH.^j.A A@ej.AQ AHj.A AYi.A` Aܒi.A Ah.Ax Ah.Ai APoh.A AXBh.A` AxCh.A ApRh.AB AV7i.AA Agh.A@ A $h.A AXg.A`I APg.Ag Ah.AF AHTk.A5 Ao.A Ahn.A Appk.A`o A(k.A A)/l.A@ A(Vl.A A/k.A I A0]~k.AmY A( k.A' Apk.AH A:.k.Ax Af.AM0 A?c.A Ac.A`R Ajc.A@ Aȭc.Ay A`~c.A` AQfc.A AQ0c.A A?2Lc.A& AF@4e.AK|, A*&{1;f.A03 A%hh.A౬P Ad l.A+l AGÕp.A7K_ Azʙt.Auv A_Gݿy.A@ Ab"n.A A 0.Au AG.A`6 AD".A Aأ.A2 A¥.A6 Ap.A A A.A`d A(.A H An.A A@/.A A~.A A$~.A ~ Aƥ~.A^ A%~.A`26 A~.A A0}.A1 A>z.Aݧ A:x.Aɗ Aw.A֓ A }bw.A A +u.AĽ At.Ak A0&t.A A(s.A`D AXs.Aྮ AX&r.A@dv Ah՚q.Apuq A`&q.Am Ap.A@}j AX59o.A 8b An.ABc AP'Tn.A` Ayn.A``` Am.A@ؙ] APm.AtX] Am.A\ A(9|m.A`[ AP/Zm.A`mZ A/6m.A c+Z AHj.AK AGj.A@ G Ai.AE Adi.AfE Axii.A`D Ai.A`yD Api.A D AX[i.A C Ani.A /B Ah.A`m= ATg.A`7 Af.AW2 AF@4e.AK|, A FV.A Le AvQw/A / AE5AA`S/A?/ AJIN/AĒL A6x,[G/AT,ѕ A+#@/A| A=/AV Ap>/A@r A0.>/A 8 A#>/A@g0 Ax'>/A`K AP'A>/A AE>/A- A`>/A  Ahd>/A H APk>/AY A݂>/Aqn Ap,>/A A>/A) A(>/Acʍ Ab>/Aߍ APx>/A@p{ A(l>/A  A>/AF1 AF>/A F A@?/A Ap?/A@` A:?/A@ؽ Ah>?/AՐ Ah^[?/A` Aߝ?/A@ѐ Axd?/A@ A@?/A@ AB>/Aw AiG?/A A=/Aq)}; Ap:'=/A~ A0H=/A AB=/A@P A6/A@} AH6/A[ Aj6/A' Axǒ6/Aa Af6/A` AH.6/A` A@W97/A`9ӳ AP87/A`\ A 8/AE+ At8/A`Y A`8/A 1 Ah359/A' A9/A A':/A`m A;/AԷ AH)v;/A^ Aз;/A A /A / A'c>/AFJ A>/A A>/A AP>/A: AL?/A`2 A?/A A @/A@Ƒ Ad@/AD A<@/AY ApA@/A` AН@/A A8#@/A Ax-?/A A(?/Anz A0=/A A'O=/A} A/A`| A` >/A@߂ AQ>/AO Ah>/A} A0+>/Aq A8>/AFa AE>/AK Ae>/A A@h?/Aw AFu?/Am AH?/A QZ A>?/AJ A"?/A}? A̹?/A@p6 AU?/A@? AN?/AK A@?/A` ] AP?/A@ t A@/A$ A0@/AH A?'@/A AA/A@% AB/A#\ AB/Aβ ApC/A` AC/AY A8sC/A AxC/A |? AC/A A C/A AB/A) Ax$0A/A  A,?/A AKq?/A A0?/A` Aa>/A@WE A0{K=/A A2%/A4p Ah%/Ap A%/A@ p AP%/Ap AP%/A p AH%/Ap A@d%/A`p A %/Ap A8%/A`p A@`%/A Ap AX%/A@!p A%/A p A\%/Ap Aذ%/Ap A8%/A@p Ap=%/Ap AY%/A@p Au%/Ap A%/Ap A%/Asp A(,%/Ajp AXV%/Aap A%/A Zp A%/A Qp AԷ%/AIp A%/A`Bp AS%/A4p A}%/A /p A%/A`)p AP%/Ap AP%/A p A0%/Ap A%/A@ p A N%/Ap Apx%/Ap A%/Ap A%/Ap AxK%/Ap Au%/A`p A%/A p A%/A p AH%/Ap Ah%/Ap A%/A@%p A@F%/A0p A%/AKp A`C%/A[p Ah@%/Ap A%/Ap A྿%/Ap A%/Ap A=%/A@p A8%/A@p A`%/A`p Ap9%/A6p A%/A@yp A5%/Ap A%/Ap A%/Ap A@1%/Ap A%/AZp A%/Ap A%/Ap A%/Ap A%/Ap AP'%/A&p Az%/AZp A@%/A@p A!%/Ap At%/Ap A%/A;p A(%/A`vp AHn%/A@p Ap%/A`p A0%/A4p Af%/A`vp AH%/A`p A%/A`p A %/Ap AH %/Ap A_%/A@Gp A0%/Akp A%/A@p A%/Ap A,%/A`p AV%/A&p AH%/A@Np AX%/Atp Ax%/A`p AhL%/Ap AP%/A`fp A %/A`p AA%/Aq A%/Agq A%/A q A`6%/Aq A%/A`vq A)%/A`2q AR%/A cq Az%/Aq A%/A`q AH%/AXq AD%/Aq Al%/Aq Ap%/A@%q A %/Aq A^%/A`q A0%/A cq A8%/Aq A(%/Aq A %/A q AM%/A?q Au%/Avq Ah%/Aq A%/A q AP<%/Aq Ad%/Aq A%/Aq A%/Aq A)%/Aq Ax%/A@p q A%/A q A0%/A q A%/A`) q A%/Ag q Ad%/A` q AH%/Ad q A%/A q Ah%/A q ApN%/Ah q A(%/A q A%/Aq q A%/A q A7%/A q A]%/A=q Ah%/Aq Ah%/A q A@A%/Aq Ac%/A q Aз%/A@q A%/A@q A%/A`Bq A`P%/Aq Ax%/A@gq AX%/Aq A84%/Aq A%/A (q A%/Aq A%/Aq A%/A[q AHa%/A`q Aث%/A@q A%/A`q A%/A@#q A%/Azq A@%/Aq Ae%/A@#q A%/Atq A %/Aq A%/A`q A%/A@iq A%/Aq AC%/Aq AXh%/Aaq A %/Aq Aȱ%/Aq A%/A`q A%/A q AC%/A`[q AXh%/A`q A%/Aq Ah%/A`q A%/Aaq AA%/Aq Ae%/Aq A%/A q Ax%/At q AP%/A q A=%/A(!q A%/A"q A %/AL#q AX%/A$q A@%/A$q A`%/A|%q AP+%/A9&q Ap%/A&q Aȓ%/AX'q Ax%/A'q A%/Az(q A@%/A=)q A%/A@*q A%/A`d*q A%/A*q AX%/A*+q Ap%/A+q Ap0%/A+q AXR%/AU,q Ap%/A ,q A%/A@ -q A8%/A -q A%/A.q A0%/A.q A%/A`;0q A%/AR0q A%/A"1q AH%/A`1q A %/A`1q A'%/A`1q Ai%/A@2q A`%/A$3q A%/A@3q A%/A3q A'%/A-5q Ao%/A6q A%/A6q Ap%/A`6q A%/A7q A0*%/A@8q A@1%/A8q A(q%/A9q A(%/A@,;q Ah %/A`;q Ah/%/A$q A`%/Aďq AL%/Aq AP%/A$q A%/Aq A%/AĖq A%/A,q A8c%/Aq A%/Aq A%/A=q A%/A`q A%/Aq APM%/Avq A%/A@ q AP%/Aжq A%/A@q Ah/%/Aq A %/Aq A0%/Aq AXF%/A cq A(h%/Aq A%/A[q A؛%/A q A0%/A@q AX%/Aq A#%/A`q A:%/Aq A}%/A q A%/Ajq A%/A q Ap%/Aq A%/A q A%/A]q Ax%/Ar A%/Acr A%/A r AH%/Ar AP%/A Or A%/Ar A%/Ar A %/Ar A%/Ar A%/A,r A(%/A}r A %/A Sr A`%/A`#r A%/A`D#r A%/A`$r AX%/A`"%r A%/A+&r A0{%/A3'r Aq%/Ai(r AXh%/A)r A[%/A"+r AhQ%/A@W,r AF%/A-r AX,%/A[0r A%/A2r A%/A6r A%/A7r Ap%/Aw9r A%/A`:r A%/A;r A%/Ar Av%/A?r A@S%/A}Br A%/AHr Ap%/AJr A%/A8Jr A]%/AQr APZ%/A@Qr A%/A`[Yr A@%/AZr AH%/A_r A,%/A`r Ah%/Agr A0%/A@,gr Ah;%/Atr A%/Awr A%/Adr Ah"%/Ar A(%/Ar A%/Awr A؟%/A@ r A%/A@Er A$%/AVs A%/As Aں%/A`s A%/As A%/AVs AΘ%/A@Er A%/Ar A(%/Ar A%/Azr A%/Asr A%/A@r AТ%/Ar Ap%/A r A0ԓ%/A r A%/Ar Al%/Aqr A0H%/A r A%/A`r A%/Ar A9%/A@r A %/A Zr A%/A&r A%/Ar A?%/A/r A%/A`r Ad%/Ar A%/A`r AC%/A5r Aω%/A Zr AΉ%/A Qr AY%/Ahr Au%/Ar A0;%/A r A%/A@r A%/Ar Aʇ%/Ar AЯ%/Ar AȔ%/Ar A[%/Ar A%/Ar A҆%/Ar AP%/Ar AL%/Ar AHK%/Ar A%/Ar A%/Azr A8D%/A@pr AB%/Ahr A%/A^r A%/Aܭr A܄%/AZr AXۄ%/A Sr A%/Aͬr A%/AFr A@B%/A+r A%/Ar Aރ%/A r A8Ń%/Ar Ap%/Ar Aȓ%/A qr A{%/Ar Ab%/A^r AJ%/Aդr A1%/A`Kr A%/A r A@%/A5r A%/Aઢr Aт%/A` r A%/A r AH%/A@ r Ap%/A ~r A@΁%/Ar AP%/Ar A@%/A`r Ar%/Ar A\%/AUr AxF%/A`ʘr A]%/A@r A%/A@pr A`B%/A'r Ay~%/ARr Af~%/Ar AS~%/A &r A?~%/Ar A,~%/Ar A~%/A cr AX~%/Ár A}%/A@5r A}%/A r A}%/AD~r A7}%/A{r Ah|%/AFxr A|%/Awr A|%/Awr A y%/A` Tr Aw%/A5r Ah#/AR#q A "/A省 A^#/AO Ay$/Ab A&&/A  A`&/A` AcF'/A A.&/A` A0&/A S Ar&/A4X A[$/A Ap.#/A{ A p /Ao A</AZ A*/AX AY/A j AQ/AjEc A!/A@nc Ah^/AWc A/Aad A@d/A e Ah /A@e A/A`md Ap}P/Agf A]/A@5f AHmU/Af AxA/AUf A@ /Ag A /AJh A0 /A}hh Ac /A@g A/A d A/A@ed A/AQd A|/AMd AQ/A"c A0|/Ac AHr/Ab A/A`\ A@W#/AK[ A/ADN AXj/ACO Aج/APO A//A`;TP AP8/A` A'.A`V A.AO A.Ad Axy.AO! AP .A@E$ A.A5S& Axǖ.A' A .Ap) A.At) AH.Aށ- Axv.A07 A@_.AD; A2.At.= A.A ZA Ah.AB A M-.A@B A$Jw .A_7"B AWhr .ATGB Ae.AwB ACu .AB A0݁.A(7B A[t.AQ|MB Af.A(!ZB ARX.A1]B AZQ J.AHQ?XB A,%1<.AFUdIB A4 /.Ap;1B Aܭ2#.ANB A.AA A8.A`VB A.A B A .A0C Ah3.A D A.AD AȺ.A@5D A.A_D A?y8 .A?D Am.ACV{E A3Q.A1t!E A7.Ad0E A ڱ".AZRbJE ABZ&.AfE Ah(.Ap,EE Ag*.AE Aȥ+.A E Af/.Aħ8E ASU[}2.AzE A$5.AWRUE AG9.AF E A8<.AE AǕ:@.AČGF AGLC.AF f3F AG.A}I F A}I.A2F A J.Ah>F AW_K.AHJF A+K.A?ȏNWF AJ.AcF AxX.AwF Ad.A f6F A6p.AvF A y.A'AF A.AF A5A.Ami+G A{.AI_G A&ƌ.AzG AH.A |G AOj.A-嬜G Afj)6.AG Ag.AG A1.Ar!G AJwN.AJ!G A٤.A/G A.A/H Ab .AVfH A0.A ;H A v.AvaaH A{Z.AA%tH A9x\.AĦ-(H AFJ.Ax>H A.A]2I A.ACS7I A7l.A, @I A.A ILI Aɠ.A[I Aٴ .A:nI Aa.A%KΝI Ar%.ADI A`ω.A3I A0.A I A y.AI A *,.A`I A)?7.AE"q5 J A64@.AO2J AH.A4P?]J AܰO.AύJ Ae-;S.A!J A 4U.A`bJ AyY.Ad=/J A`8].A}ݳJ Aaa.AGRJ Ave.AeeJ Ah.A;UJ AVc2 l.A͋K A\n.AvK AycMWq.AӸK ADl$s.A<##,K AqMt.Asd2;K A֒t.AoVKK A0ot.A5[K ALs.AekK A&r.A HzK A O~.Au=K A|.A5T?K A>ŕ.A5үK A{.AIFK As.AG;K A:.A4"L AXdz.A̬}QL Ay.A#LWL Ap.AkL AAV.A<2L Ao.AL A0w.A`TM A=d.A9>M A.A@ eM A.~X.AP2zM A.AȾM Al4.AJYM Axh.AM A;o.AkN Az.ABN Aȱm.A`!uN Aa:.AㅢN A{La.AN AHv.A|[D O Ari.AKY:O Ah~.A"bO A6.AׄO A.AO A<.ASO Af.AlGO AjO.A%IO A7~.AS.O Anlҥ.AC=O A`.AIxP A.AHPP Aţ.A!P A5.A-P Ax.A@%7P As.A{hP A*Z.AHP Am.AP A"y.A٣P A_O6.A0, /Q ALst.AGeiZQ AI.Ap9Q AW".AS`Q ANƿ.AQQ A.AAQ A .A}tQ Ar[F.A?;Q AÕ..ASGQ AI9s.A?&GQ Ah.AÜQ AL.A@#R ApP.Aƾ)R A8eS.AպfNR A U.A$uR AU.A[R Ae1T.AJR AE~Q.AH4R A *M.A2S A(G.ACS A阿?.A:eS AM*6.AXS A "1-.AfS A.".A CʦS AZ.A}TS A p .A@#S Ao .AWatS A`p .A?S AV .AcAS AY .A{W A+.AaGW A&.A~k|W ADL.AW A1.AW Are .Aן W A}.A=X A3Vn.A}5X Ah.ADX Ao .A&bX Af].Ap}X A\.AX A.Aڂ X A+.AbX A.A|X A$d].ACX A .A{++Y A%3ƭ.AI\WY AM .A~Y APۘ.AtFY Agt㝌.AhgUY AH.AY ANqx.A?Y Ao.AC~ Z AG^f.Ap"Z A[.A@4Z A\V.A=LuoZ AEO.AZ A(F.AmZ AV^:.A[ A!-.A@o"[ A3.Aɓ:[ Ap.AI[ AQ .Acmb[ Aw .Abx[ A,>.A$ tg[ AG.A|[ A=%.A[ AH.A[ A.A l[ Ah.Ab A%.A!c A[.A@W(a A.AZ_ A.Ap` A.A_d A).A4i AZ.A`]h Ay.ALd Ap=t.Aac AH.A z\ AK.A\ A`d.Ap\ A.A\ A0~q.A9] A.A9M] Aca.AWv] A8.A] APUXz~.A] A$ʗw.A] Aq/p.AlMk] Ag9qg.AJ^ AL].AH!$^ AYS.Ae1/^ AzI.A<8^3^ A@?.A1^ A0?.A,E^ A}>.AeDY^ Aڅ=.AZRl^ AX:.Aʀ)~^ AzMp7.AR^ A^ZL4.A((_^ A;0.A2^ A+.A^ AZt&.A^ A m$".A) ^ Ax .A^ A p<.At^ A.A-'_ A .AMX_ A.A |<_ A?F.A@_ A|7.A:U_ Ab.A`_ AvyF.AlϞ_ Aoi.A@#_ Ag-.A=K` Ab,.A` A .Az$` AW̦.Ar(` Aj+.Au/0` AE.AF4` Av$`.A(#v` Azˬ.A#b` A͠.A|_E` A齒.AkFa A`.Ay5a Ar.AKa AF8o.AVea ALk.A:oO}a Asf.Ay(a AY `.A*a A`Z.A̶4a AS.Aa A?fP.Ala A tJ.A|Gb AoC.AHGb A6:.At3Xnb AWc0.A薰b A[$.Azb Aj.A^Xb At .AÚɉb AJ.Af?b AE.A ub AA?.A'Ҋ'b AsLjz.A]&b AI .A^b A,.A}kb A)d.Ac AQ3j.A%xc A0.A\hc Ac.A Jc A1.AAn}%/c Aذ.A|Tc AN?.Ayrc A}ډ.AӾ:c A.A>ٔc AmN}.A-Ӹc ASqo.A@ޑc A~*Tb.A"t[c A^U.AlQGjc A)`J.AnIc A"O!A.AF@!c AcV9.AS1Ub AB3.Afg7b Ax0.A`b AxI{*.AVˎb A<%.A{o݀b AVx .Aub A&.AW,gb A .AǓVb AH.A VCb A #f.A=3V.b A.Ah "b A.ARb A.AT6Ca A\ .AXaa AS.A3pa AvCt.A*7a AQ.Ad[|a A .AΤpOa A-.A,a Ap.A 1` Aџ.A,` Aj V*.As` A].Arİ` A).AHquE` Avl.Av` A+.AU` Am.ArܡB` AG.A&` A59.AX` A_b u.AJ_ Ah4l.Ae_ AmPf.A]n_ AWc.A5_ A*1].A_/_ Aq%X.A4`$d&_ A6 r.S.AZF_ A$mN.A W _ A9K.A^ ArvH.AC^ A]F.A91i^ AE.A^ Ao7.A'g^ A=Z*.A}pY~^ A0Jr.Alt%X^ AQ.A\p\+^ Aj .AS"J] A! .AW] Aأ.A`f] Aq.A] A}.AtZu] At9.AFSf] A }.AoYoU] A-.A0jA] A0a[.AcOt+] A0e#.A(]<] A.A\ Aa.Aީ^\ A:.AQR]\ A,T.A{z*\ Ae.AH4y\ Aa.ApF\ As$.A|\ Aȩ.A}[ A7ۢ.A%[ A.AOW[ Aoӊ.Ab[ A#WU.A_)[ AϞ.AQ[ AJ.AO-/w[ Ax}.A@_[ Ay5o.A' J[ AXa.Am孬+[ Ap/U.A=[ AjJ.A淾Z AuB.A$5}Z Ai4wg=.A.bZ A:.AV鶐$Z A`X:.AY A^/2.AY Ač",.A?=#Y Ap%J&.A0٣Y A_:v!.A(8߆Y A.A \gY A.AMɎQY A*R.A(]7Y AOb.A.agY A.AX A.A`vY A.AKY AU.A`Y Ah.A 0Z A(.ACZ AP8.Ap}Z A`.AqZ A1.A@PZ A.A`vZ A+.AZ A.A )[ A)~.Aa A$q.A/Ca A0.A@[ Ah3.A[ A`P.A`][Z A@.AIZ A۷.A Z A".A a A(.A@!af A0H.A g AL}.Ag Aiw.A<_(g A>㥠q.A000g A$8 k.Ab4g AP}d.Ao3g A].A|/g Ae#^.A7g A ^.A}g@g A].AHg AC\.A7YSg A[.A(\g A|)Z.AEeg A̡KX.A {mg A| &V.A/tg A0eS.A7=yg A=Uh ATٹ .A% kh Aj8.Ax%h AVڛ.A ?h AN^.AWQh Aj.AgLh AFP.AMh A.Ah AP.A Q'i A1T0;.A >i AJ.A_Ui Az.Ayli A(6.AZi AT .Ai A\w5.A i Ad.ADi A.A!g i A!և.Aӂi AK[.A\Ci A.A~yi A`ð.Ai AŰ.Ai AG~.A`:i AԲ8.Ati AG!.AtYi Ajb.A)i A4[֬.A5i A< k.AAsi AJ(ߩ.Ai A=.ANVi AX.A i A{.Ai AT.A i AƤ .Adsq j AҒͿ.AӬ>!j ANu.A8j AXQ4.AUNj A,.A=bj A0.Ah&Ztj A9u.Aoj Ar?.Aj AN.Aj A-.Aj A$}.ACwj Axw.A@j A˹w.Aj AQw.A]Ϧj AW|v.A2 0j A+cu.Ajj Agbs.AHj A[@0_r.A'=|j Ap.A7.j An.ALHSj AKil.A?F7j Aj.A@j ALk.AnBj Aj.AH8j Ay*ii.Ak A3Rg.A'ik Awd.A*k A_R`.AVx:k A}\.A߆Gk Ay"X.A1qRk Ah"S.AYk A.AfSl APͪ.AfSl AP.AfSl A.AfSl A(.AUl Ax.AUl A8E.AUl A8#.A@`Xl Aa.AZl A.AZl A~.A[]l A@.A@_l A.AVbl Ax:.A`dl A`.A@Pgl AW.Ail AЕ.A`Kll A.Anl A@.Asl AQ.A@vl A.A@xl A@.A}l Ahn.Al A(.A0l A.A+l A*.A@%l A.A` l A.Al A.Al AP.Al A.A l A$.A`l Ah.Al Ac.A@l A.A`l A.Al AA.A l AH.A cl Ah.A`]l A.AWl A9.A@m A2;.A@m Ap<.A@m AN>.A@m AP?.A@!m A@.A@!m A B.A@!m AC.Am AP'E.Am AfF.A@m AG.A@m AI.Am ApJ.An AL.A@n A>M.An Ap}N.A` n AO.A n AP.An AX9R.A n AwS.A@n AT.An AVU.An AV.An A@W.At"n AY.Ao'n A@Y.A@i,n AZ.A.n A@[.A@3n A\.A`8n A(m].A=n A ^.ABn Aث^.A`Gn A_.ALn A`.AQn A)a.AVn Aa.A@[n Aa.A``n Ahb.Aen A0c.A jn Ac.Arn Ac.Awn A].A o A;.A o A(z.Ajo AX.Ajo A06.Ajo At.A@o AR.A@o A0.Ado Aho.Ado AM.Ao Ap+.A_o A0j.Aݜo A0H.A Zo A.Aסo A.AVo AX.A@Po A.Aͫo A .A`Ko A.AEo Ap=.A?o A|.A@o AȺ.Ao AX.A@o A8.A@o Av.Ao AX.Ao AT.Ao A`.A o A .Ao A@q.Ao A.Ao A`O.Ao A.A@o A.A@rp Ax-.A`mp A.A p Al.Ap AH .AWp A.A p A .Adr Ax.Adr Ax.Adr A8.Adr A .Ar A .Ar A .Ar A }.A_r A[.A_r A.Ar Aw.A Zr AU.Ar A3.ATr A`r.A r A`P.AOr A(.A`r Al.A Jr A.A@Er A.Ar AP.A@r A.A;r A.A@5s A#.A/ s Ahb.A)s A .A$s A .A s AP .As A] .A"s A.A 's A:.A.s Ay.A@3s AP.A:s AW.A ?s A.A`mGs A5.AgLs A.A@Ss AXt.AXs A.AQ`s A.A`gs A8R.Als A8R.A.At Ahޠ.A-t A}.A(t A0.A t AP.At A[.A@u A0.Au AP.Au A9.A u A9.A uu A٥.A "u A٥.Af*u A٥.A1u Apx.A6u Apx.AO>u Apx.AEu Apx.A?Mu Apx.ATu A٥.A@Yu A٥.A*au A9.Ahu A9.Apu AP.Awu A0.A|u A0.Au A[.A}u AP.Axu A0.Au Ahޠ.Au A>.Aau A؟.A]u Aa.Aհu A.Aеu A .A`_v A_.A Zv AP.A$Uv A.A)Pv AȺ.A/Kv Ap.A4Fv A88.A9Av A8.A>v AT.A9v A.A4v Aq.A J2v AO.A O-v A .A *v A(l.A`T(v AJ.A%v A.A Z#v Af.A` v AD.A^v A".A v A.A v A.A`dv A.A`dv A.Av AP.Av A.Av Ah.Av As.Av AhQ.Av AP/.A`dv Ah .A`dv AP .A v AP .A v A8.A^v AX.A` v A.A` v A.A Z#v A.A%v A^.A *v A.A O-v Az.A`/v AX.A4v AX.A`D7v A.A?+w A_4.A2w Ah3.A@.:w A؁2.A(?w A p2.A@w A1.A`Fw A`C1.ANw A0.A@Sw A`e/.AZw A&..A_w A`-.A`fw AH,.Akw A +.Apw Ax).Akxw A(.Af}w AN'.A@`w A`&.A`[w A$.AVw A #.A@Pw A !.AΓw AXu .A ɘw Ax.A`Ýw AX.A@w Az.A`;w A <.Aw A^.A@5w A.A0w ApA.Aw Ac.A+w A.Aw A.Aw AP .A&w A .Aw Aج .Aw A.A w A.A w A.A w A4.A w AV.A w Ax.A w Aؚ.A w A.A w A.Aw A.Aw A@.A&w A@.A`w A@.A`w AX(.A*w AXJ.Aw A .A0w Ax-.A@5w AO.Aw A.A;w A3.A@w A8.A`Ýw AX.A ɘw A.AΓw A.A@Pw A.AUw AP|.A`[w A=.A@`w A.Af}w Ah.Akxw A.Apw AB.Akw Ah.A`fw Ad.A`_w AP&.AZw A.A@Sw AH.ANw A.AFw A.A@w A .A(?w A.A@.:w A+.A2w AH.A@>+w AH.A@#w A.A@Nw AM.A@w AM.A`w AH.A cw AH.Aw AH.Asv A.Av A.Av A.A v AH.Av AH.Av AH.A@v AM.A@,v AM.A@v A.A;v AH.AĮv A+.Aɩv A.A Qv A0j.Aؚv A .Aav A.A@gv A0H.A`v A.Avv A.Au AI.Au A( .Au A̓.Au A؍.A@u A(O.A`u A.Au Aю.A`u A.Au A@.Av AX׉.Ayv A.At v Ax.A v A{.Aov A.A@iv A _.Av A@.A`dv A ~.A v A8|.A v A{.A^v Ay.A` v Aw.A` v Au.A Z#v At.A Z#v A0r.A Z#v Ap.A Z#v Ao.A Z#v A5m.A Z#v AWk.A Z#v Azi.A Z#v Ag.A` v AH]f.A` v Ahd.A^v AHb.A^v A``.A v A`^.A`dv AЦ].Av A[.A@iv AY.Av AX.Aov AV.At v A`U.A`v AS.Av ArR.Av A3Q.A`u AVO.Au AXN.A u AL.Au A8K.A`u Ap[J.Au AI.A"u A G.Au ApF.Au AF.Au ApD.A`u AȂC.A`Bu AXB.A@Gu ADB.Aеu AA.Aհu Af@.A]u A?.Au AP'?.Au A0>.A@ru A=.Au A k.A t A.A t A .At A.At A.At A.At A.At A.A ~t A.A ~t A.A` |t A.Ayt A.A &wt AH.Att AH.A+rt A`%.Aot A.A`jt A.A6ht A.A`;ct A(.A`t A(.A`[t An.A Vt A/.AQt A@.A`Lt A.AGt As.ABt A@5.A=t A.A8t A跸.A3t AXy.A`v,t Aڶ.A |'t AX.A"t A.A t A@.At A.At A~.A t A@߱.A#t A@.As A.A4s Ap.A9s A].A?q A.A?q A.A.A q Ah.Aq A.A q A.Aq A.A@q A0f.Ap A'.Ap A.A`p AP.Avp A .A |p Ah̙.A p A.Ap AH.Ap A.Ap A`.Ap Ap.A$p A`2.Ap A.A4p A<1.A@po A^/.A@po A( ..A@po AB,.A@po A@d*.Ao A%).Ao A`G'.A uo Axi%.A uo A*$.Ao AL".Azo A !.A`~o A 0.A|o A`.Azo A.A wo A.Auo A8.A po A8.Amo Apy.A@ko A:.Afo A .Aao Ap.A ^o A~.AYo AP@.ATo A.A Oo A .AJo A8 .AEo A .A@o A0 .A@;o Ag .A 6o A0.AR/o A(.AX*o A0.A`]%o AK.Ao A.Ao AH .A so Al.A@y o A.A@,m APw.A@,m AP.A@,m AZ.A@,m A|.Am A.Am A_.A 1m A.A 1m A`C.Am A`e.Am A&.A6m AH.A`m A .A7 Yk A.A Ik ATPY.AM9k A0.A`K'k Aw.Aj Ay.AH*j AY.AG[j A?}.AiSYj Aŝw.A|j ACms.Aaj AIo.Aßj Aal.Aayj APIj.A`cj A(N.A`fj A*EJ.AIj AE.A!j AGA A.Ap%j A .Ҷ<.Aa_j AJ8.A6i AX15.Ai Aj'g42.AM.lmi A/.AIi A;(..Aրi A5-.Ai A`.A@>]i AdT .AVi A.A69Ki A.Av:i AS/.A&i A޷^.AI{ i A=.Ah AXh.A h AA.Aa|0Wh Av.Ach A).Aspah A2(.AEh AM.A'1qh Ae* .AJj[h Axذ.A`Ch A.A g A$Q.A"NYg A~.AQg A@.Aig Ap;.Arg Az.A,,g A>v.Ag Ax 8s.Aʤ`g AtM@q.Ag A`o.A` g AY.ACg A U}R.A*Bg ALJK.Aq .A>1#g ADwz9.A2{g A6^(4.AVnf A`>t1.A7f A`/.Af A( .A@ef A.AZg A@B.A^ A.AM` A*.A#` Ae.A@y^ Ay<.A 8] A-.A\ A0.A\ A-.AF;B[ A*&{1.AP9[ A;W^.A=$-a AT.A8f A3.A g AZ.Av Av.Aw APf.Ay AP.Ay Aɪ.A`mx A.Ax A.A ɽx Ax.Ax A[.Ax A.A`y A.Ay A`.A q!y A".A =y A=.A@y Ap .A\y A.A`y APM.A@Gۂ Ab..A Aot.Aṡ Ax5.Ax AG.A ˸ AQ.Ao Ahe.A. Alq.A3 A&.A@y> A.A`ˆ AXd*.AzW A)5.A`f A[.Ap AH:.A AXR.A A.A`2D Aj.A@ A].A`^ AX.AH A.A Ҍ A.At AX.A` A0ٕ.A` A.A4 AXX.A`~ Ahj.A`Ғ Ap=<.As3 A.ATA Axu|.A( Ak.A & A.A" A(.A@y! APg.A` AHz.Av A.Av A'c.A AS.A`bq A(ۘ.A Q APZ.A 8/ A].A}w AV.A Ad3N.A_) AE]K.A}9 A*&{1.A0> A%hH.A౬. A?.AFA4 A`].A+4 A7.A`}% A EM.A઱" A0.A! A蕲.A A$.A& Ad.A4 At[.ȀF A$].Ai~HB Ad .A+I AGÕ.A7K_%d A.A o A,.A?m A.Am At.AQo A&䐄i.AС2s Ap.A`gs A݉.Aq A0.An A.Am A.Al Ap.A@l A.Am A7.AOn AH.ABrq A軤.A[{r Aω.Ap]t A Y.AZx A8I.Ar Ah.AKl AB".A`~l A>.Aq A.Agq A.A?q A $.A@5o Ae Ap|s.AJf APq.A6f Ao.A g Apr.Al AJ.Ak A.A`s AH.A`s A0 .A`s A.Ajs A@$.A ~-k A 4?.A@!k A.An A.A=n A.Azn AY,.A`Vn A~.A@yAn A@.AQn A.A`m AH2@.Am Ah/k.A@"m A.Afl A.Atl A.A`~l Ah.A@=l AH2<.A@l Aa.A@l Apr.AR>l Ap.A@l AHܔ.A m A8.Aam Ax.Am AH.A n AP*.A n Av.A Zn Ap.Am AHX.ALm A:.A@n A .Ac A`.A`vb A}`.Ab A.A 1e A`.A` e A(.A`B?j Aп.A@j A{ .Am A .Aj A`Ò.A-i A8.AĤg A.A.f A^.A Ed A.A_rb A`i.Aa A.Ad A}.Ac` AMQ.A_ A }.A`R] A`.A] AX!.A@C\ AR.AZ A.A@.Z AE.A l%X Af.A2V Ap/.A@T Au.A /hS A^.AQ A .AMQ Aem.AabP Ap}.As`O Ap .AgYN ApA.AM Aw/AL A6/AK A /AI ApZ/AoH A/A@F A8/A@E Aӟ/A}aE A/A@E A/A|E AIy/AF A^/A@NE A8U/ApE Aef/AD A/AD A0ș/A E AΜ/A 8=E A /A@ED A;/Ay~C A`/A@ AHl/AA A/AA A/AB@ A/Aǔ@ A/A u@ A /A /@ A/AfA A5/AWeA A/AA AP /A@yUA AТ/A5A ATn/A`A A}/A^B Ax/AْB A/A` B A~/A`B A੬/AC A/A  /A ~I A0]f /A DJ A  /AFNJ A /A \J Ax. /A \J A0X /A`J A /A`J A( /A`J A!/A Ap\/A-> A*/A/J> A/A`U: A /A`K7 A/A; A?/A cD; Ah/A@9 A/A Ɂ6 A/A5 A@=/AB6 A/A S8 Ap9/A@Ps8 A/A 7 A@ /A 8v7 A/A@6 A@ /A?S6 AX/A35 AU/A e4 Al/A S4 AXj/Aj4 A0;3/A-;. Aȥ7/A@i8. A0%/A_- A ͑/A |r- AH./A S4 AЕ/A4 AL/Aa^4 A8E}/A: A/A = A&+/A@V? A</Aj? A&/A`0A AHY/A@ A#/A9bZ A(-%/Aco ADz&/AOx A0Lf!/A6k AP!/Aݭ AG]!/A A /A`D A/AL A _/A` Al/A AL/Ax Ag./A`m4 A8 /AF A/Aj A`P/AH A/A? A-h/A} A/-/A| A@/A  AS/A2 Ar/AX A:/A6 Ay/A / A/A@iJ Aho/A As /A`KX A#/A @ Am%/Ae A8'/A8 AL./A@,| A2/A^ AH3/A l A)M6/Aי A!6/AȆ A[ϩ6/Aw~3 A2| 7/AF,Ș A^G7/AfhLK Ap.7/A7Ԟ AwU7/Aze# AK67/Afy Ad8/A"rT A26L8/A_} AA/v8/Axg+ A,8/AO# O AE*8/AZ0h A`L8/AtYy A[8/As4 A'9/A胏 AEM09/AOލ A$WJ9/A+΃3 Aj_9/AB An9/A҈ Acr9/A7L AEY9v9/Af3 AjH|9/A@VY A_΁9/A2 A9/Ay AM٭w9/A< Ax9/Aā APSd9/A(#T̓ AH9/A` Ao9/AU A~9/A𘧛 A39/A^Wݪ AbV9/Aݼ A]9/A'7ш At |9/AAj A9/A A*69/A*6 A4ū9/A? Ai̠9/AƂ AMN9/A7 AQ9/A| A+u9/A.>AА AWɟ9/AI) Ap9/A? A~߮9/Ab~ AV9/Am Aܰ9/A5 ͑ A Xn9/Aݤ{* A|n`9/Aw A<Ǭ9/AQ A9/A ' A)1H9/A6H/ A{V^9/A?& A99/A@y? AP 9/A9 A8/A A08/AnG Av8/A A8/Aھ A8/A@/ A88/AI A@8/Af Axe8/A֘ Axzc8/A ATQ8/A A0668/A9 A)8/A@p AЄ'8/A?ޗ Ar8/Aa A87/AA A 7/A A8/A^ AH8/Az4 A849/A`V A0%_9/A@iT Ap,9/A徜 A(:/Aڜ AH9:/A@## A Q:/A A8Ia:/Aӛ Ac:/A@ AX~:/A끠 A:/A@ AD8;/A@J A ;/AЙ Ah;/A` AT;/AR~ AN!;/AT\y A0:/A sy Ar:/A` _y A@N::/A'qy A:/AKy A@9/A z A`X9/A{ A9/A`| A|9/A@#~ A9/A@X Az9/A+ AP9/A@io A跹9/A`Á A9/A A9/AH A0 9/Aag AS9/A3~ AXے9/Ah| Aྜ9/AЈz A9/ATx A8b9/A`w A=9/Av A9/At AM9/Ar A%":/Aq ACd:/A o A3x:/A }o Ax؎:/A`o A`:/A@Wo A:/A o AH::/A`o AH:/ADIp AP:/A@p A:/A`Xq A:/A q A`v:/Ar APg:/A]5s AbN:/A0s AH:/A t A'::/Aଇt AXR5:/A`mt AxW9:/AOWu A@D:/A`u A8EL:/AKv At]:/AXv Al:/A*w A 0:/A@w APo:/Aw A`:/A`Tw A:/Aw Ah;/A@Ew A>;/AXw AZ;/A{w A|s;/A $w AQw;/A v Az;/A Sv A8Iv;/Atu Ar{;/A Qt AԒ;/A@,t A;/Avt AB;/A@t AB;/AjBt AB;/A`s A;/A*2s A;/A`r A8;/A!r A0پ;/Adq A؛;/A`9#q AE;/Ap Ap,;/A1p Ap,;/AFo A;/Ao Aa;/A׊o Aќ;/A &Vo A:;/Ao AX;/ASn Az;/A m AHe;/Am AuU;/A/n A:;/A in A0!;/A=n AX ;/At!o A:/A 1@o A:/A`̠o A@:/Ao A@:/A0o A:/AZo A:/An A:/A9n A06:/AÈm A^;/A@ l A;/Ak A=;/AXj A(L/A`Te AX>/A=f ARv>/Af A|C>/A`m;g AXh5>/A h AL>/A9h Apy>/A`9Gh Am>/Ag AP|>/Ag A8?/A@g AC?/Ag A3?/A@g Ax?/A |Ag A@?/A`DEg A @/Axg AH@@/Aർg AI@/A g App@/AZg A%A/A0uf AhA/Ae AA/A>e AHA/A`8d ApA/A=2c AA/Ab A4A/A)a A֒A/A |` A8QA/A` AXyA/Agfa A`@/A]a AH?@/Ab AR@/A 2c A @/Ac A?/Ac AH:?/A?c A?/Ab A8?/A-b A-:?/A>(a A`t:_@/AӴ` AD@/AOj]_ A;8A/A@_ A ߿A/A@N^ A!:B/A@Eq^ AB/A^ AsB/AX^ A8B/A`_ AC/A}f_ A`%C/Af_ AxD/A _ A$D/A@ ^ A`D/Ay^ AxE/A@a^ A>E/Ao^ A PH/Aw_ A8H/AT_ ApsI/A`2` AH2 J/A`;_ Ax˰J/A`2` AK/Aw_ Ah@&K/A`@_ AhQK/A^ AK/A ^ A2zL/A ^ AM/A@E_ Ax'M/A`_ A@sM/A} _ AXtM/A`2P_ AOM/AQ_ A_M/A@,q` A`M/A`K` A0ٲM/Ab AXM/Ac AxM/A &Le A`%DN/AFe AN/AFe A@N/A൚e AH)N/A`[e A8O/ANe A(O/A`` AHK[O/A<` AQO/A-6e AHO/A`)d A@~O/A`v`d AO/A`2c AO/Awc AO/Ab A O/A`Rb AP/A"b A)P/A@,a AP>P/Aa A@RP/Ad b A0&fP/A`Rb A0&fP/Aib A0&fP/Ab AyP/A|c A6P/Atc AѮP/Ac A0P/AHb APȰP/Aa A(>P/A@G7a AP/A@a ANQ/A@>Jb AUQ/Ac Ah@Q/A}c A t R/A |c AGjR/A d A8'R/Akf Ah9S/Ahh A\OS/Adi A?S/A$l A.4S/A3o A@R/A #p A =R/A@p A:R/A5q A(yQ/Aq AQ/A (r A@`Q/A !t A8UQ/A`du AqQ/A@w AQ/A y A {Q/A { A$fQ/A ~} AXKQ/A A/Q/A9 AP/A AӂP/A A P/A'z AO/A[) A _nO/Aބ Ah;0O/Ac A0O/A`v4 AH O/A} AHN/A`Ti A@uO/Asu A(\O/AN AP/A4 A@P/A` A,O/A`x AO/AY APMN/A1 AN/A]Z A୍N/A AN/AtΈ AqN/A AjN/AU A eN/A`m A0LbN/A@W؉ AXdQN/AF A&N/A- A3N/A`p A`N/A2 A(N/Az AM/A`׌ AM/A@C[ AM/A׍ A0M/A`;, A8M/Ay ALM/A` A8EN/A3 AP^N/A B AM/A` AM/A@9 A@rN/A AgN/AR. AHrM/A \ A0M/A AH.M/A` A0GM/Aas A M/AN AM/A9 AxM/A@ A8sM/A A{M/A`K A8sM/A`Ü A(KM/A6֔ A0N/A A8/N/A3 AM/Aq ARRM/A AĴM/A9ʖ A3M/A q AtM/A^{ AP N/AK A8N/A#T AN/A~ AxNN/A앙 AQN/As Ax5`N/A AV\N/A`;ș A N/Aw AJ"N/Ak A؛6N/AB A/CN/A@ A8'DN/A@ AJN/A Z AgZN/AX A\yN/A - AN/AR AN/A (- AN/Ax A@dN/A- APN/AgZ AxO/AR AO/A > A =O/A A[O/A& AyO/A?o A(O/AN[ A8VO/A AxO/A AO/A@Ɵ AyO/AU A^O/A@Ū A3O/A@ AF.O/A@5 A4O/A۬ A~EO/A@v ARhO/AR4 A(}O/AΜ A8O/A R AMO/AU APO/A@% A2O/A A(O/A` h AQO/A@ A(O/A, AO/A AO/Aa A'O/Aq}` A{fO/A(A AO/Ajb A0bO/AuX A v5O/A]} A6O/Aof\ AN/Aį+& A/9;N/AC A^N/A`뎽 Aނh"N/A_, A.;M/Aq APM/A6 AنM/A?b AW_M/A8 Ap9M/Ar AiM/A'%k AL/Am Ah`L/A; AZY(XL/Av[* A7L/AM' A RK/A& AT$tvK/A v A1>K/A AJ/A@ AhoJ/A`g A!J/A`d AXI/A lS A`_I/A@C A{H/AI A0eH/A\ A{NH/As AI1H/A A -H/Ap{g A G/A AG/Ar AQG/A4 AlíG/A: A.G/AL A:OG/Al1 AG/A雿ٿ A0hF/Agt A F/Ao?y< AVF/A- AE/A@& A8D/A AbAD/A AXC/Aa? AB/A A@TB/A A`3B/AqL AVB/A AB/A6 A؎C/A7 A]C/A`) A oD/A`my AE/A- AXF/A@5j AdG/AT A ҡG/A c Ap,yG/Az3 A0yG/A` AJG/A@߲ A/H/A A _H/A@. AG/A A[G/A ˿ AG/AO A*H/A AxH/A A2H/A; A(K/A@m Ap[K/A A K/A A麢v/A}9 A̱5w/A_) AvQw/A yi/AR Ati/A AWi/A冷 AqSi/A`T AGi/Aݹ AhCi/AƷ Ai/A@ A8'=i/A+ AL A(b/Aoc Ac/A A0Le/A A0ng/A A&h/A# A@h/A` Af/A+` A&f/A AXVAe/AAP A3e/A A(b/Ag A b/A AXWb/A AP?c/A uO A@b/Az Ax-`/A A8`/A AXV`/A Z0 Ap `/A` A(`/A^ A n`/A6 A"V`/A@q AR`/A A``/A` AP`/A`Q Aכ`/AD AKx`/A`z AV`/A; A:`/AO A`/Afu AP_/Af Ah_/A' A̳_/A A_/A APo_/Ax A8F_/A$- A`(_/A@.( Ap= _/A8# AJ^/A AXWY]/A@ AH;]/A AP^/A් Al{]/A A"\/A r Ar\/A< AM]/AX Ae]/A@`M A8 _]/A " AK]/A A076]/A- Ah\/AP Az\/A Ao\/AE A\/A Ah\/A`J Axg\/A AN\/A> AG\/A` AL\/An Ay^\/A A`W\/A A<\/A`K A`"\/A@F Ao[/A Aج|[/A@ AW[/A&v AP^I[/A;p A[/AD^ A`ǼZ/AZn AlZ/Ads AjZ/Ad_ AY/A A AY/A AwY/A@[ A-Y/A AxY/A` Ap,X/A A?X/A@W. Ax(X/A AyX/AE AGQX/Am A#X/A AW/A' AxW/AF AW/A A8cW/A AnW/A AȩhW/A Ap{W/A AP}W/A4t AYlW/A AONW/A9 A=W/A+ AV/A Sk A tV/A@q AxV/A` AYSV/A@J A V/A@ A`U/A` A@U/A` AxU/A@G\ AyuU/A A BU/Aં AT/A 7 AxFT/A A2T/A A#T/At AS/A, A( lS/A A@S/A3 A`R/A ApjR/A _ A R/A A0Q/A AbQ/Ad7 A( Q/A l AȃP/Am A`6~P/A AlP/Ac A[P/AN A^P/A AVlP/AƳ AhP/A` AhP/A`m A5P/AZ A0̏P/A A,P/Av AP/A> A =O/A0 ABP/A A.P/AF, A(OP/AE A[P/AKw AgP/Ax AplP/A AeP/AX APP/At A?P/Aa A "P/Aa} AaO/A " AO/A " AxO/Av, A_O/A; AQO/A@P AN/A M AxzN/A APGN/A1 AkM/A AHM/A O Aȓ:M/A0 AoL/AOa AaL/Ac A35L/A2 AK/A`; APK/A A#K/A$+ AJ/A A`}J/A$W AKJ/A A0J/A` AI/A`K AI/A A`PI/A@{ ALI/A@n AТ I/A Z. AH/A APkH/A I AH/A` Ap tH/A@,V ALfH/AŃ AXH/AX A RH/AD A0D/Ar Ah9D/Aa' A85D/Aٻ AK3D/A h A/D/Aj A@-D/A ~r A,D/A< A*D/AV A)D/AF6 Aq)D/A@ . A&D/A` A(-D/A ABD/A A[ D/A- ArD/Aa A(C/A AC/Aj A>C/A AC/A A/Aԓ As>/A &ӓ A>/A Г A>/A`f͓ A(>/Aɓ A>/A A^>/A A>/A6 A>/A u A@1>/A A>/A् Ah>/A AI>/A A>/Am APZ>/A@ A>/A3 AȎ>/A} A0~>/A 10 Au>/A; A؟b>/AO AYZ>/A`Z A_R>/A`2k ApO>/At ApAL>/A A`4>/AF A_0>/A A`>/A A >/A֦ A>/AX• A(=/A$ A=/AE AX=/A H A=/A A=/Aǖ A=/AӖ A=/A A@$=/A A W=/Av A@ J=/A@ A>=/A APk=/A g A@ A':/A` AO:/Aֆ A0&\:/A  Ah9/A`߆ AX9/A A@Ι9/Afʆ A(9/A A^9/A+ A[9/A@NІ AX9/A ( A8s9/A9 AB :/A6 A:/AX AC:/A_х A@F:/Az A=:/Az AAm:/A@Ձ Aȃ:/A AS:/A AN:/A AUL:/A?ƅ Aa:/A@, A0:/A` A,;/Ad Ap_;/A`C AA;/A' Ax;/A@% AP;/A A;/A ߂ AF;/A Ap;/A} A(;/Aρ A@;/A)؀ AN;/Az A/A@]n A@>/ABn A&>/A ~1n A1>/A]'n A8V<>/A/$n AНt>/A@+n AЏ>/A3n A`e>/AM?n AP8>/A@Yn AP>/A`mn AHa?/Arn A ?/Asn A ?/Asn AP^ ?/A`tn A?/A`rn A?/AHln AQ"?/ABbn A)?/ATn A/?/ADn A4?/A1n AH.8?/A cn AC?/A6m AXEO?/A/m Ae?/Al Apn?/AΎl As?/A al A9t?/A Pl Aw?/A@l Aq{?/Ak A,|?/A%k AX?/A@[i AX?/AUi A Ι?/A h A?/A h A?/A@[i A0Ȫ?/A Xi ApN?/A i AX?/A i A0U?/A ~ k AR?/A@k AА @/Ak AP@/Am A9@/A)1Gm AHk@/Al6m A&@/A!Vm A@/A~]m AhbA/A sm A +&A/A@m A)A/A Il A0FA/AOl AXEA/Adm A`eA/ACl A%A/A=l A%A/Ak AA/A k Aȇ1B/AIk A@UB/A_ A`GZB/A`F_ AaB/A_ AjB/A;^ A(rB/A`^ A zB/A^ AB/A`9y^ AB/Ave^ A)B/A@O^ A`2B/A`=^ AB/A0^ AXB/A'^ AX4 C/A^ A0G@C/A`"^ AcC/A3^ AxF}C/A^ A`C/A=^ A83C/A^ APfC/A@^ AC/A <^ A D/AG^ AoBD/AZ^ ApۄD/Anf^ ABD/Ae^ AHD/A\^ AD/A\^ ASD/A`d^ Ah]D/A^ A E/Aʑ^ A07E/A ^ AznE/A ~^ A8VE/A`^ AxuE/A^ AE/AƂ^ AxF/A^ AxF/A-^ A|F/A@^ AVF/AM_ AF/A@'_ AZF/A8_ ApF/A`mM_ A~G/A;e_ A,G/Ab AEwK/Ab A{K/Ab A@K/A@gb AK/Atb A0%L/A b AP@L/Ab A@uL/A 1b A(L/A`"b AL/A ub A(L/A@b AXL/A`mb A(L/Ab AXL/Ab A؎M/A b ApM/A-c Ah'M/A@g4c A/M/A>c A8s6M/A Lc A8V9M/A Uc A`;M/A/^c Aj>M/Aic A@M/A9wc AIIM/Ac AzKM/A=c AMM/A`c AxRM/Ac AXWM/Ac A0jZM/Ac AX\M/A c APY_M/A`c AaM/AMc AbM/Ac AcM/Ac AHdM/A`c AeM/A c AXfM/A sc AgM/A`c AiM/Ac A0LkM/A c A@lM/A@Cc ArM/Ac AXuM/A`;c AwM/A \c AyM/A@c AhzM/Ac A{M/A`~c A|M/A|c Ax}M/A`K{c AGM/Aksc AM/Anc A(zN/A\c A!N/A Mc A@\N/Aj$c AsN/Ac AHN/AR c AAN/Ab AN/A`bb AmN/A b A(O/Ab AO/A޵b A N/Av)T A@N/A  T A{NN/A sS AXWRN/ACS A._N/A+R AeN/A R AgN/A,Q A`hN/AQ AAhN/A@Q AygN/AP AcN/AO AcN/AO AcN/AॎO ApdN/A+sO A fN/AJO AiN/AgO AwjN/A`O AmN/A N A@qN/AN A\rN/AN A`sN/AN ApZuN/AN AwN/AN A`xN/AHN A(zN/A N AN/AvN AN/A cN ANN/A`ܻN A`%N/A SN AN/A N A'N/AZN AN/AxN A]N/A`KN AtN/A ɱN A0N/A#N AHN/AN AN/AzN A N/AFdN AEN/ALN AN/Am4N A N/A@N A8N/A#N AN/A͒M AN/A-M AHN/A?hL Ax-N/A K A AH/A}> A8H/A@ B A(M/AC AM/AXxC AdN/A@~C A0EN/AC APDN/A+C AfN/AC AȥsN/ADDB AM/AA AM/A xA AM/A`%A ApM/A A AM/AA ApiL/AE@ ApeL/A @ A_K/AK@ AK/A? AK/A`v? AZK/A |@ AYK/A? AZK/A0> Ax(K/A> A)K/A= AScK/A r= A`fK/AB%= AJ/A~< AJ/A+< AHlK/A< A!pK/Aվ< AK/A^= AK/A= A L/A^> AhL/A> AM/A |@ AXM/A@ AM/A@ AȶM/A`@ AcM/A@ AN/A+A AXFN/A@B A`vN/A@#B A=N/A`KB AN/A B APN/A`] B AxN/AB APMN/A %B AN/A0%B A\N/A@"B AN/A (B AN/AB AxzN/AB AXN/A B AxN/AA AgN/AA AX$N/A@iA AN/A@ؐA A)N/AlA A`2N/AFFA A"N/A` A A0N/A> A`N/A A> AXN/Az> AN/A;> AkN/Az> AhN/A,> APN/A Q> AN/A > AON/A@> AN/A@r> A؍N/A@i> AN/A > AʾN/A> AN/A@߅> AHiN/A q8> AՆN/A > ApN/A@= AzN/A= AȥxN/A= APvN/A?= AhuN/A4= APtN/A= AzrN/AEu= A/pN/A@%?: A6(N/A J: Ap&N/A 89 AoN/A`m9 AoN/Av9 ApN/AH9 ArN/A൰9 A0sN/A@9 AHvN/A^9 AxN/A9 A0N/A9 AN/Amp9 AuN/A c9 A0ݪN/AN9 AoN/A K9 A(1N/A`)69 A2N/A`m,9 AO/A#9 A#O/A l8 AO"O/A 18 A'(O/A 8 A2-O/A`8 A/O/AT8 A01O/A஗8 A A3O/A8 A4O/ADy8 A6O/AV8 A 06O/A/8 A4O/A8 A72O/A u7 A$.O/A`7 A-(O/A0`7 A'O/A"W7 A%O/A@yN7 A#O/A`F7 AX!O/A=7 AO/A7 A O/A`27 Ah O/A6 A}O/A=6 A`O/A=6 AO/A`6 A=O/Aଧ6 AO/Am6 AُN/A@6 AFN/A5 A؛N/A*5 APN/A@6 ApO/AN6 A0O/AE5 AXO/A 5 Ag O/A5 A O/A I5 Ap8O/A 5 AHO/A`m4 Axh#O/A@%4 AV;O/A@4 AGQO/A t3 A [O/AW>3 A`O/A H$3 AgO/A 3 AOmO/A 2 AsO/A 2 AO/A2 AIO/A2 AO/A@2 ApO/A`22 A O/A&2 AwO/A2 A'P/Ag2 A$P/AX2 AP/A=D2 A`C"P/A*2 A)P/A 2 A%0P/A@1 A0P/A1 A6P/A1 Ax>;P/A+1 Axz>P/Az1 ABP/A&1 ApEP/A`0 AFP/A ~g0 AXDP/A9/ A`

Z?P/A5 A#(P/A@  AP/Av} Ao! P/AyM AO/AL  AYO/A! Agz8O/A8 A lO/AA21 AQ膿O/AJ= Aĭ4O/AI A>+O/Av A0O/A@ AO/A`b AGP/A A?P/A AhO/A;} AN/AMK AN/A AbN/A & AhM/At AoM/A@ A`RN/A@N A(N/A AN/A# A88%N/A l AtM/A AM/At AeM/A AeM/A@ ADQM/A`K AWBM/A ASK/A/ A p[K/A[ AȥJK/AH A9K/A A`LK/A A>PK/An Ax>M/A A9M/A; AfOM/A Au\M/A` AiM/A४ A?mM/A A@S^M/A ApbM/A0 APM/AF AM/A/ A[*N/AA Ar+N/A@ A/N/A AXlN/A I AvN/A/ AWN/A`) AΛN/Az AN/A A(-N/A`[ A0N/A| A#O/AOC A`zO/A ApO/A@ AXjP/AT AP/A AP/A` AP/A AhsP/AmV AmQ/A A Q/A`; AQ/AAb A%Q/A@ A0aQ/A S AaQ/A V A7z^Q/A:p AkXQ/A;п A PQ/A$MC{ APDQ/A= A@$P/A // A89P/A]r A|P/A@z AXP/A=4 AP/Al AP/A AP/A A+(Q/AtPS A3*HQ/A-J AyM Q/A AFEcQ/A)M A~?Q/A ؀P AXKSQ/A]p= AnM Q/Ao= A$Q/AkW A*Q/A@ A wQ/A A hQ/A7cp Ad`KQ/At^gc A{Q/A AtjQ/AV AႬQ/A8L A9Q/AY AQ/AQZ:b AuR/A", AR/A@fj A$۞Q/A A^Q/AG| A'$Q/A A@Q/A | A #)Q/A j AkDQ/A | ALQ/A@pK AQQ/A  AؒzQ/A] AAQ/A AQQ/A| AQ/Ad9 AH/R/A`2P ALR/A`v ApiR/AV A؞R/AK% AoR/AL AR/A@k A8S/A A`S/A?/ A.Ab AP7.AO A.A ApW.AC A.A4 A.AB AX.A# AG.AdU A؟).A? A.A S_ Ac.A j A.Ab AHm.Av At.A Ahՙ.A A` .A` + Ab.Ay Ac.A<< AH .A% A`.Aw Ax.Azh A0.A9r A=.A6 Ax˚.A 1 A07 .A AE.A A¸.A?d AX9.Aྭ A-.A`÷ A.AT Aj.A A^;.A@A A0*.A& A.A: AH!;.A@, Ap.Ac A/b.A , AHrJ.AM A5.AG AU".A  As.A A.A A.A A.A Ahѫ.A A>.A`m{ A.A}u A.A{ A- .A AX.A  A.A( A.A  AU.A& A.A OE A M.A A'.A}u AA.A AXJ.A Axu.A Ap.A`̱ Ae.A@ A=.AXe AP.Aj Ae.A͍ A0.:.A A c.Aoq A.A AM.A] Ap.Aɽ A.A^ A%.A@ Aߖ.A ջ A.A`" Ada.A Ax.AFJ Axm.A@ A(-.A@ A/.A㊺ A.A A~%.A`D. A.Ap A%.A׿ A.Ay A8D.A`dդ Au.A/: A..A@. Az.A`Dk A興.A Ax.A`A Agg.A鼕 AxQ.A A.A 8 A.A) A(m.A89 A f.A* A.A A*/Ad A /A@ϋ A(Kp/Azs A8/AM A/A AR/Abʈ~ AՆ/AT AV`S/AƉ^ A+B1V/AOLJ A1/Ae A'L//A( A'/Aof A/Acɉ A /A- Ak/A R Agze/Ay| AXGh$/A[Ж Ag֪/Aࣟ A`!1/Ah A\J/A ~ A߸..=/AS AC/Ah A8G/AW̉ A(/A` A/Aδxv AJvHX/AMP\K A>X/Af/ A/A A`I/A@rC AFr/A` Aa/A?׉ A/A2 A`/A`S AP@/AmNJ A3v/A`v A܎/A@U Ap/A" A:/A6 A/AsR A(/A A/Av Ad}/A` AX/Aތ A/A` AZ/Ao A@/AА At/A`ߐ A>%/AUT A8/A A,/A  A{/A A/Ax A /Au A_ /Aa A /AU AmE /ALj Ae /A1Z A=E /A?7 A] /Acp A /A` AXp /A@ AYp /Ax( APy /A' A(ǐ /Aǿ AD /A8Q9 A /AĬ A /A@ߢ A /A@Ю AX /A@,X A`B /Ad Aք /A@ A8x /A@` A # /AM A  /A@iɰ A~ /AB AG~ /A`? A} /A`2o AP| /Aડ Av /A` Ai /Ad A[^ /A@ߗ AP7M /At AH; /A AH.5 /A<" Ah30 /A} AH\ /A0 A  /A 8 Ah /A` ù A /A A /A S Af/A A/Aջ A8I/A  A`/AVV A/A@.: A]/A` Aؼ4/A R A@u /A@PO A8t/AӤ A(/A A/Av APi/A' A,/A' A'/A A/AQV A /A _ AB/A- A୕/AG A\[/A` A/A AP/A B AX[/A AXև/A@P Ap/Az AX/AT A?/A A8/A Ap/AB A/A} A/A AU/A@P A /A Aq/A`b& A(i/At3 Ab/A B A=/Aʭ A8/A( A/A AJO/A͒ APF/A ۜ AX./A A/A AU/A Au/A`d AE/A A A/A@ A ^/A A/A ~0 Ac/A AA#/AMs Ah"/A A/AC A8ɧ/A \ Aho/At+ A |/A`n A E/A4 A8-/A = A@ /A`v A@B/AaE A /A A0./A A'`/A$ A02/A`l A'/AO Az/A' A(/Aa A/A@yq AP/A" Aڵ/ATg AXu/A6 A/A Ax(/AG AHY/A AI/A# A0</A@a Axv7/A A'/A l A/AF A/AV< AD/ABZ A@u/Ar Ax/AH A1/A AJ/A A/Ao: Ax/A-^ Ax/A@W A`/A@ A|/A Am/A`v3 A`a/A L A(?/A A0)/A@E A/A9 A0/A` A/A Aj/AC A/A ` A/Aa AHL/A A/AV Ax/A`Ù A2q/A A2=/A AhQ /A@W A/AA Ax/A A[/A` A/Au AxƝ/A Aзh/AW Ax//A A/A A``/A A2/AW A[/AF AK./A@ AG/A` A[/Av A(-/A@ Atj/A c ApV/AC AXE/A` A-/A AXh/AR Ah/Ac A/A` AP/A Aj/Av3 AA/A@C ApA/Ax Aw/A AV/AU A8/AE Ae/A 8 A/AF A@=y/AM A36/A  A,/A H AU/A@ AKq/A A3/A N A /A$ A0Ǽ /A` AH /A@ At /A# A Y /A6 A7 /AhF A7 /A@F A /A$Q A /A 8[ AB /A &c A[ /Apl A6 /A`| AL /A A = /A Aݙ /A A@ux /AV ANd /A@! AI7 /AR/ A" /A 8A A{ /A Oe A> /A{ AV /A ɋ Aq /A`2 AجG /A AO> /A`m A3 /A` A` /A@ A@ /A Az /A A8 /A= A` /A@ Ap /A@& AhoV /As= A7% /Au ApA/A@` A/Aa AN/A Ac/A Ah/A( APN/Avy A`/A A/A A/A^1 A/Ai AA/A@, AhQ{/AM A()N/Ay A=/A` A/A-x A5/A A/A A/A' APڨ/A q A0/A`9 A@S/A@? A6;/AN A1/AV Ah//A)Y A)/Aa A"/A Or A/A A/A AhQ/A A(\/A# A/A A(/A A虴/A?6 AS/Ah| A/A  Az/Aʵ Ai/A`$ AZ/A`{ AJ/A A=/A`2 A3/A) A//A5 A0+/A@ AL#/A lP A|/AoU A/AX A(/A`"Y Ak/A`"Y A/A+W A /A R A[ /AK A/A> A/A. A/A@ A`/A O A/A A/AQ A`/A@ A/A A(/Aw A_/A] A/AC Ap/A A@` /A@,l A/ApL A[/AZ Az'/Ai A;-/A Q1 AK1/A AxW1/AD AP^1/Az AP^1/A` AZ1/A` AR1/A$ A`C1/A`[ A1/A A0/AO Aȃ0/A AH0/Af AX//A AXt./Av Ah-/Ap A+/A4 Ap*/A@ AHa*/A A8'$/A`v A؎/A< A(/A`; A@/A , A/A5c At/A A/A A@/A^ A{/A Ak/A A)[/AH AN/A`K A 4/A`m: A /AI AH/At AZ/A@#B A/A^k A/A Ap/A@j A_/A( AH/A@ A3!/A* A/Ad A(\/AT A/A_A AЄ}/A  AL3/A A/Ao A`/AO A8Ѡ/AJ A`/A` A@Sn/A`; AGM/A@` A/A A/A AaU/A`[< A0{/A$A Ax.A`v A@W.A A0{l.A Z ApZ.A-  AXW1.A@ AxK.A` A\ .A A-.A A .A Aa.Aڏ A.A Ap<.A Aз.A, A{.AB Aav.AK A[.A` AfA.AE A.A൥ AX.A6 A.AX0 A.A`e A.A AȤy.As A`.A AB.A/! AТ".A@ A.Aq A8Q.Av A.A AxKn.AT Ax).AV A 6.A 1 A`.A 8 AH.A AU+.AV Ab@.A Aп@.AL Ar.Av AHj.A@y AHm.Av Ag$.Ax>c5 Aߑ.A{֊< A.A@V5 Ag$.Ax>c5 Ar\/AH[ A/AHA s OW_d/Aȓ A̱/AO&u A麢/A4V Aلΰ/AP99 Ak4/A)NS1 AOl/AnBx A)/A@y Al/A] Ax)/Aƞ A/A Ah;߭/A ABЭ/A & A /A` A/A A`/A Au/A Aq/Ay ABi/A A@]/A AXU/A Ax?/A") A(z7/A JG A=!/A A/A_ A/A6 AX,/A`] A8/ABQ A0Ȭ/A AȤ/AI AE/A3q Aw/Ae AM/AE A̫/A& A 0ʫ/A A0/AV] A8/Awn AȻ/A  Apʫ/A A/A`]r A[/AQ A/A A(/A AN/A@. A(˃/AT A_͟/A)/ԯh A*/Ap ' AЮ/A[c AJac/APHa!z A[R/A= A/A? AY]2/ADt8m A< /A1Q< ACk/A3 AЉ/A$@f Ac&/A`] A>/AIgm AR/A" A]hc/Aʷ Ah.q/A" A/A!˥ A@ŭ/A=\. X Ah/A Aѯ/A Zf A"/Aī A/AI Aȱ/A`D AH/A A8n/A Aֆ/A@ AȤ/At Aȓ/A` A焰/As AxKy/A Aa/A Axdv/Af A(/A3 Ap·/AFZ A/A  AU/AQ Ad/A@d Al@/A` AP[/A V Ax /AA A/A A A`ز/A AԴ/AQ AA/A A0f/A&N A`{/A@ Az/A3 A[f/A`& A(c/A5 AN/A AJ/Aǿ A6/A0 AxX3/A0C A\/A A0/A A A/A8 A/A@E A8'/A A/A` Aױ/A`A A@ձ/AL A/Aҿ A/A Aȃ/AC A{/A lN A@/A@` A@x/AR A(f/A" Ah?@Ka/Aք=: A/ASF6 A ;yɳ/A+FF Aד/A2Bj Av\./A%V A-ʝ^/AA Ax$/AuK Aڸ/A\lֻ AG/AqSc< A/AZ̼ A1Pj$/Aӥ( A//Aoz Aol'H/A| ARb/A@` A*D/A++ Ax/A+~K AxX/A@`[ Ax*/A) A,/AR AH/A-J A/AU A /A#i A/A/ A/A<6 A/A0 Aȃ/Au Ap2/A A(Ѷ/Aݿ A@/Aʂ Aǒ/A A/A 7 AH/Am A@Ӹ/A: AXJ/A J A8/A A/A A/AT A9/A`: Axz|/A? Aq/A  A/A A`(/A A`/A ~ A ;/A`;1 A[/A@ A(b/A ApM/A@9 A /At A|ȶ/A ABsg/AM AD/Ad<+?. A̕ Խ/ArG A0/A$S A;W/A vf Ah@K_/A! AP1/A` AZ/Aݛ A; /A Auߺ/A A AP/Aa A]/AU A(/A AQ/Ah( A /AМ Au/A' A=/A A /A# AӸ/Adz AhD/Aa A@ƶ/A AA/A`; Av/ALy A喝/A 8 A/A@% Ah/AF A-/A A"/A? AH/A AB/A A\/AN A_/A l Ap&/A$ AP/AN AQ/A`m: As/A Aշ /A@e A 8༱/A!F\U- AFn5r/AZY AEh*/AKmM AY_+/AC# Aأ/AlI AKAd/A77} A[W)/A A/A A/A V A욮/A`B A3-)/A(b AXt/AT A|/A, A(/AV A/AH A /Aah A $/A ! AB/A@! AU/A.# A0nQ/A JG$ AW/A}1 Ax/A 1B2 A0 ޫ/A`B\3 A֫/A@3 AHӫ/A?4 A>/A4 A/A 65 AF/A5 A`k/A@N5 A[/A.6 AP0/A@W6 AP*/A`5 AHӫ/A5 AHn/A`5 A١/A` 6 As/A7 Ais/A8 A_g/A@S9 A@n/Ag: Ah}/A: Ap/A: Ap+/AF: AHӫ/Ao: Ap/A Z'; Ap/A`De; A|/A@N; A`«/A`; AҠ/A͖; AT/A`; APe/As; A@M/Aҷ; ApAC/A6< AP77/Af= ApAC/A`n> AxU/AO? AW/A,6? Ai/At? A/A?? A@ǫ/A$? Aͫ/A`)? Ax˫/AQ@ AX/A`oA@ A/AA@ AU/AyO@ A/Ax@ Ad/A@ A0J/A+A A#=/AB A(>/A`fB A0J/AfC A_/AC A8/A9C A`v/A`[C A/A;D AHг/AVD A堫/A AD A/A AD A|/AD Ak/AD A8U/A`]xE A-I/AyF AB6/AF A+/A G A+/A`T!H A+/A@>fH A&/AyI A`C /A J A@/A`#K A8Ъ/AK Aê/AݹL AXͪ/A'M AXͪ/AzM AD/A M A/ATM A g/A` ;N AHB/A`K=N A <(/AN AQ/AiO Ax/A 88P Aک/A P A R/A`6Q A\/AyQ A/A R Ap_/A݇R AY/A R AO/A@AS Ar7/A&S A0./AaR A/A R AЦ/A? AC/AR\? AP^/A* ? ApZ2/At,= AP/A= Ahw~/A!֚> Amݛ/ATO]> A&ě/A5+BK? AFI AMۗv/AMQJ AP|e/AIJ A/A L AXڝ/ALL A>/AL AP/A`K Ap/A`DJ Aܜ/A@8K Apk/A@g}K Al/A`̟K Ac/A6K AbV/AK A0/A`[K AP/ARL A/A$L A/A@^M A/A@,M A /A@pAN At/AdrN AS/AJTN AZC/AA.O A/AJk}O A/AO AJ_/Ap CP AY/A·8KP ApIg/A9<P Aϛ/AP A1/AQ Axޜ/A@wS Aٜ/A`ÔS A/AMW Ax(D/A;-X A#%J/AQSX A6sM/AX AckM/Aq6Y A/N4I/AC`Y AO=qA/Ax" Y AXW6/A2Z A'/AbmcyZ AA/A>Z A@/AZ A'V/A 5[ ANF/A9SO[ APߛ/A[ A؛/A~~[ A:(՛/AAXR\ A nԛ/Aܹf\ A@+֛/AG\ A.ܛ/A\ Ar/Ahzb1:] A\/AXNv] A2#/AruU] A4/ /A(] AȤ/A ] A~ҍ>/At[^ A#^/A[^ AΈ|/AOld A<{4/AlMFe Ax/A@Gf An}u/Atrf A6i/A2Qf A b/AÊDg A8x_/A`g A ?[/A,6@"g AEZ/Ah A"F[/AŽQh Aī^/Ah A>d/Aڑh Apk/A8Õ/h A,t/A򅸃i AN;/A#&i A[c*~/A<:>i Ap/Agi At7/A@&i AH^k/Ahi AL/Aj AEA͟/Atj A5/A j A2/AOj A"f/A\k A ]5/A 6!k A/kϠ/Azk A/A k AT>/A2j A^{-x/ABj AU)/A/j AN/AWj A/A Ui ApM/A6ei AN/A@E,i Ad/A@Ei A/A Sh A+/Ah A,/Ah A/A@h A=/A ~Bi A/A Ri Aȶ/Aj A /A` k A./AGLŸj ARD/Am j AFԣ/A'Li AF/Aebi A /A]i A8/AA;i A&{S+/A2gi A3À8/ApHIi AIaC/A0i A*L/Aj AʨR/Asaj AxC0U/A]Gj AjU/AXj A^/A0k AhNe/A|Xl Aei/A; m A\&xi/A4m A :g/Ax[m AHbd/Ay8b:m As_/AFRm AY/Am AHuR/Aױm AEJ/AXŚXm Af@/A3;n A(%?7/Ae2 n A;M-/AHpn A`a#/A@ n A]h/A dDAn A{5/A0Een A,c/Au0zn A!$7/AƟv~n AWP{/AO.rn ACL/A}Vn AF/A{f*n Ax:K/AWm A8V3/An A/A`n A0 /A Po AH!/Ao A!/A@%p A/Ap AX%/Ap A(/Ap A",/AOq AP1/A q A/A@>wr AP /A (r Ax/A`vr A/As A8Ν/As A/Ar Ax/A ur A(y/Ar Ap/A@s A/A`s Ax/AKs Aꦝ/A@ t A0/A5t A/A5t A苝/As A{/As A k/A@s AMd/A@ t A^/A | AF/A z| A4D/A 8| A0/A0} AЕ}/Ad} A@?/A`| A7/AH} A/A~} A0/A@} AX/AV]~ Aԛ/A@,R A ֛/A@5 A)/A` A-/A@ R AO/A` A;/A A/A@5; Ax /Ao Aq/As- Aq/A| AL/Af AXW/A A@כ/A 8@ Ah^̛/Ao A /A A@/A AH헛/A`< Aa}/A`k A\/AVك A @/A> Ah/A` A/A A/AXՅ A/Aj? A@/AO A؎/A A؁/A A A /A  Ax/AΆ A/Av A/AzT A/A@W AP/A A[ۚ/A AP7К/A A š/A A/A$| A(/Aه AHn/A`K Ax:/A Ap{/AՈ Ah_/A$ AP c/Ao AdΛ/A`0 AGś/A` AX/AWY A\/A < APov/A[ Ah/Ag. AU/Aj AtC/A Ax--/AE A`/Aെ AH /Aӊ A84/Aq# AÙ/A  AP+/A׎ Ah/A@- AR/A`P A/A` A(/A3c A{/A@n Aq/AQ A(h/A A}/A AXz/AΑ AGy/A AP&t/A  A-q/A ( Ah/Ah3 A^/AG AP/A_ A D/A|ْ A8 Ah3/A AW./A" AJ*/A5 AW./Asf A~:/A v A`a/A@ Apn/A7 A`2/Acq A֗/A A/A A`/A APk8/A AN/AD AN/A@ AXB/A? Ah*/A? A /A׷ A؛/A`t Aڗ/Ama Ah/A; A/A  Acx/AD Ah/e/A@ AE/A` A0{/A`x A/A{ A*/A`) Ax)Ֆ/A A)ɖ/A1 A˖/AM A]ܖ/A x A*/A` A͖/AA A[/A]{ AZ/A`b A0Y|/A AXp/A Aa/A A$]/A AS/Af8 AN/AqX AWL/A` A#E/A AV@/A@ie A8//A A8//A u A =/A}E A(U/A A%u/A@#o A߅/A@ A&/A& A'/A% A'/A A&/A A{/A JA A0Y|/A A/Az A'/A A͖/A A/A` A/Ar AJ/ADl Au/A AX/Ao A/A AǗ/A A/A` Ai/AWL A71/A`] A؞?/A` AR/A`] A؟W/A` AVX/A  Af/A Amt/A` AԂ/A2 A@0/A Ahؙ/A - Ah//Aq A/A` A/A6 A(/AH A/A 8g AX/A K A]ݚ/A 8g AАٚ/A4 A/A@ AX/A A(/A$a Av/ATp A3/A A/AH A2/A A2/A ( AF/A`D Ah/Ao A/A+ Ah//A A/A ( A/AN A/AN A/A2 A/A@ A?/A@ Aߙ/A Aؙ/AN Aә/A  Aϙ/AoG A(/A Av/A+ A8ə/A 8w Am/A AxZ/A AfB/A Ah?l/A 1 A8m/A A /Aa A/A AP/A A8/AT Al/AT A%/A$ A8sh/A`K A5i/A` A?/A A 8/A i Ay8/A Al4/A u1 AO+/A A %/A A A /!/A`$ Ax /A A /A / A0/A ( A`/A` A0f/A@ AOۘ/A`9. A8Ԙ/A & AOۘ/AAA0f/AAl/AAȤ/A `Ah/A`[Aߙ/AA8Iə/AgAR̙/AsA ܘ/A@A˘/A@WA0/A@lA뻘/A@GA纘/A AA`Դ/A@A/A`̫AR/AAo/A HrAЦV/A 1AH9/A@yqA /AEA/AA/AZ A/A S% A( &/A3 Ap#/A`] Ap#/A@# A,/A@D AH9/Am A(iC/A q AV/A4h AW/A@k Aj/A Ay/A0*A`z/A4{Au/A@AXe/A@G!AXB/A|=A(/A`Ax՗/A`v]Ax/AA0/A[Acʗ/AvA(Kz/AklAUu/AAHnb/AVApR/A@>AB/A@A؞!/AADAi/A@ߏA`ܖ/AHAg/A A3/AJA?/AA/A@A n/A`oApJg/A 4AXWk/AAl/A@LAzi/AwAd/AAW/A@W*AE/A[Ao=/A- Ab9/A`~A`!/AkA(:/AցAX/A A /Ag A7ܕ/AIA7ܕ/AA7ܕ/AIAPޕ/A@B Aԕ/A  A0Õ/A" AL/A A𴰕/A`v A`ǔ/A<2 A"e/A=) Aò/Au( A:G=/AV. A!/A2< AVN/App Afj/A0Tt*!AU/A !A̩J/A"A]T/A+.$Aȩ/Ao$AM~/A|*m$A:/AeP_$AVŔ/A@G$AHɔ/AnF$A$!͔/A(%C$AД/APe=$A8?Ԕ/Aӻ4$Aה/Agk*$AN׉ٔ/AnH$Ad|w۔/A$A#?ܔ/A$AA/}ݔ/Aj#m#A+mݔ/A=c#Akܔ/A-,#A%۔/APF#Aٔ/A#A<_LԔ/AIb2#A%Д/A'T#AΔ/A,!#AHӔ/A֮k"AN0۔/Abȹ"A-b/Adʤ"A{I/ARK9h"A;/A5J"A=/A`5"A/A׋$)"AmĔ[%/A)Q3'"Az3/AS^."A]A/Ae>"Az>N/A[X"AN$Y/Awz"Ac/A"As/Aق "AQNσ/Aƞ"Abt/A{A"A/Ai"Ac/A#{"AÕ/AZ"A7mѕ/AqCRk"A-ݕ/AH<"A}/A[.=@"AA[/A\H"AFz/A_=S"A<}/A&b"A@$/At"A\l/Ap\}"A/A`"AL/Ab>"AC/A Q"A4 /A?nC"A;oP< /AT"Aw/Au |"AHLZ/A] "AF/A"A`!/A"Ahg/ARGD"Ab/Aە"A?-/AT"AQ?/Aa"AI /AzoD{"A[h/A5p"A/lM/A 'e"A/A["A'Q/A("Au/A+!A?"/AC+!A*(/Aqc<5!A//A q!A`e8/A`"N!AI/A͂u*!A\,\/Al!2!A"Eo/AR Ax/A A7/A z5!AR/A gM4!A*7tՖ/A˂Ƕi!A'fm/A;/!AAa*/Ax!A90 /AsY"Ap2#/A෾"A./Aa+#A)6/Anj#Aj[/Apt<$Asf;/AD$A5/A`$AsY_2./A18%AU$/A 5%A5#/AKCM%A _!#/Aof%A>$/A_M.~%A}&/A{<%A)/A鈔%A ,/Av%Ae{1/Aܬ%A(<6/AlMI%AU`%At ח/A%%A. /A8%A%/A|oi%AOY/A"$?%A`/A %AoV/AyA[$Abn/An/$Ax]/Aa$A.#M/A9xm.$A'/A~m#A؁1/A`#ACU/Ar#A\xw/AA|#A))/Aƛb_"Aj,/A-d!Aj3Ș/Aa!AO<ܘ/Au % A3/AbN A /A,5sA7/A˒A /AA֖/A֨AZ/APVmAI/A `f Aҿ/A{_A@~,/AAo1;/AyAM/Ab̛,AU d/A*AL}/AЋUAV/AmA7︙/AzTyE#Aٙ/AZB2A\/A`AT</A tzAd?/AG$AM]/A`fA[|/A`AO/A`Aз/A A#뻚/ALAۋš/AED(A hhǚ/A(_SAyp]ʚ/ALkAb%T˚/A:A4Eʚ/ABAh9ǚ/A! A0Gš/A8A-;#ǚ/AlAК/A 4FALHޚ/AZ,Aq &/A:A4/AJ]:A/A5AO:/A UA-XpRY/A1~A ëy/ApA:oU/ADA8/AA#ޛ/An1AS</A Ai謄!/AVu A1B/A]3ASZa/A35A0~/A`tA䠜/AA=/A,RA<Ȝ/AŦNA&5 ֜/As#iApޜ/AׅlA߫,/AaݖAS/Aa./ A !Cڜ/A A`Ϝ/A lY!A Ĝ/A`!AP/A`B"APM/Ad#AH/A`$A{/A x$A/A %A/Ao!%Aw/A %Asd/A %AȘA/A@:&A R/A@f&A /A6&Aޛ/A`9~&A;›/A*n&Ab/A9v&AG/A$k&A7 j/A,xE&ACӼR/AoG'Ax?1>/Aת'Ax-/AϵRr(A׊!/Aen(Ar/Az夆)A@/A@y)Aik/A@y)A@/A *A |/A`)A'/A *A/A ;*A*/A*APf&/Ac*A(K/A[*A /A`f +Aɛ/A *A(̛/A*AhD@/A)AhQ/A)A#/A3JL#*AuL0/A{*A\@/A*A )T/AnXN+A.Qj/Aq+A G/A뷊}+A,;/A,A/A0,A؛/As>A,A /A`[b,AX /AQ",AX/AҰ,A!@/Ao,AUܡ/A{! -A9L/A"Wd&-A %/A<-AI1/A~EJ-A`i>/A'P-A K/AN-AbPb/AMP-1-A@Nz/A-Az/A0U,A}b/Ax3,A̘/A,b,A0;/Av,A՟f/A+Aq/Ai+AX~/A+AD/A +AwݶM/A+AcV/A>ۈ>+AYl^/AĘ*+AbMe/A*h?+Adk/A8WY,A,Cp/As@R2,As/A@5T,A /Ak,AXp/A`; )A( [/A q!)AXZ/A!)AWg/A)Au/A*Auh/A`;*A/A` *Aт/Af)Aĕ/A)AHᐝ/Ao)Akv/A@ )ABk/A )A(\/A(AѾ/A (Aڝ/A (Aߝ/A \)AG۝/A)A/Ad)A7l/A(A(\Q/A&APN/A &AWN/A&AWI/A@&AG/A&A7/A&A-/A&Ap=./A H&A8//A 'A8/A |'A]/AZ'A@/A 7'A/A&A/A&A&/A&A=*/A&AW./A]&A/AP%Az /A=:$A#/A=.$Ap$/Ag$A#%/An$Apk%/A@t$Ap%/A|$A(\&/A $Aȥ@/AU&ALA/A̜&A8C/A H&A( C/A H&APfG/A&Ax\I/Aޕ&AH/A=&AV"/A@#AV"/A`#A&"/AH#A0.(/A#Ae/A`#Ap=m/Az$A{/AR$A/A %A/A=%Ap/A (A/A ](A@/An(A83۞/A *A/A]+A/A`)*+A/AI.A#/Az.Ap!/A.Af"/A`.AL/A]0APP/A/1AGv/A3?A'/A 1@Ap/Az?A(/A*@A/Az@Aأޞ/Ar@A(/A?A/A >A /A@g>A/A@T>A/A@ >AWğ/A>A0/A=A(/A=AQ/Ay=A(p/Ap7Ah/AR7AQ֞/A@g/A(\/A/Ap/A[/A(ޞ/A@ ]/Ap/An,Av/A,A6v/AQGx,Aȝs/A x,A&>o/A-Ai/A@A-A:E/AD -A?8/A;-A0/A!.AP/AH.A~/A`f-Â/A@".A(/AH.AT /ATa/A|/A` /A:/A.0AS/A K0A(,/A 1A./A`%1A /A@2AJ/A q2A(L/A$2AСX/A"^3ApZ/Aj{3Ah/A 1)4Adk/A@H4A)z/AO5A|/AR"5AP/A5AP/A`v5A|/A6Ap/A6A/A@N@7Aã/AY7A0/AH 8Aѱ/A@&8A/A@8A@/A8A8Ǟ/A`9AnϞ/A9A0՞/A:A( מ/A<:AO۞/A y:AB/AQ:A/A`;Ao/A3;A/A$<A/A7>Az4/A>A`6/A =>A/A׋>A/A Hk>A-/AR@>A/A`x=Aٞ/A:AΞ/A F:Ap̞/A-:Awž/AX9A/A=8A /A`f6AU/Aྐ6Aƀ/A6AZq/A A05A8R/A 3Az2/A`)1Af/A /A#/A@ .A/A6/AR/A/A(V/Ah.A֫/A H/A/A.A0;/A}0.AdQ̝/AeLX.Aܪ/Aw.A(X/A,x.A*/Aߗ_.Aq/A>s.A6`/A|Ky.ASh4&P/A!.AB/A5##/AxK6/Ab/Ak"/AU/A//A 0A00/A"0A2/A 80A09/A0Ah?/AB0AK/A y1A@L/A1AT/A`m1A@[/A N2A0f/A@%2Aph/A |3Av/Aܶ3A/A-5Ac/A5A/A5A /A@!z6AӲ/Aq6A\ĝ/A`Df7AH:ǝ/A7A@՝/A08A؝/AK8A@/A (:A/A U;A(4AXp/A`;3Axn/A@3A+a/A03AQ/A ZA2AbD/A@Ϝ1AaB/A1AH27/A0A'/A*0A$/A`)/A,/A,P/Ap/A@ /A(R/A@#/AXX/A/AY/A`90A5[/AB 0AXa/A ^0AXb/AMi0Af/A0A8g/A@0Aui/A`0Au/A 71Aw/AHO1A/A1A酜/A`1A/A{2AX/A͘2Ap/A2Ax/A D3Aຨ/A@~3A(/A`T 4Aݜ/A_5A`v/A=5A/A-46Ax /A cO6AJ/A_6Aأ/AN6A /AfR7A(./AX 7A@ 2/A@y(7A /A`y7AU/A 6AP/A@6Ap/A_6Af/AF6Aɜ/A lO5A@ǜ/A`65A/A`[4A0/A`4A(/A4AP/A (N3A8/AM2A@‰/A@%2A(]/Aߝ0AeY/An0A`W/AX0AT/A <0A0GR/A 0AJ/A`/A((/A 0A֛/A /1A0*/A@1AJ/A)2AΛ/Akd1AH/A2A螛/Av2AHn4/A`4A,/A %4A@/A2APom/A&X3Ar/A3AxW/A?3Ah/At7Ah{/A J7AM/A4AP u/A3A{/A?4A/A5AАW/A9A?/A`9:A/A~6A/A 6A /A"8A`aG/A3O2AP8&/Ac3A_/A 5A4/A@`z5Aޙ/A5A0{/A &6A0%0/AX6A@/Az7Aٓ/A&:Aؒz/A :A/AdY:A/AkW=A)/A=A(;/A r?A@䎚/A 8o@A/A u{@AX͚/A`-AA{/A@`fAAhM9/A`osBA>/A`BAqN/AzBA=j/ACAiy/A5CA땛/ACA/ACAPٿ/A DA`1ϛ/A}9DAs/ADAF/A@DA&/A@yEA$/A]=EAp=A/A=EAJ/A`fEApc/A(EAn/Ap#EA(\S/A`fEA*/AFAX/AmFA:/A`fEA83:/A`EAP8/A HkEA"/Am@EA/ADA/A?DAVǛ/AbDAЀ/A`8DAhޤ/A CAP /ACAq/A ^CAb/A@`1CA(F/A 8BAu8/A3BAج#/ApBA /A`.BA/AAA/AAA Ś/ADQAA/A (AAذ/A)@A( /Aࣦ@Ah]t/A[@Aa/A 8"@Ah]D/A?A7/A3?A( /A>A/A@=A 4/A@E=ATg/A=A@U/A+%>A(>/A;>AY,/A@g0>A/A;=A/A@5=A8/A`m;A /Al;A/A+V;A/AT;A/A QE;AXhϗ/AN;API֗/A`m;Aڗ/A~;A/A<A _!/A`<AM/A=A(m=/A`o=Ad/A@r>A83/A[?AG/A?@A4g/At"BAh"/A BA,/ADA\>/A@,DA(~U/A@nDA(a/A ۚDA //A@%EA`T /A EA#/AMEA!/A`TEA7/A5CAw/A`BBA=/AAAp뙘/A?A kV/A0>Aؗ/A;A0ٗ/A`;AƗ/A`T;A(\/A@w;Aq/A;AU/A<A/A <A/Ad(=Ax/A=A`/A`T=Aؖ/A ^?A0Ζ/A`AA̖/ACAؖ/A@/AjAG/A-jA/A`kAh/A@>clAd/A@SmAx\q/AjmAPꛗ/A൷mA ʗ/AUSnA(O˗/A@nA0—/A@!nA/A@ nAhy/AnA`Z/AzunA9/A DnAr /AbnAH/A nA̖/AroA –/AoA8/A |oA/AoA!/AeoAo/AoA:@/A=oAP /A`oAԕ/A[7pAs/AM_pAs/A QUpAI/A JipAP(/A-pA( /A1qA0/A@qAڔ/A`)rAɔ/A?VsAǵ/A sAǵ/A`tAǵ/A`muAÿ/A@NuA|ɔ/A@qvAɔ/A@#uvAɔ/A wApӔ/A@wAȱ/AwA)/A`wA"/A@yTxAP(/A cxA !/AQxA /ATxA/Aj|xA?̔/AXxA@½/A}xAȰ/A;xA`T/AyAX/A`QzAhՍ/AzAQ/A[s{A Ρ/A-{A/A||AȰ/A|AH/A\}AȰ/A u}A=/Aମ~A. /Ai 4Aɝ/Ay?*A /Ao^9A@/A1jAhDʔ/A@,A/Aqc^AH/AvAN8/ANd/AV]/AA`A /A 7AwJ/A`Ax/AZ AM=/AtA(/A@5A_(/A@AT% /AîA/AHATxqa/AzA/A`)A3C/A@A,/Aa{A{/A`Ad/A@WA>P/AA¯A*/A A:,W/Ao3Alŧ/APA0*/A`KYA&/ApAhb/AA,/A3A0w /ApAȺF/A'QAU/A &Ad/AA/A_A#/A喫Ap0 /AAyA>ۆM/AA0a}Nڱ/Al5A`Ŷ±/A/AX /AóڡA5n,/AdA`k/AqQ`AJ/AYSd=A07(/At{-A/A /Ahqݰ/AiA*#/AَϠAږE/AAXNpo/A0)Ad'BP/Az"A֏4/A6,lFAwo/A'~A/AKb2AXD/A- AO?/AJAGg/AW{XA /AA׮/A ƋA/AA */ApA%4q/AlAo6/A M}ADbK/A\LzA%9/A>ExA*r>/A6 NuA^Tt/A+*rAKδ/AgWoAs%/Ao9;lA5Tw/A+z%jAŵ/A HgA3׻ڵ/AjAfA/AJN7eAtnP/A/)dAk/AMyCqcA /A bA1/A\`A h)/A2*_AVM/Ak2~^A"/AI]S]A=b"/A ,\Apw6/A [AOFyP/A`YSYA%n/AԑXA^Xϐ/A]MkWAm*/A VAT/ALUAW~/A/YTAb$F/A>G TA}/Aa=SAIKio/ACWS~RAJb/AdDQA63"5/A_k@1QAw/A;PA0/A@,+PAZ޸/A`mHPAlf/A4vPAMF0(/AȳPA-\9/A[0QAPS/A 8ZQA.f/AQARx/AHRA0/ARtRA軟/A!UA!/ATUA_Ǔs˹/AxpVAhܹ/A@ WAg/AflWA/A`XAx/Ao~rnYA/AЎA/AWId̏Aۭ/A}ϐAW`/AuؑAdKϻ/A-A»/A SAGu/AΝA%޼/A؞A4{/A(xA@F /AA`p7/AQ͖ڞA-g/A<6A_' /A[KRA3Ҿ/A}A/AW}?A،wP/A@Ao/AW}ШAH޿/AUTA\+/Axi>o̫Az|/A(7AR/A_\AJ/A=m~aAH|/AzcAk/A AH.+/A@Aip/AAa/AA`v^/AA&/A@CA/AA2/A_AT/AwΑA`v/AܑA /AWAtc/A;OAc/AjAt/AvA/A@SA0i/A qA 2/AގAQ/AAX/AH~Ah"/AjA/AżA(/At|A/A}A(/A}AmW/A@A/A?AxX/A Q{A0;/AdA/A dA/A@dAv/AQdA@/A%eA8/AeA/AndA8/A`A0n/A\APn/A3VYA:/AVZA(/AuZA \/AYA@&/A6{XA(/AWAP/AXA؞/AdpXA@/A~WA/AͅUA[/ATA`/A;SAh/AQAPw/A 7PAH/AOA( /AOAg{/AOA/AfKA3/A5KA/A@JA/ABJA0e/A &IA/A IAXK/A`OIAp[E/AGAP7/A`;5GAP7/A@FAb/AFA46/A FA46/A`pFA@/A`pFA =/A@FAհ/A3FAo?/ATGA8&/A4`CA(_/A`)HCAuc/AzCAh/A lBAhD/A|NBA` w/A^2AA8#}/A@@AB*/A`[AAXK/Ar@Aa/A`o@A8Ҁ/Ad?AxE/A?AxO/AƲ>Aj~/A>A/A>AN/Ap>AP+/A >A/AqU=AX/A@6=A9/A@<AX /A@;As%/A*Q;AH/Ah<A/Ah<AC/A@<AbR/A;ApU/AD;Ag/AU;A0J/A u;A`/A 6A+/A+5A^,A/A+A4@/AKq'A /A%A/A %A/A@ !AE/A!A/A=s!A/A!Apʢ/A JA t/A`AH/A AbAK/AiAx/A?Ad/AN*AH2/ATA/Az/A8/A`A/A^A8/AAP/AoA( /AD AH/ABA /A 1A /AA~/AAhc/AAv/AUNAH\/A SAؽ/A`AM{/A?AH./AA0;/A@A/AAhD/A]AXۊ/A&YA/A`DaA/A@A/AtA/A8Ap /A SA/A` A !/AA7/AAl/AKAH/A |'AP A`1/A| Ap6/A྅ Ah/A' A@~/AO A`-/A A6/A 2 AHH/AR6 AhR/A  A=F/A A`c/A Ax)/A APm/A Ac`/AM AxV/AO A /A@i A@/AW A@Y/A& A8sW/A@a A/A AL/Ah A/AR A8R/A`v A/A3H A /A A(:/A@ A/A AP/A Apk/A| A/A@ AX/A= A`+/AR AP /A Ay/A AHr//A / A>/A AP8/A AE/A Ax/AK AX/A`; AP/A A6/A A8Ң/A- A/A@r A3/A [ A/A Aj /A A'/A- A/A/ A/A+ A/A`| A/A A/A Aq/A AS/Ah Ap0/Av3 A /A 8| A`P/A$ A>]/A0 AgF/AR APM/A`m AH/A`= A}/A Al/AT A`Z/Azi A/A`B A/AY AxG//A@ A\!/A`B Ad/At APx/A2 A/A` AA/A K A4/A`ob A@/A uD A`/A` Aؽ/A`9 AXW/A & Ae/A A@S&/A@ AX4/A A/Az AX/A A@Q/AV Ah;/A / AD/A` Aؼ!/AH A%+/A L A"/AR A/A J AJ/A@ A!/A`]l A`/A@ Az/A Zx A Ex/AV A؟/AI A/A AX/A@ Z A/A AS/A $ AQ/AaE A0X/Ap A(J/A APx/A E A/A@ A/A+ A0{/A cj A/A ~ A/A A/A A/AQ A/A@Z A$/A AH/A AP/A=3 AP/Al AH/AE A /A \ A/As Ap/A ZE A 0/A Aa/A AX[/A@y AR/A S A(J/A^' Aݯ/A@yJ Ahޡ/A@, Axm/A AȘ /A@ AXu /AW AP/A ' A/Ao AT/A O)A8/AJAu/A APt/A`"A@K/AyA(/Ao Ahb/A@A2/A@ ADn/A 'A`/AAN/A AAп5/A2A0/A A`/A;A /Aj=A /AA /AAH./A .A(W/A@~AI/AoN A2/A@  AK2/Aq7 AD/A ? AТK/A A/A@ A_]/ATA /AA8B/A e AȺ6/Ab A2/A@ A(/A= AP7/A A/A e A/A* A/A AԻ/A@W~ Ag/Ao AHC/A A9/A A /A@g A=t/ABS A*\/AQ2 A-/A A(@/A Aa/A@ Ay/A A 0//A` A /Aમ A@/Aa2 A/A Ah/Ad Ah./A`: A8E{/A@ Al/Aޜ A@ϐ/A9 AP/A` A//A@* A/A@ A@B/A A(/Aृ A4/AA/AɮA/AA/A@APf/AƈA/ApA/AAĥ/A AT/AAHaU/AA#:/A A./AAv/AƈA0/A@ wA/A,A/AA8E/AɮA^/A A0X,/AAl/A`ÕA;/A`A AhU/A`P A/A`A A /A@X AB/AoN Axm/A5 Ah /A;A/A Am/AM Aȋ/A` AX,/A, A/AK A`e/AF A0/AQ A/A8; A/A) A/A A"/A3 A/A@G A/Ac AX/A A.&/A Aho(/A  A1/AgA@}/A |(AY/A@,AؼW/A`;GAh5/A2#A38/A A>/A AS/AA(>\/A |(AؼW/A`UA( /A A/A`AS/AǻA/A`bA/Ao;AZ/A$jAxG /AA/AAP $/Ai/AG/A`A`/AA`/A1A/A36A(K/AfA A^/AfA A^/A};A/A ɂA 6/AMAX(%/A ;AŌ/A@yA0a/AKA0U/A AP/AļAKP/Aj5A-/AA/AAPZ/AFA/A  A /AOA5/A A/AA /A JA(/A *A;/A A/A`A"/A A/A`TA(1}/AAA/AyAȽ/AVAȥ/AoGAxFE/A A 8/A |tAP /A#A/A OA/A^AxW/AA2/A@Al/A LA/AAг}/A A`/A7A&/AAH/A Ax/A A4A l/A4AxW/A 3A3D/A@0Al0/A`0AX5L/A@>3AL/A@&3A(/A`4AN/A5A(i/A`7AP/A@q8A /ARM=Ai/Aj=A0/A`>ApY/A ;?A`Q/Aྒ?A/AAA/AvAA0F/A&@A7/A [=AȘD/AO=AhU/A?Apx/A.AAki/AN;Aq~/AI{;A/A`AA"_/A&)AA\ AVx/A:i Aŧu y/ALYe Agy/AЕP A{y/A~@S* A,Yn#z/Az͐ A,~&z/A@9d' Atz/AjLG AE{N{/A:>.լ Aõ{/A`KP A TU|/A= A 5|/Am( AŐ|/AagE A׷]}/A]n AC?}/A}k AB4~/A# AV g~/Am 0l AT] /AA AzbLv/A9 AT/Au AxH/A H A׬/A[" AX(/A A9a/Ay A£g/A7ȣ A(l/A Aؿ{r/A A;w/AN A(9|/A[ʬ AK/A AϠ/AY!\0 A:e/AX9qh AHā/A#!땮 As؁/Aɷ A0/A AA/A`ǫ AС/A ۮ A(/A"e Aj/Ag5 A]/A A€/Aྼ A/A9̫ A@/A' Aȇ/Ay AA/A AՂ/A`" Ah/AF A𖣂/A; Ap=/A  A8/AM AA/A`) AYX/A lį Au/A A$/A`" A@6/Aé A8/A; A84/AF, A`/A@ A`/AJ Ax͂/A 䶰 A =j/A A8#w/A A ق/A԰ Ax~ /AG AU/A A(/A A/A} Aa/A 8M A-/AVq A/Ag5 Aأ/A + Ab/A A8/Atc A /A` Aa/A`Tٯ A/A`"r A-/A@$ AXH/AA APS/A ZV AkɃ/ADz AUރ/A A0 /A  A0//A  AF/A ApN/A At[/A Ai/A Q A䉄/AL A᱄/Añ AɄ/A 8 Aτ/A A˄/A` Ahل/A`; A\/A ۓ A(L/As A/A A84/A Q AxX/AD Aw/A n AҬ/A ApJ/A  AP</A  Ah/A  Aw/A`DI AX(/A@ Ap5/Ad A A0h/A Aѩ/A Aޏ/A Ao/A O AX/A` Ac/A An/A Aޑ/A X Ap/Avt AX#/A@, A8/A` A`ɒ/Aj Ahے/A A6ޒ/A A6ޒ/A Ap/A SK Aw!/A`bc Ac/A` A"/A ApJ!/AJ A/AJ A/A ¡ AY./A ۞ A00/A|> A(*/A 1G A,,/AI A00/A` A6/An A0B/A@t AP|/A@> A@ /AMz A8/A`y A8;/A AP<\/Aj Asc/A$ A8l/A$ Ae/A Abo/Az AP/A2) A跀/A` Axؾ/AP A(/A 8] A 0S/A A/A A/A A^/A5 A /A@W A@j/A@ AϘ/AM8 AC}/Aq AÙ/Aq AD֙/Aq Ah*/A#3 A0/A3 A/A@1 AX5/A@% A![/AN A![/A AȺ/Ac A/A@Ph AP뜚/A ? A /A 9 A /AS AH/A A'/A Ah/AԿ Aٲ/A ޿ A`Sš/A@1 A߅D/A A]h/AD Ap_I/A,0 A`Y/A0: A!uڄ/Aх}_ Aﮛ/A A0 ϛ/A&_N A`T/A  A /Am- Ap4/AoJ AU/AZ] AZWs/A A/A5\F AЦ/A 9 AБ/A ± Ax/A@t AБ/A AF/A  APȜ/Af AV֜/AA AD/A A/A A( Ý/A A/Ae A/A A887/A`; A\/A A1/A  AW/A AMӞ/A`) A/A A` /A`a AF/ATM Au/A3 A8/A Aݟ/A AN/A A`C%/A@` As3/Ay A?/AD AaI/A@Ͽ AW/A; Aa/A൵ APj/A`m As/AMj A8#/A AX[/A AX/A@ Apy/A` A(/A A/A AP$/AV AC/A@ APu/A Ax/A@.F A/A Agġ/A AXxС/AP؞ A/|gRߡ/Au_ A /A/g* A@/A`" A /A Aԉ:$/A^a AoJT4/Aݛ AB/A/ ARS/AҐ) Afw7c/AG A\7r/A k A-/ALپ AdDU/Ax A /AI y A_+֝/A<+J? AD7//An$ A/A@W A/A` A /A AfB/A Al/Aܓ1$ A`/A,i0F Aã/A\ A/A; APM4/A Asa/A0ſ AG/A A/AFE% AxmY/A A.Jʥ/Alv2PN AcM8/A AA S/Ax A=7 /AijL A$o/AAٶ Aѧ/A9gW A`./Adz AF/ q/AW0u AZ\K/AJ3h AЀ/A`T. A$@/AwXBx Am/Aٶ A{d8ԩ/A۹T AG/A@D A0LR/A0z- A5/A Υ AX$/A֡ AY/A9Ԟ A?T /AqP' Ah2W/Ab0p AD/A@> A;/AsL.& A` e/A?|L~: A84/A@NF AP/A A /Asї A/A A@$/A@q A8 /A A!/A A/A A(%/A` A/A> p A}/A AЦ:/A6 A2(/A4- AX /A A:/A+ AjE/A3zp A*U/AOȎ AЅ9i/A+( AH/A Ғ AEiJ/A {v A?/Akc AB2!-/A|k[ A6?/AjH^ AO|б/Aek Ah ߱/AP AX/AXzǥ A $E/A`Ѝ A9/AU AWy /A/; A/A 8x Am/A Ax+/A`D A9/A0 Ak/AĐ AŨ/A`)) Aq/A A@=P/Am˒ AXy/A sғ A;/A qV AXa/A@p A ȴ/Aн A|3/AW A੕/A@B A׵/Aߓ A*ʑ?/AC, ASc4/AYw A25a/Ac.. Aqn/Ab}֒ A*S~ǵ/A6aq Aܶ/A| Aܶ/A A0'/A@,{ ADո/A& A/A=K~ A/Ay AhD+/Aiu Axp/At A#ں /A@t AѧZkd/At f AGÕ`/Aȴa* AI^/ANH A^/Am A =^/A@ϲ AU^/A`m A#_/Av AH8_/A` A؁F_/A`: Ax\_/A Zn Ax_/A  A `/A&w AxZ`/A@i A[`/A@ A@u`/A@ AB`/A  Aq`/A Z AU`/A`; A0,a/A@ A}ra/A`  Aa/A2 Aa/A`7 Ala/A \ A%b/A3j ALb/AcK Atb/A Ab/A`KK Ax)b/A A@Fb/A APb/A\ Ab/A Aac/AV~ A`nc/A( Ac/Aܗ AP@Lc/A, AP5c/A: AOc/Ab APc/Ax AHpc/A Af;a/A? A1a/A@ AXa/A`C A`/A@`F Apl`/AlG Au`/A mI A@j`/AX#J A`B_/A Q A߶_/A1Q A0G_/A^Z AX4^/AY Apb^/A`Qa A^/A{c A _^/Ad AH]/AT7e A`v]/A f A]/A g A ]/Abh Ax]/AXi A@]/AI k A]/A m Aj]/AfSn A+]/App A@0 ]/Aq Ah]/A@߭q A`]/A;r A\/At A@\/A J Ad/Ao# Ad/A` Aw.d/A8 Aw.d/A`BK AX d/A@/ Ac/Ac A c/A^ A`c/A A8 d/A@% Ad/A1 Adc/A@% A8#c/A@C A%d/A AHd/A# Ac/Ah Ac/A@Q APd/Ay A1(d/A O A]-d/A A6d/A A1d/Ajg A d/A(r Ad/A(r Ad/A Ac/Aޓ Ac/A@J Ac/AXs AGc/A, Ac/A&y AͿc/A A$c/A2 A$c/AR A@c/A@iA Asc/A Afc/A A8c/AH Asc/A@ AEc/A@ X A d/Aʚ A*d/A} Ald/A} Ad/A`7 A\d/Ak: A\d/A Ad/A Ac/A`F Ajc/A9@ APc/AN Ac/AN A8tc/Ak: Ac/A 8 Ac/A Ac/A@Wr Amc/A r AHc/A A`c/A A4rc/A AXc/Aj AXc/A- A>c/A< A`c/A@ Aӭc/A i Ac/A i A0c/A6w Ac/As A {c/A;b AYTc/A@\ A@$nc/A@r Aq c/A { Apc/A@g A /c/A APc/A AOc/A` AWEc/A`H AO A Dc/A A Ac/Aa1 Akc/A= A0fb/AH Ab/A Ab/An A8 b/A@8 Ab/A A c/A@Q AP<8d/A`) ABd/A` A e/A@p A|&e/A߽ ADe/A l A_ e/A ZS Ad/A- Ad/A@, A d/A Ap<e/A Ahd/A`s AЕe/AW APd/A + A e/As7 A Ce/A; AOe/A Ahe/AQ Abe/A@g AGke/Aj Ae/A  Aږe/A l A0e/Am Ae/A) Axe/A A}e/AԳ A@e/A. A@c/AA A8c/A0@ Axm,c/Av=EQ@ A//=c/AJ*? ApjQc/A? Ahc/Ap? A:c/AH? A&%c/A/? Ac/A&? A0c/AhB> Alc/ACu> Ayc/A[> Ad/A`< A?d/A; Ac/A[F Aw`c/A9,_ Ac/Aq AQc/A&m Ac/AdJn AN[c/A- ADDc/AO0 A=Ic/Ap( ALc/A! Asd/Ai.' Ad/A Asd/A ARc/A: AB3d/A߉'G A0vnd/Asy A;:.d/A A _d/A@El A2Z.d/ALr Am@d/A j_ APHTd/A@` A0sd/A A(Nd/A  A Hd/A A83xd/A AѧZkd/At f Am+:/AHؤ`Ax/AGp\Ax/AO\Ao/A`K\AXe/AU]AbR/AiL]A\:/A`]Am+:/AHؤ`A /A_|RA{V /AD TQAh]0/A |QAN/A QAN/A%RA(>p/AVRA8/A sRA /A_|RAd/An'QA6]/A\ݲOA)/AnNAXE/AV>PAd/An'QAe/A?GA Z/A<ŮTBA/A`9OBA~/A JBA/A@>aBAx/A@,BAM/A?AA/Ao@A 1/ADvBAHs/AoCA`/A@gCAT/A SDA/A@EAw/AGAe/A?GA; /A8AC40ATA6gmr^ C40AqAx40AOfRAu]Qڂ30A44AlBg20AP9QA5mI10A)NSqAo6/0AnBA\5-0AɴajA?e+0ARȪAQ\(0A| uA%0A F)wAg˳"0A Wn2mA@r0AĒLn]A<%0AT,QPA؉0AEAo90A[u->A+0A98 :A3 0A8A^ 0A98 :AB0A[u->A9'0AEAʇT/AT,QPA Z:/ArQA0/ApyoAO/A`[Axz/A`Av/A`^A7]/A ŒA89/A`A,iO0AVApA/AA /AЪAX/AA/AsAX#/A@A i/AA/A`)Ap=/AAx-/A QcAX/A@AM/A >AM/A;Ap=/AWAh/A Ah/A`fA8/A)[Ah/AV;Ah/A@>bA(/AjA@u/AAh/A`8A`/A^5Ap=/AAX/A AH/A lEA(/A ҁAU/A Ae/A`Ax-/A=$Aո/AaZAС/AgAؽ/A`A/AӒAxƷ/A}AxƷ/A@vA=}/AaA1/A`rA@q%/AA}#/AOAcw/A@AVy/A>A0v/ACA(o/A IA}/A Aؽ/AA/AA/ApA/A1AZ/A`AP8/AŢA/AA@/A %AH?=/A@Nd~A"/A"0A/AAx-/AaAq/AA`N/A촁A@F/AAv/AA/A@~AV/AtZAV/A_nA'0/A9A#/AdiA2&/A~A=/AQAȶ?/A(A`/A@~A/AT~A/A M~A8/A~A/AxAxi/AbAso/A AP^r/AA|/A@EyA/AosApʩ/AA@/AsA*/AVA*/AAxu/A@A /AzA@/AOA/A 4A/A QAP/AzA`%/A`AY4/A+A@/AAK/A/AxZ/A FAs/A A(/AAX/AAA/A YA /A 8A`/A`A/ASA0/A@>A/A A /ATA/AA@/AfA(~/A@A^/AA /A@-A /AA /A_4A t/AazAh/AAJ,/A AS/A`A`/AXAh/AALr/A@ƞA07u/AIA07u/A#Av/A`Apm/AVAP/A7A=/AAk9/A^AP6/AA3/AgVAX5//A4A-/AqA/A@!oAI /AAh/AA/A@yA`2/AA(/A`"A/A LA:m/AA(P/A HAXyA/A A2/A:AТ/AKiAb/AKiAHa/A J#A@/A@A/AB.AH/A Ai/AhyA=/A@>AX[ /AbAy/A`;A`X/A An/A 8AU7/A6gA/AaaA/A6gA/A@[AXۆ/AAXP/AAW /A`[AX/A A8t/A@.OA(hZ/A@WAuA/AVzBAX/A EA]/A)GA/AHAX[/AEHA/A 8IGA{/A0EA(-/ABA/A`a@A/A =AHL/Aaz9AO/A66AD/A 0<A/A@A`/A@PsDA$9/A)GAxF/AHAl/AIA@S/AwLAY/A NA`c/ANA c>/A`OA/AɄQA/A@zTAxi/A`mUAxi/A`-XAȤ/AEZA2/A`]]A`+/A hbAe/A`adA͐/A JfAX/A` jA`/AnA/A cqA, /A@%tA,/AjtA,/A`sALM/A2iAn/A ¶]A8c/AXbAЦ/A`eAHr/A3\mA((/ARrAd/Ax0rAk/A&mAi/AviA@/AgA؟K/AdAP/A@ZfA@A/A`hAR/AEmA( z/A@rAV/A /zA{/AOA /A JAҗ/AX>Aû/A@ ǡA8I/AӻA/AA0f0/A uAP /A?SAzZ/A` Ap/A3AN/AAs/AOϮA,/A3A/A@AX /AʇA`/A3A੹/AVA@>/AA/A$ZA+=w/Al A*&{1/A0ƾA%h8/A౬Ad /Aȕ+AGÕp/A/RAz/A:A_G'/ABypE'Ab"/A kL1A2-h/Aqyn:A/A6PBAʇT/Aִi.HA9'0A NAB0AuEiQA^ 0A7cTA3 0ATA+0A7cTAo90AuEiQA؉0A NA<%0Aִi.HA@r0A6PBAg˳"0Aryn:A%0A kL1AP\(0ACypE'A?e+0A:A\5-0A/RAo6/0Aɕ+A5mI10A౬AlBg20A0ƾAu]Qڂ30A}9Ax40A_AC40AqAC40AqA0/AjA0{B/A`̇A`/A9AЄ/A'ApA/A` ʞA0/AjA6/ABA\/A`oAH/A _A/A}A6/ABA R/A@A{/AA{q/AAg/AA0^/AAp8N/AAD/AA7/AɔA+/A`uA#/AHKA`/AA2/A`AE/A@#A/AA/AlA/A@PA`X/AA`X/A@A N/A$A/AzpA/A6A/A=A/AA`/A0rA/A3pA/A@gRA/AA(J/AA^/AKApl/AAF/A`bgAhU/A l!A%z/A`Aذs/A`APa/A [A ^Y/A$&AhV/AvAS/A`AL/AA~F/A cmA@/A/A1/AA(./AKA ="/AA/A஬A0/A`~A0/Aj?A /AA` /A@5Al/A`vAn/A`An/ASAn/AaA/AAn/A`An/AXAPk/A ^Ab /A:A/AA0/A`DAx#/AwA(./AA9/AͳA@E/A`AQ/A$ANb/AбAPj/A AXn/AwAr/AA1s/ARA t/A_Apw/AA/AA/A Q%A/A@\A/AbAq/AzAb/AैAئ/AAHЫ/A|A(>/A;A`/A2A`/A`A/A A@/A+AP/AgAAx/A)eA(~/AyA(~/AʜA/A@A/A@AP/AtAP/A AA8 /AAP/A@[A8 /AAH/A@A/AAU/A_A/A*Ax/A@gA9/A^A(1/A A/AAx( /AZ0A/AZA/A@Ah/ARAH'/AAf3/AA(1/A;lAU=/AA=K/AA%Y/AAd/A A8l/A@Aw/AfA|/A@ A|/A`dAPڄ/AAp΋/A}AŐ/A` 9A8/AA8/A`;AP7/AA/A#Ap0ATLbAi*|:0A*KAZ70Ab@&WA4L30A+79%A_J100A#[.A-0A8AH=<+0A|)DDAM(0AdZ0UPAX2&0A7j!|]A˒e4X%0AXkA $0AwyA%#0A AcA2'"0A{PӗA_"0A$A2'"0AtXtA%#0A土A $0AWcA˒e4X%0AkXVAX2&0AȕAM(0A/AH=<+0A:C A-0ABypEA_J100A kA4L30Aqy)AZ70A60Ai*|:0Aִi.7A9'>0A <ABA0AuEic@AԚE0A7cBAoI0AdCA+CM0A7cBAo9 Q0AuEic@AT0A <A´@J.G&,A'@w9&,At@%)ʞ&,A@9Q&,Ak)p@Db&,A_ @& $MR&,Aς@K[@&,AG@,&,AQ@j&,A@Os&,A{@%,A@!%,Aruބ@xG%,A@^D@\A%,Ae@(%,Afw@%%,AXz@z%,Ap@(O%,AKtI@A$%,A;@$,A@ &$,A|`@^$,A~W,@ޥ$,A~Vp8~@3}$,AUP?@(e$,A@1q(Y$,Aj@SK$,AA1@);$,A@s)$,A{$@+W}$,Av@$,A g@12W#,Aɫt|@,t #,AaGx@x#,AR@,Aъ@,Av@#,Ax_@x,AD@Xym,A@N'@@Wb,A@;R,AF@hB,A@1,A0@P!,A_@,A@XW,AHs@5,A@Nq@,Ad@W,AT@(,A8@,A@N,A@`!,A@,A@0t,A>@0k,A4@8c,A@^Z,A@@Q,A@@AI,A9@ .,A6@,A@,Ag@8D,A6@D,A@ @,AI@(,A@@b,A"@X,A@sN,Aq@uD,A@ֽ@:,A@x0,A]u@',A@L@B,A @9,A@Y@(:,A2@X ,A@@,A@p,A@,A@@أ,A@@,An@,A8@,A@%@hL,As@=,A^@D,A?[@0,Am@,A@,A@b@ ,Ab@g,A=@@,A @2,A@p&@O,AT@,A@,AV@,A:@,A@3@y,A @H,A#@ ,A9@#,A@pL@P,Ab@,Au@(h,Aф@,A@@ɐ,A@Ϙ@,A@r@ ,Az@(v,A@ce,AX@T,A@XD,Am@~3,A@sW@",A>@,A-#@;,A@,A@0,Aq@,A@`,A4@8,AD@X ?,A@v@83+,A%@e,AA@Hi,A@,A@0 ,A@,A@+^,A64@ʶ,AL0#@,A`@ϟn,Ar85@u ,AQM@. ,A{B@zY ,ARmd@O ,Akm@ ,A@E@sk ,ARbT@a\E ,Ai@킝 ,A "@.,A৊ݱ@L,A@g^@ t,Aa@0",A @,Ai@*,A@Q@X[,A@"@|,A@sӭ@ yV,ALD6@sV&,AٓAw@Xn,A8̣ӗ@%Ю,A9v@nV,AbI~@|Zj,A-[H@أ],A@g@0p,A5 Ф@P,At&ݮ@PF,AZ:@h,AM&ѝ@#b3,A %5U@,Aǘ@أ ,A@⦗@8,A9%@X ,A@v$,à @,A@r |,Ab/R@ [,A8ͣ@iB<,A9?@#,Aκ@(_,Ak@6,Aڄ@2B,A];@W1,A|@,Agw@j,A6B{v@J>},A{B}u@],At@ވM,Am t@;,A1txt@2o),A+C7Xt@ <,A#KPt@,AN-_t@'|+AB)t@!;(+AMVt@83+A@u@4D+A㕩u@֏+AVu@m+Awlu@.K+Au@8)+A]u@|+A̕M[Z+Azr@vA+A3r@k)+A&&s@ڷ+A?Gs@F *+A;8بs@+AS$t@+At@w+AM6Zu@LT+Au@vz +A=u@Z[+A v@W(+AuGu@+A@u@߃3+AV(qHu@R+A&$t@O+AMt@(A?=+A9 t@'w+A>pt@p+Ast@MV+Aet@!Ԃ6+Aet@+A=~t@&+ɑ+At@5 R?R+AQ~s@BX+AНr@@v+ASq@+Ap@Ym6+AdҬn@.> +A:}m@D/l+AnXj@I +AEh@V+A]A^af@O+Ac@X!+A@b@&a+Af`@j+AKa2q]@:oC+AΎZ@.4+A&X@Uv+AN3V@8E+AT@B(+AѽS@8 `{+A%S@}+A_٦S@ը+AM S@pm@L+A_1T@dP>+Aj+T@&++A+\U@*F#+AdK-V@:+Az$W@]c+ACBX@s+AL@Y@d,S+A=YZ@S+A@y\@ +A^@22+Ad@ +Ag@0b+A@5l@+Ao@ш+A2.t@f!$+A\Q"w@c,i+A($<{@@1d+A~@`+AHa@+AW-|@+A@@!+AV@0+A8fZ4̌@j+A,y;@+Av=@ +A@?=+AYW@9+Ap@{>i+A!ɗ@}Z=+AH@;+Angޙ@p}+Aš@d+A@+@(+AΝ@8+A@ƨ@+Ah@@+A@_f'S]+A$~5%@+An@+A@6@9a+APХ@](+A*X@K̠+ATO@-9.+AQ`-3@$-+AeE,@$e+A9@Kx[>+A[Y@'C+A@=Re+A]|ɵ@ W+A@P+A@so@B+A1`9@P:+A@@p$+A2^@<*3 +A3k'@H#+AC@C=7+A+AH@|.a+AN!L@@H+A@:+A@I@0S+A@@XJ+A88@#[+A@@Q+A=0@@S]+A\@P+Aj@V+A$@k+Abi@M+A@>@h.@+Aj0@d+A@g@+AL@%t+Aō@$b+A@$@_+A@@a+A5@جj+A@ @+AE@+A@+A@P@ R$+A@6+A@@i+Aʓ@+A@+AfK@0+A@E+A@G+Axݨ@X+AV`@(+A@ +A@@+A@S@Ș+A@#ȯ@s+A8F@+AZ@M+Ah@n+A@^+A5@+A@%@ A+A@@ү+A@ @X+Aʵ@Ʋ+A@J@+A@`Ѷ@pw+At@+A@0@+A@b@ +A@p}+A~@H +Aȹ@|+A6V56e@+AY @\'@( ?+Ah/@`T+AE@_ʼnu +A|tS @~)$+Aok+AӝS@6p6q+Ae@iq x+AQ%@?)+AC݆>@#J|+AdZM@0.+AQ@+A51@M+A n@M+f+Ad@]Pϵ+AQ@+A@3+A4'@ C+A=F@+A_@}$+Ax@XI+A<@!M+A@+A@P< @+A` @+A@ @@+A@# @`%+A-0 @+A @/+A$@g+AX]@h+A_b @(+A2R @ho+A @h+Ak# @ +A @PI+A) @\+A_:@;+A@-+A@h~+A@+A@J@@dj+A@+A9 @+A@@+AF@+Az@؟+A@+A@@x+A$@H)l+A @0+A)@6+A@/z+Au@'h+AE@r+A@+AB@؝+AM,@ p+A @+A@@+Ae@`q+AM@+A@fU+A@0/+AT@4+A|@+A@p@x+A@N@B+A0@db+A`@X+AQ @B$+A @P+AT@H̋+A?@Ϡ+Az@.~+A_ @o+A@N@8E +AZ#@+A[3@+A+.@(+Av.@C+Af:@b+A2x9@+A@3@C+A@%#@(+Ae@3+A4@0+A8@+A@x +A@֏@+A@i@t+A2>@0G+Ax@+AB@+A@P@+A@@+A@@+A@ے+A@%@`2+A@ |n+A@@+A{ @H+A @`vu+A#@+AM%@p +A@@'@ R+Ad,@ Ε+A]s,@@$+A@,@Q+A7,@L+A1M3@%+A@ 3@`Ǜ+A-@+A@,@hD+A,@g+A@߫,@p +A@(@ +AZ(@`S++AD(@)E+A@//)@+A@g-@+A8.@ȓT+A)@!+A@-@1+A@Q-@x5+Aj5@+AA@h+AB@G+AE@`a+AF@(+A|G@ +AG@0. +AG@ +AG@5+A8G@w+AHF@p+A F@x(+AF@H+A@F@+A@ZG@n+AH@W+A[I@+AJ@u+AK@z$+A@gM@(\U+A@LO@+A]P@P8+A4{Q@+AQQ@xW+A"YQ@hr+A+O@(+AhEM@@3+AxL@+A@gL@0+A}L@h+A=K@+AD"M@!+AP@+A>V@g+A`@/+AzGd@+Ah@sA+ABg@8t~+A-c@+A)a@+A@`@ؚH+AQb@`1+A"'h@˼+A@N{k@x+A@!p@T,A@\s@(y,A(s@ۈ,AU/u@0S,Aau@IS,Av@(-,ASBz@(9m+A@,r|@+A8X}@+A@W@P+A @x+AzU@&+A'@@S +Afa@H+A@R@+A&@v+AĈ@ C+AH@P^&+A(@X+AĈ@H +A$z@+A5@X+A@8 +Ag@{&+A%cc@5x,+AXӦ@J,Azfz@җg ,AQ#;[@Bkݽ,As+6@<"l,A  @}n5,AZ@ ,A6Uo@Q,A)o@,Abo@,AÅvp@0 ,A"Xmp@0u,Ap@۰ K,A"8q@A2,A!wq@w],Aq@QAƄ,APaq@#,Ahq@P7en,A&q@C,Ap@qA,AbTp@-@G>,Aaqp@6:,AnwWp@=56,AHsBp@}d81,AE4Y4p@",,A-,p@zc',Ar+p@M",A4'`1p@E,AP=p@Am,A Pp@F,Aip@?{6,AL%Gp@x,A*p@x ,A6p@3,A4t@x1,A -u@=,Arv@Ñ,Am;zov@`崃,Aw@沃,A@w@[,A2x@,A{@`,A2}@n,Al ~@(D,A@A~@0,A@A~@/,AS~@гg,A@y~@,AD@P/,A 2@7,A*@`,Am@|F,A}@ ~,A@ߑ{@ \~,A(z@M~,A@8z@@:~,A@z@`O ~,Az@0},Az@Jg},Ax@;|,Au@xR|,AIr@ {,Am@e{,Adm@ {,A=n@0z,Ak@0y,Ah@Lhy,A@g@)y,A.g@ w,AIa@?Aw,A@.\@v,A@IX@u,A@Q@2t,AE@q,A}0@+ވq,AUv,@a!q,As5)@~׶p,A%wm%@ir]p,A !@Ƹp,AAÙ@o,A:@ Ro,Aoq@أo,A@?@ޝn,A @%Ȉn,A5p @+Pn,A@p:o,A}@- ;o,A+1^t@Io,Aj8R@W\>o,AE@6L>o,AT;9@p=o,AG`,@(J`=o,A @h'@*0om,A@Ym,Am@83Gi,A)l@>1i,ARmd@j+i,AF@C[Ih,A]  ^@4'h,A@fh,A%I;Z,A~@3eY,A1Yj@-Y,A(@SY,A@@{'=Y,A>q@xX,AHT@Ms;X,AD֬@q 1lX,A2@@%X,A@i@@ FX,A6B@WfsnX,A)O2@}W,A6y8@9 W,AHPU@"mW,A7H@tBvW,A@ѫέ@ mW,A2{N(@q"SW,A\=q@sW,As @{V,AѦ@U#W,A@s@Po2W,Az@0QY,A@?Y,A@5h@HY,AX@X,A@RX,A@@X,A@ _X,Al~2@''5X,A]@ X,AP1^@-W,AFO5@JW,AD@wGW,A@8W,A@G2@XIcW,As@Β0q@U,Ar @U,Amհ@cU,ANb@/U,A-@#T,A@G2@b)Q,A%@hO,A4}@@yN,AO*@eM,A@AM,A@ߏ@L,A@dL,AF@(L,A@L,A?]@8L,A^10@_BpL,Aw)@`QL,A@=z<(L,A-K@2"K,A1*@pTK,AT@ޱK,A@rWK,A@W@mI,Aʾ@0yF,AXƷ@![F,A ~L@dߏE,A(v@j)E,A@oAD,A?a@(POD,A?F@+,A:d@b"%,AS*@_Gg,A>T@zA,A@GÕ,AiAԲ@d ,Aܨp@%hx,A(NSqA*&{1 ,AP9Q,AE]K ,A4JAd3N,AOfhA,AAd3N,A_AE]K ,A}9A*&{1 ,A0ƾA%hx,A౬Ad ,A+WAGÕ,A7K_4AzA,AuvGMA팛,AWSA ,A`SA ,A@SAx,A`;SA؟,A`FTA<,A@XTA(,AIbTA,A ZWTA(y,A TTA ',AIbTA8,A`TA\,A`oTA,AKTAƐ,ATAPk,A`VUAP|,A@ϽUA0,A`VAH,AVA8,A UAa,AUAx k,A`)UAP,A UAͬ,ARUA_#,AhQAXu#,A*TA_$,ASApJO$,A4nRA8#,A JRA̰#,A JRAU#,ARA #,A ɰQAh.$,APA$,A ҝPA) %,AdPA%,A PAأ%,A)nOAW%,ANA8=%,A qoNAT%,Ai4NApd%,A`bMALd%,AaMA•%,A@>MAz%,ALAp%,ALAp &,AH_LA@&,A@>DLAf`&,ALA x&,A LA&,A@MA &,A/MA&,A %MAh&,A 8LA',ALAP',AHLA8',A`õLAf<',A`LA=z',A@6LA',A LA(\',A_LA# (,A`)LA#)(,AYMAأ9(,A`]MA`A(,A@gTNA](,A NAe(,A)NAo(,A)OA~(,ANAW(,A[NA`(,ARNAp(,A`]MA(,AMA(,AMAxF(,A@yLA(,ApQLA`(,A KA(,AKA`a(,A@KA$),AKAj),ALAq5),ALA-9),ARLA ;),A@%MA>),Ay%NA8EA),ANA@F),A@#dOA@S),AOAx),ApOA),AjOA),AýOA@),A]PAxW),APAx),AQAĸ),AQAP),AJRA),AORA*,A &,RA'-*,AQA;V*,A |QA@O*,A QAxF*,A-QA*,AxQA&*,APAX*,APPA*,A`[PAX*,AOAx*,AޤOA0*,ANA*,A NA`C+,A |NA0.3+,A3fNAPg9+,At*NA=+,A@%MAC+,A@GMAC+,AjEMA^+,A`LAPkq+,A@PoLAȇt+,A`dKAp r+,ABKAr+,A`ʘJAxs+,A@IAx>}+,A&UIA҇+,A`THAh.+,AYHAu+,AEHAdž+,AGA8+,AFAV+,A]EAz+,ApDAP,,Az&DA!,,AHCAfD,,A`ECA8e,,A +CA,,A@gTCAp,,AHCA,,AH/DA,,A3_DAp8,,A3iDAB-,A@rDAN-,A@DAt-,A׫DA̠-,A@>DAPf-,AMEA-,AEA-,A FA-,AHGGA-,A qaHAр-,AHAps-,AXIÁ-,AJA8-,ALAP8-,AHLAw-,ALA(_-,A#MA(W-,AfMAQ-,A NAH-,A3qOAZ-,A`fPABe-,APAp-,ARPAW-,A qPA8-,A"PAf-,A)OA`a-,A OA-,ANA-,AzMAǥ-,A uMA0.-,AMA-,A`ÕMA-,AVMA-,ALA-,A`)KA-,A )JAX.,A qHA`.,AEAk2.,AEAJ.,A@>DAp8|.,ACAp.,AzCAh3/,A 8DAh3/,A@DAf.,A]EA(\.,AFA8/,A`FAQ.,A3HA(.,ApJA0..,ARLA8.,AOAu.,APA!i.,AQAR.,AQAr.,AoRA蕏.,A`TBSA8V.,A hSA .,ASA.,AQSAHPI/,AOAH/,AhOAPZ/,AaNA/,A [MAx$/,A LA(0,A`JA)^0,AFHA˷0,A@ƘGAMu1,AHA;1,A UIA'lvWj2,A$&JAA2,Al_bW KAV3,A /KA4,A`LA(o4,A`ձMA4,A`NApw5,A3PAPwD7,AUAȓ6,A cXA_6,AGZA6,ANw[Ab6,A[A†7,A@]Aۜ7,A`B]Aؒ7,A[A7,AZAp7,A`ZAp!8,A [AC8,A ZAnb8,A0YAN8,A`2YAu8,ARYA8,A`f ZA8,A`ZA8,A`VZA`8,A |RZA)9,A@gYAk,9,A aYAأ9,A YA9,AVXA)9,A|XA59,A cXA8;9,A3XAkF9,A WA T9,AgWA\9,AHaWAc9,A@ [WAs9,AWA9,AWAB9,AaXA9,A XA9,AXA9,AXA9,A XAp:,A)XAz0:,A`4YA#S:,AYA(s:,A YA:,A`{ZAW:,A3ZA:,A^[A:,A[AV:,A[A`:,A=\A :,A\A8:,A \Aϣ:,A\A:,A]AX:,A<]Ap:,A,e]AБ:,A@]A؟:,A u]A7:,A ]AW:,A ^AXW:,A@ $^A˵:,A@<^A:,A`U^A :,A@n^Aӹ:,A`^A( :,Ap^A:,A^A :,A`^A:,A^A:,A ^A :,A |_A8:,A_Ap:,A@,:_Ap:,AW\_A@N:,A`f}_A:,AE_A:,A _Aj:,A2_A;,A_A#;,A`A@N;,A+`A;,AtF`Aȃ;,A_`A0&;,A@ x`A.;,A@`AL7;,A `A@;,Aྷ`AH;,A`A`R;,A@`Ap `;,A `A n;,A`A|;,A@>`AO;,A@`Ap;,A`A@q;,A`A`G;,A `A;,A`AK;,A&`Ag;,A`AX4;,AaA0;,AaA;,AR$aA.;,A#.aA¬;,A`5aAt;,A ;aAx:;,A`?aAp ;,A |@aA;,AJaA+;,AVaAX;,A baA(:;,AdpaAp;,AaAh;,AaA ;,AޞaA c;,A@aA@;,AaA0;;,AaA`;,A@aA };,A@aA;,A QbA8';,AbA;,A@+bAD;,A;bAhD;,AKbAP;,A[bA=;,A`]ibA;,AbA;,A cApI;,Ax[cA3;,A`cA؟;,A`cA;,A dA;,A@>HdA;,AqdAG;,A`dA*;,A dA0;,A`)dAXl;,AeAʿ;,A@eA;,AieA;,A`)eA;,A eA{;,AeA[;,A@fAأ;,AFfAQ;,AwpfAa;,AfA;,A@fA;,A@gfAS<,AgAp[<,A4gA<,AXgAY <,A&|gAK)<,A4gAЄ2<,AgA<<,AgAj?<,AgAE<,AgAȵV<,A@`.hAPZg<,AzahAp<,A)hAp,<,AhAHP<,A OhA$<,A SiA襟<,A)iA@Ϥ<,ACiAH<,A@P_iA <,A{iA<,AiA@µ<,A iA0̸<,A iA<,AiAPٻ<,AviAD<,AjA@<,A6jAp<,AVjA<,AxjA`<,A /jAH<,AjjAH<,AkA(<,A;kA07<,A |nkA <,A@ϢkApA<,A@ kAX,=,A@!lA0 =,A ElA8=,A|lAp=,A lA=,A@,lAO'=,A#*mAx.=,AemAН5=,A lmA+<=,A mAhQB=,AnAH=,A\nA`M=,A0nAHR=,AnAV=,AoAZ=,A@[oAu^=,A oAa=,A@oAhod=,A pAf=,AapAh=,ApA#j=,ApA/t=,ARqA~=,A@CFrA N=,A rAa=,A+sA̟=,ATtA=,AuA=,AڰuA=,A@^vA_=,A@rvAX9=,AvAЦ=,A`vAH=,A 1vAB=,A lvAu=,A@vA E=,A vA@=,ATvAʷ=,AwA=,AwA=,A`$wA#=,A`1wA=,A68wA/=,A@y@wAߓ=,AJwA=,A`DVwA`=,AcwA{=,A rwA@Ot=,A@wAl=,A`wAe=,A@wAa=,A@wA@^=,A ~wAZ=,A[wA8xW=,A-wA@T=,AxAOR=,AFxAHKP=,AX,xAN=,A@AxA0nM=,A`WxAL=,AFnxA(-L=,A@ƄxA)L=,AVxAL=,A@رxA]M=,A,xAN=,A6xA0.P=,AxAhT=,A ZAyAZ=,A`fyA_=,A@yAȥe=,A`&zA#l=,ARrzAp=,A zAؒt=,AzAPx=,A@{A+{=,AN{A }=,A`{A=,A {A =,A`{A =,A|A@=,A*=|Ax=,Ae|AΕ=,A@|AR=,A|A3=,A|Axm=,A$}A=,A |$}A8V=,AhR}A@=,Aq}AH)=,A}Ah=,A}Ax=,A~A=,A)~A 0=,AQ~Ah =,A,x~Ap==,A@~A}=,A ~A`>,A~A >,AA`>,A|'A>,AMA >,A@sA%>,AAPI2>,A`A2>>,AHAI>,A֠AT>,A A8_>,A SAd>,AAi>,A;Ao>,A8߁Au>,Ap A2|>,A@8A>,A`]dAp8>,AA>,AA>,AGA>,ABA>,AĥA>,A A>,A oA>,A@A>,A@߲A>,AՄA>,AA?,ADA@?,AQCA?,A@hA ?,AڎA` ?,A0A ?,AۅA ?,AA8#?,A )A#?,AzPA ?,AAl?,A` AC?,A@WeA?,AAT ?,AA ?,AyAЄ ?,A9A ?,AA ?,AA?,AʈA?,A`܈Ab?,A@%A?,A]AX>,AA>,AgAB>,A,A>,A`"7AЄ>,ABAz>,A`)PA>,A^A3>,AnA@>,ATA&>,A A>,AA>,A A>,A*܉A>,A Av>,A@E Aa>,A` CA>,AafA>,AA>,AAh^>,A}ӊA8>,AA>,A@y Ae>,AqGAȺ>,AnA>,AוAXy>,AAԶ>,As;A>,A`TAHܶ>,A3Az>,A@A@>,A@QAH>,A͉A>,A@Ap=>,A@A(>,A'A >,AXUAp>,A`A>,AA?>,AiߎA88>,A@ A>,A`2:A>,A`fAм>,A`KA>,A@ AP'>,AFAt>,A#ϐAw>,AXAH.?,AA;?,A A`?,A9A?,AeA`?,AǑA?,AjA?,A@Au ?,AAu ?,A=>A[?,A@Ah?,AA?,A`ZA"?,AA&?,AA*?,A 8=AR.?,A3AU1?,AA3?,A4A5?,A` GA`7?,A?A8?,A͖APY9?,AA09?,A@WTAp=9?,A×AT9?,AA@9?,AAAXF8?,A`mA 7?,A@5A5?,A?A3?,AmAp81?,AA83?,A3A?,AӛA ?,AA ?,AUAP?,AHAh>,A`T֜A >,A A(>,A@TAX>,A<A&>,AwA`%>,A@A>,A AAHK>,AR*A8t>,AfA@>,A@A>,A AP>,AAHz>,A]A0>,AA>,A`ڡA>,AA̪>,AXA>,A`ӣAC>,AA2>,Ao2AP>,A`aA Ҭ>,AA>,A Aɫ>,A`AЦ>,A@Ap>,A@ OA0%>,A`}AǤ>,AApΣ>,A`KץAp>,A/A@>,AyA>,AA ޝ>,AGA(>,A ۮAHܜ>,AA0f>,A`ZAe>,A螨A0ٮ>,AmAп>,A;%A`>,AFgA`>,A@A>,A AH>,A &4Ap>,A2A>,A ̪A8V>,A}A8g>,AhA`?,AAhQ?,AA)?,AVA?,A ݬA8E,?,A6eAA:?,AMAG?,AvAU?,ARA a?,A8A ^n?,AAmz?,AA?,A@>*Aγk?,A-D!tA;sN?,AXg:A 1hG,A҆A=N,Ao3AV,AA:,WN^,Ao3A{re,A҆A+Sm,AXg:A5x,t,Ai\AJy{,A=m~Aҗg ,A݊ABk5,A֘xA<",AdA}n5 ,A uvGMA-AȜAB>-AHdA̱-AOEA麢-A4P'Aل-AP9 Ak4K-A)NSAOl-AnBA-AHdA>-AHdA8v,A A;|-A@rA(8W-AlAoX-A`+mA tX-AnAx5Y-AmA#Y-ATMlAPgpZ-A ukAZ-A kA`1n\-AlA]-A@pjA@2^-A iA Tb-A`91_Ab-AB_Ab-A]A0c-A`\A;d-A`9YAسd-AWA`/f-A$PAPM h-AFAGh-A ~GAxi-ARHAeKi-A &GAi-AFAj-AGAi-AhIAFsi-A_IA؟ci-AIAi-A-JA`i-AJAyTi-AwJAgi-A@+KAitj-AOA8֧j-A@$NA0!j-AaLAGj-A4KAði-A IAH-j-A@,YGA`vj-AFAгmj-A@^FA0j-AдEAk-AGAH!k-A@GA`Pj-AzEA((k-A`GDAk-A CAxIj-A`LFA88Cj-A$ FAh/j-A0*EA oj-A`iDAj-ABAXk-A cDAPrk-A QWDAbk-A^EAHl-AaCA/k-A2BAh/k-A3AA0l-ACA !l-ABA@l-AjBA l-A@RBAHl-A`vBA 6l-A[BAl-AAAx-k-AfBA,l-A "BAl-A0,BAk-A5AA&l-A AA,l-A?$AA2l-A AAl-A@E@A l-Aw@A,l-A`B@Ap0k-A5AAk-A@AA^k-A@Ak-A4T@AXk-A`?Al-A?AAjm-A@W]FAp m-A 8xFA +n-A@FAKn-A`}FAX n-A@'FA +n-AvBFAîl-A@MAATl-AX@AHm-A}XAA Pm-Ai?Am-A >Aذ#m-Aݒ>ATl-A>A,l-A4=Ayl-A@;Afm-A<A8nm-A<Afm-A =A`1m-AR:Axm-Ao:Am-A l?:Ad|m-A`9Awm-A9Ax9m-A"9A8sYm-A`T;8Am-Ai8Am-A@y8A m-As 8ATn-A uK8A=n-AU6AHP(n-A 5A~9n-A4An-A@4Ajn-A&4A@tn-A@P74A:~n-A o3Ann-A`3Ayn-A2A@n-A`3An-A A@3Ao-A3APo-Ay3Ao-A~3AP*o-A E3Ah^o-Av#3A+o-Ay2An-AF2A5n-A`2An-A2j2AYn-Ag1A_n-AE1An-A1A05o-A@`02A;o-A@P1Amn-A0A*{n-A&-Ahn-A-An-A+A(m-A+Am-Az-APl-A ,APm-AN:)Ap-A?+A{p-At)Ahj-A&Aj-AI"A+&q-AL&A>Cq-A$ATj-AvA'jk-AAlm-ApA([bm-AAzm-AAm-A@Ah;q-A@yAq-AVA@m-A /A> &m-A4ܨAm:`m-AԘAm-A0A7 ?m-A`{AJKTm-A tnAͰCAm-A]JdAm-AKs[Am-A+KUA"sm-AmQA +m-A[nPAcm-AmLRA@m-AWA@m-AsAXk-AVAXk-A5Ak-AAȓj-AwAk-A$ARk-A*A@Tk-A@ qA"k-AFA#k-A@APZQk-A`T#AqVk-AͳAErk-AAIk-AAk-AA k-AͳAH)l-A <A-l-AAl-A^\AVl-AA~l-A@5Al-ADAl-AAl-A2Aol-A`BAj-A@A-j-A{Auj-A"XAp[j-AQA@€p-A@bAљp-A A(qp-A+ACj-AqA9[j-AߚA0.j-A@Aj-A A0j:l-AൈA=l-A`YAPyl-A@mAPyl-A@Am-AEA m-A Aql-A@,Aql-AAAAPk-AvA7ak-A[A7ak-A2-AHrj-AA+j-A}Ax {j-A QA@cj-ARA}k-AAk-A9A0qj-A@ƲA@k-A@3A8'/k-A`ڞAHk-A`]#Avu-AAu-A@ApxWk-AKAQNk-A ؚAh3l-A lA8l-AFA@Ql-A@AhVl-A AzSl-AAxrl-A\AȲl-AzAk-A[zApk-A yA8gk-AD\xA8"k-A@GwA&l-AZpAl-A]A{n-A LAXo-AgILA&o-A JAp8Vo-AEIA[~o-AOHA}o-AGA`Go-A3WGAPo-A@6GAo-AGAXp-A FAp]p-AMDAhp-ACASip-A[CA hp-A@oCAop-ACA([p-ABA.p-A XBA(p-Af@AA&p-AI@A`p-A@{@A8p-A?A`q-A>Aq-A`;>A@q-A=A(q-AО=AFq-Af=Arq-A;Avq-AІ;Avq-A;;AP q-A` :Ahēq-A`:A]q-A՚9Aq-A՚9Axq-A09Aq-A@9Abq-A9AUq-A@v9APq-AZ9A(zq-A(R9AXq-AaE9A@q-A 8Aq-A 8Aq-A@i{8Aq-A`D8A."r-A7Ar-A`7A0*r-A@77Ar-A5AhWs-A&3A#Qs-A3AJTt-A/Au-A,A +u-A,Aw-A@>#Ax-AAx-A@>A0{h0-AV-5A@5%-AAAQ-A A#-AA` -AR A`-AA0*V-A@PA00-AfA(>-A#A-AnA-AA88-AX=A--A` AB؅-A;A0{-AmA6-AhwApN-A4AP ˆ-A`AB-A`)|A#"-A`LA(K-ARAI-A qAB-A`m}A`C-A`A@$-ARAB-A@AxKz-A`AX#i-A#A`d-AdA#S-AAXM-AApD-AwA7-ApTAp4-ÄABL-A HApE-ApA-AEA-ArAP8O-AR>A`·-AQAxK;-A൪AB-AjAh-AMDAP<#-AbA,-A ZvAQ-AAݰ%b-AItAOCeq-AVyArR~-AlA#UN-A#4WaAW-A 1"Af-AE6\A}W-A8WGWAf-A)A:!-AnuA :-AfAl-AAȺ-A൐A-AAq-A9AX_-A ۫A4-AAh;5-A AA8VI-AAb-A`A9-A_Ah⚊-A'A(>-AQAP'-A ܯA;|-A}8AOl|-AnB8AO-A98 AG-A A=?-A98 A 1X8-A[u- AA%hh,A౬Ad -A+AGÕ-A7K_%Az1 -AuvA_GW-AAb"-A3A2-h.-A]EA J K-AO LAC-A cLA`!-A@yLA i&-ALA&&-AMAp'-A`NAH='-A NA'-A`NAX'-A MA''-AMA(-A"zMA)(-A9aMA U(-A?\MA_}(-AHRMAK(-A@5fMAP|(-AMA8(-AMA)-AMA+-ALLAp--AJA--AKA8s --A`KA&--A-KAj,-ADLAP,-A@xLA --A@*LA(\$--A[LA:--AB8LA--A KAi--A-AOA?-A;QA{?-A [QA>?-A`ӈRAxmdC-AYAx@D-A@ZAuF-A;_A cG-AA`A¯I-AfAHiI-AXgA0%J-A}}gA&J-AFgAJ-A uMiAaaK-A 8kACN-A WpAqO-AerAh.P-A@rAQ-A`rAQ-AzurA8ZQ-ARrAQ-A1rA0UQ-A1qAR-AVqA` 0UR-A1&;pAS2R-AaMkpARR-AoAK S-AސoA1LS-Ap,A Ap0,AA;|-A}8A 0-A`Aps-A7AP<-AAw-A`)A +ύ-AAa-ABԣAh.-AfAM-A ZA0wY-AwA8-A賛AX-A&1AU-ARAW-AߒA-AzAHn4-A@yAQ-A`AH܄-AA`ˑ-A`KA*-AIAh$-A TAج-A}A-A(2AP-A AQ-A tA-A`íA`đ-A`DA&-AbAؒ-AAƑ-A`K$Aϑ-A⹍Az-A@tA -AOA-AA -A@`xA1)-A$NA(-A$A@C-Ah}A(-A@`=yA Ȕ-A@%QwA`֔-AZvAf-AItA-AAsAf1-A|pA>-AľoA N-A@`oAG-A]A3E!-Ayя'\AWN7-Ag,[AsH-A~,ZA!MU-AA@(YA^-A#v!XAc-AWALKf-Ag1=}ZVA(pd-AWW &UA3.U^-AbiTAdAS-Ak{0&TAȔC-A \rSAxǕ-A (*QA㐕-AଧMA:Y-A~%MAڹk-AWRLA[-AsumLAPM-A`)KA-A@HA;t-A`{GA ×-A+DAW-AzCAh#-ACA$-ACA/-ABA-A@BA-AxBA@-A lhBAԗ-A sBA-A,BAJ-ABA R-AKCAp-A`CAHi-A/7DAo-A 1%DA8ک-ACA@Wk-A@>#CAB-A`BAH-As88A1-A7AU-A@E7A@D-A`)7AX1-Ai AG]-A4A ݇-AVAxч-A Ax -A@A-A A8Ƈ-A(AY-A@A-ASA]-A A-A A@S~-A ;AV|-AhcA W|-ATuA0g}-A& Aȓ}-A&Aw}-A cҿAHN}-AZAl }-A`{AV|-AکA_z-AzAw-A$Ar-A 8oAEr-ABA0q-A@Aq-A0cAuo-A`JA8gn-A}AhQOn-AzAAp-AOuAPp-AtA,]n-A%yAH m-A`KowACj-AiAxh9i-A=eAPg-A4VA(g-AXeVAg-A:UAP h-Aj%UA(h-A'UAh-A`UAf%h-AzTA/h-A`"TAH8h-A |TA`Bh-A TA Kh-A@`TAISh-AaTAj[h-A TAech-A@ TA5kh-ATArh-ANJTA:zh-A (zTAXdh-A@gTAxKh-A/TTAxh-A`=TAxh-A$TAhh-A TAh-A@ySAPh-ASAh-A&SAh-A@SA0h-A|SAh-A\SA`Ph-A`Bi-A<RAIi-ARA^Ui-AQAai-A)QAxhi-A@>QA`oi-AQA0vi-AQA?}i-A QAТi-AޗQAPi-AdQA0{i-AiQAi-A`PQAi-AX6QAi-A QA舤i-APAྪi-APA0Hi-ADPA8"i-A PAXJi-AyPA i-A]PAxi-A@APAwi-A&PAXi-A@ PA8i-AOAi-A OA6i-A@OA@i-AOAj-AOA j-A OAbj-AOAj-A`OAp[$j-A c'PA+j-A-TPA2j-APA0v5j-A]PAe8j-A JPA0;j-A PA>j-A`]PA/Bj-A`]PAXEj-A PAHj-APApJLj-A PAyOj-APAPxRj-APAPPAx^j-A`0PAH^j-A#PA`]j-APAXj-AOARj-A@OALj-A@.OAȶEj-ApOAUFj-ASOA_Gj-AK7OAHj-A@OAIj-AG OAx-Jj-ARNA0Jj-A`DNAIj-AKNA]Ij-A6NA0Hj-A@NA0Gj-A JNAEj-A NA8Cj-A@NAFAj-ANAl>j-A@rNAx>;j-A`eNA7j-AKZNA4j-A 8PNAH 0j-A GNA+j-A@NA@#j-A`;NA`Cj-Ad3NAj-A`)NA0j-A MAti-A`MAi-AMAi-A MAhi-A@MA8i-AMAȘi-AMAi-AുMAi-AFMAi-AMAi-A@MAHi-A`BMAi-AjMA8'i-A`MAi-A MAi-AMA^i-A`TMAh;i-A@MAi-AMA@i-A`MAHni-AMA؎i-A(NAi-A`TPNAi-A wNAPs,AC eAop,A0deA ],AH^ eANQn,A=%dA,}H,A GhdAپ,A3dA(q,Aa"eAf%|,AsveA))0,AeAuZo4,AqNLfAtOa,Ai3fA ,A$Y`gA`=,A^hAy,A3hALon*,A3iA6v,,AQzA,A`zA虙,A}zAn,A׋{A,A@rxAX ,A3wA,A wA),AJwA.,A`7wAHL[,AvA``,AևvA؎,A@uA,A`uA5,AFuAH,A vA8,AuA,A`uA,A`uA=,A@)uAؚ,AhtAX,AJtA ,A`\tA߱,AuÄ,A2uA8 ,A@wA,A`wA,A3wA,AGsxA',AuAQ#,A uAX0,A quA83,A=(tA),AtAW,AwA.,AzA ,A A4,A`KAf_,A@ងA,A3Au,ARAf,A \ُA,A A,A Au,A`)|A,AA`,A쥙A;,AHA)L,A Ak,AhA`a ,A3wAG,A`gAM ,AWA,A`fݗAL,A9AP,A s˕A~,A@2A8,A`)AHa,AMAp8,A:A,A@ᔓAx,A \A,AfA,A`ߖA,AAz,AA),A=A@d,AɶA:,AA>,A*ɑA,A4AP",AA',A3ӑA,A =A,AQA؞,AA80,A̓A 0,A|AV,AAX ,AA,A@ːA(:,A[/A /,AKA ,AA,ALaA0{,AAs,AA@F,A]!ATo,AԎAu,AAj,A^A8e,A`rAJ,A=A/,Ag~AT',A@CXA!,AV?AH,AA,A`)4AHK<,AA`Ǽ,AďAh,AhA8,A@WRA,A@ A|,ARAHnK,Aa^A,ApAB,A`΍AA,AA8,A"A' ,A`K A7,AoAh>,A WAi,A`)A,AApp,AˋAX ,A`AǼ,AA0ȷ,A`bmAx,AjAho,AފA0{,A uՊA=x,A`SAxk,A4A e,A.AG,AAff,A4uAu,A`A,A |:A,A NA`a#,AׯAg(,AĝAS,AvAY,AA,AMA,A 87AH,AqA0H,A A R,A |A,AAW,AA# ,ApaAȶ,AIA,A@pԇApJ,A㺇AX$,AoAf,A)*A,AAX,AGAf,ALJA,A`XA`Ы,AjA3,A sAH{,AF~A0 ,A@A)C,A=6AXp,A |Av,AxA,A܆Ap,A džAh,A0-A,A^A,A]}A ,A@%gA`#,A@A;,A9Ap_,A@W9AHm,A`A@F,AZAsF,A 8\A ,AA ,AϚA&T,,AkwiA_6,A3AK,AsDğA/I8,AJܵ߄A虉,APAT`|,A` A%),A"A|$,AJ?0MAd엒O,AfRA&}")v,ADūA@,A@,Ae8,ALA*,A\Az ,AQ)2A,AfųA6x,AdYA9,A`APf,AA,0,A_2_A{,A?Y<AfAR,A9Ab,AA<,AA,,A@A,A0Ap,A qA,A A8,AAp8,A`)AH0,AAhv,AzA RS,A@NAhs,AvAp8,A`)At,AHA8,A A,A,A8,AAm^礢,AH.A2V,AHQAO,A ;AI,A?$5tAIGY,A<0NAp,A`KAkS,A)A(.P,AY%A &-,A'w_A,A@gA,b,AmAl,,AKAc ,A̹A,ALAzw,ALANQ5,AF8?}wA,A@yHA:,AVuj,Ar0K,A=,Av,A~$AP,AyAYB,AA +,A'RAI%EO,A)DAkZ,AƌAޱ^V,A&^ >A[,AAC,A@-Ag%*;,A AD^+,A5Ax},AIA&,A{?A,A`"QA,A`VA@H,A`VA9v,A<Aԁ,A}'*Aso,AshA,Ŧ,A"oߘA[,A3FAC,AmAA`,AukUApC,AEfA7j,A+e*Ac,A:AZy,AAD,AAh,A6zAh3*,A[A@q ,AhAH,A;A`Ci,AOA ,A@]A@,A@*A(~,A`sAn,A]WA8,AAγ,AA R,A lA^,AyKA,AA,AApJ,A Ap,A@gAXt,A =A?,A`mAP,A@,A,A A(%,AVAxzA,A`mKAK,AA8Qf,A`BA0p,A@iAf,AAБ,A`A,A@,9A@},AA@,AA8,A@DAP,AA,Ak[AC2,AAB,ApA,AA+,AzA,AAHR,ADA7H,AA0,A#A`],A`2A(z6,A0CAI4,AMA,ANA ,A@A,A-A@B,AA!,A2A,AA ,A6Aȃ,A &A,A@#Ax,A@TA%,A@WA,A`A6,A:A>,AAL,AAR,AAnt,AAHv,A`AR,ASAT,A@>JAb,AApg,AvA`2,A6-A>,A@ A,AA,AA,A4Ah,A9A X,A@AxhB,AmA8,AALJ,A`mAX,A_AX ,A@υA,A}OA,A`A,Aj&Ac,A` Ac,A`Aj,AjA@,AA,AAp,,AVA,,AAX,A`A,AtA,A`ܪA,A൭AxzT,A`KTA؎.,AQA,AZ0AC-,A:AFa,ABsijXA`#u,A"A,BS,AOqAAc^6,ASA]z.,AYdA },A bBAP(,Ajb=Axm,A`AUN,A@/As3,AAk?,A{AG,AnyA,R,AOAg,AA s,AQA8,AyA",AIA(,AAZ,A A,ArAx-,A-?A,A*%AH),AIA@,A@A m,AB+A0v,AA0;,A sAp0,A`Ai,A`A ,A`!A ,A@A W,AoA(J,A A,A൭A,A@A@s,A Ahb`,A@5RA19,A`A&,A@#*AR,A}PA,AAh,A`A,A@Ap,AHAq,A@A{d,A5AE,A@ UA@9,A ɁA,AA(,AAx),A?Ah,A9A0,AB6A,A ZA,AA,A@Ak,AiwAc,AACA,A`A7,As5AX 8,AZA,,AA~,A`Al,AAxK,A஫A),A;A,A A,AA ,A`nA@(,AA`4,A`BAKQ,AaAo,A 0AHP,Ac"A,AA0,AA,ATA,A A p ,A@A2,A |A@,AqAg,ALYA 0u,A'A,A uA7,A 8XA,A<A\,AA ,A`A,AA@ ,AױA,A}A$,AA(-,A@AH,AA,A Ahճ,AA,A@AP*,AEAd,A`WA,AA0x,AFAj,AcA,AzA0,A@A`,A A8,A?A:,A$A0,AxA`,AoA,A`A ,A@A,AA8 ,A1A 6,AA;,A@AO,AZA]S,AAg,A AKA`1P,A@iAYS,A` A k,A@WAdl,Ad]AH,A<A0,A`ôA;p,A q'A(~e,AA &,AA`%,A`TA! ,A`Ax-,AQA0{,AH A,A+A0,AA,A|eAX,A YA,A BA[,A ZA,A8=A,,A|0Al,A`A@q,A@AhQ,A cA`,A /A,AAL,A[*AHM,AAH,A^AJ1,A6AP,,AqA@(,AׂA,ApKA6,A@.A2),AA(9/,AAذ_,AAu,A@Aث,A޽A@~,A Aؚ,AhYA0f,AոA},AVA,A`0A`,ALA,AteAH,AAH,A`vA,AmyA`,AӭA,AAq ,AK"AȤ,,AA/,A@iAH:,AfbAp0,A@G8A,A@A),AAg,A֞Ar,A lAvx,AtA,A@ AH,AേA ,AA@ ,AsA,A,A4:,AA<,AA`c,A lLAq,A A4,APA ,A _A,A}A0,AA,AA,AA,AvA/,AAH.,AA0,Ah+A,Ah+A@Ӭ,A`"Aى,A|A,A RA8Iq,AA` R,A@gA`E,A[A ;,A@0A.,ABAHP%,AOA@,A@ A ,AનA,AarAԢ,A5A8,A@dA,A<A,A`A,A =A,A`SAu,A+A8g,AA,AyAX,AtAE),A{A R;,A`AN\,A@QA\,A~A8b,,A;A83,A`TaA(m{,A LA,A@]A(,AA,AAȃ,AMA,AAN,A ZA,AA,A A ,A lA(,AّAi,ASA,AէA`,AzbA`,AA,AA#,A |Ap,A |XA@W,A GA,AA0Ԇ,AA@v,AA,AŶA09,AIA ,AAې,A`Av,AAk,ADAM,A ZAR<,A`A7%,AaFA,AɳA,AA,AAd,AtAa,A`vA,A`A8,A A0;,A9AY{,AAX9g,A@Ah,A@CA@,A)A0,A`]qAF,AAh,A AN,A:A!,AAV,A)AI,AA@,APA,AcA",AA,A&AP,A[<Ay+,AAAP|,AgAx(,A`Ao,AA,A@9A`,A Axz,A`*Aep,AA Z,APAX ,A@A+,AA[,A`NA,A`]A(,ATA8w,Ao'A,AA`,A Ax,A@EAh,A A,A`)A,A Ah,AA,A{A0G,A`TSA@,AA,A4AX,ARA%,A`d4A؎2,A`A(I,AAV,A qzAbp,AAu,A@Ab,A`A,AA,A@Aw,AAU,A5AT,A`A,AމA( ,A,zA(),ApWA8,AA:,AA _(,AjAE,A=A,A}A ,A̻Ap,AA8x,A`vA8,A@5gA{,A`Ag,A`ݾA0w,A@NA,AOMAג,A@dAr,A ޿Al,A@AK,ArAyF,AA8',A lA!!,AA,AӆAXh,AA,AjA,A cAY,AդA٦,AgAج,A`3AL,AOA8k,A@Ac,AA,AvA,AAp,A !AP^,A_A0,A bAXJ,AA <,AWRAh ,Ay6A@B2,A`A@59,AADb,AANh,A˿AO,A*AV,AAH,AuA,A u^Apk,AA,A@GA@q!,A 8A,',A`KAP,A QAV,A:Ao,A@ڻA,AAXۣ,A@A,AտAa,AAw,ANAȺh,AA,AA(z,ATAj,A@AK,A@AD,A Ap,AA,A Z A,Am+Ab,A`PA>,AA`1,AA^Z,A՚A%,A`eA,A@,A0*,AA,AiA,AվAhL,AA S,A`A,A Q:A,A"A#,AA#,A`HA.,AjA(,AwA@),A 8˺A@F5,A A(U,A`A]m,A ƹAxw,A JA,ADA3,AʇAB,ANAPk,A@G7A{,A nA0,A@ynA ,A?A,AڼAX,AAX2,AA0X,A$A7],A`!AxiS,A@5A&,A A*,AsCA,A@pA襪,AXAL,A dA,AA,A@,A,AɶA~,A4A,ACAȋ,A 8XA ,ARA(,AHAA,AմA(K,A`AE,AA,A@OAX ,AZA,AA8ɓ,A A]j,A`AV,A`An,AA8Q,A`2fAo,A`-A@,A@A6,A?Axif,AϽAЕ,A,A؛,A@YA@%,AלA`,ApA,AH-A o,A}Aa,A A,A`լAw,A`dA(q,A A,A۸A,AׯA ,A ߸A̚,A@赸A$,A@A,AR~A,AR8A,A |ηAxh,A`A,A 8<Ax ,A@i"A1,A`ܑAP9,A uAc,A׵AHi,AA,A`"AM,A?A,,A`]JA(,A1A,AxA,A4_A/9,A@²AH,A^ҲAs,A ۛA$,A_0A8I,AزA07,A AG,A hA(im,AֲAha,A`ٲA`J,A@vA3,AAJ),ApA",A SA0,ARAO,AA@=,A$NA`*,A`XAH,A6A8E,A?̲A,Av-AD,AkAX,AAN,AȵAG,AAXWA,A A ,A@ؽAP8,AAi,AʗAp,A@A2,A ҾA I,A[Ah3,A`f0AB,ARA0>,A`;Ap",AA,A)zA,A3WA`,AٶA,AյA,AQuAHP,A@y>Aȩ,A GAև,AmAHm,ADA,AٮAp_,A ®AH`,A첫AS,A |A,AAy,AA,AMA#p,A`Afd,A4Ax:,AzA(,Az8A,AkDiDAG,Am½IAG,A`THAK,AÁLAge j,AMB<ANO,A'*$A`6,AA,AsA,A?1A o,A AXW,A A,AA,AA_,A@ A`,A`)A0٪,A{A0,A;APڕ,AVAՏ,AtA,A@A[,AVA^,A`pA,A`AV,AOAW},A=AX{,A.A =y,AAPw,A Av,AAPu,A`Au,A cٴAHu,A`ǴAZv,A ALw,A AHx,A`KAUz,Az|A0|,A|iAXR,AWA|,AFA@,AH7A跋,A!A ,A- A0,A;Aȥ,A@%ڳA`,AA8,A`)A3,AܲAm,A ~IJA2,A@!AH,AAU,A`{A,AbA؛,AzJA,A2AR,AA(,,A`A(,A@pAx,A ɱAG,AAX,A` Ae,A^A,AqiA@,A A,A԰A,A`;A,A^AÌ,A3rA,ARAX ,A@>4An,A3APl,A`gA~j,AAA g,A@Aod,AAh`,A`ϮA0Y^,AAhb\,AࣰAZ,AA`Y,AAY,A}AX,ALkAX,A}YA Y,A GAأZ,A6Ah,A APi,ATdAh,ACAHh,A"Ag,AFA؁e,AA c,A"¬AP8a,AൢAu^,A׃A{Y,A^AT,A c:A@BN,A /Ap H,AA(mA,A`2ԫAp:,AA`5,AAȔ0,AA,,A A',AwA( $,AeA؎ ,A`mQA0v,A <A,A4'A(~,A A0,A`A@,AAL,A ʪA,AtA,A Ap,AA,AA,A@yAx),A[jA,A`[A4,AMAp,A`@A ,A 5A8$,AcA8(,A A+,AީAH-,ATA`a/,A4AP1,AnAE3,A"fA5,AiIA8,A@`-A`<,A@!A=@,AAhD,AdިAH!I,AƨAN,AA@T,AzAZ,AtAs_,A`"VA(~d,Ai6A`%i,AAfm,A=A@s,A=ŧAPz,A3Axi,A-jA,A=>Al,AA虗,AA`q,A@`ǦA,A띦A`,AV~Aª,A^A ,AM=Ah,AA ,AxA(ܰ,A@إA@1,A`A@,AՕA0],A`BuA8,AfUA؛,Aa6AX,ARA,A`APo,AѤA ,AAL,A_As,ALA,AwAHˎ,A}gA,AWAPk,A`IA),A@><At,AR"Ap,A q A,AA(,AۣA,A@ţA6,AoA,A@Ah,AィA,A eA(q,A-GAp,A3'A0,AA,A=Aذ,A`vA,AAX,A΢A,A`2A ,A@ؤAH ,A 䎢A ,AwxA ,AaAL,AJA,A7Apx ,A Z%Ao ,A A ,A (A(, ,AA,AHߡA8g,A ϡA,AAM,AA0j,AAP,A0A,AQAH,A`A,A`rA,A#bA>,AQAB,AAAh,A@A,A A,AƠA,A`KA ,A@xAH,ARA,A-A,A`TAp,A@GAS$,AA),A؟A83/,A4ǟA5,A@AH<,AॴA0B,A A0H,AAN,AAkP,AvAXQ,A@C_AR,AGAR,A/AR,A Ah@R,AA4Q,AAO,AОA`N,AAuM,A 䔞A@L,AvAL,AHXAM,A`9A8M,AAN,A@AkP,A`A`S,AaA!W,A A8[,A }A_,A^Ad,A AAP|k,AA0r,AAz,AҜA,AA.,AZA8I,A@rAx>,AA@,A (A,AAʒ,AAP,A"A8V,AuA,A iAXl,A0]Aߘ,AMPA,AGCA0,A5A,A (A(,AA8,AA(ʓ,AA(,AAB,A A,AAج,A A,A@ܛA],A2ٛA ,AכA |,A֛A]y,A כAv,AڛAr,A ߛA n,AdԛA@k,ARțAg,A`A@d,AVAb,AAP_,A@ A],A zA'\,AhAx[,A@UAVZ,ACAZ,A@/AGZ,A A=Z,AAZ,AޚAq[,AൿA\,AAXW^,A@A0j`,A@dAb,AGAe,A`)*Ai,A AXl,AۙAo,ApA|r,A 늙APt,AbA{v,A8A`w,A \AP7x,A`A x,A` Aw,AȘAhv,A`;A8Vu,A6A`s,AAXlq,AmA`n,A@NXAXk,ACAh,A0A e,A@pAa,A A8\,AxA@AX,A`A1,AA#,A@A #,A$A9,A}A$,A= AA,A@A _`,AJAxd{,A ԔA,A9nA0,A`.A2,AA,A拓Ap,A@(A@X,A iA8b ,AAK,A+ґA,A@ǑAV,A@ϜA },A 3Awq,A@5ӐAe,A]`A0,A`,Ax],AKsA },A@ A,A`ΊA,A=Ah,AsAZ,A@!A,A Ap c,A qAps,AoAP,AA 4,A@$A?,A`A,AA ,AjA(,A@5Ah,A@ApZ,A@>Axh,A |"|A ,A |"|A,AWg}A,,A0}A,A{A8,A3zAp,AzAx,A {A,A`K{A@,A@qA,AA ,A@AP@,AՁA ,A>zA,A2yA0j,A`{A,A`]|Ai,A|A+蒞,AJ|{AEׂ,A({AEf,AB{AJ.dK,Ao?4t{A"e2,Ae1+{A p,A zA|/,AIL$zAD,AQvfyA&,AxA[I4z,AKjýwARjZ,A@vALH~=,Aӹˆ vAP$,A|#uA"#,A q\tA,AsA0G,AerA0,AqAX,A`MmAг",AkAH,A@EkA,A kA8tl,AHlA,A HlA^u_,A!>kA,A#jA,A \iA+,AiAX([,A`hA,A`KvgAg,A`DfApx,A`ohA,A@DgAh1,A?fAC-,ARaA},A%^A@$,A`T]Ah,A YA(h0,A;UA/,Ap;UAP,AlQAh';:,A PAx,AZ)OA렁,Ab9OA +,A"NA,A@WMAp,A MAk,ALAI ,A 8JAZ!,A`)JA`,,A`JAȺ;,A`JAO,A A4JA,A`mJAX9,AJAh,AdJAТ,AvHA +0,AEFAy,AMHA_M,A6HAJD,AHA ,A A=HA,A@y>GAO,A EAځd>߿,ADAxgݿ,A]X@ADAlܿ,A%DA8Qܿ,AvDAԤܿ,A74_DA ݿ,AB JDA?vR߿,A=`ͫDAi,A ۟DAx,AfDA,A/DAh.,A<#DA!>,A&DAo,ADA ,A ECA3 ,A*CA5 ,AʝCAH ,A;CA8\+ ,AіҝCA{} ,AwCAf#,AYvCAX,AfCA Rٿ,AJEBA,AVBA%_,Aζ?BA1 ,A*4BAS^u,AI6BA-Rc,A DBAQ,AkD]BAA,ABAFe4,ACBA',A{BA ,A@lBAb_,AmVFBA,ABAKf [ݾ,AHAAjm¾,A8>AA,A`ՖAA`,A@֒AA .^,ANAADn,A,gAA|\,A /AAAHz,A [>A(,A&7A,A`D_6APޖ,A 5APZ,A4A,A Z*3AϷ,A}1AH,Aw1A+,A1A@,A0AH,A`0A!,A0Aߺ,A d0A(Һ,A@h0Ah",A6=1AF,AP1AȺ,Ah<1Ay,AV1AV,A{0A@3,A//A0+,A`/A6,A /A >,A[/A(@,A=6/Axֹ,A@6-A8,A;k-AW,A-Ax,Aྚ-AÛ,A@֍-A!,A V-Ap|,A@,A,A@*Aѹ,A`9s*AȘ,AP,AcKF&,A~g+A A:{,A*An>,A#&TŻ)A 1,AR(Aњη,A'A,AxGAA,A`v^Ax԰,A@G'AW,AOA,AHA(-,A| APE,A`m Aa,A Ap5,A6 Ap,A@ A(,A | A ,A \ A`ޭ,A Aխ,A@ A(ŭ,Aa A@ӫ,A@ A(薭,A A8,A > A;,A8v Aw,A` AXys,A@r Ahe,A AXa,A A؁K,A@,A7,A AŪ,A`_A&,AAh@,A`AD,AA_,A@AG,A@A",A`K?A,A[A9թ,AzAo,A@AxNJ,A@`0 AAؽe,A2-AI@,A@,A[@ȶd,A@>z@x,A3@,A@x\,A@w@k,A@4),Aw@,Aa@P,Aq-@(ß,A@@,A@/@8,A@@O,A)@<,A@p@,A@@k,A@N@Z,A[@t,AǼ@i,Ai @(9/,A&޺@],A@͹@p,AVD@,A@@ 7,AA@P ,A2@0,Aƴ@ ",A;̴@Ol,Aܨp@k4Ӟ,A)NSqAلh,AP9Q,A麢E,A4JA̱e,AOfhAƤ,AAƤ,AA̱e,A_A麢E,A}9Aلh,A0ƾAk4Ӟ,A౬AOl,A+WAAf{B,A ÂB,A|A`aB,AA)B,A AأB,AAB,A`)DAB,A@ApB,AA)B,A`)@AP8B,A@AB,A@gAB,A@A̖B,A aAB,A AѣB,AA8B,A ApB,AߡAB,ARApB,A AfB,AA8B,AA)B,AA C,A:AC,A A`"C,A qA8'C,AeA8'C,AA&C,Az,AG:C,A4AWA`@C,A AIC,AqA`VC,A oAhC,AA`uC,A`fA0C,A \AAC,A AC,AAWC,A8ALC,AAH\C,AAfC,A@ALC,A`AC,A@AD,AAD,A`AD,A`A!D,AAH&D,A AB(D,A Ap8(D,ArAQ(D,AzA'D,AA("D,A`)DAuD,A]ApD,AA D,A |XAp D,AAp D,A-Ap8D,AA8 D,A`A D,A@>DAD,A`'AD,AzAH.D,AR*ApC,AzAC,A]A#D,AaA#D,A Ap8'D,ARAH.2D,A=HAH>D,ARtAMD,AHAHXD,A A8jD,AA(\xD,A AD,A A(ܓD,A 9AGD,A@>AD,A@AD,AzAfD,A]AtND,ATQAaҕD,AX'AD,AGɐAjqD,A"o%Ahh E,Ac]AlqE,Af AB.E,AAXEE,AAp=]E,A5ApE,A qoADŽE,A@>A`aE,A@><Ap E,AA؞E,AA`aE,A`)NA0.E,AAE,A3AE,A\ApE,A)AWE,A ABE,A3A`E,A`f4Ap E,A dAfE,A=AzE,AåA8E,AAѹE,A3A`E,AA(E,A 1MAp9^F,A`AXG,Ap AG,Aa AuG,A_E!AG,A@W!AG,A "AH,A@"AKH,A0[#AKH,A9#AG,AiX$AG,A*$AG,A3$AۿG,A $AG,A,%AG,AU%A`G,A%AG,Ak&A)G,AĂ&Am H,Ad'A# H,A(AȩG,A0(AWG,A@߾)AG,A~*AGG,At+AG,A4,AxG,A`b,Ap_G,AS-AG,A-A+G,A<.AG,At.AQG,A`/AAH,A 0A8D]H,A1AyH,A`2AH,AĽ3AH,A4A H,A |5AH,AN5AH,AR6AXJH,A6A0݉H,A@7AH,A)8AzH,Ao8AsH,A@i8A8\H,Ac39ADH,AO9A(~(H,A l9APH,Ao9A@G,A s9APH,A9A* H,A<:A@H,A:ApH,A ;AP,H,A5X;A?H,A ;AVH,A;AKXH,A@;AKXH,A@S<A]H,A<AHiH,Aӽ<A@uH,A4<ARH,Aw<AAH,AL<AبH,AL<AH,Az<AjH,AV<AfH,A<Ap I,AV=AI,A=Ap,I,A{>A\7I,A 7?ApwAqQL,A wAPL,A-xAXdSL,AךxAbL,A`yA(OlL,A (TyAnL,A&yA( ]L,A^wzAGPL,AzA)ML,A@{AhbLL,Ajm{AOL,A?{Ah[L,A6{Ap=`L,A#|AaL,A|A/bL,A|AHdL,A }AoL,A ;}AvL,A`]}APwL,A}AX~L,AsL~AL,A}~A؋L,A$A/L,A4cAjL,A;AL,A@ZA0L,AA8ҩL,AAHL,A?ACL,A!AEL,A JA L,AtAM,A xAx(M,AuAM,A QA(M,A 볂AM,AAM,A%AM,A]A()M,AaAM1M,AA8M,A6A4A, M,A cAeM,A`<A0"M,AwaA%M,AmA8g&M,A އA (M,A` A[/M,A` $A(m;M,A!A`eRM,A`A `M,A (@AXhM,AeAPkM,A예APkM,AЈAPkM,A}ArM,A4AXwM,A sJA |M,A`iAM,A3AM,AQdA0%M,A AM,AAhoM,A9AxFM,AzA0M,A AM,A@i{A(M,AE(AM,AApM,A&Ap+M,A AM,AA@M,AAM,A@NAؒM,AA@׶M,ATAM,A^6A`oM,A@KAgM,AŔAheM,A<APkgM,A@hAPvM,AAoM,A3VApM,A`KAPZM,AjAH\M,A AM,A@*AM,AAxM,A@9AM,AADM,A@A M,A@GAx)M,AAbM,A)AM,A"bA M,A6AM,AAM,AaBAqM,A`խA:M,A ApM,A8A9M,AcA M,A ŢAM,A`ANM,A s4A N,A`]MATN,A@lAxN,A͞AX9N,A AhbN,A@AN,Ag֣ApxN,A ٣AGN,A Z A@` N,AOAXN,A AN,A`դA` N,AH]AN,A}A}?N,AA3gN,A#A`GzN,ATSA-N,A`KAN,A0AyN,A`msA&iN,AgħAUN,AXAKN,A5A08N,A A/N,A6rA\.N,AǩAHn:N,AA8N,A@A8N,A`TlA0;N,AAhbCN,A`9֫A8INN,AAQN,A2AxhXN,A@NAxhXN,A@[A PN,A0BAFN,A`AEN,A 2Ah AN,AAh AN,A OAh AN,A ZAx>N,A@`سA:N,AvA(6N,A`9:A!(N,AApN,A`K AM,AALM,A A(M,A$ALM,A}AM,A?A(M,Ag&A`M,A@ nAM,AAM,A@ߟAHPM,A`dAۑM,AHAM,AsGA(JM,A srA@M,A`<A:M,AA M,A 6AHM,A AM,AHA7 M,AAƨxN,AEA ON,AAo}"N,AGAH l,N,A,BA 4N,A6HuA".f:N,AAA#F>N,A"Abo,?N,Ax"AS>N,A _Ar];N,AB"6A;N,AA4EN,AV@AE;ON,A"ݗ`AkY[N,Ar= zAgN,A[AuN,Aq.!)A8sN,AA(VB!N,A `AO70N,Al_AF¡N,AiA{N,AW65AmN,Aׄ4A(,\N,A OWA0N,A`A跍M,A0(AHDM,AA tM,AnA8M,A@AfM,A A7M,A eA}]N,A`ܪA`N,A AЮO,AAuO,AA N,A@AO,AhJA(Q,A?AS"oQ,A!.A9%Q,ARiAwQ,AY8A=Q,AMOdA=V*'R,AgA:)R,AJA [i?R,A -A!UR,AP AiR,A0D)A|R,AJ1VAR,AྎAo R,A_H2AuwR,A5AgtR,A3q!]AS,AAe#R,AV?AQ,A^A@P,A`AQ,A`A*R,AApNS,A'Ah^S,AMA4QR,Av&AtR,A@A\_&R,AXAiR,AǢYAOR,AcNAR,A"A=R,A#iSJASR,ANAX%)R,A㱭A,*R,ARmAR,AA61R,A&RAe8R,AJ7߈AS,AgA S,A#,A&{3S,Ayr+ AbJS,A1GA`C`S,AZAزzoS,A%A1l]zS,AAoAHS,AAnȇS,A"_BAaBS,AAVS,AqogA}S,AގPAUwS,Ai jףAXujS,A`;AqgSS,A)6Ak̗?S,AڊA.S,A/A&S,A`TAR,A`"lAwR,A@AƈR,AAxR,Aw Ah|R,A`]$AR,AAR,AAS,A A(S,A|HA`#S,A@,A)HS,A sAXhS,A`AȊS,AA'S,A uAS,AgAT,A~AִT,A iAU,ALAiU,AAe4V,AߜiAB"V,A Aa8BV,A׀nN.At`V,Avc|dALά{V,ACQkAfV,A@AfHZV,AI&aAnV,AAƓ锝V,AA_B V,AUA2V,A?xAuQ V,A{IlAV,A HAfEW,A;;4A+W,ABAfPiW,AkAЭ\ZW,A%JAW,A! AԇX,A(:A[)ZDX,AS[P]Ar=[rX,AMնA9X,A녨Ag6X,A=lAX,A@E?AX,A1AjX,A58AX[X,A=AvK6X,At0AnX,Ac|ABX,AaNAzX,AA 6X,Aι0AbUVX,Aw?AX,A@2ACaX,AfKjA-X,AHAݢX,AAX,AFAX,A -A/U?X,AeUA5!3_X,AAlX,Ay.AoX,A 5^A0 Y,A A(:Y,A\ AHY,A A(Y,A@` A@Y,A, AY,Aw A*Y,A@ AY,A`A!Z,A@ AHm/Z,A@r#A(K1Z,A`/A5EZ,A`ܮA YZ,A FA2fZ,AA'pZ,A`A9}Z,A?AZ,AAPx~Z,AtAZ,AHAKZ,AAZ,A/AZ,AApZ,A#gAxZ,A}AZ,A AZ,A$AoZ,ANAp[,AQAW2[,AʃA8P[,AAa[,A@|Ahs[,A AX[,AA[,A2A[,AHAX9[,A0VA#[,A@A[,A)JAp[,A6Ai[,AJAG[,A`A` [,A`FA \,AA \,A( Ax"\,A; A#\,A@yA A %\,AsF A;:\,A AM\,A7!A@_\,A}!At\,A!A\,A@GL"Ad\,AH"Aq\,A$"A\,A$"A\,A#AH\,Av#A^\,Av#A\,A#Az\,AHE)AP\,Ar)Ay\,At)AС],A`B)A 4 ],AT*Ap],A+AI],A+A`],A@K,A8t\,A,AT\,A`N-A8\,Aa-AH],A -Ap],Av-A 6],A w.A,;],A@5.A?C],A p/A2M],A 0A^],A]0Af],A 0AUb],AZ1AR],As1Aȶ;],A`bv2A$],A@N2AD],A5A[,AqGQA0&AZ,A@PZA(VY,AVbA@W,ApqA0̓T,A@Q|A@NR,AfA`6Q,AdA(Q,A4A(iQ,A qA R,AWAVR,AAnCV,A AV,A gA@5bV,A_c A *V,A A*V,A AU,A@ AhU,A`# AU,A AU,A) A|U,A A_ AU,A A(U,A ۬ A(U,A@5G AeU,A` AU,A@ϔAU,A#4AU,AA8ֻU,A@EzAzU,A`AU,AAcU,A(AU,AzA NU,A AjU,A`mAxuU,A OA6U,A@)AzU,A@yyAxuU,AAU,AE AxU,AŗAZU,AA0U,AAU,A`"AHiV,AWAH{V,AAx!V,A@!TA``)V,A A6Ah@GV,A$A@fV,A3ApkxV,A[A~V,A@jA$V,A@A8V,AMA(V,A@pAV,AKA9V,A#AQV,AAsV,A`UA0V,AAH-V,A`A =V,A$AV,A@`A`V,A,AȔV,A?AqV,AAPJV,A`\A#V,AAxU,AAU,A0AhUU,A (ArU,A;AU,Aw?AV,A`Ax!V,A /A``)V,AaBA *V,A(A-V,A` AU3V,Aj!AU3V,A@!A-V,A`9"Ae$V,A "Aȩ V,A (0#AV,A|#Az V,A`@$AH V,A n$AU,A%AXU,A`%A(U,A@NO&AU,A&AxuU,A`w'AجU,A`'Ax~U,A^(A"U,A)AaU,A`)AXU,A`?*A U,Ac*AP|zU,A`g+AأsU,A',AoU,A ZO,AtU,A,AȵU,A ,AOU,A,AX(U,A1-AXU,A`t-AgU,A-A舌U,Ax.AXJU,A.AU,A`2?/AjmU,A]/A(]U,A`u/A;]U,A Z/A@1gU,A9/ApU,A/A8ĮU,A /AU,A/AHV,A/A *V,Ays/AKV,A/A7mV,Aø.Ap}V,Aj.AxKV,Aݟ.AV,A}.AV,A 2/A8V,A/AV,A|/AV,A`0AV,Anl0A5V,A0Ap_V,A@0AV,A0AV,AxQ1APoV,AMy1AxV,A 1AhtV,At1AKV,Az1A"V,A@1A*V,A1AU,Ad1AU,A`K1A(U,A42A(U,A`d2AxuU,A2AU,A3AU,A q43AU,A&z3A0U,A3AgU,A@ 4A U,A%4A舌U,A`B4AqU,Ag5A=yU,A 5A_U,A@56AFSU,A`6APAU,Aq7Ah?U,A7A>U,A/8A`8U,A98AJ2U,A8Ap"U,A^9A0&U,A@:A0U,A:AHzU,A;N;AHzU,A;A;U,A``<A(T,A <APT,AJ=AT,A@`=AtT,Aq=AT,AF=A3T,A@,=AثT,A@=AߦT,AI >ATT,AD>A'T,A9>AT,Ay>Ah"T,A#?AhT,A ?APT,Ak@APxT,Av@A@T,AAAmT,A@y!AA(T,A |AA@T,A5BAU,ABA06 U,AtyCAU,AFDA@T,ADAT,AYEA84T,A EAhT,AFAT,Ak:GAT,A`GA(OT,AHA'T,AIA9T,A`fIA8T,AIAT,A JAh"T,A@iJApT,A IAT,AIAdT,AIAYT,A}IA\PT,ARJAP@MT,A JA8IT,A4KAKCT,A?LA9T,ALA0T,A }MAX+T,A@NA@)T,ANNA@)T,A hOA')T,A` PAe*T,A@PA!.T,AWQAx4T,AQAHrU,AXAFSU,A0YA(_U,A@YA;]U,ANZA@AXU,A3ZAYU,A}[APPU,A`T>\A}=U,A6\A`a:U,A@p]A;U,AX^A}=U,A^A@U,Ay]_AHU,A `APPU,A@`A ZU,ABaAdU,AaA+lU,AbAhsU,A*cA_wU,AcA{U,AqdA0wU,A`TeAqU,AeA\U,A fA͈U,A |VfAgU,A@GfA@U,A@gAH\U,AEhAx~U,AhA U,A iAwU,AaiAoU,A@5iA(jU,AjA(jU,AK)kAMjU,AkAhiU,AllA(jU,A?mA+lU,AmAkU,AaWnAbU,A3nA@AXU,AoAVDU,AzpAx-/U,AkpAT(U,ApAU,A pAU,Ag&pA@T,A9XpAHT,A`rAhU,AaAkU,AxAV,AEtAeV,A3_AYkV,AkA|V,A0٘AEV,A AP V,A (AYV,A@5ÙAV,AbAV,AA(V,A(AIV,ALAIV,A ڜA(V,A2AV,A@!A'V,A`žAh"V,A$gAX(V,AAP8/V,A?A #-V,AKAp,0+V,A>A8k(V,A.hAH^1%V,A/A Y"V,AGA䎦V,A^A9pMV,A˦vAAV,A9OA iV,AMSAfmV,Al0AM V,A֒rAV,A<A bhV,AE4A:w`V,A|GAV,A*AX(V,AaA V,Af&A V,A2942A>GV,A`;A4V,A=CASV,A r?IAWPV,A,LA4V,A}=MAWgV,AU>LAN"V,AZHAwS&V,A BAg(V,Aop:A +V,AEJ0Av-V,AĨ%AP8/V,AKA.V,AKAV,A dAV,A@TAV,A`A@$V,A9AV,A@9A`XW,A`[ۣARW,AॄAo W,Aq+AH W,A?ҥA W,AqAW,AA!W,A`A)W,A@ WA@1W,A`A9W,A0AsAW,A 7AIW,A֪AQW,A@.vAXYW,A AXYW,A ~A[W,Ag/A`aV,AнAV,A-ACMRV,AũvAb,A;A0jJb,A^Ac,A 8`Ac,A@,tAXc,AЩAxc,A@A]c,A"Ac,AWAc,A<A(9c,A@Ac,A`̭AX9c,A`KA"c,A JxAc,A=iAc,A6Apc,A Ax:c,A OAc,A@Ac,ApAc,AA҃c,A Atc,A@iAhc,A`[AZac,A QA@~\c,A@3A8VTc,A AMc,A;AGc,A A6c,AA}!c,A`bAHc,Ad%Ac,AA!c,A"A !c,AOA@*c,AEA4c,AAp8tc,A`mAc,A`ZAmc,AtsAc,AiAlc,AAA%c,A3*APod,AA{d,AApd,A`AId,A`Ad,A`ofAd,A=A:!d,A`;A1d,A`K]A`?d,A`fvAwKd,A@yA \d,A PANmd,A3,ACd,AAhd,A A0d,A'Afe,ATAX.e,AAxXKe,AA8Dhe,A HAye,A_Ae,AtAie,A Ap xf,ADA`Ts,A@0A u,A:5A u,A`2APu,AQ3A v,A5AP^:v,Aͣ6A(bv,AQ7A5v,A9Aw,A@9AHEw,A@:A8Ew,A 9Aw,A9APHw,A9AP&w,A@W9AhQx,A 9A>x,A 9A@*x,A`J:Ah!x,A@Ϡ:AXw,A`2;AxKw,A ҅;Aw,A;Aw,A (;AhQx,A;A2fx,A<Axx,A<A(Kx,A<AXx,A" =AUx,A=A>x,AU=Ax,A}=APx,A`<A@x,A`<A0x,AUs<A0x,AA<A(x,A$<Azx,A Z;Acx,A;Ax,A;Anx,Av;Ax,AW;AQy,AOK;A y,AM@;Axy,A`&;A(m#y,A:A@$*y,Ak:A_+y,A::AX/y,A:AP6y,A9A 1AN{,A@1A{,AtF2A7{,A@q2AL{,A2AP8{,A@3A`{,A H3A8{,A'3Ax{,A`3A{,A&4Ae{,A@`4A{,A=4A`G{,AB4A0&{,A 1Y5Aĩ{,Am5A8b{,A5A {,AD5A`C{,A "6AW{,AK6A ={,A*h6A{,A`}6A }{,AM6AP{,A6AP{,A@`6AHK{,A 7A{,A r7A^{,Aկ7Aأ{,A7A{,A`[8A{,AT8AH.{,AR8A@~{,A u9AP͜{,A@9AT{,A 8g9Ax{,A#9A`{,A`9A{,A@6:AHЄ{,As:Aj{,A@r:Ap{,A,:A'{,A@:A{,AM;Aoy{,A;Aj[{,A`s<AYN{,A=A@FA{,A=A.6{,AX\>A`-{,A@N>Ap=){,Aq.?A%{,A0?A${,A?Apx {,A|?A~{,A?A{,A ?A{,A @A0*z,A@Ahoz,AyAAxhz,A"ZAAU{,AƨAA {,A AA{,A@N=BA{,AfaBA,{,Aq}BAL7{,A JBA,?{,ABA>{,A@BAX4>{,A(BAX@{,A[CA(D{,A`m%CA*S{,A@CA`h{,AAMCAz{,ARCA{,A`QCA07{,A bCAX({,AmCAź{,A?CAhQ{,ACA{,ADCAP{,A`KCA`O{,ACAp|,A`KCA@p|,ACA,+|,A`"DA`5|,A 9DA>|,A]DAM|,A|DA_|,ADASx|,AvDAZ|,A@.+EA|,A`;EA#},A /EAH J},A EAs},A`]EAP},AvDAH},ADA},ADA#~,A`vDAxB~,A+DAc~,A;DA~,ADA@~,A DAh~,ACA@,A@iCA,A FACQۯ,A;4%hFA 7,Az#^FA@>,AFA跤,A` GA{,A%GA=,A?pOGA긢,A  H#GA*:RǢ,AOV,GAΆ3,A0;5GAŪ,A%{=GAx,AEGA_,AGAHCԄ,AcGA(ބ,AGA@,A^HA83,AGAЦ,AGA },A@GA(1 ,A`̛GA,AOGA,AFA$,A FA5,AAP&=,A6<AE,A;AG,Av;AfO,A`n:AT,A :A@X,A@ߴ9AnZ,AH9A8\,A8A\,A,u8AY,A"8A@S,AO7AI,A`K#6AHI,A)5A0D,A4AX8,A 4A0,A4A$,AP4A,A4AP ,AT4A*,A4A,A4A ,A@53APل,A3AЄ,AK3A`XDŽ,AXn3AX,Ab3A,A `3A ,AfV3A堄,AUH3A8Z,A*3Aˌ,A`2A,Ao2As,A`P2A)|,A$2A0z,AE1A{,A[1A,A1AXh,Ay1AH,Ad1A0,A@81Ad,A %1AH,A1Ay,A1Aq,A@0Aj,A@0Ak,Aʔ0A(r,A`0A(Jz,AH00A@$u,Aޒ/Ahx,AO/Aez,A/A`r{,A.Ak,Aה.Aτ,A@.A,A /c.A,A--Ab},A߻-A|,Aʞ-A{y,AH -A_u,A ,AGn,Ac@,ABh,A@ ,A_,A ɻ+A@W,A@ߍ+AU,A}}+A0LO,A>+A\I,AR*A9,A)AX 6,A_)Ap,4,A@)A@.,A X)A(.,A%)AX ,,A J(Aг(,A6(A&,A+(A,An'A_,Aj'A,A@5G'A ,A&A ,A-&Ap ,A`-&Az,A%A,A%A ,A $A ,A@p$A@_,A[g$A),A[$AJ,A`Y$AP%,A`bm$A',A$A _*,A6$A-,A%A3,A &AL9,A A]'AA,A'AN,A@K(ApJO,A`](AHU,A l.)Ah@[,AV)A/{,A+A,A=b+A0H,A@.+A`6,A`),Ac,A#-AX,A`]V-A,A -Aɬ,A-Ah@,A .A ,A Ҋ.Aų,A@]/A,A/AHÄ,A@W/A(Մ,AO0A,A`l0A,A0A,Aj0A,A0Ar,A0A*A؎O,A*APU,A@%)AZ,At*APW,A;*AhX,A@k*A[,A u*A `,A*A0a,A*Aъ,A3)AX,A`f&)A,Ap(A&,A-(A,A`)A,A5)Ah,A'[)AQ,A*As,A`+Af,AG+A_,A3+AO,A`<.AxK,AOM0A P,A1A:V,At2A0T,Aj3AuS,Aa3ATU,A`3Ab,A;4Af,A4Axk,A5A(lu,A)5Ax:,A`Dd5A,A@>|5A0X,A`]f5A,A@M5AHʊ,A@r<5A`׊,A5L5AH,At5AZ,As5A#,A$5Aq>,A 5AƤ,A`5A0,A5AE,A@5A01,A@y5AxX,AY5As,A`5A,A 4A0,A4A c,A@>4AG,A$j4Ap,A<4AE,A$4Ap[,A#4AS,A'4AX׺,A '4AŌ,A#X4A,A`4AHP,A4Ah,A`)15A ,A5A,A5A`,A`G6A@,Ak7A N,A`7A8,AzS8A,Ar8AF,At8A0]ތ,A`b8AHř,A 8A@qŌ,A#9A0jČ,A@x9ANЌ,A9AՌ,A9A,A09A0,A` :Ax\,Az:A{,A`:APw,AG;A,A;A,A<A,A Q<A0,A`<A,A =A],A<=A,A@d=A,AM=A,A=A@0,A q=A ,A =A@ڌ,A >A8Ό,A6>A,A5?AN,A@#BÁ,A=XCA8҆,A3zCA7,ACApJ,ACA`C,ADAė,A`m+DA,A@CAz,AhCA8,AbCAÌ,A:CAPˌ,ACA@Ԍ,ACA,ABA,A#CA ,AZCA,A`CAc*,AiDA7;,A@`DAH,A ɻEA([K,AFA ,AtHA0G,AHA[,AHAk,A HAh,ACHA 0,A (GAX,A uGAR,AәGA 4$,A iGAP/,A`mGA8,ApFAOR,A}FAȺu,AFA},A@>=EA,A`NDA,ACA(2,A tCAU,A`BAq,ABA0j,A /BAHK,AAAܑ,A @A ,AF?A%.,A lr?AxG,A`v)?Aq`,A>A0n,Az>A r,A>A7,AF>A(,A@>=API,A =A,Ag<A <,A<Ae,AMH<A,A` <A,A ;AE,Aq];AHy,A;A i,A@i:A_,A9AV,A`T=9A>P,A8AT,A&7A^,Ap:7An,A 6Aq|,Al6A[,A8,6A2,AX5AH,A5A,A5A,A`T5Ap,A 5Aג,A@#6A!,A@y*6A,AX96A,A?*6A =),A`5An,A5A#q,A`5A,AZ5A,A@&5A,A 4A/,A4Ahb,AR4A@,A`24An,A,4A,A63A },A=3A,ATx3A8ż,A`63A <,A@2A/,A2A`!,A2AB,A@#2A88p,A`T2Ahe,A@2A[,A2A84],A02Ah,A@2A%t,A y2Apy,A}O2AKi,A@2A@Y,A2A U,A (2AV,A1A'M,A@ O1A=7,A@i&1A(,A & 1A3 ,A0As,Aॐ0Aw,A`d0A,AR20AP<,Aj 0Af,A/A(> ,A0A+$,A@I0A]B,Af0AHrL,Ah0AXQ,A`2D0A S,Aq/A@$W,A /A^,A`O/Ap,A@.Ay,A@ּ.AXW,A .AX,AN.AC,Ai.Ap,A|z.Ad,A6.Aȓ,A6.A8Eԓ,A S.Arܓ,A.AX,A@.Aȱ,A`v.A8 ,A.AN7,A9.AcS,A`].Aȃl,A.Aw,A}/A@…,A&/AՑ,A/Ah,A /.A,A /AH̰,A@E/AX˔,A /AД,A!0A#ڔ,Ac0As,A 0Ag,A$51Ap,A`;1APG,A1Aw,A` 2A8,A2A,A2AH,A@1A,A`Ә1Aƕ,A/X1Aɕ,A`0A0{ĕ,A0AHл,A@y0A ,A0Aۨ,A{0A`O,A[0A0,A@NU0A ,A`]0Aŕ,A,>0AHKӕ,Ah/A,A[/AO,A.A,A@߰.AHK,A.A0ٕ,A{.A,A-A8,A -AM,A`,Ah@ ,AR+Ak,A@N+Ap,A``+Ahה,A C+Aذ֔,A+A8',At+A,A@'+A ,A`!+A,AF +Av,Aw*Ak,A*A#,A#'*Abޔ,A )Axܔ,A J})A,A2)AXu,A(Aj,Ah(Ac,A஧(A,A (AxX,A`(A",A@a(A,A (A,Az'A@,A^f'A8g ,A@<'A,A'A`$,Ay&A]1,A&A9,A cB&A >,A`%A:,A@>%AH)2,A $A(,A$A",Aw$A(,AWM$Ap00,A@,$ATB,A@,#AV,A`#Ac,A`v#Ak,A#Ap}q,A O#A0w{,A@`#Ah,A u#A⇕,AN#Ah,A4 #A@,A3"Axv,AӉ"A(,AV<"Aܦ,A@."AW,A !Aذ,A3!A8t,A[!A8ɥ,As0!A8D,A \ Ap,A9 Aث,A 8wA׶,AA,A`A(,AAa,AA,A+A,AN|A,A`7A,AA,ARAXJ,AՐAp,AdA,AB#AQ,A`A8,AA,A`TLA #,A@-A~,A*AU,A A={,A A(r,AfAx k,AAJi,AAd,AnAa,A3Awe,A`AXj,AAo,AAht,ApA ps,ANAp,AoJA3l,A`Aȥf,A`A@d,A`AHW,AtAH,A`A(m*,A AAxi,A@A8,AzA@~,A AL,AAB,AAP,A@EAX,z,AFA`h,AqA#U,A40A@,A@1AЦ,A6AkΓ,AA`,A;A`,A]A͟,AZ/A`X,AaA ,A`A6,A-A(܆,A@sAy|,AGAwk,AAW,A`A/,AAXV,AA@,A$A,ALAP̒,A@Aj,ARhA`ǐ,A *AD},AEAm,AAhb,A@Ax[,A]A Y,A /Ax[,A-A`,AApa,AAgf,A@AhDm,A Apq,AA{,A?A`2,A tA,AҶAX,AAy,A@5*Aʒ,ANA@,AlA_,ArA *,AWA@WQ,A=ATp,A/A,A ZA@ӓ,A4AAط,AAۓ,ÃAB,A$AI,A@Ad,A@Afy,ADA*,A ^A,A_kA,ArA,A}AsҔ,A@ƃAH,A AA`1,A6A,AAp+1,A@AE,AA1X,A^Axvd,A6Aim,AxAu,AXAh{,AoA~,A`LAY,A`APk,AAhw,A@i>Ahsx,AtA |,A@yA䂕,AA,AټAhQ,A AP,A`DA,A |A,ATA[,A`)1A,ATA#,A@WA𒾕,AAcÕ,A JAXϕ,AIA֕,AszA(ܕ,A A`rܕ,A$:Aӕ,A@sAHiڕ,A`]r"AP&,A H&A,A H&A^,A)AQ,A޽)A,A+A,A`m+A8,A@k&AT>,A@%A@=H,A ."AhM?,A"A,A &A,AX#AX,Az&A`SU,A@,%AHr,An$Ap,AE<$A7Ü,A6#A,Am#Ax+,A >#A ĝ,As!Ax,AQ#Aܟ,AkO A,A@y#Apa,A~AP,A@iAm,A`)AxS,A)Aؠ,A AmI,ABA`p,A@A0,A 1Ap,A`o:AT,A-A8,A>(A֣,A;l'A88J,A@Az,A l5AG,AA颤,A(kA,A *Axd,A`AH.,A tAY,Aw1Aޥ,AdAP+,A =A å,A Akߥ,A:%jNu A,AH AT1,A? A Bg,A AT,Aj AAFզ,Az A8 ,A A,A O%ApQ,A`AH]Y,AAv,AA,A AAPΨ,A?A,AAx$D,A4Ax,A@3Ap,A OA;,A,AH,A2-Ap9$,AjA,A ZAH.e,A;A𴶫,AOA@,A?A0ث,A`7A,A WA:,A8A,AA9,A)2A0 0,A@AD,A`DA2Ƭ,A@iAHi,A`2A,A@EVAP,A ~AHƭ,AzA@,A҂ Aȩծ,A ^ A,A`]AP,A&Ai ,A`mIA,,A@A@A,AYAJ,AzAQ,A Ah,AA_X z,AwBAخܤ,A -AVƟ,AAj0 ,A4KAÍŮ,AqXAxخ,A A,A`A?,A@!A*,A`KcAذ#,A@yAP>,A@`nAH;O,A-"AT,AXAT,AAbr,A TA0,ATAX,AྦA#,AA8ɯ,A@ZA,A@A(h4,AA(lW,AxA%Y,AA`Tb,A`)(Al,A|AEu,A`BAN},A$Apy,A@#^A7,A`ÉAX,A A,A 8wAb,A At,A Aȃ,A@A,A AIذ,AziA0w,A<AP},A<A0*,AWA),A A_,A`A7,AVA r,A4eAf[,AtAؒ,A`A[Ҵ,A A3,ApA,AoAPg,A@NKAHi,A$A8,AA`,A`A8ڷ,A Au,A@AXu,ADAX,N,A A(+,AA,AA/,AIAn,A`2A(-,A@5A1 ,AAٹ,A+Aq",A|A>,AA_X,A TA@,A^Ahs,A#(A,A`Ai1,ADApx,A OAN˻,A2A ,A\AؼV,AA,Aa Aoμ,Aa A(,A0AUh,AGA,A Ap ǽ,A cA,A Ad,A Aw,A`vA$,A pA\Z,AA(\,AVA8S,A@AX5M,AFAX5M,A ~MAX5M,A`AX5M,ADiA\,A s;Ap,A@yA`f,A|ApJ;,A@>mAP,A`A%ÿ,A`;Apƿ,A A,A8AXz,AfA1,A0AP,A sUA,A`AxK,AXA(B,Ax,A0]C,A`;$ARR,AA\,A" A,A9yA,A`vPAH{H,AHA,A Ah,AAH:F,A A,ǎN,ALFAjX,A~GA7#sb,A@A7Gpm,A.ݐABɴx,APy)A:m,A# Agǎ,A~A,A`A,A@A f,A 1A`ey,A@ϔAX,AtA;,A`1A~,AA~,A A~,A3}Ap,AA ,AfJA,AAh,A+HAV,AA(f,ApAm,AFAh,AM:Ae,A A,A`A,A`BAh,A@5Av,A`Ag,A`A ,A AP^,A`A},A^A,AA p,A@rAD,AAi,A,A@,AHA@,,AgoA8T,A5A(x,A@AΊ,AM"3JA7t/,AES\Aդ ,AjxvA#h/,A2|3A@_0,Axb)Aቾ,A)A,AZ A ,A`2AX,ANA,A`,A,A`7A,A`7AP,A A2AS',A $Ap|V,A?DApy,A ZAIi,A@A,A٣ A,A Z Ah,A`o A,AM A ;,A ' A8,AX A0,A ZY AP,Ah AX,AA,AAh3P,A A(Je,AA,A  A,AH A,A`, A",A@  A,AN As,A@WAH ,A/A`q,AA,AhA(9<,A@A( [,AAHs,AMA(,AAh,AvA,AyAH;R,A VA8,A`ܭA@,A AG,A~A ,A?A0jn,A Ap,A3CA8Q,A``Aح,A`BAˆ,A A`G*,ABAUE,Ah5.gAa_,A47YAi^v,A|1AA,AJVh.Af%,A*?A ,A ApΜ,AAH7,AK A 6,A AHn,A@'"A,A!A:(,AM#AC,AL!A`,A [!AH,AA0,AkAWg,AA,AdA ,A?A,A A(T,AA؟w,AVA,A`JAb,AA` ,A<A#,AAnX,AF AIb,A2!A,Ax A ,A A8,A Al,A@A8Q?,A vAC,A@tA0,AAP P,AAPoJ,AׇAPY,Af.Au,AyA,AA,A;A@,AfA,B,AzDA,A2vAPZ,A`2kAB,AWJA,A ZAE!,A RA),A@TA0*,A ˕A,A)TA@H,A`m{Ah,A`A8;,Aq A(=,A APV,Ax Ap,,A A,A A8g,AA,A5A,AA5,Az Ax,A@# AP,A A,A$ A0,A+ A,A ~ A,A 6AX5,AA ,A9zA$,ApAI,AjAf,AjA`,A (A@,AAx,A`A,ArA8,A@,#A,A0A,AA,A Auw,AA@ ,A |A ,AA,AA&E,A@>XALi,A@A@,AA,A{A,,AVbA`l,A`wAx,A@A@,A`"AH,A@nAh,AkAIC,A:AHr,A JAh,AA`,AA ,At]A(B,A@9A],AjA,A@EA@ ,AJA7,AA$,A"AP/b,A@#AV,AA,A|LA`,AA,A#EA,A@PA`,AbAk^,AxWA%,A`A>,A@qA~,AA0*,AwAM,A \AR,AjFA065,AA[,A A,AIGAp,A`Ax,AAP,A3A?,A A %,A`A%,AigA&,AiAh7,A`AH.>,AvAI,A`A_,A AA],A@#SAR,A MAxI,A(Ahe,AA@m,AZAi,AHXAm,A@iA-A"A-A Ad&-A`]A`H-AڪApF-A`WA:-A H`A -A DA-A` Al-A@AE-A\A`X7-AxA6-AAXA-A[AE-A KAP^L-A`DTA8b[-A@Av-A AHf-AaAo-AjA`j-AAHr-AA@u-A A(-A@ A-A A u-A |Ac-A A"-AAiD-AAH?f-A'Aq-A`AK-A` wApm-A&1Aޕ-AηAߘ-A`A/-A; A(-A AH-AA-A`A-A Ae-A@AP-A cgA-A̍A-A@A@-A@A -AǾA-AЛAhj-A EAV-AAP& -A@rYA-A#A^-A$&A -A'A%K-A@`^A8n-AKxA@w-AXA`-AA-A9AK;-AkgA8g-A@5r(AP-A(ANo-AVMA?F-A@ AL-A kA -A@ "A2-A 7"AP-Aj(A-A`(A -A $*A`2-A*Ax6-A@%$AX-Af[$A(-A@`ApN-A A0.F -A@A( -A l!A] -A+"Ax(F -A %A ҁ -A-&A -Ag"A` -A"AP -A6"AX/ -Am"Apj -A A -A@U Ah -AO4 A` -AW Ad -AAx' -AhAНh -A0 A( -Afu A&} -A AL -A["Aٮ -A*)Aȩ -A{)A{ -A7#A`Bb -A$AY -A c %A  -A`T(A( -A`+A$L -A0A(-v -AS0A -A-A@ -A+Ap -A@)A؟X -AՓ'AM -A%A} -A8%A -A`)%AL -Ah&A; -AHs'AI -Ag'AQ -AV1A -A1Aj -A`Q(A`ry -A]g(Aޛ -A''A? -A`"&AX -AOY'A-Am](A7v -A6?3Ah͡ -A 3A؁-A`ܟ(Aج[-A@`)A-A3J-AΉ-A`x/Ax-A Z@/A@-A`2A_-A6Am4-A`;;Ax>f-A@5<A-A(AA -A@ }@A@F-A^;A`-A՝;AX -A7AXׄ-A9A0]-Ah7A-A>A -A BAH?-A CAus-A`ʏGA0]-A`TFA6#-A CA:-A`BAx6U-AaBA-A"CAP-AcGA-A]KA0-A@`LAhon-A JMAm-ALAZ-ALA J K-AO LA2-h.-A]EAb"-A3A_GW-AAz1 -AuvAGÕ-A7K_%Ad -A+A%hh,A౬A*&{1,A0>AE]K,A}9~Ad3N,A_)`Av,AAAd3N,AO"AE]K,A4ƐA*&{1,AP9A%hh,A(NSAd -AnB8AGÕ-AȴaAz1 -ARH{A_GW-A| udAb"-AF)OA2-h.-A Wn=A"-AÒL-AʇT)-AT, AO-A98 A|rV-A[u- A+C^-A|A6x,{e-AT, AJil-AĒL-Aҗgr-A Wn=ABk%y-A F)OA<"~-A| udA~n5-ARH{A-AHdA>-AHdA̱-A_A麢-A}9?Aل-A0Ak4K-A౬AOl-A+A-AHdA#eҞwd,A47.b@~od,Aev@>4d,AZ@\c,A+@c,A @]a,A@0;_,A9?@7!Ӗ_,A4O-@ha_,AUs=@;"R,_,A o@^,AFÊ@^|^,A};8@h6d^,Ag P͋@a^,A@=!^,As,@f9],AYs@MB],Ahg1@ q],A.@1j\,A& B@'h;p\,ANriy@P<[,A@[,A+Juh@ q[,Ai1@ @:,WN^,A#sq@#eҞwd,A47.b@(POD,A?F@pAD,ArB@NqUC,A^`ݬ٬@3,inC,A@ҥd C,ArH(@;mB,A'@רHB,AV@;A,A;@B,A@p@IB,A@C,A@j@B,A"@գB,A@@YB,Aa@A,Aȟ@0A,Aa@CA,A@@C&@,Ac2+@ è@,ALq@ M$a@,AHP7@PV@,AbU@Qݦq?,A$k@^n?,Ask4>@-Ci?,A@@6?,AF-@ِ?,A]vZ|@a>,A+x@X6>,AiHx@p>,Aw@^[ >,A &}w@@s>,A؞[w@~fD>,AU!v@pΔ>,A@v@ Bن>,ARF[v@%y>,Av@D sn>,AV8u@ڊd>,Alku@v\>,A@u@WS>,A?t@ȺI>,As@LZjA>,AMs@8>,A@r@=,AWħEp@$=,A0m@ZW.=,A{uj@P7[=,Ag@ؼX=,Ag@U=,AΗg@0GP=,A@N_g@hJ=,A@f@xhG=,A@ f@`H=,AXXf@J=,A f@XT=,A@>e@Z=,At#e@Z=,A e@V=,A]d@~O=,Aid@A=,A-ed@hs6=,Ad@ }/=,Ad@ )=,A\d@}#=,A֩d@ `6n=,AAd@ ==,AmV8d@8=,Admd@P<,A}Fb@Jh<,AGjva@Vמ<,A`h_@I~<,A}L,^@PQb<,ACl\@I<,A@Z@lM<,A "rZ@důP<,A+BZ@wR<,AU'Z@8S<,A(Y@EWR<,A+kY@5Q<,Av:WpY@*N<,A)?Y@$@J<,A$[QY@SCSF<,AyX@ZrI@<,AiX@ª ;<,A.fX@ZӪ4<,A(͡X@t.<,AޡX@_%W'<,A;[X@ <,AKX@<,ArX@&A<,AR0X@_# <,AÏX@<,A2T@wX@Ŭ1<,AWGWX@Ԋf;,A0X@- q;,AFSmX@ k;,AW@Z;,A>V@U Ԇ;,AYT@;,A@GTS@J;,A^zR@Rx;,A4 R@p;,A@rQ@f[];,Aϐ7"O@@:,A@L@p:,AiK@I:,AUJ@㪄:,AJ@h:,A@hI@Q[8:,AJgG@ :,AHPuFF@9,AjzD@&~̷9,A4T7B@9,AP A@wuĎ9,A,;@@39,A/kt@@/9,Arf@@v9,An)@@Às9,AUp?@i9,A@?@9,Ag6]>@!gh9,A[R8=@V`9,Aڊ=@09,A@4<@|9,AsJ*Y<@ tAbn9,Az<@)3a9,A<@KU9,AGL+-=@ȇK9,A@=@09,A@?@8,A2@@8,Ap4@@G8,AҾ?@@C;8,APFA@t}@S3(0,A8@&jI 0,A0Cs@ ۵M/,AYE;@/,AB@0E/,AnHE`@B,,A`l@W,,A:@M',,Au"ڮ@j+,A"A@+,ALc@-+,Aξ~@Oa+,AVU@]$/+,A%@*,A@@I*,AU@@R*,A:3u@Y)-O*,A]@*,A7m@ ),AG@ل(,A*^a@k4[&,AcYB@Ol#,A$Wz/z@@8+A8@H+A2@+A@+AU@P+A@d@ث+AΡ@+A "@+A@@Є+A@8}+Au@,l+A@@@I+A@@hb+AÔ@*+A@(+Av@`+A@@+Aa@+A@h+A@Y+A@Q@@5+A@ّ+AT @舑+A%@I+A@g@0L+AA@B+A2A1o+AcçAi+AlMABw0+A۝b AU+A_ A|+Aǻ Ay+Ay A+AKY A+A` A:\#+Az:d$% AH +A&N AP($+A,DAc$+AB._OA +A Ae_+AA07+AAI+ATDA+AA +AA +A^FAa+A}A#+A`pA8+A2A+AAx)+A AP+A`;BA( +A A+AA+AEAi+A AXh7+A0AX9+Az<A(KX+A$Am+AyAy+A@E3A+A [A8D+A qrA`6+AvAД+A@Al+AmA+A}uAH2+A A+AA+A@>>AP+A<A+AA+A[A+AA7+A=Ax +A{A+AA`+A ZA+AH.AP+AA+A A+A`BAu+A LA+AKpA +A@A+AAp,+AAy+A  AZ+Ak Af+A A0+A`!A0$+Aզ!A4+AT"AB+A (#AR+A=#Afk+A 8_$Ar+A$A_x+A@5%A|+A%Ah@+A`&Ao+A@>'A(τ+A'AX5+A(A0f+AM(A9x+A(Aq+AV(Apb+A &)A>]+A!)AY+A@EH)A`X+A@Ca)AOY+AVs)A}_+A)Au+A+,AP8+A@,AX+Aח-A+A>.AX,+AH /A9+Ap/AA+A/Ap8H+A50A)N+A^0A(\Y+AÝ0A8d+A 0Afo+AH!1Az+AV1Au+A`1A+A1A(+A`B2A(+A q2A`+A2A8+A 73Ak+A3A+A3A(+A(4Ap+AW4Ap=+A 4A`a+Ai5A`a+A |6A+A6Ap +A`fN7A+A7A+A;8A8+Aw8A+A48A0.+A`)`9A+A9Ah/+AR9Ap+A0<A^+AM=A+AYAAp|P+AMHAhD+AHA`L+A3 RAp=V+A@:RA#`+A_RAP8i+AxRAv+ARA3+A̴RA3+ARA`+A RA+ARAh+ARRA`+A`SA`+ApMSA+AறSAP+AٳSAp+A SA#+A"TA83+A@>`TA8+A` TAX +A TAX +A UA+A1UA+AXUA@+ARUAu+A`UAP8+AGVA+A@>hVA+A@VA`+AgVA+A@ WAH\+A3WAأ+A jWA@+ARWA+A XA+AXA+AXA +A3/YA+A xYA0.+A |YA+AH+ZA+AR~ZAأ+A=ZAX+AR[A+A[AL+A`\Ah3+Az|\Ah3+A@\A`a+Az\AB+A(]AP+AzV]Aѻ+Aஃ]A0+AR]A+A]A8+A]A(ܠ+A!^A0.+AZ^A#+A |^AG+A^A3+AHA_AP}+AR_Aw+A |_AP8m+A`A0.a+A E`A0.X+A@>^`A`aN+ARz`A#C+A `A8+A@>`A`a4+A`Apk,+A aA&+A@>aAX +A^aAp+ApaA +A3saA+AdaA+A`)^aA+A@gdaAL+A`kaA+AuaAu+A q}aAǻ+A͂aA+A aA@+AHaA)+A4aA+A`]#bA+AH?bA0+AhbA+A@gbAp+AGbAX +AzbA+AcA)+A)cA ҭ+AIcA +AbcA@+AGcAX +A͖cA+A cA̕+AcȀ+A3dA+A >dA(t+ARdAp+A |dAi+AdA0d+A dA`+AeA#\+A`)2eA`aU+A`^eAO+A`eAG+A`eAA+AeA8:+A@eAW5+AfA2+A@ 1fA(1+A _fAB.+A@fA*+AfAf$+AfA+AgA@+A6gA#+A@glgAP8 +AHgA`+A@gA+A]gA+A`hA+A@>*hA+AEhA+A@nhAP+A]hA+AhAQ+A hApk+AhAH+A@hA+ARiA+A#iA+A |FiA+A aiA+AHiA+AʹiA8+A3iAp=+AiA8+A qjAP+A9jAǍ+A jA`a+A`ÝjAW+AjA`ay+AjAr+A \kAe+A%kAX ]+Ag2kAU+AqA+AzqAǚ+AqA+A@gqA+A qAx+A qApn+AqA`d+AqA83\+AqA`e[+AqApU+AqA83Q+A rAHN+A ,rAHO+AWrA)S+A grAfZ+A@rA`x+A | sA`+A@>\sA0+AsAp+A@sA8+A7tAਡ+A`wtAW+A`tAp=+A`)uA3+AmuA+A uA+A q vAਘ+AfvA+ARvA0+A wA`a~+A)rwAps+AwA@Wj+A MxA#f+AxAQ_+AxAZ+AH9yA(\S+A |yAzO+A`)yAHE+A`yA<+AyA83(+AJzA+A^zA +AtzA+A`)vzA8+A3ozA+A@hzA+A@>VzAp+ADzA8+A`2zA+A \/zA+A9zAx+AOzA`d+AtzA8]+AgxzApQ+A@tzAG!+A`zA+A@zAp+AHyA+A |yAB+A3iyA(\+A |*yA8+A3xA8+AíxAL+A@xAX+A kxA+A]AxA#+A`"xA)+A)xA-+A`wA`+AணwAP8+A`|wAG+AjwAŽ+A fwAH\+AnwAt+ĀwA`ai+AwAhH+A wAأ;+AwAz/+ARxA+A $xA`+AGMxAC +ASxA8+A |nxA+ApxAf+AxA+AxA83+A yAp=+A`çyA+A`yA+A@ +zA`+A@>JzAp+AozA(\+A@zAѧ+A@zA(ܤ+A {AH.+AE{A8+Ad{A+A {A+A@{A+Aň|AH+AT|Ah+A@}Aأ+A}A+A (}A #+A=}A=+A-}Am+A}A`w+A}A +A~AX+A` ~AP*+A&F~A(+An~Aӫ+A~Ah+A^A8+A`As+AA+AA+A@A2Q+ARAS+A (cAKV+AsA(]+AʔA$y+AAH?+AgA與+A -A+AX;A@+ALAz+A첄A'+A`mAP+AA}+A`Aȇ+A@aAз+AɆAw+A/A¥+AIA`+AA+A@4AP<+AjhAő+A~A(O+A ۔Aȓm+A.AX`+AmcAhA+A`щAPx4+A@A5(+A`m?AH2$+A\A/ +AzA+A@Ax(+A@ߒA+A`vA@+AA +ÅA$+AA++AwfA++ArAt,+A~AP ,+A ۖA_(+AaA@$+AIA+AjDAH+A@uA, +AA/+AʌA+A@A0T+A GAؽ+A`vA8Q+A uA((+A ֎A+AZA(f+AA0+A`NA+AAAP +A@KA@+A`wAhѽ+A@aA,+AAD+A`;A`T+A@.BAث+A 8hA+A@ؚA+AwڗAx+AA+A@pJA(+A}A`+A2Aӛ+A`vԘAX+AA(+A@=A`+A,}A6+AٙAP͐+A@A+A,AH+AzAL+A]ךA+AzA+AA+A@>6A+AA{+AӟAk+AɡALw+ALAj+AA{S+A`A8++A qA%+A6Aأ"+A`SA`+ARpA8+A샨A+A짨A#+A@ƨA(\+AA+A3A`+A 1A̳+A OA̪+AgA@W+A A+A@A83M+A`fA/+A`A(\'+A@>^AG+AAk+AA+AחAB+A AW+A`A`+A qeAW+AzpA8+AA@h+AApG+A bA<+A A8-+A=ܶAf"+AHA +A A+A.Ag+A@[A+A;A+A_A`+A`AIl+A-Ahb+A@A9 +A@+A8+A@%A0̙+A}A+A@CgA@u+A@A+A`DAw+A`A+AOA[+A ?A+A@A8'+AA`4+AsxA+A Z"AS+A`0A8Ҕ+A&AN+AQAe+A%AS+A=A(^+AHAp+A|AP8+AmAPf+A pA@+A`)A`+AAH.:+A A#U+A qABt+AA@W+AA@׮+AA+AA+ApAh5+A APP+A3As+AeA”+A@A+A`A+A8A`a+A qA`a+A A`'+A4'A0.?+AOAP8T+A zAz+A`A#+A'AX +A@gAP+A A+A3)Ap+A tAW^+AAu+AA(ܘ+A3A`a+A`#A+A3uA+A@A-+A A,A@>AhUJ,AOAYg,A`;A#s,A QA~,A A,AeAx ,AAݴ,A`dA,AA`,A2A,A@A,AA.,AA@,A`A\,A4qA t,AŬAP,A Ahќ,A AP,AnA,A`DA ,A 1Ar,AA,A1Ap,A XA,A`LAP,AKAp=#,AjAv7,AŮAE,AH AVR,A`vA`vd,AO Ai~,A@A8 ,A`dA ,A` AX.,A SAK,A`d}A&q,A@%A,A@PA,AFbA,AA0,AMAm,AA`,A@NAp:,A4A8e,A(Ad,A`A0,A@r A,Ay A,A# A`!,As A #,A A><,AAY,A~AXs,A A.,AwA,AA8E,AZ8A(O,AaAp,A qA,A=A^4,AApS,A (A h,A`;jAw,AAx,A@߻A(ۛ,A A4,A"A,A@Af,A@5A ,AQA',A#oA*K,AVA,A%At,A%A,A6&A+_,Ay6&AKM,A\5D/'Amk5,A'A ,A-v>(A9,ACw(A!,Aґ3)Aq7,Ao)AQ,A)A&{ ,A51Ap ,A71A ,A41A ,A'#1A8# ,A0A ,A`]0A@ ,A0Au ,Aො0Aȩ ,A Q0Ax ,Afr0A` ,Akm0AHP ,A`j0A8 ,A`e0A2 ,A`e0A(6 ,Aph0ANC ,Ao0A0G ,Ao0A0I ,Ao0AfK ,Akm0ADM ,A`j0A"O ,A`e0AQ ,A`0AR ,A`[0Ar] ,A@./0A.a ,A"0A ,A@,/A( ,A@ c/A> ,A G/AQ ,A?1/A ,A /AT ,A`.A(( ,A`.A ,A,.A8 ,A`].A ,A@r.A^# ,A}.A;% ,Av.AX' ,Aj.A@) ,A].A/ ,A@.A1 ,A .A3 ,A.A0A ,A S-Ax~^ ,AA-Ahv ,A@>,A( ,A,Ah ,A4,As ,A +A ,A+A ,A+A8I ,A+A` ,A 8+A,A`T+AK,A`%+A_,A++Ao,A5 +AP ~,A+AX,A5 +AI,A@+A0٪,A +Ap,A`-+AH,A}>+Ao,A`T+Ae,AMk+APZ,A +Aq,A+A`2,A^,AHr,Ay.Ap,A`2.A`%,A`/A,A/A,A/Ab,A ~/Ay,An/Ax,,A@0A6,A@'0APf?,A>0Ah"C,A@H0AE,A@W0Aa,Ah0A,A1A@,A03A`%,A A;5A2,A5Ah3?,A@5AK,A`O6Ax:V,A (6A@`,A@*7Ack,A&7Au,A`28Ap ,A@N8A,A@9AP+,AK:A(m,AI;A,A<A,A<A ,A [=A`q,Ad,>AD,A+>AP.,A@?AHy,A%AA0],AoBA,A`)CA@S/,A&DAf|,AR@FA@W,A7GAG,ATHA,AIA<,AKAO,ALAذl,A@ߜMA~,AMAԗ,A`B,NA5,A`K_NAhU,A@iNA,A`D=NA:,A`MA",AuMAh6,A@MA`b,ALAP,A7LA,AVALAs ,A#LAvK,A@WMA,AMA*,A?MA,AMA6H,A`^MA@{,A FMA,AcMAP/,ABMA(,A |>NAh,A`ܼNA8V,A  OA,AkOAc$,AJuOAfʩ,AwOA\[+,AOA,A@OAD޲,A4hnOAB,A/OALߍ,AA3 PAC,A_6u0PAҊ,AVPA!ē,A6$ܻPAĠ,AЉQA #v,A(ATmQAd,AQA=7,A'M-QAuRO,AQAـ#,A,QA,A4QAXI&,AWQA{|,AAQA;y,A0bQA,A ¹QA(z,A`;6RAh,ATtRA8&,ARA$,A*SAP!,A`SA%,A)SA 2,A@dTAO,A /TAk,A`;UA,A\UA,AFVA,A`ʋVAxz,AଲVAPL,AVA8Io,A@VAy,AVA_,AVA+,A` WA@,AFWAXy,AyWA!,A WAx,AWAp[,AWA!,AT@b"%,AS*@2-h>+,A:d@1,A%@ʇT8,A,YF@ Alq.A3 Ahe.A. AQ.Ao AG.A ˸ Ax5.Ax Aot.Aṡ Ab..A APM.A@Gۂ A.A`y Ap .A\y A=.A@y A".A =y A`.A q!y A.Ay A.A`y A[.Ax Ax.Ax A.A ɽx A.Ax Aɪ.A`mx AP.Ay APf.Ay Av.Aw AZ.Av A3.A g AT.A8f A;W^.A=$-a AE]K.A4Ɛy Ad3N.AO A].A}w A-.AF;B[ A0.A\ A-.A\ Ay<.A 8] Ae.A@y^ A*.A#` A.AM` A@B.A^ A.AZg A( .A@ef A`/.Af A`>t1.A7f A6^(4.AVnf ADwz9.A2{g A.>.A>1#g A ]D.A~ 2g ALJK.Aq v.Ag Az.A,,g Ap;.Arg A@.Aig A~.AQg A$Q.A"NYg A.A g Axذ.A`Ch Ae* .AJj[h AM.A'1qh A2(.AEh A).Aspah Av.Ach AA.Aa|0Wh AXh.A h A=.Ah A޷^.AI{ i AS/.A&i A.Av:i A.A69Ki AdT .AVi A`.A@>]i A5-.Ai A;(..Aրi A/.AIi Aj'g42.AM.lmi AX15.Ai AJ8.A6i A .Ҷ<.Aa_j AGA A.Ap%j AE.A!j A*EJ.AIj A(N.A`fj APIj.A`cj Aal.Aayj AIo.Aßj ACms.Aaj Aŝw.A|j A?}.AiSYj AY.AG[j Ay.AH*j Aw.Aj A0.A`K'k ATPY.AM9k A.A Ik AH,.A>7 Yk A@µ.A@Efk AYgh.Aqk AG.AjByk A_.Aeu~k A 2Gx.AҠk A0 .A|k A.A3k AC!N.Abk A].Al ATC.Aա(l Ar*.AZy;l A|i .AHl A`.A`Ol At.A?em At.A;jm AFu.Aqm Au.Avm Av.A$~m A$w.A m Aw.Am Ay.A`m Ay.Am ApAz.Am A{.Am A|.A |m A^}.Awm A~.Aqm AX.A`mm A8.Agm AX.Aam A.A]m A8ք.Am A.Am A`S.Am A .ALm AЉ.A m A`.Am AH.AAm A,.Am A .A.A q A.AFq A[.A Aq A9.A;!q A8x.A #q A8V.A6&q A84.A 1+q A8.A-q AQ.A@,0q A/.A2q A .A&5q A.A@7q AȺ.A@7q A覼.A :q AЄ.A.A@ru AP'?.Au A?.Au Af@.A]u AA.Aհu ADB.Aеu AXB.A@Gu AȂC.A`Bu ApD.A`u AF.Au ApF.Au A G.Au AI.A"u Ap[J.Au A8K.A`u AL.Au AXN.A u AVO.Au A3Q.A`u ArR.Av AS.Av A`U.A`v AV.At v AX.Aov AY.Av A[.A@iv AЦ].Av A`^.A`dv A``.A v AHb.A^v Ahd.A^v AH]f.A` v Ag.A` v Azi.A Z#v AWk.A Z#v A5m.A Z#v Ao.A Z#v Ap.A Z#v A0r.A Z#v At.A Z#v Au.A Z#v Aw.A` v Ay.A` v A{.A^v A8|.A v A ~.A v A@.A`dv A _.Av A.A@iv A{.Aov Ax.A v A.At v AX׉.Ayv A@.Av A.Au Aю.A`u A.Au A(O.A`u A؍.A@u A̓.Au A( .Au AI.Au A.Au A.Avv A0H.A`v A.A@gv A .Aav A0j.Aؚv A.A Qv A+.Aɩv AH.AĮv A.A;v AM.A@v AM.A@,v AH.A@v AH.Av AH.Av A.A v A.Av A.Av AH.Asv AH.Aw AH.A cw AM.A`w AM.A@w A.A@Nw AH.A@#w AH.A@>+w A+.A2w A.A@.:w A .A(?w A.A@w A.AFw AH.ANw A.A@Sw AP&.AZw Ad.A`_w Ah.A`fw AB.Akw A.Apw Ah.Akxw A.Af}w A=.A@`w AP|.A`[w A.AUw A.A@Pw A.AΓw AX.A ɘw A8.A`Ýw A3.A@w A.A;w AO.Aw Ax-.A@5w A .A0w AXJ.Aw AX(.A*w A@.A`w A@.A`w A@.A&w A.Aw A.Aw A.A w Aؚ.A w Ax.A w AV.A w A4.A w A.A w A.A w A.A w Aج .Aw A .Aw AP .A&w A.Aw A.Aw Ac.A+w ApA.Aw A.A0w A^.A@5w A <.Aw Az.A`;w AX.A@w Ax.A`Ýw AXu .A ɘw A !.AΓw A #.A@Pw A$.AVw A`&.A`[w AN'.A@`w A(.Af}w Ax).Akxw A +.Apw AH,.Akw A`-.A`fw A&..A_w A`e/.AZw A0.A@Sw A`C1.ANw A1.A`Fw A p2.A@w A؁2.A(?w Ah3.A@.:w A_4.A2w AH4.A@>+w AH4.A@#w A5.A@Nw A=6.A Sw A=6.A`w A06.A cw A06.Aw A06.Asv A|7.Av A|7.Av A|7.A v A06.Av A06.Av A06.A@v A=6.A@,v A=6.A@v A5.A v AH4.A Av A_4.Aɩv Ah3.AQv AH!3.Aؚv A؁2.A@ߕv A1.A@gv AC1.A`v A0.A v A`e/.A|zv A&..Auv A,.A@ nv AH,.Aiv A +.Adv A().A\v A(.A`Wv AN'.A Rv A@&.AMv A`1$.AHv A".A Cv Aг!.A9Av A.A?v A88.A9Av Ap.A4Fv AȺ.A/Kv A.A)Pv AP.A$Uv A_.A Zv A .A`_v A.Aеu Aa.Aհu A؟.A]u A>.Aau Ahޠ.Au A0.Au AP.Axu A[.A}u A0.Au A0.A|u AP.Awu A9.Apu A9.Ahu A٥.A*au A٥.A@Yu Apx.ATu Apx.A?Mu Apx.AEu Apx.AO>u Apx.A6u A٥.A1u A٥.Af*u A٥.A "u A9.A uu A9.A u AP.Au A0.Au A[.A@u AP.At A0.A t A}.A(t Ahޠ.A-t A>.At Ah.At Aa.At Ah".AHt A.A@Nt AhD.ARt A.A ۼt Aǘ.At A.At AI.At A( .A@t A̓.Ast A؍.A@yt A.A}t A(q.At A`2.At AT.A`t A.A t A7.A`t A(.At AX.A t A =.At A.At A .A~t AB.A |t Axd}.A |t A%|.Ayt AGz.Ayt Aix.A &wt Av.A &wt At.A &wt APos.A &wt A8q.A &wt A8R.AM.An AL.A@n ApJ.An AI.Am AG.A@m AfF.A@m AP'E.Am AC.Am A B.A@!m A@.A@!m AP?.A@!m AN>.A@m Ap<.A@m A2;.A@m A9.A@m A.AWl Ah.A`]l AH.A cl AA.A l A.Al A.A`l Ac.A@l Ah.Al A$.A`l A.A l AP.Al A.Al A.Al A.A` l A*.A@%l A.A+l A(.A0l Ahn.Al A@.A}l A.A@xl AQ.A@vl A@.Asl A.Anl AЕ.A`Kll AW.Ail A`.A@Pgl Ax:.A`dl A.AVbl A@.A@_l A~.A[]l A.AZl Aa.AZl A8#.A@`Xl A8E.AUl Ax.AUl A(.AUl A.AfSl AP.AfSl APͪ.AfSl A.AfSl Ah"S.AYk Ay"X.A1qRk A}\.A߆Gk A_R`.AVx:k Awd.A*k A3Rg.A'ik Ay*ii.Ak Aj.AH8j ALk.AnBj Aj.A@j AKil.A?F7j An.ALHSj Ap.A7.j A[@0_r.A'=|j Agbs.AHj A+cu.Ajj AW|v.A2 0j AQw.A]Ϧj A˹w.Aj Axw.A@j A$}.ACwj A-.Aj AN.Aj Ar?.Aj A9u.Aoj A0.Ah&Ztj A,.A=bj AXQ4.AUNj ANu.A8j AҒͿ.AӬ>!j AƤ .Adsq j AT.A i A{.Ai AX.A i A=.ANVi AJ(ߩ.Ai A< k.AAsi A4[֬.A5i Ajb.A)i AG!.AtYi AԲ8.Ati AG~.A`:i AŰ.Ai A`ð.Ai A.A~yi AK[.A\Ci A!և.Aӂi A.A!g i Ad.ADi A\w5.A i AT .Ai A(6.AZi Az.Ayli AJ.A_Ui A1T0;.A >i AP.A Q'i A.Ah AFP.AMh Aj.AgLh AN^.AWQh AVڛ.A ?h Aj8.Ax%h ATٹ .A% kh AZ{.A>=Uh ApQ.AU8h A.A8h A,z.AVfh A؟.A`mMh AW*.Ad7Jh AF.AڈEh A_).A 6}?h Aܢ.A&68h A"[ .A/h A !.A&h A%.A@h Amv*.Aǝh A/0.Aǧh A׮[~5.Aoءh Aų:.Adh Al ?.A?͘Ph A&dD.Ah A.TG.A78g AK.AGEIg Aa+M.A?fg A\,O.Aog A9O.A[3j g ArO.Apȓg AN.A~g A㥠q.A000g Aiw.A<_(g AL}.Ag A0H.A g A(.A@!af A".A a A۷.A Z A@.AIZ A`P.A`][Z Ah3.A[ A0.A@[ A$q.A/Ca A)~.Aa A.A )[ A+.AZ A.A`vZ A1.A@PZ A`.AqZ AP8.Ap}Z A(.ACZ Ah.A 0Z AU.A`Y A.AKY A.A`vY A.AX AOb.A.agY A*R.A(]7Y A.AMɎQY A.A \gY A_:v!.A(8߆Y Ap%J&.A0٣Y Ač",.A?=#Y A^/2.AY A`X:.AY A:.AV鶐$Z Ai4wg=.A.bZ AuB.A$5}Z AjJ.A淾Z Ap/U.A=[ AXa.Am孬+[ Ay5o.A' J[ Ax}.A@_[ AJ.AO-/w[ AϞ.AQ[ A#WU.A_)[ Aoӊ.Ab[ A.AOW[ A7ۢ.A%[ Aȩ.A}[ As$.A|\ Aa.ApF\ Ae.AH4y\ A,T.A{z*\ A:.AQR]\ Aa.Aީ^\ A.A\ A0e#.A(]<] A0a[.AcOt+] A-.A0jA] A }.AoYoU] At9.AFSf] A}.AtZu] Aq.A] Aأ.A`f] A! .AW] Aj .AS"J] AQ.A\p\+^ A0Jr.Alt%X^ A=Z*.A}pY~^ Ao7.A'g^ AE.A^ A]F.A91i^ ArvH.AC^ A9K.A^ A$mN.A W _ A6 r.S.AZF_ Aq%X.A4`$d&_ A*1].A_/_ AWc.A5_ AmPf.A]n_ Ah4l.Ae_ A_b u.AJ_ A59.AX` AG.A&` Am.ArܡB` A+.AU` Avl.Av` A).AHquE` A].Arİ` Aj V*.As` Aџ.A,` Ap.A 1` A-.A,a A .AΤpOa AQ.Ad[|a AvCt.A*7a AS.A3pa A\ .AXaa A.AT6Ca A.ARb A.Ah "b A #f.A=3V.b AH.A VCb A .AǓVb A&.AW,gb AVx .Aub A<%.A{o݀b AxI{*.AVˎb Ax0.A`b AB3.Afg7b AcV9.AS1Ub A"O!A.AF@!c A)`J.AnIc A^U.AlQGjc A~*Tb.A"t[c ASqo.A@ޑc AmN}.A-Ӹc A.A>ٔc A}ډ.AӾ:c AN?.Ayrc Aذ.A|Tc A1.AAn}%/c Ac.A Jc A0.A\hc AQ3j.A%xc A)d.Ac A,.A}kb AI .A^b AsLjz.A]&b AA?.A'Ҋ'b AE.A ub AJ.Af?b At .AÚɉb Aj.A^Xb A[$.Azb AWc0.A薰b A6:.At3Xnb AoC.AHGb A tJ.A|Gb A?fP.Ala AS.Aa A`Z.A̶4a AY `.A*a Asf.Ay(a ALk.A:oO}a AF8o.AVea Ar.AKa A`.Ay5a A齒.AkFa A͠.A|_E` Azˬ.A#b` Av$`.A(#v` AE.AF4` Aj+.Au/0` AW̦.Ar(` A .Az$` Ab,.A` Ag-.A=K` Aoi.A@#_ AvyF.AlϞ_ Ab.A`_ A|7.A:U_ A?F.A@_ A.A |<_ A .AMX_ A.A-'_ A p<.At^ Ax .A^ A m$".A) ^ AZt&.A^ A+.A^ A;0.A2^ A^ZL4.A((_^ AzMp7.AR^ AX:.Aʀ)~^ Aڅ=.AZRl^ A}>.AeDY^ A0?.A,E^ A@?.A1^ AzI.A<8^3^ AYS.Ae1/^ AL].AH!$^ Ag9qg.AJ^ Aq/p.AlMk] A$ʗw.A] APUXz~.A] A8.A] Aca.AWv] A.A9M] A0~q.A9] A.A\ A`d.Ap\ AK.A\ AH.A z\ Ap=t.Aac Ay.ALd AZ.A`]h A).A4i A.A_d A.Ap` A.AZ_ A[.A@W(a A%.A!c Ah.Ab A.A l[ AH.A[ A=%.A[ AG.A|[ A,>.A$ tg[ Aw .Abx[ AQ .Acmb[ Ap.AI[ A3.Aɓ:[ A!-.A@o"[ AV^:.A[ A(F.AmZ AEO.AZ A\V.A=LuoZ A[.A@4Z AG^f.Ap"Z Ao.AC~ Z ANqx.A?Y AH.AY Agt㝌.AhgUY APۘ.AtFY AM .A~Y A%3ƭ.AI\WY A .A{++Y A$d].ACX A.A|X A+.AbX A.Aڂ X A\.AX Af].Ap}X Ao .A&bX Ah.ADX A3Vn.A}5X A}.A=X Are .Aן W A1.AW ADL.AW A&.A~k|W A+.AaGW AjF2.A>W A.8.Afyn1W AXTȇ=.A!W A*k1B.A6t(W AvF.A(V AI.A V AnTeZ.AQ%V Aށj.A…bV AZx.A/jvV AhD.A?V AXż.AR W AH.AV A.ATW A8.A`W A=.A@ U Aq\y.A%uU AdiC.ApxU Av.ARU A.A +U A6[).AAio*U A,.AhT"U A.A5U A0.ACnT A~d.Aeq T AC0.AuXhT Av:.A$6T AW.A tT A .AM&\IT A.AT A.A ST Aj.AT Az.AGcT A '.A޻T ADh;.A3T A̮.Aڹ T A̟ .A5S AY .A{M A0w.A`TM Ao.AL AAV.A<2L Ap.AkL Ay.A#LWL AXdz.A̬}QL A:.A4"L As.AG;K A{.AIFK A>ŕ.A5үK A|.A5T?K A O~.Au=K A&r.A HzK ALs.AekK A0ot.A5[K A֒t.AoVKK AqMt.Asd2;K ADl$s.A<##,K AycMWq.AӸK A\n.AvK AVc2 l.A͋K Ah.A;UJ Ave.AeeJ Aaa.AGRJ A`8].A}ݳJ AyY.Ad=/J A 4U.A`bJ Ae-;S.A!J AܰO.AύJ AH.A4P?]J A64@.AO2J A)?7.AE"q5 J A *,.A`I A y.AI A0.A I A`ω.A3I Ar%.ADI Aa.A%KΝI Aٴ .A:nI Aɠ.A[I A.A ILI A7l.A, @I A.ACS7I A.A]2I AFJ.Ax>H A9x\.AĦ-(H A{Z.AA%tH A v.AvaaH A0.A ;H Ab .AVfH A.A/H A٤.A/G AJwN.AJ!G A1.Ar!G Ag.AG Afj)6.AG AOj.A-嬜G AH.A |G A&ƌ.AzG A{.AI_G A5A.Ami+G A.AF A y.A'AF A6p.AvF Ad.A f6F AxX.AwF AJ.AcF A+K.A?ȏNWF AW_K.AHJF A J.Ah>F A}I.A2F AG.A}I F AGLC.AF f3F AǕ:@.AČGF A8<.AE AG9.AF E A$5.AWRUE ASU[}2.AzE Af/.Aħ8E Aȥ+.A E Ag*.AE Ah(.Ap,EE ABZ&.AfE A ڱ".AZRbJE A7.Ad0E A3Q.A1t!E Am.ACV{E A?y8 .A?D A.A_D AȺ.A@5D A.AD Ah3.A D A .A0C A.A B A8.A`VB A.AA Aܭ2#.ANB A4 /.Ap;1B A,%1<.AFUdIB AZQ J.AHQ?XB ARX.A1]B Af.A(!ZB A[t.AQ|MB A0݁.A(7B ACu .AB Ae.AwB AWhr .ATGB A$Jw .A_7"B A M-.A@B Ah.AB A.A ZA A2.At.= A@_.AD; Axv.A07 AH.Aށ- A.At) A .Ap) Axǖ.A' A.A5S& AP .A@E$ Axy.AO! A.Ad A.AO A'.A`V Av].A> Ag.Af AXo.ABl A(iD.A A#).A k A.Ao  A .A A.A A .A^ A/.Ax( API.A` A.A`b A.Al AЄ*.A  A8X.A A.Ay AS.A@P A8S.A@ A 4.Ag# A.A% A` .A`d& A/(.A ' Ai.A( A.A) A[?.A) Ab.A* Axx.AF+ At.AdP- AX.Ak. AQ.Ac. A =F.At=. Ai.AB ( A#.AB6 A5.A`] A.A@! AQo.AI AXk.AŘ A`.A A\.A AN.A AP.A Ax:.A M Aʸ.A Z A@T.A Ax-.AD" AH.A A3.A` Ahn.AdY A.A AS.A@ Aa.A (S Au.AD A=.A A.A`f Ab/A`t A/A` Ap/A & A/A A}/A?r A7/A< Aߕ/Acl A(/Aߍ AH\/A A@/A AH/A`T= A/A$; A/A A/A Ae/A` A/A@ Ah//A@" AP/A0 Ah/A 8 AH/A A/A4 A/A  AH2/AG A/AE A/A A/AU Al/A_O AM/A`A A/A qU A+/A43 Aח/A Aq/Aa A/A` A/A| A/AQ AG/Axi A(/A Ad/A@S Ap9/A` AC/A`R A/A  A/A0g A/A[ Aٚ/AD A/A` A/Ab AX/AQ A'/A@c AXVD/Ay A+x/A`D A /A A/A` Ax/A A0L/A Ar/A@8 A[/A  A/AC( AX/A?4+ Am/AL/ AL/A 8h2 Aج5/A*4 Ax(/A ^5 AH\/A*6 A /A=6 A@/A-7 Ax/A7 A&/AG8 A/AWe8 AP)/A9 A*/A@< A`/A&@ A=R/A JD AKj/AF A/A`KaG A/AG AX[v/AzJI Ax/AJ A /AJ A _/A`@K A*/AK A/AI=J A8/A J A#'/AJ A`/AaJ A]/A@ K APMk/A׾K A8/AL AZ/A`L A/AM A" /AQr A%/Aq API%/A@߉q A8D%/Aq A@%/A q A|%/Aq A*%/Aq A%/A@Nq A؁%/A}q A0%/Azq AP+%/A zq A0%/A xq A%/Axq A%/A`bwq AM%/A&uq Ap%/A@ tq A8%/A`qq A`%/Aqq Af%/Aoq A0%/Aqmq Axv%/A2jq AE%/Aiq A%/A;hq A`%/A hq AP%/A?gq A%/A sfq A~%/Adq A%/Abq A%/A `q AP%/A^q ApJ%/A@^q A%/A ]q A%/A@[q Av%/A`vZq A(m%/AUq A5%/ATq A%/A`mSq A %/A@Qq A%/AQq AR%/A@Pq A%/A@Nq A%/AMq Ax%/AHq A%/AHq A%/AFq A%/A 8Cq AH)%/A@?q A'%/A?q A%/A=q AHn%/A =q AN%/A%/A4p A%/AIp Aܱ%/Agp A%/A p AHm%/A@p A%/A`p Ar%/A 8p AH%/ARp A %/A lp Ax%/Ap A˯%/Ap A%/Ap Ax%/Ap A`%%/A@p A%/A0p A Ү%/A@Np Ax%/A lp A~%/Ap A,%/A p A%/A@p A٭%/Ap A%/A3p A%/AIp Aj%/A_p Ah3%/Ap A%/Ap A%/A`p Ad%/A`;p A;%/A_p A8%/Ap A%/A`p Aп%/Ap A%/A`p Axm%/Ap AhD%/A?p AH%/A p A(%/Ap Aw%/Ap A N%/A /p A0%%/AXp AP%/A@p A%/A`p AX%/A,q Aȵ%/Aq A %/A q AXd%/A 8q A(`%/A=q A9%/Ahq A@%/A q A§%/Aq Ap%/ARq AxF%/A q A( %/A`q A@Ϧ%/A q A˦%/Aq A%/A`Kq A~%/Azq AXV%/A@q A0.%/A q Aݥ%/A@Gq A%/A |q Ah%/A q A@=%/Aq Ah%/A Qq AH%/Aq A8Ť%/Aq AH%/A q AM%/A cq A%%/Aq A%/Aq A8֣%/A q Ah%/AFq A%/AKq A _%/A`q A%/A/q AX%/Ajq A%/Aq A%/A q Aq%/A q AJ%/A[ q A #%/A` q Aԡ%/A q A%/A q A7%/A  q A%/A O q Ax%/A` q AJ%/A q A%/A q A`%/A q A%/A` q Ad%/A &q A%/Aq A}%/Aq AW%/A q A@1%/ARq A %/Aq A%/A@q A%/A)q A`%/Aqq AHr%/Aq A0L%/A q A&%/A@Nq A%/A q A8%/A@,q Ah%/A q AΘ%/A@q A%/A@6q A@%/AOXq Ah%/A4q A z%/A`;q AXz%/A_q AH#/A"p A@!/Aa A/A_ A1/A@Q AX/A[P AX/AM Ah) /A G Ap O"/A/B Axc"/AA A(&/A^6 A8'/Aas5 APo*/A2, A؎+/A 8S, A+/A൮) A F-/AQ;% A/./AД" AŶ3/A  A:/A AL:/A@, A(~:/A A:/A` A:/A`9 AȰ:/A  AY;/A^ Ay;/A@`` A2/A@WE A0?/A` AKq?/A A,?/A Ax$0A/A  AB/A) A C/A AC/A AxC/A |? A8sC/A AC/AY ApC/A` AB/Aβ AB/A#\ AA/A@% A?'@/A A0@/AH A@/A$ AP?/A@ t A@?/A` ] AN?/AK AU?/A@? A̹?/A@p6 A"?/A}? A>?/AJ AH?/A QZ AFu?/Am A@h?/Aw Ae>/A AE>/AK A8>/AFa A0+>/Aq Ah>/A} AQ>/AO A` >/A@߂ A>/A`| A=/A@`Y Ae=/A`3 A=/Az AR=/A} AU=/A AP=/AD A=/A A5=/A ~ Ah=/AZ AX(=/A& A=/A ~ Ap=/A A=/Ak A=/AZ A=/A`Q A=/AN Ax=/AR A(1|=/A [ A@=/A Ah8=/A AH=/A AXl=/Az A8 =/A AXy/A: A>/A A>/A A'c>/AFJ AV;>/A / Atq=/A`md A86/A@} A(B6/A`˵ A/6/A@ AQ 6/A A5/A+ A5/A* A5/A" A@S5/A`ֲ Ak4/Aܲ A~4/AK A 4/Aϲ Au3/ATβ A3/A@Wʲ Ahћ3/A; A3/A A3/A` A'3/A A2/A@ A22/A$a Av2/A@C A2/A@ A)3/AҢ A 3/A 똪 Aۼ3/A A4/A`, AhL4/A`̣ A-5/A A5/AǗ Ah7/A, Ag8/AK A k8/A@p A;9/A Le A|rγ8/A[u- A:,W1/A98 Av)/A~ A=!/A98 A 18/A[u- A A-.AF;B[ A+==/AW6g A R =/A Am A=/A} A/Aw A@?/A@ Axd?/A@ Aߝ?/A@ѐ Ah^[?/A` Ah>?/AՐ A:?/A@ؽ Ap?/A@` A@?/A AF>/A F A>/AF1 A(l>/A  APx>/A@p{ Ab>/Aߍ A(>/Acʍ A>/A) Ap,>/A A݂>/Aqn APk>/AY Ahd>/A H A`>/A  AE>/A- AP'A>/A Ax'>/A`K A#>/A@g0 A0.>/A 8 Ap>/A@r A=/AV A=/Aq)}; A`S/A?/ A8S/A AR/A@k AoR/AL A؞R/AK% ApiR/AV ALR/A`v AH/R/A`2P AQ/Ad9 AQQ/A| AAQ/A AؒzQ/A] AQQ/A  ALQ/A@pK AkDQ/A | A #)Q/A j A@Q/A | A'$Q/A A^Q/AG| A$۞Q/A AR/A@fj AuR/A", AQ/AQZ:b A9Q/AY AႬQ/A8L AtjQ/AV A{Q/A Ad`KQ/At^gc A hQ/A7cp A wQ/A A*Q/A@ A$Q/AkW AnM Q/Ao= AXKSQ/A]p= A~?Q/A ؀P AFEcQ/A)M AyM Q/A A3*HQ/A-J A+(Q/AtPS AP/A AP/A AP/Al AXP/A=4 A|P/A@z A89P/A]r A@$P/A // APDQ/A= A PQ/A$MC{ AkXQ/A;п A7z^Q/A:p AaQ/A V A0aQ/A S A%Q/A@ AQ/AAb A Q/A`; AmQ/A AhsP/AmV AP/A AP/A` AP/A AXjP/AT ApO/A@ A`zO/A A#O/AOC A0N/A| A(-N/A`[ AN/A AΛN/Az AWN/A`) AvN/A/ AXlN/A I A/N/A Ar+N/A@ A[*N/AA AM/A/ APM/AF ApbM/A0 A@S^M/A A?mM/A AiM/A४ Au\M/A` AfOM/A A9M/A; Ax>M/A A>PK/An A`LK/A A9K/A AȥJK/AH A p[K/A[ ASK/A/ AWBM/A ADQM/A`K AeM/A@ AeM/A AM/At AtM/A A88%N/A l AN/A# A(N/A A`RN/A@N AoM/A@ AhM/At AbN/A & AN/A AN/AMK AhO/A;} A?P/A AGP/A AO/A`b A0O/A@ A>+O/Av Aĭ4O/AI AQ膿O/AJ= A lO/AA21 Agz8O/A8 AYO/A! AO/AL  Ao! P/AyM AP/Av} A#(P/A@  A>Z?P/A5 AQP/A`s Ax-P/An Ap=O/AM AO/A` ApP/A% AYP/A ) AV#P/Ar* AP-P/A@+ AȘ9P/A- A;P/A`܅- A`

P/Az1 Ax>;P/A+1 A6P/A1 A0P/A1 A%0P/A@1 A)P/A 2 A`C"P/A*2 AP/A=D2 A$P/AX2 A'P/Ag2 AwO/A2 A O/A&2 ApO/A`22 AO/A@2 AIO/A2 AO/A2 AsO/A 2 AOmO/A 2 AgO/A 3 A`O/A H$3 A [O/AW>3 AGQO/A t3 AV;O/A@4 Axh#O/A@%4 AHO/A`m4 Ap8O/A 5 A O/A I5 Ag O/A5 AXO/A 5 A0O/AE5 ApO/AN6 APN/A@6 A؛N/A*5 AFN/A5 AُN/A@6 AO/Am6 A=O/Aଧ6 AO/A`6 A`O/A=6 A}O/A=6 Ah O/A6 A O/A`27 AO/A7 AX!O/A=7 A#O/A`F7 A%O/A@yN7 A'O/A"W7 A-(O/A0`7 A$.O/A`7 A72O/A u7 A4O/A8 A 06O/A/8 A6O/AV8 A4O/ADy8 A A3O/A8 A01O/A஗8 A/O/AT8 A2-O/A`8 A'(O/A 8 AO"O/A 18 A#O/A l8 AO/A#9 A2N/A`m,9 A(1N/A`)69 AoN/A K9 A0ݪN/AN9 AuN/A c9 AN/Amp9 A0N/A9 AxN/A9 AHvN/A^9 A0sN/A@9 ArN/A൰9 ApN/AH9 AoN/Av9 AoN/A`m9 Ap&N/A 89 A6(N/A J: A/pN/A@%?: AzrN/AEu= APtN/A= AhuN/A4= APvN/A?= AȥxN/A= AzN/A= ApN/A@= AՆN/A > AHiN/A q8> AN/A@߅> AʾN/A> AN/A > A؍N/A@i> AN/A@r> AON/A@> AN/A > APN/A Q> AhN/A,> AkN/Az> AN/A;> AXN/Az> A`N/A A> A0N/A> A"N/A` A A`2N/AFFA A)N/AlA AN/A@ؐA AX$N/A@iA AgN/AA AxN/AA AXN/A B AxzN/AB AN/AB AN/A (B A\N/A@"B AN/A0%B APMN/A %B AxN/AB APN/A`] B AN/A B A=N/A`KB A`vN/A@#B AXFN/A@B AN/A+A AcM/A@ AȶM/A`@ AM/A@ AXM/A@ AM/A |@ AhL/A> A L/A^> AK/A= AK/A^= A!pK/Aվ< AHlK/A< AJ/A+< AJ/A~< A`fK/AB%= AScK/A r= A)K/A= Ax(K/A> AZK/A0> AYK/A? AZK/A |@ AK/A`v? AK/A? A_K/AK@ ApeL/A @ ApiL/AE@ AM/AA ApM/A A AM/A`%A AM/A xA AM/AA AȥsN/ADDB AfN/AC APDN/A+C A0EN/AC AdN/A@~C AM/AXxC A(M/AC A8H/A@ B AH/A}> AXH/A}> AH/AzB Ap|H/AB A/H/A7B AN/A#C A@cN/AD AHWN/A@mE AHWN/A@roE AXN/ApE AYN/AjqE APYN/A rE A8V[N/A[sE A]N/AsE AgN/A sE A1lN/A@tE AKnN/A@uE ApN/A@wE AqN/A`zE Ax(rN/A|E A0rN/A~E AhN/A cE APN/AE AHN/A`F ApN/A,0F AୣN/A`fHF APN/AE[F AXtN/A@hF AN/A`omF A=N/AoF AN/AmF AwN/AmF AxN/AnF AN/AqF AN/A-uF AxdN/A zF AHN/A@F AN/AF A"N/A F AsN/A ɜF A N/A@F A(`N/AF AXVN/A`F A@N/A F AN/AoG AN/A0G AN/A'2G A8N/A͏G AP&N/ADG A)N/AG AP+N/AG AN/ARG A^N/A G A@N/A@rrH ARM/AVH ARM/AnsH AX 1M/A``H A@/M/AH AM/A@H AM/A``H A"M/A[H AأM/A4F AL/A4F A؁L/A`fG AH}J/A DG A`vJ/AwF AOXJ/A sF A(bJ/A[H AH}J/A RH APf|J/An|G AWL/AG A)L/AГH AmM/A H AM/AQH A04M/AH AH5M/A ɡH AjN/A@iH AN/AiH A0O/A ZH AO/A0H A+O/A(H AO/AH APO/AI A0O/A@I A(-O/Aa)I A O/ACI AQO/A lI ApO/AI AN/A-J AN/A PJ A(N/A[rJ ApN/A`J AN/ApJ ApN/A@WJ A(N/AK AN/Av)T A=N/Aa@T AUM/Aic A`;M/A/^c A8V9M/A Uc A8s6M/A Lc A/M/A>c Ah'M/A@g4c ApM/A-c A؎M/A b AXL/Ab A(L/Ab AXL/A`mb A(L/A@b AL/A ub A(L/A`"b A@uL/A 1b AP@L/Ab A0%L/A b AK/Atb A@K/A@gb A{K/Ab AEwK/Ab A`rK/A@>b AnK/Ab A'jK/A;b AaK/Ac A83^K/A&c AZK/AM)c AVK/A/A`mn AP8>/A@Yn A`e>/AM?n AЏ>/A3n AНt>/A@+n A8V<>/A/$n A1>/A]'n A&>/A ~1n A@>/ABn A8# >/A@]n AH2=/A{n At=/An A=/A`n A=/Adn A=/A &o A8t=/Aaio A@=/A@o A=/Aܬo A6=(=/A $7Sp Ax=/Ap A"1.X=/AsFq A%A+=/AAq A8I A:/AA,@ AvIځ:/AK AKr:/AG-)xa AY^xd:/A C AݛW:/A[糎 A3 L:/AM `և AxyqC:/A*ؑ A坁<:/ApE AA7:/Ay& A@5:/AÈ A=6:/A؈ A`7:/A AȲ9:/A A@<:/A- AЯ@:/A6 AE:/AL AU:/A A`X:/AӇ Aج[:/A} APg:/Aw Ah:/Aa Am:/A@ᮉ AX$r:/A AHs:/A A~u:/A ȉ A7x:/A@ډ Ay:/A As;:/A@Eu ABG:/Aʊ ApN|:/AD A=|:/AR AXuy:/A@ Aw:/A AHq:/A8 Ao:/A`[ Axn:/A ( A8n:/A: A(n:/Ab APp:/A@ At:/A Qŋ A8w:/A A@{:/A  AO:/A`m A&:/A@8 AH?:/A OS A:/A`)m Aୱ:/A@y A!:/Aی AB:/A A;/A`7 A ;/A \ AnA;/A} AБA;/A@~ AH;/Ak AN;/A- AX(W;/A㦍 Ap^;/A Aza;/A`͍ Ad;/Aۍ A7f;/A J A`g;/A9 APj;/A Al;/A B AxFp;/A Ap;/A` A0p;/As) A pn;/A@P Axii;/A AEb;/A AX;/AA AL;/A AHzF;/AȐ A7;/A | Ap1;/A`o@ A(,;/A o A(;/A@ AP';/A A';/Aɑ A(;/Aؑ A*;/A` AT/;/A7 AH.5;/A`;i Ap<;/A- AW;/AE A-o;/A`˓ A{;/A A;/AӦ A@;/Aʔ A`;/A% AxK;/AoP Au;/A@X A(;/A@y` A1;/A A%;/A@y A`;/A" A(;/A`2" A=/A A@ J=/A@ A W=/Av A@$=/A A=/A A=/AӖ A=/Aǖ A=/A AX=/A H A=/AE A(=/A$ A>/AX• A >/A֦ A`>/A A_0>/A A`4>/AF ApAL>/A ApO>/At A_R>/A`2k AYZ>/A`Z A؟b>/AO Au>/A; A0~>/A 10 AȎ>/A} A>/A3 APZ>/A@ A>/Am AI>/A Ah>/A A>/A् A@1>/A A>/A u A>/A6 A^>/A A>/A A(>/Aɓ A>/A`f͓ A>/A Г As>/A &ӓ A>/Aԓ A?/A@pӓ A"?/A˓ Ap*?/A_͓ Aث;?/Aړ AFW?/A8 A@Bt?/A`" Ax?/A |D A?/AW Ax6?/Atl AP?/Adz A?/A A(ܢ?/A`v A8?/AOԔ A(?/A@ AP?/A_ A?/Ah A+?/AK A?/A x AH?/A Ao?/A A(?/A@. A`?/A A?/A` A؍?/A A ?/A A(?/A A?/A@ A?/A` Ax?/A A@/A H AP|@/A  AP@@/A A @/A A@/Aa AQ'@/A / A2@/A`mG A07@/A`U A;@/Ase AtC@/AƉ A@J@/A" A^@/AX Apg@/AV A,p@/A AÇ@/A, A @/AL A@/ALk A`@/Aྰ Ap@/A A@/AL A,@/A՞ A0̝@/AX A؛@/A`; A@/A sݜ Ay@/A`" A˛@/A@, AH\@/A 4 A,@/AF= A<@/A HE Ap@/AL A@/ADT A@/A` b A p@/A`vm Aٮ@/A x A@/A鞝 A8@/A`] A`e@/A A(@/Aȝ Aho@/A@ A@/Ah Aܙ@/Acd A|@/AC Aא@/A/%: A#dA/A\ AhY A/AM}r A/ A/A) AY.:KA/AZ՞ Ax A/A A&W1A/A:E AMNA/Ak$ A^A/A`4 AA/A?EC AxA/ABO AbaUA/A&X A*U#A/A^ Ah(A/Aeb A5,A/A%b A:ν1A/Af5^ A) ^6A/AhX A:A/A JO A~2iA/A.۳6 A A=A/A@,_ AogB/AWC/A AC/Aj A(C/A ArD/Aa A[ D/A- ABD/A A(-D/A A&D/A` Aq)D/A@ . A)D/AF6 A*D/AV A,D/A< A@-D/A ~r A/D/Aj AK3D/A h A85D/Aٻ Ah9D/Aa' A>D/Ar ACD/A`ʣ A0FD/A`] ADJD/A@ɼ AMD/A`ؼ AHQD/A ATD/A AЄ[D/A` AaD/A`T AD/A`y A%E/Ag AE/As AyE/A AP F/A ACF/A} A+mF/A AXƒF/A`K A`F/A`4 AF/ABk AF/A A~G/A`B A3.G/A & AЄAG/AT A0rG/A`D AG/A@ AhG/Ax AHG/A AaH/AM A@uH/A _ A H/A@} AH/A A!H/A` A(&H/A A0G+H/A` A"/H/A 1: AY2H/Ae A4H/A u A78H/A AF8H/AW A8'5H/A߶ A.H/Av AP%H/A&y AH/A AHH/A`p Ax:G/A AG/A@S AG/AI AG/AA AkG/A AG/AF AG/A & AxRG/A 8 A#G/An AmG/A@ AG/A`d AF/A8 ApF/A AFtF/A% A-8F/A3 A-F/A@% AE/A AE/A AHrE/A@i AHE/A * AxE/A 4 A@E/A'` A E/A{ AGE/A, AE/A A[E/A@PY AE/A / AE/A" AXE/A . AFE/A AE/A AE/AqT AE/A A,F/A AF/A e Ah F/A AF/A Q7 A[F/A A`F/A AF/AL Ah3 F/A@ AzE/A AdE/A@Ʒ AHE/A" AE/A  AtE/A AE/AZ AE/A@ A-F/A ɍ AȘJF/Aj ARF/A AHaF/Ai A #F/A APZG/A`U AQG/A AG/A@a AG/A AeH/A`9 A9H/A A@@H/A S A`AH/A Ae.H/AM Ag-H/A A0 A,P/Av A0̏P/A A5P/AZ AhP/A`m AhP/A` AVlP/AƳ A^P/A A[P/AN AlP/Ac A`6~P/A AȃP/Am A( Q/A l AbQ/Ad7 A0Q/A A R/A ApjR/A _ A`R/A A@S/A3 A( lS/A AS/A, A#T/At A2T/A AxFT/A AT/A 7 A BU/Aં AyuU/A AxU/A@G\ A@U/A` A`U/A` A V/A@ AYSV/A@J AxV/A` A tV/A@q AV/A Sk A=W/A+ AONW/A9 AYlW/A AP}W/A4t Ap{W/A AȩhW/A AnW/A A8cW/A AW/A AxW/AF AW/A' A#X/A AGQX/Am AyX/AE Ax(X/A A?X/A@W. Ap,X/A AxY/A` A-Y/A AwY/A@[ AY/A AY/A A AjZ/Ad_ AlZ/Ads A`ǼZ/AZn A[/AD^ AP^I[/A;p AW[/A&v Aج|[/A@ Ao[/A A`"\/A@F A<\/A`K A`W\/A Ay^\/A AL\/An AG\/A` AN\/A> Axg\/A Ah\/A`J A\/A Ao\/AE Az\/A Ah\/AP A076]/A- AK]/A A8 _]/A " Ae]/A@`M AM]/AX Ar\/A< A"\/A r Al{]/A AP^/A් AH;]/A AXWY]/A@ AJ^/A Ap= _/A8# A`(_/A@.( A8F_/A$- APo_/Ax A_/A A̳_/A Ah_/A' AP_/Af A`/Afu A:`/AO AV`/A; AKx`/A`z Aכ`/AD AP`/A`Q A``/A` AR`/A A"V`/A@q A n`/A6 A(`/A^ Ap `/A` AXV`/A Z0 A8`/A Ax-`/A A@b/Az AP?c/A uO AXWb/A A b/A A(b/Ag A3e/A AXVAe/AAP A&f/A Af/A+` A@h/A` A&h/A# A0ng/A A0Le/A Ac/A A(b/Aoc Ac/A@> A e/AM A[f/A H Ag/A O A`Ei/A@ APi/A@ Ayi/AR Ab}i/Aݶ Axzi/A A@i/A] A8i/A0 Asi/Aﴵ Ai/A A |i/A@N Aȏi/A Api/Av AHi/A`H A\i/AE< Ai/A/ Awi/A # Ai/A= A0i/A`ͳ AJi/A A(i/Af Ai/A? Ai/A A`i/A Ƀ Avi/A' Aгi/A#˲ A #i/A`fq Axzi/A' A8i/A^ AX i/A A~i/A+y AIi/A@ԯ A}i/Ax_ Apyyi/Aܮ ACyi/Aˮ A8Qyi/A`Ʈ A@`yi/AĮ Awyi/A® A8yi/A &® Ayi/A A`yi/A Ayi/A A0yi/A} APyi/Ai Ayi/A c Ayi/A \ Ayi/A c Ayi/Ap Ayi/A@ Axyi/A A(yi/A AXzi/A9® AIzi/Avî A@zi/A 8Ů A`{i/AVʮ A ti/A J A@`i/Aϯ AHܮi/A@q Ai/A`[ Ai/A Ai/A4 Ai/A_ A@i/A@T Ai/A APji/A A[i/A@H Ai/A@` Ai/A` Ai/A@ô AǷi/Aд AHi/Azݴ Ai/A` AЀi/A Ai/A A0i/Aq Avi/A! ASi/A + Aii/A 0 Ai/A/ Aأi/A@- Aj/A A(j/A  A-j/A"ߴ APIYj/A A8Enj/A6f A=j/A N A j/A AB Axj/A 6 A@$k/A" A(k/A Q A7k/Aq A_k/A A؞lk/A A"xk/A@i Ajk/A AJk/A A(k/A& A(k/Ao Ak/A A؁7l/A ִ AHvl/A AUl/AZ* A8l/A`)8 Al/A< AXl/A/8 A8l/A1 AZm/AV& Am/A| ACm/A  A8Z&m/A A .m/AѴ A@4m/A@# A6m/A饴 A08m/A A @m/A?N AGm/A? AHMm/Aʧ AHSm/A`KC AȺTm/A& ABUm/A AUm/A@Nx AP'Vm/A(U ApXm/A6 AN\m/A 8 Agbm/A`K A0]jm/A@5 A3tm/A@Ɲ Am/AH| Aom/A[ Ap AOl\n/AnB8# A.AMűAxz8.AѱAQ/.AA(&.A`A.A`[ Af.A.A.AXAh3.A@A.AæA.A@ȲAX.A'Ap.A`fA.A6Ap.A@bAH.AA.AaAݫ.A4ʳA0{ث.AAӫ.A QAΫ.A`?A0ƫ.ARoAz«.A A k.A9AB.A ܴA8#.AaAН.Aa!A8.AdMA@B.AnA.AAh.A`vA(.A`vӵAh.AAy.A@.A匫.AA .AEA`.AqAቫ.A$A֌.AͶAh.AA2.A@ Ax\.AAa.A7A@.A@`A`.A`Ap .A cAꨫ.AAp쯫.AA`ಫ.A`;A8ѷ.A@nA,.AoA_.AAH2ū.AAxKʫ.AAЫ.A \UA֫.AFAxګ.AٳAh߫.A@ A".A3'A8.A IA.A+fAP .AכAZ.AϺAi.AA0L.A O*AH.ASA6.A{A .A3AЦ.AֻAН.AA`.A.A}.AfA8%.A@֥Ak'.A@NƼA(~+.A AP0.A0A t5.A;bAd:.AđA~A.AwȽA`6F.AAK.A[AN.AA V.A/QA2].AA`.A@NAA"Ǭ.AEkAyʬ.AॆAVϬ.A<A@Ԭ.AfAZڬ.A@ AXެ.A (A.A9A.A &XA.A 1yA.A`Ax.A@AG.A ZAXL.A AXY.A`AUg.AA0v.A@ A.A O AА.AAHK.A`AH.A` A.A A8#.ARAȭ.A OA֭.A A.AAO.AF!A}.A`]'AX.Aa*A.A`/Ax.A7AX#.Aq;AK..A`=AX9.A>A`D.A H=AOM.A (:AZV.A8Ata.A@2Ak.A.A!v.A//AP .A&2A.A ;A.Ag@A冷.A@5EAZ.A SJA0̻.ANAƮ.A RA(~Ю.AXAPٮ.A]A.A`bA.AgA.AkA.A`oA0.A@ qAH.AqA'$.A pA8-.A@nA(z5.A mA!>.AoAOG.AqA@Q.A tA\.A OxAj.A}A0ew.A A.AAH.A Ah.AؑAK.AA舺.AAȶï.A@%A ί.AFAtد.AA.A Ap.A`AK.A As.AଫA.A A@.A`̸An'.A`)A3.AHAG.AAV.AwAb.A[Al.AA@u.AA}.A AXۉ.A A7.AdA8.A&A.A ||A.A.A`ÙA K.A@~AxU.AomAP_.AI_Ac.AYAhj.ADA@x.A@.Aہ.AgAG.A A,.AxA.AAxF.A A[.AA³.AATγ.A`jAHٳ.A CA.AA .AA6.AAج .AA.AFA.A)A .AA($.AA@-.AA5.A@AsE.A A`M.AՕA].A A7e.A@zAQl.A QtAXx.AfApۄ.AxXA.A@IAP.A/A0.A'AТ.A`A.A3AX(ɴ.A Ap[״.AEA2.AA;.AA.A /A.A`bAp.A ˾A.AA .AAX%.AzAؒ,.A@Ax-2.A@5A7.A?Ai<.AcAX(A.A@AC.AׯAF.AAFH.AEAXVJ.Ac AL.A |BA8R.AH{A V.A@AAW.AA[.AEAd.AAm.A l!Av.AAAh{.AHA.AXIA.A?FA`6.A@y6A.A@8Aˢ.ALA.AgAx鴵.AtAP.A A8ĵ.A ‡A,յ.A@PAH.AAPZߵ.AAXֵ.A|Apε.AAxŵ.A OA張.A"An.A /A1.A`:A꡵.AGAU.A9VA8.AfAt.A`)xAx.AAk.A`A].A HAU.A A@FN.A Ap?.A OA1.AhA,$.A@A.AXA' .AA.AA.A OA.A@E AԴ.AA˴.AA2Ĵ.AAA.A Ao.A@GA.A`mA軚.A@ Aߏ.A)A<.A 8Ahu.AJAj.A XA_`.A@gAUW.A@rAO.A`A`C.AA:.AݰA0.AnAd$.A@AH;.AA .A#A.A|6Aج.ABA`.A`DMA0.AWAxٳ.A^cA@γ.A lAdų.A@ sAPZ.A&zAho.A=A[.A*Ae.A=Aj.AA .A@AX(.AA.AA`y.A`$AHq.A6AHi.A?APZg.A2EAX^.A@YAX.A;jAhUN.A@A7F.A At=.A A5.A A..A@A&.AAȘ.A` 'A.A9As .A`JAj.AWAX.AHcA.ApA.A|Aز.A@>A0Ͳ.AA².A;AHP.A`DA(`.A2A2.AEA؍.A@A.A JA.A@yA.A`A`.A޺Ay.AAs.A`AZj.AA_.A`fA(U.AxA`PJ.A A(?.ANA0j5.A`!A8V+.A*AA!.A5Ad.A$BA .A ZMA<.AQAP.ATVAZ.AZA`.AcA@_ڱ.AsA@б.A ADZ.A@AXt.A`A.A@A趪.AAx-.A@yA.A@A.AA #.A܄A,t.A$Ag.A@yA`[.A@AP.ATA(F.AA2>.A A 4.A`A`+.A`bAP#.AA.A AS.A lA8.A@A.A@Aj.A A_.A&A.A7A@F.A3EA`ݰ.AQAҰ.A[ApAɰ.AbA覾.Ac`AZ.A`_A.AcA".AfhA.AvAȘ~.A@{At.AAx-j.AAp<`.AՉAXW.AAR.AAh"F.AXAs+.A`A߯.AA.AA0꺯.ArA.A`pA;.A@PiA` 1.AiA.A`xA_ˮ.A@A.A6At.AA.A`bA.A A.AA0국.AAh.A A̜.A`Aȱi.AAhA.AA.AvoA0LҬ.A`]Ah.AA8։.A*A`.AVA.A A .AWA.AAh.AA~.AA>@.AAȔ5.A"A.AիA..AתAPM.A`]A2_.A<'A0C.A|A~3.A`Ah/}.A@#A.t.A A0 Z.A [A.A-$APjV.A`&A:.A A.A@pA.A`AA.A`Ahޭ.A@AH.AQA@׾.AA '.A QA(z1.A@AH. .Af+Ao.AA.A@AN.A -A _i.A`A07.ATA.Ad AXW.A&Aȶ%.A@,A .A O{A0}.A@A.AA q.AbA@J.ATA7.ARA .AA@.AfG A.A AQ6.A` AF.A@  A( 0.A  Al.A AX>.A`ܽ A .A` AL.Af; A`P.A@,T Ap-.ADZ AB.A@ A .A AӀ.A࣑ A`K.A`~ A@'.A` A`.A6 A».A" A@.A # A(.A`X$ A#.At% A@y.Ar& At.A& Aj.A;=' A.A' AL.A l^( A{.A`( A([[.A`dS* A`.AR- Ab.A@. Aԋ.Aa/ AK.A1 A.A3 A\K.A`2 AH=.A"23 A%.Az4 AP.A x5 A&.A5 AR.A`6 AP:.A (E7 AТ0.A$7 AP#.A8 AX7.A֜8 A%.AՊ= Ab.A 1>@ A.A Z/D A|.AྀG A#.A`ܽK AX؇.AO Aч.A@P AL؇.AP Aۇ.A@-P A0Hׇ.AFP AGu.AQ A`<.A`;R A.A`ʏR A E.A R AP.A R A ؆.A`S A ͆.AES A.AS A@આ.AS APځ.A}-T A@1l.A_T Aa.A`yT Ax$.A \T ALЅ.AU A`尅.AU A(\.AFV A`؞.AV A.AV A.A@V A|.A@V Aw.AE$V An.Ap4V Ac.AELV A#%.A AV A .A!W AƄ.ALW Ah.ARX A(~V.AX A@<.AX A.AN@Y Ao.AqY A`eۃ.ADY A@.AdZ A풃.A`K>Z A,.A2Z AȂ.A[ A؁.Ax] A0݀.A^ A.Aj a A 9.A@a A@u~.A`b AÙ~.A`2b Ah~.A c A8~.Ac A9~.A sc A~.A@N!c A@~.A&c A~.A+c A 4~~.A1c Ap}~.A7c A@u|~.A \Gc A`{~.A lXc A{~.A`mjc Ahz~.Apc A|~.A6c A;~.Adc A~.Ac A[~.A@c AP~.A`c Aߔ~.Aac Ac~.Ac Aؗ~.ARc AA~.AEc A~.Ac A~.A@%c AȬ~.Ac A୶~.Ayc Ah@~.A@c A ~.A c A8~.Ac Ah~.Aקc AP~.A@c A~.Ac A~.A6c A~.A.A6 AH*.A@ A' .A_ A.AFى ALj.A0 AP.A@~ AOLJ.A@~ A`.A q| A`.A&{ ArՆ.Az A(.A@ z A@3.A"y A .A y Ap+ .A`)x A.Ax Ay.Ax A݅.A x A`eɅ.A@x Aߺ.ALx Ahb.A@5x A.A`Px Aӝ.Aw Ah⌅.A4w A.A4ew A{.A@w AYr.Ajv AO.Aov Ai{.A;u A`.A u A\~.A`ʊu AHO.Au A(.A 8Ft A:.ASt A䴃.A=Ys Aas.Aas Aj.AL߮]s AzWw.A-t&Os A@5d.A@5s A;.As A tؘ.AI$her Ah.A@.}r AH܂.A@]r A5%íт.A.r AȂ.AMq A8 ‚.A^q AX.A2p Ai.A`^o A =.A`̈n A8.A2l AL.Ajl A.Axk A|.AD0k AxV~.A?4j Ao{.Axg A }t.AtEt Axt.A Zt AH)"t.Au Ar.A`w A0{r.Ax A``q.Avy A#p.AIz Ahp.A-y Atp.AMz AHnp.A`_z A( Kp.A@z A@1%p.Az Ao.Az A0o.A{ A(-o.A@W<{ A_o.A k{ AX(co.A{ A),o.A9{ Amn.A[| AOn.A Q| An.A| AHm.AD8} A@5m.A@n} Am.Az} Azm.A`} Atm.A} A0Xnm.A c} A0Xm.A@t} AePm.Aq} A@ m.A uq} Asl.A@yk} Ael.AQ} A\l.AEJ} A@Ll.AD} AXRl.AH} AMk.A`U} Ak.A 8i} APMok.Au} A`Wk.Au} A@Qk.A Zr} AIk.A"k} Aȥ/k.A@F} A.%k.A@;} A k.A@%*} A k.A 1"} Aj.A`K} Aj.A`2} A@$j.A} Aoj.A`; } AXaj.AT} AN?j.A| A/j.A | AVj.A@y| AH)i.A| A6i.AW| A`i.A| Ai.A@} A`2i.A } A@$i.A`v } Ax\i.A } A`vi.A`m} Ai.A0} Ai.A | A֮i.A^| Ai.AF| Ai.A| Aɡi.A4| Aȩi.Aw| AP*i.Aٳ| Aыi.Ac| Axdi.A@>| AMi.A || Agi.A@| Akbi.A<| A(]i.A#| AP8h.AFb| A(h.AC| Ap=th.A1| Ach.A '| AH?h.A | A/h.AX| A@`g.A{ AXۉg.Af{ APg.A}{ A vg.A`{ Ax\Vg.A 8{ ApJg.A`z{ Ag.A`)N{ Apf.A`0{ Af.A@.){ A f.A %{ A0]f.A`m&{ A( f.AN+{ A ޸f.A`v,{ A f.A |.{ Af.AU5{ APf.A T{ A8f.A\{ Af.A`Kb{ Axf.A`c{ Apf.A`d{ Af.A;d{ A*&{1;f.AP9~ AE]K^d.A4P A\:Qc.AQ A0Vc.A AZc.A6' A( ec.Aҹ AKTEc.Ak5 Ad3N>c.AO Aт,c.A|O AWc.A|O Ac.A` A"Œc.AQ0Cc An@Ѷc.A+.{ Aohoc.A\ A]Sc.A"HW AHc.AN AYc.A`Q A^!c.A< A?2Lc.A& AQ0c.A AQfc.A A`~c.A` Aȭc.Ay Ajc.A@ Ac.A`R A?c.A Af.AM0 A:.k.Ax Apk.AH A( k.A' A0]~k.AmY A/k.A I A(Vl.A A)/l.A@ A(k.A Appk.A`o Ahn.A Ao.A AHTk.A5 Ah.AF APg.Ag AXg.A`I A $h.A Agh.A@ AV7i.AA ApRh.AB AxCh.A AXBh.A` APoh.A Ah.Ai Ah.Ax Aܒi.A AYi.A` AHj.A A@ej.AQ AH.^j.A Ap8j.A ASlj.A` A`Էj.A@,: Ak.A`K A@1k.A@P AVl.A` A4'm.AI A m.A` Asm.A@ Aؽm.A@E Apm.Ah AX?o.A0 A*o.A Axo.A`f AHo.A Ap.A Q A _,q.A2 Aȋq.A A(r.A! APr.A $ A"ys.AX+ Ax-s.AiA, A s.A, As.A[- Apt.A- At.A]. Al=t.AR/ Aw.A /D A y.AaO A\z.A`W Apf~.A#r Av~.As A~.A u@t Ahs.A4փ AX=.A4 APs/.A Aڸ.A A A7އ.A Au.A AK.A@P At.A@y AHئ.AI ASN.Aw AH.A> A.A@C As..A` A.A l AH/.A4 Apw.A@.X AH2k.A@ A?.A`< A;.A AH.A? AǗ.Aි AV.A AdŖ.A` Aʕ.A&e AЄ.A u A0=.Az A=.A@z AP.A l A`!.A u A8.A A j.A Ac.A A A8g5.A` AB.A Av.A`Be APr.AM A;.A`J AȶT.A` A8|.A]. AŅ.AГ A.A` Axȕ.A# Ahԓ.A1 AHŐ.AA AG}ݐ.Ar5 AʇT.Ai\ A;sNP.AXg:S A 1.A A=U.Ao3F A.A A:,W.Ao3F A{r;.A A+.AXg:S A5x,.Ai\ AJ.A=m~ Aҗge.A ABkݍ.A A<"<.A@ A}n5b.A uv AAp/AA(#q/A %A Edq/A %Ahq/AAiq/AjAx(q/AGAX5r/AGA`Bxr/A ZZA&r/A qA0r/A`Ar/A A;s/AAXl(s/AAhAs/AAKs/A XAQs/AA|s/A`Aݝs/A AOs/A ADs/AHDAs/A:A`vs/AAxps/AA`s/A`AUs/AAS@t/AhAXmt/AhAt/A@A=t/AmA t/A+Aht/A`Aەt/AOA`Xt/A`]At/AA0t/A`AhZt/A@zANt/AfAz9t/A@A3t/AA _t/A As/AAs/AoAs/A@SAjs/AAs/AAPs/A`kAs/A@ ADs/A \AvTs/AApds/AhAxNs/A`[cAx#s/AVA0&r/A` 9A0r/AA`Wr/A AP(r/AAH Tr/A}A,Nr/A6AMHr/AଂAHr/AAPoq/A,Aq/AOAhq/AAp_q/A`wAq/A`AXtq/A9A@`_q/AQA$q/AAСSq/AKA4gq/A`Axq/AvAxץq/AA@q/AHAq/AYA(pq/AOA$q/A=A0f0q/A#&AOq/A@A~q/A=ADq/A A8q/A`]A`'r/A 8A8r/AAɻr/AAr/A2 A@ s/AA0;1s/A`5AK\s/A)AP/s/AAjs/A uEAPs/A`As/AOAPIt/A ɥA8)t/A@A3t/AW0AKt/A@lA(t/A@lA0Ut/A0ZA(~t/A#&At/AAt/A@֖A t/AAt/A.At/A0A0t/A(At/AդAhbt/A |sA(mt/AྯAt/A-At/A At/A`-A6t/AyAAt/AA&t/AcABt/A`o.A^t/A`A#t/A;$As/A;$Axxs/Af.A@03s/A Ar/A 1ALr/A ShAr/A@ [A`Wr/A`mA0#r/A ShAxdq/AuAhq/A Q'A?eq/A`A$q/AvNAp/Ap}Ap/A`XAAp/A"AYo/AIAo/AmA83o/A6Ao/AAdo/A`Ao/A?A@o/A@WA3p/AAhxp/AZAbp/AzAXFp/AxAp/AA@q/A`Aq/AAhq/A`)A3q/A6Ar/A@%/A0r/A6Ax~mr/A=sA7r/A{Ar/A@cAs/A.ARs/A`TAPs/AAQ-s/A`dAHr/AAXr/ATAr/A qAr/AeAQr/A iAq/AoAq/A@sAq/A@EA oq/A@EA8Dq/A6A8p/AA^p/A cA^p/A]&Aho/A`BA`o/A9@APro/A`AZ9o/A_Ap5o/A'An/A"Aan/AAon/AHAh]%n/A_AXcm/A_A=m/Af8Aߩm/A`Adlm/A ALm/A OA&m/A A m/A A{l/AAh@l/A'Al/AqAll/AUAXMl/ALAr(l/AsA07l/A$=Ak/A`AHk/AA8k/A AHxk/A7AZk/A AYGk/AAk/A@A0j/AࣕA@j/A@ύA$j/A@A/j/A@5aA/j/AAj/A=sAªj/A+A/j/A Q'A8sj/A œAHqj/AAXl^j/A@yA%=j/A,A+j/AjAPi/AMA,i/A}Aci/AAh/A@Ajh/AnAh/AA`gh/ADA Qh/A\Ag/A@Axmg/ARA1g/AA]g/A]Ang/A SAlg/AAutg/A 5A]g/AiAxe/AsAle/AHAle/AA =3e/AAd/AAh d/A@ DAod/AoApd/APAe/A3AXye/AtAe/A@~Ae/AA@We/A=Aqe/AKAe/A ZA3d/A6AIyd/A`DA`d/AAKc/A1AOc/A`A`Sb/A)AXƗb/AaA8b/AAhѕb/AKAb/AAtxb/AaA0b/A@p Ab/A@=A[a/A`;Aa/A3A06pa/A`Aha/A`KAHòa/A-A`a/AAb/A Ak~a/AN\Asa/AHAd`/A sAHr`/A;A3`/A[A(w`/AFApi`/A`IAB`/A8AP!`/ARAO_/A`fKAȱ_/A@#A4_/AA@q_/A 8Ae_/A 8A;H_/A &PA_/A 8.A_/A cA^/A`JA^/AOAx^/AyA^^/AmAw^/A@mA07X^/A[A<^/AVA'^/AAd]/A0Axv]/A6A]/A?Ap]/A(qA]/AAhn]/AA9]/AA7]/AA8\/A A\/A@A\/A@>A\\/A J(A88<\/A6AP\/A@֘Ag\/A :AXs\/A@rAxq\/AA.}\/Aj~Ax6\/A`'AM\/A&Ap\/AsA\/AAȲ\/A=AȲ\/A5A\/A@?A\/Av9A(m"]/AAL]/A A@T]/AAj]/AW=A>]/AA0]/AA`]/A_A(F^/AAp^/AgOAXt^/Ai Aq^/A*C A`^/A@ A _/A`2 AKC_/A$ A>_/AD A0_/A AX_/Aӆ A_/A A`/A AHX`/A Ag`/A` AO`/A A a/Aq APkHa/AA0Ta/A[AUa/AAa/A AXb/ARAoTb/A`AxKb/A`*Ahb/AA0b/A APMLc/A*A kc/A/Ac/AArd/A2A@Fd/AXhAh:e/A QA8֦e/AXrA8Ve/AA7^f/A Apڲf/A%ANf/A%A 0dg/A q A(g/A@iAP/h/A`2AX[h/A2Axh/AHA-i/A`AHLi/A`)FAi/AOA)j/AAq]j/AAxj/AAA`j/AnAk/A sA`6k/A|Alk/AWAPk/A gA`ek/A Ak/A@NA0k/A A8'k/AN AP縥/AfHA姥/A@GA/A`)GAX/AGAX/AeGANm/A`FAo/A`dFA8t/AEA/ArEA/ALEA8q/A`DEA`/A/EA?/A EA -/A=EA#/ADA!/A%DA#/A?CA)+/A`xCA0{/A CA\/A CA/A@iaCA/A 8BA0 /A@%BBAp /A AAp /AAAp /A@, @Axv/A 8?AW/AX>A`/A>Axv/A=[=A@/A@<A@$ /A#;Axv/A /Y;AUҤ/A ;A7ä/A:AƤ/A:A/A?f9A8d/A 9Ax-/A{8A/A`78Aͣ/A 7Aض/AE/7AԔ/A6Apxg/A6Ax=/A@'7A/AF,7A1/AE/7AGТ/A@p6A/A]=6A`/A 5A@o/A һ4A(\/AB4ARW/A`3Ae/A`2AБ@/A22A/A3A0ɡ/A34AXlv/A`4AF4/A J5A@٠/A6Aנ/A@'7A:ߠ/Am7Aנ/A@7AT̠/A`V8A/A 8A³/A89AHw/A@߲:A +/A,<A88/AS=A/A`m>A5/A3 AAs /AVAA/A0BA8Q/A`DAf/A~FAX"/AHA8/A IAP۝/A[KAH/A4 LA`t/AMA)/AtNAX/AOA/AJQAꄜ/A`DiRA9V/A@>SA`ܛ/A"UA駛/A`VAo/A XAPJ/Ag1ZA-%/A/\Ah /Ai]A`aĚ/A՟^AXlؚ/A_A?Ԛ/AaAĚ/A@bcA/ABdA/ATeAΚ/A_fAXݚ/A|xgAXݚ/AqhA/A@ߏiA/A=jA``/AkA /AmA/A`mroAC/A`pA/AavrA"/A`tA(7/A`"uAl/A5vAÛ/A`vA/A&vAxK/A_wA/AaexA0T/AxyA0nC/A@xAݫ/ApxAxu/A lwAW/ATzuAٟ/AKsARr/Az]rA)/A]}qA//A qkpAҌ/A`܅nA\A/AUmA~ɣ/A@#kA(/A *kA/AiA/A`gAF/A-gAXƐ/AseAw/A@`dAt/AbAPz/A@w`A^إ/A ^A/A\Ayѥ/At[A姥/AޞZA/AfYA/A XAxƘ/AWApZ¥/AWAx$ץ/A VVA"ƥ/A@gUAȩ/ATA07/ASA/ASA`S/A,RAxƘ/AQA;/A@N2PAɥ/A@ OA0ϥ/AlNA@_/A\MAa/A`ӽLA /A xKA /AĻJAɥ/A3JA/AIA姥/AgIAU/A ZHAP縥/AfHA@5/A &AȺ/AۗA//AA/AA>/AAF/AbAxGV/A$Aȇl/A$:A0و/AAL/AAP/AfAp/AAPο/ArA.ƿ/A@%BA`!/A A/A@ÑA/AquAP/A)$A@F/AʷA@“/AhHA/A`ƏA+z/ATAk/A 1AxKU/A`rA&/A@`AH!%/A A/A@cA0/A A/A3A([վ/A@TA ľ/AAʾ/A 8Aʾ/A A;/A`TA;/A"A;/AAӾ/A ZaA/A߈A0/A@A8/A A"A/AAP /AIA/AȆA0 /A隆AH!%/A[A0/A1A/Aj A/A (AX/A[A@о/A`A/A@CAx霾/A@dA/A`̝AXe/A&A(hR/A^CA//AvA/AjA/AaFA ݽ/A`9A(mƽ/AAR/AЇA}/AЇAȩy/A@AW/ARA?/A0Ao7/AjA'/AfAPM/AA/ALAd/A A@/AwAPޑ/Aa݄Af/AjAX/AAЦR/A^A:/AA*/AA/AAO/A`A߻/A AȻ/AڃA޶/At΃Aƛ/AAgx/A7A@a/AA X/A ZԄA~J/A +A/9/A`A3/A@#AX0/A@aA*/AA/A 8A/AOA/A A*/AA 9/A lŃAL/AAQ`/AAPw/A_A8/A"qA/A_A/AJA8g/A@@AXtл/A YA8/ASAXW/A:A /AA?&/A 4A8ZD/A"AlY/A 4A0we/A'wA؁q/Aq˃AGx/AAт/A CAX5/A@+Apȼ/A CA@/AdA/A }A/A/APj1/A`mA88A/AA88A/AjAI/A (APT/Aj AhUb/AdAu/A`A}/AHAԩ/A@`"A5н/A@p4AH/AOAX/AyAj /AA0;/AAR:/A@dA0.Y/Aj.AHw/A(A@`/A旆A/AjAUϾ/AfA@p/A` AP/AjA(2/A`7A NX/A@:A#q/A4AP/A@Ax׫/A=A,ÿ/A`jA0{Կ/A`ApN/A ZAS/A$tA/A\Ax) /AA /AAp/A JA./AAf/ApA`/A@ AP^/AA/AjYA/AAH/A`̤AC޿/AkA8s˿/ABAܱ/A`~A/A~A_/A~APH/A`~Axh-/A~A/A+~A/Aj5A /AzGA/ABA/A ~Ae/A~A;/A~A/A~Axm/A~Aي/A~A |/A΃~A0u/AY~Af/A@,~AȤN/A`>~AX9/A h~A 2/AӉ~A,/A@~AX9/A=AX9/A@PAH2/AAE+/A`̤Ah@%/AٳAh@%/A#A)/A@`MA//AzA="/A@A/AAHm/A cPAX#/A sbA /A`A@/AA`޽/A sbAݽ/A@,A/A AȔ/AɡA /A zA(`/AZ#A/A (~Axzս/A@~Ag/A~AE/A@~A{/A~Aa/À~AK/Aw~A5>/A@S~A6/Aޕ~A//A (~A $/A@NAW/A`~AG /A~AH/Aޕ~AXѼ/AJ~A MǼ/A?}AOʼ/A}AH/A`}A2/A}A؋/AP}A?o/A&}AlY/A}AЦR/A l|AC/A \|AH4/A}A#/A`}At/A Z|A/A` |A/A q|AP޻/A@Y|A0%/A@Y|AL/A`V|A(:/A`/|AXdu/A{AP [/A{AhF/A{Ai2/A)|A'0/A{|A/A}A(O/A_}AH?/A}A/Aj~A/A V~Aa/AӉ~A /A~A$/A~A/A }~Aw/AD~AP~AϺ/A)~A/AM}An/A}A/A/}A@/AI|A/A 5|A/A@{A/A?{A/9/Ao{A@Wf/A&~{A4/A){A/A2{A7Ի/AD{A /AD{A?&/As{A0=/A{A*W/A#|Ay/A _|A@/A@ |A/A"|A˼/A4|A0j/A`D|A/Aq|A/A}A88A/A5}AhUb/A`S}A{/A_}AE/A@h}AZ/A }AĽ/A}A/A}A0/A@}}A/A M}A/A5}A/A@y|A,/Aa|A?/A O|A p[/AQ|A8 x/AQ|Ax霾/Ao|AH/A l|A/At|A/A}A`/AJ}A؟#/A`e}A3:/Aw}A@FO/Ab}Ac/A/}Aq/A}A/A}AH /A}Amſ/A#}A/AG}A/A}A/A@E}A`1/Ad ~A/AM~A8(/A` ~A(//Ag2A=/AɡAcK/AqA0R/AA6a/A[kADp/A%A`o/A݂AȺe/AhAxc/AA!I/A*A2/A|A`1/A @Aؚ/AAX/A;ԅA3̿/A_Aⷿ/A OAH /A`Aз/A A({/A`;AX[g/Az@A(S/AשA0 V/A&A8t/A`AC/A 万A/AA/ApXAVa/A෩A@>F/A`APx?/AhsA7/AA//A)OA./ANJA//AA;C/A@%mA S/A`mAX[g/A APky/ABA;/A@5A/A`mAp[/AA,ÿ/A iAܿ/A&A/A s A/A@TA/A A/A AA 4/A cA"/A{A7/AʓA@G/AɏA8b/AAV/A`ُA/A@AD/Az9AX/A&َAf/AlAx/AxA /A@,A/A~A+/ATA/AA@./A@A7$/A`BA/AǛA/A`vWAX4/A3Aw/AgYA #/AA2 /A@PAH /AR;A/A@A /Af?A/A%AH/A *A8#/A@VAx/AߥAb/A A _/AA@/AAX9~/A}App/A`]A0Ȟ/A@A/AfA8D/A=A6/A`vA`G/AGAP/A Ah/AA/ARvA(/Ah8A/A A-/AV~AC/A~AE/A~AأB/AQi~AP9/A S~AH)3/A}A8:/A}AiK/AH}A\/A`}A"j/A`o}A5s/Ag+~A|/A/~AHn/Ag+~A/A@~A0/AF}A c/A@}A@B/A }AP/Aҿ/A EwA𣧿/A`ywAk/AvA!U/A?vA F/AvAX9-/A vA(h /A`vA5/AvAھ/AvA¾/AvAS/A`vA\/A`vAn/AXvAV/A4wA((5/A3YwA, /AcwAȥ/A]wApJ½/A_2wA/A4wAXV/A@ wAѳ/A AvA0/A`vA[/A{vA0/AgIvA?/A]-vAxK/A /vAP/AMvA,u/A`;pvAm/AvAwk/A wAlO/A`ywA`P9/AwA !/A@WxAP/A /xA/A`2wAH߼/A wA$ɼ/Ap*xAh̯/A AzxA /AyA`/AXyA/AxA(O/AbxAgٸ/A`UxA@/AxAը/AuxAHC/AxA/A@wAՆ/AtlwA*p/A əwApO/A@wA+,/A wA/A_wAٷ/A8wAO/AvAܷ/AhivAO/At\vA/ABvA֘/AvA/AEwA/A@wAtx/AxAPQ/A@wA:/A_wAD$/A@wAX/AhvAѶ/A8wA88/AgywAP/A6wAs/A6wAs/A`[xA@uh/A`[xAj/A`B8yAx\/A /nyAN/A ZyAL/A`jyAXuF/AxA4/A`[xA/A[wAP/AwAֵ/A ;xADе/AxA8/A@xAӏ/ALAyA8Q/AլyAh]r/AAHzA Ej/A@NzA8U/AqzAK/AzA93/A zA/A4zA0vд/A`)bzAE/AozAu/A`|zA`vj/A@5zAh/A AX{AW/Ak{AhK/AzA>/A@rzA@/A syA9/A 1yA/AxA/A`oxAjͳ/A OxAj/A !xA`/AbxAR=/AixAxz%/A ¹xAk/AEyA/A yAp*/Av.zA:/AGzA8 E/AH{A&O/A|Apq/A|A{/A u|A摳/A|A,/AI|AU/A J|AXг/A}A/A J|A/A \|Ah/A T|A`!/Aa|Ah/AW}A/A`K}Aڳ/Aj~AO/A | Aɇ/AمAU_/AA&O/AHA`C/A OAP8/A BA0"/A" A/A A /A`Aa/A]ًA[/A ҳA[/A]AX#/A_AA/AH@AK/A@A>b/AcA~/AA8/A5AX2/AֆA.g/AϐA(/AzAP//A@5YA@/A IAu1/A0A f/AzA/A cA/AzA<(/A`vA^/AAk/A`A(/AAʷ/A\A`/ANwAp !/AIAO/A9ӔA@>/ATAĸ/A 'Ap/AЖA?>/A@zA8l/AkA\/A Aѹ/AA˹/A$A(/A A /A 'A /ATA@/AĪAQ/A@PAX/A ~9A'6/A}A'6/A}.AX%/AA'6/ALAd/ALaA9/AzqAѹ/AߐAh/A@FA./A A͹/A sA/A3AV/A`vAxt/AzAJB/A אA/A@Ay/AKA~/A`vA/A`KAX9/AnAؼ^/ARAn/A`A/A؜A@ /AzA3/A;]A$/A38Ah./A@ʉA/AAO0/AɅA/AAX/A@"Ap%/A{AZ/AA@d/A}A(/A A{h/A /A/AA/A@AXc/A@A(/A (UA{/A@A/A@#AP&D/A@.TA{/AIAXִ/AA0n/A (UAp /AA/AbA /AzA/A 8wA@q]/AsA&/A'ŽA/A`AE/A A/A=<A(/A޶AX4Q/A;A$/AԑAȶ/AA/AAR/A`KYAp/AA}/AڱA@/A~APr/A`2ApAZ/A@AM=/A`)\A*/AX”A!/A@(A/AA޿/AhA/AhA@$/AhAhr/AAxS/AAH;/AǰA /A`A /AGA/A Ao;/A?֕A@q/AA _/AwMAHa/A@yApt/A'}AM3/A'}AQ/A@yA 0ؽ/ArAX諽/AOeA`/A Aro/A`AP/A/!A /A_Aq/AvA/AgA( /A'A`/AAf۷/AAҷ/A@!A/AbA/AI3A/A:Aԙ/AzA/AA/A_Axhķ/AAx/A@A/A Aذ/AǰAj/ApbAY/A@yAT/A AA\/A9A_]/AڱAH/AUA( /AAx/A@#AG/AA]/A`Az/ADAEg/A:A(L/A:AX /A`KYAp/AtAp/AA&/A\Aw/A sAA8R/A+A@O1/AAh/A`;]A5/A`;]A(o/A`;]AH\:/AAA/AA/AA6γ/A`A⾳/A@W(A)/A ֎A(y/AߕA #m/AH,AX9J/AتAh /A Ae/AAԌA /AA`v/A3A=Ѳ/A@BAh/A@BAؽ/A`:AТ/A A/AA/AWAH/A jA/A@.TA0{/AA /AגAPF/AwAd/A@ A(z/AΕA|/A`AJ/A іAٳ/AabA/A` AE/AKA@/A@A`/Af;At/A@5nAho/A ApD/A<A:_/A`<A@/A ƟAx:̵/As_AZ/A_1AY/AqA؍/A$A/A`yAA/A "A /A`dAPƷ/AAP/A`bApr/AèA$/A6A@1 /A0AH޺/AzyA /A쩫Ax/A`TCA7 /A`A0X/A@pA|/AEAd@/A@,A/AA/A A/A'A/AoAy/A`[IAKhz/A AZw/A ഊAPEt/AA@}m/AA gp/A|ØA0p/AyuA6r/AA@ϕr/AOAPt/A`;״A'w/A8A@¿x/AAy/A`[IA@/A A/A@RA0/A@ϛA q~/A@\A@/A (CA@/A A0!0AtAC0A uAH0A HAl0AfAt0A`LAܨ0A&^A)0A@#,Au0A`[Al0ALAt +0A AP70A@EA\D?0A2AvH0A AK0AlADP0Ah#A@]0A@A3b0AAl0A~Ahr0A5ATA0AA0A@#Adl0A`mA0A@#A|0AAy0A mA0A`OAl0ACAK0A62AT0AA~0A A)0A஭AF0A A0AvA] 0A[At0A[A"0AA%0AhvADk(0A@LAL$00A@Au10A` bAL20A`A80A`ðAC0AAG0AxAH!P0Ah@A\0Ac-A̔_0A Ali0AAm0A`Aȶt0AAw0AAh0A|dA0AaA0A`ܑA0Y0Az1A<Ñ0ATAp0AA 0A`A0A zAHp0A`EA\0A`Aɏ0AA0AךA0A@A$0A@~A0AqA0AdAJ0AFOAh3|0A@!Azx0A Aw0A;ALv0AA<=u0A?AAdj0A@.+A|0A"A40AA 0AXAW0A@.wAd 0AAB0A@.A0AA'0AA!0AHAZ.0AyAp0<0AGAXnJ0A AsZ0A@A(~l0AA\0A@PA\0AA&0A6AT0A٭AL0A Ap10A`NA@B0A(uAt0AWA0A ޹A0A]A0A AY0Aj(AX[0A??AOp0ARA\}0A QAA0AaA(0A $A|V0A(A(0A JAM0A`]ATh0AJAt0ARA, 0A` <At 0AЅA`P 0AACxT 0AA|ыA\X 0A3AGy-_ 0A$=A7e 0A,rA&k 0A2A{<q 0A8`A‰cw 0A4'1Aݠ{ 0AܒpVAhU 0AX:JA  0AxA- 0AWA-Z 0A~ýA!T 0AhI:Ar 0AA~N 0AKA 0A`0AN+= 0AO?XAl 0AQoANw 0AskuA 0AiAr 0AodA 0AQA 0A?A4 0A$A\ 0A^APk( 0A` A2 0A A7; 0AAE 0A@>AAܦ 0AzDA[ 0ANA 0AT6A& 0AA/ 0A A9 0A AC 0AIAM 0AhAT 0A AY 0A@.A8e 0AIA k 0AA#t 0A lA5{ 0A A@ 0A;Aƌ 0A@A 0A@޼A\M 0AɼA| 0AA0{ 0AA 0A`wAt 0A 2A޼ 0AfA8 0A sA 0AaA 0A`)@AP 0AtSAP 0AgZA| 0ABAT 0AYAHE 0AwA 0Ah}A 0AA 0A<A 0AA 0A`9A% 0AA8' 0A`Ax|2 0An¼A: 0AԼAC 0AA0H 0A A$O 0A$ALS 0A ؼA[Y 0AA1^ 0AkAl` 0A@CA0b 0AAb 0A@ԼApc 0A@AdRf 0A``Ag 0AzAp 0A 8AQz 0A@ǽAL 0A@нA< 0A½A 0AdA 0A`ouA 0ADA$W 0A A( 0AܼA֫ 0AA 0A SATc 0AAL 0A(ٻAy 0AAp= 0ALA 0A`3A 0Ax5APm 0A`AA 0AuAN 0A൅AH6 0A`AL 0A9Am 0AzɻAs 0Af{Aɀ 0A@%A| 0A@ƖA` 0AAS 0AA$ 0A<}AXh 0Aq`Aǰ 0A&WA9 0AUA+ 0AYAˠ 0AFnA@ 0A`2ĽA 0AjAp 0AA" 0AaCrA`z0A'򺦻A1WF0AXwA 0A A.V'0A A0I/0A+R$A!70AGS%3AЉF@0Ax6A/aH0A-AQH Q0AUA[X0AaCA_0AѻAd0A֋ʻA6j0AMKȻAȐo0AwDͻAPat0AٻAky0AE*eA~0A`KAM0A Aŧ0A[nAf0A~A]0AzA $0A{ALf0A`ÑA0A`A]0AA0ALAt0A $A'0AsA0AgAĀ 0AAt0AEAz0A OWA@40A2A4&G0A wALlv0A`?A`0A`iAT0AHA-0AEA40AA0A=AHv[0AwAp0AA 0AٝAQ0ApAhl0A FAx)m0A5}A0AG xAo 0AgA 0A &[A 0A~6A4 0A@4AX 0A@.4APϡ 0A4At 0AjI2A- 0A@3-AH 0A ,Af 0AK,A 0Anߛ,Aze 0A-Aj/ 0A;*-A. 0A-A] 0AI-ASm 0A$D0-AƠ 0ADžq-A(Ց 0A,Ao:1 0AR"(,A- 0A,As 0A&#­,A"ak 0A8h,AЁ 0A;,Aַ" 0A1,A2g} 0AȈO+Any 0AG_+Au 0A+Ab 0A`f +A(|a 0A*ALA 0A=*A 9dEC 0AGYcE*A'C 0A)A"Q 0A Q)A\R 0A)Ad9V 0Až)ATxe 0A*A i 0A`*A( d 0A@p*A_ 0A O*AZ 0A;)A(U 0A c)A\@T 0AF)A0N 0A@ (AG 0A)N(A D 0AG(Ah> 0A'AP; 0Av'A{: 0ARa'A\3 0AF&A1 0AF&A+ 0A ?&A) 0A])&A$ 0A@<&ApQ 0A)Ad!N 0A u)A|O 0Ad)A(uC 0A0)AQC 0An'~)AA 0Aa8)AL 0A$&A 0Aօɠ&ATy` 0A۲&A1 0A2&AYA 0Ap7&Af£ 0AԹz&A 0ABv&A 0A0et&AvJ 0Ak u&A{ 0AO-x&A: 0AB}&A8`N 0A%sJw&A) 0Adz{9&Aa 0A蜰&AC 0A7&A(o 0A`T&A m0A4D'AL̷0A?'AĒ30AL:(AE10AL(A~0A:w(Abç0Ad{.)A 0AMZ)As%r0Ar)AIt[0A8w)A0D0Ag)AX-0A )A1Z0ALR (A0A'K](A20AD'ARd_0A˵V'AA0A-&A,A0A5&Aܮ0A;g%A,0A@-4$Auһ0AQ$A<0Aܽ$A$d0A˛#AA z0A;3)#AO0A"AH0AJ"AN:0A5L!AMc0ABc!A2[0A-!A0AOLW Aq0A A0A< Au0AJ%U Akh0Aor AYڛ0A[d Ak0A5ՖU A0AUyE A0A6 AL0A[Aȼ0AAͳ0AA,+0A`AT0AAs0A@A0A`AWq0AMrApZ0A`cAY0AAf0A^qAf0A`]A0j0AAx0AAx0AN Ab0A@#$AU\0AnKj$A(T0A~$AK0A/$A %8B0A@%AR70Ao%A-0A"%Ai +0AU=%Ah(0A%Al&0A%ATL$0A!%AU"0A$%AwY 0Adx,%A#0A)A7%AA0A@ C%ATx0AQ%AN10A|V%Aړ|0A=%A7K20A] I%ARƁ0A׊&A40A &M&AN"0AC+c&A0Aax&A8b0ANZ8&A$&e0A &A00A׶T&A$}0A`T&A@90A&Av0A!&A"0AQvw&A a0AE&AZfP0Aŗw9&A0AFT.&A>0A$&A40Aeh &A10AB&A<00A(&Aps0Au.&Add0Af枞&&Av,X0A{&Aֳ0AV%Ac0A K%AXe0Aɣ%A?'S0AD%ARad0AT %A0Aw2w%A@ 0As_%AiS0A%A 0A7!s%AN20A-%AЭ0A(%AzI0A͂%AVAH0Ap-%AL̯0A%A̖00AP%AjK0Ao%A e0A҅%A-0A1mw%A|W 0AO%Ah0A_2%A0AL%A'0A/MV%A;0AW%A-0Aa9}%A|Ǹ0Aൠ%Aw0A٨%Aq10AӋc%A 0AsӤ%AP0AM%Ang0A~5բ%AV0A+u%A׬c0A"<%AbE0Aу?%A6q0A$O%A\0Aɷj%A^0A,҉%A䃿0A@%AUs0A%_Q%A };0Af"%A0A$AXY0A$Ao0A3]$A@0AYߔB$Amx0Aʨ$AXW0A@$Am>}0A=5$AjKw0A$ARq0A$Ak0A<$AD+j0Al;$AEr`j0A4`j$A7;i0A=(q$A@h0Aնa$Ah0Aa=l~$ADg0Ay$A,e0A! kv$AD&,d0At$A1c0A:lNGt$Azb0AY0u$AřZa0AT'6;x$AR'2M`0ALBb|$A#Y_0A@ہ$A^0A`$A\0ADЁŘ$Ap,6aZ0AqK$A B>W0AB$AdAU0A$Ae.~R0AWb$AO0Aʺ$A$8hM0A;%>O$AZgK0A$A?#I0A$AݤvH0A_$AX$G0A2gN%AƎz(G0A+%Aa*G0Avp]B%A̝G0A`X%AfB0AQ9%A};0A@i%Aq;0ANl[%AUr;0A%Az9;0A^X%AA::0A4J%Az4k:0A!I%AH\90A%A!690A%AY 80A{/%A'70Az%A;60AQJk%AnXG360Aar%AAAt50AM %A40AV%A_ <;/0AP-%AjT)0Aֳ%AJsq"0A}:%A~H0A|%Al0A@W%A#0A!SH%AR10A1&.%A0Ab%A•V0AdI^œ%A0Ac4%Ao0A2_F%ARS0A,%ABO/0AR%A8n0AoL%Aj)0A/a6y%A(0AGʿs%A. 0A)sm%A #0A;g%Aز+ 0A$ %%AD0ALX$AXl0Aþ$Apv0A?犄$AЬP0Am_$AP2#0A\RUD$AF0Aն3$A 0A.$A0A{i@$Aq0AME#A_=0A/#A90Ark#A.0A|2#AH0A"Ap0A\KW"AHl0A"AL&0A`l"A=0AHtD"A`0Als!"AN0A "A*S0AY!AP0A !AR0A苃!A8~?0AB0!Ag0APg!A秳0A"!A%_0AoYM!AY00Az$!ASI@0Ay!AV0A!AL0A@!A 0A!AHԜ0A4d!AD"0A;N!At0A@yr!A0Aേ!A#UR0Azl!A$0A)X!A{ 0AcS!AXD0Abl!A-t0A,Rd!Ay"B0A/?!A0A`!Ay|0A3]t!Ag0A !AFh0Az!AjP~0AR~!AR0A|!AP*0A9!A׫z0AlV!A$8(0A}K!A 40A !A|-60A0+4!A}70Ac>!A480A0A%!Avq90A!AsO:0AY!A;0Aݥ!A\h;0A Ǚ!AU r;0A&*u!Ab6;0AmE!AAZ:0AQu!Az790A3Vj!A490A&a!A70AY!AP/0A˱ A=AR(0A_7v j A"0A>IAN^L0A fAP^0AA}60AiAM0AnaAِ/!0A~Ae/'0A[ǎAT/0AA,30A A590A A|;0AAL&>0A'A0B0AAHG0A`AL0AA8'T0A]Als0A=Av0AAX0AmAlx0AZAQ0A`vA0A9A40AtA;0A`"AH0An!A20A+$A0[[0ABW%AV0A`Kx&AT0A Qs&ApR0A&A\O;0A~&A390As&AP\0A%A/0A`K%Ah 0Ai%A 0A&W%A0Ao$Al0A$A0A$A 0Af|$Ap0AT$A0A"AP0A"A0A O!Am0A !A<&0A!A<](0A !A"0A!A 0A!A 0A@ !A 0A!A0A"A00As"A*0A@,"A,0A`"A0A"A$N0A;d#Al-0At#A0A`]#AC0A |#Ax0A@P $A0Awa%A$0A@&A0A '&A,)0A2&Ay+0A&A890AZ&A<0A@&A8%D0A&AAL0A"'AN0A.'ApR0A`5'A,]0A|>'Aԩa0A A'AtDd0A`A'Ak0A4A'Acm0AA'A4v0A`)2'A8ޘ0Aa&Aӟ0A&A 0A&A0A \&Ap0AoU&AT0A&A0A &A0A@%A0A`BR%A`0A J.%A0A@L%AN0A@%A<0AA%Aԫ0AF%A60A&A0A`o=&A\0AF&A 0A &AԚ0A@&Al0A׿&A`É0A@&A|0A &Arm0A ('A _0A, 'A(s`0A]x&Ao]0A_z&A b0Aྎ%A̬0A s%A0AO$A 0AG$A0A $A0Avh$A\M 0A`$A| 0A$A0E 0AU%AQ 0Aq%AȾL 0A `%A0b 0A%A n 0A@%&A x 0AQ{&AJ 0A*Ap 0A?*Ahf 0AK+A< 0A@`)Az 0AXcA,=!0A= A=F!0A< A\dY!0AX ALV!0AT AIV!0AV A\T!0A qb A(mQ!0A[ A0 O!0A AK!0A@ AF!0A 6 ADiD!0Ai? A=!0Ac A4!0A@ A*!0A@ߟ A!0A ATp!0A A` 0A At 0A@5 Axګ 0AЛ AŢ 0A A 0A = AՄ 0A< A} 0AH A|w 0Ak At 0Aw At 0AAq 0A@Al8m 0AAg 0AAd 0A ( Atc 0Apn At&` 0Ad AW 0A A, > 0A A0[8 0A@ An$ 0Ay A" 0A0 A` 0AƲ A̽ 0A K AD 0A@G A 0AA 0A`AA 0AS A/ 0A@N Al 0A+ A 0Aʝ A( 0A@ A:0A A|0A`A AГ0A ^ Al0A ^ AP 0A(T AW0A_? AL0A` A0A A0A A܄0AY AT0A@> A0A S Al0A J AQ0A A 0A+ A0A@5 A40A? A}0Ag Aey0A`T Aȉp0A Ag0Aͯ Ax:_0A AIU0A A(L0A` AA0A AA0A,{ APE0A@5{ AZE0AAl Al;0A@,l A;0Aw A>0Aw A$>0A A(;0AR Al20Af+ A,0AF2 AI#0A@g= A}0A (F A!0A@L A 0AB A0A] A0A A@0A@g A䲻0A@g A0A0f A0A' A80A`] A0A _ A(D0Ao` A@0Aq A£0A@, A:0A J, A0A AT20A A|0A@r AFm0AO Adc0A+ A[0A9 AmT0A@E At5<0A A4B0A| Ap0A@ Aw 0A` A 0Au AA0A J0 APz0A+ A̎0A4 A0AF AT0A ~ A0AV A(O0A A40AF A 0A`C A a0A A0Ai A40A J AD0A@m A0A+ A40AU AhB0A~ A0A A:0Ay A|Ǫ0A@= A0Aw A0A@> A0A A0A A\uj0AF A a0Ao AȖZ0A AcK0A@ A10A A('0A[ AX0A' AT0A A0A w A0AK A0A@ APk0Ai3 AD0A A8G0A A^0A A$0A`) A0A Al0A C A,+0A@ A0A A܄0A& Ab0AL A50A`ʍ A ٴ0A`K A0Ah A0AHA0ATADkt0A JAXY^0A`A T0AA\C0AA%0AaAX10A Ađ0A@ A0A` A 0A`| AD0A ۦ A^0A A0A@ A<0A AL0A A0A A˄0AQ Ad|0A` At0A` Ak0A`o~ Ab0A@ l A`Y0Ahu A4R0A`T AAJ0A@ A70AR AH.0A At&0A A0A4| A0A`e A0A`G A` 0Af Am0A` A 0A AT 0A A( 0A@go A 30A?E A50A`0 Ax0AU Ay0A A<;0A Ag0A` A,s0A. A}G0A@/ A60A` A`0A AXy0A / AaG0A A10A  Axk0A A,0A Aڅ0A  Af0AA$L0AAhq30A@ϘALH0A`KA(/0A@EAlG0A`A(0A A0AADs0ARA\0AAR0A cfAZF0A@A|40A@A#"0AfwA(0AuA 0AqA0AjA0A bA0A`ZA0J0A@AA\-0AZ+A Q0A /Ad0A*Ah"0Aa~Az0A J'A`4`0A ˴A@0A c/AA>0A (AP0A`Aڨ0A`)A(0AÃ0AWAQ0A ,Ah>0AAA,Z00A`MA\*0ARA0A;YAp0A WA]0A-CA0A5AY0A"ALW0AAv0AAN0AA<0AAt0A@#Ax0A&A$w0AAz0A`A.0A`A90A AYw0A \Ao0AAL0AftA\80A`nA*0AiA?0A SkA0AqA 0A͇A8 0A؀A 0A`{A^ 0A@yyAH 0A~A 0A`A䥞 0AA 0A@pA 0AA} 0A Ahk 0A`ôA2c 0A A|[ 0AAT 0A*A@L 0A@`HAC 0A\A: 0AvA|0 0AZA( 0A |A 0AA 0AiA 0A`A 0A@G A 0A <Ae 0A SA 0A@GfAx 0AzA 0A@,A`c 0A6A 0A@A4 0A A4¶ 0A6Ad 0A`TA7 0A`A0ً 0A Aq 0A@Ai 0A[AZ 0AՈA`Q 0A;AI 0AA; 0AAL2 0A]A* 0A (AD# 0A@>A 0A IA B 0A NA  0A`KhA 0AzA 0A?A4 0AhA 0A@,AL 0A@`Ah( 0AF"A$ 0A`TKAL 0A nAQ 0AfA 0A ZAn 0AHA|?c 0A@A|VQ 0AQ ApH 0A@>AA 0A!A9 0AQ5A$&& 0A` BA 0A@,EAx 0A@iCAz 0AbA4 0A A s~ 0AAi 0A>ANV 0AALLe0A`KAw;0AAPš0A %A0A@As0AA0AOAo0A-A0AA0AAT0A`KVA@QB0A2A0A`AP\0AJA|t0A@Ad[0A@2A0ArA}0AoA80AA0A@ At0A47A0AKAzX0A APz=0A tAs!0A@OA0A_ Ah0AAt0A[hAxL0A cJA0Ah-A20AAy0AAp0A9GAL]0AAt0AADX0AAqG0A@5A]/0A (lA 0A`TA//A`dAp/A c5A8D/A8A /A%A/A Axz[/AA /AoRA/AVAk/A`A/AANt/A`qA /AA /A A@/A`AC/A7Ae/A`KA/AA/AA#/A@,A /AכA/AA0e/A ɒAQ/A٫Aq/AA`/A@APM/AEAZ/AA(/AAPh/AA]./A@A( /A0A/A A&/AAA/ATA/AڹA`/AA'A/AxA4&/AlAO/AgAP8/A mAht/AZkAho\/A@dAHL/A\Ah;/AsPA@/AA/AA/AA`2/AZA/A@Ac/A@AJ/AA/A@A/AzAP/x/A&eAd/A`JA`BY/A_BAD/A=As./ADA/ATAأ/A`AO/A@A /A A /A AP/AAX/A@`A ~/A^uAke/A@FA0/AjAXW/A A0/AzA@/A@ mA/A]jAX]/A3MAt /ADAN4/A39A/AL)AH/A?AH\/AAp+/A\A/A@ BA/A/A p/A/A/A`8A83/AcRA8x/AAH/A Ah/AadAp/AA /A@A0/A A[/A A8/AA /AA-/AApM/AEAw/AA/A4Az/A;"As/A`A_/AAR/ARTA[=/A"A#/A|A0/AAXV/A SA8/A@`A@F/A-AV/A>AK/A`9HAG/A0KAo/AaGA`²/A<Axˎ/A0A U/AA"=/AAD/A@AP/A` VAJ/A AsV/AAXy/ABA/A@ A0T/ApA0 /ARSA*Q/A@A /AAC/AfAН/A@@A8R/A@A0 /A6A`/AʘA8/A@sAr/A\A`/A|YA(4/A@rA"/AkA/A` LAj/A2dAe>/A?A_/A A/AEA0f/A?A~/ARAU/AzAX#%/AqA/A@!APk/A`[vA_/ANAo/A]$APf/AFAl/AAZ/A[A./A`TiA/A (A8/A AH/A@rAxƕ/A`T=AȘu/A@AXS/A AP0/A A@/AAP/A஼A@/A`AUS/A`2|A8*/AňA/AzA0̭/A0AX/AA<A/AHzA|/A@[A#G/A1A/A@Az/A@yAr/A㓿A0/AfA /AIAiA0H/A9AV/AA@/A@ A /AAȱ/A}8Ap/AXRA旮/A ZmA@F/A AkAHM/A`OA/AdAX/AܖAX/A@AO/A0A$/A`MAK /A(Az/AA۬/A`A0/A@ҔA q/A֬Aޫ/A_AX U/A`)AX A/A`)A/A (AA8t/AZAPYB/A =Axw/A 1uAZ/AmA@W/A`Aث/AA /A#AB/AA3v/AAH-T/A@~A0{5/A4pAX/A[A8R/A;A@Bۧ/A@Ahݼ/AAXV/A AU/A@vAA/AjA8$/AA /A6APϦ/A`$A@/A An/AA`r/A@،A/A'AΞ/A~AW/ACAX/A ~Apٝ/A`A؎/AEtA/A@gˈAP/A ZyAG/A^\A/A@־Ax;/A jA/AۅAPd/Ä́A`2/A,A/AOAY/A`A/A΀A8/A@!AxF/AAI/A~Ap/A ~A F/A+3}A/A {Aȃ/A S{AH/AqyAȩU/A yAّ/A wAP/A׌wA]/A7wAא/AqvA`G/A5xtAf/A tA/AtAu)/A qA؋/ApA]m/A`OpA=/ATpA0/AoAxъ/A`=oAS/A@ nA /A`)kA/AMkA(z/AiA8Vs/AhAX/A0hAA/AgA/A fAh̅/ACfAsQ/A`dA}܄/AdA0߃/AbA/A`AH/Am_A +/A^A /A s\Ap/A@[A߿~/A;ZA}/A`[zYAz<}/AXA}/ARXA8P|/AWA{/A`VVA֍{/AHUAHS{/AH^UA6{/AOJUAx{/A J>UA0z/AF:UAјz/AOUASz/A`T9UAz/ARTAy/A{TA([Wy/ASAx/A@`RA0nx/A |RAȓx/A^QAw/A`2QA7w/A@5:QA@v/AgPAyv/AAXJm/A` W>A2_m/A>Al/A=Al/A<Ae|l/A`]<Ak/A@8Aj/A@gw8A`i/A`5A i/A4A i/A3Azh/A S;3A@Bh/A`(2Ag/Apl1A/ g/A@u0A7e/A`è-Ac/AD*A+a/A&A7`/A 87$Ap^/A Au\/AnAHZ/A`DA(FY/AApYW/A Ax4W/AdAHU/A`AS/A- ABR/A` A Q/ARAQ/A ÄQ/AA0aQ/A A@=/Q/AcAP/AxAP/AAТaP/AAP/A'AxFO/A@AНO/A |=AzO/AA(`O/A)AТzN/A`KA4-N/A`×AM/AA xM/AqZA)M/AA~L/A@AylL/ATA/A`vuAP 5>/A@AB%>/A`]A=/A`26Ax:=/A9AG:/A`fA07:/A@Ah9/A@ZA(9/AA>=9/A c%A8/A SCA6/AA4/AfAH4/AEAk4/AsA%4/A (~A)3/AŔA 3/AxAp/3/AAȂ3/A A`T3/AEA3/A2A3/A=A`S}3/AAdw3/AAxq3/A@AP3/AA(\2/AA2/A`fSAd2/A`KkAxK 2/A`A`1/AAv1/A@>Ap 1/AE A<1/A?}A 0/AaAHn0/A`kAe//AA Z//A8A//A JA./AڟA@B./A`̖AH./A@AI./A൅A0-/AtoAh-/A>APP-/AAh;-/AAHP-/AdAЕ,/AN Axi,/A A@,/An>A+/A@P_AoW+/AgAP$+/A A*/AYA%+/AApΩ+/A@5A*+/A #A E+/A A8c*/A@TAj*/A`DA3*/A`@AP*/A`DAq*/A&IA`Ge*/AAA9*/A"AXW*/ApA)/AּAp)/AqAo)/A`"A@Q)/AAk8)/AXAHz)/A}A(/A`+A(/A` AM(/A`A@(/A AK(/ASAX[;(/A@,3A(#(/AA(/AA7'/A`A0L'/AA֕'/AAxv'/A 8`A-D'/AAP /'/A ͽAXV('/AiŽA@B'/AAQ '/A ½A&/AǽA =&/AҽA&/AA,&/AA&/A`νAj#'/AɻAppy'/AwƸAn'/AdA'/AӶA&/AA2&/AnAp&/AAAz&/A`Aج%/AzIAj%/A A%/AAVC%/A CA $/A`Az$/AKAT$/AA#/A+A#/AzA@`"/AAH"/A`fA`"/A9AtI"/AཱུA "/A害A0Y#/A`KA@ #/A`yAy"/ADAh?"/AA0f<"/A`oAH)5"/A@A0"/AjõA!/A"|A̧!/A.A w!/AܴAhQ9!/AzWAV!/A,Av /AӳA /A_A /ACAp= /AֲA() /A 1A k/A Aђ/AA7/AIA2/A֭A` /A`;A/A4jA@/AAE/A)«AТz/ARAo/A A@/AUAk/A`A/A@AP/AجA/A`A( /A|AHP/AxsA`/A A3/A`̳A`/AA/A íA|/A߿A_/AAA/A@Ax)/AA/A@ A(`/A`;iA0/AVA/A`;-Az/A}Aog/AŬAh7/AjA(/ALA(/A *A0/A@rAP/ARիA(/AEAP/AwAP~/A`7Ad/A AI/A JԪA!/AAh/Ad?Au /A@%A#/A`A/AF9A/A@*A/A-Axư/A٩A,/AĩA/A`A_/A`T]A H/AR7A$/A@A/AרA/A qA@/AA/AzgA/A;A@/AdAe/A JܧAG/A@ߟA@/A;hA</A`2ަA|/AaA0 /A?APIy/AAXt\/AG,AG/A@AXu!/AA/AЦAz/AĦAX[/A@>Apy/AVA8/AාAP /AgAf/A;AK/AfA8V?/A`A-3/A`AP (/A@ ХAp=/AgA /A`BAu/A`Ah/AqAQ /A AqA/AgA0H/A ZdA؎/A AWA`a/A6A/A %A֮/A@A0/A@A[/AפAP/AAl/A6Ah]/AT{AxzN/A`_Ah=/A@A,/A!A/AHA /AߣAP8/AॽAL/ARA/A@yiA/As6Ah̬/A A`G/A`o A(/A2Ax-y/A#ǢAč/A@A9/A աAp/A` Af/A2AHV/A APB/AൃA,4/AtA8/A?WA/A@?A/AAp=/AApp/A`)ϡAQ/A 뼡A/AAX۰/A5A֥/AjA0G/A`WA p/A*7A@/AhAw/A`oAo/A@ՠAf/A A]/AAT/A AM/A5rAFF/AZA7?/A@EA8./AA&/AA/A`)A/AʟA[/A A/A=Ae /A@A/A`8A/Aग़Au/A@pA=/AAA/AnAhQ/A#XA/A@$A/AtAB/AA(z/A̞AТ/A APv/A+A(n/A A`X/A`nAO/A S]A R6/A'Ae-/A|A'%/AA/A A@/A@۝A /A ƝA/A A/A`AXF/A@PAe/AoAX /A@[A`/AqJA/A@;AH/Ap$Ap/A`A/AӜA/AǜAp/ABAf/AAxK/AAQ/A+}AH/A@5AX/A A /ANAPk/A@yݚAU/A|A(/A !A /AzיAP/AcAx/AAPy/A]A7/A?Ao/AuA/A_A``i/A`KrAh@/AA/A A/A (A`eJ/A+A/ARIAP/A` DA/A ;AH/A 0A@/A^"A`/AHAѻ/AA/AA()/AڒA/AɒAX[/A@yA~/AAkw/AjAp q/AjgAi/AIHA`\/A)'A#U/AAtM/A@yAE/A` A>/AKA6/A^ΑAȓ./A`A#&/A3A/AٚAB/AjA k/A`|AP/AVmA@ /ATA' /AHA /AT3AL /AqAX /AA /A@A /AːAo /A AAК /AAo /A}Ax) /A`;iAHLy /A@`PA,l /A5A_ /A A`S /AAL /AfA7K /AAI /AHԏAH /AAK /ARAO /A jAAT /A,?AU /AAGX /A ŎA X /A6ŽAp0U /A@ƗAL /AyAؽ> /A eA7 /ARgAH, /A JjA# /A A /A3ȎAs /A A /A#A /ATAk /AFA /A問A( /AAp /AAQ /A偏A /A`}A@ /AjyA /AqxA /A`wA /AsvA( /A^nA(| /A}gAhs /A@>aAj /A XA`a /AqPAV /A'GAxzL /A=AxzB /A,5A`B9 /A`-AA0 /A'AX& /AA /AUA` /A@Ap /A|AG /A`[A /A@#A /AAx /AA7 /A`Ap /A`A /A`ێAP /A͎Aho /AŎA /AॽA̎ /A츎Au /AA} /A?As /A`KA@g /AA^ /A*AV /AAXVL /A@,AjB /AEA8 /A@AP1 /AzA ) /AnA /A?[A /A|GAF /A2Ap /A Ao /A A /A6A /A@A /AٍAG /ApA /AAP /A6Az /AQoA /AMAV /A +Az /A  Ap n /A@Akb /A@ȌAU /AA RG /AA? /A@vA@7 /A (cA/ /AOOAV' /A@<A /A-AP /AA /A`AP /AʋA(- /AзAu /A 8Ax /A?AX /AzAH. /A@rhA0L /A VAt /A3DA`Ǿ /A1AV /A ZA# /A# A¨ /AA@ /AA[ /A qԊA /A |Ay /AЍAm /AsA,_ /A|UA(W /A`CAPO /Az1AG /A !ApA /A6A(4 /AA(( /A?߉AH2 /AʼnA /A 䧉AH/AFA/A vAX/AcA0/A@5XA /A`NAp/A@GAp/A*A{/A(A/A@&A/A`DŇA(/AAI/A A/A`tA/ADvA/A;xA/A`A8D/AMA/A S͇Ah/AՇAx/A2A͓/A?A[/A`ˆAǀ/A@ψAhy/AԈAWo/AԈA)f/AԈA[/A`҈A O/A҈AC/AgшA7/AψAB./AˈAa&/A@ ƈA/A@5ˆA( /AA0/AA/AؾA/A A(/A ˆA/A`]ˆAh/AfAh/AA(ܳ/A"Aأ/AĈA/ÄA /A ՈA7/A[؈Az/A?ۈAj/AhAH_/AAhS/AAHE/A@,A0{8/A`؈A2/A ψA0"/ALjA/A`2ƈAV/A`A/A`AH/A3AP/A`A/A@iA؞/AGA/A?wA/AI^A-/A@iCA/A+Af/A`2A[/A AQ/A A/A҇A/AٺA:}/A^Apx/A \Ap/A׊Af/A AqA ]/AVA8R/A;A"G/A A:/A`A./A A["/A@yÆA/A@`AG /AA`/ApbAV/AAA/A`""A/A@A/A(Ax)/A AG/AŅADz/A`Apۥ/A ˁAۘ/A`]A(ܐ/AKAˈ/A@8Aj/A&A`w/A`Ak/A`Ah_/A A0T/A@>̈́AH/A̷A;/AVA//A` A#/AxA8 /A*KAx-/A3A/A A(z/A JA/AA/A`׃A#/A ƃA /A A/A좃AX[/A@Aج/A A~/A?mA8֋/AYA-/AEA py/A4A((q/A]$APi/A`Ata/A@AS/A APG/A@>قAQ;/AÂA`//A@,A%/A@A3/AA8/AtA8/A[A/A`>Ae/A/A/A`] A/ARA`/A JAx/A@A/AցA0/AA(/A ۪AH{/A A/A uAp=/A`vAx\/AbA[/A PA/A}1A~/A|Aos/AdA88g/A AX/AT؀AXg/A`KPA 4w/A@Ax/A ˺AZx/A ۙAkr/AjdApf/A@AAY/A <A Q/A`OAL/A`9AȲD/AVAE7/ANjA؁3/AsA%/AA/AAH/A A/AAL/A@A(/AfAx/A |AP/AtA/A@kA6/AHcA/Aj[AP/ARA臟/AjHA9/A>A(:/AW9A0./AQ/As/A'Api/AQ$Ax`/AA(W/AtAN/A# A"C/AA8s5/A?A`'/A`AH/AAD/AA/A@AHK/AײA/AAH-/AXA/AtA0/A@AE/A yA@q/AkA®/A^A/A;RAȺ/AEA/A <AXh/A3A z/A$A(q/A`)Ah/A`mAh_/AAV/A`[~AvL/A,~AhA/A~AT6/A~AQ(/A~A /A~A/A~A! /A ~AP/A@o~A`/AyA@.AyAh..A yA@= .A=yAo.A_yA.AyA@p.A@yA.A@yA.AyA.AyA.A yAP.A@yA.A yAێ.A`yAP.AayAw.A=yAxzm.A`yA d.AHyAP8Z.AyA'R.A`oyA`M.A΋yAx?.A JyAp3.A`yA8*.AyAxu".A|yA8V.AwyA .A@>qyA.AgyA .As^yAH.A WyAP8.AJyA.A J8yA0{.A)yA.AyA୿.A@ yA-.AxA =.AxAV.A6xA.AxAXۗ.AxAТ.A xAأ.AxA`{.AfxAr.AQQxAأj.A;xAa.A$xAW.A xA)O.A`owAE.AwA;.AwA(4.A`wAX(-.AwAF .AiwA.AjwA .A`wA.AmwAf.A\wA.A4FwA R.A q2wA.A wAp.AwA.A-wA.AdvAL.AvAp .AvA@u.AvA؎.AvAe.AGvvA0.A@jvA.A[vAw.AKvA#g.A %vA^.A3vAaV.A`vA(O.AduAHG.AhuA@.AuA1.A6uA[%.A`uA(.AtuA .A ~]uAo.A@ BuAA.A3,uA.AuAX.A`uAPj.A^tA.AtA.AftA.A3tA@.A`]tA@`.A^tAH2.A3tA.A"tA@`.AytAxu.AitA(-.AjYtAج}.A`2HtAt.A^8tAhk.A&'tAb.AtA(zX.A@tAO.A |sAF.A` sA >.A (sA؞8.AsA10.AsAA%.A@}sAP.A`bsA(z.A}MsA = .A9sA8#.A@isA.A`2sAQ.AorAX.A`rA-.ArA.A rAȓ.A־rA[.AնrAK.A_rA.A@اrA7.ArA.ArA.A QmrA.AYrA7.A?rA|.A \*rAv.A<rAPo.AqAg.A`qAk`.A^qAW.AqAR.AqAaG.A`KqA@?.AzqAx5.A`;uqA,.A^qA0j&.AGqA8 .A .qAf.AqA.A@pAhs!.A@oAr.A`ToA_.A=oA@.A pA8.A JpA`.ApA؎.A`pA.ApA(z.A*pAv.A ApAHa.ApAP.A`fpA .ApAs.A`pAh.ApA0L^.A2qAT.A@qA K.AqAA.A_qAx6.A<qA-.AqA$.AqA.A@qA.A qA#.ApqA@.A@!qA[.AqA`.A)qAp.A A qAj.A qAТ.A`qA.AtqAF.A`[qAXW.A,'qA .A@-qA.AqA8Ҳ.AqA.AOpAp=.A@pAf.A pAё.ARpA.A`pAp=.A`fpAn.A pAe.A?pA`\.ArpApS.AfpAI.AXpAjA.AMpAB9.A3BpAo/.A`f1pA'.A $pA.A6pAx .A oAp.AoA.AHoAh.AoAV.A`ժoA=.AzoA.A`oAh.AtoAV.A`oAF.AJoAp[.A >oA R.A@#.oA[.AoAۍ.A oA ҄.AnA@|.A nAGv.A@nAl.A^nAd.A nAX.A nAHM.A ynAC.A|cnA(z:.A2PnA@1.A@>nA).A30nA.A qnA..AnA0 .AmAP.A`mAt.A mA.AηmA.AࣧmA`e.AmA.AmA0.ApmA8.A^mA.ALmA .A?9mA8.A#mAU.A@mA.A 8lA@`.A@GlAxu.A (lAф.A (lAP}.A lAu.AؚlAh3k.AlAQc.A ulA8Z.A clAQ.A+SlAE.A?lAL;.A.lA2.A"lA8*.A`lAp".A lA(z.AQkA.AQkAX.AkA`.A@`kA.A`)kA(.AfkA(.AkA.AQykA`e.AkkA.A@akA[.A QkA.A (?kA~.A ~#kAp.A kAH.A (jAV.AjAa.A` jAj.A@PjAX׆.A jAA.A sjA~v.A`~jAo.AfjA(-i.A`PjA a.AZ6jAj[.A |#jAWU.A jA؎M.A6iA`I.AViAz:.A siA[1.A`iA).A iA`2 .AiA.AiA .A?iAp[ .A_gA.A TgA.A HgA<.Ap2gAƦ.A JgAt.AfAਔ.AfA`.AfA&.A]fAX[.AffA.AФfA(.A_fA v.A`fAH.l.A_ffA`d.A,WfA`e[.AGfAQ.A@y5fAI.A`fAB.A`eAf<.AeAe5.A@eAx,.AźeA =$.AeA<.AْeA``.AUeA( .A{eA@u.AxeAu.AreAp.A=ceAP.A_NeA p.A=1eA.A@eA.A@eA.A?eA.AdA̺.ApdA.AdAX֩.AdA,.A dA.AdA7.AdAhQ.AodA8.A@ \dAz.AOCdAr.A0dAXi.AdAb.A dA~Y.A cA2Q.A cAjJ.A cA[A.AcAG;.A`cA)4.A@cA*.A+wcA#.A ccA.A@NcA.AQ=cA( .A%cA.A cA.AbA.AbA(.AbA.A |bA(z.A^bAP.A@֠bA7.AbA.A Z|bA.A fbA(.A`MbA(`.A=bAh.A 'bA跊.A"bA.A`]bAaw.AZaAPo.A aAFh.A`)aA(z_.A`aAV.AŶaA0O.A[aA3G.AaA?.AgaAp1.A`aaA8).A3NaA.A`5aAt.AgaAp.A aA-.A+`A.A`AP.A `AH.A `Ap.A`2`A.A`A.A+u`A.AZ``A0̽.A` J`Ap.A/`AK.A`A.A+_A.A<_Ap<.A@_AH).A`)_A;.A_AȘ.A_A.A _A.A_Ah~.A_A`~.A _A}.Aʹ_Ap}.A@G_A.A@r5^A].AB]Aۢ.A`]Aբ.AI]AC.A f]Ax.A?]A k~.A]Aux.A`^A@[.Av_AH[.A x_A.