pax_global_header00006660000000000000000000000064150343013160014506gustar00rootroot0000000000000052 comment=361089b0be45f72f73696f7bbf1454b23b66b44f mapclassify-2.10.0/000077500000000000000000000000001503430131600141015ustar00rootroot00000000000000mapclassify-2.10.0/.git-blame-ignore-revs000066400000000000000000000001031503430131600201730ustar00rootroot00000000000000# black-ification of code 71bfea486c64d3e87d0677f824ee6b17d576d028 mapclassify-2.10.0/.gitattributes000066400000000000000000000000451503430131600167730ustar00rootroot00000000000000mapclassify/_version.py export-subst mapclassify-2.10.0/.github/000077500000000000000000000000001503430131600154415ustar00rootroot00000000000000mapclassify-2.10.0/.github/dependabot.yml000066400000000000000000000011251503430131600202700ustar00rootroot00000000000000# To get started with Dependabot version updates, you'll need to specify which # package ecosystems to update and where the package manifests are located. # Please see the documentation for all configuration options: # https://help.github.com/github/administering-a-repository/configuration-options-for-dependency-updates version: 2 updates: - package-ecosystem: "github-actions" directory: "/" schedule: interval: "daily" reviewers: - "jGaboardi" - package-ecosystem: "pip" directory: "/" schedule: interval: "daily" reviewers: - "jGaboardi" mapclassify-2.10.0/.github/release.yml000066400000000000000000000004511503430131600176040ustar00rootroot00000000000000changelog: exclude: labels: - ignore-for-release authors: - dependabot - pre-commit-ci categories: - title: Bug Fixes labels: - bug - title: Enhancements labels: - enhancement - title: Other Changes labels: - "*" mapclassify-2.10.0/.github/workflows/000077500000000000000000000000001503430131600174765ustar00rootroot00000000000000mapclassify-2.10.0/.github/workflows/build_docs.yml000066400000000000000000000035501503430131600223330ustar00rootroot00000000000000 name: Build Docs on: push: # Sequence of patterns matched against refs/tags tags: - 'v*' # Push events to matching v*, i.e. v1.0, v20.15.10 workflow_dispatch: inputs: version: description: Manual Doc Build Reason default: test required: false jobs: docs: name: Build & Push Docs runs-on: ${{ matrix.os }} timeout-minutes: 90 strategy: matrix: os: ['ubuntu-latest'] environment-file: [ci/313-latest.yaml] experimental: [false] defaults: run: shell: bash -l {0} steps: - name: Checkout repo uses: actions/checkout@v4 with: fetch-depth: 0 # Fetch all history for all branches and tags. - name: Setup micromamba uses: mamba-org/setup-micromamba@v2 with: environment-file: ${{ matrix.environment-file }} micromamba-version: 'latest' - name: Install package run: pip install . - name: Make Docs run: cd docs; make html - name: Commit Docs run: | git clone https://github.com/ammaraskar/sphinx-action-test.git --branch gh-pages --single-branch gh-pages cp -r docs/_build/html/* gh-pages/ cd gh-pages git config --local user.email "action@github.com" git config --local user.name "GitHub Action" git add . git commit -m "Update documentation" -a || true # The above command will fail if no changes were present, # so we ignore the return code. - name: push to gh-pages uses: ad-m/github-push-action@master with: branch: gh-pages directory: gh-pages github_token: ${{ secrets.GITHUB_TOKEN }} force: true mapclassify-2.10.0/.github/workflows/release.yml000066400000000000000000000022611503430131600216420ustar00rootroot00000000000000name: Release Package on: push: # Sequence of patterns matched against refs/tags tags: - 'v*' # Push events to matching v*, i.e. v1.0, v20.15.10 workflow_dispatch: inputs: version: description: Manual Release default: test required: false jobs: build: runs-on: ubuntu-latest steps: - name: Checkout repo uses: actions/checkout@v4 - name: Set up Python uses: actions/setup-python@v5 with: python-version: '3.x' - name: Install Dependencies run: | python -m pip install --upgrade pip build twine python -m build twine check --strict dist/* - name: Create Release Notes uses: actions/github-script@v7 with: github-token: ${{secrets.GITHUB_TOKEN}} script: | await github.request(`POST /repos/${{ github.repository }}/releases`, { tag_name: "${{ github.ref }}", generate_release_notes: true }); - name: Publish distribution 📦 to PyPI uses: pypa/gh-action-pypi-publish@release/v1 with: user: __token__ password: ${{ secrets.PYPI_PASSWORD }} mapclassify-2.10.0/.github/workflows/testing.yml000066400000000000000000000065331503430131600217050ustar00rootroot00000000000000name: Continuous Integration on: push: branches: - "*" pull_request: branches: - "*" schedule: - cron: "59 21 * * *" jobs: testing: name: (${{ matrix.os }}, ${{ matrix.environment-file }}) runs-on: ${{ matrix.os }} defaults: run: shell: bash -l {0} strategy: matrix: os: ["ubuntu-latest"] environment-file: [ 311-oldest, 311-latest, 311-numba-latest, 312-latest, 312-numba-latest, 313-latest, 313-numba-latest, 313-dev, ] include: - environment-file: 313-latest os: macos-13 # Intel - environment-file: 313-numba-latest os: macos-13 # Intel - environment-file: 313-latest os: macos-14 # Apple Silicon - environment-file: 313-numba-latest os: macos-14 # Apple Silicon - environment-file: 313-latest os: windows-latest - environment-file: 313-numba-latest os: windows-latest fail-fast: false steps: - name: checkout repo uses: actions/checkout@v4 with: fetch-depth: 0 # Fetch all history for all branches and tags. - name: setup micromamba uses: mamba-org/setup-micromamba@v2 with: environment-file: ci/${{ matrix.environment-file }}.yaml micromamba-version: 'latest' - name: environment info run: | micromamba info micromamba list - name: spatial versions run: | python -c "import geopandas; geopandas.show_versions();" - name: Download test files run: | python -c ' import libpysal libpysal.examples.fetch_all() ' - name: Run pytest run: | pytest \ mapclassify \ -r a \ -v \ -n logical \ --color yes \ --cov-append \ --cov mapclassify \ --cov-report xml \ --cov-report term-missing - name: run docstring tests if: contains(matrix.environment-file, '312-numba-latest') && contains(matrix.os, 'ubuntu') run: | pytest \ mapclassify \ --doctest-only \ -v \ -r a \ -n logical \ --color yes \ --cov-append \ --cov mapclassify \ --cov-report xml . \ --cov-report term-missing - name: zip resultant image comparisons - Ubuntu & macOS run: zip result_images.zip result_images -r if: matrix.os != 'windows-latest' && (success() || failure()) - name: zip resultant image comparisons - Windows shell: powershell run: Compress-Archive -Path result_images -Destination result_images.zip if: matrix.os == 'windows-latest' && (success() || failure()) - name: archive & upload resultant image comparisons uses: actions/upload-artifact@v4 with: name: result_images-${{ matrix.os }}-${{ matrix.environment-file }} path: result_images.zip if: success() || failure() - name: codecov (${{ matrix.os }}, ${{ matrix.environment-file }}) uses: codecov/codecov-action@v5 with: token: ${{ secrets.CODECOV_TOKEN }} mapclassify-2.10.0/.gitignore000066400000000000000000000004671503430131600161000ustar00rootroot00000000000000*.pyc .CHANGELOG.md.swp .ropeproject/ dist/ examples/notebooks/.ipynb_checkpoints/ examples/python/ mapclassify.egg-info/ mapclassify/.ropeproject/ mapclassify/datasets/calemp/.ropeproject/ mapclassify/tests/.ropeproject/ .DS_Store .vscode/settings.json __pycache__ /notebooks/.ipynb_checkpoints/ result_images/mapclassify-2.10.0/.pre-commit-config.yaml000066400000000000000000000003511503430131600203610ustar00rootroot00000000000000files: 'mapclassify\/|notebooks\/' repos: - repo: https://github.com/astral-sh/ruff-pre-commit rev: "v0.12.2" hooks: - id: ruff-check - id: ruff-format ci: autofix_prs: false autoupdate_schedule: quarterly mapclassify-2.10.0/CHANGELOG.md000066400000000000000000000234501503430131600157160ustar00rootroot00000000000000# Version 2.4.1 (2020-12-20) This is a bug-fix release. We closed a total of 9 issues (enhancements and bug fixes) through 3 pull requests, since our last release on 2020-12-13. ## Issues Closed - BUG: support series in sampled classifiers (#99) - BUG: FisherJenksSampled returns ValueError if Series is passed as y (#98) - REGR: fix invariant array regression (#101) - REGR: UserDefined classifier returns ValueError("Minimum and maximum of input data are equal, cannot create bins.") (#100) - [DOC] add example nb for new classify API (#91) - 2.4.0 Release (#97) ## Pull Requests - BUG: support series in sampled classifiers (#99) - REGR: fix invariant array regression (#101) - 2.4.0 Release (#97) The following individuals contributed to this release: - Serge Rey - Martin Fleischmann - Stefanie Lumnitz # Version 2.4.0 (2020-12-13) We closed a total of 39 issues (enhancements and bug fixes) through 15 pull requests, since our last release on 2020-06-13. Issues Closed - Remove timeout on tests. (#96) - BUG: HeadTailBreaks RecursionError due to floating point issue (#92) - Handle recursion error for head tails. (#95) - Add streamlined API (#72) - [API] add high-level API mapclassify.classify() (#90) - BUG: Fix mapclassify #88 (#89) - exclude Python 3.6 for Windows (#94) - CI: update conda action (#93) - EqualInterval unclear error when max_y - min_y = 0 (#88) - BUG: fix unordered series in greedy (#87) - BUG: greedy(strategy='balanced') does not return correct labels (#86) - Extra files in PyPI sdist (#56) - MAINT: fix repos name (#85) - DOC: content type for long description (#84) - MAINT: update gitcount notebook (#83) - Update documentations to include tutorial (#63) - build binder for notebooks (#71) - current version of mapclassify in docs? (#70) - 404 for notebook/tutorials links in docs (#79) - DOC: figs (#82) - DOCS: new images for tutorial (#81) - DOC: missing figs (#80) - DOCS: update documentation pages (#78) - Make networkx optional, remove xfail from greedy (#77) ## Pull Requests - Remove timeout on tests. (#96) - Handle recursion error for head tails. (#95) - [API] add high-level API mapclassify.classify() (#90) - BUG: Fix mapclassify #88 (#89) - exclude Python 3.6 for Windows (#94) - CI: update conda action (#93) - BUG: fix unordered series in greedy (#87) - MAINT: fix repos name (#85) - DOC: content type for long description (#84) - MAINT: update gitcount notebook (#83) - DOC: figs (#82) - DOCS: new images for tutorial (#81) - DOC: missing figs (#80) - DOCS: update documentation pages (#78) - Make networkx optional, remove xfail from greedy (#77) The following individuals contributed to this release: Serge Rey Stefanie Lumnitz James Gaboardi Martin Fleischmann # Version 2.3.0 (2020-06-13) ## Key Enhancements - Topological coloring to ensure no two adjacent polygons share the same color. - Pooled classification allows for the use of the same class intervals across maps. ## Details We closed a total of 30 issues (enhancements and bug fixes) through 10 pull requests, since our last release on 2020-01-04. ## Issues Closed - Make networkx optional, remove xfail from greedy (#77) - BINDER: point to upstream (#76) - add binder badge (#75) - Binder (#74) - sys import missing from setup.py (#73) - [WIP] DOC: Updating tutorial (#66) - chorobrewer branch has begun (#27) - Is mapclassify code black? (#68) - Code format and README (#69) - Move testing over to github actions (#64) - Add pinning in pooled example documentation (#67) - Migrate to GHA (#65) - Add a Pooled classifier (#51) - Backwards compatability (#48) - Difference between Natural Breaks and Fisher Jenks schemes (#62) - ENH: add greedy (topological) coloring (#61) - Error while running mapclassify (#60) - Pooled (#59) - Invalid escape sequences in strings (#57) - 3.8, appveyor, deprecation fixes (#58) ## Pull Requests - Make networkx optional, remove xfail from greedy (#77) - BINDER: point to upstream (#76) - add binder badge (#75) - Binder (#74) - [WIP] DOC: Updating tutorial (#66) - Code format and README (#69) - Migrate to GHA (#65) - ENH: add greedy (topological) coloring (#61) - Pooled (#59) - 3.8, appveyor, deprecation fixes (#58) ## Acknowledgements The following individuals contributed to this release: - Serge Rey - James Gaboardi - Eli Knaap - Martin Fleischmann # Version 2.2.0 (2019-12-21) This releases brings new functionality for [formatting of legend classes](https://github.com/sjsrey/geopandas/blob/legendkwds/examples/choro_legends.ipynb). We closed a total of 21 issues (enhancements and bug fixes) through 9 pull requests, since our last release on 2019-06-28. ## Issues Closed - 2.2 (#54) - 2.2 (#53) - conda-forge UnsatisfiableError on windows and python 3.7 (#52) - [MAINT] updating supported Python versions in setup.py (#49) - BUG: RecursiveError in HeadTailBreaks (#46) - BUG: HeadTailBreaks raise RecursionError (#45) - BUG: UserDefined accepts only list if max not in bins (#47) - BUG: avoid deprecation warning in HeadTailBreaks (#44) - remove docs badge (#42) - Remove doc badge (#43) - Docs: moving to project pages on github and off rtd (#41) - BUG: Fix for downstream breakage in geopandas (#40) ## Pull Requests - 2.2 (#54) - 2.2 (#53) - [MAINT] updating supported Python versions in setup.py (#49) - BUG: RecursiveError in HeadTailBreaks (#46) - BUG: UserDefined accepts only list if max not in bins (#47) - BUG: avoid deprecation warning in HeadTailBreaks (#44) - Remove doc badge (#43) - Docs: moving to project pages on github and off rtd (#41) - BUG: Fix for downstream breakage in geopandas (#40) The following individuals contributed to this release: - Serge Rey - James Gaboardi - Wei Kang - Martin Fleischmann # Version 2.1.0 (2019-06-26) We closed a total of 36 issues (enhancements and bug fixes) through 16 pull requests, since our last release on 2018-10-28. ## Issues Closed - ENH: dropping 3.5 support and adding 3.7 (#38) - ENH: plot method added to Mapclassify (#36) - ENH: keeping init keyword argument to avoid API breakage. (#35) - mapclassify.Natural_Break() does not return the specified k classes (#16) - Fix for #16 (#32) - Mixed usage of brewer2mpl and palettable.colorbrewer in color.py (#33) - Chorobrewer (#34) - conda-forge recipe needs some love (#14) - generating images for color selector (#31) - doc: bump version and dev setup docs (#30) - environment.yml (#29) - add color import and chorobrewer notebook (#28) - Chorobrewer (#26) - chorobrewer init (#25) - add badges for pypi, zenodo and docs (#24) - add geopandas and libpysal to test requirement (#23) - adjust changelog and delete tools/github_stats.py (#22) - add requirements_docs.txt to MANIFEST.in (#21) - gadf and K_classifiers not in __ini__.py (#18) - rel: 2.0.1 (#20) ## Pull Requests - ENH: dropping 3.5 support and adding 3.7 (#38) - ENH: plot method added to Mapclassify (#36) - ENH: keeping init keyword argument to avoid API breakage. (#35) - Fix for #16 (#32) - Chorobrewer (#34) - generating images for color selector (#31) - doc: bump version and dev setup docs (#30) - environment.yml (#29) - add color import and chorobrewer notebook (#28) - Chorobrewer (#26) - chorobrewer init (#25) - add badges for pypi, zenodo and docs (#24) - add geopandas and libpysal to test requirement (#23) - adjust changelog and delete tools/github_stats.py (#22) - add requirements_docs.txt to MANIFEST.in (#21) - rel: 2.0.1 (#20) The following individuals contributed to this release: - Serge Rey - Wei Kang # Version 2.0.1 (2018-10-28) We closed a total of 12 issues (enhancements and bug fixes) through 5 pull requests, since our last release on 2018-08-10. ## Issues Closed - gadf and K_classifiers not in __ini__.py (#18) - rel: 2.0.1 (#20) - fix doctests (interactive examples in inline docstrings) (#19) - complete readthedocs configuration & add Slocum 2009 reference (#17) - prepping for a doc based release (#15) - new release on pypi (#10) - prepare for release 2.0.0 (#13) ## Pull Requests - rel: 2.0.1 (#20) - fix doctests (interactive examples in inline docstrings) (#19) - complete readthedocs configuration & add Slocum 2009 reference (#17) - prepping for a doc based release (#15) - prepare for release 2.0.0 (#13) The following individuals contributed to this release: - Serge Rey - Wei Kang # Version 2.0.0 (2018-08-10) Starting from this release, mapclassify supports python 3+ only (currently 3.5 and 3.6). This release also features a first stable version of mapclassify in the process of pysal refactoring. There is a big change in the api in that we no longer provide an api module (`from mapclassify.api import Quantiles`). Instead, users will directly `from mapclassify import Quantiles`. GitHub stats for 2017/08/18 - 2018/08/10 These lists are automatically generated, and may be incomplete or contain duplicates. We closed a total of 8 issues, 4 pull requests and 4 regular issues; this is the full list (generated with the script :file:`tools/github_stats.py`): Pull Requests (4): * :ghpull:`12`: b'Clean up for next pypi release' * :ghpull:`11`: b'move notebooks outside of the package' * :ghpull:`9`: b'ENH: move classifiers up into init' * :ghpull:`8`: b'Moving to python 3+' Issues (4): * :ghissue:`12`: b'Clean up for next pypi release' * :ghissue:`11`: b'move notebooks outside of the package' * :ghissue:`9`: b'ENH: move classifiers up into init' * :ghissue:`8`: b'Moving to python 3+' # Version 1.0.1 (2017-08-17) - Warnings added when duplicate values make quantiles ill-defined - Faster digitize in place of list comprehension - Bug fix for consistent treatment of intervals (closed on the right, open on the left) v<1.0.0dev> 2017-04-21 - alpha release mapclassify-2.10.0/LICENSE.txt000066400000000000000000000027021503430131600157250ustar00rootroot00000000000000Copyright 2018 PySAL-mapclassify Developers Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. 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. 3. Neither the name of the copyright holder 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 HOLDER 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. mapclassify-2.10.0/README.md000066400000000000000000000225631503430131600153700ustar00rootroot00000000000000# mapclassify: Classification Schemes for Choropleth Maps [![Continuous Integration](https://github.com/pysal/mapclassify/actions/workflows/testing.yml/badge.svg)](https://github.com/pysal/mapclassify/actions/workflows/testing.yml) [![codecov](https://codecov.io/gh/pysal/mapclassify/graph/badge.svg?token=QJnoJwxLuo)](https://codecov.io/gh/pysal/mapclassify) [![PyPI version](https://badge.fury.io/py/mapclassify.svg)](https://badge.fury.io/py/mapclassify) [![DOI](https://zenodo.org/badge/88918063.svg)](https://zenodo.org/badge/latestdoi/88918063) [![License](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](https://opensource.org/licenses/BSD-3-Clause) [![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/pysal/mapclassify/main) `mapclassify` implements a family of classification schemes for choropleth maps. Its focus is on the determination of the number of classes, and the assignment of observations to those classes. It is intended for use with upstream mapping and geovisualization packages (see [geopandas](https://geopandas.org/mapping.html)) that handle the rendering of the maps. For further theoretical background see [Rey, S.J., D. Arribas-Bel, and L.J. Wolf (2020) "Geographic Data Science with PySAL and the PyData Stack”](https://geographicdata.science/book/notebooks/05_choropleth.html). ## Using `mapclassify` Load built-in example data reporting employment density in 58 California counties: ```python >>> import mapclassify >>> y = mapclassify.load_example() >>> y.mean() 125.92810344827588 >>> y.min(), y.max() (0.13, 4111.4499999999998) ``` ## Map Classifiers Supported ### BoxPlot ```python >>> mapclassify.BoxPlot(y) BoxPlot Interval Count -------------------------- ( -inf, -52.88] | 0 ( -52.88, 2.57] | 15 ( 2.57, 9.36] | 14 ( 9.36, 39.53] | 14 ( 39.53, 94.97] | 6 ( 94.97, 4111.45] | 9 ``` ### EqualInterval ```python >>> mapclassify.EqualInterval(y) EqualInterval Interval Count -------------------------- [ 0.13, 822.39] | 57 ( 822.39, 1644.66] | 0 (1644.66, 2466.92] | 0 (2466.92, 3289.19] | 0 (3289.19, 4111.45] | 1 ``` ### FisherJenks ```python >>> import numpy as np >>> np.random.seed(123456) >>> mapclassify.FisherJenks(y, k=5) FisherJenks Interval Count -------------------------- [ 0.13, 75.29] | 49 ( 75.29, 192.05] | 3 ( 192.05, 370.50] | 4 ( 370.50, 722.85] | 1 ( 722.85, 4111.45] | 1 ``` ### FisherJenksSampled ```python >>> np.random.seed(123456) >>> x = np.random.exponential(size=(10000,)) >>> mapclassify.FisherJenks(x, k=5) FisherJenks Interval Count ---------------------- [ 0.00, 0.64] | 4694 ( 0.64, 1.45] | 2922 ( 1.45, 2.53] | 1584 ( 2.53, 4.14] | 636 ( 4.14, 10.61] | 164 >>> mapclassify.FisherJenksSampled(x, k=5) FisherJenksSampled Interval Count ---------------------- [ 0.00, 0.70] | 5020 ( 0.70, 1.63] | 2952 ( 1.63, 2.88] | 1454 ( 2.88, 5.32] | 522 ( 5.32, 10.61] | 52 ``` ### HeadTailBreaks ```python >>> mapclassify.HeadTailBreaks(y) HeadTailBreaks Interval Count -------------------------- [ 0.13, 125.93] | 50 ( 125.93, 811.26] | 7 ( 811.26, 4111.45] | 1 ``` ### JenksCaspall ```python >>> mapclassify.JenksCaspall(y, k=5) JenksCaspall Interval Count -------------------------- [ 0.13, 1.81] | 14 ( 1.81, 7.60] | 13 ( 7.60, 29.82] | 14 ( 29.82, 181.27] | 10 ( 181.27, 4111.45] | 7 ``` ### JenksCaspallForced ```python >>> mapclassify.JenksCaspallForced(y, k=5) JenksCaspallForced Interval Count -------------------------- [ 0.13, 1.34] | 12 ( 1.34, 5.90] | 12 ( 5.90, 16.70] | 13 ( 16.70, 50.65] | 9 ( 50.65, 4111.45] | 12 ``` ### JenksCaspallSampled ```python >>> mapclassify.JenksCaspallSampled(y, k=5) JenksCaspallSampled Interval Count -------------------------- [ 0.13, 12.02] | 33 ( 12.02, 29.82] | 8 ( 29.82, 75.29] | 8 ( 75.29, 192.05] | 3 ( 192.05, 4111.45] | 6 ``` ### MaxP ```python >>> mapclassify.MaxP(y) MaxP Interval Count -------------------------- [ 0.13, 8.70] | 29 ( 8.70, 16.70] | 8 ( 16.70, 20.47] | 1 ( 20.47, 66.26] | 10 ( 66.26, 4111.45] | 10 ``` ### [MaximumBreaks](notebooks/maximum_breaks.ipynb) ```python >>> mapclassify.MaximumBreaks(y, k=5) MaximumBreaks Interval Count -------------------------- [ 0.13, 146.00] | 50 ( 146.00, 228.49] | 2 ( 228.49, 546.67] | 4 ( 546.67, 2417.15] | 1 (2417.15, 4111.45] | 1 ``` ### NaturalBreaks ```python >>> mapclassify.NaturalBreaks(y, k=5) NaturalBreaks Interval Count -------------------------- [ 0.13, 75.29] | 49 ( 75.29, 192.05] | 3 ( 192.05, 370.50] | 4 ( 370.50, 722.85] | 1 ( 722.85, 4111.45] | 1 ``` ### Quantiles ```python >>> mapclassify.Quantiles(y, k=5) Quantiles Interval Count -------------------------- [ 0.13, 1.46] | 12 ( 1.46, 5.80] | 11 ( 5.80, 13.28] | 12 ( 13.28, 54.62] | 11 ( 54.62, 4111.45] | 12 ``` ### Percentiles ```python >>> mapclassify.Percentiles(y, pct=[33, 66, 100]) Percentiles Interval Count -------------------------- [ 0.13, 3.36] | 19 ( 3.36, 22.86] | 19 ( 22.86, 4111.45] | 20 ``` ### PrettyBreaks ```python >>> np.random.seed(123456) >>> x = np.random.randint(0, 10000, (100,1)) >>> mapclassify.PrettyBreaks(x) Pretty Interval Count ---------------------------- [ 300.00, 2000.00] | 23 ( 2000.00, 4000.00] | 15 ( 4000.00, 6000.00] | 18 ( 6000.00, 8000.00] | 24 ( 8000.00, 10000.00] | 20 ``` ### StdMean ```python >>> mapclassify.StdMean(y) StdMean Interval Count -------------------------- ( -inf, -967.36] | 0 (-967.36, -420.72] | 0 (-420.72, 672.57] | 56 ( 672.57, 1219.22] | 1 (1219.22, 4111.45] | 1 ``` ### UserDefined ```python >>> mapclassify.UserDefined(y, bins=[22, 674, 4112]) UserDefined Interval Count -------------------------- [ 0.13, 22.00] | 38 ( 22.00, 674.00] | 18 ( 674.00, 4112.00] | 2 ``` ## Alternative API As of version 2.4.0 the API has been extended. A `classify` function is now available for a streamlined interface: ```python >>> classify(y, 'boxplot') BoxPlot Interval Count -------------------------- ( -inf, -52.88] | 0 ( -52.88, 2.57] | 15 ( 2.57, 9.36] | 14 ( 9.36, 39.53] | 14 ( 39.53, 94.97] | 6 ( 94.97, 4111.45] | 9 ``` ## Use Cases ### Creating and using a classification instance ```python >>> bp = mapclassify.BoxPlot(y) >>> bp BoxPlot Interval Count -------------------------- ( -inf, -52.88] | 0 ( -52.88, 2.57] | 15 ( 2.57, 9.36] | 14 ( 9.36, 39.53] | 14 ( 39.53, 94.97] | 6 ( 94.97, 4111.45] | 9 >>> bp.bins array([ -5.28762500e+01, 2.56750000e+00, 9.36500000e+00, 3.95300000e+01, 9.49737500e+01, 4.11145000e+03]) >>> bp.counts array([ 0, 15, 14, 14, 6, 9]) >>> bp.yb array([5, 1, 2, 3, 2, 1, 5, 1, 3, 3, 1, 2, 2, 1, 2, 2, 2, 1, 5, 2, 4, 1, 2, 2, 1, 1, 3, 3, 3, 5, 3, 1, 3, 5, 2, 3, 5, 5, 4, 3, 5, 3, 5, 4, 2, 1, 1, 4, 4, 3, 3, 1, 1, 2, 1, 4, 3, 2]) ``` ### Binning new data ```python >>> bp = mapclassify.BoxPlot(y) >>> bp BoxPlot Interval Count -------------------------- ( -inf, -52.88] | 0 ( -52.88, 2.57] | 15 ( 2.57, 9.36] | 14 ( 9.36, 39.53] | 14 ( 39.53, 94.97] | 6 ( 94.97, 4111.45] | 9 >>> bp.find_bin([0, 7, 3000, 48]) array([1, 2, 5, 4]) ``` Note that `find_bin` does not recalibrate the classifier: ```python >>> bp BoxPlot Interval Count -------------------------- ( -inf, -52.88] | 0 ( -52.88, 2.57] | 15 ( 2.57, 9.36] | 14 ( 9.36, 39.53] | 14 ( 39.53, 94.97] | 6 ( 94.97, 4111.45] | 9 ``` ### Apply ```python >>> import mapclassify >>> import pandas >>> from numpy import linspace as lsp >>> data = [lsp(3,8,num=10), lsp(10, 0, num=10), lsp(-5, 15, num=10)] >>> data = pandas.DataFrame(data).T >>> data 0 1 2 0 3.000000 10.000000 -5.000000 1 3.555556 8.888889 -2.777778 2 4.111111 7.777778 -0.555556 3 4.666667 6.666667 1.666667 4 5.222222 5.555556 3.888889 5 5.777778 4.444444 6.111111 6 6.333333 3.333333 8.333333 7 6.888889 2.222222 10.555556 8 7.444444 1.111111 12.777778 9 8.000000 0.000000 15.000000 >>> data.apply(mapclassify.Quantiles.make(rolling=True)) 0 1 2 0 0 4 0 1 0 4 0 2 1 4 0 3 1 3 0 4 2 2 1 5 2 1 2 6 3 0 4 7 3 0 4 8 4 0 4 9 4 0 4 ``` ## Development Notes Because we use `geopandas` in development, and geopandas has stable `mapclassify` as a dependency, setting up a local development installation involves creating a conda environment, then replacing the stable `mapclassify` with the development version of `mapclassify` in the development environment. This can be accomplished with the following steps: ``` conda-env create -f environment.yml conda activate mapclassify conda remove -n mapclassify mapclassify pip install -e . ``` mapclassify-2.10.0/ci/000077500000000000000000000000001503430131600144745ustar00rootroot00000000000000mapclassify-2.10.0/ci/311-latest.yaml000066400000000000000000000003721503430131600171600ustar00rootroot00000000000000name: test channels: - conda-forge dependencies: - python=3.11 # required - networkx - numpy - pandas - scikit-learn - scipy # testing - geopandas - libpysal - pytest - pytest-cov - pytest-xdist - codecov - matplotlib mapclassify-2.10.0/ci/311-numba-latest.yaml000066400000000000000000000004211503430131600202530ustar00rootroot00000000000000name: test channels: - conda-forge dependencies: - python=3.11 # required - networkx - numpy - pandas - scikit-learn - scipy # testing - geopandas - libpysal - pytest - pytest-cov - pytest-xdist - codecov - matplotlib # optional - numba mapclassify-2.10.0/ci/311-oldest.yaml000066400000000000000000000004321503430131600171530ustar00rootroot00000000000000name: test channels: - conda-forge dependencies: - python=3.11 # required - networkx=3.2 - numpy=1.26 - pandas=2.1 - scikit-learn=1.4 - scipy=1.12 # testing - fiona - geopandas - libpysal - pytest - pytest-cov - pytest-xdist - codecov - matplotlib mapclassify-2.10.0/ci/312-latest.yaml000066400000000000000000000003721503430131600171610ustar00rootroot00000000000000name: test channels: - conda-forge dependencies: - python=3.12 # required - networkx - numpy - pandas - scikit-learn - scipy # testing - geopandas - libpysal - pytest - pytest-cov - pytest-xdist - codecov - matplotlib mapclassify-2.10.0/ci/312-numba-latest.yaml000066400000000000000000000004501503430131600202560ustar00rootroot00000000000000name: test channels: - conda-forge dependencies: - python=3.12 # required - networkx - numpy - pandas - scikit-learn - scipy # testing - geopandas - libpysal - pytest - pytest-cov - pytest-xdist - pytest-doctestplus - codecov - matplotlib # optional - numba mapclassify-2.10.0/ci/313-dev.yaml000066400000000000000000000010341503430131600164400ustar00rootroot00000000000000name: test channels: - conda-forge dependencies: - python=3.13 # testing - pytest - pytest-cov - pytest-xdist - codecov # optional - pyproj - pip - pip: - --pre --index-url https://pypi.anaconda.org/scientific-python-nightly-wheels/simple --extra-index-url https://pypi.org/simple - scipy - scikit-learn - pandas - networkx - matplotlib - shapely - fiona - git+https://github.com/pysal/libpysal.git@main - git+https://github.com/geopandas/geopandas.git@main mapclassify-2.10.0/ci/313-latest.yaml000066400000000000000000000005571503430131600171670ustar00rootroot00000000000000name: test channels: - conda-forge dependencies: - python=3.13 # required - networkx - numpy - pandas - scikit-learn - scipy # testing - geopandas - libpysal - pytest - pytest-cov - pytest-xdist - codecov - matplotlib # docs - nbsphinx - numpydoc - sphinx - sphinx-gallery - sphinxcontrib-bibtex - sphinx_bootstrap_theme mapclassify-2.10.0/ci/313-numba-latest.yaml000066400000000000000000000004501503430131600202570ustar00rootroot00000000000000name: test channels: - conda-forge dependencies: - python=3.13 # required - networkx - numpy - pandas - scikit-learn - scipy # testing - geopandas - libpysal - pytest - pytest-cov - pytest-xdist - pytest-doctestplus - codecov - matplotlib # optional - numba mapclassify-2.10.0/codecov.yml000066400000000000000000000005441503430131600162510ustar00rootroot00000000000000codecov: notify: after_n_builds: 10 coverage: range: 50..95 round: nearest precision: 1 status: project: default: threshold: 2% patch: default: threshold: 2% target: 80% ignore: - "tests/*" comment: layout: "reach, diff, files" behavior: once after_n_builds: 10 require_changes: true mapclassify-2.10.0/docs/000077500000000000000000000000001503430131600150315ustar00rootroot00000000000000mapclassify-2.10.0/docs/Makefile000066400000000000000000000015361503430131600164760ustar00rootroot00000000000000# Minimal makefile for Sphinx documentation # # You can set these variables from the command line. 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k.>shsM$$$j痔?)ߏ \v aaaN;C:E.++C}}=z-̘1gv:k"""Ᏻƞ38Zau8lP𗋡VHIKKI'".X{'$-3}^_$ i&\.on2`\$@cc#D"+~!Mn _h"$R)Uxu!IDATS]mqqq2re"""Xwq@) ?j*jjW \ BB ~VוWkjw{f?5K.GEii)|Mj*L&߿-[/wCqq1fˑ==2ݕdR ^`aw7[RX/DV.ufWLvQHQA&.2vW]]@k1118y$*++1sL <ܹ.… pBlڴ :6lz 'Ot+Z())_|˗/u/&""# D*K"xe(gO{ [class*='col-'] { display: flex; flex-direction: row, column; } .row.equal-height.row:after, .row.equal-height.row:before { display: flex; } .row.equal-height > [class*='col-'] > .thumbnail, .row.equal-height > [class*='col-'] > .thumbnail > .caption { display: flex; flex: .9 .1 auto; flex-direction: column; } .row.equal-height > [class*='col-'] > .thumbnail > .caption > .flex-text { flex-grow: 1; } .row.equal-height > [class*='col-'] > .thumbnail > img { width: 350px; height: 200%; /* force image's height */ /* force image fit inside it's "box" */ -webkit-object-fit: cover; -moz-object-fit: cover; -ms-object-fit: cover; -o-object-fit: cover; object-fit: cover; } } .row.extra-bottom-padding{ margin-bottom: 20px; } .topnavicons { margin-left: 10% !important; } .topnavicons li { margin-left: 0px !important; min-width: 100px; text-align: center; } .topnavicons .thumbnail { margin-right: 10px; border: none; box-shadow: none; text-align: center; font-size: 85%; font-weight: bold; line-height: 10px; height: 100px; } .topnavicons .thumbnail img { display: block; margin-left: auto; margin-right: auto; } /* Table with a scrollbar */ .bodycontainer { max-height: 800px; width: 100%; margin: 0; padding: 0; overflow-y: auto; } .table-scrollable { margin: 0; padding: 0; } .label { color: #E74C3C; font-size: 100%; font-weight: bold; width: 100px; text-align: left; vertical-align: middle; } div.body { max-width: 1080px; } table.longtable.align-default{ text-align: left; }mapclassify-2.10.0/docs/_static/references.bib000066400000000000000000000026171503430131600212640ustar00rootroot00000000000000%% This BibTeX bibliography file was created using BibDesk. %% http://bibdesk.sourceforge.net/ %% Created for Wei Kang at 2018-10-25 22:16:36 -0700 %% Saved with string encoding Unicode (UTF-8) @article{Jiang_2013, Author = {Jiang, Bin}, Doi = {10.1080/00330124.2012.700499}, Issn = {1467-9272}, Journal = {The Professional Geographer}, Month = {Aug}, Number = 3, Pages = {482--494}, Publisher = {Informa UK Limited}, Title = {Head/Tail Breaks: A New Classification Scheme for Data with a Heavy-Tailed Distribution}, Url = {http://dx.doi.org/10.1080/00330124.2012.700499}, Volume = 65, Year = 2013, Bdsk-Url-1 = {http://dx.doi.org/10.1080/00330124.2012.700499}} @article{Rey_2016, Author = {Rey, Sergio J. and Stephens, Philip and Laura, Jason}, Doi = {10.1111/tgis.12236}, Issn = {1361-1682}, Journal = {Transactions in GIS}, Month = {Oct}, Number = 4, Pages = {796--810}, Publisher = {Wiley}, Title = {An evaluation of sampling and full enumeration strategies for {Fisher Jenks} classification in big data settings}, Url = {http://dx.doi.org/10.1111/tgis.12236}, Volume = 21, Year = 2016, Bdsk-Url-1 = {http://dx.doi.org/10.1111/tgis.12236}} @book{Slocum_2009, Author = {Slocum, Terry A. and McMaster, Robert B. and Kessler, Fritz C. and Howard, Hugh H.}, Publisher = {Pearson Prentice Hall, Upper Saddle River}, Title = {Thematic cartography and geovisualization}, Year = {2009}} mapclassify-2.10.0/docs/api.rst000066400000000000000000000015221503430131600163340ustar00rootroot00000000000000.. _api_ref: .. currentmodule:: mapclassify API reference ============= .. _classifiers_api: Classifiers ----------- .. autosummary:: :toctree: generated/ mapclassify.BoxPlot mapclassify.EqualInterval mapclassify.FisherJenks mapclassify.FisherJenksSampled mapclassify.greedy mapclassify.HeadTailBreaks mapclassify.JenksCaspall mapclassify.JenksCaspallForced mapclassify.JenksCaspallSampled mapclassify.MaxP mapclassify.MaximumBreaks mapclassify.NaturalBreaks mapclassify.Percentiles mapclassify.PrettyBreaks mapclassify.Quantiles mapclassify.StdMean mapclassify.UserDefined Utilities --------- .. autosummary:: :toctree: generated/ mapclassify.KClassifiers mapclassify.Pooled mapclassify.classify mapclassify.gadf mapclassify.util.get_color_array mapclassify-2.10.0/docs/conf.py000066400000000000000000000245011503430131600163320ustar00rootroot00000000000000# giddy documentation build configuration file, created by # sphinx-quickstart on Wed Jun 6 15:54:22 2018. # # 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 os # 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. # import sys import sphinx_bootstrap_theme sys.path.insert(0, os.path.abspath("../")) # import your package to obtain the version info to display on the docs website import mapclassify # -- 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 = [ #'sphinx_gallery.gen_gallery', "sphinx.ext.autodoc", "sphinx.ext.autosummary", "sphinx.ext.viewcode", "sphinxcontrib.bibtex", "sphinx.ext.mathjax", "sphinx.ext.doctest", "sphinx.ext.intersphinx", "numpydoc", "matplotlib.sphinxext.plot_directive", "nbsphinx", ] bibtex_bibfiles = ["_static/references.bib"] # Add any paths that contain templates here, relative to this directory. templates_path = ["_templates"] # The suffix(es) of source filenames. # You can specify multiple suffix as a list of string: # # source_suffix = ['.rst', '.md'] source_suffix = ".rst" # The master toctree document. master_doc = "index" # General information about the project. project = "mapclassify" # string of your project name, for example, 'giddy' copyright = "2018-, pysal developers" author = "pysal 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. # # The full version. version = mapclassify.__version__ # should replace it with your PACKAGE_NAME release = mapclassify.__version__ # should replace it with your PACKAGE_NAME # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. # # This is also used if you do content translation via gettext catalogs. # Usually you set "language" from the command line for these cases. language = "en" # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This patterns also effect to html_static_path and html_extra_path exclude_patterns = ["_build", "Thumbs.db", ".DS_Store", "tests/*"] # The name of the Pygments (syntax highlighting) style to use. pygments_style = "sphinx" # If true, `todo` and `todoList` produce output, else they produce nothing. todo_include_todos = False # -- 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 = 'alabaster' html_theme = "bootstrap" html_theme_path = sphinx_bootstrap_theme.get_html_theme_path() html_title = f"{project} v{version} Manual" # (Optional) Logo of your package. # Should be small enough to fit the navbar (ideally 24x24). # Path should be relative to the ``_static`` files directory. # html_logo = "_static/images/package_logo.jpg" # (Optional) PySAL favicon html_favicon = "_static/images/pysal_favicon.ico" # 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 = { # Navigation bar title. (Default: ``project`` value) "navbar_title": project, # string of your project name, for example, 'giddy' # Render the next and previous page links in navbar. (Default: true) "navbar_sidebarrel": False, # Render the current pages TOC in the navbar. (Default: true) #'navbar_pagenav': True, #'navbar_pagenav': False, # No sidebar "nosidebar": True, # Tab name for the current pages TOC. (Default: "Page") #'navbar_pagenav_name': "Page", # Global TOC depth for "site" navbar tab. (Default: 1) # Switching to -1 shows all levels. "globaltoc_depth": 2, # Include hidden TOCs in Site navbar? # # Note: If this is "false", you cannot have mixed ``:hidden:`` and # non-hidden ``toctree`` directives in the same page, or else the build # will break. # # Values: "true" (default) or "false" "globaltoc_includehidden": "true", # HTML navbar class (Default: "navbar") to attach to

element. # For black navbar, do "navbar navbar-inverse" #'navbar_class': "navbar navbar-inverse", # Fix navigation bar to top of page? # Values: "true" (default) or "false" "navbar_fixed_top": "true", # Location of link to source. # Options are "nav" (default), "footer" or anything else to exclude. "source_link_position": "footer", # Bootswatch (http://bootswatch.com/) theme. # # Options are nothing (default) or the name of a valid theme # such as "amelia" or "cosmo", "yeti", "flatly". "bootswatch_theme": "yeti", # Choose Bootstrap version. # Values: "3" (default) or "2" (in quotes) "bootstrap_version": "3", # Navigation bar menu "navbar_links": [ ("Installation", "installation"), ("Tutorial", "tutorial"), ("API", "api"), ("References", "references"), ], } # 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"] # Custom sidebar templates, maps document names to template names. # html_sidebars = {} # html_sidebars = {'sidebar': ['localtoc.html', 'sourcelink.html', 'searchbox.html']} # -- Options for HTMLHelp output ------------------------------------------ # Output file base name for HTML help builder. htmlhelp_basename = project + "doc" # -- 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': '', # Latex figure (float) alignment # # 'figure_align': 'htbp', } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, # author, documentclass [howto, manual, or own class]). latex_documents = [ ( master_doc, f"{project}.tex", f"{project} Documentation", "pysal developers", "manual", ), ] # -- Options for manual page output --------------------------------------- # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [(master_doc, project, f"{project} Documentation", [author], 1)] # -- 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 = [ ( master_doc, project, f"{project} Documentation", author, "PySAL Developers", "map classification schemes.", "Miscellaneous", ), ] # ----------------------------------------------------------------------------- # Autosummary # ----------------------------------------------------------------------------- # Generate the API documentation when building autosummary_generate = True # avoid showing members twice numpydoc_show_class_members = False numpydoc_use_plots = True class_members_toctree = True numpydoc_show_inherited_class_members = True numpydoc_xref_param_type = True # automatically document class members autodoc_default_options = {"members": True, "undoc-members": True} # display the source code for Plot directive plot_include_source = True def setup(app): app.add_css_file("pysal-styles.css") # Example configuration for intersphinx: refer to the Python standard library. intersphinx_mapping = { "geopandas": ("https://geopandas.org/en/latest/", None), "libpysal": ("https://pysal.org/libpysal/", None), "matplotlib": ("https://matplotlib.org/stable/", None), "networkx": ("https://networkx.org/documentation/stable/", None), "numpy": ("https://numpy.org/doc/stable/", None), "pandas": ("https://pandas.pydata.org/pandas-docs/stable/", None), "python": ("https://docs.python.org/3.13/", None), "scipy": ("https://docs.scipy.org/doc/scipy/", None), } # This is processed by Jinja2 and inserted before each notebook nbsphinx_prolog = r""" {% set docname = env.doc2path(env.docname, base=None) %} .. only:: html .. role:: raw-html(raw) :format: html .. nbinfo:: This page was generated from `{{ docname }}`__. Interactive online version: :raw-html:`Binder badge` __ https://github.com/pysal/mapclassify/blob/main/{{ docname }} .. raw:: latex \nbsphinxstartnotebook{\scriptsize\noindent\strut \textcolor{gray}{The following section was generated from \sphinxcode{\sphinxupquote{\strut {{ docname | escape_latex }}}} \dotfill}} """ # noqa: E501 # This is processed by Jinja2 and inserted after each notebook nbsphinx_epilog = r""" .. raw:: latex \nbsphinxstopnotebook{\scriptsize\noindent\strut \textcolor{gray}{\dotfill\ \sphinxcode{\sphinxupquote{\strut {{ env.doc2path(env.docname, base='doc') | escape_latex }}}} ends here.}} """ # List of arguments to be passed to the kernel that executes the notebooks: nbsphinx_execute_arguments = [ "--InlineBackend.figure_formats={'svg', 'pdf'}", "--InlineBackend.rc={'figure.dpi': 96}", ] mathjax3_config = { "TeX": {"equationNumbers": {"autoNumber": "AMS", "useLabelIds": True}}, } mapclassify-2.10.0/docs/index.rst000066400000000000000000000037251503430131600167010ustar00rootroot00000000000000.. documentation master file mapclassify =========== mapclassify is an open-source python library for Choropleth map classification. It is part of `PySAL`_ the Python Spatial Analysis Library. .. raw:: html .. toctree:: :hidden: :maxdepth: 3 :caption: Contents: Installation Tutorial API References .. _PySAL: https://github.com/pysal/pysal mapclassify-2.10.0/docs/installation.rst000066400000000000000000000031671503430131600202730ustar00rootroot00000000000000.. Installation Installation ============ mapclassify supports python `3.9`_+. Please make sure that you are operating in a python 3 environment. Installing released version --------------------------- mapclassify is available in on `conda`_ via the `conda-forge`_ channel:: conda install -c conda-forge mapclassify mapclassify is also available on the `Python Package Index`_. Therefore, you can either install directly with `pip` from the command line:: pip install -U mapclassify or download the source distribution (.tar.gz) and decompress it to your selected destination. Open a command shell and navigate to the decompressed folder. Type:: pip install . Installing development version ------------------------------ Potentially, you might want to use the newest features in the development version of mapclassify on github - `pysal/mapclassify`_ while have not been incorporated in the Pypi released version. You can achieve that by installing `pysal/mapclassify`_ by running the following from a command shell:: pip install git+https://github.com/pysal/mapclassify.git You can also `fork`_ the `pysal/mapclassify`_ repo and create a local clone of your fork. By making changes to your local clone and submitting a pull request to `pysal/mapclassify`_, you can contribute to mapclassify development. .. _3.9: https://docs.python.org/3.9/ .. _conda: https://docs.conda.io/en/latest/ .. _conda-forge: https://anaconda.org/conda-forge/mapclassify .. _Python Package Index: https://pypi.org/project/mapclassify/ .. _pysal/mapclassify: https://github.com/pysal/mapclassify .. _fork: https://help.github.com/articles/fork-a-repo/ mapclassify-2.10.0/docs/references.rst000066400000000000000000000001441503430131600177030ustar00rootroot00000000000000.. reference for the docs References ========== .. bibliography:: _static/references.bib :all: mapclassify-2.10.0/docs/tutorial.rst000066400000000000000000000005751503430131600174350ustar00rootroot00000000000000Tutorial ======== .. toctree:: :maxdepth: 1 :caption: Contents: notebooks/01_maximum_breaks.ipynb notebooks/02_legends.ipynb notebooks/03_choropleth.ipynb notebooks/04_pooled.ipynb notebooks/05_Greedy_coloring.ipynb notebooks/06_api.ipynb notebooks/08_manual_coloring.ipynb notebooks/09_legendgram.ipynb notebooks/value_by_alpha.ipynb mapclassify-2.10.0/environment.yml000066400000000000000000000006331503430131600171720ustar00rootroot00000000000000# Run `conda-env create -f environment.yml` name: mapclassify channels: - conda-forge dependencies: - python - geodatasets - geopandas - git - ipywidgets - jupyterlab - libpysal - lonboard - matplotlib - nbconvert - networkx - numba - palettable - pip - pyarrow - pydeck - scikit-learn - seaborn - shapely - pip: - git+https://github.com/pysal/mapclassify.git@main mapclassify-2.10.0/mapclassify/000077500000000000000000000000001503430131600164145ustar00rootroot00000000000000mapclassify-2.10.0/mapclassify/__init__.py000066400000000000000000000013561503430131600205320ustar00rootroot00000000000000import contextlib from importlib.metadata import PackageNotFoundError, version from . import legendgram, util from ._classify_API import classify from .classifiers import ( CLASSIFIERS, BoxPlot, EqualInterval, FisherJenks, FisherJenksSampled, HeadTailBreaks, JenksCaspall, JenksCaspallForced, JenksCaspallSampled, KClassifiers, MaximumBreaks, MaxP, NaturalBreaks, Percentiles, PrettyBreaks, Quantiles, StdMean, UserDefined, gadf, load_example, ) from .greedy import greedy from .pooling import Pooled from .value_by_alpha import shift_colormap, truncate_colormap, vba_choropleth with contextlib.suppress(PackageNotFoundError): __version__ = version("mapclassify") mapclassify-2.10.0/mapclassify/_classify_API.py000066400000000000000000000137241503430131600214420ustar00rootroot00000000000000from .classifiers import ( BoxPlot, EqualInterval, FisherJenks, FisherJenksSampled, HeadTailBreaks, JenksCaspall, JenksCaspallForced, JenksCaspallSampled, MaximumBreaks, MaxP, NaturalBreaks, Percentiles, PrettyBreaks, Quantiles, StdMean, UserDefined, ) __author__ = "Stefanie Lumnitz " _classifiers = { "boxplot": BoxPlot, "equalinterval": EqualInterval, "fisherjenks": FisherJenks, "fisherjenkssampled": FisherJenksSampled, "headtailbreaks": HeadTailBreaks, "jenkscaspall": JenksCaspall, "jenkscaspallforced": JenksCaspallForced, "jenkscaspallsampled": JenksCaspallSampled, "maxp": MaxP, "maximumbreaks": MaximumBreaks, "naturalbreaks": NaturalBreaks, "quantiles": Quantiles, "percentiles": Percentiles, "prettybreaks": PrettyBreaks, "stdmean": StdMean, "userdefined": UserDefined, } def classify( y, scheme, k=5, pct=[1, 10, 50, 90, 99, 100], pct_sampled=0.10, truncate=True, hinge=1.5, multiples=[-2, -1, 1, 2], mindiff=0, initial=100, bins=None, lowest=None, anchor=False, ): """ Classify your data with ``mapclassify.classify``. Input parameters are dependent on classifier used. Parameters ---------- y : numpy.array :math:`(n,1)`, values to classify. scheme : str ``pysal.mapclassify`` classification scheme. k : int (default 5) The number of classes. pct : numpy.array (default [1, 10, 50, 90, 99, 100]) Percentiles used for classification with ``percentiles``. pct_sampled : float default (0.10) The percentage of n that should form the sample (``JenksCaspallSampled``, ``FisherJenksSampled``) If ``pct`` is specified such that ``n*pct > 1000``, then ``pct=1000``. truncate : bool (default True) Truncate ``pct_sampled`` in cases where ``pct * n > 1000``. hinge : float (default 1.5) Multiplier for *IQR* when ``BoxPlot`` classifier used. multiples : numpy.array (default [-2,-1,1,2]) The multiples of the standard deviation to add/subtract from the sample mean to define the bins using ``std_mean``. mindiff : float (default is 0) The minimum difference between class breaks if using ``maximum_breaks`` classifier. initial : int (default 100) Number of initial solutions to generate or number of runs when using ``natural_breaks`` or ``max_p_classifier``. Setting initial to ``0`` will result in the quickest calculation of bins. bins : numpy.array (default None) :math:`(k,1)`, upper bounds of classes (have to be monotically increasing) if using ``user_defined`` classifier. Default is ``None``. For example: ``[20, max(y)]``. lowest : float (default None) Scalar minimum value of lowest class. Default is to set the minimum to ``-inf`` if ``y.min()`` > first upper bound (which will override the default), otherwise minimum is set to ``y.min()``. anchor : bool (default False) Anchor upper bound of one class to the sample mean. Returns ------- classifier : mapclassify.classifiers.MapClassifier Object containing bin ids for each observation (``.yb``), upper bounds of each class (``.bins``), number of classes (``.k``) and number of observations falling in each class (``.counts``). Notes ----- Supported classifiers include: * ``quantiles`` * ``boxplot`` * ``equalinterval`` * ``fisherjenks`` * ``fisherjenkssampled`` * ``headtailbreaks`` * ``jenkscaspall`` * ``jenkscaspallsampled`` * ``jenks_caspallforced`` * ``maxp`` * ``maximumbreaks`` * ``naturalbreaks`` * ``percentiles`` * ``prettybreaks`` * ``stdmean`` * ``userdefined`` Examples -------- >>> import libpysal >>> import geopandas >>> from mapclassify import classify Load example data. >>> link_to_data = libpysal.examples.get_path("columbus.shp") >>> gdf = geopandas.read_file(link_to_data) >>> x = gdf['HOVAL'].values Classify values by quantiles. >>> quantiles = classify(x, "quantiles") Classify values by box_plot and set hinge to ``2``. >>> box_plot = classify(x, 'box_plot', hinge=2) >>> box_plot BoxPlot Interval Count ---------------------- ( -inf, -9.50] | 0 (-9.50, 25.70] | 13 (25.70, 33.50] | 12 (33.50, 43.30] | 12 (43.30, 78.50] | 9 (78.50, 96.40] | 3 """ # reformat scheme_lower = scheme.lower() scheme = scheme_lower.replace("_", "") # check if scheme is a valid scheme if scheme not in _classifiers: raise ValueError( f"Invalid scheme: '{scheme}'\n" f"Scheme must be in the set: {_classifiers.keys()}" ) elif scheme == "boxplot": classifier = _classifiers[scheme](y, hinge) elif scheme == "fisherjenkssampled": classifier = _classifiers[scheme](y, k, pct_sampled, truncate) elif scheme == "headtailbreaks": classifier = _classifiers[scheme](y) elif scheme == "percentiles": classifier = _classifiers[scheme](y, pct) elif scheme == "stdmean": classifier = _classifiers[scheme](y, multiples, anchor) elif scheme == "jenkscaspallsampled": classifier = _classifiers[scheme](y, k, pct_sampled) elif scheme == "maximumbreaks": classifier = _classifiers[scheme](y, k, mindiff) elif scheme in ["naturalbreaks", "maxp"]: classifier = _classifiers[scheme](y, k, initial) elif scheme == "userdefined": classifier = _classifiers[scheme](y, bins, lowest) elif scheme in [ "equalinterval", "fisherjenks", "jenkscaspall", "jenkscaspallforced", "quantiles", "prettybreaks", ]: classifier = _classifiers[scheme](y, k) return classifier mapclassify-2.10.0/mapclassify/classifiers.py000066400000000000000000002545151503430131600213110ustar00rootroot00000000000000""" A module of classification schemes for choropleth mapping. """ import copy import functools import warnings import numpy as np import scipy.stats as stats from sklearn.cluster import KMeans from .legendgram import _legendgram __author__ = "Sergio J. Rey" __all__ = [ "MapClassifier", "quantile", "BoxPlot", "EqualInterval", "FisherJenks", "FisherJenksSampled", "JenksCaspall", "JenksCaspallForced", "JenksCaspallSampled", "HeadTailBreaks", "MaxP", "MaximumBreaks", "NaturalBreaks", "Quantiles", "Percentiles", "PrettyBreaks", "StdMean", "UserDefined", "gadf", "KClassifiers", "CLASSIFIERS", ] CLASSIFIERS = ( "BoxPlot", "EqualInterval", "FisherJenks", "FisherJenksSampled", "HeadTailBreaks", "JenksCaspall", "JenksCaspallForced", "JenksCaspallSampled", "MaxP", "MaximumBreaks", "NaturalBreaks", "Quantiles", "Percentiles", "PrettyBreaks", "StdMean", "UserDefined", ) K = 5 # default number of classes in any map scheme with this as an argument SEEDRANGE = 1000000 # range for drawing random ints from for Natural Breaks FMT = "{:.2f}" try: from numba import njit HAS_NUMBA = True except ImportError: HAS_NUMBA = False def njit(_type): # noqa ARG001 def decorator_njit(func): @functools.wraps(func) def wrapper_decorator(*args, **kwargs): return func(*args, **kwargs) return wrapper_decorator return decorator_njit def _format_intervals(mc, fmt="{:.0f}"): """ Helper methods to format legend intervals. Parameters ---------- mc: MapClassifier fmt: str (default '{:.0f}') Specification of formatting for legend. Returns ------- tuple: edges : list :math:`k` strings for class intervals. max_width : int Length of largest interval string. lower_open : bool True: lower bound of first interval is open. False: lower bound of first interval is closed. Notes ----- For some classifiers, it is possible that the upper bound of the first interval is less than the minimum value of the attribute that is being classified. In these cases ``lower_open=True`` and the lower bound of the interval is set to ``numpy.NINF``. """ lowest = mc.y.min() if hasattr(mc, "lowest") and mc.lowest is not None: lowest = mc.lowest lower_open = False if lowest > mc.bins[0]: lowest = -np.inf lower_open = True edges = [lowest] edges.extend(mc.bins) edges = [fmt.format(edge) for edge in edges] max_width = max([len(edge) for edge in edges]) return edges, max_width, lower_open def _get_mpl_labels(mc, fmt="{:.1f}"): """ Helper method to format legend intervals for matplotlib (and geopandas). Parameters ---------- mc : MapClassifier fmt : str (default '{:.1f}') Specification of formatting for legend. Returns ------- intervals : list :math:`k` strings for class intervals. """ edges, max_width, lower_open = _format_intervals(mc, fmt) k = len(edges) - 1 left = ["["] if lower_open: left = ["("] left.extend("(" * k) right = "]" * (k + 1) lower = ["{:>{width}}".format(edges[i], width=max_width) for i in range(k)] upper = ["{:>{width}}".format(edges[i], width=max_width) for i in range(1, k + 1)] lower = [_l + r for _l, r in zip(left, lower, strict=False)] upper = [_l + r for _l, r in zip(upper, right, strict=False)] intervals = [_l + ", " + r for _l, r in zip(lower, upper, strict=False)] return intervals def _get_table(mc, fmt="{:.2f}"): """ Helper function to generate tabular classification report. Parameters ---------- mc: MapClassifier fmt: str (default '{:.2f}') specification of formatting for legend. Returns ------- table : str Formatted table of classification results. """ intervals = _get_mpl_labels(mc, fmt) interval_width = len(intervals[0]) counts = list(map(str, mc.counts)) count_width = max([len(count) for count in counts]) count_width = max(count_width, len("count")) interval_width = max(interval_width, len("interval")) header = f"{'Interval': ^{interval_width}}" header += " " * 3 + f"{'Count': >{count_width}}" title = mc.name header += "\n" + "-" * len(header) table = [title, "", header] for i, interval in enumerate(intervals): row = f"{interval} | {counts[i]: >{count_width}}" table.append(row) return "\n".join(table) def head_tail_breaks(values, cuts): """Head tail breaks helper function.""" values = np.array(values) mean = np.mean(values) if len(cuts) > 0 and cuts[-1] == mean: # this fixes floating point from GH#117 return cuts cuts.append(mean) if len(set(values)) > 1: return head_tail_breaks(values[values > mean], cuts) return cuts def quantile(y, k=4): """ Calculates the quantiles for an array. Parameters ---------- y : numpy.array :math:`(n,1)`, values to classify. k : int (default 4) Number of quantiles. Returns ------- q : numpy.array :math:`(n,1)`, quantile values. Notes ----- If there are enough ties that the quantile values repeat, we collapse to pseudo quantiles in which case the number of classes will be less than ``k``. Examples -------- >>> import mapclassify >>> import numpy >>> x = numpy.arange(1000) >>> mapclassify.classifiers.quantile(x) array([249.75, 499.5 , 749.25, 999. ]) >>> mapclassify.classifiers.quantile(x, k=3) array([333., 666., 999.]) """ w = 100.0 / k p = np.arange(w, 100 + w, w) if p[-1] > 100.0: p[-1] = 100.0 q = np.array([stats.scoreatpercentile(y, pct) for pct in p]) q = np.unique(q) k_q = len(q) if k_q < k: warnings.warn( f"Not enough unique values in array to form {k} classes. " f"Setting k to {k_q}.", UserWarning, stacklevel=2, ) return q def binC(y, bins): # noqa N802 """ Bin categorical/qualitative data. Parameters ---------- y : numpy.array :math:`(n,q)`, categorical values. bins : numpy.array :math:`(k,1)`, unique values associated with each bin. Return ------ b : numpy.array :math:`(n,q)` bin membership, values between ``0`` and ``k-1``. Examples -------- >>> import numpy >>> import mapclassify >>> numpy.random.seed(1) >>> x = numpy.random.randint(2, 8, (10, 3)) >>> bins = list(range(2, 8)) >>> x array([[7, 5, 6], [2, 3, 5], [7, 2, 2], [3, 6, 7], [6, 3, 4], [6, 7, 4], [6, 5, 6], [4, 6, 7], [4, 6, 3], [3, 2, 7]]) >>> y = mapclassify.classifiers.binC(x, bins) >>> y array([[5, 3, 4], [0, 1, 3], [5, 0, 0], [1, 4, 5], [4, 1, 2], [4, 5, 2], [4, 3, 4], [2, 4, 5], [2, 4, 1], [1, 0, 5]]) """ # TODO: consider renaming ``binC`` to ``bin_c`` to resolve N802 (gh#185) if np.ndim(y) == 1: k = 1 n = np.shape(y)[0] else: n, k = np.shape(y) b = np.zeros((n, k), dtype="int") for i, _bin in enumerate(bins): b[np.nonzero(y == _bin)] = i # check for non-binned items and warn if needed vals = set(y.flatten()) for val in vals: if val not in bins: warnings.warn( f"\nValue not in bin: {val}\nBins: {bins}", UserWarning, stacklevel=2 ) return b def bin(y, bins): # noqa A001 """ Bin interval/ratio data. Parameters ---------- y : numpy.array :math:`(n,q)`, values to bin. bins : numpy.array :math:`(k,1)`, upper bounds of each bin (monotonic). Returns ------- b : numpy.array :math:`(n,q)`, values of values between ``0`` and ``k-1``. Examples -------- >>> import numpy >>> import mapclassify >>> numpy.random.seed(1) >>> x = numpy.random.randint(2, 20, (10, 3)) >>> bins = [10, 15, 20] >>> b = mapclassify.classifiers.bin(x, bins) >>> x array([[ 7, 13, 14], [10, 11, 13], [ 7, 17, 2], [18, 3, 14], [ 9, 15, 8], [ 7, 13, 12], [16, 6, 11], [19, 2, 15], [11, 11, 9], [ 3, 2, 19]]) >>> b array([[0, 1, 1], [0, 1, 1], [0, 2, 0], [2, 0, 1], [0, 1, 0], [0, 1, 1], [2, 0, 1], [2, 0, 1], [1, 1, 0], [0, 0, 2]]) """ # TODO: consider renaming ``bin`` to ``bin_int`` to resolve A001 (gh#185) if np.ndim(y) == 1: k = 1 n = np.shape(y)[0] else: n, k = np.shape(y) b = np.zeros((n, k), dtype="int") i = len(bins) if not isinstance(bins, list): bins = bins.tolist() binsc = copy.copy(bins) while binsc: i -= 1 c = binsc.pop(-1) b[np.nonzero(y <= c)] = i return b def bin1d(x, bins): """ Place values of a 1-d array into bins and determine counts of values in each bin. Parameters ---------- x : numpy.array :math:`(n, 1)`, values to bin. bins : numpy.array :math:`(k,1)`, upper bounds of each bin (monotonic). Returns ------- bin_ids : numpy.array 1-d array of integer bin IDs. counts : int Number of elements of ``x`` falling in each bin. Examples -------- >>> import numpy >>> import mapclassify >>> x = numpy.arange(100, dtype = "float") >>> bins = [25, 74, 100] >>> bin_ids, counts = mapclassify.classifiers.bin1d(x, bins) >>> bin_ids array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]) >>> counts.tolist() [26, 49, 25] """ left = [-float("inf")] left.extend(bins[0:-1]) right = bins cuts = list(zip(left, right, strict=False)) k = len(bins) bin_ids = np.zeros(x.shape, dtype="int") while cuts: k -= 1 _l, r = cuts.pop(-1) bin_ids += (x > _l) * (x <= r) * k counts = np.bincount(bin_ids, minlength=len(bins)) return (bin_ids, counts) def _pretty_number(x, rounded=True): exp = np.floor(np.log10(x)) f = x / 10**exp if rounded: if f < 1.5: nf = 1.0 elif f < 3.0: nf = 2.0 elif f < 7.0: nf = 5.0 else: nf = 10.0 else: if f <= 1.0: nf = 1.0 elif f <= 2.0: nf = 2.0 elif f <= 5.0: nf = 5.0 else: nf = 10.0 return nf * 10.0**exp def _pretty(y, k=5): low = y.min() high = y.max() rg = _pretty_number(high - low, False) d = _pretty_number(rg / (k - 1), True) miny = np.floor(low / d) * d maxy = np.ceil(high / d) * d return np.arange(miny, maxy + 0.5 * d, d) def load_example(): """ Helper function for doc tests """ from .datasets import calemp return calemp.load() def _kmeans(y, k=5, n_init=10): """ Helper function to do k-means in one dimension. Parameters ---------- y : numpy.array :math:`(n,1)`, values to classify. k : int, (default 5) Number of classes to form. n_init : int, (default 10) Number of initial solutions. Best of initial results is returned. """ y = y * 1.0 # sklearn.cluster.KMeans needs float or double dtype y.shape = (-1, 1) result = KMeans(n_clusters=k, init="k-means++", n_init=n_init).fit(y) class_ids = result.labels_ centroids = result.cluster_centers_ binning = [] for c in range(k): values = y[class_ids == c] binning.append([values.max(), len(values)]) binning = np.array(binning) binning = binning[binning[:, 0].argsort()] cuts = binning[:, 0] y_cent = np.zeros_like(y) for c in range(k): y_cent[class_ids == c] = centroids[c] diffs = y - y_cent diffs *= diffs return class_ids, cuts, diffs.sum(), centroids def natural_breaks(values, k=5, init=10): """ Natural breaks helper function. Jenks natural breaks is k-means in one dimension. Parameters ---------- values : numpy.array :math:`(n, 1)` values to bin. k : int, (default 5) Number of classes. init: int, (default 10) Number of different solutions to obtain using different centroids. Best solution is returned. """ values = np.array(values) uv = np.unique(values) uvk = len(uv) if uvk < k: warnings.warn( f"Not enough unique values in array to form {k} classes. " f"Setting k to {uvk}.", UserWarning, stacklevel=2, ) k = uvk kres = _kmeans(values, k, n_init=init) sids = kres[-1] # centroids fit = kres[-2] class_ids = kres[0] cuts = kres[1] return (sids, class_ids, fit, cuts) @njit("f8[:](f8[:], u2)") def _fisher_jenks_means(values, classes=5): """ Jenks Optimal (Natural Breaks) algorithm implemented in Python. Notes ----- The original Python code comes from here: http://danieljlewis.org/2010/06/07/jenks-natural-breaks-algorithm-in-python/ and is based on a JAVA and Fortran code available here: https://stat.ethz.ch/pipermail/r-sig-geo/2006-March/000811.html Returns class breaks such that classes are internally homogeneous while assuring heterogeneity among classes. """ n_data = len(values) mat1 = np.zeros((n_data + 1, classes + 1), dtype=np.int32) mat2 = np.zeros((n_data + 1, classes + 1), dtype=np.float32) mat1[1, 1:] = 1 mat2[2:, 1:] = np.inf v = np.float32(0) for _l in range(2, len(values) + 1): s1 = np.float32(0) s2 = np.float32(0) w = np.float32(0) for m in range(1, _l + 1): i3 = _l - m + 1 val = np.float32(values[i3 - 1]) s2 += val * val s1 += val w += np.float32(1) v = s2 - (s1 * s1) / w i4 = i3 - 1 if i4 != 0: for j in range(2, classes + 1): if mat2[_l, j] >= (v + mat2[i4, j - 1]): mat1[_l, j] = i3 mat2[_l, j] = v + mat2[i4, j - 1] mat1[_l, 1] = 1 mat2[_l, 1] = v k = len(values) kclass = np.zeros(classes + 1, dtype=values.dtype) kclass[classes] = values[len(values) - 1] kclass[0] = values[0] for count_num in range(classes, 1, -1): pivot = mat1[k, count_num] _id = int(pivot - 2) kclass[count_num - 1] = values[_id] k = int(pivot - 1) return np.delete(kclass, 0) class MapClassifier: r""" Abstract class for all map classifications :cite:`Slocum_2009` For an array :math:`y` of :math:`n` values, a map classifier places each value :math:`y_i` into one of :math:`k` mutually exclusive and exhaustive classes. Each classifer defines the classes based on different criteria, but in all cases the following hold for the classifiers in PySAL: .. math:: C_j^l < y_i \le C_j^u \ \forall i \in C_j where :math:`C_j` denotes class :math:`j` which has lower bound :math:`C_j^l` and upper bound :math:`C_j^u`. Map Classifiers Supported * :class:`mapclassify.classifiers.BoxPlot` * :class:`mapclassify.classifiers.EqualInterval` * :class:`mapclassify.classifiers.FisherJenks` * :class:`mapclassify.classifiers.FisherJenksSampled` * :class:`mapclassify.classifiers.HeadTailBreaks` * :class:`mapclassify.classifiers.JenksCaspall` * :class:`mapclassify.classifiers.JenksCaspallForced` * :class:`mapclassify.classifiers.JenksCaspallSampled` * :class:`mapclassify.classifiers.MaxP` * :class:`mapclassify.classifiers.MaximumBreaks` * :class:`mapclassify.classifiers.NaturalBreaks` * :class:`mapclassify.classifiers.Quantiles` * :class:`mapclassify.classifiers.Percentiles` * :class:`mapclassify.classifiers.StdMean` * :class:`mapclassify.classifiers.UserDefined` In addition to the classifiers, there are several utility functions that can be used to evaluate the properties of a specific classifier, or for automatic selection of a classifier and number of classes. * :func:`mapclassify.classifiers.gadf` * :class:`mapclassify.classifiers.K_classifiers` """ def __init__(self, y): y = np.asarray(y).flatten() self.name = "Map Classifier" self.fmt = FMT self.y = y self._classify() self._summary() def get_fmt(self): return self._fmt def set_fmt(self, fmt): self._fmt = fmt fmt = property(get_fmt, set_fmt) def _summary(self): yb = self.yb self.classes = [np.nonzero(yb == c)[0].tolist() for c in range(self.k)] self.tss = self.get_tss() self.adcm = self.get_adcm() self.gadf = self.get_gadf() def _classify(self): self._set_bins() self.yb, self.counts = bin1d(self.y, self.bins) def _update(self, data, *args, **kwargs): """ The only thing that *should* happen in this function is 1. input sanitization for pandas 2. classification/reclassification. Using their ``__init__`` methods, all classifiers can re-classify given different input parameters or additional data. If you've got a cleverer updating equation other than the intial estimation equation, remove the call to ``self.__init__`` below and replace it with the updating function. """ if data is not None: data = np.asarray(data).flatten() data = np.append(data.flatten(), self.y) else: data = self.y self.__init__(data, *args, **kwargs) @classmethod def make(cls, *args, **kwargs): """ Configure and create a classifier that will consume data and produce classifications, given the configuration options specified by this function. Note that this implements a *partial application* of the relevant class constructor. ``make`` creates a function that returns classifications; it does not actually do the classification. If you want to classify data directly, use the appropriate class constructor, like ``Quantiles``, ``Max_Breaks``, etc. If you *have* a classifier object, but want to find which bins new data falls into, use ``find_bin``. Parameters ---------- *args : required positional arguments All positional arguments required by the classifier, **excluding** the input data. rolling : bool A boolean configuring the outputted classifier to use a rolling classifier rather than a new classifier for each input. If ``rolling``, this adds the current data to all of the previous data in the classifier, and rebalances the bins, like a running median computation. return_object : bool Return the classifier object (or not). return_bins : bool Return the bins/breaks (or not). return_counts : bool Return the histogram of objects falling into each bin (or not). Returns ------- A function that consumes data and returns their bins (and object, bins/breaks, or counts, if requested). Notes ----- This is most useful when you want to run a classifier many times with a given configuration, such as when classifying many columns of an array or dataframe using the same configuration. Examples -------- >>> import libpysal >>> import mapclassify >>> import geopandas >>> import numpy >>> import pandas >>> df = geopandas.read_file(libpysal.examples.get_path("columbus.dbf")) >>> classifier = mapclassify.Quantiles.make(k=9) >>> cl = df[["HOVAL", "CRIME", "INC"]].apply(classifier) >>> cl["HOVAL"].values[:10] array([8, 7, 2, 4, 1, 3, 8, 5, 7, 8]) >>> cl["CRIME"].values[:10] array([0, 1, 3, 4, 6, 2, 0, 5, 3, 4]) >>> cl["INC"].values[:10] array([7, 8, 5, 0, 3, 5, 0, 3, 6, 4]) >>> data = [ ... numpy.linspace(3,8,num=10), ... numpy.linspace(10, 0, num=10), ... numpy.linspace(-5, 15, num=10) ... ] >>> data = pandas.DataFrame(data).T >>> data 0 1 2 0 3.000000 10.000000 -5.000000 1 3.555556 8.888889 -2.777778 2 4.111111 7.777778 -0.555556 3 4.666667 6.666667 1.666667 4 5.222222 5.555556 3.888889 5 5.777778 4.444444 6.111111 6 6.333333 3.333333 8.333333 7 6.888889 2.222222 10.555556 8 7.444444 1.111111 12.777778 9 8.000000 0.000000 15.000000 >>> data.apply(mapclassify.Quantiles.make(rolling=True)) 0 1 2 0 0 4 0 1 0 4 0 2 1 4 0 3 1 3 0 4 2 2 1 5 2 1 2 6 3 1 4 7 3 0 4 8 4 0 4 9 4 0 4 >>> dbf = libpysal.io.open(libpysal.examples.get_path("baltim.dbf")) >>> data = dbf.by_col_array("PRICE", "LOTSZ", "SQFT") >>> my_bins = [1, 10, 20, 40, 80] >>> cl = [mapclassify.UserDefined.make(bins=my_bins)(a) for a in data.T] >>> len(cl) 3 >>> cl[0][:10] array([4, 5, 5, 5, 4, 4, 5, 4, 4, 5]) """ # only flag overrides return flag to_annotate = copy.deepcopy(kwargs) return_object = kwargs.pop("return_object", False) return_bins = kwargs.pop("return_bins", False) return_counts = kwargs.pop("return_counts", False) rolling = kwargs.pop("rolling", False) if rolling: # just initialize a fake classifier data = list(range(10)) cls_instance = cls(data, *args, **kwargs) # and empty it, since we'll be using the update cls_instance.y = np.array([]) else: cls_instance = None # wrap init in a closure to make a consumer. # Qc Na: "Objects/Closures are poor man's Closures/Objects" def classifier(data, cls_instance=cls_instance): if rolling: cls_instance.update(data, inplace=True, **kwargs) yb = cls_instance.find_bin(data) else: cls_instance = cls(data, *args, **kwargs) yb = cls_instance.yb outs = [yb, None, None, None] outs[1] = cls_instance if return_object else None outs[2] = cls_instance.bins if return_bins else None outs[3] = cls_instance.counts if return_counts else None outs = [a for a in outs if a is not None] if len(outs) == 1: return outs[0] else: return outs # for debugging/jic, keep around the kwargs. # in future, we might want to make this a thin class, so that we can # set a custom repr. Call the class `Binner` or something, that's a # pre-configured Classifier that just consumes data, bins it, & # possibly updates the bins. classifier._options = to_annotate return classifier def update(self, y=None, inplace=False, **kwargs): """ Add data or change classification parameters. Parameters ---------- y : numpy.array (default None) :math:`(n,1)`, array of data to classify. inplace : bool (default False) Whether to conduct the update in place or to return a copy estimated from the additional specifications. **kwargs : dict Additional parameters that are passed to the ``__init__`` function of the class. For documentation, check the class constructor. """ kwargs.update({"k": kwargs.pop("k", self.k)}) if inplace: self._update(y, **kwargs) else: new = copy.deepcopy(self) new._update(y, **kwargs) return new def __str__(self): return self.table() def __repr__(self): return self.table() def table(self): fmt = self.fmt return _get_table(self, fmt=fmt) def __call__(self, *args): """ This will allow the classifier to be called like it's a function. Whether or not we want to make this be ``find_bin`` or ``update`` is a design decision. I like this as ``find_bin``, since a classifier's job should be to classify the data given to it using the rules estimated from the ``_classify()``. function. """ return self.find_bin(*args) def get_tss(self): """Returns sum of squares over all class means.""" tss = 0 for class_def in self.classes: if len(class_def) > 0: yc = self.y[class_def] css = yc - yc.mean() css *= css tss += sum(css) return tss def _set_bins(self): pass def get_adcm(self): """ Absolute deviation around class median (*ADCM*). Calculates the absolute deviations of each observation about its class median as a measure of fit for the classification method. Returns sum of *ADCM* over all classes. """ adcm = 0 for class_def in self.classes: if len(class_def) > 0: yc = self.y[class_def] yc_med = np.median(yc) ycd = np.abs(yc - yc_med) adcm += sum(ycd) return adcm def get_gadf(self): """Goodness of absolute deviation of fit.""" adam = (np.abs(self.y - np.median(self.y))).sum() # return 1 if array is invariant gadf = 1 if adam == 0 else 1 - self.adcm / adam return gadf def find_bin(self, x): """ Sort input or inputs according to the current bin estimate. Parameters ---------- x : numpy.array, int, float A value or array of values to fit within the estimated bins. Returns ------- right : numpy.array, int A bin index or array of bin indices that classify the input into one of the classifiers' bins. Notes ----- This differs from similar functionality in ``numpy.digitize(x, classi.bins, right=True)``. This will always provide the closest bin, so data "outside" the classifier, above and below the max/min breaks, will be classified into the nearest bin. ``numpy.digitize`` returns :math:`k+1` for data greater than the greatest bin, but retains 0 for data below the lowest bin. """ x = np.asarray(x).flatten() right = np.digitize(x, self.bins, right=True) if right.max() == len(self.bins): right[right == len(self.bins)] = len(self.bins) - 1 return right def get_legend_classes(self, fmt=FMT): """ Format the strings for the classes on the legend. Parameters ---------- fmt : str (default '{:.2f}') Formatting specification. Returns ------- classes : list :math:`k` strings with class interval definitions. """ return _get_mpl_labels(self, fmt) def plot( self, gdf, border_color="lightgray", border_width=0.10, title=None, legend=False, cmap="YlGnBu", axis_on=True, legend_kwds={"loc": "lower right", "fmt": FMT}, file_name=None, dpi=600, ax=None, ): """ Plot a mapclassifier object. Parameters ---------- gdf : geopandas.GeoDataFrame Contains the geometry column for the choropleth map. border_color : str (default 'lightgray') Matplotlib color string to use for polygon border. border_width : float (default 0.10) Width of polygon border. title : str (default None) Title of map. cmap : str (default 'YlGnBu') Matplotlib color string for color map to fill polygons. axis_on : bool (default True) Show coordinate axes. legend_kwds : dict (default {'loc': 'lower right', 'fmt':FMT}) Options for ``ax.legend()``. file_name : str (default None) Name of file to save figure to. dpi : int (default 600) Dots per inch for saved figure. ax : matplotlib.Axis (default None) Axis on which to plot the choropleth. Default is ``None``, which plots on the current figure. Returns ------- f, ax : tuple Matplotlib figure and axis on which the plot is made. Examples -------- >>> import libpysal >>> import geopandas >>> import mapclassify >>> gdf = geopandas.read_file(libpysal.examples.get_path("columbus.shp")) >>> q5 = mapclassify.Quantiles(gdf.CRIME) >>> q5.plot(gdf) # doctest: +SKIP Notes ----- Requires ``matplotlib``, and implicitly requires a ``geopandas.GeoDataFrame`` as input. """ try: import matplotlib.pyplot as plt except ImportError: raise ImportError( "Mapclassify.plot depends on matplotlib.pyplot, and this was" "not able to be imported. \nInstall matplotlib to" "plot spatial classifier." ) from None if ax is None: f = plt.figure() ax = plt.gca() else: f = plt.gcf() ax = gdf.assign(_cl=self.y).plot( column="_cl", ax=ax, cmap=cmap, edgecolor=border_color, linewidth=border_width, scheme=self.name, legend=legend, legend_kwds=legend_kwds, ) if not axis_on: ax.axis("off") if title: f.suptitle(title) if file_name: plt.savefig(file_name, dpi=dpi) return f, ax def plot_histogram( self, color="dodgerblue", linecolor="black", linewidth=None, ax=None, despine=True, **kwargs, ): """Plot histogram of `y` with bin values superimposed Parameters ---------- color : str, optional hue to color bars of the histogram, by default "dodgerblue". linecolor : str, optional color of the lines demarcating each class bin, by default "black" linewidth : int, optional change the linewidth demarcating each class bin ax : matplotlib.Axes, optional axes object to plot onto, by default None despine : bool, optional If True, to use seaborn's despine function to remove top and right axes, default is True kwargs : dict, optional additional keyword arguments passed to matplotlib.axes.Axes.hist, by default None Returns ------- matplotlib.Axes an Axes object with histogram and class bins Raises ------ ImportError depends matplotlib and rasies if not installed """ try: import matplotlib.pyplot as plt if ax is None: _, ax = plt.subplots() except ImportError as e: raise ImportError from e( "You must have matplotlib available to use this function" ) # plot `y` as a histogram ax.hist(self.y, color=color, **kwargs) # get the top of the ax so we know how high to raise each class bar lim = ax.get_ylim()[1] # plot upper limit of each bin for i in self.bins: ax.vlines(i, 0, lim, color=linecolor, linewidth=linewidth) # despine if specified if despine: ax.spines["right"].set_visible(False) ax.spines["top"].set_visible(False) return ax def plot_legendgram( self, *, ax=None, cmap=None, bins=50, inset=True, clip=None, vlines=False, vlinecolor="black", vlinewidth=1, loc="lower left", legend_size=("27%", "20%"), frameon=False, tick_params=None, bbox_to_anchor=None, **kwargs, ): """Plot a legendgram, which is a histogram with classification breaks. Legendgrams are map legends that visualize the distribution of observations by color in a given map. Parameters ---------- ax : matplotlib.Axes, optional Matplotlib axes on which to draw the plot, by default None cmap : str | matplotlib.colors.Colormap, optional Matplotlib colormap for the histogram, by default ``None``. If ``None``, defaults to the most recent colormap associated with the input ``ax``. If there is no colormap associated with ``ax`` then ``'viridis'`` is used. bins : int, optional Number of bins for histogram, by default 50 inset : bool, optional Whether to plot the legendgram as an inset axis (True) or directly on provided ax (False), by default True clip : tuple, optional Min and max values to clip the histogram x axis, by default None vlines : bool, optional Whether to draw vertical lines for class breaks, by default False vlinecolor : str, optional Color for vertical lines showing breaks, by default "black" vlinewidth : int, optional Width of vertical lines showing breaks, by default 1 loc : str, optional Location for the inset axes if inset=True, by default "lower left" legend_size : tuple, optional tuple of floats or strings describing the (width, height) of the legend. If a float is provided, it is the size in inches, e.g. ``(1.3, 1)``. If a string is provided, it is the size in relative units, e.g. ``('40%', '20%')``. By default, i.e. if ``bbox_to_anchor`` is not specified, those are relative to the `ax`. Otherwise, they are to be understood relative to the bounding box provided via ``bbox_to_anchor``. By default ("27%", "20%") frameon : bool, optional Whether to draw a frame around the inset legend, by default False tick_params : dict, optional Dictionary of parameters to customize tick appearance, by default None bbox_to_anchor : tuple or ``matplotlib.trasforms.BboxBase`` Bbox that the inset axes will be anchored to. If None, a tuple of ``(0, 0, 1, 1)`` is used. If a tuple, can be either ``[left, bottom, width, height]``, or ``[left, bottom]``. If the ``legend_size`` is in relative units (%), the 2-tuple ``[left, bottom]`` cannot be used. By default None **kwargs Additional keyword arguments passed to ``Axes.hist``. Returns ------- matplotlib.Axes The axes object containing the legendgram plot Examples -------- >>> import mapclassify >>> import geopandas as gpd >>> from libpysal import examples Load example data: >>> data = gpd.read_file(examples.get_path('south.shp')).to_crs(epsg=5070) Plot the map with the legendgram included. >>> ax = data.plot('DV80', k=10, scheme='Quantiles') >>> classifier = mapclassify.Quantiles(data['DV80'].values, k=10) >>> lg_ax = classifier.plot_legendgram( ... ax=ax, ... legend_size=("50%", "20%"), # legend size in percentage of the axis ... loc='upper left', # matplotlib-style legend locations ... clip=(2, 10), # clip the displayed range of the histogram ... ) """ return _legendgram( self, ax=ax, cmap=cmap, bins=bins, inset=inset, clip=clip, vlines=vlines, vlinecolor=vlinecolor, vlinewidth=vlinewidth, loc=loc, legend_size=legend_size, frameon=frameon, tick_params=tick_params, bbox_to_anchor=bbox_to_anchor, **kwargs, ) class HeadTailBreaks(MapClassifier): """ Head/tail Breaks Map Classification for Heavy-tailed Distributions. Parameters ---------- y : numpy.array :math:`(n,1)`, values to classify. Attributes ---------- yb : numpy.array :math:`(n,1)`, bin IDs for observations. bins : numpy.array :math:`(k,1)`, the upper bounds of each class. k : int The number of classes. counts : numpy.array :math:`(k,1)`, the number of observations falling in each class. Examples -------- >>> import mapclassify >>> import numpy >>> numpy.random.seed(10) >>> cal = mapclassify.load_example() >>> htb = mapclassify.HeadTailBreaks(cal) >>> htb.k 3 >>> htb.counts.tolist() [50, 7, 1] >>> htb.bins array([ 125.92810345, 811.26 , 4111.45 ]) >>> numpy.random.seed(123456) >>> x = numpy.random.lognormal(3, 1, 1000) >>> htb = mapclassify.HeadTailBreaks(x) >>> htb.bins array([ 32.26204423, 72.50205622, 128.07150107, 190.2899093 , 264.82847377, 457.88157946, 576.76046949]) >>> htb.counts.tolist() [695, 209, 62, 22, 10, 1, 1] Notes ----- Head/tail Breaks is a relatively new classification method developed for data with a heavy-tailed distribution. Implementation based on contributions by Alessandra Sozzi . For theoretical details see :cite:`Jiang_2013`. """ def __init__(self, y): MapClassifier.__init__(self, y) self.name = "HeadTailBreaks" def _set_bins(self): x = self.y.copy() bins = [] bins = head_tail_breaks(x, bins) self.bins = np.array(bins) self.k = len(self.bins) class EqualInterval(MapClassifier): """ Equal Interval Classification. Parameters ---------- y : numpy.array :math:`(n,1)`, values to classify. k : int (default 5) The number of classes required. Attributes ---------- yb : numpy.array :math:`(n,1)`, bin IDs for observations. Each value is the ID of the class the observation belongs to :math:`yb[i] = j` for :math:`j>=1` if :math:`bins[j-1] < y[i] <= bins[j]`, otherwise :math:`yb[i] = 0`. bins : numpy.array :math:`(k,1)`, the upper bounds of each class. k : int The number of classes. counts : numpy.array :math:`(k,1)`, the number of observations falling in each class. Examples -------- >>> import mapclassify >>> cal = mapclassify.load_example() >>> ei = mapclassify.EqualInterval(cal, k=5) >>> ei.k 5 >>> ei.counts.tolist() [57, 0, 0, 0, 1] >>> ei.bins array([ 822.394, 1644.658, 2466.922, 3289.186, 4111.45 ]) Notes ----- Intervals defined to have equal width: .. math:: bins_j = min(y)+w*(j+1) with :math:`w=\\frac{max(y)-min(j)}{k}` """ def __init__(self, y, k=K): """ see class docstring """ if min(y) == max(y): raise ValueError( f"Not enough unique values in array to form {k} classes. " "All values in `y` are equal." ) self.k = k MapClassifier.__init__(self, y) self.name = "EqualInterval" def _set_bins(self): y = self.y k = self.k max_y = max(y) min_y = min(y) rg = max_y - min_y width = rg * 1.0 / k cuts = np.arange(min_y + width, max_y + width, width) if len(cuts) > self.k: # handle overshooting cuts = cuts[0:k] cuts[-1] = max_y bins = cuts.copy() self.bins = bins class Percentiles(MapClassifier): """ Percentiles Map Classification Parameters ---------- y : numpy.array Attribute to classify. pct : numpy.array (default [1, 10, 50, 90, 99, 100]) Percentiles. Attributes ---------- yb : numpy.array :math:`(n,1)`, bin IDs for observations. bins : numpy.array :math:`(k,1)`, the upper bounds of each class. k : int The number of classes. counts : numpy.array :math:`(k,1)`, the number of observations falling in each class. Examples -------- >>> import mapclassify >>> cal = mapclassify.load_example() >>> p = mapclassify.Percentiles(cal) >>> p.bins array([1.357000e-01, 5.530000e-01, 9.365000e+00, 2.139140e+02, 2.179948e+03, 4.111450e+03]) >>> p.counts.tolist() [1, 5, 23, 23, 5, 1] >>> p2 = mapclassify.Percentiles(cal, pct = [50, 100]) >>> p2.bins array([ 9.365, 4111.45 ]) >>> p2.counts.tolist() [29, 29] >>> p2.k 2 """ def __init__(self, y, pct=[1, 10, 50, 90, 99, 100]): self.pct = pct MapClassifier.__init__(self, y) self.name = "Percentiles" def _set_bins(self): y = self.y pct = self.pct self.bins = np.array([stats.scoreatpercentile(y, p) for p in pct]) self.k = len(self.bins) def update(self, y=None, inplace=False, **kwargs): """ Add data or change classification parameters. Parameters ---------- y : numpy.array (default None) :math:`(n,1)`, array of data to classify. inplace : bool (default False) Whether to conduct the update in place or to return a copy estimated from the additional specifications. **kwargs : dict Additional parameters that are passed to the ``__init__`` function of the class. For documentation, check the class constructor. """ kwargs.update({"pct": kwargs.pop("pct", self.pct)}) if inplace: self._update(y, **kwargs) else: new = copy.deepcopy(self) new._update(y, **kwargs) return new class PrettyBreaks(MapClassifier): def __init__(self, y, k=5): """ Pretty breakpoints Computes breaks that are equally spaced round values which cover the range of values in `y`. The breaks are chosen so that they are 1, 2, or 5 times a power of 10. Parameters ---------- y : array (n,1) attribute to classify k : int The number of desired classes Notes ----- The number of classes may be different from the specified `k`, as the rounding of the upper bounds takes precedent. The lower bound of the first interval will be equal to the minimum of the data. """ self.k = k MapClassifier.__init__(self, y) self.name = "Pretty" def _set_bins(self): bins = _pretty(self.y, self.k) self.bins = bins[1:] class BoxPlot(MapClassifier): """ BoxPlot Map Classification. Parameters ---------- y : numpy.array Attribute to classify hinge : float (default 1.5) Multiplier for *IQR*. Attributes ---------- yb : numpy.array :math:`(n,1)`, bin ids for observations. bins : array :math:`(n,1)`, the upper bounds of each class (monotonic). k : int The number of classes. counts : numpy.array :math:`(k,1)`, the number of observations falling in each class. low_outlier_ids : numpy.array Indices of observations that are low outliers. high_outlier_ids : numpy.array Indices of observations that are high outliers. Notes ----- The bins are set as follows:: bins[0] = q[0]-hinge*IQR bins[1] = q[0] bins[2] = q[1] bins[3] = q[2] bins[4] = q[2]+hinge*IQR bins[5] = inf (see Notes) where :math:`q` is an array of the first three quartiles of :math:`y` and :math:`IQR=q[2]-q[0]`. If :math:`q[2]+hinge*IQR > max(y)` there will only be 5 classes and no high outliers, otherwise, there will be 6 classes and at least one high outlier. Examples -------- >>> import mapclassify >>> import numpy >>> cal = mapclassify.load_example() >>> bp = mapclassify.BoxPlot(cal) >>> bp.bins array([-5.287625e+01, 2.567500e+00, 9.365000e+00, 3.953000e+01, 9.497375e+01, 4.111450e+03]) >>> bp.counts.tolist() [0, 15, 14, 14, 6, 9] >>> bp.high_outlier_ids.tolist() [0, 6, 18, 29, 33, 36, 37, 40, 42] >>> cal[bp.high_outlier_ids].values array([ 329.92, 181.27, 370.5 , 722.85, 192.05, 110.74, 4111.45, 317.11, 264.93]) >>> bx = mapclassify.BoxPlot(numpy.arange(100)) >>> bx.bins array([-49.5 , 24.75, 49.5 , 74.25, 148.5 ]) """ def __init__(self, y, hinge=1.5): """ Parameters ---------- y : numpy.array :math:`(n,1)`, attribute to classify hinge : float (default 1.5) Multiple of inter-quartile range. """ self.hinge = hinge MapClassifier.__init__(self, y) self.name = "BoxPlot" def _set_bins(self): y = self.y pct = [25, 50, 75, 100] bins = [stats.scoreatpercentile(y, p) for p in pct] iqr = bins[-2] - bins[0] self.iqr = iqr pivot = self.hinge * iqr left_fence = bins[0] - pivot right_fence = bins[-2] + pivot if right_fence < bins[-1]: bins.insert(-1, right_fence) else: bins[-1] = right_fence bins.insert(0, left_fence) self.bins = np.array(bins) self.k = len(bins) def _classify(self): MapClassifier._classify(self) self.low_outlier_ids = np.nonzero(self.yb == 0)[0] self.high_outlier_ids = np.nonzero(self.yb == 5)[0] def update(self, y=None, inplace=False, **kwargs): """ Add data or change classification parameters. Parameters ---------- y : numpy.array (default None) :math:`(n,1)`, array of data to classify. inplace : bool (default False) Whether to conduct the update in place or to return a copy estimated from the additional specifications. **kwargs : dict Additional parameters that are passed to the ``__init__`` function of the class. For documentation, check the class constructor. """ kwargs.update({"hinge": kwargs.pop("hinge", self.hinge)}) if inplace: self._update(y, **kwargs) else: new = copy.deepcopy(self) new._update(y, **kwargs) return new class Quantiles(MapClassifier): """ Quantile Map Classification. Parameters ---------- y : numpy.array :math:`(n,1)`, values to classify. k : int (default 5) The number of classes required. Attributes ---------- yb : numpy.array :math:`(n,1)`, bin IDs for observations. Each value is the ID of the class the observation belongs to :math:`yb[i] = j` for :math:`j>=1` if :math:`bins[j-1] < y[i] <= bins[j]`, otherwise :math:`yb[i] = 0`. bins : numpy.array :math:`(k,1)`, the upper bounds of each class. k : int The number of classes. counts : numpy.array :math:`(k,1)`, the number of observations falling in each class. Examples -------- >>> import mapclassify >>> cal = mapclassify.load_example() >>> q = mapclassify.Quantiles(cal, k=5) >>> q.bins array([1.46400e+00, 5.79800e+00, 1.32780e+01, 5.46160e+01, 4.11145e+03]) >>> q.counts.tolist() [12, 11, 12, 11, 12] """ def __init__(self, y, k=K): self.k = k MapClassifier.__init__(self, y) self.name = "Quantiles" def _set_bins(self): y = self.y k = self.k self.bins = quantile(y, k=k) class StdMean(MapClassifier): """ Standard Deviation and Mean Map Classification. Parameters ---------- y : numpy.array :math:`(n,1)`, values to classify multiples : numpy.array (default [-2, -1, 1, 2]) The multiples of the standard deviation to add/subtract from the sample mean to define the bins. anchor : bool (default False) Anchor upper bound of one class to the sample mean. Attributes ---------- yb : numpy.array :math:`(n,1)`, bin IDs for observations. bins : numpy.array :math:`(k,1)`, the upper bounds of each class. k : int The number of classes. counts : numpy.array :math:`(k,1)`, the number of observations falling in each class. Notes ----- If anchor is True, one of the intervals will have its closed upper bound equal to the mean of y. Intermediate intervals will have widths equal to the standard deviation of y. The first interval will be closed on the minimum value of y, and the last interval will be closed on the maximum of y. The first and last intervals may have widths different from the intermediate intervals. Examples -------- >>> import mapclassify >>> cal = mapclassify.load_example() >>> st = mapclassify.StdMean(cal) >>> st.k 5 >>> st.bins array([-967.36235382, -420.71712519, 672.57333208, 1219.21856072, 4111.45 ]) >>> st.counts.tolist() [0, 0, 56, 1, 1] >>> st3 = mapclassify.StdMean(cal, multiples = [-3, -1.5, 1.5, 3]) >>> st3.bins array([-1514.00758246, -694.03973951, 945.8959464 , 1765.86378936, 4111.45 ]) >>> st3.counts.tolist() [0, 0, 57, 0, 1] >>> stda = mapclassify.StdMean(cal, anchor=True) >>> stda.k 9 >>> stda.bins array([ 125.92810345, 672.57333208, 1219.21856072, 1765.86378936, 2312.50901799, 2859.15424663, 3405.79947527, 3952.4447039 , 4111.45 ]) >>> float(cal.mean()), float(cal.std()), float(cal.min()), float(cal.max()) (125.92810344827588, 546.6452286365233, 0.13, 4111.45) """ def __init__(self, y, multiples=[-2, -1, 1, 2], anchor=False): self.multiples = multiples self.anchor = anchor MapClassifier.__init__(self, y) self.name = "StdMean" def _set_bins(self): y = self.y s = y.std(ddof=1) m = y.mean() if self.anchor: min_z = int((y.min() - m) / s) max_z = int((y.max() - m) / s) + 1 self.multiples = list(range(min_z, max_z)) cuts = [m + s * w for w in self.multiples] y_max = y.max() if cuts[-1] < y_max: cuts.append(y_max) self.bins = np.array(cuts) self.k = len(cuts) def update(self, y=None, inplace=False, **kwargs): """ Add data or change classification parameters. Parameters ---------- y : numpy.array (default None) :math:`(n,1)`, array of data to classify. inplace : bool (default False) Whether to conduct the update in place or to return a copy estimated from the additional specifications. **kwargs : dict Additional parameters that are passed to the ``__init__`` function of the class. For documentation, check the class constructor. """ kwargs.update({"multiples": kwargs.pop("multiples", self.multiples)}) if inplace: self._update(y, **kwargs) else: new = copy.deepcopy(self) new._update(y, **kwargs) return new class MaximumBreaks(MapClassifier): """ Maximum Breaks Map Classification. Parameters ---------- y : numpy.array :math:`(n,1)`, values to classify. k : int (default 5) The number of classes required. mindiff : float (default 0) The minimum difference between class breaks. Attributes ---------- yb : numpy.array :math:`(n,1)`, bin IDs for observations. bins : numpy.array :math:`(k,1)`, the upper bounds of each class. k : int The number of classes. counts : numpy.array :math:`(k,1)`, the number of observations falling in each class. Examples -------- >>> import mapclassify >>> cal = mapclassify.load_example() >>> mb = mapclassify.MaximumBreaks(cal, k=5) >>> mb.k 5 >>> mb.bins array([ 146.005, 228.49 , 546.675, 2417.15 , 4111.45 ]) >>> mb.counts.tolist() [50, 2, 4, 1, 1] """ def __init__(self, y, k=5, mindiff=0): if min(y) == max(y): raise ValueError( f"Not enough unique values in array to form {k} classes. " "All values in `y` are equal." ) self.k = k self.mindiff = mindiff MapClassifier.__init__(self, y) self.name = "MaximumBreaks" def _set_bins(self): xs = self.y.copy() k = self.k xs.sort() diffs = xs[1:] - xs[:-1] idxs = np.argsort(diffs) k1 = k - 1 ud = np.unique(diffs) if len(ud) < k1: warnings.warn( "Insufficient number of unique diffs. Breaks are random.", UserWarning, stacklevel=3, ) mp = [] for c in range(1, k): idx = idxs[-c] cp = (xs[idx] + xs[idx + 1]) / 2.0 mp.append(cp) mp.append(xs[-1]) mp.sort() self.bins = np.array(mp) def update(self, y=None, inplace=False, **kwargs): """ Add data or change classification parameters. Parameters ---------- y : numpy.array (default None) :math:`(n,1)`, array of data to classify. inplace : bool (default False) Whether to conduct the update in place or to return a copy estimated from the additional specifications. **kwargs : dict Additional parameters that are passed to the ``__init__`` function of the class. For documentation, check the class constructor. """ kwargs.update({"k": kwargs.pop("k", self.k)}) kwargs.update({"mindiff": kwargs.pop("mindiff", self.mindiff)}) if inplace: self._update(y, **kwargs) else: new = copy.deepcopy(self) new._update(y, **kwargs) return new class NaturalBreaks(MapClassifier): """ Natural Breaks Map Classification. Parameters ---------- y : numpy.array :math:`(n,1)`, values to classify. k : int (default 5) The number of classes required. initial : int (default 10) The number of initial solutions generated with different centroids. The best of initial results are returned. Attributes ---------- yb : numpy.array :math:`(n,1)`, bin IDs for observations. bins : numpy.array :math:`(k,1)`, the upper bounds of each class. k : int The number of classes. counts : numpy.array :math:`(k,1)`, the number of observations falling in each class. Examples -------- >>> import mapclassify >>> import numpy >>> numpy.random.seed(123456) >>> cal = mapclassify.load_example() >>> nb = mapclassify.NaturalBreaks(cal, k=5) >>> nb.k 5 >>> nb.counts.tolist() [49, 3, 4, 1, 1] >>> nb.bins array([ 75.29, 192.05, 370.5 , 722.85, 4111.45]) """ def __init__(self, y, k=K, initial=10): self.k = k self.init = initial MapClassifier.__init__(self, y) self.name = "NaturalBreaks" def _set_bins(self): x = self.y.copy() k = self.k values = np.array(x) uv = np.unique(values) uvk = len(uv) if uvk < k: warnings.warn( f"Not enough unique values in array to form {k} classes. " f"Setting k to {uvk}.", UserWarning, stacklevel=3, ) k = uvk uv.sort() # we set the bins equal to the sorted unique values and ramp k # downwards. no need to call kmeans. self.bins = uv self.k = k else: res0 = natural_breaks(x, k, init=self.init) self.bins = np.array(res0[-1]) self.k = len(self.bins) def update(self, y=None, inplace=False, **kwargs): """ Add data or change classification parameters. Parameters ---------- y : numpy.array (default None) :math:`(n,1)`, array of data to classify. inplace : bool (default False) Whether to conduct the update in place or to return a copy estimated from the additional specifications. **kwargs : dict Additional parameters that are passed to the ``__init__`` function of the class. For documentation, check the class constructor. """ kwargs.update({"k": kwargs.pop("k", self.k)}) if inplace: self._update(y, **kwargs) else: new = copy.deepcopy(self) new._update(y, **kwargs) return new class FisherJenks(MapClassifier): """ Fisher Jenks optimal classifier - mean based. Parameters ---------- y : numpy.array :math:`(n,1)`, values to classify. k : int (default 5) The number of classes required. Attributes ---------- yb : numpy.array :math:`(n,1)`, bin IDs for observations. bins : numpy.array :math:`(k,1)`, the upper bounds of each class. k : int The number of classes. counts : numpy.array :math:`(k,1)`, the number of observations falling in each class. Examples -------- >>> import mapclassify >>> cal = mapclassify.load_example() >>> fj = mapclassify.FisherJenks(cal) >>> float(fj.adcm) 799.24 >>> fj.bins.tolist() [75.29, 192.05, 370.5, 722.85, 4111.45] >>> fj.counts.tolist() [49, 3, 4, 1, 1] """ def __init__(self, y, k=K): if not HAS_NUMBA: warnings.warn( "Numba not installed. Using slow pure python version.", UserWarning, stacklevel=3, ) nu = len(np.unique(y)) if nu < k: raise ValueError( f"Fewer unique values ({nu}) than specified classes ({k})." ) self.k = k MapClassifier.__init__(self, y) self.name = "FisherJenks" def _set_bins(self): x = np.sort(self.y).astype("f8") self.bins = _fisher_jenks_means(x, classes=self.k) class FisherJenksSampled(MapClassifier): """ Fisher Jenks optimal classifier - mean based using random sample. Parameters ---------- y : numpy.array :math:`(n,1)`, values to classify. k : int (default 5) The number of classes required. pct : float (default 0.10) The percentage of :math:`n` that should form the sample. If ``pct`` is specified such that :math:`n*pct > 1000`, then :math:`pct = 1000./n`, unless truncate is ``False``. truncate : bool (default True) Truncate ``pct`` in cases where :math:`pct * n > 1000.`. Attributes ---------- yb : numpy.array :math:`(n,1)`, bin IDs for observations. bins : numpy.array :math:`(k,1)`, the upper bounds of each class. k : int The number of classes. counts : numpy.array :math:`(k,1)`, the number of observations falling in each class. Notes ----- For theoretical details see :cite:`Rey_2016`. """ def __init__(self, y, k=K, pct=0.10, truncate=True): self.k = k n = y.size if (pct * n > 1000) and truncate: pct = 1000.0 / n ids = np.random.randint(0, n, int(n * pct)) y = np.asarray(y) yr = y[ids] yr[-1] = max(y) # make sure we have the upper bound yr[0] = min(y) # make sure we have the min self.original_y = y self.pct = pct self._truncated = truncate self.yr = yr self.yr_n = yr.size MapClassifier.__init__(self, yr) self.yb, self.counts = bin1d(y, self.bins) self.name = "FisherJenksSampled" self.y = y self._summary() # have to recalculate summary stats def _set_bins(self): fj = FisherJenks(self.y, self.k) self.bins = fj.bins def update(self, y=None, inplace=False, **kwargs): """ Add data or change classification parameters. Parameters ---------- y : numpy.array (default None) :math:`(n,1)`, array of data to classify. inplace : bool (default False) Whether to conduct the update in place or to return a copy estimated from the additional specifications. **kwargs : dict Additional parameters that are passed to the ``__init__`` function of the class. For documentation, check the class constructor. """ kwargs.update({"k": kwargs.pop("k", self.k)}) kwargs.update({"pct": kwargs.pop("pct", self.pct)}) kwargs.update({"truncate": kwargs.pop("truncate", self._truncated)}) if inplace: self._update(y, **kwargs) else: new = copy.deepcopy(self) new._update(y, **kwargs) return new class JenksCaspall(MapClassifier): """ Jenks Caspall Map Classification. Parameters ---------- y : numpy.array :math:`(n,1)`, values to classify. k : int (default 5) The number of classes required. Attributes ---------- yb : numpy.array :math:`(n,1)`, bin IDs for observations. bins : numpy.array :math:`(k,1)`, the upper bounds of each class. k : int The number of classes. counts : numpy.array :math:`(k,1)`, the number of observations falling in each class. Examples -------- >>> import mapclassify >>> cal = mapclassify.load_example() >>> jc = mapclassify.JenksCaspall(cal, k=5) >>> jc.bins array([1.81000e+00, 7.60000e+00, 2.98200e+01, 1.81270e+02, 4.11145e+03]) >>> jc.counts.tolist() [14, 13, 14, 10, 7] """ def __init__(self, y, k=K): self.k = k MapClassifier.__init__(self, y) self.name = "JenksCaspall" def _set_bins(self): x = self.y.copy() k = self.k # start with quantiles q = quantile(x, k) solving = True xb, cnts = bin1d(x, q) # class means if x.ndim == 1: x.shape = (x.size, 1) n, k = x.shape xm = [np.median(x[xb == i]) for i in np.unique(xb)] xb0 = xb.copy() q = xm it = 0 rk = list(range(self.k)) while solving: xb = np.zeros(xb0.shape, int) d = abs(x - q) xb = d.argmin(axis=1) if (xb0 == xb).all(): solving = False else: xb0 = xb it += 1 q = np.array([np.median(x[xb == i]) for i in rk]) cuts = np.array([max(x[xb == i]) for i in np.unique(xb)]) cuts.shape = (len(cuts),) self.bins = cuts self.iterations = it class JenksCaspallSampled(MapClassifier): """ Jenks Caspall Map Classification using a random sample. Parameters ---------- y : numpy.array :math:`(n,1)`, values to classify. k : int (default 5) The number of classes required. pct : float (default 0.10) The percentage of :math:`n` that should form the sample. If ``pct`` is specified such that :math:`n*pct > 1000`, then :math:`pct = 1000./n`. Attributes ---------- yb : numpy.array :math:`(n,1)`, bin IDs for observations. bins : numpy.array :math:`(k,1)`, the upper bounds of each class. k : int The number of classes. counts : numpy.array :math:`(k,1)`, the number of observations falling in each class. Examples -------- >>> import mapclassify >>> import numpy >>> cal = mapclassify.load_example() >>> numpy.random.seed(0) >>> x = numpy.random.random(100000) >>> jc = mapclassify.JenksCaspall(x) >>> jcs = mapclassify.JenksCaspallSampled(x) >>> jc.bins array([0.20108144, 0.4025151 , 0.60396127, 0.80302249, 0.99997795]) >>> jcs.bins array([0.19978245, 0.40793025, 0.59253555, 0.78241472, 0.99997795]) >>> jc.counts.tolist() [20286, 19951, 20310, 19708, 19745] >>> jcs.counts.tolist() [20147, 20633, 18591, 18857, 21772] # not for testing since we get different times on different hardware # just included for documentation of likely speed gains #>>> t1 = time.time(); jc = Jenks_Caspall(x); t2 = time.time() #>>> t1s = time.time(); jcs = Jenks_Caspall_Sampled(x); t2s = time.time() #>>> t2 - t1; t2s - t1s #1.8292930126190186 #0.061631917953491211 Notes ----- This is intended for large :math:`n` problems. The logic is to apply ``Jenks_Caspall`` to a random subset of the :math:`y` space and then bin the complete vector :math:`y` on the bins obtained from the subset. This would trade off some "accuracy" for a gain in speed. """ def __init__(self, y, k=K, pct=0.10): self.k = k n = y.size if pct * n > 1000: pct = 1000.0 / n ids = np.random.randint(0, n, int(n * pct)) y = np.asarray(y) yr = y[ids] yr[0] = max(y) # make sure we have the upper bound self.original_y = y self.pct = pct self.yr = yr self.yr_n = yr.size MapClassifier.__init__(self, yr) self.yb, self.counts = bin1d(y, self.bins) self.name = "JenksCaspallSampled" self.y = y self._summary() # have to recalculate summary stats def _set_bins(self): jc = JenksCaspall(self.y, self.k) self.bins = jc.bins self.iterations = jc.iterations def update(self, y=None, inplace=False, **kwargs): """ Add data or change classification parameters. Parameters ---------- y : numpy.array (default None) :math:`(n,1)`, array of data to classify. inplace : bool (default False) Whether to conduct the update in place or to return a copy estimated from the additional specifications. **kwargs : dict Additional parameters that are passed to the ``__init__`` function of the class. For documentation, check the class constructor. """ kwargs.update({"k": kwargs.pop("k", self.k)}) kwargs.update({"pct": kwargs.pop("pct", self.pct)}) if inplace: self._update(y, **kwargs) else: new = copy.deepcopy(self) new._update(y, **kwargs) return new class JenksCaspallForced(MapClassifier): """ Jenks Caspall Map Classification with forced movements. Parameters ---------- y : numpy.array :math:`(n,1)`, values to classify. k : int (default 5) The number of classes required. Attributes ---------- yb : numpy.array :math:`(n,1)`, bin IDs for observations. bins : numpy.array :math:`(k,1)`, the upper bounds of each class. k : int The number of classes. counts : numpy.array :math:`(k,1)`, the number of observations falling in each class. Examples -------- >>> import mapclassify >>> cal = mapclassify.load_example() >>> jcf = mapclassify.JenksCaspallForced(cal, k=5) >>> jcf.k 5 >>> jcf.bins array([1.34000e+00, 5.90000e+00, 1.67000e+01, 5.06500e+01, 4.11145e+03]) >>> jcf.counts.tolist() [12, 12, 13, 9, 12] >>> jcf4 = mapclassify.JenksCaspallForced(cal, k=4) >>> jcf4.k 4 >>> jcf4.bins array([2.51000e+00, 8.70000e+00, 3.66800e+01, 4.11145e+03]) >>> jcf4.counts.tolist() [15, 14, 14, 15] """ def __init__(self, y, k=K): if min(y) == max(y): raise ValueError( f"Not enough unique values in array to form {k} classes. " "All values in `y` are equal." ) self.k = k MapClassifier.__init__(self, y) self.name = "JenksCaspallForced" def _set_bins(self): x = self.y.copy() k = self.k q = quantile(x, k) solving = True xb, cnt = bin1d(x, q) # class means if x.ndim == 1: x.shape = (x.size, 1) n, tmp = x.shape xm = [x[xb == i].mean() for i in np.unique(xb)] q = xm xbar = np.array([xm[xbi] for xbi in xb]) xbar.shape = (n, 1) ss = x - xbar ss *= ss ss = sum(ss) down_moves = up_moves = 0 solving = True it = 0 while solving: # try upward moves first moving_up = True while moving_up: class_ids = np.unique(xb) nk = [sum(xb == j) for j in class_ids] candidates = nk[:-1] i = 0 up_moves = 0 while candidates: nki = candidates.pop(0) if nki > 1: ids = np.nonzero(xb == class_ids[i]) mover = max(ids[0]) tmp = xb.copy() tmp[mover] = xb[mover] + 1 tm = [x[tmp == j].mean() for j in np.unique(tmp)] txbar = np.array([tm[xbi] for xbi in tmp]) txbar.shape = (n, 1) tss = x - txbar tss *= tss tss = sum(tss) if tss < ss: xb = tmp ss = tss candidates = [] up_moves += 1 i += 1 if not up_moves: moving_up = False moving_down = True while moving_down: class_ids = np.unique(xb) nk = [sum(xb == j) for j in class_ids] candidates = nk[1:] i = 1 down_moves = 0 while candidates: nki = candidates.pop(0) if nki > 1: ids = np.nonzero(xb == class_ids[i]) mover = min(ids[0]) mover_class = xb[mover] target_class = mover_class - 1 tmp = xb.copy() tmp[mover] = target_class tm = [x[tmp == j].mean() for j in np.unique(tmp)] txbar = np.array([tm[xbi] for xbi in tmp]) txbar.shape = (n, 1) tss = x - txbar tss *= tss tss = sum(tss) if tss < ss: xb = tmp ss = tss candidates = [] down_moves += 1 i += 1 if not down_moves: moving_down = False if not up_moves and not down_moves: solving = False it += 1 cuts = [max(x[xb == c]) for c in np.unique(xb)] cuts = np.reshape(np.array(cuts), (k,)) self.bins = cuts self.iterations = it class UserDefined(MapClassifier): """ User Specified Binning. Parameters ---------- y : numpy.array :math:`(n,1)`, values to classify. bins : numpy.array :math:`(k,1)`, upper bounds of classes (have to be monotically increasing). lowest : float (default None) Scalar minimum value of lowest class. Default is to set the minimum to ``-inf`` if ``y.min()`` > first upper bound (which will override the default), otherwise minimum is set to ``y.min()``. Attributes ---------- yb : numpy.array :math:`(n,1)`, bin IDs for observations. bins : numpy.array :math:`(k,1)`, the upper bounds of each class. k : int The number of classes. counts : numpy.array :math:`(k,1)`, the number of observations falling in each class. Examples -------- >>> import mapclassify >>> cal = mapclassify.load_example() >>> bins = [20, max(cal)] >>> bins [20, 4111.45] >>> ud = mapclassify.UserDefined(cal, bins) >>> ud.bins.tolist() [20.0, 4111.45] >>> ud.counts.tolist() [37, 21] >>> bins = [20, 30] >>> ud = mapclassify.UserDefined(cal, bins) >>> ud.bins.tolist() [20.0, 30.0, 4111.45] >>> ud.counts.tolist() [37, 4, 17] Notes ----- If upper bound of user bins does not exceed ``max(y)`` we append an additional bin. """ def __init__(self, y, bins, lowest=None): if bins[-1] < max(y): bins = np.append(bins, max(y)) self.lowest = lowest self.k = len(bins) self.bins = np.array(bins) self.y = y MapClassifier.__init__(self, y) self.name = "UserDefined" def _set_bins(self): pass def _update(self, y=None, bins=None): if y is not None: if hasattr(y, "values"): y = y.values y = np.append(y.flatten(), self.y) else: y = self.y if bins is None: bins = self.bins self.__init__(y, bins) def update(self, y=None, inplace=False, **kwargs): """ Add data or change classification parameters. Parameters ---------- y : numpy.array (default None) :math:`(n,1)`, array of data to classify. inplace : bool (default False) Whether to conduct the update in place or to return a copy estimated from the additional specifications. **kwargs : dict Additional parameters that are passed to the ``__init__`` function of the class. For documentation, check the class constructor. """ bins = kwargs.pop("bins", self.bins) if inplace: self._update(y=y, bins=bins, **kwargs) else: new = copy.deepcopy(self) new._update(y, bins, **kwargs) return new # We have to override the plot method for additional kwargs for UserDefined def plot( self, gdf, border_color="lightgray", border_width=0.10, title=None, legend=False, cmap="YlGnBu", axis_on=True, legend_kwds={"loc": "lower right", "fmt": FMT}, file_name=None, dpi=600, ax=None, ): try: import matplotlib.pyplot as plt except ImportError: raise ImportError( "Mapclassify.plot depends on matplotlib.pyplot, and this was" "not able to be imported. \nInstall matplotlib to" "plot spatial classifier." ) from None if ax is None: f = plt.figure() ax = plt.gca() else: f = plt.gcf() if "fmt" in legend_kwds: legend_kwds.pop("fmt") ax = gdf.assign(_cl=self.y).plot( column="_cl", ax=ax, cmap=cmap, edgecolor=border_color, linewidth=border_width, scheme=self.name, legend=legend, legend_kwds=legend_kwds, classification_kwds={"bins": self.bins}, # for UserDefined ) if not axis_on: ax.axis("off") if title: f.suptitle(title) if file_name: plt.savefig(file_name, dpi=dpi) return f, ax class MaxP(MapClassifier): """ MaxP Map Classification. Based on Max-p regionalization algorithm. Parameters ---------- y : numpy.array :math:`(n,1)`, values to classify. k : int (default K==5) Number of classes required. initial : int (default 1000) Number of initial solutions to use prior to swapping. seed1 : int (default 0) Random state for initial building process. seed2 : int (default 1) Random state for swapping process. Attributes ---------- yb : numpy.array :math:`(n,1)`, bin IDs for observations. bins : numpy.array :math:`(k,1)`, the upper bounds of each class. k : int The number of classes. counts : numpy.array :math:`(k,1)`, the number of observations falling in each class. Examples -------- >>> import mapclassify >>> cal = mapclassify.load_example() >>> mp = mapclassify.MaxP(cal) >>> mp.bins array([3.16000e+00, 1.26300e+01, 1.67000e+01, 2.04700e+01, 4.11145e+03]) >>> mp.counts.tolist() [18, 16, 3, 1, 20] """ def __init__(self, y, k=K, initial=1000, seed1=0, seed2=1): if min(y) == max(y): raise ValueError( f"Not enough unique values in array to form {k} classes. " "All values in `y` are equal." ) self.k = k self.initial = initial self.seed1 = seed1 self.seed2 = seed2 MapClassifier.__init__(self, y) self.name = "MaxP" def _set_bins(self): x = self.y.copy() k = self.k q = quantile(x, k) if x.ndim == 1: x.shape = (x.size, 1) n, tmp = x.shape x.sort(axis=0) # find best of initial solutions solution = 0 best_tss = x.var() * x.shape[0] tss_all = np.zeros((self.initial, 1)) while solution < self.initial: remaining = list(range(n)) seeds = [ np.nonzero(di == min(di))[0][0] for di in [np.abs(x - qi) for qi in q] ] np.random.seed(self.seed1) rseeds = np.random.permutation(list(range(k))).tolist() [remaining.remove(seed) for seed in seeds] self.classes = classes = [] [classes.append([seed]) for seed in seeds] while rseeds: seed_id = rseeds.pop() current = classes[seed_id] growing = True while growing: current = classes[seed_id] low = current[0] high = current[-1] left = low - 1 right = high + 1 move_made = False if left in remaining: current.insert(0, left) remaining.remove(left) move_made = True if right in remaining: current.append(right) remaining.remove(right) move_made = True if move_made: classes[seed_id] = current else: growing = False tss = _fit(self.y, classes) tss_all[solution] = tss if tss < best_tss: best_solution = classes best_it = solution best_tss = tss solution += 1 classes = best_solution self.best_it = best_it self.tss = best_tss self.a2c = a2c = {} self.tss_all = tss_all for r, cl in enumerate(classes): for a in cl: a2c[a] = r swapping = True while swapping: np.random.seed(self.seed2) rseeds = np.random.permutation(list(range(k))).tolist() total_moves = 0 while rseeds: _id = rseeds.pop() growing = True total_moves = 0 while growing: target = classes[_id] left = target[0] - 1 right = target[-1] + 1 n_moves = 0 if left in a2c: left_class = classes[a2c[left]] if len(left_class) > 1: a = left_class[-1] if self._swap(left_class, target, a): target.insert(0, a) left_class.remove(a) a2c[a] = _id n_moves += 1 if right in a2c: right_class = classes[a2c[right]] if len(right_class) > 1: a = right_class[0] if self._swap(right_class, target, a): target.append(a) right_class.remove(a) n_moves += 1 a2c[a] = _id if not n_moves: growing = False total_moves += n_moves if not total_moves: swapping = False xs = self.y.copy() xs.sort() self.bins = np.array([xs[cl][-1] for cl in classes]) def _ss(self, class_def): """Calculates sum of squares for a class.""" yc = self.y[class_def] css = yc - yc.mean() css *= css return sum(css) def _swap(self, class1, class2, a): """Evaluate cost of moving ``a`` from ``class1`` to ``class2``.""" ss1 = self._ss(class1) ss2 = self._ss(class2) tss1 = ss1 + ss2 class1c = copy.copy(class1) class2c = copy.copy(class2) class1c.remove(a) class2c.append(a) ss1 = self._ss(class1c) ss2 = self._ss(class2c) tss2 = ss1 + ss2 return False if tss1 < tss2 else True # noqa SIM211 def _fit(y, classes): """Calculate the total sum of squares for a vector :math:`y` classified into classes. Parameters ---------- y : numpy.array :math:`(n,1)`, variable to be classified. classes : array :math:`(k,1)`, integer values denoting class membership. """ tss = 0 for class_def in classes: yc = y[class_def] css = yc - yc.mean() css *= css tss += sum(css) return tss kmethods = {} kmethods["Quantiles"] = Quantiles kmethods["FisherJenks"] = FisherJenks kmethods["NaturalBreaks"] = NaturalBreaks kmethods["MaximumBreaks"] = MaximumBreaks def gadf(y, method="Quantiles", maxk=15, pct=0.8): r""" Evaluate the Goodness of Absolute Deviation Fit (*GADF*) of a classifier and find the minimum value of :math:`k` for which ``gadf > pct``. Parameters ---------- y : numpy.array :math:`(n, 1)`, values to be classified. method : str (default 'Quantiles') The classification method in: ``{'Quantiles', 'Fisher_Jenks', 'Maximum_Breaks', 'Natrual_Breaks'}``. maxk : int (default 15) Maximum value of :math:`k` to evaluate. pct : float (default 0.8) The percentage of *GADF* to exceed. Returns ------- k : int Number of classes. cl : object Instance of the classifier at :math:`k`. gadf : float Goodness of absolute deviation fit (*GADF*). Examples -------- >>> import mapclassify >>> cal = mapclassify.load_example() >>> qgadf = mapclassify.classifiers.gadf(cal) >>> qgadf[0] 15 >>> float(qgadf[-1]) 0.3740257590909283 Quantiles fail to exceed 0.80 before 15 classes. If we lower the bar to 0.2 we see quintiles as a result >>> qgadf2 = mapclassify.classifiers.gadf(cal, pct = 0.2) >>> qgadf2[0] 5 >>> float(qgadf2[-1]) 0.21710231966462412 Notes ----- The *GADF* is defined as: .. math:: GADF = 1 - \sum_c \sum_{i \in c} |y_i - y_{c,med}| / \sum_i |y_i - y_{med}| where :math:`y_{med}` is the global median and :math:`y_{c,med}` is the median for class :math:`c`. See Also -------- KClassifiers """ y = np.array(y) adam = (np.abs(y - np.median(y))).sum() for k in range(2, maxk + 1): cl = kmethods[method](y, k) gadf = 1 - cl.adcm / adam if gadf > pct: break return (k, cl, gadf) class KClassifiers: """ Evaluate all :math:`k`-classifers and pick optimal based on :math:`k` and *GADF*. Parameters ---------- y : numpy.array :math:`(n,1)`, values to be classified. pct : float (default 0.8) The percentage of *GADF* to exceed. Attributes ---------- best : MapClassifier Instance of the optimal ``MapClassifier``. results : dict Keys are classifier names, values are the ``MapClassifier`` instances with the best ``pct`` for each classifier. Examples -------- >>> import mapclassify >>> cal = mapclassify.load_example() >>> ks = mapclassify.classifiers.KClassifiers(cal) >>> ks.best.name 'FisherJenks' >>> ks.best.k 4 >>> float(ks.best.gadf) 0.8481032719908105 Notes ----- This can be used to suggest a classification scheme. See Also -------- gadf """ def __init__(self, y, pct=0.8): results = {} best = gadf(y, "FisherJenks", maxk=len(y) - 1, pct=pct) pct0 = best[0] k0 = best[-1] keys = list(kmethods.keys()) keys.remove("FisherJenks") results["FisherJenks"] = best for method in keys: results[method] = gadf(y, method, maxk=len(y) - 1, pct=pct) k1 = results[method][0] pct1 = results[method][-1] if (k1 < k0) or (k1 == k0 and pct0 < pct1): best = results[method] k0 = k1 pct0 = pct1 self.results = results self.best = best[1] mapclassify-2.10.0/mapclassify/datasets/000077500000000000000000000000001503430131600202245ustar00rootroot00000000000000mapclassify-2.10.0/mapclassify/datasets/__init__.py000066400000000000000000000000561503430131600223360ustar00rootroot00000000000000""" Datasets module """ from . import calemp mapclassify-2.10.0/mapclassify/datasets/calemp/000077500000000000000000000000001503430131600214655ustar00rootroot00000000000000mapclassify-2.10.0/mapclassify/datasets/calemp/README.md000066400000000000000000000004601503430131600227440ustar00rootroot00000000000000calemp ====== Employment density for California counties ------------------------------------------ * calempdensity.csv: data on employment and employment density in California counties. Polygon data, n=58, k=11. Source: Anselin, L. and S.J. Rey (in progress) Spatial Econometrics: Foundations. mapclassify-2.10.0/mapclassify/datasets/calemp/__init__.py000066400000000000000000000000241503430131600235720ustar00rootroot00000000000000from .data import * mapclassify-2.10.0/mapclassify/datasets/calemp/calempdensity.csv000066400000000000000000000156271503430131600250560ustar00rootroot00000000000000"Geographic Area","Geographic Area","Geographic Name","GEONAME","GEOCOMP","STATE","Number of Employees for All Sectors","Number of employees","Class Number","sq. km","emp/sq km" "05000US06001","06001","Alameda County, California","Alameda County, California","00","06",630171,630171,5,1910.1,329.92 "05000US06003","06003","Alpine County, California","Alpine County, California","00","06",813,813,1,1913.1,0.42 "05000US06005","06005","Amador County, California","Amador County, California","00","06",9061,9061,2,1534.7,5.9 "05000US06007","06007","Butte County, California","Butte County, California","00","06",59578,59578,3,4246.6,14.03 "05000US06009","06009","Calaveras County, California","Calaveras County, California","00","06",7344,7344,2,2642.3,2.78 "05000US06011","06011","Colusa County, California","Colusa County, California","00","06",4000,4000,1,2980.5,1.34 "05000US06013","06013","Contra Costa County, California","Contra Costa County, California","00","06",338156,338156,5,1865.5,181.27 "05000US06015","06015","Del Norte County, California","Del Norte County, California","00","06",4303,4303,1,2610.4,1.65 "05000US06017","06017","El Dorado County, California","El Dorado County, California","00","06",44477,44477,3,4432.8,10.03 "05000US06019","06019","Fresno County, California","Fresno County, California","00","06",257975,257975,4,15444.7,16.7 "05000US06021","06021","Glenn County, California","Glenn County, California","00","06",4487,4487,1,3405.5,1.32 "05000US06023","06023","Humboldt County, California","Humboldt County, California","00","06",36962,36962,3,9253.5,3.99 "05000US06025","06025","Imperial County, California","Imperial County, California","00","06",34156,34156,3,10813.4,3.16 "05000US06027","06027","Inyo County, California","Inyo County, California","00","06",5820,5820,1,26397.5,0.22 "05000US06029","06029","Kern County, California","Kern County, California","00","06",183412,183412,4,21086.8,8.7 "05000US06031","06031","Kings County, California","Kings County, California","00","06",23610,23610,2,3598.8,6.56 "05000US06033","06033","Lake County, California","Lake County, California","00","06",10648,10648,2,3259.4,3.27 "05000US06035","06035","Lassen County, California","Lassen County, California","00","06",3860,3860,1,11803.9,0.33 "05000US06037","06037","Los Angeles County, California","Los Angeles County, California","00","06",3895886,3895886,5,10515.3,370.5 "05000US06039","06039","Madera County, California","Madera County, California","00","06",24957,24957,2,5538.5,4.51 "05000US06041","06041","Marin County, California","Marin County, California","00","06",101358,101358,4,1346.2,75.29 "05000US06043","06043","Mariposa County, California","Mariposa County, California","00","06",3739,3739,1,3758.6,0.99 "05000US06045","06045","Mendocino County, California","Mendocino County, California","00","06",24898,24898,2,9089,2.74 "05000US06047","06047","Merced County, California","Merced County, California","00","06",43369,43369,3,4995.8,8.68 "05000US06049","06049","Modoc County, California","Modoc County, California","00","06",1467,1467,1,10215.9,0.14 "05000US06051","06051","Mono County, California","Mono County, California","00","06",7289,7289,1,7885.2,0.92 "05000US06053","06053","Monterey County, California","Monterey County, California","00","06",108660,108660,4,8603.8,12.63 "05000US06055","06055","Napa County, California","Napa County, California","00","06",56029,56029,3,1952.5,28.7 "05000US06057","06057","Nevada County, California","Nevada County, California","00","06",29805,29805,3,2480.3,12.02 "05000US06059","06059","Orange County, California","Orange County, California","00","06",1478452,1478452,5,2045.3,722.85 "05000US06061","06061","Placer County, California","Placer County, California","00","06",133427,133427,4,3637.4,36.68 "05000US06063","06063","Plumas County, California","Plumas County, California","00","06",4863,4863,1,6614.8,0.74 "05000US06065","06065","Riverside County, California","Riverside County, California","00","06",556789,556789,5,18669.1,29.82 "05000US06067","06067","Sacramento County, California","Sacramento County, California","00","06",480346,480346,5,2501.1,192.05 "05000US06069","06069","San Benito County, California","San Benito County, California","00","06",12163,12163,2,3597.9,3.38 "05000US06071","06071","San Bernardino County, California","San Bernardino County, California","00","06",579135,579135,5,51961.2,11.15 "05000US06073","06073","San Diego County, California","San Diego County, California","00","06",1205862,1205862,5,10889.6,110.74 "05000US06075","06075","San Francisco County, California","San Francisco County, California","00","06",497485,497485,5,121,4111.45 "05000US06077","06077","San Joaquin County, California","San Joaquin County, California","00","06",179276,179276,4,3624.1,49.47 "05000US06079","06079","San Luis Obispo County, California","San Luis Obispo County, California","00","06",88413,88413,3,8558.7,10.33 "05000US06081","06081","San Mateo County, California","San Mateo County, California","00","06",368859,368859,5,1163.2,317.11 "05000US06083","06083","Santa Barbara County, California","Santa Barbara County, California","00","06",145202,145202,4,7092.6,20.47 "05000US06085","06085","Santa Clara County, California","Santa Clara County, California","00","06",886011,886011,5,3344.3,264.93 "05000US06087","06087","Santa Cruz County, California","Santa Cruz County, California","00","06",76488,76488,3,1154.3,66.26 "05000US06089","06089","Shasta County, California","Shasta County, California","00","06",52804,52804,3,9804.8,5.39 "05000US06091","06091","Sierra County, California","Sierra County, California","00","06",324,324,1,2469.4,0.13 "05000US06093","06093","Siskiyou County, California","Siskiyou County, California","00","06",9992,9992,2,16284,0.61 "05000US06095","06095","Solano County, California","Solano County, California","00","06",108653,108653,4,2145,50.65 "05000US06097","06097","Sonoma County, California","Sonoma County, California","00","06",165261,165261,4,4082.4,40.48 "05000US06099","06099","Stanislaus County, California","Stanislaus County, California","00","06",141928,141928,4,3870.9,36.67 "05000US06101","06101","Sutter County, California","Sutter County, California","00","06",20430,20430,2,1561,13.09 "05000US06103","06103","Tehama County, California","Tehama County, California","00","06",13809,13809,2,7643.2,1.81 "05000US06105","06105","Trinity County, California","Trinity County, California","00","06",1668,1668,1,8233.3,0.2 "05000US06107","06107","Tulare County, California","Tulare County, California","00","06",94949,94949,4,12495,7.6 "05000US06109","06109","Tuolumne County, California","Tuolumne County, California","00","06",14519,14519,2,5790.3,2.51 "05000US06111","06111","Ventura County, California","Ventura County, California","00","06",273745,273745,5,4781,57.26 "05000US06113","06113","Yolo County, California","Yolo County, California","00","06",63769,63769,3,2622.2,24.32 "05000US06115","06115","Yuba County, California","Yuba County, California","00","06",11374,11374,2,1632.9,6.97 mapclassify-2.10.0/mapclassify/datasets/calemp/data.py000066400000000000000000000005161503430131600227520ustar00rootroot00000000000000from os.path import abspath, dirname import pandas as pd def load(): """ Load the data and return a DataSeries instance. """ df = _get_data() return df["emp/sq km"] def _get_data(): filepath = dirname(abspath(__file__)) filepath += "/calempdensity.csv" df = pd.read_csv(filepath) return df mapclassify-2.10.0/mapclassify/greedy.py000066400000000000000000000254071503430131600202550ustar00rootroot00000000000000""" greedy - Greedy (topological) coloring for GeoPandas Copyright (C) 2019 Martin Fleischmann, 2017 Nyall Dawson """ import operator __all__ = ["greedy"] def _balanced(features, sw, balance="count", min_colors=4): """ Strategy to color features in a way which is visually balanced. Algorithm ported from QGIS to be used with GeoDataFrames and libpysal weights objects. Original algorithm: Date : February 2017 Copyright : (C) 2017 by Nyall Dawson Email : nyall dot dawson at gmail dot com Parameters ---------- features : geopandas.GeoDataFrame GeoDataFrame. sw : libpysal.weights.W Spatial weights object denoting adjacency of features. balance : str (default 'count') The method of color balancing. min_colors : int (default 4) The minimal number of colors to be used. Returns ------- feature_colors : dict Dictionary with assigned color codes. """ feature_colors = {} # start with minimum number of colors in pool color_pool = set(range(min_colors)) # calculate count of neighbours neighbour_count = sw.cardinalities # sort features by neighbour count - handle those with more neighbours first sorted_by_count = sorted( neighbour_count.items(), key=operator.itemgetter(1), reverse=True ) # counts for each color already assigned color_counts = {} color_areas = {} for c in color_pool: color_counts[c] = 0 color_areas[c] = 0 if balance == "centroid": features = features.copy() features.geometry = features.geometry.centroid balance = "distance" for feature_id, _ in sorted_by_count: # first work out which already assigned colors are adjacent to this feature adjacent_colors = set() for neighbour in sw.neighbors[feature_id]: if neighbour in feature_colors: adjacent_colors.add(feature_colors[neighbour]) # from the existing colors, work out which are available (ie non-adjacent) available_colors = color_pool.difference(adjacent_colors) feature_color = -1 if len(available_colors) == 0: # no existing colors available for this feature; add new color and repeat min_colors += 1 return _balanced(features, sw, balance, min_colors) else: if balance == "count": # choose least used available color counts = [ (c, v) for c, v in color_counts.items() if c in available_colors ] feature_color = sorted(counts, key=operator.itemgetter(1))[0][0] color_counts[feature_color] += 1 elif balance == "area": areas = [ (c, v) for c, v in color_areas.items() if c in available_colors ] feature_color = sorted(areas, key=operator.itemgetter(1))[0][0] color_areas[feature_color] += features.loc[feature_id].geometry.area elif balance == "distance": min_distances = {c: float("inf") for c in available_colors} this_feature = features.loc[feature_id].geometry # find features for all available colors other_features = { f_id: c for (f_id, c) in feature_colors.items() if c in available_colors } distances = features.loc[other_features.keys()].distance(this_feature) # calculate the min distance from this feature to the nearest # feature with each assigned color for other_feature_id, c in other_features.items(): distance = distances.loc[other_feature_id] if distance < min_distances[c]: min_distances[c] = distance # choose color such that min distance is maximised! # - ie we want MAXIMAL separation between features with the same color feature_color = sorted( min_distances, key=min_distances.__getitem__, reverse=True )[0] feature_colors[feature_id] = feature_color return feature_colors def greedy( gdf, strategy="balanced", balance="count", min_colors=4, sw="queen", min_distance=None, silence_warnings=True, interchange=False, ): """ Color GeoDataFrame using various strategies of greedy (topological) colouring. Attempts to color a GeoDataFrame using as few colors as possible, where no neighbours can have same color as the feature itself. Offers various strategies ported from QGIS or implemented within NetworkX for greedy graph coloring. ``greedy`` will return ``pandas.Series`` representing assigned color codes. Parameters ---------- gdf : GeoDataFrame GeoDataFrame strategy : str (default 'balanced') Determine coloring strategy. Options are ``'balanced'`` for algorithm based on QGIS Topological coloring. It is aiming for a visual balance, defined by the balance parameter. Other options are those supported by ``networkx.greedy_color``: * ``'largest_first'`` * ``'random_sequential'`` * ``'smallest_last'`` * ``'independent_set'`` * ``'connected_sequential_bfs'`` * ``'connected_sequential_dfs'`` * ``'connected_sequential'`` (alias for the previous strategy) * ``'saturation_largest_first'`` * ``'DSATUR'`` (alias for the previous strategy) For details see https://networkx.github.io/documentation/stable/reference/algorithms/generated/networkx.algorithms.coloring.greedy_color.html balance : str (default 'count') If strategy is ``'balanced'``, determine the method of color balancing. * ``'count'`` attempts to balance the number of features per each color. * ``'area'`` attempts to balance the area covered by each color. * ``'centroid'`` attempts to balance the distance between colors based on the distance between centroids. * ``'distance'`` attempts to balance the distance between colors based on the distance between geometries. Slower than ``'centroid'``, but more precise. Both ``'centroid'`` and ``'distance'`` are significantly slower than other especially for larger GeoDataFrames. Apart from ``'count'``, all require CRS to be projected (not in degrees) to ensure metric values are correct. min_colors: int (default 4) If strategy is ``'balanced'``, define the minimal number of colors to be used. sw : 'queen', 'rook' or libpysal.weights.W (default 'queen') If min_distance is None, one can pass ``'libpysal.weights.W'`` object denoting neighbors or let greedy generate one based on ``'queen'`` or ``'rook'`` contiguity. min_distance : float (default None) Set minimal distance between colors. If ``min_distance`` is not ``None``, slower algorithm for generating spatial weghts is used based on intersection between geometries. ``'min_distance'`` is then used as a tolerance of intersection. silence_warnings : bool (default True) Silence libpysal warnings when creating spatial weights. interchange : bool (default False) Use the color interchange algorithm (applicable for NetworkX strategies). For details see https://networkx.github.io/documentation/stable/reference/algorithms/generated/networkx.algorithms.coloring.greedy_color.html Returns ------- color : pandas.Series ``pandas.Series`` representing assinged color codes. Examples -------- >>> from mapclassify import greedy >>> import geopandas >>> world = geopandas.read_file( ... "https://naciscdn.org/naturalearth/110m/cultural/ne_110m_admin_0_countries.zip" ... ) >>> africa = world.loc[world.CONTINENT == "Africa"].copy() >>> africa = africa.to_crs("ESRI:102022").reset_index(drop=True) Default: >>> africa["greedy_colors"] = greedy(africa) >>> africa["greedy_colors"].head() 0 1 1 0 2 0 3 1 4 4 Name: greedy_colors, dtype: int64 Balanced by area: >>> africa["balanced_area"] = greedy(africa, strategy="balanced", balance="area") >>> africa["balanced_area"].head() 0 1 1 2 2 0 3 1 4 3 Name: balanced_area, dtype: int64 Using rook adjacency: >>> africa["rook_adjacency"] = greedy(africa, sw="rook") >>> africa["rook_adjacency"].tail() 46 3 47 0 48 2 49 3 50 1 Name: rook_adjacency, dtype: int64 Adding minimal distance between colors: >>> africa["min_distance"] = greedy(africa, min_distance=1000000) >>> africa["min_distance"].head() 0 1 1 9 2 0 3 7 4 4 Name: min_distance, dtype: int64 Using different coloring strategy: >>> africa["smallest_last"] = greedy(africa, strategy="smallest_last") >>> africa["smallest_last"].head() 0 3 1 1 2 1 3 3 4 1 Name: smallest_last, dtype: int64 """ # noqa if strategy != "balanced": try: import networkx as nx STRATEGIES = nx.algorithms.coloring.greedy_coloring.STRATEGIES.keys() # noqa: N806 except ImportError: raise ImportError("The 'networkx' package is required.") from None try: import pandas as pd except ImportError: raise ImportError("The 'pandas' package is required.") from None try: from libpysal.weights import Queen, Rook, W, fuzzy_contiguity except ImportError: raise ImportError("The 'libpysal' package is required.") from None if min_distance is not None: sw = fuzzy_contiguity( gdf, tolerance=0.0, buffering=True, buffer=min_distance / 2.0, silence_warnings=silence_warnings, ) if not isinstance(sw, W): if sw == "queen": sw = Queen.from_dataframe( gdf, silence_warnings=silence_warnings, use_index=False ) elif sw == "rook": sw = Rook.from_dataframe( gdf, silence_warnings=silence_warnings, use_index=False ) if strategy == "balanced": color = pd.Series(_balanced(gdf, sw, balance=balance, min_colors=min_colors)) elif strategy in STRATEGIES: color = nx.greedy_color( sw.to_networkx(), strategy=strategy, interchange=interchange ) else: raise ValueError(f"'{strategy}' is not a valid strategy.") color = pd.Series(color).sort_index() color.index = gdf.index return color mapclassify-2.10.0/mapclassify/legendgram.py000066400000000000000000000062731503430131600211030ustar00rootroot00000000000000import warnings import numpy as np def _legendgram( classifier, *, ax=None, cmap=None, bins=50, inset=True, clip=None, vlines=False, vlinecolor="black", vlinewidth=1, loc="lower left", legend_size=("27%", "20%"), frameon=False, tick_params=None, bbox_to_anchor=None, **kwargs, ): """See ``classifiers.MapClassifier.plot_legendgram()`` docstring.""" try: import matplotlib.pyplot as plt from matplotlib import colormaps from matplotlib.collections import Collection from mpl_toolkits.axes_grid1.inset_locator import inset_axes except ImportError as e: raise ImportError from e("you must have matplotlib ") def _get_cmap(_ax): """Detect the most recent matplotlib colormap used, if previously rendered.""" _child_cmaps = [ (cc.cmap, cc.cmap.name) for cc in _ax.properties()["children"] if isinstance(cc, Collection) ] has_child_cmaps = len(_child_cmaps) n_unique_cmaps = len({cc[1] for cc in _child_cmaps}) if has_child_cmaps: _cmap, cmap_name = _child_cmaps[-1] if n_unique_cmaps > 1: warnings.warn( ( f"There are {n_unique_cmaps} unique colormaps associated with " f"the axes. Defaulting to most recent colormap: '{cmap_name}'" ), UserWarning, stacklevel=2, ) else: warnings.warn( "There is no data associated with the `ax`.", UserWarning, stacklevel=2 ) _cmap = colormaps.get_cmap("viridis") return _cmap if ax is None: f, ax = plt.subplots() else: f = ax.get_figure() if cmap is None: cmap = _get_cmap(ax) if isinstance(cmap, str): cmap = colormaps[cmap] k = len(classifier.bins) breaks = classifier.bins if inset: if not bbox_to_anchor: bbox_to_anchor = (0, 0, 1, 1) histpos = inset_axes( ax, loc=loc, width=legend_size[0], height=legend_size[1], bbox_to_anchor=bbox_to_anchor, bbox_transform=ax.transAxes, ) histax = f.add_axes(histpos) else: histax = ax _, bins, patches = histax.hist(classifier.y, bins=bins, color="0.1", **kwargs) colors = [cmap(i) for i in np.linspace(0, 1, k)] bucket_breaks = [0] + [np.searchsorted(bins, i) for i in breaks] for c in range(k): for b in range(bucket_breaks[c], bucket_breaks[c + 1]): patches[b].set_facecolor(colors[c]) if clip is not None: histax.set_xlim(*clip) histax.set_frame_on(frameon) histax.get_yaxis().set_visible(False) if tick_params is None: tick_params = {} if vlines: lim = histax.get_ylim()[1] # plot upper limit of each bin for i in classifier.bins: histax.vlines(i, 0, lim, color=vlinecolor, linewidth=vlinewidth) tick_params["labelsize"] = tick_params.get("labelsize", 12) histax.tick_params(**tick_params) return histax mapclassify-2.10.0/mapclassify/pooling.py000066400000000000000000000064021503430131600204370ustar00rootroot00000000000000import numpy from .classifiers import ( BoxPlot, EqualInterval, FisherJenks, FisherJenksSampled, MaximumBreaks, Quantiles, StdMean, UserDefined, ) __all__ = ["Pooled"] dispatcher = { "boxplot": BoxPlot, "equalinterval": EqualInterval, "fisherjenks": FisherJenks, "fisherjenkssampled": FisherJenksSampled, "quantiles": Quantiles, "maximumbreaks": MaximumBreaks, "stdmean": StdMean, "userdefined": UserDefined, } class Pooled: """Applying global binning across columns. Parameters ---------- Y : numpy.array :math:`(n, m)`, values to classify, with :math:`m>1`. classifier : str (default 'Quantiles') Name of ``mapclassify.classifier`` to apply. **kwargs : dict Additional keyword arguments for classifier. Attributes ---------- global_classifier : mapclassify.classifiers.MapClassifier Instance of the pooled classifier defined as the classifier applied to the union of the columns. col_classifier : list Elements are ``MapClassifier`` instances with the pooled classifier applied to the associated column of ``Y``. Examples -------- >>> import mapclassify >>> import numpy >>> n = 20 >>> data = numpy.array([numpy.arange(n)+i*n for i in range(1,4)]).T >>> res = mapclassify.Pooled(data) >>> res.col_classifiers[0].counts.tolist() [12, 8, 0, 0, 0] >>> res.col_classifiers[1].counts.tolist() [0, 4, 12, 4, 0] >>> res.col_classifiers[2].counts.tolist() [0, 0, 0, 8, 12] >>> res.global_classifier.counts.tolist() [12, 12, 12, 12, 12] >>> res.global_classifier.bins == res.col_classifiers[0].bins array([ True, True, True, True, True]) >>> res.global_classifier.bins array([31.8, 43.6, 55.4, 67.2, 79. ]) """ def __init__(self, Y, classifier="Quantiles", **kwargs): # noqa: N803 method = classifier.lower() valid_methods = list(dispatcher.keys()) if method not in valid_methods: raise ValueError( f"'{classifier}' not a valid classifier. " f"Currently supported classifiers: {valid_methods}" ) self.__dict__.update(kwargs) _y = numpy.asarray(Y) n, cols = _y.shape y = numpy.reshape(_y, (-1, 1), order="f") ymin = y.min() global_classifier = dispatcher[method](y, **kwargs) # self.k = global_classifier.k col_classifiers = [] name = f"Pooled {classifier}" for c in range(cols): res = UserDefined(Y[:, c], bins=global_classifier.bins, lowest=ymin) res.name = name col_classifiers.append(res) self.col_classifiers = col_classifiers self.global_classifier = global_classifier self._summary() def _summary(self): self.classes = self.global_classifier.classes self.tss = self.global_classifier.tss self.adcm = self.global_classifier.adcm self.gadf = self.global_classifier.gadf def __str__(self): s = "Pooled Classifier" rows = [s] for c in self.col_classifiers: rows.append(c.table()) return "\n\n".join(rows) def __repr__(self): return self.__str__() mapclassify-2.10.0/mapclassify/tests/000077500000000000000000000000001503430131600175565ustar00rootroot00000000000000mapclassify-2.10.0/mapclassify/tests/__init__.py000066400000000000000000000000001503430131600216550ustar00rootroot00000000000000mapclassify-2.10.0/mapclassify/tests/baseline_images/000077500000000000000000000000001503430131600226655ustar00rootroot00000000000000mapclassify-2.10.0/mapclassify/tests/baseline_images/test_legendgram/000077500000000000000000000000001503430131600260315ustar00rootroot00000000000000mapclassify-2.10.0/mapclassify/tests/baseline_images/test_legendgram/legendgram_cmap.png000066400000000000000000000127121503430131600316470ustar00rootroot00000000000000PNG  IHDR Xvp:tEXtSoftwareMatplotlib version3.10.3, https://matplotlib.org/f% pHYsaa?i6IDATx?o\gzg)"酁0VB Y"pcn L@7[ȅVgˌf$0vfI!K+JHs]{KC 1l6+b#@ F1 @b#@ F1 @b#@ F1 @b#@ F1 @b#@ F1 @b#@ F1 @b#@ F1 @b.u=Ӷmݹs^Z+++]S&I=x>Z__zג#wܩ%O$@իWtv"vww/z Bbߋ~/hT;;;qzd{:Yݺ~n]Qڶ=r͓O;C۶5L~ݾٯdz:5{23R@{cwylz! 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pytest.image_comp_kws = {"extensions": ["png"], "tol": 0.05, "remove_text": True} pytest.image_comp_kws_legend_text = { "extensions": ["png"], "tol": 2.8, "remove_text": True, } mapclassify-2.10.0/mapclassify/tests/test_classify.py000066400000000000000000000065411503430131600230120ustar00rootroot00000000000000import geopandas import libpysal import pytest import mapclassify def _assertions(a, b): assert a.k == b.k assert a.yb.all() == b.yb.all() assert a.bins.all() == b.bins.all() assert a.counts.all() == b.counts.all() class TestClassify: def setup_method(self): link_to_data = libpysal.examples.get_path("columbus.shp") gdf = geopandas.read_file(link_to_data) self.x = gdf["HOVAL"].values def test_box_plot(self): a = mapclassify.classify(self.x, "box_plot") b = mapclassify.BoxPlot(self.x) _assertions(a, b) def test_equal_interval(self): a = mapclassify.classify(self.x, "EqualInterval", k=3) b = mapclassify.EqualInterval(self.x, k=3) _assertions(a, b) def test_fisher_jenks(self): a = mapclassify.classify(self.x, "FisherJenks", k=3) b = mapclassify.FisherJenks(self.x, k=3) _assertions(a, b) def test_fisher_jenks_sampled(self): a = mapclassify.classify( self.x, "FisherJenksSampled", k=3, pct_sampled=0.5, truncate=False ) b = mapclassify.FisherJenksSampled(self.x, k=3, pct=0.5, truncate=False) _assertions(a, b) def test_headtail_breaks(self): a = mapclassify.classify(self.x, "headtail_breaks") b = mapclassify.HeadTailBreaks(self.x) _assertions(a, b) def test_quantiles(self): a = mapclassify.classify(self.x, "quantiles", k=3) b = mapclassify.Quantiles(self.x, k=3) _assertions(a, b) def test_percentiles(self): a = mapclassify.classify(self.x, "percentiles", pct=[25, 50, 75, 100]) b = mapclassify.Percentiles(self.x, pct=[25, 50, 75, 100]) _assertions(a, b) a = mapclassify.classify(self.x, "prettybreaks") b = mapclassify.PrettyBreaks(self.x) _assertions(a, b) def test_jenks_caspall(self): a = mapclassify.classify(self.x, "JenksCaspall", k=3) b = mapclassify.JenksCaspall(self.x, k=3) _assertions(a, b) def test_jenks_caspall_forced(self): a = mapclassify.classify(self.x, "JenksCaspallForced", k=3) b = mapclassify.JenksCaspallForced(self.x, k=3) _assertions(a, b) def test_jenks_caspall_sampled(self): a = mapclassify.classify(self.x, "JenksCaspallSampled", pct_sampled=0.5) b = mapclassify.JenksCaspallSampled(self.x, pct=0.5) _assertions(a, b) def test_natural_breaks(self): a = mapclassify.classify(self.x, "natural_breaks") b = mapclassify.NaturalBreaks(self.x) _assertions(a, b) def test_max_p_classifier(self): a = mapclassify.classify(self.x, "max_p", k=3, initial=50) b = mapclassify.MaxP(self.x, k=3, initial=50) _assertions(a, b) def test_std_mean(self): a = mapclassify.classify(self.x, "std_mean", multiples=[-1, -0.5, 0.5, 1]) b = mapclassify.StdMean(self.x, multiples=[-1, -0.5, 0.5, 1]) _assertions(a, b) def test_user_defined(self): a = mapclassify.classify(self.x, "user_defined", bins=[20, max(self.x)]) b = mapclassify.UserDefined(self.x, bins=[20, max(self.x)]) _assertions(a, b) def test_bad_classifier(self): classifier = "George_Costanza" with pytest.raises(ValueError, match="Invalid scheme: 'georgecostanza'"): mapclassify.classify(self.x, classifier) mapclassify-2.10.0/mapclassify/tests/test_greedy.py000066400000000000000000000133361503430131600224540ustar00rootroot00000000000000import geopandas import libpysal import pytest from ..greedy import greedy world = geopandas.read_file( "https://naciscdn.org/naturalearth/110m/cultural/ne_110m_admin_0_countries.zip" ) sw = libpysal.weights.Queen.from_dataframe( world, ids=world.index.to_list(), silence_warnings=True ) def _check_correctess(colors): assert len(colors) == len(world) for i, neighbors in sw.neighbors.items(): if len(neighbors) > 1: assert (colors[neighbors] != colors[i]).all() @pytest.mark.filterwarnings("ignore:Geometry is in a geographic CRS.") class TestGreedy: def test_default(self): colors = greedy(world) assert set(colors) == {0, 1, 2, 3, 4} assert colors.value_counts().to_list() == [36, 36, 35, 35, 35] assert (colors.index == world.index).all() _check_correctess(colors) def test_rook(self): colors = greedy(world, sw="rook") assert set(colors) == {0, 1, 2, 3, 4} assert colors.value_counts().to_list() == [36, 36, 35, 35, 35] _check_correctess(colors) def test_sw(self): colors = greedy(world, sw=sw) assert set(colors) == {0, 1, 2, 3, 4} assert colors.value_counts().to_list() == [36, 36, 35, 35, 35] _check_correctess(colors) def test_min_distance(self): europe = world.loc[world.CONTINENT == "Europe"].to_crs(epsg=3035) colors = greedy(europe, min_distance=500000) assert set(colors) == set(range(13)) assert colors.value_counts().to_list() == [3] * 13 def test_invalid_strategy(self): strategy = "spice melange" with pytest.raises(ValueError, match=f"'{strategy}' is not a valid strategy."): greedy(world, strategy=strategy) @pytest.mark.filterwarnings("ignore:Geometry is in a geographic CRS.") @pytest.mark.parametrize("pysal_geos", [None, 0]) class TestGreedyParams: def test_count(self, pysal_geos): colors = greedy( world, strategy="balanced", balance="count", min_distance=pysal_geos ) assert set(colors) == {0, 1, 2, 3, 4} assert colors.value_counts().to_list() == [36, 36, 35, 35, 35] _check_correctess(colors) def test_area(self, pysal_geos): colors = greedy( world, strategy="balanced", balance="area", min_distance=pysal_geos ) assert set(colors) == {0, 1, 2, 3, 4} assert colors.value_counts().to_list() == [55, 49, 39, 32, 2] _check_correctess(colors) def test_centroid(self, pysal_geos): colors = greedy( world, strategy="balanced", balance="centroid", min_distance=pysal_geos ) assert set(colors) == {0, 1, 2, 3, 4} assert colors.value_counts().to_list() == [39, 36, 36, 34, 32] _check_correctess(colors) def test_distance(self, pysal_geos): colors = greedy( world, strategy="balanced", balance="distance", min_distance=pysal_geos ) assert set(colors) == {0, 1, 2, 3, 4} assert colors.value_counts().to_list() == [38, 36, 35, 34, 34] _check_correctess(colors) def test_largest_first(self, pysal_geos): colors = greedy(world, strategy="largest_first", min_distance=pysal_geos) assert set(colors) == {0, 1, 2, 3, 4} assert colors.value_counts().to_list() == [64, 49, 42, 21, 1] _check_correctess(colors) def test_random_sequential(self, pysal_geos): """based on random, no consistent results to be tested""" colors = greedy(world, strategy="random_sequential", min_distance=pysal_geos) _check_correctess(colors) def test_smallest_last(self, pysal_geos): colors = greedy(world, strategy="smallest_last", min_distance=pysal_geos) assert set(colors) == {0, 1, 2, 3} assert colors.value_counts().to_list() == [71, 52, 39, 15] _check_correctess(colors) def test_independent_set(self, pysal_geos): colors = greedy(world, strategy="independent_set", min_distance=pysal_geos) assert set(colors) == {0, 1, 2, 3, 4} assert colors.value_counts().to_list() == [91, 42, 26, 13, 5] _check_correctess(colors) def test_connected_sequential_bfs(self, pysal_geos): colors = greedy( world, strategy="connected_sequential_bfs", min_distance=pysal_geos ) assert set(colors) == {0, 1, 2, 3, 4} _check_correctess(colors) def test_connected_sequential_dfs(self, pysal_geos): colors = greedy( world, strategy="connected_sequential_dfs", min_distance=pysal_geos ) assert set(colors) == {0, 1, 2, 3, 4} _check_correctess(colors) def test_connected_sequential(self, pysal_geos): colors = greedy(world, strategy="connected_sequential", min_distance=pysal_geos) assert set(colors) == {0, 1, 2, 3, 4} _check_correctess(colors) def test_saturation_largest_first(self, pysal_geos): colors = greedy( world, strategy="saturation_largest_first", min_distance=pysal_geos ) assert set(colors) == {0, 1, 2, 3} assert colors.value_counts().to_list() == [71, 47, 42, 17] _check_correctess(colors) def test_DSATUR(self, pysal_geos): colors = greedy(world, strategy="DSATUR", min_distance=pysal_geos) assert set(colors) == {0, 1, 2, 3} assert colors.value_counts().to_list() == [71, 47, 42, 17] _check_correctess(colors) def test_index(self, pysal_geos): world["ten"] = world.index * 10 reindexed = world.set_index("ten") colors = greedy(reindexed, min_distance=pysal_geos) assert len(colors) == len(world) assert set(colors) == {0, 1, 2, 3, 4} assert colors.value_counts().to_list() == [36, 36, 35, 35, 35] mapclassify-2.10.0/mapclassify/tests/test_legendgram.py000066400000000000000000000142271503430131600233020ustar00rootroot00000000000000import geopandas as gpd import matplotlib import matplotlib.pyplot as plt import numpy as np import pytest from libpysal import examples from matplotlib.testing.decorators import image_comparison from mapclassify import EqualInterval, Quantiles from mapclassify.legendgram import _legendgram no_data_warning = pytest.warns( UserWarning, match="There is no data associated with the `ax`", ) class TestLegendgram: def setup_method(self): np.random.seed(42) self.data = np.random.normal(0, 1, 100) self.classifier = EqualInterval(self.data, k=5) def test_legendgram_returns_axis(self): """Test that _legendgram returns a matplotlib axis""" _, ax = plt.subplots(figsize=(8, 6)) with no_data_warning: histax = _legendgram(self.classifier, ax=ax) plt.close() assert isinstance(histax, matplotlib.axes.Axes) def test_legendgram_standalone(self): """Test that _legendgram works without providing an axis""" with no_data_warning: histax = _legendgram(self.classifier) plt.close() assert isinstance(histax, matplotlib.axes.Axes) def test_legendgram_inset_false(self): """Test that _legendgram works with inset=False""" _, ax = plt.subplots(figsize=(8, 6)) with no_data_warning: histax = _legendgram(self.classifier, ax=ax, inset=False) plt.close() # When inset=False, histax should be the same as ax assert histax is ax def test_legendgram_clip(self): """Test that _legendgram applies clip parameter correctly""" _, ax = plt.subplots(figsize=(8, 6)) clip_range = (-2, 2) with no_data_warning: histax = _legendgram(self.classifier, ax=ax, clip=clip_range) xlim = histax.get_xlim() plt.close() assert xlim[0] == clip_range[0] assert xlim[1] == clip_range[1] def test_legendgram_tick_params(self): """Test that _legendgram applies tick_params correctly""" _, ax = plt.subplots(figsize=(8, 6)) custom_tick_params = {"labelsize": 20, "rotation": 45} with no_data_warning: _ = _legendgram(self.classifier, ax=ax, tick_params=custom_tick_params) plt.close() def test_legendgram_frameon(self): """Test that _legendgram applies frameon parameter correctly""" _, ax = plt.subplots(figsize=(8, 6)) with no_data_warning: histax = _legendgram(self.classifier, ax=ax, frameon=True) is_frame_on = histax.get_frame_on() plt.close() assert is_frame_on ################################################################################ # # * baseline images are initially generated in top directory `result_images/` # * they must be moved to the appropriate location within `mapclassify/tests/` # ################################################################################ @image_comparison(["legendgram_default"], **pytest.image_comp_kws) def test_legendgram_default(self): """Test default legendgram appearance""" _, ax = plt.subplots(figsize=(8, 6)) with no_data_warning: _legendgram(self.classifier, ax=ax) @image_comparison(["legendgram_vlines"], **pytest.image_comp_kws) def test_legendgram_vlines(self): """Test legendgram with vertical lines""" _, ax = plt.subplots(figsize=(8, 6)) with no_data_warning: _legendgram( self.classifier, ax=ax, vlines=True, vlinecolor="red", vlinewidth=2 ) @image_comparison(["legendgram_cmap"], **pytest.image_comp_kws) def test_legendgram_cmap(self): """Test legendgram with custom colormap""" _, ax = plt.subplots(figsize=(8, 6)) _legendgram(self.classifier, ax=ax, cmap="plasma") @image_comparison(["legendgram_cmap"], **pytest.image_comp_kws) def test_legendgram_cmap_class(self): """Test legendgram with custom colormap""" _, ax = plt.subplots(figsize=(8, 6)) _legendgram(self.classifier, ax=ax, cmap=matplotlib.cm.plasma) @image_comparison(["legendgram_position"], **pytest.image_comp_kws) def test_legendgram_position(self): """Test legendgram with custom position""" _, ax = plt.subplots(figsize=(8, 6)) with no_data_warning: _legendgram( self.classifier, ax=ax, loc="upper right", legend_size=("40%", "30%") ) @image_comparison(["legendgram_map"], **pytest.image_comp_kws) def test_legendgram_map(self): """Test with geopandas map""" data = gpd.read_file(examples.get_path("south.shp")).to_crs(epsg=5070) ax = data.plot("DV80", k=10, scheme="Quantiles") classifier = Quantiles(data["DV80"].values, k=10) classifier.plot_legendgram( ax=ax, legend_size=("50%", "20%"), loc="upper left", clip=(2, 10) ) ax.set_axis_off() @image_comparison(["legendgram_kwargs"], **pytest.image_comp_kws) def test_legendgram_kwargs(self): """Test legendgram keywords.""" _, ax = plt.subplots(figsize=(8, 6)) with no_data_warning: _legendgram( self.classifier, ax=ax, legend_size=("20%", "30%"), orientation="horizontal", ) @image_comparison(["legendgram_most_recent_cmap"], **pytest.image_comp_kws) def test_legendgram_most_recent_cmap(self): """Test most recent colormap legendgram appearance""" data = gpd.read_file(examples.get_path("south.shp")).to_crs(epsg=5070) ax = data.plot("DV80", k=10, scheme="Quantiles", cmap="Blues") data.plot("DV80", ax=ax, k=10, scheme="Quantiles", cmap="Greens") classifier = Quantiles(data["DV80"].values, k=10) with pytest.warns( UserWarning, match=( "There are 2 unique colormaps associated with the axes. " "Defaulting to most recent colormap: 'Greens'" ), ): classifier.plot_legendgram( ax=ax, legend_size=("50%", "20%"), loc="upper left", clip=(2, 10) ) ax.set_axis_off() mapclassify-2.10.0/mapclassify/tests/test_mapclassify.py000066400000000000000000000545341503430131600235150ustar00rootroot00000000000000import types import numpy import pytest from matplotlib.testing.decorators import image_comparison from ..classifiers import ( BoxPlot, EqualInterval, FisherJenks, FisherJenksSampled, HeadTailBreaks, JenksCaspall, JenksCaspallForced, JenksCaspallSampled, KClassifiers, MaximumBreaks, MaxP, NaturalBreaks, Percentiles, PrettyBreaks, Quantiles, StdMean, UserDefined, bin, bin1d, binC, gadf, load_example, quantile, ) from ..pooling import Pooled RTOL = 0.0001 class TestQuantile: def test_quantile(self): y = numpy.arange(1000) expected = numpy.array([333.0, 666.0, 999.0]) numpy.testing.assert_almost_equal(expected, quantile(y, k=3)) def test_quantile_k4(self): x = numpy.arange(1000) qx = quantile(x, k=4) expected = numpy.array([249.75, 499.5, 749.25, 999.0]) numpy.testing.assert_array_almost_equal(expected, qx) def test_quantile_k(self): y = numpy.random.random(1000) for k in range(5, 10): numpy.testing.assert_almost_equal(k, len(quantile(y, k))) assert k == len(quantile(y, k)) class TestUpdate: def setup_method(self): numpy.random.seed(4414) self.data = numpy.random.normal(0, 10, size=10) self.new_data = numpy.random.normal(0, 10, size=4) def test_update(self): # Quantiles quants = Quantiles(self.data, k=3) known_yb = numpy.array([0, 1, 0, 1, 0, 2, 0, 2, 1, 2]) numpy.testing.assert_allclose(quants.yb, known_yb, rtol=RTOL) new_yb = quants.update(self.new_data, k=4).yb known_new_yb = numpy.array([0, 3, 1, 0, 1, 2, 0, 2, 1, 3, 0, 3, 2, 3]) numpy.testing.assert_allclose(new_yb, known_new_yb, rtol=RTOL) # User-Defined ud = UserDefined(self.data, [-20, 0, 5, 20]) known_yb = numpy.array([1, 2, 1, 1, 1, 2, 0, 2, 1, 3]) numpy.testing.assert_allclose(ud.yb, known_yb, rtol=RTOL) new_yb = ud.update(self.new_data).yb known_new_yb = numpy.array([1, 3, 1, 1, 1, 2, 1, 1, 1, 2, 0, 2, 1, 3]) numpy.testing.assert_allclose(new_yb, known_new_yb, rtol=RTOL) # Fisher-Jenks Sampled fjs = FisherJenksSampled(self.data, k=3, pct=70) known_yb = numpy.array([1, 2, 0, 1, 1, 2, 0, 2, 1, 2]) numpy.testing.assert_allclose(known_yb, fjs.yb, rtol=RTOL) new_yb = fjs.update(self.new_data, k=2).yb known_new_yb = numpy.array([0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1]) numpy.testing.assert_allclose(known_new_yb, new_yb, rtol=RTOL) class TestFindBin: def setup_method(self): self.V = load_example() def test_find_bin(self): toclass = [0, 1, 3, 5, 50, 70, 101, 202, 390, 505, 800, 5000, 5001] mc = FisherJenks(self.V, k=5) known = [0, 0, 0, 0, 0, 0, 1, 2, 3, 3, 4, 4, 4] numpy.testing.assert_array_equal(known, mc.find_bin(toclass)) mc2 = FisherJenks(self.V, k=9) known = [0, 0, 0, 0, 2, 2, 3, 5, 7, 7, 8, 8, 8] numpy.testing.assert_array_equal(known, mc2.find_bin(toclass)) class TestMake: def setup_method(self): self.data = [ numpy.linspace(-5, 5, num=5), numpy.linspace(-10, 10, num=5), numpy.linspace(-20, 20, num=5), ] self.ei = EqualInterval.make() self.q5r = Quantiles.make(k=5, rolling=True) def test_make(self): assert isinstance(self.ei, types.FunctionType) assert isinstance(self.q5r, types.FunctionType) assert hasattr(self.ei, "_options") assert self.ei._options == {} assert hasattr(self.q5r, "_options") assert self.q5r._options == {"k": 5, "rolling": True} def test_apply(self): ei_classes = [self.ei(d) for d in self.data] known = [numpy.arange(0, 5, 1)] * 3 numpy.testing.assert_allclose(known, ei_classes) q5r_classes = [self.q5r(d) for d in self.data] known = [[0, 1, 2, 3, 4], [0, 0, 2, 3, 4], [0, 0, 2, 4, 4]] accreted_data = set(self.q5r.__defaults__[0].y) all_data = set(numpy.asarray(self.data).flatten()) assert accreted_data == all_data numpy.testing.assert_allclose(known, q5r_classes) class TestBinC: def test_bin_c(self): bins = list(range(2, 8)) y = numpy.array( [ [7, 5, 6], [2, 3, 5], [7, 2, 2], [3, 6, 7], [6, 3, 4], [6, 7, 4], [6, 5, 6], [4, 6, 7], [4, 6, 3], [3, 2, 7], ] ) expected = numpy.array( [ [5, 3, 4], [0, 1, 3], [5, 0, 0], [1, 4, 5], [4, 1, 2], [4, 5, 2], [4, 3, 4], [2, 4, 5], [2, 4, 1], [1, 0, 5], ] ) numpy.testing.assert_array_equal(expected, binC(y, bins)) class TestBin: def test_bin(self): y = numpy.array( [ [7, 13, 14], [10, 11, 13], [7, 17, 2], [18, 3, 14], [9, 15, 8], [7, 13, 12], [16, 6, 11], [19, 2, 15], [11, 11, 9], [3, 2, 19], ] ) bins = [10, 15, 20] expected = numpy.array( [ [0, 1, 1], [0, 1, 1], [0, 2, 0], [2, 0, 1], [0, 1, 0], [0, 1, 1], [2, 0, 1], [2, 0, 1], [1, 1, 0], [0, 0, 2], ] ) numpy.testing.assert_array_equal(expected, bin(y, bins)) class TestBin1d: def test_bin1d(self): y = numpy.arange(100, dtype="float") bins = [25, 74, 100] bin_ids = numpy.array( [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, ] ) counts = numpy.array([26, 49, 25]) numpy.testing.assert_array_equal(bin_ids, bin1d(y, bins)[0]) numpy.testing.assert_array_equal(counts, bin1d(y, bins)[1]) class TestNaturalBreaks: def setup_method(self): self.v = load_example() def test_natural_breaks(self): # assert expected, natural_breaks(values, k, itmax)) assert True # TODO: implement your test here def test_NaturalBreaks(self): nb = NaturalBreaks(self.v, 5) assert nb.k == 5 assert len(nb.counts) == 5 numpy.testing.assert_array_almost_equal( nb.counts, numpy.array([49, 3, 4, 1, 1]) ) def test_NaturalBreaks_stability(self): for _ in range(10): nb = NaturalBreaks(self.v, 5) assert nb.k == 5 assert len(nb.counts) == 5 def test_NaturalBreaks_randomData(self): for i in range(10): v = numpy.random.random(50) * (i + 1) nb = NaturalBreaks(v, 5) assert nb.k == 5 assert len(nb.counts) == 5 class TestHeadTailBreaks: def setup_method(self): x = list(range(1, 1000)) y = [] for i in x: y.append(i ** (-2)) self.v = numpy.array(y) def test_HeadTailBreaks(self): htb = HeadTailBreaks(self.v) assert htb.k == 4 assert len(htb.counts) == 4 numpy.testing.assert_array_almost_equal( htb.counts, numpy.array([975, 21, 2, 1]) ) def test_HeadTailBreaks_doublemax(self): v = numpy.append(self.v, self.v.max()) htb = HeadTailBreaks(v) assert htb.k == 4 assert len(htb.counts) == 4 numpy.testing.assert_array_almost_equal( htb.counts, numpy.array([980, 17, 1, 2]) ) def test_HeadTailBreaks_float(self): v = numpy.array([1 + 2**-52, 1, 1]) htb = HeadTailBreaks(v) assert htb.k == 2 assert len(htb.counts) == 2 numpy.testing.assert_array_almost_equal(htb.counts, numpy.array([2, 1])) class TestPrettyBreaks: def setup_method(self): self.V = load_example() def test_pretty(self): res = PrettyBreaks(self.V) assert res.k == 5 numpy.testing.assert_array_equal(res.counts, [57, 0, 0, 0, 1]) numpy.testing.assert_array_equal(res.bins, list(range(1000, 6000, 1000))) class TestMapClassifier: def test_Map_Classifier(self): # map__classifier = Map_Classifier(y) assert True # TODO: implement your test here def test___repr__(self): # map__classifier = Map_Classifier(y) # assert expected, map__classifier.__repr__()) assert True # TODO: implement your test here def test___str__(self): # map__classifier = Map_Classifier(y) # assert expected, map__classifier.__str__()) assert True # TODO: implement your test here def test_get_adcm(self): # map__classifier = Map_Classifier(y) # assert expected, map__classifier.get_adcm()) assert True # TODO: implement your test here def test_get_gadf(self): # map__classifier = Map_Classifier(y) # assert expected, map__classifier.get_gadf()) assert True # TODO: implement your test here def test_get_tss(self): # map__classifier = Map_Classifier(y) # assert expected, map__classifier.get_tss()) assert True # TODO: implement your test here class TestEqualInterval: def setup_method(self): self.V = load_example() def test_EqualInterval(self): ei = EqualInterval(self.V) numpy.testing.assert_array_almost_equal( ei.counts, numpy.array([57, 0, 0, 0, 1]) ) numpy.testing.assert_array_almost_equal( ei.bins, numpy.array([822.394, 1644.658, 2466.922, 3289.186, 4111.45]) ) with pytest.raises( ValueError, match="Not enough unique values in array to form 5 classes." ): EqualInterval(numpy.array([1, 1, 1, 1])) class TestPercentiles: def setup_method(self): self.V = load_example() def test_Percentiles(self): pc = Percentiles(self.V) numpy.testing.assert_array_almost_equal( pc.bins, numpy.array( [ 1.35700000e-01, 5.53000000e-01, 9.36500000e00, 2.13914000e02, 2.17994800e03, 4.11145000e03, ] ), ) numpy.testing.assert_array_almost_equal( pc.counts, numpy.array([1, 5, 23, 23, 5, 1]) ) class TestBoxPlot: def setup_method(self): self.V = load_example() def test_BoxPlot(self): bp = BoxPlot(self.V) bins = numpy.array( [ -5.28762500e01, 2.56750000e00, 9.36500000e00, 3.95300000e01, 9.49737500e01, 4.11145000e03, ] ) numpy.testing.assert_array_almost_equal(bp.bins, bins) class TestQuantiles: def setup_method(self): self.V = load_example() def test_Quantiles(self): q = Quantiles(self.V, k=5) numpy.testing.assert_array_almost_equal( q.bins, numpy.array( [ 1.46400000e00, 5.79800000e00, 1.32780000e01, 5.46160000e01, 4.11145000e03, ] ), ) numpy.testing.assert_array_almost_equal( q.counts, numpy.array([12, 11, 12, 11, 12]) ) class TestStdMean: def setup_method(self): self.V = load_example() def test_StdMean(self): std_mean = StdMean(self.V) numpy.testing.assert_array_almost_equal( std_mean.bins, numpy.array( [-967.36235382, -420.71712519, 672.57333208, 1219.21856072, 4111.45] ), ) numpy.testing.assert_array_almost_equal( std_mean.counts, numpy.array([0, 0, 56, 1, 1]) ) class TestMaximumBreaks: def setup_method(self): self.V = load_example() def test_MaximumBreaks(self): mb = MaximumBreaks(self.V, k=5) assert mb.k == 5 numpy.testing.assert_array_almost_equal( mb.bins, numpy.array([146.005, 228.49, 546.675, 2417.15, 4111.45]) ) numpy.testing.assert_array_almost_equal( mb.counts, numpy.array([50, 2, 4, 1, 1]) ) with pytest.raises( ValueError, match="Not enough unique values in array to form 5 classes." ): MaximumBreaks(numpy.array([1, 1, 1, 1])) class TestFisherJenks: def setup_method(self): self.V = load_example() def test_FisherJenks(self): fj = FisherJenks(self.V) assert fj.adcm == 799.24000000000001 numpy.testing.assert_array_almost_equal( fj.bins, numpy.array([75.29, 192.05, 370.5, 722.85, 4111.45]) ) numpy.testing.assert_array_almost_equal( fj.counts, numpy.array([49, 3, 4, 1, 1]) ) class TestJenksCaspall: def setup_method(self): self.V = load_example() def test_JenksCaspall(self): numpy.random.seed(10) jc = JenksCaspall(self.V, k=5) numpy.testing.assert_array_almost_equal( jc.counts, numpy.array([14, 13, 14, 10, 7]) ) numpy.testing.assert_array_almost_equal( jc.bins, numpy.array( [ 1.81000000e00, 7.60000000e00, 2.98200000e01, 1.81270000e02, 4.11145000e03, ] ), ) class TestJenksCaspallSampled: def setup_method(self): self.V = load_example() def test_JenksCaspallSampled(self): numpy.random.seed(100) x = numpy.random.random(100000) jc = JenksCaspall(x) jcs = JenksCaspallSampled(x) numpy.testing.assert_array_almost_equal( jc.bins, numpy.array([0.19718393, 0.39655886, 0.59648522, 0.79780763, 0.99997979]), ) numpy.testing.assert_array_almost_equal( jcs.bins, numpy.array([0.20856569, 0.41513931, 0.62457691, 0.82561423, 0.99997979]), ) class TestJenksCaspallForced: def setup_method(self): self.V = load_example() def test_JenksCaspallForced(self): numpy.random.seed(100) jcf = JenksCaspallForced(self.V, k=5) numpy.testing.assert_array_almost_equal( jcf.bins, numpy.array( [ 1.34000000e00, 5.90000000e00, 1.67000000e01, 5.06500000e01, 4.11145000e03, ] ), ) numpy.testing.assert_array_almost_equal( jcf.counts, numpy.array([12, 12, 13, 9, 12]) ) with pytest.raises( ValueError, match="Not enough unique values in array to form 5 classes." ): JenksCaspallForced(numpy.array([1, 1, 1, 1])) class TestUserDefined: def setup_method(self): self.V = load_example() def test_UserDefined(self): bins = [20, max(self.V)] ud = UserDefined(self.V, bins) numpy.testing.assert_array_almost_equal(ud.bins, numpy.array([20.0, 4111.45])) numpy.testing.assert_array_almost_equal(ud.counts, numpy.array([37, 21])) def test_UserDefined_max(self): bins = numpy.array([20, 30]) ud = UserDefined(self.V, bins) numpy.testing.assert_array_almost_equal( ud.bins, numpy.array([20.0, 30.0, 4111.45]) ) numpy.testing.assert_array_almost_equal(ud.counts, numpy.array([37, 4, 17])) def test_UserDefined_invariant(self): bins = [10, 20, 30, 40] ud = UserDefined(numpy.array([12, 12, 12]), bins) numpy.testing.assert_array_almost_equal(ud.bins, numpy.array([10, 20, 30, 40])) numpy.testing.assert_array_almost_equal(ud.counts, numpy.array([0, 3, 0, 0])) def test_UserDefined_lowest(self): bins = [20, max(self.V)] ud = UserDefined(self.V, bins, lowest=-1.0) numpy.testing.assert_array_almost_equal(ud.bins, numpy.array([20.0, 4111.45])) numpy.testing.assert_array_almost_equal(ud.counts, numpy.array([37, 21])) classes = ["[ -1.00, 20.00]", "( 20.00, 4111.45]"] assert ud.get_legend_classes() == classes class TestStdMeanAnchor: def setup_method(self): self.V = load_example() def test_StdMeanAnchor(self): sm = StdMean(self.V, anchor=True) bins = numpy.array( [ 125.92810345, 672.57333208, 1219.21856072, 1765.86378936, 2312.50901799, 2859.15424663, 3405.79947527, 3952.4447039, 4111.45, ] ) counts = numpy.array([50, 6, 1, 0, 0, 0, 0, 0, 1]) numpy.testing.assert_array_almost_equal(sm.bins, bins) numpy.testing.assert_array_almost_equal(sm.counts, counts) class TestMaxP: def setup_method(self): self.V = load_example() def test_MaxP(self): numpy.random.seed(100) mp = MaxP(self.V) numpy.testing.assert_array_almost_equal( mp.bins, numpy.array([3.16000e00, 1.26300e01, 1.67000e01, 2.04700e01, 4.11145e03]), ) numpy.testing.assert_array_almost_equal( mp.counts, numpy.array([18, 16, 3, 1, 20]) ) with pytest.raises( ValueError, match="Not enough unique values in array to form 5 classes." ): MaxP(numpy.array([1, 1, 1, 1])) class TestGadf: def setup_method(self): self.V = load_example() def test_gadf(self): qgadf = gadf(self.V) assert qgadf[0] == 15 assert qgadf[-1] == 0.37402575909092828 class TestKClassifiers: def setup_method(self): self.V = load_example() def test_K_classifiers(self): numpy.random.seed(100) ks = KClassifiers(self.V) assert ks.best.name == "FisherJenks" assert ks.best.gadf == 0.84810327199081048 assert ks.best.k == 4 class TestPooled: def setup_method(self): n = 20 self.data = numpy.array([numpy.arange(n) + i * n for i in range(1, 4)]).T def test_pooled(self): res = Pooled(self.data, k=4) assert res.k == 4 numpy.testing.assert_array_almost_equal( res.col_classifiers[0].counts, numpy.array([15, 5, 0, 0]) ) numpy.testing.assert_array_almost_equal( res.col_classifiers[-1].counts, numpy.array([0, 0, 5, 15]) ) numpy.testing.assert_array_almost_equal( res.global_classifier.counts, numpy.array([15, 15, 15, 15]) ) res = Pooled(self.data, classifier="BoxPlot", hinge=1.5) numpy.testing.assert_array_almost_equal( res.col_classifiers[0].bins, numpy.array([-9.5, 34.75, 49.5, 64.25, 108.5]) ) def test_pooled_bad_classifier(self): classifier = "Larry David" message = f"'{classifier}' not a valid classifier." with pytest.raises(ValueError, match=message): Pooled(self.data, classifier=classifier, k=4) class TestPlots: def setup_method(self): n = 20 self.data = numpy.array([numpy.arange(n) + i * n for i in range(1, 4)]).T ################################################################################ # # * baseline images are initially generated in top directory `result_images/` # * they must be moved to the appropriate location within `mapclassify/tests/` # ################################################################################ @image_comparison(["test_histogram_plot"], **pytest.image_comp_kws) def test_histogram_plot(self): ax = Quantiles(self.data).plot_histogram() return ax.get_figure() @image_comparison(["test_histogram_plot_despine"], **pytest.image_comp_kws) def test_histogram_plot_despine(self): ax = Quantiles(self.data).plot_histogram(despine=False) return ax.get_figure() @image_comparison(["test_histogram_plot_linewidth"], **pytest.image_comp_kws) def test_histogram_plot_linewidth(self): ax = Quantiles(self.data).plot_histogram( linewidth=3, linecolor="red", color="yellow" ) return ax.get_figure() mapclassify-2.10.0/mapclassify/tests/test_rgba.py000066400000000000000000000030511503430131600221010ustar00rootroot00000000000000import geopandas import numpy as np import pytest from numpy.testing import assert_array_equal from mapclassify.util import get_color_array @pytest.mark.filterwarnings("ignore:Geometry is in a geographic CRS.") class TestRGBA: def setup_method(self): world = geopandas.read_file( "https://naciscdn.org/naturalearth/110m" "/cultural/ne_110m_admin_0_countries.zip" ) world["area"] = world.area # columns are equivalent except for nan in the first position world["nanarea"] = world.area world.loc[0, "nanarea"] = np.nan self.world = world def test_rgba(self): colors = get_color_array(self.world["area"], cmap="viridis")[-1] assert_array_equal(colors, np.array([94, 201, 97, 255])) def test_rgba_hex(self): colors = get_color_array(self.world["area"], cmap="viridis", as_hex=True)[-1] assert_array_equal(colors, "#5ec961") def test_rgba_nan(self): colors = get_color_array( self.world["nanarea"], cmap="viridis", nan_color=[0, 0, 0, 0] ) # should be nan_color assert_array_equal(colors[0], np.array([0, 0, 0, 0])) # still a cmap color assert_array_equal(colors[-1], np.array([94, 201, 97, 255])) def test_rgba_nan_hex(self): colors = get_color_array( self.world["nanarea"], cmap="viridis", nan_color=[0, 0, 0, 0], as_hex=True ) assert_array_equal(colors[0], np.array(["#000000"])) assert_array_equal(colors[-1], np.array(["#5ec961"])) mapclassify-2.10.0/mapclassify/tests/test_value_by_alpha.py000066400000000000000000000111341503430131600241420ustar00rootroot00000000000000import geopandas import libpysal import matplotlib import matplotlib.pyplot as plt import pytest from matplotlib.testing.decorators import image_comparison from mapclassify import shift_colormap, truncate_colormap, vba_choropleth class TestValueByAlphaChoropleth: def setup_method(self): self.gdf = geopandas.read_file(libpysal.examples.get_path("columbus.shp")) self.x = "HOVAL" self.y = "CRIME" @image_comparison(["no_classify_default"], **pytest.image_comp_kws) def test_no_classify_default(self): fig, ax = vba_choropleth(self.x, self.y, self.gdf) assert isinstance(fig, matplotlib.figure.Figure) assert isinstance(ax, matplotlib.axes.Axes) @image_comparison(["pass_in_ax"], **pytest.image_comp_kws) def test_pass_in_ax(self): fig = plt.figure() ax = fig.add_subplot(111) fig, ax = vba_choropleth(self.x, self.y, self.gdf, ax=ax) assert isinstance(fig, matplotlib.figure.Figure) assert isinstance(ax, matplotlib.axes.Axes) @image_comparison(["classify_xy_redblue"], **pytest.image_comp_kws) def test_classify_xy_redblue(self): fig, ax = vba_choropleth( self.x, self.y, self.gdf, x_classification_kwds={"classifier": "quantiles"}, y_classification_kwds={"classifier": "quantiles"}, cmap="RdBu", ) assert isinstance(fig, matplotlib.figure.Figure) assert isinstance(ax, matplotlib.axes.Axes) @image_comparison(["divergent_revert_alpha_min_alpha"], **pytest.image_comp_kws) def test_divergent_revert_alpha_min_alpha(self): fig, ax = vba_choropleth( self.x, self.y, self.gdf, x_classification_kwds={"classifier": "fisher_jenks", "k": 3}, y_classification_kwds={"classifier": "fisher_jenks", "k": 3}, cmap="berlin", divergent=True, revert_alpha=True, min_alpha=0.5, ) assert isinstance(fig, matplotlib.figure.Figure) assert isinstance(ax, matplotlib.axes.Axes) @image_comparison(["userdefined_colors"], **pytest.image_comp_kws) def test_userdefined_colors(self): color_list = ["#a1dab4", "#41b6c4", "#225ea8"] fig, ax = vba_choropleth(self.x, self.y, self.gdf, cmap=color_list) assert isinstance(fig, matplotlib.figure.Figure) assert isinstance(ax, matplotlib.axes.Axes) @image_comparison(["shifted_colormap"], **pytest.image_comp_kws) def test_shifted_colormap(self): mid08 = shift_colormap("RdBu", midpoint=0.8, name="RdBu_08") assert mid08.name == "RdBu_08" fig, ax = vba_choropleth(self.x, self.y, self.gdf, cmap=mid08) assert isinstance(fig, matplotlib.figure.Figure) assert isinstance(ax, matplotlib.axes.Axes) @image_comparison(["truncated_colormap"], **pytest.image_comp_kws) def test_truncated_colormap(self): trunc0206 = truncate_colormap("RdBu", minval=0.2, maxval=0.6, n=2) assert trunc0206.name == "trunc(RdBu,0.20,0.60)" fig, ax = vba_choropleth(self.x, self.y, self.gdf, cmap=trunc0206) assert isinstance(fig, matplotlib.figure.Figure) assert isinstance(ax, matplotlib.axes.Axes) def test_legend_error(self): with pytest.raises( ValueError, match=( "Plotting a legend requires classification for both the `x` and `y` " "variables. See `x_classification_kwds` and `y_classification_kwds`." ), ): vba_choropleth(self.x, self.y, self.gdf, legend=True) @image_comparison(["legend"], **pytest.image_comp_kws_legend_text) def test_legend(self): fig, ax = vba_choropleth( self.x, self.y, self.gdf, x_classification_kwds={"classifier": "quantiles"}, y_classification_kwds={"classifier": "quantiles"}, legend=True, ) assert isinstance(fig, matplotlib.figure.Figure) assert isinstance(ax, matplotlib.axes.Axes) @image_comparison(["legend_kwargs"], **pytest.image_comp_kws_legend_text) def test_legend_kwargs(self): fig, ax = vba_choropleth( self.x, self.y, self.gdf, x_classification_kwds={"classifier": "quantiles"}, y_classification_kwds={"classifier": "quantiles"}, legend=True, legend_kwargs={"x_label": self.x, "y_label": self.y}, ) assert isinstance(fig, matplotlib.figure.Figure) assert isinstance(ax, matplotlib.axes.Axes) mapclassify-2.10.0/mapclassify/util.py000066400000000000000000000062751503430131600177550ustar00rootroot00000000000000import numpy as np from ._classify_API import classify as _classify def get_color_array( values, scheme="quantiles", cmap="viridis", alpha=1, nan_color=[255, 255, 255, 255], as_hex=False, **kwargs, ): """Convert array of values into RGBA or hex colors using a colormap and classifier. This function is useful for visualization libraries that require users to provide an array of colors for each object (like pydeck or lonboard) but can also be used to create a manual column of colors passed to matplotlib. Parameters ---------- values : list-like array of input values scheme : str, optional string description of a mapclassify classifier, by default `"quantiles"` cmap : str, optional name of matplotlib colormap to use, by default "viridis" alpha : float alpha parameter that defines transparency. Should be in the range [0,1] nan_color : list, optional RGBA color to fill NaN values, by default [255, 255, 255, 255] as_hex: bool, optional if True, return a (n,1)-dimensional array of hexcolors instead of a (n,4) dimensional array of RGBA values. kwargs : dict additional keyword arguments are passed to `mapclassify.classify` Returns ------- numpy.array numpy array (aligned with the input array) defining a color for each row. If `as_hex` is False, the array is :math:`(n,4)` holding an array of RGBA values in each row. If `as_hex` is True, the array is :math:`(n,1)` holding a hexcolor in each row. """ try: import pandas as pd from matplotlib import colormaps from matplotlib.colors import Normalize, to_hex except ImportError as e: raise ImportError("This function requires pandas and matplotlib") from e if not (alpha <= 1) and (alpha >= 0): raise ValueError("alpha must be in the range [0,1]") if not pd.api.types.is_list_like(nan_color) and not len(nan_color) == 4: raise ValueError("`nan_color` must be list-like of 4 values: (R,G,B,A)") # only operate on non-NaN values v = pd.Series(values, dtype=object) legit_indices = v[~v.isna()].index.values legit_vals = v.dropna().values bogus_indices = v[v.isna()].index.values # stash these for use later # transform (non-NaN) values into class bins bins = _classify(legit_vals, scheme=scheme, **kwargs).yb # normalize using the data's range (not strictly 1-k if classifier is degenerate) norm = Normalize(min(bins), max(bins)) normalized_vals = norm(bins) # generate RBGA array and convert to series rgbas = colormaps[cmap](normalized_vals, bytes=True, alpha=alpha) colors = pd.Series(list(rgbas), index=legit_indices).apply(np.array) nan_colors = pd.Series( [nan_color for i in range(len(bogus_indices))], index=bogus_indices ).apply(lambda x: np.array(x).astype(np.uint8)) # put colors in their correct places and fill empty with specified color v.update(colors) v.update(nan_colors) # convert to hexcolors if preferred if as_hex: colors = v.apply(lambda x: to_hex(x / 255.0)) return colors.values return np.stack(v.values) mapclassify-2.10.0/mapclassify/value_by_alpha.py000066400000000000000000000376271503430131600217600ustar00rootroot00000000000000""" functionality originally written by Stefanie Lumnitz as part of the ``splot`` package. TODO: * add Choropleth functionality with one input variable * merge all alpha keywords in one keyword dictionary for vba_choropleth """ import collections.abc import numpy as np from ._classify_API import classify from .classifiers import _format_intervals try: import matplotlib.pyplot as plt from matplotlib import colormaps as cm from matplotlib import colors, patches MPL_AVAILABLE = True except (ImportError, ModuleNotFoundError): MPL_AVAILABLE = False MPL_NOT_AVAILABLE = ImportError("you must have matplotlib") __author__ = "Stefanie Lumnitz " ############################################################ # # stuff from ``splot/_viz_value_by_alpha_mpl.py # ############################################################ def vba_choropleth( x, y, gdf, *, cmap="GnBu", x_classification_kwds=None, y_classification_kwds=None, divergent=False, revert_alpha=False, ax=None, legend=False, legend_kwargs=None, min_alpha=0.2, ): """Generate Value by Alpha Choropleth plots. A Value-by-Alpha Choropleth is a bivariate choropleth that uses the values of the second input variable ``y`` as a transparency mask, determining how much of the choropleth displaying the values of a first variable ``x`` is shown. Parameters ---------- x : str | numpy.ndarray | pandas.Series The color determining variable. It can be passed is as the name of the column in ``gdf`` or the actual values. y : str | numpy.ndarray | pandas.Series The alpha determining variable. It can be passed is as the name of the column in ``gdf`` or the actual values. gdf : geopandas dataframe instance The dataframe containing information to plot. cmap : str | list[str] | matplotlib.colors.Colormap Matplotlib colormap or list of colors used to create the vba_layer x_classification_kwds : dict Keywords used for binning and classifying color-determinant input values with ``mapclassify.classify``. y_classification_kwds : dict Keywords used for binning and classifying alpha-determinant input values with ``mapclassify.classify``. divergent : bool, optional Creates a divergent alpha array with high values at the extremes and low, transparent values in the middle of the input values. revert_alpha : bool, optional If ``True``, high ``y`` values will have a low alpha and low values will be transparent. Default is False. ax : matplotlib Axes instance, optional Axes in which to plot the figure in multiple Axes layout. Default is None. legend : bool, optional Adds a legend. Currently, only available if data is classified (i.e., if ``alpha`` and ``rgb`` are used). legend_kwargs : dict, optional Keyword arguments for the legend. min_alpha : float = 0.2 Minimum alpha threshold to prevent fully transparent masking. Returns ------- fig : matplotlip Figure instance Figure of Value by Alpha choropleth ax : matplotlib Axes instance Axes in which the figure is plotted Examples -------- >>> from libpysal import examples >>> import geopandas as gpd >>> import matplotlib.pyplot as plt >>> import numpy as np >>> from mapclassify import vba_choropleth Load Example Data >>> link_to_data = examples.get_path('columbus.shp') >>> gdf = gpd.read_file(link_to_data) Plot a Value-by-Alpha map >>> fig, _ = vba_choropleth('HOVAL', 'CRIME', gdf) >>> plt.show() # doctest: +SKIP >>> plt.close() Plot a Value-by-Alpha map with reverted alpha values >>> fig, _ = vba_choropleth('HOVAL', 'CRIME', gdf, cmap='RdBu', ... revert_alpha=True) >>> plt.show() # doctest: +SKIP >>> plt.close() Plot a Value-by-Alpha map with classified alpha and rgb values >>> fig, axs = plt.subplots(2,2, figsize=(20,10)) >>> vba_choropleth('HOVAL', 'CRIME', gdf, cmap='viridis', ax = axs[0,0], ... x_classification_kwds=dict(classifier='quantiles', k=3), ... y_classification_kwds=dict(classifier='quantiles', k=3)) # doctest: +SKIP >>> vba_choropleth('HOVAL', 'CRIME', gdf, cmap='viridis', ax = axs[0,1], ... x_classification_kwds=dict(classifier='natural_breaks'), ... y_classification_kwds=dict(classifier='natural_breaks')) # doctest: +SKIP >>> vba_choropleth('HOVAL', 'CRIME', gdf, cmap='viridis', ax = axs[1,0], ... x_classification_kwds=dict(classifier='std_mean'), ... y_classification_kwds=dict(classifier='std_mean')) # doctest: +SKIP >>> vba_choropleth('HOVAL', 'CRIME', gdf, cmap='viridis', ax = axs[1,1], ... x_classification_kwds=dict(classifier='fisher_jenks', k=3), ... y_classification_kwds=dict(classifier='fisher_jenks', k=3)) # doctest: +SKIP >>> plt.show() # doctest: +SKIP >>> plt.close() Pass in a list of colors instead of a cmap >>> color_list = ['#a1dab4','#41b6c4','#225ea8'] >>> vba_choropleth('HOVAL', 'CRIME', gdf, cmap=color_list, ... x_classification_kwds=dict(classifier='quantiles', k=3), ... y_classification_kwds=dict(classifier='quantiles')) # doctest: +SKIP >>> plt.show() # doctest: +SKIP >>> plt.close() Add a legend and use divergent alpha values >>> fig = plt.figure(figsize=(15,10)) >>> ax = fig.add_subplot(111) >>> vba_choropleth('HOVAL', 'CRIME', gdf, divergent=True, ... x_classification_kwds=dict(classifier='quantiles', k=5), ... y_classification_kwds=dict(classifier='quantiles', k=5), ... legend=True, ax=ax, ... legend_kwargs={"alpha_label": "CRIME", "rgb_label": "HOVAL"}) # doctest: +SKIP >>> plt.show() # doctest: +SKIP >>> plt.close() """ # noqa: E501 if not MPL_AVAILABLE: raise MPL_NOT_AVAILABLE if legend and (not x_classification_kwds or not y_classification_kwds): raise ValueError( "Plotting a legend requires classification for both the `x` and `y` " "variables. See `x_classification_kwds` and `y_classification_kwds`." ) x = gdf[x].to_numpy() if isinstance(x, str) else x y = gdf[y].to_numpy() if isinstance(y, str) else y if ax is None: fig = plt.figure() ax = fig.add_subplot(111) else: fig = ax.get_figure() if x_classification_kwds is not None: classifier = x_classification_kwds.pop("classifier") x_bins = classify(x, classifier, **x_classification_kwds) x = x_bins.yb if y_classification_kwds is not None: classifier = y_classification_kwds.pop("classifier") # TODO: use the pct keyword here y_bins = classify(y, classifier, **y_classification_kwds) y = y_bins.yb rgba, vba_cmap = _value_by_alpha_cmap( x, y, cmap=cmap, divergent=divergent, revert_alpha=revert_alpha, min_alpha=min_alpha, ) gdf.plot(color=rgba, ax=ax) if legend: left, bottom, width, height = [0, 0.5, 0.2, 0.2] ax2 = fig.add_axes([left, bottom, width, height]) if legend_kwargs: legend_kwargs |= {"ax": ax2} else: legend_kwargs = {"ax": ax2} legend_kwargs |= {"min_alpha": min_alpha} _vba_legend(x_bins, y_bins, vba_cmap, **legend_kwargs) return fig, ax def _value_by_alpha_cmap( x, y, *, cmap="GnBu", divergent=False, revert_alpha=False, min_alpha=0.2, ): """Calculates Value by Alpha rgba values Parameters ---------- x : array The color determining variable values. y : array The alpha determining variable values. cmap : str | list[str] | matplotlib.colors.Colormap Matplotlib colormap or list of colors used to create the vba_layer divergent : bool, optional Creates a divergent alpha array with high values at the extremes and low, transparent values in the middle of the input values. revert_alpha : bool, optional If ``True``, high ``y`` values will have a low alpha and low values will be transparent. Default is False. min_alpha : float = 0.2 Minimum alpha threshold to prevent fully transparent masking. Returns ------- rgba : ndarray (n,4) RGBA colormap, where the alpha channel represents one attribute (x) and the rgb color the other attribute (y) cmap : matplotlib.colors.Colormap Colormap for the VBA layer """ if not MPL_AVAILABLE: raise MPL_NOT_AVAILABLE # option for cmap or colorlist input if isinstance(cmap, str): cmap = cm.get_cmap(cmap) elif isinstance(cmap, collections.abc.Sequence): cmap = colors.LinearSegmentedColormap.from_list("newmap", cmap) # normalize rgb (x) and alpha (y) x_norm = (x - x.min()) / (x.max() - x.min()) y_norm = min_alpha + ((y - y.min()) / (y.max() - y.min()) * (1 - min_alpha)) rgba = cmap(x_norm) if revert_alpha: rgba[:, 3] = 1 - y_norm else: rgba[:, 3] = y_norm if divergent is not False: a_under_0p5 = rgba[:, 3] < 0.5 rgba[a_under_0p5, 3] = 1 - rgba[a_under_0p5, 3] rgba[:, 3] = (rgba[:, 3] - 0.5) * 2 return rgba, cmap def _vba_legend( x_bins, y_bins, cmap, *, ax=None, x_label=None, y_label=None, min_alpha=0.2, ): """Creates Value by Alpha heatmap used as choropleth legend. Parameters ---------- x_bins : MapClassifier Object of classified values used for color. y_bins : MapClassifier Object of classified values used for alpha. cmap : matplotlib.colors.Colormap Colormap for the VBA layer ax : matplotlib Axes instance, optional Axes in which to plot the figure in multiple Axes layout. Default is None x_label : str, optional Label for the x-axis; the color variable. y_label : str, optional Label for the y-axis; the alpha variable. min_alpha : float = 0.2 Minimum alpha threshold to prevent fully transparent masking. Returns ------- fig : matplotlip Figure instance Figure of Value by Alpha heatmap ax : matplotlib Axes instance Axes in which the figure is plotted """ if not MPL_AVAILABLE: raise MPL_NOT_AVAILABLE # VALUES rgba, legend_cmap = _value_by_alpha_cmap( x_bins.yb, y_bins.yb, cmap=cmap, min_alpha=min_alpha ) # separate rgb and alpha values alpha = rgba[:, 3] # extract unique values for alpha and rgb alpha_vals = np.unique(alpha) rgb_vals = legend_cmap( (x_bins.bins - x_bins.bins.min()) / (x_bins.bins.max() - x_bins.bins.min()) )[:, 0:3] # PLOTTING if ax is None: fig = plt.figure() ax = fig.add_subplot(111) else: fig = ax.get_figure() for irow, alpha_val in enumerate(alpha_vals): for icol, rgb_val in enumerate(rgb_vals): rect = patches.Rectangle( (irow, icol), 1, 1, linewidth=3, edgecolor="none", facecolor=rgb_val, alpha=alpha_val, ) ax.add_patch(rect) values_rgb, _, x_in_thousand = _format_intervals(x_bins, fmt="{:.1f}") values_alpha, _, y_in_thousand = _format_intervals(y_bins, fmt="{:.1f}") ax.plot([], []) ax.set_xlim([0, irow + 1]) ax.set_ylim([0, icol + 1]) ax.set_xticks(np.arange(irow + 1) + 0.5) ax.set_yticks(np.arange(icol + 1) + 0.5) ax.set_xticklabels( [f"$<${val}" for val in values_rgb[1:]], rotation=30, horizontalalignment="right", ) ax.set_yticklabels([f"$<${val}" for val in values_alpha[1:]]) x_label = x_label if x_label else "x-rgb variable" y_label = y_label if y_label else "y-alpha variable" if x_in_thousand: ax.set_xlabel(f"{x_label} ($10^3$)") if y_in_thousand: ax.set_ylabel(f"{y_label} ($10^3$)") else: ax.set_xlabel(x_label) ax.set_ylabel(y_label) ax.spines["left"].set_visible(False) ax.spines["right"].set_visible(False) ax.spines["bottom"].set_visible(False) ax.spines["top"].set_visible(False) return fig, ax ############################################################ # # stuff from ``splot/_viz_utils.py # ############################################################ # Utility function #1 - forces continuous diverging colormap to be centered at zero def shift_colormap(cmap, *, start=0, midpoint=0.5, stop=1.0, name="shiftedcmap"): """Offset the "center" of a colormap. Useful for data with a negative min and positive max and you want the middle of the colormap's dynamic range to be at zero. Parameters ---------- cmap : str or matplotlib.colors.Colormap Colormap to be altered start : float, optional Offset from lowest point in the colormap's range. Should be between 0.0 and ``midpoint``. Default is 0.0 (no lower ofset). midpoint : float, optional The new center of the colormap. Should be between 0.0 and 1.0. In general, this should be ``1 - vmax/(vmax + abs(vmin))``. For example, if your data range from -15.0 to +5.0 and you want the center of the colormap at 0.0, ``midpoint`` should be set to 1 - 5/(5 + 15)) or 0.75. Default is 0.5 (no shift). stop : float, optional Offset from highest point in the colormap's range. Should be between ``midpoint`` and 1.0. Default is 1.0 (no upper ofset). name : str, optional Name of the new colormap. Returns ------- new_cmap : matplotlib.colors.Colormap A new colormap that has been shifted. """ if not MPL_AVAILABLE: raise MPL_NOT_AVAILABLE if isinstance(cmap, str): cmap = cm.get_cmap(cmap) cdict = {"red": [], "green": [], "blue": [], "alpha": []} # regular index to compute the colors reg_index = np.linspace(start, stop, 257) # shifted index to match the data shift_index = np.hstack( [ np.linspace(0.0, midpoint, 128, endpoint=False), np.linspace(midpoint, 1.0, 129, endpoint=True), ] ) for ri, si in zip(reg_index, shift_index, strict=True): r, g, b, a = cmap(ri) cdict["red"].append((si, r, r)) cdict["green"].append((si, g, g)) cdict["blue"].append((si, b, b)) cdict["alpha"].append((si, a, a)) new_cmap = colors.LinearSegmentedColormap(name, cdict) cm.register(new_cmap) return new_cmap # Utility #2 - truncate colorcap in order to grab only positive or negative portion def truncate_colormap(cmap, *, minval=0.0, maxval=1.0, n=100): """Truncate a colormap by selecting a subset of the original colormap's values. Parameters ---------- cmap : str or matplotlib.colors.Colormap Colormap to be altered minval : float, optional Minimum value of the original colormap to include in the truncated colormap. Default is 0.0. maxval : float, optional Maximum value of the original colormap to include in the truncated colormap. Default is 1.0. n : int, optional Number of intervals between the min and max values for the gradient of the truncated colormap. Default is 100. Returns ------- new_cmap : matplotlib.colors.Colormap A new colormap that has been shifted. """ if not MPL_AVAILABLE: raise MPL_NOT_AVAILABLE if isinstance(cmap, str): cmap = cm.get_cmap(cmap) new_cmap = colors.LinearSegmentedColormap.from_list( f"trunc({cmap.name},{minval:.2f},{maxval:.2f})", cmap(np.linspace(minval, maxval, n)), ) return new_cmap mapclassify-2.10.0/notebooks/000077500000000000000000000000001503430131600161045ustar00rootroot00000000000000mapclassify-2.10.0/notebooks/01_maximum_breaks.ipynb000066400000000000000000000514731503430131600224650ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction to mapclassify\n", "\n", "`mapclassify` implements a family of classification schemes for choropleth maps. \n", "Its focus is on the determination of the number of classes, and the assignment of observations to those classes.\n", "It is intended for use with upstream mapping and geovisualization packages (see [geopandas](https://geopandas.org/mapping.html) and [geoplot](https://residentmario.github.io/geoplot/user_guide/Customizing_Plots.html) for examples) that handle the rendering of the maps.\n", "\n", "In this notebook, the basic functionality of mapclassify is presented." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T16:51:55.127728Z", "start_time": "2022-11-04T16:51:54.017906Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:08:50.298194Z", "iopub.status.busy": "2025-07-11T20:08:50.297864Z", "iopub.status.idle": "2025-07-11T20:08:51.743169Z", "shell.execute_reply": "2025-07-11T20:08:51.742941Z", "shell.execute_reply.started": "2025-07-11T20:08:50.298170Z" } }, "outputs": [ { "data": { "text/plain": [ "'2.9.1.dev9+gde74d6f.d20250614'" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import mapclassify as mc\n", "\n", "mc.__version__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example data\n", "`mapclassify` contains a built-in dataset for employment density for the 58 California counties." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T16:51:55.397263Z", "start_time": "2022-11-04T16:51:55.130764Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:08:51.743710Z", "iopub.status.busy": "2025-07-11T20:08:51.743588Z", "iopub.status.idle": "2025-07-11T20:08:51.753079Z", "shell.execute_reply": "2025-07-11T20:08:51.752855Z", "shell.execute_reply.started": "2025-07-11T20:08:51.743702Z" } }, "outputs": [], "source": [ "y = mc.load_example()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Basic Functionality\n", "All classifiers in `mapclassify` have a common interface and afford similar functionality. We illustrate these using the `MaximumBreaks` classifier.\n", "\n", "`MaximumBreaks` requires that the user specify the number of classes `k`. Given this, the logic of the classifier is to sort the observations in ascending order and find the difference between rank adjacent values. The class boundaries are defined as the $k-1$ largest rank-adjacent breaks in the sorted values." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T16:51:55.407290Z", "start_time": "2022-11-04T16:51:55.401874Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:08:51.753495Z", "iopub.status.busy": "2025-07-11T20:08:51.753407Z", "iopub.status.idle": "2025-07-11T20:08:51.755663Z", "shell.execute_reply": "2025-07-11T20:08:51.755478Z", "shell.execute_reply.started": "2025-07-11T20:08:51.753488Z" } }, "outputs": [ { "data": { "text/plain": [ "MaximumBreaks\n", "\n", " Interval Count\n", "--------------------------\n", "[ 0.13, 228.49] | 52\n", "( 228.49, 546.67] | 4\n", "( 546.67, 2417.15] | 1\n", "(2417.15, 4111.45] | 1" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mc.MaximumBreaks(y, k=4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The classifier returns an instance of `MaximumBreaks` that reports the resulting intervals and counts. The first class has closed lower and upper bounds:\n", "\n", "```\n", "[ 0.13, 228.49]\n", "```\n", "\n", "with `0.13` being the minimum value in the dataset:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T16:51:55.413265Z", "start_time": "2022-11-04T16:51:55.408990Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:08:51.756019Z", "iopub.status.busy": "2025-07-11T20:08:51.755953Z", "iopub.status.idle": "2025-07-11T20:08:51.757684Z", "shell.execute_reply": "2025-07-11T20:08:51.757528Z", "shell.execute_reply.started": "2025-07-11T20:08:51.756012Z" } }, "outputs": [ { "data": { "text/plain": [ "np.float64(0.13)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y.min()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Subsequent intervals are open on the lower bound and closed on the upper bound. The fourth class has the maximum value as its closed upper bound:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T16:51:55.419714Z", "start_time": "2022-11-04T16:51:55.415775Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:08:51.758076Z", "iopub.status.busy": "2025-07-11T20:08:51.757988Z", "iopub.status.idle": "2025-07-11T20:08:51.760282Z", "shell.execute_reply": "2025-07-11T20:08:51.760063Z", "shell.execute_reply.started": "2025-07-11T20:08:51.758069Z" } }, "outputs": [ { "data": { "text/plain": [ "np.float64(4111.45)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y.max()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Assigning the classifier to an object let's us inspect other aspects of the classifier:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T16:51:55.426490Z", "start_time": "2022-11-04T16:51:55.421539Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:08:51.761781Z", "iopub.status.busy": "2025-07-11T20:08:51.761689Z", "iopub.status.idle": "2025-07-11T20:08:51.764139Z", "shell.execute_reply": "2025-07-11T20:08:51.763953Z", "shell.execute_reply.started": "2025-07-11T20:08:51.761773Z" } }, "outputs": [ { "data": { "text/plain": [ "MaximumBreaks\n", "\n", " Interval Count\n", "--------------------------\n", "[ 0.13, 228.49] | 52\n", "( 228.49, 546.67] | 4\n", "( 546.67, 2417.15] | 1\n", "(2417.15, 4111.45] | 1" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mb4 = mc.MaximumBreaks(y, k=4)\n", "mb4" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `bins` attribute has the upper bounds of the intervals:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T16:51:55.433994Z", "start_time": "2022-11-04T16:51:55.429143Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:08:51.764596Z", "iopub.status.busy": "2025-07-11T20:08:51.764533Z", "iopub.status.idle": "2025-07-11T20:08:51.766608Z", "shell.execute_reply": "2025-07-11T20:08:51.766403Z", "shell.execute_reply.started": "2025-07-11T20:08:51.764589Z" } }, "outputs": [ { "data": { "text/plain": [ "array([ 228.49 , 546.675, 2417.15 , 4111.45 ])" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mb4.bins" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and `counts` reports the number of values falling in each bin:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T16:51:55.441325Z", "start_time": "2022-11-04T16:51:55.437014Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:08:51.767004Z", "iopub.status.busy": "2025-07-11T20:08:51.766930Z", "iopub.status.idle": "2025-07-11T20:08:51.768853Z", "shell.execute_reply": "2025-07-11T20:08:51.768659Z", "shell.execute_reply.started": "2025-07-11T20:08:51.766997Z" } }, "outputs": [ { "data": { "text/plain": [ "array([52, 4, 1, 1])" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mb4.counts" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The specific bin (i.e. label) for each observation can be found in the `yb` attribute:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T16:51:55.447878Z", "start_time": "2022-11-04T16:51:55.443824Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:08:51.769165Z", "iopub.status.busy": "2025-07-11T20:08:51.769105Z", "iopub.status.idle": "2025-07-11T20:08:51.771164Z", "shell.execute_reply": "2025-07-11T20:08:51.770960Z", "shell.execute_reply.started": "2025-07-11T20:08:51.769158Z" } }, "outputs": [ { "data": { "text/plain": [ "array([1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 1, 0, 1, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mb4.yb" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Changing the number of classes\n", "\n", "Staying with the the same classifier, the user can apply the same classification rule, but for a different number of classes:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T16:51:55.454514Z", "start_time": "2022-11-04T16:51:55.449706Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:08:51.771674Z", "iopub.status.busy": "2025-07-11T20:08:51.771567Z", "iopub.status.idle": "2025-07-11T20:08:51.774175Z", "shell.execute_reply": "2025-07-11T20:08:51.773986Z", "shell.execute_reply.started": "2025-07-11T20:08:51.771666Z" } }, "outputs": [ { "data": { "text/plain": [ "MaximumBreaks\n", "\n", " Interval Count\n", "--------------------------\n", "[ 0.13, 146.00] | 50\n", "( 146.00, 228.49] | 2\n", "( 228.49, 291.02] | 1\n", "( 291.02, 350.21] | 2\n", "( 350.21, 546.67] | 1\n", "( 546.67, 2417.15] | 1\n", "(2417.15, 4111.45] | 1" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mb7 = mc.MaximumBreaks(y, k=7)\n", "mb7" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T16:51:55.461787Z", "start_time": "2022-11-04T16:51:55.456906Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:08:51.774568Z", "iopub.status.busy": "2025-07-11T20:08:51.774500Z", "iopub.status.idle": "2025-07-11T20:08:51.777199Z", "shell.execute_reply": "2025-07-11T20:08:51.776971Z", "shell.execute_reply.started": "2025-07-11T20:08:51.774560Z" } }, "outputs": [ { "data": { "text/plain": [ "array([ 146.005, 228.49 , 291.02 , 350.21 , 546.675, 2417.15 ,\n", " 4111.45 ])" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mb7.bins" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T16:51:55.471152Z", "start_time": "2022-11-04T16:51:55.466248Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:08:51.777602Z", "iopub.status.busy": "2025-07-11T20:08:51.777529Z", "iopub.status.idle": "2025-07-11T20:08:51.779968Z", "shell.execute_reply": "2025-07-11T20:08:51.779769Z", "shell.execute_reply.started": "2025-07-11T20:08:51.777594Z" } }, "outputs": [ { "data": { "text/plain": [ "array([50, 2, 1, 2, 1, 1, 1])" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mb7.counts" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T16:51:55.477524Z", "start_time": "2022-11-04T16:51:55.473430Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:08:51.780322Z", "iopub.status.busy": "2025-07-11T20:08:51.780260Z", "iopub.status.idle": "2025-07-11T20:08:51.782337Z", "shell.execute_reply": "2025-07-11T20:08:51.782155Z", "shell.execute_reply.started": "2025-07-11T20:08:51.780315Z" } }, "outputs": [ { "data": { "text/plain": [ "array([3, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 1, 0, 0, 0, 6, 0, 0, 3, 0, 2, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mb7.yb" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One additional attribute to mention here is the `adcm` attribute:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T16:51:55.483597Z", "start_time": "2022-11-04T16:51:55.479867Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:08:51.782728Z", "iopub.status.busy": "2025-07-11T20:08:51.782658Z", "iopub.status.idle": "2025-07-11T20:08:51.784700Z", "shell.execute_reply": "2025-07-11T20:08:51.784526Z", "shell.execute_reply.started": "2025-07-11T20:08:51.782721Z" } }, "outputs": [ { "data": { "text/plain": [ "np.float64(727.3200000000002)" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mb7.adcm" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`adcm` is a measure of fit, defined as the mean absolute deviation around the class median. " ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T16:51:55.489640Z", "start_time": "2022-11-04T16:51:55.485845Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:08:51.785004Z", "iopub.status.busy": "2025-07-11T20:08:51.784940Z", "iopub.status.idle": "2025-07-11T20:08:51.786769Z", "shell.execute_reply": "2025-07-11T20:08:51.786565Z", "shell.execute_reply.started": "2025-07-11T20:08:51.784996Z" } }, "outputs": [ { "data": { "text/plain": [ "np.float64(1181.4900000000002)" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mb4.adcm" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `adcm` can be expected to decrease as $k$ increases for a given classifier. Thus, if using as a measure of fit, the `adcm` should only be used to compare classifiers defined on the same number of classes." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Next Steps\n", "`MaximumBreaks` is but one of many classifiers in `mapclassify`:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T16:51:55.496548Z", "start_time": "2022-11-04T16:51:55.492318Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:08:51.787157Z", "iopub.status.busy": "2025-07-11T20:08:51.787086Z", "iopub.status.idle": "2025-07-11T20:08:51.789246Z", "shell.execute_reply": "2025-07-11T20:08:51.789062Z", "shell.execute_reply.started": "2025-07-11T20:08:51.787151Z" } }, "outputs": [ { "data": { "text/plain": [ "('BoxPlot',\n", " 'EqualInterval',\n", " 'FisherJenks',\n", " 'FisherJenksSampled',\n", " 'HeadTailBreaks',\n", " 'JenksCaspall',\n", " 'JenksCaspallForced',\n", " 'JenksCaspallSampled',\n", " 'MaxP',\n", " 'MaximumBreaks',\n", " 'NaturalBreaks',\n", " 'Quantiles',\n", " 'Percentiles',\n", " 'PrettyBreaks',\n", " 'StdMean',\n", " 'UserDefined')" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mc.classifiers.CLASSIFIERS" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To learn more about an individual classifier, introspection is available:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T16:51:55.537870Z", "start_time": "2022-11-04T16:51:55.499084Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:08:51.789653Z", "iopub.status.busy": "2025-07-11T20:08:51.789560Z", "iopub.status.idle": "2025-07-11T20:08:51.804577Z", "shell.execute_reply": "2025-07-11T20:08:51.804347Z", "shell.execute_reply.started": "2025-07-11T20:08:51.789646Z" } }, "outputs": [ { "data": { "text/plain": [ "\u001b[31mInit signature:\u001b[39m mc.MaximumBreaks(y, k=\u001b[32m5\u001b[39m, mindiff=\u001b[32m0\u001b[39m)\n", "\u001b[31mDocstring:\u001b[39m \n", "Maximum Breaks Map Classification.\n", "\n", "Parameters\n", "----------\n", "\n", "y : numpy.array\n", " :math:`(n,1)`, values to classify.\n", "k : int (default 5)\n", " The number of classes required.\n", "\n", "mindiff : float (default 0)\n", " The minimum difference between class breaks.\n", "\n", "Attributes\n", "----------\n", "\n", "yb : numpy.array\n", " :math:`(n,1)`, bin IDs for observations.\n", "bins : numpy.array\n", " :math:`(k,1)`, the upper bounds of each class.\n", "k : int\n", " The number of classes.\n", "counts : numpy.array\n", " :math:`(k,1)`, the number of observations falling in each class.\n", "\n", "Examples\n", "--------\n", "\n", ">>> import mapclassify\n", ">>> cal = mapclassify.load_example()\n", ">>> mb = mapclassify.MaximumBreaks(cal, k=5)\n", ">>> mb.k\n", "5\n", "\n", ">>> mb.bins\n", "array([ 146.005, 228.49 , 546.675, 2417.15 , 4111.45 ])\n", "\n", ">>> mb.counts.tolist()\n", "[50, 2, 4, 1, 1]\n", "\u001b[31mFile:\u001b[39m ~/github_repos/mapclassify/mapclassify/classifiers.py\n", "\u001b[31mType:\u001b[39m type\n", "\u001b[31mSubclasses:\u001b[39m " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mc.MaximumBreaks?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "-------------------------\n", "\n", "For more comprehensive appliciations of `mapclassify` the interested reader is directed to the chapter on [choropleth mapping](https://geographicdata.science/book/notebooks/05_choropleth.html) in [Rey, Arribas-Bel, and Wolf (2020) \"Geographic Data Science with PySAL and the PyData Stack”](https://geographicdata.science/book).\n", "\n", "-------------------------" ] } ], "metadata": { "anaconda-cloud": {}, "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": "3.13.5" } }, "nbformat": 4, "nbformat_minor": 4 } mapclassify-2.10.0/notebooks/02_legends.ipynb000066400000000000000000000174751503430131600211070ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Legends in mapclassify\n", "\n", "`mapclassify` allows for user defined formatting of legends" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T18:03:55.559087Z", "start_time": "2022-11-04T18:03:53.594867Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:09:07.380511Z", "iopub.status.busy": "2025-07-11T20:09:07.380236Z", "iopub.status.idle": "2025-07-11T20:09:08.852814Z", "shell.execute_reply": "2025-07-11T20:09:08.852614Z", "shell.execute_reply.started": "2025-07-11T20:09:07.380490Z" } }, "outputs": [ { "data": { "text/plain": [ "'2.9.1.dev9+gde74d6f.d20250614'" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import mapclassify\n", "\n", "mapclassify.__version__" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T18:03:56.030661Z", "start_time": "2022-11-04T18:03:55.564369Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:09:08.853204Z", "iopub.status.busy": "2025-07-11T20:09:08.853090Z", "iopub.status.idle": "2025-07-11T20:09:08.861325Z", "shell.execute_reply": "2025-07-11T20:09:08.861048Z", "shell.execute_reply.started": "2025-07-11T20:09:08.853197Z" } }, "outputs": [], "source": [ "cal = mapclassify.load_example()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T18:03:56.041172Z", "start_time": "2022-11-04T18:03:56.034966Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:09:08.861775Z", "iopub.status.busy": "2025-07-11T20:09:08.861651Z", "iopub.status.idle": "2025-07-11T20:09:08.865236Z", "shell.execute_reply": "2025-07-11T20:09:08.864973Z", "shell.execute_reply.started": "2025-07-11T20:09:08.861764Z" } }, "outputs": [ { "data": { "text/plain": [ "Quantiles\n", "\n", " Interval Count\n", "--------------------------\n", "[ 0.13, 1.16] | 10\n", "( 1.16, 3.38] | 10\n", "( 3.38, 9.36] | 9\n", "( 9.36, 24.32] | 10\n", "( 24.32, 70.78] | 9\n", "( 70.78, 4111.45] | 10" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "q6 = mapclassify.Quantiles(cal, k=6)\n", "q6" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The default is to use two decimal places for this dataset.\n", "\n", "If the user desires a list of strings with these values, the `get_legend_classes` method can be called\n", "which will return the strings with the default format:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T18:03:56.047765Z", "start_time": "2022-11-04T18:03:56.042764Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:09:08.865763Z", "iopub.status.busy": "2025-07-11T20:09:08.865626Z", "iopub.status.idle": "2025-07-11T20:09:08.868002Z", "shell.execute_reply": "2025-07-11T20:09:08.867811Z", "shell.execute_reply.started": "2025-07-11T20:09:08.865721Z" } }, "outputs": [ { "data": { "text/plain": [ "['[ 0.13, 1.16]',\n", " '( 1.16, 3.38]',\n", " '( 3.38, 9.36]',\n", " '( 9.36, 24.32]',\n", " '( 24.32, 70.78]',\n", " '( 70.78, 4111.45]']" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "q6.get_legend_classes()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To set the legends to integers, an option can be passed into the method:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T18:03:56.055615Z", "start_time": "2022-11-04T18:03:56.050635Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:09:08.868326Z", "iopub.status.busy": "2025-07-11T20:09:08.868260Z", "iopub.status.idle": "2025-07-11T20:09:08.870404Z", "shell.execute_reply": "2025-07-11T20:09:08.870187Z", "shell.execute_reply.started": "2025-07-11T20:09:08.868319Z" } }, "outputs": [ { "data": { "text/plain": [ "['[ 0, 1]',\n", " '( 1, 3]',\n", " '( 3, 9]',\n", " '( 9, 24]',\n", " '( 24, 71]',\n", " '( 71, 4111]']" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "q6.get_legend_classes(fmt=\"{:.0f}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that this does not change the original object:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T18:03:56.064112Z", "start_time": "2022-11-04T18:03:56.058884Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:09:08.871564Z", "iopub.status.busy": "2025-07-11T20:09:08.871488Z", "iopub.status.idle": "2025-07-11T20:09:08.873453Z", "shell.execute_reply": "2025-07-11T20:09:08.873252Z", "shell.execute_reply.started": "2025-07-11T20:09:08.871557Z" } }, "outputs": [ { "data": { "text/plain": [ "Quantiles\n", "\n", " Interval Count\n", "--------------------------\n", "[ 0.13, 1.16] | 10\n", "( 1.16, 3.38] | 10\n", "( 3.38, 9.36] | 9\n", "( 9.36, 24.32] | 10\n", "( 24.32, 70.78] | 9\n", "( 70.78, 4111.45] | 10" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "q6" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The format can be changed on the object by calling the `set_fmt` method:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T18:03:56.073242Z", "start_time": "2022-11-04T18:03:56.067329Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:09:08.873838Z", "iopub.status.busy": "2025-07-11T20:09:08.873773Z", "iopub.status.idle": "2025-07-11T20:09:08.875806Z", "shell.execute_reply": "2025-07-11T20:09:08.875598Z", "shell.execute_reply.started": "2025-07-11T20:09:08.873831Z" } }, "outputs": [ { "data": { "text/plain": [ "Quantiles\n", "\n", " Interval Count\n", "--------------------\n", "[ 0, 1] | 10\n", "( 1, 3] | 10\n", "( 3, 9] | 9\n", "( 9, 24] | 10\n", "( 24, 71] | 9\n", "( 71, 4111] | 10" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "q6.set_fmt(fmt=\"{:.0f}\")\n", "q6" ] } ], "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": "3.13.5" } }, "nbformat": 4, "nbformat_minor": 4 } mapclassify-2.10.0/notebooks/03_choropleth.ipynb000066400000000000000000015066071503430131600216370ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Choropleth\n", "\n", "`mapclassify` is intended to be used with visualization packages to handle the actual rendering of the choropleth maps defined on its classifiers. In this notebook, we explore some examples of how this is done. The notebook also includes an example that combines `mapclassify` with [ipywidgets](https://ipywidgets.readthedocs.io/en/latest/) to allow for the interactive exploration of the choice of:\n", "\n", "- classification method\n", "- number of classes\n", "- colormap" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T01:00:43.535547Z", "start_time": "2022-11-05T01:00:40.391594Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:08:15.318782Z", "iopub.status.busy": "2025-07-11T20:08:15.318399Z", "iopub.status.idle": "2025-07-11T20:08:17.242431Z", "shell.execute_reply": "2025-07-11T20:08:17.242214Z", "shell.execute_reply.started": "2025-07-11T20:08:15.318758Z" } }, "outputs": [ { "data": { "text/plain": [ "'2.9.1.dev9+gde74d6f.d20250614'" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import geopandas\n", "import libpysal\n", "import matplotlib.pyplot as plt\n", "\n", "import mapclassify\n", "\n", "mapclassify.__version__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The example in this notebook uses data on sudden death infant syndrome for counties in North Carolina. It is a built-in dataset available through `libpysal`. We use `libpysal` to obtain the path to the shapefile and then use `geopandas` to create a geodataframe from the shapefile:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T01:00:43.545340Z", "start_time": "2022-11-05T01:00:43.539863Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:08:17.242920Z", "iopub.status.busy": "2025-07-11T20:08:17.242764Z", "iopub.status.idle": "2025-07-11T20:08:17.244703Z", "shell.execute_reply": "2025-07-11T20:08:17.244501Z", "shell.execute_reply.started": "2025-07-11T20:08:17.242913Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sids2\n", "=====\n", "\n", "North Carolina county SIDS death counts and rates\n", "-------------------------------------------------\n", "\n", "* sids2.dbf: attribute data. (k=18)\n", "* sids2.html: metadata.\n", "* sids2.shp: Polygon shapefile. (n=100)\n", "* sids2.shx: spatial index.\n", "* sids2.gal: spatial weights in GAL format.\n", "\n", "Source: Cressie, Noel (1993). Statistics for Spatial Data. New York, Wiley, pp. 386-389. Rates computed.\n", "Updated URL: https://geodacenter.github.io/data-and-lab/sids2/\n", "\n" ] } ], "source": [ "libpysal.examples.explain(\"sids2\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T01:00:43.733714Z", "start_time": "2022-11-05T01:00:43.549872Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:08:17.244980Z", "iopub.status.busy": "2025-07-11T20:08:17.244916Z", "iopub.status.idle": "2025-07-11T20:08:17.291889Z", "shell.execute_reply": "2025-07-11T20:08:17.291690Z", "shell.execute_reply.started": "2025-07-11T20:08:17.244972Z" } }, "outputs": [ { "data": { "text/html": [ "

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AREAPERIMETERCNTY_CNTY_IDNAMEFIPSFIPSNOCRESS_IDBIR74SID74NWBIR74BIR79SID79NWBIR79SIDR74SIDR79NWR74NWR79geometry
00.1141.44218251825Ashe370093700951091.01.010.01364.00.019.00.9165900.0000009.16590313.929619POLYGON ((-81.47276 36.23436, -81.54084 36.272...
10.0611.23118271827Alleghany37005370053487.00.010.0542.03.012.00.0000005.53505520.53388122.140221POLYGON ((-81.23989 36.36536, -81.24069 36.379...
20.1431.63018281828Surry3717137171863188.05.0208.03616.06.0260.01.5683811.65929265.24466871.902655POLYGON ((-80.45634 36.24256, -80.47639 36.254...
30.0702.96818311831Currituck370533705327508.01.0123.0830.02.0145.01.9685042.409639242.125984174.698795MULTIPOLYGON (((-76.00897 36.3196, -76.01735 3...
40.1532.20618321832Northampton3713137131661421.09.01066.01606.03.01197.06.3335681.867995750.175932745.330012POLYGON ((-77.21767 36.24098, -77.23461 36.214...