[.Az_A`Z.A{_A8L.A_AB.A@Ƙ_A:.Aପ_A2.A^_Ax).A_A!.A_A.A``A.A `AȔ.A@9`Ah.A``A`.A`A0L.Ad`A .Af`A(.Av`A .A`Au.A@`A.AaAXR.A%aA.A@`4aAX.AGaA@B.A NaA׽.A@PaAp=.A@`RaA( .ARaA.A`NaA蔖.A LaA.A LaA.AzIaA({.AoFaAp s.A-CaAn.A BaA_.Ag=aAR.A[:aAI.A6aAP@.A@1aA06.A 'aA$+.AqaA .A aA.AaA.A `A.A@``A(.Ag`AH..A `A.AZ`A(.A ~`A.A`A.A`A.A^`A@.A<`AP.A?`A`.Ah`A`B.A`|`A2.A``A7.AO`Ax(.A@5`A`ǃ.A`A|.A+`At.A@_A-n.A_Af.A _A@].AG_A((X.A_AeO.A 1|_A[@.ARi_A(7.Ag]_A,..ApP_A".A?_A.A,_At .A_Aho.A _Ap.A^AF.A ^AF.A^Ay.Ap^A .A`^A\.A^A.A Ak^A.A@^AX(.A ^A0fu.A@]Am.A]A kf.A]A7`.A`"]AV.At]AM.A]A2B.A@ ]A@p4.Ax]A'.ARa]A.AG]AX.A 9]AP .A *]A.A]AG.Az\A07.A\Ah.A}\A.Aú\AP.AĮ\AA.AƠ\A(۩.A\AM.A ۂ\A.Ao\Au.A S\A0.A@7\A~.A4&\Apx.A`[\A`o.A@[Ape.A` [Ac.A[AZ.A[A`L.As[AP8E.A,[A=.A[A..Az[A%.A@k[A0.A@\[A.A` @[A .A?%[A.Ay[AG.A`ZA[.AZAأ.AZA.A ZAx(.AsZA.ATZA.A(ZA.A_YAa.AQYA.A-YA(-.A4YAP.A gYA.A8FYA.A@YA().A@EYAh.A`XAo.AؔXA.AXA.AXA.AtXA`.A ɿXA`.AXAx.A@gXA.A YA0L.A?;YA0.A`YA M.A`YA<.A@YA.AYAxƵ.A@ZA.A 8$ZAXװ.A?IZAP8.AiZA.AZA3.AЧZAl.AZA".AZA'.AZA›.A@>ZAu.A[A.A@?[A =.Am[AX9y.A@ϐ[Av.A [A8o.A[Ai.A@>[A`d.A \AH2^.A\A_V.A B\AP P.A]\AH.A@u\A0f9.A֒\A1.A]\A*.A\AH-".A \A .A`\A`2.A J\Ap=.A@\Ak.A \AȔ.A@,]A.A]A``.A`"]A7.A &]A.A-]A8.AF6]A(.A<]Ax\.AsD]AL.AK]A .A@Q]A.AX]A88w.A]]An.A`"b]AQe.Ae]A\.Ai]APM.A` p]AxWA.Ax]A@=;.A@y]A`.A]A8.A`]A .A@]A.Aߧ]A.A]A07.A]A(z.A@`]AQ.A ]A.A]A.A]A.Aq]A`.A ]A.At]AX[.An]A.An]Atx.A n]Aho.Ag]AP`.AZ]A@X.A P]A0Q.A G]ApC.A36]A5.A#]A ).A`]AH'.A O ]A`.A\AH.A\A`.AQ\A@.Az\A(.A\A.A@ \Ax.A {\A(.Ap\Ak.A$p\AX.A@5k\A.Ah\A.At\\AX֦.AG\A.A`:\AH).A ,\AF.A@\A.A \A7x.A[APr.A[A2l.A[A_.A`[AȔQ.At[A2D.A[Ah.8.A[AL+.Azm[Ah.A-U[A.A'?[A.A-[A0.A [Ax.A[A.A[ZAHa.AZA.A@iZA.AZAh.A ZA.AZA3.A }ZA`.A@mZA .A_ZA.ARZA@.AHZAu.A"0ZAجm.A JZAww.Al_YA(.A@CXA¬8~.AeXAS |.A~!XAZ{jy.A3wXAef'Iv.ApmXA+;r.AkeXAh[n.A T'`XA/j.A}]XA*~f.Au\]XA۽jb.Ah`XA骫_.Al\GfXAz[.AjnXAX.AM,yXAU.AXA7JI.AbXAv@.A.YA].A HYAlO.Ad wNYAp*q.A[@YA^.A3YAuQ.A6KXA5b$.A~XAM.ABXAQ?.A( WA.AٓWA`v.A@;WA.AO 0WA .A<%WAz͝.AsWA a.A }WA6U .A62WAKR.AU WAdj.A WAm.A WAAJ.A\gWACV.A)R%WA(.AWA̒.AؚlWA妃.Aޅ[WA8.A!XA.AN1XA1ehY.AEN.XAUc%.AjyXA{w.A)WAڿ.AF{WAv&.AkRiWA8Z`.A@y WA}_.AVAʧ`.AbPVAhc.AfVA@^b.AfMaKVA_.A%2VAo;\.AtijVAҸW.A$VAFiR.AyUAqsL.A*|UA`F.A@CUA$uQBA.ACLUA<@<.Av5UAa8.A]UAF=h5.Aol VA;2.AwwVA%s0.A2Qd-VAt/.A[AVAHr..A |UVAmI&.AcHVA Y!.Ae7jWA.AWA@.AEXAB.AvXA.A|XAFg.A#7YA=z .AYA.AFYA(J.AYAތ.AYA]%n.AE:&YAk_ƙ.A0\ ZA .A@ZAh.A@5YAku.A|MYAż.A`WeYAmz.ACbYAH.AӋYAjn.AJXAݽs".A!WXA0;.A~WA;m.ALS]GeVAq.As$;UAt}.AgsuTA~03J.AZRA@6[}.AWYQAe.A`fOAQ.A%1NAӿ @.AtAMA&Ɖ.AiM|LAjΌ.ATv\1KAߍ#.ATxIA"s.Aeń_cHA.AOFA*.AqkMEAs.A`TCA=.A6AC.A #Ah .AA8.A )AE .AtAD.AA6.A`KAW.A@A%.AlA@.A |Aݛ.AfA.A@lAҗ.AwWAyP.AZuL1AǗU.AǐA/.Al6Apߔ.AyH*WA`)'.A]JvA Sq.ARE%rsAL+.A,Ax .A.MAY~].AG&YAcư.A)QA.AfA(.A1 AoK.A$A.A-AW5h.A0$.Av.A!XA&.AW2 Aw&M.AtS2޽A{EV$.A1:A u.AvᦼA+.A$Aږ.AA2)&.AR\kUA@.AA܅.A`DAH#.A5NAPoq.AAq.A@gA3.A}AMd'i.A AU) N.A- NAxX%8.AOA*J>'.A|AEn.Aʦ`AY.A.Mڵ A$.A%#A6*.AT|%AP.Aà"'As/-.A(7*A!9?.AZ,A"W.AL.Aͱt.A, >1ASP.Apl3AS!J.A.Xg5Ai.AW"8Ar:p.AA;]:AO.A{Fp<A쉌.AI>Am8Ȍ.AM^@AV?D} .ACHCAT.AŢ$EA8.A$0GA 0.A*g2IAFYpG.A;(KApߠ.A@pMA.A@EYARy5.AsZApG%].A 6c[AD œ.Av&\A80.A(\A h~.A:}w]AɷV .As^ANHy.A!^A@.AQ^An_^[.A;_A*@͖.AWX}_A0=T@.A+g_A}.A_A=T.AN\_Az.A [_A޾Yޘ.A_A5%A.A5M_AI.A-_AGl.A-`A& i.ApU>`Ab`ɚ.A`AZ(.AXaAja.A|RraA.AlEZybA|m=.Aeg6Q#cA6'.A ~cA.A ndAli>.A ceA(.AmfA/r.AiA$ .A}A^..A~A# .A]0AuΞ.A~#XA{N.ACἂAOav.A.AW..AK&A![.A;A;;§.A6eA'.A`bJAg.A@AXa.A0A$ƪ.A9A.ABAFH.A`mA`1.A At .Aq7AH.A`GA.A`.A(.A@4A .AXAs.AAxŴ.AApP.AA p.AAA.ACAx.AwA.AcA.A+A۶.AzA.A@A-̶.A;A|.A%A.A%A؎d.A=ŗA>.AAz.A^A.A9A00.A`ßA!.A /tA .A븢Aq.A sA7.AAp9|.AAP.ARAh.A AX R.AĮA.A Aj.AA`G.A~A@.A@KA.A| A.AjA&.A /PAU.AA.A@`Aà.A qA .AA.A`]Aِ.A`A.AA '.A JA.A`Ah".A QAx6 .A[A .A"A.A"Ab.AMϿA;.AA0.A A@.A;A(.A;A0.A^AH.A A.A`3AXhE.A`KAHz.AwAp.AAp.A3A.AA$.AѠA`%%.A~Ax\.A Appn.AƎA؎}.A[ʶAF.AA虗.A A\.A ȶAp.AA@$.AA0.AspAٺ.AA.AA.AXApP.A 퓺AF.AܢAFAޠ0.AgAXL.A} 79AnQ@l.A,jǤA W.A2eA}P.AO=hAeH.A*ߊأA؂3o.A~s A0.AtyAp[.A As!`N.AnRA9H.A&A{{/J.A@b*AL#.AwzvA1J\.A^џAͩ .AnC;A.u.A 8Af.A CAZ.AEQAq.At AN.ACQA,*.A(&A֦].At )A6.oo.A>AI2.AQfA9_w.AHyA;Nq.AALj.Ax>NAf{U.AdAM.AwCA5.Ap`ٟAb.AlȊA=.A9Aex.AvLAW_.ADܢAAE.AzA7ǣAAQTO.A;A.A˥A.AAx.A?A J.AØAL.AA.AMgA k.A|<A2 .A`:$ApA5.AZfKA3c.AG9A}g.AlAI.AZZAS.A٭AR¾-.AOaAc.AHÁǝAZp.A\ݾA}.A0eʝAF.AA<.Ap)"Ao.AfA5.AFhs㿞AT>.Ag ,Aj .AL_#A_].A%<A&j.A{A11:L.A^@8A;(.A^AF.A7AzE.A!A2.AA-.A /AX.AA.A A0m.AjApN.A &A.A A@.A ۓAx.AAr.A@iA.A`;?A{.AiAP.AAp.A`xApp.A AAP@ .A`AL.A Aa.A=Ak.AA`ԉ.ARA.AA.A 4A7C.AA.A?AHP.AA<.A`A.AAؽ.AA(?.AnAp_.A AX E.A@,A趶.A`mnAhb!.AoA`d.ABA`.A`AH.ARsAd.A AP/.A&AP<.AAhA.A`mA`.AA.ANWA(h4.Af(AhUs.A3Ae.AʀA˖.AA@>.Ag>A茑.AMA(>.AAї.AAH.AdA/.AAx.A$rAH?+.A AH.[.A A.AAA .A`"Am.A@Ao.AA$.A JAX.A@>A .A`2A`q.AqA.A A(.A'A.AA.A l:A@.AkA0;.AuA.AMrA(.AZA:.A .A@.A@A".A,AV.A`A$.A 1Adn.AA@.A]Ab.A@?A#.AMAh.AjAP^.A@0A.Ao1Ax.AAo@/A#A`S/AMAп /A |ZA$/AA\/AqA R&/A=ATR/AAo/A`SAľ/AAP/A3A/A$A`T/AAI/A`A(/A"A/AHiAx$O/A3A,/A$Aث/AA/A`bAH/A |A c/AXA(܀/A@GiA/A \A/A}A8E/AAǢ/A A(z/A`A#=/A sAh/AVA/Ai,AF/A(AP/AMAX/A`]A`/AA8b/Ai,A0/A@ A7/AAȃB/AA0>/AA?8/AA`v/A@PAX(/A`T2A`M/A=AD/AAJ/AA~/AEAP/A AA(/AA//A@rA?/AׯA( /AAl/A@.AX$/AzOAX/A`bAr/A ˊAp'/A@AXJ/A[]A]/A@A/AvbAn/AAp/A-As/A=wAe/ANA0/A;A`/A@4A(/A ;A((*/A=A@5/A@AA8 ?/A[@A`J/A4>AW/A:AЄc/A`d3A k/A*Am/A(Axw/A2$A@}/A !A\/A A/AAPo/AA(/A" A/AAU/A3A跷/AA/AA/A`AX/A*A/A@A0/A@A(/AA$/AjAP</A JA/A`AW/AA##/AA@+/AA؎5/AyAP?/A$AXWI/A[6AhQ/A@rFAZ/A@_Ad/AHzA[m/AŒA(t/AƤA{/A Aփ/A6Aň/A@5A(\/A AAۘ/A A /A AX/A:Ax/ASA,/AiA/A|A/A`KA(/A`oA/A@A/AAH./AHAP /AA /A_A /AtA /AF4A /ADA% /A ZTAX. /A hA7 /AtAQ@ /A6~AAX /AZA7 /AA /ARA /AڪAX /A AXW /A`2A0 /A@`A* /A A6 /A/A7 /A QAD /AAO /AA؎W /A QAV_ /AZAH.l /A@yA0Gt /AA=} /AqA@… /A@ A8֏ /A@`A0 /AwAU /ApA /A`"hA( /A`]`A[ /AXAu /A (KA0 /AdAA /A ;A /A7A[ /A}1A\ /A +Aj /A`"A, /A@App) /A@AV* /A`A4 /A`AB /AA`O /A AZ /AEA(c /AA pm /A?A0v /AA83 /AA8 /A`A /ARAPk /A`fA R /AľAP /A ˳AX /A@ A(z /A@!APo /AA /A@AP /AA0. /AEA /AA  /ARAP8 /A@5A /A`[Ap /A`Ah( /A`KAؼ3 /A<A7 /AA> /AנAHI /AA0jT /A At] /A`APh /A6Ar /AA| /A A@u /AAp /A,Ap /A`A /AA /A A /ApAV /AdA8# /A`A /AAp8 /A{Ap /AuA/AqA( /A`KnA=/AmA%/AjAH1/A AiA8V</AfA(D/AeAeM/AzcA`W/AcABa/A`fcAj/AcAp/A_cAs/A)cAxz~/AbA/A`fcA/AeA`/A gAѤ/AjA0{/AmA/AoA/A/AHrA)L/A@uA[/AzAg/A~A(\n/A}Ay/A|A2/AxA/AtA/A tAa/A@tA88/A vA/A@xA/A@yA"/AzA/AzA/AzA/AqzA /AdyAG/A@yyAX/A@ryA /A QyAX[/A@yAHz/AyA0(/A/A qUAX#@/A@]ZAnA/A^AlC/AacA0E/AhAmF/A෗lAVH/A-qAp}7J/AT~tA> L/AwA8N/A@-{AgxP/A` {A(Q/A`{ARR/A` {AT/A)yAH) W/A!wA\Y/AtA.Y/A JuA@[/AЇuA`]/AvAd_/ABwvAP7_/AvA0&a/AZwAc/A@wA`f/A=xAb1f/AQxADf/A.N}yAAzWZf/AmimzA,3tf/ANž{ABT큒f/A|#|Af/A A}At9yѼf/AW{d~Af/A@#~A6$tf/AX iGAf?f/AwA[Nx g/A1_'A&g/A*A-:g/A}kACPg/A@rAXg/AhAxg/AvԁAg/A_A0 g/A@Ag/A@`(Ahg/AAg/AAhg/A@%NA( Yh/AA@F`h/A |AXҖh/AOA`1h/A7A@ j/A;A0Cl/A$FADl/A`\A@Fm/A]A-w/A.A[w/A AЀw/AӒAHy/A`d5Aaqz/A`muAЕ{/AkpA(O|/A`Ah |/AʇA } |/A`;iA̍}/A AƘ}/A dA}/AAH}/AAG}/A`APf~/A3LAh~/A äA(~/AE?AXF4~/A`2AC~/AHA#L~/AfAXO~/AAy]d~/A3[<Ad~~/AFA~/Abc0AO/x~/AeEA~/Af#AX/A'Ah/A0A/A 1AH/AٲA/AzA/A@C³A8֯/A` ƳA 0/AA/AʴA(/AѳA/AѳA( /A`A(/AcA8/A@PA0/A AK/A@,AЄ`/A@5A{/AůA0/AA/AdA‰/A A(Ӊ/A {Aȋ/A9A8'/AAkM/AAw/A+AH/A@AZ/AAߌ/A xA/A<A`,/AEŶA!/AAH/AAs/AA8'/A@iQA(/AhAˑ/A@p7AXh/AAA/A|bAڦp/AfҿAے/AA/Aʅ~aAz_-/AmAxW/A AS/ALAxR9ē/AUA/AڼLAi(/A0A0W/AAh]/ANA/AA̢/AAHˆ/AhAhA/A0gA&/A.A./Ag@A7/AAWa/AYAPMr/A@E~Al/A`A@m/AA/AAἕ/AAP/A+A#/A A0,/A AA/A` Ah[/A6A/A"A`/A}A/AjAy/A}A/A/AXF4/A@AX/AADk/A A(r/AAU/AA([/AApʭ/AA /AA˗/A`A"ؗ/A`WA>/A@A/AsDA/AbA/A`QA8$/A9Aȥ,/A`;A`]/A@ϬA/AQAⲘ/AAƘ/AxAPԘ/AA{/A@A /AA/AQAXhI/A`Ah/U/A@;APh/AEAȲu/A@PA(/A&A8/A sAxd/AA!ƚ/AAܚ/A *A/A A/A`6A0 /A A4/A (4A5/Aq4ACj/AAn/AQA8/AAPכ/AAH]/A A@O/A ZAh/AA!/AA5/AgAwF/AtAHnT/A@Ad/A Ahx/A@ A(~/AnAq/AdA/AdA՜/ArAa/A|Ay/A`APH>/A9Ao/AĀA /AA=/AgA՝/A9lA/A JAȋ/A`vA@06/A^AP/A@>AVc/AvA@$p/ATA/AsAե/AoNA}/AaA0/A 8A/A`ÕAN.Ϛɞ/Ap8Afܞ/AVA /AA:/A`̲A$A A(u/AwuAh}/AxA@/A`bAx~/AA߅/A XA/A|Aʚ/AA@/A YAP*/A@!A oբ/AhPA+/A/sA8/A@yAH/A4A0/A`Apƣ/AẠ/A,A@_/AA/AA/A5A(/A<A/A८A/AݭAף/A,Aɣ/AvA)ţ/A@Ap/AHA/A@A8/A@AHR/A`)AQ/A`åA`rM/A`A{H/A A/A=`A̲/A`)<A#/AHoA=/A@rA@׮/A ~Az/AA#/A=AQ/A@ !A@׵/A/A_/A`)Af/AAm/ARAx/A@rAR/A`A P/A"A8,/A AH\(/A qA/AzAu /AzA(ܸ/AAf/A`)4A@/A-]A/A=cAG/A܄A/A`AJ/AAV/A |A~/A A7/AjA/AA0/A#Af/AqA؞/A:A/A&A/AfA/AA ֣/A@A@W/AA¢/A \wAp /AzA0./A@A'/AzAf&/AAp/A@:A/AAX /A3AH./A ^A8/AnA@W/AsAջ/A@;Aã/AA(ģ/ABAXΣ/A (AxFϣ/A A5أ/AtA٣/A@YAHK/AA/AA/AA/AcA/A/BA/AFAP/A}3AHn/AAXH/AA S/AyA@]/AA$ڳg/A@Ū>A9q/A A+4C/AzjAAu/A)Aȥ/A Auե/A`A0/AOAxם/AAĴ/AAP/A` AD/A A`//AA8I/AA~7/A`AX!/AA( /A QAhD*/ASA+/ApAB/Af$AU/A Q<A/AAƦ/AhA1Ȧ/A 5Apʹ/A`AX/A 1A/A,A\/A/A/A:A`/A9;A~/A@A=/A@A/AAA/A<A/A2A8/A/,A¦/AO$AĦ/AHAH2Ǧ/A@AϦ/A |Aզ/AA Rۦ/A`2tA/AA/A@A/A@WA8/AA;/A TA /A6A/AA/A@GA/A`TA /AxA0*#/A`Aw%/AA'/AAh-/A9A4/AAQ/AA8X/AAz_/A)Ap/A`[ A}/A!A7/AAY/ARA-/A@5A/AjA(/A/Ap_Ƨ/AA0jԧ/AIAx/A$2A,/A \A౨/A@*A(/A@{A/AAUX/A@CAa/A@g-Aq/A+Axw/A@Ap /ApA/AA|F/A AXh/AAq/A@Apyz/AAl/A`A7/A3A/AxAأ«/AcA /ͫ/AcCA/AAi/A@,A8/AhA([/AAe_/A`Ac/AױAsq/A@tA0v/AK_A|/AMA/AVFA/A AAx䆬/A@<A튬/A:Ah⓬/Ak:A/A$@A/AMJA/ASA0ݯ/AQAt/AnTA0w/AWA(ܽ/AXAqͬ/AZA1ܬ/A`T\Ah/AjZAk/A3QA`/AIA A/A@?Ah/AAho9/AAW/A&kAAd/A@SA怭/A3%A1/A`) Ah/A@,A8/A;A/A`A/A Ap/A`A0+/A Ap7/AhA@/AઢA J/AA0S/A@hA`/A@7AxWh/AAo/A@5Au/AA8u/ARAz/AAހ/A@GA؍/A`AĈ/A lAy/A`TA/A*AH/A`A(/A@WA0叮/A`Aю/AA /AA@d/A (0AP/A)A/AŒA/A AX/A?A/A@yA/A |A0/AAP/A@rAtƮ/AAؼծ/A^ARخ/A9Aڮ/A`mAڮ/AFAڮ/A ZALخ/AAp׮/A`2BAP֮/Aa#AYծ/A A0jӮ/A@,AҮ/A A Ӯ/AoAi֮/A \Acخ/AA<ۮ/AEA`ۮ/A@AA,ܮ/A<Aܮ/A`8AȺݮ/A(5Aޮ/A1A߮/A.AЦ/AX)Ah/A A%AGA/A`]AX(+/A A0/A`A 6/A AȶQ/AA[/AA/A JPAƬ/A A3/AلAK/AA1/A`AL/A@5AD/A6}A0/A {A8"/AAx/A J~A( /A?AR/A@A`/A OA/A`AP+/AA&"/AZA $/A`AȘ*/AA ./A1A";/A@%A>/A` AA/A`IAI/A@,?A8"Q/A ZAZ/A`oA]/A A0&_/A4aAȶ_/AA_/A`Ap_/AFJA(K_/AA,_/AA@_/A@WAF^/A AX/AAZW/AAT/ABA0R/AAC/A A8/A O-A/ALA /AA"ܱ/AAHб/AA@/A PAA/A΁AX^/A`]AT/AAX/A5A/AxAJ/A/JAp^/AAN/AAT=/A A*/A[A[ׯ/A@!*AxK˯/AAAd/AfA r/A AU/A^Ap4/AhA/A_AҮ/A_Aa/AKPA;/A3AG/A=iAXҭ/A൧A`a/A Aݡ/A lAxGJ/AHAH/AAu/AA #//A8A$ /ATA8b/A-hA{/AsAV/AuA /AZuAx/AtAp/AsA /ArAm/A`oA/AjAp/AaA@/AaBAY/A Z;AP/A5A>/A@>2A`/A /A/A .AhD/AT.Ap/A.A/A .AZƫ/A7Ah/A`dIA`Б/AZA/A`cA/AiA߄/A pAXw/A Af/AAXFI/A`;A00/A@iA7/A :AP /A ZAp ɪ/AAx/A^rA/AV~AD/AAp=}/A`ܰAs/AA@n/AA8j/AA8"g/A'Ap8d/AA P/A̓A85/A7AH//ANhA"/A AA@~/A<A/A@Aذ/AkA/A Ap/A`CA8/ANAؒ/A@yA;/A@>A=%/ANAH,/A@A 0/A0(A9/A@DAdA/AcAȃD/A tA#G/AAEX/A@AQ[/AhA h/A@πAjn/AsApo/AvAxp/AA q/A|A]q/AmAq/A`Ar/AA w/A@iAw/AnAw/A AНw/A@>)A`aw/A 0Av/A O8AYv/A ?Au/AFAgs/A@WTAsp/AaAi/AxA,V/A@A0jQ/A@ApO/AAM/ApAoK/A`KA)I/AA0@/ALA/A`AxA/A"AB/A@N$AXC/A@&AhC/A<)AD/A},A cE/A?0AF/A`4A(G/A>AG/AJAgH/AXAH/A |AؚG/AA.C/A A`A/A`A@/A AEA @/Aq`AhA/AlAA/AxAB/A A@C/AXAoD/A`A [/A`]A0L{/AlA/AA(J/AfAxi/AA /AAXۖ/At(A/A6Aꘪ/AHhAз/A^zA c/ARA(:/A?AA/A[A(z/AAΟ/A A/AũAh/A`;A A/A 8AZ/A[AV٪/AW Aܪ/A a A/A /k A/A AV/A A/A& AP/A2 AV/A A A /A AU/At A8/A5 A /A A/Af AB/A`̱ A t-/Ay+ A"=/A@y A\>/Aj AN?/A A Ahb@/A Ahb@/A AP?/AQ A(9/AT AX 7/A A }6/Aj A~6/A © A 7/A` AH 8/A`9 A'8/A` A:/A A?/A`) A=D/A AJ/A@0 AhR/AF Ae/A`;r Aj/A~ Am/Az Aew/A` A/AR A$/A#AV/A +A1/A(A1/ApzAe/ATAPë/A`;-A5ȫ/A`IAݫ/AA/AAh/A3A>/A@A%/AA/AdA/AwA8/A߇Ax/A`9Ax/AHA0/A A/A@iA(O/AA/A Ah/AٸA6/A`A$/AAH/AA~/A |A8/A"APE/A`@A@uW/A@sAdY/A@WyAp9[/A`A^/AA4g/AAAxhj/AAPm/A=Ao/AAq/AAP{/A@Ao/A@%AH̯/A?AUì/AdKAHn/A`)]A/AH`A< /AQqA+/A]AO/AA#u/A Az/AAXۆ/A JA3/A_AU/A,Axɭ/A9qA٭/A`KA/AwA /A;A)/A`AP6/AAܯ/A=A/AzA3/AAt&/A Ax*/A A M4/AA8/A@A@L/A@W|A6i/A`CA/A`A/AA/AwzAP۰/AAi/A`Ap+/AA;/A A/A.Ad+/A Ah@/A3 AQ/Al A^R/A-i AS/AQe AS/A`2a AS/A\ AT/AX A1T/A'S A T/A"H AzS/A<> AR/A2 A@M/A@ AL/A APkL/A@ A{K/A8 AHK/A@ AL/A l A[O/A`v AO/ARi A(P/Ae AQ/A`)a ATX/AjO A_/A@,> A{/A& Ax-/A@y A"/A A(욱/A l AP랱/A A'/AV A+/A A0f/A`) A,±/A Aȱ/A A0G/A A </A Ax /AVA,/A A:%/A@C5A2/A@AX>/A A2T/AIAm/AgAأv/A A}/A@Ap/AcAho/AA/A|BAơ/AoAp/AA/A@AЮ/A>Ac/A`)A̱/A?A8I/A+wAН/AA$/AA#/A`9A¨/A-oAx/AUA(/A`KCAۦ/A >A0٦/A A/A`)qA }/A VA(>/A9A/A@APޡ/AAh/AA/AA/A`A%/AA/A AՐ/AQA~/A \AXz/AXAv/AAu/A6A]t/AoAfs/AOAr/AA\r/AAhr/A`A0q/AA@q/AjA0q/AA`ؖ/AAxƚ/AЩA/A@ #AW/A@yA&Բ/A@#,AX/AA/A2A8'/AjAt/AA/A@NtA/A OhAt/Af[At//A A1/AjA3/A@A_:/AA( A@W/A` AW/A@l A/A@ { Ak/AG A/A A# /Ap3 AF/AM8 AB!/AR Aq/A- Ao/A` A /A A /A A0o/A` Am/A@AHr/A@ϔA /AiAm/A`Ah/A6+A|2/A4A@2/AcA//A"_AU/A`GA`/ADA/ABAPx/A(>A/A0A³/A@!A/AA/A`A$/A A}/A APHk/A`]Ad/A ɼAH{=/A@yHA|7/A6A(/A AX/A@Az/A dAX /AGAp=h/AAuv/A`f4A1/A@WAP1/A`AP)/A}A(9/AHA˳/AAf/AWgA/A^A0;/A]AX/Ap`AV/AlAUy/AAxm/AoAc/AAp/A~A/AA/A5A% /AWA/AA(iֱ/A@IA0;/AXAXRv/AAki/A"Ad:/A@PA 2/A A*/ADA$/AA/AAJ/AO4A/AYAd/AjA/AyAu/A3D A%/Aab AȓӰ/A-"A` Ұ/AU7"Aȃ/A /A $A/A$A`/A`$A/A$A`/A &$A/A9%A/A%A /A Sd'AP/Ah'AQ/AG0'Au/A/ 'A/A&A/A`ܵ&An!/Am&A0!/AT&A #/Af&A#/A[%Ao$/Az%A$/A %A@%/AZX%APw&/A$A&/A@$A<&/A@$A%/A#AHn%/AO#A`$/A`"e#AH!$/AJ#A(:"/Af"AP@,/A "A0n//Aѝ"A7+/A`m"A",/A|"A3/A`D{"A:/A7#APM/Aq/#AhM/A4#A/A #A(/A"A~/A`m"Ap/A "A /AH"A E/A"Aз/A@{"Ax˰/Ab"Ai/A/#A(\W/A6e&Ah"H/A&A>W/A1nH'Axg/A'A0{z/A'Ap,/A='A ]R/A_Q(A%/A ((Ab"{۰/A{W)A/A[G+A /A+A /A ^,Am/A! },A3/A,A/A/0,A0!/A,Aec%/A,A7*/Af-A0/A3-AwJ7/A@$,-A@=/A9-A>/A:-AEJ/ARvVJ-A]W/AdQ-A&d/AO-A0m/A@rL-Ax/A Q-Ah3/AT-Az/Ab-A{/A=n-A R/A=t-A/A`fz-A/A-AB/A@-A/A=-AHܼ/A]}-A/Az~-Ap=±/A-A#ű/A-AXƱ/A-Ap=ȱ/Aש-AQϱ/A-AH.ر/Az-A`aޱ/A`f-Ap=/A |-A8/A.A/A@$.A/A`*.AG/A..A/A=.A`/A@L.A/AW.AB /A \[.AX /A@ W.A8/AV.A8/A^.A8/A`fb.A`/Ak.A`'/A@z.A+/A`).A@./A.A//A.AH//A.A`a//A.A1/A@ .A7/A .A1Ap=/AGK1A8/A3]1A{/AGm1A-/A1A R/A1AL/A`f1A/A1A /A 1Aأ/A1AL/A̰1A/Az1A/A`)1AP/A31AP8#/A 1A(/A@1A//Aֳ1AVP/A@r1AR/A1AZ/A2A( [/A[2A]/Ap2A_/A(L2A_/Aw2Apk^/A`f2A`/A2ALb/Aõ2A@Wf/A2A@j/A`f2AXn/A(2A)q/A`~2Ar/A`f2As/A`)2A`t/Aק2Afu/A2Aw/A`2Ap|/A2A8/A2A𣅳/Az2A#/A`f2AP/A2A̘/A` 3A/A(3AP8/A@ M3A`a/A@ e3Aǘ/A3A/A`f3AQ/A33A/A3A/A3AP8/A 4A0./A4AP/A 3A/A@3A׸/A`3A`ẳ/A4AB/A4A؞/A@ +4Ak/AB4Adz/AnX4Aȳ/A q[4A؞ͳ/A qm4A޳/A=4AP8/A@ 4A/A4Ap/A \ 5A/A`f,5Az/A`:5Aأ/AA5Au/A@>B5A`/AR@5Ah3/A@>5A`/AJ5A R/AW5A0./A`)^5A/ARh5AP/AHw5A/A@g5A/A`ñ5A(\/Az5A؞/A`)5AP/A@>5A/A`5AP/A'6A R/A76A/A P6A/AGe6Ak/A@>|6Ah3/AR6AX /A36A/A6Ak/A6A0./A֑6A%/A֏6A{,/AR6AP85/A6A/AdC;AЄ>/A9;AWK/A0;Aj/A ;A/A:Ax~/Ag:A Ab/A 0>A@0m/AXa>Af/A`K?Ak/AH?Aj/Ax?Ab/A?Ak_/A@A@Fq/AՑ@At/A@,?A(\o/AK?Aq/A8?Ap=x/A?A0~/Aa>AXے/A,?>AѪ/AG>A/A=A"/A>A t /A`_>AH/A@in>A(@/A?AT/A;?AhDX/A@y??A8[/Az@?A0La/A/>AHj/A>AP /A?A(&/A?A`%./A?A<*/A3?A/A?A/A U@Ad/A`W@AXt/A@Ac7/A A@A0:/Aa@Ah?/Aa@APC/A>AiU\/AC>AIJt/A.+`>Ay6/Au;>AraS/Aڨ%>A@þ/A >AXl޾/A])>A_6c/A~pC>AYt-/A6n>Aa:0S/A谟>A,w/A0>A0t/AM?A/A_?A^/A@?A`B_/A`K?AH\/Ax@A8]/Ay@AHa/A`o@Ao/A`?AA/A=DAA/AvAA/A@!AA`/AAA 2/AAAH3/A3AAD/AqAAU/A,cAA;?D /AЮ&AAzo\R/AaZ@A/Ay@A%|/Af@A\!/A'Dt@A$de/A&AA]/A-gbAAWZ/A0>[ñAAb*/A`.BA h/A9(!BAFB?/A41CA, /AxgCAqk/A?!iPDA`G/A@EEA+X/A +EATl/AEA/A0EA8/AFA()/A FA/ABFA@/A@FAp/A`FAٸ/A`FA/A GAx/AGA@~/AGAp_/A;>GA/A`vFGA/A}CGA6/A>GA!/A}(GAȇ./A@1GA:/A\GAE/AxGA@R/Aw|GAt/A`vcGAH{/A0iGAY/A9|GAp/AGA؁/A dGAx/AB*GA/A GA_/AGA/A$GAЀ/AGA8/A AFA /AFAE/AGA:/AGA0/ARGA8//A=GAhMC/AFGAhU/A@NGAZ/AGA]/AGAHa/A#HA8Zj/A \>HA@qp/A;HA2/A'HAԊ/AV2HAR/A ZHA/A}GA@1/AVGAxƷ/AGA0/AךGA/A 9GAu/AWGAH/A"GA/A`mGAp/A GA(O!/A 8GAh$/A|GA0]//AGA@/AGA(I/A sGAH/A@NHAЕJ/A@9HA`\/AGHAg/A cHAhm/AdHAz/AqHA/A`HAv/A3IA`ϔ/AtIA[/AIA/A`]IA4/AJA/A&0JA/A`TJA0H/A AJAD/AJA$/AfJA}/A KA%/AKAp6/A@$KAA/Ad0KA(Q/ABKA`/AKKA(p/A`)WKA;/A{KAأ/A KAP7/AKAT/A|KA/Ah LA3/A SlLA/AMLA+ /A@LA0;/A sMA`X3/AdMAw/A6|NA0&/A@ NA&/A`22OAm/A߃OAp/A OA/A 'PAp+/A`fPAk/A PA/A@QA@ /A&bQA/A cQA/AQA0'/A +RAy5/A@NkRAHB/A 1RA@FT/ARAذd/ARAذq/A+SA{/A`oSA-/A SA /A`SA/AoTA@/A)TA@/A`TGTA/A9eTA }/A`TAP/A@TA0]/ATA/AݩTA+/ATA9/ARTA R/A@TAg*/ARTA8g9/ATA8F/A ~UAT/A@FUAxWd/A`|UAH r/A@UA|/AkUA/A@GMWA88 /AwWAw/ALXA!/AӈXA+/AoXAH)=/A;YA%]/A YA@}z/AXjZA/AZA(/A[A/A[A'/A ]AV/A^A 0{/A^A/A}Q_AP /A@N`A-/At7aAIK/AaAH q/A\bA/A`bA/ABbAd/A`}cAh /A}KdA/A`!eAU@/A eAP]/A`fA>v/A dfA /A`vfAp=/A/!gA/AgA]/AgAX/A &hA/AhA/ARhAȥ!/A}&iA12/AjiAXWG/AբiA}i/AjAx}/AhjA0/AjA/A@#+kAܿ/AQkA/AnkA8/A]tkA3/A@NakA/ATkA8/A8kA؞/A6kAX0/AqOkAVI/ApkAWd/A&kA/A`BkA/A lAV/A?lAo/A@dlA/A0lA`B/AğlAmQ/AjlA /AlAp,/A &lA' /A kA /AmkAP/A ‚kAP/ATkA/A@!A(Q/A@~>AQ/A@=A8R/A |=AHzT/A}M=A1W/A (#=AY/A@ <AX[Z/A`à<A(`X/AX<A[S/A;A8"K/Ad;A@u@/Ag:A`:/A`d:Aj7/A`(:A<5/AT9AX[2/AП9AH-./A@R9A(`+/Am9A'/A8A/A`2P8A8/A.2A/A@1A/A|1A/A0A07/A@iw0AHu/A/A0j/A`6/AX/Ay.A:R/At.AL/A3-A@/A,A6/A`;),AX#*/A +AU#/A]v+Ap/A,+A/A*A/A@*Ap[/A*A /A9*A`/A *A/A)A`/AO)AP/Adc)A/A(AX/A@ Z(Ax/A'A/A7'AA/A st&A`/A^&A`/A Q%APj/A+1%A`/AI$AP/A`K$A-/AT$Ax-/AT$Aj/AR#A/A#Ap/AQ#AЄ/A`f!#AH/A"Ax-/A@%"AG/A Z!A/Ab!A/A? A/ARA((/AAP/A JnA/A A/A^A~/AA/A AP/AA2/A` A/AA-/A GAX/AAX/A`̃Ah/AAP/AA(` /A`]zA /A Q3A /A |A=/AcA" /A!AP /AA8x/A@GA@/AflAH./A`A@5/AAЄ/A`;A/A}AX/A#A/A (A/A@OA/AAM/A JnAo/AAx/A3lA /AAh/A SA/A`KA/AAأ(/AdA@//A`$A5/A A8?/A٧AG/A{AxX/A`!A%f/A@ Ar/A A0/A@ A/Ak A0/Av1 As/A$ A`؝/A A/A A'/AJ A/A Ax/A9 APٽ/A H A|/AH A 4/A- A3/Aa A /A*A(/AA]/A xAH/A+Aǣ/A mAĢ/AA8R/AH\A8Q/AA8I/AA/A,Axm/A` nAH/A (:AЦ/A@_A跋/A`-Aw/AbAh/AA+/A@?AX/A@p Ar/A.Az/AsAy/A At/AA`t/A6 A@$o/A`ANn/AA{i/A?A0h/A`mA8c/A@EA (/AAu'/A@gAhL/A,A0/ADAY/A`dAh/AA@/AAw/AսA({/A?A/A RAT/AA/AAʾ/AAX/A`vA@/A@ A`P/A SR AU/A`O A@/A` A/Ak Aأ/A`2 AH-/AE AH2/A A@I/A AL/A` A_/A Ad/AX A b/A@G AX^/A` AdO/A` AV/A Arf/Aj Ai/A@G Ask/A@p Ah/A" A/A Ah/A q A0w/A Aȱ~/A`; Ax/A A/A A /Aܾ AXƝ/A_ A/A* A8 /A= AIWѸ/AsE_A A#A8 /All A|r/Aߙb AG+/A?& A/A A+/A Av/A` A״/A A_/A@A/Aa2AP|/AAC/A JAH/AaAp/A A/A&{AH/AAP/AA`/A#A(`/AjA0/AVA`T/AA@ƌ/A`TA?/A AO/AKA/AA/A`AP/AA@d|/AAxx/A@,A@s/A"A^/AAYY/A~A/AgA/AvAq/A A/A٥Afk/A|Aq/A@Aq/AnAt/A`KA0y/A#=AX(~/AcA@>/AA/A/MA/A'A _/A`3Ah"/AvA`/A@AhĒ/A@PA/A (AY/A`bA/A<Ap /AAЮ/AA/AA/AAHa/A଼A3/AAP/A?Adz/A`A-/A?A/AA/A]7A/A@W<A/A@ Ah]/A@>8 A@/A1 A@/A_AT/A@JAxZ/A .A@a/A7AFe/A ]AXa/A A [/A AT/A( AT/ALALk/AAon/AVAȱm/AApo/A A7|/A@AZ/A@ Aؒ/ARAH/AaA4/AA/A`A/AA/A A0/AX A/A,AHm/AsA/A A0/A@p0Ah/A*AhU/A=/A/A A /AU3A/AA`/AA /AIA/AAb/A$A/A`A/A %APM/ALeA0/A SA/A@NA`/A91A/A oA/AAh/A@A/Aq>AHi/AzA/AA/A+A /A1AL/A^A/A`A0/AAP/A@!AXy/AF`A(/AA/A?AXy/A<AhU/A@WA(/A A'/AA/A4A8t/AGfAp=/A`A;/A A/A&iAp/AA(/AA/A HAd/AAֵ/AA`/AA/AWAHP/A AxG/A^A"/A1 AA/A A o/A=!Aƾ/A@>!A0/A!Aѹ/A`!A8/A`d"A߼/AE"A0L/A"A7/Ap"A /A@5"A,/A#Aȶ/A` &#A/A]#AH-/A A#A6/A@#A/A@i $Aѯ/AHR$A/A`f$A/Am$Ap/A`7%A/A`x%AZ/A`%Ae/A &A/AzU&A1/A&Aq/A+&A/Ao&A/A@P)'A /Al'AU/A'A /A'A /A?(A/Ad(A/A J(A/A)AL/A 9)AhU/Al)A0/A`)A"/A|)A(/A@,)A/A)*Ap/A@,C*Ap</A^\*A/A *A/A@*A /A*A/A +A@F/A[+A A/A +A/Aza,A0f/A,A 5AТT/A@5A8Vb/A 6Aw/AD6AH~/A@!X6A@A/A6Aڨ/Aլ6A`/A6Ao/A7A@B/A E7A0/A@n7A/A`f7A/A7Ae/A-8A/Aod8A<*/A8Az2/Ao8A@=/A(9A8E/AT9APP/A9Ao[/AF9A(c/Af9Ajo/A`2:Aw/A!:AL/A`]:A,/A֚:A'/A@:A1/A:Ai/Ag ;A/ALg;Aj/A Z;A-/A S;A#/A"<A/AH <AX[/A=<Ah/A y<A/A@`<Ah$/A=A&/AJ=A+/A y=App6/A =AF/A=A`GF/A@ =AoL/Ad'>AW/Ah>A@d/AR>Apyg/A>A`Pr/A@ >AЄy/A>AF/A>Ah"/A@ 0?AhQ/AC?AH/AP?A/A e?A/A;?Axz/A?Ak/A(@A_/A@@A /AQ@A`%/A1A !/A`hAL,/AoA;/AAЄS/A A0\/A@A8t_/A ҆A;Y/A`dApQ/A AQ/AfUApZ/A~Aj/AߏAv/A`}AA/AtAph/A` AwV/AൊA6/A`At/A 펩A{/AhAБ/A iA/A 7A(`/A A/A A/A l A{/A(Ail/AE&A]/A`b7A8O/A`@AvC/A?A0L9/A`4A/AڧA>/AɧA/AħA/A@ߺA/AAX/ApA8/AhpA/AGA/A`'AQ*/A36A@0/AAb9/A@ڤA C/A笤A_/A`cA/Ah5A`/A`Ah"/A߶Ae/Af{A0/A;CA/AAAx-"/A9Ah +/A :A2/AּA;/AAg/A@.!iA0+/A`hAX/A@hA/AgA/AGgA/AsfAR/AfA0U/A`eAUt/AQKeAb/ARdA]/A@EdACS/AdA0%G/A@dA9/AdA0+/A,jdAX/A ZdAx/AQdAX/A#PdA6/AEOdA0/A TdA@q/A``dAP|/AjdAx/A`qdAN2/AWdA*/A,FdA/At@AdA/AcdA'+[/AZ૔dAz3!/A"dA/A@ydA`6/AudAȁv/A7-YSdAx)o/A0,dAH]/A`cA6F/AgfcA@z=S3/Ay3bAHP#/A@`bA@k"/AhbX(aA /A@# aA~/A1aA./A9d= aA u4/AX `A\c/A |`A\ i%/AC"`A7+/A`m`APA/A$PA/A@OAxW/A@OA }/AViOA~/ANA@/A@NA/A@MAH/AdtMA/ALAث/A`LA/ALA@/AEXLAU/A3.LAPk/AsLAЮ/ALA`Á/ALAl/A0 LApS/ARLAH/Ad*LA,=/A/AcNAT/A`NA/A`NA)/A@`NA{/A OAΣ/A (SOAx>l/A`OAx/AX6PAX/A NPAH/A |gPAx/A qvPAx/A{PA/ApwPA/A`RPA/A1PA@]/A SOA./A`POA/A?NA/A&MAA ji/Aז>A?n}4/A5>A?c/AW =A((/A9T=A!y/AH<Am/A@P:<AK[h/A5';Ax)e/A}n;AXWd/A :Ac/Ag:An/AE8Aۍ/A@&7Aܞ/At6AvT/AT6AB7_/A5Ah/AY5Ah/A5AU/A`o@4A8x/AΆ4AЮN/A2Ap}w/A 1Ai/A@M1A/A 0AX/A`1A ҭ/A@1Au/A1AB/A1AH/A`0A~/A`]0A/AE0AP'/A?.Ah/AN.A\/A Ɍ,A3/AI+A;l/Au)A+x/A'P)A]S,/A^4)Ażה/A#")A/A@>)A/A$O!)A&/A ")A]M/A,)AP7/A`)A#c/AUԡ(APg/Af(A@/Ag(A]/A@!-(A f/A (A@o/A('Aq/A@'A@s/Am'APMp/A k&A+k/A %Ad/A@$ANe/A@,$A f/AH$Ai/As$Am/A e$Ags/A _$Aً/A ^$Ah/AY$AH/AL$AΞ/A %$A/A+.$A/A@NJ$A``/A@ r$Af/A`}$A(;@/Aiu%A&ν/AT%A$~/AWٸ:&A\_/A&A@/Ae D'A/A 'A/AG'A~*/A(A`4/A`K(A8IM/A (A[/A@)A\/AH)AP_/A)AZt/A*A/Ap*A/A`̲*A0*/A I+A/A+A>/A`+Ax-/A,AE/AX -A/A,-A/AW-A/Ay-Ah/A Z-AP/A-A6Q/Aj&+.Azb/A.;.A A/A %\.A`/A{.A9tva/AŚ.AOU/A])܄.A/AJO.A/A.Ao~/Ayc.A󏚣/A;.A/A^A.Am Î/A].Ar/A".A`Pm/ALJ.A@/A.AHn/A@0AQZp/A0A|r/AWG0At/A`d0AjhGw/Al0A.j`y/A50A0|/As0A`T~/A0A*cX/Aʩ0AHЍ/A`0AXW/AH0APͷ/A0A/A0A/A`1Ah@/A1A"/A-1A1/Ak1Ax/A`1A/A"1A/AL1A0j/A2A/A`'2A/A@H2A/A@yg2AG'/A O2AS7/A2Ah"[/A$2AH2i/A2APkx/A@ 2A`/A /3A(/A`f,3Ax/ARI3Ax/At3A/A3A(/A?3A~/A@ 4A@/A"4A=/A4Ax/A5AA-/Asd?A/A?A/A|@A՝/AE@A%/A )CA/A 1^CA/A CA/ADAi/A`mwDA,/ADAN6/A EAG/AŎEAr]/AFA^/AFAd/AFFA0ݷ/AIAȓ/AMA83./A }NAH/A0LOAȲ/ARA(/A=RA/A6SA/A@SA0f/A`TAho/A@UTAP/A`fiTAh/A~TAH/A2TAp/ATA/ATA/A@%TA\/AUA A /A7UA (/A ZUA8/AVAxP/A}VA^/A@VAc/A*WAd/A1WAXa/A3jWA0_/A WA(`/A`WA~h/A WA!r/A@ XA}/A 7XA/A`eXAX[/A XA/A:YA/AYAP/A NZAF/A ZAp[/AZA%t/AZA/AhZAE/A ZA/A 8ZA8/AZZAQ/A+[Ah/A@C[A /A@V[Ah/A@ c[AxKV/A /[A/A`;[AH/A [A٨/Ayn[A*/A%f[Ac*/ApޥZA0g4/A7umZA{/A?)ZAߨ/AYA/AYA(D/Ar*YA/A]YAX/AYA ./A/XALH/A]P);XAÖ^/AaWA_/A,TWA/AdVAw/AȚxVAE/A`VA(/AxUA$ /AhUvUA~K9/A}0TA*/AtsTAx)5/ASAO6/ASAy6/ASA;6/ASAH5/A@SA +3/ASA%*/A`ASA$/ASAXj/ASAЯh/AaSAwq/AdSApr/A1SA8/AQHSAС/ARSA]/A`SAK/A@RA@=s/A)RA`Pt/A RA`j/ARAhLh/AmRA(!/AKRA/AQA]/A@`QA`]/AQAXd/A&QAU4/AmPA $/AnPAȰ 0/A,\PA,>/AxUOA"Q/AF%OA_/ANAUk/ẠNA(t/A 1%NA8xx/A"MAz/A'aMAz/AiMAx/A3MA~Ay/A=9T MA8{/A ۻLA2~/Ad]LAV/AsLA /AU6~LAlF}/A?hnLAי/A\LA/A$LAۑ;/AOLA /ALAZL/AݱyKAj/A^/KAt;c/A&KA!/A@rKA"{/AWKA/A`/KAYt/A^c^ KAh/AgJA`!/A JA /A&JA(/A`JA؎/A@JArn/AI{}JAU/A OcJA/ABh JAج//A`DIA1/A`IAJX6/AEO/IA:/AIA=/ANsIA?6?/AƱn[IA@/A9}AIACc@/Ab.(IA2?i?/A IAhn=/A`2HAf/Ay6HAi A/AhpeHAF/A%879HAM/AHA\/A?GAj/A`GAyt/AXb/AI&zDAx/A^DAcn/A0DAؒb/AɧCAbb/A#CAYb/AǠCAcb/A-CAyb/A`CAb/A/CAb/A̚CAb/AtCA5c/A`2CAc/A@֕CAd/A൓CAe/A ґCAxg/A@CAk/A ACA@z/A ZCA/A qCA/A lCA/A@}CA軄/A|zCA/AwCA/AuCAH-/AQtCAi/ArCA/A pCAhՆ/AlCA/A`jCA/AhCA8/A CAx/A`KeBApu/A`BA(u/AAAPu/APAAPYt/A"AAq/A`[@AXJo/A`@Ah/A @Af/A @A0fa/A@Nd@A]/A`@A`]/A@A(]/A@A]/A@AD^/A @AH^/A @A{_/A@AV`/A+@A(\a/A?Ac/A ?A`g/Aj?Ajv/Ax?Ay/A2?A}|/A?AH!/A?Ap,/A?A /A?Ax/A ?Ap=/A@`?A/A?A@‚/A߳?A@/A`?A`e/A@ N?Ap<|/A ?A ku/A`T>A]/A@y=ApR/A=A[L/AFF=A`H/A@=A(H/A@G<A(-H/A<ANH/A<AH/A <A`I/Az<AI/A<APMJ/A<A,K/A<AxdM/A<AO/A0<AS/A&<AVa/AÍ<Ap,g/A`<Ai/As<AP'l/A {<Axm/A3Au)/A3AmM/Au3A*/Aʮ#3Aq%3/AT)2A~:/A2AKQC/A¡}2AQdsM/A 2AZ X/A.ؘ2ACid/A{Yw2AeMp/Ak t2A|/Ay2A/Ag2A{/SZ/A$Bi2A$k{/A 6~2A/A 2AXp/A`]2Ax/AO2A!/A2A~/A 2A/AL2Ax~/Aa2A&/A2AH/A3Ax-/A^3A(,/A3AXJ/A`3AXT/A` 3A f/A^(4An/A |84Aw/AD4A8t/AL4A/A P4AX/A@P4Ah/AK4AŪ/A C4Ah/A@G64At/A4A~/A@3A@u/A@3AX,/As3A98/A43A8c/A`2Aώ/A2A/A&U2A_/A`2A!/Ay1A9/At81Ag/A |0A4/A=0A؎/A s0A`Ǽ/AL0A/A`K?0Ae/A@,0A/AF0AX0AM0A0A/A8"0A/A@00A`2j/AL$60A|`/A:0A\/AX>0A ]/ATr@0A_/AB0A@c/AC0ATi/AD0ABp/AF0Ax/AHG0As/AJ0At/AXL0A /A S0A@d0A`E[0A 81A(]0A`)u1A|2c0A=1Alc0A1AHd0A 1AlOe0A`b2Ak0A02A<=l0A92A%m0AC2A3s0A 2Av0A;2Av0A2A`w0A2Ajw0A2ADXw0A$2Ah(w0A3Av0A 3Ayv0A`3Av0A3At0A+*3As0A@C83A, q0AE3ADl0A[Z3A܆`0A@P3A5Y0A3AG0A@W3A?0AR4AL>0A4A$<0A$4AԼ90A <4A60AV\4At;/0A`4AW,0A=4A*0A4AQ)0A@r4AP"0A5Ar0AZ15A,+0A@>H5A 0A X5A/Aw5A/A`95AH/AÆ5Aȼ/A 5Aa/Ad|5Ap/A@}5Apۏ/A5A/A5Aj/Aα5Ac/A5A@]/Až5AD/A 5A0*/A5A@/A 5A/AD;Aj3/A`b;AQ'/A`5;AU/A`;;An /A:Ay/A":A0;/A}:A@/A`՛:A7/AR:A(/AF:A(i/A@:A(:/A:A7/A:Ap~/A:A8m/A`d;A=^/AN;A=Q/At;AF/A;A A/AV;A;/A@<A؁2/A`<AX*/AO<A $/A@G=A"/Aj=A"/A =A%/A@>A!&/A?>A'%/A%?A@"/A_Z?AI/A@?As/At?A0 /A@?A /AM@A@5/AVv@A(`/A`@AXy/A[@AS/AA4AAX/A@`AA07/A&{A e/A{{A+K\/Asa;M|AN/Axb|A\;/A4[}ALE0/Ao}Ak*/AsF~AHa)/Ap~AAa3-/ArH;A/V6/A&䝱A#?D/AV=AV/AAcHp/A#uAn/AUAQ/Aȓ7A/Af_Ah/A<Acfp/A~"@AR/A< IA^U'/AUAy/A#}xfAk, /ATzAh/A<A/AnCgA/AUłAr/AqA/A`AV/Ar^lSAWe%/AcKAJ2/A;Ap[B/A@58AN܆L/Aul][ARH8(U/AA;\/A%wA`/A^A!c/ARryAEd/AXPAeu-c/AskA!_/AxAHZ/A<CAPR/AFA(H/AufҰAYA/A MA?/A0AZ%oOB/A AH/AB$AS/AA+p/A{A qV/AmNAIcB/A#"Aͫ/A0uA ޶/A@lcڍAo`/AWyʻA'UJ/ABA1Ș/Ao򑚀A]O/AB!RaA$z/A>Ah//AAF/AG7e Ai?yr/Ai&Z,A=vif/A"&=A⋌]/APApZY/AkxeAPY/A`[{AB#9W/AA9Z/AcIA6*_/AЉAqXi/AnAc_fu/ApiAA/A@yAD*/AA`/A+jAu(/A܂fA$/AcV~ĝA}e/A!qEA7/Aɜ})AY/Ann\A87#/Ao"A8k/ATA/AVA/AG{ iA{5/ACp,AgO/A.!rA}Ʊ/A{#AF/AN }A񳱸/AOjA/Au!vAҦA4r/A^!AO/A:0AoW:/A߃oKA/A;WAG/Abh{AOO^/A֎mڨA/AccާA- /A֡A8kS/AVx\Ay/AV2䠨Ap"/ApAV5/A7s-A/A@PrA\а /AVyA%U,/A!A)W5/Agw9mAQM9/A\ͪA:/A/A/ALAm/AlAx /A61#Ay v/A'A~/AjiA/A`A/A<ԯA?/Am%AvJ|/AnF˃jAn/AL˷Ac\/AA%AZ/AMA/A`A(ё/AɼA)Ha/Ad`ӼAD/AxؼAM/AtdؼA/A[z'ӼA'/A]ȼAsnۮ/A`ӹA?>/A;^Ay/A`AJ-C/A2xlA|/A EMA4A1/A:{C3Ahه/A,A~/AIA$u/A=A-Jk/A6{&xAxza/A A!1eZ/A#! AiCS/AT ASJL/AXeAE/AA(?/AAw7/A-O]ۻA庄W//A,лAnֵ&/AFӁ˻A~/AٍEʻAN/A@GϻAx /AA A/A`A/A )A/A@mA ^/A6Ag/A@,8AV/A?WAxi/AOAX/AAܓ/A׽A|/A`KAk^/A:A@5J/A`;WAXt7/A`)hA./A kA2&/AkA"/AjAU/A@gA/AX]AI/A+A/AA(/AA(/AMA@/AAf/AhAP/A-A@/A`GA|/AUA`ǥ/A ZfAPg/A ҶA[h/AMTAX/AِA/A9aAH/A aAȇ/A Ap/ADA(i/A A AZ/ATA`6Z/A UAQ/A@AJ/AA C/A1A*A/A ObAX;/AkA@$8/A/A@3/A@N=Ap%/A Ah"/A^A /AA`/A |A`/A`A /AaA /A8A/AA/A@PMA/A`RAd/A iApN/AAP/AĨA/AA/A8A/AXAP/AA/A9 Alu/AAZh/A?Az/AvAs)-/ARѰiA7ׅo_0ADAp0A}A{0A`]A0A A{0AA0A A0AA0!0AtAPo/AAP|/AAg/A ZFA/AA/AMpA/AdA/A2A" /AAu+/AA0/AqgA/AHA()/ABA/A@5A|/AQPA?/AA0YM/A 2AK/A@d A(6/A A5/A A"/A` A6/Aq A/A A(~/A` A`/A A/A@>|Af]/AANj/A AP//A4A_/A`8A՛/AAt/AAX/A3.A/A A@/AbA*/A@>A*)/A,2AP&/A`2 A(X/A-A/A^mA/A@LAP/AIA/A >A;/A:A/AA0/A@A'/A0AAf/A`Aq/A (AP/A lA/AB6Ax/AaAr&/AAPx*/A@AU/A`;Aؒ/AYA8b;/A Ah?v/A|AF/AAh/AAS/A@A/AdA@1/AA/A A)/A3A//AdAN/A@cAX/A@WA/A1Ahn/AfA R/AgA/A@yA/AAD/AAA/A@WAh/4/A`AXd/A1AHLo/AsA/A A/AoA@/ATaA/APA/AKA@5#/AMAW*/A8A$/AqA/A c'A//ANAH:/A A8'U/A Ax/AAP/A@eA/A A/A 82A/A*\A /A A;/A`A(/APA /A@=A(-/AA0&/A@A/AA/AnAx&/ARCA((/A@`A06/A@Azc/Aa)Ax/AAl/A@9AӺ/A1AW/AAx/AA Z/AsAl!/AsA8/A?A /A`)>A/A ɋAl/A@hA /A`ʃA /AߴA -/AIA/As,A"^/A AF/AAh?v/AAx/AAP+/Am&A/AJA`/AtAh/A AGU/A)A`Q/A`TzANg/AAXhU/A6SAEN/AA/A֠A`/A֠A@F/A 8KAb/A AH/A&AЄ/A AAݪ/A Ab/A-eA pv/A`A[/A`DAT/A}AP/AHvA˵/A YAp/A sA!/A"AP^-/A@AP/A ܨA4/AA( /AA8/A;Ai/A ZAi/A@Ay/A6AA/AwA/A@AP/A3UA/AA#/A`̷At/A}A/s/AA/A ɵA/A&.AЄ/A@AKv/A`յA(/AgݶA/AA /AA#/A`,A;/AA\/AA@k/A-aA@ӆ/AhA/AA/A ĹA/AA@k/A@A5/Aa{A0/A@iA$/AzA /ATbA/A`]Ax~%/A`fA@`i/AXAN/AAxW}/A`vA0/AAHD/A<A p/AoA(d/AA/A 8A /Aw*A@`/A*AxG'/AA/AA/A:Ab/A3 Aq /A4HAh/A AP/A)Ah\/AAB/AA/A@EAHv/ADA$/AA/A`*A/Af~AxQ/A A /A2A蕼/Ay A1/A A'/A@w AxmW/AtA=/A@#A'/Ai[A/AY!Ap8M/A$A؞/A(APfg/A}*Axd,/A@*AI/A:,Aȋ/A !+Aؒ}/Af)Aa/A'Ar/A=c%Ai/A ~#A2O/A "A0w]/A`!A m/A&!Ad/A@HA+h/AyA8q/A \\APk/AHA?/At}A#/A0gA7>/A#ApZE/A$A8A/A@A=/AA0*./A cA0*./AtAPoKA`Xt/A KAY-t/A;JAHt/A@yJAht/AJA>s/A`)YIAs/AUHA Rs/AHAss/A*tGA@s/AFAms/AFApNds/A`PEAWs/AྞDAVs/Ac"DA2as/ACA;bs/A{BA}is/A AApNds/A /AAOs/AeAA7s/AAA(s/A`KAA!s/A`#AA8s/Ao@A7s/AG@A8Qs/AfX@A8Ecs/Ao@AȘls/Aw@Aps/A@d@As/A Q?ARs/AdW?Avs/At>A8ts/AN>Ashs/A@=ATs/AaU=AX6s/A/<Ah]s/A H<AFr/AX<Ar/At;Ar/A`B;A,|r/A |;A6*r/A lQ:AIr/Aa9HA1~r/A GA8r/A`mGAUs/A?3HA*s/AHAVs/A VIABts/A_ JAps/A`RKAQzs/A,cLA s/ALMAys/A[MApxs/AMAHVs/A@%OATs/A$OA7s/AtsPAX#s/AHPA0*.s/A'QA@5Ps/AQAP^s/A@RA@s/A 8RAks/A`ogSAxs/ASA舏s/A 3TA s/AQTAbs/A`TA"s/ATAps/A@$UAs/ASUAt/A$UAHCt/A;UACt/AUA@@t/A`/VADWA0{Gt/AV_WA(Ct/A&WA@Bt/AXWA3;t/AWAh;9t/A XAP/;t/A2XA,;t/A\XA`2t/A_XA)t/AgXA8 t/A`DXAHt/AmXA"t/A>XAX(#t/A XAt/A ZWAt/A`WA*t/A@ߩWA&t/A@|WAX(#t/A`2>WA#t/AWA Nt/A@VAnt/AVA, t/A[VA(t/A9VAs/AVA\s/A`VAs/A sVAas/A@,VA0*s/A'*VAOs/A@VAs/A5UA@s/AUAs/A`UAps/A@UAs/A sUAs/A@lUAs/AN;UAs/A@UA8s/ATAXs/A@ TAs/ATA s/AoTA~s/ATArs/ATAgs/A`vTA\`s/AR UAUs/ATAvDs/AR UAH9s/A@-UAhU3s/AkdUA.s/A'UA%s/A UA`+s/A`KUA*s/A sVAps/A2(VAs/A`7VAts/AUAs/A@UA0 s/AyUAs/AJUASs/AR UAns/ATAP'r/A TAr/AoTAr/A HTAr/AbTAr/A/TA Ns/A@TA"s/AhSAxs/ASAP'r/ASAx r/ASAv/A6]AaCv/A\AKBv/AO\A@1Ev/A+[A-v/A[[Av/A`B+[A0u/AZAh@u/A`)ZA޿/A`bAȩݿ/A^{A/A5|A@0/A@pAd/A@.YAx/A bAP@/AzkA0/A@ fA%X/ANAF/A`8Ag/A`A؟{/A*A(`&/AWAp,y/ADAf/AhA8/A/fAe:/A TAp!/A`<A0/AMApĮ/AuA/AFAhդ/A`b8Aj/A>A t/A@DA` /Aj&WA/A cA o/A``AM/A`÷nA/A 6sA/AzA c)/A؀A޿/A`bAО/A4dA؝/A 8 ~Ar/AFAx /A `Ap/AmLA/AHBA/A,7AС/A2Ap[Ρ/A ,ApE/A`(A,/Ac2Ap/A#A^/AsA 3/A A@/A`ZAJ/AjA~/A:Adś/A Z1FAp/A`TAD/A2`Ah7/A@gAŖ/A`*sA@//AhnA1/ApAؼ/A`?|Afؕ/AɍA@/A ΣA8祗/AF_A@z/A-Ab/A`AО/A4dAȃj/A@AI/AA/AdgA@ RA/Al*\Aʁ {/Ac9AU/A=3z|Au/AYA0/AANu6/A;As&Y/AkmVyA*x/AxAe/AM^A)/AA؈x/A>59A/A^ASVNj/A7h= Aab_ɋ/AXWvAHŋ/A`߳Alu/AA/A AA(:/A`okA@$P/A9A𴝏/A`AUΏ/A@y}A/A ZAa/AA/A^Ah8/A {AH!/AtAP͔/AnA0/A`/hAx>/A3R_A O/AL\AD/A`էWA/AVA>C/A 8wYAk-/A`dNA:/AHAx/A@Ax/Aa=Ah/A;A@/A+@A[/AAA!/A;Aq/A E3AH /AH<,A@/AO&Aз/A$A\/A "Aye/A@y{Aye/AF]Ah/AA]/AjA/A`;5 AXE/A@A@/AAF/AA/A=*A(h/A\A-DY/AocAV8N/AvN\A*G/AAu APF/AuCAI/AYyAQ/AO{uA<&^/A[uCA ^o/AK/AO/A'x"yAܞ/A?Ax /A6Ah@/A _AF/AAv/AjjAD6/A`~A`T/A ZA0/AA6/A`A/A<AК/A`A(U/A`DBAຝ/AA/A$A()p/AAѷ/A@A /A@5AX5/A@A(/ARA-/AgApj/A TAH/AAH/A`A@/AAj/A ZA /A`AP/A A /A A/ANAZP/A A/ADA`r/A nA0,/AnA@/A HA/A SAx/A@A6/A uA/A@A1"/AoA/A A@}/A Api/A@A,/A PA/AOAذϘ/A nAF/A YA9/ARAx/AvA‚/AAʫ/AAߘ/A AذϘ/AA4/A@A/ApRA&/A"A]/A[Au/A2A`r/AAߙ/AIpA,/A@A/A@,PA/AdA o/AAқ/A AP՛/A@rAȋM/AAhͲ/A㯱APY/AOAiA/A A K/A@AF/AA8"/AA@/AAÚ/ANAXؚ/A4Aj/AAh]R/A`AiF/A=xAh/Aj5Apo/A@wA)/A`ΐAt;/AA tA/A` A(\/A@JA¦/A=zAƙ/AcA렙/A _A/Ap{A/A 'AX/A`fA~/A~`A/AgVAe`/AHA=/A`93@Ad(/A@[3A/A+A&/AK*AB/A@y#AР/A[:"A05b^/AC*(!A @/AM" A2&/A^ A$</A5KA[/Aֆ'A /A-A&/A:ADƗ؎/A6GAԎ/AQ]!AL8+Ԏ/At/[UnA"u؎/A /A{QO/AY- Aq%/A_vbAQ/A̾A/AAUvNI/AfAI7/AUS AБ^/AfD̵AP!/Af9y;aAZ'/AUn\A/A ~Ax/A6 A /A@A0{/Ak1A/AFAU/AiAH{r/A= AB/AA/A8iA@B/A \ AHc/A A /Aྦྷ A@d_/AU AZ%/A6 AA/A`) A2t/A A/A`) Axil/A \ A/A@A/AZ A1/A6 APn/A@ AP/A`) AԪ~/A- A0}/A A(|/A Ax+|/A A0{/A  Az/A | A@y/A 8M A0[w/A Au/A٣ A Rs/A Ar/A \ A1p/ANAo/Aj!Ap/A 0ADp/AMAp/A`A p/AAj,q/A A؁q/AW"Axmr/A $Ar/Ack'As/A )A0Xt/A`b*Au/AF+Asu/A ~,A{u/A -Av/A`/APw/A .Aw/Ay-Ah4x/A-%,AFx/Aj)A@_x/A@߷)Aw4y/An*A/y/A`+AXoy/A%-Aby/A -A)+z/AK.A Θz/A .A`z/A<0A(J{/A@W$1Abf{/A1Az{/A1A*{/A/A${/A7.A=|/Aa-AX|/Aa-AH}/Aa-A0{a}/A Ғ,A}/A-%,Ap|~/A Ғ,A~/A@.A~/A`[/A~/A 60AHj/A`B1A~/AБ2AN/A`K3A@/A#5AH;/A#6Aj/A57A/A8Av/A>7Ag/AlyL7AVջ/A0s7Agˀ/A\&Z97ArdWڀ/Ahv87Al6U/AD7AD/A8Ab /AB8Aln /A^\q8A/A8Ah/Atk28AO/A=09Ap /AY9A:WFSL/A_<:A {/A?o;AԬ/AՖ;A7/A~zv<AL/APV=A@R/A =A,\/A@>A5ʑj/A!2?A>.|/A,?AeW/A?A /A X@Ai;/A ]@AUcƂ/Ag @AZ؂/Al@AIg/A\hy@A`Z/AgAA8/A@A/$/AEm@A6/AˡyQ@ATG/A@A\w7/Am @A+/Ap;p?AFt5$/Ar>A!/Atd*>A 4"/A=Au'/A<AT1/AlC<Ar6?/A/Aൂ2AV8`/AV2AT/AS]X3Ag/ARf3AXh/A&a4Ǎqdž/AK!4AB׆/AArR5AP/A@46A 8/A`K8A`a/A9D:Ax)/AHh:AH/A@#:Ah//A:A/A:AY/A:A(h(/A`v;A /A`:A=/A8AF/Ai8AE/Az\8An/A 7AX/At7Ap0/Aiq7Aڅ/A+7A0*υ/A6Aȅ/A 26A ą/A5A…/A@!5Aż/A5A`G/AS5A讅/A4APM/A34A/A@4Ahb/Aߺ4A`/A4A(/A ^5Aܬ/A9j5A/A5Aw/AOO6A( ą/A6ANŅ/AO6Aυ/A@.7AЅ/A`97Amх/A`E7A@1܅/A@y7Ax݅/A7A/Al8A//A9|8Av/A 8A{/A@9A/A 9A/Af9A`/A@:Ax/A`):A/A:A/A:A/A9;A/AG;A1/AFh<A2/A@y<Al;/A <A`#/A=A/&/A =AA/A ~=A M/A`m<Aq/A <AЄo/A2<A0 G/A<A@3/A;A _+/AD;A[/A`;A`/A;APގ/A;A@O͆/A$:As/A:A/A`:A/A ۡ:AX#/A :A_/A;Aц/A<A/A>A//A?A /A>A"/A@W>AxzB/A@>@Ap=/A@\EAˇ/AEGA /A3wGA7/A`FAH/AfGA7/ABGAH/A_GA?/A SGAe/A}HA@3/A@GA3/A@.GA*/A@#GAh.*/A@5GA"/A cGA(/A@5FAb/A AGFACr/AGA88^/A@CGA88^/A$GA/AFGA/AGAi/AfGACr/A9FA`/AEA@j/A@iFAx؊/A;1FA0/AEAiG/A`;EA(yQ/A SEAY/A3EA a/AEA"/ADA@ԋ/ADA( /A/EA;/A"!FA(L/AБFAS/A9FAxƌ/A`GAxƌ/A _GA Œ/AGA/A9GAX4/AGA:/AӄHAPߊ/A!IA/AIAQ/A@IAI/A#^IAHN/A[IA0L/A1IA"/A`IA`/AIAȈ/AT#JAˆ/AIAIV/AJA=f/AMAL/A?PAC/AOA@/APA/A 3QAM/A uRAq/ASA+/A`FUA/A$YVAp/A@VA"/A`WAVV/AXAxn/AYAu/A@[APQ/A` \A8,/A%]AX/A1^A:0/A `A/A4aAU/A /aAs/A@`bAh/ANzdA7/AvfAѨ/A`$hA/A`mhA@/A BiA /AsrlAOډ/ATtAH/A vA/A?zA0Չ/A:{A@/A`o|APÉ/A 8}AXhӉ/ASA0&/A)`Ar /AnA$/A@NAN/A( Ai/A!AQp/AAPc/A0 An/A`mAP//AkA7/AǥA06J/A͇AP/A`gAW/AA젉/A@yA0Y$/A`APwΊ/AA; /A,A/A5A 4W/A`ŐA'/A@WAKI/ADAз/AAǕA -/A?ԖAQ/A=A-0/A`pA~a/A`ApIV/A8A@q/A J(A0*/A AAh/AٞA/A`A} /A ]A pC/A èAb׏/A;A؟ُ/A` A0H/A&A/AiA/A [A/AA0H/AFAXyR/AWA7/AtoA@5/AjmA/A AĎ/At*AX/A (A/AaA`a͎/A@@A /AcAe/A`A>/A@A؁/AA`/AèA0Ӎ/AApÍ/A̴AP8/Af/AP/AXAݩ/A[AP/A`A/A`o;A@S/A_BA!g/ATA F/AwAP|)/A`At/A6A/ApAI/A'A/AA2/A@,AҊ/A`]Axm/A@޽A`َ/A@AÎ/A^A걎/A^kAч/A@VA)G/A AP-/A@؝A /A`m4Al/A GAȘO/A@ZA/A A /A dAoi/A`大A/A .Ax/A@,MAHnC/APA`/AA/A%A//AA8/A<AG/A@AX#/A(APZ/A@OA/A qvA@$/A^^A8_/AgAen/AAH;>/AA//A@7As/A`qA6/AgA/AA4/AA@=/AAK/A@ANz/AMA`*/A0AXӃ/AAf/AA(l/ABAs/A[Awp/AsAwp/A@.4AH/A&AK/AQAeH/A<A`q/AA8b_/A PAf%/A PA,/A AX/A IA0/A}ZAX5p/AcA;>/A-A{ /AtAI~/AtA./A3AX5p/A$A)/A`VAP,/A`!A/AAP</A Ax\~/AAH}/A A4Z|/A`iAH{/A9Az/AA`ey/A`[A(y/AA0x/A,A #v/A,A}v/A 85Ahu/AFAt/A@r`APs/A4Axq/A]Aɛq/AJA`q/A@CA r/A 5ASr/Ao6A0]r/A 6Atr/A+7Ar/A`%7A(r/A [7Aplr/AЕ7Ar/A`7Ar/A@8Afs/A?8A8s/Au8A؁Ms/A Җ8A`fs/A8Aȓs/A@8As/A;j9Ads/A@:As/As:At/A@N:Azt/AV;A%t/A cp;A"'t/A <A:t/A}<AYt/A <Akht/Aa=Ayt/AI=At/A =AxXt/AF>At/A`K>Axt/A>A;t/A>At/A`>A؎u/Ak>A,)u/Ags>AhCu/A@^>Aou/A>AXhu/A`>Au/A@-?Au/A>?Agu/A@-?A`u/A@؀?Av/A?A(v/A?AX>v/A?A(Vv/AG@Aeiv/A@Axv/A@Av/A @Av/A`@AA`6v/AFrAAȥv/AOAAv/A@.AA@v/AAAv/AAA(v/ABAIfv/AAAXEIv/AAA A,v/AߟAAbv/A@~AApyu/A_AAku/A |@Au/A@Agu/A`@A`u/Ay@A+u/AP@AhLu/AC@Amu/A.@A Zu/A Z@A Bu/Az?A@"u/A?A`u/A@@AP@t/A<7@ANt/Aف@At/A AT@AhѬt/AC@AHt/AC@AHt/A4?AAt/A Q?APmt/A ?AWt/A ?A&Dt/AC@A`t/A`#AA2t/A &AAp+(t/ABA t/A ^BAs/ABAs/Aa!CAs/A@NCAhs/ACA(s/A"TDAs/A3DA(ms/A'EAt/A@#UEAt/A'rEA2t/ALvEAP8t/AEAP8t/A=FA=t/A fFA=t/A`FAY-t/A_FA4)t/AF>GA3t/A@GAgKt/AOxGAP[t/A`TGA]t/A@GAPmt/A HAXtt/AHAyt/A^HAt/AE#IA t/A/IAEt/A=HA@Ot/AzHA8 t/A QyHAt/AqHAt/A ZHAxt/A AIAt/A@PIAxt/A2IAt/A;IA6t/A`IA(u/A@IA u/AJAp[.u/Ah JAJA:0x/A JA@$\x/AIA@x/AHA0Gx/AGAx/A GABy/A:FASy/A`EAty/ApEAy/ANEA(-Ny/A@EA9y/AFAXRy/A \xGA8ły/A@`GApy/A@`GAhy/A@%HA Ay/AIAhy/ANJAy/A`KARy/AELAxy/A)MA8Zy/A`MMAy/A>NAy/ANA,y/AOAעy/AjPA;y/A@ZQAy/AQAy/A`;QAz/A /QAz/A RA-z/A"SAO z/A@SA*z/A7TA};z/A TAhJz/A TANz/ASAAz/ARAIz/A RAVAy/AUAy/A`UAPHy/A`2pTA8y/A@TAy/A@.SAH]y/A`SAH]y/A"SA~y/A 8tRAy/A RAy/A&QAhy/AnQA`sy/A?PApy/A`]}PA0ry/A@OAcy/A'cOA'Wy/AOASJy/A@*OAzFy/A@#NAHGy/A@,NA1y/A9NAp.y/A`MA87y/AWMA87y/AMA'y/A@ؽLAhy/ABLA Ry/AkKA5y/AFGKAy/A` JAy/AJAp.y/AJASJy/ANJAHky/A@pJAly/A`zJAly/AgJA]y/AIAUy/AdIASJy/A SHA>y/A`՜HA%y/AHAHx/AHAEx/A IAx/A`BIA!x/AFJA6x/AjJA@ex/AИJA@}Hx/A JAx/A@ؽLAMAjv/A@NlMA4Tv/A@,gMAe>v/AMA7v/A`NAxu#v/AӹNA7v/A@ENA`Gu/AOAu/A@ NALu/A`NAu/AMA7v/AMApI0v/A`MMA`Cv/ALAUYv/ALA@Ftv/A${LAv/A cLAXqv/AKA /]v/A`KAk5v/AxKA v/A |aLAu/ALAu/A ACMAٽu/AoMA`u/AྤMAТu/A^ NAxu/A8NA0j{u/AHhOAvu/A @PAu/A GRA(iu/A RA v/A A@TAH$v/ATALv/A@NUAv/A@WVA v/A(XAXv/A`EYAw/AZA@1Mw/A ZAh^w/A\Aw/A l\Axzw/A0]AhQ x/A^Abx/A _Ap.x/A`A&x/AOaA"x/A`ܠaA7x/ACbA8w/A ŒbA0qw/A_bA6w/A`TcAv]w/AĭdAgw/A@%eA(>w/A4eA`w/AeAx/A HeA~Qx/AfAvx/AgAh3|x/AggAvx/A hAUx/A@hA@5x/A@hAXx/A0iAPkx/A@yiAXx/AMjA0x/AjAKEx/AgrkAex/A lkAgx/A@lAx/A0lA@$x/A`[mA(>x/A@rnAXx/A@E2oA`x/ApA@$x/A@&rApߤx/A'lsAUx/AftA<~x/AKuA<~x/AvvAtx/A`wAUx/AR0xA"x/A@xA`w/A8yA@ήw/AneyA*w/AmyA~_w/AyAp2w/A8zA w/A@EzAv/A5{Ah^v/A@,v{A0v/A {A(Fv/Aמ{AHv/A l {A u/A@zAԏu/AazA( eu/AyA8Z6u/AAzAhu/A5{A0u/AVr|A"u/AO}AFu/A@r}A\u/A2~A\u/AX~AXDu/A-A"u/AfA(>t/A@,>AXt/A AЀt/AV:Agt/A݀A t/A`At/AX[At/A6At/A@A`t/A`ՄAu/AɅAu/A`Ah&u/A 7ApNu/AAu/A @APMu/A@pކAFu/A@NֆAv/AABv/A`̩A~mv/AmAHyv/A@rAv/A@#A8xv/AI$A v/A Av/A(Aźv/AۋA8v/A Aov/AgzAv/AvA@1[v/AkAB_v/A`"jAvkv/A_Av/AAv/A?׊Auv/AzALv/A‰Av/A@#Av/AA|0v/A dAv/AAu/AfAPu/Ai AËu/A-Avyu/A c8A9ku/AA7.u/A@A(it/A_Apt/AƈAÙt/A@ΈAjt/A<A( st/Ai Aot/A`xATt/A`pA /:t/A$At/A4‡AFt/A'Aثs/A@ ADs/AxAvs/AńA}s/A@Axs/A@#څA9s/A潆ABs/A`xAds/A@GA0s/AAxs/AUAxs/A >As/AamAxWs/A@>As/A AxHt/A`>AUt/A`WACt/A$AH\Zu/A[mA]u/AAxv/AʜA 0 v/A'Au/AgAbv/A`֪A徯|v/A){lAvv/A愕A} v/A?#mA3:v/A^|>A1zw/AnArFw/Ak7˵AK}w/AAhdw/A)cAҭY7w/Aa5Ab}w/A"A}4w/AcɻAw/A _2ݼA$w/A3AKw/AIAC8uw/ANH* A 4`w/A@AKw/A-h_A^2w/AAwv/Aq?A(mv/A`AP/u/AܳA`u/AAu/AVAHu/AT(Ayu/A Apu/AHApΫt/A@ABt/ADnA0YZt/AAhs/A&WAUZs/A(Aīr/A`<APs/AAQs/A\A(s/A Al?u/AAh{/A A0|/AOA8~/A`zAp/AAhS/AAꄣ/AvwjA?/A\K1A? lG/AI AtY'/AB5A/A7Amƈ?/A8٭A2/AKA'L2/A8Jf5A~v2/A'A]&ƀ/A_ RAF:̈́/A*'dAR/AmdA|0Z_/AZ Ap/A.~AX/AHAK%/A Z`A ņ/AAх/AAc/A Aȃj/A@A~>/A Ap;/A A8/AAJ7/A`AW7/A bA`m7/A]A7/AxA 7/AhA6/A ANRT6/Ae ,Au6/A?Y=A7/A?A > /7/A$A>1A-:JG7/A=lIA"_7/A@A7/A nA(`F8/A@ ѷA?`8/A@YA c8/AJADg8/A@7Aly8/AٶAp,8/A RAb9/A@A8w9/A zAx :/AA7:/AA7:/A@譫A$:/A +A]:/AMAx~:/A@PϤA:/A A;/A%A`O;/A`A';/A/9A;/AAh/Ao|AX>/AލA@>/A@A ?/A‰A(~f?/A@5A(?/AzAey?/AE9A5e?/A6LA.N?/ApޅA-7?/A@DžAm1x#?/AEoAi8?/AO;m A7 ?/AIkAǥl?/AD0AY#0>/AI˻A>/AFA!?/A!??A?/Af.7A?/An.A7t?/A%AW?/ASp A.?/A͆A?/AE A7>/AD=AyE@>/Az-A+;x>/AA~>/A ACAy/A@5A*%y/AA؁y/AAy/A畻Ay/A`KA/y/A˼A0?y/A&WA0by/AAԌy/A`KAy/A`;A@~y/AkAXcz/A@,OA3z/AA0z/AAy/A ACAy/A@5A]/A7 AT/A4 A1B/A`S4 AHN/A@i4 A`V/A{4 A_/A4 Aзh/A 4 A0p/A@4 AFx/A4 Aȓ/A 4 A@u/AE5 A`/Ax#5 AQ/AP5 A/Ae5 A(/A8w5 A@B/A@5 A/A`5 ApŎ/A 5 A[ώ/A5 A َ/AX5 A/A-5 A/A5 AX(/A` 6 A/A$6 A/A AF6 A k/A]6 A(/A6 A/A@,6 A/A`6 A/A6 A/A 7 A/A 1:7 A/A`;I7 Ap/AR7 A1/A8W7 A /AL7 Aj /AR7 A`/Aj7 A/A z7 Ap/A z7 AȘ"/A7 A(/A7 A0/AVz7 A 3/A5e7 AН6/AC7 A08/A !7 A #;/A`6 A@/A`6 A`F/A 6 AM/A6 A8VX/Amh6 Ac/AEJ6 A8n/A46 A #w/A*6 A/A8!6 A/A@6 A(/A6 A8/A@.6 A/A 6 AƲ/A6 A0L/A5 A@=Ï/A95 Aˏ/A5 Aԏ/A5 A@ޏ/A 5 A/A r5 A/A@25 AL/A4 A/A4 Ax /AM4 A/A 4 Ap/A4 A@$/A`4 Ah./AVf4 Ax7/ABF4 AB/A"4 A0fN/A 3 AT/A J3 A@Z/A3 AXya/A 3 Axzg/A`D3 Aجl/A߅3 A@Fv/A`3 Ah}/AxC3 AX/A@(3 A劐/A3 A(/A2 A@/A2 AF/A2 AXV/A@s2 A/AN2 A-Ő/A2 A`ϐ/A Z1 AՐ/A1 Adܐ/A1 A/A-1 A-/AVn1 AL/AjP1 A /A#-1 A0/A 1 Ax- /A;0 A/A`0 Ao/A@`0 A /A s0 A'/AP0 AU./A .0 Ap4/A0 A:/AF/ AX(@/A@/ AF/A/ AM/A / A8U/A@i/ A ]/Ai/ Ae/AS/ A0n/A@y=/ ALx/A%/ AQ/A/ Aѕ/A. AxF/A`. A/A. A/A h. A(z/A M. A3/A6. A/A@%. Aȑ/A- A8ϑ/A@- AUؑ/AЮ- A/Aa- AP/A`vk- A'/A S- A/AB- A/A- AP/A, A /A, A`G/A, A7#/A`x, A8./A L, A4/AV4, Axz:/A, A8@/A, AL/AO+ AR/A+ A@Y/A`+ Ah/A@l+ A(p/A`vO+ A~w/A`5+ A/Ax+ AXƇ/A+ Aڎ/A * Ae/A@* A6/AѾ* AhL/A* Ah/Ay* A趷/Ab* A/AiJ* Aƒ/A 81* Ah͒/A * AWՒ/Aa) A1ݒ/A ) Ai/AA) A@F/A) Ao/A) A8 /Ap) Ah/A0V) Aȃ /A`:) AX/A c) AV/A ~) Ax&/A( A(//A( A>/AՔ( AE/A}( AK/Af( AR/A@O( A(Y/A4( A`/AV( A@uf/A( A0fp/A`' Az/A`' A/A$' A/Ao' AL/AW' A'/A9' A/A@!' AXV/A' Aᮓ/AV& AH/A@& Aﻓ/A A& Au“/A & Atȓ/AT& AГ/Ao& Azؓ/A S& A`/A,2& A8V/A& A7/AL% Ap/AX% A0j/A@% AP7/A {% Aj/A`R% A'/A (% A[2/A`% Ax$ As/A`v!$ Aج/Ax$ Ah̍/A# A'/A# A A0f/A0 A<'/A`K! A2/A A0L=/A ApF/A@  ApAR/A A[/A`" ApAf/A A pn/A A,v/A A0ꃛ/A A0j/A`" Ah/Ai A(z/AS Ap=/A< A /A, A /A&  A,͛/A, A<כ/A  A3ݛ/A A`/Aţ A/A A/A { A/Ad A /AL A0/A`8 AF/A! A"/A`  AL,/A AТ3/A@% Ao:/A APD/A, AK/A ɣ AU/A  AKb/A O AȘj/A| A(zr/Ao Ax-{/A0Z A/AB A`/A* A`/At  A@W/A A1/A` AՖ/A  A #/Axy A/AW AТ/A1 A/A A/A@ A舓/A൹ A"/A A /A@^ AX /A: A/A` Aց/A@W A'/A J A@|/A A`w/A ld At/A`@ A'r/A`[ Ao/A Axl/A Aj/Aٷ Atf/A A_/A F A0\/A`& A@W/A AU/Aa A`V/A` APg/A`L Aȃp/A A" /A@ A!1/A A"/A@ A!"/A Aȱ/An A2'/A@, AHKZ/AY Ah/A@a A/A@ A@BF/A AN/A A8W/A2x AA`/A`j APj/Aj` Ahr/A2Z A|/AN Ax/A: A8/A + AX/A` A/A Ahѧ/A' A7/A A/A' Ap/A` Aʤ/AN A@Ӥ/AV Auݤ/A A-/A A0/A A/A` A/A A,/A` A/Aܡ Ah/AV A(/A` A01/Aj A:/A AC/A`~ APoM/A| AjV/A'y Aa/A@i Ak/A;e A0y/Aa A`~/A^ AF/A`S A`e/AH A/A@? AB/A@,6 A߳/A`+ Ax/A AΥ/AM AX[ۥ/A` A/A`9 A"/A A/A A/At A /A A`/A@ A/A A&/AF A p//A྄ A7/A 8q A B/AZ AM/AC AxQ/A6 AZ[/A! AFe/A@ A8k/A As/AL APt/A A}/A9 AXƈ/A@N APf/A A/A A-/A A/A A/AL Aպ/Al AĦ/AW A83ͦ/AH Aצ/A4 Ah/A$ Ae/A A0j/A AH/A AV/A A /A Ah"/AT AQ/A@ Aȶ!/A ( AU(/A A@B1/A@ A9/A  AA/A@ AJ/A AhS/A Aj^/A  A0f/A@N Ap/A; APw/A` Ak/A ۅ A(-/A Ap/A6 A/A AhѦ/A՘ AHЯ/A` A༧/A AƧ/A A AZͧ/A  Ap=ا/A Ap As9/A@> A!/A@ AX#&/A A@-/A@ AU5/A A@/A6 AG/A@ A@pQ/A- AX/A A`/A@y A"j/A  A/A=H AH/A=H AP{/A` A /AF, A /A"; AV/A N APo/A a A/At A /AA A`/A Z A'±/A  A`б/A? A1ͱ/A? A`ѱ/A9 A/A9 A@1߱/A? A@ٱ/A? A(α/A Aӱ/AA A[ܱ/A@ AH/A 1 A/A A/A  A"/A + A8Q /AA Ap</A@U AU /Ah A8V)/A0z AH2/A A/E/A; A?/A; AB/A 8 AP/W/A 8 AhQ/A; AhoM/A; AX(C/A AзL/AX AppR/A AxY/A` Ahb/AM Ak/A  Aoq/A`( A Ax/A`; AZ/A@#K A/Ab Ap/A } A/A AP暲/A` AꜲ/A & A /A`  A/AX2 A0/A9U A`/A$ A /A Axu/A`; A /A` A/A A8/A> AP/AUb APf/A` AP/A AH/A A/A  Ap=Ʋ/A Aβ/A  AfӲ/A A۲/A  A/A`" A /Af A(/A AI/A Ah/AT A/A  A%/A2 A./A= A`9/A) A8DC/A; A0J/A^ AS/A@ A@]/A= AP8h/A A s/A A͒/A@ Ak/A} AHm/A A/A A(/A A뺳/A Aó/A_y AXʳ/A`m Apӳ/A` AX޳/A R A/A`I A/A: A0j/A-. A/Aa A < /A@ A`2/A A /A& A#/A " A0f-/Ac A6/A  A@/A A@uK/A  AR/A AX]/A A0`/A` Ac/At AX[e/AD Ag/Af! AXl/A 8 Aq/A`m A``t/AE A7w/Aّ A@y/A g A/AS A,/A JO AX[/A`[J A/AC A t/A`; AA/A /2 A(/AX& A Ĵ/A A`ʹ/AD AТԴ/A  Aߴ/A@ ApA/A A_/A@W A /A / A/A Aj/A; A /AO Af'/A A_0/A`m A:/A  AFD/A@ A#P/A / A AY/A AVc/A An/A AQx/A A/A6 A7/A- A(/A` AL/A`  A/A` Aవ/A ~ AxK/A0 Ax(Ƶ/A`( Aѵ/A@0 Aݵ/A: A(/AA AP A /A6 A3/A@% A A"=/A@1 A 0/A@ B A/A A־/A Aʾ/A AӾ/A Aܾ/A Aз/Ad A/A A'/A A/A@ A/A A/Ads Ah/A-b A*/AP A4/A@C Ah>/A83 A0F/A@& A`Q/A" AHY/AO AZc/A` A@l/A As/A AF~/A@ A/A@ A;/A A(/A AP/A} A軮/At A/A; A/A A˿/At APؿ/A App/A` A/A A(/A Z A@/A` A/A- A /A A /A_ A/A׫ A`/A Ah"/Acg A /A=Z AP /AA A=/A A(/A A8 /A  A/Aࣸ A/A` A/A@ A(/A` A( /Axw Aج!/A@e A*/AX A؎2/AN A:/A 8A AC/A&3 A1L/A@$ A`T/A A([\/A A0ef/A` APo/A@ A`w/A; A/AM Az/A6 Aȶ/Am A_/A AU/A A /A[ A@~/AM A/A`D Aȶ/A Ah/Ay A /A`t A/Aj` A/A@,X Ap/A: Ah%/A A/A@x A;/A@P Aà/A# AX/A Aw/A Ax>/A@y A`/A AU/A A`/AQ Aȫ/A`\ A 6/A AhF/A A`eN/Av A8V/A@ A\/A Af/A6 Aq/A0v A[y/A@Cd Aj/A`BR A0/A`B Aя/A3 A/A! A`/A A`±/A A/A2 A0/A@ A/A; A'/A@` A/A`ܽ AP/A@Ƶ A /Av A/A` Ap/A྘ A/A A/Ahx A8"/A`j A=*/A^ Ax4/AO A@/A= AhK/A- A(U/A! AV`/A Ag/A Ar/A A( }/A`K A7/A' AAsow'/AN  A?w}/A:w AdH/A7o A)/A Ax AǟIG/AWNDŽ A[rc/A+}ʢ Ax~/A A4Ά/AT& A/Ahq AQV/A/5v A )/AOz AZ/A-| A /A{ Asb/AC/y A/A=u A]sV/A,pPo A^50/Ach A*/AT_ ADJ/A1V A/~/A}BK A?/A? A8Ž/A6 A/Al3u A8/A" AT|\j/A|R Ap2"/A% A>zA#/Axue AEuFE/AL?Mn A|Wg/Ak A/Ajx Am8t(/A*D A幗/AmOx A \h/A2C1 AF4/Af, A߾u/Av A0/A A7/A0 A8M /Ad AD/A{ Am,/AtrW A#/Ax A/A| AW/A~& A2 /AS A"/A2- A{#/Ab[ AX$/Ay ANAj$/ADq A2/AviK AX/A8=/A@ A@?/A | A'D/A W AH/A 2 AM/A AhQ/A`m AHT/A8ͳ AجW/A㪳 A6Z/A AP^/A O A(b/A  A@g/Ao A@i/A 1ʲ Ah"l/A Ar/A Zf Au/A@ A({/A A(/Aױ A/A A/A > AX(G/A AtO/A൫ A-/AM A~'/Ax A/A|߾ A8/AT A`/A A/A ( A5/Au A@/A A/A A4/A ACu/A AP/A AP/Au A0f/A AF/Aଠ Aִ/A APj /A0 Am/A A/AX& A/Aj Ax/A A֋/Ai AP/A` A/A`W A/A6L A8/AZ A;/A`9 Ah/Anj A~/A"a A(m/A A/Ax A/A6 A/A@5 A`/A@ A/A AD/A Aڤ/A A-/A@r A/A A /A A0o/A Aw;/A A&/A`Ө Ah/A A 3/AK A/A@ A/A@ A(/Ao A6~/A A8ge/A AaS/Aِ Ae A/A A  A;/Ao AZ/AB A=,/A9Y A./Ad A6/A~ APo@/A AI/A AP/A AhL[/A` A@=e/A@ Am/A Apv/A  AX/A A/A/ A/A@ AX/AK Ad/AU AP/A lb A/A@Pm A@/A | AX/Ao A/A@֌ Az/A@ A؎/AV A/A A`/A AX/A@ A /A A/A c Ap/A Aؒ/A& A 4U/A0 A/A | A1`A/A  A̕(p/A9„{ ADΜ/A_@ A/Aa A`c/A`2 AP;I/A9 m A)0/A]d AI82:/A1( A:@/Ag A"/A@ As/A~g AO/A~$:[ ASA/AZ AH /A AU*/A>5U A X4/A6 Aa$=/AL AhwD/A`(Co AQJ/AB#J AcN/A-/! Au AN%H/ASKM AP/B/A) ABF/Al' AlL/A& A_sa/A~[ܹ AϬz/AoQ AO /A; A=~/A A/A@9 Ab /AE A/Ah].W Ag/Af A`/A` A@#/AM A/8H/A 9 Ar0o/AGƣ A@/A@ A/A  A/A] A"W/A` A/A  A/A Ax(/AB Adp/A0F A;/AB Avpa/A>H A/AG Ag{Z/Ab A*/A;S A0L/A Q A/A + A"j|/A4gK A/UX/A3R A)/Ax A+/A] A*/A${ݎ A#/A@! AS0Oe//A^%' A%x>/A A`Q/A-Y* A8Dg/A \S AH.x/A_ż AOy/A: ; A`6/AN Aj/AuΊ A)ݑ/AB{ܺ Aمy]/A As+/AMZ1 A:/AR˹ A;PC/A1O A!hF/AZYи A:1cE/AVYP A>/A6ӷ AM/Aq Ai/A A/ADҳ A(1/A` AX /Ad A//A`ҩ A @/A@Z AV/A١ Aqn`X/AC{ A,~1/A` A /A ~ AY/Av=ߟ AJ/Aq7! A`G/Ak AXl/A`m AUs/A U1 Aj /A A3:P/A{ A_/A A0U/A?& Ahu/A A/A@Eg A/AP A@/A} AO/A  Aأ/At A(/A`F Ax/A( Ax/A`vc A/Aә A8/A@y Ak/A ~ Ah./A Aql/A@WF AQ/A A^T/A@Ϫ Af/A B Ak/AM A_/A A/A@E? Ah/A) Ap/A밗 A,/A A6H/Aa A h/Ap A^/A; AP/A AV//A  A:/AdÌ Adf/A`]$y Av/AXr A/A ˏk A@F/A`]&a AЦ~/A`CX A/A5gR A/AZO Au3/AN Apk*/A HH AF/A ~]E A0/A Z3E AP/AaE A`"/AD AF/AC AP/A ~2C A410AG A0A@5H A~0AH Al0A lZJ A0ALjJ A0AH A0A#iH Aȅ0A Z G AH! 0A HlG A 0A`G A@Q0AٲF A0AF A0AE A40AǝF AX/A B AB/A AA A/A@; A %0AH,6 AZ!0A&2 AH0A`v. AXΧ0A]& A0AӠ% A0A?# A|@0A l A0A`v= A0A Atļ0A_ Ay0Awi A_00A# A0A A P0A` A0A@= A0A0 A=0A A(0A ( AL5 0A`TW AP|y0A` A0A. A,0A| A0A` A.e0Aw AL0A V A: 0A9Z Ai 0AG A, 0A` A 0A 13 A,# 0A; A`ǯ 0A A$} 0Ay A 0A. A 0A l A 0A4ι Av 0A@ A 0A` Aj 0A% A` 0Ak A 0A 1 A.= 0A ALI 0A Aov 0A4: Ap 0A`ó A(3 0A y Ap 0A)α A M 0A`B AM 0A@!7 A M 0A`)+ AM 0A AM 0A A4L 0A' AHJ 0Aϯ AB 0Ag A? 0A@? A4 0A`Kͮ A2 0A`K A%1 0A` A0 0A@胮 Af0 0Aր AK0 0A`} A<0 0A`vy A80 0Au AB0 0Ar AW0 0Aao AXy0 0ABl A0 0A`Di A0 0Ahf A\&1 0A c Ax1 0Aa AdA2 0A$\ A;3 0AW Ag4 0AS A7 0A`M A}: 0AH A ; 0A G AbF 0A0@ ARO 0A: AS 0A`)8 A` 0A`9 Ag 0AA Ao 0A Q ALLx 0Aj A 0A`v A 0A A' 0A@ A 0A&ܮ AX 0A`f A 0A`] Aܸ 0A< Au 0A`) AT Q 0A AT 0A  A(0A$ Ax 0A` A?0A Ad0Az A0AM AԸ 0A J As0A@ AA0A A)0AЍ A0Ae A$ 0A`ܟ A//0An AKE0A Þ ASe0Aޘ A<=~0A c A A0A@` A蹪0A A0A` A`0Aߥ AhB0A`d AXJ'0A Apb0A  AU0A |A Ac0Aݔ A0A$ A0A |( AX0A쇒 Ad[0A Α Ap0A磐 AI 0Ax AH0A Z AI#0Ax A40A ATH0A`2V AĀS0A`f/ AȖ0A@% A$Ƞ0A ADX0AN Av0AHC~ At0A } A60A'| A@10A{ A!0A.{ Az0A z AG0A Nz AI40A|ky A,4H0A@E(y AdL^0A@ _y AHpx0AIz AǢ0A`Yy A70A/uw A\ 0A@؂v A[F0AFxu A<0A`mt A.0A@Ns AH%0AjCq A,40AZp A0ATp AJ0Ao A0AT o A .0Al Av0AFi A0Ai A0Ag Aࢷ0A f Ax|0Ae A0A@Ưd AA0A&d Al20Ac A*T0Ab AԚ0A&a A'0A_ At0A ^ A"0A] AH0A@[ AI0A,[ AI0A Z At$H0A@pY Ap>0A-X A:0AjW Ad?0A@V AhWE0A@yV A$60AU A&0A`BT A/0AT A)0A#"S A''0A2R A|'0AkR A, $0A@Q A '0A`P A20A@ O A~>0AOO AB0A6O AO0AO A`6U0AN AtV0AN A dW0AN ASX0A@N AX0AN ATX0A@N ATX0AN ATX0AN ATX0A`N ATX0AN ASX0A N AS0A`=N AnM0AaM A0L0AM AK0AM A AK0A3~M A AK0A@>tM AK0AHjM AL0A`XM AnM0AtBM AQ0A M ASX0A`L AL]0A_L A`0A DL A`0A =L AXYa0A8L AL&d0A`!L A4g0AL A\i0AK APk0AXK An0AaK ADZo0AaK A88q0AgK As0A K AG A80AG A̽0A@WQG Ax0AaGG Ax0A@gBG A|0Av3G Ap0A@G Adʧ0A@F AX0A`DF Av0A`F Ap0AzF APܴ0A`F A,0AF A0A={F A0A PF A0A@:F A,0A$F A 0A?F A0AvE ALf0AE Aq0AkE AO0AKE A>0A7E AP0A#E Al0AD Al0A D A 0AମD AJ0A?D AD0AƕD A90A`̐D AD0AыD A(0AֆD Aض0A_D A0AdzD A0ArD Ax)0AtkD As0AcD A40AaD A|0A\D A0AWD A]0APD ApL0AHD A,%0ALD A0A@C A 0ApC Aȇ0A cC A0A}B A\0A B AC0A`B A20AB A20A`B A20A`;B A20A`KB A0A[B A\0AhB Av0AeB A'0A`VB AX0AGB AX0AN;B Aȇ0Ac'B Aȇ0AB Aȇ0A@A Aȇ0AA Av0ATGA Ae0A<@ A0A`b@ A'0A@ AX0A@ A0A+@ A0A_@ A0A@!@ AX0A ? Ad0As? A`0A ? Ak0A`2? AZ0A0? A 0A? A!0A ? A\>0A /? A0A4? AD0A@? A*0A`2? A[,0Az? A0/0A-@ AL,0A@ A,0A`@ A}*0A5A AH.*0ATGA A(0A@gA A'0Ac'B A'0AtB ATr&0AB A<%0A,C A#0A@YC AH!0AjC A0A@C A0A C A!0AwC A 0AC AT0AD A'0A@D A0A/D A H0A`܁D Ax+0AƕD A0A9D Ac0AD Apn0A7E A0A-E A\0AvE A8E0AZF A8x0A`}F A0A`F A 0AF A0A F AI0A?F A0A@0G A|0AJG A\0AbG A\ 0AlG A|00AG A0AG A̬0A H A0A3H AE0A VH A80A0cH A8~0AhH Ar0A3H AJ A*N0A`)FJ A`L0A HJ A]K0APJ AJ0AUJ A 0I0A`\J A4Q90AJ A*0ApiK A8#0A@5K AL0A=L Aj0A =L A0A`TqL AT0AL A0A"M AA0A]M A0A@PM AЄ0A}'N A 0A 8hN A̔0AN A8V0AzN A0A uN A0AjN A0AfN A4"0AN A4f0AfN A0AN A<0A`N AH0AN A\0A SON A60A N A50A@M A`ڛ0A M A$n0AvM A0ADoM A0AHjM Ahs0ANeM A,0ANeM A0ANeM Ag0ANeM Ax0AgM A:0AHjM Ap 0A@>tM Aވ0A@.M A"0AM A }0A0M Aq0A 8hN AP|m0AN A_j0A@N Api0A?N Ah0AN Af0A 8WO Ae0AmO ALe0A wO Avd0AO Ac0A`O Ab0AO Aa0AO AXj`0AdO AL]0A ZO A\0A`TO ATZ0A ҵO A1Z0A ҵO AS0A4O AdyC0A`K2P AD0A IQ A\FF0A@sR AtH0A`KtS A\N0A@oT Ac0A@ߵU A}0A`KV A0A@?X Aл|0AY Ak0A l>[ A*Q0A c\ A(50AD^ A0A`` Aܵ0A ` A0Aa ABj0Adb Ae0Ad A0A O f A(0Af APK0A`9g A0Ah AxZ0A`h AL0A Ai A0A`̣k A A0lR0A`U AP0An AKD0AI At>0Ao Aخ70A A0/0Aj A'0A ̵ A`#0A A0A@p A0A / Ab 0ABͶ A\~0A AH0A AȾ0A`" A 0A A 0A AĀ 0A  A! 0AI A0 0A AhB 0A^ A 0AI A 0A A 0A`d۹ A 0AjA A8M 0AN AlI 0A A 0A A< 0Aa A 0Az A< 0Am AX 0A d AK 0A 8t AL 0Az A 0A@ A4O 0Aw A8 0A@,G A 0Aז A" 0A@ Aw 0A AC 0AD A 0AB Ap 0A AD 0A A\ 0A A 0A@5 A 0A (1 A}0A Ax0Ai Ax0A` Ax0AZ Ax0AX Ax0A` Al0A@ A0A J A}0A A0 0A` A 0A - AD 0A@h A 0Ajn Af 0Aஆ A" 0A`B A 0A@! A 0AT A8 0A@ Al 0A@Ơ A8 0A` A 0A@y Atf 0A A\ 0A A 0A^ A 0A A3 0AD A 0A A P 0A A0A`fA ATi0AE A4 0A@M A(s 0A`KZ A8 0Aw\ Ab 0A Ad A| 0A`p A~0Am AW0A A:0A`KI A '0Au A0A A90A A0A< A00A` A0A A; 0A Z) AlG$0A I A|&0A c[ A B)0Aj A-0A H| A+/0A A030A[ Ad50A A 60A A;0AV A>0Aե AH'B0Aե AD0AV AM0A A09O0A@ߛ AO0Aޛ AQ0A@ߛ A0Q0A@ߛ A4R0A@ߛ A3T0A[ AT0AV A.Y0AΪ AXY0A@ AZ0AK Ax^0A`; A=_0A6 A,c0A A$(i0Ak A̬i0A@y Apk0A`f Ahm0A` App0A@>1 A s0AjI A s0AO Au0Aj Aw0A@` Adx0A`ã A<y0A@ A A 0A E Ax 0AO Ax 0AQ Ax 0AW A(O 0A`\ A; 0Ay^ AQ 0A$j Ax 0A`̈ A !0A0 A`z!0AL A`z!0A[ A`z!0A A`z!0A` A 0A Ax 0A Ah0A8x A0A@ A0A AS0A̟ A A0A A00A A\0A A`6%0A { A'0A& A 1*0A A}*0Ah5 Ap*0AL A +0A`w A*0A@ Ap*0A 8 A,)0A`f AT(0A H A4$0A&< Aؖ$0A`M AlG$0A`K AlG$0A A%0AfZ A(0A 8 A B)0At: Ad(0A} A=(0A} AT(0A Ah'0A Au&0AHQ A$0Af A6#0A | A"0A A0A A\0Aҭ A\>0A Ҿ A0Ah Al0A@y Ahh0AI A0A A(@ 0A` A90AЋ A 0AN A 0A At 0AЩ AH0s 0A`V Ap_ 0A & AP` 0Ay AWA 0Aú A|2; 0A0 A` 0A AHa 0A@k Ađp 0A A^ 0A`f A/Z 0A{ AC 0A@ Ah3 0Av At0 0A` AC 0A. A4Y 0A- AD 0A A$ 0A| A 0A A 0A@ AȘ 0A A 0A` A\ 0A A7 0Af A< 0A A 0A A4 0At A0A I A@y0A`< A40AA Ad0A A0A A0A# A,t0A AL0A AH0A A4/0AF AvF0A O AȶY0A@P AVk0A# A|?z0A Aͅ0Ah A70A AHp0A@y AR0AF A @r0A A [0A`] AJ0A  A3E0A  A\:0A ? A%0A`/ Ae0AT6 AHn0A`D A0A A90A& AlV0A  A%0A A 0A8] Ad0A A,ɾ0A AI0A`]p A,T0AVG A|0A@h A+0A Q A0A A AHv0Aͳ A0A` A|(0Ajp A00A= A<0A`2 Ap0A(% Ap0A  AD@ 0A2 A  0A  A 0A`Î A 0A ALh 0As A` 0A@ A 0A`b A 0A A 0A@y A 0A@! AQ 0A@ ! Aг 0A{" Al 0A@" AK 0A!# AI 0Aƍ# AX 0A_?$ A8G 0AY$ Aȅ 0A% AI 0Aپ% Af 0Aw|% Ay 0A8٪Q<& A 0AdS7& A,w 0A5x[& A 0A' AWO 0As>' A&%cI 0A2d$l' A'\ 0Am' Az 0A' A^y 0Ac 'y' A 0A ' A5 0A -( A@cw 0Avwf( A,j 0Azݛ( ANWO 0A]( A,ק 0Aӆ( A 0A` ) A-Ff 0AozB:) A 0A-V) Ao? 0A2Sڣu) A7 0Al>) A`N 0A]x%̷) A  0A) A0L 0A ) A4B 0AD- Aԑ 0A4- A y 0A 1v- A 0A. A0Y 0A`. A̿T 0A. A!S 0A`Q. A\>9 0A`Q. A\>9 0A/ A5T 0A_/ AS 0A@N/ A̟ 0A. A(0A/ A\0AW/ A-0AXs/ A؟%0A?/ A ,0A-/ A60A0 A =0A lQ0 ALB0Aa0 AW0A 1 A:)0A2 Ab0A 4 Au0A Z6 A-0A@ϴ7 A00A Z7 A0A8 Az0A#q9 A 0A@i9 Al֓0A?J: Aė0A@: A0Ah:; AXH0A&> A20A> Ap0A$@ A(޷0A@B AG0AYE A 0A@(G AP<0AWG A\0A JL A0A`ӾS A50AX AZ40A@,[ A0A6-^ AL0A@j A0A@r8f A tZ0A>P Ad^0AiB A0A+: A|F0A@6 A^0A-) A10A#$ Axڞ0A! Ai0A`T  A0A= AH0A@R AK@0A  A`E 0A- A4f!0A= A$$0AI AH%0A`; A`ػ&0Az AX]'0A} ATC(0AI A.)0A/< AC*0A AVV+0A`2 A`,0A@> A$-0A8 AX/0A A d00A` AX10A Agx20A S{ A20A4 A$20A3 A20A> A20A* AX30A@% A]z30AÖ Am70A9k{ A:0Ai A`\:0A g AL:0A`"{f Ar:0A^e A<:0A&d AE;0A^ A$}=0A@R A\=0A (nP AT@0AM< A`TC0A?+ ARE0AK AqF0A@5 A G0AɌ Ax:G0A AG0A@ A>G0AS ADxG0A` A('H0Ay AuH0A AwH0A: AܙH0A`G AH0A" A%I0Af ArI0A} A(vI0A S3| AsI0A 6{ A(>!I0AQ%x A?H0A@r AG0A`]g A|AG0A@W[ A$G0A AW ApF0AzP AGE0A@>E A0E0AA A ;E0A : ADD0A A,/ AD0A V( AHvC0A@`V$ A86/C0A@5 AtB0A  A wA0Aӕ Al;A0A!Ar@@0AA<>0AUA )>0A,A\u>0AIA)0A (չA0a(0A`A_(0A 0A@r)0A@פA( )0AA)0A@AY*0AهA4*0A Ҽ|Aݩ*0A |Ax*0A@`zAlV*0AtyAm+0AIA+0A HA),0A@Ahc,0A ck6AIg,0AV5ALi,0A 5A{,0A-A[},0AV-AȼS,0AZ'Am,0A@eAh,0A lA8,0AxA8,0AAi,0AB*A@S,0A[A`e,0AvA1,0A gA ,0AM(Ap],0AAt,0AzA,0A IAB-0ARAHC-0APAD-0ALA0U-0A@iU>A$=]-0A6:A`-0AJ8A|ǐ-0A5&A|-0A? AJ-0ABA-0AAܵ-0A A-0AA-0A[A}-0A@LA-0AAض-0A@A -0A A-0A^A-0A+Ac-0A 9Ap-0AK AX`,0AZ/A$N,0A@ؔA +0A !A+0A`fA+0A`bAܨ+0A'vAD+0A 7oA +0A@@gAx+0A^_AI+0AWA~7+0A@COA=+0AGA02+0A ?AL0+0A|=A, @+0A@7AU+0A@3A]+0A%1A86+0A@-A8+0A &)A0H+0Ag(A+0A`"A3+0A!A}+0A`A8+0A@EAQ$+0A`oAWV+0A:Aht+0A AȜ+0A s4A@+0A A^,0A+A,0A@AP4-0AA|u-0A`A4-0AMAG-0A@ԄA.0A@A*.0A Ap/.0A@HA=.0A ~PA,.0A APӻ.0A`BuA .0A 9AD.0ALA̿--0AkAp)0A Aķ(0AAh$'0ASA8%0A0A>#0A03A"0A`wA0AHmA0A^A0AyXAx0A`]UA0ASA]0AOA0A@`KA(0AB'/AV0AWA)A$ ]0A !A@b 0AA 0A`vA;0A@%A>0AtA 0A-Ap2/0A6gA:0A/,AHC0A+AǛ0AAql0A`~Apd0AAP<0A?GA\0A4yA0A`KA30AHYA0A cA p0A Ah0ALA0A`TA$.0A͸A(^0A(A0A@,A8*0A=AP0A@A0A_A;0AvA0{0ARAG0A`A0A Ah0AճA`' 0A`DA 0A@iA(0A`vA\0A /A40A`vA0A`"A0A?Al0A0EA:0A,A0AQAlx0A cAlI0AIALr0A`Al70AQxAg0A@A0AAŽ70ABZ4A^%0AI9AB0AjA5Ҽ0A@ ABԵ0AA-0AAk7i!0A35@A0AA@\0AA10A/HYARi0Ao A0A$A0A AT;0AӓA%AK0A[6A#0ARNLA0AŘͭeA0AHA0AvA  0AA0AA0A$vAHV0AŐA70A@%A\0A`NA"0A (qA70A@,fA0A`]=A܆0A`;AA0AdAt0AľA-0AHA<0A@%A@ 0A*6AD`0AA}-0AڮA8A0A HA@sZ0AA\0A@A0AA 0A;Aź0AA`v0A"A 0A ɂA0 0AA"B0AUAS0ARAlV`0ABA0AAj0ArA`0A8AXJZ0A`AT0A<AP0AAPL0AI0AH0A_AC0AA<>0AA30A+A;00A@QA/0AUAl/,0A sA4'0A`KA#0A@5A:"0A@Aԫ 0AA8~0A`A|?0AA0AA/0AA(0AAf0AAw0A@ A|0AA0A@A0AA0A$A>0A$AH0A$AH 0AA0A@A 0A`A@0A@AXH0AaA0AgA0A?A8ػ0AA\0A@Ad*0A`[Al0AAg0A0APO0A`A70AgCA0A=kA0A`An0A@Al0A*AЧ0AzA0AADo0A@,iAv0AAR0AA=0AAd&0A`At0A-zA00A HaA 0A^^AQ0A@GAK0AA:90A@=A~0AaIA0A,A}L0AzAhN0A HA.O0AvAZAT!20A@>TA-0A YAh,0A@YA#*0AYA8<$0AdA 0A`YA0A`̞A0AA:0AR~A0AsAHa0A@gA0AAX0A=vA0A Al0AA0A 1|A0AKcAp0AVAྠ0AGA0A'AF0A A0A`A̓0AAL(z0AZAy0ASAX9y0A IAx0A &3AX9y0AAwz0A`Awz0AA0AA40A[]AX0AmA90AWAxi0An>A`0A A~0A Ag0A`TA[0A`AX~0A~A|x0A@ALlv0AXAHq0AA`'l0AǮA\e0A@aApc0A MAh`0AB<AdY[0A`AT0AAM0A`AA0A+A|70AA0.0AA&0A`AU#0A` A0AA!0A`mA0AA'0A`*A{0A>A 0ACA1 0AHAT0A`2KAg0A JA0AHA0A 8FA`<0A>A 0A`*A}0AAO0AuAN0AOAk0A@A0A2A80A#AC0A@AT0A AATT0A[Al0A AU0AEoAu0A ,ATc0AA+\0AsA>0A@A/0AUAP 0A AT0AWAL,0AMA 0ApAx0A@A0A@>A 0A@gtAT0Ax,Axk0AAQ0A`A0AZA0A A0A@5A$0AxAH0A یAё0AD A?ÿ0AR AMlɐ0A!tk`AQ0A:AIp0A1A_%9]0A?(AK0AA%Nn?F0Al]AAA0AC)A߼>0A%kA<0A\A|<0AZ2A!=0A;AQiG@0A&_~ApCD0AAd0A@RAu0A4=Aړ0A"<A(0A cA0A`;A>0A`;A0AA R0A@`A0AKA0A A/0A Ap0A AD"0AAdf0AeAX0A Axܥ0A$ A@0A AD 0A`iAD 0A (AG0AAl0A uApP0A5Az0A@NyA؎0A qA0A@A!0AޥA50Af?A;0ANAOl0AmB$A'_0AнA0AxAl0AA D0A ]AFu0AzWAi%0A mAs"^0A=uAEun(0AAmAor0ALA$0A@> A<0A`A0AANs0AA0AAu0A~Adf0AfZAT0A`;KA{0AA?U0A ƻAmc=0A1DcA20Ak"AѦZ0A/L=(Aŭ0AȽoAh0A J[AP0AlDAXٷ0A (~A80AjiAml0Aʸ/Aiҧ0A$AY0AAu@0AŃ\AĂ0AA}&0A2A:0AA _0Ak\*Ao0ARCA0AANLz0A_r Aˏp0AAȅd0A`At$]0A sA7N0A|/Ad@0A@A10ARLA!0A<AhAG0AgȂ5AM 0A\A0a0AA#$0A W1A.O0Aݨ_+A {3 0A4uA 0ANA>0A)A0A$(qA0AU!AM0Av +A}0A_QA0A\eyA0A A*~0A]HAβ0AኚAO@0A A&0A bA 0A@gA0A`sAT!0A@>Ax0AKA0A|AY0A̘AHa0AןA0A@>^A8E0A_Ad0AgA&0AWA$W0A@A80AA 0AHA\50A`AV0A At0A`A0A`]A(s0A;A0A$A,0A`cAe0A6@AU0A@&A&0AT~A40AA90A;AwC0A@!A=0AA^ 60A0`A|00AWA>+0AGfAK9ن(0AeAu&0A\01(AI&0Ae=sA#7/(0AjXA9+0AwYA0/0A-FAmK$70AOA@?0AeيA]I0AF'A;P0A7aWA W0A}{A_0AoAh0A Afqsn0ATe A%t֚r0AhGe_AjAu0A0p|Au0AA0A`DAX*0A ZoAEk90A=AYn0A]A{0AW\Au20AB.A؎0A&aA78+z0A*VAUXe0AnپAzB0A'A0A 8Af܅0AV Ar 0Aɚ`#Ao0AHi<Aǔ0A],]XA~0A:wduAۇͽ0A;A~0AňAǔ0AFAo0AsAr 0A6e Af܅0AQ A0A=+ A%:p0A2^ AXZ0AU An@Y0A= Am0A | A؃0A\`l A7=0AI A0A0L AOԊ0AUAOg0AwLA{0A.(~Ay0Al~,A0AJyA/?0A?Ax0A@AI0A®AƱ0A AAt 0A`AfL0AIEA(0AAr0AA,F30AA¯A£sx0A7\ASu0Ar A$0AAx0A \+A*A0Aǣ /AS%0AU(4A 0A74V9Aq|0A%>A80AOCA!$r0A0GA&¸0Ag$LACj'0AiOA/0A4SA"0AUARa0AċA[0AuW5A н0AA#0Ao~ A0A ALȲ0A# Aʮ0A~M AAJ0Aۜp-w A4W0A) A40A/ !Aڭ0AJr!A%nl0A\9kN!A !0A$"A0A@ "A~0Aa[#At0A$Ab0A&AX0A̜'A0A *A!Đ70AK*A/0Ae+A\'0AE+Ai/$0A&^P+An~"0A: ^+A( 0Ao+Ak7+0A&5憂+AC0AC +Az6p0A3'+Ak2;0A-!+Ay0A+A(+0AG,+A0L0AT,A0Ap_,A%s0AZ} ,A0A-A$}Y0AEG-A8)0AVEu-A㶠%0AY-A@qZ 0AsXؽ-AL0A-A0Aj-A0A-A}0A+-A)0A@ .A|P0A2.Al0A O0A$0A1Aƚ0A H3R2A90A7U3Am0A3AӬ0A!vT^4AXU' 0Any4A60A5A84$0As5A͍0Al5A\0A5A0Ag5A0A5AΤ0A.P5AV|0Abq5AT x0A`16A؞t0A=F!6AS!q0Ad2vC6A o0Ajh6Aa %n0A֎6Am0A|6A n0Ab 6Aɝn0A6Am0AZda7ASl0AF7A[Ak0A07A?Ai0ADsD7A, g0A^V7A=e0AV`{ V7AEY+4c0ArV]_X7A^4]a0Ab@H ]7Av_0ALd7An^0A#m7A \0AZQ#x7Aǜ[0AW7AVZ0Aۛ7AmD4Z0A?(Yd7AvY0Ax,n7AZ0A8*"7A`Z0AK7A T0A C8A K0AV8A B0AL9AP60A1k9A)0AH9A؄'0ABp9A&0Apƅ9A^$0A@Ͼ9A.!0A9Az0AA5:A?0A@K:A0AG :A@p 0Ab:A 0AA;A 0A$˟;A 0Af\;AQZ0A x@;AWa0AѴ'<A!0AZ:#SQ<A-F 0AEnq<Ae0A <ATpT0AЏ<AԳ0Avj<A` 0A`윲<A0A <AK0A3ek<Aj0A2l =A 0A@2=A 0A WNuu=AME0APuz=ASך0A=A/<0À2>Ax0Ayg>AC0A[䷕>A!Y0ANq>A'0A)>AH0A}/>A0A>AsШ0Ar ?AM)0A6a1 4?AZB0A'_S?A\0A u?A90AՏ?A+̪0A e2?At0A?Alw0A  @A zwp0Ak=@A\i0A@x@Ae0A @AT0A8AA%M0A&EAAg0ABA(0A}xCAXS0A~(CAU0A`lDAX0A.-j2EA<4 0AEA~$0A`"FAn(&0AYƪVA|F0A,VAl@0Aa:WA<60ATWAe00AEWA^%0A@ WAx1 0AXAL{ 0AXAHV0AwXA0j0AiYA0A@YAX_0AYA00AZA쾗0A[A0A [AhT0A \A?0A^A 0A)`Ao0AbAa0A`;bA3-0AocAX0A9{aAD>0Au`Ap70A :`A #0AFaA0A@bALU0A AaAO'0AcA`\0A!cAΔ0A9)3cAB|T0Ao2dA=0AldA0A`"dAۡ].0A[eA60A51eA-0AE!"GeAz0AZDVeA9#0AEEE^eA0A^eA[p0A`eAUC,0AӑseA\0AgeAI0AheADQ0A;hAt$0A iAh0A ɷiAd}0AAjAp0AhAfn0A@kgA,4a0AfA)0A`aA0A}E\A@0A(^A$0A`^A 0A,_Atd 0Ap]A00A %]A80A`]A`0A| \AO0A@iEA50A@ DA$9a0A BA0A AAA0Acg9A0A#V8Aqӭ~0ANQ/H7Aa0AmwX7A#0Ae6A#0A Q6A4+:0A?i4AnGR0Aΰܺ2A%l0A^81AQz0AXuq/A 0Aئ!-A0Aƾ|F,A0A@5*AĂ*0A'A F;0A0g&A-RN0AÜ$AhSb0A j#A@c0A`S#Ac0A`dO#AYf0Aei2"Al0Ao!Aٹt0Ax X!A*~0A"VR A$Ɗ0A- AA0AN9A10AtgW/A%x0A+ A?#z0AH~K7 A)]-{0AMx A&bx0AC A6>t0Abd3 A,#m0Ai AF}j0AuõW A'1h0AfV A*h0AEo Ahi0A Aam0AmK= ANqo0Ab?_A) o0A).(cA kl0A=#ZA1Rg0A}jA`0A@2ALU0AA R0AAV0AA;S0AjAI0AAF0A@kAXE0A&A <0A9Ap40AIA90AtA90AྙA,+0AAw*0AdA(0AF?A4q!0AIA0AAe0AaA0A@HA$0A@g2A0A %Ae0AA|2 0A?A4 0AOA4 0ATA4 0AA0A6A$0A&A70AA00A6A<_0AA 0A5A0AI0Ac0ARAL0AAX0A,AW0A =A0A A0AtAH0A;A0A IA0AoxAP0A@UAh0A<0Ah0A ZA0A@aAh0AR-At0A`AO0A Ap0AAp0A%Al80A 3AxZ0AA,0A QvAl0AagAt0A`A}0A@GYAY0A@ KA0A#A0A A0A`)Al0AA(0AA@|0AA w0A ATp0AA|pw0A"Arx0A`As0A`Ad{f0A Aԫ]0AwTATcV0A@A(R0AhA<<0AtA%10A OAؖ)0AA4%0A`CA]0AqA0A`iA80AѐA^0A A(D0AA p0A AA(0A@\A0A AD+0A ~Ax0A}AXb0A @AXM0An7A &0A A0A0Aw}A@0A.Al0AfALu0AAa0AaAҳ0A >A0A`9AL{0A^)AT0A@NA?0AAp"0A_OA0A@iA[0A ~A0AAA,0A&A<0A[A0A Aպ0AAPK0A@#A40A zA$q0AA$(`0A^At\0AAP0AuA @0A@WA (0AٗAH!0A <AW0A,A0A@bA0A@7AJ0Az Al0ARA=0A`'A0A@A0A`D^Ax0AA m0A`ADf0A0\A`6R0AH#AH>0AtA^/0AmAP"0A AH0AA0A@A0AxA`0A-A*0AlAe0A\A0AxHA 0A3A80A` A8m0A A0AwAX0AKA0AA0A@A[0A`Al0AlA8g0A (AU0AGAlS0AR;AI0A` AA0A A850AA/&0A`vAE0A?yA0A`MAl0A6:A0A Ax0A@>A 0AA0A` A0A9A@0AfA0AA B0AA\0A@VAl0AhA̝0A` A0A>A0AAp0AW޾AL0A}Aw0AAp0AகAlOc0A`;A^_0AuAL\0AmAIO0AEYAXG0A0IA>0A l-A4 90AA40A`A|.0AιAU*0AෙA$0AwEAh 0A(A0A@A0A,A 0A(AT 0A 1A0A_3AQ0A`@A( 0A`v_A 0A`ӒAS 0A@謽A 0A@NA; 0A圽Axi 0A@wAԫ 0A4WAY 0A` Au 0AA 0A}yA@± 0A`4A( 0A&A4 0AaA 0AƕA 0AuATɚ 0AzAH 0A_Al' 0AzHAt 0A<.A 0A cAy 0A!Ap.q 0A ADn 0A@"A$i 0A /A0c 0A`A[ 0A"ܺAeM 0A ںA`%F 0A`ºA< 0AAZ6 0A A1 0AAX) 0A`m`A" 0AvUA|! 0A OAz 0A ~BA2 0A`;Ae 0A@i>A] 0A`-A 0AzAl 0A2A* 0A׹A 0A絹A( 0A AX 0AyA1 0A ~qAX 0A@ƆAl 0AA 0A`啹AA|P 0A7Ah 0A1AT 0A OAp 0A8A 0AIA 0A`fAC 0A9Adݣ 0A@A 0AqA 0A@yZAi 0AtA 0A`)A! 0A3ATo 0A@wA^ 0AhAxN 0A#_A D 0AV\A40 0Ah>A@ 0A`dAp 0AA 0AA\> 0AfAXH 0A@/APM 0A$A+ 0A ͭALf 0AiAD 0A`TA 0A A 0A`kAh 0A`mGAH 0AA< 0AA 0A` A 0AA 0A |A0 0A@Ap 0A A q 0A@#As 0ATŪA8l 0A@ A c 0A {A[ 0A$9AtI 0AA ( 0A)A܌ 0A`] Ah 0AA% 0A (A@ 0A?=Ab 0A|CA 0A 8A0A[ A@0AA,C0A`Ax0A|zA0A OIAP0A@&A 0A@AӼ0A ~A8ֶ0A@Aë0A@#RAʥ0A=9AD@0A@NAؿ0A̦APz0A=A~0AaAw0A<(Aاr0AAf0A uA^0AwApLT0A iAHH0ApA50A`dAK00AMA*0A@A7!0AwA0AAt0AڐÃ0A|A 0AVPA 0A-A<0A0A0AФA880AĮAd0A`A0A&xAhH0A]ZAl0A*,A00A}AA0A`ޣAո0AAf0A`[A"0A`£Ad0A^ͣA$0AA20AڣA-0A@߷A|0A2ALp0A jAc0A@5qAȇS0A[NAQM0A1AhQE0A`KA8:0AA.0AA 0A+A)0A ҢA0AA(0A>A 0ATAq0AϡALu0A`2áA80AsȡAH0AnġA0A@EA"0A ~A0A Q2A0AAy0A@`A0A@A°0A@֠A۬0AgA0A A{0A@#Ax0AӠAP0A@yA.0A?AL0ATA`E{0AǹAXJv0AՑA,ci0A /^A\0AAO0A"A50A?A #0A@؝A0AK~A`0A@P"A|0A[A0A GA0AA;0A@A,)0A!A(0A &Aj0A (Ap0A3A,t0A יA0AՙA|0A+A:0A0A0$AV;0A3 AX80ARAR50A`mAJ20A`AE/0AAhH,0A A8^'0A@A r"0A A0A"A0AF#At0A"Ad0A`A 0A`A0A A 0AA\u0AA 0AAd0A Ap0AAD0A A٘A0AИAt0A]ȘA&0A=`A;nA|6]0A9mA\#0AjA8 0AiAd,0AiAxo 0A*iAt0A5hA0AhA/A`hAP|/A|hA/ATWhA /A& hAP/A~gAB/AyEgA/AfA /A@p`fA/A 3fA/AeAС/AFeAm/AQeA̮/AĬdA/A]dAd/AZ!dA]/A`cAy/AincAz^/AbAH/A aA@/A`Az=/A`Aȃ>/A_Ah"?/A_ApV?/A@_A?/Ax_ACm 7/A{_A*/AVQ^A12W:/Apy^A/A@]A/A%]Aɒ/A%mS]A/Arj]A>/AQ]A 9/Aړx4]Ab/AB]AD/AT'\AB^/A\A/Aw\A/A[A}/AE-[A? :/Ao9[AA9/A}8N[AH/AN[At:/A+n[A 9*/A !W[A/A!qA[Ax/A@/[A\R/A- [Ahz]M/A ZA/A]ZA$/AZA,F'/AZj_ZA/Ax4ZA8/A ZA/AbLYA[/A^lYAk\XA*A5n/A쯓LXA_^/A*TXA`"Q/A炨XA z /AXA`"Q/Ae}XA_^/A4XA*A5n/APlXA7/AXA/A@i\WAP-/A{b:WAl3>/Ar.ZeWA/As VAn/AB VA!m/A߰VAج/AVA&/A̺TAx6/A`[{SA4/AwRAaEG/AcnGRATrU/A^hʘQA`/AkPAP7g/A`2/PAPg/A O)PAXg/A^OAg/A@iOAg/AOAPf/A mOAd/A@?OA2a/A OA0j]/A@%NA4Y/AҋNA`T/AQNAO/ANA`J/A MA7D/AMA>/A/MA(9/AࣻLA/5/A@ GLA0/A`2KA/-/A"]KA+/AH+KA~(/AJA |%/AJA "/AJA(/A@gJA/A^8JA/A| JA/ANIAxh /A`IA`r/AOIA/A@UIA3/A`4IA/A)IAhD/A`2HA>/A (HA/AHA&/A$HAP/AMHA /A jHA/A ARHA( /A+;HA/Ap%HAho/A` HAx/A@EGA!2/A FAp/AEA؎ /AsEA/Ao9EAb/ADA8V/AФDA/AEXDAڴ/A8DA[/A lDA`2/ACA``/ACA/ACAΒ/A#CA/ANeCA/A>CAHÂ/A CA3~/A`BA }/A$BA|/ABAB{/ABA`y/ABA w/A 1BAAu/ABAxr/ABA`ep/ABA@m/A`;BAhk/A)BAei/A`BA8tg/A@BAXe/A JBA{a/ACA@`/A-CA3_/ACAj]/A9CA7[/ACAhX/ADAأU/ADA]S/A& DAfQ/A`DApO/ACAN/ACA`M/A CAfM/AECA{M/A CA`N/ACAN/A CAM/AzCAJ/A@CAxG/ACA`6D/A BAHK@/A BA;/A\BAh7/A$%BA1/AHAA1/A@AA R1/AնAAt0/A JAA"//A AA\-/A6eAA`%+/AJAA(/A@C1AAm%/A_AA!/A[AA/AR@A=/A+@A8 /A O@Ap/Ȁ@A/An@A`B/A`L@A2/A'@A( /Ac@Ax/A?Ax>/A?Av/A ?A#/A \?AG/A?A/A൉?AX/A@q?A`/AY?AP/A@>B?Ap /A+?A/A@r>A^/A@yr>Aأ/A>AY/A->Ah/A>AԵ/A=A/A=Aڳ/A@=Ay/AH=A/A`ʺ=A/Af=Aϴ/AH=A/A=Aͮ/AP=A]/A=A]/A&<A/A@<A]/A`u<A/A><A /A +Ac/A+AY/A@%,AF/A@N-A/A`u.A/A/A8/A@0A^/Al3A/AV6Ax:/AK;A|/A&>A@7/A>Apq/At@Ax/AUAAE/ACA@F/A$DA/ADA /AxEA[)/AoFAxzI/At0GAO/A GA@C/A]HAPgM/AIA5/AHJAsA/A`ӕJAV/A`1KAc/ALA3v/A`LA/AhMAH]r/A`MAU/A@CNA)J/A@yNADX/AOA(~r/AJPA(|/AA3QA=f/AQAGB/A`2'RA)/ALQAЦ /A@%QA/AޞQA`/A@QAPk/A_QA`/AkQAx/A -QA(`/A`PA6/A PA/A[PAp/A@PA/A5QA&u/Am4QAR/AZQA0/A*QA$/A c4QApN/A@LQAD/A RA/AൖRA8t/A qSA/A`KTA8c/ASAh/A+SAP//ARAZ/ASAoX/A JqSAR/A` QA5/A@rPA[//AEPA/APA/A"yNA/AMA/AAMA@>/AMA`r/A?MA0]/AFAg/A @AP /A;Ahod/A@8A\/A1Af/A 1+A`/A`@$A_1/A#A$/AzZ#A@/A` #A_1/Au#AQO/A`@$AH݌/A`%Ao/A`)&Au/A@[&AU/A &AH./A^(ApN/A@(Aȇ/AT)A0/A(AHY/A`I+A=/A@+AC/A+Ap_/A`v+A/A -As/AH<.AK/Ai.A0/A c#.A&%/A (0AP/Af0A /A1A_+/Az0A0؋/A Y3AXm/A3A1t/A 3Aذ/A3A(K/Ah6A/A@N6A8/A}6A#/A836Ah]W/A 8A>8/A`;8AV@/A29A5^/A8A`/A;AHм/A g;Ap/A S;Ap/A@;A /Ah;A"/A>A$/A`>A /A6>AH'/A M>AU/A@Auj/A AAp/AX?AA؎/A&@A/AtyCA/A`)CA/ACAH/ACAQ/A2FAP6/A`;xFAr=/A FAZ/AV_FAM/A,HA/A_"IA8/A`)TIA/AtIA(`/AƋKAp/AKA /AFLAP%/A`KA y/ANA /A OAZ$/AOA/A`NAP/ANA`/APAx/A-QA/A@QAPȎ/A RA/A@!QA@/A?(QA0XP/ASAQ/ASAHzN/ASAb/A`TA/AaTA =/A"TAT/AaSAp}/A TA( :/A VA߶/Aoo^Ap{/AV_Ap/A bA(O/A=n 2AK(/A671A@/A l0A/Apg2A/A@0A@/A+A&/A&(A(J/A@,.#Ay/A@C1A3/A_A0٢/A KAP*m/A@w A@}r/A@59 AHw/A2A/AA'/AnA(/A@~A/A*Ap/A`RA8E/A@`A1/A@`A/A@`AȘ/A@>A }|/A`DhAQ/A@Ap/A |A/A |Ai/A SA/A@yAPHn/A`#Ah]#/A$A /A@A9/AAh./AA /A@A/AՂA(/AyAz/Af Ap/A JnAP/A`|AQ/A`|A@o/A`|A =N/A`|A/A`|AP/AӊA//A-A/A$7A8R/AAX/AhAPȤ/A`!A?/A@A/A`2EA1p/A@ZA8/A6A Ծ/A*PA̾/AxSAuž/A@^A /A`A9/A`v[Ap/A A8 z/AA 9/A`KA /A@ϪA,/A"Az/A ?AТ\/A"Aذ*/AફApy,/AApy,/A`_A(3/ALA/AoA/AoA؎/Ag(A`/AApl/A`APx/AZRA /AaA/A Ap޻/AA/A`9CA`/A cA`/A4A(`>/A JA|/AzA/AzA0/A`A֝/A$Ao/A@PlAj/A@PlAXT/AAA4/A A`"/A`A؟/A$Ap /AAp /ABAp /A AX#/AِA/AxAh۹/A`9CAo¹/AA?/A#Aω/A[ZA0/AAPM/ARA#͹/A@DAH?/A |aAX#/AhA=/A/A]/AmA/AA`/A Ax׺/AAP|ں/A`AϺ/A`MADܺ/A@ AD/A@BAD/AA8g%/AA0,/A@5AJ/A`ܦA/AA(@/AgNAI/At$A/i/A@P%Ax܌/AeA@ξ/AzA޻/A@A/ABAx /ABAB/A A_A0e/AA`k/AA؎/AA/A`2AƼ/A@hAx /A=wA/AwAP̈́/AAri/AA8D/A@ A`C&/A@)A'/A^A/AqA/A H]ApJ /ApA6"/A`"#A(JO/A`oAxzf/A&AT/A@Aȱd/AAPx/AN[AT/A cAQ/A$A0R/A@,3AT/A@AV/A@Am/A SAeo/A 82Ad/AAX5X/A (lAeo/A@5BApx/A`vsAW/ACA/AA8/AʷA/Aa/Aȼ/A@Aqμ/AA&/A AX(/AOyAx-7/AkAI/A$AqR/AF#A7Xع[/AQ6Ayc/AGOAj/A`lA k/A"RAWh/A=Ac/A@5EAP]/A ~AI U/AwgAL/A Ae>M/Aj;!AEP/A1ZAv&QV/Aa;G[AWk]/A ZAVg/AAA{/AJ6h A=/AjDA/AWA3/ACAky/AZ;AW|/A:AO?Ľ/ArzAA(>"Ͻ/ACX %PAҬHٽ/ALJ_eAl/ATAN/Aēz8AC/A/A${/AzA/y/AިA/AKA0H/A@AP/A3pAm/AߝAK^/AA'23/AaP=K+A?{E/A0]h$AS/AA~]/A`\A-,b/As݄gA w7Bh/AE[oAn/Azk9sA s/A.ψ/Ak AǾ/A:wA~޾/AjBA:6/AׂAP</ApAs#/AtmA`S9/A AuAwA ^/A &2A`h/AhAۓo/AAUr/A|8 oA(q/AAp)%h/AJ~AQ^/A:AiR}S/AɰA/I/A>?APm @/A@A 8/A'Y$AUz0/A؏"A;%*/AW;AF~5%/AX_A17!/AbAc7/ /AA/AA=!/AibAd'/AHArB2/AMqAFA/A'A[6U/A7%bA@l/AA k/A5㢳AX^j/AK-4AX|k/A[>9A Um/A1f :A;=p/ARxTA_!t/A:,lAx/AAv~/A`ÒA@/A@AhaϿ/A!AR/AA;8/AeAi/A A`/AQ*A&S/A}?8A/A@3AAD\/A0:PA81/AZzA /Aͪ EAܤ,/AfAN3o'/Ab5<Ai//AۑA3/A@*A4/Aϑ7A71/AIARu+/A/A!/A,Av/A;}$Ac /A_"A}/Aa!'A|T/A#1AFN5/ATAA7/AOcWAYa/A٥iqA'/A,@LA/AXAM3/AA*5 /AX.A/A -A/A$AqW?/A2GHL A/A` A;l@/A+ AA/A A /A& A/Az+ AV]p/A|MI A{1F/Ap}h AK!_/Aٞ AO@/A:7 AN/A2 AF/A> A4/A v A/A A!M{/A A /A" Ahѭ/A%PZ As6=/AXK Axme/A+ AGn/AI49 A0{u/AFr Amje/AB A{R/A]R RAP/>/A@%A!h:/A >AP8/A@>XAn6/AXxA0i5/AzޅAWL6/A&rdA4'8/A'Av;/AA?/A`AhnD/AA$K/AZAR/A8mA#Z/A/{Aʥb/AAJxbk/AAs/AA /AeAP~A/A 7SZA Ǣ/AnI\AP/Am_rhA/AAC/A19Aoz/Ax AI/A#Aijh/A _AmU/Aӏ A /ABA?}l%/A(xrAG+/AaϰAH70/AÐվAU3/AfsA/6/Avz0hAW7/A<~"AO.7/A{AAW5/Ae`A 2/AQh9~A./AcAU)/AAvP%/AѱA}~\!/A;3vA $/AMo{Aۦ/A!ZA H@/ACAlI/Afd]A/AHwF.A^/A`DAҺn'/AmA2//A^A0-3/AYA3/AA,0/Anc>Apl+/A |MA@$/A*ώ<A/A A:΀/AIA/AabA/A! .A@/AqA/AAP/A$A0 /ABA0 /AZA/A AH/A A /AA /A A2/AAB/A@A'_/AдAy}/A`As/Aa<A /A`A/A0gA/AଵA/AA /A@EAh]#/AAh./A==A1/A6A0&%/AdA*/A`0AAE/AA:/AAH/A`jA%g/A@NAj/A`2AJ/A}kAJ/AaAPHn/AnA Ox/A&4: Aȉ/A2З AmW/A8 A"T+/Aml!AZ |/A (E!ABs/ADvAN"Aaf/AQ"Aq\/A"AAE/A#A0]e/Ai$A@/An8%A/A%A@/A`m@&A8V~/AB&Ac/A'A/A@,(A/A@5(A/A(A/A`(AP /A+)A/A@y-A+/A+H/A M/AVI1A$/A3A/A`!4ABA/AYF{4A/Am4A$/AĐ C5A/A 85A.*/Aŗ5A/A5A6/A5A/A6AL/AQ")6AxF/A86ApI/A_7\86A_e//A#H6A`H/AiPU_i6As+`/A|R6AhUv/AμQ*6AfF/A1 E#7Ax/A y7A/A`;,8A/Ar8A1/A#Q8AqJ/AJ$9A0/Aymf$9A/Aك9A</A4t:APM /A:AM3 /AN;Ag /A@>;Ati/A-N<AE/Ark<A)+/Aj<AN/Au;<A0/A`0=A{k/AKSg=AYH&*/A*'=A(/AѢ=A@Š/A`B*>A:/A<.>AiL/A u>AM/AaA?Aȥ0/Ay?A 0M/A 1l@AT/A8ɟ@Aq]/Aiƭ@Akhۯg/A[@Ar/AN@A> I}/AScӢ@A7[/Aw@AG&'/A0C@A'q/AI@A5t/A(Ep@A4p/Ax @A`/A@AK\/A=ra@A1/A+<@A[O/AZ@A/Aez?A(\l/Afz?ANa/AINv?AMR/A d?A /AT b?A/Aѹ?A N/A ¹?Aȶ6L/A+;?AӜ /A>`?AX'/AqL7?A׆0/AQ?Ap8/AV@A?/A>@A!E/Ae@AUH/Aw@AN/A)AA.U/A@&BAˣdO/Ao;zBAmG/AVRBAqJ=;/ACAIp'-/Ac?QCA(-/Ah@WCA< /A*{CAH/ACAkي/A@gCA=gr/AtDA~/A;V.DANzf/A{ 9_DA|p=/ADAk'/A0DAѵ/A`EAPȵ/A`;EA/AްsEAǻ/AEA"ŋ/AGEFASB/AXFA-/AyGAŷ/A8 HAϭ/A!HAJ[/A*IAв/A- IA5/AhIAROU/AԊqLJA x/A@JAVB/AJAQp/A 4JAvZ/A#KA6g1/A?:KAw/AIaKAG<_?/AΧJA/A߭JA"/A@{HAPoA/A@cGAx/ATFA`/Ah?FAa/AEA /A@NEA@/A EAhD/AGGAU/A HAذ;/A`]HA;/AtHAX/AHA/AHA$/A`HAaN/A@HAf/A.IAO/AJA]/A`GLA #/A |yMAK/AMAV/A` OA(#/APAz/AQAJ/ATAh/A@,OVA/AwA/AVA`%^/AA/At<AwU/A`A/AA` /A Ax%/A`3AX5#/A1Aq/AAm/A@A`X/A Ae/A<A(>/AAt/A@IAN~/A`6A{Yzd/AdAtyL/ATA7n7/A?Ac%/A fEA vz/A"A7! /ASGNA_/ACg~A;*/AܒA>/A5)jL`A/AA`/A`KA/AQdA7/Aj5A '/AZpASWN/Ar:A>/A+A1,/A~A/A$A/AA/A@A`L/A A8#h/A|A/A&A@S;/AoA/ANXA68/A`A-^/A@A=6/AxAl/A@AX/AAx9/AuAT/APAwx/AlA0H1/AA/AWA/AA/AgAk%/A ˇA/AA/A AP+/AmA/AgEAƔ/A A1/A>A$/A)pA/AAw[/A|A]/AKxA3/A0A/R/A`)A >/A`]AA/A A,/AA=1/A_UA_&/A KA^/AA E/A AP;/A@ A /AA(~/AEA/A4A/A |A(\9/AA/A /A/AqAx/A Ahr/A@VAiJ/AA=/AA/A`2A/A෍Ax/A`jA /A@FAB/A`ܔA=4/A`A/A@ypAif/A#A/AA /A/A$]/AA(M/AA@>/A A@5/AAҽ/AzAPĽ/AA{/AXA9/A`aA"¼/A@A8/A`)bAvú/ALJAp*/A`A !/AjA@/A@`$ AP룴/AAd/AA)Ю/A@!Ax-/A Al/A !AR/A!A8(/A m"A//Az"A`/A@y'A T/A.A})/A1AV/A`mq4Ap0#/A4A/Au6A 4/A8A1/A=<A/A?AxZ/A AAp?/A@5BAǝ/AoCA,/AEAfh/AMFA:/AJA@/AIOAYw/ArrOAzOOR/AsPA(.&*/AEKQAo/AI[a8QAwEҗ/A.yRAX㣗/A}RA n/AzRAm|8/AL5SAu/AU5)wSAI})xƖ/AmԨSA+OBV/AU_SAQ/A'SAV>&/A]ogvSAxܕ/A SA 19/AmySVSA@1\/A-TA/A`9TA>ޔ/Af3{TA/AF/AAFv/A]3A+JF/APAc/AcAYR/A a>Ah@҈/Af Ap/A@GAW/AA/AMuA0A/AA؎p/A AD/A A[Y/ACmAdg=q/A3A5*$/A0.A~/A±A~ϊ/A_CU1PA/A OA/AA#/A9A/AxA /A`vA@}/A=AO/A UAr/A`rAR/AA /AA74/A`]A8)/AsIAc6/AsAe/A5m6A$}/AOjIAˌ/AaAA/A{A*/A ~8A/A A؞/Am}A/A •Aج/A A2:/A/APP/Am}ARC/AeAY/A@A/AAV'˳/A*TA֢/ADob]AǧA/AB!A6o/AAG4k/Ak;A!?/A= A竐B/AA/AbYA%5/A`A˶/AA`Թ/AA`/AA`/A@Ag/A@Aj/AA؍/AAh/AVWA/A`6A/AAd/A࣐Ag/A \A/A CA؁$/AAFf]/Ak-fA/A`!A>6і/AB'%A]g /A'sAH/AA/AtrA*/A<AH/A@HA/A AA/A3A/AA@OQ/AA3I/A) A//A 8$A0*/A@#hAh^r/AA\>/A#8 AH!lw/A(cdA/AqAT#/A<2A@0)/AA/A@Ah/A)fA8"%/A@bA%&/AQA`/A AV/A /AH/A /AB/A@AXl/A0Adޜ/AdA`G"/A@WA(/A@yMA@/A`vA=/A@EA/A A/A (As/A0A)I/A`ʯA/A@عA/A`̺A,/A A/AA/A[A /A AS+/A@HA(3/AVAz/AAH/AIAv/A2An/AhkeAPoe/A@ƘAq!5!W/A3S@xAI/AA׌=/A1A] 3/A,U AN1F+/AA USA%/A@- Ap!/A`AE/A@A/ATAث/A |jAy/A Al&/AA`/A$A(/A^Am/AvA;W/A A8VP/AAJ/AVAxi/A pAJ=/A&AHЙ/AfDA1Ș/A`d#A`˗/A AX/A@߃APb/A 8 A@`Q/A5A/ANAX/A4A濕/AAؒ/A2A/A<A/AA/A AT/A@-Ap/AIvAxhД/A3Aۙ/AWA@/A]A9y/A`A[w/A Am/AIA/A2_A@/ADAcÓ/A'Aґ/A@AhG/AA/AA/AW AP//A` Axے/A` APՒ/A@p: Az/AF AX/A AxR/A@ϙ A`V/A# AfI2/A[ Av/A8bI A`/Ah` A(ӑ/Ag& Ađ/Am A /A`"AZ/Ap\i\As/A\4AÐ/A EA{0l/Az AG݄C/AۆAk.Ə/A$8E/A!TA6!N/AIAV/A AŴ/A 1<Ac/AAA}/A;AxS/AKDA(,/AA*6/ASDiA`喋/A`TAN3pg/AD፳A9/AgIAx/A^*DAp/ABhN7kAC/A@ A5d/AF4AU"ٍ/A(|@Aĺq/A2+AR/A8KGAp2/AAY/AA7K/A\/CA` Ɖ/Ao712jAp/A@8AJM/Ao&APm/A@A7/ALtnpA*~`/A$DA;ψ/A5kAJ/A_kQArXr/Ah2A1GG/A4a[AUp/AY A/AAކ/Aq5Aц/AA Ƙ!dž/A3HAv$/Ak5MAg~/A\PA /AA7/AʣA@6/A@TA/AmhA/A 8|AÆ/A`AĆ/ACA k/A^tA$/A bAZ/An2DA0f/A`\AK^1}/AA~p/AABg/AhvFAp b/AA/AA/A*A0݆/AaAhӆ/A?A` /AA0/A q9A`/A@A˚8U/A)ޟAM/AĝA#VlJ/A/\AJ/A@AXrJ/ATwAKH/A9LAD/A@#AK(9/AQ _A))/A%GA;D/AlyYAp=/A`<A/AbA /ބ/AŸAׄ/A#Aa/AA0/AحA^)惲/AӴ`vAF/AAF Xą/AsA0ԅ/AA /A+ϫAh/AA-/AAh*/AHdA!@H/AHko"A}/A'~GA}g/APF AP /AXA?V /A4cA+/ATetAqߪ/AcA$ x)/A|A:/A`)AhN/A!yAY^/AxA Ԣi/A"ԬcAsp/AaDA_s/Ap/Aڂr/A$yAl/A֡AfGDu/AmߝsA{3/A~A h/AkAS 7/AW@ZA84Æ/A@pAPۆ/A'eA"|a /AjAnf/AKKA͆ /AŹ/xZAo^/AuA /A?A(/A.AA/AnAbj%/AqAK3 A/AྜAH/AgYA5/Aa A1/A=ĊA m/A&UAxq/AFˣAi/A61wA/AW*( AKq/A CA/A JA oЇ/AeAA| /A0 A| /AA? /A$΁AϠ/AdA(/A`28A{y/AA'/AzlIA=oO /A l~AcR/A|1;e~A }AN/AE+}A ]F/AJ}A&-L@/A&|Ã;/A);.|A%E7/Af7|A(5/AЅ|A@>/A:$|AK/A{A2jY/A~a{Ad/AzA` j/A,zA1gm/Ap*)zA_o/A`~zA9:o/AgyhSzAb-;m/A-J)zA{1Bqi/AϗzAc/AyA^/ADyARUX/A`bܫyAq`x=R/A1\yA K/AFyA3T5/A xZyA)/Asɉ9yA=/AZMc/(yA/A3B%yAք/AQ3yA]8/A!wyAps/A3z;yA8;/AV.zAGn/AzAS\/Aj{AsP/A.D{A8G/A@"|AQ /AMAƃ/A@A/Ad|A&Ϣ/A|A*'Ӡ/A⬃{AÁ/A?v{AY/A[h-{A5/A'`zAc݁z/AqzA Cj/AXj)~zA&]ZX/AdWzAWE/A u=zAvy!2/A&E7Xق/AǕzAH)#̂/A:zAV/AKHzAL/Aa"?{A)/AG@ {AƮ/A{A7/A$|Aj‰/A!S|A["͑/A K&&|A9[/AB'|Av/AI}AI/A`}APp/A }AӚEo/A|.%A}?l/AπAFg/A.fAqr6a/AzAY/A4A$FyJ/AP7A*iz׸G/A[ѲzAD.B/A7kzA:/A }\zAc1/A <9zA='P'/AzA/AzA^#/A5zAL |/ATXyAV/A.3yA/A}jyAج܁/AyA=bˁ/A yA O/A~XPxAsAʔ/AU5stA\/AaA?tAȚ/A6XtAo/AgtApiR/AvtA.to/Adm}tAF_/At{ytA@O/A%GltA`?/AVtACc:=/ADtAՕZ:/AVV|4tAՋ6/A7XC&tAd2/AtH>tAKJC./A?tAv5}v)/A/ tA|~$/A tAl~/Ac tA/AG9tA(/A-tA/A̍[!tA /A.tAx~/A`Q~tAl~/AT1׿tA+S`6~/A0tA2~/A6uAO- y~/A%uAFdA]u/A`ҰAجu/A@,Ahu/A AHu/A@A54u/AA)u/AHAEu/A Au/AO1Au/A|AUu/A=A)~v/A (OA5v/A*Afv/AQAP'v/A'QA)v/AA@w/A`myA w/AAh{qw/AĔAgw/AjA8Qw/A@>APox/A A@5'x/A 8A8QUx/AQAЯx/ALAux/A Ax/AAy/AaA!y/AAP^"y/AʊA>3y/A`APWy/AAly/A6Asy/A`[Aby/AAy/ALA3y/A`HAy/AABy/A6A8y/A`Ay/AR'A z/ATA2z/AVAJz/AyAp,dz/A`Az/A`)AТz/AOAz/ApAHz/A \A%z/A`Avz/A@5ZA؎z/ANcAH){/A A-O{/AKAu{/AAxm{/ApA`{/AAH]{/A`KAӖ{/ATAh"{/AAߎ{/A@Au{/A A2{/AoKA0{/AAAw{/A`wA[s{/A %A{/A|A0n{/AAњ{/AA`{/ASA[{/A cAH2{/AA{/ABA8{/AhA8E{/A`vA{/A A%|/AA ||/A ZAe(|/A$ A0|/AsBA@4|/AA0&2|/A@Aa)|/AHA 0|/A`]AR{/AA{/AA {/A`]A0f{/A$GApʎ{/AAx)j{/A|A(F{/AA%6{/AA {/A}%A{/A[SAz/A u}A^z/A AUz/A A Az/A^ A"z/A`LA0z/A APwz/A`TAp{z/A`KAnz/A`v Afz/AeAfaz/AA( Uz/AsAx=z/AA`v z/A!A z/A@Az/A@rcA`z/AĐA z/A`̻Ay/AA-y/AAy/A%Ay/A``Apy/AdAy/AaAȺy/A"A(y/A iAxy/A 1A y/A A8 y/AyAiy/AAh@y/AAx z/AA([ z/AA03z/A@ACz/AΞAYz/AhApz/AAdžz/AAPz/AIA0*z/A@>A/z/AA@Nz/A`f!Az/AtAQz/Aq`A0;z/AYAH?z/A@A'z/AxAyz/AAXz/A`A Sz/A Av7z/AA$ z/AXrAz/AμAz/AnASz/A Az/A!A0z/A^A`z/AsAy/AWA(y/A`vbA(y/AFA ty/A \A8y/ADApy/AAH)y/A^A84y/ADAhy/AA[y/AAALy/A_A`!y/AAy/AAy/A@,A@y/A@NAy/A Apy/A`AUy/AtA@y/A`A(qy/AIFA0fy/AA0ٳy/Aज़A0Ly/A`Apy/AA y/A`vAy/A A0y/A JA0y/AAny/AAz/A`KAe*z/A Z;A03z/AARz/A@<Ahz/AaPAtz/AAvz/A AXz/AGAz/A |Aϔz/A/&AX5ez/AA9z/AAYz/A`ANy/AX A@By/AAy/AVA@`y/A-Ay/AAjy/A@,-Ajy/A`]EA*y/AAly/Ap<A"z/A4A`e/z/A sAȔRz/A`Aqz/AAz/A@sAFz/A`A p{/ABA{{/A A8O{/AA({/AVAX{/ApA{/A@gAQ{/A͹Aھ{/A@A{/AA\^{/AAx2{/A JA8 {/AA {/A JA{{/ANJA{{/A`bA{/A QA.z/A&A^z/A`bAoz/AsAP0z/AAA+z/A@gAgo/AnZNkA&jo/Ad.kAm&yn/AXlA:n/AX#lA(Jn/A`!mAƅ(n/AߎmA) n/A'EnA;m/AS:vnAj.m/A:UoAvA$m/AѴoAbƏm/A|PpA2m/AOpAЛm/A&qAhDm/AJrA{BRm/AS:rAks6m/AssA]0m/A4-ɍ,tAH1m/AJ/A8h3*qA79J/A^qApJ/A6rAPJ/ArAJ/A@>rAJ/A0{sA(J/AsAP^K/A !tAu K/A~tAK/AٹtA%K/A@tA0 2K/AH@uAJ3K/AjuAh0K/A`uAb;K/A ɷuA8DK/AVuA RJK/AquALK/AuAxPK/AauA@dWK/AuA<^K/AuAPYaK/AuAcK/A`uA@}{K/A[uAK/AQuAڛK/A@uAK/A cvA୧K/A@1vAXtK/AyvA +K/A@vA`vK/A@`vAHK/A\vANK/A@yvAHK/A@uAK/AquAK/AwuA(cK/AxXuAhL/A~BKuA]-L/A ?PuA3NJL/AΉ(guA.fL/AˇLuA! ̀L/A^;uAvL/A5VvAL/A@hvA pL/A@%vAаL/A`vAzL/AmwA$L/A xA0L/A@xAL/A 0yAXL/AfyA L/AzAjL/A zA`L/A zA`eL/AzAHL/A]LzA(L/AF`zAL/AzAL/A`zAL/A6^{AXyL/A@{AsL/Ap|AQL/A`B|A@L/Ay|A%L/Ay|AL/Ay|AL/At}AL/A 9}AnL/AZ}AL/A;}A``M/AĤ}A }M/Aɟ}A`P(M/AՕ}A#4M/A@g}A%PM/A@ߋ}AWM/Aஸ}AT`M/A@P~A]M/A`~A]M/Ax~AhUM/A~A[M/A+AhbM/AdApcM/A6A\M/AA(XM/A GA`RM/AځA@dQM/A`|ASM/AA\M/A`9A2bM/A@ރAnM/AAp[wM/AfA}M/A@APM/A<A`ÍM/A@ARM/A@>A(M/A4APfM/AAM/AAM/A@/A,M/AanAM/AAM/A@AM/A AM/AA`vM/AQA'M/A]vAM/AA@$M/AͅA`6M/A ZAȺ N/A JAN/A4A(N/A |GA4N/A A8c@N/A A@ABN/AAXKN/A`[Ap|dN/AxA N/AAx:N/AA`N/AAN/AAGN/AOMAHN/A@A0N/A AcN/A`)_AxO/A[A8O/A`AMO/A`AOO/A`m A@PO/A@iAryO/A_Ah;O/AqAO/AjAܫO/A@`AO/AZA}O/A牊A4O/A uAX$O/A`zAO/A@A$P/AdA=P/AA,PP/A@6A&UP/AN~A`!ZP/AČASP/A6A@uGP/AMmA@P/A`Ah//P/A`DA'P/A@YAP+P/A A"P/A&A P/A`A3P/A`A8 P/A>APZP/AAPP/AHŏA`O/A<ATO/AAqK$AgmO/AΐAO/A`A*GO/A$o,ANO/ADCAiO/A IAhN/AĒA-CN/AjA9N/AAyN/A*˾A^N/AA@N/A`בAM/AAttM/A<AHM/A}qA$LPM/A-A M/APaAJ'oM/A@ƑAsħUM/Aʺ>A>M/A Q?AS7M/A&A4/M/A8AX&M/A'sAM/AxGJA!OAM/AZ KASb` M/A6AήM/A^&Aw)nL/As!A:-L/A19Ap L/A`UAtEL/AAA8ڣrL/AQ&A%8L/AtAkK/A)zA|K/A"ߎACBK/A2$\7AeK/A`[AJ/AIA{9]J/Ahf&A0J/Ag@ A_ųJ/A` AqΚJ/A=͍AЦJ/A?A ~J/A-A:pJ/AnnpAȻcJ/AoA0XJ/A@.7A#t?J/AT%ACo(J/AZA9IJ/AV҈AżdJ/Agx֒ALv=I/AZ9GA-I/A@A pI/A ҸA zI/ASVA4Z[I/AHκ)A"I/AĀAعI/A`mvA %lI/A7OrIA OI/A tz-A41I/Af "A JI/Aoq(AɱH/A^I4@AH/AhAstH/APB†AkH/AѨ]o AAG/A.?AĵAG/AͼbALF/ArAx:F/ANlA5F/ANAF/A"%AGH9YF/A,AG.x1F/AᦻAT F/AG?4AohE/A5ZLYAHE/AAE/A$AUE/AAE/As- A)D/AޢoAbD/AM_ÉApD/A AP@D/A(CAI C/A4見AٹC/A# AyC/AA1W@C/A:G:A\Ż8 C/AVK،AB/A 냍A6DB/AhA'EB/AA| SB/A!TA^ܸB/AP'eAʭB/ASarA)湨B/A|yA[9 GB/AR#{A@B/A=wAx3hjB/Ao`KA|uB/A)AB1bB/AA)NB/Ar^ A;B/A= Aá'B/A+gAB/A1AҤ 7A/A4,A/AU]AiA/A#AbA/AVAD@:@/APA$-@/A@ A`]q@/A9+A?@/AŅjiA@/A`dA=@/A<A@/AJ֖A& ê@/AFpAe[@/A2A1,@/AeЂA|2Q@/A@Q%AN@/Aͩ&AԜ@/AmA@/AMA~@/Ak;LAx@/AAGl@/A ݜA\\@/AGAYH@/Al)YA/@/Av A@/AƘb2Az?/AVAKA5?/AHؠAأ?/AVA%WB?/Aڴ{A o?/A^8>A:RST?/AAo9?/AR.}ۀA^+?/AZ S_A!n?/AH-Aպ>/AA>/AAes>/Aj Aڶ^8>/A62tADT>/AqaA;>/A5NAO>/A ]=Av>/A .AW>/AA5L>/A# ATyM >/AEJ A^->/A>Ax6>/AA">/AACI>/AK A1u>/AaAtT>/AL][A.4>/A 鳵AzӒb>/AaW)xAFi=/A:+A(=/AўA(-=/A ҰAP6/A`)Aa6/AැA6/A=jA6/A`7Au6/A@(A6/A`BA6/ApA6/AzAp= 7/A oA7/A`fnA77/AA[7/A@gA`w7/A̴A7/A`)A07/ARlA7/AHYA7/ApWAf7/A \wA)7/A \A`a7/A 3A7/AXAk7/AA87/AAH\7/AzA7/A@>AB7/A3A 7/A@8A7/AR`A 7/A`fzA7/A HAX7/A]A#7/A A#7/A=<A7/AA*8/A@ AC8/AFAP8/A`A`e8/A`Ap o8/A`DA8y8/A qA8/A@A)8/A AL8/AcA88/AAp 8/A@Ah8/A`FAz8/A=Aأ8/A AQ8/AA8/A`)A8/AfA؞8/A \A8/AA8/A@ A08/A`!A8/A`A9/AAP 9/A@A.9/A A RB9/A=A8I9/A/AH.B9/A@hA8,9/A`ÛA)9/A@ Au9/Ap A8/AA 8/AAأ8/AAf8/A`Ah38/AA8/A \A`a8/A@A8/ASA8/A`)pAf8/AסA8/A3A9/AeA8 9/AAH\9/AA{9/A HA(-9/A@ϏAU9/ARAP*[9/AAH.`9/A]AfW9/A qAk9/AQA9/AzAh 9/A3A9/A@ A/9/AAZ9/A`fdAQ9/A@ A9/AzA(ܤ9/AAHP9/A A9/AGX1An9/A[A=9/AYv=A5/g9/AA9/A@CYA8l 9/A,QYA7{9/A6AA?9/A3˚Alm9/ALUA@19/A A@9/ALpA]9/AܾA!d9/A61ACcE9/A{>CVA`S%9/A A4P!8/A'=SnA?8/A YA08/A |A3n8/ASNUAـ8/AR dA {8/A"AEj8/A$p6AxX[8/A AZ;K8/AAI>8/A A.T8/AqAi8/AmDFA<{8/A]A#|8/A5A@ ~8/AgqA/Y~8/AGmA\~8/AA~`d~8/Aqq;A~}8/AA"|8/A"`,Aϔ:Gz8/A+Aix8/ANAOKu8/A^Azr8/AdAo8/A\? A֒l8/A;o Ai8/A` AfF8/Aޞ.À%8/A@A8/A$3A׽8/A*Ar477/AwAjh9M7/AAƕ$7/AȸAz:7/AAJ7/AJrAIG7/A,Ae7/AM30Aׁ7/A A7/A@iA ʤ7/AAApft 8/AvljA8/AAo8/AUAN)8/AA酽 8/A*ۋA6`8/A1]Ax8/A~A@7/A$qqA87/AOF+gA7/AD`AE'7/A|RcAg o7/A,SbAYv7/Aί/]A7/AMwTA(p7/AhծHAǪ7/AD9As!~7/Al'A]#-7/A\2;A7/A A+b7/A?An,7/A? Ak7/Akn(A77H7/A;Ad7/AAE57/ArqA7/AaTA0u7/AiAN­7/AHA@7/AՙAw~^7/AR2F}AӍn7/A2(*dA7/Ag QAzak7/Ac\vBAl7/A@r9AC7/ACDAhR7/A1o,RAf7/AmvaAʣ7/A$rAfY 7/A$@{AO7/AAA'v[7/AAf'K7/AybA* T=7/A)=?#A .>a27/A'PA؟*7/AA`r&7/A/G At$$7/AAf%7/ACA%7/APy{ Ansy"7/ALA=7/AA`ASg7/AԥlA=7/A% >A7/AzALU6/AUAp6/A 7A=b6/AAbBI6/A[ʊAsZ56/A TA$6/AFA2>wAg8d6/AppA&f6/AK HA 6/A Acp6/A=aAP^6/AAA`6/A{cwA6/AWA Y6/AElmA\P~6/A5RAʠ(6/ASq)Ap66/A@G]A6v6/A$AH6/AcAX?o6/A-IhAj6/AoԖAS6/A$;̂A6/AtA6/AsvA,&7/A~ AзW7/A͍Az{7/AVl*A./7/A9A&7/A`A37/Ae%XAm?7/A,Ag7/A鈉,A88/AAw8/A.A18/A+YA8/AdTxAFܬ8/ARƴA?8/A8AP8/AA%9/AH[AF9/A7ABAPL9/AS<A4|+R9/AE|=AW9/Aa:Ay[Қ]9/A|4Ab9/A;)AKDg9/ADAhk9/A  A, h9/A}Af9/AQA[f9/Aw=8Aaf9/AAɭg9/A2\jA+g9/AJKA3|f9/AiANKf9/A\˾Am߂f9/A|ڗA'f9/Ax͎A"Be9/A w/A=Jt8/AsAVq8/AMA`ap8/A&Ar8/ADŽA t8/A AK1t8/AwAu8/AAj.|We8/ACAyT8/AJA~^C8/AgpvAE18/Ad]NaA0{8/ARA8/A̧qAG8/AANA#8/AR(A#7/A@ Ay'B7/A AO67/A3qAu7/A AF7/A&A7/A"5A ~07/A/TANݮ7/AA.07/A5AEv 7/AyˆAeW7/A@ A-ѫ7/A[iA {7/ABA9Y7/AV"A|j7/AdAb7/AxA%7/Ax cAʬ7/AcAbt/7/A Anq7/ApAod"7/AGEH\A( 8/AA8 8/A@>VA\\8/A"GA6$8/AOJ) 7A88/A@A@8/A \3A<8/AA.8/AHCA8*8/AAX 8/AQAx{8/A6A[8/AXُA- 7/AˎAL17/As|A7/AmAs7/AA3\~A=7/AAA`a7/A 4A7/A GAHܙ7/AzA R7/AAL7/AkAYT|7/A\}Qy:AD{7/AUۨbA}7/A@iA\~7/A0A,~7/A0. A n7/AR5Ads~7/AiJA;~7/AkZaA{W³}7/AbGAVk|7/AgA {7/A. AHy7/A&z AQw7/Ab OAm@3u7/A7Ar7/A8Ap7/AzA@+f7/A!j߄A'< \7/Ay|AKDS7/AyEmA~J7/AXA'7/AA{GC7/Aۿ̙AZs7/AtڝA7/AmzA B7/AxAFi 7/A݊tpAŭ# 7/A8jA8 7/A^UmeA7/A3,cA?*V7/AFbA87/AdAp )6/ApA¯5/AzlA83m5/AbA'd5/A,Ay_5/AޔAu]5/AbQ)A`5/AM7Ae5/AvZAՊj5/AAОj5/ASbdAHnf5/ANv?A6G\5/AɌNAM5/Av}A/M5/Ai5AtK5/ApJ-TAH5/A|A E5/AB~Aڡ/@5/AӰA\Sv:5/AAE45/A^2&A-5/A0A3/ArA83/ArAЪ 2/A_Aج 5/AZ}AC6/A϶A!8/A@AX=:/A`A!/A@Ah=/A 1A@ /=/A`9A3/A AH{H@/A OA7OA/AA?B/A AMA/A`a0Apc%A/A74A]@/A@AAX@/AKA@/A`MA?/A@iSA0>/A_dA@>/A,yeAH>/A efA>/A8(gApű>/A:hA=/A`WvA;/A@tA(9/A -sA^8/AqA<,7/AbA,6/Az4A8]6/AAh&5/AyA(5/A=1A5/A cAo4/A5A3/A`A`3/AALf2/A *A1/AA1/AA1/AAh0/A-AL*0/A`f5AJ//ACAx./AAh*-/A`zA`T+/ABAp,-+/AA&+/A sA0+/A5A*/A'A*/A96A)/A IA=)/A"A)/Aho-Aڝ(/A+5AB(/A?S=Ap'/A`oDApŢ'/AmyLA0&O'/ATA"&/ADsAp%/A[tAB&/A)|Aw2%/A@ErA"/AHAqa/AivAH/A/TA/A SAc/A|SAh^ /AZ]EA0/A;AD/A .A(/A|!AXy.A_AQ.A9A+.AQ A.A ,AHu.A Aȃ3.A`APUU.A@AH .A`A .ATAt.ApA.A@ AI.A AاM.AVA0.A+ A..AAG.A Ar.A@DAPQ].A`Ay..AEAL.A A(S.A@`wA0.A`A.AA .A`wA.A[:A .A .A0r2.A2A2.AcOAHvo.AWA].A dXAg.A@?YA0.Al]A.A`0bAmn.ATgA ɹ.A_5iA .AkA,.A`[pAЈ.A uA8r.A@rfAЯ.A@#@A` .A늇Ap.A-A?.A`LAc.A ~A.A@r/A.A_{A.A AA.A`]-ApS.A)ۚA.AkќAڴ.AA.AAn.A\A@,.A@5A.A ~AO.A`YXAXT.A ZA@W.AQA(\.ANAX}.A`ՈA.A (AF.AஃA.A`;zA?.A |AHu.A#tAH.A Aھ.A@`rA".A Ax.A@A>.AkAHvq.AAZ.A`kAP.A A غ.AZ Aȭ.A@ri AX0=.AATk.AWA.A AXѶ.A@^A6.A XA0L.A G A.A /R#A(.AH0AxL.AT2A.A@33A[`.A6A(Ȳ.A|>AY.A JAd.A@PPA.AWA@.A XAH?.A`ܪXA0̘.A@!ZAQ.A&aA =ɭ.AdAX֭.A)eAp.AeA.AfAF.AVgAjs.A`"hA ͏.A`hAPfĮ.A5iA.AiA.AiAB .AiA.AiA.A iAh.A`DjA.A!jA0v.AD:lA.Ao`oA(R.AkAz.A 0nAB{.A &3nAQ{.AjnAz.A@nAȓx.AnAuu.A@nA=g.AnA.W.A@ pA t.A (pA.A pA.A qA@S.AOqA(.AࣳrAoݱ.A0rAı.A`tAT.A@uAX.AuAA.A`vAB.A$vA`z.AHwA`T.AWXyA@.AzAxG.A q{AH.A]t}A#.A)A.ADAP.A@nA`I.A@E`A`B3.A@GA84.AцA07A.AvAl@.A`Ao=.AO+A$8.AvMA((2.AoA((-.AטA).AA#.A·A.AkA.A A.A@W8Axz.A`]A83 .A@yA .AA.AƈA.A A3.A@ A0.A`-A.A0NAF.AzuA.A ˝AV.AHʉA.AA.A`,A .A /^A R.AAj.AĊA..A`fA.A9Apޮ.ApAzڮ.AA׮.A&A8Ԯ.ARAxЮ.A`A̮.A`3A0Hɮ.ASA2Ʈ.AqAB®.A`A.A &A.ARیA.AA .A`A.Aj8A.A`XAP<.A cAୢ.A@A8.AύA.AA`.AA.A)A8Q.ARAТ.A@uA|.AෑAu.A`)A@um.AAH)f.A&A[a.A0PAP \.AӀAKX.AWA kT.A q̏ABP.A/AtM.AAyH.A:ABE.A@pZAP@.AA0=.AHA@8.A ɻAأ4.A@CؐA`0.AvA@,.A"AH)*.A cSAp(.A}Ah'.A@A8&.A0ƑAp &.ANApk$.A&A#.A/Aj#.A@ ZAF!.A;A.AA\.A ӒA.AA`a.A34A.A`gAp.AA@B.A@WГAh.AAL.AH@A.A@XAH.AA.AA p.A(AHz.AA3.A`WAp.AՔA(.AЕAU.AA.A Q;A.AmA.A<A.A`mA7.A lږA .A \Ah.A/Ap.A[`A.A෋A0v.A鰗Ah.A3ؗA.AAН.A%AQ.AMA@.AVvAQ.A@Ax.AȘAh.AA.A(AH.AXA .A9AX!.AWAp!.A@G֙A#.A0Aг$.A(A`&.A5AW0.A@%OAН8.A q`AȓB.A3vA J.A猚AQ.A墚AZ.A=A#b.AAc.AA_.A cAZ.Av5ApU.A Z^AP.A&A`M.ḀAfL.A͛Ap=M.A`)AO.A@AQ.A/>AT.A ZbApW.A@A@Y.A`ծA[.A̜A`^.AA`.A AQb.ABA pc.AcA`ed.AOAe.AѸA c.A`[Apk`.AAz].A /<A`Z.AbABW.A@APS.A@A0fO.A AJ.AݞAE.AAp.A PA:.A@pA0f6.AAh2.AdAp=/.A@A,.AA).AE6Ap8%.A`^A0!.A ⃠A.A9AH.AA.A@A0v.A`A.A (=AQp.A}[A.AȠA:,W.A98IA|r;.A[u-ɦA+.A<A6x,.AT,AJ.AĒLAҗge.A WnrABkݍ.A F)A<"<.A| uOA~n5b.AR A Ad .AnB8# AGÕ.Aȴa Az.ARH A_G7.A| u Ab".AF) A2-h.A Wn A/AÒL AʇT /AT,ѕ A A`/A` A>/A A>/A@P A>/A@Nj A>/Ag^ A>/A ΃ A>/AL A`/?/AC AP?/A`] A Q?/A`[ A0w?/A Ap}?/A,{ AU?/Aw/ A?/AF A@?/A. Ah?/Aﻀ AȂ_?/A Ԁ A0?/A A A9t?/Ao; A(mm?/A@L A0]P?/A Ah@J?/AN A-?/A c A&?/A`K A.>/A A >/A Ah;>/AH& A҇>/A A`Â>/A AhS>/A@" AM>/A 0 A>/A@Ϯ A0>/Ag A/=/A A5 A=/AB A؁=/Ax A`=/Au A@=/A㉆ Af=/A6 Ab=/A= AOF=/Ao AhoA=/A`ք A =/A` A@p=/A A/A`{ A?.>/Az As>/A$z AN>/A@yy Ax->/A@!y A>/A`x A@J?/A`v/x Al?/Aw A07?/A`Lw Ak?/A*3w A(?/Av Ax:?/Av A ^k?/Aw A(`d?/A`]w A(90?/A_x A'?/A`3x A:>/A@x A@>/AVx AȾ>/A (9y AXV>/A6Ny A(>/A0y A~>/A 8y AM>/A`2Sz A@E>/A Zgz AN>/Az A_ >/A@z A=/A@Pv{ A==/AO{ AXh=/A | AE=/A| Ac=/A| AH[=/A+| A8E*=/A@)} A@"=/A`<} A``Z/A/q A @/A`9un APuB/AOk AB/A?l AB/A`2l AxuB/Am AB/A`im AB/Aǎl A(B/Al AB/Ak A@B/A@ߗk A؞GC/A`̧h Ax-D/A`'f APD/A`f AD/A g A7E/ANg A F/Ag A(`F/Ae A@pvG/A@d A -O/A[d AXJO/A@nf AhO/A f ADO/A g AN]N/Ap AJ*J/AzFh A<J/A4g A>I/Ag AלI/Ah A` N/A}r A`O/Ag ApO/Ag ArP/A Hng AP/A`e AB'Q/AWf A88Q/AHi A R/AWj A6U/As AW/A| AgX/A AVuY/AU A`XY/A AsZ/A, A@Z/A@  A~[/A@ A}[/A" A[/As AX[/A . A[/A@ A[/A൬ A[/A d AH\/AN AI\/A`t A\/Akƌ A]/A@WH Ap8]/A AC]/A A6]/A@pܑ A]/A` AТ"]/A@ AkB]/Aվ AQ]/A9 Ah^/A \ Ap^/Af A"_/A1 AH_/A Apea/A@ A-b/A`á A,b/Aˠ Ap=Zc/Atݠ Ac/AW A0\d/A Ad/A; Ad/AIp Axe/A  A(Qe/A6$ A d/A@%V Axzd/Ao( A\d/A`;7 A_Fd/A' AhoHd/A`" A^d/A" APɩ A=a/AΩ A@ a/Ab A( `/AE An_/A4 Ax`/A A`/A A/`/AZ A `/A@< AF_/A`m& Au_/A` Aj_/A O A-u_/A AP_/AF AP_/A A!_/A! AS_/A@>ܧ A_/A 1 Ax:_/Aȧ A^/A AQ^/A API^/A A'^/AӦ A^/Aa A^/A`ަ Ap^/Ao Aw^/A Ap^/A AE^/A@2 A Aل8t/AP9[ A麢v/A4Ɛy A̱5w/AO Aw/A Aw/A AvQw/A {/A87 A`t{/A AH{/A 1 Aj{/AM AP{/A1 A)|/A` Ah"S|/A/ Ahs_|/AF Abi|/A` A(r|/AH A0||/A  A |/A`K% Ae|/A`KM AXW|/A t A8|/A AНz/A s Az/AS Az/A`9 A8z/A Ax>z/A AAz/A$} A0|/A@> A|/A A k|/A@, Ax|/A Ah|/A@N AH|/Aq A|/A AQ}/A A(}/Ap A8R}/A, A8#}/A`d= Ap&}/A X AX6}/A`{ AZ}/A` A8j}/A Apq}/A`; A-x}/A"+ A(}/As A`a}/A A p}/A`b A0̮}/A` A}/A`v Aأ}/A ۋ Ah}/A`X A83}/A/ A}/A@ߗ A}/AD A}/A@ AТ}/A$s A}/A` AH.}/A;# A}/A A}/A` Ax}/A AH}/Aa A(}/A  A}/A Q AH}/A A}/A A`}/A A}/A A(-}/A@& A0}/A`o_ Ao}/AW A(}/A cu Am}/A` A}/Aaa Ak}/AĖ A}/A A-}/A` A8'}/A` Ay}/A` A<}/A@WL A`}/Aj A(}/A`o AH-}/A] A}/A 8 Ae}/A@ A%}/A A0̐}/A  AH2}/A`r Ad}/A Ap}/ANe A,}/A Af}/AD A}/A< A}/A Ah@}/A Ad}/A \ A@}/A5 A}/A8 Ah}/A A}/A A A4}/A A}/A@ A}/A A舁}/Aj Aj}/A Ae}/A | Ab}/A Ah`}/Ac A)`}/A` A_}/A`T A5_}/A 8 A^}/Af A^}/A@y Aq^}/A A ^}/A A]}/A` A^]}/A AP]}/A|( A]}/AU0 A]}/A7 A<_}/AE AOa}/AfT A(i}/A| Aأm}/As A@Ox}/A@ A@{}/A@. A0}}/Aq A}/A A #}/A A}/A Ah}/A A؞}/A@ A,}/A` Ah}}/AX Av}/AƏ Ag}/AT A"V}/Ad ApP}/A~ ApAI}/A@N A8RD}/A A18}/A@N A0w5}/A`b A,3}/Aw A+}/Aj A#}/A> A#}/A@` A9}/A`B A }/A A`}/Ap A3|/A@i A|/A$ A|/A= A|/AO! A|/A` A@|/A A/|/A}E Ap|/A` A&|/A & A0|/A^ A'|/Ah| A |/AB At}|/A`ܫ Apu|/A@ Ad|/Az AXW^|/A` AȃX|/A@y AؼN|/A APF|/A AmD|/A A A|/A* A87|/AO A4|/A@ Ao2|/A AH1|/A AX#1|/A A/|/A` Au.|/A  A-|/A A8+|/A A|*|/Ac ApJ)|/A Aq(|/A A;(|/A A A%|/A A#|/A@ Aa"|/A! Aؒ!|/A@.6 A8g!|/Ag Ap #|/Aʡ AG$|/A@ A)|/A AT+|/AXK A+|/A`mi A)|/A A(|/Af A}%|/Aγ A3|/A A|/AJ Ap[|/A } AP|/A AHa|/A A8|/A? A{/A A({/A A(-{/A`B Ah{/A`] A{/A AX{/A@5 AF{/A A{/A A{/A A{/Ad A`%{/AR AG{/A} A{/A |j AA{/A c] A`{/A\ A({/Ac\ AM{/Ac\ A{/A\ A{/AV] A +{/AN^ A{/A+a A({/A@We Ap{/A j AP{/Ap A0.{/A`"w AP{/A< A{/A A{/A Ap{/A_ A{/A A={/A Ae{/A@. Au{/AT A5{/A@ A{/A, A`{/AH A0{/A`BZ A{/A m Ad{/AǗ A({/AO A{/AЄ AȤ{/Az AX{/A` Ah{/A A8ڼ{/A A`%{/A A_{/AO Ap|{/Aj A@{/A A{/AH AhD{/A A{/A` A(z{/A A{/A` Ah{/AN A8z{/A A@o{/AE A@d{/A`T AW{/A A`L{/A[ A"B{/A@C A>{/AN AH7{/Ak` A\){/AX AQ{/A  A( {/A  A8{/A A{/A < A AF/A@o AjC/A  Ap=@/A  A#>/A@% A,=/Aa  Ax=/A 9 Aؼ>/Ah A@/A ɏ AjC/A_ A0E/A  AtJ/A A2P/A AW/A> Apc/At A3k/AW A =u/A@ֲ A /A AX(/A`" A/A`" AV/A`"  A؎/A A/A`mN A!/Ao Ap/A` A/AЌ A?/Ai AxG/A A0/A A(/Az A/A@y A0/Aa A-/AX AP8&/A@y AV0/A@ A#;/A  AG/A@, A~S/A A#`/A Aj/A Au/A` A~/A A/A@N A趧/AjH A/A À/A$ Axր/Aao A/A A/AF A2/A] A(z /A O A /A A@/A A'/A` Ak&/Av A-/A@_ A,6/A`vI AX?/A5 AhI/A Z$ AЄS/A A[^/A Aeh/A@ Ap/AD Au/A & A-x/A A0e/A@ A/A AP/A A AH2/Ab AP/A / A(/A Az /A Ay/A / AQ/A A/A = AB/A`Ka Ax/A` A/A`; APk/AM Ap[)/A A//A & Ap[8/A A8gB/A AC/A  A]/Afs A#/A@, AL/Ai AT/A9 AZ/A0  A2^/A} AX^/A Ac/A@ Ag/Aྦ Aȃj/A  ALm/A@e AUp/AD As/A@%" A0t/A A`{/A# A=/A5 A~/A E Aі/AU AX(/Aj A =/A A7/A A/A൩ A[dž/A ˻ A׆/A@ A8݆/A Ap/A A>/A A">/A  A9/A@, AxG4/A`* AH3/AV A78/AX A:/A@#Y Ae?/AoL AD/A`+ AJ/A  AUQ/Aa AxW/A  A`/A, Apx׆/A \ A8x/A[@ A0n/A 8 Al/A`H APm/A 8a A7q/Ao Ax/A{ A/Aq A/A`v_ Ax/A@,V A̗/AZ AQ/AZj Aǰ/A@ AX۶/A`K AÇ/A Ahʇ/A A@·/A Ahև/A`m  A`ڇ/A, A<ۇ/A,D A8 އ/Af A/A A/A@N A/A@ A/A; A/Ad A /A` A@/A6 A/AU A/Au A /A@ƙ A /A A/A A/A; A8V/AN A/AL1 Aj/A8 A/A2 A@/Aj" A@u/ATP At/A St APf2/A Apy;/A AuD/A AN/AN AXyY/A APf/Av Ahl/A Axzx/A@ A(/ANA A([/A`i A /A Z A̫/AT AP/A  A[/A A/A@W AÈ/A: APʈ/A`oZ AtЈ/Av APk׈/A/ AUވ/A& A/A A0./AE A[/A@ Ap/Ax A/AV AX /An8 A/A] A"'/A Z A-/A  A'4/A޽ A`9/A AG>/A  A G/A` A N/AvC Apʈ/ADd$ Ap݈/A@C$ AY/AV APb/A AQf/A Ao/A! AQz/ANG! A8/A`m`! A /Aix! A /A ! A攉/A! Ao /A?d% APo0/A?% A/A " A~Ƥ/AOaI" Av؉/A`6# A)܉/AM# A/A@0$ A.D/A&@0*$ A`P+/A@h% AE/A6& AxQ/A`& AD]/A& A 8/A 8( AJ/A ) A軺/AO) APˊ/A) A|#/A{^* A~l/A̜K+ AoK/A u, A/AV, A|R/A- A /AQE'. A./Aw/ A /P/Ai0 A(zp/Aд0 AK|/A=.1 AȄ/AD\91 AE͌/AoÍ1 ABZR/A,K2 A? K/A9&p22 A9>/A2 A0Lg/A@2 A6p/A 82 A8{/AN2 AXu/A2 A`/A@2 Aأ/A;3 Ap[/A3 AX(/A`3 A0./A`D,3 AXƍ/A@W83 AXuэ/ANE3 Aڍ/A2R3 A^/A7 A]/A7 Af.A;d{ A`f.Ac{ A(if.AZ{ AxQf.A&I{ Au9f.A /{ AH:+f.A{ AP f.A z AXe.Az Ae.A`z Ae.A ɝz A((e.A@z A@e.A Xz Ake.A*z A0LKe.Ay AHe.A y AEe.Ay A #Be.A \y A\?e.A`y AP/:e.A@߫y AX,e.A Iy A)e.A`:y A&e.A.y A$e.A`$y AP!e.Ay A e.AUx AWd.Ax AH{d.Ax A(\d.A`fx A(d.A+x A0wd.Acw A0jd.A mw Ax)]d.AoPw A@Md.Af5w A&d.Aw AXd.Axv Ad.A v Ahc.A&?v A0{c.Aou A3_c.A A].A`D' A].AI AC:].Al,Vl A&,].A6`e A ].A'( A^? ].AMWz͔ A4&].A Af].A@W A(].A A\.A8 Aؚx].Ao Ax].AҞ A].A4 A].A AO^.A Ah^.A( AhԹ^.Aњu A 4V^.AAF A7J^.A{>U A1 _.Aǁ A51)_.A#Zӧ A(qhJ_.AK Al_.A2L A< _.AP A&_.A AQ`.A[ A4`.A A8x`.A ⼩ AǙ`.A= Ap`.A A^.A` A"^.A@iL A^.AKi A]^.A` AO^.A@ A@^.AE A_.A`i A _.A@i A~6`.AKb A0`.A A`.A?U Aa.AA AVma.A A9Va.A A2]a.AM AP6a.A@h A1a.AX A`.AF AP+`.A`, AH_.AF A_.A2! AS`.A Ap<_.A U Ay`.AӰ AP`.A@ Aa.Aؗ A`qa.AH Apia.A@ư A@Ϗa.A@Ű Aga.Aٛ AP8a.Atʰ Aݘa.A#QD AgVa.AN; A#b.A Ax8kKCb.A zp8 Ap9db.ArZ Axb.An Azb.A߲ AQb.A@ h A a.AD& Ahݺa.AXb AQa.Aγ A(Na.Aӳ APfJa.A ٳ APFa.A A8Ca.A@ A@a.A A=a.A A8x;a.A6 A`9a.A A7a.A ) AF6a.A@8 APM5a.A@pG A4a.A/W A4a.A`g A4a.A@v A 5a.A A5a.A 8 A06a.Af A`8a.A AO:a.A̴ Aia.Av̻ A8a.AmYٻ A(a.A| A [$a.Az A&Mͼa.Ab(5 A2a.Ac Ax-a.A| A€~a.AU9 A6xa.A Bf A2wa.A@ҽ ADza.A:> Aw÷Ӂa.AQrW Apca.AKC A`qa.A`` Ad!a.A.2Ͽ Aa.A$e3 A6a.A&. A6GRa.A, A`b.A AHRb.A> A@b.A A|$c.A|O Aт,c.A|O Ad3N>c.AO AKTEc.Ak5 A^!c.A< A}8b.A) Ab.A Axb.A, A?2Lc.A& A^!c.A< A @h-AHA..Az^ A;H-A:rAj-A A0=-AAP-A`dNAxǥ-A@mA@h-A PA8-A) A8ӹ-A7A-A0〽A]&[-AV;Aw-A<4ұ APw-A A9X6-AK.Aix-AAݽ-Ar&A@p-A GAH?q-A_A |-A`"Aι-AAHĹ-A.A;Ჽ-AK A#-A%`$OAn-A|A-A >=A;-AJAX-AI`WAeVɹ-ANXAԹ-A#߅A`-A`A.¾-A@8AB-A`]9Ay-AA>Ax(?-A5A؟-AU7A-A@E7Apf-A`ܬ5AXu-AFu6A_-A8Ad-AR8Af-A:A~-A];AxK-A:Ap-AM9A`6L-AA9A-A <AI-AIAdg-A?A-A?A>-A!AA-A;^DApl-ADAXa-A XAAOd-A`%AA4-A,@A0-A@`<A)-A \=A>-A ~?AU-A;9?AЀ-AH<AhM-A6:AsH-AD@Aw-A`$HAO-A ?TAȘe-AAlA(-AЕmAP-A |4qA*-AMoAx-AGXAp0m-A`[AHi-AYA`-A@>\AX-A@]A0-A _^Ajq-A$iA8-AvjA-AjA-A@jAH-ASaA~-AhcA-A`2aA8-A@cAP-A@kAS-AnA-A@T|A@-A͢A-A A[-AڅAO-A!APY^-A`DA>-AoA-A A c-A@ՍA-AAps-AgA-A[TA-A`f8A-A`At-A@rA RJ-A`{A]-ACAm-A AhD-A@xA-A@!\A8t-AzAذ+-AA[-A`ӔA@ -A=`AX-A98A,-AAN-A@ȍA8tv-AA`-A 8A-A 8CA2-A@A8gu-A@`˝A`a-AIA1-AApi-AqA"-ArA^.-AAPQ-A?Ad-ANAT-A,AT-A|AX(-AщA0&-A'A`f-AWAچ-A`bA-AAТ-A`MA^.-A&A-AFA'7-A']A[-AAR-AA`%-A"A;-AxALN-AlAȘo-A0A-A@MAa-A Axh-AA(-A AZ-AA8-AA -AA@c-ADZA-A`6Ahbm-AǁAp[-A"Ah&-A pAHu-AHA-AA#-AAo-Ap2Aʐ-AwMA?P-A A8i-A@ԭA+-A A$-AIгAv-AYA(-A FAH2-AWA-AdA-AŦA-AVήAʒ-AޮA~ -A`8A@-A`8A\-A UA3-AA~-A HAx\-A3A -A@Aȓ(-A֪A\V-A֪AUo-A`AH˔-A^A-A$A-AZA8-A Ax,-A`A0L-ABAȓ-AA-A A E-A(AJ-AA-AoA@-AA-A ɹA(-A"A-A`8A\-AXAA-A ȾAj-A`fAPv-A Ax-AgA_-A`A -A5MA-AA(-A`BA'-A JcA@1-AAŒ-A`A0;-A A-A@Axi1-AghA-AbA -AFA`-AA(-AAp8m-AAx-A BAұ-A@A0{`-AJAP/-A@AF-A J3A-A J3AD-AA-A.A-A@uAh-AA@OI-AA-A A1-A`AX-AAȲ-A Ax)-AॣA!-A@ At-A A+.A@ Aa.A$ARw.AHiA>.AAs;.A 'A+7.A`5A%7.A@5A-.AvSA-A@,"A0-A@A)C-Ao0Ar7-ASA'-AA!-A A-AA0f-A AY-Af A@`W-A AIW-A@ AV-A A^O-A@ Ah -Az AcE-A  Ah"-A A(-AA-A A`-A AP<-A A0]-A!A-A2A-A@r_A-A_AP-AeAA-A`A(-A AH-AA--AOA覟-A5Az-AV A_K.Aj A .A`m%A.A` A&.A`A2.AZAX.A$ A.A`d A(C.A@A-A2A9-AA-A`A8-AjAPo-AAhb-A`mA-A Ahё-A@PA(ː-A@A`z-A $A+-A A8gx-A HA֚-A`]%A(1-A# Ax\-A`mA-AHLAG-ASAw{-A |Aȱ-At{Aʨ-A{A0-AhA-AAw-A ɇA(m-A|Ap2-A&A2-AA0-AA83/-A@yA`.-AA.-A`KAZ-A`A83-A"A8-AAH6-A`A-AiwAТ/-A lA0t-AA-AAv-A`AYC-A ZAqD-AA(\E-A@WADI-AAȩ-A (A6-AAPo-A A -AA$-A AP8-AaA-AA8X-A@Y$A8-A%AǷ-AH[&A~-Ay&A-A.'AX-A<'A-A@i$A,-A~%A\-AV%A-A&A"-A`{&A|-A$(AȺO-A |g$A-AN"A8t-A@"A`a-A@ "A@-AHP A I-A !A(-A@!AHy-A#A@-A`Ab-A@WA0-A AKA(-A`A AY-A ZAPv-A Aq-AvAF-AA A-A@,A6-A`DKAx -A@A-A@5OA-AqA _-AyA]-A W A-A!AN-A J"A6-A "AH-A"AHi-A`#A4-Am#A -AW#A-A1$A9-A |9$A4-A Aq5-A}A88-A`Ad9-A}A:e-ActA!.AUAx$.A@,Ap4.A@A.6.A`9A0.A@A`.AAp-At(A].A@A-A A%AW-AK*A0.AAh.AA w.AvA;.A$A .AAXa.A6]AI.ARAȘ.AAP8c .A@W#AhI .AhA(< .AHA .A_A .A` Aw .A"A(K.A4:A.A`=A@ .A=Am.A=Ap.Aப=A.A @ATG.ABBAPj.A@FAD~ .A=tEA7p .A JAd .A`PA .A@,iUA9 .A VA5 .A0WAՂ .A@4VA@ .A 'VA`ӆ .AUA }{ .A`TUAl .Az TA}= .AZOA; .AOA5 .AOAe .A43OAI .AVSAr .AWAp .AXA`] .A@\Aa .A@]A .A}{^A .A `_AH .A B`Aذ .AQaA .AHCeA麢 .A}9[A̱ .A_i=A..AA..AA̱ .AO&A麢 .A4Aل .AP9Ak4;.A)NS1AOl.AnBxAh_-A~ oA6-AEoA !G_-A[oAv#@-AuoA!-ApA-A`v2rAH-AMjA-A_kA(O-AsA-AuAwc-A`wAЮ-A`vzA( -A`fmA9u-ARnA@}-A|AX-A |{A`2U-AaA~-A`aAX#8-A SA(-A@y {AXR-AA-AॿAHn-A` A -A$A>-AۆAC-A`xA(`-A`[ |A()-A@`AD-A l΋AS-AVA8 -A8A -AbA`X-A`A-A@ƋA@-A ˴Al-A@P Aȃ-A`KA{-AA-ABA^-AײA-Ap A0 2-A AA-A` A-A`CA@Ww-A@rAp-AXAHL-AA<-A AA)-A &A -A/A-AzAV-Az^ A<"L-AA}n5r-A uvA0AUWF&.A A W&.A\.A%7ld&.A 5>%Am&.A`ӻA&.AHAQ&.A@Aj&.AMA*.A`;)AR,.A@AS-.APAbZ-.A@ƍA\e-.AJAz-.A ?;A c-.A ?A8..A@jA..A|A..A@OAP ..A@#A..A FA,..AF A`rr/.AAr/.A(A8/.A+A/.ADAZ2.A 0Ad2.ARAH+3.A`A@>L4.A@pAB4.AA5.AtA5.AAx:6.AFAU6.A@eAx-Y6.AvnA$z6.AOAXp6.AA6.A A6.AAh7.AƘA7.A`KA6.A`AXi6.ACAxƭ6.A cA 7.A;Aȇ;7.A@WAXU7.A^A7.AţAd7.AbA7.AKCA 7.A rA7.A \\Aޘ7.A|/A؟7.Ak)A@7.A`KA+8.AsdA +8.A@ƷA8.A ZAV"8.A`B&APg18.A6AC8.A@qAPg18.AOA(m68.AAH8.A,AA8.A A <8.A`Ax=8.A`).AX@7.A@ A:B7.A 8AA8.A@?A:.A`8A̴:.A@PA:.A \ AhL*:.A ~vA88~:.AA؁:.A `A":.AIA';.AVBA`!;.A<A0<.A@#qAP/=.AAXn;.A A <.A`~Aׯ<.A >A=.AgAr>.A@A>.A5A=.A@GAx5=.AAx{=.AAm=.AF:AȪ=.A AA%=.AA0*=.AAR>.AʸAN>.A@rAxH>.ANA>.A@%A>.AA @.A0.APH>.A@Ax=.A@A=.A3n޴AF=.A0{Af=.A0[AuPW=.AkGA,g=.A7>mAz>5 >.A *$AD5>.A7% AbH5>.A,e滲AY|M>.A:[A|f>.AJAw~>.AAC)Ȗ>.AWAD>.At3ӲAWY>.A֌A>.A`KA86?.A@AA.AA8xA.AAp B.A`KnA0B.A jAJJB.A8aAp0B.AH<APB.AfAQC.A/AD.A@nAD.A`fAJBE.A"AP8D.A@ibAvC.A AA\C.A A9C.A AhB.AA0B.A0AMB.A A7C.A A%1D.Ax5AbFD.A@iA G.AQA@G.A%APQH.A`sA /H.AT׷ADH.A1AxD.A`AXE.A@AأG.A`AmE.A`A@1E.A?HAAHG.A`A H.A)yAjH.A OZA0{:H.AzAC.AwAȲY@.A ;A`@.A]A@B.AA0D.AAxIC.A (UAg@.AaAAA.ANoAhC.AzQ AX[E.A%A@F.A+A0*G.AAUH.A`A@5BH.A6AH %H.A`+AuD.AA0gE.A }AH.A A?CI.A la AhI.A`Aq`I.A`AJ.A@!Ab J.A@5A*O.A7AO.A:AO.AAR N.A"ApK.A`KAL.A`UA"K.A&AA@K.A`AJ.A34ApNJ.A"A GK.Az+AhLK.A65-Ay K.A--AwJ.A@#v.AHG=J.AQ/AlJ.A@ 0A(BJ.A /AHJ.A /A9!K.A`/A8x>K.A`1AK.A@1AJ.A@k2AX}J.A3A(K.Aյ3AxqK.AŽ4AXpK.A`5Ax>J.A7A=J.A7AxK.A]7A@9-K.A7AuhK.AH7A0K.Av6A(-K.A i7AL.A'8A`-K.A}8A L.AxT:APxIL.A`:AhbdL.A`B;A0*mL.A z<A.L.A@!BAL.A`CAM.ACAL.ACAL.A`@EAL.A EAL.A0$FARL.A{FAL.A}FAhL.A}MGA L.A@EGAOL.A HAL.A HA@W M.AIA8(M.AZHA;.M.AHA0M.A*IAIM.A@!bJA0ݒM.A JAx M.AIAM.A@JA NM.AinJAxM.A9KAM.A@DKALJM.A`KA(M.AWEMAxqL.ANA?M.AwZAWN.A$?dAHPO.AlA>"P.A3sAIP.A 8!tAdP.AtAYP.A@,uAP.AzxA֗P.AxAKWP.A{yAlQ.A AhQ.AAX0Q.A6A0Q.AtAXQ.AkAQ.AfA ZQ.A ATRR.A`A0*R.A`vAHÏR.AAͪR.A 8qAR.A厉A$R.A`tډAR.A@AR.AAXS.AơA@dS.A@֍A@S.A sA9S.A }AS.A`AȩT.AKAHT.AM~AHT.AAHT.AAHXdT.AzږAhT.AAAhQjT.AoIAJpT.AߜA(T.A cAf8U.AA#tU.AA2V.A &6AH iV.AAuV.A@eAhȤV.AAPgV.A^A`V.AuAV.Aw0AV.A@>iApc9W.AiAkW.A*Ax]X.AiWAiX.A6A茡X.A@AxX.A_ҭAY.AAZ.Aa!Ad1[.A@A;[.AwA{[.AtA0[.ADA[.AzA V[.A@A8 z\.A`(A}[.A`nAq[.A`KeAB\.A"ZAh/\.AA].A (A].AAv"_.A@. AF_.AJAb.AA c.AAd.A AxWQd.AA>A8d.AAd.A`]-A(c.A Aǣc.AA~zc.A@RAH_.AdAs_.A /AZ].A^A8|\.A=A؅}\.A@itA"\.AA\.A ZA8+\.A A0L].A (A(:].AB7AI].AAȏ].AAH].AſAH2].AX A].A ApAz].A Aأm].AQR A$].A` AD].A` A8H].A` A?ZE9F].A AJY].A=m~ Aҗgc.AABkj.A֘A<"o.AA}n5t.A uvGAg.AVߝq#A0C.Aՠu Au8/l.A zu Axml.A@Geu Apl.A@Geu Al.A-du ApAl.Adu ATl.A`9Ru A{l.Au A-m.A"u A#qm.At A2m.At Ah/m.A`Dt An.A{t A(~in.A Lt A n.A;t A_o.ANps A|o.A@`Is A2o.A s Ao.A r AHo.A6r A +p.A`r A(\p.A#r Ap.Aq A2p.A q Aq.A`q A %q.Ahq APLq.A|kq A0Uq.ATaq A\q.AZq AP+fq.AtOq Ahlq.Aq Anq.Aq A0xq.Ap A{q.Ap A~q.A`p Ap q.Ahp Aأq.AŸp Aq.Atp Aq.A@ p AXq.AŮp Aq.Ap A8ҡq.Ap Aq.Ap A0&q.Aap ALq.Aӷp Anq.AMp Apq.A p Aq.Azp AHݢq.A@ߺp A@q.A@yp Apq.AVp A-q.Ap Aq.Ap Aɡq.Ap Aq.A-p A؟q.Ap AHq.A`p Aáq.Ap A8ҡq.ARp A q.A p Aq.A`p Ax q.AQp A$q.Ap Ah?q.Ap A\q.Ap A{q.Ap A¢q.Ap Aq.A p ADq.A`p A8Rq.A p ApIq.A_p A^]r.Ao ANt.A`]l At.A@zl At.ATl Aft.A@ l A:u.AFTk AVu.A@yj A0u.A9j A`wv.A`h A( v.Ah Aw.A0g AOw.A g A@>5x.A.A`S AS.AyS A_.A]S Ar.A >T A w.A_T A.AT AD.A@yT AТ.A-T A@.A?T A.AҨT A@Ӆ.AT A.A`;T Ad.A 1T A.AسT AxK.AеT AQ.A`T AP/.AoT AP擁.ART A(z.AT AĔ.AT A.AFT A.AmT A .A`T A8.A@T Aꔁ.AT A֔.A@T A.A T Al.AFT A.A |T Ao.A@T A N.AVT Aː.A 1T Ap.A`vT AD.A_T A.A T A .A &T A.A uT AH?.AT Ax.AT A.A`bT A.A ZU A0.A@rU A؟.A U A.A AU A8.A U A0.A l"U A⇁.A*U A@.A8U ApɌ.A?U A .A cDU AI.A`KU Aߔ.A MU A.A@IU AP.A`bEU A0ح.A@U Ay.A@,2U Aǁ.AU Ap߁.AT AP.AtT A6.AP APf.A3O Ah.A+mO AԢ.AO ABل.A`N AH.A4N Ab.AaM A4.AM A,.A@.K AÆ.AZI AH.͆.A@0I A(.A` H A兇.A }F AXҘ.A "F A{.AFE AB~.AE A.AWE A0.AD A0.A/$C A.A`2C A`.A? AТ.A/= A3.A8 A8܈.At8 A.AE7 Ae.A@}5 AR.A5 AY.A5 Ab`.Ap5 A.A2 A.A@3 Aʉ.A#?3 A .A1 A.A0 A!.A`)0 A.A@E 0 Ak.A@& A=.A Axŋ.A A(.At A .A@' A2|.A QO A3z.A= A#.A@ Ap:.A& AB.A A<.Aä A86.Aj A(Ғ.A`D A(ړ.A`9 A@uҔ.A՘ANޔ.AyAꚕ.A.A #Au.A`]Ax~.ABA.AAPk.A`)A8 .AA@.A A0..AXA2.A@ Aɥ.A`AQΥ.AA;.AA0C.AVߝq#A=U.A98IA 1.A[u-ɦAg.ARf,:s A}i.A`t Ai.At APwi.At A i.AUt AVi.A@t Axi.A`2t Aجsj.A!u Ak k.AXu A`GKk.Au Ack.Aדu Af~k.Au AIk.Aՠu A\k.A`u Ak.Au A@Ol.Au A8/l.A zu A Dl-A AA8jr/ARf,:s AS/3     )4]@)/A@`vzA((/AyAݕ'/A@QyA%/A uAh"-%/A AgxA>$/A@.|A(#/AA;"/A@AP v"/AྱAhp"/AAp7"/A5A` !/A^\Aa!/A cA^!/A@Ah#/A_^A&/A΋AH(/AnAx>*/APAq+/AA./AǏA//A`A+1/AqA2/A_AiG3/A@yAQ3/ArA864/A@` Aȱ 5/A=A}5/AA6/A`նAH{A6/A嶶AHa5/AIAذ5/A`JA5/A渨A5/AKAh4/AǕAh3/A A(\3/AAw]2/AoA&2/A+A0/A@OA`r//AAT./AOA['-/A`+A&,/A+A@+/AA+/A |A@)/A@`vzAH.$/A7AP7$/A AV #/A #AH.$/A7A:.AIAA83.A@A .Az@A .A@A( .A[@Af.A@A33.A`k@ApN.Af@Ag.Ap@A#.A@t@A.A^@A.Aa@Af̷.A[@A.A @AV.AAAf!.Az8AAp-.ApSAA`<.A_AAQ.AkAA`h.A`fjAAp=w.A ^AA.AFAAz.ABAA.ADAA.A]YAA`͸.AaAA`׸.A dAA`ݸ.A [AA8.ApIAA.A1AAأ.A`)AA`a.AzAA`.A@A.AAAp.A AA.A+AA.A /AA83.A]#AA%.AAA#1.A@A=.A@AD.A@Ap8K.A@@AO.A@g@AXS.A @ABW.A@AY.Ap@ApY.A @AU.A@@A(P.AG}@AM.ARb@AN.A[C@AVR.A`@AY.A?Aa.A ?AHak.A`)?Au.A q?Ap~.A@?A慹.A෶?A8.A@?A(\.Az?A`.A?A`.A@Aœ.A`@A.A?A@W.A?A.A@ @Aɹ.A@A0.ٹ.A`?A0.A@?Ah.A?A R .A ?A`.AR|?A8.A`fd?A.Az2?AH.A?A`a.A3>AX#.AH>AH\*.A=>AP87.Aj>A;.AU>AuD.Ap>A[.A`̔=A].A3=A#[.A@p=AP8X.A m=AS.Apo=Ap=@.A@G=AL=.A \=A<.A@=A R=.A=Ap?.AGg=AA.A=R=AP8D.A \1=AC.A=ALE.A<ABI.AQ<A{c.A o<Ah.A][<Ap=o.AGM<A8|.A <<A`a.A 3<Ap.A 2<A8.A ?<Ap=.AE<A֑.AK<Ap.A@N<Au.AI<A#.Az*<A83ƺ.A[;A83Ժ.A@g;Akܺ.A;A`.A3y;AQ.ARl;A.A3W;A.AF;A8.Ap+;A.A` ;AB.A:A .A:Ap0.A:AzA.A\:A`^.AR:A؁p.A9A@.A ¯9A8.A9AG.Aj9Au.AC9AHλ.A=9Aٻ.A8A`.A8A.A8A8.Ap8A.Aw8A@.A o8A` .A=Z8A.AJ8AQ.A 58A&.AR8Ak0.A7A8.A7A`?.A7AuF.A=7AN.As7AV.AL7A`a.A3!7A0i.A=7Ap .A 6A0.A 6A.AGm6A R.A@F6A R.Ap#6AX ü.A3 6A)м.AR5AU޼.A5A.Az5A .Azz5A(\.A@H5A+.A3+5Ap=.A@4A@H.A4AU.A@ 4AQ`.A`)4A@g.ARl4A`at.A O4AP8.A=84Ap .A 4AǙ.Ap 4A0..A`3A0..A3A.A3AL.A@3A .A@3A@$.AG 3A-.A 2Af7.Az2A?.A]2AG.A=2AQ.A@`2A[.AD2Af.A )2Aus.A 2AX.A=1Ap둾.A1A`a.A`f1AH.Az1A0.A=1Aھ.A]1Az.A1Aأ.A1Az.A 1A؞.A 1Az.A 1A.AGw1A.AZ1A.A 51A.A`)1A.A0A+.A@L0A@1+.A:0A8g+.A(0Ap+,.A0Az-.AH0Ap9/.A@/Aؒ0.A`B/A81.A;/A`2.A/A72.A4m/A2.A@r4/A3.A.A5.A=.AH6.A@.A8.AM.AX9.At.A,<.A -A>.A -AGB.A [-A@F.Am-A0J.A`;,AP^O.A},AT.A@>_,AX Z.A !,A_.A+A@e.A+Al.A[+Ar.A+A`z.A*AX.A*AH݈.A`*AH.A &*A𒘿.A@y)A8.A)A誩.A+)Ap.ARJ)Aп.A=(AԿ.A$(Anٿ.A#(AN޿.AMn(Av.A\(A.AiL(A.A=(Ak.A`0(A.A((Ap+.A`#(Ap.A` (A\.A (A .A(AXy .A#(A".A%(A.A`&(A .A%(A@.A"(AH.A@(A@.A@y(A.A3(A.A (A8.A(AP.A(A.A(Ap.A(AQ.A(A{ .AR(AP .A'A.A 'A.A'A.A 'Ah.A'A.A'A.A'AM.AX'A.A A'A.A'Ay.A p'AX[ .A@Z'A(.A@`E'AD.A1'AB.A'A>.AB&Ap A.A#0&A@dD.A&AH.A@5&Ax)L.A0%AP.A%A)U.A%A_X.A@%AS[.A%AP].A@%A>`.A@%Ab.A%Aȃc.A-%A0Ye.A%APg.Az%ApAj.Aq%AGm.A@b%A`p.AU%A Rt.AJ%Any.As9%A~.A`m*%A|.A%A_.A@C%As.A`B%Ah.A@ $APڛ.A|$AƠ.A`m$Ap.A$Aҩ.A$AP.A`$A0.A$A .A`b$A.A}$A.Ag$A.AR$A8.Ap#A@.A@M#A .A`v#A.A"Ai.A@"A.Az"AP.AjR"A".A@>+"A`a.A "At.A?!A( .A*`!A#!.A !A'.Am Aj..A Aذ5.A A _=.A@l A`rE.A cH AM.A3% AxhS.Aa A }X.A?Ap].AAJa.A uAd.A@ Am.A@^Aw.A@+AH.A`AP.A A.A^A.A`A.A֍A0.A`uAxv.AK_Ah.AJAh.A`f8A.A .A }.A<#Ap.AA0.A A8.AGA.ARAxd .AଌA>.Ah{AxX.AkA.A?]A4$.AvPA*.AGEAF.AA@J.A APMM.AAhP.A 8A}R.AA8T.A@Aa.AVAd.A9AHg.A Ak.A`KAfp.AA8s.A@WAw.A@.AH{.A@AG.AA.AgA.A A@.AA.AA.A`BA+.A AC.A-A3.A@Ah.AwA.A 8dA.AOAp.A8A.A#A`.AA`.AA.A=A .A)AP.A`KAP.A@A.A@A`.AA0..A`|AW.AAx6.AAH.A6A!.A`A0$.AQA&.AA).A`ܶA+.AA,.A Aw..AoA0.ANAo3.Ad.A@6.AA:.A@A`=.AGAh^@.AoA(hB.AǨA(C.A |AE.A }AE.A@gAE.ApQAE.A5BAD.A@2AD.A/#AE.AAHF.A-AhH.A3AJ.A@>A`Q.AA8W.A3A0].A`pA`ab.AEAPn.A AQ.A'A.AQAz.A@WA(ܳ.A`fA}.A@A(z.A@GA.AHuA c.A9AXW-.A+A0@.A=AD.A]AH H.Ao2AqL.A@ALQ.AA\T.A@Ap W.A(AMY.AତA&[.AA`\.AyAHg.A A8Zj.A A(Jm.A A(p.Aq Ast.A5X AȤx.Ah@ AP8}.A`) A.A6 A.AV Aأ.A3y A`.A@NP A0.A( Aȫ.A? A.AH A.A As.A`K A.A@. A.A`br Apl.AZ Ap8.A@D A`.A > A.A`: A(.A7 A.A u6 Aȇ.A6 Av.A8 AR.A`< A.A A A.A`H A0 .A@PM A .A P A.A@P A`.A0O Ah.AK A.AF A@u.A: Ap.A, A%#.A A%.A | A=(.A`K A*.A= A_1.A AX9.A ABA.A3i A ^E.AT AI.A \A AN.A`/ A#T.A A:X.Ai A[.A AQ_.A AGb.A3 A`e.A| A0h.A& Ak.AF[ Am.A4 A`o.A= Af.AzAQ.AAp.A`mApJ.AwA`.AcA88.A 1>AP.AA0.A@jAh.AHAP.A9&A.A`TA.AAH.A A.A@ A`.A=tA.A-WA.A 8;A.A| A.AA.A3A.A@A.AEApl.A`A.Aq~A`q.A;oA.A_A .AMAP .A@ <A..A@C)A.AA8c.AA.AA{.A AL.A@ gAr.AKA6.A0A .A@A .AA@.AA.A@gA .AA[.A`sAN.AOA.A@ ,Au.AAp=.A`)~A .ATnAh].A^A.AOA@p.A`AAȥ.A`]4AB.A`f(AD.A 8AP.AXAp.A A.A4AX.A@EA.ARA.A@AX.AA.A?gA8.AKA.A`/A.A@A.AhAؼ.ARA.A`mlA.A`4A.AtA.AzA.A A.AMA.A^A.A|AH.AAW.AA.AA(.A{A.A:A.A=.A.AA.AAT.A As.AǞAP'.AAv.ADAg.A|A8.AsA.AMA@$.Ao%Aȵ.AA.AA.AQA8.A@`~AX.ARA t.A Z<A@.A%A(.A A.AvA.A`dA.A@Ah.AzAxv.A@A.AߞAh.A JA.AA.A̜AR.AA0.A@A@`.A tA).AgAu.AYA .AA.AAX.AyA-.A`"AHr.A OA .A6A83.A@ Ah.A@NA.AmA8.AWAu.AdDAh.A_1A.AA`.A A.A`9Ap.AAxd.AA.A0AБ.A`Ah.AjvA .A WA.A?8AP.AAk.A`A_.A@AǼ.AAȤ.AA.AV`A.A9A.A[A@.AA(.AA.A`̠A( .ApA.A`DAAu.A@AT.AsA.AzAhb.AA@.A VAذ.A%Af.AA.AAH.A A8.A^A.A +AP.A oAX9.ARA8.A@C6A.ALAp.AAp=.AA.AA(K.AnA.A3A9.A`Ah.A9A .A}AP8.A?A.AAE.A`6Aǭ.A A0.A<AX.A?gAD.A`@A8.AA`.AAV.A@>A.A`A`.A`9Am.AjAذ.ALA(z.A.AǬ.AA.A`"AHܶ.AoA Ҽ.AaAP.A`LAБ.A8A.A%A.AA.A3AD.A@EA.AA.AA.AhAP.A@A.A[A(.AA.AA .AA`2.A AL1.AfApkD.A`A pW.A`)AZ.A \AX\.AAM^.AVA_.A@NA0`.A;A8Va.AfA؞a.A &Aha.AӸA`.A ɱA`.A \AT.ABnAB.A2A`4.AA.A AX.AA =.AphA7.A@4Aj.AAu.AAM.A0A]8.A ҞA3.A A81.A&AJ/.AokAs+.A SA(.A@iIA +.A@&A.A@AO.AAȥ.A`dxAí.A@^A.A@>A .AA ^.A`fAho.AiA4.A//A(- .AzA@F.A@iAH.AfaA(.A3A.A Am.AF~AL.A@^AP&@.ALA2.Am=A[%.A`m2A.A^.Ae.A &Ax:.A@'A.A1AP.A4AI.A`9A8.A>APxg.A4AU.AE;AȩC.AOA0j9.AzbA8V3.A spA,.A (Ah%.A A.A A.A Ab .ARAf.A<Ah.AA@.A@y A .A@%mA(w.AaxAr.A 8{A@o.AxAlm.AuA(j.A@gAjf.A`)Ac.AA kZ.AAp}V.A4A =X.AAZ.A@`AX.AAfS.A`A)=.A Aq+.A Ah.A@YA.ALyA .A A.AgA.AĿA`6.AfA0.AzA(>.A?A.A%A.A@,A 0.A0A.A`2A.AGAh.AdAXһ.A jA.A cAB.A ¸A/.AA(.A@pAHn.AеA`.A@A@W.A/AFP.A`=APxH.A]A;.AA-.A@>AO.A sA.A`Ax.AfAF.AjASֿ.AAp8ο.AAʿ.AAɿ.AAǿ.AApɿ.Ap1A83Ϳ.A@NAp8п.A=fApͿ.A qAp8ʿ.A \A.A qA.AHA8.A qAѡ.AAš.AA.A`)6Ak.AUAX.A wAז.AA.AA𣔿.ARAX .A`A#.A:A~.A ZAX}.A Ap=.AAp.AA(܏.A AX.A&A.A=AP.A`)RA(ܮ.AQdA뱿.AA8.A=A̭.A3A.A`A板.AAX.A AP.AR"Aa.AAh3Y.AGAS.AAQ.AAM.A`)AWG.AA?.A"A 5.A`f6A-.A@ KAأ&.A`lA0".A A!.AzA#.AͪAh'.AA`*.A A),.A@A؞+.A@2AG*.A@A#.A A]A0w#.A]A( .A@yjA .AuA.A A(\.AAأ .A׮AX4.AA.AQAP8.A'AЮ.Ap8A.A`IA.A&\A@.AnA@.AAf.A A0e.AAx.AA.A@A.AFA.A^AD.A ZwA.A ۏAT.AA.A`A.ApA޾.AQAھ.A@A?׾.A#A5Ծ.AN?AѾ.A[A(\Ͼ.AxAž.A@A#ž.AAHiƾ.AA?Ǿ.AAǾ.A -A`Ǿ.A@AǾ.A$TA*ƾ.A gAľ.AyAþ.AgA.A`A@>.AլAL.AA.A@A84.AA8.A`TA0v.A AЦ.A!A =.AX9Ap=.AQA8".A A.AXAΊ.A A.A!Au.AAg.AA(yZ.A>AXM.AGAvJ.AA`F.A ApB.A JAh>.AyA9.A0A4.A`JAQ/.AcA^*.A0~A%.A AX!.AA1.A lA.A@ A .AN1A' .A@nA.A A.A A.A@ !A.ATAٽ.AyA؎н.A<Aǽ.A@AP޾.A-A.A@>fA0f.ApAxˡ.A`AŖ.A KA@W.AgA.AA@z.AAYu.A@(Ao.A`KKAi.A`]mAc.A?A\.AݭAT.A&ABM.A@ AC@.A)A2.AiA@%.AA.AA.A@ A`6.AnA@.AA ռ.A A Rż.A^Ax鵼.A+A.A6Aݕ.A`gA<.A`A6t.AAb.A`)dAYU.AyADH.AA;.A9FAp=/.AA.AAT.A` A.A2A0*һ.AAH .A% AB.A`̠ A .Ak A0e.A A`S.A AH܊.A! Aˇ.A2 AS.A 8B A0{.A^P AM|.A\ Aw.Ag A s.Ap Ah3n.Aw AK.A AC.A A;.Aj A4.A. A+.AL A 4#.Ag A`a.A`f A/.A@ؙ A.A ¯ AH.A` AD.A@ A=.A` A( .A \ A.A \w AXʙ.A3 A/.AɎ AՎ.A A@Ɖ.A A0.AG A.A A(|.A Ax.AR A`h.A AP8T.A*AQ.A`f@AA``.AQAf.AfA8.AA.A \AR.AAX$.AA`.A@A.A  A.A&A).A |,Ax.A@,9A𒟹.A@CGA@Ƣ.AVA0.A#gA8.A@xA(\.A`A`X.AɑA.AAX[.AA`r.AίAN.A;A.A3A.AAiy.AK Ak.A`6A;i.A c<Af.A`@A c.A`CAp,a.A DA`^.A lDA[.A`fBAX.A>AZV.A ,A0HS.AAO.A A`K.AAG.A)ABC.ARA@.A Ah>.AAX<.A@A9.AA87.AAp06.A`A4.AA(3.A`T A`3.AAh2.AcA2.A(A83.A1A5.AXAP6.AzAH7.A@gA6.A/A>5.AjAU3.AA@0.AVA@..AA%+.A AP'.A@ Aa.A c$A.A;AP .A@PA.AB^A.A`dmAX.A}A.AAG.AjA@S.A Aݸ.AA.AGA9.A@qA .A`A`.AAko.ApYAj.AnAe.A@PAR`.A4APZ.AAT.AAN.AANH.A@WAA.A3AP<.A A8.A ZA3.AAXE/.A cAG+.AA83.ApiA: .A`̏A.AA).AA.A`A`.ANA(ڷ.A)A`Ϸ.A qAA.A@5|Aʯ.AݸAk.A \A.AAh.A`%A.A :AH.A`NAP8.AGaA~.A@ynA3w.A/}Ahp.AaAj.A`Ac.AA].AUA)X.AAТS.AA(N.AAI.A ZA=D.A$A>.A@53A08.A`f@A5.AyFAC2.A@ KA0..A NAf+.A lOAX'.A-OAy$.ANMA!.AIA(.A@DA.A@N>A.A`f6A.A@51A`.Ay-Ap|.A`K+A .A*A+ .A+A8.A@W.AH!.A |2Ap.A8A .A>A.AFAh.AOA؎.AYA(.AdA.AxnAP.A3xA.AA.AUA.AAA$.A@Au.AA.A@A.AAX.A@PAy.A|A@5.A sAL.AA8t߶.AA߶.AA:߶.A@A߶.A A.AA.A`AP.AA.A`f A.A`A.AQ A8.A*A.A@E5AX.A?AȘ.A4HAH.ARPAͶ.AÓA<̶.AA,ʶ.AAXǶ.A AHŶ.AA¶.A@A0.A`A(ۼ.AA.AAH.AA.A A0j.A Ah.AA.A@AP.A/A.A@yA`a.A`fA(l.A A .AA`%.A/Aߞ.AAA4.A6RA-.A \aAH\.A ‹Aނ.AܖAX.AРA}.A}Ay.A̰A@`v.AࣶApr.A`A#o.AAPwl.A}Ai.A`vAf.A`AFd.AսAPa.ATAhs_.A`2Ap].AA@dW.A@ AQ.AĐAkJ.A@AhQG.A@օA5D.A A'A.AA06>.AA p;.AA8.AęA6.A?A4.AA`3.A`A1.AA=%.AAx~.A+LA].AHyA .AA8.AA`.AA`O.A AP.A@.Aݵ.A)A:ֵ.A`o=Aε.A sOA8ǵ.A`A.A fA^.AkA㶵.AoAx\.AMpAӫ.AoAU.A`lAP꠵.ANhA.AaAhѓ.AWA).AOLAP.A ?A^}.AE0AXFv.AAio.A@ Ah.AAf.A`mA d.AAFa.AvAX^.A`AR[.AAFX.AA0HU.A`;AiR.AUAO.A@,AQM.AAPL.A@AL.A` AxJ.A@AGI.AqAiG.AA RE.A@gAi?.AA839.A3Ah7.A ( A06.A?(A6.A 0Ax6.A`9A R7.ARBA7.A@A36.A@>A4.AA02.A%A0.AWA-.A Af*.AA@&.A=A@$.A 8A؎!.AA.A@PA_.A 8A.A8,A.A 8CA0..A YAu .A ZlAj.A~A.AAo.A?A(.AAxi.AൺA.A JA.A`]A.A@A޴.A@Aٴ.AtA8մ.AAȘд.A3Ab̴.A  A}ȴ.A`f AĴ.A#& A.A 7 A̭.A@ Ap.At)!AӇ.A!ABt.A !A0q.AE"Ahn.A@P"Ak.A "Ah.A@$"Aad.A-"Ao`.A4"A@S\.A:"A8X.A >"AhS.A6A"AiO.A@A"AK.A@@"AH.A;"A E.A8"AA.A@>7"AH>.A7"A;.A9"AБ8.AH="A`5.A`B"A3.A`I"A0.AQ"A9-.Af"A 0*.AR{"A'.AV"A@%.A "A@_#.AN"A!.A "A.A"A.A #A(.A@%#A.A@>#Az.A@V#A.An#A .A_#A.A#APw.A#Aj.A@$A.A9M$A@ӳ.A$A`̳.A $A0YƳ.A $A}.A$AU.A$AP氳.A$A}.A$AX۩.A@$A.A%AH .A` %A.A%Ah.A`]%A.A@%AB.AR%AX,.A A%A2.A %A0f.A%Aم.A&%A(.A /%A.A9%A=.AD%Ah3.ARP%A#|.A%APYz.A-%Ax.A&A0*u.A "&Aq.A;&Am.AR&A(>l.Ah&Aj.Ax|&Ag.A 1&AȲd.A`&Appa.A`&A].A&AY.A`o&A U.A&AP8Q.A&A'Ax.AM'AG.A]'A.A (d'A`.A k'A0.At'AQ.A~'A%.A'A^ .A;'A .A'A .AB'AXu .A'AL.A'A((.Am'AV.A''A.A(A.Az(A@ #.A@ (AS&.A,(A /).A:(AP+.A H(Ap-.AW(A/.Ag(A`e1.Av(A02.Af(Ag3.A`D(A3.A_(A 3.A(A+3.A@(Ap82.AR(A$0.A`)A-.A q[)Ap|*.A`)A&.A@!)A@".AH*AЮ.A@[*Ah.A Q*A.As*A.A$+A(.A@f+A.A+A8E.A+A`a.A!,AЮ.A B,Ax.A`Kb,A.A4,A.A෡,AЄ.A,A.A3,A5.A 0-A\.A`;-Aܲ.A3-Aղ.A 1-Alϲ.A A$.A@ɲ.ANO.A8IJ.A@G{.Ad.AV.AԵ.AT.A׭.A3 /A .A//A@ࣲ.AQ/AW.At/AϚ.A/A×.AX/A8.A/A83.Az/AB~.AR0AH|.A0A؁z.A 0A x.A ~0Anu.A0Ap}r.A c0AUo.A0Al.Aa0AXh.Ap0AHe.A0Apa.Az0Aȱ].A`0AhY.A`0ARV.A0AR.A@50AO.A00AL.A@0AJ.A`շ0A@H.Aٸ0AF.Aॻ0AhC.A 0A0B.A0A @.A c0A?.A0Au<.A`0A`9.A (1Av7.A s1A`5.A O-1A3.A@D1AP+2.A^1A0.Aqy1AP/.A]1Ac/.Ax1A(\/.A@1A..A`1AX-.A@&2AQ,.AT2AY(.AO2A$.A@2A(!.A2A.A S3A.A+3A.ARV3AH.As3A3.A@3A9.A@3A.A Q3A .A`)3A.A@3A.A@ 4A.A(4Ax.AF4A .Axd4A(!.AH4AJ1.A5ABA.Aઍ5AQ.AR6AW.A?6A(>^.Al6Ae.A6A(Kl.A6As.A@i6A{.A7A'.A:7Aό.A`7A̕.Ap7A(.A`7A˵.A 8A0Ų.AP8AH.Բ.Ap8A(-޲.A8AP.A`8AK.A@'9Af.AT9A8&.AR9A0<.AO:AP8y.A;A׸.A ;A).A(~<A87.A3=A#z.A`=A.A M>A0..A`>Aأ.AY?A R!.A`)?A R%.A ?Ap%.A?A".A \?Af.A"@AH\.AGI@A.A[[@AG .A@gz@AG,.Ap@AH5.A @AG.AÍ@AT.Az|@A8`.A p@Api.Ape@Ao.ApQ@AX x.A`)6@A`a.A$@A.A`)@A.A(@A@Wѵ.A[@A.A`g@A.A@>l@A.A i@Az6.Az`@A RX.Ab@Ac.Ap@Am.A̒@Aft.A@Au.Az@A@w.AR AA|.AG+AAp.AR.AA.A'AA`a.A=AA’.A3!AA.A*AA`᝶.A6AAB.A>AAL.A ?AAX.A .AA.AAAf.A`)AAL¶.A@A(\ж.A@A@.A]AA83.A +AA`.AG]AA0.#.A(AA,.AAAP83.ApAAp:.A~AAQ=.AnAA=.A=ZAA:.AIAA#/A9:A@%!/A@9Ag"/A@>dAY"/A@,~YA#/A9:AɜI/A<(A`GI/Aa&A`GH/A`o7'AF/A"Ah9F/A AjE/Af AD/A@A`O5D/A ZtAJA/A 'AXI@/AVAU\?/AR|A>/ARAp<8/APA8/A@ %A`6/A,AmX5/A@3A93/At;A 2/AGDA1/A}JA2/A RA5/A`_Ap5/AaA؞6/A bAȋ6/A`dcA8y7/A,dA8/A aA.7;/A ]AlfD/A[AXD/A@VYAE/AVAF/A`[VAGH/A eQA@1HI/A`MAppJ/AsHAK/A)CAP+8M/A|h9Ah{K/A4AJJ/A@,AɜI/A<(A`)/A@WqA-(/AXiAh]%'/AjA'/AzsA5)/A^uA`)/A@WqA`OU/A}AU/A3}AV/AХ}AuV/A`}AV/Aqu}ApV/AwI}AdV/A/}ARV/A`}AV/A}A("V/A@`}AV/A|AHLV/A ~|AV/A 8|AV/A|A#V/A|A(V/An|A/V/A` c|A$6V/A@h|A؟=V/A`ok|A >V/A,O|A@FDV/A7|A/GV/A Q|APGV/A {A0nHV/A{AHIV/A@5{APV/A{AXV/A{A0aV/A{ApZfV/A{AhnhV/A`mk{AhV/A0E{A kmV/A9{AW/AyA4W/A@yA-W/AyA)W/A$OyA#W/AR yA W/AxAW/AxAAW/A`TxAW/A|xAV/AhxAV/Ao\xAV/A@>xA(V/AxAHV/A ZwApV/A`mwAV/AڷwApV/A@wA٨V/A@EwAV/AwA̜V/A`bkwAV/A(wAMV/ATvAxV/A qvA( V/A`;vA`rV/A@i\vAV/A`չvA0LV/A vAV/A`7wAV/A@ swA8s W/A@ swA.W/AqwAX+W/AwA(>W/AwAH]TW/AwA(bW/AwA` dW/AxA NXW/A@y_xAUVW/A8xA/`W/A sxAPrW/AyAtW/A@_yAtW/AyAyW/AyAhW/AHyAP^W/A LzA(OW/AzARW/AjzAeW/A T{A88W/A`ܢ{AW/A@{A W/A@(|A}W/A /T|Ax W/A;|AW/A|AW/A}AW/AAX}AX/A@N}A X/A`]}AhoX/Aw}ATX/As}A`2&X/A`m}A3X/Ao}A7X/AD}APCX/A@r }ATX/A|A;fX/A`|A(uX/A@,|AȃX/A;|A|X/A`k|ACX/A`\|AX/A`\|AX/A{|AOX/Aߪ|A(X/A`)|AY/A%}ApY/AAX}Aг Y/A@N}A0Y/A }APoY/A}A7Y/A,}ADY/A }AOY/A`]}A8VY/Ah}AUhY/A@#`}AxwY/AAX}A߆Y/A QT}AY/A@}A`GY/A}AX Y/A@G|AY/A |AhY/A c }AY/A|AHnY/A@,|ALY/Ag|A _Y/A<|A؟Y/A<|AhY/A@`|AY/A Z|AtY/A |AXY/A 8|A(JkY/A |AJY/A|A5Y/A}AH\$Y/A@G|A(`#Y/A;|AvY/A]H|Ak Y/A_|APoY/AD{AvY/At{{AY/Ag{AY/A {AX/A@{AjX/Ac{A@X/A@{AX/A@{AX/Ao{AX/A;H{A`X/A{AX/AzA0X/A@zA(X/A`zAiX/A@ؚzA`ЁX/AzAHvX/AozA|X/A,zAHX/A zANX/AzAX/AT8zA؟X/A@(zA@X/AEpAKY/A VpAxY/A`pAY/AQpApY/A@!7qA(~Y/A@qA$uY/AtqAUhY/A CrA(QY/A@rA oA؎PX/AoAppXX/A QoAQ`X/A`oAppXX/A`moAXNX/ApAOX/A6pAT/A yAl5T/AzA'T/A`d4zAT/A@czA` T/A3zA` T/A^zA/T/A`zAUS/A {AhsS/AP{AS/At{{AS/A{AS/A S{A"S/AF{A0*S/A[{A5S/A@({AjS/AID{AS/A{A@ƙS/A{ApS/A@{A S/A |A0~S/A4|A0~S/A@`|A!S/A J|A!S/A@W|A(-S/A}AS/A(}AS/AAX}AS/A/}A`%S/A}A S/A}AšS/AW~AS/A@P~AQS/AH~AXtS/A@,m~AGS/A X~AS/A@g8~A`6S/A`m ~AS/As}A@qS/A@%}AS/A`}AS/Al}A`S/AÒ}A0;S/A }ATS/A}AXS/A J~A@T/A8~AXyT/A~AH;%T/A ~A5T/A~AAT/A@P}AQT/A0}Ah;`T/A }ApT/A}A(`T/A}AȲT/A@P}AT/A*~AxT/A ~AT/A8~AT/A,~A'T/AD>~Ap=U/A`E~A U/A`<~Ap%U/A~A8'U/A@ }Ab0U/A }A2U/A*~A>6U/Ax5~A`=U/A@`d~AFU/A`)~A]U/A 8~AlU/A~AzwU/A 1~A(,U/A`~AU/AAU/A A@OU/A A߶U/A~A@WU/A`b~AX,U/A`"~AU/A~AU/A ~APjU/A@,m~A`OU/A`I~A`OU/A~A`OU/A}A~R/A OAR/A`yA^R/A`zAR/AAA`R/A㍄A0R/A֐A0R/A֐A>R/Ai~AR/A?]A(R/A`KLAR/A@r5ADR/AA8R/AA(R/A@A`R/A^փAR/AAAR/A AhR/A`A(R/AEA(R/A A S/A@.AS/A 1AsS/A q'A%S/AA>)S/A@G A*S/A A04S/A كA:S/AăACAS/A@AX9KS/A |AlSS/AzAx]S/A;@A8eS/AA3mS/AׂAXtwS/A`AXzS/A^A8bzS/AAPxS/AAӀS/A/ցAȃS/AA֌S/A_AqS/AZ:AȃS/A$ AS/AՀAxS/AA@S/A@莀AS/A vA pS/A3zAȔcS/A@yA^]S/A&AX(WS/AAIS/A@i0Ah"9S/A#Ah"9S/AA4;S/A@yA-S/A`TAX S/AAx(R/AYAh"R/A |AR/AĀAR/AA0ԺR/A# Ax(R/A4AR/ArAOR/AAFR/A@́AR/A@́AR/A сA`R/AA`R/AAR/A@ Ah R/AN/AR/AIA(R/AWAXlR/ApA!R/A@,~AXdR/Ao}AxR/A`9{A8VR/A@EjA R/AyAHR/AA0UR/AXAxR/A@ւAR/A A~R/AAyR/AA(oR/A]AgR/A@$AHngR/A2AiR/A @AmR/AZA`2qR/AuAPrR/AAHmR/A A$mR/AjŃA nR/A׃AX9sR/AڃAxR/A`ՃA|R/A6߃A(:R/A 8A,R/AAR/A@pA}yR/A 1AsR/A3ApR/AXIAZoR/A]APhR/A wAhR/AAnnR/A^AuR/A@yAR/A AH܇R/A@A'R/A`A~R/A OA?PR/A AJR/A 8 ApCR/AZA?R/A@rىApCR/AƉAnIR/A ЉANR/AANOR/A ۣA(\VR/A ۣAP<\R/AQAbR/A AhR/AωAspR/A9͉AsR/AMA5tR/A@A{R/A@,AX|R/AyAX|R/AiAX|R/A`SA`R/AMNA@R/A@WAxdžR/A@XAR/AGAR/A6A=R/A'1AxR/A2AHR/A`#AhљR/AAx:R/A4AXhR/AA(,R/A=AR/AƈAx R/A`dAR/A A88R/ARAxGR/AAxuR/AuA(R/A`hAHR/AgApR/AgAR/A ~Ah^R/A܈A{R/A A5tR/AAlR/A əA(9hR/A㧈A_R/AqAPjYR/A@AWR/AA(\VR/A QЈAMSR/AA"NR/A4APGR/AA``FR/A)ALR/A`(A RR/A60A"NR/A QA``FR/A UA':R/AYAPI,R/A`rA'R/A`rAY'R/A bAi&R/AIA!R/A;Ax>R/A 3A 0R/A@yA"R/AA|R/A-A@R/AAR/AxAR/A痈A"R/AAhnR/AA R/AAXR/AaAHQ/A AhQ/A-tAQ/A ~AhQ/A५A(>Q/A`ՈA-Q/AqAHQ/A@yA@Q/AU.AsQ/AHAR/AW]AdQ/A}zA Q/A 䃉AHQ/AyAXQ/A clAQ/A^|A8xQ/AfA8xQ/AkAHKQ/AщA(Q/A@yA/Q/A@ AQ/AAQ/A0AЀQ/A@AQ/AԉAIQ/AA8Q/AAQ/A@yA:Q/AwAQ/AtA.Q/AtA֩Q/AA0ءQ/AVAPQ/AAխQ/A`AgQ/A@AqQ/A sGAQ/AmArR/AA@R/AA(R/A$A5=R/A`uAMR/A@EJA@SR/A/AMSR/AA?PR/A A )/Ag@Ay/&/A`wAN&/A3AXyG(/AA )/Ag@A gp/A|ØA@}m/AAl/A ⬚A0p/AyuA gp/A|ØAh0$/AѐA!/A{A@+!/AA@ /AA(/A`dЮAh/A#~A/A@``A1!/A A!/A;uAh0$/AѐAM/A@$AM/A-A(\M/AAM/AghA@dN/A`#A88&N/A@A7N/AAHN/A ZAx6TN/AA gN/AAxwN/AmAN/A;{AΜN/A AN/A;{A9N/ANApN/A`9)AN/AApN/A@A@1N/A`AN/A AX9N/AXA O/A`AO/A`A.O/AA:O/AA^FO/AAX[KO/ABAHWO/A@A lO/A &AXV~O/A`AOO/AAO/A ]AtO/At!AfeO/AA-:O/A״A@S,O/AA`$O/AbA0]O/A AO/AAx'O/A` Ax'O/AlA+O/A*3A-:O/A0AEO/A%AXOO/AAQZO/A@AX9bO/A AhhO/A3EAZrO/AAHwO/A@Ax{O/AAHO/AwAdO/AHAO/AྻA@O/ARA[O/A`AO/AqAO/A)_AoO/A GAO/A 6A`6O/A`'AO/A@W!A(O/AAP@O/A@ A O/A@yA O/A AZO/A?AHO/A cuAvO/AVAFlO/AH=AbO/A@A(ZO/A@GAUO/A |ApXO/AėAdO/ABAgO/A[A`%nO/A2;AsO/A ATvO/A@A {O/A lAdO/A`AO/AtAO/AAO/AAO/A`AO/A@A@O/AAO/AAp0O/AŢAHO/AFpAO/A@JA[O/A`AHO/A}A|O/A`A@O/A`DAXO/AWA`O/A`*A"O/A`dAO/AAO/A`mAh3O/AAO/A)kA@O/A&@AO/AFApJO/AIA=O/AZAO/AfA |O/AtAx~O/A`AtO/A cAXWkO/A@kAeO/A`PA2fO/A7AأjO/A`DA@sO/A@GAyO/A@rA@}O/AAȁO/AAO/A@؅AO/AnAWO/A'VAWO/A4*AO/AAO/A`ADzO/A AHgO/AHA$UO/AAhCO/A@A 5O/A@A+O/A@AA(O/AZAг'O/A`.A"O/AqA &O/AAp,O/A AV3O/AIiA7O/A SA4O/A@CA8O/AAȩO/A@AO/AnA0O/ARjAPO/A0sAx O/A`oAeO/AவA8'O/A&ټAHO/A`;AO/A`;AhQO/A$ܼAhO/AҼAO/AѬA=O/A@AqO/AguAO/A=_AO/A`=AO/A AO/A$AO/AAO/A A(mO/Ay.APO/A`mBAO/A *AXhO/A$AO/AջA O/AA(yO/AઌApNkO/AzAPdO/A@ SAMO/A PA o9O/AV`AA(O/AvAPjO/A&}A O/ArAN/ArAN/AfAH)N/A?AN/AA(N/A=ACN/AA(~N/A@#TA`N/A`AXtN/AA@N/AzA EN/A`bA7N/ADtA@N/AݎAhN/AAeN/A$ϹAN/A`AN/AR7AN/AMjA7N/AAȇN/A`A8bN/A?~A@N/A@#TAwO/A_#Ap O/AAO/A qܹAСO/A@AȂO/AaA O/A rA(O/A&JAO/A/AA(O/A8AV3O/AA>O/A ɸAxGO/A飸ARO/A`hAfO/AIAGuO/AaBAȉO/A Z#ALO/AA8'O/A'APH/AAH/AAСH/A cAH/AA8#H/A@AH/A AAH/ADKAH/AtAH/AA'H/A-AkoH/AO?AUH/A+A _0H/A A H/A VApH/AA88G/AnA(G/A=A`G/AA߿G/A`A>G/AAG/AFAG/A;ɡAXG/A ⁡A`ӍG/A/AG/A ͠A>G/AHzAPG/ANAG/A AG/A`)7A G/A@yAG/AAG/AzAYG/A@NA`G/A`;AG/A`f'AxG/A ˑAxG/A`mAG/A@RAhoeG/AUAPZG/A'ŠAQG/AAHEG/A`vnA9G/A ApJ G/AOAHKF/A@bAmF/A`[3A(F/A ZAF/A`mAdF/A AF/A AF/A ApIfF/A AMF/AGA0PF/A0AXF/AAhs[F/A^AȤNF/AFA`3F/ABAp_F/ABAF/A@bAF/A@pA'E/A@WȟA8E/A`mApE/AA̟AE/A@pAXtE/AQA8gE/Aw՟AȥE/A`#AȥE/A9~A +E/AXvA@)E/Aj/A0-E/A A0E/A@pA0E/A&zA9E/A A@BE/A lATZE/A@5+AnE/A AE/AfܝAhE/A@AE/AJAE/A AxE/AAfE/A@qAXE/AmAF/AXRA0F/A`FA?F/A`6ASF/AA8'oF/ABAhF/A`6A/F/AAF/A`mțAF/AaApF/A A F/AŨAmF/A`YAF/A̙AG/A;ApF/A`JAiF/A`YApF/A@,Ah F/AmAFF/A&A`F/AȘA@F/A (}AEgF/AFuA=F/AFuAxF/A`̔AE/AUqA|E/A 8yA E/AAFE/A]AyE/AAoSE/A;6A AE/AX.A +E/A@QADE/AA`vE/A_A0D/AAPAyvD/A QrAjD/A@nA pED/A tAh+D/A@AAD/AMĪAp[C/AcATC/AXA|C/AsAYC/AA؎/C/AA C/AAB/A ѬA%B/A-DA5B/A@AB/A3A@B/A AC/AIAa8C/AA"LC/A` (A {C/AhAH|C/A8AHvC/A`MA(hrC/A)5AVC/AOɳA4C/A0ӳA C/A lpAбB/AAPkB/A`A`LB/A`A!B/A@ApB/AAA/Ah!AA/A AKA/A#'AwA/AǓArA/AAhA/A`EAa B/ARA RB/AA@B/A|A@BB/A?A B/A_A0A/A`A(,A/AFA0A/A_A^A/A@r.AHrA/AL@AA/AvA((A/A?AA/AUηA0A/A*AȂA/A?AݫA/Aj4AxA/A nAA/A*A0A/A?A A/AθA((A/A?AHzA/AA0qA/A A8iA/AUAeA/A?*A0X`A/A?*A0X`A/A SAp=jA/AjAmA/A uAؒrA/AAؒrA/AjA(hnA/AӹAp=jA/AApcA/A*"Ap]A/A 0APA/A@5uAQA/AjAQA/AͺA((OA/AAȲCA/A&A"BA/A3Ai7A/A\AR:A/A*A?A/A A0?A/A»A];A/AۻAr=A/AA88DA/AAp}>A/AA;A/A =A((6A/AiAxm0A/AA0X.A/A ӼA(/A/AA-A/A ;AM-A/A iA3A/AA.A/AAؒ'A/AjŽAR!A/AjA0H A/AA(h#A/A*[AA/A`5AMA/A?+A A/A@AA/A?]A A/A|AA/A?A8x@/AjA8x@/A ѽAxA/AA A/A`A8 A/A AA/A3AA/AQAxmA/AUrA A/AA@/A`zA@/AUA@/A*A@/A`ARA/A*A0A/A?AMA/A HAA/A?AȂA/AA(A/A?οAr A/A AA/AAB@/AA]@/AA@/Aj6A(@/AEA8@/A8A(@/AQAM@/AjA@/AAx@/A`A8@/AA@/A*A@/AUA @/AA»@/AAȂ@/AAݱ@/A #AТ@/A?,A@/A?EAؒ@/A?^A8@/AdA@/AAb@/A*Aؒ@/AA8@/A`A@/A'A8x@/AAp=@/AjA@/AA@/A?&Ax-@/A,AȂ@/AGA0@/A`A@/A*A(h@/A AB@/A`A0H@/AA(@/A*A0@/AA@/AUAp@/A?A0HA/AUA0A/AA0A/A A"A/AAp}%A/A(Ap}%A/A ^AxA/A?ABA/AA(A/AAA/A9A.A/AUgA88DA/A qARA/AjiAТgA/A?LA0sA/A`$AƒA/A?A0A/AjoAA/AU"A]A/AA(A/AAA/AUA A/AA8xA/A`AA/ATA0A/A*VArA/A*VAA/A*=Ap}B/A`ArB/A`AB/AA" B/AUGA8xB/AAxB/A Ab)B/A`Ar7B/A*ACB/A?Ap}QB/AA`B/A?A(hhB/A A(tB/A?dA]B/A %A0B/A?οAȲB/A AB/A_AТB/A`Ap=B/AھAp}B/AA0ȣB/AUrAp=B/A*UAp=B/A*#AxB/AAȂB/AɽAp}B/AjA0B/AjAB/A iAC/AYA0C/AYA((*C/ARA3C/A A3C/A A81C/AjA((*C/AUeA"C/A?A C/A7A8B/A AB/A*AxB/AAB/A uA cB/A^AB/A?PAB/A,A0B/AٺA B/A*Ax-B/AdA(hB/A2ARB/A? A0HC/A 뷹Ap=EC/A 뷹A0eC/AU¹A8zC/AKڹAx-C/AA0C/A AC/A*TAbC/AkA]C/AA0ȶC/AAC/A?AC/AiA(C/AU?AxC/Ac AC/A AC/A?ٹAxC/AjݹAMC/A ̹A8C/A߹A0C/AAC/A &"AȲC/A7A D/ACA]D/ARAD/AUqA(hD/AAxmD/AA-D/A`A;D/AA0H@D/A {ABLD/AwAxVD/A`yARZD/AՖAr]D/A*ABeD/AͺA(huD/AjAD/AկA0D/A ApD/AUAHܭD/A@A0D/AcAhD/A0AsD/AAND/A@ADD/A@A"D/AAE/AN,A@E/A$A }E/A`A#E/AmѹA *E/AmѹA0E/A$A26E/A`2A 9E/A4[A+@E/A/AtRE/AAkaE/AtuAlE/ALAXsE/A`2AˆE/AAH E/A 1A0E/AhA`qE/AhAE/AApyE/A[AxE/AAPME/AA@~E/A AE/A,(AhE/A@yBAXE/A DA 0F/A!A83 F/AAxF/A`)A]#F/AعAPZ(F/A@AȺ'F/A A&F/AA@AH;,F/A'hA1F/AwAJ/ASA`J/A HAxJ/A`1A0J/A:A誖J/AxA pJ/A"AJJ/A qVA8J/A ݼAXJ/ABJAJ/AAWJ/AAPJ/AA(J/A˾AkJ/A$A@RJ/ACA7J/A@>A5J/A@ ArAXJ/A@yAJ/A A(J/AVAiJ/AApJ/AAJ/AARJ/AU+AȥJ/AfiAJ/A`vAHaJ/AA J/A@,A1J/A@yA0J/A@yA؁K/A3AK/A 8]AbK/AؖA0K/AAK/AAK/A@-A K/AV^AK/A@A(K/AAK/A`AwK/A9 A$ K/A'RA`K/A zA K/A AH-K/AMA:K/A?AGK/AAZK/AAgK/A`bA mK/AHA`wK/AAPK/AA8K/AAH.K/A`mAK/A :AK/ALAjK/AxAK/AA@K/A`DAK/AA _K/AcA`CK/AAwK/A֪A RK/A AK/A`nA&K/A`wA@K/A;>AIL/AAL/A? Ap,0L/AAML/ALAML/A cAML/AANL/ApiAPPL/A=4AQL/A@ASL/A 8A]L/AVAbL/A$ABdL/AA$hL/A^}AxL/A84AL/A84A%L/AjiA@AL/A sAȶL/AAPL/A`AL/A A@`L/A A]L/A`AYL/A`vA7L/AKA0L/AAkL/AAXšL/A@wAL/A AL/A8AL/AA}L/AAL/AVA'L/A@ AЀL/A 8A}L/A AL/A+AL/A uAL/AɾAL/A APL/AAL/AAL/A`] AL/A-AL/ALA,L/AൊA8L/AmA8L/A@AHL/AAL/A`APWA[/AkKA[/A BA[/A9A@5\/Ag Ax\/A ~A8\/A A1'\/AA1'\/A-A0-\/A*As7\/A[ AA\/AAJ\/AA0{V\/AA@d\\/AAȔb\/A`KAXt\/A@A;\/A-A\/AA\/AvA\/AaAPZ\/AaA\/A@8A\/AA\/A@ءA\/A`A\/A@A\/A^AV\/A ZwA\/A_A\/A@EApp\/A A\/A@EA\/A A范\/A[A\/AkA–\/A}A0&\/AQ%A0&\/A@Ai\/AfYA\\/A9Ah\/AIA`C\/A@A`\/AAL]/AxjAf^/A A`%_/A4A`/A`m<AD`/A`$Ag`/AtIA@1`/A@6Ah`/A`A`/A 8AȘ`/AmA a/A ALa/AAha/AxA`O a/A@tATA5g/A`AXyg/A AAHg/AxAf/AhZAsf/A@NA g/A@֊A|g/A`}AXg/AAg/A Z+Aأg/A /AHuh/A`Ahh/A AX2i/A Axi/AUA/j/AA6j/A yAWk/A3Aql/A,Afl/A8Al/A@Al/A0A@>l/AAhl/A |As l/A@MAk/A'Azk/AACk/AUA5j/AAԕj/AAAj/A`uA!j/AA`i/A | Ax\i/A@ tAi/A@wA _i/AZAi/AAi/AA0 i/A@NAXcj/A@yvA`Xi/A@A0i/ApA0Ei/A`K>Ahi/A A0i/A@ݵA)h/A` HAh/AAh/AòAxwh/A|A Nh/A=A+h/AZANg/ANAEg/A@iA g/A-A؍yg/A@yڬA3qg/A`Amg/AKCAdg/AVĨAOg/A@jAJg/AAJg/A`LA:g/A AP*1g/AzA#g/AաA@uf/AРAf/AVAff/AƠA f/AA0jf/AkA0jf/A pAhf/A#ZA`f/A;A{f/AZrAIf/A'AHf/A`)vA;e/AA(ܡe/A A%ee/A̙AXFe/AौA4e/AjAm!e/AܤA8e/AAd/A AWd/A@.A0d/A4Axod/A׈Aad/A_A`Zd/AAh{d/A  Axod/AAad/A QA>3d/AA)d/A6өAEVd/AAHdd/A[Aad/AW2APQd/A"ѬA7d/AǬAAAd/ALA'd/A@rAc/A AXc/AGAxvc/A Asc/A.A@ec/ANAac/A)-Azc/A`{Aʖc/A?A@c/A Ah d/AA5d/AzcAh@:d/AdAȘJd/AAad/AkAd/AOAd/A)AHad/AաAxi e/AnAH;e/AGApe/A`巟Aޯe/A`巟A e/AAe/AtAe/AAe/A ~Ae/A APɮe/A^MA،e/AXATe/A Ae/A`A`d/A`Ad/A ANd/AA8Vd/A`KAvd/A`>A5d/AA8d/AۘAc/A!Aͤc/AoA"c/A@#Alc/A`TAx\c/A Ab/AjsAb/AzǖAVb/A`)ږAa/ANAXa/AtAa/AA a/A cAPa/A}AHa{a/AAxia/A AӎAa/AA%a/AVA0b/AA4Kb/A@2A8pb/A@Ab/Ak`AHib/A@Ab/AAb/ALA#c/AڍA7c/A`f1APgNc/AʍAxzIc/A\AX4c/AތAX4c/AXA6c/A֋AGc/A4AGc/A ⃌AHVc/AAmc/A2xAHc/AA0c/A |?A#c/A֌A#c/A@5AP/c/AAP/c/A9dA`c/A`Adc/A`mҊAxcc/AkAHvc/AWAoc/AŲA#c/A&A#c/Ax{AhQc/A4ƇAMc/AAsc/A'<A&_c/ACArKc/A콄A?c/AKA,c/ASAP c/AA!c/A<A<c/A ݅Ab/AAb/AA7b/AAdb/A lA%b/A…AUa/A㳅AQa/A3;APa/A]%AXa/AvA(>a/AҨAH8b/AH,APAb/A`KA(b/AdxAa/A`APa/ANAa/AAga/ARAa/AGA@ b/A`~Ap b/A}A}b/A@,|A`b/A*|AXa/A*|A֜a/A`Ӻ{A8Ea/AzA0Ra/AzyAP/a/A5yA`/A@yzA`/A`p{A`/A\|A`/AU}AR`/A #}A$`/AC|AO`/A`v{AX _/Ac{A8_/A 8zAТr_/A` LyAX_/A?yA^/A?yAY^/AyA0Ts^/AhxxAJ^/A`YwA`"^/A`vAs]/AvAJ]/AwA"w]/A OyAQE]/A kxAS\/A`wA\/A/vA@¬\/A vAX<\/AoTvA[/ABtuA{[/AuA4[/A "vAxZ/A vA'Z/A@WwAUZ/A|ZxA\Z/AxA [/AxA*[/A AxAW[/AxA(z[/A` 5xA0[/AxA> \/AxA0LS\/A|ZxA@W\/AxA \/AxAp\/A`ryAh]/A6zA]/A`E{Ah"1]/A |AH-i]/A%|AА]/A`{AD]/AhX{A*^/AhX{A\W^/A}{Af^/Ak{A@­^/A s{A8^/A 8|Ap^/A|A^/A@yc}Ahs^/A ~A^/A^~A^/A-IA@­^/A AX^/AAv^/A A(z^/A@>A_/A~A- _/AH~A4C_/A=}Ax:__/ATV~AH_/Ai~A_/At~A_/AA%`/A^~AR`/Aൗ~AY`/Ao~AD`/A@>Ac1`/A`2AP`/A XA`T_/AƢAM_/A.AJ_/ÁAF_/ApPA =m_/A /AE_/A;A_/AA^/A`A^/AA q^/AuAbz^/A Abz^/AA9^/A`)A0^/A@tAЦ!^/AAN^/AA`^/A RAas^/A {~Abz^/A`}Af^/A|A(`l^/A|ApR^/A@N|AU4^/A |AO^/A}A(J]/A>}Ap]/A`"Z}A]/A}A ]/A`}A6]/Ai~A84]/AA2]/A@Ahމ]/AdaA݂]/A`)A؁d]/A?TAP|H]/AOAX]/A.AH-i]/A uAH-i]/A<A()T]/A@A5]/A`9A ]/AIAr]/Aj}AP]/AAAp]/AA\/ÁA\/AA`\/Aj4AX[\/A)A@W\/AA@W\/AaA0\/A`HA0\/AwAHPh\/A㜄AU\/AdA@\/A@5AU\/AA@s\/AEA\/A lA\/AAX[\/ASA(\/A@WA\/AlA(\/A6Ap_\/A@qAX\/AňA(\/AQA \/A=ˉAX[\/A1Ap\/AcASv\/A@$AHPh\/A AMZ\/AASv\/AOA\/AیASv\/A/Aq\/AॖAxe\/AAHPh\/A#dAMZ\/A@AN\/AAx9\/A`VA\/A@sA?\/AǏA`\/A`1Ap\/AʎAؒ\/AגA\/A>A?\/AAB"\/A A> \/ẠA`$\/AAH&\/AxAؒ\/AxA;[/AڊAp\/AcA+\/AAB\/A S;ApIE\/A=ˉAx9\/A ~dAU\/A,wAx\/A ~dA0\/AAx\/AனAj\/ABA@G\/A`A(2\/A~AH;\/A AXE0\/AAP\/AA[/A AyA[/AAh/[/A}Ah/[/A鸄A06[/A`v#A[/A ƃA =\/AU{A\/A@P_AB"\/A? A\/A`vlAH&\/A@!AI\/AZAc\/AA\/A.A(\/A ڀAг\/A@NA\/ANAX[\/AA\/AzAг\/A`d~Ap_\/A@N:~Ap_\/A}A \/An+}A \/A|A \/A A|A0\/A@y{A(\/AO{AY\/AAzAq\/A`IzA`P\/A@yAp\/A@,$zA[/AzA-[/AzAx([/A c<{A%[/A|AX$[/A@N|Atn[/A|AM[/Al}Af([/A }A [/A_~AZ/AAXRZ/A (-ALZ/AAHZ/A`T AZ/AA0Z/A A`Z/AAZ/A_AXRZ/A`vlAЦZ/A܂A@NZ/A0AXZ/A଄A0tZ/AkAAkZ/AwAZ/A`AHZ/A`AZ/A0%AhZ/ALAXZ/AFALZ/A AXfZ/A^A@}Z/A@#A(JZ/AA؟Z/A `AZ/A@$AHZ/A@AvZ/A^*A?]Z/A`dFA?]Z/A@#A0tZ/A AZ/AzAOZ/AibAZ/A̋AZ/AAZ/ADAZ/A@A(`[/A@A&[/A2A&[/AAT[/AhAvu[/AhA}[/A@yHA@~[/A=ˉAҚ[/AAҚ[/A@$A}[/AËAX$[/A =A!z[/ATA@F[/A@A8[/A3Ai6[/AیAf([/A@>9Ah#[/A`TAe![/AzmA [/A:A^Z/A`A0YZ/A蝐AxZ/AAЦZ/AAxKZ/A`A0tZ/A~AhZ/AA_Z/A;A_Z/ApA`CrZ/A A`GZ/A J(AZ/AʎA?]Z/A@A;HZ/A:AH.Z/A`D A0Z/A /AxY/AA`Y/A ZōA8xY/AIqAY/ANAH ZY/A`΍A>Y/A@p5A`TY/A vA@ Y/A@A@ Y/AॖAY/A`AQY/AOAHPY/AHAQY/A`7A@ Y/AAp"Y/A(AY2Y/AAгIY/A5A aY/A,wA@}Y/AA–Y/A`mAkY/A`AjY/A@qAiY/A6ApzY/AɇAWY/A`A>Y/A`B0A@W$Y/ARAHPY/ARA0HX/AMhA0HX/A &A0HX/AնA`X/AbA@ Y/AAH-Y/A A^GY/ALAccY/AQ͆AY/AQ͆AXY/A`A–Y/AA–Y/A lAY/A`ëAccY/AA\@Y/AqAY/AajAQY/A`KAQY/A@5AQY/A A0Y/A@ AгIY/A`KCAཱྀY/AAY/A`AY/A =A'Y/A JA'Y/AAP|Y/A@tALY/A]AY/AzAjY/A]A0wY/A4A bY/AwAFRY/AA>Y/AހAX+Y/A$AEY/A?TA8X/A \A&X/A \ApX/A?TA`X/A(Ax)X/AAKX/A좀A(-X/A cAKX/A@> AxuX/A`AfX/A_A@ZX/AAH?KX/AAXRFX/AwAp>X/A@r€A@,X/A@AX/A3AYX/A |DAW/AsAW/A \ApW/A6AW/A@AiW/A /AW/A[ApAoW/A RAlW/A}A0oW/AAثwW/AAoW/A`6ApgW/Aj AiW/A~AmW/A~A~wW/A~A|W/A[~AiW/A`B~AX]W/AI}AXW/A }AQW/A W}AWW/A}AbW/A |A`SW/A@߰|A0>W/A |A9W/A}AzW/A@,}AH W/A/}AV/A(}ARV/A@E }AȔV/A|AV/A|AAV/A}|APkV/AT|AV/A@E||AV/A|AV/A|AHV/AE}A V/Aq}AjV/A4}A2V/AH}ḀV/A@`}A٦V/A~AH?V/A^~AV/A@N~AБnV/A~AX^V/A/A0fIV/AFA,V/AAx V/A`fCA(U/A@WAU/A`̀A(U/AŀAh"U/AŀAU/A ҷApAU/AIAHyU/A AfU/AxAZU/A ҷAX[RU/A |ـAFU/AA$U/A@ApU/A AU/A`vAU/A QAU/A΀A`CU/AqĀAH;T/A6AT/A@̀AxT/A AUT/A Ah@T/A AAXT/A`AީT/A@ᅀAT/A삀AT/A zAhzT/A &tA cT/A_wAYT/AA`PT/AhǀAH!OT/A`ART/ABAx\XT/A@fA`\T/A-A(VT/A AP UT/AhLA`PT/AAOT/A AH?RT/A ALT/A`,AKT/A@yXAIT/A-˃AcKT/A=AKT/A昄A1NT/AلATT/A@A`SdT/A_A`SdT/A)A^T/A}A^T/AхAdT/AslALnT/A`AA$zT/AXA規T/AA|AT/AApT/AA@T/AeA0T/A l;AT/ALA8T/AeA@T/AvAp0T/A`LJAU/ACAT/A,_AT/A,_AXhT/AvAZT/A-A`TT/AHA@T/A qAT/A@WCAXU/A uvA8U/A@֥A_]U/A@ȉAȘU/AAU/A@.DAH{U/A`2AXU/AAhU/AhA U/A`HA)U/A`mjAV/AlA"V/A @A 0 V/AApJV/A QҊA@V/AjA U/AiAU/A`;0AX9U/AR AU/A͉ApU/A A"U/A@AhU/A |lA@U/A@PMAU/A@5ABU/A \وASU/A`vA{U/A`DAPMU/A&ɈA@uU/A8AU/A JAU/A`TA |U/A0߉AV/A)A6V/AЯA(V/A@bAyBV/AwAJV/A"A>V/A@eA4V/AAT1V/A@݉A@V/A AP,V/AGAXl>V/AlA[?V/AɊA=V/A0A0CV/A=A:V/AuA,V/A`A!V/AEA'V/AA2V/A A`XDV/A DA(i[V/A`5A( iV/A`AȔzV/A0AV/AXMAV/AxAH.V/AȌAYV/AAV/A?5AV/A`KAH{V/A^hA W/AA@W/AύAPMW/AwAW/AMAW/A`pA`a*W/A ujA)7W/A`TAAW/A3ΎA0JW/A֎A[W/A0ӎAqW/AA{W/A^AW/AMFAW/A;AW/AMAXW/A TAW/A@CUADW/AotApAW/A@A8W/A@ǎA0W/A@юAƄW/A A0yW/A@AqW/ArAiW/A ZWAiW/A1APaW/A?ACWW/A A[LW/At/AHrAW/A JAn.W/A JAW/A]APZW/A jAV/AAqV/ADڏA]V/AA W/A`QASW/AEA W/AݐAW/A`)'AhW/A TAg W/AsyA(W/A]A(W/AőA03W/AaAZ4W/A@C$A(h8W/A`)LA?W/A@SAxHW/ATAIW/AvAaBW/AA;W/A`mAM0W/AA@*W/AܒAd%W/Aj A)W/A/A(0W/A |TA(W/A`[A6#W/A AAW/A6A(9W/AA@V/AēA0V/A@yAV/A)ѓA+V/A@AV/A` A(V/A-,A8V/A HAV/AMAЄV/A3A\V/AA(~V/A6AOV/A`KA(V/AA-V/A@A8'V/A` A8R~V/A@E APjV/A@E AeV/A@AXV/AAFV/A`Ao9V/A~A,V/A Ah%V/A@NAP$V/A-̓A 'V/A@E A,V/A=A2V/AzA7V/AߣADV/AAQV/A9ݔA7^V/A@ AkV/A (AUyV/A`mRAV/AqAwV/AzAV/A`A V/A#AV/A`KIAV/A`tA tV/A \A޴V/AHAYV/AϖAXVV/AAfV/A` AHV/ACAH.V/AoAzV/A6A0ԓV/A 䨗AЀV/A@ җApqV/A@A@bV/A@A7^V/A |HA0^V/AAptV/A̐A؞V/A;AP V/A@AHzV/A]:AƏV/AtA V/AAEV/A@NA tV/A@ƙAxV/A9љAhV/A`ApV/A AЀV/AF]AHV/A AoV/A ØADbV/A ØATV/AAhPV/A֘AQV/AWAHPV/A |HA[V/AwAlV/A`mA(OV/AjAV/A`AzV/A4ARV/ATAǧV/AʖApV/AA`V/A}A`V/A[0As|V/A`TڕApYV/A ZpA3KV/AvA@6V/A ~A#V/AEA"V/A@YAXV/A&AV/AoA0 V/A锔AV/A`2ACV/Aj2AV/A@#ۓA V/AA'V/A`6A/V/AAJ(V/AA)V/A u9AP(V/AAl&V/A`BđAV/A`БAo V/AAU/ArA=U/A` A8"U/A@.AU/AAPkU/ARВA(U/AA(U/A@,GAnU/A u9A8U/A [AU/AÒA0U/A"AU/A@JAxرU/AFA+U/A Z&AIU/A@ ޒA`U/A@ABU/A5AؒU/A (A@U/AAYU/A*A U/AMAU/A/AU/A`D_AU/A`vtAPMU/A 8sAU/AuAV/A`vtA6V/Ak#AJ(V/A OA/V/A`fA9V/AA@7V/A!ADV/AXAOV/A`"VAplVV/AAaV/AXƒA(\oV/A`BA`|V/A@A(ʊV/AAAV/A;ARV/A@ >A1V/A <AV/A`*AW W/A,AW/A ~AW/A@hAW/A@C$AW/A |/ASV/A!AV/AёA؟V/AcAV/AqAcV/ABA.V/A` AHV/A@A`V/A^A V/AAXʢV/A@AƏV/AAlnV/A@lA|=V/A@rJAN#V/A AU/A@C0A`%U/ATAXU/A AȩU/AAnU/AxAx\JU/AєAAU/AAp_-U/A ՋA`U/A<AXT/A SAbT/A SA@T/ALA@T/AՏAIT/AApnT/A}QA`PT/A;UA@$2T/A-iAT/AzVA*T/A ,AKT/A֊A0 T/A汊AAT/AI|A0HS/AjJAUS/AAS/A J6ANS/AYAXS/A쐊APS/AA0S/A&ĊA'S/AۊAcS/AAA|S/AA(iS/A l6A=TS/AUA _KS/AA>S/A`9ȋAȶ4S/A sA0S/AA#S/AA88S/AA@qS/A`6AȵR/AeAR/A AR/A@AP R/A@AUR/AAR/AgA@ӨR/A"ApR/ABAgR/A`qArR/AaAP[R/A$Ap[CR/A qA`3R/A qAxR/A`A4 R/AA|Q/A AQ/AذAQ/ARAQ/A@謍AUQ/A_ЍAh]Q/AA8tQ/A 2AQ/AXVAQ/A jApQ/A`mA8'Q/A`qAT~Q/AfAhsvQ/A`AmQ/AͼAȤiQ/A &AhQ/AoAZQ/A2'AYQ/AAHaQ/A@ A[Q/A`mAQQ/AJAj6Q/A`+A()Q/AA(Q/AA Q/A@.AZP/A`6AhP/AhRAxP/A}AXP/A AP/AȎA P/A}A((P/A`6A`P/A@.A˘P/A[A˘P/AɴAϗP/AyAޓP/AdJA#P/A@.A`6}P/AA0P/AǨA0P/AAP/A^AP/A`BJAP/AAEyP/AA0wlP/A`9ȋA@]P/A`vAQP/A)̋AJP/A`AJP/A*A@DP/AQFA@8P/AaAn,P/AqAH P/A^AP/AAP/A`mARO/AaAh^O/AAO/AЋAO/AЋA`O/AA O/A@yuA0O/A EAO/A;AؒO/AۋA`O/A"ABO/AeA O/AAPO/A-ԌA O/A A/O/A@>AX$O/A@CAX$O/A@A O/A`mA O/A(AhO/A S܎AO/A A#AO/A्A O/AЏA RO/Av܏ApO/AA8xO/A@VA@NO/A A(O/AAO/AAPO/A*;AO/A`^AO/AAxO/ȂAPO/A`A`O/AKAeO/A+AO/AtAO/AAЦO/ADA@OO/A SAP/AAP/AqAn,P/A SAX9:P/A3ACP/AjAXSP/A`ZA@]P/A3AXhP/A4AiP/AؒAfP/AAcP/AI~A@]P/A`OA_P/A'AdP/A^A@dqP/AApIxP/AfAP/A`v'AP/A@AP/A97ApJP/AH3A,P/AAP/A@ԐA8P/A@ԐA P/A A@uP/A CAxGP/A CAHQ/A CAQ/A@y*Ap%Q/A@Af7Q/A AX9CQ/A APRQ/AA fQ/A-AX}Q/AKAX#Q/AܐAQ/AA@Q/ANAQ/AA/Q/ABAQ/A@ԑApQ/AAQ/A`OAQ/A`AТQ/A`ܒA@uQ/AARR/A`ZARR/AARR/A 7AHKR/AnA R/A QAȶ+R/A QAHr=R/A׭AUR/A |ŔAHjR/A@NєAPuR/AAQR/AZAۦR/A@yAR/A`]͔AR/A QAR/AjA'R/AnApR/A'AۦR/A'AxR/A?AaR/A@rCA/R/A/A` R/A8A07R/A`KɓA07R/Ak~A R/Ak~A`R/Ak~A0R/AjAN S/A AGACS/A]A S/A൭AS/A@,ѓA#S/AAX'S/A#AX'S/A`[A%S/A橔A0"S/AA)S/A)_A@qS/AHWAR/A gAR/AvA07R/A 뱕AQR/AAYR/A@!(AR/AGAR/A-gA˪R/AIA@ӨR/A (AϩR/AD(AR/AcAHR/A@YAQR/A`UAR/A@IAR/A@AؒS/A9AX$"S/A*A3S/A A DS/A@זAHS/A@ƐAiRS/A:A@]S/AAXiS/AA0wS/A AS/ATA}S/AA S/ATAS/AA0S/AAS/AA8xT/A@AX$+T/A@p"A3'T/A|aA3T/A-ABT/A@A|VT/A"A,kT/A"A}T/A'>APT/ADyAqT/A`AxKcT/A@חAxKcT/AACeT/A NAP8hT/A 8A zT/AܜA@†T/AeAxT/AYA[T/A@ APT/A@5ȘAX9T/AAAT/A` RAT/A ZAT/AङAzT/A ܙApIT/AA(:T/AbAT/A@ĚAXU/A-ܚAU/A`v AxU/AsNAXJ*U/A@}A89U/A`A89U/AmԛA8LU/A@Ah]U/A`AidU/A@6A^U/A`yATU/AĜA8LU/AAEU/A@#3AFU/A@>nAHRU/A` vA`qbU/AčAHKlU/A`;A {U/A@WAɍU/A@E3AU/A@^AU/A ɕAU/AAU/A`A͌U/A*CA }U/AZAY/A1AGY/AAY/AEʢAY/A` AAY/A6-AY/A AeAQY/A`nAw'Y/A@AX/AپA`X/A.AX/A`ͦAX/A ~PAX/A 8ANUX/AlA(-X/A@AW/AJAW/A AXҎW/A tA]W/A2Aj1W/A`Ax V/A A0V/A]AaW/A@,A%W/AகA`AW/AApxwW/A&GA*W/A@A3W/A LAW/AtϨA:W/A`[AW/AaA՜W/A.A0ٱW/A@p!AW/A4Ah;W/AqA:W/A@5A@X/AA@X/AvACX/A`KAKX/A l*APzX/AA0XX/AAsX/A A PX/A]AXF+X/A]ApX/AAX/A 1dAX/A`AX/A&AX/A@A;X/Ah3AgX/Ah3Ax\X/AcAaX/A`̵A X/AcA`X/A`"~AUxX/A 8AXWX/AKA4X/AoAh6X/A緸AXWX/A`VAX/AA`X/AƹAX/AA`X/AA[X/A@ATqX/A)ANUX/AptAG2X/A;A(X/A[ApX/A`lApX/AܽAؚ&X/AADX/AܽA8eX/A AA0XX/AAUxX/A@ ARjX/A?AKGX/AﯿApX/A`˿AX/AĆAh X/A JA/X/AAO\X/AAX/AAX/A ۭAPMNX/A'6AXF+X/A`TA~!X/AOA?X/AAh X/AApX/AkA@A%X/A A8I9X/AA PX/AUAUxX/AA X/AFAX/AA0Y/A A~JY/A`djAP{Y/A,Ap=Y/ArAY/ArAY/A@HA Y/AsAY/A*A0*OY/AUAY/AމA`v Y/A AHr Y/Aa*ApY/AzAw'Y/AA{>ABl/AzBAl/A Apl/AAl/A3AX l/A@A`al/A`)xAzl/A6Al/AAl/AAp8l/A Al/ARA#l/A 1ABl/A31Al/AA Rzl/AA ll/A A`aml/A(ALl/A3qA`l/A Al/A@ Al/AAl/AzDAXxl/ARApWl/AAAl/A]A=&l/A Azl/A Ak/A Ak/AA`k/A `Ak/AAp k/AAk/A aA8k/A@>FApk/A^AX k/A Aztk/AATk/A uAP8Lk/AA=k/A A,k/ApA) k/A Aj/A HA8j/A AQj/AAj/A:Apj/A3qA(wj/AA@Wlj/A`A@Wnj/A}A(ej/AYAB[j/AtAHaGj/A A3j/A1A(j/ApOAj/A \YAXi/AUAX i/AAki/A@ Ai/A3 A0i/A0Ah3xi/A@RAfdi/A eA#Vi/A AKi/AHAfHi/AzAzJi/AmAW\i/AA0.mi/A{Ahti/AzAki/AABi/AAWi/A]7Aj/A GALYj/AkAtj/AAp=j/A HA`aj/ApKAp j/A A`j/A@vAk/AHAP81k/A A@k/A Abk/AֻALk/AzApk/ARJAP8k/AHAl/AbA8(l/A`)A5l/A`)A8nl/A=Akl/A@GApl/AzAQJm/A8A83m/A0Ap n/A A 'n/A 8Adn/A`f A0.n/A 9 An/A \k An/A- Ao/AAp=.o/A ABo/A |A]o/A Apho/A`LA@lo/AAso/AA@no/AAGio/Az Axfo/AhUAXeo/A@^A_o/A`ftAKo/AzZA6o/A HMA%o/Ap]AXo/AxAH\o/A׻A0n/AA Rn/A7APn/A`fAo/AåA o/AýAXo/A AB$o/ApA#5o/A`)AfAo/A AHo/A@VApPo/A=APo/A =AXo/AzA(\do/AR^Afao/A#Ago/A#A83ho/A`)A_o/A׉A?o/A -Ap=!o/AAko/ARAn/A2ABn/A`).An/AAn/A3An/AALn/A qAzn/AAp=n/AABn/A An/ARXAPn/A Apn/A A3n/A9Azn/A]Akn/A`)A(\`n/AQA(Qn/AQVA8@n/AR>A:n/A m AQ?n/A@g~!A>n/AL"A>n/A "A8:n/A H"A0n/A}#An/A` $A|n/A`@$A)m/A$A`m/AR$AH\m/A=J%Apm/Ap%AGm/A`ù%A)m/A`"&A`m/A&Am/A!'Am/Ay'AP8m/A'Ap=m/A H%(Am/AY(AX n/A(An/A`(AH n/AR)Af&n/A3)Apk(n/A)A4n/A*Ap=>n/A`*A{n/A@G)An/A)A8n/AR*An/A *An/A)An/A@G)Ap=n/A3m)Ap=o/A@R)A"o/A \U)A85o/A M)AXIo/A;)AX Uo/A)A`go/A (Aho/A(AH.io/A(~(AHaio/A`t(Aio/Ai(Aio/A^(AX jo/ApS(AGjo/AH(A8jo/A@<(Apjo/Ap1(Ajo/A@&(A jo/A%(Ajo/A(Ajo/A](Afjo/A (A]jo/A`v(Aio/A 'A83io/A'A#io/Az'Aho/A'ALho/A'AH.ho/A H'Afo/A`f'Afo/A'Aeo/A@>'A ceo/A@>'Ado/A'Abo/A`)'A)ao/AR'Aao/A`d'A`o/A 'A^o/AÓ'Ap^o/A@'Ap\o/A['A8\o/A@>'AuZo/A z'AGZo/Ay'Ap8Xo/A@gn'AX Xo/Apm'AWo/A@gl'AWo/A[k'AUo/A&a'AUo/A`)`'AkUo/A3_'AH.So/A@>T'ASo/A[S'APo/A@gH'APo/A G'AVNo/Az<'AH.No/A;'AKo/Az0'AVKo/A-'A83Ko/A -'AHo/A"'A8Ho/A 'ApFo/A`'A@o/A`)&A>o/A3&A=o/A&AL;o/A &A9o/AR&A7o/A[&AT7o/A&A(5o/A&A4o/AR&AȘ3o/A`ܷ&A1o/A&A/o/A&A-o/A&A,o/A@>&A*o/A`Õ&A&o/Ap&Af&o/A`&AB#o/A H}&A o/A q&A o/Af&A{o/AZ&Ap=o/A`fN&Ao/ARJ&A`ao/A@B&Apo/A6&AHo/A`m1&Afo/A +&Ap o/A &A@$ o/A&AP o/A&Ap o/A@&A#o/A`%AHo/A@%Ao/A@%AXo/A`%Ao/A%Ao/A%AP8o/A%APn/A`Û%APn/A%AP8n/A`Ï%APn/A ƒ%AP8n/A w%A n/A8t%ABn/A`)l%A8n/A``%A`n/A@X%An/AT%An/AH%A n/A@?%A)o/A=<%Ao/A5%Ap=o/A3/%A o/A`)%Ao/Ap!%AxKo/A@%Ap=o/A%Afo/AR %A(ho/A%ALo/A`%A@$ o/A A$A8 o/A $A@o/A`K$Ao/A@$Ao/AV$A;o/A$Ao/A`$Ao/A[$Ao/A@$A6o/A@$Ao/A@$Ao/A@$Ao/A@ $A`a#o/A=$Aj#o/A Q$A&o/A$A)o/A$A*o/A$AX +o/A3$AG,o/A`f$A`a-o/A $AW.o/A$A-/o/A $A1o/A 1%A2o/A3%AH.3o/AH %Ap3o/A3 %Ap=4o/A@ %AP4o/A@%A)5o/A`)%A@6o/A%A(->o/A@y8%AFo/A`ff%A(\Go/ARh%ApGo/A=j%AHo/Al%A8Ho/A \o%A8Io/Ap%AQIo/A`r%AIo/At%AIo/Au%AJo/A@x%AJo/A=z%A}Lo/A(%ALo/A%AuNo/A`%AuRo/Aן%ARo/A%ATo/Aק%ApVo/A%A`Xo/A q%AZo/A %A]o/A%Ap_o/AH%Axh`o/A`%A`ao/AH%Apkdo/A@ %Ago/A%Aio/A%Ap8lo/A@%A(no/A%AQqo/A`)&Auro/A` &Aгso/A  &A#to/A@ &AБuo/A&A`vo/A`&Axo/A@ &AHxo/A`&AQo/A`&Aho/A&Adžo/A&Aho/A`&Ao/A@ &A8o/A@ &A‡o/A@ &Ao/A@ &AP|o/A`&AH.o/A&APZo/A&Ao/A&Ao/A &Ao/A&AP8o/A &Avo/A&Avo/A&A8o/A@&Ap=o/A`&AǏo/A"&Ao/A@"&Ao/A@ #&Ao/A #&Ao/AH#&Ao/A/&A2o/A`m1&A`o/Az<&A`o/A`<&Apo/AXE&Ao/AI&Ao/A`I&Apo/AV&Auo/AV&A-o/A=^&Ap=o/A q_&A@Wo/A qa&Ap=o/A \s&Ap=o/As&AXlo/A`"w&A̘o/A`)&A(Jo/A&A#o/Ap&A#o/A@>&Ao/A@ &Ao/A&Aљo/A &A̙o/A&Ao/A &Ao/A@g&Ao/A&AQo/A@>&AGo/A`&Ax o/A'A`o/A@g 'A8o/A H'ALo/A%'A)o/A('Auo/Az4'AQo/A6'Ao/A C'Auo/AE'AOo/AAH'A)o/A[Q'Ao/A@>T'Ao/A _'A@הo/Ab'Aǔo/Ae'Apo/A Hi'Apo/A@k'AHo/A l'AHo/Azz'AHo/Az~'Apo/A'A`o/A H'Ao/A['AOo/A@i'Afo/Ap'A@o/A֫'A̗o/AN'Ax o/A@ƺ'Axǜo/A3'A@¡o/A`'AP8o/A'A#o/A H'Ao/A'AQo/A 'A`o/A`f'Aǧo/A'Ąo/A`f'A̩o/A 'A̪o/A'A«o/A='Ao/A`)'ALo/A(A0o/Ap(Ao/A H (Aأo/A H (A0no/Ak(Ao/A(Azo/A(AFo/A%(Ao/A@,(A(ܾo/A(4(Ao/A@7(A`o/A(<(A^o/A@,K(Ao/A{(A8o/Am(A8o/A`(Ap=o/A((A`So/A`՚(Ao/Aק(A o/A`(A؞o/A(A@1o/A (A@1o/A(A(\o/A')A`So/A@()A`o/A4)Ao/A_)A0.o/A@ i)A`o/A`fr)AXo/Au)AWo/A)A*A"o/A`?*AP8o/ARD*Ao/AI*Aњo/A3O*Ax o/A |S*A`o/A`*A`o/Af*Ao/A m*Afo/An*Ao/An*Ap=o/AHu*Aho/A Q{*A8o/A q*Ao/A@ *AQo/A*A|o/AR*Ahzo/A`*Ayo/A *Ap=wo/A \*Ano/A*Ano/A@!*Ako/A*Afko/A`*AHaho/A3*Abo/A*Af_o/A \*A\o/A`*A`Xo/A*ApUo/A@*ARo/A*Ap/A.Ap/A (.Ahp/At.AQo/Aa.Ao/A .A0o/A -AHo/A t-Aho/At-Ao/A ,A! p/A2,Ap/A@E+A7p/A@E+ABp/A`;n+AXp/A +Arp/A (*Amp/A}}*AHp/A7*Awp/A)Ap߰p/A`ܗ)AQp/A@E;)Ap/A(Ap/A (A4p/A(Ap/A(Ap/A (A q/A2(A"q/A (h(A2q/A`F(A:q/A@N4(AQq/AW'Ameq/Aa'AZq/A2a'A8q/A 'AP+q/A2&Aq/Atp&A4q/A@&Aq/A`&Ahq/A &AXq/A%'Aq/Ah'A0q/A@E'Aq/A`'AP+q/A ((Aq/AC(Apq/A `(ATq/A (A=q/A@E(A(>Bq/A`;)A8Eq/Ad)ApFq/A})AJq/A`*ARq/Aj*ASq/A}*ApFq/A`*A8q/A2*A46q/Aj3*A46q/A)A0q/AW)A4q/AX)AQq/A}Q)App/Ad)Awp/Aj)Ap/A`:*AQp/A@n*AP+p/A`Ӟ*A!p/A+Ap/A`>+AŒp/AL+A8}p/Aɥ+Atp/A@E+Ahp/AWA,A4p/A@E,Ap/A@E#-Ap߰p/A-Ap/A`2-A!p/A`Z-A8p/A`-A4q/A -A q/A <.A q/A.Aq/AK/AQp/A/AHp/A0A8p/A o0AXp/At0A!p/A1AP+p/A1Adp/A2Q2AQq/Aa2AQAq/A`;2Akq/A`3AP+q/A@N$3Aq/Aj3AXq/A23A4q/A_4A8 r/A4A r/A 5A0o/A` wHAo/A\HA83 p/AiHA\p/A c.HA0]'p/A`BGA(i>p/AGAxQp/AGA tfp/Aq/A k9Aq/A`9AQq/A@F9AQq/A8AGq/A@~8A¸q/Aa8Aq/A`7Aq/At<7Adq/AW6A0vq/A`;^6AWq/A (6AIq/A@E6A=q/At6A46q/A`5Aq/A`5Amq/A25Amp/Aa5Ap/AW5A!p/A5Adp/A5Aip/At6AHKp/A@E6AXp/A"6Ao/At6Apo/Aj5Ao/A 5Avo/A5A)Vo/A 6Au\o/A@g-6Axto/AK6AAOo/A>A o/A&J?ASo/A`o?Ap/A@8@ADp/A@AH2p/AXOAA@p/AAAho/AIBAh3o/ABA=o/AVCAzo/AfCA@o/A`"CAo/ADAPMo/A`'DA!o/A?jDAЄo/ADAo/A0EAHܝo/A`]OEAo/A EAXo/A3/FA΅o/A FA0\o/AdGA0]o/AGA _o/AiHAXo/AfHAcLo/A[HAAo/A HAxBo/A6IAVIo/A3IAh Qo/Ak;IA0o/A` wHACN/A $PAAN/A`tOACN/A NARN/A@vNABiN/A NA@N/ALMA`N/AMAXN/A?MALO/A \qMA؁;O/A QMA AO/A`THLAxKO/A0uKAsUO/AJAsfO/A@yZJAȶlO/A yIA`}O/A HA/O/A>HAqO/A5GAO/ACGAxP/A;FASP/AW>FA0{P/A FA(zP/AFAyP/A= FAX5Q/A^FAA;Q/A FA Q/A`:FAQ/A`FAQ/ALEA"R/A EAR/A EAR/A`JEAxR/A@DA0Q/A@,BDALQ/A CAtQ/A NCAQ/A ABA Q/A (MBAثQ/A`AAv}Q/A|AAgQ/A}KAA0XQ/A@r@A&MQ/A j@A6Q/A`?A#Q/A`+?A4"Q/A`>A'Q/A qq>AP^P/A@">AؼP/AX=ADP/A`=AhDP/A=AQP/A`D=A@_P/A<AlP/A l<AhP/A(<AP/Aj=A^Q/A=APQ/A@<AЮQ/A"<A(P/Aݬ;AyP/AX:;A P/A2:A*P/A@:ARP/A:A@SwP/A`:ApaP/A-:AxJP/A 9Ah-P/A Q:AxP/A :ApO/A@9A-O/Ae9A@O/A`̞9AO/A 9AO/A9A"iO/A9A8(O/A!9Ag O/A/8APN/A 8AN/A@7AiN/A*7A֎N/A`n6AȆN/Aq.6A8xxN/AX5AfN/A5A]WN/AX5A 8N/AŴ5AX/N/A i5AC%N/A`B4A8Q-N/A׽4A5?N/Aʤ4ATN/A@o4A k_N/A54A]N/A;3AAN/A3A8Q-N/A 3AxN/A`o3AM/An94A`M/A@ m4A`M/A`)4A8M/A`m}4A |M/AN4AȤM/A+4AM/A@,4AM/Aܭ3AXM/A@3AHM/AB3AM/A`3A*M/A2ApM/A 2AnM/A A2A,M/Ak2A0G{M/A a2A,kM/A.2AbiM/A1AȥoM/A@E1ATrM/A|1ATrM/A`)1ApM/A0ApM/A0AnM/A A0A`gM/A0AUaM/A@i/A\M/A'/AHKM/A/A9M/A/A*M/A/A"M/Au/A M/A l?/AH&M/A)/A;M/A@`.A@M/AT.AM/A@.AP*AP&C/A!*AiC/Ax*APC/A *AؒC/A)AC/A)A8'C/A`c)AC/A@9)AC/A} )A@C/A)AxC/AF.)Ak}C/A)Ax|C/A` (A rC/Aީ(A(fC/AА(A XC/A(AxIC/A(A=C/A<(AТ7C/A@.(A#C/A@.(AC/A~(AC/A 10(ARC/A`(AB/AZ'AnB/A(AB/A`fP(AFB/A`"[(A۾B/A@e(AhB/A@e(AB/A>(ApΔB/A(A(܋B/A'AH-xB/A'AhB/A(AHUB/A`(AH.EB/A(AH!,B/A(A!B/A(AB/A?(A/A/A(AHA/A?(AA/A`(A0A/A 'AA/A 'AXůA/A`'AA/Ah'A>A/AZ'A`ӕA/A!(AӄA/A@S(AuA/A@S(APaA/A)(AP@_A/A`'A0%`A/A'AkA/A'AkA/A@5d'APA/A U'A BA/A U'A6A/A@'A%A/A&A& A/A`m&A4A/A5'AO@/A@yY'AP@/AG'Ax@/A'AH@/A@'AX(@/A &A@/An&AP@/Ab&A`@/A&AP @/A=%A@/A@#%A@ߪ@/A;e%AH@/A@P%A@/A@R%AH}@/A ҋ%A@/A४%Ax)@/A"%A@/A7&Aȇ@/A|&A @/A'A6@/A@yY'AQ@/A`'AQ@/A>(Al@/Ad(Al@/A@(A؅@/Ao5)A(mx@/A̠)AЕn@/A`)ARW@/A*A@SF@/AO*A;@/A@L*A;@/A *A:@/A#*A)@/AG+AP@/A@+A(9@/A`,A@F-@/A a,AH/@/A,A'@/Ai-A'@/A?-A)@/A6.AxF@/A 8.A@/A/A9?/A(J/A?/A(J/AP?/A(J/AX?/A l?/Ah?/AR /Au?/A1/A`e?/AD8/AXJ?/AD8/As$?/A/A?/A@.AY?/A`h.A8>/A-A>/A@_-A0%>/At2-A >/A@y?-AH>/A k-A >/A-AH>/A@(.A5>/A0.A脻>/A`.AG>/Aa5/Ax>/A/A>/A/AH>/A`b-0Af>/A`}0A>/Ao0AP>/A0A>/A@>.1A >/A1A0>/A1A>/A1A>/A1A`?/A :1A?/A1Ax)(?/A`$1Ah4?/A211A`@?/A1A@BW?/A c1A@f?/A`o2A?/Ap2A06?/Aſ2A?/A53Af?/A@3A?/A'4AXu?/A q4Asf?/A4A[?/A (R5A(h?/Aǧ5AH.w?/A 5A~?/Ax5Ax5?/A`]5A?/A5A?/A 6A6?/A``6Aȇ?/A c6A ?/A`d6AXR?/A@6A0{?/AW7AH?/A@6Ag @/A6A@/A7A8@/AgH7A @/A37Ab'@/A7A=@/AgH7A K@/A7AHa@/At+7As@/A@@7Ah]t@/A7Ajp@/AR8Ax]@/Aq8AJ@/A08Ao<@/A<8Ab'@/A8A$@/A9A.@/A`279Ax:L@/A9Ahg@/A8APIz@/A`f8A}@/A[G9Au@/A9Ac@/Az9Ae@/A:Aw@/AH:Ah]t@/A@:Aw@/A:AHr@/A;As@/Ah;Ah@/A :AHz@/A`:Aȩ@/A@ߗ:A@@/A H:A@/A`3:A@/AV[:AA/A`]:AA/A":AXA/A`;Aa A/A'e;Ap,A/A ;Ah$A/A;Ap*A/A <Ahb#A/Aa<A,A/A ғ<AЦAA/A /_<AwOA/A`T<A\A/A`o,<APzA/A@p;AA/A,;AA/A<AA/A@Eu<AA/A5<AA/A e=A tA/A@">A"A/A@p>AB/A>AV;B/A@>AUnB/A |?AB/A |?APoB/A>ASB/A=AأC/Ao=A6C/Az=ApGC/A=Al9C/A >Ax-C/A@,>A)3C/As?ApGC/A?AX^C/A7@APkC/A`f@A&C/Aҍ@A C/A Zx@A&C/A@N@A@NC/A@?A C/A S ?AC/A;>AC/A=AC/Ah=A%C/A=A3D/A <A#TD/A`<APwD/A`B<AȶD/A`<A޳D/A@j<AXD/A;AxD/A cA;AXJD/A@:AsD/A4g:AD/A*9AѼD/Aa^9AP/D/A/8AؚD/AU8AXD/A 8AD/A ɩ7AID/AQP7AXD/Am6A`D/AF6AD/A@.96AXJD/A@5A 3A UE/A3AE/A 2AE/A`[2A(E/AR2A7E/A ( 2A`6 F/Ak2A4F/A2AcF/A 81AՠF/A1AF/A`0AF/A (0AF/A/AG/A@/A)G/A'/Ap=XG/A@/AݧG/A/AxG/A/A 6H/A &/AcH/A2/0AXRH/A-0A`H/AFq1APH/A@,?1AI/A0A@RI/A/AӔI/A (0A@1I/A A0AhsJ/A@#Z0A0J/Aя0AWKJ/A0ArJ/A0ATJ/A@W0AJ/A1AHJ/Az1AJ/A`v2ASJ/A`[2A K/As)3A+K/A`3A)CK/A3A`gK/A@t3A ^K/A73AK/A@t3AK/A`#4AK/A@4A0]K/A 5A8xK/A6AּK/Al7AּK/A@8A8'K/A8AYK/A P9AؒK/A l9AXK/A O;AK/AM<A K/A=A AK/Am>AK/A9?AxK/A@AּK/A@@AK/AAAK/AٶAA K/A _BAoK/AL CAUK/AdCAUAP4/A`UA3/A7VA(K3/AVAX3/ALWAT3/AVAh3/A 8VA3/A |BVAs3/AUA؟w3/AUAlk3/A`TA\3/AjTA NP3/ASAbE3/A`KSAA93/A`5SAȔ3/AzRAHr3/A \RAX( 3/A` QA~3/AQAz23/A`v'QA3/A`QAHr3/AjPA3/AoPA2/A}tPA82/AIPAx2/A$#PA|2/AOAXWM2/ATOAX(.2/A8dPA2/AaPA 1/A PA`1/A@PAXc1/APAyx1/A PAHq1/ABQAHq1/AQA0}1/AQAj1/A@RAX#d1/A SA~e1/A@.qSAPb1/ASA(ma1/AŅTA\1/A3UA`OF1/A઄UA,1/A"UA(1/A |BVA0/AAVAx0/A WAЄ1/A@WWAj$1/A WA8D1/A_VAf1/A-XVAc1/A-XVA&1/A@VAض1/A\WAض1/A@WAਗ1/AXA0}1/A JZYAR1/AYAx˟1/AiZA}1/AYA1/A 9YA1/AXA 2/AwXA`/2/A0rWAxm>2/A WAN2/A +WA(Y2/AWAcd2/A`2WA0&2/A cXA0 2/A@EWA)2/A QWA2/A`;WAe2/A WA3/A WAh.3/A\WAp_T3/A`6WAVz3/A`WA0*3/AEXA^3/AHXAG3/AYA{3/A@ZA3/A3NZA3/A-ZA4/AZA@94/A`<[Aؽo4/AX[Aj4/A][AHK4/AZA64/ADYA84/A qXA@4/A`2WAw4/A'WAW/A cAMW/A bA X/A;bApp@X/A@ aAhzX/A aAX/AaA Y/A`XaAG.Y/AOPaA@~fY/AqaA0Y/A(bA(Y/A@yBbAZ/AqaA(&Z/A@yaAh@Z/Aar`Ah Z/A`_A[Y/A,q^A@Y/A O]AY/Aٌ\AЮY/A`U[AZ/A`ZAP+RZ/A3ZAx~Z/A3ZA`Z/AYAX [/A@YAX9([/A AYA8b[/A]YAX[/AYA[/AH}ZAP[/A ZA~[/Af[Ap \/AL[A`2%\/AZAP\/A@ RZAw[/AYA$[/Ac YA[/A XA[/AXWA$[/A HVAP\/AUAx-\/A3UAP@[\/ATAъ\/AZUAؼ\/A@@UAP\/ANTAb ]/A@TA N1]/A`hTAS]/ATA~v]/A c TA8]/A ASA]/A@,1SA2]/ARA$]/AjRA]/A9QA ^/A QA+^/A@*QAU^/AߟPAU^/A cIPA]/A cIPA]/AkPA]/A ҹPA0|]/APAH5]/APAh@\/AZPAx\/ANOAhb\/A`NA0b\/AXNA\/AMAxǔ[/ALA0L*[/ALAUZ/A]mLA|Z/A@pLA+Z/A@yLAY/A@pLAxY/ALA -Y/A@ZLAHrX/A JMAP&X/A NAX/AT6OA8Y/A?OAkY/A`PAdY/A aQAX*Y/A [RAxGX/A`SAhX/A@yUAqX/A JWAX9oX/A`XApZX/A VYAX/A`XA&Y/A`XA?Y/A`f>ZAh(Y/A@[A`X/A J]Ax@X/A]AcW/A^AW/A@p?`A0̀W/A bA@nW/A`bcAW/AAcAP7W/AcAW/A cA5ac/A /A*qc/AA8c/A@>Ac/A`Ac/AA8c/A |Ac/AAxKc/A.Ah3c/A@~Ac/A@ذ~A=d/A~A$d/A@~ASOd/A`d~Add/AM~Ad/A~A`d/A஺~Aݓd/AK~Aid/A~A^d/A"~A8td/A}`~Ad/A8~A(9d/A}Ae/A9}A e/A~Ae/A1~AP8 e/A|z~A8d/A@5~Ahsd/A4~A( d/A $A"d/A Ad/AAd/A`Ad/AAe/AFAp e/AA,(e/AaCA@Ce/A@5~A0ce/A|z~AXwe/A@~Ae/A}Aze/A@,}AFe/A}Ake/A@?|Abe/A{A Ae/A {AP< e/A@5!{Ane/A@EzAp=0e/A4yAd/AayAd/A=yA@1d/A` $yAtd/A@IxA@d/A`-xA0d/A`dwAxd/A+wA@ d/AtmvAc/A uAxc/A>uAc/A@rdtAc/AmsA|c/AqrA Mc/A@YqAho+c/A`oRpA0; c/A[oA0c/AnAxc/A_nA؞c/A]mAb/A`?mAb/AfmAob/AmAh b/AV$nAb/AnAb/AoAb/Av[pA@Slb/AapABb/A LpAp|b/AypA; b/A 1qA@a/AෘqAXa/A"qA8a/AqA&Ra/A`mqA(1a/AqAX a/A AnrAȃ a/A c+sA0U`/A}1sAPZ`/AIsAX`/A sA `/A=/A}^A8#@/A ёAPoA/A ɆAH%F/A`AȵI/AQA`0M/A pAlP/A4AR/Ah&AnT/AAp}Q/A`A(Q/A痢A@$ R/AOA/R/A`]A0R/A,AP$R/AxA(R/AhAH)R/A`@Ac%R/A Ac%R/AIAH"R/A ұA!R/A@qAJR/AKRA@$ R/A CAQ/A9APQ/A`\AQ/A`2Ax)Q/A ೡAjQ/AٲAxǯQ/A@NAX9Q/A@#rATQ/AoAxzQ/A qA?Q/A-AÒQ/AAQ/AHAQ/A`lA#Q/AA Q/AfТAIQ/A ZA Q/A`.AhbQ/A %A0fQ/A!AjQ/A`"AXQ/AA(Q/AAQ/A /AQ/A`բA(-Q/AڻA0Q/A,Ap}Q/A`A7H/A,AVU.H/A6xAPn'H/A8Ae#H/A.gA4"H/AՏF AwZ$H/Ad1ٱA(H/A#Aiy+0H/APlRAC9H/A@aA2EH/A0OASH/A0"⵰A@cH/A AT+rH/AjfrAͨH/AkHAFH/A@ApH/A9ޯAxJH/A7AfL<,H/A:]AQH/As(ZA}NH/AT+ԮAXRH/AAy&UH/AȭAZH/Ak AkH/A[eKAlR2H/AeFAQ9I/As A;)I/A&[PAqMI/AIs罩AS#sI/A`J8AI/A:?.¨AJI/A\ZA)b&I/AATK=(J/AAoYJ/AA`%J/AOfAʩJ/A9eAV갾J/A\!-SAŇJ/Aq/Ar J/A"AJ/AMfA9K/AlkAq!{K/A_A(bf%K/AA()K/A`NAK/A w%A5K/AE夤ףAvJ/Ay)ABJ/A;7A}Wk^J/AS3̎A^DJ/A,lAWJ/AG!Af +J/ArАAI/A@>AUNI/A[-cARxI/A=AcƠI/AAUI/AA.2aI/A ACI/A 4A .&I/AlkAX^ I/Ḁ<ȱA@SH/AAďH/A5||.A i)H/A~NARG/A SdAfo{G/Aa7A4G/A@`ĥAqgC!G/A7 A4 G/A2Q#A -F/A"AOF/A%A@"aF/AjAUF/AAvBF/A,vޥA?xF/AҬAAu{F/AvIpAmF/A )AGF/A}hp?A!F/AA9E/A-ѢA &}E/A) kAܟ 'E/AqDBA҆\D/A43ȟAgpD/A|yAvD/A[(A*=C/A Ф^AULC/A3ļAȓB/A| AB/A%~៛AB/AK3ASA/AطښAk~EBA/A 閚A@/A*LgAH>j@/AmMAP\?/A% HAs?/AVXAN&?/A !}A