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" ], "text/plain": [ " AREA PERIMETER CNTY_ CNTY_ID NAME FIPS FIPSNO CRESS_ID \\\n", "0 0.114 1.442 1825 1825 Ashe 37009 37009 5 \n", "1 0.061 1.231 1827 1827 Alleghany 37005 37005 3 \n", "2 0.143 1.630 1828 1828 Surry 37171 37171 86 \n", "3 0.070 2.968 1831 1831 Currituck 37053 37053 27 \n", "4 0.153 2.206 1832 1832 Northampton 37131 37131 66 \n", "\n", " BIR74 SID74 NWBIR74 BIR79 SID79 NWBIR79 SIDR74 SIDR79 \\\n", "0 1091.0 1.0 10.0 1364.0 0.0 19.0 0.916590 0.000000 \n", "1 487.0 0.0 10.0 542.0 3.0 12.0 0.000000 5.535055 \n", "2 3188.0 5.0 208.0 3616.0 6.0 260.0 1.568381 1.659292 \n", "3 508.0 1.0 123.0 830.0 2.0 145.0 1.968504 2.409639 \n", "4 1421.0 9.0 1066.0 1606.0 3.0 1197.0 6.333568 1.867995 \n", "\n", " NWR74 NWR79 geometry \n", "0 9.165903 13.929619 POLYGON ((-81.47276 36.23436, -81.54084 36.272... \n", "1 20.533881 22.140221 POLYGON ((-81.23989 36.36536, -81.24069 36.379... \n", "2 65.244668 71.902655 POLYGON ((-80.45634 36.24256, -80.47639 36.254... \n", "3 242.125984 174.698795 MULTIPOLYGON (((-76.00897 36.3196, -76.01735 3... \n", "4 750.175932 745.330012 POLYGON ((-77.21767 36.24098, -77.23461 36.214... " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pth = libpysal.examples.get_path(\"sids2.shp\")\n", "gdf = geopandas.read_file(pth)\n", "gdf.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once created, the geodataframe has a `plot` method that can be called to create our first choropleth map. We will specify the column to classify and plot as `SIDR79`: SIDS death rate per 1,000 births (1979-84). The classification scheme is set to `Quantiles`, and the number of classes set to `k=10` (declies):" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T01:00:44.072165Z", "start_time": "2022-11-05T01:00:43.736051Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:08:17.292415Z", "iopub.status.busy": "2025-07-11T20:08:17.292263Z", "iopub.status.idle": "2025-07-11T20:08:17.350460Z", "shell.execute_reply": "2025-07-11T20:08:17.350245Z", "shell.execute_reply.started": "2025-07-11T20:08:17.292407Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = gdf.plot(column=\"SIDR79\", scheme=\"Quantiles\", k=10, figsize=(16, 9))\n", "ax.set_axis_off()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can peak under the hood a bit and recreate the classification object that was used in the previous choropleth:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T01:00:44.081602Z", "start_time": "2022-11-05T01:00:44.075185Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:08:17.350934Z", "iopub.status.busy": "2025-07-11T20:08:17.350853Z", "iopub.status.idle": "2025-07-11T20:08:17.353396Z", "shell.execute_reply": "2025-07-11T20:08:17.353228Z", "shell.execute_reply.started": "2025-07-11T20:08:17.350925Z" } }, "outputs": [ { "data": { "text/plain": [ "Quantiles\n", "\n", " Interval Count\n", "--------------------\n", "[0.00, 0.56] | 10\n", "(0.56, 1.15] | 10\n", "(1.15, 1.40] | 10\n", "(1.40, 1.79] | 10\n", "(1.79, 2.08] | 10\n", "(2.08, 2.18] | 10\n", "(2.18, 2.38] | 10\n", "(2.38, 2.81] | 10\n", "(2.81, 3.40] | 10\n", "(3.40, 6.11] | 10" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "q10 = mapclassify.Quantiles(gdf.SIDR79, k=10)\n", "q10" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For quick, exploratory work, the classifier object has its own `plot` method that takes a geodataframe as an argument:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T01:00:44.264108Z", "start_time": "2022-11-05T01:00:44.084642Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:08:17.354587Z", "iopub.status.busy": "2025-07-11T20:08:17.354499Z", "iopub.status.idle": "2025-07-11T20:08:17.390728Z", "shell.execute_reply": "2025-07-11T20:08:17.390525Z", "shell.execute_reply.started": "2025-07-11T20:08:17.354579Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "q10.plot(gdf);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Back to working directly with the dataframe, we can toggle on the `legend`:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T01:00:44.637932Z", "start_time": "2022-11-05T01:00:44.266642Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:08:17.391181Z", "iopub.status.busy": "2025-07-11T20:08:17.391097Z", "iopub.status.idle": "2025-07-11T20:08:17.453742Z", "shell.execute_reply": "2025-07-11T20:08:17.453498Z", "shell.execute_reply.started": "2025-07-11T20:08:17.391172Z" } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = gdf.assign(cl=q10.yb).plot(\n", " figsize=(16, 9),\n", " column=\"cl\",\n", " categorical=True,\n", " k=10,\n", " cmap=\"OrRd\",\n", " linewidth=0.1,\n", " edgecolor=\"white\",\n", " legend=True,\n", ")\n", "ax.set_axis_off()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we see the 10 classes, but without more specific information on the legend, the user has to know that 0 is the first decile and 9 the 10th. We also do not know the values that define these classes. \n", "\n", "We can rectify this as follows:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T01:00:44.646109Z", "start_time": "2022-11-05T01:00:44.640625Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:08:17.454112Z", "iopub.status.busy": "2025-07-11T20:08:17.454041Z", "iopub.status.idle": "2025-07-11T20:08:17.455997Z", "shell.execute_reply": "2025-07-11T20:08:17.455814Z", "shell.execute_reply.started": "2025-07-11T20:08:17.454103Z" } }, "outputs": [ { "data": { "text/plain": [ "['[0.00, 0.56]',\n", " '(0.56, 1.15]',\n", " '(1.15, 1.40]',\n", " '(1.40, 1.79]',\n", " '(1.79, 2.08]',\n", " '(2.08, 2.18]',\n", " '(2.18, 2.38]',\n", " '(2.38, 2.81]',\n", " '(2.81, 3.40]',\n", " '(3.40, 6.11]']" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "q10.get_legend_classes()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T01:00:44.653754Z", "start_time": "2022-11-05T01:00:44.648642Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:08:17.456301Z", "iopub.status.busy": "2025-07-11T20:08:17.456227Z", "iopub.status.idle": "2025-07-11T20:08:17.458208Z", "shell.execute_reply": "2025-07-11T20:08:17.458037Z", "shell.execute_reply.started": "2025-07-11T20:08:17.456293Z" } }, "outputs": [ { "data": { "text/plain": [ "{0: '[0.00, 0.56]',\n", " 1: '(0.56, 1.15]',\n", " 2: '(1.15, 1.40]',\n", " 3: '(1.40, 1.79]',\n", " 4: '(1.79, 2.08]',\n", " 5: '(2.08, 2.18]',\n", " 6: '(2.18, 2.38]',\n", " 7: '(2.38, 2.81]',\n", " 8: '(2.81, 3.40]',\n", " 9: '(3.40, 6.11]'}" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mapping = {i: s for i, s in enumerate(q10.get_legend_classes())}\n", "mapping" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T01:00:44.659702Z", "start_time": "2022-11-05T01:00:44.656459Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:08:17.458596Z", "iopub.status.busy": "2025-07-11T20:08:17.458505Z", "iopub.status.idle": "2025-07-11T20:08:17.460077Z", "shell.execute_reply": "2025-07-11T20:08:17.459925Z", "shell.execute_reply.started": "2025-07-11T20:08:17.458589Z" } }, "outputs": [], "source": [ "def replace_legend_items(legend, mapping):\n", " for txt in legend.texts:\n", " for k, v in mapping.items():\n", " if txt.get_text() == str(k):\n", " txt.set_text(v)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T01:00:45.026943Z", "start_time": "2022-11-05T01:00:44.662743Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:08:17.460477Z", "iopub.status.busy": "2025-07-11T20:08:17.460385Z", "iopub.status.idle": "2025-07-11T20:08:17.526970Z", "shell.execute_reply": "2025-07-11T20:08:17.526753Z", "shell.execute_reply.started": "2025-07-11T20:08:17.460471Z" } }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = gdf.assign(cl=q10.yb).plot(\n", " figsize=(16, 9),\n", " column=\"cl\",\n", " categorical=True,\n", " k=10,\n", " cmap=\"OrRd\",\n", " linewidth=0.1,\n", " edgecolor=\"white\",\n", " legend=True,\n", " legend_kwds={\"loc\": \"lower right\"},\n", ")\n", "ax.set_axis_off()\n", "replace_legend_items(ax.get_legend(), mapping)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Interactive Exploration of Choropleth Classification\n", "\n", "Next, we develop a small application that relies on `mapclassify` together with [palettable](https://jiffyclub.github.io/palettable/) and [ipywidgets](https://ipywidgets.readthedocs.io/en/latest/) to explore the choice of:\n", "\n", "- classification method\n", "- number of classes\n", "- colormap\n", "\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T01:00:45.128715Z", "start_time": "2022-11-05T01:00:45.033912Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:08:17.527397Z", "iopub.status.busy": "2025-07-11T20:08:17.527314Z", "iopub.status.idle": "2025-07-11T20:08:17.569315Z", "shell.execute_reply": "2025-07-11T20:08:17.569094Z", "shell.execute_reply.started": "2025-07-11T20:08:17.527388Z" } }, "outputs": [], "source": [ "from ipywidgets import (\n", " Button,\n", " Dropdown,\n", " FloatSlider,\n", " HBox,\n", " IntSlider,\n", " Label,\n", " Output,\n", " RadioButtons,\n", " Tab,\n", " VBox,\n", " interact,\n", ")\n", "from palettable import colorbrewer" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Declare that our application shall have 3 options for color scheme..." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T01:00:45.139393Z", "start_time": "2022-11-05T01:00:45.131309Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:08:17.569692Z", "iopub.status.busy": "2025-07-11T20:08:17.569609Z", "iopub.status.idle": "2025-07-11T20:08:17.571871Z", "shell.execute_reply": "2025-07-11T20:08:17.571655Z", "shell.execute_reply.started": "2025-07-11T20:08:17.569683Z" } }, "outputs": [], "source": [ "data_type = RadioButtons(options=[\"Sequential\", \"Diverging\", \"Qualitative\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "... and define thise 3 colormaps based on [colorbrewer](https://colorbrewer2.org/#type=sequential&scheme=BuGn&n=3)." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T01:00:45.146386Z", "start_time": "2022-11-05T01:00:45.142688Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:08:17.572168Z", "iopub.status.busy": "2025-07-11T20:08:17.572101Z", "iopub.status.idle": "2025-07-11T20:08:17.573584Z", "shell.execute_reply": "2025-07-11T20:08:17.573419Z", "shell.execute_reply.started": "2025-07-11T20:08:17.572160Z" } }, "outputs": [], "source": [ "sequential = colorbrewer.COLOR_MAPS[\"Sequential\"]\n", "diverging = colorbrewer.COLOR_MAPS[\"Diverging\"]\n", "qualitative = colorbrewer.COLOR_MAPS[\"Qualitative\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, create value and name options to bind the radio button to the dropdown menus..." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T01:00:45.162086Z", "start_time": "2022-11-05T01:00:45.148913Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:08:17.574001Z", "iopub.status.busy": "2025-07-11T20:08:17.573904Z", "iopub.status.idle": "2025-07-11T20:08:17.576775Z", "shell.execute_reply": "2025-07-11T20:08:17.576549Z", "shell.execute_reply.started": "2025-07-11T20:08:17.573994Z" } }, "outputs": [], "source": [ "bindings = {\n", " \"Sequential\": range(3, 9 + 1),\n", " \"Diverging\": range(3, 11 + 1),\n", " \"Qualitative\": range(3, 12 + 1),\n", "}\n", "cmap_bindings = {\n", " \"Sequential\": list(sequential.keys()),\n", " \"Diverging\": list(diverging.keys()),\n", " \"Qualitative\": list(qualitative.keys()),\n", "}\n", "class_val = Dropdown(options=bindings[data_type.value], value=5)\n", "cmap_val = Dropdown(options=cmap_bindings[data_type.value])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "... and define 7 `mapclassify` objects data classification. " ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T01:00:45.168640Z", "start_time": "2022-11-05T01:00:45.165083Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:08:17.577218Z", "iopub.status.busy": "2025-07-11T20:08:17.577152Z", "iopub.status.idle": "2025-07-11T20:08:17.579073Z", "shell.execute_reply": "2025-07-11T20:08:17.578857Z", "shell.execute_reply.started": "2025-07-11T20:08:17.577211Z" } }, "outputs": [], "source": [ "k_classifiers = {\n", " \"equal_interval\": mapclassify.EqualInterval,\n", " \"fisher_jenks\": mapclassify.FisherJenks,\n", " \"jenks_caspall\": mapclassify.JenksCaspall,\n", " \"jenks_caspall_forced\": mapclassify.JenksCaspallForced,\n", " \"maximum_breaks\": mapclassify.MaximumBreaks,\n", " \"natural_breaks\": mapclassify.NaturalBreaks,\n", " \"quantiles\": mapclassify.Quantiles,\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now it all together with helper functions for visualizing & updating plot options." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T01:00:45.175681Z", "start_time": "2022-11-05T01:00:45.170573Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:08:17.579369Z", "iopub.status.busy": "2025-07-11T20:08:17.579307Z", "iopub.status.idle": "2025-07-11T20:08:17.582328Z", "shell.execute_reply": "2025-07-11T20:08:17.582123Z", "shell.execute_reply.started": "2025-07-11T20:08:17.579359Z" } }, "outputs": [], "source": [ "def k_values(ctype, cmap):\n", " k = list(colorbrewer.COLOR_MAPS[ctype][cmap].keys())\n", " return list(map(int, k))\n", "\n", "\n", "def update_map(method=\"quantiles\", k=5, cmap=\"Blues\"):\n", " classifier = k_classifiers[method](gdf.SIDR79, k=k)\n", " mapping = {i: s for i, s in enumerate(classifier.get_legend_classes())}\n", " plt_kws = {\n", " \"figsize\": (16, 9),\n", " \"column\": \"cl\",\n", " \"categorical\": True,\n", " \"legend\": True,\n", " \"lw\": 0.1,\n", " }\n", " ax = gdf.assign(cl=classifier.yb).plot(\n", " k=k, cmap=cmap, ec=\"grey\", legend_kwds={\"loc\": \"lower left\"}, **plt_kws\n", " )\n", " ax.set_axis_off()\n", " ax.set_title(\"SIDR79\")\n", " replace_legend_items(ax.get_legend(), mapping)\n", "\n", "\n", "def type_change(change):\n", " class_val.options = bindings[change[\"new\"]]\n", " cmap_val.options = cmap_bindings[change[\"new\"]]\n", "\n", "\n", "def cmap_change(change):\n", " cmap = change[\"new\"]\n", " ctype = data_type.value\n", " k = k_values(ctype, cmap)\n", " class_val.options = k" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, let's play with the interactive plot!\n", "* *the plot will not render in the docs, but may be accessed directly by running this notebook locally or as a binder.*" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T01:00:45.475549Z", "start_time": "2022-11-05T01:00:45.177737Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:08:17.582814Z", "iopub.status.busy": "2025-07-11T20:08:17.582674Z", "iopub.status.idle": "2025-07-11T20:08:17.647820Z", "shell.execute_reply": "2025-07-11T20:08:17.647567Z", "shell.execute_reply.started": "2025-07-11T20:08:17.582807Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "ca73a75bb1274855a3ebf74e3ac4afef", "version_major": 2, "version_minor": 0 }, "text/plain": [ "interactive(children=(Dropdown(description='method', index=6, options=('equal_interval', 'fisher_jenks', 'jenk…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "3c32cfe1dab34c9a82d604b467dc6fc9", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(RadioButtons(options=('Sequential', 'Diverging', 'Qualitative'), value='Sequential'), Output())…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data_type.observe(type_change, names=[\"value\"])\n", "cmap_val.observe(cmap_change, names=[\"value\"])\n", "out = Output()\n", "Tab().children = [out]\n", "interact(update_map, method=list(k_classifiers.keys()), cmap=cmap_val, k=class_val)\n", "display(VBox([data_type, out]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Changing the type of colormap (sequential, diverging, qualitative) will update the options for the available color maps (`cmap`). Changing any of the values using the dropdowns will update the classification and the resulting choropleth map.\n", "\n", "It is important to note that the example variable is best portrayed with the sequential colormaps. The other two types of colormaps are included for demonstration purposes only." ] } ], "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": "3.13.5" } }, "nbformat": 4, "nbformat_minor": 4 } mapclassify-2.10.0/notebooks/04_pooled.ipynb000066400000000000000000001226321503430131600207420ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Pooled Classification\n", "\n", "A common workflow with longitudinal spatial data is to apply the same classification scheme to an attribute over different time periods. More specifically, one would like to keep the class breaks the same over each period and examine how the mass of the distribution changes over these classes in the different periods.\n", "\n", "The `Pooled` classifier supports this workflow." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:41.941529Z", "start_time": "2022-11-05T19:18:40.603589Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:09:26.436870Z", "iopub.status.busy": "2025-07-11T20:09:26.436634Z", "iopub.status.idle": "2025-07-11T20:09:27.889059Z", "shell.execute_reply": "2025-07-11T20:09:27.888847Z", "shell.execute_reply.started": "2025-07-11T20:09:26.436845Z" } }, "outputs": [ { "data": { "text/plain": [ "'2.9.1.dev9+gde74d6f.d20250614'" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy\n", "\n", "import mapclassify\n", "\n", "mapclassify.__version__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Sample Data\n", "We construct a synthetic dataset composed of 20 cross-sectional units at three time points. Here the mean of the series is increasing over time." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:41.949728Z", "start_time": "2022-11-05T19:18:41.945010Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:09:27.889460Z", "iopub.status.busy": "2025-07-11T20:09:27.889336Z", "iopub.status.idle": "2025-07-11T20:09:27.891484Z", "shell.execute_reply": "2025-07-11T20:09:27.891297Z", "shell.execute_reply.started": "2025-07-11T20:09:27.889453Z" } }, "outputs": [ { "data": { "text/plain": [ "(20, 3)" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "n = 20\n", "data = numpy.array([numpy.arange(n) + i * n for i in range(1, 4)]).T\n", "data.shape" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:41.955945Z", "start_time": "2022-11-05T19:18:41.951635Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:09:27.891751Z", "iopub.status.busy": "2025-07-11T20:09:27.891690Z", "iopub.status.idle": "2025-07-11T20:09:27.893577Z", "shell.execute_reply": "2025-07-11T20:09:27.893379Z", "shell.execute_reply.started": "2025-07-11T20:09:27.891745Z" } }, "outputs": [ { "data": { "text/plain": [ "array([[20, 40, 60],\n", " [21, 41, 61],\n", " [22, 42, 62],\n", " [23, 43, 63],\n", " [24, 44, 64],\n", " [25, 45, 65],\n", " [26, 46, 66],\n", " [27, 47, 67],\n", " [28, 48, 68],\n", " [29, 49, 69],\n", " [30, 50, 70],\n", " [31, 51, 71],\n", " [32, 52, 72],\n", " [33, 53, 73],\n", " [34, 54, 74],\n", " [35, 55, 75],\n", " [36, 56, 76],\n", " [37, 57, 77],\n", " [38, 58, 78],\n", " [39, 59, 79]])" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Default: Quintiles\n", "The default is to apply a [vec](https://en.wikipedia.org/wiki/Vectorization_(mathematics)) operator to the data matrix and treat the observations as a single collection. Here the quantiles of the pooled data are obtained." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:41.965023Z", "start_time": "2022-11-05T19:18:41.957991Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:09:27.893896Z", "iopub.status.busy": "2025-07-11T20:09:27.893838Z", "iopub.status.idle": "2025-07-11T20:09:27.896375Z", "shell.execute_reply": "2025-07-11T20:09:27.896222Z", "shell.execute_reply.started": "2025-07-11T20:09:27.893889Z" } }, "outputs": [ { "data": { "text/plain": [ "Pooled Classifier\n", "\n", "Pooled Quantiles\n", "\n", " Interval Count\n", "----------------------\n", "[20.00, 31.80] | 12\n", "(31.80, 43.60] | 8\n", "(43.60, 55.40] | 0\n", "(55.40, 67.20] | 0\n", "(67.20, 79.00] | 0\n", "\n", "Pooled Quantiles\n", "\n", " Interval Count\n", "----------------------\n", "[20.00, 31.80] | 0\n", "(31.80, 43.60] | 4\n", "(43.60, 55.40] | 12\n", "(55.40, 67.20] | 4\n", "(67.20, 79.00] | 0\n", "\n", "Pooled Quantiles\n", "\n", " Interval Count\n", "----------------------\n", "[20.00, 31.80] | 0\n", "(31.80, 43.60] | 0\n", "(43.60, 55.40] | 0\n", "(55.40, 67.20] | 8\n", "(67.20, 79.00] | 12" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res = mapclassify.Pooled(data)\n", "res" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that the class definitions are constant across the periods." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:41.971895Z", "start_time": "2022-11-05T19:18:41.967042Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:09:27.896709Z", "iopub.status.busy": "2025-07-11T20:09:27.896648Z", "iopub.status.idle": "2025-07-11T20:09:27.898438Z", "shell.execute_reply": "2025-07-11T20:09:27.898270Z", "shell.execute_reply.started": "2025-07-11T20:09:27.896702Z" } }, "outputs": [], "source": [ "res = mapclassify.Pooled(data, k=4)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:41.978331Z", "start_time": "2022-11-05T19:18:41.974160Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:09:27.899550Z", "iopub.status.busy": "2025-07-11T20:09:27.899475Z", "iopub.status.idle": "2025-07-11T20:09:27.901214Z", "shell.execute_reply": "2025-07-11T20:09:27.901053Z", "shell.execute_reply.started": "2025-07-11T20:09:27.899543Z" } }, "outputs": [ { "data": { "text/plain": [ "array([15, 5, 0, 0])" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res.col_classifiers[0].counts" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:41.984738Z", "start_time": "2022-11-05T19:18:41.980393Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:09:27.901580Z", "iopub.status.busy": "2025-07-11T20:09:27.901470Z", "iopub.status.idle": "2025-07-11T20:09:27.903176Z", "shell.execute_reply": "2025-07-11T20:09:27.903011Z", "shell.execute_reply.started": "2025-07-11T20:09:27.901574Z" } }, "outputs": [ { "data": { "text/plain": [ "array([ 0, 0, 5, 15])" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res.col_classifiers[-1].counts" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:41.990334Z", "start_time": "2022-11-05T19:18:41.986702Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:09:27.903491Z", "iopub.status.busy": "2025-07-11T20:09:27.903432Z", "iopub.status.idle": "2025-07-11T20:09:27.905163Z", "shell.execute_reply": "2025-07-11T20:09:27.904974Z", "shell.execute_reply.started": "2025-07-11T20:09:27.903484Z" } }, "outputs": [ { "data": { "text/plain": [ "array([15, 15, 15, 15])" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res.global_classifier.counts" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:42.001119Z", "start_time": "2022-11-05T19:18:41.997311Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:09:27.905412Z", "iopub.status.busy": "2025-07-11T20:09:27.905355Z", "iopub.status.idle": "2025-07-11T20:09:27.907365Z", "shell.execute_reply": "2025-07-11T20:09:27.907172Z", "shell.execute_reply.started": "2025-07-11T20:09:27.905405Z" } }, "outputs": [ { "data": { "text/plain": [ "Pooled Classifier\n", "\n", "Pooled Quantiles\n", "\n", " Interval Count\n", "----------------------\n", "[20.00, 34.75] | 15\n", "(34.75, 49.50] | 5\n", "(49.50, 64.25] | 0\n", "(64.25, 79.00] | 0\n", "\n", "Pooled Quantiles\n", "\n", " Interval Count\n", "----------------------\n", "[20.00, 34.75] | 0\n", "(34.75, 49.50] | 10\n", "(49.50, 64.25] | 10\n", "(64.25, 79.00] | 0\n", "\n", "Pooled Quantiles\n", "\n", " Interval Count\n", "----------------------\n", "[20.00, 34.75] | 0\n", "(34.75, 49.50] | 0\n", "(49.50, 64.25] | 5\n", "(64.25, 79.00] | 15" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Extract the pooled classification objects for each column." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:42.007476Z", "start_time": "2022-11-05T19:18:42.003714Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:09:27.907807Z", "iopub.status.busy": "2025-07-11T20:09:27.907737Z", "iopub.status.idle": "2025-07-11T20:09:27.910340Z", "shell.execute_reply": "2025-07-11T20:09:27.910143Z", "shell.execute_reply.started": "2025-07-11T20:09:27.907800Z" } }, "outputs": [ { "data": { "text/plain": [ "Pooled Quantiles\n", "\n", " Interval Count\n", "----------------------\n", "[20.00, 34.75] | 15\n", "(34.75, 49.50] | 5\n", "(49.50, 64.25] | 0\n", "(64.25, 79.00] | 0" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c0, c1, c2 = res.col_classifiers\n", "c0" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compare to the unrestricted classifier for the first column..." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:42.016243Z", "start_time": "2022-11-05T19:18:42.010510Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:09:27.910677Z", "iopub.status.busy": "2025-07-11T20:09:27.910620Z", "iopub.status.idle": "2025-07-11T20:09:27.912837Z", "shell.execute_reply": "2025-07-11T20:09:27.912653Z", "shell.execute_reply.started": "2025-07-11T20:09:27.910671Z" } }, "outputs": [ { "data": { "text/plain": [ "Quantiles\n", "\n", " Interval Count\n", "----------------------\n", "[20.00, 24.75] | 5\n", "(24.75, 29.50] | 5\n", "(29.50, 34.25] | 5\n", "(34.25, 39.00] | 5" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mapclassify.Quantiles(c0.y, k=4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "... and the last column comparisions." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:42.024230Z", "start_time": "2022-11-05T19:18:42.018980Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:09:27.913187Z", "iopub.status.busy": "2025-07-11T20:09:27.913134Z", "iopub.status.idle": "2025-07-11T20:09:27.915033Z", "shell.execute_reply": "2025-07-11T20:09:27.914840Z", "shell.execute_reply.started": "2025-07-11T20:09:27.913181Z" } }, "outputs": [ { "data": { "text/plain": [ "Pooled Quantiles\n", "\n", " Interval Count\n", "----------------------\n", "[20.00, 34.75] | 0\n", "(34.75, 49.50] | 0\n", "(49.50, 64.25] | 5\n", "(64.25, 79.00] | 15" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c2" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:42.034362Z", "start_time": "2022-11-05T19:18:42.027227Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:09:27.915302Z", "iopub.status.busy": "2025-07-11T20:09:27.915242Z", "iopub.status.idle": "2025-07-11T20:09:27.917452Z", "shell.execute_reply": "2025-07-11T20:09:27.917276Z", "shell.execute_reply.started": "2025-07-11T20:09:27.915296Z" } }, "outputs": [ { "data": { "text/plain": [ "Quantiles\n", "\n", " Interval Count\n", "----------------------\n", "[60.00, 64.75] | 5\n", "(64.75, 69.50] | 5\n", "(69.50, 74.25] | 5\n", "(74.25, 79.00] | 5" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mapclassify.Quantiles(c2.y, k=4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Non-default classifier: BoxPlot" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:42.046090Z", "start_time": "2022-11-05T19:18:42.037414Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:09:27.917751Z", "iopub.status.busy": "2025-07-11T20:09:27.917691Z", "iopub.status.idle": "2025-07-11T20:09:27.920192Z", "shell.execute_reply": "2025-07-11T20:09:27.920005Z", "shell.execute_reply.started": "2025-07-11T20:09:27.917744Z" } }, "outputs": [ { "data": { "text/plain": [ "Pooled Classifier\n", "\n", "Pooled BoxPlot\n", "\n", " Interval Count\n", "------------------------\n", "( -inf, -9.50] | 0\n", "( -9.50, 34.75] | 15\n", "( 34.75, 49.50] | 5\n", "( 49.50, 64.25] | 0\n", "( 64.25, 108.50] | 0\n", "\n", "Pooled BoxPlot\n", "\n", " Interval Count\n", "------------------------\n", "( -inf, -9.50] | 0\n", "( -9.50, 34.75] | 0\n", "( 34.75, 49.50] | 10\n", "( 49.50, 64.25] | 10\n", "( 64.25, 108.50] | 0\n", "\n", "Pooled BoxPlot\n", "\n", " Interval Count\n", "------------------------\n", "( -inf, -9.50] | 0\n", "( -9.50, 34.75] | 0\n", "( 34.75, 49.50] | 0\n", "( 49.50, 64.25] | 5\n", "( 64.25, 108.50] | 15" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res = mapclassify.Pooled(data, classifier=\"BoxPlot\", hinge=1.5)\n", "res" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:42.054064Z", "start_time": "2022-11-05T19:18:42.048325Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:09:27.920544Z", "iopub.status.busy": "2025-07-11T20:09:27.920476Z", "iopub.status.idle": "2025-07-11T20:09:27.922486Z", "shell.execute_reply": "2025-07-11T20:09:27.922281Z", "shell.execute_reply.started": "2025-07-11T20:09:27.920537Z" } }, "outputs": [ { "data": { "text/plain": [ "array([ -9.5 , 34.75, 49.5 , 64.25, 108.5 ])" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res.col_classifiers[0].bins" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:42.060428Z", "start_time": "2022-11-05T19:18:42.056889Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:09:27.922812Z", "iopub.status.busy": "2025-07-11T20:09:27.922756Z", "iopub.status.idle": "2025-07-11T20:09:27.924242Z", "shell.execute_reply": "2025-07-11T20:09:27.924055Z", "shell.execute_reply.started": "2025-07-11T20:09:27.922806Z" } }, "outputs": [], "source": [ "c0, c1, c2 = res.col_classifiers" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:42.067574Z", "start_time": "2022-11-05T19:18:42.062869Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:09:27.924636Z", "iopub.status.busy": "2025-07-11T20:09:27.924571Z", "iopub.status.idle": "2025-07-11T20:09:27.926389Z", "shell.execute_reply": "2025-07-11T20:09:27.926225Z", "shell.execute_reply.started": "2025-07-11T20:09:27.924630Z" } }, "outputs": [ { "data": { "text/plain": [ "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2])" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c0.yb" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:42.076215Z", "start_time": "2022-11-05T19:18:42.070735Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:09:27.926767Z", "iopub.status.busy": "2025-07-11T20:09:27.926702Z", "iopub.status.idle": "2025-07-11T20:09:27.928352Z", "shell.execute_reply": "2025-07-11T20:09:27.928166Z", "shell.execute_reply.started": "2025-07-11T20:09:27.926761Z" } }, "outputs": [], "source": [ "c00 = mapclassify.BoxPlot(c0.y, hinge=3)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:42.085022Z", "start_time": "2022-11-05T19:18:42.078938Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:09:27.928735Z", "iopub.status.busy": "2025-07-11T20:09:27.928618Z", "iopub.status.idle": "2025-07-11T20:09:27.930507Z", "shell.execute_reply": "2025-07-11T20:09:27.930321Z", "shell.execute_reply.started": "2025-07-11T20:09:27.928728Z" } }, "outputs": [ { "data": { "text/plain": [ "array([1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4])" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c00.yb" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:42.093521Z", "start_time": "2022-11-05T19:18:42.088235Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:09:27.930763Z", "iopub.status.busy": "2025-07-11T20:09:27.930695Z", "iopub.status.idle": "2025-07-11T20:09:27.933175Z", "shell.execute_reply": "2025-07-11T20:09:27.932986Z", "shell.execute_reply.started": "2025-07-11T20:09:27.930755Z" } }, "outputs": [ { "data": { "text/plain": [ "BoxPlot\n", "\n", " Interval Count\n", "----------------------\n", "( -inf, -3.75] | 0\n", "(-3.75, 24.75] | 5\n", "(24.75, 29.50] | 5\n", "(29.50, 34.25] | 5\n", "(34.25, 62.75] | 5" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c00" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:42.100363Z", "start_time": "2022-11-05T19:18:42.095608Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:09:27.935748Z", "iopub.status.busy": "2025-07-11T20:09:27.935658Z", "iopub.status.idle": "2025-07-11T20:09:27.938444Z", "shell.execute_reply": "2025-07-11T20:09:27.938219Z", "shell.execute_reply.started": "2025-07-11T20:09:27.935741Z" } }, "outputs": [ { "data": { "text/plain": [ "Pooled BoxPlot\n", "\n", " Interval Count\n", "------------------------\n", "( -inf, -9.50] | 0\n", "( -9.50, 34.75] | 15\n", "( 34.75, 49.50] | 5\n", "( 49.50, 64.25] | 0\n", "( 64.25, 108.50] | 0" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c0" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Non-default classifier: FisherJenks" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:42.111872Z", "start_time": "2022-11-05T19:18:42.103537Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:09:27.938976Z", "iopub.status.busy": "2025-07-11T20:09:27.938786Z", "iopub.status.idle": "2025-07-11T20:09:27.941939Z", "shell.execute_reply": "2025-07-11T20:09:27.941747Z", "shell.execute_reply.started": "2025-07-11T20:09:27.938965Z" } }, "outputs": [ { "data": { "text/plain": [ "Pooled Classifier\n", "\n", "Pooled FisherJenks\n", "\n", " Interval Count\n", "----------------------\n", "[20.00, 31.00] | 12\n", "(31.00, 43.00] | 8\n", "(43.00, 55.00] | 0\n", "(55.00, 67.00] | 0\n", "(67.00, 79.00] | 0\n", "\n", "Pooled FisherJenks\n", "\n", " Interval Count\n", "----------------------\n", "[20.00, 31.00] | 0\n", "(31.00, 43.00] | 4\n", "(43.00, 55.00] | 12\n", "(55.00, 67.00] | 4\n", "(67.00, 79.00] | 0\n", "\n", "Pooled FisherJenks\n", "\n", " Interval Count\n", "----------------------\n", "[20.00, 31.00] | 0\n", "(31.00, 43.00] | 0\n", "(43.00, 55.00] | 0\n", "(55.00, 67.00] | 8\n", "(67.00, 79.00] | 12" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res = mapclassify.Pooled(data, classifier=\"FisherJenks\", k=5)\n", "res" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:42.119626Z", "start_time": "2022-11-05T19:18:42.113905Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:09:27.942365Z", "iopub.status.busy": "2025-07-11T20:09:27.942291Z", "iopub.status.idle": "2025-07-11T20:09:27.944501Z", "shell.execute_reply": "2025-07-11T20:09:27.944319Z", "shell.execute_reply.started": "2025-07-11T20:09:27.942358Z" } }, "outputs": [ { "data": { "text/plain": [ "FisherJenks\n", "\n", " Interval Count\n", "----------------------\n", "[20.00, 23.00] | 4\n", "(23.00, 27.00] | 4\n", "(27.00, 31.00] | 4\n", "(31.00, 35.00] | 4\n", "(35.00, 39.00] | 4" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c0, c1, c2 = res.col_classifiers\n", "mapclassify.FisherJenks(c0.y, k=5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Non-default classifier: MaximumBreaks\n", "\n" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:42.127177Z", "start_time": "2022-11-05T19:18:42.121621Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:09:27.944780Z", "iopub.status.busy": "2025-07-11T20:09:27.944718Z", "iopub.status.idle": "2025-07-11T20:09:27.946888Z", "shell.execute_reply": "2025-07-11T20:09:27.946706Z", "shell.execute_reply.started": "2025-07-11T20:09:27.944773Z" } }, "outputs": [ { "data": { "text/plain": [ "array([[20, 40, 60],\n", " [10, 10, 10],\n", " [22, 42, 62],\n", " [23, 43, 63],\n", " [24, 44, 64],\n", " [25, 45, 65],\n", " [26, 46, 66],\n", " [27, 47, 67],\n", " [28, 48, 68],\n", " [29, 49, 10],\n", " [30, 50, 70],\n", " [31, 51, 71],\n", " [32, 52, 72],\n", " [33, 53, 73],\n", " [34, 54, 74],\n", " [35, 55, 75],\n", " [36, 56, 76],\n", " [37, 57, 77],\n", " [38, 58, 78],\n", " [39, 59, 79]])" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data[1, 0] = 10\n", "data[1, 1] = 10\n", "data[1, 2] = 10\n", "data[9, 2] = 10\n", "data" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:42.136098Z", "start_time": "2022-11-05T19:18:42.128885Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:09:27.947183Z", "iopub.status.busy": "2025-07-11T20:09:27.947120Z", "iopub.status.idle": "2025-07-11T20:09:27.949682Z", "shell.execute_reply": "2025-07-11T20:09:27.949473Z", "shell.execute_reply.started": "2025-07-11T20:09:27.947177Z" } }, "outputs": [ { "data": { "text/plain": [ "Pooled Classifier\n", "\n", "Pooled MaximumBreaks\n", "\n", " Interval Count\n", "----------------------\n", "[10.00, 15.00] | 1\n", "(15.00, 21.00] | 1\n", "(21.00, 41.00] | 18\n", "(41.00, 61.00] | 0\n", "(61.00, 79.00] | 0\n", "\n", "Pooled MaximumBreaks\n", "\n", " Interval Count\n", "----------------------\n", "[10.00, 15.00] | 1\n", "(15.00, 21.00] | 0\n", "(21.00, 41.00] | 1\n", "(41.00, 61.00] | 18\n", "(61.00, 79.00] | 0\n", "\n", "Pooled MaximumBreaks\n", "\n", " Interval Count\n", "----------------------\n", "[10.00, 15.00] | 2\n", "(15.00, 21.00] | 0\n", "(21.00, 41.00] | 0\n", "(41.00, 61.00] | 1\n", "(61.00, 79.00] | 17" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res = mapclassify.Pooled(data, classifier=\"MaximumBreaks\", k=5)\n", "res" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:42.143133Z", "start_time": "2022-11-05T19:18:42.138961Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:09:27.950163Z", "iopub.status.busy": "2025-07-11T20:09:27.950044Z", "iopub.status.idle": "2025-07-11T20:09:27.951905Z", "shell.execute_reply": "2025-07-11T20:09:27.951722Z", "shell.execute_reply.started": "2025-07-11T20:09:27.950156Z" } }, "outputs": [ { "data": { "text/plain": [ "Pooled MaximumBreaks\n", "\n", " Interval Count\n", "----------------------\n", "[10.00, 15.00] | 1\n", "(15.00, 21.00] | 1\n", "(21.00, 41.00] | 18\n", "(41.00, 61.00] | 0\n", "(61.00, 79.00] | 0" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c0, c1, c2 = res.col_classifiers\n", "c0" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:42.149335Z", "start_time": "2022-11-05T19:18:42.144915Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:09:27.952286Z", "iopub.status.busy": "2025-07-11T20:09:27.952216Z", "iopub.status.idle": "2025-07-11T20:09:27.953962Z", "shell.execute_reply": "2025-07-11T20:09:27.953796Z", "shell.execute_reply.started": "2025-07-11T20:09:27.952279Z" } }, "outputs": [ { "data": { "text/plain": [ "array([20, 10, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,\n", " 37, 38, 39])" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c0.y" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:42.154525Z", "start_time": "2022-11-05T19:18:42.151983Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:09:27.954242Z", "iopub.status.busy": "2025-07-11T20:09:27.954185Z", "iopub.status.idle": "2025-07-11T20:09:27.955631Z", "shell.execute_reply": "2025-07-11T20:09:27.955443Z", "shell.execute_reply.started": "2025-07-11T20:09:27.954235Z" } }, "outputs": [], "source": [ "import warnings" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:42.163717Z", "start_time": "2022-11-05T19:18:42.156794Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:09:27.955900Z", "iopub.status.busy": "2025-07-11T20:09:27.955844Z", "iopub.status.idle": "2025-07-11T20:09:27.958018Z", "shell.execute_reply": "2025-07-11T20:09:27.957842Z", "shell.execute_reply.started": "2025-07-11T20:09:27.955893Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Insufficient number of unique diffs. Breaks are random.\n" ] } ], "source": [ "with warnings.catch_warnings():\n", " warnings.filterwarnings(\"error\")\n", " try:\n", " mapclassify.MaximumBreaks(c0.y, k=5)\n", " except UserWarning as e:\n", " print(e)\n", " with warnings.catch_warnings():\n", " warnings.filterwarnings(\"ignore\")\n", " mapclassify.MaximumBreaks(c0.y, k=5)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:42.174401Z", "start_time": "2022-11-05T19:18:42.166025Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:09:27.958377Z", "iopub.status.busy": "2025-07-11T20:09:27.958313Z", "iopub.status.idle": "2025-07-11T20:09:27.961102Z", "shell.execute_reply": "2025-07-11T20:09:27.960907Z", "shell.execute_reply.started": "2025-07-11T20:09:27.958370Z" } }, "outputs": [ { "data": { "text/plain": [ "Pooled Classifier\n", "\n", "Pooled UserDefined\n", "\n", " Interval Count\n", "----------------------\n", "[10.00, 62.80] | 20\n", "(62.80, 66.60] | 0\n", "(66.60, 71.40] | 0\n", "(71.40, 75.20] | 0\n", "(75.20, 79.00] | 0\n", "\n", "Pooled UserDefined\n", "\n", " Interval Count\n", "----------------------\n", "[10.00, 62.80] | 20\n", "(62.80, 66.60] | 0\n", "(66.60, 71.40] | 0\n", "(71.40, 75.20] | 0\n", "(75.20, 79.00] | 0\n", "\n", "Pooled UserDefined\n", "\n", " Interval Count\n", "----------------------\n", "[10.00, 62.80] | 4\n", "(62.80, 66.60] | 4\n", "(66.60, 71.40] | 4\n", "(71.40, 75.20] | 4\n", "(75.20, 79.00] | 4" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res = mapclassify.Pooled(\n", " data, classifier=\"UserDefined\", bins=mapclassify.Quantiles(data[:, -1]).bins\n", ")\n", "res" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:42.181788Z", "start_time": "2022-11-05T19:18:42.176573Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:09:27.961595Z", "iopub.status.busy": "2025-07-11T20:09:27.961527Z", "iopub.status.idle": "2025-07-11T20:09:27.964196Z", "shell.execute_reply": "2025-07-11T20:09:27.964003Z", "shell.execute_reply.started": "2025-07-11T20:09:27.961587Z" } }, "outputs": [ { "data": { "text/plain": [ "Quantiles\n", "\n", " Interval Count\n", "----------------------\n", "[10.00, 62.80] | 4\n", "(62.80, 66.60] | 4\n", "(66.60, 71.40] | 4\n", "(71.40, 75.20] | 4\n", "(75.20, 79.00] | 4" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mapclassify.Quantiles(data[:, -1])" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:42.188254Z", "start_time": "2022-11-05T19:18:42.183388Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:09:27.964521Z", "iopub.status.busy": "2025-07-11T20:09:27.964460Z", "iopub.status.idle": "2025-07-11T20:09:27.966457Z", "shell.execute_reply": "2025-07-11T20:09:27.966222Z", "shell.execute_reply.started": "2025-07-11T20:09:27.964514Z" } }, "outputs": [ { "data": { "text/plain": [ "array([60, 10, 62, 63, 64, 65, 66, 67, 68, 10, 70, 71, 72, 73, 74, 75, 76,\n", " 77, 78, 79])" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data[:, -1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Pinning the pooling\n", "\n", "Another option is to specify a specific subperiod as the definition for the classes in the pooling.\n", "\n", "### Pinning to the last period\n", "\n", "As an example, we can use the quintles from the third period to defined the pooled classifier:" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:42.198019Z", "start_time": "2022-11-05T19:18:42.190998Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:09:27.966763Z", "iopub.status.busy": "2025-07-11T20:09:27.966702Z", "iopub.status.idle": "2025-07-11T20:09:27.969472Z", "shell.execute_reply": "2025-07-11T20:09:27.969276Z", "shell.execute_reply.started": "2025-07-11T20:09:27.966756Z" } }, "outputs": [ { "data": { "text/plain": [ "Pooled Classifier\n", "\n", "Pooled UserDefined\n", "\n", " Interval Count\n", "----------------------\n", "[10.00, 62.80] | 20\n", "(62.80, 66.60] | 0\n", "(66.60, 71.40] | 0\n", "(71.40, 75.20] | 0\n", "(75.20, 79.00] | 0\n", "\n", "Pooled UserDefined\n", "\n", " Interval Count\n", "----------------------\n", "[10.00, 62.80] | 20\n", "(62.80, 66.60] | 0\n", "(66.60, 71.40] | 0\n", "(71.40, 75.20] | 0\n", "(75.20, 79.00] | 0\n", "\n", "Pooled UserDefined\n", "\n", " Interval Count\n", "----------------------\n", "[10.00, 62.80] | 4\n", "(62.80, 66.60] | 4\n", "(66.60, 71.40] | 4\n", "(71.40, 75.20] | 4\n", "(75.20, 79.00] | 4" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pinned = mapclassify.Pooled(\n", " data, classifier=\"UserDefined\", bins=mapclassify.Quantiles(data[:, -1]).bins\n", ")\n", "pinned" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:42.206186Z", "start_time": "2022-11-05T19:18:42.200535Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:09:27.969874Z", "iopub.status.busy": "2025-07-11T20:09:27.969800Z", "iopub.status.idle": "2025-07-11T20:09:27.971653Z", "shell.execute_reply": "2025-07-11T20:09:27.971467Z", "shell.execute_reply.started": "2025-07-11T20:09:27.969867Z" } }, "outputs": [ { "data": { "text/plain": [ "UserDefined\n", "\n", " Interval Count\n", "----------------------\n", "[10.00, 62.80] | 44\n", "(62.80, 66.60] | 4\n", "(66.60, 71.40] | 4\n", "(71.40, 75.20] | 4\n", "(75.20, 79.00] | 4" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pinned.global_classifier" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Pinning to the first period" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:42.215909Z", "start_time": "2022-11-05T19:18:42.207832Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:09:27.972019Z", "iopub.status.busy": "2025-07-11T20:09:27.971958Z", "iopub.status.idle": "2025-07-11T20:09:27.974710Z", "shell.execute_reply": "2025-07-11T20:09:27.974519Z", "shell.execute_reply.started": "2025-07-11T20:09:27.972012Z" } }, "outputs": [ { "data": { "text/plain": [ "Pooled Classifier\n", "\n", "Pooled UserDefined\n", "\n", " Interval Count\n", "----------------------\n", "[10.00, 23.80] | 4\n", "(23.80, 27.60] | 4\n", "(27.60, 31.40] | 4\n", "(31.40, 35.20] | 4\n", "(35.20, 39.00] | 4\n", "(39.00, 79.00] | 0\n", "\n", "Pooled UserDefined\n", "\n", " Interval Count\n", "----------------------\n", "[10.00, 23.80] | 1\n", "(23.80, 27.60] | 0\n", "(27.60, 31.40] | 0\n", "(31.40, 35.20] | 0\n", "(35.20, 39.00] | 0\n", "(39.00, 79.00] | 19\n", "\n", "Pooled UserDefined\n", "\n", " Interval Count\n", "----------------------\n", "[10.00, 23.80] | 2\n", "(23.80, 27.60] | 0\n", "(27.60, 31.40] | 0\n", "(31.40, 35.20] | 0\n", "(35.20, 39.00] | 0\n", "(39.00, 79.00] | 18" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pinned = mapclassify.Pooled(\n", " data, classifier=\"UserDefined\", bins=mapclassify.Quantiles(data[:, 0]).bins\n", ")\n", "pinned" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that the quintiles for the first period, by definition, contain all the values from that period, they do not bound the larger values in subsequent periods. Following the [mapclassify policy](https://github.com/pysal/mapclassify/blob/a7770fb98bf945dad3c62ccf2c0f8b53abb1774a/mapclassify/classifiers.py#L589), an additional class is added to contain all values in the pooled series." ] } ], "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": "3.13.5" } }, "nbformat": 4, "nbformat_minor": 4 } mapclassify-2.10.0/notebooks/05_Greedy_coloring.ipynb000066400000000000000000133533521503430131600226050ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Getting started with mapclassify.greedy\n", "\n", "Greedy or topological coloring (or sequential coloring) is a cartographic method of assigning colors to polygons (or other geometries, `mapclassify.greedy` supports all geometry types) in such a way, that no two adjacent polygons share the same color.\n", "\n", "`greedy` is a small toolkit within `mapclassify` providing such a functionality on top of GeoPandas GeoDataFrames. `mapclassify.greedy()` is all we need." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T20:25:49.035556Z", "start_time": "2022-11-04T20:25:46.646113Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:02:50.956203Z", "iopub.status.busy": "2025-07-11T20:02:50.955916Z", "iopub.status.idle": "2025-07-11T20:02:52.437597Z", "shell.execute_reply": "2025-07-11T20:02:52.437386Z", "shell.execute_reply.started": "2025-07-11T20:02:50.956175Z" } }, "outputs": [ { "data": { "text/plain": [ "'2.9.1.dev9+gde74d6f.d20250614'" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from mapclassify import __version__, greedy\n", "\n", "__version__" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T20:25:50.439227Z", "start_time": "2022-11-04T20:25:49.038378Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:02:52.437995Z", "iopub.status.busy": "2025-07-11T20:02:52.437876Z", "iopub.status.idle": "2025-07-11T20:02:52.961990Z", "shell.execute_reply": "2025-07-11T20:02:52.961729Z", "shell.execute_reply.started": "2025-07-11T20:02:52.437987Z" } }, "outputs": [], "source": [ "import geopandas\n", "import seaborn\n", "\n", "seaborn.set()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Few of `greedy`'s methods of coloring require either measuring of areas or distances. To obtain proper values, our GeoDataFrame needs to be in a projected CRS. Let's use Africa and reproject it to 'ESRI:102022':" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T20:25:50.942770Z", "start_time": "2022-11-04T20:25:50.444949Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:02:52.962600Z", "iopub.status.busy": "2025-07-11T20:02:52.962433Z", "iopub.status.idle": "2025-07-11T20:02:53.672513Z", "shell.execute_reply": "2025-07-11T20:02:53.672296Z", "shell.execute_reply.started": "2025-07-11T20:02:52.962590Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "world = geopandas.read_file(\n", " \"https://naciscdn.org/naturalearth/110m/cultural/ne_110m_admin_0_countries.zip\"\n", ")\n", "africa = world.loc[world.CONTINENT == \"Africa\"]\n", "africa = africa.to_crs(\"ESRI:102022\")\n", "ax = africa.plot(figsize=(8, 12), edgecolor=\"w\")\n", "ax.set_axis_off()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Default\n", "\n", "The default usage of `greedy` is extremely simple. Greedy returns a Series with color codes, so we can assign it directly as a new column of our GeoDataFrame:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T20:25:51.398283Z", "start_time": "2022-11-04T20:25:50.947778Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:02:53.672996Z", "iopub.status.busy": "2025-07-11T20:02:53.672862Z", "iopub.status.idle": "2025-07-11T20:02:53.770429Z", "shell.execute_reply": "2025-07-11T20:02:53.770217Z", "shell.execute_reply.started": "2025-07-11T20:02:53.672988Z" } }, "outputs": [ { "data": { "text/plain": [ "1 1\n", "2 0\n", "11 0\n", "12 1\n", "13 4\n", "Name: greedy_default, dtype: int64" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "africa[\"greedy_default\"] = greedy(africa)\n", "africa[\"greedy_default\"].head(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using resulting color codes as plotting categories gives us following the plot:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T20:25:51.740911Z", "start_time": "2022-11-04T20:25:51.400845Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:02:53.770843Z", "iopub.status.busy": "2025-07-11T20:02:53.770766Z", "iopub.status.idle": "2025-07-11T20:02:53.825619Z", "shell.execute_reply": "2025-07-11T20:02:53.825393Z", "shell.execute_reply.started": "2025-07-11T20:02:53.770834Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = africa.plot(\n", " figsize=(8, 12),\n", " column=\"greedy_default\",\n", " categorical=True,\n", " cmap=\"Set3\",\n", " legend=True,\n", " edgecolor=\"w\",\n", ")\n", "ax.set_axis_off()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Strategies\n", "\n", "### Balanced\n", "\n", "Greedy offers several strategies of coloring. The default strategy is `balanced` based on `count` attempting to balance the number of features per each color. Other balanced modes are `area` (balance the area covered by each color), `distance` and `centroid` (both attemtps to balance the distance between colors). Each of them attempt to balance the color assignment according to different conditions and hence can result in a different number of colors." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T20:25:51.747414Z", "start_time": "2022-11-04T20:25:51.743068Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:02:53.826888Z", "iopub.status.busy": "2025-07-11T20:02:53.826808Z", "iopub.status.idle": "2025-07-11T20:02:53.828656Z", "shell.execute_reply": "2025-07-11T20:02:53.828481Z", "shell.execute_reply.started": "2025-07-11T20:02:53.826879Z" } }, "outputs": [], "source": [ "africa = africa.reset_index(drop=True)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T20:25:52.098809Z", "start_time": "2022-11-04T20:25:51.750210Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:02:53.829028Z", "iopub.status.busy": "2025-07-11T20:02:53.828954Z", "iopub.status.idle": "2025-07-11T20:02:53.883836Z", "shell.execute_reply": "2025-07-11T20:02:53.883606Z", "shell.execute_reply.started": "2025-07-11T20:02:53.829019Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "africa[\"greedy_area\"] = greedy(africa, strategy=\"balanced\", balance=\"area\")\n", "ax = africa.plot(\n", " \"greedy_area\",\n", " categorical=True,\n", " figsize=(8, 12),\n", " cmap=\"Set3\",\n", " legend=True,\n", " edgecolor=\"w\",\n", ")\n", "ax.set_axis_off()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Different modes of balancing within the `balanced` strategy can be set using the `balance` keyword." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T20:25:52.491906Z", "start_time": "2022-11-04T20:25:52.101125Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:02:53.884223Z", "iopub.status.busy": "2025-07-11T20:02:53.884152Z", "iopub.status.idle": "2025-07-11T20:02:53.949703Z", "shell.execute_reply": "2025-07-11T20:02:53.949503Z", "shell.execute_reply.started": "2025-07-11T20:02:53.884214Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "africa[\"greedy_distance\"] = greedy(africa, strategy=\"balanced\", balance=\"distance\")\n", "ax = africa.plot(\n", " \"greedy_distance\",\n", " categorical=True,\n", " figsize=(8, 12),\n", " cmap=\"Set3\",\n", " legend=True,\n", " edgecolor=\"w\",\n", ")\n", "ax.set_axis_off()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## NetworkX strategies\n", "\n", "On top of four modes of balanced coloring strategy, `greedy` offers all `networkx.greedy_coloring()` strategies, like `largest_first`:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T20:25:53.134096Z", "start_time": "2022-11-04T20:25:52.494317Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:02:53.950076Z", "iopub.status.busy": "2025-07-11T20:02:53.950007Z", "iopub.status.idle": "2025-07-11T20:02:54.110010Z", "shell.execute_reply": "2025-07-11T20:02:54.109800Z", "shell.execute_reply.started": "2025-07-11T20:02:53.950067Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "africa[\"greedy_largest_first\"] = greedy(africa, strategy=\"largest_first\")\n", "ax = africa.plot(\n", " \"greedy_largest_first\",\n", " categorical=True,\n", " figsize=(8, 12),\n", " cmap=\"Set3\",\n", " legend=True,\n", " edgecolor=\"w\",\n", ")\n", "ax.set_axis_off()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Another strategy provided by NetworkX is `smallest_last`. All strategies provide different results. Check [Comparison of strategies](#Comparison-of-strategies) below for details." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T20:25:53.487667Z", "start_time": "2022-11-04T20:25:53.136233Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:02:54.110426Z", "iopub.status.busy": "2025-07-11T20:02:54.110284Z", "iopub.status.idle": "2025-07-11T20:02:54.161702Z", "shell.execute_reply": "2025-07-11T20:02:54.161486Z", "shell.execute_reply.started": "2025-07-11T20:02:54.110418Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "africa[\"greedy_s\"] = greedy(africa, strategy=\"smallest_last\")\n", "ax = africa.plot(\n", " \"greedy_s\",\n", " categorical=True,\n", " figsize=(8, 12),\n", " cmap=\"Set3\",\n", " legend=True,\n", " edgecolor=\"w\",\n", ")\n", "ax.set_axis_off()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Greedy is variable in a way how to define adjacency and which coloring strategy to use. All options are described in this documentation together with comparison of their performance." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Defining adjacency\n", "\n", "The key in toplogical coloring is the definition of adjacency, to understand which features are neighboring and could not share the same color. `mapclassify.greedy` comes with several methods of defining it. Binary spatial weights denoting adjacency are then stored as `libpysal` weights objects." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T20:25:53.498536Z", "start_time": "2022-11-04T20:25:53.495171Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:02:54.162122Z", "iopub.status.busy": "2025-07-11T20:02:54.162046Z", "iopub.status.idle": "2025-07-11T20:02:54.163666Z", "shell.execute_reply": "2025-07-11T20:02:54.163462Z", "shell.execute_reply.started": "2025-07-11T20:02:54.162113Z" } }, "outputs": [], "source": [ "from shapely.geometry import Point" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For illustration purposes, let's generate a 10x10 mesh of square polygons:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T20:25:53.505998Z", "start_time": "2022-11-04T20:25:53.502015Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:02:54.163963Z", "iopub.status.busy": "2025-07-11T20:02:54.163891Z", "iopub.status.idle": "2025-07-11T20:02:54.165741Z", "shell.execute_reply": "2025-07-11T20:02:54.165564Z", "shell.execute_reply.started": "2025-07-11T20:02:54.163954Z" } }, "outputs": [], "source": [ "def poly_lattice_gdf(dim, plot=False):\n", " polys = []\n", " for x in range(dim):\n", " for y in range(dim):\n", " polys.append(Point(x, y).buffer(0.5, cap_style=3))\n", " _gdf = geopandas.GeoDataFrame(geometry=polys)\n", " if plot:\n", " ax = _gdf.plot(edgecolor=\"w\")\n", " ax.set_axis_off()\n", " return _gdf" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T20:25:53.676696Z", "start_time": "2022-11-04T20:25:53.509107Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:02:54.166148Z", "iopub.status.busy": "2025-07-11T20:02:54.166056Z", "iopub.status.idle": "2025-07-11T20:02:54.191672Z", "shell.execute_reply": "2025-07-11T20:02:54.191445Z", "shell.execute_reply.started": "2025-07-11T20:02:54.166141Z" } }, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "gdf = poly_lattice_gdf(10, plot=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### libpysal adjacency\n", "\n", "The most performant way of generating spatial weights is using libpysal contiguity weights. As they are based on the shared nodes or edges, the dataset needs to be topologically correct. Neighboring polygons needs to share vertices and edges, otherwise their relationship will not be captured. There are two ways to define contiguity weights - `\"rook\"` and `\"queen\"`.\n", "\n", "#### Rook\n", "\n", "Rook identifies two objects as neighboring only if they share at least on edge - line between two shared points. Use rook if you do not mind two polygons touching by their corners having the same color:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T20:25:53.853264Z", "start_time": "2022-11-04T20:25:53.679119Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:02:54.192121Z", "iopub.status.busy": "2025-07-11T20:02:54.192039Z", "iopub.status.idle": "2025-07-11T20:02:54.218091Z", "shell.execute_reply": "2025-07-11T20:02:54.217856Z", "shell.execute_reply.started": "2025-07-11T20:02:54.192112Z" } }, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "gdf[\"rook\"] = greedy(gdf, sw=\"rook\", min_colors=2)\n", "ax = gdf.plot(\"rook\", edgecolor=\"w\", categorical=True, cmap=\"Set3\")\n", "ax.set_axis_off()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Queen\n", "\n", "The default option in `greedy` is `\"queen\"` adjacency. Queen adjaceny identifies two objects as neighboring if they share at least one point. It ensures that even polygons sharing only one corner will not share a color:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T20:25:54.036657Z", "start_time": "2022-11-04T20:25:53.855749Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:02:54.218545Z", "iopub.status.busy": "2025-07-11T20:02:54.218474Z", "iopub.status.idle": "2025-07-11T20:02:54.245098Z", "shell.execute_reply": "2025-07-11T20:02:54.244878Z", "shell.execute_reply.started": "2025-07-11T20:02:54.218537Z" } }, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "gdf[\"queen\"] = greedy(gdf, sw=\"queen\", min_colors=2)\n", "ax = gdf.plot(\"queen\", edgecolor=\"w\", categorical=True, cmap=\"Set3\")\n", "ax.set_axis_off()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Intersection-based adjacency\n", "\n", "As noted above, if the topology of the dataset is not ideal, libpysal might not identify two visually neighboring features as neighbors. `greedy` can utilize an intersection-based algorithm using GEOS intersection to identify if two features intersects in any way. They do not have to share any points. Naturally, such an approach is significantly slower ([details below](#Performance)), but it can provide correct adjacency when libpysal fails.\n", "\n", "To make `greedy` use this algorithm, one just needs to define `min_distance`. If it is set to 0, it behaves similarly to `queen` contiguity, just capturing all intersections:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T20:25:54.432862Z", "start_time": "2022-11-04T20:25:54.039221Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:02:54.245517Z", "iopub.status.busy": "2025-07-11T20:02:54.245441Z", "iopub.status.idle": "2025-07-11T20:02:54.273133Z", "shell.execute_reply": "2025-07-11T20:02:54.272799Z", "shell.execute_reply.started": "2025-07-11T20:02:54.245509Z" } }, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "gdf[\"geos\"] = greedy(gdf, min_distance=0, min_colors=2)\n", "ax = gdf.plot(\"geos\", edgecolor=\"w\", categorical=True, cmap=\"Set3\")\n", "ax.set_axis_off()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`min_distance` also sets the minimal distance between colors. To do that, all features within such a distance are identified as neighbors, hence no two features within the set distance can share the same color:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T20:25:54.854327Z", "start_time": "2022-11-04T20:25:54.436079Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:02:54.273611Z", "iopub.status.busy": "2025-07-11T20:02:54.273525Z", "iopub.status.idle": "2025-07-11T20:02:54.300722Z", "shell.execute_reply": "2025-07-11T20:02:54.300485Z", "shell.execute_reply.started": "2025-07-11T20:02:54.273602Z" } }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "gdf[\"dist1\"] = greedy(gdf, min_distance=1, min_colors=2)\n", "ax = gdf.plot(\"dist1\", edgecolor=\"w\", categorical=True, cmap=\"Set3\")\n", "ax.set_axis_off()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Reusing spatial weights\n", "\n", "Passing `libpysal.weights.W` object to `sw`, will skip generating spatial weights and use the passed object instead. That will improve the performance if one intends to repeat the coloring multiple times. In that case, weights should be denoted using the GeodataFrame's index.\n", "\n", "### Performance\n", "\n", "The difference in performance of libpysal and GEOS-based method is large, so it is recommended to use libpysal if possible. Details of comparison between all methods are below:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T20:25:54.860153Z", "start_time": "2022-11-04T20:25:54.857221Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:02:54.301157Z", "iopub.status.busy": "2025-07-11T20:02:54.301076Z", "iopub.status.idle": "2025-07-11T20:02:54.302743Z", "shell.execute_reply": "2025-07-11T20:02:54.302533Z", "shell.execute_reply.started": "2025-07-11T20:02:54.301149Z" } }, "outputs": [], "source": [ "import time\n", "\n", "import numpy\n", "import pandas" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T20:25:54.867962Z", "start_time": "2022-11-04T20:25:54.862767Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:02:54.303135Z", "iopub.status.busy": "2025-07-11T20:02:54.303062Z", "iopub.status.idle": "2025-07-11T20:02:54.305202Z", "shell.execute_reply": "2025-07-11T20:02:54.304996Z", "shell.execute_reply.started": "2025-07-11T20:02:54.303127Z" } }, "outputs": [], "source": [ "def run_greedy(_gdf, greedy_kws, min_colors=4, runs=5):\n", " timer = []\n", " for _ in range(runs):\n", " s = time.time()\n", " colors = greedy(_gdf, min_colors=min_colors, **greedy_kws)\n", " e = time.time() - s\n", " timer.append(e)\n", " _mean_time = round(numpy.mean(timer), 4)\n", " return _mean_time, colors\n", "\n", "\n", "def printer(m, t, c):\n", " print(f\"\\t{m}:\\t\", t, \"s;\\t\", c + 1, \"colors\")" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T20:25:54.875087Z", "start_time": "2022-11-04T20:25:54.871547Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:02:54.305510Z", "iopub.status.busy": "2025-07-11T20:02:54.305446Z", "iopub.status.idle": "2025-07-11T20:02:54.307099Z", "shell.execute_reply": "2025-07-11T20:02:54.306901Z", "shell.execute_reply.started": "2025-07-11T20:02:54.305503Z" } }, "outputs": [], "source": [ "params = {\n", " \"rook\": {\"sw\": \"rook\"},\n", " \"queen\": {\"sw\": \"queen\"},\n", " \"geos\": {\"min_distance\": 0},\n", " \"dist1\": {\"min_distance\": 1},\n", "}" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T20:36:29.324334Z", "start_time": "2022-11-04T20:25:54.878330Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:02:54.309897Z", "iopub.status.busy": "2025-07-11T20:02:54.309761Z", "iopub.status.idle": "2025-07-11T20:03:15.398692Z", "shell.execute_reply": "2025-07-11T20:03:15.398467Z", "shell.execute_reply.started": "2025-07-11T20:02:54.309886Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10 ----------------------------------------------\n", "\trook:\t 0.0017 s;\t 2 colors\n", "\tqueen:\t 0.0016 s;\t 4 colors\n", "\tgeos:\t 0.0012 s;\t 4 colors\n", "\tdist1:\t 0.0032 s;\t 10 colors\n", "20 ----------------------------------------------\n", "\trook:\t 0.006 s;\t 2 colors\n", "\tqueen:\t 0.0057 s;\t 4 colors\n", "\tgeos:\t 0.0036 s;\t 4 colors\n", "\tdist1:\t 0.0125 s;\t 10 colors\n", "30 ----------------------------------------------\n", "\trook:\t 0.0268 s;\t 2 colors\n", "\tqueen:\t 0.0129 s;\t 4 colors\n", "\tgeos:\t 0.0081 s;\t 4 colors\n", "\tdist1:\t 0.0294 s;\t 10 colors\n", "40 ----------------------------------------------\n", "\trook:\t 0.0243 s;\t 2 colors\n", "\tqueen:\t 0.0237 s;\t 4 colors\n", "\tgeos:\t 0.0158 s;\t 4 colors\n", "\tdist1:\t 0.0568 s;\t 10 colors\n", "50 ----------------------------------------------\n", "\trook:\t 0.0375 s;\t 2 colors\n", "\tqueen:\t 0.0488 s;\t 4 colors\n", "\tgeos:\t 0.0282 s;\t 4 colors\n", "\tdist1:\t 0.0975 s;\t 10 colors\n", "60 ----------------------------------------------\n", "\trook:\t 0.0664 s;\t 2 colors\n", "\tqueen:\t 0.0526 s;\t 4 colors\n", "\tgeos:\t 0.046 s;\t 4 colors\n", "\tdist1:\t 0.1666 s;\t 10 colors\n", "70 ----------------------------------------------\n", "\trook:\t 0.0861 s;\t 2 colors\n", "\tqueen:\t 0.0718 s;\t 4 colors\n", "\tgeos:\t 0.0837 s;\t 4 colors\n", "\tdist1:\t 0.2378 s;\t 10 colors\n", "80 ----------------------------------------------\n", "\trook:\t 0.1216 s;\t 2 colors\n", "\tqueen:\t 0.1078 s;\t 4 colors\n", "\tgeos:\t 0.1086 s;\t 4 colors\n", "\tdist1:\t 0.3524 s;\t 10 colors\n", "90 ----------------------------------------------\n", "\trook:\t 0.1475 s;\t 2 colors\n", "\tqueen:\t 0.1318 s;\t 4 colors\n", "\tgeos:\t 0.156 s;\t 4 colors\n", "\tdist1:\t 0.4902 s;\t 10 colors\n", "100 ----------------------------------------------\n", "\trook:\t 0.1813 s;\t 2 colors\n", "\tqueen:\t 0.1736 s;\t 4 colors\n", "\tgeos:\t 0.221 s;\t 4 colors\n", "\tdist1:\t 0.6763 s;\t 10 colors\n" ] } ], "source": [ "times = pandas.DataFrame(index=params.keys())\n", "steps = range(10, 110, 10)\n", "for step in steps:\n", " print(step, \"----------------------------------------------\")\n", " gdf = poly_lattice_gdf(step, plot=False)\n", " for method, kwargs in params.items():\n", " mean_time, colors = run_greedy(gdf, kwargs, min_colors=2)\n", " printer(method, mean_time, numpy.max(colors))\n", " times.loc[method, step] = mean_time" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T20:36:29.347774Z", "start_time": "2022-11-04T20:36:29.327020Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:03:15.399062Z", "iopub.status.busy": "2025-07-11T20:03:15.398990Z", "iopub.status.idle": "2025-07-11T20:03:15.404849Z", "shell.execute_reply": "2025-07-11T20:03:15.404649Z", "shell.execute_reply.started": "2025-07-11T20:03:15.399055Z" } }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " 10 20 30 40 50 60 70 80 90 \\\n", "rook 0.0017 0.0060 0.0268 0.0243 0.0375 0.0664 0.0861 0.1216 0.1475 \n", "queen 0.0016 0.0057 0.0129 0.0237 0.0488 0.0526 0.0718 0.1078 0.1318 \n", "geos 0.0012 0.0036 0.0081 0.0158 0.0282 0.0460 0.0837 0.1086 0.1560 \n", "dist1 0.0032 0.0125 0.0294 0.0568 0.0975 0.1666 0.2378 0.3524 0.4902 \n", "\n", " 100 \n", "rook 0.1813 \n", "queen 0.1736 \n", "geos 0.2210 \n", "dist1 0.6763 " ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "times" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T20:36:29.352569Z", "start_time": "2022-11-04T20:36:29.350031Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:03:15.405267Z", "iopub.status.busy": "2025-07-11T20:03:15.405190Z", "iopub.status.idle": "2025-07-11T20:03:15.406718Z", "shell.execute_reply": "2025-07-11T20:03:15.406517Z", "shell.execute_reply.started": "2025-07-11T20:03:15.405259Z" } }, "outputs": [], "source": [ "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T20:36:29.660070Z", "start_time": "2022-11-04T20:36:29.354841Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:03:15.407078Z", "iopub.status.busy": "2025-07-11T20:03:15.407017Z", "iopub.status.idle": "2025-07-11T20:03:15.460993Z", "shell.execute_reply": "2025-07-11T20:03:15.460756Z", "shell.execute_reply.started": "2025-07-11T20:03:15.407071Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = times.T.plot()\n", "ax.set_ylabel(\"time (s)\")\n", "ax.set_xlabel(\"# of polygons\")\n", "locs, labels = plt.xticks()\n", "plt.xticks(ticks=locs, labels=(times.columns**2)[:7], rotation=\"vertical\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plotting without the GEOS methods, the difference between `queen` and `rook` is minimal:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T20:36:29.906521Z", "start_time": "2022-11-04T20:36:29.662613Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:03:15.461505Z", "iopub.status.busy": "2025-07-11T20:03:15.461416Z", "iopub.status.idle": "2025-07-11T20:03:15.508490Z", "shell.execute_reply": "2025-07-11T20:03:15.508258Z", "shell.execute_reply.started": "2025-07-11T20:03:15.461496Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = times.loc[[\"rook\", \"queen\"]].T.plot()\n", "ax.set_ylabel(\"time (s)\")\n", "ax.set_xlabel(\"# of polygons\")\n", "locs, labels = plt.xticks()\n", "plt.xticks(ticks=locs, labels=(times.columns**2)[:7], rotation=\"vertical\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Comparison of strategies\n", "\n", "Different coloring strategies lead to different results, but also have different performance. It all depends on the prefered goal. \n", "\n", "If one want visually balanced result, `'balanced'` strategy could be the right choice. It comes with four different modes of balancing - `'count'`, `'area'`, `'distance'`, and `'centroid'`. The first one attempts to balance the number of features per each color, the second balances the area covered by each color, and the two last are based on the distance between features (either represented by the geometry itself or its centroid, which is a bit faster).\n", "\n", "Other strategies might be helpful if one wants to minimize the number of colors as not all strategies use the same amount in the end. Or they just might look better on your map." ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T20:36:29.912141Z", "start_time": "2022-11-04T20:36:29.908900Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:03:15.508938Z", "iopub.status.busy": "2025-07-11T20:03:15.508854Z", "iopub.status.idle": "2025-07-11T20:03:15.510718Z", "shell.execute_reply": "2025-07-11T20:03:15.510492Z", "shell.execute_reply.started": "2025-07-11T20:03:15.508930Z" } }, "outputs": [], "source": [ "strategies = [\n", " \"balanced\",\n", " \"largest_first\",\n", " \"random_sequential\",\n", " \"smallest_last\",\n", " \"independent_set\",\n", " \"connected_sequential_bfs\",\n", " \"connected_sequential_dfs\",\n", " \"saturation_largest_first\",\n", "]\n", "balanced_modes = [\"count\", \"area\", \"centroid\", \"distance\"]" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T20:36:29.917560Z", "start_time": "2022-11-04T20:36:29.914999Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:03:15.511079Z", "iopub.status.busy": "2025-07-11T20:03:15.511007Z", "iopub.status.idle": "2025-07-11T20:03:15.512586Z", "shell.execute_reply": "2025-07-11T20:03:15.512388Z", "shell.execute_reply.started": "2025-07-11T20:03:15.511071Z" } }, "outputs": [], "source": [ "import warnings\n", "\n", "import libpysal" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T20:36:29.979308Z", "start_time": "2022-11-04T20:36:29.920269Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:03:15.513020Z", "iopub.status.busy": "2025-07-11T20:03:15.512945Z", "iopub.status.idle": "2025-07-11T20:03:15.527861Z", "shell.execute_reply": "2025-07-11T20:03:15.527610Z", "shell.execute_reply.started": "2025-07-11T20:03:15.513011Z" } }, "outputs": [], "source": [ "sw = libpysal.weights.Queen.from_dataframe(\n", " world, ids=world.index.to_list(), silence_warnings=True\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below is a comparison of performance and the result of each of the strategies supported by `greedy`.\n", "\n", "When using the `'balanced'` strategy with `'area'`, `'distance'`, or `'centroid'` modes, keep in mind that your data needs to be in a projected CRS to obtain correct results. For the simplicity of this comparison, let's pretend that dataset below is (even though it is not).\n", "\n", "Strategies used in `mapclassify.greedy` have two origins - `'balanced'` is ported from QGIS while the rest comes from NetworkX. The nippet below generates each option 20x and returns the mean time elapsed together with the number of colors used." ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T20:39:15.844559Z", "start_time": "2022-11-04T20:36:29.982473Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:03:15.528309Z", "iopub.status.busy": "2025-07-11T20:03:15.528228Z", "iopub.status.idle": "2025-07-11T20:04:11.191207Z", "shell.execute_reply": "2025-07-11T20:04:11.190991Z", "shell.execute_reply.started": "2025-07-11T20:03:15.528302Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "balanced_count\n", "\ttime:\t 0.0004 s;\t 5 colors\n", "balanced_area\n", "\ttime:\t 0.0095 s;\t 5 colors\n", "balanced_centroid\n", "\ttime:\t 1.3561 s;\t 5 colors\n", "balanced_distance\n", "\ttime:\t 1.3759 s;\t 5 colors\n", "largest_first\n", "\ttime:\t 0.0006 s;\t 5 colors\n", "random_sequential\n", "\ttime:\t 0.0005 s;\t 5 colors\n", "smallest_last\n", "\ttime:\t 0.0011 s;\t 4 colors\n", "independent_set\n", "\ttime:\t 0.0275 s;\t 5 colors\n", "connected_sequential_bfs\n", "\ttime:\t 0.0009 s;\t 5 colors\n", "connected_sequential_dfs\n", "\ttime:\t 0.0009 s;\t 5 colors\n", "saturation_largest_first\n", "\ttime:\t 0.0096 s;\t 4 colors\n" ] } ], "source": [ "times = {}\n", "with warnings.catch_warnings():\n", " warnings.filterwarnings(\n", " \"ignore\", message=\"Geometry is in a geographic CRS.\", category=UserWarning\n", " )\n", " for strategy in strategies:\n", " if strategy == \"balanced\":\n", " for mode in balanced_modes:\n", " stgy_mode = strategy + \"_\" + mode\n", " print(stgy_mode)\n", " kwargs = {\"strategy\": strategy, \"balance\": mode, \"sw\": sw}\n", " mean_time, colors = run_greedy(world, kwargs, runs=20)\n", " printer(\"time\", mean_time, numpy.max(colors))\n", " world[stgy_mode], times[stgy_mode] = colors, mean_time\n", " else:\n", " print(strategy)\n", " kwargs = {\"strategy\": strategy, \"sw\": sw}\n", " mean_time, colors = run_greedy(world, kwargs, runs=20)\n", " printer(\"time\", mean_time, numpy.max(colors))\n", " world[strategy], times[strategy] = colors, mean_time" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, `smallest_last` and `saturation_largest_first` were able, for this particular dataset, to generate greedy coloring using only 4 colors. If one wants to use a higher number than the minimal, the `'balanced'` strategy allows the setting of `min_colors`." ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T20:39:16.475775Z", "start_time": "2022-11-04T20:39:15.847142Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:04:11.191650Z", "iopub.status.busy": "2025-07-11T20:04:11.191566Z", "iopub.status.idle": "2025-07-11T20:04:11.372191Z", "shell.execute_reply": "2025-07-11T20:04:11.371973Z", "shell.execute_reply.started": "2025-07-11T20:04:11.191642Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "times = pandas.Series(times)\n", "ax = times.plot(kind=\"bar\")\n", "ax.set_yscale(\"log\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The plot above shows the performance of each strategy. Note that the vertical axis is using log scale.\n", "\n", "Below are all results plotted on the map." ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T20:39:22.246880Z", "start_time": "2022-11-04T20:39:16.478530Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:04:11.372538Z", "iopub.status.busy": "2025-07-11T20:04:11.372465Z", "iopub.status.idle": "2025-07-11T20:04:12.371006Z", "shell.execute_reply": "2025-07-11T20:04:12.370786Z", "shell.execute_reply.started": "2025-07-11T20:04:11.372531Z" } }, "outputs": [ { "data": { "image/png": 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", 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", 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", 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ee+9lbGyMhoYGnve85/H2t78dn8+3bOuZ7N3vfjf//d//Pef9fvazn9HR0bFi2yGEEEIIsRZIwE4IIYQQYo06ePDgnPfx+XwTylfXq56eHsbHx+e8344dO6qTb4UQQgghLlYSsBNCCCGEEEIIIYQQYg2RKbFCCCGEEEIIIYQQQqwhErATQgghhBBCCCGEEGINkYCdEEIIIYQQQgghhBBriATshBBCCCGEEEIIIYRYQyRgJ4QQQgghhBBCCCHEGiIBOyGEEEIIIYQQQggh1hAJ2AkhhBBCCCGEEEIIsYZIwE4IIYQQQgghhBBCiDVEAnZCCCGEEEIIIYQQQqwhErATQgghhBBCCCGEEGIN0Vd7A4QQQgghhBBCCCHExceyLAqFwmpvxgVhGAaapi3b8iRgJ4QQQgghhBBCCCGWjeM49Pf3E41GcZzV3poLQ1EgHA7T2tqKoihLXp4E7IQQQgghhBBCCCHEsunv72d8PEogEMblcgFLD2CtbQ65XI7x8SgAbW1tS16iBOyEEEIIIYQQQgghxLKwLItotBSsCwRCq705F4xpugGIRqM0NzcvuTxWhk4IIYQQQgghhBBCiGVRKBRwHMqZdZcWl8uF47Asffskw04IIYQQQgghhBBCLLPlKYNNWQX6skkKjoWhaLS5/fg0Y1mWvfyWr/RXAnZCCCGEEEIIIYQQYk0Zyad5MjFMdzZO7dwKBeh0B9kXaKTB9K7W5q04CdgJIYQQQgghhBBCiDXjbCbGPWPdWEwdMesA57JxerMJnlXXyUbPyvXJs22bz33uM3z3u98mHk+wb99+/uqv3k1n54YVW2eF9LATQgghhBBCCCGEEGvCSD49Y7CuloXDPWPdjOTTK7Yt//7v/8add36L97znb/jsZz+PosCf//nblqVH3VwkYCeEEEIIIYQQQggh1oQnE8NzBusqLByeSgyvyHYUCgW+8pUv8cY3/gk33HAj27Zt54Mf/EeGh4e5++6fr8g6a0nATgghhBBCCCGEEEKsupRVoDsbX9BjzmXjpKzlz3g7duwo6XSKq666unpbIBBgx46dPP74Y8u+vskkYCeEEEIIIYQQQgghVl1fNjnP3LrznPLjltvQ0CAATU3NE25vbGxkcLB/2dc3mQTshBBCCCGEEEIIIcSqKzjWBX3cbLLZLACmaU643TRNcrn8sq9vMgnYCSGEEEIIIYQQQohVZyjaBX3cbFwuNwD5/MTgXD6fx+PxLPv6JpOAnRBCCCGEEEIIIYRYdW1uP8oCH6OUH7fcmptLpbAjIxOHWgwPD9PU1LTs65tMAnZCCCGEEEIIIYQQYtX5NINOd3BBj9ngDuLTjGXflm3btuPz+XnssUertyUSCY4ePcL+/Vcs+/om01d8DUIIIYQQQgghhBBCzMO+QCO92QTWPMZPaCjsDTSuyHaYpsnLXvZ7fPKTHyMcjtDa2sonPvH/aG5u5pZbbl2RddaSgJ0QQgghhBBCCCGEWBMaTC/PquvknrHuWYN2GgrPquukwfSu2La86U1/imVZfOhDHyCXy7F//xX8v//3SQxj+TP6JlMcx1noxFwhhBBCCCGEEEIIIabIZrOcPHmKhoYWTNO16OWM5NM8lRjmXDY+IWynUCqD3RtoXNFg3WLk8zlGRgbYsqULt9u9pGVJhp0QQgghhBBCCCGEWFMaTC+31m8kZRXoyyYpOBaGotHm9q9Iz7q1RgJ2QgghxEXOdhwUQFFmnrnlOA6W42A7DpZj41C6eqkqCoqioKKgKJS/LnR21+zb5jhOdT1CCCGEEELU8mkG23yR1d6MC04CdkIIcZFzHAfHAacagjlP4fxNtQEdu/wYVQG7/FgFpRTAUecfVHGc0lrPN1+o3Qqluv65gkmXOttxcHDQlIUPdy/YFmcSUU4logxlUxRtG6sclKt+tR3seTT1raWioCql35umlN4dqlL+Vw7qVf6vKuXAX+19FAWXqhMyXQQNFyHTRdjlJqCbaOr55ykBPSGEEEIIcSmSgJ0QQlwELNtBqwmkZQsWyWyRTMEinS+SLdjkCha5okXRdkpBuHI21fn/l4Ijtu3gNjRawx4MTal5rE12wleLbMHGcUrr1jUVXVXQVQVNVdE1pfy9ilbz/8rtlcdo5ccY5ccbWuk+pq7i0jVMXUWdJlBj2XYpALSAAOJaV/mdVIJTjuMQL+QZyqQYyaUZyaZJFvIA1aBXJdg5OQNOQSFdzNOXTi44GDcfNqX3DI5DYZmX7dONahAvaLoIGS5Cppuw6cZvmBPeD5ZjA8uf+SeEEEIIIcRqkoCdEEKsQXYpJW7GYJRlO9XsJsdxGIxnuefoEGOpPLFMgYJlX+AtXlmGpuA2NFy6hsfUiHhN6v0mTQE3O1uDQCnYtZYCNgvdnpxVZDibnhCcG8lmKDrr43dZKZ89n1VXzrwrf629XVXUctadWr1NUUo5lypUA44AiUKeZKFAfyaJQmlZbd4AXcEIpqqhKWo5k9OpPkYIIYQQQoj1TgJ2QgixCiqZVFSyomoCO0XL5txYmtMjKc6MJBmIZVFVBVNTMXSVOq/J5kYfWxr9NARc3H9ihLuPDpWynS5StlN6jSJeg+agm6agm9awhzqvWb2P48B84mOOUy7/rLxeCgsK9EyX7WeXa37VcgA1ls+RKOSoc3nwGee30XLsalnodMvoSyc4GR9nKJvCWgdD3PdEmnhue9eqrLt2yH1tgE8IIYQQQoiLgQTshBBihRUtm3imQDpvUbBtipZDwbLJ5Evlqqm8RTpX+prKFRlJ5rAmR99sh3zRhhyMp/KcHE7yUwZX5wmtgIjXZGO9lzqfC59Lw2vqBDw6flPHY+qYek1Ps/JrU8k+rAY/axvyleUti7xd/mdZ5GyLvFUkb9sTbq8E3BwqX2uUvzE0lY2+MO2+wISAW7KQpy+dYCCTZDCTYjCTIm9b1Z8bqkrE9FDnclPn8lDn8lDv9uLRdAxVxVA1ADy6wTWN7VzT2D7hOdw/1MP9Qz2LfWlX1JlklLxlYWrarPezHYdC+fUu2DYuVZsQyJx831q1r3WqmEdFwaMb03RknL/zvRVL75vactpKzzyoBAKlv6IQQgghhLjwJGAnhBAr6GBPlGf6YpwdTZEtrI/Sxgupq9HPa67ftKDH1JYJV8pO+9MJziSjJAr5cglljmQhT3GZs9QeGu7DUFU6fSEcx2EgkyRjFWd9TMG2GcqmGMqmpv25AhiqRsAwaXB72RGqZ2uwrvrz65s6OBwdIZrPLudTWRaJQp5PHH54SctwqRoB00XAMAkapa8Bw0RXVMbyWcZymeq/gm2zM1TP8zq2TBnAUQm0VYZdTGY7DlmrSKZYIF0skCoWyFpF0sUCGatI1iqiKypuTceladWvLk3Ho+m4NR2PbuAqB1gliCeEEEIIIVaSBOyEEGIRKhlwtVNOoZSMVQkWOI7DrrYQezrC2I7D070xfnlsmJFkbjU2eU1K56YPds3Uw69SzqpU+56Vfl60bR4a7lvx7YVSAO5UYnzZlucAedtiNJdhNJfhaGx0ws/dmkbWsqZ/8EUgZ1vksqWefbNxqRp/cfl1U263bJt4IcdoLkM0ny0F4IpF0laBbPlrplickPU4X7qiUu/20ODy0uD20uT20ukPLXg5QgghSmqz4iutDKa7yCKEELWSuSLnohnyRRtTV9kQ9uB3XfzhrIv/GQohxDKYPIV1IJbhcH8c23Gw7NI/XVPZ3xmmJeSp3l8rP0RVFHa3hdjTHuLUcJKe8QwDsQz9sSyxzHLP2FybNFWh0e+iKeimOVj62hryAOUSxZoAnaooODhYjl3NpMrbFkPlktNkIV/OiiplSY1mM6v2vFbaxRysW6iRbBrLsXl6fJjxXJbxfIZkIb/kGbgKEHG5q4G5UnDOR8Awq4NdKtN7hRAldnnYC+Ueo5J1KmrZjjOhpUDWKpIq5ImXM+CTxQJF26bdF6DTF8RQtSmPEUKIwUSOR7qjnB5LT+jXrSqwuc7LVZ1hmgOu1dvAFaY4zjroai2EEKugsnssWDaPnxtnJJknk7fIWzYnhhLMtPdsj3i4elM9l7eHqkG+ygl/pSdWbXAqV7Doj2fJFyw0VUVTS8EtTS1P1FRLkzE1VSmX+0HRcsgVLXJFm2yh9DVftMjkLR44NUo6v3aCPPU+k9/Y1sjejnD19bBse8IJXiVzrhKcK5SDc/2ZJEOZFAOZ1JosCRXrj4qCqWmYqkazx8e+umbafYHqe2+2wSBibSrtX89/P3mQD0ycrH2+T6FSvU1ML10sVANzAcM1Zfp1tljErcv1f3G+N6iqKBRsi+5knLOpGN3JOOP5zKyDlFQUmj0+Ov1BNvrCtPn8qOXsO/n7FGJ9ymaznDx5ioaGFkxzcQG1kyMpfnhkeNb9h6YoPH9nI1safIvd1AX593//LA8//CCf+tS/zXiffD7HyMgAW7Z04Xa7l7Q++YQVQohpWHbpBOWeI0M8dHqMvDX//nO94xl6x3v43lO9RLwmYa9J2GMQ8ZmEvQYRr0nIY+AxS7tgl6HRGfGgqeocS55bJl/kUH98TQTsmoNubtrWyK62ILbDhAzFynOtZC3lLIvDsWEG0ikGMkmi+eySs6bEpSlsunnD9v3zvv/kzLnJvfHE2jA5UFSwbNL5IslskXi2SCpXIJmz0FWFxoCLlpCboNtAURTyxdJFluODCQqWjd9tEHDp+N0GQbdO0GPgd+m4DK26rsr74lIPFnz33DHyVpGwy81trZunDIsxNQ3LtkFRUJHgyqWo8rdZuSA5kE7y496TjOTmn/lu49CfSdKfSfLQcB9uTefySCMH6loImC5sx0aVfbMQl5TBRG7OYB2A5Tj88MgwL9unr3im3de+9mX+7d8+xf79V6zoempJwE4IIaahKjAQz3GwN7agYF2touUwnMgxnJi+Z52uKgQ9pQDeC/e2EvaYKOVsj75ohp7xNKpyvtRILWfXuQyVep+LsNcgmStyZiTFubE050bTq9Ifz2NoeE0Nr0vHa2r4TJ2drUG2NQew7NKBvDbDOVwlUOLWdfZGmtkcyDOeyxDN54jls4xk05xLxS/gsxHrnanOPrF2MsmkuzBsu5J9M7HX52STA3Pnby8FVzVVYSyV4+xoulpKbzsOo6k8D56a2P9R1xTCHpOxVH7K9OHp6FqpbL856GZLk5/L28OLeKYXh8rr9cqu3RNuq21TAOXfo/wNXdJq/14dx6HJ4+MVXbu588wR+jPJRS0zaxV5ZKSfR0f62RQIc6CuhU2BsLQmEOIS8kh3dM5gXYXlODzSHeU3dzWvyLYMDQ3xf/7PB3jyycfZsGHjiqxjJlISK4QQM6iUUPWOZzgyEOf4YIJMoZS5ppQnTShArmgteQKs21D5vas3sKneV81GS+WKHB2Ic3IoyamR5AWdMqupCrqqYGgqulb6amgquqrgNjS2NvnZ2RrE59KnHDxXytM0dXEH1bVlNZbj8KnDjyxqYIAQUMq4e07bZjb4Q3KydwFMfo0zeYvxdJ7RZI5ousB4Ok9ryM3Vm+unffzjZ8d5sieKZdvV/qB+t8GWRj/bm/00BNw1Ja0VpZYBJ4YSJLJFcuU2AZV9c65okSt/tWwHQ1MxdbW6Xyv9//x+rjXkoavRR6loVrLGhFioSnD3B90nOJWMzitgPpew6eaqhlb21q3MCbkQYnktpSQ2mSvyHw91L6jaRlXg9Vd3rsggil/+8h5+8pMf8cd//GY+97l/o7+/74KVxErATgixLtjlElUFpZqFdqHUBpBm8nfffXrJ69FUheftbuHAhgi6NrFktGDZfOynx0jOMFV1OexoCfDKa+Z31WjyEI7lUNvnrxSss/neueOcXMaJrOLSsi1Yx/M7tqApipRTXSCVDLnHz43zw4P9M2Yot4bcNAfdOJSHFzil4N7J4dkzcgJunU31PjrrvGyo99EYcKEqCna5iZ0D5c+K0oWVyZOmZ9rm8mBqqEyhXub9mxCXmsrxi2XbDGSS9KYT9KYTDKSTZKzFHctsC9bx4g3bp6zHKa/L4fw+SC7OCLG6lhKwOzSY4KfHRha8zudub+Cy5sCCH7cQH/jA+y9owE5KYoUQ64LD+b5nsUyedN7CrWt4TA23cb4ErvbAbbmCepW+LNMZTeb44dP9S1p+wK1z5cY6rtxUh9+l45Qnz6pKuQwMh75ohlR+5YJ1UMokHIhlaClPbp3NYoN1lebltSVVlmOTLOQZz2WJ5rPE8jnihRyDmRTxwoUv8RUXhxuaOriuqUOy6i6wylTdAxsiJDIFfnViZNqgXX8sS39s4YNkEtkiB3tjHOyNAWBqKu0RD5sb/OzrDBP0GIBSvdAy320+/xaR94oQy6Gy39VUlTZvgBavn2uUdgCKtk26WCBRyJEo5EkW86XJsYUCyWKegm3h0nRcqoapadWvXYFItSy7Epg7m4wxkk0TL+TQFAWfblLv9tDuDeDS9OrFwOXoEyyEuDDyxcVVFS32cWuZBOyEEMtuJU6QVeV8VlfIY+IxbL7w69P0RTMYmkLEaxLxmdWvdT6Ter+LoNtAU5UZ+yIBpHPF6lTWSmYbzJ5FZtkOecvm+GCCeKawqOe0pdHP1Zvr2N4cmDBxVlEUsvkiY6k8o6kcY8k8D58Zm3Eq7XJJ5op85p6TGJrCC/a0cWBDBNt2FpRpMlPANF0sEMtnGS8H5GLlHnWxfI5kMb8ST0dcwrYF67iuqQOQHnWroRK0u2l7I9dtaeCxs2M8dHqM8fTy/a2risLN2xtpj3hoCrgJuPXqPseySxl2kiUnxNqgKApaTTBcV1WCpoug6aoeN8yWFVeZJA+lwUC241CwLX7Yc3LWLPyI6abVG6DN66fDGyTickuJuxDrgKkvLsC+2MetZRKwE0Isu5U4QZ48uMDUVf7wpi5yBQvbgaLtMJzI0hfNcHokxa9PjBDLFFCAXW0hXrinFbehTXsC99CZMe45OgSUMseCHoOOiIdtTQG2NQcmZPBVaKqCR9W4enM9121pIJ4pkM4XSectMgWLXMEiW+6jpCqV3kgKRrlvUnvYQ9hrYpXLuCrb1RfNcHo4yXAyx7GB8z3zLpSQx6Al5AYHipY9IYA5WW3z8axVJJbPVodFxPM5YoVSYC5RyM27aawQy8FY4OAJsfwqJ8WmrnD15nqu7arn2GCCB06NcmYkteTl3767mas310/bY65ykcaybSr97Spmu3gjhLjw5jO4pBLwc8rBvd5Ugh/0HCdZLF0w1RUVt6bj1rTyVx1X+atb03EcGMqmsHGod3lkHyDEGrch7EFVwF7A6YOqQGd47iqh9UYCdkKIZVMp45zpQKiSebdcJ0yqouAxz+/Ggm6dzQ3+6slZtmBVA2kD8Sxdjf5pl3PLjiYOdIY5NpigP5Yl5DFoCLhoCbrR1Nkz7Sq3Bz1GuRTrfD+mSs+90v/KgyooHZcqkx5f0Rb20Fb+sClYNo+eGeP+k6PEs4vL4ptLa8jNvs4wrSEPzUE3rnJwcj496jRFJVsscjIxTl86wUg2zXA2TdG5+NLRxfpiyXtwTansS7Y2BdjREiSaznNuLM1ALEN/LMtALDPvoTqqAns6wlzb1TDr/UqZyhYPnBolmS2QyBbxu3VevK8dhdI+erbPKyHE2lLtiefY6KrKyzfvwq3pmJo2oc3G5MdUiuOV8lGY/M0Lsfb5XTqb67ycHE3P+zGb67wrMnBitV18z0gIccFVAnCaqnByKMmxwThjqTweQyPgNuis89LV6K+mKa/UwdLkLDy3cb6/nV3uCzdTECrkNbliYx2aqswZeKyYKfCoViNylZ/NvBy73On8zGiKiM8k5DGqGYqGpnLdlgau21I6Mf3Ij44s29AJQ1PZ0x7ihXvbyts88fnOt0edS9PYGapnV7ihWgYXL+QZzqZIFwvkLIucXSx9tcpf7fL/y18LtgRXxPKy5D21JlX2K2GvScBtcHl7qLq/+/Ez/dx/cnTGx25p9POsHU20htzomjpj64Xa/bICjCSyjKdLF25sx8XBnih7O8OSdSfEOmQ5DioKrd7pL8BOVrpITLWcVv7OhVg/ruoMc2YsM68qHU1VuKozvPIbtQokYCfEJWquDCrbcUjnipi6iq6qs/YCqgRqbAe2NPnZ0uSfcAJUmvC6utTatLYZVF6Pyte5evEtV5YgCjx+bpyne2NoqkK9z6Qh4KIx4KY97GFbedrRno7QrCe0s9FUhb0dYToiHjrrvNT7XcuS7VgKkioTvg+ZLgKGuahJbT/oPsHh2MKnQglRqy+dJJbPEjRKU8nkJG3tqZStAgzEMhwbSMx4X5eu8rtXduI21Orvcqb9SvVzx3FwmxqvmDT5upIBXft5IO8PIda++RxL2OV9SuW+BdtiJJthJJsmYJhsCoQn3Fcy7oRYu5oDLp6/s5EfHhmeNWinKQrP39FIc2Bhk2jXC8Vx5hGyFEJcFCpBunzR5uhAnCP9cfpimVKZgFIp1VTIFixSuSIO8NZbt1Hvvzh3gDOZbdDC5IPBxagEN+F8cPD0SJIv/vrMopc5l91tQV521YYp21GxnAeslYls0wXrCrZF3rJQFAVdKQeDa+7z5RMHGcwuvb+VuPQoQNh00+zxETLduFSNXZFGvLohJ2ZrlFMOnqmqQjpf5NhggpNDSU4OJav9OzVV4TmXNXNNV70MEBFCTGE7NqqiEsvn6EvHqwG6kVyaROH8oJuXbbqMDf7QlMdLhq0QKyObzXLy5CkaGlowzcWfSw4mcjzSHeX0WHpCTztVKZXBXtUZXnPBunw+x8jIAFu2dOF2u5e0LAnYCXGRqwTpUrkih/tLQbrTI6lq4KmWriq4DA2XrtIW9nDDlgZaV6B5Z2WCn8PCM7CW2+QDtcr3Z0dTnBhMMJzMoSoKuqagqyo+U2NLk58NdT5UVZn2dSwtZ+ay0sFYlv54hli6QDSdJ5Yp0L+AHk6LoakKv3Ogg7DXIOw18dX0eFjoJNi5WI5TzbpLFws8OtLP4ehwtTm0EMslYro5UN9Cs8dPo9uLrpbK7i3HZnJa7+TJxWLtqXxeOTUtDOR3JlZaZQLpdH3Qaj/jJWC8Np1LxnhkpI8zydis96tzubmivpVkIY+qKNWBFG5Np87lwW+Y1d9xZXLtdJUZSs2NciFIiJktV8CuIpkr0h3NkC/amLpKZ9izZnvWScBOCDGnysnOiaEED5wc5eRwcsp96v0mG+p8dNZ52VTvI+IzJ/y8NpBTyVCpXNlYbLPuXNHiye4o0XSeoNugMeBmS9P8epEsRm0/unzRJlMoYmilzC5dOx8snPxcoXRwni/apPNFsgWLdL48+bVg4+DgNjR6xzMUp+mX1Rx0s6c9jDFpvHhl2ZbtYNulk9Ji+Wu6UCSZLU2aTeeK5Io2hqbiMlRMXcWla7gNFbeu4TJK3+uqQro8XCOWKZDIFIiXG6zHswVSuSJ1PheddV46I14667yYulrN8ptvr7rFqkySTRbyHIuNcjYZozsVX/BgCoVywAWl/LUUGJQBF5e2y8INPK99i5xICyFm5Til1hyVIIxKKRhcsC0G0in6MgmGMykyVpGMVSRnFclbFi5NZ3uojptbNs65DrH8Khn7UwJnTumY4NGRfu4dPLfk9QQMkzds24+mquSsIueSMTJWsTIqrDrEtjo8jNL6691e6l0eVEVZlgoMIS4myx2wW08kYCeEmJf7T45wbCCBqasE3Dp+l47fbRDylAZBuA1tzsBN5ednR1OcHEqiqgohj0HYaxDxmgQ9BoZ2Pihl2065vHbq8mzbIWfZfO/JXg71xau3/8F1G9nc4F9ylpdl2xQth2zRQlNVCkWbs2MpesbSdI+lGU7kpu2lt605wG/vb8c7j6s058tZHTRVJZ0r8uDpUR45M0Y6b024777OML99oGNBz6F2+ZTzEEuUWYOkldKymbIWrfLvZbUOJGszGCzHZiiTwnKcalmsrqhoqoKmqGiKgqqoqDCvDEzLsbFsB8uxKTo2qWKBvlSC/kyS/nSSeCF3YZ6kWBWmqrHRH2JzIExXIFItgZWTJiEuftMFSSzbJm9bFGybnFUka58fepQvD0FKFPL0pxOM57MEDBdh003YrHx1U+f2EDRcEzKuZJ+ycNVAaem72kMalNK85mrwtPb+lQDYUDZFTypOplistm2pXLTLWRZPjg1QXKZT2WaPD8eB4WxqQX2XdUWl2eOjxeOn1eun3RvAZ5jynlmEyrEinH9/nP/hxPuudoWOmJsE7CRgJ4SYQeWKZG0JWG3ftPlkx1UCb78+McK9x4fJF2fOZPIYGiGvQdhj0BR0s7stRFPQPW3wrlJyeqgvxvef6iOdt3DpKq++bhOddV4ACpZNMltE1xRMTcVVnvQ6F9t2ODGc5JfHhjB1DVNXMTWV0WSO3mhm1sf+3tUb2NESWPCHf+Xgciie5TP3nJzws9aQm9sua6Yt7MVjlp7DXMM+FrTecoDu/PGvUi7PuLRLNCrvf61cHpkpFuhLJ+hLJxnIlP7JZNqLV6Pby7NbN9Hm9aNOU+ImhFifJgfnkoU8A5kkg5kUQ5kUw9k0GaswbXNyn25Q5/KU/7mpc3mImBPLIKc7dhILUxugU4GzyRg526Joly6oTfhq2/gMg83+MPVuLwXboj+dpCcVpzedoD+dXLdZ9AHDZF9dMwfqW9AUVQJL85ApFhjPZxnLZYjms+UKmVJ2o6ao1axGVVFQUfDqBvVuL2HThaGWjrFn66EsLjwJ2EnATgixQmzbIZ23+NZj3ZwZWdwAgDqfye62EJd3hGgKTL+jSuaKfOJnx8gVbXRVYWO9j2g6z1gqXw1CuXSVv7h9B4amznkAPVPj4JFkjk/+/PiMjwt5DN5667Zp+yVNl5k2U9Dtn350hFSuOO06gm6DjoiHyztCbG8Ozjmhd3IPvIJlE8sUyBYs8kWbXNGmYNkUyl8VpRTcNHQFQ1NxGxptYQ+Gpi5bkHA9qz3RsxybLx5/ivF8dpW3SqyU/XXNPLt1k5x0C7FOTe4rF8tn6U8nGcqWgnND2TRZa+rnbdBw0eT2EikH5xrcHiIuD2btCf2krC4xP5N/JwXbpmhb5Mv/clbpX6H8/fH4GGfn6CtX4dZ08pZVza66GIRNNy/q3EaD2yvBo1k4jsPjowPcPXB20cvw6QYR003Y5SZieoi43NS7PARNF5qiTsl2rPa5rSmPF8tPAnbLE7Bbm136hBCrSlVLQY3ruurZ2RLEsm00VUVXlVLZYvmfriroWul2Q1dpDronfCB+94lePvWLE9T5TBr8LkxdxdDUcmBJLQfYwO/SMXWVVL6Iz6UT8ZnVzLobtzWgq3MH62DmrDKPobG3I8xoMkc0U8DQFNyGVv13zaY6lElX1C3boWDZHBtMMBDLMBDLMhjPUu93ceXGCJe3h4HzQTXbdnjW9kbuOtg/7TbEswUO9Rc41B/H0FQ8hlYaZKEpGGrp9XDranXoh6GpJLJFxlI5xtN5Ujlr2uXOxtAUdrQE2d8ZZktTYMGPv5jUvi81RWV3pJEnxwYnTJAT61urx8+LN2zHqxtyciTEOjPhoopt05+ZmGmVt2f+DGzx+NkSiLAtVEedy1NdnjPNIAlFUdCmmyQggEpQjtLkLIBydhPAWC5T7UPbk4qTm+V3slDTBV/Xm4jppsXrp8MbYFMgQsAwZQLtHCqvT6Pby55IEyfiY2QW8V5IFQukigV60onqbZqi8KLObXQFIlOOCTRFBQVyVpFYPoeDg1c38GhGdXhVRSW4J5m3YrVIwE4IMa2QxyToNsoHvbX9fs+XXcLspZe942nchobfpRP0GDQFXDQH3TQEXJiaiq6pPGdXy6zbYS3DBFO3ofE7V8zcS266SbGaqpDMWhSKNtlCafhDY8BFrmhz77FhbAcObIhUH6OqCldvrqct7GE8nSeaLg2BqEyBjaYLFKxSaUfBsqv/X0mmrhHPFBhJ5tjSFJADxzLbcbi6oY1rGtsZyaY5Hh/j4eG+dVt6I0rihRypYh6/nCQJse6oisLR2CiPjfQzmJ1+kn2Frih0+kJsCUbYGqyr9q1UJi1v2hGfYka241CwLU7Ex0u9YR2Hom0zUA6eLiaQcrHy6QYtHn+1b12Lx4+pVVqf2NWWHPI5NLvK69PuC9LhC/Kcts30pOIcjY0ymssQMt3VzLk6lwe/bpzviVgtv3aq7REdzreLMVQNr25M+zuoZIu6NJ0mj066WGAgnSRrlXolGqqGqWqETTdB0wUKU/YxQlwoUhIrhFgWRcumL5ZhMJbFcaDe76Il5MZXHuSwHnvDzLbNswUESiWtUx+XLVgkc0VS5X+ZvEU6X6RoO+Ug6MQedKqi4NIrE2JVPKZWzsA7359PUxUyBYtUeSpsrmhR73MR8ZnVYSBSEjuzSr+d4WyKO88ckROSdUxVFExV5ZrGdq5qaMNyHLR1sq8R4lJW+Zw9ND7MD3tPTnsfU9XYGqxjSyDC5kAYXVWrU8jF0tiODSg8MTrAA8M9ZK3ly5y7mLR7A1weaWKTP4TPMIFS9pWUVC6vSmCs8ppatn1BetJNniRdOxFYfr+LIyWxUhIrxCVrpmBRwSpNSc1bNvmiTb5o4TgQ8ZnVwFntMkofTNVbqj9bSFDNtssZeIpCe9hDW9hTbQxbuwxFUdbdyfNs2zzb66OWIm5Tbq+U4Db4XdWg3sQMgInXTyo/VVBmHSThMTQ8hka936xOV6slwbqZVYKkjW4fr96yh2+eOUxUetutG/UuD6/YvBuXpk35+9AURTLthFijaqeBDmdTPD0+zJHoyLT37QpEuL29C4+mT/iMk2Ddcim1QYnmsxKsm4amKPzZ7mtn+Jm8B5fb1GPYC/MaV44Hpzt+F2uDZVkUClnscvWVYbjRtPkNJlzPJGAnxDqkKEp1Z3ViKMH3nuwjninM2qrX0FTqfCb1PhO3oaGpCmqlH50ysTddZ8RLe8QzYT0zmfiztfshV0kmXszJe+2JRSpXZDCeRS0Poqi8jqqiYKgKfreBqZ8/uKgENCcHzaYP6i3t9asebIgFUxUFv2Hyqi2X899njtCfSa72Jol5iOazPDE2wO5wI4Fprt5KsE6ItaWSFZexihwcG+JQdHjGAUBuTefW1k3sDDdUJz/KX/Tsqj3oAKV82a/ymlX2hxMyicq3G6rGrW2bURSFx0cHLvRmr2m24/D0+BBbg3UkCjn8uolHN6o/t2y7GmSqDOWYPORALD8ZIHPpKBTypNMJ8tN8VpimG683gFHOel0psViMT3/6E9x33y9JpVJs3bqNN7/5bezff2BF1wtSEivEReGnhwb41Ynpr0wvlsfU2N4c4JpyXza7PC31Qn0ozpQZU9sbZD4q5aD9sQyW5dAUdE8IqNUqWvaUSbG2XSqLvefYMMcG4gwlcnOu021obKr38pv72vG79BkP3Krj51FmDIrO/tipgcDa8tfK/zN5C01VMLTpD2os20G9gL/btayS+fj97uOcSIyv9uaIGi5Vm3DCrioKXt0gYJgEDBc7Qw20+6RXoxBrVe3fZsG2+NThR2fsHbo9WMdtbV24NE0CH/Ng2TbJYp5YPkeykK9eZFTLlQIK5YuzioJNachDziqStyyydulrzrI4nYxeFAMgVpquqNS7PNS7vTS4PDS4vSiKwkA6SbKYL00phwlTdSuKtl3tsTabxV5oth0bdZrMv0qAa8rV/XUwKbX2tYjmsqStAuligZxl0eD20lR+/aVMfm1ZjpLYXC5DPD425/2CwTpc5aFDK+Htb38z4+Nj/OVfvotIJMI3v/lffPe73+YLX/gymzZtnnJ/KYkVQlQPfJ/sHueh06PLtlxDU+lq9LG1KcDWJj9hb/mKRbnhqlae4LYSveim6/2WzhW578QwI8k8fpfOC/e0LmiZuYLFVx86S894pnpbyGNQ5zPJWzaZvEXRcri8I8SNWxtR1YlX8FVV4bP3nKQ/Nr8ySU1VuHpTHTdvb6w+h8knG5VAWipX5FBfnGgmTyJbJJEtkMgW2dLo54V726qvReW1UcslfrFMgaF4luFEjuFkjuFEjpFkjga/ixftbaM17Cm9P4B4psDdR4d4sjuK7TjomoKplfrimbqK32WwvdnPrrYQAbdxyQfv1PLJzIs3bOfugbOSabBG7AzV88LObTP+vHIiIsE6IdauSpuHvnRixkE/QcPFbW2b2ByIyN/zPGSLRf7j+BPSf/UCKzo2g9kUg9nUjPe5rW0zfckEB8cHSRYLpAp5UsXChInHhqpilgccmJp2/v+qRqc/yO5w44K3zXHgibEBHhzuxaMbeDW9/NXAo0899ddVla5AhDqXZ0r/uLVg8n7AZxgYmopXM8jZRSzHZjyfpc7lkWDdRaZQyM8rWAcQj48RDjeuSKZdd/c5HnroAf71X/+DvXv3AfAXf/FX3H//r/jxj3/Im970p8u+zloSsBNiHbLt0snpXU/28fi5pWcBNfhdbGv2s605wIY6H5qqTEjxzxdtusdSnBtLM57Os6HOx1Wb6iYso5LxVTFT3zSrfKfanzuOg2U7nBhKkswVqfeZNAfdeF06LkPjubtaONwfpyXkXtDEWMdx8Lp0XnZlJ3cd7OfYYGnceyxTmuBa8ZJ97RzYOPPJwXg6P+M6wl6D3W0hGgIumgOlCbh6eRtnmkx1bizFAydHOT6UqOkheN6e9olXXW3bwXIcvvFYNycGExTt6ROj+6IZ/u3ekxzYGOG5u1owNZWAW+fF+9p49s4mHjo1SjxbJOQxCHsNAu5SSYdlO3SPpWkOuqn3l66AXcrlHJXA6LNbN9Hm8XP3wFlSxcLcDxQr5lQiymMj/eyvb5n2fakoCpoUywmx5lTLL3E4Eh3l8dEBhqYJcqiKwpX1rVzf1EHlY34tBQ0qKhcHagMDtZlLF3qwlqlpEqxbg54cG+RMMkosP3tVRsG2Kdg2KaYeYxyPj1Hn8lDv8qAr6ryrSzRVJVEODs732OXegXPUudxsC9azI1RPg9u7ZgLmk7fBULVSdqJEMS566XRiwfcPheqXfTvC4Qj//M8fY+fOy6q3ld6XDvF4bNnXN5mUxAqxztjlMsov3X+Ws6MzX9mbi9+l8xtbG9jVFiLoMbCd8sGmqhDP5Dk1nKJ7PE33WJrhSWWgv3NFB3s7wtXvk9kCo6k8Y8k80UyelpCb7c1BVOX8FfXKgIuneqJ4TY2tTYEpmVxf+PVpzoycf05Bt8HezjDXbK7Da+qAM+8DloJlV6ekVnz4h4dJ56c2VA55DPZ2hNneHKAt4kFVlGoWnG07HO6P881Hu6c87sCGCC+4vBVNVabtUzeZbTucGknylQfPThuoq3XVpjpecHkrigLpvMWX7j/DQHz+wxA8psbzdreyrzMMTOzDVwqaTgzITehnUw58LCQ4erGyHYeibfPlkwdn7LMkVkaD28v1jR14dYP+TIKBdIqsVeDySFO5p9X0ZT9CiLXDcRx+PdTDwbEh0tb0wYMOb4DntncRNktlQ2shSDATx3HKwZgYqUIen2Hg0038holPN/DrJgHTJKC7cE+TzQSlz5WCbaEparVsdSm+ePxJRnKZue8o1j1dUdBVFV1R0VWt/FWtuU1BU1TOJKLk7MUPELmqoZWbWzYu45YvTm2APF3OUnTrOi5Nx5yhpNh2HBzOH89eqhef14KllMRalsXY2MKrXOrqWi7IIIqf/ewnvPe97+LDH/4oN930rCk/l5JYIS5RpZJR+PpD5xYdrNNVheu3NHDT9kY0pdQ7rZL19uCpUX59coRUbvartfceHeJwf5yRRI7xdL76+FpuQ2N3W5B9nRHqfCaPnBnj4TOjpHKlAwiPoXH77hb2dYarwy1eekUHn/z5cXLFUplMPFvgvuPD3Hd8mF2tQV5+9YY5n18lMyyRLXJ0IM6ZkRQjyRy240wbrINSxt0vjw/zy+PDuA2NvR0hXrCnDSgFrXa3hzg7muL4UIKiVQrkvWBPKztagvO+AmnZDqPJHN94uHvOYB3AI2fGGIxnuWpTHT87PEg8s7AMr0ze4thAvBqwUxQFHAfLtlEUBVVRiWXyPHRqjONDCZqDbjrrvGys99EYcFUDe5dyeWxF2ipIT58LrMnt5Q+27q1+3+4LVP+ftYpkioXSwfql/dYUYl14cLh3xp95dYOXb95VnTa/ljmOwwPDvdw/1HP+xhmu4xiqypsvu2raEr1KFt53zx3jtzZuZ7od2flJ8mVK7fgIqmWLtuPw8s27+Mbpw4zk0ot8ZmK9KDoORcsCLJgmK2+5DGVSq14ea5ff/0+MDnIsNjplGJiKgkvTcGs6Hl3Hrem4NQN35TbNwK3reMolwR5Nx6VpM/YOrP2bU5YhkC6WplBY3EXyQiGLpvmWeWsmevLJJ/iHf/g7br75lmmDdctNAnZCrBOVQNTPjwxyYmhxEyy3Nvl5yf7SIAQ4P202ms7zlQfPMpaaufSz1mgqz+gc980WLB49O86jZ6cv2c0ULP7nyT4iPpPOiBdVVQi4DXa3h3hsmscc6o/zdG+UXa2h2afWlj9gI16DazbXc/2WBtL5Iof74hwdiHNyOFU9CJhpu2OTgmOO4/DCvW0TbrPthTUDVhV4/Nw4eWv6BtvT6R4rZTguVth3vo9DIltgOJFjNJljNJVnKJ7lzEiq2nt4OJHj6d5SWrepqbRHPHTWednWHKAj4l31A7fV4DgOPak4/3Pu2JKuVIuFG81l+OXAOSIuNw1uLw0uL3o5u9at6bP+DQsh1oZkIc/JOYb3ZIoFCo49Y7bMWmE7DgOZJA/UButmoCkKz23rmjZYV8l211BIFfPVIJzl2NXG/1mrSE8qTiyfw3LsUqZ3+atlO5iaRlcgQovHh6ooeHSDV3Tt5s4zh2XCuViyTl+Q391UKv1bzWO+yvF8vJCb9n1t45CximSsIuPzO32pLrcU3CsF89y1//Tzt3l1gzZvYMK2iAvHnqH9z1xWunj03nvv5n3v+/+4/PI9fOAD/2dF11UhJbFCrBO1U1pzBYtz5WDOubE0fdE0BWvuP+U/f+4OAm69+gFsOw4Fy+a/H+uhZzxdzX67kDyGxpuetYWw1+SZ3hjffqKH4gzPxefSeOut23Hp6oIPIiolrtF0nh8/M8Dh/vjE7TA1NtR52VDnZUdLkLDXrJa42rZDwbYZT+UJuHXchl4qg13ABC/bcTg3muILvz6zoO1eClVRqPOZRNP5GfvezUfIY3B5e4i9HWGagktL615PUsU8d3WfoGjbhF1uwqabWD7H8fgoBXv+gVcxf4aqoqJMCZAqQLPHx5X1rWwP1VfLu4UQa9P/nDvG2WRsQoP9mfzOxh1s9IfX5N905WJpbyrO93tOkCzMHhnwaDq/tXEHLR7/hOdTmV45nstwcHyIw9ERdoUb+Y3mzurP7+4/S286zlhufpklXt1gSyDClmCEjb4Qg9kUXzv1zOKfrBCAVzN48YZttPvmV0VS2/N4timtlQy22bLXKhfjKheIKwHu/zp9iL4F9jNbDi0ePy/s3ErQcK3J/dNat5SS2Gw2RSIRXfA6A4EwbvfKZNh94xtf46Mf/SduueVW/vZvP4hpzjzgYjlLYiVgV2M8l6XoWJiqRtBwVXdQa6Xp5mqqbagracJrQ23POdt2eKJ7nLsO9lfLU72mRkvITXPQTXPQQ1vYTYPfNeG9PLnkMVe0iKULBNw6HlOvuZ/NB793aMWeS9hrsKHOx1M90Tnve+O2Rp69s2nR78HKgUW2YJHKFUnmivhdenXYQu1rYtsOqqrw+LlxfnpooFpSqwABj8Gu1iDXbakn5DGr951r3Q+cHKn28wM43B+nL7p+es+87bbtRLzGhPdR0bLJFiz8buOiGVZR2/OvopIBUXRsjkRHeSY6vCoHkBezW1s3cXmkiYPjQzw60k+8MLVhd9h0cWV9G3vqFr8fEEKsnNrj5kyxwFguy1guw7lUjKOxqVPttwXreGHnVpRyvtlaOOau9MjsTsa4f6iHnnnu629t3cT++pbq95XXYjyX4Yc9JydkCr1u6z7qXO7q8/1hzwkORUcWtb2GqqIpqrRvEMtmd7iRW1o3YqjarAG2aD7Lt88eocntY1MgTFcgglc3qvex7NIU15FsmkQhT9B00eDyEjZd1b7Ulb+TvnSCaD6LVzPwG2a11PWRkX5+NTi1l/SFoCsq1zd1cFm4Ab9hXpIVJ4u1mIBdpff5Wuth961vfYMPf/hD/N7v/T7veMdfos7RU10Cdivkp32nUSh96IUMN/vqm1d7k1Zd5YAlUchzNDYCDoRdbiIuDyHDha6qF80J+npnOw6D8SzpXJGWkAdfuey10vduroEIswWmTw0n+dL9Z1gLOwuPofEXt+9AU1d+Elu2YPG1h85ydnTmslQF2Nka5MX72jB1bdbX2SpnSao1FwN+9MwAD56aegKzVm2o89Ie8RBNF4im88QyhWogc097iOfvacWta5fMwIp4PsfT40Mcio5MG1wSC9PpC/LyzbuA0r7raGyUh4Z7GZ2mofrNLRu4or5VPn/EkkztwVr7vTLrxHPp8Tk/tY3jv3XmML2pBEVnYpayR9PZW9fMgfoWvPrqXfyprPdMIsoDwz30pRdWYurXDW5o7mR3uLF60aeyzHPJGD/tO020PMBoR6ieTf4wTR4vEdPD6USU/+k+tgLPSojF8Wg6t7RuZGeoAUVRJpRuW45NpljkKyefJlmcmHna4PLiNwzGclkShdy05w8KEDJd1Lk8BAwXZ5Ox6t/GWlXv8tAViNAViNDqLWXRzpZVWOGUB2FMaEtZHsy3GtOlL5TFBOwsxyFvWXh0nVhslPwC3hOm6V6RKbHnzp3lVa96Ob/xGzfx13/9ngk/c7lc+P2BKY+RgN0Kqo6fL3+4Xox/PLVqs0gqU6tMVUNRFDLFAkdioxyJjszYE6PTF+S5bV2EXaU3YtYqoilKdfKVuLCWcgJRCdgNxbMMxLMMxDIksqUrtYZWmjxl6CqGpmKoCoamkrfsatAmmi4Qy+TnVZq7VLvbQtywtZ7WkAcAex4BycXoHU/z2V+emtd9Qx6DV127kQa/a9ZgVeV1jqbzfO2hcwwuYPLreuDSVZ69s5ldbUEC7torrOv75Ha+J4/fOXt0zp5NYnq6orI7Urqif/6EwEFTFI7FRnlqbBBT1fAbJgHDRaPby6ZAeLU3W6xTlb/ps6Mpjg8m0FTl/L/yQKZUrkg8UyCeLRDLFEhkiqgqdES8bKjzsbHBS3vYW5ooXpP1LqY3eT9q2TZFx6Zo2xTKXy3Hptnjr97nQle5WI7N2WSMb589uqTl7Ktr5tbWTRO23XZsHOAH3Sc5Fp94oU6BNXFRVIjpuFSNdl+QDb4gG/1h6t0essUiXz31NONrPMi2UlyqxqZAmM3lrEK3pmNV9me2TcG2yNsWOav0tfR96faCXbqfqWp49dJQDJ9emjTt0XUMVbsoEmKWUhILUCjkiUaH533/cLgRw5i5THWxPv/5z/HpT39y2p+98IUv5n3v+7spt0vATiyY4zilFN6actaCbdGXTtKbitObTtCfTlJ0bBTApWnkLGteBw+aorAz1MBoLsNATWBPATYHIjy3fTOmOvNUnlqWY1/SI7grO+fVSreu9Hlbqrue6uPhM2PLsEWz85oa25oDbG8OsLUpgKmrE3r9LYXjOBwbTPC1h87N+zGGpvK7V3awvTkw6/orv1/HgXS+VJYbzxRI5ooks0XG0nnGUnnGU3mSc0zsXYyAWydbsFY8uGpqKg0BF40BF43+0tfmkJug28BxWJbf04VgOw5juQx3njlSzQrx6QYh003IdNHg8rCnrpSR/dBwL/etUtnGeqUA1za2c0VDK25Nn/cJupSliMWofM4OJ7L8+JmBeQ9x2tLoYySZI5aZuE/WVIUdLQGec1kLEd/ynyhcqla7Hc2dZ45wJhld9ON1ReVNO6/ArU2d7/ezvtM8OTa4hK0TYnV5NYOCY0k/3xqV87elUoCdoQaub+ogZLrW9THOUgN2ALlchnh87nPKYLAOl8uzqHWshOUM2MmU2ItcJR6btSyOxUeJ53MkCjnGchmGs+lpA3JO+f7zZTkOz0wT/XaAU4lxPnOklG3iUjUC5cwIgGQxT6pYwHEcwqabOpeHiMtNxHRT7/YSMs73NpjMLqcWKyhTGphWdpZrLehXCZrWZm7WBsgcx8G2Hb75WDd1PpNrNtcTcJ+f5jrZSmQs1QbrZsoWKFo2ijJzmRBAf+zC9GRL5y2e7I7yZHcUVVHoiHjY3OhnS6Of9rAHVVXmDEJWfieTX0/HgfaIl5u2NfJ0b4zx9NwjqAqWzdO9MbY2BVCZ+WSj8t5UFPC7Dfxug+agm0plVu32FiybaLrASDJLIlMkW7DIFi2yhVLfuGzBIlc8//9swZ5wwGBqKh5Tw2NqeE2NvR1h9nVGyBUsHj07xpPdUZK50nKXMJdiWnnLpi+amdKjz+fSuXZzPdd01WFoaz8bV1UU7h/qmVDykbWKE8o0f9J3GoCw6WZ3uJGTibEF7UcvZX7D5IqGVlzlizrz3adJsE7UKn3GUt2PW7ZDrmChaaWMOV0rHU+kckV+fniQJ7ujM16U9JoqV22qp6vBT2PQhcfQqg3QswWL0yMp7j85Qs94Bst22NYUkGDdMlvNv23bcXh+xxb+49gTi54QXpnqOnm5J+JjEqwT617aKqz2Jqw5yzW93gEOx0ZQFYXb27uWZZlLUUmmcRwHFKoVEBeKy+UhHG4knU5MWx5rmm683sCKZNatFZJhdxGzHYdkIc/DI308Mz5EcR3+qg1VrWbnmaqKoWq4NI2Q6abB5cGtGSQKOeLlf3nLosHtpdnjo9UTmHBlohLYu1BXbWvLjRPZAgOxLCPJHCPJHKPJHEXb4fZdLWyoL02yGU/nuefoEE92R4FS0ObKjRFu2dGEy9CqJ6eVAFTPWJqOOu+U9U6XQr3QzLnKMuKZAvceG2I8XaiWBeWL56+m6WrpJChfXP5gz2LU+UzchoZLV/G7dbY3B7m8PTTtfSu/n3TO4p5jQ4S9BlubAjQH3aWfOef7S6iKQl80w91HBjk+SzbG627YxKYG/4w/X4rKdC2glIU6y++zaNkUbQdTU6fcr/a9MHlQRu94mh8c7Kf3Ag3BMHWV37uqk82N/jUdtHMch0dH+3lwqHfa6aUtHj9dwQjbgnXUla/uSWns4qko1aCLRzPYGoywI1RPq7fUI2Q+/WLEpaGyP7Nsm8F4jt7xNP2xLH3RDMOJ7JTPJa08pGm2jytdVfnr5+/A0DUsyyKTyZDNZslmsxiGgd/vx+v1oigKj50d43+e7MPQVG7a1sANWxvPr2dSBujFUN60Fk0+ppvPJMr5+uLxpxjJzdzDdjYuVeMtu66ufm85NrF8ji+fPChZSUKIOd3UvIGrGlpX/eJFNJfl4PgQXt0o/9PxGyZezcCjG7PuZydn2FU+exf7jCzLolDIVvfxhuFekQETy0FKYsWcKg1+P3v0cVLFS/cqiKGq1JkegqaLrkCE3ZFGcgWLdN4i6DEmZLc58+w9U7mKXzkgT+ctPIY6IRvQdhx6xtIcHUhwbDDBSHLmZvSbG3wkssUZ72NqKtdtqec3tjZi6ipnRpL84sgQ3WNp3vObu9AnDV94+PQoD58Zw9RUTF3Fa2psrPexrTlA2GvO+7lWdg1DiRw/ONiH40DYa6KrCgPxLIPx7DTNupeXoam4dBWHUlbEbDY3+HjtDZun3D75JKkSpCpYNg+eGuWXx4cnBCErZbb7O8NsavCfL1MuP+7kUIIfPj0w7e/r1p3NtIbdhL0mAbeOSy99iCw1SBxN5/n8r07j0lVchoZbV/G5dCJek6DXIOjWaQ6WBo3MZ1rtTConv4+fG+dnhwdI5VY+Q0xTFV55zQa61nDQrjJ8x3EcBjMpelJx3LpOvctDg9tb7TVSe3L+qcOPkJFpfYvi1Qw2B8JsD9WxwR9CU2S4kTivcrHFdhwePzvOY+fGGYqfD87pKjxnVys7mgMcGYjzk2cGWEh45HU3bGZjvZfu7m6y2el7M0UiERoaGvjuE730x7JsqPOyod7H1iY/bkPDcRy6U3GOxkY5nYjS7guwO9zIRn+oOgFPskOXrvLZ2pOK89BwL26tdCLp081yr8vSP69mVI/R5lOFUXmPxQs50sUCw9k0PytnUc9Xq8fP72+5fMJtUgorhJivZo+Pqxva6PAFJ0zdrcgUCySLBexyBZfjOFiUvnr10pTd6UrypzPTMZbtOMTyOb5z7ghjuek/D12qhqcczIPSxYlSf1IH3bLYntMI1zVhmqVhlbqiVr9WVrnUQN5aJAE7MaNK5kGqWOBXg+d4enz+jRovdvUuD6/bto/vPNHDE+eiQKmXV9hr0hJ0s6MlwKYGf/lq/dSMtEogZDSZ4+hAgtMjSc6NpslbpVMBUysFUTymxlgqR7awvFdQPaZG0G1MGFKwtyNMyGMwni71OxtL58nkZw6yhL0GWxr9bKz30Rx0U+czq2VClWmyk8tsJ78WlQNky3YYSeY4N5ri7qND1UmhC2VoKk1BFy1BN81BNy0hD40BFy5dnbAduaLFaDLPUDxbzVKMZ4ukckVS+SKW7fCbe9q4clPdrAGy2oBrPFPgmb4YRcsm7C1l5/VHM3SPp+kZz7CzJcCL97cD5w/uK5Nev/N4L0/1RGd9bqamsq05wMuu6pzyei4ksDYQy/CZe07Oeb+N9T72dYa5vD2Epi4tu2A4keWOX5xY9OPnoijQGvKwucHH1qYAmxp86yIoU7kYMtNUr0q/uy+eeGqVtnB9avH42ByIsCUQocnjm5ChLASc71mYLZYutjx8enTC587bb9uGz61jqBM/OyzL5mBvjAdOjeIyVNy6htvQSOUKnBxOVe8X8Zq8eF8bmxp8jI2NMTo6/fRuVVXp6uoqZexTeo86jkPOshjLZzibjHJofJhYYWobBZ9usDPUwK5wAyHTjTlNZsByZohdCiqnMclinp/1neZUIjrt/dyaTpPbR5vXT5s3QJsvgDnNxZaZPDU2yK8He+ZdCnh5pJHb27dUv7cdh5xVpC+dJGsVSRbzPDLct+iSWyHEpSNouGj3lvpjj+cyjOUy89p3aIpSHWbhM4xSqwi7SM6yyNkWecvCUFWubmhjX33ztH3kHceh6Nh88/ThGYdQzsRjw37bRaiuCXWaoKOqKNUAnqGquFS9Wt203j/9JGC3gtZzqU3BtjgWG+NwdJjuVFymTU0yXcBuMlNX2droZ3tLkE0NPnymVg1o9YyluefY0LwbVK8XIY9Bg99FQ8BFg99FU8BFxGfic+kznixUgk6V4Fc8U+Bbj3ZTtJ1SFpiuYupa9eRowldDw2OUTpg8pladJDq5x18lE9ChFNyZEDBjaoZgwbJJ54sEy8ubT/ZCZfsp90NUlIkTZ3NFC7P8+5+8vPkO1uis8/KGG7uqJ2C942l+fmSQbU0Brt5cGj0+W7my4zicGU3xxV+fmXNdFXU+k7fdtn3e95/JiaEEQ/Ecw4ksQ4kc/bEMS/3E0FSF3znQwbbm8pCQNdpzcjEq7+FvnTlMTzqx2puzprlUjY3+0nS1LYEIbl2XIRJiWpWLG9F0nl+fGCFbsBhL5xlN5MiWs6M3Nfh43Q2bORobxaPptHsDM/bAnbBsxyGdK5Ir2NT5SxnosViM4eGZL3b6fD7a29sZTCerw7YOR4fJLaLMUVOU0nRArZSdUJoUWJoSWO/ysMEXQlPVdX1sWjF5X7/QzPPK/qFiusf2p5N89dTT815mi8fP1mCE7aF6wqZ71n1QJYvl0ZF+fj3UPecxdrs3wO9s3ImuTuzTWm1toSh84fiTE3qhCiHEavHrJtc2trOnrql6W2V/mC4W+GHPCc4kYwta5lwBu+mYqoZb0/Ho63vUggydWCE/7DnBBl+ILcEIrvJo5rV0gFQ5uDkWG+WB4V4su9TQ1qZ0EJG1ilgSf51R9XBplpcoX7Q51B/nUH+8elulLHMlpnWuBbFMgVimwMnhiYFIVYGgxyDsMdE1pTrQoM5ncmBDmO3NwfKUz9L9/ujmLVOWXRt0qw2KKdMcvObLgxPyllMNvkXTedI5iw31XjbU+UrNTmcYtGFoKiHPwhqOKoqC4pSu41QCgApO9QSxUtI6+TnlizZPlHsNzqXy/DJ5ix8fGqj2KDw1nOLB06P87hWdtEc8M564KIrCowuYuNvgd/HyqzqXpVfjlkY/mxt81RPfbMHicH+cI/1xTg4nF10Sraql3yNQzepcj2qzwGzHYTCT5FeDPRKso/SaBHSTgOkiYJgEDRdBwyRouggZboKmqzTspeZz9mII2orlU7kwNBDPct/xYY70x3nZVZ3sajvfl9R2HCy71BAb4Gd9p6rDXnaHG7mivoV6t7f63hodHSWVSmHbNrqu43a78Xg8BN0mY2NjRKNRrDmGxbjdpT6n/3PuGPHi3MOIZn2O5V7DyWmy8aA0aXSDP0hXIMLWYB1e3Vi2wPZKZ/LVBujytkVvKk7BttkSiExZX22P1lqVbcvbFmcTUc4kY3h1g6sb2qqBsMp6Hh8d4IGhngVt40AmyUAmyX2D3dS5PKXgXbB+2ixfVSm9y65taudobISROQJtvekEnz32ONc1trO/vuX8MsoXJB8Z6ZNgnRBizUgW8/ys/zQPj/RxdUMbHl2nOxXnXDLG+DTDHlZK3rYo2Na6D9gtJ8mwq/GJQw+Tty1UFNp9AbYGI2wL1uM3zDVRqlV7kFawLfrSSc4kojw9PiQp9fPQ4PLw2m37+M7jPfMOtoiJDE3hstYQeztCbG70o8C8e//N5cFTozx4anTGaawuXeXarnpu3NpYKvecpkx3sR4+PcqxwQT5ok17xEN7xMvGOi9+tzHhJEIpnxz8+sQIPzs8vz40CrC7PcTxwQS54tQMjLfeuo2Iz5xx/3LP0SHuPjo0r3U1Bly86eYtqMrsQylmM7kE2i6XAE+ebFywbI70x/nh0/2LKofWVIUNdV62NvnZ1hygMeCeUK681tmOw3guw/H4GN2pOP3pJEXn0mokHjBMdoUb2RqMoKta9aKIW9PxaPrEskTHnrGMWIhalYndxwYTPHJmnP2dIVyGBg5sbvRxMjFeaoCtlTLRKo2wk4U8v54mYGOqKre0bGJnuB5d1cjlcoyPjxOPx6dZ+9yCwSDNzc0AjGTT3DtwjrOphWUdLFaT28eWYIStgQiNiywdrxzPDmZS9KbiBAwXoXJg3VPOgFjMMe+EAJ1l0Z2K05OK052KM5xN4dMN/mjHFdUSYtspTRzUFJWzySij2Qw2pYt8lYvRRdumN51gIJ0stSIoc2s61zS0sb++hd50nF/0n5mxv9Ji+A2TrYEI20L1pTI0ShceLcfmB90nOLHAgUI3t2zgyvqJzeN/OXCOh0f6lm2bhRBirVlMhh2Uzp2aPL6V27ALQEpiV0glYDdZk9vHZeEGdocby2U7pQbkq63yqys6Dk+PD/LoyADxwszDDS51DS4vr922l28/3lPNchIL81sH2tnfGal+P1eg7HzJaclM00kryynaNv/8o6NkCjMHgLymxrO2N7GzNUi2aJHOWWQLFi2h0rAHmH4q7vlsv0pnhNJXTVW487FuDvZMPeF65TUb2N5c6hlxZiSFZTs4OHz78Z5lGcjQ4Hfxllu3Tfszx3E41B/nm490z3t5HkPjdb+xmcaAa1EXGCzbYTiR5b7jI4wmc1iOw+62EDdua5i2vMyyHeKZAv/xq1MkskvLQPW7dLY0+dnS6Kct7CHkMarl6Gutn1mlT91XTz19SU77a/X4ubFlA52+oJSximVTKTl84tw4958cJZrJ847n7MDn0qvHZkXb5hunDy36av8V9S1c1dCG3zAZGBhYdNBO0zRCoRCRSARN00gXC3yv+xg9qQuXXevXjVLfx2CEjb5Qdf8433YQBcfmk4cerobBDFUlYrrZGqzjuqaOOYN2lmOjUgrAZ60i3ck4PelSkG4km562mGGzP0zQdOHR9GqD8oPjQwxnFzeJ9UJwazpdgTAtHj+Pjw4s+L3X6vHze127qq8VnD/m+UnvKQ6Oz++CnBBCrDcSsJOS2AtmKJtiaCDFLwfP0eUPszvSxOZAuJpNsFonKpX1GorCvroW9kSauXvgLE/JBCqxQn56aIDjAwlCXoOQxyTkMajzmQTcpTKdgmWTL9rkija5okW+aFdvS+aK9I5nGEvluXpzHddurq+GzlRVYSSR455jQ7MG6wDSeYsfPN3PD57un/KzoNtgU4OPTQ2laX0Bt1Fed4F4pkAsUyRXsKo98SpBw97x6ctSHKf0d/ZMX2xBgbP5Gk3m+PWJYa7f0nC+tLKc0Xbf8ZF5Z9ZVZAoWn733JC/a18a+msDqfGmqwjcf6WY0VcpyVICBeLZabjbd/YMegxfta+erD55d8PpqJXNFnuyOTgimB9w6dT6TpoCbm7c34XVpqx60q7QfuPPskUsyWHdlfSs3tWyofr/avw+x/lUCQw+eGuW+48PVjN233roNn0vnO2ePcjoZXZZ1PTY6wGOjA/z57muXdOxmWRZjY2OMj4/T2dmJ1+1mZ6jhggbsksUCB8eHODg+hK6oXN3YxjWNbeA4c7ZzURSFX/SeqQbVVEXhTTuuwDXLRMHa8vVoLktPOk5vKkFfOjHvINZy/R4vpKxV5FB0hEPRkQU/tnZ/OaEVCKXp49LGRggh5k9xYhicRCGLg5sCW3CU0NwPXOckYLcAtuNwIjHOicQ4Xt3gslADe+uaiLg8q14yW+mt8Zy2zWwL1nFwbIgzyei0GYOXrJqeWWJxUjlrQn+/xfrxMwM8cmaM2y5rxmNq3H9ylOODSz/RiWcLPNUTrU5v1VWF4iL7rAHkijbDiSzfebx3yds2HQf4yaFBjg0meekVHQQ9BvFsgTsf66F7bHEZBx5TK2UCLqJM2HEcXrC3jXzRwqVr1PtNQh4Te5bXUFVge3OAgFtfcpbdZIlskUS2yNnRNE/1RHnOrmau2lS/oAm7y01VFH412D1jz6mLlUvVeH7HFrYEZ5/CLMRCWHZ5UMtj3RM+W/7ops3U+Ux+0HNixYI8S30Pq6pKW1sbLpeLw9ERftp3epm2bOGKjs39Qz0cj4/x2q17Z72v7ZQyxZ2aHDgVZUqwbvIxraao/LDnJKcS42Sti7On73Lx6gYv6NjCRn942v3l0dgI9w12k7jEPkeEEGIxNKcHN/dgcASF8xfLHVQKzk6yPAtL6VjFLVxZErBbpHSxwKOj/Tw62s/t7V1cHilNVJkrcLeS5bSVA4IOX4CN/hC243AuGeNQdJgjsdEVWacQizWWyvONFchaq7WUYB3A957sBQWK1spGec+OprjjF8fZ1Rbi6d4YBWthmVsuXeWy1iD7OsNsrPctaUL05nrflMEgMwXHKoHB7z/Vu+zBuslyRZvvP9XPwZ4Yv7W/nTq/C5g82GTl+99ZjsNz27sIGOa0/bIuRu3eAC/o2IrfKJWcS7BOLAfbdkhmC3zlwbMMJc6383jF1Rtoj/j4Rd+ZFT12Wer7uKGhAa/XyyPDfdw7eG5ejzFUlVZPgHq3B5eqMZrLMJrLEM1lJ/RoW6zCHAMzgHIPOXh+x1Y2+kP8oOcklmOTs4oY5V6Uta9NpTfdWC7DoejM03NFySZ/iBd0bK0GQCe/lo+PDnDPwNIy0oUQ4lJhOM/g479QmHqeoWBjcgiDY6ScV1BQdq3YdoyNjfGxj/0zDzzwa3K5HAcOXMnb3vYONm/uWrF1VkjAbhmYqla9gvbgcC8PDvViaiqmqmOqGqam4VI13LrOnkgTHb7gsk6gdcrNeVUqUy7PT7Ta4A+xKRCmKxDhJ32nLskSrgo5xRQLtdSA30LkijaPn5vYyLot7OGlV3TQPZ7mxGCCE0PJ0lRet0HQYxDyGHQ1+tnREkRVzpfwLva9riilKb7zYTsO0XSerz98juHE4ntn6pqC36UTcBv4XTrRdJ7+2MzlVefG0txx9wm2NPpxGxqGpmDqKjdubcRtTJ3qu9y0csN0l7by61pNrR4/20J17AjVEzBcq55FvtKs8sAQpfwJKkHJleU4DmdGU3zjkW6yNW0QXnB5Kztbgzw03MvjYwMrt/5lWEY0GiUQCLC/vplnxocZzc898fN57VvYHqovbUNN5pXtOIznswxnUozk0oxmM4zm0sTyuXlta73LwzWNbewINczrb7UyqXRzIIKuqBQdm38/9gR765o5UN+CVzco2BaxfI6xXIZoPivBujmoisKNzZ1c1dBW/R3U9l+1HJveVIIHh1cmY18IIS42mtMzY7CulkIRH18n4bxxxTLt/uqv3oGqqnz0o5/A7Xbzr//6Kd72tj/hm9/8Dm63Z0XWWSEBuxoKEDJdtHr8eHSDJ0YH5nWg9MBQL0XbZnuonnqXBxuHrGWRneZK5+HoCK0eP9c1tbM5EFlUxp3t2KWTCkXBsm0Gsyn6UgnihRydviDbygeDcL6kYWe4gWaPj2+cOXzJlXJNJnNWxHqgAC/a10bEZxL2mhOGfVRMnqp6IWMMT/fG+N6TfQvOBqx1++4Wrt/SMOX2J86N85NDAzNOn7Vsh2M1JdRbGv14XRfu48wBHhmZ2kNxPTNVjUa3l23BUpDOZ5jVzxpY/V510wUhKgGP2QIUlaEYDs6Ez9rKhS5NUUkXC5xORIFSBpShqkRcHsLm0poEi6kqv7P7T47w08ODE1pU3LKjias31/HM+DD3Da5s9rVl24RCIWKxGPYiL2Tm83nOnTtHZ2cnr966h6+ePMhwbvag3T0DZzFVjU5fsBSsK78eqqJQ7/IQMd1sp776fi7aNuP5DEOZNKPlQF7WKlLn8lDv9lDv8tLo9uIv/72qNQFAVVFIFwvE87nS5GZdx6Xp1WERDvDds0er060zVpEHh3t5eKQPl6qRkbLXBdkVbuCqhjagtL+pXO97aLiXM8kog5mU9KwTQogFcHPPnMG6CoUibu4lxauWfTtisShtbe38r//1R3R1bQHgDW94I695zSs5deoUu3btXvZ11pKAXY03X3bVhKvqh6PD0wbdJhvJpflh70l+2nd6Qk+QmfRnkvz32aNs9Id4ced2zDkSNWqn8MXzOXrScfrTSfrTSUay6QllFJbj0BWM4Dhg45RP6J3qwVvIcF3yATsh1jpdU3jZlZ20BN2lfdIMsRJFUdAucBzFth2e6Yvx348tvRx0V2tw2tv3doTZ2Rrkp4cGePTs+LT3ATA0lefuaubqzReur51T3pde09jOvQNn11XWsgKETTcN7tJJfpPbR7PHh1c3qp99tdnfa2EaOpQ+Awu2xd39Z4kXcqSKeVKFAkXHZnuwnisbWmny+Krbfj5IB2eSUXpScRrdPjq8AQJmqZQ6VSxwJDbC8dgY/ZnklHW+qHMbQWNx05bF9Gy7FCT9zmM9PN07cSr3S/a1sX9DhJPxcX7Se2pZ16sAmwJhGt3eaoBLVRRUw6Crq4tUKkUikSCVSi34gl6hUKC7u5vOzk5etWUPnz/xJLH8zBnHiUKeO88ewa1p7K1r5sr6Vjy6UQ1kTn6/6apKo9tHvcsDNFR/XltZUfnbVRW1+jcwkEny2MgAJxJj1ZJWBej0BdkdaWRLoI6f95+mJz21d6ztOBKsW4RD0RHi+Rwh003IdOE3TA5HRzibnDqBXgghxOxKAyaOLOgxBodRnNiyD6IIhcL8/d9/qPr96OgoX/7yF2lqapaS2Autdtx60bHZGWogb1sEDBeBcu+e0VyGsVyGwUxySjCvcpVysk5fkEa3F7dWurrp0jQ8msEGfxAFZcYG3pXb85bF/cM9nIyPEy/MXnpWmRgmhFifvKbGq6/bREvIXS5xLd2+moMWaqmqQsRrLn05CvjdxrTPS1UVXIrKi/a1c2BDhB8/M8C5SUM4NjX4+O0D7QTcRvUxF0JlX70n0kRXIMwPe07SnVr6IJaV4NMN9kSaqC8H6EKmqxqMs8rZc9M1ll9LKgHSrFVkoz9EopAnWciTKOQYy2c4HBvhcGyENq+fK+pb2RqsYyiT4pnoMMdio1MCD17dwKvpjMyRCVWwrWpJm5THLp1tOyRyBb720DkGJpW8v+rajWxrDvDU2CA/6zu9LOWqFQHD5IUdW2n3BauBq9r3vKIo+P1+AoEAtm0zPj7O6OjC+uYVCgWSySShUIg60z1rwK4ia1k8NNzHoyP9/GbnNjYHwmizNDOYHDxXFKV6/0qQ0XIcDkeHeWJskOHs1KFFDnAuFedcKg6cnP8TFPNiO07ptV2jnwdCCLGelKbBLuyiuIKNwUnyXLFCWwUf+tDf853v/DemafLhD38Uj2dly2FBAnZTVA7OdVRubdsMnO9tA1RPcDLFAv9+7Aly85jC+pIN2zHKfe7gfI+puU4CKqU+bl3nQH0LRdvm6fGhZT2YFUKsHX6Xzh/e1EXAbZSCBTj86Ol++qIZXnvD5ur+ybKdZRuusJjeZBGfiaaWtmPx64XP/+oUz7+8lfaId8p2VPaPLSEP/+vGLuKZAgd7ohzuj7N/Q/j8tNhVCqaoioJPN3n55l08MtLHvQPzazp/oXQFIjy/YwumWkrhXuuBuZlU3gch003AcOE4Dpp6fts/ffhR0laBvnSSvvTxambdTNLFAuliYc71/qj3FGeSMZ7dugm3pkum3SJV9lnHhxJ8+/EesoWJB99vvLmLtrCXB4Z6ln2Iy7ZgHbe3b8FQZy/rrmaoqSp1dXWMj48vqEzW7/cTDoc5Hhvj9AKzqSzHYSCTZHMgvKDH1XKAvG3xxeNPkpzHe1sIIYRY6xRm7mc9++MW31d7Pl75ylfzO7/zu3zrW9/kr//6L/nMZz7Hzp2Xreg6JWBXo/aEsTaYNt2JjUvT+e2NOziZGKc/nWQwk5oxw85QtdJyF3HAX9mekOHiOW2b2eAL8oOek4uaJuY3zGqmn4LC4eiwBP+EWCMU4MX72gi4jWowzrIdHjs3Tr5o84ODfUS8Jv2xLPs3hNnaFKhOSF1sdplTLh382eFBktkCDqX9oOOUBlgULJtswSJbtMgVSv/PFZevBLRnPMNnf3mKXW1Bbt/VQshrTglGVv4fcOtct6WB39jWeD5TZpUzDiv756sa2nhwqHdeF3AuhJubN3BVY9tFNSii9L6cmu1mahpp63yQYjk/047FRgmZLm5s3nBRvZYXSuXv9KfPDPDrkyMTfqYCb71tG2Gvyc/6TvPk2OCyrrvB7eXFG7bPO0Oy9n6qqs47YKeqKi0tLWSKBf6n+9iitjVbLFaHhk1W+76bLkOw8r2pajy3vYvvnD22LNNmhRBCiNVglgd1Oiyuj7CDa5m3aKJKCex73vO/efrpp/jmN7/O//7ff7ui65SAXY2FHIyrikKrN0CrN4BazoTrSyd4ZKSPU+Xm1YtZ7kwqB5LbQ/V0BSOM5TIMZ9OM5TLE8jlMVcOj63g0A4+ukyjkOZOIoikKVza00eYtDdKo1Z2KkbiE+tnJqZZYqzrrvPzmnjaaQ+c/nGzH4fhQgnw5QFbby+2ZvhhBt8GOlgCXtQXZWO8rTaFbYOadoij88tgQ9x2fffqfqam8/TnbKVg2D54a5VB/nHhm+TI5DvXFOdwfZ1O9j91tIXa3h3Ab2oTnU9uvby0GTiIuDwPT9EK70AxV5arGUuPztfg6LUZp0uLEzLpkIc+R2Ag+3SCaX9xV2Nl0+oLc1rqZiMtdLcsV82fZDtmCxX89fG5KOTtAR52XsNekN53gqWUO1gEkC7l5Besq90mn0yQSCZLJ5IKy62zbJpPJ4PV6eVHnNr7XfXzB25qzrRm3s2BbfPnk03g0nU5/kA2+EO3eAJo6sVejAmwORIi43IzOUe4thBBCrLaw6UZRzicKALg0rVxhBAVnS7lT6/w/kx1UCmxZ9m0dGxvjkUce4rbbnoumlStXVJXNm7sYHl75VmQSsFuC2gN4VVFo8wb47Y07iedzxAs5CrZN0FjeKK+iKBiKRrPHT4PbO6EHUSUDAaVUunt9U0f19sknG/f0n72kgnW15NqzWE2GpmDqKqam4TZUrt/SwJ6OMPak8lLHgcH4zGnd8WyBh8+McWQgjmU7bKj3saXRz/bmAAG3Pq+sEtt2uGl7E531PpLZIm5DxW1ouA0Nl65i6iqGpk7Yfzx3dwvPu7yVvmiagz0xnumLkcguvUG548DpkRSnR1LcdbCPzQ1+nrOrmebK4I01rsntXRMBu/VS6roQtf26Kny6wVUNbVzV0MaPe0/y9PjsQee5hE03Hb4Abd4AG3whgqarFBBZB++9taTSk/LcWIpvPdpNKjcx6zTo1tE1lWg6z3gqT4c/SL3LM2dPwYXwagZXl4PWM6m0KEkmk4yNjZHLLb6Epre3l4aGBrbX1fMa081/njy4oMdXBoFNl0FnqBov3biTfz/+BP2ZJA8N96GVLxhv8AXZ4A9hOQ6n4uOcTo4zllv+4LUQQgix3BwcXKpe8/355BoFcJQQBWcnJofmvcwCly37wAmAkZFh3ve+/4+6ujquuuoaAIrFAkePHuGmm25e9vVNJgG7ZVQ5yAqaLoLlSXQLnTi2EJNPzGYqu50crHMchysbWnlstF+CV0JcAD6Xxs3bm7hyY2RClhBQ7QM3ubxTUxWG4jOffIU8BrfsbGJfR5i+aIavP3yOI/1x7veZvO227dVA/WxZd5V1bqjzQnnAxVwBisr+pDXkoSXk4XmXt3JuNMXB3hiH+mKk83OXhSoKzLZrtB04OZzk1L1J/vDGLlpCnmXr2bcSbMfhppYNnEnG5hwMtNK6ApFVXf+FUHsRKpbPYjvQ6vGTKOZJFfJzfq4pQKPbS7svSLs3QKcvWJ3UaePUTMldu++5tabyOxlN5fjZ4UGODkydPvqifW1csSEyYR/z6Ej/sgXrPJrO1Q1t7K9vQVWUafdllWOyeDzO2NgYhcLyZAqPjIzg9XrL01wXpjed4Ksnn6bTH6TDG6TNF8BUtWoGnTWp3YrlOPSk4vSk4sve908IIYS4EGL5HBm1SNB0oZX7+0+W5VkYHENh7sQAB50sKxM827ZtO9dddz0f/vD/5T3v+d8EAkE+//nPkUjEeeUrX70i66ylOCsZURLTshy7lOBZzoa70BkRlQPrLx5/ipHc1FKVi1WT28sfbN3Ltx7t5unehTWGFmKxLm8P8ZL97WiKMq+ea7UBtlPDSc6MpPhlTclqY8DFlRsjXLWpHqA6/CFftDkxlGAslWd7c4CxVJ7xdJ5rNtdh6tqKTrusDaDc+Vg3B3tm/vtSgPe95HIAnuyO8tDpUfqiM5+wN/hd/MktW1HnEUxcTbZjM5bLcueZw6vW+L3R7eX3uy4vHfis4ddqOdT2tKsNrDmOQ7pYIF7IEy/kSBRyJAp5ssUidS4PrV4/rV5/dRCUgwTmlqJyCBnLFPj54UGe7o1NGzDdUO/ldTds5nh8jCPREVRFoWjbnE5Gl7wNfsPkirqWaqBuut+nU36/xGIxxsfHKRaXnhU8YRv8ftra2nhkuI97B5c2gEYBmj1+tgfr2Baq46mxIR4e6VueDRVCCCEuEI8N+20Xobom1EmtuWrVuzxo6vTdXA3nED6+PmvQzkEnxSsoKLuWYaunl0wmuOOOT3Dvvb8gkUiyf/9+/uzP/pKurulLcPP5HCMjA2zZ0oXbvbh+fBUSsFtDan8Vy3WyNV057NlkjCdGBziZGJ/hURcnCdiJ1fBHN3XRFvbMu/F5KeHOQUGpZrx945FzBNwG+zvDtIQ81bKzyWy7FICozUarZGmsdACnEjz59YkRfnF0kKI180fLrTubuWl7Y/X7VK7I071RDvcnODeWmpJ9d8uOJm7e3rjmg1C241CwLX7Uc5ITF3D/GjRcXNPYxuWRJkACUBWTg3qVi2Vr/X20HlQuLCRzRe4+Msjj58aZaWi0CrzzBTspOhZfOPEU+WUaztLs9nFlQyvbQ6WLFzMF6gAymQwDAwPLHqirCIfDNDU1Ydk2JxPj/LT3NFl7ZdYlhBBCrAfzDdhpikK9yzvjfE7N6cHNvRgcntDTrtSz7jKy3IyldCz35i+JBOwuIss1ea42e6b2//F8jp/3n+ZMMoZfN7Fxqv1SLjVNbh9/sHWPBOzEijJ1lc6Il6DHIOjWuX5LAy5DW/TyKsG5yq56LQcbKtuaK1o8cS7KkYE43WPpatlvLZeucttlzVy9uX7C7Zm8xZGBOOdGU/SMZxhJ5njpFR3sagut6bLYiso+/cmxQe7pPzvj9PDlEDZdXNPYzq5wKfgpgTqxkipBunzR5thAnMMDcY4NJCjOFKkru3FbI7dd1sw3Th+iOxVf0jYolMq+r2popd0XxHZs1BmqFCr7zJGREcbHVz6A7nK5iEQiBAIBHEqlrt85e4T8AoZYCCGEEBeL+QbsANyaXm0pNtPRrOLEMDiJQg4HFwW2rEjPuuWwnAE76WG3yiaX8izmZLxainX2COliAU1RaPH48Rsmx2OjFCs9W1a5t9JaITFqsVK2Nwd4yf52fC69JltuadSaKamT2Y6DZTkUbRtdKw2IWE2VbXXpGldtquParnqKls2Z0RQnh5KcHE4ynCjth3JFm7sO9vPY2XFetK+N9ogXx3HwmBp7O0Ls7wyjKKXgn6mpazpQWauyT98TaWKDL8j3uo8znF3+1gNXN7TxG82dE9YpxHKq7MM0VSGTtzjcH+Nwf5zTI6lpg/AzqQTax5ahV93vbrqMDf5QzYCGmfd5iqIwMDBAPL60IOF85XI5BgYGGBkZobm5mU5fkL2RFh4ZlXJWIYQQK8unG7g1vfpvMJNctRYtc1EAvTzt3Cp/nmetIoWsRZ1r5qokRwmR54oLuKVrgwTs1pDFBesccpbF97uPT5g0ttSr2EKI+XMbGs+/vIV9nZHqiaSiKGgrFEepBPePDyb4xZEhtjcHuH5LPfpswT270q9r4s+n6+Nl2aUy2unKbp3y+PXJpbeTVX6maypdDX66Gv08T1FI5YqcGk4ykswxmswxmszzxfvPcFlrkNt3teA2tQmDOVz64rMTV5OqKIRMN6/acjnfO3d82VsQ+HTjgpQ6i0uTZTsULJsnu8c53B/n3Fh61kExs8mWB9EY6tIuKGwOhNngL11JnytI7TgOtm2TSEwdfrHSDMPA7XaTKuQlWCeEEGLF3dS8Ycp09CdHB/lZ/+lV2qKpDFXFVDVcmo4+qV9dJXBnOTbTj5+4tEnAbp2qnLCfSozzk95TZCzplSLEauiIeHjlNRvxlMteL0S2k6KUSmR3tATZ0RKcs0+d4zik80X6Ylm2NwemLOuhU6M8dHoUl67S4HfREHDR4HfRHHQT9BgTMvdGkjmODiRoDLjY2hSgErObLXBUG/jzuXR2tZVOumsDfqlckXi2gNd18XwsqYqC48DzOrbw+eNPkl7GK50Pj/Sxr64ZTQJ2YhlVji1OjyT59uO9pHJLP7ZI50vLMNSlBd9vbN4w7zYiiqKgqiqhUIhoNLqk9c6Xqqo0NjYSCoXIW0W+fvrQBVmvEEKIS9fmQHhKsM5xHJ4cG1ilLSqJmG42u/14Y1nqTA8ulwuH6ctdKwOjdFa3UmitunjOjC4xiqLwy4FzMjlsAeS0Viy3trCH11y/GV2bfjLhSqoNkM1n3bmiTdCtT5hCC6WrWuPpPGOpUoZufyw75bGGpuJ369i2QyxzPujkMTT2doS5YWsDQY8x75Pp6TLzfC4dr6lN2b71TlEUTFXj9rYuvn3u6LItN1Us0J9J0uYNSEmsWFYPnhrlh0/3L9vyEuWgX1cgsujy8G3BOhrd3gU/rr6+nlgstuKtMILBII2NjaiqyqHxYX7Ye3JF1yeEEGL982g6IdNNyHSV/hluwqaboOnioeFeDo4Pzfr4Zo+PF3RsnXD8bTsOB8eHGFmGNhTzZagqu8KNNHt8hE03dS4PXt0gk8lwKnG6OkxirqNVOZqdngTs1iHbcbAdh970+ix79WoGjR4vZ5OrM/hBOtiJ5dAcdPOa6zehqxc+WLdQiqIQ8ZrTlrE6DrSHPbM+vmDZjKemDqvJFCwePD3Kw2dGubw9zE3bG2nwu5a0nStVRryaVEWhKxjhxuZOHhjqXZZBFK0ePx2+4DJsnRDn2Q7kisszxbXizEiK3vE0v9HcScR08+PeU9gL/CS+sblzwUO6FEVB07RqRvJKcLlcNDc343a7ieWyfPvcUUYv4EmSEEKI9aXB5eXWtk00e3wTMs8txwbn/EX4W9s20ZtOTOn/qgBbg3Vc2dBKmzeAVfPZ6Dilvta/Huy+IM/FqxkcqG9hf30zpqph46CiVJMKzicXXIpn38v3nCVgtw5V/ihf2XU5j4/084uBs6u8RfPT6PZyRX0ruyOliYYPDvXyq6HZdygKYKoaOXt5TyCEWIoGv4vX3bAJQ1On7fO2Fs20nZqqsKFu4ZkrtWwHnuqJ8lRPlBfva+PAhvO9/Ch3o1CUS7vfmuM4XN3Qxq5wI/cOnOVEfBxwJnycO1Dzus3u5pb5lwcKMV8KEPGay77cz/7yFK+6diO7mht5ZKRvQVf+VUVBV1WYsZhmZvl8HnsFprSqqkpDQwOhUAjLsflZ32meHBtc9vUIIYS4OCjA1Y1t3NDUCThThiZpijrxI86BF3Rs4Ssnn8YB3JrGnkgzB+pb8Btm9XhRqwnWATww3LPirbIippsrG0rn9Er1OF9Bm/QZbRgGilIaymSaS5uUut7kcjkUpfQaLJUE7Napykna7kjTmg/YhU03t7d3TckGcWvTv/06fUG2h+pp8fiod3nRFIXedIJD0WGOx8YkeCdWlQL8/rUbcOnaugnWzSXkNfG5NFK5pf9t3XWwn8F4Fr/LwNQVjPL02g11Xvxu46Iqd12ISrDSqxu8sHPbjPdLFvL8cvAch6MjM95nkz9Mu2TXiRWgqsqSsmRnY2oqWau44Aw023H4r9OHeMXm3Xh1fdbJsLUcx6FYLKKq6rIG7UKhEA0NDaiqyvH4GHd1H2f5Q4JCCCEuFvUuDy/o2Fpt7aDM43NMUxSa3D5ubtlYLTlVlfMjGdRJgbqCbfP46ACPjS6+d52pahQde9qLxwqwORDhivqW6rT2uS4aa5pGOBxmfDwKlLLSL/7CV4dcLkciESUSCaNpSx+eJwG7dU5XVW5q3sDh6AgjucX1hllJdS4Pr9i8qxqcO5OIcnB8iHPJ2IyBt+uaOujwlhrjV05y27wB2r0BbmvbzNlEjN50gt50nMFMqjoOer52tgTxu3QyeYujgwnyRTnUFvO3uz1EnW9lTmhXi+04XLmxjnuPDS95WZbt8NDpsSm3/+6VnexqW/pVpvVuroMbn27wgo6tXNXQyt39Z6ed+H1Ty8LLA4WYr4hv+TPsAFrCbk4nxhdVJBLL5/jaqWd4RdcufLo57/e+x+Nhy5YtpNNpRkZGyOVys95f13XcbjdutxvTNFFVFVVVq0MsKlfK81aRLx1/itG8lL8KIYSYngJc1dDGDc0dpUy0RRy3XdnQiuXYpQy8GpXjwHghxyMj/RyKDlNYxMWpiOlmSzDC1mAdrR4/aavAQ8N9HBwboujYKMAV9a1cUd9CwHRVg3nz/RxubW0FIBqNsgqD21eFokAkEq4+9yUvz1npTrxiWWWKBTRFxdS06mRIh9IfzbHYKL8cPEcsXzog9Wg6EZeHrFWcUv9+IWzwBfnNzu24NA1VUSjYFp858hj5WTLkdEXl5pYN7K1rnnFHULujsBybwUyKnlScsVwWU1VxaTouTUNXVMbzWQbSSYazaXy6wf/avn/Ccm3HIZUrcnokyY+eHiCdl+w9Mbu33LqNOt/8TxjXA8dxyBVtPvrjo+StlQlg/97VG9jZEljUwUrloORiG0gxm8pzPp0Ypz+dxFA1DFXFb5hsDdat9uaJi9z/vesQuWW8mLWtyc/vX7uRH/We5NAs2aNzMVWN6xrbuaKhFQdnygnMTBzHwXEcent7yWQy6LqOYRjVfy6XC4/HU70SXjk0nm5/VZmkO5xN8cuBbs4ko4t+PkIIIS5OdS4PL+jYQpPbByxfW5jK8WFfOsHDw32cmuNCWNBwcaC+hTqXm1/0nyGez9PuC9AViLA1GCFkuqsxhdqer1nL4pGRPvyGyf665iU/B8uyKBQKc9/xImAYxrJk1lVIwG6dyRaLnEvFaPX6CRgTs3wqgazRbJqQ6caseaMcjg7zy8FukoWpjeOX2wZfiBuaO2jzBqo7FdtxeGykn3sHz024b6vHz0Z/iEa3l2aPn4BhVncW890pOI6DXT5wr/y/sudSFaW6vLFcltFcmmg+y2g2Q6KQo9UboCsQprW8rfcdG+aeGbKMTF3lhi0N7GwJ8ER3lAdOjS7pdRLrj64qvPdFu1d7M1bM95/q45EzU7PjlsMtO5q4aVvjgsuILduhaNncc2yIW3Y0oavrp2/gcrAqAyqqLQEVVC7tfoBi5f3rPSemnRi9GL+xtYFbL2smms/y1ZNPL0tbi+ubOri+qWNBj6k93K39+5ktODfbshRF4VRinG+fXb7pz0IIIdaOgGHi101SxQKpYn7eVV2XhRu4vb0LUKo95paq8lmVLOT5Wf8ZTiXGJ/z82sZ2DtS3kCzkiRdyJAt5fLrB1mBd9RCyWM7AMzUNy7EnDIiYaX1yvLn6pCR2nXHrOluDddNWf1cyfho9vik/2xlqwKsbfOvMkRXbtg5vgJtbNtLi9U9Jl1UVhe50HE1RaHB76fQF2RNpIuLyYDs2MHHS5kJ2DrVNLqv/V6bep97tIexyVXdOtuPQk4pzXzmQ+Zz2zdyys5k9HWH+/b5T1Wy7zQ0+XryvjZC3lFWVLRZ57u4WPIbGL47OPm5bXFxMfX7ZHOuNZTvkihZnR1Mrto5Tw0metaNpQY+x7FIG7OfuO0U8U2AgluUPrtu0oID+elfNILo0nq5YAxzHoSXkWVTAblO9j93tIe4+OkQqV+R3rmhnT3uYM8ko3+8+MWuG/Xy1ewNc29i+4P3ATPetHA/Ytl0qfS3fz7IdFGVi2U9lndF8jgeGeziyhGxBIYQQK8+j6Xh1A4+mU3QcclaRvG2RsyyKztRMchWFrmCYvZFmNvpDEz47claRdLHALwfPlYeHTa/B5a0mkiwH23FwcHhouI+Hh3spTlquR9O5trEdXVXx6gaNbm91GrtS0/fOKLd4AObMUL9UjrPXAwnYrUOLLcU7WbNjURWFzf5wOeNs9p4u83Vb22bqy800J2+j5Ti8oH0rZrk8tnYHNt8G0suhduekKgodviCv6NpNdzLGr4Z6OBwd4ba2zbzjudv58TMD7O8M0xb2kioWuH+oh+PxUaL5HL/ZsY2btjfS1ejjqw+dk1LaS8TFGLCzbId4psAX7z9NNL1yqeo94xkKlo2hze81tB0Hy7b50gNniGdK23V6JMW3n+jhdw50XFJBOyEuJAfY1Rbk8XMzn4xMFvQY3L67hd1tIRzHYWuTn2d6Y+xpD/Pk2CC/6D+zqN51kwUMk5ds2A4ssTSnprw+ms7TF80QyxSIZQrEMwW08vCNer9JxGti2Q4Bt0HEZ+I4Dp8//sSyPB8hhBCL59Y0NvnD+HQTr27g1Q18uoHfKH3v1vRZz5sdx6Fg26UAnm2Rs4qETTde3SiViU56rEvTMVWNZo9/2oCdR9MJGC76MwkGMymaPT5sx170uW6lUu1MMsov+s9U217VUhWFZ7dumpL4Mnlia+V2sf5ISewl5L7Bc5xNxtgerOfySBMeXSeez5Eo5MhYRX7UcxIbh13hRgbSSUxNo9MXZJM/TN62+OaZw7MuP2i4eF77Fjr9wXV3Ml3ZIZ5LxjgcHeHKhlYa3F6Kts2Dw708OtI34WqGAlzT2M51je04wNM9Mb7/VH811VhcnOp8Jm+7bTu2XbrSVSm5Xq9sx6E/muHLD54lcwGCzq+6diNbm/zYDqjKzAcOpX5T8KUHznB6ZGrW396OML99oB2Qgw8hVoJtO3z4R0fIFmbfL+iqwg1bG7hpW2PpBEGdmJ3Wk4rzrTOHlxTc6vAG2BQI0+4N0OzxoyrKknuI5os233y0m57x9Lz2fZsafLzi6g1oqsL3uo9yOhlb0vqFEEIszeWRRm5u2Yhb00sZaI5zQdqGOI7DQCZJLJ/Dpel4dR2PVgoUaur5wFxlm5RFfmbZ5WDiT3pPcSw+fRsmr27wWxu20+Lxy/HwRUwCdpeI2uh+bWPJ2sEVyUIeRWFKbzyAB4Z6+PVQz6zrCBouXrxhO01u77rdaVRep75UAl1VuW/wHGdmOTAPGS5ubtnItlAdRdvm6Z4Y33mi9wJusbjQtjcHaPC7CHoMgh6D5qCLsHd9DqFwHIfxdJ7/ebKPM9MExpZbwK2zvTlAU8BNZ52XlpB72l5StgPffaKXp3qiMy7r8vYQL72i1MNqve5vhJis9m+gpnEhlbaNF+q97jgOp0dSPHx6lGODyWqbi1o7WgK8YE8rQbcx7bbZjsOh6DA/7j216O0wVY03X3ZV6QLJIifszeTHz/Tz2Nlxgh6D4cTslQZvuHEzrWEPnz/2BPHiyvcCFkIIMb0mt5fntHXR4vWvSoKI7diUYoNK9Xx6OVWOA3pScX7Qc5LkDJ85zR4fv71hBx7dWJfnIGL+JGAnqqbrJWc7Dsdjo3y/58SMj9MVlR2hem5p3Yihqhe0xHWl1AY1s1aR4Uway7FLfW5wSuV6jk3WKqVPZ4pF2nwBdoTqOTmU5EsPnFntpyAuELeh8Re375h3qedaY9sOqqrwVHeUHz3Tf0HLu5+1vZFbdjZXM1wHY1kePTvGwd7YnJk9UCrb+90rO4HFtwoQYq25/+T/z95/R0mSnued6O+LiIz03pT3baf9+AHGwczAkzB0IAkakRQlStqVdrV377l395y756zWaeVWJEVJNKABJYIECQKEB2YGMxiHmZ6ZnmnfXdXlfVV6HxHf/SMrs6u6fHdVl+n4nVOnuzLDfJGVGRnfE+/7PLPkygZ+Z61qoCnopi/ua3xO7hb1ttFS1eTd0RQz2TIJv5OmoIsmvwunQ113TFJK/uT6u8zdZlJ9ly/I57qP3u4hrMqtZtr/+ruXyZaMVZd/+nCCxw/G+Z3Lb9iV9DY2NjY7gFNReX9TB6ciTQtdLnvzunst6jfHXpoc5uzcxKrLxZxufuHACQR3XnFus/uxPexsGqx04lOE4NWZlSvGmtxejocSHA3H0JWbF+6mtBCIOyoD3mkWj9mlanT4Ao3f6xf6kpsG1BLJF6+dY65U4H1NHXz8RAvffG/1E63N/qFUNfnxjTke643tyfTS+piPtwVpCbn4zy/2UzXvzn2cH16dYTxVpDPq5cJYmsnM5kzuL45nkHKEn36wY8+14dvYrMQLV6b54aIwo56Yl4d7o3ddrAMa7a0uh8oDXREUcdM+ov5ZW29MFpInmjr56vDtJanW0uZv3/9nNW49V3z2/g6aAi4ypSqXJjK8NTS/RMC7NJHhqcMJzkSaeWN2fEvHYmNjY2OzNveFYjzV3IVT1RYq2/bH9V49TKLusZ6qlPjGyDVmSoU11wvprnVDI2z2D7ZgZ7MmFdNkftGd8XZvgEOBCAcDEbwOHVNK1IXWWkUIyqbBQDbJbKmIR3NwIpxAU5Q9KdqtRv1CX9R+AWon3Cebu/j68FUCupMHu+PM5yu8NrCy54DN/uLV/jke7o4i1vBl2+0oiiDqc/KTp9v5q7Mjd22/16ZzXJvO3fb6lyYyfO2dMX7SDqKw2cNYluTt4eQSsa454OLnH+lCU7Yuae52qYt36noCXd1mQ9bOKapQ6A2EafX4GS9kN73fVo//jidmi6vpbj1HSFmrmG8LOqlWy4RdGk8ejPPkwTgXx9OMJotIJB3hWqCWz+G4o7HY2NjY2GycmNPDh9t6Fm7e3P0bV9uFJSUl02AolyJZLpGqlEhWSsyUCitaUNzKVCnPfLlIxOlubG872nNtdge2YGezDCklktqH/+XpmxP3bl+Qz3YfxZRWQ9Wvi3WpSolXp0e5npnHlJJmt48nmjrQVXVDJ569jiIUDgYi9PhDfH/sBj5N55ljzWRKVS6OZ3Z6eDbbTL5s8IPLU3zkWPOSx2uTwZuT3d2OIgTH2oKMJgt7Smx+ZySFW1d59ljLTg/Fxua2UBTBqwOzSx8UcGUqS6ZYJZmvkC5WkVIS8Tn56PHd9V6vC2ETqSJuXSXk1hvPWVLybFsvf3b9PQy58XZSAbR67txI2zAMMpkMhmGQSCSWiXbpVIrZ2ZuvvaqqJBIJjrQEuK8tuHB8tWM0rf1/PWNjY2Oz0+iKymOJds5Eb15X7xexrs7F1AwvTg7f1rrZaoUvXjuHrqjEXR4+0tZHyOna4hHa7BZswc5mCXWF/s3Zcc7OTlA0DSJOF4cCUU5Fal5Tt5bgCmonVinhZKSJk+EEUZenIdTttxPsalhS8unOw5ybn+K7Y/18uusIn72/nWzxBiPJ2/Pvsdk7/PjGHKc7QjQH3ZiWRapQZTZbpmSYdEQ8RLy1MBfTkmsmpO40Ukru7wrvKcEOalWOXl3j/QfjOz0UG5tNIaVkOlNmLrfUWHoyXeKv3lxe7fpE0L0rKg3qwpclJfmywbffm6BsWPzCo11LllOEIKS7+EBLF98bv7Hh7bd7AzgU9Y7Gl81mmZycbDxmGAatra1Lqu4KhaWtR6ZpMjGx1NJCVVVaW1s5Hk7w4tTtTbBsbGxsbNbnUCDCB1t7cKnajn/PbTX1783hXJo3Zu7cXqFimYwVsuyzl8nmFmzBzga46Uszms/w2vQomWqF09FmDgejRJzuNUtthRB4NAef7Dy4pG1nv51k16N+vCcjTRwKRnl1epSHYq184X3dfPfCJG8OJnd4hDbbiZTwRz+6gUtXyRSry5736CodEQ+dEQ9dUS+tITcSdl0JuxCCgHtvtn1VTMtui7XZc1gSTClxqGJd/8iDTX6eOpzYcfceS0ouL7Sjl42bVXOPH4gBy89pihCciDQxnM9wJb3+zQAFwQdbuu9ImDQMg6mpqSWP5fN5hoeHcbvdtQpoy1om2K2EaZrkcjlisRhBh5N0de1UWRsbGxubzRHUnXyopYduf2hX3JTaDiwkL44P8fb85PoLbwK3tjev2202hp0Se49jLfi3XEjN8PbcJPPlIm5V41cOnqoZe7K7xIS9Qv2L5r35KTp9QYK6i6ppMTJX4LkrU4zZFXf3PD6nxqmOEPd3hYl4nbtGaLIsSa5s8G++d3sm8TuFR1f57549squrF21sVsOSkhszOf7staFVlznY5OdnH+rcNe/xf/ntS8tSpb1OjX/+kSMAy85p9XT1P7r6Djlj6U2NuMtDk9tL0TAoGFW6fEEeS7Tf9nFKKSkWi6TTaTweD273gs+PZWFZVkOsU1UVTdNQVZV0Ok0mk6FSqay4TVVV6enpIVMt8+Ubl8gZKy9nY2NjY7NxVCF4MNbKo4m2fZ16akqLC8kZvr+JSvO18GgOgg4nuqpuS5q6ze7BrrC7B6l71AlgKJfiW6P9lEwDVQgO+MM8FG/FuQ/LkO8myoK5dacvxB9cfZt2b4BjoTiHYlF+Ld5LsWrynfOTvDua2umh2uwQubLBy9dnefn6LKc6QnzseAuaquzoZLx+bvjym3uv5atUtTg7OM+ZrjDaQtf+bhA1bGw2giIEfQk/uqpgITFuqbRrD7t3lVgHLPNzU4TgU6daG7/Xx7nUy1PhcDDG2bmbLach3cXP9R5b1v56p/eTPR4PHo8Hw7SYz1ewpERTBKqqoSkCRRNUTYt0wcTvEkQiESKRCIZhMDc3RyaTQUqJoij4/X78fj+KohDUXfzG4TOY0mC2VOavBy/jdThIlots3KHPxsbGxqbDG+DDrb2E9JptzG75ftsOVKFsqMJ8IwQcTn7l4Ck0xU6KvRewK+zuAep/4qq0yFbKFIwqAd2FT3NwLTNPplrGqWgcCUVxqtqSUAmbO+dPr7/biOd2KLVwiiebuyiWLH77uWs7PDqb3YLPqfHEoTjdMS8xnxNFCCxL3rXkWcuSKIrg6+fGeGto77ZvOzWF051hHu2NEvLo+7atwmZ/MpYsEPU5+YsfDzM4l2883hRw8dThBB5dxaOrxP2uHa/K/b+/c5l82QBqYt3PPNTBwSb/ks+baUnGU0WG5/K4HCr3d4WZLxf54+vv1tZD8Pm+48RdbpQtvO6QUlKumnzxlUGmMqUNrdPkd9IZ9fJYX+3cYZom1WoVl2vByFsWEJV3oPw6qC3g6EXqD9SOQihIaVIwTCaLeb433k/BMLbseGxsbGz2Ex7VwVMtnRwNxe+Z67SCUeU/Xj7LVggvz7T2ciwcvydeNxtbsNvX1P+0JdPgjdlx0pUyvf4wBwJhnKqGJS2gVgmGwBbptoF6y/G1zDwXUzMM5dIA/EzPffiFi3/7/as7PEKb3YiuKrSG3fTGfNzfFcbr1DAtuW1ps5aUZIpV/u7cOP0zuW3Zx05wX2uAj59oxe1QUfZIUq/NvU3dL3Y6W+b3Xri+6nIPdIX55Km2uzewFfibt0YbVeJnOsP8xOnl4/nehUleG5jFkvBQd4SPn6xV4KXKJTRFwaEo6Iq6bcLj7z5/jZns5v3m+hJePnOmA4+uICpnofgCWGPLF9TP1MQ7YwzUJqTjAGgHuZqe4xujq//9bPYeQd3JA9EWunxBstUKmWqZTKVMplomW60wUchtKgXZxuZe5WQ4wRPNXTgU5Z4RnExpcW5uihcmV7e92ChBh5NfPXT6nnntbGzBbt+xOJl1rlRkppRHIunzR9BV1a6e2yHqr/uNbJJMpcKpaBPXp7N8aQ2/IhsbAEXUvKvOdIYJuXUcqkBTFTRV1Ca8qljSerbRia+UEilBAi9fn+GlqzMY1v77OnBqCs/c18wD3RGg3p5Xu5u7n1svbHYnt1YS1CtbYenn989eHVxXPP/cA+3c1xLcETFaylrb7vcvTTKVKRFwO/js/R1LjqF+eZksVPjxjXmeua/5rrT0WtJCLMRyCCGoGhYzuTIDMzneHk4yn1/bey7q1XmgO8KZzjC6Ckrqv9/E3gUy+D+TrLr44kIVoc3eQ0EQ0J2EdRchp4t2j58DgUitXb1SRVEUhKIghEBdaEk7n5zmu2MDOzxyG5vdS9zl4ZnWXpo9vh2vEL/bmNLi/PwMP5i4c/+6uMvDL/SdsH3m7yFswW6fsHgSMF8ukiwX8TucJNxeW6TbRdT/TgPTOf70tcGdHo7NPkAI8OoaTx9OcLozvGYVXr1KT8pam9qVySwXxtPrTmD3A16nRsLvJO53EvM5SQRctIbcqIqwL3ps7gqWlBSNKl6Hzmv9s1wYT/Ngd4RTHeElFbRSSsqGxVyuzGyuzGyuwtnBeYrVpQEPuqrwm0/3EXTr21Z9uxamZS0Rvk1LIqh5vi2mLkreSZVw/VJVLPjD1rn1ZkWuWmEkn2Y0nyVTKdPi8dHs9hFxugk5XaQLlTUr2z26yj995jCKANUcheLXwdiEdYXjCPj/AdfS83x9xK6g3wtoQnAoGCPu8hDWXURdbvwOZ+Oa2qqnCefzTE9PY1lLq+gURaG9vR0cGv/h0ptb0u5mY7PfOBSI8omOA/f0tVaqXOIPr71z2+vrikqXL0iPP8ShYBT9Ft9Xm/2LHTqxT1h8xz6suwguutiwxbrdQz2M4s9eH9zpodjsE6SsBVj4XBrrXQepiiBbqvLFl2/cEyLdYvJlgxtlgxuzN33BnJrCo30xnj6c2MGR2ex3zIWKr7OzE/x4Zox/cOQBLCkZTRYZTY7xxuA8v/RYN0IoDQHM5VBpC3toDroRAk53hPjjV26QLd30RauYFn/xxjBfeLQbn8tx9ysWBLw1N8FoPgNASHfzVEtX45gVasdSrwAUYunja7FYoDNNk0KhQKFQoFqtous6Xq8Xr9fbWK5eOQuQcHkJ6C6QtX0KatYgUkqqluRgwkf/TL6xfB1FCN5/IIaqCJT0/wrWbZiDVweQ1UEOBDo4GIhwLTO/+W3YbDmqEHg0B15Nx6s5Fv5f+/1gMIJb1TClRFompmGSy2YplUoUi0XK5bVbqi3LIpVK0dzczIFABEUI2jx+HIrKSD7DcC69oUThDm+AZ9p6CekuvjZ8heuZvesla2NzK8lKkfPJadwLnz+P5sCjOtDuobbYkNOFV3OQvyUlfT1iLg8faO6izRtAEcIuxLkHsSvsbGy2kLr/ELDQbihrEeWLqgqklMzmyvzduXGG5ws7M1Cbfcffe7yX9rB73YmwZUnmCxV+xw48afBzD3dyIOHfkSolm/1LvaL6RjbJCxNDJCsl7o8283RLN7O5MvO5MgOzeTwOlSfXEY1NS5IvG/zBjwbIFJdf7Ee9Op881UpHxHvX3sdSSiqWye9feZuyVav+82oOWj1+ToQTdPtDSwS6mVKekVyGQ8EoPoe+bNKxWKQrlUrk83ny+Tyl0sqhEaFQCFVVG+uoqoqqqjgcDlRVXVKJVxczFVVDVQRXJjO82j+LlKBrCkeaAxxrC+JyqEhjBJH5V7f/wggX0v9PQG1moljgrwYvYVi2t9ndIOhwcirahE/T8TlqPx7Vga4uTyC2pERKiWkYTE9PUywW72jffQcPNN7PhmWBlKgLbbOpcom35yd5e25y2XjvC8c5Ho7jdzgbj//xtXPMle9sPDY2ux2HovALvScIOZ1LQodWuvm0kWCKvdBm+42Ra5tOiv3JzkP0+MP3jLBpsxxbsLOx2ULqLT/XprKUqiYxv5OmgGvZSbbeInRuJMn3Lk6SL5urbNHGZmM8eSjOU4cSG/az+oOX+hlN2hMCgPawm197ou+eSSqz2X6klOSMKt8bG2Awl2o83u7185G2PgAciopL1RqV1xuZaPzt26O8M5Ja8bmmgIvffKpv2ycs9bFaUlI2Df7k+rvLKgZaPX6ebO5kvlxkplhgsphjophb8vypSKKWEGhZCCEoFApks1ny+TymuX3fifF4nFAotOR1Mi0T1RyG0vNQ3QLvOeEG36+B4wBl0+C9+WlenBq+8+3arEqXL8inOg7VbB9Mq+azaBiYpolhGFSrVSqVSuPfrSYQCKDrOplMprF9TdMIhUKEQiHmKyW+1P8eTlWj2xfieDhOuzeAJSXVSoVkMomu6wRDISwkr06P8tbsJJbdZGuzjwnpLn6m5z58Dr3mrUxNnPv++AAj+QzOhe/J9zd10Orxr/j9theEOljwsUvO8IPxjfvYaULwW0cfQlPsirp7GVuws7HZQqSU5Msmf/ijAZKFCr/4aDc9ce+qIoBlScbTRf7gJduo2ObOSASc/MOnD25oWdOS/PjGHN+9MLn+wvcID3VH6Ev4aAm6CbgdOz0cmz2OlJKiafD7V97CWOMyy6Vo/L1Dp3FpSx1KLKs2cWlUhi2ENayXevqZM+0cawtuSZWdaUkUUa8Wh4plMlvK41BUmj0+hnJpvjV6ncIm23sWc18oxkfbDzA7O8v8/N1pH1UUhQMHDkD5CpSfA0wwtiHRVbjB/RGk4yhCbWK+VLCDKLaRD7Z0czrazPz8PLOzszs9nCX09vaiLfqMSykxTZNcLsfs7OwSXzxN02htbcXpdJKslPjq0BVSlZWrTG3uXUK6i0OBSM170enGq+moC3MNgUAIMKRFulJivJClP5tkNJ/d4VGvTlh30eb1E3V6uJCaYba0tANJAI83dfJQvHXJzVVTSlQhmC7mSbi9OzDyzbFZH7sef4jPdB3ZvgHZ7AlsDzsbmy1ECIFbV/mtDx7gO+cn6Yp61qzYURRBa8iNQxVUTVs7t7l9pjNlkoUKQbdj3SoxRcDxtqAt2C3ijcF53hisCQYfP9HCg92RPXHH1mZ3IoTArWqcjDTx1tzqn7OSZTBTLtCm+pd8bhVF8Gr/LAJwOlRcDoVixVxTrAN47vIU97UGkHLzQSr15GhFEeTKBpcn0kxnymiq4HRHmIBH4y8HL6EgaPX4GC3c+eRvvJDDkhKfz3fXBDvLssjlcni9BxHFL9+eV916OO5Den8esEBWkNIiqOsotUdstoEfTg7hdzjpDYcBdpVoNzs7i9frxTRNTNNcs9XbMAyGh4fxer00t7TwU91H+VL/exRNY8Xlbe4tNBQ+2XmQbn+o5mdmSdLFCmNzRQpVE8uSC0EpEHA7aA+7eSDm54FYK6YlSZaLvDw9Qn92d3kkJislkmsI0xJ4aWqYsUKWj7UfQFMECoKJQpYXJ4eZLOb4XPdROhZ83nYr6/nYdfmCxJweoi5341/bs87GFuxsbLaYWgonfOJkK7B+qbYiBI/0RHlzKEmparfG2tw+335vgs8/0rXuckII/C4HbSE3Yym7LfZW3h1N8VBPdKeHYbMPeCjWuqZgdywUp8MbWPZ4xbB47tIUhrW5GznpYpVvn5/gk6faGj5di5Nc10IIwSvXZ7gwnmYivXTi1BX14vfULhkt5JaIdQBHQzEUIe7YP2yzJJNJfD4fOA5D+ZWt34HaglB85KsVcoZCrpriO6MDtli3TQQcTt7f1EGLx4cQgkAgsKsEu0wmQyaT2dQ6+XyesdFR2trb+Wz3Ef5i4CKGtN9B9zJxp5uf6z2Opij8+MYcrw/MkypU1m2a9ugqLUE3XVEP93dF+MmuwxSqVb547Rwla28JwQPZJH8xcJ5fOngKgNdnxphcsFu4mJqhyxfcyeGti5SSHn+I88mZZc8dDET4VOehmr8mckMBTTb3BrZca2OzDQghsCy57P8rYVmSDx5t4n/4yBF+4dEuTneE1k37tLFZiatTWc6PpdZ8v9WxpKQv4bsLo9p7FG3h3GaLuLpGSmi7x88zbb2s5EyiqYLPP9KFdhutrWeHkvynH17n6+fGeX1gjvl8BXOdc4JpSS6Mpfn+pallYp0ioCfmY6qUW2Xt2+eNmXGGc2mCoRDKXfLoaWtro6Ojo/a6mxPbs5PSC0hzFlNafKn/PC9N1vzrEi4PR4JRHoi24LQ9ibaMQ8EIR0MxXIrKzMwMg4ODOz2kLaFUKjE9NUXc5eWTHQexLw3vbdya3rgBUzEkyQ2IdQCFikn/TI7nLk/zr797ha+cHcGpany669C2j3k7SFVuVpqXF1We1hPLdzMS6PGFV3yuzx+uBTUJgSoUW6yzaWBX2NnYbBOKctNIfPH/V1oOQAjojfk4kPCTKxtcn976yZHN/udb701wIOHHuc6XvSIEAzP2e2wlgm59p4dgs8expGS6mOeHk0NLHtcVlb5AmEOBKN3+WiXAap/T3riPw81+LoxvfhIykS4xkS6hKoK2sIewd/X3dL0Sb7UW+QMJP7qmcCm19RVLhrT49mg/v374DE1NTUxMbJOAtoh6gqxI/29gLa9yuGOcj4N2CKGECagqv3bwNEGna9lij8bb+POB82u2gdmsjU/TeTjeyslIEwDT09ObrmTb7WSzWTRNoycW4wMt3Tw3MbjTQ7rrPNnUyZXMHFPF/E4PZUcZzqf5nctv8huHztARcd/WNiwpOT+WJurVeepwgh5/iBvZ1NYOdJtxL/KDLC0KKNrNrbB1FCHo9gdRhcBcdLNOAL2BsN36arMitmBnY7ONLJ6IbeROSV28W8+nyMZmNQoVk6+cHeGnHujAoSorpsaalqR/JmenxN5CS9DF00eaONTkbyQ+29hslnob6my5wPFQnJxRwalqDZFOFcqaicTlqsn5sTRXp7Jcm7r91tOwV+cLj3YT8jjW/f754ZVpMqWlnjqfONlCW9hDxKNTNKrbItgB5IwKqUoJr353hPKpqSm6urpAfwiQmM6nQOgIBAiQ0gJpIqigWDNgXIPy22CtLyZK3z9C6AcxTItSxaJiVNA1jReuTJPMVyhUDHJlA5em8rMPd/LLB07xp/3vMlfe++fioO4k6nSjIFCEaFQCzZeL2ya0fKbrMHG3FyklQ0NDlMv789qpniB7OtpMtlrhjdnxnR7SXeNXD54i7HTzQKyFkmlwNTPPG9NjZIytT/rdCzye6EBX1Dv2vf7R9VlOdYT5aNsB/uPlN/dMu74qBD+x0DZaq0QTi57bG2KXQ1Fp8/gZXqgI1BWVJ5o7cam2LGOzMvY7w8ZmF6Jre+NLx2Z3cn06x79/7hofO97CsbbgEnFAypoQ9YNLduAEgN+l0R72cKYzzMEmP5aUDbFuPf9JG5uVEIApLY4GYxwLxRvvocWfw9XEOiklDk3hQJOfRMDFA90RQm4Hrw/M8dbw5kzCnZpCyLN24rGUknSxyvXpHGc6w0R9Om8PJ5nLVTiQ8ONzacyXi3x3rH9T+74dVFXd9n0AVCq1ib50P4MQgrG5PFenpht+gW6HikdXFwzbO/C6e8D9LNZCXK7EQpVJLBFBWDOI6nkQXhAKltbHuaF5vn5ufUFlMl2kO+Yj4fbuacHO79B5LNG+5L1+K/lqhWuZea5l5hnLZ7E21Mi3Pt8avc4DsVaOhKJ0dHZSrVSYnZ0ln99/lVhTU1M4HA6eaO4kWy1zOT2HQ1FocvsYy2e26BXdXfzqwVOEdBeTk7XrlUAgwKlIE6ciTQvf1RZ5o8JEMU9/Zp7rmfk9IzzdDj/Xc4xWr5/LExm+ff7OqpFNS/K1c2P80mPdfL73OF8aOL9Fo9xenm7pJu7yogiBJS0eb+rkb4evALX22L1w3WZJyfFwguF8hh5fiGfaevFoa39X29zbCLmSeYqNjc2OYVqS8VSRP/zRwE4PxWYfcKjJzydPteJ1ajVz94rJD69M8/qNbUhG3ANEfTqHmwK0hd10Rjz4XLWLJMuSKIqgVDW5OpXlZHtoT1z42dwbGKbFv/jGxU2to6sKv/hYNx0Rz5LHV3tf11NiLSn53sVJDiT8tEZc/McrZ+9o7OvxeKKDkNPFoWCUXC7H+Pj2Vw+1tbXh9nj40qtDDMyubQ0QcDloC7vxuzRURRB0OzjUFGA8VeRIS6BRiVt/Xf/6rRHeG02vO4afPN3GyY4QhmVhSclLk0O8l9qGFt1twq1qPBxv43S01o5ayOWZm5vDsqyF91JtehEIBAgEAjh0HVVRKJsG/Zkk1zPzDObSKwYpRJ1uvFqtMrRuvC6AiWKOwgrpim5V40QkwelIMz6HTjqdZmpqaluPf6fo6u7G4XDwd8NXeSTRRpPbR65a4e25Sc4np5ekyToVlZjLg0QyUcjtKVFvsViXzd6sNNY0DbfbjaZp6LqO0+nE6XQiRO0m29eGr9C/x1o8N8JH2no5Fk7wnfMTvDawdddvD3ZH+MTJVl6bHuWV6dEt2+520OMP8ZmuI8se/5vBy9zIpQD4bNcROn0BlD1QbdefmacvEFmz4t7GBmzBzsZm1/LGjTkujmcYSxXuuPTd5t5G1xQe7omSKVa5MJ5e14B+P6IqgicOxnniYLxWAiVvtqDXJ9rPX57i9YE5fuHRblpDbrsl1mZHqF+WjaWKvDWUZGguz3y+gq4pGKbF4o+vz6kR8zuJePSGV6qU4HQovP9AHLeuLpkI1N/rlUqFTCaDEAKHw0GpVGp4f7W3t+Ny1TzXDNPi/7n04y0/xiPBKCcjTTS53DjUpZUFlXKZXD7fGON24HA46O7upn86x5deH1p/hVVwagpCQNmoGYXH/U6mMyU2cooNeRycaAvhcqj0xr3E/S7+4sZ5Jne5T5euqDwQa+HBWAuqUCgWCkxMTGBZ69c2+f1+gsEgusuJpqhULZPrmSRX0nOMF7IcDEQ4FWki4fauuL4lJdcz85ybn2KikOWReDtuTcOwLEwpcWsax8MJJicn952X3WJ6entxaBqmtEgnU3i9Xhy6jkRyPZPEIRSa3F68jptt5kWjynMTg1xJb9/Nula3n1PRJtKVEj+eHcfYwHtiJR6Jt/L+pk5mZmZIJtevLBZC4HQ6aWtrY7yU5y9vbO7mxm7ngWgLTzZ38ubgPN98b+t9Pr+wcGPnP1x587b/ZneDR+NtPJJoW9L6aknJfLnIn1x/F4Bef4hPryDq7TZMaSEW7ANsbNbDFuxsbHYJ9QofqFXZCWqCgiUls9kSN2bzDM8XuDKZvScFFxub26U15OYzZ9qI+pyrVhZ9/dw4bw8n+eTJVs50he2LKJsdwbIkCHjp6gwvXp1uCD+P9UX50NFmilWT/ukcMZ9OzOfE6ai1kS6+lKtXmkhqrbemJbk6leF7F6c40uznw0ebEQIsy2JsbIxSaXnogc/nI5FIIBSFf3dx6wS7o8EYD8Vba1U/VhGqVxCV18HKgJUG/STS/SmEUjNULxQKFAoFNE2jWCwuqbS5E4QQtLS04HZ7+F83Wbm4Hbh1lf/XR49iSclbsxO8PTdJdsGjK6y7qFgm+RUqy+4GTW4vhmUxVy7iUR388sGTOFWNcqnExMQEhmGsv5EVcLvdRCIRnG4XmnLzfWwYBplMhnw+v6RSTwhBJBLB4/WiKgoV00RTlJtC4UIVXqVSZmR4ZCsOfdeiqirt7e1L2n81TSORSOByu5HSwqgalEolCoUCqqoSjUZRNY0v37jIeGFrPkewWHz34qgHuojatWu6UqKw0KZoSck7c5Ncz64vwOmKwj848gBm1WBoaOOCektLC26vh+fHB7mQmlm1PdajasRcXkK6k2SlyEQxv2uFqqDu5FcOnGZwNs+XXh9kO2btiYCTf/j0Qc7NTfKDXRxq8nRzF6ciTai3JGznqhX+05W3gNr92F8/dAavQ7ev42z2DbZgZ2OzS6gLdl97Z4yQ20F7xENHxINDrX0x1S54IFus8nfvjtNvJ3za7EPOdIY50RbEo2u4dLXm5yhB1pt5JFRMi0ypSqZYJVcyyJYNciWDXLmKrir4XA58Tg2fUyPgdnAg4cOSrFgxZ0nJa/2zfO/iFCfag3z2/o67fMQ2NjUsS1KsmgzPF+iNedFUQaZkYJqSmN/ZEC4sCYrYWJBRnT94qX9JyEzUq/MbT/ahawqzs7PLqlgURaGjowOhqfz2pTfv+Nh6/CE+3Xl4QUwsIQrfhPJLsFqTnvBA+H8DlrbwViplhodHNlTNtRrBYLAmRgrBeKrAf35xd9hPPHEwzqmOEFFf7W9dMS1UIdBUBdOS/Hh2lFenx+7qmCJON7/YdwJVCM4np5kuFfhQaw+jo6MUCoUt24/X68Xn85FKpTYUHBEKhQiFQqRSKVKp1JaNY7/T29cLQmEgm2Qgm+JSaua22mR1ReEDLd0cDkbRFLXmJZfLkcvlyOfzOBwOAoEAXq8XRakl1gshUBSFG9kUXxu+sqbXXEh38fneY7g1B4ODgw3fyfXweDy0tLSgqiqWlGQqZYbyaXyag5jTg1dzoC6MZzF1QXGqmOM7YwNbmtwcd7r5cGsvAd1Jqlrm/PwUFzYR4HM8FOfZ9j5+/8V+xlLb53X5K+/vIR7Q+b1ttkC4Ez7a1seRUGyZEJeqlPjDq+80fj8SjPLxjoN3eXQ2NtuHLdjZ2OwiLEtSNkzGU0WmMiUGZvJky1WCLgdBj87ThxO4HCqqIrg2leW5y1NMprfuwsLGZqcQwEeON/NIb2xdj616JaplSSxkLZVQWdr2V6tMko2kwpWwLEmmVOV3nr9GxKvzD58+uGQbi/dlY7OdLK5Kqb/j7tQ/sR6eMpEu8p9+uDw0QtcUvvBoF21hD9VqFSEEhmEwOzuL3+8nFAohpcUfXHnnjhMZWz1+fq73GFQuQu73YSPW8MIHzodAbYbC34Lrg+D+MABSmiSTaWZnN5dcG41GiUajTKeL/PDaDBfHd1/bZNDtoC/hoyvqJVeqMp4qcaI9yOHmACXDQCIpmQaThRzXs0n6t8loXxWCX+w7QUh3Ucjn8fq8KELBlBb9165vwx5tthtN02hubkZ3OlEVhSvpOX44OYRXc+Bz6Hg1neF8mnRlZdE0pLv4UGsPHd4AAsjn86RSqQ2Jt0IIotEo4XC4UQUMtdbAomGQrJSYKuaQEh6MtSClZHJy8rYCRFwuV0MEdjqdWJZFuVymXC5TrVYxDAPDMDBNE1VV0XUdXdcJBoMIIZgq5vjGyDXS1ds/77W6/Xy4tZuoy4OUkmKxiNPpRNM05koF5ssl3JqGW9VwqRrnkzOcnZ2gZNUqVju9QZ5u7iLicmNakt99/hqpwvZV2Z7qCPGTp9v4+vA1rmfnt20/d8JPdx+l3RtY9t04Wyo0WmLr/HzvcRJur11lZ7MvsAU7G5tdSF1wEAL+6s0RLk/UEsBiPie/8v4eXAttUKoiuDyR4fkrU0xn1r8rbXNnBFwauYrBLu2c2LPEfE6eOdbMwYRvVZFiNRHPsiRik9VGi/njl28wOJfnVEeIT59p51vvjZMuVvm5h7vs0AmbXcutLbB16iJdqWpyaTzDhfE0N2Zza3qqPdAV5v0HYhQqJnG/E11Tl7z3/274Klczdz6B+41DZ/Apc4jsb4O8neosFbQ+UPxI1wcQWjulUgkpJbOzsxSLRXS95tlVqVRwOp14PB6y2SyGYZBIJAiFQlybyvLnd+Bbt1M81B2hK+rFkpK430ki4KolJVqSolnlT6+/S8G8vfbUlfhASzenIk1MjI+Tz+fRNI2WlhZMy2J87O5W+tlsPbFYrCGeLaZgVPlS/3tkF8SqoO6kxxfiRDixEGABmXSaZDJJtbp5AakeElGvvNM0rfFYPSm6UCgwOTl52+3Wi1EWt01vYNlwOEw4HEYCfzt0hcH8+gEyXs1Bi9tPwu0h4nSTcHkJ6jWhMJlMkkqlGmMIhULEYjGgZkugqCpyoQPAkpJ8tYIQAq/moFg1eX1gjjcG5ylWzNt+DTaCQ1X4Z88cwq1rmJZFxTJJV8tMFfNcSc0yuoVt1LeDJgT/6OhDy9phASaLOf68f2nKbYvbx+f7jt+t4dnYbCu2YGdjs4upT76ypSpnh5K8fH2GgMvBL7+vB79LQ0Lji/7CeJofXplmJmsLd9vBbzzZR2vITaFi8C+/fXmnh7PnCbodHG8LcrI9RCLgWuLhuBZ1IUFKSdmwGuI1sKmkLcuSvD2c5O/eXZ5I2RJ08dn7O4j5nY1tVgwLXVOwFr4y7bu2NruBXKnaSDouV00uTmS4MJbmxmy+8V7dLB88kuD9B2IoigLSYrJY4HxymnS1TLpSJlstY97Gth+Jt/K+RDtgIorfhNILrNoSuxbO9yE9n0YIHaSkYlk1L7OFapl6pWL9M2pJiWkYOBwOZrIlfvf5/VEdpqmC1qCbk+0hHuiO8PXhq1zbAmEVIOb08EsHT1KpVBgcHNySbdrsPuoVaJVKpdGK3NHZSVVaqAhURTTSNk3TXCY+bTWqqqJp2obaorcTTdNob2/H4XBQNg3EQt1z2TLIGlXS5RJ5o0KHN0jM6UZTb16HSCmpVqukUinS6TRrTbObm5sJBAL8ySs3yBSrHGkJcF9rAFVReH1gjndHU3fVs9qjq3REPDQHXDQFXbQG3QQ9OlJKpop5/nb4yo55aXb5gnyu++iyx01pcSk1y3fHBmhye3myuYuZYp6JYo6T4QRt3oB9vWaz57EFOxubPUDdQPzqZJa/fWcMw7I40RbikZ4oTUEXVr19T7BrjXP3OqqiNL70//ClfkaS2+clst95qDvCR0+0ALVW2Pod/rpADTQ8nEpVC4+uogqxTNC73bZV05IUKwa//dw1ysbqn5ewR+dQs59CxeD8WJqo18mH72viUJPfrryz2RX8ySs30LWax9nAzO2LdCvRHnbTG/Px2IHYQhrqzc/mVDHPnw+cX2cLywnrLj7ecYAmtw9ZuYAo/DVYc6CEkZ7P1CrozHFE+RWoXql52VlzoLaAcIHjBLg/QNU0+a8DF8ibVaqWyRNNnUScbi6nZymbJiHdxXy5SLJSotcf4snmrsYY/pevbX7cuxVNFfzGE32EvTr/6cqblLfo+18Vgl8/dAa3pnHdbn+9Z9B1nfaODkBQMizcmsLU1CSVSmXDPnL7BUVRakEdi8Q4TdPQdb1xY8AwDPL5PMVikUqlQrVaxTQ3Vgnncrlob+/g3EiKr53bvRWrTk3hwe4ITx1OoAh4LznNczsQTPFUcxeno01LEmLr/O3QFfqzST7deZhufwiJXHE5G5u9ii3Y2djsIeof14l0iWtTWa5PZwm6HfzUg51LlhvLZ5go2KEUi9mKE914IctH2vtwKArlqkXFsMgUDaazJUaTBa5MZihVbcF0LZ4+nOCpw4llnl2GJTk/lubt4SQz2RLlqtX4m3l0lU+dauNIS+CO21SllIwli/zV2RHSxc3fKf6HTx8g7nfa/nY224ppySXBEoWKgVNTG4J2vYLsxavTPH95etvHoyjQFnRzoiPEQ91R+jNJ/nb4ym1vr+ZF5AMUqF4GRw9SOhhNlmgK6DgdjhXXk9IiU6nwp/3vUllBnIo5PRwPx1GEYCiXZjifxqGo/MahM1RNyVtD83zv4tSmxxvz1VptZ3O7S7SoB+V8Z/T6pozs1+N0pIkPtvYwPz+/aZ9Am72Frut4PB50XccfCFCqWvzZq4M0BV18+kw7ExMTW5bOvJ9QVXXD4txK9Pb2ka+a/O7z16msceNwtxBwO/jIsWbuaw0yls/wFzc2nq6todDq9TNZzK543t4Iv3rwNCHdueL13+9dPotDKPy9Q6ftm6k2+xJtpwdgY2OzcepfRC1BF00BJ48fiiHk0gmcJWt3ll6cGt7Joe5b/vjaOU5Hmwk6nPgcOpGAi85ohAe7I1hSkisZXJ3KMp0p0RZyE/Y6mcuVuDyZ5fpUtmEO3h3z8lhvlKDbwRuD85wdSq65372OAD52ooWHeqJALelSFTCTLfPm4DzvjaZWrXY71OSnL+FbUoF3O0gpMS3J198duy2xThHgdztIFaromoJHV+2LQ5ttQRHw3liaN27MMZMtUzYshICQWyfq04n5nER9OjdmNm/IfjtYFowki/zsw53kqxW+M7Y8xGIj+DQHz7b10eELMjiTI1Wscqz1ALmCxRdfvkamVPOsOpjw0Rv3UayaPH04TsGo8u3RAWbLhSUtWaoQNLt9tHsDOBWVk5Em1IXP5OloM5asBTQIIZhMFXjp2syGxnkw4eNEe4i+hA+PXrtUllKSK1V5/cY8AzM5JnZB4NPofK3Su9UT2DLBzq1qPNXS1Qggsdm/6LpOV1dX43tsMl3kv/54mHSxyly+zPsPxGhubiYYDDI+Pr5trbB7kTsR69ra2lBVha+8NrQnxDqATLHKX745wofvq/BYbwyv5li1PfZMtJlOb5Co04VH09EWOlQupWb51ujmK3aPBmOEna5Vn3erGsfCcSRgX5HZ7EfsCjsbmz1OtlrG73ACNS+Hehn4H1x5m3TV9rO7GzgUhbDuptMX4FAgSrPHB9QmeAWjikdzNKrJKoaJqggcqoppWRRNA59Dp2qa3JjJ872Lk7uuiuNOUYTgM/e3cbwt1KiQOzeS4vWB2XUnvY/0Rvno8ZYtCYCo++T9zVujvDuauqNtffhoE4/2xe5IQLSxWQ3TsuifzvHClWkm06UtqRC+Uzy6wj//yFG+P36D95LT+DWdsmVsqmKi0xvkp3qO0j+d489eG9zQOv/9s4dx6Spvzo7z7vw0ZcsgrLv5SHsvcZe3sZxpWRjVKiMjI1iWhdvtJhQK4XQ6qVareL3ehr2EaVmUKlbtnGxaPHd5Cp9T40DCT9ijE/M7G+36lpQU8nny+TzhcLgRbFGqmvzt26Ncnrz71UcxnxOvU8Ojq3z6TDvT5Rxf3kTFy1oI4KPtfRwNxSkWi4yMjGzJdm12H16vl7a2Nr78xjDXprMY5tIzjaoIHj8Q44lDCUBiVKsUCgXS6fQ91yK7lRw8eJBzoymevzSN16kyn69QNiw0RXCmM8xDPRGmMiXODiYZnLs7N2U2it+l8U+fOcyNbJLvjQ80HldReCzRzuFgFIeqUjUtZrJlJlJFumNeQh6dP71+jvnK5m50HA/Heaa1F1g9XOxrQ1f4aPsB9EXtyzY2+wlbsLOx2aNIKTGkxR9cfQcFQZvXzwdbunGpGkKILTWgttkcPs2BU9VIVUqYUuJQFJrdPprdPiJON4ZlMZRPMZhNY0iLDm+AE+EEB4MRFATpYpU3bszxSv/cTh/KHeN2qPz0Qx30xGoipmlJ/u7cGO+MpDa0/gNdYT5xsvWOxbq64HdxPM3wfAHLklgLacxSSnRNwampOB0K6UKVwbl8I8DFoQqePdZCe9jN9y9O0T+TI+Rx8E8+eMhui7XZFurCkiIEZcNkaDbPjdk850ZSFKvbmxZ4Kz6XRnfUS9zn5MnDCWaKeQwpaXbXxLK8UWUkn2Ywm2KuXKRsmiTcXqqWSdWyiLs8dHgDeDQHVWnR7Qvx5uA831gh8GUxTx2K8/SRplpqumUhFvmIQu01KhaLZDIZMpnMqtsJhUJEYlE0RaVgVDk3P4VL1QjrLoQQRHQXfr1206tqmWSrFUbzGV6YGMKhKHys/QCdvmAjLVXXdXRdJx6Pg6Lyv31ja4SyjdAacvOx4y20RzyNx0zL4i9vXGK8uLXC4ZloM081dzXeiyCZnZ5Z87W22VsEAgGam5v53eevrRlYFvXpPNgVoS/hI+6vVToZpsX83CypVOoujXb/0NXVha7rS65r8mUDRQhcDgXDMBCKiqYqTGdKfPnNYeZ20Y3cn3qgg2NtwWWPSym5OpXlleuzDM/XksCbAi5+86k+rmeSfH3k6qb2cybSzAdau9e9YfvO3CSno82bOwgbmz2ELdjZ2OxhLCl5aXKYs3MTAJwIJ3imrXYn6vWZMV6esu+M7yVcqsbRUIxTkSYiTjdVs1Zl850LE6QKO5PMdSc0BVx8/pFOgu5aRUpt4id5rX+O1pAHj67ytXNjjK0R4BFwO/hnzxzekvGYVs0vr94zsTjwoh7cArXWciEEhYrBq9dnOdUZJuLVEcC7o2m++vYoAL/y/h46Ih47gcxm27EsCQKKFZOvvj3K9em751H6a0/00h6uCUSmaTa8VAuFAuVyGZ/Ph9vtXnVSJaWkZJiUqxY+p4amKrx6fYbvruMl91hflGePtWBZFqOjoxiGQSgUQlEUSqUShUIBwzDW3IbD4aCzu4vpYp7XZsYYyqWXBXMoCA4FIxRNg6Fcetk2NKHwC33HCekuhgYHG/tsaWnB5/NxaSLNX745uuY4top/9sxhPE6VG9kkqUqZd+cnSVe3byLf5vHTFwhjWDWhNeH2MjkxQS5ne+TuBTRNo6WlBdM0KRaLjc9snfb2djweD//6u5fJltb+LNXxOlV6Yj4e7onSFnIzPj5GoVDYrkPYl2iaRlNTE6VSiUqlgsvlwums+bPNzs5SLNauiUKhEJFoDCnhq++McnF8d4jlbl2lJ+pd0n8qqPlrz+dvno9iPie//L4edIfg9y6dxWDj1dgPxVp5orlzQ90Vfzt0hVaPn4firZs9FBubPYEt2NnY7GGklMyXS/zx9XNA7QvzN488gEdz2BV2e5w2j59TkSYOBWueb/P5Cj+4OLkj7Ve3w/1dYT51qq3xe/2iK10pEdRdlE0DS0qcqsbfvDXK+bHlE+U6v/WBA8R8K5sN3w3q3nlSSr5+bpy3h5NEvDr/+IMHbQ87m7tK3av01f5Zvnthctv39/GTLTzYFcE0Taanp8nn86x02djb24umaczny4Q8Oi9cnsK0JPd3RXBqCk6HQr5s8jvPXef+rhA/vrH+d9OvvK+brpiPgYGBdYW51ejp6cFUBF+8do7CKn5LGyGku/h87zEcQmF4aAjDMFBVlXg8jt/v519/5wq5yu2NcS0UITjVEeJgk4+gW6c15F7y/J/3n2eyeHfEM11R+eme+4i73Az0DyzzM9M0Db/fj8vlolAokM/na5VCQqAoCpZlrfjesdk+Wltb8Xi9mJbEodbsUixLNv52mqbednCNU1P49Sf7CHscDA8NUa3uvZuKewFFUejs7ETXdb53YZJX+veOr+Tff7KPmF/nzwfOM1de/cbsrTwcb+Xxpo2JdQB/dv1dpksFPtjSzalIk31dZrPvsAU7G5t9wB9efYfUgi9EWHcRcbrpz+7vEIN7BbeqcTyc4Ey0GY/m4Pdf7N8VZudr0Rpy8xtP9jV+r190fXesn0upWQIOJ6lKCYei8ltHH+TSeJq/Ort6hcpThxM8eTAO1JJZ66LFWtT96m6HxReJt14wXp/Ocm0qy6GmAN0xr+1hZ7Nj/Hhgjm+dn9i27X/h0S56E37S6TTT09Nrii3BYJBoNIqmaUs+M4ZlMVHIkq1WuC8cp1gxmM9XmEiX6J/Jcn0qj7EgHhxu9vNITxSHpuBQBImAE4Fk4MbQbQl2HR0duN1uvjp0hYEt+D6MOd38bO8xhGUxeGMQqIlUPT09GJbFf36xn7lchb6En4hXJ+zRSRYqvD6wcWuDjoiH7qgXl6PWon+wyU/A7Wi89ovPRcO5NN8YuUbR3HqhcDWOBmN8rONAQ0TVNA23200gEMDj8SxZVgix5L1g++HdfXr7DnBxIsPfvDVKyOMg7ncR9zuJ+51EPDo/vjHPhfHVb5atR9Dt4L/58CEK+Tzj42u3uNusjdvtJhKJ4HA4UBQFiVi4hhFIy0JVFd4eTvL1c3vjdQ64HPyzZw/z9twEz08MbXg9v0Pn1w6d2VTnwn+8fJb7QnEcikJId3E4GLVFO5t9hZ0Sa2OzDwg7XQ3BLlkpkdykqavN7qVoGrwxO87l9Cy/cfh+/v5TB5BS8qXXBum/SwmRm2U6U+IrZ0eYypRwaQq/+ngvL04OcT5ZS2isvz8rlrmhiouzg/M1wU5A1bQalQKmZQECAUvEuZueS7fH4gu9Wy/6emM++uK+WsqtLdbZ7CAP90axpOQ721Rp1xHxYJomqqo2xJeV6O7pRghBtVpFAg5N4+WpEXSlFhRRF5TGCll6/CGafF7awrVk7do2a+EPqqIhrRLIAkKN3Nx+dzeZTIbp6Y1VAbndbqLRKG63m+cnBrdErAOYLRe5kJzhZDhRa7Xt7Gz46zlUlZ97uIvB2Tz3d0UabbeWJUkXKsxky8wXKtRfQk0VSwz+T7QHeX9fnKagC9OSsHAGU245F+WrFb47NsBIPo2xA/fbvQ4HAJ1dXSAlmnYzQXelCfLix9xuN6qq3lG6ps3GCYVCaKrC2cFaNWuqUCVVqHJtauuq9H1ODUUIuyX2DnC5XLS0tuLQNMqGyXS2TKZYJV82yJcNTAkRj07Q4+Ds0N65EX+42Y+UkjdmNndT6eFY2/oL3cLjTZ21lFgpyVUrtlhns++wBTsbmz2OlJKQY/W4c5v9QbZa4cs3LnJ/tJkDgQg/90gXuZLBf37xOoXKxn1B7gaGJRstrr/0WDeGtDg3v7Zf1VrkygbvjaU42R5aMoFVFWXZsnVR4ctvDPPJU614dW1LgyHq21Lt60GbXcCjfTEqpnVbLW3rYVgSp6Y2kiTHxsawLAuv10uiqdZ2ZJkmukNnKJcm6nRTMqu8Mz3K2/PLRcT3ktO8l6yNU1dUfvXgKTxKElF+A1XxgzGMqLwDjsPg//tYlsFUqUjJNOgJhfH5/STn50mn01iWhaZpRKNRPF4vqqJiSYkQtfOClJLXp8d4e25rxUy3VhOs/H4/qqrWwp8siQok8xXu76oJjY3zlICffbgLqLXWJ/MVFEUQ8eqkCxUuT2YwLMn7D8QbIp+6UFWzmPoE1OvQEYIdEesA3p6bpNMbpMsXXPPGxmq4XC7y+d15o2m/EQqFmcuVG+b/28F9rQFMS9rBE7eJ1+uluaWFXNng+fdGuTCeXpbUu1c50R6kaBjkjI17bD6WaOdUtGlT+7GkbIh1Qgi8Dn2zQ7Wx2fXYgp2NzR7HkpLgQsKdzf5mNJ9hNJ8h5nTz6a4jhDxOfumxHn7/pRuNtrLdREvQRWfUw8XUDNU1xreRyd7fvTvO8HyBB7oitIbcjZAIRbAw0RUooratr58b4+pUlr94Y5hfeqwH3a6Es9nHPNQd3RbB7v/69mUU4Gcf7uRQc4Devj4sy0JVFMqWiZQWpoBzM2O8tMGAI00IjobihJ0uvJoGlgal50BtBnMOHEeQns9gWQb/7uKbjfVaPbUU9EQ8TiwWw7QslIXU2KG5PNemsqiK4HhbkKjPyYXkNC9Pb337ZcTpAimJRCLM5cr89nPXADiQ8PGzD3cua9dffLNAVQQxv3Ph3CWpiCoPdEXQFiqG12sBk1IykE0yuEIwxnaiCYUDgTAxl4eY00O3P3Tb27oTnzNFUVAU5bb9DO8ldF3H4dB448r2+lweawthbGPoyX7F6/USCATwen3M5Mr86auD5Mv75319f2eYjoiXVzb4vaAKwbNtvRwNxW9jbxJL3jx/2iFgNvsRW7CzsdnjKEIQsAW7e4rZcpH/euMCH23rozMY5H/8+BGuT+X4izeGd3poALQG3ZxoD3J/d5iSafCjNS7a5spFDjf7ub8zxFvDKaDWLubVNVRFoAiBqtR+rk1leWsoSdzv5P7OME1BV6NtJF82yZUNZnNl5vMVPnq8mQe7o3fpiG1sdo7iNoQd1LGAa9NZDjUHADifmmGuXOR8chpzk1Vej8bbuD/WglNRsaiJ7KhRiPxrAKQsI4QTKS1+MHZjybqThSx/1v8ecZeHNo+fZo8PXagcCEa4MZvn5es1I/aXr8/yUw90cF9TnIlCjvOpmTt+DRZzPZMknmhHAIYp+WfPHGJkvsCRliBCrD5ZrFd/WFIyls/wyvQoY4Vaa+JDsVYeb+oAWNJ6LLm5PUtKBrMpvj5ybVnK7XaiCYXPdR+hzRvAlBYKtz8ZllJSqWxc3NF1nUAggNvtxuHQURaEzWqlwszMjN2GuQL11ywQDGJaknOjqW3dn2lZqLqG0+lckj5rs5R6QI3b7UZRa9c2piW5MpXhq2+PUTF23w3XjRLzOfHoKnP5CvmyQdij89ETLcyVCrw2M7bu+i5V4yc7D9Hi8d/W/gXCboG12ffYoRM2NnscU0qupGb59lj/Tg/FZgeION08FGvlWDjOdy9M8Gr/xg3ON4qmKDzcE8blUBECEDXfuJUukbqiXlpDboQQTBZyfH98gOnS6hMrl6ryk52HafH4yRSruB0qToe64rIz2RK/+/z1ZY+riqAj4qEv7uNAwkdTwFWrvrMr62z2OVJKBufy/Mkrg9u2j86oh19+Xw8zpQJf6n/vtrbhUBT+yX0PL/E6M6WFKpa2teerFS4kZ7gvHMWpqiiImredlJjSIFs1KJoGihCkyyXiLi8Rl5svvTbI9elaWmrUp/NLj/UQcDsoVKv83pWzd/YCLEJXVD7U2sPhYJSKZZKtlIm7vesG4dSPu2hUuZKe493kNLOLzose1cGHWns4GIwsW8e0LK5k5vje2MCmRdI7QRWCT3cdocMb2FTVSn3cUspGMIUQglKpxPDw6jeVNE0jFovhcrlQVK1ReZgtVRmczTOaLODUVB7pjeLRVaqGwfzcHJlM5o6PdS9ST+X1eDzouo5Q1MZrNjJf4IUrUwxss89t3O/k15/oQ9cULClrvrKWhWmaGIZBtVqlWq2SyWSWpQrvJ9xuN/F4nGq1SqlUIpvNYhgGiqLQ1NSEx+tDAGOpIjdmctyYyzM6X8Cw9uYUXADH2oI83BOlI3IzbKZqWBhWzWf496+9RWGdatiQ7uKz3UcIOJy3XRlXP/duNFHWxmYvYlfY2djseSTpqn1n815lvlzkO2P99AXCHGoKbLlg99MPdnC42b+iX9xqTBSyfH342oa8S0qmyVeHrvCFAyfxux3LJvBQ8346N5LkRwtVNAARr87hZj99cR9dUS+aqmBastEWa1+32dwLSAnZ0va2Uj11KIEiBN8YuXrb2zAsa5moJbhZPVZ//EdTI1hIfA4X0sqCLEP++wgcaFobIbWTkOYGLJrdsca2FreTzeUq/JvvXeGRnigfPdHC8VCcwVyag4Ew5+anuBPZoGKZfGv0OoPZFB9t70Mu+CWtN9msTyRdqsbpaDO6qvLt0X40oXAkFOX+aAsxl6fxeoBEEQqWtLCQjBdy3M3b64oQfKrz0KbFOqgd6/T0dMPXTAiBruvrhk20tLTgcrmYzpQZmksyPF9gZL5AprS0jfaV/llOtod4/GCM5uZmorE46VSS+fn5TY1zL+P1emltbUUIQblqMpIqMr7oJ128/dbjzTCTLfPvf3CV1pCbiFcn6tOJeJ0E3Q58Ljc+nw+AaCxGPpdjcnJ7W3R3Ak3TaG1to2pJ3A4dv99PPB7HMC3EwvXI20NJfnh1etvP1XeLo60BPvdAByXD4M2ZccYKGZrdfuIuD36HzqsTY+uKdW0eP5/uOoymqLct1pnS4vz8DMlKkadbum3RzmbfYgt2NjZ7HFUojYRYm3uXsXyWlsDttRSsRHvYzecf6cKja7w3P80785PkqhXqzVprTR4rlrmhlFa3qnEq0tRok6uvU095tSzJm4PzvNo/R6ZURRFwX0uAh3oidMd8jdaw+sWendpqc69hIclt8yTwneEkvXEfAYeTVOXmzSEFQZPbS7ZaWVecj7u8yyZl9aoIZVHF3ZPNnbw8NYKUJlhZRPY/gFxItSzfUtWrxCH0/wVWFi3PjSZ5pDfKs+19jYncqWgzX7x2bvMvwi20ewMIIRohFBulPpnUFZUnmjo5GUmgLzr3Qf18VvdjUhBIPtzawxNNHVzLzHMtPc9QPr2trbHHwwl6/eFNr1d/nRe3vkopN9QuKYQgma/wez9cXkW9GNOSvD2c5O3hJIeb/TxxME5bLEYoHCGTTjE7O7vm+vuBYrGIJSVXJjL85Ztb79W4GXJlg6urJM86VIWoT+f9B2Icbwvh9fnIZbOkUql900Lb0dlJxZL8px9eJ1WoEvHqtATdtIZc6JrCa/1zzOX3l8/fRLo253h3foofLXiF9mdTG17/SDDKR9r7EIg78pxThcLVzBwj+Qy5aoWPdRxASNvHzmb/YQt2Njb7gLQt2N3zjBUy9PhDKArcaefJT55u42RHiIJR5S9vXGQkv3UtRy5V42AgwuFglHZvzRer1mUrGpNxS0oGZwv89VsjFCq1qoy+uI+fON1GwO3AspYKdTY29yoKglx5e6tp3htL86nTbXys/QAD2RQvTQ3T6w/zgZYunKq2EIaQ4r3kNCP59IoBM/PlAqlKaVnr0+JqCFUouDWFD7f18rWhK3yq8wDS98uI7O/ASrcArBksI4lUQvQlfJwbSS15ulS1+O3nrnKyPcQjvVGag26Cjo35vWoodPtD9PhDNLu9OBSFV2fGuJSqiUEdC+eu26XHH6bXv/zctxL118ipahwNxTgeTpCrVvj2aD/D+e0JoLienufBWAsBh46yQtXzahSLRcrlMqXS5q9JamLfxvcFcGUyy5XJLJ0RD08cinMgEUHXdcbHxze9/72EZVkU8nmOtgSIevVdKwhVTYvJdImvnB3lpWszfPBIE4ebgwSDQUzLYnRkZM8Ld0IolMoGxYVrlfl8hfl8hQvjdzcc5m6SzFeYzZU5GIw0BLuN8mi8jfc1dWxJNZyUkslizQrhamae/I1LfKi1h5jLs8Ry4aYvqNzU+czGZrdge9jZ2OwD/uPls+SNu9MCYbM7afX4+Lne43zl7Ajnx27/QvHnH+niYJOf88lpXpgYomKt3ca0EZrcXnr9YXp8IZrcXmCpofqtmJbFW0NJvvneBG6HykeON3OqI4xlSduXzsbmFv7ijWEuT2yvj9d9LQGePdZM0KMzXsjS6vEvmXAt9hF6cXKYs3MTy7bR5vHzue6jKGL1qor6Nn8wfgNVCJ5q7oLqRUT+T0GuJALpyOD/hBR+/sU3LrCaJdRD3RE+frIVgLJZq8ZLV8pMF/MUzSoVy6JimVRMk/vCcdo9/oYPWz3ZVNd1CtUKY4XcEq+5naD+er87P8WLk8Nbcp6+FZ9D5/O9x/Bqjg1NcuvTiaGhoU2FS9Tp6OigIlX+7fdvv/X66cMJnjqcoFAoMDo62nhcURQ0TUNV1SU/1WqVbHbl6rDdjqIo9PT2cmkiw1fOjq6/wi4h6HbQHHTx6TPtYFbX9DXcC7jdbtra2nm1f5bvX5ra6eHcNT57fzsHm3387uU311+Y2s2JD7f2ciKS2LIxzJeKfPH68orpdm+A05EmevwhHIpK1TKZKOToWKiMtrHZa9gVdjY2e5yKadpinQ1TxTyWlBxu9q8p2B1q8vOp0614dG3VSfNr06O8Mr01E4CHYq080dyJJWWjmgRWDqyoIxCc7gzTE/PhdWk4F4y0bbHOZq9yq3+blHLFlkaxhpi10javTGa2RKwLeRwE3Q5ShSqZYnVZPVumVG34Qja7fY2x1qmPWQjBg7GWFQW7sUKWP7z2Du9LtHMsFF+x2qEukn2otYfzyWkup2c5EjyCDP5/EPm/hOri0AsFfL+KUAPMZkqrinUAqUJNQLIsi0qhiJSSkNtNIuJdtqyUkmQyST6fp1QqNYQov99PJBLhYDCy415J9df7eDjBkWAMU1rkqhUmijlG8xmminmSd1h5n6tW+PKNi/x873Fc6vpJjPW/XTQaZWJi+d9/PaSU3Kn56AtXpikbJs8ea6Gvrw9rIXxoLQ/WaDTK+Pj4bYmMO4llWeRzOY63hXjp2gzTmb1RqZYuVkkXq7zaP8tThxPour7nXvs6brebpqYmhIDWkHunh3NXCXocjZsf6+FQFD7WfoC+22izXw1TWowXVxbbR/MZRvMZVCFo8fhJlouEdBedvce2bP82NncTW7CzsdnDWFJyMTWz08Ow2QV0eIMoQjCRKq653JnOMD6ng0upWUbzGeQtU/OSYXA9m9ySMR0NxXiiuXOJT9VGUBSBgiDm31j7mo3NbqUu7GSrZV6ZHmU4lyakuwg7XYR0Fw5FZbEnpFvTiLk8hHU32oLIYFi14AFlwe9n8WdpPHV7okxTwMWBhI/2iIfOiAePfvNy0LQkmWKV2VyZZL6C7lA4vVDhCuu3onsdOs1uL5PF5QmVuWqF744N8NbsBJ/vO45gufBV//2+UHzRvnzg+3uQ+yOovgvoyND/D6HUghpifiefPt3GDy5N8WtP9KAq8NV3xumfro3h+nSOt4eTnOkMY5om09PTjX0JIVAUpfF/0zRXDEjIZrNEIjsv1i1GEQJNUdCFiltzEHd7ORlpAlhI7ZQY0mK2XOD16bFNt9CmK2UKRnXDXn1CCPx+P9lsllwudxvHs+lVlvFq/xyFisnJ9lBNHCpUSBWrFMoGFdOiYliNf3tiPj56vJnOzi7y+dxtCY0bJRKJEAwGUVQVyzTJ5/OkUqk7EqtmZ2fx+f184HATf/HG3qpUe31gjvcdiNHa1sbgjRs7PZxN09TUhD8QIF82+MH5Cd4a3prrpr1C2KOTMUv4HTrvT3QQcbr51uj1JTcK/A6dM5FmTkaa0BbOsXfC4nOvKhRG82tXx+qKigIcCcZo8fjuaN82NjuJ3RJrY7PH+ZNr55gtry3S2OxvNKHw833H8alO/s9vXVpz2UNNfj7/SBevTo/y6hZV0a1EzOnhFw4cR2H9ygwbm/2IlJKqZfHC5CAXk7NYG4piuYlL1ahaJuYKl2kOReEnOg/T5QtSNS3ShSqDc3nOjSQZTRZpD7vpinp5fWAW4xZLOVUR/L8/XmtNRa5euXqzClCsGehSD4mpi2uWlFxOz/Lt0f41j+9MtJmnm7vWPT/UL1MFtbHI6gWElQPXo4yNjVGpVGhqasLj8SyZ0ElpcWUy1xAyPnd/O8fagqTT6YZgtxkCgQDNzc2bXu9u0HiNFo79tf5Z5vMVgm4HIa/OwYQfXVMoGyYjhTQvTg6TqpQI6651K/H+0dEHcaobv79f/xtYlkU+n9+weNfe3o6l6vyr71xedZnemJfRVJHKrW/qO8DlUPjgkSYe7I5QKpUYGbm9EAdFUQgEAng8NRG5UCiQyWSwLAu3201HRwczpTxDuTQd3gAJV62607SsJZW1i8OXGv9KWfs8WhbpdJpUKkUikSAQrN2oe3s4ydfeGbuzF2IHONzs52ce7MQwqszOzqIsqoQslUq7rvJOURRCoRCBYBDd4eDtoSTfPD+OYe7/qfSnTrUS9Tl5dzTFyfYQnREPppS1JFxuBge9Nj2GYVm0ev0cDNSsA7bCb9iSkuuZec7NT1ExTcqWsSQE6VY+0tbHsXC8sS62f53NHsausLOx2cMUjKot1tnw8Y4DRJxuvnJ25YmGz6XxoSNNHGkJ4HKolE2DsS0MkliJLl8QYYt1Nvc4uqpSNs1Ni3UApTXajaqWxd8MXabPH6bdG6DTG+DB7ggPdkcwLKtRifeBIwnGkkW+f3GSTKlKS8hNT9TbqN5bqzddCIG6zufXkpKqYeF0qI3HFCE4GoyhKyrfHLmOIVcWV96Zm+R4OEHU6V5zQnfTJ6+2beE4ulBvCPl8Hk3TcDqdIC0EFrL4PKL4PYT3pzjS8jD/w0cO81dnRzneHrptsQ7YddV1i1k8JsuSPNoX46tvjzY8tTRFcLDJz8n2IAebwvQdDGNKiaYomJZF0TQYzWf45ujyhNazsxO8r6lj02NRFAWfz4ff72dsbIx8fnnF5WKklKw2nQ55HPzy+7oJeZxIaTKRrvDC5SmuTW++iu9WSlWLb743QXvYQ9Sjrr/CKnT39KCpNb8sgcDn8xGPxzGlhZC1z8p/HbjQCGXxqA66/SG6fUGKpsFcuYjCzc+dQOBQFFyq1vgJ6i7i8TiBQABVVSlXTf7jD/tJF/emLcqVySx/eXaEn36wg9bW1mXPy4Uq0ZpgaWGaZq0aVlEQQkERAqv+nGGQz+eZm5u77fHU37Mej6dRcVv/cTgcqKqGogjShQrfeG+Ud0dTd3D0e4cznWHu74pgSUlX9KaNgLbsXKjweFMHNXlsc50Va2FJSdk0+P74AKWF6uc2j59f6DvBtcw8b89NMlMqNJZ/oqmT+0Kxm6NalLxtY7MXsSvsbGz2OFud4mmzt7g/2sxTzV388OoMP7xSm4gebvbzxME4QY8Dp0NtXFQN5dKcT07Tn02uWLWzlTwYa+Hxpk47ydXmnsaUFiO5DOlKmZJp8PImE/U2g0vVaPP4afcGKJsGg7kU94Xi3BeKo6tLhYitEJ5MSzKXK/NHL9/gnz5zCKe2dB+WlCTLRf5u5Bpzq9xYcqkaz7b1ciCwOTGsvmwmk8HpdOF06khpARai+E2oXgfnw+B6HBZ5o83OzjI/P7+p46yLI+FwGFVVd6VgdytSSqazZX7vheUCXMDl4EP3NZEtVplIl0j4nXRGPXTHfAxkknx1+MqydT7Q0s3pSNNtHbuUklKpRLVaxeVy4XBoIC0KxTIzMzONKqq2tjaEw8m//PbNCjtFgQ8eaeKx3ghCVBGFr4ESRzofQSgeytUqX35jlIHZtcXAjfBPnzmEjrnpCrtAIEAkUkunfX16jFcWPuNRp4dmj5dmt49Wj5/pUn7dqtON8GCshfclOtAUBcO0+BffuHjH29xpgm4HHl3FlBLLqgnMfreDgEsj4HYQcNU8Nv1uB8WKSbpYIVsyyJaqeHSNqE+nKeAi4nViWhalYnHJe2s1NE0jEongdrtRNQ11oW2z3k5uWRJz4f+pQpUrkxmuTGaZze0Nv8DbxeVQ6I356Ev4ONTkx+dyUKlUyOVyjRsXwF07F5qWxfnUDD8Yv9k6/bM999Hi8VOvnBvNZ3hrdgKfQ+eDrT13ZVw2NncLu8LOxmYPY0qLE+GELdjdw7wv0UGhYvLDK9M83BPhiYNxfC4HBaPKRCFLMlNivlJkMJsmZ9y99pL+TJLHmzobv+/WyhQbm+1EFQrd/lAjdGK2XEBKSZcvhCFNBrNpbuRSW7KvkmnQn03Sv8iDcrKY58XJYXr9YUCSrVYI6S4+1nFgS/Y5ky1TNkxMUy67olSEIOR08Yt9J3hhcohz88sTFEumwdeGr3Iy0sQHmrsQSCR1j6L1q+4CgQAAP7o2w0vXZvjNp/qIeH8CWGjptQwylSoFs0pIdxGNRhsTz7Vo+I1pKuqiNqp6eM7tIqXEQtaqj285lluDSe4EIQRNARdRn85cbul5P1Oq8jdv3bRDuLDw74ePNvH+g3H+6bFHmCnmeWd+igsLHrnPTwziUjWOLqpa2cxY3G43bvciU/7qJTyeQ3R1dTI7O0cymURK2fibe3SVn3qgg66oC0XRkJUriPyfg6x58Ini34HjOLr3Z/jwfc38pxfvXAhzaiq66qCnp5cbNwbWXT4YDBKNxtAWCdVB3dmopZ0tF5gtFzif3Dqf4R5/qPZZXtjJhfHbT4TfTdSDKBYzmdm8P2dz0MVD3RFOtofo6urCMAwW16UYhsH4+DgOh4NEIoHT6UJRBHO5MsNTGcaSBcaSRaaza4fY7CccqiDmczKRrr3eTx6K89ThBIoQGKaFUa0wM5NCURSi0ehdF+ug9l0SdNz0NO7wBmjzBhZ+q42j1eOnvav2mH29abPfsAU7G5s9jCoUgrprp4dhs4OMFTL0+MP8T5+8D1VRmC8V+dboEFfScyumUN4NQrqTkO5ivJClvXFRZWNz72FJyUy2zO+/OMD/8NHDfKLjIFALkgA4GWnij66eI1PdvooNQ1pczdTaxHr8IZ5u6doScUhVBIeb/Tx+II7HufLlpCoUJLXU10OBCM9NDK5Ybffu/BRj+Qz3R1tQhGh4D20EKSV9CR8/uDTFaLJAxONgtlziz6+fx+BmO64mFP7xfQ/h9/sbgl0gECAQCJDP50kmbwqd0WgEsSDU/deB8xQMg7DTxYdaevA79E1NBuuvtZSS2XKR4VyaTKWMRc0jUC6EjiTcXo6F4ziEssQT8E54rDdG2bCQSN4eSjKXX/2mzfcvTTGXr/ATp9to8vh41u2l2xfkGwttsiWjemeT9Xqlo7TAmkGk/gzp/WVisUNUqzWxpr7VJw8l6In7oPwmlF5AmLf6rZpQPQfGo/hcfZsfywp8891xehM+TneEcbvdFIsrV4V6vV4SiSYcDo2pTImAS+LWNS6lZvne2PpC3+3wwZbuRqVsxTB5byzNhbE0A7N33hK8n5hMl/j6uXG+e2GSUx0hjrUGcag3BfeY30VPby+KEFRMi5evz/LjG3PkyhtLO91v6KrCF97XTXvYw3ujKc6PpWkL1zwYh4aGKJdr30uBQIB4vHZO3gkhTAhBk9vH081dKELQ6QtiSWuJJ93i86Ut1tnsN+yWWBubPc5sqcCfXH93p4dhs0MIoM8fJuH2MlHIbVm1zkZwKip9gTBh3U3U5cYhFOJuL55bEgXtu5029yqWJZnMlPjPL/bj0hRaQ24sYHA2z0PdET5+spV35iZ5bmJwW/YvgIDuJOr00OcPcyKS2NJKLtOSqIrAsuSq4RV1LGkhEFxMzTJXLpCrVrieSa7ocfe57qN0+YIbGqtpSd4amueb703w60/0EvE7+A+XzzaedyoqYaebkO7k4x0HkdLCskwURSCEhpQVhNAxzSrVqomqqmhaTYDMGRX+85W3G9s64A/zodYevA593fNafezz5SKvTI0wmEtTsZanzy5GEwpHQlFORZppcnsxpbXp4J76fi2rJgha1CrXFCE4N5rileuzAI02w5jfyQNdESbSRd4aSvJoX5SWoJtyuYzT6WS2mMdA0uz2kc1mURSl4fF1RxSfh9J3kP5/DGoL0gKhCMqGiSoEDk2D3J9C5ezq23D/JNL1FKPJEqYlcai1gJT3RlO80r95LzNdU/gfP3aUcqlENpvFsiyklGSztTTKRCJBKBQima/wg0uTjCaL/LcfPoSF5JWpURQhGt5ZZ+cmuZicWdXDcS2cikLU6SXidNEbCHMgEOHKZIa3h5Ncn87VfN1sNk3Yo/Pk4TjTmRJnB5NUzK0LL9lraKrgC4/WxLpisYDb7Wmcw6WU3LhxA8OoCZnNzc34/f4dvY4zraV/q8VBLTY2+x1bsLOx2ePMl4p88fq5nR6GzT1EUHfya4fO7PQwbGz2DL/z3LUlvkcxn85vfeAg8+Ui3xq9zvQiw+ytwKmofK77KHGXB3UhYGIrhbo6DXFoE9s2F4Q7RQimijm+Mnh5WcCGALp9IU5Gmuj1hxbyYVevnPjK2RHOj6V5X1+UZ4618PbcJJPFHFGnm9OR5oaHn2WVUaqXgQrIElSvAga4ngVHzfdISgshq0ih8/zEIO/MT9Hi9vH+pg48mgNdUQnozhXHsZh8tcKPpka4mJq5jcgRSLhqFXe9/hBB3dVI4xUstOYuTFgXt9kqQjCcS3N2dmLJzRtdUXimtZe+QORm4Ag3k4Br21maGFwul0mn08RiMRRFYX5+ntnZWYQQtLe343K57nwCXzkP+f+K9P4CCK2W/ou18GNC8Tmw1ggJEW5wfxypP1BbXlYAgVAjXBxP85dvbt4z8lff30PnImN9gEq1ytjoKJ1d3VwcT/N358boivroiXvpjftI+GvvBwmkChVURRBwOahYJu/MTfH6zNiGhDtNUfj7h+7HpS2tWH1raJ6vnxvf9LHY7G98To1THSHGU0VubNDHURGCUx0hnjwUJ+ByMD09RSZTs9XRdR1FUbAsa4n/X1NTE4FAYEcFu7pckaqUmCrmaXb7COhOW7SzuSewBTsbmz2MaVlcTM3yvfHtacOwsbmV9yXaeTTRvtPDsLHZU/z+i/2MpW622B1M+Pn5R7v4L/3nmShufVvb6UjTthpv14WebLXCG7PjPNHUia6qm548WQvbGc6neXtukqHcck8un0PnZDjBqUgTTlVbcR9/9KMBhudroufPP9JFX8KHIgSmZaJY04ji9wAB1QtACdRO8P0SqAuebHKh8k2ojePLViv8yfV3eSDWwiPxNmBp25UlJdcy83xz5BrOhRRPt6rhVDU0IRjMpW+rumol/A6dLl+QLl8Ij+ogVSmRrpbIVMo4FBWv5sChqFxKzTJbXl38dSoqf+/QaVyiiMj9AZhjoDQhg/8cIQTT09ONCsO5ublGhY2maY3/13/v6uqqJXZu5m8uTUABrNprXb0O2d+t/b6VeD+P1B/k//rWFUrG5ratKgKfU0NVatV6PTEvHzvRimVZVE3JeKpAd8y35Lirpsl/eL6fYtWgVK3t7wOHEzxxKI4Qgr8ZvLxi9fsBf4ROX4D5cpHJYo5HE+30+EJ8491x5vMV8mWDXNmgUFm7MtPm3qIl6OKJQ3EONwcWBHz48hvDXJ3Krrne/Z1hnjqcIOB2UKlWmZmeXjO9WdM0mpub8Xg8u6JT4ntjA7yXrAn4x8Nxnmnt3fEx2djcDWwPOxubPYyqKIzk94fpsM3ux+/QG2Ld4ioZGxub1amaFtPZpQbqUZ8OgHVbtVdrczLSxAdaurelom4xWaPCH157B4C5cpGf6z226W0oC1Vi3b4Qnd4gf3L9XVKVm6+VJgSf7TqCR3MwWyrS4at5YprSWhIG4XfdbMMPeRw1L7TCD1BLX7+5M+djEP4XkP8vYAyDcN30VRO3pOgC8+UiP993nPAin9hbJ60/mhxGUgvPKJkGqU2/AhsjW61wPjlzxwEGZcukYpm41TKYQ7UHrTGEcQMcvei6zszMDLfey18s1tV/n5qaorW1dRMH8UegtYLWA8ZgzZ9ureq5O6H4PdAf4hOnWvnK2Vv979bGtOSSAISZbBmfUyPqc3JjNs/HT7SQzWZJp9NUKhV6e3spVS0+cryZGzN5UsUKHzraRMznJFUp88Lk4BKxLqy7+EhbLwm3b0m1Y53XB+Y4O5Rc9riNjVNT+NDRJh7sjmDJmzcQFCQ/81An/88PrpK5JbwDaq3enznTzpGWAJVKhbGxsTWFOgC3201LSwvqQnXyTgtjyXKxIdYBXEnP8YGWbhy3nLttbPYjtmBnY7OHsaTkgVgLA9nUut44NjZ3SrZaYbZUIOp0L5ks29jYrIxpSa5MZqiaSwWQR3qjFI0qyfLmkxDX4mAgwodbe5DbLNYJIQgsSu1L3eFxKKKWD/srB081qtYk8Fiig4jTjSIELq/WECEXn39MS/LpM22EPA7GkkXifheUXoDFYh2Afn9NmPP+ApRfqlXbOR+5KdrdMp5ufwjLshBCcDk1y3ghiyklprQwpWS2VCC9jWEh24UlJahxzND/jVq9APk/huxvQ/B/JhQKk8vlKBTWb9F2OBzrLtNAmuA4DNV3ofitOxj9BrFmofI297Wc5mpbkPfG7uzG5nOXa0LBP3z6AFJK3B4PHp8PufD+8DpVDjcHONxcE5TLpsHzE4O8Oz/dEOV9ms6nOg7S5PFhmpI3b8xzdSrLRLqIz+kg5HHgdzk4f4djtdmfHGsN8PETrbh0FSEE6i2n9+vTWXKlpWKdW1dpD3v4yLFmwh6dubk55uZW93bUNI1YLIbX60VV1V1RVVfHpS6VLKqWxeXULPeF4/b1qM2+xxbsbGz2MIoQxF1ePt5+gK8OX9np4djsczQhcCr23Uwbm40iBIzO32yFVYDPPtCO3+XgYmpmy2+0vC/Rvu2VdXUUIRDUqtF8mxFvVqE+6ToUiDAWaeJKeo4HYi2N5ND6MdXDK+oTSVWp+a99+L7m2vNmAaXwzeU7qLwH2oHa/53vq/1bSzpYcTxSShRF4aXJYd6Y3T/+YX987V06fAFOR5roC5xC6v8SqtdAOEFKHA4HYsEbr46maWiaRql0U5jdXJucUqtwFC6oXt6Go1qOKH4D1FY+c387jx+M8eevD5Eubi4NNOLVua81SKFc5UhLkETAxfT0NPF4nGy10vAyVITCn/efp2wa+B06U8U85Vs+2x9p76PF6+fNwTmeuzRNsXrz+VK1vMTj0sZmMY/0Rvno8ZZVP2+vDczxvQuTCAEPdIXpjvnojHgIuGvnZdOSVKsVDMNA1/Ul/nR1dF2ns7MTIW6eW3eLWAfgXvAPXfydOZzPcCLStIOjsrG5O9iCnY3NHqcecW5js9V4NQe/eeQBLiRn+OHkEB9r78Pr0HfVRZyNzW7FtCSZYpXz4zcrZnwujftag5Qtk8vp2S3dX48vRNTl2dJtroemKOiKSovHv6XbfaK5k/5sklemR3g41oamKDfbv1YQ2IQQmJZEYKGk/3dg+YSUyllwP1sLKhBrX/7WJ8bfHxvg3eQ2tW3uEBYSl6rR7QstiLsq6EeA2nE3NTURj8cZGhpCSkkikcDn8wGQSqWYnq69Hm63e+PfBfXlKu9s9eGsjjWPyPxLcD5O3P8J/tsPH+T1gSTfuTC5odV1TeG3nj6Aqt58vyWTSYrFYq3C9JbgkZ/puY+xQobhXIaI041bc+BWNdyaA6/moNXtxzAtXr4+u0Sss7G5FbdDxefScGoKnREvzxxrXlMcf3c0RUvIzU+ebiPudy4LkFEVgaLrNDU1IaVkaGhomWjn8XhQVmjR3k2EdOeSgCbVvha1uUewBTsbm32ApigEHE4ye7A9x2b38dmuI4zkM5ydnQDgWDjOsXB8V7VH2NjsdlRF8EcvD5Av36zqyZQM+mdy9MV9y5JR75SA7rzrn9Enmjo5Hk6gKcqW7VsIgYrCs219fGXwEjeyKb5w4OS666mKAKmA58NQ+JvlC8g8FP66FjaxQhvsrWOwpOTRRDvjxRyzW5ziu9N4VAeqovAHL/Xzi49243Qsan+rvIeinyAajTaEujcG5zjWGiQQCDAzM0MoFLq9yb33Z6DUAqXnWVFU3XIsKL+IqLyD9HyaR/vu51Czjz96aZBcZe3P30ePNaOqCvl8nkwmQ7lcbogcY2NjQM3Lr1qt4vF4CAQCtLp8dHiDjSRfKSUs/CuRvDuaIlVY7jFmY1PneFuQT59pr53PFljv3PqbT9VatWunNbHiqW1x1dxKeZMej2dD+9oJpJRULItUZekcp9nt25XjtbHZana3lG5jY7MhpJS8v8lO7rS5c6JON93+EE80d2IheWVqpPGcfVFkY7NxKoZFtrRcFPjxwDxCCEKLwgy2gvPJaVKVMtYWJZOuh5SS09HmRpXDVp8f2jx+nmnt5QsHTq44wVwRIcD1FLg+sPLz2sGby62DIgQ+h86hQGSDI947DOVTAHTHfPzu89coVozG3086auEhfr+fmWyZf/P9q4zMFfDoGslkkkgkQjwe3/jfZDHCC56PgX5qqw5lY8gMIv8nkPszwh6F/+4jB/nCY90E3avXLbw5mGQmU8Lt9tDc3LzkuXw+Tz6fp1wuY1kWuVyO8fFxbgwMcP3aNa5du0b/9esM9PczMDDAjRs3qFYqHEj4aQ64UOyvUpsVONEW5HMPdCx7f6x3bq2LVso6bywpJcVikWp1uWg8PT1NMplsLLfbeGN2bEk77P3RZk5Hm+3rUpt7Aluws7HZBwghOBKM8XRz104PxWaP43foS35/bWaMqeLaaWI2NjbL0TWFx/qihDxL/d0+cCROyTQYyG5tEuSJcIKg7gTuzgRmO32OFCG4kJrhRCSx+X1ICe6fAMeJ5c8V/uZmW+Y6k1IpJUWj2qg03k/Ml0vkqxWOtgTIlAz+7fev8mr/LOOpIlVTki8bfPv8BL/3w35yJYPJTBHDtAiFQkQikduvaqmvY6W29Hg2TOVNRPp/R5RfoSfm4r/98CF+86k+HugKL1t0PF3kd1+4zv/57UsAjWrDW3E4HLS1tdHU1ISu6ysuAzWRL+B28JtPH+Cff+QIHz7adJc+qTZ7BY+zJiBv9rO10eWFECuKdYqi4Ha7MQyj0fJt7RLRTkpJ2TJ5e+5mK/vRUIynW7p3pbBoY7Md2C2xNjb7BCEE98daMKXkR1PD2F9jNpvlkx0HORSMNn5/tq2XG9kUibvsi2Vjsx+wLMmzx1r44JEm/o9vXcK0amdlTa35vvX4Q1zPbI1o1+z28cGFdNjdXHFQn2DdOsZ6+6BYCLIQQtDlDd5egIYQtTAJ709B6r1bnqxA7kvgvAGej4PUasmxK25GcDk9uyw8YL+QN6oE3DWBqWJYfHcNb7eZbIV//4Or/ObTB/CoAsuSGylSXB3rFv9GtRPUZpBFcD6CVDsRMgfmDBS/DdYWiqZWEgpfQRS/A64nafI/xidPtfGJk83MZCu8MTjPm4M3P5cVwyJfNvB6vczPzy/ZVN2oX1KTyYPBINeuXVtRSJibm0NVVSqVCuFwmPcfjPNK/yyFyv58f9lsntcH5uiL++iL+9atltssUkoMw2BychJVVTHN2vtO13U6OjtRb2lxVxZaZ3fD98mPZ8aoWrXK8ajTzUfa+nbN2Gxs7ga2YGdjs894MNZCzOXhb4buThKbzf7hxzNjSwS74+EEx8MJLCntSgAbm01Sn3BpqkLEqzOTrfnvfP2dMX718V5a3P4tE+yOhmJY0loxkGE3YUiLiUIOr+bArTmQUpKtVshUy+SqFTp9QSJON4KaJ99tp90KBUQQlNhScUjtgOB/D7ICxhhoq1elSyk5E21BEQovTAxi7qNqjoDDScLtZWAmt+F1MiWDf/Xty5zuDPGp03dowWFlbv7f8zNYzscaf2vTkgzP5fG7IoS9zQj9JMKcQhS+CsYWXtfIHBS/iSh+C/TTCMdR4r7DfOJkGxfH0xQqNYEg5HHgcWpk0jcrzWvthwrBYBCE4PevvMWnOg/R6vETj8cbwRy3Un9c0zQikf3Xam1z5/z1WyP8/CNdtIU9KKImjt8q3kkpsSRLfO42gsPh4NChQwt+d7ImNAsoGgZfH7xKrlqlYFQxpMX90WaebuneugPbJPWbNe/MT/LWouq6O/pesLHZo9iCnY3NPkMIQY8/hFNR921lgM32MF0q8K/Pv8YHW7o5Hb3p2WNfHNnY3Bl+l8ZMtkzApfGF9/WQM6qcnbuzqiGXqvFkcye9/jCeBfFrNyOlRCBIV8q8Oj3KWCG75HmnonIy0rQoDfYOzzvSAv00lL5/87F6OqzQQetcc/V69caJcIKqZfLi5PCdjWeHUBaqFV2aRq5aIVutkKuWuZic4UgsRmvQzXi6uKFtWcBbwylOd0ZoC7vhljTKDaP1gBJFup5GaC2cG0ry+o05HKpgPl9pVJ05NYUHuyM81hfD4/9NLKuCQh5hzkL+SyDT6+xoQ4NBej4HwoGwaqLcU4cSTGVKvDOa4qcf6AApmZubw+msBbt0dnY2QjdminnyRpW/HrzEPzr60LKqH4/HQzgcxjRNTNNESomm1d6H3VEvFycy2NjUKVUt/vBHN3CogvcdiPPUofiS5+uV2pcnMxxq8qMqN6uS1+LW54UQKEKQq1b4yuAl5spLzwFvzU3S7QvR6Qve9WtAS0oKRpVvjV5nJJ9Z9pyNzb2GLdjZ2OxDLCl5MN7Ky4sCA2xsNkq99cDGxmZrKFZMFCHoiftwqArnkrMEHU4My1pipL1RHIrC57qPEHd5G5OpnWoPMi3ZqPRYq01JCIEmBPeFY5yIJMhWy1xOzTFWyDKST6MsTCC3DgGup6H0AmCA4wj4/0HtKWmu2gp7K4oQ3B9t4XxyhvnyxoStu40A2r0BWj1+LClxKAoh3UXE6SbsdOFQlh6rJSUXktMoQvD5Rzr5V9+9sqn9ffmNYU52hGgPu+mKenE71A2//yzLAv8/QhECw7T43rvjvDE4v+KyZcPi5euzvDYwx4m2IJ1RL1Gvk5bQAZTg/4ya/yJUz29q7A2cjyH1h0HrAFRSlRJhZwSkxcO9MQA+fqoVVShYlkVHZye6oyaMm1Ly4+lRTCl5Y6aWGFuxrEbbIdRaDYPBIOFwmIppokoLTVEQ1AQW07J48nCCofnCkiRpGxuAqim5MJbmTEeIoEdvVNqNJgt87Z0x5vMVXA6V+7vCtIc9RLw6cf/Gqs/qn1VLSnRFxVwlqOhbo9f59cP3b5tgVxfflEXjUYTgYmqGFyaGVvxu3O03pmxstgMh7Xe+jc2+xJKSL147R6pS2umh2OwxHoq18kRzp+0RYmOzhVRNi4ph4XIoKEIghMCUkr8evLSsikAAMZcHyVJ/N4XaZOvJ5k46vFtX+SClxILG9usTp5JpUDCqjf1HnO5Gi7wEDNPivdE0746mMCzJ5x5oJ+zRN3TeqO1TogqFsmlwdnaCh+Kty8SlOzwwKL8IhW/UqutC/wsIx/rr3YIpLcbzWX4wcQOFmrCYqpSXTSh1RSWo14RYU0qKZnXNGyCKEJyONFE2TYZyaXJGBXXBx6/F4yfu8jCcS9PmDdDpDZBwexnKpRnKpZELr12HN8ChYBSXqtXadhda3QxT4nKoWJbF5OQkhUIBp9OJ0+nE5/Phdrsb3mt//voQ16c33h67GCFqFWlPHopjSVDEyuKxadXeN4oiePnaDBfG00xlyrdVMRPx6vzcw51EfU6U8stQ+KtNDtoL4X9ByagyWshyITlNfzYFgCZqn8+I083BQJgz0WY0RSVTKXMxNcPBQISXp0a4vkJozH9z9CGQEtM0G9V42WqFc/NTeDQHfodOly+IU9Uar4lh1vwD3xre2hAam/1DZ8TDsdYgE+ki74ykGo83B1y0hNy0BF10x7zE/beXPD6Wz/AXNy6u+Nyzbb0cDcVQt8luYbKQo9njw5KSsmnwnbGBNQOZnmzu5P5oi935YXNPYQt2Njb7FEtaDOcy/LXtZWezSTSh8HhTB2eizbZgZ2OzjVhSMlXM8V8GLjQe6/YFebqlm4jTvep6GxXT6z5Fiyc3UkqKpkHJNKiYJmXLpGIaVCyLkO6kxeNnqpjj7blJrmXml3i3tbh9dPtDnAgncAiV//vblzGsm8//zEMddEd9uHV1U4ERDT8ltrhSUEpAgixA6TnQz4DauuHqurWomCavzYzy9twkppRoQvArB08T0J2NZeqttOfmp5at71I1fnLB9wxqx102DZyqxny5SMGo0u4NLByGpGSYZEsGEa+OtsggvmpaTKZLvDk4z7ujKQRwoj3EEwfjRLw6Y2OjFIvLKwM1TcMwDHp7+0gWq/yH56/fUVhVZ8TD6c4wIY+DsEfH73KgKgLTkszlykxmSsxkSoyniwzM3HnyuKYKPnmylVMdYaRVQMhyzRsv+++oNe+ugeMk+P8ef97/HpMbSEF3KgrlDVSeJ1xefqHvOLD0fWxJiSUtsCTVapX5+Xny+TyaptHW1obT6eSrb49ybpEYY2OzFp8+01Z770vZONdu5txZP+feyKa4nJ7lSnpuxeUOBiJ8qvPQFo36Jqa0mC7m+S8DFzgajNHq8fPK9AhFc/VqU5eq8huH79/amzo2NnsAW7Czsdnn/N3wVa5mVm43sbFZjZjLwy8dOLnTw7CxuSd4Y2acV6dHeTTRxsPxtttLR11Eff3pYp5rmXnmygVy1Sq5aoW8UVlTmKlXz63Fbx15kKlUmS++cmPZc08eivPUocSWpxzeEfVL3XqC7BZUi9Qvn3NGhZFchmaPj7DuWjJprguro/kMN7Ip3JqGS639tLh9uDXHin/nl6dGOBlJoEmVF6/O8O5oshGCABD26EgkliXJlJZOcI+3BfncAx1UDYPZmRmy2eytm19CIBCgubl5WwQjj65SrJps50zjdEeIQ81+Ai4HbWEPZH4bjOurLC3AcQTp+QyWiPDvLr65ZeNwqxqf7DhEu9dPtVqlXC6Tz+fJ5XK1NuA16OntZSpb4Q9eGtiy8djsT4SADx5p4vGD8fUX3gBfvPYO8+XVO3F6/WE+3XV4S/a1GCklX+p/j+lSYcPrHA3G+FjHgS0fi43Nbsf2sLOx2cdYUvKx9gMUhi4zmreNjW02TtpupbaxuWs8FG/loXhr4/fbFevq7apvz03y1twE2Wpl09vYiLaSrBQJuPUVn1OEYNfFSi9+PbeotasuzHk1ncOh6IotY/VlWjw+Wj3+msgnQGH1ahgpJY/E2xDAX50d4fLEcsEtWVj971pcCGyYGB+nVFr/PJ7JZIhEo3zsRAsht4MfD843tnGnFLZoO2vxzkiKd0ZSxHxO/tEHD4LavFyw0x9BOh8FrQ0hdKpmle+Obq04diwcp8MXYH5+ntnZ2fVXWEQ2k6E9EuGjx1u4MZtjaK5AqWqHhtncRADH2oJ88EgTYe/K597NYkmL05FmnpsYXGOprVfbLSk5n5zelFgHMFG8vbZ9G5u9ji3Y2djsYxQhsIDPdB3mr25csr/sbDZM1bIomQYu1f6asLG5m2zGO7JeSWdJSaZaZqZY4M3Z8W0/18+UChwOeFZ87p2RJCfaQwTdjkYYxa5kiyrtauLq2sfZEPNW+Lve+veWsuZt9scv32AivfkbJ5lSFQCXy7UhwQ5gdGSE1tZWnjyc4ExXmH/7vaub3u9OU6jUKg2l8wmELEDlPaAKSgLp/VlKpslELsdgdox3VmhRvlMuJGd4LNGOy7V5H7HZ2Vk8Hg8PdIV5pDeKlJKZXJkbMzle6Z8jU6xu+Xht9g66qvAr7++hJeTe0pRURSgcDcXWFOycyuavAettuvUk2jqmZaEIQdWy+NFthOI1ub2bXsfGZj9gz8RsbPY5tS9Lhc91H+UvblxgZpN3tGzuTR6ItthinY3NXUBKSTabZXp6Gk3TaGpqwu12L1sGlqb7KUIwnEvzyvQoM6X8Eq+57cZco2U3VagyMJPjTGeI3Vdqt8BGxLpNJMluaJerCLG3PqYogi+/PnJbYh1AS7D23ikUNv5dbxgGw8PDxGIxIpEIXqdKvry3Krw6o7XJfFVE0H2/hJRlkCZC8YA0+ePr5ygY25fGWjQN+jNJDvjDt7X+8PAwUBNag8EgMb+fqDfK4eYAv/9S/577e9hsHZ841UpTsCYEb2XYgpSSdKW86vMe1cETzZ0bsmioL1M0qiQrJebLRebLRZLlEjmjQkR3E3d7SLi8vJecprSGV91KNLm8PNvWa4eh2dyT2LMxG5t7AEUINEXh0XgbXx+5ttPDsdnFKEJwJBjjSTsl1sZm26kLcXNzc1iWRaVSYWRkBJ/PV0uRNU0sy8LpdOJyuXC73aiqiqIovDEzxku3UaWwFfT6Q6Tyq7dlxv3O3Z3iJ5Tlot0yEW9rUxGFEBs+p1rW7YuvR1r8GKZJpbK5dmhd1/H7ayEXtYrAvSUQHW0JUDFNfvvSWTq8fh6JtyOEYLIwxsXk7LaKdVCr/un0BddtIFRVFdNc/bWtVCo4HA6EEFzLzNHnj/CFx3r4ox8NUDbWD76w2V/c1xLgZHtoW7ZtIRnOp5c81uL2cSrSRJvXT1B3bVisK5sGXxm8tGqb61Qxz6X0ik+tilvVOBKKcTwcJ+7yNqr2bGzuNWzBzsbmHkERgnZvkITLQ7pSpmztrYtxm+3laCjGwUCELl8Qh7K5hEcbG5vbZ25ujmp1actbLre0pbVUKpFO35ztdHd3czLctCOCXYc3QFB38dLg9LLnVEXw7H3NdEX3QOvSErFOLq+42+JKlnzFpFgxiPtdjcdWm3x+9EQLbw7O8+bg5gKjNEVwIOGnVNx8JX1beweGJfnrHw812mr3CqoiONIcYLJY8/sbyWcZyV+6q2P4bNcRHEJhdGT1z2S9glFK2QimqFQqmKaJw+HA4XDgcrlQVJUfjA/ybnKKA/4wn+g4xOcf6eJPXx3EvAMx12bvYViSqmGhKmJbgnxUoRB1ugk4nJyONtHjDy+5/tuIWFcwqnz5xkVSC97HAvA6dIIOJwHd2fi3P5OkP5tcsr5b1XiiuZOgw4lDUdEUpfYjFDyaY0mNtn1NanOvYgt2Njb3EE5V4RcXkj8Ny2Igm+T74wOU1rjba7O/URA809bLsXB8UxdpNjY2d0Z90j4/v/kU79nZWVpbW2lyeZkq5bdhdCtzwB/mE50HKVQMfnR9ubH+zz3cSW/cd9fGs2Vs8/lOCIFXV/Ho6pLHbqUu4oW8Gh873sLliQy58sYqww42+XnyUBxNEUylUpsan8PhQFMVvvneGJcn106V3Y0cSPjQNYW35yZ2ZLTgDKYAAQAASURBVP+qEDgUlVKhsGplYywWIxwOM5xLUzCqRJ1ufG4XXq+3Vk0rLcqmyXy1zIsjQwwvBIVdzyZ5fuIGH2zt4aPHm/nGuztzjDY7w9WpLP/m+1d4f1+Mh3ujqGLrhDsFwZloM2eizUAthAI2dv1nSYlEcm5uitdnxiiaBq0eHyfCTRwORtEUpeFZZyFRELhVbYlgl3B5+XTXYTyrpGXb2NjUsAU7G5t7CGVRBYGmKBwIRDgUjFI2DV6YGOJCamYHR2dzN4k4XXT5QhwIRGj3+AFbpLOxuVvUW2EnJydva/16RV7E5b5rgp1TUflk5yFypSq/8/x1Kiu059nnkNURQqzr6CeE4GJyhr5AGAT4Xdqagp0QcLwtyBMH48T9LgzDZHZ2dlP+dQDhcBghBNen92Yw1emOMBXTpD+bumv7PBNt5v5oM4aUaAvm+ourYG9FCIEEvjp8BcPaXGvru8lpHk20E/Y473DUNnuRYsXk+5emeG1gjn/yoUPoWyTYLfPP3EQIjyIE78xNUTQN7o+1cDAQIay7sJDUz3SDuRQh3U3UVfPVXOyLfDQU49nW3mXBFDY2NsuxBTsbm3uY+pekrqh8qLWHvv8/e/8dJcl133minxuR3rvKzMosX+0NuhtoeEuABEBPiqQoUSvvZmd2zM7O2Tm757z3/n37ZnZ39uxoZrRjpNGIokRRdJLoDQACIOGBRjfal+nyJrPS+4j7/sjK6qou312uu+/noE83MiMjIqsiI+/93t/v+/UFeXVqlNmqCqa4kzkVivNEexcCgUR5gigUO0WrgqpWqzE9Pb3hFM8bqdfrSCnp8wQ4n1le6bYdGLJZLXFpqrCiWAdwZjSzUGFnmnJbWrjuVExpMlkust8XplBp8DdvDa8aPGHRBKe6gjy6P4LfaaNebzA1NbWmYLQW1WrTeP5Y0s8vBlI3/R52A7tF40DMS9VsELA5FtrythspJX6bA8MwMAyDdCpFsbi6eJ7NZgkGgzzS1sFLU9c2fTyr0MmUNudLqLizKFQbvDcyx73doV1L4JZSYiLRhcbJcLxZaTffnSGE4IP0DHZdp2o0eHlqBE0Ifnv/SWy6TtLt46Md/VSMBqfC7conWaHYIEqwUygUCCGwCEGfN0i3x883hy8yMt+Oobiz2O8L8aFEz6KBkhosKRTbTevz1mqBzeVu7f5qmialUoluj3+LznB9GlJSaTToDrtW3ebMSIa5Yo3OkIsPH4nv2LndCWhCw2e1Y9U1/ubtEcYy5RW2EZzuCfLkwShOq069XmdsbGxNoWgjZLNZfD4fHz4SZyhVZPImE2p3g4YpuTyd52Dcx4fae/jG8IUdOe5YKU/dNEBKhoaG1t2+VqtRLpc5HorelGCnCQi6bTdxpoo7ibeG57i/N7yjxzSlXBgpXsnN8fejlznoC/PhZC+60NC1ZmXea9NjvJue5LlkPwf8YQ74w5QadSza9cq9g/7Iwr6UWKdQbIytjcBSKBS3NZoQ6ELjcz2HSc63SSruDARwPBjl2WQfpjTVQEmh2CGklFQqFcbGxhgaGrplsa5FPp/HrlsI2Rxbsr+N8FZqgjavg3/+7EFCK4gHEriWLvHKlVnOjWeR89UXrRZgxcq0fj4eq41CtcHY3HKx7kjCxz9+Zj/PH2vHYdWZm5tjaGjolsW6FmNjY0hp8sX7u9gfvX18CA1TEnLbqBsmPxi7uiPHfKAtwa/vuwdNCCrl5b+r1Uin09h1C6fD7Zs+5uuzY/RG3Dzcv7NijWJvMZWrcGkqv6PhI5oQnJ2b4Y8vvs3fjlzClJLz2Vn+7MoZ5qplzPn712y1xMPRDrrmF5Ksmo7f5ljS8tqqxFMoFBtHVdgpFIolaPPmx4/FOhkp5nBarLh0K06LBaduwa43//7R+ADndqgVS3FrtDs9PJPoJep0qxYEhWIHkVJSKBSYmNh6o/hisYiUkl/pO8p/uvQOtU36Yt0Mr82MMV0p8rGOffz3H9rHv/rehVXbY7/25gj/y8cOI40GpmnicDiW3HtaItV23I9u3Pd2HmsrEEJQqVSw2Wx47BaSAedChV132MWzR9tJBJwU6zW+fe0SH0n24XBsrVBrmiYT4+O0JxJ86aEeUoUqr16Z5b3RzJ5OJtU1QdBlY6JSoNjY/nTbdqeHR6KdlMtlRtZIhF2JYrFIpVLh0VgnFaPB2Q36Bj8e60QgqJsm3WE3P796e7UtK7aWv3tvjH/09H40oe3IPc2Ukg63t7kis4hsrUpjPqjClCYf79y/5HnlTadQbA1CqmVPhUKxDnXDpFwzKNUaaJog6m1OFKbLRc7OTXMmPcX2TxUVN8PpSDuPx7qQqMGTQrGTSCkpl8uMjo5u+b41TcNisRAKhfB6vfzni++Qa+ycv1WvJ8Bnew4xk6vwX14ZoFJf/g3Q5rXzD57cRyYzx+zsLJqm4XQ6MeZTyR0OBw6HA4/Hg6ZpW7KY0NpHoVCgWCxSr9exWCzYbDasVisulwtN25lJ7s3Seg9zpRqZYo3eNg9Vw+DV6RHeSTVDSp5NNJO9BwYGFn6eW4nf7ycYCmG1WMiU6vyHF1cOGdkrfOpkkuMdfv7tB69v+1jkC71HaHe4Gbh6c9V8NpuNeDyOw+EgVSnxlYGz64rt/+zIgwt+kG8Pp/nb98Zv6tiKO4cTnQE+c6pjx47XSoV9Pz3NCxPDmEgidhe/sf+eHTsHheJuRVXYKRSKFZFS8vLlGV68NLNsdb0z5OJYws+RpJ+nE708Ge8mU6twMZvi7dTEjlR6KNbnmUQvJ0KxBUNghUKxcwghmJub27L96bpOLBbD6XSi6/rC4xPF/I6KdQCDhQwvTQ7zaLST//HZg/yr7y5Nvnz6UJTH9rdhmiaFQjN51DTNJe2brcANIQQulwuv14vX6114bLNIKTEMg6mpqVXbRO12O11dXQvb7wXhTkqJKZsTYquu8X+de41T4XaOh6Ikgg7emp3gxcnhhe33eUP4bHZMCd29vZiNBtVqlUKhQD6f35JzymazZLNZ3G437e0Jnjsa39Mi0VtDaU51BTkRji+ImtuFlBJ5C2OcWq3G+Pg4XV1dhB0u+rxBLmTXrpirGA1SmTo/vTDFdL5608dW3Dm8N5KhN+LmeEdgR8Z32rzn8clwnJ9Pj1I2GhwLtmFKc1PpsgqFYvMowU6hUKzKqa4gL1xc2rLhsOo8f7SdoVSBP3l5gLjPQTLo5EjCzyOxTh5q66DYqDGQn+PN2XGy9fUnkk/FuzkSaFv2uCEl3xu9wnDx5pLv7mZOR9o5EYoBe7cNTKG4kzFNk/Im/K1Ww+v1Yrfb8fl8aLrGSDFPulpiplxiolQgVbv1Y9wMb85OMFUu8oXeI9zT4ePtaxksmsYv3ZfkUNxHoVBgamoKcx1xQ0pJsVhcaBdsa1v+XbBRxsbGFhJPV6JarTI0NITVasXn8+FyudB1HSHErgl4Qgh0Afq8FbtJ0yvwrVSzjXqfN8gnOvfT7vTgsljRNQ3DlAzOFpjJV+kIukgE3Hi9XtqiUXLZLLOzW2NXUSwWKRTy3Nsd4uxYlsHZrfHL22omsk0frZjDveX77nL7iDhcBG1Oio0aUYf7ljwZmyJo08PuxYnhdcU6gKlKgQ6/n+FUkT3cnazYYb797jheh5WesHtHErmllMzVKpSNBroQHA22KbFOodgBlGCnUChWRAiB227hvu4gbwylFx6/p8NPe8BB3O/gkX3NidXFyRx//oshNCE4GPdxuN3HiXCce0IxKo0GU9UihjmfMiVauaTXBxcdbi+1uuTy1NLqgK6wm890H+SFyWHeS09t+3u+U4g7Pc022D1SQaJQ3G1IKclms+uKVethsVhob29vVqc16nx76DxTlb0jmowWc9RNg0PtfmqG5DMnk2iaIJvNMj09ven9ZbNZQqHQgoi2GaSUa4p1Ler1OvV6nVKptPCY2+0mmUyuuL0hTfR1JqVSSkwkGtcN1Q1pIhBrVr8svkebUjJeWvod+Dv7TxKwOzBMydhcibdnswzNFhmdK9FYpNzomqA34ubRfW30REL4/AGKhTxTU7f+vTk5OYnX6yXqc+xZwc6UkCvXORSI0OcNMlTI8J3RKze9v26PH6duocPt455QDFPKhUoiwzAYGxu76X37/X4MJH9y6T0KG6yMvZBJ0esN8pEjcX58YYqGoVQ7RfOe8VdvXONffvTwzhyP5j1KAA9HO7DrSkZQKHYC9UlTKBRr8vThGGfHspTrTZ8cm64hJUtW8/ZFveyPefkvLw/w8uUZXr48g8du4WDcy6F2H51Bb7OCAblgWrt4uGkY8P5ohu+fW9rK4rLp/MoD3TyT6OVUKM5XB89RMhrb/ZZve/b5gpjIdSeZCoViexBCkMlkbnk/rdbX748NcD6790J+JDBRKhD3uzClH13XGB4e3pBwtuL+pGRqaopYLIbFYll4bCPiXcsj72aqGkOhEIZp8vXhC1SMxrxfE4TtTno8fnq9QTxWG6ZsLjwtPp+WMPduaop0tUyxUcemabS7PJyaTwOV8/tbLN61khWzmQxerw8EzFZKLOZKLs19kXY+GM/yjbdHb/R8v34OpuTKdIEr0wWSQSeP7WvjULsfj9fLzPT0LSUTe73e+SrAvb348+c/H2J/zMuRhJ8DwfBNC3aPxjp5sO26eFutVhkeHl7jFZtDSkndMDYs1gGcz85yMBDmgb4wh9p9fOudMYZSe1M8VewstYa5Y5YnAsHRQBtJl5eAzaEWhRWKHUIJdgqFYlWEENh0jacORfnu+832nPMTOZ45El+ynT4v3kU8dsbmmpOlQrXBW8NzvDV88x5OpZrBf3l5gHu7gzx/tJ3fPXAv3x29wpV8ev0X38UkXF401CBKodhNbqVtzmKx4PV68fl8AGTre9e3arSYIxn1MjZX5mDcd8tVhcVikYGBASwWCw6HA5fLhcfjwWKxrJv22hL5NovVamWuWmGkuFTYSlfLXM6lgUHCdid93iD7fEHiTg9CCEwpuZxN8/rs+DKx7UI2RdThJub08OLkMH3eAAmXd6EqpWI0cFmsCCEYHBygr79/mYjz0tQ1NCE4lYzTMCV/++7YqqJdi7G5Mn/1xjXavHY+fTJJIhbD6/XedFVYSzT+yNE4Px+YZa9G1aWKNVIDKXKVOl843YXfZidb29zn5v5IggfbkhQKBSYnmwuIt3o9L6YVfFK4iTTbbw5fpNvt5+Od+/nNR3s5P5HlhYvTTOf27r1Bsb0ciHl58mB0x46nzVsH+G0OJdQpFDuIEuwUCsWaaJrgdE+INwbTzBaqpIo1GoaJRV9avWWYkn1RD++NZLb8HN4enmNotsgv39/Fxzv38SeX3t1xk/WtpOmH42hO1oBcvUauVkUI8FpteK12bJrOlVyauVpl0/u3avr6GykUim3F4XAsBC5sFCEEiUQCt7vpk1Wo13hvemxZq+ReYqyURxcaj+2PYJom9frmxYiVaDQaFAoFCoUC09PT2O123G43Pp8Pm822UN3R+juTydx06EKlUsHrdKy5TapaJlUt88bsOG6LlYTLy2gxR3mNqu+vDZ1HE4K6aS7YOgjArutUDIPP9xwm4fFQr9fRhOBqbvkC1wuTw7Q53ZzqCjJXrPGzyzPLtlmJmXyV//LyAE8ciPLEgTb6+voZHx9bCPvYKJlMBpvNRiAQwGHVqTVMAk4rqeLe+w522XSeOhjFME2qjc2n594Tii6EQmw1VquVzs5OpIC/Gb5wU/sYLmb5dxfe5MOJXo5E2zjc7ueD8aZw57LpdIfdnJ/IMaOCKe4KPnEigce+s1N5IYRaDlYodhgl2CkUinWREp47FufLv2i2heQqdUJu+5JtdE1wNOHnx+enyJS2ZsK2mHSxxp//fIh//Mx+PttziP965cyWH2O7cegWfqXvKCG7E2DVapFW+9Tj8S5Gijm+O3J5YUXeY7Vx0BdmulJcVg3SYqSYJeJwLpiYKxSKnUVKeVOCXVtbGy6Xi/fT07wyfY1SY+9bAEyUCk3RTJpMTGzet26jVKtVqtUq6XQat9tNOBzG4XBgmiapVOqmW5B1Xcftds9X0m2MYqO+oe0NKTFuKEmTQMVoiknvz03T5fHj9/vJ1qqkqsvbeeNON+0OD5W6wfAm2yBNCS9cnGZgpsDn7uuko6OTbDbDzMzGRD+LxYLL5aLRaGCakl9/qIdqw6An4uHiZI4PxnP0hN1cnSlwbnx3w6FcNp3ferSXkNvGt0cuUTE3/9mxajr16tYLkRaLhY7OTtA0vnzlfTI3sRC3mB+ND/LC+DDPJHs4GI9wJOEHmvedDx2KMZ4p88LF6WW+wIo7i3euzfHYvjZUsZtCcWejBDuFQrEuuibYF/VyIObl0lSed69l+NCh6ApCE3z0eIKvvLZ1fi+LKVQb/PTCNM8ejRN3upksL5287PeFuDfc3vTaEfDO7OS2+D4JIOb0EHW68Vpt1AyDq/k50tUyFiHw2xxUjAbFG9peTCkJ2Z0LFSGrtRQsXsFMurz8St8xXpgYYr8/xEF/ZMFD6Uouzd9eu7SsRWq4kOV0JLHVb1uhUGwCq9W65vNCCGw2GzabbaHtU9d1zs/N8MPxgR06y1unIU0K9Ro22Wxn3QlaqbI2m21eTLr5tkW73b5wP90OAjYHj8e6+OnkEIUbUtOv5tLUDAOLxcLl+VTYFp/pOki3J4CuCRqmyZ+8PMD0TVZOXUuX+Hc/vcwnTiQ4lgzidrsZGRnBMFavQtM0jc7OziXXccznWMiL2hf1NlugpeRI0sfATGHB63anEcBvPNJLyG3n5alrDOYzm3p9u9PDfl8Ih26hJLe+Oi2RSKBpGl8ZOLtlqc4NTK4VcxzyNytbs9ksqVSKUChE1OfjSw92c3Ysw3ffn6BU253fi2J7+cVAin1RL4mAE9OUO5IUq1Aodh4hb8VkRaFQ3DWYpqRUN/jhuUnOjmX5pXs7OJLwrSg6/f2Zcd4c2p7Jj1XX+J+ePYgpTP722iVG51vF2p0efrn3KLWGSaVuYNUFHoeVTLXMt4Yvbckg2WO18Xisiz5vALtuWUgGbCUBVo3Ggj9RwzT56cQQ789drzgRwGe6D9Hj8W/K/8OUEm3eL2mxubCUkvOZWb43dnXJ9gL4w0P34dQtymdEodhhpJRIKRkdHV2x/dDn8xEMBrHbr1cpG6bJZLnIO6kJLm2TcLSdPBrt5MFokqGhIWq1vdcquRZCCPr6+piqlvjLgXNbvv/jwSgfSfZxJZfm29cuLXv+6fYejoei/GT8+vfF47FOTkcSvHstw/nJHBOZMoXq1lRb3tMR4OP3JNAFzMysHkiRTCZxulx8dfADrELjsz2HVjW3N03J64OpZcFRO8nvP9FHzOdE1wT5eo0ruRRXcxnqpkHI7iRod2BISa5W5d5InLrZFJo73D5cFiuGNGnU6kxOTt50aMpKBAIB2tra+MHYVc5ltnYB8Z8eeYD6fAtv44Zq3Gg0itfnp2aYvHplloGZAuOZrRELt5JjST+FSkOFaNwCnzqZ5J6OwIKftEKhuLNQgp1CodgwLcFoOlfhy68N88yhGMc7lopPUkoMU/J//PAi5W1a1d0X9fCpk0ncdguFeo3zmVlOhuNUagZ//MJVynUDAZzqDvLhwzE0TfDHl96icQtVGAmXh093HcSu62gbSF9tVdENF7LMVIpoCI4G2xYEva1CSsnbqUlenFxa1fhotJP72xI7lh6mUChWF+s0TcPv9xMMBrFYLBTrNa7k55gs5blWyJG/jT05ASyaxj8+fD8zMzNbko6700SjUXw+H//mg9e3fN92TecfHbkfgP948W3yi6rsdCH47/qPE7A70IWGKSWGaWLVdc5P5PjrN66tGzJxMwRcVj5/XyeJgBPDMDBNs7kAZZqYprmQuDtSyPLK9ChT5QL/5MgDay4Amabk3/70MnO75G3ntOr8zmN9BN02GvUaFqsVXbv+XW2YJgjQhYZhGBiGgaZrNOoNstks2ezWt/RaLBZ6enpIVcv8t6vvb+m+7wu382R7NyMjI6smI9tsNtrbE1itVjRN8OLFaV64uH1t62uRCDj5yJE4Ea+dy1N5rqWKnO4JkQy6qBsmf/ziFVKF2/s+uFv8ygNd7I951XhPobhDUYKdQqHYNIYpKVTq/NefD/FQb5gH+sLLtvm798ZuKSF2Pax6MwzjZGeQqM9BwzD5Tz8bYCq3tKKl3e/gdx/vZ6ZS4C9uonpCAMeCUZ5J9AJsekBkSrOZqidYkty61ZVvX7l6lonydb8sr9XGb+8/ib5G661Codg6WoLH2NhSY/9AIEAkEmkGI9QqvDA5vOmWvduBf3rkAbKZDLOzW29DsN34/X6i0Sj/57nXtmX/n+w8wH5/iDPpKX40Prjw+D5fkE91HSRfrvN3Z8YJuW2E3DbGM2Xe3YYAp8UIAY/ua6M34sZm0bDpGjaLhlVv/rFZrotdb81OkHR5iTrdq34HGqbk0lSOr74xsq3nvRYBl5V/+KH9mI06w8PDuN1uAMrl8kLbtN1u39IKutUQQpBMJnE4Hfzni+9uuSj/+wdO4RAag4OD628MdHR04HQ6+emFaa5M55nMVrZFDL4Rv9PKM4djHO8IUDMM5mplgjYnNl2nZhi8MTvGA5EkmVKdf//ClT2bQryX+RfPHcK9w+ETCoVi51CfboVCsWl0rdlu+ruP9fFvf3KZgNvGgZh34XkpJR+7J0GpZnB+YuV2m1ulbkh+fjXFz6+miHqbHkQ3inUAE9kKP/pgkueOtXNPMMaZuakN7d+m6RwNtnFfuB2fzb5QMbdZNKGx3dkPpjR5KNrBNxYlz+XrNV6dHuHxWNf2HlyhuMtZnFKaSqWW+akFg0GqpsHXBj9gZoVQgTsBu6Y101C3KCH2TuPt1AT7/SHuCcV4bWZsocqu1xPAME1G5kpc2uGAACnh5cszvLxK6qxFE3idVj56rJ2TbXEa0ljzq0zXBIfb/Rxpz/LBNn3vr0emVOeH5yb52D0J+vr6yWabn8nF7JRYl0gkcDqdvDA5vC0VtLoQWK1Went7yWQyZLPZNb0cR0dH6e7p4alDUZ4+HKNQafDTC1O8MzK3bSLZyc4AnzjR9NM9NzfND8cGaJ1h0OYgW6ugoXFfOIHPYcWqa9QazS0E7IigeLuTDDqVWKdQ3OGoT7hCobgpdE3gtlvwOix87c0R/tePH1l4TgiBlJLPn+7kb98d2/ZKgfWMuH8xkOJ4R4An4l2cnZtivcbYXk+Aj3fux7qonWZvV6kJbJq+7NG3Zyd5oC2JY4vbcBUKRZNWk8L4+PiqibBSSjL12h0r1gEc8IURQqzo2aeAsVKekWKOTrePz/Uc5itXz9KQJvt8ITQhKFQbWDRBw9w7EkXDlMwVa3zv7AQfO57AlJLOkAu7RVv1+1BKySdPJhmZK5Gv7E7C8RtDaXKVOo/tb6MjHMbvDzA0NHhLwSSboSXWuVwuXpwc5p3U9vj6/YeLb3MyFOPecJxIJEI4HGZubo65ublV3+vw0BDAQnv+J08meWRfhDOjGcYzZcYz5RUDKsJuG7lKnYYpOZrw83B/hLph8sF4luFUiUrdIFu+LtYLAc8eifNQf4RUpcRfD35AyVh6PczNJ+X2ev3YdR0hBP/y+cPkKnVsFg2HVecrrw1zZXpzSdt3C3aLxr3dQQ61+zBMqfzrFIo7GNUSq1AobgpTSkxT8q+/f4FqwyTstvE/PHNgyTatypOrMwXG58q8dHmahrE7t5xEwMnvPt5Hvlblv14+Q2MV2e5YsI2PJPqQbL79dbcwpeRnk9d464aUQYDf2X8Sv82+xwVHheL24MZK20ajwfj4+JpCVU9PD+lGjb8YOLsTp7grPBHr4nRbgqtXr66ZPLpX2e6WWACf1c7vHTwFwHS5yFAhw/2RxML1NJOv8P+8dHXXviM3wqP7Ijx9OLbmd6NhSsYzJX58fpqRdJHd1CBPdgb49KmONX3ebpVoNIrX613w/xPzlW8vTg7z9jaJdTcSsDn4WLKfmMuDaZpkMhkymcy6n0Wv10skEkHTLQuCT7HaoFhtUJj/OxFwEvbY+dmlGdq8Ng61+6nPB1xY5oU2gNG5Em8Mpgm4rOyLeukIOrmUTfH3o1fWPf+4002n20/M6abfFwRTIoFcpcEf/eQKppqqLuNLD3bTH/UgJWhiry8qKxSKW0GVXSgUik3RCp6QEr7y+jWq8+0LqWKNkXSRRMC1MPBrDSD6Im76Im5ylfq2pceux3imzNfeHOHz93XyWwdO8J8uvbNsG6/VdtNedbvNaud7JZfm3kj7dnflKhR3PK31zZmZGYrFIvV6nfXWPG02GzabDWHc2a2iM9US0DTZvx0Fu50gV6/y8tQ1Hot1EXW6iTrdS66fsMfO80fb+bsz47t4lmvz1nCaJw9Gm56sq1T06JogEXDxW4/2Um00bTF+eG5yxcqt7Wa20Ky+17T1g6Juhvb2djweD5PlApKmlYZVaPxsYoj30huz39gKMrUKfzF4jjaHi+eT/URCIUKhEPl8nkwms+qCQj6fJ59vtmI7nU7cbjcOhwO/XSfotKPpbkxp0jBNHuoPY9EEqVRqoc3YYrHgcrmw2WzE/QE+e28HhimpmcamBMvJcpHJcpFPdx1EQ1AoFcnlciQSCf7wyX6++c4oE1lVvdviZGeA/S0bGjW4UyjueJRgp1AoNkVLGBpOFRlJF5c891dvXOPXH+4h4nEsKc8XQmBKyYmOwK4JdgDnJ3J8451RPndfJ0cDEc5llpqjnwjFENx+IQ0COB1p5730FDVz6aTozNw0p9sSu3NiCsUdhBCCsbExisXi+hsDuq7jdDoB8FlthOxO0ndoW2y70wOgPOzW4e3ZCe4Nt+PULYgbAoE0IbivJ8RQqsjZsa1PLN0KKnWTP//5EB8+Eqcz5Fp1u9b3v92iczwZYF+bh6++OcJIurTm/nVNEPc7KFYbZEq3fi3VjOaCYiwWZ3Z2hlxua7z1hBC0JxK4XS5enxnnlendC9pYzEylxH+7+j5ei40PJXro9QTw+XyYpkmj0Vj4U6lUlqU5l8vlZVWI+/bt41oxS7vLg8tiI5fLLfEEbDQaCz/TVCpFLBbD6/XyJ5fepWJurCXaoVu4NxzHqun4bXYMw2BiotktMDU1RTQa5cmDUf7y9Wu38JO5c/A5rHz0ePtN+yorFIrbDyXYKRSKm6I34uZfPHeY90bmeOnSzHz7hME33h7j95/owzSXrsBrQtARcvGJexJ85/2JXWtxODee5eP3JDgWii0T7Jy6pVnxcJsNgoQQ2HULj8Y6+OnE8JLnMrUK1wpZOty+Vavw1MBPoVgbKSX5fH6ZWKdpGsFgEKvVitVqpdFoUC6XsdlsBAKBhe3sus6v7zvOL6bHeGN2/I5r8Yo4XDQajR3zCbtdaUjJGzPjPBFfOQxISsmnTiSZyJRJFbc+qGAruJYu8SevDPA/PL0fv9O2rneWrglcNgu/9UgvPz4/xRtDaerG9etEAEcSfh7oDZEMXq/QvzKd5y9fv4ZxCz2107kq33xnlMf2txGJtG2ZYNfyqHt5aoQ3ZvdeRWS+UePb1y6hAQ+0JYk7PbisVpwWCw6bC5/Ph91uZ2pq7SrAer1OyO4kW6vhstgQQuB0OqnX6zidTnRdxzCMhara1ud/I2KdRWjcF2nn/kgCi6YhpUTXNBqLKnRzuRzhcBiHdblH793KJ08m0LXVfSQVCsWdhxLsFArFTSGEwGZpVgTc3xtmaLbAt98dZypX4b+8PMivPtCN06YvG8zf2x0kEXDywsXpHU/Fg2Yy3rmxLCe7AngsNgqL0tuEELdte4EmBCdDcc7NzTBdWVrFcDmXptPtW/j/VlvzjSjhTqFYjpQS0zSZmVmephkKhZopsA2DUs3A63Th8TSrzS5N5vhgIsdnTsbRKi8hLT08Eu3lkD/M98euMlneWKXe7UCuViXp8q6/oYIzc1M8GE1i1/Rl91shBJoGX3ygi//nxat7KoRiMVLCn74yyMeOJzjU7lv3u0PTmkFUHzka5+nDMUbnSlyZLlCpGzzSHyHotmGacski376ol/0xLxduIXE25LYRcNmaQRlb3BYrhOCAL8R76Ulqe1SoNoFfzIwte/zp9h5OhJrVcPV6nVqtRrVapVqtUqlUFgS4er2O02Hn60PneTzeTZ83gNd7/XO+0u+9toGW+IP+ME+39+DQLVSrVYYnJhZEwBtFfyklVn17WppvN460+9gXVfdZheJuQ4VOKBSKLcE0JaaU/Pj8FK8NpPA4LPzTDx9ccfW9NTD/5jujvLfNCbIr4XVY+MfPHMCQJu+lp/j59Agm8GS8m5OhGPo2+d1sN6Y0mamU+IurZ1l8Yz8VjvNkvHtBpDOkiS60Ja/TxO35nhWK7URKiZSSycnJZSmwQgj6+vqYyFX5zz8bWHUf/+9PHEJUfgiV74P1FKb7VxHCytupSV6ZGqEh9+Zkf6NowO8cOInXaufKlSvr+vrtRXYidGIxD7QleCTauWbV8w8/mOTnV1MrPr+X+OSJBCc6A5v63mx+rq4LeYtFn9aCkmFKLk7m+Os3N99uejTh45F9bSQCTkxTUq/XmJub27IKO4BAIEBbWxsXMrN8d+zqlu13pzjkD3M40IbXasNjsWLTLQvXY+szLITAME00IZguF/nO6BV6vAHcupWzmRkytQouiwW/1YEhTSqNBoVGbZVIr+v8xr57CFrtjI2NrZss3dPTw0S+zp++MrgVb/u2xWbR+CfPHMBp0287j2WFQnFrqAo7hUKxJWiaQEh49micIwkff/LKIJemchyI+ZaJdq1V9Fx5d/yO8pUGX33jGk8fivFgNMl9kXYytQp107xtxToATWjEnB7uCcWWGF47dAvNzLXmz12/QZxTYp1CsTK5XI7Z2dllQQp2u51EIoGmaVg0wa8+0IUpm2KDYcqFBQxDsrTFvv4OWuZ98Pw2p8JH2O8L8YOxq1wrbp2QsNN8JNmHz+ZgcnLythTrdoN3U1M8EEliXaO17fEDUd4anqPW2NuCrmCp2CZYP7Gy6d93/d+LX98SI3RNcCDuxWbRNvUzcNl0fum+TkzDJJ1Ok06nt6VVO5PJEAgEiDhX9/Lby1zIpriQXSoItzs99HuDTS85KUHAQV+EiWyFiMfJb+47QcmoM1spkZn37Ss1GpQahZUOsSoN08QwjHXFOmjaDlTrKsjmQwejSqxTKO5SlGCnUCi2jNbAuzPkJhlwcmY0y+F2/6rb7+bU7sp0gSvTBWI+Byc6Axxp9xFx2XbxjLYGKSXPJHoJ2Z38dGIIgLDduWRStdrrJLdfOq5CsR1IKanVaqt6PAWDQSwWC0LWiHklMa8NhEZTFNcQ4gbPJTOz6H8aUPiPaJb9eDy/zed7j3B2bpqXJoep3IYJq+/PzTQrdbzeLa1gupNppWh+JNm34vNCCOwWjS892M1fvn6Nyh4WLOxWbeH7RROCq7k5+n3BTe9Hzot9i7FoGn/45D5m8xVylTrpYo3xTJnxTGWJD95iDrX7EMDIyLVtD0GpVqsEXC7smkZ1j7bFboaJcoGJ8nXx7dlEH7omeOXKDIOzRR7ui9DX5qYnGMBlsVBqbCxYYjEJl4eY072sYnkl4vE4uq4zlatu+jh3Eh1BJw/2hZVliUJxl6IEO4VCseWYpqQn4uHVK7PM5CuE3PZlVXaGKfnNR3q5NJXnZ5emGZ3bnfTEqVyFH5yb5AfnJnnuaJyH+iO7ch5bRWtAdyoc50Jmlkytwj5faF0hToj1JD2F4u5BCEE6vXKitaZpeL3e5mct/x8QjdVatZriXfPPCsJB4zJa5n8F95c4EjhNvzfIj8YHuZzbvSTtm2G8lOdnk9d4It5FOBxekiKpWJ3356Y54A/R6fatWOWsCUFn0MWHDkX57vsTu3CGG+Ot4TSH232YUjJeyvPTiaFNC3ZSSiqVCpVKhUAgsESYCLltBF3WZvWeEGjzqfPpQo2JbJnZQpWZfJXZQpVUocaRdj8Nw6DRaGCxWLBYLGjzlfOt/Ta9AjV0XcdqtS75+8Yk1LXIZDIk3W5+7+C9/LfLZ8g19mZQyM3yVmqCfb4Qn7uvk5+cn+InF6a4NOXkdx/v54lYNxcys4yX8zRME4umYdKsnlsNXQieS+7DMM2FJNjV6OjowOVy8fpgip9eWDsc407GZdP54v1d3IZ5aAqFYotQgp1CodhyWs2XppR8/a1R/uDJ/mXbtAS8/jYPB2JevvXOKO/ugp9dCwEbMs++XTCkya/2H6Nurl6Z0XqvN3raKRQKlrXBtmiFSjRZ614hAWP+zypYDoIxgVZ9Dbv9QT7ZdYAruTQ/Hh+k2Ngdy4Cb4a3UBCfDMdxutxLsNsFPxof4zf0nVn1e0wSnu0O8dGmaYnVvVtkNzBSbC17H2qmbBnZ9c4meUkpKpRLpdJpgMLji968QAn3R45oQRLx2Qm4bkuvjCXNRld7+/fs3dOzFxwAIh5uVTLOzs6u9bIFyuczo6CgdHR18ousAfzFwdt3X3E6kqmXeSo3zSLSTpw5GeWMozWyhSqnW4EiwjSPBtiXbSympGgaXc2l+OL7c1/NYMErAZmd8fO1k3WQyicvl4ocfTPLqlfV/D3cqQsAXTnfitFmWBLK0aLWQrxYkplAo7gyUYKdQKLYcISDitWPVBZO5Cm8Mpjndu3KVlz5vOv2JE0kmcxUms+t7mmwHMb+DwB3QEtuiJcBZtdUnTxKYLOVpV+mOCsUSTNNc0V9J13WCwSANw8Rq0WlWz9080v1F0AKARCCR0qDXG+C395/khckhzs4tT6bdq6SrFXo8fqxWK1JKGjfRLne3MVer8MbsOA9EEqsuFGmaIOC0UazuThX6RnhzKM1j+9vocPl4Kt69KQEhm80yPT1Nb18fVktzWrLRhbMbRYzNiharHSMUCgFsSLSrVCoUCgXCbvemjn078EAkwaOxLq6livz9mfEFL8F/9b0LeB0WQm47AZcVARhSYrfoPHM4xkF/eEXBLmhrhlMUi2snZNvtDs6NZe9qsQ7g6UMxusPuFa9TU5pUDIPvj13ls92HduHsFArFTqFKKhQKxZYjgONJP//swwc5FPdyaSq/5kC6NRj5jYd76Y3szqC3O+zGvMsM0zUhKNTr5OvVu+69KxQr0UqFzWazy4zqHQ4H3d3dWK02fn61NZG8xWGUzFOvN7h8+erCn+HBIWSjwbPJfr7Qcxi/zX5rx9ghvj96BVNKurq66Ovro6enB5fr9jTk30lenRrhWjG36j3YMCVPHIiumLi+V2iYkr9+c4R6Q9Lp8a8bPtJ6fnZ2lunpaQCEJng3NcmrU5tPhd0OQqEQVqt14f+FEITDYdra2pb9sVqtWDQNh3Zn1UGcjiRomCZvDKWZyS/1kctXGgynirw3kuHdkQzvj2aZyVdxWHXen1vewmoRggP+MGZjA5WiQpAp31ntxZvBZtH4xD0JHtvftqpYV2zU+crA2TVbkBUKxZ2BEuwUCsWW00yAEzhsOl98oJsnDlxvmzDMlQfyutY02f71h3t4dN/O+8i1+x3cjZpVvy+Ix2KbT5FVKO5eWiJCOp1eVlkTCATo7OykIeE/vHiFi5Pzhum32EoujDS6vnRC1mg0GB4eZmZmhoTLy2/uO8F94fY97zFZMhq8ODlMyWjwfnoaU9Po6OggHo/v9qntaSTw9yOXKNRrmHL55LuVlvovnjvEkwfa9ux1MJwq8m9+dIm/eWtkIW29JYDf+KfVctryiXQ4HFg0nU63j9dnxpmtlnd9EUlKSTDY9OITQpBMJgmFQnj9vut/fM0/VpuN2m0YGLMer0yPUDcNPndfJ8c6Vg8Q89gtfO6+Tr5wupOq0eDFyWsAtDlcfDjRyyF/mPvbkrgt1lWDfBajaQKXzcIDvSE+eryd/jbPuq+5U+iNuPlHH9rPqe7VfSAFgm8NXyRbq3LQH8ZY4b6hUCjuHO6spSCFQrGnaFXVdQRdzOar/N2ZMT57bwceu3XFagFtvj32w0fiJANOvvbWCKvoe1uOw6qzhwsYtg3BfIXj3ahWKhSLEEIwNTVFNptd8ng8Hsfn8zE2V+JPXxmgYULM26p6u8V1T3Nu1erjubk5stksHR0dPBHv4lAgzPdHrzK7h1sj301P8W66OSH/4fgAH070ck8oRqVSIZPJ7O7J7WEqhsGbs+M8nehddRuHVefJg1ESASdffXNk1cWv3aRumFyYyFE3TKy6Rj6fp1KpIKVE07SFsIdqtUo+n194XSKZBODM3DQmkr8e/ICn23s4FIgsCHc77dElhCAQCOB2uzEMA7vdzo/HBzkzN72j57GbvJeeQheCp9p7mFkjqfUjR+IcbvfRkAaXs9dDc/q8Qe4JxbgnFAOa7cPl8vr3LwGc6gpiygAACb+TqzPrp8rezlh1jY8ciXF/bxjTXL2l3JSSM+kppislLELjUCCiPIgVijscJdgpFIptR9MEQbeNz9/XxbfeHeULpzsRQltxQNIq/z/U7uMTJ5J8+92xHTlHh1W/I8ImNotoGRbv2boNhWL7abXB3ijWuVwufD4frw+k+O7ZCdr9DvbHvMR8jvktblWwy65ZpWeaJteuXcPn89EWjfLf7buH12bGeH1mDOM2ENl/ND5Il9u30DZYKpUolUprtkzquo7f76dSqVAqlXbwbHcXj9W2rvebEIL9MS8P9Ib4+dW9Ge7RMCXnxrMcT/iZnJzc0GuEELw1O8E7qeb2FaPBd0av8G56iqTLS5vDRbfHj9Ni3XGD/VbSbNmoM1Ve23vtTiTmbFa3Zcsrh+AEXFaOd/iZqZRoc7g4FoqSrVd5bWYMi6bRME3GRkbw+XyrJm/fyPj4OKZpUi6XicfjJAJebLpGzdiaSrL+Ng+zheqq72mnOdzu46PH23Hbm9PylQImWjRMk1fm28b3+YLY1vApVigUdwZKsFMoFDuCrgmcNp2DcS/nJ3Kc6Fy93B+aA/hTXUFy5TovXNz+FW2H9fYe9EgpMZEgmz+7zUxoVLqYQsGK6aZOpxPTlHz37AQJv5Pfe6IXMS+wSbOAMG9RNDHnEELDYrGsGdKQy+UoFAokEgkeakty0N+stpso792qkzaHiyfj3QTsThqGgc/vJxgMIqWkXC5TLBbJZDIL4p2maQSDwYWkUCEElUqFVCq1rkn9nUB9E15Ujx+I8tbQ3JYJGFvNu9cynOwM0tbWRiqVWuYHuZhkMokQgguZ5QED46U846XrlXgH/WE+3rl++utW0lrIc+hWfrX/GC9PXePN2YkdPYfd5LXpUQ76w5zuCfGzy8tDcArVBmOZMsmACyEEdcPkSCDCazNjWIUGUlKtVpmZ2XiAzuLPezabxefz8dyxOO+OZBhJ37yIH3BZ+fg9CfZFvVTrBt96d4zLU3kau1StGnLb+NjxBP1Rz4aFaBNJ1Wy2Xx8LRlVCrEJxF6AEO4VCsWPomuB0TxjYeArckwejZEo13h3JbOu5uWy3l2AnZdN1rjVQE0KQrpSomyalRh2rpiEQOC1W2hwuTCmvt78qFIoFpJQUCgWMFTyoXC4XhWqzCuMTJ9pBViH7r8HMINgCzyozAzQ9vAqFtcU30zQZHR3F7XYTi8f5lb6jvJ2a5JWpERp7yMPIY7HySKyTo4E2GqbJixenFxZd+tvc3NsdoivkIhKJ4Ha7GR8fx+fzEQ6H0TSN8VKe74xc4XAgwv2RBMlkkmq1SjqdXtJGeadxrZDlobYkJmsvogjR9Ht9sC+8ooCyFxhOFZnKVYjNC7ANw0SaJo1GnWq1uiDYmqaJ3eFgIDfHVGV9UfZiNkXI7uShtuSOf5dpomnZ8US8m6TLy7evXbornF/TtQrpSpmHbrjevA4Lzx6NIyXMFWt0BF1M5SpcmMjx2P42HJqFfl8QeYuhCOVymXK5zInOIPd2h/izVwcZnF39WrHogoMxHzZLc2HFpmv4XVb8TisHYl6EkFB5Eav1FL98fxemlKQKVd69luHVq9uXSqsJ6Il48DuteB0WAi4b93QEFj2/sevZoVtwW6wIIeh0+9SYTqG4C1CCnUKh2BU2OsiQUvKxexIMp0rMlbYnNcxu0fA6rOtvuEeQUlJs1Hl/bpo2h4t9vhAAbY5mwu5Lk8NLKgACNgeHAxHuDcexaBq60JYJfgrF3cxK9yMhBA6Hg6GJHAB+pxXRuAC3WlW3mHnBzm63ryvYtSgWiwxcvUoikeBUOM4BX4jvj13lWjG3ded1E1g1jfsjCU5HEgjg3HiWb70zSmPRfP3qTJGrM83J9gO9IZ4/1k5/f//C8+/PTfGj8SEAXp8d5/XZcU6EYjwc7aC9vZ1wJEI6lSKX2933uh1MlAt8eeAsn+46gNdqX1u0Ax7dF9mzgh3Af3zpKlGvnajPQZvXTtTrIOZzEAg4CQQCSCkxTIlF1+jxBrCg0WB9cefn06NEHW76fUFMaaLtoH9X6z7R5w1yOpLgjdnxHTv2bnI+O8vj8S5CbhvpYnMc5ndaOZYMYM4HiQC8MZhiJF3iyYNRnk704Lc5FlKAb4WRkRHi8Tger5epXGXFbbrDbg7EPJzqDuFc1DFhSok0DQRVNHMQ8n8CFND4BljvQ7MdJuI5xZGEb8sFu3s6AjzQG+KFi9N86FCMRMDZ7IaYV3pvJvnZlJKHox3N9wXKzEShuAtQgp1CTdwVe5pmeyd85Gicr75xbVuO0Qq7uB1WKk0pma2UyNYqHPSHCdocC8+13kOvJ4DXaidfr5Kr1bhWzPLz6VEuZGb5dPdBbJpOtlbFkCZdHv9t894Viu1iJW8lv9+PEIKzY01fu2LNwOkMb+0ESeaR0sBq3fyCwfj4OE6nk/b2dj7fe4Szc9O8ODG80C61UwjgaLCNx2JdOHQLY3MlvvbmCLnK6i2+AK8PpmkYJg/0RfjB2Ql++YEuvFbHsu3eS0/xXnqKw/4Ij8c6icfjRCIR0uk02Wx2TT+8zRK2O7dsXzeDlJLvjl7luWQ/Qfvyn0ULIQQWfW/fsw1TMpGtMJFdKrDYdI2I107Ua6fN6yDssXEw7uNT3Qf4+vCFDe37b69d4lf7jy4sUu0kre9Lh373TKGu5tI8Hu9iX9TD28NzRH0ObJamKKYJAfPVh88da+ePfnKZdLFKrzcAgM1m25JzcLrcjKZLlGrL72+H2n188f4uDFM2w8MKfwG11wENbS0RuP4W1N/CtN7LTH71UI3NYtEFj/ZHeOpQDCklv/ZQz0JIjBCCW/noakJwTyimWmEViruIu+fbRrEqLbFOTdoVexVdE/S1ubHqgrqx9U0o5ZrB2FyZRNC5ZwdArc+nJgRhh5OIw7WsxbX17w63j4TLu7B9udHgzdlxBvJz/Onl95bsd583yHMd/Vg0HX2PvneFYjtofaZSqRSVylJRwe12E41GGZsr8cF8hV2mVCfiCW31WYAsYLHcXIVvuVxmYGCAWCzGkUAbfd4gPxof4EpubovPc2W6PX6eincTdrhIF6t85e0BRuc2nmL79rUMb1/LADCbrxJw2Vfd9nx2lvPZWfq9AZ6K99DW1kY4HF4Q7tbySdsov7n/xC3vY6fYju/CnaBmmIxnyoxnrl8nnzyRWNfXdjF+m52Iw7Ur39cSKNVrd0V1nd9qx221IQDDNPno8QQfPZ4AmoKsacqFgISWEHW6J8SlyTz394Yol8vMzm5R1ZqUdIXd/OYjPbx4cYahVJHeiJujCT8zhabY9nfXLvKJrn1ojqcRtXeAjYVKaOY4J7uaVWtnx7KMzJVoGJKYz0GtYW6ou0MAPRE393QEOJr0Y9WblZ+tcdnNVNOthrmFixQKhWLvI+RWLk0qFArFNmFKyXsjmW1LjT3c7uOX7+/aln1vhrWE81awhL7JFqDW4E4TgmK9Rl2aCOBrg+fJ1qt4rTY+3rmfdqdHifaKuwYpJYZhkE6nF0IONE1DCIHb7UZoOv/+hSsLn58nD0Q51R2Cuf8F5MZFqXXPw/fPqJsJhoaGbmk/drudRCKB1WrlcjbNjycGKTW2JwXRY7HxkWQvvd4g5VqD752d5Mxo5pb2+amTSY4kfPzRhTc2tH2n28sz7b0E7U5M0ySTyTA3N3dTwp3f7ycajfLt93YmlXwjCOD5Y4kFL66V+P9+5wOqjb3jX3iz/NajvUR8Nv744lsb2v6Xug/R5fHtaDvsYkwpydYq/PXgeQqN1cUcu6bveMXrreCyWHks1knU4SZkd2LRrv98bxybrDRWaT2Wr9Rx2XSuXrmypecXDofxBwJYdJ1qw8A+X+X31lCa+3pCvDgxTLZW4ZNd+xGVH0L5uxvfufu3MazH0TUNw5SUaw0881YpxWqDwdki11JFPpjIUqwaeOwW9kU9hNx2Qh4bPWE3brsFw5RbKs4pFAqFqrBTrElLz1WTeMVuo82nxn7v7AS1bZignJ/IcXW6QE/Eva2DrcWDuXylTr1hYtE1dE2QLdfxOSw4bZYVz0EIgX4TDXmLqxDc1mZ7iiklJ8IxXpq8Rr5e468GzvFwtIMH25ILvijqc6+407FYLLS1tS15rDXpFELwTz58cPmLhHdLBTthpNEtyVveT7VaZXBwkEgkQl8wQJfnJC9MDHEus7U+Z4cDEZ5p70UXgpcvT/Pj81uT4j2br2LVNR6JdvDq9Oi6248U8/zplTPEnG4+0t5LWyhEMBhcEO5WChFZj3fnq/32Cj0RD0cT/lW/k7rCbi5P3d5BHG67ha6Qi3Nz61+nNk3nE5376fb4d/X7SRMCv83Bl/qP8dXBD8jUrlfo2jSdw4EIp8Jx/DY7//nSuxTq2+O/u9UknB6OBaMrPnfjz3s1308Ar8OKsQ0JxqlUilQqRTAYxOPxUDU0LFYbR5N+oJlM/VZqgqlykZjzKUTlhY3fq4t/gq6FwPPfo1va8NivT5HddgtH2n0cS/qJ+x387Xvj/NajvYQ9dgzTRCAWKg2VWKdQKLYaJdgp1mTxF/LiKh2FYqdpGVS3fEC2g3PjWfrats8Tp1w3ODOSYWi2wOhcmUJ1ucdTszUowHZbCWtCcDIUZ7JUZKyUo9So8+r0KNcKWU6F47gsVpJu37aeg0KxmwiAxgii/ANYZN8tlm5xAzUwt9jo38xs6ffq7OwsmUyGZEcHz3X0czgQ4QdjA+Tqt+bR5NKtfDjZyz5fiHSxyp+9Oki2vLZP3WZ4azhNT8TNg9Ekl7MpZqobm2hPlYv8+cBZQjYHz3b00z6fTJrNZkmn0zQaW3eOO025ZqwqAJim5BP3JPijn17elkWsneJwe/N75rWZ9UXax2KddO2yWNdCEwKXxcqv9h3lTHqadK1MwunlSDCCRVxvh0w4PVyqL/fI3ItUjK37rIyPb1+1aj6fx+v1YrPZyNdrWDWNakMyWW4G93x/bIDf2HccnJ+E+llo5fk2xkCuFFgjwP4EuD4BzFcV3nCNtQS5poefnbCn2b6va7tT5alQKO4elGCn2DBKqFPsJkII3hpOb6tgNzhb2JaJgGFKzk9k+eY7Y2uef3fYxYnOIDu1QKsJwSe69gNQNRr8bPIaZ+amGS01KzY+2tHP4UCz+khV2yruSCydYDsGxa/s3jmYGdji1r5Go8Hw0BDBYJBkOMxv7T/B14cvMHqTSbL7fSE+kujDqmm8fGWGH38wtaXnC1BtmHz/3AT7Ywd4vmMfX776/gYyQ6+TrlX4y4Fz+Cw2PtLRT6ffj9/vJ5fLkU6nqde3pz14O2mJWSuhaQKP3cLzx9q3zSpiJziW9FNqNMiuUYXW5fbhtdq5JxTbU2NRTQjsuoXTbe3oQsOQ5hLLCkOaxJ0eLuVuD8Gu3eXdsn1t5+ctHo9js9n43thVzmeWe+SlqmXGSnk63I+A45GFx2VjApH7327YWoD7N8E271+5zvXlc1qJ+RxL/PsUCoViO1GCnWLTqIm7YqdptajN5Cvrb3wLZEp13huZ43hHYEsnBbomuDJdWFOsC7ptfOnB7h1tRV38Hm2azoeTfUyUC8xUSgD8YGyAd1KT2DSdhMvLA21JLOpzr7hTaF3L9geh+jY0Lu7OeZgZhNCwWCxbXg02NzdHNpulp7eXp+LdfGXgLMYmrIsdus7T7b0cCkTIlWv8558PMFvYvva+VKHG985O8Pyxdp5N9vO9saub3keuUeNvhs7j0i18JNlHry+Az+cjn8+TTqep1W6P9kSATKmG12FZ9TtB05pWEW6bzstXZhlJl3b4DG+NVjvsB2u0bR/2R/ho5z5gb5rtN79H59shbxDeNQQRh2sXzmrzHAu28Xi8a0sC6KSUJJNJrl27tqUpzoFAgGAwiNVqZapc4Ooa4TpfHTxP0OZAmw/fOuQPc39bEiz7oXH5+oauTzfFug28Z8OUfPpkEouubWRzhUKh2BKUYKfYNOuZzioUW03rGpvJ31pL10b49rvjeB1WesLuLV09LVTWnog/2BtC17RdWbFtfY6rRoMH2pI4NB1DSnL1KnXTxKHreK12lSKruDORJtgf2kXBrjnpdDgcFAqFrd+9aZKanSUajfIHB+/l7dQk5zIz6/pq9XoCPNfRj1238MZgiu+8P7Hl57YSUkqklJyduzVvvJLR4FvXLmHTNJ5J9HHAF8Ln81EoFEin07S1tWG1Whf8CvciqUKNZNCFvs7p7Yt66Y96+as3rt0WnnZ2i8bBuI9TXc1k2F/MrF4heLotsSQl/XZiL19b0JQZD/jDPNiWJOJwbdmYXgiBzWYjkUiQSqWoVqs3LdwJIbBYLMRiMZxOJ3OlGrOpHPtiXu6PJHhlemTV184t8hZMVUrcF4kjHE8hCvOCnf1JcDy1qfOxW/WFf5tS3nbXpEKhuP1Qgp3ipvje6BUsms4z7T1KtFPsCKYp+cLpTv7Di1coVrcvdc2Ukr964xr/8Kl9+JzWLbu2Zwuri40CONEZ3DWzYklzUm8RGvt9oQXnrsXVDNomJh51w6RUa+C2W5qDWdlcvFb3CcXexARzF1vWzAzQTHndDsEOIJvNUqvViEajPBzt4NFYJz8cG+D9G0QxTQi63D6OBNo4FIhQqNb5z69cZSK7vdXNi+mPeig3Ggut+bdKzTT57ugVfojGU4lujgTa6PJ4AMiV68zmK0jY9grum2GmUNlQJY+mCUwp+eL9XXtWtGuJdEcTfvqjHnRNUGk0eDs1Sba28vejQ7fQdptUqK3OHqwKRHAs2MYDbUl8NvvCd/1WfkcLIXC5XLjdbmq12qZTsDVNo6urC5ttPijLNPnu+xO8MdS8V//Pzx8i4fLQ6fYxWylRXsN/73S4ncfiSTShI5kfP2pt4PoMSLlmdV1rjpMt17g6XWBwtshsocrxpJ9H9rWt+jqFQqHYKpRgp9g0cn5FyTBNhBCYspmQpFBsJ5omcNos/NqDPXzr3TGmcts3uao1TF4bTPHhI/FbvrJNKRlOFcmWV/dzkfPHtOm7U2GnNdW0ZY+vV1EnpUQC46U83xy+yAF/iGeT/Xz1jWtcmS7gsOrc2x3k/p4QPqe1OfBFCXeKvcYuX4+ygJQGVqt1Ww9TLpcZHh5G13V6enuJOFy4dCsui4Wg3ck+X4h+bxCbrtMwTd67Nsc3d8EbLeF3kqlv/f29gcmPxgf5yfggT8S7ORWOMzCT51vvjm/5sbaKK9MFnj26setTEwKJ5HP3dvCvvn9hW/1eN4rNonEw7uVYwk9/1Lsg0g3k53h9doypcnHN11eNBoZp3tbG/nuti1cAH+vcx35faEFK3K4qMSEEUkpMc/OhKD6fD6vVyjvDabLlOu+OzC0JucmUanQG/HR6/IwV8/zV4LkV9xNzunk83gGNa1D6OsKYr8iznaC5mrj2tSWE4EcfTPLKleteeW1eO/d2h5SPnUKh2BGUYKfYNBJ4Ntnf/LeUaFtslq1QrIauCaI+B//gqX2cHc3wk4vTzBW3x4/o7eE5HuqL4LFbbnpAZpqSumnywoX1W7tW0cz2NEI0pfqQ3YlN17mSnePZJHjsza+WSt3g1SuzvDWU5uP3JDjeEQBUK71ir6GBpW8Xjy/BzGO12nfkaIZhIKXkVDjOqXB84fFaw2B8rszrgynOT+xOhZbLpuNxWLk4u8VJvIswgZenRrg30k6tscfUlBuYyVfJlmobrvYWQmC36vS3ebi0g1V2dotG0G0j6LIt/B322OgKuTct0rXo8fjp94Zua7EOwGXZXiF+s3wk2despBc7s9QuhMBut+N0OimXN5b8DE2/uly5zrffW1lQ/39eGsDvtPCPnj7AXG31/Tq1+amupatpfVApNG0IrIfYyGJNtlxbItYBPHUwinWXFlgVCsXdhxLsFJtGE2LBt0FNuhU7Tatt9HDCz5GEn6+/Pcq58eyWH6faMPnK68P87mN9CAmmZEMtq4Yp0ebbPy9N5fj7MxMUqiu3arhsGh1BF5qm4XXsrUH9aqwktjl0C7/ad5TifCrc5A3Vj9WGydffHuWVK7M82BumM+wi5LYp7xfF3kAI0CO7ew7mHLqe3LHDzc7M4HK5qNfrOBwO3G43/8cPLlJtbL4SZiuJ+x0ADOYz23ocOV9bdDtMuM9P5Li/N7yuj10Lw5Sc6Axsm2DnsOrc1x2k3e8k7GmKc0t8vUyJIU00QCD5ytWzTGxQpIPm98lzyT76fSEMaS5LXr3diDhcWIRGQ+7uZwvg6fYejgbadmXsHo/HGRoa2pCXXSAQwGaz8aMLa1e/GhIsmmB8jfb5oWKWP77wDs929NPreQjsDyHKP4D6WbDuW/dcLDcIxgdiXg7FfbfFvUOhUNwZKMFOcVOoibZit9G1ZqvFL93bgduuc248R3EVYexmmcxW+L9/fJm+Njc9ETf9US8um75w/S8W5wAKlTqDs0UGZ4tcnSmQW6MN1mbR+OfPHkLXtC1NUdsMN4pvUkpM5KqTo5ZQX27UcS6qGtCEwGu145hfyU4Vajy6L8L9vWHeHErTHXbR7ndimBJdE1yazBPx2JccfyXzZmXorNgxhBvQaNZf7cLhzTS6pWPHjpfL5cjlcgBEIhHcbveOHXst4n4npim5Vsxt63Fanl0bFcF2k3dHMtzbHQI2tmika4IjCT/d4RTDqa1LjXXZdB7uj/BAbxiLLpqVmqZJrVqmmK9RrVYpl8sLScd2u53u7m4O+MObEuxOhGL0epthFLezUNdCE4J2l4eRbb6m1+OJWBcnF1XU7iSt4Ii2tjamp9fuOIjH4/h8PrKl2oJf3WocT/oRQjBWXC7YtTs9HPSHKRl1crUq3x6+hEPX+VT3QRKuj0L+P0H9Elj6Qegr7L2J227h95/o5/3RDAfjXnoiHsxdbjdfPDZS4ySF4s5HCXYKheK2peWP8vyxdp4/1s5YpszgTIFsuc7FyfyqlW2bIVep8+5IhndHMlg0wZce6qY34qHWMLk4mWNwtki6WCVdrJFfIQnW57h+mzVpGic/vj/Kg33hBbGqOXfc+TZRSbMh5NWpEaYqRUI2Jw+0JXBatBUr6QTzIt8qbSRWvTnofWRfhKcORgF45nBsic+LlM3qjxsHmZoQTJTylI0GVqHR6fGrQahi5xAaCC/Ira/W3RBmRl3vQNznoGZuX6hQi9Z0+3boEpjKVfijn1zmkycT7It6N2QpYJqST51M8u9+emVLvOzsFo0/fHIfHruFarXC+OgUtdradhTVapViscg9wRgvTl5bc1uLEMSdHhCCfb7gLZ/vXsKUkoTLu2uCnUUInkv2czCwu1XEQggCgQCpVArDWP0zXqlU8Hq9fO/s+qnUDktzzFEzDQ4HIjzYlmSqXGCmUuJkKI7bagMp0TWNXu8M3x29yl8OnOMPD57C5f5VRP6PwPuPAOeaXnZxv4N2f5zWR2m3q+s0IXhpcphrhRwH/WFOhGLYdF3ZjSgUdyhKsFMoFLc1iwdOyYCTdr8DTQgeP1Dny78YZia/ejrrZmmYki//YpiYz8FUrrLuROj3Hu8jGVyecNcaVM3NzeHz+dD11Vd3t5NWe/sjsU4qRgNTyoXKuZUGfa3HHJa1vzqeOhhdIsgt/h0JIVb06jOlSdRxvcrndjcaV9yGyI37K205ZmZd8/O7gTavnVJj9crkraTpwbsjh7plcpU6P7s0Q2/EvaH7oqYJgi4bX3qwm2++M7qwmBR026jUDcq1zYmizx1tx2O3MDo6QqWy8UCQdDpNZ2cnD7YleW1m5QCTiN3Fp7oPELA126HvxIqhpMu7K8f1We18sms/bY69UUEL4HQ610zDzmQyBINBPnEiwYXJi2vuq240K6J/ufcIQbuTQqXOfl+YA/4whin5yi+GuDpT5NmjcR7qi1A1DH4yMcRfD57nN/cfRzo/gSj+OXj/cM3jtK7HvVKR2zBNzqSnqZkG05Uib8yO8w8Pn1ZinUJxh6IEO4VCcccghFhINvXYrfyDp/bx5mCaFy5OU65vTdWGYUrGM+tP7I8l/YQ9dlKVEq9OjzbPb/4cn0/2k89mmZ2dJRjc3WqC1kDUoVu2pDW3lRy72QmXJrRdD+tU3MWYRWB7Amw2dvwMQmjYbLZ1K5fuZGy6Rl3ukGDH7lfKrIdF0/jIkRg9ETdhj41Co0bNMAg7XOveY4UQdIfd/OGT+/g3P7qIVdP4wyf7kRIGZwoE531EW/YSl6byC5XiDVMipWRwtsiBmJdT3UGy2eymxDpoJhNXKhVOh9tXFezujcTxLQpcudPEulZL7E5zMhTjiXg3mhB75mcqpcThcKwp2AHMzMyQSCT43H0d/M1bo6tul5/vonDpNr5zZnzVFtofnJvEZdU52RUnVS3zXnqKX8yM83D0CNTPQeUlsD92WyyamFIyXMgsqUSuGA1mKiXCduee+V0rFIqtQwl2CoXijqTl9XO6J8TJriDvjczxxlB6SyvuVkMDPndfJwDjlSqXc0sHkc8n+zFNc9cq61ZjK1Zndyp5TqHYMqQEc3b97bYTMwM0fb/uZsHOomuUzJ3xEZR7vJLLZtH4x0/vx+OwkqlWOJ+d5RfTY4TtTj7bc2hD+9A1gdtuoTvkpjvswqpp1Bt19kc9mGYzLVhKiaZpPNgXXhYmdmY0w/6Yl3q9wdTU1E29j1QqRTKZ5HigjfczMzh1CyfDcXQhqJkG+32hm9rv7YRdtxCwOcjUNid43gw2Teez3QdJun17skXSskaFvhACXdcpFArMzc1xNBFgNl/lxUsrp0a/N5Kh3jD5YGL9duNvvjvGwXYf7S4P76Wn+Pn0GPt9IcKuzyJy/wasB0BrW9PPbi8gpaS4QhXyL6ZH+WTXgV04I4VCsd0owU6hUNzRaJrApgnu7Q5xf2+Ya6kiFyZzTOUqjM2VtyUV0QReuDjNUwejxBxuEi7vshSzvTiQVijuSoSAyqvX/19rA+ECWQHz5kSKTWPOAWCz2XbmeItoHXOXsm+WYNU16juUVGuytwW7xw+04XFY+Zuh8wwXrnsr5upVpsoF2hzuDZ2/aUo+f7qTwdlCszpnaGjN7W02G5qmEY1GOZbwU6/XGB1fO61zLYrFIrVajcfiXURdHo4G2tCEQCLR5pd37obvwrjTvSOCXZfHR9LtA/bmz7VcXrlDwWazkUgksFgsDA0NMTMzg8Vi4cmDUYZTJYZSKweXbESsa1E3TOza9anvVwfO84eHTqK5fw1R+HPw/bPmjXAP/twWUzGW+yXXd8D7U6FQ7A5KsFMoFHcFrYq7jqCLjlCznahhmLwxlObVK7NbElCxmBcvTjM8W+RXHujis90H+drgeaYqzQGnJpoJe17v7vjaKBSKpUjbSYS1H6lFEdae649X30KU/3ahAm77TqCIlMaOC3Z+vx+3282bQylqxu4k5ELz/vzskThOm04mvzNegnt9Xt7uc1I1GkvEuhZn0tN8ONG7of1omsAmNA7FfQvpuGvRqvC8dm3toIiN4HQ6iUQi2Gw2pJQcC7Qt8uDbwz/8LcYwTdpdHi5kU9t6nC63nwciyT27ICiEoFRanlzs8/mIRqML5xwKhZienmZycpKenh4+f7qTf/39C7d8/Lph4rNev8dWzAbfGR3gE537kM6PI0rfBtdnQJp7ttJOCEF1hdCOfb4QhjTviGRlhUKxFCXYKRSKu4rFnkUWXePB3jAP9IZ5fzTDwEyBwdniMvGuO+zmob4w7QEHTquFodkC5ydyXJjMUakvn+R+7r4OIp6mJ0/NMPFarXR5/ExVilg0DSEE4XAYXaV6KRR7Amk5SMM0aJiSt67MMJYucSDm5WTXSaTtFNQvIMrfAWN1P6VbPAMw5/B4drY9MNLWxrV0ie++P7mjx11MyG3jl0930eazczEzy08nhnfkuHKPV9hFvDZS1ZXFy4H8HEL0bXxnUmJIyejIyBad3caIx+MLLZCLPWbvNpo+dlu/QGcRGj6bjT5vkBOhGH6bA3OPjimklBiGQb1+vZ3TZrMRi8VwOp1LxkJ+v590Ok2j0WB6eppkMsmDvWFeG7w1wfPcWJbHD0R5It7FS/PJxZdzaX42Ocrj8YNI4UAU/iO4Pg9acE962mlCLKmws2s6D7YlORaM7un7mUKhuHmUYKdQKO5qWgLe8Y4AJ7uaARDDqSJnRjPYLRpHE36SQReGKReq9PZFveyPeXmu0c73z07w7khmYX/PHIlxLBlgrlrGBGqyzkylxmix2bbhmm/HWOxfZ5iyWekxX/EhhMA05YqG6IbZTDbciwNyheJ2Q0pJwzD5o59eIVte6gt0YTLPj85P8eyROEeTB9C9vYjcvwZze6pkRGME0xrYln2vhNPpRNc0Xrw4vaHKq+3gSLuPz5zqAAHfGbnMpdzKpvFbhUO30OH2Um40J7x7dYJr0cDrsHJueuWW7GKjzlS5SNsGwieg+X1Rq1R21B/R6XRitVp37Hh7GSEEUYebqMPFdGV5hdnNELQ5+LX+49h0vSnSzT++V69paPrX9ff3UywWMU0Tv9+/8NziMY0QApfLRS6XQ5uvyJwt3Ho78U8uTBNy2zidTFAxGrw+02z1fjM1Ttmo82yyB2l/DJH938D9ebDdv+fKcCuNBpPl66Edz3f00+sN7unfu0KhuDWUYKdQKBRcb5kF6Ay66A67m5NYufz5lpBmt2h8+lQHnSEXf/feOHarxkN9YQbzc3xj+OKKx8k1avxiepQ2h5t+XxAhBJlilfMTOZxWnZDHhkXTyFXqnB/PMp2v0u53kq/UEQL2R72c7g0h5NJzUigUm0cCP7s8u0ysa1GqGXzz3TFeuGTlf3i6H837B4js/wFUAQ3sD4NwQO19MKcBK+jtoMeR9geYz4Zmof1PLPo3N/xbj1Epb72g4vP5sFqt6Lq+8MdqtSK05qJBqrA7IRcOq8Yv3ddJqVHjywPvU2psrS3BjQjgC71HaHO4Fh5r7FDAxWa5tzuEJgSD+cyq23xv9Aqf7zmM02Ld0GS9Wt3+wCVoii0ej4dQKKQqyG/g+Y59/PnV97dEIL8nFMMyL2bdDmJN6zrQdX2JHchK14eUckHsDQaDlGoNrs6s7GG3Wb721igum85jsS5GCjkm5sWvc5kZOt0+DgcOg7BD8S9Bi4Klc0+1x3518ByziypvO9y+2+L3r1Aobh4l2CkUCsUNtAQ5TYg1bXZaA81TXUFsFg1TgkXTeHVq7ba5yXKB48EoppS8cnmGn1yYXnP7xcm2AzNF3hqe4/P3dRLzO9SESKG4SQxTki3V+PnA+gmxmVKdr7w+wq892IX0/h4i/8dgPQLuLyCliXB9EtkYAT2KEM12eNMwMG70Glplni6lBMNkZmblNMS1sNvtBAIBLBYLU1NTNOaFL5vNRiKZxLaoyqlumFQbJqPZClPZCiPpErnKymLldnMg5kPXBN+6dnHbxTqAk+E4EbuT75+dYCZfwe+08sH4xg3rd5LjHQHKjfqC7yk008cXy4upapkvD5zls92HiNida34PCCFwOBzbdr6tiii3243X61V2DyugCUHY7uTBtiQ/n7611npNCI4F225boWYj14XdbieZTOJwOHjh4tpjpM3yww+m+IMnvdj1pdPgn0+PcDgQBts9UH0FCn8C/n8JOPdMpV3Y4VoQ7HxW+7L3oFAo7jzUp1yhUChuESEERxP+hUHoWuM6DcHHO/djGPCnLw8wMrcxg/XOkItDcS8XJvO0+x2kS1VifoeaECkUN0HT5wn+5u1RGsbGql2uThf53tkpnj/Wj/T8DoIG0mxwdWCIcDiMxxOnVqqRz89hsVjIZDKY21jBFQgECAaDCx5hppT09jaDCCTz4TamyV+/cY2hVJFK3dy11teVOJLwUWk0tqxFcC0cus5jsU6mchV+MbC9xv9bQdRn51Lu+nl6LTZ+9+ApCvUafzlwDpfFQp83yOszY/zF1fd5ONrB/ZHEwu99JVrXyVahaRperxePx4PL5UIIsUSkU99NK/NgW5LLuTSzt3Dd60Lc0UJNS2DWdZ3ZfIUXt1iw89ibixi1G5JVy0aD5irt/H1SZqH2JtgfBXa/ys6UkgciCYr1GhXToLZC+IRCobjzuHPv9gqFQrGDtCYnhjTJ1lZvPUq4vVg1ndFUYcNiXcRj53ceaxqMP7KvDSklddOk1KjjsiiPIIViM0jZDBt4cyjFeGZziaSvD6ax6RpPHz6ERJDP5zHNZmXczVTH3Sy9fX1YLRbS1TJvjY9wOZfCplno9vg5GY7h0x18451RZvJV0sXdaXldC5tFY1/Uy2BhbkeOdzQQRRcaX397Z0MXbhYBhO1Oej1+hgtZnmrvRgAei5XfO3iq2UwtBA+0JaibJjXT4P25aXo8ATxW24qindVqxWq1LjH9v1lsNhvJZHJJoMTivxUr0xQ1TZ5p7+GvBj+46f3UTZNio4bbsrOp0juJrusUi0VCbjcxn4Op3M152HnsFkJuG36XlXy5wWyhykN9YaSUFOo1wnYnz3f0M5DPUG7Um9ewMX59B/Ur4Hhii97VraEJQdjh4pf7jgLw9aELZGsVfFa7+uwpFHcwSrBTKBSKLUQg+PV99/DDsQEGC5klzx0PRvlIsim89USaybNrVXt4HRYMU/Lpk0kAfjB6FY/VxrupKSpmgwO+MJ/o2r9t70WhuBNpVQKd7gnz9vAcE9nNTQRfvjLLY/sj1KsVJid3Nl3VarWSTCbRdZ1vDV/kav664FUxmqJNvzeI22Hn4mR+R89tM7R57eia2LEFh4P+MIYpmcnvPfFyJUwT2l1ePttzuNkuDeTzeVKpFKFQiEqlQqPRwOv1IoTAabdzTyhG3TCoGg0cumXZBF5KicvlIpvN3tQ5tT43Xq+XWCyGEEKJBDeBJjQSLi92Tadq3nyFVKpSxuW23rG/AyEExWIRh8PB7zzWy//5w4tU6hurWA66bfzSvR3EvA6sluVJr1JKpitFDGnyyY79tDnctDnc14XuxiLBrnF1K97OlrFYjG9Ikyu5Oe6LtGNIE4G47op6h14XCsXdiBLsFAqFYgvRRHMS+tmeQ5QadSpGg2K9htNiJeJw0TAMJsbHiUQiPHesneeOtWOaJufGc3z97VF0TbA/5uVQ3MeJzgDQ9Np6YWKIs5mlFTwPR5OY89VCCoVi4wghMEzJM4dj/PkvhoGmR9h/93APYY+dS1N5/v7MOPf3hCjVGpy7weus0jCx6TvbIhWPx/F4vdRNkx+MDSwR6xYzVMjQ6w1wJOHbsEfbic4APWE3fqcVr8NCuW7y8uUZLk1tj+g3NlfmJ+enePpwjEejnbwyvX2Vb4Jm5bNFE4TdNlJ7sOLwRiyaIJPJkM/nsdvt2Gw25ubmqNfrTE1dT44tFq973LlcLsLhMA6Hg3S1TGgFXzufz7dpwc5qtdLe3o7d3vRmvLH1VbF5hBB0enxcyd18hWmqWibp9qKvZbR7GyOlxGazMTY2RmdnJ3/45D7+rx9dWvd1Fk3wK/d3EfbYKJdK5KpVKpUK5XIZh8OB0+nE4/HQ5nDzBwfvBWBqaopisbjgmYcWBHP+cyaLYEw1Ayj2yDVvSJNL2TSTpTyVRgOv1YbbYmMgP4cpJfdG4nitdjU+VCjuEJRgp1AoFFtMa4DkslhxWawEbI6Fx7KZDFarlbGxMcLhMLquY7fbOZb08/W3R9kf9fDF+7sAyNWqaELwjaHzzFSXtu55rTbCi9IOFQrF5tA1QW+bh//l44cRND+3+nzq4umeEPd2B5s+cNJkOn9lSXXWcKrI8WQAXdeXB0tsEZqmEQqFKJfLeDwefD4fZ+emeWnyGhVj9ZCGs3MzHAtG+ey9HcwVB9atIPzSg13sj/mQUlIxGuTrNdpcdn71wW6qDYMzIxm+8/7Eps+/I+gkXaxRql3/+fgcFn7toR7cDgt/995Y8zHr9rb1+W12km4f749mbguxzuewoOvagshQLm+sbbtUKlEul+nq6loQ6270lHM6netes7quo2kaNpsNt9uNz+dbVk2nxLpbw5AmcafnlgS7bK2yqJ7qzsTr9TI7O8vMzAyxWIw2r42ZfI2Yz4HNoiGAuVKNfOX6/fBj9ySIeO1MjI8vEbSh+RkplUqkUiksFguxWAzDMMjlmgsbo6Oj9Pf3Iuz3Qvm7119YvwT2CHvBxw6anRyHAxEOByILj0kpOTs3jSYEP50YQiB4Kt6Ne5UWeYVCcfugBDuFQqHYZlqDJSkloVAIgGg0Si6Xo1qtLqT3PX+sHZ+zeVv+r5ffI1VdfaIWtG1f4p9CcbfQ+mxWTQPPDcJR6zmBIOC0LxHsXro4w/FkgGQyydjY2LaIdj29vVgWVfFdzqb5wdjAuq9rSJNvDF/g1/qP86UHu/nff3Bx1W01DfqjXt5NTfLC5PBCKIUmBH2eAMdCUe7vDRP1OrgyU+BkZ4Cg20amVOPFizOcGc0s7OtAzMunTibQNYGuCazz594wTUbTZa6lijyyP4JEYpiSz57qwJSSw8E2DgUivDA5zDuprW8xLjcaSCmx6stb4/YiTx+KIaWkVNp8KIGUkpGREaLRKN75akyLpi2ZsMtVgkfa29vxeDxLxDhVSbc9aAhiTs8t7aNiGHe0ECOEQNd14vH4gj/o4XY/dkuB3328f2G76VyFf//CFQCCLhunuoLkcrllYt2NNBoNxsbGljxmmiamaaJrwRs2HgHH3hDrbqyaW/wZfa6j+XMxTJP/dvUMJaOx7HtNoVDcfijBTqFQKHaIGysU/H7/kucf7AsDUGrU1xTroOmDo1Aobg0pJTZdx6brNEyTiUyF774/zqnuIHGfk1rD5Dvvjy8LbkgVa3zznTE+fTJJKBTa8sAJn8+HZd6nriFNyo36ptJUi4065zOz3BuOc39PiPOTOQqV5VV593WF0ITgQnZ2SYKsKSVX8nNcyc/xSLSDh6IddEfclBp13klNcsAX4rP3dvDR43GGUyWcVgsdISeZWoXxQh6BYCA/hy4EXR4/B0NheiJuJkp5/n7kCk7dwpf6jzGVq2BKSAScsE0Jtg+2JZHAzy7tXCjIrXAk4aNYLNJorF5FuRamaTI5OYnVasVut5OtVfAvqvJ2Op0LYoYQArvdTnt7OxbLct87JdZtD0IIYk73Le3DlNuXQL1XEELg9XpJpZpev06rjs953fdSSkmmfP3eXKg2xfnVROmNoAkB5g1t48IG0oQ9MO5qfY5/NnkNq6bxULQDuN6qLpv/w+d6DlOo19VnWKG4A1CCnUKhUOwSKw2kLmVTfGfk8rqvzdRuLjFNoVBcRwjBW0Mpwh47Fl2jzWvnNx7uwaQ5GTRN+O1HexFCUK41GJwt8tLFGaI+O7WGQanWWPD22krcbjeGNBksZJYIaZthslxA0mwR+9g9CeqGyQsXpnj1anPya7NofORonNlKiYlSYdX9vDo9ytupCayaTqlRx5CSlyaH6fMGORWOsy/qpSFNzqSneGlymMYN53shm+In40NEnU3BTgK5epV301OcDMUW7oNxlxfSUyucwa1xX6QdIQRHkj7Gs5tLBd5pHt0XxmrRmcpkbnlfExMTdHd345/3spKy6ecXi8WoVqvour5Q3a0q6XYeh24h5nQzVV67Emw1DgciGNJE3wMi0nZjmiZSSgIuG7OF6sL1akroDLr4hx/aR92QvDaQIlOq43LcQgeC0FYQ7NyASdPpdPeRUvJwtIMrufSSijshrjdJuy02vNat/25SKBQ7jxLsFAqFYo9gSpNeb4B9vhCXcuk1t+1we3forDbO9UG0MjpW3B6YUnJfT3ihpbVUKi1cx63Jj9PpBCEoS5P7e8Pc1xNacn0bhkY8HiebzS74jQUCAYLz7e8T4+NUKhsX2L1eL26Ph8F5A/Gb5XIuzZUPXidsdxJ1unkm0cvjB6K47VauzuRx2yxYdY2fDA+y3lEqhkFlUduvBK7m51YNvriRhjQZLy0NsHhlaoRDvjBOa7Ni5pA/zGvTo6S3eDHi7dQkfd4AD/WFeenSDLXGzlcm2Swav/ZgN+W6wS+uphhKLRdpPna8ndM9Tc/Cm2mHvZFGo0GtVsPpdKIBxWoDt92CplnQbwhMUWLdzmNKyROxbv566INNvzZoc9DrDa6/4R2AlJJGo0E+n2d/1MNEtowpQRdNH1KnzYLTZsGUks/e26w2a9yERYGmNe/jQmhg3hDWo91aNeRWI4RAAw4FIqtWE7a+o1rjsbpp0DDNFROkFQrF3kYJdgqFQrFHaLa5Sj7RdYCvDn7AaHH1hMf9vvCeqopotWJ87/1xTveECLvtaNp17z5Qk0LF3qN1RU5OTq7qedTf30+pUefPrpzBa7Wx3xdCFxpT5QIf7diH22rD5/Ph8XopFYs4nA4suoXZSgmHbiGRTDJw9eqGzsftdhOLx5ipFPnu6MZesxYSmK2Wma2Webq9F4dV58G+EI/sizCeaYqL2Xr1lo9zM9RMg/90+R2+1HeMsMOFEIJTkXZ+PD64pcd5cXKYgfwcX+g9QkfAycDszVU03QxBl43ff7IPp/X6cDvisfNvf7K0ivrpQ02vwGw2y/T09JYcOxwO43Q6+eEHk7w3kuFIu4+P3ZMAtudebEqJRKIh1L1+A2jzSbG9ngCDhcymXns8FL1rFsZa44dMJoPP5+NDh2IrLmQsFqiEEFgslg21ldtsNqLRKE6nHRDI6huI+g0iqvDAHgv40Da4ONraDuCrgx/wfMc+2hyuu+LaUSjuFJRgp1AoFHuI1uDqk50H+ObwBSbKK7equSx75/ZtmpK6adIwJB89nljynJQSwzCw7KHzVShatHx/2traaDQaVKtN8cpiseBwOPD6fOi6TnVe1MrXa7y9KBjhz66cIenyMlEu8KH2Hnq9AaqmwbeHzjNUyNLh9vHLvUcWwinWI97eTrpa4W+GzlMztybI4pA/TJ83iE3XyefzTExMkEgkaPc3q0bKN+mVthXUTZNsrUrQ5kAIsW2JsQ2zWVXXEXLtqGBnmOaCWPfDsQEej3WRK9eXbGPRNB7pj1AsFpma2pqWYIvFQiAY4tJUnlevzPKbj/TSE3Fvi8jTWqwpNmp4rfZmm+YeEzf2KqaUPJ3o4c+unKFubrzy06lb162KvVNo+f0urn5e6xrWhEACoVBoTfFb13U6Ozqw2qxAA1F5EaovgZm5YUsL6FH2SjvsYm78Oay2iNvcTuPRWCcXMrO0xbt26AwVCsVWoGZQCoVCscfQhMCu6/xy7xF+OjnMdLnA5CKfG5/VTsTh2sUzbGKaEk0TvDsy16yO6Vq5RUcIQbFYxO1uCgR7qTJQoRBCYLVa6e7uplqtMjMzQ3t7O7quY5gmk6UC3xm9suJry0aDK/NtoX837z0pYGEyPVrM8YvpUR6KdhAOhxfM01dC0zR0TeOd1OSS9tNbYb8vxMc699MwjQWxDmB8fJzOzk4sdhuNXTSvPxWK0esNUCwWcTgc2PTtGZZOV4pMlgs8dTDKRKbC5en8+i/aAnKVBhcmcuyPebiUTfGRZB+zhaUVjZ+9N4mmiS0NLrFYLOia4J3hprXCG4MpusNOmlLGLaRdSgPE9dcbpslUpchYMc/rM2N8oms/XW7/GjtQLEYTAq/VztPtvXx/7NYrau9UQqEQHk8zVXcj44eWyJdOp1etsvN4PNjsdqidheKXQa7gb6kFwPO7oCdgl8csa73v1nO5ehWbpuO0WJdtownBPl+I2UpJVdcpFLcZSrBTKBSKPUhrlfjDiV4A5qplaqaBQHA+M7vjRtOGKdHnW1wrdQNNCPKVOn/33jgBt5VP3nO9sm5xC2y9XiebzZLJZHA6nQghCAQCuFwuJdop9gyta9Fms9HR0YGUkq8NfsB4sUCDzQlaN1a+tD6rtVpt+caLSCQSGNJkZJVW+COBCHGnB10IdNEU93QhMKSk0mhwwB+iahhk61UapolE0un202g0GBgYWLa/arWK0+nEZ7WT24W22EejnTzQlqBcLjMxMUFfXx+1LRIqF+O2WPlM90HaHM0qvqMJ344JdtAUtXRNw6Jp1E1jScolQH+bh0KhsO71sRkqlQqmlHzsngQfOWIS8sybz9cHwNKz8bTLlpgrtOa/a++A7V4QGt8avshwIbtE8A3MV0oqNo4mBEcCEV6ZGqHQ2Ng1INhrDZrbi67rSxb8NkpbW9vCIkULTdMIhUJYLJbmvqwHEMKxXLCznQTXF0A4dj0ddiOVsdcKWb42dJ4PtfdwKhwHlop8c9UyQbtzIVVWoVDcPijBTqFQKPY4V6fzhD123JoFXdd4fL6dYScq1VrHODeeZTRdolBtcH7iuqBwb1eAT5xIUqlUmJycpLe3mag5NzdHNptdMgltGakXi0VisRg+n++mz//GQbsQAtM00TRtyXkrFJuh1SIrhCBkd3JtDR/JjeKdb/MMh8Pk88uFIpvNRq1Ww2a3czmbXjUB+qn2HmyahmlKQLb+a07eNY16rUbA4cBntVOqNQMGDMNYtRV3dnYWn9/PQX+YN2bHb/l9boZ93hAPtCUoFApMTk422yqlJGjb2lTDLrefz3bvRxMGovoa0naCjtDOVSc/caCNwwk/V3Jpio06c9UKnSEXNotG2G3jNx7pxW7VSc+untJ7sxTyeex2O3abQEoTam8iSt+AwP8HpG25CCElYADiehWdOQPVN6BxFYxpkEUwxpDOT6wYOKLdVTLS1iGBI8EIr8/s7OfwdqAVANT6ft/o97oQAq/Xi2EYFItFDMMgEongdNoRQm9+JmQFZB7EovuO3g3uXwJLd1Ok3gMpvOuJdZJma/0vdR+i23O9wrX1szKlJGh3bucpKhSKbUQJdgqFQrFHaQ22Xrw0w0i6KXbZLBofP96O32WjO7z9yWVCCH5yfoqfXV65Xetj9ySoVCqMjIwsSR7M5/NrVozMzc3h9y9vnWqlda42MJdSUi6XMef9flptMgBSwGB+jkytyslQbGNvUKG4gZZod08oxrvpW/cU++nEEO0uD16rnWg0yuzsLKZp0t7ejsvtQdcE+XweTQjCDiceq41CfflnxyI0ioXisoqRxbS1tREIBHhvNMOj+9ooFAoLvnw34na70ebbqHaaR2MdGIax5L2kUilisRjHAm2czdx6e6hDszTFOjmHyP1HMKcRxhRhzy/xy6c7+eqbIwvbasDHTyToDLnwOqy8PpDipxc3Hv7wSH+YTLnOB+PXBd7+qJsnD0YZzM8ttEu/PjPGRzv38c+fPUi2VMeqwcjIyEK68FYyOdn0WoxEIoRCIai+2awiyv8H8P6DpmgH89VzDWgMgjEBZrbp42WMgTG5+gFuIOJw4dkmD8I7HQEcD0bXFewsQuPeSJy4y7Pmdnci+UoDj8O6UOm/EaSU+P1+AoHAdfGt8ipUfoww0yyth9bB/UWwP9Bs/YY9IdZtpLpOAAmXF0NKTKDSqOG2ND+Lcv71rYUoQ5o73qGhUChuDSXYKRQKxR6n1jB5fH8bfqeV756d4BvvNCtmPnwkxqP72rb9+G/N+yDdSGfQia5ppNPN59vb2xcGhR6Ph0pl5UohaLYHXr16dWH1vBVOIaVE0zTi8Tgej2dJe225XGZ0dHRJdV1XVxd2ux0x3ybY6w2Sr1cxkcr4XHHTCCGIOFw8Guvk9ZmxZYbwbXYnH+3cj2Pecy1dLfO1ofMr7qtsNLiUTXN/W4JAIEAgEFj4nIymS3SEXHi9XmoNg5Ddye/sP8lfXD3LbLWZMuu12ig26lSNxrrhLTMzMzidTu7vCXF5Kk9/1E+9Xmdubnk1lM3WnNBNl3cuhAGaQlrI7iSVSqHrOsFgkFqthq7rmKbJh9p7tkSw+1zvITTBglgHNE3lNT+HE8/wSH+JV682PQU/d7qTIwk/s5USBaPKo/sjXJ7OMzq3vpD2Pz17EI/DipSS4Z4iX/7FEABfON1Ftlbl70euLKQ0XsqlyV49x6/0HaXNayeXy22LWLeY2dlZAgEfwv15RPZfQWMIsv872I41N2iMQ2MAqK+1m3U5GYqp++5NIoTAb3MQd7qX+NXeyKOxTk6F40jkXfVTllLitFk2JdbBDWnIrbZuaz9UfspSsU4Dz2+D9cj8trfg87iFLBbr1uoaaD2uz/89kJvjeChG3Wzal+jz7pXNVuq76cpRKO4MlGCnUCgUexjDlPyDp/Yt/H+mVOPlK7MA/OiDKewWndM9oW07frZUo1Rb2VdqX9QLXPfCcrmut5qFQiGy2Sz1+uqTQGMVvyrTNBkfH8fhcGC327HZbJTLZQqF5W1jExMTdHV1NSf7stkq6LVubVud4u5ESsn9kQT93iBfHfyAitE0L//tfScIOpxU6gYj6RKagP6on092HuBvRy6tuC+P1YphmvzJy4PEfA6EgJphcm4sR3vAQdht5+xYFpdV55985ACf7j7AtUKWY8HodUFbyg1VfJRKJXz+AH/5+jW+eH8n+2LNBNIbK159Ph9Vo7HwvnaKe0LN91QoFAiHw0sqbVtivAVt096BizkeaCPqcCPK374u1rUo/y3S8RhdYTevXk3hsmkcavdxJj3Fj8YHceoWfq3/OL/xSC//7qeXOZrwEXbbKdcNLk7luZZqVjsfjHv58OE4HoeV749exWO18XC0g//5o0cAia5pfHf48rJQj6n5AIyk20exuDNi6cTEFIlEO9L1OUTpL5utrpWfbukxejwBVblzC5hScsAXXlWwC9udnArH5wWcZpq83ED11Z3A+YkcRxJbEGYiNNDawPkcFP98/kENPL/RFOv20PV7Y2XdRlqBpZS8NTvBS1PXCDtcxJ2ehX20/r7zrxaF4s5DCXYKhUKxh7lxRfmZI3EOxn38+PwUQ6kiZ0YzGxbsFq/QLq5cW2v7THl1we1Ud7MyptFoYJrmEg850zRXTWfbKJVKZc0qPYB6vc7Vq1fRdR2LxUIgEMDv9ysPO8UtI0SzFiFkd/L5nsN8ZeAs94XbCTqc/OiDSd4byVCoNq/x54+1c39PiIDNsaIH3WSpyCF/BLfdwtvXlla7jc2VGZuv5CrUGnztzRE+eSLB0WCUwZkiZ8Yy9EbcHGr3YdE2PqE0peTH56c4EPfhcDiWCHYdHR1YLBa+MXyR8g4Kdg7NwsPRDmq1GrVaDa/XS8Vo8F+vvEelcSsSXRONptffPaE2MEag8sIKG4UAG7lKcwHggd4ImhC8Ndtszy0bDf5m6Dxf6j/GP3nmwJL7yMP9EXLlOhIIuGzUTYOXp65xbr4icCif4aFokmKjzpVcelXxpTV53u7quhbFYpFcLo/f/xCYc1D5/s3vTCxfEPFabfi22H/wbkMAB/1hXpq6tuLzJ8PxZWKLJsSGWiZvZ+qGyQ8/mNwawQ6aolzt/fl/e8Dz62A5sOspsIvZaGXdjYj5sLIut4+Ey7viNq1KO1OaaHtIoFQoFKujBDuFQqG4zUgEnPzmo738/ZlxGkZzijucz9DtDSzbVkqJpDmwv5hNMVstcTIUZ6SYJe70ELQ7lw0IF9r15sr8YiC14jm0+x14HVbq9Tp+v38hCTYYDALNKp/NpLndKoZhYBgGU1NTFAoFksnkjh1bcWejCUGbw8UXe48SdbgZnSvxynyVa4sXL03zYF+YB9sSfH9seSLre3NTPBRN8sSBNi5NrZ1Qemkqz//+g4tLXz+S4chUni+c7sJqta5ZudoUzpuf50ypTrVuEAqHyeWa/mrxeByXy8WLE8MMFTIb+RFsGfdF2tE1jZHxplfX9PQ08XicL/Ye5StXz1ExNy4e9nuDPBRNoguNmmnQME2SLje6ZkHWPkCUvsbyzF7Ash8hBC9faopsh9u95GpV5hYJrXO1Cn925Qz7fCFmKiXGi3ncVitPxLpwWZopr9lCiRcnrzFbKS28bqpS5FvXVq6yXMx4qUC7y7uj98ipqSksFgtu90eR1kOI2mtQe295OuZaaGGk40Nkb6jW7PUE1CLJLSKEwGuzE3O4mapcF3otQtDvCxGxO5f8fDUh+ObwRR6JdhBxuO5I0c4wJZen8mTLdRqGiUXXbv06kybUL4ClHzy/BcK1x8S660LazYix97clOBmOrfnaK7k0XquNNof7jrxuFIo7DSXYKRQKxW2GpjVb5B7f38ZfvDYMgNtqo2o0sM97ahnSRENQMRq8OTvBYH6Oz/Yc4lAgAjSrhr517SJJl48PJ3oXBnetwfB3zozzxtDK3nUAqWKNgZkCyaCTSCRCNptlbm4Om82GYRhkMplt/zmsRisRbnEIhkJxKwghiDndXJ7K87W3Rpc9X60bNAyTkG3lJD5TSi7n0hz237zn5GS2KSh5PJ4VPela1Ot1NCFw2XRKNYPXBlI8sj+y8LzL7eZyNsVbqdXDK7aLA/7QQnUdsJCaG4/H+e8P38fbqUlenBze0L6eTfbitNiQjXEQThAOaFyA8vcQxvLf0QJGUyz8vSf6KFQNIl4H76SWhyvk67Ulj+frNf5+9MpG3+qajJVy3Ec7mqYtBOjsBGNjY/NtyB3orh5wfR7qZxHVN5oiBivbFDSxIj2/jZQ6fzV4dskzTV811W53q5hS8kisk29fu9hsgQeeTfZzKBDBuKG1utyoM5CfY7JU4LcOnMCm6Xec+KJrgstTeaSEf//CFYIuGw/2helr82zaz24J3j8ASy8g94xfHbQCIrRF/978e5RSogtt2WtbYztNCMZLeWYrJX6p5/CWnLdCodhelGCnUCgUtyk+p5Uj7T4AsrUKXx38AF0I+rxBog436WqZDzIzRJ1uPpzsw2O5nuDX5nDzsY79/N3IJX44PsCzyX4ApIRvvDPC+6PZNY9da5j8t58PcbIrwKdPdhAMBpmbm2N8fO2Uu52iXC4vJMiqyg/FViCE4IOJHHVjucBiSnh/NMup7iB/ePAUDt3K341c4mo+AzQTNDvdPqy6RsxrZyq/+WTWuWKNumHidDrXFOxaqbAfO97O194abRaPzBdy2Ww2LLrOldzqr98ObJrGr/QdI2h3MjOzNFAin89TqVSIxWKcCEU3JNidDMWw6zrSmELk/n+bOxljBApfweP+Il6HjUytwqvTI+u/bguwahoHfGG6PX5MKXdUrGuRSqVIpVLYbDYikQgu1zGE7STSLCFqbzXTZI3lvwPp/hLoCb4zcoVi43qFZ4fbR9jhWra9YvNoQtDt8fOprgN8+9olAjYHB/1hgCX+gIY0GZy/t5SMOj8ZH+Rjnft345S3nZDbjiYE6WKNdLFGqdZgf2zlds+NIcDSN//PvdUSuniccrNjlpbnKbBkIdaUkv9y6R3KjQYNaeKZrxRWKBR7HyXYKRQKxW2IEALDlDxxMApAvy/EH3gCjBZzfJCZ4ccTgwDcF27nyfZuTGkua6eJOt08m+znb69dQiYkV2cKfO3NEaqNjU8i372W4aHeMG3h8Joiwk4zNTVFNpslFothsVg25NmnUKyFYZrEfI5Vn08Gm9V1dqGjaxof69jPvz//Fg1MTgRjBO1OXr4yc1NiHTQ1t0ypht9uW3O7Wq1GPp/nULwp5muLKlFaVaf5Rm3F124XH+vcT9juZHJycqE1dzH1ep10Ok1HRwf3BGOcmZtadV8fna84ojGEKH755k6o9hrCTGO6f4OAzcunug7w3ZGrlIxbS0pdj+eS/RzwhzGlpFQs7opg16JWqy0ssLjdbkKhEA7HIwjH40hjFlF9HRqDgADrYYT9FG/MjHEpt7Ty+t5QHEOaKnBii2iKdgE+030Qt8WGCdxYA6YLjeHC9UW1C9kU90Xa78gWx8cPtBH12fnrN0cwTMlEtsJIukQy6Ly597pHfz5bsbDYGucU6jXeSk3wZLwbgJlKiddmxsjXr9/3C/PJ462uDIVCsXdRn1KFQqG4TWm1hLQGehZNo9vjp8vjx2u105AmTkvzNr+aufD7c9NEnW5mKiUSAeemxLoW45kyMb9zx9u71kIIQSweRwiN777fnJR+6FAMq67dWiuN4q5FE4KTnUFeujRDpb60dfBAzEub1061WmV4eBi73U53dzdPJrpJV8t0un00TJMff7C6ELUecZ+DNq+DbHbt6ldoCnOl+XPUF00CW8nM1h0WVzpcPgqFwopiXYtSqUStVuOhaJIzc1Mc8ofp8wb58fgAYbubeyNxutxeHBYbVF6E0jdZ0aNuozQuo2X/X+B4lk738/zG/nv48tX3l0xqt5IOl5cD/jCNRoOZmZmFduC9QLFYXEisDQaD+P1+rM7nEYuuk4lSnp9NLa1EDNoc9PuCaiFki9GEoMvtR7J6W+SNLbKvz4zzya4DO3F6O87+mJcvnO7kq29cw5RwdaZAIuC8o3qwb/UzZEqTQr3O67PjfDA3w0F/mPFSnp9ODDGzyGdzMelqmbjToz6/CsUeRwl2CoVCcZtzYxuFKU0ej3cB8O/Pv8kDbSsHMBjS5MG2JCF7szLozMjNVch1hFyYprmjBurrEYlE0ITGf/zZ1QXvr4uTeX7v8T6cNosS7RSbRgiB3aLxGw/38F9fHVwQt6NeO58/3Um90WB4uNlK2AqFaHd6OBGKAVCp31oa65MHoximydTU2qKfEAKH08mlyaYgpGkCydIKU3krQtcmOR1OYNN1ZtYQ61pUq1U8Hg+/te8EQbsNIXQO+kMIoSGlAY2rUHgDam9s3QlWfoBWv4jL/z+yzxvinfRyP7utQAJz1TJeq514PI7dbmd2dnbd1+00c3NzzM3NoWkawWCQcDjMbLnIVwbOLdv2gbak8q7bJpop1Sv/ZE0pl6XyXsmlydQq+Kz2O6bKbrHv2oGYl8+c6uDrb48yPldS3+E3oAmNH4xdpd3l5eFoB6cj7Qgh+GjHPr597SKZ2vLK7nS1QtTpWVbBqVAo9hZKsFMoFIo7jMWtSb9/8F5g5XYLXWgEbQ5MKfnWO6OcWce3bjWGUyXavA46OjqYmppC13V0XUfTNOr1OpVKZcfFPCEEmibwOawLgl22XOdPXx3k95/oR9fUEFWxeTRNEPM5+N3H+/ju+xMMzhZ5ZF8bGjA4fN33yzRNKpUKUad74bHvn735kAe3XedQu2/NCrUWut40n5/MNa97XYiFQjRNa94bGttUCdvr8RN1eriUTS2krp4Kx6hUKgsVXKths9nweNwIWSRoyUBtEqqvI2zHoDGAqF8AWVlzHzeNMYxp5HmqvRuLpvHG7NZ7cY6V8vzJ5ffQEHy0o58DwWa13W4G9KyF0+kkFAqRqZb5s6vvL3veZ7VzOBC5Y8Sh2wkpJT7rUsFOArOV0rLHb2duXIw83hHg8nSey1N5qnUDq64tafm/WzGlpG4afDjZh89qR84vyQggbHfycLST784H5liExme6D1Js1MnXq6Akd4Viz6MEO4VCobiDscxP0FdreRBCgJT4nWv7Yq3F358ZZzZf5bljcXp6epY9b5om4+PjlEort2VsNW63e0GYePxAG5emrreepf7/7P1nlCTZdeB5/p+ZudYitE4tSleWBgpaaxAgQXQ3GpxpDqfZ07s7fc7szJmzM7P7YebM7BnV073NZrOpQJAAm02CIAlZBQIoAKVQOiuzUoXM0NLDtTJ7+8EjIiMyQ4eHzPs7SGSGh7nZ86hw82fX7rs3W2YqU6I9JkXSxfYYhiIR8PCVJ3vom8rSmfBTKhbvWA5+8+ZNfD4f1WqV7u5u2mIB3ri5vaB4R7wW+NtMnchqtYrjOLREvEvjvTX22vuiousfsPtQ6zHuiTWglOKJxnZemx6jJRAi6HIzO7t2x+lFzc3NQBnm/0eULtz6RvVa3ce6GmP+f0CH/gve3XyMqNvLM6N9u3IcB813h28Q9XhJJBLkcrmljMyDwufz0dLaSq5a4Y+uv7nqNo80tO7xqMQiQ6k7Muyg1v39KAdQHUfzUGeci8PzfO2FAf7xkz1YikP/mm3HAcWO6kB6TAvXUqfglT+PV6dv3Sz6ZOdJOhea3kwUcmtmcQohDg4J2AkhhOC9Zxp5dXCWfNneeONVvNQ/w2yuTMhrMl+oMJevkC5UOdEY4JP3t9HS0sLQ0NCuX5j6fD7a2tqo2A5Xx9N8f5Wspksj8/hcJomgRzrIim1ZDIJ1JWqBtNWC0Vpr8vk8oVCto+HgzPoZZuvpjPup2s5SB9iN5PN5jiVrY8sUK0uBusWmE/XMsOsJRnikoY32QJhUKsXc3ByNjY1caGjFcRzm5uY2DNi53W48Hjeq+ENYHqzbUw4q83+B/4vcE3uSgl3h5xO70z1WAz8bH+IzXafp6u5memrqwGTaeTwe2traKNlV/uj6G6z2mxL3+Lg31njoAyWHlVKK9kAYv+Uiv9Cx11IGkSOUXbcaw1B0JwP8d586T8V2uDw6TyzgpiXiw2WqQ/lZ7mhNtlrmZi7NPbHGbe1j8X242vtRw0ImHXgMk+5gFKiF9Fr8wW0dTwixtyRgJ4QQd7larRxoifjoncpuez/XJ+8son5lPMNo6gb//AOnaGtrI5vNYprmUvF7r9eLbdvMzMxQLu+82LvL5QLgT14Y4Obs6hl9L/bN8GLfDI8dS/DRe1okaCe2zTQUWmuSySRKKWZmZu7YxuVyobXmsWMJShWb65Nbf491xP1oZ/PB9FQqRXt7kCePJxicyfH0qUba29uXAnef7jyFvbBM3VAK23H48dgAw/mtNULwGhaf6TqDopb9NzU1BcDIyAg+n49SqbRhIxrDMOjsbAddqjWT2G/5v0AZjTycPM6r02MU7J3VHlzLUC7N7119nc91naYhmTgQATu32017eztV7fDH19+kvMZ/u/e3dC9bdCf2g6UMnm7q5OXpEToDER5KtNw1n2OGUngskwc6Y2RLFf7PZ67y0XtbuLctcuh+BoZSfH+4l5F8hny1smbN4e1SwEOJFn4xeZOSY3M9PcuJcKyWWSdzHyEOBenBLoQQAsfRNC8soau3dLHKN18exOVyEY1G8QWCRKJRItEoVWURDAZpbNzeneXbJRsayJWqTKY3rnX1Ut8Mf/nqTbTmjqYZWuulr5f/W4jbLV7wxONxOjs7cbtXLi+fnZ1lbm6OlrCHX3+siwc6ols+xmy2jLFQF3IzKpUKtm3zgbNN/KMnetBa4/P58HhqGTgxj4+k10/S6yfu8ZHw+vmV7rP0hLY2to+0H0MB/f39S8G6RYVCYVNdo4PBIIZhobL/fh+z626T/yYKxQdaewhYrl07TNGu8pOxQSzDrNs5cLtcLhft7e04wNeuv0V+jUDl8VCMzmBkR8v3xM4ZSnEu1sBXTz7A+1q6CbuPTrOJjThaM5hN8b2bNwh6XHz03mZifvfSudg5JJ/XWmtenxljZOFGydX5maXH6+mxxja+cuI+Tobj9GXmMJWBoQ5nRqIQdyPJsBNCCIFSUK7uTiF6gN6pHP/z99654xhey+D/+bGzdatvZ1ereF1uWqI+BqY3XoL49sg8n7q/FW1XsW0bt9uNZVkUCgWy2Sy2bePxeAgEArhcbgxDYTtaOtSJOyil8Hg8dHV1MT09vaLe3PT0NDMzM7S3t/OpB9p442ZqS/t+7vok97ZHaGxsZHx89S6mhmEQDAaJRCL4fL4VF30bXZgZSuEAn+k8zbcGrzCY3bjWntew6AlFSafTVKvbz0JbCnDa9W/0sG3ODEbpRU6EH+NYKMYLk8O70ogCYKyQ5fr8LMfCUebn5ze97LmeLMuivb0dZRj86Y2LpKurZztbSvH+lm4cre+a4NBhsJglfzcpVKtcmZ/mQ23HuK89BsCLk8OM5DN8tus0jj64te0Ws/rfmJ3gx2O3mhVNFfM8Nz7Iw8kWApYbRzsYOwyML91Q8vj4VOcpHK3l/SvEISMBOyGEECileHtkewXxN2u1gOCXH+8CIJPZ2lK8tQwODtJz7Bi//mgXrw7W6mY9d22SYmXtYKTLNJieSy8FWAzDWJEZlMlksCwLj8fDs5fHaY36ONkUwjLkDrVYSanaEtmGhgY8Hg8TExMrMjXT6TRNPh9Bt0W2fGeQK+S1SAQ8RP0uTEPhaBiayTGTLXN5NM3p5tCqx4zFYsTj8Tu6Km6FoRSO1ryrqWNTAbt3N3eiUJtqKLGexbp66N1Zero+BSoMer72t+dxMJPgzEK1D6M6hOH/OO9u7mQoO89Ecft1CNdiKoXLqGW8dHV1kclkGBvbfkfhrTIMg/b2dkzT5Jv9l5gpr53l+GhDG0GXW857ou70QiBJKbVhMEmjyVTKaOAv+i9jKMV0MU9poWzA3w5d47NdZ/Zg1Fu3GKx7bXqMn4wP3vH9V6bHeG16nGPhKOeiDRwL1YKROw2wLT5fsfXPBiHE/pKAnRBCCKBWJ2t5R9W90Br1kUql6tqMYnBggO7uHh7piWOgiAfcfPPloTu2e6Q7ztOnal0ti8VbS2hvX8ZnWRaBYIg3hub4xY3p2mOG4ulTDbz7VKPcrRYrLF4MhUIhXC4XY2NjSxloXq+Xqu0sBessU3FPa4SHu+M0hb24zFvZFIuBPqUUmWIFayGrMxKJkMvlME2TYDBIOBzGsqwVx94uQymafEHaA2GGc+l1tz0ZjlEoFHb83s3n80QiEbDaoDqwtSebHWj/50BZqOLPofw6sJnxuMBzAe19P8psQFeugxEGI0nFdnC5FUrdmiLb2uFjHSf4+o2LVOvYYddSik93nqYzGOG5a5Pc2x4j5N2d0gRQC841NDTgcruXlsRZloUyDL41cIXxwtoBya5ghMca2uRiX9TND94eo28qS8TnIup3E/XX/j7bEgbW6W6P4t54I69MjzJWuLMmaF8mxVuzE9xzwBqjLJ8rvDM/vfZ2aGJuHxG3h6rjYG2yFMJmyPtXiMNHAnZCCCGwHU3nPgTsDKXq3jnW6/XWlvg6NqDwusxVt/vYvbUi3ePj4xQKa2eVBINBTEPxYt+thgJVR/P3VyYZnS/y+Qfb0QayTFasoJTC6/XS09NDOl3L4LQsi3y5SlvMxwMdUe5ti+K2DDR3ZlAsv7AKeWt11LTWNDY2Ln1veVCvXhzt8PH2E3y99+JSB0pDKYKWG43mVDjBuWgSj2kxkb6zycZWLQYbtzwldT+GDnyJqmNTqTr4g19GO59DFb4PpZ/B7f1NXfejA58HewbMJlB+MsUyA9NznGnpRjvwnTdGubiQaex1GYS8Fm7LJOC2+LVHO3lvSxfPjvbXMuKoZQKdiSbJVspcT28t09BSBp/rOkNbIMQzl8Z5sW+GzniAqM+Pz+ejUqngOM6m6gBuhsfjobW1FdOySFdqzUCqjqZaKvPS5DCDubWzKoOWm090nJQ2E6JuHEfz/jNN3JzN39GI58HOGJ9+YO3mC4ZSuA2TR5KtPDdx5804qHVhPh9r4CD8xi5m1f2yf4aZbJmP39fKXGn9ep2NvgBJj18CbEIICdgJIYSo1bDLrbJEbzd5rdpd480UWLYsC9u2V912MTASDAYJhUJYlkXZrvInNy7y5WP30JUI8P4zjfz9lcml50R8tY+/iYkJ0un1M4kWa0r53XcG/q6Mpfl3mV5+4109+FymTK7FCou/D+FwuJZFtuCfvPv4ilqIm/2tuf33azd+3wxl4LdcfKH7LL3pORw056MNhN21hhWO1kvjjcViZDKZHRVJn5+fJ5GIoYL/GJX+38GZ2/hJZjs68KvMZEv8zk9v4DjQnQjwsftaaAh+FrxPoyrXwJmB6k2wb6IDX6Ti+ChUfRTyVX7w9gADMwsZZa/feYhixaFYuVXL7a2bKR7obKLZFyTm8eIyzKUL8arjMHbtDbJr1H67ndc0+UznaVr8Ib5/cYxfDtSCfd9+Y5jfft9JOjo6gNq5cW5ujpmZmQ1/xqZpEgqFyGazSxmdPp8Pr9eLaZrEYjEqjsM3+t5mYp1MutsZKD7ZeRL3wrJdIerBWDj3/cMnuvnDX/Qxmb5Vu/H1oTmCHov3n21a8/mKWlBrLSXHplCtEnS519xmr2hgcr5AyOviTHMY29E8kGimyRtc2sbWDjOlApOFHEGXmxZfcO0dCiHuKhKwE0IIsS8XYsWqQ7nqEAgEmJ9fO7sjGo0udVCsVKsU8nmy2SxaaxKJBB6PB6UUjuMwVcrz5vgEb6dqHSv/3bXX+PVj53nieJIX+2bIl2s1bp44XlsKm8ttfOFa63apubc9Sv8qjSymsyUGZ/KcagphyvWsWMVqgbWDnJFpKEXM4+VCQwtQW4K2/HuL3G438XicmZntZ9o5jsPQ0DBdXR3gfR/k/2rjJ3nfj+Nofu9nfSwmoA3M5PidH9/gTEuID51rJuS5gGUaKGUuBbu+8dKyIN0WffuNEYoVm9PNYabSJYZm8ngsg9eH5vjHT/Xwq8fO8TdD15gurt5Ax2OYKAUJj5+Pd5wkYLmYy5aXgnUA84Uq/8cPr/JAZwxDwfGGED0NMYLBIGNjY6s2pHC5XMTjccLhMEopEokEc3NzKKWIx+NALfA3U8zzzf5LlLeYsfeB1h5afEG5GSHqzjAUbgz+8ZM9/MHP+pjJ3Qp4/+z6FAGPyWPHkkuB8eWUUjR4/evu3z4A3WK11kwXczSGAzSGvcxXSozm0zzV2EHZdqgs1PY1DcXJSBxTGbuSOS2EOLwkYCeEEAKtNR8+38KbN1NLQa29MDCT41RTCL/fv2an2OXLVdPVMuFAgHC4VuOmWK1yZX6G/swcV+ZXDxp4zNpHnWUaQO21XZ9I82hP7SJ3M0XzM5k0D3bGeHskRd/Uygv+roSfsy1hrs/P0B4I4zUtmWiLQ8/cZHfCRCJBJBJhaGho291iy+Uy5XIVt/shVOEHoJe9x6xzOMF/hNIlVHUQ7GG01UmqYK/ayObKWIYrY7eW9p9qCnF/R5R82d52sG7RDy6N84NLd3bp/fqLg/z6o538g+P38Mr0GOciSWyteWd+Go9hcjwcI+K+VZuuatuk5+eJRyI81BnltaHU0veKVWdp+f3zvTOcbg7x+Yfa6ezsJJVKUa1WqVar5HI5DMOgq6vWuOfGZJbne6f5wsMdxBMJFFB1bH7nndeo3r48eJMeSbZyb7xxW88VYjMMQ+GxTH7zPcf569eGuTJ+6737/bfHyZZsPnC2adWgnc9y8WRjO89PDt+xX49hElnICt4vjtZMFHL8ef8l3tXYgWkofjw2yNlokvZAmN//WS9TmVtBylNNIX79sS6ZPwghVlB6J+sYhBBCHAmO1rw6MMt3L+5dd0IAyzD4rz56GhybgYGBNbcLBAK0tbXxR9feYLZcpNUfoiMQ5pdTI+teioYtN//kzEN87+IoL/evDMz9xlM9dMT9pNNpZmZm1g02mKbJsWPHePbyBM/33ioWbRmK337fSXweg39z5RWeauzgkYZWWTom7ipaa7LZ7I66m7rd7lqWXeUaKvuHQBk8T+L4v8B8uUS+WqHZH1wKJE6mi/zOT27U6RXsnNsy+K2njxMPerAdB7taxXK50FpTrVTI5/NorWvZbgsZiZ2dnVQx+d9+eHXdfVsG/Ppj3XQnAijFihqGjtb8X89eI12snb8MpfhP3tVDU8TL1268RapcXG/XazoVTvDJzpOrBkqEqLfF37MXeqf54W1B8XvbI3zmgXYUt5bSLvf94V4uL2TVLzKU4p+dvYDLWL2G7W5ztCZdLvGNvrcp2CvnFp/qOEmHP8L/8r0rS4+5TIP/4v0nCXotmT8IIVaQDDshhLjLOY5mNle6Y5K8F862hHCZBoXynUu9ljMWuqTlF4Jqo/kMo/mNG2Qcj9SWhN2eFQfwjZcH+a2njxMJhymVSqRSqTX3k0gkUEpxcWTlNg91xYj6XXxroDbxvpae4bHGtYtlC3EUKaUIhULMzc2t6Li8FeVymenpWZLJ0+jIf4PKfRPH/wXG8ln+evAqJcfGUIqQ5eY/Pf0gY/PrF23fa+Wqw0+uTvL5hzsYvnlz1eWrt8tmsyQSCQwD1lupWnXgT14YWPq6JxngeGOQmN/NLwdml4J1AB8+30Rr1McPR/q2Haxr9Qf5WMdxCdaJPbP4e/bE8SRDs3mujN2qLXtxeJ7pTIlfe6STkNe1ImintebdTR13BOwcrZks5Gn17+1ybq01GijaVf7jwDt3BOu6ghGOhWP0Ta6ck7RFfYR9rj0bpxDi8Khfn2ghhBCHk4K/em2YqrP3CdeffbAdrTUTExPrbrfYSbY7FFl3u9u1+UM4jmYmd+fFc7Hi8C9/dJ1S1SYQWL149eJE3+fzkcqXyRRXTr4f7UmQqZQZWOiwOFXMk6mUdlSEX4jDajGwvl1zc3MMD4+AEUOH/ilVR/PtoWuUnNpSdkfrpeYX4/PbC0btJr+7dh98s51dc7kcSinedaJhS8fpn87x7OUJ/uKVmwwsq6vZGffz2LEk1+ZnuHRbAGOzuoNRPt91FoWSYJ3Yc47WfPzeFlzmynPJ2HyR332ul/lCBXvZXEUpRcDl5kQodse+UuUiu/1JvJjlCrUg3c1cmtdnxvlm3yXSlZXzjpjby+e6zlAo2fzVazdXfG9gJsel0XmcfZiHCSEONsmwE0KIu9DiXeDZXJmX+mYY26eL31cHZ3m4O05zczM3b95cc7tSqRYE+1j7CT7cdoyCXeWPr7+5YQH10XyGU5EEJxqCXJ/MrrrN64NzPHGiYanDYndPz1KJfcuyKBaLuNxu3h6c4wNnm5jJlnjjZooL3XESQQ8/HhtYsb+r8zM8lGheUahfiLtBMBjE5/MtLf20LAvLsqhUKuRyOTweDz6fj1KphG3bOI6D4zgLjV1q7+VCoYBSCq01L0wOU7wtQ+U9zV0Uq/ZSnbeDZLGTtGmaSzcZ1lMqlSiVSjzcFeO5a9sLsC0XC9Q6Yv5gtG9bz38k2cq7mjrQ7E8jIiEMpQh4LD77YBvffmNkRZ3KQtnmT14Y4DefPobHMpcy7Ryt+XTXaS7NTfGTsYGlAP+r06N0BSP4LQtjkzU5t0opxZsz47w8Nbphl+jzsQY0mn/199dXrb/5N6+P0PIeLxG/+0A3JRJC7C0J2AkhxF3q59en+PGVyX0dw7PvTNAQ8tKV8BMKhchkVl/mqrVmeHiYZDKJz+cjZJgcD8V5Z3561e0XvTYzzhMN7VzoTqwZsLs8lua+jhhNTbXC1i7LomRXmS4VSGeKHAvHUMCDXbGli9gHO2N0JgJM5LO8PrNyKfH1+VkuJFu3/sMQ4pCLRqMrvl7MPjENY6ljKUAoFFqxnaM1drVKNptlbm6OarVKSTu8MbvyvdXo9dPoC/Cjy3u/fH8zJtJFtNZ0dnYyMDBAubz+BTzA/Pw8DQ0NxANuZnMbb7+e7Sb2egyTD7b2cDpa68gpwTqxnwylONMcpu19Pv7q1WGGZm81pJrLl/n6i4P8xlM9oKn9UbVzzdloku5ghO+P9DKYnWe6VOCvBq7wlZP37ep4NxOsU8D5aAPT6dKqwTqAsu3w3Ytj/MMnuus/yE2SZfBCHDwSsBNCiLtU1d6bpRdel8mTx5OYhlq4gF+4kAdONgZpi/nRWhMMBtcM2EEt283n81G2ba6nZzYM1i2aKxdpj/kIeCxypVvZOvGAm4+cb+ZUc63jrNaa1tZaoG2ymOcv+i/XNhyBnmCExxrbeWlymPc2dxMNWVxNTfOd4TuL3o8VsmQrZfyWCwUy+RVHnq0dLs9N89z4IIZSGEpxOpLgvS3d/E9/dwmf26Ql4iNdrPCfvOsYs+U83xvuxVIGLsOgLRDidCRBUyy2FPQbz6Sxb4tAnQjHsbXDi32be+/vte6G2tL6XC63qWAdQD6fRynFQ10xnr28fmmAzdpKLlFPMMqH24/hM2v1s+R8JQ4Cw1CEPC6++lQPf/HKTd5ZVtNuNFXgT18c5IHO6NL55lhDALdl4rNc/Er3WV6YHObFyWGmS3mGsvO0B0I7yrJzHI1hKBytl3LnNZCtlDcM1gG0B8IEXG6e7b+zo+2K7WK+fQuaORKsF+JAkoCdEELcZRYng/3Tq2ec1Vtn3M+7TzVQsW/dVVYL/6c1DGRSdIeiBAKBpaVwyymlCAQCxONxyrbNv37nl1s6/ktTI3yy4yT/9L0n+N7FURrDXu5pixAPeLAdzStTo+SrVcIuN0opwm43PxkbXLGP/uw8/dlanbr+G29ueMxv9l3igXgTFxok004cfaYyuJ6eXVqKBtxayqogV7K5sZDh2j+V40RjkMcb2gi7PfSm53hleoxXpscIWm56QlF6QlGuzt+55LXZFyRftFkjQWVffeHhds63RUmn0xvW5FwuHA6jteaVgdmNN64jj2Hy3pZuzsca5EJdHEiLAbIPnG1aEbCDWs23gZlb9RvDPhdffrSLhnCtxuXjDW20+II8M9pHb2aOzuDW6t8ut5gprDRMF/OEXG7y1QpDuTRvz91apXBfvImT4Rghy8NQbp6+zByTxTytviAPJ1up2A5vDKXWPdbV8QyP9iTwuMy6LotdnPetFww0lKJYsfG6TMm0E+IAkYCdEELcRZZfmP3DJ7p59vI4rwzM7foxAW7m53lzdoL+TOqObf7FPY9jLCybm5mpXahblkVjYyP+QABDKcp2lZ/eVi9uM3ozc3y99yK/2nOOL1zoBKBYrfLGzDi/mBiitMkC8VuRrpR4Y3ZCAnbirtHmD9HmD6EXyrwnvX4ADAwcbr3HfvTOBCeagpyJJilVHdqaa895bWacbLXMxblJLs6tvlQ/7vGRzm1cG26vnWkJca41wtzcHFNTm69FZ1kW0WiUoZk8qfzOX9fiz36jy+yuYISPth9fyqqTYJ04qAylSAQ9nGkJr+gce7t0ocK//3kvX3y4gxONIQxD0RmM8JunHwJ2ttRTKcW33xjmI+ebibq9/N3Nawxk5wlYrqU/TzZ2cG+8kWyxQrFic0+skQcSzUv7qNg2L/ZunBk8ni7yuz/t5cuP1YKPq703F1+L42iU2jgr1nb0UvBPa1hr81S+zL/58XUaQ15+9ZFO6VorxAEhATshhLgLLAbqctUyPxkbJFet8P6Wbj5xXxsfPNfM8zem61L0fDVTmRKFss2xUIzuYJT/89JLd2zzytQo52ONJBIJXC4Xtm3j9/txuV30Z1K8NjPKzdzay2U3MlMq8DtXXqXFFyTq9nJ1fprdTtK5kGyRu9TiruBozYVkC85CPanF3/hipUr1toD4dLbE//d7V6g6DqebQvzao13Ml+/s4nw7BQRdbgZyqXoPf8c+cW8rlUqF6emtLdVtbW1Fwx0dI7dtE1UOpLGEOGwcR/NId3zdgB3Uynz81WvD/LP3nyTgtpaaUsDOl3r3TmX53ed6+a2nj/O5rjNMFfM0+m51l1/Mkv3OW6NLj51qCtGTDHBpdJ7hucKmjnNfe5TzrWFMU636/nS0Bg3ff3uUoMfFg51Rgl7XHd9fHsibyZWI+mqNLNbK2tNaM5UpUbE1I6kC6UJFAnZCHBASsBNCiLuAoRTfG77BtfmZpbpQX++9yLlokqebu3j3qQZsR9M3laU16sPntvj59foE8D51fxu+he6Jty93XfTcxBDPTQzxifYTnAzHcdBUHc2PRvq5mKpfIHGskGWssPtLgR9vaFtxd12Io8xQin/5o2ubzhJbXB7/QGeMUtWmN7Nxlu/imeOgBZmaQh6CXhfj4+Nrnt9Wk0wm8Xg8/M0bI6SL1Y2fsAmLR1cYcNstCUspPtR2nLPSWEIcMoah6E4G8LtN8mV73W1LVYdvvTbMV57sqdsNs4rtUFg47v/xw6v8gye6SAQ9vDIwy/BcnmTQzUiqwJWxlTcVr01kuDax+RuNYa+LTz/QhrFG1pzj1OZF3397jEyxQsXWPHdtiojfRTzgIR5wE/BYTGWKDM3kyRQrJIIemsNekgHPigDm7ZRStMV8uC2DprCXgEdCBEIcFPJuFEKIu4DWmt703B1F3C+nphnNZ/j1Y/fwwXMrA0yPdMf45stDjM0Xd3Tsjrif6WKen40PUtHr57Wt1sThsOkORnmyqWO/hyHEntBaky5Utryk0+82OdkY4kZmc7XbDBS2dvC5DtbUtSniA6BY3Px50ufzEYvFuD6Z5Y2bqV0a2S2mUny++yyt/lp3Xsn6FYeNAh7uivOzTdxI7J/O8e3Xh/nIPS24TWPdQNVm2M6teZMD/MkLg2tvvANPnkiu26jKMBRuQ/HpB9qWHtNas5BUx2LL3J5kkJ5kcGllxWLDjI343RaffbCdsy3herwcIUSdHKxZjxBCiLpztOat2QnKzup3plPlEv/2yquE3R7iHh9osAyDD7b28E+ePk65YtM7leXv3hqjWFn/7vZqlKp1Ults2nDUzZYK5KsVvKa1dIdfslnEUeVouLzBUrXVJIK1jI+CvbnssmOhKC7D5JWBO5tR7KfFZWPOFmphBoO1i+lvvFTfC//F+zHLr80V8LH2E7T6Q3IeEofa+882UaravNy/cZD/jZsprk1k+GfvP4nXZe7od9/rMvG5TArbmP9sxbGGwJqBtcW5xPJ6dFAL7t16aSufu/iatxKwPNsSllIeQhwwErATQogjrqodnp8cXncbDcyXSytqSQ1m5/l05ynaAyHOtEToSgT45stDjKQ2V4tl0dBMnp6GCE83dfLcxNB2XsKhkq6U+NPei5yLNuC3XPgtF0HLRcDlJuLyyERYHCmmobg0uvVg/M3ZPJdG5rm3tZG+9OyGAf2I24ujNVfGt1/Lcjc83BnFtm2q1a0ta93C6tmt7LX217JTzPtaujkZjst5Rxxqix1OP3ZvK27L3FTJjqawF7+7Ppe6jWEPgzP5uuxrLeV12l8vlAdds2FEPSwGA9drTLEWZyHTr56dbYUQNcZ+D0AIIcTusbVDX3qO4iazWJYrO/ZCR1fF8M0hvC6DLz3aicfa2kfHN14e5Np4hgfvoppumUqZl6ZG+PHYAN+5eZ0/77/MH1x7YymbaLFzrtZ66d9CHDa2oxlNFRjZZEH12337jWHypSpPNXWuu13AcnE+1rCtDN/d5HUZRAOepc7W6zEMA9M0cblcGMbuTr/VQsTu0YZWHkg0S7BOHAmLQbsPnG3i3CaWbT7Wk9hSXcm12I7myePJHe9nQ+u8TQ2l6Jtav/6us3zprrP1170YbNtsRp7j6KU/r/TPcml0nlypNsfRWq9YSiyE2D7JsBNCiCPK0Q6mMnhnfmudC1fsA40CqtUqI8PDdHR08tWnjvHdi6PcnN3c3Wbb0bzYN82ZljBnIgmuzB+sJW17abFj5ny5yA9H+mjw+jkWitERCGMaBo52MJTcSxOHg2kofnZtctvPr9i1gF9bwrvmNl3BCB9vP4HbMPmbN0bX3G4/uM3ae7VarZJMJgkGg7hctzorLi4tWy1gprXmPacbef7GFBW7Phe2cwt1BN/b3MULUyO8q6lTlreJI0UphaM1HzrfzJXxzLo3vF4emKU15iPgqZWnMLcZKDcNxanmMN3JAAPTue0OfU0hr8XnH2qndaEe5u201ozPF/mzlwb50qOdHGsI1mrTLdao05q5XJmIz4WxXtRvExYDnGudMxaPOZcrM5IqMJku8s5YmunsrdUZiaCbnmSQD55rwjTMHY1HCCEBOyGEOLIUil9M3FzIkts6A8WFZAuVSgXbtrFtm8nJCZINDXz1qR7+7U9uMJUpbbwjYDZXBiDqXvvC/G6QrpSYr5T4m8GrlBybkXyGN2YnsJSiJxTjXLSBnlB0acotF9riILMdzbnWyI6WqaYKFbqNwKrf6wyE+VzXGQrlKv/2uRtMZ8vbPs5uONMSBm3T2tIMC4H2XKXMUC6NQS0rpqo1FcembNuUndqfquNwT7SRp081cKErzrPvjPNmHZpPjKYKvNw3w4WeBAmvf+niWoijxFCKiM/Fw10xfjmwdj27vqks/+pH13mkO86FnjhRn2vbn6mOo/nCwx08c7k+79VFPckAv/pIJy7TWHNsGnh7ZB7b0Xzz5SG+9GgnJxpDOI7mr98Y5p2xNImgh994qgfTWKibu82lqRv9fBbPJ2PzBfqnsoykCiuCdQAz2TIz2VlaIz7u64jKMlkhdkgCdkIIcUQ5WhOwXBtvuAoFfKT9GEHLzejorayWdDpNOp3m+PETfOR8M19/cXNF0xfryOQqB+uCe6/9x4F3Vs0IqGrN9fQs19Oz+EyLM5Ek52MNNPpWD2QIcRCYhuLe9ii/HJjddMbt7SI+11Lm6XJRt5fPdJ0mX67yL5+5yjrlnfbNR+9pQuscqnIDSr8A98P4PY/zwuQwqfL6XWPfmpuk1R/iY23H+eyD7TxxLMn33h5jcGZnGTy/6J3mwa4YcY9PgnXiSHvfmUbeHE6tW/utYjs83zuNx2Xw1IkGzG2+JQxD4XObfPbBdooVm6t1qqX5xPEkHmvtYB3UgmQDC+eFxaDdu082cHU8jc9t8qVHuzjeGMR29i6b9nRzmLMtYZRSvNw3w/feHrtjm/F0gQdUdFP7qy2fXcjuY/sBRyGOIll3I4QQR5ShVK3rK+AzN39/xlCKT3ac4kwkydzcHLncnReQqdQcxxtDHG8ILj0W9rroTgQW9gHWsglXMugGYCi/9W6SR8lm6tUV7Cqvz47z9d6LvDk7UZcaPELsFsfRfOK+1g2LlHfE/fwPn76nlpW2wDQUHTE/85U7g1uPNrSiUPzOj28cyGCd2zJQykTlvw25r0G1F8pvoJSBf5M3SkbzGX7/+hs8M9xLNGDx1ad6+NKjncQD7m2N6XxrhH/2vhOY0plaHHFKKTwuk6dObK623NXxzI4zvYyFLq0NofqtFHBvEKyD2jLVqcytc6TtaH5ydZJsqco/eqKHnobavGsvM9lM49ZS/0ePJXigI0pH3L/0pzXqYypT2vC12U6t1t3bIyle7p/l5f5ZhufyS98TQkiGnRBCHFlKKara4d5YIx9qO8a3B6/Sm5lb9zmmUnyq8xQ9wSizs7NrFlOfmZkhEonyq490MDxXoFC2OdMSxjQUhYqNxzSwdW1S+WLvNA0hL1XHWdGFVmzsx2MDnIsmcalbdWCkJpU4SAxD0Rjy8MGzTTxzeeKO74e9Lk40BrmnLQLAr17o4JWBWbKlKg0hLz63yQ+GRlY8J2C5OBdt4PpEhnz5YDWaWPTp+1tr/3Dm0IGvgpkAIwxaM13cWpbcxdQUF1NTvLe5i/sam/hnjSd5uX+Gn16b2lSjDaWgPebnMw+2YRmr18wT4qhRwLtONHBpdJ7J9Ppzi9FUgdcGZ3mgM7bQbXX775GIr36Xz9YGQTbH0UxmiqvWucwUq7w1nFo6t+4Xx9F85sH2Ox5/5tL4ms+xHY2h4OJIip9enSS1UH9zUTLo4ZHuOA91xTAMuQEh7m4SsBNCiCOsJxjlWCiG1poLyZY1A3ZN3gAnI3HORJIEXW6mp6eZm1s/uDcyMkxDQwMdUQ/KMCjkc+TzeYLBINlCFbfbzQfPNvFgR5RCxaFiH8wL74Ms7vbiWla0eb5cxNEQ83jRWqPR0qRC7DulFE+eaGA0VeTS6PzS46eaQvzKwx24TEXVcRjIzOE2LB7ojKKoBZZupGfvqLN5X7wJgO++dbCaTCzXEfej7SmU2YryPIBdrWIqg97MHOVVlvhuxk/GB3lh8iYf7zjFoz0JHuiM8ZOrk/yyf3bd7NyP3tPCoz2JPV0SJ8R+W2xA8bkH2/m953rZKCHrh5fGyZdtEkEPHXE/Ppe55aw0Q0HYt71SI6sJedffl2Eovnvx1nJTy1Q80BHj4a7aueE7b47SEfPXGk7s0zJSw1Cr3kisOg75cnWpJMoiR2uqjsMf/byf8fTqpQOmsyW+9/YYP702yRPHk5xrDRP1uzEWOgXD3tf4XXyNUhtU7DUJ2AkhxBG2fEIzXSzc8f2OQJiPth0n5PZga4dqucLE+DiZzMb1WUqlEsPDw3c8nkqlbu2/o4NE0ItSiqktZp0IyFbLvDY9xlQxz1BunsxCDcCg5aYrGKErGKE7FMW7xpJnmViKvaK15rMPtjFfKDM8VzvXnG0J4zIVX7/xFlOlO88/a4l7fBQrNulidbeGu2N+t4kyGyDwBSqVMoODQxw7doxG787qTpYch28NXiHh9vHJzpN85Hwzj/Uk+Nbrw2vWCexJ7v2SOCEOAtNQNIW9fObBdr712p3zkeVKVYcfvVPLAo4H3PzWe06gFFv6jFRKcbwhxG+95zjT2TKmofC7TX5xfYrrk1kAAh6TjpifgZn8uhmyYZ9r3eBfbSlsacX7/p++9yQxf615Rszv5qqd4T+8MsQ/effxumTfr7UPrTVas2ZQUCnFs5fHeWVgltKyGgaNYS8Pd8VXzEUMpfjLV4fXDNYtly/b/OidCX70zgSWoTjZFOJXH+kkX63gt1zY2sHco5uWmlpW5+LrkPmV2CsSsBNCiCPO0Zp0pcRzEysbRPhMi092nMSlDCYnJ1cE2urB6/Xi8Xp5dXCWBzvjzK4SMBTrK9o2Pxm/s7FHtlrmUmqKS6kpABq9AaJuD7bWC38cDKV4ONFCdygqE0ux62qdCeErT/Tw9RcHGJrN87PrU9zbHuXplm7+cuCdTe+r4tgb1sTbby/3z/DkiUYymQxTU1M4jsPc3BzxeLwu+58pF/jjG29xPBTj4+0n+PD5Zn7/Z313bBf2ukgEPXU5phCHkVKKe9siFMo231+l+cFqZnNlXh+a40J3HLZ4rjENRXPER2PIu3Sear3QyfO905xsCtIS8aGUIl+u8tOrk8zlyszmyszkVjbdWqz5u97ragx7Od8aYWg2R6ZYJeA2lwJqi+UCxueLvNQ3w5ObrOe3msVAndYwnS1ybTyD2zJwWQaWYdAQ8uA2DSJ+16pzCdvRPH2qVsZgMlNCKdAa/u7NUfqncnzknmYCHgsF/PTaFNcntt60o+ropY60Px8fYrqU595YI2ejDZhKodla8HWrFve9+LO6/WshdosE7IQQ4gjTC4GaV6bHqNy2TOvDbcdwmyY3B4col+vfvbWtrQ1DKd4anudCd4KxQrbuxxA1k8Uck6tkMA5m5+kKRnhvcxcJr18mlmJXGUqBAf/wiW7+7MVBGkK1QFLMvbUi7WGXB7XVq+g99szlSZ443kA+n6dareL1eonFYhTtysZP3oLezBzjxRzJgB+PZazIXlHArz3aiV5M/RDiLqWU4rFjCTLFCr+4Mb2p5zSEPOwkKXV5tplhwLtPNayoj+d1mXzs3talbb7+wgC9U1kCHosHOqJ88FwztqPXzYy1Hc0XLnQwXyjzfz5zrVYj2FUrkxHw3CqX0Rr1benGnO1oylWHy6PztER9tERq52gNxAMenjjuQXNrjfFigEprveqYTUNhGiZtMT+WafDVp3q4OJzi2csTXBqd5+pEmqeOJ/G7LX56dXJTY9zIeCHHeKGfn44PcTqS4HQkQXsgXGsOsouZd+q2DDuZU4ndJgE7IYQ4om7dMdVEXSszMI6FYhwPx5mZmdmVYF1NbRLzuYVixIOZ+fU2FrtkMDvP1268xT2xRt7T3IVlGJJtJ3aNYShw4CtPdqOUYrKQ4z/0X9rcc5XiAy3ddAYjPFeni7rdZhgGhmHQ0tJCVTv8/rU36n6Mt2cn+UjbcX77fSf5y1dvMrSwRK4t5qM16qv78YQ4jLTWvP9ME/3TOUZTG2f0t0V9dQu2rBZ0W/456ziaTz/Qxkgqz+mm8FJm3kbL2Be/H/G58VgG+bJN1F8LFn34fAsey6w19+qbpjvZtaXxel1GrQmHuhWEWjmeO8fm6LXHbDua820RHj+WwDRqtfbOtkb4wdtjvHkzxU+vTW16fGtZLOW5/Gdbdmwuzk1ycW4Sj2HSE4pyIhynJxTFZZi7dqPSuK2endwQFbtFKlULIcQRpLWmWCxSqVRwqE0ouoK3OomdCMeoOvaaXWB3wjAMIpEI1WoF27aJ+l3YjmamLEti94sGLs5N8o2+t8lXK+sWsBdipxYzTyq2zbcGryw1YTgRii1tYyqFyzCWLgndhskXus9yT6yRVwdn+fEhCNhprXG5XLS3t2NZFn81cHXbDSfW8878NP+h/zJul+KrT/XwwbNNmIbibEsEe6NK+0LcJRaDJV96tHOpruNaon7XUqbaXjAMRdBrcaopjLHQyVlv8XM4GfSQL9fqei4Gid5zupFHuuNcHc/w/I2tBcSUUphb7MC6XoDRNBTHG4Ikgh4MpTAMhdcy+OyD7fzGUz20xXx4rJ2FHm5l/a0+jpJjc2V+hr+7eZ1/884rfO/mDSqOg6Pre15eanwBS/uWM7HYLZJhJ4QQR5BSiqmpKZLJJD7L4oFEEw8lW3hjZpy35yY5EY5TLdd36VY4HCYej+N2uwEo21XmK2W8pkW2ultZfGIrZkoF/rT3Ip/vPkvC45NMO7FraheDBp/qPMWPRvr5tWNncZsuZot5fjI+yKc6T+EyTLKVMn94/Q0+13WaFn+I710c45cDs/s9/E2xNUSjUbTWfG/4BqOFrddl2qzRQoZ/e+UVPt11midPJLmnPULE597yRb8QR5lhKAIei6882cPrQ3M8c2mcwiqNH8IbdGfdlbEptSLOtNVsrLDPRdXWd2RyuUyDD5xt4sHO2g2RjZbZ7rblx14cZ1vMzz9593EAKrZD31SWF3pnGJzZYjMyvbjfjTe1tead+WlG8hk+1XmKBq+/rnOeV6fHaPUHafYFgd2tnyfubkrLJ70QQhw5juPQ29tbq2FnGDiOQ3NzM+FwGEdrHMdhZHiYUqm042N5PB6amprwer1kyiX6sineSU0zmt+9i1exMy7D4FMdp+gKRna9ULO4u2mt0TgoXUShwahd3OTKVW7O5DnTEiZXLeMzXXznzRFeG0rt74C34MuPddGV9PP13oukyht3PKyX+2NNvK+1W963QqzDcTSlqs3fvTnK5bH0iu/1JAN85cmefRrZ9vxvP7jCR+5p4VxLeCmLeTE4txjEe2VghgvdiX0e6cYWxz2RLvJC7zRvj8xvKls4HnDzzz9wir8f7eeN2YlNH89SBp/qPEl3MFqXTroAv5i8yfFQjBZ/6FbGnZyTxS6QJbFCCHEEKaWIRGpLYJ2FJVrj4+PkcjmKhQID/f11CdYlk0k6OzsxXS6eHenn9669zo9G+yVYd8BVHIe/HrzKs6P9TBRW3uGW+3iinmrFyk1UdQitAvRNZbk6nubf/v0N/vyXQ/zs2iTYBj+6PH6ognUAQzM5TGXsabAOwGPt3VI+IQ4rw1B4XSZffKSTR7pXdm/ezwy0rdJaMzKXJ1uqYjvOiqWXi69DA+lChb+/cvBLCcCtcTeEPHz2wXb+xYdP857TjXhd64cmlhbEbjEwVtUO3x68RsGubme4SxytcdA8PznMWD5Liz+0NJ7bxyRzKVEvsiRWCCGOqHg8zvz8/IpJw8jISN32r5QiFosxWczxjd63qX/lJrGbHPRSoeao20PY5cVtGHSFotwfb9rv4YmjRDvgPku+VOFPXhhY8a2/vzJ5aC4ybzedK2MaioTHx0xp72p03htrlKawQmzCYq24j99X69a6uNz+cAXsIOp38/4zTXTG16jNp8HvNvmtp48fqOYHWmu0XtlRd7nFLGGfy+TpUw10Jfx87fmBdXZY+2s7r85BM5BNcSaSXNHNd8U2Wtfq0qExUDjopW6zi3Pp5yeGORmJ82hD66Y68zrawdiljrXi7iABOyGEOIKUUliWhcvl2rUusIFAAKUUb8xMSLDukEuVS6TKtYzLG5k5OgJhYm7vgZn0i6PBbZp86dFOWqNePNay3y29smD34r/HUkW+9sLgXg5xS+IBN46j9zRY1+ILEnF79+x4Qhx2y4N2i11R/XvYcGKnDEPhc5k8eSKJUquXsDAMBRpCPteB+tyuZZ5tcjugJxnkVFOIaxOrr9JYbDqhtnnL4rmxIfKVCudiDfgtF7Z2lgJycOtnO1PIk6mUyFYqjBUyRN1einaVkMvDA4kmApZ7w0Dd4n+H2kpfCdqJ7ZOAnRBCHEFaayqVCpVKfRtLLNfQ2EiuUuZSamudycTBN5BJEUk0YUoej6gHZYB2cJmKU00+sEdR1dGFyuGLv2PL/1ZgxOhO9hD2WqSLO1vGtFsWL788hkFpF7rDribs9uzJcYQ4ShaDJ61R39JjjtZMZ0pUbAePZeJ1mwQ9Fo6j18wI2y+bGc9Bq2mptSaVr6BULUNwMxxH89F7Wrg+mWG1FaV6Bxl2AHm7wnMTQ/xsYojuYJRzsQZOhGMobnXLtbVDoy+A17SYKk6hqS2pfSTZSsDl3lRW3dLr0ZpctUzQ5T5QmY/icJGAnRBCHFEul4uenh4mJibI5bbYiWsTtONQdA7mhbTYmcHcPA8lW/Z7GOIoUQbkv4MqPrPJ7SOo2P+Hd59q5Dtvje7u2Lbp8liaD5xr5j3N3fxwtG9PjpmtSMdtIXbKdjRz+TK/97