/A< 7A` Q>/A'A=/A@hAٚ=/AgAAWe=/At~AT)=/Aqk2$A+/A}AE>/A A`>/AC QA>JB`~>/A Aɛ>/AM}ȻAS7->/A@ Ak>/AA{?/A A#B/A@5Af`B/A[AaB/A oA52B/A#kIA-(C/Au@'A؇C/A 2 Am.%1C/A LuKA!q`DC/A%AHtUC/A%$YȼALAbC/AAlC/AnA7$rsC/Aw˽Au̩OuC/Am'*AsC/A AOJ~C/AXA-qC/A;"AsiC/A+6>A1C/ABLIA:B0C/A{BXAmC/ARfuAȊD/A$8Alޛ8D/ADžN9AaD/A AʡD/AhAHu sD/AS8AJJE/A¤ׁA3+E/A sAOE/A ,PAzKCF/A9}AaF/Aև8AkF/A.AB;DG/A8 BAvJG/A_9gA9G/AAFH/A7AwZH/AJxAnhDH/Ač$APAI/A@Aj`I/A6A{I/AbA]rI/A&B*AړI/AYAIeñI/AFA1I/A2AQI/AT5=AkI/A@vAJYI/AS*AI/A@W AKJu}I/Ae\B{4Aa=PI/AMATQI/AwSS&mA)H/AV-AH/AwA.vH/A\8A7H/A,A݋V/AkAWwV/AlKctA = dV/AGoA#5>PV/A9^AGP7>V/A!?:=@Ae,V/AI)An V/AkplA)MV/AAjʆV/A?>`A:U/A'uδAU/AwAU/AwA8\CV/Ar=rK:AV/A:6J6AS) U/Ah{풾AU/AKpAU/AQyIAaU/AhliA)j$U/A;ԖAU/A)A06~U/A`DA t$eU/AyFATMU/A#A-U/AzwbA U/A奣AS T/AB-A0{T/A@AisiT/Ai,ATE޴T/AC&AaT/A]ƄA#^yT/A9_AVT/AgAsm/T/AA8T/ATAvցGS/A. ;>AS/AKKfAOS/AYAS/A:ATyS/A`\AS/ASQA[S/As0)AmS/AAqS/A=A]aS/A$<:A]gwS/A"D]oA0S/AYAۏcPS/A5fAS/A=1AQS/AD[cA\ynS/A+H޻A9KS/AHFFA %%S/A8A2R/A2lu?AI~R/AmҹAwR/AEݕvA|OEqR/A.+A8?R/A3CA R/A`B˸ArQ/AARQ/A E|AQ.4Q/A.=A$X@Q/AhA ,8Q/AA)ۅQ/A:.6AP6 }rQ/A;ʶAךaiQ/A+YAhbdQ/A@A4Q/A<aA͏XQ/AA5P/AsAMMP/A֘fr7ANk`P/ANZAY'P/A,0ijADXO/A/d^AӴO/A~sAzO/AJfAWaVAO/AԬA7RO/A'ڳAh?N/A@Ak"\N/AoQA)N/AJ"AVN/Aϗ=A~$xN/AɨJAskN/A6VAL3N/Aj6A xN/A%RkltAN/A;׶A)N/A/\A?N/A|ALWN/A_"oA2N/AA~kN/AS+؆AN/A=AN/AkzpFAԗ0N/AqxAVN/ABx/)AֈWN/AAA hC O/A4iAWQ&O/AZA}CO/A"K:A6'eO/AOAO/AzZA⥋O/A"]A8O/Apae UA8P/AplpAA1D†GP/A>3%"AGP/A`Aa~P/AI0&AiP/AAec)qP/AM8.AQ/AAvX.0Q/A@aA2GQ/AΌ3AYQ/Aǿ~UAo4zhQ/ANm{AXqQ/A@EAV}gzQ/A|e*AcQ/AQ)tAXߚQ/AGgtAu%Q/AzA_-Q/Aث'A9lQ/A:7AWW[R/AFBAWj>R/AuPHAخmR/A*`GAP7R/A@y?APR/AeMtdAR/ALRAS/ASeAD!4S/A]ƠA#%cS/AuȠAܕS/A!d6AS/Aݡ5}AWMT/A*WAiOhBT/AUҲ$Am T/AVҴAl9bT/A>iQA#1 U/A/AI99PU/AtA];U/A= r:AU/AjABV/AopAo)A@W/A`A6W/AK@X AW/A"Aa\wW/Auņq AUHiW/AAW/AKAա.Aj{AС.A`{Aš.Ai;{A!.A` zA`.AzA.AezA@}.AVKzAh.A/zAL.AzAU.A@yA.A`yA.A yA k.A෇yAP.A -uAH!.AtuA#.A^ uAM%.A@5tApA&.AtA&.A tA%.AxtA$.AastAXd$.AetA(!.A$tA(.AoAT.AWoA鈢.AnA.AanA}.A/nAFy.A mA(p.AKmApd.A@lAwZ.A@G]lApO.AclA=D.AkA7.A@ dkA2.A`HkA+.A@-kAL$.AZkAx.A HjA(.AjA(z .A ZjA.AjA.A3jA)h.A`hAҝ.AameAPǝ.A SweA\.A]}eA(ܺ.A`}eA`!.AeA8.A ~eAv.AMmeA\.A]eA8'D.AoDeA,.A"eA8.AXdAXd.A`dA7.A@0dA제.AcAp.AccA.AֈcAq.A`BWcAX[c.A?cA3L.A8bAPxG.A bAHC.A@bA<.AWQbA:.A 'bA@7.AaA7.A`aAF7.AaAp8.AOaAB:.AkNaAB.A`A؞L.Ac`AY.A_Ac.A#_AXd.A`_Af.A}_Ahk.A`k_An.A (e_AzB.A[`A.AL7\A(௝.A %\A.A@![A( ٝ.A`m1ZAo_.AsTA.AGQA°.APAx$.APAQ.A@N/PANʜ.A )OA[.A`MA虉.A\MAz.A OJMA8p.A@:MAIh.A 'MA`d.A@>MA`a.AMA\.A`]LA0{X.A@LAU.ALAT.ALAS.A LAfS.ALAR.A tLA #T.AHLAp.A:<AH.A`))8AQ.A@ B5A(.A\9Abv.AY3Ar˝.Ay/AȶS.AD)A֞.A"A .A஠ A(>Y.Ai A0Z.A@E A``].A@A].AA_.AfAHa.A6AP7c.A`Axe.A@iAg.AALi.AoAh.A AQi.A@AP+k.AsAl.AAn.A lpAQx.A%A.A"A(>.A`A`e.AGAI.A`BAԟ.A@iA #.AA(z.A \A.A@A0.A@ƱA .A`uA`.A8Au.AA`J.A@,A"C.A@A@A.A@A0{>.A@A_<.A.A(O<.A[EA0<.A`[A(\?.A3AlC.A-AH.AAO.AAr.A`rA(z~.A A".A`TA8⃠.AAF.A`A@A.A G!A .A;)A8.A J+1A.A͆5A".A@?Ay#.A`AA83.A0AAx@.A ZAAK.A JAAP P.A`DAAS.A@>BAr[.A:BA/^.A@NTBAh`.AWmBA8a.A BA}a.A BA(`.A`BAU`.A@BA_.A2BAZ.A`BAH2T.A |CAHM.A>CA!J.ALCAjF.A`bWCAA.A bCA0:.A@lCA(5.AjCAx1.A bCA p.A`mTAA.A@NYFA.AzFA #.AFAH.A; GA(.A@NSGA_.AGA@`.A@` HA.Af[HA.AHA0 .A@HA$.A@GHA-.A HAB9.AO&IABN.ANmIAk.A߇LAX ӟ.ALA.A LAo.A@LA.AMA.AMAP.AWMA.A,MAp,.A@QMA@ .AMAhL.A MA.A`;NAX .A)!NAF#.Ap0NAPI$.A8NA#%.ADANA0L%.A xNAZc.AVA.A@p[AX[Ǟ.A q^AN6.AaA<.A`)dAeD.A@|dA@.A0AfA4.AzfA-.AfAo.A@iAh.n.A`tA`1l.A?uA*l.AuAn.AࣾuAq.A;uAPx.A@,4vAh.A |qvA.A vA.AvA.A mwAx.AFwA"ţ.A 1wA8أ.A@"xA.AdxA0v.A@xA.AxA #.A@ yA0S.AyAgp.A yAu.AஎzAˤ.ADzA ֤.A$zA E.A`{A@.A"{A>.Aa3{A.A`f@{A@.AI{A.A \O{Au,.A@%Q{Azѩ.A@>A pש.A`Ah.AWA`e.A A0.AB Ax.AA.A A .A H,A`.A@9Al.A`?A,.AXNA";.A3^AC.AwgA0R.A`zA0{_.AAk.AXA"w.A构Ap.AA0Ȋ.AתA.AAe.A@Ax.A`vA.AࣻA ͼ.AAŬ.A`A8Ǭ.A 8AKЬ.A`Aج.A萄A.A9}A.ABfA.AVRA7.A`);Au.A`!A .A@pA.A`mAf.AW΃A(.A@PA3.AWA(>.A eAxH.A OEAR.A@$Ap].AAh.AނAr.AcÂAB{.AA@B.A`mA@.AwAf.AcAj.AZDA.A8'A7.A@A@u.AAH.A`؁Az.A ZAȭ.AAhҭ.AxAܭ.AzMAH.A %Ax.A ZAH.AЀAz.ABA .A#A.AcA,.A@@A.AfA$.AAH,.AAH1.AaAA.AxA0LF.A]rA0Q.AHAp].AA`h.A3~A`r.A~Av.A~A3}.A~AL.A~A.A`~A.A@CX~AQ.A`/~Aأ.A~A.A@}A.A}A7Ȯ.AM}A`aӮ.A}Aڮ.A`d}A3.AB}A.A@g}AB.A|A.A`|A[.A q|APk.Aݍ|A.A ld|Ak .A`5|AP.An|Az.A{A2.A@G{AH2#.AX{A`).A{A8/.A@^{A85.A8{A;.Ao{AVB.AzAH.AzA8N.A@zAR.ALJzAxU.A`czA@W.Ad?zAu\.AzA`.AyAȘd.A (yAh.A`ܫyA(k.A@yA([r.A~yA.AHwAX(.A /vvA(ٯ.A`sA#.A=sAp.AsA.AQsAB.AߣsA8 .A ZrAG.A`frA`v.AfWqA.A`mqA=}.AqAk.ApA`.A@ypA`V.ApAG.AvpA81.A*pA.AoA.A nA^.AlA0{ì.A/iAx>?.AoRfA".A$fA4.A\eAX.ABcAQd.A`bwcAg.A RcAef.A`%cAe.AbA0f.AbAx.AA[`.A6A.A@33AxL.AT2A(.AH0A.A /R#A0L.A G A6.A XAXѶ.A@^A.A ATk.AWAX0=.AAȭ.A@ri A غ.AZ AP.A AZ.A`kAHvq.AA>.AkAx.A@A".A Aھ.A@`rAH.A AHu.A#tA?.A |A.A`;zAF.AஃA.A (AX}.A`ՈA(\.ANA@W.AQAXT.A ZAO.A`YXA.A ~A@,.A@5An.A\A.AAڴ.AA.AkќApS.A)ۚAA.A`]-A.A A.A_{A.A@r/Ac.A ~A?.A`LAp.A-A` .A늇AЯ.A@#@A8r.A@rfAЈ.A uA,.A`[pA .AkA ɹ.A_5iAmn.ATgA.A`0bA0.Al]Ag.A@?YA].A dXAHvo.AWA2.AcOA0r2.A2A .A .A.A[:A .A`wA.AA0.A`A(S.A@`wAL.A Ay..AEAPQ].A`Ar.A@DAG.A A..AA0.A+ AاM.AVAI.A A.A@ At.ApA .ATAH .A`APUU.A@Aȃ3.A`AHu.A A.A ,A+.AQ AQ.A9AXy.A_A(/A|!AD/A .A0/A;Ah^ /AZ]EAc/A|SA/A SAH/A/TAqa/AivA"/AHAw2%/A@ErAB&/A)|Ap%/A[tA"&/ADsA0&O'/ATApŢ'/AmyLAp'/A`oDAB(/A?S=Aڝ(/A+5A)/Aho-A=)/A"A)/A IA*/A96A*/A'A0+/A5A&+/A sAp,-+/AA`T+/ABAh*-/A`zAx./AAJ//ACAL*0/A`f5Ah0/A-A1/AA1/AA1/AALf2/A *A`3/AA3/A`Ao4/A5A5/A cA(5/A=1Ah&5/AyA8]6/AA,6/Az4A<,7/AbA^8/AqA(9/A -sA;/A@tA=/A`WvApű>/A:hA>/A8(gAH>/A efA@>/A,yeA0>/A_dA?/A@iSA@/A`MAX@/AKA]@/A@AApc%A/A74AMA/A`a0A?B/A A7OA/AAH{H@/A OA`<>/A AL/A@A!AqW'2/Ac,Aj֎Y&2/Ah8A[m3$2/AwH A_q!2/A:AMA&2/Ao(Ah2/A1XeAQ2/A3A2/A fA81/AAՑ1/AAA!1/A!JAyH1/A 5A1/A;A?#V1/A]NAA= 1/A[tC&A>?1/AƞAjF1/AjRxA,55c1/AyxAEv1/AGxrA!R1/AnA1/AlAKW1/A`%-5AZ~1/A{A81/A ATr]1/A{GDAH1/AMHA92/AnAB2/A`wA:2/AAH\V2/A {AH\k2/A@>vA0.[2/AAi!IW2/ACqA;9S2/A؀ANQ2/A<`AqbO2/A8WA=N2/A{NyApM2/A$eAegN2/A8QAxO2/A,i>=AOQ2/Ai+AEtT2/AtNA%X2/A" A8\2/A \AH%Se2/AGAHѥl2/ANj2AHq2/A@FA[Wo2/A5-A@m2/AjIJAZCi2/AkAwtd2/AךALB_2/A#%A[Y2/A.Ai‹S2/A(PA?L2/Az^A8F2/A HA'2/AtA0#$2/A1gSAln2/AǀUAj]2/AXA2/A6GAO2/A,ЏA 2/A A2/ApъAb 2/A$>qAeY1/AVknVA4@1/Aۤ9AK1/A9A&1/AfAA1/AiAh2/A(Ah32/A=AM(2/A~{oAةuP2/AoB9A3n/|2/A*Fv A ѫ2/A Aʴ:2/A9zAD3/AzA43/A@ =A8,5/A A95/AEcA#~5/AI̻A"K&6/APpAuvx6/AGR[A6/A A 7/AhA7/AxA`m7/A]AW7/A bAJ7/A`A8/AAp;/A A~>/A A>/A&AC>/AAX,->/AxAп=/AЀqA`=/ATslAXJ}/Ac6AP>/A?5AeÕ=/A:5ADHAcREjA/AYUAO/AYEVAu/A AfKc/AN#Atc/AN-A@/A mAi[/A`*&AgI/ABKAu/A4xA9/A_=2A?/A+A ZtJ/AUFAg1/AfڃARˇ/A+Atk/A2pA2Z/A*%A*/A AA@>/A`3A /A~A /AA = /A lA /A A /A@AX /A@ϜA /A`AhL /A`AX /AA /A@.2A /A@ =A@5 /AQA(> /A A /AHAP /AXKA( /AOxAx˭ /A0A4 /AxAi /A A@m /A`2 Axi /A 0AP8f /APAP` /AA?] /AAT\ /AAC\ /A@,AG /AAG /AAbF /A 1AD /AXAxzF /A`A`aD /A@GA C /AA]C /A `A;E /AAzA /ABA)8 /A@ A8 /AM A 6 /Ax A0 /A}A/ /A`A0+ /A@ A%' /AAD /A@@Ap /A&NAHa" /A"\A /AHA /ADAB /A A( /AA0 /AưAϝ /A`dAp /AAPړ /AA5 /A (A՘ /Ax_Aߎ /A A@~ /AqSA"o /AAPb /A &AHU /A AnE /A@߷A0 * /A`1A3# /A ɺAH /A 8AA /AuAk /AךA /AuA0.( /A`AnE /AVAcO /A /\AP7[ /A Z4At /AyAr} /AN>A+ /A Z4A /A`Ak /AMA` /A@A@ /A@A /AA /AxAJ /AA8 /A{A)8 /A@|AXh9 /A A V /A AXk /AuA"z /A  A /A A0 /A 3!Ax /AR!A /A ~S!AĖ /A`!AĖ /AD"AĖ /Av"A /A#AH /A@i#AQ /A^#AH\ /AJ$A8 /AR$A| /Ar$Ah^ /A $AD /A%%A)8 /AM%A8 /A?N%A$ /A%AP| /A%Ap_ /A%AP /A`%A /A`%Al /AЦ%A` /A`%Ar /A` %A`T /A  %A /A%A84 /A-%Apʢ /A>%Aj /AA%A# /A C%A{ /A%Ax /A`%At /A%A@j /A |%%ANc /A/%A`Q /A}%A= /A ۤ%A0/ /A`v%A( /A %Ar /A&%A( /A%ApN /Al&A" /A[l&A* /AK&AU /A&A0 /A&A /A஻&A؁ /A?&A0{ /AE&Ax{ /A&ACp /AV&Aa /A&'AP T /A lR'AA /AΌ'Ax>, /A'A, /A'A% /Ah'A /A(AP /A(Aѿ /A ('(A /A`d5(Aw /A`o5(AP /AX(A /A(A( /A@(Aȇo /A)A( g /A:)Ahf /A`;)AHiP /A)A: /A)A38 /A *A#8 /A *A([5 /AL.*A-. /A*A+ /A@*A+ /A*A& /ABP+A(" /A`ܮ+A /A@,A  /A`2,A`e /A`T,A` /A (,A1 /A |,AP /As-A /A ɉ-A /A-A/ /A@-Aؚ /A@.A` /A.AO /A.A /A.A /A Z.AHr /AT/Axz /A lY/Aӓ /A /A /A%0Ax /AT0A l /A0A(zR /A \1AD /AO1As; /A21A" /A_2A /Aj2Aп /Aӱ2AR /A3G3APk/A`;3Ab/A3A/A`e4A/Aj4A7/A65A/AQ5A1/Av5A/A}C6A0L/Apv6A(K/A 6A/AR7At/AH7App/A@7Ap/A7A0&/A<8Aؚ/A;A/AA|/A@A4/A`AAX/A d@A/A$?A9/A`v>Ax\/A@yR?AA/Atn>A>/A`9K<A/A@9;A3/A`2:A؁/AД;A M,/ANAPM.A`NAp.A` OA%.AൂOA@B.A AOAp.AOAP.AGPA$.A$QA .A-tQA@.A6QAx.A`RAx.Af SAo.A`JSA"n.A&SAhG.ASA8.A@WUA(.AZWA .A`)XAR.A VYA.A[ZA0.Aל]A#.A@^A .Am_AX4.AJ`AhG.AaA^P.AaA/1.AْbA.AD&cA .A_cA.A`mXdA.A /eA .A@ bfAH:.A3gA.A@fgAd.A@W?gAX.A`DgA8V.ADgAxV.A \EgAP7.A܈gA.A܈gA07.AKgA.Az(gA0.A"gAR.A[hA@N.A@yhA@.AhAh.AO"iA4.AliAh.AsiA`Ǽ.AtiA0 .AqiA |.ADjAxd.A`RjA8.A |^jA .A0jA!.A@jA.A`jA`6.A jA .A`.A`mAPo>.AmAW6.AfmAW+.AnAHm.AoAh.A5[oA.AoA Ҝ.ApA@.AqA.AO+qA.A>qA؞.A`PqA|u.AmnqA(j.AqA^.A qAR.AqAF.AqAm:.ADqAX ..A \qAR$.ArA.A`%rA.A;rA(~.ASrA.AkrA4.A ZrAx.A)rA.A \rA .ArAA.ArAH\.AsA`.AZsA.A 1sA|.AsA.AtA.A`TVtA.AtA".AxtAx .AtA0y.A`tA(s.A tAj.AuA8a.A#uAY.A6uA0 S.ANuAgD.AuAoA.A`ܝuAxG<.AuAp7.AuA3.A`uA&/.ALvAx+.A7vAu(.A YvAx!.A{vA.AvA.A 8vA .A#vA3.AvA.AwA.A`6wAHK.AaVwA.AtwA.AwA؁.A wA$.A(wA.A`wA0գ.A`wA.A xA ҋ.A` xA.A3xAh3s.ADxAl.A FxAwf.A`ExA`.A@#CxAY.A >xAS.A9xAhM.A@i1xAG.A (xA0A.AxA8b=.AxA0]<.AjxA7.ADxAP2.AwA<.AEuA+.A^tA-.A YvA .A@y\vAP.A`vAP@.AdvA.A ivA`Ü.ApvA .A|tvAĘ.A#yvA.AvA8.A|vA8E.AʟvA E.AӰvAl.AvA#c.A wAZ.A@9wAТQ.AgwAH.A qwAD.AwA@.AwA0<.A 8wA 8.AqwA3.A wA0*..AmxA(.A` xA@q#.A!xA .AT,xA.A5xAh.A =xA .A`DxAh?.AWIxA0.AaLxAho.AMxA.A@ MxAX.A`oIxA.ACxA.AgV.A@uA8T.A@>uA RR.AuA8P.AwuAO.A ~luAsO.AKVuARO.A@uA O.Ah.uAO.A )uAQP.AuAoQ.A@tAR.A`KtA(T.A ZtAH.W.A tA=Y.A ~tA[.A ?tA(^.AvsA b.A;sAe.AdsAj.A`BsAn.AsAs.A`rAHPy.ArA9.ANrA.A 8rAF.AoqAf.A`fqA&.A`qA@.A@pAȤ.A pAX.AVvpA.AoA@.A`oA.A XoAp.ApnAH.A nAG.A@>nA0Y'.A"6nAp7.A`"mAȇ8.A@mAsH.AFmA(zY.A`XmAH2`.AGA4o.AB9GA0j.AmGA^.A@GAkY.AGATY.A'GAYL.AGAdB.A@,.AEA>.A2EA>.A,DAȘG.AT]DAK.AXDAO.AhCAD^.AБCA4i.AaCAz.ABA@S.A$7BAH.A@ϗAAŔ.AhAA4.A7AA.AAAw.A ~~@A.A@B@A޸.A)@A8.A`f@Aj.A.@Aw.A_/@A`X.AO@A`e.Ae@A.A @Aj.A@A.A`@A .A`AAXW.A AAx.Af AA0U.A S@A~.A l@AC!.A0?An!.A`?AP$.A?A2.A?Ax):.At?A >.A@?A>.A?A'F.A ?AX.A>AHaZ.A>A)[.A]>A+l.Aq>Apj.A">AP ].At>Ao.A=Am.A@M=A8.A@K=A.A@;=A@.A&=AѺ.A =At.A<A*.A`e<A.A H<AX.A`;Aп.A+;A.A,;A`.A;A0/A:A /A Z9Ap_/AI9A /A9AȘA/A8A(Q/A@N8AU/A஭8A(~Z/A}8Af/A<8A@Au/A7Aau/A7A0e/A lA7A/A?@7Aٷ/A>7ApJ/A>7A N/A>7Ax~/A>7AW/A47A|/A@ &7Ah/A6A /A`6A/A@ߜ6A/A@|6A@4/AR]6A8C/AxH6AVJ/A66A`PU/A+6Aa/Aj5A)o/A5A+x/A०5A/A;5AȂ/Ae5A`ӫ/A;5Ap}/A 5AH]/Aa4A8/A4A/A`4A </A4As/A@4A`/A`e4AP8</A.4A_/A@ 3AH/A3AJ/A a3A/A03A /A`2AX/AE2AH/A |2A/A^O2AHr/AK2A /A&@2Aذ/A?2A/A"1A &/A61API8/A@1AR/A`q1A]/A?_1A8 t/A 1A`x/Aa1APf/A0APj/Aו0AP/A.AO/A@5~)Ap/A@"Ap9-/AAP /A`[A /AaA؟ /A0 A /A@Ah"r /A@.A//AA /AZAw/ARA8 /A'A/AA5/A`;TAϳ/A`AXF/AA /AzAHPR/AQANv/A A/A A:A/A&[A0/AMAS/A`Av/A"Aг/A A&W/AeAp/AgA /A#0A\ /AA(/A@yBA0/AKAE/A9AB/A 5A.AK>iA&':.AhLA&5.AmrA.AsAhb.ALqA /.A\A(# I.AJM[A{?#?.AȅYAv".A0kcXAH6).Ar WAtQ.Aa^.A`fvA@Z.AovAU.Af{vAA.AwAx.AgzAhj.A`8{A@.A`~Ax .AqSAj.AAd.A`A0]Y.A`Ap[.A@WA8.AlAp_.A JAR.A yAЄL.AA~.AஅA.AʡA0j.A2Aa.AġA׀F.AzVAx.A\|UA[~D.A{QAw0.A;;Am.AϻA V޲.AlUAbG솽.A ,ݰA ].A_VAg8.AEA.AA=i.AD'A4LӼ.Av*A儚W.AEAdN.AޭAH.AYr.LjA$p.AAZУa.AbA9V.AyӫAzeQ.AW@ASP.A}AX.A`A`.AUAX6.As{Ah.A{A]e.A$vA6A.ARdA0.Aӵ`A.AaAP.AbA.A@5@bA.AdA\u.A hAyy.AqikA.A`՝kA.AkkA.AyplA.A]lA8t.ASkA.A jAh.AjA( .A AQjAx .A`>jA6.A iAN@.A@#iAm.A`8iAt.AK$iA8.A`hAPީ.AΗhA.Ag!hA.A@ hAt .AgAz.A`fxgAD.A fA`6J.AfAPw.A wfAp.A afAƬ.AeA.A`eA.A^eAp.A`vCeA.AdAq .AdAL.A |CdA|U.A A,dAh́.A`cA5.AcA舸.A&&cA8־.A@ycA7.AbAX.A0bAh.A`TbAo%.A@bA@L.A@aAhS.A SaA]y.A 'aA(>.AdaAȦ.A`A .Aՙ`A.A5`Aa.A@!`A.A@p`Ah .A_A/.AD_AX6.A`22_A;].A@^AX .A`_A(.A_A].A`^A`|.A5^AS.A`B_AH.A1_A>.A E^APx.A@]Ah.A^]Ax.A@`+]A0.A`\A.A@i\AX[A.A&k\AXI.A V\Aq.A [Apx.A[A8Q.A6r[Axu.AW\A-.AjJ\A.A5[A0؇.A^Ap.A ۍ]Ao`.A ]A>;.A\A.Aj[A 0.AZA{.AYAx..A WA(iH.A@>WA}Y.A |XAm.AWA.A&\AD].A[AW.A[Ao.A`fE\A(p.A@5WA8I.A WA¢.AVAm.AnWA@5_.ATWA).A@PVA.A _VA:w.A VAq.A uVAذ-.AWA0-.AWA*.AQWA#(.A q%WAx&.A *WAhD.A|VA.AſVA`G.AVA .A`VA.AٷVAb.AVA[.AUAt.AVA([.A`2VA{b.AhCVA`.ACUAJ!.Aٳ>KUA}.AFdTA.A%{ITAR.ASA.ASAX.A0SA9.A\SA.A9SA#.A@SA.ARA.ARA.A@yRA8.AhRAx .A5RA .ARA-.AjQA".A`QA +.AUrQAy3.ACQA@=.A`,QAG.A`QAhsR.AQA0*].APA0 h.A/PAs.A?PA@~.APA.A`PAh.AîPA5.APAXh.APA8.APA.A`PAh.A`PA*.A`;PAX.AaQA(:.AQA.A"-QA.A`BQAЀ .AYQA`1.ArQA =.ArQAG.AUqQAE#.A cnQAh/(.AiQA,.A`fcQA1.Aq[QA6.A QQA N:.A`FQAH>.At:QAHA.A ,QAh^E.A@QAkH.A@ QAK.A PAPoM.APA[O.APApP.APAQ.APAR.AܜPA0LV.ANPA^Z.AOAX^.AOAc.A@cOAh.AOAn.A`NAt.AT}NA({.Ay1NA.A,MA;.ATMAٚ.AcAMAէ.A`dLA0.A ZLA.A@NQLA.AFLAk.AHKA3.A^jKA`T.A@KA .AJA.AJAh".AJAȱ-.AJA8.A@ JAD.A JAh@O.AJAZ.AJAe.AƤJAx q.A&JAI|.A3JA~.AJA0.A OJA.A` JAh.A`]JA0.AjJA.A@NJA.A 0JA0.A4JA7.AXJA(:.AIA`%.A IAC.AaIA.AIAx.AIA(.A`IAs7.AIAXF.AqIAPU.AwTIAxzd.A`5IAHs.AIAh.AHAŏ.AHA0ǝ.AHA@.AHA.A aHAe.A@8HAp.ABHA9.A@pGA(.A^GA@.A qGA .AGA.A\GA .A39GA.A,GA.A$FA '.A?FA +.A@ϷFA0.A9FA4.AFA8.AyFA^<.AacFA?.A KFAB.A3FA"E.AFAPG.A FAI.AEAJ.AEAK.AEAL.AaEA8RL.A@}EAL.A`bEAK.A HEAJ.A-EAI.AQEA(mH.A@DA0&H.AxDAEH.ADAH.ADAI.AH{DA K.A?]DAL.A?DA(N.A;"DA]Q.AgDA;T.A &CAPxW.ACA[.ACA.a.AÚCAf.ACAol.AMgCAq.A@KCA0Hv.Ag/CAz.ACAp~.ABA,.A`BAR.AʹBAP .AdBAPZ.AtsBA7.ARBA.A;?BAX.A[0BA@.A cBAR.A@AAPH.A@AA8|.AAAw.A@,AAp s.A`mAAq.A`mAAm.A@AAh.A@xAAЄd.A^AA`.ACAAr].AA(AAhZ.A AA0W.A`@AX.A@A@Z.A@A \.A`p@A'^.AXG@A`.A0@Ac.A u?APf.A@5?AXj.A?An.Af~?AFs.A W?AHx.A;2?AHP}.AF ?A0݂.A&>Aˆ.A>A.A>A.AF>Ac.A`K~>Axi.Aa>A.AB>A.A>Ah.A@=A(>.AF=A@.A`f=A1.Ao=Ap.AD=A ^.A=AF.A<AȺ.A <A(.AА<AA.A`c<A.AQ<A".A A<Ak.A@1<Ax.A?$<A8.A <A(l.A_ <A.A ~ <APf.AD<A~.A;A.A`;A.A@;A A .A;A.A;A.A;Ax:.A<A3.A[<A(.A<As.An+<A.A`6<AxW.A` A<AV.AJ<AȤ.A`R<A7.A`vY<A .A@^<A".ANb<A&.A3d<A).AVd<A8#,.A`b<A" r4.Az:9A<AE9=.Aa$<Ad3OG.A#ò <AR.A;A/orq.A] *7;A.AE R<AE.A0<Av9v.A&tхZ<A~pf.A<A.A;<A0e.AE<AX!.A`=A(z..A=A;.A9/=AFJ.AL=AX.A`fh=Ag.A=A@v.A@5=A.A-=A@u.A@=A .A@,=A.A`)=A`P.A`v=A(.A` >A.A`>A(.A $>A0.A`->AU.AF5>A8$.A@>;>Aؽ4.A`v?>A D.AA>AL.A->AcT.Af>Af\.A A >Ad.A`=Al.Aa=A{u.A=A"~.A=A.A =Ahď.A=Aȶ.A=A.A=Aƪ.A=Aڳ.A@y=A.A=A.A@y=A@.A=A@.A=A .A@=A .A@>A`T.A B>A(.A`)>A.A8v>A.Ak>A+.A@`c>Ap.A\>A`´.A@pW>A.AT>AЕ.AR>A.AR>AHr.AT>A0T.A`X>A`%.A^>A.A`be>Ap|.Acn>A.Ay>A`C.A"}>A .A>A.A9>A`T.AD>A$.A@|>A,.A Zx>AH25.Ajr>AX=.Aj>A cE.A a>ANM.AV>AU.A@J>AV.A6>>AVX.A2>AxZ.A(>A8\.A>A_.AK>Ab.AV>Ae.A@ >Ah.A>A8l.AV>AНo.Az>As.A`;>A\v.A>Ay.A@ >AP|.A@>A).A`v0>A̩.A`O>A .Al>A@.A 1>A.A@֑>A.A>AH.Ac>Ah/.A`K>AE.A>AH?j.A>An.A@N>A.A`>Ap.A<>A.A >A.A lc>AF.AD>Ah@u.A=AW}.A =A@d.A=A(.A=A.AA=A誘.A=Az.A`(=A.A= =A@W.A<Ak.A<Apl.A`<A.A@<AH.A<A.A<A%..A1<A0E.A;AHna.A:A(-.A:Aª.AHe:AH..A@f:A.A@2:A.A q:Ak.A 9A.A9Ak .A9A..A`;%8A6.Ap7A\.A 5A([.A@N5A(\X.AR5AT.A4A8R.A@4AhT.A#3AY.A2A[.A]k2Ab.A#2AX h.AR2Api.A!2A`l.Apq2Ahn.Ap2AXw.AR43A8.A[3A`a.A=R3A).A =3AL.A2AH\.A H1A=.A0A.Ap0AX.Ą0A R.A0Au.A30AW.A/AG.A`/A.A3O/A.AN/A.A@p]/A.A I/A.A=/AX .A.AQ#.A@.AH.7.Al.A(G.A3Q.A(T.A \+.Ai.A]-AH.À,A.A*,Af.A`),A8.A+Az.Az+A.A2+Ap.Az*A`a.A*AG.A@*A}.A Q*AH..A`)AG.A@)A“.A`)B)Aњ.A#)A.A")A`.A1)Af.A2)A`a.A`)A).AR(A.A`f(Ah3.A(Ap.A`f(A`.AY(A8.A?(A.A5(A.A (A).A'A0.A&A .A@\&AHܭ.A1%A.A`$A@ן.A@h$AL.A`@$Ap=.A`$AǬ.AR#A.A]#A.A=#A.A#AW.A@"Au.A^"Ap.A3"A.A"A.A"A0..A"AL.A!Af.A`)!Ak.A`[!Ar.Az Ad.A`f` AV.A3 AN.A \A8I.ADAu;.AࣼA#<.A@ZAp@.A A:.A@Ak/.A AP!.A@gjA.AA .A[Ap.A KAH.AA.ARA{.AVA.AAP.ARAX .A`fA.AzA.AA8.A xA.A@(A R.AA.A`)XA#.AzA.A`)XAQ.A A`v.AדA83\.AAPS.A`AP.A@GAp=I.A@AG.A A@.A]=A2.A A&.A`A.A'A.AAz .AA .A3A.A@XAH".A HAp=.AzAX.AA@%.A`)<Ap(.A \A%.AAL.A`)rAk .ASAp.AH/AH.A@Ap8.AAf.A͜A8.AVA .AAp=.A`A.AAH.A@ A.AtA.A`DAp.A@AG.AzA.A(A.A= Aǵ.A`)Z A.A[% A.ARV A0.A׍ Ah3.Ap A).AQ A.Ac A{.A`) Ap=.AAH\.A wAp.AA.A"AB.A |A.AR~Ak .AzAH .A HA .A=HA.ARAX .AbAG.AAp.A=Ap8.A@.A.A@A.AjAu.ARA.A@AX .A`fA.A`hA.A@XAH.A@6A.AAX .A]Ap8 .AzA.AQ AH.ApA .A*A.A`)A8.A̰Az .A`A#.A@<A@1.AzAk?.A@A`O.AHAQ`.A`Ae.AHAul.AzAQo.A:Ax.A?Aƒ.AAL.A`)Au.A A`a.Az:Az.A A(\.AAk.A \]A.A`(Az.AA~.AQAG.A`A0..ApeA`a.AHA=.A]7Au.AA.ApA`.A AL.AA.AHA.A+AL.A qA.A Ak.A@AH.A@A.A@Ap.AIAX .A$A8.Az Ah3.AHA.AAf.A}A8.APA.A`A .AAp .A/A{ .A HA{ .A@8Ap.A HA(!.A HAu0.AMAk=.A \AHE.A=A#`.AAr.AGA(y.A \Az.A`)A`{.AaAQ.A6Ap=.A]Az.A`fA( .AiAG.Ap9A.A AX .AA`~.ARAp8t.A`éApkg.AzA^.A[AZ.AAP.A[AC.A"AL8.A=@AL$.A VA).A=\A.A=JAX .AR(AX.AA.A HAL.AcA#.Ap!Ap=.AA+.A \kA5.AA:.A`AD.AIAzR.A@AT.A3AuU.A`XAf^.A/Azn.A3 A`.AA0.AAL.A A`.AA.A oAP8.A GA#.AR&A8.ApAB.AA0..AzAk.A@tA .AA` .AA.A`)A .AAh3.A@gA=.A`)A.A @A).APA=6.A HOAK.Az*Ap.AA.AUA.AAk.ARAPN.A<Ay.AAأ.A`LA.A3A.AA`.A`)PAz:.AAe.AA(܁.A@Az.ARHAL.A A.AA.AA+.A̼ALU.AzA@Wz.A`A8.A@AQ.A`A8.AA.A@gA.AA.AA&.ARA@.AA)N.AAB_.A@gA~.AAp.A.Aǻ.A)A`.AA(\.ARA`.AAh.A3+A7.A9A0.d.AA.AAk.AA.A`÷A.ARAL..ApAkA.A@jA8b.A^A0.AzBAHܠ.ARA.AA .A QA(\O.AA`a.A`VA.A@AP.AQA.AeAB(.A`)AJ.AA(\t.A@ A8.A Ap.A qA.A@|A).A`@A$.AAأ2.A@AH\G.A`jAa.A`) Aj.A`Asm.A@Ap.AA8z.A+A8.AzA#.AR0A̱.A`A.A#A.AA.A9Aأ.A`|A.A=A@W.ApAX .A`vA.A Ap=.AzAp8.AA.A5A.ARA( .AAf.A5AQ.AA.A`)Aֆ.A Ao.AzA`.A 9AT.A`fAH\K.A A6.A=dAH#.AA.A }A#.A`hA.A 'AP.AA(\.A[A.AAp=.AAB.A A{.A=dAr.A SAp=i.A`)lA@Wk.A@ AQm.AAf.A A)S.A6AF.Az2A=.AAV2.AA8(.A AG .AxA.AAأ.A A8.AA׎.ATAp=.A@DAXw.A \EALp.A@ZAkw.AA{.A \Ap.A%Ȧ.AkA.A@gAt.ApAb.A̎A`U.AmA@G.A@g@A8.AAu%.A=AQ.AR8A83.AAp.ApCAX~.A AU.A?AE.AA0/.AA#.ARA@.A3AHa.AA.A@A83.AA.AAy.A(Aa.AAL.AA9.AAG.A@AP.AMA.A HAp.ARA`.ApA.A \mAd.A q APC.AA &.A q[A# .A /A0.A=.AQ.AMA.A@lA`.AkA8u.A GA@W`.A ApN.A A#J.A0Ap=K.AAJ.AAXP.AzDAh.A3A8s.A A(t.A3sA`x.A=,Aft.AA`u.A=A`.A`AG.ApKA8.A H;A`.A HMAp.A׋AL.AA.AA`.AzA`.ApA&.A=pAfC.A AAJ.A3A8N.A=A#J.AAO.ARfAp=O.A` AK.A \A(B.AAp;.ARA8<.A`)A@.A@ AXL.A@ }AP8\.ASA)m.A |fA83.A@GA.AAp.AAu.A@"A`.ASAp=.AדAQ.AzA .AzA84.ARAC.A'AL.A=PA[.AׅAu.AAǐ.AA.AA(\.A Ap8.AAB.AA.AR`A`a.A3A8 .A@A.A]EA؞7.A HoA`I.AAX].AAx.AA.AA.AA.A=FA%.A@AX.A A|.ApA.A \A«.A=A Ҹ.AA.AzAu.A@ sAk.A@\AX .AkA.A AX.AA R.AA8 .A=4A.A A*.ARA89.AA?.A^AGB.A@AH?.AA9.AAH.9.A3'AK.AyA)d.AQA`a.AWA0..AAP8.AñA.A`A.A]AX.AAG.AA.AA!.AA/.AAI.AAT.AAd.A GAy.ALA.AwA83.A@A.A@ A.A`)LA`.ApSAz.A@ }A.A HA).AA8.A Ap8.A HeAp=.A֕AB.AA83.AAX .A)A.A=tA(\.A`̲A.AzAu.A HAp8.A`̲A.AsA$.A@A=A.A@Al.AA.AA¨.A@gnA.A?A.AAp.AA#.AzbAA.A`) A(T.AAXY.AAأf.ARRA8.ARAB.AqAH\.AA.AA88.A( A6.AýAT.AAus.AzJAG.Az*A#.A3AW.AzA׬.A \-A.A \AL.A=2Ak.A A#.AA.AAP8.A~A.A HA`.ARbA8.A=A.ARA.A5A—.A@ A.A@VA.A@A|.ARAb.AAP.AR|A82.AA.AAQ.A`pA`a.AA.AmA.AA.A=VA).A]}A`h.A A`%.A@ oA.A`)Af.A=θA.Ap7Aƒ.AݷA([.A|Az2.A3A .A A.A`)&A.A޵Ap.A`Ap.AҴAR.A@GAu.A |0Ak.A VA`.A`)Ab.A@JAp.A`Au.A$A`.A`)AW.A Ap.A@Aǿ.AGAu.A]Ap.AAP.A1A0.A@tA.AAh.AAX.A$A.A3eAk.A׫A.A3A.A`<A@.A \sAk.AA=.A`A R.A@g԰A(.A@A8.AA.A@APf.AAL.A`)A83.AA.A3A!.AA`,.A@ A8:.Az^Ap8K.AAF.AA`aC.AzA=.ApA88.A`fбA0.ADZA,.A`fAp=+.AGA0.A]A(\:.A2Ap>.AeA9.AzAk0.AQA{.AWA.A?A.A Aأ.AA R.A3WAǸ.A3Ap .A A0.A@ᆰAp.A 1AB.A`A.AHAP8~.A AAg.AAP.AۮAB..AA%.ApAH*.A`XA5.A`)TAu6.A`fAG8.AGAu&.A=ȬA.A A.A3A8.A AH.ARFA.A \A.AҩA.A`vA.A=A.A= AH.AAL%.AóA( ;.A sA(=.AiA>.AzPA4.A?AG$.AGAp8.A`fTAX.Az\A.A33Ak.A`)A.A`fAp.AGAB.AjA.A3WA.AAp8.ARA8.A[A.AA#.A@ ٦A'.A@A/.APA(=.ARAuJ.AAX.A@>A8Z.ArA83\.A`ApS.A[ABK.A@AfH.A \A(@.A q7A3.AԥAp1.AQA'.AAp$.A \ŤAp'.AtA".AA.AA.A`̾A.AzA.AzzA`a.A qAp.A(^A ҷ.AGA̮.A A@.A kAk.A[Aמ.AA0.A8AHܜ.A8AG.A@ጞA0.ApEAB.A@A`}.A ĝAu{.AAy.A@:Akt.AzA؞r.A`)6Ah3o.AΛAQ_.A`f@ABU.A=ʚA(\O.A@vAPK.A`f6ApH.ApA؞C.ApAB8.A`VAP/.A`fA&.A^A(.A A.ApAf.AjA.AHAX.AA.A@ A#.A`ŖAut.AAe.A qAR.A q_AG.A AG@.A`f̕A6.A [A؞$.AA.A 3A0 .AӓAf.AAp.AJA`a.AzA.A[ےA83.AޒA0..A Ap.A ϒAp=.A A.AuABz.A>AXi.A@6AZ.A3AV.A AU.AAP8>.A ˑA8,.A@>A.A`ÑA  .A`NAH.Az*A0..A`A.A A.A \Ap .A@Aأ.AzA¹.AHAש.A ݐA.A@ A`.A`)A@W.AzpAh.AIA`aQ.A 1AA.A`f$Ak#.AA.A |ȏA.A3A.AGoA.A`TA8.AHA.A=Aq.AGCA\.A_A0.?.A=HA(5.A]A$.A3A(.AAH.AA`.A3uAP8.ABA`.A=Ap=.AQA .A`)Av.AQ܎A)b.A@ᘎAP.A@ZAh3?.A 1A..A3AG.AzAp.AAz.A HAP8.AA.A@ḌAp.AA.AaAX.A!Ay.ARAWU.A`fAk?.A@GъA'.A`̀A=.AAp.AӉAu.ApAu.ApWA83u.AALS.AA?.A A".AqA) .A ZAH.AMA8.A \A5.ARaAPf.AeAأ.AfA.AVAu.A&A.AA(u.A`)ԇAp=d.AAQ.A=A@.A@ᖇA)*.ARA .A@ qAP.ARDA.A`A.A qӆA.AzA.A``A}.A`)8Ai.A`),ApF.A qA@W".ARA(.A`fA83.A A8.ARAG.A ݅A8.A3A#.AA8.AcAfe.A^AX O.A`PA=:.AGA).A3SA(\.AࣂA.A3A.A`A0.A=nA@W.A=@Au.AAk.AԄA)I.AAX 9.ÅA(.AhAP.A /A#.A`fAW.AA.A |Au.A`f AB.A%Az.A$A(\}.A A8i.A`fAK.A3AG7.A dA1.ApGA*.AA'.A A.AAf .A@A{.A`iAȢ.A`UAۅ.AjNA6R.AjXAhB.A_A,/.A'AԺ.AA .A3AB.A*A0..A :APy.A (VAn.A{A0.c.AAH.AR҄AP84.AAk!.A`A.A=ބA.A`AH.AoAB.AAA.AQ6Ap.A@XA-o.A`AHc.AA`^.AĄAY.A=ބAD.A3AH.A A).AA.A`A.A@A.A`fA.AGAp .A@A@W.AτAB.AބA.AA.A@A.A4A).A3IAz.AAp8.A@GA.AӅA8.A@څAB.AA.AA(.A`),Ap.AXAt.A뉆Ak.A`)A;d.A ؆A].AzAXM.A}A:.A A8 $.AyA.AوA.A^ωA.AñA`.AAΑ.AOAHY.A)AB.A`T{Ax.AA.A ÏA .A`sA.AYAH;.A`A;~.A@ȔAPz.A`yA~.AsA.A wA@`.AA.AA8.AA&.A AAR).ADA(.AAH&.AA0{8.AVA`<.A^Ah@?.ABAI.A@vAN.AkAK.A 8zAD.A@PIA D.AA>T.AA`.A OƦA k.A^A8f.A`DAZ.ABAQ.A q#AW.A`A0e.A`ܠA0;g.Af4A`q].A`A0R.A4FAP.AG4AuU.AT\AV.A AD[.A&AVj.AXA@r.A{A4{.A`bA.A/A1.A'A.A`tA.AMA.AƱAh.AH^A.A삳AXl.A`9AX.A`mA!.A Ad.A`[A7.A⻸A.A`K AH.A8A .A`BCA.A`.A.A/A.A5A .AAz.A<Ak.A`AQd.AvA8Vc.AX?Ao.A 휻Aؒ{.AV"AXv.A͉Ape.A`A5.A@lA.A A-.AZA.A@վAx.A ޿Ahg.AA9`.A`A8X.AA^@.AAs".A8A .A&A 4.A l A؞.A6A.A ZA".A@A6.AiA).A`A.AlA.AxA.AA .AA p.AApxp.A@>AwY.A`A(F.A A>.AAB.A AC.AAG.AA(S.A^A,V.A Ab.A Am.AHpAz.AA.AA.A@A@5.A@WAH\y.AiAV.A AA(0.AA` .AMA`.A ەA .A?AC.A0AH.AfAׯ.A AX4.A-A^.AA E.A Al .A` A.AApA.ANA .A}A؍.A uAe.A,A\.ADAH2W.A`mA?N.Aj,AS.AHOAc.A A.A`]A@.AAA``.A gAp.AjyAS.A EA.A+Ah.A@A9.A߽A`.AMAs.ANA0.A@oAD|.A2TA0s.AA਋.AԽA Ά.AA`B.A@`HA@O.AuAP.A} A8.AաA@p.AA8.A AX.AA@.Ah9A.A J)A@q.A`qAp.AzA>.A`cA8$.A[A@F.A$Ax.A$A&.AA`.A Aw.A֠AHr.A uA.AtiAxi.AAa.A@GA\.AA {.AEA r.AnAa.A A0Y.A"AG.A5AЄ?.A [A/2.AFA.AlA`.AැA .AA" .AA;.AA@ .A HAoG.A4A`~.A@A`r.AA .AA.A@Aث.A#A.AsA.A 8zA{.AlA$M.A,@AJ.ALAE.AahA̞.AtA.Aj,A.AA0.AQgAh.APA.A+Ax.AA.A@A .AA6.A[NA@.A`]A8xX.A Ax~R.A@AD.A3A0_.AAm.AdA.AAi.AA0.A;YAp .A@EOA*.AӝAm.A@qAk.A{A؁>.A?A?.A@ihAT.AA 0.Aa8AHn2.AAP/.A{AhL.A4A.AnAh.AA.A MA .AA}.A A>F.AAXF5.A APZ.A@iiA.A 1/A8.AB APkc.A(A.A A =.A;AZ.A6^A.AbA.AANl.AA".AB8A>.AEA.AXAu.ABAȔ.AॺA.A`A0&.AhOA.A A.AsA.AοA{.AAxf.A sA{.AVA3.AAP .A@JAx.A Apxx.A@#u Ař.A` A1w.A A0.A= AL(.A A.A@`" A@.A A`G.A[r A`O.AB A(:.A9? Ad.A`d APk.A1 A?..A Ah".A`fM A0:.A}$A/.A AJF.A@%A].AJA].A}$Aȩ.A AP .A@A.A`TA84.AGA.A`AF.AA(`.AAH.A!Ax.A-A٢.A"A0&r.A4A@dv.ABrA(:.A ;A.A;aA@u.A@yAC.AA@>.AAn.AעAr.A@Ap.A@rAf.AA.AA.A A0H.A9A@=.AA@F.AAA.A`AP.A`AJ.A@A.AྋAgs.AXTAxK.A AX.A@,"Aؾ.A`A8'.AMAh.AA Q.AqAЄO.A`]jALU.A`ZAPM.AҟA<.AA *.A`AC.A(A.AѺA .A;^A!.A2AV.A A؎.A[A <.AAu.AA.AՔA.A^XAPo.A`շAV.AvHA0y.A`Ab.AiA#.A@NA.AA.AQAh.A#Ao/.ArAP>.A#Ao .A"A .AA.A3A.A<,A.AA@ .AOA.AA0]'.A Ai.A ~qAi.A`KAh.AhA..A UAPHT.A OAsx.A Ax .A@AB.AA8o.AAՖ.AaA+.A@ϵAX.A &A.A A.A!A.A0A.AcA.ARA[.AA .A JA .A@|A.A` bA.A}YA.ABA('.AzA8.AfAM.ATA2:.A \A1;.AզA H.AA1O.A A =.A螙AC.AXApS.AXXAX.ACA0L.A?Aɲ.AԌA.A`mbAƹ.AdA(˫.AhA.A_ڈA`a.A⭈A`B.AA.A`;A.A@rAs.A@yA ^.ANAR.AUAXP.A AE.A`;:A .A TAXy.AˁA,.AnAAx.A}A.Ax}Ah.A_%~A`.A8xA.AjAM.A AP8.AA.A (UA.AxAh.AAl.A`AP.AwKAK.A*XA.A_|Aݗ.Ai+A% HX.A;~AF'.AZtA~қ.AJAzf.A7jA.AVMAK.A[A.AGA.A-~APkh.A@#ՎA7.A@ŽA!.A@N@A@.A` DA.AjzA.AVAȶ.A?đAP.A Am.A`A(h.A@+AH:.ARA@.AQ^APD.A@-A8R.AࣂAp8F.ArA@WZ.AA`l.AЍA~.A@API.A`Ak.A \A.AAأ.AAA.ASA.A<>A.A߉A(.Aj`AP.A@yA #.AAB.AG׈AW.ARA.A һAؽ.A'AL .A AL.A@ᾆAK.A͆A: .AA *.A`eAP@-.AMA0..A7Ak'.A@GمA..AAX0.AօA*3.AA=.A@VAP@.A?A3.A)AX =.A=΄A>.AA8D.A@nA(\N.A`A0X].AvAPkl.A@Aht.A 뒃A`v.A`zAn.AmAx.AA(y.A`m Ay.AAp8.A3A`.AR2AȺ.AA(.A*A  .ARAc.AVAZ.A=APP.AൢA|C.A@!4A8.A`vA 5.AɃA`v/.A A,.A/A$.A·A".AA R0.AA '.A(Ax.A@A.ABAp.ArAx.ATA).AAl.A AH.A`ӘAX.AmA.A0AP͹.A RAǮ.A]ωA`ӑ.AӊA.A4ՊA@.AAp=.AA(.A|A`P.A`jA|.ANAxr.A@A0o.A JAQy.A A`p.AHsA@n.AA@Be.AA88Y.A@%AH.Q.AQۍAXN.A|A@W].AG AW.ATAA.A@A?.A@1A;.A QA3.A`fA%.A3A.AvAf.A AX#.A`̼A83".AяAk,.AߏAL.AGYA.A`ËAa.A`ËA8.A෾A(s.AۏA0Y.A_A(D.A`cAx) .AA.A%AH.A'A.AAZ.A:A-.A`6Ah.A |VAv.A`lA0.A2A.AaAW.A jAHn,.AAv.A l{Al.A/{A.A ~ZyA .A`tyAPx.A@xA@q .A=xA].AtA`T.A |OtAH.A |rA.A@3sAA.ARrA.ArAxX.AUmApU.A`pAJ .A@`qAXW.A rA .A ZrA.A@:sA.AsA(>.AuA\.A8uA.A / A.AAr.AwAF.AقA8E.AB|A0d.AXAHٿ.A4AXǿ.A PA.As1AH.A`vkA0.A OrA(.A ɰA.A`~AXW,.A۔A.AmAo\.AAH#.AkApV.AKA(8.A$2A<.A`AX5G.A2A.A`A-.A@iA.A`A8m.A&AO.ApMA`ؽ.AQA8.AAp8.A[A뼽.AzAϽ.AAŽ.A`̠A߽.ABAP8.A@ AHb.A5AzN.AIAo.A`) Aأ.A׋Ap=.A MAk.A෤Ak.A=A#.AֵAW0.A AkF.A뉲A 9.AAp.ApPAӾ.A`oA0H.A A1O+a'.ARڹA}.wA.AAx^.A`vA .A`hA.A͞A5N.AAww.AA.A@CKA`.A J&AH.A`oAu.A AT9.ATA.A`KA.A@Ac.AAh.A`OAa.AtA8V.A`HA3.A^AhbV.AAE.A&A#.AA@5.AA.A A.A`o2A.A`nAJ.A@,A.AcAi.A_qA .AJA .AN$AX.A@kA.AA(W.A{VƿA?.AN*A Da.A#hA@.AA'7 .AbA$P.A#WtA2qa.AaiA.AAk.AsA0&.AN$A.AA胥.A)A.A@AmY.AAppM.A AH.A@A`=.A CA .AMA^.A@A˽.AZAǾ.A(A؟V.A0Aο.AAֿ.A@Aο.A`m5A@.A AP^(.AWAS2.AgA.A@A.ALA.AAp<*.AvA4.A?BA0Ժ.A`A(~.A A&.A+gAЦ.AAо.A@AH2.A@`A˾.A ~A@䫿.AAH!.AaA0.AAJ.A,GAhb.AsAx.A`ôA֎.A86A .AvtAr.AA(J.A@A=.A`A .AQ!A@).A@`AX4.AʺAXb.A "ApyP.AA?.A J*A.AgA3.AQ*A@).Aj AȔ.AA.A}Aܽ.A*qA5.A`]ֺA.AA^.AA.AවAq.A*q^A}.Ad:A0t.A?r(A.A@`A0.A c Ak.AA.AA.A޳Ah.A A\.A |A8.A@ȯA(.AݯA(iK.A@N̪AA H.At>A$#C.A5ޟxA9$i>.AyADb18.A-8dAh?1.ARAU.A`A.A@AQ.AGA.A6Ak.A١A.A`{A0l.A|YA.g.A#GA#a.A9:d8Au[.A5-AU.AV'Axp^O.A%AxI.A&A3%.A@Axʺ.A?A.AA`S.AAPڋ.A@A4q.A`Ap.A~APx.AM٣Ad.AiAL.A`AH..A=PAɺ.A(A>.AZAu6.AׁA(\v.A@NAl[.AKA>.AAp!.AٳA.AAHP.A`A.ANA(ܣ.A@Ag .A@WA8En.AԖA@Bٹ.A`(A.A1A.A Aq.AAx.A!A<.AQA.ArAX.AorA.ArAZ.Ay"lA6.AHRkA.A |fA.A@%eA.A`{eA .AgeAؒ@.A`dAS.A`)LeAY.ADeA=t.AfAi.AࣦfAi.AfAp.AfA8.AVfA.A`6fAG|.A`ffAQa.A@BeAj.Af5eA tg.AQeALV.AÃdA`e.A=DdA}`.A7dA`.A6dA.A@scAhb.A cA(.A4bAН{.A 8[eAȇ!.AfAذ+.A gA茌.A`eA.A-_bA.A`HbA`e.A`aApI.AaA k.A@iaA.AR`Axu\.ATaA`.ADZaA8u.AQbAn.A3bA@ƚ.A2bAHmZ.AcA.A@eA.AykeAl.A@cAH\k.A@ cA֞.A@cA.A`cAH'.A @eA$1.A|eAߋ.AtbA.ANbA[.A`aA}.A caAXg.A aAH\k.A`DaAx).Aע`APQ.A2`Ad.A`_A.A@i_Aʋ.A؂_A0.Ax_A.Ac_AP.A+_Ah.A=8bAe.AAbAf.AdA8.AGdA.A@ qbAp.AaA.A`fzaAP8p.A`.bA`Z.AࣜaAp\.A \aA.A_A8V.A_A6.A ^AbAE.A bA;,.AbA^;.AbAH)J.AbA].A bAk.AbA}.A`bA.A@bA ߜ.A]}bA8.AywbA@.A4fbAȥ.A3`bAR.AFTbA4.AKbA(.A<.ABbA(\J.A`;IbA_.ATbAi.A \YbAؼ.AdbAȩ.A`ebA().ApbA0.AqbA(z.AࣁbAw.AΈbA0w.AbAo.A (bA.A`bA8.A@ bA.AॳbA&.A ¹bA>.AbAD.A}bA Q.AbAa.A`bA8Ip.AbAج{.A`bA{.AbA.A bA0;.AbA8.AcA`2.A@W cAj.A`)cA5.AFcA.A#cA@}.A@+cAh.A2cA@$.A;cA0L4.A3DcAX>.A /IcAM.A2QcA].A`TYcAl.A4acApx.A3gcA .ApcA.A@ucA.A΀cA .AઆcAp=.AcA0..A@ idAG.AhdA.AzbA.A bA .A`f.cA.A \GcA.ABcA0.A bA8.AbA@$.A ۥbA{.AơbAHp.AbA`.A`bA@L.A@`bAH.AabAH9.A܀bAP&+.A@pybA* .A sbA.A kbA".AebA7.A6^bAxK.A/WbA0L.AQbAȤ.A@>KbA =.ACbA@.A@p?bAp.A`6bA( .A`0bAE.A?*bA~.Ad"bAu.A bA`i.AbAȶZ.A 8bA0{O.A bAc?.A`mbAl5.AdaAx((.AaA@.AaA0T .A$aA.AaA.A aAk(.AuaAu".A}^AV&.A@^^A8+.A`)T^AE.A Z^A(V.A _^A[.A`^AYl.Apd^A p.A e^AK.A`i^Af.A`;k^AX.A|o^A'.Ap^A .A`t^AT.Av^Ad.A {^Ax.A|^A].A Ҁ^Ah.Ac^A.A^A].A^A.Aj^A.AΎ^A(.A^AH,.A ɔ^A8>.AK^AF.A`9^A_U.A^A _^.A6^A(l.A@^APy.A@!^A`.AO^Ah.A ۮ^Ar.A ^A.A@^A`ص.A-^A.Ax^A .A ~^A@.A^A .A^AH!.AQ^Axd.A^Apx.A^AH.A@^A".A^A0-.AT^A<.A^AȃH.A^AU.A]^A^.A`^At.A-^Ax.A`^Ap=.A^A.A@^Ae.A^A.A^A.A`_AB.A_A.A@_Ap=.A@_A.A^AX'.A@^A.A_;^A^.A` ]A#.A \]A.A4\A8Q<.Aʬ\AWj.A#\A".AmT\A t.A)\A.A#\AP|.A`1\A#M.A`k\A.A@ϐ\A.A#\A@.AM]AX.AH4]A.A;]A$.Ad.]A?:.A ]AO.A\A_.A\Ax.AMv\A#.A@^\Apʿ.A 3\Ar.A[A+.A8;[AxE.AZAu.AUZAТ.AӧYAu.AxYAH.AXXA.AILXAP.A9XA .AILXA+.AʵXA7.A1YA`1[.AoYAd.A`YA.AYA8.AsZA +.A [A.A[AV#.A`v3\AV.A\Ap.Ah\Ad.AZ]]A.Ai]A8.Ai]A.A ]A.A /I^Aȥ,.A`^AxK.A`^Ahd.A6h^Aw.A /I^A([.Aܙ^AH?.A@5_AXl.A_A .A`A.An`APM1.AF`ATP.A^`Aw.A _AJ.A`_Aչ.A_AG.AHL`A`.A `AP2.A Z]aAV.A@%WaA .A aAչ.AxaA .A+aAX.A `AV#.A@,`AxE.AxaA`m.AqaAp .AaA=.A.AcA .A9dAp=.AcAL1.A OcAZ.A 8;cA>.A OcAp,.A`GcAx.A bAr.A 8;cAx\o.A^cA.AhdA.ABeA.AdA)<.A QkdA f.AndAH\o.AcA.AlcAH.A 8;cA.A bA`2..A@>bA0,.ALaA9.A#.A@kA1.A@dkAJ.A kAQ_.A[lAPt.A'lA.AlA.AmA0G.A&mAP.A/mA.A@ImA .A QgmA'.AmA.A`mAp.AmA9:.AmAP.AʠmA@~p.A tmAHL.A mA .AlA7 .AlA^C.A`lA0X\.AwlAn.AulA.A`{lA,.A=lA.A$}lAhs.AFlA.A lA.AlA.AclA7<.AplA0R.AlAHg.AElA.AmA8ڍ.A@W:mA ߡ.AcmA^.AimA*.A`zmA.AmA .A mA.AnAH{-.AnA=.A`$nA #O.A SnAp].A nAp_f.AwnAXn.A-mA(:.AmAp9.A nA.AG@nAX.A PnA .AdnA).A$nA0$.AnA0e%.A nA4.A nAI.A #oAU.A;oAhoe.AKJoAXo.A-oAXu.A`ToAp|.AzoAP.A0 oA8.A>oA1.A2oA͒.A`vpA@.A dpAĠ.A@pA:.ApA0.A`m+qA.A`dwqA.A4qAj.A}%rA.A@PArA.AΞrA(.ArA89.A=rAH.A A8sAWX.AnsAo.AHsAx.AsA.A&sAh.A@rtA(.AtCtA0G.A itA.A tA.A tAHz1.A'uAPV.A@]uA@Fn.A"uAz.A3uA@.A uA 0.AuAg.A3uA.A`uAp8.AuA[ .AIuA#.AvAe>.A@TvAW.A ZvAXh.A"vAHy.A0vA`e.A@g wA8 .A7wA軷.A&PwAx.AcwAP&.AywA 0.AxwA.AwA%0.A@!wAJ.A KxAY.A `xAq.ATxAb.AmxA`×.AĴxAa.AxA..AxA.AvqyA.A yAP.AyAhM.A`TyAH.A yA".AyAHa,.A@yA 3.A_yA(<.AyAUH.AyAM.A`yAZQ.AXyApU.AzAV.A3zABU.A`YzA0*R.A zA0TP.AzAP.A{APU.A`F{A0\.A{A8h.AU{A v.A`|Aȩ.A JC|A.Awx|A@ӝ.A@i|A.A0|A:.A }A|.AI}A.A@b}A>.Aj}AC.A`}Apr.AA~A.Av~A0.A ۚ~APk.A ~A.A@~AX.A ~A虹.AAP.AA.AL*A`.A@>;A8 .AJA[.A+jA`e.AA.AA0.A !AH;.APA0L.AAh].A@ Axs.A,A #.A ̀A.A@AX.AƁAt.A-A0.A 89AP@^.A jA].A@A.AHA!.A`[A>.A$Aa.AAP.AgA?.A@A.AqA`.AhFAX.A)APM.A)Ap8.A@Awj.A`TAƨ.AAp.A=A .A{A-/A`^Ap9W/ALA{/A`?A_/A#AH/AA /A@rAH{I/AـA|/A/ˀAr/ADƀAk/AƀA0f/A@A`6/AAN/AA@/A_A /A4ـAG.7/ATIA=yP/At yA Ei/AAu/A܆̀A} //Aj.AIU/Ak+AyJbg/A;8TpA/Am]Ay_/AX%A /A{A.R/AsA./Ag4Aϧ/AC(݂Ax</AvWYA:/A%rATJ/A.AXŽ/AFIɜBA"/AQA,/A\ACM/A^zA@/AuAXd@/ApACb/AŎmAl/A shAֈ=|/AAfAE2/AvIA i /A|HA9O/AoHA/A=a;zGA˶/AyZFA6( /AàHFA¢ /AJcEA > /A+@EA{\M /AoPDA욣X /AڈwDA:߄` /Anv DAOd /A>CAe /A+CA qSe /AN\ίBAeh /AJU?ALW( /Az)?A /A,ĄO?AKѭ /AA(u /AF/٫>A /A9L>A ' /Ab=A /A 8=A瘍 /AP ,=A\rV /Aڢ<Ao5 /A@B<A3å- /AGUJ;AW /A/m;A(  /A];A< /Ah5:A4 /AT:A]% /AeV9A7@% /A"˧9A\ /AG8A ͝ /A2|8Ap /A`[7A~ /A5AM\ /A74AE /AK Z2ANyȘ /A# 0A(lM' /A.Am:"= /AN;-A~t X /A;g+A1w /A]/l)A.xڜ /APmh (AHL /Askg&Aq' /A@$A8b( /Aqv]9#A} 2` /AS2!Aw /A2 AQH7 /AFDGqVAYT /AbrAZ /AYņAIS /AA1 /AKhA뜞 /AԖA|V /AzG0A" /A~8A`} /A59A|(} /AZvA /A@A /A!A /A0Ax /Ah_A /AʏAAb /A Ae& /Aj_ AxK( /A` A/AAW/AA /A`UAH? /A`bA"r/A@POA@W9/AXA /A@%AX/A A8*/A`A]/A3A@$x/A@tAP/~/A lA/AA/A=A/A7A([/ACA(z/A`RAg/AA8/A@A`/A@A.N{/Aq^4AP//A9Awr/AչA$v0/A|\ A2R/ABAh(x/AA˱/A@AyS_/AdtAY/Ax<ALIX@/AZZ Aa}/A/A8)$PAw /ABȕ*PA@}/A-,PAHXx/AzU}*PAs/A&PAp/A`PA/4/Ah>چOA^==_/A .OA?/AM\NA)w/AvNAW9(5/A(HNAK.Ay(gNADA.A쓛 NAEk.ANA"`.A@NA5.AûNAg .AHn-MA\N.At0MA͝<.ApMAГ.A DMAp.AQ?MA\.ALA %.ANLAL.A3LA.Af.A7lrLA)3~.AeqLAF.AZLA..AcLAH.AjPLA.AWLA@ƞ.AhLA3td.AZKLAL;).ALA.AZG='MA|bů.A, EMA9Ur.Aɮ2 QMA#4.A-MKMA.AP4MAo].Ab MAh.ApVLAPwD.ÄLA,.ASKA +l.A2wKAI.A?JAW .A@nJA?.A!K5JAo˅.A>IA4].AbHIA04.AHvIAYA.A9^yQIA6R5.A UAXO.Ab>Aovo.A5% >A)N.AIz=AŅ@.AJ=A3mC.A?'Kp=AM.Ag- _=A8+}.AQ_=AEWu.A]q=A1.A@`=A7^\.A Wa?A .AZ9bBAf".AssDAߞ,.AB!GAqu.A@,yIAטB.ASKAI .AYNA3.ANi`PAZ!C.AlRA P.A4ATAQ .Ax WArs+.AQ/~WA ~.AXA}.A~ fYADäo[.AiGZAv9.Ap3[A .AdPO'\A}.A@P$]Ak;.A.m^A uk.Aȹ_A,t.AWx aAIRS.A.abA=.AUjcAY |.A$ eA=.p.AIcfA.A9gA .A iA4ڞ.AcZjAcqܩ.AwkA[.A(/lA4.Aw0&*nA,W.AØboA~.A3pA6e.A&AH}.A@WGAe.A`fAx)F.AA.A`ܶA .A`MAX.A uķA.A"A@.A/A j.A`KA 8.A׶A0H5.A@,A.AJA.A A.AkAx.A$AH/.A`A.A@A@>.A`)AJ.A@ʹAh.AzA.AضAs.AAd.A(2AP.AA $.A`6A`e .A@JAhM.AADF.AA.A@i*A55U.A'*%kAҠw.AUAp [.AAAV<.AVqA36.AWеA(;f].AUA`.AَAx.A@'AH.AR AM.A`,A.AFA.A?A`!.A`"A.AkA.A 4A.A)As.A|APo.AՀAIl.ARAH{M.A`)sA .AWA.A@/A>;.A=IA.A`A .AǶA(K.AɈAxG.A@[A8s.A_A'.A cAX.AAX`.AUθA%s.AA).A@AY.AެAh`.A A.A`KAг.A` ANJ.AHϽA.AݽAE.A As*.AYAO.AVAp[.A lA.A|Ag.A@ևAЮ.A 끾A@Az.AlAxT.A`iA02.AdA.ARlA.A`iA.AQAc-_*.A>"!AE &.A*Aj".A ֣ A.AA.A AX.A`9ϲA(j.A ZQA&B.A@AP.AA+.A(Ae.AVAۯ.AQDAϩ.Ac7A.A˷AhΠ.AQAPg.A/A.A%A.A2=AH16.A;[ARՓ.A9_Aa.A =cAwEo.A`ٸA.Ao A0.A`?Aэ.Az(A8.A%A.A`TA99l.AuxA+.A0A(.A@lAJ.AfZ핽A.A|A4 t.A ݮA.L.Aґg{A-%.A%+{A[.A[13EA.AA "ų.A YAGR$.AhzA6~ m.ACS jAbN.AK+5˺Aٺ"4.Avl:"A*9.AnpAZ>i .Aɜ|:A.A@A.AcőA7.AOb4A_ (.AA$>.A(r-ӞAF‰W.AHjA r.Ab5HAg.A z8Ab.A`o;A .AeYA`.A`mA{T.A=/xA'P.A@=8vA0Y.A gAٔV.A ]bnbA).A9ZQAG:;.A(5AK.AA(Z.A)I޵Atg.AtŤARq.Aג@eAy.A@ AxG.AA0ٲ.ArA୕.AA.A$A.A`޲A0;.AӲAʼn.A`庲AXh.A@Ay.ATAe.A .A@.A`A.A0ޱA.AaqA`.AAxi>.A@ΰA .AfA`.A |EAxv..A`A.AʜAxo.A Al.A`{Aٯ.AZЭAP|$.AAp.AF8A@.A@˫A<.A@rAP.A0Ah.A'A.A AH?.A͔A@J.A S A.A@PAp.A@yhAz.AAp<.AA( .A@P=AH.A`TAS.AǻAT!p.AAm>.A[V_^'Ayz.AX^Arf1.AߐA[.A<)#A;5.AućiAD`.AC2AAf;.AݿfAZ.AAo.A`m|A.AA`T.A l}A .A`aAXyw.ArA@z.A sA`.AOAx5.ACA t.AݰA0.AvA.AA.AIA`.A 8iA.A ZA.A2}A.AiA .A<A`%.AfA( .A`vA.AӱAd.A`ñA.AA.ABıAhs.ABڱA@.AEA8.A^A$.A A*3.A9IAp,5.A &A`C6.A,A8;.A0A`aJ.ASAhO.A`A P.A若A S.AiAHLQ.A`.A8I.A8_AE.A A`?.A/AH!+.AA.AQAp.A<ԷA .AͼA.A`A`.A޹A.AZAw.A>A.A42Av.A@PA8b.ABA .AR2AG.A@A.AkA`T.A`A؛.A3A0.A 8=AY.A,eAH.AuAx.A1A舲.A LAp8.A@]A.A`)jAV.A |}Ag.AͮAu.AA .AA.AA`й.A@-A؟.A Z_Ap.A=qAx-.A`|A.A@APo.A|A7.A quA@W.A-A`.AvA".AA.AAx .Ah A.A@eAp|.AAv.AδA@.AAȓ .A`TA8.A`KA.AA?.AQ,A(m.AEA.A A]AP .A \sA.ABAn.A&Apl .A sA`a .AA@.AsAK.A`A.A.Ap,.A`9BA.A TAp|.A hAp+.AkA( .A`)A..AA.AA.AAH.AhA.AA.AA,.A)Axh.A$=A.AhA.A@zA(.ADA@.AA#.A A0.A$A.A@A@.AIAo.A` )A.A`JAo.A`wA.AA8 .A`A.A A@.A@WA.A@AX9 .A Ap .A)A.AIA.A`zA".A AH +.A@ A9.A ZhA=.AA*D.A ZAH.A@AXN.AGA9S.A`KAT.A@iqAP.A`A8L.A` A2G.AAyC.AATC.AE^AF.A APO.A4AN.A@nA@1L.A[AbH.A3'Ax:H.AMA;O.AфAS.AձA0&T.A sAP X.AA0X.A+A(S.A>A8S.AqNAXS.A wA`X.AAh[.AAhb].AAs[.A|-AY.A@iXAxZ.AwA(a.AAKf.A`2Ah.A@Ai.Aa(A0Yk.A`TA(){.AA.A@A<.A A#{.A?Aux.A Az.AAz.A4A y.AuAu.A`[AH].A4APT.AAUH.A`],AGB.AIA@.AfAB.AOA0HF.A3AX4H.AjA`H.A`B APC.A:AL).A 7A$.A]A.A ҠAhs.A3A .A4;A> .AAx.AAX[.A Z;A.AA0v.AIA.AA.A CAp=.AwA.ARAP .A Ah".AA5.A@A.AyAhb.A'eAx.A@5A.AvA@.A'A~ .AJA.A XA(.A~A.AA.AAXh.A4A8t.A׳A`.A2A(\.AA.AAx.A@Ah.A5A.AzQA.A@plA.A@A.A ғA@$.A@ߟA0*.AAP.A Ac.A AH.AAN.AAѻ.AAhD.AA8.AAhs.A`AXJ.A@,A N.A ~Aȓ.A QA(-.AhA).AzA趨.AA.A@A(.A A.Ap)Az.A`AA.A6ZAܰ.A 8sAU.AqAQ.A AѰ.AA.AHA.A A88.A'AX.A q(A.A`@A.AOWA`.AnA.A AAX.A`bA.AAHa.A``AX.AUAX.AjA#.AA8.A`AH{.A ZAR.A:AP.A\AhQ.A~Au.A@A(.AAp.ARAʝ.A2A0.AhIA4.AtAB.A@AH.A@gAX .A3AX.A@Ahğ.AA.A&/A.A3LA@.AhA{.A`fA-.A`Ap}.A SA@Wz.A`A x.AA4v.A.A t.A=DAs.ASAȶr.A bAq.A qAn.Ag~Ael.AൊA@i.A •Aze.AAHa.AnAP^.A@ AP8\.AAH Z.A AMX.A Z AhV.A6A#V.A MAHU.AcAU.A`zA(U.A?AdV.AAW.A`KAHaY.A@g,Ah"].AL>A``.AOQAHc.AReAx)f.A4zA^h.A׏A$j.AA |k.A`ռAHal.AA0&l.A Axzk.AAp_j.AAAh.Ag.Af.A@DA8d.AXABS.A=AP.AA@N.AA`XM.Aq APfL.Ag=A(K.A`ZAL.AwAN.AATQ.AABS.A`AR.AACR.A -A_R.A @AR.A$SAS.A eAPIV.A@{A@dX.A@AZ.A`A(m[.A`AW\.AA\.A@ A\.A=A%\.ALoA)[.AAJ\.A A\.A@5Ah].AGA`\.AAأ[.AGAY.A`AW.AA3T.A AP.A >Ah3M.A_AH<.AA(-;.Ao&A&:.A'`A9.A`Ap9.AA9.AjAp:.A )AH;.AXA`<.AAkD.A@ AE.A+A{G.ALA0I.AmA88L.AAh3O.ARAR.AAoU.A`pAY.AAxZ.A`AZ.A`A[.AA[.A@AZ.A@9AY.A`SAX.ApmA0.W.AA(W.A"A8tW.AA@5X.AAX[Y.A-A`Z.A AXl].A@NA_.AA`ea.A`(Ab.A?6A8c.ADAe.A`fAxig.AAh.A`A>i.ALAri.A`"A(-i.AAmh.A6A4g.A-YAe.A \{AH\c.AAh3X.A A'U.A NApR.AAh@P.AA`eN.A@NAL.A:A K.A`\AH.A@.~Ah.F.A&AC.A`A?.A`2A;.AAL7.A`)AH1.A2A*.A (GA$.A`YA.A@gjA,.A rAH.A|Ap.A@χA.AAP.A?A(.AAأ.A33 Ap.A`\ Ax).A@ A`.Aɲ Ag.A A/.A Aj.A7 A.A@Ge A.A} A(:.A A.A#3 A6.Ayx AH.A  A.A A.A% A.A@CG AB.A^h A.A Ao.A@ A.A` AP.AM A.A Q AP^.A" Az.A> A0.A a An.A@ A.A AB.A AH..A Ah.A JAH.Ao1A~.A sRA).A=tAH.A`9AZ}.A`Ap{.A`Az.A AXhz.A@iAHz.A`'Az.A 5A8{.AAA(}.A`TNA~.A`ZA8 .ALhA.AmwA].A=Al.AAH.A<AF.AA.AA.AQAp=.A0A.AT A8g~.A@AG{.A2AXv.A c\AhQp.AAH.j.AͬAHd.AA~^.A;AW.Az4AR.ANWAuL.A@C{A0]G.A@CAHB.A6A>.AA@:.AA7.A <Ah5.A@ dA0*3.ÅAF0.AA,.A`AD).AA)%.Az A .ADA.AgAJ.A Az.A 8AB .A A`.A{A0.A AP7.A`A.A@A.ApA.A@N=Ar.A `A.A@5AQ.A A.A@!A|.A`)A.AA.AAXy.AApJ{.A<Afs.A`)^AXp.AlAm.Av|A_k.A &Ai.A@Ag.A/A Re.A`Ab.A`vA `.A߼A].A@A0X.A QADT.AATP.AAL.A@AI.A@A <.A?A 0.AxzAH$.A@>A~".A@5AТ .AfA$.AA .AA@`.A`A`%.AA(\.AA.AkA8.AJA.ARbA.A`yA.AA0{.AɦA@.A`KA.AA@ .AAP' .AdA.AAh.AAF.AH&A .A6A.AHA .A[Ah.AwAj.A$A.A`As.AiAp.A@%A{.A@ A R.A!A.A .A.A`9A.ADA0..A@LAg.AWA.AWcA.AoA{.Ac|A_.ANAh.A`)AЕ.AA.AA .A Ax:.A A.A&Az.A |AAX .A \[Aݷ.ApAM.A@߄A`.A`A.A෨Aͥ.AEAݢ.AA`S.A`;A3.A`dA.A`)Au.AA@=.AA(~.A`A9.A`Ap.AAH\.A^A.AOA.AzAɒ.A&Am.AǫAȂ.A@#A .A@Ap .A A.AAȇ.A u Ap+.A3AC.A+A`|.AK:Aw.AGAr.A]SAf.A`fAXF_.AҺAPX.A(AR.AXAHM.ATQA(H.A@ AF.AїAE.A`A E.AAD.A/AE.A`AE.ANA@F.Az AG.A@) AhH.AG A@H.Ae AhG.A@P AF.A̠ AC.A@ AH?.A@ Aȱ;.A8!Ap 7.A@ 9!A84.AN!A1.A/c!APZ..Av!A*.A!AX&.A&!Ak".Ap!AX.A!A .A+"A.Apm"A0.AB{"A^.A"AHz.A`"AF.A"A@.A-"A.A "A( .A "AX.A"A0 .Ap"A).A@#A.A!#AH.Ap;#A.Al#A8.A`[#A.Ap#Ad.A#A0.A#Ak.A$A.AQ$A<.A`K&$A.A9$A.AL$A.A e$A!.A~$A.Aྗ$Aa.A@$Ap=.Az$A.A$A.A$Aho.A`$A.A@%$A.A-$A.A$AX.A`"$A).A$Ax.A$A.A`) %Aa.A%A.A )%AЦ{.A <%A}l.A@.P%Az].Af%ApY.Ati%AU.Atn%A:R.Au%AN.A}%AK.A@%A@H.A@N%AF.A<%AC.A5%A`A.A%Aȃ@.A+%Aؼ>.A@,%A<.A %A*:.A%A@7.A`2%A4.A%A1.A%Ah*.A %A`a".A%AH.Aq%A`X.A%Ap .A` &Ap.Ax3&AxK.A \&Ah.A&A`.A`ó&Ap.A &AD.A 'AY.A 9'Aĵ.A h'Ax.AH'A.A'AH.A'A.A@(A0.A9(A.A`(A`.A(A8.A (A M.AB(AhL.A=(A.A@(Ah.A(A~.A)A~.Af)Ap~.A`).)A=.A@G)A.A|`)APZ.Ax)A.A)A _.A)A].A`)A.A@W)A.A@)Aш.A)A <.A A)A_.A-)A@O.A)A.A=)Amv.A`)APk.A)A(`.A`f)A"\.A`)A}W.A)AR.A)APN.A`v)AJ.A)APF.A \*AP.An,*Aw:.AA*A:7.A@yX*At4.Ap*A()2.A[*AH\0.A \*AB/.A *A..Aq*AH\..A@ *A`..A*A(>/.A*AU0.A*A1.A*A03.A*A7.A}+A:.A+AH<.A +A?.A|.+Ax@.A@>+A cB.AO+A[C.A \a+AHC.A3s+AHPC.A`+A(>B.A}+A@.A+AБ>.A+A<.A+A _7.A$+A.3.A ,Axu/.A ",Ap9,.A Z;,A).A U,A'.Ah,A&.Ak|,A0$.A,A =$.A@,A$.Aù,A%.A ,Ah@'.A,A7(.A,Aȱ(.A,A(.A,A"(.A,A'.A-AP".A-Ahn.AL.-AH.A(B-A0.A]L-A.AX-Aj .A&e-A` .A@ys-A).A@-A0.AL-A.A-AB.A3-A`.A@-A.A.A.A,.A8E.AD.A`.A`;\.A.A`s.AP.A.A`a.A=.A.A`.A".A྽.A.A.Ak.A`f.A.A`.A.A .Ah3.A`K.A.A .Ap.A`).AX.A/Ah.A/A.A&/A.A@ 9/A.A L/A.A`/A0.A@ t/A.A /A0..A \/A.A`]/AX.A@y/A@.A 0Ap.AK0A8.Au0Aq.A0A.A@0A.Ap0Aج.A@i0A.A` 1A.Aj 1A.A`)1A.A@>1A0 |.A 1AXyx.A$1At.A1AQq.A1Ak.A1AHf.A` 1A{a.A'1Ar\.A/1AW.AX91AR.A`D1A~N.A Q1AQJ.A^1A@H.Avn1AE.A`|1A |B.A1Ah?.A 1Aq;.Aෟ1A؁7.A 1A(\3.A1A1.A̭1A@..A`ܮ1A,.A1A*.A 81A(.A 81A'.Ax1A8&.A1A&.A1A*'.A2AQ(.A /2A?).A@A2A).AMT2A).A@,g2Ah).A`y2A(.A2Ax'.A2A@&.A2A$.A 2AP!.A=2A.A 2A.AV 3AP.A@$3A .A=3AX.A``3A.A3AL.A3A.A-3Axi.A#4AH\.Ab4A#.A4A.A5A`e.A 55A0.AQ5A.A m5A8.A`95AP.A 5AH.A5A.A`5A.A 5A.A5A`.A@>6ApN.AI6A(.A6AG.Az6A@.Ao6A.AK6A茚.A7A0.A?!7Ax.A;7A.AGU7AL.A@7Aӗ.A7A.A7AHr.A7A0.A7A 4.A7Ar.Ah7AQ.A8A(.A8A.A8A[.A&8A.A-8AR|.A68A0w.A@A8Ahs.AMN8Ap.Ap\8Al.Al8Ai.A@|8Ae.AǕ8A(a.Aǭ8AG].A8AL.A@ 8A8I.A8AYG.A#8AQE.A 9AC.A@E 9A-B.A9A8#A.A 9Az@.A(9A>.AE9Ap<.Aa9AH9.A \}9AxG6.A ˆ9A(2.A9A8..A`"9A*.A9A@B$.A9AH.A@ְ9A.A9A.A 9A.A9A.A`9Ax .Aa9AH. .A@ 9Ap=.A`:Au.A:A.A:AP8.Ap;AǢ.A ;A.AT<A|.A3<A)g.A<AF.A@<A).A3=Au .A`X=A\.ALk=A.A=A@.A`>A`a.A,>A.A%>ApͿ.A`f >A.A>A.A >ALr.A?A U.AH?A=.A3?A`*.A@?A.A?A`a.AH?AQ .A?A0.Az?A.A \?AG.Ap@A.A@A8.A"@Aܾ.Az(@AӾ.A`)6@A`Ǿ.AzT@AǾ.An@A.A`)|@AG.A`)@A.AR@AHᗾ.A̮@Ap.A̸@AX.A@Auy.A @AQt.A@An.A@A#[.AIAAT.AAAuP.A(AA`aG.A AAB=.AAA86.AGAA-.A]AAX'.AAA83 .A AAX.AQAA@.A BA .A]?BA).A_BAG.A@pBA .ABA#ҽ.A=BA(\ʽ.ABAʽ.A`)CA̽.ACAн.A 3CAp Խ.A`fHCALԽ.A=jCA`ѽ.A`)CA=ͽ.ACAĽ.A CA#.ADAP.ADAp .ADA .A=DA`.A@CAB.ACA`ွ.A CA`w.A`)CA^.ADA`S.A`DAF.A3)DA8.AGDA-.AlDAp).A@DAp8*.A@DA,.ADAu..A \DAk,.A \DA(.A@g EAH.%.A@ EAp.ADA( .A DA.A3DAP.A=DAf.ADA.A@>DAH\.A qDA޼.A3DA׼.A3DA0Լ.ADAӼ.ADAѼ.Az EAB̼.A@ !EAh3ļ.A3EA0.A@>|EAfm.AEA3X.A EAp K.A FAأ?.A?FA3.ArFA().A]FA`.AFA.A@FA.AGA.A`f GA@W.AzDGAp .AGAB .A3)HA .AMHA(.A wHA8.AHAp.A@IAXӻ.A=|IA˻.AHIAk.A3SJAp.A@tJA毻.ApJA.AJAf.ApJA̎.AzJA0.ApJAׂ.A mJA0~.A3EJA@x.A@>2JAzp.A7JAX h.AOJAL_.AxJAU.AGJA8F.ApJA؞7.A`)JAP80.AJA'.A@>JA8.AJA.AJAp.AzJA؞.A@JA8.A1KA0..A `KAp.ARKA0. .A֧KAf.AzKA.A`fKAL.A KAQ޺.ApKA`Ժ.A KA8Ϻ.A= LAƺ.A@\LA83.ApLAp=.AMAf.A{MAuw.ApMAf.A=MA?.Az.NA3.AHQNA0.+.A`frNA$.A3NAG.ANA.ANA`a.A3NA.A NAz.A`)NAk.ANA8.ANAع.AzNA).ANA).A OA.AOAf.AOA𣈹.A`%OA@ׂ.A`9OAzq.AחOA#i.A[OAu_.AOAQ.AOAC.A׽OA9.A̤OA8.A OA1.A3OA+.A@OA{.AOA.A`DOAp.AROAX.A@>OA.A3PAB.A%PAL.A@0PAkӸ.A TPA(̸.A gPAL˸.A sPAP˸.APAuӸ.A`fPAP߸.AzPA.AzPA0..APA R.AzPA.APA.A3PAƸ.A`fPA).APA؞.Az QA8.A`PAˆ.A*PAਅ.A3PA#|.A`)PAp=N.APAp=F.AzPAB@.A 0PAp7.A PAp4.APA@W4.APA833.APAX-.ApPA!.ApPAL.AGQA.AQ$QA(.A4QABӷ.A3QAP.A3+QA .A( QAP.A(QA.A(PA.AzQAP.A]!QA ~.A?QA{.A }QAp|.A`)QAp .AQA).AQAp.AQAp.AQA`a.AQA(܂.AQAy.AQAo.A@RAd.AQAuT.AQA0.F.A@QAأ;.A@QA..ApQA`(.A`fQAQ%.A`QA@W".A` RAG.A5RA.A`)VRAL.ARA.A@ RA.A@RA(.A`fRAڶ.A |jRA̶.A3?RAQǶ.A |.RA.A@"RADz.A)RAʩ.A A4RA8.A8RA.ABRA롶.ARPRA⡶.A YRA(ܡ.A3aRA(\.AlRA®.A@RAֺ.ARAp=ƶ.A`)LSAQ̶.ASAH.̶.AHSAƶ.ASAH.A`)TA.AR.TAB.AoTAL.A@gTA`av.ATA#j.ATAc.ARTA0_.A TApZ.A \kTA`aT.ApQTA#H.A=TA:.A0TA/.A1TA8&.A GTAB.A`)lTA.A@TAP .A TA .A@gTAz.A@>TA.A@gTA؞.ATA.A3TA0.ܵ.A 'UAе.A QUA`.ApkUA.A@gxUA`᥵.A@UA8.A`)UAf.A]UAL.A(VA.A(YA.A[YA.A@YA .A[YA.A YA`a~.A@|YAXx.ARvYAzs.A`zYA`\.AYAP8U.A YAkL.A YAp E.AYAQ>.AGYA8+.A@ YAp.A`TYA.A CYA`.AEYA .ARVYAp .AkYA(\.A}YA.AYA8.A@g~YA`a.A wYAu.A3cYAݱ.AR6YA(ı.AzXA`ḱ.A \XA.A@XAp=.A@XAB.A`ÇXA.AnXAH..AQXA8ʱ.A 1XAϱ.A=XA)ұ.A XAuұ.AWAб.A`WAp=̱.A@WAƱ.A@WAf.A@ WA`a.AWA.AWAV.A@WA.A \WAB.A׻WAz.A@GWA(\x.A=WAp=w.A`fWA{u.AWAp=r.ARWAd.AXA_.A`f XAp=Y.A \XAQ.ARXA K.A= XAE.A@ XALB.AXAH;.AXA5.A3WAX +.A`WAh3$.A \WA`.AWA0..A`fWAp .AVAB.A`VA(\۰.AzfVA(\Ȱ.AQ"VA(\.ApUAG.A]UA(\.A (UA.AUA`.A TA.A`)|TAz.ATAp=.A`)SAx.A@ SAj.ASAVd.A@fSAV].AWSAW.A USAHT.AVSAp8Q.A@RSAN.AHSApI.A3/SAuE.A@SA8A.A qSA;.A \RA6.A qRAB3.A@SA{-.ARSAP'.A RA!.ARA .ARA .ARAp=.A@rRAX .AXRAB .ADRA.A1RA#.AR,RA.A`.RA@ .Ap1RA.A@4RAأ.A@G3RAH.A |0RAأ.A \RAH\.A3QAG.A@QA@.ApeQA#.A`fHQAu.Ap/QA.AQAk.AQQA.A PAf.APAh3ٯ.A`fPAЯ.A PA`aʯ.APAp.A RPA².AROAh.AOA².AOA.AOAѸ.AGOA`.A OAů.AשOAL̯.A̮OA(Я.A[OALӯ.A̞OAԯ.AOAկ.Az\OA Rӯ.A@.OAWЯ.A3OA R˯.A qOABï.A@ NAѺ.A`NA.A`)NAW.ANA.A`xNA`.ANAQ.A3MA8.A`)MAp .A3MA8.AMAB.AMA`.A MAP8.AuMA.A`)DMA(܆.A*MA؞.A=MA0.ALA8.AGLA8.ALAk.AoLAP8}.A``LA`as.AZLAe.A``LA[.AgLAp=V.ApLA`R.A~LA)N.A`̄LAJ.A]LA`D.A@tLA>.A`f^LAp8.A;LA#+.A@KA'.A@KA$.A=KA`%.A`)KAf0.A&KAp7.AJA;.A@JA<.AJAp=<.A mJAL9.AzNJA3.A`),JAz..AIA*.AIA'.AIAB".A3IA.A`fIA .A3IA.AqIAk.A \]IA`.A \?IA.AIAX .AHA`.A |tHA#.A@ UHA.A@G3HA.AHA.AGApݮ.A`)GA֮.A \GApϮ.A`fVGA`î.A]FA@.A 1FAר.AëEAp.AEAz.A`mEAh3.A[[EAP8.AIEA.AG7EA.A EAأ.A@gDAǍ.ARDA8.A׫DAP抮.A@DAp .A|DAB~.A@dDA|.A=ZDAz.A q1DA#~.ACAP8~.ARCA=z.ACAGt.A`fCA(k.A wCAe.A`)hCA)`.ASCA8\.A`.CApW.A3BA(\T.A \BA3O.A`fBA0.G.A@BA<.ApBAP1.A}BA`).AgBAP%.ASBAu!.A,BA`.A@BA`.A@AA`a.AGAAh.AoAA@.AfAA.AAA8.AAAͭ.AAAH.AUAA𣴭.AzAA8.A`fAAQ.A`BAX .ABAѩ.A0BAB.Ap=BA.AzFBAQ.A@VBAV.A@fBAk.A |BA̫.A BAB.A \BA̖.A@ BAp.A`)BALJ.ARBA.A |BA|.A \BAP8u.A}BAo.AkBA)i.AbBA^.A=fBAS.A`zBAأJ.A`fBA4.A@ BAG#.ARBAB.A3BA.ABA.ABAP8.ABAh.A`CA .A6CAz .AKCA`.A@dCAp.A`rCA.A |rCA(\.A _CA߬.A [CA`aج.AeCApϬ.AtCA83Ȭ.AxCAp.A@dCAѵ.A \]CA̫.A=jCAz.ACAǛ.AדCA.A@CA{.A׻CAu.ACAHm.ACAd.A`fCA8Y.A` DA`aA.A>DAP!.AׅDAk.A`)DAh3.ADAPf.Ap3EA.AEAث.A[EAPʫ.A FA؞.ABFA.A`fXFA沫.A ]FA.A gFA.AFAp.AFAfv.AFAHV.A`ftGA(\>.ARGA!.A@ 7HA.AHA`Ъ.AIA R.AhIA.A@IA`%n.AkJAn.A&lJA]j.A`JAa.A (JAK].A JAU\.A@rKA(m[.AKAX.AvKAJV.AKAU.AjKA@R.A@`$KAL.A`v1KAI.A5KAI.A 6KA,.A@%[KA&.AxeKA".AlKA@.AKA ߩ.A KAcϩ.A_LA.A (HLA囹.A vLAX.A`fLAΞ.ALAh.A]LAs.ALAn.AwLAHj.ALAf.ALA/d.A@LAc.A LA\.AoLADW.AMAC.A~MA3A.AMAB;.AMA0.AMA&.A@NNAs.Aj\NA.A@ NAxm.AuOAX.A OA蠨.AVOA{.A`$PAl.A`;PA`e.APA3[.Ag)QARN.AaQA?.AQA..AQA0%-.AQA%.A`[QA.A<@RA' .A QRA.A@RA ç.A RA.A@SA0{.A@gSAг.A߃SAȋ.AOSAx${.A`2SA o.ASAl.ASAH?b.ASA].A TAW.A@%TAhM7.A@TA.AVRUA4.A`mUUAh.AXUA> .A6UA.AUA.A@UAY.A@5vVA.A VA.A`2VAXR٦.A ZBWA0Ԧ.A@]WAPϦ.A$tWA¦.AqWA虴.A WAh.A@XAu.A@9XA.A@>JXAXz.A@gcXAHM.AYA .AYA.A&_Aɤ.A @_A.A@E_A>.A$\_A<.AZ_A;.A Y_A6.A*W_A,.AK_A,.A@J_AH,.AJ_A(`+.AG_AH\#.A6_Aذ.A_AU .A _A .A^AH!.A@^A.A^A.A^A0.A^A .A^A +.A^A.A^A).A^A@.A^A&.A@N^A@1.A^A .A^A@S.A ^A@.A@^A@u.A_A.A__AxX.At_A;.A"_As.A#1_A@A.AJ_A`e.AfV_A$.A@ u_A أ.A_Aף.A@_AhУ.A`)F`A.A^1aA`ǿ.A?aAH-.A҄aA@乣.AaA .AaAX.A`baAa.A bA.AbA0H.A =bA@`.AGbAH.A_bA`e.ATzbA.AbAXh.A$bA؁.A@EbA84x.AbAhm.AbA[.A@bAQ.AbA=.A AbAx6.AbA3.AbAp}.AbAU.A+cA .A6cApk .A 1 cA .A cAN.A@,cAPТ.A@.cA.AcAs.A`%dAz.AROdApa.AdAV.A`)dA@NT.A`dA83N.A eAp A.A`\eA(@.A]eA6.ApeA8..A eA8 .A`ZfA#.A \fA.A fAG.AJgA-.A׏gA`a.A hAW.A`ÑhAp.AhA8.A-iAH.A}iAp.AiA%.AjAo..A`mbjA0.AtjAQ?.AjA^.A`*kApp.ARpkAp.AkA`.AkA`a.ARkAQ.AkA@ל.A@,lA8.A`f^lA(\.A͒lAz.AlAW.A lAp=Ţ.A3mAQ΢.AmAP8Ԣ.A -mABޢ.A`)ZmAk.A@>mAh3.A@nAN.A oAe.A=foAH.p.AQoABt.AoA`au.A3oAp=s.AGpAq.Az"pAm.ADpAf.AGepApd.A |pA83d.A pA`a].A`pAأH.A@qA0.<.A \1qA2.AR:qA'.ABqA#.A`)VqA.A_xqA.AqAx.A`vqA8.A`fqA8.AqAu.AzrAu.A`fNrA .A@trAP.AzrAP8.A@rAk.A sA.A`;sA`aӢ.AJsA(.ARsA`.AXsAp.ALsA𣅢.A1sAXx.ArApi.ArAc.AFxrApR.AR&rA=.A`fqA&.A͢qA.A \qA#.A`~qA.A@dqA).A@G'qA̡.ApA=.ApAأ.ARrpAє.A`f^pAG.A apAy.A`fxpA@W^.AppA4.A=qA@W-.A!qAz$.A@ kqAp".A`fqA .A@&rA.ArA@W.AsAp8.A@gZsA .A@gsAX .A3sA.AןsA(.AzsAWؠ.A:sAX ͠.A@>rAH\.A@ YrA.A`)rAP8.ARqAX .A=^qȀ.A`)qAPf~.A`pA{v.AطpAr.AGpAX.AlpA0?.AzJpA(*.A0JAh=.A@ )JA K.A3QJAL.A{JAGI.A`çJAأD.A JA0C.A JAPfC.AqKA>.AG5LAP@.A ]LA`C.AiLA`aN.ApLAT.A`tLAU.A`LALN.ApLAp M.AࣰLAM.A@ELA)N.A`LAX Y.ARLA_.ALA@f.ALAȃh.ALA)l.AzLAs.ALAp=.AqLAL.ASLA.ALAp8ɤ.A=KA83Ф.AKAVӤ.ApmKA)Ӥ.A8KAp=Ф.A[JAp8Ƥ.A@>JA).AzJA.AJAP8.A kJAp.AMJA.A %JAVƤ.A IA֤.AIA(.AכIA.AIA( .A̜IA.A`ÿIAQ'.AIAP8;.A JAfJ.A JAuV.A@JAe.A`IAy.AIA`a.AIA0ҥ.A`fIA.A gIA.A_IA(.A1IA .A`HA.A@HA8.A \HA.A=fHA .A=FHA.A0HAp8#.AHA#,.AGA2.AGGA?.AoGAHG.AeGAQ.A@nGA`Z.A GA8e.AGApv.A GA(.A࣌GA‘.AiGAf.AUGA.A@2GAP8.AQ(GA.A`),GA̼.A(FA0.Ϧ.AFAH.ARlFA .A EAP".AEA4.ANEA8?.A  EA?.A DA@.ADAPD.A`DAK.A`)DA8V.AHDAj^.ADA(\c.A`DAi.A DA8j.ADAh.AnjDA8h.AgDA_.A \EDALU.A0DAHP.A`)DAQ.A3DA(\U.ACApa.ACA#t.ACAz.A=CAp8}.A׳CA8.A*CA0n.A`CAk.A`fBAȧ.A`)>BAҧ.AAAܧ.AAA.A CAA.A3#AAf.A@A.Ap@A .A@>@AP.A @A.AÓ@A(.Aps@A4.AL@A ?.A@AfV.A?AWd.AR?A8m.Aׯ?A-~.A`a?AQ.A`:?A.A3?Au.A=>A@W֨.Aͦ>Ah{ب.A>Az.A>A.Ae>As.AB>A8.A >A`a.A@>A.A=A .A`=AP8.Al=A).A-=AW2.A`f<A5.A<A`:.A <A<.At<A(=.AU<A=.AM<AQ@.A &<AF.A <AWP.Ap;A\.AG;Ab.A`);ALj.Ap;At.A ;Az|.AG;A.A׋;AG.Ap;A̅.AJ;A.A@>$;A`ᤩ.A:Af.A`):Aɩ.AK:A8.AH:A.A9A(>.A@9A0.A9Ap<.AnO9AH.A?9A*".A 59A$.A4)9A$.A &9A%.A`9Ac(.AN9A3.AŢ8A6.A߉8A 9.ABv8A89.A u8A(z;.An8AM.AZ8A0Z.A`7Ahc.A 87Ahk.A`d7An.A ۟7A.A@%Z7A0Y.A T7A2.A47A`ý.Aw6AHKϪ.A`06A@ݪ.A$5A( .A5A@.A@Ϛ5A0.A2r5A .A35AF.A@P4Ab.A`4AX{.A ]4A.A`)4A.A4AH!.A 4A3.AV3Ah.A3ASث.A 13A.A2A0.A 2A.AD2A('.A 1AI.A@/1AثV.A0A0].Aǿ0AHb.Aa0A8x.A 0A|.Av/A`.Ag/A(.A`/AH.Ao/A>.AH.A`.A .AXV.A`.Ak.Az.A@έ.A b.Aӯ.AXV.A`嵬.A`0.AX(.A.A(.A.A.A@-ARǬ.AT-A\ͬ.A-A ج.A-A /.A-A6.Ahi,AN.A,A`W.A`+A@ ^.A|+A `(A'.A@PB(AUG.A'Adu.A6&A0ju.A@&AY.Af_&A̋.A2&A0.A@C&Ab.A %A۹.A%A0î.A@,%A.A qf%A(\.AU%A.AA%A/.A3%A .A %A.A`$A` .A$AHC).AՒ$A..A r$A5.AJ$A#G.A`#A(Y.A A@#AP`.A "APf.A͠"Azl.A6ABʰ.A@A.A eA .A AXc.A@iApy.A@_A8.A`[?A.AA؁$.A`Ah2.AAA.A ATI.AQA`P.AfAh].A-Ac.AAAi.ADA0m.AyAt.AA|.A tA!~.AhAX.AAA0؆.AAHP.AA0ꓱ.A oAp.A`9Ag.A͜A8.A"EAs.A(Ax.A@A8'.AAd.A JA .AUAHi.A SAP8.ApA0.AzJA .A$A.A A@.AHA .A A`.A(A.A@ihA#.A`7A+.ARA3..A A0j0.AA22.AA3.AvAk4.AA\4.A)A4.AiA#6.AKAPo7.A (=A#9.A (/A;;.A"AЮ=.AAv@.A  A8C.A`vA(F.A[A=K.A@AXhO.A+AQS.A@AhV.AA9Z.AA%].AtA0_.A3A@h.AiAXr.A0Aw|.AAP.A`)A{.A"Aᙲ.A hA碲.A@`:A.A AH˳.AAأ.A=A{Ų.AIAв.AtA8Ӳ.AA@޲.AA.AGA.A@Av.A@yAX.ALA0L.A sA.A}AH?.AwA(.AtpAh.AjA(\.AdAHK.A]Ag.A WA.ARAj.AOAA.A PAx.A RA.A SA@~.ATA.AUA.A UAH.AHWA.AWA.A-UAج.AQAk .A@yLA .AEA.A@P7A.A'A`.A@`A.AEA8.A@ A".A ~ APY$.A]m AЮ&.A\ Ax).AdM AH,.A[? A0.A A8 A3.A2 AU7.A. A;.A), A>.A`]+ AB.A6, AxF.A. A.AdAxiB.AAuF.AAF.A`vA`N.A AؒR.A`fAo.A@At.A@PAxK.A$AO.A Aj.A 8A.AAㅷ.AŐA|.A c~AH.A}ANj.A@`zA.A@_AН.A0$A߯.A`A.AQAt.A@AA( .AݕAhMH.A` A_\.AA\.A`AP].AFA^.A͵A`.AVAa.A?ApZa.AަAP8c.A`vAd.A`2vAXf.AJAg.AwCA3h.A@Aqi.A6A`%j.A 2Apj.A^/Aj.A/Apl.A'As.A AP.AoApո.AA R.A`A(~.A`A.A@XAs.A/LA,.ADA .As9AX.A.A|.A@A.A^A\/.A AC.A !AY.AAs.A@(A|.A Ab.AA㎹.AW|APo.APAXۛ.AA{.A3A窹.AA.AAs.AA.A/A0ǹ.AtA /ѹ.A[APӹ.A NA cܹ.A s"A`6.A+A+.AA.A`AP.AzA.A@]ApN%.A`9A2.A AZ.AAb.AAx(l.AAp<.AGAP.AIlAB.ARA@.AtDAz.A4Aﾺ.AAϺ.AQABϺ.AA(Ϻ.AA޺.AA.AA( .A/A.ADžA.Aj_Aw.A_UA`.A#A.A Ah".A+A(.AA0-.ArA(E.AA 0F.AAYJ.AAO.AFA^.AwAxk.A`OA.r.A99Ax)w.A "Apx{.A  A.A A.AMA`1.A`fA.AZ,AHr.AA̯.AnA׻.AKA`ܻ.A$A.A@EA.AA.A A.AAX#1.AApA1.A A_1.A2AJ.A`\APc.AA`h.A@iAh.A/A n.AǨA-.A_A@.A@[AP<.A@>;A.AA .A Aљ.AA .AA.A<A㸼.AyAK.A<Aﾼ.Ax*A8ü.A-Aļ.A AƼ.AHAɼ.A cA\ʼ.A`AXʼ.AA޼.AÝA8.A@Ak.A`xA8.A^Aa.AT?A.A@2A.AA`.A@A.AÉA-.AuA( .A dAp/.A (ApP.A AXa.AAH.d.AALr.AA`w.AA`ᅽ.A@> A.AA(.AA(.A q;A#.A qA(\.AA0..ARAp.A qAx.A4AB.A`)A(ܯ.A|A.ASAG.AR(Az.A`)A(.A Au.A \AP.AAp Ľ.A=A`ǽ.A@GAhʽ.A͈Aҽ.A 1Ah3ܽ.A`AB.A`AG.AGA.A@ZA#.A 7A0..AAP8.A Ap.A@gA .ARA`.A@pAX".A3[A8*.AYA1.ASAX4.A[QA@.A[%AkU.AA_.A{ALg.AHMAq.A@As.A AVs.AMApo.A Aq.A |Av.AͲA).AA8.A OAX .A A}.AA}.A AX.A]sA.AaA.ApAAp.A(NAH..AeA.A Af.AA`a.AQA뾾.AAþ.A3gA(Ⱦ.AQHAо.AAzھ.A AW.AA0.A A.ABA.A A.AA.AA %.A०A c(.A AH(.AanA8).AUAp/.A:A:.A@APA.AA{D.A`ATM.ApAPX.AA@d.AHyAm.A`KAh y.AZApJ.AhAh.AAl.A@A@W.Av^AJ.A |EA线.A:A[п.A A1A+ۿ.A c$Ap .AIA.AdAu.AAп.A AJ.A`[A` .AOAh{.A;A0H/.A =A:.A;AM.A@`%AV.A AP^.A+Ae.A Axf.A@EAe.A AA|h.A`]AHn.AAP+t.AAxu.A`WAhbx.A@=Aā.AjA .AA#.AA0.A A.AA].A`AH?.A A.AA.AA0f.ArAl.AfA5.AeA@.A O]A@S.A<Ap.A@ AH .A`A8.A`A.AFA .AA.AA.A@A7".AtAx+.AlA2.AdA=.AoAJ.A@kAпW.ASA@1s.A`A.AA+.A4A.A@AЦ.A A.A`Ax.A?A.A An.A A.A@A.A-ApN.A`"AxK.A@AP.A@ A8D.A`AA.A`TXA0.A9A.A&A.AF A .A6Ax.A`A!.A Ay#.AॆA@ .AtA.A@#aAK.AtTAHL.A;AX.A5A`C.AA(i.A@,A@u .AA.A c[A@ 2.A ZAH.A`rA`h.AAx׈.AAXW.A |qA`v.AflA@.A rAp.AA .AAX.A<A.AzA@.AdA.Ad>AXW.A@AH].AA@.A?AP .AaA( .A0A4.AZAe.AR}AP.A At.A`A.A@?AHP.AoA R.A`9AK.A9Ax.AAv.A@WAx.AБAL.A;A`.A@A0{.A A`.A |kA؁.AyA(.A A.AA k.A@g|A&.A@hA.AjA8c.A tA8.AtAp.AdA&.A`>AQ.AwA.A`A8ɸ.A@5Aྵ.A@vA .A ]Aߺ.A\A0ٿ.AdkA R.A-Ap.AA.A4Ao.AaA2.A cA".A@>A.AѽA.AA\ .A3A.AAj.AwAp/.A`ZA`%>.A`YAoH.A@aAX U.AtACk.A$Au.AA,y.A[A|.A`uAX9.A}A8#.A|xA.A`\A.A@"A .AA .A@`Axؙ.AxfA/.A[3AhQ.A2A`.A1A.A Ab.A A}.AA=u.AA8.A/A~.AGA#p.A AH.AA..A MAk.A`)&AH.A \A=.ARA)Ͻ.A wAأ.A 9A.A`A憽.A |ҸA`a.ApAA.A=A˛.AawJA>.K¼.AwtAk.A.ʫA\JLO.A!KAWy.AdbA(%.AkoyAd.AVA(Q.AsA}1ݔn.AM PA5M.A~J%A/.AA.A <AD.A mA`!5.A aA#.AA.A+Ac.A ;A.A`AH.A`"ݪAHߺ.A@AѺ.A@EA(.AANJ.Aj{A@o.A6 AU.AA`10.A`ީAP8!.AsA .A c0Aٹ.AAȩ.A[A𴡹.ACAx.A`)"A(`u.A AY.A﫧A I.A(AP&8.ATAH.AAθ.AAp.A ҌA8".AŝAX.A൝AP^X.AaAF.AӼAk.A A.A4Ax.A^A0.AAh.AA*H.A|A.AA.A AXh.A`A).AAw.AAw.AծA.A A.A`UAV.AA.A PAP.A` Ap.AA(:.AAp.AA(q.AAH .A9AhQ.ABAȇ.AAA.AAB.A`Ar.AżA r.A@*Ah.A`.A(\D.A@5A+.A9vA(.AӦAڵ.A ҌAl.AIdA.A;AfD.AfcA@}D.AApk*.AAug.ANAhc.A JEAPx.AA`.A9!APژ.A`;ܣAor.A8]Ad.A6Aor.AףA(\.A 4AZ.A*A.A@ UA.A<A.AAA3.A AA.A?ΣA.AsA`%.A`2A.A AơA@.A`mpAXڴ.A)Aڴ.A A̴.A oA8.AXAk.A }A.AAPD.A@ϔA۳.A@Aó.A AXh.AƣA 9.A&A..AAM<.A`ģAT/.AeAH.A Ax.AկAP<.AA.A`KAҲ.A@W%A4 .AA.A Ah.AନA0.A=dAt.A DA\.A ɤAА .AWA( .ANAs.AoA`.AAق.AA0}.A`AX.AXyAh'.AbAK.A^ܥAް.AAXE.A7A.A@AT@.AA̯.A\A`.An)A^.AӿA 㭮.A 8ͤA1.A$A.A$Aan.AAXhU.A1A"G.AEAP;.A`6A09.A sAC.AA@Y.A=0AX.AAR.A]գA.A`B̢Al.A8A@#.A`aA.ATHAg.A@9A.AvA'.AˢA耬.Az$AxU.AA"7.A "A`.A A@«.A2AD.ADA.AAX.A`)>Ah0.AsAJ.AAP.AA.A ~GA˪.A@5Axm.AA.A ¸A{.A=A+.A4A.AAX5.AO$A1ݩ.A "A.AA0.ASA8g.AA[j.A6|As^.AfA9.APApx.AXAȨ.A`]VAh.A`]߲A-ߨ.A`'AX.A KAI.A A l.A`KA@3.A@AQV.AA 8.AA8R.AapAh.AAڧ.A`)|A.A9Ae.A A𴦧.A 'AB.AA.A`A.A͵Aps.A$A0|.A0HAጧ.A`) A͠.A0HA .A_A.A׀A(.A@θAx.A@MAXg.A`A K.AιA8.A0AS.AV@A .AA.A6ȸAxm.AAH)֦.A`\AlĦ.A#A.AA.AA.AŤAh.AOA7r.AjAh.A ܵAp`.A AP^i.A-AY.A`A@.A&APY .A|EAH.A-fAe.AvA&.A 䖹A4.AgǹAH.A.A@5.AQ.A`mYAXT.AjA@).AsAG.A}ӻAP?.A@A.A`hAQ.A$AXE٦.AAPȦ.AlA`¦.A`XAJ.A Aߘ.A`ӻA0t.A#]An.A@>LA[H.A@PкA C.A/A`.A-MA(ͥ.ADA.A`wAH.A$RAPMޥ.A Aă.A`ùA`Ǟ.AzA8gȥ.A[A.A /A.A@RA@c.AD^Ahc.A_Ah.A|ݻA.A AX[֦.A@ټA$Ӧ.A@A .AKAz.A9׾A,.A0A,.AA.AAPf.A`vA¦.A<A(y.AKA-.A ͽAe.A$A@S.AླA0.AA0U.A@Ax.AA 0ڥ.A-A.A A.AA(#.A A`6'.AM Ah/.A=(AX.AA`.AAv.A/AU.A?Ağ.A?ÿA+.A[ֿA䪦.A=Apή.AϿAڶ.AAr.AA`.Ay̿AHл.A ٿA .AAFæ.A59AXhԦ.ArA@Aަ.ABA̦.ApA3.A[A`.A OA.A Aгz.A`daA3.A`A6.AxA}.A[Ah.AAA`v.AA.A 9A<.AQLA.AoA8.AAX:.AA.AAq.A,AHi,.A u]A:ҭ.AAt.AAТu.A`)A3.A@!+Ax.A@{A0ʭ.AAPx.A.A٘.AA`r.A@NAk.A?Aؒb.A lnAз;.AA6.A Av .ABA.A@`!A.AA.AUA.AAb.A lAs\.A Aw?.AЄAF.A@W]Ap.A@,dA =.A quA^f.A@ƛA@BC.ANAxJ.A Ax.A`A.AAr?.A`]A.A-AB.A`[AxI.A A+.AA1.A@#kAV.A`"As.A .A.Ay%AHƤ.A`Aɤ.A)AtҤ.AAX.A൚A0Ӥ.A_Aܤ.A=A@.A3A .ALFA6.AA1.A6A=.A |AH.AA.A$gA #.AXA(횤.A`TAK.A ZAH..AdlA&x.AJAZ.AoA( 9.A-aAQ7.A ~EAU.A ZAP.AAY.AA.AA`C .AA.A@A,.AjAG.A AVR.A 8Aгb.A |;A Mv.ABA.AA8g.A;A`6.AA.A4wA&֤.A@A(Ӥ.A&A@_.ARAʸ.A`A.AAPYڤ.A+HA@ݤ.A`RA.AAxKV.AAC.AZA`6E.AA.AAl.A@A.A@xAx .A 8DA8֣.AA.A ۝A.A3uAΣ.A@Aeţ.AAP.AA؁.A Ap.AAP.AA<.A`*AP..A@NA8.AX5Aʣ.A 80AX8.A'A@.A^Ad.AAH9.A@WZAdģ.A#QAף.AFA(zӣ.AAD.AAHۣ.A=A .A`YA".A@, APM .A6FA@ƣ.AAeأ.A`,Ap£.AgA@.A &A3`.A SAЄT.A`Ap .A A.AA(.AkAxKڡ.A6Aɍ.AAȵa.A@fAPO.AA`.A஌A 0.A A[ޡ.AoA.AM,A/.A A0.A@aA.A@%A`D.AAp١.AAXD.A_A@N.A ZAX.A@yA|.AA+l.A ZjAc.A`[[A8.A`A&.A`AH~.A`SA3.AAؠ.A c[A@.AaAhn.AA;.AlA.A:AX誠.AGAPچ.A:A@ౠ.A NA(l.AwAxb.A`v#AZ.A`UA~.A"AP$.A@ jA@.A]AP.AA@.AA.A@,dAPS.A̚A`-.AEAf1.AziA8t.AbA}.A`b?A`.A 7App.AFAj.AFA}.A`KA 3.A AK.AAhP.AVAp Q.AOAO.A>AM.AacAA.AacAA.AR~Ax$4.AR~AH.3.A@WA@.AF]A(.A AU.A@A8.A A5.A AH.3.AΤAD.AA8C.A@#A0Q.AA 0W.A`NA(՟.AIJAӟ.A)A(KY.A A@\.A`o0A\.A2Aß.A@Aß.A@OA=.A[A=.A ZAp_.A A8 .AײAhi.A AT/.A utAH.3.AA*.A/A 4.ANxAp.AhA.AAP .A qAxG.AA72.A`]Ax$4.A AT.A@ggA-A.AMAN.A@AO.AzA()u.A@ AU.AA 4.A MA.A@#tA`=.A@sA@S.A}AV.A΁AY.A47A[.AWwA8j.A0{A`h.AAe.AMAIc.A}Ax.AA(.A@Ap.AVAX .AAH:.A.A".A`A|.AhA–.A`A.AA@.A`A0.AsAh@~.AqA~.A଩A&.AA.AApI.A`VApڄ.AA.AA7.A`A].A ۇA.AAxˊ.A Ap}.A $A .A`A Ҍ.A:A8Ů.A9/A-.AAö.A An.A`A.A@MA ܠ.A}A.A?Axz.A AX .A@PA@.AaGAp.AAQ.A@GA(q.ARA.AshAP'.A6A .AͺA83ɠ.A ۠A.A`)A8g.A@!AC .A@ A Ԡ.AG;AXѠ.A0AIݠ.AA|ߠ.A` A@.A 1AM.A@#A.A@8AI.AtAF.AtzA.F.A{A]E.A}AKE.A}AHa.A`A!.A JBA*#.AH>A(K$.AEA%.AWA(.A@AAߠ.AfAm.A@A.AsA".A.AA؟F.A ABJ.ACA`a.AALc.A Am.AAy.AA`b.A3 A3.AzAأš.AA`!.A ~A֡.A 0A}С.A"Ad.AwAН.A@!OA.A<A`.A`&A(?.A AXA.Ay?A|.AmXAġ.A΅Aϡ.A A.A6 AҤ.AA.A lA5.AA.AQA*.A AMt.AaAj.A@߃AK.AA8ݠ.A#A`.A`AX.A`A.AAQ.A $AP'.A@ A.A`A.AA.A^zA+.A`K[A #.AAP).A9A+.AAd9.A`A'.AAz .Az(A@.A`IAh.A@!A8.AyA.A`bAج֠.AAW.A``A#.A@GqAx5.A VAV.AUA1.A-A9.AAB.A`f,A^.A` A&'.Ao@AX$.A@NA4%.A`A1.AxA3.AjAGF.AAI.A |A0_.A`KAc.ADkA{k.A sCA.AA𴒠.AftAX .AbA.ANAǕ.A`)@As.A A̮.AA >.AʤAZ.A`GA.A&+A.AFAd.AA..AoA.AA.AAP8.AAx.AAN.AA`ʟ.A34AΟ.A@ A.AA=.AA0.A^A.A APo.AA.A Aן.A6Aҟ.A AHz.A9Aؽ.A1A.A A4.AA0L.A'Aȓ.AVAp.AA|.A 8AH̟.A`KA.A A c.A҇Aؒ.AxA.A@XAWM.AA?.AxxA,.A 0Ap.A@AG.AAk.AZA̿.A3A(\.A@8AB.A`)A.ANA8.AApl.ATAQ}.A`vAxt.A@AVy.A A(܏.AG]Ap룞.A]A`a.AAu.AlA?.A6}A.A A.A3!A`e*.AjA`8.AA8B.A A$.A`A0.AzApʞ.A3A̞.AR,Aeɞ.A.AАÞ.A`2A.AJA.AxMAlt.AgA8n.A@`kA@].AwA @.A$_A@S:.A`mZAxF .A 5A.AHApӝ.AA`a.A@ A&.AA،.AmA#.AA .AAL.A=Auʝ.AAȵΝ.ARAԝ.A +Ah.A`ZA ).A@A\.ApAɞ.A\Ax:.AQ0A!0.A@AP~.AA8Ţ.A AW.A`QA跍.A xAc|.A_ALi.AfAȩ.AhwAxuܞ.AA.AkA A:.A`A A.A e AN.AX AA.AG Ap2.A`= A".A Q A4.A S A˜.A`z A.A A.AB Ap.A@ Azq.A` AXhR.A` A<.A@ A>!.A`" AXy.A A0f.A AЄ .A`D A"/.A A`$.A~ Axu.A@N Aě.A A𣼛.A@` A0 .A Ap.A` A ț.A A.A A.A A8B.A AȘ2.A֔ A6.A= A`T=.A@ A@.A`AȔL.A@A@r.A;A <.AA@.AAٛ.A`dA.A0AP;.AA@q.AuA3.ApSA2.A@AȤ_.A@rAX.AKAX.A`*Aϳ.A@cA{.A-AX.AyAǒ.A@GAB.A`)A.A+$A.A A2.AAX.A@AΝ.A@>AP.AA7.AAŽ.A@>Ax).A|APx.A@2Ah.A6A=ԝ.A_A.AOA.A!A(.AA'.AjA'.A5A..A3bA:.A@r<A 8.A`A`~.AA.A(Ax诞.A)Aў.AA`6ݞ.AA;.AA <.ALFAz.A@A`.AA(L.ArA0H%.A0AS(.AܾA`*.AܾA-.ARA0 .A}AhD .AA֞.A`mAP՞.A@A О.AA@f.A JAh.A,Afd.AAP.AArH.AA>.A)A07.AjA+.A6A컞.A`A.AAh.AtQAP.AA.AZA.A3A.A@A!.A Aު.AOA`.A cA0.AeAt.A[cAX.AAF@.AMA2.AmAo@.A} A<.AA .A?3A .A^AL.A` FA .AoMA.AAh'.AjA:.A Ai-.A A@#.AA8s".A`-Ay.A!APk.A}A-.AAȶ.A.A0=.AVAeO.AfAkI.A=A@uD.A@Am.A`9AXz.A`AA.A߻Aݵ.AAJ.A <A.AFAxi%.AA@T.AgoA(j.A`AX.A`hAlП.A Ax)۟.A~A1.A&A@.A@`A0.A@,Ap0%.AeA1.A9AHC..A A).A 8A.A`;ApA/.AVA.AA?.A;A(.AJA >.A6A0Te.AAp=z.AoAB.AaAV.A@pAhL.ApA .A Z!A`.A-A΋.A@y>A'.A`*Aȇ.AGAo.A>A#.AA.APA/.A A.A`A8Ơ.A@A.A@A.A`!AJ.A@NǷA0Hߠ.AV.Ax.AaAsk.A {Apb.AūA[j.A@)AY}.A ێAC.A5IA.A@Ap.A ASɞ.A`BtA.A uAc .AհA.AQA(y.A,AV#.AAH.AA .A=A0'.A`A@.A UAό.AAX^.A+A^.AwAE.AhA0*G.AXAxW$.A`qA@&.AѯA3.A A8 .A J?AБ..A@ᆱAK.A)AH.AHcA.A@A%S.AײA.A;A.A-˲A8..A ۱AP@q.AvAu.A@W"Ad.AzűA7.A`KOAQ .A԰A`%.AzZAI.AǯAp`.AHAp.A WA.ALSAx.A۬AD.A٫Ar.A`pAy.A qA`u.A A8n.A!A~.AdA⚥.A AR.AAz.A@ ʩA4s.A=Ar].A`hAc.A (A pݥ.A A<ڥ.A sA U.A쭧AhV.AA0P.AnYALD.AWAp.A$AY.A֤A.AAp.AqAb?.A@WADx.A A.AA`.AŐAX.ALAH.A=Ap.AMvA!.A@ئA".AVApN.A@iGAh.AA7.A A@Ͽ.AvA|.AAX5@.A?A(1.AΟA)*.A^AL.A@fA.AQAT .A3A0.A|A*.A0*A =3.A`vܤAe.A@A.A@Ap.A'AXh.A 3AH).A|}A8"`.A@yסAV.A;A8痨.A$A8痨.A cRApʨ.AşA4.AZ`AV^.A@N AL.ASA.AAͨ.AZA8tר.A׎Aϩ.A`vAP.A ӛA!.A<<A9.A ĚA@FȪ.A|A2o.A`]AJH.AI'ANk.AĝA@ׇ.AVgAǡ.ATAX.AƜAP.AF›A`d.A@zAf.AAת.AgxA .AA ..A APH.AA`a.A뵗AXh.ARTA`v.ApA~.AA.ARA8.AGAf.Ap A`ᔫ.A`)4A83.AGOA.AjA#ƫ.AVA0.ӫ.AjAX.AA؞.A[Ah3 .AAP8%.AALE.A[Apc.A Ar.A |AV.A`)ZA淬.A %A@.AږAW .A`˖AX ".AץA0.,.A@gAz2.A@ AQ5.AԖAW7.ARAأJ.A$A#X.A"AP8p.AGAp.A A.A&AP.A`)0A.A@ ;Akڭ.A35Ap.A0A.A`)6A` .A<A`.A 0A`(.A*Aأ4.A =AP8F.A wA8O.AA\.AzA`al.A@ЗAz.A]їA̔.AAB.A=A@.AA{ή.A]A.AAX.ApA.A&AP.AQ>Af.AUA.ArA.AAp.AÇA.AA@+.AA(;.AA`K.AGAf^.ApA@p.AӘA(\.A`fAP8.A A̾.AA).AzԘAأ.AGɘA.A̶A0.A@ᢘA.AQA8.A`fA1.A cAkL.A TALZ.Ap_Af.ApAp=x.AcA8.A )Aΰ.ApAp.AǗA.A뗗AQ.A{AW.A]AL(.ANA4.AQAE.A `Apc.AzTAfp.ATA.A3EAX.A3/AQڱ.A2A.A=,A).AKA`.AJA.A@ GA*.AZA?.AGcA[.AgAw.A`)VA .A=RA{Ȳ.Az\AV.A jA.AUAQ.A;A4.A@ =AY.A`+Au.AA`.AA.A &A Գ.AA.A[AH.AA.AAQ3.A[AHP.AA`.A`A`an.A@Apy.A=A@׎.AAp=.A`Aਲ਼.A3ӖAp.A`Aƴ.AzZA=۴.A6A.A`fDAX .A`)AB,.AږAG.A`)A_.A 2Azw.A@gPA(܂.A3OA.A =A@W.A=Aŵ.A@ Az۵.A9A.A@ 3Ap.AAQ.A`A0 .A['AP88.A DAH\J.A>AfZ.A@8An.A@2AHa.AA듶.A=A8.A3A֦.A= A{.A:A溶.AZAʶ.A\Aڶ.AoAL.AYAp= .A[=A(.ASA+.AAX<.A`)ƗAK.A]AfN.A>AV.AOAa.AKAP8j.ABAp.ATAWs.AAkz.A׭AL.AؘA0..A@A.ARAHɷ.A AHѷ.A`f AH\Է.A4Aҷ.AGQAӷ.A]Aݷ.AqA.ApAs.AA.A`9 A? .A AX .AߙA` .A ՙA.AA%.AfA(.A8A,.AAF7.AjA@0N.AAV.ABAPn.A`92A(y.A-A.AAPk.A4A{.ADA .AFA>ʸ.AIkAHθ.AAX[ָ.At}A/.A A.AA(y.AA` .A-A(q.A cA8x.AA&.A`KAȩ..A@A12.A{A8.A@uA֐.AΎAX(.AÛAbp.A 8Ax.AQA.A3A .A@rÜA@䈹.AʜA&.A ɏA0.AAW.ARA8穹.AΑA2.A A8ʹ.AܝAxhع.A`BA.Ah%Ao.AKA`.A@EfA\(.AǀA=.A sAQ.AmAqg.A A y.A;NA.AXAǗ.A}A̺.A@ ПAۺ.A@A9ߺ.A@g֟A.A;A.AמA޺.AxAP'ں.A]5Aк.AKAXFͺ.A؞AȲϺ.AqAպ.AA޺.A sA0X.AAg.AAp .A3Ah .ARA.A |A(.AğAf .AA.AAV.A#A8,.A92A0K.A}CAȩX.A.AYm.A`:A(O.A`fA挻.A uAF.AݠAhb.A A'.A,A`C.Ah5A(\˻.Ap'Apֻ.A@N+A 0ܻ.Ao8A8.AmdA(.A 8APk.AA.A@Ap.AңA/.AA( .AޡA.AVA.A@%A7.Az5ABS.A OA j.A@yHAP|q.A0A(x.A A؟.A A.A@ϡA0.AA`Ƽ.A 룡AѼ.A6AP׼.A áAxܼ.A ߡAi޼.AAּ.A`9A̼.A@pAPȼ.A@ AGǼ.A@rAh@̼.ACAӼ.AUAX޼.AQ٢A.AA{.A;Apl.AޢA84.AfɢAp.ANA .A@5Aȶ .ATƢAx.AԢA!.ARݢA:.AA(D.A`AN.AA@X.AA(c.A@A i.A A@k.At5Ak.AKAg.AcAHf.A`pAh.A@xA y.A A|.AA.A@rڣA.A@CA`6y.A,Ay.A6ZAO.A0A8ւ.AA8.AͶAl.AҤA@1.AAD.A"A軜.A3A3.A8AĶ.A@N;AŽ.A;A{˽.A lMA8Eҽ.A``AW.A`ՀA6.AA.AאA .A|A.A`{A.A)}A%.A`mA;.A@EAU.A@yߥAu.AKA .Ah,Aƴ.AeAa׾.A쎦AP.A;AQ.AϦA<.AA-.AA<9.AAA.A`AXG.AAQ.AAn.A OA\|.A[qA,.AA0.AAP.AĽA` .A@AHPʿ.AAlٿ.AN#A.A@A.A`A.AA).A@gЧA2.A֧A@=.AA4B.A`AD.A9AS.ABAP\.AըA0d.A@NAv.A@A.AA.AwAS.AAz.AA8.A` 5A.Ah_A.A[AY!.ASAпK.ATAh[.A eA`d.A}uA k.A@5A@p.A AH)z.AAP.AA0.A;A8.A`A3.AAHa.A`A*.AA .A)xA`2(.AkA()3.AsA9.AࣂA8?.A坩AH.A` A(W.AAPjb.A~A`vl.AfA`Px.A l]A8}.A_]A@‡.ASA^.A?A 0.A@-Ad.AZA.AhƨA).AA N.A@A .A@`רAP^.A`A.A@pA.AD&A .A.A(.A 0AH-.A`Ai.AAPx.ApA#.A`hAHn.A;oAd.A(tAe.A`oA0j.AMA@.A@pA8#.AA=.AA@.A@ϓA`.A/A .AzA.AhAR.AvA/.AjZAB.A$AP.W.A zjAA{n.Ak*A1….A5٦AM.AoAh˳.A A@0.A@A-m.A.&)AǑ!.ApXu6AdyA.ASEAq.A TAf7٨.A;̙DcA8z.Aǵ"rA.AQAK.AdnAH.AA~ .AR;!A.AMlAtn.AktoAs;.A2\i<AX_M.Ao3ǧA.AӍ.˧Anb.ALZDͧA _[.AĶA.AʧAzӀ0.A1JAicپ'.AndA]3H.AxQu_Aog.AA $.A.ӦAMa_.AseA.A#^#APK.A0'AUt:.AR!>A^.A|A~.A)A.A`Ah.A'A.A 6ޢA R.A@rA 5.AA@.A6A>i5.Aq5>A2/.AoA2..Ad,5<Afe3.A++Avכ[<.AlӺAfMfJ.AGUDLA\.AAjb.AmlA9c.AڍHAa?Ja.ADjA [.A AS.A&.Ap|R.A ~AP.AɗA U.AAs`.A NAs.A$Ax.A SA.AA .AԖA.ADA.Ac!A.A A.A|A .AlA.A`tAH\.AwALO.A?AH.AmA (.A  A.Ac`AA.A /A.AēAp.A6KAA .AkA`.A`ǑA@ .A첑A.AvAP.A@ A .AcAq%.A@A$.A |A .AA.A@A.A l[A' .A*A.AA ".A[ǎA8?.AǎAHH.AAHR.A?AR.A@pAHM.AQAdA.A QA0..ApAf.AÈAI.AAx.A)uAX.A@OA(.A \A7.A`΍A.A QAh.A`QA.A Ah.AwAX.A 8rA.A c?Ax.A:AA .AFA(`.A@EMA:.AhA8.A@,A(&.AˋA+1.A`DЋA<.AA0{F.A&}A^.A͊Ao.A A@.A6A@.A lAH.AXAhވ.A/A.A ~҈Aw.AA8#k.A ZA`W.AM1A@-.A5A.AA(.A` pA.A %AP.AAx.A Ax).AA9.AgA(J.A&AQ.AA%.AMnA.AzoA,.ÀAP.AAȲ.AʄAx.A ZApp .A;A"..ALA!8.AхA0=.A@A9.A`RA00.ArA,!.A|xA.APAh.A` A@ .A ܅AY.AhA.AA0.A4AX.AaWAXI.A AA`#.A`f΄Ap=.AରAp.A qA.A&A.A 䨄Ar.A /A .Af=Ak.A`AhQR.A@)A8.A7A.A8A .A(.A.AAL.A`KA(.AA.A@iAX(.A؃A.A)A.A@wA.AGANt.A*7APj.A$AHe.AAb.A AW.AAR.AAJ.A`A@.AʂA #1.AЂA'.A AP$.A@WA,.A@dA c..A`0Aw'.A`m A0.A`BA.A"Ah.A@(A.A A.A`́A.A=AP.AA A.A@pA.AApq.A@ɁA$_.AdAQ.A#A L.A-@A&J.ApAL.A`AO.A@AI.A4ςA 08.AڂAj*.AقA!.AA:.A@WAp.A"Ap(.ASA }6.AA;.AA7.A, Ah+.AfdA>+.A@A/.A߄A)5.A@,AH/.A gAE!.A A&.A A0.A;Ap.A؅A .AAЦ.A@A.A@ AP.A s A@.AЅA .AA.A`A8E.A@A.AA֭.AA\.A`]AH.A`Aq.A@hA@pY.A@QAK.A0A#.A`A8 .AՄA.AτA.A`ƄAP.A=AP.AࣔAP.AjA8.A=6A`a.A Ah3.AAh}.AЃAWd.AAR.AAG.A AB.AGAkA.A YA7.AGA`..ABA`.A GAuտ.A]CAz.A (A֣.A[Aأ.AAy.A3A#n.AAp=f.A Ah3a.AAP8b.A@Apc.A@>nAa.A֓AQ.A߃Az/.A Ah3.A˄A.AA.AAp ޾.A-A(.A`A`.A@AH̃.A A8m.A`)!AM.AMA`.A܅A.AAIڽ.AnAЮѽ.A pAQƽ.AA.A+A`C.A`KA8.AtA.A A𸌽.A`Aw.AA".A`0Ab.A$A.AAJ.A A؛.AʆA^.AB{A0b.A`Aս.ApA0;.A@A.A@ւAH.AA.A@W=A p.AA R.A ՁAx.AA.A pAx0.AJA \.A3Au.Ad1Aޅ.ABA0H.A3݁AIξ.A@*Aؾ.A@,)A.A (#APo.A#A.A"A.A@gBAP.AGAA#.A@2A.AA؞.A@ AP8.A@A.A qAp $.A@A,.AA3.A@ A8H.AA c.A@A`am.A |A@v.A`)Aأ~.AA׆.AA-.ARAu.ABAB.Ap3AV.A@G+AP8.AR*AL.A`f&Ap.AAB.AA8.AA).ARAQ.A@HA.A@2A.Az(A֥.AAf.A~Aּ.A ~A.A ~A8.Apo~Ap=ÿ.A:~Aſ.A3~AH.̿.Az}A8ҿ.Ap}A8ܿ.A }AH.A@>}A.A}A.A@g}A`.A}A8.A }AH.A@g}A.A}A .A}A(.A@}A.A@ }A*.A }Ap=7.Ap}Ap?.A d}AK.A[Y}AQS.A`)f}AzY.A }AP8_.A}A)v.A}A0.A@g}A.A@}A.A}}A8.A@\}AL.AR>}A.Az6}AP.Ap5}A .ARd}AX .A}A.A@>}A.A}A#.A@>j}A.AN}A.Az,}Az.A@>}A8.A}A.AR}AX ..A`)}AC.A`+}ALX.A%}A(܁.A`}A).A@>}Ak.A)}A.AGI}AH.A[y}A".Aj}AH).A6~AB.A l~A%.AR~A9.A֑~ABQ.A }~A@Wh.Ap[~Ak.AG}A.A@>}AP.A@>}A.A3}AP*.A L}A=.A O}AH\N.Av}ALX.A}A-^.A~AZ.Ak~AkV.Ap~AGJ.AQ~AG9.A~A/.A~AL$.AAk.AyA.AAW.A`fA.ARA.A@G AW4.AA`9.A%AA.A@:APE.A=4AH.A`fA RI.AAP.A A83U.A \A R].AAg.AA(n.A`A(v.A࣬AG.AçA.A׿AQ.AAHa.A@ A.A qA.A'A.AzAH.AAp.AA.A@A.A`/AX.A`) A.A`)ځAL.AA(\.AzA#.A@`A0.A3SA`.A qUA`:.A`8A`V.A,A=g.Az.A z.A=:Ap.A \5A.A/Af.A@@A.A[Ap=.AA.AAQ.A3Ak.AA`a.A@<A8.AGAM.A A`x.AуAk.AȃAL.AûA.AA`.ApA .A ⤃AX.ARA.ARAH.AA.A`)ƒA.AzʃAk.AσA8,.A׃A<.A A@K.A`fApP.A3 A`].A=ڃA`g.A=Af.A |A#^.AGGA@WV.A@gA#O.A@A`J.A@lAp=B.AA<.A`)A;.AǁA$.Az|A!.A \UA(.Az,A5.A qA)C.AAI.A@ AT.AA`].ACAH.b.A A(^.A ՁAY.A`)A`W.A`)FAX.A@dA8].AyA`ae.AGA(q.A@ኂAp={.AA`}.AGقA}.ApA~.A A}.AA8z.A(Af|.AQA….AmAp.Az|A.A`fxAf.A]mAp.A3CAW.A`fAf.AGAh.AA0..A3A.A(Ah3.Ap AB.A=,AW'.AGA#D.AAN.A?AT.AR@AY.A \!AS.ApAVQ.A3AH.Q.AτA`V.ApÄAuc.AʄAX q.AAL.A`AG.A`fAH..A`ԄA.A`nA#.A@>A`a.AzA.AGA.Az܃Ap.A܃A).A݃A.AƒAB.AApn.AÃAp8^.A qALV.AzBAU.A*A`Z.A Ap=^.A At.AA̋.A3!A8.AA.Az$AG.A 8A.A@>4A`.A'A.A#A.A 2A`.ApUA RS.AaAm.A3eA Ru.A@rAWx.AA8y.ARAf{.A`fڃAu.A`fAǃ.AA.A Aœ.AAª.Ap˃A8.A A(\.AAp=.AuAH.A]A8.AzJA.AA.A3A4.AEA(a.A׿Au.A`nA.A`f*A.A@ĀAأ.A`A8.A(~Ap=.A@J}A8.A|Aj.A@Y|A.Az{A.A~{A.A |zA.A`yA.ARxAG.ARwAp.A q1wA.AwA.AvAp x.AHvAj.AvAGZ.AzvAP8G.A`)rvA.A \CvAp.A =vAX{.A?vA( .A`fXvA.AjvA\.A`VvA R.AKvAxz.A6vA.A1vA)J.AvAf.AuA.A@ {uA`.AuA`.AtA(\.A(tA.A+tA.A=tAy.AtAz-.AR,uAp.A`huA`.A`uAk|.A uA>.AuA8 .A@GuA.A@uA.AR#uA0.AsA.A@sA .A sAX.A $tA.AktA`.A`ftA}.AtAB..A  uA.AG}uAX p.A |uAI.AuA.AuAzѾ.A0vA.A6vAp8.ArvAV.A@ vA0.A@6wAP.A wAP8.ARwAQq.A@.xAG3.A`ÁxA.A xA.A3YyAf.A]yAud.AQyA%.AzA.AQ.zA.AQ4zAz.A&zA`].A 3zAp?.APzA.A zA.AzALa.A@{A=,.A@|A.A`@|Au.A=R|A3.A|AH..A!}A(.A}Au.A -~Ak.A~AY.Az Ak.AA83Ʒ.A`A.A3]AL.AA.A AQ.A`.Am.AzbA88.AA3.A ́AL̵.A@g A.ARfAkH.AAh.ARVA(ܪ.A=܃Ap0.A`A.A`A`.A[Aw.A |A.A`$Aϲ.A~Ap=.AĆA`aA.A!A8.AGA𣔱.ApɇA O.AAp.A iA.ARĈAp8X.A3A.A(NAG.AGiA(\.ApA›.AzA(\f.A‰AX-.AA.A \A`Į.A`*A̍.A`f<A`].A`JA1.A@GOA83.A@JAQ.AtA.A@GA@n.AÛA)?.AA.A@ AŬ.A|Aч.A`)`A83I.Ap=A R.AA8E.AZAh.A@yA.AA.A|Az.A`mA ܪ.AFiA\Ԫ.A~APZҪ.AAΪ.AωAxK.A2Aot.A`fAt`.AވA.A@ԊAG2.A6A .A@A@.AuA`.AxzA׃.A A_'.A@Aج.ASA\s j.A݅$AuK-;.A\φA5.A,+sAZ.AA&A(0.Af A8.A`ACy.A(ἋA"%y.A%% A Rn.A^A l.AiA8l.AkIkJAm.A[*A9o.A8 A9,ls.AA#Ax.A~@\ׂA F}.A0vA9.A+HAX[.AXAh@.AA|.AsyxAJ也.AzA.AA^ſ.AjAAW14Ÿ.A廄~A3ğ.A,½sA ş.A1wAgAIgƟ.AZ㗭ZAv&A ǟ.A`]AMAPƟ.A#+O?AƟ.A@2A$5B.AEOAк.A@EفAx.AvAH.A eA.A@`eAd.AeA.A"vA_.AA \.A`A`CF.AAH.A Ap<.A AB.A A΂Ah.A`̂A< ߂.AXA&u.A6Aȶh.A@1AfUc.AROA6`.ApAM1].AhިғA׳].A0Aګ!].AyԥۃA7)_.AO]Ab.AT; AM8g.A6?Aom.A`ZAQp.AMG_At.AsfA٧v.AoA}Z]y.ASyA{R{.A* A@(|.A]3A}.AKA}.A Ax-P.A$A83$.AsA.AFuA`,.AAh3.ADA5.A`A.A`K[AX .A`mAX.AvAL.AanAr0>.ADGGdA%Y;1.A3' QA! $.A305A\[2.A>Ac.AAi.A፨ޭA_+:r.A,_Ѡ~A(Л.AYA0Ʒ.AV4FA5.A@AA:;.ATyJA#09k.AcaA@S.A`ՅA%L.At]Al:/vE.AuPAW=.AxpA̋5.A#ÙAe*..A̞A&.A(A.AA.Ar@{Aݨaš.ANA7u.AsA.A=̓AxF0.A[AXu.A狄AǙ.A@ۄAPș.AKɃARL.A |ЃAp8.AkA9.A`BAPߗ.A;pA8Iڗ.A@>ąA.A@`A0&v.AAȔt.A`ÚA u.AiA0.A@,HAXJ.A`fAx>.AA.AxA.A@NA.AApJ.AA.AAP .A[A .A`eA@Ʒ.A@.jAN.A`A@۔.A ZA@ה.AUA>ה.AUA.AzA9ѥ .AW_Aw.AVPA2.Atz8Aa.AK߸A.AthfAKOړ.A/EnAJrӓ.ApDLA͓.Az%Aɓ.AfA0ȓ.A@DAн.A`"<A .A|؄Av.AzۄA~.AA( `.A3Af.A@Aq.A@>Axh.A-A𢿒.A A.AAБ.A A0.AA.AVڃA@ .A@yA p .A3}AB .A!A(ː.AQBA`ː.AyAh .AYAM .A@ A.AA.A^{A.AZA'.A lYA'.AyA@(.AA@".A`DA`X".A҆A3.A؆AP.A AK.A A@F.A#Axx.A A'v.AAr\.AA=.A`A0Ǽ.AT Aʏ.Av}Ay.Av}Apo.AWA)G.A Aˎ.AaA.A@.AP.A ZA8֌.AAp.A=ƅA0.AąA`ᗎ.A`)^Au.A`fTA~.AAX#J.AAHPL.AX<Ah.A,A.A` A.A`DZApA.A+A`S.A@pA0{.AA..AAH+.AQAx.A \A@.A`]Aq.A@$A@.AA.ABA`!g.AAVh.A[Ahh.A"DA@i.A@A"j.AA`k.AAk.AׅAxl.AsAm.A#Az.A&Az.AH6A.A@6Ax.A #A83k.A@4Azk.A`Ah/b.A@A 1.AAx*.A Ax.AA .A`TAފ.A3A֊.A@A@d.A@Ap.A(A0p.A@yAWh.AAXl:.A@A20.A`A8/.AA0b.AAb.A uA8a.AÅAa.AA0a.A^A`.AVAJ.Av{AUF.AAH".A`߇A$.A}AѪ.AA .A A.A $AH .A@.A .A SA5.ALAډ.A AAȤщ.AaAx.A`A(:.A SA?z.A@An.A A((n.A@A@.A&&APf.AEAW.A@ A8.AA@1%.AqA".A`)FA(\0.AA3.A A?.A ¿AwA.AAE.A8A+.AQHA(.ARA( .A+AG .A`DA&.AQnAȇ$.A/Ap.A`$A8#.ABA4.A Ah.AAPψ.A 8A(.AIAm.A(AM.A AV=.AzA@}.AAE.AdA.AYAʅ.AA .A滉A.AhA0҅.AA@丄.A`A.A}A.AA(.A`A.A9A0.A`sA'Ԃ.AA'ă.AȅA^|.ANƅAȶw.AൖA@>.AƉA㘂.AHA@G.AaA J.A Am.A"Am.A@5A`$.AA .AA$A08.AQA,:.A`]2Al.A0A(~.A@PhAh~.A |AH:.A`TA~.A9SA ^}.A (A8|.A@`Ap8}.AxA }.A@rAH|.A@SAh {.A-A!|.AIAo|.A?@Aпr{.AAty.AFA ov.A 6APiu.A ]Acs.Ag[A p.AAX$`n.A`oA.n.A^A[n.AA(n.A@yxAo.A)yAx(p.A OyA`1q.A`zA^s.A{Au.A`{Av.A X|AxiPw.AŤ{AIz.AjxA |.AsvA~.AZtA̾.A@F.ApAG.A`oAM.AoAޘ.A pA`! .A pA < .A`oA #.AoAP.ApAl1.AspA1.AoAi.A@yoA[i.A@oA^.A`;oA.AKoAi.ADoAi.A oA 1.A@oA4.AnAך.AoA'ؚ.A`TpA.AڦpA$.A@PoA .A@%oA .AEpA _ .A`pAP".A oApE.A`"oAȱT.A`ܮoAe.ApAՁ.A}pA``.AFpA`Û.AxppARқ.A(pA.AVoA.AfoAh. .A &mAX.A\A.A`2\A`rm.A0VApf.ASAx-.AOMAΝ.A`8MAF.A7A.A`]6A`>.AF1A@.A;0Ahަ.A`/AP.A`Ì/A.A '/AӖ.A/Ap–.A.AH.A.A.A c+A.A@+A.A`.Ah"Z.A.Aѕ.A/A.A_1Aד.A2AMT.A2A".ALj2A .A1A.AP1Axz.A`v-0A'.A`D-A5ޒ.AH-AHp+6ؒ.A*-A*ђ.Aȩ--Afʒ.AF-A&’.A6B-A ?".A"-Aҵ.A-Aе.A-Aj.A@5/AS\9.AP)C/A1磀.AmO/AMAK.A@Z/AH.AEGcd/Ah..Aol/A ,g.Aq/Aƍ.AbFu/AJf.A#rv/AlPو.A0,Iu/A)[Ke.A^q/A .A&l/AБؐ.A"/A.Ah.A9.A`KN-A<4.AaףHg-AŒ/.AM-A$L,.A@9N-A{/*.A@$-A9%c).A\'N-A).A.Ae;+.A5x%.AVI..AcC.Ad2.A2U_.Ae7.APxx.Aq=.A2|.A>D.AVTF.AL.A@.A\bH.Ak.Am,B.AQe/A/;.Ac*/Ay/A.A@Ǝ/AQ-.AfN/Ae q.Ak\1/A?.Ag/Ae.AU{9/As~.A4g.Ah~.A@.A}>~.A@EU.Aw~.AHd<-Aw ,a~.AfX#y-A?xN~.AN,A sA~.A}Uo,Ax8~.A9e+A54~.AL+ApN~.A O1+AS~.A@r+A~.AL+A~.A@*A ~.A#Y*A<}.A`T@)A}.ABB)A[|.A :)A(>|.A@4)Am|.At)AK|.A_(Aجz.A'Ax~x.A&A7x.A3~&A8'Lw.A@{%Axmv.A %Aw%v.A#Au.A$A,u.A@g#ALu.AZ#AxXt.Ac#A't.A`T#AA5t.A@9#A@s.Ak"As.A s"Apr.A s"A`r.A s"A$r.A Zl"Auq.A@'"A@q.Ai!App.Af A Asp.A c* Azo.A` AHo.Aov Ax 4o.A` AZn.A`o An.A A@Pn.A AXm.Av A0Ym.A@y Am.AE A nm.A@_ A0&Mm.A` ACm.A@iP Ap<8m.AUAm.AOqAhm.AA`6m.A Al.A g!All.A A!Aj.A A!A&j.A A!AAi.AR!Ah.A@ A3h.A&A%h.A`KANh.AHA Fh.A@RA3h.A4KAPh.A AAg.A/Asg.A AWf.A QA`d.A=Ac.A əA1c.A,AXb.A`}Aзb.A@AH}b.A*Ab.A`fAOwb.AA`ab.A`YAGb.A`Ax:b.AR ATa.AÕA`.AqA_.A5A4_.A`)FAk^.AX/A^.AA`vK^.A Apl[.A?PA@uZ.AAxY.AlA W.AAP/lV.A`"A c;U.A`fAu2S.A AP.A#AX:P.AAD!P.AA1IP.AҭA8P.ATAh&P.AxAP.A`[CAO.A`WAO.AnAx[O.A PAXN.A`1AN.A`9AhM.AAM.AAL.AaiA ML.A`AKL.AUAwK.A@GAK.Aj'AFL.A S A3L.A> AL.Av6 AK.A AABK.A`kAp0J.AAfJ.A{ANH.ATA`4H.A A`G.A A NG.Ah AxG.A@ؖ AH.AYAH.AOAH.A=RAvG.AAG.A@YAP^gG.A; A`CEG.A@ye A0G.A  AVF.A  Ah%E.AT A 2D.Aa, A-C.Ax} A8#NC.A m AgDC.Ad APC.AAC.AA$C.AՄA`TlC.A uAhQYC.ARAYOC.AA`LC.AAЀBC.A` A15C.AA0H C.Ap AC.A A`wB.A@ A@uA.A@ Aأ@.AXx AI @.A@iAP?.ATA>.AiAd>.AgAd>.A ,A(>.A ,A>.A Ap >.A@YA[>.AxHAg>.A;)AHL=>.A`CAxi=.A@A=.AA;.A 8,Apʰ;.A@5&A@%{b;.AkVA0+;.AL~A:.A4A}~:.AA}B5:.A9 @AX9.AjA#:.A`.A0:.A[A9.AQAp9.A॥Aȱ 9.AjA9.A =A`9.A@A9.AŪA9.AsA 9.ADAb9.ADb!nAͫ9.AtpAi9.A{AHݎ9.A@5AS u9.A~9nA`CZ9.A@A";49.AqAp 9.A`;Awΐ8.A ;A;"8.AY@YkA78.Aǧ*Ag7.A4@<AR7.ABsAbN^7.A;8gAh?7.AD05AH6.A Aa 6.ApAB5.AAXF_5.A' A15.A`DA *5.AAuH5.A Av5.AOAh5.A})A6.A`Ah] 6.A{>AyaN6.AA\6.A1pzPAl_6.Ak As!26.AҔA!6.Ad^+AOiݲ%6.A lAS7 )6.ACA9f+6.AQA)y-6.AUANk.6.AWnA6/6.AiZA6.A|A 5.AAxd5.A#6AX4.AIaAȓ4.AEApJ4.A(RA4.A@ϥAz'4.A;At3.A(A:2.AA(1.AOARO1.A@>\Ah@0.AAn0.AdA8p0.AYApJ|0.A0A0.A ^A@0.AxAX-1.A A".AfAb".ARAF".A/EA0*!.A$@A!.AAP7!.A`Ah{!.A A@5!.A̓AU .AmAp.AAPK.A֘A0.A<APk.AXA.A wAz.AA@.A A.ATAho.AgA\.AAsH.AOAxK.AA.A@As .A A^.AA`,.A@Aԫ.ALAN<.A@A.A $A.AA.A 4Aܝ.A qcAA.A`!A.AZAN.A@%JA.AQA0I.A@,pA^..AvA(n.AgA.AhA.A@!A .A`Ap.A@A8.A`AН.A@Ap.AA*.A@6Ab.AA0Ǽ.AAxd.A OA0.A`AM.A@,pA".A@,pA?.A:A.AT3AJ.AM$A.AAA.AAؚ.AAx)F.AעA?1.A`!A?1.A5A).AAx .A<A.A@`8A.A@rA=.A:A`!.AA .A@A`Bc .A`5Ak& .A [A\ .A \A@k .AFAz .AA0L! .AA`vJ .AEA@; .ADAH1 .AQA .A]Ag .AAK .A@yA .A[A0 .AછAeP .A;Azx .AˢFpAiH.Ap@AZ8.AA@~.A A gu.ARAF$.AA.AԒ\A.A@A`.AAvAȔ`.AA;.AWA`.A@ Ah%.A ^A`6.A?Ah.A`lA.A/A0[.A#A.A`fA)yU.AebAmޥ.A 'A(<.AaMAI.A@ Al.A-ˬA;޽N.ACmSA*-.A߁0A6V.A&dtfAzl.A,An[.An9: Aw .AKR A1W.AGO A}s'.AM AX.A AG.A ` A<.And A8.A6 A,D.Ak A.A&D A~yVq.A٘ A|B Y.ABr AB.Aŋs9 ATT/.Ac Asr.A]> A. .A|: / Ap.Ad Ar.A A.AXL A0,.AO A^.AZX߲ AVڻ.A6JQAcrP.AkA`C/.A8&7aAz}=-AvA-A0A4t-A˦|A]8-Aj-A(-A`A-AMA ^-AA,-A A3A-A@,A3-A@EAP@=-A`hA;-Aҁ&A6`-A@i'A -AS0A_y-A}Ц2A:-Aq4A x-A#‹U7Adn-A\X9Ai-AB <A-A@-A{ocA5jZ4-AF eAʍ-A@#fADv-ABZgAf e9-A/ܠgA0v-AhA,,-ApMiAN)6H-Aٻ(tiAy*-AVZviAT-AcjAxAO-AOcjAeĢ-AP bkA#-A8oOSlA(B-A`;mA6b-AmAx-A[znA*j-A?9oA@*-A@pA6-At*pAW^-AjqAREJ-ArA@S'-A!rAx|-ABKsA-A ItA6 .-A( UtA6p-A4vuA&p5-AoԼuAX(G-AYvA)d-A;wAppT-A fxA;-AvA0.A iuAH.A rA .A]qAxj.AqAp.A rA@.AyqAnHWX.A`pA.A pA Z.A WpAj.AI,,pAx*?.ApAB{X( .AY pA\Ctm .Av93pAj{R .An)pAV= .A'BRpAU: .AippAh| .A pA( .A7qAid6 .AaPqA' .A rA(A .AjVrA6 .ACrA!; .A0erAFr .AmKrA%Ϫ .AܵrA;6̧ .AJrALy6 .Aw'rA,Q .AۙrA(9 .A .A`6iA4DE .A@@ևhAq.A'hA[m5.Ang.hAM.AhAXz.AFZAu/.A >YAi:].ACYA Ȯ.Arq YA\.AYA.AqYA&|.Ap|+YAoK.AVYAKHz.AviYA~h.A>'YAj.A 5ZA.AdZA~^.AD[A.AXAH? .AXA]Z .AWAMX!.ATAhݐ".A VTA/#.A TAh"#.AUAbX$.AVApS$.A*WAH#.A}zVA#.AVAHr$.A@,WA$.A@NWA $.A@WAxƩ$.AכWAs^$.A 1WAd$.AVAX$.A@*WAY$.AmWA$.A XAS&.A@`_A v4&.A5bKG`A]]e&.A%`A@ %.A-|FaA/N%.AHSaAd%.A bA(#&.A+eAy'.A@lA7(.A`nA (.AVoA'.A/ApA0'.A-juA _&.AoA`%.AnA|%.A GfmAc%.AsulAx*7%.A4ޠAkA6A%.AMFjAP$.A^FhA15p$.AsgAг$.A`!fAak$.A9.bAf $.At9`AWy#.A^A`=#.A,^AH!.AtaAa!.AaDdA & .AfAf.A@NfA@.AaZiA@.A mAP.A nAR_.A u{oA.AqAe.ArAPk1.A&rA .A NtAPk1.AuAhy.A`DvA@k.A OؑA4ىod.A͹(&A1a.A2b.ACc.A[@ړAZi.AZCr҄As.Ax+AW.A"͕APk.AhA(.A`;XAxY.A (Ac|+<.A3 j^A#q".A1SA , .A꓉A`.AAV .AxI͈AM.AX/DA*+.AH^WA๕.A1AS.AV*Ah~.A;"A<t.A"ׇAؽm.A>A4Mg.A@{A0c.A'HAES.A<AA.A!:A[D-.AlņA.A챆A$R.A|A3t.ACjAq.A+ Ab.AZ~ABJTk.AUdAVo.AUWA|.A0HAN.AN A1B1.A"CAjd.A0ݙAG.An1Aӑ.ALacAX.AA c.A5U#~A9Gm.A67n~AdN.A~-~AB0J.Ai/}Ahi.AHG}AaP.A@}A2.AG9}Anĕ.AFn}Aw.A)Ui}A):.A'}AϨ.A\|A.A;u|Afk.AIn#|Ahm٢.Ay9{A;g7.Ar骔{A;.Ay[{A^_Vq.An-{Al/\.A:H {A'G.AtJIzA(j1.A zA$.A 9 zA.A`{Ax[.A_zA t.A zA(.AzA.A x{A.A z}A2.A ۇAn .AτA`r.AdA.A@/AQ.A`A`G.AjzA.A`[A@< .A@CA} .A@DA .AA؎ .A`A@@ .AjAH\x .A XAH .AA@ .A'AJ .AA .A`A~ .A@>{Ax| .A \A@ .A@WAh8 .AtJA .AA .AA(+ .A=A8V .A]A@ .A`Aɚ .A&A8V .A&Aw .ASA}{ .A u`A /? .A}Ae .A"Ahb .AwA06 .A uAp .A@5Ash .A A-/ .A SAXQ .A SA(Kz .A-A.A@NA@.A@AAa.A`vA.A JAX.A@+AM.AA/.A?Aj[.AA[.A`Ao.ADA /A.A$A9=.AHA.A A.A@5 AH.Aa_Ae.A@iA0-.A`IAp,.AAj+.A+XAPI(.A`}AF.A@iAP8 .A[A .AoA .AA.A@ϯA.A_A@.A A.AAใ.A  As.A Ae4 .AAX: .A$ڬA7 .AA: .A@߫A8.A{Aȶ.A ~IAi.AAX.AzSAH.A`]A3o.AܰAKd.AA`Ÿ.AA( .AqAp .A@5qA .A SA)U .A@A .A Az .A`A .A@ AP .AAxz .A@gA .A A0e .AA`T .A@p6Ax .A@,A .A4Ao .A@A P .A$A . .AaA( .AOAH .A QA8 .A>Ax3 .AzAxL .A _A_ .A A܀ .A@.BAܢ .A Aȩ .A@r"A .A sfA .AAv .AzAfP.AAH\.A@7A 7.APAʺ.A?6A3.A0+#A%.A,Al.AW91_AN.A#UAXw.AՀA+ .A`oAHr .A`VAR.AveA\Y.A@0A.AA .A+AH\B .AAa .AAx .A@`As .AyƺAc{ .AFAa+\ .A姟`AsB@ .AEAs-( .AMxlCAh1 .A5 AS| .A`5SA2.ADA+5.A/@A.A`A0S .A0A* .A /Aj .AxAH .AA] .AAA7 .A@AHכ .A{QveAIɷg .AouA2X .ACA=K .AsWAj .A#DA`) .A/<- AK .Aj>AR .AQXAL7)~ .A`mAu .Ahc_|AȔk .A@,A .A@pA:JK .AG%AZ .AxTEA .AP6E} A r .AOAA .AĝAP.A lAH(.AA.A^KA6B.A$As.A@A(.AApʲ.A`DA:t.A dAyֽ2.A/e} AeA(t-A@,CA(-A`KEA'?-A*GA-As HAXXb-A7IAz-A YJA%V-ARLAoD-A`lMA,]3-A OA`-AwPAׅ/W)-AX)Q~QA@-A}RA+H-AhSA-A`"TAH@-A`BGWA4-A@ZWAHC5-A@ilZA8a-A;]AGZ'A-A :z^A-AǨ_AF5]-AJ`A\BͲ-A'8`A U-Aݨ\RaAá>-AJ daAJ-AbAk-AaA-AeaA-AfaAX-AoHcAy-AŰcA-AƉeAHN-A:fAH-AhA-AjA&-A{AbkA t5-A 0alA--AݙlA~j-ADAm2-A7ĩA`2-A {AA(߼-A]=&ƪA|6ļ-A~A~-Ad=A+~-ABz-AIi$N+AP@a-A:1La,ALFM-A8wy-Ay@ =-AtS.A1-Aٶ/A/iu.-AWV0A--A@.!2AI 0-A 4AT9-Ac 5A/F-AF7A<;-Ay:AH-AAAh-AHEA0-A@6IA-A@NAp-ANA -AtV WPA}5mͮ-AѨQAΐ~-Aؗ-0SA0뫮-All9XTAw-AVJVA3C-AmWAl-AFXA4/-A AZA蛮-A7z[A-A$\AN-A@pbAP7-A ™fAE%-AmAO-AvA,-Ai1A(>-AA8-A -A-A4AX-AAQ-A AO-Ah(A,S-A8E8A[-AɑzGAyh-ATSAߩy-AZA*䑐-A,8[AANث-AZUACo˰-A'FA^-A`MW-A@-AU AH-A?FAhQ-AܱA`-AA-A A?-AeAX[-AAh]-A-<AH-A:rA=e-A98A-AHA:,W-A98A|rK-A[u-IA+-AżA6x,-AT,AJ-AĒL. Aҗgu-A WnABkݝ-A F)7-A<"L-A| uAA~n5r-ARXA .A`VjA .A AqA= .AsAx .A tA8 .AଗrAo .A?8“oAل .A0~yAk4;.A౬^AOl.A+A-AA-A/A@B-AA,-Aޘ AXFA-A!Ay-AL*A_GG.A| uAb".AF)w A2-h .A Wn2A.AÒLnAʇT.AT,QA A[[.A@r@ A[.A zB A p[.ARC A.>\.A@]E Ahѥ\.A@yH A{].AFwM A^.AU A^.AU A? _.AV A$_.A@y W AHzj_.A fX A _.AX Ap_.A=Y Ax_.AoVY A A_.AY AȘ_.A$Z A0v_.AZ Ay_.A`TZ A@u_.A}[ Ap `.Af[ Af<`.A\ AXH`.A`#] A0``.Af] A l`.A^ A4t`.A5^ A}}`.AW^ A3`.Aw^ A`.A@C^ A #`.A^ A(`.A@W_ A0`.AV_ A`r`.A`` A!`.A F` A`.AZp` A3a.A@#1a AYa.Aa Aka.Aa A̎a.A (b A Ra.A c Axa.A+c AHKa.A d Ab.A0d A (b.A`d AЄ>b.AOd A vb.A3e Ab.A cSf AEb.Abf A b.Aqf A0b.AUf A#b.A@f Ab.A3f Ab.A?g A@ub.A@>g APZc.Ag.ARf,:s A%hh.A(NSa Ad l.AnBE AGÕp.Aȴa+ Azʙt.AR A_Gݿy.A| uOAb"n.AF)A2-h.A WnrA*.AÒLAʇT.AT,AA؃-AM BA;sN`-AXg:IA -Af2o13JAc-A@kHAY%j-A9K'Ah6-A;&A-A 'A-A 'AE-AZh%A-A`)"A-A"A(<-AAȇb-A`fA˟-A`TA@-AA tq-AmMAS-A"A -A$AP8x-A iAE-A|A-A@A-A`A.-A3A0{[-AC"Ah-A`KgA-AaAH-A@ d AH-Aa AHJ-A Ak-A`FA0 -AFA-A AH}-A$ A(-A` Ax-A6A@-A_ A`"-A AH?C-A A -AaSAg-A@AЄ-AfA,-A@AA~-A@Ah1-A/Aj-A`meA#-A,$A0-A`"ApS-A3QA-AZAc-A^A-ADOAȘq-A`DA-AAЄ-A`SAP-AxEAփ-A bA -AƩA[-A A}L-A ZA-A`AD-AzAh-AA0]-Ay2A<-AAK-A`A 0-A@E A d-A A)-A A-A Az-A} Ah-AA -A@KA8g-A-Ah-A`mA-A AB -A@ PA=2-A@AM-A@PAp-A AH-AfAu-AAx-AӸAl-A!Aг-AR $A8r-A%A-A(A-Ay(AQ-A E*A`_-Aq*A-A-AW;-A /A v-A*#0Ah-A`U1A@-A`1A0-A`2A1-Aྣ3Ã-A@I5AH-AP5A-A`7A(-A@yw8Aku-A;9A{-A /d;ApT-A1>AN-AfBAH2-A:DA8-AREA #-AW~ByFA5x,-Ai\~?AJ-A=m~a2AY%j-A9K'ALf-AL*A0[.A4 A $/4:ELT\gpuz-A*Ay-AL*Aڔ-Af*A-A*A-.A:A-A1A^-A27A˃\-AID1ɻ;A-AnBA-.A:AH.A KA.A`KAA-A`HA-AVRAH.A KAĔ.AVA@F.A'MA-A ?ZAPo-A` bA/.A,[AĔ.AVAp.AqeA.A/aA.AHjA؟V.Ap\oA.AQtA.A0kAp.AqeAh.AvAM`.AAtAXt.A#|AP.A <AB.A7A.Aڹ~Ax1.A`[%xAh.AvA.A{AU .A@,hAw .A@,{AX .AହAhk .A,CAX .A@,A0 .AŒA .A`vA0!| .A A d.A A.A{A@ .A A0 .AA:..A`9Ae.A A@ .A A` .AsZA{ .AA(i .A`oAX .AA.A9A` .AsZA|s.A`f"AxO.AAhnV.ARA.AXA!N.A@A7.ATA | .A@yUAH.A@ ~Ax.AzAxm.A@ A|s.A`f"A<.A A5/.A4A0Q.A@NUAp.A@ρAR.A@A].AA<.A A.AQ A0݉.A+AQz.AFA0o.AAl8.AfA .A ҮA`.AmA.AQ A8/.A+Ar/.A(A/.AWA{2.Ay8A#2.AA@/.A_A/.ADA8/.A+AȗP.A OAX0Q.A6AhQ.AA ҅P.A`,AP.A AAP.A@CA@kP.AvAQP.A`ӇAeP.A A P.A`AȗP.A OATKR.AgA@dS.A@֍AXS.AơAxWR.AޡA88HR.A 8MArQ.AA _Q.AAdR.ADATKR.AgAV.A@>iAV.Aw0AȇU.A*APiAkW.A*Apc9W.AiA1$U.A/CA0 zU.AAkW.A*AZ.AfA0[.ADA{[.AtA0TZ.AxAZ.A YAhZ.AIApAZ.A6AZ.AfA- ^B.A 1 AX\B.A1 ACWB.A@1 APB.Ak1 AEB.Aa1 Ai:B.A41 AH\+B.A*1 AX#B.A 0 AB.A`v0 A\B.A_0 AB.A0 AP>B.A>, AТrB.A@N' AB.Ag& Ax5B.A J% AŘB.A$ AHB.A`K! A0 B.A" AxC.A< A@C.A ASC.A AV-C.A+ A^:C.A A(1MC.AN A;gC.A` AD{C.A=6 ARC.AǓ AȓC.A<  AC.A AC.A`b AD.A`- AOGD.A@e A wD.A5 AH\D.A l AXFD.A A@ E.A3 AE.A  A5\E.A A0E.AC A$E.A5 A@FE.A A E.Av Ap F.A`' AX)F.A A0JF.A AdF.AQ9 A0 sF.A O A\F.A/ A-F.A`m A8F.A AhoF.A A;F.A AG.AA A%G.A A/5G.A AAG.A@r/ ApxKG.A`Y AOG.A  AUG.A`5 AZG.A A]G.AA4^G.A AVG.A`XARG.AgADG.A2AVBG.AA 9G.A@Ap$G.A A G.A}AhG.A`dbAG.A`AF.A`A8F.AjA#F.AAjF.AA(F.A=AF.A6APG.A AG.AAG.AAh#G.ARA.G.A A/G.A@xANF.AUAAȩF.AA84F.AiAqF.A}A F.AAF.A 3AF.AAUF.AAn{F.A@,AhcF.A`YAl[F.A`2qAؚUF.A@ƋAGF.AA@:F.AAC7F.A_A5+F.A@+APF.A@AF.AApE.AAHE.AFoA E.A`;AuE.A@ƧAE.A`&AE.A CA(E.A@AE.A AE.ANALE.AA bE.A8ANE.A~A88E.AߢAȥD.A@}A)D.A,AЦ D.AAjC.A7/6ʠA\qC.AW4#A*[C.AkGAG]C.A^ݡAIB.A`ՍA0B.A;AO+B.A]1-lA6DA.A}ߙ}A!IA.A%)PwA>B@.A| qAwQU@.AR&zjA ?.AuX&dA`6N?.Ac^A A?.A_]A`8?.A ]A0?.A2]A@u"?.A\Ah?.A I\A?.A \[A?.A5[A=.A`'NA0]=.ANAl=.A`IAWS=.AGAxi*=.A`KEAXW=.AOzEAw<.AO BAc<.A J?AV<.Af>A"><.A`=AG;.A@.h;A.;.A q:AhI;.A:8A`e%;.A`m7A:.A6A :.A „3Ap9.A2A}9.AD1A$R9.A 0A:9.A@gN0A58.A/A 8.Aw/AX8.AR/AuL8.AZ~/A7.AO/A`l7.A51/A56.A=.A }6.A.A@O6.A`m.AO4.A 9.AX(4.A.A0 4.A.Ax 4.A`T.-AP|3.A -A`61.Ae,A`2.A@ 'A$..A%A,.AB%Ao+.AR$AН&.A"Aa#.A4M Ap#.A32 Ap9".A/A0;z".A`2 Ag".A cJ A^".A A".A`o(A ".A(A!.A`(A@!.A /)A!.A@L)A1!.A@p)A!.A_)A!.Ab)Ax .A(AXG .Aӣ(Ax .Azw(A.A-(A .AX'Aذ.A &A@g.A4]&A.A%A/.A`v%Al.A$AHH.A$AJ.A~$A.Ae$A(:.AL$APju.A0$A.A#AXh.A&"A.A QP!Ax\n.A`7!AH$.A *!Ah/.AW A1.A@ A.AA@.A@WApl.A}Ahĭ.AA V.A`TtAج .A 8Ab* .AA@q .A#AhՑ .A`ʶAP8 .AA& .A൫A .A*gAl .A OAN .AA .A -AP< .A@~AS .AAأ2 .AtA .A<%A .A"A .A@RAX .AA> .A2AX[| .A@kA .A`fCAr .AA@s .AgAl .A@Aؚa .A@AL .A-A+A`V-A@NAO-AOA=-A` Ap/-AA-A` Avi-A)AP&-AI^A8-AA-A /5A[*-AVAb-A`]A-AौA|-ARA /-AA-A \AX({-AA8-A gA-A@A@-A`RAZ-AcA-A&0A1-A`A-A2AXK-AAQ-AAM-A AuU-A`GAPR-A`]kA@W-A {A W-A@ơAXZ-AAX-AEA^v-A@)AXj-A`Ax(-AvA(z}-A`Ai-A@A-A|VAb-A&A0wD-AbA?-A UA-As]A}-AA:M-A&9A -A qAj*-AgA(%-A@yA-A^AA-AbA`B-A`IA( -A *A"-A@A0-A A2-AAKP-A *AC-A@NA0-A4AȱI-ALA-AA3-AVA4-A Q*A`C-A6A]-AA-AUA8-A`IA26-AbA-AĺAs-AAȋC-AAj-AnA]-A 8~AY-AnA4-A@GAx+-AApX-A,Aqt-AAz-AvAc-AOAPk-AAhl-A_A(>t-AcA-A@IA~-A/A-AA[-A[|Ah-A Z_A0̔-A(A.-A%A-AA%-A`ԿA@-AjA-AѶA-A@eAM-A`DA-A`AD-AA-A 8A%-AE`A0-A +Ah-A A-AA`-AMApI-A/A-A@ᱺAx-A#hAC-AjTA'-A@`úAx)-A@>ƼAڝ-A@A(-AҿAhv-AfA8f-AGAU-A AL-AFA+-AA-A`lA-A`ݾA -A=A-A ℾA-Az}Ap0-AqA-A`žAx-AhAp-A`;A -A SAy -AAȥ-AxCAB%-A JA.-A`NAA-A0_AzM-AZAJK-A`mAA-ACA'-A`vNA0-AdAp9 -A@iAP<-AA-A Ax-A`AW-AQܿAy-A`[Ak-A-vApy-A`ABE-A`TοA #F-AaAN-AA_-A@CA07-A`[A0-A졺A-ADA茛-AšA[-AyAx^-AA;D-AA.-AA-AtA-A AX-A A -A3A0-AЛAq-AAX(-A@EA+-A ~A--AAA-A 1%ApO-A AH)W-A&A0W-A SùAH-AA -A HHA -A-\A8-A|Ah-AhA@-AwwA }-ALAؔ-AA?-A`A-A#A R}-AAI-AA&-A ӽA -AlA -A@'A-AޘAx6-A9Ax<-A ZAhD-A#A?-A App2-Aq|A -AzA-AϵA\-AHA( -A@DA>-A@A-A9AxX-AǺA-A/Ah"-A@5ٻAh-A#As=-A`~AP+@-A A>-A A4-ANW+A -AhlnA#-A@ oA$-A@ƗA-A0)A -A37A-A Z9A`%-AMA$-AxfA-A@`AX[-A_ A^(-AA(-AJAP-A@A-A@ýA,-A$ȽAз -A$aA/-AtA5-A lA0 R+-AG8qAd3N-AOfAf-AAd3N-A_AE]K-A}98A*&{1-A0ƾVA%hX-A౬sAd -A+WAGÕ-A7K_Az!-AuvGA_GG.AAb".A֘A2-h .AA.Az.Aݧ A0}.A1 A~.A A%~.A`26 Aƥ~.A^ A$~.A ~ A~.A A@/.A An.A A(.A H A A.A`d Ap.A A¥.A6 Aأ.A2 AD".A AG.A`6 A 0.Au A2-h.A A#ӯ.AV A8.A A0Hۊ.A`2 Aˊ.A Ad.A@W AP.A@ A/.A0 A߈.Aa A`.Aޛ AP8.AQ Ae.AHU A -Az^ A`K#.A)ޮAl 908J~jw{A+d-A4 Aj-A RAp-A9A-AA Y-A@#5A-AA2-AA -A9A(\-A+AV-Az^ AA+d-A4 Ay-AL*AXFA-A!AT{-Ak(Ap[`-A 1Aڔ-Af*Ay-AL*A^-A27A-A8A˃\-AID1ɻ;A^-A27A؃-AM BA@ -AEAx>-A@.AA -A`bVEA Z-A@ FAk-A@DAۣ-A@rFA0-A"KA b-AKAH_-AMAP^:-A@NA03-A 1NAŜ)-ARA`k-ARA`2y-AkSA)˕-A%-L SA= -AkRAͥ-ARAȢ-Ao6tRAX=-A-QRAϨ-A LA -Af2o13JA;sN`-AXg:IA؃-AM BA+-ADlA-A@iA0-A@jA-A@kA-A@5kA@Ɠ-AMGkAȶ-AjA@qg-A6jA=I-AVhAd-AhA8-AM`jA-A0jAN-A!kAH)-A OjA?-A@iA@}-A`iA-A6iA-A`bhA-A VfA@-A^A&m'D-A8x^A]H-Atg^AAK4M-AY^A5cR-AS5O^A:mK;X-AX4`H^AH ^-AZlYE^A_d-Af4F^Ai-AJ^A]-A`ml`A{R-A`A0-AbAx(-A"{bAHP-AbAC{ܥ-AUkO cADT-A 5cA`-A`ׯWcA؞-A`ocAp2-AcA{3-A`]cAh;-AcAJ\-A`cA&-AbA R-A@bA-A@ylbA-A6bA@q-A` aA-A`AP-A`A-Ah`Ap-A@_API-A^A$-A`^Ah-A@^A-A8O^A07-An]AH-A`]]A8-A`T]A(-Am]A M(-A Jm]A0%-A`i]AP!-Af]AX-A cz]A-AR^A-A`1^A)-AO^^Aܑ-A~^AXd-A` ^Aȓt-A Oc_AI-A5_A8s>-A *_A-A^A`-AZ]A-A[]A`-Aޕ]A-A ]A@-A*t]AP8-Aj]Ap-A@`]AQ-AT]A-AN]Aѓ-A&G]As-A=]AW-A2]A@-A@#]AЏ-A`]A-A@]Ag-A\A--A\AQ-A@\As-A\APg-A@i;\AH.-A@#\A0-A \A <-Af\AZ-A3}\Arz-A SrYAȺq-AGYA-ArXA-A&XA-A!XA -AWAe-AWAۯ-AFWA-A@WA} -A J!VA(-ARAP-ABm8Aأ-AM7A-Aa01A-A&0AC-A|0A0;a-A0A[C-A/AY%j-A9K'AJ-A=m~a2A5x,-Ai\~?A #-AW~ByFA>-AךGA-ABKA0H-AOAf-AOA؎~-ARA-A@ JUA3-A5XA$-A@>?XA -AoZA@-A`]]AQo-A`KgA)-A[hA-A@!hAp|-AGiA-AĩiA7 -A3?iA(9-A^HjADį-AWʂjA-AjAb&-AͷjAYX-A(jAZot-AᨚijA?-A>KjA(-A jA"tcC-AԍjAЮ-AQjA-A` jAɺ-A` jAp -ACkA0ي-A`TlkAg-A@kA+-ADlAU-A BAB-A c0Ax$-AA0·-A`դA0-A`-Aע-A@AR-AʎA8:-AAx\-AkAj?-A AhD-AڲA -A@mAPM-A)Aq3-A cAת-A AH-A` A8-A_A-AyAp`-AAptl-AټAx-A.AXp -A?AX-A Af-AؿAX-A@ AXFñ-A AwA -ABFA`K-A#A--A)sAU-A BAj[.A<1 A([.A . A'Z.A4. AY.Ah. APX.A`- AX.A`. Ap+mX.A`;- AgX.A7. A[YX.A4. AXTX.A?. APX.A . A83NX.A`. AXHX.A. A}IX.Ac / AX.A/ A>X.A &- AvQW.A`3- AUGW.A>- A>W.A-r- A(2W.A / A7W.AO4/ AGBW.A4W/ A((W.A@/ Aȥ%X.A / AH$X.A / A:OX.A`b/ A8PX.A@%/ AdX.A/ AceX.A/ AyZ.A0 A0Z.A1 AH]Z.A1 Aj[.A<1 A@Y].A-Z A!Q].A`- AV\.A@` A2\.A A\.A Ax-@].At AxY].A|] A@Y].A-Z AV.A AV.A@ Ax\V.A) A`V.A`w A8kV.Aീ A V.A AV.A A0&4V.A@ AZV.A= AȺV.A Ax$V.A c2 A(,V.Akb APfV.A<\ A` W.A} AW.Au A8 W.A` A V.Ak AV.A Ap#c.AbK( A c.A Ab.A@O A!b.AF- A@$b.A) A`.A  A8@`.A Q A0_.AM A4F^.A^6 A*m<^.A Ak\X4^.A Ұ A.^.A`B AYz?"^.Ac A)^.A U& A ^.A_ AS].A*N A<].AΒ A~̈].Ay |$| A ].A#o AQ|].A4(l A a].A Fs AHƝ].A֮M Ai].ANVĞ A].A AH(].A= AU\.A`: A ].A Av\.Aj= A+\.A^q} A}\.A" h A\.A O Aa\.Aofrz@ AͦJ\.AW&s AH\.A39 A; M\.AhҦ A].Ax AZ ].A&n AG].A؀^E A].At A3].A`d8 AX^.A  A`.A@= A(E`.AD AL`.A`R A`va.A  AX)b.A R A^[b.A A^c.A[ AxX e.A L( AF@4e.AK|, AE]K^d.A}9? Ap#c.AbK( A 0.Au A$.A# Al.A A"..A Q Ak.A A* .A; Ap[.Aل A`C[.Ay Aw1.A= A؞.Aa AX.A+Y AR.A6z Ap,^.A3 AP .A' A*.A? A(.A A.A@ A0B.Aa AD.Aoe AY.AEe A#ӯ.AV A2-h.A A 0.Au Am.AgP AϏ.AƙO A@ .A`O A.AR A\.A`fvR AX.A P Auw.AcL A 4 .A #K AH.AcJ AX.A J AfF.AJ A8E.AD A.A C A@J.AmA AЄ,.A@:B AC.A D A(~^.A+E A៌.AF Aƌ.ApF AP.A@%5G AH .A$L A@ȍ.AM A1΍.AO Ah.A R A&.A/V AXVk.A5W Aڪ.AU Aؚ}.AS As.AQ Am.AgP A1.A^4 A` .AL0 AB.A`- A̭.Av+ AH>.A' AL.A`R$ A@е.A`v# A&%.A) A0X.A 1 A.AE A.A AP.A A_ڸ.A` A޺.Aax A.A`dE Aʽ.A` A;J.A@ ApNU.A AX(.A u A.AAv A0.A`B A6.A@߲ A0f.A A`.A9 A54.A@a AH..Ah A`̾.A AL.Ao0 AFּ.AC AXRǼ.Af A 0.Ap AW.A`̃ A`.A}Ҷ A .A A@B.A API.A@ Ao.AR Aȶ6.A@ Aʱ.A_ AC.A`} Ax].Ao A.A@G9m Aܭ.Ase Aڭ.A 1je A.A d Ai.A c Aذ_.A`Kc AjY.A`Íc APk6.Ac A|ج.A@a Am.A̵` A.Axt` AX.Aa` A%k.A` Ac.A"` A02.A a A.A a Ah+.A_ A.AT A+.AS Ag8.AS AHC.AR A.AR A.A@fR A.A ҚQ A+.AR Ax.A-W A;.A/^ A텟.A"` A@ϟ.Aidb A .Aje Ah.AOi A4.A`m AR.A Pq A`.As AÓ.A`w A0j͓.A`rz A\.AM| A.A l} A\.A@ A?m.A Ax.A Ap.A B A@.AЗ AX.A@Cĵ A! .A cz AHU.A A0;.A@i A$.A ? AfN.A`D AY.A > Az.A( A0.Ag A.A AH.A| A .A[ A8".A7 Ap.A Aʧ.A A<.AZ A .A@, A.A AP.A0S Ag.Ak A`Ce.A@ AY.Al A"X.A@m Ap.A`e An.A3f A\Y.A Zh A@K.A`  A4.A@P A0.A@yo AH0.A} A.AK A_.Ap AY.A`D AN.A% Al.AՖ A0.A@a AHi8.A A.AY A .A$ AXR.Aા Am.A Abq.A ARb.A@ A(-+.AI A0囨.AD AH,.A`& A.A AXF.AV A@.A- A(&.A_b A H.A`i Axhp.AR- A.A ALҨ.A Aʨ.A 1 A@.A) A@A{.Aѡ Ah/P.A`$ Ac.A A@`.A  A蕾.A ? Ap~.A^ APT.A^ A .Aa A@.A`V AMڦ.A`V AH̰.A t A.A`V AHl.A@ ABE.A@ A(.At A.A \ A舭.A ( Ah~.A A( q.A@ AP/l.Az Atg.A`; Ap.A g A.A@U A1.Aߜ A𴭦.AH AƦ.A ALަ.Aa A.A- AO.A A4.A)F Ax).A} Ay.A A* A).A`mx? A.AN A A.A*A A.AxA Aʰ.A&@ Aӱ.Ae= AD[.A@g; A1.A^4 A;.A (wL A-.A@!L A}.A@%L A.AҠL A .AN A.A@uO Ap.A (P At.A@Q A.A=Q AX.A (Q A ʡ.A@RQ As.A qQP A.A3O AH@.A (?M A;.A (wL A.AoR A8{.ALR An_.A ZSR A@3.A ZR Aؚ.AT AP^.A@U AX.A@V Af£.AoW A>ѣ.AX A.A W AK.A@yW Ap6.AIV AH)M.AU A𒎤.A=wS A.AoR A(s.A A7.A=GA0.AAh̨.A͝AG.A^mXAƯ.AA_.A_MLAxa߰.AQA{H.Aq>LAiɬ.AkPA +.AHAMo.A|bA8.At@AA.A&A`Xz.AA{sk.AHDAu`.AfbQAY.AᰴA6W.A$yAp a.AJ7AqzTo.A_A弁.AoP67ABoʗ.AYrZAY1.A󐜘Ą.A|gAM9.A@d|DAE;5.At&5AH&.A@8Ab.A*j)AJHc.As8.A(#lة.AHGA̴.A ltAZm;K.A,.NAS.A0u AY׸.A_,EpA.A5qAR.ArAX$ .AtĚAb&.AAb 1.A& pA?.AE ƛA P.AAT.AAn.AqǛAa>.AG@A_p".A9Aݫ.A ]A' .A!xA Σ;.Ac}ANJ k.A}SNnAS .A PJAH.Ǭ.AAɣ.A |*A9.AAf\Q .AKg갈Akn".AWAŵ9.AJքA>1Q.A Aqg.A|A|.A QAt.A@AP,.A ZAh.A`Ahċ.AߴA.A uAj.Ag-A]Я.ATA ѯ.A@A0.A`<A8.A@AU.A=ܗAU.AA-.A`堗Ap8.A A@<.AAA.AAm:.AsAp7.ARA`A.AIA L.A <AXJY.A Am.A`ȖAԅ.A@>A.AA۠.A ɖAxW.AAp,.AൖAb.A@`A _.AA`.A~AX.AaAPɹ.A_Ah@.A ,A汰.A@A.A@Aϰ.A@A.AוAN.A०A.AMAi.A= A%.A A".A AޔA8(.A єA(.A@AiW).A oA56&.A~WAּ~.Al+Ao.A7`wAг.A։A.A5@A.A"1]SA[qW.AA2>-.AgA0v.AAPɰ.A1A汰.A^טhzAK.Ah1Aa~H.AdwApň.A䕀AV터.A/EA$&.AQ(}A%%.Adx|Ai..AU{A0 .A-N zAB&뻰.A 8xA]S}հ.A;wAC.A{HvAѼ.A8 VuA5l=.A/issA)&i.A?HrAFW.AÉqARk'ͱ.A pA zj.AF}oA9;@.A nA~.AemA1.A^ mA .AؖIlAk)9M.AD$WkAlY.AF| jAȵ.A`ijA֙.A2?%rjA:!.A2?%rjA0.A`ijAI[3.A.A@6jA.AgjAP.A jAdb.AVjAw.AFjA0U.AjA .A`kA.AT>kA .A(kAᴵ.A`>kA0.A@kkAѵ.A qkA;.AjkA@.AkA@F.A`lA.A wA.A@ wA@ .A (zwA0.AwAh..AxAM.AspwA(`.A`twA.AwAxX.AwAmߴ.AxAuڴ.A xAѴ.A@yA˴.A cyAؒô.AyA ɴ.AyA.A zAٻ.AtRzAX챴.A6zA.A@!zAPM.AzA.A@P<{A.Aw{Ai.A@%{AX.A 8{A.A@{AXF.A A{Ap|.A@,&|A.A`B|A.A`]h|A.Ai~|A.A||Az.A@g|APk|.A|A6z.A/|AH?u.A|A`%l.A c}Amd.AA}A7b.A@p}A`v_.A@}A@W.A |}A8P.A }A\N.A@~A`R.A@L~AH:R.A~AM.A`~AXK.A@~AE.A~A@.A@A2.A`T~A*.A`AA*.ABtA0X#.AAЄ.A A.AA.AYAc.A@A@0.AEA0ճ.AZAͳ.AAƳ.AUAX³.AnAX³.A#A.A_AhMų.ApAhMų.A3A0ó.A ZAų.Af͂AhMų.AxA˳.AAʳ.A@Aأӳ.AAҳ.A@'A(ٳ.A 0ABڳ.A SA8ݳ.AyA.A A.A`oAp.A Z؃A .A`A0&.AQ A(.A`]#A.AQA(.A pA6.A/A.A`A.AEA(.A0A3/.A\Ax)4.AA;.AAxI.AȅA:J.A<AJP.AAS.A4AX.AA].AVAPg.A@5A#.AA{ܴ.A`KA.A4}A).A wA@uB.A#A@uB.A@Ax.A@A-%sB.AXcCA)ok.A$A"߷.A/AA(+.A |Ui~A*Ys.A ]}A.AH|ACej.A |A-*6¸.A&=|AfMŸ.A0|AplǸ.A |A0 ɸ.A7܄|Aɸ.An|AH75ɸ.Ak Y|A*Ǹ.AwC|AŸ.Ag/|A¸.A?|A=-.A} |AẸ.A{A}=8ڔ.AQ6l^{AO|r.ALzA6OR.A&PUyA37.A/)=yAH.AuxAKm .AvPxwAX.AUOvA.A ~uA}/S.AWtAc3/.AmzDDsA@.ABqA~5.A)qA+.AԆyqAD-p߷.A)䤭qAqݷ.AqAX=ڷ.Ay[qAz9b׷.AO}qA(~ӷ.A`qqAڷ.A@qAmj߷.AN8LqA.A \VqAme.A^qAX.ADcqA_W.AhPWqA^M.ADqA.A*qA~.A1LqA◄.Ag5qAԕ".A.hpAVaC%.AHRpAk^c'.A7pAc_(.AzpA0(.AgpA(.AlDpA`+.AepA[T0.AoA4"c7.AoAc@.AgoA}g@.A^doA;@.AqaoA@.A'_oA3A.AK\oAA.A ]ZoAECB.AR}XoA'LB.A@ WoA;՜C.AGKVoAwXD.ATiUoA5E.A,tUoAE.A) i&VoAuۃF.An'D0WoA'G.ASMXoAG.A@ZoAjI.A ӈ^oAO>K.A}XaoA(M.AboApsO.A2ĿfcoAQ.AhboAeR.A `oAT.A;g]oAcV.A &YoA>V.A!NUoACKoA! W.AjHoA렒W.A7=PFoA2X.A~ DoA X.At'oAs.AoAx v.A膆, oA` z.AmPknAF~.A6}nA:(.AEnAҖ.AWnA2,ˌ.A|zV_nAP'.A nAd֕.AIBnAmR`.AnAh.Agd߯nA?L.AnA: P.At]vnA.AnAy ۤ.A`nAI.Aڱ4snAϣ.A%I^nA? .A0InAܙ.Ag]>6nAG.A%nAig.AFnA.A`K nABⒸ.Aq9k9nAzב.AlnAc䐸.AnnA .AunA^.AN=mAO׎.A7mAի~.ARcmAU.AڊfmA].AdmArq.AdohmA .A&,mA딏.A4>FmA R.AmA7a.AÈdmA`8.ADmAǘ.ACW[mA.A۶mA֜.A>)mA@.AmAD.AmA虯.A SmAX.A SmA0Ƚ.AmA.A4mAĸ.AmAʸ.AmAи.A=mAո.AmApٸ.AmmA߸.AnAp}.AWnA,.AznAx.AamA .A@mAb.A@`mAA.A mA0.A&mA.A@`mA.A`ʋmAM.AmAM.AwmA).AbmA0.AYmA.AFmA.AR8mA.A`$mAH.A`mmAXF.AlAp<.A`dlA.Ag.AlAPf.AXlApf.AslAP8o.AǔlA z.AWzlAX|.AzlAP@.A{lAX솹.A3glA3.A@ OlA`.A@zA ^$.A@#MzA.A/0yA.AھwA@.A wAPZE.A@vAy.A@vA8D).A`muAY.AXuA.AatA.A@WtAP.A@ytA.A9-tA-.A@sA,8.AsApV.A HsA x.A sAp.A@sA.A 'sAjD.A;rA&.AJrA0].ACqA&k.A6pA^.A@WmA( .A3mAxǢ.A lAX(.A@N.lA[.AkAx.A(kAp(.AjAhC.AmjAZV.A`viAZ.AiAXc_.AviA3\.A@iA@c.A@iAk.AMwiA.ADhA׈.A`dhAh/.AkhAY.A;#hApJ.AgA.A`ӃgAR.A?gA@5.AMfA].AǕfA.A>fA..AfAp_.A@>eA.AeA0j.ACeAP.A /dAHaC.A cAX,h.A@EbAw.A@ZbA.AbAX.A \aAȺ.A`AU.Af`Ah.Ae_Ax-.A_A.A4^Av.A^ApN.A ]A.A l\AX.A`\Ax.A&[AXt.AD[Ax-.AZA(`.A`YA.A`YA N.AXAG.A=XA.AqXAw.AXA0.APXA`.AWA`q.AWA(`.AHWA.A WA7.A`)iWA.A`!WA06.AaVAE.A@>VA0%.A`VA.AdVA8.ASVA.AK!VA.A cUA8.A`)]UAG.ACUA襝.A TAV.ATAH.ATA.A=SA|.A4SAJq.A@SAxj.A?SAe.ALSAY^.ASA`TZ.ARA_.AvRAp|g.AARA s.AQA`s.AQAȔl.A`fQAk.A+QA^.APA\.AjPAHX.APA0R.A9 PAVN.A`9OAppY.A cUOA@L.ABNA8bJ.AaNA7H.ANAJ.A@PNAE.A`NAHD.AMA(L.A0MA8H.A*MAH?.A@CLA1G.A \LA(D.A-LA&B.A`KKA)D.A`m,KA$M.A`BJAX[A.AJA ?.A[OJA0;.A`IAE;.AIAZ..AIA/.A@WHAP/.A@.OHAu+.AW/HA..AGA`).A cGA (.A ~EGA@+.A`8GAx".A FAT.A@pFA`G.AFA..A&OFA.A` FA.AEAa.A` EA.A3EAP.A \EAxz.A@DA@q.A DA(.ADA".A `DA.ADAp.AqCA.A CA.ACA.A@\CA.A ,CA?.A CA.ABAX.ABA@.AiBA@1.A$BA.A*AA.A QAA.A@,AAA0.Ap0AA@ƽ.AYAAXƬ.A_AA8.A9AADy.AAAY.AXAA`4.ABAP.A9aBA.ABAK.ABAhQ.A3BA.A`AA.AAA臽.AEAA5.A@A.A@A(1.A0@A88.A@A.A@5}@A.Ae@A.A#@A~.Aj?A.AM>A8.A>Apڠ.A y>AԱ.AW>A0j.A=A8'.Av=AH.A E=A .An=AHr.A<A@.Ak<A 0.A`T<Ax.A B<AR.A <Au.A?;AH{.A@E;A`t.AH`;A`p.A@#5;APg.AN+;AhT.A;A&8.A`:A&.A:AX.A`:Apy .A R:Ap .A6 :Aa.A 9A .A`]9AH .A09Ax.A\9Ax .A@69Ax.A@58Ap .A8A`C.A 8A*.A`KI8AЄ.A 8A.A7Al.A7A؁.A|7AH.A@6A.A"6A.A`5A.As5A.A95Aѫ.A 4A.Ayb4A^.A34A3.A14A0Y.A@ 3AȂ.A 3Aȥv.ABM3Ai.A +3A)Z.A02AR.A` 2A`9.A@R2A(0.Aa2A..A1A/.A`T1A0-.A81A&2.A81A&2.Ai1A2.A81AB,.A 1A ,.AV0A0!.A0A?#.Ap0Ap=.A$10A.A`/A .A`/Ag.A@ /A_ .Ap/A .AB/Ah".A@/A.A O.A.A ۡ.A.A`p.A.AN.A=.A.A.A -AP.A ~s-A.AT+A d.Az *A.As>(A`.A%AE.Ay"A.AJ!A j.Ak Al.AC Al.A@W0A0j0.A&AS.A A@1.A lG Asw.A` Ah@n.A`] A5.A@> App.A`m4 A8ɾ.A@ A.A AX5s.A AXyK.A`8 AH.A A.A#OAhD.A(Aig.A@Ah .A A~.AA.AIAW.A3AV.AAp2.A^AU4.A ?AP^1.AAp#.AA&.A@ hApÿ.A A-.AA.A@pAh◿.AA}.A@A].A`fjA`E.AVPA+0.A /A##.A A.A)\A0e.A@Ax\!.AA0.AtDA?.AAI.A AU.AAl.AAXJ.A`ܳA.AnA@.AdAͿ.A vA޿.A@hA7E.A0.]A.A$\Ah..AAp_A.A /A M.AAR.AăA)ZZ.A|Anb.AŤpA< i.A_A p.A;JA@u.Aw1A\$x.AXA|.AŽAV.AkˁA.AA(-Aҍ.AA.AIAb.AUA.A0A0Թ.AqAP|.A<A;.AhA0.AA`.A`]0A.A@CA8I.A`A.A IA.AqA.AAT.A AJ.A`mBA}.A A@w.A A(yc.AOA@Y.AsA/S.A=AP;.A-RA5.AA.A` YAt.AV)A.AA.AiA`#.A%A ).AA`&.A@,A]8.AnA?.AvrA(yc.AhA?.AVMA0{L.A.A).A`KAx.A@%A(.AV2A.A@poA.A`CA.A/A(.A- Apܿ.A$Aֿ.AAٿ.A@5A`Կ.AA̿.AHA0̮.A A@>.AA.AAʔ.A(A0 s.AWA].AbAR.A(A 4H.A AoD.AtAA.AcA<.A@7A +.ARA.AHAr.AIcAx.A`A([.A4A}.AqA`.AA!.A@yAe.A JAP.AAج.AAX׽.AAX9ҽ.AA .AA@.A~A8㬽.A`2qApp.AMAј.AgA(.A@AӋ.A;Ah.AoKAd.Ad8A8\.A[AX.AAT.A AI.A0A=G.A ªA@A.AྑA6#.A@hA.A KA`.A@ A.A &A@uټ.A&Ax˼.AaA Eȼ.A 1At.AA .AoAQ.A?;A ^|.AAcQ.A8Ai;.A,XA`-.A +Ad.AvA.AVAO.AAS.AAһ.ARAJǻ.AA@_.A`ӑA.A jAd.A ZAAW.A A&.A A .A`"A(q.A-A}.A gA/f.A`;AT.AyA=.A`Ap.A~AxF.Aq'A.AA .A@gAP7.A 8eA; .A5A .A@>A7.A SA.ALA.AXAQ.AzA.AAp ݺ.A`mA}޺.AAhӺ.A#qAQӺ.AOA.A -Aк.AAhú.A`A.A@PAT.AA@q.AA 4.AEAع.A9Apҹ.A A̹.A"A(`Ϲ.A`AXŹ.A"Aﻹ.A Ao.A (A3.Ak>AX9.A A@.AA˘.A`ܴA`P.A yA.AWAks.AtAc.A`A@W.AzAZ.A*AW.AQAQ.AAE.A@dA[8.AA`1.A+A((.A Ag).AA +$.A5A%-.ARAȥ.A`Ab.AA.A`mA.AhA@.AA.A`iAX4~.A 7A4.A@GAP.A-Aŷ.A;<Af.A AX֍.A Axdv.A Aj.ADA^.A.A؟R.AEA8ER.A`"ZA.AA(.AA8.A@NAxõ.AXAx-.A (EAz.A;zAPr.ANA(f.AAa.A@CAW.AXA.A@i"A.A-Ahѵ.A AH.AӗAXd.A`~A!.A`mAX0.A=A+o.AgYAxz.A /IA𴉶.Ad!Ax.AAAն.A.A A`%/.A@A.A AA.AkA.A`#A).A%AP.AAPβ.A`Aǿ.A`Aг.A1A}.A?WA*.A`Ag.A@A.AHA.AHAP.A@A .AA.A?Ap.A@A׫.A җA.A Aս.Aj_A(m.AX>Ag.A%A Dz.A (!AƲ.AA0.A@A\.AoA2.A@rA9.ATA.Av:AH̉.A`8A@.A$Ap.A$Ap.A@AȲi.A@A /j.AAu`.A}A_.AA ].A8AYd.ARjAsr.A`fCA`{.A AIA`{.A@Ax.A Apw.AA.|.A?A.A`)A.A ONA.A?PAњ.A AP.AAp .AA}.A'Asr.A@Ax)k.A AY.AAhc.AAv.AAo.A _A_.AzOA0\.Af AS.A&AMT.AǵAO.A?^A0K.AAwN.A` A(1X.AzAm.Ag<AHac.AsAx6i.AApu.AzA跌.A`KA8.A`Ab.AA@.AyA(m.A@ydAȱ².A@}A`ʲ.A`bhA-ղ.A@gCA.AvAA?.A`A.A`A.A`AX4.A`A".A8A01.AgAxF<.A:A oH.A@AL.AA"S.AAPX.A@A0e.A@AIq.A@!AH{.AA@.A@5|A.ACA\.A-6APn.A^SA.A0AP.A`mQAPg.A@EAh.A@y,A.AAp .A@`A .A;A0.A-A.A A .AA.A_A.A \A .AڗAp/.A@AXE5.A@,A(N.A |A^U.A'A].AA_P.A@pA`v.A@/AQ.AoA.A Axd.AXqA.A`vAK.A`mA9.A`yAݍ.A!A8R.A`yAL.A5AHKl.AA0i.AA@F.A "A>.A`vA>.A9AQ.A@AhN.A׉A8.AzA#.AA5.A@ѿA01.A|A@B".AпAhb.A@aA*.A qBA.AؾA01.AA"/.A`A.A뾾A .AaAM.A<A.AA.A_Ahb.AѽAJ.A@,AJ.A )A.AAȲ.A@>&Ax5Ǵ.AA}.AfVAY.AA(̴.AAlʴ.AȼASѴ.A A0ߴ.A`o3A8.A ,AN״.AdA0.AA.Aq̼A(9ش.A@A״.Aj|Ah.A`oA.A UA3޴.A%ASѴ.AػAD̴.AwApJƴ.AA.A ջAx.AAδ.A xAfд.A@A8ʴ.AA ɴ.A@A@.A鯼Ah.AA@|.AA.AkA.A/AȒ.A Ap,}.A DApa.A@ixAH\.A++A8E.A HAP^G.A`^Ap).A`vA&.A iA0-.A-A08.A/APo5.A@AD.AҼAJ.A`Ax.AmAI.AAH:C.AA>.A@gA6.AA84.AXfA@;.A=AH:C.AoNAV.AI"AT.A@CAP8Z.A`A\.AA2`.A A].ATAPkc.AwA0d.ATAPj.A Aei.AAXs.AػAu.AAy.AjAv.A |ĻA@g.AdܻAc.A |ĻA_.A (ȻA b.A;޻A\.AAxX.A ջA=T.A;޻ApW.A@NAgR.A0AE.AAk.A0lA(\^.AAXj.AٺAtn.A@yA(.A ƺAB.APA8.ACA@.A@pAȲ.A@ Aˣ.A_A؎.A ZAɴ.AAHaִ.AA1.AR5A.A@iAˣ.A DA( .AL*A0e.A ~Aei.A@ A[.AºAU.AA8.AXպA0-.ADAL.A`IA0I.A|3A7).ArA|&.AdA).A ]A%.ABBA>!.A HA`!.A@.A!f.A}A `.A PAQ.A;iAsM.A7A00.A`A.A@iݸAi'.AA.A4A.AYA4.A8A#Գ.A@,ոAh.A AH.A@Ap.A*A0ȳ.A@!ԹAW.AιAO.AXAe.AiAP.A@KA0.Af As.AԷA`.A A.A@`(AA.Az۶A.A@Ah?.ADA.A٘A.A޴A.AAFٲ.A Q{AHɲ.AZAH:.AdCAX.AsAp_.A&As.A@5سA(`.AųAQ.A<óAD.ADAX(=.A@NA A3.AUApp&.AwA.A ײAx-.A1Axܱ.AXA0.AD1Ah̨.A6A".AAeɱ.A ZA୩.AA.AHƯA}.A;uAd.A`Ah N.AcAЦ5.AaAX8.AAG.A JYA`%7.A@EA6.AX8A#H.A@٤A(yI.A`AL.A@A(\P.AAP.A A cQ.AA`a.A@p A£.AKAxi.A9TAl.A]AҠ.AgA.AqAҠ.A |A(m.A APj.AaAã.AAp.AǟA0f.AVA.A`A.AXA`v.AA.A3A.A3A.A3A۱.A`f$A .A +AL.AsApD.AABk.AzALa.A A/.A`Ap[.AAP.AX AxX.A@`QA.A A>.A'A滲.A@GA.A`AH\.AA)h.A7Ah3.A@Ay.A`mA06.A lA].A@ AH!.A@ A}.AxAp.A@5nA.AcA.AZA޾.APAxG.AHA8b.AjAA:.A;Ap߶.A/7Ap_.A^4A˱.A03A83.A3AhL.A A.A#Af.AܥAJw.AWAH.p.AA>k.A`"AFg.A lƤA@g.A4ƤAb.A ɣAȘl.A Ae).A \AД.A`) A8A.A@A0.AƛAȥ.AfAa.A@,BA[.A/mAH{.AEAH?¬.A`]zA`qC.A笀A襛.AM{A&.A@&{Az.AdzAd.A ?zAPP.A`yA=<.A yA7.AqxA%..A`ܧxA.A@wA3.AvA<.A mvA0 ͪ.AVuAâ.A StA~.AF A5.Aբ A |.A¢ A.As AL.AR A`e.A" A .A J Ap.A(k Ah.A@f AЦ.A \2 A.A Au.Aՠ A.A A(.AHC Aٻ.AI A Е.A u AX9ƕ.Aw Aʕ.A`] AЄՕ.A= A.A@a A.A A.A@6 Ap6.A AHY.A@r,~ A(k.A}} A8j.A} AXp.A} Ahn.A} Apu.A f} A{.A`+} A8.A | AU.AWt| A@.Ad9| Aȶ.Az{ A?.A@{ A͍.A{ A(.A@={ A[.Adz A\.A |z A.A y A`ӥ.A*y As.AIy AXҟ.Ax A`.Ax AH.AӉx A@.A=w A.A@v At.A`v APH.A@Eyv A3.A}v Ab.AjKv A4.A@y@v A.A`v Aج.A u Aٷ.AZiu AL.A H!u A5.A`Tt A.A@Qt Ap.A`fs A( Ɩ.As A<і.A {q AӖ.A`mpq Aؖ.A@Zq AqՖ.Aq AԖ.A* q AՖ.A q AHٖ.A` p Aۖ.Alp ANٖ.ATp AӖ.A p AUՖ.AKo AԖ.A@Oo AȲӖ.Asn Aؖ.A@Gun Am֖.A&n AH?ږ.A m AՖ.Aom A ۖ.Aւm AH?ږ.ARm Aޖ.A@%m A@.A։l Aܖ.A ql A@.ALl Ax)ߖ.A +l A ۖ.ANk AP.A2k A@.A^k A.Apk A .A j A.A A"j A.A4i A.A`B:i AP .A /h A8.A|h A.Ag A.A@f A c .A@f A<.A@i^ A.Ag^ A8t&.Afg\ AP|:.A`T$X A\.A`KP A`b.A`P At.AJ A,.A@oC A".AXA A.A@ A A}.A= Ap<.A9 A ͗.A1 Abؗ.A0 Ahؗ.A/0 APߗ.Ad. A.A, A(.A@>+ A`T.A' Ax.AF& A.A`Dv& A.A:% A7.A# A.A " A(q.A A! A.AB AP.A A.A`j A.A Y A.A A((.A@ A*.A`[ Ah4.A  A2.A$ AP:.Am Ad.AP A8.A A.A? A؊.A ANj.A@- A.A AΞ.A1 A.A  AP.A` AD.A A> AsK.AD AsK.A@$ AM.A A@I.A AI.A AM.A AM.A qt A2H.A$= AhbG.A` AQ.AO AsK.Aෝ AO.AO A@I.A Z% AsK.A AE.A AH;J.AЊ AH;J.A}F AH;J.A ApG.AT A(F.A;B A(L.Aa AH;J.A` A K.A@W A`B.A, A?.A A`B.Az] A@>.A` A5.A` A3.Ad Ah7.Ac A 7.A`% A;.A Ah7.A@ A<.A( A 3.A< A@_:.Aj A3.A`ܘ A0.A`a A8.AF7 Ah7.A6 AM6.AL] A/;.A A@.A A 3.A<5 A &.A Ax}.A` Az.A` Ay.A@#c A@y.A@ Av.A@X A@ u.A AجU.A A59.A@5 Ax*.A@WY A.ADM A.A  A.A2% A(×.A A@.A A.A! Am.A@.Q At.A`қ Aplu.Aթ A@º.A@ Ayė.A; Aq.A (C A.A/Z A$.AU A쥙.A!~ AXh̙.A3} A.A`F| A.A'| A-.A@Wz A9.A8y A(.Aiu A0.A`Kr AX.ADh A.A g A8.A@yg A.A9g Ap.A g A8#.A-f Ax͝.A@f Aם.Adf APx.A@,=e Ax:.Ad A0X.Ay]c AEj.Ab Ah^q.A` b AXWz.Acb A .A/b Aԝ.Aga AX,.ADa A8.AO` AL.Ac` AǞ.A.` A՞.A`_ A.Ah_ A(.A *^ A .A Z] APM.Ah\ A@FQ.A`=\ AhQY.A\ Ah^.Ap[ Ax.AZ([ Ah|.AZ Apۈ.A`Z A.AZ A.AY APY.ARY AX.AX A0Y.A=uW AX#.AV A".A`MV A/.AU A0@.AVU AJ.A5U Ap.AS Au.AS Aʹ.A}Q Ah/Ԡ.As@P A.A27O A.A %O A .A/N AX(֠.A@yoM A.AL A3 .A oK A`.A? K AQ.AI A녡.A`F Ahơ.A`KC A(m.A 4B A| .AΊ@ A.A`@ AxA.A= AppN.A= A^.A}; A0m.A+v: A0ݒ.A`7 AĢ.A 7 A0.A@"7 Ar.A 5 Ax.A 5 A07.A}I4 A(.A c3 Al.A`2 A.AE1 Ab .A Z. A>).AQ, Am=.A`m.) A3.A /$ Ah{&.Ao A.A q A8.A0 An.A A@^.A` A.A Af.AE A`l.AE A@F%.AK A;7.A0 AB2.A`m AwI.AZ A@<.A@P Ah].A A.ADJ AXW.A@P A.Az A`! .A AH.A| Aȃ.A Z AUs.AC A@.A Aw.Aෙ A ʝ.A@ AhJ.A@= A.A^f ApkҜ.A A.A Axa.A`K Aʛ.A` A8gs.A A(-c.AW A.A@ Aħ.Aa AТ(.A Ahۙ.AQ A(͙.A@ APxW.AӇ A8ј.Ax APx.AJ A.A A.A0 AY~.A A8D .AL A.A A.A$ AXՕ.A` A`.A@5 A|.A`mT ApA~.AM AX.A@Ϯ Ap0.AQ A.A AhE.A d AxWړ.A@#^ A6.Aƽ A8Eh.A A(\.A> AP&.A) A0.A Am.A(y A`.A1 Aȥ.A3 A .A}׬ AS.A APo.AE A j.A@6 Ab.A 87 A8k.A`s A?.A"~ A`.A: A&ؐ.A@ A .A` A_.A@E8 AB.Aoء A>.A A`$.A{ A.A 뭟 A8.A@G A.A1 A.A` A@.AM AV.A$ AU.Aܘ AK.AX AJ*.A@ݗ A .A9 A_k.A`v Aٌ.A q Ah.A Apɞ.Afނ A83.A ւ AH.A A^.Aa A瀋.A At.Ac Aj.A F A@1\.A`]" A0H.A A;.AÁ A5.A2 Ap'.A A.Aw AX.A@W( Aq.A` A܊.A A2ӊ.A A8Ɗ.A(} Au.A@e A`.A9 Apz.A ALH.Ai A.A~ A3.At~ Ac.A`N~ A8E.A (:~ Ap.Aq~ A6Ή.A`o} A8.A*y} A0峉.A*y} A.A`)S} AN.A5} Ah.A@} AP8w.A-| A .A&{ A.A{ A;܈.A b{ A0͈.A9{ A@่.Ad{ A(".A~y A.A Jy AP'.A`o'x A@.A`w A .A<%v Anj.A )u AX4B.A\t AX[.A@s A(m.A@s A.A@es A-.A@r A _.A`Qr Az.Aq Al?.A`"p A.A|;p A@F.A@o A3.A o Ax.A`dDn Am.A;m AT.Aem A8A.A Zl A0.A@wl AH.A Qk A.A /Bk A.A@nj AX҃.A`վi AG.A`h A(K.Ag A_.AEe A@߂.A?xX A,.AOU A(;.A 2N Ag{.A`H AQ.A B? A8ń.AXB AA.A iA Ae.AA At.A S? Ag.A> AɅ.A6< A.A`@7 A0懅.A`1 A(.A`) Ac.Aw( AP+=.AT' A3+.A-& ArK.AN߆& Am j.Ae:& AIF.A4+% A(.An*s% A.Ak$ A ۿ.AC)z$ A^ʆ.A`# AV.A AXƆ.Az A.Aab A.A AX#.A Jk A8.A`) A[.A A㶂.A ^ A\.A` A@ƺ.A-l A5#.A I Agn}.A@ AW|.A Ap{.Av Aey.Ac Azx.A$ A8bw.A ۊ Aw.A  Av.A% Au.A`D A8t.A A8s.A Ap=q.A Ap.A@ A8an.AH A@Nl.A!| Ai.Ap A07h.A Am ACh.A` i Ag.A h A f.A-b Ae.A,^ A49e.A[ A#e.A@[[ A;Xd.A`W A8c.A AU Ac.AS A؟c.AS Anc.AQZS A@_c.AVS AJc.A JR A.c.AR A5b.AO Av b.A K Aha.AVJ A a.A J AX`.A D A`.A |~C A_.Af= AH=_.A)8 A(^.A4 A:N^.Axy3 A'N^.A y3 Af].A?C1 AT\.A`) , Ah[.Ar% AH[.A$ A83[.A@$ A`7Z.A A%Y.A? AY.A AE1Y.A9 AW.A| AV.A A@1U.A ~ AX9T.A L AN.AB A\wN.Aw AXN.AK AH6N.A  AM.AE# A 8N.Ae A:N.A AH.N.A@m AN.AhP A2 N.AR AN.Ah AM.A A>M.A@ A M.A^ A]M.A AЀM.AF A%M.A AM.A A"M.Ad AM.A4 Ax׋M.A A&sM.A`Î A0qM.AUq AlM.A@M A`gM.A : AdcM.A A(OWM.A` AIM.Au A8EM.AU A9M.A A@3M.A A/M.A_ AeM.A@E AHL.Ap AݡL.A A@L.A AЄL.Aג AL.A AL.A`)g ASL.A@K A(L.A@1 A0gL.AE AFL.A A[L.A@# AТ L.ARX AXL.A . AHL.A3 AXK.A A`aK.A AK.Ak AXWK.A9 AK.A` AdK.A`2 AH)K.A u A8K.A 8 AzK.A`. AP'oK.A R Au^K.A`o A8DK.A W A5K.A@ AY1K.A` AK.A Ap} K.A[ ApJ.A} APJ.AǤ AJ.A AJ.A AHJ.A A0tJ.A& A`%KJ.A] A2J.Al AJ.A@# API.A AI.A AI.Aظ AI.A AI.A AdpI.A A_fI.A A_I.A A0wMI.A&k Az*I.A?_ A@ I.A`T A ߰H.A况 AHˊH.A AH.A@ AȺxH.A" AitH.AE7 A@mH.A; AliH.A`dP AX$cH.A`dP A`v\H.Ag AlEH.A쭱 A3H.A  A lE.A& AlfE.A`2 AZE.A A(E.A A7D.A`v AjD.A"v AX D.A@,t AD.A?Nt A0D.As A9D.AZr AD.A@q AD.A S&q AD.Aap AȘD.AyMp AD.A{o AD.AFn AD.A`fm A(D.A@Gm AD.A8m AD.A`[ m APD.A@5l A୸D.A ~k A( D.A$^k A8ֳD.AEk A0ȹD.Ad1k AD.A@9i A&D.Ai AD.A@h AD.Ag AvD.Ag AD.A f AXD.Alf AD.A d A5D.Ab AD.A b AD.A@,`a A!D.AV` AD.A P` A'D.A_ A |D.At_ A8D.A |_ AdD.A`9_ A8D.A_ AD.A^ AD.A3r^ AD.A@`] A3D.A*[ ApD.A൶Z AD.AmZ AD.A`Y AYD.AOY AD.A4W AD.A W AE.A"O AD.A O A1D.AaWO AW@.A V A@.A@%KU A@.AU A8@.A`1U AP@.A9.U APޯA.AaS A(ܤA.AS A@.A T A@.AT A5@.AU A@.AgR A@.A R Ax@.ARR Ag@.A`ÎR ApA.A$qQ AjA.Ah1Q AH@.A SR A@W@.A@W`R Aȶ@.A rR Ap@.A`bmR A@.AP AT@.A QO AH@.AЇP A@.AtP AP@.A P AXd4A.A@,O AxX6A.AjqO AH/A.A`IO A!@.A O A@.A O A@.A`܇O AH@.A` N AH@.AD'N A|@.A4)N AH@.A6N A,A.A4N A%,A.AAN AGTA.A NN AUA.AN AgA.A N AmA.AF5N A0jA.A`BM A0A.A@M A&C.A J A B.ArJ AMC.AI AxC.AକH A _TD.AL Ah; D.A@%cM AD.A{M A@lD.APL AC.AH A>C.A`K}H AD.AH A(D.A@ϝH A4D.AFH AYCD.A H AD.A |NG AwC.AuG AD.A@rG A`C.AfH AnC.AZH AC.A sH AC.AfH ACC.A@RI Ap6C.AH A9C.A H ACC.A H A0NC.AM>H AIRC.AG AOC.A#wG A0EC.AaG A2C.AF AC.A pF A؁C.A& AlaA.A = A@ukA.A = AA.A J; AA.Az9 ADA.AY7 AA.A6 AA.A4 AA.A`ma4 A=A.A 3 ApA.A3 A`A.A`3 AA.A O3 A~ B.A`U3 A` B.AO3 AB.A83 AxiB.A`2 A( B.Aޘ2 AB.AA2 AxKB.AK2 AB.Ap2 A B.A_2 A(B.Af2 AF7B.A@a2 A؞,B.A42 AF7B.Ah2 ApAB.A 72 AOB.A`,2 AhVB.AK2 A0LaB.A92 ATkB.A@H2 AXrB.A`"2 AhB.A`2 AjB.A(1 A- ^B.A 1 A:,W.@.Ao33 A8.A4 A=0.Ao33 A 1H).A҆. A;sN!.AXg:' AʇT.Ai\ A.AA`vU-A`cA\A-A`dSAj*-A (YA R$-A EA-A QVA -A@=Ax-AjIAX-A@AH-A`K+A0-A <A-A1A-A"As-AR+APZ-A`A-AA0G-A@,A-A"A-AA覇-AA8E-A@!AP|-AZAhw-A`A"r-A@Ap,i-A@A^-AAhX-A@,A}R-AA7-AA-A*Ax-AAXEu-A@ iA-A ,AX-A@NpAP-A@AH\-AR{Ah-AA-AA'-AAx9-A 8A*f-AQAj-AA0&o-A`m%A#u-ABA~-A?}AHm-A ˻Ae-A`]A-AF AS-A@56 Ah-A@yS A=-Atc Ap-A` Ax~ -A ^ AL-AX A(4-A=* AG-A A;:-A`AE&-AAN-A^A-AAP-A`ӯA-AvA-A`ӯA8-A`As-AA-A@RAH-ABAp|-A` A.-A9Aߥ-AAP-AfA7-A'kA,-A/_A(-AྍA-AAm-ABA-AAx-AA(-A4AP-AA-AAG#-AA'-AA(-A`vxA(.-A6KA5-AH/A9-A@A:;-AA=-A ۰AF-A /A`1N-A@yAXP-A1Aȓ-A Aȓ-A 4Ax-A/OAx -AAА -A@FAa-A5A#-A AXũ-A!Ax-A *Aם-A |dAH-A߮A-AA-A0A_s-A,=%A-A+A8-A@i.A0;'-A_4A::-A`B5A^-A79A-A@>=Ap΢-A AA-ADDAA-A.JA-AVJAh -AYJA-A uLJA9-A@ߛIAk-AQDAHr-AkxBA@-A EAJ-AFAu-AHAu-AIIAĄ-A`}JA-A@AKA-AKA#-A`ofLA-A`3MAXV-APAà-AQAH-AFQA8t-AmSA@`g-A ZRAba-A cQA:-AiNA07-A lMA.-ALA,;zP--AE}XLAo;+-A$xLA5>)-AhoLA~'-A лLA:%-AU@tLAG8"-A(GLA+-AkLA1-AjLA `-ALAm'-A-2LA$_-A LAs-A~HLA -ADŽLAf-AH*LAW-A`LA c-AgMAK-A SAx-AߏXAp-A `A@-AE4bA@-AeA*-AiA؎_-ARtrA(q-A {A-A琂AC-A AL-A``A8_-AA=p-AA`-AA-A@A-AMAX-AAy-AAp-AwA-A_A0-A@ÊA -A`AQ-A@AK-A`K-A-A`f݊Av-A@A'-AH|A$-A(A;-A`2ޑAE-A ҒAc-AÕAb-AA<|-A`ooAk-A@}A(j-AMAR>-A#rAxX?-A@衘AU-AFA+u-A>A5-AA-AiAb -A`;ɞA5-A?Aj-AA-A`գA-A СA@-AyPAx-A=A0-AA-A 8A(l-A %A5 -AAx7-A A@O5-AwA+l-AeA p-A_A-A`DA-A@N4A-A@GA(l-A@A-AcAX-AA@3-A`bAR7-A@#UA_-AA-A?Ah@-A`ݥA-AAP<-A@A-A)A -A 1[A@!-A`bZA4-AXA0G-A AxL-A`ެAf-AAx-A`oA-AXA)C-A$Aw-Aa AU-A`iA7-AA 8-A@`A-AA-AsAH-AAs-ADGA ^-A /A-AN1AL-A`b5A 0-AA~-A@C)Ak-A`bA8y-A!Af-A#A-AOA0u-A(A-ArAJ-AA(qd-A@A-A/ AH-A AЄ-A s) A0-A A-Ab AH-A`) Ar-AH A`-A( Ad-A`/ Ah"-A0 ApA$-A3 A"-A &3 AP/-A 3 A0D-A7P43 AZ-A/3 AH-AU3 AoT-A ^ 4 A2t-A)04 A)-A c]4 AM*1-A4M4 A軋/6-AwwHk4 ALr8-A 5 A\㍝8-Ay=G5 Ako5-A)#5 Ay0-A 5 AGQB)-A`Z5 AЕ-A s#6 Ay-AgB6 A -A#\6 A|m;-Ap6 A`-A”}6 A1-A6 Ah-A :; AHn-A@>; Aw-A? A`G -A=`> A-A@.dA A8D-A_J A8 i-AKyR AV-AgR AZ-A`vS AQP-AA7S A!5*G-Af8cS A?-A0CS Ai:-AN#S A+8-AJNT A4&7-AF(u>T Ay9-Ap%wT AM>-A_ îT AЕD-AKT AL-AWKU AV-A[8*7U AKb-AWU Ab-A U ACv-A U A-A\ A؞-ALd Ae-Aji A s-Ai AЮ}-A'l A"$r-A/2BNl A6$7h-A h{l AC"`-A+@l AIZ-AM{l AX5V-A!m APLc-AP\'m Ab}p-A.'m AȘ}-A!m AA-Am Ap-A 84m A8-Al A-Ap A`-A@Cv A@N-A@ v A/-A2v A/-A#v A(`-A@v Aٖ-A w Aa-Aw Ak]-A0+w A^-ARy AQD-Ay A(mG-A{z Aؽo-A Ao-A r AT8-A`{ A&-Afm A-A A ^-A` ٔ A-A Axh-AQ As-Ax Aؼr-A@y Ax-A A(-A Ap,-A An7-A A--A@ A'-A AG-A ֫ AbS-A AXn-AoQ A -A&E At-A`9P Ac-A O A~-A A-A` A-A@r? APx/-A@ Ae-A A8'-A@Ŷ A-A` A@-A8Z A -AMN A-A\ A-Aʬ AP^-A A-A`; A0-Aû Ap-A i A-A@ A@ -A p A-A ڹ A-Ax A`v-A A -Au A(-A Ag-A`T As-A`o A-Ao A q-A` A(r-A@ AT-Aݷ A˸-A Ax -A`) A8Z-A A`-A ~ A`-A@u AhL-Az Axu-Az A-ABػ A-A` A+-Af8 A"-A|ʼ AЄ-A H A8-A`dn Aº-AB A6-Aj A-A AЀ-Af AP/-A` A-A A-AF Ah]-Ak A-AzP Aȶ-A AP&-A@q A(-Am A+~-AW Ap}-AҞ AZ-A? A-A@H A-A (E A-A  A^-AV A٧-A ¼ A-A'ڼ Ahj-A,| A`c-AZ AX^-A l A[-AK AW-A | AYS-A/ AYM-Ajg AJ-Ap APD-A |G AN-At+ AP7-A@  AH;-A A-A8 A5-Ac AE$-A`m@ Ap/-A;U A81-A AP;-Aǖ A?-A A`G-AfƼ AN-A lx A`G-A ( A -AN A8-A_& A+-A A21-Aֹ A8E0-A@ Ai-A ҹ AX-A O] A` -A@ AJ-A, A 4-A@< A-A@ A(-AHU AX+-A`T A0eD-A4 A}-A0N Aȩ-AЪ A$-A% A$-A9 A-A A-AH API-AZ A-A` A(-Ag A8-A Ap, -A AP/-AH AX-A@߭ A-A@ A-A c AP"-A' AЕ%-A y A`'-AǾ A.-A@ A?|-A`< AXhi-A_ App\-AX Ap]-A A|-A Az-A AP -A \ AH-A Ahq-At Ah^-A`@ A-AM A-Ac A-A@ϒ A-A`a Axz-A` A-A@W A[-A@u Ac-A`B A-A0 A-Ad& Ay-A s A(h-AU Ax-A@V A-AF A_-A A-A޵ A-A_ A-A  A@u-AQ; A-A= A-AM A8-A&5 A-A J Aȇ-An A-Aj A(-A AH-A" A0G-A@r AH]8-A A(,-A |C A(,-A`]z AQ-A 9 AZ-A Zg Ag-A^ A8y-A A+-At' AU-A AXFh-A` Axme-A A0H-A Axz-A AhM.-Af A-A A AR+-A AEM-Aj Ah-A A@O~-A`2 Acx-A@W A@-AS A-A  AP<-A < A%-AI+ ABl-A% Aɶ-A A@1-A Z Ap-A` A-A A,-A Ah-A  A-A` A0}-A`} AϪ-A1 A -A,j A`Å-A A"-Ae A؎-A A -A? A p-A`f A@B-A; A-Adn AP<-A A -A@ A-A@5 A8R-A< A؟-A@ AB -A`" A(-AV Aw-A` A -ABg Ax -A@> A-A  A;-A`! A#C-A$V A-A sK A6-A c A@]-A A(,f-A A0Q-A9+ AXK-A A@5-A AX#-A` Ax( -A< AЯ-AH A@-A@` A@-A0x A-A  A-Aǂ AC-A AH -AgZ Ap=(-A" AH,-At; A0W-A@i AS-A AP Al-A  A Υ-A3 AX_-A A-A Zc AY-A c A߳-AU A-A" A-A&" A-A$ A G-A H$ A`-A<' AU-A`T:' Ah-A' Ax-A* A-A=x) A8c-AK, AX-Aݲ. A0-A L0 A R7-A`9I2 A`-A 8#8 Aȱ=-AO9 Aɉ-A`K; A-A`D< A-A@#> A(~-AD A,,-A?C A<-Aj:J At-A K A -AL Aj-A@EuP ALa-A (Y AP-A Y AHG-A`mZ A`K-A@ykZ A/R-A@,a A^-Aa Ao-Aa A@-Aa A-A@`a A-AX A oY.A}] A@.A=\ A6.Aҗ] A(y.A^ AB.A;_ A؎[.A q` Acy.AKa AD.AQa A;.A |a A(.A@%b A6.A;b AW.A Nb A`P.A@,b AĂ.A@a A.Aa A..A_ A虸.Aja AV.A 2c Ab,.Ad A0.A`mNh A .A?vk A .Apo A`k.A lm AX#f.A4t AÝ-A A0-A Ap-AU A-AA A5-A AZ-A A.Av A<.A |I} A".AE ANR.A` A.A`* At.Aǜ A`X.A@ A.A~ A .AR AH.A`H A].A A4.A Az.A# A.A A뿤.Az Az$֌.Ap# AOа.A*, A ]_.AP1 Aa(> .A2 A8.A . Aȵ&.An AX.A` A@` .A A`C< .AT̋ AE .A6 A9 .A?\ A .Ax A .A AHaJ .A@ A.A` Ae.A { A8Q.A[ A(`.A`B A;/ .A`2 A@b .A Q? A .A/ A` .A A{ .A ANh .A ArW .A G A@K .A| A@F .A@i AH{J .Av A&V .A@ɞ Ah .AU A8 .A< A .AAz AX5 .A AN .A  A0 .A٠ AX& .A A1 .A` A, .A 덢 A(.A 1; AZ .A㙲 A.A)Ҳ Aȃo.A@z A0&.A@, A.AI A+.A` A*q .AD Au .A f A .A`K A0eb.A@Ns Ap .A ٰ Ah.A A .AR A- .A A(w.A@ A.A A;.A@ A.A@S A@W.A@ A8.A,_ Aȓ .A A@N .A@e Ajj .A. AXd .A^ AX4 .AU A7.A Ag.A@M A.A A.A Ax--.Aif Aw.A Jw A .A`J A! .Ai A`n.A- Ap[^.A A.AD2 AR.A` Apʜ.AU A؎Z.A@i AǢ.A; AN.A`f A.A`̞ A R.A@R A)L.A \ AC.A A.A \ A RS.A AH\W.AR~ AXl.A`) A.A3 A{.A=4 Az.A \ AH.Ao A.A \ AH.A \= AЄ.A AP+.A AKK.Ak Ax].A Ai.Aл Ap.A@ A(n.AjG A@@.AQ A`Q.A@ AR.A`m Ax.AE A$.AE A.A` A=y.A A AI.A u A.A@x A#.A+ AV.A5 A(.An An.A A@.A= AP.A A.A@ A@W.A A(`.A AL.AV AM.Ae A.AЫ AP.A6 At.Az A h.AR A,.A`mO A0$.A AH.A c A.A A!.A A06.A A.A$ A8.A, A(`.A A<.A A>.A3@ A).Aྎ A{.A}* A@N.A AX.Aw A.Az A.A@E Ar.A॒ A8.A | A(.A@p AP .A A9.A A_.A`)i AX.Af Ad.AX A0.A@y A.A@8 A`.Af> AА.A A8D.A AD.AR A@.A`_ A@u.A A .A^> A( .A> A!.A!@ A!.A aC APڇ!.A`9C A .A`oC AH .A cC AN^ .AC A, .AC A }.A pC AW.A 4C A.A@!"C A^n.A =C A5.AkC A.A?C A.A,uC AC.AiBC A\.ApC AH{(.AC A.A C ApZ.A/C AKz.A@!"C A3.ALB AR.AB AA.A B A.AC Af.A@!"C Aث_.A@!B ApI.A (B Ap:.AƮB A%.A(B Aݻ.AB A.AD A!.AE A8Ib!.A`F Ai!.A`bG Aq!.A}G Aq!.A;EH A؎m!.A`sI A".A`; R A".A@T A0".A T A#.AFmV A>#.AV A$#.AV A(S#.AaX A 3#.A Y A>@#.A@Y A`#.A{Y A(K#.A [ Ap#.A[ A#.A k\ A0&.A xs AP2'.A@$w Ap '.A`mky Ap(.Ayl A(.A_ A 0^'.A Aa&.A E A8l%.A`9 A+R%.A |3 A(&.A` Ab'.A Av'.A Al'.A A'.A̍ AÇ'.A3ۋ A(.A Aĺ).A`TN A@y).A`TЊ AB).AX A@o).A AxiZ).Aq AAa).A` A@q]).A`]Ɗ AhK).Ay Ab).AQ Al(.A  AEZ(.A`2 A?(.A m Asm(.A A`C(.A Z A 0t(.A A(.A`ܱ Av).A Ar).A A0).A@ o AǞ).A AL).A`b[ A" *.A A*.AH AK(.A`9 A`v'.A Aq'.AsY A'.AR A%(.A 7 Ap).A` Ae*.A ޑ A,.A AH/.Ak Ao0.A` AM1.A AX1.AVg At2.A A3.AX AK3.AH Apn4.A A̝4.A@1 AA6.A@O Aj8.A@N[ Axd|9.AM A&;.A`o A<.A` AXۿ<.A A=.A2F A=.A! A.=.A?! A0X=.A /" A0Z>.A`=% A>.A) AX5W?.A, A?.A@. A$?.A0 A`B@@.A3 A@.AЦ6 AaA.A<; ACA.A@5? AA.Az!@ Ax>-B.A3A A5KB.A@KC AأB.AD AHB.A qE AHC.A`mH AuhC.AOJ AՒC.A`{L Ah޲C.AgM AC.A]M AD.AO A#D.AlP A D.A@ P AТD.AU AE.A@ [ A*E.A2^ AlF.A q7_ AhgF.A`T` A+rF.Aa AF.A@b A cF.Aad A؎F.Axd A8G.AZg A ͋G.A[i A(G.A |l ApWI.Av AXI.Ay A8J.Ay~ AJ.A@M A(IK.Aa AAL.A@yV A ML.A@؛ AxL.A@ A4L.A@ F A:M.A AItM.A A {M.A A`CM.A- AM.A ҕ A*$N.A줘 ASN.Aљ AhN.A-I A_N.A鶚 AsN.AП AxXN.AǞ AN.A`C A,O.A" AȵBO.A m AX9LO.A@y Ax`O.A` AzO.A?% AzO.A㪣 AO.A JJ AO.A AP.AH A)P.A˭ AP.A AP.A J AQQ.Aɯ AЕ\Q.A- AQ.A` AQ.A A8c"R.A AaR.A APR.A@ AER.A@5 A0.X.A@ Aw0Y.Aq AGY.A/E A#Z.Ae AҎ[.A A].A AP|].A A(^.A` A_.A A Y`.A ACa.A` Apga.A 8% Aa.A4 A0fa.At Axa.A AxWa.Ap Ap+ta.AR A/a.AQ AEa.A A v.A | Av.A# Aw.At Alw.A A&4w.Aá) Agw.A  Atw.A*%x AEw.A  Aosw.A>jgJ* A@Qw.Ad5 AMKw.A`m@ Afww.A9I ACKw.A/FQ A+ w.A`W Av.Ax A8"v.A`) AԿv.A ~ Aȩjv.AO A0ԩv.AR AcTv.A Aɂv.ATH Ahv.A@ Aqv.A;uF A$v.An AXv.A`T A7Pw.A A_w.A A_w.A@4 A*w.A Ah{ x.A@S Ap9x.A Apx.A- Ax.A`K- Ax.A@X Ax.A` Aȶ#y.A  A`Dy.A A][y.Ax Ay.A`)q Ay.A`Ӕ A)z.A V A8_z.A@ A`|z.AAz.A@./A8z.AqA@z.A`vA1{.A`;A0Xm{.A@ A{.As Ap0{.Az A{.A AU{.A?) A0{.A`g A|.A A(|.AAp=M|.AA(Kz|.A1ApJ|.AAN|.A@5A|.A@ƆA|.AA60}.A`1AЕa}.A@zA]}.A*AH }.A A?}.AA}.AjA}.A8vA}.A@`Ap}.AP A}.A] Aȩ}.A`!A}.A@G?"A}.A6#A,}.A#A}.A9%AH}.A`v]%A~.A }&A}.Av)AW}.A3)Ah3}.A3*A}.A@b*Ap}.A3i+A}.A,Ah3}.A3S-A=}.A µ-Aױ}.A .Az}.A.A`}.A A A|.A`?Afw|.A JAAd|.A ^CARl|.A@yEAP{|.A`EAxz|.AvFA8|.A@GAȂ|.A`!JA|.A`KAk|.ALA"|.A@NA|.A ~OAh|.A`9ePA83|.AQA|.ATA |.A`VA|.A WA`a|.AXAk|.A!YAh|.A]YA|.A?[A|.A[Aأ|.A`f\A#j|.A\A`%[|.A\AQ|.A@z]A0.R|.Az^A(B|.A`\_A(;|.A_A8|.AH`Ap=|.AaAB|.A@gFbA|.A`)dbA|.ARbAP|.A ~cA|.AcA(|.A`cA` |.AcAp8{.AzxdA{.AdAH{.A3dAp|.A@eA(\|.AeAz|.ADeA` |.AXeAp{.ApeAX{.A |eAp{.A37fA{.A fAH.{.A@ fAp={.AzgA{.ApgAU{.A hA|.A-ehA |.AhA |.AKiAh3*|.AjAHI|.ADjAqh|.AbkA(n|.A`kA }.AoAS}.A2} oA}}.A]+oAH%}.A`7oAi-}.A==oA6}.A?oApJ>}.A=oAKÆtF}.A1b6oA0xTN}.AV+oARװU}.A+oAx\}.A+ oAo}.AnA0 y}.A`nA t}.A |KnA(g}.A@*nAذg}.A&mAho}.AkmAxu}.AnmA{}.AmA%}.A@{mA8g}.AĒmAP}.A`nAh}.AkOnA}.A`nA@}.AoAd}.A&oAH}.AoA?}.A SoA}.AAoA&}.AॽoA}.A@5xoAh}.A3oA>}.AhoA@}.AhoA0}.AR*oA}.AzoA`}.AoA/}.A_oA}.A`spA8}.Ap%qAp}.AYqA~.A&qAP=~.A`WqAQ~.A qAhd~.A`ӄqA@p~.A@PqA2~.APqAP~.AqAH~.AqAp~.A |qA}~.AhqA`.ArA8 .A@ sA`/.A@ sA'4.A sAYI.A6sA'[.AAtAi.Av>tAx.ADtA/.A`>tA|.A ɊtAb.AitA8.AtAʸ.ANuA.A@`tA.A@,MuAH.A3uA#.AuA(.A "vAؒ.A=vAh/.AvA?;.A@wAP P.AawAЦ_.A wAx.A#wAW.A]wAH΀.AHuxA Հ.A@.xApl.A xA.A@pxA(>.A@`xApp.A`f6yA .A`ayA.AyA .AyAH.A syA87.A`yAP'=.A@ zAe.AQJzAto.A8yAJf.A2yA1e.AyAn.ARyA`}.A`;yAث.AyA8.AƱyA0堁.AyA.A yAc.A`mxAȁ.A`xA!́.A #yAہ.ApyA.A yA.A`yA.AzA.A`zA(2.A@N{A9U.AzAQu.AzAs.A@A{AhĒ.A"|A(.A|A].A`d }AsY.A j}A`X.A@y}A.A`vA.A@A.A@zAp<}.AAȅ.A? Ah.A)AH.AA-.AhsAc.A̒A.A ۄA·.A@߆A}.ABA.AZA{.A \AxI.A2Ac.A6QAxu.A@A.A mAG.A@ A .ANA.AIAF.AE Av.A'A.A@ A8֟.AAȉ.AwAБ.AAH{?.A@A@].A@7A&}.AA͊.A ֱA@.A A A.A)A;.A}A .AThA͋.AHAH.A ?Ai.AOAW~.A*AА.A`A@$͌.AAЯ .AӖA,1.A@lAw.A A.A{AТ.A (A9[.A@EA~.A`)YA0X.AgApZŎ.AA.A@>AH.AX%A&e.AAt.A$As.A -A0g.AOA T.A=A@X.A7AP8Ր.A+AN.A(Ae.A`pAH.AAY.AAr.A@`;A.AxA@>.AAx.AnAh.AA ӓ.A?As.AӬAU<.AHAq.AA.AAg".A3 AM.A App.A A0*.A z AF.AAh@ .ARBAh@ .A`vA -.A|A"^.A"A߆.A`oAuޘ.A@AP .AOA.AoA*.AǟAI.AHAI.A A\.AA 4.AAެ.A`mA .A`2A@O.A AP.A`m%Ax.A`dU(A(.Aɤ(A.A (App.A@%(A@.A`\*AW.AM)A@.AHc)A0.A.)A/4.AX*A`vD.A (O*Ax.A,APw.Aa-A.A2w/A!.A`0A().A1APޗ.Ar1A.Aા1AV˛.At6AIǛ.A7AΛ.Al7A?.A7A.A7Ak.A 8AR.A08AQ.AI8A@5=.A`8A9.A#7A`%(.A`G7Ap0".A`7A.A k6A .AE6A .A66AXE.A:6A`.AP6A5.AO8A`G2.AT8A@(.A@Cf8A%A.A@>9A{M.A`'9AhJ.A@9AhP.Ad9Aꟛ.A c8AP.A /8Aƽ.Ad8AU؛.AM8A֛.A8A@ɛ.A9A0Û.A:A.A`;AJ.Az;Ae.A9;Ag9.A <A'.A+<A8g(.A c<A0XK.A=A0.A<>A@.A`>A.AF@Ap^.A`AA`.AEAX$ϛ.A)DA舛.A@,/BA8.A0%AA@~%.A`AA؟7.A@BAA.A CAȱD.A?CAF.A`dCA=.A|_DA?-.AEAȱ.AEAI.A/FA.ApHA.ALAzG.AnNA8#P.A`jPA.ATA`؜.AVA=.AYA().AmD\A୹.A_AP.AcbA g.AH*fAp0ߞ.AhA@8.A \kAX.A 6yA.AzA).A (Apv.AxA Ң.A |'A.A9A kL.A(A0Ա.AA.AĒA }.A |+A.A:A8c.A4AH.A OA..A-AI.A@pA(1n.A`zA(s.A A4-ANW+A3-AVA`6#-A HHAY-AAB-AA-A{A--AaAS-A@An-A ׺APZ-AձAs-A6iA -AhlnA4-ANW+A-A0)As-AܔA -A37A-A0)A<.A`9,ATy.A=Aou.A@yAln.A/9AW.AXXA^.AqŬAW.A`ˬAg`.AQApe.AQA Rr.A JAPZ.A ȭAr|.At2A<.A`9,A`K#.A@>~A.AA2.A@P?yAb.A`pA.AchA;.AeA0Y.AoZaAh38.A@`A.AEMaAX# .AR bA.AbA0..ATcAq.A.AmA.A }nA.AoA.AjoAҾ.A9pAp,.A.AB}A.AB}Ah.A}A0.A}AX#.A}A&.A}Ah.A`թ}A.Ay}A0].A }A(.A l}AL.A}A.A`}A&}.Af}A0v.A}Ar.A`}A(p.A@N~Apd.A ~A b.A`~A,N.A`0~A@5?.Ap]~AX1.Ai~A .A`x~Ab.Ay~A.Ax~A.A@~Aw.A@~AHi.A@~A.A ~A0H.A`~A@.A~A.A~Ah.A~Ap0.A~A(:.A* A.Ap"AX.A A/.A8AG.AtAu.A)A@.Aa@Ax.Aa@A.Af;Ar.A`m'AhW.AaA].AZFAW.A@AxR.ABAR.A ~^A0D.A`fA0=.A`xA1.A`tA84*.AXA#'.A`ÜAp|.A`A.AA8.A`A.AA*.A@NA.AAv.A@FA.AFA.AUA.AAH.A;AgW.A;AE.AA2;.A A:..A@ćA.AAc.A A`G .A |Ah.A+Ap.A}2Ag.AJAx)ۿ.A WA(ֿ.A`dAJĿ.A菈A .AA8.A6Ax:.A^A~.AA8g.AAl~.A OAȔ.A@Axm.AkԓAx}.AA8r.AvdAP.A`A,.A)ޮA).A`fAh.A@GA(r.A"A.A A(L.AA }.A`DAt.A@ݓA.AAh&.AA`K#.A@>~A*b EM.A Ak*W/A AH 6C.Ab Ac.A j A.A S_ A؟).A? AG.AdU AX.A# A.AB A.A4 ApW.AC A.A AP7.AO A.Ab AHm.Av AHj.A@y Ar.Av Aп@.AL Ab@.A AU+.AV AH.A A`.A 8 A 6.A 1 Ax).AV AxKn.AT A.A A8Q.Av A.Aq AТ".A@ AB.A/! A`.A AȤy.As A.A A.A`e A.AX0 AX.A6 A.A൥ AfA.AE A[.A` Aav.AK A{.AB Aз.A, Ap<.A A.A Aa.Aڏ A .A A-.A A\ .A AxK.A` AXW1.A@ ApZ.A-  A0{l.A Z A@W.A Ax.A`v A0{/A$A AaU/A`[< A/A A/A AGM/A@` A@Sn/A`; A`/A` A8Ѡ/AJ A`/AO A/Ao AL3/A AЄ}/A  A/A_A A(\/AT A/Ad A3!/A* AH/A@ A_/A( Ap/A@j A/A A/A^k AZ/A@#B AH/At A /AI A 4/A`m: AN/A`K A)[/AH Ak/A A{/A A@/A^ A/A At/A A/A5c A@/A , A(/A`; A؎/A< A8'$/A`v AHa*/A Ap*/A@ A+/A4 Ah-/Ap AXt./Av AX//A AH0/Af Aȃ0/A A0/AO A1/A A`C1/A`[ AR1/A$ AZ1/A` AP^1/A` AP^1/Az AxW1/AD AK1/A A;-/A Q1 Az'/Ai A[/AZ A/ApL A@` /A@,l Ap/A A/AC A_/A] A(/Aw A/A A`/A@ A/AQ A/A A`/A O A/A@ A/A. A/A> A[ /AK A /A R A/A+W Ak/A`"Y A(/A`"Y A/AX A|/AoU AL#/A lP A0+/A@ A//A5 A3/A) A=/A`2 AJ/A AZ/A`{ Ai/A`$ Az/Aʵ A/A  AS/Ah| A虴/A?6 A(/A A/A A(\/A# AhQ/A A/A A/A A"/A Or A)/Aa Ah//A)Y A1/AV A6;/AN A@S/A@? A0/A`9 APڨ/A q A/A' A/A A5/A A/A-x A=/A` A()N/Ay AhQ{/AM AA/A@, A/Ai A/A^1 A/A A`/A APN/Avy Ah/A( Ac/A AN/A A/Aa ApA/A@` A7% /Au AhoV /As= Ap /A@& A` /A@ A8 /A= Az /A A@ /A A` /A@ A3 /A` AO> /A`m AجG /A Aq /A`2 AV /A ɋ A> /A{ A{ /A Oe A" /A 8A AI7 /AR/ ANd /A@! A@ux /AV Aݙ /A A = /A AL /A A6 /A`| A[ /Apl AB /A &c A /A 8[ A /A$Q A7 /A@F A7 /AhF A Y /A6 At /A# AH /A@ A0Ǽ /A` A /A$ A3/A N AKq/A AU/A@ A,/A H A36/A  A@=y/AM A/AF Ae/A 8 A8/AE AV/AU Aw/A ApA/Ax AA/A@C Aj/Av3 AP/A A/A` Ah/Ac AXh/AR A-/A AXE/A` ApV/AC Atj/A c A(-/A@ A[/Av AG/A` AK./A@ A[/AF A2/AW A``/A A/A Ax//A Aзh/AW AxƝ/A A/Au A[/A` Ax/A A/AA AhQ /A@W A2=/A A2q/A Ax/A`Ù A/AV AHL/A A/Aa A/A ` Aj/AC A/A A0/A` A/A9 A0)/A@E A(?/A A`a/A L Am/A`v3 A|/A A`/A@ Ax/A@W Ax/A-^ A/Ao: AJ/A A1/A Ax/AH A@u/Ar AD/ABZ A/AV< A/AF A'/A l Axv7/A A0</A@a AI/A# AHY/A Ax(/AG A/A AXu/A6 Aڵ/ATg AP/A" A/A@yq A(/Aa Az/A' A'/AO A02/A`l A'`/A$ A0./A A /A A@B/AaE A@ /A`v A8-/A = A E/A4 A |/A`n Aho/At+ A8ɧ/A \ A/AC Ah"/A AA#/AMs Ac/A A/A ~0 A ^/A A/A@ AE/A A Au/A`d AU/A A/A AX./A APF/A ۜ AJO/A͒ A/A A8/A( A=/Aʭ Ab/A B A(i/At3 Aq/A`b& A /A AU/A@P A/A A/A} Ap/AB A8/A A?/A AX/AT Ap/Az AXև/A@P AX[/A AP/A B A/A A\[/A` A୕/AG AB/A- A /A _ A/AQV A'/A A,/A' APi/A' A/Av A(/A A8t/AӤ A@u /A@PO Aؼ4/A R A]/A` A/A@.: A`/AVV A8I/A  A/Aջ Af/A A /A S A /A Ah /A` ù A  /A 8 AH\ /A0 Ah30 /A} AH.5 /A<" AH; /A AP7M /At A[^ /A@ߗ Ai /Ad Av /A` AP| /Aડ A} /A`2o AG~ /A`? A~ /AB A  /A@iɰ A # /AM A8x /A@` Aք /A@ A`B /Ad AX /A@,X A /A@Ю A /A@ߢ A /AĬ AD /A8Q9 A(ǐ /Aǿ APy /A' AYp /Ax( AXp /A@ A /A` A] /Acp A=E /A?7 Ae /A1Z AmE /ALj A /AU A_ /Aa A /Au A/Ax A{/A A,/A  A8/A A>%/AUT At/A`ߐ A@/AА AZ/Ao A/A` AX/Aތ Ad}/A` A3v/A`v AP@/AmNJ A`/A`S A/A2 Aa/A?׉ AFr/A` A`I/A@rC A/A A>X/Af/ AJvHX/AMP\K A/Aδxv A(/A` A8G/AW̉ AC/Ah A߸..=/AS A\J/A ~ A`!1/Ah Ag֪/Aࣟ AXGh$/A[Ж Agze/Ay| Ak/A R A /A- A/Acɉ A'/Aof A'L//A( A1/Ae A+B1V/AOLJ AV`S/AƉ^ AՆ/AT AR/Abʈ~ A/A A8/AM A(Kp/Azs A /A@ϋ A*/Ad A.A A f.A* A(m.A89 A.A) A.A 8 AxQ.A Agg.A鼕 Ax.A`A A興.A Az.A`Dk A..A@. Au.A/: A8D.A`dդ A.Ay A%.A׿ A.Ap A~%.A`D. A.A A/.A㊺ A(-.A@ Axm.A@ Ax.AFJ Ada.A A.A`" Aߖ.A ջ A%.A@ A.A^ Ap.Aɽ AM.A] A.A A c.Aoq A0.:.A Ae.A͍ AP.Aj A=.AXe Ae.A@ Ap.A`̱ Axu.A AXJ.A AA.A A'.A}u A M.A A.A OE AU.A& A.A  A.A( AX.A  A- .A A.A{ A.A}u A>.A`m{ Ahѫ.A A.A A.A A.A As.A AU".A  A5.AG AHrJ.AM A/b.A , Ap.Ac AH!;.A@, A.A: A0*.A& A^;.A@A Aj.A A.AT A-.A`÷ AX9.Aྭ A¸.A?d AE.A A07 .A Ax˚.A 1 A=.A6 A0.A9r Ax.Azh A`.Aw AH .A% Ac.A<< Ab.Ay A` .A` + Ahՙ.A At.A AHm.Av At[.ȀF Ad.A4 A$.A& A蕲.A A0.A! A EM.A઱" A7.A`}% A`].A+4 A?.AFA4 Ag$.Ax>c5 A.A@V5 Aߑ.A{֊< A$].Ai~HB At[.ȀF Ak*W/A闕 ApAW/A AEW/A APxV/A & AV/A S AqV/AB A( V/A N AהT/A, A,S/A l2 ASS/A@% AhS/A, AR/A A@R/A ' Ax-Q/A`) A;P/A` A+O/Ah AM/A`D A eM/A S A`!L/A}X AL/A % A L/AH A K/AK/A AT$tvK/A v A RK/A& A7L/AM' AZY(XL/Av[* Ah`L/A; AL/Am AiM/A'%k Ap9M/Ar AW_M/A8 AنM/A?b APM/A6 A.;M/Aq Aނh"N/A_, A^N/A`뎽 A/9;N/AC AN/Aį+& A6O/Aof\ A v5O/A]} A0bO/AuX AO/Ajb A{fO/A(A A'O/Aq}` AO/Aa AO/A A(O/A, AQO/A@ A(O/A` h A2O/A APO/A@% AMO/AU A8O/A R A(}O/AΜ ARhO/AR4 A~EO/A@v A4O/A۬ AF.O/A@5 A3O/A@ A^O/A@Ū AyO/AU AO/A@Ɵ AxO/A A8VO/A A(O/AN[ AyO/A?o A[O/A& A =O/A AO/A > AxO/AR APN/AgZ A@dN/A- AN/Ax AN/A (- AN/AR A\yN/A - AgZN/AX AJN/A Z A8'DN/A@ A/CN/A@ A؛6N/AB AJ"N/Ak A N/Aw AV\N/A`;ș Ax5`N/A AQN/As AxNN/A앙 AN/A~ A8N/A#T AP N/AK AtM/A^{ A3M/A q AĴM/A9ʖ ARRM/A AM/Aq A8/N/A3 A0N/A A(KM/A6֔ A8sM/A`Ü A{M/A`K A8sM/A AxM/A@ AM/A9 A M/AN A0GM/Aas AH.M/A` A0M/A AHrM/A \ AgN/AR. A@rN/A AM/A@9 AM/A` AP^N/A B A8EN/A3 ALM/A` A8M/Ay A0M/A`;, AM/A׍ AM/A@C[ AM/A`׌ A(N/Az A`N/A2 A3N/A`p A&N/A- AXdQN/AF A0LbN/A@W؉ A eN/A`m AjN/AU AqN/A AN/AtΈ A୍N/A AN/A]Z APMN/A1 AO/AY A,O/A`x A@P/A` AP/A4 A(\O/AN A@uO/Asu AHN/A`Ti AH O/A} A0O/A`v4 Ah;0O/Ac A _nO/Aބ AO/A[) A P/A'z AӂP/A AP/A A/Q/A9 AXKQ/A A$fQ/A ~} A {Q/A { AQ/A y AqQ/A@w A8UQ/A`du A@`Q/A !t AQ/A (r A(yQ/Aq A:R/A5q A =R/A@p A@R/A #p A.4S/A3o A?S/A$l A\OS/Adi Ah9S/Ahh A8'R/Akf AGjR/A d A t R/A |c Ah@Q/A}c AUQ/Ac ANQ/A@>Jb AP/A@a A(>P/A@G7a APȰP/Aa A0P/AHb AѮP/Ac A6P/Atc AyP/A|c A0&fP/Ab A0&fP/Aib A0&fP/A`Rb A@RP/Ad b AP>P/Aa A)P/A@,a AP/A"b A O/A`Rb AO/Ab AO/Awc AO/A`2c A@~O/A`v`d AHO/A`)d AQO/A-6e AHK[O/A<` A(O/A`` A8O/ANe AH)N/A`[e A@N/A൚e AN/AFe A`%DN/AFe AxM/A &Le AXM/Ac A0ٲM/Ab A`M/A`K` A_M/A@,q` AOM/AQ_ AXtM/A`2P_ A@sM/A} _ Ax'M/A`_ AM/A@E_ A2zL/A ^ AK/A ^ AhQK/A^ Ah@&K/A`@_ AK/Aw_ Ax˰J/A`2` AH2 J/A`;_ ApsI/A`2` A8H/AT_ A PH/Aw_ A>E/Ao^ AxE/A@a^ A`D/Ay^ A$D/A@ ^ AxD/A _ A`%C/Af_ AC/A}f_ A8B/A`_ AsB/AX^ AB/A^ A!:B/A@Eq^ A ߿A/A@N^ A;8A/A@_ AD@/AOj]_ A`t:_@/AӴ` A-:?/A>(a A8?/A-b A?/Ab AH:?/A?c A?/Ac A @/Ac AR@/A 2c AH?@/Ab A`@/A]a AXyA/Agfa A8QA/A` A֒A/A |` A4A/A)a AA/Ab ApA/A=2c AHA/A`8d AA/A>e AhA/Ae A%A/A0uf App@/AZg AI@/A g AH@@/Aർg A @/Axg A@?/A`DEg Ax?/A |Ag A3?/A@g AC?/Ag A8?/A@g AP|>/Ag Am>/Ag Apy>/A`9Gh AL>/A9h AXh5>/A h A|C>/A`m;g ARv>/Af AX>/A=f A>/A`Te Ax?/A@Pe AF?/Ad Ap?/Axd Asq?/A@rc AhT?/AIc AI?/A|c AK;/AXw Ah;/A@Ew A:/Aw A`:/A`Tw APo:/Aw A 0:/A@w Al:/A*w At]:/AXv A8EL:/AKv A@D:/A`u AxW9:/AOWu AXR5:/A`mt A'::/Aଇt AH:/A t AbN:/A0s APg:/A]5s A`v:/Ar A:/A q A:/A`Xq AP:/A@p AH:/ADIp AH::/A`o A:/A o A`:/A@Wo Ax؎:/A`o A3x:/A }o ACd:/A o A%":/Aq AM9/Ar A9/At A=9/Av A8b9/A`w A9/ATx Aྜ9/AЈz AXے9/Ah| AS9/A3~ A0 9/Aag A9/AH A9/A A跹9/A`Á AP9/A@io Az9/A+ A9/A@X A|9/A@#~ A9/A`| A`X9/A{ A@9/A z A:/AKy A@N::/A'qy Ar:/A` _y A0:/A sy AN!;/AT\y AT;/AR~ Ah;/A` A ;/AЙ AD8;/A@J A:/A@ AX~:/A끠 Ac:/A@ A8Ia:/Aӛ A Q:/A AH9:/A@## A(:/Aڜ Ap,9/A徜 A0%_9/A@iT A849/A`V AH8/Az4 A8/A^ A 7/A A87/AA Ar8/Aa AЄ'8/A?ޗ A)8/A@p A0668/A9 ATQ8/A Axzc8/A Axe8/A֘ A@8/Af A88/AI A8/A@/ A8/Aھ Av8/A A08/AnG A8/A AP 9/A9 A99/A@y? A{V^9/A?& A)1H9/A6H/ A9/A ' A<Ǭ9/AQ A|n`9/Aw A Xn9/Aݤ{* Aܰ9/A5 ͑ AV9/Am A~߮9/Ab~ Ap9/A? AWɟ9/AI) A+u9/A.>AА AQ9/A| AMN9/A7 Ai̠9/AƂ A4ū9/A? A*69/A*6 A9/A At |9/AAj A]9/A'7ш AbV9/Aݼ A39/A^Wݪ A~9/A𘧛 Ao9/AU AH9/A` APSd9/A(#T̓ Ax9/Aā AM٭w9/A< A9/Ay A_΁9/A2 AjH|9/A@VY AEY9v9/Af3 Acr9/A7L An9/A҈ Aj_9/AB A$WJ9/A+΃3 AEM09/AOލ A'9/A胏 A[8/As4 A`L8/AtYy AE*8/AZ0h A,8/AO# O AA/v8/Axg+ A26L8/A_} Ad8/A"rT AK67/Afy AwU7/Aze# Ap.7/A7Ԟ A^G7/AfhLK A2| 7/AF,Ș A[ϩ6/Aw~3 A!6/AȆ A)M6/Aי AH3/A l A2/A^ AL./A@,| A8'/A8 Am%/Ae A#/A @ As /A`KX Aho/A A/A@iJ Ay/A / A:/A6 Ar/AX AS/A2 A@/A  A/-/A| A-h/A} A/A? A`P/AH A/Aj A8 /AF Ag./A`m4 AL/Ax Al/A A _/A` A/AL A /A`D AG]!/A AP!/Aݭ A0Lf!/A6k ADz&/AOx A(-%/Aco A#/A9bZ AHY/A@ A&/A`0A A</Aj? A&+/A@V? A/A = A8E}/A: AL/Aa^4 AЕ/A4 AH./A S4 A ͑/A |r- A0%/A_- Aȥ7/A@i8. A0;3/A-;. AXj/Aj4 Al/A S4 AU/A e4 AX/A35 A@ /A?S6 A/A@6 A@ /A 8v7 A/A 7 Ap9/A@Ps8 A/A S8 A@=/AB6 A/A5 A/A Ɂ6 Ah/A@9 A?/A cD; A/A; A /A`K7 A/A`U: A*/A/J> Ap\/A-> Af/A@#> AB/A B A 1/A`;LD A/A.E AXF/A@FF A/A`]H A8g/A rI Ad/AJ A/A ~J AGT/AJ AD/Aq K APl/AJ A!/A /A ~I A /ABI AXU /AH AXl /A iH A  /A&H As /A|H A`& /AG A /AG A`! /A lG A0N /A"G AP /AVF A/AF Ai/AdF Aq/A F A0/AF A$C/AFF A/A F A(/A(F AC/A@rF A޵/A?F A/A E Ap/AdD A,/A@E A5d/AxE A/A&E A/AQE AHn/ACE A8\/AD Av/AD A-Y/A D A3/A`mC A/A hC A/A l Aa.A@l AH2<.A@l Ah.A@=l A.A`~l A.Atl A.Afl Ah/k.A@"m AH2@.Am A.A`m A@.AQn A~.A@yAn AY,.A`Vn A.Azn A.A=n A.An A 4?.A@!k A@$.A ~-k A.Ajs A0 .A`s AH.A`s A.A`s AJ.Ak Apr.Al Ao.A g APq.A6f Ap|s.AJf A.A@>e A .A`ve A.A_(e A.A`9e A`.Ae A.Ad Aҋ.A+d A.A`bGd A0.A`5d AÖ.A@5c AH.A`[c A.Ac A2.A d Aȇ.Ad Aؽ.A ge A0n.Ad A]T.A |e Ah.Ae AÜ.ARg Ac.A@k AN.Ak AX5P.A (wk AZ-.AHYk A(,.A3mk A.A@%k Aj.A@Us A.Az.s Ah.A s AȈ.A}s Aw6.A(_s A.Ar Ax.A@,r A .Ar Aن.Ar AhQ.A@Wr A.A :r A&.A@ Br Aȓ.A^Pr A`{.A`r Axz;.A[{r A.A3r AY.A r A W.A@!r A.Aq AB".A`~l Ah.AKl A8I.Ar A Y.AZx Az.Auv| A_G7.A Ab".A A2-h.A] A/A A;sN/AXg: A 18/A A=!/Ao3dž Av)/A A:,W1/Ao3dž A{rγ8/A A+#@/AXg: A5x,[G/Ai\> AJIN/A=m~! AҗgT/A] Ak*W/A闕 Aω.Ap]t A軤.A[{r AH.ABrq A7.AOn A.Am Ap.A@l A.Al A.Am A0.An A݉.Aq Ap.A`gs A&䐄i.AС2s Aω.Ap]t At.AQo A.Am A,.A?m A.A o At.AQo AS/A@t A(/AîAo   /A~ A/A+ Ar/A g A k/A^Ͼ A /A~ A@u/A`KA@/A@y;AXFW/A@ A Ū/A}Ax/A.AG/A nA ձ/A` A@u/A`KAm+:/AHؤ`A\:/A`]AbR/AiL]AXe/AU]Ao/A`K\Ax/AO\Ax/AGp\A}n5R/A uvZA /A_|RA8/A sRA(>p/AVRAN/A%RAN/A QAh]0/A |QA{V /AD TQAd/An'QAXE/AV>PA)/AnNA6]/A\ݲOAe/A?GAw/AGA/A@EAT/A SDA`/A@gCAHs/AoCA 1/ADvBAA/Ao@AM/A?Ax/A@,BA/A@>aBA~/A JBA/A`9OBA Z/A<ŮTBA"_/A&)AA/A`AAq~/AI{;Aki/AN;Apx/A.AAhU/A?AȘD/AO=A7/A [=A0F/A&@A/AvAA/AAA`Q/Aྒ?ApY/A ;?A0/A`>Ai/Aj=A /ARM=AP/A@q8A(i/A`7AN/A5A(/A`4AL/A@&3AX5L/A@>3Al0/A`0A3D/A@0AxW/A 3A l/A4A8'j/A>4AS/AER4Al~/Ak4A/A_4AH /A4AK /A A.6Aȋ/A5A@/A4A/A`Ӫ4Ah/A=^4A` /A4A( e/A S2A`%T/A` 1A`/AH0AcM/A`D-/A`/A:.A/A R1A((/AN2A/Aǿ2A@/A1AX/Ag-Ap7/ANQ/A@E/A`0A-/Aa0A/A8-AxGL/A'-Ap/A൞)A`ظ/A &A/A r&A /A%A`/A%A/Aֹ%Ad/A%A/A%A-/A &AK/A9&AXa/A&Ai/A ˑ%AB}/A#A\/A"AxO/AW A@q/A@Ajx/A`;2A/AAh./A AX/A)$!A/AC'AP/AC'A/A'AH!/A'A(/A A /AK A@ /A'A(/A`'AH3/AO!A$/AF!A`/A&APM/Ap%A/AW A0e/A E AI/Aj%A/A@[$A( /AsAx/A`mA|/A C$A(/A"#AY/A 'AT /AA4/A#Au/Ag!AȘ6/AA$/A vAp/A^!Ap9/A@ Aq/AA[^/AAh?/A@A@Q/A ZGA1/A@A-/A`A;/AdkA\/A |(AS/AA>/A A38/A Ah5/A2#AؼW/A`;GAY/A@,A@}/A |(A1/AgAho(/A  A.&/A AX/A A/Ac A/A@G A"/A3 A/A A/A) A/A8; A0/AQ A`e/AF A/AK AX,/A, Aȋ/A` Am/AM A/A Ah /A;Axm/A5 AB/AoN A /A@X A/A`A AhU/A`P A;/A`A Al/A`ÕA0X,/AA^/A A8E/AɮA/AA/A,A0/A@ wAv/AƈA./AA#:/A AHaU/AAT/AAĥ/A A/AA/ApAPf/AƈA/A@A/AA/AɮA4/AA(/Aृ A@B/A A/A@ A//A@* AP/A` A@ϐ/A9 Al/Aޜ A8E{/A@ Ah./A`: Ah/Ad A/A A@/Aa2 A /Aમ A 0//A` Ay/A Aa/A@ A(@/A A-/A A*\/AQ2 A=t/ABS A /A@g A9/A AHC/A Ag/Ao AԻ/A@W~ A/A A/A* A/A e AP7/A A(/A= A2/A@ AȺ6/Ab A8B/A e A /AA_]/ATA/A@ AТK/A AD/A ? AK2/Aq7 A2/A@  AI/AoN A(W/A@~AH./A .A /AA /AA /Aj=A`/A;A0/A Aп5/A2AN/A AA`/AADn/A 'A2/A@ Ahb/A@A(/Ao A@K/AyAPt/A`"Au/A A8/AJAT/A O)A/Ao AP/A ' AXu /AW AȘ /A@ Axm/A Ahޡ/A@, Aݯ/A@yJ A(J/A^' AR/A S AX[/A@y Aa/A A 0/A Ap/A ZE A/As A /A \ AH/AE AP/Al AP/A=3 AH/A A$/A A/A@Z A/AQ A/A A/A A/A ~ A0{/A cj A/A+ A/A@ APx/A E A(J/A A0X/Ap AQ/AaE AS/A $ A/A AX/A@ Z A/A A؟/AI A Ex/AV Az/A Zx A`/A@ A!/A`]l AJ/A@ A/A J A"/AR A%+/A L Aؼ!/AH AD/A` Ah;/A / A@Q/AV AX/A A/Az AX4/A A@S&/A@ Ae/A AXW/A & Aؽ/A`9 A`/A` A@/A uD A4/A`ob AA/A K A/A` APx/A2 Ad/At A\!/A`B AxG//A@ A/AY A/A`B A`Z/Azi Al/AT A}/A AH/A`= APM/A`m AgF/AR A>]/A0 A`P/A$ A /A 8| Ap0/Av3 AS/Ah Aq/A A/A A/A A/A`| A/A+ A/A/ A'/A- Aj /A A/A A3/A [ A/A@r A8Ң/A- A6/A AP/A AX/A`; Ax/AK AE/A AP8/A A>/A AHr//A / Ay/A AP /A A`+/AR AX/A= A/A@ Apk/A| AP/A A/A A(:/A@ A /A A/A3H A8R/A`v A/AR AL/Ah A/A A8sW/A@a A@Y/A& A@/AW A /A@i AxV/AO Ac`/AM APm/A Ax)/A A`c/A A=F/A AhR/A  AHH/AR6 A6/A 2 A`-/A A@~/AO Ah/A' Ap6/A྅ A`1/A| Ap /A O> A0/A A0/A a Ae/A Aܓ/AO:Axu/AA('/AAP/A@A`v/A@A/AVAO/AAv/AA0t/AAL/A A(5/A@yWA-/AmA`/A@yWA/A[A)/A@A8c)/A A8c)/AEA,/AA7/AOAx H/A@pAc`/AAv/AA/A@LAխ/A`2A/AI As/A`2A/AKAXʙ/A@yy A7/A7 A/A`TA0/A &Aȩ/A[nA/AA/A&A /A 1A/AA+/A sA /A`{Ap/A1A(\/A`AP,A8U/AJ,A /A=/AL/A Z2A/A@5A/A@ 5A^AN/Ap>A/A>Aj~/A>AxO/AƲ>AxE/A?A8Ҁ/Ad?Aa/A`o@AXK/Ar@AB*/A`[AA8#}/A@@A` w/A^2AAhD/A|NBAh/A lBAuc/AzCA(_/A`)HCA8&/A4`CAo?/ATGAհ/A3FA =/A@FA@/A`pFA46/A`pFA46/A FAb/AFAP7/A@FAP7/A`;5GAp[E/AGAXK/A`OIA/A IA0e/A &IA/ABJA/A@JA3/A5KA/AfKAg{/AOA( /AOAH/AOAPw/A 7PAh/AQA`/A;SA[/ATA/AͅUA@/A~WA؞/AdpXAP/AXA(/AWA@&/A6{XA \/AYA(/AuZA:/AVZAPn/A3VYA0n/A\A8/A`A/AndA8/AeA@/A%eAv/AQdA/A@dA/A dA@/A>dA/AdA W/A`cA_/A@ #cAPK/A cA5/AdA /AdAx/AyxdApc/A;dAa/AQdA،/A` eAP@/A@dA//A%eA/APeAH /A(|eAs/A UeAP͡/AeAM/A$iA@/AZiA7/AiA(/AGiAP/AT4iA`/AMhA軻/AhAX/A@GgA/A0gA4w/A!iAH/AiAv/A$jAe/ANiA(/A 8jA88/AjA9/AKcjAxK/A 8jA/A`iAI/AiAA/A c+kAx/AlA,$/AjA /AjA}/A )mAD/A#nA./A@gpA84/A^PpA/AkkpA@0-/AкpA0H&/A@5=qAp/A@pA/ApA#/A-[qA/A̓sAb/A`)tAP'G/AvA/AvA/A"vA8'/AvA /AxfvAg/AvA#/A@3vAH/A@3vAHm/A@GuA/AtuA/A}euA@/AZuAN/AuA@/A@ uA/AvAY5/A"vAK/A_tA:/AYtA#/A4 vAHm/A@.uA/A sA)/A sA}/A}uAU/AtA/A (sAd/A sA{/A tAd3/A 8gtA\1/A AtAXWv/A tAh/AuAy/A4 vA/A JtAH/A uAq/AvA~/AwAh/AuA`!/A=VvA /A6wAp,/AfLwA8/A@kvA`/A`)wA/AMOyA;/A@TxA/A 8xAxvg/A AwA Y/A xA\/A^yA/AzA,/AzA/AKzA X/A yAP'T/A $zAH/AIzA~/A`zA/A zA/AzA(z/AzA趪/A{AD/Aߦ{AŮ/A q|A/A`|A(%/A1}A/A6A84/A@lA0/A@ B}A A/A &}A0L/A`VAX-/A~Ap=Z/AB|A;/A|A /AX~A/At}ApJ/A{A;/A|A /A (|A8/AB5{A\/A {APx/A{A8/A@r{AP{/A`}A,/A`b}A 2/A/}A`/A|A/A`2H{A/A`h{A0%/A 2{AH?/A zAX/A zAPx/A%{A/AK|A!/AK|AX*/A&|A+P/AUOzA\u/AzAz/ADzA7 /ApyAX /A`yAB?/A`9zA/A|A;/AJ{A/AzAk/A RxA`q/A`)xAX/ATwA8/AwA%a/AJxA]/A`xxA蕂/A1zAo/A 2{A /AozAO/AZzA_/AAX/AA/Acj}A/AAhq/A`~Am/A Q~A/A@`:A:/A&A0fb/AYA`/A[mAժ/AAȤ/A A@s/AzA8/A xAX/A wA8^/AzA ҃/A`zAL/A{AT/A{A0;/A/A@SA/AAY/AaA%/A A`./AA($/A@W~AM/A`oA-/A=HAQ/AKA7/A$A/A@56AϤ/A&AP|/A6A/AA{/ATA/A5AP;/AA(./A@ۄA/A`߆A@F/A9A/A=A@S/A@y'AHt/Ai A/A SA`/AAp/AkA0{/AlA4/A ~AY/A`A=/A`aA/A QAxW0/Aä́Axd/Ao̫AH޿/AUTAo/AW}ШA،wP/A@A/AW}?A3Ҿ/A}A_' /A[KRA-g/A<6A`p7/AQ͖ڞA@F /AA4{/A(xA%޼/A؞AGu/AΝA»/A SAdKϻ/A-AW`/AuؑAۭ/A}ϐA/AWId̏A/0/A9>ЎAhDS/AEŬݍAY]y/AB;/AY /A JAѼ/AAAl/A#2|Au]/A؛A/Aى-cA/A2?AX /AAHu/A|A(/AlA2/AdA8Z;/Ab$cA ׺/AbAFt/AaA֑/Aj8`A*h/Ay_AfK/A7s^A R2/Ak]AGY]/A `90\AS푗 /Af[A/AG TAW~/A/YTAT/ALUAm*/A VA^Xϐ/A]MkWA%n/AԑXAOFyP/A`YSYApw6/A [A=b"/A ,\A"/AI]S]AVM/Ak2~^A h)/A2*_A1/A\`A /A bAk/AMyCqcAtnP/A/)dA/AJN7eA3׻ڵ/AjAfAŵ/A HgA5Tw/A+z%jAs%/Ao9;lAKδ/AgWoA^Tt/A+*rA*r>/A6 NuA%9/A>ExADbK/A\LzAo6/A M}A%4q/AlA */ApA/AA׮/A ƋA /AAGg/AW{XAO?/AJAXD/A- A/AKb2Awo/A'~A֏4/A6,lFAd'BP/Az"AXNpo/A0)AږE/AA*#/AَϠAhqݰ/AiA/A /A07(/At{-AJ/AYSd=A`k/AqQ`A5n,/AdAX /AóڡA`Ŷ±/A/A0a}Nڱ/Al5A>ۆM/AAS/A {>yA#S /AOeAFT/A~Ap/AAP/AA¯Ad/A@WA{/A`A,/Aa{A3C/A@A/A`)ATxqa/AzA*/A AT% /AîA_(/A@A(/A@5AM=/AtAx/AZ AwJ/A`A /A 7AV]/AA`AN8/ANd/AH/AvA/Aqc^AhDʔ/A@,A@/A1jA /Ao^9Aɝ/Ay?*A. /Ai 4A=/Aମ~AȰ/A u}AH/A\}AȰ/A|A/A||A Ρ/A-{AQ/A[s{AhՍ/AzAX/A`QzA`T/AyAȰ/A;xA@½/A}xA?̔/AXxA/Aj|xA /ATxA !/AQxAP(/A cxA"/A@yTxA)/A`wAȱ/AwApӔ/A@wAɔ/A wAɔ/A@#uvA|ɔ/A@qvAÿ/A@NuAǵ/A`muAǵ/A`tAǵ/A sAɔ/A?VsAڔ/A`)rA0/A@qA( /A1qAP(/A-pAI/A JipAs/A QUpAs/AM_pAԕ/A[7pAP /A`oA:@/A=oAo/AoA!/AeoA/AoA8/A |oA –/AoA̖/AroAH/A nAr /AbnA9/A DnA`Z/AzunAhy/AnA/A@ nA0—/A@!nA(O˗/A@nA ʗ/AUSnAPꛗ/A൷mAx\q/AjmAd/A@SmAh/A@>clA/A`kAG/A-jA0>/AjAHK8/ARiAXu/A`iAx\/A |iAԎ/A iA0/AgA𴛖/A@fAPF/A bA/AJaAO/A@F`A/ܗ/AzV_Ax/Af!^AD0/A\A\/A S]\A//AW\Aȶ/A\A,ܘ/A`f\A0ܘ/A@ \A/A`\A/A[AP@˘/A43ZA0/A`;YAr/A@{XAP/A cWA`/AWA٠/AVA|/A2VA7/AXUAW/ATA&3/A SA0T,/A+RA8/APA@͗/ANPA嫗/A`PA /A*PA/A~PAcr/A OA^/ANAa/AsMAa/ALAu/A`3LA;/A"KA+җ/A3KA@/A;lKA/AL:KA/A lJA/A ~JA+җ/AsJA0/A lJA A/AZKAk/AZKA@/AJAb7/AsJAr /A JA/A3IA/A GAؖ/A@Ap뙘/A?A=/AAAw/A`BBA7/A5CA!/A`TEA#/AMEA`T /A EA //A@%EA(a/A ۚDA(~U/A@nDA\>/A@,DA,/ADAh"/A BA4g/At"BAG/A?@A83/A[?Ad/A@r>A(m=/A`o=AM/A=A _!/A`<A/A<Aڗ/A~;API֗/A`m;AXhϗ/AN;A/A QE;A/AT;A/A+V;A /Al;A8/A`m;A/A@5=A/A;=AY,/A@g0>A(>/A;>A@U/A+%>ATg/A=A 4/A@E=A/A@=A( /A>A7/A3?Ah]D/A?Aa/A 8"@Ah]t/A[@A( /Aࣦ@Aذ/A)@A/A (AA Ś/ADQAA/AAA/AAA /A`.BAج#/ApBAu8/A3BA(F/A 8BAb/A@`1CAq/A ^CAP /ACAhޤ/A CAЀ/A`8DAVǛ/AbDA/A?DA/ADA"/Am@EAP8/A HkEA83:/A`EA:/A`fEAX/AmFA*/AFA(\S/A`fEAn/Ap#EApc/A(EAJ/A`fEAp=A/A=EA$/A]=EA&/A@yEAF/A@DAs/ADA`1ϛ/A}9DAPٿ/A DA/ACA땛/ACAiy/A5CA=j/ACAqN/AzBA>/A`BAhM9/A`osBA{/A@`fAAX͚/A`-AA/A u{@A@䎚/A 8o@A(;/A r?A)/A=A/AkW=A/AdY:Aؒz/A :Aٓ/A&:A@/Az7A0%0/AX6A0{/A &6Aޙ/A5A4/A@`z5A_/A 5AP8&/Ac3A`aG/A3O2A /A"8A/A 6A/A~6A?/A`9:AАW/A9A/A5A{/A?4AP u/A3AM/A4Ah{/A J7Ah/At7AxW/A?3Ar/A3APom/A&X3A@/A2A,/A %4AHn4/A`4A螛/Av2AH/A2AΛ/Akd1AJ/A)2A0*/A@1A֛/A /1A((/A 0AJ/A`/A0GR/A 0AT/A <0A`W/AX0AeY/An0A(]/Aߝ0A@‰/A@%2A8/AM2AP/A (N3A(/A4A0/A`4A/A`[4A@ǜ/A`65Aɜ/A lO5Af/AF6Ap/A_6AP/A@6AU/A 6A /A`y7A@ 2/A@y(7A(./AX 7A /AfR7Aأ/AN6AJ/A_6Ax /A cO6A/A-46A`v/A=5Aݜ/A_5A(/A`T 4Aຨ/A@~3Ax/A D3Ap/A2AX/A͘2A/A{2A酜/A`1A/A1Aw/AHO1Au/A 71Aui/A`0A8g/A@0Af/A0AXb/AMi0AXa/A ^0A5[/AB 0AY/A`90AXX/A/A(R/A@#/Ap/A@ /A,/A,P/A$/A`)/A'/A*0AH27/A0AaB/A1AbD/A@Ϝ1AQ/A ZA2A+a/A03Axn/A@3AXp/A`;3A0/A@>4AP냝/AE4AM/A l5Ah^/Aֺ5AQ/A`C7A&/Aq7Ap/A7A͝/A&<8AЯϝ/A@rY8ANߝ/A9A0/A %9A /A9A&/A9A /A:A( /A:A(/A@f;A`/Az;A0/A`;A?/A`N;A(s.A*/Aߗ_.A(X/A,x.Aܪ/Aw.AdQ̝/AeLX.A0;/A}0.A/A.A֫/A H/A(V/Ah.AR/A/A/A6/A#/A@ .Af/A /Az2/A`)1A8R/A 3AZq/A A05Aƀ/A6AU/Aྐ6A /A`f6A/A=8Awž/AX9Ap̞/A-:AΞ/A F:Aٞ/A:A/A`x=A-/AR@>A/A Hk>A/A׋>A`6/A =>Az4/A>A/A7>A/A$<Ao/A3;A/A`;AB/AQ:AO۞/A y:A( מ/A<:A0՞/A:AnϞ/A9A8Ǟ/A`9A@/A8A/A@8Aѱ/A@&8A0/AH 8Aã/AY7A/A@N@7Ap/A6A|/A6AP/A`v5AP/A5A|/AR"5A)z/AO5Adk/A@H4Ah/A 1)4ApZ/Aj{3AСX/A"^3A(L/A$2AJ/A q2AP >/A@2A o/A-Aȝs/A x,A6v/AQGx,Av/A,Ap/An,A(ޞ/A@ ]/Ap/A[/A(\/A/AQ֞/A@g/Ah/AR7A(p/Ap7AQ/Ay=A(/A=A0/A=AWğ/A>A/A@ >A/A@T>A /A@g>A/A >A(/A?Aأޞ/Ar@A/Az@A(/A*@Ap/Az?A'/A 1@AGv/A3?APP/A/1AL/A]0Af"/A`.Ap!/A.A#/Az.A/AI.A/A`)*+A/A]+A83۞/A *A@/An(A/A ](Ap/A (A/A=%A/A %A{/AR$Ap=m/Az$Ae/A`#A0.(/A#A&"/AH#AV"/A`#AV"/A@#AH/A=&Ax\I/Aޕ&APfG/A&A( C/A H&A8C/A H&ALA/A̜&Aȥ@/AU&A(\&/A $Ap%/A|$Apk%/A@t$A#%/An$Ap$/Ag$A#/A=.$Az /A=:$A/AP%AW./A]&A=*/A&A&/A&A/A&A/A&A@/A 7'A]/AZ'A8/A |'A8//A 'Ap=./A H&A-/A&A7/A&AG/A&AWI/A@&AWN/A&APN/A &A(\Q/A&A7l/A(A/Ad)AG۝/A)Aߝ/A \)Aڝ/A (AѾ/A (A(\/A(ABk/A )Akv/A@ )AHᐝ/Ao)Aĕ/A)Aт/Af)A/A` *Auh/A`;*Au/A*AWg/A)AXZ/A!)A( [/A q!)AXp/A`; )A /Ak,As/A@5T,A,Cp/As@R2,Adk/A8WY,AbMe/A*h?+AYl^/AĘ*+AcV/A>ۈ>+AwݶM/A+AD/A +AX~/A+Aq/Ai+A՟f/A+A0;/Av,A̘/A,b,A}b/Ax3,Az/A0U,A@Nz/A-AbPb/AMP-1-A K/AN-A`i>/A'P-AI1/A~EJ-A %/A<-A9L/A"Wd&-AUܡ/A{! -A!@/Ao,AX/AҰ,AX /AQ",A /A`[b,A؛/As>A,A/A0,A,;/A,A G/A뷊}+A.Qj/Aq+A )T/AnXN+A\@/A*AuL0/A{*A#/A3JL#*AhQ/A)AhD@/A)A(̛/A*Aɛ/A *A /A`f +A(K/A[*APf&/Ac*A*/A*A/A ;*A'/A *A |/A`)A@/A *Aik/A@y)A@/A@y)Ar/Az夆)A׊!/Aen(Ax-/AϵRr(Ax?1>/Aת'ACӼR/AoG'A7 j/A,xE&AG/A$k&Ab/A9v&A;›/A*n&Aޛ/A`9~&A /A6&A R/A@f&AȘA/A@:&Asd/A %Aw/A %A/Ao!%A/A %A{/A x$AH/A`$APM/Ad#AP/A`B"A Ĝ/A`!A`Ϝ/A lY!A !Cڜ/A AS/Aa./ A߫,/AaݖApޜ/AׅlA&5 ֜/As#iA<Ȝ/AŦNA=/A,RA䠜/AA0~/A`tASZa/A35A1B/A]3Ai謄!/AVu AS</A A#ޛ/An1A8/AA:oU/ADA ëy/ApA-XpRY/A1~AO:/A UA/A5A4/AJ]:Aq &/A:ALHޚ/AZ,AК/A 4FA-;#ǚ/AlA0Gš/A8Ah9ǚ/A! A4Eʚ/ABAb%T˚/A:Ayp]ʚ/ALkA hhǚ/A(_SAۋš/AED(A#뻚/ALAз/A AO/A`A[|/A`AM]/A`fAd?/AG$AT</A tzA\/A`Aٙ/AZB2A7︙/AzTyE#AV/AmAL}/AЋUAU d/A*AM/Ab̛,Ao1;/AyA@~,/AAҿ/A{_AI/A `f AZ/APVmA֖/A֨A /AA7/A˒A /A,5sA3/AbN AO<ܘ/Au % Aj3Ș/Aa!Aj,/A-d!A))/Aƛb_"A\xw/AA|#ACU/Ar#A؁1/A`#A'/A~m#A.#M/A9xm.$Ax]/Aa$Abn/An/$AoV/AyA[$A`/A %AOY/A"$?%A%/A|oi%A. /A8%At ח/A%%A/HƗ/A8>%A./A@ϵ%A=Y/Aa!%Aa1q/A(%A }N/A@%A22tH/An$%A$+[B/Aa%AU`$/A_M.~%A _!#/Aof%A5#/AKCM%AU$/A 5%AsY_2./A18%A5/A`$Asf;/AD$Al>/Apt<$A?/A+#Aj["A/A`"A\l/Ap\}"A@$/At"A<}/A&b"AFz/A_=S"AA[/A\H"A}/A[.=@"A-ݕ/AH<"A7mѕ/AqCRk"AÕ/AZ"Ac/A#{"A/Ai"Abt/A{A"AQNσ/Aƞ"As/Aق "Ac/A"AN$Y/Awz"Az>N/A[X"A]A/Ae>"Az3/AS^."AmĔ[%/A)Q3'"A/A׋$)"A=/A`5"A;/A5J"A{I/ARK9h"A-b/Adʤ"AN0۔/Abȹ"AHӔ/A֮k"AΔ/A,!#A%Д/A'T#A<_LԔ/AIb2#Aٔ/A#A%۔/APF#Akܔ/A-,#A+mݔ/A=c#AA/}ݔ/Aj#m#A#?ܔ/A$Ad|w۔/A$AN׉ٔ/AnH$Aה/Agk*$A8?Ԕ/Aӻ4$AД/APe=$A$!͔/A(%C$AHɔ/AnF$AVŔ/A@G$A:/AeP_$AM~/A|*m$Aȩ/Ao$Aۅ>T/A+.$Adu!/Ar$AH/A^%A8/Af%ATX/A`"7%AesV/A.z%A%V/A/t$ARLmW/At3 $AJ7AZ/AWAHnb/AVAUu/AA(Kz/AklAcʗ/AvA0/A[Ax/AAx՗/A`v]A(/A`AXB/A|=AXe/A@G!Au/A@A`z/A4{Ay/A0*Aj/A AW/A@k AV/A4h A(iC/A q AH9/Am A,/A@D Ap#/A@# Ap#/A`] A( &/A3 A/A S% A/AZ A/AA /AEAH9/A@yqAЦV/A 1Ao/A HrAR/AA/A`̫A`Դ/A@A纘/A AA뻘/A@GA0/A@lA˘/A@WA ܘ/A@AR̙/AsA8Iə/AgAߙ/AAh/A`[AȤ/A `Al/AA0f/AAOۘ/AAA8Ԙ/A & AOۘ/A`9. A0f/A@ A`/A` A0/A ( A /A / Ax /A A /!/A`$ A %/A A AO+/A Al4/A u1 Ay8/A A 8/A i A?/A A5i/A` A8sh/A`K A%/A$ Al/AT A8/AT AP/A A/A A /Aa A8m/A Ah?l/A 1 AfB/A AxZ/A Am/A A8ə/A 8w Av/A+ A(/A Aϙ/AoG Aә/A  Aؙ/AN Aߙ/A A?/A@ A/A@ A/A2 A/AN A/AN A/A ( Ah//A A/A+ Ah/Ao AF/A`D A2/A ( A2/A A/AH A3/A Av/ATp A(/A$a AX/A A/A@ AАٚ/A4 A]ݚ/A 8g AX/A K A/A 8g A(/AH A/A6 A/A` Ah//Aq Ahؙ/A - A@0/A AԂ/A2 Amt/A` Af/A AVX/A  A؟W/A` AR/A`] A؞?/A` A71/A`] Ai/AWL A/A` AǗ/A A/A AX/Ao Au/A AJ/ADl A/Ar A/A` A͖/A A'/A A/Az A0Y|/A A{/A JA A&/A A'/A A'/A% A&/A& A߅/A@ A%u/A@#o A(U/A A =/A}E A8//A u A8//A AV@/A@ie A#E/A AWL/A` AN/AqX AS/Af8 A$]/A Aa/A AXp/A A0Y|/A AZ/A`b A[/A]{ A͖/AA A*/A` A]ܖ/A x A˖/AM A)ɖ/A1 Ax)Ֆ/A A*/A`) A/A{ A0{/A`x AE/A` Ah/e/A@ Acx/AD A/A  Ah/A; Aڗ/Ama A؛/A`t A /A׷ Ah*/A? AXB/A? AN/A@ AN/AD APk8/A A`/A A/A A֗/A A`2/Acq Apn/A7 Ax>/A@ Ae/A  A_/A A,/A` A,/AK1 A/Ax A0"/A Ax&/A@ A7/An AE/A w Ah/e/A w A/An A/An A֗/An A/A@ A5/A@ A6/A@ AН'/Aֺ AP7%/A; Ai/A* Ag/A* A—/A6! A/A AhQ/A` A-/A@W A%/AO7 A/A ھ Ap /A A^/A| A /A s' Ap /Aq A_$/A@g Aq-/Aк A` A D/A|ْ AP/A_ A^/AG Ah/Ah3 A-q/A ( AP&t/A  AGy/A AXz/AΑ A}/A A(h/A Aq/AQ A{/A@n A(/A3c A/A` AR/A`P Ah/A@- AP+/A׎ AÙ/A  A84/Aq# AH /Aӊ A`/Aെ Ax--/AE AtC/A AU/Aj Ah/Ag. APov/A[ A\/A < AX/AWY AGś/A` AdΛ/A`0 AP c/Ao Ah_/A$ Ap{/AՈ Ax:/A AHn/A`K A(/Aه A/A$| A š/A AP7К/A A[ۚ/A AP/A A/A@W A/AzT A/Av Ax/AΆ A /A  A؁/A A A؎/A A@/AO A/Aj? A/AXՅ A/A Ah/A` A @/A> A\/AVك Aa}/A`k AH헛/A`< A@/A A /A Ah^̛/Ao A@כ/A 8@ AXW/A AL/Af Aq/A| Aq/As- Ax /Ao A/A@5; A;/A AO/A` A-/A@ R A)/A` A ֛/A@5 Aԛ/A@,R AX/AV]~ A0/A@} A/A~} A7/AH} A@?/A`| AЕ}/Ad} A0/A0} A4D/A 8| AF/A z| A(O/A@>| ABV/A~{ Ab/A`{ Avo/Az AhD/Aaz APx/Aez Ay/A@z Ap/A`bz AGœ/A`z A0Hݜ/A`2z A/Az AP|/A@z Ah/A@,mz A8/Az AzҜ/A'z AĜ/Ay A@F/A`iy Aߪ/Ax AXy/ALx Ay/ABx Aǜ/A ەw A /A ҽv AX/AJv A@!/Acu A:/Au AT/A-u Ar/Au A苝/Aau A趨/A2v AЄŝ/AJv A@5˝/AJv AXܝ/AJv A@S/A3wr AP1/A q A",/AOq A(/Ap AX%/Ap A/Ap A!/A@%p AH!/Ao A0 /A Po A/A`n A8V3/An Ax:K/AWm AF/A{f*n ACL/A}Vn AWP{/AO.rn A!$7/AƟv~n A,c/Au0zn A{5/A0Een A]h/A dDAn A`a#/A@ n A;M-/AHpn A(%?7/Ae2 n Af@/A3;n AEJ/AXŚXm AHuR/Aױm AY/Am As_/AFRm AHbd/Ay8b:m A :g/Ax[m A\&xi/A4m Aei/A; m AhNe/A|Xl A^/A0k AjU/AXj AxC0U/A]Gj AʨR/Asaj A*L/Aj AIaC/A0i A3À8/ApHIi A&{S+/A2gi A8/AA;i A /A]i AF/Aebi AFԣ/A'Li ARD/Am j A./AGLŸj A /A` k Aȶ/Aj A/A Ri A=/A ~Bi A/A@h A,/Ah A+/Ah A/A Sh Ad/A@Ei AN/A@E,i ApM/A6ei A/A Ui AN/AWj AU)/A/j A^{-x/ABj AT>/A2j A/A k A/kϠ/Azk A ]5/A 6!k A"f/A\k A2/AOj A5/A j AEA͟/Atj AL/Aj AH^k/Ahi At7/A@&i Aư>/Agi AtI͞/AMQi Api AN;/A#&i A,t/A򅸃i Apk/A8Õ/h A>d/Aڑh Aī^/Ah A"F[/AŽQh AEZ/Ah A ?[/A,6@"g A8x_/A`g A b/AÊDg A6i/A2Qf An}u/Atrf Ax/A@Gf A<{4/AlMFe A/A>Old A@Ә/A@c AI/A@Ɖb A+/A 6b A!t/Aa A@/Aka Am*ޜ/AG` Aɜ/AgS` A8/A q_ AĬ/AQx_ A%F/A{E _ AΈ|/A/At[^ AȤ/A ] A4/ /A(] A2#/AruU] A\/AXNv] Ar/Ahzb1:] A.ܛ/A\ A@+֛/AG\ A nԛ/Aܹf\ A:(՛/AAXR\ A؛/A~~[ APߛ/A[ ANF/A9SO[ A'V/A 5[ A@/AZ AA/A>Z A'/AbmcyZ AXW6/A2Z AO=qA/Ax" Y A/N4I/AC`Y AckM/Aq6Y A6sM/AX A#%J/AQSX Ax(D/A;-X A$#Z>/AMW A6/AzhW Ap})/AzX A(/A X A/A@gX A/A@QX A./AoW Axd,/A W A3/A 8JW A%/AVáV Ax/ArV AHW/Ax&tl+V AT/A@!U APԛ/A\נU A/A$S-U AdԹ/AH\U AJ|K@/A"wU AÒ/AmtT AGMG/A}T A+jO/A*(T A-䇛/AKS A/AHxS A:k君/AIR A/AR;PR AS̔/AQ A/AL AXڝ/ALL Ax>/A L Axu/ApO AV/A@O A /AFI Al Ɏ/AS eI A=qe^/Ap I A1/Aʨ8H A/AOH A/AVIG Ak)v/A'8Y.{G AJރ_/AɪpG AsF/AgF A^/AF AfS_/Aw8EeE A(r/AaD A&\T/A6nC A /A@. C A&E/AR[B A-(/AlA A]/A]ĐA Aq/ArW@ A Ahw~/A!֚> AP/A= ApZ2/At,= AP^/A* ? AC/AR\? ARj/A>? A~Uǝ/AI` @ A|ߝ/A'T@ AR7R/A .0@ A`S/A@ A&&/A/4A A \2/APZ7lA Am7;/Al)@A Aj?/Ao7B Aq/Х@/AQ͠B AM!G=/AyW C A(5/A`nC ApkH./AN; D As*/A(4D A`9+/A{]LE A//AXE ArY3/Az F A S8/A&F Am=/A=F A(D/ATQF A.b'L/A? o_F Auq&T/AHZiF A^m\/A*mF AHd/A@PkF Aәlo|/A=F A|ߧ/ADH}E AfH A+/A`T!H A+/A G AB6/AF A-I/AyF A8U/A`]xE Ak/AD A|/AD A/A AD A堫/A AD AHг/AVD A/A;D A`v/A`[C A8/A9C A_/AC A0J/AfC A(>/A`fB A#=/AB A0J/A+A Ad/A@ A/Ax@ AU/AyO@ A/AA@ AX/A`oA@ Ax˫/AQ@ Aͫ/A`)? A@ǫ/A$? A/A?? Ai/At? AW/A,6? AxU/AO? ApAC/A`n> AP77/Af= ApAC/A6< A@M/Aҷ; APe/As; AT/A`; AҠ/A͖; A`«/A`; A|/A@N; Ap/A`De; Ap/A Z'; AHӫ/Ao: Ap+/AF: Ap/A: Ah}/A: A@n/Ag: A_g/A@S9 Ais/A8 As/A7 A١/A` 6 AHn/A`5 AHӫ/A5 AP*/A`5 AP0/A@W6 A[/A.6 A`k/A@N5 AF/A5 A/A 65 A>/A4 AHӫ/A?4 A֫/A@3 A0 ޫ/A`B\3 Ax/A 1B2 Ax>/A}1 AhH/Aan0 Aq/A,0 AF/A?!1 AӬ/AJ1 A/AJ1 AO /A0 A/A,0 A@/Aan0 A/A@W70 A/A/ As/A 1[/ As/A*x. Az/AH- Aլ/Ap * APլ/Aa * AWշ /A@e AX/A Ap=g/A`D Aզ/ANI Ap=/A5 A(/A$ A@Bk/A A[/A 8 A/A`" A7'/A@W( APC/A| A`S/A@WJ AU/A Ap@Ka/Aք=: A1}S./AMؘS Ab !/AF A`ʲ/Aw A隲/A A/A@A A0{/Ao Aw/AM{ Ag`/AM{ A pX/AY AL/A)& AH/A` A@;/A0 AK(/A@ A /A9 A/A`s A /AX A/A A/A`TW AK/A߾ AТ[/A@ Ax#/A Aa/A $ APẔ/A@T A8'/A A/A`[ AU/A+ Aϱ/A@ AXı/A AHr/A A/A A(n/A 8 A`[/A AT/AE+ AP/A0- AͰ/A; Aİ/A A'/A F A胊/Ah A0ݞ/A$ Ac/Ax AЀ/A` Ah/A AѰ/A+ A/AI Al/A| Avp/A@ Ah/Am AP'/A^) Ax$/A A/A`] A E̱/A]# AH]/A A//A Z Ah./A# AE/A A`1'/A- A8_/A¿ A}/A AH?/A AԲ/A`T A/A8 A/A A0/A@,% AJ/A AH{/A`K? A/A A!/A`H AN/A b A0j/AF A/A@ A(1ز/A` APk/A A1/A A~/Af A8Z/A 7 A [/A AW/A Z A(D/A@ A77/A` A8/A d A/A*x Ar/A' A/AM7 Axmb/AV~ A7/A+3 AX2/AZ A (/A;| A/AQ A/A 7 AP/AN Axi/Ax A/A A/A ɷ A0 ۰/Aj AŰ/A P AH/A A_/A4 AFT/A1 A./A A0)/A+ A_/A~ AXF/A Z A`̰/Ay A߰/A l Ah/A A/ /A`TT AX/A`) Ah?/AIgm Ac&/A`] AЉ/A$@f ACk/A3 A< /A1Q< AY]2/ADt8m A/A? A[R/A= AJac/APHa!z AЮ/A[c A*/Ap ' A_͟/A)/ԯh A(˃/AT AN/A@. A(/A A/A A[/AQ A/A`]r Apʫ/A AȻ/A  A8/Awn A0/AV] A 0ʫ/A A̫/A& AM/AE Aw/Ae AE/A3q AȤ/AI A0Ȭ/A A8/ABQ AX,/A`] A/A6 A/A_ A=!/A A(z7/A JG Ax?/A") AXU/A A@]/A ABi/A Aq/Ay Au/A A`/A A/A A /A` ABЭ/A & Ah;߭/A A/A Ax)/Aƞ Al/A] A>)/A@y Ap3/A AA/A A0jR/A6 A/Aq A`/A} ApI/AS A( /A`K A //AC A(?/A A5G/A A5b/At! A`Tj/A \ Ax:/A| Ap/A (d AН/A` A-/A@ A/A A#/A- A,/A A@ԯ/AM_ A/A? A/A A p/A Aү/A6 Aʯ/A@c A/A AhQ/A A0ꕯ/As AX/A A*/Az A1/A`) A/A@ AP /A`\ A@/A`܅ A0.ˮ/A@# AP7Į/A3K AS/A AX/A A/A A/A` A=_/A@7 AuQ/AEp AhG/A8 AP2/A@v A p A(%/A` A/A A!/A A8 /A A@$/A@q A/A A /Asї AP/A A84/A@NF A` e/A?|L~: A;/AsL.& AD/A@> Ah2W/Ab0p A?T /AqP' AY/A9Ԟ AX$/A֡ A5/A Υ A0LR/A0z- AG/A@D A{d8ԩ/A۹T Am/Aٶ A$@/AwXBx AЀ/A`T. AZ\K/AJ3h AF/ q/AW0u A`./Adz Aѧ/A9gW A$o/AAٶ A=7 /AijL AA S/Ax AcM8/A A.Jʥ/Alv2PN AxmY/A A/AFE% AG/A Asa/A0ſ APM4/A A/A; Aã/A\ A`/A,i0F Al/Aܓ1$ AfB/A A /A A/A` A/A@W AD7//An$ A_+֝/A<+J? A /AI y AdDU/Ax A-/ALپ A\7r/A k Afw7c/AG ARS/AҐ) AB/A/ AoJT4/Aݛ Aԉ:$/A^a A /A A@/A`" A /A/g* A/|gRߡ/Au_ AXxС/AP؞ Agġ/A A/A Ax/A@.F APu/A AC/A@ AP$/AV A/A A(/A Apy/A` AX/A@ AX[/A A8#/A As/AMj APj/A`m Aa/A൵ AW/A; AaI/A@Ͽ A?/AD As3/Ay A`C%/A@` AN/A Aݟ/A A8/A Au/A3 AF/ATM A` /A`a A/A AMӞ/A`) AW/A A1/A  A\/A A887/A`; A/A A/Ae A( Ý/A A/A AD/A AV֜/AA APȜ/Af AF/A  AБ/A Ax/A@t AБ/A ± AЦ/A 9 A/A5\F AZWs/A AU/AZ] Ap4/AoJ A /Am- A`T/A  A0 ϛ/A&_N Aﮛ/A A!uڄ/Aх}_ A`Y/A0: Ap_I/A,0 A]h/AD A߅D/A A`Sš/A@1 Aٲ/A ޿ Ah/AԿ A'/A AH/A A /AS A /A 9 AP뜚/A ? A/A@Ph AȺ/Ac A![/A A![/AN AX5/A@% A/A@1 A0/A3 Ah*/A#3 AD֙/Aq AÙ/Aq AC}/Aq AϘ/AM8 A@j/A@ A /A@W A^/A5 A/A A/A A 0S/A A(/A 8] Axؾ/AP A跀/A` AP/A2) Abo/Az Ae/A A8l/A$ Asc/A$ AP<\/Aj A8;/A A8/A`y A@ /AMz AP|/A@> A0B/A@t A6/An A00/A` A,,/AI A(*/A 1G A00/A|> AY./A ۞ A/A ¡ A/AJ ApJ!/AJ A"/A Ac/A` Aw!/A`bc Ap/A SK A6ޒ/A A6ޒ/A Ahے/A A`ɒ/Aj A8/A` AX#/A@, Ap/Avt Aޑ/A X An/A Ac/A AX/A` Ao/A O Aޏ/A Aѩ/A A0h/A AP7/A8> A }/A c A導/A= A@B/A AH/A APx|/A A Aߍ/Ah A6Ӎ/A Qܿ A /A`ve Axm/Aa A/AR A(-/A@k A0e؍/Ak A%/Ai A ō/A Zټ A]ύ/A A8/A A()/A Af1/Ao A`Xk/A APb/A@] A/A`;ι A]ύ/A  A(:G/A Ap[C/A` AÍ/AH A%/Aɼ Aӭ/A/U A(/A@@ A0/At A`/A 3 Ar/A`f A/A' AϢ/A`m Am/A` AE/Aſ A0*/Ad A /Ayc A /A A/A A/ARݾ A/A; A5/A A5/A 폾 AV/A v A/A v A/A@p A/A J{ A׋/A 폾 AË/A`] A`/A`; A,/A`T\ AX/A A A/A Zټ A/A@ļ A0p/A m A 7/A_= A/A AP /AXZ Ap!/A (r A /A[X A/AW AEt/A P A*(/A AP|2/A@ A"/Aܻ A/A Awȉ/AK A8D/A`] A/A?̸ A()ω/A Azى/A A:/A@NB AZ/A6Q A؟ӈ/A҂ A/AV Ao/A`m5 A&C/A A`/Aع AՇ/A ( A/A\ A0/A | Aja/A?̸ AtB/A_o A`@/AE AK=/A` AP:/A$ AP:/A`mN A.լ Atz/AjLG A,~&z/A@9d' A,Yn#z/Az͐ A{y/A~@S* Agy/AЕP Aŧu y/ALYe AVx/A:i Aax/A>\ Ahx/A @ Aaŵw/AZߣ A~B^w/A:$; r Aѡ w/AF A @ĸv/Aq Ay(kv/Au: A]!v/A1rMC A 9Du/A A# vu/A A\u/A%;ϔ0 A"u/Amm Axhu/AD Ab"^p/AF)7 A_Gݯj/A| u϶ Azʉe/AR AѧZkd/At f A83xd/A A Hd/A A(Nd/A  A0sd/A APHTd/A@` Am@d/A j_ A2Z.d/ALr A _d/A@El A;:.d/A A0vnd/Asy AB3d/A߉'G ARc/A: Asd/A Ad/A Asd/Ai.' ALc/A! A=Ic/Ap( ADDc/AO0 AN[c/A- Ac/AdJn AQc/A&m Ac/Aq Aw`c/A9,_ A>c/A[F AEȪ!c/A^! Ac/A A0Ic/AQ A?c/A\%X A v:c/A&3 Ah9c/Al AY&=c/A%~ A(CFc/AAU2 A|fSc/Ah AKdc/A: Azc/A$r. AAc/A AX c/AdS AHc/Ax A0$e/A  A:e/A`2 A f/A4 AmGf/A AZf/A`m Ate/A@Q AHmye/A`b A%e/A`q A0e/A. Aږe/A  Ae/Av Ae/A  A}e/A< Ae/A% APe/A@N& A(e/A&' A(e/A( Ae/A) A8me/A7) Are/A) A e/Aa-* A8Ve/A* Are/A* Aȥve/A, Aȥve/A - Axe/A@y.. A@Pe/A q/ A(e/A O0 AXe/Aye0 AKd/A&0 AUd/Ak1 Asod/A`&2 Aqd/A2 A@d/A@3 AUd/A@W54 Ad/Ay4 AQd/A@i6 Au4d/A`9 A Alc/ACu> A0c/AhB> Ac/A&? A&%c/A/? A:c/AH? Ahc/Ap? ApjQc/A? A//=c/AJ*? Axm,c/Av=EQ@ A8c/A0@ A>@c/AA A!A!c/AGPXA A]c/A-A A@b/A`TE Aa/A&M A~a/A`Q Axa/AOU AHP`/AW A0`/A\ A <:`/A`^ A _/Aɀc AF_/A`uh A 0_/A Li A _/A[j A_^/A 8l A^/A~n A,^/A@n AXy^/A`2o A w^/Ax~q ACr^/Aq Aзm^/Aőr A1e^/A0s AAa^/A)u AW^/A`)v Ah^/A 1r A9_/Aw APK_/A A Ra/AM Aa/A Z Ab/A  Ad/Ab A!e/A[ Ăe/A Ae/Ajm Ae/Apr A e/AϬ Ahe/A"~ A AWEc/A`H AOc/A` APc/A A /c/A Apc/A@g Aq c/A { A@$nc/A@r AYTc/A@\ A {c/A;b Ac/As A0c/A6w Ac/A i Aӭc/A i A`c/A@ A>c/A< AXc/A- AXc/Aj A4rc/A A`c/A AHc/A Amc/A r Ac/A@Wr Ac/A Ac/A 8 A8tc/Ak: Ac/AN APc/AN Ajc/A9@ Ac/A`F Ad/A A\d/A A\d/Ak: Ad/A`7 Ald/A} A*d/A} A d/Aʚ AEc/A@ X Asc/A@ A8c/AH Afc/A Asc/A A@c/A@iA A$c/AR A$c/A2 AͿc/A Ac/A&y AGc/A, Ac/AXs Ac/A@J Ac/Aޓ Ad/A Ad/A(r A d/A(r A1d/Ajg A6d/A A]-d/A A1(d/A O APd/Ay Ac/A@Q Ac/Ah AHd/A# A%d/A A8#c/A@C Adc/A@% Ad/A1 A8 d/A@% A`c/A A c/A^ Ac/Ac AX d/A@/ Aw.d/A`BK Aw.d/A8 Ad/A` Ad/Ao# Ah7d/A > A8d/A  A:d/AF Ad/A ApCd/A ApCd/Aqw Ad/Ab A`d/Aﯾ AhDHd/A6پ A%Od/A, Am"d/A`x Ah%d/A/ ApNTd/A AVd/A`ڽ Aw.d/A@̽ AY5d/A  A/[d/A@PC A\d/A ( A,d/A A3d/AgY Acd/At A9gd/AR AY5d/A^" A?d/A Ahd/A@ A0jd/A`B} A:d/A`Z ADd/A@ A@od/A`DѺ A@od/A A/d/A@ A/d/Aj And/Aj And/A9 AId/Af A%Od/A Alxd/A썹 Alxd/Ak ARd/A I ApNTd/AjƸ A0{d/A ۸ Avd/A@# A,d/A`f A,d/A@ A0{d/A`C A}d/A`m A 00d/A@η Ah7d/AOu AMd/A@y Axd/A 1| AY5d/A (E A:d/A Axd/A) AȎd/A^T ACd/A?[ Ahd/Aʵ A8vd/A`B Alxd/A` Ax( APg'b/A;y8 AXpb/A 85 Ab/A 4 Azb/A`2 A8 hb/A-2 Ab/A c1 AX$c/A@+ A,c/A Z) A|ac/A`}( A\c/Aq' A΁c/AR& Arc/A@o% AH)c/A@G\# Ac/Ar! Ahc/AM A`c/A@ AIc/A Axc/AT AБuc/AX AHpc/AAcw/A@A}#/AOA@q%/AA1/A`rA=}/AaAxƷ/A@vAxƷ/A}A/AӒAؽ/A`AС/AgAո/AaZAx-/A=$Ae/A`AU/A A(/A ҁAH/A lEAX/A Ap=/AA`/A^5Ah/A`8A@u/AA(/AjAh/A@>bAh/AV;A8/A)[Ah/A`fAh/A Ap=/AWAM/A;AM/A >AX/A@Ax-/A QcAp=/AA/A`)A i/AAX#/A@A/AsAX/AA /AЪApA/AA,iO0AVA89/A`A/A@!oA-/AqAX5//A4A3/AgVAP6/AAk9/A^A=/AAP/A7Apm/AVAv/A`A07u/A#A07u/AIALr/A@ƞAh/AA`/AXAS/A`AJ,/A Ah/AA t/AazA /A_4A /AA /A@-A^/AA(~/A@A@/AfA/AA /ATA/A A0/A@>A/ASA`/A`A /A 8A/A YAX/AAA(/AAs/A AxZ/A FAK/A/A@/AAY4/A+A`%/A`AP/AzA/A QA/A 4A@/AOA /AzAxu/A@A*/AA*/AVA@/AsApʩ/AA/AosA|/A@EyAP^r/AAso/A Axi/AbA>/AxA8/A@PAɆ/A`tA8 ~/A`tAP^r/A}]A(m/A A]/AzA(V/AzAȇI/AKAHR/A`ApS/A`AHR/A@,A R?/A@AAp0/A@AAP'/AA(/A`AF /AA/A`A /A+A0/AA/A/A(/A^A$/AGA/A@jA/A@jAX/A YA@N/A@ 6A8/A@ 6Am/A^A0/A A"/AA,/A`Aaj/A6A;O/A{A0./AྟA /A@A/AA\/AA/A AAy 9/AmFAuAi/AhyAH/A A/AB.A@/A@AHa/A J#Ab/AKiAТ/AKiA2/A:AXyA/A A(P/A HA:m/AA/A LA(/A`"A`2/AA/A@yAh/AAI /AA/A@!oA6/ABA/A}AH/A _A\/A`oA6/ABA R/A@A?/AӎAp/A;AHЫ/A|Aئ/AAb/AैAq/AzA/AbA/A@\A/A Q%A/AApw/AA t/A_A1s/ARAr/AAXn/AwAPj/A ANb/AбAQ/A$A@E/A`A9/AͳA(./AAx#/AwA0/A`DA/AAb /A:APk/A ^An/AXAn/A`A/AAn/AaAn/ASAn/A`Al/A`vA` /A@5A /AA0/Aj?A0/A`~A/A஬A ="/AA(./AKA1/AA@/A/A~F/A cmAL/AAS/A`AhV/AvA ^Y/A$&APa/A [Aذs/A`A%z/A`AhU/A l!AF/A`bgApl/AA^/AKA(J/AA/AA/A@gRA/A3pA`/A0rA/AA/A=A/A6A/AzpA N/A$A`X/A@A`X/AA/A@PA/AlA/AAE/A@#A2/A`A`/AA#/AHKA+/A`uA7/AɔAD/AAp8N/AA0^/AAg/AA{q/AA{/AA R/A@A#/A$A%/A\A1/A\AX$:/A^AA/A PAp0N/A`@AX/AgA8\/AHA\/A@.A@c/A*ADp/A&APHv/A@5AJz/ALAP/A@WAӉ/A?AW/AYA֎/A`A0Ջ/A@Aْ/AAH]/A lkA@`/A9A_/AA\/AAH]/AhAۖ/AAݙ/AA^/A cAݙ/ABzAp[/A&JAۖ/AAޛ/A`A/AA/AྜྷA/AoAh^/A@7APޚ/A`f A _/A AAPo/A}kA@/A@lA8E/A^{As/A@rA

/AA੹/AVA`/A3AX /AʇA/A@A,/A3As/AOϮAN/AAp/A3AzZ/A` AP /A?SA0f0/A uA/AA8I/AӻAû/A@ ǡAҗ/AX>A /A JA{/AOAV/A /zA( z/A@rAR/AEmA@A/A`hAP/A@ZfA؟K/AdA@/AgAi/AviAk/A&mAd/Ax0rA((/ARrAHr/A3\mAЦ/A`eA8c/AXbAn/A ¶]ALM/A2iA,/A`sA,/AjtA, /A@%tA/A cqA`/AnAX/A` jA͐/A JfAe/A`adA`+/A hbA2/A`]]AȤ/AEZAxi/A`-XAxi/A`mUA/A@zTA/AɄQA c>/A`OA`c/ANAY/A NA@S/AwLAl/AIAxF/AHA$9/A)GA`/A@PsDA/A@AD/A 0<AO/A66AHL/Aaz9A/A =A/A`a@A(-/ABA{/A0EA/A 8IGAX[/AEHA/AHA]/A)GAX/A EA/AVzBA`Gc/ABI>A@/A ;A; /A):Ad3N/AOfRAF/AqAd3N/A_AE]K/A}9A+=w/Al AJxD.A` AX'/A,1A I &-4@`?.AFA4 A0.Ak5 Ag$.Ax>c5 A?.AFA4 Aߑ.A{֊< Ah.A@WA A$].Ai~HB Aߑ.A{֊< A~O1/AAb/A )Am/AEAb/A CAh/AAz/ARȪA~O1/AA/AAP/A@A/A`AQ/A?A@B/A@P+A//A`9As/A-AТ/AAp/A@>A/{/AAG/AD>Ar~/AAX}/AAX}/AA/AJA`1/AkA/AūA|/ArAƗ/Ad A/A`A/A]ApJ/A3AHr/A gA"/A$A/AZAP//AH%A@/AT޿AX/AxA/A AH/A``Ax/A`ܽA/AHA|/AA(-/AEA>/AFкAȔ/AËA /AA0H /AtrA;/AA48/A׻A/A%AЄ/AAHA/A TAh>/A |A(/AZAp/AFAg/A-RA/AiAg/A߸A/A@=Ap/A`vA/A?A0/AKAl/A ਵAg/A<Af/AĴA0/AA`/A`oA@_/A?tA`/AAc/AqA@_/AA8Z/AྫAս/AiAR/A?AѮ/AA@S/AaaAW/A 1A]/AAL/AA@5/A,YAf/AA k /AحA/A׭Aq"/AҭA5/AҭA |F/AAHP/A@y0AhU/AXAH]/A:Av/A0.A/AAH/A 7AȔ/AaWAЙ/A A/A`ЧAP/A`֧A/A@y A/AAh/A!Az/A[Ao/AA(qx/Az&A/AA`/A@Aؚ/A@jAH!/A |AP&/AAH/AXA*/A lAX,/A 8,A/A`Ah/A`;A0/AA0/AÜAX,/AēA8'/A]A` /AA(/AߛA/AfIAh/A 8Ah/A@#A(/AA/A 힙A/A A@/A઴A@/AxA/A*AX/A`TA/A6A/AA/AF[A/A ˺A/AܖAc/A`~Ah"f/A@gAp/A@Ax/AA>/A`gA@ /AA1/A列Aਙ/A@,Ag/AvOAp[j/A0;A./AA /AƔAБ /AxA35/A@A83F/AʖA}/A`9A{N/A@%ĕA8`/AʕAhq/A߯Ax/A߯A`/A AxF/A` A\9/A AP/A@ ՕAQ_/A[Al/ATA |/A]ÔA*/A#Axm/ApAp/A^A0/AAx)w/AgA8gY/AA*/A`˒A`/A`ܯA8/A YA0s/A@A /A@PAhn/AѐAp/AСA0/A`PA /A|xAH]X/AwA+/AwA@ߙ/AwAx\/ASwAЄb/AwA8b/A twA6/A xAH>/ApwAI/AwAl/AwA@5o/A QwAG/A&xAh/AAP/A A؁/ArAp,/AAA/A@QA/AMAZ/AA0{/AApl/AApIt/A`zA }/AMA`/A`̄A /Ax>A8z/AFA0{/AjA /AMA`/A@yA M/A ^A//Ap"A0/AA0/A_5A8/AݬAP@/AEwA8/A`AY/A@Ao/ATAh"/AjlA4/AAY/A |A/A %AB/A`A@/A (tA,/A`EA*0/AqA/A`KAhD/A@A@כ/AA୰/A /A(/AŌA}/A<ٲAU4/A@ȰAsG/A AP/AձAX/A/A(/AxwA /A ɍA؎[/A`mAFl/A`Aq /A AP$/A`,Axp/A@5AXh/A@AF/A`]UA0:/AA@S/A`oAU/A ˳AX/A,A\/AA@/A`mAH/A`YA~z/AAP/AjA)/A`A o /A޴A`1e/Az2A]q/AA ~/AHkA`/A(A./AgAx-/AӷAF/A`A/AAH/A dA%/A@ A7/A \AHk/A@rA F/A AA@I/AٵAB~/A HvA"/A'uAp+/A@r;A/A{Ar/AA2/A-A{/AӽAg/A ʼA/A"ڼAƼ/A@A/A NA^!/AT=AH(/AA/AA0Ԇ/A@%.Ai~/ApAȱ>/A SAP*/A|AHO/A`A/A`ùA/AAT/A`ApA/A6A//AA@ߙ/AaA2r/AջAw/A SAh/A A~/AZAȃn/A΅ApN/A`A./AA=/AAp|/A`AX~/A@A/A=A/AؽA~/AA0F/A-AԔ/A`AA/Ad۾AhU/A/(A /A`A'/A XA/AAv/AAF/AGAh/A-hAs/A 1A/AAx/AGp\A/AX]A8/Ad:]A/AkR]A/AkR]A/AkR]A)/A c4]AX9/A`K\Ag/A[AĪ/A^A((+/Aŵ\AXo/A]A|/A@d]A(8/Ao\AgL/A"\Ah"/A]Ap/A\Ap/AE]A/Ak]A0/A`u^A/A W^A؟1/AV^A*/Aྲ^A(/A J_Ao/A qb_A#?/A`bM^Aa8/Ak^AC#/A@.^A}'/A S^A /A ]Ax/Ao']AΝ/A\AU/Ah[Aأ/A@ZAQ/A0ZA /A@8ZAH/A`ZZAL/A |YAx/A_YA}/A݇YA!R/AXA(Kz/A};XA0T/A=4YA/AYA/AXA/A`oWAa/AʵWA/A@XA~/A`XA/A QWA/A ɕWA/A VA/A 1=UA( /AjTA!/A`XSA3/A`KSA ߂/ATA/AoUAx6/AUA~/ATAHz/A =TAo/A@SAXy4/AiSA /A_|RA}n5R/A uvZAx/AGp\A{V /AD TQA/A?(SA(/A`TpSAp/A}TA/AYSA|/ASA/A`?TAC/A_TA8/A UAPg/A_UA/A_UApl/AT7UApl/AH UAI/A@TAi'/A`HTAf/A-TAf/AxSA/A)TAX/A 1TA/A 1TA{/A@TA/AqTAv/A6TA(/AzSA8b/ASAX(%/AQgSAi'/A#SA!~/A 8 SAd/A#SA/ASA/ARAȇ/A&RAhD/A&RA@y/A(RA@y/ARAP+/ARA`a /ARA /A@E:SA&=/ARA/A =RAd/An'QA{V /AD TQA6]/A\ݲOA/AaOA|/ANAy/A KA@/A @IAe/A?GA6]/A\ݲOA Z/A<ŮTBA@B/Ay3CA/ADAh]/A 8EAf/AKCA"_/A&)AA Z/A<ŮTBA Z:/ArQAu/AKAB/A` FAh/Ad4AX'/A0A/A`f.A/A _8A/A`CA0/A`IA7/ALOAo%/A'XA Z:/ArQAy 9/AmFAn/AAK/A A8#%/A Aج/AAA/AྟA`v/A@qA@u/A6A _/A8A~z/A40AX/A[Ax0/A@,A&/AӗA /A (iA/ANA(/A uEAο/A@A/AAB/AŊA׉/AaA6k/AgA5H/AosA3/AA+/A`]A /A Ai /A`9As/AVA/A@!tAp/A@yA/A}]Ah^/AA /A@AA?/A`A /A_*AhU2/A;A``B/A|Ax[/A A\/A AXWx/A@GA8/AA8/AzA@d/AA8/AWAp/A AP/A(A/AKAhb/A@QA/A@yA/A cA7/AfA8/A@A /A@{AXc/A@iAp/AAC/AgLA/A;/Aho/A@FA/A?Axz/AnAA`e/AA%/A`K_A%/A@A@/A SwA1/A`Au/A@ cA=/AJOfAK>/A`l+iAx/>/Ax}iA>/AMiA=/A 0iAx=/AjAp[V=/AjA=/Ap*mAO*>/A&2mA蝴>/A YlAP>/A%lAr?/A%kAЮ?/A0flA(Bi@/AImAIh@/AInAX#?/A1nA(K?/AKnA@X?/AnAxKo>/A9qA>/Ap,rA9?/AhrA@/A{rAx@/ArA@/Ap,rA #sA/AБgsAXIB/A }-vA;D/A0!wALE/A0Tn{A^E/A}|A8>D/A|Ahf4C/AҪ|AhC/A }A\C/A 9}Ap=B/A@o~AX_vC/A@ AD/AA7E/Ab[AP/E/AGɅA@ F/A0EAxF/A@ AnbG/AWAxG/ẢAyG/AIAWH/A@<=AtH/A;4A7I/AA$J/A4Af=J/AA` K/A(A?H/AwAMH/AxA(F/A |A E/A@^yA G/APxAuH/A0kwAZNJ/AuAteK/A`etAL/A@<6xAM/AyAN/AwA@RP/AuAS/A1rA U/A:pAxm]W/AZoA&X/AnABXX/AiAXmX/AgAP/vY/AifAƨ[/AfAp]/A`CfAO_/AxfA̳`/A }aA`/A 96_A)`/A]Am`/A 9\Aa/AКZAjLa/AEYAԉa/A WAxqa/A0kVA%b/Ap UA;b/A@MSAb/AxRA&d/A ]LA.Rd/AhSKAOd/ALwiKAqܸd/A5KAld/ALJA(d/A0JARd/AIA d/A" UNIARqd/A-eHANVd/AboHAd/AHAP c/A**CApc/Ad@AEe/A`6APf/Ao-AȩEg/AK-A`h/A,Ak/AF*AJm/A)AHp/A@L&A0r/Ap_$%A0t/A_#AEv/Ap!A)|x/A4 A{/A`nAH{/A@&A }/ApApV~/APҎA/AZADυ/A BA0X/AA@|/A`?A/AA8"7/AVA؞/APA/A HA/AAG//A<A_/A%AT/AEAW/APAȶp/A (Aɕ/Ap>A`/ApAx/AdA\/ABA/A!A/Ap,Ah/APAا/AG A#/A  A0/A0" A/Apg A𒥦/A"3A(/Ap=Ag/AăA' /AA/A $AV/A HA5/AVApV/A@gAP/AAi/A ]A( G/A'A/A2SAٗ/A`AKZ/A`]A/A@EAў/A>A$/A`bA_/AQ0A/ATAP/AA '/A@iZAx(H/A㟸AH/A@sA%Ŀ/AIAH?/A@xA/AzAD/A{A5/Acu~Al/A@%xA׹/A7qA/ATiA /A ˠ[A⟶/A&ZA%/ABOAx)/Ap\A/A`bAHA/AeA`B/A /VA/AzmTA@}۱/A"HAP|/AZJA*/A?A|/A >A8'*/A@yAA쟭/A`=A%/A >A(0/AEA@h/A@rVAy/A OA0M/AMAP|/AyFA /AEAL/A@g;AXʬ/A6A/A@T5Aꔯ/A F#AJ/AQ4)A /A8'Ah/A`vAX/A`T~A/A3A(`/A`0WAsT/A@EcA5/AtxgA/AnA /A१}Avr/A\A 0d/AM0A`P/AA8 /AAT/A@ߨA)/A`]AX/AұA/A&)A؁/A3EA oi/AAH!/AԸAP^/AOA/A@.LAHib/AkA؎//AAx/AdA/A 俾A@`/AmAD/AgA3/A ҪAf/A \A/A@iA؍j/A`A+=w/Al AE]K/A}9Ad3N/A_AF/AqAd3N/AOfRA; /A):A 7/AX4AI/AN AН/AWA@/A@yA`4/A%A@'/A@KA֪DE/AhԷA;7ئ_/A֖7Ac%/A)EAWPi/ASd9qA\,A*/A +A@.c/APA X/AAx/AAXd/AAr/A2gAAq/A (AHah/AQAHÔ/AAU/A`A8/AhA0/A+;AHah/A`A/A Z}A/AJA88/AAek/AjApA_/A`A@Z(/AYƄ[Alō1/A iAwu/A"AB^}/AcAP|/AzAf/AοAC}/A%A_ /A@&AQqG/A\Aq/A$Ac/AA /A,{Ax`)/AJB;AQ/A$4AɣY/A6nA/A?@AlID /AҳAj\/AZ_AdH/AgzF~A/ALUDrA_2/A9AH/A@A /Ap,A/AzаA8/AAAB/A$AVj/A9fAIn/AQ(TFA) R/A4 L8Aآ6/Ae<A|/ALRAT4/AUzA/AAȺ/A`夰Aa8/AAR/A_\A5x,ӿ/Ai\~A+/AXg:ӾA{r+/AFAlŧ/APAH/AAl/AoA /A`0A@/A`TA䌰/AqA t]/A@r<A*/AA p/A3Ab̯/A@A}/A;A}/AA/AIDA(/A@AD/A@AP맯/A`QA(K/A@#tA/A?Am/ApA?O/A/#AYR-/AZ.vA/A7\NA՜/A=kAx/Ad$KA@C/AF'A\X/AqAAj'/AbдMA Y/AAi/AHAt/A"QAQ& OX/A}/A%BT$/ANϖA /AnAٴy̬/A1 A`lΩ/A-+vAIzԬ/AJ+URA|-o/AAo6W/ARJAM=D/Ar;BACO6/A/wAfn,/AiwAHn(/A A/ARAj/A6A3/A$|AT-Ӈe/A8нA`M/AI$ A8/Agf: cAǺ&/A5OA`!/A-A/AT(A/AdAڪ/A9A뭪/A#AaR,/Ap:A}|/AχAGV/A79 A./A܇}A@/AAة/AzW A]/ANfpA'~/AA/PQ/A촙_A_%/A OLAM/A'"A,Ҩ/AC0A/ABA?6n/A)#^A~i/A ANL`M/A QAC5/AH%3иAS!/A{cA#W/A4>lAa4i/A˲tAI+v/A=( A3/AltTAxh/A ɠA:@/AigeAx\~/A[;0Ar$/Aj(A8 /AuߣA*/A ATxqa/AzA̦/A`Aqbաb/AؔAA /A.LۂAJߠ/A AIֻ/Aۢc^Ahז/A5?ۚA4,p/A͂AWT|I/AAX!/A`AxK/AAn=/AA/A'5A} /A\AI/AfAcB/Ak[Aq0t/A:AEAXy/A@hk3A ojt/A5RAp/AsAFܝk/A;UA ad/A3XA]/A.vAV/A`DmA=:2O/AMqA(G/ANA)B/A]A- =/AMAkJ8/AEAoX3/A\A+P./Aa\AX)/A`At%/AA A)"/AWAre/ATALi/A0MA /A517A7|/A0A#/A<,A_x/AC>A8g/A OAmGm/A?2FAf/AAA_%\/A/oGA` c/AA"d/ /A'R#.AQP/A֚PA/A;lA~۞/A\uA@ӿ/A<A=/Aɏ$Ar;F/AwLŋA`/A˸ @AÝ/AA/결/A2(AX&/AJ73AOGw/A1uDrIAߓ-p/A0QAY/AALA'FC/AUV8A`1//A sA7(/AyAOz!/AūkA /A0Au$/A8Ad /AA/A[A8ٸ/ABA3X/AU]A*/A/Ah /AHAx/AtdAP!*Ŝ/A oyAHn#/A7AgAqx/A ~RAX$N/A |Ad;/AFA+/A%EAf)/A pAn4/A1T=A/AIoAPo/AyA%]; /Al/A.RA<S/A'AWT/AJ-;AYhԛ/AAh;_ƛ/A=A-/A ZA&t/AcGAY/A.A=L/A^APYS/A`Ap=k/AA&}/A cAgy/A !AX]/A`AB/AA~/A@-AH/AfAa/A@A/A6EAЄ/A JAx/AvAxW/A ¯A`v/A sA/A@EeAÙ/AZA᜙/A Ay/AvAH`/AApQ/A`6A+A/AVA N0/AA/A`5A0"/A+A//AA^/A &AH:Y/AZAn-/ADA8E/A$A/AAA/AHA̛/A`AT% /AîA=E/Ao3A 1/AFA;sN@/AXg:ӾAʇT/Ai\~A}/A A/lZ/AM A`Y/A! APY/A1' ATmY/A6 A-Y/A>g AHX/AO AQOSX/AGW ACX/As Al_X/A{ݖ A5X/AI A X/A@ܱ AW/Ay Ak*W/A闕 AҗgT/A] AJIN/A=m~! A5x,[G/Ai\> A+#@/AXg: A{rγ8/A A:,W1/Ao3dž Av)/A A=!/Ao3dž A 18/A A;sN/AXg: AʇT /Ai\> A/A.Aa Ay:.A4F^da AңIF7.ADa A4.A_a A3.A0 b A@>3.AHb A1.A b A&.AZ'b A0*.A@.Hb Ax.Ab Ak.A .A^( AX.A Z#' A.A & Aj.A& AGx.A& A(h.A& AJ.AB"& A d.A,& Ar.A% A0Ǻ.A`T% AP.A;% A0X.A$ Ax0.Af$ AH#.A`f $ AP.A$ Ah.A@.$ A.AC% ACv.A &x% AW.Ak% A8:.A@% A0.A$ Ap*.A$ Axh.A" A"e.AR" A0.AG# A83.A@ # A.A`9$ A.A`m$ AEl.A@G# A0B.A# A7e.A" Aୢ.A`;! A .A! AP/.A A.A ~ A<.A&B A>.At A .A@ % AP.Aa A .A A.A^ A0j.AQ Aǰ.A6 Ah.Aྶ A0{v.A`2 AR.Af A >.A@R A44.A A7 .A`] AH.A`D Abm.A A.A AH.A A@p.A Ask.AN A3.A@ A,.A A0.A  AH.Ax AP'.Ap A`.A@, A$d.A Z Ah.A@ A@.A@d A .A ApI{.A` Ax [.A AC.A ( A1.A ( A.A ( A`.A@% A࠻.AO A.A@ A=}.A # AZy.A J+ APJ.AD AX.AÖ Ah\.A Az.A` AUC.A c4 A߬.A Q A .A@d A(1 .Ao A Ⱦ.A: Am.A& A<.Aߏ AH.A) A7.A@s A4d.A`" A].A~& AH.A ( A0ʹ.Am. A^l.Aܴ1 A.A@9 Ahֲ.A< A.A@@ Axm߰.A+C A (.A@E A.A@߂G AXX.AG A4.ATHF AV.AjqG AP.A@I A?.Ad~Ah&.AA.AAt.A@ݓA }.A`DA(L.AA.A A(r.A"Ah.A@GA).A`fA,.A)ޮAP.A`A8r.AvdAx}.AAxm.AkԓAȔ.A@Al~.A OA8g.AAC7.AT A.AAq.A ʉAH.AA0J.A lA$.A`bA0.AXA.A:Aʺ.A Ax.AyApco.AAp.A@#As.ApAX_.A Aȭ.A@{A.AZAF.A@`AHö.A4AǶ.A`ݑAy.AAv.ABgAj.A_lA8\.AVrAa;.AA9%.A sUA#,.A A-.AA#.AA0.AA .AA($.AoA].A`A.AApѲ.AA0?ò.A0Ay.AХAwZ.A Aұ.A#xA(.A`)A0x.A`LA7.A A7.A}A/ .AA8.A A`.A@mApϰ.A@%A.AȫAR.AѬA_.AAAhb>.A`A.AtƭA.A@ѭAp.AA.A@%AA[.A_sAP3t.AAg.A@%AG.AAL?.A nA :.A5]A&.A GAPZ.A ȭA Rr.A JA&0.A $A9.A S@A9.AAۭ.AxApŭ.A $AI.APA.A A}A吭.A@iAх.A@ߍA.AgAxA.AaAP.ATA>.A lۮA.A`AhQ}.A@A{.A@ߍA(dd.A\AQ.ATA:.A A}A,.A'At.AA.A;A.A Ab.AhAPYR.AAE.AA.A@A.A`Aj.AhAH˹.AA.A}Ax.A`Ai.AK$A M7.A0*AA.A J,A@.AX.A.Am/A\w uں.AB 0Aú.An0A0Z.A1A` ټ.A;A .A%D=AY.A?A] R.A%?AO.A1bu"@AY4N.A_`H@AWQ.A@n@A0l.APQ@A).AWA….AgXAW.A[YA .A@I`Ah.Ai2fA8.A@$ fAo.AzPfA8'w.AjA.AӦyA:ǯ̚.A+DyAa.AyAd.A* #zAUF}.A2"s4zAo.A@OzAPL yU.A-WszA2J>.A ?"zAn*.Ae=~zAM.A {A+<.Aw{Av.AE-{A.f.AtA{A曚.A!U{A{Aa.Axjii{A.A]s{{AȎR".AZn{A=).A%{AV.AQ{Avq(U.A)V|A .Arf(|Ak.AD|A#.A³|A#.ASAµ.A łAH..AA.A0"AP.AMA.Aa~A؞.A9A0..A`)юA`b.A`\ٓAߋ.A A.AhAfz.A{˔A\.A6_A.AėA.ABAx.A0nAX r.A`\*A8.A0w%A.A!Au볯.A*ZA P.A蘝A='.AdܝAl0.A6T$A.A#pA0.AAdn1.A'؞AN1.Ar-Ac/.AU: A+.A AT&.Aw5A.ApIA$Y1.Ak AӂH.As#AG[d.AЊA\E'.AnqZAߚ.A=iJA.A^Ai .A2HAЕ.AG̡Aɯ.A {A)D-.A*KAx=.Aw;AP+X.A AX.A,1AH.A@nAZ.A.A.A@qA|.A@<Ah.}.AOAؖ{.A`:A|R.Apw.Ac.Ap5ԏAx.AvAGg.A$N~AȤ.A8~AH.Ar~ANw/Av}A:/A 8 |As/A{A/AzA /AxA!f /AP&vA#/APZqAh/ApAx/AypA/A pA`6/ApA5/Ap5YpAp I/A`]toAq;/A`mA/A^lAP/ABkA0Y7/A-jA/A?hA,,g/A2u Am~/A A/A`4 A/Az؀ Aʯ/Az A/Af{ A /AҐ { AW+/A#7{ AyK/A&Rk{ AXi/A{ A׃/A 1Fx| Au/A,g| Ab/A"| A»/A[ } AZ*/Ay-I} A+/A9} Aһ/A;Q} AGZ/ACy~ A/A d~ Aۊ/AqW~ AE`S/A~ Aw/A.n~! A}Eѳ/Ao,*V A/A A9s/An Az/AD AU~l/AGS A,xU/A^ A;\zB/AT A01/A@  A7O3/A]Y,J Av7/A]Qpv A; =/A A0D/AA>_ā A;XL/AJw AV/AZ?> A/a/AF Al/Az@  AM4$x/A5;>! Ah̃/A  A~/A񝅏 Ar;] /AN ABq/Au A/AR A>/AɾK AT#/Ab @~ AaW p/A,/} Ay /A;7t| A=/Ad{ Aw/A/gy AfJn/AHx AP /A@rw AX/At A/AHq A`/A@wn Ax/A`k A/AWj AX(R/Af Am /A՘c Al/A (` A(:/A-_ Axmi/A@E] Ah1/AQ[ A(/A#[ A"/Aiw[ A1/AxiE[ A&/Ap[ A#Z/A][ A(>2/Ag[ A?;D/A~-Z Ax/A`K6Z AI{/Aw=Z AOu@M/A'[ A1 /Ai[ AЀ/AA[ AtU~/A $\ AZ64/AD5Z\ A灘i/A08~\ A5/AFqm\ AhR/As\ A/A3] A/A v^ A8/A@y^ A}/A`E_ AH/A{a A@N/Ac A`/Ae AD/Asi A/Afk Au̹/A iՄl A7o/Am Au!/A 0o A0_T/ASsp A1X|/Aݧq Ah%/A r A0/A@Gr A /Ay p AaB/Ap A06/Ap A/A r Ah/A@t A`Ì/ACt A#ں /A@t AJ/AĒL. A,,g/A2u Axhu/AD Ayt/A6 AHjt/Ap A~b`t/Ax醪 AyZt/A,:9q A8Yt/A^7 Av]t/Ae7 ATqft/AzƂ Ahtt/A"/ A?Ft/A֩` AO 1t/AX&4 At/AAq~ AYt/A| AEt/APmv{ A.T%u/A z AQu/A1 zy Au;u/A*x APu/Aw Au/A;r Ap v/Af Acu/A Fe Ahu/Ad4d AX[|u/A^d A o\t/A@4i?d AӈVt/A䦥}4d Azs/A9O=d AKqG0s/AG[d ACOr/A^r_d A%0!] r/A\%sd A {q/A>/e Avjp/Afڝe Afi[p/A2 f AƨNo/AJES f Ad@o/Azy(ag A ln/Abh A+n/ARah Arm/Ai Aecl/A J^l AG9k/A n Ax6j/Aѥ%n As.j/A@,fo Axi/Ao A|i/AdH Aȩb/A? Ab/A@j A`2b/A qe APMb/Ad A c/A`f A0'c/A Aic/A  A@™c/A`ܳ A誴c/A| A((c/A` Ac/A A?d/A^ Ax1d/A Z AtCd/A}[ A8d/A@g- Ag3d/A^ A7d/A A`Xd/A Add/A? AѧZkd/At f Azʉe/AR A_Gݯj/A| u϶ Ab"^p/AF)7 Axhu/AD A+A@N,,A@?N,,AS@̱+,A/AM@麢*,A#i@ل(,Aۡs"@k4[&,AR>bd@Ol#,Aܨ,@ꞙ@Bkݽ,ASnp@җg ,A;K@J,A%\,@6x,+A,YF#@++AQ1ًy@|rk+A(Z@:,W+A#sq@.+A@=+A"sq@ 1+A(Z@ꞙ@zɡ+A@GÕ8+AiAT@d G+Aܨ,@%h+AQ>bd@*&{1k+Aڡs"@E]K+A"i@d3Nn+A.AM@+AS@d3Nn+AaҐ@E]K+As~@*&{1k+A$^a@%h+AcYB@d G+A#Wz/z@GÕ8+An<ˮ@zɡ+AZ@_G+A @b"+As+6@2-hƲ+AQ#;[@Z+Axfz@ʇTH+AWӦ@;sN+A&t@ 1+AJ@=+A݌g@.+A@:,W+Aތg@{rk+AK@++A&t@{&+A%cc@8 +Ag@X+A@+A5@H +A$z@X+AĈ@P^&+A(@ C+AH@v+AĈ@+A&@H+A@R@@S +Afa@&+A'@x+AzU@P+A @+A@W@+A8X}@(9m+A@,r|@(-,ASBz@IS,Av@0S,Aau@ۈ,AU/u@(y,A(s@T,A@\s@x+A@!p@˼+A@N{k@`1+A"'h@ؚH+AQb@+A@`@+A)a@8t~+A-c@sA+ABg@+Ah@/+AzGd@g+A`@+A>V@!+AP@+AD"M@h+A=K@0+A}L@+A@gL@@3+AxL@(+AhEM@hr+A+O@xW+A"YQ@+AQQ@P8+A4{Q@+A]P@(\U+A@LO@z$+A@gM@u+AK@+AJ@W+A[I@n+AH@+A@ZG@H+A@F@x(+AF@p+A F@w+AHF@5+A8G@ +AG@0. +AG@ +AG@(+A|G@`a+AF@G+AE@h+AB@+AA@x5+Aj5@1+A@Q-@!+A@-@ȓT+A)@+A8.@+A@g-@)E+A@//)@`S++AD(@ +AZ(@p +A@(@g+A@߫,@hD+A,@+A@,@`Ǜ+A-@%+A@ 3@L+A1M3@Q+A7,@@$+A@,@ Ε+A]s,@ R+Ad,@p +A@@'@+AM%@`vu+A#@H+A @+A{ @ |n+A@@`2+A@ے+A@%@+A@+A@@+A@@+A@P@+AB@0G+Ax@t+A2>@+A@i@x +A@֏@+A@0+A8@3+A4@(+Ae@C+A@%#@+A@3@b+A2x9@C+Af:@(+Av.@+A+.@+A[3@8E +AZ#@o+A@N@.~+A_ @Ϡ+Az@H̋+A?@P+AT@B$+A @X+AQ @db+A`@B+A0@x+A@N@+A@p@4+A|@0/+AT@fU+A@+A@`q+AM@+Ae@+A@@ p+A @؝+AM,@+AB@r+A@'h+AE@/z+Au@6+A@0+A)@H)l+A @x+A$@+A@@؟+A@+Az@+AF@+A@@+A9 @@dj+A@+A@J@h~+A@-+A@;+A@\+A_:@PI+A) @ +A @h+Ak# @ho+A @(+A2R @h+A_b @g+AX]@/+A$@+A @`%+A-0 @@+A@# @+A@ @+A` @+A@P< @!M+A@XI+A<@}$+Ax@+A_@ C+A=F@3+A4'@+A@]Pϵ+AQ@M+f+Ad@M+A n@dt+A>51@WBh=+A 9D@A\+A3"E@x+AV@ov+A#^3A@yeKV+A1$@@4$d3+AW@Z+Asj6@q+AaJ3@{+A8@@x+A@sQ+Aq! @` (+A@--@|uZ+Aoph@Wm+A|@+A7@+A@iq x+AQ%@6p6q+Ae@a>k+AӝS@Qf+AT@&ݏb+AmUg@(`+Aǜ)@_+Aa@ƵN+A{C@`;+A@~)$+Ao'@H+A@0+A@X+AO@8|+A1@"+A@%@ݷ+A@0+AD@[ +A;@k.+A@^!+A@@`+A@b@.+At@+A@@+Acb@dm+A ==@`+Ado@X2k+Ag8*@ZY+A .t@:+A!0@+AV@'+A5@z+Aϸ@s+A95/@\@k+Abi@V+A$@P+Aj@@S]+A\@Q+A=0@#[+A@@XJ+A88@0S+A@@:+A@I@H+A@ +A>@+AE@  +AJf@h+A@Nǂ@+A3@+A&ڀ@X+A@@\+A@@p}h+A&}@H+A@@y@tC̭+Av@tM'y@rudL+Aװx@ٞm+ADjWx@ 0+Az-x@3ʐ+Ay.x@܂+A<Gx@ DGn+AiKx@:c+A%y@o+At@0+At@8VM+A6-r@0(+Aqs@+A^s@1+A[Nt@+APt@H+At@ +A@.t@0+AGs@+At@+Ams@8+A@r@G+Adq@(+A@~l@`+Ak@5+A,h@|+A@ydc@(Q+A_@O+A}_@hI+A^@7.+A\@zL/+ASJFZ@n5+A< Y@m@+AZuW@+Q+AV@^4Oo+AMV{R@+AH@qD+A3 E@+A@PPA@ֻ+AN4@R+A@y4"@@S+A;(@ Ϥ+A<@3+A&{a@ ϴ+A!; @1E+AL) @#YY+AX@+A@@+Ab@+Aޢ@г+A&$@hG+Av)@jv+A{@K?+A֍v@C=7+A+A[Y@$e+A9@$-+AeE,@-9.+AQ`-3@K̠+ATO@](+A*X@9a+APХ@+A@6@+An@_f'S]+A$~5%@@+A@+Ah@8+A@ƨ@(+AΝ@d+A@+@p}+Aš@;+Angޙ@}Z=+AH@{>i+A!ɗ@9+Ap@?=+AYW@ +A@+Av=@j+A,y;@0+A8fZ4̌@!+AV@+A@@d>+AW-|@u+AQgV@r# +A\T'W@*+AoXTʃ@d)8+A F@+Aj+T@pm@L+A_1T@ը+AM S@}+A_٦S@8 `{+A%S@B(+AѽS@8E+AT@Uv+AN3V@.4+A&X@:oC+AΎZ@j+AKa2q]@&a+Af`@X!+A@b@O+Ac@V+A]A^af@I +AEh@D/l+AnXj@.> +A:}m@Ym6+AdҬn@+Ap@@v+ASq@BX+AНr@5 R?R+AQ~s@&+ɑ+At@+A=~t@!Ԃ6+Aet@MV+Aet@p+Ast@'w+A>pt@(A?=+A9 t@O+AMt@R+A&$t@߃3+AV(qHu@+A@u@W(+AuGu@Z[+A v@vz +A=u@LT+Au@w+AM6Zu@+At@+AS$t@F *+A;8بs@ڷ+A?Gs@k)+A&&s@vA+A3r@1L>[Z+Azr@9r+A 2r@6,+AM.s@8+As@g9+AY.Ht@P*+ACxrt@ƅq+AG ^Vu@+_IJ+ARӠu@+A ru@'"VJ+As1u@w+Au@If Ŭ+A,cu@ a+AUv@+A:v@5@+A$v@0+AEu@A6+Au@ f+Avlu@y+Ah̚Ru@=)l+Af9[u@|+A̕M},A{B}u@j,A6B{v@,Agw@W1,A|@2B,A];@6,Aڄ@(_,Ak@#,Aκ@iB<,A9?@ [,A8ͣ@r |,Ab/R@,A@v$,à @X ,A@8,A9%@أ ,A@⦗@,Aǘ@#b3,A %5U@h,AM&ѝ@PF,AZ:@P,At&ݮ@0p,A5 Ф@أ],A@g@|Zj,A-[H@nV,AbI~@%Ю,A9v@Xn,A8̣ӗ@sV&,AٓAw@ yV,ALD6@|,A@sӭ@X[,A@"@*,A@Q@,Ai@0",A @ t,Aa@L,A@g^@.,A৊ݱ@킝 ,A "@a\E ,Ai@sk ,ARbT@ ,A@E@O ,Akm@zY ,ARmd@. ,A{B@}I ,AY>QM@Z{ ,AQJι@w ,A@/@j ,A !_@ ,A4@" ,A@0; ,A 4@,AZ@Є,A@@h,A@|@0,A@@u ,A@~3,A@sW@XD,Am@T,A@ce,AX@(v,A@ ,Az@,A@r@ɐ,A@Ϙ@,A@@(h,Aф@,Au@P,Ab@#,A@pL@ ,A9@H,A#@y,A @,A@3@,A:@,AV@,A@O,AT@2,A@p&@@,A @g,A=@ ,Ab@,A@b@,A@0,Am@D,A?[@=,A^@hL,As@,A@%@,A8@,An@أ,A@@,A@@p,A@@,A@X ,A@(:,A2@9,A@Y@B,A @',A@L@x0,A]u@:,A@uD,A@ֽ@sN,Aq@X,A@b,A"@(,A@@,AI@D,A@ @8D,A6@,Ag@,A@ .,A6@AI,A9@@Q,A@@^Z,A@8c,A@0k,A4@0t,A>@,A@`!,A@N,A@,A@(,A8@W,AT@,Ad@5,A@Nq@XW,AHs@,A@P!,A_@1,A0@hB,A@;R,AF@@Wb,A@Xym,A@N'@x,AD@#,Ax_@,Av@>,Aъ@,A@,A"@Px,A]@N,AI@,A@c,Az@,AzC@6,A[@`vM,Ao@fd,Az@(`{,AɊ@T,Al@Pk,A@`@Z,AP@!,AՒ@),Aϗ@2,A@r@i;,A@@)D,A@@L,A_@@U,A~@^,A@o@f,Ad@o,A[@(w,AW@@,A@U@ֈ,A@5X@`e,A@^@X,AQg@i,Ap@9^,AE@A1,A_A@.ˋx,A@ c@# ,As%@,A@@P(Q,Ao][|Z@;뼝,AL5s@ps\,Ad@$6,A꺥.@1\u,A~|r@G[L,ACF'M@[qG,A0'@ _,A|.6@PaB,AQ$@d,AFqQ@+,A@c5x,AGp@}LN,AA o@)+,AA @ ,A@1l% ,A2p@|vQK ,A?@ ~q ,A7@lw ,AX!X@x ,Aߟ@͞2 ,AT`-@'!,A:ŀ@v !,AU@$!,Au@Aq",Avg @ӊO",Aُx@ -",AF_K@",AZ@{p",A^*@"#,AHX@´@7b!',A@ @',A@@(v',A@',AfKf@);I',AWݐ@wzf',A1(@P',A@U@sn(,Az4@_@(,A@@iSe(,A%xO@^#(,A5@jl(,A8#9@y(,Aܙ;}@e),Af3ϟ@1),A@3h),A^6J@),A7@4O),Aw1@ ),AG@麢*,As~@̱+,AaҐ@N,,AS@N,,AS@팛,A@ ",A-D!tA ",A;̴@T@Bk5,AS*@җg ,A;d@Jy{,A%@6x,t,A,YF@+Sm,AQ1ً@|re,A(Z@#eҞwd,A47.b@d,AW7c@#4Wd,AvTʭ@J8e,A|Ej@"Me,AX@0Ue,A|@@f,AZ@8sf,A@83g,A@;9h,A/@=FPh,A/2D@l]h,A % @fh,A%I1i,ARmd@83Gi,A)l@Ym,Am@*0om,A@ Ɔm,A>'@ļm,A+hLT@m,A$_@'um,Ag97H@2m,Ab}x@n,A{@(4o,A&@l5o,A/Pg@3T7o,Ab@ a8o,AKD@:f<:o,Ap@{;o,Apn @ho,AT;9@W\>o,AE@.%>o,Aj8R@_=o,AS^@I,Aaqp@qA,AbTp@C,Ap@P7en,A&q@#,Ahq@QAƄ,APaq@w],Aq@A2,A!wq@۰ K,A"8q@0u,Ap@0 ,A"Xmp@,AÅvp@,Abo@Q,A)o@> ,A6Uo@:,A8n@F,Ab@`l,AYa@m,A@w_@E&,Au _@ 3 ,A#_@L,Arj_@',A {^@!9!D,A{ ^@X,A(_@W,A_@:,Am_@ʇ,A_@xi,A `@Ĺ,A{`@&,A@`@%,Ak7a@,ATIa@ $,An@@2,Az@(~Đ,A@@*,A@Z,A@@ѓ,Ab?@``S,A@9@瀕,AT@Poؕ,A@#@4,A@ѝ@зE,A@hv,A\~@,A@``,A1y@,AQ׸@,Aj@&,AX@@Z,Aӵ@xv,Ao7@1,A@ʹ@ ",A;̴@ q[,Ai1@ @V,A@=N,A"sq@ 1hG,A(Z@(POD,A?F@oAD,A?a@j)E,A@dߏE,A(v@![F,A ~L@0yF,AXƷ@mI,Aʾ@rWK,A@W@ޱK,A@pTK,AT@2"K,A1*@=z<(L,A-K@`QL,A@_BpL,Aw)@8L,A^10@L,A?]@(L,A@dL,AF@L,A@AM,A@ߏ@eM,A@@yN,AO*@hO,A4}@b)Q,A%@#T,A@G2@/U,A-@cU,ANb@U,Amհ@U,Ar @3U,A>0q@"2V,AS\@dV,AˉF/@xV,A T@V,A@@MŅV,Aq@lW,APf@Βq@SY,A@@-Y,A(@3eY,A1Yj@ 9>;Z,A~@uZ,A@i]@/E+,A݊A1,A*Amz?,AA ^n?,AA a?,A8AU?,ARAG?,AvAA:?,AMA8E,?,A6eA?,A ݬA)?,AVAhQ?,AA`?,AA8g>,AhA8V>,A}A>,A ̪Ap>,A2AH>,A &4A>,A A`>,A@A`>,AFgAп>,A;%A0ٮ>,AmAe>,A螨A0f>,A`ZAHܜ>,AA(>,A ۮA ޝ>,AGA>,AA@>,AyAp>,A/ApΣ>,A`KץAǤ>,AA0%>,A`}Ap>,A@ OAЦ>,A@Aɫ>,A`A>,A A Ҭ>,AAP>,A`aA2>,Ao2AC>,AA>,A`ӣA̪>,AXA>,AA>,A`ڡA0>,AAHz>,A]AP>,AA>,A A@>,A@A8t>,AfAHK>,AR*A>,A AA`%>,A@A&>,AwAX>,A<A(>,A@TA >,A Ah>,A`T֜AP?,AHA ?,AUA ?,AA?,AӛA83?,A3Ap81?,AA3?,AmA5?,A?A 7?,A@5AXF8?,A`mA@9?,AAAT9?,AAp=9?,A×A09?,A@WTAPY9?,AA8?,A͖A`7?,A?A5?,A` GA3?,A4AU1?,AAR.?,A3A*?,A 8=A&?,AA"?,AA?,A`ZAh?,AA[?,A@Au ?,A=>Au ?,AA?,A@A?,AjA`?,AǑA?,AeA`?,A9A;?,A AH.?,AAw>,AXAt>,A#ϐAP'>,AFA>,A@ Aм>,A`KA>,A`fA>,A`2:A88>,A@ A?>,AiߎA>,AAp>,A`A >,AXUA(>,A'Ap=>,A@A>,A@AH>,A͉A@>,A@QAz>,A@AHܶ>,A3A>,A`TAԶ>,As;AXy>,AA>,AוAȺ>,AnAe>,AqGA>,A@y A8>,AAh^>,A}ӊA>,AA>,AA>,AafAa>,A` CAv>,A@E A>,A A>,A*܉A>,A A>,AA&>,A A@>,ATA3>,AnA>,A^Az>,A`)PAЄ>,ABA>,A`"7AB>,A,A>,AgAX>,AA?,A]Ab?,A@%A?,A`܈A?,AʈA ?,AA ?,AAЄ ?,A9A ?,AyAT ?,AA?,AAC?,A@WeAl?,A` A ?,AA#?,AzPA8#?,A )A ?,AA ?,AۅA` ?,A0A ?,AڎA?,A@hA@?,AQCA?,ADA>,AA>,AՄA>,A@߲A>,A@A>,A oA>,A A>,AĥA>,ABA>,AGA>,AAp8>,AA>,A`]dA2|>,A@8Au>,Ap Ao>,A8߁Ai>,A;Ad>,AA8_>,A SAT>,A AI>,A֠A2>>,AHAPI2>,A`A%>,AA >,A@sA>,AMA`>,A|'A >,AA`>,A~A}=,A ~Ap==,A@~Ah =,A,x~A 0=,AQ~A=,A)~Ax=,A~Ah=,A}AH)=,A}A@=,Aq}A8V=,AhR}A=,A |$}Axm=,A$}A3=,A|AR=,A|AΕ=,A@|Ax=,Ae|A@=,A*=|A =,A|A =,A`{A=,A {A }=,A`{A+{=,AN{APx=,A@{Aؒt=,AzAp=,A zA#l=,ARrzAȥe=,A`&zA_=,A@yAZ=,A`fyAhT=,A ZAyA0.P=,AxAN=,A6xA]M=,A,xAL=,A@رxA)L=,AVxA(-L=,A@ƄxAL=,AFnxA0nM=,A`WxAN=,A@AxAHKP=,AX,xAOR=,AFxA@T=,AxA8xW=,A-wAZ=,A[wA@^=,A ~wAa=,A@wAe=,A@wAl=,A`wA@Ot=,A@wA{=,A rwA`=,AcwA=,A`DVwAߓ=,AJwA/=,A@y@wA=,A68wA#=,A`1wA=,A`$wA=,AwAʷ=,AwA@=,ATvA E=,A vAu=,A@vAB=,A lvAH=,A 1vAЦ=,A`vAX9=,AvA_=,A@rvA=,A@^vA=,AڰuA=,AuA̟=,ATtAa=,A+sA N=,A rA~=,A@CFrA/t=,ARqA#j=,ApAh=,ApAf=,AapAhod=,A pAa=,A@oAu^=,A oAZ=,A@[oAV=,AoAHR=,AnA`M=,A0nAH=,A\nAhQB=,AnA+<=,A mAН5=,A lmAx.=,AemAO'=,A#*mA=,A@,lAp=,A lA8=,A|lA0 =,A ElAX,=,A@!lApA<,A@ kA <,A@ϢkA07<,A |nkA(<,A;kAH<,AkAH<,AjjA`<,A /jA<,AxjAp<,AVjA@<,A6jAD<,AjAPٻ<,AviA<,AiA0̸<,A iA@µ<,A iA<,AiA <,A{iAH<,A@P_iA@Ϥ<,ACiA襟<,A)iA$<,A SiAHP<,A OhAp,<,AhAp<,A)hAPZg<,AzahAȵV<,A@`.hAE<,AgAj?<,AgA<<,AgAЄ2<,AgAK)<,A4gAY <,A&|gA<,AXgAp[<,A4gAS<,AgA;,A@gfA;,A@fAa;,AfAQ;,AwpfAأ;,AFfA[;,A@fA{;,AeA;,A eA;,A`)eA;,AieAʿ;,A@eAXl;,AeA0;,A`)dA*;,A dAG;,A`dA;,AqdA;,A@>HdA;,A dA؟;,A`cA3;,A`cApI;,Ax[cA;,A cA;,AbA=;,A`]ibAP;,A[bAhD;,AKbAD;,A;bA;,A@+bA8';,AbA;,A QbA };,A@aA`;,A@aA0;;,AaA@;,AaA c;,A@aA ;,AޞaAh;,AaAp;,AaA(:;,AdpaAX;,A baA+;,AVaA;,AJaAp ;,A |@aAx:;,A`?aAt;,A ;aA¬;,A`5aA.;,A#.aA;,AR$aA0;,AaAX4;,AaAg;,A`AK;,A&`A;,A`A`G;,A `A@q;,A`Ap;,A`AO;,A@`A|;,A@>`A n;,A`Ap `;,A `A`R;,A@`AH;,A`A@;,Aྷ`AL7;,A `A.;,A@`A0&;,A@ x`Aȃ;,A_`A;,AtF`A@N;,A+`A#;,A`A;,A_Aj:,A2_A:,A _A:,AE_A@N:,A`f}_Ap:,AW\_Ap:,A@,:_A8:,A_A :,A |_A:,A ^A:,A^A :,A`^A:,A^A( :,Ap^Aӹ:,A`^A :,A@n^A:,A`U^A˵:,A@<^AXW:,A@ $^AW:,A ^A7:,A ]A؟:,A u]AБ:,A@]Ap:,A,e]AX:,A<]A:,A]Aϣ:,A\A8:,A \A :,A\A`:,A=\AV:,A[A:,A[A:,A^[AW:,A3ZA:,A`{ZA(s:,A YA#S:,AYAz0:,A`4YAp:,A)XA9,A XA9,AXA9,AXA9,A XAB9,AaXA9,AWAs9,AWAc9,A@ [WA\9,AHaWA T9,AgWAkF9,A WA8;9,A3XA59,A cXA)9,A|XA9,AVXAأ9,A YAk,9,A aYA)9,A@gYA`8,A |RZA8,A`VZA8,A`ZA8,A`f ZAu8,ARYAN8,A`2YAnb8,A0YAC8,A ZAp!8,A [Ap7,A`ZA7,AZAؒ7,A[Aۜ7,A`B]A†7,A@]Ab6,A[A6,ANw[A_6,AGZAȓ6,A cXAPwD7,AUApw5,A3PA4,A`NA(o4,A`ձMA4,A`LAV3,A /KAA2,Al_bW KA'lvWj2,A$&JA;1,A UIAMu1,AHA˷0,A@ƘGA)^0,AFHA(0,A`JAx$/,A LA/,A [MAPZ/,AaNAH/,AhOAHPI/,AOA.,AQSA .,ASA8V.,A hSA蕏.,A`TBSAr.,AoRAR.,AQA!i.,AQAu.,APA8.,AOA0..,ARLA(.,ApJAQ.,A3HA8/,A`FA(\.,AFAf.,A]EAh3/,A@DAh3/,A 8DAp.,AzCAp8|.,ACAJ.,A@>DAk2.,AEA`.,AEAX.,A qHA-,A )JA-,A`)KA-,ALA-,AVMA-,A`ÕMA0.-,AMAǥ-,A uMA-,AzMA-,ANA`a-,A OAf-,A)OA8-,A"PAW-,A qPAp-,ARPABe-,APAZ-,A`fPAH-,A3qOAQ-,A NA(W-,AfMA(_-,A#MAw-,ALAP8-,AHLA8-,ALÁ-,AJAps-,AXIAр-,AHA-,A qaHA-,AHGGA-,A FA-,AEAPf-,AMEA̠-,A@>DAt-,A׫DAN-,A@DAB-,A@rDAp8,,A3iDA,,A3_DA,,AH/DAp,,AHCA,,A@gTCA8e,,A +CAfD,,A`ECA!,,AHCAP,,Az&DAz+,ApDAV+,A]EA8+,AFAdž+,AGAu+,AEHAh.+,AYHA҇+,A`THAx>}+,A&UIAxs+,A@IAr+,A`ʘJAp r+,ABKAȇt+,A`dKAPkq+,A@PoLA^+,A`LAC+,AjEMAC+,A@GMA=+,A@%MAPg9+,At*NA0.3+,A3fNA`C+,A |NA*,A NA0*,ANAx*,AޤOAX*,AOA*,A`[PAX*,APPA&*,APA*,AxQAxF*,A-QA@O*,A QA;V*,A |QA'-*,AQA*,A &,RA),AORAP),AJRAĸ),AQAx),AQAxW),APA@),A]PA),AýOA),AjOAx),ApOA@S),AOA@F),A@#dOA8EA),ANA>),Ay%NA ;),A@%MA-9),ARLAq5),ALAj),ALA$),AKA`a(,A@KA(,AKA`(,A KA(,ApQLAxF(,A@yLA(,AMA(,AMAp(,A`]MA`(,ARNAW(,A[NA~(,ANAo(,A)OAe(,A)NA](,A NA`A(,A@gTNAأ9(,A`]MA#)(,AYMA# (,A`)LA(\',A_LA',A LA=z',A@6LAf<',A`LA8',A`õLAP',AHLA',ALAh&,A 8LA&,A %MA &,A/MA&,A@MA x&,A LAf`&,ALA@&,A@>DLAp &,AH_LAp%,ALAz%,ALA•%,A@>MALd%,AaMApd%,A`bMAT%,Ai4NA8=%,A qoNAW%,ANAأ%,A)nOA%,A PA) %,AdPA$,A ҝPAh.$,APA #,A ɰQAU#,ARA̰#,A JRA8#,A JRApJO$,A4nRA_$,ASAXu#,A*TA_#,AhQAͬ,ARUAP,A UAx k,A`)UAa,AUA8,A UAH,AVA0,A`VAP|,A@ϽUAPk,A`VUAƐ,ATA,AKTA\,A`oTA8,A`TA ',AIbTA(y,A TTA,A ZWTA(,AIbTA<,A@XTA؟,A`FTAx,A`;SA ,A@SA ,A`SA팛,AWSA J K-A}8A-AzA-AAA̱U-AO"A麢5-A4ƐAلX-AP9Ak4Ï-A)NSA;|-A}8AP'-A ܯA(>-AQAh⚊-A'A9-A_Ab-A`A8VI-AAh;5-A AA4-AAX_-A ۫Aq-A9A-AAȺ-A൐Al-AA :-AfA:!-AnuAf-A)A}W-A8WGWAf-AE6\AW-A 1"A#UN-A#4WaArR~-AlAOCeq-AVyAݰ%b-AItAQ-AA,-A ZvAP<#-AbAh-AMDAB-AjAxK;-A൪A`·-AQAP8O-AR>A-ArA-AEApE-ApABL-A HAp4-ÄA7-ApTApD-AwAXM-AA#S-AA`d-AdAX#i-A#AxKz-A`AB-A@A@$-ARA`C-A`AB-A`m}AI-A qA(K-ARA#"-A`LAB-A`)|AP ˆ-A`ApN-A4A6-AhwA0{-AmAB؅-A;A--A` A88-AX=A-AA-AnA(>-A#A00-AfA0*V-A@PA`-AA` -AR A#-AAQ-A A@5%-AAA>h0-AV-5Ap7-AYѯA5Q_}9-A*B,A6-A_Am{xM(-A(uvAke-A@AT -A=yA~-AA@p~-A`Ap5~-Az$A }-A #AН~-A$ AX[~-A? APx}-A AA|-A` A(~-A SAH}-A tA1|-AA@0|-A]{A,|-A`jAv{-A@AP7z-AD+AN2z-AmAgy-A@7A;x-ApA."z-AA@{-A[ Ap;{-A,!Ay-AAjy-AA(|y-A`vAXz-A!A@dz-A#"A 4hy-A Aey-A@#Acy-A (A0{Ax-AAw-A@>#A +u-A,Au-A,AJTt-A/A#Qs-A3AhWs-A&3Ar-A5A0*r-A@77Ar-A`7A."r-A7Aq-A`D8Aq-A@i{8Aq-A 8A@q-A 8AXq-AaE9A(zq-A(R9APq-AZ9AUq-A@v9Abq-A9Aq-A@9Axq-A09Aq-A՚9A]q-A՚9Ahēq-A`:AP q-A` :Avq-A;;Avq-AІ;Arq-A;AFq-Af=A(q-AО=A@q-A=Aq-A`;>A`q-A>A8p-A?A`p-A@{@A&p-AI@A(p-Af@AA.p-A XBA([p-ABAop-ACA hp-A@oCASip-A[CAhp-ACAp]p-AMDAXp-A FAo-AGAPo-A@6GA`Go-A3WGA}o-AGA[~o-AOHAp8Vo-AEIA&o-A JAXo-AgILA{n-A LAl-A]A&l-AZpA8"k-A@GwA8gk-AD\xApk-A yAk-A[zAȲl-AzAxrl-A\AzSl-AAhVl-A A@Ql-A@A8l-AFAh3l-A lAQNk-A ؚApxWk-AKAu-A@Avu-AAHk-A`]#A8'/k-A`ڞA@k-A@3A0qj-A@ƲAk-A9A}k-AA@cj-ARAx {j-A QA+j-A}AHrj-AA7ak-A2-A7ak-A[APk-AvA(k-A@>AA=l-A A &m-A4ܨA@m-A /Aq-AVAh;q-A@yAm-A@Azm-AA([bm-AAlm-ApA'jk-AATj-AvA>Cq-A$A+&q-AL&Aj-AI"Ahj-A&A{p-At)Ap-A?+APm-AN:)APl-A ,Am-Az-A(m-A+An-A+Ahn-A-A*{n-A&-Amn-A0A;o-A@P1A05o-A@`02An-A1A_n-AE1AYn-Ag1An-A2j2A5n-A`2An-AF2A+o-Ay2Ah^o-Av#3AP*o-A E3Ao-A~3APo-Ay3Ao-A3An-A A@3A@n-A`3Ayn-A2Ann-A`3A:~n-A o3A@tn-A@P74Ajn-A&4An-A@4A~9n-A4AHP(n-A 5A=n-AU6ATn-A uK8A m-As 8Am-A@y8Am-Ai8A8sYm-A`T;8Ax9m-A"9Awm-A9Ad|m-A`9Am-A l?:Axm-Ao:A`1m-AR:Afm-A =A8nm-A<Afm-A<Ayl-A@;A,l-A4=ATl-A>Aذ#m-Aݒ>Am-A >A Pm-Ai?AHm-A}XAATl-AX@Aîl-A@MAA +n-AvBFAX n-A@'FAKn-A`}FA +n-A@FAp m-A 8xFAjm-A@W]FAl-A?AAXk-A`?Ak-A4T@A^k-A@Ak-A@AAp0k-A5AA,l-A`B@A l-Aw@Al-A@E@A2l-A AA,l-A?$AA&l-A AAk-A5AAl-A0,BA,l-A "BAx-k-AfBAl-AAA 6l-A[BAHl-A`vBA l-A@RBA@l-AjBA !l-ABA0l-ACAh/k-A3AA/k-A2BAHl-AaCAbk-A^EAPrk-A QWDAXk-A cDAj-ABA oj-A`iDAh/j-A0*EA88Cj-A$ FAxIj-A`LFAk-A CA((k-A`GDA`Pj-AzEAH!k-A@GAk-AGA0j-AдEAгmj-A@^FA`vj-AFAH-j-A@,YGAði-A IAGj-A4KA0!j-AaLA8֧j-A@$NAitj-AOAgi-A@+KAyTi-AwJA`i-AJAi-A-JA؟ci-AIAFsi-A_IAi-AhIAj-AGAi-AFAeKi-A &GAxi-ARHAGh-A ~GAPM h-AFA`/f-A$PAسd-AWA;d-A`9YA0c-A`\Ab-A]Ab-AB_A Tb-A`91_A@2^-A iA]-A@pjA`1n\-AlAZ-A kAPgpZ-A ukA#Y-ATMlAx5Y-AmA tX-AnAoX-A`+mAW-A@>lAW-ANkA8iW-AkA(8W-A?-A`ӈRA{?-A [QA?-A;QAx>-AOA t,<-AzLA/99-AzJA3-AJA8?3-A FAxF1-A`FA\0-A@GA/V/-A GA--AJAi--AbA;sN0-AXg:lA 1X8-AtA=?-Ao3džxAG-AzA:,W>O-Ao3džxA{rV-AtA+C^-AXg:lA5x,{e-Ai\>bAJil-A=m~!UAҗgr-A]EABk%y-A3A<"~-AA}n5-A uvAA麢5-A}9~A̱U-A_)`A-AAA-AAA(q-AHA..AHWAB..AA̱ .AO&A麢 .A4Aل .AP9Ak4;.A)NS1AOl.AnBxAADl-AL6AkI-Ad5A4--A@%6Aj-A q=8A+-A}=AF-A<&BA-Aq?FAO-AvEAPA-A@EA`X-A@ HA8-A`"GAhݬ-AFA1-ABAc-A@kHA -Af2o13JA 1а-AFQA=e-Ao3UA-AHWA:,W-Ao3UA{rK-AFQA+-AXg:IA #-AW~ByFA8-AREAH2-A:DAN-AfBApT-A1>A{-A /d;Aku-A;9A(-A@yw8A-A`7AH-AP5Ã-A@I5A1-Aྣ3A0-A`2A@-A`1Ah-A`U1A v-A*#0AW;-A /A-A-A`_-Aq*AQ-A E*A-Ay(A-A(A8r-A%Aг-AR $Al-A!Ax-AӸAu-AAH-AfAp-A AM-A@PA=2-A@AB -A@ PA-A Ah-A`mA8g-A-A -A@KAh-AAz-A} A-A A)-A A d-A A 0-A@E AK-A`A<-AA0]-Ay2Ah-AAD-AzA-A`A}L-A ZA[-A A -AƩAփ-A bAP-AxEAЄ-A`SA-AAȘq-A`DA-ADOAc-A^A-AZApS-A3QA0-A`"A#-A,$Aj-A`meAh1-A/A~-A@A,-A@AAЄ-AfAg-A@A -AaSAH?C-A A`"-A A@-A_ Ax-A6A(-A` AH}-A$ A-A A0 -AFAk-A`FAHJ-A AH-Aa AH-A@ d A-AaAh-A`KgA0{[-AC"A.-A3A-A`A-A@AE-A|AP8x-A iA -A$AS-A"A tq-AmMA@-AA˟-A`TAȇb-A`fA(<-AA-A"A-A`)"AE-AZh%A-A 'A-A 'Ah6-A;&AY%j-A9K'Aҗgu-A򨑝"ABkݝ-AXAA+d-A4 A-A`A -A/A)-A &A<-A AAHL-AAp-AXA@Ww-A@rA-A`CAf>-A` A-A-Ac-A@Ah -A`AA|-AA8(-AmAh/-A@A R-A ApŬ-AA<-A {AX-A@AA-A`A-A;A0.-A?A-AྒAb-AYA?a-AVA6-AB4A\ -A@5AM-AEAp-AŔA9"-AMA-A2YAu-A`MA;-AH )A0-Ak At,-AxAb,-A%TA*a-AĸAݸ!-A~A (-AedAW-E^0-AӑOA ?8-A@@AKA-Au6AJ-AGvP3A)T-A 5A-AA-AA1-AsA*-A A-AgA-AAH-A@rAߊ-AA-AAjW-A A]9-A`A(-A@A%-A@A$-A@A-AUAf-AA؟-A[A-A u{A-A{Ap-A ZA-A ZAP^-A֥AHz-AnA(-A`2gA O-A]AMM-A]AJ-A=]A9-AA37-AA5-AtA8g$-A`PAX-AGA(\-A c:Aq-AAX-AAq-A`;Ah-A`7A-A`m0A(m}-AH)A[-A8$A Z-A@#A8V(-A A-A@A -A` A-A`Ah"-AAA-A A <-AA0{M-A A7Q-A@Aea-A@5A7-AA -A@AhD-AZAxX-AZA-AZA(-A@A-A7A<-A@¼Aу-A`܉Ap 5-A.A 1-A`-A#-AA-A3A@5-A`Ac-A A^-AR Ax-A`)A-AA-A@rnAX-AɳA7-Aj_A-A˭A(-Ao֭A -AA}-A@yRA-A`;A3-AaAb<-ADԫA"-AM@AA_-As6Aδ-AT fA`*-A ϭA9-Ak=ͧA˓-A4i!A -A ˧FAXJh-AA-A@WqASh-AlA{-A* i^A$3o-A -AۆA -A$AHn-A` A-AॿAXR-AA(-A@y {AX#8-A SA~-A`aA`2U-AaAX-A |{A@}-A|A9u-ARnA( -A`fmAЮ-A`vzAwc-A`wA-AuA(O-AsA-A_kAH-AMjA-A`v2rA!-ApAv#@-AuoA !G_-A[oA6-AEoA>h_-A~ oA-A@oA=5-A;D/pAY-A5dvpAY-A`pA-A@lAw{5-A<1slA]oS-A`:lA濓s-A:ݳmA/m-AiK|6mA,gԺ-AUmAP-Ave&cmA-A*fR^mAGM$-A87dGmAnF-A/mA:g-AlAR-A lAU-ABlA5-A\-kA<-Ac84skAx-A-jAU-A9~jA-A@iAv-AO`AHP-A`Kg_A-A@$_A(9B-A`iA-A@gA$D-AcA(o-A`AD-AT0_A -A`EYA:-A`EYA -A@`WAH-Ax SA@d-AxRA(-A;1Aa-A.A--A(AR-A(A-A@i*APH-A.Ah3-AFnA؇-AtA-A`uAp -AnA(1-AfAP^-AsA3Ah-A@,A{-A@!'AQ-A$Ap-A්*A -A`QA-ArRA06-AM&BA-A`f@AH-ArRAr-ANRAPMc-A`AF-AlAЄ&-A`jA-A@i]AY-A"RLA-AGA-A`EA-A ;AX-A|7A]-A]7Ax\-A}6A-As6AТ-Aax6A~-Afx6Aez-A'v6A/^ws-A7p6A&.l-ABe6A*?f-AiV6AT!a-AZC6Av~X<]-A!-6A Y-A 6AkW-A*5AzV-A5AV-Ah5A]W-A 5A[Z-Ax5Ag[]-AfAVq5A8Cyb-Aw[5AgŌg-AI5A4m-A8:5A1t-AIk05A&C{-A *5AT=-A5*5AP-AS.5AP-A3&75A|Atʖ-AϒwkD5A=#l-A(oU5Ay=-A~Ej5AS -A p5A -Ar5A-AѠO5A[Y-A Ǯ5AA-AB5AvQ-A~?N 6A,@-A8,%6A$-A&=6AR-A&;Q6AM-Aa6Ahё-A4i6A-AR7A]-AF7A0-A&DAQ-Aa.DAr-A cwJAU-A`JAH-AKA@Oy-A`JAx-A(KAe-A@oKAe-AnKAW-A2IA8.A (GAЕ.A$FAr.A[2EAx.ASCA-.AKDAx.A MRAb.AOA ).AzVA.ASAxz .A@\AHPB .A QcA F .A ccA A .AGkAo .A?8“oA .AHCeAذ .AQaAH .A B`A .A `_A .A}{^Aa .A@]A`] .A@\Ap .AXAr .AWAI .AVSAe .A43OA5 .AOA; .AOA}= .AZOAl .Az TA }{ .A`TUA`ӆ .AUA@ .A 'VAՂ .A@4VA5 .A0WA9 .A VA .A@,iUAd .A`PA7p .A JAD~ .A=tEAPj.A@FATG.ABBA.A @Ap.Aப=Am.A=A@ .A=A.A`=A(K.A4:Aw .A"A .A` A .A_A(< .AHAhI .AhAP8c .A@W#AȘ.AAI.ARAXa.A6]A .AA;.A$A w.AvAh.AA0.AAW-AK*A-A A%A].A@Ap-At(A`.AA0.A@A.6.A`9Ap4.A@Ax$.A@,A!.AUA:e-ActAd9-A}A88-A`Aq5-A}A4-A A9-A |9$A-A1$A -AW#A4-Am#AHi-A`#AH-A"A6-A "AN-A J"A-A!A]-A W A _-AyA-AqA-A@5OAx -A@A6-A`DKA A-A@,AF-AAq-AvAPv-A A AY-A ZA(-A`A0-A AKAb-A@WA@-A`AHy-A#A(-A@!A I-A !A@-AHP A`a-A@ "A8t-A@"A-AN"AȺO-A |g$A|-A$(A"-A`{&A-A&A\-AV%A,-A~%A-A@i$AX-A<'A-A.'A~-Ay&AǷ-AH[&A8-A%A8X-A@Y$A-AAP8-AaA$-A A -AAPo-A A6-AAȩ-A (ADI-AA(\E-A@WAqD-AAYC-A ZAv-A`A-AA0t-AAТ/-A lA-AiwAH6-A`A8-AA83-A"AZ-A`A.-A`KA`.-AA83/-A@yA0-AA2-AAp2-A&A(m-A|Aw-A ɇA-AA0-AhAʨ-A{Aȱ-At{Aw{-A |AG-ASA-AHLAx\-A`mA(1-A# A֚-A`]%A8gx-A HA+-A A`z-A $A(ː-A@Ahё-A@PA-A Ahb-A`mAPo-AA8-AjA-A`A9-AA-A2A(C.A@A.A`d AX.A$ A2.AZA&.A`A.A` A .A`m%A_K.Aj Az-AV A覟-A5A--AOAH-AA(-A AA-A`AP-AeA-A_A-A@r_A-A2A0]-A!AP<-A A`-A A-A A(-AAh"-A AcE-A  Ah -Az A^O-A@ AV-A AIW-A@ A@`W-A AY-Af A0f-A A-AA!-A A'-AAr7-ASA)C-Ao0A0-A@A-A@,"A-.AvSA%7.A@5A+7.A`5As;.A 'A>.AARw.AHiAa.A$A+.A@ At-A A!-A@ Ax)-AॣAȲ-A AX-AA1-A`A-A A@OI-AAh-AA-A@uA-A.AD-AA-A J3AF-A J3AP/-A@A0{`-AJAұ-A@Ax-A BAp8m-AA(-AA`-AA -AFA-AbAxi1-AghA-A@A0;-A AŒ-A`A@1-AA'-A JcA(-A`BA-AA -A5MA_-A`Ax-AgAPv-A Aj-A`fAA-A ȾA\-AXA-A`8A(-A"A-A ɹA@-AA-AoAJ-AA E-A(A-A Aȓ-AA0L-ABAx,-A`A8-A A-AZA-A$AH˔-A^AUo-A`A\V-A֪Aȓ(-A֪A -A@Ax\-A3A~-A HA3-AA\-A UA@-A`8A~ -A`8Aʒ-AޮA-AVήA-AŦA-AdAH2-AWA(-A FAv-AYA$-AIгA+-A A8i-A@ԭA?P-A Aʐ-AwMAo-Ap2A#-AA-AAHu-AHAh&-A pAp[-A"Ahbm-AǁA-A`6A@c-ADZA -AA8-AAZ-AA(-A Axh-AAa-A A-A@MAȘo-A0ALN-AlA;-AxA`%-A"AR-AA[-AA'7-A']A-AFA^.-A&AТ-A`MA-AAچ-A`bA`f-AWA0&-A'AX(-AщAT-A|AT-A,Ad-ANAPQ-A?A^.-AA"-ArApi-AqA1-AA`a-AIA8gu-A@`˝A2-A@A-A 8CA`-A 8A8tv-AAN-A@ȍA,-AAX-A98A@ -A=`A[-A`ӔAذ+-AA8t-AzA-A@!\AhD-A@xAm-A A]-ACA RJ-A`{At-A@rA-A`A-A`f8A-A[TAps-AgA-AA c-A@ՍA-A A>-AoAPY^-A`DAO-A!A[-AڅA-A A@-A͢A-A@T|AS-AnAP-A@kA8-A@cA-A`2aA~-AhcAH-ASaA-A@jA-AjA8-AvjAjq-A$iA0-A _^AX-A@]A`-A@>\AHi-AYAp0m-A`[Ax-AGXA*-AMoAP-A |4qA(-AЕmAȘe-AAlAO-A ?TAw-A`$HAsH-AD@AhM-A6:AЀ-AH<AU-A;9?A>-A ~?A)-A \=A0-A@`<A4-A,@AOd-A`%AAXa-A XAApl-ADA-A;^DA>-A!AA-A?Adg-A?AI-AIA-A <A`6L-AA9Ap-AM9AxK-A:A~-A];Af-A:Ad-AR8A_-A8AXu-AFu6Apf-A`ܬ5A-A@E7A؟-AU7Ax(?-A5Ay-AA>AB-A`]9A.¾-A@8A`-A`AԹ-A#߅AeVɹ-ANXAX-AI`WA;-AJA-A >=An-A|A#-A%`$OA;Ჽ-AK AHĹ-A.Aι-AA |-A`"AH?q-A_A@p-A GAݽ-Ar&Aix-AA9X6-AK.APw-A Aw-A<4ұ A]&[-AV;A-A0〽A8ӹ-A7A8-A) A@h-A PAxǥ-A@mAP-A`dNA0=-AAj-A AH-A:rA#0 R+-A*A8d.A 1 As]b-A*Aڔ-Af*Az!-AR5A^-A27A-A1A-A*A-.A:A-AnBA˃\-AID1ɻ;AGÕ-AȴajNAd -AnBhA%hX-A(NSqA*&{1-AP9QA4-ANW+A>-A AP+@-A As=-A`~Ah-A#Ah"-A@5ٻA-A/AxX-AǺA-A9A>-A@A( -A@DA\-AHA-AϵA -AzApp2-Aq|A?-A AhD-A#Ax<-A ZAx6-A9A-AޘA -A@'A -AlA&-A ӽAI-AA R}-AA-A#A?-A`Aؔ-AA }-ALA@-AwwAh-AhA8-A|A -A-\A -A HHAH-AA0W-A SùAH)W-A&ApO-A AA-A 1%A--AA+-A ~AX(-A@EAq-AA0-AЛA -A3AX-A A-A A-AtA.-AA;D-AAx^-AA[-AyA茛-AšA-ADA0-A졺A07-A`[A_-A@CAN-AA #F-AaABE-A`TοApy-A`Ak-A-vAy-A`[AW-AQܿAx-A`A-A AP<-AAp9 -A@iA0-AdA'-A`vNAA-ACAJK-A`mAzM-AZAA-A0_A.-A`NAB%-A JAȥ-AxCAy -AA -A SAp-A`;Ax-AhA-A`žAp0-AqA-Az}A-A ℾA -A=A-A`ݾA-A`lA+-AAL-AFAU-A A8f-AGAhv-AfA(-AҿAڝ-A@Ax)-A@>ƼA'-A@`úAC-AjTAx-A#hA-A@ᱺApI-A/A`-AMA-AAh-A A0-A +A%-AE`A-A 8AD-AA-A`AM-A`DA-A@eA-AѶA@-AjA%-A`ԿA-AA.