NeqvatyNCxhgDvOdVIZyKwboDFcXTtvoIEYNblc5t4t5DNaBiKWMBNa8THSGq9zOUdNo4A+rMpZksFbqRn+UTHyaXvLWbchVxuHm1ow1jI0Fwa4xb+mxtKEXF7Gc6lafEFUWjJtBNbJr8xQghxBC0WwDVNk9bWVmKxWN327XK5aGtrw+12b3tZgjjYhnNpHCmYLOpJa/B9BMy2TW4/j670cW9baFOb39sW4fMPtfPxe1v2bHI7myvz9kiK87EGnm7q3JNjjuYzvD1Xq/lnaylGIMRWaV3rUDqXK/PEsSR+962AUt9Ujj/8RT//17PXGJrN42hN1XGwHY3j6KXAjWEo+qeylKo2sNCMQD4zV1Cq1vRjq80XHK3pSqxeq+9W04kdDg44G03yn55+cClYd/s4aw2T1NK/t5sdp7WmPRBmvlJaMWeW3xexWZJhJ4QQR9hi7ZaGhgZcLheTkzsv7h6JRPD7/bw6PcrPxofqMEpx0FQch/FCtnZHWDIIRD0oBVqB94OQ++PNPaX4LJ7Qf8ZjPQle6p9ZdZvfeKqb5ogHt+VaesxtGqQKFQwFVycyjMztXo25b702DMDDrS1MlfK8k5retWNB7YL1hyN93EjP8tmuM7t6LCGOKkdrjjUEON4YpDHk4S8X3seL5vJl/vaNEc63RfBYBm7LxG0ZeCwDr8tkNFXgh5fGUQpaIj56kgGONQTpTgRQautdTI+qbS0D1dCV8PN87+r7A6jHiuVUaWV37936b7Y4D4+4PSuOIdmZYrMkYCeEEEfc4gQhGo2SSqV21ITCsiyCwSAVx+GnEqw70kZyGZp8AaljJ+pHmeA+DzkTsDfevnIZXb3Jh861cHFkjnx5ZUbZ+dYwnYkglN+CwjToHPg/xf2dtzKKH+qK8r/+4FqdX8gtjoa/enWYtqifJxvbdz1gt6jJF9xSLSUhRM1ig4PFNK172qN0JPwMzuS5OVv7M5kpkipUuDKWZr5QoVRdPZtVaxhNFRhNFfjFjWnec6qBp09LF+dF2w2CxQMb1erc3H49hknA5WZ2lcZAY4Us2UoZv+Xa9fOoUuqOudRiMFNq24mNSMBOCCHuElprYrEYExMT296Hy+XC7XZzcXbnmXriYEt4fLLkWdSfcoPrJFSubG7z7J9gRP4rfuOpY/z/fnyDjpiPd51soGI7dCb8aGcelf0jWOxVXXqJ2sWcBv/nsYx7d+mF3KKBl/pm+Mg9zTR4/UwV87t+zPtiEhQQol4iPjfnW13c2xZBKUXFrp1PXGZtgX2xYtM7meXS6DzXJzMr6t4t93zvNPd1RFcEnOxam1DMA9bI4qDRWlN1NK8MzPJi3+oZ1ZtpOmGgOBWJcyaapDsYxVCKdLnElflprs7PrDg/f/fmdT7VeQqPae35zQ+1bLktrMxGlJsxYjkJ2AkhxF1CKUU4HCaTyZDPb++CslAoUCwWORGO8czBrAMv6qQtEJIJo6g/bYPrnk0H7HAmUbn/SDL06/z3nzqHUgZaVwEDpQzI/TVLwToAnV323DncbgO/2yRf3kRG3zZ4LYO2mA+/x0Qpxftbuvnz/su7cqzlKo4jWRlC1NHygNpioG6R12VypiXM+bYIFduhdzLLaKrA2HyB6WyJfMmmbDtUbM2//vvrhL0uIj4XUb+bqN9Fe8zPyaYQtqM3DNztJOPqsGdrPXt5nJf7Z/G6TL7wcDtVB8bmC4ylCtycy29Yw64nFOV9zd1EPd4VQa+Qy82FZCuPNrQxVczxzb5LVByH4XyGP77+Fh/rOE5nILKvP7vl2XbGKkE8cfdSequVIIUQQhxaWteKJg8ODlKpVLa1j1AoREtLCz8a7efN2e1n64mDK+nx8ZWT9+/3MMRR5WQg9d9zq4T4JrgvgPKBMwvVvlrgz2wC++bazzESEP3veLlvhu+9PbbjYS+6rz3Kh8834XOZGMbKC3utNZdTUzwz0sdutoT47bMX8Jpy312I/eA4t5pPLLIdTcV2MJTCNKBia94emeet4RQ3Z/N0xv188ZFOgh5rzQwqZ6lG252ZVut9bzOBwNuPo3Wt0615ALqWLgamUvkyCvC4TNymgaYWnDOUYmgmx7ffGOGff+AUL04O8/zk8B37+b+ffxSFWvdmo6M1r06P8bOJlWVdPtlxkhPh+IG4UeloZ0U32cX/1hLAuzvJJ70QQtxFFj/oY7HYthtQZDIZotEo72vpZr5UZCA3X88higOgLRCWiaHYHVqDCgAuYAv1NMuv3PnYesE6ABUEahka9WIY8OkHWqlWKszNpalUKkt/TNMkFotxPtbI2UiSiWKeG+lZXp8dp+rUN3xXrFZxG+aBuLgU4m5jrBIcMw2FaZjLvoYHO2Nc6I6TKVbwuy1MQ60aXHO0ZmA6R7nqcH0yw+PHEvjdFgGPtSLrKluqcmlkHq/L4P6O2Ipjb2Qx6FOoVhjOpQm63LT4N9eFe7ct1Vr2u9fcpj3m5x8+3l3bfo1FsZlymajHu+6xDKV4ONnC23OTzJVvNZ748dgAPaEoCmPf5z7GbUHUxdFoNlu9TxwlkmEnhBB3Ia01fX192Pb2lokZhkFnZyemZfGvLr+8q5kkYu99sfssrYEwpgQDxG7IfxuKP97943geh8CX+B//7nLdAmYfPt/ME8eTDA4OUiqVVj+sx0M0GiUQCGBZFrbjcC09w/MTN5mvbL/pz3Inw3E+1XmqLvsSQuwfR2scR1OqOgQ81tJjWq8MxK1VC8929FIW2mqW33x7dqQPB835aANtgfChq5W2GOwcy2cYzM5TcRymi3lG8xlOhOO8u7kTn2ltGHBztMNsqch/6L9M0a4uPf5gopn3Nnctfb3fgTshQDLshBDirhWLxZie3l5HQ8dxqFQquFwunm7u4uWpEfLLJj0AcbeXNn+Ii6mpegxX7JFHG1rpCEb2exjiKKvsXtfWFZxaPbtYwGIqU59AWXPYu3T+W0upVFpq7uPxeIjH45yNNnA22oCjq+QrNt8ducFwLrPtcVxPzzJZyJH0+g/VBbcQYiVDKQxTYS2rm2cotSKVSmuNYu3MvtUsBuoyxSpXx9M80pPgg23HaqVRlh/nEDGN2rLQRm+ARm9gRb03qAU6NxNkM5RB3OPj13rO8R8H3iFXrZ3P35gZJ+Hx0eQL0uQL7Nrr2ApbOxisbFAh7i77v2hdCCHEnlNKEY1G76i/tFVaax5KttxR7+zxhja+cvJ+Pth2bEv7C1luToXjPN3UyWc6T/PZztM7Gp/YmhPhGE81diDJ92LXaBs8j+3NsexxAD59f1vddvmDS+MopQiFNreUrFQqMTY2RjqdBsBQFgGzwOe7TuHe4fn3tZmxQ3fBLYTYvMXP4mLFZniu1izM2cLn83NXJ/mXz14l5HUt7ev2INdho5TCNAxMw7jjdWzldRlKEfP4+AfH76XR6wdqS06fHe2v1SDd53mQozUlu0q2UlnRPRaQOdpdRgJ2Qghxl1oM2m3XyMgIN27cYGRkBL/l4j8/8zBfPXEf/+TUgzzZ1IHewhI0v2nx1ZP385tnHuKTnae40NBKTyjKsXAMryHJ4HvhZDjOJztOLRR5PryTeXHAKbO2VBXP7h/LmYb8t2mPBzjdXJ9aTbliFaUUzhaX2I6Pj5PP59G6jMr+IaYy+GLPuR2N5er8DEW7KhdvQhxRStVq3vncFhVb89Ork2h9a3nsWmxHMzxX4NWhOT52byunm0Pyub4KQyn8losvHbuHBxPNNPuCeAyTyWKOf3flNeyF8/x+nGMNpXAZJhF37bNyMdC62SxCcXTIVZAQQtzFYrEYc3NzO5qM5HI5JicnCYVC+JWB0jA5OblUgH09bsPgfS3dnA4n0cD33x7j5myeVL7MI91x3nWqgaJTXXcfYudOhRN8vOMECgnWib3gAu+Te1PHrnK9dkSzPveouxtqy6TK5a0vsZ2bm8PvbwPlQRW+Q6PvU0TdXlLLCp9vha01L02O8J6WLmkSI8QRtbjktTsZ4HhjrZGOs0HAzjQUHXE//+WHTsu5YQOGqrWweF9L99JjRbvKfLnESD6D2zTxmy7Cbs+e1/xb7VjSLfbuIwE7IYS4SymlMAyDWCzG7OzsjvaVSqVIpVIrHksmk6w3pfxAaw/now2YSnF1PM0zlyeYzd26CK51R9vRsMQmnI4k+Hj7CUCCdWIP+T4JlT6wB3f3OKrWdTBfuhX4D3st0sXt3Qhoi/oA1q1ht5ZSqVS70LI6ofwGyv9pTkfivDQ1uq2xxN1eLiRbAXnvCnHULa9Vt1otu+WWB3TuhnPDTgNYtz/Xa1p4fVYtmw3ILDQLOig/ycXxSuDu7iABOyGEuMvF43FSqdSWl3htxDAMlFJ8pO0YHYEwActN0a5SsKuYShF1e7k0muYnVyaYyd2ZrTI0m+eRngQJt4+ZcqGuYxM1p8JxCdaJvadUrViQ5wLkdztgV1tO9GuPdlKs2AQ9BoZhobUDKC6PzvMfXx3e9O4aw15s297W+bKhoQFwoPwKOCm0LtIeiGw5YPdgvIn74k1E3T5KVZs/e3GAX3u0a83i80KIu8vd9Hm+m1lvi/sNL1uWepActPGI3SEBOyGEuIstftgnEgmmpurbzTWfzxMIBDgfa6RcLtc6oWkTu+xQcTSvjs3y3bfG1szCW8yuKzl2XcclarymxYfajgMy6RP7QYER3v3DOGm0k8elR3FZGlW6CfYIyvMYWMc51xrhv2uJYDsO2VKVv3jlJmPzay9Rjfhc28quA/B6PVAdBCdVe6A6RIOnm1PhOBG3l8Fsisli/o7nXUi08lhDrXGGaSgsw2C+UOb5G1O81D9LrlSVYJ0QYs8tD5YtZqPt9XwiVS6SKhXpCkUwlZTnF0ePBOyEEOIut9h8Ym5ujmq1fvXistks2WwWtVBvo7m5mWAwxNee7ydf3jgIF/JaaK0Juzxkq1uvFyXW93hDG66FLEgh9p4Gvb3A15bYI6jUf3vn0d0PoLVmbm4WrTWWZREKBvnKkz383nO9zObKNIQ8fPbBNrLFKt94eQgAn9ukUtpexq9lmaji9aWvFeB3uflk5ykA3k0nFdshVy1RXTj3KRSWoRiazTOZLlJ1NO+Mpbk5eyuwZ5nyHhZC7L3lmW2GUpRtG8tQGHsUOLO1w81cmlSpSHcouifHFGKvScBOCCEEUMuym5iYqPt+FxtaTE9PEwqF+JWHOviTFwc2fN47Y2keO5bgiz3n+On4AG/M1n9sd6uY28sDieY9LZ4sxB2c6f07thHCsTUzMzNLD6XTaTo6OvjnHziF4zi3gtkRaAh5mMqUMBS4vF78fj/5/J3ZcOvTLJ9622YPfRMZfnJ1kkLFpjnspTPupysZIORx8dbNFMWKw3yhwquDs2vW9DQOTGUlIcTdarKQ42+GrvHpzlM0+gLY2sFUxpp11uqxlNVUBlfnZzCXdVCVeY04aiRgJ4QQAqUU4XCYubm5bXU/3IxqtUoqlaKnIUo84F7RYGI184UK/+65Xn7tkU7e19JNsy/I90d6d2VsdxMDxYfaju33MMTdTplQHdq/w5cvYvq6MAxjqR5dsVikv78fr9eLx+NBa006naa7u5t/+t4TVB2HXNHG6zZob28nn8/jdrsZHx/fVPCuWnWwXMdRBcD1MKbh4qW+EUZTtYy9uVyZd8bSW34tZduhVLHxuMwtP3c7nIXIoVwYC3F3snXtnGmgcNCYysDRmnSlxJ/1vU13MMLxUIxjoRglxybm9qKpnTPS5RIe08JjmjsKsGmtmS+XGM7VzplvzIxzb7yRg9MaQoj6kICdEEKIJc3NzQwN7d5F9OzsLJFIhE/f38YfPd+/4faFss3Xnu/nY/e2cKG7gTPRJFXHoWRXGczO88PRvl0b61H1gdYe2vwhWQor9o+2ofwWVC7v3xDMZtB3No+oVqtLy/kXDQ4O4vf78Xq9RCMRBqezDM3mefxYAssy8fl8mwrYKRQYydoXRgSAVKE+N0hmcmVaFzrY7iatNSW7Sr5aIerxSs0oIe4ii9lyE4Uck4UcSa+f6WKesUKWwcw8UAvo92VS9GVSQG2eF3V7uSfWwEguQ382BUDc4+WrJx/Y/liAG5nZpa/7MikeSDRve39CHFQSsBNCCAHUsuw8Hg/RaJRUKrUrx7AsC6UUmdLma1c5Gr7z1hhvj8zTHPaRCLppDHu5J9FI0uvjmdF+plYp1C5WClgu7o83LdyBFmKfaLu2FDb/53tzOP+vghGndnlX+6PQKPc9OM7manZWKhXm5+eZn5+nXC7T1dDA4Eye/+m77/DffuIchrFx0KqxsRHL5YLsNxZ2+jrwadqifmayOw/azRfKtES8exKIH86l+cXkMF85cd+uH0sIcXAopfjx2ACvz4xv6XmpcpGfT9xc8djDiZYdL2Ft9YeW/j2QTfFOaorTkaRk/4ojRQJ2QgghVkgmk+RyuW13QlyPz1fLAPnWq8Nbfu7gTJ7BmVuBucePJfjQ+Wb+0Yn7KFVtfjo+wNup+na6PUp+rec8UY93zXoyQuw6bYPOQ+Z3QK/dibV+TJT3SRzHwbZrjW6WfverVUql0pb3ODc3h8/n412nGjjfFsE0FKa58VLUYDCArvSiyq/VHnDmsO0q7TEfbw2ntjyO21Vtjdaw229tTa1z92ypwFuzE9wbb5QsOyHuAlprejNzWw7W3a4jEOZUJMG98aYd7cdQiudvCwK+Oj3G2WjDjvYrxEEjATshhBBLFi9mGxoaGB0drfv+DaNWgNjZeNMNvdg3w5s3U5xoDPLYsQQfajvG+1t6QNXu5v5gpJeJQq4ORzr82vwhoh4vgATrxP5RJmT+EJzUHh2wFkian59naqp+wfzx8XGampvxmyblUolCYf2usZZlYZoWKv/6baOb42xLlFLV4adXJ6k6a3SV2AR7rY4UdaS1xtYOv5yqfTa8PTclS9CEuEsopeoSnP9Ex0n8lmvHNw+11ozkMysey1R2pwazEPtJAnZCCCH2TC1gV7/9FSo2F0fmeXt0noc6Y4S8Llym4sGuOL9+7B7+Q/9lRm+b0N2NzkSSSx3bhNgX2obKNajuZd3J2u+7rnMwy3EcxrZwQyMYDKKUQrsfQdlTUL0KgCq+gM/7EZ48kaQrEeBrz/dvO2jnOJrdCtktXlgrpfju0A3myrXsyMlijkK1gs9y7dKRhRC7zdEaxcY38y7OTvCz2zLatiNVKuI1rR0vWy3Y1TtuVLiMvWm8I8Rekpm7EEKIO1jW7tzPUarWUazetIZXB+f4ydVJnrk8we/+5AaGUnQHI3U/1mHkNs1awXsh9osyofzyHh9zf6e5gUCAlpYWYvF47aLY1YUT+s/B9ylQESj9Peb8f42R+ybtMR9PHE9u+1i1QN/uhOwWL+S/NXCF3szciu/NlYt1D4gKIXbX4nu24tgMZFNUtbPU/fl2tnYYy2d4ZrSfor25up9r6QyEaQ2E6lJjzm+58NwWoEtXSqRKt85Jcm4SR4Fk2AkhhFhBKYXX68Xv92+q8+FWmKZZ1wy7tWSKFWxH0xGMwOTW6+UdNVXHppZ/I0E7sU90Gcpv7/FBdyfDbi0ul4toNIrf78dyuTANA9vRDM3k6J3K0j+d44njCe5p+wD4PoBj5zEyvwPll8DzJO8+1cEbN+fIFLd+UTyXK+/acndHa67Pzyx1d1xx3FKRJl8QyWsR4vBQSjGYTfHXg1extSZouflAaw/HQtE7ziOmMvjRaP+Oj+kxTD7afmLHjSaW81kWpbK94rFfTo/ywdYetK7NenQdjyfEfpCAnRBCiDtorWlpaWF0dHTD+kybFQgECIfDXBtP12V/63E0vNQ3w+PHE7t+rMOgqh0kXif2jdZQHQLq38hmfXsTsLMsi87OLiyrFraazhS5PjxL72SWwdkcVfvW8f/y1WGuTWTwuyyePt2AP/SPYP5/QmX/ADP6/+Z8a4QX+2a2PIYbk1k+ck/93+C2dqg6Dj8dH1r1+/PlopxWhDgEFstijOTSPDvaz3y5tLSkNFst8+2hq3yk7Tjnosk7gnYec+chg4eTLfgtV12DZxXnzorIl1NTHA/FGM6nuTY/y68fP4/XdGFK0A5gKZNSgpiHhwTshBBC3EEphWEYtLe3MzU1RSqV2vE+I5EIVdvhGy+vfuFXTyGvxZMnasvLDKhLk4vDbLVJrRB7xwG7/k1sNrZ3ATvLMvnF9Sle7JshW1o/Q+7i8DwAxxqDnEgGa6PU82jt4HdvL1ftZFOwrpkrUPu5TRfz/O3QdbLV1Yu5X0/P8kRje92OKYSoP601Boo/632b8UJ2ze1+ONKLozWdwTCGUvhMF5Zh0OwLcjO3s5ut+Wql7sH9Nn+Ya+mVNzhsrfnroatLX//pjbf5Qs9ZIm6P1PEFNHqhbqEhTcgOCQnYCSGEWJVSCq01jY2NBINBJicnKZe334GrVCoRDAZxWwbl6t4EkEp29a4P1gFUHUey68T+USaUfrk/x91ltWCdhdaaVKGyYbBuUdBjcaIhiFG5eOtBbeN1bX3MhoLHehJ1f4u/MTvBT8cH16xtdS7aQJMvwGg+Q6s/JBd/QhxAi01jfjw6sG6wDmqJ+M+MrmwMFLBc5Ko7z45+a26SsMvDw8kWNPXJ8Joq5jbcJlstk6mUiLm9Oz7ekaAhX60ScXv2eyRikyRgJ4QQYk2LF2A+n4+uri7m5uaYmZnZVsZKOBwmV6rsSbAuU6wyPJsnGpKPOajdcZamE2JfaAecabB33l1w62q/87uRYWcYBg0NDUQitcY2VcfhVFNo6fvXJjKkC2tf5Eb9LgxDQfmVZaOtbitg93BXnLDPVdeA2QuTw7ywTv1Pt2Hy0fbjdTueEGJ3KKX4/nAvl1NT23p+PYJ1UFuK+dzEEH3ZFB9rP07I5dl2VrCjNROF7FLH6o2UbVuqgiwwlJJg3SEjVzILFidzcndQCCHutHhujMVihMNhJiYmyOU2vrO5XD6fJxQK43UZFCu7H7S7NDrPh843cyaSIF+tUtUOVcem6jgU7Sr5HXY7O0xiHq80nRD7REP5zX06di34Ve+5nVKKzs5OXC4Xl+em+OX0KJ9oP8nJphAnGoMAfOzeFl7sneGZy+Or7mOpsYTZBBUDJ/zfogwfVae0pbEEPRbvO9u0o9ezeNGcrZSZLubpz6Z4fWb1cS+ytbNUE0sIcXDZ2uGdbQbrdsNwLs3vX3uDE6E4DyaaaAuEl7IAN0sB76Smt3DMDCfC8W2M9uhZ/nPe6s99Lx3kse01CdgtkF8IIYTYmFIK0zRpa2sjl8sxPT1NqbS5C8zZ2VnC4TCffbCdb+5BHbvLY2k+dL6Zj3ecvON7Wmu+P9K7pQnfYdboDciFtdgfygR7/eDPrnFm0PY0yUSMTCZDtbrzIL3H4yGRSOB2u/m7oWtcS88C8LXet5bqZfpNi4+2n+DJE0nu64gyNJPj2XcmmMvdKimQKVbIlap4vR/GNBswrCTfuzjKq4Nzmx5L2Oviq0/14Da3X4vI1g43s2kupaa4Pj+Lw+ayEW2tGc1laA+EZQ4txAFye6AjVSpt8l29dxytuZae4Vp6hpPhOB9qO4bbMDedbaeUYjiX2fTxbubScp5axWLpm4P0s1m8gXSQxrTfJGAnhBBiSxY/RP1+P11dXWSzWWZmZjYM3FWrVWzbpiG4N6n46UKFf/2j6wQ8tUmgadT+9CQDPHmigflNBhoPOwUkPL79Hoa4m9mTy74wAXuvDozK/Dt05F/Q2dlBX1//jvbm9/tpbW1FA33puaVg3aLFvOG8XeWvBq/waLKVnlCMU80hTjeH+cZLg/RO1WpIORr+6Bf9fPWpHgKeRxmezfNy/8r9rcU0FOdbI3zgbBMBj4VpbP3Cxtaa6/MzfHf4xpafuyhvVyVvV4gDZPkS05vZeX46PrTpIPx+uZ6eZayQ5bOdp0l6/RsG7RytyVRKzJTymz7GdClPrlIm4HLvdLhHzkELjEn32jtJwE4IIcS2LH7IBwIBgsEgmUyGmZmZNRtTRKNRTNPk+2+P7NkY5/Jl5m6b0zVHfDhaM1rY/N3Zwyzq9mIakl0n9pHrDHifQrvOo4wQWjtgj6GyfwzO5MbP3wlnEpX9I8zgf0Z3dxcDA4Pb2k0gEKC1tZVCtcofXX+TorNxtt7L06O8PD2K17D4z848xPHG4FLADmA6W+Lf/6yXzz/Uzo+vbPxzCPtcPNId5+GuOD63iePoWh28bTCoLU3brp5glBOhmFxcCXFAONphuljAMgziHh8dwQhe02Roh91d90K2UuabfZf4YGsPJ8Jx3Ka5Zn07Bbw6PbblMOSrM2M83dxVl/GK3VPrYHvwAon7SQJ2QgghdmTxQzUYDBIKhUilUkxOrrz4NE2TRCLBxHyB65P7GygbmctjKMU/PnEf3xu+wWRx83dpD6Ok17/fQxB3Ma01yv8JbKfKVKnIZGECl2FyKtyEEfmvwR6hlptm3PZHgVK3fb3wtz2Nyv05OBObG0TlCir3dVyBf0hPTzfz82n8fj8ejwfbtimVSuvebFgM1mUrZf7wxpu1rstbUHSqa3ZaTeUr/MHP18/864z7efxYgjMtYbRe+LHAtoN1jtbMl4tc2kFdq4eSzRKsE+KA0FpTsm3+evAq7YEQH+84SemQ1emtaofvj/TCSC+dgQif7DiJxzTvCNw4aK7Mb72cySvTY0TdXs7HGqREyAGzfFmufK7cSQJ2Qggh6mLxwzYajTIzM4Nt31r2lkwmUUrxF6+u3XVwr/RP5/jhpTGePtXIl47dw7+5/ApVdr8Jxn5JeHxSHF7sGwdN7/wMf3dz5dLLn5oWX+g5S8BsouhoQKO1xqn9i4X/4aDRuvbY4t8tvk6MyH+FKjwD9hjoIujCyr+pggqC1Q1WN9o6BmhcLjeJRIJctcJYMUfAchFddrOhXC5TrVapVqsUi0U8Hg8tLS3kKmV+/9rre3qmMJTi8w+1cb4tiu3ULmjqcS2jgNdnxrF30D3Xb9W3K60QYvs0MJidJ1stc2V+hoJdZTSfobLFmwsHxVBunmdG+3h/SzcBlxtbOwtL7xXPTwxTtLdXVuFGeo774jtr0iPqw9Gakl3Fa1qUHBuPcWdwVtRIwE4IIURdaa3p7u5menqa+fl5PB4P4XAYDVzojvPDS/tUgH6ZF3pn6J3K8k/fe5LHGtv4xeTN/R7SrjCV4kQ4jpIqU2K/aMiv0uwhb1f52o2L29qlxzD4Ys85GnwfXXOCr7WNUubCFzYKYyk17bWZMaaKBd7b3IXXqk2FbcchHImsWIrjLFzsVrXmazcu7jhYt5V3oWUofvWRTo4vdJ3dTp26tTho3Ka5o334TVedRiOE2CmNpuTcCmINZuf3cTT1cT09y/X0LO3+EGeiSRyteWV6jHRl+/WHp4u5Oo5Q7IRG8wfX3sDWmlORBB9tP77fQzqwJGAnhBCirpRSGIZBU1MT8Xgcy7KwtcNgNs0Tx5O8PTLPaKqw38NkMl1iLlfmeDh2ZAN2jzW0baqIsxC7RSnFuWgDb8yOM1sq1mWfJcfh671vYwAhl5uAy0PQchGwXMQ9fu5PNN0K1gEOBqWKzXS2jFLwcLIVgNlciV/2zXJpZJ75YoX/8kNnmCnn+O7wjYWLxAYcrfnJ2MCmatatxW9ZuAyD2dzqS25v5zINvvxYJ52JwO68d/XOAm4Jj0+KtwtxgCgU6fLRbKQ1nM8wnK9PKZWYNOA6MExl8J+ffZiK4+A1LVkJsg4J2AkhhKi7xQwVy7IYzWf51uA7lB2H/8e5x+hJBg5EwM5QEPJaDOSyG298SLUHwpJbJ/aVoRSWofhc1xn+tPdtinWsq+QA85Uy85VbgbAWX5D7E00MTOeYzpYYnstzaSS9ou7cJ+5rZTpT4qX+mRX7e7l/hiePJ+kORnlzdoJLqa3XSVrNg/EWlFKbqt/ZGvXxiftaaQ57dy3QbihFWyC07eefiiTWLAgvhNhbWmuGc2nenN1kTc+72FypKOeuA8RUBqZpLP1brE4CdkIIIXaNozXjhSzlhYvlsmPTGffzi30eF9QujC3ToDc9t99D2TVRt1dqgoh9ZyiDkMvDpztP8Rf9l7fc3W8rFn/df35jkt7J1Zc/feet0VUff+7aFJ1xP+9p6uKd1NTSeWunGn0BShWbVL6y7nZPnUjywXPN2Dvo/roZSimafEEClotcdf0xreZMJCk3AoQ4ABytuZGe5bs3b+Ds6pn1aMhWy1xOTXEu2iBBO3FoSChTCCHErjGUIub2Ln3dl5njVHOYL17owNrFC9KWiJd3nUjya4908tUne/jKE938g8e7eLgrtrTNB842UbUdrqZm1tnT4WUoRcCSOlPiYDCUoj0QpicU23jjHVis16i3EWur2A5/++YopqH4RMepuo3JY5oUq+sP6InjCT54rhmtdV3r1a3F0ZoHE81bfl7S6yfmkRsBQuw3vdA05idjgxKs24IXJoeZLuYB1uzeLcRBIhl2Qgghdo1SioT3Vs2Q74/04mjNPa2NvDWc4up4feqSeF0GX3mih0TQwmUaS/WrtK7UukZigHJxorGN955u4JnLk3Qng7w1O3FkO8TGJLtOHDC2djgTSdCX2b2s1qWGEdt8/nS2xC9uTPOukw18ov0E3xm+sfGTNqA1eFwGLtOgYq8cmddl8N7TjTx2LInWes/eswo4G23g5xNbq995NpKUJWVC7DNHaxTwk7EBstXN1cYUNZlKma/3XqQ7GOWJxjZa/CE5p4kDTQJ2QgghdlXI5VnxtWkYOI5mcCZft2N88UInzRE3qvwalMfBrv1Rziws3XlW4H6QgP/TfO7BNgB8R7TToYHiw23HZBIqDhRTGZyMxPGNWRTqWMtuucXf9p1kTvzonQlMQ/HE8SSmYfA3Q9d2NKY3Z8f5eMdJ2mM++qdXLtP9/EMdHG+odYLdywC7Uor8Fi/0FXAuKsthhdgPWms0tWzlsmPzvZs36M+m9ntYh9ZANsVANkV3MMqH247ht1wyXxIHkgTshBBC7CpDKSylqC5cQHcHIvRP5yhW7LrsPxl005P0o4o/hcK319lSQ/k1VPktiP6/QEVJV45mV7Wnmztp9gUlw04cOArFvfFGXp5avY7czvdf4zg7W+r0w0vjVG3Nu0818CvdZ/nboavbrmnXEYgAtey927VEvLtar24tjtZMF7fW/Kc9EJbusELssVS5iNaakmMzkKkFmcbyWVkEWycD2RR/dP1N3tvSxT2xxkN/o3MxU9vRDoY0cjgSJGAnhBBi11mGSXUho8ZtWkxn5+u2788+2A6UofjMJp9RhdLraO/TR7Kr2qlwgoeSLfs9DCFWpYCHEi28Mj22K/WDlpbE1mGl+99fmcB2HN57ponfPvsIr02P8dzE0Jb24TYMzkUbGJ8vkCnemVXo9+z9VLyWqaN5eWpkS887E0lia0e6+QmxS5Zn0eUqZS6lpra8bF1sXdmx+eFIH1dSM3yh5+yelieoN03tc3amVKDBGzjUr0XUyCeuEEKIXecybn3cTBWzPNAZw23V5yMo4nOhKtdBb2GJbfFZAN7d1FGXMRwUUbeHj7QfWypGLcRBo5TCb7k4HUnszv4Xm07UKf/kp9em+J2fXKdUsWkNhLb8/Mca2jANxV++Orzq9/unstg7zAbcjpcmR5grFze9vYHiVCQuwToh6mjxpsXi3xXH4ZfTo/zulVf53auvSbBujw3l5slUSoc6e9FQCkdrbEfzk7EBHLQ01zjk5FNXCCHErnMb5tK/nxntx2UaPNIdr8u+PRYLjSW2QOfYfln6g+t4qHZBLXdTxUHmaM17mrtWnBfqpV5LYpebTJfon87R4AlsaeLsNgzujTcxPl9YdTkswN+9NYpG72mQXSnFlfmtdcduD4TwmLIwR4h6WAyg9Kbn+LPei/zLSy/xx9ff5N9dfY1fTNwkV63s8wjvXn81cIWK4xzqIJehFI2+ACfDcb41cJWiXT3Ur+duJwE7IYQQu255VsZUMc9sqcBTJ5KYO6zd1BLx4rJcULm6xWcqlHJRdupTR++gSHh9dcssEmK3GErhNS2e2oUM18VYtVPn98HzN6ZxmQa/efohPtt1mlPh9W84GMBXTtyPS5l87+LYmtul8hWevz69J+9aR2sqjs2bsxOky1ur33kynMDWR+8mhxB7zdGaol3lT3sv8rc3rzFeyKGpLWE8anOSw2imVODbg1cOdR07qH3OtvhDfKith+8N32CykJPVF4eU3CoTQgix63K3dSN8YXKYT3WeojPuX9E1MeJz8d7TjWRLVcbni4ynC8xmy0sXs1G/C5dpULU1Fdvh6VMNaG2jKpe2OKJa4fSifbQmx43eAIb0cBSHgKEUD8SbGM6luZ6e3dG+Im4PTzV24DJMwgtNEepRw265kVSBH7w9xtnWMB2RCD3BKA/mMvzlwBWqq2TrfuXE/QRdbv785SGGZtdfrv/mcIqnTzfWd8BreH5imFdn1g4grkaBLIcVYpsWgySLtcUGMil+NNZPprK1Ls1i7wznM0wWcjR4/Yd6xYKhFCGXm890nualqRHGChkeTLRIXbtDRgJ2QgghdlXVce5Y3nE9PUvVcTjRGFoK2AU8Jv/4yR7CPhdaayyzdnFYqtr8xS9vksqX+e33n1x511Pb6MpV0JuvxQSAql3U3xdvoCMQZrqYpyMQwmua9GfTPDc2SLp6eCbT7f4QTzd30egL7PdQhNiST3Sc5M96LzJZ3EINytt0BiKciSbJFCrYWjM8l2cmV//374t9M7zYN4NS8PF7W7nQHee3z10gXSlxIz2LpQw8pkWTN0Dc6+Nbrw1zbSKz4X5nd2GsqzGUYjS/8Xhu1+oP4bNcuzAiIY625R1H35mb4vnJ4SPbnf6ouZya4j3NXUsB18Ma4DKUgULzZGM7s6UCV+dnOBWOH/puuHcTCdgJIYTYVfNrTE7TlRKnmkM8c3kcj2Xwjx7vIeyzGBm+SbFYxLIsgsEgsXicLz7Swc3ZPFprxicmMAwD0zSJx6NgNoGRAGcLNZl0BvJ/jWV10+DuoMGbBGcO7BFOhs9zMhzlj69fZHYLRdn3y7loAx9tPy71ScSho5RCa82JcHxHATuoZbH8789sdWn81oW8Fu851ciDnTFsx8GuVAhgcCHZgtYsZC4YvD44x1vDqU3v9+Zsno64f/cGvsDcxgXayXBcusMKsQ2LAZFUucgzo33Y8jl9aFydn+Hp5i4qjo3HtHC0RnE4A3eLY455fCS8tc8ZWR57eEjATgghxK5xtGa2uHpDiL70HBcaWvn8Q+10xv2EvC7GxkYpFmtBsmq1SiqVIpvN0tXVzYnGEJlMhnQ6vbSPXC5HR3srOvwvUJl/C/YWOqoVf7Js8agLqAIajCg68t/wobbj/Hn/Vpfa7j5LGQQXlv0BC4ECuVMqDicFNPuC+z2MDRlK8f4zjTx+PAlAPp9jfHwcZ5W1t93dPbTGfFva/+tDc3sSsIu4vQxvMcvuWCgmS+2F2IaKYzOYneeV6TEJ1h0yuWqFP77+JplKmQvJFp5s6lgK2h1Wy+eJMmc8PCRgJ4QQYtdorUmtkaX22sw498ebOdUcwmUaaK3J5XJ3bFetVhkdHaGhoYGJiYkV3ysWi/QPDNHd3Qnh/xsq+wdQeWcbI122ZNdJQXWYkKt9G/upP7dh0uoP0R4I0RGI0OQLyERLHBlKqV3pFltPPrfJrz3SSWfcTz6fZ3x8HHud+pfZbIameJx4wL3p5a4uU+16XSFHa85Gk1xKTW36OSGXm6jHu2tjEuIocxkmPx4bkHp1h9Tcwvz1xakRKo7Du5s793lE4m4kATshhBC7xlBqzYBdtlrmX73zMgBfPnYPCZdnzf0UCgWGhoZW/V61WqWvb4Duri7M4G+icn8O5Zd2NG7lpPBY+zsx85kW72nu4mw0iVIKWzsYqEO5HEOI9RzkAHQy6OEfPN5FyOtiYmJiRYbvWmZnZ4lGY9zTFuG5a5sLjh1rCOJoMHfxR2EoRWcwQtLjY7q0eubz7bqDUSlQLsQ2OVpTqFb3exiiDmZLhQP9WSWOLilGIYQQYteodQJ2i0IuN83+INlsdtvHcRyHvv5+yuUyBH8dfB8DFdn2/nBmcRsGFgaPNbRyIhTb/r62KOn1cyHZwm+ceoAzC8E6AFMZctEsjqSDehFkKGrBOk+ttuZmgnVQOx9VqxXubY9u+lixgHtPllrZ2uFUJLHp7buDEWQhnxBb52jNzVyaqq5zy2qxL8YKGXLVstQLFntOMuyEEELsqrnS2gG7qNvLu5s6a7XuZmd3fKzBwSHa2toIBD4Cvo+gnTxULqLyf1drNLFZlaso34f55+cvoBYKrV+fn+Fvb17f8RhXE3V7ebKxne5QFO9CcWM4uIEMIerJZx3M6eh97VGifjcjIyNLtTU3K51Ok0wmaY54GZ/f+LnvjKZ596mG7Q518zQENtnx1W2Y9IRich4SYhsMpXh2tG+/hyHqpGjb/Gnv2/xK1xliHp+cF8WeOZgzJCGEEEdCxbHJVu+s3dLkC/B4QxvHQjEcrcmk0+vWhNqKkZERPB4PwWAQn8+Hz3cB7X4Alf8OlH4ObOJud7UXMr+PMhNQ6UP7P0uLf3dq2j2YaObppk6UUksTQJkIirtJyOXBZ1oU7IOzdCzgsfjA2WbKlcqqtTU3Mjs7Sywe5772KOPz42tuZxmKx44leHovgnXUzi1ec3MBuzORxLa6ygohIFcpM18u7fcwRB1lK2W+0XeJT3acpCsYkVUPYk9IwE4IIcSuWWs57Oe6zuAxTNLpNFNTU6t2WtyJUqlEqVSbKFuWRVtbK27/58DzBKrwdwuNKTY4ZuXisl4UVXSdF4aFXR4+2n6c9kBYakSJu16zP0h/JrWjfbhMRcXe2fu0JeLl4/e10hDyYBmKm2vUztwMrTVuc/XqM7GAm0e64zzUGcPjMvfsHKCUoiMYxmUYVDY4794Xb0LDoe6KKMR+CbjcHAvF6MvM7fdQRB2VHZtvDV7hPc1dPJRs2e/hiLuABOyEEELsCkdrpourFzY3lKJYKNzR9XU3VKtVBgeHCIVCNDU1oEK/iXayqNJLUPpZrSvsuhSYnczlt7Ykbj1Rt4cvH793qTumBOvE3czWDi2+nQXslFL85tPH+d2f9mI72w/ave9ME60RL4VCgcnZ2VpdzG0yDIPp7MoMm464nyePJzjdHK41mTDU0vj3iscw+WjbCX4xeZPZ25pPBCwXx8NxjoeiNPoCezYmIY4aR2seiDdJwO4I0sBPxgdx0FxItu73cMQRJwE7IYQQu0KjmVujE+F+FO3NZDJkMhlCoRCxWAyP973gOo9K/8/rP9FsQxle+jNrL2vbCq9p8vmus7gMU5a+CgEYKFr9oR3tQ2tNMujhyeNJfnZ99c6sJxqDvPtkAx6XyTtjaV7snaZUvZVl5nebHG8Mks1kGB/f2fvd6/ViKMVUphawawx7+PT9bbTF/NhOLZtuNzvCrkcpxfFwlJOROP2ZOV6ZHiNdKfFIspV7Yo0okEYTQuyQoRTdoSjdwQgD2fn9Ho7YBc+ND2Epg/vjTXLjVewaCdgJIYTYFaYy7sjeWORovW/BqsXAXUNDA7FYMxgxcNa5A+5+ELTNu5o7SZVL9O7gbrmhFJ/uPE3Y7ZFgnRALlFK0+UObWqa5nmKxyHtON/L26DxzuZWZcaebQ3zp0S6qVRvHsXn6VAOP9sS5PpGhVHFwuwzOtdQ6S++kAY7b7aa5uRmXq1YnLluq0pUI8A8e61rKplv8ez8ZC810OoMReha6YC8/L+//CIU43PTCjckPtR7jD6+/Kd1ij6i/HxugOxgl4vZI0E7sitULawghhBB1sFbBZecA1GwLh4Noew6c7LrbaddZHK3QjsMnOk7SvINlYheSLbT6QxKsE+I2pmFwfCFwtF0jIyOA5l0nkiseD/tcfOhcM5Vqlb6+XgYGBrg5NISpbc61hHmwK8o9rRGq5SIjw8M7Wgbb0tKC4XKRX2ig8fixBPd3RDFNhXEAAnW3M9WtSwE5LwlRP0oplFKE3B56QtH9Ho7YRZdTU5KVLHaNBOyEEELsmtwqHWItpXAZ+/vx09TUhGm6ULlvsKyzxCoMMJvI5XIM9A+ggKeaOrd93I5ARDJXhFiFozXHw/Gd7cNx0BqqyxpP3NMW4b94/0mifheTy2pmlkolBgcH6e/rpb+3l77eGwwPD1MorJ4VvBnhcBi3282LU8P83rXXuZlNc7I5xMhcHkOppYwbIcTRdvt7PWi592kkYi/0ZVJyw0PsGlkSK4QQYtfkq9U7HjsbbcBnuZiY2f6ys51wu9217LrSS6jqtfU3trpRyiSfz+M4Do5t7+hOV5MvsO+ZhUIcRIZSdATCO96PUgrDqHV7Pd0c5j2nGymXywwOD1Nd5XxUT/F4nPlyiVenxwDoy8zR1hyiULZ39bhCiINl+ef8K1Oj9GdT+zcYseuK9u5+toi7mwTshBBC7IqiXcVZZZFAf2aOimMTi8eYn9/7Qsztba2gC6j8t9ff0HUGHfwNtFMlk8lgGAaGYVDdQZaM7ThgbvvpQhxpfstFyOUmU9n+ktRSscCF7gQXuhNorcnn8wwPD9dxlGuzbZugx0NXMMpANlVb+g988ZFaVq4E64W4+zw3MbTfQxC7zJRzu9hFErATQgixK/LVO5eaKuDxxnZchkkml9/zMfl8PiyXG+2UwfdxqFwDe3Sh6cRCFowKgOcRtO/T2HaVgYEBwuEwiWQSFFyaW70D5Wb0ZeY4F2tYUTdKCFGjteZEOM7rM9vv0Do8PEwikaBcLlMoFHY9q265kZERurq6+GzXaYZzaVr8QarVKrOzszQ1Ne3ZOIQQB8eDieYdndPEwRd0yZJnsXskYCeEEKLutNZkV8mS+XjHCU6FE8zPzzOxrJ7UXikUCszMzBAMBnG7H0d53wWA1g7oDKBRRhSAUrHI2NgYHR0deDweRvMZfjDcy1y5uO3jW4aBkip2Qqzp8YZ23p6b3FG32JmZmTqOaPMcx6G/v5/2jg5avAEqxRKTk5PE43H0AWi0I4TYW1prLiRaJGB3xKUrpRVdtoWoJwnYCSGEqDuNZqa0MoPufLSB05Eks7OzTE9P79PIahfzixf0Ho8Hn8+H1+vFtXCHtFCYJZfL4ff76eruoupofjTaz5uzOw8wNnmDMqETYg1KKTymydNNXfxorB+oLTWyD1mzhuGbN1d87fF4JFgnxF1IKUXQ5cZSakflNMTBNl8u8dbsBPfFm2SOJ+pOAnZCCCHqTqFW3FGOuDy8v7WbUqm0r8G625VKJUql0h2Pt7S0EAwG6cvM8cxIP3l7vU6ym2Mpg6jHu+P9CHGUGUpxf6KJimPTl5njc91nGMqm+eFIL4VDWtjb5XLt9xCEEPtEKcWZaJK3d1BOQxx8L0wOcz7WgKGkULGoLymiI4QQoq5srbkyP0OqfCsQ9qG2Yxgobt6WeXIQdXZ2EgqFeGV6jG8PXatLsA7gfKxBFsMKsUkXGlr5Qs85lIaeUIQPtR5bdTuvadIVjOzZuCzLIhzefDdbv9+PYch0W4i7ldaaD7cd53Qksd9DEbuoYFcp2dIRXNSfZNgJIYSoK1MpXp4aWfo65vbSGYwwNzeHs4O6VLvNMAy6urowLYtnRvq4ODdZt32fDMd5urmzbvsT4m4xNjpKJBLheDjGr/acYziXpj+Toqod2gNhnlxsYpPJ7Oo4DMOgra0Nj9eLoRSJRIKbN2+u29TC4/HQ3Nws9euEuIsppXC05lQ4wdX5/amvKfaG25TsOlF/ErATQghRV/lqhZlSYenre2ONONrZt0Lwm9XV1YVhmvz14FUGsqm67fepxg4ea2zDkYt2IbZEAfF4nLGxMQzDoMnjo9Uf5PHGdqCWuVIulxkaG6ZcvrPJTb34fD5aW1vBqN2MmC0VeH9rD13d3YwMD1Ms3tmIJhqN0tDQUHsd8r4X4q5mKMWJcIwGr5+pYn7jJ4hDx2dauA0J2In6k4CdEEKIuvKaKz9aTkcTlEvlA51d193Tg2HVgnWD2fm67fdYKMZjjW0AUohYiC1SShEIBPB6vYyOji49vrgkNZ/Pr5vhVg+WZdHa1ka2WuZv+q8tXWyXbJvPdZ8hGo0yPr6yA2RTUxORSEQy64QQSzSa97d08+f9l/d7KGIXnIkm5ZwvdoUU1RBCCFFXhlL4lgXtHK3RB7g7WndPD5Zl8TeD1+oarHMZBh9s7cE5wK9diINOa72UqbYonU6TTqd3PVgHtZqWFe3wH/vfWZEZ82hDK7bjMDm5cul8LBYjEqnV1JMLNyHEIkMZRNzSeOqoOhWOI7M9sRskYCeEEKLuAtatrojZShnLOpgJ3SdOnMDtcmEoRcWpb7Hgpxo78FsuyawTYgeUUng8nqUg2F4yTRPDNEmVi+SqteYzpyMJPt15irZAmLnZ2RWZwy6Xi2QyeaBvUAgh9k+5zvMMcTB4TYsWf0jme2JXSMBOCCFEXTlak6ncqidlH9AlApFIZKl7Y8V2+ELPOS4kW+qy7yZfgAcTzTJ5E6IOtNYkk8k977Zq2zaTExM0egN8tus0D8Sb+ETHSboDETKZDLOzsyu2l5p1Qoj1RN1ePFLn7MjpCIRlvid2zcFMeRBCCHEoOVrTl56jtHAXuSMQpjMYIZVK7e/AVhEOh8mVqvyvP7iCYcBXn+zh6eYuWv0hetNzjBeyK5pnbJYCPtx2HL3wbyHEziilMAyDZDJ5xxLU3ZZOpzEMg/aGBjqDEcqVCgP9/XdsF41GCQaDezo2IcThYijFqUiirl3oxf6z9vhmkri7SMBOCCFE3RhKcT0zu/TvD7T2ULXtPb/I3gyX28ObN1MAOA78wc/7ed/pRt51qoET4TiO1rw6PcYLk8NU9eYbZryvpZukxydZNkLUkVKKaDRKLpcjl8vt6bFTqRTFYhHTNFc9tt/vp6GhQQqOCyHW5WjNE43tXE5NYcvS+SPDkNuzYhdJwE4IIUTdOFovNZw4H20g5vYyNja2z6O6k9frxTINrk9mVjz+46uT/Oz6NJYJv/JwBw83tHAmmuCZkX4Gsql192kqxUfbT3AqHJeLdiF2gdaa5uZmBgYGsO29rQVVLBZXfTwQCNDa2grIUlghxPoMpQhYLu6LN/H6zPjGTzgAvKaFz7RwmyZuY+GPaVK0qwxkUtJoAWgPhLG1g6kk007UnwTshBBC1I3WmpjHB9QCWAD5fH69p+yLSCSCozUD03dmy1Qdh6oDf/riIN2JAF94pIPPd5/hjZlxnhsfWjXbzmtafKrjJG2BsFy0C7FLli+NnZiY2O/hEA6HaWpqAiRYJ4TYvMca2g5swM5UijZ/mO5QhJ5gjITXt+a2s6UCv5i4yfX07JrbHHWGUhwLRSVYJ3aNBOyEEELUjaEUiYWA3VBuHqUUkUiEubm5fR7ZSj6fj4n5IqXq+ktdB2Zy/K/fv8LnH2rnvrYmuoIR/nLgCulKaWmbnmCUj7Yfx2NaUnRYiF222DV2v8ViMVkGK4TYMqUUfstF2OVZMZfYS5ZSeC0XxWqVqnYIudz0BKMcC8foDESwDANbOxsu9Yy6vXyq8xT5aoWiXSVfrXBxdpIr89N3TebduWgSn+Xa72GII0wCdkIIIepGLQvYzZaK5CplgsHggQvYKcNkKrv5zL+/em2YN4fn+NIjXXy++wx/1vs2ZcfmeCjGZ7pO42gtwToh9sh+B8gaGxuJRqMSrBNCbFvU7d3zgJ3HMHkg0cyFZAuehfIli0s5tdZoWJrLbCZjbHFbv+XCb7mIur20B8I81dTBy9OjXJqbPNK1+hS1bEn5LBC7SXI3hRBC1NXySctAdh6X272Po1mL3nKJ4N7JHN98eZCI28snO04C8FRThwTrhNhjpdL+ZKUYhkFbWxuRSATY/8ChEOLw2qtgnaEUbf4QTzd18ptnHuKJxnbchrn0/cXAnFJqx3OZxecHXW4+2NrDf3rqQawjulTUZ1p8ouMkEbdXPgvErpIMOyGEEHVja4eBTGrp66HsPOdjDbjdbsrl8v4N7DYK2M78ajpbwnE0Sa+fY6EYSa+/7mMTQqxvfn5+z48ZiURIJpMYhiEXZ0KIHbG1w3x59UY29XQh2cKTjR1YhoGj9cLcZ/fPX4ZSaK0JutzEPF6miuuvaOgKRvhI23H+vP8S8+X9uSGzFZ2BMJ/sPLUi8CnEbjmaIW8hhBD7wlTGim6qA9kUZcemvbNj/wZ1m2g0immaTMxvfbL85ce6UQqeGenjfS1dOEd4qYcQB5HWmng8vifHsiyLWCxGd3c3TU1NEqwTQtSFgdr1Gm+WMniisR3LqF3uG0rt6flLKYWjNV8+fg+dgcia27X5Q3ym8zRBl5uk5+DfBDVQfKT9OB7DlNUVYk9IwE4IIURdDWRvZb8U7CrPjvRhGSaWdTCSugOBAMWKzc9vTG/5uYMzOSzD4OMdJwi53DJZE2KPKaVwuXZW4NuyLExzZWaEaZoYhoHH46G5uZmOjg56enpIJpNLx5NgnRBiJ+yFLvN9y1YibJfHMNdtCvFkU/u+L0c1lEKh+HTnqaX6xsu1+IJ8vvvMUkZeyHUQS6isdDqSIOTyyOeB2DMH4+pJCCHEoae1ZrqUJ1+trHi84qzfiXWvuVwu8lUH01DYztbucf/ixjQPdcVwyZ1VIfaN2+3Gsiyq1eqG23o8HsLhMOFweKkw+GKwzrbtpXp4Pl/tYnJ58XC5IBNC1JPW8J3h69xIz27peWGXh8caWvFbbgKWi6jHi9e0sB2HqVKeyUIOn2kR9fhwGwamMggekOCXoRSWYfAr3Wf5g2tvUNUOfsvFU40d3BNrWGp0obXecOnsfrOUwZNN7VK7WOwpCdgJIYSom3dSd2atecyDVePDcRzCPg9ffqyL77w1imkocqUq+bK97vMMBZ+8vxXYeWFmIcTOHDt2jMHBQcrlMnrZ0nSXy4XP58Pj8eD3+/F4PGt28DNNcylQJ0E6IcRuUwoSHh8jlotsZeO6vpZSxD1+fqX7DB7TuqMGnWkYNHkDNHj8dWkasVsMpQi63ARdLlLlEp/pPEWTL4hSCkXtRsmLUyMU7CqmUge2s+xTTR2EXJ4D+3MWR5ME7IQQQtSFrTVvz02ueMxQisca2qna1U1lw+wFvVB4uTsR4J9/4BQAtqN58+Ycz9+YZjZfJup3ky5UsJ3aXdQHOqI8fbqBsNclF/RCHABaa7q6uqhWqwwPD1OtVkkmk0sdXJdb7z0r72chxF4xUDza0MaFZCsvTA7z6szYqrVwPYbJY41tPJRowVioBbdWkEgphXlIzmMPJpoZzmVo8YdWPK6B4Vyar5y4j1enx/jZxND+DHAdLsPg/niTBOuWWfy9XOummKgPpfUBDWELIYQ4NJyFYN2zo/0rHn8o0cx7mrsYHR0ll8vt0+hWcrlctLS04PF4gFsX7LXgXO1vyzSo2g6DM3kaQh5CXmtp2YYQ4uBYfqEgFw1CiMNg8fI7VS7x8tQIZcdGoynbDn7L4r0t3XhN68jNORYDPMsDkI7WTBfzuA2TqMdLplLi966+vs8jvdOZSIKPd5zc72EcKMsDdiA3wHaLZNgJIYTYMUMpXp8ZX/GYpQyebOygVCodmGAdQKVSYWhoiFgsRjKZvFXXyqhNNCxz8W+DnmQAFEvLNoQQB8vyCwS5WBBCHAaL56qI28NH2o/f8f2jWiNt8TXdCtY5GMqg0RdYyjQMuTxEXB7mK6V9G+dq7o03Hdn/Ltu1+LMYyWdo9YdAa7m5vQukS6wQQogdcbRmKDvPTKmw4nGXYeA2zQMVrFtubm6OwcHBdS/yDePg1oQRQgghxOG11vzibpl3LL8Vuph5V7SrRNweAtbOuoHX0yPJVjoC4bvmv8tWaK1p9Yf4zs3rPDPaR7pSWnWZt9g+CdgJIYTYEUMp3pgdv+NxW1LkhRBCCCHEKlabH3pNiy/0nOO3zjzME43t+zCq28dj8lRTB1JFbHWL/w0/0nac8XyOV6fHZEVKnUnATgghxI4U7Sp9mdQdjz+YaAZqXVkPKsOofQzKREwIIYQQYv/cnsH2RGM772/p3p/BLDgRjt/RnVesZCiFZRh8oecs8+Wi/KzqTAJ2Qgghts3RmstzUyvS3w0UH247xlNNHeRyOWZnZ/dxhOsrFosMDw9j27YE7YQQQgghDpD74k2EXZ59O/7JcByZHW7MUAqvafGpzlPMl4uyLLaOJGAnhBBi2wyleDs1teKxk5E498QaSaVSjIyM7NPINi+fzx/ooKIQQgghxN3qfKxh347d4g9J7bpNMpTCUgYThRwlu7r0uK0P7kqbw0ACdkIIIbZFa81UMcd0Mb/icbdhorVmcnJyn0a2NUopotHofg9DCCGEEEIso4COQHhfjh2wXHhNa1+OfZgdD8V4cfLWDXtTSchpJ+SnJ4QQYltG81l+Mja46vcOU/2KxsZGXC7XoRqzEEIIIcRRp5SixR8k6vbu+bETHv+eH/OwU0qhFDT6AlycneDFyWFylfJ+D+tQk5CxEEKIbbk4N8nNXPqOx8uODYDb7aZcPtgf0uFwmEgkst/DEEIIIYQQq1Aofv3Yef5D/2VmSoUtPTfq9qK1Zr5SWne7rmCEE+E4bsPAVAYe06QzEMHRWpbEbpGhDJJeH3/a+zYAPtPi/kQzttaYy36WtnYk+24TJGAnhBBiyxyt8a2xTGAoO4/WmlgsxsTExB6PbPN8Ph+NjY1orSW7TgghhBDiADKUwmNafPn4PdxIz9L7/2/vzp8jyc87v3++edV94Eajz+menp4hZ4YciZfkkbiUpZBWpLxaHbFaykdsrO0IRzjCf4wdjliF7B821nZ4tfSuFSutLOogRVISRXKG19wz3Y0+cQN1V2Vl5tc/1DE4u4EG0Cig3689Hct/AAA7kklEQVSIDjTqykQ1uirrk8/3eaobmq9vqNM/QbybmUxOvzJ3VdOZnO7WK/qj2+/sertPlqf02ak5jacyim0iI6PBEaExH/8dB7N5KfFfPryt96tr+tT4jJ4vjg8D0NV2S99YmNdvX35RrkNwtxcCOwDAgVlZ5f1g1+tacaSHrbqmcqO3lMD3fRWLRRWLRfm+T1gHAAAw4hxj5BhXL5Qm9FK5N4SiFXW12mnpdn1Dt2obWu73VL5RmtCvnb8mY8zwOG8qnR1eL0mecfTrF5/X88Xx4URTqr2OhrVWlXBrRePdRlV3G1W5xijvB8q6vhZbDU1nsoR1j0FgBwA4MEdGzxXK+ubCzh52zxfHVArSchz3qexLOp2W7/uKokhxHCtJEnmet+NPEARKp3tLIwYI6wAAAE6HzaFaxvN13vU0ly3o9ZlLqnU7Wmg2dL00PgzqEms1ly3ov3r+Vd2tV/WtxXlF1uorF68P++Kx5PVoWUkPmrVdr4v7YV5FvUDvhdIEy44fg8AOAHBgxhiNpzIq+IFqm5rJPlco67+4dEPdblf37917KvsyPT2tdHr3ZsSbw7kBQjoAAIDTb/Oy1bwX6GrBH14ubQ3j5nJ5ffXaK8OKOkKio2WtlVWvbc6H1bU9bzebyWkqnZOV1Wvjs/w7PAaBHQDgic1lC3qvsjr8/oXihKIk1q1bt57aPjQaDaVSqV2DOMI5AACAs88Ys2WowXaD6jwCouNhjNH3lu/rBysP1YqjXW9zPlvQ7z73ieG/QWyTp7mLpxKBHQDgwBJr1Ylj3alXhpcZSdcKY+p2nu5k2DAMCeYAAACAE/RiaVKxtXp7fXnXybzXi+Nbvqdv4OPxDAEADswxRl+//9GWM2hT6ZzSnqdqtfpU9yWbze669BUAAADA01HwA31+6rz+5Y3XdKM0seN6z3GYvHtABHYAgAOrhR19WFvfctn5bEHWWtVquzeaPS579a8DAAAA8HQYY+T0p/P+0rkrCjYNoJvN5PXy2PQJ7t3pRGAHADiQxFotths7Lp/LFRQniZLk6PtReN7eHRza7faRbw8AAADA7hJrhwM8tjPGKOP5upArSpJ8x9GvX3x+eB32jx52AIADsbJabjd3XD6TySnqdo90W57naW5uTul0Wp1OR5VKRY1GQ57nyfd9ZTIZFYvFI90mAAAAzpbYJlt6pm2fFptYy0CKfbDWyhijhVZdc9nCjsul3nO50KrrVn81zpfOXVHRT/H8PgECOwDAgbjG0XJrZ4VdM+oq66WOdFvlclmpVO8xgyDQ1NSUpqc/LqfffHAAAAAADIRxrP/9gx9qMpXRi+VJ3ShNyHdchXGse42q7jaqWmw3dClX1BemL5z07p4KVtKbKw/1jYV5ffnC83qhNCFjjLpJMuxR5xijv1+6J8cYfWZyjqWwh0BgBwA4sN0q7NY7bU2nske6nSj6eKjFbsEcYR0AAAB24zmOvjB1Xn/18LbuNKr6qwe3VArSWuu0tHkxp5H0hZPayVPE9qsQ395YkSR9a/GuMp6vtzaW9aBZ07984TXZ/lLZ37j0gsI4VtbzOcF+CPSwAwAcSDeJVel2dkx5qoTtI38z7na7vMEDAADgwBxj9OmJWV3oL92MrNXqtrBOkjpxtPPO2MJaKyvpO4t3tdTvZV3tdvTvbr+jdzZWNBb0hsANBk+4xlHW82WM4Vj+EKiwAwDsm7VWD5t1/czErF6fuaT3Kqv64eqCsp6v1ybOHfkbcqfTke33GDH9qVO86QMAAGA/Emv1mak53Zt/b8/bXCuOP8U9Or2+tXBHP1h9uOPycpDSly9e39IHkH51R4PADgCwb4msHjRrer44LtcYvVSe1LXimH6ytqSU6+re3btHur0oijQ/P6+xsTEVCgWFYSjf9+U4DsEdAAAAHskxRnOZwp7XT6ay+jn61z1SYq0qYVtvri7suC7jevrtKy/Jc1xCumPAklgAwL65xtFyu6lz2XyvxF3SSrupxVZdjjGK4/jItxmGoSqVisIwVDqdluPw1gUAAID9SXueni+M7bj8XCavz03NncAenR6xtQqTWH9690Ml2xYT+46j37ryogp+Si5h3bGgwg4AcCAXc0W5pheaWUl36hUt9qfG5vN5ra+vH+n2yuWypqamht9TWQcAAID9SqzVr164poUPfqR61JUkXcqV9E8v35DLieA9Jdaq3u3oa7ff0UbY2XLdWJDWb16+oVKQprLuGBHYAQD2LbaJPj0xO+xR4Riju42qKt2O2nGk8lhZlUpFSZIcyfYmJyc1Pj5O7zoAAAA8EccY+Y6r37z8ou7UK3IdR6+OTQ+PLTnO3CqxiYyMfrS2oO8s3lOY7FxB82sXrqkcpHnejhmBHQBg3waVdYMzaXGSaKFVlyT92b0P9ZWLL+jylSu6dfPmkWyvWCxKoqoOAAAAT84xRhPpjCZSmeFx5eB4luPMnsEJ+aVWU3/x4NZwGuxms5mcXipPKnBcnrengMAOALBvm6c/WdsbQBH3p7jWuqGM0ZH1sTPGyPN4mwIAAMDhucaRyJi2sNbKqhdeVsK2vrVwRx/Wdra3uZgr6vWZizqXLSi2vZU0mz8X4HjwSQgAsC/WWoVxLNfpLSswxuhOozq8Puf5kpX8IFChUFCtVjvU9oIgOOwuAwAAANhFYq2srL63/EDvV9e00m7uejvPOPonl27I6/f7G6y4wfEjsAMA7EtirX66saTvLN7VpXxJF3NFvbWxPLz+dr2iP7r9tn7nyic0NjZ26MAun8/TUwQAAAA4Yom1akZd/fGd97TQ2rn0dbMbpQn5jsMx+QkgsAMA7IvrOFpsNRRbq1u1Dd2qbWy93hj97pVPSEZqNnc/Q/comUxG09PT8n1fEv1EAAAAgKNmrdX9Rk3/8e77asXRI2/ryOjzU+dlxWrik0BgBwDYt8X+gIndxNYqsomiVkcrKyv7fkxjjKamplQul6moAwAAAI6JtVZvbSzr6/dvyu7j9q+OT6sUpDg+PyEsPgYA7EsYx9oIO4+8TSPqHnhQxPj4uEqlkiSq6gAAAIDjkFir1U5Lf/ng1r7CupTj6j+buXjs+4W9UWEHAHgsa61+ur70yNtkXE/jqYw2NjYO9NiDsA4AAADA8XCM0U/Wl5TzArXjSGES73nba4Ux/aNzl4eD5nAyCOwAAI/15uqCvrEw/8jbXM73greDBHbpdPrAFXkAAAAADia2ib507oq+dO6KJKkTR/r+ykO9ubowDO8mU1l96dxlXcyXlFgrh7DuRPEpCQDwSM2oq2q3owu5opZbDXX2OBt3OV9WFMcKw3Dfj93pdFSr1VQoFOhfBwAAABwT12ztiBY4rn5u+oJ+bvqCYpsosVaB48r2F8wS1p08AjsAwCOlXVe/OHt5+Kb9lw9u6Udriztu83xxTN0DhHVSb6ntw4cP1Wq1NDU1RWgHAAAAPAXGmOHkV8e4H1/OPNiRwdAJAMAjOcaRY4ystVrrtDRfr+y4zeszl+Q7rhYXF3d5hMfb2NjQnTt31Gq1lCSJpF6YBwAAAADPIirsAACPNOhf8ZP1JX3j4bwim2y5/lwmr1fHZ1StVg+0HHa7Tqeje/fu9R7z3Dnl8/lD7TcAAAAAnFYEdgCAR3KM0Z/e/UDvVlZ3vf5ivqjEWi0sLBzJ9owxyuVyLI0FAAAA8MwisAMA7CmxVvcbtT3DOklaabfkGKN0Oq12u/3E2zLGqFgsanx8nLAOAAAAwDONwA4AsCfHGP1g9cEjb3OtMKY4SQ61HLZUKmliYkKu22t4S2AHAAAA4FlGYAcA2FNirW7WNna9zkh6ZXxGnxybUqNeHw6LOKhMJqOZmRkmxAIARt6grysAAMeNwA4AsCfHGHnG2TFoYiqd1a9feF7jqYzCMHzi6bCSND09TVgHABhp1lr9Xzff0kvlCc1lCyoFaaVdPkoBAI4P7zIAgEfKeJ5q3a3LXV8qT2osldHCwoJqtdoTP3ahUFAqlTrsLgIAcKw6Saxm1NWP15b0zsaqSkFKX754/aR3CwBwhhHYAQAeKePuDOzacSRr7aHDuqmpKarrAAAjL+16+m9vvLblstgmco1zQnsEADjrCOwAAI+Udv0dl7Wi6Il7+JTLZY2Pj8vzPMI6AMCpRVgHADhOvMsAAPZkrdXlfGnH5bVuR8YYzczMKJPJKJ/P7+vxyuWypqenmQYLAAAAAI9AYAcA2JMxRp+ZPLcjtLtdr+in60sqlUq6ePGi5ubmVCwWH/lYruuyBBYAAAAA9oHADgDwSFbSL8xc2nH51+/f1Nduv6M/u/ehJMlxHv2WEsfxSIR1sU1krT3RfQAAAACARyGwAwA81t1GdcdlVtJ8vaLFVkPS/pa31uv1pxaWJZu2Y60dfr/UaiiyyZbrAQAAAGCUMHQCAPBIjjH6oLq25/XrnbZW2k2NT0yoWq0qjuM9b1uv1x+7dPawEmvlGKONTls/Xl9UOUirGXVVDtK626jqg+qani+O69cuXBveFgAAAABGibGsCwIAPEJird6rrOo/9Ze+7mYildHvX3tFnuMMq9msJPXfYqy1Muoti02lUse+v40o1PuVNX16YkaucdSJI31QXdNcpqCxVFrGGMXWyiWsAwAAADCCCOwAAPuy1GroH5Yf6IPqqnZ745hIZTSdzilwXQWOq5TrKuV4ul4aV9bzn+q+Dpa7DqrnEmtlrZX7mD57AAAAADAKCOwAAPsyWD7aTWL9/dJ9fX/lwa7BnST5jqNPj8/qU+MzKgYpxTaRa5wdjwUAAAAA2InADgBwIEl/eetqu6U/f3BTC636jtu8PnNRn5mcGw6Y+On6sn6w+lD1bqhfu3BN14pjcmROfGIsAAAAAIwihk4AAA5kUBk3ns7on1/9pN6rrOrbi3dV7XaGt5nN5GUkOY6jr91+R/P1yvC6Tn8oBWEdAAAAAOyOwA4AcCCJtYptotu1DV3IFXWjNKEbpQk1o64+qq3rdn1D05mcrKTlVmNLWDdgE6uN2oZKpRLBHQAAAABsQ2AHADiQXoWdo6vFMf14bVGtKNK14pim0zm9Oj6jT45NycioFXUfOVl2eXlZuVxOntd7KyK4AwAAAIAeAjsAwIH1QjujT43PKkoS/cPKff3xnfc1m8nrUr6krOfrrx/eVr0b7vkY1lrNz8+rVCppbGxMrutKIrgDAAAAAIZOAAAOZfA20rWJfry2qDdXF1R7RFD3K3NX9VJpQvfu3h1eFsex8vm8xsbGFATBlsclwAMAAADwrCGwAwAcmaT/lvJBdU33GlVthG1thG1Vw44GbzZfOndFr03M7rxvkmhxcVHNZlOpVEr5fF7lclnWWkI7AAAAAM8UAjsAwJGLbSJHZhi01buhfri2oLc3VhQlic5l8pKkciqtT5SnNJPJDYO5JElkTO++hHUAAAAAnkUEdgCAY2etlVWv9103SdSMQsXWajyVUWKtjFj6CgAAAAADBHYAAAAAAADACHFOegcAAAAAAAAAfIzADgAAAAAAABghBHYAAAAAAADACCGwAwAAAAAAAEYIgR0AAAAAAAAwQgjsAAAAAAAAgBFCYAcAAAAAAACMEAI7AAAAAAAAYIQQ2AEAAAAAAAAjhMAOAAAAAAAAGCEEdgAAAAAAAMAIIbADAAAAAAAARgiBHQAAAAAAADBCCOwAAAAAAACAEUJgBwAAAAAAAIwQAjsAAAAAAABghBDYAQAAAAAAACOEwA4AAAAAAAAYIQR2AAAAAAAAwAghsAMAAAAAAABGCIEdAAAAAAAAMEII7AAAAAAAAIARQmAHAHgmWWtlrT3p3QAAAACAHQjsAADPjMQmstaq1u3oTqOidhyd9C4BAAAAwA7eSe8AAADHyVorK8kxRh9W1/Xd5fsq+Sk9VyhrJpNXYhM5hvNXAAAAAEaHsawHAgCcEZvDue1+uLogI6OXx6fkGkexTeQS1AEAAAAYQQR2AIBTz1orsy2k2xzeJZve6nYL8wAAAABglBDYAQBOlUEQZyS14khZzx9el1hLIAcAAADg1COwAwCcCoO3q3Yc683Vh/rp+pIu58v61QvXCOoAAAAAnCkMnQAAnArGGH136b6+u3xfkU0kSS+PTRPWAQAAjKjBcC+O14CDo9s2AGBkWWuH/ef+fumevrN0dxjWSZLrGA7+AOAUGbyus8gHR21wvNBN4uH3/J6Nhtv1DVXDzpaewgAejwo7AMDIGZyFrXY7+sn6kt7ZWFGtG+643TsbK5rN5DlrCwAjZvDBfPDaHCax6t1Q9W6oZtSVa4wynq+M6ynnB0q73vB+RtoxSAjYyyCUW++09BcPbul+s6aJVEZXC2N6eWxKY6nMsR0nJNbKymrQXNeodyKxHUcKHLc/+KpXYfbsMvrrB7c1mc7qNy69cNI7A5wq9LADAIyMwQH1QrOu7y7f10e19S3XT6dz+vTEjHKer5wXqBykFbiuYpvIfaYPhgHgZFlrlcjKNY7acaR7jaruNapabje11mmpEXUfeX/XGI2lMnqhOK5Xx2eUdj1OxJyQweT1UT8ZZocVdYm+vXhHP1pb1G4fbC/mivrF2UuaOcITfIMQbr3T1p16RZFNFCWJ2nGku42KltpN+Y6juWxBn5s8r4v54sg/n48zrFjsB5Pbf5LtIXtirdpxpB+sPNT3Vh7ISPrNyzd0OV8+1c8D8DQR2AEATtzgragRdfWNh/N6v7q65fqM6+nnpi/oU+MzSmTlyFB9AQAjJLFWP1xd0PvVNT1o1g71WD8zMasvzl6WRKXdSfnmw3ndKE9oOp0buXBlcMxQCTv64dqC3t5YUTuOHnu/q4WyvjB1QbPZgwV3gzBaklzjKEoSfVRd0083ljVfr+zrMV4ojutzU+c1ncntuO4wQd7gvp04UjPqqhl1lfF8lfyUXOfjE5mDEHYgTvrtRYy2nPDc/rNKUmwTVcOOVjstVcKOOnE0DCg/blLSe8xGFKrWDdWIurv+mwSOq9+68qLOZfK9zY/Y7xYwagjsAAAnKrFWkU3090v39MPVBUWb3pYcGX1m6pw+P3VernFG7kMDADxrtvagsr1Km/5QoO8s3T2SbbjG6LWJWb0yNt1fzvisLyk8etbawSpOGWOG3zvG6I2Vh/rGwrwCx9XvXHlJs9m8YpvsebJsexi02/XSwcOZ3e6X9Hsgfnvxjt5cXdi1ou5xLuaK+vzUeV3Klx55u14A1VUl7KjSbasSdrTSbmq+X1H3JOayBY0FaXWTRJGNlXI9vVSa1OV8qV/VmEh6fH/eQVB3v1HVd5fv6/a24NBIKvgp5TxfeT9Q1vMVJrFW2y2tdVpb9j9wXBX8QAU/UN4PlPcC+Y6rlXZTi+2G1jutJ3qe9+Iao89Pnde1wpgm0tldf9ZBNZ+VFNlEYRyr3Q8KN7PWKrZWsU1kZOQ5jjzjyHMcBY6rlOsqcNxTUTEK7IbADgBwYhJr1Ykj/dHtd7TSbm65bixI68sXr2sqnZXEWVgAOCmDD86SdLO2rm6SyDFGsU10t17V7frGY5e8Pqlzmbxem5jVC6UJOf1gaXsF0H6d9Q/sjwrVBqIkUa3bUSXsqBVH8h1HadeT5zi6U6/orfVlrYft4e1dY3QpV9L5XEHnswUVg5TSriffcRUniepRqErYkZGU9XxlPF+e48jph05hHGu101RkrbKup7wfKOP5O3ocbteOI611WrLWqhyklfMDxUmiH60t6nsrD47k9y3n+fIdV67ZGpBZK9WjcF9Ve0cl7XqazeQ1m8npXLagqXRWGdfbUiXX27feMtN3K6v6ydqiVjqtp7aPx8EzRqUgPQzlukm85cTtUSn5KX1++rxeHps+8scGjhOBHQDgxFhrVeuG+sP339xyuZH03934GWU8X+4Z/nAFAKNicw866eNm+q5xVAs7+vH6kn6yvqTmMQVzj5P3Ak1lssp7gXKer1KQ1gulcXnGGVaG7WVzNVLW8zWWykj6+Gc2+6hoGmWDSvWb1XXN1yt62KqpGnaU8XzlvUCB6w4HfnT6E1QPyzVG8RN+jJxIZXQ5X5JrHK11WloP2wo37Vc3jnfs5yBU6yZPVtl2WnnGUcbz5BlHrThSJ46OtNrtWfLa+Ky+NHflsRWhwCghsAMAnLh/f/td3apvDL+fSef0+8+/cnI7BADPgM0TWdc6Ld2qbehuoyLXOCoFKWU9X7drFd1p7K9P19PmO45eLE3qpfKkzmcLW5Z2ShpWAX5UXdd3l+9ruV/JnXY9zWRyKvkpFYKUSn5KpSClUpBW1vP33N6ofNDfHK4m1uqN1Yf6u6V7z1yYBRyEZ4xeGZ/RZyfnlPeDx1Z5AqOAwA4AcKKstfrW4h19f+Xh8DLXGP3uc5/QbCbPgRQAHAHb7/3lmN6SyW4Sa75e0a3ahm7XN1Trhie9i4eS9XydzxaUdj2lXFdGRg9bdS00awdaYucYo4zrKeP6ynjecBLmhVxRX5i+cDw7vw+DaejWWi23m7rTqOhBo6b7zZpaT3HpJnDaGUmzmbzOZfM6l+0t9c77gaT9LSsHniYCOwDAiRlUQvxv77+548Ni2vX0Gxev62K+dOb7DgF4Nmxvot+rlNKRL/3fPqShHUdabbe00m5qtdPUUruhhWZj2AsO+/OPLzyvG/1eek/L4P3vdm1DP15b1N1G9ciWtQLoyfuB5jJ5zWULms7ken0TPX/ra7W1ktFjA72kPwDjcUv1gf0gsAMAHLmkH8S5/Ylnu033G7z9/Nm9j/ROZWXPx7peHNenxmd0IVeU84jHA4BRNphu+d3l+3q3sqJykNZYkNZn+suzDvPBbvMSyShJdKu2ro9qG1ptN7URtgl4johnHH312ssaS6UPPPDiSSTWqpvE+uM77+tuo3rs2wPwMdcYFfvL5Qt+qj9JN6VykNJ4KqPMLsvnrbV6Z2NF95s1XS2M6UqhJIfwDodAYAcAODKD/j7z9Q3db9S01mnplfFpXc6Xt9xu0Mz8z+/dfGRYt5nvOLqcL+nVsRldKZSpugNwaiTWKraJ/t2td/SwVd9yXdb19cVzl3W1UFbK9Xa5794nKQYh4INmTYuthh40a5qvVxRZepkdl9dnLupnJ87tmN551BJrZa3Vv59/V3cI64CRk3JclYK0PMfIMY4cSdVuRxthZ3gb33H0UmlSn56Y1WQ6O1xZwvEr9ovADgBwpP5mYX5LPzpJ+vLF67peHN9ygPIf5t/Tzdr6E21jLEjrt668qIKf4qAHwEjaXPVWDTv6k7sf7AjrNjOSptM5jaXSasex2nFXvuPqc1PndTlfUmytHPWW0w6apVtZ/dubbz/ycXE00q6nS7mifmH2kop+6kh7XA2W2xnz8bTaWrej/3f+PS31B2UAON3GUxldzpV0KV/SZDqjjOsrcN0tt9k+rRsgsAMAHKn/9Z3vqR1vXX6VcT199drLKvjBsFLkD997U9VuZ7eH2Je8F+hfvPApecahOTDwFAwqYw/bkHt7H7dHbm/TVE6zy3023+akwvvtkwbDOFYjClXvhlrttPReZVX3m7VDbWMmndOL5Um9WJ6U7zjDYQM3a+vDyac4Hs8XxvTZqfM6l81L+nj4w0HsNl02ThJFNlErilTptlUNe5U5652W1sK2NjptegwCZ5wjo4znKev5ynuB8n6gG6UJXcwVObaFJAI7AMAR+1fv/kCNqLvj8oIf6J9ffVlZz5eR9LdL9/Td5fuH2tanx2f0S3PPHeoxADzaYAlPO470fmVVzxfHhxP1oiRRK+qqEKQeuUx987LO1XZLa52WAteR77hKO57G05nhdiRpud1UJWyr3g3ViLqK+uFGZBNZa+UYR64x8hxn+CGn4AfKuJ4C11XguEq53q5ByWGfi0GI4hpHibW616jqdn1DS62GlttNJnaeEUU/pd++8qLGUplDtWAYfNT69uJd3aytqxJ2WLIMYE9G0uenzusT5SmVU2lJvRNDu520wtlHYAcAOFL/89v/oG6y+4eR2UxOv3f1ZRlJVtLXbr9z6EbaX+kvt5U4kAEOa1BFN6ggim2ih826btU29MO1heH/7fFUWu0oVjPuhfM3ShP6z+eeU+C4O4KNxFp14kjfXJjXfL2ya6D/sxPn9Pnp83prfVlvri4cqvp24MXShH557qr8fq+xzUtJDxq+xEnS703UVq0bqtYNtd5pa76+wUCHM2o2k9NXr71yJI/1p3c/0LuV1SN5LADPjsBxNZPJ6VK+pE+PzyhwXHrgPWMI7AAARyKxVmudlv71hz9+5O0+P3VePz99QVZSmMT6Pz/6qTbC9hNv10j6zOScXp+52PuegxicAkdd+fUk20+slYyG4VwlbOtBs671TksbYVsbYUfL7YbifR4qZlxPX5y9rE+MTQ0va8eRFpp1/cWDW0cSwh1UynH16viMXh2fVpgkWmk3tdZpqdrtDKv24mTnwsO4//wk1qoZdVWPwqe+7zh5/+y5T2ginVVirVKuK6P9L70eVOX9w/J9fXvx7jHvKYCzznccfbI8pblsQWOpjMaC9I4eeAcxCi0l8HgEdgCAAxl80HdMr4/V5olXX7v9jubrlUfe3zVG/8OLn1HgukqsVb0b6v/46CeHXkb2hanz+rnpCwR2GHmJtaqEHbWirnK+r5wXyDHacxLo5vvZfsjmyMjq4+V2g/+Pm+3WvDpOEtWjrjbCtqphW5VuR6vtlh40a0e2lHMmnVPgulpheSjOACPpc/33l/18qB28P96sruu7y/cZCALg2HjGkZXtV8f3jg0C11HgePIdZ1g1H1sr1xilHE8p11PadZXzAxX9lEpBSpOp7HD5bWyTA52cwPHaOTseAIBtNody62FbD5o1LTTriq2V5zjyjKNqt/PYsE7qVa5Uux1NOBk5xijvB/rKxRf0R7ffPtQ+flBd08/3q+yAp2nwAb3SXy6ZclwFrtsP4rYe9CbW6k6joj+ef09RP2ybSGX0+9dekbPHsXEr6qoSdlTrdtSIumpEXXXiSL7jynccFfyUPjk2tSWga8eRHjZrWmm3tNJuaj1sq9rtqLnLctSjtthuHPs2gKfBM46+fPG6rhbK+7p9Yq02Om19/cHNQw8aAYDH2d4PM5HtTxnfq1XD3pXuKcfVuWxBc9m8ptM5zWRyyvnBvgdF4XgQ2AE4U3ZrDL25KoUzRk8mkdU/LD/QOxsrey5f9fY5Ne9SrqTJdHb4vWOMLuaLOpfJH6oSYSNsn/gyQzx7Emu13G7oGw/nd3xAz3uBfvn8c7qUK8ntV8A5xuj9yuowrJOk1U5Lf/DeG8r7gQLH6feB64XglbC9Z0/Ize43q3p1fEZ361V9VFvXw2aN+ZLAIX3l0nVdLYzt67ZJ/2TU/33rLSpLAZw6nSTW7fqGbtc3hpdlPV8vlSb1henzChyXY+wTQGAH4Ex6d2NFD5o1dZJYWddX3g+UGk4OdJV2eyPUBxUwm3WTWK5xCPY2cY2j89mCNjptTaWzwxL6gh+oHKQ10Q/g/urBLf1wbVFSryx/LJXWZDqrUpBS0e/9mcsWdg1WJ9PZQwV2cb+H3lgqw78djt3gd/hhs6b/Z/7dXUO1ehTqP8y/N/ze6Z8w2G1CZDuO1D7Eh/yfri/rp+vLT3x/ADt9VF3vVZl4wY5lYoP2EIMg/sPqmr5+/yZDSACcGc2oq8gm8hyHsO6E0MMOwJmzuXQ7tomqYUeOMfIdV55x5PV7OsQ2kbXqNZLedAA+WPpJtdZWm0O2QdXi9r5Zrf5yPdcYlYL08PZxP6BwtHufrW6S6A/ff/NQgYUkTadz+uq1lwnscGxim8g1jjY6bf3t0l29V1mlkg04wxwZXS2WdTFXVMb1lfN8Zf1AzW5Xa52W1sKWlttN3TvkxHMAGCWOMfryxeu6XhznM9EJIrADcGYMwrbNL2umvwx2eBtZmV1Co6cl6Z+hN8bsWmX2LEmslZHUiLr6+oObulXbOJLH/eq1lzWTznFggSORbBrq0I4jfVhd00fVdd2srRPUAQCAM8eR0VcuXde1whjH0yeMwA7AqRZbq9V2U99beaC025uIFDiuPMeRtVKYxOomsbpJorTrqRSkVA56yzSznr/lw/hhPe7sU2ITNaNIX7v9jmYyef3i7CVlPX/L1NVh4NgPGo0O3+T1JIPBvUbGN6Ou7jeqmm9U9db6kuIjfCu6VhjTP7l848geD8+O7a8H9W6oB82aHjbrvUErrTohHQAAOLNcY/TrF67r+SJh3Sighx0wonYLf+Ikkes8vrH/4L57LVvcz7ZOC9cYTWdy2gjbWmwdbDLhRCqj5wplfaI8pYlU5rHPwWA0urtpuEKcJGrGXTW6XbXjSDOZnDKev+v91zptfe32O2pEXa12Wnq/sqrZTE5jqYzGUxmVg5Ria9WKI7WirqykV8amlfeDYTXaQf+dBk2ws66vwHWHj7O9EvFxvyO72TyRcvNlnSQeLo1t9v/UuqHq3VC1KNRG2Fa9Gx5oWwfxUW1drai7578DsN1gmeuDZk03a+taajW13G7QOB4AADwTjKSin9KvXrimuWzh1H42PGuosANG1FGEaAvNujbCtnKer1KQVs73t4Qrh93WbgHWSUhsokrY0X+8+4GW280D398xRv9o9rJeLE/KN86WUDROEjWirmrdjqrdXti00WlrPWxrI2zv2nOtHKR0LlOQjBTGsTpJrE4cabXTGlbwHGTfXipN6kqhLNcY+Y4jzzgKXFc5L1Da9XY0wB5U6hlJ71ZW9BcPbslaq+vFCb1UnpQxUiuKhk3uM56vuWxBE/1hDXv9PmyuPrLWaqnd1J16RcvthmrdsBfKReGBf8aj9tnJOf3C7KUT3QecHolNFFmrP7nzgW5tmowGAABwVnnG0Wcmz+lyvqxSkFLW8+XQsmfkENgBp9B+ArbNL7bWWnVtom4SK+16w4Attlbu5rCn399N0q5B3OaKqm6S6GZ1TZG1eqk8KelolpU+qcQmkozuN6pqxZHCJFY7jnS7tqE7B2wEbSR5jiNH5lRMe8u43nCZ71Q6q6l0Tqudpr6/8lAbYXvfj+MZRzdKE/rC9HmVgvTw8ihJ1I4jLbbqWmg1tNCq62GzrnDEnptykNYvnbuiK4Xyqa4axdO1+bVyvdPWn9x9X0tPEPwDAACMKqPesfJEOqOJVFafGp9R1vOPpP0Ojg+BHXAAcX9gwOZgalBlZmR27dU1uH6v6ZhJvyPS5usH95GOro/ZdpuXWNa7oVY7LVXCtiphR4m1mkhnNJXODauuat2OVtstrYdt3W9Udbu+oaj/8lH0U/rtKy+pFKR2hHYncZZm8/PnGkdvrDzU3y7dG7mAaVQZSWOpjMI4VjvuDv+dR8lkKqPpTE45L1DB7/15rjAm6WSDY5xeg6E1/+bDn2ilQ2AHAABOP0dGX5q7opfHpobFGEfZwxvHi8AO2MUgSBuEaHGSaK3T0mK7oZV2U2udthxjlHE95XxfBS+lwHWGPbsGXwPHVSlIqRSkVQ7SSruu4n5fucRaRTZRvdtVPer192rHkQLHVdr1lPY8ZVxPKddT2u39fTBUIbZWsU16X5NEMh8Hfk4/UDSbLhv0LIttoijp3a/eDXWrvqFbtY3HVmE5MsNgcS9Zz9eXZi/rRnlyGFyGcaz7zZpm+33dYpvsGlweN2utwiTW3y3d0xurC0912zh6n52c0+szF7f0adwelAP7NehfFyWJ/vrhbf1kfemkdwkAAODQxoK0fnnuOV3IFamiO6UI7PBMGPya210u2/5BP4xjrXSaWmo1tNRuaLHV0Gq79djACj2TqayuFErDyYqDXmrnsnldK4zrYr6ovBco43rDXnG7DS/YbHNvtt3ebAbXbw4ud72NrP7Vu2/s2ncOo+9irqhfnL2smUyOJa84lMHrSRjHer+6qg+ra7pTr4xkNSkAAMBBlIKUPjM5p1fGpmWlYQsknD4EdjhV4iTZdcnpbktQB+FPJ4600GoojGNZ2eH1Yb8vV7vffL8RhVppt1SPjm96JbbyHUd5L9BctqDzuYIu5ooq+KlhJWCUJAptokqnN+BhI2yr0u1ItlfC7RpHrmMUOK5ynq+cFyjvByoGKRX9QL7jDrfVjiP98fx7utesneBPjIOYTGV1of97cTFfUtr1aISLJ7K5BUCUJLrXqOqtjWV91O/DCQAAcNqVg7S+OHtZVwtlWbHk9SwgsMOpYa3VfL2i2/UNVcKONsK2xlK9F6VSkFYnjlTtdrTR6YU6K+2mHvSnpOLZlHY9lfyUCkGgh826GlH3pHcJ25T8lCbSWU2kMioFKRX9lEpBSvl+4DroK8YBB/Zre8VuvRvqXqM6rPpdbjepmAYAAGdKOUjr965+UmnX47j5DCGww6lhrdX71TX9yd0PtlxuJPmOy0ABYISVg5QmUlmNpzKaSGU0me793XM+bn6713AWYDebJ1sPDkxbUVcLrcaWicZNgnoAAHCGecbRf339VRX9nQMAcbp5J70DwH4ZY3SjNKFvPpzfsmzVSoR1wIg6l8nr9ZmLupgvSRpMpbJytvUr7B1ccIDxLNs8QORRk7GttWrHkTbCtlbaLa11en+W2g2qaAEAwDPntYlZwrozisAOI2mvZXCDiagAjtZ0OqfPTs3tGMjhqFfBGjiuAtdVynXlGUeJtf1pxVaJTRT1e4R5/b6CrnHkOY7KQVqJTT5+PIK5Z9IgjNs+OCZKEjX6U7Kr3VC1bkfNqKuoPwE76U/EDvu3a/YncLM0AAAAoOdirsjR9RlFYIddJf2V0k8rpd/cc8haq4VWXR/V1rXWaakTx2rHkTpxrFbcVTdJHv+AAA5kIp3RjdLE8PvBElVZPbbi6XG2V9Ph2RFbK9cYLbbqetCsq9IfHFMNO6p1Q6qjAQAADinn+bSUOaMI7LArxxjdrm/oQrYoz3EU26S39tRIRrt/eLfWbpng+KjlTMmmSovEWq12Wrrfbwp+u17ZUeUD4Hi9s7GiC9miXhmfltR7DbBW/R5hTx7W4dmy+WRPlCR6v7KqN1YXtNRunPCeAQAAnE33mzVNZXLD1iI4Oxg6gT39xzvv62ZtQ9eKY7qUKym2idpxpDCJ1YljdeJIrTgaLlFqx5Fc4+i1iVl9buq8Uq675fGSft+hlXZTq52mVtstrXZaWmw1FFmq5oBRkHY9Ffyg/6c3tfVcNq9zmbxcx9kSygObA7owiXW/UdO9RlX3GlUtthvD6wEAAHB8XpuY1S/OXmKA2xlDYIddxUmij2rr+tO7Hyp5gm5BKcfVRDrTD/ZidZKIpazAKeAao4KfUmQThXE8XLLoGqPn8mV98dxllYI0wd0RGSw9HrQDSPpnRkfpuR20LNh8ABjbRLUw1HK7oXvNXki30m7SWw4AAOCEfHZyTq/PXCSwO0MI7M6wOEkO3Xuq0Q31xuqCPqqtaa3TPtodBHDixoK0rpfGNZnKaiaTUylI7wiLukmsbpIo43oyxii2iVz60h3Y5pAzTGJVwrZW2y1thG1thG25xtFYkFY5ldZ4KqNSkJJrHCU2eap9ADdvr94N+1XRvUmsg32td0PCOQAAgBHiyOgXZi/pZyfPDQd+Sdox9AunB4HdGTRYu/7W+rIcI2U9XzkvUMbzlHF9Of2+cfsJ8ga/HsYYtaNIdxsV3W1U9fbGCs3CgTPgd668pAu5oqSnN2TmWTN4HV3ptPTn9z7S4j77uXnG0dVCWS+WJ/VcofxUljjENtFap6VvPpzXw1adymgAAIBT5kKuqJl0ThnPU9r1lHE9ZT1fU+mcXMdw4v0UIbA7gwZVHKvtpv7k3odaaTe3XD+Zzup6cVzXi+OaTGcPVC0z6EfUiSN9Z/GuHjTrCpPesrluEivm1wk4Vf77Gz+jvB+c9G6cWYm1ipJE31m6qx+tLj5RiwGp12bgn155UbOZ/LEGq4m1+ubCvN5cXTi2bQAAAODoODLyHEelIKVykB7+KQUp5f1AGbcX3FFld/oQ2J1hg3DtJ2tLer+6qgfN2o5AbTaT02cm53S9OH6g/8B7TaBJrFW3P5RiMIyiFUdqxV114lhRkii2iWJrFdlEcZIoslaVsK21ToslVsBT9j998nM0pz0m1lrdbVT1J3c/UOsIJl8X/ZT+m+uvynfcx9/4EP764e09A7uU4yrj+b0Dv37Vdsb15DuOHNM7Y9v72uvDd6u2oY9q68e6vwAAAM+CF0sTen3mktKuJ6d/rLXbidxBj2SO8U8/ArtnwKDiLkoS3WtUdb9Zk2uM0v2kPeN5OpcpyHecI/8P3WtWLslayUhGe/fUi5JES+2GFpp1Lfa/rof0zQOO089OnNNL5UlNZ3KSRH+6IzBoOfCDlYf69uLdJ66q280/vvC8XiiNH9u/0eBkzG6HBnu9PwwOCnsP8PHljjG616jpj26/fRy7CgAA8EzIuJ5+5fxVPV8c37NwBmcTgd0zZlB1t7k33WGGUhy1wZRE1+l9GA3jWA9bdd1tVPT95YdH+sEXwMeyrq8rhbJ+Ze65M9GYdnCiYr/9Oo9CbK1cY1QJ2/r/7n2ke83akW/jc1Nz+vnpi6em36C1Vt9avKMfrDzk1RsAAOAJ/JfXXtFkOntqjv9wdAjsMPIGv6I/XV/S1x/cOuG9Ac62V8am9frMRWU8/6mGXUclsYmMjN5YXdCP1xb1YnlSnx6fObK+HYOTCmbbEoQoSfRhdU0/XV/SnUb10NuRpLTraTKV0XQmp9lMXueyeZWC9Kk6szrY141OW3+3dE/vVFZOepcAAABOjfPZgv7Z1U+e9G7ghBDY4VQYBAd/8N4bakTdk94d4MybyeR0tTCma4Uxjacy8pydSzAH4dVxV+QNtrEfd+oVfWNhfjhsxzNG/+L6p5Xzg0OflUys1XqnrQfNmirdtiphR9Wwo0rYUTN+steltOuq6KdUDFIq+WmNpzKaSGc0nsoo7XrD7Uqnc4rv9tD3X3/44x2DkAAAALDTtcKYfv3i88MewXj2ENhhJPV631m5xlFird6rrOqHqwt62Kqf9K4BzyTXmP6EKf/jEfGer3KQ0njQC5kKfmp4MBEnyaGCvM1VZOudlm7XN7TWaStw3H7/TVdp1+8PPvDkGqONsKNbtXW140idOFY7jnSjNKHXJmaPLFC01mqp3dSdekX3mlV14kixtYoTq9gmSqwdDtbxHVc5z1fO85X1g+Hfc56vcpBWIUgp2DRAIrFW9ikEoIc12E9JOyoNN9+m1u1oud3Uarul1U5TK+2mVjqtp727AAAAp4prjL44e1mfnpg90IlrnD0EdtiX7UuwNv/aDC4fhGyykoy2TKV5VIXI4H5GZthzaqXd1L1GVQ+avf51RzFhEcDxcoxRyU9pKp3VbDav89mipjNZucY5VDXe4DXCkRn2QbOD15rNtr3uHJfNJxQOeh/p6ezjQT1q/8IkVieO1I5jtfuTv4ffx9HH08A3fe0k8Un9KAAAAKfaXDav37v6siQGwj3rCOxwaGudlurdcMsHuPbwA12kMEk0nkrrfLaoC7mCMp4/vG8Yx1rttLTSbmq109RSq6GFVkORTU7wJwJwVFxjNJXOajKd1WSq93Uqnd3yOiBtmjS6S+CP/dk+tGc/ukmsejdUtRuq1u2otsvXbsLrMQAAwNNU9FO6kCvqYq6oy/mS8n5wqvoY42gQ2OFQBpVzt+sbWm43tdZf+rTWae8ZupWDlAp+Suudlur0owOeSRnX01gqrbTrKeV6SvWXuqZcTynXVcrxlPN9FfxAOW9r/7nBksy9lmOOkuHyUaNhGLmr/nVGB5vcPahajJJES+2GHjRrWmm3FNtE1vYrEdUL86x630eJVT0KVe+GCqmEAwAAGHn0s3s2EdjhSAw+lA4qO6y1Wmw19G9vvU21HIBDy3m+8n6ggh+o4KdU8AONBWlNZ3Iq+ClJT28IxnaD6sDNVYHdJNZ6p63VTlO1bjg8uWG1NTyztleFGLiuUo6rYEt46Srl9nr0me2BZX9J7t16RX+zcEdL7caOFcIAAAA4O8aCtD4/fV4vliYl6UAneXE6Edjh2Fhr9Y2H83pzbeGkdwXAGeY7jiZSWU2mM5pMZTWVzmk6k1XqCKes7vYYzairSthRJWyr2u1NjF0P21rrtI50mrWR+mFlSsVNgeViq6G3NpaPbDsAAAAYfVnP1ytj03q+OKbpdE7GGMVJcqC2KDgdCOxwrL7x8LbeWCWwA/D05TxfU+legPeJ8pQm0pnHTtraXqVnrVUl7Gix3dBKu9lb+t9pqdbtKObtEwAAACco7Xq61O9zdyFX1FgqI6k3rIKe0KcfgR2OzfdXHuhvFu6c9G4AgKRe748vTF/QdDorqbeEILF2uJygHUdabjW13G5opdPUSruplXaLZf0AAAA4FdKuq9lMQXPZvM5lCzqXyStwXUmEeKcRgR2OnLVWb20s68/v3zzpXQGAHT47OafXZy4qtlZ3GhXdrK7rdr2iardz0rsGAAAAHKlykNZsJqeZTF5jQVrjqYwKfrCl/3zS74+M0UJgh2Px7saKqt2OmlFXrThSK4rUirtqRZGaUaiIXzsAJyjr+QrjiNciAAAAPJPyfqBykFY5SOlcpqBXxqdlraUCb4QQ2D1Fg8l+spKMZGSGvZSOoin6qBgk9LK9ZWbbf6bEWr1bWdH3lx9opdM6ob0EAAAAAACS9LMT5/TFc5e3XLb9sz2TaZ8uArttEpvI6ZeC7haixTbZUSq6vYm5tVa2f79m1FW9G6odR2rHkTpxrE7S+xrGsdpJpG4cayyV0cVcUedzBfmOe6YCvN0MnufldlONbqhWHKndr8AbPFet/td2FKmTxMPntffcWCX6+LkGAAAAAABPLuN6yvmBMq6ntOsp43rKeP7w72nP00Qqq1KQ2pGNJNbKWisZ7dorL7ZJL/Cjj96+Edj1DUo/7zeq+puFO8p4vibTGU2msprO5FQO0gqTWDer6/qotq75ekWuMbqcL+m5wpiuFspKuZ4kqRK29e7Gqt6vrmq53TzQfhhJM5m8LuQKOpcp6HyuoKznS+r9gu9VtXaaDUK3R/3n3vdj9NP/ThLr33z0E9W74bHsMwAAAAAAz6K5bEGvjk3ramFMnSRSvdtVPQrVjEI1oq6aUVeNbu+rMUblIKVSkFY5SGss1fs6yDmkj1cjMhRjKwK7Tb61cEffW3mw63WOjPoLWnc1CNqiJD7yZZ4FP9BctjD8pc56vvKer5wfKOv6w6kvA4Nk2zHP5i+7tVZhEusP3ntD3YTpjgAAAAAAjBLPOFuCvHIqrYlURhdyxZPetZFBYLfJ//L29xQm8UnvxoG5xgyDvKKfGjaOHEtlNBaklfODHffZ3k/vUUn243rS7XWfzRVvkqTBmncdbt375oq83fYnShJ9c2FeP1pbfOJtAAAAAACApydwXP2Pn/jsSe/GyPBOegdweLG1qnVD1bqhFluNHdd7xqgYpJXzfKUcVynXU+C6SrueAsdVqv/3lNNbk552XAWuKyup0Q1Vj0LVu101+uWtvdLWUPWoq9gm8h1XvuMo6H/1HXfPv/uOo4zrq5xKKecFw7DNWqukH8DZ3gU7KgTjJFE9Coc/ayMK1eiX3vb2q/f1NIauAAAAAAAAAwR2z4DIWq11WlobsYmsRlLBT6kU9P4MqgOTQQAZhap1O6r3A7p2HJ30LgMAAAAAABw7AjucGCup2u2o2u3o7s7CQAAAAAAAgGeS8/ibAAAAAAAAAHhaCOwAAAAAAACAEUJgBwAAAAAAAIwQAjsAAAAAAABghBDYAQAAAAAAACOEwA4AAAAAAAAYIQR2AAAAAAAAwAghsAMAAAAAAABGCIEdAAAAAAAAMEII7AAAAAAAAIARQmAHAAAAAAAAjBBjrbUnvROjohNHJ70LAAAAAAAAz6SU6530LowMAjsAAAAAAABghLAkFgAAAAAAABghBHYAAAAAAADACCGwAwAAAAAAAEYIgR0AAAAAAAAwQgjsAAAAAAAAgBFCYAcAAAAAAACMEAI7AAAAAAAAYIQQ2AEAAAAAAAAjhMAOAAAAAAAAGCEEdgAAAAAAAMAIIbADAAAAAAAARgiBHQAAAAAAADBCCOwAAAAAAACAEUJgBwAAAAAAAIwQAjsAAAA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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt_kws = {\"categorical\": True, \"figsize\": (16, 12), \"cmap\": \"Set3\", \"legend\": True}\n", "for strategy in times.index:\n", " ax = world.plot(strategy, **plt_kws)\n", " ax.set_axis_off()\n", " ax.set_title(strategy)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "------------------------------" ] } ], "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": "3.13.5" } }, "nbformat": 4, "nbformat_minor": 4 } mapclassify-2.10.0/notebooks/06_api.ipynb000066400000000000000000000513471503430131600202370ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Overview of the mapclassify API\n", "\n", "There are a number of ways to access the functionality in `mapclassify`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We first load the example dataset that we have seen earlier." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T15:10:19.167785Z", "start_time": "2022-11-05T15:10:14.404320Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:09:46.155836Z", "iopub.status.busy": "2025-07-11T20:09:46.155682Z", "iopub.status.idle": "2025-07-11T20:09:47.825654Z", "shell.execute_reply": "2025-07-11T20:09:47.825424Z", "shell.execute_reply.started": "2025-07-11T20:09:46.155819Z" }, "tags": [] }, "outputs": [], "source": [ "import geopandas\n", "import libpysal\n", "\n", "import mapclassify" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Current `mapclassify` version." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T15:10:19.182165Z", "start_time": "2022-11-05T15:10:19.171353Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:09:47.826059Z", "iopub.status.busy": "2025-07-11T20:09:47.825935Z", "iopub.status.idle": "2025-07-11T20:09:47.828633Z", "shell.execute_reply": "2025-07-11T20:09:47.828449Z", "shell.execute_reply.started": "2025-07-11T20:09:47.826051Z" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "'2.9.1.dev9+gde74d6f.d20250614'" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mapclassify.__version__" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T15:10:19.586837Z", "start_time": "2022-11-05T15:10:19.187232Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:09:47.828952Z", "iopub.status.busy": "2025-07-11T20:09:47.828888Z", "iopub.status.idle": "2025-07-11T20:09:47.878217Z", "shell.execute_reply": "2025-07-11T20:09:47.878008Z", "shell.execute_reply.started": "2025-07-11T20:09:47.828944Z" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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"gdf = geopandas.read_file(pth)\n", "y = gdf.HOVAL\n", "gdf.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Original API (< 2.4.0)\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T15:10:19.595711Z", "start_time": "2022-11-05T15:10:19.589037Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:09:47.878751Z", "iopub.status.busy": "2025-07-11T20:09:47.878597Z", "iopub.status.idle": "2025-07-11T20:09:47.881121Z", "shell.execute_reply": "2025-07-11T20:09:47.880928Z", "shell.execute_reply.started": "2025-07-11T20:09:47.878743Z" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "BoxPlot\n", "\n", " Interval Count\n", "----------------------\n", "( -inf, -0.70] | 0\n", "(-0.70, 25.70] | 13\n", "(25.70, 33.50] | 12\n", "(33.50, 43.30] | 12\n", "(43.30, 69.70] | 7\n", "(69.70, 96.40] | 5" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bp = mapclassify.BoxPlot(y)\n", "bp" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Extended API (>= 2.40)\n", "\n", "Note the original API is still available so this extension keeps backwards compatibility." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T15:10:19.603460Z", "start_time": "2022-11-05T15:10:19.598526Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:09:47.881418Z", "iopub.status.busy": "2025-07-11T20:09:47.881360Z", "iopub.status.idle": "2025-07-11T20:09:47.883786Z", "shell.execute_reply": "2025-07-11T20:09:47.883564Z", "shell.execute_reply.started": "2025-07-11T20:09:47.881412Z" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "BoxPlot\n", "\n", " Interval Count\n", "----------------------\n", "( -inf, -0.70] | 0\n", "(-0.70, 25.70] | 13\n", "(25.70, 33.50] | 12\n", "(33.50, 43.30] | 12\n", "(43.30, 69.70] | 7\n", "(69.70, 96.40] | 5" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bp = mapclassify.classify(y, \"box_plot\")\n", "bp" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T15:10:19.611996Z", "start_time": "2022-11-05T15:10:19.608075Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:09:47.885008Z", "iopub.status.busy": "2025-07-11T20:09:47.884942Z", "iopub.status.idle": "2025-07-11T20:09:47.886786Z", "shell.execute_reply": "2025-07-11T20:09:47.886599Z", "shell.execute_reply.started": "2025-07-11T20:09:47.885002Z" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "mapclassify.classifiers.BoxPlot" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(bp)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T15:10:19.619168Z", "start_time": "2022-11-05T15:10:19.614412Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:09:47.887068Z", "iopub.status.busy": "2025-07-11T20:09:47.887010Z", "iopub.status.idle": "2025-07-11T20:09:47.889333Z", "shell.execute_reply": "2025-07-11T20:09:47.889155Z", "shell.execute_reply.started": "2025-07-11T20:09:47.887061Z" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "Quantiles\n", "\n", " Interval Count\n", "----------------------\n", "[17.90, 23.08] | 10\n", "(23.08, 30.48] | 10\n", "(30.48, 39.10] | 9\n", "(39.10, 45.83] | 10\n", "(45.83, 96.40] | 10" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "q5 = mapclassify.classify(y, \"quantiles\", k=5)\n", "q5" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Robustness of the `scheme` argument" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T15:10:19.627988Z", "start_time": "2022-11-05T15:10:19.621853Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:09:47.889601Z", "iopub.status.busy": "2025-07-11T20:09:47.889524Z", "iopub.status.idle": "2025-07-11T20:09:47.891745Z", "shell.execute_reply": "2025-07-11T20:09:47.891561Z", "shell.execute_reply.started": "2025-07-11T20:09:47.889593Z" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "BoxPlot\n", "\n", " Interval Count\n", "----------------------\n", "( -inf, -0.70] | 0\n", "(-0.70, 25.70] | 13\n", "(25.70, 33.50] | 12\n", "(33.50, 43.30] | 12\n", "(43.30, 69.70] | 7\n", "(69.70, 96.40] | 5" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mapclassify.classify(y, \"boxPlot\")" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T15:10:19.634396Z", "start_time": "2022-11-05T15:10:19.629847Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:09:47.892023Z", "iopub.status.busy": "2025-07-11T20:09:47.891966Z", "iopub.status.idle": "2025-07-11T20:09:47.894385Z", "shell.execute_reply": "2025-07-11T20:09:47.894186Z", "shell.execute_reply.started": "2025-07-11T20:09:47.892017Z" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "BoxPlot\n", "\n", " Interval Count\n", "----------------------\n", "( -inf, -0.70] | 0\n", "(-0.70, 25.70] | 13\n", "(25.70, 33.50] | 12\n", "(33.50, 43.30] | 12\n", "(43.30, 69.70] | 7\n", "(69.70, 96.40] | 5" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mapclassify.classify(y, \"Boxplot\")" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T15:10:19.641115Z", "start_time": "2022-11-05T15:10:19.636017Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:09:47.894748Z", "iopub.status.busy": "2025-07-11T20:09:47.894682Z", "iopub.status.idle": "2025-07-11T20:09:47.897179Z", "shell.execute_reply": "2025-07-11T20:09:47.896985Z", "shell.execute_reply.started": "2025-07-11T20:09:47.894741Z" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "BoxPlot\n", "\n", " Interval Count\n", "----------------------\n", "( -inf, -0.70] | 0\n", "(-0.70, 25.70] | 13\n", "(25.70, 33.50] | 12\n", "(33.50, 43.30] | 12\n", "(43.30, 69.70] | 7\n", "(69.70, 96.40] | 5" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mapclassify.classify(y, \"Box_plot\")" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T15:10:19.691302Z", "start_time": "2022-11-05T15:10:19.645124Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:09:47.897522Z", "iopub.status.busy": "2025-07-11T20:09:47.897410Z", "iopub.status.idle": "2025-07-11T20:09:47.899898Z", "shell.execute_reply": "2025-07-11T20:09:47.899723Z", "shell.execute_reply.started": "2025-07-11T20:09:47.897515Z" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "StdMean\n", "\n", " Interval Count\n", "----------------------\n", "( -inf, 1.50] | 0\n", "( 1.50, 19.97] | 5\n", "(19.97, 56.90] | 37\n", "(56.90, 75.37] | 3\n", "(75.37, 96.40] | 4" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mapclassify.classify(y, \"Std_Mean\")" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2022-10-26T03:01:45.977181Z", "start_time": "2022-10-26T03:01:45.931234Z" }, "execution": { "iopub.execute_input": "2025-07-11T20:09:47.900260Z", "iopub.status.busy": "2025-07-11T20:09:47.900198Z", "iopub.status.idle": "2025-07-11T20:09:47.902657Z", "shell.execute_reply": "2025-07-11T20:09:47.902454Z", "shell.execute_reply.started": "2025-07-11T20:09:47.900253Z" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "StdMean\n", "\n", " Interval Count\n", "----------------------\n", "[17.90, 19.97] | 5\n", "(19.97, 38.44] | 24\n", "(38.44, 56.90] | 13\n", "(56.90, 75.37] | 3\n", "(75.37, 93.83] | 3\n", "(93.83, 96.40] | 1" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mapclassify.classify(y, \"Std_Mean\", anchor=True)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "execution": { "iopub.execute_input": "2025-07-11T20:09:47.903002Z", "iopub.status.busy": "2025-07-11T20:09:47.902932Z", "iopub.status.idle": "2025-07-11T20:09:47.905671Z", "shell.execute_reply": "2025-07-11T20:09:47.905465Z", "shell.execute_reply.started": "2025-07-11T20:09:47.902994Z" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "(np.float64(38.43622446938775),\n", " np.float64(18.466069465206047),\n", " np.float64(17.9),\n", " np.float64(96.400002))" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y.mean(), y.std(), y.min(), y.max()" ] } ], "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": "3.13.5" } }, "nbformat": 4, "nbformat_minor": 4 } mapclassify-2.10.0/notebooks/08_manual_coloring.ipynb000066400000000000000000236437251503430131600226550ustar00rootroot00000000000000{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "1d5cebc1-08a5-4f67-94b0-3bdb849b820d", "metadata": { "execution": { "iopub.execute_input": "2025-07-11T20:21:52.934608Z", "iopub.status.busy": "2025-07-11T20:21:52.934157Z", "iopub.status.idle": "2025-07-11T20:21:54.233120Z", "shell.execute_reply": "2025-07-11T20:21:54.232893Z", "shell.execute_reply.started": "2025-07-11T20:21:52.934579Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Author: eli knaap\n", "\n", "pandas : 2.3.1\n", "geodatasets: 2024.8.0\n", "geopandas : 1.1.1\n", "mapclassify: 2.9.1.dev9+gde74d6f.d20250614\n", "\n" ] } ], "source": [ "import geodatasets\n", "import geopandas as gpd\n", "import pandas as pd\n", "\n", "import mapclassify\n", "from mapclassify.util import get_color_array\n", "\n", "%load_ext watermark\n", "%watermark -a 'eli knaap' -iv" ] }, { "cell_type": "code", "execution_count": 2, "id": "8aa8876b-aafe-456b-895a-b34f7311746f", "metadata": { "execution": { "iopub.execute_input": "2025-07-11T20:21:54.233590Z", "iopub.status.busy": "2025-07-11T20:21:54.233468Z", "iopub.status.idle": "2025-07-11T20:21:54.422610Z", "shell.execute_reply": "2025-07-11T20:21:54.422332Z", "shell.execute_reply.started": "2025-07-11T20:21:54.233582Z" } }, "outputs": [], "source": [ "df = gpd.read_file(geodatasets.get_path(\"geoda cincinnati\")).to_crs(4326)" ] }, { "cell_type": "code", "execution_count": 3, "id": "baa23e00-83ea-4962-90c5-695893d17f03", "metadata": { "execution": { "iopub.execute_input": "2025-07-11T20:21:54.422994Z", "iopub.status.busy": "2025-07-11T20:21:54.422905Z", "iopub.status.idle": "2025-07-11T20:21:54.498187Z", "shell.execute_reply": "2025-07-11T20:21:54.497976Z", "shell.execute_reply.started": "2025-07-11T20:21:54.422987Z" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# use mapclassify under the hood\n", "df.plot(\"DENSITY\", scheme=\"quantiles\", cmap=\"YlOrBr\")" ] }, { "cell_type": "code", "execution_count": 4, "id": "bb6b3e10-4053-4b54-845e-d1fdea1fdc95", "metadata": { "execution": { "iopub.execute_input": "2025-07-11T20:21:54.498543Z", "iopub.status.busy": "2025-07-11T20:21:54.498474Z", "iopub.status.idle": "2025-07-11T20:21:54.503866Z", "shell.execute_reply": "2025-07-11T20:21:54.503678Z", "shell.execute_reply.started": "2025-07-11T20:21:54.498535Z" } }, "outputs": [], "source": [ "# get colors directly and pass them to geopandas\n", "colors = get_color_array(\n", " df.DENSITY.values, scheme=\"quantiles\", cmap=\"YlOrBr\", as_hex=True\n", ")" ] }, { "cell_type": "code", "execution_count": 5, "id": "545ce6dd-2b2a-42ad-989f-4efae3f46277", "metadata": { "execution": { "iopub.execute_input": "2025-07-11T20:21:54.504221Z", "iopub.status.busy": "2025-07-11T20:21:54.504156Z", "iopub.status.idle": "2025-07-11T20:21:54.506349Z", "shell.execute_reply": "2025-07-11T20:21:54.506163Z", "shell.execute_reply.started": "2025-07-11T20:21:54.504213Z" } }, "outputs": [ { "data": { "text/plain": [ "array(['#fee290', '#fd9828', '#fee290', '#662505', '#fee290', '#fd9828',\n", " '#662505', '#fd9828', '#fd9828', '#ca4b02', '#fd9828', '#662505',\n", " '#662505', '#fd9828', '#ffffe5', '#662505', '#662505', '#ca4b02',\n", " '#662505', '#662505', '#fee290', '#662505', '#ca4b02', '#662505',\n", " '#ffffe5', '#fd9828', '#fee290', '#ca4b02', '#fd9828', '#fd9828',\n", " '#ffffe5', '#ca4b02', '#fd9828', '#ca4b02', '#ffffe5', '#662505',\n", " '#662505', '#ca4b02', '#fd9828', '#ca4b02', '#fee290', '#ca4b02',\n", " '#662505', '#ffffe5', '#fd9828', '#fd9828', '#fd9828', '#fee290',\n", " '#fee290', '#fee290', '#662505', '#ffffe5', '#ffffe5', '#fee290',\n", " '#ffffe5', '#ca4b02', '#fd9828', '#fd9828', '#ffffe5', '#ca4b02',\n", " '#fd9828', '#fee290', '#ca4b02', '#ca4b02', '#ca4b02', '#ca4b02',\n", " '#662505', '#fd9828', '#fee290', '#fee290', '#fee290', '#fee290',\n", " '#fee290', '#ffffe5', '#fd9828', '#ffffe5', '#ffffe5', '#fee290',\n", " '#fd9828', '#ffffe5', '#ffffe5', '#ffffe5', '#ffffe5', '#ffffe5',\n", " '#ffffe5', '#fee290', '#ffffe5', '#fee290', '#ca4b02', '#ffffe5',\n", " '#ffffe5', '#ffffe5', '#ca4b02', '#fee290', '#ffffe5', '#662505',\n", " '#662505', '#fee290', '#fd9828', '#ffffe5', '#ca4b02', '#ffffe5',\n", " '#fd9828', '#fd9828', '#662505', '#662505', '#fd9828', '#fee290',\n", " '#fd9828', '#662505', '#fd9828', '#ffffe5', '#ca4b02', '#fd9828',\n", " '#662505', '#ca4b02', '#662505', '#fd9828', '#fee290', '#ca4b02',\n", " '#fee290', '#ffffe5', '#ffffe5', '#ffffe5', '#fd9828', '#662505',\n", " '#ca4b02', '#ffffe5', '#fd9828', '#fee290', '#ffffe5', '#ca4b02',\n", " '#ffffe5', '#fee290', '#fee290', '#ffffe5', '#662505', '#ffffe5',\n", " '#fd9828', '#662505', '#fd9828', '#662505', '#662505', '#ffffe5',\n", " '#662505', '#ffffe5', '#662505', '#ffffe5', '#fd9828', '#ffffe5',\n", " '#ffffe5', '#ca4b02', '#662505', '#662505', '#ca4b02', '#662505',\n", " '#ffffe5', '#662505', '#ca4b02', '#fee290', '#fd9828', '#ca4b02',\n", " '#662505', '#ffffe5', '#fd9828', '#fd9828', '#ca4b02', '#ca4b02',\n", " '#662505', '#ffffe5', '#fee290', '#fee290', '#662505', '#ca4b02',\n", " '#ffffe5', '#662505', '#fd9828', '#fd9828', '#ffffe5', '#ffffe5',\n", " '#fee290', '#fee290', '#fee290', '#ffffe5', '#ffffe5', '#fee290',\n", " '#ffffe5', '#ffffe5', '#ffffe5', '#ffffe5', '#ca4b02', '#ffffe5',\n", " '#fee290', '#ffffe5', '#662505', '#fee290', '#fee290', '#662505',\n", " '#fd9828', '#fd9828', '#ca4b02', '#ca4b02', '#ffffe5', '#fee290',\n", " '#fee290', '#662505', '#ffffe5', '#fd9828', '#ffffe5', '#ffffe5',\n", " '#fee290', '#fd9828', '#fd9828', '#fee290', '#ca4b02', '#ca4b02',\n", " '#fd9828', '#fee290', '#fee290', '#fee290', '#fee290', '#fee290',\n", " '#fee290', '#ca4b02', '#fee290', '#662505', '#fee290', '#662505',\n", " '#ca4b02', '#662505', '#662505', '#fd9828', '#ffffe5', '#ffffe5',\n", " '#ffffe5', '#ffffe5', '#ca4b02', '#662505', '#ca4b02', '#662505',\n", " '#662505', '#ffffe5', '#ffffe5', '#ca4b02', '#ca4b02', '#ca4b02',\n", " '#ca4b02', '#ca4b02', '#662505', '#ffffe5', '#fee290', '#fee290',\n", " '#fee290', '#ffffe5', '#fee290', '#ffffe5', '#ffffe5', '#fee290',\n", " '#fee290', '#fee290', '#ca4b02', '#662505', '#fd9828', '#fd9828',\n", " '#662505', '#fd9828', '#ca4b02', '#ffffe5', '#662505', '#fd9828',\n", " '#fee290', '#ffffe5', '#fee290', '#ca4b02', '#ca4b02', '#ca4b02',\n", " '#ca4b02', '#fd9828', '#fd9828', '#662505', '#ca4b02', '#ca4b02',\n", " '#ca4b02', '#ca4b02', '#ca4b02', '#fd9828', '#fd9828', '#ca4b02',\n", " '#662505', '#ca4b02', '#662505', '#ca4b02', '#ca4b02', '#ca4b02',\n", " '#fee290', '#ffffe5', '#ffffe5', '#662505', '#ca4b02', '#fee290',\n", " '#fee290', '#ffffe5', '#fd9828', '#ca4b02', '#fd9828', '#fd9828',\n", " '#fee290', '#fee290', '#662505', '#fd9828', '#ca4b02', '#ffffe5',\n", " '#fee290', '#fee290', '#662505', '#662505', '#ffffe5', '#662505',\n", " '#662505', '#ca4b02', '#fee290', '#fd9828', '#fee290', '#662505',\n", " '#ffffe5', '#662505', '#ffffe5', '#ffffe5', '#ffffe5', '#fee290',\n", " '#ffffe5', '#ffffe5', '#fee290', '#ffffe5', '#fd9828', '#fee290',\n", " '#662505', '#ffffe5', '#fee290', '#ffffe5', '#662505', '#ffffe5',\n", " '#ffffe5', '#fee290', '#ca4b02', '#fd9828', '#fee290', '#fd9828',\n", " '#fee290', '#662505', '#fee290', '#662505', '#fd9828', '#662505',\n", " '#662505', '#fd9828', '#fee290', '#fd9828', '#662505', '#662505',\n", " '#fee290', '#fd9828', '#ca4b02', '#fd9828', '#ffffe5', '#fee290',\n", " '#ca4b02', '#ca4b02', '#fee290', '#ffffe5', '#fd9828', '#fee290',\n", " '#fd9828', '#ffffe5', '#662505', '#fee290', '#fd9828', '#fee290',\n", " '#ca4b02', '#ffffe5', '#fd9828', '#662505', '#fd9828', '#ca4b02',\n", " '#662505', '#662505', '#fd9828', '#662505', '#662505', '#662505',\n", " '#ca4b02', '#fd9828', '#fee290', '#fee290', '#ca4b02', '#ffffe5',\n", " '#662505', '#fd9828', '#ca4b02', '#662505', '#ca4b02', '#ffffe5',\n", " '#ca4b02', '#662505', '#ca4b02', '#662505', '#662505', '#ca4b02',\n", " '#ca4b02', '#fd9828', '#662505', '#fd9828', '#fee290', '#662505',\n", " '#662505', '#662505', '#fee290', '#fd9828', '#ca4b02', '#fee290',\n", " '#ca4b02', '#662505', '#fd9828', '#fd9828', '#ca4b02', '#662505',\n", " '#fd9828', '#fd9828', '#fee290', '#ca4b02', '#fd9828', '#ca4b02',\n", " '#ca4b02', '#ca4b02', '#fd9828', '#ca4b02', '#fd9828', '#ca4b02',\n", " '#ca4b02', '#ca4b02', '#fd9828', '#fd9828', '#fd9828', '#662505',\n", " '#fee290', '#ca4b02', '#fd9828', '#fee290', '#662505', '#662505',\n", " '#fd9828', '#662505', '#fd9828', '#fd9828', '#ffffe5', '#ca4b02',\n", " '#fee290'], dtype=object)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "colors" ] }, { "cell_type": "code", "execution_count": 6, "id": "e45fcd62-a96a-4601-a355-783cd0323d32", "metadata": { "execution": { "iopub.execute_input": "2025-07-11T20:21:54.507489Z", "iopub.status.busy": "2025-07-11T20:21:54.507411Z", "iopub.status.idle": "2025-07-11T20:21:54.564317Z", "shell.execute_reply": "2025-07-11T20:21:54.564110Z", "shell.execute_reply.started": "2025-07-11T20:21:54.507482Z" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.plot(color=colors)" ] }, { "cell_type": "markdown", "id": "a76cea95-34e0-4f3a-a945-1b1a4477fec2", "metadata": {}, "source": [ "geopandas `explore` method can also use mapclassify under the hood or take a list of colors" ] }, { "cell_type": "code", "execution_count": 7, "id": "adcc5cbd-fa95-40c9-9532-7ad9a4898ce4", "metadata": { "execution": { "iopub.execute_input": "2025-07-11T20:21:54.564701Z", "iopub.status.busy": "2025-07-11T20:21:54.564631Z", "iopub.status.idle": "2025-07-11T20:21:54.752936Z", "shell.execute_reply": "2025-07-11T20:21:54.752690Z", "shell.execute_reply.started": "2025-07-11T20:21:54.564693Z" } }, "outputs": [ { "data": { "text/html": [ "
Make this Notebook Trusted to load map: File -> Trust Notebook
" ], "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# json doesnt like numpy arrays\n", "df.explore(color=list(colors), tiles=\"CartoDB Positron\")" ] }, { "cell_type": "markdown", "id": "533b18dc-33aa-42d1-b160-dc693272eb0b", "metadata": {}, "source": [ "For some visualization libraries, you *need* to pass the colors explicitly. Their examples usually punt on classification schemes, but rather use linear or logarithmic scalers " ] }, { "cell_type": "code", "execution_count": 8, "id": "151d7f77-042f-40b2-b0d0-a1aa90268ed2", "metadata": { "execution": { "iopub.execute_input": "2025-07-11T20:21:54.753415Z", "iopub.status.busy": "2025-07-11T20:21:54.753297Z", "iopub.status.idle": "2025-07-11T20:21:54.804046Z", "shell.execute_reply": "2025-07-11T20:21:54.803594Z", "shell.execute_reply.started": "2025-07-11T20:21:54.753408Z" } }, "outputs": [], "source": [ "from lonboard import Map, PolygonLayer" ] }, { "cell_type": "markdown", "id": "da5f4fee-3487-4fd8-bd8a-b1cf4bf3deb4", "metadata": {}, "source": [ "lonboard requires a 2-dimensional array of integers" ] }, { "cell_type": "code", "execution_count": 9, "id": "f4eed567-9259-47b6-a251-4a5266dbb59f", "metadata": { "execution": { "iopub.execute_input": "2025-07-11T20:21:54.804899Z", "iopub.status.busy": "2025-07-11T20:21:54.804744Z", "iopub.status.idle": 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Forms-B\":u=>u>=65136&&u<=65279,\"Halfwidth and Fullwidth Forms\":u=>u>=65280&&u<=65519};function o0(u){for(let a of u)if(dh(a.charCodeAt(0)))return!0;return!1}function a0(u){for(let a of u)if(!zx(a.charCodeAt(0)))return!1;return!0}function zx(u){return!(Oe.Arabic(u)||Oe[\"Arabic Supplement\"](u)||Oe[\"Arabic Extended-A\"](u)||Oe[\"Arabic Presentation Forms-A\"](u)||Oe[\"Arabic Presentation Forms-B\"](u))}function dh(u){return!(u!==746&&u!==747&&(u<4352||!(Oe[\"Bopomofo Extended\"](u)||Oe.Bopomofo(u)||Oe[\"CJK Compatibility Forms\"](u)&&!(u>=65097&&u<=65103)||Oe[\"CJK Compatibility Ideographs\"](u)||Oe[\"CJK Compatibility\"](u)||Oe[\"CJK Radicals Supplement\"](u)||Oe[\"CJK Strokes\"](u)||!(!Oe[\"CJK Symbols and Punctuation\"](u)||u>=12296&&u<=12305||u>=12308&&u<=12319||u===12336)||Oe[\"CJK Unified Ideographs Extension A\"](u)||Oe[\"CJK Unified Ideographs\"](u)||Oe[\"Enclosed CJK Letters and Months\"](u)||Oe[\"Hangul Compatibility Jamo\"](u)||Oe[\"Hangul Jamo 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u>=1424&&u<=2303||Oe[\"Arabic Presentation Forms-A\"](u)||Oe[\"Arabic Presentation Forms-B\"](u)}function c0(u,a){return!(!a&&l0(u)||u>=2304&&u<=3583||u>=3840&&u<=4255||Oe.Khmer(u))}function bf(u){for(let a of u)if(l0(a.charCodeAt(0)))return!0;return!1}let u0=\"deferred\",iA=\"loading\",nA=\"loaded\",ph=null,us=\"unavailable\",_u=null,Bc=function(u){u&&typeof u==\"string\"&&u.indexOf(\"NetworkError\")>-1&&(us=\"error\"),ph&&ph(u)};function h0(){Od.fire(new as(\"pluginStateChange\",{pluginStatus:us,pluginURL:_u}))}let Od=new Nl,f0=function(){return us},v_=function(){if(us!==u0||!_u)throw new Error(\"rtl-text-plugin cannot be downloaded unless a pluginURL is specified\");us=iA,h0(),_u&&cl({url:_u},u=>{u?Bc(u):(us=nA,h0())})},ua={applyArabicShaping:null,processBidirectionalText:null,processStyledBidirectionalText:null,isLoaded:()=>us===nA||ua.applyArabicShaping!=null,isLoading:()=>us===iA,setState(u){if(!Li())throw new Error(\"Cannot set the state of the rtl-text-plugin when not in the 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oA{constructor(a){this._properties=a,this._values=Object.create(a.defaultPossiblyEvaluatedValues)}get(a){return this._values[a]}}class nr{constructor(a){this.specification=a}possiblyEvaluate(a,h){if(a.isDataDriven())throw new Error(\"Value should not be data driven\");return a.expression.evaluate(h)}interpolate(a,h,A){let x=Da[this.specification.type];return x?x(a,h,A):a}}class dr{constructor(a,h){this.specification=a,this.overrides=h}possiblyEvaluate(a,h,A,x){return new Mo(this,a.expression.kind===\"constant\"||a.expression.kind===\"camera\"?{kind:\"constant\",value:a.expression.evaluate(h,null,{},A,x)}:a.expression,h)}interpolate(a,h,A){if(a.value.kind!==\"constant\"||h.value.kind!==\"constant\")return a;if(a.value.value===void 0||h.value.value===void 0)return new Mo(this,{kind:\"constant\",value:void 0},a.parameters);let x=Da[this.specification.type];if(x){let E=x(a.value.value,h.value.value,A);return new Mo(this,{kind:\"constant\",value:E},a.parameters)}return a}evaluate(a,h,A,x,E,P){return a.kind===\"constant\"?a.value:a.evaluate(h,A,x,E,P)}}class wf extends dr{possiblyEvaluate(a,h,A,x){if(a.value===void 0)return new Mo(this,{kind:\"constant\",value:void 0},h);if(a.expression.kind===\"constant\"){let E=a.expression.evaluate(h,null,{},A,x),P=a.property.specification.type===\"resolvedImage\"&&typeof E!=\"string\"?E.name:E,D=this._calculate(P,P,P,h);return new Mo(this,{kind:\"constant\",value:D},h)}if(a.expression.kind===\"camera\"){let E=this._calculate(a.expression.evaluate({zoom:h.zoom-1}),a.expression.evaluate({zoom:h.zoom}),a.expression.evaluate({zoom:h.zoom+1}),h);return new Mo(this,{kind:\"constant\",value:E},h)}return new Mo(this,a.expression,h)}evaluate(a,h,A,x,E,P){if(a.kind===\"source\"){let D=a.evaluate(h,A,x,E,P);return this._calculate(D,D,D,h)}return a.kind===\"composite\"?this._calculate(a.evaluate({zoom:Math.floor(h.zoom)-1},A,x),a.evaluate({zoom:Math.floor(h.zoom)},A,x),a.evaluate({zoom:Math.floor(h.zoom)+1},A,x),h):a.value}_calculate(a,h,A,x){return x.zoom>x.zoomHistory.lastIntegerZoom?{from:a,to:h}:{from:A,to:h}}interpolate(a){return a}}class aA{constructor(a){this.specification=a}possiblyEvaluate(a,h,A,x){if(a.value!==void 0){if(a.expression.kind===\"constant\"){let E=a.expression.evaluate(h,null,{},A,x);return this._calculate(E,E,E,h)}return this._calculate(a.expression.evaluate(new un(Math.floor(h.zoom-1),h)),a.expression.evaluate(new un(Math.floor(h.zoom),h)),a.expression.evaluate(new un(Math.floor(h.zoom+1),h)),h)}}_calculate(a,h,A,x){return x.zoom>x.zoomHistory.lastIntegerZoom?{from:a,to:h}:{from:A,to:h}}interpolate(a){return a}}class Bd{constructor(a){this.specification=a}possiblyEvaluate(a,h,A,x){return!!a.expression.evaluate(h,null,{},A,x)}interpolate(){return!1}}class Hn{constructor(a){this.properties=a,this.defaultPropertyValues={},this.defaultTransitionablePropertyValues={},this.defaultTransitioningPropertyValues={},this.defaultPossiblyEvaluatedValues={},this.overridableProperties=[];for(let h in a){let A=a[h];A.specification.overridable&&this.overridableProperties.push(h);let x=this.defaultPropertyValues[h]=new sA(A,void 0),E=this.defaultTransitionablePropertyValues[h]=new d0(A);this.defaultTransitioningPropertyValues[h]=E.untransitioned(),this.defaultPossiblyEvaluatedValues[h]=x.possiblyEvaluate({})}}}Ge(\"DataDrivenProperty\",dr),Ge(\"DataConstantProperty\",nr),Ge(\"CrossFadedDataDrivenProperty\",wf),Ge(\"CrossFadedProperty\",aA),Ge(\"ColorRampProperty\",Bd);let uo=\"-transition\";class ji extends Nl{constructor(a,h){if(super(),this.id=a.id,this.type=a.type,this._featureFilter={filter:()=>!0,needGeometry:!1},a.type!==\"custom\"&&(this.metadata=a.metadata,this.minzoom=a.minzoom,this.maxzoom=a.maxzoom,a.type!==\"background\"&&(this.source=a.source,this.sourceLayer=a[\"source-layer\"],this.filter=a.filter),h.layout&&(this._unevaluatedLayout=new Nx(h.layout)),h.paint)){this._transitionablePaint=new Ah(h.paint);for(let A in a.paint)this.setPaintProperty(A,a.paint[A],{validate:!1});for(let A in a.layout)this.setLayoutProperty(A,a.layout[A],{validate:!1});this._transitioningPaint=this._transitionablePaint.untransitioned(),this.paint=new oA(h.paint)}}getCrossfadeParameters(){return this._crossfadeParameters}getLayoutProperty(a){return a===\"visibility\"?this.visibility:this._unevaluatedLayout.getValue(a)}setLayoutProperty(a,h,A={}){h!=null&&this._validate(s0,`layers.${this.id}.layout.${a}`,a,h,A)||(a!==\"visibility\"?this._unevaluatedLayout.setValue(a,h):this.visibility=h)}getPaintProperty(a){return a.endsWith(uo)?this._transitionablePaint.getTransition(a.slice(0,-11)):this._transitionablePaint.getValue(a)}setPaintProperty(a,h,A={}){if(h!=null&&this._validate(rA,`layers.${this.id}.paint.${a}`,a,h,A))return!1;if(a.endsWith(uo))return this._transitionablePaint.setTransition(a.slice(0,-11),h||void 0),!1;{let x=this._transitionablePaint._values[a],E=x.property.specification[\"property-type\"]===\"cross-faded-data-driven\",P=x.value.isDataDriven(),D=x.value;this._transitionablePaint.setValue(a,h),this._handleSpecialPaintPropertyUpdate(a);let F=this._transitionablePaint._values[a].value;return F.isDataDriven()||P||E||this._handleOverridablePaintPropertyUpdate(a,D,F)}}_handleSpecialPaintPropertyUpdate(a){}_handleOverridablePaintPropertyUpdate(a,h,A){return!1}isHidden(a){return!!(this.minzoom&&a=this.maxzoom)||this.visibility===\"none\"}updateTransitions(a){this._transitioningPaint=this._transitionablePaint.transitioned(a,this._transitioningPaint)}hasTransition(){return this._transitioningPaint.hasTransition()}recalculate(a,h){a.getCrossfadeParameters&&(this._crossfadeParameters=a.getCrossfadeParameters()),this._unevaluatedLayout&&(this.layout=this._unevaluatedLayout.possiblyEvaluate(a,void 0,h)),this.paint=this._transitioningPaint.possiblyEvaluate(a,void 0,h)}serialize(){let a={id:this.id,type:this.type,source:this.source,\"source-layer\":this.sourceLayer,metadata:this.metadata,minzoom:this.minzoom,maxzoom:this.maxzoom,filter:this.filter,layout:this._unevaluatedLayout&&this._unevaluatedLayout.serialize(),paint:this._transitionablePaint&&this._transitionablePaint.serialize()};return this.visibility&&(a.layout=a.layout||{},a.layout.visibility=this.visibility),le(a,(h,A)=>!(h===void 0||A===\"layout\"&&!Object.keys(h).length||A===\"paint\"&&!Object.keys(h).length))}_validate(a,h,A,x,E={}){return(!E||E.validate!==!1)&&fh(this,a.call(za,{key:h,layerType:this.type,objectKey:A,value:x,styleSpec:ee,style:{glyphs:!0,sprite:!0}}))}is3D(){return!1}isTileClipped(){return!1}hasOffscreenPass(){return!1}resize(){}isStateDependent(){for(let a in this.paint._values){let h=this.paint.get(a);if(h instanceof Mo&&_f(h.property.specification)&&(h.value.kind===\"source\"||h.value.kind===\"composite\")&&h.value.isStateDependent)return!0}return!1}}let w_={Int8:Int8Array,Uint8:Uint8Array,Int16:Int16Array,Uint16:Uint16Array,Int32:Int32Array,Uint32:Uint32Array,Float32:Float32Array};class mh{constructor(a,h){this._structArray=a,this._pos1=h*this.size,this._pos2=this._pos1/2,this._pos4=this._pos1/4,this._pos8=this._pos1/8}}class kn{constructor(){this.isTransferred=!1,this.capacity=-1,this.resize(0)}static serialize(a,h){return a._trim(),h&&(a.isTransferred=!0,h.push(a.arrayBuffer)),{length:a.length,arrayBuffer:a.arrayBuffer}}static deserialize(a){let h=Object.create(this.prototype);return h.arrayBuffer=a.arrayBuffer,h.length=a.length,h.capacity=a.arrayBuffer.byteLength/h.bytesPerElement,h._refreshViews(),h}_trim(){this.length!==this.capacity&&(this.capacity=this.length,this.arrayBuffer=this.arrayBuffer.slice(0,this.length*this.bytesPerElement),this._refreshViews())}clear(){this.length=0}resize(a){this.reserve(a),this.length=a}reserve(a){if(a>this.capacity){this.capacity=Math.max(a,Math.floor(5*this.capacity),128),this.arrayBuffer=new ArrayBuffer(this.capacity*this.bytesPerElement);let h=this.uint8;this._refreshViews(),h&&this.uint8.set(h)}}_refreshViews(){throw new Error(\"_refreshViews() must be implemented by each concrete StructArray layout\")}}function wn(u,a=1){let h=0,A=0;return{members:u.map(x=>{let E=w_[x.type].BYTES_PER_ELEMENT,P=h=Sf(h,Math.max(a,E)),D=x.components||1;return A=Math.max(A,E),h+=E*D,{name:x.name,type:x.type,components:D,offset:P}}),size:Sf(h,Math.max(A,a)),alignment:a}}function Sf(u,a){return Math.ceil(u/a)*a}class Es extends kn{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(a,h){let A=this.length;return this.resize(A+1),this.emplace(A,a,h)}emplace(a,h,A){let x=2*a;return this.int16[x+0]=h,this.int16[x+1]=A,a}}Es.prototype.bytesPerElement=4,Ge(\"StructArrayLayout2i4\",Es);class gh extends kn{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(a,h,A){let x=this.length;return this.resize(x+1),this.emplace(x,a,h,A)}emplace(a,h,A,x){let E=3*a;return this.int16[E+0]=h,this.int16[E+1]=A,this.int16[E+2]=x,a}}gh.prototype.bytesPerElement=6,Ge(\"StructArrayLayout3i6\",gh);class Wo extends kn{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(a,h,A,x){let E=this.length;return this.resize(E+1),this.emplace(E,a,h,A,x)}emplace(a,h,A,x,E){let P=4*a;return this.int16[P+0]=h,this.int16[P+1]=A,this.int16[P+2]=x,this.int16[P+3]=E,a}}Wo.prototype.bytesPerElement=8,Ge(\"StructArrayLayout4i8\",Wo);class p0 extends kn{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(a,h,A,x,E,P){let D=this.length;return this.resize(D+1),this.emplace(D,a,h,A,x,E,P)}emplace(a,h,A,x,E,P,D){let F=6*a;return this.int16[F+0]=h,this.int16[F+1]=A,this.int16[F+2]=x,this.int16[F+3]=E,this.int16[F+4]=P,this.int16[F+5]=D,a}}p0.prototype.bytesPerElement=12,Ge(\"StructArrayLayout2i4i12\",p0);class Fd extends kn{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(a,h,A,x,E,P){let D=this.length;return this.resize(D+1),this.emplace(D,a,h,A,x,E,P)}emplace(a,h,A,x,E,P,D){let F=4*a,V=8*a;return this.int16[F+0]=h,this.int16[F+1]=A,this.uint8[V+4]=x,this.uint8[V+5]=E,this.uint8[V+6]=P,this.uint8[V+7]=D,a}}Fd.prototype.bytesPerElement=8,Ge(\"StructArrayLayout2i4ub8\",Fd);class Tf extends kn{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)}emplaceBack(a,h){let A=this.length;return this.resize(A+1),this.emplace(A,a,h)}emplace(a,h,A){let x=2*a;return this.float32[x+0]=h,this.float32[x+1]=A,a}}Tf.prototype.bytesPerElement=8,Ge(\"StructArrayLayout2f8\",Tf);class Ho extends kn{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)}emplaceBack(a,h,A,x,E,P,D,F,V,q){let X=this.length;return this.resize(X+1),this.emplace(X,a,h,A,x,E,P,D,F,V,q)}emplace(a,h,A,x,E,P,D,F,V,q,X){let rt=10*a;return this.uint16[rt+0]=h,this.uint16[rt+1]=A,this.uint16[rt+2]=x,this.uint16[rt+3]=E,this.uint16[rt+4]=P,this.uint16[rt+5]=D,this.uint16[rt+6]=F,this.uint16[rt+7]=V,this.uint16[rt+8]=q,this.uint16[rt+9]=X,a}}Ho.prototype.bytesPerElement=20,Ge(\"StructArrayLayout10ui20\",Ho);class lA extends kn{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)}emplaceBack(a,h,A,x,E,P,D,F,V,q,X,rt){let at=this.length;return this.resize(at+1),this.emplace(at,a,h,A,x,E,P,D,F,V,q,X,rt)}emplace(a,h,A,x,E,P,D,F,V,q,X,rt,at){let ct=12*a;return this.int16[ct+0]=h,this.int16[ct+1]=A,this.int16[ct+2]=x,this.int16[ct+3]=E,this.uint16[ct+4]=P,this.uint16[ct+5]=D,this.uint16[ct+6]=F,this.uint16[ct+7]=V,this.int16[ct+8]=q,this.int16[ct+9]=X,this.int16[ct+10]=rt,this.int16[ct+11]=at,a}}lA.prototype.bytesPerElement=24,Ge(\"StructArrayLayout4i4ui4i24\",lA);class bi extends kn{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)}emplaceBack(a,h,A){let x=this.length;return this.resize(x+1),this.emplace(x,a,h,A)}emplace(a,h,A,x){let E=3*a;return this.float32[E+0]=h,this.float32[E+1]=A,this.float32[E+2]=x,a}}bi.prototype.bytesPerElement=12,Ge(\"StructArrayLayout3f12\",bi);class T extends kn{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer)}emplaceBack(a){let h=this.length;return this.resize(h+1),this.emplace(h,a)}emplace(a,h){return this.uint32[1*a+0]=h,a}}T.prototype.bytesPerElement=4,Ge(\"StructArrayLayout1ul4\",T);class l extends kn{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)}emplaceBack(a,h,A,x,E,P,D,F,V){let q=this.length;return this.resize(q+1),this.emplace(q,a,h,A,x,E,P,D,F,V)}emplace(a,h,A,x,E,P,D,F,V,q){let X=10*a,rt=5*a;return this.int16[X+0]=h,this.int16[X+1]=A,this.int16[X+2]=x,this.int16[X+3]=E,this.int16[X+4]=P,this.int16[X+5]=D,this.uint32[rt+3]=F,this.uint16[X+8]=V,this.uint16[X+9]=q,a}}l.prototype.bytesPerElement=20,Ge(\"StructArrayLayout6i1ul2ui20\",l);class d extends kn{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(a,h,A,x,E,P){let D=this.length;return this.resize(D+1),this.emplace(D,a,h,A,x,E,P)}emplace(a,h,A,x,E,P,D){let F=6*a;return this.int16[F+0]=h,this.int16[F+1]=A,this.int16[F+2]=x,this.int16[F+3]=E,this.int16[F+4]=P,this.int16[F+5]=D,a}}d.prototype.bytesPerElement=12,Ge(\"StructArrayLayout2i2i2i12\",d);class v extends kn{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(a,h,A,x,E){let P=this.length;return this.resize(P+1),this.emplace(P,a,h,A,x,E)}emplace(a,h,A,x,E,P){let D=4*a,F=8*a;return this.float32[D+0]=h,this.float32[D+1]=A,this.float32[D+2]=x,this.int16[F+6]=E,this.int16[F+7]=P,a}}v.prototype.bytesPerElement=16,Ge(\"StructArrayLayout2f1f2i16\",v);class b extends kn{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)}emplaceBack(a,h,A,x){let E=this.length;return this.resize(E+1),this.emplace(E,a,h,A,x)}emplace(a,h,A,x,E){let P=12*a,D=3*a;return this.uint8[P+0]=h,this.uint8[P+1]=A,this.float32[D+1]=x,this.float32[D+2]=E,a}}b.prototype.bytesPerElement=12,Ge(\"StructArrayLayout2ub2f12\",b);class M extends kn{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)}emplaceBack(a,h,A){let x=this.length;return this.resize(x+1),this.emplace(x,a,h,A)}emplace(a,h,A,x){let E=3*a;return this.uint16[E+0]=h,this.uint16[E+1]=A,this.uint16[E+2]=x,a}}M.prototype.bytesPerElement=6,Ge(\"StructArrayLayout3ui6\",M);class O extends kn{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)}emplaceBack(a,h,A,x,E,P,D,F,V,q,X,rt,at,ct,mt,bt,Pt){let jt=this.length;return this.resize(jt+1),this.emplace(jt,a,h,A,x,E,P,D,F,V,q,X,rt,at,ct,mt,bt,Pt)}emplace(a,h,A,x,E,P,D,F,V,q,X,rt,at,ct,mt,bt,Pt,jt){let Rt=24*a,Gt=12*a,Yt=48*a;return this.int16[Rt+0]=h,this.int16[Rt+1]=A,this.uint16[Rt+2]=x,this.uint16[Rt+3]=E,this.uint32[Gt+2]=P,this.uint32[Gt+3]=D,this.uint32[Gt+4]=F,this.uint16[Rt+10]=V,this.uint16[Rt+11]=q,this.uint16[Rt+12]=X,this.float32[Gt+7]=rt,this.float32[Gt+8]=at,this.uint8[Yt+36]=ct,this.uint8[Yt+37]=mt,this.uint8[Yt+38]=bt,this.uint32[Gt+10]=Pt,this.int16[Rt+22]=jt,a}}O.prototype.bytesPerElement=48,Ge(\"StructArrayLayout2i2ui3ul3ui2f3ub1ul1i48\",O);class B extends kn{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)}emplaceBack(a,h,A,x,E,P,D,F,V,q,X,rt,at,ct,mt,bt,Pt,jt,Rt,Gt,Yt,ce,Ne,ir,Fe,Re,Me,Ye){let Ie=this.length;return this.resize(Ie+1),this.emplace(Ie,a,h,A,x,E,P,D,F,V,q,X,rt,at,ct,mt,bt,Pt,jt,Rt,Gt,Yt,ce,Ne,ir,Fe,Re,Me,Ye)}emplace(a,h,A,x,E,P,D,F,V,q,X,rt,at,ct,mt,bt,Pt,jt,Rt,Gt,Yt,ce,Ne,ir,Fe,Re,Me,Ye,Ie){let Ae=32*a,hr=16*a;return this.int16[Ae+0]=h,this.int16[Ae+1]=A,this.int16[Ae+2]=x,this.int16[Ae+3]=E,this.int16[Ae+4]=P,this.int16[Ae+5]=D,this.int16[Ae+6]=F,this.int16[Ae+7]=V,this.uint16[Ae+8]=q,this.uint16[Ae+9]=X,this.uint16[Ae+10]=rt,this.uint16[Ae+11]=at,this.uint16[Ae+12]=ct,this.uint16[Ae+13]=mt,this.uint16[Ae+14]=bt,this.uint16[Ae+15]=Pt,this.uint16[Ae+16]=jt,this.uint16[Ae+17]=Rt,this.uint16[Ae+18]=Gt,this.uint16[Ae+19]=Yt,this.uint16[Ae+20]=ce,this.uint16[Ae+21]=Ne,this.uint16[Ae+22]=ir,this.uint32[hr+12]=Fe,this.float32[hr+13]=Re,this.float32[hr+14]=Me,this.uint16[Ae+30]=Ye,this.uint16[Ae+31]=Ie,a}}B.prototype.bytesPerElement=64,Ge(\"StructArrayLayout8i15ui1ul2f2ui64\",B);class U extends kn{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)}emplaceBack(a){let h=this.length;return this.resize(h+1),this.emplace(h,a)}emplace(a,h){return this.float32[1*a+0]=h,a}}U.prototype.bytesPerElement=4,Ge(\"StructArrayLayout1f4\",U);class W extends kn{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)}emplaceBack(a,h,A){let x=this.length;return this.resize(x+1),this.emplace(x,a,h,A)}emplace(a,h,A,x){let E=3*a;return this.uint16[6*a+0]=h,this.float32[E+1]=A,this.float32[E+2]=x,a}}W.prototype.bytesPerElement=12,Ge(\"StructArrayLayout1ui2f12\",W);class Z extends kn{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)}emplaceBack(a,h,A){let x=this.length;return this.resize(x+1),this.emplace(x,a,h,A)}emplace(a,h,A,x){let E=4*a;return this.uint32[2*a+0]=h,this.uint16[E+2]=A,this.uint16[E+3]=x,a}}Z.prototype.bytesPerElement=8,Ge(\"StructArrayLayout1ul2ui8\",Z);class $ extends kn{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)}emplaceBack(a,h){let A=this.length;return this.resize(A+1),this.emplace(A,a,h)}emplace(a,h,A){let x=2*a;return this.uint16[x+0]=h,this.uint16[x+1]=A,a}}$.prototype.bytesPerElement=4,Ge(\"StructArrayLayout2ui4\",$);class st extends kn{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)}emplaceBack(a){let h=this.length;return this.resize(h+1),this.emplace(h,a)}emplace(a,h){return this.uint16[1*a+0]=h,a}}st.prototype.bytesPerElement=2,Ge(\"StructArrayLayout1ui2\",st);class At extends kn{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)}emplaceBack(a,h,A,x){let E=this.length;return this.resize(E+1),this.emplace(E,a,h,A,x)}emplace(a,h,A,x,E){let P=4*a;return this.float32[P+0]=h,this.float32[P+1]=A,this.float32[P+2]=x,this.float32[P+3]=E,a}}At.prototype.bytesPerElement=16,Ge(\"StructArrayLayout4f16\",At);class pt extends mh{get anchorPointX(){return this._structArray.int16[this._pos2+0]}get anchorPointY(){return this._structArray.int16[this._pos2+1]}get x1(){return this._structArray.int16[this._pos2+2]}get y1(){return this._structArray.int16[this._pos2+3]}get x2(){return this._structArray.int16[this._pos2+4]}get y2(){return this._structArray.int16[this._pos2+5]}get featureIndex(){return this._structArray.uint32[this._pos4+3]}get sourceLayerIndex(){return this._structArray.uint16[this._pos2+8]}get bucketIndex(){return this._structArray.uint16[this._pos2+9]}get anchorPoint(){return new w(this.anchorPointX,this.anchorPointY)}}pt.prototype.size=20;class yt extends l{get(a){return new pt(this,a)}}Ge(\"CollisionBoxArray\",yt);class dt extends mh{get anchorX(){return this._structArray.int16[this._pos2+0]}get anchorY(){return this._structArray.int16[this._pos2+1]}get glyphStartIndex(){return this._structArray.uint16[this._pos2+2]}get numGlyphs(){return this._structArray.uint16[this._pos2+3]}get vertexStartIndex(){return this._structArray.uint32[this._pos4+2]}get lineStartIndex(){return this._structArray.uint32[this._pos4+3]}get lineLength(){return this._structArray.uint32[this._pos4+4]}get segment(){return this._structArray.uint16[this._pos2+10]}get lowerSize(){return this._structArray.uint16[this._pos2+11]}get upperSize(){return this._structArray.uint16[this._pos2+12]}get lineOffsetX(){return this._structArray.float32[this._pos4+7]}get lineOffsetY(){return this._structArray.float32[this._pos4+8]}get writingMode(){return this._structArray.uint8[this._pos1+36]}get placedOrientation(){return this._structArray.uint8[this._pos1+37]}set placedOrientation(a){this._structArray.uint8[this._pos1+37]=a}get hidden(){return this._structArray.uint8[this._pos1+38]}set hidden(a){this._structArray.uint8[this._pos1+38]=a}get crossTileID(){return this._structArray.uint32[this._pos4+10]}set crossTileID(a){this._structArray.uint32[this._pos4+10]=a}get associatedIconIndex(){return this._structArray.int16[this._pos2+22]}}dt.prototype.size=48;class Ft extends O{get(a){return new dt(this,a)}}Ge(\"PlacedSymbolArray\",Ft);class Ht extends mh{get anchorX(){return this._structArray.int16[this._pos2+0]}get anchorY(){return this._structArray.int16[this._pos2+1]}get rightJustifiedTextSymbolIndex(){return this._structArray.int16[this._pos2+2]}get centerJustifiedTextSymbolIndex(){return this._structArray.int16[this._pos2+3]}get leftJustifiedTextSymbolIndex(){return this._structArray.int16[this._pos2+4]}get verticalPlacedTextSymbolIndex(){return this._structArray.int16[this._pos2+5]}get placedIconSymbolIndex(){return this._structArray.int16[this._pos2+6]}get verticalPlacedIconSymbolIndex(){return this._structArray.int16[this._pos2+7]}get key(){return this._structArray.uint16[this._pos2+8]}get textBoxStartIndex(){return this._structArray.uint16[this._pos2+9]}get textBoxEndIndex(){return this._structArray.uint16[this._pos2+10]}get verticalTextBoxStartIndex(){return this._structArray.uint16[this._pos2+11]}get verticalTextBoxEndIndex(){return this._structArray.uint16[this._pos2+12]}get iconBoxStartIndex(){return this._structArray.uint16[this._pos2+13]}get iconBoxEndIndex(){return this._structArray.uint16[this._pos2+14]}get verticalIconBoxStartIndex(){return this._structArray.uint16[this._pos2+15]}get verticalIconBoxEndIndex(){return this._structArray.uint16[this._pos2+16]}get featureIndex(){return this._structArray.uint16[this._pos2+17]}get numHorizontalGlyphVertices(){return this._structArray.uint16[this._pos2+18]}get numVerticalGlyphVertices(){return this._structArray.uint16[this._pos2+19]}get numIconVertices(){return this._structArray.uint16[this._pos2+20]}get numVerticalIconVertices(){return this._structArray.uint16[this._pos2+21]}get useRuntimeCollisionCircles(){return this._structArray.uint16[this._pos2+22]}get crossTileID(){return this._structArray.uint32[this._pos4+12]}set crossTileID(a){this._structArray.uint32[this._pos4+12]=a}get textBoxScale(){return this._structArray.float32[this._pos4+13]}get collisionCircleDiameter(){return this._structArray.float32[this._pos4+14]}get textAnchorOffsetStartIndex(){return this._structArray.uint16[this._pos2+30]}get textAnchorOffsetEndIndex(){return this._structArray.uint16[this._pos2+31]}}Ht.prototype.size=64;class St extends B{get(a){return new Ht(this,a)}}Ge(\"SymbolInstanceArray\",St);class Bt extends U{getoffsetX(a){return this.float32[1*a+0]}}Ge(\"GlyphOffsetArray\",Bt);class Qt extends gh{getx(a){return this.int16[3*a+0]}gety(a){return this.int16[3*a+1]}gettileUnitDistanceFromAnchor(a){return this.int16[3*a+2]}}Ge(\"SymbolLineVertexArray\",Qt);class $t extends mh{get textAnchor(){return this._structArray.uint16[this._pos2+0]}get textOffset0(){return this._structArray.float32[this._pos4+1]}get textOffset1(){return this._structArray.float32[this._pos4+2]}}$t.prototype.size=12;class oe extends W{get(a){return new $t(this,a)}}Ge(\"TextAnchorOffsetArray\",oe);class pe extends mh{get featureIndex(){return this._structArray.uint32[this._pos4+0]}get sourceLayerIndex(){return this._structArray.uint16[this._pos2+2]}get bucketIndex(){return this._structArray.uint16[this._pos2+3]}}pe.prototype.size=8;class he extends Z{get(a){return new pe(this,a)}}Ge(\"FeatureIndexArray\",he);class be extends Es{}class Ze extends Es{}class Kr extends Es{}class Ee extends p0{}class pr extends Fd{}class tr extends Tf{}class Gi extends Ho{}class Jr extends lA{}class Vr extends bi{}class ei extends T{}class On extends d{}class tn extends b{}class Gs extends M{}class hs extends ${}let Bn=wn([{name:\"a_pos\",components:2,type:\"Int16\"}],4),{members:qo}=Bn;class jr{constructor(a=[]){this.segments=a}prepareSegment(a,h,A,x){let E=this.segments[this.segments.length-1];return a>jr.MAX_VERTEX_ARRAY_LENGTH&&Ke(`Max vertices per segment is ${jr.MAX_VERTEX_ARRAY_LENGTH}: bucket requested ${a}`),(!E||E.vertexLength+a>jr.MAX_VERTEX_ARRAY_LENGTH||E.sortKey!==x)&&(E={vertexOffset:h.length,primitiveOffset:A.length,vertexLength:0,primitiveLength:0},x!==void 0&&(E.sortKey=x),this.segments.push(E)),E}get(){return this.segments}destroy(){for(let a of this.segments)for(let h in a.vaos)a.vaos[h].destroy()}static simpleSegment(a,h,A,x){return new jr([{vertexOffset:a,primitiveOffset:h,vertexLength:A,primitiveLength:x,vaos:{},sortKey:0}])}}function ql(u,a){return 256*(u=ut(Math.floor(u),0,255))+ut(Math.floor(a),0,255)}jr.MAX_VERTEX_ARRAY_LENGTH=Math.pow(2,16)-1,Ge(\"SegmentVector\",jr);let 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q=[],X=[],rt=V.vertexLength;for(let ct of D)if(ct.length!==0){ct!==D[0]&&X.push(q.length/2);for(let mt=0;mten)||u.y===a.y&&(u.y<0||u.y>en)}function yK(u){return u.every(a=>a.x<0)||u.every(a=>a.x>en)||u.every(a=>a.y<0)||u.every(a=>a.y>en)}let JF;Ge(\"FillExtrusionBucket\",wC,{omit:[\"layers\",\"features\"]});var vK={get paint(){return JF=JF||new Hn({\"fill-extrusion-opacity\":new nr(ee[\"paint_fill-extrusion\"][\"fill-extrusion-opacity\"]),\"fill-extrusion-color\":new dr(ee[\"paint_fill-extrusion\"][\"fill-extrusion-color\"]),\"fill-extrusion-translate\":new nr(ee[\"paint_fill-extrusion\"][\"fill-extrusion-translate\"]),\"fill-extrusion-translate-anchor\":new nr(ee[\"paint_fill-extrusion\"][\"fill-extrusion-translate-anchor\"]),\"fill-extrusion-pattern\":new wf(ee[\"paint_fill-extrusion\"][\"fill-extrusion-pattern\"]),\"fill-extrusion-height\":new dr(ee[\"paint_fill-extrusion\"][\"fill-extrusion-height\"]),\"fill-extrusion-base\":new 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x=this.layers[0].layout.get(\"line-sort-key\"),E=!x.isConstant(),P=[];for(let{feature:D,id:F,index:V,sourceLayerIndex:q}of a){let X=this.layers[0]._featureFilter.needGeometry,rt=S(D,X);if(!this.layers[0]._featureFilter.filter(new un(this.zoom),rt,A))continue;let at=E?x.evaluate(rt,{},A):void 0,ct={id:F,properties:D.properties,type:D.type,sourceLayerIndex:q,index:V,geometry:X?rt.geometry:y(D),patterns:{},sortKey:at};P.push(ct)}E&&P.sort((D,F)=>D.sortKey-F.sortKey);for(let D of P){let{geometry:F,index:V,sourceLayerIndex:q}=D;if(this.hasPattern){let X=vC(\"line\",this.layers,D,this.zoom,h);this.patternFeatures.push(X)}else this.addFeature(D,F,V,A,{});h.featureIndex.insert(a[V].feature,F,V,q,this.index)}}update(a,h,A){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(a,h,this.stateDependentLayers,A)}addFeatures(a,h,A){for(let x of this.patternFeatures)this.addFeature(x,x.geometry,x.index,h,A)}isEmpty(){return 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P=this.layers[0].layout,D=P.get(\"line-join\").evaluate(a,{}),F=P.get(\"line-cap\"),V=P.get(\"line-miter-limit\"),q=P.get(\"line-round-limit\");this.lineClips=this.lineFeatureClips(a);for(let X of h)this.addLine(X,a,D,F,V,q);this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,a,A,E,x)}addLine(a,h,A,x,E,P){if(this.distance=0,this.scaledDistance=0,this.totalDistance=0,this.lineClips){this.lineClipsArray.push(this.lineClips);for(let Pt=0;Pt=2&&a[F-1].equals(a[F-2]);)F--;let V=0;for(;V0;if(Ne&&Pt>V){let Me=rt.dist(at);if(Me>2*q){let Ye=rt.sub(rt.sub(at)._mult(q/Me)._round());this.updateDistance(at,Ye),this.addCurrentVertex(Ye,mt,0,0,X),at=Ye}}let Fe=at&&ct,Re=Fe?A:D?\"butt\":x;if(Fe&&Re===\"round\"&&(YtE&&(Re=\"bevel\"),Re===\"bevel\"&&(Yt>2&&(Re=\"flipbevel\"),Yt100)jt=bt.mult(-1);else{let Me=Yt*mt.add(bt).mag()/mt.sub(bt).mag();jt._perp()._mult(Me*(ir?-1:1))}this.addCurrentVertex(rt,jt,0,0,X),this.addCurrentVertex(rt,jt.mult(-1),0,0,X)}else if(Re===\"bevel\"||Re===\"fakeround\"){let Me=-Math.sqrt(Yt*Yt-1),Ye=ir?Me:0,Ie=ir?0:Me;if(at&&this.addCurrentVertex(rt,mt,Ye,Ie,X),Re===\"fakeround\"){let Ae=Math.round(180*ce/Math.PI/20);for(let hr=1;hr2*q){let Ye=rt.add(ct.sub(rt)._mult(q/Me)._round());this.updateDistance(rt,Ye),this.addCurrentVertex(Ye,bt,0,0,X),rt=Ye}}}}addCurrentVertex(a,h,A,x,E,P=!1){let D=h.y*x-h.x,F=-h.y-h.x*x;this.addHalfVertex(a,h.x+h.y*A,h.y-h.x*A,P,!1,A,E),this.addHalfVertex(a,D,F,P,!0,-x,E),this.distance>e6/2&&this.totalDistance===0&&(this.distance=0,this.updateScaledDistance(),this.addCurrentVertex(a,h,A,x,E,P))}addHalfVertex({x:a,y:h},A,x,E,P,D,F){let V=.5*(this.lineClips?this.scaledDistance*(e6-1):this.scaledDistance);this.layoutVertexArray.emplaceBack((a<<1)+(E?1:0),(h<<1)+(P?1:0),Math.round(63*A)+128,Math.round(63*x)+128,1+(D===0?0:D<0?-1:1)|(63&V)<<2,V>>6),this.lineClips&&this.layoutVertexArray2.emplaceBack((this.scaledDistance-this.lineClips.start)/(this.lineClips.end-this.lineClips.start),this.lineClipsArray.length);let q=F.vertexLength++;this.e1>=0&&this.e2>=0&&(this.indexArray.emplaceBack(this.e1,this.e2,q),F.primitiveLength++),P?this.e2=q:this.e1=q}updateScaledDistance(){this.scaledDistance=this.lineClips?this.lineClips.start+(this.lineClips.end-this.lineClips.start)*this.distance/this.totalDistance:this.distance}updateDistance(a,h){this.distance+=a.dist(h),this.updateScaledDistance()}}let r6,i6;Ge(\"LineBucket\",SC,{omit:[\"layers\",\"patternFeatures\"]});var n6={get paint(){return i6=i6||new Hn({\"line-opacity\":new dr(ee.paint_line[\"line-opacity\"]),\"line-color\":new dr(ee.paint_line[\"line-color\"]),\"line-translate\":new nr(ee.paint_line[\"line-translate\"]),\"line-translate-anchor\":new nr(ee.paint_line[\"line-translate-anchor\"]),\"line-width\":new dr(ee.paint_line[\"line-width\"]),\"line-gap-width\":new dr(ee.paint_line[\"line-gap-width\"]),\"line-offset\":new dr(ee.paint_line[\"line-offset\"]),\"line-blur\":new dr(ee.paint_line[\"line-blur\"]),\"line-dasharray\":new aA(ee.paint_line[\"line-dasharray\"]),\"line-pattern\":new wf(ee.paint_line[\"line-pattern\"]),\"line-gradient\":new Bd(ee.paint_line[\"line-gradient\"])})},get layout(){return r6=r6||new Hn({\"line-cap\":new nr(ee.layout_line[\"line-cap\"]),\"line-join\":new dr(ee.layout_line[\"line-join\"]),\"line-miter-limit\":new nr(ee.layout_line[\"line-miter-limit\"]),\"line-round-limit\":new nr(ee.layout_line[\"line-round-limit\"]),\"line-sort-key\":new dr(ee.layout_line[\"line-sort-key\"])})}};class PK extends dr{possiblyEvaluate(a,h){return h=new un(Math.floor(h.zoom),{now:h.now,fadeDuration:h.fadeDuration,zoomHistory:h.zoomHistory,transition:h.transition}),super.possiblyEvaluate(a,h)}evaluate(a,h,A,x){return h=kt({},h,{zoom:Math.floor(h.zoom)}),super.evaluate(a,h,A,x)}}let rT;class IK extends ji{constructor(a){super(a,n6),this.gradientVersion=0,rT||(rT=new PK(n6.paint.properties[\"line-width\"].specification),rT.useIntegerZoom=!0)}_handleSpecialPaintPropertyUpdate(a){if(a===\"line-gradient\"){let h=this.gradientExpression();this.stepInterpolant=!!function(A){return A._styleExpression!==void 0}(h)&&h._styleExpression.expression instanceof sh,this.gradientVersion=(this.gradientVersion+1)%Number.MAX_SAFE_INTEGER}}gradientExpression(){return this._transitionablePaint._values[\"line-gradient\"].value.expression}recalculate(a,h){super.recalculate(a,h),this.paint._values[\"line-floorwidth\"]=rT.possiblyEvaluate(this._transitioningPaint._values[\"line-width\"].value,a)}createBucket(a){return new SC(a)}queryRadius(a){let h=a,A=s6(We(\"line-width\",this,h),We(\"line-gap-width\",this,h)),x=We(\"line-offset\",this,h);return A/2+Math.abs(x)+te(this.paint.get(\"line-translate\"))}queryIntersectsFeature(a,h,A,x,E,P,D){let F=_e(a,this.paint.get(\"line-translate\"),this.paint.get(\"line-translate-anchor\"),P.angle,D),V=D/2*s6(this.paint.get(\"line-width\").evaluate(h,A),this.paint.get(\"line-gap-width\").evaluate(h,A)),q=this.paint.get(\"line-offset\").evaluate(h,A);return q&&(x=function(X,rt){let at=[];for(let ct=0;ct=3){for(let bt=0;bt0?a+2*u:u}let CK=wn([{name:\"a_pos_offset\",components:4,type:\"Int16\"},{name:\"a_data\",components:4,type:\"Uint16\"},{name:\"a_pixeloffset\",components:4,type:\"Int16\"}],4),LK=wn([{name:\"a_projected_pos\",components:3,type:\"Float32\"}],4);wn([{name:\"a_fade_opacity\",components:1,type:\"Uint32\"}],4);let 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Hs=24,a6=rn,l6=function(u,a,h,A,x){var E,P,D=8*x-A-1,F=(1<>1,q=-7,X=h?x-1:0,rt=h?-1:1,at=u[a+X];for(X+=rt,E=at&(1<<-q)-1,at>>=-q,q+=D;q>0;E=256*E+u[a+X],X+=rt,q-=8);for(P=E&(1<<-q)-1,E>>=-q,q+=A;q>0;P=256*P+u[a+X],X+=rt,q-=8);if(E===0)E=1-V;else{if(E===F)return P?NaN:1/0*(at?-1:1);P+=Math.pow(2,A),E-=V}return(at?-1:1)*P*Math.pow(2,E-A)},c6=function(u,a,h,A,x,E){var P,D,F,V=8*E-x-1,q=(1<>1,rt=x===23?Math.pow(2,-24)-Math.pow(2,-77):0,at=A?0:E-1,ct=A?1:-1,mt=a<0||a===0&&1/a<0?1:0;for(a=Math.abs(a),isNaN(a)||a===1/0?(D=isNaN(a)?1:0,P=q):(P=Math.floor(Math.log(a)/Math.LN2),a*(F=Math.pow(2,-P))<1&&(P--,F*=2),(a+=P+X>=1?rt/F:rt*Math.pow(2,1-X))*F>=2&&(P++,F/=2),P+X>=q?(D=0,P=q):P+X>=1?(D=(a*F-1)*Math.pow(2,x),P+=X):(D=a*Math.pow(2,X-1)*Math.pow(2,x),P=0));x>=8;u[h+at]=255&D,at+=ct,D/=256,x-=8);for(P=P<0;u[h+at]=255&P,at+=ct,P/=256,V-=8);u[h+at-ct]|=128*mt};function rn(u){this.buf=ArrayBuffer.isView&&ArrayBuffer.isView(u)?u:new 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h=0;h=128&&f6(h,A,this),this.pos=h-1,this.writeVarint(A),this.pos+=A},writeMessage:function(u,a,h){this.writeTag(u,rn.Bytes),this.writeRawMessage(a,h)},writePackedVarint:function(u,a){a.length&&this.writeMessage(u,OK,a)},writePackedSVarint:function(u,a){a.length&&this.writeMessage(u,BK,a)},writePackedBoolean:function(u,a){a.length&&this.writeMessage(u,NK,a)},writePackedFloat:function(u,a){a.length&&this.writeMessage(u,FK,a)},writePackedDouble:function(u,a){a.length&&this.writeMessage(u,zK,a)},writePackedFixed32:function(u,a){a.length&&this.writeMessage(u,UK,a)},writePackedSFixed32:function(u,a){a.length&&this.writeMessage(u,VK,a)},writePackedFixed64:function(u,a){a.length&&this.writeMessage(u,jK,a)},writePackedSFixed64:function(u,a){a.length&&this.writeMessage(u,GK,a)},writeBytesField:function(u,a){this.writeTag(u,rn.Bytes),this.writeBytes(a)},writeFixed32Field:function(u,a){this.writeTag(u,rn.Fixed32),this.writeFixed32(a)},writeSFixed32Field:function(u,a){this.writeTag(u,rn.Fixed32),this.writeSFixed32(a)},writeFixed64Field:function(u,a){this.writeTag(u,rn.Fixed64),this.writeFixed64(a)},writeSFixed64Field:function(u,a){this.writeTag(u,rn.Fixed64),this.writeSFixed64(a)},writeVarintField:function(u,a){this.writeTag(u,rn.Varint),this.writeVarint(a)},writeSVarintField:function(u,a){this.writeTag(u,rn.Varint),this.writeSVarint(a)},writeStringField:function(u,a){this.writeTag(u,rn.Bytes),this.writeString(a)},writeFloatField:function(u,a){this.writeTag(u,rn.Fixed32),this.writeFloat(a)},writeDoubleField:function(u,a){this.writeTag(u,rn.Fixed64),this.writeDouble(a)},writeBooleanField:function(u,a){this.writeVarintField(u,!!a)}};var MC=c(a6);let EC=3;function WK(u,a,h){u===1&&h.readMessage(HK,a)}function HK(u,a,h){if(u===3){let{id:A,bitmap:x,width:E,height:P,left:D,top:F,advance:V}=h.readMessage(qK,{});a.push({id:A,bitmap:new Vx({width:E+2*EC,height:P+2*EC},x),metrics:{width:E,height:P,left:D,top:F,advance:V}})}}function qK(u,a,h){u===1?a.id=h.readVarint():u===2?a.bitmap=h.readBytes():u===3?a.width=h.readVarint():u===4?a.height=h.readVarint():u===5?a.left=h.readSVarint():u===6?a.top=h.readSVarint():u===7&&(a.advance=h.readVarint())}let p6=EC;function A6(u){let a=0,h=0;for(let P of u)a+=P.w*P.h,h=Math.max(h,P.w);u.sort((P,D)=>D.h-P.h);let A=[{x:0,y:0,w:Math.max(Math.ceil(Math.sqrt(a/.95)),h),h:1/0}],x=0,E=0;for(let P of u)for(let D=A.length-1;D>=0;D--){let F=A[D];if(!(P.w>F.w||P.h>F.h)){if(P.x=F.x,P.y=F.y,E=Math.max(E,P.y+P.h),x=Math.max(x,P.x+P.w),P.w===F.w&&P.h===F.h){let V=A.pop();D=0&&A>=a&&sT[this.text.charCodeAt(A)];A--)h--;this.text=this.text.substring(a,h),this.sectionIndex=this.sectionIndex.slice(a,h)}substring(a,h){let A=new I_;return A.text=this.text.substring(a,h),A.sectionIndex=this.sectionIndex.slice(a,h),A.sections=this.sections,A}toString(){return this.text}getMaxScale(){return this.sectionIndex.reduce((a,h)=>Math.max(a,this.sections[h].scale),0)}addTextSection(a,h){this.text+=a.text,this.sections.push($x.forText(a.scale,a.fontStack||h));let A=this.sections.length-1;for(let x=0;x=63743?null:++this.imageSectionID:(this.imageSectionID=57344,this.imageSectionID)}}function nT(u,a,h,A,x,E,P,D,F,V,q,X,rt,at,ct,mt){let bt=I_.fromFeature(u,x),Pt;X===n.ai.vertical&&bt.verticalizePunctuation();let{processBidirectionalText:jt,processStyledBidirectionalText:Rt}=ua;if(jt&&bt.sections.length===1){Pt=[];let ce=jt(bt.toString(),IC(bt,V,E,a,A,at,ct));for(let Ne of ce){let ir=new I_;ir.text=Ne,ir.sections=bt.sections;for(let 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this.symbolInstances.length===0&&!this.hasRTLText}uploadPending(){return!this.uploaded||this.text.programConfigurations.needsUpload||this.icon.programConfigurations.needsUpload}upload(a){!this.uploaded&&this.hasDebugData()&&(this.textCollisionBox.upload(a),this.iconCollisionBox.upload(a)),this.text.upload(a,this.sortFeaturesByY,!this.uploaded,this.text.programConfigurations.needsUpload),this.icon.upload(a,this.sortFeaturesByY,!this.uploaded,this.icon.programConfigurations.needsUpload),this.uploaded=!0}destroyDebugData(){this.textCollisionBox.destroy(),this.iconCollisionBox.destroy()}destroy(){this.text.destroy(),this.icon.destroy(),this.hasDebugData()&&this.destroyDebugData()}addToLineVertexArray(a,h){let A=this.lineVertexArray.length;if(a.segment!==void 0){let x=a.dist(h[a.segment+1]),E=a.dist(h[a.segment]),P={};for(let D=a.segment+1;D=0;D--)P[D]={x:h[D].x,y:h[D].y,tileUnitDistanceFromAnchor:E},D>0&&(E+=h[D-1].dist(h[D]));for(let D=0;D0}hasIconData(){return 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A=this.symbolInstances.get(h);this.featureSortOrder.push(A.featureIndex),[A.rightJustifiedTextSymbolIndex,A.centerJustifiedTextSymbolIndex,A.leftJustifiedTextSymbolIndex].forEach((x,E,P)=>{x>=0&&P.indexOf(x)===E&&this.addIndicesForPlacedSymbol(this.text,x)}),A.verticalPlacedTextSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.text,A.verticalPlacedTextSymbolIndex),A.placedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,A.placedIconSymbolIndex),A.verticalPlacedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,A.verticalPlacedIconSymbolIndex)}this.text.indexBuffer&&this.text.indexBuffer.updateData(this.text.indexArray),this.icon.indexBuffer&&this.icon.indexBuffer.updateData(this.icon.indexArray)}}}let w6,S6;Ge(\"SymbolBucket\",C_,{omit:[\"layers\",\"collisionBoxArray\",\"features\",\"compareText\"]}),C_.MAX_GLYPHS=65535,C_.addDynamicAttributes=kC;var OC={get paint(){return S6=S6||new Hn({\"icon-opacity\":new dr(ee.paint_symbol[\"icon-opacity\"]),\"icon-color\":new dr(ee.paint_symbol[\"icon-color\"]),\"icon-halo-color\":new dr(ee.paint_symbol[\"icon-halo-color\"]),\"icon-halo-width\":new dr(ee.paint_symbol[\"icon-halo-width\"]),\"icon-halo-blur\":new dr(ee.paint_symbol[\"icon-halo-blur\"]),\"icon-translate\":new nr(ee.paint_symbol[\"icon-translate\"]),\"icon-translate-anchor\":new nr(ee.paint_symbol[\"icon-translate-anchor\"]),\"text-opacity\":new dr(ee.paint_symbol[\"text-opacity\"]),\"text-color\":new dr(ee.paint_symbol[\"text-color\"],{runtimeType:Us,getOverride:u=>u.textColor,hasOverride:u=>!!u.textColor}),\"text-halo-color\":new dr(ee.paint_symbol[\"text-halo-color\"]),\"text-halo-width\":new dr(ee.paint_symbol[\"text-halo-width\"]),\"text-halo-blur\":new dr(ee.paint_symbol[\"text-halo-blur\"]),\"text-translate\":new nr(ee.paint_symbol[\"text-translate\"]),\"text-translate-anchor\":new nr(ee.paint_symbol[\"text-translate-anchor\"])})},get layout(){return w6=w6||new Hn({\"symbol-placement\":new nr(ee.layout_symbol[\"symbol-placement\"]),\"symbol-spacing\":new nr(ee.layout_symbol[\"symbol-spacing\"]),\"symbol-avoid-edges\":new nr(ee.layout_symbol[\"symbol-avoid-edges\"]),\"symbol-sort-key\":new dr(ee.layout_symbol[\"symbol-sort-key\"]),\"symbol-z-order\":new nr(ee.layout_symbol[\"symbol-z-order\"]),\"icon-allow-overlap\":new nr(ee.layout_symbol[\"icon-allow-overlap\"]),\"icon-overlap\":new nr(ee.layout_symbol[\"icon-overlap\"]),\"icon-ignore-placement\":new nr(ee.layout_symbol[\"icon-ignore-placement\"]),\"icon-optional\":new nr(ee.layout_symbol[\"icon-optional\"]),\"icon-rotation-alignment\":new nr(ee.layout_symbol[\"icon-rotation-alignment\"]),\"icon-size\":new dr(ee.layout_symbol[\"icon-size\"]),\"icon-text-fit\":new nr(ee.layout_symbol[\"icon-text-fit\"]),\"icon-text-fit-padding\":new nr(ee.layout_symbol[\"icon-text-fit-padding\"]),\"icon-image\":new dr(ee.layout_symbol[\"icon-image\"]),\"icon-rotate\":new dr(ee.layout_symbol[\"icon-rotate\"]),\"icon-padding\":new dr(ee.layout_symbol[\"icon-padding\"]),\"icon-keep-upright\":new nr(ee.layout_symbol[\"icon-keep-upright\"]),\"icon-offset\":new dr(ee.layout_symbol[\"icon-offset\"]),\"icon-anchor\":new dr(ee.layout_symbol[\"icon-anchor\"]),\"icon-pitch-alignment\":new nr(ee.layout_symbol[\"icon-pitch-alignment\"]),\"text-pitch-alignment\":new nr(ee.layout_symbol[\"text-pitch-alignment\"]),\"text-rotation-alignment\":new nr(ee.layout_symbol[\"text-rotation-alignment\"]),\"text-field\":new dr(ee.layout_symbol[\"text-field\"]),\"text-font\":new dr(ee.layout_symbol[\"text-font\"]),\"text-size\":new dr(ee.layout_symbol[\"text-size\"]),\"text-max-width\":new dr(ee.layout_symbol[\"text-max-width\"]),\"text-line-height\":new nr(ee.layout_symbol[\"text-line-height\"]),\"text-letter-spacing\":new dr(ee.layout_symbol[\"text-letter-spacing\"]),\"text-justify\":new dr(ee.layout_symbol[\"text-justify\"]),\"text-radial-offset\":new dr(ee.layout_symbol[\"text-radial-offset\"]),\"text-variable-anchor\":new nr(ee.layout_symbol[\"text-variable-anchor\"]),\"text-variable-anchor-offset\":new dr(ee.layout_symbol[\"text-variable-anchor-offset\"]),\"text-anchor\":new dr(ee.layout_symbol[\"text-anchor\"]),\"text-max-angle\":new nr(ee.layout_symbol[\"text-max-angle\"]),\"text-writing-mode\":new nr(ee.layout_symbol[\"text-writing-mode\"]),\"text-rotate\":new dr(ee.layout_symbol[\"text-rotate\"]),\"text-padding\":new nr(ee.layout_symbol[\"text-padding\"]),\"text-keep-upright\":new nr(ee.layout_symbol[\"text-keep-upright\"]),\"text-transform\":new dr(ee.layout_symbol[\"text-transform\"]),\"text-offset\":new dr(ee.layout_symbol[\"text-offset\"]),\"text-allow-overlap\":new nr(ee.layout_symbol[\"text-allow-overlap\"]),\"text-overlap\":new nr(ee.layout_symbol[\"text-overlap\"]),\"text-ignore-placement\":new nr(ee.layout_symbol[\"text-ignore-placement\"]),\"text-optional\":new nr(ee.layout_symbol[\"text-optional\"])})}};class T6{constructor(a){if(a.property.overrides===void 0)throw new Error(\"overrides must be provided to instantiate FormatSectionOverride class\");this.type=a.property.overrides?a.property.overrides.runtimeType:Ca,this.defaultValue=a}evaluate(a){if(a.formattedSection){let h=this.defaultValue.property.overrides;if(h&&h.hasOverride(a.formattedSection))return h.getOverride(a.formattedSection)}return a.feature&&a.featureState?this.defaultValue.evaluate(a.feature,a.featureState):this.defaultValue.property.specification.default}eachChild(a){this.defaultValue.isConstant()||a(this.defaultValue.value._styleExpression.expression)}outputDefined(){return!1}serialize(){return null}}Ge(\"FormatSectionOverride\",T6,{omit:[\"defaultValue\"]});class aT extends ji{constructor(a){super(a,OC)}recalculate(a,h){if(super.recalculate(a,h),this.layout.get(\"icon-rotation-alignment\")===\"auto\"&&(this.layout._values[\"icon-rotation-alignment\"]=this.layout.get(\"symbol-placement\")!==\"point\"?\"map\":\"viewport\"),this.layout.get(\"text-rotation-alignment\")===\"auto\"&&(this.layout._values[\"text-rotation-alignment\"]=this.layout.get(\"symbol-placement\")!==\"point\"?\"map\":\"viewport\"),this.layout.get(\"text-pitch-alignment\")===\"auto\"&&(this.layout._values[\"text-pitch-alignment\"]=this.layout.get(\"text-rotation-alignment\")===\"map\"?\"map\":\"viewport\"),this.layout.get(\"icon-pitch-alignment\")===\"auto\"&&(this.layout._values[\"icon-pitch-alignment\"]=this.layout.get(\"icon-rotation-alignment\")),this.layout.get(\"symbol-placement\")===\"point\"){let A=this.layout.get(\"text-writing-mode\");if(A){let x=[];for(let E of A)x.indexOf(E)<0&&x.push(E);this.layout._values[\"text-writing-mode\"]=x}else this.layout._values[\"text-writing-mode\"]=[\"horizontal\"]}this._setPaintOverrides()}getValueAndResolveTokens(a,h,A,x){let E=this.layout.get(a).evaluate(h,{},A,x),P=this._unevaluatedLayout._values[a];return P.isDataDriven()||Yp(P.value)||!E?E:function(D,F){return F.replace(/{([^{}]+)}/g,(V,q)=>D&&q in D?String(D[q]):\"\")}(h.properties,E)}createBucket(a){return new C_(a)}queryRadius(){return 0}queryIntersectsFeature(){throw new Error(\"Should take a different path in FeatureIndex\")}_setPaintOverrides(){for(let a of OC.paint.overridableProperties){if(!aT.hasPaintOverride(this.layout,a))continue;let h=this.paint.get(a),A=new T6(h),x=new Jm(A,h.property.specification),E=null;E=h.value.kind===\"constant\"||h.value.kind===\"source\"?new t0(\"source\",x):new wt(\"composite\",x,h.value.zoomStops),this.paint._values[a]=new Mo(h.property,E,h.parameters)}}_handleOverridablePaintPropertyUpdate(a,h,A){return!(!this.layout||h.isDataDriven()||A.isDataDriven())&&aT.hasPaintOverride(this.layout,a)}static hasPaintOverride(a,h){let A=a.get(\"text-field\"),x=OC.paint.properties[h],E=!1,P=D=>{for(let F of D)if(x.overrides&&x.overrides.hasOverride(F))return void(E=!0)};if(A.value.kind===\"constant\"&&A.value.value instanceof ln)P(A.value.value.sections);else if(A.value.kind===\"source\"){let D=V=>{E||(V instanceof Gl&&Ki(V.value)===gt?P(V.value.sections):V instanceof Ti?P(V.sections):V.eachChild(D))},F=A.value;F._styleExpression&&D(F._styleExpression.expression)}return E}}let M6;var tJ={get paint(){return M6=M6||new Hn({\"background-color\":new nr(ee.paint_background[\"background-color\"]),\"background-pattern\":new aA(ee.paint_background[\"background-pattern\"]),\"background-opacity\":new nr(ee.paint_background[\"background-opacity\"])})}};class eJ extends ji{constructor(a){super(a,tJ)}}let E6;var rJ={get paint(){return E6=E6||new Hn({\"raster-opacity\":new nr(ee.paint_raster[\"raster-opacity\"]),\"raster-hue-rotate\":new nr(ee.paint_raster[\"raster-hue-rotate\"]),\"raster-brightness-min\":new nr(ee.paint_raster[\"raster-brightness-min\"]),\"raster-brightness-max\":new nr(ee.paint_raster[\"raster-brightness-max\"]),\"raster-saturation\":new nr(ee.paint_raster[\"raster-saturation\"]),\"raster-contrast\":new nr(ee.paint_raster[\"raster-contrast\"]),\"raster-resampling\":new nr(ee.paint_raster[\"raster-resampling\"]),\"raster-fade-duration\":new nr(ee.paint_raster[\"raster-fade-duration\"])})}};class iJ extends ji{constructor(a){super(a,rJ)}}class nJ extends ji{constructor(a){super(a,{}),this.onAdd=h=>{this.implementation.onAdd&&this.implementation.onAdd(h,h.painter.context.gl)},this.onRemove=h=>{this.implementation.onRemove&&this.implementation.onRemove(h,h.painter.context.gl)},this.implementation=a}is3D(){return this.implementation.renderingMode===\"3d\"}hasOffscreenPass(){return this.implementation.prerender!==void 0}recalculate(){}updateTransitions(){}hasTransition(){return!1}serialize(){throw new Error(\"Custom layers cannot be serialized\")}}class sJ{constructor(a){this._callback=a,this._triggered=!1,typeof MessageChannel<\"u\"&&(this._channel=new MessageChannel,this._channel.port2.onmessage=()=>{this._triggered=!1,this._callback()})}trigger(){this._triggered||(this._triggered=!0,this._channel?this._channel.port1.postMessage(!0):setTimeout(()=>{this._triggered=!1,this._callback()},0))}remove(){delete this._channel,this._callback=()=>{}}}let BC=63710088e-1;class dA{constructor(a,h){if(isNaN(a)||isNaN(h))throw new Error(`Invalid LngLat object: (${a}, ${h})`);if(this.lng=+a,this.lat=+h,this.lat>90||this.lat<-90)throw new Error(\"Invalid LngLat latitude value: must be between -90 and 90\")}wrap(){return new dA(Et(this.lng,-180,180),this.lat)}toArray(){return[this.lng,this.lat]}toString(){return`LngLat(${this.lng}, ${this.lat})`}distanceTo(a){let h=Math.PI/180,A=this.lat*h,x=a.lat*h,E=Math.sin(A)*Math.sin(x)+Math.cos(A)*Math.cos(x)*Math.cos((a.lng-this.lng)*h);return BC*Math.acos(Math.min(E,1))}static convert(a){if(a instanceof dA)return a;if(Array.isArray(a)&&(a.length===2||a.length===3))return new dA(Number(a[0]),Number(a[1]));if(!Array.isArray(a)&&typeof a==\"object\"&&a!==null)return new dA(Number(\"lng\"in a?a.lng:a.lon),Number(a.lat));throw new Error(\"`LngLatLike` argument must be specified as a LngLat instance, an object {lng: , lat: }, an object {lon: , lat: }, or an array of [, ]\")}}let P6=2*Math.PI*BC;function I6(u){return P6*Math.cos(u*Math.PI/180)}function C6(u){return(180+u)/360}function L6(u){return(180-180/Math.PI*Math.log(Math.tan(Math.PI/4+u*Math.PI/360)))/360}function k6(u,a){return u/I6(a)}function R6(u){return 360*u-180}function FC(u){return 360/Math.PI*Math.atan(Math.exp((180-360*u)*Math.PI/180))-90}class lT{constructor(a,h,A=0){this.x=+a,this.y=+h,this.z=+A}static fromLngLat(a,h=0){let A=dA.convert(a);return new lT(C6(A.lng),L6(A.lat),k6(h,A.lat))}toLngLat(){return new dA(R6(this.x),FC(this.y))}toAltitude(){return this.z*I6(FC(this.y))}meterInMercatorCoordinateUnits(){return 1/P6*(a=FC(this.y),1/Math.cos(a*Math.PI/180));var a}}function D6(u,a,h){var A=2*Math.PI*6378137/256/Math.pow(2,h);return[u*A-2*Math.PI*6378137/2,a*A-2*Math.PI*6378137/2]}class zC{constructor(a,h,A){if(a<0||a>25||A<0||A>=Math.pow(2,a)||h<0||h>=Math.pow(2,a))throw new Error(`x=${h}, y=${A}, z=${a} outside of bounds. 0<=x<${Math.pow(2,a)}, 0<=y<${Math.pow(2,a)} 0<=z<=25 `);this.z=a,this.x=h,this.y=A,this.key=Kx(0,a,a,h,A)}equals(a){return this.z===a.z&&this.x===a.x&&this.y===a.y}url(a,h,A){let x=(P=this.y,D=this.z,F=D6(256*(E=this.x),256*(P=Math.pow(2,D)-P-1),D),V=D6(256*(E+1),256*(P+1),D),F[0]+\",\"+F[1]+\",\"+V[0]+\",\"+V[1]);var E,P,D,F,V;let q=function(X,rt,at){let ct,mt=\"\";for(let bt=X;bt>0;bt--)ct=1<1?\"@2x\":\"\").replace(/{quadkey}/g,q).replace(/{bbox-epsg-3857}/g,x)}isChildOf(a){let h=this.z-a.z;return h>0&&a.x===this.x>>h&&a.y===this.y>>h}getTilePoint(a){let h=Math.pow(2,this.z);return new w((a.x*h-this.x)*en,(a.y*h-this.y)*en)}toString(){return`${this.z}/${this.x}/${this.y}`}}class O6{constructor(a,h){this.wrap=a,this.canonical=h,this.key=Kx(a,h.z,h.z,h.x,h.y)}}class Nc{constructor(a,h,A,x,E){if(a= z; overscaledZ = ${a}; z = ${A}`);this.overscaledZ=a,this.wrap=h,this.canonical=new zC(A,+x,+E),this.key=Kx(h,a,A,x,E)}clone(){return new Nc(this.overscaledZ,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y)}equals(a){return this.overscaledZ===a.overscaledZ&&this.wrap===a.wrap&&this.canonical.equals(a.canonical)}scaledTo(a){if(a>this.overscaledZ)throw new Error(`targetZ > this.overscaledZ; targetZ = ${a}; overscaledZ = ${this.overscaledZ}`);let h=this.canonical.z-a;return a>this.canonical.z?new Nc(a,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y):new Nc(a,this.wrap,a,this.canonical.x>>h,this.canonical.y>>h)}calculateScaledKey(a,h){if(a>this.overscaledZ)throw new Error(`targetZ > this.overscaledZ; targetZ = ${a}; overscaledZ = ${this.overscaledZ}`);let A=this.canonical.z-a;return a>this.canonical.z?Kx(this.wrap*+h,a,this.canonical.z,this.canonical.x,this.canonical.y):Kx(this.wrap*+h,a,a,this.canonical.x>>A,this.canonical.y>>A)}isChildOf(a){if(a.wrap!==this.wrap)return!1;let h=this.canonical.z-a.canonical.z;return a.overscaledZ===0||a.overscaledZ>h&&a.canonical.y===this.canonical.y>>h}children(a){if(this.overscaledZ>=a)return[new Nc(this.overscaledZ+1,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y)];let h=this.canonical.z+1,A=2*this.canonical.x,x=2*this.canonical.y;return[new Nc(h,this.wrap,h,A,x),new Nc(h,this.wrap,h,A+1,x),new Nc(h,this.wrap,h,A,x+1),new Nc(h,this.wrap,h,A+1,x+1)]}isLessThan(a){return this.wrapa.wrap)&&(this.overscaledZa.overscaledZ)&&(this.canonical.xa.canonical.x)&&this.canonical.ythis.max&&(this.max=X),X=this.dim+1||h<-1||h>=this.dim+1)throw new RangeError(\"out of range source coordinates for DEM data\");return(h+1)*this.stride+(a+1)}unpack(a,h,A){return a*this.redFactor+h*this.greenFactor+A*this.blueFactor-this.baseShift}getPixels(){return new zc({width:this.stride,height:this.stride},new Uint8Array(this.data.buffer))}backfillBorder(a,h,A){if(this.dim!==a.dim)throw new Error(\"dem dimension mismatch\");let x=h*this.dim,E=h*this.dim+this.dim,P=A*this.dim,D=A*this.dim+this.dim;switch(h){case-1:x=E-1;break;case 1:E=x+1}switch(A){case-1:P=D-1;break;case 1:D=P+1}let F=-h*this.dim,V=-A*this.dim;for(let q=P;q=this._numberToString.length)throw new Error(`Out of bounds. Index requested n=${a} can't be >= this._numberToString.length ${this._numberToString.length}`);return this._numberToString[a]}}class z6{constructor(a,h,A,x,E){this.type=\"Feature\",this._vectorTileFeature=a,a._z=h,a._x=A,a._y=x,this.properties=a.properties,this.id=E}get geometry(){return this._geometry===void 0&&(this._geometry=this._vectorTileFeature.toGeoJSON(this._vectorTileFeature._x,this._vectorTileFeature._y,this._vectorTileFeature._z).geometry),this._geometry}set geometry(a){this._geometry=a}toJSON(){let a={geometry:this.geometry};for(let h in this)h!==\"_geometry\"&&h!==\"_vectorTileFeature\"&&(a[h]=this[h]);return a}}class N6{constructor(a,h){this.tileID=a,this.x=a.canonical.x,this.y=a.canonical.y,this.z=a.canonical.z,this.grid=new Na(en,16,0),this.grid3D=new Na(en,16,0),this.featureIndexArray=new he,this.promoteId=h}insert(a,h,A,x,E,P){let D=this.featureIndexArray.length;this.featureIndexArray.emplaceBack(A,x,E);let F=P?this.grid3D:this.grid;for(let V=0;V=0&&X[3]>=0&&F.insert(D,X[0],X[1],X[2],X[3])}}loadVTLayers(){return this.vtLayers||(this.vtLayers=new uA.VectorTile(new MC(this.rawTileData)).layers,this.sourceLayerCoder=new F6(this.vtLayers?Object.keys(this.vtLayers).sort():[\"_geojsonTileLayer\"])),this.vtLayers}query(a,h,A,x){this.loadVTLayers();let E=a.params||{},P=en/a.tileSize/a.scale,D=r0(E.filter),F=a.queryGeometry,V=a.queryPadding*P,q=V6(F),X=this.grid.query(q.minX-V,q.minY-V,q.maxX+V,q.maxY+V),rt=V6(a.cameraQueryGeometry),at=this.grid3D.query(rt.minX-V,rt.minY-V,rt.maxX+V,rt.maxY+V,(bt,Pt,jt,Rt)=>function(Gt,Yt,ce,Ne,ir){for(let Re of Gt)if(Yt<=Re.x&&ce<=Re.y&&Ne>=Re.x&&ir>=Re.y)return!0;let Fe=[new w(Yt,ce),new w(Yt,ir),new w(Ne,ir),new w(Ne,ce)];if(Gt.length>2){for(let Re of Fe)if(ne(Gt,Re))return!0}for(let Re=0;Re(Rt||(Rt=y(Gt)),Yt.queryIntersectsFeature(F,Gt,ce,Rt,this.z,a.transform,P,a.pixelPosMatrix)))}return ct}loadMatchingFeature(a,h,A,x,E,P,D,F,V,q,X){let rt=this.bucketLayerIDs[h];if(P&&!function(bt,Pt){for(let jt=0;jt=0)return!0;return!1}(P,rt))return;let at=this.sourceLayerCoder.decode(A),ct=this.vtLayers[at].feature(x);if(E.needGeometry){let bt=S(ct,!0);if(!E.filter(new un(this.tileID.overscaledZ),bt,this.tileID.canonical))return}else if(!E.filter(new un(this.tileID.overscaledZ),ct))return;let mt=this.getId(ct,at);for(let bt=0;bt{let D=a instanceof oA?a.get(P):null;return D&&D.evaluate?D.evaluate(h,A,x):D})}function V6(u){let a=1/0,h=1/0,A=-1/0,x=-1/0;for(let E of u)a=Math.min(a,E.x),h=Math.min(h,E.y),A=Math.max(A,E.x),x=Math.max(x,E.y);return{minX:a,minY:h,maxX:A,maxY:x}}function oJ(u,a){return a-u}function j6(u,a,h,A,x){let E=[];for(let P=0;P=A&&X.x>=A||(q.x>=A?q=new w(A,q.y+(A-q.x)/(X.x-q.x)*(X.y-q.y))._round():X.x>=A&&(X=new w(A,q.y+(A-q.x)/(X.x-q.x)*(X.y-q.y))._round()),q.y>=x&&X.y>=x||(q.y>=x?q=new w(q.x+(x-q.y)/(X.y-q.y)*(X.x-q.x),x)._round():X.y>=x&&(X=new w(q.x+(x-q.y)/(X.y-q.y)*(X.x-q.x),x)._round()),F&&q.equals(F[F.length-1])||(F=[q],E.push(F)),F.push(X)))))}}return E}Ge(\"FeatureIndex\",N6,{omit:[\"rawTileData\",\"sourceLayerCoder\"]});class pA extends w{constructor(a,h,A,x){super(a,h),this.angle=A,x!==void 0&&(this.segment=x)}clone(){return new pA(this.x,this.y,this.angle,this.segment)}}function G6(u,a,h,A,x){if(a.segment===void 0||h===0)return!0;let E=a,P=a.segment+1,D=0;for(;D>-h/2;){if(P--,P<0)return!1;D-=u[P].dist(E),E=u[P]}D+=u[P].dist(u[P+1]),P++;let F=[],V=0;for(;DA;)V-=F.shift().angleDelta;if(V>x)return!1;P++,D+=q.dist(X)}return!0}function W6(u){let a=0;for(let h=0;hV){let ct=(V-F)/at,mt=Da.number(X.x,rt.x,ct),bt=Da.number(X.y,rt.y,ct),Pt=new pA(mt,bt,rt.angleTo(X),q);return Pt._round(),!P||G6(u,Pt,D,P,a)?Pt:void 0}F+=at}}function lJ(u,a,h,A,x,E,P,D,F){let V=H6(A,E,P),q=q6(A,x),X=q*P,rt=u[0].x===0||u[0].x===F||u[0].y===0||u[0].y===F;return a-X=0&&Gt=0&&Yt=0&&rt+V<=q){let ce=new pA(Gt,Yt,jt,ct);ce._round(),A&&!G6(u,ce,E,A,x)||at.push(ce)}}X+=Pt}return D||at.length||P||(at=Z6(u,X/2,h,A,x,E,P,!0,F)),at}Ge(\"Anchor\",pA);let L_=gl;function 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xt=this._layers[vt.id]=n.aC(vt);xt._featureFilter=n.a6(xt.filter),this.keyCache[vt.id]&&delete this.keyCache[vt.id]}for(let vt of nt)delete this.keyCache[vt],delete this._layerConfigs[vt],delete this._layers[vt];this.familiesBySource={};let ht=n.bl(Object.values(this._layerConfigs),this.keyCache);for(let vt of ht){let xt=vt.map(se=>this._layers[se.id]),_t=xt[0];if(_t.visibility===\"none\")continue;let Dt=_t.source||\"\",Mt=this.familiesBySource[Dt];Mt||(Mt=this.familiesBySource[Dt]={});let Vt=_t.sourceLayer||\"_geojsonTileLayer\",ie=Mt[Vt];ie||(ie=Mt[Vt]=[]),ie.push(xt)}}}class c{constructor(tt){let nt={},ht=[];for(let Dt in tt){let Mt=tt[Dt],Vt=nt[Dt]={};for(let ie in Mt){let se=Mt[+ie];if(!se||se.bitmap.width===0||se.bitmap.height===0)continue;let ae={x:0,y:0,w:se.bitmap.width+2,h:se.bitmap.height+2};ht.push(ae),Vt[ie]={rect:ae,metrics:se.metrics}}}let{w:vt,h:xt}=n.p(ht),_t=new n.q({width:vt||1,height:xt||1});for(let Dt in tt){let Mt=tt[Dt];for(let Vt in Mt){let ie=Mt[+Vt];if(!ie||ie.bitmap.width===0||ie.bitmap.height===0)continue;let se=nt[Dt][Vt].rect;n.q.copy(ie.bitmap,_t,{x:0,y:0},{x:se.x+1,y:se.y+1},ie.bitmap)}}this.image=_t,this.positions=nt}}n.bm(\"GlyphAtlas\",c);class f{constructor(tt){this.tileID=new n.O(tt.tileID.overscaledZ,tt.tileID.wrap,tt.tileID.canonical.z,tt.tileID.canonical.x,tt.tileID.canonical.y),this.uid=tt.uid,this.zoom=tt.zoom,this.pixelRatio=tt.pixelRatio,this.tileSize=tt.tileSize,this.source=tt.source,this.overscaling=this.tileID.overscaleFactor(),this.showCollisionBoxes=tt.showCollisionBoxes,this.collectResourceTiming=!!tt.collectResourceTiming,this.returnDependencies=!!tt.returnDependencies,this.promoteId=tt.promoteId,this.inFlightDependencies=[],this.dependencySentinel=-1}parse(tt,nt,ht,vt,xt){this.status=\"parsing\",this.data=tt,this.collisionBoxArray=new n.a3;let _t=new n.bn(Object.keys(tt.layers).sort()),Dt=new n.bo(this.tileID,this.promoteId);Dt.bucketLayerIDs=[];let Mt={},Vt={featureIndex:Dt,iconDependencies:{},patternDependencies:{},glyphDependencies:{},availableImages:ht},ie=nt.familiesBySource[this.source];for(let ni in ie){let Hr=tt.layers[ni];if(!Hr)continue;Hr.version===1&&n.w(`Vector tile source \"${this.source}\" layer \"${ni}\" does not use vector tile spec v2 and therefore may have some rendering errors.`);let jn=_t.encode(ni),Bi=[];for(let xn=0;xn=es.maxzoom||es.visibility!==\"none\"&&(_(xn,this.zoom,ht),(Mt[es.id]=es.createBucket({index:Dt.bucketLayerIDs.length,layers:xn,zoom:this.zoom,pixelRatio:this.pixelRatio,overscaling:this.overscaling,collisionBoxArray:this.collisionBoxArray,sourceLayerIndex:jn,sourceID:this.source})).populate(Bi,Vt,this.tileID.canonical),Dt.bucketLayerIDs.push(xn.map(oa=>oa.id)))}}let se,ae,lr,vr,Xe=n.aH(Vt.glyphDependencies,ni=>Object.keys(ni).map(Number));this.inFlightDependencies.forEach(ni=>ni?.cancel()),this.inFlightDependencies=[];let cr=++this.dependencySentinel;Object.keys(Xe).length?this.inFlightDependencies.push(vt.send(\"getGlyphs\",{uid:this.uid,stacks:Xe,source:this.source,tileID:this.tileID,type:\"glyphs\"},(ni,Hr)=>{cr===this.dependencySentinel&&(se||(se=ni,ae=Hr,zi.call(this)))})):ae={};let wr=Object.keys(Vt.iconDependencies);wr.length?this.inFlightDependencies.push(vt.send(\"getImages\",{icons:wr,source:this.source,tileID:this.tileID,type:\"icons\"},(ni,Hr)=>{cr===this.dependencySentinel&&(se||(se=ni,lr=Hr,zi.call(this)))})):lr={};let xi=Object.keys(Vt.patternDependencies);function zi(){if(se)return xt(se);if(ae&&lr&&vr){let ni=new c(ae),Hr=new n.bp(lr,vr);for(let jn in Mt){let Bi=Mt[jn];Bi instanceof n.a4?(_(Bi.layers,this.zoom,ht),n.bq({bucket:Bi,glyphMap:ae,glyphPositions:ni.positions,imageMap:lr,imagePositions:Hr.iconPositions,showCollisionBoxes:this.showCollisionBoxes,canonical:this.tileID.canonical})):Bi.hasPattern&&(Bi instanceof n.br||Bi instanceof n.bs||Bi instanceof n.bt)&&(_(Bi.layers,this.zoom,ht),Bi.addFeatures(Vt,this.tileID.canonical,Hr.patternPositions))}this.status=\"done\",xt(null,{buckets:Object.values(Mt).filter(jn=>!jn.isEmpty()),featureIndex:Dt,collisionBoxArray:this.collisionBoxArray,glyphAtlasImage:ni.image,imageAtlas:Hr,glyphMap:this.returnDependencies?ae:null,iconMap:this.returnDependencies?lr:null,glyphPositions:this.returnDependencies?ni.positions:null})}}xi.length?this.inFlightDependencies.push(vt.send(\"getImages\",{icons:xi,source:this.source,tileID:this.tileID,type:\"patterns\"},(ni,Hr)=>{cr===this.dependencySentinel&&(se||(se=ni,vr=Hr,zi.call(this)))})):vr={},zi.call(this)}}function _(gt,tt,nt){let ht=new n.a8(tt);for(let vt of gt)vt.recalculate(ht,nt)}function w(gt,tt){let nt=n.l(gt.request,(ht,vt,xt,_t)=>{if(ht)tt(ht);else if(vt)try{let Dt=new n.bw.VectorTile(new n.bv(vt));tt(null,{vectorTile:Dt,rawData:vt,cacheControl:xt,expires:_t})}catch(Dt){let Mt=new Uint8Array(vt),Vt=`Unable to parse the tile at ${gt.request.url}, `;Vt+=Mt[0]===31&&Mt[1]===139?\"please make sure the data is not gzipped and that you have configured the relevant header in the server\":`got error: ${Dt.messge}`,tt(new Error(Vt))}});return()=>{nt.cancel(),tt()}}class I{constructor(tt,nt,ht,vt){this.actor=tt,this.layerIndex=nt,this.availableImages=ht,this.loadVectorData=vt||w,this.fetching={},this.loading={},this.loaded={}}loadTile(tt,nt){let ht=tt.uid;this.loading||(this.loading={});let vt=!!(tt&&tt.request&&tt.request.collectResourceTiming)&&new n.bu(tt.request),xt=this.loading[ht]=new f(tt);xt.abort=this.loadVectorData(tt,(_t,Dt)=>{if(delete this.loading[ht],_t||!Dt)return xt.status=\"done\",this.loaded[ht]=xt,nt(_t);let Mt=Dt.rawData,Vt={};Dt.expires&&(Vt.expires=Dt.expires),Dt.cacheControl&&(Vt.cacheControl=Dt.cacheControl);let ie={};if(vt){let se=vt.finish();se&&(ie.resourceTiming=JSON.parse(JSON.stringify(se)))}xt.vectorTile=Dt.vectorTile,xt.parse(Dt.vectorTile,this.layerIndex,this.availableImages,this.actor,(se,ae)=>{if(delete this.fetching[ht],se||!ae)return nt(se);nt(null,n.e({rawTileData:Mt.slice(0)},ae,Vt,ie))}),this.loaded=this.loaded||{},this.loaded[ht]=xt,this.fetching[ht]={rawTileData:Mt,cacheControl:Vt,resourceTiming:ie}})}reloadTile(tt,nt){let ht=this.loaded,vt=tt.uid;if(ht&&ht[vt]){let xt=ht[vt];xt.showCollisionBoxes=tt.showCollisionBoxes,xt.status===\"parsing\"?xt.parse(xt.vectorTile,this.layerIndex,this.availableImages,this.actor,(_t,Dt)=>{if(_t||!Dt)return nt(_t,Dt);let Mt;if(this.fetching[vt]){let{rawTileData:Vt,cacheControl:ie,resourceTiming:se}=this.fetching[vt];delete this.fetching[vt],Mt=n.e({rawTileData:Vt.slice(0)},Dt,ie,se)}else Mt=Dt;nt(null,Mt)}):xt.status===\"done\"&&(xt.vectorTile?xt.parse(xt.vectorTile,this.layerIndex,this.availableImages,this.actor,nt):nt())}}abortTile(tt,nt){let ht=this.loading,vt=tt.uid;ht&&ht[vt]&&ht[vt].abort&&(ht[vt].abort(),delete ht[vt]),nt()}removeTile(tt,nt){let ht=this.loaded,vt=tt.uid;ht&&ht[vt]&&delete ht[vt],nt()}}class R{constructor(){this.loaded={}}loadTile(tt,nt){return n._(this,void 0,void 0,function*(){let{uid:ht,encoding:vt,rawImageData:xt,redFactor:_t,greenFactor:Dt,blueFactor:Mt,baseShift:Vt}=tt,ie=xt.width+2,se=xt.height+2,ae=n.a(xt)?new n.R({width:ie,height:se},yield n.bx(xt,-1,-1,ie,se)):xt,lr=new n.by(ht,ae,vt,_t,Dt,Mt,Vt);this.loaded=this.loaded||{},this.loaded[ht]=lr,nt(null,lr)})}removeTile(tt){let nt=this.loaded,ht=tt.uid;nt&&nt[ht]&&delete nt[ht]}}function N(gt,tt){if(gt.length!==0){j(gt[0],tt);for(var nt=1;nt=Math.abs(Dt)?nt-Mt+Dt:Dt-Mt+nt,nt=Mt}nt+ht>=0!=!!tt&>.reverse()}var Q=n.bz(function gt(tt,nt){var ht,vt=tt&&tt.type;if(vt===\"FeatureCollection\")for(ht=0;ht>31}function Li(gt,tt){for(var nt=gt.loadGeometry(),ht=gt.type,vt=0,xt=0,_t=nt.length,Dt=0;Dt<_t;Dt++){var Mt=nt[Dt],Vt=1;ht===1&&(Vt=Mt.length),tt.writeVarint(rr(1,Vt));for(var ie=ht===3?Mt.length-1:Mt.length,se=0;segt},ih=Math.fround||(Uo=new Float32Array(1),gt=>(Uo[0]=+gt,Uo[0]));var Uo;let Si=3,Ns=5,ll=6;class kc{constructor(tt){this.options=Object.assign(Object.create(No),tt),this.trees=new Array(this.options.maxZoom+1),this.stride=this.options.reduce?7:6,this.clusterProps=[]}load(tt){let{log:nt,minZoom:ht,maxZoom:vt}=this.options;nt&&console.time(\"total time\");let xt=`prepare ${tt.length} points`;nt&&console.time(xt),this.points=tt;let _t=[];for(let Mt=0;Mt=ht;Mt--){let Vt=+Date.now();Dt=this.trees[Mt]=this._createTree(this._cluster(Dt,Mt)),nt&&console.log(\"z%d: %d clusters in %dms\",Mt,Dt.numItems,+Date.now()-Vt)}return nt&&console.timeEnd(\"total time\"),this}getClusters(tt,nt){let ht=((tt[0]+180)%360+360)%360-180,vt=Math.max(-90,Math.min(90,tt[1])),xt=tt[2]===180?180:((tt[2]+180)%360+360)%360-180,_t=Math.max(-90,Math.min(90,tt[3]));if(tt[2]-tt[0]>=360)ht=-180,xt=180;else if(ht>xt){let se=this.getClusters([ht,vt,180,_t],nt),ae=this.getClusters([-180,vt,xt,_t],nt);return se.concat(ae)}let Dt=this.trees[this._limitZoom(nt)],Mt=Dt.range(Jn(ht),ki(_t),Jn(xt),ki(vt)),Vt=Dt.data,ie=[];for(let se of Mt){let ae=this.stride*se;ie.push(Vt[ae+Ns]>1?Rc(Vt,ae,this.clusterProps):this.points[Vt[ae+Si]])}return ie}getChildren(tt){let nt=this._getOriginId(tt),ht=this._getOriginZoom(tt),vt=\"No cluster with the specified id.\",xt=this.trees[ht];if(!xt)throw new Error(vt);let _t=xt.data;if(nt*this.stride>=_t.length)throw new Error(vt);let Dt=this.options.radius/(this.options.extent*Math.pow(2,ht-1)),Mt=xt.within(_t[nt*this.stride],_t[nt*this.stride+1],Dt),Vt=[];for(let ie of Mt){let se=ie*this.stride;_t[se+4]===tt&&Vt.push(_t[se+Ns]>1?Rc(_t,se,this.clusterProps):this.points[_t[se+Si]])}if(Vt.length===0)throw new Error(vt);return Vt}getLeaves(tt,nt,ht){let vt=[];return this._appendLeaves(vt,tt,nt=nt||10,ht=ht||0,0),vt}getTile(tt,nt,ht){let vt=this.trees[this._limitZoom(tt)],xt=Math.pow(2,tt),{extent:_t,radius:Dt}=this.options,Mt=Dt/_t,Vt=(ht-Mt)/xt,ie=(ht+1+Mt)/xt,se={features:[]};return this._addTileFeatures(vt.range((nt-Mt)/xt,Vt,(nt+1+Mt)/xt,ie),vt.data,nt,ht,xt,se),nt===0&&this._addTileFeatures(vt.range(1-Mt/xt,Vt,1,ie),vt.data,xt,ht,xt,se),nt===xt-1&&this._addTileFeatures(vt.range(0,Vt,Mt/xt,ie),vt.data,-1,ht,xt,se),se.features.length?se:null}getClusterExpansionZoom(tt){let nt=this._getOriginZoom(tt)-1;for(;nt<=this.options.maxZoom;){let ht=this.getChildren(tt);if(nt++,ht.length!==1)break;tt=ht[0].properties.cluster_id}return nt}_appendLeaves(tt,nt,ht,vt,xt){let _t=this.getChildren(nt);for(let Dt of _t){let Mt=Dt.properties;if(Mt&&Mt.cluster?xt+Mt.point_count<=vt?xt+=Mt.point_count:xt=this._appendLeaves(tt,Mt.cluster_id,ht,vt,xt):xt1,ie,se,ae;if(Vt)ie=Xi(nt,Mt,this.clusterProps),se=nt[Mt],ae=nt[Mt+1];else{let Xe=this.points[nt[Mt+Si]];ie=Xe.properties;let[cr,wr]=Xe.geometry.coordinates;se=Jn(cr),ae=ki(wr)}let lr={type:1,geometry:[[Math.round(this.options.extent*(se*xt-ht)),Math.round(this.options.extent*(ae*xt-vt))]],tags:ie},vr;vr=Vt||this.options.generateId?nt[Mt+Si]:this.points[nt[Mt+Si]].id,vr!==void 0&&(lr.id=vr),_t.features.push(lr)}}_limitZoom(tt){return Math.max(this.options.minZoom,Math.min(Math.floor(+tt),this.options.maxZoom+1))}_cluster(tt,nt){let{radius:ht,extent:vt,reduce:xt,minPoints:_t}=this.options,Dt=ht/(vt*Math.pow(2,nt)),Mt=tt.data,Vt=[],ie=this.stride;for(let se=0;sent&&(cr+=Mt[xi+Ns])}if(cr>Xe&&cr>=_t){let wr,xi=ae*Xe,zi=lr*Xe,ni=-1,Hr=((se/ie|0)<<5)+(nt+1)+this.points.length;for(let jn of vr){let Bi=jn*ie;if(Mt[Bi+2]<=nt)continue;Mt[Bi+2]=nt;let xn=Mt[Bi+Ns];xi+=Mt[Bi]*xn,zi+=Mt[Bi+1]*xn,Mt[Bi+4]=Hr,xt&&(wr||(wr=this._map(Mt,se,!0),ni=this.clusterProps.length,this.clusterProps.push(wr)),xt(wr,this._map(Mt,Bi)))}Mt[se+4]=Hr,Vt.push(xi/cr,zi/cr,1/0,Hr,-1,cr),xt&&Vt.push(ni)}else{for(let wr=0;wr1)for(let wr of vr){let xi=wr*ie;if(!(Mt[xi+2]<=nt)){Mt[xi+2]=nt;for(let zi=0;zi>5}_getOriginZoom(tt){return(tt-this.points.length)%32}_map(tt,nt,ht){if(tt[nt+Ns]>1){let _t=this.clusterProps[tt[nt+ll]];return ht?Object.assign({},_t):_t}let vt=this.points[tt[nt+Si]].properties,xt=this.options.map(vt);return ht&&xt===vt?Object.assign({},xt):xt}}function Rc(gt,tt,nt){return{type:\"Feature\",id:gt[tt+Si],properties:Xi(gt,tt,nt),geometry:{type:\"Point\",coordinates:[(ht=gt[tt],360*(ht-.5)),ts(gt[tt+1])]}};var ht}function Xi(gt,tt,nt){let ht=gt[tt+Ns],vt=ht>=1e4?`${Math.round(ht/1e3)}k`:ht>=1e3?Math.round(ht/100)/10+\"k\":ht,xt=gt[tt+ll],_t=xt===-1?{}:Object.assign({},nt[xt]);return Object.assign(_t,{cluster:!0,cluster_id:gt[tt+Si],point_count:ht,point_count_abbreviated:vt})}function Jn(gt){return gt/360+.5}function ki(gt){let tt=Math.sin(gt*Math.PI/180),nt=.5-.25*Math.log((1+tt)/(1-tt))/Math.PI;return nt<0?0:nt>1?1:nt}function ts(gt){let tt=(180-360*gt)*Math.PI/180;return 360*Math.atan(Math.exp(tt))/Math.PI-90}function Vo(gt,tt,nt,ht){for(var vt,xt=ht,_t=nt-tt>>1,Dt=nt-tt,Mt=gt[tt],Vt=gt[tt+1],ie=gt[nt],se=gt[nt+1],ae=tt+3;aext)vt=ae,xt=lr;else if(lr===xt){var vr=Math.abs(ae-_t);vrht&&(vt-tt>3&&Vo(gt,tt,vt,ht),gt[vt+2]=xt,nt-vt>3&&Vo(gt,vt,nt,ht))}function cl(gt,tt,nt,ht,vt,xt){var _t=vt-nt,Dt=xt-ht;if(_t!==0||Dt!==0){var Mt=((gt-nt)*_t+(tt-ht)*Dt)/(_t*_t+Dt*Dt);Mt>1?(nt=vt,ht=xt):Mt>0&&(nt+=_t*Mt,ht+=Dt*Mt)}return(_t=gt-nt)*_t+(Dt=tt-ht)*Dt}function xo(gt,tt,nt,ht){var vt={id:gt===void 0?null:gt,type:tt,geometry:nt,tags:ht,minX:1/0,minY:1/0,maxX:-1/0,maxY:-1/0};return function(xt){var _t=xt.geometry,Dt=xt.type;if(Dt===\"Point\"||Dt===\"MultiPoint\"||Dt===\"LineString\")Pa(xt,_t);else if(Dt===\"Polygon\"||Dt===\"MultiLineString\")for(var Mt=0;Mt<_t.length;Mt++)Pa(xt,_t[Mt]);else if(Dt===\"MultiPolygon\")for(Mt=0;Mt<_t.length;Mt++)for(var Vt=0;Vt<_t[Mt].length;Vt++)Pa(xt,_t[Mt][Vt])}(vt),vt}function Pa(gt,tt){for(var nt=0;nt0&&(_t+=ht?(vt*Vt-Mt*xt)/2:Math.sqrt(Math.pow(Mt-vt,2)+Math.pow(Vt-xt,2))),vt=Mt,xt=Vt}var ie=tt.length-3;tt[2]=1,Vo(tt,0,ie,nt),tt[ie+2]=1,tt.size=Math.abs(_t),tt.start=0,tt.end=tt.size}function Nl(gt,tt,nt,ht){for(var vt=0;vt1?1:nt}function mn(gt,tt,nt,ht,vt,xt,_t,Dt){if(ht/=tt,xt>=(nt/=tt)&&_t=ht)return null;for(var Mt=[],Vt=0;Vt=nt&&vr=ht)){var Xe=[];if(ae===\"Point\"||ae===\"MultiPoint\")gi(se,Xe,nt,ht,vt);else if(ae===\"LineString\")oi(se,Xe,nt,ht,vt,!1,Dt.lineMetrics);else if(ae===\"MultiLineString\")du(se,Xe,nt,ht,vt,!1);else if(ae===\"Polygon\")du(se,Xe,nt,ht,vt,!0);else if(ae===\"MultiPolygon\")for(var cr=0;cr=nt&&_t<=ht&&(tt.push(gt[xt]),tt.push(gt[xt+1]),tt.push(gt[xt+2]))}}function oi(gt,tt,nt,ht,vt,xt,_t){for(var Dt,Mt,Vt=lo(gt),ie=vt===0?bo:hl,se=gt.start,ae=0;aent&&(Mt=ie(Vt,lr,vr,cr,wr,nt),_t&&(Vt.start=se+Dt*Mt)):xi>ht?zi=nt&&(Mt=ie(Vt,lr,vr,cr,wr,nt),ni=!0),zi>ht&&xi<=ht&&(Mt=ie(Vt,lr,vr,cr,wr,ht),ni=!0),!xt&&ni&&(_t&&(Vt.end=se+Dt*Mt),tt.push(Vt),Vt=lo(gt)),_t&&(se+=Dt)}var Hr=gt.length-3;lr=gt[Hr],vr=gt[Hr+1],Xe=gt[Hr+2],(xi=vt===0?lr:vr)>=nt&&xi<=ht&&ul(Vt,lr,vr,Xe),Hr=Vt.length-3,xt&&Hr>=3&&(Vt[Hr]!==Vt[0]||Vt[Hr+1]!==Vt[1])&&ul(Vt,Vt[0],Vt[1],Vt[2]),Vt.length&&tt.push(Vt)}function lo(gt){var tt=[];return tt.size=gt.size,tt.start=gt.start,tt.end=gt.end,tt}function du(gt,tt,nt,ht,vt,xt){for(var _t=0;_t_t.maxX&&(_t.maxX=ie),se>_t.maxY&&(_t.maxY=se)}return _t}function Ul(gt,tt,nt,ht){var vt=tt.geometry,xt=tt.type,_t=[];if(xt===\"Point\"||xt===\"MultiPoint\")for(var Dt=0;Dt0&&tt.size<(vt?_t:ht))nt.numPoints+=tt.length/3;else{for(var Dt=[],Mt=0;Mt_t)&&(nt.numSimplified++,Dt.push(tt[Mt]),Dt.push(tt[Mt+1])),nt.numPoints++;vt&&function(Vt,ie){for(var se=0,ae=0,lr=Vt.length,vr=lr-2;ae0===ie)for(ae=0,lr=Vt.length;ae24)throw new Error(\"maxZoom should be in the 0-24 range\");if(tt.promoteId&&tt.generateId)throw new Error(\"promoteId and generateId cannot be used together.\");var ht=function(vt,xt){var _t=[];if(vt.type===\"FeatureCollection\")for(var Dt=0;Dt1&&console.time(\"creation\"),ae=this.tiles[se]=gn(gt,tt,nt,ht,Mt),this.tileCoords.push({z:tt,x:nt,y:ht}),Vt)){Vt>1&&(console.log(\"tile z%d-%d-%d (features: %d, points: %d, simplified: %d)\",tt,nt,ht,ae.numFeatures,ae.numPoints,ae.numSimplified),console.timeEnd(\"creation\"));var lr=\"z\"+tt;this.stats[lr]=(this.stats[lr]||0)+1,this.total++}if(ae.source=gt,vt){if(tt===Mt.maxZoom||tt===vt)continue;var vr=1<1&&console.time(\"clipping\");var Xe,cr,wr,xi,zi,ni,Hr=.5*Mt.buffer/Mt.extent,jn=.5-Hr,Bi=.5+Hr,xn=1+Hr;Xe=cr=wr=xi=null,zi=mn(gt,ie,nt-Hr,nt+Bi,0,ae.minX,ae.maxX,Mt),ni=mn(gt,ie,nt+jn,nt+xn,0,ae.minX,ae.maxX,Mt),gt=null,zi&&(Xe=mn(zi,ie,ht-Hr,ht+Bi,1,ae.minY,ae.maxY,Mt),cr=mn(zi,ie,ht+jn,ht+xn,1,ae.minY,ae.maxY,Mt),zi=null),ni&&(wr=mn(ni,ie,ht-Hr,ht+Bi,1,ae.minY,ae.maxY,Mt),xi=mn(ni,ie,ht+jn,ht+xn,1,ae.minY,ae.maxY,Mt),ni=null),Vt>1&&console.timeEnd(\"clipping\"),Dt.push(Xe||[],tt+1,2*nt,2*ht),Dt.push(cr||[],tt+1,2*nt,2*ht+1),Dt.push(wr||[],tt+1,2*nt+1,2*ht),Dt.push(xi||[],tt+1,2*nt+1,2*ht+1)}}},Te.prototype.getTile=function(gt,tt,nt){var ht=this.options,vt=ht.extent,xt=ht.debug;if(gt<0||gt>24)return null;var _t=1<1&&console.log(\"drilling down to z%d-%d-%d\",gt,tt,nt);for(var Mt,Vt=gt,ie=tt,se=nt;!Mt&&Vt>0;)Vt--,ie=Math.floor(ie/2),se=Math.floor(se/2),Mt=this.tiles[Dr(Vt,ie,se)];return Mt&&Mt.source?(xt>1&&console.log(\"found parent tile z%d-%d-%d\",Vt,ie,se),xt>1&&console.time(\"drilling down\"),this.splitTile(Mt.source,Vt,ie,se,gt,tt,nt),xt>1&&console.timeEnd(\"drilling down\"),this.tiles[Dt]?ve(this.tiles[Dt],vt):null):null};class Mr extends I{constructor(tt,nt,ht,vt){super(tt,nt,ht),this._dataUpdateable=new Map,this.loadGeoJSON=(xt,_t)=>{let{promoteId:Dt}=xt;if(xt.request)return n.f(xt.request,(Mt,Vt,ie,se)=>{this._dataUpdateable=Us(Vt,Dt)?La(Vt,Dt):void 0,_t(Mt,Vt,ie,se)});if(typeof xt.data==\"string\")try{let Mt=JSON.parse(xt.data);this._dataUpdateable=Us(Mt,Dt)?La(Mt,Dt):void 0,_t(null,Mt)}catch{_t(new Error(`Input data given to '${xt.source}' is not a valid GeoJSON object.`))}else xt.dataDiff?this._dataUpdateable?(function(Mt,Vt,ie){var se,ae,lr,vr;if(Vt.removeAll&&Mt.clear(),Vt.remove)for(let Xe of Vt.remove)Mt.delete(Xe);if(Vt.add)for(let Xe of Vt.add){let cr=gr(Xe,ie);cr!=null&&Mt.set(cr,Xe)}if(Vt.update)for(let Xe of Vt.update){let cr=Mt.get(Xe.id);if(cr==null)continue;let wr=!Xe.removeAllProperties&&(((se=Xe.removeProperties)===null||se===void 0?void 0:se.length)>0||((ae=Xe.addOrUpdateProperties)===null||ae===void 0?void 0:ae.length)>0);if((Xe.newGeometry||Xe.removeAllProperties||wr)&&(cr=Object.assign({},cr),Mt.set(Xe.id,cr),wr&&(cr.properties=Object.assign({},cr.properties))),Xe.newGeometry&&(cr.geometry=Xe.newGeometry),Xe.removeAllProperties)cr.properties={};else if(((lr=Xe.removeProperties)===null||lr===void 0?void 0:lr.length)>0)for(let xi of Xe.removeProperties)Object.prototype.hasOwnProperty.call(cr.properties,xi)&&delete cr.properties[xi];if(((vr=Xe.addOrUpdateProperties)===null||vr===void 0?void 0:vr.length)>0)for(let{key:xi,value:zi}of Xe.addOrUpdateProperties)cr.properties[xi]=zi}}(this._dataUpdateable,xt.dataDiff,Dt),_t(null,{type:\"FeatureCollection\",features:Array.from(this._dataUpdateable.values())})):_t(new Error(`Cannot update existing geojson data in ${xt.source}`)):_t(new Error(`Input data given to '${xt.source}' is not a valid GeoJSON object.`));return{cancel:()=>{}}},this.loadVectorData=this.loadGeoJSONTile,vt&&(this.loadGeoJSON=vt)}loadGeoJSONTile(tt,nt){let ht=tt.tileID.canonical;if(!this._geoJSONIndex)return nt(null,null);let vt=this._geoJSONIndex.getTile(ht.z,ht.x,ht.y);if(!vt)return nt(null,null);let xt=new class{constructor(Dt){this.layers={_geojsonTileLayer:this},this.name=\"_geojsonTileLayer\",this.extent=n.N,this.length=Dt.length,this._features=Dt}feature(Dt){return new class{constructor(Mt){this._feature=Mt,this.extent=n.N,this.type=Mt.type,this.properties=Mt.tags,\"id\"in Mt&&!isNaN(Mt.id)&&(this.id=parseInt(Mt.id,10))}loadGeometry(){if(this._feature.type===1){let Mt=[];for(let Vt of this._feature.geometry)Mt.push([new n.P(Vt[0],Vt[1])]);return Mt}{let Mt=[];for(let Vt of this._feature.geometry){let ie=[];for(let se of Vt)ie.push(new n.P(se[0],se[1]));Mt.push(ie)}return Mt}}toGeoJSON(Mt,Vt,ie){return et.call(this,Mt,Vt,ie)}}(this._features[Dt])}}(vt.features),_t=zl(xt);_t.byteOffset===0&&_t.byteLength===_t.buffer.byteLength||(_t=new Uint8Array(_t)),nt(null,{vectorTile:xt,rawData:_t.buffer})}loadData(tt,nt){var ht;(ht=this._pendingRequest)===null||ht===void 0||ht.cancel(),this._pendingCallback&&this._pendingCallback(null,{abandoned:!0});let vt=!!(tt&&tt.request&&tt.request.collectResourceTiming)&&new n.bu(tt.request);this._pendingCallback=nt,this._pendingRequest=this.loadGeoJSON(tt,(xt,_t)=>{if(delete this._pendingCallback,delete this._pendingRequest,xt||!_t)return nt(xt);if(typeof _t!=\"object\")return nt(new Error(`Input data given to '${tt.source}' is not a valid GeoJSON object.`));{Q(_t,!0);try{if(tt.filter){let Mt=n.bC(tt.filter,{type:\"boolean\",\"property-type\":\"data-driven\",overridable:!1,transition:!1});if(Mt.result===\"error\")throw new Error(Mt.value.map(ie=>`${ie.key}: ${ie.message}`).join(\", \"));_t={type:\"FeatureCollection\",features:_t.features.filter(ie=>Mt.value.evaluate({zoom:0},ie))}}this._geoJSONIndex=tt.cluster?new kc(function({superclusterOptions:Mt,clusterProperties:Vt}){if(!Vt||!Mt)return Mt;let ie={},se={},ae={accumulated:null,zoom:0},lr={properties:null},vr=Object.keys(Vt);for(let Xe of vr){let[cr,wr]=Vt[Xe],xi=n.bC(wr),zi=n.bC(typeof cr==\"string\"?[cr,[\"accumulated\"],[\"get\",Xe]]:cr);ie[Xe]=xi.value,se[Xe]=zi.value}return Mt.map=Xe=>{lr.properties=Xe;let cr={};for(let wr of vr)cr[wr]=ie[wr].evaluate(ae,lr);return cr},Mt.reduce=(Xe,cr)=>{lr.properties=cr;for(let wr of vr)ae.accumulated=Xe[wr],Xe[wr]=se[wr].evaluate(ae,lr)},Mt}(tt)).load(_t.features):function(Mt,Vt){return new Te(Mt,Vt)}(_t,tt.geojsonVtOptions)}catch(Mt){return nt(Mt)}this.loaded={};let Dt={};if(vt){let Mt=vt.finish();Mt&&(Dt.resourceTiming={},Dt.resourceTiming[tt.source]=JSON.parse(JSON.stringify(Mt)))}nt(null,Dt)}})}reloadTile(tt,nt){let ht=this.loaded;return ht&&ht[tt.uid]?super.reloadTile(tt,nt):this.loadTile(tt,nt)}removeSource(tt,nt){this._pendingCallback&&this._pendingCallback(null,{abandoned:!0}),nt()}getClusterExpansionZoom(tt,nt){try{nt(null,this._geoJSONIndex.getClusterExpansionZoom(tt.clusterId))}catch(ht){nt(ht)}}getClusterChildren(tt,nt){try{nt(null,this._geoJSONIndex.getChildren(tt.clusterId))}catch(ht){nt(ht)}}getClusterLeaves(tt,nt){try{nt(null,this._geoJSONIndex.getLeaves(tt.clusterId,tt.limit,tt.offset))}catch(ht){nt(ht)}}}class sa{constructor(tt){this.self=tt,this.actor=new n.C(tt,this),this.layerIndexes={},this.availableImages={},this.workerSourceTypes={vector:I,geojson:Mr},this.workerSources={},this.demWorkerSources={},this.self.registerWorkerSource=(nt,ht)=>{if(this.workerSourceTypes[nt])throw new Error(`Worker source with name \"${nt}\" already registered.`);this.workerSourceTypes[nt]=ht},this.self.registerRTLTextPlugin=nt=>{if(n.bD.isParsed())throw new Error(\"RTL text plugin already registered.\");n.bD.applyArabicShaping=nt.applyArabicShaping,n.bD.processBidirectionalText=nt.processBidirectionalText,n.bD.processStyledBidirectionalText=nt.processStyledBidirectionalText}}setReferrer(tt,nt){this.referrer=nt}setImages(tt,nt,ht){this.availableImages[tt]=nt;for(let vt in this.workerSources[tt]){let xt=this.workerSources[tt][vt];for(let _t in xt)xt[_t].availableImages=nt}ht()}setLayers(tt,nt,ht){this.getLayerIndex(tt).replace(nt),ht()}updateLayers(tt,nt,ht){this.getLayerIndex(tt).update(nt.layers,nt.removedIds),ht()}loadTile(tt,nt,ht){this.getWorkerSource(tt,nt.type,nt.source).loadTile(nt,ht)}loadDEMTile(tt,nt,ht){this.getDEMWorkerSource(tt,nt.source).loadTile(nt,ht)}reloadTile(tt,nt,ht){this.getWorkerSource(tt,nt.type,nt.source).reloadTile(nt,ht)}abortTile(tt,nt,ht){this.getWorkerSource(tt,nt.type,nt.source).abortTile(nt,ht)}removeTile(tt,nt,ht){this.getWorkerSource(tt,nt.type,nt.source).removeTile(nt,ht)}removeDEMTile(tt,nt){this.getDEMWorkerSource(tt,nt.source).removeTile(nt)}removeSource(tt,nt,ht){if(!this.workerSources[tt]||!this.workerSources[tt][nt.type]||!this.workerSources[tt][nt.type][nt.source])return;let vt=this.workerSources[tt][nt.type][nt.source];delete this.workerSources[tt][nt.type][nt.source],vt.removeSource!==void 0?vt.removeSource(nt,ht):ht()}loadWorkerSource(tt,nt,ht){try{this.self.importScripts(nt.url),ht()}catch(vt){ht(vt.toString())}}syncRTLPluginState(tt,nt,ht){try{n.bD.setState(nt);let vt=n.bD.getPluginURL();if(n.bD.isLoaded()&&!n.bD.isParsed()&&vt!