-A%A0̔-A(Ah-A Z_A[-A[|A-AA~-A/A-A@IA(>t-AcAhl-A_APk-AAc-AOAz-AvAqt-AApX-A,Ax+-AA4-A@GAY-AnA]-A 8~Aj-AnAȋC-AAs-AA-AĺA26-AbA8-A`IA-AUA]-AA`C-A6A4-A Q*A3-AVA-AAȱI-ALA0-A4AC-A@NAKP-A *A2-AA0-A A"-A@A( -A *A`B-A`IAA-AbA-A^A(%-A@yAj*-AgA -A qA:M-A&9A}-AA-As]A?-A UA0wD-AbAb-A&A-A|VAi-A@A(z}-A`Ax(-AvAXj-A`A^v-A@)AX-AEAXZ-AA W-A@ơA@W-A {APR-A`]kAuU-A`GAM-A AQ-AAXK-AA-A2A1-A`A-A&0AZ-AcA@-A`RA-A@A8-A gAX({-AA-A \A /-AA|-ARA-AौAb-A`]A[*-AVA-A /5A8-AAP&-AI^Avi-A)A-A` Ap/-AA=-A` AO-AOA`V-A@NAx>-A+A-AA@-AA-A@WAXy-A)A*,-A@ߌA4f-AA-AA˥-AtA-A A -AA8*-A`AR-A)A ^X-A)Ah"a-AA0n-A}A)o-A`dA3-A ~6A+-A A-AA`-A@N5AA-A`A0 .A`Ah.AAP$.A 1eA2.A `A(.AyAV.A wA0o.AAr.A@ARe.A AQc.A@A"-A`fA-AAP*-A 3AxR-A@dAPE-A`:A.-AhwA-A AXup-AcAa-A |A7-A Q_A-A#PA-A JAP -A`A8.A<A07e.AaA.A+Ah/.AEA(-AAք-AAh5.A AK-ABBA.A`TAa&.AkA.A"A؍.AA/.AEAг.A (gA.A`mOA07.Az AH.AAJ.A@A8.AA`.A8ANA.A, AЄH.A@iA@B.A`BA8g.A`A .AA0.A MA5.A@ETAe.A Ah f.A@AX.A`~A.AV}A@/.AaAh3/.AAXJ.A$Am8.A pAU.ANA .AdA.A A@.AA.AHA!.A଴A.AaA^.A`APgz.A@bApI.A݇AЄM.A`AP^W.A@`A.AsAЀ+.A$A0.AAvh.A)cA g.A leAh.AgA0 S.A` A.AA.AA@.AxAU.A lAp.AAH.A@rA.AaAw.AAh.AsA.A$A.AAќ.A@:A.A̦A0A.AI A .A@ϳ AZ.A A' .A@Y Au .A)V Af .A ( A .Ap A .A@APw .AU_A .A/yAx .A@,AL .A .A2AX .AA .A@RA .A"A .A<%Aأ2 .AtAS .AAP< .A@~A .A -AN .AAl .A OA .A*gA& .A൫AP8 .AAhՑ .A`ʶA@q .A#Ab* .AAج .A 8A V.A`TtAhĭ.AApl.A}A@.A@WA.AA1.A@ Ah/.AW AH$.A *!Ax\n.A`7!A.A QP!AXh.A&"A.A#APju.A0$A(:.AL$A.Ae$AJ.A~$AHH.A$Al.A$A/.A`v%A.A%A@g.A4]&Aذ.A &A .AX'A.A-(Ax .Azw(AXG .Aӣ(Ax .A(A!.Ab)A!.A_)A1!.A@p)A!.A@L)A@!.A /)A!.A`(A ".A(A".A`o(A^".A Ag".A cJ A0;z".A`2 Ap9".A/Ap#.A32 Aa#.A4M AН&.A"Ao+.AR$A,.AB%A$..A%A`2.A@ 'A`61.Ae,AP|3.A -Ax 4.A`T.-A0 4.A.AX(4.A.AO4.A 9.A@O6.A`m.A }6.A.A56.A=.A`l7.A51/A7.AO/AuL8.AZ~/AX8.AR/A 8.Aw/A58.A/A:9.A@gN0A$R9.A 0A}9.AD1Ap9.A2A :.A „3A:.A6A`e%;.A`m7AhI;.A:8A.;.A q:AG;.A@.h;A"><.A`=AV<.Af>Ac<.A J?Aw<.AO BAXW=.AOzEAxi*=.A`KEAWS=.AGAl=.A`IA0]=.ANA=.A`'NA?.A5[A?.A \[Ah?.A I\A@u"?.A\A0?.A2]A`8?.A ]A A?.A_]A`6N?.Ac^A ?.AuX&dAwQU@.AR&zjA>B@.A| qA!IA.A%)PwA6DA.A}ߙ}AO+B.A]1-lA0B.A;AIB.A`ՍAG]C.A^ݡA*[C.AkGA\qC.AW4#AjC.A7/6ʠAЦ D.AA)D.A,AȥD.A@}A88E.AߢANE.A~A bE.A8ALE.AAE.ANAE.A A(E.A@AE.A CAE.A`&AuE.A@ƧA E.A`;AHE.AFoApE.AAF.AAPF.A@A5+F.A@+AC7F.A_A@:F.AAGF.AAؚUF.A@ƋAl[F.A`2qAhcF.A`YAn{F.A@,AUF.AAF.AAF.A 3A F.AAqF.A}A84F.AiAȩF.AANF.AUAA/G.A@xA.G.A Ah#G.ARAG.AAG.AAPG.A AF.A6A(F.A=AjF.AA#F.AA8F.AjAF.A`AG.A`AhG.A`dbA G.A}Ap$G.A A 9G.A@AVBG.AADG.A2ARG.AgAVG.A`XA4^G.A A]G.AAZG.A AUG.A`5 AOG.A  ApxKG.A`Y AAG.A@r/ A/5G.A A%G.A AG.AA A;F.A AhoF.A A8F.A A-F.A`m A\F.A/ A0 sF.A O AdF.AQ9 A0JF.A AX)F.A Ap F.A`' A E.Av A@FE.A A$E.A5 A0E.AC A5\E.A AE.A  A@ E.A3 AXFD.A AH\D.A l A wD.A5 AOGD.A@e AD.A`- AC.A`b AC.A AȓC.A<  ARC.AǓ AD{C.A=6 A;gC.A` A(1MC.AN A^:C.A AV-C.A+ ASC.A A@C.A AxC.A< A0 B.A" AHB.A`K! AŘB.A$ Ax5B.A J% AB.Ag& AТrB.A@N' AP>B.A>, AB.A0 A\B.A_0 AB.A`v0 AX#B.A 0 AH\+B.A*1 Ai:B.A41 AEB.Aa1 APB.Ak1 ACWB.A@1 AX\B.A1 A- ^B.A 1 A{rG.A҆. A+3O.AXg:' A5x,kV.Ai\ A?ZE9F].A A8H].A` AD].A` A$].A` Aأm].AQR ApAz].A A].A AH2].AX AH].AſAȏ].AAI].AA(:].AB7A0L].A (A8+\.A A\.A ZA"\.AA؅}\.A@itA8|\.A=AZ].A^As_.A /AH_.AdA~zc.A@RAǣc.AA(c.A Ad.A`]-A8d.AAxWQd.AA>Ad.A A c.AAb.AAF_.AJAv"_.A@. A].AA].A (Ah/\.AAB\.A"ZAq[.A`KeA}[.A`nA8 z\.A`(A V[.A@A[.AzA0[.ADAZ.AfApAZ.A6AhZ.AIAZ.A YA0TZ.AxA{[.AtA;[.AwAd1[.A@AZ.Aa!AY.AAxX.A_ҭA茡X.A@AiX.A6Ax]X.AiWAkW.A*A0 zU.AA1$U.A/CApc9W.AiAV.A@>iAP"P.A3sAHPO.AlAWN.A$?dA?M.AwZAxqL.ANA(M.AWEMALJM.A`KAM.A@DKAxM.A9KA NM.AinJAM.A@JAx M.AIA0ݒM.A JAIM.A@!bJA0M.A*IA;.M.AHA8(M.AZHA@W M.AIAL.A HAOL.A HA L.A@EGAhL.A}MGAL.A}FARL.A{FAL.A0$FAL.A EAL.A`@EAL.ACAM.ACAL.A`CA.L.A@!BA0*mL.A z<AhbdL.A`B;APxIL.A`:A L.AxT:A`-K.A}8AL.A'8A(-K.A i7A0K.Av6AuhK.AH7A@9-K.A7AxK.A]7A=J.A7Ax>J.A7AXpK.A`5AxqK.AŽ4A(K.Aյ3AX}J.A3AJ.A@k2AK.A@1A8x>K.A`1A9!K.A`/AHJ.A /A(BJ.A /AlJ.A@ 0AHG=J.AQ/AwJ.A@#v.Ay K.A--AhLK.A65-A GK.Az+ApNJ.A"AJ.A34AA@K.A`A"K.A&AL.A`UApK.A`KAR N.A"AO.AAO.A:A*O.A7Ab J.A@5AJ.A@!Aq`I.A`AhI.A`A?CI.A la AH.A A0gE.A }AuD.AAH %H.A`+A@5BH.A6AUH.A`A0*G.AA@F.A+AX[E.A%AhC.AzQ AAA.ANoAg@.AaAxIC.A (UA0D.AA@B.AA`@.A]AȲY@.A ;AC.AwA0{:H.AzAjH.A OZA H.A)yAAHG.A`A@1E.A?HAmE.A`Ap,>G.A`A.L.AA-L.A|AL.AjA\xF.AAD.A`mAID.A-AtK.A@`AJ.AAKD.AX%Aأ.A`KAWY>.A֌AD>.At3ӲAC)Ȗ>.AWAw~>.AA|f>.AJAY|M>.A:[AbH5>.A,e滲AD5>.A7% Az>5 >.A *$A,g=.A7>mAuPW=.AkGAf=.A0[AF=.A0{A=.A3n޴Ax=.A@APH>.A@A @.A0.A>.AA>.A@%AxH>.ANAN>.A@rAR>.AʸA0*=.AA%=.AAȪ=.A AAm=.AF:Ax{=.AAx5=.AA=.A@GA>.A5Ar>.A@A=.AgAׯ<.A >A <.A`~AXn;.A AP/=.AA0<.A@#qA`!;.A<A';.AVBA":.AIA؁:.A `A88~:.AAhL*:.A ~vA:.A \ A̴:.A@PAx>:.A`8APހ:.A`ӢAp9.AqAs8.AAX׻8.A Z#AH8.AzA~8.A1Ak&8.AAw7.AٕA7.AIAP.8.ATA(8.A@_A48.A=ApZB8.A2A}E8.A@A@1:8.AA K8.A@qAZ8.AbAp]8.A A R8.A@A K8.A@yASX8.A@AX\8.Aa`AL4.A@pAH+3.A`Ad2.ARAZ2.A 0A/.ADA@/.A_A#2.AA{2.Ay8A/.AWAr/.A(A`rr/.AA,..AF A..A FAP ..A@#A..A@OA..A|A8..A@jA c-.A ?Az-.A ?;A\e-.AJAbZ-.A@ƍAS-.APAR,.A@A*.A`;)Aj&.AMAQ&.A@A&.AHAm&.A`ӻA%7ld&.A 5>%A W&.A\.AUWF&.A Ao2&.A>0A@&.AkrA; &.A'9A%.A A(1%.AA3#.AAm".AAX".A@EAxX".A@#A!.A`mAxM!.A@rA !.ADA .A@A8" .A}A06 .AAH.A`]TA`6.A8A1.A`sAr.Ag&A`.A@`SA.AA.A`A .AA.AA.A A.A@A.ApAh3.A qA4.A`}AH.A`A .AA`.AAIB.A@\AX.AaA3 .AAh/.AA8'.A`óAG.A`A<.AA.AMAX07.AOAt.A`KdA.A ?A.A@5bA.A`uA.AOApλ.AApλ.A\AM.Az%AP.A@A.AQ A`.AmA .A ҮAl8.AfA0o.AAQz.AFA0݉.A+Ax1K.AA.A AȘ.AA].AAR.A@Ap.A@ρA0Q.A@NUA5/.A4A$.AOWAx.AzAH.A@ ~A | .A@yUA7.ATA!N.A@A.AXAhnV.ARAxO.AA l.AA*.A@?Ap .AA.AA0'.AA.A9AX .AA(i .A`oA{ .AA@ .A Ae.A A:..A`9A0 .AA`%! .ABAЈ .AA .A@6AȠ .AA9 .A@,AX( .AA .A`vA0 .AŒAX .A@,Ahk .A,CAX .AହAw .A@,{AU .A@,hA.A{A d.A A0!| .A A0 .AAѵ .A`]sA8 .AWA\ .A`vA.Aڹ~AB.A7AP.A <AXt.A#|AM`.AAtA.A0kA.AQtA؟V.Ap\oA.AHjA.A/aA.A|_A/.A,[APo-A` bA-A ?ZA@F.A'MA^.AଧGAX9p.A;JA3.A`GAH.A KA-AVRA-A`HA.A`KAA-.A:A -AhlnA-A0)A$-A@ƗA#-A@ oA -AhlnA -A37AE]K-A4A0 R+-AG8qA5-A lA/-AtAз -A$aA,-A$ȽA-A@ýAP-A@A(-AJA^(-AAX[-A_ A-A@`A$-AxfA`%-AMA-A Z9A -A37A#0 R+-A*A8d.A 1 As]b-A*Aڔ-Af*Az!-AR5A^-A27A-A1A-A*A-.A:A-AnBA˃\-AID1ɻ;AGÕ-AȴajNAd -AnBhA%hX-A(NSqA*&{1-AP9QA4-ANW+A>-A AP+@-A As=-A`~Ah-A#Ah"-A@5ٻA-A/AxX-AǺA-A9A>-A@A( -A@DA\-AHA-AϵA -AzApp2-Aq|A?-A AhD-A#Ax<-A ZAx6-A9A-AޘA -A@'A -AlA&-A ӽAI-AA R}-AA-A#A?-A`Aؔ-AA }-ALA@-AwwAh-AhA8-A|A -A-\A -A HHAH-AA0W-A SùAH)W-A&ApO-A AA-A 1%A--AA+-A ~AX(-A@EAq-AA0-AЛA -A3AX-A A-A A-AtA.-AA;D-AAx^-AA[-AyA茛-AšA-ADA0-A졺A07-A`[A_-A@CAN-AA #F-AaABE-A`TοApy-A`Ak-A-vAy-A`[AW-AQܿAx-A`A-A AP<-AAp9 -A@iA0-AdA'-A`vNAA-ACAJK-A`mAzM-AZAA-A0_A.-A`NAB%-A JAȥ-AxCAy -AA -A SAp-A`;Ax-AhA-A`žAp0-AqA-Az}A-A ℾA -A=A-A`ݾA-A`lA+-AAL-AFAU-A A8f-AGAhv-AfA(-AҿAڝ-A@Ax)-A@>ƼA'-A@`úAC-AjTAx-A#hA-A@ᱺApI-A/A`-AMA-AAh-A A0-A +A%-AE`A-A 8AD-AA-A`AM-A`DA-A@eA-AѶA@-AjA%-A`ԿA-AA.-A%A0̔-A(Ah-A Z_A[-A[|A-AA~-A/A-A@IA(>t-AcAhl-A_APk-AAc-AOAz-AvAqt-AApX-A,Ax+-AA4-A@GAY-AnA]-A 8~Aj-AnAȋC-AAs-AA-AĺA26-AbA8-A`IA-AUA]-AA`C-A6A4-A Q*A3-AVA-AAȱI-ALA0-A4AC-A@NAKP-A *A2-AA0-A A"-A@A( -A *A`B-A`IAA-AbA-A^A(%-A@yAj*-AgA -A qA:M-A&9A}-AA-As]A?-A UA0wD-AbAb-A&A-A|VAi-A@A(z}-A`Ax(-AvAXj-A`A^v-A@)AX-AEAXZ-AA W-A@ơA@W-A {APR-A`]kAuU-A`GAM-A AQ-AAXK-AA-A2A1-A`A-A&0AZ-AcA@-A`RA-A@A8-A gAX({-AA-A \A /-AA|-ARA-AौAb-A`]A[*-AVA-A /5A8-AAP&-AI^Avi-A)A-A` Ap/-AA=-A` AO-AOA`V-A@NAx>-A+A-AA@-AA-A@WAXy-A)A*,-A@ߌA4f-AA-AA˥-AtA-A A -AA8*-A`AR-A)A ^X-A)Ah"a-AA0n-A}A)o-A`dA3-A ~6A+-A A-AA`-A@N5AA-A`A0 .A`Ah.AAP$.A 1eA2.A `A(.AyAV.A wA0o.AAr.A@ARe.A AQc.A@A"-A`fA-AAP*-A 3AxR-A@dAPE-A`:A.-AhwA-A AXup-AcAa-A |A7-A Q_A-A#PA-A JAP -A`A8.A<A07e.AaA.A+Ah/.AEA(-AAք-AAh5.A AK-ABBA.A`TAa&.AkA.A"A؍.AA/.AEAг.A (gA.A`mOA07.Az AH.AAJ.A@A8.AA`.A8ANA.A, AЄH.A@iA@B.A`BA8g.A`A .AA0.A MA5.A@ETAe.A Ah f.A@AX.A`~A.AV}A@/.AaAh3/.AAXJ.A$Am8.A pAU.ANA .AdA.A A@.AA.AHA!.A଴A.AaA^.A`APgz.A@bApI.A݇AЄM.A`AP^W.A@`A.AsAЀ+.A$A0.AAvh.A)cA g.A leAh.AgA0 S.A` A.AA.AA@.AxAU.A lAp.AAH.A@rA.AaAw.AAh.AsA.A$A.AAќ.A@:A.A̦A0A.AI A .A@ϳ AZ.A A' .A@Y Au .A)V Af .A ( A .Ap A .A@APw .AU_A .A/yAx .A@,AL .A .A2AX .AA .A@RA .A"A .A<%Aأ2 .AtAS .AAP< .A@~A .A -AN .AAl .A OA .A*gA& .A൫AP8 .AAhՑ .A`ʶA@q .A#Ab* .AAج .A 8A V.A`TtAhĭ.AApl.A}A@.A@WA.AA1.A@ Ah/.AW AH$.A *!Ax\n.A`7!A.A QP!AXh.A&"A.A#APju.A0$A(:.AL$A.Ae$AJ.A~$AHH.A$Al.A$A/.A`v%A.A%A@g.A4]&Aذ.A &A .AX'A.A-(Ax .Azw(AXG .Aӣ(Ax .A(A!.Ab)A!.A_)A1!.A@p)A!.A@L)A@!.A /)A!.A`(A ".A(A".A`o(A^".A Ag".A cJ A0;z".A`2 Ap9".A/Ap#.A32 Aa#.A4M AН&.A"Ao+.AR$A,.AB%A$..A%A`2.A@ 'A`61.Ae,AP|3.A -Ax 4.A`T.-A0 4.A.AX(4.A.AO4.A 9.A@O6.A`m.A }6.A.A56.A=.A`l7.A51/A7.AO/AuL8.AZ~/AX8.AR/A 8.Aw/A58.A/A:9.A@gN0A$R9.A 0A}9.AD1Ap9.A2A :.A „3A:.A6A`e%;.A`m7AhI;.A:8A.;.A q:AG;.A@.h;A"><.A`=AV<.Af>Ac<.A J?Aw<.AO BAXW=.AOzEAxi*=.A`KEAWS=.AGAl=.A`IA0]=.ANA=.A`'NA?.A5[A?.A \[Ah?.A I\A@u"?.A\A0?.A2]A`8?.A ]A A?.A_]A`6N?.Ac^A ?.AuX&dAwQU@.AR&zjA>B@.A| qA!IA.A%)PwA6DA.A}ߙ}AO+B.A]1-lA0B.A;AIB.A`ՍAG]C.A^ݡA*[C.AkGA\qC.AW4#AjC.A7/6ʠAЦ D.AA)D.A,AȥD.A@}A88E.AߢANE.A~A bE.A8ALE.AAE.ANAE.A A(E.A@AE.A CAE.A`&AuE.A@ƧA E.A`;AHE.AFoApE.AAF.AAPF.A@A5+F.A@+AC7F.A_A@:F.AAGF.AAؚUF.A@ƋAl[F.A`2qAhcF.A`YAn{F.A@,AUF.AAF.AAF.A 3A F.AAqF.A}A84F.AiAȩF.AANF.AUAA/G.A@xA.G.A Ah#G.ARAG.AAG.AAPG.A AF.A6A(F.A=AjF.AA#F.AA8F.AjAF.A`AG.A`AhG.A`dbA G.A}Ap$G.A A 9G.A@AVBG.AADG.A2ARG.AgAVG.A`XA4^G.A A]G.AAZG.A AUG.A`5 AOG.A  ApxKG.A`Y AAG.A@r/ A/5G.A A%G.A AG.AA A;F.A AhoF.A A8F.A A-F.A`m A\F.A/ A0 sF.A O AdF.AQ9 A0JF.A AX)F.A Ap F.A`' A E.Av A@FE.A A$E.A5 A0E.AC A5\E.A AE.A  A@ E.A3 AXFD.A AH\D.A l A wD.A5 AOGD.A@e AD.A`- AC.A`b AC.A AȓC.A<  ARC.AǓ AD{C.A=6 A;gC.A` A(1MC.AN A^:C.A AV-C.A+ ASC.A A@C.A AxC.A< A0 B.A" AHB.A`K! AŘB.A$ Ax5B.A J% AB.Ag& AТrB.A@N' AP>B.A>, AB.A0 A\B.A_0 AB.A`v0 AX#B.A 0 AH\+B.A*1 Ai:B.A41 AEB.Aa1 APB.Ak1 ACWB.A@1 AX\B.A1 A- ^B.A 1 A{rG.A҆. A+3O.AXg:' A5x,kV.Ai\ A?ZE9F].A A8H].A` AD].A` A$].A` Aأm].AQR ApAz].A A].A AH2].AX AH].AſAȏ].AAI].AA(:].AB7A0L].A (A8+\.A A\.A ZA"\.AA؅}\.A@itA8|\.A=AZ].A^As_.A /AH_.AdA~zc.A@RAǣc.AA(c.A Ad.A`]-A8d.AAxWQd.AA>Ad.A A c.AAb.AAF_.AJAv"_.A@. A].AA].A (Ah/\.AAB\.A"ZAq[.A`KeA}[.A`nA8 z\.A`(A V[.A@A[.AzA0[.ADAZ.AfApAZ.A6AhZ.AIAZ.A YA0TZ.AxA{[.AtA;[.AwAd1[.A@AZ.Aa!AY.AAxX.A_ҭA茡X.A@AiX.A6Ax]X.AiWAkW.A*A0 zU.AA1$U.A/CApc9W.AiAV.A@>iAP"P.A3sAHPO.AlAWN.A$?dA?M.AwZAxqL.ANA(M.AWEMALJM.A`KAM.A@DKAxM.A9KA NM.AinJAM.A@JAx M.AIA0ݒM.A JAIM.A@!bJA0M.A*IA;.M.AHA8(M.AZHA@W M.AIAL.A HAOL.A HA L.A@EGAhL.A}MGAL.A}FARL.A{FAL.A0$FAL.A EAL.A`@EAL.ACAM.ACAL.A`CA.L.A@!BA0*mL.A z<AhbdL.A`B;APxIL.A`:A L.AxT:A`-K.A}8AL.A'8A(-K.A i7A0K.Av6AuhK.AH7A@9-K.A7AxK.A]7A=J.A7Ax>J.A7AXpK.A`5AxqK.AŽ4A(K.Aյ3AX}J.A3AJ.A@k2AK.A@1A8x>K.A`1A9!K.A`/AHJ.A /A(BJ.A /AlJ.A@ 0AHG=J.AQ/AwJ.A@#v.Ay K.A--AhLK.A65-A GK.Az+ApNJ.A"AJ.A34AA@K.A`A"K.A&AL.A`UApK.A`KAR N.A"AO.AAO.A:A*O.A7Ab J.A@5AJ.A@!Aq`I.A`AhI.A`A?CI.A la AH.A A0gE.A }AuD.AAH %H.A`+A@5BH.A6AUH.A`A0*G.AA@F.A+AX[E.A%AhC.AzQ AAA.ANoAg@.AaAxIC.A (UA0D.AA@B.AA`@.A]AȲY@.A ;AC.AwA0{:H.AzAjH.A OZA H.A)yAAHG.A`A@1E.A?HAmE.A`Ap,>G.A`A.L.AA-L.A|AL.AjA\xF.AAD.A`mAID.A-AtK.A@`AJ.AAKD.AX%Aأ.A`KAWY>.A֌AD>.At3ӲAC)Ȗ>.AWAw~>.AA|f>.AJAY|M>.A:[AbH5>.A,e滲AD5>.A7% Az>5 >.A *$A,g=.A7>mAuPW=.AkGAf=.A0[AF=.A0{A=.A3n޴Ax=.A@APH>.A@A @.A0.A>.AA>.A@%AxH>.ANAN>.A@rAR>.AʸA0*=.AA%=.AAȪ=.A AAm=.AF:Ax{=.AAx5=.AA=.A@GA>.A5Ar>.A@A=.AgAׯ<.A >A <.A`~AXn;.A AP/=.AA0<.A@#qA`!;.A<A';.AVBA":.AIA؁:.A `A88~:.AAhL*:.A ~vA:.A \ A̴:.A@PAx>:.A`8APހ:.A`ӢAp9.AqAs8.AAX׻8.A Z#AH8.AzA~8.A1Ak&8.AAw7.AٕA7.AIAP.8.ATA(8.A@_A48.A=ApZB8.A2A}E8.A@A@1:8.AA K8.A@qAZ8.AbAp]8.A A R8.A@A K8.A@yASX8.A@AX\8.Aa`AL4.A@pAH+3.A`Ad2.ARAZ2.A 0A/.ADA@/.A_A#2.AA{2.Ay8A/.AWAr/.A(A`rr/.AA,..AF A..A FAP ..A@#A..A@OA..A|A8..A@jA c-.A ?Az-.A ?;A\e-.AJAbZ-.A@ƍAS-.APAR,.A@A*.A`;)Aj&.AMAQ&.A@A&.AHAm&.A`ӻA%7ld&.A 5>%A W&.A\.AUWF&.A Ao2&.A>0A@&.AkrA; &.A'9A%.A A(1%.AA3#.AAm".AAX".A@EAxX".A@#A!.A`mAxM!.A@rA !.ADA .A@A8" .A}A06 .AAH.A`]TA`6.A8A1.A`sAr.Ag&A`.A@`SA.AA.A`A .AA.AA.A A.A@A.ApAh3.A qA4.A`}AH.A`A .AA`.AAIB.A@\AX.AaA3 .AAh/.AA8'.A`óAG.A`A<.AA.AMAX07.AOAt.A`KdA.A ?A.A@5bA.A`uA.AOApλ.AApλ.A\AM.Az%AP.A@A.AQ A`.AmA .A ҮAl8.AfA0o.AAQz.AFA0݉.A+Ax1K.AA.A AȘ.AA].AAR.A@Ap.A@ρA0Q.A@NUA5/.A4A$.AOWAx.AzAH.A@ ~A | .A@yUA7.ATA!N.A@A.AXAhnV.ARAxO.AA l.AA*.A@?Ap .AA.AA0'.AA.A9AX .AA(i .A`oA{ .AA@ .A Ae.A A:..A`9A0 .AA`%! .ABAЈ .AA .A@6AȠ .AA9 .A@,AX( .AA .A`vA0 .AŒAX .A@,Ahk .A,CAX .AହAw .A@,{AU .A@,hA.A{A d.A A0!| .A A0 .AAѵ .A`]sA8 .AWA\ .A`vA.Aڹ~AB.A7AP.A <AXt.A#|AM`.AAtA.A0kA.AQtA؟V.Ap\oA.AHjA.A/aA.A|_A/.A,[APo-A` bA-A ?ZA@F.A'MA^.AଧGAX9p.A;JA3.A`GAH.A KA-AVRA-A`HA.A`KAA-.A:A -AhlnA-A0)A$-A@ƗA#-A@ oA -AhlnA -A37AE]K-A4A0 R+-AG8qA5-A lA/-AtAз -A$aA,-A$ȽA-A@ýAP-A@A(-AJA^(-AAX[-A_ A-A@`A$-AxfA`%-AMA-A Z9A -A37A3@b.A}[AH.Ar5 Adkq|(Qp.A}[A0C.AVߝq#A;.AAQΥ.AAɥ.A`A2.A@ A0..AXA@.A A8 .AAPk.A`)A.AAx~.ABAu.A`]A>.A #AG.A HA.AA.AAHJ.A ~OA?z.AǹA.AAͪ.AM9AGa.A$(A0*F.A`A0.AvA .A`Ahޡ.A@EAա.AAס.A/DAӡ.AVAơ.A A ¡.AAb.A*AP4.A`fA7R.AAؒv.A?BAH.AXAi.AeA(.A@NEAX j.AfAa.A@ClA0=.AFA.A u%Aǜ.Av}A .AAg.A4At.AD"A2.A Aۛ.A A(Л.AܷAV.AA #.AA~.A rAB.A`AG|.AaAAz.AfAr.A&Aac.AAޘ.A`A.AcAޗ.A`~Aۗ.A 1Aݗ.AAϗ.A8AA.A9AHZ.A;Ap,.AAꚕ.AT A_.A]S AS.AyS A>.A`S A3.A@R A8.A;WR A .AR Ae.AQ A8Q.A`)Q A.AP AԀ.A/O A Ȁ.AjgO A..A@ N A.A`9M AhՍ.A+M A.AL A|.AL A:y.AK Aev.A@ƎK A".A`!L Ap,.A^P AX.A@V AX.A W Axu.A,W Apxg.AW A8\.AV A06.AW Ax.AW A`6.A@ W AX4.A RX As.A /X A.A@ W AY.A /X Ag.AX A`.AX AK.AY Aq~.A7S Ap}.A@]T Ax}.AS Axܳ}.AR Ap8}.AP Ao}.A^.P A`h}.AO A}.A4P A`|.AN A[|.A;J A`|.A (I A8|.A`J AP}.A A A\}.A@Q@ Ac|.A? A|.A@5A A`{.AG A{.A8KI Aj{.A@ƵJ A73|.A`]N A8Ei|.AQ A(|.AU A`6|.A7V Ah}.AzZ A;}.A@\ A}.A@\ A`}.Aྔ\ A2|.A c\ A|.A`o2] A@_|.AA] AY|.A0^] AB|.A`m] A|.A؅] Ae|.A@] AE|.A@y ^ A 9|.A"^ A`C0|.A@=^ Ah |.A6w^ AH\|.A{^ A|.A`f^ Axm{.A^ A{.A`_ A{.Az3_ A{.AE_ Ah{.AT_ A{.AX` Ax(y.A@b A0y.A`^b Ay.A3a Ab(y.AGc A88x.Ad Apx.A`5d A@>5x.Ag.ARf,:s Af.A;d{ Apf.A`d{ Axf.A`c{ Af.A`Kb{ A8f.A\{ APf.A T{ Af.AU5{ A f.A |.{ A ޸f.A`v,{ A( f.AN+{ A0]f.A`m&{ A f.A %{ Af.A@.){ Apf.A`0{ Ag.A`)N{ ApJg.A`z{ Ax\Vg.A 8{ A vg.A`{ APg.A}{ AXۉg.Af{ A@`g.A{ A/h.AX| AH?h.A | Ach.A '| Ap=th.A1| A(h.AC| AP8h.AFb| A(]i.A#| Akbi.A<| Agi.A@| AMi.A || Axdi.A@>| Aыi.Ac| AP*i.Aٳ| Aȩi.Aw| Aɡi.A4| Ai.A| Ai.AF| A֮i.A^| Ai.A | Ai.A0} A`vi.A`m} Ax\i.A } A@$i.A`v } A`2i.A } Ai.A@} A`i.A| A6i.AW| AH)i.A| AVj.A@y| A/j.A | AN?j.A| AXaj.AT} Aoj.A`; } A@$j.A} Aj.A`2} Aj.A`K} A k.A 1"} A k.A@%*} A.%k.A@;} Aȥ/k.A@F} AIk.A"k} A@Qk.A Zr} A`Wk.Au} APMok.Au} Ak.A 8i} AMk.A`U} AXRl.AH} A@Ll.AD} A\l.AEJ} Ael.AQ} Asl.A@yk} A@ m.A uq} AePm.Aq} A0Xm.A@t} A0Xnm.A c} Atm.A} Azm.A`} Am.Az} A@5m.A@n} AHm.AD8} An.A| AOn.A Q| Amn.A[| A),o.A9{ AX(co.A{ A_o.A k{ A(-o.A@W<{ A0o.A{ Ao.Az A@1%p.Az A( Kp.A@z AHnp.A`_z Atp.AMz Ahp.A-y A#p.AIz A``q.Avy A0{r.Ax Ar.A`w AH)"t.Au Axt.A Zt A }t.AtEt Ao{.Axg AxV~.A?4j A|.AD0k A.Axk AL.Ajl A8.A2l A =.A`̈n Ai.A`^o AX.A2p A8 ‚.A^q AȂ.AMq A5%íт.A.r AH܂.A@]r Ah.A@.}r A tؘ.AI$her A;.As A@5d.A@5s AzWw.A-t&Os Aj.AL߮]s Aas.Aas A䴃.A=Ys A:.ASt A(.A 8Ft AHO.Au A\~.A`ʊu A`.A u Ai{.A;u AO.Aov AYr.Ajv A{.A@w A.A4ew Ah⌅.A4w Aӝ.Aw A.A`Px Ahb.A@5x Aߺ.ALx A`eɅ.A@x A݅.A x Ay.Ax A.Ax Ap+ .A`)x A .A y A@3.A"y A(.A@ z ArՆ.Az A`.A&{ A`.A q| AOLJ.A@~ AP.A@~ ALj.A0 A.AFى A' .A_ AH*.A@ A`>.A6 A.Aʺ A.As ApyM.A`) A`ߋ.A /n A0.A`] A،.A Af.A A(9.A~ AP^F.A4o Ao.ADǓ Ah;.A A(༊.A`K AZ".A@d AH).Ar AТ?.A l AX.A~ A0%.A@5{ Am.Ap[x A=M.Axw A/.Aw A.A%u AXх.Atxt AI.Ar AMH.A@r AXŅ.A^#r Aѥ.A Sl A0T.A0j A=.A`[j A0.Aj A.Axj A@.A@j A0H.A@N j A`.Aj AX9.Aj A0{w.A &j Aje.AZ A@.AdZ A`eۃ.ADY Ao.AqY A.AN@Y A@<.AX A(~V.AX Ah.ARX AƄ.ALW A .A!W A#%.A AV Ac.AELV An.Ap4V Aw.AE$V A|.A@V A.A@V A.AV A`؞.AV A(\.AFV A`尅.AU ALЅ.AU Ax$.A \T Aa.A`yT A@1l.A_T APځ.A}-T A@આ.AS A.AS A ͆.AES A ؆.A`S AP.A R A E.A R A.A`ʏR A`<.A`;R AGu.AQ A0Hׇ.AFP Aۇ.A@-P AL؇.AP Aч.A@P AX؇.AO A#.A`ܽK A|.AྀG A.A Z/D Ab.A 1>@ A%.AՊ= AX7.A֜8 AP#.A8 AТ0.A$7 AP:.A (E7 AR.A`6 A&.A5 AP.A x5 A%.Az4 AH=.A"23 A\K.A`2 A.A3 AK.A1 Aԋ.Aa/ Ab.A@. A`.AR- A([[.A`dS* A{.A`( AL.A l^( A.A' Aj.A;=' At.A& A@y.Ar& A#.At% A(.A`X$ A@.A # A».A" A`.A6 A@'.A` A`K.A`~ AӀ.A࣑ A .A AB.A@ Ap-.ADZ A`P.A@,T AL.Af; A .A` AX>.A`ܽ Al.A A( 0.A  AF.A@  AQ6.A` A.A A@.AfG A .AA7.ARA@J.ATA q.AbA.AA0}.A@A .A O{Aȶ%.A@,AXW.A&A.Ad A07.ATA _i.A`AN.A -A.A@Ao.AAH. .Af+A(z1.A@A '.A QA@׾.AAH.AQAhޭ.A@AA.A`A.A`A.A@pA:.A APjV.A`&A.A-$A0 Z.A [A.t.A Ah/}.A@#A~3.A`A0C.A|A2_.A<'APM.A`]A..AתA.AիAȔ5.A"A>@.AA~.AAh.AA.AA .AWA.A A`.AVA8։.A*Ah.AA0LҬ.A`]A.AvoAhA.AAȱi.AA̜.A`Ah.A A0국.AA.AA.A A.A`bAt.AA.A6A_ˮ.A@A.A`xA` 1.AiA;.A@PiA.A`pA0꺯.ArA.AA߯.AAs+.A`Ah"F.AXAR.AAXW.AAp<`.AՉAx-j.AAt.AAȘ~.A@{A.AvA".AfhA.AcAZ.A`_A覾.Ac`ApAɰ.AbAҰ.A[A`ݰ.AQA@F.A3EA.A7A_.A&Aj.A A.A@A8.A@AS.A lA.A AP#.AA`+.A`bA 4.A`A2>.A A(F.AAP.ATA`[.A@Ag.A@yA,t.A$A #.A܄A.AA.A@Ax-.A@yA趪.AA.A@AXt.A`ADZ.A@A@б.A A@_ڱ.AsA`.AcAZ.AZAP.ATVA<.AQA .A ZMAd.A$BAA!.A5A8V+.A*A0j5.A`!A(?.ANA`PJ.A A(U.AxA_.A`fAZj.AAs.A`Ay.AA`.A޺A.A`A.A@yA.A JA؍.A@A2.AEA(`.A2AHP.A`DA².A;A0Ͳ.AAز.A@>A.A|A.ApAX.AHcAj.AWAs .A`JA.A9AȘ.A` 'A&.AA..A@A5.A At=.A A7F.A AhUN.A@AX.A;jAX^.A@YAPZg.A2EAHi.A?AHq.A6A`y.A`$A.AAX(.AA .A@Aj.AAe.A=A[.A*Aho.A=APZ.A&zAdų.A@ sA@γ.A lAxٳ.A^cA0.AWA`.A`DMAج.ABA.A|6A .A#AH;.AAd$.A@A0.AnA:.AݰA`C.AAO.A`AUW.A@rA_`.A@gAj.A XAhu.AJA<.A 8Aߏ.A)A軚.A@ A.A`mAo.A@GAA.A A2Ĵ.AA˴.AAԴ.AA.A@E A.A OA.AA' .AA.AXA,$.A@A1.AhAp?.A OA@FN.A AU.A A].A HAk.A`Ax.AAt.A`)xA8.AfAU.A9VA꡵.AGA1.A`:An.A /A張.A"Axŵ.A OApε.AAXֵ.A|APZߵ.AAH.AA,յ.A@PA8ĵ.A ‡AP.A Ax鴵.AtA.AgAˢ.ALA.A@8A`6.A@y6A.A?FA.AXIAh{.AHAv.AAAm.A l!Ad.AA[.AEAAW.AA V.A@A8R.AH{AL.A |BAXVJ.Ac AFH.AEAF.AAC.AׯAX(A.A@Ai<.AcA7.A?Ax-2.A@5Aؒ,.A@AX%.AzA .AA.AAp.A ˾A.A`bA.A /A;.AA2.AAp[״.AEAX(ɴ.A A.A3AТ.A`A0.A'AP.A/A.A@IApۄ.AxXAXx.AfAQl.A QtA7e.A@zA].A A`M.AՕAsE.A A5.A@A@-.AA($.AA .AA.A)A.AFAج .AA6.AA .AA.AAHٳ.A CATγ.A`jA³.AA[.AAxF.A A.AA,.AxAG.A Aہ.AgA@x.A@.Ahj.ADAc.AYAP_.AI_AxU.AomA K.A@~AA>.A`ÙAHP0.AA".AAA.AA .A/A.A3A.A AJ.A@+A۲.A`@AhԲ.A ZKA`1Ͳ.ARVAOŲ.A_A廲.AlAo.AzA@.AʈA0.AA7.AaAX.AAȃx.A[Ahh.AA_`.AAW.A<AN.A AdE.A`A;<.A[A2.A@NA_).A@ %A!.A H7A .A|BA _.A`)FA .A|BAi.A 7A.A H#Aڱ.AAѱ.AdA'ɱ.A@NA±.A`̤AA.A{AP.AOAh".AdDAA.A:A0.A5A߉.A l/AP7}.A@%*Ao.A&A@e.A`%A[[.A"A@1O.A A C.AxA7.AAX-.AA`#.A$AH.A<0AP.A&<A.AIA8.AWA .AbA`ְ.AfjAoʰ.A}pA.A.AoA(z5.A mA8-.A@nA'$.A pAH.AqA0.A@ qA.A`oA.AkA.AgA.A`bAPٮ.A]A(~Ю.AXAƮ.A RA0̻.ANAZ.A SJA冷.A@5EA.Ag@A.A ;AP .A&2A!v.A//Ak.A.Ata.A@2AZV.A8AOM.A (:A`D.A H=AX9.A>AK..A`=AX#.Aq;Ax.A7A.A`/AX.Aa*A}.A`]'AO.AF!A.AA֭.A Aȭ.A OA8#.ARA.A AH.A` AHK.A`AА.AA.A O A0v.A@ AUg.AAXY.A`AXL.A AG.A ZAx.A@A.A`A.A 1yA.A &XA.A9AXެ.A (AZڬ.A@ A@Ԭ.AfAVϬ.A<Ayʬ.AॆA"Ǭ.AEkAP¬.A>A0̻.A@ Ap,.A`]AK.A@A;.AwAȘ.AHOAH.A!A˦.AHA@d.AfAȘ.A`Ax˞.AA.AVAo.AA.AA0.A` A@d.A JAƌ.A ZsAXۉ.ANA.A0/AV.AA.A` A`.A`ʿA|.AA(,x.AEAt.A`A"r.A@?AZo.A`Ah.A@A.AMűA(-G.A(A pO.A⥱AzX.A`mAc.AHAAq.A rA}.A `A֋.A@FAP8.A(Ao.AA跧.AxAk.A$A.A﫰A.A A .AsA=Ǭ.A@WXAh̬.A3AxWЬ.A AzЬ.A`DޯA(Ь.A`ApѬ.AAӬ.A sAhL٬.A&CAݬ.A&A.AA`.A lȮAF.A眮A.A[rAP .AEA`.A@p"A(`.A`2Ae&.AAk/.AŭA0.8.A&A-@.AՌA3G.A`m^AhK.ABAS.A`;AhQ[.A A8b.A@A`Gj.A 8ǬA=s.A`mA|.AuA.ANA=.A`!A.A$ALJ.A`ԫA.A 8AX.A/A8.AE`AH.A9?A0.AA.A`;AX֔.A ΪAF.A Aۛ.A@yAH.AXAG.A@.$A.AA@.A@.ʩA.AAA.A /pAH.ANA@.A'A跫.ADAf.ARAh.A@AH{.AuAX֣.A@8A@p.AA.A Aؽ.A`§AX.A`DA`.A A.AXAXV.A:Ah.AkA".AA@.A̦A0.AxA.A슦A.AymAH.AaKA(.A"A.AAё.A@gեA.A;Ax.A (Ap.AaoA`G.ATA .A@g5A.AAA.A`AA.A3ؤA =ĭ.A@NAPMʭ.A;AЭ.A`iA ׭.A@?Afۭ.AA0߭.AA .AϣA0.A cA`v.A`]AK.A@N]A`a.A3A".A`2Aȓ.AAL.AAp=.A cAj.A{A(.A`XA.A9A.A ZA.A Ax.AfAH .A`܏Ap.A@yeAQp.A}[A\:Qc.AQ AKTEc.Ak5 A( ec.Aҹ AZc.A6' A0Vc.A A\:Qc.AQ Aт,c.A|O A^!c.A< AYc.A`Q AHc.AN A]Sc.A"HW Aohoc.A\ An@Ѷc.A+.{ A"Œc.AQ0Cc Ac.A` AWc.A|O Aт,c.A|O A?2Lc.A& Ab.AH Ad3N>c.A_ Ap#c.AbK( AHd.Ak Af.A`P AKf.AV Ah"i.A@N Anj.A' Ax)j.A& ( Al.A1 AMn.A 8 Ann.A@#F9 AHn.A99 A]n.AI: A`an.AՖ: An.A; Aȃ,p.A4B AHp.AG Ahp.A@J A$q.AhmL Aq.AQ A=q.A@Q A.tr.AT Aur.AU AX[t.A ca A(iJv.A`]g A v.Ai A w.ANo A0Fy.A S7x AxJy.APx A%%z.AǙ} A9?z.AB;~ A{.AX܈ A|.A} A[|.A@iH A}.A`) A}.A  ADӀ.A A0(.A53 A .A& A]T.A. A.A A Ҽ.A`D+ Ae.AHU AP8.AQ A`.Aޛ A߈.Aa A/.A0 AP.A@ Ad.A@W Aˊ.A A0Hۊ.A`2 A8.A A#ӯ.AV A*.A ASN.Aw AHئ.AI At.A@y AK.A@P Au.A A7އ.A Aڸ.A A APs/.A AX=.A4 Ahs.A4փ A~.A u@t Av~.As Apf~.A#r A\z.A`W A y.AaO Aw.A /D Al=t.AR/ At.A]. Apt.A- As.A[- A s.A, Ax-s.AiA, A"ys.AX+ APr.A $ A(r.A! Aȋq.A A _,q.A2 Ap.A Q AHo.A Axo.A`f A*o.A AX?o.A0 Apm.Ah Aؽm.A@E Asm.A@ A m.A` A4'm.AI AVl.A` A@1k.A@P Ak.A`K A`Էj.A@,: ASlj.A` Ap8j.A AH.^j.A A@ej.AQ AHj.A AYi.A` Aܒi.A Ah.Ax Ah.Ai APoh.A AXBh.A` AxCh.A ApRh.AB AV7i.AA Agh.A@ A $h.A AXg.A`I APg.Ag Ah.AF AHTk.A5 Ao.A Ahn.A Appk.A`o A(k.A A)/l.A@ A(Vl.A A/k.A I A0]~k.AmY A( k.A' Apk.AH A:.k.Ax Af.AM0 A?c.A Ac.A`R Ajc.A@ Aȭc.Ay A`~c.A` AQfc.A AQ0c.A A?2Lc.A& AF@4e.AK|, A*&{1;f.A03 A%hh.A౬P Ad l.A+l AGÕp.A7K_ Azʙt.Auv A_Gݿy.A@ Ab"n.A A 0.Au AG.A`6 AD".A Aأ.A2 A¥.A6 Ap.A A A.A`d A(.A H An.A A@/.A A~.A A$~.A ~ Aƥ~.A^ A%~.A`26 A~.A A0}.A1 A>z.Aݧ A:x.Aɗ Aw.A֓ A }bw.A A +u.AĽ At.Ak A0&t.A A(s.A`D AXs.Aྮ AX&r.A@dv Ah՚q.Apuq A`&q.Am Ap.A@}j AX59o.A 8b An.ABc AP'Tn.A` Ayn.A``` Am.A@ؙ] APm.AtX] Am.A\ A(9|m.A`[ AP/Zm.A`mZ A/6m.A c+Z AHj.AK AGj.A@ G Ai.AE Adi.AfE Axii.A`D Ai.A`yD Api.A D AX[i.A C Ani.A /B Ah.A`m= ATg.A`7 Af.AW2 AF@4e.AK|, AFV.A Le AvQw/A / AE5AA`S/A?/ AJIN/AĒL A6x,[G/AT,ѕ A+#@/A| A=/AV Ap>/A@r A0.>/A 8 A#>/A@g0 Ax'>/A`K AP'A>/A AE>/A- A`>/A  Ahd>/A H APk>/AY A݂>/Aqn Ap,>/A A>/A) A(>/Acʍ Ab>/Aߍ APx>/A@p{ A(l>/A  A>/AF1 AF>/A F A@?/A Ap?/A@` A:?/A@ؽ Ah>?/AՐ Ah^[?/A` Aߝ?/A@ѐ Axd?/A@ A@?/A@ AB>/Aw AiG?/A A=/Aq)}; Ap:'=/A~ A0H=/A AB=/A@P A6/A@} AH6/A[ Aj6/A' Axǒ6/Aa Af6/A` AH.6/A` A@W97/A`9ӳ AP87/A`\ A 8/AE+ At8/A`Y A`8/A 1 Ah359/A' A9/A A':/A`m A;/AԷ AH)v;/A^ Aз;/A A /A / A'c>/AFJ A>/A A>/A AP>/A: AL?/A`2 A?/A A @/A@Ƒ Ad@/AD A<@/AY ApA@/A` AН@/A A8#@/A Ax-?/A A(?/Anz A0=/A A'O=/A} A/A`| A` >/A@߂ AQ>/AO Ah>/A} A0+>/Aq A8>/AFa AE>/AK Ae>/A A@h?/Aw AFu?/Am AH?/A QZ A>?/AJ A"?/A}? A̹?/A@p6 AU?/A@? AN?/AK A@?/A` ] AP?/A@ t A@/A$ A0@/AH A?'@/A AA/A@% AB/A#\ AB/Aβ ApC/A` AC/AY A8sC/A AxC/A |? AC/A A C/A AB/A) Ax$0A/A  A,?/A AKq?/A A0?/A` Aa>/A@WE A0{K=/A A2%/A4p Ah%/Ap A%/A@ p AP%/Ap AP%/A p AH%/Ap A@d%/A`p A %/Ap A8%/A`p A@`%/A Ap AX%/A@!p A%/A p A\%/Ap Aذ%/Ap A8%/A@p Ap=%/Ap AY%/A@p Au%/Ap A%/Ap A%/Asp A(,%/Ajp AXV%/Aap A%/A Zp A%/A Qp AԷ%/AIp A%/A`Bp AS%/A4p A}%/A /p A%/A`)p AP%/Ap AP%/A p A0%/Ap A%/A@ p A N%/Ap Apx%/Ap A%/Ap A%/Ap AxK%/Ap Au%/A`p A%/A p A%/A p AH%/Ap Ah%/Ap A%/A@%p A@F%/A0p A%/AKp A`C%/A[p Ah@%/Ap A%/Ap A྿%/Ap A%/Ap A=%/A@p A8%/A@p A`%/A`p Ap9%/A6p A%/A@yp A5%/Ap A%/Ap A%/Ap A@1%/Ap A%/AZp A%/Ap A%/Ap A%/Ap A%/Ap AP'%/A&p Az%/AZp A@%/A@p A!%/Ap At%/Ap A%/A;p A(%/A`vp AHn%/A@p Ap%/A`p A0%/A4p Af%/A`vp AH%/A`p A%/A`p A %/Ap AH %/Ap A_%/A@Gp A0%/Akp A%/A@p A%/Ap A,%/A`p AV%/A&p AH%/A@Np AX%/Atp Ax%/A`p AhL%/Ap AP%/A`fp A %/A`p AA%/Aq A%/Agq A%/A q A`6%/Aq A%/A`vq A)%/A`2q AR%/A cq Az%/Aq A%/A`q AH%/AXq AD%/Aq Al%/Aq Ap%/A@%q A %/Aq A^%/A`q A0%/A cq A8%/Aq A(%/Aq A %/A q AM%/A?q Au%/Avq Ah%/Aq A%/A q AP<%/Aq Ad%/Aq A%/Aq A%/Aq A)%/Aq Ax%/A@p q A%/A q A0%/A q A%/A`) q A%/Ag q Ad%/A` q AH%/Ad q A%/A q Ah%/A q ApN%/Ah q A(%/A q A%/Aq q A%/A q A7%/A q A]%/A=q Ah%/Aq Ah%/A q A@A%/Aq Ac%/A q Aз%/A@q A%/A@q A%/A`Bq A`P%/Aq Ax%/A@gq AX%/Aq A84%/Aq A%/A (q A%/Aq A%/Aq A%/A[q AHa%/A`q Aث%/A@q A%/A`q A%/A@#q A%/Azq A@%/Aq Ae%/A@#q A%/Atq A %/Aq A%/A`q A%/A@iq A%/Aq AC%/Aq AXh%/Aaq A %/Aq Aȱ%/Aq A%/A`q A%/A q AC%/A`[q AXh%/A`q A%/Aq Ah%/A`q A%/Aaq AA%/Aq Ae%/Aq A%/A q Ax%/At q AP%/A q A=%/A(!q A%/A"q A %/AL#q AX%/A$q A@%/A$q A`%/A|%q AP+%/A9&q Ap%/A&q Aȓ%/AX'q Ax%/A'q A%/Az(q A@%/A=)q A%/A@*q A%/A`d*q A%/A*q AX%/A*+q Ap%/A+q Ap0%/A+q AXR%/AU,q Ap%/A ,q A%/A@ -q A8%/A -q A%/A.q A0%/A.q A%/A`;0q A%/AR0q A%/A"1q AH%/A`1q A %/A`1q A'%/A`1q Ai%/A@2q A`%/A$3q A%/A@3q A%/A3q A'%/A-5q Ao%/A6q A%/A6q Ap%/A`6q A%/A7q A0*%/A@8q A@1%/A8q A(q%/A9q A(%/A@,;q Ah %/A`;q Ah/%/A$q A`%/Aďq AL%/Aq AP%/A$q A%/Aq A%/AĖq A%/A,q A8c%/Aq A%/Aq A%/A=q A%/A`q A%/Aq APM%/Avq A%/A@ q AP%/Aжq A%/A@q Ah/%/Aq A %/Aq A0%/Aq AXF%/A cq A(h%/Aq A%/A[q A؛%/A q A0%/A@q AX%/Aq A#%/A`q A:%/Aq A}%/A q A%/Ajq A%/A q Ap%/Aq A%/A q A%/A]q Ax%/Ar A%/Acr A%/A r AH%/Ar AP%/A Or A%/Ar A%/Ar A %/Ar A%/Ar A%/A,r A(%/A}r A %/A Sr A`%/A`#r A%/A`D#r A%/A`$r AX%/A`"%r A%/A+&r A0{%/A3'r Aq%/Ai(r AXh%/A)r A[%/A"+r AhQ%/A@W,r AF%/A-r AX,%/A[0r A%/A2r A%/A6r A%/A7r Ap%/Aw9r A%/A`:r A%/A;r A%/Ar Av%/A?r A@S%/A}Br A%/AHr Ap%/AJr A%/A8Jr A]%/AQr APZ%/A@Qr A%/A`[Yr A@%/AZr AH%/A_r A,%/A`r Ah%/Agr A0%/A@,gr Ah;%/Atr A%/Awr A%/Adr Ah"%/Ar A(%/Ar A%/Awr A؟%/A@ r A%/A@Er A$%/AVs A%/As Aں%/A`s A%/As A%/AVs AΘ%/A@Er A%/Ar A(%/Ar A%/Azr A%/Asr A%/A@r AТ%/Ar Ap%/A r A0ԓ%/A r A%/Ar Al%/Aqr A0H%/A r A%/A`r A%/Ar A9%/A@r A %/A Zr A%/A&r A%/Ar A?%/A/r A%/A`r Ad%/Ar A%/A`r AC%/A5r Aω%/A Zr AΉ%/A Qr AY%/Ahr Au%/Ar A0;%/A r A%/A@r A%/Ar Aʇ%/Ar AЯ%/Ar AȔ%/Ar A[%/Ar A%/Ar A҆%/Ar AP%/Ar AL%/Ar AHK%/Ar A%/Ar A%/Azr A8D%/A@pr AB%/Ahr A%/A^r A%/Aܭr A܄%/AZr AXۄ%/A Sr A%/Aͬr A%/AFr A@B%/A+r A%/Ar Aރ%/A r A8Ń%/Ar Ap%/Ar Aȓ%/A qr A{%/Ar Ab%/A^r AJ%/Aդr A1%/A`Kr A%/A r A@%/A5r A%/Aઢr Aт%/A` r A%/A r AH%/A@ r Ap%/A ~r A@΁%/Ar AP%/Ar A@%/A`r Ar%/Ar A\%/AUr AxF%/A`ʘr A]%/A@r A%/A@pr A`B%/A'r Ay~%/ARr Af~%/Ar AS~%/A &r A?~%/Ar A,~%/Ar A~%/A cr AX~%/Ár A}%/A@5r A}%/A r A}%/AD~r A7}%/A{r Ah|%/AFxr A|%/Awr A|%/Awr A y%/A` Tr Aw%/A5r Ah#/AR#q A "/A省 A^#/AO Ay$/Ab A&&/A  A`&/A` AcF'/A A.&/A` A0&/A S Ar&/A4X A[$/A Ap.#/A{ A p /Ao A</AZ A*/AX AY/A j AQ/AjEc A!/A@nc Ah^/AWc A/Aad A@d/A e Ah /A@e A/A`md Ap}P/Agf A]/A@5f AHmU/Af AxA/AUf A@ /Ag A /AJh A0 /A}hh Ac /A@g A/A d A/A@ed A/AQd A|/AMd AQ/A"c A0|/Ac AHr/Ab A/A`\ A@W#/AK[ A/ADN AXj/ACO Aج/APO A//A`;TP AP8/A` A'.A`V A.AO A.Ad Axy.AO! AP .A@E$ A.A5S& Axǖ.A' A .Ap) A.At) AH.Aށ- Axv.A07 A@_.AD; A2.At.= A.A ZA Ah.AB A M-.A@B A$Jw .A_7"B AWhr .ATGB Ae.AwB ACu .AB A0݁.A(7B A[t.AQ|MB Af.A(!ZB ARX.A1]B AZQ J.AHQ?XB A,%1<.AFUdIB A4 /.Ap;1B Aܭ2#.ANB A.AA A8.A`VB A.A B A .A0C Ah3.A D A.AD AȺ.A@5D A.A_D A?y8 .A?D Am.ACV{E A3Q.A1t!E A7.Ad0E A ڱ".AZRbJE ABZ&.AfE Ah(.Ap,EE Ag*.AE Aȥ+.A E Af/.Aħ8E ASU[}2.AzE A$5.AWRUE AG9.AF E A8<.AE AǕ:@.AČGF AGLC.AF f3F AG.A}I F A}I.A2F A J.Ah>F AW_K.AHJF A+K.A?ȏNWF AJ.AcF AxX.AwF Ad.A f6F A6p.AvF A y.A'AF A.AF A5A.Ami+G A{.AI_G A&ƌ.AzG AH.A |G AOj.A-嬜G Afj)6.AG Ag.AG A1.Ar!G AJwN.AJ!G A٤.A/G A.A/H Ab .AVfH A0.A ;H A v.AvaaH A{Z.AA%tH A9x\.AĦ-(H AFJ.Ax>H A.A]2I A.ACS7I A7l.A, @I A.A ILI Aɠ.A[I Aٴ .A:nI Aa.A%KΝI Ar%.ADI A`ω.A3I A0.A I A y.AI A *,.A`I A)?7.AE"q5 J A64@.AO2J AH.A4P?]J AܰO.AύJ Ae-;S.A!J A 4U.A`bJ AyY.Ad=/J A`8].A}ݳJ Aaa.AGRJ Ave.AeeJ Ah.A;UJ AVc2 l.A͋K A\n.AvK AycMWq.AӸK ADl$s.A<##,K AqMt.Asd2;K A֒t.AoVKK A0ot.A5[K ALs.AekK A&r.A HzK A O~.Au=K A|.A5T?K A>ŕ.A5үK A{.AIFK As.AG;K A:.A4"L AXdz.A̬}QL Ay.A#LWL Ap.AkL AAV.A<2L Ao.AL A0w.A`TM A=d.A9>M A.A@ eM A.~X.AP2zM A.AȾM Al4.AJYM Axh.AM A;o.AkN Az.ABN Aȱm.A`!uN Aa:.AㅢN A{La.AN AHv.A|[D O Ari.AKY:O Ah~.A"bO A6.AׄO A.AO A<.ASO Af.AlGO AjO.A%IO A7~.AS.O Anlҥ.AC=O A`.AIxP A.AHPP Aţ.A!P A5.A-P Ax.A@%7P As.A{hP A*Z.AHP Am.AP A"y.A٣P A_O6.A0, /Q ALst.AGeiZQ AI.Ap9Q AW".AS`Q ANƿ.AQQ A.AAQ A .A}tQ Ar[F.A?;Q AÕ..ASGQ AI9s.A?&GQ Ah.AÜQ AL.A@#R ApP.Aƾ)R A8eS.AպfNR A U.A$uR AU.A[R Ae1T.AJR AE~Q.AH4R A *M.A2S A(G.ACS A阿?.A:eS AM*6.AXS A "1-.AfS A.".A CʦS AZ.A}TS A p .A@#S Ao .AWatS A`p .A?S AV .AcAS AY .A{W A+.AaGW A&.A~k|W ADL.AW A1.AW Are .Aן W A}.A=X A3Vn.A}5X Ah.ADX Ao .A&bX Af].Ap}X A\.AX A.Aڂ X A+.AbX A.A|X A$d].ACX A .A{++Y A%3ƭ.AI\WY AM .A~Y APۘ.AtFY Agt㝌.AhgUY AH.AY ANqx.A?Y Ao.AC~ Z AG^f.Ap"Z A[.A@4Z A\V.A=LuoZ AEO.AZ A(F.AmZ AV^:.A[ A!-.A@o"[ A3.Aɓ:[ Ap.AI[ AQ .Acmb[ Aw .Abx[ A,>.A$ tg[ AG.A|[ A=%.A[ AH.A[ A.A l[ Ah.Ab A%.A!c A[.A@W(a A.AZ_ A.Ap` A.A_d A).A4i AZ.A`]h Ay.ALd Ap=t.Aac AH.A z\ AK.A\ A`d.Ap\ A.A\ A0~q.A9] A.A9M] Aca.AWv] A8.A] APUXz~.A] A$ʗw.A] Aq/p.AlMk] Ag9qg.AJ^ AL].AH!$^ AYS.Ae1/^ AzI.A<8^3^ A@?.A1^ A0?.A,E^ A}>.AeDY^ Aڅ=.AZRl^ AX:.Aʀ)~^ AzMp7.AR^ A^ZL4.A((_^ A;0.A2^ A+.A^ AZt&.A^ A m$".A) ^ Ax .A^ A p<.At^ A.A-'_ A .AMX_ A.A |<_ A?F.A@_ A|7.A:U_ Ab.A`_ AvyF.AlϞ_ Aoi.A@#_ Ag-.A=K` Ab,.A` A .Az$` AW̦.Ar(` Aj+.Au/0` AE.AF4` Av$`.A(#v` Azˬ.A#b` A͠.A|_E` A齒.AkFa A`.Ay5a Ar.AKa AF8o.AVea ALk.A:oO}a Asf.Ay(a AY `.A*a A`Z.A̶4a AS.Aa A?fP.Ala A tJ.A|Gb AoC.AHGb A6:.At3Xnb AWc0.A薰b A[$.Azb Aj.A^Xb At .AÚɉb AJ.Af?b AE.A ub AA?.A'Ҋ'b AsLjz.A]&b AI .A^b A,.A}kb A)d.Ac AQ3j.A%xc A0.A\hc Ac.A Jc A1.AAn}%/c Aذ.A|Tc AN?.Ayrc A}ډ.AӾ:c A.A>ٔc AmN}.A-Ӹc ASqo.A@ޑc A~*Tb.A"t[c A^U.AlQGjc A)`J.AnIc A"O!A.AF@!c AcV9.AS1Ub AB3.Afg7b Ax0.A`b AxI{*.AVˎb A<%.A{o݀b AVx .Aub A&.AW,gb A .AǓVb AH.A VCb A #f.A=3V.b A.Ah "b A.ARb A.AT6Ca A\ .AXaa AS.A3pa AvCt.A*7a AQ.Ad[|a A .AΤpOa A-.A,a Ap.A 1` Aџ.A,` Aj V*.As` A].Arİ` A).AHquE` Avl.Av` A+.AU` Am.ArܡB` AG.A&` A59.AX` A_b u.AJ_ Ah4l.Ae_ AmPf.A]n_ AWc.A5_ A*1].A_/_ Aq%X.A4`$d&_ A6 r.S.AZF_ A$mN.A W _ A9K.A^ ArvH.AC^ A]F.A91i^ AE.A^ Ao7.A'g^ A=Z*.A}pY~^ A0Jr.Alt%X^ AQ.A\p\+^ Aj .AS"J] A! .AW] Aأ.A`f] Aq.A] A}.AtZu] At9.AFSf] A }.AoYoU] A-.A0jA] A0a[.AcOt+] A0e#.A(]<] A.A\ Aa.Aީ^\ A:.AQR]\ A,T.A{z*\ Ae.AH4y\ Aa.ApF\ As$.A|\ Aȩ.A}[ A7ۢ.A%[ A.AOW[ Aoӊ.Ab[ A#WU.A_)[ AϞ.AQ[ AJ.AO-/w[ Ax}.A@_[ Ay5o.A' J[ AXa.Am孬+[ Ap/U.A=[ AjJ.A淾Z AuB.A$5}Z Ai4wg=.A.bZ A:.AV鶐$Z A`X:.AY A^/2.AY Ač",.A?=#Y Ap%J&.A0٣Y A_:v!.A(8߆Y A.A \gY A.AMɎQY A*R.A(]7Y AOb.A.agY A.AX A.A`vY A.AKY AU.A`Y Ah.A 0Z A(.ACZ AP8.Ap}Z A`.AqZ A1.A@PZ A.A`vZ A+.AZ A.A )[ A)~.Aa A$q.A/Ca A0.A@[ Ah3.A[ A`P.A`][Z A@.AIZ A۷.A Z A".A a A(.A@!af A0H.A g AL}.Ag Aiw.A<_(g A>㥠q.A000g A$8 k.Ab4g AP}d.Ao3g A].A|/g Ae#^.A7g A ^.A}g@g A].AHg AC\.A7YSg A[.A(\g A|)Z.AEeg A̡KX.A {mg A| &V.A/tg A0eS.A7=yg A=Uh ATٹ .A% kh Aj8.Ax%h AVڛ.A ?h AN^.AWQh Aj.AgLh AFP.AMh A.Ah AP.A Q'i A1T0;.A >i AJ.A_Ui Az.Ayli A(6.AZi AT .Ai A\w5.A i Ad.ADi A.A!g i A!և.Aӂi AK[.A\Ci A.A~yi A`ð.Ai AŰ.Ai AG~.A`:i AԲ8.Ati AG!.AtYi Ajb.A)i A4[֬.A5i A< k.AAsi AJ(ߩ.Ai A=.ANVi AX.A i A{.Ai AT.A i AƤ .Adsq j AҒͿ.AӬ>!j ANu.A8j AXQ4.AUNj A,.A=bj A0.Ah&Ztj A9u.Aoj Ar?.Aj AN.Aj A-.Aj A$}.ACwj Axw.A@j A˹w.Aj AQw.A]Ϧj AW|v.A2 0j A+cu.Ajj Agbs.AHj A[@0_r.A'=|j Ap.A7.j An.ALHSj AKil.A?F7j Aj.A@j ALk.AnBj Aj.AH8j Ay*ii.Ak A3Rg.A'ik Awd.A*k A_R`.AVx:k A}\.A߆Gk Ay"X.A1qRk Ah"S.AYk A.AfSl APͪ.AfSl AP.AfSl A.AfSl A(.AUl Ax.AUl A8E.AUl A8#.A@`Xl Aa.AZl A.AZl A~.A[]l A@.A@_l A.AVbl Ax:.A`dl A`.A@Pgl AW.Ail AЕ.A`Kll A.Anl A@.Asl AQ.A@vl A.A@xl A@.A}l Ahn.Al A(.A0l A.A+l A*.A@%l A.A` l A.Al A.Al AP.Al A.A l A$.A`l Ah.Al Ac.A@l A.A`l A.Al AA.A l AH.A cl Ah.A`]l A.AWl A9.A@m A2;.A@m Ap<.A@m AN>.A@m AP?.A@!m A@.A@!m A B.A@!m AC.Am AP'E.Am AfF.A@m AG.A@m AI.Am ApJ.An AL.A@n A>M.An Ap}N.A` n AO.A n AP.An AX9R.A n AwS.A@n AT.An AVU.An AV.An A@W.At"n AY.Ao'n A@Y.A@i,n AZ.A.n A@[.A@3n A\.A`8n A(m].A=n A ^.ABn Aث^.A`Gn A_.ALn A`.AQn A)a.AVn Aa.A@[n Aa.A``n Ahb.Aen A0c.A jn Ac.Arn Ac.Awn A].A o A;.A o A(z.Ajo AX.Ajo A06.Ajo At.A@o AR.A@o A0.Ado Aho.Ado AM.Ao Ap+.A_o A0j.Aݜo A0H.A Zo A.Aסo A.AVo AX.A@Po A.Aͫo A .A`Ko A.AEo Ap=.A?o A|.A@o AȺ.Ao AX.A@o A8.A@o Av.Ao AX.Ao AT.Ao A`.A o A .Ao A@q.Ao A.Ao A`O.Ao A.A@o A.A@rp Ax-.A`mp A.A p Al.Ap AH .AWp A.A p A .Adr Ax.Adr Ax.Adr A8.Adr A .Ar A .Ar A .Ar A }.A_r A[.A_r A.Ar Aw.A Zr AU.Ar A3.ATr A`r.A r A`P.AOr A(.A`r Al.A Jr A.A@Er A.Ar AP.A@r A.A;r A.A@5s A#.A/ s Ahb.A)s A .A$s A .A s AP .As A] .A"s A.A 's A:.A.s Ay.A@3s AP.A:s AW.A ?s A.A`mGs A5.AgLs A.A@Ss AXt.AXs A.AQ`s A.A`gs A8R.Als A8R.A.At Ahޠ.A-t A}.A(t A0.A t AP.At A[.A@u A0.Au AP.Au A9.A u A9.A uu A٥.A "u A٥.Af*u A٥.A1u Apx.A6u Apx.AO>u Apx.AEu Apx.A?Mu Apx.ATu A٥.A@Yu A٥.A*au A9.Ahu A9.Apu AP.Awu A0.A|u A0.Au A[.A}u AP.Axu A0.Au Ahޠ.Au A>.Aau A؟.A]u Aa.Aհu A.Aеu A .A`_v A_.A Zv AP.A$Uv A.A)Pv AȺ.A/Kv Ap.A4Fv A88.A9Av A8.A>v AT.A9v A.A4v Aq.A J2v AO.A O-v A .A *v A(l.A`T(v AJ.A%v A.A Z#v Af.A` v AD.A^v A".A v A.A v A.A`dv A.A`dv A.Av AP.Av A.Av Ah.Av As.Av AhQ.Av AP/.A`dv Ah .A`dv AP .A v AP .A v A8.A^v AX.A` v A.A` v A.A Z#v A.A%v A^.A *v A.A O-v Az.A`/v AX.A4v AX.A`D7v A.A?+w A_4.A2w Ah3.A@.:w A؁2.A(?w A p2.A@w A1.A`Fw A`C1.ANw A0.A@Sw A`e/.AZw A&..A_w A`-.A`fw AH,.Akw A +.Apw Ax).Akxw A(.Af}w AN'.A@`w A`&.A`[w A$.AVw A #.A@Pw A !.AΓw AXu .A ɘw Ax.A`Ýw AX.A@w Az.A`;w A <.Aw A^.A@5w A.A0w ApA.Aw Ac.A+w A.Aw A.Aw AP .A&w A .Aw Aج .Aw A.A w A.A w A.A w A4.A w AV.A w Ax.A w Aؚ.A w A.A w A.Aw A.Aw A@.A&w A@.A`w A@.A`w AX(.A*w AXJ.Aw A .A0w Ax-.A@5w AO.Aw A.A;w A3.A@w A8.A`Ýw AX.A ɘw A.AΓw A.A@Pw A.AUw AP|.A`[w A=.A@`w A.Af}w Ah.Akxw A.Apw AB.Akw Ah.A`fw Ad.A`_w AP&.AZw A.A@Sw AH.ANw A.AFw A.A@w A .A(?w A.A@.:w A+.A2w AH.A@>+w AH.A@#w A.A@Nw AM.A@w AM.A`w AH.A cw AH.Aw AH.Asv A.Av A.Av A.A v AH.Av AH.Av AH.A@v AM.A@,v AM.A@v A.A;v AH.AĮv A+.Aɩv A.A Qv A0j.Aؚv A .Aav A.A@gv A0H.A`v A.Avv A.Au AI.Au A( .Au A̓.Au A؍.A@u A(O.A`u A.Au Aю.A`u A.Au A@.Av AX׉.Ayv A.At v Ax.A v A{.Aov A.A@iv A _.Av A@.A`dv A ~.A v A8|.A v A{.A^v Ay.A` v Aw.A` v Au.A Z#v At.A Z#v A0r.A Z#v Ap.A Z#v Ao.A Z#v A5m.A Z#v AWk.A Z#v Azi.A Z#v Ag.A` v AH]f.A` v Ahd.A^v AHb.A^v A``.A v A`^.A`dv AЦ].Av A[.A@iv AY.Av AX.Aov AV.At v A`U.A`v AS.Av ArR.Av A3Q.A`u AVO.Au AXN.A u AL.Au A8K.A`u Ap[J.Au AI.A"u A G.Au ApF.Au AF.Au ApD.A`u AȂC.A`Bu AXB.A@Gu ADB.Aеu AA.Aհu Af@.A]u A?.Au AP'?.Au A0>.A@ru A=.Au A k.A t A.A t A .At A.At A.At A.At A.At A.A ~t A.A ~t A.A` |t A.Ayt A.A &wt AH.Att AH.A+rt A`%.Aot A.A`jt A.A6ht A.A`;ct A(.A`t A(.A`[t An.A Vt A/.AQt A@.A`Lt A.AGt As.ABt A@5.A=t A.A8t A跸.A3t AXy.A`v,t Aڶ.A |'t AX.A"t A.A t A@.At A.At A~.A t A@߱.A#t A@.As A.A4s Ap.A9s A].A?q A.A?q A.A.A q Ah.Aq A.A q A.Aq A.A@q A0f.Ap A'.Ap A.A`p AP.Avp A .A |p Ah̙.A p A.Ap AH.Ap A.Ap A`.Ap Ap.A$p A`2.Ap A.A4p A<1.A@po A^/.A@po A( ..A@po AB,.A@po A@d*.Ao A%).Ao A`G'.A uo Axi%.A uo A*$.Ao AL".Azo A !.A`~o A 0.A|o A`.Azo A.A wo A.Auo A8.A po A8.Amo Apy.A@ko A:.Afo A .Aao Ap.A ^o A~.AYo AP@.ATo A.A Oo A .AJo A8 .AEo A .A@o A0 .A@;o Ag .A 6o A0.AR/o A(.AX*o A0.A`]%o AK.Ao A.Ao AH .A so Al.A@y o A.A@,m APw.A@,m AP.A@,m AZ.A@,m A|.Am A.Am A_.A 1m A.A 1m A`C.Am A`e.Am A&.A6m AH.A`m A .A7 Yk A.A Ik ATPY.AM9k A0.A`K'k Aw.Aj Ay.AH*j AY.AG[j A?}.AiSYj Aŝw.A|j ACms.Aaj AIo.Aßj Aal.Aayj APIj.A`cj A(N.A`fj A*EJ.AIj AE.A!j AGA A.Ap%j A .Ҷ<.Aa_j AJ8.A6i AX15.Ai Aj'g42.AM.lmi A/.AIi A;(..Aրi A5-.Ai A`.A@>]i AdT .AVi A.A69Ki A.Av:i AS/.A&i A޷^.AI{ i A=.Ah AXh.A h AA.Aa|0Wh Av.Ach A).Aspah A2(.AEh AM.A'1qh Ae* .AJj[h Axذ.A`Ch A.A g A$Q.A"NYg A~.AQg A@.Aig Ap;.Arg Az.A,,g A>v.Ag Ax 8s.Aʤ`g AtM@q.Ag A`o.A` g AY.ACg A U}R.A*Bg ALJK.Aq .A>1#g ADwz9.A2{g A6^(4.AVnf A`>t1.A7f A`/.Af A( .A@ef A.AZg A@B.A^ A.AM` A*.A#` Ae.A@y^ Ay<.A 8] A-.A\ A0.A\ A-.AF;B[ A*&{1.AP9[ A;W^.A=$-a AT.A8f A3.A g AZ.Av Av.Aw APf.Ay AP.Ay Aɪ.A`mx A.Ax A.A ɽx Ax.Ax A[.Ax A.A`y A.Ay A`.A q!y A".A =y A=.A@y Ap .A\y A.A`y APM.A@Gۂ Ab..A Aot.Aṡ Ax5.Ax AG.A ˸ AQ.Ao Ahe.A. Alq.A3 A&.A@y> A.A`ˆ AXd*.AzW A)5.A`f A[.Ap AH:.A AXR.A A.A`2D Aj.A@ A].A`^ AX.AH A.A Ҍ A.At AX.A` A0ٕ.A` A.A4 AXX.A`~ Ahj.A`Ғ Ap=<.As3 A.ATA Axu|.A( Ak.A & A.A" A(.A@y! APg.A` AHz.Av A.Av A'c.A AS.A`bq A(ۘ.A Q APZ.A 8/ A].A}w AV.A Ad3N.A_) AE]K.A}9 A*&{1.A0> A%hH.A౬. A?.AFA4 A`].A+4 A7.A`}% A EM.A઱" A0.A! A蕲.A A$.A& Ad.A4 At[.ȀF A$].Ai~HB Ad .A+I AGÕ.A7K_%d A.A o A,.A?m A.Am At.AQo A&䐄i.AС2s Ap.A`gs A݉.Aq A0.An A.Am A.Al Ap.A@l A.Am A7.AOn AH.ABrq A軤.A[{r Aω.Ap]t A Y.AZx A8I.Ar Ah.AKl AB".A`~l A>.Aq A.Agq A.A?q A $.A@5o Ae Ap|s.AJf APq.A6f Ao.A g Apr.Al AJ.Ak A.A`s AH.A`s A0 .A`s A.Ajs A@$.A ~-k A 4?.A@!k A.An A.A=n A.Azn AY,.A`Vn A~.A@yAn A@.AQn A.A`m AH2@.Am Ah/k.A@"m A.Afl A.Atl A.A`~l Ah.A@=l AH2<.A@l Aa.A@l Apr.AR>l Ap.A@l AHܔ.A m A8.Aam Ax.Am AH.A n AP*.A n Av.A Zn Ap.Am AHX.ALm A:.A@n A .Ac A`.A`vb A}`.Ab A.A 1e A`.A` e A(.A`B?j Aп.A@j A{ .Am A .Aj A`Ò.A-i A8.AĤg A.A.f A^.A Ed A.A_rb A`i.Aa A.Ad A}.Ac` AMQ.A_ A }.A`R] A`.A] AX!.A@C\ AR.AZ A.A@.Z AE.A l%X Af.A2V Ap/.A@T Au.A /hS A^.AQ A .AMQ Aem.AabP Ap}.As`O Ap .AgYN ApA.AM Aw/AL A6/AK A /AI ApZ/AoH A/A@F A8/A@E Aӟ/A}aE A/A@E A/A|E AIy/AF A^/A@NE A8U/ApE Aef/AD A/AD A0ș/A E AΜ/A 8=E A /A@ED A;/Ay~C A`/A@ AHl/AA A/AA A/AB@ A/Aǔ@ A/A u@ A /A /@ A/AfA A5/AWeA A/AA AP /A@yUA AТ/A5A ATn/A`A A}/A^B Ax/AْB A/A` B A~/A`B A੬/AC A/A  /A ~I A0]f /A DJ A  /AFNJ A /A \J Ax. /A \J A0X /A`J A /A`J A( /A`J A!/A Ap\/A-> A*/A/J> A/A`U: A /A`K7 A/A; A?/A cD; Ah/A@9 A/A Ɂ6 A/A5 A@=/AB6 A/A S8 Ap9/A@Ps8 A/A 7 A@ /A 8v7 A/A@6 A@ /A?S6 AX/A35 AU/A e4 Al/A S4 AXj/Aj4 A0;3/A-;. Aȥ7/A@i8. A0%/A_- A ͑/A |r- AH./A S4 AЕ/A4 AL/Aa^4 A8E}/A: A/A = A&+/A@V? A</Aj? A&/A`0A AHY/A@ A#/A9bZ A(-%/Aco ADz&/AOx A0Lf!/A6k AP!/Aݭ AG]!/A A /A`D A/AL A _/A` Al/A AL/Ax Ag./A`m4 A8 /AF A/Aj A`P/AH A/A? A-h/A} A/-/A| A@/A  AS/A2 Ar/AX A:/A6 Ay/A / A/A@iJ Aho/A As /A`KX A#/A @ Am%/Ae A8'/A8 AL./A@,| A2/A^ AH3/A l A)M6/Aי A!6/AȆ A[ϩ6/Aw~3 A2| 7/AF,Ș A^G7/AfhLK Ap.7/A7Ԟ AwU7/Aze# AK67/Afy Ad8/A"rT A26L8/A_} AA/v8/Axg+ A,8/AO# O AE*8/AZ0h A`L8/AtYy A[8/As4 A'9/A胏 AEM09/AOލ A$WJ9/A+΃3 Aj_9/AB An9/A҈ Acr9/A7L AEY9v9/Af3 AjH|9/A@VY A_΁9/A2 A9/Ay AM٭w9/A< Ax9/Aā APSd9/A(#T̓ AH9/A` Ao9/AU A~9/A𘧛 A39/A^Wݪ AbV9/Aݼ A]9/A'7ш At |9/AAj A9/A A*69/A*6 A4ū9/A? Ai̠9/AƂ AMN9/A7 AQ9/A| A+u9/A.>AА AWɟ9/AI) Ap9/A? A~߮9/Ab~ AV9/Am Aܰ9/A5 ͑ A Xn9/Aݤ{* A|n`9/Aw A<Ǭ9/AQ A9/A ' A)1H9/A6H/ A{V^9/A?& A99/A@y? AP 9/A9 A8/A A08/AnG Av8/A A8/Aھ A8/A@/ A88/AI A@8/Af Axe8/A֘ Axzc8/A ATQ8/A A0668/A9 A)8/A@p AЄ'8/A?ޗ Ar8/Aa A87/AA A 7/A A8/A^ AH8/Az4 A849/A`V A0%_9/A@iT Ap,9/A徜 A(:/Aڜ AH9:/A@## A Q:/A A8Ia:/Aӛ Ac:/A@ AX~:/A끠 A:/A@ AD8;/A@J A ;/AЙ Ah;/A` AT;/AR~ AN!;/AT\y A0:/A sy Ar:/A` _y A@N::/A'qy A:/AKy A@9/A z A`X9/A{ A9/A`| A|9/A@#~ A9/A@X Az9/A+ AP9/A@io A跹9/A`Á A9/A A9/AH A0 9/Aag AS9/A3~ AXے9/Ah| Aྜ9/AЈz A9/ATx A8b9/A`w A=9/Av A9/At AM9/Ar A%":/Aq ACd:/A o A3x:/A }o Ax؎:/A`o A`:/A@Wo A:/A o AH::/A`o AH:/ADIp AP:/A@p A:/A`Xq A:/A q A`v:/Ar APg:/A]5s AbN:/A0s AH:/A t A'::/Aଇt AXR5:/A`mt AxW9:/AOWu A@D:/A`u A8EL:/AKv At]:/AXv Al:/A*w A 0:/A@w APo:/Aw A`:/A`Tw A:/Aw Ah;/A@Ew A>;/AXw AZ;/A{w A|s;/A $w AQw;/A v Az;/A Sv A8Iv;/Atu Ar{;/A Qt AԒ;/A@,t A;/Avt AB;/A@t AB;/AjBt AB;/A`s A;/A*2s A;/A`r A8;/A!r A0پ;/Adq A؛;/A`9#q AE;/Ap Ap,;/A1p Ap,;/AFo A;/Ao Aa;/A׊o Aќ;/A &Vo A:;/Ao AX;/ASn Az;/A m AHe;/Am AuU;/A/n A:;/A in A0!;/A=n AX ;/At!o A:/A 1@o A:/A`̠o A@:/Ao A@:/A0o A:/AZo A:/An A:/A9n A06:/AÈm A^;/A@ l A;/Ak A=;/AXj A(L/A`Te AX>/A=f ARv>/Af A|C>/A`m;g AXh5>/A h AL>/A9h Apy>/A`9Gh Am>/Ag AP|>/Ag A8?/A@g AC?/Ag A3?/A@g Ax?/A |Ag A@?/A`DEg A @/Axg AH@@/Aർg AI@/A g App@/AZg A%A/A0uf AhA/Ae AA/A>e AHA/A`8d ApA/A=2c AA/Ab A4A/A)a A֒A/A |` A8QA/A` AXyA/Agfa A`@/A]a AH?@/Ab AR@/A 2c A @/Ac A?/Ac AH:?/A?c A?/Ab A8?/A-b A-:?/A>(a A`t:_@/AӴ` AD@/AOj]_ A;8A/A@_ A ߿A/A@N^ A!:B/A@Eq^ AB/A^ AsB/AX^ A8B/A`_ AC/A}f_ A`%C/Af_ AxD/A _ A$D/A@ ^ A`D/Ay^ AxE/A@a^ A>E/Ao^ A PH/Aw_ A8H/AT_ ApsI/A`2` AH2 J/A`;_ Ax˰J/A`2` AK/Aw_ Ah@&K/A`@_ AhQK/A^ AK/A ^ A2zL/A ^ AM/A@E_ Ax'M/A`_ A@sM/A} _ AXtM/A`2P_ AOM/AQ_ A_M/A@,q` A`M/A`K` A0ٲM/Ab AXM/Ac AxM/A &Le A`%DN/AFe AN/AFe A@N/A൚e AH)N/A`[e A8O/ANe A(O/A`` AHK[O/A<` AQO/A-6e AHO/A`)d A@~O/A`v`d AO/A`2c AO/Awc AO/Ab A O/A`Rb AP/A"b A)P/A@,a AP>P/Aa A@RP/Ad b A0&fP/A`Rb A0&fP/Aib A0&fP/Ab AyP/A|c A6P/Atc AѮP/Ac A0P/AHb APȰP/Aa A(>P/A@G7a AP/A@a ANQ/A@>Jb AUQ/Ac Ah@Q/A}c A t R/A |c AGjR/A d A8'R/Akf Ah9S/Ahh A\OS/Adi A?S/A$l A.4S/A3o A@R/A #p A =R/A@p A:R/A5q A(yQ/Aq AQ/A (r A@`Q/A !t A8UQ/A`du AqQ/A@w AQ/A y A {Q/A { A$fQ/A ~} AXKQ/A A/Q/A9 AP/A AӂP/A A P/A'z AO/A[) A _nO/Aބ Ah;0O/Ac A0O/A`v4 AH O/A} AHN/A`Ti A@uO/Asu A(\O/AN AP/A4 A@P/A` A,O/A`x AO/AY APMN/A1 AN/A]Z A୍N/A AN/AtΈ AqN/A AjN/AU A eN/A`m A0LbN/A@W؉ AXdQN/AF A&N/A- A3N/A`p A`N/A2 A(N/Az AM/A`׌ AM/A@C[ AM/A׍ A0M/A`;, A8M/Ay ALM/A` A8EN/A3 AP^N/A B AM/A` AM/A@9 A@rN/A AgN/AR. AHrM/A \ A0M/A AH.M/A` A0GM/Aas A M/AN AM/A9 AxM/A@ A8sM/A A{M/A`K A8sM/A`Ü A(KM/A6֔ A0N/A A8/N/A3 AM/Aq ARRM/A AĴM/A9ʖ A3M/A q AtM/A^{ AP N/AK A8N/A#T AN/A~ AxNN/A앙 AQN/As Ax5`N/A AV\N/A`;ș A N/Aw AJ"N/Ak A؛6N/AB A/CN/A@ A8'DN/A@ AJN/A Z AgZN/AX A\yN/A - AN/AR AN/A (- AN/Ax A@dN/A- APN/AgZ AxO/AR AO/A > A =O/A A[O/A& AyO/A?o A(O/AN[ A8VO/A AxO/A AO/A@Ɵ AyO/AU A^O/A@Ū A3O/A@ AF.O/A@5 A4O/A۬ A~EO/A@v ARhO/AR4 A(}O/AΜ A8O/A R AMO/AU APO/A@% A2O/A A(O/A` h AQO/A@ A(O/A, AO/A AO/Aa A'O/Aq}` A{fO/A(A AO/Ajb A0bO/AuX A v5O/A]} A6O/Aof\ AN/Aį+& A/9;N/AC A^N/A`뎽 Aނh"N/A_, A.;M/Aq APM/A6 AنM/A?b AW_M/A8 Ap9M/Ar AiM/A'%k AL/Am Ah`L/A; AZY(XL/Av[* A7L/AM' A RK/A& AT$tvK/A v A1>K/A AJ/A@ AhoJ/A`g A!J/A`d AXI/A lS A`_I/A@C A{H/AI A0eH/A\ A{NH/As AI1H/A A -H/Ap{g A G/A AG/Ar AQG/A4 AlíG/A: A.G/AL A:OG/Al1 AG/A雿ٿ A0hF/Agt A F/Ao?y< AVF/A- AE/A@& A8D/A AbAD/A AXC/Aa? AB/A A@TB/A A`3B/AqL AVB/A AB/A6 A؎C/A7 A]C/A`) A oD/A`my AE/A- AXF/A@5j AdG/AT A ҡG/A c Ap,yG/Az3 A0yG/A` AJG/A@߲ A/H/A A _H/A@. AG/A A[G/A ˿ AG/AO A*H/A AxH/A A2H/A; A(K/A@m Ap[K/A A K/A A麢v/A}9 A̱5w/A_) AvQw/A yi/AR Ati/A AWi/A冷 AqSi/A`T AGi/Aݹ AhCi/AƷ Ai/A@ A8'=i/A+ AL A(b/Aoc Ac/A A0Le/A A0ng/A A&h/A# A@h/A` Af/A+` A&f/A AXVAe/AAP A3e/A A(b/Ag A b/A AXWb/A AP?c/A uO A@b/Az Ax-`/A A8`/A AXV`/A Z0 Ap `/A` A(`/A^ A n`/A6 A"V`/A@q AR`/A A``/A` AP`/A`Q Aכ`/AD AKx`/A`z AV`/A; A:`/AO A`/Afu AP_/Af Ah_/A' A̳_/A A_/A APo_/Ax A8F_/A$- A`(_/A@.( Ap= _/A8# AJ^/A AXWY]/A@ AH;]/A AP^/A් Al{]/A A"\/A r Ar\/A< AM]/AX Ae]/A@`M A8 _]/A " AK]/A A076]/A- Ah\/AP Az\/A Ao\/AE A\/A Ah\/A`J Axg\/A AN\/A> AG\/A` AL\/An Ay^\/A A`W\/A A<\/A`K A`"\/A@F Ao[/A Aج|[/A@ AW[/A&v AP^I[/A;p A[/AD^ A`ǼZ/AZn AlZ/Ads AjZ/Ad_ AY/A A AY/A AwY/A@[ A-Y/A AxY/A` Ap,X/A A?X/A@W. Ax(X/A AyX/AE AGQX/Am A#X/A AW/A' AxW/AF AW/A A8cW/A AnW/A AȩhW/A Ap{W/A AP}W/A4t AYlW/A AONW/A9 A=W/A+ AV/A Sk A tV/A@q AxV/A` AYSV/A@J A V/A@ A`U/A` A@U/A` AxU/A@G\ AyuU/A A BU/Aં AT/A 7 AxFT/A A2T/A A#T/At AS/A, A( lS/A A@S/A3 A`R/A ApjR/A _ A R/A A0Q/A AbQ/Ad7 A( Q/A l AȃP/Am A`6~P/A AlP/Ac A[P/AN A^P/A AVlP/AƳ AhP/A` AhP/A`m A5P/AZ A0̏P/A A,P/Av AP/A> A =O/A0 ABP/A A.P/AF, A(OP/AE A[P/AKw AgP/Ax AplP/A AeP/AX APP/At A?P/Aa A "P/Aa} AaO/A " AO/A " AxO/Av, A_O/A; AQO/A@P AN/A M AxzN/A APGN/A1 AkM/A AHM/A O Aȓ:M/A0 AoL/AOa AaL/Ac A35L/A2 AK/A`; APK/A A#K/A$+ AJ/A A`}J/A$W AKJ/A A0J/A` AI/A`K AI/A A`PI/A@{ ALI/A@n AТ I/A Z. AH/A APkH/A I AH/A` Ap tH/A@,V ALfH/AŃ AXH/AX A RH/AD A0D/Ar Ah9D/Aa' A85D/Aٻ AK3D/A h A/D/Aj A@-D/A ~r A,D/A< A*D/AV A)D/AF6 Aq)D/A@ . A&D/A` A(-D/A ABD/A A[ D/A- ArD/Aa A(C/A AC/Aj A>C/A AC/A A/Aԓ As>/A &ӓ A>/A Г A>/A`f͓ A(>/Aɓ A>/A A^>/A A>/A6 A>/A u A@1>/A A>/A् Ah>/A AI>/A A>/Am APZ>/A@ A>/A3 AȎ>/A} A0~>/A 10 Au>/A; A؟b>/AO AYZ>/A`Z A_R>/A`2k ApO>/At ApAL>/A A`4>/AF A_0>/A A`>/A A >/A֦ A>/AX• A(=/A$ A=/AE AX=/A H A=/A A=/Aǖ A=/AӖ A=/A A@$=/A A W=/Av A@ J=/A@ A>=/A APk=/A g A@ A':/A` AO:/Aֆ A0&\:/A  Ah9/A`߆ AX9/A A@Ι9/Afʆ A(9/A A^9/A+ A[9/A@NІ AX9/A ( A8s9/A9 AB :/A6 A:/AX AC:/A_х A@F:/Az A=:/Az AAm:/A@Ձ Aȃ:/A AS:/A AN:/A AUL:/A?ƅ Aa:/A@, A0:/A` A,;/Ad Ap_;/A`C AA;/A' Ax;/A@% AP;/A A;/A ߂ AF;/A Ap;/A} A(;/Aρ A@;/A)؀ AN;/Az A/A@]n A@>/ABn A&>/A ~1n A1>/A]'n A8V<>/A/$n AНt>/A@+n AЏ>/A3n A`e>/AM?n AP8>/A@Yn AP>/A`mn AHa?/Arn A ?/Asn A ?/Asn AP^ ?/A`tn A?/A`rn A?/AHln AQ"?/ABbn A)?/ATn A/?/ADn A4?/A1n AH.8?/A cn AC?/A6m AXEO?/A/m Ae?/Al Apn?/AΎl As?/A al A9t?/A Pl Aw?/A@l Aq{?/Ak A,|?/A%k AX?/A@[i AX?/AUi A Ι?/A h A?/A h A?/A@[i A0Ȫ?/A Xi ApN?/A i AX?/A i A0U?/A ~ k AR?/A@k AА @/Ak AP@/Am A9@/A)1Gm AHk@/Al6m A&@/A!Vm A@/A~]m AhbA/A sm A +&A/A@m A)A/A Il A0FA/AOl AXEA/Adm A`eA/ACl A%A/A=l A%A/Ak AA/A k Aȇ1B/AIk A@UB/A_ A`GZB/A`F_ AaB/A_ AjB/A;^ A(rB/A`^ A zB/A^ AB/A`9y^ AB/Ave^ A)B/A@O^ A`2B/A`=^ AB/A0^ AXB/A'^ AX4 C/A^ A0G@C/A`"^ AcC/A3^ AxF}C/A^ A`C/A=^ A83C/A^ APfC/A@^ AC/A <^ A D/AG^ AoBD/AZ^ ApۄD/Anf^ ABD/Ae^ AHD/A\^ AD/A\^ ASD/A`d^ Ah]D/A^ A E/Aʑ^ A07E/A ^ AznE/A ~^ A8VE/A`^ AxuE/A^ AE/AƂ^ AxF/A^ AxF/A-^ A|F/A@^ AVF/AM_ AF/A@'_ AZF/A8_ ApF/A`mM_ A~G/A;e_ A,G/Ab AEwK/Ab A{K/Ab A@K/A@gb AK/Atb A0%L/A b AP@L/Ab A@uL/A 1b A(L/A`"b AL/A ub A(L/A@b AXL/A`mb A(L/Ab AXL/Ab A؎M/A b ApM/A-c Ah'M/A@g4c A/M/A>c A8s6M/A Lc A8V9M/A Uc A`;M/A/^c Aj>M/Aic A@M/A9wc AIIM/Ac AzKM/A=c AMM/A`c AxRM/Ac AXWM/Ac A0jZM/Ac AX\M/A c APY_M/A`c AaM/AMc AbM/Ac AcM/Ac AHdM/A`c AeM/A c AXfM/A sc AgM/A`c AiM/Ac A0LkM/A c A@lM/A@Cc ArM/Ac AXuM/A`;c AwM/A \c AyM/A@c AhzM/Ac A{M/A`~c A|M/A|c Ax}M/A`K{c AGM/Aksc AM/Anc A(zN/A\c A!N/A Mc A@\N/Aj$c AsN/Ac AHN/AR c AAN/Ab AN/A`bb AmN/A b A(O/Ab AO/A޵b A N/Av)T A@N/A  T A{NN/A sS AXWRN/ACS A._N/A+R AeN/A R AgN/A,Q A`hN/AQ AAhN/A@Q AygN/AP AcN/AO AcN/AO AcN/AॎO ApdN/A+sO A fN/AJO AiN/AgO AwjN/A`O AmN/A N A@qN/AN A\rN/AN A`sN/AN ApZuN/AN AwN/AN A`xN/AHN A(zN/A N AN/AvN AN/A cN ANN/A`ܻN A`%N/A SN AN/A N A'N/AZN AN/AxN A]N/A`KN AtN/A ɱN A0N/A#N AHN/AN AN/AzN A N/AFdN AEN/ALN AN/Am4N A N/A@N A8N/A#N AN/A͒M AN/A-M AHN/A?hL Ax-N/A K A AH/A}> A8H/A@ B A(M/AC AM/AXxC AdN/A@~C A0EN/AC APDN/A+C AfN/AC AȥsN/ADDB AM/AA AM/A xA AM/A`%A ApM/A A AM/AA ApiL/AE@ ApeL/A @ A_K/AK@ AK/A? AK/A`v? AZK/A |@ AYK/A? AZK/A0> Ax(K/A> A)K/A= AScK/A r= A`fK/AB%= AJ/A~< AJ/A+< AHlK/A< A!pK/Aվ< AK/A^= AK/A= A L/A^> AhL/A> AM/A |@ AXM/A@ AM/A@ AȶM/A`@ AcM/A@ AN/A+A AXFN/A@B A`vN/A@#B A=N/A`KB AN/A B APN/A`] B AxN/AB APMN/A %B AN/A0%B A\N/A@"B AN/A (B AN/AB AxzN/AB AXN/A B AxN/AA AgN/AA AX$N/A@iA AN/A@ؐA A)N/AlA A`2N/AFFA A"N/A` A A0N/A> A`N/A A> AXN/Az> AN/A;> AkN/Az> AhN/A,> APN/A Q> AN/A > AON/A@> AN/A@r> A؍N/A@i> AN/A > AʾN/A> AN/A@߅> AHiN/A q8> AՆN/A > ApN/A@= AzN/A= AȥxN/A= APvN/A?= AhuN/A4= APtN/A= AzrN/AEu= A/pN/A@%?: A6(N/A J: Ap&N/A 89 AoN/A`m9 AoN/Av9 ApN/AH9 ArN/A൰9 A0sN/A@9 AHvN/A^9 AxN/A9 A0N/A9 AN/Amp9 AuN/A c9 A0ݪN/AN9 AoN/A K9 A(1N/A`)69 A2N/A`m,9 AO/A#9 A#O/A l8 AO"O/A 18 A'(O/A 8 A2-O/A`8 A/O/AT8 A01O/A஗8 A A3O/A8 A4O/ADy8 A6O/AV8 A 06O/A/8 A4O/A8 A72O/A u7 A$.O/A`7 A-(O/A0`7 A'O/A"W7 A%O/A@yN7 A#O/A`F7 AX!O/A=7 AO/A7 A O/A`27 Ah O/A6 A}O/A=6 A`O/A=6 AO/A`6 A=O/Aଧ6 AO/Am6 AُN/A@6 AFN/A5 A؛N/A*5 APN/A@6 ApO/AN6 A0O/AE5 AXO/A 5 Ag O/A5 A O/A I5 Ap8O/A 5 AHO/A`m4 Axh#O/A@%4 AV;O/A@4 AGQO/A t3 A [O/AW>3 A`O/A H$3 AgO/A 3 AOmO/A 2 AsO/A 2 AO/A2 AIO/A2 AO/A@2 ApO/A`22 A O/A&2 AwO/A2 A'P/Ag2 A$P/AX2 AP/A=D2 A`C"P/A*2 A)P/A 2 A%0P/A@1 A0P/A1 A6P/A1 Ax>;P/A+1 Axz>P/Az1 ABP/A&1 ApEP/A`0 AFP/A ~g0 AXDP/A9/ A`

Z?P/A5 A#(P/A@  AP/Av} Ao! P/AyM AO/AL  AYO/A! Agz8O/A8 A lO/AA21 AQ膿O/AJ= Aĭ4O/AI A>+O/Av A0O/A@ AO/A`b AGP/A A?P/A AhO/A;} AN/AMK AN/A AbN/A & AhM/At AoM/A@ A`RN/A@N A(N/A AN/A# A88%N/A l AtM/A AM/At AeM/A AeM/A@ ADQM/A`K AWBM/A ASK/A/ A p[K/A[ AȥJK/AH A9K/A A`LK/A A>PK/An Ax>M/A A9M/A; AfOM/A Au\M/A` AiM/A४ A?mM/A A@S^M/A ApbM/A0 APM/AF AM/A/ A[*N/AA Ar+N/A@ A/N/A AXlN/A I AvN/A/ AWN/A`) AΛN/Az AN/A A(-N/A`[ A0N/A| A#O/AOC A`zO/A ApO/A@ AXjP/AT AP/A AP/A` AP/A AhsP/AmV AmQ/A A Q/A`; AQ/AAb A%Q/A@ A0aQ/A S AaQ/A V A7z^Q/A:p AkXQ/A;п A PQ/A$MC{ APDQ/A= A@$P/A // A89P/A]r A|P/A@z AXP/A=4 AP/Al AP/A AP/A A+(Q/AtPS A3*HQ/A-J AyM Q/A AFEcQ/A)M A~?Q/A ؀P AXKSQ/A]p= AnM Q/Ao= A$Q/AkW A*Q/A@ A wQ/A A hQ/A7cp Ad`KQ/At^gc A{Q/A AtjQ/AV AႬQ/A8L A9Q/AY AQ/AQZ:b AuR/A", AR/A@fj A$۞Q/A A^Q/AG| A'$Q/A A@Q/A | A #)Q/A j AkDQ/A | ALQ/A@pK AQQ/A  AؒzQ/A] AAQ/A AQQ/A| AQ/Ad9 AH/R/A`2P ALR/A`v ApiR/AV A؞R/AK% AoR/AL AR/A@k A8S/A A`S/A?/ A.Ab AP7.AO A.A ApW.AC A.A4 A.AB AX.A# AG.AdU A؟).A? A.A S_ Ac.A j A.Ab AHm.Av At.A Ahՙ.A A` .A` + Ab.Ay Ac.A<< AH .A% A`.Aw Ax.Azh A0.A9r A=.A6 Ax˚.A 1 A07 .A AE.A A¸.A?d AX9.Aྭ A-.A`÷ A.AT Aj.A A^;.A@A A0*.A& A.A: AH!;.A@, Ap.Ac A/b.A , AHrJ.AM A5.AG AU".A  As.A A.A A.A A.A Ahѫ.A A>.A`m{ A.A}u A.A{ A- .A AX.A  A.A( A.A  AU.A& A.A OE A M.A A'.A}u AA.A AXJ.A Axu.A Ap.A`̱ Ae.A@ A=.AXe AP.Aj Ae.A͍ A0.:.A A c.Aoq A.A AM.A] Ap.Aɽ A.A^ A%.A@ Aߖ.A ջ A.A`" Ada.A Ax.AFJ Axm.A@ A(-.A@ A/.A㊺ A.A A~%.A`D. A.Ap A%.A׿ A.Ay A8D.A`dդ Au.A/: A..A@. Az.A`Dk A興.A Ax.A`A Agg.A鼕 AxQ.A A.A 8 A.A) A(m.A89 A f.A* A.A A*/Ad A /A@ϋ A(Kp/Azs A8/AM A/A AR/Abʈ~ AՆ/AT AV`S/AƉ^ A+B1V/AOLJ A1/Ae A'L//A( A'/Aof A/Acɉ A /A- Ak/A R Agze/Ay| AXGh$/A[Ж Ag֪/Aࣟ A`!1/Ah A\J/A ~ A߸..=/AS AC/Ah A8G/AW̉ A(/A` A/Aδxv AJvHX/AMP\K A>X/Af/ A/A A`I/A@rC AFr/A` Aa/A?׉ A/A2 A`/A`S AP@/AmNJ A3v/A`v A܎/A@U Ap/A" A:/A6 A/AsR A(/A A/Av Ad}/A` AX/Aތ A/A` AZ/Ao A@/AА At/A`ߐ A>%/AUT A8/A A,/A  A{/A A/Ax A /Au A_ /Aa A /AU AmE /ALj Ae /A1Z A=E /A?7 A] /Acp A /A` AXp /A@ AYp /Ax( APy /A' A(ǐ /Aǿ AD /A8Q9 A /AĬ A /A@ߢ A /A@Ю AX /A@,X A`B /Ad Aք /A@ A8x /A@` A # /AM A  /A@iɰ A~ /AB AG~ /A`? A} /A`2o AP| /Aડ Av /A` Ai /Ad A[^ /A@ߗ AP7M /At AH; /A AH.5 /A<" Ah30 /A} AH\ /A0 A  /A 8 Ah /A` ù A /A A /A S Af/A A/Aջ A8I/A  A`/AVV A/A@.: A]/A` Aؼ4/A R A@u /A@PO A8t/AӤ A(/A A/Av APi/A' A,/A' A'/A A/AQV A /A _ AB/A- A୕/AG A\[/A` A/A AP/A B AX[/A AXև/A@P Ap/Az AX/AT A?/A A8/A Ap/AB A/A} A/A AU/A@P A /A Aq/A`b& A(i/At3 Ab/A B A=/Aʭ A8/A( A/A AJO/A͒ APF/A ۜ AX./A A/A AU/A Au/A`d AE/A A A/A@ A ^/A A/A ~0 Ac/A AA#/AMs Ah"/A A/AC A8ɧ/A \ Aho/At+ A |/A`n A E/A4 A8-/A = A@ /A`v A@B/AaE A /A A0./A A'`/A$ A02/A`l A'/AO Az/A' A(/Aa A/A@yq AP/A" Aڵ/ATg AXu/A6 A/A Ax(/AG AHY/A AI/A# A0</A@a Axv7/A A'/A l A/AF A/AV< AD/ABZ A@u/Ar Ax/AH A1/A AJ/A A/Ao: Ax/A-^ Ax/A@W A`/A@ A|/A Am/A`v3 A`a/A L A(?/A A0)/A@E A/A9 A0/A` A/A Aj/AC A/A ` A/Aa AHL/A A/AV Ax/A`Ù A2q/A A2=/A AhQ /A@W A/AA Ax/A A[/A` A/Au AxƝ/A Aзh/AW Ax//A A/A A``/A A2/AW A[/AF AK./A@ AG/A` A[/Av A(-/A@ Atj/A c ApV/AC AXE/A` A-/A AXh/AR Ah/Ac A/A` AP/A Aj/Av3 AA/A@C ApA/Ax Aw/A AV/AU A8/AE Ae/A 8 A/AF A@=y/AM A36/A  A,/A H AU/A@ AKq/A A3/A N A /A$ A0Ǽ /A` AH /A@ At /A# A Y /A6 A7 /AhF A7 /A@F A /A$Q A /A 8[ AB /A &c A[ /Apl A6 /A`| AL /A A = /A Aݙ /A A@ux /AV ANd /A@! AI7 /AR/ A" /A 8A A{ /A Oe A> /A{ AV /A ɋ Aq /A`2 AجG /A AO> /A`m A3 /A` A` /A@ A@ /A Az /A A8 /A= A` /A@ Ap /A@& AhoV /As= A7% /Au ApA/A@` A/Aa AN/A Ac/A Ah/A( APN/Avy A`/A A/A A/A^1 A/Ai AA/A@, AhQ{/AM A()N/Ay A=/A` A/A-x A5/A A/A A/A' APڨ/A q A0/A`9 A@S/A@? A6;/AN A1/AV Ah//A)Y A)/Aa A"/A Or A/A A/A AhQ/A A(\/A# A/A A(/A A虴/A?6 AS/Ah| A/A  Az/Aʵ Ai/A`$ AZ/A`{ AJ/A A=/A`2 A3/A) A//A5 A0+/A@ AL#/A lP A|/AoU A/AX A(/A`"Y Ak/A`"Y A/A+W A /A R A[ /AK A/A> A/A. A/A@ A`/A O A/A A/AQ A`/A@ A/A A(/Aw A_/A] A/AC Ap/A A@` /A@,l A/ApL A[/AZ Az'/Ai A;-/A Q1 AK1/A AxW1/AD AP^1/Az AP^1/A` AZ1/A` AR1/A$ A`C1/A`[ A1/A A0/AO Aȃ0/A AH0/Af AX//A AXt./Av Ah-/Ap A+/A4 Ap*/A@ AHa*/A A8'$/A`v A؎/A< A(/A`; A@/A , A/A5c At/A A/A A@/A^ A{/A Ak/A A)[/AH AN/A`K A 4/A`m: A /AI AH/At AZ/A@#B A/A^k A/A Ap/A@j A_/A( AH/A@ A3!/A* A/Ad A(\/AT A/A_A AЄ}/A  AL3/A A/Ao A`/AO A8Ѡ/AJ A`/A` A@Sn/A`; AGM/A@` A/A A/A AaU/A`[< A0{/A$A Ax.A`v A@W.A A0{l.A Z ApZ.A-  AXW1.A@ AxK.A` A\ .A A-.A A .A Aa.Aڏ A.A Ap<.A Aз.A, A{.AB Aav.AK A[.A` AfA.AE A.A൥ AX.A6 A.AX0 A.A`e A.A AȤy.As A`.A AB.A/! AТ".A@ A.Aq A8Q.Av A.A AxKn.AT Ax).AV A 6.A 1 A`.A 8 AH.A AU+.AV Ab@.A Aп@.AL Ar.Av AHj.A@y AHm.Av Ag$.Ax>c5 Aߑ.A{֊< A.A@V5 Ag$.Ax>c5 Ar\/AH[ A/AHA s OW_d/Aȓ A̱/AO&u A麢/A4V Aلΰ/AP99 Ak4/A)NS1 AOl/AnBx A)/A@y Al/A] Ax)/Aƞ A/A Ah;߭/A ABЭ/A & A /A` A/A A`/A Au/A Aq/Ay ABi/A A@]/A AXU/A Ax?/A") A(z7/A JG A=!/A A/A_ A/A6 AX,/A`] A8/ABQ A0Ȭ/A AȤ/AI AE/A3q Aw/Ae AM/AE A̫/A& A 0ʫ/A A0/AV] A8/Awn AȻ/A  Apʫ/A A/A`]r A[/AQ A/A A(/A AN/A@. A(˃/AT A_͟/A)/ԯh A*/Ap ' AЮ/A[c AJac/APHa!z A[R/A= A/A? AY]2/ADt8m A< /A1Q< ACk/A3 AЉ/A$@f Ac&/A`] A>/AIgm AR/A" A]hc/Aʷ Ah.q/A" A/A!˥ A@ŭ/A=\. X Ah/A Aѯ/A Zf A"/Aī A/AI Aȱ/A`D AH/A A8n/A Aֆ/A@ AȤ/At Aȓ/A` A焰/As AxKy/A Aa/A Axdv/Af A(/A3 Ap·/AFZ A/A  AU/AQ Ad/A@d Al@/A` AP[/A V Ax /AA A/A A A`ز/A AԴ/AQ AA/A A0f/A&N A`{/A@ Az/A3 A[f/A`& A(c/A5 AN/A AJ/Aǿ A6/A0 AxX3/A0C A\/A A0/A A A/A8 A/A@E A8'/A A/A` Aױ/A`A A@ձ/AL A/Aҿ A/A Aȃ/AC A{/A lN A@/A@` A@x/AR A(f/A" Ah?@Ka/Aք=: A/ASF6 A ;yɳ/A+FF Aד/A2Bj Av\./A%V A-ʝ^/AA Ax$/AuK Aڸ/A\lֻ AG/AqSc< A/AZ̼ A1Pj$/Aӥ( A//Aoz Aol'H/A| ARb/A@` A*D/A++ Ax/A+~K AxX/A@`[ Ax*/A) A,/AR AH/A-J A/AU A /A#i A/A/ A/A<6 A/A0 Aȃ/Au Ap2/A A(Ѷ/Aݿ A@/Aʂ Aǒ/A A/A 7 AH/Am A@Ӹ/A: AXJ/A J A8/A A/A A/AT A9/A`: Axz|/A? Aq/A  A/A A`(/A A`/A ~ A ;/A`;1 A[/A@ A(b/A ApM/A@9 A /At A|ȶ/A ABsg/AM AD/Ad<+?. A̕ Խ/ArG A0/A$S A;W/A vf Ah@K_/A! AP1/A` AZ/Aݛ A; /A Auߺ/A A AP/Aa A]/AU A(/A AQ/Ah( A /AМ Au/A' A=/A A /A# AӸ/Adz AhD/Aa A@ƶ/A AA/A`; Av/ALy A喝/A 8 A/A@% Ah/AF A-/A A"/A? AH/A AB/A A\/AN A_/A l Ap&/A$ AP/AN AQ/A`m: As/A Aշ /A@e A 8༱/A!F\U- AFn5r/AZY AEh*/AKmM AY_+/AC# Aأ/AlI AKAd/A77} A[W)/A A/A A/A V A욮/A`B A3-)/A(b AXt/AT A|/A, A(/AV A/AH A /Aah A $/A ! AB/A@! AU/A.# A0nQ/A JG$ AW/A}1 Ax/A 1B2 A0 ޫ/A`B\3 A֫/A@3 AHӫ/A?4 A>/A4 A/A 65 AF/A5 A`k/A@N5 A[/A.6 AP0/A@W6 AP*/A`5 AHӫ/A5 AHn/A`5 A١/A` 6 As/A7 Ais/A8 A_g/A@S9 A@n/Ag: Ah}/A: Ap/A: Ap+/AF: AHӫ/Ao: Ap/A Z'; Ap/A`De; A|/A@N; A`«/A`; AҠ/A͖; AT/A`; APe/As; A@M/Aҷ; ApAC/A6< AP77/Af= ApAC/A`n> AxU/AO? AW/A,6? Ai/At? A/A?? A@ǫ/A$? Aͫ/A`)? Ax˫/AQ@ AX/A`oA@ A/AA@ AU/AyO@ A/Ax@ Ad/A@ A0J/A+A A#=/AB A(>/A`fB A0J/AfC A_/AC A8/A9C A`v/A`[C A/A;D AHг/AVD A堫/A AD A/A AD A|/AD Ak/AD A8U/A`]xE A-I/AyF AB6/AF A+/A G A+/A`T!H A+/A@>fH A&/AyI A`C /A J A@/A`#K A8Ъ/AK Aê/AݹL AXͪ/A'M AXͪ/AzM AD/A M A/ATM A g/A` ;N AHB/A`K=N A <(/AN AQ/AiO Ax/A 88P Aک/A P A R/A`6Q A\/AyQ A/A R Ap_/A݇R AY/A R AO/A@AS Ar7/A&S A0./AaR A/A R AЦ/A? AC/AR\? AP^/A* ? ApZ2/At,= AP/A= Ahw~/A!֚> Amݛ/ATO]> A&ě/A5+BK? AFI AMۗv/AMQJ AP|e/AIJ A/A L AXڝ/ALL A>/AL AP/A`K Ap/A`DJ Aܜ/A@8K Apk/A@g}K Al/A`̟K Ac/A6K AbV/AK A0/A`[K AP/ARL A/A$L A/A@^M A/A@,M A /A@pAN At/AdrN AS/AJTN AZC/AA.O A/AJk}O A/AO AJ_/Ap CP AY/A·8KP ApIg/A9<P Aϛ/AP A1/AQ Axޜ/A@wS Aٜ/A`ÔS A/AMW Ax(D/A;-X A#%J/AQSX A6sM/AX AckM/Aq6Y A/N4I/AC`Y AO=qA/Ax" Y AXW6/A2Z A'/AbmcyZ AA/A>Z A@/AZ A'V/A 5[ ANF/A9SO[ APߛ/A[ A؛/A~~[ A:(՛/AAXR\ A nԛ/Aܹf\ A@+֛/AG\ A.ܛ/A\ Ar/Ahzb1:] A\/AXNv] A2#/AruU] A4/ /A(] AȤ/A ] A~ҍ>/At[^ A#^/A[^ AΈ|/AOld A<{4/AlMFe Ax/A@Gf An}u/Atrf A6i/A2Qf A b/AÊDg A8x_/A`g A ?[/A,6@"g AEZ/Ah A"F[/AŽQh Aī^/Ah A>d/Aڑh Apk/A8Õ/h A,t/A򅸃i AN;/A#&i A[c*~/A<:>i Ap/Agi At7/A@&i AH^k/Ahi AL/Aj AEA͟/Atj A5/A j A2/AOj A"f/A\k A ]5/A 6!k A/kϠ/Azk A/A k AT>/A2j A^{-x/ABj AU)/A/j AN/AWj A/A Ui ApM/A6ei AN/A@E,i Ad/A@Ei A/A Sh A+/Ah A,/Ah A/A@h A=/A ~Bi A/A Ri Aȶ/Aj A /A` k A./AGLŸj ARD/Am j AFԣ/A'Li AF/Aebi A /A]i A8/AA;i A&{S+/A2gi A3À8/ApHIi AIaC/A0i A*L/Aj AʨR/Asaj AxC0U/A]Gj AjU/AXj A^/A0k AhNe/A|Xl Aei/A; m A\&xi/A4m A :g/Ax[m AHbd/Ay8b:m As_/AFRm AY/Am AHuR/Aױm AEJ/AXŚXm Af@/A3;n A(%?7/Ae2 n A;M-/AHpn A`a#/A@ n A]h/A dDAn A{5/A0Een A,c/Au0zn A!$7/AƟv~n AWP{/AO.rn ACL/A}Vn AF/A{f*n Ax:K/AWm A8V3/An A/A`n A0 /A Po AH!/Ao A!/A@%p A/Ap AX%/Ap A(/Ap A",/AOq AP1/A q A/A@>wr AP /A (r Ax/A`vr A/As A8Ν/As A/Ar Ax/A ur A(y/Ar Ap/A@s A/A`s Ax/AKs Aꦝ/A@ t A0/A5t A/A5t A苝/As A{/As A k/A@s AMd/A@ t A^/A | AF/A z| A4D/A 8| A0/A0} AЕ}/Ad} A@?/A`| A7/AH} A/A~} A0/A@} AX/AV]~ Aԛ/A@,R A ֛/A@5 A)/A` A-/A@ R AO/A` A;/A A/A@5; Ax /Ao Aq/As- Aq/A| AL/Af AXW/A A@כ/A 8@ Ah^̛/Ao A /A A@/A AH헛/A`< Aa}/A`k A\/AVك A @/A> Ah/A` A/A A/AXՅ A/Aj? A@/AO A؎/A A؁/A A A /A  Ax/AΆ A/Av A/AzT A/A@W AP/A A[ۚ/A AP7К/A A š/A A/A$| A(/Aه AHn/A`K Ax:/A Ap{/AՈ Ah_/A$ AP c/Ao AdΛ/A`0 AGś/A` AX/AWY A\/A < APov/A[ Ah/Ag. AU/Aj AtC/A Ax--/AE A`/Aെ AH /Aӊ A84/Aq# AÙ/A  AP+/A׎ Ah/A@- AR/A`P A/A` A(/A3c A{/A@n Aq/AQ A(h/A A}/A AXz/AΑ AGy/A AP&t/A  A-q/A ( Ah/Ah3 A^/AG AP/A_ A D/A|ْ A8 Ah3/A AW./A" AJ*/A5 AW./Asf A~:/A v A`a/A@ Apn/A7 A`2/Acq A֗/A A/A A`/A APk8/A AN/AD AN/A@ AXB/A? Ah*/A? A /A׷ A؛/A`t Aڗ/Ama Ah/A; A/A  Acx/AD Ah/e/A@ AE/A` A0{/A`x A/A{ A*/A`) Ax)Ֆ/A A)ɖ/A1 A˖/AM A]ܖ/A x A*/A` A͖/AA A[/A]{ AZ/A`b A0Y|/A AXp/A Aa/A A$]/A AS/Af8 AN/AqX AWL/A` A#E/A AV@/A@ie A8//A A8//A u A =/A}E A(U/A A%u/A@#o A߅/A@ A&/A& A'/A% A'/A A&/A A{/A JA A0Y|/A A/Az A'/A A͖/A A/A` A/Ar AJ/ADl Au/A AX/Ao A/A AǗ/A A/A` Ai/AWL A71/A`] A؞?/A` AR/A`] A؟W/A` AVX/A  Af/A Amt/A` AԂ/A2 A@0/A Ahؙ/A - Ah//Aq A/A` A/A6 A(/AH A/A 8g AX/A K A]ݚ/A 8g AАٚ/A4 A/A@ AX/A A(/A$a Av/ATp A3/A A/AH A2/A A2/A ( AF/A`D Ah/Ao A/A+ Ah//A A/A ( A/AN A/AN A/A2 A/A@ A?/A@ Aߙ/A Aؙ/AN Aә/A  Aϙ/AoG A(/A Av/A+ A8ə/A 8w Am/A AxZ/A AfB/A Ah?l/A 1 A8m/A A /Aa A/A AP/A A8/AT Al/AT A%/A$ A8sh/A`K A5i/A` A?/A A 8/A i Ay8/A Al4/A u1 AO+/A A %/A A A /!/A`$ Ax /A A /A / A0/A ( A`/A` A0f/A@ AOۘ/A`9. A8Ԙ/A & AOۘ/AAA0f/AAl/AAȤ/A `Ah/A`[Aߙ/AA8Iə/AgAR̙/AsA ܘ/A@A˘/A@WA0/A@lA뻘/A@GA纘/A AA`Դ/A@A/A`̫AR/AAo/A HrAЦV/A 1AH9/A@yqA /AEA/AA/AZ A/A S% A( &/A3 Ap#/A`] Ap#/A@# A,/A@D AH9/Am A(iC/A q AV/A4h AW/A@k Aj/A Ay/A0*A`z/A4{Au/A@AXe/A@G!AXB/A|=A(/A`Ax՗/A`v]Ax/AA0/A[Acʗ/AvA(Kz/AklAUu/AAHnb/AVApR/A@>AB/A@A؞!/AADAi/A@ߏA`ܖ/AHAg/A A3/AJA?/AA/A@A n/A`oApJg/A 4AXWk/AAl/A@LAzi/AwAd/AAW/A@W*AE/A[Ao=/A- Ab9/A`~A`!/AkA(:/AցAX/A A /Ag A7ܕ/AIA7ܕ/AA7ܕ/AIAPޕ/A@B Aԕ/A  A0Õ/A" AL/A A𴰕/A`v A`ǔ/A<2 A"e/A=) Aò/Au( A:G=/AV. A!/A2< AVN/App Afj/A0Tt*!AU/A !A̩J/A"A]T/A+.$Aȩ/Ao$AM~/A|*m$A:/AeP_$AVŔ/A@G$AHɔ/AnF$A$!͔/A(%C$AД/APe=$A8?Ԕ/Aӻ4$Aה/Agk*$AN׉ٔ/AnH$Ad|w۔/A$A#?ܔ/A$AA/}ݔ/Aj#m#A+mݔ/A=c#Akܔ/A-,#A%۔/APF#Aٔ/A#A<_LԔ/AIb2#A%Д/A'T#AΔ/A,!#AHӔ/A֮k"AN0۔/Abȹ"A-b/Adʤ"A{I/ARK9h"A;/A5J"A=/A`5"A/A׋$)"AmĔ[%/A)Q3'"Az3/AS^."A]A/Ae>"Az>N/A[X"AN$Y/Awz"Ac/A"As/Aق "AQNσ/Aƞ"Abt/A{A"A/Ai"Ac/A#{"AÕ/AZ"A7mѕ/AqCRk"A-ݕ/AH<"A}/A[.=@"AA[/A\H"AFz/A_=S"A<}/A&b"A@$/At"A\l/Ap\}"A/A`"AL/Ab>"AC/A Q"A4 /A?nC"A;oP< /AT"Aw/Au |"AHLZ/A] "AF/A"A`!/A"Ahg/ARGD"Ab/Aە"A?-/AT"AQ?/Aa"AI /AzoD{"A[h/A5p"A/lM/A 'e"A/A["A'Q/A("Au/A+!A?"/AC+!A*(/Aqc<5!A//A q!A`e8/A`"N!AI/A͂u*!A\,\/Al!2!A"Eo/AR Ax/A A7/A z5!AR/A gM4!A*7tՖ/A˂Ƕi!A'fm/A;/!AAa*/Ax!A90 /AsY"Ap2#/A෾"A./Aa+#A)6/Anj#Aj[/Apt<$Asf;/AD$A5/A`$AsY_2./A18%AU$/A 5%A5#/AKCM%A _!#/Aof%A>$/A_M.~%A}&/A{<%A)/A鈔%A ,/Av%Ae{1/Aܬ%A(<6/AlMI%AU`%At ח/A%%A. /A8%A%/A|oi%AOY/A"$?%A`/A %AoV/AyA[$Abn/An/$Ax]/Aa$A.#M/A9xm.$A'/A~m#A؁1/A`#ACU/Ar#A\xw/AA|#A))/Aƛb_"Aj,/A-d!Aj3Ș/Aa!AO<ܘ/Au % A3/AbN A /A,5sA7/A˒A /AA֖/A֨AZ/APVmAI/A `f Aҿ/A{_A@~,/AAo1;/AyAM/Ab̛,AU d/A*AL}/AЋUAV/AmA7︙/AzTyE#Aٙ/AZB2A\/A`AT</A tzAd?/AG$AM]/A`fA[|/A`AO/A`Aз/A A#뻚/ALAۋš/AED(A hhǚ/A(_SAyp]ʚ/ALkAb%T˚/A:A4Eʚ/ABAh9ǚ/A! A0Gš/A8A-;#ǚ/AlAК/A 4FALHޚ/AZ,Aq &/A:A4/AJ]:A/A5AO:/A UA-XpRY/A1~A ëy/ApA:oU/ADA8/AA#ޛ/An1AS</A Ai謄!/AVu A1B/A]3ASZa/A35A0~/A`tA䠜/AA=/A,RA<Ȝ/AŦNA&5 ֜/As#iApޜ/AׅlA߫,/AaݖAS/Aa./ A !Cڜ/A A`Ϝ/A lY!A Ĝ/A`!AP/A`B"APM/Ad#AH/A`$A{/A x$A/A %A/Ao!%Aw/A %Asd/A %AȘA/A@:&A R/A@f&A /A6&Aޛ/A`9~&A;›/A*n&Ab/A9v&AG/A$k&A7 j/A,xE&ACӼR/AoG'Ax?1>/Aת'Ax-/AϵRr(A׊!/Aen(Ar/Az夆)A@/A@y)Aik/A@y)A@/A *A |/A`)A'/A *A/A ;*A*/A*APf&/Ac*A(K/A[*A /A`f +Aɛ/A *A(̛/A*AhD@/A)AhQ/A)A#/A3JL#*AuL0/A{*A\@/A*A )T/AnXN+A.Qj/Aq+A G/A뷊}+A,;/A,A/A0,A؛/As>A,A /A`[b,AX /AQ",AX/AҰ,A!@/Ao,AUܡ/A{! -A9L/A"Wd&-A %/A<-AI1/A~EJ-A`i>/A'P-A K/AN-AbPb/AMP-1-A@Nz/A-Az/A0U,A}b/Ax3,A̘/A,b,A0;/Av,A՟f/A+Aq/Ai+AX~/A+AD/A +AwݶM/A+AcV/A>ۈ>+AYl^/AĘ*+AbMe/A*h?+Adk/A8WY,A,Cp/As@R2,As/A@5T,A /Ak,AXp/A`; )A( [/A q!)AXZ/A!)AWg/A)Au/A*Auh/A`;*A/A` *Aт/Af)Aĕ/A)AHᐝ/Ao)Akv/A@ )ABk/A )A(\/A(AѾ/A (Aڝ/A (Aߝ/A \)AG۝/A)A/Ad)A7l/A(A(\Q/A&APN/A &AWN/A&AWI/A@&AG/A&A7/A&A-/A&Ap=./A H&A8//A 'A8/A |'A]/AZ'A@/A 7'A/A&A/A&A&/A&A=*/A&AW./A]&A/AP%Az /A=:$A#/A=.$Ap$/Ag$A#%/An$Apk%/A@t$Ap%/A|$A(\&/A $Aȥ@/AU&ALA/A̜&A8C/A H&A( C/A H&APfG/A&Ax\I/Aޕ&AH/A=&AV"/A@#AV"/A`#A&"/AH#A0.(/A#Ae/A`#Ap=m/Az$A{/AR$A/A %A/A=%Ap/A (A/A ](A@/An(A83۞/A *A/A]+A/A`)*+A/AI.A#/Az.Ap!/A.Af"/A`.AL/A]0APP/A/1AGv/A3?A'/A 1@Ap/Az?A(/A*@A/Az@Aأޞ/Ar@A(/A?A/A >A /A@g>A/A@T>A/A@ >AWğ/A>A0/A=A(/A=AQ/Ay=A(p/Ap7Ah/AR7AQ֞/A@g/A(\/A/Ap/A[/A(ޞ/A@ ]/Ap/An,Av/A,A6v/AQGx,Aȝs/A x,A&>o/A-Ai/A@A-A:E/AD -A?8/A;-A0/A!.AP/AH.A~/A`f-Â/A@".A(/AH.AT /ATa/A|/A` /A:/A.0AS/A K0A(,/A 1A./A`%1A /A@2AJ/A q2A(L/A$2AСX/A"^3ApZ/Aj{3Ah/A 1)4Adk/A@H4A)z/AO5A|/AR"5AP/A5AP/A`v5A|/A6Ap/A6A/A@N@7Aã/AY7A0/AH 8Aѱ/A@&8A/A@8A@/A8A8Ǟ/A`9AnϞ/A9A0՞/A:A( מ/A<:AO۞/A y:AB/AQ:A/A`;Ao/A3;A/A$<A/A7>Az4/A>A`6/A =>A/A׋>A/A Hk>A-/AR@>A/A`x=Aٞ/A:AΞ/A F:Ap̞/A-:Awž/AX9A/A=8A /A`f6AU/Aྐ6Aƀ/A6AZq/A A05A8R/A 3Az2/A`)1Af/A /A#/A@ .A/A6/AR/A/A(V/Ah.A֫/A H/A/A.A0;/A}0.AdQ̝/AeLX.Aܪ/Aw.A(X/A,x.A*/Aߗ_.Aq/A>s.A6`/A|Ky.ASh4&P/A!.AB/A5##/AxK6/Ab/Ak"/AU/A//A 0A00/A"0A2/A 80A09/A0Ah?/AB0AK/A y1A@L/A1AT/A`m1A@[/A N2A0f/A@%2Aph/A |3Av/Aܶ3A/A-5Ac/A5A/A5A /A@!z6AӲ/Aq6A\ĝ/A`Df7AH:ǝ/A7A@՝/A08A؝/AK8A@/A (:A/A U;A(4AXp/A`;3Axn/A@3A+a/A03AQ/A ZA2AbD/A@Ϝ1AaB/A1AH27/A0A'/A*0A$/A`)/A,/A,P/Ap/A@ /A(R/A@#/AXX/A/AY/A`90A5[/AB 0AXa/A ^0AXb/AMi0Af/A0A8g/A@0Aui/A`0Au/A 71Aw/AHO1A/A1A酜/A`1A/A{2AX/A͘2Ap/A2Ax/A D3Aຨ/A@~3A(/A`T 4Aݜ/A_5A`v/A=5A/A-46Ax /A cO6AJ/A_6Aأ/AN6A /AfR7A(./AX 7A@ 2/A@y(7A /A`y7AU/A 6AP/A@6Ap/A_6Af/AF6Aɜ/A lO5A@ǜ/A`65A/A`[4A0/A`4A(/A4AP/A (N3A8/AM2A@‰/A@%2A(]/Aߝ0AeY/An0A`W/AX0AT/A <0A0GR/A 0AJ/A`/A((/A 0A֛/A /1A0*/A@1AJ/A)2AΛ/Akd1AH/A2A螛/Av2AHn4/A`4A,/A %4A@/A2APom/A&X3Ar/A3AxW/A?3Ah/At7Ah{/A J7AM/A4AP u/A3A{/A?4A/A5AАW/A9A?/A`9:A/A~6A/A 6A /A"8A`aG/A3O2AP8&/Ac3A_/A 5A4/A@`z5Aޙ/A5A0{/A &6A0%0/AX6A@/Az7Aٓ/A&:Aؒz/A :A/AdY:A/AkW=A)/A=A(;/A r?A@䎚/A 8o@A/A u{@AX͚/A`-AA{/A@`fAAhM9/A`osBA>/A`BAqN/AzBA=j/ACAiy/A5CA땛/ACA/ACAPٿ/A DA`1ϛ/A}9DAs/ADAF/A@DA&/A@yEA$/A]=EAp=A/A=EAJ/A`fEApc/A(EAn/Ap#EA(\S/A`fEA*/AFAX/AmFA:/A`fEA83:/A`EAP8/A HkEA"/Am@EA/ADA/A?DAVǛ/AbDAЀ/A`8DAhޤ/A CAP /ACAq/A ^CAb/A@`1CA(F/A 8BAu8/A3BAج#/ApBA /A`.BA/AAA/AAA Ś/ADQAA/A (AAذ/A)@A( /Aࣦ@Ah]t/A[@Aa/A 8"@Ah]D/A?A7/A3?A( /A>A/A@=A 4/A@E=ATg/A=A@U/A+%>A(>/A;>AY,/A@g0>A/A;=A/A@5=A8/A`m;A /Al;A/A+V;A/AT;A/A QE;AXhϗ/AN;API֗/A`m;Aڗ/A~;A/A<A _!/A`<AM/A=A(m=/A`o=Ad/A@r>A83/A[?AG/A?@A4g/At"BAh"/A BA,/ADA\>/A@,DA(~U/A@nDA(a/A ۚDA //A@%EA`T /A EA#/AMEA!/A`TEA7/A5CAw/A`BBA=/AAAp뙘/A?A kV/A0>Aؗ/A;A0ٗ/A`;AƗ/A`T;A(\/A@w;Aq/A;AU/A<A/A <A/Ad(=Ax/A=A`/A`T=Aؖ/A ^?A0Ζ/A`AA̖/ACAؖ/A@/AjAG/A-jA/A`kAh/A@>clAd/A@SmAx\q/AjmAPꛗ/A൷mA ʗ/AUSnA(O˗/A@nA0—/A@!nA/A@ nAhy/AnA`Z/AzunA9/A DnAr /AbnAH/A nA̖/AroA –/AoA8/A |oA/AoA!/AeoAo/AoA:@/A=oAP /A`oAԕ/A[7pAs/AM_pAs/A QUpAI/A JipAP(/A-pA( /A1qA0/A@qAڔ/A`)rAɔ/A?VsAǵ/A sAǵ/A`tAǵ/A`muAÿ/A@NuA|ɔ/A@qvAɔ/A@#uvAɔ/A wApӔ/A@wAȱ/AwA)/A`wA"/A@yTxAP(/A cxA !/AQxA /ATxA/Aj|xA?̔/AXxA@½/A}xAȰ/A;xA`T/AyAX/A`QzAhՍ/AzAQ/A[s{A Ρ/A-{A/A||AȰ/A|AH/A\}AȰ/A u}A=/Aମ~A. /Ai 4Aɝ/Ay?*A /Ao^9A@/A1jAhDʔ/A@,A/Aqc^AH/AvAN8/ANd/AV]/AA`A /A 7AwJ/A`Ax/AZ AM=/AtA(/A@5A_(/A@AT% /AîA/AHATxqa/AzA/A`)A3C/A@A,/Aa{A{/A`Ad/A@WA>P/AA¯A*/A A:,W/Ao3Alŧ/APA0*/A`KYA&/ApAhb/AA,/A3A0w /ApAȺF/A'QAU/A &Ad/AA/A_A#/A喫Ap0 /AAyA>ۆM/AA0a}Nڱ/Al5A`Ŷ±/A/AX /AóڡA5n,/AdA`k/AqQ`AJ/AYSd=A07(/At{-A/A /Ahqݰ/AiA*#/AَϠAږE/AAXNpo/A0)Ad'BP/Az"A֏4/A6,lFAwo/A'~A/AKb2AXD/A- AO?/AJAGg/AW{XA /AA׮/A ƋA/AA */ApA%4q/AlAo6/A M}ADbK/A\LzA%9/A>ExA*r>/A6 NuA^Tt/A+*rAKδ/AgWoAs%/Ao9;lA5Tw/A+z%jAŵ/A HgA3׻ڵ/AjAfA/AJN7eAtnP/A/)dAk/AMyCqcA /A bA1/A\`A h)/A2*_AVM/Ak2~^A"/AI]S]A=b"/A ,\Apw6/A [AOFyP/A`YSYA%n/AԑXA^Xϐ/A]MkWAm*/A VAT/ALUAW~/A/YTAb$F/A>G TA}/Aa=SAIKio/ACWS~RAJb/AdDQA63"5/A_k@1QAw/A;PA0/A@,+PAZ޸/A`mHPAlf/A4vPAMF0(/AȳPA-\9/A[0QAPS/A 8ZQA.f/AQARx/AHRA0/ARtRA軟/A!UA!/ATUA_Ǔs˹/AxpVAhܹ/A@ WAg/AflWA/A`XAx/Ao~rnYA/AЎA/AWId̏Aۭ/A}ϐAW`/AuؑAdKϻ/A-A»/A SAGu/AΝA%޼/A؞A4{/A(xA@F /AA`p7/AQ͖ڞA-g/A<6A_' /A[KRA3Ҿ/A}A/AW}?A،wP/A@Ao/AW}ШAH޿/AUTA\+/Axi>o̫Az|/A(7AR/A_\AJ/A=m~aAH|/AzcAk/A AH.+/A@Aip/AAa/AA`v^/AA&/A@CA/AA2/A_AT/AwΑA`v/AܑA /AWAtc/A;OAc/AjAt/AvA/A@SA0i/A qA 2/AގAQ/AAX/AH~Ah"/AjA/AżA(/At|A/A}A(/A}AmW/A@A/A?AxX/A Q{A0;/AdA/A dA/A@dAv/AQdA@/A%eA8/AeA/AndA8/A`A0n/A\APn/A3VYA:/AVZA(/AuZA \/AYA@&/A6{XA(/AWAP/AXA؞/AdpXA@/A~WA/AͅUA[/ATA`/A;SAh/AQAPw/A 7PAH/AOA( /AOAg{/AOA/AfKA3/A5KA/A@JA/ABJA0e/A &IA/A IAXK/A`OIAp[E/AGAP7/A`;5GAP7/A@FAb/AFA46/A FA46/A`pFA@/A`pFA =/A@FAհ/A3FAo?/ATGA8&/A4`CA(_/A`)HCAuc/AzCAh/A lBAhD/A|NBA` w/A^2AA8#}/A@@AB*/A`[AAXK/Ar@Aa/A`o@A8Ҁ/Ad?AxE/A?AxO/AƲ>Aj~/A>A/A>AN/Ap>AP+/A >A/AqU=AX/A@6=A9/A@<AX /A@;As%/A*Q;AH/Ah<A/Ah<AC/A@<AbR/A;ApU/AD;Ag/AU;A0J/A u;A`/A 6A+/A+5A^,A/A+A4@/AKq'A /A%A/A %A/A@ !AE/A!A/A=s!A/A!Apʢ/A JA t/A`AH/A AbAK/AiAx/A?Ad/AN*AH2/ATA/Az/A8/A`A/A^A8/AAP/AoA( /AD AH/ABA /A 1A /AA~/AAhc/AAv/AUNAH\/A SAؽ/A`AM{/A?AH./AA0;/A@A/AAhD/A]AXۊ/A&YA/A`DaA/A@A/AtA/A8Ap /A SA/A` A !/AA7/AAl/AKAH/A |'AP A`1/A| Ap6/A྅ Ah/A' A@~/AO A`-/A A6/A 2 AHH/AR6 AhR/A  A=F/A A`c/A Ax)/A APm/A Ac`/AM AxV/AO A /A@i A@/AW A@Y/A& A8sW/A@a A/A AL/Ah A/AR A8R/A`v A/A3H A /A A(:/A@ A/A AP/A Apk/A| A/A@ AX/A= A`+/AR AP /A Ay/A AHr//A / A>/A AP8/A AE/A Ax/AK AX/A`; AP/A A6/A A8Ң/A- A/A@r A3/A [ A/A Aj /A A'/A- A/A/ A/A+ A/A`| A/A A/A Aq/A AS/Ah Ap0/Av3 A /A 8| A`P/A$ A>]/A0 AgF/AR APM/A`m AH/A`= A}/A Al/AT A`Z/Azi A/A`B A/AY AxG//A@ A\!/A`B Ad/At APx/A2 A/A` AA/A K A4/A`ob A@/A uD A`/A` Aؽ/A`9 AXW/A & Ae/A A@S&/A@ AX4/A A/Az AX/A A@Q/AV Ah;/A / AD/A` Aؼ!/AH A%+/A L A"/AR A/A J AJ/A@ A!/A`]l A`/A@ Az/A Zx A Ex/AV A؟/AI A/A AX/A@ Z A/A AS/A $ AQ/AaE A0X/Ap A(J/A APx/A E A/A@ A/A+ A0{/A cj A/A ~ A/A A/A A/AQ A/A@Z A$/A AH/A AP/A=3 AP/Al AH/AE A /A \ A/As Ap/A ZE A 0/A Aa/A AX[/A@y AR/A S A(J/A^' Aݯ/A@yJ Ahޡ/A@, Axm/A AȘ /A@ AXu /AW AP/A ' A/Ao AT/A O)A8/AJAu/A APt/A`"A@K/AyA(/Ao Ahb/A@A2/A@ ADn/A 'A`/AAN/A AAп5/A2A0/A A`/A;A /Aj=A /AA /AAH./A .A(W/A@~AI/AoN A2/A@  AK2/Aq7 AD/A ? AТK/A A/A@ A_]/ATA /AA8B/A e AȺ6/Ab A2/A@ A(/A= AP7/A A/A e A/A* A/A AԻ/A@W~ Ag/Ao AHC/A A9/A A /A@g A=t/ABS A*\/AQ2 A-/A A(@/A Aa/A@ Ay/A A 0//A` A /Aમ A@/Aa2 A/A Ah/Ad Ah./A`: A8E{/A@ Al/Aޜ A@ϐ/A9 AP/A` A//A@* A/A@ A@B/A A(/Aृ A4/AA/AɮA/AA/A@APf/AƈA/ApA/AAĥ/A AT/AAHaU/AA#:/A A./AAv/AƈA0/A@ wA/A,A/AA8E/AɮA^/A A0X,/AAl/A`ÕA;/A`A AhU/A`P A/A`A A /A@X AB/AoN Axm/A5 Ah /A;A/A Am/AM Aȋ/A` AX,/A, A/AK A`e/AF A0/AQ A/A8; A/A) A/A A"/A3 A/A@G A/Ac AX/A A.&/A Aho(/A  A1/AgA@}/A |(AY/A@,AؼW/A`;GAh5/A2#A38/A A>/A AS/AA(>\/A |(AؼW/A`UA( /A A/A`AS/AǻA/A`bA/Ao;AZ/A$jAxG /AA/AAP $/Ai/AG/A`A`/AA`/A1A/A36A(K/AfA A^/AfA A^/A};A/A ɂA 6/AMAX(%/A ;AŌ/A@yA0a/AKA0U/A AP/AļAKP/Aj5A-/AA/AAPZ/AFA/A  A /AOA5/A A/AA /A JA(/A *A;/A A/A`A"/A A/A`TA(1}/AAA/AyAȽ/AVAȥ/AoGAxFE/A A 8/A |tAP /A#A/A OA/A^AxW/AA2/A@Al/A LA/AAг}/A A`/A7A&/AAH/A Ax/A A4A l/A4AxW/A 3A3D/A@0Al0/A`0AX5L/A@>3AL/A@&3A(/A`4AN/A5A(i/A`7AP/A@q8A /ARM=Ai/Aj=A0/A`>ApY/A ;?A`Q/Aྒ?A/AAA/AvAA0F/A&@A7/A [=AȘD/AO=AhU/A?Apx/A.AAki/AN;Aq~/AI{;A/A`AA"_/A&)AA\ AVx/A:i Aŧu y/ALYe Agy/AЕP A{y/A~@S* A,Yn#z/Az͐ A,~&z/A@9d' Atz/AjLG AE{N{/A:>.լ Aõ{/A`KP A TU|/A= A 5|/Am( AŐ|/AagE A׷]}/A]n AC?}/A}k AB4~/A# AV g~/Am 0l AT] /AA AzbLv/A9 AT/Au AxH/A H A׬/A[" AX(/A A9a/Ay A£g/A7ȣ A(l/A Aؿ{r/A A;w/AN A(9|/A[ʬ AK/A AϠ/AY!\0 A:e/AX9qh AHā/A#!땮 As؁/Aɷ A0/A AA/A`ǫ AС/A ۮ A(/A"e Aj/Ag5 A]/A A€/Aྼ A/A9̫ A@/A' Aȇ/Ay AA/A AՂ/A`" Ah/AF A𖣂/A; Ap=/A  A8/AM AA/A`) AYX/A lį Au/A A$/A`" A@6/Aé A8/A; A84/AF, A`/A@ A`/AJ Ax͂/A 䶰 A =j/A A8#w/A A ق/A԰ Ax~ /AG AU/A A(/A A/A} Aa/A 8M A-/AVq A/Ag5 Aأ/A + Ab/A A8/Atc A /A` Aa/A`Tٯ A/A`"r A-/A@$ AXH/AA APS/A ZV AkɃ/ADz AUރ/A A0 /A  A0//A  AF/A ApN/A At[/A Ai/A Q A䉄/AL A᱄/Añ AɄ/A 8 Aτ/A A˄/A` Ahل/A`; A\/A ۓ A(L/As A/A A84/A Q AxX/AD Aw/A n AҬ/A ApJ/A  AP</A  Ah/A  Aw/A`DI AX(/A@ Ap5/Ad A A0h/A Aѩ/A Aޏ/A Ao/A O AX/A` Ac/A An/A Aޑ/A X Ap/Avt AX#/A@, A8/A` A`ɒ/Aj Ahے/A A6ޒ/A A6ޒ/A Ap/A SK Aw!/A`bc Ac/A` A"/A ApJ!/AJ A/AJ A/A ¡ AY./A ۞ A00/A|> A(*/A 1G A,,/AI A00/A` A6/An A0B/A@t AP|/A@> A@ /AMz A8/A`y A8;/A AP<\/Aj Asc/A$ A8l/A$ Ae/A Abo/Az AP/A2) A跀/A` Axؾ/AP A(/A 8] A 0S/A A/A A/A A^/A5 A /A@W A@j/A@ AϘ/AM8 AC}/Aq AÙ/Aq AD֙/Aq Ah*/A#3 A0/A3 A/A@1 AX5/A@% A![/AN A![/A AȺ/Ac A/A@Ph AP뜚/A ? A /A 9 A /AS AH/A A'/A Ah/AԿ Aٲ/A ޿ A`Sš/A@1 A߅D/A A]h/AD Ap_I/A,0 A`Y/A0: A!uڄ/Aх}_ Aﮛ/A A0 ϛ/A&_N A`T/A  A /Am- Ap4/AoJ AU/AZ] AZWs/A A/A5\F AЦ/A 9 AБ/A ± Ax/A@t AБ/A AF/A  APȜ/Af AV֜/AA AD/A A/A A( Ý/A A/Ae A/A A887/A`; A\/A A1/A  AW/A AMӞ/A`) A/A A` /A`a AF/ATM Au/A3 A8/A Aݟ/A AN/A A`C%/A@` As3/Ay A?/AD AaI/A@Ͽ AW/A; Aa/A൵ APj/A`m As/AMj A8#/A AX[/A AX/A@ Apy/A` A(/A A/A AP$/AV AC/A@ APu/A Ax/A@.F A/A Agġ/A AXxС/AP؞ A/|gRߡ/Au_ A /A/g* A@/A`" A /A Aԉ:$/A^a AoJT4/Aݛ AB/A/ ARS/AҐ) Afw7c/AG A\7r/A k A-/ALپ AdDU/Ax A /AI y A_+֝/A<+J? AD7//An$ A/A@W A/A` A /A AfB/A Al/Aܓ1$ A`/A,i0F Aã/A\ A/A; APM4/A Asa/A0ſ AG/A A/AFE% AxmY/A A.Jʥ/Alv2PN AcM8/A AA S/Ax A=7 /AijL A$o/AAٶ Aѧ/A9gW A`./Adz AF/ q/AW0u AZ\K/AJ3h AЀ/A`T. A$@/AwXBx Am/Aٶ A{d8ԩ/A۹T AG/A@D A0LR/A0z- A5/A Υ AX$/A֡ AY/A9Ԟ A?T /AqP' Ah2W/Ab0p AD/A@> A;/AsL.& A` e/A?|L~: A84/A@NF AP/A A /Asї A/A A@$/A@q A8 /A A!/A A/A A(%/A` A/A> p A}/A AЦ:/A6 A2(/A4- AX /A A:/A+ AjE/A3zp A*U/AOȎ AЅ9i/A+( AH/A Ғ AEiJ/A {v A?/Akc AB2!-/A|k[ A6?/AjH^ AO|б/Aek Ah ߱/AP AX/AXzǥ A $E/A`Ѝ A9/AU AWy /A/; A/A 8x Am/A Ax+/A`D A9/A0 Ak/AĐ AŨ/A`)) Aq/A A@=P/Am˒ AXy/A sғ A;/A qV AXa/A@p A ȴ/Aн A|3/AW A੕/A@B A׵/Aߓ A*ʑ?/AC, ASc4/AYw A25a/Ac.. Aqn/Ab}֒ A*S~ǵ/A6aq Aܶ/A| Aܶ/A A0'/A@,{ ADո/A& A/A=K~ A/Ay AhD+/Aiu Axp/At A#ں /A@t AѧZkd/At f AGÕ`/Aȴa* AI^/ANH A^/Am A =^/A@ϲ AU^/A`m A#_/Av AH8_/A` A؁F_/A`: Ax\_/A Zn Ax_/A  A `/A&w AxZ`/A@i A[`/A@ A@u`/A@ AB`/A  Aq`/A Z AU`/A`; A0,a/A@ A}ra/A`  Aa/A2 Aa/A`7 Ala/A \ A%b/A3j ALb/AcK Atb/A Ab/A`KK Ax)b/A A@Fb/A APb/A\ Ab/A Aac/AV~ A`nc/A( Ac/Aܗ AP@Lc/A, AP5c/A: AOc/Ab APc/Ax AHpc/A Af;a/A? A1a/A@ AXa/A`C A`/A@`F Apl`/AlG Au`/A mI A@j`/AX#J A`B_/A Q A߶_/A1Q A0G_/A^Z AX4^/AY Apb^/A`Qa A^/A{c A _^/Ad AH]/AT7e A`v]/A f A]/A g A ]/Abh Ax]/AXi A@]/AI k A]/A m Aj]/AfSn A+]/App A@0 ]/Aq Ah]/A@߭q A`]/A;r A\/At A@\/A J Ad/Ao# Ad/A` Aw.d/A8 Aw.d/A`BK AX d/A@/ Ac/Ac A c/A^ A`c/A A8 d/A@% Ad/A1 Adc/A@% A8#c/A@C A%d/A AHd/A# Ac/Ah Ac/A@Q APd/Ay A1(d/A O A]-d/A A6d/A A1d/Ajg A d/A(r Ad/A(r Ad/A Ac/Aޓ Ac/A@J Ac/AXs AGc/A, Ac/A&y AͿc/A A$c/A2 A$c/AR A@c/A@iA Asc/A Afc/A A8c/AH Asc/A@ AEc/A@ X A d/Aʚ A*d/A} Ald/A} Ad/A`7 A\d/Ak: A\d/A Ad/A Ac/A`F Ajc/A9@ APc/AN Ac/AN A8tc/Ak: Ac/A 8 Ac/A Ac/A@Wr Amc/A r AHc/A A`c/A A4rc/A AXc/Aj AXc/A- A>c/A< A`c/A@ Aӭc/A i Ac/A i A0c/A6w Ac/As A {c/A;b AYTc/A@\ A@$nc/A@r Aq c/A { Apc/A@g A /c/A APc/A AOc/A` AWEc/A`H AO A Dc/A A Ac/Aa1 Akc/A= A0fb/AH Ab/A Ab/An A8 b/A@8 Ab/A A c/A@Q AP<8d/A`) ABd/A` A e/A@p A|&e/A߽ ADe/A l A_ e/A ZS Ad/A- Ad/A@, A d/A Ap<e/A Ahd/A`s AЕe/AW APd/A + A e/As7 A Ce/A; AOe/A Ahe/AQ Abe/A@g AGke/Aj Ae/A  Aږe/A l A0e/Am Ae/A) Axe/A A}e/AԳ A@e/A. A@c/AA A8c/A0@ Axm,c/Av=EQ@ A//=c/AJ*? ApjQc/A? Ahc/Ap? A:c/AH? A&%c/A/? Ac/A&? A0c/AhB> Alc/ACu> Ayc/A[> Ad/A`< A?d/A; Ac/A[F Aw`c/A9,_ Ac/Aq AQc/A&m Ac/AdJn AN[c/A- ADDc/AO0 A=Ic/Ap( ALc/A! Asd/Ai.' Ad/A Asd/A ARc/A: AB3d/A߉'G A0vnd/Asy A;:.d/A A _d/A@El A2Z.d/ALr Am@d/A j_ APHTd/A@` A0sd/A A(Nd/A  A Hd/A A83xd/A AѧZkd/At f Am+:/AHؤ`Ax/AGp\Ax/AO\Ao/A`K\AXe/AU]AbR/AiL]A\:/A`]Am+:/AHؤ`A /A_|RA{V /AD TQAh]0/A |QAN/A QAN/A%RA(>p/AVRA8/A sRA /A_|RAd/An'QA6]/A\ݲOA)/AnNAXE/AV>PAd/An'QAe/A?GA Z/A<ŮTBA/A`9OBA~/A JBA/A@>aBAx/A@,BAM/A?AA/Ao@A 1/ADvBAHs/AoCA`/A@gCAT/A SDA/A@EAw/AGAe/A?GA ; /A8AC40ATA6gmr^ C40AqAx40AOfRAu]Qڂ30A44AlBg20AP9QA5mI10A)NSqAo6/0AnBA\5-0AɴajA?e+0ARȪAQ\(0A| uA%0A F)wAg˳"0A Wn2mA@r0AĒLn]A<%0AT,QPA؉0AEAo90A[u->A+0A98 :A3 0A8A^ 0A98 :AB0A[u->A9'0AEAʇT/AT,QPA Z:/ArQA0/ApyoAO/A`[Axz/A`Av/A`^A7]/A ŒA89/A`A,iO0AVApA/AA /AЪAX/AA/AsAX#/A@A i/AA/A`)Ap=/AAx-/A QcAX/A@AM/A >AM/A;Ap=/AWAh/A Ah/A`fA8/A)[Ah/AV;Ah/A@>bA(/AjA@u/AAh/A`8A`/A^5Ap=/AAX/A AH/A lEA(/A ҁAU/A Ae/A`Ax-/A=$Aո/AaZAС/AgAؽ/A`A/AӒAxƷ/A}AxƷ/A@vA=}/AaA1/A`rA@q%/AA}#/AOAcw/A@AVy/A>A0v/ACA(o/A IA}/A Aؽ/AA/AA/ApA/A1AZ/A`AP8/AŢA/AA@/A %AH?=/A@Nd~A"/A"0A/AAx-/AaAq/AA`N/A촁A@F/AAv/AA/A@~AV/AtZAV/A_nA'0/A9A#/AdiA2&/A~A=/AQAȶ?/A(A`/A@~A/AT~A/A M~A8/A~A/AxAxi/AbAso/A AP^r/AA|/A@EyA/AosApʩ/AA@/AsA*/AVA*/AAxu/A@A /AzA@/AOA/A 4A/A QAP/AzA`%/A`AY4/A+A@/AAK/A/AxZ/A FAs/A A(/AAX/AAA/A YA /A 8A`/A`A/ASA0/A@>A/A A /ATA/AA@/AfA(~/A@A^/AA /A@-A /AA /A_4A t/AazAh/AAJ,/A AS/A`A`/AXAh/AALr/A@ƞA07u/AIA07u/A#Av/A`Apm/AVAP/A7A=/AAk9/A^AP6/AA3/AgVAX5//A4A-/AqA/A@!oAI /AAh/AA/A@yA`2/AA(/A`"A/A LA:m/AA(P/A HAXyA/A A2/A:AТ/AKiAb/AKiAHa/A J#A@/A@A/AB.AH/A Ai/AhyA=/A@>AX[ /AbAy/A`;A`X/A An/A 8AU7/A6gA/AaaA/A6gA/A@[AXۆ/AAXP/AAW /A`[AX/A A8t/A@.OA(hZ/A@WAuA/AVzBAX/A EA]/A)GA/AHAX[/AEHA/A 8IGA{/A0EA(-/ABA/A`a@A/A =AHL/Aaz9AO/A66AD/A 0<A/A@A`/A@PsDA$9/A)GAxF/AHAl/AIA@S/AwLAY/A NA`c/ANA c>/A`OA/AɄQA/A@zTAxi/A`mUAxi/A`-XAȤ/AEZA2/A`]]A`+/A hbAe/A`adA͐/A JfAX/A` jA`/AnA/A cqA, /A@%tA,/AjtA,/A`sALM/A2iAn/A ¶]A8c/AXbAЦ/A`eAHr/A3\mA((/ARrAd/Ax0rAk/A&mAi/AviA@/AgA؟K/AdAP/A@ZfA@A/A`hAR/AEmA( z/A@rAV/A /zA{/AOA /A JAҗ/AX>Aû/A@ ǡA8I/AӻA/AA0f0/A uAP /A?SAzZ/A` Ap/A3AN/AAs/AOϮA,/A3A/A@AX /AʇA`/A3A੹/AVA@>/AA/A$ZA+=w/Al A*&{1/A0ƾA%h8/A౬Ad /Aȕ+AGÕp/A/RAz/A:A_G'/ABypE'Ab"/A kL1A2-h/Aqyn:A/A6PBAʇT/Aִi.HA9'0A NAB0AuEiQA^ 0A7cTA3 0ATA+0A7cTAo90AuEiQA؉0A NA<%0Aִi.HA@r0A6PBAg˳"0Aryn:A%0A kL1AP\(0ACypE'A?e+0A:A\5-0A/RAo6/0Aɕ+A5mI10A౬AlBg20A0ƾAu]Qڂ30A}9Ax40A_AC40AqAC40AqA0/AjA0{B/A`̇A`/A9AЄ/A'ApA/A` ʞA0/AjA6/ABA\/A`oAH/A _A/A}A6/ABA R/A@A{/AA{q/AAg/AA0^/AAp8N/AAD/AA7/AɔA+/A`uA#/AHKA`/AA2/A`AE/A@#A/AA/AlA/A@PA`X/AA`X/A@A N/A$A/AzpA/A6A/A=A/AA`/A0rA/A3pA/A@gRA/AA(J/AA^/AKApl/AAF/A`bgAhU/A l!A%z/A`Aذs/A`APa/A [A ^Y/A$&AhV/AvAS/A`AL/AA~F/A cmA@/A/A1/AA(./AKA ="/AA/A஬A0/A`~A0/Aj?A /AA` /A@5Al/A`vAn/A`An/ASAn/AaA/AAn/A`An/AXAPk/A ^Ab /A:A/AA0/A`DAx#/AwA(./AA9/AͳA@E/A`AQ/A$ANb/AбAPj/A AXn/AwAr/AA1s/ARA t/A_Apw/AA/AA/A Q%A/A@\A/AbAq/AzAb/AैAئ/AAHЫ/A|A(>/A;A`/A2A`/A`A/A A@/A+AP/AgAAx/A)eA(~/AyA(~/AʜA/A@A/A@AP/AtAP/A AA8 /AAP/A@[A8 /AAH/A@A/AAU/A_A/A*Ax/A@gA9/A^A(1/A A/AAx( /AZ0A/AZA/A@Ah/ARAH'/AAf3/AA(1/A;lAU=/AA=K/AA%Y/AAd/A A8l/A@Aw/AfA|/A@ A|/A`dAPڄ/AAp΋/A}AŐ/A` 9A8/AA8/A`;AP7/AA/A#Ap0ATLbAi*|:0A*KAZ70Ab@&WA4L30A+79%A_J100A#[.A-0A8AH=<+0A|)DDAM(0AdZ0UPAX2&0A7j!|]A˒e4X%0AXkA $0AwyA%#0A AcA2'"0A{PӗA_"0A$A2'"0AtXtA%#0A土A $0AWcA˒e4X%0AkXVAX2&0AȕAM(0A/AH=<+0A:C A-0ABypEA_J100A kA4L30Aqy)AZ70A60Ai*|:0Aִi.7A9'>0A <ABA0AuEic@AԚE0A7cBAoI0AdCA+CM0A7cBAo9 Q0AuEic@AT0A <A 0.3] assert isinstance(P2, GeometryArray) P3 = P[[1, 3, 5]] assert len(P3) == 3 assert isinstance(P3, GeometryArray) assert [p.x for p in P3] == [1, 3, 5] P4 = P[1::2] assert len(P4) == 5 assert isinstance(P3, GeometryArray) assert [p.x for p in P4] == [1, 3, 5, 7, 9] P5 = P[1] assert isinstance(P5, shapely.geometry.Point) assert P5.equals(points[1]) @pytest.mark.parametrize( "item", [ geopandas.GeoDataFrame( geometry=[shapely.geometry.Polygon([(0, 0), (2, 0), (2, 2), (0, 2)])] ), geopandas.GeoSeries( [shapely.geometry.Polygon([(0, 0), (2, 0), (2, 2), (0, 2)])] ), np.array([shapely.geometry.Polygon([(0, 0), (2, 0), (2, 2), (0, 2)])]), [shapely.geometry.Polygon([(0, 0), (2, 0), (2, 2), (0, 2)])], shapely.geometry.Polygon([(0, 0), (2, 0), (2, 2), (0, 2)]), ], ) def test_setitem(item): points = [shapely.geometry.Point(i, i) for i in range(10)] P = from_shapely(points) P[[0]] = item assert isinstance(P[0], shapely.geometry.Polygon) def test_equality_ops(): with pytest.raises(ValueError): _ = P[:5] == P[:7] a1 = from_shapely([points[1], points[2], points[3]]) a2 = from_shapely([points[1], points[0], points[3]]) res = a1 == a2 assert res.tolist() == [True, False, True] res = a1 != a2 assert res.tolist() == [False, True, False] # check the correct expansion of list-like geometry multi_poly = shapely.geometry.MultiPolygon( [shapely.geometry.box(0, 0, 1, 1), shapely.geometry.box(3, 3, 4, 4)] ) a3 = from_shapely([points[1], points[2], points[3], multi_poly]) res = a3 == multi_poly assert res.tolist() == [False, False, False, True] def test_dir(): assert "contains" in dir(P) assert "to_numpy" in dir(P) def test_chaining(): # contains will give False for empty / missing T = from_shapely(triangle_no_missing) assert T.contains(T.centroid).all() def test_pickle(): import pickle T2 = pickle.loads(pickle.dumps(T)) # assert (T.data != T2.data).all() assert T2[-1] is None assert T2[-2].is_empty assert T[:-2].geom_equals(T2[:-2]).all() def test_raise_on_bad_sizes(): with pytest.raises(ValueError) as info: T.contains(P) assert "lengths" in str(info.value).lower() assert "12" in str(info.value) assert "21" in str(info.value) def test_buffer_single_multipolygon(): # https://github.com/geopandas/geopandas/issues/1130 multi_poly = shapely.geometry.MultiPolygon( [shapely.geometry.box(0, 0, 1, 1), shapely.geometry.box(3, 3, 4, 4)] ) arr = from_shapely([multi_poly]) result = arr.buffer(1) expected = [multi_poly.buffer(1)] equal_geometries(result, expected) result = arr.buffer(np.array([1])) equal_geometries(result, expected) def test_astype_multipolygon(): # https://github.com/geopandas/geopandas/issues/1145 multi_poly = shapely.geometry.MultiPolygon( [shapely.geometry.box(0, 0, 1, 1), shapely.geometry.box(3, 3, 4, 4)] ) arr = from_shapely([multi_poly]) result = arr.astype(str) assert isinstance(result[0], str) assert result[0] == multi_poly.wkt # astype(object) does not convert to string result = arr.astype(object) assert isinstance(result[0], shapely.geometry.base.BaseGeometry) # astype(np_dtype) honors the dtype result = arr.astype(np.dtype("U10")) assert result.dtype == np.dtype("U10") assert result[0] == multi_poly.wkt[:10] @pytest.mark.skipif(not HAS_PYPROJ, reason="pyproj not installed") def test_check_crs(): t1 = T.copy() t1.crs = 4326 assert _check_crs(t1, T) is False assert _check_crs(t1, t1) is True assert _check_crs(t1, T, allow_none=True) is True @pytest.mark.skipif(not HAS_PYPROJ, reason="pyproj not installed") def test_crs_mismatch_warn(): t1 = T.copy() t2 = T.copy() t1.crs = 4326 t2.crs = 3857 # two different CRS with pytest.warns(UserWarning, match="CRS mismatch between the CRS"): _crs_mismatch_warn(t1, t2) # left None with pytest.warns(UserWarning, match="CRS mismatch between the CRS"): _crs_mismatch_warn(T, t2) # right None with pytest.warns(UserWarning, match="CRS mismatch between the CRS"): _crs_mismatch_warn(t1, T) @pytest.mark.skipif(HAS_PYPROJ, reason="pyproj installed") def test_missing_pyproj(): with pytest.warns(UserWarning, match="Cannot set the CRS, falling back to None"): t = T.copy() t.crs = 4326 assert t.crs is None @pytest.mark.parametrize("NA", [None, np.nan]) def test_isna(NA): t1 = T.copy() t1[0] = NA assert t1[0] is None def test_isna_pdNA(): t1 = T.copy() t1[0] = pd.NA assert t1[0] is None def test_shift_has_crs(): t = T.copy() t.crs = 4326 assert t.shift(1).crs == t.crs assert t.shift(0).crs == t.crs assert t.shift(-1).crs == t.crs def test_unique_has_crs(): t = T.copy() t.crs = 4326 assert t.unique().crs == t.crs @pytest.mark.skipif(HAS_PYPROJ, reason="pyproj installed") def test_to_crs_pyproj_error(): t = T.copy() t.crs = 4326 with pytest.raises( ImportError, match="The 'pyproj' package is required for to_crs" ): t.to_crs(3857) @pytest.mark.skipif(HAS_PYPROJ, reason="pyproj installed") def test_estimate_utm_crs_pyproj_error(): with pytest.raises( ImportError, match="The 'pyproj' package is required for estimate_utm_crs" ): T.estimate_utm_crs() class TestEstimateUtmCrs: def setup_method(self): self.esb = shapely.geometry.Point(-73.9847, 40.7484) self.sol = shapely.geometry.Point(-74.0446, 40.6893) self.landmarks = from_shapely([self.esb, self.sol], crs="epsg:4326") def test_estimate_utm_crs__geographic(self): pyproj = pytest.importorskip("pyproj") assert self.landmarks.estimate_utm_crs() == pyproj.CRS("EPSG:32618") assert self.landmarks.estimate_utm_crs("NAD83") == pyproj.CRS("EPSG:26918") def test_estimate_utm_crs__projected(self): pyproj = pytest.importorskip("pyproj") assert self.landmarks.to_crs("EPSG:3857").estimate_utm_crs() == pyproj.CRS( "EPSG:32618" ) def test_estimate_utm_crs__antimeridian(self): pyproj = pytest.importorskip("pyproj") antimeridian = from_shapely( [ shapely.geometry.Point(1722483.900174921, 5228058.6143420935), shapely.geometry.Point(4624385.494808555, 8692574.544944234), ], crs="EPSG:3851", ) assert antimeridian.estimate_utm_crs() == pyproj.CRS("EPSG:32760") def test_estimate_utm_crs__out_of_bounds(self): pytest.importorskip("pyproj") with pytest.raises(RuntimeError, match="Unable to determine UTM CRS"): from_shapely( [shapely.geometry.Polygon([(0, 90), (1, 90), (2, 90)])], crs="EPSG:4326" ).estimate_utm_crs() def test_estimate_utm_crs__missing_crs(self): pytest.importorskip("pyproj") with pytest.raises(RuntimeError, match="crs must be set"): from_shapely( [shapely.geometry.Polygon([(0, 90), (1, 90), (2, 90)])] ).estimate_utm_crs() def test_reduce_keepdims(): arr = geopandas.GeoDataFrame(geometry=[]).geometry.array assert arr._reduce("all", keepdims=True) == np.array([True], dtype=object) assert arr._reduce("all", keepdims=False) geopandas-1.2.0/geopandas/tests/test_compat.py000066400000000000000000000016331525650651600214700ustar00rootroot00000000000000from geopandas._compat import import_optional_dependency import pytest def test_import_optional_dependency_present(): # pandas is not optional, but we know it is present pandas = import_optional_dependency("pandas") assert pandas is not None # module imported normally must be same import pandas as pd assert pandas == pd def test_import_optional_dependency_absent(): with pytest.raises(ImportError, match="Missing optional dependency 'foo'"): import_optional_dependency("foo") with pytest.raises(ImportError, match="foo is required"): import_optional_dependency("foo", extra="foo is required") @pytest.mark.parametrize( "bad_import", [["foo"], 0, False, True, {}, {"foo"}, {"foo": "bar"}] ) def test_import_optional_dependency_invalid(bad_import): with pytest.raises(ValueError, match="Invalid module name"): import_optional_dependency(bad_import) geopandas-1.2.0/geopandas/tests/test_config.py000066400000000000000000000022361525650651600214520ustar00rootroot00000000000000import geopandas import pytest def test_options(): assert "display_precision: " in repr(geopandas.options) assert set(dir(geopandas.options)) == { "display_precision", "io_engine", } with pytest.raises(AttributeError): geopandas.options.non_existing_option with pytest.raises(AttributeError): geopandas.options.non_existing_option = 10 def test_options_display_precision(): assert geopandas.options.display_precision is None geopandas.options.display_precision = 5 assert geopandas.options.display_precision == 5 with pytest.raises(ValueError): geopandas.options.display_precision = "abc" with pytest.raises(ValueError): geopandas.options.display_precision = -1 geopandas.options.display_precision = None def test_options_io_engine(): assert geopandas.options.io_engine is None geopandas.options.io_engine = "pyogrio" assert geopandas.options.io_engine == "pyogrio" with pytest.raises(ValueError): geopandas.options.io_engine = "abc" with pytest.raises(ValueError): geopandas.options.io_engine = -1 geopandas.options.io_engine = None geopandas-1.2.0/geopandas/tests/test_crs.py000066400000000000000000000631111525650651600207730ustar00rootroot00000000000000import random import warnings import numpy as np import pandas as pd from shapely.geometry import LineString, Point, Polygon from geopandas import GeoDataFrame, GeoSeries, points_from_xy, read_file from geopandas.array import GeometryArray, from_shapely, from_wkb, from_wkt import pytest from geopandas.testing import assert_geodataframe_equal pyproj = pytest.importorskip("pyproj") def _create_df(x, y=None, crs=None): y = y or x x = np.asarray(x) y = np.asarray(y) return GeoDataFrame( {"geometry": points_from_xy(x, y), "value1": x + y, "value2": x * y}, crs=crs ) def df_epsg26918(): # EPSG:26918 # Center coordinates # -1683723.64 6689139.23 return _create_df( x=range(-1683723, -1683723 + 10, 1), y=range(6689139, 6689139 + 10, 1), crs="epsg:26918", ) def test_to_crs_transform(): df = df_epsg26918() lonlat = df.to_crs(epsg=4326) utm = lonlat.to_crs(epsg=26918) assert_geodataframe_equal(df, utm, check_less_precise=True) def test_to_crs_transform__missing_data(): # https://github.com/geopandas/geopandas/issues/1573 df = df_epsg26918() df.loc[3, "geometry"] = None lonlat = df.to_crs(epsg=4326) utm = lonlat.to_crs(epsg=26918) assert_geodataframe_equal(df, utm, check_less_precise=True) def test_to_crs_transform__empty_data(): df = df_epsg26918().iloc[:0] lonlat = df.to_crs(epsg=4326) utm = lonlat.to_crs(epsg=26918) assert_geodataframe_equal(df, utm, check_less_precise=True) def test_to_crs_inplace(): df = df_epsg26918() lonlat = df.to_crs(epsg=4326) df.to_crs(epsg=4326, inplace=True) assert_geodataframe_equal(df, lonlat, check_less_precise=True) def test_to_crs_geo_column_name(): # Test to_crs() with different geometry column name (GH#339) df = df_epsg26918() df = df.rename(columns={"geometry": "geom"}) df.set_geometry("geom", inplace=True) lonlat = df.to_crs(epsg=4326) utm = lonlat.to_crs(epsg=26918) assert lonlat.geometry.name == "geom" assert utm.geometry.name == "geom" assert_geodataframe_equal(df, utm, check_less_precise=True) def test_to_crs_dimension_z(): # preserve z dimension arr = points_from_xy([1, 2], [2, 3], [3, 4], crs=4326) assert arr.has_z.all() result = arr.to_crs(epsg=3857) assert result.has_z.all() # pyproj + numpy 1.25 trigger warning for single-element array -> recommendation is to # ignore the warning for now (https://github.com/pyproj4/pyproj/issues/1307) @pytest.mark.filterwarnings("ignore:Conversion of an array with:DeprecationWarning") def test_to_crs_dimension_mixed(): s = GeoSeries([Point(1, 2), LineString([(1, 2, 3), (4, 5, 6)])], crs=2056) result = s.to_crs(epsg=4326) assert not result[0].is_empty assert result.has_z.tolist() == [False, True] roundtrip = result.to_crs(epsg=2056) # TODO replace with assert_geoseries_equal once we expose tolerance keyword # assert_geoseries_equal(roundtrip, s, check_less_precise=True) for a, b in zip(roundtrip, s): np.testing.assert_allclose(a.coords[:], b.coords[:], atol=0.01) # ----------------------------------------------------------------------------- # Test different supported formats for CRS specification @pytest.fixture( params=[ 4326, "epsg:4326", pytest.param( {"init": "epsg:4326"}, ), "+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs", {"proj": "latlong", "ellps": "WGS84", "datum": "WGS84", "no_defs": True}, ], ids=["epsg_number", "epsg_string", "epsg_dict", "proj4_string", "proj4_dict"], ) def epsg4326(request): if isinstance(request.param, int): return {"epsg": request.param} return {"crs": request.param} @pytest.fixture( params=[ pytest.param(26918, id="epsg_number"), pytest.param("epsg:26918", id="epsg_string"), pytest.param( {"init": "epsg:26918", "no_defs": True}, id="epsg_dict", ), pytest.param( "+proj=utm +zone=18 +ellps=GRS80 +datum=NAD83 +units=m " "+no_defs +nadgrids=null", id="proj4_string", ), pytest.param( { "proj": "utm", "zone": 18, "datum": "NAD83", "units": "m", "no_defs": True, "nadgrids": "null", }, id="proj4_dict", ), ] ) def epsg26918(request): if isinstance(request.param, int): return {"epsg": request.param} return {"crs": request.param} @pytest.mark.filterwarnings("ignore:'\\+init:DeprecationWarning") @pytest.mark.filterwarnings("ignore:'\\+init:FutureWarning") def test_transform2(epsg4326, epsg26918): df = df_epsg26918() lonlat = df.to_crs(**epsg4326) utm = lonlat.to_crs(**epsg26918) # can't check for CRS equality, as the formats differ although representing # the same CRS assert_geodataframe_equal(df, utm, check_less_precise=True, check_crs=False) # pyproj + numpy 1.25 trigger warning for single-element array -> recommendation is to # ignore the warning for now (https://github.com/pyproj4/pyproj/issues/1307) @pytest.mark.filterwarnings("ignore:Conversion of an array with:DeprecationWarning") def test_crs_axis_order__always_xy(): df = GeoDataFrame(geometry=[Point(-1683723, 6689139)], crs="epsg:26918") lonlat = df.to_crs("epsg:4326") test_lonlat = GeoDataFrame( geometry=[Point(-110.1399901, 55.1350011)], crs="epsg:4326" ) assert_geodataframe_equal(lonlat, test_lonlat, check_less_precise=True) def test_skip_exact_same(): df = df_epsg26918() utm = df.to_crs(df.crs) assert_geodataframe_equal(df, utm, check_less_precise=True) # Test CRS on GeometryArray level class TestGeometryArrayCRS: def setup_method(self): self.osgb = pyproj.CRS(27700) self.wgs = pyproj.CRS(4326) self.geoms = [Point(0, 0), Point(1, 1)] self.polys = [ Polygon([(random.random(), random.random()) for i in range(3)]) for _ in range(10) ] self.arr = from_shapely(self.polys, crs=27700) def test_array(self): arr = from_shapely(self.geoms) arr.crs = 27700 assert arr.crs == self.osgb arr = from_shapely(self.geoms, crs=27700) assert arr.crs == self.osgb arr = GeometryArray(arr) assert arr.crs == self.osgb arr = GeometryArray(arr, crs=4326) assert arr.crs == self.wgs def test_series(self): s = GeoSeries(crs=27700) assert s.crs == self.osgb assert s.values.crs == self.osgb arr = from_shapely(self.geoms) s = GeoSeries(arr, crs=27700) assert s.crs == self.osgb assert s.values.crs == self.osgb # manually change CRS s = s.set_crs(4326, allow_override=True) assert s.crs == self.wgs assert s.values.crs == self.wgs s = GeoSeries(self.geoms, crs=27700) assert s.crs == self.osgb assert s.values.crs == self.osgb arr = from_shapely(self.geoms, crs=27700) s = GeoSeries(arr) assert s.crs == self.osgb assert s.values.crs == self.osgb with pytest.raises( ValueError, match="CRS mismatch between CRS of the passed geometries and 'crs'", ): s = GeoSeries(arr, crs=4326) assert s.crs == self.osgb def test_dataframe(self): arr = from_shapely(self.geoms, crs=27700) df = GeoDataFrame(geometry=arr) assert df.crs == self.osgb assert df.geometry.crs == self.osgb assert df.geometry.values.crs == self.osgb arr = from_shapely(self.geoms) s = GeoSeries(arr, crs=27700) df = GeoDataFrame(geometry=s) assert df.crs == self.osgb assert df.geometry.crs == self.osgb assert df.geometry.values.crs == self.osgb # different passed CRS than array CRS is now an error match_str = "CRS mismatch between CRS of the passed geometries and 'crs'" with pytest.raises(ValueError, match=match_str): df = GeoDataFrame(geometry=s, crs=4326) with pytest.raises(ValueError, match=match_str): GeoDataFrame(geometry=s, crs=4326) with pytest.raises(ValueError, match=match_str): GeoDataFrame({"data": [1, 2], "geometry": s}, crs=4326) with pytest.raises(ValueError, match=match_str): GeoDataFrame(df, crs=4326).crs # manually change CRS arr = from_shapely(self.geoms) s = GeoSeries(arr, crs=27700) df = GeoDataFrame(geometry=s) df = df.set_crs(crs="epsg:4326", allow_override=True) assert df.crs == self.wgs assert df.geometry.crs == self.wgs assert df.geometry.values.crs == self.wgs with pytest.raises(ValueError, match="Assigning CRS to a GeoDataFrame without"): GeoDataFrame(self.geoms, columns=["geom"], crs=27700) with pytest.raises(ValueError, match="Assigning CRS to a GeoDataFrame without"): GeoDataFrame(crs=27700) df = GeoDataFrame(self.geoms, columns=["geom"]) df = df.set_geometry("geom", crs=27700) assert df.crs == self.osgb assert df.geometry.crs == self.osgb assert df.geometry.values.crs == self.osgb assert df.geom.crs == self.osgb assert df.geom.values.crs == self.osgb df = GeoDataFrame(geometry=self.geoms, crs=27700) assert df.crs == self.osgb assert df.geometry.crs == self.osgb assert df.geometry.values.crs == self.osgb # new geometry with set CRS has priority over GDF CRS df = GeoDataFrame(geometry=self.geoms, crs=27700) df = df.set_geometry(self.geoms, crs=4326) assert df.crs == self.wgs assert df.geometry.crs == self.wgs assert df.geometry.values.crs == self.wgs arr = from_shapely(self.geoms) s = GeoSeries(arr, crs=27700) df = GeoDataFrame() df = df.set_geometry(s) assert df._geometry_column_name == "geometry" assert df.crs == self.osgb assert df.geometry.crs == self.osgb assert df.geometry.values.crs == self.osgb arr = from_shapely(self.geoms, crs=27700) df = GeoDataFrame() df = df.set_geometry(arr) assert df.crs == self.osgb assert df.geometry.crs == self.osgb assert df.geometry.values.crs == self.osgb arr = from_shapely(self.geoms) df = GeoDataFrame({"col1": [1, 2], "geometry": arr}, crs=4326) assert df.crs == self.wgs assert df.geometry.crs == self.wgs assert df.geometry.values.crs == self.wgs arr = from_shapely(self.geoms, crs=4326) df = GeoDataFrame({"col1": [1, 2], "geometry": arr}) assert df.crs == self.wgs assert df.geometry.crs == self.wgs assert df.geometry.values.crs == self.wgs # geometry column name None on init df = GeoDataFrame({"geometry": [0, 1]}) with pytest.raises( ValueError, match="Assigning CRS to a GeoDataFrame without a geometry", ): df.crs = 27700 # geometry column without geometry with warnings.catch_warnings(): warnings.filterwarnings( "ignore", "Geometry column does not contain geometry", UserWarning ) df = GeoDataFrame({"geometry": [Point(0, 1)]}).assign(geometry=[0]) with pytest.raises( ValueError, match="Assigning CRS to a GeoDataFrame without an active geometry", ): df.crs = 27700 with pytest.raises( AttributeError, match="The CRS attribute of a GeoDataFrame without an active", ): assert df.crs == self.osgb def test_dataframe_getitem_without_geometry_column(self): df = GeoDataFrame({"col": range(10)}, geometry=self.arr) df["geom2"] = df.geometry.centroid subset = df[["col", "geom2"]] with pytest.raises( AttributeError, match="The CRS attribute of a GeoDataFrame without an active", ): assert subset.crs == self.osgb def test_dataframe_setitem(self): # new geometry CRS has priority over GDF CRS arr = from_shapely(self.geoms) s = GeoSeries(arr, crs=27700) df = GeoDataFrame() with pytest.warns( FutureWarning, match="You are adding a column named 'geometry'" ): df["geometry"] = s assert df.crs == self.osgb assert df.geometry.crs == self.osgb assert df.geometry.values.crs == self.osgb arr = from_shapely(self.geoms, crs=27700) df = GeoDataFrame() with pytest.warns( FutureWarning, match="You are adding a column named 'geometry'" ): df["geometry"] = arr assert df.crs == self.osgb assert df.geometry.crs == self.osgb assert df.geometry.values.crs == self.osgb # test to_crs case (GH1960) arr = from_shapely(self.geoms) df = GeoDataFrame({"col1": [1, 2], "geometry": arr}, crs=4326) df["geometry"] = df["geometry"].to_crs(27700) assert df.crs == self.osgb assert df.geometry.crs == self.osgb assert df.geometry.values.crs == self.osgb # test changing geometry crs not in the geometry column doesn't change the crs arr = from_shapely(self.geoms) df = GeoDataFrame( {"col1": [1, 2], "geometry": arr, "other_geom": arr}, crs=4326 ) df["other_geom"] = from_shapely(self.geoms, crs=27700) assert df.crs == self.wgs assert df.geometry.crs == self.wgs assert df["geometry"].crs == self.wgs assert df["other_geom"].crs == self.osgb def test_dataframe_setitem_without_geometry_column(self): arr = from_shapely(self.geoms) df = GeoDataFrame({"col1": [1, 2], "geometry": arr}, crs=4326) # override geometry with non geometry with pytest.warns(UserWarning): df["geometry"] = 1 # assigning a list of geometry object doesn't have cached access to 4326 df["geometry"] = self.geoms assert df.crs is None @pytest.mark.parametrize( "scalar", [None, Point(0, 0), LineString([(0, 0), (1, 1)])] ) def test_scalar(self, scalar): df = GeoDataFrame() with pytest.warns( FutureWarning, match="You are adding a column named 'geometry'" ): df["geometry"] = scalar df = df.set_crs(4326) assert df.crs == self.wgs assert df.geometry.crs == self.wgs assert df.geometry.values.crs == self.wgs @pytest.mark.filterwarnings("ignore:Accessing CRS") def test_crs_with_no_geom_fails(self): with pytest.raises(ValueError, match="Assigning CRS to a GeoDataFrame without"): df = GeoDataFrame() df.crs = 4326 def test_read_file(self, nybb_filename): df = read_file(nybb_filename) assert df.crs == pyproj.CRS(2263) assert df.geometry.crs == pyproj.CRS(2263) assert df.geometry.values.crs == pyproj.CRS(2263) def test_multiple_geoms(self): arr = from_shapely(self.geoms, crs=27700) s = GeoSeries(self.geoms, crs=4326) df = GeoDataFrame(s, geometry=arr, columns=["col1"]) assert df.crs == self.osgb assert df.geometry.crs == self.osgb assert df.geometry.values.crs == self.osgb assert df.col1.crs == self.wgs assert df.col1.values.crs == self.wgs def test_multiple_geoms_set_geom(self): arr = from_shapely(self.geoms, crs=27700) s = GeoSeries(self.geoms, crs=4326) df = GeoDataFrame(s, geometry=arr, columns=["col1"]) df = df.set_geometry("col1") assert df.crs == self.wgs assert df.geometry.crs == self.wgs assert df.geometry.values.crs == self.wgs assert df["geometry"].crs == self.osgb assert df["geometry"].values.crs == self.osgb def test_assign_cols(self): arr = from_shapely(self.geoms, crs=27700) s = GeoSeries(self.geoms, crs=4326) df = GeoDataFrame(s, geometry=arr, columns=["col1"]) df["geom2"] = s df["geom3"] = s.values df["geom4"] = from_shapely(self.geoms) assert df.crs == self.osgb assert df.geometry.crs == self.osgb assert df.geometry.values.crs == self.osgb assert df.geom2.crs == self.wgs assert df.geom2.values.crs == self.wgs assert df.geom3.crs == self.wgs assert df.geom3.values.crs == self.wgs assert df.geom4.crs is None assert df.geom4.values.crs is None def test_copy(self): arr = from_shapely(self.geoms, crs=27700) s = GeoSeries(self.geoms, crs=4326) df = GeoDataFrame(s, geometry=arr, columns=["col1"]) arr_copy = arr.copy() assert arr_copy.crs == arr.crs s_copy = s.copy() assert s_copy.crs == s.crs assert s_copy.values.crs == s.values.crs df_copy = df.copy() assert df_copy.crs == df.crs assert df_copy.geometry.crs == df.geometry.crs assert df_copy.geometry.values.crs == df.geometry.values.crs assert df_copy.col1.crs == df.col1.crs assert df_copy.col1.values.crs == df.col1.values.crs def test_rename(self): arr = from_shapely(self.geoms, crs=27700) s = GeoSeries(self.geoms, crs=4326) df = GeoDataFrame(s, geometry=arr, columns=["col1"]) df = df.rename(columns={"geometry": "geom"}).set_geometry("geom") assert df.crs == self.osgb assert df.geometry.crs == self.osgb assert df.geometry.values.crs == self.osgb df = df.rename_geometry("geom2") assert df.crs == self.osgb assert df.geometry.crs == self.osgb assert df.geometry.values.crs == self.osgb df = df.rename(columns={"col1": "column1"}) assert df.column1.crs == self.wgs assert df.column1.values.crs == self.wgs def test_geoseries_to_crs(self): s = GeoSeries(self.geoms, crs=27700) s = s.to_crs(4326) assert s.crs == self.wgs assert s.values.crs == self.wgs df = GeoDataFrame(geometry=s) assert df.crs == self.wgs df = df.to_crs(27700) assert df.crs == self.osgb assert df.geometry.crs == self.osgb assert df.geometry.values.crs == self.osgb # make sure that only active geometry is transformed arr = from_shapely(self.geoms, crs=4326) df["col1"] = arr df = df.to_crs(3857) assert df.col1.crs == self.wgs assert df.col1.values.crs == self.wgs def test_array_to_crs(self): arr = from_shapely(self.geoms, crs=27700) arr = arr.to_crs(4326) assert arr.crs == self.wgs def test_from_shapely(self): arr = from_shapely(self.geoms, crs=27700) assert arr.crs == self.osgb def test_from_wkb(self): L_wkb = [p.wkb for p in self.geoms] arr = from_wkb(L_wkb, crs=27700) assert arr.crs == self.osgb def test_from_wkt(self): L_wkt = [p.wkt for p in self.geoms] arr = from_wkt(L_wkt, crs=27700) assert arr.crs == self.osgb def test_points_from_xy(self): df = pd.DataFrame([{"x": x, "y": x, "z": x} for x in range(10)]) arr = points_from_xy(df["x"], df["y"], crs=27700) assert arr.crs == self.osgb # setting CRS in GeoSeries should not set it in passed array without CRS def test_original(self): arr = from_shapely(self.geoms) s = GeoSeries(arr, crs=27700) assert arr.crs is None assert s.crs == self.osgb def test_ops(self): arr = self.arr bound = arr.boundary assert bound.crs == self.osgb cent = arr.centroid assert cent.crs == self.osgb hull = arr.convex_hull assert hull.crs == self.osgb envelope = arr.envelope assert envelope.crs == self.osgb exterior = arr.exterior assert exterior.crs == self.osgb representative_point = arr.representative_point() assert representative_point.crs == self.osgb def test_binary_ops(self): arr = self.arr quads = [] while len(quads) < 10: geom = Polygon([(random.random(), random.random()) for i in range(4)]) if geom.is_valid: quads.append(geom) arr2 = from_shapely(quads, crs=27700) difference = arr.difference(arr2) assert difference.crs == self.osgb intersection = arr.intersection(arr2) assert intersection.crs == self.osgb symmetric_difference = arr.symmetric_difference(arr2) assert symmetric_difference.crs == self.osgb union = arr.union(arr2) assert union.crs == self.osgb def test_other(self): arr = self.arr buffer = arr.buffer(5) assert buffer.crs == self.osgb interpolate = arr.exterior.interpolate(0.1) assert interpolate.crs == self.osgb simplify = arr.simplify(5) assert simplify.crs == self.osgb @pytest.mark.parametrize( "attr, arg", [ ("affine_transform", ([0, 1, 1, 0, 0, 0],)), ("translate", ()), ("rotate", (10,)), ("scale", ()), ("skew", ()), ], ) def test_affinity_methods(self, attr, arg): result = getattr(self.arr, attr)(*arg) assert result.crs == self.osgb def test_slice(self): s = GeoSeries(self.arr, crs=27700) assert s.iloc[1:].values.crs == self.osgb df = GeoDataFrame({"col1": self.arr}, geometry=s) assert df.iloc[1:].geometry.values.crs == self.osgb assert df.iloc[1:].col1.values.crs == self.osgb def test_concat(self): s = GeoSeries(self.arr, crs=27700) assert pd.concat([s, s]).values.crs == self.osgb df = GeoDataFrame({"col1": from_shapely(self.geoms, crs=4326)}, geometry=s) assert pd.concat([df, df]).geometry.values.crs == self.osgb assert pd.concat([df, df]).col1.values.crs == self.wgs def test_merge(self): arr = from_shapely(self.geoms, crs=27700) s = GeoSeries(self.geoms, crs=4326) df = GeoDataFrame({"col1": s}, geometry=arr) df2 = GeoDataFrame({"col2": s}, geometry=arr).rename_geometry("geom") merged = df.merge(df2, left_index=True, right_index=True) assert merged.col1.values.crs == self.wgs assert merged.geometry.values.crs == self.osgb assert merged.col2.values.crs == self.wgs assert merged.geom.values.crs == self.osgb assert merged.crs == self.osgb # make sure that geometry column from list has CRS (__setitem__) def test_setitem_geometry(self): arr = from_shapely(self.geoms, crs=27700) df = GeoDataFrame({"col1": [0, 1]}, geometry=arr) df["geometry"] = list(df.geometry) assert df.geometry.values.crs == self.osgb df2 = GeoDataFrame({"col1": [0, 1]}, geometry=arr) df2["geometry"] = from_shapely(self.geoms, crs=4326) assert df2.geometry.values.crs == self.wgs def test_astype(self): arr = from_shapely(self.geoms, crs=27700) df = GeoDataFrame({"col1": [0, 1]}, geometry=arr) df2 = df.astype({"col1": str}) assert df2.crs == self.osgb def test_apply(self): s = GeoSeries(self.arr) assert s.crs == 27700 # apply preserves the CRS if the result is a GeoSeries result = s.apply(lambda x: x.centroid) assert result.crs == 27700 def test_apply_geodataframe(self): df = GeoDataFrame({"col1": [0, 1]}, geometry=self.geoms, crs=27700) assert df.crs == 27700 # apply preserves the CRS if the result is a GeoDataFrame result = df.apply(lambda col: col, axis=0) assert result.crs == 27700 result = df.apply(lambda row: row, axis=1) assert result.crs == 27700 class TestSetCRS: @pytest.mark.parametrize( "constructor", [ lambda geoms, crs: GeoSeries(geoms, crs=crs), lambda geoms, crs: GeoDataFrame(geometry=geoms, crs=crs), ], ids=["geoseries", "geodataframe"], ) def test_set_crs(self, constructor): naive = constructor([Point(0, 0), Point(1, 1)], crs=None) assert naive.crs is None # by default returns a (shallow) copy result = naive.set_crs(crs="EPSG:4326") assert result.crs == "EPSG:4326" assert naive.crs is None result = naive.set_crs(epsg=4326) assert result.crs == "EPSG:4326" assert naive.crs is None # with inplace=True result = naive.set_crs(crs="EPSG:4326", inplace=True) assert result is naive assert result.crs == naive.crs == "EPSG:4326" # raise for non-naive when crs would be overridden non_naive = constructor([Point(0, 0), Point(1, 1)], crs="EPSG:4326") assert non_naive.crs == "EPSG:4326" with pytest.raises(ValueError, match="already has a CRS"): non_naive.set_crs("EPSG:3857") # allow for equal crs result = non_naive.set_crs("EPSG:4326") assert result.crs == "EPSG:4326" # replace with allow_override=True result = non_naive.set_crs("EPSG:3857", allow_override=True) assert non_naive.crs == "EPSG:4326" assert result.crs == "EPSG:3857" result = non_naive.set_crs("EPSG:3857", allow_override=True, inplace=True) assert non_naive.crs == "EPSG:3857" assert result.crs == "EPSG:3857" # set CRS to None result = non_naive.set_crs(crs=None, allow_override=True) assert result.crs is None assert non_naive.crs == "EPSG:3857" geopandas-1.2.0/geopandas/tests/test_datasets.py000066400000000000000000000007071525650651600220160ustar00rootroot00000000000000from geopandas import GeoDataFrame, read_file from geopandas.datasets import get_path import pytest @pytest.mark.parametrize( "test_dataset", ["naturalearth_lowres", "naturalearth_cities", "nybb", "foo"] ) def test_read_paths(test_dataset): with pytest.raises( AttributeError, match=r"The geopandas\.dataset has been deprecated and was removed", ): assert isinstance(read_file(get_path(test_dataset)), GeoDataFrame) geopandas-1.2.0/geopandas/tests/test_decorator.py000066400000000000000000000027021525650651600221650ustar00rootroot00000000000000from textwrap import dedent from geopandas._decorator import doc @doc(method="cumsum", operation="sum") def cumsum(whatever): """ This is the {method} method. It computes the cumulative {operation}. """ @doc( cumsum, dedent( """ Examples -------- >>> cumavg([1, 2, 3]) 2 """ ), method="cumavg", operation="average", ) def cumavg(whatever): ... @doc(cumsum, method="cummax", operation="maximum") def cummax(whatever): ... @doc(cummax, method="cummin", operation="minimum") def cummin(whatever): ... def test_docstring_formatting(): docstr = dedent( """ This is the cumsum method. It computes the cumulative sum. """ ) assert cumsum.__doc__ == docstr def test_docstring_appending(): docstr = dedent( """ This is the cumavg method. It computes the cumulative average. Examples -------- >>> cumavg([1, 2, 3]) 2 """ ) assert cumavg.__doc__ == docstr def test_doc_template_from_func(): docstr = dedent( """ This is the cummax method. It computes the cumulative maximum. """ ) assert cummax.__doc__ == docstr def test_inherit_doc_template(): docstr = dedent( """ This is the cummin method. It computes the cumulative minimum. """ ) assert cummin.__doc__ == docstr geopandas-1.2.0/geopandas/tests/test_dissolve.py000066400000000000000000000267561525650651600220520ustar00rootroot00000000000000import warnings import numpy as np import pandas as pd from shapely import MultiPolygon, Polygon import geopandas from geopandas import GeoDataFrame, read_file from geopandas._compat import GEOS_GE_312, HAS_PYPROJ, PANDAS_GE_30 import pytest from geopandas.testing import assert_geodataframe_equal, geom_almost_equals from pandas.testing import assert_frame_equal @pytest.fixture def nybb_polydf(nybb_filename): nybb_polydf = read_file(nybb_filename) nybb_polydf = nybb_polydf[["geometry", "BoroName", "BoroCode"]] nybb_polydf = nybb_polydf.rename(columns={"geometry": "myshapes"}) nybb_polydf = nybb_polydf.set_geometry("myshapes") nybb_polydf["manhattan_bronx"] = 5 nybb_polydf.loc[3:4, "manhattan_bronx"] = 6 nybb_polydf["BoroCode"] = nybb_polydf["BoroCode"].astype("int64") return nybb_polydf @pytest.fixture def merged_shapes(nybb_polydf): # Merged geometry manhattan_bronx = nybb_polydf.loc[3:4] others = nybb_polydf.loc[0:2] collapsed = [others.geometry.union_all(), manhattan_bronx.geometry.union_all()] merged_shapes = GeoDataFrame( {"myshapes": collapsed}, geometry="myshapes", index=pd.Index([5, 6], name="manhattan_bronx"), crs=nybb_polydf.crs, ) return merged_shapes @pytest.fixture def first(merged_shapes): first = merged_shapes.copy() first["BoroName"] = ["Staten Island", "Manhattan"] first["BoroCode"] = [5, 1] return first @pytest.fixture def expected_mean(merged_shapes): test_mean = merged_shapes.copy() test_mean["BoroCode"] = [4, 1.5] return test_mean def test_geom_dissolve(nybb_polydf, first): test = nybb_polydf.dissolve("manhattan_bronx") assert test.geometry.name == "myshapes" assert geom_almost_equals(test, first) @pytest.mark.skipif(not HAS_PYPROJ, reason="pyproj not installed") def test_dissolve_retains_existing_crs(nybb_polydf): assert nybb_polydf.crs is not None test = nybb_polydf.dissolve("manhattan_bronx") assert test.crs is not None def test_dissolve_retains_nonexisting_crs(nybb_polydf): nybb_polydf.geometry.array.crs = None test = nybb_polydf.dissolve("manhattan_bronx") assert test.crs is None def test_first_dissolve(nybb_polydf, first): test = nybb_polydf.dissolve("manhattan_bronx") assert_frame_equal(first, test, check_column_type=False) def test_mean_dissolve(nybb_polydf, first, expected_mean): test = nybb_polydf.dissolve("manhattan_bronx", aggfunc="mean", numeric_only=True) # for non pandas "mean", numeric only cannot be applied. Drop columns manually test2 = nybb_polydf.drop(columns=["BoroName"]).dissolve( "manhattan_bronx", aggfunc="mean" ) assert_frame_equal(expected_mean, test, check_column_type=False) assert_frame_equal(expected_mean, test2, check_column_type=False) def test_dissolve_emits_other_warnings(nybb_polydf): # we only do something special for pandas 1.5.x, but expect this # test to be true on any version def sum_and_warn(group): warnings.warn("foo") # noqa: B028 return group.sum(numeric_only=False) with pytest.warns(UserWarning, match="foo"): nybb_polydf.dissolve("manhattan_bronx", aggfunc=sum_and_warn) def test_multicolumn_dissolve(nybb_polydf, first): multi = nybb_polydf.copy() multi["dup_col"] = multi.manhattan_bronx multi_test = multi.dissolve(["manhattan_bronx", "dup_col"], aggfunc="first") first_copy = first.copy() first_copy["dup_col"] = first_copy.index first_copy = first_copy.set_index([first_copy.index, "dup_col"]) assert_frame_equal(multi_test, first_copy, check_column_type=False) def test_reset_index(nybb_polydf, first): test = nybb_polydf.dissolve("manhattan_bronx", as_index=False) comparison = first.reset_index() assert_frame_equal(comparison, test, check_column_type=False) @pytest.mark.parametrize( "grid_size, expected", [ ( None, MultiPolygon( [ Polygon([(0, 0), (10, 0), (10, 9)]), Polygon([(0, 0.4), (4.6, 5), (0, 5)]), ] ), ), (1, Polygon([(0, 5), (5, 5), (10, 9), (10, 0), (0, 0)])), ], ) def test_dissolve_grid_size(grid_size, expected): gdf = geopandas.GeoDataFrame( geometry=[ Polygon([(0, 0), (10, 0), (10, 9)]), Polygon([(0, 0.4), (4.6, 5), (0, 5)]), ] ) dissolved_gdf = gdf.dissolve(grid_size=grid_size) assert dissolved_gdf.geometry[0].equals(expected) def test_dissolve_none(nybb_polydf): test = nybb_polydf.dissolve(by=None) expected = GeoDataFrame( { nybb_polydf.geometry.name: [nybb_polydf.geometry.union_all()], "BoroName": ["Staten Island"], "BoroCode": [5], "manhattan_bronx": [5], }, geometry=nybb_polydf.geometry.name, crs=nybb_polydf.crs, ) assert_frame_equal(expected, test, check_column_type=False) def test_dissolve_none_mean(nybb_polydf): test = nybb_polydf.dissolve(aggfunc="mean", numeric_only=True) expected = GeoDataFrame( { nybb_polydf.geometry.name: [nybb_polydf.geometry.union_all()], "BoroCode": [3.0], "manhattan_bronx": [5.4], }, geometry=nybb_polydf.geometry.name, crs=nybb_polydf.crs, ) assert_frame_equal(expected, test, check_column_type=False) def test_dissolve_level(): gdf = geopandas.GeoDataFrame( { "a": [1, 1, 2, 2], "b": [3, 4, 4, 4], "c": [3, 4, 5, 6], "geometry": geopandas.array.from_wkt( ["POINT (0 0)", "POINT (1 1)", "POINT (2 2)", "POINT (3 3)"] ), } ).set_index(["a", "b", "c"]) expected_a = geopandas.GeoDataFrame( { "a": [1, 2], "geometry": geopandas.array.from_wkt( ["MULTIPOINT (0 0, 1 1)", "MULTIPOINT (2 2, 3 3)"] ), } ).set_index("a") expected_b = geopandas.GeoDataFrame( { "b": [3, 4], "geometry": geopandas.array.from_wkt( ["POINT (0 0)", "MULTIPOINT (1 1, 2 2, 3 3)"] ), } ).set_index("b") expected_ab = geopandas.GeoDataFrame( { "a": [1, 1, 2], "b": [3, 4, 4], "geometry": geopandas.array.from_wkt( ["POINT (0 0)", "POINT (1 1)", "MULTIPOINT (2 2, 3 3)"] ), } ).set_index(["a", "b"]) assert_frame_equal(expected_a, gdf.dissolve(level=0)) assert_frame_equal(expected_a, gdf.dissolve(level="a")) assert_frame_equal(expected_b, gdf.dissolve(level=1)) assert_frame_equal(expected_b, gdf.dissolve(level="b")) assert_frame_equal(expected_ab, gdf.dissolve(level=[0, 1])) assert_frame_equal(expected_ab, gdf.dissolve(level=["a", "b"])) def test_dissolve_sort(): gdf = geopandas.GeoDataFrame( { "a": [2, 1, 1], "geometry": geopandas.array.from_wkt( ["POINT (0 0)", "POINT (1 1)", "POINT (2 2)"] ), } ) expected_unsorted = geopandas.GeoDataFrame( { "a": [2, 1], "geometry": geopandas.array.from_wkt( ["POINT (0 0)", "MULTIPOINT (1 1, 2 2)"] ), } ).set_index("a") expected_sorted = expected_unsorted.sort_index() assert_frame_equal(expected_sorted, gdf.dissolve("a")) assert_frame_equal(expected_unsorted, gdf.dissolve("a", sort=False)) def test_dissolve_categorical(): gdf = geopandas.GeoDataFrame( { "cat": pd.Categorical(["a", "a", "b", "b"]), "noncat": [1, 1, 1, 2], "to_agg": [1, 2, 3, 4], "geometry": geopandas.array.from_wkt( ["POINT (0 0)", "POINT (1 1)", "POINT (2 2)", "POINT (3 3)"] ), } ) # when observed=False we get an additional observation # that wasn't in the original data none_val = "GEOMETRYCOLLECTION EMPTY" if PANDAS_GE_30 else None expected_gdf_observed_false = geopandas.GeoDataFrame( { "cat": pd.Categorical(["a", "a", "b", "b"]), "noncat": [1, 2, 1, 2], "geometry": geopandas.array.from_wkt( [ "MULTIPOINT (0 0, 1 1)", none_val, "POINT (2 2)", "POINT (3 3)", ] ), "to_agg": [1, None, 3, 4], } ).set_index(["cat", "noncat"]) # when observed=True we do not get any additional observations expected_gdf_observed_true = geopandas.GeoDataFrame( { "cat": pd.Categorical(["a", "b", "b"]), "noncat": [1, 1, 2], "geometry": geopandas.array.from_wkt( ["MULTIPOINT (0 0, 1 1)", "POINT (2 2)", "POINT (3 3)"] ), "to_agg": [1, 3, 4], } ).set_index(["cat", "noncat"]) assert_frame_equal(expected_gdf_observed_false, gdf.dissolve(["cat", "noncat"])) assert_frame_equal( expected_gdf_observed_true, gdf.dissolve(["cat", "noncat"], observed=True) ) def test_dissolve_dropna(): gdf = geopandas.GeoDataFrame( { "a": [1, 1, None], "geometry": geopandas.array.from_wkt( ["POINT (0 0)", "POINT (1 1)", "POINT (2 2)"] ), } ) expected_with_na = geopandas.GeoDataFrame( { "a": [1.0, np.nan], "geometry": geopandas.array.from_wkt( ["MULTIPOINT (0 0, 1 1)", "POINT (2 2)"] ), } ).set_index("a") expected_no_na = geopandas.GeoDataFrame( { "a": [1.0], "geometry": geopandas.array.from_wkt(["MULTIPOINT (0 0, 1 1)"]), } ).set_index("a") assert_frame_equal(expected_with_na, gdf.dissolve("a", dropna=False)) assert_frame_equal(expected_no_na, gdf.dissolve("a")) def test_dissolve_dropna_warn(nybb_polydf): # No warning with default params with warnings.catch_warnings(record=True) as record: nybb_polydf.dissolve() for r in record: assert "dropna kwarg is not supported" not in str(r.message) def test_dissolve_multi_agg(nybb_polydf, merged_shapes): merged_shapes[("BoroCode", "min")] = [3, 1] merged_shapes[("BoroCode", "max")] = [5, 2] merged_shapes[("BoroName", "count")] = [3, 2] with warnings.catch_warnings(): warnings.simplefilter(action="error") test = nybb_polydf.dissolve( by="manhattan_bronx", aggfunc={ "BoroCode": ["min", "max"], "BoroName": "count", }, ) assert_geodataframe_equal(test, merged_shapes) @pytest.mark.parametrize("method", ["coverage", "disjoint_subset"]) def test_dissolve_method(nybb_polydf, method): if method == "disjoint_subset" and not GEOS_GE_312: pytest.skip("Unsupported GEOS.") manhattan_bronx = nybb_polydf.loc[3:4] others = nybb_polydf.loc[0:2] collapsed = [ others.geometry.union_all(method=method), manhattan_bronx.geometry.union_all(method=method), ] merged_shapes = GeoDataFrame( {"myshapes": collapsed}, geometry="myshapes", index=pd.Index([5, 6], name="manhattan_bronx"), crs=nybb_polydf.crs, ) merged_shapes["BoroName"] = ["Staten Island", "Manhattan"] merged_shapes["BoroCode"] = [5, 1] test = nybb_polydf.dissolve("manhattan_bronx", method=method) assert_frame_equal(merged_shapes, test, check_column_type=False) geopandas-1.2.0/geopandas/tests/test_explore.py000066400000000000000000001170221525650651600216630ustar00rootroot00000000000000import uuid from packaging.version import Version import numpy as np import pandas as pd import shapely import geopandas as gpd from geopandas._compat import HAS_PYPROJ import pytest folium = pytest.importorskip("folium") branca = pytest.importorskip("branca") matplotlib = pytest.importorskip("matplotlib") mapclassify = pytest.importorskip("mapclassify") geodatasets = pytest.importorskip("geodatasets") from branca.colormap import StepColormap from matplotlib import cm, colors FOLIUM_GE_019 = Version(folium.__version__) >= Version("0.19.0") @pytest.fixture(scope="class") def _setup_class_test_explore( nybb_filename, naturalearth_lowres, naturalearth_cities, request ): request.cls.nybb = gpd.read_file(nybb_filename) request.cls.world = gpd.read_file(naturalearth_lowres) request.cls.cities = gpd.read_file(naturalearth_cities) request.cls.chicago = gpd.read_file(geodatasets.get_path("geoda.chicago_commpop")) request.cls.world["range"] = range(len(request.cls.world)) request.cls.missing = request.cls.world.copy() np.random.seed(42) request.cls.missing.loc[ np.random.choice(request.cls.missing.index, 40), "continent" ] = np.nan request.cls.missing.loc[ np.random.choice(request.cls.missing.index, 40), "pop_est" ] = np.nan @pytest.mark.usefixtures("_setup_class_test_explore") class TestExplore: def _fetch_map_string(self, m): out = m._parent.render() out_str = "".join(out.split()) return out_str def test_simple_pass(self): """Make sure default pass""" self.nybb.explore() self.world.explore() self.cities.explore() self.world.geometry.explore() def test_choropleth_pass(self): """Make sure default choropleth pass""" self.world.explore(column="pop_est") @pytest.mark.skipif(not HAS_PYPROJ, reason="requires pyproj") def test_map_settings_default(self): """Check default map settings""" m = self.world.explore() assert m.location == [ pytest.approx(-3.1774349999999956, rel=1e-6), pytest.approx(2.842170943040401e-14, rel=1e-6), ] assert m.options["zoom"] == 10 if not FOLIUM_GE_019: assert m.options["zoomControl"] is True else: assert m.options["zoom_control"] is True assert m.position == "relative" assert m.height == (100.0, "%") assert m.width == (100.0, "%") assert m.left == (0, "%") assert m.top == (0, "%") assert m.global_switches.no_touch is False assert m.global_switches.disable_3d is False assert "openstreetmap" in m.to_dict()["children"].keys() @pytest.mark.skipif(not HAS_PYPROJ, reason="requires pyproj") def test_map_settings_custom(self): """Check custom map settings""" m = self.nybb.explore( zoom_control=False, width=200, height=200, ) assert m.location == [ pytest.approx(40.70582377450201, rel=1e-6), pytest.approx(-73.9778006856748, rel=1e-6), ] assert m.options["zoom"] == 10 if not FOLIUM_GE_019: assert m.options["zoomControl"] is False else: assert m.options["zoom_control"] is False assert m.height == (200.0, "px") assert m.width == (200.0, "px") # custom XYZ tiles m = self.nybb.explore( zoom_control=False, width=200, height=200, tiles="https://mt1.google.com/vt/lyrs=m&x={x}&y={y}&z={z}", attr="Google", ) out_str = self._fetch_map_string(m) s = '"https://mt1.google.com/vt/lyrs=m\\u0026x={x}\\u0026y={y}\\u0026z={z}"' assert s in out_str assert '"attribution":"Google"' in out_str m = self.nybb.explore(location=(40, 5)) assert m.location == [40, 5] assert m.options["zoom"] == 10 m = self.nybb.explore(zoom_start=8) assert m.location == [ pytest.approx(40.70582377450201, rel=1e-6), pytest.approx(-73.9778006856748, rel=1e-6), ] assert m.options["zoom"] == 8 m = self.nybb.explore(location=(40, 5), zoom_start=8) assert m.location == [40, 5] assert m.options["zoom"] == 8 def test_simple_color(self): """Check color settings""" # single named color m = self.nybb.explore(color="red") out_str = self._fetch_map_string(m) assert '"fillColor":"red"' in out_str # list of colors colors = ["#333333", "#367324", "#95824f", "#fcaa00", "#ffcc33"] m2 = self.nybb.explore(color=colors) out_str = self._fetch_map_string(m2) for c in colors: assert f'"fillColor":"{c}"' in out_str # column of colors df = self.nybb.copy() df["colors"] = colors m3 = df.explore(color="colors") out_str = self._fetch_map_string(m3) for c in colors: assert f'"fillColor":"{c}"' in out_str # line GeoSeries m4 = self.nybb.boundary.explore(color="red") out_str = self._fetch_map_string(m4) assert '"fillColor":"red"' in out_str def test_choropleth_linear(self): """Check choropleth colors""" # default cmap m = self.nybb.explore(column="Shape_Leng") out_str = self._fetch_map_string(m) assert 'color":"#440154"' in out_str assert 'color":"#fde725"' in out_str assert 'color":"#50c46a"' in out_str assert 'color":"#481467"' in out_str assert 'color":"#3d4e8a"' in out_str # named cmap m = self.nybb.explore(column="Shape_Leng", cmap="PuRd") out_str = self._fetch_map_string(m) assert 'color":"#f7f4f9"' in out_str assert 'color":"#67001f"' in out_str assert 'color":"#d31760"' in out_str assert 'color":"#f0ecf5"' in out_str assert 'color":"#d6bedc"' in out_str def test_choropleth_mapclassify(self): """Mapclassify bins""" # quantiles m = self.nybb.explore(column="Shape_Leng", scheme="quantiles") out_str = self._fetch_map_string(m) assert 'color":"#21918c"' in out_str assert 'color":"#3b528b"' in out_str assert 'color":"#5ec962"' in out_str assert 'color":"#fde725"' in out_str assert 'color":"#440154"' in out_str # headtail m = self.world.explore(column="pop_est", scheme="headtailbreaks") out_str = self._fetch_map_string(m) assert '"fillColor":"#3b528b"' in out_str assert '"fillColor":"#21918c"' in out_str assert '"fillColor":"#5ec962"' in out_str assert '"fillColor":"#fde725"' in out_str assert '"fillColor":"#440154"' in out_str # custom k m = self.world.explore(column="pop_est", scheme="naturalbreaks", k=3) out_str = self._fetch_map_string(m) assert '"fillColor":"#21918c"' in out_str assert '"fillColor":"#fde725"' in out_str assert '"fillColor":"#440154"' in out_str # UserDefined overriding default k m = self.chicago.explore( column="POP2010", scheme="UserDefined", classification_kwds={"bins": [25000, 50000, 75000, 100000]}, ) out_str = self._fetch_map_string(m) assert '"fillColor":"#fde725"' in out_str assert '"fillColor":"#35b779"' in out_str assert '"fillColor":"#31688e"' in out_str assert '"fillColor":"#440154"' in out_str def test_categorical(self): """Categorical maps""" # auto detection m = self.world.explore(column="continent") out_str = self._fetch_map_string(m) assert 'color":"#9467bd","continent":"Europe"' in out_str assert 'color":"#c49c94","continent":"NorthAmerica"' in out_str assert 'color":"#1f77b4","continent":"Africa"' in out_str assert 'color":"#98df8a","continent":"Asia"' in out_str assert 'color":"#ff7f0e","continent":"Antarctica"' in out_str assert 'color":"#9edae5","continent":"SouthAmerica"' in out_str assert 'color":"#7f7f7f","continent":"Oceania"' in out_str assert 'color":"#dbdb8d","continent":"Sevenseas(openocean)"' in out_str # forced categorical m = self.nybb.explore(column="BoroCode", categorical=True) out_str = self._fetch_map_string(m) assert 'color":"#9edae5"' in out_str assert 'color":"#c7c7c7"' in out_str assert 'color":"#8c564b"' in out_str assert 'color":"#1f77b4"' in out_str assert 'color":"#98df8a"' in out_str # pandas.Categorical df = self.world.copy() df["categorical"] = pd.Categorical(df["name"]) m = df.explore(column="categorical") out_str = self._fetch_map_string(m) for c in np.apply_along_axis(colors.to_hex, 1, cm.tab20(range(20))): assert f'"fillColor":"{c}"' in out_str # custom cmap m = self.nybb.explore(column="BoroName", cmap="Set1") out_str = self._fetch_map_string(m) assert 'color":"#999999"' in out_str assert 'color":"#a65628"' in out_str assert 'color":"#4daf4a"' in out_str assert 'color":"#e41a1c"' in out_str assert 'color":"#ff7f00"' in out_str # custom list of colors cmap = ["#333432", "#3b6e8c", "#bc5b4f", "#8fa37e", "#efc758"] m = self.nybb.explore(column="BoroName", cmap=cmap) out_str = self._fetch_map_string(m) for c in cmap: assert f'"fillColor":"{c}"' in out_str # shorter list (to make it repeat) cmap = ["#333432", "#3b6e8c"] m = self.nybb.explore(column="BoroName", cmap=cmap) out_str = self._fetch_map_string(m) for c in cmap: assert f'"fillColor":"{c}"' in out_str with pytest.raises(ValueError, match="'cmap' is invalid"): self.nybb.explore(column="BoroName", cmap="nonsense") def test_categories(self): m = self.nybb[["BoroName", "geometry"]].explore( column="BoroName", categories=["Brooklyn", "Staten Island", "Queens", "Bronx", "Manhattan"], ) out_str = self._fetch_map_string(m) assert '"Bronx","__folium_color":"#c7c7c7"' in out_str assert '"Manhattan","__folium_color":"#9edae5"' in out_str assert '"Brooklyn","__folium_color":"#1f77b4"' in out_str assert '"StatenIsland","__folium_color":"#98df8a"' in out_str assert '"Queens","__folium_color":"#8c564b"' in out_str df = self.nybb.copy() df["categorical"] = pd.Categorical(df["BoroName"]) with pytest.raises(ValueError, match="Cannot specify 'categories'"): df.explore("categorical", categories=["Brooklyn", "Staten Island"]) def test_bool(self): df = self.nybb.copy() df["bool"] = [True, False, True, False, True] df["bool_extension"] = pd.array([True, False, True, False, True]) m1 = df.explore("bool") m2 = df.explore("bool_extension") out1_str = self._fetch_map_string(m1) assert '"__folium_color":"#9edae5","bool":true' in out1_str assert '"__folium_color":"#1f77b4","bool":false' in out1_str out2_str = self._fetch_map_string(m2) assert '"__folium_color":"#9edae5","bool":true' in out2_str assert '"__folium_color":"#1f77b4","bool":false' in out2_str def test_datetime(self): df = self.nybb.copy().head(2) date1 = pd.Timestamp(2022, 1, 1, 1, 22, 0, 0) date2 = pd.Timestamp(2025, 1, 1, 1, 22, 0, 0) df["datetime"] = [date1, date2] m1 = df.explore("datetime") out1_str = self._fetch_map_string(m1) assert '"__folium_color":"#9edae5","datetime":"2025-01-0101:22:00"' in out1_str assert '"__folium_color":"#1f77b4","datetime":"2022-01-0101:22:00"' in out1_str df2 = df.set_index("datetime") m2 = df2.explore() out2_str = self._fetch_map_string(m2) assert '"datetime":"2025-01-0101:22:00"' in out2_str assert '"datetime":"2022-01-0101:22:00"' in out2_str def test_non_json_serialisable(self): df = self.nybb.copy().head(2) u1 = "12345678-1234-5678-1234-567812345678" uuid1 = uuid.UUID(u1) u2 = "12345678-1234-5678-1234-567812345679" uuid2 = uuid.UUID(u2) df["object"] = [uuid1, uuid2] m1 = df.explore("object") out1_str = self._fetch_map_string(m1) assert f'"__folium_color":"#9edae5","object":"{u2}"' in out1_str assert f'"__folium_color":"#1f77b4","object":"{u1}"' in out1_str df2 = df.set_index("object") m2 = df2.explore() out2_str = self._fetch_map_string(m2) assert f'"object":"{u2}"' in out2_str assert f'"object":"{u1}"' in out2_str def test_string(self): df = self.nybb.copy() df["string"] = pd.array([1, 2, 3, 4, 5], dtype="string") m = df.explore("string") out_str = self._fetch_map_string(m) assert '"__folium_color":"#9edae5","string":"5"' in out_str def test_column_values(self): """ Check that the dataframe plot method returns same values with an input string (column in df), pd.Series, or np.array """ column_array = np.array(self.world["pop_est"]) m1 = self.world.explore(column="pop_est") # column name m2 = self.world.explore(column=column_array) # np.array m3 = self.world.explore(column=self.world["pop_est"]) # pd.Series assert m1.location == m2.location == m3.location m1_fields = self.world.explore(column=column_array, tooltip=True, popup=True) out1_fields_str = self._fetch_map_string(m1_fields) assert ( 'fields=["pop_est","continent","name","iso_a3","gdp_md_est","range"]' in out1_fields_str ) assert ( 'aliases=["pop_est","continent","name","iso_a3","gdp_md_est","range"]' in out1_fields_str ) m2_fields = self.world.explore( column=self.world["pop_est"], tooltip=True, popup=True ) out2_fields_str = self._fetch_map_string(m2_fields) assert ( 'fields=["pop_est","continent","name","iso_a3","gdp_md_est","range"]' in out2_fields_str ) assert ( 'aliases=["pop_est","continent","name","iso_a3","gdp_md_est","range"]' in out2_fields_str ) # GeoDataFrame and the given list have different number of rows with pytest.raises(ValueError, match="different number of rows"): self.world.explore(column=np.array([1, 2, 3])) def test_no_crs(self): """Naive geometry get no tiles""" df = self.world.copy() df.geometry.array.crs = None m = df.explore() assert "openstreetmap" not in m.to_dict()["children"].keys() def test_style_kwds(self): """Style keywords""" m = self.world.explore( style_kwds={"fillOpacity": 0.1, "weight": 0.5, "fillColor": "orange"} ) out_str = self._fetch_map_string(m) assert '"fillColor":"orange","fillOpacity":0.1,"weight":0.5' in out_str m = self.world.explore(column="pop_est", style_kwds={"color": "black"}) assert '"color":"black"' in self._fetch_map_string(m) # custom style_function - geopandas/issues/2350 m = self.world.explore( style_kwds={ "style_function": lambda x: { "fillColor": ( "red" if x["properties"]["gdp_md_est"] < 10**6 else "green" ), "color": ( "black" if x["properties"]["gdp_md_est"] < 10**6 else "white" ), } } ) # two lines with formatting instructions from style_function. # make sure each passes test assert all( ('"fillColor":"green"' in t and '"color":"white"' in t) or ('"fillColor":"red"' in t and '"color":"black"' in t) for t in [ "".join(line.split()) for line in m._parent.render().split("\n") if "return" in line and "color" in line ] ) # style function has to be callable with pytest.raises(ValueError, match="'style_function' has to be a callable"): self.world.explore(style_kwds={"style_function": "not callable"}) def test_tooltip(self): """Test tooltip""" # default with no tooltip or popup m = self.world.explore() assert "GeoJsonTooltip" in str(m.to_dict()) assert "GeoJsonPopup" not in str(m.to_dict()) # True m = self.world.explore(tooltip=True, popup=True) assert "GeoJsonTooltip" in str(m.to_dict()) assert "GeoJsonPopup" in str(m.to_dict()) out_str = self._fetch_map_string(m) assert ( 'fields=["pop_est","continent","name","iso_a3","gdp_md_est","range"]' in out_str ) assert ( 'aliases=["pop_est","continent","name","iso_a3","gdp_md_est","range"]' in out_str ) # True choropleth m = self.world.explore(column="pop_est", tooltip=True, popup=True) assert "GeoJsonTooltip" in str(m.to_dict()) assert "GeoJsonPopup" in str(m.to_dict()) out_str = self._fetch_map_string(m) assert ( 'fields=["pop_est","continent","name","iso_a3","gdp_md_est","range"]' in out_str ) assert ( 'aliases=["pop_est","continent","name","iso_a3","gdp_md_est","range"]' in out_str ) # single column m = self.world.explore(tooltip="pop_est", popup="iso_a3") out_str = self._fetch_map_string(m) assert 'fields=["pop_est"]' in out_str assert 'aliases=["pop_est"]' in out_str assert 'fields=["iso_a3"]' in out_str assert 'aliases=["iso_a3"]' in out_str # list m = self.world.explore( tooltip=["pop_est", "continent"], popup=["iso_a3", "gdp_md_est"] ) out_str = self._fetch_map_string(m) assert 'fields=["pop_est","continent"]' in out_str assert 'aliases=["pop_est","continent"]' in out_str assert 'fields=["iso_a3","gdp_md_est"' in out_str assert 'aliases=["iso_a3","gdp_md_est"]' in out_str # number m = self.world.explore(tooltip=2, popup=2) out_str = self._fetch_map_string(m) assert 'fields=["pop_est","continent"]' in out_str assert 'aliases=["pop_est","continent"]' in out_str # keywords tooltip m = self.world.explore( tooltip=True, popup=False, tooltip_kwds={"aliases": [0, 1, 2, 3, 4, 5], "sticky": False}, ) out_str = self._fetch_map_string(m) assert ( 'fields=["pop_est","continent","name","iso_a3","gdp_md_est","range"]' in out_str ) assert "aliases=[0,1,2,3,4,5]" in out_str assert '"sticky":false' in out_str # keywords popup m = self.world.explore( tooltip=False, popup=True, popup_kwds={"aliases": [0, 1, 2, 3, 4, 5]}, ) out_str = self._fetch_map_string(m) assert ( 'fields=["pop_est","continent","name","iso_a3","gdp_md_est","range"]' in out_str ) assert "aliases=[0,1,2,3,4,5]" in out_str assert "${aliases[i]" in out_str # no labels m = self.world.explore( tooltip=True, popup=True, tooltip_kwds={"labels": False}, popup_kwds={"labels": False}, ) out_str = self._fetch_map_string(m) assert "${aliases[i]" not in out_str # named index gdf = self.nybb.set_index("BoroName") m = gdf.explore() out_str = self._fetch_map_string(m) assert "BoroName" in out_str def test_escape_special_characters(self): # check if special characters are escaped gdf = self.world.copy() gdf["name"] = """{{{what a mess}}} they are so different.""" m = gdf.explore() out_str = self._fetch_map_string(m) assert """{{{""" in out_str assert """}}}""" in out_str def test_default_markers(self): # check overridden default for points m = self.cities.explore() strings = ['"radius":2', '"fill":true', "CircleMarker(latlng,opts)"] out_str = self._fetch_map_string(m) for s in strings: assert s in out_str m = self.cities.explore(marker_kwds={"radius": 5, "fill": False}) strings = ['"radius":5', '"fill":false', "CircleMarker(latlng,opts)"] out_str = self._fetch_map_string(m) for s in strings: assert s in out_str def test_custom_markers(self): # Markers m = self.cities.explore( marker_type="marker", marker_kwds={"icon": folium.Icon(icon="star")}, ) assert ""","icon":"star",""" in self._fetch_map_string(m) # Circle Markers m = self.cities.explore(marker_type="circle", marker_kwds={"fill_color": "red"}) assert ""","fillColor":"red",""" in self._fetch_map_string(m) # Folium Markers m = self.cities.explore( marker_type=folium.Circle( radius=4, fill_color="orange", fill_opacity=0.4, color="black", weight=1 ), ) assert ""","color":"black",""" in self._fetch_map_string(m) # Circle m = self.cities.explore(marker_type="circle_marker", marker_kwds={"radius": 10}) assert ""","radius":10,""" in self._fetch_map_string(m) # Unsupported Markers with pytest.raises( ValueError, match="Only 'marker', 'circle', and 'circle_marker' are supported", ): self.cities.explore(marker_type="dummy") def test_vmin_vmax(self): df = self.world.copy() df["range"] = range(len(df)) m = df.explore("range", vmin=-100, vmax=1000) out_str = self._fetch_map_string(m) assert 'case"176":return{"color":"#3b528b","fillColor":"#3b528b"' in out_str assert 'case"119":return{"color":"#414287","fillColor":"#414287"' in out_str assert 'case"3":return{"color":"#482173","fillColor":"#482173"' in out_str # test 0 df2 = self.nybb.copy() df2["values"] = df2["BoroCode"] * 10.0 m = df2[df2["values"] >= 30].explore("values", vmin=0) out_str = self._fetch_map_string(m) assert 'case"0":return{"color":"#fde725","fillColor":"#fde725"' in out_str assert 'case"1":return{"color":"#7ad151","fillColor":"#7ad151"' in out_str assert 'default:return{"color":"#22a884","fillColor":"#22a884"' in out_str df2["values_negative"] = df2["BoroCode"] * -10.0 m = df2[df2["values_negative"] <= 30].explore("values_negative", vmax=0) out_str = self._fetch_map_string(m) assert 'case"1":return{"color":"#414487","fillColor":"#414487"' in out_str assert 'case"2":return{"color":"#2a788e","fillColor":"#2a788e"' in out_str def test_missing_vals(self): m = self.missing.explore("continent") assert '"fillColor":null' in self._fetch_map_string(m) m = self.missing.explore("pop_est") assert '"fillColor":null' in self._fetch_map_string(m) m = self.missing.explore("pop_est", missing_kwds={"color": "red"}) assert '"fillColor":"red"' in self._fetch_map_string(m) m = self.missing.explore("continent", missing_kwds={"color": "red"}) assert '"fillColor":"red"' in self._fetch_map_string(m) def test_categorical_legend(self): m = self.world.explore("continent", legend=True) out_str = self._fetch_map_string(m) assert "#1f77b4'>Africa" in out_str assert "#ff7f0e'>Antarctica" in out_str assert "#98df8a'>Asia" in out_str assert "#9467bd'>Europe" in out_str assert "#c49c94'>NorthAmerica" in out_str assert "#7f7f7f'>Oceania" in out_str assert "#dbdb8d'>Sevenseas(openocean)" in out_str assert "#9edae5'>SouthAmerica" in out_str m = self.missing.explore( "continent", legend=True, missing_kwds={"color": "red"} ) out_str = self._fetch_map_string(m) assert "red'>NaN" in out_str def test_categorical_legend_custom_labels(self): # GH3496: custom legend labels must be honored for categorical and # boolean columns labels = [ "Cat0", "Cat1", "Cat2", "Cat3", "Cat4", "Cat5", "Cat6", "Cat7", ] m = self.world.explore("continent", legend=True, legend_kwds={"labels": labels}) out_str = self._fetch_map_string(m) for label in labels: assert label in out_str # original category names must no longer appear as legend rows assert "'>Africa" not in out_str assert "'>SouthAmerica" not in out_str # a length mismatch must raise a clear error with pytest.raises(ValueError, match="number of legend labels"): self.world.explore( "continent", legend=True, legend_kwds={"labels": ["only", "two"]} ) # custom labels combined with a missing (NaN) row: the NaN entry must # still render after the custom labels and not consume a custom label m = self.missing.explore( "continent", legend=True, legend_kwds={"labels": labels}, missing_kwds={"color": "red"}, ) out_str = self._fetch_map_string(m) assert "Cat0" in out_str assert "red'>NaN" in out_str # GH3496: also works for boolean columns (the original reporter's case) nybb_bool = self.nybb.copy() nybb_bool["is_manhattan"] = nybb_bool["BoroName"] == "Manhattan" m = nybb_bool.explore( "is_manhattan", legend=True, legend_kwds={"labels": ["Outer", "Core"]} ) out_str = self._fetch_map_string(m) assert "Outer" in out_str assert "Core" in out_str def test_colorbar(self): def quoted_in(find, s): return find in s or find.replace("'", '"') in s m = self.world.explore("range", legend=True) out_str = self._fetch_map_string(m) assert "attr(\"id\",'legend')" in out_str assert quoted_in("text('range')", out_str) m = self.world.explore( "range", legend=True, legend_kwds={"caption": "my_caption"} ) out_str = self._fetch_map_string(m) assert "attr(\"id\",'legend')" in out_str assert quoted_in("text('my_caption')", out_str) m = self.missing.explore("pop_est", legend=True, missing_kwds={"color": "red"}) out_str = self._fetch_map_string(m) assert "red'>NaN" in out_str # do not scale legend m = self.world.explore( "pop_est", legend=True, legend_kwds={"scale": False}, scheme="Headtailbreaks", ) out_str = self._fetch_map_string(m) assert out_str.count("#440154ff") == 100 assert out_str.count("#3b528bff") == 100 assert out_str.count("#21918cff") == 100 assert out_str.count("#5ec962ff") == 100 assert out_str.count("#fde725ff") == 100 # scale legend accordingly m = self.world.explore( "pop_est", legend=True, scheme="Headtailbreaks", ) out_str = self._fetch_map_string(m) assert out_str.count("#440154ff") == 16 assert out_str.count("#3b528bff") == 50 assert out_str.count("#21918cff") == 138 assert out_str.count("#5ec962ff") == 290 assert out_str.count("#fde725ff") == 6 # discrete cmap m = self.world.explore("pop_est", legend=True, cmap="Pastel2") out_str = self._fetch_map_string(m) assert out_str.count("b3e2cdff") == 63 assert out_str.count("fdcdacff") == 62 assert out_str.count("cbd5e8ff") == 63 assert out_str.count("f4cae4ff") == 62 assert out_str.count("e6f5c9ff") == 62 assert out_str.count("fff2aeff") == 63 assert out_str.count("f1e2ccff") == 62 assert out_str.count("ccccccff") == 63 def test_colorbar_max_labels(self): import re # linear m = self.world.explore("pop_est", legend_kwds={"max_labels": 3}) out_str = self._fetch_map_string(m) tick_str = re.search(r"tickValues\(\[[\',\,\.,0-9]*\]\)", out_str).group(0) assert ( tick_str.replace(",''", "") == "tickValues([140.0,471386328.07843137,942772516.1568627])" ) # scheme m = self.world.explore( "pop_est", scheme="headtailbreaks", legend_kwds={"max_labels": 3} ) out_str = self._fetch_map_string(m) assert "tickValues([140.0,'',184117213.1818182,'',1382066377.0,''])" in out_str # short cmap m = self.world.explore("pop_est", legend_kwds={"max_labels": 3}, cmap="tab10") out_str = self._fetch_map_string(m) tick_str = re.search(r"tickValues\(\[[\',\,\.,0-9]*\]\)", out_str).group(0) assert ( tick_str == "tickValues([140.0,'','','',559086084.0,'','','',1118172028.0,'','',''])" ) @pytest.mark.skipif(not HAS_PYPROJ, reason="requires pyproj") def test_xyzservices_providers(self): xyzservices = pytest.importorskip("xyzservices") m = self.nybb.explore(tiles=xyzservices.providers.OpenStreetMap.DE) out_str = self._fetch_map_string(m) assert '"https://tile.openstreetmap.de/{z}/{x}/{y}.png"' in out_str assert ( 'attribution":"\\u0026copy;\\u003cahref=\\"https://www.openstreetmap.org' in out_str ) assert '"maxZoom":18' in out_str @pytest.mark.skipif(not HAS_PYPROJ, reason="requires pyproj") def test_xyzservices_query_name(self): pytest.importorskip("xyzservices") m = self.nybb.explore(tiles="OpenStreetMap DE") out_str = self._fetch_map_string(m) assert '"https://tile.openstreetmap.de/{z}/{x}/{y}.png"' in out_str assert ( 'attribution":"\\u0026copy;\\u003cahref=\\"https://www.openstreetmap.org' in out_str ) assert '"maxZoom":18' in out_str @pytest.mark.skipif(not HAS_PYPROJ, reason="requires pyproj") def test_xyzservices_providers_min_zoom_override(self): xyzservices = pytest.importorskip("xyzservices") m = self.nybb.explore(tiles=xyzservices.providers.OpenStreetMap.DE, min_zoom=3) out_str = self._fetch_map_string(m) assert '"minZoom":3' in out_str @pytest.mark.skipif(not HAS_PYPROJ, reason="requires pyproj") def test_xyzservices_providers_max_zoom_override(self): xyzservices = pytest.importorskip("xyzservices") m = self.nybb.explore(tiles=xyzservices.providers.OpenStreetMap.DE, max_zoom=12) out_str = self._fetch_map_string(m) assert '"maxZoom":12' in out_str @pytest.mark.skipif(not HAS_PYPROJ, reason="requires pyproj") def test_xyzservices_providers_both_zooms_override(self): xyzservices = pytest.importorskip("xyzservices") m = self.nybb.explore( tiles=xyzservices.providers.OpenStreetMap.DE, min_zoom=3, max_zoom=12, ) out_str = self._fetch_map_string(m) assert '"maxZoom":12' in out_str assert '"minZoom":3' in out_str def test_linearrings(self): rings = self.nybb.explode(index_parts=True).exterior m = rings.explore() out_str = self._fetch_map_string(m) assert out_str.count("LineString") == len(rings) def test_mapclassify_categorical_legend(self): m = self.missing.explore( column="pop_est", legend=True, scheme="naturalbreaks", missing_kwds={"color": "red", "label": "missing"}, legend_kwds={"colorbar": False, "interval": True}, ) out_str = self._fetch_map_string(m) strings = [ "[140.00,21803000.00]", "(21803000.00,66834405.00]", "(66834405.00,163046161.00]", "(163046161.00,328239523.00]", "(328239523.00,1397715000.00]", "missing", ] for s in strings: assert s in out_str # interval=False m = self.missing.explore( column="pop_est", legend=True, scheme="naturalbreaks", missing_kwds={"color": "red", "label": "missing"}, legend_kwds={"colorbar": False, "interval": False}, ) out_str = self._fetch_map_string(m) strings = [ ">140.00,21803000.00", ">21803000.00,66834405.00", ">66834405.00,163046161.00", ">163046161.00,328239523.00", ">328239523.00,1397715000.00", "missing", ] for s in strings: assert s in out_str # custom labels m = self.world.explore( column="pop_est", legend=True, scheme="naturalbreaks", k=5, legend_kwds={"colorbar": False, "labels": ["s", "m", "l", "xl", "xxl"]}, ) out_str = self._fetch_map_string(m) strings = [">s<", ">m<", ">l<", ">xl<", ">xxl<"] for s in strings: assert s in out_str # fmt m = self.missing.explore( column="pop_est", legend=True, scheme="naturalbreaks", missing_kwds={"color": "red", "label": "missing"}, legend_kwds={"colorbar": False, "fmt": "{:.0f}"}, ) out_str = self._fetch_map_string(m) strings = [ ">140,21803000", ">21803000,66834405", ">66834405,163046161", ">163046161,328239523", ">328239523,1397715000", "missing", ] for s in strings: assert s in out_str def test_given_m(self): "Check that geometry is mapped onto a given folium.Map" m = folium.Map() self.nybb.explore(m=m, tooltip=False, highlight=False) out_str = self._fetch_map_string(m) assert out_str.count("BoroCode") == 5 # should not change map settings assert m.options["zoom"] == 1 def test_highlight(self): m = self.nybb.explore(highlight=True) out_str = self._fetch_map_string(m) assert '"fillOpacity":0.75' in out_str m = self.nybb.explore( highlight=True, highlight_kwds={"fillOpacity": 1, "color": "red"} ) out_str = self._fetch_map_string(m) assert '{"color":"red","fillOpacity":1}' in out_str def test_custom_colormaps(self): step = StepColormap(["green", "yellow", "red"], vmin=0, vmax=100000000) m = self.world.explore("pop_est", cmap=step, tooltip=["name"], legend=True) strings = [ 'fillColor":"#008000ff"', # Green '"fillColor":"#ffff00ff"', # Yellow '"fillColor":"#ff0000ff"', # Red ] out_str = self._fetch_map_string(m) for s in strings: assert s in out_str assert out_str.count("008000ff") == 304 assert out_str.count("ffff00ff") == 188 assert out_str.count("ff0000ff") == 191 # Using custom function colormap def my_color_function(field): """Maps low values to green and high values to red.""" if field > 100000000: return "#ff0000" else: return "#008000" m = self.world.explore("pop_est", cmap=my_color_function, legend=False) strings = [ '"color":"#ff0000","fillColor":"#ff0000"', '"color":"#008000","fillColor":"#008000"', ] for s in strings: assert s in self._fetch_map_string(m) # matplotlib.Colormap cmap = colors.ListedColormap(["red", "green", "blue", "white", "black"]) m = self.nybb.explore("BoroName", cmap=cmap) strings = [ '"fillColor":"#ff0000"', # Red '"fillColor":"#008000"', # Green '"fillColor":"#0000ff"', # Blue '"fillColor":"#ffffff"', # White '"fillColor":"#000000"', # Black ] out_str = self._fetch_map_string(m) for s in strings: assert s in out_str def test_multiple_geoseries(self): """ Additional GeoSeries need to be removed as they cannot be converted to GeoJSON """ gdf = self.nybb gdf["boundary"] = gdf.boundary gdf["centroid"] = gdf.centroid gdf.explore() def test_map_kwds(self): def check(): out_str = self._fetch_map_string(m) if not FOLIUM_GE_019: assert m.options["zoomControl"] is False else: assert m.options["zoom_control"] is False assert "dragging:false" in out_str or '"dragging":false' in out_str assert ( "scrollWheelZoom:false" in out_str or '"scrollWheelZoom":false' in out_str ) # check that folium and leaflet Map() parameters can be passed m = self.world.explore( zoom_control=False, map_kwds={"dragging": False, "scrollWheelZoom": False} ) check() with pytest.raises( ValueError, match="'zoom_control' cannot be specified in 'map_kwds'" ): self.world.explore( map_kwds={ "dragging": False, "scrollWheelZoom": False, "zoom_control": False, } ) def test_none_geometry(self): # None and empty geoms are dropped prior plotting df = self.nybb.copy() df.loc[0, df.geometry.name] = None m = df.explore() self._fetch_map_string(m) def test_empty_geometry(self): # None and empty geoms are dropped prior plotting df = self.nybb.copy() df.loc[0, df.geometry.name] = shapely.Point() m = df.explore() self._fetch_map_string(m) def test_all_empty(self): with_crs = gpd.GeoDataFrame( geometry=[shapely.Point(), shapely.Point()], crs=4326 ) with pytest.warns( UserWarning, match="The GeoSeries you are attempting to plot is composed of empty", ): m = with_crs.explore() out_str = self._fetch_map_string(m) if HAS_PYPROJ: assert "center:[0.0,0.0],crs:L.CRS.EPSG3857" in out_str no_crs = gpd.GeoDataFrame(geometry=[shapely.Point(), shapely.Point()]) with pytest.warns( UserWarning, match="The GeoSeries you are attempting to plot is composed of empty", ): m = no_crs.explore() out_str = self._fetch_map_string(m) assert "center:[0.0,0.0],crs:L.CRS.Simple" in out_str def test_add_all_empty_named_index(self): gdf1 = gpd.GeoDataFrame(geometry=[shapely.Point(0, 0), shapely.Point(1, 1)]) gdf2 = gpd.GeoDataFrame(geometry=[shapely.Point(), shapely.Point()]) m = gdf1.rename_axis(index="index_name").explore() with pytest.warns( UserWarning, match="The GeoSeries you are attempting to plot is composed of empty", ): m = gdf2.rename_axis(index="index_name").explore(m=m, color="red") out_str = self._fetch_map_string(m) assert "center:[0.5,0.5],crs:L.CRS.Simple" in out_str geopandas-1.2.0/geopandas/tests/test_extension_array.py000066400000000000000000000453111525650651600234200ustar00rootroot00000000000000""" This file contains a minimal set of tests for compliance with the extension array interface test suite (by inheriting the pandas test suite), and should contain no other tests. Other tests (eg related to the spatial functionality or integration with GeoSeries/GeoDataFrame) should be added to test_array.py and others. The tests in this file are inherited from the BaseExtensionTests, and only minimal tweaks should be applied to get the tests passing (by overwriting a parent method). A set of fixtures are defined to provide data for the tests (the fixtures expected to be available to pytest by the inherited pandas tests). """ import itertools import operator import numpy as np import pandas as pd from pandas.api.types import is_object_dtype from pandas.tests.extension import base as extension_tests import shapely.geometry from shapely.geometry import Point from geopandas._compat import PANDAS_GE_30 from geopandas.array import GeometryArray, GeometryDtype, from_shapely import pytest from pandas.testing import assert_frame_equal, assert_series_equal # ----------------------------------------------------------------------------- # Compat with extension tests in older pandas versions # ----------------------------------------------------------------------------- not_yet_implemented = pytest.mark.skip(reason="Not yet implemented") no_minmax = pytest.mark.skip(reason="Min/max not supported") # ----------------------------------------------------------------------------- # Required fixtures # ----------------------------------------------------------------------------- @pytest.fixture def dtype(): """A fixture providing the ExtensionDtype to validate.""" return GeometryDtype() def make_data(): if PANDAS_GE_30: size = 10 else: size = 100 a = np.empty(size, dtype=object) a[:] = [shapely.geometry.Point(i, i) for i in range(size)] ga = from_shapely(a) return ga @pytest.fixture def data(): """Length-100 array for this type. * data[0] and data[1] should both be non missing * data[0] and data[1] should not be equal """ return make_data() @pytest.fixture def data_for_twos(): """Length-100 array in which all the elements are two.""" raise NotImplementedError @pytest.fixture def data_missing(): """Length-2 array with [NA, Valid]""" return from_shapely([None, shapely.geometry.Point(1, 1)]) @pytest.fixture(params=["data", "data_missing"]) def all_data(request, data, data_missing): """Parametrized fixture giving 'data' and 'data_missing'""" if request.param == "data": return data elif request.param == "data_missing": return data_missing @pytest.fixture def data_repeated(data): """ Generate many datasets. Parameters ---------- data : fixture implementing `data` Returns ------- Callable[[int], Generator]: A callable that takes a `count` argument and returns a generator yielding `count` datasets. """ def gen(count): for _ in range(count): yield data return gen @pytest.fixture def data_for_sorting(): """Length-3 array with a known sort order. This should be three items [B, C, A] with A < B < C """ return from_shapely([Point(0, 1), Point(1, 1), Point(0, 0)]) @pytest.fixture def data_missing_for_sorting(): """Length-3 array with a known sort order. This should be three items [B, NA, A] with A < B and NA missing. """ return from_shapely([Point(1, 2), None, Point(0, 0)]) @pytest.fixture def na_cmp(): """Binary operator for comparing NA values. Should return a function of two arguments that returns True if both arguments are (scalar) NA for your type. By default, uses ``operator.or`` """ return lambda x, y: x is None and y is None @pytest.fixture def na_value(): """The scalar missing value for this type. Default 'None'""" return None @pytest.fixture def data_for_grouping(): """Data for factorization, grouping, and unique tests. Expected to be like [B, B, NA, NA, A, A, B, C] Where A < B < C and NA is missing """ return from_shapely( [ shapely.geometry.Point(1, 1), shapely.geometry.Point(1, 1), None, None, shapely.geometry.Point(0, 0), shapely.geometry.Point(0, 0), shapely.geometry.Point(1, 1), shapely.geometry.Point(2, 2), ] ) @pytest.fixture(params=[True, False]) def box_in_series(request): """Whether to box the data in a Series""" return request.param @pytest.fixture( params=[ lambda x: 1, lambda x: [1] * len(x), lambda x: pd.Series([1] * len(x)), lambda x: x, ], ids=["scalar", "list", "series", "object"], ) def groupby_apply_op(request): """ Functions to test groupby.apply(). """ return request.param @pytest.fixture(params=[True, False]) def as_frame(request): """ Boolean fixture to support Series and Series.to_frame() comparison testing. """ return request.param @pytest.fixture(params=[True, False]) def as_series(request): """ Boolean fixture to support arr and Series(arr) comparison testing. """ return request.param @pytest.fixture(params=[True, False]) def use_numpy(request): """ Boolean fixture to support comparison testing of ExtensionDtype array and numpy array. """ return request.param @pytest.fixture(params=["ffill", "bfill"]) def fillna_method(request): """ Parametrized fixture giving method parameters 'ffill' and 'bfill' for Series.fillna(method=) testing. """ return request.param @pytest.fixture(params=[True, False]) def as_array(request): """ Boolean fixture to support ExtensionDtype _from_sequence method testing. """ return request.param @pytest.fixture def invalid_scalar(data): """ A scalar that *cannot* be held by this ExtensionArray. The default should work for most subclasses, but is not guaranteed. If the array can hold any item (i.e. object dtype), then use pytest.skip. """ return object.__new__(object) # Fixtures defined in pandas/conftest.py that are also needed: defining them # here instead of importing for compatibility _all_numeric_reductions = [ "count", "sum", "max", "min", "mean", "prod", "std", "var", "median", "kurt", # "skew", # skew is defined as non-reduction geospatial method, so skipping here "sem", ] @pytest.fixture(params=_all_numeric_reductions) def all_numeric_reductions(request): """ Fixture for numeric reduction names. """ return request.param _all_boolean_reductions = ["all", "any"] @pytest.fixture(params=_all_boolean_reductions) def all_boolean_reductions(request): """ Fixture for boolean reduction names. """ return request.param _all_reductions = _all_numeric_reductions + _all_boolean_reductions @pytest.fixture(params=_all_reductions) def all_reductions(request): """ Fixture for all (boolean + numeric) reduction names. """ return request.param @pytest.fixture(params=[True, False]) def skipna(request): return request.param # only == and != are support for GeometryArray # @pytest.fixture(params=["__eq__", "__ne__", "__le__", "__lt__", "__ge__", "__gt__"]) @pytest.fixture(params=["__eq__", "__ne__"]) def all_compare_operators(request): """ Fixture for dunder names for common compare operations * >= * > * == * != * < * <= """ return request.param @pytest.fixture(params=[None, lambda x: x]) def sort_by_key(request): """ Simple fixture for testing keys in sorting methods. Tests None (no key) and the identity key. """ return request.param # ----------------------------------------------------------------------------- # Inherited tests # ----------------------------------------------------------------------------- class TestDtype(extension_tests.BaseDtypeTests): # additional tests def test_array_type_with_arg(self, data, dtype): assert dtype.construct_array_type() is GeometryArray def test_registry(self, data, dtype): s = pd.Series(np.asarray(data), dtype=object) result = s.astype("geometry") assert isinstance(result.array, GeometryArray) expected = pd.Series(data) assert_series_equal(result, expected) class TestInterface(extension_tests.BaseInterfaceTests): def test_contains(self, data, data_missing): # overridden due to the inconsistency between # GeometryDtype.na_value = np.nan # and None being used as NA in array # ensure data without missing values data = data[~data.isna()] # first elements are non-missing assert data[0] in data assert data_missing[0] in data_missing assert None in data_missing assert None not in data assert pd.NaT not in data_missing class TestConstructors(extension_tests.BaseConstructorsTests): pass class TestReshaping(extension_tests.BaseReshapingTests): # NOTE: this test is copied from pandas/tests/extension/base/reshaping.py # because starting with pandas 3.0 the assert_frame_equal is strict regarding # the exact missing value (None vs NaN) # Our `result` uses None, but the way the `expected` is created results in # NaNs (and specifying to use None as fill value in unstack also does not # help) # -> the only change compared to the upstream test is marked @pytest.mark.parametrize( "index", [ # Two levels, uniform. pd.MultiIndex.from_product(([["A", "B"], ["a", "b"]]), names=["a", "b"]), # non-uniform pd.MultiIndex.from_tuples([("A", "a"), ("A", "b"), ("B", "b")]), # three levels, non-uniform pd.MultiIndex.from_product([("A", "B"), ("a", "b"), (0, 1)]) if PANDAS_GE_30 else pd.MultiIndex.from_product([("A", "B"), ("a", "b", "c"), (0, 1, 2)]), pd.MultiIndex.from_tuples( [ ("A", "a", 1), ("A", "b", 0), ("A", "a", 0), ("B", "a", 0), ("B", "c", 1), ] ), ], ) @pytest.mark.parametrize("obj", ["series", "frame"]) def test_unstack(self, data, index, obj): data = data[: len(index)] if obj == "series": ser = pd.Series(data, index=index) else: ser = pd.DataFrame({"A": data, "B": data}, index=index) n = index.nlevels levels = list(range(n)) # [0, 1, 2] # [(0,), (1,), (2,), (0, 1), (0, 2), (1, 0), (1, 2), (2, 0), (2, 1)] combinations = itertools.chain.from_iterable( itertools.permutations(levels, i) for i in range(1, n) ) for level in combinations: result = ser.unstack(level=level) assert all( isinstance(result[col].array, type(data)) for col in result.columns ) if obj == "series": # We should get the same result with to_frame+unstack+droplevel df = ser.to_frame() alt = df.unstack(level=level).droplevel(0, axis=1) assert_frame_equal(result, alt) obj_ser = ser.astype(object) expected = obj_ser.unstack(level=level, fill_value=data.dtype.na_value) if obj == "series": assert all(is_object_dtype(x) for x in expected.dtypes) # <------------ next line is added expected[expected.isna()] = None # -------------> result = result.astype(object) assert_frame_equal(result, expected) class TestGetitem(extension_tests.BaseGetitemTests): @pytest.mark.xfail(reason="read-only not yet implemented") def test_getitem_propagates_readonly_property(self, data): super().test_getitem_propagates_readonly_property(data) class TestSetitem(extension_tests.BaseSetitemTests): @pytest.mark.xfail(reason="read-only not yet implemented") def test_readonly_property(self, data): super().test_readonly_property(data) @pytest.mark.xfail(reason="read-only not yet implemented") def test_readonly_propagates_to_numpy_array(self, data): super().test_readonly_propagates_to_numpy_array(data) class TestMissing(extension_tests.BaseMissingTests): def test_fillna_series(self, data_missing): fill_value = data_missing[1] ser = pd.Series(data_missing) # Fill with a scalar result = ser.fillna(fill_value) expected = pd.Series(data_missing._from_sequence([fill_value, fill_value])) assert_series_equal(result, expected) # Fill with a series filler = pd.Series( from_shapely( [ shapely.geometry.Point(1, 1), shapely.geometry.Point(2, 2), ], ) ) result = ser.fillna(filler) expected = pd.Series(data_missing._from_sequence([fill_value, fill_value])) assert_series_equal(result, expected) # Fill with a series not affecting the missing values filler = pd.Series( from_shapely( [ shapely.geometry.Point(2, 2), shapely.geometry.Point(1, 1), ] ), index=[10, 11], ) result = ser.fillna(filler) assert_series_equal(result, ser) # More `GeoSeries.fillna` testcases are in # `geopandas\tests\test_pandas_methods.py::test_fillna_scalar` # and `geopandas\tests\test_pandas_methods.py::test_fillna_series`. def test_fillna_limit_pad(self, data_missing): super().test_fillna_limit_pad(data_missing) def test_fillna_limit_backfill(self, data_missing): super().test_fillna_limit_backfill(data_missing) def test_fillna_series_method(self, data_missing, fillna_method): super().test_fillna_series_method(data_missing, fillna_method) def test_fillna_no_op_returns_copy(self, data): super().test_fillna_no_op_returns_copy(data) @pytest.mark.xfail(reason="read-only not yet implemented") def test_fillna_readonly(self, data_missing): super().test_fillna_readonly(data_missing) from pandas.tests.extension.base import BaseReduceTests class TestReduce(BaseReduceTests): def _supports_reduction(self, ser: pd.Series, op_name: str) -> bool: # Specify if we expect this reduction to succeed. return op_name in ("count", "any", "all") _all_arithmetic_operators = [ "__add__", "__radd__", # '__sub__', '__rsub__', "__mul__", "__rmul__", "__floordiv__", "__rfloordiv__", "__truediv__", "__rtruediv__", "__pow__", "__rpow__", "__mod__", "__rmod__", ] @pytest.fixture(params=_all_arithmetic_operators) def all_arithmetic_operators(request): """ Fixture for dunder names for common arithmetic operations Adapted to exclude __sub__, as this is implemented as "difference". """ return request.param class TestArithmeticOps(extension_tests.BaseArithmeticOpsTests): @pytest.mark.skip(reason="not applicable") def test_divmod_series_array(self, data, data_for_twos): pass @pytest.mark.skip(reason="not applicable") def test_add_series_with_extension_array(self, data): pass class TestComparisonOps(extension_tests.BaseComparisonOpsTests): def _compare_other(self, s, data, op_name, other): op = getattr(operator, op_name.strip("_")) result = op(s, other) expected = s.combine(other, op) assert_series_equal(result, expected) def test_compare_scalar(self, data, all_compare_operators): op_name = all_compare_operators s = pd.Series(data) self._compare_other(s, data, op_name, data[0]) def test_compare_array(self, data, all_compare_operators): op_name = all_compare_operators s = pd.Series(data) other = pd.Series([data[0]] * len(data)) self._compare_other(s, data, op_name, other) class TestMethods(extension_tests.BaseMethodsTests): @pytest.mark.parametrize("dropna", [True, False]) def test_value_counts(self, all_data, dropna): pass def test_value_counts_with_normalize(self, data): pass @pytest.mark.parametrize("ascending", [True, False]) def test_sort_values_frame(self, data_for_sorting, ascending): super().test_sort_values_frame(data_for_sorting, ascending) @pytest.mark.skip(reason="searchsorted not supported") def test_searchsorted(self, data_for_sorting, as_series): pass @not_yet_implemented def test_combine_le(self): pass @pytest.mark.skip(reason="addition not supported") def test_combine_add(self): pass @not_yet_implemented def test_fillna_length_mismatch(self, data_missing): msg = "Length of 'value' does not match." with pytest.raises(ValueError, match=msg): data_missing.fillna(data_missing.take([1])) @no_minmax def test_argmin_argmax(self): pass @no_minmax def test_argmin_argmax_empty_array(self): pass @no_minmax def test_argmin_argmax_all_na(self): pass @no_minmax def test_argreduce_series(self): pass @no_minmax def test_argmax_argmin_no_skipna_notimplemented(self): pass # https://github.com/geopandas/geopandas/issues/1906 @pytest.mark.xfail(reason="JSON serialization not yet working") def test_values_for_json(self, data): super().test_values_for_json(data) @pytest.mark.xfail(reason="JSON serialization not yet working") def test_json_roundtrip(self, data): super().test_json_roundtrip(data) class TestCasting(extension_tests.BaseCastingTests): pass class TestGroupby(extension_tests.BaseGroupbyTests): @pytest.mark.parametrize("as_index", [True, False]) def test_groupby_extension_agg(self, as_index, data_for_grouping): super().test_groupby_extension_agg(as_index, data_for_grouping) def test_groupby_extension_transform(self, data_for_grouping): super().test_groupby_extension_transform(data_for_grouping) @pytest.mark.parametrize( "op", [ lambda x: 1, lambda x: [1] * len(x), lambda x: pd.Series([1] * len(x)), lambda x: x, ], ids=["scalar", "list", "series", "object"], ) def test_groupby_extension_apply(self, data_for_grouping, op): super().test_groupby_extension_apply(data_for_grouping, op) class TestPrinting(extension_tests.BasePrintingTests): pass @not_yet_implemented class TestParsing(extension_tests.BaseParsingTests): pass geopandas-1.2.0/geopandas/tests/test_geocode.py000066400000000000000000000126371525650651600216200ustar00rootroot00000000000000import pandas as pd from shapely.geometry import Point from geopandas import GeoDataFrame, GeoSeries, geocode, reverse_geocode, tools from geopandas._compat import HAS_PYPROJ from geopandas.tools.geocoding import _prepare_geocode_result import pytest from geopandas.testing import assert_geodataframe_equal from geopandas.tests.util import assert_geoseries_equal, mock from pandas.testing import assert_series_equal geopy = pytest.importorskip("geopy") class ForwardMock(mock.MagicMock): """ Mock the forward geocoding function. Returns the passed in address and (p, p+.5) where p increases at each call """ def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self._n = 0.0 def __call__(self, *args, **kwargs): self.return_value = args[0], (self._n, self._n + 0.5) self._n += 1 return super().__call__(*args, **kwargs) class ReverseMock(mock.MagicMock): """ Mock the reverse geocoding function. Returns the passed in point and 'address{p}' where p increases at each call """ def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self._n = 0 def __call__(self, *args, **kwargs): self.return_value = f"address{self._n}", args[0] self._n += 1 return super().__call__(*args, **kwargs) @pytest.fixture def locations(): locations = ["260 Broadway, New York, NY", "77 Massachusetts Ave, Cambridge, MA"] return locations @pytest.fixture def points(): points = [Point(-71.0597732, 42.3584308), Point(-77.0365305, 38.8977332)] return points def test_prepare_result(): # Calls _prepare_result with sample results from the geocoder call # loop p0 = Point(12.3, -45.6) # Treat these as lat/lon p1 = Point(-23.4, 56.7) d = {"a": ("address0", p0.coords[0]), "b": ("address1", p1.coords[0])} df = _prepare_geocode_result(d) assert type(df) is GeoDataFrame if HAS_PYPROJ: assert df.crs == "EPSG:4326" assert len(df) == 2 assert "address" in df coords = df.loc["a"]["geometry"].coords[0] test = p0.coords[0] # Output from the df should be lon/lat assert coords[0] == pytest.approx(test[1]) assert coords[1] == pytest.approx(test[0]) coords = df.loc["b"]["geometry"].coords[0] test = p1.coords[0] assert coords[0] == pytest.approx(test[1]) assert coords[1] == pytest.approx(test[0]) def test_prepare_result_none(): p0 = Point(12.3, -45.6) # Treat these as lat/lon d = {"a": ("address0", p0.coords[0]), "b": (None, None)} df = _prepare_geocode_result(d) assert type(df) is GeoDataFrame if HAS_PYPROJ: assert df.crs == "EPSG:4326" assert len(df) == 2 assert "address" in df row = df.loc["b"] # TODO we should probably replace this with a missing value instead of point? assert len(row["geometry"].coords) == 0 assert row["geometry"].is_empty assert pd.isna(row["address"]) @pytest.mark.parametrize("geocode_result", (None, (None, None))) def test_prepare_geocode_result_when_result_is(geocode_result): result = {0: geocode_result} expected_output = GeoDataFrame( {"geometry": [Point()], "address": [None]}, crs="EPSG:4326", ) output = _prepare_geocode_result(result) assert_geodataframe_equal(output, expected_output) def test_bad_provider_forward(): from geopy.exc import GeocoderNotFound with pytest.raises(GeocoderNotFound): geocode(["cambridge, ma"], "badprovider") def test_bad_provider_reverse(): from geopy.exc import GeocoderNotFound with pytest.raises(GeocoderNotFound): reverse_geocode([Point(0, 0)], "badprovider") def test_forward(locations, points): from geopy.geocoders import Photon for provider in ["photon", Photon]: with mock.patch("geopy.geocoders.Photon.geocode", ForwardMock()) as m: g = tools.geocode(locations, provider=provider, timeout=2) assert len(locations) == m.call_count n = len(locations) assert isinstance(g, GeoDataFrame) expected = GeoSeries( [Point(float(x) + 0.5, float(x)) for x in range(n)], crs="EPSG:4326" ) assert_geoseries_equal(expected, g["geometry"]) assert_series_equal(g["address"], pd.Series(locations, name="address")) def test_reverse(locations, points): from geopy.geocoders import Photon for provider in ["photon", Photon]: with mock.patch("geopy.geocoders.Photon.reverse", ReverseMock()) as m: g = tools.reverse_geocode(points, provider=provider, timeout=2) assert len(points) == m.call_count assert isinstance(g, GeoDataFrame) expected = GeoSeries(points, crs="EPSG:4326") assert_geoseries_equal(expected, g["geometry"]) address = pd.Series( ["address" + str(x) for x in range(len(points))], name="address" ) assert_series_equal(g["address"], address) def test_top_level_api(locations, points): with mock.patch("geopy.geocoders.Photon.geocode", ForwardMock()) as m: g = geocode(locations, provider="photon", timeout=2) assert len(locations) == m.call_count assert isinstance(g, GeoDataFrame) with mock.patch("geopy.geocoders.Photon.reverse", ReverseMock()) as m: g = tools.reverse_geocode(points, provider="photon", timeout=2) assert len(points) == m.call_count assert isinstance(g, GeoDataFrame) geopandas-1.2.0/geopandas/tests/test_geodataframe.py000066400000000000000000001776771525650651600226520ustar00rootroot00000000000000import json import os import re import shutil import tempfile from enum import Enum import numpy as np import pandas as pd from shapely.geometry import Point, Polygon, box import geopandas import geopandas._compat as compat from geopandas import GeoDataFrame, GeoSeries, points_from_xy, read_file from geopandas.array import GeometryArray, GeometryDtype, from_shapely import pytest from geopandas.testing import assert_geodataframe_equal, assert_geoseries_equal from geopandas.tests.util import PACKAGE_DIR, validate_boro_df from pandas.testing import assert_frame_equal, assert_index_equal, assert_series_equal @pytest.fixture def dfs(request): s1 = GeoSeries( [ Polygon([(0, 0), (2, 0), (2, 2), (0, 2)]), Polygon([(2, 2), (4, 2), (4, 4), (2, 4)]), ] ) s2 = GeoSeries( [ Polygon([(1, 1), (3, 1), (3, 3), (1, 3)]), Polygon([(3, 3), (5, 3), (5, 5), (3, 5)]), ] ) df1 = GeoDataFrame({"col1": [1, 2], "geometry": s1}) df2 = GeoDataFrame({"col2": [1, 2], "geometry": s2}) return df1, df2 @pytest.fixture( params=["union", "intersection", "difference", "symmetric_difference", "identity"] ) def how(request): return request.param @pytest.mark.usefixtures("_setup_class_nybb_filename") class TestDataFrame: def setup_method(self): N = 10 # self.nybb_filename attached via _setup_class_nybb_filename self.df = read_file(self.nybb_filename) # TODO re-write instance variables to be fixtures self.tempdir = tempfile.mkdtemp() self.crs = "epsg:4326" self.df2 = GeoDataFrame( [ {"geometry": Point(x, y), "value1": x + y, "value2": x * y} for x, y in zip(range(N), range(N)) ], crs=self.crs, ) self.df3 = read_file( os.path.join(PACKAGE_DIR, "geopandas", "tests", "data", "null_geom.geojson") ) def teardown_method(self): shutil.rmtree(self.tempdir) def test_df_init(self): assert type(self.df2) is GeoDataFrame if compat.HAS_PYPROJ: assert self.df2.crs == self.crs @pytest.mark.skipif(not compat.HAS_PYPROJ, reason="Requires pyproj") def test_different_geo_colname(self): from pyproj.exceptions import CRSError data = { "A": range(5), "B": range(-5, 0), "location": [Point(x, y) for x, y in zip(range(5), range(5))], } df = GeoDataFrame(data, crs=self.crs, geometry="location") locs = GeoSeries(data["location"], crs=self.crs) assert_geoseries_equal(df.geometry, locs) assert "geometry" not in df assert df.geometry.name == "location" # internal implementation detail assert df._geometry_column_name == "location" geom2 = [Point(x, y) for x, y in zip(range(5, 10), range(5))] with pytest.raises(CRSError): df.set_geometry(geom2, crs="dummy_crs") @pytest.mark.filterwarnings("ignore:Geometry is in a geographic CRS") def test_geo_getitem(self): data = { "A": range(5), "B": range(-5, 0), "location": [Point(x, y) for x, y in zip(range(5), range(5))], } df = GeoDataFrame(data, crs=self.crs, geometry="location") assert isinstance(df.geometry, GeoSeries) df["geometry"] = df["A"] assert isinstance(df.geometry, GeoSeries) assert df.geometry[0] == data["location"][0] # good if this changed in the future assert not isinstance(df["geometry"], GeoSeries) assert isinstance(df["location"], GeoSeries) df["buff"] = df.buffer(1) assert isinstance(df["buff"], GeoSeries) df["array"] = from_shapely([Point(x, y) for x, y in zip(range(5), range(5))]) assert isinstance(df["array"], GeoSeries) data["geometry"] = [Point(x + 1, y - 1) for x, y in zip(range(5), range(5))] df = GeoDataFrame(data, crs=self.crs) assert isinstance(df.geometry, GeoSeries) assert isinstance(df["geometry"], GeoSeries) # good if this changed in the future assert not isinstance(df["location"], GeoSeries) def test_getitem_no_geometry(self): res = self.df2[["value1", "value2"]] assert isinstance(res, pd.DataFrame) assert not isinstance(res, GeoDataFrame) # with different name df = self.df2.copy() df = df.rename(columns={"geometry": "geom"}).set_geometry("geom") assert isinstance(df, GeoDataFrame) res = df[["value1", "value2"]] assert isinstance(res, pd.DataFrame) assert not isinstance(res, GeoDataFrame) df["geometry"] = np.arange(len(df)) res = df[["value1", "value2", "geometry"]] assert isinstance(res, pd.DataFrame) assert not isinstance(res, GeoDataFrame) def test_geo_setitem(self): data = { "A": range(5), "B": np.arange(5.0), "geometry": [Point(x, y) for x, y in zip(range(5), range(5))], } df = GeoDataFrame(data) s = GeoSeries([Point(x, y + 1) for x, y in zip(range(5), range(5))]) expected = s.copy() # setting geometry column for vals in [s, s.values]: df["geometry"] = vals assert_geoseries_equal(df["geometry"], expected) assert_geoseries_equal(df.geometry, expected) # non-aligned values s2 = GeoSeries([Point(x, y + 1) for x, y in zip(range(6), range(6))]) df["geometry"] = s2 assert_geoseries_equal(df["geometry"], expected) assert_geoseries_equal(df.geometry, expected) # setting other column with geometry values -> preserve geometry type for vals in [s, s.values]: df["other_geom"] = vals assert isinstance(df["other_geom"].values, GeometryArray) # overwriting existing non-geometry column -> preserve geometry type data = { "A": range(5), "B": np.arange(5.0), "other_geom": range(5), "geometry": [Point(x, y) for x, y in zip(range(5), range(5))], } df = GeoDataFrame(data) for vals in [s, s.values]: df["other_geom"] = vals assert isinstance(df["other_geom"].values, GeometryArray) def test_geometry_property(self): assert_geoseries_equal( self.df.geometry, self.df["geometry"], check_dtype=True, check_index_type=True, ) df = self.df.copy() new_geom = [ Point(x, y) for x, y in zip(range(len(self.df)), range(len(self.df))) ] df.geometry = new_geom new_geom = GeoSeries(new_geom, index=df.index, crs=df.crs) assert_geoseries_equal(df.geometry, new_geom) assert_geoseries_equal(df["geometry"], new_geom) if compat.HAS_PYPROJ: # new crs gs = new_geom.to_crs(crs="epsg:3857") df.geometry = gs assert df.crs == "epsg:3857" def test_geometry_property_errors(self): with pytest.raises(AttributeError): df = self.df.copy() del df["geometry"] df.geometry # list-like error with pytest.raises(ValueError): df = self.df2.copy() df.geometry = "value1" # list-like error with pytest.raises(ValueError): df = self.df.copy() df.geometry = "apple" # non-geometry error with pytest.raises(TypeError): df = self.df.copy() df.geometry = list(range(df.shape[0])) with pytest.raises(KeyError): df = self.df.copy() del df["geometry"] df["geometry"] # ndim error with pytest.raises(ValueError): df = self.df.copy() df.geometry = df def test_rename_geometry(self): assert self.df.geometry.name == "geometry" df2 = self.df.rename_geometry("new_name") assert df2.geometry.name == "new_name" df2 = self.df.rename_geometry("new_name", inplace=True) assert df2 is None assert self.df.geometry.name == "new_name" # existing column error msg = "Column named Shape_Area already exists" with pytest.raises(ValueError, match=msg): df2 = self.df.rename_geometry("Shape_Area") with pytest.raises(ValueError, match=msg): self.df.rename_geometry("Shape_Area", inplace=True) def test_set_geometry(self): geom = GeoSeries([Point(x, y) for x, y in zip(range(5), range(5))]) original_geom = self.df.geometry expected = geom.copy() expected.crs = self.df.crs df2 = self.df.set_geometry(geom) assert df2 is not self.df assert_geoseries_equal(df2.geometry, expected) assert_geoseries_equal(self.df.geometry, original_geom) assert_geoseries_equal(self.df["geometry"], self.df.geometry) # unknown column with pytest.raises(ValueError): self.df.set_geometry("nonexistent-column") # ndim error with pytest.raises(ValueError): self.df.set_geometry(self.df) @pytest.mark.skipif(not compat.HAS_PYPROJ, reason="Requires pyproj") def test_set_geometry_crs(self): geom = GeoSeries([Point(x, y) for x, y in zip(range(5), range(5))]) # new crs - setting should default to GeoSeries' crs gs = GeoSeries(geom, crs="epsg:3857") new_df = self.df.set_geometry(gs) assert new_df.crs == "epsg:3857" # explicit crs overrides self and dataframe new_df = self.df.set_geometry(gs, crs="epsg:26909") assert new_df.crs == "epsg:26909" assert new_df.geometry.crs == "epsg:26909" # Series should use dataframe's new_df = self.df.set_geometry(geom.values) assert new_df.crs == self.df.crs assert new_df.geometry.crs == self.df.crs def test_set_geometry_col(self): g = self.df.geometry g_simplified = g.simplify(100) self.df["simplified_geometry"] = g_simplified.copy() df2 = self.df.set_geometry("simplified_geometry") # Drop is false by default assert "simplified_geometry" in df2 assert_geoseries_equal(df2.geometry, g_simplified) # If True, drops column and renames to geometry with pytest.warns(FutureWarning): df3 = self.df.set_geometry("simplified_geometry", drop=True) assert "simplified_geometry" not in df3 assert_geoseries_equal(df3.geometry, g_simplified) def test_set_geometry_inplace(self): geom = [Point(x, y) for x, y in zip(range(5), range(5))] ret = self.df.set_geometry(geom, inplace=True) assert ret is None geom = GeoSeries(geom, index=self.df.index, crs=self.df.crs) assert_geoseries_equal(self.df.geometry, geom) def test_set_geometry_series(self): # Test when setting geometry with a Series that # alignment will occur # # Reverse the index order # Set the Series to be Point(i,i) where i is the index self.df.index = range(len(self.df) - 1, -1, -1) d = {} for i in range(len(self.df)): d[i] = Point(i, i) g = GeoSeries(d) # At this point, the DataFrame index is [4,3,2,1,0] and the # GeoSeries index is [0,1,2,3,4]. Make sure set_geometry aligns # them to match indexes df = self.df.set_geometry(g) for i, r in df.iterrows(): assert i == r["geometry"].x assert i == r["geometry"].y def test_set_geometry_empty(self): df = pd.DataFrame(columns=["a", "geometry"], index=pd.DatetimeIndex([])) result = df.set_geometry("geometry") assert isinstance(result, GeoDataFrame) assert isinstance(result.index, pd.DatetimeIndex) def test_set_geometry_np_int(self): self.df.loc[:, 0] = self.df.geometry df = self.df.set_geometry(np.int64(0)) assert df.geometry.name == 0 def test_get_geometry_invalid(self): df = GeoDataFrame() # no column "geometry" ever added df["geom"] = self.df.geometry msg_geo_col_none = "active geometry column to use has not been set. " with pytest.raises(AttributeError, match=msg_geo_col_none): df.geometry # "geometry" originally present but dropped (but still a gdf) col_subset_drop_geometry = ["BoroCode", "BoroName", "geom2"] df2 = self.df.copy().assign(geom2=self.df.geometry)[col_subset_drop_geometry] with pytest.raises(AttributeError, match="is not present"): df2.geometry msg_other_geo_cols_present = "There are columns with geometry data type" msg_no_other_geo_cols = "There are no existing columns with geometry data type" with pytest.raises(AttributeError, match=msg_other_geo_cols_present): df2.geometry with pytest.raises(AttributeError, match=msg_no_other_geo_cols): GeoDataFrame().geometry def test_get_geometry_geometry_inactive(self): # https://github.com/geopandas/geopandas/issues/2574 df = self.df.assign(geom2=self.df.geometry).set_geometry("geom2") df = df.loc[:, ["BoroName", "geometry"]] assert df._geometry_column_name == "geom2" msg_geo_col_missing = "is not present. " # Check that df.geometry raises if active geometry column is missing, # it should not fall back to column named "geometry" with pytest.raises(AttributeError, match=msg_geo_col_missing): df.geometry @pytest.mark.skipif(not compat.HAS_PYPROJ, reason="Requires pyproj") def test_override_existing_crs_warning(self): with pytest.warns( FutureWarning, match="Overriding the CRS of a GeoSeries that already has CRS", ): self.df.geometry.crs = "epsg:2100" with pytest.warns( FutureWarning, match="Overriding the CRS of a GeoDataFrame that already has CRS", ): self.df.crs = "epsg:4326" def test_active_geometry_name(self): # default single active called "geometry" assert self.df.active_geometry_name == "geometry" # one GeoSeries, not active no_active = GeoDataFrame({"foo": self.df.BoroName, "bar": self.df.geometry}) assert no_active.active_geometry_name is None assert no_active.set_geometry("bar").active_geometry_name == "bar" # multiple, none active multiple = GeoDataFrame({"foo": self.df.geometry, "bar": self.df.geometry}) assert multiple.active_geometry_name is None assert multiple.set_geometry("foo").active_geometry_name == "foo" assert multiple.set_geometry("bar").active_geometry_name == "bar" def test_align(self): df = self.df2 res1, res2 = df.align(df) assert_geodataframe_equal(res1, df) assert_geodataframe_equal(res2, df) res1, res2 = df.align(df.copy()) assert_geodataframe_equal(res1, df) assert_geodataframe_equal(res2, df) if compat.HAS_PYPROJ: # assert crs is / is not preserved on mixed dataframes df_nocrs = df.copy().set_crs(None, allow_override=True) res1, res2 = df.align(df_nocrs) assert_geodataframe_equal(res1, df) assert res1.crs is not None assert_geodataframe_equal(res2, df_nocrs) assert res2.crs is None # mixed GeoDataFrame / DataFrame df_nogeom = pd.DataFrame(df.drop("geometry", axis=1)) res1, res2 = df.align(df_nogeom, axis=0) assert_geodataframe_equal(res1, df) assert type(res2) is pd.DataFrame assert_frame_equal(res2, df_nogeom) # same as above but now with actual alignment df1 = df.iloc[1:].copy() df2 = df.iloc[:-1].copy() exp1 = df.copy() exp1.iloc[0] = np.nan exp2 = df.copy() exp2.iloc[-1] = np.nan res1, res2 = df1.align(df2) assert_geodataframe_equal(res1, exp1) assert_geodataframe_equal(res2, exp2) if compat.HAS_PYPROJ: df2_nocrs = df2.copy().set_crs(None, allow_override=True) exp2_nocrs = exp2.copy().set_crs(None, allow_override=True) res1, res2 = df1.align(df2_nocrs) assert_geodataframe_equal(res1, exp1) assert res1.crs is not None assert_geodataframe_equal(res2, exp2_nocrs) assert res2.crs is None df2_nogeom = pd.DataFrame(df2.drop("geometry", axis=1)) exp2_nogeom = pd.DataFrame(exp2.drop("geometry", axis=1)) res1, res2 = df1.align(df2_nogeom, axis=0) assert_geodataframe_equal(res1, exp1) assert type(res2) is pd.DataFrame assert_frame_equal(res2, exp2_nogeom) @pytest.mark.skipif(not compat.HAS_PYPROJ, reason="Requires pyproj") def test_to_json(self): text = self.df.to_json(to_wgs84=True) data = json.loads(text) assert data["type"] == "FeatureCollection" assert len(data["features"]) == 5 assert "id" in data["features"][0].keys() # check it converts to WGS84 coord = data["features"][0]["geometry"]["coordinates"][0][0][0] np.testing.assert_allclose(coord, [-74.0505080640324, 40.5664220341941]) def test_to_json_missing_geom_errors_nicely(self): df = self.df.copy() # mock doing some operation where the tracking of the active geometry # column is lost df._geometry_column_name = None msg = re.escape( "You are calling a geospatial method on the GeoDataFrame, but the " "active geometry column to use has not been set" ) with pytest.raises(AttributeError, match=msg): df.to_json() def test_to_json_wgs84_false(self): text = self.df.to_json() data = json.loads(text) # check it doesn't convert to WGS84 coord = data["features"][0]["geometry"]["coordinates"][0][0][0] assert coord == [970217.0223999023, 145643.33221435547] def test_to_json_no_crs(self): self.df.geometry.array.crs = None with pytest.raises(ValueError, match="CRS is not set"): self.df.to_json(to_wgs84=True) @pytest.mark.filterwarnings( "ignore:Geometry column does not contain geometry:UserWarning" ) def test_to_json_geom_col(self): df = self.df.copy() df["geom"] = df["geometry"] df["geometry"] = np.arange(len(df)) df.set_geometry("geom", inplace=True) text = df.to_json() data = json.loads(text) assert data["type"] == "FeatureCollection" assert len(data["features"]) == 5 def test_to_json_only_geom_column(self): text = self.df[["geometry"]].to_json() data = json.loads(text) assert len(data["features"]) == 5 assert "id" in data["features"][0].keys() def test_to_json_na(self): # Set a value as nan and make sure it's written self.df.loc[self.df["BoroName"] == "Queens", "Shape_Area"] = np.nan text = self.df.to_json() data = json.loads(text) assert len(data["features"]) == 5 for f in data["features"]: props = f["properties"] assert len(props) == 4 if props["BoroName"] == "Queens": assert props["Shape_Area"] is None def test_to_json_bad_na(self): # Check that a bad na argument raises error with pytest.raises(ValueError): self.df.to_json(na="garbage") def test_to_json_dropna(self): self.df.loc[self.df["BoroName"] == "Queens", "Shape_Area"] = np.nan self.df.loc[self.df["BoroName"] == "Bronx", "Shape_Leng"] = np.nan text = self.df.to_json(na="drop") data = json.loads(text) assert len(data["features"]) == 5 for f in data["features"]: props = f["properties"] if props["BoroName"] == "Queens": assert len(props) == 3 assert "Shape_Area" not in props # Just make sure setting it to nan in a different row # doesn't affect this one assert "Shape_Leng" in props elif props["BoroName"] == "Bronx": assert len(props) == 3 assert "Shape_Leng" not in props assert "Shape_Area" in props else: assert len(props) == 4 def test_to_json_keepna(self): self.df.loc[self.df["BoroName"] == "Queens", "Shape_Area"] = np.nan self.df.loc[self.df["BoroName"] == "Bronx", "Shape_Leng"] = np.nan text = self.df.to_json(na="keep") data = json.loads(text) assert len(data["features"]) == 5 for f in data["features"]: props = f["properties"] assert len(props) == 4 if props["BoroName"] == "Queens": assert np.isnan(props["Shape_Area"]) # Just make sure setting it to nan in a different row # doesn't affect this one assert "Shape_Leng" in props elif props["BoroName"] == "Bronx": assert np.isnan(props["Shape_Leng"]) assert "Shape_Area" in props def test_to_json_drop_id(self): text = self.df.to_json(drop_id=True) data = json.loads(text) assert len(data["features"]) == 5 for f in data["features"]: assert "id" not in f.keys() def test_to_json_drop_id_only_geom_column(self): text = self.df[["geometry"]].to_json(drop_id=True) data = json.loads(text) assert len(data["features"]) == 5 for f in data["features"]: assert "id" not in f.keys() def test_to_json_with_duplicate_columns(self): df = GeoDataFrame( data=[[1, 2, 3]], columns=["a", "b", "a"], geometry=[Point(1, 1)] ) with pytest.raises( ValueError, match="GeoDataFrame cannot contain duplicated column names" ): df.to_json() def test_copy(self): df2 = self.df.copy() assert type(df2) is GeoDataFrame assert self.df.crs == df2.crs def test_empty_copy(self): # https://github.com/geopandas/geopandas/issues/2765 df = GeoDataFrame() df2 = df.copy() assert type(df2) is GeoDataFrame df3 = df.copy(deep=True) assert type(df3) is GeoDataFrame def test_no_geom_copy(self): df = GeoDataFrame(pd.DataFrame({"a": [1, 2, 3]})) assert type(df) is GeoDataFrame assert type(df.copy()) is GeoDataFrame def test_empty(self): df = GeoDataFrame({"geometry": []}) assert df.geometry.dtype == "geometry" df = GeoDataFrame({"a": []}, geometry="a") assert df.geometry.dtype == "geometry" df = GeoDataFrame(geometry=[]) assert df.geometry.dtype == "geometry" def test_bool_index(self): # Find boros with 'B' in their name df = self.df[self.df["BoroName"].str.contains("B")] assert len(df) == 2 boros = df["BoroName"].values assert "Brooklyn" in boros assert "Bronx" in boros assert type(df) is GeoDataFrame def test_coord_slice_points(self): assert self.df2.cx[-2:-1, -2:-1].empty assert_frame_equal(self.df2, self.df2.cx[:, :]) assert_frame_equal(self.df2.loc[5:], self.df2.cx[5:, :]) assert_frame_equal(self.df2.loc[5:], self.df2.cx[:, 5:]) assert_frame_equal(self.df2.loc[5:], self.df2.cx[5:, 5:]) def test_from_dict(self): data = {"A": [1], "geometry": [Point(0.0, 0.0)]} df = GeoDataFrame.from_dict(data, crs=3857) if compat.HAS_PYPROJ: assert df.crs == "epsg:3857" else: assert df.crs is None assert df._geometry_column_name == "geometry" data = {"B": [1], "location": [Point(0.0, 0.0)]} df = GeoDataFrame.from_dict(data, geometry="location") assert df._geometry_column_name == "location" def test_from_features(self, nybb_filename): fiona = pytest.importorskip("fiona") with fiona.open(nybb_filename) as f: features = list(f) crs = f.crs_wkt df = GeoDataFrame.from_features(features, crs=crs) validate_boro_df(df, case_sensitive=True) if compat.HAS_PYPROJ: assert df.crs == crs else: assert df.crs is None def test_from_features_unaligned_properties(self): p1 = Point(1, 1) f1 = { "type": "Feature", "properties": {"a": 0}, "geometry": p1.__geo_interface__, } p2 = Point(2, 2) f2 = { "type": "Feature", "properties": {"b": 1}, "geometry": p2.__geo_interface__, } p3 = Point(3, 3) f3 = { "type": "Feature", "properties": None, "geometry": p3.__geo_interface__, } df = GeoDataFrame.from_features([f1, f2, f3]) result = df[["a", "b"]] expected = pd.DataFrame( [{"a": 0, "b": np.nan}, {"a": np.nan, "b": 1}, {"a": np.nan, "b": np.nan}] ) assert_frame_equal(expected, result) def test_from_features_empty_properties(self): geojson_properties_object = """{ "type": "FeatureCollection", "features": [ { "type": "Feature", "properties": {}, "geometry": { "type": "Polygon", "coordinates": [ [ [ 11.3456529378891, 46.49461446367692 ], [ 11.345674395561216, 46.494097442978195 ], [ 11.346918940544128, 46.49385370294394 ], [ 11.347616314888, 46.4938352377453 ], [ 11.347514390945435, 46.49466985846028 ], [ 11.3456529378891, 46.49461446367692 ] ] ] } } ] }""" geojson_properties_null = """{ "type": "FeatureCollection", "features": [ { "type": "Feature", "properties": null, "geometry": { "type": "Polygon", "coordinates": [ [ [ 11.3456529378891, 46.49461446367692 ], [ 11.345674395561216, 46.494097442978195 ], [ 11.346918940544128, 46.49385370294394 ], [ 11.347616314888, 46.4938352377453 ], [ 11.347514390945435, 46.49466985846028 ], [ 11.3456529378891, 46.49461446367692 ] ] ] } } ] }""" # geoJSON with empty properties gjson_po = json.loads(geojson_properties_object) gdf1 = GeoDataFrame.from_features(gjson_po) # geoJSON with null properties gjson_null = json.loads(geojson_properties_null) gdf2 = GeoDataFrame.from_features(gjson_null) assert_frame_equal(gdf1, gdf2) assert "properties" not in gdf1.columns def test_from_features_no_properties(self): data = { "type": "FeatureCollection", "features": [ { "type": "Feature", "geometry": {"type": "Point", "coordinates": [0.0, 90.0]}, }, { "type": "Feature", "geometry": {"type": "Point", "coordinates": [0.0, -90.0]}, }, ], } gdf = GeoDataFrame.from_features(data) assert gdf.shape == (2, 1) assert "properties" not in gdf.columns def test_from_features_empty(self): # GH3777: an empty feature list should still produce a "geometry" column, expected_crs = GeoDataFrame(geometry=[], crs="EPSG:4326").crs gdf = GeoDataFrame.from_features([], crs="EPSG:4326") assert isinstance(gdf, GeoDataFrame) assert len(gdf) == 0 assert list(gdf.columns) == ["geometry"] assert gdf.crs == expected_crs # without a CRS, an empty feature list still yields a "geometry" column gdf_no_crs = GeoDataFrame.from_features([]) assert list(gdf_no_crs.columns) == ["geometry"] assert gdf_no_crs.crs is None # an empty FeatureCollection behaves the same as an empty list gdf_fc = GeoDataFrame.from_features( {"type": "FeatureCollection", "features": []}, crs="EPSG:4326" ) assert list(gdf_fc.columns) == ["geometry"] assert gdf_fc.crs == expected_crs def test_from_features_geom_interface_feature(self): class Placemark: def __init__(self, geom, val): self.__geo_interface__ = { "type": "Feature", "properties": {"a": val}, "geometry": geom.__geo_interface__, } p1 = Point(1, 1) f1 = Placemark(p1, 0) p2 = Point(3, 3) f2 = Placemark(p2, 0) df = GeoDataFrame.from_features([f1, f2]) assert sorted(df.columns) == ["a", "geometry"] assert df.geometry.tolist() == [p1, p2] def test_from_feature_collection(self): data = { "name": ["a", "b", "c"], "lat": [45, 46, 47.5], "lon": [-120, -121.2, -122.9], } df = pd.DataFrame(data) geometry = [Point(xy) for xy in zip(df["lon"], df["lat"])] gdf = GeoDataFrame(df, geometry=geometry) # from_features returns sorted columns expected = gdf[["geometry", "name", "lat", "lon"]] # test FeatureCollection res = GeoDataFrame.from_features(gdf.__geo_interface__) assert_frame_equal(res, expected) # test list of Features res = GeoDataFrame.from_features(gdf.__geo_interface__["features"]) assert_frame_equal(res, expected) # test __geo_interface__ attribute (a GeoDataFrame has one) res = GeoDataFrame.from_features(gdf) assert_frame_equal(res, expected) def test_dataframe_to_geodataframe(self): df = pd.DataFrame( {"A": range(len(self.df)), "location": np.array(self.df.geometry)}, index=self.df.index, ) gf = df.set_geometry("location", crs=self.df.crs) assert isinstance(df, pd.DataFrame) assert isinstance(gf, GeoDataFrame) assert_geoseries_equal(gf.geometry, self.df.geometry) assert gf.geometry.name == "location" assert "geometry" not in gf with pytest.warns(FutureWarning): gf2 = df.set_geometry("location", crs=self.df.crs, drop=True) assert isinstance(df, pd.DataFrame) assert isinstance(gf2, GeoDataFrame) assert gf2.geometry.name == "geometry" assert "geometry" in gf2 assert "location" not in gf2 assert "location" in df # should be a copy df.loc[0, "A"] = 100 assert gf.loc[0, "A"] == 0 assert gf2.loc[0, "A"] == 0 with pytest.raises(ValueError): df.set_geometry("location", inplace=True) def test_dataframe_not_manipulated(self): df = pd.DataFrame( { "A": range(len(self.df)), "latitude": self.df.geometry.centroid.y, "longitude": self.df.geometry.centroid.x, }, index=self.df.index, ) df_copy = df.copy() gf = GeoDataFrame( df, geometry=points_from_xy(df["longitude"], df["latitude"]), crs=self.df.crs, ) assert type(df) is pd.DataFrame assert "geometry" not in df assert_frame_equal(df, df_copy) assert isinstance(gf, GeoDataFrame) assert hasattr(gf, "geometry") # ensure mutating columns in gf doesn't update df gf.loc[0, "A"] = 7 assert_frame_equal(df, df_copy) gf["A"] = 3 assert_frame_equal(df, df_copy) def test_geodataframe_geointerface(self): assert self.df.__geo_interface__["type"] == "FeatureCollection" assert len(self.df.__geo_interface__["features"]) == self.df.shape[0] def test_geodataframe_iterfeatures(self): df = self.df.iloc[:1].copy() df.loc[0, "BoroName"] = np.nan # when containing missing values # null: output the missing entries as JSON null result = next(iter(df.iterfeatures(na="null")))["properties"] assert result["BoroName"] is None # drop: remove the property from the feature. result = next(iter(df.iterfeatures(na="drop")))["properties"] assert "BoroName" not in result.keys() # keep: output the missing entries as NaN result = next(iter(df.iterfeatures(na="keep")))["properties"] assert np.isnan(result["BoroName"]) # test for checking that the (non-null) features are python scalars and # not numpy scalars assert type(df.loc[0, "Shape_Leng"]) is np.float64 # null result = next(iter(df.iterfeatures(na="null"))) assert isinstance(result["properties"]["Shape_Leng"], float) # drop result = next(iter(df.iterfeatures(na="drop"))) assert isinstance(result["properties"]["Shape_Leng"], float) # keep result = next(iter(df.iterfeatures(na="keep"))) assert isinstance(result["properties"]["Shape_Leng"], float) # when only having numerical columns df_only_numerical_cols = df[["Shape_Leng", "Shape_Area", "geometry"]] assert type(df_only_numerical_cols.loc[0, "Shape_Leng"]) is np.float64 # null result = next(iter(df_only_numerical_cols.iterfeatures(na="null"))) assert isinstance(result["properties"]["Shape_Leng"], float) # drop result = next(iter(df_only_numerical_cols.iterfeatures(na="drop"))) assert isinstance(result["properties"]["Shape_Leng"], float) # keep result = next(iter(df_only_numerical_cols.iterfeatures(na="keep"))) assert isinstance(result["properties"]["Shape_Leng"], float) with pytest.raises( ValueError, match="GeoDataFrame cannot contain duplicated column names" ): df_with_duplicate_columns = df[ ["Shape_Leng", "Shape_Leng", "Shape_Area", "geometry"] ] list(df_with_duplicate_columns.iterfeatures()) # geometry not set df = GeoDataFrame({"values": [0, 1], "geom": [Point(0, 1), Point(1, 0)]}) with pytest.raises(AttributeError): list(df.iterfeatures()) def test_geodataframe_iterfeatures_non_scalars(self): # When some features in geodataframe are non-scalar values df = GeoDataFrame( {"geometry": [Point(1, 2)], "non-scalar": [[1, 2]], "test_col": None} ) # null expected = {"non-scalar": [1, 2], "test_col": None} result = next(iter(df.iterfeatures(na="null"))).get("properties") assert expected == result # drop expected = {"non-scalar": [1, 2]} result = next(iter(df.iterfeatures(na="drop"))).get("properties") assert expected == result # keep expected = {"non-scalar": [1, 2], "test_col": None} result = next(iter(df.iterfeatures(na="keep"))).get("properties") assert expected == result def test_geodataframe_geojson_no_bbox(self): geo = self.df.to_geo_dict(na="null", show_bbox=False) assert "bbox" not in geo.keys() for feature in geo["features"]: assert "bbox" not in feature.keys() def test_geodataframe_geojson_bbox(self): geo = self.df.to_geo_dict(na="null", show_bbox=True) assert "bbox" in geo.keys() assert len(geo["bbox"]) == 4 assert isinstance(geo["bbox"], tuple) for bound in geo["bbox"]: assert not isinstance(bound, np.float64) for feature in geo["features"]: assert "bbox" in feature.keys() def test_pickle(self): import pickle df2 = pickle.loads(pickle.dumps(self.df)) assert_geodataframe_equal(self.df, df2) def test_pickle_method(self): filename = os.path.join(self.tempdir, "df.pkl") self.df.to_pickle(filename) unpickled = pd.read_pickle(filename) assert_frame_equal(self.df, unpickled) assert self.df.crs == unpickled.crs def test_estimate_utm_crs(self): pyproj = pytest.importorskip("pyproj") assert self.df.estimate_utm_crs() == pyproj.CRS("EPSG:32618") assert self.df.estimate_utm_crs("NAD83") == pyproj.CRS("EPSG:26918") def test_to_wkb(self): wkbs0 = [ ( # POINT (0 0) b"\x01\x01\x00\x00\x00\x00\x00\x00\x00\x00\x00" b"\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00" ), ( # POINT (1 1) b"\x01\x01\x00\x00\x00\x00\x00\x00\x00\x00" b"\x00\xf0?\x00\x00\x00\x00\x00\x00\xf0?" ), ] wkbs1 = [ ( # POINT (2 2) b"\x01\x01\x00\x00\x00\x00\x00\x00\x00\x00" b"\x00\x00@\x00\x00\x00\x00\x00\x00\x00@" ), ( # POINT (3 3) b"\x01\x01\x00\x00\x00\x00\x00\x00\x00\x00" b"\x00\x08@\x00\x00\x00\x00\x00\x00\x08@" ), ] gs0 = GeoSeries.from_wkb(wkbs0) gs1 = GeoSeries.from_wkb(wkbs1) gdf = GeoDataFrame({"geom_col0": gs0, "geom_col1": gs1}) expected_df = pd.DataFrame({"geom_col0": wkbs0, "geom_col1": wkbs1}) # set fixed byte-order (the expected WKB blobs are in little-endian) assert_frame_equal(expected_df, gdf.to_wkb(byte_order=1)) def test_to_wkt(self): wkts0 = ["POINT (0 0)", "POINT (1 1)"] wkts1 = ["POINT (2 2)", "POINT (3 3)"] gs0 = GeoSeries.from_wkt(wkts0) gs1 = GeoSeries.from_wkt(wkts1) gdf = GeoDataFrame({"gs0": gs0, "gs1": gs1}) expected_df = pd.DataFrame({"gs0": wkts0, "gs1": wkts1}) assert_frame_equal(expected_df, gdf.to_wkt()) @pytest.mark.parametrize("how", ["left", "inner", "right"]) @pytest.mark.parametrize("predicate", ["intersects", "within", "contains"]) def test_sjoin(self, how, predicate, naturalearth_cities, naturalearth_lowres): """ Basic test for availability of the GeoDataFrame method. Other sjoin tests are located in /tools/tests/test_sjoin.py """ left = read_file(naturalearth_cities) right = read_file(naturalearth_lowres) expected = geopandas.sjoin(left, right, how=how, predicate=predicate) result = left.sjoin(right, how=how, predicate=predicate) assert_geodataframe_equal(result, expected) @pytest.mark.parametrize("how", ["left", "inner", "right"]) @pytest.mark.parametrize("distance", [0, 3]) @pytest.mark.skipif( not compat.GEOS_GE_310, reason="`dwithin` requires GEOS 3.10", ) def test_sjoin_dwithin(self, how, distance): """ Basic test for predicate='dwithin' availability of the GeoDataFrame method. Other sjoin tests are located in /tools/tests/test_sjoin.py """ left = GeoDataFrame(geometry=points_from_xy([0, 1, 2], [0, 1, 1])) right = GeoDataFrame(geometry=[box(0, 0, 1, 1)]) expected = geopandas.sjoin( left, right, how=how, predicate="dwithin", distance=distance ) result = left.sjoin(right, how=how, predicate="dwithin", distance=distance) assert_geodataframe_equal(result, expected) @pytest.mark.parametrize("how", ["left", "inner", "right"]) @pytest.mark.parametrize("max_distance", [None, 1]) @pytest.mark.parametrize("distance_col", [None, "distance"]) @pytest.mark.filterwarnings("ignore:Geometry is in a geographic CRS:UserWarning") def test_sjoin_nearest( self, how, max_distance, distance_col, naturalearth_cities, naturalearth_lowres ): """ Basic test for availability of the GeoDataFrame method. Other sjoin tests are located in /tools/tests/test_sjoin.py """ left = read_file(naturalearth_cities) right = read_file(naturalearth_lowres) expected = geopandas.sjoin_nearest( left, right, how=how, max_distance=max_distance, distance_col=distance_col ) result = left.sjoin_nearest( right, how=how, max_distance=max_distance, distance_col=distance_col ) assert_geodataframe_equal(result, expected) def test_clip(self, naturalearth_cities, naturalearth_lowres): """ Basic test for availability of the GeoDataFrame method. Other clip tests are located in /tools/tests/test_clip.py """ left = read_file(naturalearth_cities) world = read_file(naturalearth_lowres) south_america = world[world["continent"] == "South America"] expected = geopandas.clip(left, south_america) result = left.clip(south_america) assert_geodataframe_equal(result, expected) def test_clip_sorting(self, naturalearth_cities, naturalearth_lowres): """ Test sorting of geodataframe when clipping. """ cities = read_file(naturalearth_cities) world = read_file(naturalearth_lowres) south_america = world[world["continent"] == "South America"] unsorted_clipped_cities = geopandas.clip(cities, south_america, sort=False) sorted_clipped_cities = geopandas.clip(cities, south_america, sort=True) expected_sorted_index = pd.Index( [55, 59, 62, 88, 101, 114, 122, 169, 181, 189, 210, 230, 236, 238, 239] ) assert not ( sorted(unsorted_clipped_cities.index) == unsorted_clipped_cities.index ).all() assert ( sorted(sorted_clipped_cities.index) == sorted_clipped_cities.index ).all() assert_index_equal( expected_sorted_index, sorted_clipped_cities.index, exact=True ) def test_overlay(self, dfs, how): """ Basic test for availability of the GeoDataFrame method. Other overlay tests are located in tests/test_overlay.py """ df1, df2 = dfs expected = geopandas.overlay(df1, df2, how=how) result = df1.overlay(df2, how=how) assert_geodataframe_equal(result, expected) def check_geodataframe(df, geometry_column="geometry"): assert isinstance(df, GeoDataFrame) assert isinstance(df.geometry, GeoSeries) assert isinstance(df[geometry_column], GeoSeries) assert df._geometry_column_name == geometry_column assert df.geometry.name == geometry_column assert isinstance(df.geometry.values, GeometryArray) assert isinstance(df.geometry.dtype, GeometryDtype) class TestConstructor: def test_dict(self): data = { "A": range(3), "B": np.arange(3.0), "geometry": [Point(x, x) for x in range(3)], } df = GeoDataFrame(data) check_geodataframe(df) # with specifying other kwargs df = GeoDataFrame(data, index=list("abc")) check_geodataframe(df) assert_index_equal(df.index, pd.Index(list("abc"))) df = GeoDataFrame(data, columns=["B", "A", "geometry"]) check_geodataframe(df) assert_index_equal(df.columns, pd.Index(["B", "A", "geometry"])) df = GeoDataFrame(data, columns=["A", "geometry"]) check_geodataframe(df) assert_index_equal(df.columns, pd.Index(["A", "geometry"])) assert_series_equal(df["A"], pd.Series(range(3), name="A")) def test_dict_of_series(self): data = { "A": pd.Series(range(3)), "B": pd.Series(np.arange(3.0)), "geometry": GeoSeries([Point(x, x) for x in range(3)]), } df = GeoDataFrame(data) check_geodataframe(df) df = GeoDataFrame(data, index=pd.Index([1, 2])) check_geodataframe(df) assert_index_equal(df.index, pd.Index([1, 2])) assert df["A"].tolist() == [1, 2] # one non-series -> length is not correct data = { "A": pd.Series(range(3)), "B": np.arange(3.0), "geometry": GeoSeries([Point(x, x) for x in range(3)]), } with pytest.raises(ValueError): GeoDataFrame(data, index=[1, 2]) def test_dict_specified_geometry(self): data = { "A": range(3), "B": np.arange(3.0), "other_geom": [Point(x, x) for x in range(3)], } df = GeoDataFrame(data, geometry="other_geom") check_geodataframe(df, "other_geom") with pytest.raises(ValueError): df = GeoDataFrame(data, geometry="geometry") # when no geometry specified -> works but raises error once # trying to access geometry df = GeoDataFrame(data) with pytest.raises(AttributeError): _ = df.geometry df = df.set_geometry("other_geom") check_geodataframe(df, "other_geom") # combined with custom args df = GeoDataFrame(data, geometry="other_geom", columns=["B", "other_geom"]) check_geodataframe(df, "other_geom") assert_index_equal(df.columns, pd.Index(["B", "other_geom"])) assert_series_equal(df["B"], pd.Series(np.arange(3.0), name="B")) df = GeoDataFrame(data, geometry="other_geom", columns=["other_geom", "A"]) check_geodataframe(df, "other_geom") assert_index_equal(df.columns, pd.Index(["other_geom", "A"])) assert_series_equal(df["A"], pd.Series(range(3), name="A")) def test_array(self): data = { "A": range(3), "B": np.arange(3.0), "geometry": [Point(x, x) for x in range(3)], } a = np.array([data["A"], data["B"], data["geometry"]], dtype=object).T df = GeoDataFrame(a, columns=["A", "B", "geometry"]) check_geodataframe(df) df = GeoDataFrame(a, columns=["A", "B", "other_geom"], geometry="other_geom") check_geodataframe(df, "other_geom") def test_from_frame(self): data = { "A": range(3), "B": np.arange(3.0), "geometry": [Point(x, x) for x in range(3)], } gpdf = GeoDataFrame(data) pddf = pd.DataFrame(data) check_geodataframe(gpdf) assert type(pddf) is pd.DataFrame for df in [gpdf, pddf]: res = GeoDataFrame(df) check_geodataframe(res) res = GeoDataFrame(df, index=pd.Index([0, 2])) check_geodataframe(res) assert_index_equal(res.index, pd.Index([0, 2]), exact="equiv") assert res["A"].tolist() == [0, 2] res = GeoDataFrame(df, columns=["geometry", "B"]) check_geodataframe(res) assert_index_equal(res.columns, pd.Index(["geometry", "B"]), exact=False) with pytest.raises(ValueError): GeoDataFrame(df, geometry="other_geom") def test_from_frame_specified_geometry(self): data = { "A": range(3), "B": np.arange(3.0), "other_geom": [Point(x, x) for x in range(3)], } gpdf = GeoDataFrame(data, geometry="other_geom") check_geodataframe(gpdf, "other_geom") pddf = pd.DataFrame(data) for df in [gpdf, pddf]: res = GeoDataFrame(df, geometry="other_geom") check_geodataframe(res, "other_geom") # gdf from gdf should preserve active geometry column name df = GeoDataFrame(gpdf) check_geodataframe(df, "other_geom") def test_only_geometry(self): exp = GeoDataFrame( {"geometry": [Point(x, x) for x in range(3)], "other": range(3)} )[["geometry"]] df = GeoDataFrame(geometry=[Point(x, x) for x in range(3)]) check_geodataframe(df) assert_geodataframe_equal(df, exp) df = GeoDataFrame({"geometry": [Point(x, x) for x in range(3)]}) check_geodataframe(df) assert_geodataframe_equal(df, exp) df = GeoDataFrame( {"other_geom": [Point(x, x) for x in range(3)]}, geometry="other_geom" ) check_geodataframe(df, "other_geom") exp = exp.rename(columns={"geometry": "other_geom"}).set_geometry("other_geom") assert_geodataframe_equal(df, exp) def test_no_geometries(self): # keeps GeoDataFrame class (no DataFrame) data = {"A": range(3), "B": np.arange(3.0)} df = GeoDataFrame(data) assert type(df) is GeoDataFrame gdf = GeoDataFrame({"x": [1]}) assert list(gdf.x) == [1] def test_empty(self): df = GeoDataFrame() assert type(df) is GeoDataFrame df = GeoDataFrame({"A": [], "B": []}, geometry=[]) assert type(df) is GeoDataFrame def test_column_ordering(self): geoms = [Point(1, 1), Point(2, 2), Point(3, 3)] gs = GeoSeries(geoms) gdf = GeoDataFrame( {"a": [1, 2, 3], "geometry": gs}, columns=["geometry", "a"], geometry="geometry", ) check_geodataframe(gdf) assert list(gdf.columns) == ["geometry", "a"] # with non-default index gdf = GeoDataFrame( {"a": [1, 2, 3], "geometry": gs}, columns=["geometry", "a"], index=pd.Index([0, 0, 1]), geometry="geometry", ) check_geodataframe(gdf) assert list(gdf.columns) == ["geometry", "a"] def test_do_not_preserve_series_name_in_constructor(self): # GH3337 # GeoDataFrame(... geometry=...) should always create geom col "geometry" geoms = [Point(1, 1), Point(2, 2), Point(3, 3)] gs = GeoSeries(geoms) gdf = GeoDataFrame({"a": [1, 2, 3]}, geometry=gs) check_geodataframe(gdf, geometry_column="geometry") # still get "geometry", even with custom geoseries name gs = GeoSeries(geoms, name="my_geom") gdf = GeoDataFrame({"a": [1, 2, 3]}, geometry=gs) check_geodataframe(gdf, geometry_column="geometry") def test_overwrite_geometry(self): # GH602 data = pd.DataFrame({"geometry": [1, 2, 3], "col1": [4, 5, 6]}) geoms = pd.Series([Point(i, i) for i in range(3)]) # passed geometry kwarg should overwrite geometry column in data res = GeoDataFrame(data, geometry=geoms) assert_geoseries_equal(res.geometry, GeoSeries(geoms)) def test_repeat_geo_col(self): df = pd.DataFrame( [ {"geometry": Point(x, y), "geom": Point(x, y)} for x, y in zip(range(3), range(3)) ], ) # explicitly prevent construction of gdf with repeat geometry column names # two columns called "geometry", geom col inferred df2 = df.rename(columns={"geom": "geometry"}) with pytest.raises(ValueError): GeoDataFrame(df2) # ensure case is caught when custom geom column name is used # two columns called "geom", geom col explicit df3 = df.rename(columns={"geometry": "geom"}) with pytest.raises(ValueError): GeoDataFrame(df3, geometry="geom") @pytest.mark.parametrize("dtype", ["geometry", "object"]) def test_multiindex_with_geometry_label(self, dtype): # DataFrame with MultiIndex where "geometry" label corresponds to # multiple columns df = pd.DataFrame([[Point(0, 0), Point(1, 1)], [Point(2, 2), Point(3, 3)]]) df = df.astype(dtype) df.columns = pd.MultiIndex.from_product([["geometry"], [0, 1]]) # don't error in constructor gdf = GeoDataFrame(df) with pytest.raises(AttributeError, match=r".*geometry .* has not been set.*"): gdf.geometry res_gdf = gdf.set_geometry(("geometry", 0)) assert res_gdf.shape == gdf.shape assert isinstance(res_gdf.geometry, GeoSeries) def test_default_geo_colname_none(self): match = "You are adding a column named 'geometry' to a GeoDataFrame" gdf = GeoDataFrame({"a": [1, 2]}) gdf2 = gdf.copy() geo_col = GeoSeries.from_xy([1, 3], [3, 3]) with pytest.warns(FutureWarning, match=match): gdf2["geometry"] = geo_col assert gdf2._geometry_column_name == "geometry" gdf4 = gdf.copy() with pytest.warns(FutureWarning, match=match): gdf4.geometry = geo_col assert gdf4._geometry_column_name == "geometry" # geo col name should only change if we add geometry gdf5 = gdf.copy() with pytest.warns( UserWarning, match="Geometry column does not contain geometry" ): gdf5["geometry"] = "foo" assert gdf5._geometry_column_name is None with pytest.warns(FutureWarning, match=match): gdf3 = gdf.copy().assign(geometry=geo_col) assert gdf3._geometry_column_name == "geometry" # Check that adding a GeoSeries to a column called "geometry" to a # gdf without an active geometry column some time after the init does not # warn / set the active geometry column gdf6 = gdf.copy() gdf6["geom2"] = geo_col gdf6["geom3"] = geo_col gdf6 = gdf6.set_geometry("geom2") subset = gdf6[["a", "geom3"]] # this has a missing active geometry col assert subset._geometry_column_name == "geom2" subset["geometry"] = geo_col # adding column called geometry shouldn't auto-set assert subset._geometry_column_name == "geom2" def test_multiindex_geometry_colname_2_level(self): # GH1763 https://github.com/geopandas/geopandas/issues/1763 crs = "EPSG:4326" df = pd.DataFrame( [[1, 0], [0, 1]], columns=[["location", "location"], ["x", "y"]] ) x_col = df["location", "x"] y_col = df["location", "y"] gdf = GeoDataFrame(df, crs=crs, geometry=points_from_xy(x_col, y_col)) if compat.HAS_PYPROJ: assert gdf.crs == crs assert gdf.geometry.crs == crs assert gdf.geometry.dtype == "geometry" assert gdf._geometry_column_name == "geometry" assert gdf.geometry.name == "geometry" def test_multiindex_geometry_colname_3_level(self): # GH1763 https://github.com/geopandas/geopandas/issues/1763 # Note 3-level case uses different code paths in pandas, it is not redundant crs = "EPSG:4326" df = pd.DataFrame( [[1, 0], [0, 1]], columns=[ ["foo", "foo"], ["location", "location"], ["x", "y"], ], ) x_col = df["foo", "location", "x"] y_col = df["foo", "location", "y"] gdf = GeoDataFrame(df, crs=crs, geometry=points_from_xy(x_col, y_col)) if compat.HAS_PYPROJ: assert gdf.crs == crs assert gdf.geometry.crs == crs assert gdf.geometry.dtype == "geometry" assert gdf._geometry_column_name == "geometry" assert gdf.geometry.name == "geometry" def test_multiindex_geometry_colname_3_level_new_col(self): crs = "EPSG:4326" df = pd.DataFrame( [[1, 0], [0, 1]], columns=[ ["foo", "foo"], ["location", "location"], ["x", "y"], ], ) x_col = df["foo", "location", "x"] y_col = df["foo", "location", "y"] df[("geometry", "", "")] = GeoSeries.from_xy(x_col, y_col) df2 = df.copy() gdf = df.set_geometry("geometry", crs=crs) if compat.HAS_PYPROJ: assert gdf.crs == crs assert gdf._geometry_column_name == "geometry" assert gdf.geometry.name == "geometry" # test again setting with tuple col name gdf = df2.set_geometry(("geometry", "", ""), crs=crs) if compat.HAS_PYPROJ: assert gdf.crs == crs assert gdf._geometry_column_name == ("geometry", "", "") assert gdf.geometry.name == ("geometry", "", "") def test_assign_cols_using_index(self, nybb_filename): df = read_file(nybb_filename) other_df = pd.DataFrame({"foo": range(5), "bar": range(5)}) expected = pd.concat([df, other_df], axis=1) df[other_df.columns] = other_df assert_geodataframe_equal(df, expected) def test_geometry_colname_enum(self): # ensure that other classes to geometry arg in GeoDataFrame # with `name` attribute are not assumed to be (Geo)Series class Fruit(Enum): apple = 1 pear = 2 df = pd.DataFrame( {Fruit.apple: [1, 2], Fruit.pear: GeoSeries.from_xy([1, 2], [3, 4])} ) res = GeoDataFrame(df, geometry=Fruit.pear) assert res.active_geometry_name == Fruit.pear def test_geometry_nan_scalar(self): gdf = GeoDataFrame( data=[[np.nan, np.nan]], columns=["geometry", "something"], crs="EPSG:4326", ) assert gdf.shape == (1, 2) assert gdf.active_geometry_name == "geometry" assert gdf.geometry[0] is None if compat.HAS_PYPROJ: assert gdf.crs == "EPSG:4326" def test_geometry_nan_array(self): gdf = GeoDataFrame( { "geometry": [np.nan, None, pd.NA], "something": [np.nan, np.nan, np.nan], }, crs="EPSG:4326", ) assert gdf.shape == (3, 2) assert gdf.active_geometry_name == "geometry" assert gdf.geometry.isna().all() if compat.HAS_PYPROJ: assert gdf.crs == "EPSG:4326" @pytest.mark.skipif(not compat.HAS_PYPROJ, reason="pyproj not available") def test_geodataframe_crs(): gdf = GeoDataFrame(columns=["geometry"]) gdf.crs = "IGNF:ETRS89UTM28" assert gdf.crs.to_authority() == ("IGNF", "ETRS89UTM28") def test_geodataframe_nocrs_json(): # no CRS, no crs field gdf = GeoDataFrame(columns=["geometry"]) gdf_geojson = json.loads(gdf.to_json()) assert "crs" not in gdf_geojson # WGS84, no crs field (default as per spec) gdf.crs = 4326 gdf_geojson = json.loads(gdf.to_json()) assert "crs" not in gdf_geojson @pytest.mark.skipif(not compat.HAS_PYPROJ, reason="pyproj not available") def test_geodataframe_crs_json(): gdf = GeoDataFrame(columns=["geometry"]) gdf.crs = 25833 gdf_geojson = json.loads(gdf.to_json()) assert "crs" in gdf_geojson assert gdf_geojson["crs"] == { "type": "name", "properties": {"name": "urn:ogc:def:crs:EPSG::25833"}, } gdf_geointerface = gdf.__geo_interface__ assert "crs" not in gdf_geointerface @pytest.mark.skipif(not compat.HAS_PYPROJ, reason="pyproj not available") @pytest.mark.parametrize( "crs", ["+proj=cea +lon_0=0 +lat_ts=45 +x_0=0 +y_0=0 +ellps=WGS84 +units=m", "IGNF:WGS84"], ) def test_geodataframe_crs_nonrepresentable_json(crs): gdf = GeoDataFrame( [Point(1000, 1000)], columns=["geometry"], crs=crs, ) with pytest.warns( UserWarning, match="GeoDataFrame's CRS is not representable in URN OGC" ): gdf_geojson = json.loads(gdf.to_json()) assert "crs" not in gdf_geojson def test_geodataframe_crs_colname(): # https://github.com/geopandas/geopandas/issues/2942 gdf = GeoDataFrame({"crs": [1], "geometry": [Point(1, 1)]}) assert gdf.crs is None assert gdf["crs"].iloc[0] == 1 assert getattr(gdf, "crs") is None # https://github.com/geopandas/geopandas/issues/3501 gdf = GeoDataFrame({"crs": [1]}, geometry=[Point(1, 1)]) assert gdf.crs is None assert gdf["crs"].iloc[0] == 1 assert getattr(gdf, "crs") is None # test multiindex handling df = pd.DataFrame([[1, 0], [0, 1]], columns=[["crs", "crs"], ["x", "y"]]) x_col = df["crs", "x"] y_col = df["crs", "y"] gdf = GeoDataFrame(df, geometry=points_from_xy(x_col, y_col)) assert gdf.crs is None assert gdf["crs"].iloc[0].to_list() == [1, 0] assert getattr(gdf, "crs") is None @pytest.mark.parametrize("geo_col_name", ["geometry", "polygons"]) def test_set_geometry_supply_colname(dfs, geo_col_name): df, _ = dfs if geo_col_name != "geometry": df = df.rename_geometry(geo_col_name) df["centroid"] = df.geometry.centroid res = df.set_geometry("centroid") assert res.active_geometry_name == "centroid" assert geo_col_name in res.columns # Test that drop=False explicitly warns deprecated = "The `drop` keyword argument is deprecated" with pytest.warns(FutureWarning, match=deprecated): res2 = df.set_geometry("centroid", drop=False) assert_geodataframe_equal(res, res2) with pytest.warns(FutureWarning, match=deprecated): res3 = df.set_geometry("centroid", drop=True) # drop=True should preserve previous geometry col name (keep old behaviour) assert res3.active_geometry_name == geo_col_name assert "centroid" not in res3.columns # Test that alternative suggested without using drop=True is equivalent assert_geodataframe_equal( res3, df.set_geometry("centroid") .drop(columns=geo_col_name) .rename_geometry(geo_col_name), ) @pytest.mark.parametrize("geo_col_name", ["geometry", "polygons"]) def test_set_geometry_supply_arraylike(dfs, geo_col_name): df, _ = dfs if geo_col_name != "geometry": df = df.rename_geometry(geo_col_name) centroids = df.geometry.centroid res = df.set_geometry(centroids) assert res.active_geometry_name == geo_col_name # drop should do nothing if the column already exists match_str = ( "The `drop` keyword argument is deprecated and has no effect when " "`col` is an array-like value" ) with pytest.warns( FutureWarning, match=match_str, ): res2 = df.set_geometry(centroids, drop=True) assert res2.active_geometry_name == geo_col_name centroids = centroids.rename("centroids") res3 = df.set_geometry(centroids) # Should preserve the geoseries name # (and old geometry column should be kept) assert res3.active_geometry_name == "centroids" assert geo_col_name in res3.columns # Drop should not remove previous active geometry colname for arraylike inputs with pytest.warns( FutureWarning, match=match_str, ): res4 = df.set_geometry(centroids, drop=True) assert res4.active_geometry_name == "centroids" assert geo_col_name in res4.columns @pytest.mark.filterwarnings("error::FutureWarning") def test_reduce_geometry_array(): """ Check for a FutureWarning. `geopandas.array.GeometryArray._reduce` issues a FutureWarning if the parameter `keepdims` is not set. `GeometryArray` inherits from `pandas.api.extensions.ExtensionArray` and its `_reduce` is overridden in `GeometryArray`. This warning is issued with pandas 2.2.2 (tested). """ GeoDataFrame({"geometry": []}).all() class GDFChild(GeoDataFrame): def custom_method(self): return "this is a custom output" def test_inheritance(dfs): df, _ = dfs df.loc[:, "col2"] = ["a"] * len(df) dfc = GDFChild(df) dfc2 = dfc.rename_geometry("geometry2") children = [ dfc, dfc.iloc[[0]], dfc.loc[dfc.col1 == 1], dfc.dissolve(), dfc[["col2", "geometry"]], dfc.copy(), dfc2, dfc2.iloc[[0]], dfc2.loc[dfc.col1 == 1], dfc2.dissolve(), dfc2[["col2", "geometry2"]], dfc2.copy(), ] for v in children: assert isinstance(v, GDFChild) assert v.custom_method() == "this is a custom output" df2 = dfc2.drop(columns=["geometry2"]) assert not isinstance(df2, GDFChild) geopandas-1.2.0/geopandas/tests/test_geom_methods.py000066400000000000000000003033221525650651600226570ustar00rootroot00000000000000import re import string import warnings import numpy as np from pandas import DataFrame, Index, MultiIndex, Series, concat import shapely from shapely import wkt from shapely.geometry import ( LinearRing, LineString, MultiLineString, MultiPoint, MultiPolygon, Point, Polygon, box, ) from shapely.geometry.collection import GeometryCollection from shapely.ops import unary_union from geopandas import GeoDataFrame, GeoSeries from geopandas._compat import GEOS_GE_312, HAS_PYPROJ from geopandas.base import GeoPandasBase import pytest from geopandas.testing import assert_geodataframe_equal from geopandas.tests.util import assert_geoseries_equal, geom_almost_equals, geom_equals from numpy.testing import assert_array_equal from pandas.testing import assert_frame_equal, assert_index_equal, assert_series_equal def assert_array_dtype_equal(a, b, *args, **kwargs): a = np.asanyarray(a) b = np.asanyarray(b) assert a.dtype == b.dtype assert_array_equal(a, b, *args, **kwargs) class TestGeomMethods: def setup_method(self): self.t1 = Polygon([(0, 0), (1, 0), (1, 1)]) self.t2 = Polygon([(0, 0), (1, 1), (0, 1)]) self.t3 = Polygon([(2, 0), (3, 0), (3, 1)]) self.tz = Polygon([(1, 1, 1), (2, 2, 2), (3, 3, 3)]) self.tz1 = Polygon([(2, 2, 2), (1, 1, 1), (3, 3, 3)]) self.sq = Polygon([(0, 0), (1, 0), (1, 1), (0, 1)]) self.sqz = Polygon([(1, 1, 1), (2, 2, 2), (3, 3, 3), (4, 4, 4)]) self.t4 = Polygon([(0, 0), (3, 0), (3, 3), (0, 2)]) self.t5 = Polygon([(2, 0), (3, 0), (3, 3), (2, 3)]) self.t6 = Polygon([(2, 0), (2, 0), (3, 0), (3, 0)]) self.inner_sq = Polygon( [(0.25, 0.25), (0.75, 0.25), (0.75, 0.75), (0.25, 0.75)] ) self.nested_squares = Polygon(self.sq.boundary, [self.inner_sq.boundary]) self.p0 = Point(5, 5) self.p3d = Point(5, 5, 5) self.g0 = GeoSeries( [ self.t1, self.t2, self.sq, self.inner_sq, self.nested_squares, self.p0, None, ] ) self.g1 = GeoSeries([self.t1, self.sq]) self.g2 = GeoSeries([self.sq, self.t1]) self.g3 = GeoSeries([self.t1, self.t2]) self.gz = GeoSeries([self.tz, self.sqz, self.tz1]) self.g3.crs = "epsg:4326" self.g4 = GeoSeries([self.t2, self.t1]) self.g4.crs = "epsg:4326" self.g_3d = GeoSeries([self.p0, self.p3d]) self.na = GeoSeries([self.t1, self.t2, Polygon()]) self.na_none = GeoSeries([self.t1, None]) self.a1 = self.g1.copy() self.a1.index = ["A", "B"] self.a2 = self.g2.copy() self.a2.index = ["B", "C"] self.esb = Point(-73.9847, 40.7484, 30.3244) self.sol = Point(-74.0446, 40.6893, 31.2344) self.landmarks = GeoSeries([self.esb, self.sol], crs="epsg:4326") self.pt2d = Point(-73.9847, 40.7484) self.landmarks_mixed = GeoSeries([self.esb, self.sol, self.pt2d], crs=4326) self.pt_empty = wkt.loads("POINT EMPTY") self.landmarks_mixed_empty = GeoSeries( [self.esb, self.sol, self.pt2d, self.pt_empty], crs=4326 ) self.l1 = LineString([(0, 0), (0, 1), (1, 1)]) self.l2 = LineString([(0, 0), (1, 0), (1, 1), (0, 1)]) self.g5 = GeoSeries([self.l1, self.l2]) self.g6 = GeoSeries([self.p0, self.t3]) self.g7 = GeoSeries([self.sq, self.t4]) self.g8 = GeoSeries([self.t1, self.t5]) self.empty = GeoSeries([]) self.all_none = GeoSeries([None, None]) self.all_geometry_collection_empty = GeoSeries( [GeometryCollection([]), GeometryCollection([])] ) self.empty_poly = Polygon() self.g9 = GeoSeries(self.g0, index=range(1, 8)) self.g10 = GeoSeries([self.t1, self.t4]) # Crossed lines self.l3 = LineString([(0, 0), (1, 1)]) self.l4 = LineString([(0, 1), (1, 0)]) self.crossed_lines = GeoSeries([self.l3, self.l4]) # Placeholder for testing, will just drop in different geometries # when needed self.gdf1 = GeoDataFrame( {"geometry": self.g1, "col0": [1.0, 2.0], "col1": ["geo", "pandas"]} ) self.gdf2 = GeoDataFrame( {"geometry": self.g1, "col3": [4, 5], "col4": ["rand", "string"]} ) self.gdf3 = GeoDataFrame( {"geometry": self.g3, "col3": [4, 5], "col4": ["rand", "string"]} ) self.gdfz = GeoDataFrame( {"geometry": self.gz, "col3": [4, 5, 6], "col4": ["rand", "string", "geo"]} ) self.g11 = GeoSeries( [ self.p0, self.p3d, self.pt_empty, self.t1, self.tz, self.empty_poly, self.l1, ] ) # expected coordinates from g11 self.expected_2d = np.array( [ [5.0, 5.0], [5.0, 5.0], [0.0, 0.0], [1.0, 0.0], [1.0, 1.0], [0.0, 0.0], [1.0, 1.0], [2.0, 2.0], [3.0, 3.0], [1.0, 1.0], [0.0, 0.0], [0.0, 1.0], [1.0, 1.0], ] ) self.expected_3d = np.array( [ [5.0, 5.0, np.nan], [5.0, 5.0, 5.0], [0.0, 0.0, np.nan], [1.0, 0.0, np.nan], [1.0, 1.0, np.nan], [0.0, 0.0, np.nan], [1.0, 1.0, 1.0], [2.0, 2.0, 2.0], [3.0, 3.0, 3.0], [1.0, 1.0, 1.0], [0.0, 0.0, np.nan], [0.0, 1.0, np.nan], [1.0, 1.0, np.nan], ] ) self.squares = GeoSeries([self.sq for _ in range(3)]) self.l5 = LineString([(100, 0), (0, 0), (0, 100)]) self.l6 = LineString([(5, 5), (5, 100), (100, 5)]) self.g12 = GeoSeries([self.l5]) self.g13 = GeoSeries([self.l6]) self.lines = GeoSeries( [ LineString([(0, 0), (1, 1)]), LineString([(0, 0), (0, 1)]), LineString([(0, 1), (1, 1)]), LineString([(1, 1), (1, 0)]), LineString([(1, 0), (0, 0)]), LineString([(5, 5), (6, 6)]), LineString([(0.5, -1), (0.5, 2)]), Point(0, 0), ], crs=4326, index=range(2, 10), ) self.l5 = LineString([(100, 0), (0, 0), (0, 100)]) self.l6 = LineString([(5, 5), (5, 100), (100, 5)]) self.g12 = GeoSeries([self.l5]) self.g13 = GeoSeries([self.l6]) self.g14 = GeoSeries( [ MultiLineString([[(0, 2), (0, 10)], [(0, 10), (5, 10)]]), MultiLineString([[(0, 2), (0, 10)], [(0, 11), (5, 10)]]), MultiLineString(), MultiLineString([[(0, 0), (1, 0)], [(0, 0), (3, 0)]]), Point(0, 0), ], crs=4326, index=range(2, 7), ) def _test_unary_real(self, op, expected, a): """Tests for 'area', 'length', 'is_valid', etc.""" fcmp = assert_series_equal self._test_unary(op, expected, a, fcmp) def _test_unary_topological(self, op, expected, a, method=False): if isinstance(expected, GeoPandasBase): fcmp = assert_geoseries_equal else: def fcmp(a, b): assert a.equals(b) self._test_unary(op, expected, a, fcmp, method=method) def _test_binary_topological(self, op, expected, a, b, *args, **kwargs): """Tests for 'intersection', 'union', 'symmetric_difference', etc.""" if isinstance(expected, GeoPandasBase): fcmp = assert_geoseries_equal else: def fcmp(a, b): assert geom_equals(a, b) if isinstance(b, GeoPandasBase): right_df = True else: right_df = False self._binary_op_test(op, expected, a, b, fcmp, True, right_df, *args, **kwargs) def _test_binary_real(self, op, expected, a, b, *args, **kwargs): fcmp = assert_series_equal self._binary_op_test(op, expected, a, b, fcmp, True, False, *args, **kwargs) def _binary_op_test( self, op, expected, left, right, fcmp, left_df, right_df, *args, **kwargs ): """ This is a helper to call a function on GeoSeries and GeoDataFrame arguments. For example, 'intersection' is a member of both GeoSeries and GeoDataFrame and can take either GeoSeries or GeoDataFrame inputs. This function has the ability to test all four combinations of input types. Parameters ---------- expected : str The operation to be tested. e.g., 'intersection' left: GeoSeries right: GeoSeries fcmp: function Called with the result of the operation and expected. It should assert if the result is incorrect left_df: bool If the left input should also be called with a GeoDataFrame right_df: bool Indicates whether the right input should be called with a GeoDataFrame """ def _make_gdf(s): n = len(s) col1 = string.ascii_lowercase[:n] col2 = range(n) return GeoDataFrame( {"geometry": s.values, "col1": col1, "col2": col2}, index=s.index, crs=s.crs, ) # Test GeoSeries.op(GeoSeries) result = getattr(left, op)(right, *args, **kwargs) fcmp(result, expected) if left_df: # Test GeoDataFrame.op(GeoSeries) gdf_left = _make_gdf(left) result = getattr(gdf_left, op)(right, *args, **kwargs) fcmp(result, expected) if right_df: # Test GeoSeries.op(GeoDataFrame) gdf_right = _make_gdf(right) result = getattr(left, op)(gdf_right, *args, **kwargs) fcmp(result, expected) if left_df: # Test GeoDataFrame.op(GeoDataFrame) result = getattr(gdf_left, op)(gdf_right, *args, **kwargs) fcmp(result, expected) def _test_unary(self, op, expected, a, fcmp, method=False): # GeoSeries, (GeoSeries or geometry) if method: result = getattr(a, op)() else: result = getattr(a, op) fcmp(result, expected) # GeoDataFrame, (GeoSeries or geometry) gdf = self.gdf1.set_geometry(a) if method: result = getattr(gdf, op)() else: result = getattr(gdf, op) fcmp(result, expected) @pytest.mark.skipif(not HAS_PYPROJ, reason="pyproj not available") def test_crs_warning(self): # operations on geometries should warn for different CRS no_crs_g3 = self.g3.copy().set_crs(None, allow_override=True) with pytest.warns(UserWarning): self._test_binary_topological("intersection", self.g3, self.g3, no_crs_g3) def test_alignment_warning(self): with pytest.warns( UserWarning, match="The indices of the left and right GeoSeries' are not equal", ): self.g0.intersection(self.g9, align=None) with warnings.catch_warnings(record=True) as record: self.g0.intersection(self.g9, align=True) self.g0.intersection(self.g9, align=False) assert len(record) == 0 def test_intersection(self): self._test_binary_topological("intersection", self.t1, self.g1, self.g2) self._test_binary_topological( "intersection", self.all_none, self.g1, self.empty, align=True ) assert len(self.g0.intersection(self.g9, align=True)) == 8 assert len(self.g0.intersection(self.g9, align=False)) == 7 @pytest.mark.parametrize( "grid_size, expected", [ ( None, Polygon([(2.2, 2.2), (2.2, 0.8), (0.8, 0.8), (0.8, 2.2), (2.2, 2.2)]), ), (1, Polygon([(2.0, 2.0), (2.0, 1.0), (1.0, 1.0), (1.0, 2.0), (2.0, 2.0)])), ], ) def test_intersection_grid_size(self, grid_size, expected): poly1 = GeoSeries([Polygon([(0, 0), (2.2, 0), (2.2, 2.2), (0, 2.2)])]) poly2 = GeoSeries([Polygon([(0.8, 0.8), (3.2, 0.8), (3.2, 3.2), (0.8, 3.2)])]) self._test_binary_topological( "intersection", expected, poly1, poly2, grid_size=grid_size ) def test_clip_by_rect(self): self._test_binary_topological( "clip_by_rect", self.g1, self.g10, *self.sq.bounds ) # self.g1 and self.t3.bounds do not intersect self._test_binary_topological( "clip_by_rect", self.all_geometry_collection_empty, self.g1, *self.t3.bounds ) def test_union_series(self): self._test_binary_topological("union", self.sq, self.g1, self.g2) assert len(self.g0.union(self.g9, align=True)) == 8 assert len(self.g0.union(self.g9, align=False)) == 7 def test_union_polygon(self): self._test_binary_topological("union", self.sq, self.g1, self.t2) def test_union_polygon_grid_size(self): poly1 = GeoSeries([Polygon([(0, 0), (0, 1.0), (1.8, 1.0), (1.8, 0)])]) poly2 = GeoSeries([Polygon([(2.2, 0), (2.2, 1.0), (3.0, 1.0), (3.0, 0)])]) expected_no_grid_size = MultiPolygon( [ Polygon([(0, 0), (0, 1.0), (1.8, 1.0), (1.8, 0)]), Polygon([(2.2, 0), (2.2, 1.0), (3.0, 1.0), (3.0, 0)]), ] ) expected_grid_size_one = Polygon([(0, 0), (0, 1), (3, 1), (3, 0)]) self._test_binary_topological("union", expected_no_grid_size, poly1, poly2) # Check no difference in polygon coverage assert ( poly1.union(poly2, grid_size=1).difference(expected_grid_size_one)[0] == self.empty_poly ) def test_symmetric_difference_series(self): self._test_binary_topological("symmetric_difference", self.sq, self.g3, self.g4) assert len(self.g0.symmetric_difference(self.g9, align=True)) == 8 assert len(self.g0.symmetric_difference(self.g9, align=False)) == 7 def test_symmetric_difference_poly(self): expected = GeoSeries([GeometryCollection(), self.sq], crs=self.g3.crs) self._test_binary_topological( "symmetric_difference", expected, self.g3, self.t1 ) @pytest.mark.parametrize( "grid_size, expected", [ ( None, MultiPolygon( [ Polygon([(0, 1), (1.2, 1), (1.2, 0), (0, 0), (0, 1)]), Polygon([(1.8, 1), (3, 1), (3, 0), (1.8, 0), (1.8, 1)]), ] ), ), ( 1, MultiPolygon( [ Polygon([(0, 1), (1, 1), (1, 0), (0, 0), (0, 1)]), Polygon([(2, 1), (3, 1), (3, 0), (2, 0), (2, 1)]), ] ), ), ], ) def test_symmetric_difference_poly_grid_size(self, grid_size, expected): poly1 = GeoSeries(Polygon([(0, 0), (0, 1), (1.8, 1.0), (1.8, 0), (0, 0)])) poly2 = GeoSeries(Polygon([(1.2, 0), (1.2, 1), (3.0, 1.0), (3.0, 0), (1.2, 0)])) self._test_binary_topological( "symmetric_difference", expected, poly1, poly2, grid_size=grid_size ) def test_difference_series(self): expected = GeoSeries([GeometryCollection(), self.t2]) self._test_binary_topological("difference", expected, self.g1, self.g2) assert len(self.g0.difference(self.g9, align=True)) == 8 assert len(self.g0.difference(self.g9, align=False)) == 7 def test_difference_poly(self): expected = GeoSeries([self.t1, self.t1]) self._test_binary_topological("difference", expected, self.g1, self.t2) @pytest.mark.parametrize( "grid_size, expected", [ ( None, GeoSeries( [ Polygon([(1, 1), (1, 0), (0, 0), (1, 1)]), Polygon([(1, 1), (1, 0), (0, 0), (0.8, 1.0), (1.0, 1.0)]), ] ), ), ( 1, GeoSeries( [ Polygon([(0, 0), (1, 0), (1, 1)]), Polygon([(0, 0), (1, 0), (1, 1), (0, 1)]), ] ), ), ], ) def test_difference_poly_grid_size(self, grid_size, expected): poly = Polygon([(0, 0), (0.8, 1.0), (0.0, 1.0), (0, 0)]) self._test_binary_topological( "difference", expected, self.g1, poly, grid_size=grid_size ) def test_shortest_line(self): expected = GeoSeries([LineString([(1, 1), (5, 5)]), None]) assert_array_dtype_equal(expected, self.na_none.shortest_line(self.p0)) expected = GeoSeries( [ LineString([(5, 5), (1, 1)]), LineString([(2, 0), (2, 0)]), ] ) assert_array_dtype_equal(expected, self.g6.shortest_line(self.g7)) expected = GeoSeries( [LineString([(0.5, 0.5), (0.5, 0.5)]), LineString([(0.5, 0.5), (0.5, 0.5)])] ) crossed_lines_inv = self.crossed_lines[::-1] assert_array_dtype_equal( expected, self.crossed_lines.shortest_line(crossed_lines_inv, align=False) ) def test_snap(self): expected = GeoSeries([Polygon([(0, 0.5), (1, 0), (1, 1), (0, 0.5)]), None]) assert_array_dtype_equal( expected, self.na_none.snap(Point(0, 0.5), tolerance=1) ) expected = GeoSeries( [ Point((5, 5)), Polygon([(0, 2), (0, 0), (3, 0), (3, 3), (0, 2)]), ] ) assert_array_dtype_equal(expected, self.g6.snap(self.g7, tolerance=3)) def test_geo_op_empty_result(self): l1 = LineString([(0, 0), (1, 1)]) l2 = LineString([(2, 2), (3, 3)]) expected = GeoSeries([GeometryCollection()]) # binary geo resulting in empty geometry result = GeoSeries([l1]).intersection(l2) assert_geoseries_equal(result, expected) # binary geo empty result with right GeoSeries result = GeoSeries([l1]).intersection(GeoSeries([l2])) assert_geoseries_equal(result, expected) # unary geo resulting in empty geometry result = GeoSeries([GeometryCollection()]).convex_hull assert_geoseries_equal(result, expected) def test_boundary(self): l1 = LineString([(0, 0), (1, 0), (1, 1), (0, 0)]) l2 = LineString([(0, 0), (1, 0), (1, 1), (0, 1), (0, 0)]) expected = GeoSeries([l1, l2], index=self.g1.index, crs=self.g1.crs) self._test_unary_topological("boundary", expected, self.g1) def test_area(self): expected = Series(np.array([0.5, 1.0]), index=self.g1.index) self._test_unary_real("area", expected, self.g1) expected = Series(np.array([0.5, np.nan]), index=self.na_none.index) self._test_unary_real("area", expected, self.na_none) @pytest.mark.skipif(not HAS_PYPROJ, reason="pyproj not available") def test_area_crs_warn(self): with pytest.warns(UserWarning, match="Geometry is in a geographic CRS"): self.g4.area def test_bounds(self): # Set columns to get the order right expected = DataFrame( { "minx": [0.0, 0.0], "miny": [0.0, 0.0], "maxx": [1.0, 1.0], "maxy": [1.0, 1.0], }, index=self.g1.index, columns=["minx", "miny", "maxx", "maxy"], ) result = self.g1.bounds assert_frame_equal(expected, result) gdf = self.gdf1.set_geometry(self.g1) result = gdf.bounds assert_frame_equal(expected, result) def test_bounds_empty(self): # test bounds of empty GeoSeries # https://github.com/geopandas/geopandas/issues/1195 s = GeoSeries([]) result = s.bounds expected = DataFrame( columns=["minx", "miny", "maxx", "maxy"], index=s.index, dtype="float64" ) assert_frame_equal(result, expected) def test_union_all(self): p1 = self.t1 p2 = Polygon([(2, 0), (3, 0), (3, 1)]) expected = unary_union([p1, p2]) g = GeoSeries([p1, p2]) self._test_unary_topological("union_all", expected, g, method=True) g2 = GeoSeries([p1, None]) self._test_unary_topological("union_all", p1, g2, method=True) g3 = GeoSeries([None, None]) assert g3.union_all().equals(shapely.GeometryCollection()) assert g.union_all(method="coverage").equals(expected) if GEOS_GE_312: assert g.union_all(method="disjoint_subset").equals(expected) def test_unary_union_deprecated(self): p1 = self.t1 p2 = Polygon([(2, 0), (3, 0), (3, 1)]) g = GeoSeries([p1, p2]) with pytest.warns( DeprecationWarning, match="The 'unary_union' attribute is deprecated" ): result = g.unary_union assert result == g.union_all() def test_intersection_all(self): expected = Polygon([(1, 1), (1, 1.5), (1.5, 1.5), (1.5, 1), (1, 1)]) g = GeoSeries([box(0, 0, 2, 2), box(1, 1, 3, 3), box(0, 0, 1.5, 1.5)]) assert g.intersection_all().equals(expected) g2 = GeoSeries([box(0, 0, 2, 2), None]) assert g2.intersection_all().equals(g2[0]) g3 = GeoSeries([None, None]) assert g3.intersection_all().equals(shapely.GeometryCollection()) def test_contains(self): expected = [True, False, True, False, False, False, False] assert_array_dtype_equal(expected, self.g0.contains(self.t1)) expected = [False, True, True, True, True, True, False, False] assert_array_dtype_equal(expected, self.g0.contains(self.g9, align=True)) expected = [False, False, True, False, False, False, False] assert_array_dtype_equal(expected, self.g0.contains(self.g9, align=False)) def test_contains_properly(self): expected = [False, False, True, False, False, False, False] assert_array_dtype_equal(expected, self.g0.contains_properly(Point(0.25, 0.25))) expected = [False, False, False, False, False, True, False, False] assert_array_dtype_equal( expected, self.g0.contains_properly(self.g9, align=True) ) expected = [False, False, True, False, False, False, False] assert_array_dtype_equal( expected, self.g0.contains_properly(self.g9, align=False) ) @pytest.mark.skipif(shapely.geos_version < (3, 10, 0), reason="requires GEOS>=3.10") def test_dwithin(self): expected = [True, True, True, False, True, True, False] assert_array_dtype_equal(expected, self.g0.dwithin(self.p0, 6)) expected = [False, True, True, True, True, True, False, False] assert_array_dtype_equal(expected, self.g0.dwithin(self.g9, 1, align=True)) expected = [True, True, True, True, False, False, False] assert_array_dtype_equal(expected, self.g0.dwithin(self.g9, 1, align=False)) def test_length(self): expected = Series(np.array([2 + np.sqrt(2), 4]), index=self.g1.index) self._test_unary_real("length", expected, self.g1) expected = Series(np.array([2 + np.sqrt(2), np.nan]), index=self.na_none.index) self._test_unary_real("length", expected, self.na_none) @pytest.mark.skipif(not HAS_PYPROJ, reason="pyproj not available") def test_length_crs_warn(self): with pytest.warns(UserWarning, match="Geometry is in a geographic CRS"): self.g4.length def test_count_coordinates(self): expected = Series(np.array([4, 5]), index=self.g1.index) assert_series_equal(self.g1.count_coordinates(), expected, check_dtype=False) expected = Series(np.array([4, 0]), index=self.na_none.index) assert_series_equal( self.na_none.count_coordinates(), expected, check_dtype=False ) def test_count_geometries(self): expected = Series(np.array([4, 2, 1, 1, 0])) s = GeoSeries( [ MultiPoint([(0, 0), (1, 1), (1, -1), (0, 1)]), MultiLineString([((0, 0), (1, 1)), ((-1, 0), (1, 0))]), LineString([(0, 0), (1, 1), (1, -1)]), Point(0, 0), None, ] ) assert_series_equal(s.count_geometries(), expected, check_dtype=False) def test_count_interior_rings(self): expected = Series(np.array([1, 2, 0, 0])) s = GeoSeries( [ Polygon( [(0, 0), (0, 5), (5, 5), (5, 0)], [[(1, 1), (1, 4), (4, 4), (4, 1)]], ), Polygon( [(0, 0), (0, 5), (5, 5), (5, 0)], [ [(1, 1), (1, 2), (2, 2), (2, 1)], [(3, 2), (3, 3), (4, 3), (4, 2)], ], ), Point(0, 1), None, ] ) assert_series_equal(s.count_interior_rings(), expected, check_dtype=False) def test_crosses(self): expected = [False, False, False, False, False, False, False] assert_array_dtype_equal(expected, self.g0.crosses(self.t1)) expected = [False, True] assert_array_dtype_equal(expected, self.crossed_lines.crosses(self.l3)) expected = [False] * 8 assert_array_dtype_equal(expected, self.g0.crosses(self.g9, align=True)) expected = [False] * 7 assert_array_dtype_equal(expected, self.g0.crosses(self.g9, align=False)) def test_disjoint(self): expected = [False, False, False, False, False, True, False] assert_array_dtype_equal(expected, self.g0.disjoint(self.t1)) expected = [False] * 8 assert_array_dtype_equal(expected, self.g0.disjoint(self.g9, align=True)) expected = [False, False, False, False, True, False, False] assert_array_dtype_equal(expected, self.g0.disjoint(self.g9, align=False)) def test_relate(self): expected = Series( [ "212101212", "212101212", "212FF1FF2", "2FFF1FFF2", "FF2F112F2", "FF0FFF212", None, ], index=self.g0.index, ) assert_series_equal(expected, self.g0.relate(self.inner_sq)) expected = Series(["FF0FFF212", None], index=self.g6.index) assert_series_equal(expected, self.g6.relate(self.na_none)) expected = Series( [ None, "2FFF1FFF2", "2FFF1FFF2", "2FFF1FFF2", "2FFF1FFF2", "0FFFFFFF2", None, None, ], index=range(8), ) assert_series_equal(expected, self.g0.relate(self.g9, align=True)) expected = Series( [ "FF2F11212", "2FF11F212", "212FF1FF2", "FF2F1F212", "FF2FF10F2", None, None, ], index=self.g0.index, ) assert_series_equal(expected, self.g0.relate(self.g9, align=False)) def test_relate_pattern(self): expected = Series([True] * 4 + [False] * 3, index=self.g0.index, dtype=bool) assert_array_dtype_equal( expected, self.g0.relate_pattern(self.inner_sq, "2********") ) expected = Series([True, False], index=self.g6.index, dtype=bool) assert_array_dtype_equal( expected, self.g6.relate_pattern(self.na_none, "FF0******") ) expected = Series( [False] + [True] * 5 + [False, False], index=range(8), dtype=bool ) with pytest.warns(UserWarning, match="The indices of the left and right"): assert_array_dtype_equal( expected, self.g0.relate_pattern(self.g9, "T********", align=None) ) expected = Series( [False] + [True] * 2 + [False] * 4, index=self.g0.index, dtype=bool ) assert_array_dtype_equal( expected, self.g0.relate_pattern(self.g9, "T********", align=False) ) def test_distance(self): expected = Series( np.array([np.sqrt((5 - 1) ** 2 + (5 - 1) ** 2), np.nan]), self.na_none.index ) assert_array_dtype_equal(expected, self.na_none.distance(self.p0)) expected = Series(np.array([np.sqrt(4**2 + 4**2), np.nan]), self.g6.index) assert_array_dtype_equal(expected, self.g6.distance(self.na_none)) expected = Series(np.array([np.nan, 0, 0, 0, 0, 0, np.nan, np.nan]), range(8)) assert_array_dtype_equal(expected, self.g0.distance(self.g9, align=True)) val = self.g0.iloc[4].distance(self.g9.iloc[4]) expected = Series(np.array([0, 0, 0, 0, val, np.nan, np.nan]), self.g0.index) assert_array_dtype_equal(expected, self.g0.distance(self.g9, align=False)) @pytest.mark.skipif(not HAS_PYPROJ, reason="pyproj not available") def test_distance_crs_warning(self): with pytest.warns(UserWarning, match="Geometry is in a geographic CRS"): self.g4.distance(self.p0) def test_hausdorff_distance(self): # closest point is (0, 0) in self.p1 expected = Series(np.array([np.sqrt(5**2 + 5**2), np.nan]), self.na_none.index) assert_array_dtype_equal(expected, self.na_none.hausdorff_distance(self.p0)) expected = Series(np.array([np.sqrt(5**2 + 5**2), np.nan]), self.na_none.index) assert_array_dtype_equal(expected, self.na_none.hausdorff_distance(self.p0)) expected = Series(np.array([np.nan, 0, 0, 0, 0, 0, np.nan, np.nan]), range(8)) assert_array_dtype_equal( expected, self.g0.hausdorff_distance(self.g9, align=True) ) val_1 = self.g0.iloc[0].hausdorff_distance(self.g9.iloc[0]) val_2 = self.g0.iloc[2].hausdorff_distance(self.g9.iloc[2]) val_3 = self.g0.iloc[4].hausdorff_distance(self.g9.iloc[4]) expected = Series( np.array([val_1, val_1, val_2, val_2, val_3, np.nan, np.nan]), self.g0.index ) assert_array_dtype_equal( expected, self.g0.hausdorff_distance(self.g9, align=False) ) expected = Series(np.array([52.5]), self.g12.index) assert_array_dtype_equal( expected, self.g12.hausdorff_distance(self.g13, densify=0.25) ) @pytest.mark.skipif( shapely.geos_version < (3, 10, 0), reason="buggy with GEOS<3.10" ) def test_frechet_distance(self): # closest point is (0, 0) in self.p1 expected = Series(np.array([np.sqrt(5**2 + 5**2), np.nan]), self.na_none.index) assert_array_dtype_equal(expected, self.na_none.frechet_distance(self.p0)) expected = Series(np.array([np.nan, 0, 0, 0, 0, 0, np.nan, np.nan]), range(8)) assert_array_dtype_equal( expected, self.g0.frechet_distance(self.g9, align=True) ) # expected returns val_1 = 1.0 val_2 = np.sqrt(2) / 4 val_3 = np.sqrt(2) / 2 val_4 = (np.sqrt(2) / 2) * 10 expected = Series( np.array([val_1, val_1, val_2, val_3, val_4, np.nan, np.nan]), self.g0.index ) assert_array_dtype_equal( expected, self.g0.frechet_distance(self.g9, align=False) ) expected = Series(np.array([np.sqrt(100**2 + (100 - 5) ** 2)]), self.g12.index) assert_array_dtype_equal( expected, self.g12.frechet_distance(self.g13, densify=0.25) ) def test_intersects(self): expected = [True, True, True, True, True, False, False] assert_array_dtype_equal(expected, self.g0.intersects(self.t1)) expected = [True, False] assert_array_dtype_equal(expected, self.na_none.intersects(self.t2)) expected = np.array([], dtype=bool) assert_array_dtype_equal(expected, self.empty.intersects(self.t1)) expected = np.array([], dtype=bool) assert_array_dtype_equal(expected, self.empty.intersects(self.empty_poly)) expected = [False] * 7 assert_array_dtype_equal(expected, self.g0.intersects(self.empty_poly)) expected = [False, True, True, True, True, True, False, False] assert_array_dtype_equal(expected, self.g0.intersects(self.g9, align=True)) expected = [True, True, True, True, False, False, False] assert_array_dtype_equal(expected, self.g0.intersects(self.g9, align=False)) def test_overlaps(self): expected = [True, True, False, False, False, False, False] assert_array_dtype_equal(expected, self.g0.overlaps(self.inner_sq)) expected = [False, False] assert_array_dtype_equal(expected, self.g4.overlaps(self.t1)) expected = [False] * 8 assert_array_dtype_equal(expected, self.g0.overlaps(self.g9, align=True)) expected = [False] * 7 assert_array_dtype_equal(expected, self.g0.overlaps(self.g9, align=False)) def test_touches(self): expected = [False, True, False, False, False, False, False] assert_array_dtype_equal(expected, self.g0.touches(self.t1)) expected = [False] * 8 assert_array_dtype_equal(expected, self.g0.touches(self.g9, align=True)) expected = [True, False, False, True, False, False, False] assert_array_dtype_equal(expected, self.g0.touches(self.g9, align=False)) def test_within(self): expected = [True, False, False, False, False, False, False] assert_array_dtype_equal(expected, self.g0.within(self.t1)) expected = [True, True, True, True, True, False, False] assert_array_dtype_equal(expected, self.g0.within(self.sq)) expected = [False, True, True, True, True, True, False, False] assert_array_dtype_equal(expected, self.g0.within(self.g9, align=True)) expected = [False, True, False, False, False, False, False] assert_array_dtype_equal(expected, self.g0.within(self.g9, align=False)) def test_covers_itself(self): # Each polygon in a Series covers itself res = self.g1.covers(self.g1) exp = Series([True, True]) assert_series_equal(res, exp) def test_covers(self): res = self.g7.covers(self.g8) exp = Series([True, False]) assert_series_equal(res, exp) expected = [False, True, True, True, True, True, False, False] assert_array_dtype_equal(expected, self.g0.covers(self.g9, align=True)) expected = [False, False, True, False, False, False, False] assert_array_dtype_equal(expected, self.g0.covers(self.g9, align=False)) def test_covers_inverse(self): res = self.g8.covers(self.g7) exp = Series([False, False]) assert_series_equal(res, exp) def test_covered_by(self): res = self.g1.covered_by(self.g1) exp = Series([True, True]) assert_series_equal(res, exp) expected = [False, True, True, True, True, True, False, False] assert_array_dtype_equal(expected, self.g0.covered_by(self.g9, align=True)) expected = [False, True, False, False, False, False, False] assert_array_dtype_equal(expected, self.g0.covered_by(self.g9, align=False)) def test_is_valid(self): expected = Series(np.array([True] * len(self.g1)), self.g1.index) self._test_unary_real("is_valid", expected, self.g1) def test_is_valid_reason(self): expected = Series(np.array(["Valid Geometry"] * len(self.g1)), self.g1.index) assert_series_equal(self.g1.is_valid_reason(), expected) s = GeoSeries( [ Polygon([(0, 0), (1, 1), (1, 0), (0, 1)]), # bowtie geometry Polygon([(0, 0), (1, 1), (1, 1), (0, 1)]), None, ] ) expected = Series(["Self-intersection[0.5 0.5]", "Valid Geometry", None]) assert_series_equal(s.is_valid_reason(), expected) @pytest.mark.skipif(not GEOS_GE_312, reason="GEOS 3.12 and shapely 2.1 needed.") def test_is_valid_coverage(self): s = GeoSeries( [ Polygon([(0, 0), (1, 1), (1, 0), (0, 0)]), Polygon([(0, 0), (1, 1), (0, 1), (0, 0)]), ] ) assert s.is_valid_coverage() s2 = GeoSeries( [ Polygon([(0, 0), (1, 1), (1, 0), (0, 0)]), Polygon([(0, 0), (0.5, 0.5), (1, 1), (0, 1), (0, 0)]), ] ) assert not s2.is_valid_coverage() @pytest.mark.skipif(not GEOS_GE_312, reason="GEOS 3.12 and shapely 2.1 needed.") def test_invalid_coverage_edges(self): s = GeoSeries( [ Polygon([(0, 0), (1, 1), (1, 0), (0, 0)]), Polygon([(0, 0), (0.5, 0.5), (1, 1), (0, 1), (0, 0)]), ] ) expected = GeoSeries( [LineString([(0, 0), (1, 1)]), LineString([(0, 0), (0.5, 0.5), (1, 1)])] ) assert_geoseries_equal(s.invalid_coverage_edges(), expected) def test_is_empty(self): expected = Series(np.array([False] * len(self.g1)), self.g1.index) self._test_unary_real("is_empty", expected, self.g1) def test_is_ring(self): expected = Series(np.array([False] * len(self.g1)), self.g1.index) self._test_unary_real("is_ring", expected, self.g1) expected = Series(np.array([True] * len(self.g1)), self.g1.index) self._test_unary_real("is_ring", expected, self.g1.exterior) def test_is_simple(self): expected = Series(np.array([True] * len(self.g1)), self.g1.index) self._test_unary_real("is_simple", expected, self.g1) def test_is_ccw(self): expected = Series(np.array([False] * len(self.g1)), self.g1.index) self._test_unary_real("is_ccw", expected, self.g1) def test_is_closed(self): expected = Series(np.array([False, False]), self.g5.index) self._test_unary_real("is_closed", expected, self.g5) def test_has_z(self): expected = Series([False, True], self.g_3d.index) self._test_unary_real("has_z", expected, self.g_3d) @pytest.mark.skipif(shapely.geos_version < (3, 12, 0), reason="requires GEOS>=3.12") def test_has_m(self): s = GeoSeries.from_wkt( [ "POINT M (2 3 5)", "POINT Z (1 2 3)", ], ) expected = Series([True, False]) self._test_unary_real("has_m", expected, s) def test_xyz_points(self): expected_x = [-73.9847, -74.0446] expected_y = [40.7484, 40.6893] expected_z = [30.3244, 31.2344] assert_array_dtype_equal(expected_x, self.landmarks.geometry.x) assert_array_dtype_equal(expected_y, self.landmarks.geometry.y) assert_array_dtype_equal(expected_z, self.landmarks.geometry.z) # mixed dimensions expected_z = [30.3244, 31.2344, np.nan] assert_array_dtype_equal(expected_z, self.landmarks_mixed.geometry.z) def test_m_points(self): s = GeoSeries.from_wkt( [ "POINT M (2 3 5)", "POINT M (1 2 3)", "POINT (0 0)", ] ) expected = [5, 3, np.nan] assert_array_dtype_equal(expected, s.m) def test_xyz_points_empty(self): expected_x = [-73.9847, -74.0446, -73.9847, np.nan] expected_y = [40.7484, 40.6893, 40.7484, np.nan] expected_z = [30.3244, 31.2344, np.nan, np.nan] assert_array_dtype_equal(expected_x, self.landmarks_mixed_empty.geometry.x) assert_array_dtype_equal(expected_y, self.landmarks_mixed_empty.geometry.y) assert_array_dtype_equal(expected_z, self.landmarks_mixed_empty.geometry.z) def test_xyz_polygons(self): # accessing x attribute in polygon geoseries should raise an error with pytest.raises(ValueError): _ = self.gdf1.geometry.x # and same for accessing y attribute in polygon geoseries with pytest.raises(ValueError): _ = self.gdf1.geometry.y # and same for accessing z attribute in polygon geoseries with pytest.raises(ValueError): _ = self.gdfz.geometry.z def test_centroid(self): polygon = Polygon([(-1, -1), (1, -1), (1, 1), (-1, 1)]) point = Point(0, 0) polygons = GeoSeries([polygon for i in range(3)]) points = GeoSeries([point for i in range(3)]) assert_geoseries_equal(polygons.centroid, points) @pytest.mark.skipif(not HAS_PYPROJ, reason="pyproj not available") def test_centroid_crs_warn(self): with pytest.warns(UserWarning, match="Geometry is in a geographic CRS"): self.g4.centroid def test_normalize(self): polygon = Polygon([(0, 0), (1, 1), (0, 1)]) linestring = LineString([(0, 0), (1, 1), (1, 0)]) point = Point(0, 0) series = GeoSeries([polygon, linestring, point]) polygon2 = Polygon([(0, 0), (0, 1), (1, 1)]) expected = GeoSeries([polygon2, linestring, point]) assert_geoseries_equal(series.normalize(), expected) def test_orient_polygons(self): polygon = Polygon( [(0, 0), (0, 10), (10, 10), (10, 0), (0, 0)], holes=[[(2, 2), (2, 4), (4, 4), (4, 2), (2, 2)]], ) linestring = LineString([(0, 0), (1, 1), (1, 0)]) point = Point(0, 0) series = GeoSeries([polygon, linestring, point]) polygon2 = Polygon( [(0, 0), (10, 0), (10, 10), (0, 10), (0, 0)], holes=[[(2, 2), (2, 4), (4, 4), (4, 2), (2, 2)]], ) expected = GeoSeries([polygon2, linestring, point]) assert_geoseries_equal(series.orient_polygons(), expected) polygon_cw = Polygon( [(0, 0), (0, 10), (10, 10), (10, 0), (0, 0)], holes=[[(2, 2), (2, 4), (4, 4), (4, 2), (2, 2)]], ) expected = GeoSeries([polygon_cw, linestring, point]) assert_geoseries_equal(series.orient_polygons(exterior_cw=True), expected) def test_make_valid(self): polygon1 = Polygon([(0, 0), (0, 2), (1, 1), (2, 2), (2, 0), (1, 1), (0, 0)]) polygon2 = Polygon([(0, 2), (0, 1), (2, 0), (0, 0), (0, 2)]) linestring = LineString([(0, 0), (1, 1), (1, 0)]) series = GeoSeries([polygon1, polygon2, linestring]) out_polygon1 = MultiPolygon( [ Polygon([(1, 1), (0, 0), (0, 2), (1, 1)]), Polygon([(2, 0), (1, 1), (2, 2), (2, 0)]), ] ) out_polygon2 = GeometryCollection( [Polygon([(2, 0), (0, 0), (0, 1), (2, 0)]), LineString([(0, 2), (0, 1)])] ) expected = GeoSeries([out_polygon1, out_polygon2, linestring]) assert not series.is_valid.all() result = series.make_valid() assert_geoseries_equal(result, expected) assert result.is_valid.all() @pytest.mark.parametrize( "method, keep_collapsed, expected", [ ( "linework", True, MultiLineString([[(0, 0), (1, 1)], [(1, 1), (1, 2)]]), ), ( "structure", True, LineString([(0, 0), (1, 1), (1, 2), (1, 1), (0, 0)]), ), ("structure", False, Polygon()), ], ) def test_make_valid_method(self, method, keep_collapsed, expected): polygon = Polygon([(0, 0), (1, 1), (1, 2), (1, 1), (0, 0)]) series = GeoSeries([polygon]) expected = GeoSeries([expected]) assert not series.is_valid.all() result = series.make_valid(method=method, keep_collapsed=keep_collapsed) assert_geoseries_equal(result, expected, check_geom_type=True) assert result.is_valid.all() def test_reverse(self): expected = GeoSeries( [ LineString([(0, 0), (0, 1), (1, 1)]), LineString([(0, 0), (1, 0), (1, 1), (0, 1)]), ] ) assert_geoseries_equal(expected, self.g5.reverse()) @pytest.mark.skipif(shapely.geos_version < (3, 10, 0), reason="requires GEOS>=3.10") def test_segmentize_linestrings(self): expected_g1 = GeoSeries( [ Polygon( ( (0, 0), (0.5, 0), (1, 0), (1, 0.5), (1, 1), (0.6666666666666666, 0.6666666666666666), (0.3333333333333333, 0.3333333333333333), (0, 0), ) ), Polygon( ( (0, 0), (0.5, 0), (1, 0), (1, 0.5), (1, 1), (0.5, 1), (0, 1), (0, 0.5), (0, 0), ) ), ] ) expected_g5 = GeoSeries( [ LineString([(0, 0), (0, 0.5), (0, 1), (0.5, 1), (1, 1)]), LineString( [(0, 0), (0.5, 0), (1, 0), (1, 0.5), (1, 1), (0.5, 1), (0, 1)] ), ] ) result_g1 = self.g1.segmentize(max_segment_length=0.5) result_g5 = self.g5.segmentize(max_segment_length=0.5) assert_geoseries_equal(expected_g1, result_g1) assert_geoseries_equal(expected_g5, result_g5) def test_segmentize_wrong_index(self): with pytest.raises( ValueError, match="Index of the Series passed as 'max_segment_length' does not match", ): self.g1.segmentize(max_segment_length=Series([0.5, 0.5], index=[99, 98])) def test_transform(self): # Test 2D test_2d = GeoSeries( [LineString([(2, 2), (4, 4)]), Polygon([(0, 0), (1, 1), (0, 1)])] ) expected_2d = GeoSeries( [LineString([(4, 6), (8, 12)]), Polygon([(0, 0), (2, 3), (0, 3)])] ) result_2d = test_2d.transform(lambda x: x * [2, 3]) assert_geoseries_equal(expected_2d, result_2d) # Test 3D test_3d = GeoSeries( [ Point(0, 0, 0), LineString([(2, 2, 2), (4, 4, 4)]), Polygon([(0, 0, 0), (1, 1, 1), (0, 1, 0.5)]), ] ) expected_3d = GeoSeries( [ Point(1, 1, 1), LineString([(3, 3, 3), (5, 5, 5)]), Polygon([(1, 1, 1), (2, 2, 2), (1, 2, 1.5)]), ] ) result_3d = test_3d.transform(lambda x: x + 1, include_z=True) assert_geoseries_equal(expected_3d, result_3d) # Test 3D as 2D transformation expected_3d_to_2d = GeoSeries( [ Point(1, 1), LineString([(3, 3), (5, 5)]), Polygon([(1, 1), (2, 2), (1, 2)]), ] ) result_3d_to_2d = test_3d.transform(lambda x: x + 1, include_z=False) assert_geoseries_equal(expected_3d_to_2d, result_3d_to_2d) @pytest.mark.skipif(shapely.geos_version < (3, 11, 0), reason="requires GEOS>=3.11") def test_concave_hull(self): assert_geoseries_equal(self.squares, self.squares.concave_hull()) @pytest.mark.skipif(shapely.geos_version < (3, 11, 0), reason="requires GEOS>=3.11") @pytest.mark.parametrize( "expected_series,ratio", [ ([(0, 0), (0, 3), (1, 1), (3, 3), (3, 0), (0, 0)], 0.0), ([(0, 0), (0, 3), (3, 3), (3, 0), (0, 0)], 1.0), ], ) def test_concave_hull_accepts_kwargs(self, expected_series, ratio): expected = GeoSeries(Polygon(expected_series)) s = GeoSeries(MultiPoint([(0, 0), (0, 3), (1, 1), (3, 0), (3, 3)])) assert_geoseries_equal(expected, s.concave_hull(ratio=ratio)) def test_concave_hull_wrong_index(self): with pytest.raises( ValueError, match="Index of the Series passed as 'ratio' does not match" ): self.g1.concave_hull(ratio=Series([0.0, 1.0], index=[99, 98])) with pytest.raises( ValueError, match="Index of the Series passed as 'allow_holes' does not match", ): self.g1.concave_hull( ratio=0.1, allow_holes=Series([True, False], index=[99, 98]) ) def test_constrained_delaunay_triangles(self): input = GeoSeries([Polygon([(0, 0), (1, 1), (0, 1)])]) expected = GeoSeries( [GeometryCollection([Polygon([(0, 0), (0, 1), (1, 1), (0, 0)])])] ) assert_geoseries_equal( input.constrained_delaunay_triangles(), expected, check_geom_type=True ) def test_convex_hull(self): # the convex hull of a square should be the same as the square assert_geoseries_equal(self.squares, self.squares.convex_hull) def test_delaunay_triangles(self): expected = GeoSeries( [ Polygon([(0, 1), (0, 0), (1, 0), (0, 1)]), Polygon([(0, 1), (1, 0), (1, 1), (0, 1)]), ] ) dlt = self.g5.delaunay_triangles() assert_geoseries_equal(expected, dlt) def test_delaunay_triangles_pass_kwargs(self): expected = GeoSeries( [ LineString([(0, 1), (1, 1)]), LineString([(0, 0), (0, 1)]), LineString([(0, 0), (1, 0)]), LineString([(1, 0), (1, 1)]), LineString([(0, 1), (1, 0)]), ], ) dlt = self.g5.delaunay_triangles(only_edges=True) assert_geoseries_equal(expected, dlt) def test_voronoi_polygons(self): expected = GeoSeries.from_wkt( [ "POLYGON ((2 2, 2 0.5, 0.5 0.5, 0.5 2, 2 2))", "POLYGON ((-1 2, 0.5 2, 0.5 0.5, -1 0.5, -1 2))", "POLYGON ((-1 -1, -1 0.5, 0.5 0.5, 0.5 -1, -1 -1))", "POLYGON ((2 -1, 0.5 -1, 0.5 0.5, 2 0.5, 2 -1))", ], crs=self.g1.crs, ) vp = self.g1.voronoi_polygons() assert_geoseries_equal(expected, vp) def test_voronoi_polygons_only_edges(self): expected = GeoSeries.from_wkt( [ "LINESTRING (0.5 0.5, 0.5 2)", "LINESTRING (2 0.5, 0.5 0.5)", "LINESTRING (0.5 0.5, -1 0.5)", "LINESTRING (0.5 0.5, 0.5 -1)", ], crs=self.g1.crs, ) vp = self.g1.voronoi_polygons(only_edges=True) assert_geoseries_equal(expected, vp, check_less_precise=True) def test_voronoi_polygons_extend_to(self): expected = GeoSeries.from_wkt( [ "POLYGON ((3 3, 3 0.5, 0.5 0.5, 0.5 3, 3 3))", "POLYGON ((-2 3, 0.5 3, 0.5 0.5, -2 0.5, -2 3))", "POLYGON ((-2 -1, -2 0.5, 0.5 0.5, 0.5 -1, -2 -1))", "POLYGON ((3 -1, 0.5 -1, 0.5 0.5, 3 0.5, 3 -1))", ], crs=self.g1.crs, ) vp = self.g1.voronoi_polygons(extend_to=box(-2, 0, 3, 3)) assert_geoseries_equal(expected, vp) def test_exterior(self): exp_exterior = GeoSeries([LinearRing(p.boundary) for p in self.g3]) for expected, computed in zip(exp_exterior, self.g3.exterior): assert computed.equals(expected) def test_interiors(self): original = GeoSeries([self.t1, self.nested_squares]) # This is a polygon with no interior. expected = [] assert original.interiors[0] == expected # This is a polygon with an interior. expected = LinearRing(self.inner_sq.boundary) assert original.interiors[1][0].equals(expected) no_interiors = GeoSeries([self.t1, self.sq]) assert no_interiors.interiors[0] == [] assert no_interiors.interiors[1] == [] def test_interpolate(self): expected = GeoSeries([Point(0.5, 1.0), Point(0.75, 1.0)]) self._test_binary_topological( "interpolate", expected, self.g5, 0.75, normalized=True ) expected = GeoSeries([Point(0.5, 1.0), Point(1.0, 0.5)]) self._test_binary_topological("interpolate", expected, self.g5, 1.5) def test_interpolate_distance_array(self): expected = GeoSeries([Point(0.0, 0.75), Point(1.0, 0.5)]) self._test_binary_topological( "interpolate", expected, self.g5, np.array([0.75, 1.5]) ) expected = GeoSeries([Point(0.5, 1.0), Point(0.0, 1.0)]) self._test_binary_topological( "interpolate", expected, self.g5, np.array([0.75, 1.5]), normalized=True ) def test_interpolate_distance_wrong_length(self): distances = np.array([1, 2, 3]) with pytest.raises(ValueError): self.g5.interpolate(distances) def test_interpolate_distance_wrong_index(self): distances = Series([1, 2], index=[99, 98]) with pytest.raises( ValueError, match="Index of the Series passed as 'distance' does not match" ): self.g5.interpolate(distances) @pytest.mark.skipif(not HAS_PYPROJ, reason="pyproj not available") def test_interpolate_crs_warning(self): g5_crs = self.g5.copy() g5_crs.crs = 4326 with pytest.warns(UserWarning, match="Geometry is in a geographic CRS"): g5_crs.interpolate(1) def test_project(self): expected = Series([2.0, 1.5], index=self.g5.index) p = Point(1.0, 0.5) self._test_binary_real("project", expected, self.g5, p) expected = Series([1.0, 0.5], index=self.g5.index) self._test_binary_real("project", expected, self.g5, p, normalized=True) s = GeoSeries([Point(2, 2), Point(0.5, 0.5)], index=[1, 2]) expected = Series([np.nan, 2.0, np.nan]) assert_series_equal(self.g5.project(s, align=True), expected) expected = Series([2.0, 0.5], index=self.g5.index) assert_series_equal(self.g5.project(s, align=False), expected) def test_affine_transform(self): # 45 degree reflection matrix matrix = [0, 1, 1, 0, 0, 0] expected = self.g4 res = self.g3.affine_transform(matrix) assert_geoseries_equal(expected, res) def test_translate_tuple(self): trans = self.sol.x - self.esb.x, self.sol.y - self.esb.y assert self.landmarks.translate(*trans)[0].equals(self.sol) res = self.gdf1.set_geometry(self.landmarks).translate(*trans)[0] assert res.equals(self.sol) def test_rotate(self): angle = 98 expected = self.g4 o = Point(0, 0) res = self.g4.rotate(angle, origin=o).rotate(-angle, origin=o) assert geom_almost_equals(self.g4, res) res = self.gdf1.set_geometry(self.g4).rotate(angle, origin=Point(0, 0)) assert geom_almost_equals(expected, res.rotate(-angle, origin=o)) def test_scale(self): expected = self.g4 scale = 2.0, 1.0 inv = tuple(1.0 / i for i in scale) o = Point(0, 0) res = self.g4.scale(*scale, origin=o).scale(*inv, origin=o) assert geom_almost_equals(expected, res) res = self.gdf1.set_geometry(self.g4).scale(*scale, origin=o) res = res.scale(*inv, origin=o) assert geom_almost_equals(expected, res) def test_skew(self): expected = self.g4 skew = 45.0 o = Point(0, 0) # Test xs res = self.g4.skew(xs=skew, origin=o).skew(xs=-skew, origin=o) assert geom_almost_equals(expected, res) res = self.gdf1.set_geometry(self.g4).skew(xs=skew, origin=o) res = res.skew(xs=-skew, origin=o) assert geom_almost_equals(expected, res) # Test ys res = self.g4.skew(ys=skew, origin=o).skew(ys=-skew, origin=o) assert geom_almost_equals(expected, res) res = self.gdf1.set_geometry(self.g4).skew(ys=skew, origin=o) res = res.skew(ys=-skew, origin=o) assert geom_almost_equals(expected, res) def test_buffer(self): original = GeoSeries([Point(0, 0)]) expected = GeoSeries([Polygon(((5, 0), (0, -5), (-5, 0), (0, 5), (5, 0)))]) calculated = original.buffer(5, quad_segs=1) assert geom_almost_equals(expected, calculated) with pytest.warns( DeprecationWarning, match="The `resolution` argument to `buffer` is deprecated", ): calculated = original.buffer(5, resolution=1) assert geom_almost_equals(expected, calculated) def test_buffer_args(self): args = {"cap_style": 3, "join_style": 2, "mitre_limit": 2.5} calculated_series = self.g0.buffer(10, **args) for original, calculated in zip(self.g0, calculated_series): if original is None: assert calculated is None else: expected = original.buffer(10, **args) assert calculated.equals(expected) def test_buffer_distance_array(self): original = GeoSeries([self.p0, self.p0]) expected = GeoSeries( [ Polygon(((6, 5), (5, 4), (4, 5), (5, 6), (6, 5))), Polygon(((10, 5), (5, 0), (0, 5), (5, 10), (10, 5))), ] ) calculated = original.buffer(np.array([1, 5]), quad_segs=1) assert_geoseries_equal(calculated, expected, check_less_precise=True) def test_buffer_distance_wrong_length(self): original = GeoSeries([self.p0, self.p0]) distances = np.array([1, 2, 3]) with pytest.raises(ValueError): original.buffer(distances) def test_buffer_distance_series(self): original = GeoSeries([self.p0, self.p0]) expected = GeoSeries( [ Polygon(((6, 5), (5, 4), (4, 5), (5, 6), (6, 5))), Polygon(((10, 5), (5, 0), (0, 5), (5, 10), (10, 5))), ] ) calculated = original.buffer(Series([1, 5]), quad_segs=1) assert_geoseries_equal(calculated, expected, check_less_precise=True) def test_buffer_distance_wrong_index(self): original = GeoSeries([self.p0, self.p0], index=[0, 1]) distances = Series(data=[1, 2], index=[99, 98]) with pytest.raises( ValueError, match="Index of the Series passed as 'distance' does not match" ): original.buffer(distances) def test_buffer_empty_none(self): p = Polygon([(0, 0), (0, 1), (1, 1), (1, 0)]) s = GeoSeries([p, GeometryCollection(), None]) result = s.buffer(0) assert_geoseries_equal(result, s) result = s.buffer(np.array([0, 0, 0])) assert_geoseries_equal(result, s) @pytest.mark.skipif(not HAS_PYPROJ, reason="pyproj not available") def test_buffer_crs_warn(self): with pytest.warns(UserWarning, match="Geometry is in a geographic CRS"): self.g4.buffer(1) with warnings.catch_warnings(record=True) as record: # do not warn for 0 self.g4.buffer(0) for r in record: assert "Geometry is in a geographic CRS." not in str(r.message) def test_simplify(self): s = GeoSeries([shapely.LineString([(0, 0), (1, 0.1), (2, 0)])]) e = GeoSeries([shapely.LineString([(0, 0), (2, 0)])]) assert_geoseries_equal(s.simplify(0.2), e) def test_simplify_wrong_index(self): with pytest.raises( ValueError, match="Index of the Series passed as 'tolerance' does not match" ): self.g1.simplify(Series([0.1], index=[99])) @pytest.mark.skipif(not GEOS_GE_312, reason="GEOS 3.12 and shapely 2.1 needed.") def test_simplify_coverage(self): s = GeoSeries( [ shapely.Polygon( [(0, 0), (10, 1), (20, 0), (20, 10), (10, 5), (0, 10), (0, 0)] ), shapely.Polygon( [(0, 10), (10, 5), (20, 10), (20, 20), (0, 20), (0, 10)] ), ] ) e = GeoSeries( [ shapely.Polygon([(0, 0), (20, 0), (20, 10), (0, 10)]), shapely.Polygon([(0, 10), (20, 10), (20, 20), (0, 20)]), ] ) assert_geoseries_equal(s.simplify_coverage(8), e.normalize()) e_boundary = GeoSeries( [ shapely.Polygon([(0, 0), (10, 1), (20, 0), (20, 10), (0, 10)]), shapely.Polygon([(0, 10), (20, 10), (20, 20), (0, 20)]), ] ) assert_geoseries_equal( s.simplify_coverage(8, simplify_boundary=False), e_boundary.normalize() ) def test_envelope(self): e = self.g3.envelope assert np.all(e.geom_equals(self.sq)) assert isinstance(e, GeoSeries) assert self.g3.crs == e.crs def test_minimum_rotated_rectangle(self): s = GeoSeries([self.sq, self.t5], crs=3857) r = s.minimum_rotated_rectangle() exp = GeoSeries.from_wkt( [ "POLYGON ((0 0, 0 1, 1 1, 1 0, 0 0))", "POLYGON ((2 0, 2 3, 3 3, 3 0, 2 0))", ], crs=3857, ) assert np.all(r.normalize().geom_equals_exact(exp, 0.001)) assert isinstance(r, GeoSeries) assert s.crs == r.crs def test_extract_unique_points(self): eup = GeoSeries([self.t6]).extract_unique_points() expected = GeoSeries([MultiPoint([(2, 0), (3, 0)])]) assert_series_equal(eup, expected) def test_minimum_bounding_circle(self): mbc = self.g1.minimum_bounding_circle() centers = GeoSeries([Point(0.5, 0.5)] * 2) assert np.all(mbc.centroid.geom_equals_exact(centers, 0.001)) assert_series_equal( mbc.area, Series([1.560723, 1.560723]), ) assert isinstance(mbc, GeoSeries) assert self.g1.crs == mbc.crs def test_maximum_inscribed_circle(self): gs = GeoSeries( [ Polygon([(0, 0), (1, 0), (1, 1), (0, 1)]), Polygon([(0, 0), (0.5, -1), (1, 0), (1, 1), (-0.5, 0.5)]), ] ) mic = gs.maximum_inscribed_circle() assert (mic.geom_type == "LineString").all() assert (shapely.get_num_points(mic) == 2).all() expected_centers = GeoSeries([Point(0.5, 0.5), Point(0.466796875, 0.259765625)]) expected_length = Series([0.5, 0.533203125]) assert_geoseries_equal( shapely.get_point(mic, 0), expected_centers, check_less_precise=True ) assert_series_equal(shapely.length(mic), expected_length) # with tolerance for second polygon -> stops earlier with smaller circle, thus # just assert the length of the resulting line is lower mic_tolerance = gs.maximum_inscribed_circle(tolerance=np.array([0, 10])) assert (shapely.length(mic_tolerance) <= 0.5).all() def test_total_bounds(self): bbox = self.sol.x, self.sol.y, self.esb.x, self.esb.y assert isinstance(self.landmarks.total_bounds, np.ndarray) assert tuple(self.landmarks.total_bounds) == bbox df = GeoDataFrame( {"geometry": self.landmarks, "col1": range(len(self.landmarks))} ) assert tuple(df.total_bounds) == bbox def test_explode_geoseries(self): s = GeoSeries( [MultiPoint([(0, 0), (1, 1)]), MultiPoint([(2, 2), (3, 3), (4, 4)])], crs=4326, ) s.index.name = "test_index_name" expected_index_name = ["test_index_name", None] index = [(0, 0), (0, 1), (1, 0), (1, 1), (1, 2)] expected = GeoSeries( [Point(0, 0), Point(1, 1), Point(2, 2), Point(3, 3), Point(4, 4)], index=MultiIndex.from_tuples(index, names=expected_index_name), crs=4326, ) assert_geoseries_equal(expected, s.explode(index_parts=True)) @pytest.mark.parametrize("index_name", [None, "test"]) def test_explode_geodataframe(self, index_name): s = GeoSeries([MultiPoint([Point(1, 2), Point(2, 3)]), Point(5, 5)]) df = GeoDataFrame({"col": [1, 2], "geometry": s}) df.index.name = index_name test_df = df.explode(index_parts=True) expected_s = GeoSeries([Point(1, 2), Point(2, 3), Point(5, 5)]) expected_df = GeoDataFrame({"col": [1, 1, 2], "geometry": expected_s}) expected_index = MultiIndex( [[0, 1], [0, 1]], # levels [[0, 0, 1], [0, 1, 0]], # labels/codes names=[index_name, None], ) expected_df = expected_df.set_index(expected_index) assert_frame_equal(test_df, expected_df, check_index_type=False) @pytest.mark.parametrize("index_name", [None, "test"]) def test_explode_geodataframe_level_1(self, index_name): # GH1393 s = GeoSeries([MultiPoint([Point(1, 2), Point(2, 3)]), Point(5, 5)]) df = GeoDataFrame({"level_1": [1, 2], "geometry": s}) df.index.name = index_name test_df = df.explode(index_parts=True) expected_s = GeoSeries([Point(1, 2), Point(2, 3), Point(5, 5)]) expected_df = GeoDataFrame({"level_1": [1, 1, 2], "geometry": expected_s}) expected_index = MultiIndex( [[0, 1], [0, 1]], # levels [[0, 0, 1], [0, 1, 0]], # labels/codes names=[index_name, None], ) expected_df = expected_df.set_index(expected_index) assert_frame_equal(test_df, expected_df, check_index_type=False) def test_explode_index_parts_dtype(self, monkeypatch): # GH3865 get_parts returns platform-sized (int32 on 32-bit) indices, # but we want to be consistent in using int64 for the index level get_parts = shapely.get_parts def get_parts_int32(geoms, return_index=False): parts, idx = get_parts(geoms, return_index=True) return parts, idx.astype(np.int32) monkeypatch.setattr(shapely, "get_parts", get_parts_int32) s = GeoSeries([MultiPoint([Point(1, 2), Point(2, 3)]), Point(5, 5)]) result = s.explode(index_parts=True) assert result.index.levels[1].dtype == np.int64 @pytest.mark.parametrize("index_name", [None, "test"]) def test_explode_geodataframe_no_multiindex(self, index_name): # GH1393 s = GeoSeries([MultiPoint([Point(1, 2), Point(2, 3)]), Point(5, 5)]) df = GeoDataFrame({"level_1": [1, 2], "geometry": s}) df.index.name = index_name test_df = df.explode(index_parts=False) expected_s = GeoSeries([Point(1, 2), Point(2, 3), Point(5, 5)]) expected_df = GeoDataFrame({"level_1": [1, 1, 2], "geometry": expected_s}) expected_index = Index([0, 0, 1], name=index_name) expected_df = expected_df.set_index(expected_index) assert_frame_equal(test_df, expected_df) def test_explode_pandas_fallback(self): d = { "col1": [["name1", "name2"], ["name3", "name4"]], "geometry": [MultiPoint([(1, 2), (3, 4)]), MultiPoint([(2, 1), (0, 0)])], } gdf = GeoDataFrame(d, crs=4326) expected_df = GeoDataFrame( { "col1": ["name1", "name2", "name3", "name4"], "geometry": [ MultiPoint([(1, 2), (3, 4)]), MultiPoint([(1, 2), (3, 4)]), MultiPoint([(2, 1), (0, 0)]), MultiPoint([(2, 1), (0, 0)]), ], }, index=[0, 0, 1, 1], crs=4326, ) # Test with column provided as arg exploded_df = gdf.explode("col1") assert_geodataframe_equal(exploded_df, expected_df) # Test with column provided as kwarg exploded_df = gdf.explode(column="col1") assert_geodataframe_equal(exploded_df, expected_df) def test_explode_pandas_fallback_ignore_index(self): d = { "col1": [["name1", "name2"], ["name3", "name4"]], "geometry": [MultiPoint([(1, 2), (3, 4)]), MultiPoint([(2, 1), (0, 0)])], } gdf = GeoDataFrame(d, crs=4326) expected_df = GeoDataFrame( { "col1": ["name1", "name2", "name3", "name4"], "geometry": [ MultiPoint([(1, 2), (3, 4)]), MultiPoint([(1, 2), (3, 4)]), MultiPoint([(2, 1), (0, 0)]), MultiPoint([(2, 1), (0, 0)]), ], }, crs=4326, ) # Test with column provided as arg exploded_df = gdf.explode("col1", ignore_index=True) assert_geodataframe_equal(exploded_df, expected_df) # Test with column provided as kwarg exploded_df = gdf.explode(column="col1", ignore_index=True) assert_geodataframe_equal(exploded_df, expected_df) def test_explode_pandas_fallback_multiple_columns(self): # GH2753: exploding multiple non-geometry columns falls back to pandas gdf = GeoDataFrame( { "col1": [[1, 2, 3], [4, 5], [6]], "col2": [[7, 8, 9], [10, 11], [12]], "geometry": [Point(0, 0), Point(1, 1), Point(2, 2)], } ) expected_df = GeoDataFrame( { "col1": [1, 2, 3, 4, 5, 6], "col2": [7, 8, 9, 10, 11, 12], "geometry": [ Point(0, 0), Point(0, 0), Point(0, 0), Point(1, 1), Point(1, 1), Point(2, 2), ], }, index=[0, 0, 0, 1, 1, 2], ) # exploding object-dtype list columns keeps the object dtype expected_df[["col1", "col2"]] = expected_df[["col1", "col2"]].astype(object) exploded_df = gdf.explode(["col1", "col2"]) assert_geodataframe_equal(exploded_df, expected_df) # A singleton list of a non-geometry column behaves like the scalar form assert_geodataframe_equal(gdf.explode(["col1"]), gdf.explode("col1")) def test_explode_multiple_columns_with_geometry_raises(self): # GH2753: exploding multiple columns including a geometry is unsupported gdf = GeoDataFrame( { "col1": [[1, 2, 3], [4, 5], [6]], "geometry": [ MultiPoint([(1, 2), (3, 4)]), MultiPoint([(2, 1), (0, 0)]), MultiPoint([(5, 5), (6, 6)]), ], } ) with pytest.raises(ValueError, match="geometry column is not supported"): gdf.explode(["col1", "geometry"]) @pytest.mark.parametrize("outer_index", [1, (1, 2), "1"]) def test_explode_pandas_multi_index(self, outer_index): index = MultiIndex.from_arrays( [[outer_index, outer_index, outer_index], [1, 2, 3]], names=("first", "second"), ) df = GeoDataFrame( {"vals": [1, 2, 3]}, geometry=[MultiPoint([(x, x), (x, 0)]) for x in range(3)], index=index, ) test_df = df.explode(index_parts=True) expected_s = GeoSeries( [ Point(0, 0), Point(0, 0), Point(1, 1), Point(1, 0), Point(2, 2), Point(2, 0), ] ) expected_df = GeoDataFrame({"vals": [1, 1, 2, 2, 3, 3], "geometry": expected_s}) expected_index = MultiIndex.from_tuples( [ (outer_index, *pair) for pair in [(1, 0), (1, 1), (2, 0), (2, 1), (3, 0), (3, 1)] ], names=["first", "second", None], ) expected_df = expected_df.set_index(expected_index) assert_frame_equal(test_df, expected_df, check_index_type=False) @pytest.mark.parametrize("outer_index", [1, (1, 2), "1"]) def test_explode_pandas_multi_index_false(self, outer_index): index = MultiIndex.from_arrays( [[outer_index, outer_index, outer_index], [1, 2, 3]], names=("first", "second"), ) df = GeoDataFrame( {"vals": [1, 2, 3]}, geometry=[MultiPoint([(x, x), (x, 0)]) for x in range(3)], index=index, ) test_df = df.explode(index_parts=False) expected_s = GeoSeries( [ Point(0, 0), Point(0, 0), Point(1, 1), Point(1, 0), Point(2, 2), Point(2, 0), ] ) expected_df = GeoDataFrame({"vals": [1, 1, 2, 2, 3, 3], "geometry": expected_s}) expected_index = MultiIndex.from_tuples( [ (outer_index, 1), (outer_index, 1), (outer_index, 2), (outer_index, 2), (outer_index, 3), (outer_index, 3), ], names=["first", "second"], ) expected_df = expected_df.set_index(expected_index) assert_frame_equal(test_df, expected_df) @pytest.mark.parametrize("outer_index", [1, (1, 2), "1"]) def test_explode_pandas_multi_index_ignore_index(self, outer_index): index = MultiIndex.from_arrays( [[outer_index, outer_index, outer_index], [1, 2, 3]], names=("first", "second"), ) df = GeoDataFrame( {"vals": [1, 2, 3]}, geometry=[MultiPoint([(x, x), (x, 0)]) for x in range(3)], index=index, ) test_df = df.explode(ignore_index=True) expected_s = GeoSeries( [ Point(0, 0), Point(0, 0), Point(1, 1), Point(1, 0), Point(2, 2), Point(2, 0), ] ) expected_df = GeoDataFrame({"vals": [1, 1, 2, 2, 3, 3], "geometry": expected_s}) expected_index = Index(range(len(expected_df))) expected_df = expected_df.set_index(expected_index) assert_frame_equal(test_df, expected_df) # index_parts is ignored if ignore_index=True test_df = df.explode(ignore_index=True, index_parts=True) assert_frame_equal(test_df, expected_df) def test_explode_order(self): df = GeoDataFrame( {"vals": [1, 2, 3]}, geometry=[MultiPoint([(x, x), (x, 0)]) for x in range(3)], index=[2, 9, 7], ) test_df = df.explode(index_parts=True) expected_index = MultiIndex.from_arrays( [[2, 2, 9, 9, 7, 7], [0, 1, 0, 1, 0, 1]], ) expected_geometry = GeoSeries( [ Point(0, 0), Point(0, 0), Point(1, 1), Point(1, 0), Point(2, 2), Point(2, 0), ], index=expected_index, ) expected_df = GeoDataFrame( {"vals": [1, 1, 2, 2, 3, 3]}, geometry=expected_geometry, index=expected_index, ) assert_geodataframe_equal(test_df, expected_df, check_index_type=False) def test_explode_order_no_multi(self): df = GeoDataFrame( {"vals": [1, 2, 3]}, geometry=[Point(0, x) for x in range(3)], index=[2, 9, 7], ) test_df = df.explode(index_parts=True) expected_index = MultiIndex.from_arrays( [[2, 9, 7], [0, 0, 0]], ) expected_df = GeoDataFrame( {"vals": [1, 2, 3]}, geometry=[Point(0, x) for x in range(3)], index=expected_index, ) assert_geodataframe_equal(test_df, expected_df, check_index_type=False) def test_explode_order_mixed(self): df = GeoDataFrame( {"vals": [1, 2, 3]}, geometry=[MultiPoint([(x, x), (x, 0)]) for x in range(2)] + [Point(0, 10)], index=[2, 9, 7], ) test_df = df.explode(index_parts=True) expected_index = MultiIndex.from_arrays( [[2, 2, 9, 9, 7], [0, 1, 0, 1, 0]], ) expected_geometry = GeoSeries( [ Point(0, 0), Point(0, 0), Point(1, 1), Point(1, 0), Point(0, 10), ], index=expected_index, ) expected_df = GeoDataFrame( {"vals": [1, 1, 2, 2, 3]}, geometry=expected_geometry, index=expected_index, ) assert_geodataframe_equal(test_df, expected_df, check_index_type=False) def test_explode_duplicated_index(self): df = GeoDataFrame( {"vals": [1, 2, 3]}, geometry=[MultiPoint([(x, x), (x, 0)]) for x in range(3)], index=[1, 1, 2], ) test_df = df.explode(index_parts=True) expected_index = MultiIndex.from_arrays( [[1, 1, 1, 1, 2, 2], [0, 1, 0, 1, 0, 1]], ) expected_geometry = GeoSeries( [ Point(0, 0), Point(0, 0), Point(1, 1), Point(1, 0), Point(2, 2), Point(2, 0), ], index=expected_index, ) expected_df = GeoDataFrame( {"vals": [1, 1, 2, 2, 3, 3]}, geometry=expected_geometry, index=expected_index, ) assert_geodataframe_equal(test_df, expected_df, check_index_type=False) @pytest.mark.parametrize("geom_col", ["geom", "geometry"]) def test_explode_geometry_name(self, geom_col): s = GeoSeries([MultiPoint([Point(1, 2), Point(2, 3)]), Point(5, 5)]) df = GeoDataFrame({"col": [1, 2], geom_col: s}, geometry=geom_col) test_df = df.explode(index_parts=True) assert test_df.geometry.name == geom_col assert test_df.geometry.name == test_df._geometry_column_name def test_explode_geometry_name_two_geoms(self): s = GeoSeries([MultiPoint([Point(1, 2), Point(2, 3)]), Point(5, 5)]) df = GeoDataFrame({"col": [1, 2], "geom": s, "geometry": s}, geometry="geom") test_df = df.explode(index_parts=True) assert test_df.geometry.name == "geom" assert test_df.geometry.name == test_df._geometry_column_name assert "geometry" in test_df.columns def test_get_coordinates(self): expected = DataFrame( data=self.expected_2d, columns=["x", "y"], index=[0, 1, 3, 3, 3, 3, 4, 4, 4, 4, 6, 6, 6], ) assert_frame_equal(self.g11.get_coordinates(), expected) def test_get_coordinates_z(self): expected = DataFrame( data=self.expected_3d, columns=["x", "y", "z"], index=[0, 1, 3, 3, 3, 3, 4, 4, 4, 4, 6, 6, 6], ) assert_frame_equal(self.g11.get_coordinates(include_z=True), expected) def test_get_coordinates_m(self): s = GeoSeries.from_wkt( [ "POINT M (2 3 5)", "POINT ZM (1 2 3 4)", ], ) # only m expected = DataFrame( data=np.array([[2.0, 3.0, 5.0], [1.0, 2.0, 4.0]]), columns=["x", "y", "m"], ) assert_frame_equal(s.get_coordinates(include_m=True), expected) # only z expected = DataFrame( data=np.array([[2.0, 3.0, np.nan], [1.0, 2.0, 3.0]]), columns=["x", "y", "z"], ) assert_frame_equal(s.get_coordinates(include_z=True), expected) # both expected = DataFrame( data=np.array([[2.0, 3.0, np.nan, 5.0], [1.0, 2.0, 3.0, 4.0]]), columns=["x", "y", "z", "m"], ) assert_frame_equal(s.get_coordinates(include_z=True, include_m=True), expected) def test_get_coordinates_ignore(self): expected = DataFrame( data=self.expected_2d, columns=["x", "y"], ) assert_frame_equal(self.g11.get_coordinates(ignore_index=True), expected) def test_get_coordinates_parts(self): expected = DataFrame( data=self.expected_2d, columns=["x", "y"], index=MultiIndex.from_tuples( [ (0, 0), (1, 0), (3, 0), (3, 1), (3, 2), (3, 3), (4, 0), (4, 1), (4, 2), (4, 3), (6, 0), (6, 1), (6, 2), ] ), ) assert_frame_equal( self.g11.get_coordinates(index_parts=True), expected, check_index_type=False, ) def test_minimum_bounding_radius(self): mbr_geoms = self.g1.minimum_bounding_radius() assert_series_equal( mbr_geoms, Series([0.707106, 0.707106]), ) mbr_lines = self.g5.minimum_bounding_radius() assert_series_equal( mbr_lines, Series([0.707106, 0.707106]), ) def test_minimum_clearance(self): mc_geoms = self.g1.minimum_clearance() assert_series_equal( mc_geoms, Series([0.707107, 1.000000]), ) mc_lines = self.g5.minimum_clearance() assert_series_equal( mc_lines, Series([1.0, 1.0]), ) def test_minimum_clearance_line(self): mcl_geoms = self.g1.minimum_clearance_line() expected = GeoSeries( [LineString([(1, 0), (0.5, 0.5)]), LineString([(0, 0), (1, 0)])] ) assert_geoseries_equal(mcl_geoms, expected) @pytest.mark.parametrize("size", [10, 20, 50]) def test_sample_points(self, size): for gs in ( self.g1, self.na, self.a1, self.na_none, ): output = gs.sample_points(size, rng=0) assert_index_equal(gs.index, output.index) assert ( len(output.explode(ignore_index=True)) == len(gs[~(gs.is_empty | gs.isna())]) * size ) x = output.get_coordinates()["x"] assert not x.equals(x.sort_values()) def test_sample_points_array(self): output = concat([self.g1, self.g1]).sample_points([10, 15, 20, 25], rng=0) expected = Series( [10, 15, 20, 25], index=[0, 1, 0, 1], name="sampled_points", dtype="int32" ) assert_series_equal(shapely.get_num_geometries(output), expected) @pytest.mark.parametrize("rng", [None, 1, np.random.default_rng(seed=2)]) @pytest.mark.parametrize("size", [10, 20, 50]) @pytest.mark.parametrize("method", ["cluster_poisson", "cluster_normal"]) def test_sample_points_pointpats(self, method, size, rng): pytest.importorskip("pointpats") for gs in ( self.g1, self.na, self.a1, ): output1 = gs.sample_points(size, method=method, rng=rng) assert_index_equal(gs.index, output1.index) assert ( len(output1.explode(ignore_index=True)) == len(gs[~gs.is_empty]) * size ) if rng is not None: output2 = gs.sample_points(size, method=method, rng=rng) if rng == 1: assert_geoseries_equal(output1, output2) else: with pytest.raises(AssertionError, match="2 out of"): assert_geoseries_equal(output1, output2) x = output1.get_coordinates()["x"] assert not x.equals(x.sort_values()) with pytest.raises( AttributeError, match=re.escape("pointpats.random module has no") ): gs.sample_points(10, method="nonexistent") @pytest.mark.parametrize("rng", [None, 1, np.random.default_rng(seed=2)]) @pytest.mark.parametrize("method", ["cluster_poisson", "cluster_normal"]) def test_sample_points_pointpats_array(self, method, rng): pytest.importorskip("pointpats") output = concat([self.g1, self.g1]).sample_points( [10, 15, 20, 25], method=method, rng=rng ) expected = Series( [10, 15, 20, 25], index=[0, 1, 0, 1], name="sampled_points", dtype="int32" ) assert_series_equal(shapely.get_num_geometries(output), expected) def test_offset_curve(self): oc = GeoSeries([self.l1]).offset_curve(1, join_style="mitre") expected = GeoSeries([LineString([[-1, 0], [-1, 2], [1, 2]])]) assert_geoseries_equal(expected, oc) assert isinstance(oc, GeoSeries) def test_offset_curve_wrong_index(self): with pytest.raises( ValueError, match="Index of the Series passed as 'distance' does not match" ): GeoSeries([self.l1]).offset_curve(Series([1], index=[99])) def test_polygonize(self): expected = GeoSeries.from_wkt( [ "POLYGON ((0 0, 0.5 0.5, 0.5 0, 0 0))", "POLYGON ((0.5 0.5, 0 0, 0 1, 0.5 1, 0.5 0.5))", "POLYGON ((0.5 0.5, 1 1, 1 0, 0.5 0, 0.5 0.5))", "POLYGON ((1 1, 0.5 0.5, 0.5 1, 1 1))", ], name="polygons", crs=4326, ) result = self.lines.polygonize() assert_geoseries_equal(expected, result) assert_index_equal(self.lines.index, Index(range(2, 10))) def test_polygonize_no_node(self): expected = GeoSeries.from_wkt( ["POLYGON ((0 0, 1 1, 1 0, 0 0))", "POLYGON ((1 1, 0 0, 0 1, 1 1))"], name="polygons", crs=4326, ) result = self.lines.polygonize(node=False) assert_geoseries_equal(expected, result) assert_index_equal(self.lines.index, Index(range(2, 10))) def test_polygonize_full(self): expected_poly = GeoSeries.from_wkt( [ "POLYGON ((0 0, 0.5 0.5, 0.5 0, 0 0))", "POLYGON ((0.5 0.5, 0 0, 0 1, 0.5 1, 0.5 0.5))", "POLYGON ((0.5 0.5, 1 1, 1 0, 0.5 0, 0.5 0.5))", "POLYGON ((1 1, 0.5 0.5, 0.5 1, 1 1))", ], name="polygons", crs=4326, ) expected_cuts = GeoSeries([], name="cut edges", crs=4326) expected_dangles = GeoSeries.from_wkt( [ "LINESTRING (5 5, 6 6)", "LINESTRING (0.5 1, 0.5 2)", "LINESTRING (0.5 -1, 0.5 0)", ], name="dangles", crs=4326, ) expected_invalid = GeoSeries([], name="invalid ring lines", crs=4326) result = self.lines.polygonize(full=True) assert_geoseries_equal(expected_poly, result[0]) assert_geoseries_equal(expected_cuts, result[1]) assert_geoseries_equal(expected_dangles, result[2]) assert_geoseries_equal(expected_invalid, result[3]) assert_index_equal(self.lines.index, Index(range(2, 10))) @pytest.mark.skipif(shapely.geos_version < (3, 11, 0), reason="requires GEOS>=3.11") @pytest.mark.parametrize( "geom,expected", [ ( GeoSeries(LinearRing([(0, 0), (1, 2), (1, 2), (1, 3), (0, 0)])), GeoSeries(LinearRing([(0, 0), (1, 2), (1, 3), (0, 0)])), ), ( GeoSeries(Polygon([(0, 0), (0, 0), (1, 0), (1, 1), (1, 0), (0, 0)])), GeoSeries(Polygon([(0, 0), (1, 0), (1, 1), (1, 0), (0, 0)])), ), ], ) def test_remove_repeated_points(self, geom, expected): assert_geoseries_equal(expected, geom.remove_repeated_points(tolerance=0.0)) def test_remove_repeated_points_wrong_index(self): with pytest.raises( ValueError, match="Index of the Series passed as 'tolerance' does not match" ): GeoSeries([self.l1]).remove_repeated_points(Series([1], index=[99])) def test_force_2d(self): expected = GeoSeries( [ Point(-73.9847, 40.7484), Point(-74.0446, 40.6893), self.pt2d, self.pt_empty, ], crs=4326, ) assert_geoseries_equal(expected, self.landmarks_mixed_empty.force_2d()) def test_force_3d(self): expected = GeoSeries( [ self.esb, self.sol, Point(-73.9847, 40.7484, 0), self.pt_empty, ], crs=4326, ) assert_geoseries_equal(expected, self.landmarks_mixed_empty.force_3d()) expected = GeoSeries( [ self.esb, self.sol, Point(-73.9847, 40.7484, 2), self.pt_empty, ], crs=4326, ) assert_geoseries_equal(expected, self.landmarks_mixed_empty.force_3d(2)) expected = GeoSeries( [ Polygon([(0, 0, 1), (1, 0, 1), (1, 1, 1), (0, 0, 1)]), Polygon([(0, 0, 2), (1, 0, 2), (1, 1, 2), (0, 1, 2), (0, 0, 2)]), ], ) assert_geoseries_equal(expected, self.g1.force_3d([1, 2])) def test_shared_paths(self): line = LineString([(0, 0), (0.5, 0.5), (0, 1)]) expected = GeoSeries.from_wkt( [ "GEOMETRYCOLLECTION (MULTILINESTRING ((0 0, 0.5 0.5))," " MULTILINESTRING EMPTY)", "GEOMETRYCOLLECTION (MULTILINESTRING EMPTY," " MULTILINESTRING ((0 1, 0.5 0.5)))", ] ) assert_geoseries_equal(expected, self.crossed_lines.shared_paths(line)) s2 = GeoSeries( [ LineString([(0, 0), (0.5, 0.5), (1, 0), (1, 1), (0.9, 0.9)]), LineString([(1, 1), (0, 1), (1, 0)]), ], index=[1, 2], ) expected = GeoSeries.from_wkt( [ None, "GEOMETRYCOLLECTION (MULTILINESTRING ((0.5 0.5, 1 0))," " MULTILINESTRING EMPTY)", None, ] ) with pytest.warns( UserWarning, match="The indices of the left and right GeoSeries' are not equal", ): assert_geoseries_equal( self.crossed_lines.shared_paths(s2, align=None), expected ) expected = GeoSeries.from_wkt( [ "GEOMETRYCOLLECTION (MULTILINESTRING ((0 0, 0.5 0.5))," " MULTILINESTRING ((0.9 0.9, 1 1)))", "GEOMETRYCOLLECTION (MULTILINESTRING ((0 1, 1 0))," " MULTILINESTRING EMPTY)", ] ) assert_geoseries_equal( self.crossed_lines.shared_paths(s2, align=False), expected ) def test_force_3d_wrong_index(self): with pytest.raises( ValueError, match="Index of the Series passed as 'z' does not match" ): self.g1.force_3d(Series([1], index=[99])) def test_line_merge(self): expected = GeoSeries( [ LineString([(0, 2), (0, 10), (5, 10)]), MultiLineString([[(0, 2), (0, 10)], [(0, 11), (5, 10)]]), GeometryCollection(), LineString([(0, 0), (1, 0), (3, 0)]), GeometryCollection(), ], crs=4326, index=range(2, 7), ) assert_geoseries_equal(expected, self.g14.line_merge()) @pytest.mark.skipif(shapely.geos_version < (3, 11, 0), reason="requires GEOS>=3.11") def test_line_merge_directed(self): expected = GeoSeries( [ LineString([(0, 2), (0, 10), (5, 10)]), MultiLineString([[(0, 2), (0, 10)], [(0, 11), (5, 10)]]), GeometryCollection(), MultiLineString([[(0, 0), (1, 0)], [(0, 0), (3, 0)]]), GeometryCollection(), ], crs=4326, index=range(2, 7), ) assert_geoseries_equal(expected, self.g14.line_merge(directed=True)) @pytest.mark.skipif( shapely.geos_version < (3, 11, 0), reason="different order in GEOS<3.11" ) def test_build_area(self): # test with polygon in it s = GeoSeries.from_wkt( [ "LINESTRING (18 4, 4 2, 2 9)", "LINESTRING (18 4, 16 16)", "LINESTRING (16 16, 8 19, 8 12, 2 9)", "LINESTRING (8 6, 12 13, 15 8)", "LINESTRING (8 6, 15 8)", "LINESTRING (0 0, 0 3, 3 3, 3 0, 0 0)", "POLYGON ((1 1, 2 2, 1 2, 1 1))", "LINESTRING (10 7, 13 8, 12 10, 10 7)", ], crs=4326, ) expected = GeoSeries.from_wkt( [ "POLYGON ((0 3, 3 3, 3 0, 0 0, 0 3), (2 2, 1 2, 1 1, 2 2))", "POLYGON ((13 8, 10 7, 12 10, 13 8))", "POLYGON ((2 9, 8 12, 8 19, 16 16, 18 4, 4 2, 2 9), " "(8 6, 15 8, 12 13, 8 6))", ], crs=4326, name="polygons", ) assert_geoseries_equal(expected, s.build_area()) # test difference caused by nodign s2 = GeoSeries.from_wkt( [ "LINESTRING (8 6, 12 13, 15 8)", "LINESTRING (8 6, 15 8)", "LINESTRING (0 0, 0 15, 12 15, 12 0, 0 0)", "LINESTRING (10 7, 13 8, 12 10, 10 7)", ], crs=4326, ) noded = GeoSeries.from_wkt( ["POLYGON ((12 0, 0 0, 0 15, 12 15, 12 13, 15 8, 12 7.142857, 12 0))"], crs=4326, name="polygons", ) assert_geoseries_equal(noded, s2.build_area(node=True), check_less_precise=True) non_noded = GeoSeries.from_wkt( [ "POLYGON ((0 15, 12 15, 12 13, 15 8, 12 7.142857, 12 0, 0 0, 0 15), " "(12 7.666667, 13 8, 12 10, 12 7.666667))" ], crs=4326, name="polygons", ) assert_geoseries_equal( non_noded, s2.build_area(node=False), check_less_precise=True ) @pytest.mark.skipif( shapely.geos_version < (3, 9, 5), reason="Empty geom bug in GEOS<3.9.5" ) def test_set_precision(self): expected = GeoSeries( [ Point(-74, 41, 30.3244), Point(-74, 41, 31.2344), Point(-74, 41), self.pt_empty, ], crs=4326, ) assert_geoseries_equal(expected, self.landmarks_mixed_empty.set_precision(1)) s = GeoSeries( [ LineString([(0, 0), (0, 0.1), (0, 1), (1, 1)]), LineString([(0, 0), (0, 0.1), (0.1, 0.1)]), ], ) expected = GeoSeries( [ LineString([(0, 0), (0, 1), (1, 1)]), LineString(), ], ) assert_geoseries_equal(expected, s.set_precision(1)) expected = GeoSeries( [ LineString([(0, 0), (0, 0), (0, 1), (1, 1)]), LineString([(0, 0), (0, 0), (0, 0)]), ] ) assert_series_equal( expected.to_wkt(), s.set_precision(1, mode="pointwise").to_wkt() ) expected = GeoSeries( [ LineString([(0, 0), (0, 1), (1, 1)]), LineString([(0, 0), (0, 0)]), ] ) assert_series_equal( expected.to_wkt(), s.set_precision(1, mode="keep_collapsed").to_wkt() ) def test_get_precision(self): expected = Series([0.0, 0.0, 0.0, 0.0], index=self.landmarks_mixed_empty.index) assert_series_equal(expected, self.landmarks_mixed_empty.get_precision()) with_precision = self.landmarks_mixed_empty.set_precision(1) expected = Series([1.0, 1.0, 1.0, 1.0], index=with_precision.index) assert_series_equal(expected, with_precision.get_precision()) mixed = concat([self.landmarks_mixed_empty, with_precision]) expected = Series([0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0], index=mixed.index) assert_series_equal(expected, mixed.get_precision()) def test_get_geometry(self): expected = GeoSeries( [ LineString([(0, 2), (0, 10)]), LineString([(0, 2), (0, 10)]), None, LineString([(0, 0), (1, 0)]), Point(0, 0), ], index=range(2, 7), crs=4326, ) assert_series_equal(expected, self.g14.get_geometry(0)) expected = GeoSeries( [ LineString([(0, 10), (5, 10)]), LineString([(0, 11), (5, 10)]), None, LineString([(0, 0), (3, 0)]), None, ], index=range(2, 7), crs=4326, ) assert_series_equal(expected, self.g14.get_geometry(1)) expected = GeoSeries( [ LineString([(0, 10), (5, 10)]), LineString([(0, 11), (5, 10)]), None, LineString([(0, 0), (3, 0)]), Point(0, 0), ], index=range(2, 7), crs=4326, ) assert_series_equal(expected, self.g14.get_geometry(-1)) expected = GeoSeries( [ LineString([(0, 2), (0, 10)]), LineString([(0, 11), (5, 10)]), None, LineString([(0, 0), (3, 0)]), Point(0, 0), ], index=range(2, 7), crs=4326, ) assert_series_equal(expected, self.g14.get_geometry([0, 1, 1, -1, 0])) def test_make_grid(self): # test make_grid exposed as a method, otherwise in test_make_grid.py expected = GeoSeries([Polygon([(0, 0), (1, 0), (1, 1), (0, 1)])]) assert_geoseries_equal(self.g1.make_grid(1), expected) assert_geoseries_equal(self.gdf1.make_grid(1), expected) geopandas-1.2.0/geopandas/tests/test_geoseries.py000066400000000000000000000716031525650651600221760ustar00rootroot00000000000000import json import os import random import shutil import tempfile import warnings import numpy as np import pandas as pd from shapely.geometry import ( GeometryCollection, LineString, MultiLineString, MultiPoint, MultiPolygon, Point, Polygon, ) from shapely.geometry.base import BaseGeometry import geopandas._compat as compat from geopandas import GeoDataFrame, GeoSeries, clip, read_file from geopandas.array import GeometryArray, GeometryDtype import pytest from geopandas.testing import assert_geoseries_equal, geom_almost_equals from geopandas.tests.util import geom_equals from numpy.testing import assert_array_equal from pandas.testing import assert_index_equal, assert_series_equal class TestSeries: def setup_method(self): self.tempdir = tempfile.mkdtemp() self.t1 = Polygon([(0, 0), (1, 0), (1, 1)]) self.t2 = Polygon([(0, 0), (1, 1), (0, 1)]) self.sq = Polygon([(0, 0), (1, 0), (1, 1), (0, 1)]) self.g1 = GeoSeries([self.t1, self.sq]) self.g2 = GeoSeries([self.sq, self.t1]) self.g3 = GeoSeries([self.t1, self.t2], crs="epsg:4326") self.g4 = GeoSeries([self.t2, self.t1]) self.na = GeoSeries([self.t1, self.t2, Polygon()]) self.na_none = GeoSeries([self.t1, self.t2, None]) self.a1 = self.g1.copy() self.a1.index = ["A", "B"] self.a2 = self.g2.copy() self.a2.index = ["B", "C"] self.esb = Point(-73.9847, 40.7484) self.sol = Point(-74.0446, 40.6893) self.landmarks = GeoSeries([self.esb, self.sol], crs="epsg:4326") self.l1 = LineString([(0, 0), (0, 1), (1, 1)]) self.l2 = LineString([(0, 0), (1, 0), (1, 1), (0, 1)]) self.g5 = GeoSeries([self.l1, self.l2]) self.esb3857 = Point(-8235939.130493107, 4975301.253789809) self.sol3857 = Point(-8242607.167991625, 4966620.938285081) self.landmarks3857 = GeoSeries([self.esb3857, self.sol3857], crs="epsg:3857") def teardown_method(self): shutil.rmtree(self.tempdir) def test_copy(self): gc = self.g3.copy() assert type(gc) is GeoSeries assert self.g3.name == gc.name assert self.g3.crs == gc.crs def test_in(self): assert self.t1 in self.g1 assert self.sq in self.g1 assert self.t1 in self.a1 assert self.t2 in self.g3 assert self.sq not in self.g3 assert 5 not in self.g3 def test_align(self): a1, a2 = self.a1.align(self.a2) assert isinstance(a1, GeoSeries) assert isinstance(a2, GeoSeries) assert a2["A"] is None assert a1["B"].equals(a2["B"]) assert a1["C"] is None @pytest.mark.skipif(not compat.HAS_PYPROJ, reason="pyproj not available") def test_align_crs(self): a1 = self.a1.set_crs("epsg:4326") a2 = self.a2.set_crs("epsg:31370") res1, res2 = a1.align(a2) assert res1.crs == "epsg:4326" assert res2.crs == "epsg:31370" res1, res2 = a1.align(a2.set_crs(None, allow_override=True)) assert res1.crs == "epsg:4326" assert res2.crs is None def test_align_mixed(self): a1 = self.a1 s2 = pd.Series([1, 2], index=["B", "C"]) _res1, res2 = a1.align(s2) exp2 = pd.Series([np.nan, 1, 2], index=["A", "B", "C"]) assert_series_equal(res2, exp2) def test_warning_if_not_aligned(self): # GH-816 # Test that warning is issued when operating on non-aligned series # _series_op with pytest.warns(UserWarning, match="The indices .+ not equal"): self.a1.contains(self.a2) # _geo_op with pytest.warns(UserWarning, match="The indices .+ not equal"): self.a1.union(self.a2) def test_no_warning_if_aligned(self): # GH-816 # Test that warning is not issued when operating on aligned series a1, a2 = self.a1.align(self.a2) with warnings.catch_warnings(record=True) as record: a1.contains(a2) # _series_op, explicitly aligned self.g1.intersects(self.g2) # _series_op, implicitly aligned a2.union(a1) # _geo_op, explicitly aligned self.g2.intersection(self.g1) # _geo_op, implicitly aligned user_warnings = [w for w in record if w.category is UserWarning] assert not user_warnings, user_warnings[0].message def test_geom_equals(self): assert np.all(self.g1.geom_equals(self.g1)) assert_array_equal(self.g1.geom_equals(self.sq), [False, True]) def test_geom_equals_align(self): a = self.a1.geom_equals(self.a2, align=True) exp = pd.Series([False, True, False], index=["A", "B", "C"]) assert_series_equal(a, exp) a = self.a1.geom_equals(self.a2, align=False) exp = pd.Series([False, False], index=["A", "B"]) assert_series_equal(a, exp) def test_geom_equals_exact(self): # TODO: test tolerance parameter assert np.all(self.g1.geom_equals_exact(self.g1, 0.001)) assert_array_equal(self.g1.geom_equals_exact(self.sq, 0.001), [False, True]) with warnings.catch_warnings(): warnings.filterwarnings( "ignore", "The indices of the left and right GeoSeries' are not equal", UserWarning, ) assert_array_equal( self.a1.geom_equals_exact(self.a2, 0.001, align=True), [False, True, False], ) assert_array_equal( self.a1.geom_equals_exact(self.a2, 0.001, align=False), [False, False] ) def test_geom_equals_identical(self): assert np.all(self.g1.geom_equals_identical(self.g1)) assert_array_equal(self.g1.geom_equals_identical(self.sq), [False, True]) assert_array_equal( self.a1.geom_equals_identical(self.a2, align=True), [False, True, False], ) assert_array_equal( self.a1.geom_equals_identical(self.a2, align=False), [False, False] ) def test_equal_comp_op(self): s = GeoSeries([Point(x, x) for x in range(3)]) res = s == Point(1, 1) exp = pd.Series([False, True, False]) assert_series_equal(res, exp) def test_to_file(self): """Test to_file and from_file""" tempfilename = os.path.join(self.tempdir, "test.shp") self.g3.to_file(tempfilename) # Read layer back in? s = GeoSeries.from_file(tempfilename) assert all(self.g3.geom_equals(s)) # TODO: compare crs def test_to_json(self): """ Test whether GeoSeries.to_json works and returns an actual json file. """ json_str = self.g3.to_json() data = json.loads(json_str) assert "id" in data["features"][0].keys() assert "bbox" in data["features"][0].keys() # TODO : verify the output is a valid GeoJSON. def test_to_json_drop_id(self): """ Test whether GeoSeries.to_json works when drop_id is True. """ json_str = self.g3.to_json(drop_id=True) data = json.loads(json_str) assert "id" not in data["features"][0].keys() def test_to_json_no_bbox(self): """ Test whether GeoSeries.to_json works when show_bbox is False. """ json_str = self.g3.to_json(show_bbox=False) data = json.loads(json_str) assert "bbox" not in data["features"][0].keys() def test_to_json_no_bbox_drop_id(self): """ Test whether GeoSeries.to_json works when show_bbox is False and drop_id is True. """ json_str = self.g3.to_json(show_bbox=False, drop_id=True) data = json.loads(json_str) assert "id" not in data["features"][0].keys() assert "bbox" not in data["features"][0].keys() @pytest.mark.skipif(not compat.HAS_PYPROJ, reason="Requires pyproj") def test_to_json_wgs84(self): """ Test whether the wgs84 conversion works as intended. """ text = self.landmarks3857.to_json(to_wgs84=True) data = json.loads(text) assert data["type"] == "FeatureCollection" assert "id" in data["features"][0].keys() coord1 = data["features"][0]["geometry"]["coordinates"] coord2 = data["features"][1]["geometry"]["coordinates"] np.testing.assert_allclose(coord1, self.esb.coords[0]) np.testing.assert_allclose(coord2, self.sol.coords[0]) def test_to_json_wgs84_false(self): """ Ensure no conversion to wgs84 """ text = self.landmarks3857.to_json() data = json.loads(text) coord1 = data["features"][0]["geometry"]["coordinates"] coord2 = data["features"][1]["geometry"]["coordinates"] assert coord1 == [-8235939.130493107, 4975301.253789809] assert coord2 == [-8242607.167991625, 4966620.938285081] def test_representative_point(self): assert np.all(self.g1.contains(self.g1.representative_point())) assert np.all(self.g2.contains(self.g2.representative_point())) assert np.all(self.g3.contains(self.g3.representative_point())) assert np.all(self.g4.contains(self.g4.representative_point())) @pytest.mark.skipif(not compat.HAS_PYPROJ, reason="pyproj not available") def test_transform(self): utm18n = self.landmarks.to_crs(epsg=26918) lonlat = utm18n.to_crs(epsg=4326) assert geom_almost_equals(self.landmarks, lonlat) with pytest.raises(ValueError): self.g1.to_crs(epsg=4326) with pytest.raises(ValueError): self.landmarks.to_crs(crs=None, epsg=None) def test_estimate_utm_crs__geographic(self): pyproj = pytest.importorskip("pyproj") assert self.landmarks.estimate_utm_crs() == pyproj.CRS("EPSG:32618") assert self.landmarks.estimate_utm_crs("NAD83") == pyproj.CRS("EPSG:26918") def test_estimate_utm_crs__projected(self): pyproj = pytest.importorskip("pyproj") assert self.landmarks.to_crs("EPSG:3857").estimate_utm_crs() == pyproj.CRS( "EPSG:32618" ) @pytest.mark.skipif(not compat.HAS_PYPROJ, reason="pyproj not available") def test_estimate_utm_crs__out_of_bounds(self): with pytest.raises(RuntimeError, match="Unable to determine UTM CRS"): GeoSeries( [Polygon([(0, 90), (1, 90), (2, 90)])], crs="EPSG:4326" ).estimate_utm_crs() @pytest.mark.skipif(not compat.HAS_PYPROJ, reason="pyproj not available") def test_estimate_utm_crs__missing_crs(self): with pytest.raises(RuntimeError, match="crs must be set"): GeoSeries([Polygon([(0, 90), (1, 90), (2, 90)])]).estimate_utm_crs() def test_fillna(self): # default is to fill with empty geometry na = self.na_none.fillna() assert isinstance(na[2], BaseGeometry) assert na[2].is_empty assert geom_equals(self.na_none[:2], na[:2]) # XXX: method works inconsistently for different pandas versions # self.na_none.fillna(method='backfill') def test_coord_slice(self): """Test CoordinateSlicer""" # need some better test cases assert geom_equals(self.g3, self.g3.cx[:, :]) assert geom_equals(self.g3[[True, False]], self.g3.cx[0.9:, :0.1]) assert geom_equals(self.g3[[False, True]], self.g3.cx[0:0.1, 0.9:1.0]) def test_coord_slice_with_zero(self): # Test that CoordinateSlice correctly handles zero slice (#GH477). gs = GeoSeries([Point(x, x) for x in range(-3, 4)]) assert geom_equals(gs.cx[:0, :0], gs.loc[:3]) assert geom_equals(gs.cx[:, :0], gs.loc[:3]) assert geom_equals(gs.cx[:0, :], gs.loc[:3]) assert geom_equals(gs.cx[0:, 0:], gs.loc[3:]) assert geom_equals(gs.cx[0:, :], gs.loc[3:]) assert geom_equals(gs.cx[:, 0:], gs.loc[3:]) def test_geoseries_geointerface(self): assert self.g1.__geo_interface__["type"] == "FeatureCollection" assert len(self.g1.__geo_interface__["features"]) == self.g1.shape[0] @pytest.mark.skipif(not compat.HAS_PYPROJ, reason="pyproj not available") def test_proj4strings(self): # As string reprojected = self.g3.to_crs("+proj=utm +zone=30") reprojected_back = reprojected.to_crs(epsg=4326) assert geom_almost_equals(self.g3, reprojected_back) # As dict reprojected = self.g3.to_crs({"proj": "utm", "zone": "30"}) reprojected_back = reprojected.to_crs(epsg=4326) assert geom_almost_equals(self.g3, reprojected_back) # Set to equivalent string, convert, compare to original copy = self.g3.copy().set_crs("epsg:4326", allow_override=True) reprojected = copy.to_crs({"proj": "utm", "zone": "30"}) reprojected_back = reprojected.to_crs(epsg=4326) assert geom_almost_equals(self.g3, reprojected_back) # Conversions by different format reprojected_string = self.g3.to_crs("+proj=utm +zone=30") reprojected_dict = self.g3.to_crs({"proj": "utm", "zone": "30"}) assert geom_almost_equals(reprojected_string, reprojected_dict) def test_from_wkb(self): assert_geoseries_equal(self.g1, GeoSeries.from_wkb([self.t1.wkb, self.sq.wkb])) def test_from_wkb_on_invalid(self): # Single point LineString hex WKB: invalid invalid_wkb_hex = "01020000000100000000000000000008400000000000000840" message = "point array must contain 0 or >1 elements" with pytest.raises(Exception, match=message): GeoSeries.from_wkb([invalid_wkb_hex], on_invalid="raise") with pytest.warns(Warning, match=message): res = GeoSeries.from_wkb([invalid_wkb_hex], on_invalid="warn") assert res[0] is None with warnings.catch_warnings(): warnings.simplefilter("error") res = GeoSeries.from_wkb([invalid_wkb_hex], on_invalid="ignore") assert res[0] is None def test_from_wkb_series(self): s = pd.Series([self.t1.wkb, self.sq.wkb], index=[1, 2]) expected = self.g1.copy() expected.index = pd.Index([1, 2]) assert_geoseries_equal(expected, GeoSeries.from_wkb(s)) def test_from_wkb_series_with_index(self): index = [0] s = pd.Series([self.t1.wkb, self.sq.wkb], index=[0, 2]) expected = self.g1.reindex(index) assert_geoseries_equal(expected, GeoSeries.from_wkb(s, index=index)) def test_from_wkb_with_missing(self): s = pd.Series([self.t1.wkb, None, self.sq.wkb]) result = GeoSeries.from_wkb(s) expected = GeoSeries([self.t1, None, self.sq]) assert_geoseries_equal(result, expected) def test_from_wkt(self): assert_geoseries_equal(self.g1, GeoSeries.from_wkt([self.t1.wkt, self.sq.wkt])) def test_from_wkt_on_invalid(self): # Single point LineString WKT: invalid invalid_wkt = "LINESTRING(0 0)" message = "point array must contain 0 or >1 elements" with pytest.raises(Exception, match=message): GeoSeries.from_wkt([invalid_wkt], on_invalid="raise") with pytest.warns(Warning, match=message): res = GeoSeries.from_wkt([invalid_wkt], on_invalid="warn") assert res[0] is None with warnings.catch_warnings(): warnings.simplefilter("error") res = GeoSeries.from_wkt([invalid_wkt], on_invalid="ignore") assert res[0] is None def test_from_wkt_series(self): s = pd.Series([self.t1.wkt, self.sq.wkt], index=[1, 2]) expected = self.g1.copy() expected.index = pd.Index([1, 2]) assert_geoseries_equal(expected, GeoSeries.from_wkt(s)) def test_from_wkt_series_with_index(self): index = [0] s = pd.Series([self.t1.wkt, self.sq.wkt], index=[0, 2]) expected = self.g1.reindex(index) assert_geoseries_equal(expected, GeoSeries.from_wkt(s, index=index)) def test_from_wkt_with_missing(self): s = pd.Series([self.t1.wkt, None, self.sq.wkt]) result = GeoSeries.from_wkt(s) expected = GeoSeries([self.t1, None, self.sq]) assert_geoseries_equal(result, expected) @pytest.mark.parametrize("missing_value", [None, np.nan, pd.NA]) def test_from_wkt_with_missing_object(self, missing_value): s = pd.Series([self.t1.wkt, missing_value, self.sq.wkt], dtype="object") result = GeoSeries.from_wkt(s) expected = GeoSeries([self.t1, None, self.sq]) assert_geoseries_equal(result, expected) def test_to_wkb(self): assert_series_equal(pd.Series([self.t1.wkb, self.sq.wkb]), self.g1.to_wkb()) assert_series_equal( pd.Series([self.t1.wkb_hex, self.sq.wkb_hex]), self.g1.to_wkb(hex=True) ) def test_to_wkt(self): assert_series_equal(pd.Series([self.t1.wkt, self.sq.wkt]), self.g1.to_wkt()) def test_clip(self, naturalearth_lowres, naturalearth_cities): left = read_file(naturalearth_cities) world = read_file(naturalearth_lowres) south_america = world[world["continent"] == "South America"] expected = clip(left.geometry, south_america) result = left.geometry.clip(south_america) assert_geoseries_equal(result, expected) def test_clip_sorting(self, naturalearth_cities, naturalearth_lowres): """ Test sorting of geodseries when clipping. """ cities = read_file(naturalearth_cities) world = read_file(naturalearth_lowres) south_america = world[world["continent"] == "South America"] unsorted_clipped_cities = clip(cities, south_america, sort=False) sorted_clipped_cities = clip(cities, south_america, sort=True) expected_sorted_index = pd.Index( [55, 59, 62, 88, 101, 114, 122, 169, 181, 189, 210, 230, 236, 238, 239] ) assert not ( sorted(unsorted_clipped_cities.index) == unsorted_clipped_cities.index ).all() assert ( sorted(sorted_clipped_cities.index) == sorted_clipped_cities.index ).all() assert_index_equal(expected_sorted_index, sorted_clipped_cities.index) def test_from_xy_points(self): x = self.landmarks.x.values y = self.landmarks.y.values index = self.landmarks.index.tolist() crs = self.landmarks.crs assert_geoseries_equal( self.landmarks, GeoSeries.from_xy(x, y, index=index, crs=crs) ) assert_geoseries_equal( self.landmarks, GeoSeries.from_xy(self.landmarks.x, self.landmarks.y, crs=crs), ) def test_from_xy_points_w_z(self): index_values = [5, 6, 7] x = pd.Series([0, -1, 2], index=index_values) y = pd.Series([8, 3, 1], index=index_values) z = pd.Series([5, -6, 7], index=index_values) expected = GeoSeries( [Point(0, 8, 5), Point(-1, 3, -6), Point(2, 1, 7)], index=index_values ) assert_geoseries_equal(expected, GeoSeries.from_xy(x, y, z)) def test_from_xy_points_unequal_index(self): x = self.landmarks.x y = self.landmarks.y y.index = -np.arange(len(y)) crs = self.landmarks.crs assert_geoseries_equal( self.landmarks, GeoSeries.from_xy(x, y, index=x.index, crs=crs) ) unindexed_landmarks = self.landmarks.copy() unindexed_landmarks = unindexed_landmarks.reset_index(drop=True) assert_geoseries_equal( unindexed_landmarks, GeoSeries.from_xy(x, y, crs=crs), ) def test_from_xy_points_indexless(self): x = np.array([0.0, 3.0]) y = np.array([2.0, 5.0]) z = np.array([-1.0, 4.0]) expected = GeoSeries([Point(0, 2, -1), Point(3, 5, 4)]) assert_geoseries_equal(expected, GeoSeries.from_xy(x, y, z)) @pytest.mark.skipif(compat.HAS_PYPROJ, reason="pyproj installed") def test_set_crs_pyproj_error(self): with pytest.raises( ImportError, match="The 'pyproj' package is required for set_crs" ): self.g1.set_crs(3857) @pytest.mark.filterwarnings("ignore::UserWarning") def test_missing_values(): s = GeoSeries([Point(1, 1), None, np.nan, GeometryCollection(), Polygon()]) # construction -> missing values get normalized to None assert s[1] is None assert s[2] is None assert s[3].is_empty assert s[4].is_empty # isna / is_empty assert s.isna().tolist() == [False, True, True, False, False] assert s.is_empty.tolist() == [False, False, False, True, True] assert s.notna().tolist() == [True, False, False, True, True] # fillna defaults to fill with empty geometry -> no missing values anymore assert not s.fillna().isna().any() # dropna drops the missing values assert not s.dropna().isna().any() assert len(s.dropna()) == 3 def test_isna_empty_geoseries(): # ensure that isna() result for empty GeoSeries has the correct bool dtype s = GeoSeries([]) result = s.isna() assert_series_equal(result, pd.Series([], dtype="bool")) @pytest.mark.skipif(not compat.HAS_PYPROJ, reason="pyproj not available") def test_geoseries_crs(): gs = GeoSeries().set_crs("IGNF:ETRS89UTM28") assert gs.crs.to_authority() == ("IGNF", "ETRS89UTM28") @pytest.mark.skipif(not compat.HAS_PYPROJ, reason="Requires pyproj") def test_geoseries_override_existing_crs_warning(): gs = GeoSeries(crs="epsg:4326") with pytest.warns( FutureWarning, match="Overriding the CRS of a GeoSeries that already has CRS", ): gs.crs = "epsg:2100" # ----------------------------------------------------------------------------- # # Constructor tests # ----------------------------------------------------------------------------- def check_geoseries(s): assert isinstance(s, GeoSeries) assert isinstance(s.geometry, GeoSeries) assert isinstance(s.dtype, GeometryDtype) assert isinstance(s.values, GeometryArray) class TestConstructor: def test_constructor(self): s = GeoSeries([Point(x, x) for x in range(3)]) check_geoseries(s) def test_single_geom_constructor(self): p = Point(1, 2) line = LineString([(2, 3), (4, 5), (5, 6)]) poly = Polygon( [(0, 0), (1, 0), (1, 1), (0, 1)], [[(0.1, 0.1), (0.9, 0.1), (0.9, 0.9)]] ) mp = MultiPoint([(1, 2), (3, 4), (5, 6)]) mline = MultiLineString([[(1, 2), (3, 4), (5, 6)], [(7, 8), (9, 10)]]) poly2 = Polygon( [(0, 0), (0, -1), (-1, -1), (-1, 0)], [[(-0.1, -0.1), (-0.1, -0.5), (-0.5, -0.5), (-0.5, -0.1)]], ) mpoly = MultiPolygon([poly, poly2]) geoms = [p, line, poly, mp, mline, mpoly] index = ["a", "b", "c", "d"] for g in geoms: gs = GeoSeries(g) assert len(gs) == 1 # accessing elements no longer give identical objects assert gs.iloc[0].equals(g) gs = GeoSeries(g, index=index) assert len(gs) == len(index) for x in gs: assert x.equals(g) def test_non_geometry_raises(self): with pytest.raises(TypeError, match="Non geometry data passed to GeoSeries"): GeoSeries([True, False, True]) with pytest.raises(TypeError, match="Non geometry data passed to GeoSeries"): GeoSeries(["a", "b", "c"]) with pytest.raises(TypeError, match="Non geometry data passed to GeoSeries"): GeoSeries([[1, 2], [3, 4]]) def test_empty(self): s = GeoSeries([]) check_geoseries(s) s = GeoSeries() check_geoseries(s) def test_data_is_none(self): s = GeoSeries(index=range(3)) check_geoseries(s) def test_empty_array(self): # with empty data that have an explicit dtype, we use the fallback or # not depending on the dtype # dtypes that can never hold geometry-like data non_geom_compat_dtypes = [ np.array([], dtype="bool"), np.array([], dtype="int64"), np.array([], dtype="float32"), ] # dtypes that can potentially hold geometry-like data (object) or # can come from empty data (float64) geom_compat_dtypes = [ np.array([], dtype="object"), np.array([], dtype="float64"), ] if compat.PANDAS_INFER_STR: # in pandas >=3 future string, str is not converted to object # so is non geom compatible non_geom_compat_dtypes.append(np.array([], dtype="str")) else: geom_compat_dtypes.append(np.array([], dtype="str")) for arr in non_geom_compat_dtypes: with pytest.raises( TypeError, match="Non geometry data passed to GeoSeries" ): GeoSeries(arr) for arr in geom_compat_dtypes: with warnings.catch_warnings(record=True) as record: s = GeoSeries(arr) assert not record assert isinstance(s, GeoSeries) def test_from_series(self): shapes = [ Polygon([(random.random(), random.random()) for _ in range(3)]) for _ in range(10) ] s = pd.Series(shapes, index=list("abcdefghij"), name="foo") g = GeoSeries(s) check_geoseries(g) assert [a.equals(b) for a, b in zip(s, g)] assert s.name == g.name assert s.index is g.index @pytest.mark.skipif(not compat.HAS_PYPROJ, reason="pyproj not available") def test_from_series_no_set_crs_on_construction(self): # https://github.com/geopandas/geopandas/issues/2492 # also when passing Series[geometry], ensure we don't change crs of # original data gs = GeoSeries([Point(1, 1), Point(2, 2), Point(3, 3)]) s = pd.Series(gs) result = GeoSeries(s, crs=4326) assert s.values.crs is None assert gs.crs is None assert result.crs == "EPSG:4326" # https://github.com/geopandas/geopandas/issues/3382 s2 = pd.Series(gs.set_crs("EPSG:4326")) result = GeoSeries(s2, crs=4326) assert result.crs == "EPSG:4326" with pytest.raises( ValueError, match="CRS mismatch between CRS of the passed geometries and 'crs'", ): GeoSeries(s2, crs=4283) def test_copy(self): # default is to copy with CoW / pandas 3+ arr = np.array([Point(x, x) for x in range(3)], dtype=object) result = GeoSeries(arr) # modifying result doesn't change original array result.loc[0] = Point(10, 10) if compat.PANDAS_GE_30 or getattr(pd.options.mode, "copy_on_write", False): assert arr[0] == Point(0, 0) else: assert arr[0] == Point(10, 10) # avoid copy with copy=False arr = np.array([Point(x, x) for x in range(3)], dtype=object) result = GeoSeries(arr, copy=False) assert result.array._data.flags.writeable # now modifying result also updates original array result.loc[0] = Point(10, 10) assert arr[0] == Point(10, 10) # GH 1216 @pytest.mark.parametrize("name", [None, "geometry", "Points"]) @pytest.mark.parametrize("crs", [None, "epsg:4326"]) def test_reset_index(self, name, crs): s = GeoSeries( [MultiPoint([(0, 0), (1, 1)]), MultiPoint([(2, 2), (3, 3), (4, 4)])], name=name, crs=crs, ) s = s.explode(index_parts=True) df = s.reset_index() assert type(df) is GeoDataFrame # name None -> 0, otherwise name preserved assert df.geometry.name == (name if name is not None else 0) assert df.crs == s.crs @pytest.mark.parametrize("name", [None, "geometry", "Points"]) @pytest.mark.parametrize("crs", [None, "epsg:4326"]) def test_to_frame(self, name, crs): s = GeoSeries([Point(0, 0), Point(1, 1)], name=name, crs=crs) df = s.to_frame() assert type(df) is GeoDataFrame # name None -> 0, otherwise name preserved expected_name = name if name is not None else 0 assert df.geometry.name == expected_name assert df._geometry_column_name == expected_name assert df.crs == s.crs # if name is provided to to_frame, it should override df2 = s.to_frame(name="geom") assert type(df) is GeoDataFrame assert df2.geometry.name == "geom" assert df2.crs == s.crs def test_explode_without_multiindex(self): s = GeoSeries( [MultiPoint([(0, 0), (1, 1)]), MultiPoint([(2, 2), (3, 3), (4, 4)])] ) s = s.explode(index_parts=False) expected_index = pd.Index([0, 0, 1, 1, 1]) assert_index_equal(s.index, expected_index) def test_explode_ignore_index(self): s = GeoSeries( [MultiPoint([(0, 0), (1, 1)]), MultiPoint([(2, 2), (3, 3), (4, 4)])] ) s = s.explode(ignore_index=True) expected_index = pd.Index(range(len(s))) assert_index_equal(s.index, expected_index) # index_parts is ignored if ignore_index=True s = s.explode(index_parts=True, ignore_index=True) assert_index_equal(s.index, expected_index) geopandas-1.2.0/geopandas/tests/test_merge.py000066400000000000000000000227161525650651600213110ustar00rootroot00000000000000import warnings import pandas as pd from shapely.geometry import Point from geopandas import GeoDataFrame, GeoSeries from geopandas._compat import HAS_PYPROJ import pytest from geopandas.testing import assert_geodataframe_equal from pandas.testing import assert_index_equal class TestMerging: def setup_method(self): self.gseries = GeoSeries([Point(i, i) for i in range(3)]) self.series = pd.Series([1, 2, 3]) self.gdf = GeoDataFrame({"geometry": self.gseries, "values": range(3)}) self.df = pd.DataFrame({"col1": [1, 2, 3], "col2": [0.1, 0.2, 0.3]}) def _check_metadata(self, gdf, geometry_column_name="geometry", crs=None): assert gdf._geometry_column_name == geometry_column_name assert gdf.crs == crs def test_merge(self): res = self.gdf.merge(self.df, left_on="values", right_on="col1") # check result is a GeoDataFrame assert isinstance(res, GeoDataFrame) # check geometry property gives GeoSeries assert isinstance(res.geometry, GeoSeries) # check metadata self._check_metadata(res) # test that crs and other geometry name are preserved self.gdf.crs = "epsg:4326" self.gdf = self.gdf.rename(columns={"geometry": "points"}).set_geometry( "points" ) res = self.gdf.merge(self.df, left_on="values", right_on="col1") assert isinstance(res, GeoDataFrame) assert isinstance(res.geometry, GeoSeries) self._check_metadata(res, "points", self.gdf.crs) def test_concat_axis0(self): # frame res = pd.concat([self.gdf, self.gdf]) assert res.shape == (6, 2) assert isinstance(res, GeoDataFrame) assert isinstance(res.geometry, GeoSeries) self._check_metadata(res) exp = GeoDataFrame(pd.concat([pd.DataFrame(self.gdf), pd.DataFrame(self.gdf)])) assert_geodataframe_equal(exp, res) # series res = pd.concat([self.gdf.geometry, self.gdf.geometry]) assert res.shape == (6,) assert isinstance(res, GeoSeries) assert isinstance(res.geometry, GeoSeries) @pytest.mark.skipif(not HAS_PYPROJ, reason="pyproj not available") def test_concat_axis0_crs(self): # CRS not set for both GeoDataFrame res = pd.concat([self.gdf, self.gdf]) self._check_metadata(res) # CRS set for both GeoDataFrame, same CRS res1 = pd.concat([self.gdf.set_crs("epsg:4326"), self.gdf.set_crs("epsg:4326")]) self._check_metadata(res1, crs="epsg:4326") # CRS not set for one GeoDataFrame, but set for the other GeoDataFrame with pytest.warns( UserWarning, match=r"CRS not set for some of the concatenation inputs.*" ): res2 = pd.concat([self.gdf, self.gdf.set_crs("epsg:4326")]) self._check_metadata(res2, crs="epsg:4326") # CRS set for both GeoDataFrame, different CRS with pytest.raises( ValueError, match=r"Cannot determine common CRS for concatenation inputs.*" ): pd.concat([self.gdf.set_crs("epsg:4326"), self.gdf.set_crs("epsg:4327")]) # CRS not set for one GeoDataFrame, but set for the other GeoDataFrames, # same CRS with pytest.warns( UserWarning, match=r"CRS not set for some of the concatenation inputs.*" ): res3 = pd.concat( [self.gdf, self.gdf.set_crs("epsg:4326"), self.gdf.set_crs("epsg:4326")] ) self._check_metadata(res3, crs="epsg:4326") # CRS not set for one GeoDataFrame, but set for the other GeoDataFrames, # different CRS with pytest.raises( ValueError, match=r"Cannot determine common CRS for concatenation inputs.*" ): pd.concat( [self.gdf, self.gdf.set_crs("epsg:4326"), self.gdf.set_crs("epsg:4327")] ) @pytest.mark.skipif(not HAS_PYPROJ, reason="pyproj not available") def test_concat_axis0_unaligned_cols(self): # https://github.com/geopandas/geopandas/issues/2679 gdf = self.gdf.set_crs("epsg:4326").assign( geom=self.gdf.geometry.set_crs("epsg:4327") ) both_geom_cols = gdf[["geom", "geometry"]] single_geom_col = gdf[["geometry"]] with warnings.catch_warnings(): warnings.simplefilter("error") pd.concat([both_geom_cols, single_geom_col]) # Check order of mismatch doesn't matter with warnings.catch_warnings(): warnings.simplefilter("error") pd.concat([single_geom_col, both_geom_cols]) # Side effect of this fix, explicitly provided all none geoseries # will not be warned for (ideally this would still warn) explicit_all_none_case = gdf[["geometry"]].assign( geom=GeoSeries([None for _ in range(len(gdf))]) ) with warnings.catch_warnings(): warnings.simplefilter("error") pd.concat([both_geom_cols, explicit_all_none_case]) # Check concat with partially None col is not affected by the special casing # for all None no CRS handling with pytest.warns( UserWarning, match=r"CRS not set for some of the concatenation inputs.*" ): partial_none_case = self.gdf[["geometry"]].copy() partial_none_case.iloc[0] = None pd.concat([single_geom_col, partial_none_case]) def test_concat_axis0_crs_wkt_mismatch(self): pyproj = pytest.importorskip("pyproj") # https://github.com/geopandas/geopandas/issues/326#issuecomment-1727958475 wkt_template = """GEOGCRS["WGS 84", ENSEMBLE["World Geodetic System 1984 ensemble", MEMBER["World Geodetic System 1984 (Transit)"], MEMBER["World Geodetic System 1984 (G730)"], MEMBER["World Geodetic System 1984 (G873)"], MEMBER["World Geodetic System 1984 (G1150)"], MEMBER["World Geodetic System 1984 (G1674)"], MEMBER["World Geodetic System 1984 (G1762)"], MEMBER["World Geodetic System 1984 (G2139)"], ELLIPSOID["WGS 84",6378137,298.257223563,LENGTHUNIT["metre",1]], ENSEMBLEACCURACY[2.0]],PRIMEM["Greenwich",0, ANGLEUNIT["degree",0.0174532925199433]],CS[ellipsoidal,2], AXIS["geodetic latitude (Lat)",north,ORDER[1], ANGLEUNIT["degree",0.0174532925199433]], AXIS["geodetic longitude (Lon)",east,ORDER[2], ANGLEUNIT["degree",0.0174532925199433]], USAGE[SCOPE["Horizontal component of 3D system."], AREA["World.{}"],BBOX[-90,-180,90,180]],ID["EPSG",4326]]""" wkt_v1 = wkt_template.format("") wkt_v2 = wkt_template.format(" ") # add additional whitespace crs1 = pyproj.CRS.from_wkt(wkt_v1) crs2 = pyproj.CRS.from_wkt(wkt_v2) # pyproj crs __hash__ based on WKT strings means these are distinct in a # set are but equal by equality assert len({crs1, crs2}) == 2 assert crs1 == crs2 expected = pd.concat([self.gdf, self.gdf]).set_crs(crs1) res = pd.concat([self.gdf.set_crs(crs1), self.gdf.set_crs(crs2)]) assert_geodataframe_equal(expected, res) def test_concat_axis1(self): res = pd.concat([self.gdf, self.df], axis=1) assert res.shape == (3, 4) assert isinstance(res, GeoDataFrame) assert isinstance(res.geometry, GeoSeries) self._check_metadata(res) def test_concat_axis1_multiple_geodataframes(self): # https://github.com/geopandas/geopandas/issues/1230 # Expect that concat should fail gracefully if duplicate column names belonging # to geometry columns are introduced. # _constructor_from_mgr changes mean we now get the concat specific error # message in this case too expected_err = ( "Concat operation has resulted in multiple columns using the geometry " "column name 'geometry'." ) with pytest.raises(ValueError, match=expected_err): pd.concat([self.gdf, self.gdf], axis=1) # Check case is handled if custom geometry column name is used df2 = self.gdf.rename_geometry("geom") expected_err2 = ( "Concat operation has resulted in multiple columns using the geometry " "column name 'geom'." ) with pytest.raises(ValueError, match=expected_err2): pd.concat([df2, df2], axis=1) if HAS_PYPROJ: # Check that two geometry columns is fine, if they have different names res3 = pd.concat([df2.set_crs("epsg:4326"), self.gdf], axis=1) # check metadata comes from first df self._check_metadata(res3, geometry_column_name="geom", crs="epsg:4326") @pytest.mark.filterwarnings("ignore:Accessing CRS") def test_concat_axis1_geoseries(self): gseries2 = GeoSeries([Point(i, i) for i in range(3, 6)], crs="epsg:4326") result = pd.concat([gseries2, self.gseries], axis=1) # Note this is not consistent with concat([gdf, gdf], axis=1) where the # left metadata is set on the result. This is deliberate for now. assert type(result) is GeoDataFrame assert result._geometry_column_name is None assert_index_equal(pd.Index([0, 1]), result.columns, exact="equiv") gseries2.name = "foo" result2 = pd.concat([gseries2, self.gseries], axis=1) assert type(result2) is GeoDataFrame assert result._geometry_column_name is None assert_index_equal(pd.Index(["foo", 0]), result2.columns) geopandas-1.2.0/geopandas/tests/test_op_output_types.py000066400000000000000000000364301525650651600234720ustar00rootroot00000000000000import numpy as np import pandas as pd from shapely import Polygon from shapely.geometry import Point import geopandas from geopandas import GeoDataFrame, GeoSeries from geopandas._compat import PANDAS_GE_31 import pytest from geopandas.testing import assert_geodataframe_equal pyproj = pytest.importorskip("pyproj") crs_osgb = pyproj.CRS(27700) crs_wgs = pyproj.CRS(4326) N = 10 @pytest.fixture(params=["geometry", "point"]) def df(request): geo_name = request.param df = GeoDataFrame( [ { "value1": x + y, "value2": x * y, geo_name: Point(x, y), # rename this col in tests } for x, y in zip(range(N), range(N)) ], crs=crs_wgs, geometry=geo_name, ) # want geometry2 to be a GeoSeries not Series, test behaviour of non geom col df["geometry2"] = df[geo_name].set_crs(crs_osgb, allow_override=True) return df @pytest.fixture def df2(): """For constructor_sliced tests""" return GeoDataFrame( { "geometry": GeoSeries([Point(x, x) for x in range(3)]), "geometry2": GeoSeries([Point(x, x) for x in range(3)]), "geometry3": GeoSeries([Point(x, x) for x in range(3)]), "value": [1, 2, 1], "value_nan": np.nan, } ) def _check_metadata_gdf(gdf, geo_name="geometry", crs=crs_wgs): assert gdf._geometry_column_name == geo_name assert gdf.geometry.name == geo_name assert gdf.crs == crs def _check_metadata_gs(gs, name="geometry", crs=crs_wgs): assert gs.name == name assert gs.crs == crs def assert_object(result, expected_type, geo_name="geometry", crs=crs_wgs): """ Helper method to make tests easier to read. Checks result is of the expected type. If result is a GeoDataFrame or GeoSeries, checks geo_name and crs match. If geo_name is None, then we expect a GeoDataFrame where the geometry column is invalid/ isn't set. This is never desirable, but is a reality of this first stage of implementation. """ assert type(result) is expected_type if expected_type == GeoDataFrame: assert geo_name is not None _check_metadata_gdf(result, geo_name=geo_name, crs=crs) elif expected_type == GeoSeries: _check_metadata_gs(result, name=geo_name, crs=crs) def assert_obj_no_active_geo_col(result, expected_type, geo_colname=None): """ Helper method to make tests easier to read. Checks result is of the expected type. Asserts that accessing result.geometry.name raises, corresponding to _geometry_column_name being in an invalid state (either None, or a column no longer present) This amounts to testing the assertion raised (geometry column is unset, vs old geometry column is missing) We assert that _geometry_column_name = int_geo_colname """ if expected_type == GeoDataFrame: if geo_colname is None: assert result._geometry_column_name is None else: assert geo_colname == result._geometry_column_name if result._geometry_column_name is None: msg = ( "You are calling a geospatial method on the GeoDataFrame, " "but the active" ) else: msg = ( "You are calling a geospatial method on the GeoDataFrame, but " r"the active geometry column \(" rf"'{result._geometry_column_name}'\) is not present" ) with pytest.raises(AttributeError, match=msg): result.geometry.name # be explicit that geometry is invalid here else: raise NotImplementedError() def test_getitem(df): geo_name = df.geometry.name assert_object(df[["value1", "value2"]], pd.DataFrame) assert_object(df[[geo_name, "geometry2"]], GeoDataFrame, geo_name) assert_object(df[[geo_name]], GeoDataFrame, geo_name) assert_obj_no_active_geo_col(df[["geometry2", "value1"]], GeoDataFrame, geo_name) assert_obj_no_active_geo_col(df[["geometry2"]], GeoDataFrame, geo_name) assert_object(df[["value1"]], pd.DataFrame) # Series assert_object(df[geo_name], GeoSeries, geo_name) assert_object(df["geometry2"], GeoSeries, "geometry2", crs=crs_osgb) assert_object(df["value1"], pd.Series) def test_loc(df): geo_name = df.geometry.name assert_object(df.loc[:, ["value1", "value2"]], pd.DataFrame) assert_object(df.loc[:, [geo_name, "geometry2"]], GeoDataFrame, geo_name) assert_object(df.loc[:, [geo_name]], GeoDataFrame, geo_name) assert_obj_no_active_geo_col( df.loc[:, ["geometry2", "value1"]], GeoDataFrame, geo_name ) assert_obj_no_active_geo_col(df.loc[:, ["geometry2"]], GeoDataFrame, geo_name) assert_object(df.loc[:, ["value1"]], pd.DataFrame) # Series assert_object(df.loc[:, geo_name], GeoSeries, geo_name) assert_object(df.loc[:, "geometry2"], GeoSeries, "geometry2", crs=crs_osgb) assert_object(df.loc[:, "value1"], pd.Series) @pytest.mark.parametrize("geom_name", ["geometry", "geom"]) def test_loc_add_row(geom_name, nybb_filename): # https://github.com/geopandas/geopandas/issues/3119 nybb = geopandas.read_file(nybb_filename)[["BoroCode", "geometry"]] if geom_name != "geometry": nybb = nybb.rename_geometry(geom_name) # add a new row if PANDAS_GE_31: expected_crs = nybb.crs else: expected_crs = None # this should be nybb.crs, regressed in #2373 nybb.loc[5] = [6, nybb.geometry.iloc[0]] if PANDAS_GE_31 or geom_name == "geometry": assert nybb.geometry.dtype == "geometry" assert nybb.crs is expected_crs else: assert nybb.geometry.dtype == "object" @pytest.mark.xfail(reason="failing because crs is lost") @pytest.mark.parametrize("geom_name", ["geometry", "geom"]) def test_loc_add_row_empty_df(geom_name): # https://github.com/geopandas/geopandas/issues/3109 gdf = GeoDataFrame(geometry=[], crs="EPSG:4326") if geom_name != "geometry": gdf = gdf.rename_geometry("geom") p = Polygon([(0, 0), (1, 0), (1, 1), (0, 1)]) gdf.loc[0] = [p] assert gdf.active_geometry_name == geom_name assert gdf.crs == "EPSG:4326" # Series case ser = GeoSeries(crs="EPSG:4326") ser[0] = p assert ser.crs == "EPSG:4326" def test_iloc(df): geo_name = df.geometry.name assert_object(df.iloc[:, 0:2], pd.DataFrame) assert_object(df.iloc[:, 2:4], GeoDataFrame, geo_name) assert_object(df.iloc[:, [2]], GeoDataFrame, geo_name) assert_obj_no_active_geo_col(df.iloc[:, [3, 0]], GeoDataFrame, geo_name) assert_obj_no_active_geo_col(df.iloc[:, [3]], GeoDataFrame, geo_name) assert_object(df.iloc[:, [0]], pd.DataFrame) # Series assert_object(df.iloc[:, 2], GeoSeries, geo_name) assert_object(df.iloc[:, 3], GeoSeries, "geometry2", crs=crs_osgb) assert_object(df.iloc[:, 0], pd.Series) def test_squeeze(df): geo_name = df.geometry.name assert_object(df[[geo_name]].squeeze(), GeoSeries, geo_name) assert_object(df[["geometry2"]].squeeze(), GeoSeries, "geometry2", crs=crs_osgb) def test_to_frame(df): geo_name = df.geometry.name res1 = df[geo_name].to_frame() assert_object(res1, GeoDataFrame, geo_name, crs=df[geo_name].crs) res2 = df["geometry2"].to_frame() assert_object(res2, GeoDataFrame, "geometry2", crs=crs_osgb) res3 = df["value1"].to_frame() assert_object(res3, pd.DataFrame) def test_reindex(df): geo_name = df.geometry.name assert_object(df.reindex(columns=["value1", "value2"]), pd.DataFrame) assert_object(df.reindex(columns=[geo_name, "geometry2"]), GeoDataFrame, geo_name) assert_object(df.reindex(columns=[geo_name]), GeoDataFrame, geo_name) assert_object(df.reindex(columns=["new_col", geo_name]), GeoDataFrame, geo_name) assert_obj_no_active_geo_col( df.reindex(columns=["geometry2", "value1"]), GeoDataFrame, geo_name ) assert_obj_no_active_geo_col( df.reindex(columns=["geometry2"]), GeoDataFrame, geo_name ) assert_object(df.reindex(columns=["value1"]), pd.DataFrame) # reindexing the rows always preserves the GeoDataFrame assert_object(df.reindex(index=[0, 1, 20]), GeoDataFrame, geo_name) # reindexing both rows and columns assert_object( df.reindex(index=[0, 1, 20], columns=[geo_name]), GeoDataFrame, geo_name ) assert_object(df.reindex(index=[0, 1, 20], columns=["value1"]), pd.DataFrame) def test_drop(df): geo_name = df.geometry.name assert_object(df.drop(columns=[geo_name, "geometry2"]), pd.DataFrame) assert_object(df.drop(columns=["value1", "value2"]), GeoDataFrame, geo_name) cols = ["value1", "value2", "geometry2"] assert_object(df.drop(columns=cols), GeoDataFrame, geo_name) assert_obj_no_active_geo_col( df.drop(columns=[geo_name, "value2"]), GeoDataFrame, geo_name ) assert_obj_no_active_geo_col( df.drop(columns=["value1", "value2", geo_name]), GeoDataFrame, geo_name ) assert_object(df.drop(columns=["geometry2", "value2", geo_name]), pd.DataFrame) def test_apply(df): geo_name = df.geometry.name def identity(x): return x # axis = 0 assert_object(df[["value1", "value2"]].apply(identity), pd.DataFrame) assert_object(df[[geo_name, "geometry2"]].apply(identity), GeoDataFrame, geo_name) assert_object(df[[geo_name]].apply(identity), GeoDataFrame, geo_name) res = df[["geometry2", "value1"]].apply(identity) assert_obj_no_active_geo_col(res, GeoDataFrame, geo_name) assert_obj_no_active_geo_col( df[["geometry2"]].apply(identity), GeoDataFrame, geo_name ) assert_object(df[["value1"]].apply(identity), pd.DataFrame) # axis = 0, Series assert_object(df[geo_name].apply(identity), GeoSeries, geo_name) assert_object(df["geometry2"].apply(identity), GeoSeries, "geometry2", crs=crs_osgb) assert_object(df["value1"].apply(identity), pd.Series) # axis = 0, Series, no longer geometry assert_object(df[geo_name].apply(str), pd.Series) assert_object(df["geometry2"].apply(lambda x: str(x)), pd.Series) # noqa: PLW0108 # axis = 1 assert_object(df[["value1", "value2"]].apply(identity, axis=1), pd.DataFrame) assert_object( df[[geo_name, "geometry2"]].apply(identity, axis=1), GeoDataFrame, geo_name ) assert_object(df[[geo_name]].apply(identity, axis=1), GeoDataFrame, geo_name) # TODO below should be a GeoDataFrame to be consistent with new getitem logic # leave as follow up as quite complicated # FrameColumnApply.series_generator returns object dtypes Series, so will have # patch result of apply assert_object(df[["geometry2", "value1"]].apply(identity, axis=1), pd.DataFrame) assert_object(df[["value1"]].apply(identity, axis=1), pd.DataFrame) def test_apply_axis1_secondary_geo_cols(df): geo_name = df.geometry.name def identity(x): return x assert_obj_no_active_geo_col( df[["geometry2"]].apply(identity, axis=1), GeoDataFrame, geo_name ) def test_expanddim_in_apply(): # https://github.com/geopandas/geopandas/pull/2296#issuecomment-1021966443 s = GeoSeries.from_xy([0, 1], [0, 1]) result = s.apply(lambda x: pd.Series([x.x, x.y])) assert_object(result, pd.DataFrame) def test_expandim_in_groupby_aggregate_multiple_funcs(): # https://github.com/geopandas/geopandas/pull/2296#issuecomment-1021966443 # There are two calls to _constructor_expanddim here # SeriesGroupBy._aggregate_multiple_funcs() and # SeriesGroupBy._wrap_series_output() len(output) > 1 s = GeoSeries.from_xy([0, 1, 2], [0, 1, 3]) def union(s): return s.union_all() def total_area(s): return s.area.sum() grouped = s.groupby([0, 1, 0]) agg = grouped.agg([total_area, union]) assert_obj_no_active_geo_col(agg, GeoDataFrame, geo_colname=None) result = grouped.agg([union, total_area]) assert_obj_no_active_geo_col(result, GeoDataFrame, geo_colname=None) assert_object(grouped.agg([total_area, total_area]), pd.DataFrame) assert_object(grouped.agg([total_area]), pd.DataFrame) def test_expanddim_in_unstack(): # https://github.com/geopandas/geopandas/pull/2296#issuecomment-1021966443 s = GeoSeries.from_xy( [0, 1, 2], [0, 1, 3], index=pd.MultiIndex.from_tuples([("A", "a"), ("A", "b"), ("B", "a")]), ) unstack = s.unstack() expected_geo_name = None assert_obj_no_active_geo_col(unstack, GeoDataFrame, geo_colname=expected_geo_name) # https://github.com/geopandas/geopandas/issues/2486 s.name = "geometry" unstack = s.unstack() assert_obj_no_active_geo_col(unstack, GeoDataFrame, expected_geo_name) def test_delitem(): # https://github.com/geopandas/geopandas/issues/2932 df = GeoDataFrame({"geometry": GeoSeries.from_xy([1], [1]), "a": [1]}) del df["geometry"] assert_object(df, pd.DataFrame) df2 = GeoDataFrame({"geometry": GeoSeries.from_xy([1], [1]), "a": [1]}) df2["geom2"] = df2.geometry del df2["geometry"] assert_obj_no_active_geo_col(df2, GeoDataFrame, geo_colname="geometry") # indexing / constructor_sliced tests test_case_column_sets = [ ["geometry"], ["geometry2"], ["geometry", "geometry2"], # non active geo col case ["geometry", "value"], ["geometry", "value_nan"], ["geometry2", "value"], ["geometry2", "value_nan"], ] @pytest.mark.parametrize( "column_set", test_case_column_sets, ids=[", ".join(i) for i in test_case_column_sets], ) def test_constructor_sliced_row_slices(df2, column_set): # https://github.com/geopandas/geopandas/issues/2282 df_subset = df2[column_set] assert isinstance(df_subset, GeoDataFrame) res = df_subset.loc[0] # row slices shouldn't be GeoSeries, even if they have a geometry col assert type(res) is pd.Series if "geometry" in column_set: assert not isinstance(res.geometry, pd.Series) assert res.geometry == Point(0, 0) def test_constructor_sliced_column_slices(df2): # Note loc doesn't use _constructor_sliced so it's not tested here geo_idx = df2.columns.get_loc("geometry") sub = df2.head(1) # column slices should be GeoSeries if of geometry type assert type(sub.iloc[:, geo_idx]) is GeoSeries assert type(sub.iloc[[0], geo_idx]) is GeoSeries sub = df2.head(2) assert type(sub.iloc[:, geo_idx]) is GeoSeries assert type(sub.iloc[[0, 1], geo_idx]) is GeoSeries # check iloc row slices are pd.Series instead assert type(df2.iloc[0, :]) is pd.Series def test_constructor_sliced_in_pandas_methods(df2): # constructor sliced is used in many places, checking a sample of non # geometry cases are sensible assert type(df2.count()) is pd.Series # drop the secondary geometry columns as not hashable hashable_test_df = df2.drop(columns=["geometry2", "geometry3"]) assert type(hashable_test_df.duplicated()) is pd.Series assert type(df2.quantile(numeric_only=True)) is pd.Series assert type(df2.memory_usage()) is pd.Series def test_merge_preserve_geodataframe(): # https://github.com/geopandas/geopandas/issues/2932 ser = GeoSeries.from_xy([1], [1]) df = GeoDataFrame({"geo": ser}) res = df.merge(df, left_index=True, right_index=True) assert_obj_no_active_geo_col(res, GeoDataFrame, geo_colname=None) expected = GeoDataFrame({"geo_x": ser, "geo_y": ser}) assert_geodataframe_equal(expected, res) geopandas-1.2.0/geopandas/tests/test_overlay.py000066400000000000000000000771571525650651600217040ustar00rootroot00000000000000import os import numpy as np import pandas as pd from shapely import make_valid from shapely.geometry import GeometryCollection, LineString, Point, Polygon, box import geopandas from geopandas import GeoDataFrame, GeoSeries, overlay, read_file from geopandas._compat import HAS_PYPROJ, PANDAS_INFER_STR import pytest from geopandas.testing import assert_geodataframe_equal, assert_geoseries_equal try: from fiona.errors import DriverError except ImportError: class DriverError(Exception): pass DATA = os.path.join(os.path.abspath(os.path.dirname(__file__)), "data", "overlay") @pytest.fixture def dfs(request): s1 = GeoSeries( [ Polygon([(0, 0), (2, 0), (2, 2), (0, 2)]), Polygon([(2, 2), (4, 2), (4, 4), (2, 4)]), ] ) s2 = GeoSeries( [ Polygon([(1, 1), (3, 1), (3, 3), (1, 3)]), Polygon([(3, 3), (5, 3), (5, 5), (3, 5)]), ] ) df1 = GeoDataFrame({"col1": [1, 2], "geometry": s1}) df2 = GeoDataFrame({"col2": [1, 2], "geometry": s2}) return df1, df2 @pytest.fixture(params=["default-index", "int-index", "string-index"]) def dfs_index(request, dfs): df1, df2 = dfs if request.param == "int-index": df1.index = [1, 2] df2.index = [0, 2] if request.param == "string-index": df1.index = ["row1", "row2"] return df1, df2 @pytest.fixture( params=["union", "intersection", "difference", "symmetric_difference", "identity"] ) def how(request): return request.param @pytest.fixture(params=[True, False]) def keep_geom_type(request): return request.param def test_overlay(dfs_index, how): """ Basic overlay test with small dummy example dataframes (from docs). Results obtained using QGIS 2.16 (Vector -> Geoprocessing Tools -> Intersection / Union / ...), saved to GeoJSON """ df1, df2 = dfs_index result = overlay(df1, df2, how=how) # construction of result def _read(name): expected = read_file(os.path.join(DATA, "polys", f"df1_df2-{name}.geojson")) expected.geometry.array.crs = None for col in expected.columns[expected.dtypes == "int32"]: expected[col] = expected[col].astype("int64") return expected if how == "identity": expected_intersection = _read("intersection") expected_difference = _read("difference") expected = pd.concat( [expected_intersection, expected_difference], ignore_index=True, sort=False ) else: expected = _read(how) # TODO needed adaptations to result if how == "union": result = result.sort_values(["col1", "col2"]).reset_index(drop=True) elif how == "difference": result = result.reset_index(drop=True) assert_geodataframe_equal(result, expected, check_column_type=False) # for difference also reversed if how == "difference": result = overlay(df2, df1, how=how) result = result.reset_index(drop=True) expected = _read("difference-inverse") assert_geodataframe_equal(result, expected, check_column_type=False) @pytest.mark.parametrize("keep_geom_type", [True, False]) def test_overlay_invalid_input(keep_geom_type): invalid_polygon = Polygon([(0, 0), (1, 1), (1, 2), (1, 1), (0, 0)]) square = Polygon(((0, 0), (1, 0), (1, 1), (0, 1), (0, 0))) df1 = geopandas.GeoDataFrame({"A": [1, 2]}, geometry=[invalid_polygon, square]) df2 = geopandas.GeoDataFrame({"B": [5, 6]}, geometry=[square, square]) df1_df2 = df1.overlay(df2, keep_geom_type=keep_geom_type) # As overlay only supports inputs with uniform geometry types, `make_valid` of # `invalid_polygon` should always result in POLYGON EMPTY, so the number of results # will should always be 2. assert len(df1_df2) == 2 if keep_geom_type: assert df1_df2.geom_type.isin(["Polygon"]).all() @pytest.mark.filterwarnings("ignore:GeoSeries crs mismatch:UserWarning") def test_overlay_nybb(how, nybb_filename): polydf = read_file(nybb_filename) # The circles have been constructed and saved at the time the expected # results were created (exact output of buffer algorithm can slightly # change over time -> use saved ones) # # construct circles dataframe # N = 10 # b = [int(x) for x in polydf.total_bounds] # polydf2 = GeoDataFrame( # [ # {"geometry": Point(x, y).buffer(10000), "value1": x + y, "value2": x - y} # for x, y in zip( # range(b[0], b[2], int((b[2] - b[0]) / N)), # range(b[1], b[3], int((b[3] - b[1]) / N)), # ) # ], # crs=polydf.crs, # ) polydf2 = read_file(os.path.join(DATA, "nybb_qgis", "polydf2.shp")) result = overlay(polydf, polydf2, how=how) cols = ["BoroCode", "BoroName", "Shape_Leng", "Shape_Area", "value1", "value2"] if how == "difference": cols = cols[:-2] # expected result if how == "identity": # read union one, further down below we take the appropriate subset expected = read_file(os.path.join(DATA, "nybb_qgis", "qgis-union.shp")) else: expected = read_file(os.path.join(DATA, "nybb_qgis", f"qgis-{how}.shp")) # The result of QGIS for 'union' contains incorrect geometries: # 24 is a full original circle overlapping with unioned geometries, and # 27 is a completely duplicated row) if how == "union": expected = expected.drop([24, 27]).reset_index(drop=True) # Eliminate observations without geometries (issue from QGIS) expected = expected[expected.is_valid].reset_index(drop=True) if how == "identity": expected = expected[expected.BoroCode.notnull()].copy() boro_code_dtype = result["BoroCode"].dtype if boro_code_dtype in ("int32", "int64"): # Depending on the pandas version, the dtype might be int32 or int64 expected["BoroCode"] = expected["BoroCode"].astype(boro_code_dtype) # Order GeoDataFrames expected = expected.sort_values(cols).reset_index(drop=True) # TODO needed adaptations to result result = result.sort_values(cols).reset_index(drop=True) if how in ("union", "identity"): # concat < 0.23 sorts, so changes the order of the columns # but at least we ensure 'geometry' is the last column assert result.columns[-1] == "geometry" assert len(result.columns) == len(expected.columns) result = result.reindex(columns=expected.columns) # the ordering of the spatial index results causes slight deviations # in the resultant geometries for multipolygons # for more details on the discussion, see: # https://github.com/geopandas/geopandas/pull/1338 # https://github.com/geopandas/geopandas/issues/1337 # Temporary workaround below: # simplify multipolygon geometry comparison # since the order of the constituent polygons depends on # the ordering of spatial indexing results, we cannot # compare symmetric_difference results directly when the # resultant geometry is a multipolygon # first, check that all bounds and areas are approx equal # this is a very rough check for multipolygon equality kwargs = {} pd.testing.assert_series_equal( result.geometry.area, expected.geometry.area, **kwargs ) pd.testing.assert_frame_equal( result.geometry.bounds, expected.geometry.bounds, **kwargs ) # There are two cases where the multipolygon have a different number # of sub-geometries -> not solved by normalize (and thus drop for now) if how == "symmetric_difference": expected.loc[9, "geometry"] = None result.loc[9, "geometry"] = None if how == "union": expected.loc[24, "geometry"] = None result.loc[24, "geometry"] = None # missing values get read as None in read_file for a string column, but # are introduced as NaN by overlay expected["BoroName"] = expected["BoroName"].fillna(np.nan) assert_geodataframe_equal( result, expected, normalize=True, check_crs=False, check_column_type=False, check_less_precise=True, ) def test_overlay_overlap(how): """ Overlay test with overlapping geometries in both dataframes. Test files are created with:: import geopandas from geopandas import GeoSeries, GeoDataFrame from shapely.geometry import Point, Polygon, LineString s1 = GeoSeries([Point(0, 0), Point(1.5, 0)]).buffer(1, resolution=2) s2 = GeoSeries([Point(1, 1), Point(2, 2)]).buffer(1, resolution=2) df1 = GeoDataFrame({'geometry': s1, 'col1':[1,2]}) df2 = GeoDataFrame({'geometry': s2, 'col2':[1, 2]}) ax = df1.plot(alpha=0.5) df2.plot(alpha=0.5, ax=ax, color='C1') df1.to_file('geopandas/geopandas/tests/data/df1_overlap.geojson', driver='GeoJSON') df2.to_file('geopandas/geopandas/tests/data/df2_overlap.geojson', driver='GeoJSON') and then overlay results are obtained from using QGIS 2.16 (Vector -> Geoprocessing Tools -> Intersection / Union / ...), saved to GeoJSON. """ df1 = read_file(os.path.join(DATA, "overlap", "df1_overlap.geojson")) df2 = read_file(os.path.join(DATA, "overlap", "df2_overlap.geojson")) result = overlay(df1, df2, how=how) if how == "identity": raise pytest.skip() expected = read_file( os.path.join(DATA, "overlap", f"df1_df2_overlap-{how}.geojson") ) if how == "union": # the QGIS result has the last row duplicated, so removing this expected = expected.iloc[:-1] # TODO needed adaptations to result result = result.reset_index(drop=True) if how == "union": result = result.sort_values(["col1", "col2"]).reset_index(drop=True) assert_geodataframe_equal( result, expected, normalize=True, check_column_type=False, check_less_precise=True, ) @pytest.mark.parametrize("other_geometry", [False, True]) def test_geometry_not_named_geometry(dfs, how, other_geometry): # Issue #306 # Add points and flip names df1, df2 = dfs df3 = df1.copy() df3 = df3.rename(columns={"geometry": "polygons"}) df3 = df3.set_geometry("polygons") if other_geometry: df3["geometry"] = df1.centroid.geometry assert df3.geometry.name == "polygons" res1 = overlay(df1, df2, how=how) res2 = overlay(df3, df2, how=how) assert df3.geometry.name == "polygons" if how == "difference": # in case of 'difference', column names of left frame are preserved assert res2.geometry.name == "polygons" if other_geometry: assert "geometry" in res2.columns assert_geoseries_equal( res2["geometry"], df3["geometry"], check_series_type=False ) res2 = res2.drop(["geometry"], axis=1) res2 = res2.rename(columns={"polygons": "geometry"}) res2 = res2.set_geometry("geometry") # TODO if existing column is overwritten -> geometry not last column if other_geometry and how == "intersection": res2 = res2.reindex(columns=res1.columns) assert_geodataframe_equal(res1, res2) df4 = df2.copy() df4 = df4.rename(columns={"geometry": "geom"}) df4 = df4.set_geometry("geom") if other_geometry: df4["geometry"] = df2.centroid.geometry assert df4.geometry.name == "geom" res1 = overlay(df1, df2, how=how) res2 = overlay(df1, df4, how=how) assert_geodataframe_equal(res1, res2) def test_bad_how(dfs): df1, df2 = dfs with pytest.raises(ValueError): overlay(df1, df2, how="spandex") def test_duplicate_column_name(dfs, how): if how == "difference": pytest.skip("Difference uses columns from one df only.") df1, df2 = dfs df2r = df2.rename(columns={"col2": "col1"}) res = overlay(df1, df2r, how=how) assert ("col1_1" in res.columns) and ("col1_2" in res.columns) def test_geoseries_warning(dfs): df1, df2 = dfs # Issue #305 with pytest.raises(NotImplementedError): overlay(df1, df2.geometry, how="union") @pytest.mark.skipif(not HAS_PYPROJ, reason="pyproj not available") def test_preserve_crs(dfs, how): df1, df2 = dfs result = overlay(df1, df2, how=how) assert result.crs is None crs = "epsg:4326" df1.crs = crs df2.crs = crs result = overlay(df1, df2, how=how) assert result.crs == crs @pytest.mark.skipif(not HAS_PYPROJ, reason="pyproj not available") def test_crs_mismatch(dfs, how): df1, df2 = dfs df1.crs = 4326 df2.crs = 3857 with pytest.warns(UserWarning, match="CRS mismatch between the CRS"): overlay(df1, df2, how=how) def test_empty_intersection(dfs): df1, _df2 = dfs polys3 = GeoSeries( [ Polygon([(-1, -1), (-3, -1), (-3, -3), (-1, -3)]), Polygon([(-3, -3), (-5, -3), (-5, -5), (-3, -5)]), ] ) df3 = GeoDataFrame({"geometry": polys3, "col3": [1, 2]}) expected = GeoDataFrame([], columns=["col1", "col3", "geometry"]) result = overlay(df1, df3) assert_geodataframe_equal(result, expected, check_dtype=False) def test_correct_index(dfs): # GH883 - case where the index was not properly reset _df1, df2 = dfs polys3 = GeoSeries( [ Polygon([(1, 1), (3, 1), (3, 3), (1, 3)]), Polygon([(-1, 1), (1, 1), (1, 3), (-1, 3)]), Polygon([(3, 3), (5, 3), (5, 5), (3, 5)]), ] ) df3 = GeoDataFrame({"geometry": polys3, "col3": [1, 2, 3]}) i1 = Polygon([(1, 1), (1, 3), (3, 3), (3, 1), (1, 1)]) i2 = Polygon([(3, 3), (3, 5), (5, 5), (5, 3), (3, 3)]) expected = GeoDataFrame( [[1, 1, i1], [3, 2, i2]], columns=["col3", "col2", "geometry"] ) result = overlay(df3, df2, keep_geom_type=True) assert_geodataframe_equal(result, expected) def test_warn_on_keep_geom_type(dfs): _df1, df2 = dfs polys3 = GeoSeries( [ Polygon([(1, 1), (3, 1), (3, 3), (1, 3)]), Polygon([(-1, 1), (1, 1), (1, 3), (-1, 3)]), Polygon([(3, 3), (5, 3), (5, 5), (3, 5)]), ] ) df3 = GeoDataFrame({"geometry": polys3}) with pytest.warns(UserWarning, match="`keep_geom_type=True` in overlay"): overlay(df2, df3, keep_geom_type=None) @pytest.mark.parametrize( "geom_types", ["polys", "poly_line", "poly_point", "line_poly", "point_poly"] ) def test_overlay_strict(how, keep_geom_type, geom_types): """ Test of mixed geometry types on input and output. Expected results initially generated using following snippet. polys1 = gpd.GeoSeries([Polygon([(1, 1), (3, 1), (3, 3), (1, 3)]), Polygon([(3, 3), (5, 3), (5, 5), (3, 5)])]) df1 = gpd.GeoDataFrame({'col1': [1, 2], 'geometry': polys1}) polys2 = gpd.GeoSeries([Polygon([(1, 1), (3, 1), (3, 3), (1, 3)]), Polygon([(-1, 1), (1, 1), (1, 3), (-1, 3)]), Polygon([(3, 3), (5, 3), (5, 5), (3, 5)])]) df2 = gpd.GeoDataFrame({'geometry': polys2, 'col2': [1, 2, 3]}) lines1 = gpd.GeoSeries([LineString([(2, 0), (2, 4), (6, 4)]), LineString([(0, 3), (6, 3)])]) df3 = gpd.GeoDataFrame({'col3': [1, 2], 'geometry': lines1}) points1 = gpd.GeoSeries([Point((2, 2)), Point((3, 3))]) df4 = gpd.GeoDataFrame({'col4': [1, 2], 'geometry': points1}) params=["union", "intersection", "difference", "symmetric_difference", "identity"] stricts = [True, False] for p in params: for s in stricts: exp = gpd.overlay(df1, df2, how=p, keep_geom_type=s) if not exp.empty: exp.to_file('polys_{p}_{s}.geojson'.format(p=p, s=s), driver='GeoJSON') for p in params: for s in stricts: exp = gpd.overlay(df1, df3, how=p, keep_geom_type=s) if not exp.empty: exp.to_file('poly_line_{p}_{s}.geojson'.format(p=p, s=s), driver='GeoJSON') for p in params: for s in stricts: exp = gpd.overlay(df1, df4, how=p, keep_geom_type=s) if not exp.empty: exp.to_file('poly_point_{p}_{s}.geojson'.format(p=p, s=s), driver='GeoJSON') """ polys1 = GeoSeries( [ Polygon([(1, 1), (3, 1), (3, 3), (1, 3)]), Polygon([(3, 3), (5, 3), (5, 5), (3, 5)]), ] ) df1 = GeoDataFrame({"col1": [1, 2], "geometry": polys1}) polys2 = GeoSeries( [ Polygon([(1, 1), (3, 1), (3, 3), (1, 3)]), Polygon([(-1, 1), (1, 1), (1, 3), (-1, 3)]), Polygon([(3, 3), (5, 3), (5, 5), (3, 5)]), ] ) df2 = GeoDataFrame({"geometry": polys2, "col2": [1, 2, 3]}) lines1 = GeoSeries( [LineString([(2, 0), (2, 4), (6, 4)]), LineString([(0, 3), (6, 3)])] ) df3 = GeoDataFrame({"col3": [1, 2], "geometry": lines1}) points1 = GeoSeries([Point((2, 2)), Point((3, 3))]) df4 = GeoDataFrame({"col4": [1, 2], "geometry": points1}) if geom_types == "polys": result = overlay(df1, df2, how=how, keep_geom_type=keep_geom_type) elif geom_types == "poly_line": result = overlay(df1, df3, how=how, keep_geom_type=keep_geom_type) elif geom_types == "poly_point": result = overlay(df1, df4, how=how, keep_geom_type=keep_geom_type) elif geom_types == "line_poly": result = overlay(df3, df1, how=how, keep_geom_type=keep_geom_type) elif geom_types == "point_poly": result = overlay(df4, df1, how=how, keep_geom_type=keep_geom_type) try: expected = read_file( os.path.join( DATA, "strict", f"{geom_types}_{how}_{keep_geom_type}.geojson", ) ) # the order depends on the spatial index used # so we sort the resultant dataframes to get a consistent order # independently of the spatial index implementation assert all(expected.columns == result.columns), "Column name mismatch" cols = list(set(result.columns) - {"geometry"}) expected = expected.sort_values(cols, axis=0).reset_index(drop=True) result = result.sort_values(cols, axis=0).reset_index(drop=True) # some columns are all-NaN in the result, but get read as object dtype # column of None values in read_file for col in ["col1", "col3", "col4"]: if col in expected.columns and expected[col].isna().all(): expected[col] = expected[col].astype("float64") assert_geodataframe_equal( result, expected, normalize=True, check_column_type=False, check_less_precise=True, check_crs=False, check_dtype=False, ) except DriverError: # fiona >= 1.8 assert result.empty except OSError: # fiona < 1.8 assert result.empty except RuntimeError: # pyogrio.DataSourceError assert result.empty def test_mixed_geom_error(): polys1 = GeoSeries( [ Polygon([(1, 1), (3, 1), (3, 3), (1, 3)]), Polygon([(3, 3), (5, 3), (5, 5), (3, 5)]), ] ) df1 = GeoDataFrame({"col1": [1, 2], "geometry": polys1}) mixed = GeoSeries( [ Polygon([(1, 1), (3, 1), (3, 3), (1, 3)]), LineString([(3, 3), (5, 3), (5, 5), (3, 5)]), ] ) dfmixed = GeoDataFrame({"col1": [1, 2], "geometry": mixed}) with pytest.raises(NotImplementedError): overlay(df1, dfmixed, keep_geom_type=True) def test_keep_geom_type_error(): gcol = GeoSeries( GeometryCollection( [ Polygon([(1, 1), (3, 1), (3, 3), (1, 3)]), LineString([(3, 3), (5, 3), (5, 5), (3, 5)]), ] ) ) dfcol = GeoDataFrame({"col1": [2], "geometry": gcol}) polys1 = GeoSeries( [ Polygon([(1, 1), (3, 1), (3, 3), (1, 3)]), Polygon([(3, 3), (5, 3), (5, 5), (3, 5)]), ] ) df1 = GeoDataFrame({"col1": [1, 2], "geometry": polys1}) with pytest.raises(TypeError): overlay(dfcol, df1, keep_geom_type=True) def test_keep_geom_type_geometry_collection(): # GH 1581 df1 = read_file(os.path.join(DATA, "geom_type", "df1.geojson")) df2 = read_file(os.path.join(DATA, "geom_type", "df2.geojson")) with pytest.warns(UserWarning, match="`keep_geom_type=True` in overlay"): intersection = overlay(df1, df2, keep_geom_type=None) assert len(intersection) == 1 assert (intersection.geom_type == "Polygon").all() intersection = overlay(df1, df2, keep_geom_type=True) assert len(intersection) == 1 assert (intersection.geom_type == "Polygon").all() intersection = overlay(df1, df2, keep_geom_type=False) assert len(intersection) == 1 assert (intersection.geom_type == "GeometryCollection").all() def test_keep_geom_type_geometry_collection2(): polys1 = [ box(0, 0, 1, 1), box(1, 1, 3, 3).union(box(1, 3, 5, 5)), ] polys2 = [ box(0, 0, 1, 1), box(3, 1, 4, 2).union(box(4, 1, 5, 4)), ] df1 = GeoDataFrame({"left": [0, 1], "geometry": polys1}) df2 = GeoDataFrame({"right": [0, 1], "geometry": polys2}) result1 = overlay(df1, df2, keep_geom_type=True) expected1 = GeoDataFrame( { "left": [0, 1], "right": [0, 1], "geometry": [box(0, 0, 1, 1), box(4, 3, 5, 4)], } ) assert_geodataframe_equal(result1, expected1) result1 = overlay(df1, df2, keep_geom_type=False) expected1 = GeoDataFrame( { "left": [0, 1, 1], "right": [0, 0, 1], "geometry": [ box(0, 0, 1, 1), Point(1, 1), GeometryCollection([box(4, 3, 5, 4), LineString([(3, 1), (3, 2)])]), ], } ) assert_geodataframe_equal(result1, expected1) def test_keep_geom_type_geomcoll_different_types(): polys1 = [box(0, 1, 1, 3), box(10, 10, 12, 12)] polys2 = [ Polygon([(1, 0), (3, 0), (3, 3), (1, 3), (1, 2), (2, 2), (2, 1), (1, 1)]), box(11, 11, 13, 13), ] df1 = GeoDataFrame({"left": [0, 1], "geometry": polys1}) df2 = GeoDataFrame({"right": [0, 1], "geometry": polys2}) result1 = overlay(df1, df2, keep_geom_type=True) expected1 = GeoDataFrame( { "left": [1], "right": [1], "geometry": [box(11, 11, 12, 12)], } ) assert_geodataframe_equal(result1, expected1) result2 = overlay(df1, df2, keep_geom_type=False) expected2 = GeoDataFrame( { "left": [0, 1], "right": [0, 1], "geometry": [ GeometryCollection([LineString([(1, 2), (1, 3)]), Point(1, 1)]), box(11, 11, 12, 12), ], } ) assert_geodataframe_equal(result2, expected2) def test_keep_geom_type_geometry_collection_difference(): # GH 2163 polys1 = [ box(0, 0, 1, 1), box(1, 1, 2, 2), ] # the tiny sliver in the second geometry may be converted to a # linestring during the overlay process due to floating point errors # on some platforms polys2 = [ box(0, 0, 1, 1), box(1, 1, 2, 3).union(box(2, 2, 3, 2.00000000000000001)), ] df1 = GeoDataFrame({"left": [0, 1], "geometry": polys1}) df2 = GeoDataFrame({"right": [0, 1], "geometry": polys2}) result1 = overlay(df2, df1, keep_geom_type=True, how="difference") expected1 = GeoDataFrame( { "right": [1], "geometry": [box(1, 2, 2, 3)], }, ) assert_geodataframe_equal(result1, expected1) @pytest.mark.parametrize("should_make_valid", [True, False]) def test_overlap_make_valid(should_make_valid): bowtie = Polygon([(1, 1), (9, 9), (9, 1), (1, 9), (1, 1)]) assert not bowtie.is_valid fixed_bowtie = make_valid(bowtie) assert fixed_bowtie.is_valid df1 = GeoDataFrame({"col1": ["region"], "geometry": GeoSeries([box(0, 0, 10, 10)])}) df_bowtie = GeoDataFrame( {"col1": ["invalid", "valid"], "geometry": GeoSeries([bowtie, fixed_bowtie])} ) if should_make_valid: df_overlay_bowtie = overlay(df1, df_bowtie, make_valid=should_make_valid) assert df_overlay_bowtie.at[0, "geometry"].equals(fixed_bowtie) assert df_overlay_bowtie.at[1, "geometry"].equals(fixed_bowtie) else: with pytest.raises(ValueError, match="1 invalid input geometries"): overlay(df1, df_bowtie, make_valid=should_make_valid) def test_empty_overlay_return_non_duplicated_columns(nybb_filename): nybb = geopandas.read_file(nybb_filename) nybb2 = nybb.copy() nybb2.geometry = nybb2.translate(20000000) result = geopandas.overlay(nybb, nybb2) expected = GeoDataFrame( columns=[ "BoroCode_1", "BoroName_1", "Shape_Leng_1", "Shape_Area_1", "BoroCode_2", "BoroName_2", "Shape_Leng_2", "Shape_Area_2", "geometry", ], crs=nybb.crs, ) assert_geodataframe_equal(result, expected, check_dtype=False) def test_non_overlapping(how): p1 = Polygon([(0, 0), (2, 0), (2, 2), (0, 2)]) p2 = Polygon([(3, 3), (5, 3), (5, 5), (3, 5)]) df1 = GeoDataFrame({"id": [1], "col1": [1], "geometry": [p1]}) df2 = GeoDataFrame({"id": [2], "col2": [2], "geometry": [p2]}) result = overlay(df1, df2, how=how) if how == "intersection": expected = GeoDataFrame( { "id_1": np.array([], dtype="int64"), "col1": np.array([], dtype="int64"), "id_2": np.array([], dtype="int64"), "col2": np.array([], dtype="int64"), "geometry": [], } ) elif how == "union": expected = GeoDataFrame( { "id_1": [1, np.nan], "col1": [1, np.nan], "id_2": [np.nan, 2], "col2": [np.nan, 2], "geometry": [p1, p2], } ) elif how == "identity": expected = GeoDataFrame( { "id_1": [1], "col1": [1], "id_2": [np.nan], "col2": [np.nan], "geometry": [p1], } ) elif how == "symmetric_difference": expected = GeoDataFrame( { "id_1": [1, np.nan], "col1": [1, np.nan], "id_2": [np.nan, 2], "col2": [np.nan, 2], "geometry": [p1, p2], } ) elif how == "difference": expected = GeoDataFrame( { "id": [1], "col1": [1], "geometry": [p1], } ) assert_geodataframe_equal(result, expected) def test_no_intersection(): # overlapping bounds but non-overlapping geometries gs = GeoSeries([Point(x, x).buffer(0.1) for x in range(3)]) gdf1 = GeoDataFrame({"foo": ["a", "b", "c"]}, geometry=gs) gdf2 = GeoDataFrame({"bar": ["1", "3", "5"]}, geometry=gs.translate(1)) expected = GeoDataFrame(columns=["foo", "bar", "geometry"]) if PANDAS_INFER_STR: expected = expected.astype({"foo": "str", "bar": "str"}) result = overlay(gdf1, gdf2, how="intersection") assert_geodataframe_equal(result, expected, check_index_type=False) def test_zero_len(): # https://github.com/geopandas/geopandas/issues/3422 gdf1 = GeoDataFrame( { "geometry": [ Polygon([[0.0, 0.0], [2.0, 0.0], [2.0, 2.0], [0.0, 2.0], [0.0, 0.0]]) ] }, crs=4326, ) # overlay with empty geodataframe shouldn't throw empty = GeoDataFrame({"geometry": []}, crs=4326) res = gdf1.overlay(empty, how="union") assert_geodataframe_equal(res, gdf1) res = gdf1.overlay(empty, how="union") assert_geodataframe_equal(res, gdf1) res = empty.overlay(gdf1, how="union", keep_geom_type=False) assert_geodataframe_equal(res, gdf1) res = empty.overlay(gdf1, how="difference", keep_geom_type=False) assert_geodataframe_equal(res, empty) # keep_geom_type should warn gracefully with an empty input # https://github.com/geopandas/geopandas/discussions/3738 with pytest.warns( UserWarning, match="`keep_geom_type=True` is invalid when df1 is empty" ): empty.overlay(gdf1, how="union", keep_geom_type=True) class TestOverlayWikiExample: def setup_method(self): self.layer_a = GeoDataFrame(data={"id": ["a"]}, geometry=[box(0, 2, 6, 6)]) self.layer_b = GeoDataFrame(data={"id": ["b"]}, geometry=[box(4, 0, 10, 4)]) self.intersection = GeoDataFrame( data={"id_1": ["a"], "id_2": ["b"]}, geometry=[box(4, 2, 6, 4)] ) self.union = GeoDataFrame( data={"id_1": ["a", "a", np.nan], "id_2": ["b", np.nan, "b"]}, geometry=[ box(4, 2, 6, 4), Polygon([(4, 2), (0, 2), (0, 6), (6, 6), (6, 4), (4, 4), (4, 2)]), Polygon([(10, 0), (4, 0), (4, 2), (6, 2), (6, 4), (10, 4), (10, 0)]), ], ) self.a_difference_b = GeoDataFrame( data={"id": ["a"]}, geometry=[ Polygon([(4, 2), (0, 2), (0, 6), (6, 6), (6, 4), (4, 4), (4, 2)]) ], ) self.b_difference_a = GeoDataFrame( data={"id": ["b"]}, geometry=[ Polygon([(10, 0), (4, 0), (4, 2), (6, 2), (6, 4), (10, 4), (10, 0)]) ], ) self.symmetric_difference = GeoDataFrame( data={"id_1": ["a", np.nan], "id_2": [np.nan, "b"]}, geometry=[ Polygon([(4, 2), (0, 2), (0, 6), (6, 6), (6, 4), (4, 4), (4, 2)]), Polygon([(10, 0), (4, 0), (4, 2), (6, 2), (6, 4), (10, 4), (10, 0)]), ], ) self.a_identity_b = GeoDataFrame( data={"id_1": ["a", "a"], "id_2": ["b", np.nan]}, geometry=[ box(4, 2, 6, 4), Polygon([(4, 2), (0, 2), (0, 6), (6, 6), (6, 4), (4, 4), (4, 2)]), ], ) self.b_identity_a = GeoDataFrame( data={"id_1": ["b", "b"], "id_2": ["a", np.nan]}, geometry=[ box(4, 2, 6, 4), Polygon([(10, 0), (4, 0), (4, 2), (6, 2), (6, 4), (10, 4), (10, 0)]), ], ) def test_intersection(self): df_result = overlay(self.layer_a, self.layer_b, how="intersection") assert_geodataframe_equal(df_result, self.intersection) def test_union(self): df_result = overlay(self.layer_a, self.layer_b, how="union") assert_geodataframe_equal(df_result, self.union) def test_a_difference_b(self): df_result = overlay(self.layer_a, self.layer_b, how="difference") assert_geodataframe_equal(df_result, self.a_difference_b) def test_b_difference_a(self): df_result = overlay(self.layer_b, self.layer_a, how="difference") assert_geodataframe_equal(df_result, self.b_difference_a) def test_symmetric_difference(self): df_result = overlay(self.layer_a, self.layer_b, how="symmetric_difference") assert_geodataframe_equal(df_result, self.symmetric_difference) def test_a_identity_b(self): df_result = overlay(self.layer_a, self.layer_b, how="identity") assert_geodataframe_equal(df_result, self.a_identity_b) def test_b_identity_a(self): df_result = overlay(self.layer_b, self.layer_a, how="identity") assert_geodataframe_equal(df_result, self.b_identity_a) geopandas-1.2.0/geopandas/tests/test_pandas_accessor.py000066400000000000000000000032301525650651600233300ustar00rootroot00000000000000"""Ensure geopandas methods can be accessed via pandas 'geo' accessor.""" import re import pandas as pd from shapely.geometry import Point # Use this import to register the "geo" accessor. import geopandas.accessors # noqa: F401 # pylint: disable=unused-import from geopandas.array import GeometryDtype import pandas.testing import pytest @pytest.fixture def s(): return pd.Series( [Point(x, y) for x, y in zip(range(3), range(3))], dtype=GeometryDtype() ) @pytest.fixture def s2(): return pd.Series( [Point(x, y + 1) for x, y in zip(range(3), range(3))], dtype=GeometryDtype() ) def test_series_geo_buffer(s): """Ensure returned geometry values have the expected dtype.""" got = s.geo.buffer(0.2) assert isinstance(got.dtype, GeometryDtype) # Double-check that the .geo accessor works on the result. radius = got.geo.minimum_bounding_radius() for row in radius.index: assert radius[row] >= 0.1999 # Allow for some rounding error. def test_series_geo_distance(s, s2): got = s.geo.distance(s2) pandas.testing.assert_series_equal( got, pd.Series([1.0, 1.0, 1.0]), ) def test_series_geo_x(s): x = s.geo.x pandas.testing.assert_series_equal( x, pd.Series([0.0, 1.0, 2.0]), ) def test_series_geo_x_attributeerror_for_not_geo_dtype(): s = pd.Series([1, 2, 3]) with pytest.raises( AttributeError, match=re.escape("Can only use .geo accessor with GeometryDtype values"), ): s.geo.x def test_series_geo_y(s): y = s.geo.y pandas.testing.assert_series_equal( y, pd.Series([0.0, 1.0, 2.0]), ) geopandas-1.2.0/geopandas/tests/test_pandas_methods.py000066400000000000000000000676531525650651600232140ustar00rootroot00000000000000import os import numpy as np import pandas as pd import shapely from shapely.geometry import GeometryCollection, LinearRing, LineString, Point import geopandas import geopandas._compat as compat from geopandas import GeoDataFrame, GeoSeries from geopandas.array import from_shapely import pytest from geopandas.testing import assert_geodataframe_equal, assert_geoseries_equal from numpy.testing import assert_array_equal from pandas.testing import assert_frame_equal, assert_series_equal @pytest.fixture def s(): return GeoSeries([Point(x, y) for x, y in zip(range(3), range(3))]) @pytest.fixture def df(): return GeoDataFrame( { "geometry": [Point(x, x) for x in range(3)], "value1": np.arange(3, dtype="int64"), "value2": np.array([1, 2, 1], dtype="int64"), } ) def test_repr(s, df): assert "POINT" in repr(s) assert "POINT" in repr(df) assert "POINT" in df._repr_html_() @pytest.mark.skipif(shapely.geos_version < (3, 9, 0), reason="requires GEOS>=3.9") def test_repr_boxed_display_precision(): # geographic coordinates p1 = Point(10.123456789, 50.123456789) p2 = Point(4.123456789, 20.123456789) s1 = GeoSeries([p1, p2, None]) assert "POINT (10.12346 50.12346)" in repr(s1) # geographic coordinates 4326 s3 = GeoSeries([p1, p2], crs=4326) assert "POINT (10.12346 50.12346)" in repr(s3) # projected coordinates p1 = Point(3000.123456789, 3000.123456789) p2 = Point(4000.123456789, 4000.123456789) s2 = GeoSeries([p1, p2, None]) assert "POINT (3000.123 3000.123)" in repr(s2) # projected geographic coordinate s4 = GeoSeries([p1, p2], crs=3857) assert "POINT (3000.123 3000.123)" in repr(s4) geopandas.options.display_precision = 1 assert "POINT (10.1 50.1)" in repr(s1) geopandas.options.display_precision = 9 assert "POINT (10.123456789 50.123456789)" in repr(s1) def test_repr_all_missing(): # https://github.com/geopandas/geopandas/issues/1195 s = GeoSeries([None, None, None]) assert "None" in repr(s) df = GeoDataFrame({"a": [1, 2, 3], "geometry": s}) assert "None" in repr(df) assert "geometry" in df._repr_html_() def test_repr_empty(): # https://github.com/geopandas/geopandas/issues/1195 s = GeoSeries([]) assert repr(s) == "GeoSeries([], dtype: geometry)" df = GeoDataFrame({"a": [], "geometry": s}) assert "Empty GeoDataFrame" in repr(df) # https://github.com/geopandas/geopandas/issues/1184 assert "geometry" in df._repr_html_() def test_repr_linearring(): # https://github.com/geopandas/geopandas/pull/2689 # specifically, checking internal shapely/wkt/wkb conversions # preserve LinearRing s = GeoSeries([LinearRing([(0, 0), (1, 1), (1, -1)])]) assert "LINEARRING" in str(s.iloc[0]) # shapely scalar repr assert "LINEARRING" in str(s) # GeoSeries repr # check something coercible to linearring is not converted s2 = GeoSeries( [ LineString([(0, 0), (1, 1), (1, -1)]), LineString([(0, 0), (1, 1), (1, -1), (0, 0)]), ] ) assert "LINEARRING" not in str(s2) def test_indexing(s, df): # accessing scalar from the geometry (column) exp = Point(1, 1) assert s[1] == exp assert s.loc[1] == exp assert s.iloc[1] == exp assert df.loc[1, "geometry"] == exp assert df.iloc[1, 0] == exp # multiple values exp = GeoSeries([Point(2, 2), Point(0, 0)], index=[2, 0]) assert_geoseries_equal(s.loc[[2, 0]], exp) assert_geoseries_equal(s.iloc[[2, 0]], exp) assert_geoseries_equal(s.reindex([2, 0]), exp) assert_geoseries_equal(df.loc[[2, 0], "geometry"], exp) # TODO here iloc does not return a GeoSeries assert_series_equal( df.iloc[[2, 0], 0], exp, check_series_type=False, check_names=False ) # boolean indexing exp = GeoSeries([Point(0, 0), Point(2, 2)], index=[0, 2]) mask = np.array([True, False, True]) assert_geoseries_equal(s[mask], exp) assert_geoseries_equal(s.loc[mask], exp) assert_geoseries_equal(df[mask]["geometry"], exp) assert_geoseries_equal(df.loc[mask, "geometry"], exp) # slices s.index = [1, 2, 3] exp = GeoSeries([Point(1, 1), Point(2, 2)], index=[2, 3]) assert_series_equal(s[1:], exp) assert_series_equal(s.iloc[1:], exp) assert_series_equal(s.loc[2:], exp) def test_reindex(s, df): # GeoSeries reindex res = s.reindex([1, 2, 3]) exp = GeoSeries([Point(1, 1), Point(2, 2), None], index=[1, 2, 3]) assert_geoseries_equal(res, exp) # GeoDataFrame reindex index res = df.reindex(index=[1, 2, 3]) assert_geoseries_equal(res.geometry, exp) # GeoDataFrame reindex columns res = df.reindex(columns=["value1", "geometry"]) assert isinstance(res, GeoDataFrame) assert isinstance(res.geometry, GeoSeries) assert_frame_equal(res, df[["value1", "geometry"]]) res = df.reindex(columns=["value1", "value2"]) assert type(res) is pd.DataFrame assert_frame_equal(res, df[["value1", "value2"]]) def test_take(s, df): inds = np.array([0, 2]) # GeoSeries take result = s.take(inds) expected = s.iloc[[0, 2]] assert isinstance(result, GeoSeries) assert_geoseries_equal(result, expected) # GeoDataFrame take axis 0 result = df.take(inds, axis=0) expected = df.iloc[[0, 2], :] assert isinstance(result, GeoDataFrame) assert_geodataframe_equal(result, expected) # GeoDataFrame take axis 1 df = df.reindex(columns=["value1", "value2", "geometry"]) # ensure consistent order result = df.take(inds, axis=1) expected = df[["value1", "geometry"]] assert isinstance(result, GeoDataFrame) assert_geodataframe_equal(result, expected) result = df.take(np.array([0, 1]), axis=1) expected = df[["value1", "value2"]] assert isinstance(result, pd.DataFrame) assert_frame_equal(result, expected) def test_take_empty(s, df): # ensure that index type is preserved in an empty take # https://github.com/geopandas/geopandas/issues/1190 inds = np.array([], dtype="int64") # use non-default index df.index = pd.date_range("2012-01-01", periods=len(df)) result = df.take(inds, axis=0) assert isinstance(result, GeoDataFrame) assert result.shape == (0, 3) assert isinstance(result.index, pd.DatetimeIndex) # the original bug report was an empty boolean mask for result in [df.loc[df["value1"] > 100], df[df["value1"] > 100]]: assert isinstance(result, GeoDataFrame) assert result.shape == (0, 3) assert isinstance(result.index, pd.DatetimeIndex) def test_assignment(s, df): exp = GeoSeries([Point(10, 10), Point(1, 1), Point(2, 2)]) s2 = s.copy() s2[0] = Point(10, 10) assert_geoseries_equal(s2, exp) s2 = s.copy() s2.loc[0] = Point(10, 10) assert_geoseries_equal(s2, exp) s2 = s.copy() s2.iloc[0] = Point(10, 10) assert_geoseries_equal(s2, exp) df2 = df.copy() df2.loc[0, "geometry"] = Point(10, 10) assert_geoseries_equal(df2["geometry"], exp) df2 = df.copy() df2.iloc[0, 0] = Point(10, 10) assert_geoseries_equal(df2["geometry"], exp) def test_assign(df): res = df.assign(new=1) exp = df.copy() exp["new"] = 1 assert isinstance(res, GeoDataFrame) assert_frame_equal(res, exp) def test_astype(s, df): # check geoseries functionality with pytest.raises(TypeError): s.astype(int) assert s.astype(str)[0] == "POINT (0 0)" res = s.astype(object) assert isinstance(res, pd.Series) and not isinstance(res, GeoSeries) assert res.dtype == object df = df.rename_geometry("geom_list") # check whether returned object is a geodataframe res = df.astype({"value1": float}) assert isinstance(res, GeoDataFrame) # check whether returned object is a dataframe res = df.astype(str) assert isinstance(res, pd.DataFrame) and not isinstance(res, GeoDataFrame) res = df.astype({"geom_list": str}) assert isinstance(res, pd.DataFrame) and not isinstance(res, GeoDataFrame) res = df.astype(object) assert isinstance(res, pd.DataFrame) and not isinstance(res, GeoDataFrame) assert res["geom_list"].dtype == object def test_astype_invalid_geodataframe(): # https://github.com/geopandas/geopandas/issues/1144 # a GeoDataFrame without geometry column should not error in astype df = GeoDataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}) res = df.astype(object) assert isinstance(res, pd.DataFrame) and not isinstance(res, GeoDataFrame) assert res["a"].dtype == object def test_convert_dtypes(df): # https://github.com/geopandas/geopandas/issues/1870 # Test geometry col is first col, first, geom_col_name=geometry # (order is important in concat, used internally) res1 = df.convert_dtypes() expected1 = GeoDataFrame( pd.DataFrame(df).convert_dtypes(), crs=df.crs, geometry=df.geometry.name ) # Checking type and metadata are right assert_geodataframe_equal(expected1, res1) # Test geom last, geom_col_name=geometry res2 = df[["value1", "value2", "geometry"]].convert_dtypes() assert_geodataframe_equal(expected1[["value1", "value2", "geometry"]], res2) if compat.HAS_PYPROJ: # Test again with crs set and custom geom col name df2 = df.set_crs(epsg=4326).rename_geometry("points") expected2 = GeoDataFrame( pd.DataFrame(df2).convert_dtypes(), crs=df2.crs, geometry=df2.geometry.name ) res3 = df2.convert_dtypes() assert_geodataframe_equal(expected2, res3) # Test geom last, geom_col=geometry res4 = df2[["value1", "value2", "points"]].convert_dtypes() assert_geodataframe_equal(expected2[["value1", "value2", "points"]], res4) def test_to_csv(df): exp = ( "geometry,value1,value2\nPOINT (0 0),0,1\nPOINT (1 1),1,2\nPOINT (2 2),2,1\n" ).replace("\n", os.linesep) assert df.to_csv(index=False) == exp @pytest.mark.filterwarnings( "ignore:Dropping of nuisance columns in DataFrame reductions" ) def test_numerical_operations(s, df): # df methods ignore the geometry column exp = pd.Series([3, 4], index=["value1", "value2"]) res = df.sum(numeric_only=True) assert_series_equal(res, exp) # series methods raise error (not supported for geometry) with pytest.raises(TypeError): s.sum() with pytest.raises(TypeError): s.max() with pytest.raises((TypeError, ValueError)): # TODO: remove ValueError after pandas-dev/pandas#32749 s.idxmax() # numerical ops raise an error with pytest.raises(TypeError): df + 1 with pytest.raises(TypeError): s + 1 # boolean comparisons work res = df == 100 exp = pd.DataFrame(False, index=df.index, columns=df.columns) assert_frame_equal(res, exp) def test_where(s): res = s.where(np.array([True, False, True])) exp = GeoSeries([Point(0, 0), None, Point(2, 2)]) assert_series_equal(res, exp) def test_select_dtypes(df): res = df.select_dtypes(include=[np.number]) exp = df[["value1", "value2"]] assert_frame_equal(res, exp) def test_equals(s, df): # https://github.com/geopandas/geopandas/issues/1420 s2 = s.copy() assert s.equals(s2) is True s2.iloc[0] = None assert s.equals(s2) is False df2 = df.copy() assert df.equals(df2) is True df2.loc[0, "geometry"] = Point(10, 10) assert df.equals(df2) is False df2 = df.copy() df2.loc[0, "value1"] = 10 assert df.equals(df2) is False # Missing values def test_fillna_scalar(s, df): s2 = GeoSeries([Point(0, 0), None, Point(2, 2)]) res = s2.fillna(Point(1, 1)) assert_geoseries_equal(res, s) # allow np.nan although this does not change anything # https://github.com/geopandas/geopandas/issues/1149 res = s2.fillna(np.nan) assert_geoseries_equal(res, s2) # raise exception if trying to fill missing geometry w/ non-geometry df2 = df.copy() df2["geometry"] = s2 res = df2.fillna(Point(1, 1)) assert_geodataframe_equal(res, df) with pytest.raises((NotImplementedError, TypeError)): # GH2351 df2.fillna(0) # allow non-geometry fill value if there are no missing values # https://github.com/geopandas/geopandas/issues/1149 df3 = df.copy() df3.loc[0, "value1"] = np.nan res = df3.fillna(0) assert_geodataframe_equal(res.astype({"value1": "int64"}), df) def test_fillna_series(s): # fill na with another GeoSeries s2 = GeoSeries([Point(0, 0), None, Point(2, 2)]) # check na filled with the same index res = s2.fillna(GeoSeries([Point(1, 1)] * 3)) assert_geoseries_equal(res, s) # check na filled based on index, not position index = [3, 2, 1] res = s2.fillna(GeoSeries([Point(i, i) for i in index], index=index)) assert_geoseries_equal(res, s) # check na filled but the input length is different res = s2.fillna(GeoSeries([Point(1, 1)], index=[1])) assert_geoseries_equal(res, s) # check na filled but the inputting index is different res = s2.fillna(GeoSeries([Point(1, 1)], index=[9])) assert_geoseries_equal(res, s2) def test_fillna_inplace(s): s2 = GeoSeries([Point(0, 0), None, Point(2, 2)]) arr = s2.array s2.fillna(Point(1, 1), inplace=True) assert_geoseries_equal(s2, s) assert s2.array is arr def test_dropna(): s2 = GeoSeries([Point(0, 0), None, Point(2, 2)]) res = s2.dropna() exp = s2.loc[[0, 2]] assert_geoseries_equal(res, exp) @pytest.mark.parametrize("NA", [None, np.nan]) def test_isna(NA): s2 = GeoSeries([Point(0, 0), NA, Point(2, 2)], index=[2, 4, 5], name="tt") exp = pd.Series([False, True, False], index=[2, 4, 5], name="tt") res = s2.isnull() assert type(res) is pd.Series assert_series_equal(res, exp) res = s2.isna() assert_series_equal(res, exp) res = s2.notnull() assert_series_equal(res, ~exp) res = s2.notna() assert_series_equal(res, ~exp) # Any / all def test_any_all(): empty = GeometryCollection([]) s = GeoSeries([empty, Point(1, 1)]) assert not s.all() assert s.any() s = GeoSeries([Point(1, 1), Point(1, 1)]) assert s.all() assert s.any() s = GeoSeries([empty, empty]) assert not s.all() assert not s.any() # Groupby / algos def test_sort_values(): s = GeoSeries([Point(0, 0), Point(2, 2), Point(0, 2)]) res = s.sort_values() assert res.index.tolist() == [0, 2, 1] res2 = s.sort_values(ascending=False) assert res2.index.tolist() == [1, 2, 0] # empty geoseries assert_geoseries_equal(s.iloc[:0].sort_values(), s.iloc[:0]) def test_sort_values_empty_missing(): s = GeoSeries([Point(0, 0), None, Point(), Point(1, 1)]) # default: NA sorts last, empty first res = s.sort_values() assert res.index.tolist() == [2, 0, 3, 1] # descending: NA sorts last, empty last res = s.sort_values(ascending=False) assert res.index.tolist() == [3, 0, 2, 1] # NAs first, empty first after NAs res = s.sort_values(na_position="first") assert res.index.tolist() == [1, 2, 0, 3] # NAs first, descending with empyt last res = s.sort_values(ascending=False, na_position="first") assert res.index.tolist() == [1, 3, 0, 2] # all missing / empty s = GeoSeries([None, None, None]) res = s.sort_values() assert res.index.tolist() == [0, 1, 2] s = GeoSeries([Point(), Point(), Point()]) res = s.sort_values() assert res.index.tolist() == [0, 1, 2] s = GeoSeries([Point(), None, Point()]) res = s.sort_values() assert res.index.tolist() == [0, 2, 1] def test_unique(): s = GeoSeries([Point(0, 0), Point(0, 0), Point(2, 2)]) exp = from_shapely([Point(0, 0), Point(2, 2)]) # TODO should have specialized GeometryArray assert method assert_array_equal(s.unique(), exp) def test_value_counts(): # each object is considered unique s = GeoSeries([Point(0, 0), Point(1, 1), Point(0, 0)]) res = s.value_counts() name = "count" exp = pd.Series([2, 1], index=from_shapely([Point(0, 0), Point(1, 1)]), name=name) assert_series_equal(res, exp) # Check crs is preserved in the output index s2 = GeoSeries([Point(0, 0), Point(1, 1), Point(0, 0)], crs="EPSG:4326") res2 = s2.value_counts() assert_series_equal(res2, exp) # CRS should now be preserved in the index array if compat.HAS_PYPROJ: assert s2.value_counts().index.array.crs is not None assert s2.value_counts().index.array.crs == "EPSG:4326" # check mixed geometry s3 = GeoSeries([Point(0, 0), LineString([[1, 1], [2, 2]]), Point(0, 0)]) res3 = s3.value_counts() index = from_shapely([Point(0, 0), LineString([[1, 1], [2, 2]])]) exp3 = pd.Series([2, 1], index=index, name=name) assert_series_equal(res3, exp3) # check None is handled s4 = GeoSeries([Point(0, 0), None, Point(0, 0)]) res4 = s4.value_counts(dropna=True) exp4_dropna = pd.Series([2], index=from_shapely([Point(0, 0)]), name=name) assert_series_equal(res4, exp4_dropna) exp4_keepna = pd.Series([2, 1], index=from_shapely([Point(0, 0), None]), name=name) res4_keepna = s4.value_counts(dropna=False) assert_series_equal(res4_keepna, exp4_keepna) def test_drop_duplicates_series(): ser = GeoSeries([Point(0, 0), Point(0, 0)], crs="EPSG:4326") dropped = ser.drop_duplicates() expected = GeoSeries([Point(0, 0)], crs="EPSG:4326") assert_geoseries_equal(expected, dropped) def test_drop_duplicates_frame(): # duplicated does not yet use EA machinery, see above gdf_len = 3 dup_gdf = GeoDataFrame( {"geometry": [Point(0, 0) for _ in range(gdf_len)], "value1": range(gdf_len)} ) dropped_geometry = dup_gdf.drop_duplicates(subset="geometry") assert_geodataframe_equal(dup_gdf.iloc[[0]], dropped_geometry) dropped_all = dup_gdf.drop_duplicates() assert_geodataframe_equal(dup_gdf, dropped_all) def test_groupby(df): # counts work fine res = df.groupby("value2").count() exp = pd.DataFrame( {"geometry": [2, 1], "value1": [2, 1], "value2": [1, 2]} ).set_index("value2") assert_frame_equal(res, exp) # reductions ignore geometry column res = df.groupby("value2").sum(numeric_only=True) exp = pd.DataFrame({"value1": [2, 1], "value2": [1, 2]}, dtype="int64").set_index( "value2" ) assert_frame_equal(res, exp) # applying on the geometry column res = df.groupby("value2")["geometry"].apply(lambda x: x.union_all()) exp = GeoSeries( [shapely.geometry.MultiPoint([(0, 0), (2, 2)]), Point(1, 1)], index=pd.Index([1, 2], name="value2"), name="geometry", ) assert_series_equal(res, exp) # apply on geometry column not resulting in new geometry res = df.groupby("value2")["geometry"].apply(lambda x: x.union_all().area) exp = pd.Series([0.0, 0.0], index=pd.Index([1, 2], name="value2"), name="geometry") assert_series_equal(res, exp) def test_groupby_agg_tuple(df): res_dict = ( df.groupby("value2") .agg({"geometry": lambda x: x.union_all()}) .set_geometry("geometry") # groupby does not set active geom ) res_tup = ( df.groupby("value2") .agg(geometry=("geometry", lambda x: x.union_all())) .set_geometry("geometry") ) exp = GeoDataFrame( geometry=[shapely.geometry.MultiPoint([(0, 0), (2, 2)]), Point(1, 1)], index=pd.Index([1, 2], name="value2"), ) assert_geodataframe_equal(res_tup, exp) assert_geodataframe_equal(res_dict, res_tup) def test_groupby_groups(df): g = df.groupby("value2") res = g.get_group(1) assert isinstance(res, GeoDataFrame) exp = df.loc[[0, 2]] assert_frame_equal(res, exp) @pytest.mark.parametrize("crs", [None, "EPSG:4326"]) @pytest.mark.parametrize("geometry_name", ["geometry", "geom"]) def test_groupby_metadata(crs, geometry_name): if crs and not compat.HAS_PYPROJ: pytest.skip("requires pyproj") # https://github.com/geopandas/geopandas/issues/2294 df = GeoDataFrame( { geometry_name: [Point(0, 0), Point(1, 1), Point(0, 0)], "value1": np.arange(3, dtype="int64"), "value2": np.array([1, 2, 1], dtype="int64"), }, crs=crs, geometry=geometry_name, ) # dummy test asserting we can access the crs def func(group): assert isinstance(group, GeoDataFrame) assert group.crs == crs df.groupby("value2").apply(func, include_groups=False) # selecting the non-group columns -> no need to pass the keyword df.groupby("value2")[[geometry_name, "value1"]].apply(func) # actual test with functionality res = df.groupby("value2").apply( lambda x: geopandas.sjoin(x, x[[geometry_name, "value1"]], how="inner"), include_groups=False, ) expected = ( df.take([0, 0, 2, 2, 1]) .set_index("value2", drop=True, append=True) .swaplevel() .rename(columns={"value1": "value1_left"}) .assign(value1_right=[0, 2, 0, 2, 1]) ) assert_geodataframe_equal(res.drop(columns=["index_right"]), expected) def test_apply(s): # function that returns geometry preserves GeoSeries class def geom_func(geom): assert isinstance(geom, Point) return geom result = s.apply(geom_func) assert isinstance(result, GeoSeries) assert_geoseries_equal(result, s) # function that returns non-geometry results in Series def numeric_func(geom): assert isinstance(geom, Point) return geom.x result = s.apply(numeric_func) assert not isinstance(result, GeoSeries) assert_series_equal(result, pd.Series([0.0, 1.0, 2.0])) def test_apply_loc_len1(df): # subset of len 1 with loc -> bug in pandas with inconsistent Block ndim # resulting in bug in apply # https://github.com/geopandas/geopandas/issues/1078 subset = df.loc[[0], "geometry"] result = subset.apply(lambda geom: geom.is_empty) expected = subset.is_empty np.testing.assert_allclose(result, expected) @pytest.mark.skipif(compat.PANDAS_GE_30, reason="convert_dtype is removed in pandas 3") def test_apply_convert_dtypes_keyword(s): # ensure the convert_dtypes keyword is accepted with pytest.warns() as record: res = s.apply(lambda x: x, convert_dtype=True, args=()) assert_geoseries_equal(res, s) assert len(record) == 1 assert "the convert_dtype parameter" in str(record[0].message) @pytest.mark.parametrize("crs", [None, "EPSG:4326"]) def test_apply_no_geometry_result(df, crs): if crs: if not compat.HAS_PYPROJ: pytest.skip("requires pyproj") df = df.set_crs(crs) result = df.apply(lambda col: col.astype(str), axis=0) assert type(result) is pd.DataFrame expected = df.astype(str) assert_frame_equal(result, expected) result = df.apply(lambda col: col.astype(str), axis=1) assert type(result) is pd.DataFrame assert_frame_equal(result, expected) def test_apply_preserves_geom_col_name(df): df = df.rename_geometry("geom") result = df.apply(lambda col: col, axis=0) assert result.geometry.name == "geom" def test_df_apply_returning_series(df): # https://github.com/geopandas/geopandas/issues/2283 result = df.apply(lambda row: row.geometry, axis=1) assert_geoseries_equal(result, df.geometry, check_crs=False) result = df.apply(lambda row: row.value1, axis=1) assert_series_equal(result, df["value1"].rename(None)) # https://github.com/geopandas/geopandas/issues/2480 result = df.apply(lambda x: float("NaN"), axis=1) assert result.dtype == "float64" # assert list of nones is not promoted to GeometryDtype result = df.apply(lambda x: None, axis=1) assert result.dtype == "object" # https://github.com/geopandas/geopandas/issues/2889 # contrived case such that `from_shapely` receives an array of geodataframes res = df.apply(lambda row: df.geometry.to_frame(), axis=1) assert res.dtype == "object" def test_df_apply_geometry_dtypes(df): # https://github.com/geopandas/geopandas/issues/1852 apply_types = [] def get_dtypes(srs): apply_types.append((srs.name, type(srs))) df["geom2"] = df.geometry df.apply(get_dtypes) expected = [ ("geometry", GeoSeries), ("value1", pd.Series), ("value2", pd.Series), ("geom2", GeoSeries), ] assert apply_types == expected def test_pivot(df): # https://github.com/geopandas/geopandas/issues/2057 # pivot failing due to creating a MultiIndex result = df.pivot(columns="value1") expected = GeoDataFrame(pd.DataFrame(df).pivot(columns="value1")) assert_geodataframe_equal(result, expected) def test_isna_empty_dtypes(): # https://github.com/geopandas/geopandas/issues/3417 # should not auto coerce isna to geometry dtype expected = pd.DataFrame({"geometry": []}).isna() actual = GeoDataFrame({"geometry": []}).isna() assert_frame_equal(expected, actual) # different geometry col name expected = pd.DataFrame({"a": []}).isna() actual = GeoDataFrame({"a": []}, geometry="a").isna() assert_frame_equal(expected, actual) def test_preserve_attrs(df): # https://github.com/geopandas/geopandas/issues/1654 df.attrs["name"] = "my_name" attrs = {"name": "my_name"} assert df.attrs == attrs # preserve attrs in indexing operations for subset in [df[:2], df[df["value1"] > 2], df[["value2", "geometry"]]]: assert df.attrs == attrs # preserve attrs in methods df2 = df.reset_index() assert df2.attrs == attrs # https://github.com/geopandas/geopandas/issues/1875 df3 = df2.explode(index_parts=True) assert df3.attrs == attrs def test_attrs_concat(): # from pandas-dev/pandas#60357 # concat propagates attrs if all input attrs are equal geoms = [Point(2, 2), Point(3, 3)] df1 = GeoDataFrame({"A": [2, 3], "geometry": geoms}) df1.attrs = {"a": 1, "b": 2} df2 = GeoDataFrame({"A": [4, 5], "geometry": geoms}) df2.attrs = df1.attrs.copy() df3 = GeoDataFrame({"A": [6, 7], "geometry": geoms}) df3.attrs = df1.attrs.copy() assert pd.concat([df1, df2, df3]).attrs == df1.attrs # concat does not propagate attrs if input attrs are different df2.attrs = {"c": 3} assert pd.concat([df1, df2, df3]).attrs == {} def test_attrs_merge(): # from pandas-dev/pandas#60357 geoms = [Point(2, 2), Point(3, 3)] # merge propagates attrs if all input attrs are equal df1 = GeoDataFrame({"key": ["a", "b"], "val1": [1, 2], "geometry": geoms}) df1.attrs = {"a": 1, "b": 2} df2 = GeoDataFrame({"key": ["a", "b"], "val2": [3, 4], "geometry": geoms}) df2.attrs = df1.attrs.copy() assert pd.merge(df1, df2).attrs == df1.attrs # merge does not propagate attrs if input attrs are different df2.attrs = {"c": 3} assert pd.merge(df1, df2).attrs == {} def test_preserve_flags(df): # https://github.com/geopandas/geopandas/issues/1654 df = df.set_flags(allows_duplicate_labels=False) assert df.flags.allows_duplicate_labels is False # preserve flags in indexing operations for subset in [df[:2], df[df["value1"] > 2], df[["value2", "geometry"]]]: assert df.flags.allows_duplicate_labels is False # preserve attrs in methods df2 = df.reset_index() assert df2.flags.allows_duplicate_labels is False # it is honored for operations that introduce duplicate labels with pytest.raises(ValueError): df.reindex([0, 0, 1]) with pytest.raises(ValueError): df[["value1", "value1", "geometry"]] with pytest.raises(ValueError): pd.concat([df, df]) def test_ufunc(): # this is calling a shapely ufunc, but we currently rely on pandas' implementation # of `__array_ufunc__` to wrap the result back into a GeoSeries ser = GeoSeries([Point(1, 1), Point(2, 2), Point(3, 3)]) result = shapely.buffer(ser, 2) assert isinstance(result, GeoSeries) # ensure the result is still writeable # (https://github.com/geopandas/geopandas/issues/3178) assert result.array._data.flags.writeable result.loc[0] = Point(10, 10) assert result.iloc[0] == Point(10, 10) geopandas-1.2.0/geopandas/tests/test_plotting.py000066400000000000000000003261631525650651600220550ustar00rootroot00000000000000import itertools import warnings from packaging.version import Version import numpy as np import pandas as pd from shapely.affinity import rotate from shapely.geometry import ( GeometryCollection, LinearRing, LineString, MultiLineString, MultiPoint, MultiPolygon, Point, Polygon, box, ) import geopandas._compat as compat from geopandas import GeoDataFrame, GeoSeries, points_from_xy, read_file from geopandas._compat import HAS_PYPROJ from geopandas.plotting import GeoplotAccessor, _check_invalid_categories import pytest matplotlib = pytest.importorskip("matplotlib") matplotlib.use("Agg") import matplotlib.pyplot as plt try: # skipif and importorskip do not work for decorators from matplotlib.testing.decorators import check_figures_equal, image_comparison MPL_DECORATORS = True # fixme: image comparison tests fail under 3.11.0 dev as of 2026-03-13 if Version(matplotlib.__version__) >= Version("3.11.0.dev"): MPL_DECORATORS = False except ImportError: MPL_DECORATORS = False @pytest.fixture(autouse=True) def close_figures(request): yield plt.close("all") try: cycle = matplotlib.rcParams["axes.prop_cycle"].by_key() MPL_DFT_COLOR = cycle["color"][0] except KeyError: MPL_DFT_COLOR = matplotlib.rcParams["axes.color_cycle"][0] plt.rcParams.update({"figure.max_open_warning": 0}) class TestPointPlotting: def setup_method(self): self.N = 10 self.points = GeoSeries(Point(i, i) for i in range(self.N)) values = np.arange(self.N) self.df = GeoDataFrame({"geometry": self.points, "values": values}) self.df["exp"] = (values * 10) ** 3 multipoint1 = MultiPoint(self.points) multipoint2 = rotate(multipoint1, 90) self.df2 = GeoDataFrame( {"geometry": [multipoint1, multipoint2], "values": [0, 1]} ) def test_figsize(self): ax = self.points.plot(figsize=(1, 1)) np.testing.assert_array_equal(ax.figure.get_size_inches(), (1, 1)) ax = self.df.plot(figsize=(1, 1)) np.testing.assert_array_equal(ax.figure.get_size_inches(), (1, 1)) def test_default_colors(self): # # without specifying values -> uniform color # GeoSeries ax = self.points.plot() _check_colors( self.N, ax.collections[0].get_facecolors(), [MPL_DFT_COLOR] * self.N ) # GeoDataFrame ax = self.df.plot() _check_colors( self.N, ax.collections[0].get_facecolors(), [MPL_DFT_COLOR] * self.N ) # # with specifying values -> different colors for all 10 values ax = self.df.plot(column="values") cmap = plt.get_cmap() expected_colors = cmap(np.arange(self.N) / (self.N - 1)) _check_colors(self.N, ax.collections[0].get_facecolors(), expected_colors) def test_series_color_no_index(self): # Color order with ordered index colors_ord = pd.Series(["a", "b", "c", "a", "b", "c", "a", "b", "c", "a"]) # Plot using Series as color ax1 = self.df.plot(colors_ord) # Correct answer: Add as column to df and plot self.df["colors_ord"] = colors_ord ax2 = self.df.plot("colors_ord") # Confirm out-of-order index re-sorted for col1, col2 in zip(ax1.collections, ax2.collections): np.testing.assert_array_equal(col1.get_facecolors(), col2.get_facecolors()) np.testing.assert_array_equal( col1.get_paths()[0].vertices, col2.get_paths()[0].vertices ) def test_series_color_index(self): # Color order with out-of-order index colors_ord = pd.Series( ["a", "a", "a", "a", "b", "b", "b", "c", "c", "c"], index=[0, 3, 6, 9, 1, 4, 7, 2, 5, 8], ) # Plot using Series as color ax1 = self.df.plot(colors_ord) # Correct answer: Add as column to df and plot self.df["colors_ord"] = colors_ord ax2 = self.df.plot("colors_ord") # Confirm out-of-order index re-sorted for col1, col2 in zip(ax1.collections, ax2.collections): np.testing.assert_array_equal(col1.get_facecolors(), col2.get_facecolors()) np.testing.assert_array_equal( col1.get_paths()[0].vertices, col2.get_paths()[0].vertices ) def test_colormap(self): # without specifying values but cmap specified -> no uniform color # but different colors for all points # GeoSeries ax = self.points.plot(cmap="RdYlGn") cmap = plt.get_cmap("RdYlGn") exp_colors = cmap(np.arange(self.N) / (self.N - 1)) _check_colors(self.N, ax.collections[0].get_facecolors(), exp_colors) ax = self.df.plot(cmap="RdYlGn") _check_colors(self.N, ax.collections[0].get_facecolors(), exp_colors) # # with specifying values -> different colors for all 10 values ax = self.df.plot(column="values", cmap="RdYlGn") cmap = plt.get_cmap("RdYlGn") _check_colors(self.N, ax.collections[0].get_facecolors(), exp_colors) # when using a cmap with specified lut -> limited number of different # colors ax = self.points.plot(cmap=plt.get_cmap("Set1", lut=5)) cmap = plt.get_cmap("Set1", lut=5) exp_colors = cmap(list(range(5)) * 2) _check_colors(self.N, ax.collections[0].get_facecolors(), exp_colors) def test_single_color(self): ax = self.points.plot(color="green") _check_colors(self.N, ax.collections[0].get_facecolors(), ["green"] * self.N) ax = self.df.plot(color="green") _check_colors(self.N, ax.collections[0].get_facecolors(), ["green"] * self.N) # check rgba tuple GH1178 ax = self.df.plot(color=(0.5, 0.5, 0.5)) _check_colors( self.N, ax.collections[0].get_facecolors(), [(0.5, 0.5, 0.5)] * self.N ) ax = self.df.plot(color=(0.5, 0.5, 0.5, 0.5)) _check_colors( self.N, ax.collections[0].get_facecolors(), [(0.5, 0.5, 0.5, 0.5)] * self.N ) with pytest.raises((ValueError, TypeError)): self.df.plot(color="not color") with warnings.catch_warnings(): # 'color' overrides 'column' warnings.filterwarnings( "ignore", message="Only specify one of 'column' or 'color'" ) ax = self.df.plot(column="values", color="green") _check_colors( self.N, ax.collections[0].get_facecolors(), ["green"] * self.N ) def test_markersize(self): ax = self.points.plot(markersize=10) assert ax.collections[0].get_sizes() == [10] ax = self.df.plot(markersize=10) assert ax.collections[0].get_sizes() == [10] ax = self.df.plot(column="values", markersize=10) assert ax.collections[0].get_sizes() == [10] ax = self.df.plot(markersize="values") assert (ax.collections[0].get_sizes() == self.df["values"]).all() ax = self.df.plot(column="values", markersize="values") assert (ax.collections[0].get_sizes() == self.df["values"]).all() def test_markersize_categorical(self): ax = self.df.plot( column="values", markersize="values", scheme="quantiles", k=10 ) for i, col in enumerate(ax.collections): assert col.get_sizes() == self.df["values"][i] def test_markersize_array_missing(self): self.df.loc[1:4, "values"] = np.nan ax = self.df.plot(column="values", markersize="values") np.testing.assert_array_equal( ax.collections[0].get_sizes(), [0.0, 5.0, 6.0, 7.0, 8.0, 9.0] ) ax2 = self.df.plot( column="values", markersize=np.arange(10), missing_kwds={"color": "r"} ) np.testing.assert_array_equal( ax2.collections[0].get_sizes(), [0.0, 5.0, 6.0, 7.0, 8.0, 9.0] ) np.testing.assert_array_equal(ax2.collections[1].get_sizes(), [1, 2, 3, 4]) def test_markerstyle(self): ax = self.df2.plot(marker="+") expected = _style_to_vertices("+") np.testing.assert_array_equal( expected, ax.collections[0].get_paths()[0].vertices ) def test_style_kwargs(self): ax = self.points.plot(edgecolors="k") assert (ax.collections[0].get_edgecolor() == [0, 0, 0, 1]).all() def test_style_kwargs_alpha(self): ax = self.df.plot(alpha=0.7) np.testing.assert_array_equal([0.7], ax.collections[0].get_alpha()) try: ax = self.df.plot(alpha=np.linspace(0, 0.0, 1.0, self.N)) except TypeError: # no list allowed for alpha up to matplotlib 3.3 pass else: np.testing.assert_array_equal( np.linspace(0, 0.0, 1.0, self.N), ax.collections[0].get_alpha() ) def test_legend(self): with warnings.catch_warnings(): warnings.filterwarnings( "ignore", message="Only specify one of 'column' or 'color'" ) # 'color' overrides 'column' # legend ignored if color is given. ax = self.df.plot(column="values", color="green", legend=True) assert len(ax.get_figure().axes) == 1 # no separate legend axis # legend ignored if no column is given. ax = self.df.plot(legend=True) assert len(ax.get_figure().axes) == 1 # no separate legend axis # # Continuous legend # the colorbar matches the Point colors ax = self.df.plot(column="values", cmap="RdYlGn", legend=True) point_colors = ax.collections[0].get_facecolors() cbar_colors = _get_colorbar_ax(ax.get_figure()).collections[-1].get_facecolors() # first point == bottom of colorbar np.testing.assert_array_equal(point_colors[0], cbar_colors[0]) # last point == top of colorbar np.testing.assert_array_equal(point_colors[-1], cbar_colors[-1]) # # Categorical legend # the colorbar matches the Point colors ax = self.df.plot(column="values", categorical=True, legend=True) for col, leg in zip(ax.collections, ax.get_legend().axes.collections): np.testing.assert_array_equal(col.get_facecolors(), leg.get_facecolors()) # # Normalized legend # the colorbar matches the Point colors norm = matplotlib.colors.LogNorm( vmin=self.df[1:].exp.min(), vmax=self.df[1:].exp.max() ) ax = self.df[1:].plot(column="exp", cmap="RdYlGn", legend=True, norm=norm) point_colors = ax.collections[0].get_facecolors() cbar_colors = _get_colorbar_ax(ax.get_figure()).collections[-1].get_facecolors() # first point == bottom of colorbar np.testing.assert_array_equal(point_colors[0], cbar_colors[0]) # last point == top of colorbar np.testing.assert_array_equal(point_colors[-1], cbar_colors[-1]) # colorbar generated proper long transition assert cbar_colors.shape == (256, 4) def test_subplots_norm(self): # colors of subplots are the same as for plot (norm is applied) cmap = matplotlib.cm.viridis_r norm = matplotlib.colors.Normalize(vmin=0, vmax=20) ax = self.df.plot(column="values", cmap=cmap, norm=norm) actual_colors_orig = ax.collections[0].get_facecolors() exp_colors = cmap(np.arange(10) / (20)) np.testing.assert_array_equal(exp_colors, actual_colors_orig) _fig, ax = plt.subplots() self.df[1:].plot(column="values", ax=ax, norm=norm, cmap=cmap) actual_colors_sub = ax.collections[0].get_facecolors() np.testing.assert_array_equal(actual_colors_orig[1], actual_colors_sub[0]) def test_empty_plot(self): s = GeoSeries([Polygon()]) with pytest.warns(UserWarning): ax = s.plot() assert len(ax.collections) == 0 s = GeoSeries([]) with pytest.warns(UserWarning): ax = s.plot() assert len(ax.collections) == 0 df = GeoDataFrame([], columns=["geometry"]) with pytest.warns(UserWarning): ax = df.plot() assert len(ax.collections) == 0 with pytest.warns(UserWarning): ax = ( GeoDataFrame( {"pop": [1.0, 2.0]}, geometry=[Point(0, 0), Point(1, 1)], crs="EPSG:4326", ) .query("pop > 5") .plot(column="pop") ) assert len(ax.collections) == 0 def test_empty_geometry(self): s = GeoSeries([Polygon([(0, 0), (1, 0), (1, 1)]), Polygon()]) ax = s.plot() assert len(ax.collections) == 1 # more complex case with GEOMETRYCOLLECTION EMPTY, POINT EMPTY and NONE poly = Polygon([(-1, -1), (-1, 2), (2, 2), (2, -1), (-1, -1)]) point = Point(0, 1) point_ = Point(10, 10) empty_point = Point() gdf = GeoDataFrame(geometry=[point, empty_point, point_]) gdf["geometry"] = gdf.intersection(poly) with warnings.catch_warnings(): # loc to add row calls concat internally, warning for pandas >=2.1 warnings.filterwarnings( "ignore", "The behavior of DataFrame concatenation with empty", FutureWarning, ) gdf.loc[3] = [None] ax = gdf.plot() assert len(ax.collections) == 1 @pytest.mark.parametrize( "geoms", [ [ box(0, 0, 1, 1), box(7, 7, 8, 8), ], [ LineString([(1, 1), (1, 2)]), LineString([(7, 1), (7, 2)]), ], [ Point(1, 1), Point(7, 7), ], ], ) def test_empty_geometry_colors(self, geoms): s = GeoSeries( geoms, index=["r", "b"], ) s2 = s.intersection(box(5, 0, 10, 10)) ax = s2.plot(color=["red", "blue"]) blue = np.array([0.0, 0.0, 1.0, 1.0]) if s.geom_type["r"] == "LineString": np.testing.assert_array_equal(ax.get_children()[0].get_edgecolor()[0], blue) else: np.testing.assert_array_equal(ax.get_children()[0].get_facecolor()[0], blue) def test_multipoints(self): # MultiPoints ax = self.df2.plot() _check_colors(4, ax.collections[0].get_facecolors(), [MPL_DFT_COLOR] * 4) ax = self.df2.plot(column="values") cmap = plt.get_cmap(lut=2) expected_colors = [cmap(0)] * self.N + [cmap(1)] * self.N _check_colors(20, ax.collections[0].get_facecolors(), expected_colors) ax = self.df2.plot(color=["r", "b"]) # colors are repeated for all components within a MultiPolygon _check_colors(20, ax.collections[0].get_facecolors(), ["r"] * 10 + ["b"] * 10) def test_multipoints_alpha(self): ax = self.df2.plot(alpha=0.7) np.testing.assert_array_equal([0.7], ax.collections[0].get_alpha()) try: ax = self.df2.plot(alpha=[0.7, 0.2]) except TypeError: # no list allowed for alpha up to matplotlib 3.3 pass else: np.testing.assert_array_equal( [0.7] * 10 + [0.2] * 10, ax.collections[0].get_alpha() ) def test_categories(self): self.df["cats_object"] = ["cat1", "cat2"] * 5 self.df["nums"] = [1, 2] * 5 self.df["singlecat_object"] = ["cat2"] * 10 self.df["cats"] = pd.Categorical(["cat1", "cat2"] * 5) self.df["singlecat"] = pd.Categorical( ["cat2"] * 10, categories=["cat1", "cat2"] ) self.df["cats_ordered"] = pd.Categorical( ["cat2", "cat1"] * 5, categories=["cat2", "cat1"] ) self.df["bool"] = [False, True] * 5 self.df["bool_extension"] = pd.array([False, True] * 5) self.df["cats_string"] = pd.array(["cat1", "cat2"] * 5, dtype="string") ax1 = self.df.plot("cats_object", legend=True) ax2 = self.df.plot("cats", legend=True) ax3 = self.df.plot( "singlecat_object", categories=["cat1", "cat2"], legend=True, marker="o" ) ax4 = self.df.plot("singlecat", legend=True, marker="o") ax5 = self.df.plot("cats_ordered", legend=True) ax6 = self.df.plot("nums", categories=[1, 2], legend=True) ax7 = self.df.plot("bool", legend=True) ax8 = self.df.plot("bool_extension", legend=True) ax9 = self.df.plot("cats_string", legend=True) point_colors0_0 = ax1.collections[0].get_facecolors() point_colors0_1 = ax1.collections[1].get_facecolors() for ax in [ax2, ax3, ax4, ax5, ax6, ax7, ax8, ax9]: point_colorsx_0 = ax.collections[0].get_facecolors() point_colorsx_1 = ax.collections[1].get_facecolors() np.testing.assert_array_equal(point_colors0_0, point_colorsx_0) np.testing.assert_array_equal(point_colors0_1, point_colorsx_1) handles1, _ = ax1.get_legend_handles_labels() for ax in [ax2, ax3, ax4, ax5, ax6, ax7, ax8, ax9]: handles, _ = ax.get_legend_handles_labels() np.testing.assert_array_equal( handles1[0].get_facecolor(), handles[0].get_facecolor() ) np.testing.assert_array_equal( handles1[1].get_facecolor(), handles[1].get_facecolor() ) with pytest.raises(TypeError): self.df.plot(column="cats_object", categories="non_list") with pytest.raises( ValueError, match="Column contains values not listed in categories" ): self.df.plot(column="cats_object", categories=["cat1"]) with pytest.raises( ValueError, match="Cannot specify 'categories' when column has" ): self.df.plot(column="cats", categories=["cat1"]) def test_check_invalid_categories_raises(self): values = np.array(["cat1", "cat2"], dtype=object) categories = ["cat1"] with pytest.raises( ValueError, match="Column contains values not listed in categories" ): _check_invalid_categories(categories, values) def test_missing(self): self.df.loc[0, "values"] = np.nan ax = self.df.plot("values") cmap = plt.get_cmap() expected_colors = cmap(np.arange(self.N - 1) / (self.N - 2)) _check_colors(self.N - 1, ax.collections[0].get_facecolors(), expected_colors) ax = self.df.plot("values", missing_kwds={"color": "r"}) cmap = plt.get_cmap() expected_colors = cmap(np.arange(self.N - 1) / (self.N - 2)) _check_colors(1, ax.collections[1].get_facecolors(), ["r"]) _check_colors(self.N - 1, ax.collections[0].get_facecolors(), expected_colors) ax = self.df.plot( "values", missing_kwds={"color": "r"}, categorical=True, legend=True ) _check_colors(1, ax.collections[-1].get_facecolors(), ["r"]) point_colors = ax.collections[0].get_facecolors() nan_color = ax.collections[-1].get_facecolors() leg_colors = ax.get_legend().axes.collections[0].get_facecolors() leg_colors1 = ax.get_legend().axes.collections[-1].get_facecolors() np.testing.assert_array_equal(point_colors[0], leg_colors[0]) np.testing.assert_array_equal(nan_color[0], leg_colors1[0]) def test_no_missing_and_missing_kwds(self): # GH2210 df = self.df.copy() df["category"] = df["values"].astype("str") df.plot("category", missing_kwds={"facecolor": "none"}, legend=True) def test_missing_aspect(self): self.df.loc[0, "values"] = np.nan ax = self.df.plot( "values", missing_kwds={"color": "r"}, categorical=True, legend=True, aspect=2, ) assert ax.get_aspect() == 2 class TestPointZPlotting: def setup_method(self): self.N = 10 self.points = GeoSeries(Point(i, i, i) for i in range(self.N)) values = np.arange(self.N) self.df = GeoDataFrame({"geometry": self.points, "values": values}) def test_plot(self): # basic test that points with z coords don't break plotting self.df.plot() class TestLineStringPlotting: def setup_method(self): self.N = 10 values = np.arange(self.N) self.lines = GeoSeries( [LineString([(0, i), (4, i + 0.5), (9, i)]) for i in range(self.N)], index=list("ABCDEFGHIJ"), ) self.df = GeoDataFrame({"geometry": self.lines, "values": values}) multiline1 = MultiLineString(self.lines.loc["A":"B"].values) multiline2 = MultiLineString(self.lines.loc["C":"D"].values) self.df2 = GeoDataFrame( {"geometry": [multiline1, multiline2], "values": [0, 1]} ) self.linearrings = GeoSeries( [LinearRing([(0, i), (4, i + 0.5), (9, i)]) for i in range(self.N)], index=list("ABCDEFGHIJ"), ) self.df3 = GeoDataFrame({"geometry": self.linearrings, "values": values}) def test_autolim_false(self): """Test linestring plot preserving axes limits.""" ax = self.lines[: self.N // 2].plot() ylim = ax.get_ylim() self.lines.plot(ax=ax, autolim=False) assert ax.get_ylim() == ylim ax = self.df[: self.N // 2].plot() ylim = ax.get_ylim() self.df.plot(ax=ax, autolim=False) assert ax.get_ylim() == ylim def test_autolim_true(self): """Test linestring plot autoscaling axes limits.""" ax = self.lines[: self.N // 2].plot() ylim = ax.get_ylim() self.lines.plot(ax=ax, autolim=True) assert ax.get_ylim() != ylim ax = self.df[: self.N // 2].plot() ylim = ax.get_ylim() self.df.plot(ax=ax, autolim=True) assert ax.get_ylim() != ylim def test_single_color(self): ax = self.lines.plot(color="green") _check_colors(self.N, ax.collections[0].get_colors(), ["green"] * self.N) ax = self.df.plot(color="green") _check_colors(self.N, ax.collections[0].get_colors(), ["green"] * self.N) ax = self.linearrings.plot(color="green") _check_colors(self.N, ax.collections[0].get_colors(), ["green"] * self.N) ax = self.df3.plot(color="green") _check_colors(self.N, ax.collections[0].get_colors(), ["green"] * self.N) # check rgba tuple GH1178 ax = self.df.plot(color=(0.5, 0.5, 0.5, 0.5)) _check_colors( self.N, ax.collections[0].get_colors(), [(0.5, 0.5, 0.5, 0.5)] * self.N ) ax = self.df.plot(color=(0.5, 0.5, 0.5, 0.5)) _check_colors( self.N, ax.collections[0].get_colors(), [(0.5, 0.5, 0.5, 0.5)] * self.N ) with pytest.raises((TypeError, ValueError)): self.df.plot(color="not color") with warnings.catch_warnings(): # 'color' overrides 'column' warnings.filterwarnings( "ignore", message="Only specify one of 'column' or 'color'" ) ax = self.df.plot(column="values", color="green") _check_colors(self.N, ax.collections[0].get_colors(), ["green"] * self.N) def test_style_kwargs_linestyle(self): # single for ax in [ self.lines.plot(linestyle=":", linewidth=1), self.df.plot(linestyle=":", linewidth=1), self.df.plot(column="values", linestyle=":", linewidth=1), ]: assert [(0.0, [1.0, 1.65])] == ax.collections[0].get_linestyle() # tuple ax = self.lines.plot(linestyle=(0, (3, 10, 1, 15)), linewidth=1) assert [(0, [3, 10, 1, 15])] == ax.collections[0].get_linestyle() # multiple ls = [("dashed", "dotted", "dashdot", "solid")[k % 4] for k in range(self.N)] exp_ls = [_style_to_linestring_onoffseq(st, 1) for st in ls] for ax in [ self.lines.plot(linestyle=ls, linewidth=1), self.lines.plot(linestyles=ls, linewidth=1), self.df.plot(linestyle=ls, linewidth=1), self.df.plot(column="values", linestyle=ls, linewidth=1), ]: assert exp_ls == ax.collections[0].get_linestyle() def test_style_kwargs_linewidth(self): # single for ax in [ self.lines.plot(linewidth=2), self.df.plot(linewidth=2), self.df.plot(column="values", linewidth=2), ]: np.testing.assert_array_equal([2], ax.collections[0].get_linewidths()) # multiple lw = [(0, 1, 2, 5.5, 10)[k % 5] for k in range(self.N)] for ax in [ self.lines.plot(linewidth=lw), self.lines.plot(linewidths=lw), self.df.plot(linewidth=lw), self.df.plot(column="values", linewidth=lw), ]: np.testing.assert_array_equal(lw, ax.collections[0].get_linewidths()) def test_style_kwargs_alpha(self): ax = self.df.plot(alpha=0.7) np.testing.assert_array_equal([0.7], ax.collections[0].get_alpha()) try: ax = self.df.plot(alpha=np.linspace(0, 0.0, 1.0, self.N)) except TypeError: # no list allowed for alpha up to matplotlib 3.3 pass else: np.testing.assert_array_equal( np.linspace(0, 0.0, 1.0, self.N), ax.collections[0].get_alpha() ) def test_style_kwargs_path_effects(self): from matplotlib.patheffects import withStroke effects = [withStroke(linewidth=8, foreground="b")] ax = self.df.plot(color="orange", path_effects=effects) assert ax.collections[0].get_path_effects()[0].__dict__["_gc"] == { "linewidth": 8, "foreground": "b", } def test_subplots_norm(self): # colors of subplots are the same as for plot (norm is applied) cmap = matplotlib.cm.viridis_r norm = matplotlib.colors.Normalize(vmin=0, vmax=20) ax = self.df.plot(column="values", cmap=cmap, norm=norm) actual_colors_orig = ax.collections[0].get_edgecolors() exp_colors = cmap(np.arange(10) / (20)) np.testing.assert_array_equal(exp_colors, actual_colors_orig) _fig, ax = plt.subplots() self.df[1:].plot(column="values", ax=ax, norm=norm, cmap=cmap) actual_colors_sub = ax.collections[0].get_edgecolors() np.testing.assert_array_equal(actual_colors_orig[1], actual_colors_sub[0]) def test_multilinestrings(self): # MultiLineStrings ax = self.df2.plot() assert len(ax.collections[0].get_paths()) == 4 _check_colors(4, ax.collections[0].get_edgecolors(), [MPL_DFT_COLOR] * 4) ax = self.df2.plot("values") cmap = plt.get_cmap(lut=2) # colors are repeated for all components within a MultiLineString expected_colors = [cmap(0), cmap(0), cmap(1), cmap(1)] _check_colors(4, ax.collections[0].get_edgecolors(), expected_colors) ax = self.df2.plot(color=["r", "b"]) # colors are repeated for all components within a MultiLineString _check_colors(4, ax.collections[0].get_edgecolors(), ["r", "r", "b", "b"]) class TestPolygonPlotting: def setup_method(self): t1 = Polygon([(0, 0), (1, 0), (1, 1)]) t2 = Polygon([(1, 0), (2, 0), (2, 1)]) self.polys = GeoSeries([t1, t2], index=list("AB")) self.df = GeoDataFrame({"geometry": self.polys, "values": [0, 1]}) multipoly1 = MultiPolygon([t1, t2]) multipoly2 = rotate(multipoly1, 180) self.df2 = GeoDataFrame( {"geometry": [multipoly1, multipoly2], "values": [0, 1]} ) t3 = Polygon([(2, 0), (3, 0), (3, 1)]) df_nan = GeoDataFrame({"geometry": t3, "values": [np.nan]}) self.df3 = pd.concat([self.df, df_nan]) def test_autolim_false(self): """Test polygon plot preserving axes limits.""" ax = self.polys[:1].plot() xlim = ax.get_xlim() self.polys.plot(ax=ax, autolim=False) assert ax.get_xlim() == xlim ax = self.df[:1].plot() xlim = ax.get_xlim() self.df.plot(ax=ax, autolim=False) assert ax.get_xlim() == xlim def test_autolim_true(self): """Test polygon plot autoscaling axes limits.""" ax = self.polys[:1].plot() xlim = ax.get_xlim() self.polys.plot(ax=ax, autolim=True) assert ax.get_xlim() != xlim ax = self.df[:1].plot() xlim = ax.get_xlim() self.df.plot(ax=ax, autolim=True) assert ax.get_xlim() != xlim def test_autolim_categorical(self): """Test categorical polygon plot respecting autolim.""" ax = self.df[:1].plot() xlim = ax.get_xlim() self.df.plot(ax=ax, column="values", categorical=True, autolim=False) assert ax.get_xlim() == xlim ax = self.df[:1].plot() xlim = ax.get_xlim() self.df.plot(ax=ax, column="values", categorical=True, autolim=True) assert ax.get_xlim() != xlim def test_single_color(self): ax = self.polys.plot(color="green") _check_colors(2, ax.collections[0].get_facecolors(), ["green"] * 2) # color only sets facecolor assert len(ax.collections[0].get_edgecolors()) == 0 ax = self.df.plot(color="green") _check_colors(2, ax.collections[0].get_facecolors(), ["green"] * 2) assert len(ax.collections[0].get_edgecolors()) == 0 # check rgba tuple GH1178 ax = self.df.plot(color=(0.5, 0.5, 0.5)) _check_colors(2, ax.collections[0].get_facecolors(), [(0.5, 0.5, 0.5)] * 2) ax = self.df.plot(color=(0.5, 0.5, 0.5, 0.5)) _check_colors(2, ax.collections[0].get_facecolors(), [(0.5, 0.5, 0.5, 0.5)] * 2) with pytest.raises((TypeError, ValueError)): self.df.plot(color="not color") with warnings.catch_warnings(): # 'color' overrides 'column' warnings.filterwarnings( "ignore", message="Only specify one of 'column' or 'color'" ) ax = self.df.plot(column="values", color="green") _check_colors(2, ax.collections[0].get_facecolors(), ["green"] * 2) def test_vmin_vmax(self): # when vmin == vmax, all polygons should be the same color # non-categorical ax = self.df.plot(column="values", categorical=False, vmin=0, vmax=0) actual_colors = ax.collections[0].get_facecolors() np.testing.assert_array_equal(actual_colors[0], actual_colors[1]) # vmin vmax set correctly for array with NaN (GitHub issue 877) ax = self.df3.plot(column="values") actual_colors = ax.collections[0].get_facecolors() assert np.any(np.not_equal(actual_colors[0], actual_colors[1])) def test_style_kwargs_color(self): # facecolor overrides default cmap when color is not set ax = self.polys.plot(facecolor="k") _check_colors(2, ax.collections[0].get_facecolors(), ["k"] * 2) # edgecolor ax = self.polys.plot(edgecolor="red") np.testing.assert_array_equal( [(1, 0, 0, 1)], ax.collections[0].get_edgecolors() ) ax = self.df.plot("values", edgecolor="red") np.testing.assert_array_equal( [(1, 0, 0, 1)], ax.collections[0].get_edgecolors() ) # alpha sets both edge and face ax = self.polys.plot(facecolor="g", edgecolor="r", alpha=0.4) _check_colors(2, ax.collections[0].get_facecolors(), ["g"] * 2, alpha=0.4) _check_colors(2, ax.collections[0].get_edgecolors(), ["r"] * 2, alpha=0.4) # check rgba tuple GH1178 for face and edge ax = self.df.plot(facecolor=(0.5, 0.5, 0.5), edgecolor=(0.4, 0.5, 0.6)) _check_colors(2, ax.collections[0].get_facecolors(), [(0.5, 0.5, 0.5)] * 2) _check_colors(2, ax.collections[0].get_edgecolors(), [(0.4, 0.5, 0.6)] * 2) ax = self.df.plot( facecolor=(0.5, 0.5, 0.5, 0.5), edgecolor=(0.4, 0.5, 0.6, 0.5) ) _check_colors(2, ax.collections[0].get_facecolors(), [(0.5, 0.5, 0.5, 0.5)] * 2) _check_colors(2, ax.collections[0].get_edgecolors(), [(0.4, 0.5, 0.6, 0.5)] * 2) def test_style_kwargs_linestyle(self): # single ax = self.df.plot(linestyle=":", linewidth=1) assert [(0.0, [1.0, 1.65])] == ax.collections[0].get_linestyle() # tuple ax = self.df.plot(linestyle=(0, (3, 10, 1, 15)), linewidth=1) assert [(0, [3, 10, 1, 15])] == ax.collections[0].get_linestyle() # multiple ls = ["dashed", "dotted"] exp_ls = [_style_to_linestring_onoffseq(st, 1) for st in ls] for ax in [ self.df.plot(linestyle=ls, linewidth=1), self.df.plot(linestyles=ls, linewidth=1), ]: assert exp_ls == ax.collections[0].get_linestyle() def test_style_kwargs_linewidth(self): # single ax = self.df.plot(linewidth=2) np.testing.assert_array_equal([2], ax.collections[0].get_linewidths()) # multiple for ax in [self.df.plot(linewidth=[2, 4]), self.df.plot(linewidths=[2, 4])]: np.testing.assert_array_equal([2, 4], ax.collections[0].get_linewidths()) # alpha ax = self.df.plot(alpha=0.7) np.testing.assert_array_equal([0.7], ax.collections[0].get_alpha()) try: ax = self.df.plot(alpha=[0.7, 0.2]) except TypeError: # no list allowed for alpha up to matplotlib 3.3 pass else: np.testing.assert_array_equal([0.7, 0.2], ax.collections[0].get_alpha()) def test_legend_kwargs(self): categories = list(self.df["values"].unique()) prefix = "LABEL_FOR_" ax = self.df.plot( column="values", categorical=True, categories=categories, legend=True, legend_kwds={ "labels": [prefix + str(c) for c in categories], "frameon": False, }, ) assert len(categories) == len(ax.get_legend().get_texts()) assert ax.get_legend().get_texts()[0].get_text().startswith(prefix) assert ax.get_legend().get_frame_on() is False def test_colorbar_kwargs(self): # Test if kwargs are passed to colorbar label_txt = "colorbar test" ax = self.df.plot( column="values", categorical=False, legend=True, legend_kwds={"label": label_txt}, ) cax = _get_colorbar_ax(ax.get_figure()) assert cax.get_ylabel() == label_txt ax = self.df.plot( column="values", categorical=False, legend=True, legend_kwds={"label": label_txt, "orientation": "horizontal"}, ) cax = _get_colorbar_ax(ax.get_figure()) assert cax.get_xlabel() == label_txt if MPL_DECORATORS: """Test that geometries are properly normalized so holes appear.""" @image_comparison( ["polygon_with_holes"], extensions=["png", "pdf"], remove_text=True, savefig_kwarg={"dpi": 300, "bbox_inches": "tight"}, ) def test_plot_polygon_with_holes(self): geoms = [ Polygon( [(0, 0), (0, 5), (5, 5), (5, 0)], [ [(1, 1), (1, 2), (2, 2), (2, 1)], [(3, 2), (3, 3), (4, 3), (4, 2)], ], ) ] _df = GeoDataFrame(geometry=geoms) _df.plot() else: def test_plot_polygon_with_holes(self): geoms = [ Polygon( [(0, 0), (0, 5), (5, 5), (5, 0)], [ [(1, 1), (1, 2), (2, 2), (2, 1)], [(3, 2), (3, 3), (4, 3), (4, 2)], ], ) ] _df = GeoDataFrame(geometry=geoms) ax = _df.plot() plotted_vertices = ax.collections[0].get_paths()[0].vertices expected_vertices = _df.normalize().get_coordinates().to_numpy() np.testing.assert_array_equal(plotted_vertices, expected_vertices) def test_multipolygons_color(self): # MultiPolygons ax = self.df2.plot() assert len(ax.collections[0].get_paths()) == 2 _check_colors(2, ax.collections[0].get_facecolors(), [MPL_DFT_COLOR] * 2) ax = self.df2.plot("values") cmap = plt.get_cmap(lut=2) # colors are repeated for all components within a MultiPolygon expected_colors = [cmap(0), cmap(1)] _check_colors(2, ax.collections[0].get_facecolors(), expected_colors) ax = self.df2.plot(color=["r", "b"]) # colors are repeated for all components within a MultiPolygon _check_colors(2, ax.collections[0].get_facecolors(), ["r", "b"]) def test_multipolygons_linestyle(self): # single ax = self.df2.plot(linestyle=":", linewidth=1) assert [(0.0, [1.0, 1.65])] == ax.collections[0].get_linestyle() # tuple ax = self.df2.plot(linestyle=(0, (3, 10, 1, 15)), linewidth=1) assert [(0, [3, 10, 1, 15])] == ax.collections[0].get_linestyle() # multiple ls = ["dashed", "dotted"] exp_ls = [_style_to_linestring_onoffseq(st, 1) for st in ls] for ax in [ self.df2.plot(linestyle=ls, linewidth=1), self.df2.plot(linestyles=ls, linewidth=1), ]: assert exp_ls == ax.collections[0].get_linestyle() def test_multipolygons_linewidth(self): # single ax = self.df2.plot(linewidth=2) np.testing.assert_array_equal([2], ax.collections[0].get_linewidths()) # multiple for ax in [self.df2.plot(linewidth=[2, 4]), self.df2.plot(linewidths=[2, 4])]: np.testing.assert_array_equal([2, 4], ax.collections[0].get_linewidths()) def test_multipolygons_alpha(self): ax = self.df2.plot(alpha=0.7) np.testing.assert_array_equal([0.7], ax.collections[0].get_alpha()) try: ax = self.df2.plot(alpha=[0.7, 0.2]) except TypeError: # no list allowed for alpha up to matplotlib 3.3 pass else: np.testing.assert_array_equal([0.7, 0.2], ax.collections[0].get_alpha()) if MPL_DECORATORS: """Test multipolygons properly orient so holes will appear. Test partly derived from shapely PR #1933.""" @image_comparison( ["multipolygon_with_holes"], extensions=["png", "pdf"], remove_text=True, savefig_kwarg={"dpi": 300, "bbox_inches": "tight"}, ) def test_multipolygons_with_interior(self): poly1 = box(0, 0, 1, 1).difference(box(0.2, 0.2, 0.5, 0.5)) poly2 = box(3, 3, 6, 6).difference(box(4, 4, 5, 5)) multipoly = MultiPolygon([poly1, poly2]) _df = GeoDataFrame(geometry=[multipoly]) _df.plot() else: def test_multipolygons_with_interior(self): poly1 = box(0, 0, 1, 1).difference(box(0.2, 0.2, 0.5, 0.5)) poly2 = box(3, 3, 6, 6).difference(box(4, 4, 5, 5)) multipoly = MultiPolygon([poly1, poly2]) _df = GeoDataFrame(geometry=[multipoly]) ax = _df.plot() plotted_vertices = ax.collections[0].get_paths()[0].vertices expected_vertices = _df.normalize().get_coordinates().to_numpy() np.testing.assert_array_equal(plotted_vertices, expected_vertices) def test_subplots_norm(self): # colors of subplots are the same as for plot (norm is applied) cmap = matplotlib.cm.viridis_r norm = matplotlib.colors.Normalize(vmin=0, vmax=10) ax = self.df.plot(column="values", cmap=cmap, norm=norm) actual_colors_orig = ax.collections[0].get_facecolors() exp_colors = cmap(np.arange(2) / (10)) np.testing.assert_array_equal(exp_colors, actual_colors_orig) _fig, ax = plt.subplots() self.df[1:].plot(column="values", ax=ax, norm=norm, cmap=cmap) actual_colors_sub = ax.collections[0].get_facecolors() np.testing.assert_array_equal(actual_colors_orig[1], actual_colors_sub[0]) def test_single_category_continuous_cmap(self): # a categorical plot with a single group must not divide by # (ngroups - 1) == 0 when stretching a continuous cmap df = self.df.copy() df["cat"] = ["a", "a"] ax = df.plot(column="cat", cmap="viridis", legend=True) cmap = plt.get_cmap("viridis") _check_colors(2, ax.collections[0].get_facecolors(), [cmap(0.0)] * 2) # a single row is the degenerate case of the same thing ax = df[:1].plot(column="cat", cmap="viridis") _check_colors(1, ax.collections[0].get_facecolors(), [cmap(0.0)]) def test_single_bin_scheme_default_cmap(self): # a constant column collapses to one bin, and `scheme` defaults to the # continuous "viridis" cmap -> same single-group division by zero pytest.importorskip("mapclassify") df = self.df.copy() df["constant"] = [0, 0] with warnings.catch_warnings(): warnings.simplefilter("ignore", UserWarning) ax = df.plot(column="constant", scheme="quantiles", k=5, legend=True) cmap = plt.get_cmap("viridis") _check_colors(2, ax.collections[0].get_facecolors(), [cmap(0.0)] * 2) class TestPolygonZPlotting: def setup_method(self): t1 = Polygon([(0, 0, 0), (1, 0, 0), (1, 1, 1)]) t2 = Polygon([(1, 0, 0), (2, 0, 0), (2, 1, 1)]) self.polys = GeoSeries([t1, t2], index=list("AB")) self.df = GeoDataFrame({"geometry": self.polys, "values": [0, 1]}) multipoly1 = MultiPolygon([t1, t2]) multipoly2 = rotate(multipoly1, 180) self.df2 = GeoDataFrame( {"geometry": [multipoly1, multipoly2], "values": [0, 1]} ) def test_plot(self): # basic test that points with z coords don't break plotting self.df.plot() class TestColorParamArray: def setup_method(self): geom = [] color = [] for a, b in [(0, 2), (4, 6)]: b = box(a, a, b, b) geom += [b, b.buffer(0.8).exterior, b.centroid] color += ["red", "green", "blue"] self.gdf = GeoDataFrame({"geometry": geom, "color_rgba": color}) self.mgdf = self.gdf.dissolve(self.gdf.geom_type) def test_color_single(self): ax = self.gdf.plot(color=self.gdf["color_rgba"]) _check_colors( 4, np.concatenate([c.get_edgecolor() for c in ax.collections]), ["green"] * 2 + ["blue"] * 2, ) _check_colors( 4, np.concatenate([c.get_facecolor() for c in ax.collections]), ["red"] * 2 + ["blue"] * 2, ) def test_color_multi(self): ax = self.mgdf.plot(color=self.mgdf["color_rgba"]) _check_colors( 4, np.concatenate([c.get_edgecolor() for c in ax.collections]), ["green"] * 2 + ["blue"] * 2, ) _check_colors( 2, np.concatenate([c.get_facecolor() for c in ax.collections]), ["red", "blue"], ) class TestGeometryCollectionPlotting: def setup_method(self): coll1 = GeometryCollection( [ Polygon([(1, 0), (2, 0), (2, 1)]), MultiLineString([((0.5, 0.5), (1, 1)), ((1, 0.5), (1.5, 1))]), ] ) coll2 = GeometryCollection( [Point(0.75, 0.25), Polygon([(2, 2), (3, 2), (2, 3)])] ) self.series = GeoSeries([coll1, coll2]) self.df = GeoDataFrame({"geometry": self.series, "values": [1, 2]}) def test_colors(self): # default uniform color ax = self.series.plot() _check_colors( 2, ax.collections[0].get_facecolors(), [MPL_DFT_COLOR] * 2 ) # poly _check_colors( 2, ax.collections[1].get_edgecolors(), [MPL_DFT_COLOR] * 2 ) # line _check_colors(1, ax.collections[2].get_facecolors(), [MPL_DFT_COLOR]) # point def test_values(self): ax = self.df.plot("values") cmap = plt.get_cmap() exp_colors = cmap([0.0, 1.0]) _check_colors(2, ax.collections[0].get_facecolors(), exp_colors) # poly _check_colors( 2, ax.collections[1].get_edgecolors(), [exp_colors[0]] * 2 ) # line _check_colors(1, ax.collections[2].get_facecolors(), [exp_colors[1]]) # point def test_style_kwds_mapping(self): poly = Polygon([(0, 0), (1, 0), (1, 1), (0, 1)]) poly2 = Polygon([(1, 1), (2, 1), (2, 2), (1, 2)]) poly3 = Polygon([(2, 2), (3, 2), (3, 3), (2, 3)]) gc = GeometryCollection([poly2, poly3]) df = GeoDataFrame({"geometry": [poly, gc]}) ax = df.plot(facecolor=["r", "g"]) np.testing.assert_array_equal( ax.collections[0].get_facecolors(), np.array( [[1.0, 0.0, 0.0, 1.0], [0.0, 0.5, 0.0, 1.0], [0.0, 0.5, 0.0, 1.0]] ), ) class TestNonuniformGeometryPlotting: def setup_method(self): pytest.importorskip("matplotlib") poly = Polygon([(1, 0), (2, 0), (2, 1)]) line = LineString([(0.5, 0.5), (1, 1), (1, 0.5), (1.5, 1)]) point = Point(0.75, 0.25) self.series = GeoSeries([poly, line, point]) self.df = GeoDataFrame({"geometry": self.series, "values": [1, 2, 3]}) def test_colors(self): # default uniform color ax = self.series.plot() _check_colors(1, ax.collections[0].get_facecolors(), [MPL_DFT_COLOR]) _check_colors(1, ax.collections[1].get_edgecolors(), [MPL_DFT_COLOR]) _check_colors(1, ax.collections[2].get_facecolors(), [MPL_DFT_COLOR]) # colormap: different colors ax = self.series.plot(cmap="RdYlGn") cmap = plt.get_cmap("RdYlGn") exp_colors = cmap(np.arange(3) / (3 - 1)) _check_colors(1, ax.collections[0].get_facecolors(), [exp_colors[0]]) _check_colors(1, ax.collections[1].get_edgecolors(), [exp_colors[1]]) _check_colors(1, ax.collections[2].get_facecolors(), [exp_colors[2]]) def test_style_kwargs(self): ax = self.series.plot(markersize=10) assert ax.collections[2].get_sizes() == [10] ax = self.df.plot(markersize=10) assert ax.collections[2].get_sizes() == [10] def test_style_kwargs_linestyle(self): # single for ax in [ self.series.plot(linestyle=":", linewidth=1), self.df.plot(linestyle=":", linewidth=1), ]: assert [(0.0, [1.0, 1.65])] == ax.collections[0].get_linestyle() # tuple ax = self.series.plot(linestyle=(0, (3, 10, 1, 15)), linewidth=1) assert [(0, [3, 10, 1, 15])] == ax.collections[0].get_linestyle() def test_style_kwargs_linestyle_listlike(self): # multiple ls = ["solid", "dotted", "dashdot"] exp_ls = [_style_to_linestring_onoffseq(style, 1) for style in ls] for ax in [ self.series.plot(linestyle=ls, linewidth=1), self.series.plot(linestyles=ls, linewidth=1), self.df.plot(linestyles=ls, linewidth=1), ]: assert exp_ls[0] == ax.collections[0].get_linestyle()[0] assert exp_ls[1] == ax.collections[1].get_linestyle()[0] assert exp_ls[2] == ax.collections[2].get_linestyle()[0] def test_style_kwargs_linewidth(self): # single ax = self.df.plot(linewidth=2) np.testing.assert_array_equal([2], ax.collections[0].get_linewidths()) def test_style_kwargs_linewidth_listlike(self): # multiple for ax in [ self.series.plot(linewidths=[2, 4, 5.5]), self.series.plot(linewidths=[2, 4, 5.5]), self.df.plot(linewidths=[2, 4, 5.5]), ]: np.testing.assert_array_equal([2], ax.collections[0].get_linewidths()) np.testing.assert_array_equal([4], ax.collections[1].get_linewidths()) np.testing.assert_array_equal([5.5], ax.collections[2].get_linewidths()) def test_style_kwargs_alpha(self): ax = self.df.plot(alpha=0.7) np.testing.assert_array_equal([0.7], ax.collections[0].get_alpha()) ax = self.df.plot(alpha=[0.7, 0.2, 0.9]) np.testing.assert_array_equal([0.7], ax.collections[0].get_alpha()) np.testing.assert_array_equal([0.2], ax.collections[1].get_alpha()) np.testing.assert_array_equal([0.9], ax.collections[2].get_alpha()) @pytest.fixture(scope="class") def _setup_class_geographic_aspect(naturalearth_lowres, request): """Attach naturalearth_lowres class attribute for unittest style setup_method""" df = read_file(naturalearth_lowres) request.cls.north = df.loc[df.continent == "North America"] request.cls.north_proj = request.cls.north.to_crs("ESRI:102008") bounds = request.cls.north.total_bounds y_coord = np.mean([bounds[1], bounds[3]]) request.cls.exp = 1 / np.cos(y_coord * np.pi / 180) @pytest.mark.usefixtures("_setup_class_geographic_aspect") @pytest.mark.skipif(not compat.HAS_PYPROJ, reason="pyproj not available") class TestGeographicAspect: def test_auto(self): ax = self.north.geometry.plot() assert ax.get_aspect() == self.exp ax2 = self.north_proj.geometry.plot() assert ax2.get_aspect() in ["equal", 1.0] ax = self.north.plot() assert ax.get_aspect() == self.exp ax2 = self.north_proj.plot() assert ax2.get_aspect() in ["equal", 1.0] ax3 = self.north.plot("pop_est") assert ax3.get_aspect() == self.exp ax4 = self.north_proj.plot("pop_est") assert ax4.get_aspect() in ["equal", 1.0] def test_manual(self): ax = self.north.geometry.plot(aspect="equal") assert ax.get_aspect() in ["equal", 1.0] self.north.geometry.plot(ax=ax, aspect=None) assert ax.get_aspect() in ["equal", 1.0] ax2 = self.north.geometry.plot(aspect=0.5) assert ax2.get_aspect() == 0.5 self.north.geometry.plot(ax=ax2, aspect=None) assert ax2.get_aspect() == 0.5 ax3 = self.north_proj.geometry.plot(aspect=0.5) assert ax3.get_aspect() == 0.5 self.north_proj.geometry.plot(ax=ax3, aspect=None) assert ax3.get_aspect() == 0.5 ax = self.north.plot(aspect="equal") assert ax.get_aspect() in ["equal", 1.0] self.north.plot(ax=ax, aspect=None) assert ax.get_aspect() in ["equal", 1.0] ax2 = self.north.plot(aspect=0.5) assert ax2.get_aspect() == 0.5 self.north.plot(ax=ax2, aspect=None) assert ax2.get_aspect() == 0.5 ax3 = self.north_proj.plot(aspect=0.5) assert ax3.get_aspect() == 0.5 self.north_proj.plot(ax=ax3, aspect=None) assert ax3.get_aspect() == 0.5 ax = self.north.plot("pop_est", aspect="equal") assert ax.get_aspect() in ["equal", 1.0] self.north.plot("pop_est", ax=ax, aspect=None) assert ax.get_aspect() in ["equal", 1.0] ax2 = self.north.plot("pop_est", aspect=0.5) assert ax2.get_aspect() == 0.5 self.north.plot("pop_est", ax=ax2, aspect=None) assert ax2.get_aspect() == 0.5 ax3 = self.north_proj.plot("pop_est", aspect=0.5) assert ax3.get_aspect() == 0.5 self.north_proj.plot("pop_est", ax=ax3, aspect=None) assert ax3.get_aspect() == 0.5 @pytest.mark.filterwarnings( "ignore:Numba not installed. Using slow pure python version.:UserWarning" ) class TestMapclassifyPlotting: @classmethod def setup_class(cls): try: import mapclassify except ImportError: pytest.importorskip("mapclassify") cls.mc = mapclassify cls.classifiers = list(mapclassify.classifiers.CLASSIFIERS) cls.classifiers.remove("UserDefined") @pytest.fixture def df(self, naturalearth_lowres): # version of naturalearth_lowres for mapclassify plotting tests df = read_file(naturalearth_lowres) df["NEGATIVES"] = np.linspace(-10, 10, len(df.index)) df["low_vals"] = np.linspace(0, 0.3, df.shape[0]) df["mid_vals"] = np.linspace(0.3, 0.7, df.shape[0]) df["high_vals"] = np.linspace(0.7, 1.0, df.shape[0]) df.loc[df.index[:20:2], "high_vals"] = np.nan return df @pytest.fixture def nybb(self, nybb_filename): # version of nybb for mapclassify plotting tests df = read_file(nybb_filename) df["vals"] = [0.001, 0.002, 0.003, 0.004, 0.005] return df def test_legend(self, df): ax = df.plot( column="pop_est", scheme="QUANTILES", k=3, cmap="OrRd", legend=True ) labels = [t.get_text() for t in ax.get_legend().get_texts()] expected = [ s.split("|")[0][1:-2] for s in str(self.mc.Quantiles(df["pop_est"], k=3)).split("\n")[4:] ] assert labels == expected def test_colorbar(self, df): ax = df.plot( column="pop_est", scheme="QUANTILES", k=3, cmap="OrRd", legend=True, legend_kwds={"colorbar": True}, ) bins = self.mc.Quantiles(df["pop_est"], k=3).bins tl = _get_ax(ax.get_figure(), "").get_yticklabels() for i in range(1, 4): assert tl[i].get_position()[1] == bins[i - 1] def test_bin_labels(self, df): ax = df.plot( column="pop_est", scheme="QUANTILES", k=3, cmap="OrRd", legend=True, legend_kwds={"labels": ["foo", "bar", "baz"]}, ) labels = [t.get_text() for t in ax.get_legend().get_texts()] expected = ["foo", "bar", "baz"] assert labels == expected def test_invalid_labels_length(self, df): with pytest.raises( ValueError, match="Number of labels must match number of categories" ): df.plot( column="pop_est", scheme="QUANTILES", k=3, cmap="OrRd", legend=True, legend_kwds={"labels": ["foo", "bar"]}, ) def test_negative_legend(self, df): ax = df.plot( column="NEGATIVES", scheme="FISHER_JENKS", k=3, cmap="OrRd", legend=True ) labels = [t.get_text() for t in ax.get_legend().get_texts()] expected = ["-10.00, -3.41", " -3.41, 3.30", " 3.30, 10.00"] assert labels == expected def test_negative_colorbar(self, df): ax = df.plot( column="NEGATIVES", scheme="FISHER_JENKS", k=3, cmap="OrRd", legend=True, legend_kwds={"colorbar": True}, ) cbar = _get_ax(ax.get_figure(), "") labels = [label.get_text() for label in cbar.get_yticklabels()] expected = ["−10.0", "−3.4", "3.3", "10.0"] assert labels == expected def test_fmt(self, df): ax = df.plot( column="NEGATIVES", scheme="FISHER_JENKS", k=3, cmap="OrRd", legend=True, legend_kwds={"fmt": "{:.0f}"}, ) labels = [t.get_text() for t in ax.get_legend().get_texts()] expected = ["-10, -3", " -3, 3", " 3, 10"] assert labels == expected def test_interval(self, df): ax = df.plot( column="NEGATIVES", scheme="FISHER_JENKS", k=3, cmap="OrRd", legend=True, legend_kwds={"interval": True}, ) labels = [t.get_text() for t in ax.get_legend().get_texts()] expected = ["[-10.00, -3.41]", "( -3.41, 3.30]", "( 3.30, 10.00]"] assert labels == expected @pytest.mark.parametrize("scheme", ["FISHER_JENKS", "FISHERJENKS"]) def test_scheme_name_compat(self, scheme, df): ax = df.plot(column="NEGATIVES", scheme=scheme, k=3, legend=True) assert len(ax.get_legend().get_texts()) == 3 @pytest.mark.parametrize("scheme", ["FISHER_JENKS", "FISHERJENKS"]) def test_scheme_name_compat_colorbar(self, scheme, df): ax = df.plot( column="NEGATIVES", scheme=scheme, k=3, legend=True, legend_kwds={"colorbar": True}, ) cbar = _get_ax(ax.get_figure(), "") assert len(cbar.get_yticklabels()) == 4 def test_schemes(self, df): # test if all available classifiers pass for scheme in self.classifiers: df.plot(column="pop_est", scheme=scheme, legend=True) def test_classification_kwds(self, df): ax = df.plot( column="pop_est", scheme="percentiles", k=3, classification_kwds={"pct": [50, 100]}, cmap="OrRd", legend=True, ) labels = [t.get_text() for t in ax.get_legend().get_texts()] expected = [ s.split("|")[0][1:-2] for s in str(self.mc.Percentiles(df["pop_est"], pct=[50, 100])).split("\n")[ 4: ] ] assert labels == expected def test_invalid_scheme(self, df): with pytest.raises(ValueError): scheme = "invalid_scheme_*#&)(*#" df.plot(column="gdp_md_est", scheme=scheme, k=3, cmap="OrRd", legend=True) def test_cax_legend_passing(self, df): """Pass a 'cax' argument to 'df.plot(.)', that is valid only if 'ax' is passed as well (if not, a new figure is created ad hoc, and 'cax' is ignored) """ ax = plt.axes() from mpl_toolkits.axes_grid1 import make_axes_locatable divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.1) with pytest.raises(ValueError): ax = df.plot(column="pop_est", cmap="OrRd", legend=True, cax=cax) def test_cax_legend_height(self, df): """Pass a cax argument to 'df.plot(.)', the legend location must be aligned with those of main plot """ # base case with warnings.catch_warnings(record=True) as _: # don't print warning ax = df.plot(column="pop_est", cmap="OrRd", legend=True) plot_height = _get_ax(ax.get_figure(), "").get_position().height legend_height = _get_ax(ax.get_figure(), "").get_position().height assert abs(plot_height - legend_height) >= 1e-6 # fix heights with cax argument fig, ax2 = plt.subplots() from mpl_toolkits.axes_grid1 import make_axes_locatable divider = make_axes_locatable(ax2) cax = divider.append_axes("right", size="5%", pad=0.1, label="fixed_colorbar") with warnings.catch_warnings(record=True) as _: ax2 = df.plot(column="pop_est", cmap="OrRd", legend=True, cax=cax, ax=ax2) plot_height = _get_ax(fig, "").get_position().height legend_height = _get_ax(fig, "fixed_colorbar").get_position().height assert abs(plot_height - legend_height) < 1e-6 def test_empty_bins(self, df): bins = np.arange(1, 11) / 10 ax = df.plot( "low_vals", scheme="UserDefined", classification_kwds={"bins": bins, "lowest": 0}, legend=True, ) colors_exp = [ (0.267004, 0.004874, 0.329415, 1.0), (0.281412, 0.155834, 0.469201, 1.0), (0.244972, 0.287675, 0.53726, 1.0), (0.190631, 0.407061, 0.556089, 1.0), (0.147607, 0.511733, 0.557049, 1.0), (0.119699, 0.61849, 0.536347, 1.0), (0.20803, 0.718701, 0.472873, 1.0), (0.430983, 0.808473, 0.346476, 1.0), (0.709898, 0.868751, 0.169257, 1.0), (0.993248, 0.906157, 0.143936, 1.0), ] for i, c in enumerate(ax.collections): assert (c.get_facecolors() == colors_exp[i]).all() # 3 collections fall into bins, other are empty if i > 2: assert c.get_paths() == [] labels = [ "0.00, 0.10", "0.10, 0.20", "0.20, 0.30", "0.30, 0.40", "0.40, 0.50", "0.50, 0.60", "0.60, 0.70", "0.70, 0.80", "0.80, 0.90", "0.90, 1.00", ] legend = [t.get_text() for t in ax.get_legend().get_texts()] assert labels == legend assert [ patch.get_facecolor() for patch in ax.get_legend().get_patches() ] == colors_exp ax2 = df.plot( "mid_vals", scheme="UserDefined", classification_kwds={"bins": bins, "lowest": 0}, legend=True, ) for i, c in enumerate(ax2.collections): assert (c.get_facecolors() == colors_exp[i]).all() # 5 collections fall into bins, other are empty if i not in range(2, 7): assert c.get_paths() == [] legend = [t.get_text() for t in ax2.get_legend().get_texts()] assert labels == legend assert [ patch.get_facecolor() for patch in ax.get_legend().get_patches() ] == colors_exp ax3 = df.plot( "high_vals", scheme="UserDefined", classification_kwds={"bins": bins, "lowest": 0}, legend=True, ) for i, c in enumerate(ax3.collections): assert (c.get_facecolors() == colors_exp[i]).all() # 3 collections fall into bins, other are empty if i < 7: assert c.get_paths() == [] legend = [t.get_text() for t in ax3.get_legend().get_texts()] assert labels == legend assert [ patch.get_facecolor() for patch in ax.get_legend().get_patches() ] == colors_exp def test_empty_bins_colorbar(self, df): bins = np.arange(1, 11) / 10 ax = df.plot( "low_vals", scheme="UserDefined", classification_kwds={"bins": bins, "lowest": 0}, legend=True, legend_kwds={"colorbar": True}, ) cbar = _get_ax(ax.get_figure(), "") labels = [label.get_text() for label in cbar.get_yticklabels()] expected_labels = ["0.0", "0.2", "0.4", "0.6", "0.8", "1.0"] colors_exp = [ (0.267004, 0.004874, 0.329415, 1.0), (0.281412, 0.155834, 0.469201, 1.0), (0.244972, 0.287675, 0.53726, 1.0), (0.190631, 0.407061, 0.556089, 1.0), (0.147607, 0.511733, 0.557049, 1.0), (0.119699, 0.61849, 0.536347, 1.0), (0.20803, 0.718701, 0.472873, 1.0), (0.430983, 0.808473, 0.346476, 1.0), (0.709898, 0.868751, 0.169257, 1.0), (0.993248, 0.906157, 0.143936, 1.0), ] assert labels == expected_labels np.testing.assert_array_almost_equal( cbar.collections[1].get_facecolors(), colors_exp, decimal=2 ) expected = np.array( [ [0.281412, 0.155834, 0.469201, 1.0], [0.267004, 0.004874, 0.329415, 1.0], [0.244972, 0.287675, 0.53726, 1.0], [0.190631, 0.407061, 0.556089, 1.0], ] ) assert all( (z == expected).all(axis=1).any() for z in ax.collections[0].get_facecolors() ) ax2 = df.plot( "mid_vals", scheme="UserDefined", classification_kwds={"bins": bins, "lowest": 0}, legend=True, legend_kwds={"colorbar": True}, ) cbar = _get_ax(ax2.get_figure(), "") labels = [label.get_text() for label in cbar.get_yticklabels()] assert labels == expected_labels np.testing.assert_array_almost_equal( cbar.collections[1].get_facecolors(), colors_exp, decimal=2 ) expected = np.array( [ [0.190631, 0.407061, 0.556089, 1.0], [0.147607, 0.511733, 0.557049, 1.0], [0.119483, 0.614817, 0.537692, 1.0], [0.20803, 0.718701, 0.472873, 1.0], [0.421908, 0.805774, 0.35191, 1.0], ] ) assert all( (z == expected).all(axis=1).any() for z in ax2.collections[0].get_facecolors() ) ax3 = df.plot( "high_vals", scheme="UserDefined", classification_kwds={"bins": bins, "lowest": 0}, legend=True, legend_kwds={"colorbar": True}, ) cbar = _get_ax(ax3.get_figure(), "") labels = [label.get_text() for label in cbar.get_yticklabels()] assert labels == expected_labels np.testing.assert_array_almost_equal( cbar.collections[1].get_facecolors(), colors_exp, decimal=2 ) expected = np.array( [ [0.421908, 0.805774, 0.35191, 1.0], [0.699415, 0.867117, 0.175971, 1.0], (0.993248, 0.906157, 0.143936, 1.0), ] ) assert all( (z == expected).all(axis=1).any() for z in ax3.collections[0].get_facecolors() ) def test_equally_formatted_bins(self, nybb): ax = nybb.plot( "vals", scheme="quantiles", legend=True, ) labels = [t.get_text() for t in ax.get_legend().get_texts()] expected = [ "0.00, 0.00", "0.00, 0.00", "0.00, 0.00", "0.00, 0.00", "0.00, 0.01", ] assert labels == expected ax2 = nybb.plot( "vals", scheme="quantiles", legend=True, legend_kwds={"fmt": "{:.3f}"} ) labels = [t.get_text() for t in ax2.get_legend().get_texts()] expected = [ "0.001, 0.002", "0.002, 0.003", "0.003, 0.003", "0.003, 0.004", "0.004, 0.005", ] assert labels == expected @pytest.mark.parametrize("legend", (True, False)) def test_missing_categorical(self, df, legend): bins = np.arange(1, 11, 2) / 10 ax = df.plot( "high_vals", scheme="UserDefined", classification_kwds={"bins": bins, "lowest": 0}, legend=legend, legend_kwds={"colorbar": False}, missing_kwds={"color": "red", "label": "missing"}, ) if not legend: ax.legend() handles, labels = ax.get_legend_handles_labels() assert len(handles) == 7 assert labels == [ "0.00, 0.10", "0.10, 0.30", "0.30, 0.50", "0.50, 0.70", "0.70, 0.90", "0.90, 1.00", "missing", ] @pytest.mark.parametrize("legend", (True, False)) def test_missing_categorical_custom_labels(self, df, legend): bins = np.arange(1, 11, 2) / 10 ax = df.plot( "high_vals", scheme="UserDefined", classification_kwds={"bins": bins, "lowest": 0}, legend=legend, legend_kwds={"colorbar": False, "labels": "abcdef"}, missing_kwds={"color": "red", "label": "missing"}, ) if not legend: ax.legend() handles, labels = ax.get_legend_handles_labels() assert len(handles) == 7 assert labels == ["a", "b", "c", "d", "e", "f", "missing"] def test_missing_colorbar(self, df): bins = np.arange(1, 11, 2) / 10 ax = df.plot( "high_vals", scheme="UserDefined", classification_kwds={"bins": bins, "lowest": 0}, legend=True, legend_kwds={"colorbar": True}, missing_kwds={"color": "red", "label": "missing"}, ) ax.legend() handles, labels = ax.get_legend_handles_labels() assert len(handles) == 1 assert labels == ["missing"] def test_norm_conflict(self, df): with pytest.raises( ValueError, match=r"Cannot set `norm` and `scheme` at the same time." ): df.plot( "low_vals", scheme="quantiles", legend_kwds={"colorbar": True}, norm=matplotlib.colors.LogNorm(vmin=0, vmax=1), ) def test_unknown_lowest(self, df): bins = np.arange(1, 11, 2) / 10 ax = df.plot( "mid_vals", scheme="UserDefined", classification_kwds={"bins": bins}, # no lowest here legend=True, legend_kwds={"colorbar": True}, ) # duplication is expected, it is a fallback expected_labels = ["0.1", "0.1", "0.3", "0.5", "0.7", "0.9"] cbar = _get_ax(ax.get_figure(), "") labels = [label.get_text() for label in cbar.get_yticklabels()] assert labels == expected_labels def test_vmin_categorical(self, df): ax = df.plot( "NEGATIVES", scheme="quantiles", legend=True, vmin=0, ) expected = [106, 18, 17, 18, 18] actual = [len(c.get_paths()) for c in ax.collections] assert actual == expected _handles, labels = ax.get_legend_handles_labels() assert labels == [ " 0.00, 2.00", " 2.00, 4.00", " 4.00, 6.00", " 6.00, 8.00", " 8.00, 10.00", ] def test_vmax_categorical(self, df): ax = df.plot( "NEGATIVES", scheme="quantiles", legend=True, vmax=0, ) expected = [18, 18, 17, 18, 106] actual = [len(c.get_paths()) for c in ax.collections] assert actual == expected _handles, labels = ax.get_legend_handles_labels() assert labels == [ "-10.00, -8.00", " -8.00, -6.00", " -6.00, -4.00", " -4.00, -2.00", " -2.00, 0.00", ] def test_vmin_vmax_categorical(self, df): ax = df.plot( "NEGATIVES", scheme="quantiles", legend=True, vmax=5, vmin=-5, ) expected = [62, 18, 17, 18, 62] actual = [len(c.get_paths()) for c in ax.collections] assert actual == expected _handles, labels = ax.get_legend_handles_labels() assert labels == [ "-5.00, -3.00", "-3.00, -1.00", "-1.00, 1.00", " 1.00, 3.00", " 3.00, 5.00", ] def test_vmin_colorbar(self, df): ax = df.plot( "NEGATIVES", scheme="quantiles", legend=True, legend_kwds={"colorbar": True}, vmin=0, ) expected_labels = ["0", "2", "4", "6", "8", "10"] cbar = _get_ax(ax.get_figure(), "") labels = [label.get_text() for label in cbar.get_yticklabels()] assert labels == expected_labels def test_vmax_colorbar(self, df): ax = df.plot( "NEGATIVES", scheme="quantiles", legend=True, legend_kwds={"colorbar": True}, vmax=0, ) expected_labels = ["−10", "−8", "−6", "−4", "−2", "0"] cbar = _get_ax(ax.get_figure(), "") labels = [label.get_text() for label in cbar.get_yticklabels()] assert labels == expected_labels def test_vmin_vmax_colorbar(self, df): ax = df.plot( "NEGATIVES", scheme="quantiles", legend_kwds={"colorbar": True}, legend=True, vmax=5, vmin=-5, ) expected_labels = ["−5", "−3", "−1", "1", "3", "5"] cbar = _get_ax(ax.get_figure(), "") labels = [label.get_text() for label in cbar.get_yticklabels()] assert labels == expected_labels def test_greedy_scheme(self, nybb): pytest.importorskip("libpysal") ax = nybb.plot(scheme="greedy", legend=True) expected_lengths = [2, 1, 1, 1] expected_labels = ["0", "1", "2", "3"] labels = [t.get_text() for t in ax.get_legend().get_texts()] assert labels == expected_labels lengths = [len(col.get_paths()) for col in ax.collections] assert lengths == expected_lengths def test_greedy_scheme_kwds(self, nybb): pytest.importorskip("libpysal") ax = nybb.plot( scheme="greedy", legend=True, classification_kwds={"balance": "area"} ) expected_lengths = [1, 2, 1, 1] expected_labels = ["0", "1", "2", "3"] labels = [t.get_text() for t in ax.get_legend().get_texts()] assert labels == expected_labels lengths = [len(col.get_paths()) for col in ax.collections] assert lengths == expected_lengths def test_greedy_column_error(self, nybb): with pytest.raises( ValueError, match=r"The `scheme='greedy'` cannot be specified together with `column`.", ): nybb.plot("vals", scheme="greedy") class TestPlotCollections: def setup_method(self): self.N = 3 self.values = np.arange(self.N) self.points = GeoSeries(Point(i, i) for i in range(self.N)) self.lines = GeoSeries( [LineString([(0, i), (4, i + 0.5), (9, i)]) for i in range(self.N)] ) self.polygons = GeoSeries( [Polygon([(0, i), (4, i + 0.5), (9, i)]) for i in range(self.N)] ) def test_points(self): from matplotlib.collections import PathCollection from geopandas.plotting import _plot_point_collection _fig, ax = plt.subplots() coll = _plot_point_collection(ax, self.points) assert isinstance(coll, PathCollection) ax.cla() # default: single default matplotlib color coll = _plot_point_collection(ax, self.points) _check_colors(self.N, coll.get_facecolors(), [MPL_DFT_COLOR] * self.N) # edgecolor depends on matplotlib version # _check_colors(self.N, coll.get_edgecolors(), [MPL_DFT_COLOR]*self.N) ax.cla() # specify single other color coll = _plot_point_collection(ax, self.points, color="g") _check_colors(self.N, coll.get_facecolors(), ["g"] * self.N) _check_colors(self.N, coll.get_edgecolors(), ["g"] * self.N) ax.cla() # specify edgecolor/facecolor coll = _plot_point_collection(ax, self.points, facecolor="g", edgecolor="r") _check_colors(self.N, coll.get_facecolors(), ["g"] * self.N) _check_colors(self.N, coll.get_edgecolors(), ["r"] * self.N) ax.cla() # list of colors coll = _plot_point_collection(ax, self.points, color=["r", "g", "b"]) _check_colors(self.N, coll.get_facecolors(), ["r", "g", "b"]) _check_colors(self.N, coll.get_edgecolors(), ["r", "g", "b"]) ax.cla() coll = _plot_point_collection( ax, self.points, color=[(0.5, 0.5, 0.5, 0.5), (0.1, 0.2, 0.3, 0.5), (0.4, 0.5, 0.6, 0.5)], ) _check_colors( self.N, coll.get_facecolors(), [(0.5, 0.5, 0.5, 0.5), (0.1, 0.2, 0.3, 0.5), (0.4, 0.5, 0.6, 0.5)], ) _check_colors( self.N, coll.get_edgecolors(), [(0.5, 0.5, 0.5, 0.5), (0.1, 0.2, 0.3, 0.5), (0.4, 0.5, 0.6, 0.5)], ) ax.cla() # not a color with pytest.raises((TypeError, ValueError)): _plot_point_collection(ax, self.points, color="not color") def test_points_values(self): from geopandas.plotting import _plot_point_collection # default colormap fig, ax = plt.subplots() coll = _plot_point_collection(ax, self.points, self.values) fig.canvas.draw_idle() cmap = plt.get_cmap() expected_colors = cmap(np.arange(self.N) / (self.N - 1)) _check_colors(self.N, coll.get_facecolors(), expected_colors) # edgecolor depends on matplotlib version # _check_colors(self.N, coll.get_edgecolors(), expected_colors) def test_linestrings(self): from matplotlib.collections import LineCollection from geopandas.plotting import _plot_linestring_collection _fig, ax = plt.subplots() coll = _plot_linestring_collection(ax, self.lines) assert isinstance(coll, LineCollection) ax.cla() # default: single default matplotlib color coll = _plot_linestring_collection(ax, self.lines) _check_colors(self.N, coll.get_color(), [MPL_DFT_COLOR] * self.N) ax.cla() # specify single other color coll = _plot_linestring_collection(ax, self.lines, color="g") _check_colors(self.N, coll.get_colors(), ["g"] * self.N) ax.cla() # specify edgecolor / facecolor coll = _plot_linestring_collection(ax, self.lines, facecolor="g", edgecolor="r") _check_colors(self.N, coll.get_facecolors(), ["g"] * self.N) _check_colors(self.N, coll.get_edgecolors(), ["r"] * self.N) ax.cla() # list of colors coll = _plot_linestring_collection(ax, self.lines, color=["r", "g", "b"]) _check_colors(self.N, coll.get_colors(), ["r", "g", "b"]) ax.cla() coll = _plot_linestring_collection( ax, self.lines, color=[(0.5, 0.5, 0.5, 0.5), (0.1, 0.2, 0.3, 0.5), (0.4, 0.5, 0.6, 0.5)], ) _check_colors( self.N, coll.get_colors(), [(0.5, 0.5, 0.5, 0.5), (0.1, 0.2, 0.3, 0.5), (0.4, 0.5, 0.6, 0.5)], ) ax.cla() # pass through of kwargs coll = _plot_linestring_collection(ax, self.lines, linestyle="--", linewidth=1) exp_ls = _style_to_linestring_onoffseq("dashed", 1) res_ls = coll.get_linestyle()[0] assert res_ls[0] == exp_ls[0] assert res_ls[1] == exp_ls[1] ax.cla() # not a color with pytest.raises((TypeError, ValueError)): _plot_linestring_collection(ax, self.lines, color="not color") def test_linestrings_values(self): from geopandas.plotting import _plot_linestring_collection fig, ax = plt.subplots() # default colormap coll = _plot_linestring_collection(ax, self.lines, self.values) fig.canvas.draw_idle() cmap = plt.get_cmap() expected_colors = cmap(np.arange(self.N) / (self.N - 1)) _check_colors(self.N, coll.get_color(), expected_colors) ax.cla() # specify colormap coll = _plot_linestring_collection(ax, self.lines, self.values, cmap="RdBu") fig.canvas.draw_idle() cmap = plt.get_cmap("RdBu") expected_colors = cmap(np.arange(self.N) / (self.N - 1)) _check_colors(self.N, coll.get_color(), expected_colors) ax.cla() # specify vmin/vmax coll = _plot_linestring_collection(ax, self.lines, self.values, vmin=3, vmax=5) fig.canvas.draw_idle() cmap = plt.get_cmap() expected_colors = [cmap(0)] _check_colors(self.N, coll.get_color(), expected_colors * 3) ax.cla() def test_polygons(self): from matplotlib.collections import PatchCollection from geopandas.plotting import _plot_polygon_collection _fig, ax = plt.subplots() coll = _plot_polygon_collection(ax, self.polygons) assert isinstance(coll, PatchCollection) ax.cla() # default: single default matplotlib color coll = _plot_polygon_collection(ax, self.polygons) _check_colors(self.N, coll.get_facecolor(), [MPL_DFT_COLOR] * self.N) assert len(coll.get_edgecolor()) == 0 ax.cla() # default: color sets both facecolor and edgecolor coll = _plot_polygon_collection(ax, self.polygons, color="g") _check_colors(self.N, coll.get_facecolor(), ["g"] * self.N) _check_colors(self.N, coll.get_edgecolor(), ["g"] * self.N) ax.cla() # default: color can be passed as a list coll = _plot_polygon_collection(ax, self.polygons, color=["g", "b", "r"]) _check_colors(self.N, coll.get_facecolor(), ["g", "b", "r"]) _check_colors(self.N, coll.get_edgecolor(), ["g", "b", "r"]) ax.cla() coll = _plot_polygon_collection( ax, self.polygons, color=[(0.5, 0.5, 0.5, 0.5), (0.1, 0.2, 0.3, 0.5), (0.4, 0.5, 0.6, 0.5)], ) _check_colors( self.N, coll.get_facecolor(), [(0.5, 0.5, 0.5, 0.5), (0.1, 0.2, 0.3, 0.5), (0.4, 0.5, 0.6, 0.5)], ) _check_colors( self.N, coll.get_edgecolor(), [(0.5, 0.5, 0.5, 0.5), (0.1, 0.2, 0.3, 0.5), (0.4, 0.5, 0.6, 0.5)], ) ax.cla() # only setting facecolor keeps default for edgecolor coll = _plot_polygon_collection(ax, self.polygons, facecolor="g") _check_colors(self.N, coll.get_facecolor(), ["g"] * self.N) assert len(coll.get_edgecolor()) == 0 ax.cla() # custom facecolor and edgecolor coll = _plot_polygon_collection(ax, self.polygons, facecolor="g", edgecolor="r") _check_colors(self.N, coll.get_facecolor(), ["g"] * self.N) _check_colors(self.N, coll.get_edgecolor(), ["r"] * self.N) ax.cla() # not a color with pytest.raises((TypeError, ValueError)): _plot_polygon_collection(ax, self.polygons, color="not color") def test_polygons_values(self): from geopandas.plotting import _plot_polygon_collection fig, ax = plt.subplots() # default colormap, edge is still black by default coll = _plot_polygon_collection(ax, self.polygons, self.values) fig.canvas.draw_idle() cmap = plt.get_cmap() exp_colors = cmap(np.arange(self.N) / (self.N - 1)) _check_colors(self.N, coll.get_facecolor(), exp_colors) # edgecolor depends on matplotlib version # _check_colors(self.N, coll.get_edgecolor(), ['k'] * self.N) ax.cla() # specify colormap coll = _plot_polygon_collection(ax, self.polygons, self.values, cmap="RdBu") fig.canvas.draw_idle() cmap = plt.get_cmap("RdBu") exp_colors = cmap(np.arange(self.N) / (self.N - 1)) _check_colors(self.N, coll.get_facecolor(), exp_colors) ax.cla() # specify vmin/vmax coll = _plot_polygon_collection(ax, self.polygons, self.values, vmin=3, vmax=5) fig.canvas.draw_idle() cmap = plt.get_cmap() exp_colors = [cmap(0)] _check_colors(self.N, coll.get_facecolor(), exp_colors * 3) ax.cla() # override edgecolor coll = _plot_polygon_collection(ax, self.polygons, self.values, edgecolor="g") fig.canvas.draw_idle() cmap = plt.get_cmap() exp_colors = cmap(np.arange(self.N) / (self.N - 1)) _check_colors(self.N, coll.get_facecolor(), exp_colors) _check_colors(self.N, coll.get_edgecolor(), ["g"] * self.N) ax.cla() class TestGeoplotAccessor: def setup_method(self): geometries = [Polygon([(0, 0), (1, 0), (1, 1)]), Point(1, 3)] x = [1, 2] y = [10, 20] self.gdf = GeoDataFrame( {"geometry": geometries, "x": x, "y": y}, crs="EPSG:4326" ) self.df = pd.DataFrame({"x": x, "y": y}) def compare_figures(self, kind, fig_test, fig_ref, kwargs): """Compare Figures.""" ax_pandas_1 = fig_test.subplots() self.df.plot(kind=kind, ax=ax_pandas_1, **kwargs) ax_geopandas_1 = fig_ref.subplots() self.gdf.plot(kind=kind, ax=ax_geopandas_1, **kwargs) ax_pandas_2 = fig_test.subplots() getattr(self.df.plot, kind)(ax=ax_pandas_2, **kwargs) ax_geopandas_2 = fig_ref.subplots() getattr(self.gdf.plot, kind)(ax=ax_geopandas_2, **kwargs) _pandas_kinds = GeoplotAccessor._pandas_kinds if MPL_DECORATORS: @pytest.mark.parametrize("kind", _pandas_kinds) @check_figures_equal(extensions=["png", "pdf"]) def test_pandas_kind(self, kind, fig_test, fig_ref): """Test Pandas kind.""" import importlib _scipy_dependent_kinds = ["kde", "density"] # Needs scipy _y_kinds = ["pie"] # Needs y _xy_kinds = ["scatter", "hexbin"] # Needs x & y kwargs = {} if kind in _scipy_dependent_kinds: if not importlib.util.find_spec("scipy"): with pytest.raises( ModuleNotFoundError, match="No module named 'scipy'" ): self.gdf.plot(kind=kind) return elif kind in _y_kinds: kwargs = {"y": "y"} elif kind in _xy_kinds: kwargs = {"x": "x", "y": "y"} if kind == "hexbin": # increase gridsize to reduce duration kwargs["gridsize"] = 10 self.compare_figures(kind, fig_test, fig_ref, kwargs) plt.close("all") @check_figures_equal(extensions=["png", "pdf"]) def test_geo_kind(self, fig_test, fig_ref): """Test Geo kind.""" ax1 = fig_test.subplots() self.gdf.plot(ax=ax1) ax2 = fig_ref.subplots() getattr(self.gdf.plot, "geo")(ax=ax2) plt.close("all") def test_invalid_kind(self): """Test invalid kinds.""" with pytest.raises(ValueError, match="error is not a valid plot kind"): self.gdf.plot(kind="error") with pytest.raises( AttributeError, match="'GeoplotAccessor' object has no attribute 'error'", ): self.gdf.plot.error() def test_column_values(): """ Check that the dataframe plot method returns same values with an input string (column in df), pd.Series, np.array, or other 1D list-like """ # Build test data t1 = Polygon([(0, 0), (1, 0), (1, 1)]) t2 = Polygon([(1, 0), (2, 0), (2, 1)]) polys = GeoSeries([t1, t2], index=list("AB")) df = GeoDataFrame({"geometry": polys, "values": [0, 1]}) numeric_index_polys = GeoSeries([t1, t2], index=[0, 1]) numeric_index_df = GeoDataFrame({"geometry": numeric_index_polys, "values": [0, 1]}) mutliindex_column_df = GeoDataFrame( {"geometry": numeric_index_polys, "values": [0, 1]} ) mutliindex_column_df.columns = pd.MultiIndex.from_arrays([[1, 1], [1, 2]]) mutliindex_column_df = mutliindex_column_df.set_geometry((1, 1)) # Test with continuous values ax = df.plot(column="values") colors = ax.collections[0].get_facecolors() ax = df.plot(column=df["values"]) colors_series = ax.collections[0].get_facecolors() np.testing.assert_array_equal(colors, colors_series) ax = df.plot(column=df["values"].values) colors_array = ax.collections[0].get_facecolors() np.testing.assert_array_equal(colors, colors_array) ax = df.plot(column=[0, 1]) colors_list = ax.collections[0].get_facecolors() np.testing.assert_array_equal(colors, colors_list) ax = df.plot(column=pd.Series([1, 0], index=["B", "A"])) colors_list = ax.collections[0].get_facecolors() np.testing.assert_array_equal(colors, colors_list) ax = mutliindex_column_df.plot(column=(1, 2)) colors_array = ax.collections[0].get_facecolors() np.testing.assert_array_equal(colors, colors_array) # Test with categorical values ax = df.plot(column="values", categorical=True) colors = ax.collections[0].get_facecolors() ax = df.plot(column=df["values"], categorical=True) colors_series = ax.collections[0].get_facecolors() np.testing.assert_array_equal(colors, colors_series) ax = df.plot(column=df["values"].values, categorical=True) colors_array = ax.collections[0].get_facecolors() np.testing.assert_array_equal(colors, colors_array) ax = df.plot(column=(0, 1), categorical=True) colors_tuple = ax.collections[0].get_facecolors() np.testing.assert_array_equal(colors, colors_tuple) ax = mutliindex_column_df.plot(column=(1, 2), categorical=True) colors_array = ax.collections[0].get_facecolors() np.testing.assert_array_equal(colors, colors_array) # Test with pd.Index ax = numeric_index_df.plot(column=numeric_index_df.index, categorical=True) colors_array = ax.collections[0].get_facecolors() np.testing.assert_array_equal(colors, colors_array) # Check raised error: is df rows number equal to column length? with pytest.raises(ValueError, match="different number of rows"): ax = df.plot(column=np.array([1, 2, 3])) def test_polygon_patch(): # test adapted from descartes by Sean Gillies # (BSD license, https://pypi.org/project/descartes). from matplotlib.patches import PathPatch from geopandas.plotting import _PolygonPatch polygon = ( Point(0, 0).buffer(10.0).difference(MultiPoint([(-5, 0), (5, 0)]).buffer(3.0)) ) patch = _PolygonPatch(polygon) assert isinstance(patch, PathPatch) path = patch.get_path() if compat.GEOS_GE_390: assert len(path.vertices) == len(path.codes) == 195 else: assert len(path.vertices) == len(path.codes) == 198 def _check_colors(N, actual_colors, expected_colors, alpha=None): """ Asserts that the members of `collection` match the `expected_colors` (in order) Parameters ---------- N : int The number of geometries believed to be in collection. matplotlib.collection is implemented such that the number of geoms in `collection` doesn't have to match the number of colors assignments in the collection: the colors will cycle to meet the needs of the geoms. `N` helps us resolve this. collection : matplotlib.collections.Collection The colors of this collection's patches are read from `collection.get_facecolors()` expected_colors : sequence of RGBA tuples alpha : float (optional) If set, this alpha transparency will be applied to the `expected_colors`. (Any transparency on the `collection` is assumed to be set in its own facecolor RGBA tuples.) """ from matplotlib import colors conv = colors.colorConverter # Convert 2D numpy array to a list of RGBA tuples. actual_colors = map(tuple, actual_colors) all_actual_colors = list(itertools.islice(itertools.cycle(actual_colors), N)) assert len(all_actual_colors) == len(expected_colors), ( "Different lengths of actual and expected colors!" ) for actual, expected in zip(all_actual_colors, expected_colors): assert actual == conv.to_rgba(expected, alpha=alpha), ( f"{actual} != {conv.to_rgba(expected, alpha=alpha)}" ) def _style_to_linestring_onoffseq(linestyle, linewidth): """Converts a linestyle string representation, namely one of: ['dashed', 'dotted', 'dashdot', 'solid'], documented in `Collections.set_linestyle`, to the form `onoffseq`. """ offset, dashes = matplotlib.lines._get_dash_pattern(linestyle) return matplotlib.lines._scale_dashes(offset, dashes, linewidth) def _style_to_vertices(markerstyle): """Converts a markerstyle string to a path.""" # TODO: Vertices values are twice the actual path; unclear, why. path = matplotlib.markers.MarkerStyle(markerstyle).get_path() return path.vertices / 2 def _get_ax(fig, label): """ Helper function to not rely on the order of `fig.axes`. Previously, we did `fig.axes[1]`, but in matplotlib 3.4 the order switched and the colorbar ax was first and subplot ax second. """ for ax in fig.axes: if ax.get_label() == label: return ax raise ValueError(f"no ax found with label {label}") def _get_colorbar_ax(fig): return _get_ax(fig, "") class TestStyleMapping: def setup_method(self, nybb_filename): from geodatasets import get_path self.guerry = read_file(get_path("geoda guerry")) self.nyc = read_file(get_path("geoda nyc")) @pytest.fixture def nybb(self, nybb_filename): # version of nybb for mapclassify plotting tests df = read_file(nybb_filename) df["vals"] = [0.001, 0.002, 0.003, 0.004, 0.005] return df def test_series(self): # test that symbology and label is correctly registered ax = self.guerry.geometry.plot(color="red", label="guerry", hatch="///") ax.legend() col = ax.collections[0] _check_colors( 1, col.get_facecolors(), [[1, 0, 0, 1]], ) assert col.get_hatch() == "///" assert col.get_label() == "guerry" handles, labels = ax.get_legend_handles_labels() assert labels == ["guerry"] assert handles[0].get_hatch() == "///" np.testing.assert_array_equal(handles[0].get_facecolor(), [[1, 0, 0, 1]]) def test_categorical(self): ax = self.guerry.plot("Region", hatch="///") ax.legend() assert len(ax.collections) == 5 # one per category cmap = plt.get_cmap("tab10") for i, col in enumerate(ax.collections): np.testing.assert_array_equal(col.get_facecolors()[0], cmap(i)) assert col.get_hatch() == "///" handles, labels = ax.get_legend_handles_labels() for i, handle in enumerate(handles): np.testing.assert_array_equal(handle.get_facecolors()[0], cmap(i)) assert handle.get_hatch() == "///" assert labels == ["C", "E", "N", "S", "W"] def test_categorical_mapping(self): hatches = { "C": "/", "E": "//", "N": "|", "S": "+", "W": "///", } cmap = { "C": "r", "E": "g", "N": "b", "S": "k", "W": "y", } expected = [ [[1.0, 0.0, 0.0, 1.0]], [[0.0, 0.5, 0.0, 1.0]], [[0.0, 0.0, 1.0, 1.0]], [[0.0, 0.0, 0.0, 1.0]], [[0.75, 0.75, 0.0, 1.0]], ] ax = self.guerry.plot("Region", cmap=cmap, hatch=hatches, legend=True) assert len(ax.collections) == 5 # one per category for col, hatch, c in zip(ax.collections, hatches.values(), expected): assert col.get_hatch() == hatch np.testing.assert_array_equal(col.get_facecolor(), c) handles, labels = ax.get_legend_handles_labels() for handle, hatch, c in zip(handles, hatches.values(), expected): assert handle.get_hatch() == hatch np.testing.assert_array_equal(handle.get_facecolor(), c) assert labels == ["C", "E", "N", "S", "W"] def test_categorical_cmap_invalid(self): with pytest.raises(ValueError, match="`cmap` type is not supported"): self.guerry.plot("Region", cmap=["r", "g", "b"]) def test_categorical_missing(self): self.guerry.loc[50:, "Region"] = None # nothing in legend if no missing_kwds ax = self.guerry.plot("Region") ax.legend() assert len(ax.collections) == 5 _, labels = ax.get_legend_handles_labels() assert labels == ["C", "E", "N", "S", "W"] # additional collection ax = self.guerry.plot("Region", missing_kwds=dict(color="y", hatch="/")) assert len(ax.collections) == 6 handles, labels = ax.get_legend_handles_labels() assert labels == ["C", "E", "N", "S", "W", "NaN"] col = ax.collections[-1] _check_colors( 1, col.get_facecolors(), [[0.75, 0.75, 0.0, 1.0]], ) assert col.get_hatch() == "/" assert handles[-1].get_hatch() == "/" np.testing.assert_array_equal( handles[-1].get_facecolors()[0], [0.75, 0.75, 0.0, 1.0] ) def test_mixed_geom_types(self, nybb): # all combinations of geom type x class show up cent = nybb.copy() cent["geometry"] = nybb.centroid bound = nybb.copy() bound["geometry"] = nybb.boundary comb = pd.concat([nybb, cent, bound]) ax = comb.plot("BoroName", legend=True) handles, labels = ax.get_legend_handles_labels() assert labels == list( np.repeat(["Bronx", "Brooklyn", "Manhattan", "Queens", "Staten Island"], 3) ) cmap = plt.get_cmap("tab10") expected_colors = cmap(range(5)) for i in range(0, 15, 3): np.testing.assert_array_equal( handles[i].get_facecolor()[0], expected_colors[i // 3] ) np.testing.assert_array_equal( handles[i + 1].get_edgecolor()[0], expected_colors[i // 3] ) np.testing.assert_array_equal( handles[i + 2].get_facecolor()[0], expected_colors[i // 3] ) def test_layers_categorical(self, nybb): ax = self.nyc.plot("forhis06", legend=True, scheme="quantiles") nybb.boundary.plot(ax=ax, color="k", label="Borough") ax.legend() handles, labels = ax.get_legend_handles_labels() assert labels == [ "10.70, 28.96", "28.96, 38.29", "38.29, 41.96", "41.96, 46.31", "46.31, 69.52", "Borough", ] np.testing.assert_array_equal( handles[-1].get_edgecolor(), [[0.0, 0.0, 0.0, 1.0]] ) def test_layers_colorbar(self, nybb): ax = self.nyc.plot("forhis06", legend=True) nybb.boundary.plot(ax=ax, color="k", label="Borough") ax.legend() handles, labels = ax.get_legend_handles_labels() assert labels == [ "Borough", ] np.testing.assert_array_equal( handles[0].get_edgecolor(), [[0.0, 0.0, 0.0, 1.0]] ) cbar = _get_ax(ax.get_figure(), "") assert len(cbar.get_yticklabels()) == 7 def test_scheme_categorical_cmap_passthrough(self): ax = self.nyc.plot("forhis06", legend=True, scheme="quantiles", cmap="Reds") cmap = ax.collections[0].get_cmap().name assert cmap == "Reds" def test_categorical_points_cmap_no_warning(self, nybb): with warnings.catch_warnings(): warnings.simplefilter("error", UserWarning) ax = nybb.set_geometry(nybb.centroid).plot("BoroName") assert ax.collections[0].get_cmap().name == "tab10" def test_empty_class_poly(self): df = GeoDataFrame( ["foo", "bar"], geometry=[ box(0, 0, 1, 1), box(2, 2, 3, 3), ], ) ax = df.plot(0, categories=["foo", "baz", "bar"], legend=True) handles, labels = ax.get_legend_handles_labels() assert labels == ["foo", "baz", "bar"] assert isinstance(handles[1], matplotlib.collections.PolyCollection) def test_empty_class_line(self): df = GeoDataFrame( ["foo", "bar"], geometry=[ LineString([(1, 1), (1, 2)]), LineString([(7, 1), (7, 2)]), ], ) ax = df.plot(0, categories=["foo", "baz", "bar"], legend=True) handles, labels = ax.get_legend_handles_labels() assert labels == ["foo", "baz", "bar"] assert isinstance(handles[1], matplotlib.lines.Line2D) def test_empty_class_point(self): df = GeoDataFrame( ["foo", "bar"], geometry=[ Point(1, 1), Point(7, 1), ], ) ax = df.plot(0, categories=["foo", "baz", "bar"], legend=True) handles, labels = ax.get_legend_handles_labels() assert labels == ["foo", "baz", "bar"] assert isinstance(handles[1], matplotlib.collections.PathCollection) def test_cbar_extends_lower(self, nybb): ax = nybb.plot("BoroCode", legend=True, vmin=2) cbar = _get_ax(ax.get_figure(), "") labels = [label.get_text() for label in cbar.get_yticklabels()] assert labels == ["2.0", "2.5", "3.0", "3.5", "4.0", "4.5", "5.0"] # no idea how to assert for extension in a more sensible way assert cbar.collections[1].get_array().sum() == 896 def test_cbar_extends_both(self, nybb): ax = nybb.plot("BoroCode", legend=True, vmin=2, vmax=4) cbar = _get_ax(ax.get_figure(), "") labels = [label.get_text() for label in cbar.get_yticklabels()] assert labels == [ "2.00", "2.25", "2.50", "2.75", "3.00", "3.25", "3.50", "3.75", "4.00", ] # no idea how to assert for extension in a more sensible way assert cbar.collections[1].get_array().sum() == 768 def test_cbar_extends_upper(self, nybb): ax = nybb.plot("BoroCode", legend=True, vmax=4) cbar = _get_ax(ax.get_figure(), "") labels = [label.get_text() for label in cbar.get_yticklabels()] assert labels == ["1.0", "1.5", "2.0", "2.5", "3.0", "3.5", "4.0"] # no idea how to assert for extension in a more sensible way assert cbar.collections[1].get_array().sum() == 640 def test_missing_mapping(self, nybb): # ensure that array assignment is preserved while dict mapping # is ignored in missing vals nybb["vals_na"] = [1, 2, 3, np.nan, 5] hatches = { 1.0: "/", 2.0: "//", 3.0: "|", 4.0: "+", 5.0: "///", } ax = nybb.plot( "vals_na", categorical=True, hatch=hatches, edgecolor="k", linewidth=np.arange(5), missing_kwds={"color": "k"}, legend=True, ) for c in ax.collections: if c.get_label() == "NaN": assert c.get_hatch() is None assert c.get_linewidth()[0] == 3 np.testing.assert_array_equal(c.get_facecolor(), [[0.0, 0.0, 0.0, 1.0]]) else: assert c.get_hatch() == hatches[float(c.get_label())] assert c.get_linewidth()[0] == float(c.get_label()) - 1 handles, labels = ax.get_legend_handles_labels() assert labels == ["1.0", "2.0", "3.0", "5.0", "NaN"] nan_handle = handles[-1] assert nan_handle.get_hatch() is None assert nan_handle.get_linewidth()[0] == 3 np.testing.assert_array_equal( nan_handle.get_facecolor(), [[0.0, 0.0, 0.0, 1.0]] ) def test_style_kwds_series(self): gdf = GeoDataFrame( {"col": ["a", "b", "a"]}, geometry=points_from_xy(range(3), range(3)).buffer(0.5), ) ec = pd.Series(["k", "r", "b"]) ax = gdf.plot(column="col", edgecolor=ec) np.testing.assert_array_equal( ax.collections[0].get_edgecolor(), np.array([[0.0, 0.0, 0.0, 1.0], [0.0, 0.0, 1.0, 1.0]]), ) np.testing.assert_array_equal( ax.collections[1].get_edgecolor(), np.array([[1.0, 0.0, 0.0, 1.0]]), ) def test_style_kwarg_colorlike(self): """Do not subdivide color tuple""" gdf = GeoDataFrame( {"col": ["a", "b", "a"]}, geometry=points_from_xy(range(3), range(3)).buffer(0.5), ) ax = gdf.plot(column="col", edgecolor=(0.2, 0.2, 0.2)) for col in ax.collections: np.testing.assert_array_equal( col.get_edgecolor(), np.array([[0.2, 0.2, 0.2, 1.0]]), ) @pytest.mark.skipif(not HAS_PYPROJ, reason="pyproj not installed") class TestAxisLabels: def setup_method(self, nybb_filename): from geodatasets import get_path self.nybb = read_file(get_path("geoda nyc")) def test_feet(self): ax = self.nybb.plot() assert ax.get_xlabel() == "Easting [US survey foot]" assert ax.get_ylabel() == "Northing [US survey foot]" def test_metres(self): utm = self.nybb.to_crs(self.nybb.estimate_utm_crs()) ax = utm.plot() assert ax.get_xlabel() == "Easting [metre]" assert ax.get_ylabel() == "Northing [metre]" def test_4326(self): geog = self.nybb.to_crs(4326) ax = geog.plot() assert ax.get_xlabel() == "Geodetic longitude [degree]" assert ax.get_ylabel() == "Geodetic latitude [degree]" def test_naive(self): naive = self.nybb.set_crs(None, allow_override=True) ax = naive.plot() assert ax.get_xlabel() == "x" assert ax.get_ylabel() == "y" def test_passed_ax(self): import matplotlib.pyplot as plt _, ax = plt.subplots() ax = self.nybb.plot(ax=ax) assert ax.get_xlabel() == "Easting [US survey foot]" assert ax.get_ylabel() == "Northing [US survey foot]" def test_series(self): ax = self.nybb.geometry.plot() assert ax.get_xlabel() == "Easting [US survey foot]" assert ax.get_ylabel() == "Northing [US survey foot]" def test_preservation(self): import matplotlib.pyplot as plt _, ax = plt.subplots() ax.set_xlabel("xlabel") ax = self.nybb.plot(ax=ax) assert ax.get_xlabel() == "xlabel" assert ax.get_ylabel() == "Northing [US survey foot]" def test_no_labels(self): # gdf route ax = self.nybb.plot("forhis06", add_labels=False) assert ax.get_xlabel() == "" assert ax.get_ylabel() == "" # gdf -> gs ax = self.nybb.plot(add_labels=False) assert ax.get_xlabel() == "" assert ax.get_ylabel() == "" # gs ax2 = self.nybb.geometry.plot(add_labels=False) assert ax2.get_xlabel() == "" assert ax2.get_ylabel() == "" class TestTilesPlotting: def setup_method(self): pytest.importorskip("contextily") from geodatasets import get_path self.nybb = read_file(get_path("nybb")) def test_tiles_series(self): ax = self.nybb.geometry.plot(tiles=True) im = next(iter(ax.images)) assert im.get_array().shape == (1023, 1030, 4) attr = next(iter(ax.texts)).get_text() assert ( attr == "(C) OpenStreetMap contributors, Tiles style by Humanitarian OpenStreetMap Team hosted by OpenStreetMap France" # noqa: E501 ) def test_tiles_dataframe(self): ax = self.nybb.plot(tiles=True) im = next(iter(ax.images)) assert im.get_array().shape == (1023, 1030, 4) attr = next(iter(ax.texts)).get_text() assert ( attr == "(C) OpenStreetMap contributors, Tiles style by Humanitarian OpenStreetMap Team hosted by OpenStreetMap France" # noqa: E501 ) def test_dataframe_choropleth(self): ax = self.nybb.plot("BoroCode", tiles=True) im = next(iter(ax.images)) assert im.get_array().shape == (1023, 1030, 4) attr = next(iter(ax.texts)).get_text() assert ( attr == "(C) OpenStreetMap contributors, Tiles style by Humanitarian OpenStreetMap Team hosted by OpenStreetMap France" # noqa: E501 ) def test_tiles_custom_xyzservices(self): ax = self.nybb.plot(tiles="Esri.WorldImagery") im = next(iter(ax.images)) assert im.get_array().shape == (1023, 1030, 4) attr = next(iter(ax.texts)).get_text() assert ( attr == "Tiles (C) Esri -- Source: Esri, i-cubed, USDA, USGS, AEX, GeoEye, " "Getmapping, Aerogrid, IGN, IGP, UPR-EGP, and the GIS User Community" ) def test_tiles_custom_attribution(self): ax = self.nybb.plot(tiles="Esri.WorldImagery", attr="Custom attribution") im = next(iter(ax.images)) assert im.get_array().shape == (1023, 1030, 4) attr = next(iter(ax.texts)).get_text() assert attr == "Custom attribution" geopandas-1.2.0/geopandas/tests/test_show_versions.py000066400000000000000000000022261525650651600231140ustar00rootroot00000000000000from geopandas.tools._show_versions import ( _get_C_info, _get_deps_info, _get_sys_info, show_versions, ) def test_get_sys_info(): sys_info = _get_sys_info() assert "python" in sys_info assert "executable" in sys_info assert "machine" in sys_info def test_get_c_info(): C_info = _get_C_info() assert "GEOS" in C_info assert "GEOS lib" in C_info assert "GDAL" in C_info assert "GDAL data dir" in C_info assert "PROJ" in C_info assert "PROJ data dir" in C_info def test_get_deps_info(): deps_info = _get_deps_info() assert "geopandas" in deps_info assert "pandas" in deps_info assert "fiona" in deps_info assert "numpy" in deps_info assert "shapely" in deps_info assert "pyproj" in deps_info assert "matplotlib" in deps_info assert "mapclassify" in deps_info assert "geopy" in deps_info assert "psycopg" in deps_info assert "psycopg2" in deps_info assert "geoalchemy2" in deps_info def test_show_versions(capsys): show_versions() out, _err = capsys.readouterr() assert "python" in out assert "GEOS" in out assert "geopandas" in out geopandas-1.2.0/geopandas/tests/test_sindex.py000066400000000000000000001063741525650651600215070ustar00rootroot00000000000000import re from math import sqrt import numpy as np import shapely from shapely.geometry import ( GeometryCollection, LineString, MultiPolygon, Point, Polygon, box, ) import geopandas from geopandas import GeoDataFrame, GeoSeries, read_file from geopandas import _compat as compat import pytest from numpy.testing import assert_array_equal try: from scipy.sparse import coo_array # noqa: F401 HAS_SCIPY = True except ImportError: HAS_SCIPY = False SCIPY_MARK = pytest.mark.skipif(not HAS_SCIPY, reason="scipy not installed") class TestSeriesSindex: def test_has_sindex(self): """Test the has_sindex method.""" t1 = Polygon([(0, 0), (1, 0), (1, 1)]) t2 = Polygon([(0, 0), (1, 1), (0, 1)]) d = GeoDataFrame({"geom": [t1, t2]}, geometry="geom") assert not d.has_sindex d.sindex assert d.has_sindex d.geometry.values._sindex = None assert not d.has_sindex d.sindex assert d.has_sindex s = GeoSeries([t1, t2]) assert not s.has_sindex s.sindex assert s.has_sindex s.values._sindex = None assert not s.has_sindex s.sindex assert s.has_sindex def test_empty_geoseries(self): """Tests creating a spatial index from an empty GeoSeries.""" s = GeoSeries(dtype=object) assert not s.sindex assert len(s.sindex) == 0 def test_point(self): s = GeoSeries([Point(0, 0)]) assert s.sindex.size == 1 hits = s.sindex.intersection((-1, -1, 1, 1)) assert len(list(hits)) == 1 hits = s.sindex.intersection((-2, -2, -1, -1)) assert len(list(hits)) == 0 def test_empty_point(self): """Tests that a single empty Point results in an empty tree.""" s = GeoSeries([Point()]) assert not s.sindex assert len(s.sindex) == 0 def test_polygons(self): t1 = Polygon([(0, 0), (1, 0), (1, 1)]) t2 = Polygon([(0, 0), (1, 1), (0, 1)]) sq = Polygon([(0, 0), (1, 0), (1, 1), (0, 1)]) s = GeoSeries([t1, t2, sq]) assert s.sindex.size == 3 def test_lazy_build(self): s = GeoSeries([Point(0, 0)]) assert s.values._sindex is None assert s.sindex.size == 1 assert s.values._sindex is not None def test_rebuild_on_item_change(self): s = GeoSeries([Point(0, 0)]) original_index = s.sindex s.iloc[0] = Point(0, 0) assert s.sindex is not original_index def test_rebuild_on_slice(self): s = GeoSeries([Point(0, 0), Point(0, 0)]) original_index = s.sindex # Select a couple of rows sliced = s.iloc[:1] assert sliced.sindex is not original_index # Select all rows sliced = s.iloc[:] assert sliced.has_sindex assert sliced.sindex is original_index # Select all rows and flip sliced = s.iloc[::-1] assert sliced.sindex is not original_index class TestFrameSindex: def setup_method(self): data = { "A": range(5), "B": range(-5, 0), "geom": [Point(x, y) for x, y in zip(range(5), range(5))], } self.df = GeoDataFrame(data, geometry="geom") def test_sindex(self): self.df.crs = "epsg:4326" assert self.df.sindex.size == 5 hits = list(self.df.sindex.intersection((2.5, 2.5, 4, 4))) assert len(hits) == 2 assert hits[0] == 3 def test_lazy_build(self): assert self.df.geometry.values._sindex is None assert self.df.sindex.size == 5 assert self.df.geometry.values._sindex is not None def test_sindex_rebuild_on_set_geometry(self): # First build the sindex assert self.df.sindex is not None original_index = self.df.sindex self.df.set_geometry( [Point(x, y) for x, y in zip(range(5, 10), range(5, 10))], inplace=True ) assert self.df.sindex is not original_index def test_rebuild_on_row_slice(self): # Select a subset of rows rebuilds original_index = self.df.sindex sliced = self.df.iloc[:1] assert sliced.sindex is not original_index # Slicing all does not rebuild original_index = self.df.sindex sliced = self.df.iloc[:] assert sliced.sindex is original_index # Re-ordering rebuilds sliced = self.df.iloc[::-1] assert sliced.sindex is not original_index def test_rebuild_on_single_col_selection(self): """Selecting a single column should not rebuild the spatial index.""" # Selecting geometry column preserves the index original_index = self.df.sindex geometry_col = self.df["geom"] assert geometry_col.sindex is original_index geometry_col = self.df.geometry assert geometry_col.sindex is original_index def test_rebuild_on_multiple_col_selection(self): """Selecting a subset of columns preserves the index.""" original_index = self.df.sindex # Selecting a subset of columns preserves the index for pandas < 2.0 # with pandas 2.0, the column is now copied, losing the index. But # with pandas >= 3.0 and Copy-on-Write this is preserved again subset1 = self.df[["geom", "A"]] if not compat.PANDAS_GE_30: assert subset1.sindex is not original_index else: assert subset1.sindex is original_index subset2 = self.df[["A", "geom"]] if not compat.PANDAS_GE_30: assert subset2.sindex is not original_index else: assert subset2.sindex is original_index def test_rebuild_on_update_inplace(self): gdf = self.df.copy() old_sindex = gdf.sindex # updating geometry columns in place gdf.replace({"geom": {Point(1, 1): Point(10, 10)}}, inplace=True) # spatial index should be invalidated assert not gdf.has_sindex new_sindex = gdf.sindex # and should be different assert new_sindex is not old_sindex # update should still have happened though assert gdf.geometry.x.tolist() == [0, 10, 2, 3, 4] def test_update_inplace_no_rebuild(self): gdf = self.df.copy() old_sindex = gdf.sindex gdf.replace({"A": {1: 2}}, inplace=True) # an inplace replace on another column shouldn't invalidate the index assert gdf.has_sindex # and the "new" should be the same new_sindex = gdf.sindex assert old_sindex is new_sindex # Skip to accommodate Shapely geometries being unhashable # TODO unskip? @pytest.mark.skip @pytest.mark.usefixtures("_setup_class_nybb_filename") class TestJoinSindex: def setup_method(self): self.boros = read_file(self.nybb_filename) def test_merge_geo(self): # First check that we gets hits from the boros frame. tree = self.boros.sindex hits = tree.intersection((1012821.80, 229228.26)) res = [self.boros.iloc[hit]["BoroName"] for hit in hits] assert res == ["Bronx", "Queens"] # Check that we only get the Bronx from this view. first = self.boros[self.boros["BoroCode"] < 3] tree = first.sindex hits = tree.intersection((1012821.80, 229228.26)) res = [first.iloc[hit]["BoroName"] for hit in hits] assert res == ["Bronx"] # Check that we only get Queens from this view. second = self.boros[self.boros["BoroCode"] >= 3] tree = second.sindex hits = tree.intersection((1012821.80, 229228.26)) res = ([second.iloc[hit]["BoroName"] for hit in hits],) assert res == ["Queens"] # Get both the Bronx and Queens again. merged = first.merge(second, how="outer") assert len(merged) == 5 assert merged.sindex.size == 5 tree = merged.sindex hits = tree.intersection((1012821.80, 229228.26)) res = [merged.iloc[hit]["BoroName"] for hit in hits] assert res == ["Bronx", "Queens"] class TestShapelyInterface: def setup_method(self): data = { "geom": [Point(x, y) for x, y in zip(range(5), range(5))] + [box(10, 10, 20, 20)] # include a box geometry } self.df = GeoDataFrame(data, geometry="geom") self.expected_size = len(data["geom"]) # --------------------------- `intersection` tests -------------------------- # @pytest.mark.parametrize( "test_geom, expected", ( ((-1, -1, -0.5, -0.5), []), ((-0.5, -0.5, 0.5, 0.5), [0]), ((0, 0, 1, 1), [0, 1]), ((0, 0), [0]), ), ) def test_intersection_bounds_tuple(self, test_geom, expected): """Tests the `intersection` method with valid inputs.""" res = list(self.df.sindex.intersection(test_geom)) assert_array_equal(res, expected) @pytest.mark.parametrize("test_geom", ((-1, -1, -0.5), -0.5, None, Point(0, 0))) def test_intersection_invalid_bounds_tuple(self, test_geom): """Tests the `intersection` method with invalid inputs.""" with pytest.raises(TypeError): # we raise a useful TypeError self.df.sindex.intersection(test_geom) # ------------------------------ `query` tests ------------------------------ # @pytest.mark.parametrize( "output_format", ("indices", pytest.param("sparse", marks=SCIPY_MARK), "dense") ) @pytest.mark.parametrize( "predicate, test_geom, expected", ( (None, box(-1, -1, -0.5, -0.5), []), # bbox does not intersect (None, box(-0.5, -0.5, 0.5, 0.5), [0]), # bbox intersects (None, box(0, 0, 1, 1), [0, 1]), # bbox intersects multiple ( None, LineString([(0, 1), (1, 0)]), [0, 1], ), # bbox intersects but not geometry ("intersects", box(-1, -1, -0.5, -0.5), []), # bbox does not intersect ( "intersects", box(-0.5, -0.5, 0.5, 0.5), [0], ), # bbox and geometry intersect ( "intersects", box(0, 0, 1, 1), [0, 1], ), # bbox and geometry intersect multiple ( "intersects", LineString([(0, 1), (1, 0)]), [], ), # bbox intersects but not geometry ("within", box(0.25, 0.28, 0.75, 0.75), []), # does not intersect ("within", box(0, 0, 10, 10), []), # intersects but is not within ("within", box(11, 11, 12, 12), [5]), # intersects and is within ("within", LineString([(0, 1), (1, 0)]), []), # intersects but not within ("contains", box(0, 0, 1, 1), []), # intersects but does not contain ("contains", box(0, 0, 1.001, 1.001), [1]), # intersects and contains ("contains", box(0.5, 0.5, 1.5, 1.5), [1]), # intersects and contains ("contains", box(-1, -1, 2, 2), [0, 1]), # intersects and contains multiple ( "contains", LineString([(0, 1), (1, 0)]), [], ), # intersects but not contains ("touches", box(-1, -1, 0, 0), [0]), # bbox intersects and touches ( "touches", box(-0.5, -0.5, 1.5, 1.5), [], ), # bbox intersects but geom does not touch ( "contains", box(10, 10, 20, 20), [5], ), # contains but does not contains_properly ( "covers", box(-0.5, -0.5, 1, 1), [0, 1], ), # covers (0, 0) and (1, 1) ( "covers", box(0.001, 0.001, 0.99, 0.99), [], ), # does not cover any ( "covers", box(0, 0, 1, 1), [0, 1], ), # covers but does not contain ( "contains_properly", box(0, 0, 1, 1), [], ), # intersects but does not contain ( "contains_properly", box(0, 0, 1.001, 1.001), [1], ), # intersects 2 and contains 1 ( "contains_properly", box(0.5, 0.5, 1.001, 1.001), [1], ), # intersects 1 and contains 1 ( "contains_properly", box(0.5, 0.5, 1.5, 1.5), [1], ), # intersects and contains ( "contains_properly", box(-1, -1, 2, 2), [0, 1], ), # intersects and contains multiple ( "contains_properly", box(10, 10, 20, 20), [], ), # contains but does not contains_properly ), ) def test_query(self, predicate, test_geom, expected, output_format): """Tests the `query` method with valid inputs and valid predicates.""" res = self.df.sindex.query(test_geom, predicate=predicate) assert_array_equal(res, expected) if output_format != "indices": dense = np.zeros(len(self.df), dtype=bool) dense[expected] = True res = self.df.sindex.query( test_geom, predicate=predicate, output_format=output_format ) if output_format == "sparse": res = res.todense() assert_array_equal(res, dense) def test_query_invalid_geometry(self): """Tests the `query` method with invalid geometry.""" with pytest.raises(TypeError): self.df.sindex.query("notavalidgeom") @pytest.mark.skipif(not compat.GEOS_GE_310, reason="Requires GEOS 3.10") @pytest.mark.parametrize( "distance, test_geom, expected", ( # bounds don't intersect and not within distance=0 ( 0, box(9.0, 9.0, 9.9, 9.9), [], ), # bounds don't intersect but is within distance=1 ( 1, box(9.0, 9.0, 9.9, 9.9), [5], ), # within 1-D absolute distance in both axes, but not euclidean distance ( 0.5, Point(0.5, 0.5), [], ), # same as before but within euclidean distance ( sqrt(2 * 0.5**2) + 1e-9, Point(0.5, 0.5), [0, 1], ), # less than euclidean distance between points, multi-object ( sqrt(2) - 1e-9, [ Polygon([(0, 0), (1, 0), (1, 1)]), Polygon([(1, 1), (2, 1), (2, 2)]), ], # multi-object test [[0, 0, 1, 1], [0, 1, 1, 2]], ), # more than euclidean distance between points, multi-object ( sqrt(2) + 1e-9, [ Polygon([(0, 0), (1, 0), (1, 1)]), Polygon([(1, 1), (2, 1), (2, 2)]), ], [[0, 0, 0, 1, 1, 1, 1], [0, 1, 2, 0, 1, 2, 3]], ), # distance is array-like, broadcastable to geometry ( [2, 10], [Point(0.5, 0.5), Point(1, 1)], [[0, 0, 1, 1, 1, 1, 1], [0, 1, 0, 1, 2, 3, 4]], ), ), ) def test_query_dwithin(self, distance, test_geom, expected): """Tests the `query` method with predicates that require keyword arguments.""" res = self.df.sindex.query(test_geom, predicate="dwithin", distance=distance) assert_array_equal(res, expected) @pytest.mark.skipif(not compat.GEOS_GE_310, reason="Requires GEOS 3.10") def test_dwithin_no_distance(self): """Tests the `query` method with keyword arguments that are invalid for certain predicates.""" with pytest.raises( ValueError, match="'distance' parameter is required for 'dwithin' predicate" ): self.df.sindex.query(Point(0, 0), predicate="dwithin") @pytest.mark.parametrize( "predicate", [ None, "contains", "contains_properly", "covered_by", "covers", "crosses", "intersects", "overlaps", "touches", "within", ], ) def test_query_distance_invalid(self, predicate): """Tests the `query` method with keyword arguments that are invalid for certain predicates.""" msg = "'distance' parameter is only supported in combination with 'dwithin'" with pytest.raises(ValueError, match=msg): self.df.sindex.query(Point(0, 0), predicate=predicate, distance=0) @pytest.mark.skipif( compat.GEOS_GE_310, reason="Test for 'dwithin'-incompatible versions of GEOS" ) def test_dwithin_requirements(self): """Tests whether a ValueError is raised when trying to use dwithin with incompatible versions of shapely or pyGEOS """ with pytest.raises( ValueError, match=re.escape("predicate = 'dwithin' requires GEOS >= 3.10.0") ): self.df.sindex.query(Point(0, 0), predicate="dwithin", distance=0) @pytest.mark.parametrize( "test_geom, expected_value", [ (None, []), (GeometryCollection(), []), (Point(), []), (MultiPolygon(), []), (Polygon(), []), ], ) def test_query_empty_geometry(self, test_geom, expected_value): """Tests the `query` method with empty geometry.""" res = self.df.sindex.query(test_geom) assert_array_equal(res, expected_value) def test_query_invalid_predicate(self): """Tests the `query` method with invalid predicates.""" test_geom = box(-1, -1, -0.5, -0.5) with pytest.raises(ValueError): self.df.sindex.query(test_geom, predicate="test") @pytest.mark.parametrize( "sort, expected", ( (True, [[0, 0, 0], [0, 1, 2]]), # False could be anything, at least we'll know if it changes (False, [[0, 0, 0], [0, 1, 2]]), ), ) def test_query_sorting(self, sort, expected): """Check that results from `query` don't depend on the order of geometries. """ # these geometries come from a reported issue: # https://github.com/geopandas/geopandas/issues/1337 # there is no theoretical reason they were chosen test_polys = GeoSeries([Polygon([(1, 1), (3, 1), (3, 3), (1, 3)])]) tree_polys = GeoSeries( [ Polygon([(1, 1), (3, 1), (3, 3), (1, 3)]), Polygon([(-1, 1), (1, 1), (1, 3), (-1, 3)]), Polygon([(3, 3), (5, 3), (5, 5), (3, 5)]), ] ) expected = [0, 1, 2] test_geo = test_polys.values[0] res = tree_polys.sindex.query(test_geo, sort=sort) # asserting the same elements assert sorted(res) == sorted(expected) # asserting the exact array can fail if sort=False try: assert_array_equal(res, expected) except AssertionError as e: if sort is False: pytest.xfail( "rtree results are known to be unordered, see " "https://github.com/geopandas/geopandas/issues/1337\n" f"Expected:\n {expected}\n" f"Got:\n {res.tolist()}\n" ) raise e def test_unsupported_output(self): with pytest.raises(ValueError, match="Invalid output_format: 'dataarray'"): test_geom = box(-1, -1, -0.5, -0.5) self.df.sindex.query(test_geom, output_format="dataarray") # ------------------------- `query_bulk` tests -------------------------- # @pytest.mark.parametrize( "output_format", ("indices", pytest.param("sparse", marks=SCIPY_MARK), "dense") ) @pytest.mark.parametrize( "predicate, test_geom, expected", ( (None, [(-1, -1, -0.5, -0.5)], [[], []]), (None, [(-0.5, -0.5, 0.5, 0.5)], [[0], [0]]), (None, [(0, 0, 1, 1)], [[0, 0], [0, 1]]), ("intersects", [(-1, -1, -0.5, -0.5)], [[], []]), ("intersects", [(-0.5, -0.5, 0.5, 0.5)], [[0], [0]]), ("intersects", [(0, 0, 1, 1)], [[0, 0], [0, 1]]), # only second geom intersects ("intersects", [(-1, -1, -0.5, -0.5), (-0.5, -0.5, 0.5, 0.5)], [[1], [0]]), # both geoms intersect ( "intersects", [(-1, -1, 1, 1), (-0.5, -0.5, 0.5, 0.5)], [[0, 0, 1], [0, 1, 0]], ), ("within", [(0.25, 0.28, 0.75, 0.75)], [[], []]), # does not intersect ("within", [(0, 0, 10, 10)], [[], []]), # intersects but is not within ("within", [(11, 11, 12, 12)], [[0], [5]]), # intersects and is within ( "contains", [(0, 0, 1, 1)], [[], []], ), # intersects and covers, but does not contain ( "contains", [(0, 0, 1.001, 1.001)], [[0], [1]], ), # intersects 2 and contains 1 ( "contains", [(0.5, 0.5, 1.001, 1.001)], [[0], [1]], ), # intersects 1 and contains 1 ("contains", [(0.5, 0.5, 1.5, 1.5)], [[0], [1]]), # intersects and contains ( "contains", [(-1, -1, 2, 2)], [[0, 0], [0, 1]], ), # intersects and contains multiple ( "contains", [(10, 10, 20, 20)], [[0], [5]], ), # contains but does not contains_properly ("touches", [(-1, -1, 0, 0)], [[0], [0]]), # bbox intersects and touches ( "touches", [(-0.5, -0.5, 1.5, 1.5)], [[], []], ), # bbox intersects but geom does not touch ( "covers", [(-0.5, -0.5, 1, 1)], [[0, 0], [0, 1]], ), # covers (0, 0) and (1, 1) ( "covers", [(0.001, 0.001, 0.99, 0.99)], [[], []], ), # does not cover any ( "covers", [(0, 0, 1, 1)], [[0, 0], [0, 1]], ), # covers but does not contain ( "contains_properly", [(0, 0, 1, 1)], [[], []], ), # intersects but does not contain ( "contains_properly", [(0, 0, 1.001, 1.001)], [[0], [1]], ), # intersects 2 and contains 1 ( "contains_properly", [(0.5, 0.5, 1.001, 1.001)], [[0], [1]], ), # intersects 1 and contains 1 ( "contains_properly", [(0.5, 0.5, 1.5, 1.5)], [[0], [1]], ), # intersects and contains ( "contains_properly", [(-1, -1, 2, 2)], [[0, 0], [0, 1]], ), # intersects and contains multiple ( "contains_properly", [(10, 10, 20, 20)], [[], []], ), # contains but does not contains_properly ), ) def test_query_bulk(self, predicate, test_geom, expected, output_format): """Tests the `query` method with valid inputs and valid predicates. """ test_geoms = [box(*geom) for geom in test_geom] res = self.df.sindex.query(test_geoms, predicate=predicate) assert_array_equal(res, expected) if output_format != "indices": dense = np.zeros((len(self.df), len(test_geoms)), dtype=bool) tree, other = expected[::-1] dense[tree, other] = True res = self.df.sindex.query( test_geoms, predicate=predicate, output_format=output_format ) if output_format == "sparse": res = res.todense() assert_array_equal(res, dense) @pytest.mark.parametrize( "test_geoms, expected_value", [ # single empty geometry ([GeometryCollection()], [[], []]), # None should be skipped ([GeometryCollection(), None], [[], []]), ([None], [[], []]), ([None, box(-0.5, -0.5, 0.5, 0.5), None], [[1], [0]]), ], ) def test_query_bulk_empty_geometry(self, test_geoms, expected_value): """Tests the `query` method with an empty geometries.""" res = self.df.sindex.query(test_geoms) assert_array_equal(res, expected_value) def test_query_bulk_empty_input_array(self): """Tests the `query` method with an empty input array.""" test_array = np.array([], dtype=object) expected_value = [[], []] res = self.df.sindex.query(test_array) assert_array_equal(res, expected_value) def test_query_bulk_invalid_input_geometry(self): """ Tests the `query` method with invalid input for the `geometry` parameter. """ test_array = "notanarray" with pytest.raises(TypeError): self.df.sindex.query(test_array) def test_query_bulk_invalid_predicate(self): """Tests the `query` method with invalid predicates.""" test_geom_bounds = (-1, -1, -0.5, -0.5) test_predicate = "test" with pytest.raises(ValueError): self.df.sindex.query([box(*test_geom_bounds)], predicate=test_predicate) @pytest.mark.parametrize( "predicate, test_geom, expected", ( (None, (-1, -1, -0.5, -0.5), [[], []]), ("intersects", (-1, -1, -0.5, -0.5), [[], []]), ("contains", (-1, -1, 1, 1), [[0], [0]]), ), ) def test_query_bulk_input_type(self, predicate, test_geom, expected): """Tests that query can accept a GeoSeries, GeometryArray or numpy array. """ # pass through GeoSeries to test input type test_geom = geopandas.GeoSeries([box(*test_geom)], index=["0"]) # test GeoSeries res = self.df.sindex.query(test_geom, predicate=predicate) assert_array_equal(res, expected) # test GeometryArray res = self.df.sindex.query(test_geom.geometry, predicate=predicate) assert_array_equal(res, expected) res = self.df.sindex.query(test_geom.geometry.values, predicate=predicate) assert_array_equal(res, expected) # test numpy array res = self.df.sindex.query( test_geom.geometry.values.to_numpy(), predicate=predicate ) assert_array_equal(res, expected) res = self.df.sindex.query( test_geom.geometry.values.to_numpy(), predicate=predicate ) assert_array_equal(res, expected) @pytest.mark.parametrize( "sort, expected", ( (True, [[0, 0, 0], [0, 1, 2]]), # False could be anything, at least we'll know if it changes (False, [[0, 0, 0], [0, 1, 2]]), ), ) def test_query_bulk_sorting(self, sort, expected): """Check that results from `query` don't depend on the order of geometries. """ # these geometries come from a reported issue: # https://github.com/geopandas/geopandas/issues/1337 # there is no theoretical reason they were chosen test_polys = GeoSeries([Polygon([(1, 1), (3, 1), (3, 3), (1, 3)])]) tree_polys = GeoSeries( [ Polygon([(1, 1), (3, 1), (3, 3), (1, 3)]), Polygon([(-1, 1), (1, 1), (1, 3), (-1, 3)]), Polygon([(3, 3), (5, 3), (5, 5), (3, 5)]), ] ) res = tree_polys.sindex.query(test_polys, sort=sort) # asserting the same elements assert sorted(res[0]) == sorted(expected[0]) assert sorted(res[1]) == sorted(expected[1]) # asserting the exact array can fail if sort=False try: assert_array_equal(res, expected) except AssertionError as e: if sort is False: pytest.xfail( "rtree results are known to be unordered, see " "https://github.com/geopandas/geopandas/issues/1337\n" f"Expected:\n {expected}\n" f"Got:\n {res.tolist()}\n" ) raise e # ------------------------- `nearest` tests ------------------------- # @pytest.mark.parametrize("return_all", [True, False]) @pytest.mark.parametrize( "geometry,expected", [ ([0.25, 0.25], [[0], [0]]), ([0.75, 0.75], [[0], [1]]), ], ) def test_nearest_single(self, geometry, expected, return_all): geoms = shapely.points(np.arange(10), np.arange(10)) df = geopandas.GeoDataFrame({"geometry": geoms}) p = Point(geometry) res = df.sindex.nearest(p, return_all=return_all) assert_array_equal(res, expected) p = shapely.points(geometry) res = df.sindex.nearest(p, return_all=return_all) assert_array_equal(res, expected) @pytest.mark.parametrize("return_all", [True, False]) @pytest.mark.parametrize( "geometry,expected", [ ([(1, 1), (0, 0)], [[0, 1], [1, 0]]), ([(1, 1), (0.25, 1)], [[0, 1], [1, 1]]), ], ) def test_nearest_multi(self, geometry, expected, return_all): geoms = shapely.points(np.arange(10), np.arange(10)) df = geopandas.GeoDataFrame({"geometry": geoms}) ps = [Point(p) for p in geometry] res = df.sindex.nearest(ps, return_all=return_all) assert_array_equal(res, expected) ps = shapely.points(geometry) res = df.sindex.nearest(ps, return_all=return_all) assert_array_equal(res, expected) s = geopandas.GeoSeries(ps) res = df.sindex.nearest(s, return_all=return_all) assert_array_equal(res, expected) x, y = zip(*geometry) ga = geopandas.points_from_xy(x, y) res = df.sindex.nearest(ga, return_all=return_all) assert_array_equal(res, expected) @pytest.mark.parametrize("return_all", [True, False]) @pytest.mark.parametrize( "geometry,expected", [ (None, [[], []]), ([None], [[], []]), ], ) def test_nearest_none(self, geometry, expected, return_all): geoms = shapely.points(np.arange(10), np.arange(10)) df = geopandas.GeoDataFrame({"geometry": geoms}) res = df.sindex.nearest(geometry, return_all=return_all) assert_array_equal(res, expected) @pytest.mark.parametrize("return_distance", [True, False]) @pytest.mark.parametrize( "return_all,max_distance,expected", [ (True, None, ([[0, 0, 1], [0, 1, 5]], [sqrt(0.5), sqrt(0.5), sqrt(50)])), (False, None, ([[0, 1], [0, 5]], [sqrt(0.5), sqrt(50)])), (True, 1, ([[0, 0], [0, 1]], [sqrt(0.5), sqrt(0.5)])), (False, 1, ([[0], [0]], [sqrt(0.5)])), ], ) def test_nearest_max_distance( self, expected, max_distance, return_all, return_distance ): geoms = shapely.points(np.arange(10), np.arange(10)) df = geopandas.GeoDataFrame({"geometry": geoms}) ps = [Point(0.5, 0.5), Point(0, 10)] res = df.sindex.nearest( ps, return_all=return_all, max_distance=max_distance, return_distance=return_distance, ) if return_distance: assert_array_equal(res[0], expected[0]) assert_array_equal(res[1], expected[1]) else: assert_array_equal(res, expected[0]) @pytest.mark.parametrize("return_distance", [True, False]) @pytest.mark.parametrize( "return_all,max_distance,exclusive,expected", [ (False, None, False, ([[0, 1, 2, 3, 4], [0, 1, 2, 3, 4]], 5 * [0])), (False, None, True, ([[0, 1, 2, 3, 4], [1, 0, 1, 2, 3]], 5 * [sqrt(2)])), (True, None, False, ([[0, 1, 2, 3, 4], [0, 1, 2, 3, 4]], 5 * [0])), ( True, None, True, ([[0, 1, 1, 2, 2, 3, 3, 4], [1, 0, 2, 1, 3, 2, 4, 3]], 8 * [sqrt(2)]), ), (False, 1.1, True, ([[1, 2, 5], [5, 5, 1]], 3 * [1])), (True, 1.1, True, ([[1, 2, 5, 5], [5, 5, 1, 2]], 4 * [1])), ], ) def test_nearest_exclusive( self, expected, max_distance, return_all, return_distance, exclusive ): geoms = shapely.points(np.arange(5), np.arange(5)) if max_distance: # add a non grid point geoms = np.append(geoms, [Point(1, 2)]) df = geopandas.GeoDataFrame({"geometry": geoms}) ps = geoms res = df.sindex.nearest( ps, return_all=return_all, max_distance=max_distance, return_distance=return_distance, exclusive=exclusive, ) if return_distance: assert_array_equal(res[0], expected[0]) assert_array_equal(res[1], expected[1]) else: assert_array_equal(res, expected[0]) # --------------------------- misc tests ---------------------------- # def test_empty_tree_geometries(self): """Tests building sindex with interleaved empty geometries.""" geoms = [Point(0, 0), None, Point(), Point(1, 1), Point()] df = geopandas.GeoDataFrame(geometry=geoms) assert df.sindex.query(Point(1, 1))[0] == 3 def test_size(self): """Tests the `size` property.""" assert self.df.sindex.size == self.expected_size def test_len(self): """Tests the `__len__` method of spatial indexes.""" assert len(self.df.sindex) == self.expected_size def test_is_empty(self): """Tests the `is_empty` property.""" # create empty tree empty = geopandas.GeoSeries([], dtype=object) assert empty.sindex.is_empty empty = geopandas.GeoSeries([None]) assert empty.sindex.is_empty empty = geopandas.GeoSeries([Point()]) assert empty.sindex.is_empty # create a non-empty tree non_empty = geopandas.GeoSeries([Point(0, 0)]) assert not non_empty.sindex.is_empty @pytest.mark.parametrize( "predicate, expected_shape", [ (None, (2, 471)), ("intersects", (2, 213)), ("within", (2, 213)), ("contains", (2, 0)), ("overlaps", (2, 0)), ("crosses", (2, 0)), ("touches", (2, 0)), ], ) def test_integration_natural_earth( self, predicate, expected_shape, naturalearth_lowres, naturalearth_cities ): """Tests output sizes for the naturalearth datasets.""" world = read_file(naturalearth_lowres) capitals = read_file(naturalearth_cities) res = world.sindex.query(capitals.geometry, predicate) assert res.shape == expected_shape geopandas-1.2.0/geopandas/tests/test_testing.py000066400000000000000000000131131525650651600216560ustar00rootroot00000000000000import warnings import numpy as np import pandas as pd from pandas import DataFrame, Series from shapely.geometry import Point, Polygon from geopandas import GeoDataFrame, GeoSeries from geopandas._compat import HAS_PYPROJ from geopandas.array import from_shapely import pytest from geopandas.testing import assert_geodataframe_equal, assert_geoseries_equal s1 = GeoSeries( [ Polygon([(0, 0), (2, 0), (2, 2), (0, 2)]), Polygon([(2, 2), (4, 2), (4, 4), (2, 4)]), ] ) s2 = GeoSeries( [ Polygon([(0, 2), (0, 0), (2, 0), (2, 2)]), Polygon([(2, 2), (4, 2), (4, 4), (2, 4)]), ] ) s3 = Series( [ Polygon([(0, 2), (0, 0), (2, 0), (2, 2)]), Polygon([(2, 2), (4, 2), (4, 4), (2, 4)]), ] ) a = from_shapely( [ Polygon([(0, 2), (0, 0), (2, 0), (2, 2)]), Polygon([(2, 2), (4, 2), (4, 4), (2, 4)]), ] ) s4 = Series(a) df1 = GeoDataFrame({"col1": [1, 2], "geometry": s1}) df2 = GeoDataFrame({"col1": [1, 2], "geometry": s2}) s4 = s1.copy() s4.array.crs = 4326 s5 = s2.copy() s5.array.crs = 27700 s6 = GeoSeries( [ Polygon([(0, 3), (0, 0), (2, 0), (2, 2)]), Polygon([(2, 2), (4, 2), (4, 4), (2, 4)]), ] ) df4 = GeoDataFrame( {"col1": [1, 2], "geometry": s1.copy(), "geom2": s4.copy(), "geom3": s5.copy()}, crs=3857, ) df5 = GeoDataFrame( {"col1": [1, 2], "geometry": s1.copy(), "geom3": s5.copy(), "geom2": s4.copy()}, crs=3857, ) @pytest.mark.filterwarnings("ignore::UserWarning") def test_geoseries(): assert_geoseries_equal(s1, s2) assert_geoseries_equal(s1, s3, check_series_type=False, check_dtype=False) assert_geoseries_equal(s3, s2, check_series_type=False, check_dtype=False) assert_geoseries_equal(s1, s4, check_series_type=False) with pytest.raises(AssertionError) as error: assert_geoseries_equal(s1, s2, check_less_precise=True) assert "1 out of 2 geometries are not almost equal" in str(error.value) assert "not almost equal: [0]" in str(error.value) with pytest.raises(AssertionError) as error: assert_geoseries_equal(s2, s6, check_less_precise=False) assert "1 out of 2 geometries are not equal" in str(error.value) assert "not equal: [0]" in str(error.value) def test_geodataframe(): assert_geodataframe_equal(df1, df2) with pytest.raises(AssertionError): assert_geodataframe_equal(df1, df2, check_less_precise=True) with pytest.raises(AssertionError): assert_geodataframe_equal(df1, df2[["geometry", "col1"]]) assert_geodataframe_equal(df1, df2[["geometry", "col1"]], check_like=True) df3 = df2.copy() df3.loc[0, "col1"] = 10 with pytest.raises(AssertionError): assert_geodataframe_equal(df1, df3) assert_geodataframe_equal(df5, df4, check_like=True) if HAS_PYPROJ: df5["geom2"] = df5.geom2.set_crs(3857, allow_override=True) with pytest.raises(AssertionError): assert_geodataframe_equal(df5, df4, check_like=True) def test_equal_nans(): s = GeoSeries([Point(0, 0), np.nan]) assert_geoseries_equal(s, s.copy()) assert_geoseries_equal(s, s.copy(), check_less_precise=True) def test_no_crs(): df1 = GeoDataFrame({"col1": [1, 2], "geometry": s1}, crs=None) df2 = GeoDataFrame({"col1": [1, 2], "geometry": s1}, crs={}) assert_geodataframe_equal(df1, df2) @pytest.mark.skipif(not HAS_PYPROJ, reason="pyproj not available") def test_ignore_crs_mismatch(): df1 = GeoDataFrame({"col1": [1, 2], "geometry": s1.copy()}, crs="EPSG:4326") df2 = GeoDataFrame({"col1": [1, 2], "geometry": s1}, crs="EPSG:31370") with pytest.raises(AssertionError): assert_geodataframe_equal(df1, df2) # assert that with `check_crs=False` the assert passes, and also does not # generate any warning from comparing both geometries with different crs with warnings.catch_warnings(record=True) as record: assert_geodataframe_equal(df1, df2, check_crs=False) assert len(record) == 0 def test_almost_equal_but_not_equal(): s_origin = GeoSeries([Point(0, 0)]) s_almost_origin = GeoSeries([Point(0.0000001, 0)]) assert_geoseries_equal(s_origin, s_almost_origin, check_less_precise=True) with pytest.raises(AssertionError): assert_geoseries_equal(s_origin, s_almost_origin) def test_geodataframe_no_active_geometry_column(): def create_dataframe(): gdf = GeoDataFrame({"value": [1, 2], "geometry": [Point(1, 1), Point(2, 2)]}) gdf["geom2"] = GeoSeries([Point(3, 3), Point(4, 4)]) return gdf # no active geometry column (None) df1 = create_dataframe() df1._geometry_column_name = None df2 = create_dataframe() df2._geometry_column_name = None assert_geodataframe_equal(df1, df2) # active geometry column ("geometry") not present df1 = create_dataframe()[["value", "geom2"]] df2 = create_dataframe()[["value", "geom2"]] assert_geodataframe_equal(df1, df2) df1 = GeoDataFrame(create_dataframe()[["value"]]) df2 = GeoDataFrame(create_dataframe()[["value"]]) assert_geodataframe_equal(df1, df2) def test_geodataframe_multiindex(): def create_dataframe(): gdf = DataFrame([[Point(0, 0), Point(1, 1)], [Point(2, 2), Point(3, 3)]]) gdf = GeoDataFrame(gdf.astype("geometry")) gdf.columns = pd.MultiIndex.from_product([["geometry"], [0, 1]]) return gdf df1 = create_dataframe() df2 = create_dataframe() assert_geodataframe_equal(df1, df2) df1 = create_dataframe() df1._geometry_column_name = None df2 = create_dataframe() df2._geometry_column_name = None assert_geodataframe_equal(df1, df2) geopandas-1.2.0/geopandas/tests/test_types.py000066400000000000000000000047101525650651600213500ustar00rootroot00000000000000from pandas import DataFrame, Series from shapely.geometry import Point from geopandas import GeoDataFrame, GeoSeries class TestSeries: def setup_method(self): N = self.N = 10 r = 0.5 self.pts = GeoSeries([Point(x, y) for x, y in zip(range(N), range(N))]) self.polys = self.pts.buffer(r) def test_slice(self): assert type(self.pts[:2]) is GeoSeries assert type(self.pts[::2]) is GeoSeries assert type(self.polys[:2]) is GeoSeries def test_head(self): assert type(self.pts.head()) is GeoSeries def test_tail(self): assert type(self.pts.tail()) is GeoSeries def test_sort_index(self): assert type(self.pts.sort_index()) is GeoSeries def test_loc(self): assert type(self.pts.loc[5:]) is GeoSeries def test_iloc(self): assert type(self.pts.iloc[5:]) is GeoSeries def test_fancy(self): idx = (self.pts.index.to_series() % 2).astype(bool) assert type(self.pts[idx]) is GeoSeries def test_take(self): assert type(self.pts.take(list(range(0, self.N, 2)))) is GeoSeries def test_groupby(self): for f, s in self.pts.groupby(lambda x: x % 2): assert type(s) is GeoSeries class TestDataFrame: def setup_method(self): N = 10 self.df = GeoDataFrame( [ {"geometry": Point(x, y), "value1": x + y, "value2": x * y} for x, y in zip(range(N), range(N)) ] ) def test_geometry(self): assert type(self.df.geometry) is GeoSeries # still GeoSeries if different name df2 = GeoDataFrame( { "coords": [Point(x, y) for x, y in zip(range(5), range(5))], "nums": range(5), }, geometry="coords", ) assert type(df2.geometry) is GeoSeries assert type(df2["coords"]) is GeoSeries def test_nongeometry(self): assert type(self.df["value1"]) is Series def test_geometry_multiple(self): assert type(self.df[["geometry", "value1"]]) is GeoDataFrame def test_nongeometry_multiple(self): assert type(self.df[["value1", "value2"]]) is DataFrame def test_slice(self): assert type(self.df[:2]) is GeoDataFrame assert type(self.df[::2]) is GeoDataFrame def test_fancy(self): idx = (self.df.index.to_series() % 2).astype(bool) assert type(self.df[idx]) is GeoDataFrame geopandas-1.2.0/geopandas/tests/util.py000066400000000000000000000106041525650651600201210ustar00rootroot00000000000000import os.path from pandas import Series from geopandas import GeoDataFrame from geopandas.testing import ( # noqa: F401 assert_geoseries_equal, geom_almost_equals, geom_equals, ) HERE = os.path.abspath(os.path.dirname(__file__)) PACKAGE_DIR = os.path.dirname(os.path.dirname(HERE)) _TEST_DATA_DIR = os.path.join(PACKAGE_DIR, "geopandas", "tests", "data") _NYBB = "zip://" + os.path.join(_TEST_DATA_DIR, "nybb_16a.zip") _NATURALEARTH_CITIES = os.path.join( _TEST_DATA_DIR, "naturalearth_cities", "naturalearth_cities.shp" ) _NATURALEARTH_LOWRES = os.path.join( _TEST_DATA_DIR, "naturalearth_lowres", "naturalearth_lowres.shp" ) # mock not used here, but the import from here is used in other modules try: from unittest import mock except ImportError: from unittest import mock # noqa: F401 def validate_boro_df(df, case_sensitive=False): """Tests a GeoDataFrame that has been read in from the nybb dataset.""" assert isinstance(df, GeoDataFrame) # Make sure all the columns are there and the geometries # were properly loaded as MultiPolygons assert len(df) == 5 columns = ("BoroCode", "BoroName", "Shape_Leng", "Shape_Area") if case_sensitive: for col in columns: assert col in df.columns else: for col in columns: assert col.lower() in (dfcol.lower() for dfcol in df.columns) assert Series(df.geometry.geom_type).dropna().eq("MultiPolygon").all() def get_srid(df): """Return srid from `df.crs`.""" if df.crs is not None: return df.crs.to_epsg() or 0 return 0 def create_spatialite(con, df): """ Return a SpatiaLite connection containing the nybb table. Parameters ---------- `con`: ``sqlite3.Connection`` `df`: ``GeoDataFrame`` """ with con: geom_col = df.geometry.name srid = get_srid(df) con.execute( "CREATE TABLE IF NOT EXISTS nybb " "( ogc_fid INTEGER PRIMARY KEY" ", borocode INTEGER" ", boroname TEXT" ", shape_leng REAL" ", shape_area REAL" ")" ) con.execute( "SELECT AddGeometryColumn(?, ?, ?, ?)", ("nybb", geom_col, srid, df.geom_type.dropna().iat[0].upper()), ) con.execute("SELECT CreateSpatialIndex(?, ?)", ("nybb", geom_col)) sql_row = "INSERT INTO nybb VALUES(?, ?, ?, ?, ?, GeomFromText(?, ?))" con.executemany( sql_row, ( ( None, row.BoroCode, row.BoroName, row.Shape_Leng, row.Shape_Area, row.geometry.wkt if row.geometry else None, srid, ) for row in df.itertuples(index=False) ), ) def create_postgis(con, df, srid=None, geom_col="geom"): """ Create an nybb table in the test_geopandas PostGIS database. Returns a boolean indicating whether the database table was successfully created """ # Try to create the database, skip the db tests if something goes # wrong # If you'd like these tests to run, create a database called # 'test_geopandas' and enable postgis in it: # > createdb test_geopandas # > psql -c "CREATE EXTENSION postgis" -d test_geopandas if srid is not None: geom_schema = f"geometry(MULTIPOLYGON, {srid})" geom_insert = f"ST_SetSRID(ST_GeometryFromText(%s), {srid})" else: geom_schema = "geometry" geom_insert = "ST_GeometryFromText(%s)" try: cursor = con.cursor() cursor.execute("DROP TABLE IF EXISTS nybb;") sql = f"""CREATE TABLE nybb ( {geom_col} {geom_schema}, borocode integer, boroname varchar(40), shape_leng float, shape_area float );""" cursor.execute(sql) for i, row in df.iterrows(): sql = f"""INSERT INTO nybb VALUES ({geom_insert}, %s, %s, %s, %s );""" cursor.execute( sql, ( row["geometry"].wkt, row["BoroCode"], row["BoroName"], row["Shape_Leng"], row["Shape_Area"], ), ) finally: cursor.close() con.commit() geopandas-1.2.0/geopandas/tools/000077500000000000000000000000001525650651600165675ustar00rootroot00000000000000geopandas-1.2.0/geopandas/tools/__init__.py000066400000000000000000000005311525650651600206770ustar00rootroot00000000000000from .clip import clip from .geocoding import geocode, reverse_geocode from .make_grid import make_grid from .overlay import overlay from .sjoin import sjoin, sjoin_nearest from .util import collect __all__ = [ "clip", "collect", "geocode", "make_grid", "overlay", "reverse_geocode", "sjoin", "sjoin_nearest", ] geopandas-1.2.0/geopandas/tools/_random.py000066400000000000000000000050331525650651600205610ustar00rootroot00000000000000from warnings import warn import numpy from shapely.geometry import MultiPoint from geopandas.array import ( LINE_GEOM_TYPES, POLYGON_GEOM_TYPES, points_from_xy, ) def uniform(geom, size, rng=None): """ Sample uniformly at random from a geometry. For polygons, this samples uniformly within the area of the polygon. For lines, this samples uniformly along the length of the linestring. For multi-part geometries, the weights of each part are selected according to their relevant attribute (area for Polygons, length for LineStrings), and then points are sampled from each part uniformly. Any other geometry type (e.g. Point, GeometryCollection) are ignored, and an empty MultiPoint geometry is returned. Parameters ---------- geom : any shapely.geometry.BaseGeometry type the shape that describes the area in which to sample. size : integer an integer denoting how many points to sample Returns ------- shapely.MultiPoint geometry containing the sampled points Examples -------- >>> from shapely.geometry import box >>> square = box(0,0,1,1) >>> uniform(square, size=102) # doctest: +SKIP """ generator = numpy.random.default_rng(seed=rng) if geom is None or geom.is_empty: return MultiPoint() if geom.geom_type in POLYGON_GEOM_TYPES: return _uniform_polygon(geom, size=size, generator=generator) if geom.geom_type in LINE_GEOM_TYPES: return _uniform_line(geom, size=size, generator=generator) warn( f"Sampling is not supported for {geom.geom_type} geometry type.", UserWarning, stacklevel=8, ) return MultiPoint() def _uniform_line(geom, size, generator): """Sample points from an input shapely linestring.""" fracs = generator.uniform(size=size) return MultiPoint(geom.interpolate(fracs, normalized=True)) def _uniform_polygon(geom, size, generator): """Sample uniformly from within a polygon using batched sampling.""" if geom.area == 0: return MultiPoint() xmin, ymin, xmax, ymax = geom.bounds candidates = [] while len(candidates) < size: batch = points_from_xy( x=generator.uniform(xmin, xmax, size=size), y=generator.uniform(ymin, ymax, size=size), ) valid_samples = batch[batch.sindex.query(geom, predicate="contains")] candidates.extend(valid_samples) generator.shuffle(candidates) # avoid the artifacts of STRTree ordering return MultiPoint(candidates[:size]) geopandas-1.2.0/geopandas/tools/_show_versions.py000066400000000000000000000073231525650651600222150ustar00rootroot00000000000000import importlib import platform import sys def _get_sys_info(): """System information. Returns ------- sys_info : dict system and Python version information """ python = sys.version.replace("\n", " ") blob = [ ("python", python), ("executable", sys.executable), ("machine", platform.platform()), ] return dict(blob) def _get_C_info(): """Information on system PROJ, GDAL, GEOS. Returns ------- c_info: dict system PROJ information """ try: import pyproj proj_version = pyproj.proj_version_str except Exception: proj_version = None try: import pyproj proj_dir = pyproj.datadir.get_data_dir() except Exception: proj_dir = None try: import shapely._buildcfg geos_version = "{}.{}.{}".format(*shapely._buildcfg.geos_version) geos_dir = shapely._buildcfg.geos_library_path except Exception: try: from shapely import geos_version_string geos_version = geos_version_string geos_dir = None except Exception: geos_version = None geos_dir = None try: import pyogrio gdal_version = pyogrio.__gdal_version_string__ gdal_dir = pyogrio.get_gdal_data_path() except Exception: gdal_version = None gdal_dir = None if gdal_version is None: try: import fiona gdal_version = fiona.env.get_gdal_release_name() except Exception: gdal_version = None try: import fiona gdal_dir = fiona.env.GDALDataFinder().search() except Exception: gdal_dir = None blob = [ ("GEOS", geos_version), ("GEOS lib", geos_dir), ("GDAL", gdal_version), ("GDAL data dir", gdal_dir), ("PROJ", proj_version), ("PROJ data dir", proj_dir), ] return dict(blob) def _get_deps_info(): """Overview of the installed version of main dependencies. Returns ------- deps_info: dict version information on relevant Python libraries """ deps = [ "geopandas", # required deps "numpy", "pandas", "pyproj", "shapely", # optional deps "pyogrio", "geoalchemy2", "geopy", "matplotlib", "mapclassify", "fiona", "psycopg", "psycopg2", "pyarrow", ] def get_version(module): return module.__version__ deps_info = {} for modname in deps: try: if modname in sys.modules: mod = sys.modules[modname] else: mod = importlib.import_module(modname) ver = get_version(mod) deps_info[modname] = ver except Exception: deps_info[modname] = None return deps_info def show_versions(): """ Print system information and installed module versions. Examples -------- :: $ python -c "import geopandas; geopandas.show_versions()" """ sys_info = _get_sys_info() deps_info = _get_deps_info() proj_info = _get_C_info() maxlen = max(len(x) for x in deps_info) tpl = f"{{k:<{maxlen}}}: {{stat}}" print("\nSYSTEM INFO") print("-----------") for k, stat in sys_info.items(): print(tpl.format(k=k, stat=stat)) print("\nGEOS, GDAL, PROJ INFO") print("---------------------") for k, stat in proj_info.items(): print(tpl.format(k=k, stat=stat)) print("\nPYTHON DEPENDENCIES") print("-------------------") for k, stat in deps_info.items(): print(tpl.format(k=k, stat=stat)) geopandas-1.2.0/geopandas/tools/clip.py000066400000000000000000000230121525650651600200660ustar00rootroot00000000000000"""Module to clip vector data using GeoPandas.""" import warnings import numpy as np import pandas.api.types from shapely.geometry import MultiPolygon, Polygon, box from geopandas import GeoDataFrame, GeoSeries from geopandas._compat import PANDAS_GE_30 from geopandas.array import ( LINE_GEOM_TYPES, POINT_GEOM_TYPES, POLYGON_GEOM_TYPES, _check_crs, _crs_mismatch_warn, ) def _mask_is_list_like_rectangle(mask): """ Check if the input mask is list-like and not an instance of specific geometric types. Parameters ---------- mask : GeoDataFrame, GeoSeries, (Multi)Polygon, list-like Polygon vector layer used to clip ``gdf``. Returns ------- bool True if `mask` is list-like and not an instance of `GeoDataFrame`, `GeoSeries`, `Polygon`, or `MultiPolygon`, otherwise False. """ return pandas.api.types.is_list_like(mask) and not isinstance( mask, GeoDataFrame | GeoSeries | Polygon | MultiPolygon ) def _clip_gdf_with_mask(gdf, mask, sort=False): """ Clip geometry to the polygon/rectangle extent. Clip an input GeoDataFrame to the polygon extent of the polygon parameter. Parameters ---------- gdf : GeoDataFrame, GeoSeries Dataframe to clip. mask : (Multi)Polygon, list-like Reference polygon/rectangle for clipping. sort : boolean, default False If True, the results will be sorted in ascending order using the geometries' indexes as the primary key. Returns ------- GeoDataFrame The returned GeoDataFrame is a clipped subset of gdf that intersects with polygon/rectangle. """ clipping_by_rectangle = _mask_is_list_like_rectangle(mask) if clipping_by_rectangle: intersection_polygon = box(*mask) else: intersection_polygon = mask gdf_sub = gdf.iloc[ gdf.sindex.query(intersection_polygon, predicate="intersects", sort=sort) ] # For performance reasons points don't need to be intersected with poly non_point_mask = gdf_sub.geom_type != "Point" if not non_point_mask.any(): # only points, directly return return gdf_sub # Clip the data with the polygon if isinstance(gdf_sub, GeoDataFrame): clipped = gdf_sub.copy(deep=not PANDAS_GE_30) if clipping_by_rectangle: clipped.loc[non_point_mask, clipped._geometry_column_name] = ( gdf_sub.geometry.values[non_point_mask].clip_by_rect(*mask) ) else: clipped.loc[non_point_mask, clipped._geometry_column_name] = ( gdf_sub.geometry.values[non_point_mask].intersection(mask) ) else: # GeoSeries clipped = gdf_sub.copy(deep=not PANDAS_GE_30) if clipping_by_rectangle: clipped[non_point_mask] = gdf_sub.values[non_point_mask].clip_by_rect(*mask) else: clipped[non_point_mask] = gdf_sub.values[non_point_mask].intersection(mask) if clipping_by_rectangle: # clip_by_rect might return empty geometry collections in edge cases clipped = clipped[~clipped.is_empty] return clipped def clip(gdf, mask, keep_geom_type=False, sort=False): """Clip points, lines, or polygon geometries to the mask extent. Both layers must be in the same Coordinate Reference System (CRS). The ``gdf`` will be clipped to the full extent of the clip object. If there are multiple polygons in mask, data from ``gdf`` will be clipped to the total boundary of all polygons in mask. If the ``mask`` is list-like with four elements ``(minx, miny, maxx, maxy)``, a faster rectangle clipping algorithm will be used. Note that this can lead to slightly different results in edge cases, e.g. if a line would be reduced to a point, this point might not be returned. The geometry is clipped in a fast but possibly dirty way. The output is not guaranteed to be valid. No exceptions will be raised for topological errors. Parameters ---------- gdf : GeoDataFrame or GeoSeries Vector layer (point, line, polygon) to be clipped to mask. mask : GeoDataFrame, GeoSeries, (Multi)Polygon, list-like Polygon vector layer used to clip ``gdf``. The mask's geometry is dissolved into one geometric feature and intersected with ``gdf``. If the mask is list-like with four elements ``(minx, miny, maxx, maxy)``, ``clip`` will use a faster rectangle clipping (:meth:`~GeoSeries.clip_by_rect`), possibly leading to slightly different results. keep_geom_type : boolean, default False If True, return only geometries of original type in case of intersection resulting in multiple geometry types or GeometryCollections. If False, return all resulting geometries (potentially mixed-types). sort : boolean, default False If True, the results will be sorted in ascending order using the geometries' indexes as the primary key. Returns ------- GeoDataFrame or GeoSeries Vector data (points, lines, polygons) from ``gdf`` clipped to polygon boundary from mask. See Also -------- GeoDataFrame.clip : equivalent GeoDataFrame method GeoSeries.clip : equivalent GeoSeries method Examples -------- Clip points (grocery stores) with polygons (the Near West Side community): >>> import geodatasets >>> chicago = geopandas.read_file( ... geodatasets.get_path("geoda.chicago_health") ... ) >>> near_west_side = chicago[chicago["community"] == "NEAR WEST SIDE"] >>> groceries = geopandas.read_file( ... geodatasets.get_path("geoda.groceries") ... ).to_crs(chicago.crs) >>> groceries.shape (148, 8) >>> nws_groceries = geopandas.clip(groceries, near_west_side) >>> nws_groceries.shape (7, 8) """ if not isinstance(gdf, GeoDataFrame | GeoSeries): raise TypeError(f"'gdf' should be GeoDataFrame or GeoSeries, got {type(gdf)}") clipping_by_rectangle = _mask_is_list_like_rectangle(mask) if ( not isinstance(mask, GeoDataFrame | GeoSeries | Polygon | MultiPolygon) and not clipping_by_rectangle ): raise TypeError( "'mask' should be GeoDataFrame, GeoSeries," f"(Multi)Polygon or list-like, got {type(mask)}" ) if clipping_by_rectangle and len(mask) != 4: raise TypeError( "If 'mask' is list-like, it must have four values (minx, miny, maxx, maxy)" ) if isinstance(mask, GeoDataFrame | GeoSeries): if not _check_crs(gdf, mask): _crs_mismatch_warn(gdf, mask, stacklevel=3) if isinstance(mask, GeoDataFrame | GeoSeries): box_mask = mask.total_bounds elif clipping_by_rectangle: box_mask = mask else: # Avoid empty tuple returned by .bounds when geometry is empty. A tuple of # all nan values is consistent with the behavior of # {GeoSeries, GeoDataFrame}.total_bounds for empty geometries. # TODO(shapely) can simpely use mask.bounds once relying on Shapely 2.0 box_mask = mask.bounds if not mask.is_empty else (np.nan,) * 4 box_gdf = gdf.total_bounds if not ( ((box_mask[0] <= box_gdf[2]) and (box_gdf[0] <= box_mask[2])) and ((box_mask[1] <= box_gdf[3]) and (box_gdf[1] <= box_mask[3])) ): return gdf.iloc[:0] if isinstance(mask, GeoDataFrame | GeoSeries): combined_mask = mask.geometry.union_all() else: combined_mask = mask clipped = _clip_gdf_with_mask(gdf, combined_mask, sort=sort) if keep_geom_type: geomcoll_concat = (clipped.geom_type == "GeometryCollection").any() geomcoll_orig = (gdf.geom_type == "GeometryCollection").any() new_collection = geomcoll_concat and not geomcoll_orig if geomcoll_orig: warnings.warn( "keep_geom_type can not be called on a " "GeoDataFrame with GeometryCollection.", stacklevel=2, ) else: # Check that the gdf for multiple geom types (points, lines and/or polys) orig_types_total = sum( [ gdf.geom_type.isin(POLYGON_GEOM_TYPES).any(), gdf.geom_type.isin(LINE_GEOM_TYPES).any(), gdf.geom_type.isin(POINT_GEOM_TYPES).any(), ] ) # Check how many geometry types are in the clipped GeoDataFrame clip_types_total = sum( [ clipped.geom_type.isin(POLYGON_GEOM_TYPES).any(), clipped.geom_type.isin(LINE_GEOM_TYPES).any(), clipped.geom_type.isin(POINT_GEOM_TYPES).any(), ] ) # Check there aren't any new geom types in the clipped GeoDataFrame more_types = orig_types_total < clip_types_total if orig_types_total > 1: warnings.warn( "keep_geom_type can not be called on a mixed type GeoDataFrame.", stacklevel=2, ) elif new_collection or more_types: orig_type = gdf.geom_type.iloc[0] if new_collection: clipped = clipped.explode(index_parts=False) if orig_type in POLYGON_GEOM_TYPES: clipped = clipped.loc[clipped.geom_type.isin(POLYGON_GEOM_TYPES)] elif orig_type in LINE_GEOM_TYPES: clipped = clipped.loc[clipped.geom_type.isin(LINE_GEOM_TYPES)] return clipped geopandas-1.2.0/geopandas/tools/geocoding.py000066400000000000000000000130101525650651600210720ustar00rootroot00000000000000import time from collections import defaultdict import pandas as pd from shapely.geometry import Point import geopandas def _get_throttle_time(provider): """ Amount of time to wait between requests to a geocoding API, for providers that specify rate limits in their terms of service. """ import geopy.geocoders # https://operations.osmfoundation.org/policies/nominatim/ if provider == geopy.geocoders.Nominatim: return 1 else: return 0 def geocode(strings, provider=None, **kwargs): """ Geocode a set of strings and get a GeoDataFrame of the resulting points. Parameters ---------- strings : list or Series of addresses to geocode provider : str or geopy.geocoder Specifies geocoding service to use. If none is provided, will use 'photon' (see the Photon's terms of service at: https://photon.komoot.io). Either the string name used by geopy (as specified in geopy.geocoders.SERVICE_TO_GEOCODER) or a geopy Geocoder instance (e.g., geopy.geocoders.Photon) may be used. Some providers require additional arguments such as access keys See each geocoder's specific parameters in geopy.geocoders Notes ----- Ensure proper use of the results by consulting the Terms of Service for your provider. Geocoding requires geopy. Install it using 'pip install geopy'. See also https://github.com/geopy/geopy Examples -------- >>> df = geopandas.geocode( # doctest: +SKIP ... ["boston, ma", "1600 pennsylvania ave. washington, dc"] ... ) >>> df # doctest: +SKIP geometry address 0 POINT (-71.05863 42.35899) Boston, MA, United States 1 POINT (-77.03651 38.89766) 1600 Pennsylvania Ave NW, Washington, DC 20006... """ if provider is None: provider = "photon" throttle_time = _get_throttle_time(provider) return _query(strings, True, provider, throttle_time, **kwargs) def reverse_geocode(points, provider=None, **kwargs): """ Reverse geocode a set of points and get a GeoDataFrame of the resulting addresses. The points Parameters ---------- points : list or Series of Shapely Point objects. x coordinate is longitude y coordinate is latitude provider : str or geopy.geocoder (opt) Specifies geocoding service to use. If none is provided, will use 'photon' (see the Photon's terms of service at: https://photon.komoot.io). Either the string name used by geopy (as specified in geopy.geocoders.SERVICE_TO_GEOCODER) or a geopy Geocoder instance (e.g., geopy.geocoders.Photon) may be used. Some providers require additional arguments such as access keys See each geocoder's specific parameters in geopy.geocoders Notes ----- Ensure proper use of the results by consulting the Terms of Service for your provider. Reverse geocoding requires geopy. Install it using 'pip install geopy'. See also https://github.com/geopy/geopy Examples -------- >>> from shapely.geometry import Point >>> df = geopandas.tools.reverse_geocode( # doctest: +SKIP ... [Point(-71.0594869, 42.3584697), Point(-77.0365305, 38.8977332)] ... ) >>> df # doctest: +SKIP geometry address 0 POINT (-71.05941 42.35837) 29 Court Sq, Boston, MA 02108, United States 1 POINT (-77.03641 38.89766) 1600 Pennsylvania Ave NW, Washington, DC 20006... """ if provider is None: provider = "photon" throttle_time = _get_throttle_time(provider) return _query(points, False, provider, throttle_time, **kwargs) def _query(data, forward, provider, throttle_time, **kwargs): # generic wrapper for calls over lists to geopy Geocoders from geopy.geocoders import get_geocoder_for_service from geopy.geocoders.base import GeocoderQueryError if forward: if not isinstance(data, pd.Series): data = pd.Series(data) else: if not isinstance(data, geopandas.GeoSeries): data = geopandas.GeoSeries(data) if isinstance(provider, str): provider = get_geocoder_for_service(provider) coder = provider(**kwargs) results = {} for i, s in data.items(): try: if forward: results[i] = coder.geocode(s) else: results[i] = coder.reverse((s.y, s.x), exactly_one=True) except (GeocoderQueryError, ValueError): results[i] = (None, None) time.sleep(throttle_time) df = _prepare_geocode_result(results) return df def _prepare_geocode_result(results): """Convert the geocode results to a GeoDataFrame. Takes a dict where keys are index entries, values are tuples containing: (address, (lat, lon)) """ # Prepare the data for the DataFrame as a dict of lists d = defaultdict(list) index = [] for i, s in results.items(): if s is None: p = Point() address = None else: address, loc = s # loc is lat, lon and we want lon, lat if loc is None: p = Point() else: p = Point(loc[1], loc[0]) d["geometry"].append(p) d["address"].append(address) index.append(i) df = geopandas.GeoDataFrame(d, index=index, crs="EPSG:4326") return df geopandas-1.2.0/geopandas/tools/hilbert_curve.py000066400000000000000000000112271525650651600220010ustar00rootroot00000000000000import numpy as np def _hilbert_distance(geoms, total_bounds=None, level=16): """ Calculate the distance along a Hilbert curve. The distances are calculated for the midpoints of the geometries in the GeoDataFrame. Parameters ---------- geoms : GeometryArray total_bounds : 4-element array Total bounds of geometries - array level : int (1 - 16), default 16 Determines the precision of the curve (points on the curve will have coordinates in the range [0, 2^level - 1]). Returns ------- np.ndarray Array containing distances along the Hilbert curve """ if geoms.is_empty.any() | geoms.isna().any(): raise ValueError( "Hilbert distance cannot be computed on a GeoSeries with empty or " "missing geometries.", ) # Calculate bounds as numpy array bounds = geoms.bounds # Calculate discrete coords based on total bounds and bounds x, y = _continuous_to_discrete_coords(bounds, level, total_bounds) # Compute distance along hilbert curve distances = _encode(level, x, y) return distances def _continuous_to_discrete_coords(bounds, level, total_bounds): """Calculate mid points & ranges of geoms and returns as discrete coords. Parameters ---------- bounds : Bounds of each geometry - array p : The number of iterations used in constructing the Hilbert curve total_bounds : Total bounds of geometries - array Returns ------- Discrete two-dimensional numpy array Two-dimensional array Array of hilbert distances for each geom """ # Hilbert Side length side_length = (2**level) - 1 # Calculate mid points for x and y bound coords - returns array x_mids = (bounds[:, 0] + bounds[:, 2]) / 2.0 y_mids = (bounds[:, 1] + bounds[:, 3]) / 2.0 # Calculate x and y range of total bound coords - returns array if total_bounds is None: total_bounds = ( np.nanmin(x_mids), np.nanmin(y_mids), np.nanmax(x_mids), np.nanmax(y_mids), ) xmin, ymin, xmax, ymax = total_bounds # Transform continuous value to discrete integer for each dimension x_int = _continuous_to_discrete(x_mids, (xmin, xmax), side_length) y_int = _continuous_to_discrete(y_mids, (ymin, ymax), side_length) return x_int, y_int def _continuous_to_discrete(vals, val_range, n): """Convert a continuous one-dimensional array to discrete integer values based their ranges. Parameters ---------- vals : Array of continuous values val_range : Tuple containing range of continuous values n : Number of discrete values Returns ------- One-dimensional array of discrete ints """ width = val_range[1] - val_range[0] if width == 0: return np.zeros_like(vals, dtype=np.uint32) res = (vals - val_range[0]) * (n / width) np.clip(res, 0, n, out=res) return res.astype(np.uint32) # Fast Hilbert curve algorithm by http://threadlocalmutex.com/ # From C++ https://github.com/rawrunprotected/hilbert_curves # (public domain) MAX_LEVEL = 16 def _interleave(x): x = (x | (x << 8)) & 0x00FF00FF x = (x | (x << 4)) & 0x0F0F0F0F x = (x | (x << 2)) & 0x33333333 x = (x | (x << 1)) & 0x55555555 return x def _encode(level, x, y): x = np.asarray(x, dtype="uint32") y = np.asarray(y, dtype="uint32") if level > MAX_LEVEL: raise ValueError("Level out of range") x = x << (16 - level) y = y << (16 - level) # Initial prefix scan round, prime with x and y a = x ^ y b = 0xFFFF ^ a c = 0xFFFF ^ (x | y) d = x & (y ^ 0xFFFF) A = a | (b >> 1) B = (a >> 1) ^ a C = ((c >> 1) ^ (b & (d >> 1))) ^ c D = ((a & (c >> 1)) ^ (d >> 1)) ^ d a = A.copy() b = B.copy() c = C.copy() d = D.copy() A = (a & (a >> 2)) ^ (b & (b >> 2)) B = (a & (b >> 2)) ^ (b & ((a ^ b) >> 2)) C ^= (a & (c >> 2)) ^ (b & (d >> 2)) D ^= (b & (c >> 2)) ^ ((a ^ b) & (d >> 2)) a = A.copy() b = B.copy() c = C.copy() d = D.copy() A = (a & (a >> 4)) ^ (b & (b >> 4)) B = (a & (b >> 4)) ^ (b & ((a ^ b) >> 4)) C ^= (a & (c >> 4)) ^ (b & (d >> 4)) D ^= (b & (c >> 4)) ^ ((a ^ b) & (d >> 4)) # Final round and projection a = A.copy() b = B.copy() c = C.copy() d = D.copy() C ^= (a & (c >> 8)) ^ (b & (d >> 8)) D ^= (b & (c >> 8)) ^ ((a ^ b) & (d >> 8)) # Undo transformation prefix scan a = C ^ (C >> 1) b = D ^ (D >> 1) # Recover index bits i0 = x ^ y i1 = b | (0xFFFF ^ (i0 | a)) return ((_interleave(i1) << 1) | _interleave(i0)) >> (32 - 2 * level) geopandas-1.2.0/geopandas/tools/make_grid.py000066400000000000000000000375561525650651600211030ustar00rootroot00000000000000import warnings from typing import Literal import numpy as np import shapely from shapely.geometry import MultiPolygon, Polygon from geopandas import GeoDataFrame, GeoSeries, points_from_xy from geopandas.array import from_shapely def make_grid( input_geometry: Polygon | MultiPolygon | GeoSeries | GeoDataFrame, /, cell_size: float, grid_type: Literal["square", "hexagon"] = "square", feature_type: Literal["centers", "corners", "polygons"] = "polygons", offset: tuple[float, float] | None = None, intersect: bool = True, flat_topped: bool = False, ) -> GeoSeries: """Provide the centers, corners, or polygons of a square or hexagonal grid. The output covers the area of the ``input_geometry``. By default, only grid elements that spatially overlap with the ``input_geometry`` are returned. This filtering can be disabled by setting the ``intersect`` parameter to ``False``. The origin of the grid is at the lower left corner of the bounding box of the ``input_geometry``. If there are multiple geometries in a GeoSeries/GeoDataFrame, the grid will be created over the total bounds of the GeoSeries/GeoDataFrame. Subsequently, the grid is filtered to only include elements that spatially overlap with any of the individual geometries when ``intersect`` is ``True``. .. versionadded:: 1.2 Parameters ---------- input_geometry : (Multi)Polygon, GeoSeries, GeoDataFrame Polygon within its boundaries the grid is made. cell_size : float Side length of the square. For hexagonal cells the distance between opposite edges (edge length is ``cellsize/sqrt(3)``). grid_type : str, one of "square", "hexagon", default "square" Grid type that is returned. All cell types reflect naive tiling of a plane, not a tiling of the globe (like H3 or S2). feature_type : str, one of "centers", "corners", "polygons", default "polygons" Grid feature that is returned. ``"centers"`` returns points at the center of each grid cell. ``"corners"`` returns points at all unique vertices of the grid cells (i.e., the points where cell edges meet). ``"polygons"`` returns the grid cell polygons. offset : tuple, optional Lower left corner coordinates (x, y) of the grid. By default uses the lower left corner of the bounding box of the input geometry. intersect : bool, default True If False, the grid is not filtered by the ``input_geometry`` and the full grid covering the bounding box is returned. flat_topped : bool, default False If True, generate flat topped hexagons. By default (False), the orientation of the hexagonal cells is such that a corner points upwards. Returns ------- GeoSeries The returned GeoSeries contains the grid-cell centers, corners, or polygons. Notes ----- When ``intersect=True``, the grid is filtered using the ``"intersects"`` spatial predicate. The filtering behavior depends on ``feature_type``: - ``"polygons"``: All grid cell polygons that share any area or boundary with the input geometries are returned. - ``"centers"``: Only center points that fall within or on the boundary of the input geometries are returned. - ``"corners"``: Only corner points that fall within or on the boundary of the input geometries are returned. As a result, the set of corners returned when ``feature_type="corners"`` may not correspond exactly to the vertices of the polygons returned when ``feature_type="polygons"``. Examples -------- >>> import geopandas >>> import geodatasets >>> world = geopandas.read_file( ... geodatasets.get_path('naturalearth land')) >>> madagascar = world.cx[45:50, -25:-15] >>> sq_grid = geopandas.make_grid(madagascar, cell_size=1) >>> sq_grid.head(3) 0 POLYGON ((43.25419 -25.60143, 44.25419 -25.601... 1 POLYGON ((43.25419 -24.60143, 44.25419 -24.601... 2 POLYGON ((43.25419 -23.60143, 44.25419 -23.601... dtype: geometry By default, grid is aligned with the bounding box of the input geometries. If instead you want to a fixed grid aligning with specific coordinates, use the ``offset`` keyword. For example, to have a grid of round degrees: >>> sq_grid2 = geopandas.make_grid(madagascar, cell_size=1, offset=(30, -30)) >>> sq_grid2.head(3) 0 POLYGON ((43 -25, 44 -25, 44 -24, 43 -24, 43 -... 1 POLYGON ((43 -24, 44 -24, 44 -23, 43 -23, 43 -... 2 POLYGON ((43 -23, 44 -23, 44 -22, 43 -22, 43 -... dtype: geometry .. plot:: _static/code/make_grid.py Specify the ``feature_type`` keyword to get the centers or corners of the grid cells, instead of the polygons: >>> sq_grid_centers = geopandas.make_grid( ... madagascar, cell_size=1, feature_type="centers" ... ) >>> sq_grid_centers.head(3) 0 POINT (43.75419 -24.10143) 1 POINT (43.75419 -23.10143) 2 POINT (43.75419 -22.10143) dtype: geometry Specify the ``grid_type="hexagon`` keyword to get hexagons instead of the default squares. .. plot:: _static/code/make_grid_types.py """ # Run basic checks _basic_checks(input_geometry, cell_size, offset, feature_type, grid_type, intersect) output_grid = None if isinstance(input_geometry, (GeoDataFrame, GeoSeries)): bounds = np.array(input_geometry.total_bounds) else: bounds = np.array(input_geometry.bounds) if offset is not None: grid_origin_x = offset[0] grid_origin_y = offset[1] else: grid_origin_x = bounds[0] grid_origin_y = bounds[1] x_dist = bounds[2] - grid_origin_x y_dist = bounds[3] - grid_origin_y if grid_type == "square": # Set corner coordinates of square grid. x_coords_corn = np.arange(grid_origin_x, bounds[2] + cell_size, cell_size) y_coords_corn = np.arange(grid_origin_y, bounds[3] + cell_size, cell_size) xv, yv = np.meshgrid(x_coords_corn, y_coords_corn) if feature_type == "corners": sq_corners_np = np.array([xv, yv]).T.reshape(-1, 2) output_grid = points_from_xy(sq_corners_np[:, 0], sq_corners_np[:, 1]) elif feature_type == "centers": sq_centers_np = ( np.array([xv[:-1, :-1], yv[:-1, :-1]]).T.reshape(-1, 2) + cell_size / 2 ) output_grid = points_from_xy(sq_centers_np[:, 0], sq_centers_np[:, 1]) elif feature_type == "polygons": # Extracting corners of all square-grid cells. bt_left_corners = np.array([xv[:-1, :-1], yv[:-1, :-1]]).T.reshape(-1, 1, 2) bt_right_corners = np.array([xv[1:, 1:], yv[:-1, :-1]]).T.reshape(-1, 1, 2) top_right_corners = np.array([xv[1:, 1:], yv[1:, 1:]]).T.reshape(-1, 1, 2) top_left_corners = np.array([xv[:-1, :-1], yv[1:, 1:]]).T.reshape(-1, 1, 2) sq_coords = np.concatenate( ( bt_left_corners, bt_right_corners, top_right_corners, top_left_corners, ), axis=1, ) sq_polygons = shapely.polygons(sq_coords) output_grid = from_shapely(sq_polygons) elif grid_type == "hexagon": if not flat_topped: x_dist, y_dist = y_dist, x_dist # Create rectangular meshgrid containing both the centers and the cornes # of the hexagonal grid dx = cell_size / np.sqrt(3) dy = cell_size / 2 # Determine number of grid points along x/y direction such that the area of # any bounding box is fully covered with polygons. n_dx = x_dist / dx n_grid_point_x = int((np.ceil((n_dx - 1) / 1.5) + 2) * 1.5) n_dy = y_dist / dy n_grid_point_y = int(n_dy) + 4 x_coords = np.arange(0, n_grid_point_x).astype(float) * dx y_coords = np.arange(0, n_grid_point_y).astype(float) * dy - dy xv, yv = np.meshgrid(x_coords, y_coords) # Shift every second row to transform the rectangular into a hexagonal grid xv[::2, :] = xv[::2, :] - (dx / 2) mask_center = np.zeros_like(xv, dtype=bool) mask_center[1::2, 2::3] = True mask_center[::2, 1::3] = True if feature_type == "centers": hex_centers_np = np.array([xv[mask_center], yv[mask_center]]).T.reshape( -1, 2 ) if not flat_topped: hex_centers_np = hex_centers_np[:, ::-1] output_grid = points_from_xy( hex_centers_np[:, 0] + grid_origin_x, hex_centers_np[:, 1] + grid_origin_y, ) elif feature_type == "corners": # The inverted center mask is the corner mask. Now consider all corners mask_corners = np.invert(mask_center) hex_corners_np = np.array([xv[mask_corners], yv[mask_corners]]).T.reshape( -1, 2 ) if not flat_topped: hex_corners_np = hex_corners_np[:, ::-1] output_grid = points_from_xy( hex_corners_np[:, 0] + grid_origin_x, hex_corners_np[:, 1] + grid_origin_y, ) elif feature_type == "polygons": hex_coords_a = _hex_polygon_corners(xv, yv, (0, 1)) hex_coords_b = _hex_polygon_corners(xv, yv, (2, 0)) hex_coords = np.concatenate((hex_coords_a, hex_coords_b), axis=0) if not flat_topped: hex_coords = hex_coords[:, :, ::-1] hex_coords[:, :, 0] += grid_origin_x hex_coords[:, :, 1] += grid_origin_y hex_polygons = shapely.polygons(hex_coords) output_grid = from_shapely(hex_polygons) if isinstance(input_geometry, (GeoDataFrame, GeoSeries)): output_grid = GeoSeries(output_grid, crs=input_geometry.crs) else: output_grid = GeoSeries(output_grid) if intersect: if isinstance(input_geometry, (GeoDataFrame, GeoSeries)): unique_indices = np.unique( output_grid.sindex.query( input_geometry.geometry, predicate="intersects" )[-1] ) else: unique_indices = np.unique( output_grid.sindex.query(input_geometry, predicate="intersects") ) return output_grid[unique_indices].reset_index(drop=True) else: return output_grid def _hex_polygon_corners( xv: np.ndarray, yv: np.ndarray, index_0: tuple[int, int] = (0, 0) ) -> np.ndarray: """Group hexagon corners that belong to the same grid cell. Parameters ---------- xv : np.array meshgrid containing x-values of all centers and corners of the hexgon grid yv : np.array meshgrid containing y-values of all centers and corners of the hexgon grid index_0 : tuple indicates index of the meshgrid, where polygon corner grouping starts Returns ------- np.array The array has the shape (n, 6, 2) where n is the number of grid cells, "6" corresponds to the hexagon corners and "2" to the corresponding x,y coordinates. """ i_x0 = index_0[0] i_y0 = index_0[1] # Determine the maximum index such that only complete hexagons # with 6 corners are grouped. if (i_y0 % 2) == 0: x_corr = 1 else: x_corr = 0 # Labeling of hexagon corners assumes that hexagon stands on a flat side: # Middle right corner of the hexagon n_poly_x = xv[(i_y0 + 1) :: 2, (i_x0 + 2 - x_corr) :: 3].shape[1] max_i_x = i_x0 + n_poly_x * 3 - 1 # Top right corner of the hexagon n_poly_y = xv[(i_y0 + 2) :: 2, (i_x0 + 1) :: 3].shape[0] max_i_y = i_y0 + n_poly_y * 2 # Extracting specific corners of all hexagon grid cells. bottom_left_corners = np.array( [xv[i_y0:max_i_y:2, i_x0:max_i_x:3], yv[i_y0:max_i_y:2, i_x0:max_i_x:3]] ).T.reshape(-1, 1, 2) bottom_right_corners = np.array( [ xv[i_y0:max_i_y:2, (i_x0 + 1) : max_i_x : 3], yv[i_y0:max_i_y:2, (i_x0 + 1) : max_i_x : 3], ] ).T.reshape(-1, 1, 2) middle_right_corners = np.array( [ xv[(i_y0 + 1) : max_i_y : 2, (i_x0 + 2 - x_corr) :: 3], yv[(i_y0 + 1) : max_i_y : 2, (i_x0 + 2 - x_corr) :: 3], ] ).T.reshape(-1, 1, 2) top_right_corners = np.array( [ xv[(i_y0 + 2) :: 2, (i_x0 + 1) : max_i_x : 3], yv[(i_y0 + 2) :: 2, (i_x0 + 1) : max_i_x : 3], ] ).T.reshape(-1, 1, 2) top_left_corners = np.array( [xv[(i_y0 + 2) :: 2, i_x0:max_i_x:3], yv[(i_y0 + 2) :: 2, i_x0:max_i_x:3]] ).T.reshape(-1, 1, 2) middle_left_corners = np.array( [ xv[(i_y0 + 1) : max_i_y : 2, (i_x0 - x_corr) : max_i_x : 3], yv[(i_y0 + 1) : max_i_y : 2, (i_x0 - x_corr) : max_i_x : 3], ] ).T.reshape(-1, 1, 2) hex_coords = np.concatenate( ( bottom_left_corners, bottom_right_corners, middle_right_corners, top_right_corners, top_left_corners, middle_left_corners, ), axis=1, ) return hex_coords def _basic_checks( input_geometry: Polygon | MultiPolygon | GeoSeries | GeoDataFrame, cell_size: float, offset: tuple[float, float] | None, feature_type: str, grid_type: str, intersect: bool, ) -> None: """Check the validity of make_grid input parameters. `cell_size` must be larger than 0. `feature_type` and `grid_type` must be a valid option. Parameters ---------- input_geometry : (Multi)Polygon, GeoSeries, GeoDataFrame cell_size : float offset : tuple feature_type : str, one of "centers", "corners", "polygons" type of return grid_type : str, one of "square", "hexagon" grid type """ if not isinstance(input_geometry, (GeoDataFrame, GeoSeries, Polygon, MultiPolygon)): raise TypeError( "`input_geometry` should be GeoDataFrame, GeoSeries or" f"(Multi)Polygon, got {type(input_geometry)}" ) if isinstance(input_geometry, (GeoDataFrame, GeoSeries)): if input_geometry.empty: raise ValueError("`input_geometry` is empty") else: if input_geometry.is_empty: raise ValueError("`input_geometry` is empty") if cell_size <= 0: raise ValueError(f"`cell_size` should be positive, got {cell_size}") allowed_feature_type = ["centers", "corners", "polygons"] if feature_type not in allowed_feature_type: raise ValueError( f""" Invalid value for parameter `feature_type`. Only {allowed_feature_type} are supported. '{feature_type}' was given. """ ) allowed_grid_type = ["square", "hexagon"] if grid_type not in allowed_grid_type: raise ValueError( f""" Invalid value for parameter `grid_type`. Only {allowed_grid_type} are supported. '{grid_type}' was given. """ ) if not isinstance(intersect, bool): raise TypeError(f"`intersect` should be a bool, got {type(input_geometry)}") if isinstance(input_geometry, (GeoDataFrame, GeoSeries)): bounds = np.array(input_geometry.total_bounds) else: bounds = np.array(input_geometry.bounds) width = bounds[2] - bounds[0] height = bounds[3] - bounds[1] if (cell_size > width) and (cell_size > height): warnings.warn( f"`cell_size` {cell_size} is larger than " f"input_geometry dimensions ({width}, {height})", stacklevel=2, ) if offset is not None and ((offset[0] > bounds[2]) or (offset[1] > bounds[3])): warnings.warn( "One of the `offset` coordinates is larger than input_geometry extent.", stacklevel=2, ) geopandas-1.2.0/geopandas/tools/overlay.py000066400000000000000000000430431525650651600206260ustar00rootroot00000000000000import warnings from functools import reduce from typing import Literal import numpy as np import pandas as pd from geopandas import GeoDataFrame, GeoSeries from geopandas._compat import PANDAS_GE_30 from geopandas.array import ( LINE_GEOM_TYPES, POINT_GEOM_TYPES, POLYGON_GEOM_TYPES, _check_crs, _crs_mismatch_warn, ) def _ensure_geometry_column(df): """Ensure that the geometry column is called 'geometry'. If another column with that name exists, it will be dropped. """ if not df._geometry_column_name == "geometry": if PANDAS_GE_30: if "geometry" in df.columns: df = df.drop("geometry", axis=1) df = df.rename_geometry("geometry") else: if "geometry" in df.columns: df.drop("geometry", axis=1, inplace=True) df.rename_geometry("geometry", inplace=True) return df def _overlay_intersection(df1, df2): """Overlay Intersection operation used in overlay function.""" # Spatial Index to create intersections idx1, idx2 = df2.sindex.query(df1.geometry, predicate="intersects", sort=True) # Create pairs of geometries in both dataframes to be intersected if idx1.size > 0 and idx2.size > 0: left = df1.geometry.take(idx1) right = df2.geometry.take(idx2) if PANDAS_GE_30: left = left.reset_index(drop=True) right = right.reset_index(drop=True) else: left.reset_index(drop=True, inplace=True) right.reset_index(drop=True, inplace=True) intersections = left.intersection(right) poly_ix = intersections.geom_type.isin(POLYGON_GEOM_TYPES) intersections.loc[poly_ix] = intersections[poly_ix].make_valid() # only keep actual intersecting geometries pairs_intersect = pd.DataFrame({"__idx1": idx1, "__idx2": idx2}) geom_intersect = intersections # merge data for intersecting geometries df1 = df1.reset_index(drop=True) df2 = df2.reset_index(drop=True) dfinter = pairs_intersect.merge( df1.drop(df1._geometry_column_name, axis=1), left_on="__idx1", right_index=True, ) dfinter = dfinter.merge( df2.drop(df2._geometry_column_name, axis=1), left_on="__idx2", right_index=True, suffixes=("_1", "_2"), ) return GeoDataFrame(dfinter, geometry=geom_intersect, crs=df1.crs) else: result = df1.iloc[:0].merge( df2.iloc[:0].drop(df2.geometry.name, axis=1), left_index=True, right_index=True, suffixes=("_1", "_2"), ) result["__idx1"] = np.nan result["__idx2"] = np.nan return result[ result.columns.drop(df1.geometry.name).tolist() + [df1.geometry.name] ] def _overlay_difference(df1, df2): """Overlay Difference operation used in overlay function.""" # spatial index query to find intersections idx1, idx2 = df2.sindex.query(df1.geometry, predicate="intersects", sort=True) idx1_unique, idx1_unique_indices = np.unique(idx1, return_index=True) idx2_split = np.split(idx2, idx1_unique_indices[1:]) sidx = [ idx2_split.pop(0) if idx in idx1_unique else [] for idx in range(df1.geometry.size) ] # Create differences new_g = [] for geom, neighbours in zip(df1.geometry, sidx): new = reduce( lambda x, y: x.difference(y), [geom] + list(df2.geometry.iloc[neighbours]) ) new_g.append(new) differences = GeoSeries(new_g, index=df1.index, crs=df1.crs) poly_ix = differences.geom_type.isin(POLYGON_GEOM_TYPES) differences.loc[poly_ix] = differences[poly_ix].make_valid() geom_diff = differences[~differences.is_empty].copy() dfdiff = df1[~differences.is_empty].copy() dfdiff[dfdiff._geometry_column_name] = geom_diff return dfdiff def _overlay_identity(df1, df2): """Overlay Identity operation used in overlay function.""" dfintersection = _overlay_intersection(df1, df2) dfdifference = _overlay_difference(df1, df2) dfdifference = _ensure_geometry_column(dfdifference) # Columns that were suffixed in dfintersection need to be suffixed in dfdifference # as well so they can be matched properly in concat. new_columns = [ col if col in dfintersection.columns else f"{col}_1" for col in dfdifference.columns ] dfdifference.columns = new_columns # Now we can concatenate the two dataframes result = pd.concat([dfintersection, dfdifference], ignore_index=True, sort=False) # keep geometry column last columns = list(dfintersection.columns) columns.remove("geometry") columns.append("geometry") return result.reindex(columns=columns) def _overlay_symmetric_diff(df1, df2): """Overlay Symmetric Difference operation used in overlay function.""" dfdiff1 = _overlay_difference(df1, df2) dfdiff2 = _overlay_difference(df2, df1) dfdiff1["__idx1"] = range(len(dfdiff1)) dfdiff2["__idx2"] = range(len(dfdiff2)) dfdiff1["__idx2"] = np.nan dfdiff2["__idx1"] = np.nan # ensure geometry name (otherwise merge goes wrong) dfdiff1 = _ensure_geometry_column(dfdiff1) dfdiff2 = _ensure_geometry_column(dfdiff2) # combine both 'difference' dataframes dfsym = dfdiff1.merge( dfdiff2, on=["__idx1", "__idx2"], how="outer", suffixes=("_1", "_2") ) geometry = dfsym.geometry_1.copy() geometry.name = "geometry" # https://github.com/pandas-dev/pandas/issues/26468 use loc for now geometry.loc[dfsym.geometry_1.isnull()] = dfsym.loc[ dfsym.geometry_1.isnull(), "geometry_2" ] if PANDAS_GE_30: dfsym = dfsym.drop(["geometry_1", "geometry_2"], axis=1) dfsym = dfsym.reset_index(drop=True) else: dfsym.drop(["geometry_1", "geometry_2"], axis=1, inplace=True) dfsym.reset_index(drop=True, inplace=True) dfsym = GeoDataFrame(dfsym, geometry=geometry, crs=df1.crs) return dfsym def _overlay_union(df1, df2): """Overlay Union operation used in overlay function.""" dfinter = _overlay_intersection(df1, df2) dfsym = _overlay_symmetric_diff(df1, df2) dfunion = pd.concat([dfinter, dfsym], ignore_index=True, sort=False) # keep geometry column last columns = list(dfunion.columns) columns.remove("geometry") columns.append("geometry") return dfunion.reindex(columns=columns) def overlay( df1: GeoDataFrame, df2: GeoDataFrame, how: Literal[ "intersection", "union", "identity", "symmetric_difference", "difference" ] = "intersection", keep_geom_type: bool | None = None, make_valid: bool = True, ): """Perform spatial overlay between two GeoDataFrames. Currently only supports data GeoDataFrames with uniform geometry types, i.e. containing only (Multi)Polygons, or only (Multi)Points, or a combination of (Multi)LineString and LinearRing shapes. Implements several methods that are all effectively subsets of the union. See the User Guide page :doc:`../../user_guide/set_operations` for details. Parameters ---------- df1 : GeoDataFrame df2 : GeoDataFrame how : string Method of spatial overlay: 'intersection', 'union', 'identity', 'symmetric_difference' or 'difference'. keep_geom_type : bool If True, return only geometries of the same geometry type as df1 has, if False, return all resulting geometries. Default is None, which will set keep_geom_type to True but warn upon dropping geometries. make_valid : bool, default True If True, any invalid input geometries are corrected with a call to make_valid(), if False, a `ValueError` is raised if any input geometries are invalid. Returns ------- df : GeoDataFrame GeoDataFrame with new set of polygons and attributes resulting from the overlay Examples -------- >>> from shapely.geometry import Polygon >>> polys1 = geopandas.GeoSeries([Polygon([(0,0), (2,0), (2,2), (0,2)]), ... Polygon([(2,2), (4,2), (4,4), (2,4)])]) >>> polys2 = geopandas.GeoSeries([Polygon([(1,1), (3,1), (3,3), (1,3)]), ... Polygon([(3,3), (5,3), (5,5), (3,5)])]) >>> df1 = geopandas.GeoDataFrame({'geometry': polys1, 'df1_data':[1,2]}) >>> df2 = geopandas.GeoDataFrame({'geometry': polys2, 'df2_data':[1,2]}) >>> geopandas.overlay(df1, df2, how='union') df1_data df2_data geometry 0 1.0 1.0 POLYGON ((2 2, 2 1, 1 1, 1 2, 2 2)) 1 2.0 1.0 POLYGON ((2 2, 2 3, 3 3, 3 2, 2 2)) 2 2.0 2.0 POLYGON ((4 4, 4 3, 3 3, 3 4, 4 4)) 3 1.0 NaN POLYGON ((2 0, 0 0, 0 2, 1 2, 1 1, 2 1, 2 0)) 4 2.0 NaN MULTIPOLYGON (((3 4, 3 3, 2 3, 2 4, 3 4)), ((4... 5 NaN 1.0 MULTIPOLYGON (((2 3, 2 2, 1 2, 1 3, 2 3)), ((3... 6 NaN 2.0 POLYGON ((3 5, 5 5, 5 3, 4 3, 4 4, 3 4, 3 5)) >>> geopandas.overlay(df1, df2, how='intersection') df1_data df2_data geometry 0 1 1 POLYGON ((2 2, 2 1, 1 1, 1 2, 2 2)) 1 2 1 POLYGON ((2 2, 2 3, 3 3, 3 2, 2 2)) 2 2 2 POLYGON ((4 4, 4 3, 3 3, 3 4, 4 4)) >>> geopandas.overlay(df1, df2, how='symmetric_difference') df1_data df2_data geometry 0 1.0 NaN POLYGON ((2 0, 0 0, 0 2, 1 2, 1 1, 2 1, 2 0)) 1 2.0 NaN MULTIPOLYGON (((3 4, 3 3, 2 3, 2 4, 3 4)), ((4... 2 NaN 1.0 MULTIPOLYGON (((2 3, 2 2, 1 2, 1 3, 2 3)), ((3... 3 NaN 2.0 POLYGON ((3 5, 5 5, 5 3, 4 3, 4 4, 3 4, 3 5)) >>> geopandas.overlay(df1, df2, how='difference') geometry df1_data 0 POLYGON ((2 0, 0 0, 0 2, 1 2, 1 1, 2 1, 2 0)) 1 1 MULTIPOLYGON (((3 4, 3 3, 2 3, 2 4, 3 4)), ((4... 2 >>> geopandas.overlay(df1, df2, how='identity') df1_data df2_data geometry 0 1 1.0 POLYGON ((2 2, 2 1, 1 1, 1 2, 2 2)) 1 2 1.0 POLYGON ((2 2, 2 3, 3 3, 3 2, 2 2)) 2 2 2.0 POLYGON ((4 4, 4 3, 3 3, 3 4, 4 4)) 3 1 NaN POLYGON ((2 0, 0 0, 0 2, 1 2, 1 1, 2 1, 2 0)) 4 2 NaN MULTIPOLYGON (((3 4, 3 3, 2 3, 2 4, 3 4)), ((4... See Also -------- sjoin : spatial join GeoDataFrame.overlay : equivalent method Notes ----- Every operation in GeoPandas is planar, i.e. the potential third dimension is not taken into account. """ # Allowed operations allowed_hows = [ "intersection", "union", "identity", "symmetric_difference", "difference", # aka erase ] # Error Messages if how not in allowed_hows: raise ValueError(f"`how` was '{how}' but is expected to be in {allowed_hows}") if isinstance(df1, GeoSeries) or isinstance(df2, GeoSeries): raise NotImplementedError( "overlay currently only implemented for GeoDataFrames" ) if not _check_crs(df1, df2): _crs_mismatch_warn(df1, df2, stacklevel=3) if keep_geom_type is None: keep_geom_type = True keep_geom_type_warning = True else: keep_geom_type_warning = False for i, df in enumerate([df1, df2]): poly_check = df.geom_type.isin(POLYGON_GEOM_TYPES).any() lines_check = df.geom_type.isin(LINE_GEOM_TYPES).any() points_check = df.geom_type.isin(POINT_GEOM_TYPES).any() if sum([poly_check, lines_check, points_check]) > 1: raise NotImplementedError(f"df{i + 1} contains mixed geometry types.") if how == "intersection": box_gdf1 = df1.total_bounds box_gdf2 = df2.total_bounds if not ( ((box_gdf1[0] <= box_gdf2[2]) and (box_gdf2[0] <= box_gdf1[2])) and ((box_gdf1[1] <= box_gdf2[3]) and (box_gdf2[1] <= box_gdf1[3])) ): result = df1.iloc[:0].merge( df2.iloc[:0].drop(df2.geometry.name, axis=1), left_index=True, right_index=True, suffixes=("_1", "_2"), ) return result[ result.columns.drop(df1.geometry.name).tolist() + [df1.geometry.name] ] # Computations def _make_valid(df): df = df.copy() if df.geom_type.isin(POLYGON_GEOM_TYPES).all(): mask = ~df.geometry.is_valid col = df._geometry_column_name if make_valid: df.loc[mask, col] = df.loc[mask, col].make_valid() # Extract only the input geometry type, as make_valid may change it if mask.any(): df = _collection_extract( df, geom_type="Polygon", keep_geom_type_warning=False ) elif mask.any(): raise ValueError( "You have passed make_valid=False along with " f"{mask.sum()} invalid input geometries. " "Use make_valid=True or make sure that all geometries " "are valid before using overlay." ) return df # Determine the geometry type before make_valid, as make_valid may change it if keep_geom_type: if len(df1) == 0: msg = ( "`keep_geom_type=True` is invalid when df1 is empty as " "there is no geometry type to keep. Setting `keep_geom_type=False`" ) warnings.warn(msg, stacklevel=3) keep_geom_type = False else: geom_type = df1.geom_type.iloc[0] df1 = _make_valid(df1) df2 = _make_valid(df2) with warnings.catch_warnings(): # CRS checked above, suppress array-level warning warnings.filterwarnings("ignore", message="CRS mismatch between the CRS") if how == "difference": result = _overlay_difference(df1, df2) elif how == "intersection": result = _overlay_intersection(df1, df2) elif how == "symmetric_difference": result = _overlay_symmetric_diff(df1, df2) elif how == "union": result = _overlay_union(df1, df2) elif how == "identity": result = _overlay_identity(df1, df2) if how in ["intersection", "symmetric_difference", "union", "identity"]: if PANDAS_GE_30: result = result.drop(["__idx1", "__idx2"], axis=1) else: result.drop(["__idx1", "__idx2"], axis=1, inplace=True) if keep_geom_type: result = _collection_extract(result, geom_type, keep_geom_type_warning) if PANDAS_GE_30: result = result.reset_index(drop=True) else: result.reset_index(drop=True, inplace=True) return result def _collection_extract(df, geom_type, keep_geom_type_warning): # Check input if geom_type in POLYGON_GEOM_TYPES: geom_types = POLYGON_GEOM_TYPES elif geom_type in LINE_GEOM_TYPES: geom_types = LINE_GEOM_TYPES elif geom_type in POINT_GEOM_TYPES: geom_types = POINT_GEOM_TYPES else: raise TypeError(f"`geom_type` does not support {geom_type}.") result = df.copy() # First we filter the geometry types inside GeometryCollections objects # (e.g. GeometryCollection([polygon, point]) -> polygon) # we do this separately on only the relevant rows, as this is an expensive # operation (an expensive no-op for geometry types other than collections) is_collection = result.geom_type == "GeometryCollection" if is_collection.any(): geom_col = result._geometry_column_name collections = result.loc[is_collection, [geom_col]] exploded = collections.reset_index(drop=True).explode(index_parts=True) exploded = exploded.reset_index(level=0) orig_num_geoms_exploded = exploded.shape[0] exploded.loc[~exploded.geom_type.isin(geom_types), geom_col] = None num_dropped_collection = ( orig_num_geoms_exploded - exploded.geometry.isna().sum() ) # level_0 created with above reset_index operation # and represents the original geometry collections # TODO avoiding dissolve to call union_all in this case could further # improve performance (we only need to collect geometries in their # respective Multi version) dissolved = exploded.dissolve(by="level_0") result.loc[is_collection, geom_col] = dissolved[geom_col].values else: num_dropped_collection = 0 # Now we filter all geometries (in theory we don't need to do this # again for the rows handled above for GeometryCollections, but filtering # them out is probably more expensive as simply including them when this # is typically about only a few rows) orig_num_geoms = result.shape[0] result = result.loc[result.geom_type.isin(geom_types)] num_dropped = orig_num_geoms - result.shape[0] if (num_dropped > 0 or num_dropped_collection > 0) and keep_geom_type_warning: warnings.warn( "`keep_geom_type=True` in overlay resulted in " f"{num_dropped + num_dropped_collection} dropped geometries of different " "geometry types than df1 has. Set `keep_geom_type=False` to retain all " "geometries", UserWarning, stacklevel=2, ) return result geopandas-1.2.0/geopandas/tools/sjoin.py000066400000000000000000000641121525650651600202670ustar00rootroot00000000000000import warnings from functools import partial import numpy as np import pandas as pd from geopandas import GeoDataFrame from geopandas._compat import PANDAS_GE_30 from geopandas.array import _check_crs, _crs_mismatch_warn def sjoin( left_df, right_df, how="inner", predicate="intersects", lsuffix="left", rsuffix="right", distance=None, on_attribute=None, **kwargs, ): """Spatial join of two GeoDataFrames. See the User Guide page :doc:`../../user_guide/mergingdata` for details. Parameters ---------- left_df, right_df : GeoDataFrames how : string, default 'inner' The type of join: * 'left': use keys from left_df; retain only left_df geometry column * 'right': use keys from right_df; retain only right_df geometry column * 'inner': use intersection of keys from both dfs; retain only left_df geometry column predicate : string, default 'intersects' Binary predicate. Valid values are determined by the spatial index used. You can check the valid values in left_df or right_df as ``left_df.sindex.valid_query_predicates`` or ``right_df.sindex.valid_query_predicates`` Available predicates include: * ``'intersects'``: True if geometries intersect (boundaries and interiors) * ``'within'``: True if left geometry is completely within right geometry * ``'contains'``: True if left geometry completely contains right geometry * ``'contains_properly'``: True if left geometry contains right geometry and their boundaries do not touch * ``'overlaps'``: True if geometries overlap but neither contains the other * ``'crosses':`` True if geometries cross (interiors intersect but neither contains the other, with intersection dimension less than max dimension) * ``'touches'``: True if geometries touch at boundaries but interiors don't * ``'covers'``: True if left geometry covers right geometry (every point of right is a point of left) * ``'covered_by'``: True if left geometry is covered by right geometry * ``'dwithin'``: True if geometries are within specified distance (requires distance parameter) lsuffix : string, default 'left' Suffix to apply to overlapping column names (left GeoDataFrame). rsuffix : string, default 'right' Suffix to apply to overlapping column names (right GeoDataFrame). distance : number or array_like, optional Distance(s) around each input geometry within which to query the tree for the 'dwithin' predicate. If array_like, must be one-dimesional with length equal to length of left GeoDataFrame. Required if ``predicate='dwithin'``. on_attribute : string, list or tuple Column name(s) to join on as an additional join restriction on top of the spatial predicate. These must be found in both DataFrames. If set, observations are joined only if the predicate applies and values in specified columns match. Examples -------- >>> import geodatasets >>> chicago = geopandas.read_file( ... geodatasets.get_path("geoda.chicago_health") ... ) >>> groceries = geopandas.read_file( ... geodatasets.get_path("geoda.groceries") ... ).to_crs(chicago.crs) >>> chicago.head() # doctest: +SKIP ComAreaID ... geometry 0 35 ... POLYGON ((-87.60914 41.84469, -87.60915 41.844... 1 36 ... POLYGON ((-87.59215 41.81693, -87.59231 41.816... 2 37 ... POLYGON ((-87.62880 41.80189, -87.62879 41.801... 3 38 ... POLYGON ((-87.60671 41.81681, -87.60670 41.816... 4 39 ... POLYGON ((-87.59215 41.81693, -87.59215 41.816... [5 rows x 87 columns] >>> groceries.head() # doctest: +SKIP OBJECTID Ycoord ... Category geometry 0 16 41.973266 ... NaN MULTIPOINT (-87.65661 41.97321) 1 18 41.696367 ... NaN MULTIPOINT (-87.68136 41.69713) 2 22 41.868634 ... NaN MULTIPOINT (-87.63918 41.86847) 3 23 41.877590 ... new MULTIPOINT (-87.65495 41.87783) 4 27 41.737696 ... NaN MULTIPOINT (-87.62715 41.73623) [5 rows x 8 columns] >>> groceries_w_communities = geopandas.sjoin(groceries, chicago) >>> groceries_w_communities.head() # doctest: +SKIP OBJECTID community geometry 0 16 UPTOWN MULTIPOINT ((-87.65661 41.97321)) 1 18 MORGAN PARK MULTIPOINT ((-87.68136 41.69713)) 2 22 NEAR WEST SIDE MULTIPOINT ((-87.63918 41.86847)) 3 23 NEAR WEST SIDE MULTIPOINT ((-87.65495 41.87783)) 4 27 CHATHAM MULTIPOINT ((-87.62715 41.73623)) [5 rows x 95 columns] See Also -------- overlay : overlay operation resulting in a new geometry GeoDataFrame.sjoin : equivalent method Notes ----- Every operation in GeoPandas is planar, i.e. the potential third dimension is not taken into account. """ if kwargs: first = next(iter(kwargs.keys())) raise TypeError(f"sjoin() got an unexpected keyword argument '{first}'") on_attribute = _maybe_make_list(on_attribute) _basic_checks(left_df, right_df, how, lsuffix, rsuffix, on_attribute=on_attribute) indices = _geom_predicate_query( left_df, right_df, predicate, distance, on_attribute=on_attribute ) joined, _ = _frame_join( left_df, right_df, indices, None, how, lsuffix, rsuffix, predicate, on_attribute=on_attribute, ) return joined def _maybe_make_list(obj): if isinstance(obj, tuple): return list(obj) if obj is not None and not isinstance(obj, list): return [obj] return obj def _basic_checks(left_df, right_df, how, lsuffix, rsuffix, on_attribute=None): """Check the validity of join input parameters. `how` must be one of the valid options. `'index_'` concatenated with `lsuffix` or `rsuffix` must not already exist as columns in the left or right data frames. Parameters ---------- left_df : GeoDataFrame right_df : GeoData Frame how : str, one of 'left', 'right', 'inner' join type lsuffix : str left index suffix rsuffix : str right index suffix on_attribute : list, default None list of column names to merge on along with geometry """ if not isinstance(left_df, GeoDataFrame): raise ValueError(f"'left_df' should be GeoDataFrame, got {type(left_df)}") if not isinstance(right_df, GeoDataFrame): raise ValueError(f"'right_df' should be GeoDataFrame, got {type(right_df)}") allowed_hows = ["left", "right", "inner"] if how not in allowed_hows: raise ValueError(f'`how` was "{how}" but is expected to be in {allowed_hows}') if not _check_crs(left_df, right_df): _crs_mismatch_warn(left_df, right_df, stacklevel=4) if on_attribute: for attr in on_attribute: if (attr not in left_df) and (attr not in right_df): raise ValueError( f"Expected column {attr} is missing from both of the dataframes." ) if attr not in left_df: raise ValueError( f"Expected column {attr} is missing from the left dataframe." ) if attr not in right_df: raise ValueError( f"Expected column {attr} is missing from the right dataframe." ) if attr in (left_df.geometry.name, right_df.geometry.name): raise ValueError( "Active geometry column cannot be used as an input " "for on_attribute parameter." ) def _geom_predicate_query(left_df, right_df, predicate, distance, on_attribute=None): """Compute geometric comparisons and get matching indices. Parameters ---------- left_df : GeoDataFrame right_df : GeoDataFrame predicate : string Binary predicate to query. on_attribute: list, default None list of column names to merge on along with geometry Returns ------- DataFrame DataFrame with matching indices in columns named `_key_left` and `_key_right`. """ original_predicate = predicate if predicate == "within": # within is implemented as the inverse of contains # contains is a faster predicate # see discussion at https://github.com/geopandas/geopandas/pull/1421 predicate = "contains" sindex = left_df.sindex input_geoms = right_df.geometry else: # all other predicates are symmetric # keep them the same sindex = right_df.sindex input_geoms = left_df.geometry if sindex: l_idx, r_idx = sindex.query( input_geoms, predicate=predicate, sort=False, distance=distance ) else: # when sindex is empty / has no valid geometries l_idx, r_idx = np.array([], dtype=np.intp), np.array([], dtype=np.intp) if original_predicate == "within": # within is implemented as the inverse of contains # flip back the results r_idx, l_idx = l_idx, r_idx indexer = np.lexsort((r_idx, l_idx)) l_idx = l_idx[indexer] r_idx = r_idx[indexer] if on_attribute: for attr in on_attribute: (l_idx, r_idx), _ = _filter_shared_attribute( left_df, right_df, l_idx, r_idx, attr ) return l_idx, r_idx def _reset_index_with_suffix(df, suffix, other): """ Equivalent of df.reset_index(), but with adding 'suffix' to auto-generated column names. """ index_original = df.index.names if PANDAS_GE_30: df_reset = df.reset_index() else: # we already made a copy of the dataframe in _frame_join before getting here df_reset = df df_reset.reset_index(inplace=True) column_names = df_reset.columns.to_numpy(copy=True) for i, label in enumerate(index_original): # if the original label was None, add suffix to auto-generated name if label is None: new_label = column_names[i] if "level" in new_label: # reset_index of MultiIndex gives "level_i" names, preserve the "i" lev = new_label.split("_")[1] new_label = f"index_{suffix}{lev}" else: new_label = f"index_{suffix}" # check new label will not be in other dataframe if new_label in df.columns or new_label in other.columns: raise ValueError( f"'{new_label}' cannot be a column name in the frames being joined" ) column_names[i] = new_label return df_reset, pd.Index(column_names) def _process_column_names_with_suffix( left: pd.Index, right: pd.Index, suffixes, left_df, right_df ): """ Add suffixes to overlapping labels (ignoring the geometry column). This is based on pandas' merge logic at https://github.com/pandas-dev/pandas/blob/ a0779adb183345a8eb4be58b3ad00c223da58768/pandas/core/reshape/merge.py#L2300-L2370 """ to_rename = left.intersection(right) if len(to_rename) == 0: return left, right lsuffix, rsuffix = suffixes if not lsuffix and not rsuffix: raise ValueError(f"columns overlap but no suffix specified: {to_rename}") def renamer(x, suffix, geometry): if x in to_rename and x != geometry and suffix is not None: return f"{x}_{suffix}" return x lrenamer = partial( renamer, suffix=lsuffix, geometry=getattr(left_df, "_geometry_column_name", None), ) rrenamer = partial( renamer, suffix=rsuffix, geometry=getattr(right_df, "_geometry_column_name", None), ) # TODO retain index name? left_renamed = pd.Index([lrenamer(lab) for lab in left]) right_renamed = pd.Index([rrenamer(lab) for lab in right]) dups = [] if not left_renamed.is_unique: # Only warn when duplicates are caused because of suffixes, already duplicated # columns in origin should not warn dups = left_renamed[(left_renamed.duplicated()) & (~left.duplicated())].tolist() if not right_renamed.is_unique: dups.extend( right_renamed[(right_renamed.duplicated()) & (~right.duplicated())].tolist() ) # TODO turn this into an error (pandas has done so as well) if dups: warnings.warn( f"Passing 'suffixes' which cause duplicate columns {set(dups)} in the " f"result is deprecated and will raise a MergeError in a future version.", FutureWarning, stacklevel=4, ) return left_renamed, right_renamed def _restore_index(joined, index_names, index_names_original): """ Set back the original index columns, and restoring their name as `None` if they didn't have a name originally. """ if PANDAS_GE_30: joined = joined.set_index(list(index_names)) else: joined.set_index(list(index_names), inplace=True) # restore the fact that the index didn't have a name joined_index_names = list(joined.index.names) for i, label in enumerate(index_names_original): if label is None: joined_index_names[i] = None joined.index.names = joined_index_names return joined def _adjust_indexers(indices, distances, original_length, how, predicate): """Adjust the indexers for the join based on the `how` parameter. The left/right indexers from the query represents an inner join. For a left or right join, we need to adjust them to include the rows that would not be present in an inner join. """ # the indices represent an inner join, no adjustment needed if how == "inner": return indices, distances l_idx, r_idx = indices if how == "right": # re-sort so it is sorted by the right indexer indexer = np.lexsort((l_idx, r_idx)) l_idx, r_idx = l_idx[indexer], r_idx[indexer] if distances is not None: distances = distances[indexer] # switch order r_idx, l_idx = l_idx, r_idx # determine which indices are missing and where they would need to be inserted idx = np.arange(original_length) l_idx_missing = idx[~np.isin(idx, l_idx)] insert_idx = np.searchsorted(l_idx, l_idx_missing) # for the left indexer, insert those missing indices l_idx = np.insert(l_idx, insert_idx, l_idx_missing) # for the right indexer, insert -1 -> to get missing values in pandas' reindexing r_idx = np.insert(r_idx, insert_idx, -1) # for the indices, already insert those missing values manually if distances is not None: distances = np.insert(distances, insert_idx, np.nan) if how == "right": # switch back l_idx, r_idx = r_idx, l_idx return (l_idx, r_idx), distances def _frame_join( left_df, right_df, indices, distances, how, lsuffix, rsuffix, predicate, on_attribute=None, ): """Join the GeoDataFrames at the DataFrame level. Parameters ---------- left_df : GeoDataFrame right_df : GeoDataFrame indices : tuple of ndarray Indices returned by the geometric join. Tuple with integer indices representing the matches from `left_df` and `right_df` respectively. distances : ndarray, optional Passed through and adapted based on the indices, if needed. how : string The type of join to use on the DataFrame level. lsuffix : string Suffix to apply to overlapping column names (left GeoDataFrame). rsuffix : string Suffix to apply to overlapping column names (right GeoDataFrame). on_attribute: list, default None list of column names to merge on along with geometry Returns ------- GeoDataFrame Joined GeoDataFrame. """ if on_attribute: # avoid renaming or duplicating shared column right_df = right_df.drop(on_attribute, axis=1) if how in ("inner", "left"): right_df = right_df.drop(right_df.geometry.name, axis=1) else: # how == 'right': left_df = left_df.drop(left_df.geometry.name, axis=1) left_df = left_df.copy(deep=False) left_nlevels = left_df.index.nlevels left_index_original = left_df.index.names left_df, left_column_names = _reset_index_with_suffix(left_df, lsuffix, right_df) right_df = right_df.copy(deep=False) right_nlevels = right_df.index.nlevels right_index_original = right_df.index.names right_df, right_column_names = _reset_index_with_suffix(right_df, rsuffix, left_df) # if conflicting names in left and right, add suffix left_column_names, right_column_names = _process_column_names_with_suffix( left_column_names, right_column_names, (lsuffix, rsuffix), left_df, right_df, ) left_df.columns = left_column_names right_df.columns = right_column_names left_index = left_df.columns[:left_nlevels] right_index = right_df.columns[:right_nlevels] # perform join on the dataframes original_length = len(right_df) if how == "right" else len(left_df) (l_idx, r_idx), distances = _adjust_indexers( indices, distances, original_length, how, predicate ) # the `take` method doesn't allow introducing NaNs with -1 indices # left = left_df.take(l_idx) # therefore we are using the private _reindex_with_indexers as workaround new_index = pd.RangeIndex(len(l_idx)) left = left_df._reindex_with_indexers({0: (new_index, l_idx)}) right = right_df._reindex_with_indexers({0: (new_index, r_idx)}) if PANDAS_GE_30: kwargs = {} else: kwargs = dict(copy=False) joined = pd.concat([left, right], axis=1, **kwargs) if how in ("inner", "left"): joined = _restore_index(joined, left_index, left_index_original) else: # how == 'right': joined = joined.set_geometry(right_df.geometry.name) joined = _restore_index(joined, right_index, right_index_original) return joined, distances def _nearest_query( left_df: GeoDataFrame, right_df: GeoDataFrame, max_distance: float, how: str, return_distance: bool, exclusive: bool, on_attribute: list | None = None, ): # use the opposite of the join direction for the index use_left_as_sindex = how == "right" if use_left_as_sindex: sindex = left_df.sindex query = right_df.geometry else: sindex = right_df.sindex query = left_df.geometry if sindex: res = sindex.nearest( query, return_all=True, max_distance=max_distance, return_distance=return_distance, exclusive=exclusive, ) if return_distance: (input_idx, tree_idx), distances = res else: (input_idx, tree_idx) = res distances = None if use_left_as_sindex: l_idx, r_idx = tree_idx, input_idx sort_order = np.argsort(l_idx, kind="stable") l_idx, r_idx = l_idx[sort_order], r_idx[sort_order] if distances is not None: distances = distances[sort_order] else: l_idx, r_idx = input_idx, tree_idx else: # when sindex is empty / has no valid geometries l_idx, r_idx = np.array([], dtype=np.intp), np.array([], dtype=np.intp) if return_distance: distances = np.array([], dtype=np.float64) else: distances = None if on_attribute: for attr in on_attribute: (l_idx, r_idx), shared_attribute_rows = _filter_shared_attribute( left_df, right_df, l_idx, r_idx, attr ) distances = distances[shared_attribute_rows] return (l_idx, r_idx), distances def _filter_shared_attribute(left_df, right_df, l_idx, r_idx, attribute): """Return the indices for the left and right dataframe that share the same entry in the attribute column. Also returns a Boolean `shared_attribute_rows` for rows with the same entry. """ shared_attribute_rows = ( left_df[attribute].iloc[l_idx].values == right_df[attribute].iloc[r_idx].values ) l_idx = l_idx[shared_attribute_rows] r_idx = r_idx[shared_attribute_rows] return (l_idx, r_idx), shared_attribute_rows def sjoin_nearest( left_df: GeoDataFrame, right_df: GeoDataFrame, how: str = "inner", max_distance: float | None = None, lsuffix: str = "left", rsuffix: str = "right", distance_col: str | None = None, exclusive: bool = False, ) -> GeoDataFrame: """Spatial join of two GeoDataFrames based on the distance between their geometries. Results will include multiple output records for a single input record where there are multiple equidistant nearest or intersected neighbors. Distance is calculated in CRS units and can be returned using the `distance_col` parameter. See the User Guide page https://geopandas.readthedocs.io/en/latest/docs/user_guide/mergingdata.html for more details. Parameters ---------- left_df, right_df : GeoDataFrames how : string, default 'inner' The type of join: * 'left': use keys from left_df; retain only left_df geometry column * 'right': use keys from right_df; retain only right_df geometry column * 'inner': use intersection of keys from both dfs; retain only left_df geometry column max_distance : float, default None Maximum distance within which to query for nearest geometry. Must be greater than 0. The max_distance used to search for nearest items in the tree may have a significant impact on performance by reducing the number of input geometries that are evaluated for nearest items in the tree. lsuffix : string, default 'left' Suffix to apply to overlapping column names (left GeoDataFrame). rsuffix : string, default 'right' Suffix to apply to overlapping column names (right GeoDataFrame). distance_col : string, default None If set, save the distances computed between matching geometries under a column of this name in the joined GeoDataFrame. exclusive : bool, default False If True, the nearest geometries that are equal to the input geometry will not be returned, default False. Examples -------- >>> import geodatasets >>> groceries = geopandas.read_file( ... geodatasets.get_path("geoda.groceries") ... ) >>> chicago = geopandas.read_file( ... geodatasets.get_path("geoda.chicago_health") ... ).to_crs(groceries.crs) >>> chicago.head() # doctest: +SKIP ComAreaID ... geometry 0 35 ... POLYGON ((-87.60914 41.84469, -87.60915 41.844... 1 36 ... POLYGON ((-87.59215 41.81693, -87.59231 41.816... 2 37 ... POLYGON ((-87.62880 41.80189, -87.62879 41.801... 3 38 ... POLYGON ((-87.60671 41.81681, -87.60670 41.816... 4 39 ... POLYGON ((-87.59215 41.81693, -87.59215 41.816... [5 rows x 87 columns] >>> groceries.head() # doctest: +SKIP OBJECTID Ycoord ... Category geometry 0 16 41.973266 ... NaN MULTIPOINT ((-87.65661 41.97321)) 1 18 41.696367 ... NaN MULTIPOINT ((-87.68136 41.69713)) 2 22 41.868634 ... NaN MULTIPOINT ((-87.63918 41.86847)) 3 23 41.877590 ... new MULTIPOINT ((-87.65495 41.87783)) 4 27 41.737696 ... NaN MULTIPOINT ((-87.62715 41.73623)) [5 rows x 8 columns] >>> groceries_w_communities = geopandas.sjoin_nearest(groceries, chicago) >>> groceries_w_communities[["Chain", "community", "geometry"]].head(2) Chain community geometry 0 VIET HOA PLAZA UPTOWN MULTIPOINT ((1168268.672 1933554.35)) 1 COUNTY FAIR FOODS MORGAN PARK MULTIPOINT ((1162302.618 1832900.224)) To include the distances: >>> groceries_w_communities = geopandas.sjoin_nearest(groceries, chicago, \ distance_col="distances") >>> groceries_w_communities[["Chain", "community", \ "distances"]].head(2) Chain community distances 0 VIET HOA PLAZA UPTOWN 0.0 1 COUNTY FAIR FOODS MORGAN PARK 0.0 In the following example, we get multiple groceries for Uptown because all results are equidistant (in this case zero because they intersect). In fact, we get 4 results in total: >>> chicago_w_groceries = geopandas.sjoin_nearest(groceries, chicago, \ distance_col="distances", how="right") >>> uptown_results = \ chicago_w_groceries[chicago_w_groceries["community"] == "UPTOWN"] >>> uptown_results[["Chain", "community"]] Chain community 30 VIET HOA PLAZA UPTOWN 30 JEWEL OSCO UPTOWN 30 TARGET UPTOWN 30 Mariano's UPTOWN See Also -------- sjoin : binary predicate joins GeoDataFrame.sjoin_nearest : equivalent method Notes ----- Since this join relies on distances, results will be inaccurate if your geometries are in a geographic CRS. Every operation in GeoPandas is planar, i.e. the potential third dimension is not taken into account. """ _basic_checks(left_df, right_df, how, lsuffix, rsuffix) left_df.geometry.values.check_geographic_crs(stacklevel=1) right_df.geometry.values.check_geographic_crs(stacklevel=1) return_distance = distance_col is not None indices, distances = _nearest_query( left_df, right_df, max_distance, how, return_distance, exclusive, ) joined, distances = _frame_join( left_df, right_df, indices, distances, how, lsuffix, rsuffix, None, ) if return_distance: joined[distance_col] = distances return joined geopandas-1.2.0/geopandas/tools/tests/000077500000000000000000000000001525650651600177315ustar00rootroot00000000000000geopandas-1.2.0/geopandas/tools/tests/__init__.py000066400000000000000000000000001525650651600220300ustar00rootroot00000000000000geopandas-1.2.0/geopandas/tools/tests/test_clip.py000066400000000000000000000435041525650651600222770ustar00rootroot00000000000000"""Tests for the clip module.""" import numpy as np import pandas as pd import shapely from shapely.geometry import ( GeometryCollection, LinearRing, LineString, MultiPoint, Point, Polygon, box, ) import geopandas from geopandas import GeoDataFrame, GeoSeries, clip from geopandas._compat import HAS_PYPROJ, PANDAS_INFER_STR from geopandas.array import POLYGON_GEOM_TYPES from geopandas.tools.clip import _mask_is_list_like_rectangle import pytest from geopandas.testing import assert_geodataframe_equal, assert_geoseries_equal from pandas.testing import assert_index_equal mask_variants_single_rectangle = [ "single_rectangle_gdf", "single_rectangle_gdf_list_bounds", "single_rectangle_gdf_tuple_bounds", "single_rectangle_gdf_array_bounds", ] mask_variants_large_rectangle = [ "larger_single_rectangle_gdf", "larger_single_rectangle_gdf_bounds", ] @pytest.fixture def point_gdf(): """Create a point GeoDataFrame.""" pts = np.array([[2, 2], [3, 4], [9, 8], [-12, -15]]) gdf = GeoDataFrame([Point(xy) for xy in pts], columns=["geometry"], crs="EPSG:3857") return gdf @pytest.fixture def point_gdf2(): """Create a point GeoDataFrame.""" pts = np.array([[5, 5], [2, 2], [4, 4], [0, 0], [3, 3], [1, 1]]) gdf = GeoDataFrame([Point(xy) for xy in pts], columns=["geometry"], crs="EPSG:3857") return gdf @pytest.fixture def pointsoutside_nooverlap_gdf(): """Create a point GeoDataFrame. Its points are all outside the single rectangle, and its bounds are outside the single rectangle's.""" pts = np.array([[5, 15], [15, 15], [15, 20]]) gdf = GeoDataFrame([Point(xy) for xy in pts], columns=["geometry"], crs="EPSG:3857") return gdf @pytest.fixture def pointsoutside_overlap_gdf(): """Create a point GeoDataFrame. Its points are all outside the single rectangle, and its bounds are overlapping the single rectangle's.""" pts = np.array([[5, 15], [15, 15], [15, 5]]) gdf = GeoDataFrame([Point(xy) for xy in pts], columns=["geometry"], crs="EPSG:3857") return gdf @pytest.fixture def single_rectangle_gdf(): """Create a single rectangle for clipping.""" poly_inters = Polygon([(0, 0), (0, 10), (10, 10), (10, 0), (0, 0)]) gdf = GeoDataFrame([1], geometry=[poly_inters], crs="EPSG:3857") gdf["attr2"] = "site-boundary" return gdf @pytest.fixture def single_rectangle_gdf_tuple_bounds(single_rectangle_gdf): """Bounds of the created single rectangle""" return tuple(single_rectangle_gdf.total_bounds) @pytest.fixture def single_rectangle_gdf_list_bounds(single_rectangle_gdf): """Bounds of the created single rectangle""" return list(single_rectangle_gdf.total_bounds) @pytest.fixture def single_rectangle_gdf_array_bounds(single_rectangle_gdf): """Bounds of the created single rectangle""" return single_rectangle_gdf.total_bounds @pytest.fixture def larger_single_rectangle_gdf(): """Create a slightly larger rectangle for clipping. The smaller single rectangle is used to test the edge case where slivers are returned when you clip polygons. This fixture is larger which eliminates the slivers in the clip return. """ poly_inters = Polygon([(-5, -5), (-5, 15), (15, 15), (15, -5), (-5, -5)]) gdf = GeoDataFrame([1], geometry=[poly_inters], crs="EPSG:3857") gdf["attr2"] = ["study area"] return gdf @pytest.fixture def larger_single_rectangle_gdf_bounds(larger_single_rectangle_gdf): """Bounds of the created single rectangle""" return tuple(larger_single_rectangle_gdf.total_bounds) @pytest.fixture def buffered_locations(point_gdf): """Buffer points to create a multi-polygon.""" buffered_locs = point_gdf buffered_locs["geometry"] = buffered_locs.buffer(4) buffered_locs["type"] = "plot" return buffered_locs @pytest.fixture def donut_geometry(buffered_locations, single_rectangle_gdf): """Make a geometry with a hole in the middle (a donut).""" donut = geopandas.overlay( buffered_locations, single_rectangle_gdf, how="symmetric_difference" ) return donut @pytest.fixture def two_line_gdf(): """Create Line Objects For Testing""" linea = LineString([(1, 1), (2, 2), (3, 2), (5, 3)]) lineb = LineString([(3, 4), (5, 7), (12, 2), (10, 5), (9, 7.5)]) gdf = GeoDataFrame([1, 2], geometry=[linea, lineb], crs="EPSG:3857") return gdf @pytest.fixture def multi_poly_gdf(donut_geometry): """Create a multi-polygon GeoDataFrame.""" multi_poly = donut_geometry.union_all() out_df = GeoDataFrame(geometry=GeoSeries(multi_poly), crs="EPSG:3857") out_df["attr"] = ["pool"] return out_df @pytest.fixture def multi_line(two_line_gdf): """Create a multi-line GeoDataFrame. This GDF has one multiline and one regular line.""" # Create a single and multi line object multiline_feat = two_line_gdf.union_all() linec = LineString([(2, 1), (3, 1), (4, 1), (5, 2)]) out_df = GeoDataFrame(geometry=GeoSeries([multiline_feat, linec]), crs="EPSG:3857") out_df["attr"] = ["road", "stream"] return out_df @pytest.fixture def multi_point(point_gdf): """Create a multi-point GeoDataFrame.""" multi_point = point_gdf.union_all() out_df = GeoDataFrame( geometry=GeoSeries( [multi_point, Point(2, 5), Point(-11, -14), Point(-10, -12)] ), crs="EPSG:3857", ) out_df["attr"] = ["tree", "another tree", "shrub", "berries"] return out_df @pytest.fixture def mixed_gdf(): """Create a Mixed Polygon and LineString For Testing""" point = Point(2, 3) line = LineString([(1, 1), (2, 2), (3, 2), (5, 3), (12, 1)]) poly = Polygon([(3, 4), (5, 2), (12, 2), (10, 5), (9, 7.5)]) ring = LinearRing([(1, 1), (2, 2), (3, 2), (5, 3), (12, 1)]) gdf = GeoDataFrame( [1, 2, 3, 4], geometry=[point, poly, line, ring], crs="EPSG:3857" ) return gdf @pytest.fixture def geomcol_gdf(): """Create a Mixed Polygon and LineString For Testing""" point = Point(2, 3) poly = Polygon([(3, 4), (5, 2), (12, 2), (10, 5), (9, 7.5)]) coll = GeometryCollection([point, poly]) gdf = GeoDataFrame([1], geometry=[coll], crs="EPSG:3857") return gdf @pytest.fixture def sliver_line(): """Create a line that will create a point when clipped.""" linea = LineString([(10, 5), (13, 5), (15, 5)]) lineb = LineString([(1, 1), (2, 2), (3, 2), (5, 3), (12, 1)]) gdf = GeoDataFrame([1, 2], geometry=[linea, lineb], crs="EPSG:3857") return gdf def test_not_gdf(single_rectangle_gdf): """Non-GeoDataFrame inputs raise attribute errors.""" with pytest.raises(TypeError): clip((2, 3), single_rectangle_gdf) with pytest.raises(TypeError): clip(single_rectangle_gdf, "foobar") with pytest.raises(TypeError): clip(single_rectangle_gdf, (1, 2, 3)) with pytest.raises(TypeError): clip(single_rectangle_gdf, (1, 2, 3, 4, 5)) def test_non_overlapping_geoms(): """Test that a bounding box returns empty if the extents don't overlap""" unit_box = Polygon([(0, 0), (0, 1), (1, 1), (1, 0), (0, 0)]) unit_gdf = GeoDataFrame([1], geometry=[unit_box], crs="EPSG:3857") non_overlapping_gdf = unit_gdf.copy() non_overlapping_gdf = non_overlapping_gdf.geometry.apply( lambda x: shapely.affinity.translate(x, xoff=20) ) out = clip(unit_gdf, non_overlapping_gdf) assert_geodataframe_equal(out, unit_gdf.iloc[:0]) out2 = clip(unit_gdf.geometry, non_overlapping_gdf) assert_geoseries_equal(out2, GeoSeries(crs=unit_gdf.crs)) @pytest.mark.parametrize("mask_fixture_name", mask_variants_single_rectangle) class TestClipWithSingleRectangleGdf: @pytest.fixture def mask(self, mask_fixture_name, request): return request.getfixturevalue(mask_fixture_name) def test_returns_gdf(self, point_gdf, mask): """Test that function returns a GeoDataFrame (or GDF-like) object.""" out = clip(point_gdf, mask) assert isinstance(out, GeoDataFrame) def test_returns_series(self, point_gdf, mask): """Test that function returns a GeoSeries if GeoSeries is passed.""" out = clip(point_gdf.geometry, mask) assert isinstance(out, GeoSeries) def test_clip_points(self, point_gdf, mask): """Test clipping a points GDF with a generic polygon geometry.""" clip_pts = clip(point_gdf, mask) pts = np.array([[2, 2], [3, 4], [9, 8]]) exp = GeoDataFrame( [Point(xy) for xy in pts], columns=["geometry"], crs="EPSG:3857" ) assert_geodataframe_equal(clip_pts, exp) def test_clip_points_geom_col_rename(self, point_gdf, mask): """Test clipping a points GDF with a generic polygon geometry.""" point_gdf_geom_col_rename = point_gdf.rename_geometry("geometry2") clip_pts = clip(point_gdf_geom_col_rename, mask) pts = np.array([[2, 2], [3, 4], [9, 8]]) exp = GeoDataFrame( [Point(xy) for xy in pts], columns=["geometry2"], crs="EPSG:3857", geometry="geometry2", ) assert_geodataframe_equal(clip_pts, exp) def test_clip_poly(self, buffered_locations, mask): """Test clipping a polygon GDF with a generic polygon geometry.""" clipped_poly = clip(buffered_locations, mask) assert len(clipped_poly.geometry) == 3 assert all(clipped_poly.geom_type == "Polygon") def test_clip_poly_geom_col_rename(self, buffered_locations, mask): """Test clipping a polygon GDF with a generic polygon geometry.""" poly_gdf_geom_col_rename = buffered_locations.rename_geometry("geometry2") clipped_poly = clip(poly_gdf_geom_col_rename, mask) assert len(clipped_poly.geometry) == 3 assert "geometry" not in clipped_poly.keys() assert "geometry2" in clipped_poly.keys() def test_clip_poly_series(self, buffered_locations, mask): """Test clipping a polygon GDF with a generic polygon geometry.""" clipped_poly = clip(buffered_locations.geometry, mask) assert len(clipped_poly) == 3 assert all(clipped_poly.geom_type == "Polygon") def test_clip_multipoly_keep_geom_type(self, multi_poly_gdf, mask): """Test a multi poly object where the return includes a sliver. Also the bounds of the object should == the bounds of the clip object if they fully overlap (as they do in these fixtures).""" clipped = clip(multi_poly_gdf, mask, keep_geom_type=True) expected_bounds = ( mask if _mask_is_list_like_rectangle(mask) else mask.total_bounds ) assert np.array_equal(clipped.total_bounds, expected_bounds) # Assert returned data is not a geometry collection assert (clipped.geom_type.isin(POLYGON_GEOM_TYPES)).all() def test_clip_multiline(self, multi_line, mask): """Test that clipping a multiline feature with a poly returns expected output.""" clipped = clip(multi_line, mask) assert clipped.geom_type[0] == "MultiLineString" def test_clip_multipoint(self, multi_point, mask): """Clipping a multipoint feature with a polygon works as expected. should return a geodataframe with a single multi point feature""" clipped = clip(multi_point, mask) assert clipped.geom_type[0] == "MultiPoint" assert hasattr(clipped, "attr") # All points should intersect the clip geom assert len(clipped) == 2 clipped_mutltipoint = MultiPoint( [ Point(2, 2), Point(3, 4), Point(9, 8), ] ) assert clipped.iloc[0].geometry.wkt == clipped_mutltipoint.wkt shape_for_points = ( box(*mask) if _mask_is_list_like_rectangle(mask) else mask.union_all() ) assert all(clipped.intersects(shape_for_points)) def test_clip_lines(self, two_line_gdf, mask): """Test what happens when you give the clip_extent a line GDF.""" clip_line = clip(two_line_gdf, mask) assert len(clip_line.geometry) == 2 def test_mixed_geom(self, mixed_gdf, mask): """Test clipping a mixed GeoDataFrame""" clipped = clip(mixed_gdf, mask) assert ( clipped.geom_type[0] == "Point" and clipped.geom_type[1] == "Polygon" and clipped.geom_type[2] == "LineString" ) def test_mixed_series(self, mixed_gdf, mask): """Test clipping a mixed GeoSeries""" clipped = clip(mixed_gdf.geometry, mask) assert ( clipped.geom_type[0] == "Point" and clipped.geom_type[1] == "Polygon" and clipped.geom_type[2] == "LineString" ) def test_clip_with_line_extra_geom(self, sliver_line, mask): """When the output of a clipped line returns a geom collection, and keep_geom_type is True, no geometry collections should be returned.""" clipped = clip(sliver_line, mask, keep_geom_type=True) assert len(clipped.geometry) == 1 # Assert returned data is not a geometry collection assert not (clipped.geom_type == "GeometryCollection").any() def test_clip_no_box_overlap(self, pointsoutside_nooverlap_gdf, mask): """Test clip when intersection is empty and boxes do not overlap.""" clipped = clip(pointsoutside_nooverlap_gdf, mask) assert len(clipped) == 0 def test_clip_box_overlap(self, pointsoutside_overlap_gdf, mask): """Test clip when intersection is empty and boxes do overlap.""" clipped = clip(pointsoutside_overlap_gdf, mask) assert len(clipped) == 0 def test_warning_extra_geoms_mixed(self, mixed_gdf, mask): """Test the correct warnings are raised if keep_geom_type is called on a mixed GDF""" with pytest.warns(UserWarning): clip(mixed_gdf, mask, keep_geom_type=True) def test_warning_geomcoll(self, geomcol_gdf, mask): """Test the correct warnings are raised if keep_geom_type is called on a GDF with GeometryCollection""" with pytest.warns(UserWarning): clip(geomcol_gdf, mask, keep_geom_type=True) def test_clip_line_keep_slivers(sliver_line, single_rectangle_gdf): """Test the correct output if a point is returned from a line only geometry type.""" clipped = clip(sliver_line, single_rectangle_gdf) # Assert returned data is a geometry collection given sliver geoms assert "Point" == clipped.geom_type[0] assert "LineString" == clipped.geom_type[1] def test_clip_multipoly_keep_slivers(multi_poly_gdf, single_rectangle_gdf): """Test a multi poly object where the return includes a sliver. Also the bounds of the object should == the bounds of the clip object if they fully overlap (as they do in these fixtures).""" clipped = clip(multi_poly_gdf, single_rectangle_gdf) assert np.array_equal(clipped.total_bounds, single_rectangle_gdf.total_bounds) # Assert returned data is a geometry collection given sliver geoms assert "GeometryCollection" in clipped.geom_type[0] @pytest.mark.skipif(not HAS_PYPROJ, reason="pyproj not available") def test_warning_crs_mismatch(point_gdf, single_rectangle_gdf): with pytest.warns(UserWarning, match="CRS mismatch between the CRS"): clip(point_gdf, single_rectangle_gdf.to_crs(4326)) def test_clip_with_polygon(single_rectangle_gdf): """Test clip when using a shapely object""" polygon = Polygon([(0, 0), (5, 12), (10, 0), (0, 0)]) clipped = clip(single_rectangle_gdf, polygon) exp_poly = polygon.intersection( Polygon([(0, 0), (0, 10), (10, 10), (10, 0), (0, 0)]) ) exp = GeoDataFrame([1], geometry=[exp_poly], crs="EPSG:3857") exp["attr2"] = "site-boundary" assert_geodataframe_equal(clipped, exp) def test_clip_with_multipolygon(buffered_locations, single_rectangle_gdf): """Test clipping a polygon with a multipolygon.""" multi = buffered_locations.dissolve(by="type").reset_index() clipped = clip(single_rectangle_gdf, multi) assert clipped.geom_type[0] == "Polygon" @pytest.mark.parametrize( "mask_fixture_name", mask_variants_large_rectangle, ) def test_clip_single_multipoly_no_extra_geoms( buffered_locations, mask_fixture_name, request ): """When clipping a multi-polygon feature, no additional geom types should be returned.""" masks = request.getfixturevalue(mask_fixture_name) multi = buffered_locations.dissolve(by="type").reset_index() clipped = clip(multi, masks) assert clipped.geom_type[0] == "Polygon" @pytest.mark.filterwarnings("ignore:All-NaN slice encountered") @pytest.mark.parametrize( "mask", [ Polygon(), (np.nan,) * 4, (np.nan, 0, np.nan, 1), GeoSeries([Polygon(), Polygon()], crs="EPSG:3857"), GeoSeries([Polygon(), Polygon()], crs="EPSG:3857").to_frame(), GeoSeries([], crs="EPSG:3857"), GeoSeries([], crs="EPSG:3857").to_frame(), ], ) def test_clip_empty_mask(buffered_locations, mask): """Test that clipping with empty mask returns an empty result.""" clipped = clip(buffered_locations, mask) expected = GeoDataFrame([], columns=["geometry", "type"], crs="EPSG:3857") if PANDAS_INFER_STR: expected = expected.astype({"type": "str"}) assert_geodataframe_equal( clipped, expected, check_index_type=False, ) clipped = clip(buffered_locations.geometry, mask) assert_geoseries_equal(clipped, GeoSeries([], crs="EPSG:3857")) def test_clip_sorting(point_gdf2): """Test the sorting kwarg in clip""" bbox = shapely.geometry.box(0, 0, 2, 2) unsorted_clipped_gdf = point_gdf2.clip(bbox) sorted_clipped_gdf = point_gdf2.clip(bbox, sort=True) expected_sorted_index = pd.Index([1, 3, 5]) assert not (sorted(unsorted_clipped_gdf.index) == unsorted_clipped_gdf.index).all() assert (sorted(sorted_clipped_gdf.index) == sorted_clipped_gdf.index).all() assert_index_equal(expected_sorted_index, sorted_clipped_gdf.index, exact="equiv") geopandas-1.2.0/geopandas/tools/tests/test_hilbert_curve.py000066400000000000000000000040471525650651600242040ustar00rootroot00000000000000import numpy as np from shapely.geometry import Point from shapely.wkt import loads import geopandas import pytest from pandas.testing import assert_series_equal def test_hilbert_distance(): # test the actual Hilbert Code algorithm against some hardcoded values geoms = geopandas.GeoSeries.from_wkt( [ "POINT (0 0)", "POINT (1 1)", "POINT (1 0)", "POLYGON ((0 0, 0 1, 1 1, 1 0, 0 0))", ] ) result = geoms.hilbert_distance(total_bounds=(0, 0, 1, 1), level=2) assert result.tolist() == [0, 10, 15, 2] result = geoms.hilbert_distance(total_bounds=(0, 0, 1, 1), level=3) assert result.tolist() == [0, 42, 63, 10] result = geoms.hilbert_distance(total_bounds=(0, 0, 1, 1), level=16) assert result.tolist() == [0, 2863311530, 4294967295, 715827882] @pytest.fixture def geoseries_points(): p1 = Point(1, 2) p2 = Point(2, 3) p3 = Point(3, 4) p4 = Point(4, 1) return geopandas.GeoSeries([p1, p2, p3, p4]) def test_hilbert_distance_level(geoseries_points): with pytest.raises(ValueError): geoseries_points.hilbert_distance(level=20) def test_specified_total_bounds(geoseries_points): result = geoseries_points.hilbert_distance( total_bounds=geoseries_points.total_bounds ) expected = geoseries_points.hilbert_distance() assert_series_equal(result, expected) @pytest.mark.parametrize( "empty", [ None, loads("POLYGON EMPTY"), ], ) def test_empty(geoseries_points, empty): s = geoseries_points s.iloc[-1] = empty with pytest.raises( ValueError, match="cannot be computed on a GeoSeries with empty" ): s.hilbert_distance() def test_zero_width(): # special case of all points on the same line -> avoid warnings because # of division by 0 and introducing NaN s = geopandas.GeoSeries([Point(0, 0), Point(0, 2), Point(0, 1)]) with np.errstate(all="raise"): result = s.hilbert_distance() assert np.array(result).argsort().tolist() == [0, 2, 1] geopandas-1.2.0/geopandas/tools/tests/test_make_grid.py000066400000000000000000000463011525650651600232700ustar00rootroot00000000000000import numpy as np from shapely.geometry import MultiPolygon, Point, Polygon from geopandas import GeoDataFrame, GeoSeries from geopandas.tools.make_grid import make_grid import pytest from geopandas.testing import assert_geoseries_equal @pytest.fixture def square(): """Square polygon with side length 2""" return Polygon([(0, 0), (2, 0), (2, 2), (0, 2)]) @pytest.fixture def multi_polygon(): polygon_1 = Polygon([(0.1, 0.1), (0.9, 0), (0.9, 0.9), (0, 0.1)]) polygon_2 = Polygon([(1.1, 1.1), (1.9, 1.1), (1.9, 1.9)]) return MultiPolygon([polygon_1, polygon_2]) @pytest.fixture def geo_series(): polygon_1 = Polygon([(0.1, 0.1), (0.9, 0), (0.9, 0.9), (0, 0.1)]) polygon_2 = Polygon([(1.1, 1.1), (1.9, 1.1), (1.9, 1.9)]) point_1 = Point((1.5, 0.4)) return GeoSeries([polygon_1, polygon_2, point_1], crs="EPSG:4326") @pytest.fixture def geo_dataframe(): polygon_1 = Polygon([(0.1, 0.1), (0.9, 0), (0.9, 0.9), (0, 0.1)]) polygon_2 = Polygon([(1.1, 1.1), (1.9, 1.1), (1.9, 1.9)]) point_1 = Point((1.5, 0.4)) d = { "col1": ["poly1", "poly2", "point1"], "geometry": [polygon_1, polygon_2, point_1], } return GeoDataFrame(d, crs="EPSG:4326") @pytest.fixture def neg_square(): return Polygon([(0, 0), (-2, 0), (-2, -2), (0, -2)]) @pytest.fixture def exotic_polygon(): return Polygon( [(0, 0), (0.75, 0), (1, 0.5), (1.25, 0), (2, 0), (2, 0.9), (1, 2), (0, 2)] ) class TestBasicChecks: def test_inputs_neg_cell_size(self, square): with pytest.raises(ValueError): make_grid(square, -3) def test_inputs_polygon(self): polygon = Point(0.0, 0.0) with pytest.raises(TypeError): make_grid(polygon, 1) def test_inputs_empty_polygon(self): with pytest.raises(ValueError): empty_polygon = Polygon() make_grid(empty_polygon, 1) def test_inputs_empty_gdf(self): with pytest.raises(ValueError): empty_gdf = GeoDataFrame() make_grid(empty_gdf, 1) def test_inputs_empty_gs(self): with pytest.raises(ValueError): empty_gs = GeoSeries() make_grid(empty_gs, 1) def test_inputs_grid_type(self, square): with pytest.raises(ValueError): make_grid(square, 1, grid_type="circle") def test_inputs_feature_type(self, square): with pytest.raises(ValueError): make_grid(square, 1, feature_type="corner") def test_inputs_intersect_type(self, square): with pytest.raises(TypeError): make_grid(square, 1, intersect="True") def test_offset_too_large(self, square): with pytest.warns(UserWarning): make_grid(square, 1, offset=(3, 3)) class TestMakeGridSquare: def test_square_centers(self, square): cell_size = 1 out = make_grid( square, cell_size, feature_type="centers", grid_type="square", intersect=False, ) exp_out = GeoSeries( [Point(0.5, 0.5), Point(0.5, 1.5), Point(1.5, 0.5), Point(1.5, 1.5)] ) assert_geoseries_equal(out, exp_out) def test_neg_square_centers(self, neg_square): cell_size = 1 out = make_grid( neg_square, cell_size, feature_type="centers", grid_type="square", intersect=False, ) exp_out = GeoSeries( [ Point(-1.5, -1.5), Point(-1.5, -0.5), Point(-0.5, -1.5), Point(-0.5, -0.5), ] ) assert_geoseries_equal(out, exp_out) def test_square_centers_multipolygon(self, multi_polygon): cell_size = 1 out = make_grid( multi_polygon, cell_size, feature_type="centers", grid_type="square", intersect=False, ) exp_out = GeoSeries( [Point(0.5, 0.5), Point(0.5, 1.5), Point(1.5, 0.5), Point(1.5, 1.5)] ) assert_geoseries_equal(out, exp_out) def test_square_centers_multipolygon_intersect(self, multi_polygon): cell_size = 1 out = make_grid( multi_polygon, cell_size, feature_type="centers", grid_type="square" ) exp_out = GeoSeries([Point(0.5, 0.5), Point(1.5, 1.5)]) assert_geoseries_equal(out, exp_out) def test_exotic_square_centers(self, exotic_polygon): cell_size = 1 out = make_grid( exotic_polygon, cell_size, feature_type="centers", grid_type="square" ) exp_out = GeoSeries([Point(0.5, 0.5), Point(0.5, 1.5), Point(1.5, 0.5)]) assert_geoseries_equal(out, exp_out) def test_square_centers_geoseries(self, geo_series): cell_size = 1 out = make_grid( geo_series, cell_size, feature_type="centers", grid_type="square" ) exp_out = GeoSeries([Point(0.5, 0.5), Point(1.5, 1.5)], crs="EPSG:4326") assert_geoseries_equal(out, exp_out) def test_square_centers_geodataframe(self, geo_dataframe): cell_size = 1 out = make_grid( geo_dataframe, cell_size, feature_type="centers", grid_type="square" ) exp_out = GeoSeries([Point(0.5, 0.5), Point(1.5, 1.5)], crs="EPSG:4326") assert_geoseries_equal(out, exp_out) def test_square_corners(self, square): cell_size = 2 out = make_grid( square, cell_size, feature_type="corners", grid_type="square", intersect=False, ) exp_out = GeoSeries([Point(0, 0), Point(0, 2), Point(2, 0), Point(2, 2)]) assert_geoseries_equal(out, exp_out) def test_square_polygons(self, square): cell_size = 1 out = make_grid( square, cell_size, feature_type="polygons", grid_type="square", intersect=False, ) exp_out = GeoSeries( [ Polygon([(0, 0), (1, 0), (1, 1), (0, 1)]), Polygon([(0, 1), (1, 1), (1, 2), (0, 2)]), Polygon([(1, 0), (2, 0), (2, 1), (1, 1)]), Polygon([(1, 1), (2, 1), (2, 2), (1, 2)]), ] ) assert_geoseries_equal(out, exp_out) def test_square_polygons_multipolygon(self, multi_polygon): cell_size = 1 out = make_grid( multi_polygon, cell_size, feature_type="polygons", grid_type="square" ) exp_out = GeoSeries( [ Polygon([(0, 0), (1, 0), (1, 1), (0, 1)]), Polygon([(1, 1), (2, 1), (2, 2), (1, 2)]), ] ) assert_geoseries_equal(out, exp_out) def test_square_polygons_geoseries(self, geo_series): cell_size = 1 out = make_grid( geo_series, cell_size, feature_type="polygons", grid_type="square" ) exp_out = GeoSeries( [ Polygon([(0, 0), (1, 0), (1, 1), (0, 1)]), Polygon([(1, 0), (2, 0), (2, 1), (1, 1)]), Polygon([(1, 1), (2, 1), (2, 2), (1, 2)]), ], crs="EPSG:4326", ) assert_geoseries_equal(out, exp_out) def test_square_polygons_geodataframe(self, geo_dataframe): cell_size = 1 out = make_grid( geo_dataframe, cell_size, feature_type="polygons", grid_type="square" ) exp_out = GeoSeries( [ Polygon([(0, 0), (1, 0), (1, 1), (0, 1)]), Polygon([(1, 0), (2, 0), (2, 1), (1, 1)]), Polygon([(1, 1), (2, 1), (2, 2), (1, 2)]), ], crs="EPSG:4326", ) assert_geoseries_equal(out, exp_out) def test_square_cellsize_too_large(self, square): cell_size = 5 with pytest.warns(UserWarning): out = make_grid( square, cell_size, feature_type="centers", grid_type="square", intersect=False, ) exp_out = GeoSeries([Point(2.5, 2.5)]) assert_geoseries_equal(out, exp_out) def test_square_cellsize_too_large_intersect(self, square): cell_size = 5 with pytest.warns(UserWarning): out = make_grid( square, cell_size, feature_type="centers", grid_type="square" ) exp_out = GeoSeries() assert_geoseries_equal(out, exp_out) def test_square_centers_offset(self, square): cell_size = 1 out = make_grid( square, cell_size, feature_type="centers", grid_type="square", offset=(0.1, 0.1), intersect=False, ) exp_out = GeoSeries( [Point(0.6, 0.6), Point(0.6, 1.6), Point(1.6, 0.6), Point(1.6, 1.6)] ) assert_geoseries_equal(out, exp_out) class TestMakeGridHexagon: def test_hexagon_centers(self, square): cell_size = 1 * np.sqrt(3) out = make_grid( square, cell_size, feature_type="centers", grid_type="hexagon", intersect=False, flat_topped=True, ) exp_out = GeoSeries( [ Point(0.5, -0.5 * np.sqrt(3)), Point(2, 0), Point(0.5, 0.5 * np.sqrt(3)), Point(2, np.sqrt(3)), Point(0.5, 1.5 * np.sqrt(3)), Point(2, 2 * np.sqrt(3)), ] ) assert_geoseries_equal(out, exp_out, check_less_precise=True) def test_hexagon_centers_offset(self, square): cell_size = 1 * np.sqrt(3) out = make_grid( square, cell_size, feature_type="centers", grid_type="hexagon", offset=(0.1, 0.1), intersect=False, flat_topped=True, ) exp_out = GeoSeries( [ Point(0.5, -0.5 * np.sqrt(3)), Point(2, 0), Point(0.5, 0.5 * np.sqrt(3)), Point(2, np.sqrt(3)), Point(0.5, 1.5 * np.sqrt(3)), Point(2, 2 * np.sqrt(3)), ] ).translate(xoff=0.1, yoff=0.1) assert_geoseries_equal(out, exp_out, check_less_precise=True) def test_hexagon_centers_intersect(self, square): cell_size = 1 * np.sqrt(3) out = make_grid( square, cell_size, feature_type="centers", grid_type="hexagon", flat_topped=True, ) exp_out = GeoSeries( [Point(2, 0), Point(0.5, np.sqrt(3) / 2), Point(2, np.sqrt(3))] ) assert_geoseries_equal(out, exp_out, check_less_precise=True) def test_hexagon_corners_cs_larger_bb(self, square): cell_size = 3 * np.sqrt(3) with pytest.warns(UserWarning): out = make_grid( square, cell_size, feature_type="corners", grid_type="hexagon", intersect=False, flat_topped=True, ) exp_out = GeoSeries( [ Point(-1.5, -1.5 * np.sqrt(3)), Point(4.5, -1.5 * np.sqrt(3)), Point(0, 0), Point(3, 0), Point(-1.5, 1.5 * np.sqrt(3)), Point(4.5, 1.5 * np.sqrt(3)), Point(0, 3 * np.sqrt(3)), Point(3, 3 * np.sqrt(3)), ] ) assert_geoseries_equal(out, exp_out, check_less_precise=True) def test_hexagon_corners_cs_eq_bb(self, square): cell_size = 2 * np.sqrt(3) with pytest.warns(UserWarning, match="`cell_size`*"): out = make_grid( square, cell_size, feature_type="corners", grid_type="hexagon", intersect=False, flat_topped=True, ) exp_out = GeoSeries( [ Point(-1, -np.sqrt(3)), Point(3, -np.sqrt(3)), Point(0, 0), Point(2, 0), Point(-1, np.sqrt(3)), Point(3, np.sqrt(3)), Point(0, 2 * np.sqrt(3)), Point(2, 2 * np.sqrt(3)), Point(-1, 3 * np.sqrt(3)), Point(3, 3 * np.sqrt(3)), ] ) assert_geoseries_equal(out, exp_out, check_less_precise=True) def test_hexagon_corners_cs_smaller_bb(self, square): cell_size = 1 * np.sqrt(3) out = make_grid( square, cell_size, feature_type="corners", grid_type="hexagon", intersect=False, flat_topped=True, ) exp_out = GeoSeries( [ Point(-0.5, -0.5 * np.sqrt(3)), Point(1.5, -0.5 * np.sqrt(3)), Point(2.5, -0.5 * np.sqrt(3)), Point(0, 0), Point(1, 0), Point(3, 0), Point(-0.5, 0.5 * np.sqrt(3)), Point(1.5, 0.5 * np.sqrt(3)), Point(2.5, 0.5 * np.sqrt(3)), Point(0, np.sqrt(3)), Point(1, np.sqrt(3)), Point(3, np.sqrt(3)), Point(-0.5, 1.5 * np.sqrt(3)), Point(1.5, 1.5 * np.sqrt(3)), Point(2.5, 1.5 * np.sqrt(3)), Point(0, 2 * np.sqrt(3)), Point(1, 2 * np.sqrt(3)), Point(3, 2 * np.sqrt(3)), ] ) assert_geoseries_equal(out, exp_out, check_less_precise=True) def test_hexagon_corners_intersect(self, square): cell_size = 1 * np.sqrt(3) out = make_grid( square, cell_size, feature_type="corners", grid_type="hexagon", flat_topped=True, ) exp_out = GeoSeries( [ Point(0, 0), Point(1, 0), Point(1.5, np.sqrt(3) / 2), Point(0, np.sqrt(3)), Point(1, np.sqrt(3)), ] ) assert_geoseries_equal(out, exp_out, check_less_precise=True) def test_hexagon_polygons(self, square): cell_size = 3 * np.sqrt(3) with pytest.warns(UserWarning): out = make_grid( square, cell_size, feature_type="polygons", grid_type="hexagon", intersect=False, flat_topped=True, ) exp_out = GeoSeries( [ Polygon( [ (0, 0), (3, 0), (4.5, 1.5 * np.sqrt(3)), (3, 3 * np.sqrt(3)), (0, 3 * np.sqrt(3)), (-1.5, 1.5 * np.sqrt(3)), ] ), ] ) assert_geoseries_equal(out, exp_out, check_less_precise=True) def test_hexagon_polygons_intersect(self, square): cell_size = 1.5 * np.sqrt(3) with pytest.warns(UserWarning, match="`cell_size`*"): out = make_grid( square, cell_size, feature_type="polygons", grid_type="hexagon", flat_topped=True, ) exp_out = GeoSeries( [ Polygon( [ (0, 0), (1.5, 0), (2.25, 1.5 * np.sqrt(3) / 2), (1.5, 1.5 * np.sqrt(3)), (0, 1.5 * np.sqrt(3)), (-0.75, 1.5 * np.sqrt(3) / 2), ] ), Polygon( [ (2.25, -1.5 * np.sqrt(3) / 2), (3.75, -1.5 * np.sqrt(3) / 2), (4.5, 0), (3.75, 1.5 * np.sqrt(3) / 2), (2.25, 1.5 * np.sqrt(3) / 2), (1.5, 0), ] ), Polygon( [ (2.25, 1.5 * np.sqrt(3) / 2), (3.75, 1.5 * np.sqrt(3) / 2), (4.5, 1.5 * np.sqrt(3)), (3.75, 2.25 * np.sqrt(3)), (2.25, 2.25 * np.sqrt(3)), (1.5, 1.5 * np.sqrt(3)), ] ), ] ) assert_geoseries_equal(out, exp_out, check_less_precise=True) def test_hexagon_centers_not_flat_topped(self, square): cell_size = 1 * np.sqrt(3) out = make_grid( square, cell_size, feature_type="centers", grid_type="hexagon", flat_topped=False, intersect=False, ) exp_out = GeoSeries( [ Point(-0.5 * np.sqrt(3), 0.5), Point(0, 2), Point(0.5 * np.sqrt(3), 0.5), Point(np.sqrt(3), 2), Point(1.5 * np.sqrt(3), 0.5), Point(2 * np.sqrt(3), 2), ] ) assert_geoseries_equal(out, exp_out, check_less_precise=True) def test_hexagon_corners_not_flat_topped(self, square): cell_size = 1 * np.sqrt(3) out = make_grid( square, cell_size, feature_type="corners", grid_type="hexagon", flat_topped=False, intersect=False, ) exp_out = GeoSeries( [ Point(-0.5 * np.sqrt(3), -0.5), Point(-0.5 * np.sqrt(3), 1.5), Point(-0.5 * np.sqrt(3), 2.5), Point(0, 0), Point(0, 1), Point(0, 3), Point(0.5 * np.sqrt(3), -0.5), Point(0.5 * np.sqrt(3), 1.5), Point(0.5 * np.sqrt(3), 2.5), Point(np.sqrt(3), 0), Point(np.sqrt(3), 1), Point(np.sqrt(3), 3), Point(1.5 * np.sqrt(3), -0.5), Point(1.5 * np.sqrt(3), 1.5), Point(1.5 * np.sqrt(3), 2.5), Point(2 * np.sqrt(3), 0), Point(2 * np.sqrt(3), 1), Point(2 * np.sqrt(3), 3), ] ) assert_geoseries_equal(out, exp_out, check_less_precise=True) def test_hexagon_polygons_not_flat_topped(self, square): cell_size = 3 * np.sqrt(3) with pytest.warns(UserWarning): out = make_grid( square, cell_size, feature_type="polygons", grid_type="hexagon", flat_topped=False, intersect=False, ) exp_out = GeoSeries( [ Polygon( [ (0, 0), (0, 3), (1.5 * np.sqrt(3), 4.5), (3 * np.sqrt(3), 3), (3 * np.sqrt(3), 0), (1.5 * np.sqrt(3), -1.5), ] ), ] ) assert_geoseries_equal(out, exp_out, check_less_precise=True) geopandas-1.2.0/geopandas/tools/tests/test_random.py000066400000000000000000000054751525650651600226350ustar00rootroot00000000000000import numpy import shapely import geopandas from geopandas.tools._random import uniform import pytest @pytest.fixture def multipolygons(nybb_filename): return geopandas.read_file(nybb_filename).geometry @pytest.fixture def polygons(multipolygons): return multipolygons.explode(ignore_index=True).geometry @pytest.fixture def multilinestrings(multipolygons): return multipolygons.boundary @pytest.fixture def linestrings(polygons): return polygons.boundary @pytest.fixture def points(multipolygons): return multipolygons.centroid @pytest.mark.parametrize("size", [10, 100]) @pytest.mark.parametrize( "geom_fixture", ["multipolygons", "polygons", "multilinestrings", "linestrings"] ) def test_uniform(geom_fixture, size, request): geom = request.getfixturevalue(geom_fixture)[0] sample = uniform(geom, size=size, rng=1) sample_series = ( geopandas.GeoSeries(sample).explode(index_parts=True).reset_index(drop=True) ) assert len(sample_series) == size sample_in_geom = sample_series.buffer(0.00000001).sindex.query( geom, predicate="intersects" ) assert len(sample_in_geom) == size def test_uniform_unsupported(points): with pytest.warns(UserWarning, match="Sampling is not supported"): sample = uniform(points[0], size=10, rng=1) assert sample.is_empty def test_uniform_generator(polygons): sample = uniform(polygons[0], size=10, rng=1) sample2 = uniform(polygons[0], size=10, rng=1) assert sample.equals(sample2) generator = numpy.random.default_rng(seed=1) gen_sample = uniform(polygons[0], size=10, rng=generator) gen_sample2 = uniform(polygons[0], size=10, rng=generator) assert sample.equals(gen_sample) assert not sample.equals(gen_sample2) @pytest.mark.parametrize("size", range(5, 12)) def test_unimodality(size): # GH 3470 circle = shapely.Point(0, 0).buffer(1) generator = numpy.random.default_rng(seed=1) centers_x = [] centers_y = [] for _ in range(200): pts = shapely.get_coordinates(uniform(circle, size=2**size, rng=generator)) centers_x.append(numpy.mean(pts[:, 0])) centers_y.append(numpy.mean(pts[:, 1])) numpy.testing.assert_allclose(numpy.mean(centers_x), 0, atol=1e-2) numpy.testing.assert_allclose(numpy.mean(centers_y), 0, atol=1e-2) stats = pytest.importorskip("scipy.stats") assert stats.shapiro(centers_x).pvalue > 0.05 assert stats.shapiro(centers_y).pvalue > 0.05 def test_uniform_zero_area_polygon(): # a collapsed ring is not `is_empty`, but its interior is, so no # candidate point is ever `contains`ed and `_uniform_polygon` never returned. collapsed = shapely.Polygon([(0, 0), (1, 1), (2, 2), (0, 0)]) assert not collapsed.is_empty assert collapsed.area == 0 assert uniform(collapsed, size=5, rng=1).is_empty geopandas-1.2.0/geopandas/tools/tests/test_sjoin.py000066400000000000000000001433761525650651600225020ustar00rootroot00000000000000import math from collections.abc import Sequence import numpy as np import pandas as pd import shapely from shapely.geometry import GeometryCollection, Point, Polygon, box import geopandas import geopandas._compat as compat from geopandas import ( GeoDataFrame, GeoSeries, points_from_xy, read_file, sjoin, sjoin_nearest, ) import pytest from geopandas.testing import assert_geodataframe_equal, assert_geoseries_equal from pandas.testing import assert_frame_equal, assert_index_equal, assert_series_equal @pytest.fixture() def dfs(request): polys1 = GeoSeries( [ Polygon([(0, 0), (5, 0), (5, 5), (0, 5)]), Polygon([(5, 5), (6, 5), (6, 6), (5, 6)]), Polygon([(6, 0), (9, 0), (9, 3), (6, 3)]), ] ) polys2 = GeoSeries( [ Polygon([(1, 1), (4, 1), (4, 4), (1, 4)]), Polygon([(4, 4), (7, 4), (7, 7), (4, 7)]), Polygon([(7, 7), (10, 7), (10, 10), (7, 10)]), ] ) df1 = GeoDataFrame({"geometry": polys1, "df1": [0, 1, 2]}) df2 = GeoDataFrame({"geometry": polys2, "df2": [3, 4, 5]}) if request.param == "string-index": df1.index = ["a", "b", "c"] df2.index = ["d", "e", "f"] if request.param == "named-index": df1.index.name = "df1_ix" df2.index.name = "df2_ix" if request.param == "multi-index": i1 = ["a", "b", "c"] i2 = ["d", "e", "f"] df1 = df1.set_index([i1, i2]) df2 = df2.set_index([i2, i1]) if request.param == "named-multi-index": i1 = ["a", "b", "c"] i2 = ["d", "e", "f"] df1 = df1.set_index([i1, i2]) df2 = df2.set_index([i2, i1]) df1.index.names = ["df1_ix1", "df1_ix2"] df2.index.names = ["df2_ix1", "df2_ix2"] # construction expected frames expected = {} part1 = df1.copy().reset_index().rename(columns={"index": "index_left"}) part2 = ( df2.copy() .iloc[[0, 1, 1, 2]] .reset_index() .rename(columns={"index": "index_right"}) ) part1["_merge"] = [0, 1, 2] part2["_merge"] = [0, 0, 1, 3] exp = pd.merge(part1, part2, on="_merge", how="outer") expected["intersects"] = exp.drop("_merge", axis=1).copy() part1 = df1.copy().reset_index().rename(columns={"index": "index_left"}) part2 = df2.copy().reset_index().rename(columns={"index": "index_right"}) part1["_merge"] = [0, 1, 2] part2["_merge"] = [0, 3, 3] exp = pd.merge(part1, part2, on="_merge", how="outer") expected["contains"] = exp.drop("_merge", axis=1).copy() part1["_merge"] = [0, 1, 2] part2["_merge"] = [3, 1, 3] exp = pd.merge(part1, part2, on="_merge", how="outer") expected["within"] = exp.drop("_merge", axis=1).copy() return [request.param, df1, df2, expected] @pytest.fixture() def dfs_shared_attribute(): geo_left = [ Point(0, 0), Point(1, 1), Point(2, 2), Point(3, 3), Point(4, 4), Point(5, 5), Point(6, 6), Point(7, 7), ] geo_right = [ Point(0, 0), Point(1, 1), Point(2, 2), Point(3, 3), Point(4, 4), Point(5, 5), Point(6, 6), Point(7, 7), ] attr_tracker = ["A", "B", "C", "D", "E", "F", "G", "H"] left_gdf = geopandas.GeoDataFrame( { "geometry": geo_left, "attr_tracker": attr_tracker, "duplicate_column": [0, 1, 2, 3, 4, 5, 6, 7], "attr1": [True, True, True, True, True, True, True, True], "attr2": [True, True, True, True, True, True, True, True], } ) right_gdf = geopandas.GeoDataFrame( { "geometry": geo_right, "duplicate_column": [0, 1, 2, 3, 4, 5, 6, 7], "attr1": [True, True, False, False, True, True, False, False], "attr2": [True, True, False, False, False, False, False, False], } ) return left_gdf, right_gdf class TestSpatialJoin: @pytest.mark.parametrize( "how, lsuffix, rsuffix, expected_cols", [ ("left", "left", "right", {"col_left", "col_right", "index_right"}), ("inner", "left", "right", {"col_left", "col_right", "index_right"}), ("right", "left", "right", {"col_left", "col_right", "index_left"}), ("left", "lft", "rgt", {"col_lft", "col_rgt", "index_rgt"}), ("inner", "lft", "rgt", {"col_lft", "col_rgt", "index_rgt"}), ("right", "lft", "rgt", {"col_lft", "col_rgt", "index_lft"}), ], ) def test_suffixes(self, how: str, lsuffix: str, rsuffix: str, expected_cols): left = GeoDataFrame({"col": [1], "geometry": [Point(0, 0)]}) right = GeoDataFrame({"col": [1], "geometry": [Point(0, 0)]}) joined = sjoin(left, right, how=how, lsuffix=lsuffix, rsuffix=rsuffix) assert set(joined.columns) == expected_cols | {"geometry"} @pytest.mark.skipif(not compat.HAS_PYPROJ, reason="pyproj not available") @pytest.mark.parametrize("dfs", ["default-index", "string-index"], indirect=True) def test_crs_mismatch(self, dfs): _index, df1, df2, _expected = dfs df1.crs = "epsg:4326" with pytest.warns(UserWarning, match="CRS mismatch between the CRS"): sjoin(df1, df2) @pytest.mark.parametrize("dfs", ["default-index"], indirect=True) def test_unknown_kwargs(self, dfs): _index, df1, df2, _expected = dfs with pytest.raises( TypeError, match=r"sjoin\(\) got an unexpected keyword argument 'extra_param'", ): sjoin(df1, df2, extra_param="test") @pytest.mark.parametrize( "dfs", [ "default-index", "string-index", "named-index", "multi-index", "named-multi-index", ], indirect=True, ) @pytest.mark.parametrize("predicate", ["intersects", "contains", "within"]) def test_inner(self, predicate, dfs): index, df1, df2, expected = dfs res = sjoin(df1, df2, how="inner", predicate=predicate) exp = expected[predicate].dropna().copy() exp = exp.drop("geometry_y", axis=1).rename(columns={"geometry_x": "geometry"}) exp[["df1", "df2"]] = exp[["df1", "df2"]].astype("int64") if index == "default-index": exp[["index_left", "index_right"]] = exp[ ["index_left", "index_right"] ].astype("int64") if index == "named-index": exp[["df1_ix", "df2_ix"]] = exp[["df1_ix", "df2_ix"]].astype("int64") exp = exp.set_index("df1_ix") if index in ["default-index", "string-index"]: exp = exp.set_index("index_left") exp.index.name = None if index == "multi-index": exp = exp.set_index(["level_0_x", "level_1_x"]).rename( columns={"level_0_y": "index_right0", "level_1_y": "index_right1"} ) exp.index.names = df1.index.names if index == "named-multi-index": exp = exp.set_index(["df1_ix1", "df1_ix2"]) assert_frame_equal(res, exp) @pytest.mark.parametrize( "dfs", [ "default-index", "string-index", "named-index", "multi-index", "named-multi-index", ], indirect=True, ) @pytest.mark.parametrize("predicate", ["intersects", "contains", "within"]) def test_left(self, predicate, dfs): index, df1, df2, expected = dfs res = sjoin(df1, df2, how="left", predicate=predicate) if index in ["default-index", "string-index"]: exp = expected[predicate].dropna(subset=["index_left"]).copy() elif index == "named-index": exp = expected[predicate].dropna(subset=["df1_ix"]).copy() elif index == "multi-index": exp = expected[predicate].dropna(subset=["level_0_x"]).copy() elif index == "named-multi-index": exp = expected[predicate].dropna(subset=["df1_ix1"]).copy() exp = exp.drop("geometry_y", axis=1).rename(columns={"geometry_x": "geometry"}) exp["df1"] = exp["df1"].astype("int64") if index == "default-index": exp["index_left"] = exp["index_left"].astype("int64") # TODO: in result the dtype is object res["index_right"] = res["index_right"].astype(float) elif index == "named-index": exp[["df1_ix"]] = exp[["df1_ix"]].astype("int64") exp = exp.set_index("df1_ix") if index in ["default-index", "string-index"]: exp = exp.set_index("index_left") exp.index.name = None if index == "multi-index": exp = exp.set_index(["level_0_x", "level_1_x"]).rename( columns={"level_0_y": "index_right0", "level_1_y": "index_right1"} ) exp.index.names = df1.index.names if index == "named-multi-index": exp = exp.set_index(["df1_ix1", "df1_ix2"]) assert_frame_equal(res, exp) def test_empty_join(self): # Check joins resulting in empty gdfs. polygons = geopandas.GeoDataFrame( { "col2": [1, 2], "geometry": [ Polygon([(0, 0), (1, 0), (1, 1), (0, 1)]), Polygon([(1, 0), (2, 0), (2, 1), (1, 1)]), ], } ) not_in = geopandas.GeoDataFrame({"col1": [1], "geometry": [Point(-0.5, 0.5)]}) empty = sjoin(not_in, polygons, how="left", predicate="intersects") assert empty.index_right.isnull().all() empty = sjoin(not_in, polygons, how="right", predicate="intersects") assert empty.index_left.isnull().all() empty = sjoin(not_in, polygons, how="inner", predicate="intersects") assert empty.empty @pytest.mark.parametrize( "predicate", [ "contains", "contains_properly", "covered_by", "covers", "crosses", "intersects", "touches", "within", ], ) @pytest.mark.parametrize( "empty", [ GeoDataFrame(geometry=[GeometryCollection(), GeometryCollection()]), GeoDataFrame(geometry=GeoSeries()), ], ) def test_join_with_empty(self, predicate, empty): # Check joins with empty geometry columns/dataframes. polygons = geopandas.GeoDataFrame( { "col2": [1, 2], "geometry": [ Polygon([(0, 0), (1, 0), (1, 1), (0, 1)]), Polygon([(1, 0), (2, 0), (2, 1), (1, 1)]), ], } ) result = sjoin(empty, polygons, how="left", predicate=predicate) assert result.index_right.isnull().all() result = sjoin(empty, polygons, how="right", predicate=predicate) assert result.index_left.isnull().all() result = sjoin(empty, polygons, how="inner", predicate=predicate) assert result.empty @pytest.mark.parametrize("dfs", ["default-index", "string-index"], indirect=True) def test_sjoin_invalid_args(self, dfs): _, df1, df2, _ = dfs with pytest.raises(ValueError, match="'left_df' should be GeoDataFrame"): sjoin(df1.geometry, df2) with pytest.raises(ValueError, match="'right_df' should be GeoDataFrame"): sjoin(df1, df2.geometry) @pytest.mark.parametrize( "dfs", [ "default-index", "string-index", "named-index", "multi-index", "named-multi-index", ], indirect=True, ) @pytest.mark.parametrize("predicate", ["intersects", "contains", "within"]) def test_right(self, predicate, dfs): index, df1, df2, expected = dfs res = sjoin(df1, df2, how="right", predicate=predicate) if index in ["default-index", "string-index"]: exp = expected[predicate].dropna(subset=["index_right"]).copy() elif index == "named-index": exp = expected[predicate].dropna(subset=["df2_ix"]).copy() elif index == "multi-index": exp = expected[predicate].dropna(subset=["level_0_y"]).copy() elif index == "named-multi-index": exp = expected[predicate].dropna(subset=["df2_ix1"]).copy() exp = exp.drop("geometry_x", axis=1).rename(columns={"geometry_y": "geometry"}) exp["df2"] = exp["df2"].astype("int64") if index == "default-index": exp["index_right"] = exp["index_right"].astype("int64") res["index_left"] = res["index_left"].astype(float) elif index == "named-index": exp[["df2_ix"]] = exp[["df2_ix"]].astype("int64") exp = exp.set_index("df2_ix") if index in ["default-index", "string-index"]: exp = exp.set_index("index_right") exp = exp.reindex(columns=res.columns) exp.index.name = None if index == "multi-index": exp = exp.set_index(["level_0_y", "level_1_y"]).rename( columns={"level_0_x": "index_left0", "level_1_x": "index_left1"} ) exp.index.names = df2.index.names if index == "named-multi-index": exp = exp.set_index(["df2_ix1", "df2_ix2"]) if predicate == "within": exp = exp.sort_index() assert_frame_equal(res, exp, check_index_type=False) @pytest.mark.skipif(not compat.GEOS_GE_310, reason="`dwithin` requires GEOS 3.10") @pytest.mark.parametrize("how", ["inner"]) @pytest.mark.parametrize( "geo_left, geo_right, expected_left, expected_right, distance", [ ( # Distance is number, 2x1 [Point(0, 0), Point(1, 1)], [Point(1, 1)], [0, 1], [0, 0], math.sqrt(2), ), # Distance is number, 2x2 ( [Point(0, 0), Point(1, 1)], [Point(0, 0), Point(1, 1)], [0, 1, 0, 1], [0, 0, 1, 1], math.sqrt(2), ), # Distance is array, matches len(left) ( [Point(0, 0), Point(0, 0), Point(-1, -1)], [Point(1, 1)], [1, 2], [0, 0], [0, math.sqrt(2), math.sqrt(8)], ), # Distance is np.array, matches len(left), # inner join sorts the right GeoDataFrame ( [Point(0, 0), Point(0, 0), Point(-1, -1)], [Point(1, 1), Point(0.5, 0.5)], [1, 2, 1, 2], [1, 1, 0, 0], np.array([0, math.sqrt(2), math.sqrt(8)]), ), ], ) def test_sjoin_dwithin( self, geo_left, geo_right, expected_left: Sequence[int], expected_right: Sequence[int], distance, how, ): left = geopandas.GeoDataFrame({"geometry": geo_left}) right = geopandas.GeoDataFrame({"geometry": geo_right}) expected_gdf = left.iloc[expected_left].copy() expected_gdf["index_right"] = expected_right joined = sjoin(left, right, how=how, predicate="dwithin", distance=distance) assert_frame_equal(expected_gdf.sort_index(), joined.sort_index()) # GH3239 @pytest.mark.parametrize( "predicate", [ "contains", "contains_properly", "covered_by", "covers", "crosses", "intersects", "touches", "within", ], ) def test_sjoin_left_order(self, predicate): # a set of points in random order -> that order should be preserved # with a left join pts = GeoDataFrame( geometry=points_from_xy([0.1, 0.4, 0.3, 0.7], [0.8, 0.6, 0.9, 0.1]) ) polys = GeoDataFrame( {"id": [1, 2, 3, 4]}, geometry=[ box(0, 0, 0.5, 0.5), box(0, 0.5, 0.5, 1), box(0.5, 0, 1, 0.5), box(0.5, 0.5, 1, 1), ], ) joined = sjoin(pts, polys, predicate=predicate, how="left") assert_index_equal(joined.index, pts.index) def test_sjoin_shared_attribute(self, naturalearth_lowres, naturalearth_cities): countries = read_file(naturalearth_lowres) cities = read_file(naturalearth_cities) countries = countries[["geometry", "name"]].rename(columns={"name": "country"}) # Add first letter of country/city as an attribute column to be compared countries["firstLetter"] = countries["country"].astype(str).str[0] cities["firstLetter"] = cities["name"].astype(str).str[0] result = sjoin(cities, countries, on_attribute="firstLetter") assert ( result["country"].astype(str).str[0] == result["name"].astype(str).str[0] ).all() assert result.shape == (23, 5) @pytest.mark.parametrize( "attr1_key_change_dict, attr2_key_change_dict", [ pytest.param( {True: "merge", False: "no_merge"}, {True: "merge", False: "no_merge"}, id="merge on string attributes", ), pytest.param( {True: 2, False: 1}, {True: 2, False: 1}, id="merge on integer attributes", ), pytest.param( {True: True, False: False}, {True: True, False: False}, id="merge on boolean attributes", ), pytest.param( {True: True, False: False}, {True: "merge", False: "no_merge"}, id="merge on mixed attributes", ), ], ) def test_sjoin_multiple_attributes_datatypes( self, dfs_shared_attribute, attr1_key_change_dict, attr2_key_change_dict ): left_gdf, right_gdf = dfs_shared_attribute left_gdf["attr1"] = left_gdf["attr1"].map(attr1_key_change_dict) left_gdf["attr2"] = left_gdf["attr2"].map(attr2_key_change_dict) right_gdf["attr1"] = right_gdf["attr1"].map(attr1_key_change_dict) right_gdf["attr2"] = right_gdf["attr2"].map(attr2_key_change_dict) joined = sjoin(left_gdf, right_gdf, on_attribute=("attr1", "attr2")) assert (["A", "B"] == joined["attr_tracker"].values).all() def test_sjoin_multiple_attributes_check_header(self, dfs_shared_attribute): left_gdf, right_gdf = dfs_shared_attribute joined = sjoin(left_gdf, right_gdf, on_attribute=["attr1"]) assert (["A", "B", "E", "F"] == joined["attr_tracker"].values).all() assert {"attr2_left", "attr2_right", "attr1"}.issubset(joined.columns) assert "attr1_left" not in joined def test_sjoin_error_column_does_not_exist(self, dfs_shared_attribute): left_gdf, right_gdf = dfs_shared_attribute right_gdf_dropped_attr = right_gdf.drop("attr1", axis=1) left_gdf_dropped_attr = left_gdf.drop("attr1", axis=1) with pytest.raises( ValueError, match="Expected column attr1 is missing from the right dataframe", ): sjoin(left_gdf, right_gdf_dropped_attr, on_attribute="attr1") with pytest.raises( ValueError, match="Expected column attr1 is missing from the left dataframe", ): sjoin(left_gdf_dropped_attr, right_gdf, on_attribute="attr1") with pytest.raises( ValueError, match="Expected column attr1 is missing from both of the dataframes", ): sjoin(left_gdf_dropped_attr, right_gdf_dropped_attr, on_attribute="attr1") def test_sjoin_error_use_geometry_column(self, dfs_shared_attribute): left_gdf, right_gdf = dfs_shared_attribute with pytest.raises( ValueError, match="Active geometry column cannot be used as an input for " "on_attribute parameter", ): sjoin(left_gdf, right_gdf, on_attribute="geometry") with pytest.raises( ValueError, match="Active geometry column cannot be used as an input for " "on_attribute parameter", ): sjoin(left_gdf, right_gdf, on_attribute=["attr1", "geometry"]) class TestIndexNames: @pytest.mark.parametrize("how", ["inner", "left", "right"]) def test_preserve_index_names(self, how): # preserve names of both left and right index geoms = [Point(1, 1), Point(2, 2)] df1 = GeoDataFrame({"geometry": geoms}, index=pd.Index([1, 2], name="myidx1")) df2 = GeoDataFrame( {"geometry": geoms}, index=pd.Index(["a", "b"], name="myidx2") ) result = sjoin(df1, df2, how=how) if how in ("inner", "left"): expected = GeoDataFrame( {"myidx1": [1, 2], "geometry": geoms, "myidx2": ["a", "b"]} ).set_index("myidx1") else: # right join expected = GeoDataFrame( {"myidx2": ["a", "b"], "myidx1": [1, 2], "geometry": geoms}, ).set_index("myidx2") assert_geodataframe_equal(result, expected) # but also add suffixes if both left and right have the same index df1.index.name = "myidx" df2.index.name = "myidx" result = sjoin(df1, df2, how=how) if how in ("inner", "left"): expected = GeoDataFrame( {"myidx_left": [1, 2], "geometry": geoms, "myidx_right": ["a", "b"]} ).set_index("myidx_left") else: # right join expected = GeoDataFrame( {"myidx_right": ["a", "b"], "myidx_left": [1, 2], "geometry": geoms}, ).set_index("myidx_right") assert_geodataframe_equal(result, expected) @pytest.mark.parametrize("how", ["inner", "left", "right"]) def test_preserve_index_names_multiindex(self, how): # preserve names of both left and right index geoms = [Point(1, 1), Point(2, 2)] df1 = GeoDataFrame( {"geometry": geoms}, index=pd.MultiIndex.from_tuples( [("a", 1), ("b", 2)], names=["myidx1", "level2"] ), ) df2 = GeoDataFrame( {"geometry": geoms}, index=pd.MultiIndex.from_tuples( [("c", 3), ("d", 4)], names=["myidx2", None] ), ) result = sjoin(df1, df2, how=how) expected_base = GeoDataFrame( { "myidx1": ["a", "b"], "level2": [1, 2], "geometry": geoms, "myidx2": ["c", "d"], "index_right1": [3, 4], } ) if how in ("inner", "left"): expected = expected_base.set_index(["myidx1", "level2"]) else: # right join expected = expected_base.set_index(["myidx2", "index_right1"]) # if it was originally None, that is preserved expected.index.names = ["myidx2", None] assert_geodataframe_equal(result, expected) # but also add suffixes if both left and right have the same index df1.index.names = ["myidx", "level2"] df2.index.names = ["myidx", None] result = sjoin(df1, df2, how=how) expected_base = GeoDataFrame( { "myidx_left": ["a", "b"], "level2": [1, 2], "geometry": geoms, "myidx_right": ["c", "d"], "index_right1": [3, 4], } ) if how in ("inner", "left"): expected = expected_base.set_index(["myidx_left", "level2"]) else: # right join expected = expected_base.set_index(["myidx_right", "index_right1"]) # if it was originally None, that is preserved expected.index.names = ["myidx_right", None] assert_geodataframe_equal(result, expected) @pytest.mark.parametrize("how", ["inner", "left", "right"]) def test_duplicate_column_index_name(self, how): # case where a left column and the right index have the same name or the # other way around -> correctly add suffix or preserve index name geoms = [Point(1, 1), Point(2, 2)] df1 = GeoDataFrame({"myidx": [1, 2], "geometry": geoms}) df2 = GeoDataFrame( {"geometry": geoms}, index=pd.Index(["a", "b"], name="myidx") ) result = sjoin(df1, df2, how=how) if how in ("inner", "left"): expected = GeoDataFrame( {"myidx_left": [1, 2], "geometry": geoms, "myidx_right": ["a", "b"]} ) else: # right join expected = GeoDataFrame( {"index_left": [0, 1], "myidx_left": [1, 2], "geometry": geoms}, index=pd.Index(["a", "b"], name="myidx_right"), ) assert_geodataframe_equal(result, expected) result = sjoin(df2, df1, how=how) if how in ("inner", "left"): expected = GeoDataFrame( {"geometry": geoms, "index_right": [0, 1], "myidx_right": [1, 2]}, index=pd.Index(["a", "b"], name="myidx_left"), ) else: # right join expected = GeoDataFrame( {"myidx_left": ["a", "b"], "myidx_right": [1, 2], "geometry": geoms}, ) assert_geodataframe_equal(result, expected) @pytest.mark.parametrize("how", ["inner", "left", "right"]) def test_duplicate_column_index_name_multiindex(self, how): # case where a left column and the right index have the same name or the # other way around -> correctly add suffix or preserve index name geoms = [Point(1, 1), Point(2, 2)] df1 = GeoDataFrame({"myidx": [1, 2], "geometry": geoms}) df2 = GeoDataFrame( {"geometry": geoms}, index=pd.MultiIndex.from_tuples( [("a", 1), ("b", 2)], names=["myidx", "level2"] ), ) result = sjoin(df1, df2, how=how) if how in ("inner", "left"): expected = GeoDataFrame( { "myidx_left": [1, 2], "geometry": geoms, "myidx_right": ["a", "b"], "level2": [1, 2], } ) else: # right join expected = GeoDataFrame( {"index_left": [0, 1], "myidx_left": [1, 2], "geometry": geoms}, index=pd.MultiIndex.from_tuples( [("a", 1), ("b", 2)], names=["myidx_right", "level2"] ), ) assert_geodataframe_equal(result, expected) result = sjoin(df2, df1, how=how) if how in ("inner", "left"): expected = GeoDataFrame( {"geometry": geoms, "index_right": [0, 1], "myidx_right": [1, 2]}, index=pd.MultiIndex.from_tuples( [("a", 1), ("b", 2)], names=["myidx_left", "level2"] ), ) else: # right join expected = GeoDataFrame( { "myidx_left": ["a", "b"], "level2": [1, 2], "myidx_right": [1, 2], "geometry": geoms, }, ) assert_geodataframe_equal(result, expected) @pytest.mark.parametrize("how", ["inner", "left", "right"]) def test_conflicting_column_index_name(self, how): # test case where the auto-generated index name conflicts geoms = [Point(1, 1), Point(2, 2)] df1 = GeoDataFrame({"index_right": [1, 2], "geometry": geoms}) df2 = GeoDataFrame({"geometry": geoms}) with pytest.raises(ValueError, match="'index_right' cannot be a column name"): sjoin(df1, df2, how=how) @pytest.mark.parametrize("how", ["inner", "left", "right"]) def test_conflicting_column_with_suffix(self, how): # test case where the auto-generated index name conflicts geoms = [Point(1, 1), Point(2, 2)] df1 = GeoDataFrame( {"column": [1, 2], "column_right": ["a", "b"], "geometry": geoms} ) df2 = GeoDataFrame({"column": [0.1, 0.2], "geometry": geoms}) result = sjoin(df1, df2, how=how) if how in ("inner", "left"): expected = GeoDataFrame( {1: [1, 2], 2: ["a", "b"], 3: geoms, 4: [0, 1], 5: [0.1, 0.2]} ) expected.columns = [ "column_left", "column_right", "geometry", "index_right", "column_right", ] else: # right join expected = GeoDataFrame( {1: [0, 1], 2: [1, 2], 3: ["a", "b"], 4: [0.1, 0.2], 5: geoms} ) expected.columns = [ "index_left", "column_left", "column_right", "column_right", "geometry", ] expected = expected.set_geometry("geometry") assert_geodataframe_equal(result, expected) @pytest.mark.usefixtures("_setup_class_nybb_filename") class TestSpatialJoinNYBB: def setup_method(self): self.polydf = read_file(self.nybb_filename) self.crs = self.polydf.crs N = 20 b = [int(x) for x in self.polydf.total_bounds] self.pointdf = GeoDataFrame( [ {"geometry": Point(x, y), "pointattr1": x + y, "pointattr2": x - y} for x, y in zip( range(b[0], b[2], int((b[2] - b[0]) / N)), range(b[1], b[3], int((b[3] - b[1]) / N)), ) ], crs=self.crs, ) def test_geometry_name(self): # test sjoin is working with other geometry name polydf_original_geom_name = self.polydf.geometry.name self.polydf = self.polydf.rename(columns={"geometry": "new_geom"}).set_geometry( "new_geom" ) assert polydf_original_geom_name != self.polydf.geometry.name res = sjoin(self.polydf, self.pointdf, how="left") assert self.polydf.geometry.name == res.geometry.name def test_sjoin_left(self): df = sjoin(self.pointdf, self.polydf, how="left") assert df.shape == (21, 8) for i, row in df.iterrows(): assert row.geometry.geom_type == "Point" assert "pointattr1" in df.columns assert "BoroCode" in df.columns def test_sjoin_right(self): # the inverse of left df = sjoin(self.pointdf, self.polydf, how="right") df2 = sjoin(self.polydf, self.pointdf, how="left") assert df.shape == (12, 8) assert df.shape == df2.shape for i, row in df.iterrows(): assert row.geometry.geom_type == "MultiPolygon" for i, row in df2.iterrows(): assert row.geometry.geom_type == "MultiPolygon" def test_sjoin_inner(self): df = sjoin(self.pointdf, self.polydf, how="inner") assert df.shape == (11, 8) def test_sjoin_predicate(self): # points within polygons df = sjoin(self.pointdf, self.polydf, how="left", predicate="within") assert df.shape == (21, 8) assert df.loc[1]["BoroName"] == "Staten Island" # points contain polygons? never happens so we should have nulls df = sjoin(self.pointdf, self.polydf, how="left", predicate="contains") assert df.shape == (21, 8) assert np.isnan(df.loc[1]["Shape_Area"]) def test_sjoin_bad_predicate(self): # AttributeError: 'Point' object has no attribute 'spandex' with pytest.raises(ValueError): sjoin(self.pointdf, self.polydf, how="left", predicate="spandex") def test_sjoin_duplicate_column_name(self): pointdf2 = self.pointdf.rename(columns={"pointattr1": "Shape_Area"}) df = sjoin(pointdf2, self.polydf, how="left") assert "Shape_Area_left" in df.columns assert "Shape_Area_right" in df.columns @pytest.mark.parametrize("how", ["left", "right", "inner"]) def test_sjoin_named_index(self, how): # original index names should be unchanged pointdf2 = self.pointdf.copy() pointdf2.index.name = "pointid" polydf = self.polydf.copy() polydf.index.name = "polyid" res = sjoin(pointdf2, polydf, how=how) assert pointdf2.index.name == "pointid" assert polydf.index.name == "polyid" # original index name should pass through to result if how == "right": assert res.index.name == "polyid" else: # how == "left", how == "inner" assert res.index.name == "pointid" def test_sjoin_values(self): # GH190 self.polydf.index = [1, 3, 4, 5, 6] df = sjoin(self.pointdf, self.polydf, how="left") assert df.shape == (21, 8) df = sjoin(self.polydf, self.pointdf, how="left") assert df.shape == (12, 8) @pytest.mark.xfail def test_no_overlapping_geometry(self): # Note: these tests are for correctly returning GeoDataFrame # when result of the join is empty df_inner = sjoin(self.pointdf.iloc[17:], self.polydf, how="inner") df_left = sjoin(self.pointdf.iloc[17:], self.polydf, how="left") df_right = sjoin(self.pointdf.iloc[17:], self.polydf, how="right") expected_inner_df = pd.concat( [ self.pointdf.iloc[:0], pd.Series(name="index_right", dtype="int64"), self.polydf.drop("geometry", axis=1).iloc[:0], ], axis=1, ) expected_inner = GeoDataFrame(expected_inner_df) expected_right_df = pd.concat( [ self.pointdf.drop("geometry", axis=1).iloc[:0], pd.concat( [ pd.Series(name="index_left", dtype="int64"), pd.Series(name="index_right", dtype="int64"), ], axis=1, ), self.polydf, ], axis=1, ) expected_right = GeoDataFrame(expected_right_df).set_index("index_right") expected_left_df = pd.concat( [ self.pointdf.iloc[17:], pd.Series(name="index_right", dtype="int64"), self.polydf.iloc[:0].drop("geometry", axis=1), ], axis=1, ) expected_left = GeoDataFrame(expected_left_df) assert expected_inner.equals(df_inner) assert expected_right.equals(df_right) assert expected_left.equals(df_left) @pytest.mark.skip("Not implemented") def test_sjoin_outer(self): df = sjoin(self.pointdf, self.polydf, how="outer") assert df.shape == (21, 8) def test_sjoin_empty_geometries(self): # https://github.com/geopandas/geopandas/issues/944 empty = GeoDataFrame(geometry=[GeometryCollection()] * 3, crs=self.crs) df = sjoin(pd.concat([self.pointdf, empty]), self.polydf, how="left") assert df.shape == (24, 8) df2 = sjoin(self.pointdf, pd.concat([self.polydf, empty]), how="left") assert df2.shape == (21, 8) @pytest.mark.parametrize("predicate", ["intersects", "within", "contains"]) def test_sjoin_no_valid_geoms(self, predicate): """Tests a completely empty GeoDataFrame.""" empty = GeoDataFrame(geometry=[], crs=self.pointdf.crs) assert sjoin(self.pointdf, empty, how="inner", predicate=predicate).empty assert sjoin(self.pointdf, empty, how="right", predicate=predicate).empty assert sjoin(empty, self.pointdf, how="inner", predicate=predicate).empty assert sjoin(empty, self.pointdf, how="left", predicate=predicate).empty def test_empty_sjoin_return_duplicated_columns(self, nybb_filename): nybb = geopandas.read_file(nybb_filename) nybb2 = nybb.copy() nybb2.geometry = nybb2.translate(200000) # to get non-overlapping result = geopandas.sjoin(nybb, nybb2) assert "BoroCode_right" in result.columns assert "BoroCode_left" in result.columns @pytest.fixture def world(naturalearth_lowres): return read_file(naturalearth_lowres) @pytest.fixture def cities(naturalearth_cities): return read_file(naturalearth_cities) def test_sjoin_inner(world, cities): # GH637 countries = world[["geometry", "name"]] countries = countries.rename(columns={"name": "country"}) cities_with_country = sjoin(cities, countries, how="inner", predicate="intersects") assert cities_with_country.shape == (213, 4) class TestNearest: @pytest.mark.parametrize( "how_kwargs", ({}, {"how": "inner"}, {"how": "left"}, {"how": "right"}) ) def test_allowed_hows(self, how_kwargs): left = geopandas.GeoDataFrame({"geometry": []}) right = geopandas.GeoDataFrame({"geometry": []}) sjoin_nearest(left, right, **how_kwargs) # no error @pytest.mark.parametrize("how", ("outer", "abcde")) def test_invalid_hows(self, how: str): left = geopandas.GeoDataFrame({"geometry": []}) right = geopandas.GeoDataFrame({"geometry": []}) with pytest.raises(ValueError, match="`how` was"): sjoin_nearest(left, right, how=how) @pytest.mark.parametrize("distance_col", (None, "distance")) def test_empty_right_df_how_left(self, distance_col: str): # all records from left and no results from right left = geopandas.GeoDataFrame({"geometry": [Point(0, 0), Point(1, 1)]}) right = geopandas.GeoDataFrame({"geometry": []}) joined = sjoin_nearest( left, right, how="left", distance_col=distance_col, ) assert_geoseries_equal(joined["geometry"], left["geometry"]) assert joined["index_right"].isna().all() if distance_col is not None: assert joined[distance_col].isna().all() @pytest.mark.parametrize("distance_col", (None, "distance")) def test_empty_right_df_how_right(self, distance_col: str): # no records in joined left = geopandas.GeoDataFrame({"geometry": [Point(0, 0), Point(1, 1)]}) right = geopandas.GeoDataFrame({"geometry": []}) joined = sjoin_nearest( left, right, how="right", distance_col=distance_col, ) assert joined.empty if distance_col is not None: assert distance_col in joined @pytest.mark.parametrize("how", ["inner", "left"]) @pytest.mark.parametrize("distance_col", (None, "distance")) def test_empty_left_df(self, how, distance_col: str): right = geopandas.GeoDataFrame({"geometry": [Point(0, 0), Point(1, 1)]}) left = geopandas.GeoDataFrame({"geometry": []}) joined = sjoin_nearest(left, right, how=how, distance_col=distance_col) assert joined.empty if distance_col is not None: assert distance_col in joined @pytest.mark.parametrize("distance_col", (None, "distance")) def test_empty_left_df_how_right(self, distance_col: str): right = geopandas.GeoDataFrame({"geometry": [Point(0, 0), Point(1, 1)]}) left = geopandas.GeoDataFrame({"geometry": []}) joined = sjoin_nearest( left, right, how="right", distance_col=distance_col, ) assert_geoseries_equal(joined["geometry"], right["geometry"]) assert joined["index_left"].isna().all() if distance_col is not None: assert joined[distance_col].isna().all() @pytest.mark.parametrize("how", ["inner", "left"]) def test_empty_join_due_to_max_distance(self, how): # after applying max_distance the join comes back empty # (as in NaN in the joined columns) left = geopandas.GeoDataFrame({"geometry": [Point(0, 0)]}) right = geopandas.GeoDataFrame({"geometry": [Point(1, 1), Point(2, 2)]}) joined = sjoin_nearest( left, right, how=how, max_distance=1, distance_col="distances", ) expected = left.copy() expected["index_right"] = [np.nan] expected["distances"] = [np.nan] if how == "inner": expected = expected.dropna() expected["index_right"] = expected["index_right"].astype("int64") assert_geodataframe_equal(joined, expected) def test_empty_join_due_to_max_distance_how_right(self): # after applying max_distance the join comes back empty # (as in NaN in the joined columns) left = geopandas.GeoDataFrame({"geometry": [Point(0, 0), Point(1, 1)]}) right = geopandas.GeoDataFrame({"geometry": [Point(2, 2)]}) joined = sjoin_nearest( left, right, how="right", max_distance=1, distance_col="distances", ) expected = right.copy() expected["index_left"] = [np.nan] expected["distances"] = [np.nan] expected = expected[["index_left", "geometry", "distances"]] assert_geodataframe_equal(joined, expected) @pytest.mark.parametrize("how", ["inner", "left"]) def test_max_distance(self, how): left = geopandas.GeoDataFrame({"geometry": [Point(0, 0), Point(1, 1)]}) right = geopandas.GeoDataFrame({"geometry": [Point(1, 1), Point(2, 2)]}) joined = sjoin_nearest( left, right, how=how, max_distance=1, distance_col="distances", ) expected = left.copy() expected["index_right"] = [np.nan, 0] expected["distances"] = [np.nan, 0] if how == "inner": expected = expected.dropna() expected["index_right"] = expected["index_right"].astype("int64") assert_geodataframe_equal(joined, expected) def test_max_distance_how_right(self): left = geopandas.GeoDataFrame({"geometry": [Point(1, 1), Point(2, 2)]}) right = geopandas.GeoDataFrame({"geometry": [Point(0, 0), Point(1, 1)]}) joined = sjoin_nearest( left, right, how="right", max_distance=1, distance_col="distances", ) expected = right.copy() expected["index_left"] = [np.nan, 0] expected["distances"] = [np.nan, 0] expected = expected[["index_left", "geometry", "distances"]] assert_geodataframe_equal(joined, expected) @pytest.mark.parametrize("how", ["inner", "left"]) @pytest.mark.parametrize( "geo_left, geo_right, expected_left, expected_right, distances", [ ( [Point(0, 0), Point(1, 1)], [Point(1, 1)], [0, 1], [0, 0], [math.sqrt(2), 0], ), ( [Point(0, 0), Point(1, 1)], [Point(1, 1), Point(0, 0)], [0, 1], [1, 0], [0, 0], ), ( [Point(0, 0), Point(1, 1)], [Point(1, 1), Point(0, 0), Point(0, 0)], [0, 0, 1], [1, 2, 0], [0, 0, 0], ), ( [Point(0, 0), Point(1, 1)], [Point(1, 1), Point(0, 0), Point(2, 2)], [0, 1], [1, 0], [0, 0], ), ( [Point(0, 0), Point(1, 1)], [Point(1, 1), Point(0.25, 1)], [0, 1], [1, 0], [math.sqrt(0.25**2 + 1), 0], ), ( [Point(0, 0), Point(1, 1)], [Point(-10, -10), Point(100, 100)], [0, 1], [0, 0], [math.sqrt(10**2 + 10**2), math.sqrt(11**2 + 11**2)], ), ( [Point(0, 0), Point(1, 1)], [Point(x, y) for x, y in zip(np.arange(10), np.arange(10))], [0, 1], [0, 1], [0, 0], ), ( [Point(0, 0), Point(1, 1), Point(0, 0)], [Point(1.1, 1.1), Point(0, 0)], [0, 1, 2], [1, 0, 1], [0, np.sqrt(0.1**2 + 0.1**2), 0], ), ], ) def test_sjoin_nearest_left( self, geo_left, geo_right, expected_left: Sequence[int], expected_right: Sequence[int], distances: Sequence[float], how, ): left = geopandas.GeoDataFrame({"geometry": geo_left}) right = geopandas.GeoDataFrame({"geometry": geo_right}) expected_gdf = left.iloc[expected_left].copy() expected_gdf["index_right"] = expected_right # without distance col joined = sjoin_nearest(left, right, how=how) # inner / left join give a different row order check_like = how == "inner" assert_geodataframe_equal(expected_gdf, joined, check_like=check_like) # with distance col expected_gdf["distance_col"] = np.array(distances, dtype=float) joined = sjoin_nearest(left, right, how=how, distance_col="distance_col") assert_geodataframe_equal(expected_gdf, joined, check_like=check_like) @pytest.mark.parametrize( "geo_left, geo_right, expected_left, expected_right, distances", [ ([Point(0, 0), Point(1, 1)], [Point(1, 1)], [1], [0], [0]), ( [Point(0, 0), Point(1, 1)], [Point(1, 1), Point(0, 0)], [1, 0], [0, 1], [0, 0], ), ( [Point(0, 0), Point(1, 1)], [Point(1, 1), Point(0, 0), Point(0, 0)], [1, 0, 0], [0, 1, 2], [0, 0, 0], ), ( [Point(0, 0), Point(1, 1)], [Point(1, 1), Point(0, 0), Point(2, 2)], [1, 0, 1], [0, 1, 2], [0, 0, math.sqrt(2)], ), ( [Point(0, 0), Point(1, 1)], [Point(1, 1), Point(0.25, 1)], [1, 1], [0, 1], [0, 0.75], ), ( [Point(0, 0), Point(1, 1)], [Point(-10, -10), Point(100, 100)], [0, 1], [0, 1], [math.sqrt(10**2 + 10**2), math.sqrt(99**2 + 99**2)], ), ( [Point(0, 0), Point(1, 1)], [Point(x, y) for x, y in zip(np.arange(10), np.arange(10))], [0, 1] + [1] * 8, list(range(10)), [0, 0] + [np.sqrt(x**2 + x**2) for x in np.arange(1, 9)], ), ( [Point(0, 0), Point(1, 1), Point(0, 0)], [Point(1.1, 1.1), Point(0, 0)], [1, 0, 2], [0, 1, 1], [np.sqrt(0.1**2 + 0.1**2), 0, 0], ), ], ) def test_sjoin_nearest_right( self, geo_left, geo_right, expected_left: Sequence[int], expected_right: Sequence[int], distances: Sequence[float], ): left = geopandas.GeoDataFrame({"geometry": geo_left}) right = geopandas.GeoDataFrame({"geometry": geo_right}) expected_gdf = right.iloc[expected_right].copy() expected_gdf["index_left"] = expected_left expected_gdf = expected_gdf[["index_left", "geometry"]] # without distance col joined = sjoin_nearest(left, right, how="right") assert_geodataframe_equal(expected_gdf, joined) # with distance col expected_gdf["distance_col"] = np.array(distances, dtype=float) joined = sjoin_nearest(left, right, how="right", distance_col="distance_col") assert_geodataframe_equal(expected_gdf, joined) @pytest.mark.filterwarnings("ignore:Geometry is in a geographic CRS") def test_sjoin_nearest_inner(self, naturalearth_lowres, naturalearth_cities): # check equivalency of left and inner join countries = read_file(naturalearth_lowres) cities = read_file(naturalearth_cities) countries = countries[["geometry", "name"]].rename(columns={"name": "country"}) # default: inner and left give the same result result1 = sjoin_nearest(cities, countries, distance_col="dist") assert result1.shape[0] == cities.shape[0] result2 = sjoin_nearest(cities, countries, distance_col="dist", how="inner") assert_geodataframe_equal(result2, result1) result3 = sjoin_nearest(cities, countries, distance_col="dist", how="left") assert_geodataframe_equal(result3, result1, check_like=True) # with max_distance: rows that go above are dropped in case of inner result4 = sjoin_nearest(cities, countries, distance_col="dist", max_distance=1) assert_geodataframe_equal( result4, result1[result1["dist"] < 1], check_like=True ) result5 = sjoin_nearest( cities, countries, distance_col="dist", max_distance=1, how="left" ) assert result5.shape[0] == cities.shape[0] result5 = result5.dropna() result5["index_right"] = result5["index_right"].astype("int64") assert_geodataframe_equal(result5, result4, check_like=True) @pytest.mark.parametrize( "max_distance,expected", [(None, [1, 3, 3, 1, 2]), (1.1, [3, 3, 1, 2])] ) def test_sjoin_nearest_exclusive(self, max_distance, expected): geoms = shapely.points(np.arange(3), np.arange(3)) geoms = np.append(geoms, [Point(1, 2)]) df = geopandas.GeoDataFrame({"geometry": geoms}) result = df.sjoin_nearest( df, max_distance=max_distance, distance_col="dist", exclusive=True ) assert_series_equal( result["index_right"].reset_index(drop=True), pd.Series(expected), check_names=False, ) if max_distance: assert result["dist"].max() <= max_distance geopandas-1.2.0/geopandas/tools/tests/test_tools.py000066400000000000000000000027131525650651600225050ustar00rootroot00000000000000from shapely.geometry import LineString, MultiPoint, Point from geopandas import GeoSeries from geopandas.tools import collect import pytest class TestTools: def setup_method(self): self.p1 = Point(0, 0) self.p2 = Point(1, 1) self.p3 = Point(2, 2) self.mpc = MultiPoint([self.p1, self.p2, self.p3]) self.mp1 = MultiPoint([self.p1, self.p2]) self.line1 = LineString([(3, 3), (4, 4)]) def test_collect_single(self): result = collect(self.p1) assert self.p1.equals(result) def test_collect_single_force_multi(self): result = collect(self.p1, multi=True) expected = MultiPoint([self.p1]) assert expected.equals(result) def test_collect_multi(self): result = collect(self.mp1) assert self.mp1.equals(result) def test_collect_multi_force_multi(self): result = collect(self.mp1) assert self.mp1.equals(result) def test_collect_list(self): result = collect([self.p1, self.p2, self.p3]) assert self.mpc.equals(result) def test_collect_GeoSeries(self): s = GeoSeries([self.p1, self.p2, self.p3]) result = collect(s) assert self.mpc.equals(result) def test_collect_mixed_types(self): with pytest.raises(ValueError): collect([self.p1, self.line1]) def test_collect_mixed_multi(self): with pytest.raises(ValueError): collect([self.mpc, self.mp1]) geopandas-1.2.0/geopandas/tools/util.py000066400000000000000000000026541525650651600201250ustar00rootroot00000000000000import pandas as pd from shapely.geometry import MultiLineString, MultiPoint, MultiPolygon from shapely.geometry.base import BaseGeometry _multi_type_map = { "Point": MultiPoint, "LineString": MultiLineString, "Polygon": MultiPolygon, } def collect(x, multi=False): """Collect single part geometries into their Multi* counterpart. Parameters ---------- x : an iterable or Series of Shapely geometries, a GeoSeries, or a single Shapely geometry multi : boolean, default False if True, force returned geometries to be Multi* even if they only have one component. """ if isinstance(x, BaseGeometry): x = [x] elif isinstance(x, pd.Series): x = list(x) # We cannot create GeometryCollection here so all types # must be the same. If there is more than one element, # they cannot be Multi*, i.e., can't pass in combination of # Point and MultiPoint... or even just MultiPoint t = x[0].geom_type if not all(g.geom_type == t for g in x): raise ValueError("Geometry type must be homogeneous") if len(x) > 1 and t.startswith("Multi"): raise ValueError(f"Cannot collect {t}. Must have single geometries") if len(x) == 1 and (t.startswith("Multi") or not multi): # If there's only one single part geom and we're not forcing to # multi, then just return it return x[0] return _multi_type_map[t](x) geopandas-1.2.0/pyproject.toml000066400000000000000000000141411525650651600164030ustar00rootroot00000000000000[build-system] requires = ["hatchling>=1.5", "hatch-vcs"] build-backend = "hatchling.build" [project] name = "geopandas" dynamic = ["version"] authors = [{ name = "Kelsey Jordahl", email = "kjordahl@alum.mit.edu" }] maintainers = [{ name = "GeoPandas contributors" }] license = "BSD-3-Clause" description = "Geographic pandas extensions" keywords = ["GIS", "cartography", "pandas", "shapely"] classifiers = [ "Development Status :: 5 - Production/Stable", "Intended Audience :: Science/Research", "Operating System :: OS Independent", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3 :: Only", "Topic :: Scientific/Engineering :: GIS", ] requires-python = ">=3.11" dependencies = [ "numpy >= 2", "pyogrio >= 0.8", "packaging", "pandas >= 2.2.0", "pyproj >= 3.7.0", "shapely >= 2.1.0", ] [project.optional-dependencies] all = [ "psycopg[binary]>=3.2.0", "SQLAlchemy>=2.0", "geopy", "matplotlib>=3.9", "mapclassify>=2.7", "xyzservices", "folium", # >=0.12 (implied minimum version, but older versions may work) "GeoAlchemy2", "pyarrow>=15.0.0", "scipy", "pointpats>=2.5.3", 'contextily', ] # Minimum supported additional deps, not installed as part of `all` extra # (useful to track for bumping minimum dep versions) # fiona>=1.8.21 # scipy>=1.10.0 [dependency-groups] dev = [ "pytest>=3.1.0", "pytest-cov", "pytest-xdist", "codecov", "pre-commit", "ruff", ] [project.readme] text = """\ GeoPandas is a project to add support for geographic data to `pandas`_ objects. The goal of GeoPandas is to make working with geospatial data in python easier. It combines the capabilities of `pandas`_ and `shapely`_, providing geospatial operations in pandas and a high-level interface to multiple geometries to shapely. GeoPandas enables you to easily do operations in python that would otherwise require a spatial database such as PostGIS. .. _pandas: https://pandas.pydata.org .. _shapely: https://shapely.readthedocs.io/en/latest/ """ content-type = "text/x-rst" [project.urls] Home = "https://geopandas.org" Repository = "https://github.com/geopandas/geopandas" [tool.hatch.version] source = "vcs" [tool.hatch.build.hooks.vcs] version-file = "geopandas/_version.py" [tool.hatch.build.targets.sdist] include = [ "/README.md", "/geopandas/**/*.py", "/geopandas/tests/data/null_geom.geojson", ] [tool.pytest.ini_options] markers = ["web: tests that need network connectivity"] xfail_strict = true filterwarnings = [ "ignore:distutils Version classes are deprecated.*:DeprecationWarning:pandas.*", "ignore:distutils Version classes are deprecated.*:DeprecationWarning:numpy.*", "ignore:The geopandas.dataset module is deprecated", # pytest-xdist incompatibility with pytest-cov "ignore:The --rsyncdir command line argument:DeprecationWarning", # dateutil incompatibility with python 3.12 (https://github.com/dateutil/dateutil/issues/1284) "ignore:datetime.datetime.utcfromtimestamp:DeprecationWarning:dateutil", "ignore:Cannot set the CRS, falling back to None:UserWarning:geopandas", "ignore:Use `@` matmul instead of `\\*` mul operator for matrix multiplication:PendingDeprecationWarning:rasterio", ] [tool.ruff] line-length = 88 extend-exclude = ["doc/*", "benchmarks/*"] [tool.ruff.lint] select = [ # pyflakes "F", # pycodestyle "E", "W", # pyupgrade "UP", # flake8-bugbear "B", # flake8-debugger "T10", # flake8-simplify # "SIM", # pylint "PLC", "PLE", "PLR", "PLW", # misc lints "PIE", # implicit string concatenation "ISC", # type-checking imports "TC", # comprehensions "C4", # Ruff-specific rules "RUF", # isort "I", # pydocstyle "D", ] ignore = [ ### Intentionally disabled # module level import not at top of file "E402", # do not assign a lambda expression, use a def "E731", # mutable-argument-default "B006", # unused-loop-control-variable "B007", # get-attr-with-constant "B009", # Only works with python >=3.10 "B905", # dict literals "C408", # Too many arguments to function call "PLR0913", # Too many returns "PLR0911", # Too many branches "PLR0912", # Too many statements "PLR0915", # Magic number "PLR2004", # Redefined loop name "PLW2901", # Global statements are discouraged "PLW0603", # compare-to-empty-string "PLC1901", # Unnecessary dict comprehension for iterable "C420", # `len(*)` used as condition without comparison "PLC1802", ### Additional checks that don't pass yet # Missing docstring in public module "D100", # Missing docstring in public class "D101", # Missing docstring in public method "D102", # Missing docstring in public function "D103", # Missing docstring in public package "D104", # Missing docstring in magic method "D105", # 1 blank line required between summary line and description "D205", # Useless statement "B018", # Within an except clause, raise exceptions with ... "B904", # Consider `elif` instead of `else` then `if` to remove indentation level "PLR5501", # collection-literal-concatenation "RUF005", # Mutable class attributes should be annotated with `typing.ClassVar`, "RUF012", # import should be at the top-level of a file "PLC0415" ] allowed-confusables = ["−"] [tool.ruff.lint.per-file-ignores] # ignore pydocstyle errors in tests "**/tests/*" = ["D"] "geopandas/conftest.py" = ["D"] # the order of imports in __init__ is a bit tricky, so keep manual "geopandas/__init__.py" = ["F401", "I"] [tool.ruff.lint.isort] extra-standard-library = ["packaging"] section-order = [ "future", "standard-library", "third-party", "geo", "first-party", "local-folder", "testing" ] [tool.ruff.lint.isort.sections] "geo" = ["shapely", "pyproj"] "testing" = ["pytest", "pandas.testing", "numpy.testing", "geopandas.tests", "geopandas.testing"] [tool.ruff.lint.pydocstyle] convention = "numpy" geopandas-1.2.0/readthedocs.yml000066400000000000000000000006621525650651600165020ustar00rootroot00000000000000version: 2 sphinx: # Path to your Sphinx configuration file. configuration: doc/source/conf.py build: os: ubuntu-22.04 tools: python: mambaforge-latest jobs: post_checkout: # we need the tags for version to work - git fetch origin --depth 150 - git fetch --tags post_build: - mamba list python: install: - method: pip path: . conda: environment: doc/environment.yml formats: [] geopandas-1.2.0/requirements-dev.txt000066400000000000000000000007471525650651600175360ustar00rootroot00000000000000# required numpy>=2 pyogrio>=0.8 pandas>=2.2 pyproj>=3.7.0 shapely>=2.1.0 packaging # geodatabase access psycopg[binary]>=3.1.20 SQLAlchemy>=2.0 # geocoding geopy # plotting matplotlib>=3.9 mapclassify>=2.7 xyzservices contextily folium # testing pytest>=3.1.0 pytest-cov pytest-xdist pytest-doctestplus fsspec codecov geodatasets # styling pre-commit ruff # PostGIS writing GeoAlchemy2 # parquet pyarrow>=15.0.0 geoarrow-pyarrow # sparse scipy # sampling pointpats # io fiona

P/Az1 Ax>;P/A+1 A6P/A1 A0P/A1 A%0P/A@1 A)P/A 2 A`C"P/A*2 AP/A=D2 A$P/AX2 A'P/Ag2 AwO/A2 A O/A&2 ApO/A`22 AO/A@2 AIO/A2 AO/A2 AsO/A 2 AOmO/A 2 AgO/A 3 A`O/A H$3 A [O/AW>3 AGQO/A t3 AV;O/A@4 Axh#O/A@%4 AHO/A`m4 Ap8O/A 5 A O/A I5 Ag O/A5 AXO/A 5 A0O/AE5 ApO/AN6 APN/A@6 A؛N/A*5 AFN/A5 AُN/A@6 AO/Am6 A=O/Aଧ6 AO/A`6 A`O/A=6 A}O/A=6 Ah O/A6 A O/A`27 AO/A7 AX!O/A=7 A#O/A`F7 A%O/A@yN7 A'O/A"W7 A-(O/A0`7 A$.O/A`7 A72O/A u7 A4O/A8 A 06O/A/8 A6O/AV8 A4O/ADy8 A A3O/A8 A01O/A஗8 A/O/AT8 A2-O/A`8 A'(O/A 8 AO"O/A 18 A#O/A l8 AO/A#9 A2N/A`m,9 A(1N/A`)69 AoN/A K9 A0ݪN/AN9 AuN/A c9 AN/Amp9 A0N/A9 AxN/A9 AHvN/A^9 A0sN/A@9 ArN/A൰9 ApN/AH9 AoN/Av9 AoN/A`m9 Ap&N/A 89 A6(N/A J: A/pN/A@%?: AzrN/AEu= APtN/A= AhuN/A4= APvN/A?= AȥxN/A= AzN/A= ApN/A@= AՆN/A > AHiN/A q8> AN/A@߅> AʾN/A> AN/A > A؍N/A@i> AN/A@r> AON/A@> AN/A > APN/A Q> AhN/A,> AkN/Az> AN/A;> AXN/Az> A`N/A A> A0N/A> A"N/A` A A`2N/AFFA A)N/AlA AN/A@ؐA AX$N/A@iA AgN/AA AxN/AA AXN/A B AxzN/AB AN/AB AN/A (B A\N/A@"B AN/A0%B APMN/A %B AxN/AB APN/A`] B AN/A B A=N/A`KB A`vN/A@#B AXFN/A@B AN/A+A AcM/A@ AȶM/A`@ AM/A@ AXM/A@ AM/A |@ AhL/A> A L/A^> AK/A= AK/A^= A!pK/Aվ< AHlK/A< AJ/A+< AJ/A~< A`fK/AB%= AScK/A r= A)K/A= Ax(K/A> AZK/A0> AYK/A? AZK/A |@ AK/A`v? AK/A? A_K/AK@ ApeL/A @ ApiL/AE@ AM/AA ApM/A A AM/A`%A AM/A xA AM/AA AȥsN/ADDB AfN/AC APDN/A+C A0EN/AC AdN/A@~C AM/AXxC A(M/AC A8H/A@ B AH/A}> AXH/A}> AH/AzB Ap|H/AB A/H/A7B AN/A#C A@cN/AD AHWN/A@mE AHWN/A@roE AXN/ApE AYN/AjqE APYN/A rE A8V[N/A[sE A]N/AsE AgN/A sE A1lN/A@tE AKnN/A@uE ApN/A@wE AqN/A`zE Ax(rN/A|E A0rN/A~E AhN/A cE APN/AE AHN/A`F ApN/A,0F AୣN/A`fHF APN/AE[F AXtN/A@hF AN/A`omF A=N/AoF AN/AmF AwN/AmF AxN/AnF AN/AqF AN/A-uF AxdN/A zF AHN/A@F AN/AF A"N/A F AsN/A ɜF A N/A@F A(`N/AF AXVN/A`F A@N/A F AN/AoG AN/A0G AN/A'2G A8N/A͏G AP&N/ADG A)N/AG AP+N/AG AN/ARG A^N/A G A@N/A@rrH ARM/AVH ARM/AnsH AX 1M/A``H A@/M/AH AM/A@H AM/A``H A"M/A[H AأM/A4F AL/A4F A؁L/A`fG AH}J/A DG A`vJ/AwF AOXJ/A sF A(bJ/A[H AH}J/A RH APf|J/An|G AWL/AG A)L/AГH AmM/A H AM/AQH A04M/AH AH5M/A ɡH AjN/A@iH AN/AiH A0O/A ZH AO/A0H A+O/A(H AO/AH APO/AI A0O/A@I A(-O/Aa)I A O/ACI AQO/A lI ApO/AI AN/A-J AN/A PJ A(N/A[rJ ApN/A`J AN/ApJ ApN/A@WJ A(N/AK AN/Av)T A=N/Aa@T AUM/Aic A`;M/A/^c A8V9M/A Uc A8s6M/A Lc A/M/A>c Ah'M/A@g4c ApM/A-c A؎M/A b AXL/Ab A(L/Ab AXL/A`mb A(L/A@b AL/A ub A(L/A`"b A@uL/A 1b AP@L/Ab A0%L/A b AK/Atb A@K/A@gb A{K/Ab AEwK/Ab A`rK/A@>b AnK/Ab A'jK/A;b AaK/Ac A83^K/A&c AZK/AM)c AVK/A/A`mn AP8>/A@Yn A`e>/AM?n AЏ>/A3n AНt>/A@+n A8V<>/A/$n A1>/A]'n A&>/A ~1n A@>/ABn A8# >/A@]n AH2=/A{n At=/An A=/A`n A=/Adn A=/A &o A8t=/Aaio A@=/A@o A=/Aܬo A6=(=/A $7Sp Ax=/Ap A"1.X=/AsFq A%A+=/AAq A8I A:/AA,@ AvIځ:/AK AKr:/AG-)xa AY^xd:/A C AݛW:/A[糎 A3 L:/AM `և AxyqC:/A*ؑ A坁<:/ApE AA7:/Ay& A@5:/AÈ A=6:/A؈ A`7:/A AȲ9:/A A@<:/A- AЯ@:/A6 AE:/AL AU:/A A`X:/AӇ Aج[:/A} APg:/Aw Ah:/Aa Am:/A@ᮉ AX$r:/A AHs:/A A~u:/A ȉ A7x:/A@ډ Ay:/A As;:/A@Eu ABG:/Aʊ ApN|:/AD A=|:/AR AXuy:/A@ Aw:/A AHq:/A8 Ao:/A`[ Axn:/A ( A8n:/A: A(n:/Ab APp:/A@ At:/A Qŋ A8w:/A A@{:/A  AO:/A`m A&:/A@8 AH?:/A OS A:/A`)m Aୱ:/A@y A!:/Aی AB:/A A;/A`7 A ;/A \ AnA;/A} AБA;/A@~ AH;/Ak AN;/A- AX(W;/A㦍 Ap^;/A Aza;/A`͍ Ad;/Aۍ A7f;/A J A`g;/A9 APj;/A Al;/A B AxFp;/A Ap;/A` A0p;/As) A pn;/A@P Axii;/A AEb;/A AX;/AA AL;/A AHzF;/AȐ A7;/A | Ap1;/A`o@ A(,;/A o A(;/A@ AP';/A A';/Aɑ A(;/Aؑ A*;/A` AT/;/A7 AH.5;/A`;i Ap<;/A- AW;/AE A-o;/A`˓ A{;/A A;/AӦ A@;/Aʔ A`;/A% AxK;/AoP Au;/A@X A(;/A@y` A1;/A A%;/A@y A`;/A" A(;/A`2" A=/A A@ J=/A@ A W=/Av A@$=/A A=/A A=/AӖ A=/Aǖ A=/A AX=/A H A=/AE A(=/A$ A>/AX• A >/A֦ A`>/A A_0>/A A`4>/AF ApAL>/A ApO>/At A_R>/A`2k AYZ>/A`Z A؟b>/AO Au>/A; A0~>/A 10 AȎ>/A} A>/A3 APZ>/A@ A>/Am AI>/A Ah>/A A>/A् A@1>/A A>/A u A>/A6 A^>/A A>/A A(>/Aɓ A>/A`f͓ A>/A Г As>/A &ӓ A>/Aԓ A?/A@pӓ A"?/A˓ Ap*?/A_͓ Aث;?/Aړ AFW?/A8 A@Bt?/A`" Ax?/A |D A?/AW Ax6?/Atl AP?/Adz A?/A A(ܢ?/A`v A8?/AOԔ A(?/A@ AP?/A_ A?/Ah A+?/AK A?/A x AH?/A Ao?/A A(?/A@. A`?/A A?/A` A؍?/A A ?/A A(?/A A?/A@ A?/A` Ax?/A A@/A H AP|@/A  AP@@/A A @/A A@/Aa AQ'@/A / A2@/A`mG A07@/A`U A;@/Ase AtC@/AƉ A@J@/A" A^@/AX Apg@/AV A,p@/A AÇ@/A, A @/AL A@/ALk A`@/Aྰ Ap@/A A@/AL A,@/A՞ A0̝@/AX A؛@/A`; A@/A sݜ Ay@/A`" A˛@/A@, AH\@/A 4 A,@/AF= A<@/A HE Ap@/AL A@/ADT A@/A` b A p@/A`vm Aٮ@/A x A@/A鞝 A8@/A`] A`e@/A A(@/Aȝ Aho@/A@ A@/Ah Aܙ@/Acd A|@/AC Aא@/A/%: A#dA/A\ AhY A/AM}r A/ A/A) AY.:KA/AZ՞ Ax A/A A&W1A/A:E AMNA/Ak$ A^A/A`4 AA/A?EC AxA/ABO AbaUA/A&X A*U#A/A^ Ah(A/Aeb A5,A/A%b A:ν1A/Af5^ A) ^6A/AhX A:A/A JO A~2iA/A.۳6 A A=A/A@,_ AogB/AWC/A AC/Aj A(C/A ArD/Aa A[ D/A- ABD/A A(-D/A A&D/A` Aq)D/A@ . A)D/AF6 A*D/AV A,D/A< A@-D/A ~r A/D/Aj AK3D/A h A85D/Aٻ Ah9D/Aa' A>D/Ar ACD/A`ʣ A0FD/A`] ADJD/A@ɼ AMD/A`ؼ AHQD/A ATD/A AЄ[D/A` AaD/A`T AD/A`y A%E/Ag AE/As AyE/A AP F/A ACF/A} A+mF/A AXƒF/A`K A`F/A`4 AF/ABk AF/A A~G/A`B A3.G/A & AЄAG/AT A0rG/A`D AG/A@ AhG/Ax AHG/A AaH/AM A@uH/A _ A H/A@} AH/A A!H/A` A(&H/A A0G+H/A` A"/H/A 1: AY2H/Ae A4H/A u A78H/A AF8H/AW A8'5H/A߶ A.H/Av AP%H/A&y AH/A AHH/A`p Ax:G/A AG/A@S AG/AI AG/AA AkG/A AG/AF AG/A & AxRG/A 8 A#G/An AmG/A@ AG/A`d AF/A8 ApF/A AFtF/A% A-8F/A3 A-F/A@% AE/A AE/A AHrE/A@i AHE/A * AxE/A 4 A@E/A'` A E/A{ AGE/A, AE/A A[E/A@PY AE/A / AE/A" AXE/A . AFE/A AE/A AE/AqT AE/A A,F/A AF/A e Ah F/A AF/A Q7 A[F/A A`F/A AF/AL Ah3 F/A@ AzE/A AdE/A@Ʒ AHE/A" AE/A  AtE/A AE/AZ AE/A@ A-F/A ɍ AȘJF/Aj ARF/A AHaF/Ai A #F/A APZG/A`U AQG/A AG/A@a AG/A AeH/A`9 A9H/A A@@H/A S A`AH/A Ae.H/AM Ag-H/A A0 A,P/Av A0̏P/A A5P/AZ AhP/A`m AhP/A` AVlP/AƳ A^P/A A[P/AN AlP/Ac A`6~P/A AȃP/Am A( Q/A l AbQ/Ad7 A0Q/A A R/A ApjR/A _ A`R/A A@S/A3 A( lS/A AS/A, A#T/At A2T/A AxFT/A AT/A 7 A BU/Aં AyuU/A AxU/A@G\ A@U/A` A`U/A` A V/A@ AYSV/A@J AxV/A` A tV/A@q AV/A Sk A=W/A+ AONW/A9 AYlW/A AP}W/A4t Ap{W/A AȩhW/A AnW/A A8cW/A AW/A AxW/AF AW/A' A#X/A AGQX/Am AyX/AE Ax(X/A A?X/A@W. Ap,X/A AxY/A` A-Y/A AwY/A@[ AY/A AY/A A AjZ/Ad_ AlZ/Ads A`ǼZ/AZn A[/AD^ AP^I[/A;p AW[/A&v Aج|[/A@ Ao[/A A`"\/A@F A<\/A`K A`W\/A Ay^\/A AL\/An AG\/A` AN\/A> Axg\/A Ah\/A`J A\/A Ao\/AE Az\/A Ah\/AP A076]/A- AK]/A A8 _]/A " Ae]/A@`M AM]/AX Ar\/A< A"\/A r Al{]/A AP^/A් AH;]/A AXWY]/A@ AJ^/A Ap= _/A8# A`(_/A@.( A8F_/A$- APo_/Ax A_/A A̳_/A Ah_/A' AP_/Af A`/Afu A:`/AO AV`/A; AKx`/A`z Aכ`/AD AP`/A`Q A``/A` AR`/A A"V`/A@q A n`/A6 A(`/A^ Ap `/A` AXV`/A Z0 A8`/A Ax-`/A A@b/Az AP?c/A uO AXWb/A A b/A A(b/Ag A3e/A AXVAe/AAP A&f/A Af/A+` A@h/A` A&h/A# A0ng/A A0Le/A Ac/A A(b/Aoc Ac/A@> A e/AM A[f/A H Ag/A O A`Ei/A@ APi/A@ Ayi/AR Ab}i/Aݶ Axzi/A A@i/A] A8i/A0 Asi/Aﴵ Ai/A A |i/A@N Aȏi/A Api/Av AHi/A`H A\i/AE< Ai/A/ Awi/A # Ai/A= A0i/A`ͳ AJi/A A(i/Af Ai/A? Ai/A A`i/A Ƀ Avi/A' Aгi/A#˲ A #i/A`fq Axzi/A' A8i/A^ AX i/A A~i/A+y AIi/A@ԯ A}i/Ax_ Apyyi/Aܮ ACyi/Aˮ A8Qyi/A`Ʈ A@`yi/AĮ Awyi/A® A8yi/A &® Ayi/A A`yi/A Ayi/A A0yi/A} APyi/Ai Ayi/A c Ayi/A \ Ayi/A c Ayi/Ap Ayi/A@ Axyi/A A(yi/A AXzi/A9® AIzi/Avî A@zi/A 8Ů A`{i/AVʮ A ti/A J A@`i/Aϯ AHܮi/A@q Ai/A`[ Ai/A Ai/A4 Ai/A_ A@i/A@T Ai/A APji/A A[i/A@H Ai/A@` Ai/A` Ai/A@ô AǷi/Aд AHi/Azݴ Ai/A` AЀi/A Ai/A A0i/Aq Avi/A! ASi/A + Aii/A 0 Ai/A/ Aأi/A@- Aj/A A(j/A  A-j/A"ߴ APIYj/A A8Enj/A6f A=j/A N A j/A AB Axj/A 6 A@$k/A" A(k/A Q A7k/Aq A_k/A A؞lk/A A"xk/A@i Ajk/A AJk/A A(k/A& A(k/Ao Ak/A A؁7l/A ִ AHvl/A AUl/AZ* A8l/A`)8 Al/A< AXl/A/8 A8l/A1 AZm/AV& Am/A| ACm/A  A8Z&m/A A .m/AѴ A@4m/A@# A6m/A饴 A08m/A A @m/A?N AGm/A? AHMm/Aʧ AHSm/A`KC AȺTm/A& ABUm/A AUm/A@Nx AP'Vm/A(U ApXm/A6 AN\m/A 8 Agbm/A`K A0]jm/A@5 A3tm/A@Ɲ Am/AH| Aom/A[ Ap{/A87 A`t{/A AH{/A 1 Aj{/AM AP{/A1 A)|/A` Ah"S|/A/ Ahs_|/AF Abi|/A` A(r|/AH A0||/A  A |/A`K% Ae|/A`KM AXW|/A t A8|/A AНz/A s Az/AS Az/A`9 A8z/A Ax>z/A AAz/A$} A0|/A@> A|/A A k|/A@, Ax|/A Ah|/A@N AH|/Aq A|/A AQ}/A A(}/Ap A8R}/A, A8#}/A`d= Ap&}/A X AX6}/A`{ AZ}/A` A8j}/A Apq}/A`; A-x}/A"+ A(}/As A`a}/A A p}/A`b A0̮}/A` A}/A`v Aأ}/A ۋ Ah}/A`X A83}/A/ A}/A@ߗ A}/AD A}/A@ AТ}/A$s A}/A` AH.}/A;# A}/A A}/A` Ax}/A AH}/Aa A(}/A  A}/A Q AH}/A A}/A A`}/A A}/A A(-}/A@& A0}/A`o_ Ao}/AW A(}/A cu Am}/A` A}/Aaa Ak}/AĖ A}/A A-}/A` A8'}/A` Ay}/A` A<}/A@WL A`}/Aj A(}/A`o AH-}/A] A}/A 8 Ae}/A@ A%}/A A0̐}/A  AH2}/A`r Ad}/A Ap}/ANe A,}/A Af}/AD A}/A< A}/A Ah@}/A Ad}/A \ A@}/A5 A}/A8 Ah}/A A}/A A A4}/A A}/A@ A}/A A舁}/Aj Aj}/A Ae}/A | Ab}/A Ah`}/Ac A)`}/A` A_}/A`T A5_}/A 8 A^}/Af A^}/A@y Aq^}/A A ^}/A A]}/A` A^]}/A AP]}/A|( A]}/AU0 A]}/A7 A<_}/AE AOa}/AfT A(i}/A| Aأm}/As A@Ox}/A@ A@{}/A@. A0}}/Aq A}/A A #}/A A}/A Ah}/A A؞}/A@ A,}/A` Ah}}/AX Av}/AƏ Ag}/AT A"V}/Ad ApP}/A~ ApAI}/A@N A8RD}/A A18}/A@N A0w5}/A`b A,3}/Aw A+}/Aj A#}/A> A#}/A@` A9}/A`B A }/A A`}/Ap A3|/A@i A|/A$ A|/A= A|/AO! A|/A` A@|/A A/|/A}E Ap|/A` A&|/A & A0|/A^ A'|/Ah| A |/AB At}|/A`ܫ Apu|/A@ Ad|/Az AXW^|/A` AȃX|/A@y AؼN|/A APF|/A AmD|/A A A|/A* A87|/AO A4|/A@ Ao2|/A AH1|/A AX#1|/A A/|/A` Au.|/A  A-|/A A8+|/A A|*|/Ac ApJ)|/A Aq(|/A A;(|/A A A%|/A A#|/A@ Aa"|/A! Aؒ!|/A@.6 A8g!|/Ag Ap #|/Aʡ AG$|/A@ A)|/A AT+|/AXK A+|/A`mi A)|/A A(|/Af A}%|/Aγ A3|/A A|/AJ Ap[|/A } AP|/A AHa|/A A8|/A? A{/A A({/A A(-{/A`B Ah{/A`] A{/A AX{/A@5 AF{/A A{/A A{/A A{/Ad A`%{/AR AG{/A} A{/A |j AA{/A c] A`{/A\ A({/Ac\ AM{/Ac\ A{/A\ A{/AV] A +{/AN^ A{/A+a A({/A@We Ap{/A j AP{/Ap A0.{/A`"w AP{/A< A{/A A{/A Ap{/A_ A{/A A={/A Ae{/A@. Au{/AT A5{/A@ A{/A, A`{/AH A0{/A`BZ A{/A m Ad{/AǗ A({/AO A{/AЄ AȤ{/Az AX{/A` Ah{/A A8ڼ{/A A`%{/A A_{/AO Ap|{/Aj A@{/A A{/AH AhD{/A A{/A` A(z{/A A{/A` Ah{/AN A8z{/A A@o{/AE A@d{/A`T AW{/A A`L{/A[ A"B{/A@C A>{/AN AH7{/Ak` A\){/AX AQ{/A  A( {/A  A8{/A A{/A < A AF/A@o AjC/A  Ap=@/A  A#>/A@% A,=/Aa  Ax=/A 9 Aؼ>/Ah A@/A ɏ AjC/A_ A0E/A  AtJ/A A2P/A AW/A> Apc/At A3k/AW A =u/A@ֲ A /A AX(/A`" A/A`" AV/A`"  A؎/A A/A`mN A!/Ao Ap/A` A/AЌ A?/Ai AxG/A A0/A A(/Az A/A@y A0/Aa A-/AX AP8&/A@y AV0/A@ A#;/A  AG/A@, A~S/A A#`/A Aj/A Au/A` A~/A A/A@N A趧/AjH A/A À/A$ Axր/Aao A/A A/AF A2/A] A(z /A O A /A A@/A A'/A` Ak&/Av A-/A@_ A,6/A`vI AX?/A5 AhI/A Z$ AЄS/A A[^/A Aeh/A@ Ap/AD Au/A & A-x/A A0e/A@ A/A AP/A A AH2/Ab AP/A / A(/A Az /A Ay/A / AQ/A A/A = AB/A`Ka Ax/A` A/A`; APk/AM Ap[)/A A//A & Ap[8/A A8gB/A AC/A  A]/Afs A#/A@, AL/Ai AT/A9 AZ/A0  A2^/A} AX^/A Ac/A@ Ag/Aྦ Aȃj/A  ALm/A@e AUp/AD As/A@%" A0t/A A`{/A# A=/A5 A~/A E Aі/AU AX(/Aj A =/A A7/A A/A൩ A[dž/A ˻ A׆/A@ A8݆/A Ap/A A>/A A">/A  A9/A@, AxG4/A`* AH3/AV A78/AX A:/A@#Y Ae?/AoL AD/A`+ AJ/A  AUQ/Aa AxW/A  A`/A, Apx׆/A \ A8x/A[@ A0n/A 8 Al/A`H APm/A 8a A7q/Ao Ax/A{ A/Aq A/A`v_ Ax/A@,V A̗/AZ AQ/AZj Aǰ/A@ AX۶/A`K AÇ/A Ahʇ/A A@·/A Ahև/A`m  A`ڇ/A, A<ۇ/A,D A8 އ/Af A/A A/A@N A/A@ A/A; A/Ad A /A` A@/A6 A/AU A/Au A /A@ƙ A /A A/A A/A; A8V/AN A/AL1 Aj/A8 A/A2 A@/Aj" A@u/ATP At/A St APf2/A Apy;/A AuD/A AN/AN AXyY/A APf/Av Ahl/A Axzx/A@ A(/ANA A([/A`i A /A Z A̫/AT AP/A  A[/A A/A@W AÈ/A: APʈ/A`oZ AtЈ/Av APk׈/A/ AUވ/A& A/A A0./AE A[/A@ Ap/Ax A/AV AX /An8 A/A] A"'/A Z A-/A  A'4/A޽ A`9/A AG>/A  A G/A` A N/AvC Apʈ/ADd$ Ap݈/A@C$ AY/AV APb/A AQf/A Ao/A! AQz/ANG! A8/A`m`! A /Aix! A /A ! A攉/A! Ao /A?d% APo0/A?% A/A " A~Ƥ/AOaI" Av؉/A`6# A)܉/AM# A/A@0$ A.D/A&@0*$ A`P+/A@h% AE/A6& AxQ/A`& AD]/A& A 8/A 8( AJ/A ) A軺/AO) APˊ/A) A|#/A{^* A~l/A̜K+ AoK/A u, A/AV, A|R/A- A /AQE'. A./Aw/ A /P/Ai0 A(zp/Aд0 AK|/A=.1 AȄ/AD\91 AE͌/AoÍ1 ABZR/A,K2 A? K/A9&p22 A9>/A2 A0Lg/A@2 A6p/A 82 A8{/AN2 AXu/A2 A`/A@2 Aأ/A;3 Ap[/A3 AX(/A`3 A0./A`D,3 AXƍ/A@W83 AXuэ/ANE3 Aڍ/A2R3 A^/A7 A]/A7 AΜl-A AA8jr/Aՠu AY/3     )4]@)/A@`vzA((/AyAݕ'/A@QyA%/A uAh"-%/A AgxA>$/A@.|A(#/AA;"/A@AP v"/AྱAhp"/AAp7"/A5A` !/A^\Aa!/A cA^!/A@Ah#/A_^A&/A΋AH(/AnAx>*/APAq+/AA./AǏA//A`A+1/AqA2/A_AiG3/A@yAQ3/ArA864/A@` Aȱ 5/A=A}5/AA6/A`նAH{A6/A嶶AHa5/AIAذ5/A`JA5/A渨A5/AKAh4/AǕAh3/A A(\3/AAw]2/AoA&2/A+A0/A@OA`r//AAT./AOA['-/A`+A&,/A+A@+/AA+/A |A@)/A@`vzAH.$/A7AP7$/A AV #/A #AH.$/A7A:.AIAA83.A@A .Az@A .A@A( .A[@Af.A@A33.A`k@ApN.Af@Ag.Ap@A#.A@t@A.A^@A.Aa@Af̷.A[@A.A @AV.AAAf!.Az8AAp-.ApSAA`<.A_AAQ.AkAA`h.A`fjAAp=w.A ^AA.AFAAz.ABAA.ADAA.A]YAA`͸.AaAA`׸.A dAA`ݸ.A [AA8.ApIAA.A1AAأ.A`)AA`a.AzAA`.A@A.AAAp.A AA.A+AA.A /AA83.A]#AA%.AAA#1.A@A=.A@AD.A@Ap8K.A@@AO.A@g@AXS.A @ABW.A@AY.Ap@ApY.A @AU.A@@A(P.AG}@AM.ARb@AN.A[C@AVR.A`@AY.A?Aa.A ?AHak.A`)?Au.A q?Ap~.A@?A慹.A෶?A8.A@?A(\.Az?A`.A?A`.A@Aœ.A`@A.A?A@W.A?A.A@ @Aɹ.A@A0.ٹ.A`?A0.A@?Ah.A?A R .A ?A`.AR|?A8.A`fd?A.Az2?AH.A?A`a.A3>AX#.AH>AH\*.A=>AP87.Aj>A;.AU>AuD.Ap>A[.A`̔=A].A3=A#[.A@p=AP8X.A m=AS.Apo=Ap=@.A@G=AL=.A \=A<.A@=A R=.A=Ap?.AGg=AA.A=R=AP8D.A \1=AC.A=ALE.A<ABI.AQ<A{c.A o<Ah.A][<Ap=o.AGM<A8|.A <<A`a.A 3<Ap.A 2<A8.A ?<Ap=.AE<A֑.AK<Ap.A@N<Au.AI<A#.Az*<A83ƺ.A[;A83Ժ.A@g;Akܺ.A;A`.A3y;AQ.ARl;A.A3W;A.AF;A8.Ap+;A.A` ;AB.A:A .A:Ap0.A:AzA.A\:A`^.AR:A؁p.A9A@.A ¯9A8.A9AG.Aj9Au.AC9AHλ.A=9Aٻ.A8A`.A8A.A8A8.Ap8A.Aw8A@.A o8A` .A=Z8A.AJ8AQ.A 58A&.AR8Ak0.A7A8.A7A`?.A7AuF.A=7AN.As7AV.AL7A`a.A3!7A0i.A=7Ap .A 6A0.A 6A.AGm6A R.A@F6A R.Ap#6AX ü.A3 6A)м.AR5AU޼.A5A.Az5A .Azz5A(\.A@H5A+.A3+5Ap=.A@4A@H.A4AU.A@ 4AQ`.A`)4A@g.ARl4A`at.A O4AP8.A=84Ap .A 4AǙ.Ap 4A0..A`3A0..A3A.A3AL.A@3A .A@3A@$.AG 3A-.A 2Af7.Az2A?.A]2AG.A=2AQ.A@`2A[.AD2Af.A )2Aus.A 2AX.A=1Ap둾.A1A`a.A`f1AH.Az1A0.A=1Aھ.A]1Az.A1Aأ.A1Az.A 1A؞.A 1Az.A 1A.AGw1A.AZ1A.A 51A.A`)1A.A0A+.A@L0A@1+.A:0A8g+.A(0Ap+,.A0Az-.AH0Ap9/.A@/Aؒ0.A`B/A81.A;/A`2.A/A72.A4m/A2.A@r4/A3.A.A5.A=.AH6.A@.A8.AM.AX9.At.A,<.A -A>.A -AGB.A [-A@F.Am-A0J.A`;,AP^O.A},AT.A@>_,AX Z.A !,A_.A+A@e.A+Al.A[+Ar.A+A`z.A*AX.A*AH݈.A`*AH.A &*A𒘿.A@y)A8.A)A誩.A+)Ap.ARJ)Aп.A=(AԿ.A$(Anٿ.A#(AN޿.AMn(Av.A\(A.AiL(A.A=(Ak.A`0(A.A((Ap+.A`#(Ap.A` (A\.A (A .A(AXy .A#(A".A%(A.A`&(A .A%(A@.A"(AH.A@(A@.A@y(A.A3(A.A (A8.A(AP.A(A.A(Ap.A(AQ.A(A{ .AR(AP .A'A.A 'A.A'A.A 'Ah.A'A.A'A.A'AM.AX'A.A A'A.A'Ay.A p'AX[ .A@Z'A(.A@`E'AD.A1'AB.A'A>.AB&Ap A.A#0&A@dD.A&AH.A@5&Ax)L.A0%AP.A%A)U.A%A_X.A@%AS[.A%AP].A@%A>`.A@%Ab.A%Aȃc.A-%A0Ye.A%APg.Az%ApAj.Aq%AGm.A@b%A`p.AU%A Rt.AJ%Any.As9%A~.A`m*%A|.A%A_.A@C%As.A`B%Ah.A@ $APڛ.A|$AƠ.A`m$Ap.A$Aҩ.A$AP.A`$A0.A$A .A`b$A.A}$A.Ag$A.AR$A8.Ap#A@.A@M#A .A`v#A.A"Ai.A@"A.Az"AP.AjR"A".A@>+"A`a.A "At.A?!A( .A*`!A#!.A !A'.Am Aj..A Aذ5.A A _=.A@l A`rE.A cH AM.A3% AxhS.Aa A }X.A?Ap].AAJa.A uAd.A@ Am.A@^Aw.A@+AH.A`AP.A A.A^A.A`A.A֍A0.A`uAxv.AK_Ah.AJAh.A`f8A.A .A }.A<#Ap.AA0.A A8.AGA.ARAxd .AଌA>.Ah{AxX.AkA.A?]A4$.AvPA*.AGEAF.AA@J.A APMM.AAhP.A 8A}R.AA8T.A@Aa.AVAd.A9AHg.A Ak.A`KAfp.AA8s.A@WAw.A@.AH{.A@AG.AA.AgA.A A@.AA.AA.A`BA+.A AC.A-A3.A@Ah.AwA.A 8dA.AOAp.A8A.A#A`.AA`.AA.A=A .A)AP.A`KAP.A@A.A@A`.AA0..A`|AW.AAx6.AAH.A6A!.A`A0$.AQA&.AA).A`ܶA+.AA,.A Aw..AoA0.ANAo3.Ad.A@6.AA:.A@A`=.AGAh^@.AoA(hB.AǨA(C.A |AE.A }AE.A@gAE.ApQAE.A5BAD.A@2AD.A/#AE.AAHF.A-AhH.A3AJ.A@>A`Q.AA8W.A3A0].A`pA`ab.AEAPn.A AQ.A'A.AQAz.A@WA(ܳ.A`fA}.A@A(z.A@GA.AHuA c.A9AXW-.A+A0@.A=AD.A]AH H.Ao2AqL.A@ALQ.AA\T.A@Ap W.A(AMY.AତA&[.AA`\.AyAHg.A A8Zj.A A(Jm.A A(p.Aq Ast.A5X AȤx.Ah@ AP8}.A`) A.A6 A.AV Aأ.A3y A`.A@NP A0.A( Aȫ.A? A.AH A.A As.A`K A.A@. A.A`br Apl.AZ Ap8.A@D A`.A > A.A`: A(.A7 A.A u6 Aȇ.A6 Av.A8 AR.A`< A.A A A.A`H A0 .A@PM A .A P A.A@P A`.A0O Ah.AK A.AF A@u.A: Ap.A, A%#.A A%.A | A=(.A`K A*.A= A_1.A AX9.A ABA.A3i A ^E.AT AI.A \A AN.A`/ A#T.A A:X.Ai A[.A AQ_.A AGb.A3 A`e.A| A0h.A& Ak.AF[ Am.A4 A`o.A= Af.AzAQ.AAp.A`mApJ.AwA`.AcA88.A 1>AP.AA0.A@jAh.AHAP.A9&A.A`TA.AAH.A A.A@ A`.A=tA.A-WA.A 8;A.A| A.AA.A3A.A@A.AEApl.A`A.Aq~A`q.A;oA.A_A .AMAP .A@ <A..A@C)A.AA8c.AA.AA{.A AL.A@ gAr.AKA6.A0A .A@A .AA@.AA.A@gA .AA[.A`sAN.AOA.A@ ,Au.AAp=.A`)~A .ATnAh].A^A.AOA@p.A`AAȥ.A`]4AB.A`f(AD.A 8AP.AXAp.A A.A4AX.A@EA.ARA.A@AX.AA.A?gA8.AKA.A`/A.A@A.AhAؼ.ARA.A`mlA.A`4A.AtA.AzA.A A.AMA.A^A.A|AH.AAW.AA.AA(.A{A.A:A.A=.A.AA.AAT.A As.AǞAP'.AAv.ADAg.A|A8.AsA.AMA@$.Ao%Aȵ.AA.AA.AQA8.A@`~AX.ARA t.A Z<A@.A%A(.A A.AvA.A`dA.A@Ah.AzAxv.A@A.AߞAh.A JA.AA.A̜AR.AA0.A@A@`.A tA).AgAu.AYA .AA.AAX.AyA-.A`"AHr.A OA .A6A83.A@ Ah.A@NA.AmA8.AWAu.AdDAh.A_1A.AA`.A A.A`9Ap.AAxd.AA.A0AБ.A`Ah.AjvA .A WA.A?8AP.AAk.A`A_.A@AǼ.AAȤ.AA.AV`A.A9A.A[A@.AA(.AA.A`̠A( .ApA.A`DAAu.A@AT.AsA.AzAhb.AA@.A VAذ.A%Af.AA.AAH.A A8.A^A.A +AP.A oAX9.ARA8.A@C6A.ALAp.AAp=.AA.AA(K.AnA.A3A9.A`Ah.A9A .A}AP8.A?A.AAE.A`6Aǭ.A A0.A<AX.A?gAD.A`@A8.AA`.AAV.A@>A.A`A`.A`9Am.AjAذ.ALA(z.A.AǬ.AA.A`"AHܶ.AoA Ҽ.AaAP.A`LAБ.A8A.A%A.AA.A3AD.A@EA.AA.AA.AhAP.A@A.A[A(.AA.AA .AA`2.A AL1.AfApkD.A`A pW.A`)AZ.A \AX\.AAM^.AVA_.A@NA0`.A;A8Va.AfA؞a.A &Aha.AӸA`.A ɱA`.A \AT.ABnAB.A2A`4.AA.A AX.AA =.AphA7.A@4Aj.AAu.AAM.A0A]8.A ҞA3.A A81.A&AJ/.AokAs+.A SA(.A@iIA +.A@&A.A@AO.AAȥ.A`dxAí.A@^A.A@>A .AA ^.A`fAho.AiA4.A//A(- .AzA@F.A@iAH.AfaA(.A3A.A Am.AF~AL.A@^AP&@.ALA2.Am=A[%.A`m2A.A^.Ae.A &Ax:.A@'A.A1AP.A4AI.A`9A8.A>APxg.A4AU.AE;AȩC.AOA0j9.AzbA8V3.A spA,.A (Ah%.A A.A A.A Ab .ARAf.A<Ah.AA@.A@y A .A@%mA(w.AaxAr.A 8{A@o.AxAlm.AuA(j.A@gAjf.A`)Ac.AA kZ.AAp}V.A4A =X.AAZ.A@`AX.AAfS.A`A)=.A Aq+.A Ah.A@YA.ALyA .A A.AgA.AĿA`6.AfA0.AzA(>.A?A.A%A.A@,A 0.A0A.A`2A.AGAh.AdAXһ.A jA.A cAB.A ¸A/.AA(.A@pAHn.AеA`.A@A@W.A/AFP.A`=APxH.A]A;.AA-.A@>AO.A sA.A`Ax.AfAF.AjASֿ.AAp8ο.AAʿ.AAɿ.AAǿ.AApɿ.Ap1A83Ϳ.A@NAp8п.A=fApͿ.A qAp8ʿ.A \A.A qA.AHA8.A qAѡ.AAš.AA.A`)6Ak.AUAX.A wAז.AA.AA𣔿.ARAX .A`A#.A:A~.A ZAX}.A Ap=.AAp.AA(܏.A AX.A&A.A=AP.A`)RA(ܮ.AQdA뱿.AA8.A=A̭.A3A.A`A板.AAX.A AP.AR"Aa.AAh3Y.AGAS.AAQ.AAM.A`)AWG.AA?.A"A 5.A`f6A-.A@ KAأ&.A`lA0".A A!.AzA#.AͪAh'.AA`*.A A),.A@A؞+.A@2AG*.A@A#.A A]A0w#.A]A( .A@yjA .AuA.A A(\.AAأ .A׮AX4.AA.AQAP8.A'AЮ.Ap8A.A`IA.A&\A@.AnA@.AAf.A A0e.AAx.AA.A@A.AFA.A^AD.A ZwA.A ۏAT.AA.A`A.ApA޾.AQAھ.A@A?׾.A#A5Ծ.AN?AѾ.A[A(\Ͼ.AxAž.A@A#ž.AAHiƾ.AA?Ǿ.AAǾ.A -A`Ǿ.A@AǾ.A$TA*ƾ.A gAľ.AyAþ.AgA.A`A@>.AլAL.AA.A@A84.AA8.A`TA0v.A AЦ.A!A =.AX9Ap=.AQA8".A A.AXAΊ.A A.A!Au.AAg.AA(yZ.A>AXM.AGAvJ.AA`F.A ApB.A JAh>.AyA9.A0A4.A`JAQ/.AcA^*.A0~A%.A AX!.AA1.A lA.A@ A .AN1A' .A@nA.A A.A A.A@ !A.ATAٽ.AyA؎н.A<Aǽ.A@AP޾.A-A.A@>fA0f.ApAxˡ.A`AŖ.A KA@W.AgA.AA@z.AAYu.A@(Ao.A`KKAi.A`]mAc.A?A\.AݭAT.A&ABM.A@ AC@.A)A2.AiA@%.AA.AA.A@ A`6.AnA@.AA ռ.A A Rż.A^Ax鵼.A+A.A6Aݕ.A`gA<.A`A6t.AAb.A`)dAYU.AyADH.AA;.A9FAp=/.AA.AAT.A` A.A2A0*һ.AAH .A% AB.A`̠ A .Ak A0e.A A`S.A AH܊.A! Aˇ.A2 AS.A 8B A0{.A^P AM|.A\ Aw.Ag A s.Ap Ah3n.Aw AK.A AC.A A;.Aj A4.A. A+.AL A 4#.Ag A`a.A`f A/.A@ؙ A.A ¯ AH.A` AD.A@ A=.A` A( .A \ A.A \w AXʙ.A3 A/.AɎ AՎ.A A@Ɖ.A A0.AG A.A A(|.A Ax.AR A`h.A AP8T.A*AQ.A`f@AA``.AQAf.AfA8.AA.A \AR.AAX$.AA`.A@A.A  A.A&A).A |,Ax.A@,9A𒟹.A@CGA@Ƣ.AVA0.A#gA8.A@xA(\.A`A`X.AɑA.AAX[.AA`r.AίAN.A;A.A3A.AAiy.AK Ak.A`6A;i.A c<Af.A`@A c.A`CAp,a.A DA`^.A lDA[.A`fBAX.A>AZV.A ,A0HS.AAO.A A`K.AAG.A)ABC.ARA@.A Ah>.AAX<.A@A9.AA87.AAp06.A`A4.AA(3.A`T A`3.AAh2.AcA2.A(A83.A1A5.AXAP6.AzAH7.A@gA6.A/A>5.AjAU3.AA@0.AVA@..AA%+.A AP'.A@ Aa.A c$A.A;AP .A@PA.AB^A.A`dmAX.A}A.AAG.AjA@S.A Aݸ.AA.AGA9.A@qA .A`A`.AAko.ApYAj.AnAe.A@PAR`.A4APZ.AAT.AAN.AANH.A@WAA.A3AP<.A A8.A ZA3.AAXE/.A cAG+.AA83.ApiA: .A`̏A.AA).AA.A`A`.ANA(ڷ.A)A`Ϸ.A qAA.A@5|Aʯ.AݸAk.A \A.AAh.A`%A.A :AH.A`NAP8.AGaA~.A@ynA3w.A/}Ahp.AaAj.A`Ac.AA].AUA)X.AAТS.AA(N.AAI.A ZA=D.A$A>.A@53A08.A`f@A5.AyFAC2.A@ KA0..A NAf+.A lOAX'.A-OAy$.ANMA!.AIA(.A@DA.A@N>A.A`f6A.A@51A`.Ay-Ap|.A`K+A .A*A+ .A+A8.A@W.AH!.A |2Ap.A8A .A>A.AFAh.AOA؎.AYA(.AdA.AxnAP.A3xA.AA.AUA.AAA$.A@Au.AA.A@A.AAX.A@PAy.A|A@5.A sAL.AA8t߶.AA߶.AA:߶.A@A߶.A A.AA.A`AP.AA.A`f A.A`A.AQ A8.A*A.A@E5AX.A?AȘ.A4HAH.ARPAͶ.AÓA<̶.AA,ʶ.AAXǶ.A AHŶ.AA¶.A@A0.A`A(ۼ.AA.AAH.AA.A A0j.A Ah.AA.A@AP.A/A.A@yA`a.A`fA(l.A A .AA`%.A/Aߞ.AAA4.A6RA-.A \aAH\.A ‹Aނ.AܖAX.AРA}.A}Ay.A̰A@`v.AࣶApr.A`A#o.AAPwl.A}Ai.A`vAf.A`AFd.AսAPa.ATAhs_.A`2Ap].AA@dW.A@ AQ.AĐAkJ.A@AhQG.A@օA5D.A A'A.AA06>.AA p;.AA8.AęA6.A?A4.AA`3.A`A1.AA=%.AAx~.A+LA].AHyA .AA8.AA`.AA`O.A AP.A@.Aݵ.A)A:ֵ.A`o=Aε.A sOA8ǵ.A`A.A fA^.AkA㶵.AoAx\.AMpAӫ.AoAU.A`lAP꠵.ANhA.AaAhѓ.AWA).AOLAP.A ?A^}.AE0AXFv.AAio.A@ Ah.AAf.A`mA d.AAFa.AvAX^.A`AR[.AAFX.AA0HU.A`;AiR.AUAO.A@,AQM.AAPL.A@AL.A` AxJ.A@AGI.AqAiG.AA RE.A@gAi?.AA839.A3Ah7.A ( A06.A?(A6.A 0Ax6.A`9A R7.ARBA7.A@A36.A@>A4.AA02.A%A0.AWA-.A Af*.AA@&.A=A@$.A 8A؎!.AA.A@PA_.A 8A.A8,A.A 8CA0..A YAu .A ZlAj.A~A.AAo.A?A(.AAxi.AൺA.A JA.A`]A.A@A޴.A@Aٴ.AtA8մ.AAȘд.A3Ab̴.A  A}ȴ.A`f AĴ.A#& A.A 7 A̭.A@ Ap.At)!AӇ.A!ABt.A !A0q.AE"Ahn.A@P"Ak.A "Ah.A@$"Aad.A-"Ao`.A4"A@S\.A:"A8X.A >"AhS.A6A"AiO.A@A"AK.A@@"AH.A;"A E.A8"AA.A@>7"AH>.A7"A;.A9"AБ8.AH="A`5.A`B"A3.A`I"A0.AQ"A9-.Af"A 0*.AR{"A'.AV"A@%.A "A@_#.AN"A!.A "A.A"A.A #A(.A@%#A.A@>#Az.A@V#A.An#A .A_#A.A#APw.A#Aj.A@$A.A9M$A@ӳ.A$A`̳.A $A0YƳ.A $A}.A$AU.A$AP氳.A$A}.A$AX۩.A@$A.A%AH .A` %A.A%Ah.A`]%A.A@%AB.AR%AX,.A A%A2.A %A0f.A%Aم.A&%A(.A /%A.A9%A=.AD%Ah3.ARP%A#|.A%APYz.A-%Ax.A&A0*u.A "&Aq.A;&Am.AR&A(>l.Ah&Aj.Ax|&Ag.A 1&AȲd.A`&Appa.A`&A].A&AY.A`o&A U.A&AP8Q.A&A'Ax.AM'AG.A]'A.A (d'A`.A k'A0.At'AQ.A~'A%.A'A^ .A;'A .A'A .AB'AXu .A'AL.A'A((.Am'AV.A''A.A(A.Az(A@ #.A@ (AS&.A,(A /).A:(AP+.A H(Ap-.AW(A/.Ag(A`e1.Av(A02.Af(Ag3.A`D(A3.A_(A 3.A(A+3.A@(Ap82.AR(A$0.A`)A-.A q[)Ap|*.A`)A&.A@!)A@".AH*AЮ.A@[*Ah.A Q*A.As*A.A$+A(.A@f+A.A+A8E.A+A`a.A!,AЮ.A B,Ax.A`Kb,A.A4,A.A෡,AЄ.A,A.A3,A5.A 0-A\.A`;-Aܲ.A3-Aղ.A 1-Alϲ.A A$.A@ɲ.ANO.A8IJ.A@G{.Ad.AV.AԵ.AT.A׭.A3 /A .A//A@ࣲ.AQ/AW.At/AϚ.A/A×.AX/A8.A/A83.Az/AB~.AR0AH|.A0A؁z.A 0A x.A ~0Anu.A0Ap}r.A c0AUo.A0Al.Aa0AXh.Ap0AHe.A0Apa.Az0Aȱ].A`0AhY.A`0ARV.A0AR.A@50AO.A00AL.A@0AJ.A`շ0A@H.Aٸ0AF.Aॻ0AhC.A 0A0B.A0A @.A c0A?.A0Au<.A`0A`9.A (1Av7.A s1A`5.A O-1A3.A@D1AP+2.A^1A0.Aqy1AP/.A]1Ac/.Ax1A(\/.A@1A..A`1AX-.A@&2AQ,.AT2AY(.AO2A$.A@2A(!.A2A.A S3A.A+3A.ARV3AH.As3A3.A@3A9.A@3A.A Q3A .A`)3A.A@3A.A@ 4A.A(4Ax.AF4A .Axd4A(!.AH4AJ1.A5ABA.Aઍ5AQ.AR6AW.A?6A(>^.Al6Ae.A6A(Kl.A6As.A@i6A{.A7A'.A:7Aό.A`7A̕.Ap7A(.A`7A˵.A 8A0Ų.AP8AH.Բ.Ap8A(-޲.A8AP.A`8AK.A@'9Af.AT9A8&.AR9A0<.AO:AP8y.A;A׸.A ;A).A(~<A87.A3=A#z.A`=A.A M>A0..A`>Aأ.AY?A R!.A`)?A R%.A ?Ap%.A?A".A \?Af.A"@AH\.AGI@A.A[[@AG .A@gz@AG,.Ap@AH5.A @AG.AÍ@AT.Az|@A8`.A p@Api.Ape@Ao.ApQ@AX x.A`)6@A`a.A$@A.A`)@A.A(@A@Wѵ.A[@A.A`g@A.A@>l@A.A i@Az6.Az`@A RX.Ab@Ac.Ap@Am.A̒@Aft.A@Au.Az@A@w.AR AA|.AG+AAp.AR.AA.A'AA`a.A=AA’.A3!AA.A*AA`᝶.A6AAB.A>AAL.A ?AAX.A .AA.AAAf.A`)AAL¶.A@A(\ж.A@A@.A]AA83.A +AA`.AG]AA0.#.A(AA,.AAAP83.ApAAp:.A~AAQ=.AnAA=.A=ZAA:.AIAA#/A9:A@%!/A@9Ag"/A@>dAY"/A@,~YA#/A9:AɜI/A<(A`GI/Aa&A`GH/A`o7'AF/A"Ah9F/A AjE/Af AD/A@A`O5D/A ZtAJA/A 'AXI@/AVAU\?/AR|A>/ARAp<8/APA8/A@ %A`6/A,AmX5/A@3A93/At;A 2/AGDA1/A}JA2/A RA5/A`_Ap5/AaA؞6/A bAȋ6/A`dcA8y7/A,dA8/A aA.7;/A ]AlfD/A[AXD/A@VYAE/AVAF/A`[VAGH/A eQA@1HI/A`MAppJ/AsHAK/A)CAP+8M/A|h9Ah{K/A4AJJ/A@,AɜI/A<(A`)/A@WqA-(/AXiAh]%'/AjA'/AzsA5)/A^uA`)/A@WqA`OU/A}AU/A3}AV/AХ}AuV/A`}AV/Aqu}ApV/AwI}AdV/A/}ARV/A`}AV/A}A("V/A@`}AV/A|AHLV/A ~|AV/A 8|AV/A|A#V/A|A(V/An|A/V/A` c|A$6V/A@h|A؟=V/A`ok|A >V/A,O|A@FDV/A7|A/GV/A Q|APGV/A {A0nHV/A{AHIV/A@5{APV/A{AXV/A{A0aV/A{ApZfV/A{AhnhV/A`mk{AhV/A0E{A kmV/A9{AW/AyA4W/A@yA-W/AyA)W/A$OyA#W/AR yA W/AxAW/AxAAW/A`TxAW/A|xAV/AhxAV/Ao\xAV/A@>xA(V/AxAHV/A ZwApV/A`mwAV/AڷwApV/A@wA٨V/A@EwAV/AwA̜V/A`bkwAV/A(wAMV/ATvAxV/A qvA( V/A`;vA`rV/A@i\vAV/A`չvA0LV/A vAV/A`7wAV/A@ swA8s W/A@ swA.W/AqwAX+W/AwA(>W/AwAH]TW/AwA(bW/AwA` dW/AxA NXW/A@y_xAUVW/A8xA/`W/A sxAPrW/AyAtW/A@_yAtW/AyAyW/AyAhW/AHyAP^W/A LzA(OW/AzARW/AjzAeW/A T{A88W/A`ܢ{AW/A@{A W/A@(|A}W/A /T|Ax W/A;|AW/A|AW/A}AW/AAX}AX/A@N}A X/A`]}AhoX/Aw}ATX/As}A`2&X/A`m}A3X/Ao}A7X/AD}APCX/A@r }ATX/A|A;fX/A`|A(uX/A@,|AȃX/A;|A|X/A`k|ACX/A`\|AX/A`\|AX/A{|AOX/Aߪ|A(X/A`)|AY/A%}ApY/AAX}Aг Y/A@N}A0Y/A }APoY/A}A7Y/A,}ADY/A }AOY/A`]}A8VY/Ah}AUhY/A@#`}AxwY/AAX}A߆Y/A QT}AY/A@}A`GY/A}AX Y/A@G|AY/A |AhY/A c }AY/A|AHnY/A@,|ALY/Ag|A _Y/A<|A؟Y/A<|AhY/A@`|AY/A Z|AtY/A |AXY/A 8|A(JkY/A |AJY/A|A5Y/A}AH\$Y/A@G|A(`#Y/A;|AvY/A]H|Ak Y/A_|APoY/AD{AvY/At{{AY/Ag{AY/A {AX/A@{AjX/Ac{A@X/A@{AX/A@{AX/Ao{AX/A;H{A`X/A{AX/AzA0X/A@zA(X/A`zAiX/A@ؚzA`ЁX/AzAHvX/AozA|X/A,zAHX/A zANX/AzAX/AT8zA؟X/A@(zA@X/AEpAKY/A VpAxY/A`pAY/AQpApY/A@!7qA(~Y/A@qA$uY/AtqAUhY/A CrA(QY/A@rA oA؎PX/AoAppXX/A QoAQ`X/A`oAppXX/A`moAXNX/ApAOX/A6pAT/A yAl5T/AzA'T/A`d4zAT/A@czA` T/A3zA` T/A^zA/T/A`zAUS/A {AhsS/AP{AS/At{{AS/A{AS/A S{A"S/AF{A0*S/A[{A5S/A@({AjS/AID{AS/A{A@ƙS/A{ApS/A@{A S/A |A0~S/A4|A0~S/A@`|A!S/A J|A!S/A@W|A(-S/A}AS/A(}AS/AAX}AS/A/}A`%S/A}A S/A}AšS/AW~AS/A@P~AQS/AH~AXtS/A@,m~AGS/A X~AS/A@g8~A`6S/A`m ~AS/As}A@qS/A@%}AS/A`}AS/Al}A`S/AÒ}A0;S/A }ATS/A}AXS/A J~A@T/A8~AXyT/A~AH;%T/A ~A5T/A~AAT/A@P}AQT/A0}Ah;`T/A }ApT/A}A(`T/A}AȲT/A@P}AT/A*~AxT/A ~AT/A8~AT/A,~A'T/AD>~Ap=U/A`E~A U/A`<~Ap%U/A~A8'U/A@ }Ab0U/A }A2U/A*~A>6U/Ax5~A`=U/A@`d~AFU/A`)~A]U/A 8~AlU/A~AzwU/A 1~A(,U/A`~AU/AAU/A A@OU/A A߶U/A~A@WU/A`b~AX,U/A`"~AU/A~AU/A ~APjU/A@,m~A`OU/A`I~A`OU/A~A`OU/A}A~R/A OAR/A`yA^R/A`zAR/AAA`R/A㍄A0R/A֐A0R/A֐A>R/Ai~AR/A?]A(R/A`KLAR/A@r5ADR/AA8R/AA(R/A@A`R/A^փAR/AAAR/A AhR/A`A(R/AEA(R/A A S/A@.AS/A 1AsS/A q'A%S/AA>)S/A@G A*S/A A04S/A كA:S/AăACAS/A@AX9KS/A |AlSS/AzAx]S/A;@A8eS/AA3mS/AׂAXtwS/A`AXzS/A^A8bzS/AAPxS/AAӀS/A/ցAȃS/AA֌S/A_AqS/AZ:AȃS/A$ AS/AՀAxS/AA@S/A@莀AS/A vA pS/A3zAȔcS/A@yA^]S/A&AX(WS/AAIS/A@i0Ah"9S/A#Ah"9S/AA4;S/A@yA-S/A`TAX S/AAx(R/AYAh"R/A |AR/AĀAR/AA0ԺR/A# Ax(R/A4AR/ArAOR/AAFR/A@́AR/A@́AR/A сA`R/AA`R/AAR/A@ Ah R/AN/AR/AIA(R/AWAXlR/ApA!R/A@,~AXdR/Ao}AxR/A`9{A8VR/A@EjA R/AyAHR/AA0UR/AXAxR/A@ւAR/A A~R/AAyR/AA(oR/A]AgR/A@$AHngR/A2AiR/A @AmR/AZA`2qR/AuAPrR/AAHmR/A A$mR/AjŃA nR/A׃AX9sR/AڃAxR/A`ՃA|R/A6߃A(:R/A 8A,R/AAR/A@pA}yR/A 1AsR/A3ApR/AXIAZoR/A]APhR/A wAhR/AAnnR/A^AuR/A@yAR/A AH܇R/A@A'R/A`A~R/A OA?PR/A AJR/A 8 ApCR/AZA?R/A@rىApCR/AƉAnIR/A ЉANR/AANOR/A ۣA(\VR/A ۣAP<\R/AQAbR/A AhR/AωAspR/A9͉AsR/AMA5tR/A@A{R/A@,AX|R/AyAX|R/AiAX|R/A`SA`R/AMNA@R/A@WAxdžR/A@XAR/AGAR/A6A=R/A'1AxR/A2AHR/A`#AhљR/AAx:R/A4AXhR/AA(,R/A=AR/AƈAx R/A`dAR/A A88R/ARAxGR/AAxuR/AuA(R/A`hAHR/AgApR/AgAR/A ~Ah^R/A܈A{R/A A5tR/AAlR/A əA(9hR/A㧈A_R/AqAPjYR/A@AWR/AA(\VR/A QЈAMSR/AA"NR/A4APGR/AA``FR/A)ALR/A`(A RR/A60A"NR/A QA``FR/A UA':R/AYAPI,R/A`rA'R/A`rAY'R/A bAi&R/AIA!R/A;Ax>R/A 3A 0R/A@yA"R/AA|R/A-A@R/AAR/AxAR/A痈A"R/AAhnR/AA R/AAXR/AaAHQ/A AhQ/A-tAQ/A ~AhQ/A५A(>Q/A`ՈA-Q/AqAHQ/A@yA@Q/AU.AsQ/AHAR/AW]AdQ/A}zA Q/A 䃉AHQ/AyAXQ/A clAQ/A^|A8xQ/AfA8xQ/AkAHKQ/AщA(Q/A@yA/Q/A@ AQ/AAQ/A0AЀQ/A@AQ/AԉAIQ/AA8Q/AAQ/A@yA:Q/AwAQ/AtA.Q/AtA֩Q/AA0ءQ/AVAPQ/AAխQ/A`AgQ/A@AqQ/A sGAQ/AmArR/AA@R/AA(R/A$A5=R/A`uAMR/A@EJA@SR/A/AMSR/AA?PR/A A )/Ag@Ay/&/A`wAN&/A3AXyG(/AA )/Ag@A gp/A|ØA@}m/AAl/A ⬚A0p/AyuA gp/A|ØAh0$/AѐA!/A{A@+!/AA@ /AA(/A`dЮAh/A#~A/A@``A1!/A A!/A;uAh0$/AѐAM/A@$AM/A-A(\M/AAM/AghA@dN/A`#A88&N/A@A7N/AAHN/A ZAx6TN/AA gN/AAxwN/AmAN/A;{AΜN/A AN/A;{A9N/ANApN/A`9)AN/AApN/A@A@1N/A`AN/A AX9N/AXA O/A`AO/A`A.O/AA:O/AA^FO/AAX[KO/ABAHWO/A@A lO/A &AXV~O/A`AOO/AAO/A ]AtO/At!AfeO/AA-:O/A״A@S,O/AA`$O/AbA0]O/A AO/AAx'O/A` Ax'O/AlA+O/A*3A-:O/A0AEO/A%AXOO/AAQZO/A@AX9bO/A AhhO/A3EAZrO/AAHwO/A@Ax{O/AAHO/AwAdO/AHAO/AྻA@O/ARA[O/A`AO/AqAO/A)_AoO/A GAO/A 6A`6O/A`'AO/A@W!A(O/AAP@O/A@ A O/A@yA O/A AZO/A?AHO/A cuAvO/AVAFlO/AH=AbO/A@A(ZO/A@GAUO/A |ApXO/AėAdO/ABAgO/A[A`%nO/A2;AsO/A ATvO/A@A {O/A lAdO/A`AO/AtAO/AAO/AAO/A`AO/A@A@O/AAO/AAp0O/AŢAHO/AFpAO/A@JA[O/A`AHO/A}A|O/A`A@O/A`DAXO/AWA`O/A`*A"O/A`dAO/AAO/A`mAh3O/AAO/A)kA@O/A&@AO/AFApJO/AIA=O/AZAO/AfA |O/AtAx~O/A`AtO/A cAXWkO/A@kAeO/A`PA2fO/A7AأjO/A`DA@sO/A@GAyO/A@rA@}O/AAȁO/AAO/A@؅AO/AnAWO/A'VAWO/A4*AO/AAO/A`ADzO/A AHgO/AHA$UO/AAhCO/A@A 5O/A@A+O/A@AA(O/AZAг'O/A`.A"O/AqA &O/AAp,O/A AV3O/AIiA7O/A SA4O/A@CA8O/AAȩO/A@AO/AnA0O/ARjAPO/A0sAx O/A`oAeO/AவA8'O/A&ټAHO/A`;AO/A`;AhQO/A$ܼAhO/AҼAO/AѬA=O/A@AqO/AguAO/A=_AO/A`=AO/A AO/A$AO/AAO/A A(mO/Ay.APO/A`mBAO/A *AXhO/A$AO/AջA O/AA(yO/AઌApNkO/AzAPdO/A@ SAMO/A PA o9O/AV`AA(O/AvAPjO/A&}A O/ArAN/ArAN/AfAH)N/A?AN/AA(N/A=ACN/AA(~N/A@#TA`N/A`AXtN/AA@N/AzA EN/A`bA7N/ADtA@N/AݎAhN/AAeN/A$ϹAN/A`AN/AR7AN/AMjA7N/AAȇN/A`A8bN/A?~A@N/A@#TAwO/A_#Ap O/AAO/A qܹAСO/A@AȂO/AaA O/A rA(O/A&JAO/A/AA(O/A8AV3O/AA>O/A ɸAxGO/A飸ARO/A`hAfO/AIAGuO/AaBAȉO/A Z#ALO/AA8'O/A'APH/AAH/AAСH/A cAH/AA8#H/A@AH/A AAH/ADKAH/AtAH/AA'H/A-AkoH/AO?AUH/A+A _0H/A A H/A VApH/AA88G/AnA(G/A=A`G/AA߿G/A`A>G/AAG/AFAG/A;ɡAXG/A ⁡A`ӍG/A/AG/A ͠A>G/AHzAPG/ANAG/A AG/A`)7A G/A@yAG/AAG/AzAYG/A@NA`G/A`;AG/A`f'AxG/A ˑAxG/A`mAG/A@RAhoeG/AUAPZG/A'ŠAQG/AAHEG/A`vnA9G/A ApJ G/AOAHKF/A@bAmF/A`[3A(F/A ZAF/A`mAdF/A AF/A AF/A ApIfF/A AMF/AGA0PF/A0AXF/AAhs[F/A^AȤNF/AFA`3F/ABAp_F/ABAF/A@bAF/A@pA'E/A@WȟA8E/A`mApE/AA̟AE/A@pAXtE/AQA8gE/Aw՟AȥE/A`#AȥE/A9~A +E/AXvA@)E/Aj/A0-E/A A0E/A@pA0E/A&zA9E/A A@BE/A lATZE/A@5+AnE/A AE/AfܝAhE/A@AE/AJAE/A AxE/AAfE/A@qAXE/AmAF/AXRA0F/A`FA?F/A`6ASF/AA8'oF/ABAhF/A`6A/F/AAF/A`mțAF/AaApF/A A F/AŨAmF/A`YAF/A̙AG/A;ApF/A`JAiF/A`YApF/A@,Ah F/AmAFF/A&A`F/AȘA@F/A (}AEgF/AFuA=F/AFuAxF/A`̔AE/AUqA|E/A 8yA E/AAFE/A]AyE/AAoSE/A;6A AE/AX.A +E/A@QADE/AA`vE/A_A0D/AAPAyvD/A QrAjD/A@nA pED/A tAh+D/A@AAD/AMĪAp[C/AcATC/AXA|C/AsAYC/AA؎/C/AA C/AAB/A ѬA%B/A-DA5B/A@AB/A3A@B/A AC/AIAa8C/AA"LC/A` (A {C/AhAH|C/A8AHvC/A`MA(hrC/A)5AVC/AOɳA4C/A0ӳA C/A lpAбB/AAPkB/A`A`LB/A`A!B/A@ApB/AAA/Ah!AA/A AKA/A#'AwA/AǓArA/AAhA/A`EAa B/ARA RB/AA@B/A|A@BB/A?A B/A_A0A/A`A(,A/AFA0A/A_A^A/A@r.AHrA/AL@AA/AvA((A/A?AA/AUηA0A/A*AȂA/A?AݫA/Aj4AxA/A nAA/A*A0A/A?A A/AθA((A/A?AHzA/AA0qA/A A8iA/AUAeA/A?*A0X`A/A?*A0X`A/A SAp=jA/AjAmA/A uAؒrA/AAؒrA/AjA(hnA/AӹAp=jA/AApcA/A*"Ap]A/A 0APA/A@5uAQA/AjAQA/AͺA((OA/AAȲCA/A&A"BA/A3Ai7A/A\AR:A/A*A?A/A A0?A/A»A];A/AۻAr=A/AA88DA/AAp}>A/AA;A/A =A((6A/AiAxm0A/AA0X.A/A ӼA(/A/AA-A/A ;AM-A/A iA3A/AA.A/AAؒ'A/AjŽAR!A/AjA0H A/AA(h#A/A*[AA/A`5AMA/A?+A A/A@AA/A?]A A/A|AA/A?A8x@/AjA8x@/A ѽAxA/AA A/A`A8 A/A AA/A3AA/AQAxmA/AUrA A/AA@/A`zA@/AUA@/A*A@/A`ARA/A*A0A/A?AMA/A HAA/A?AȂA/AA(A/A?οAr A/A AA/AAB@/AA]@/AA@/Aj6A(@/AEA8@/A8A(@/AQAM@/AjA@/AAx@/A`A8@/AA@/A*A@/AUA @/AA»@/AAȂ@/AAݱ@/A #AТ@/A?,A@/A?EAؒ@/A?^A8@/AdA@/AAb@/A*Aؒ@/AA8@/A`A@/A'A8x@/AAp=@/AjA@/AA@/A?&Ax-@/A,AȂ@/AGA0@/A`A@/A*A(h@/A AB@/A`A0H@/AA(@/A*A0@/AA@/AUAp@/A?A0HA/AUA0A/AA0A/A A"A/AAp}%A/A(Ap}%A/A ^AxA/A?ABA/AA(A/AAA/A9A.A/AUgA88DA/A qARA/AjiAТgA/A?LA0sA/A`$AƒA/A?A0A/AjoAA/AU"A]A/AA(A/AAA/AUA A/AA8xA/A`AA/ATA0A/A*VArA/A*VAA/A*=Ap}B/A`ArB/A`AB/AA" B/AUGA8xB/AAxB/A Ab)B/A`Ar7B/A*ACB/A?Ap}QB/AA`B/A?A(hhB/A A(tB/A?dA]B/A %A0B/A?οAȲB/A AB/A_AТB/A`Ap=B/AھAp}B/AA0ȣB/AUrAp=B/A*UAp=B/A*#AxB/AAȂB/AɽAp}B/AjA0B/AjAB/A iAC/AYA0C/AYA((*C/ARA3C/A A3C/A A81C/AjA((*C/AUeA"C/A?A C/A7A8B/A AB/A*AxB/AAB/A uA cB/A^AB/A?PAB/A,A0B/AٺA B/A*Ax-B/AdA(hB/A2ARB/A? A0HC/A 뷹Ap=EC/A 뷹A0eC/AU¹A8zC/AKڹAx-C/AA0C/A AC/A*TAbC/AkA]C/AA0ȶC/AAC/A?AC/AiA(C/AU?AxC/Ac AC/A AC/A?ٹAxC/AjݹAMC/A ̹A8C/A߹A0C/AAC/A &"AȲC/A7A D/ACA]D/ARAD/AUqA(hD/AAxmD/AA-D/A`A;D/AA0H@D/A {ABLD/AwAxVD/A`yARZD/AՖAr]D/A*ABeD/AͺA(huD/AjAD/AկA0D/A ApD/AUAHܭD/A@A0D/AcAhD/A0AsD/AAND/A@ADD/A@A"D/AAE/AN,A@E/A$A }E/A`A#E/AmѹA *E/AmѹA0E/A$A26E/A`2A 9E/A4[A+@E/A/AtRE/AAkaE/AtuAlE/ALAXsE/A`2AˆE/AAH E/A 1A0E/AhA`qE/AhAE/AApyE/A[AxE/AAPME/AA@~E/A AE/A,(AhE/A@yBAXE/A DA 0F/A!A83 F/AAxF/A`)A]#F/AعAPZ(F/A@AȺ'F/A A&F/AA@AH;,F/A'hA1F/AwAJ/ASA`J/A HAxJ/A`1A0J/A:A誖J/AxA pJ/A"AJJ/A qVA8J/A ݼAXJ/ABJAJ/AAWJ/AAPJ/AA(J/A˾AkJ/A$A@RJ/ACA7J/A@>A5J/A@ ArAXJ/A@yAJ/A A(J/AVAiJ/AApJ/AAJ/AARJ/AU+AȥJ/AfiAJ/A`vAHaJ/AA J/A@,A1J/A@yA0J/A@yA؁K/A3AK/A 8]AbK/AؖA0K/AAK/AAK/A@-A K/AV^AK/A@A(K/AAK/A`AwK/A9 A$ K/A'RA`K/A zA K/A AH-K/AMA:K/A?AGK/AAZK/AAgK/A`bA mK/AHA`wK/AAPK/AA8K/AAH.K/A`mAK/A :AK/ALAjK/AxAK/AA@K/A`DAK/AA _K/AcA`CK/AAwK/A֪A RK/A AK/A`nA&K/A`wA@K/A;>AIL/AAL/A? Ap,0L/AAML/ALAML/A cAML/AANL/ApiAPPL/A=4AQL/A@ASL/A 8A]L/AVAbL/A$ABdL/AA$hL/A^}AxL/A84AL/A84A%L/AjiA@AL/A sAȶL/AAPL/A`AL/A A@`L/A A]L/A`AYL/A`vA7L/AKA0L/AAkL/AAXšL/A@wAL/A AL/A8AL/AA}L/AAL/AVA'L/A@ AЀL/A 8A}L/A AL/A+AL/A uAL/AɾAL/A APL/AAL/AAL/A`] AL/A-AL/ALA,L/AൊA8L/AmA8L/A@AHL/AAL/A`APWA[/AkKA[/A BA[/A9A@5\/Ag Ax\/A ~A8\/A A1'\/AA1'\/A-A0-\/A*As7\/A[ AA\/AAJ\/AA0{V\/AA@d\\/AAȔb\/A`KAXt\/A@A;\/A-A\/AA\/AvA\/AaAPZ\/AaA\/A@8A\/AA\/A@ءA\/A`A\/A@A\/A^AV\/A ZwA\/A_A\/A@EApp\/A A\/A@EA\/A A范\/A[A\/AkA–\/A}A0&\/AQ%A0&\/A@Ai\/AfYA\\/A9Ah\/AIA`C\/A@A`\/AAL]/AxjAf^/A A`%_/A4A`/A`m<AD`/A`$Ag`/AtIA@1`/A@6Ah`/A`A`/A 8AȘ`/AmA a/A ALa/AAha/AxA`O a/A@tATA5g/A`AXyg/A AAHg/AxAf/AhZAsf/A@NA g/A@֊A|g/A`}AXg/AAg/A Z+Aأg/A /AHuh/A`Ahh/A AX2i/A Axi/AUA/j/AA6j/A yAWk/A3Aql/A,Afl/A8Al/A@Al/A0A@>l/AAhl/A |As l/A@MAk/A'Azk/AACk/AUA5j/AAԕj/AAAj/A`uA!j/AA`i/A | Ax\i/A@ tAi/A@wA _i/AZAi/AAi/AA0 i/A@NAXcj/A@yvA`Xi/A@A0i/ApA0Ei/A`K>Ahi/A A0i/A@ݵA)h/A` HAh/AAh/AòAxwh/A|A Nh/A=A+h/AZANg/ANAEg/A@iA g/A-A؍yg/A@yڬA3qg/A`Amg/AKCAdg/AVĨAOg/A@jAJg/AAJg/A`LA:g/A AP*1g/AzA#g/AաA@uf/AРAf/AVAff/AƠA f/AA0jf/AkA0jf/A pAhf/A#ZA`f/A;A{f/AZrAIf/A'AHf/A`)vA;e/AA(ܡe/A A%ee/A̙AXFe/AौA4e/AjAm!e/AܤA8e/AAd/A AWd/A@.A0d/A4Axod/A׈Aad/A_A`Zd/AAh{d/A  Axod/AAad/A QA>3d/AA)d/A6өAEVd/AAHdd/A[Aad/AW2APQd/A"ѬA7d/AǬAAAd/ALA'd/A@rAc/A AXc/AGAxvc/A Asc/A.A@ec/ANAac/A)-Azc/A`{Aʖc/A?A@c/A Ah d/AA5d/AzcAh@:d/AdAȘJd/AAad/AkAd/AOAd/A)AHad/AաAxi e/AnAH;e/AGApe/A`巟Aޯe/A`巟A e/AAe/AtAe/AAe/A ~Ae/A APɮe/A^MA،e/AXATe/A Ae/A`A`d/A`Ad/A ANd/AA8Vd/A`KAvd/A`>A5d/AA8d/AۘAc/A!Aͤc/AoA"c/A@#Alc/A`TAx\c/A Ab/AjsAb/AzǖAVb/A`)ږAa/ANAXa/AtAa/AA a/A cAPa/A}AHa{a/AAxia/A AӎAa/AA%a/AVA0b/AA4Kb/A@2A8pb/A@Ab/Ak`AHib/A@Ab/AAb/ALA#c/AڍA7c/A`f1APgNc/AʍAxzIc/A\AX4c/AތAX4c/AXA6c/A֋AGc/A4AGc/A ⃌AHVc/AAmc/A2xAHc/AA0c/A |?A#c/A֌A#c/A@5AP/c/AAP/c/A9dA`c/A`Adc/A`mҊAxcc/AkAHvc/AWAoc/AŲA#c/A&A#c/Ax{AhQc/A4ƇAMc/AAsc/A'<A&_c/ACArKc/A콄A?c/AKA,c/ASAP c/AA!c/A<A<c/A ݅Ab/AAb/AA7b/AAdb/A lA%b/A…AUa/A㳅AQa/A3;APa/A]%AXa/AvA(>a/AҨAH8b/AH,APAb/A`KA(b/AdxAa/A`APa/ANAa/AAga/ARAa/AGA@ b/A`~Ap b/A}A}b/A@,|A`b/A*|AXa/A*|A֜a/A`Ӻ{A8Ea/AzA0Ra/AzyAP/a/A5yA`/A@yzA`/A`p{A`/A\|A`/AU}AR`/A #}A$`/AC|AO`/A`v{AX _/Ac{A8_/A 8zAТr_/A` LyAX_/A?yA^/A?yAY^/AyA0Ts^/AhxxAJ^/A`YwA`"^/A`vAs]/AvAJ]/AwA"w]/A OyAQE]/A kxAS\/A`wA\/A/vA@¬\/A vAX<\/AoTvA[/ABtuA{[/AuA4[/A "vAxZ/A vA'Z/A@WwAUZ/A|ZxA\Z/AxA [/AxA*[/A AxAW[/AxA(z[/A` 5xA0[/AxA> \/AxA0LS\/A|ZxA@W\/AxA \/AxAp\/A`ryAh]/A6zA]/A`E{Ah"1]/A |AH-i]/A%|AА]/A`{AD]/AhX{A*^/AhX{A\W^/A}{Af^/Ak{A@­^/A s{A8^/A 8|Ap^/A|A^/A@yc}Ahs^/A ~A^/A^~A^/A-IA@­^/A AX^/AAv^/A A(z^/A@>A_/A~A- _/AH~A4C_/A=}Ax:__/ATV~AH_/Ai~A_/At~A_/AA%`/A^~AR`/Aൗ~AY`/Ao~AD`/A@>Ac1`/A`2AP`/A XA`T_/AƢAM_/A.AJ_/ÁAF_/ApPA =m_/A /AE_/A;A_/AA^/A`A^/AA q^/AuAbz^/A Abz^/AA9^/A`)A0^/A@tAЦ!^/AAN^/AA`^/A RAas^/A {~Abz^/A`}Af^/A|A(`l^/A|ApR^/A@N|AU4^/A |AO^/A}A(J]/A>}Ap]/A`"Z}A]/A}A ]/A`}A6]/Ai~A84]/AA2]/A@Ahމ]/AdaA݂]/A`)A؁d]/A?TAP|H]/AOAX]/A.AH-i]/A uAH-i]/A<A()T]/A@A5]/A`9A ]/AIAr]/Aj}AP]/AAAp]/AA\/ÁA\/AA`\/Aj4AX[\/A)A@W\/AA@W\/AaA0\/A`HA0\/AwAHPh\/A㜄AU\/AdA@\/A@5AU\/AA@s\/AEA\/A lA\/AAX[\/ASA(\/A@WA\/AlA(\/A6Ap_\/A@qAX\/AňA(\/AQA \/A=ˉAX[\/A1Ap\/AcASv\/A@$AHPh\/A AMZ\/AASv\/AOA\/AیASv\/A/Aq\/AॖAxe\/AAHPh\/A#dAMZ\/A@AN\/AAx9\/A`VA\/A@sA?\/AǏA`\/A`1Ap\/AʎAؒ\/AגA\/A>A?\/AAB"\/A A> \/ẠA`$\/AAH&\/AxAؒ\/AxA;[/AڊAp\/AcA+\/AAB\/A S;ApIE\/A=ˉAx9\/A ~dAU\/A,wAx\/A ~dA0\/AAx\/AனAj\/ABA@G\/A`A(2\/A~AH;\/A AXE0\/AAP\/AA[/A AyA[/AAh/[/A}Ah/[/A鸄A06[/A`v#A[/A ƃA =\/AU{A\/A@P_AB"\/A? A\/A`vlAH&\/A@!AI\/AZAc\/AA\/A.A(\/A ڀAг\/A@NA\/ANAX[\/AA\/AzAг\/A`d~Ap_\/A@N:~Ap_\/A}A \/An+}A \/A|A \/A A|A0\/A@y{A(\/AO{AY\/AAzAq\/A`IzA`P\/A@yAp\/A@,$zA[/AzA-[/AzAx([/A c<{A%[/A|AX$[/A@N|Atn[/A|AM[/Al}Af([/A }A [/A_~AZ/AAXRZ/A (-ALZ/AAHZ/A`T AZ/AA0Z/A A`Z/AAZ/A_AXRZ/A`vlAЦZ/A܂A@NZ/A0AXZ/A଄A0tZ/AkAAkZ/AwAZ/A`AHZ/A`AZ/A0%AhZ/ALAXZ/AFALZ/A AXfZ/A^A@}Z/A@#A(JZ/AA؟Z/A `AZ/A@$AHZ/A@AvZ/A^*A?]Z/A`dFA?]Z/A@#A0tZ/A AZ/AzAOZ/AibAZ/A̋AZ/AAZ/ADAZ/A@A(`[/A@A&[/A2A&[/AAT[/AhAvu[/AhA}[/A@yHA@~[/A=ˉAҚ[/AAҚ[/A@$A}[/AËAX$[/A =A!z[/ATA@F[/A@A8[/A3Ai6[/AیAf([/A@>9Ah#[/A`TAe![/AzmA [/A:A^Z/A`A0YZ/A蝐AxZ/AAЦZ/AAxKZ/A`A0tZ/A~AhZ/AA_Z/A;A_Z/ApA`CrZ/A A`GZ/A J(AZ/AʎA?]Z/A@A;HZ/A:AH.Z/A`D A0Z/A /AxY/AA`Y/A ZōA8xY/AIqAY/ANAH ZY/A`΍A>Y/A@p5A`TY/A vA@ Y/A@A@ Y/AॖAY/A`AQY/AOAHPY/AHAQY/A`7A@ Y/AAp"Y/A(AY2Y/AAгIY/A5A aY/A,wA@}Y/AA–Y/A`mAkY/A`AjY/A@qAiY/A6ApzY/AɇAWY/A`A>Y/A`B0A@W$Y/ARAHPY/ARA0HX/AMhA0HX/A &A0HX/AնA`X/AbA@ Y/AAH-Y/A A^GY/ALAccY/AQ͆AY/AQ͆AXY/A`A–Y/AA–Y/A lAY/A`ëAccY/AA\@Y/AqAY/AajAQY/A`KAQY/A@5AQY/A A0Y/A@ AгIY/A`KCAཱྀY/AAY/A`AY/A =A'Y/A JA'Y/AAP|Y/A@tALY/A]AY/AzAjY/A]A0wY/A4A bY/AwAFRY/AA>Y/AހAX+Y/A$AEY/A?TA8X/A \A&X/A \ApX/A?TA`X/A(Ax)X/AAKX/A좀A(-X/A cAKX/A@> AxuX/A`AfX/A_A@ZX/AAH?KX/AAXRFX/AwAp>X/A@r€A@,X/A@AX/A3AYX/A |DAW/AsAW/A \ApW/A6AW/A@AiW/A /AW/A[ApAoW/A RAlW/A}A0oW/AAثwW/AAoW/A`6ApgW/Aj AiW/A~AmW/A~A~wW/A~A|W/A[~AiW/A`B~AX]W/AI}AXW/A }AQW/A W}AWW/A}AbW/A |A`SW/A@߰|A0>W/A |A9W/A}AzW/A@,}AH W/A/}AV/A(}ARV/A@E }AȔV/A|AV/A|AAV/A}|APkV/AT|AV/A@E||AV/A|AV/A|AHV/AE}A V/Aq}AjV/A4}A2V/AH}ḀV/A@`}A٦V/A~AH?V/A^~AV/A@N~AБnV/A~AX^V/A/A0fIV/AFA,V/AAx V/A`fCA(U/A@WAU/A`̀A(U/AŀAh"U/AŀAU/A ҷApAU/AIAHyU/A AfU/AxAZU/A ҷAX[RU/A |ـAFU/AA$U/A@ApU/A AU/A`vAU/A QAU/A΀A`CU/AqĀAH;T/A6AT/A@̀AxT/A AUT/A Ah@T/A AAXT/A`AީT/A@ᅀAT/A삀AT/A zAhzT/A &tA cT/A_wAYT/AA`PT/AhǀAH!OT/A`ART/ABAx\XT/A@fA`\T/A-A(VT/A AP UT/AhLA`PT/AAOT/A AH?RT/A ALT/A`,AKT/A@yXAIT/A-˃AcKT/A=AKT/A昄A1NT/AلATT/A@A`SdT/A_A`SdT/A)A^T/A}A^T/AхAdT/AslALnT/A`AA$zT/AXA規T/AA|AT/AApT/AA@T/AeA0T/A l;AT/ALA8T/AeA@T/AvAp0T/A`LJAU/ACAT/A,_AT/A,_AXhT/AvAZT/A-A`TT/AHA@T/A qAT/A@WCAXU/A uvA8U/A@֥A_]U/A@ȉAȘU/AAU/A@.DAH{U/A`2AXU/AAhU/AhA U/A`HA)U/A`mjAV/AlA"V/A @A 0 V/AApJV/A QҊA@V/AjA U/AiAU/A`;0AX9U/AR AU/A͉ApU/A A"U/A@AhU/A |lA@U/A@PMAU/A@5ABU/A \وASU/A`vA{U/A`DAPMU/A&ɈA@uU/A8AU/A JAU/A`TA |U/A0߉AV/A)A6V/AЯA(V/A@bAyBV/AwAJV/A"A>V/A@eA4V/AAT1V/A@݉A@V/A AP,V/AGAXl>V/AlA[?V/AɊA=V/A0A0CV/A=A:V/AuA,V/A`A!V/AEA'V/AA2V/A A`XDV/A DA(i[V/A`5A( iV/A`AȔzV/A0AV/AXMAV/AxAH.V/AȌAYV/AAV/A?5AV/A`KAH{V/A^hA W/AA@W/AύAPMW/AwAW/AMAW/A`pA`a*W/A ujA)7W/A`TAAW/A3ΎA0JW/A֎A[W/A0ӎAqW/AA{W/A^AW/AMFAW/A;AW/AMAXW/A TAW/A@CUADW/AotApAW/A@A8W/A@ǎA0W/A@юAƄW/A A0yW/A@AqW/ArAiW/A ZWAiW/A1APaW/A?ACWW/A A[LW/At/AHrAW/A JAn.W/A JAW/A]APZW/A jAV/AAqV/ADڏA]V/AA W/A`QASW/AEA W/AݐAW/A`)'AhW/A TAg W/AsyA(W/A]A(W/AőA03W/AaAZ4W/A@C$A(h8W/A`)LA?W/A@SAxHW/ATAIW/AvAaBW/AA;W/A`mAM0W/AA@*W/AܒAd%W/Aj A)W/A/A(0W/A |TA(W/A`[A6#W/A AAW/A6A(9W/AA@V/AēA0V/A@yAV/A)ѓA+V/A@AV/A` A(V/A-,A8V/A HAV/AMAЄV/A3A\V/AA(~V/A6AOV/A`KA(V/AA-V/A@A8'V/A` A8R~V/A@E APjV/A@E AeV/A@AXV/AAFV/A`Ao9V/A~A,V/A Ah%V/A@NAP$V/A-̓A 'V/A@E A,V/A=A2V/AzA7V/AߣADV/AAQV/A9ݔA7^V/A@ AkV/A (AUyV/A`mRAV/AqAwV/AzAV/A`A V/A#AV/A`KIAV/A`tA tV/A \A޴V/AHAYV/AϖAXVV/AAfV/A` AHV/ACAH.V/AoAzV/A6A0ԓV/A 䨗AЀV/A@ җApqV/A@A@bV/A@A7^V/A |HA0^V/AAptV/A̐A؞V/A;AP V/A@AHzV/A]:AƏV/AtA V/AAEV/A@NA tV/A@ƙAxV/A9љAhV/A`ApV/A AЀV/AF]AHV/A AoV/A ØADbV/A ØATV/AAhPV/A֘AQV/AWAHPV/A |HA[V/AwAlV/A`mA(OV/AjAV/A`AzV/A4ARV/ATAǧV/AʖApV/AA`V/A}A`V/A[0As|V/A`TڕApYV/A ZpA3KV/AvA@6V/A ~A#V/AEA"V/A@YAXV/A&AV/AoA0 V/A锔AV/A`2ACV/Aj2AV/A@#ۓA V/AA'V/A`6A/V/AAJ(V/AA)V/A u9AP(V/AAl&V/A`BđAV/A`БAo V/AAU/ArA=U/A` A8"U/A@.AU/AAPkU/ARВA(U/AA(U/A@,GAnU/A u9A8U/A [AU/AÒA0U/A"AU/A@JAxرU/AFA+U/A Z&AIU/A@ ޒA`U/A@ABU/A5AؒU/A (A@U/AAYU/A*A U/AMAU/A/AU/A`D_AU/A`vtAPMU/A 8sAU/AuAV/A`vtA6V/Ak#AJ(V/A OA/V/A`fA9V/AA@7V/A!ADV/AXAOV/A`"VAplVV/AAaV/AXƒA(\oV/A`BA`|V/A@A(ʊV/AAAV/A;ARV/A@ >A1V/A <AV/A`*AW W/A,AW/A ~AW/A@hAW/A@C$AW/A |/ASV/A!AV/AёA؟V/AcAV/AqAcV/ABA.V/A` AHV/A@A`V/A^A V/AAXʢV/A@AƏV/AAlnV/A@lA|=V/A@rJAN#V/A AU/A@C0A`%U/ATAXU/A AȩU/AAnU/AxAx\JU/AєAAU/AAp_-U/A ՋA`U/A<AXT/A SAbT/A SA@T/ALA@T/AՏAIT/AApnT/A}QA`PT/A;UA@$2T/A-iAT/AzVA*T/A ,AKT/A֊A0 T/A汊AAT/AI|A0HS/AjJAUS/AAS/A J6ANS/AYAXS/A쐊APS/AA0S/A&ĊA'S/AۊAcS/AAA|S/AA(iS/A l6A=TS/AUA _KS/AA>S/A`9ȋAȶ4S/A sA0S/AA#S/AA88S/AA@qS/A`6AȵR/AeAR/A AR/A@AP R/A@AUR/AAR/AgA@ӨR/A"ApR/ABAgR/A`qArR/AaAP[R/A$Ap[CR/A qA`3R/A qAxR/A`A4 R/AA|Q/A AQ/AذAQ/ARAQ/A@謍AUQ/A_ЍAh]Q/AA8tQ/A 2AQ/AXVAQ/A jApQ/A`mA8'Q/A`qAT~Q/AfAhsvQ/A`AmQ/AͼAȤiQ/A &AhQ/AoAZQ/A2'AYQ/AAHaQ/A@ A[Q/A`mAQQ/AJAj6Q/A`+A()Q/AA(Q/AA Q/A@.AZP/A`6AhP/AhRAxP/A}AXP/A AP/AȎA P/A}A((P/A`6A`P/A@.A˘P/A[A˘P/AɴAϗP/AyAޓP/AdJA#P/A@.A`6}P/AA0P/AǨA0P/AAP/A^AP/A`BJAP/AAEyP/AA0wlP/A`9ȋA@]P/A`vAQP/A)̋AJP/A`AJP/A*A@DP/AQFA@8P/AaAn,P/AqAH P/A^AP/AAP/A`mARO/AaAh^O/AAO/AЋAO/AЋA`O/AA O/A@yuA0O/A EAO/A;AؒO/AۋA`O/A"ABO/AeA O/AAPO/A-ԌA O/A A/O/A@>AX$O/A@CAX$O/A@A O/A`mA O/A(AhO/A S܎AO/A A#AO/A्A O/AЏA RO/Av܏ApO/AA8xO/A@VA@NO/A A(O/AAO/AAPO/A*;AO/A`^AO/AAxO/ȂAPO/A`A`O/AKAeO/A+AO/AtAO/AAЦO/ADA@OO/A SAP/AAP/AqAn,P/A SAX9:P/A3ACP/AjAXSP/A`ZA@]P/A3AXhP/A4AiP/AؒAfP/AAcP/AI~A@]P/A`OA_P/A'AdP/A^A@dqP/AApIxP/AfAP/A`v'AP/A@AP/A97ApJP/AH3A,P/AAP/A@ԐA8P/A@ԐA P/A A@uP/A CAxGP/A CAHQ/A CAQ/A@y*Ap%Q/A@Af7Q/A AX9CQ/A APRQ/AA fQ/A-AX}Q/AKAX#Q/AܐAQ/AA@Q/ANAQ/AA/Q/ABAQ/A@ԑApQ/AAQ/A`OAQ/A`AТQ/A`ܒA@uQ/AARR/A`ZARR/AARR/A 7AHKR/AnA R/A QAȶ+R/A QAHr=R/A׭AUR/A |ŔAHjR/A@NєAPuR/AAQR/AZAۦR/A@yAR/A`]͔AR/A QAR/AjA'R/AnApR/A'AۦR/A'AxR/A?AaR/A@rCA/R/A/A` R/A8A07R/A`KɓA07R/Ak~A R/Ak~A`R/Ak~A0R/AjAN S/A AGACS/A]A S/A൭AS/A@,ѓA#S/AAX'S/A#AX'S/A`[A%S/A橔A0"S/AA)S/A)_A@qS/AHWAR/A gAR/AvA07R/A 뱕AQR/AAYR/A@!(AR/AGAR/A-gA˪R/AIA@ӨR/A (AϩR/AD(AR/AcAHR/A@YAQR/A`UAR/A@IAR/A@AؒS/A9AX$"S/A*A3S/A A DS/A@זAHS/A@ƐAiRS/A:A@]S/AAXiS/AA0wS/A AS/ATA}S/AA S/ATAS/AA0S/AAS/AA8xT/A@AX$+T/A@p"A3'T/A|aA3T/A-ABT/A@A|VT/A"A,kT/A"A}T/A'>APT/ADyAqT/A`AxKcT/A@חAxKcT/AACeT/A NAP8hT/A 8A zT/AܜA@†T/AeAxT/AYA[T/A@ APT/A@5ȘAX9T/AAAT/A` RAT/A ZAT/AङAzT/A ܙApIT/AA(:T/AbAT/A@ĚAXU/A-ܚAU/A`v AxU/AsNAXJ*U/A@}A89U/A`A89U/AmԛA8LU/A@Ah]U/A`AidU/A@6A^U/A`yATU/AĜA8LU/AAEU/A@#3AFU/A@>nAHRU/A` vA`qbU/AčAHKlU/A`;A {U/A@WAɍU/A@E3AU/A@^AU/A ɕAU/AAU/A`A͌U/A*CA }U/AZAY/A1AGY/AAY/AEʢAY/A` AAY/A6-AY/A AeAQY/A`nAw'Y/A@AX/AپA`X/A.AX/A`ͦAX/A ~PAX/A 8ANUX/AlA(-X/A@AW/AJAW/A AXҎW/A tA]W/A2Aj1W/A`Ax V/A A0V/A]AaW/A@,A%W/AகA`AW/AApxwW/A&GA*W/A@A3W/A LAW/AtϨA:W/A`[AW/AaA՜W/A.A0ٱW/A@p!AW/A4Ah;W/AqA:W/A@5A@X/AA@X/AvACX/A`KAKX/A l*APzX/AA0XX/AAsX/A A PX/A]AXF+X/A]ApX/AAX/A 1dAX/A`AX/A&AX/A@A;X/Ah3AgX/Ah3Ax\X/AcAaX/A`̵A X/AcA`X/A`"~AUxX/A 8AXWX/AKA4X/AoAh6X/A緸AXWX/A`VAX/AA`X/AƹAX/AA`X/AA[X/A@ATqX/A)ANUX/AptAG2X/A;A(X/A[ApX/A`lApX/AܽAؚ&X/AADX/AܽA8eX/A AA0XX/AAUxX/A@ ARjX/A?AKGX/AﯿApX/A`˿AX/AĆAh X/A JA/X/AAO\X/AAX/AAX/A ۭAPMNX/A'6AXF+X/A`TA~!X/AOA?X/AAh X/AApX/AkA@A%X/A A8I9X/AA PX/AUAUxX/AA X/AFAX/AA0Y/A A~JY/A`djAP{Y/A,Ap=Y/ArAY/ArAY/A@HA Y/AsAY/A*A0*OY/AUAY/AމA`v Y/A AHr Y/Aa*ApY/AzAw'Y/AA{>ABl/AzBAl/A Apl/AAl/A3AX l/A@A`al/A`)xAzl/A6Al/AAl/AAp8l/A Al/ARA#l/A 1ABl/A31Al/AA Rzl/AA ll/A A`aml/A(ALl/A3qA`l/A Al/A@ Al/AAl/AzDAXxl/ARApWl/AAAl/A]A=&l/A Azl/A Ak/A Ak/AA`k/A `Ak/AAp k/AAk/A aA8k/A@>FApk/A^AX k/A Aztk/AATk/A uAP8Lk/AA=k/A A,k/ApA) k/A Aj/A HA8j/A AQj/AAj/A:Apj/A3qA(wj/AA@Wlj/A`A@Wnj/A}A(ej/AYAB[j/AtAHaGj/A A3j/A1A(j/ApOAj/A \YAXi/AUAX i/AAki/A@ Ai/A3 A0i/A0Ah3xi/A@RAfdi/A eA#Vi/A AKi/AHAfHi/AzAzJi/AmAW\i/AA0.mi/A{Ahti/AzAki/AABi/AAWi/A]7Aj/A GALYj/AkAtj/AAp=j/A HA`aj/ApKAp j/A A`j/A@vAk/AHAP81k/A A@k/A Abk/AֻALk/AzApk/ARJAP8k/AHAl/AbA8(l/A`)A5l/A`)A8nl/A=Akl/A@GApl/AzAQJm/A8A83m/A0Ap n/A A 'n/A 8Adn/A`f A0.n/A 9 An/A \k An/A- Ao/AAp=.o/A ABo/A |A]o/A Apho/A`LA@lo/AAso/AA@no/AAGio/Az Axfo/AhUAXeo/A@^A_o/A`ftAKo/AzZA6o/A HMA%o/Ap]AXo/AxAH\o/A׻A0n/AA Rn/A7APn/A`fAo/AåA o/AýAXo/A AB$o/ApA#5o/A`)AfAo/A AHo/A@VApPo/A=APo/A =AXo/AzA(\do/AR^Afao/A#Ago/A#A83ho/A`)A_o/A׉A?o/A -Ap=!o/AAko/ARAn/A2ABn/A`).An/AAn/A3An/AALn/A qAzn/AAp=n/AABn/A An/ARXAPn/A Apn/A A3n/A9Azn/A]Akn/A`)A(\`n/AQA(Qn/AQVA8@n/AR>A:n/A m AQ?n/A@g~!A>n/AL"A>n/A "A8:n/A H"A0n/A}#An/A` $A|n/A`@$A)m/A$A`m/AR$AH\m/A=J%Apm/Ap%AGm/A`ù%A)m/A`"&A`m/A&Am/A!'Am/Ay'AP8m/A'Ap=m/A H%(Am/AY(AX n/A(An/A`(AH n/AR)Af&n/A3)Apk(n/A)A4n/A*Ap=>n/A`*A{n/A@G)An/A)A8n/AR*An/A *An/A)An/A@G)Ap=n/A3m)Ap=o/A@R)A"o/A \U)A85o/A M)AXIo/A;)AX Uo/A)A`go/A (Aho/A(AH.io/A(~(AHaio/A`t(Aio/Ai(Aio/A^(AX jo/ApS(AGjo/AH(A8jo/A@<(Apjo/Ap1(Ajo/A@&(A jo/A%(Ajo/A(Ajo/A](Afjo/A (A]jo/A`v(Aio/A 'A83io/A'A#io/Az'Aho/A'ALho/A'AH.ho/A H'Afo/A`f'Afo/A'Aeo/A@>'A ceo/A@>'Ado/A'Abo/A`)'A)ao/AR'Aao/A`d'A`o/A 'A^o/AÓ'Ap^o/A@'Ap\o/A['A8\o/A@>'AuZo/A z'AGZo/Ay'Ap8Xo/A@gn'AX Xo/Apm'AWo/A@gl'AWo/A[k'AUo/A&a'AUo/A`)`'AkUo/A3_'AH.So/A@>T'ASo/A[S'APo/A@gH'APo/A G'AVNo/Az<'AH.No/A;'AKo/Az0'AVKo/A-'A83Ko/A -'AHo/A"'A8Ho/A 'ApFo/A`'A@o/A`)&A>o/A3&A=o/A&AL;o/A &A9o/AR&A7o/A[&AT7o/A&A(5o/A&A4o/AR&AȘ3o/A`ܷ&A1o/A&A/o/A&A-o/A&A,o/A@>&A*o/A`Õ&A&o/Ap&Af&o/A`&AB#o/A H}&A o/A q&A o/Af&A{o/AZ&Ap=o/A`fN&Ao/ARJ&A`ao/A@B&Apo/A6&AHo/A`m1&Afo/A +&Ap o/A &A@$ o/A&AP o/A&Ap o/A@&A#o/A`%AHo/A@%Ao/A@%AXo/A`%Ao/A%Ao/A%AP8o/A%APn/A`Û%APn/A%AP8n/A`Ï%APn/A ƒ%AP8n/A w%A n/A8t%ABn/A`)l%A8n/A``%A`n/A@X%An/AT%An/AH%A n/A@?%A)o/A=<%Ao/A5%Ap=o/A3/%A o/A`)%Ao/Ap!%AxKo/A@%Ap=o/A%Afo/AR %A(ho/A%ALo/A`%A@$ o/A A$A8 o/A $A@o/A`K$Ao/A@$Ao/AV$A;o/A$Ao/A`$Ao/A[$Ao/A@$A6o/A@$Ao/A@$Ao/A@$Ao/A@ $A`a#o/A=$Aj#o/A Q$A&o/A$A)o/A$A*o/A$AX +o/A3$AG,o/A`f$A`a-o/A $AW.o/A$A-/o/A $A1o/A 1%A2o/A3%AH.3o/AH %Ap3o/A3 %Ap=4o/A@ %AP4o/A@%A)5o/A`)%A@6o/A%A(->o/A@y8%AFo/A`ff%A(\Go/ARh%ApGo/A=j%AHo/Al%A8Ho/A \o%A8Io/Ap%AQIo/A`r%AIo/At%AIo/Au%AJo/A@x%AJo/A=z%A}Lo/A(%ALo/A%AuNo/A`%AuRo/Aן%ARo/A%ATo/Aק%ApVo/A%A`Xo/A q%AZo/A %A]o/A%Ap_o/AH%Axh`o/A`%A`ao/AH%Apkdo/A@ %Ago/A%Aio/A%Ap8lo/A@%A(no/A%AQqo/A`)&Auro/A` &Aгso/A  &A#to/A@ &AБuo/A&A`vo/A`&Axo/A@ &AHxo/A`&AQo/A`&Aho/A&Adžo/A&Aho/A`&Ao/A@ &A8o/A@ &A‡o/A@ &Ao/A@ &AP|o/A`&AH.o/A&APZo/A&Ao/A&Ao/A &Ao/A&AP8o/A &Avo/A&Avo/A&A8o/A@&Ap=o/A`&AǏo/A"&Ao/A@"&Ao/A@ #&Ao/A #&Ao/AH#&Ao/A/&A2o/A`m1&A`o/Az<&A`o/A`<&Apo/AXE&Ao/AI&Ao/A`I&Apo/AV&Auo/AV&A-o/A=^&Ap=o/A q_&A@Wo/A qa&Ap=o/A \s&Ap=o/As&AXlo/A`"w&A̘o/A`)&A(Jo/A&A#o/Ap&A#o/A@>&Ao/A@ &Ao/A&Aљo/A &A̙o/A&Ao/A &Ao/A@g&Ao/A&AQo/A@>&AGo/A`&Ax o/A'A`o/A@g 'A8o/A H'ALo/A%'A)o/A('Auo/Az4'AQo/A6'Ao/A C'Auo/AE'AOo/AAH'A)o/A[Q'Ao/A@>T'Ao/A _'A@הo/Ab'Aǔo/Ae'Apo/A Hi'Apo/A@k'AHo/A l'AHo/Azz'AHo/Az~'Apo/A'A`o/A H'Ao/A['AOo/A@i'Afo/Ap'A@o/A֫'A̗o/AN'Ax o/A@ƺ'Axǜo/A3'A@¡o/A`'AP8o/A'A#o/A H'Ao/A'AQo/A 'A`o/A`f'Aǧo/A'Ąo/A`f'A̩o/A 'A̪o/A'A«o/A='Ao/A`)'ALo/A(A0o/Ap(Ao/A H (Aأo/A H (A0no/Ak(Ao/A(Azo/A(AFo/A%(Ao/A@,(A(ܾo/A(4(Ao/A@7(A`o/A(<(A^o/A@,K(Ao/A{(A8o/Am(A8o/A`(Ap=o/A((A`So/A`՚(Ao/Aק(A o/A`(A؞o/A(A@1o/A (A@1o/A(A(\o/A')A`So/A@()A`o/A4)Ao/A_)A0.o/A@ i)A`o/A`fr)AXo/Au)AWo/A)A*A"o/A`?*AP8o/ARD*Ao/AI*Aњo/A3O*Ax o/A |S*A`o/A`*A`o/Af*Ao/A m*Afo/An*Ao/An*Ap=o/AHu*Aho/A Q{*A8o/A q*Ao/A@ *AQo/A*A|o/AR*Ahzo/A`*Ayo/A *Ap=wo/A \*Ano/A*Ano/A@!*Ako/A*Afko/A`*AHaho/A3*Abo/A*Af_o/A \*A\o/A`*A`Xo/A*ApUo/A@*ARo/A*Ap/A.Ap/A (.Ahp/At.AQo/Aa.Ao/A .A0o/A -AHo/A t-Aho/At-Ao/A ,A! p/A2,Ap/A@E+A7p/A@E+ABp/A`;n+AXp/A +Arp/A (*Amp/A}}*AHp/A7*Awp/A)Ap߰p/A`ܗ)AQp/A@E;)Ap/A(Ap/A (A4p/A(Ap/A(Ap/A (A q/A2(A"q/A (h(A2q/A`F(A:q/A@N4(AQq/AW'Ameq/Aa'AZq/A2a'A8q/A 'AP+q/A2&Aq/Atp&A4q/A@&Aq/A`&Ahq/A &AXq/A%'Aq/Ah'A0q/A@E'Aq/A`'AP+q/A ((Aq/AC(Apq/A `(ATq/A (A=q/A@E(A(>Bq/A`;)A8Eq/Ad)ApFq/A})AJq/A`*ARq/Aj*ASq/A}*ApFq/A`*A8q/A2*A46q/Aj3*A46q/A)A0q/AW)A4q/AX)AQq/A}Q)App/Ad)Awp/Aj)Ap/A`:*AQp/A@n*AP+p/A`Ӟ*A!p/A+Ap/A`>+AŒp/AL+A8}p/Aɥ+Atp/A@E+Ahp/AWA,A4p/A@E,Ap/A@E#-Ap߰p/A-Ap/A`2-A!p/A`Z-A8p/A`-A4q/A -A q/A <.A q/A.Aq/AK/AQp/A/AHp/A0A8p/A o0AXp/At0A!p/A1AP+p/A1Adp/A2Q2AQq/Aa2AQAq/A`;2Akq/A`3AP+q/A@N$3Aq/Aj3AXq/A23A4q/A_4A8 r/A4A r/A 5A0o/A` wHAo/A\HA83 p/AiHA\p/A c.HA0]'p/A`BGA(i>p/AGAxQp/AGA tfp/Aq/A k9Aq/A`9AQq/A@F9AQq/A8AGq/A@~8A¸q/Aa8Aq/A`7Aq/At<7Adq/AW6A0vq/A`;^6AWq/A (6AIq/A@E6A=q/At6A46q/A`5Aq/A`5Amq/A25Amp/Aa5Ap/AW5A!p/A5Adp/A5Aip/At6AHKp/A@E6AXp/A"6Ao/At6Apo/Aj5Ao/A 5Avo/A5A)Vo/A 6Au\o/A@g-6Axto/AK6AAOo/A>A o/A&J?ASo/A`o?Ap/A@8@ADp/A@AH2p/AXOAA@p/AAAho/AIBAh3o/ABA=o/AVCAzo/AfCA@o/A`"CAo/ADAPMo/A`'DA!o/A?jDAЄo/ADAo/A0EAHܝo/A`]OEAo/A EAXo/A3/FA΅o/A FA0\o/AdGA0]o/AGA _o/AiHAXo/AfHAcLo/A[HAAo/A HAxBo/A6IAVIo/A3IAh Qo/Ak;IA0o/A` wHACN/A $PAAN/A`tOACN/A NARN/A@vNABiN/A NA@N/ALMA`N/AMAXN/A?MALO/A \qMA؁;O/A QMA AO/A`THLAxKO/A0uKAsUO/AJAsfO/A@yZJAȶlO/A yIA`}O/A HA/O/A>HAqO/A5GAO/ACGAxP/A;FASP/AW>FA0{P/A FA(zP/AFAyP/A= FAX5Q/A^FAA;Q/A FA Q/A`:FAQ/A`FAQ/ALEA"R/A EAR/A EAR/A`JEAxR/A@DA0Q/A@,BDALQ/A CAtQ/A NCAQ/A ABA Q/A (MBAثQ/A`AAv}Q/A|AAgQ/A}KAA0XQ/A@r@A&MQ/A j@A6Q/A`?A#Q/A`+?A4"Q/A`>A'Q/A qq>AP^P/A@">AؼP/AX=ADP/A`=AhDP/A=AQP/A`D=A@_P/A<AlP/A l<AhP/A(<AP/Aj=A^Q/A=APQ/A@<AЮQ/A"<A(P/Aݬ;AyP/AX:;A P/A2:A*P/A@:ARP/A:A@SwP/A`:ApaP/A-:AxJP/A 9Ah-P/A Q:AxP/A :ApO/A@9A-O/Ae9A@O/A`̞9AO/A 9AO/A9A"iO/A9A8(O/A!9Ag O/A/8APN/A 8AN/A@7AiN/A*7A֎N/A`n6AȆN/Aq.6A8xxN/AX5AfN/A5A]WN/AX5A 8N/AŴ5AX/N/A i5AC%N/A`B4A8Q-N/A׽4A5?N/Aʤ4ATN/A@o4A k_N/A54A]N/A;3AAN/A3A8Q-N/A 3AxN/A`o3AM/An94A`M/A@ m4A`M/A`)4A8M/A`m}4A |M/AN4AȤM/A+4AM/A@,4AM/Aܭ3AXM/A@3AHM/AB3AM/A`3A*M/A2ApM/A 2AnM/A A2A,M/Ak2A0G{M/A a2A,kM/A.2AbiM/A1AȥoM/A@E1ATrM/A|1ATrM/A`)1ApM/A0ApM/A0AnM/A A0A`gM/A0AUaM/A@i/A\M/A'/AHKM/A/A9M/A/A*M/A/A"M/Au/A M/A l?/AH&M/A)/A;M/A@`.A@M/AT.AM/A@.AP*AP&C/A!*AiC/Ax*APC/A *AؒC/A)AC/A)A8'C/A`c)AC/A@9)AC/A} )A@C/A)AxC/AF.)Ak}C/A)Ax|C/A` (A rC/Aީ(A(fC/AА(A XC/A(AxIC/A(A=C/A<(AТ7C/A@.(A#C/A@.(AC/A~(AC/A 10(ARC/A`(AB/AZ'AnB/A(AB/A`fP(AFB/A`"[(A۾B/A@e(AhB/A@e(AB/A>(ApΔB/A(A(܋B/A'AH-xB/A'AhB/A(AHUB/A`(AH.EB/A(AH!,B/A(A!B/A(AB/A?(A/A/A(AHA/A?(AA/A`(A0A/A 'AA/A 'AXůA/A`'AA/Ah'A>A/AZ'A`ӕA/A!(AӄA/A@S(AuA/A@S(APaA/A)(AP@_A/A`'A0%`A/A'AkA/A'AkA/A@5d'APA/A U'A BA/A U'A6A/A@'A%A/A&A& A/A`m&A4A/A5'AO@/A@yY'AP@/AG'Ax@/A'AH@/A@'AX(@/A &A@/An&AP@/Ab&A`@/A&AP @/A=%A@/A@#%A@ߪ@/A;e%AH@/A@P%A@/A@R%AH}@/A ҋ%A@/A४%Ax)@/A"%A@/A7&Aȇ@/A|&A @/A'A6@/A@yY'AQ@/A`'AQ@/A>(Al@/Ad(Al@/A@(A؅@/Ao5)A(mx@/A̠)AЕn@/A`)ARW@/A*A@SF@/AO*A;@/A@L*A;@/A *A:@/A#*A)@/AG+AP@/A@+A(9@/A`,A@F-@/A a,AH/@/A,A'@/Ai-A'@/A?-A)@/A6.AxF@/A 8.A@/A/A9?/A(J/A?/A(J/AP?/A(J/AX?/A l?/Ah?/AR /Au?/A1/A`e?/AD8/AXJ?/AD8/As$?/A/A?/A@.AY?/A`h.A8>/A-A>/A@_-A0%>/At2-A >/A@y?-AH>/A k-A >/A-AH>/A@(.A5>/A0.A脻>/A`.AG>/Aa5/Ax>/A/A>/A/AH>/A`b-0Af>/A`}0A>/Ao0AP>/A0A>/A@>.1A >/A1A0>/A1A>/A1A>/A1A`?/A :1A?/A1Ax)(?/A`$1Ah4?/A211A`@?/A1A@BW?/A c1A@f?/A`o2A?/Ap2A06?/Aſ2A?/A53Af?/A@3A?/A'4AXu?/A q4Asf?/A4A[?/A (R5A(h?/Aǧ5AH.w?/A 5A~?/Ax5Ax5?/A`]5A?/A5A?/A 6A6?/A``6Aȇ?/A c6A ?/A`d6AXR?/A@6A0{?/AW7AH?/A@6Ag @/A6A@/A7A8@/AgH7A @/A37Ab'@/A7A=@/AgH7A K@/A7AHa@/At+7As@/A@@7Ah]t@/A7Ajp@/AR8Ax]@/Aq8AJ@/A08Ao<@/A<8Ab'@/A8A$@/A9A.@/A`279Ax:L@/A9Ahg@/A8APIz@/A`f8A}@/A[G9Au@/A9Ac@/Az9Ae@/A:Aw@/AH:Ah]t@/A@:Aw@/A:AHr@/A;As@/Ah;Ah@/A :AHz@/A`:Aȩ@/A@ߗ:A@@/A H:A@/A`3:A@/AV[:AA/A`]:AA/A":AXA/A`;Aa A/A'e;Ap,A/A ;Ah$A/A;Ap*A/A <Ahb#A/Aa<A,A/A ғ<AЦAA/A /_<AwOA/A`T<A\A/A`o,<APzA/A@p;AA/A,;AA/A<AA/A@Eu<AA/A5<AA/A e=A tA/A@">A"A/A@p>AB/A>AV;B/A@>AUnB/A |?AB/A |?APoB/A>ASB/A=AأC/Ao=A6C/Az=ApGC/A=Al9C/A >Ax-C/A@,>A)3C/As?ApGC/A?AX^C/A7@APkC/A`f@A&C/Aҍ@A C/A Zx@A&C/A@N@A@NC/A@?A C/A S ?AC/A;>AC/A=AC/Ah=A%C/A=A3D/A <A#TD/A`<APwD/A`B<AȶD/A`<A޳D/A@j<AXD/A;AxD/A cA;AXJD/A@:AsD/A4g:AD/A*9AѼD/Aa^9AP/D/A/8AؚD/AU8AXD/A 8AD/A ɩ7AID/AQP7AXD/Am6A`D/AF6AD/A@.96AXJD/A@5A 3A UE/A3AE/A 2AE/A`[2A(E/AR2A7E/A ( 2A`6 F/Ak2A4F/A2AcF/A 81AՠF/A1AF/A`0AF/A (0AF/A/AG/A@/A)G/A'/Ap=XG/A@/AݧG/A/AxG/A/A 6H/A &/AcH/A2/0AXRH/A-0A`H/AFq1APH/A@,?1AI/A0A@RI/A/AӔI/A (0A@1I/A A0AhsJ/A@#Z0A0J/Aя0AWKJ/A0ArJ/A0ATJ/A@W0AJ/A1AHJ/Az1AJ/A`v2ASJ/A`[2A K/As)3A+K/A`3A)CK/A3A`gK/A@t3A ^K/A73AK/A@t3AK/A`#4AK/A@4A0]K/A 5A8xK/A6AּK/Al7AּK/A@8A8'K/A8AYK/A P9AؒK/A l9AXK/A O;AK/AM<A K/A=A AK/Am>AK/A9?AxK/A@AּK/A@@AK/AAAK/AٶAA K/A _BAoK/AL CAUK/AdCAUAP4/A`UA3/A7VA(K3/AVAX3/ALWAT3/AVAh3/A 8VA3/A |BVAs3/AUA؟w3/AUAlk3/A`TA\3/AjTA NP3/ASAbE3/A`KSAA93/A`5SAȔ3/AzRAHr3/A \RAX( 3/A` QA~3/AQAz23/A`v'QA3/A`QAHr3/AjPA3/AoPA2/A}tPA82/AIPAx2/A$#PA|2/AOAXWM2/ATOAX(.2/A8dPA2/AaPA 1/A PA`1/A@PAXc1/APAyx1/A PAHq1/ABQAHq1/AQA0}1/AQAj1/A@RAX#d1/A SA~e1/A@.qSAPb1/ASA(ma1/AŅTA\1/A3UA`OF1/A઄UA,1/A"UA(1/A |BVA0/AAVAx0/A WAЄ1/A@WWAj$1/A WA8D1/A_VAf1/A-XVAc1/A-XVA&1/A@VAض1/A\WAض1/A@WAਗ1/AXA0}1/A JZYAR1/AYAx˟1/AiZA}1/AYA1/A 9YA1/AXA 2/AwXA`/2/A0rWAxm>2/A WAN2/A +WA(Y2/AWAcd2/A`2WA0&2/A cXA0 2/A@EWA)2/A QWA2/A`;WAe2/A WA3/A WAh.3/A\WAp_T3/A`6WAVz3/A`WA0*3/AEXA^3/AHXAG3/AYA{3/A@ZA3/A3NZA3/A-ZA4/AZA@94/A`<[Aؽo4/AX[Aj4/A][AHK4/AZA64/ADYA84/A qXA@4/A`2WAw4/A'WAW/A cAMW/A bA X/A;bApp@X/A@ aAhzX/A aAX/AaA Y/A`XaAG.Y/AOPaA@~fY/AqaA0Y/A(bA(Y/A@yBbAZ/AqaA(&Z/A@yaAh@Z/Aar`Ah Z/A`_A[Y/A,q^A@Y/A O]AY/Aٌ\AЮY/A`U[AZ/A`ZAP+RZ/A3ZAx~Z/A3ZA`Z/AYAX [/A@YAX9([/A AYA8b[/A]YAX[/AYA[/AH}ZAP[/A ZA~[/Af[Ap \/AL[A`2%\/AZAP\/A@ RZAw[/AYA$[/Ac YA[/A XA[/AXWA$[/A HVAP\/AUAx-\/A3UAP@[\/ATAъ\/AZUAؼ\/A@@UAP\/ANTAb ]/A@TA N1]/A`hTAS]/ATA~v]/A c TA8]/A ASA]/A@,1SA2]/ARA$]/AjRA]/A9QA ^/A QA+^/A@*QAU^/AߟPAU^/A cIPA]/A cIPA]/AkPA]/A ҹPA0|]/APAH5]/APAh@\/AZPAx\/ANOAhb\/A`NA0b\/AXNA\/AMAxǔ[/ALA0L*[/ALAUZ/A]mLA|Z/A@pLA+Z/A@yLAY/A@pLAxY/ALA -Y/A@ZLAHrX/A JMAP&X/A NAX/AT6OA8Y/A?OAkY/A`PAdY/A aQAX*Y/A [RAxGX/A`SAhX/A@yUAqX/A JWAX9oX/A`XApZX/A VYAX/A`XA&Y/A`XA?Y/A`f>ZAh(Y/A@[A`X/A J]Ax@X/A]AcW/A^AW/A@p?`A0̀W/A bA@nW/A`bcAW/AAcAP7W/AcAW/A cA5ac/A /A*qc/AA8c/A@>Ac/A`Ac/AA8c/A |Ac/AAxKc/A.Ah3c/A@~Ac/A@ذ~A=d/A~A$d/A@~ASOd/A`d~Add/AM~Ad/A~A`d/A஺~Aݓd/AK~Aid/A~A^d/A"~A8td/A}`~Ad/A8~A(9d/A}Ae/A9}A e/A~Ae/A1~AP8 e/A|z~A8d/A@5~Ahsd/A4~A( d/A $A"d/A Ad/AAd/A`Ad/AAe/AFAp e/AA,(e/AaCA@Ce/A@5~A0ce/A|z~AXwe/A@~Ae/A}Aze/A@,}AFe/A}Ake/A@?|Abe/A{A Ae/A {AP< e/A@5!{Ane/A@EzAp=0e/A4yAd/AayAd/A=yA@1d/A` $yAtd/A@IxA@d/A`-xA0d/A`dwAxd/A+wA@ d/AtmvAc/A uAxc/A>uAc/A@rdtAc/AmsA|c/AqrA Mc/A@YqAho+c/A`oRpA0; c/A[oA0c/AnAxc/A_nA؞c/A]mAb/A`?mAb/AfmAob/AmAh b/AV$nAb/AnAb/AoAb/Av[pA@Slb/AapABb/A LpAp|b/AypA; b/A 1qA@a/AෘqAXa/A"qA8a/AqA&Ra/A`mqA(1a/AqAX a/A AnrAȃ a/A c+sA0U`/A}1sAPZ`/AIsAX`/A sA `/A=/A}^A8#@/A ёAPoA/A ɆAH%F/A`AȵI/AQA`0M/A pAlP/A4AR/Ah&AnT/AAp}Q/A`A(Q/A痢A@$ R/AOA/R/A`]A0R/A,AP$R/AxA(R/AhAH)R/A`@Ac%R/A Ac%R/AIAH"R/A ұA!R/A@qAJR/AKRA@$ R/A CAQ/A9APQ/A`\AQ/A`2Ax)Q/A ೡAjQ/AٲAxǯQ/A@NAX9Q/A@#rATQ/AoAxzQ/A qA?Q/A-AÒQ/AAQ/AHAQ/A`lA#Q/AA Q/AfТAIQ/A ZA Q/A`.AhbQ/A %A0fQ/A!AjQ/A`"AXQ/AA(Q/AAQ/A /AQ/A`բA(-Q/AڻA0Q/A,Ap}Q/A`A7H/A,AVU.H/A6xAPn'H/A8Ae#H/A.gA4"H/AՏF AwZ$H/Ad1ٱA(H/A#Aiy+0H/APlRAC9H/A@aA2EH/A0OASH/A0"⵰A@cH/A AT+rH/AjfrAͨH/AkHAFH/A@ApH/A9ޯAxJH/A7AfL<,H/A:]AQH/As(ZA}NH/AT+ԮAXRH/AAy&UH/AȭAZH/Ak AkH/A[eKAlR2H/AeFAQ9I/As A;)I/A&[PAqMI/AIs罩AS#sI/A`J8AI/A:?.¨AJI/A\ZA)b&I/AATK=(J/AAoYJ/AA`%J/AOfAʩJ/A9eAV갾J/A\!-SAŇJ/Aq/Ar J/A"AJ/AMfA9K/AlkAq!{K/A_A(bf%K/AA()K/A`NAK/A w%A5K/AE夤ףAvJ/Ay)ABJ/A;7A}Wk^J/AS3̎A^DJ/A,lAWJ/AG!Af +J/ArАAI/A@>AUNI/A[-cARxI/A=AcƠI/AAUI/AA.2aI/A ACI/A 4A .&I/AlkAX^ I/Ḁ<ȱA@SH/AAďH/A5||.A i)H/A~NARG/A SdAfo{G/Aa7A4G/A@`ĥAqgC!G/A7 A4 G/A2Q#A -F/A"AOF/A%A@"aF/AjAUF/AAvBF/A,vޥA?xF/AҬAAu{F/AvIpAmF/A )AGF/A}hp?A!F/AA9E/A-ѢA &}E/A) kAܟ 'E/AqDBA҆\D/A43ȟAgpD/A|yAvD/A[(A*=C/A Ф^AULC/A3ļAȓB/A| AB/A%~៛AB/AK3ASA/AطښAk~EBA/A 閚A@/A*LgAH>j@/AmMAP\?/A% HAs?/AVXAN&?/A !}A