=null){this.self.importScripts(vt);let xt=n.bD.isParsed();ht(xt?void 0:new Error(`RTL Text Plugin failed to import scripts from ${vt}`),xt)}}catch(vt){ht(vt.toString())}}getAvailableImages(tt){let nt=this.availableImages[tt];return nt||(nt=[]),nt}getLayerIndex(tt){let nt=this.layerIndexes[tt];return nt||(nt=this.layerIndexes[tt]=new o),nt}getWorkerSource(tt,nt,ht){return this.workerSources[tt]||(this.workerSources[tt]={}),this.workerSources[tt][nt]||(this.workerSources[tt][nt]={}),this.workerSources[tt][nt][ht]||(this.workerSources[tt][nt][ht]=new this.workerSourceTypes[nt]({send:(vt,xt,_t)=>{this.actor.send(vt,xt,_t,tt)}},this.getLayerIndex(tt),this.getAvailableImages(tt))),this.workerSources[tt][nt][ht]}getDEMWorkerSource(tt,nt){return this.demWorkerSources[tt]||(this.demWorkerSources[tt]={}),this.demWorkerSources[tt][nt]||(this.demWorkerSources[tt][nt]=new R),this.demWorkerSources[tt][nt]}}return n.i()&&(self.worker=new sa(self)),sa}),i([\"./shared\"],function(n){\"use strict\";var o=\"3.6.2\";class c{static testProp(l){if(!c.docStyle)return l[0];for(let d=0;d{window.removeEventListener(\"click\",c.suppressClickInternal,!0)},0)}static mousePos(l,d){let v=l.getBoundingClientRect();return new n.P(d.clientX-v.left-l.clientLeft,d.clientY-v.top-l.clientTop)}static touchPos(l,d){let v=l.getBoundingClientRect(),b=[];for(let M=0;M{l=[],d=0,v=0,b={}},T.addThrottleControl=W=>{let Z=v++;return b[Z]=W,Z},T.removeThrottleControl=W=>{delete b[W],B()},T.getImage=(W,Z,$=!0)=>{f.supported&&(W.headers||(W.headers={}),W.headers.accept=\"image/webp,*/*\");let 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$=l.shift();if($.cancelled){Z--;continue}let st=M($);d++,$.innerRequest=st}},U=(W,Z)=>{let $=new Image,st=W.url,At=!1,pt=W.credentials;return pt&&pt===\"include\"?$.crossOrigin=\"use-credentials\":(pt&&pt===\"same-origin\"||!n.s(st))&&($.crossOrigin=\"anonymous\"),$.fetchPriority=\"high\",$.onload=()=>{Z(null,$),$.onerror=$.onload=null},$.onerror=()=>{At||Z(new Error(\"Could not load image. 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l[0]=T[0],l[1]=T[1],l[2]=T[2],l}var J,ut=function(T,l,d){return T[0]=l[0]-d[0],T[1]=l[1]-d[1],T[2]=l[2]-d[2],T};J=new n.A(3),n.A!=Float32Array&&(J[0]=0,J[1]=0,J[2]=0);var Et=function(T){var l=T[0],d=T[1];return l*l+d*d};function kt(T){let l=[];if(typeof T==\"string\")l.push({id:\"default\",url:T});else if(T&&T.length>0){let d=[];for(let{id:v,url:b}of T){let M=`${v}${b}`;d.indexOf(M)===-1&&(d.push(M),l.push({id:v,url:b}))}}return l}function Xt(T,l,d,v,b){if(v)return void T(v);if(b!==Object.values(l).length||b!==Object.values(d).length)return;let M={};for(let O in l){M[O]={};let B=n.h.getImageCanvasContext(d[O]),U=l[O];for(let W in U){let{width:Z,height:$,x:st,y:At,sdf:pt,pixelRatio:yt,stretchX:dt,stretchY:Ft,content:Ht}=U[W];M[O][W]={data:null,pixelRatio:yt,sdf:pt,stretchX:dt,stretchY:Ft,content:Ht,spriteData:{width:Z,height:$,x:st,y:At,context:B}}}}T(null,M)}(function(){var T=new n.A(2);n.A!=Float32Array&&(T[0]=0,T[1]=0)})();class qt{constructor(l,d,v,b){this.context=l,this.format=v,this.texture=l.gl.createTexture(),this.update(d,b)}update(l,d,v){let{width:b,height:M}=l,O=!(this.size&&this.size[0]===b&&this.size[1]===M||v),{context:B}=this,{gl:U}=B;if(this.useMipmap=!!(d&&d.useMipmap),U.bindTexture(U.TEXTURE_2D,this.texture),B.pixelStoreUnpackFlipY.set(!1),B.pixelStoreUnpack.set(1),B.pixelStoreUnpackPremultiplyAlpha.set(this.format===U.RGBA&&(!d||d.premultiply!==!1)),O)this.size=[b,M],l instanceof HTMLImageElement||l instanceof HTMLCanvasElement||l instanceof HTMLVideoElement||l instanceof ImageData||n.a(l)?U.texImage2D(U.TEXTURE_2D,0,this.format,this.format,U.UNSIGNED_BYTE,l):U.texImage2D(U.TEXTURE_2D,0,this.format,b,M,0,this.format,U.UNSIGNED_BYTE,l.data);else{let{x:W,y:Z}=v||{x:0,y:0};l instanceof HTMLImageElement||l instanceof HTMLCanvasElement||l instanceof HTMLVideoElement||l instanceof 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n.E{constructor(){super(),this.images={},this.updatedImages={},this.callbackDispatchedThisFrame={},this.loaded=!1,this.requestors=[],this.patterns={},this.atlasImage=new n.R({width:1,height:1}),this.dirty=!0}isLoaded(){return this.loaded}setLoaded(l){if(this.loaded!==l&&(this.loaded=l,l)){for(let{ids:d,callback:v}of this.requestors)this._notify(d,v);this.requestors=[]}}getImage(l){let d=this.images[l];if(d&&!d.data&&d.spriteData){let v=d.spriteData;d.data=new n.R({width:v.width,height:v.height},v.context.getImageData(v.x,v.y,v.width,v.height).data),d.spriteData=null}return d}addImage(l,d){if(this.images[l])throw new Error(`Image id ${l} already exist, use updateImage instead`);this._validate(l,d)&&(this.images[l]=d)}_validate(l,d){let v=!0,b=d.data||d.spriteData;return this._validateStretch(d.stretchX,b&&b.width)||(this.fire(new n.j(new Error(`Image \"${l}\" has invalid \"stretchX\" value`))),v=!1),this._validateStretch(d.stretchY,b&&b.height)||(this.fire(new n.j(new Error(`Image \"${l}\" has invalid \"stretchY\" value`))),v=!1),this._validateContent(d.content,d)||(this.fire(new n.j(new Error(`Image \"${l}\" has invalid \"content\" value`))),v=!1),v}_validateStretch(l,d){if(!l)return!0;let v=0;for(let b of l){if(b[0]-1);U++,M[U]=B,O[U]=W,O[U+1]=De}for(let B=0,U=0;B{let B=this.entries[b];B||(B=this.entries[b]={glyphs:{},requests:{},ranges:{}});let U=B.glyphs[M];if(U!==void 0)return void O(null,{stack:b,id:M,glyph:U});if(U=this._tinySDF(B,b,M),U)return B.glyphs[M]=U,void O(null,{stack:b,id:M,glyph:U});let W=Math.floor(M/256);if(256*W>65535)return void O(new Error(\"glyphs > 65535 not supported\"));if(B.ranges[W])return void O(null,{stack:b,id:M,glyph:U});if(!this.url)return void O(new Error(\"glyphsUrl is not set\"));let Z=B.requests[W];Z||(Z=B.requests[W]=[],Sr.loadGlyphRange(b,W,this.url,this.requestManager,($,st)=>{if(st){for(let At in st)this._doesCharSupportLocalGlyph(+At)||(B.glyphs[+At]=st[+At]);B.ranges[W]=!0}for(let At of Z)At($,st);delete B.requests[W]})),Z.push(($,st)=>{$?O($):st&&O(null,{stack:b,id:M,glyph:st[M]||null})})},(b,M)=>{if(b)d(b);else if(M){let O={};for(let{stack:B,id:U,glyph:W}of M)(O[B]||(O[B]={}))[U]=W&&{id:W.id,bitmap:W.bitmap.clone(),metrics:W.metrics};d(null,O)}})}_doesCharSupportLocalGlyph(l){return!!this.localIdeographFontFamily&&(n.u[\"CJK Unified Ideographs\"](l)||n.u[\"Hangul Syllables\"](l)||n.u.Hiragana(l)||n.u.Katakana(l))}_tinySDF(l,d,v){let b=this.localIdeographFontFamily;if(!b||!this._doesCharSupportLocalGlyph(v))return;let M=l.tinySDF;if(!M){let B=\"400\";/bold/i.test(d)?B=\"900\":/medium/i.test(d)?B=\"500\":/light/i.test(d)&&(B=\"200\"),M=l.tinySDF=new Sr.TinySDF({fontSize:48,buffer:6,radius:16,cutoff:.25,fontFamily:b,fontWeight:B})}let O=M.draw(String.fromCharCode(v));return{id:v,bitmap:new n.q({width:O.width||60,height:O.height||60},O.data),metrics:{width:O.glyphWidth/2||24,height:O.glyphHeight/2||24,left:O.glyphLeft/2+.5||0,top:O.glyphTop/2-27.5||-8,advance:O.glyphAdvance/2||24,isDoubleResolution:!0}}}}Sr.loadGlyphRange=function(T,l,d,v,b){let M=256*l,O=M+255,B=v.transformRequest(d.replace(\"{fontstack}\",T).replace(\"{range}\",`${M}-${O}`),Q.Glyphs);n.l(B,(U,W)=>{if(U)b(U);else if(W){let Z={};for(let $ of n.n(W))Z[$.id]=$;b(null,Z)}})},Sr.TinySDF=class{constructor({fontSize:T=24,buffer:l=3,radius:d=8,cutoff:v=.25,fontFamily:b=\"sans-serif\",fontWeight:M=\"normal\",fontStyle:O=\"normal\"}={}){this.buffer=l,this.cutoff=v,this.radius=d;let B=this.size=T+4*l,U=this._createCanvas(B),W=this.ctx=U.getContext(\"2d\",{willReadFrequently:!0});W.font=`${O} ${M} ${T}px ${b}`,W.textBaseline=\"alphabetic\",W.textAlign=\"left\",W.fillStyle=\"black\",this.gridOuter=new Float64Array(B*B),this.gridInner=new Float64Array(B*B),this.f=new Float64Array(B),this.z=new Float64Array(B+1),this.v=new Uint16Array(B)}_createCanvas(T){let l=document.createElement(\"canvas\");return l.width=l.height=T,l}draw(T){let{width:l,actualBoundingBoxAscent:d,actualBoundingBoxDescent:v,actualBoundingBoxLeft:b,actualBoundingBoxRight:M}=this.ctx.measureText(T),O=Math.ceil(d),B=Math.max(0,Math.min(this.size-this.buffer,Math.ceil(M-b))),U=Math.min(this.size-this.buffer,O+Math.ceil(v)),W=B+2*this.buffer,Z=U+2*this.buffer,$=Math.max(W*Z,0),st=new Uint8ClampedArray($),At={data:st,width:W,height:Z,glyphWidth:B,glyphHeight:U,glyphTop:O,glyphLeft:0,glyphAdvance:l};if(B===0||U===0)return At;let{ctx:pt,buffer:yt,gridInner:dt,gridOuter:Ft}=this;pt.clearRect(yt,yt,B,U),pt.fillText(T,yt,yt+O);let Ht=pt.getImageData(yt,yt,B,U);Ft.fill(De,0,$),dt.fill(0,0,$);for(let St=0;St0?oe*oe:0,dt[$t]=oe<0?oe*oe:0}}Ke(Ft,0,0,W,Z,W,this.f,this.v,this.z),Ke(dt,yt,yt,B,U,W,this.f,this.v,this.z);for(let St=0;St<$;St++){let Bt=Math.sqrt(Ft[St])-Math.sqrt(dt[St]);st[St]=Math.round(255-255*(Bt/this.radius+this.cutoff))}return At}};class Li{constructor(){this.specification=n.v.light.position}possiblyEvaluate(l,d){return n.z(l.expression.evaluate(d))}interpolate(l,d,v){return{x:n.B.number(l.x,d.x,v),y:n.B.number(l.y,d.y,v),z:n.B.number(l.z,d.z,v)}}}let oo;class zl extends n.E{constructor(l){super(),oo=oo||new n.r({anchor:new n.D(n.v.light.anchor),position:new Li,color:new n.D(n.v.light.color),intensity:new n.D(n.v.light.intensity)}),this._transitionable=new n.T(oo),this.setLight(l),this._transitioning=this._transitionable.untransitioned()}getLight(){return this._transitionable.serialize()}setLight(l,d={}){if(!this._validate(n.t,l,d))for(let v in l){let b=l[v];v.endsWith(\"-transition\")?this._transitionable.setTransition(v.slice(0,-11),b):this._transitionable.setValue(v,b)}}updateTransitions(l){this._transitioning=this._transitionable.transitioned(l,this._transitioning)}hasTransition(){return this._transitioning.hasTransition()}recalculate(l){this.properties=this._transitioning.possiblyEvaluate(l)}_validate(l,d,v){return(!v||v.validate!==!1)&&n.x(this,l.call(n.y,n.e({value:d,style:{glyphs:!0,sprite:!0},styleSpec:n.v})))}}class No{constructor(l,d){this.width=l,this.height=d,this.nextRow=0,this.data=new Uint8Array(this.width*this.height),this.dashEntry={}}getDash(l,d){let v=l.join(\",\")+String(d);return this.dashEntry[v]||(this.dashEntry[v]=this.addDash(l,d)),this.dashEntry[v]}getDashRanges(l,d,v){let b=[],M=l.length%2==1?-l[l.length-1]*v:0,O=l[0]*v,B=!0;b.push({left:M,right:O,isDash:B,zeroLength:l[0]===0});let U=l[0];for(let W=1;W1&&(U=l[++B]);let Z=Math.abs(W-U.left),$=Math.abs(W-U.right),st=Math.min(Z,$),At,pt=M/v*(b+1);if(U.isDash){let yt=b-Math.abs(pt);At=Math.sqrt(st*st+yt*yt)}else At=b-Math.sqrt(st*st+pt*pt);this.data[O+W]=Math.max(0,Math.min(255,At+128))}}}addRegularDash(l){for(let B=l.length-1;B>=0;--B){let U=l[B],W=l[B+1];U.zeroLength?l.splice(B,1):W&&W.isDash===U.isDash&&(W.left=U.left,l.splice(B,1))}let d=l[0],v=l[l.length-1];d.isDash===v.isDash&&(d.left=v.left-this.width,v.right=d.right+this.width);let b=this.width*this.nextRow,M=0,O=l[M];for(let B=0;B1&&(O=l[++M]);let U=Math.abs(B-O.left),W=Math.abs(B-O.right),Z=Math.min(U,W);this.data[b+B]=Math.max(0,Math.min(255,(O.isDash?Z:-Z)+128))}}addDash(l,d){let v=d?7:0,b=2*v+1;if(this.nextRow+b>this.height)return n.w(\"LineAtlas out of space\"),null;let M=0;for(let B=0;B{b.send(l,d,M)},v=v||function(){})}getActor(){return this.currentActor=(this.currentActor+1)%this.actors.length,this.actors[this.currentActor]}remove(l=!0){this.actors.forEach(d=>{d.remove()}),this.actors=[],l&&this.workerPool.release(this.id)}}function Uo(T,l,d){let v=function(b,M){if(b)return d(b);if(M){let O=n.F(n.e(M,T),[\"tiles\",\"minzoom\",\"maxzoom\",\"attribution\",\"bounds\",\"scheme\",\"tileSize\",\"encoding\"]);M.vector_layers&&(O.vectorLayers=M.vector_layers,O.vectorLayerIds=O.vectorLayers.map(B=>B.id)),d(null,O)}};return T.url?n.f(l.transformRequest(T.url,Q.Source),v):n.h.frame(()=>v(null,T))}class Si{constructor(l,d){l&&(d?this.setSouthWest(l).setNorthEast(d):Array.isArray(l)&&(l.length===4?this.setSouthWest([l[0],l[1]]).setNorthEast([l[2],l[3]]):this.setSouthWest(l[0]).setNorthEast(l[1])))}setNorthEast(l){return this._ne=l instanceof n.L?new n.L(l.lng,l.lat):n.L.convert(l),this}setSouthWest(l){return this._sw=l instanceof n.L?new n.L(l.lng,l.lat):n.L.convert(l),this}extend(l){let d=this._sw,v=this._ne,b,M;if(l instanceof n.L)b=l,M=l;else{if(!(l instanceof Si))return Array.isArray(l)?l.length===4||l.every(Array.isArray)?this.extend(Si.convert(l)):this.extend(n.L.convert(l)):l&&(\"lng\"in l||\"lon\"in l)&&\"lat\"in l?this.extend(n.L.convert(l)):this;if(b=l._sw,M=l._ne,!b||!M)return this}return d||v?(d.lng=Math.min(b.lng,d.lng),d.lat=Math.min(b.lat,d.lat),v.lng=Math.max(M.lng,v.lng),v.lat=Math.max(M.lat,v.lat)):(this._sw=new n.L(b.lng,b.lat),this._ne=new n.L(M.lng,M.lat)),this}getCenter(){return new n.L((this._sw.lng+this._ne.lng)/2,(this._sw.lat+this._ne.lat)/2)}getSouthWest(){return this._sw}getNorthEast(){return this._ne}getNorthWest(){return new n.L(this.getWest(),this.getNorth())}getSouthEast(){return new n.L(this.getEast(),this.getSouth())}getWest(){return this._sw.lng}getSouth(){return this._sw.lat}getEast(){return this._ne.lng}getNorth(){return this._ne.lat}toArray(){return[this._sw.toArray(),this._ne.toArray()]}toString(){return`LngLatBounds(${this._sw.toString()}, ${this._ne.toString()})`}isEmpty(){return!(this._sw&&this._ne)}contains(l){let{lng:d,lat:v}=n.L.convert(l),b=this._sw.lng<=d&&d<=this._ne.lng;return this._sw.lng>this._ne.lng&&(b=this._sw.lng>=d&&d>=this._ne.lng),this._sw.lat<=v&&v<=this._ne.lat&&b}static convert(l){return l instanceof Si?l:l&&new Si(l)}static fromLngLat(l,d=0){let v=360*d/40075017,b=v/Math.cos(Math.PI/180*l.lat);return new Si(new n.L(l.lng-b,l.lat-v),new n.L(l.lng+b,l.lat+v))}}class Ns{constructor(l,d,v){this.bounds=Si.convert(this.validateBounds(l)),this.minzoom=d||0,this.maxzoom=v||24}validateBounds(l){return Array.isArray(l)&&l.length===4?[Math.max(-180,l[0]),Math.max(-90,l[1]),Math.min(180,l[2]),Math.min(90,l[3])]:[-180,-90,180,90]}contains(l){let d=Math.pow(2,l.z),v=Math.floor(n.G(this.bounds.getWest())*d),b=Math.floor(n.H(this.bounds.getNorth())*d),M=Math.ceil(n.G(this.bounds.getEast())*d),O=Math.ceil(n.H(this.bounds.getSouth())*d);return l.x>=v&&l.x=b&&l.y{this._loaded=!1,this.fire(new n.k(\"dataloading\",{dataType:\"source\"})),this._tileJSONRequest=Uo(this._options,this.map._requestManager,(M,O)=>{this._tileJSONRequest=null,this._loaded=!0,this.map.style.sourceCaches[this.id].clearTiles(),M?this.fire(new n.j(M)):O&&(n.e(this,O),O.bounds&&(this.tileBounds=new Ns(O.bounds,this.minzoom,this.maxzoom)),this.fire(new n.k(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})),this.fire(new n.k(\"data\",{dataType:\"source\",sourceDataType:\"content\"})))})},this.serialize=()=>n.e({},this._options),this.id=l,this.dispatcher=v,this.type=\"vector\",this.minzoom=0,this.maxzoom=22,this.scheme=\"xyz\",this.tileSize=512,this.reparseOverscaled=!0,this.isTileClipped=!0,this._loaded=!1,n.e(this,n.F(d,[\"url\",\"scheme\",\"tileSize\",\"promoteId\"])),this._options=n.e({type:\"vector\"},d),this._collectResourceTiming=d.collectResourceTiming,this.tileSize!==512)throw new Error(\"vector tile sources must have a tileSize of 512\");this.setEventedParent(b)}loaded(){return this._loaded}hasTile(l){return!this.tileBounds||this.tileBounds.contains(l.canonical)}onAdd(l){this.map=l,this.load()}setSourceProperty(l){this._tileJSONRequest&&this._tileJSONRequest.cancel(),l(),this.load()}setTiles(l){return this.setSourceProperty(()=>{this._options.tiles=l}),this}setUrl(l){return this.setSourceProperty(()=>{this.url=l,this._options.url=l}),this}onRemove(){this._tileJSONRequest&&(this._tileJSONRequest.cancel(),this._tileJSONRequest=null)}loadTile(l,d){let v=l.tileID.canonical.url(this.tiles,this.map.getPixelRatio(),this.scheme),b={request:this.map._requestManager.transformRequest(v,Q.Tile),uid:l.uid,tileID:l.tileID,zoom:l.tileID.overscaledZ,tileSize:this.tileSize*l.tileID.overscaleFactor(),type:this.type,source:this.id,pixelRatio:this.map.getPixelRatio(),showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};function M(O,B){return delete l.request,l.aborted?d(null):O&&O.status!==404?d(O):(B&&B.resourceTiming&&(l.resourceTiming=B.resourceTiming),this.map._refreshExpiredTiles&&B&&l.setExpiryData(B),l.loadVectorData(B,this.map.painter),d(null),void(l.reloadCallback&&(this.loadTile(l,l.reloadCallback),l.reloadCallback=null)))}b.request.collectResourceTiming=this._collectResourceTiming,l.actor&&l.state!==\"expired\"?l.state===\"loading\"?l.reloadCallback=d:l.request=l.actor.send(\"reloadTile\",b,M.bind(this)):(l.actor=this.dispatcher.getActor(),l.request=l.actor.send(\"loadTile\",b,M.bind(this)))}abortTile(l){l.request&&(l.request.cancel(),delete l.request),l.actor&&l.actor.send(\"abortTile\",{uid:l.uid,type:this.type,source:this.id},void 0)}unloadTile(l){l.unloadVectorData(),l.actor&&l.actor.send(\"removeTile\",{uid:l.uid,type:this.type,source:this.id},void 0)}hasTransition(){return!1}}class kc extends n.E{constructor(l,d,v,b){super(),this.id=l,this.dispatcher=v,this.setEventedParent(b),this.type=\"raster\",this.minzoom=0,this.maxzoom=22,this.roundZoom=!0,this.scheme=\"xyz\",this.tileSize=512,this._loaded=!1,this._options=n.e({type:\"raster\"},d),n.e(this,n.F(d,[\"url\",\"scheme\",\"tileSize\"]))}load(){this._loaded=!1,this.fire(new n.k(\"dataloading\",{dataType:\"source\"})),this._tileJSONRequest=Uo(this._options,this.map._requestManager,(l,d)=>{this._tileJSONRequest=null,this._loaded=!0,l?this.fire(new n.j(l)):d&&(n.e(this,d),d.bounds&&(this.tileBounds=new Ns(d.bounds,this.minzoom,this.maxzoom)),this.fire(new n.k(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})),this.fire(new n.k(\"data\",{dataType:\"source\",sourceDataType:\"content\"})))})}loaded(){return this._loaded}onAdd(l){this.map=l,this.load()}onRemove(){this._tileJSONRequest&&(this._tileJSONRequest.cancel(),this._tileJSONRequest=null)}setSourceProperty(l){this._tileJSONRequest&&this._tileJSONRequest.cancel(),l(),this.load()}setTiles(l){return this.setSourceProperty(()=>{this._options.tiles=l}),this}serialize(){return n.e({},this._options)}hasTile(l){return!this.tileBounds||this.tileBounds.contains(l.canonical)}loadTile(l,d){let v=l.tileID.canonical.url(this.tiles,this.map.getPixelRatio(),this.scheme);l.request=j.getImage(this.map._requestManager.transformRequest(v,Q.Tile),(b,M,O)=>{if(delete l.request,l.aborted)l.state=\"unloaded\",d(null);else if(b)l.state=\"errored\",d(b);else if(M){this.map._refreshExpiredTiles&&O&&l.setExpiryData(O);let B=this.map.painter.context,U=B.gl;l.texture=this.map.painter.getTileTexture(M.width),l.texture?l.texture.update(M,{useMipmap:!0}):(l.texture=new qt(B,M,U.RGBA,{useMipmap:!0}),l.texture.bind(U.LINEAR,U.CLAMP_TO_EDGE,U.LINEAR_MIPMAP_NEAREST),B.extTextureFilterAnisotropic&&U.texParameterf(U.TEXTURE_2D,B.extTextureFilterAnisotropic.TEXTURE_MAX_ANISOTROPY_EXT,B.extTextureFilterAnisotropicMax)),l.state=\"loaded\",d(null)}},this.map._refreshExpiredTiles)}abortTile(l,d){l.request&&(l.request.cancel(),delete l.request),d()}unloadTile(l,d){l.texture&&this.map.painter.saveTileTexture(l.texture),d()}hasTransition(){return!1}}class Rc extends kc{constructor(l,d,v,b){super(l,d,v,b),this.type=\"raster-dem\",this.maxzoom=22,this._options=n.e({type:\"raster-dem\"},d),this.encoding=d.encoding||\"mapbox\",this.redFactor=d.redFactor,this.greenFactor=d.greenFactor,this.blueFactor=d.blueFactor,this.baseShift=d.baseShift}loadTile(l,d){let v=l.tileID.canonical.url(this.tiles,this.map.getPixelRatio(),this.scheme),b=this.map._requestManager.transformRequest(v,Q.Tile);function M(O,B){O&&(l.state=\"errored\",d(O)),B&&(l.dem=B,l.needsHillshadePrepare=!0,l.needsTerrainPrepare=!0,l.state=\"loaded\",d(null))}l.neighboringTiles=this._getNeighboringTiles(l.tileID),l.request=j.getImage(b,(O,B,U)=>n._(this,void 0,void 0,function*(){if(delete l.request,l.aborted)l.state=\"unloaded\",d(null);else if(O)l.state=\"errored\",d(O);else if(B){this.map._refreshExpiredTiles&&l.setExpiryData(U);let W=n.a(B)&&n.J()?B:yield function($){return n._(this,void 0,void 0,function*(){if(typeof VideoFrame<\"u\"&&n.K()){let st=$.width+2,At=$.height+2;try{return new n.R({width:st,height:At},yield n.M($,-1,-1,st,At))}catch{}}return n.h.getImageData($,1)})}(B),Z={uid:l.uid,coord:l.tileID,source:this.id,rawImageData:W,encoding:this.encoding,redFactor:this.redFactor,greenFactor:this.greenFactor,blueFactor:this.blueFactor,baseShift:this.baseShift};l.actor&&l.state!==\"expired\"||(l.actor=this.dispatcher.getActor(),l.actor.send(\"loadDEMTile\",Z,M))}}),this.map._refreshExpiredTiles)}_getNeighboringTiles(l){let d=l.canonical,v=Math.pow(2,d.z),b=(d.x-1+v)%v,M=d.x===0?l.wrap-1:l.wrap,O=(d.x+1+v)%v,B=d.x+1===v?l.wrap+1:l.wrap,U={};return U[new n.O(l.overscaledZ,M,d.z,b,d.y).key]={backfilled:!1},U[new n.O(l.overscaledZ,B,d.z,O,d.y).key]={backfilled:!1},d.y>0&&(U[new n.O(l.overscaledZ,M,d.z,b,d.y-1).key]={backfilled:!1},U[new n.O(l.overscaledZ,l.wrap,d.z,d.x,d.y-1).key]={backfilled:!1},U[new n.O(l.overscaledZ,B,d.z,O,d.y-1).key]={backfilled:!1}),d.y+1{this._updateWorkerData()},this.serialize=()=>n.e({},this._options,{type:this.type,data:this._data}),this.id=l,this.type=\"geojson\",this.minzoom=0,this.maxzoom=18,this.tileSize=512,this.isTileClipped=!0,this.reparseOverscaled=!0,this._removed=!1,this._pendingLoads=0,this.actor=v.getActor(),this.setEventedParent(b),this._data=d.data,this._options=n.e({},d),this._collectResourceTiming=d.collectResourceTiming,d.maxzoom!==void 0&&(this.maxzoom=d.maxzoom),d.type&&(this.type=d.type),d.attribution&&(this.attribution=d.attribution),this.promoteId=d.promoteId;let M=n.N/this.tileSize;this.workerOptions=n.e({source:this.id,cluster:d.cluster||!1,geojsonVtOptions:{buffer:(d.buffer!==void 0?d.buffer:128)*M,tolerance:(d.tolerance!==void 0?d.tolerance:.375)*M,extent:n.N,maxZoom:this.maxzoom,lineMetrics:d.lineMetrics||!1,generateId:d.generateId||!1},superclusterOptions:{maxZoom:d.clusterMaxZoom!==void 0?d.clusterMaxZoom:this.maxzoom-1,minPoints:Math.max(2,d.clusterMinPoints||2),extent:n.N,radius:(d.clusterRadius||50)*M,log:!1,generateId:d.generateId||!1},clusterProperties:d.clusterProperties,filter:d.filter},d.workerOptions),typeof this.promoteId==\"string\"&&(this.workerOptions.promoteId=this.promoteId)}onAdd(l){this.map=l,this.load()}setData(l){return this._data=l,this._updateWorkerData(),this}updateData(l){return this._updateWorkerData(l),this}setClusterOptions(l){return this.workerOptions.cluster=l.cluster,l&&(l.clusterRadius!==void 0&&(this.workerOptions.superclusterOptions.radius=l.clusterRadius),l.clusterMaxZoom!==void 0&&(this.workerOptions.superclusterOptions.maxZoom=l.clusterMaxZoom)),this._updateWorkerData(),this}getClusterExpansionZoom(l,d){return this.actor.send(\"geojson.getClusterExpansionZoom\",{clusterId:l,source:this.id},d),this}getClusterChildren(l,d){return this.actor.send(\"geojson.getClusterChildren\",{clusterId:l,source:this.id},d),this}getClusterLeaves(l,d,v,b){return this.actor.send(\"geojson.getClusterLeaves\",{source:this.id,clusterId:l,limit:d,offset:v},b),this}_updateWorkerData(l){let d=n.e({},this.workerOptions);l?d.dataDiff=l:typeof this._data==\"string\"?(d.request=this.map._requestManager.transformRequest(n.h.resolveURL(this._data),Q.Source),d.request.collectResourceTiming=this._collectResourceTiming):d.data=JSON.stringify(this._data),this._pendingLoads++,this.fire(new n.k(\"dataloading\",{dataType:\"source\"})),this.actor.send(`${this.type}.loadData`,d,(v,b)=>{if(this._pendingLoads--,this._removed||b&&b.abandoned)return void this.fire(new n.k(\"dataabort\",{dataType:\"source\"}));let M=null;if(b&&b.resourceTiming&&b.resourceTiming[this.id]&&(M=b.resourceTiming[this.id].slice(0)),v)return void this.fire(new n.j(v));let O={dataType:\"source\"};this._collectResourceTiming&&M&&M.length>0&&n.e(O,{resourceTiming:M}),this.fire(new n.k(\"data\",Object.assign(Object.assign({},O),{sourceDataType:\"metadata\"}))),this.fire(new n.k(\"data\",Object.assign(Object.assign({},O),{sourceDataType:\"content\"})))})}loaded(){return this._pendingLoads===0}loadTile(l,d){let v=l.actor?\"reloadTile\":\"loadTile\";l.actor=this.actor;let b={type:this.type,uid:l.uid,tileID:l.tileID,zoom:l.tileID.overscaledZ,maxZoom:this.maxzoom,tileSize:this.tileSize,source:this.id,pixelRatio:this.map.getPixelRatio(),showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};l.request=this.actor.send(v,b,(M,O)=>(delete l.request,l.unloadVectorData(),l.aborted?d(null):M?d(M):(l.loadVectorData(O,this.map.painter,v===\"reloadTile\"),d(null))))}abortTile(l){l.request&&(l.request.cancel(),delete l.request),l.aborted=!0}unloadTile(l){l.unloadVectorData(),this.actor.send(\"removeTile\",{uid:l.uid,type:this.type,source:this.id})}onRemove(){this._removed=!0,this.actor.send(\"removeSource\",{type:this.type,source:this.id})}hasTransition(){return!1}}var Jn=n.Q([{name:\"a_pos\",type:\"Int16\",components:2},{name:\"a_texture_pos\",type:\"Int16\",components:2}]);class ki extends n.E{constructor(l,d,v,b){super(),this.load=(M,O)=>{this._loaded=!1,this.fire(new n.k(\"dataloading\",{dataType:\"source\"})),this.url=this.options.url,this._request=j.getImage(this.map._requestManager.transformRequest(this.url,Q.Image),(B,U)=>{this._request=null,this._loaded=!0,B?this.fire(new n.j(B)):U&&(this.image=U,M&&(this.coordinates=M),O&&O(),this._finishLoading())})},this.prepare=()=>{if(Object.keys(this.tiles).length===0||!this.image)return;let M=this.map.painter.context,O=M.gl;this.boundsBuffer||(this.boundsBuffer=M.createVertexBuffer(this._boundsArray,Jn.members)),this.boundsSegments||(this.boundsSegments=n.S.simpleSegment(0,0,4,2)),this.texture||(this.texture=new qt(M,this.image,O.RGBA),this.texture.bind(O.LINEAR,O.CLAMP_TO_EDGE));let B=!1;for(let U in this.tiles){let W=this.tiles[U];W.state!==\"loaded\"&&(W.state=\"loaded\",W.texture=this.texture,B=!0)}B&&this.fire(new n.k(\"data\",{dataType:\"source\",sourceDataType:\"idle\",sourceId:this.id}))},this.serialize=()=>({type:\"image\",url:this.options.url,coordinates:this.coordinates}),this.id=l,this.dispatcher=v,this.coordinates=d.coordinates,this.type=\"image\",this.minzoom=0,this.maxzoom=22,this.tileSize=512,this.tiles={},this._loaded=!1,this.setEventedParent(b),this.options=d}loaded(){return this._loaded}updateImage(l){return l.url?(this._request&&(this._request.cancel(),this._request=null),this.options.url=l.url,this.load(l.coordinates,()=>{this.texture=null}),this):this}_finishLoading(){this.map&&(this.setCoordinates(this.coordinates),this.fire(new n.k(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})))}onAdd(l){this.map=l,this.load()}onRemove(){this._request&&(this._request.cancel(),this._request=null)}setCoordinates(l){this.coordinates=l;let d=l.map(n.U.fromLngLat);this.tileID=function(b){let M=1/0,O=1/0,B=-1/0,U=-1/0;for(let st of b)M=Math.min(M,st.x),O=Math.min(O,st.y),B=Math.max(B,st.x),U=Math.max(U,st.y);let W=Math.max(B-M,U-O),Z=Math.max(0,Math.floor(-Math.log(W)/Math.LN2)),$=Math.pow(2,Z);return new n.W(Z,Math.floor((M+B)/2*$),Math.floor((O+U)/2*$))}(d),this.minzoom=this.maxzoom=this.tileID.z;let v=d.map(b=>this.tileID.getTilePoint(b)._round());return this._boundsArray=new n.V,this._boundsArray.emplaceBack(v[0].x,v[0].y,0,0),this._boundsArray.emplaceBack(v[1].x,v[1].y,n.N,0),this._boundsArray.emplaceBack(v[3].x,v[3].y,0,n.N),this._boundsArray.emplaceBack(v[2].x,v[2].y,n.N,n.N),this.boundsBuffer&&(this.boundsBuffer.destroy(),delete this.boundsBuffer),this.fire(new n.k(\"data\",{dataType:\"source\",sourceDataType:\"content\"})),this}loadTile(l,d){this.tileID&&this.tileID.equals(l.tileID.canonical)?(this.tiles[String(l.tileID.wrap)]=l,l.buckets={},d(null)):(l.state=\"errored\",d(null))}hasTransition(){return!1}}class ts extends ki{constructor(l,d,v,b){super(l,d,v,b),this.load=()=>{this._loaded=!1;let M=this.options;this.urls=[];for(let O of M.urls)this.urls.push(this.map._requestManager.transformRequest(O,Q.Source).url);n.X(this.urls,(O,B)=>{this._loaded=!0,O?this.fire(new n.j(O)):B&&(this.video=B,this.video.loop=!0,this.video.addEventListener(\"playing\",()=>{this.map.triggerRepaint()}),this.map&&this.video.play(),this._finishLoading())})},this.prepare=()=>{if(Object.keys(this.tiles).length===0||this.video.readyState<2)return;let M=this.map.painter.context,O=M.gl;this.boundsBuffer||(this.boundsBuffer=M.createVertexBuffer(this._boundsArray,Jn.members)),this.boundsSegments||(this.boundsSegments=n.S.simpleSegment(0,0,4,2)),this.texture?this.video.paused||(this.texture.bind(O.LINEAR,O.CLAMP_TO_EDGE),O.texSubImage2D(O.TEXTURE_2D,0,0,0,O.RGBA,O.UNSIGNED_BYTE,this.video)):(this.texture=new qt(M,this.video,O.RGBA),this.texture.bind(O.LINEAR,O.CLAMP_TO_EDGE));let B=!1;for(let U in this.tiles){let W=this.tiles[U];W.state!==\"loaded\"&&(W.state=\"loaded\",W.texture=this.texture,B=!0)}B&&this.fire(new n.k(\"data\",{dataType:\"source\",sourceDataType:\"idle\",sourceId:this.id}))},this.serialize=()=>({type:\"video\",urls:this.urls,coordinates:this.coordinates}),this.roundZoom=!0,this.type=\"video\",this.options=d}pause(){this.video&&this.video.pause()}play(){this.video&&this.video.play()}seek(l){if(this.video){let d=this.video.seekable;ld.end(0)?this.fire(new n.j(new n.Y(`sources.${this.id}`,null,`Playback for this video can be set only between the ${d.start(0)} and ${d.end(0)}-second mark.`))):this.video.currentTime=l}}getVideo(){return this.video}onAdd(l){this.map||(this.map=l,this.load(),this.video&&(this.video.play(),this.setCoordinates(this.coordinates)))}hasTransition(){return this.video&&!this.video.paused}}class Vo extends ki{constructor(l,d,v,b){super(l,d,v,b),this.load=()=>{this._loaded=!0,this.canvas||(this.canvas=this.options.canvas instanceof HTMLCanvasElement?this.options.canvas:document.getElementById(this.options.canvas)),this.width=this.canvas.width,this.height=this.canvas.height,this._hasInvalidDimensions()?this.fire(new n.j(new Error(\"Canvas dimensions cannot be less than or equal to zero.\"))):(this.play=function(){this._playing=!0,this.map.triggerRepaint()},this.pause=function(){this._playing&&(this.prepare(),this._playing=!1)},this._finishLoading())},this.prepare=()=>{let M=!1;if(this.canvas.width!==this.width&&(this.width=this.canvas.width,M=!0),this.canvas.height!==this.height&&(this.height=this.canvas.height,M=!0),this._hasInvalidDimensions()||Object.keys(this.tiles).length===0)return;let O=this.map.painter.context,B=O.gl;this.boundsBuffer||(this.boundsBuffer=O.createVertexBuffer(this._boundsArray,Jn.members)),this.boundsSegments||(this.boundsSegments=n.S.simpleSegment(0,0,4,2)),this.texture?(M||this._playing)&&this.texture.update(this.canvas,{premultiply:!0}):this.texture=new qt(O,this.canvas,B.RGBA,{premultiply:!0});let U=!1;for(let W in this.tiles){let Z=this.tiles[W];Z.state!==\"loaded\"&&(Z.state=\"loaded\",Z.texture=this.texture,U=!0)}U&&this.fire(new n.k(\"data\",{dataType:\"source\",sourceDataType:\"idle\",sourceId:this.id}))},this.serialize=()=>({type:\"canvas\",coordinates:this.coordinates}),d.coordinates?Array.isArray(d.coordinates)&&d.coordinates.length===4&&!d.coordinates.some(M=>!Array.isArray(M)||M.length!==2||M.some(O=>typeof O!=\"number\"))||this.fire(new n.j(new n.Y(`sources.${l}`,null,'\"coordinates\" property must be an array of 4 longitude/latitude array pairs'))):this.fire(new n.j(new n.Y(`sources.${l}`,null,'missing required property \"coordinates\"'))),d.animate&&typeof d.animate!=\"boolean\"&&this.fire(new n.j(new n.Y(`sources.${l}`,null,'optional \"animate\" property must be a boolean value'))),d.canvas?typeof d.canvas==\"string\"||d.canvas instanceof HTMLCanvasElement||this.fire(new n.j(new n.Y(`sources.${l}`,null,'\"canvas\" must be either a string representing the ID of the canvas element from which to read, or an HTMLCanvasElement instance'))):this.fire(new n.j(new n.Y(`sources.${l}`,null,'missing required property \"canvas\"'))),this.options=d,this.animate=d.animate===void 0||d.animate}getCanvas(){return this.canvas}onAdd(l){this.map=l,this.load(),this.canvas&&this.animate&&this.play()}onRemove(){this.pause()}hasTransition(){return this._playing}_hasInvalidDimensions(){for(let l of[this.canvas.width,this.canvas.height])if(isNaN(l)||l<=0)return!0;return!1}}let cl={},xo=T=>{switch(T){case\"geojson\":return Xi;case\"image\":return ki;case\"raster\":return kc;case\"raster-dem\":return Rc;case\"vector\":return ll;case\"video\":return ts;case\"canvas\":return Vo}return cl[T]};function Pa(T,l){let d=n.Z();return n.$(d,d,[1,1,0]),n.a0(d,d,[.5*T.width,.5*T.height,1]),n.a1(d,d,T.calculatePosMatrix(l.toUnwrapped()))}function na(T,l,d,v,b,M){let O=function($,st,At){if($)for(let pt of $){let yt=st[pt];if(yt&&yt.source===At&&yt.type===\"fill-extrusion\")return!0}else for(let pt in st){let yt=st[pt];if(yt.source===At&&yt.type===\"fill-extrusion\")return!0}return!1}(b&&b.layers,l,T.id),B=M.maxPitchScaleFactor(),U=T.tilesIn(v,B,O);U.sort(as);let W=[];for(let $ of U)W.push({wrappedTileID:$.tileID.wrapped().key,queryResults:$.tile.queryRenderedFeatures(l,d,T._state,$.queryGeometry,$.cameraQueryGeometry,$.scale,b,M,B,Pa(T.transform,$.tileID))});let Z=function($){let st={},At={};for(let pt of $){let yt=pt.queryResults,dt=pt.wrappedTileID,Ft=At[dt]=At[dt]||{};for(let Ht in yt){let St=yt[Ht],Bt=Ft[Ht]=Ft[Ht]||{},Qt=st[Ht]=st[Ht]||[];for(let $t of St)Bt[$t.featureIndex]||(Bt[$t.featureIndex]=!0,Qt.push($t))}}return st}(W);for(let $ in Z)Z[$].forEach(st=>{let At=st.feature,pt=T.getFeatureState(At.layer[\"source-layer\"],At.id);At.source=At.layer.source,At.layer[\"source-layer\"]&&(At.sourceLayer=At.layer[\"source-layer\"]),At.state=pt});return Z}function as(T,l){let d=T.tileID,v=l.tileID;return d.overscaledZ-v.overscaledZ||d.canonical.y-v.canonical.y||d.wrap-v.wrap||d.canonical.x-v.canonical.x}class ao{constructor(l,d){this.timeAdded=0,this.fadeEndTime=0,this.tileID=l,this.uid=n.a2(),this.uses=0,this.tileSize=d,this.buckets={},this.expirationTime=null,this.queryPadding=0,this.hasSymbolBuckets=!1,this.hasRTLText=!1,this.dependencies={},this.rtt=[],this.rttCoords={},this.expiredRequestCount=0,this.state=\"loading\"}registerFadeDuration(l){let d=l+this.timeAdded;dM.getLayer(W)).filter(Boolean);if(U.length!==0){B.layers=U,B.stateDependentLayerIds&&(B.stateDependentLayers=B.stateDependentLayerIds.map(W=>U.filter(Z=>Z.id===W)[0]));for(let W of U)O[W.id]=B}}return O}(l.buckets,d.style),this.hasSymbolBuckets=!1;for(let b in this.buckets){let M=this.buckets[b];if(M instanceof n.a4){if(this.hasSymbolBuckets=!0,!v)break;M.justReloaded=!0}}if(this.hasRTLText=!1,this.hasSymbolBuckets)for(let b in this.buckets){let M=this.buckets[b];if(M instanceof n.a4&&M.hasRTLText){this.hasRTLText=!0,n.a5();break}}this.queryPadding=0;for(let b in this.buckets){let M=this.buckets[b];this.queryPadding=Math.max(this.queryPadding,d.style.getLayer(b).queryRadius(M))}l.imageAtlas&&(this.imageAtlas=l.imageAtlas),l.glyphAtlasImage&&(this.glyphAtlasImage=l.glyphAtlasImage)}else this.collisionBoxArray=new n.a3}unloadVectorData(){for(let l in this.buckets)this.buckets[l].destroy();this.buckets={},this.imageAtlasTexture&&this.imageAtlasTexture.destroy(),this.imageAtlas&&(this.imageAtlas=null),this.glyphAtlasTexture&&this.glyphAtlasTexture.destroy(),this.latestFeatureIndex=null,this.state=\"unloaded\"}getBucket(l){return this.buckets[l.id]}upload(l){for(let v in this.buckets){let b=this.buckets[v];b.uploadPending()&&b.upload(l)}let d=l.gl;this.imageAtlas&&!this.imageAtlas.uploaded&&(this.imageAtlasTexture=new qt(l,this.imageAtlas.image,d.RGBA),this.imageAtlas.uploaded=!0),this.glyphAtlasImage&&(this.glyphAtlasTexture=new qt(l,this.glyphAtlasImage,d.ALPHA),this.glyphAtlasImage=null)}prepare(l){this.imageAtlas&&this.imageAtlas.patchUpdatedImages(l,this.imageAtlasTexture)}queryRenderedFeatures(l,d,v,b,M,O,B,U,W,Z){return this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData?this.latestFeatureIndex.query({queryGeometry:b,cameraQueryGeometry:M,scale:O,tileSize:this.tileSize,pixelPosMatrix:Z,transform:U,params:B,queryPadding:this.queryPadding*W},l,d,v):{}}querySourceFeatures(l,d){let v=this.latestFeatureIndex;if(!v||!v.rawTileData)return;let b=v.loadVTLayers(),M=d&&d.sourceLayer?d.sourceLayer:\"\",O=b._geojsonTileLayer||b[M];if(!O)return;let B=n.a6(d&&d.filter),{z:U,x:W,y:Z}=this.tileID.canonical,$={z:U,x:W,y:Z};for(let st=0;stv)b=!1;else if(d)if(this.expirationTime{this.remove(l,M)},v)),this.data[b].push(M),this.order.push(b),this.order.length>this.max){let O=this._getAndRemoveByKey(this.order[0]);O&&this.onRemove(O)}return this}has(l){return l.wrapped().key in this.data}getAndRemove(l){return this.has(l)?this._getAndRemoveByKey(l.wrapped().key):null}_getAndRemoveByKey(l){let d=this.data[l].shift();return d.timeout&&clearTimeout(d.timeout),this.data[l].length===0&&delete this.data[l],this.order.splice(this.order.indexOf(l),1),d.value}getByKey(l){let d=this.data[l];return d?d[0].value:null}get(l){return this.has(l)?this.data[l.wrapped().key][0].value:null}remove(l,d){if(!this.has(l))return this;let v=l.wrapped().key,b=d===void 0?0:this.data[v].indexOf(d),M=this.data[v][b];return this.data[v].splice(b,1),M.timeout&&clearTimeout(M.timeout),this.data[v].length===0&&delete this.data[v],this.onRemove(M.value),this.order.splice(this.order.indexOf(v),1),this}setMaxSize(l){for(this.max=l;this.order.length>this.max;){let d=this._getAndRemoveByKey(this.order[0]);d&&this.onRemove(d)}return this}filter(l){let d=[];for(let v in this.data)for(let b of this.data[v])l(b.value)||d.push(b);for(let v of d)this.remove(v.value.tileID,v)}}class ee{constructor(){this.state={},this.stateChanges={},this.deletedStates={}}updateState(l,d,v){let b=String(d);if(this.stateChanges[l]=this.stateChanges[l]||{},this.stateChanges[l][b]=this.stateChanges[l][b]||{},n.e(this.stateChanges[l][b],v),this.deletedStates[l]===null){this.deletedStates[l]={};for(let M in this.state[l])M!==b&&(this.deletedStates[l][M]=null)}else if(this.deletedStates[l]&&this.deletedStates[l][b]===null){this.deletedStates[l][b]={};for(let M in this.state[l][b])v[M]||(this.deletedStates[l][b][M]=null)}else for(let M in v)this.deletedStates[l]&&this.deletedStates[l][b]&&this.deletedStates[l][b][M]===null&&delete this.deletedStates[l][b][M]}removeFeatureState(l,d,v){if(this.deletedStates[l]===null)return;let b=String(d);if(this.deletedStates[l]=this.deletedStates[l]||{},v&&d!==void 0)this.deletedStates[l][b]!==null&&(this.deletedStates[l][b]=this.deletedStates[l][b]||{},this.deletedStates[l][b][v]=null);else if(d!==void 0)if(this.stateChanges[l]&&this.stateChanges[l][b])for(v in this.deletedStates[l][b]={},this.stateChanges[l][b])this.deletedStates[l][b][v]=null;else this.deletedStates[l][b]=null;else this.deletedStates[l]=null}getState(l,d){let v=String(d),b=n.e({},(this.state[l]||{})[v],(this.stateChanges[l]||{})[v]);if(this.deletedStates[l]===null)return{};if(this.deletedStates[l]){let M=this.deletedStates[l][d];if(M===null)return{};for(let O in M)delete b[O]}return b}initializeTileState(l,d){l.setFeatureState(this.state,d)}coalesceChanges(l,d){let v={};for(let b in this.stateChanges){this.state[b]=this.state[b]||{};let M={};for(let O in this.stateChanges[b])this.state[b][O]||(this.state[b][O]={}),n.e(this.state[b][O],this.stateChanges[b][O]),M[O]=this.state[b][O];v[b]=M}for(let b in this.deletedStates){this.state[b]=this.state[b]||{};let M={};if(this.deletedStates[b]===null)for(let O in this.state[b])M[O]={},this.state[b][O]={};else for(let O in this.deletedStates[b]){if(this.deletedStates[b][O]===null)this.state[b][O]={};else for(let B of Object.keys(this.deletedStates[b][O]))delete this.state[b][O][B];M[O]=this.state[b][O]}v[b]=v[b]||{},n.e(v[b],M)}if(this.stateChanges={},this.deletedStates={},Object.keys(v).length!==0)for(let b in l)l[b].setFeatureState(v,d)}}class ls extends n.E{constructor(l,d,v){super(),this.id=l,this.dispatcher=v,this.on(\"data\",b=>{b.dataType===\"source\"&&b.sourceDataType===\"metadata\"&&(this._sourceLoaded=!0),this._sourceLoaded&&!this._paused&&b.dataType===\"source\"&&b.sourceDataType===\"content\"&&(this.reload(),this.transform&&this.update(this.transform,this.terrain),this._didEmitContent=!0)}),this.on(\"dataloading\",()=>{this._sourceErrored=!1}),this.on(\"error\",()=>{this._sourceErrored=this._source.loaded()}),this._source=((b,M,O,B)=>{let U=new(xo(M.type))(b,M,O,B);if(U.id!==b)throw new Error(`Expected Source id to be ${b} instead of ${U.id}`);return U})(l,d,v,this),this._tiles={},this._cache=new Nl(0,this._unloadTile.bind(this)),this._timers={},this._cacheTimers={},this._maxTileCacheSize=null,this._maxTileCacheZoomLevels=null,this._loadedParentTiles={},this._coveredTiles={},this._state=new ee,this._didEmitContent=!1,this._updated=!1}onAdd(l){this.map=l,this._maxTileCacheSize=l?l._maxTileCacheSize:null,this._maxTileCacheZoomLevels=l?l._maxTileCacheZoomLevels:null,this._source&&this._source.onAdd&&this._source.onAdd(l)}onRemove(l){this.clearTiles(),this._source&&this._source.onRemove&&this._source.onRemove(l)}loaded(){if(this._sourceErrored)return!0;if(!this._sourceLoaded||!this._source.loaded())return!1;if(!(this.used===void 0&&this.usedForTerrain===void 0||this.used||this.usedForTerrain))return!0;if(!this._updated)return!1;for(let l in this._tiles){let d=this._tiles[l];if(d.state!==\"loaded\"&&d.state!==\"errored\")return!1}return!0}getSource(){return this._source}pause(){this._paused=!0}resume(){if(!this._paused)return;let l=this._shouldReloadOnResume;this._paused=!1,this._shouldReloadOnResume=!1,l&&this.reload(),this.transform&&this.update(this.transform,this.terrain)}_loadTile(l,d){return this._source.loadTile(l,d)}_unloadTile(l){if(this._source.unloadTile)return this._source.unloadTile(l,()=>{})}_abortTile(l){this._source.abortTile&&this._source.abortTile(l,()=>{}),this._source.fire(new n.k(\"dataabort\",{tile:l,coord:l.tileID,dataType:\"source\"}))}serialize(){return this._source.serialize()}prepare(l){this._source.prepare&&this._source.prepare(),this._state.coalesceChanges(this._tiles,this.map?this.map.painter:null);for(let d in this._tiles){let v=this._tiles[d];v.upload(l),v.prepare(this.map.style.imageManager)}}getIds(){return Object.values(this._tiles).map(l=>l.tileID).sort(mn).map(l=>l.key)}getRenderableIds(l){let d=[];for(let v in this._tiles)this._isIdRenderable(v,l)&&d.push(this._tiles[v]);return l?d.sort((v,b)=>{let M=v.tileID,O=b.tileID,B=new n.P(M.canonical.x,M.canonical.y)._rotate(this.transform.angle),U=new n.P(O.canonical.x,O.canonical.y)._rotate(this.transform.angle);return M.overscaledZ-O.overscaledZ||U.y-B.y||U.x-B.x}).map(v=>v.tileID.key):d.map(v=>v.tileID).sort(mn).map(v=>v.key)}hasRenderableParent(l){let d=this.findLoadedParent(l,0);return!!d&&this._isIdRenderable(d.tileID.key)}_isIdRenderable(l,d){return this._tiles[l]&&this._tiles[l].hasData()&&!this._coveredTiles[l]&&(d||!this._tiles[l].holdingForFade())}reload(){if(this._paused)this._shouldReloadOnResume=!0;else{this._cache.reset();for(let l in this._tiles)this._tiles[l].state!==\"errored\"&&this._reloadTile(l,\"reloading\")}}_reloadTile(l,d){let v=this._tiles[l];v&&(v.state!==\"loading\"&&(v.state=d),this._loadTile(v,this._tileLoaded.bind(this,v,l,d)))}_tileLoaded(l,d,v,b){if(b)return l.state=\"errored\",void(b.status!==404?this._source.fire(new n.j(b,{tile:l})):this.update(this.transform,this.terrain));l.timeAdded=n.h.now(),v===\"expired\"&&(l.refreshedUponExpiration=!0),this._setTileReloadTimer(d,l),this.getSource().type===\"raster-dem\"&&l.dem&&this._backfillDEM(l),this._state.initializeTileState(l,this.map?this.map.painter:null),l.aborted||this._source.fire(new n.k(\"data\",{dataType:\"source\",tile:l,coord:l.tileID}))}_backfillDEM(l){let d=this.getRenderableIds();for(let b=0;b1||(Math.abs(O)>1&&(Math.abs(O+U)===1?O+=U:Math.abs(O-U)===1&&(O-=U)),M.dem&&b.dem&&(b.dem.backfillBorder(M.dem,O,B),b.neighboringTiles&&b.neighboringTiles[W]&&(b.neighboringTiles[W].backfilled=!0)))}}getTile(l){return this.getTileByID(l.key)}getTileByID(l){return this._tiles[l]}_retainLoadedChildren(l,d,v,b){for(let M in this._tiles){let O=this._tiles[M];if(b[M]||!O.hasData()||O.tileID.overscaledZ<=d||O.tileID.overscaledZ>v)continue;let B=O.tileID;for(;O&&O.tileID.overscaledZ>d+1;){let W=O.tileID.scaledTo(O.tileID.overscaledZ-1);O=this._tiles[W.key],O&&O.hasData()&&(B=W)}let U=B;for(;U.overscaledZ>d;)if(U=U.scaledTo(U.overscaledZ-1),l[U.key]){b[B.key]=B;break}}}findLoadedParent(l,d){if(l.key in this._loadedParentTiles){let v=this._loadedParentTiles[l.key];return v&&v.tileID.overscaledZ>=d?v:null}for(let v=l.overscaledZ-1;v>=d;v--){let b=l.scaledTo(v),M=this._getLoadedTile(b);if(M)return M}}_getLoadedTile(l){let d=this._tiles[l.key];return d&&d.hasData()?d:this._cache.getByKey(l.wrapped().key)}updateCacheSize(l){let d=Math.ceil(l.width/this._source.tileSize)+1,v=Math.ceil(l.height/this._source.tileSize)+1,b=Math.floor(d*v*(this._maxTileCacheZoomLevels===null?n.c.MAX_TILE_CACHE_ZOOM_LEVELS:this._maxTileCacheZoomLevels)),M=typeof this._maxTileCacheSize==\"number\"?Math.min(this._maxTileCacheSize,b):b;this._cache.setMaxSize(M)}handleWrapJump(l){let d=Math.round((l-(this._prevLng===void 0?l:this._prevLng))/360);if(this._prevLng=l,d){let v={};for(let b in this._tiles){let M=this._tiles[b];M.tileID=M.tileID.unwrapTo(M.tileID.wrap+d),v[M.tileID.key]=M}this._tiles=v;for(let b in this._timers)clearTimeout(this._timers[b]),delete this._timers[b];for(let b in this._tiles)this._setTileReloadTimer(b,this._tiles[b])}}update(l,d){if(this.transform=l,this.terrain=d,!this._sourceLoaded||this._paused)return;let v;this.updateCacheSize(l),this.handleWrapJump(this.transform.center.lng),this._coveredTiles={},this.used||this.usedForTerrain?this._source.tileID?v=l.getVisibleUnwrappedCoordinates(this._source.tileID).map(Z=>new n.O(Z.canonical.z,Z.wrap,Z.canonical.z,Z.canonical.x,Z.canonical.y)):(v=l.coveringTiles({tileSize:this.usedForTerrain?this.tileSize:this._source.tileSize,minzoom:this._source.minzoom,maxzoom:this._source.maxzoom,roundZoom:!this.usedForTerrain&&this._source.roundZoom,reparseOverscaled:this._source.reparseOverscaled,terrain:d}),this._source.hasTile&&(v=v.filter(Z=>this._source.hasTile(Z)))):v=[];let b=l.coveringZoomLevel(this._source),M=Math.max(b-ls.maxOverzooming,this._source.minzoom),O=Math.max(b+ls.maxUnderzooming,this._source.minzoom);if(this.usedForTerrain){let Z={};for(let $ of v)if($.canonical.z>this._source.minzoom){let st=$.scaledTo($.canonical.z-1);Z[st.key]=st;let At=$.scaledTo(Math.max(this._source.minzoom,Math.min($.canonical.z,5)));Z[At.key]=At}v=v.concat(Object.values(Z))}let B=v.length===0&&!this._updated&&this._didEmitContent;this._updated=!0,B&&this.fire(new n.k(\"data\",{sourceDataType:\"idle\",dataType:\"source\",sourceId:this.id}));let U=this._updateRetainedTiles(v,b);if(gi(this._source.type)){let Z={},$={},st=Object.keys(U),At=n.h.now();for(let pt of st){let yt=U[pt],dt=this._tiles[pt];if(!dt||dt.fadeEndTime!==0&&dt.fadeEndTime<=At)continue;let Ft=this.findLoadedParent(yt,M);Ft&&(this._addTile(Ft.tileID),Z[Ft.tileID.key]=Ft.tileID),$[pt]=yt}this._retainLoadedChildren($,b,O,U);for(let pt in Z)U[pt]||(this._coveredTiles[pt]=!0,U[pt]=Z[pt]);if(d){let pt={},yt={};for(let dt of v)this._tiles[dt.key].hasData()?pt[dt.key]=dt:yt[dt.key]=dt;for(let dt in yt){let Ft=yt[dt].children(this._source.maxzoom);this._tiles[Ft[0].key]&&this._tiles[Ft[1].key]&&this._tiles[Ft[2].key]&&this._tiles[Ft[3].key]&&(pt[Ft[0].key]=U[Ft[0].key]=Ft[0],pt[Ft[1].key]=U[Ft[1].key]=Ft[1],pt[Ft[2].key]=U[Ft[2].key]=Ft[2],pt[Ft[3].key]=U[Ft[3].key]=Ft[3],delete yt[dt])}for(let dt in yt){let Ft=this.findLoadedParent(yt[dt],this._source.minzoom);if(Ft){pt[Ft.tileID.key]=U[Ft.tileID.key]=Ft.tileID;for(let Ht in pt)pt[Ht].isChildOf(Ft.tileID)&&delete pt[Ht]}}for(let dt in this._tiles)pt[dt]||(this._coveredTiles[dt]=!0)}}for(let Z in U)this._tiles[Z].clearFadeHold();let W=n.ab(this._tiles,U);for(let Z of W){let $=this._tiles[Z];$.hasSymbolBuckets&&!$.holdingForFade()?$.setHoldDuration(this.map._fadeDuration):$.hasSymbolBuckets&&!$.symbolFadeFinished()||this._removeTile(Z)}this._updateLoadedParentTileCache()}releaseSymbolFadeTiles(){for(let l in this._tiles)this._tiles[l].holdingForFade()&&this._removeTile(l)}_updateRetainedTiles(l,d){let v={},b={},M=Math.max(d-ls.maxOverzooming,this._source.minzoom),O=Math.max(d+ls.maxUnderzooming,this._source.minzoom),B={};for(let U of l){let W=this._addTile(U);v[U.key]=U,W.hasData()||dthis._source.maxzoom){let $=U.children(this._source.maxzoom)[0],st=this.getTile($);if(st&&st.hasData()){v[$.key]=$;continue}}else{let $=U.children(this._source.maxzoom);if(v[$[0].key]&&v[$[1].key]&&v[$[2].key]&&v[$[3].key])continue}let Z=W.wasRequested();for(let $=U.overscaledZ-1;$>=M;--$){let st=U.scaledTo($);if(b[st.key])break;if(b[st.key]=!0,W=this.getTile(st),!W&&Z&&(W=this._addTile(st)),W){let At=W.hasData();if((Z||At)&&(v[st.key]=st),Z=W.wasRequested(),At)break}}}return v}_updateLoadedParentTileCache(){this._loadedParentTiles={};for(let l in this._tiles){let d=[],v,b=this._tiles[l].tileID;for(;b.overscaledZ>0;){if(b.key in this._loadedParentTiles){v=this._loadedParentTiles[b.key];break}d.push(b.key);let M=b.scaledTo(b.overscaledZ-1);if(v=this._getLoadedTile(M),v)break;b=M}for(let M of d)this._loadedParentTiles[M]=v}}_addTile(l){let d=this._tiles[l.key];if(d)return d;d=this._cache.getAndRemove(l),d&&(this._setTileReloadTimer(l.key,d),d.tileID=l,this._state.initializeTileState(d,this.map?this.map.painter:null),this._cacheTimers[l.key]&&(clearTimeout(this._cacheTimers[l.key]),delete this._cacheTimers[l.key],this._setTileReloadTimer(l.key,d)));let v=d;return d||(d=new ao(l,this._source.tileSize*l.overscaleFactor()),this._loadTile(d,this._tileLoaded.bind(this,d,l.key,d.state))),d.uses++,this._tiles[l.key]=d,v||this._source.fire(new n.k(\"dataloading\",{tile:d,coord:d.tileID,dataType:\"source\"})),d}_setTileReloadTimer(l,d){l in this._timers&&(clearTimeout(this._timers[l]),delete this._timers[l]);let v=d.getExpiryTimeout();v&&(this._timers[l]=setTimeout(()=>{this._reloadTile(l,\"expired\"),delete this._timers[l]},v))}_removeTile(l){let d=this._tiles[l];d&&(d.uses--,delete this._tiles[l],this._timers[l]&&(clearTimeout(this._timers[l]),delete this._timers[l]),d.uses>0||(d.hasData()&&d.state!==\"reloading\"?this._cache.add(d.tileID,d,d.getExpiryTimeout()):(d.aborted=!0,this._abortTile(d),this._unloadTile(d))))}clearTiles(){this._shouldReloadOnResume=!1,this._paused=!1;for(let l in this._tiles)this._removeTile(l);this._cache.reset()}tilesIn(l,d,v){let b=[],M=this.transform;if(!M)return b;let O=v?M.getCameraQueryGeometry(l):l,B=l.map(pt=>M.pointCoordinate(pt,this.terrain)),U=O.map(pt=>M.pointCoordinate(pt,this.terrain)),W=this.getIds(),Z=1/0,$=1/0,st=-1/0,At=-1/0;for(let pt of U)Z=Math.min(Z,pt.x),$=Math.min($,pt.y),st=Math.max(st,pt.x),At=Math.max(At,pt.y);for(let pt=0;pt=0&&St[1].y+Ht>=0){let Bt=B.map($t=>dt.getTilePoint($t)),Qt=U.map($t=>dt.getTilePoint($t));b.push({tile:yt,tileID:dt,queryGeometry:Bt,cameraQueryGeometry:Qt,scale:Ft})}}return b}getVisibleCoordinates(l){let d=this.getRenderableIds(l).map(v=>this._tiles[v].tileID);for(let v of d)v.posMatrix=this.transform.calculatePosMatrix(v.toUnwrapped());return d}hasTransition(){if(this._source.hasTransition())return!0;if(gi(this._source.type)){let l=n.h.now();for(let d in this._tiles)if(this._tiles[d].fadeEndTime>=l)return!0}return!1}setFeatureState(l,d,v){this._state.updateState(l=l||\"_geojsonTileLayer\",d,v)}removeFeatureState(l,d,v){this._state.removeFeatureState(l=l||\"_geojsonTileLayer\",d,v)}getFeatureState(l,d){return this._state.getState(l=l||\"_geojsonTileLayer\",d)}setDependencies(l,d,v){let b=this._tiles[l];b&&b.setDependencies(d,v)}reloadTilesForDependencies(l,d){for(let v in this._tiles)this._tiles[v].hasDependency(l,d)&&this._reloadTile(v,\"reloading\");this._cache.filter(v=>!v.hasDependency(l,d))}}function mn(T,l){let d=Math.abs(2*T.wrap)-+(T.wrap<0),v=Math.abs(2*l.wrap)-+(l.wrap<0);return T.overscaledZ-l.overscaledZ||v-d||l.canonical.y-T.canonical.y||l.canonical.x-T.canonical.x}function gi(T){return T===\"raster\"||T===\"image\"||T===\"video\"}ls.maxOverzooming=10,ls.maxUnderzooming=3;let oi=\"mapboxgl_preloaded_worker_pool\";class lo{constructor(){this.active={}}acquire(l){if(!this.workers)for(this.workers=[];this.workers.length{d.terminate()}),this.workers=null)}isPreloaded(){return!!this.active[oi]}numActive(){return Object.keys(this.active).length}}let du=Math.floor(n.h.hardwareConcurrency/2),ul;function bo(){return ul||(ul=new lo),ul}lo.workerCount=n.ac(globalThis)?Math.max(Math.min(du,3),1):1;class hl{constructor(l,d){this.reset(l,d)}reset(l,d){this.points=l||[],this._distances=[0];for(let v=1;v0?(b-O)/B:0;return this.points[M].mult(1-U).add(this.points[d].mult(U))}}function Ia(T,l){let d=!0;return T===\"always\"||T!==\"never\"&&l!==\"never\"||(d=!1),d}class wo{constructor(l,d,v){let b=this.boxCells=[],M=this.circleCells=[];this.xCellCount=Math.ceil(l/v),this.yCellCount=Math.ceil(d/v);for(let O=0;Othis.width||b<0||d>this.height)return[];let U=[];if(l<=0&&d<=0&&this.width<=v&&this.height<=b){if(M)return[{key:null,x1:l,y1:d,x2:v,y2:b}];for(let W=0;W0}hitTestCircle(l,d,v,b,M){let O=l-v,B=l+v,U=d-v,W=d+v;if(B<0||O>this.width||W<0||U>this.height)return!1;let Z=[];return this._forEachCell(O,U,B,W,this._queryCellCircle,Z,{hitTest:!0,overlapMode:b,circle:{x:l,y:d,radius:v},seenUids:{box:{},circle:{}}},M),Z.length>0}_queryCell(l,d,v,b,M,O,B,U){let{seenUids:W,hitTest:Z,overlapMode:$}=B,st=this.boxCells[M];if(st!==null){let pt=this.bboxes;for(let yt of st)if(!W.box[yt]){W.box[yt]=!0;let dt=4*yt,Ft=this.boxKeys[yt];if(l<=pt[dt+2]&&d<=pt[dt+3]&&v>=pt[dt+0]&&b>=pt[dt+1]&&(!U||U(Ft))&&(!Z||!Ia($,Ft.overlapMode))&&(O.push({key:Ft,x1:pt[dt],y1:pt[dt+1],x2:pt[dt+2],y2:pt[dt+3]}),Z))return!0}}let At=this.circleCells[M];if(At!==null){let pt=this.circles;for(let yt of At)if(!W.circle[yt]){W.circle[yt]=!0;let dt=3*yt,Ft=this.circleKeys[yt];if(this._circleAndRectCollide(pt[dt],pt[dt+1],pt[dt+2],l,d,v,b)&&(!U||U(Ft))&&(!Z||!Ia($,Ft.overlapMode))){let Ht=pt[dt],St=pt[dt+1],Bt=pt[dt+2];if(O.push({key:Ft,x1:Ht-Bt,y1:St-Bt,x2:Ht+Bt,y2:St+Bt}),Z)return!0}}}return!1}_queryCellCircle(l,d,v,b,M,O,B,U){let{circle:W,seenUids:Z,overlapMode:$}=B,st=this.boxCells[M];if(st!==null){let pt=this.bboxes;for(let yt of st)if(!Z.box[yt]){Z.box[yt]=!0;let dt=4*yt,Ft=this.boxKeys[yt];if(this._circleAndRectCollide(W.x,W.y,W.radius,pt[dt+0],pt[dt+1],pt[dt+2],pt[dt+3])&&(!U||U(Ft))&&!Ia($,Ft.overlapMode))return O.push(!0),!0}}let At=this.circleCells[M];if(At!==null){let pt=this.circles;for(let yt of At)if(!Z.circle[yt]){Z.circle[yt]=!0;let dt=3*yt,Ft=this.circleKeys[yt];if(this._circlesCollide(pt[dt],pt[dt+1],pt[dt+2],W.x,W.y,W.radius)&&(!U||U(Ft))&&!Ia($,Ft.overlapMode))return O.push(!0),!0}}}_forEachCell(l,d,v,b,M,O,B,U){let W=this._convertToXCellCoord(l),Z=this._convertToYCellCoord(d),$=this._convertToXCellCoord(v),st=this._convertToYCellCoord(b);for(let At=W;At<=$;At++)for(let pt=Z;pt<=st;pt++)if(M.call(this,l,d,v,b,this.xCellCount*pt+At,O,B,U))return}_convertToXCellCoord(l){return Math.max(0,Math.min(this.xCellCount-1,Math.floor(l*this.xScale)))}_convertToYCellCoord(l){return Math.max(0,Math.min(this.yCellCount-1,Math.floor(l*this.yScale)))}_circlesCollide(l,d,v,b,M,O){let B=b-l,U=M-d,W=v+O;return W*W>B*B+U*U}_circleAndRectCollide(l,d,v,b,M,O,B){let U=(O-b)/2,W=Math.abs(l-(b+U));if(W>U+v)return!1;let Z=(B-M)/2,$=Math.abs(d-(M+Z));if($>Z+v)return!1;if(W<=U||$<=Z)return!0;let st=W-U,At=$-Z;return st*st+At*At<=v*v}}function ve(T,l,d,v,b){let M=n.Z();return l?(n.a0(M,M,[1/b,1/b,1]),d||n.ae(M,M,v.angle)):n.a1(M,v.labelPlaneMatrix,T),M}function jo(T,l,d,v,b){if(l){let M=n.af(T);return n.a0(M,M,[b,b,1]),d||n.ae(M,M,-v.angle),M}return v.glCoordMatrix}function gn(T,l,d){let v;d?(v=[T.x,T.y,d(T.x,T.y),1],n.ag(v,v,l)):(v=[T.x,T.y,0,1],vt(v,v,l));let b=v[3];return{point:new n.P(v[0]/b,v[1]/b),signedDistanceFromCamera:b}}function Ul(T,l){return .5+T/l*.5}function Ca(T,l){let d=T[0]/T[3],v=T[1]/T[3];return d>=-l[0]&&d<=l[0]&&v>=-l[1]&&v<=l[1]}function Te(T,l,d,v,b,M,O,B,U,W){let Z=v?T.textSizeData:T.iconSizeData,$=n.ah(Z,d.transform.zoom),st=[256/d.width*2+1,256/d.height*2+1],At=v?T.text.dynamicLayoutVertexArray:T.icon.dynamicLayoutVertexArray;At.clear();let pt=T.lineVertexArray,yt=v?T.text.placedSymbolArray:T.icon.placedSymbolArray,dt=d.transform.width/d.transform.height,Ft=!1;for(let Ht=0;HtMath.abs(d.x-l.x)*v?{useVertical:!0}:(T===n.ai.vertical?l.yd.x)?{needsFlipping:!0}:null}function Us(T,l,d,v,b,M,O,B,U,W,Z,$,st,At,pt,yt){let dt=l/24,Ft=T.lineOffsetX*dt,Ht=T.lineOffsetY*dt,St;if(T.numGlyphs>1){let Bt=T.glyphStartIndex+T.numGlyphs,Qt=T.lineStartIndex,$t=T.lineStartIndex+T.lineLength,oe=Dr(dt,B,Ft,Ht,d,Z,$,T,U,M,st,pt,yt);if(!oe)return{notEnoughRoom:!0};let pe=gn(oe.first.point,O,yt).point,he=gn(oe.last.point,O,yt).point;if(v&&!d){let be=gr(T.writingMode,pe,he,At);if(be)return be}St=[oe.first];for(let be=T.glyphStartIndex+1;be0?pe.point:La($,oe,Qt,1,b,yt),be=gr(T.writingMode,Qt,he,At);if(be)return be}let Bt=tt(dt*B.getoffsetX(T.glyphStartIndex),Ft,Ht,d,Z,$,T.segment,T.lineStartIndex,T.lineStartIndex+T.lineLength,U,M,st,pt,yt);if(!Bt)return{notEnoughRoom:!0};St=[Bt]}for(let Bt of St)n.ak(W,Bt.point,Bt.angle);return{}}function La(T,l,d,v,b,M){let O=gn(T.add(T.sub(l)._unit()),b,M).point,B=d.sub(O);return d.add(B._mult(v/B.mag()))}function Mr(T,l){let{projectionCache:d,lineVertexArray:v,labelPlaneMatrix:b,tileAnchorPoint:M,distanceFromAnchor:O,getElevation:B,previousVertex:U,direction:W,absOffsetX:Z}=l;if(d.projections[T])return d.projections[T];let $=new n.P(v.getx(T),v.gety(T)),st=gn($,b,B);if(st.signedDistanceFromCamera>0)return d.projections[T]=st.point,st.point;let At=T-W;return La(O===0?M:new n.P(v.getx(At),v.gety(At)),$,U,Z-O+1,b,B)}function sa(T,l,d){return T._unit()._perp()._mult(l*d)}function gt(T,l,d,v,b,M,O,B){let{projectionCache:U,direction:W}=B;if(U.offsets[T])return U.offsets[T];let Z=d.add(l);if(T+W=b)return U.offsets[T]=Z,Z;let $=Mr(T+W,B),st=sa($.sub(d),O,W),At=d.add(st),pt=$.add(st);return U.offsets[T]=n.al(M,Z,At,pt)||Z,U.offsets[T]}function tt(T,l,d,v,b,M,O,B,U,W,Z,$,st,At){let pt=v?T-l:T+l,yt=pt>0?1:-1,dt=0;v&&(yt*=-1,dt=Math.PI),yt<0&&(dt+=Math.PI);let Ft,Ht,St=yt>0?B+O:B+O+1,Bt=b,Qt=b,$t=0,oe=0,pe=Math.abs(pt),he=[],be;for(;$t+oe<=pe;){if(St+=yt,St=U)return null;$t+=oe,Qt=Bt,Ht=Ft;let Ee={projectionCache:$,lineVertexArray:W,labelPlaneMatrix:Z,tileAnchorPoint:M,distanceFromAnchor:$t,getElevation:At,previousVertex:Qt,direction:yt,absOffsetX:pe};if(Bt=Mr(St,Ee),d===0)he.push(Qt),be=Bt.sub(Qt);else{let pr,tr=Bt.sub(Qt);pr=tr.mag()===0?sa(Mr(St+yt,Ee).sub(Bt),d,yt):sa(tr,d,yt),Ht||(Ht=Qt.add(pr)),Ft=gt(St,pr,Bt,B,U,Ht,d,Ee),he.push(Ht),be=Ft.sub(Ht)}oe=be.mag()}let Ze=be._mult((pe-$t)/oe)._add(Ht||Qt),Kr=dt+Math.atan2(Bt.y-Qt.y,Bt.x-Qt.x);return he.push(Ze),{point:Ze,angle:st?Kr:0,path:he}}let nt=new Float32Array([-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0]);function ht(T,l){for(let d=0;d=1;Vr--)tr.push(Ee.path[Vr]);for(let Vr=1;Vrgn(ei,U,pt));tr=Vr.some(ei=>ei.signedDistanceFromCamera<=0)?[]:Vr.map(ei=>ei.point)}let Jr=[];if(tr.length>0){let Vr=tr[0].clone(),ei=tr[0].clone();for(let On=1;On=be.x&&ei.x<=Ze.x&&Vr.y>=be.y&&ei.y<=Ze.y?[tr]:ei.xZe.x||ei.yZe.y?[]:n.am([tr],be.x,be.y,Ze.x,Ze.y)}for(let Vr of Jr){Kr.reset(Vr,.25*he);let ei=0;ei=Kr.length<=.5*he?1:Math.ceil(Kr.paddedLength/Gi)+1;for(let On=0;On=this.screenRightBoundary||bthis.screenBottomBoundary}isInsideGrid(l,d,v,b){return v>=0&&l=0&&dv.collisionGroupID===d}}return this.collisionGroups[l]}}function vr(T,l,d,v,b){let{horizontalAlign:M,verticalAlign:O}=n.au(T);return new n.P(-(M-.5)*l+v[0]*b,-(O-.5)*d+v[1]*b)}function Xe(T,l,d,v,b,M){let{x1:O,x2:B,y1:U,y2:W,anchorPointX:Z,anchorPointY:$}=T,st=new n.P(l,d);return v&&st._rotate(b?M:-M),{x1:O+st.x,y1:U+st.y,x2:B+st.x,y2:W+st.y,anchorPointX:Z,anchorPointY:$}}class cr{constructor(l,d,v,b,M){this.transform=l.clone(),this.terrain=d,this.collisionIndex=new _t(this.transform),this.placements={},this.opacities={},this.variableOffsets={},this.stale=!1,this.commitTime=0,this.fadeDuration=v,this.retainedQueryData={},this.collisionGroups=new lr(b),this.collisionCircleArrays={},this.prevPlacement=M,M&&(M.prevPlacement=void 0),this.placedOrientations={}}getBucketParts(l,d,v,b){let M=v.getBucket(d),O=v.latestFeatureIndex;if(!M||!O||d.id!==M.layerIds[0])return;let B=v.collisionBoxArray,U=M.layers[0].layout,W=Math.pow(2,this.transform.zoom-v.tileID.overscaledZ),Z=v.tileSize/n.N,$=this.transform.calculatePosMatrix(v.tileID.toUnwrapped()),st=U.get(\"text-pitch-alignment\")===\"map\",At=U.get(\"text-rotation-alignment\")===\"map\",pt=Dt(v,1,this.transform.zoom),yt=ve($,st,At,this.transform,pt),dt=null;if(st){let Ht=jo($,st,At,this.transform,pt);dt=n.a1([],this.transform.labelPlaneMatrix,Ht)}this.retainedQueryData[M.bucketInstanceId]=new ae(M.bucketInstanceId,O,M.sourceLayerIndex,M.index,v.tileID);let Ft={bucket:M,layout:U,posMatrix:$,textLabelPlaneMatrix:yt,labelToScreenMatrix:dt,scale:W,textPixelRatio:Z,holdingForFade:v.holdingForFade(),collisionBoxArray:B,partiallyEvaluatedTextSize:n.ah(M.textSizeData,this.transform.zoom),collisionGroup:this.collisionGroups.get(M.sourceID)};if(b)for(let Ht of M.sortKeyRanges){let{sortKey:St,symbolInstanceStart:Bt,symbolInstanceEnd:Qt}=Ht;l.push({sortKey:St,symbolInstanceStart:Bt,symbolInstanceEnd:Qt,parameters:Ft})}else l.push({symbolInstanceStart:0,symbolInstanceEnd:M.symbolInstances.length,parameters:Ft})}attemptAnchorPlacement(l,d,v,b,M,O,B,U,W,Z,$,st,At,pt,yt,dt){let Ft=n.aq[l.textAnchor],Ht=[l.textOffset0,l.textOffset1],St=vr(Ft,v,b,Ht,M),Bt=this.collisionIndex.placeCollisionBox(Xe(d,St.x,St.y,O,B,this.transform.angle),$,U,W,Z.predicate,dt);if((!yt||this.collisionIndex.placeCollisionBox(Xe(yt,St.x,St.y,O,B,this.transform.angle),$,U,W,Z.predicate,dt).box.length!==0)&&Bt.box.length>0){let Qt;if(this.prevPlacement&&this.prevPlacement.variableOffsets[st.crossTileID]&&this.prevPlacement.placements[st.crossTileID]&&this.prevPlacement.placements[st.crossTileID].text&&(Qt=this.prevPlacement.variableOffsets[st.crossTileID].anchor),st.crossTileID===0)throw new Error(\"symbolInstance.crossTileID can't be 0\");return this.variableOffsets[st.crossTileID]={textOffset:Ht,width:v,height:b,anchor:Ft,textBoxScale:M,prevAnchor:Qt},this.markUsedJustification(At,Ft,st,pt),At.allowVerticalPlacement&&(this.markUsedOrientation(At,pt,st),this.placedOrientations[st.crossTileID]=pt),{shift:St,placedGlyphBoxes:Bt}}}placeLayerBucketPart(l,d,v){let{bucket:b,layout:M,posMatrix:O,textLabelPlaneMatrix:B,labelToScreenMatrix:U,textPixelRatio:W,holdingForFade:Z,collisionBoxArray:$,partiallyEvaluatedTextSize:st,collisionGroup:At}=l.parameters,pt=M.get(\"text-optional\"),yt=M.get(\"icon-optional\"),dt=n.ar(M,\"text-overlap\",\"text-allow-overlap\"),Ft=dt===\"always\",Ht=n.ar(M,\"icon-overlap\",\"icon-allow-overlap\"),St=Ht===\"always\",Bt=M.get(\"text-rotation-alignment\")===\"map\",Qt=M.get(\"text-pitch-alignment\")===\"map\",$t=M.get(\"icon-text-fit\")!==\"none\",oe=M.get(\"symbol-z-order\")===\"viewport-y\",pe=Ft&&(St||!b.hasIconData()||yt),he=St&&(Ft||!b.hasTextData()||pt);!b.collisionArrays&&$&&b.deserializeCollisionBoxes($);let be=this.retainedQueryData[b.bucketInstanceId].tileID,Ze=this.terrain?(Ee,pr)=>this.terrain.getElevation(be,Ee,pr):null,Kr=(Ee,pr)=>{var tr,Gi;if(d[Ee.crossTileID])return;if(Z)return void(this.placements[Ee.crossTileID]=new ie(!1,!1,!1));let Jr=!1,Vr=!1,ei=!0,On=null,tn={box:null,offscreen:null},Gs={box:null,offscreen:null},hs=null,Bn=null,qo=null,jr=0,ql=0,Zl=0;pr.textFeatureIndex?jr=pr.textFeatureIndex:Ee.useRuntimeCollisionCircles&&(jr=Ee.featureIndex),pr.verticalTextFeatureIndex&&(ql=pr.verticalTextFeatureIndex);let yu=pr.textBox;if(yu){let Ws=Fn=>{let fs=n.ai.horizontal;if(b.allowVerticalPlacement&&!Fn&&this.prevPlacement){let Zo=this.prevPlacement.placedOrientations[Ee.crossTileID];Zo&&(this.placedOrientations[Ee.crossTileID]=Zo,fs=Zo,this.markUsedOrientation(b,fs,Ee))}return fs},Ps=(Fn,fs)=>{if(b.allowVerticalPlacement&&Ee.numVerticalGlyphVertices>0&&pr.verticalTextBox){for(let Zo of b.writingModes)if(Zo===n.ai.vertical?(tn=fs(),Gs=tn):tn=Fn(),tn&&tn.box&&tn.box.length)break}else tn=Fn()},Eo=Ee.textAnchorOffsetStartIndex,yh=Ee.textAnchorOffsetEndIndex;if(yh===Eo){let Fn=(fs,Zo)=>{let _n=this.collisionIndex.placeCollisionBox(fs,dt,W,O,At.predicate,Ze);return _n&&_n.box&&_n.box.length&&(this.markUsedOrientation(b,Zo,Ee),this.placedOrientations[Ee.crossTileID]=Zo),_n};Ps(()=>Fn(yu,n.ai.horizontal),()=>{let fs=pr.verticalTextBox;return b.allowVerticalPlacement&&Ee.numVerticalGlyphVertices>0&&fs?Fn(fs,n.ai.vertical):{box:null,offscreen:null}}),Ws(tn&&tn.box&&tn.box.length)}else{let Fn=n.aq[(Gi=(tr=this.prevPlacement)===null||tr===void 0?void 0:tr.variableOffsets[Ee.crossTileID])===null||Gi===void 0?void 0:Gi.anchor],fs=(_n,ho,Gr)=>{let Ua=_n.x2-_n.x1,S_=_n.y2-_n.y1,zd=Ee.textBoxScale,cA=$t&&Ht===\"never\"?ho:null,Yl={box:[],offscreen:!1},Yo=dt===\"never\"?1:2,me=\"never\";Fn&&Yo++;for(let ke=0;kefs(yu,pr.iconBox,n.ai.horizontal),()=>{let _n=pr.verticalTextBox;return b.allowVerticalPlacement&&!(tn&&tn.box&&tn.box.length)&&Ee.numVerticalGlyphVertices>0&&_n?fs(_n,pr.verticalIconBox,n.ai.vertical):{box:null,offscreen:null}}),tn&&(Jr=tn.box,ei=tn.offscreen);let Zo=Ws(tn&&tn.box);if(!Jr&&this.prevPlacement){let _n=this.prevPlacement.variableOffsets[Ee.crossTileID];_n&&(this.variableOffsets[Ee.crossTileID]=_n,this.markUsedJustification(b,_n.anchor,Ee,Zo))}}}if(hs=tn,Jr=hs&&hs.box&&hs.box.length>0,ei=hs&&hs.offscreen,Ee.useRuntimeCollisionCircles){let Ws=b.text.placedSymbolArray.get(Ee.centerJustifiedTextSymbolIndex),Ps=n.aj(b.textSizeData,st,Ws),Eo=M.get(\"text-padding\");Bn=this.collisionIndex.placeCollisionCircles(dt,Ws,b.lineVertexArray,b.glyphOffsetArray,Ps,O,B,U,v,Qt,At.predicate,Ee.collisionCircleDiameter,Eo,Ze),Bn.circles.length&&Bn.collisionDetected&&!v&&n.w(\"Collisions detected, but collision boxes are not shown\"),Jr=Ft||Bn.circles.length>0&&!Bn.collisionDetected,ei=ei&&Bn.offscreen}if(pr.iconFeatureIndex&&(Zl=pr.iconFeatureIndex),pr.iconBox){let Ws=Ps=>{let Eo=$t&&On?Xe(Ps,On.x,On.y,Bt,Qt,this.transform.angle):Ps;return this.collisionIndex.placeCollisionBox(Eo,Ht,W,O,At.predicate,Ze)};Gs&&Gs.box&&Gs.box.length&&pr.verticalIconBox?(qo=Ws(pr.verticalIconBox),Vr=qo.box.length>0):(qo=Ws(pr.iconBox),Vr=qo.box.length>0),ei=ei&&qo.offscreen}let vu=pt||Ee.numHorizontalGlyphVertices===0&&Ee.numVerticalGlyphVertices===0,_h=yt||Ee.numIconVertices===0;if(vu||_h?_h?vu||(Vr=Vr&&Jr):Jr=Vr&&Jr:Vr=Jr=Vr&&Jr,Jr&&hs&&hs.box&&this.collisionIndex.insertCollisionBox(hs.box,dt,M.get(\"text-ignore-placement\"),b.bucketInstanceId,Gs&&Gs.box&&ql?ql:jr,At.ID),Vr&&qo&&this.collisionIndex.insertCollisionBox(qo.box,Ht,M.get(\"icon-ignore-placement\"),b.bucketInstanceId,Zl,At.ID),Bn&&(Jr&&this.collisionIndex.insertCollisionCircles(Bn.circles,dt,M.get(\"text-ignore-placement\"),b.bucketInstanceId,jr,At.ID),v)){let Ws=b.bucketInstanceId,Ps=this.collisionCircleArrays[Ws];Ps===void 0&&(Ps=this.collisionCircleArrays[Ws]=new se);for(let Eo=0;Eo=0;--pr){let tr=Ee[pr];Kr(b.symbolInstances.get(tr),b.collisionArrays[tr])}}else for(let Ee=l.symbolInstanceStart;Ee=0&&(l.text.placedSymbolArray.get(B).crossTileID=M>=0&&B!==M?0:v.crossTileID)}markUsedOrientation(l,d,v){let b=d===n.ai.horizontal||d===n.ai.horizontalOnly?d:0,M=d===n.ai.vertical?d:0,O=[v.leftJustifiedTextSymbolIndex,v.centerJustifiedTextSymbolIndex,v.rightJustifiedTextSymbolIndex];for(let B of O)l.text.placedSymbolArray.get(B).placedOrientation=b;v.verticalPlacedTextSymbolIndex&&(l.text.placedSymbolArray.get(v.verticalPlacedTextSymbolIndex).placedOrientation=M)}commit(l){this.commitTime=l,this.zoomAtLastRecencyCheck=this.transform.zoom;let d=this.prevPlacement,v=!1;this.prevZoomAdjustment=d?d.zoomAdjustment(this.transform.zoom):0;let b=d?d.symbolFadeChange(l):1,M=d?d.opacities:{},O=d?d.variableOffsets:{},B=d?d.placedOrientations:{};for(let U in this.placements){let W=this.placements[U],Z=M[U];Z?(this.opacities[U]=new Vt(Z,b,W.text,W.icon),v=v||W.text!==Z.text.placed||W.icon!==Z.icon.placed):(this.opacities[U]=new Vt(null,b,W.text,W.icon,W.skipFade),v=v||W.text||W.icon)}for(let U in M){let W=M[U];if(!this.opacities[U]){let Z=new Vt(W,b,!1,!1);Z.isHidden()||(this.opacities[U]=Z,v=v||W.text.placed||W.icon.placed)}}for(let U in O)this.variableOffsets[U]||!this.opacities[U]||this.opacities[U].isHidden()||(this.variableOffsets[U]=O[U]);for(let U in B)this.placedOrientations[U]||!this.opacities[U]||this.opacities[U].isHidden()||(this.placedOrientations[U]=B[U]);if(d&&d.lastPlacementChangeTime===void 0)throw new Error(\"Last placement time for previous placement is not defined\");v?this.lastPlacementChangeTime=l:typeof this.lastPlacementChangeTime!=\"number\"&&(this.lastPlacementChangeTime=d?d.lastPlacementChangeTime:l)}updateLayerOpacities(l,d){let v={};for(let b of d){let M=b.getBucket(l);M&&b.latestFeatureIndex&&l.id===M.layerIds[0]&&this.updateBucketOpacities(M,v,b.collisionBoxArray)}}updateBucketOpacities(l,d,v){l.hasTextData()&&(l.text.opacityVertexArray.clear(),l.text.hasVisibleVertices=!1),l.hasIconData()&&(l.icon.opacityVertexArray.clear(),l.icon.hasVisibleVertices=!1),l.hasIconCollisionBoxData()&&l.iconCollisionBox.collisionVertexArray.clear(),l.hasTextCollisionBoxData()&&l.textCollisionBox.collisionVertexArray.clear();let b=l.layers[0],M=b.layout,O=new Vt(null,0,!1,!1,!0),B=M.get(\"text-allow-overlap\"),U=M.get(\"icon-allow-overlap\"),W=b._unevaluatedLayout.hasValue(\"text-variable-anchor\")||b._unevaluatedLayout.hasValue(\"text-variable-anchor-offset\"),Z=M.get(\"text-rotation-alignment\")===\"map\",$=M.get(\"text-pitch-alignment\")===\"map\",st=M.get(\"icon-text-fit\")!==\"none\",At=new Vt(null,0,B&&(U||!l.hasIconData()||M.get(\"icon-optional\")),U&&(B||!l.hasTextData()||M.get(\"text-optional\")),!0);!l.collisionArrays&&v&&(l.hasIconCollisionBoxData()||l.hasTextCollisionBoxData())&&l.deserializeCollisionBoxes(v);let pt=(yt,dt,Ft)=>{for(let Ht=0;Ht
0,$t=this.placedOrientations[dt.crossTileID],oe=$t===n.ai.vertical,pe=$t===n.ai.horizontal||$t===n.ai.horizontalOnly;if(Ft>0||Ht>0){let he=es(Bt.text);pt(l.text,Ft,oe?oa:he),pt(l.text,Ht,pe?oa:he);let be=Bt.text.isHidden();[dt.rightJustifiedTextSymbolIndex,dt.centerJustifiedTextSymbolIndex,dt.leftJustifiedTextSymbolIndex].forEach(Ee=>{Ee>=0&&(l.text.placedSymbolArray.get(Ee).hidden=be||oe?1:0)}),dt.verticalPlacedTextSymbolIndex>=0&&(l.text.placedSymbolArray.get(dt.verticalPlacedTextSymbolIndex).hidden=be||pe?1:0);let Ze=this.variableOffsets[dt.crossTileID];Ze&&this.markUsedJustification(l,Ze.anchor,dt,$t);let Kr=this.placedOrientations[dt.crossTileID];Kr&&(this.markUsedJustification(l,\"left\",dt,Kr),this.markUsedOrientation(l,Kr,dt))}if(Qt){let he=es(Bt.icon),be=!(st&&dt.verticalPlacedIconSymbolIndex&&oe);dt.placedIconSymbolIndex>=0&&(pt(l.icon,dt.numIconVertices,be?he:oa),l.icon.placedSymbolArray.get(dt.placedIconSymbolIndex).hidden=Bt.icon.isHidden()),dt.verticalPlacedIconSymbolIndex>=0&&(pt(l.icon,dt.numVerticalIconVertices,be?oa:he),l.icon.placedSymbolArray.get(dt.verticalPlacedIconSymbolIndex).hidden=Bt.icon.isHidden())}if(l.hasIconCollisionBoxData()||l.hasTextCollisionBoxData()){let he=l.collisionArrays[yt];if(he){let be=new n.P(0,0);if(he.textBox||he.verticalTextBox){let Kr=!0;if(W){let Ee=this.variableOffsets[St];Ee?(be=vr(Ee.anchor,Ee.width,Ee.height,Ee.textOffset,Ee.textBoxScale),Z&&be._rotate($?this.transform.angle:-this.transform.angle)):Kr=!1}he.textBox&&wr(l.textCollisionBox.collisionVertexArray,Bt.text.placed,!Kr||oe,be.x,be.y),he.verticalTextBox&&wr(l.textCollisionBox.collisionVertexArray,Bt.text.placed,!Kr||pe,be.x,be.y)}let Ze=!!(!pe&&he.verticalIconBox);he.iconBox&&wr(l.iconCollisionBox.collisionVertexArray,Bt.icon.placed,Ze,st?be.x:0,st?be.y:0),he.verticalIconBox&&wr(l.iconCollisionBox.collisionVertexArray,Bt.icon.placed,!Ze,st?be.x:0,st?be.y:0)}}}if(l.sortFeatures(this.transform.angle),this.retainedQueryData[l.bucketInstanceId]&&(this.retainedQueryData[l.bucketInstanceId].featureSortOrder=l.featureSortOrder),l.hasTextData()&&l.text.opacityVertexBuffer&&l.text.opacityVertexBuffer.updateData(l.text.opacityVertexArray),l.hasIconData()&&l.icon.opacityVertexBuffer&&l.icon.opacityVertexBuffer.updateData(l.icon.opacityVertexArray),l.hasIconCollisionBoxData()&&l.iconCollisionBox.collisionVertexBuffer&&l.iconCollisionBox.collisionVertexBuffer.updateData(l.iconCollisionBox.collisionVertexArray),l.hasTextCollisionBoxData()&&l.textCollisionBox.collisionVertexBuffer&&l.textCollisionBox.collisionVertexBuffer.updateData(l.textCollisionBox.collisionVertexArray),l.text.opacityVertexArray.length!==l.text.layoutVertexArray.length/4)throw new Error(`bucket.text.opacityVertexArray.length (= ${l.text.opacityVertexArray.length}) !== bucket.text.layoutVertexArray.length (= ${l.text.layoutVertexArray.length}) / 4`);if(l.icon.opacityVertexArray.length!==l.icon.layoutVertexArray.length/4)throw new Error(`bucket.icon.opacityVertexArray.length (= ${l.icon.opacityVertexArray.length}) !== bucket.icon.layoutVertexArray.length (= ${l.icon.layoutVertexArray.length}) / 4`);if(l.bucketInstanceId in this.collisionCircleArrays){let yt=this.collisionCircleArrays[l.bucketInstanceId];l.placementInvProjMatrix=yt.invProjMatrix,l.placementViewportMatrix=yt.viewportMatrix,l.collisionCircleArray=yt.circles,delete this.collisionCircleArrays[l.bucketInstanceId]}}symbolFadeChange(l){return this.fadeDuration===0?1:(l-this.commitTime)/this.fadeDuration+this.prevZoomAdjustment}zoomAdjustment(l){return Math.max(0,(this.transform.zoom-l)/1.5)}hasTransitions(l){return this.stale||l-this.lastPlacementChangeTimel}setStale(){this.stale=!0}}function wr(T,l,d,v,b){T.emplaceBack(l?1:0,d?1:0,v||0,b||0),T.emplaceBack(l?1:0,d?1:0,v||0,b||0),T.emplaceBack(l?1:0,d?1:0,v||0,b||0),T.emplaceBack(l?1:0,d?1:0,v||0,b||0)}let xi=Math.pow(2,25),zi=Math.pow(2,24),ni=Math.pow(2,17),Hr=Math.pow(2,16),jn=Math.pow(2,9),Bi=Math.pow(2,8),xn=Math.pow(2,1);function es(T){if(T.opacity===0&&!T.placed)return 0;if(T.opacity===1&&T.placed)return 4294967295;let l=T.placed?1:0,d=Math.floor(127*T.opacity);return d*xi+l*zi+d*ni+l*Hr+d*jn+l*Bi+d*xn+l}let oa=0;class Um{constructor(l){this._sortAcrossTiles=l.layout.get(\"symbol-z-order\")!==\"viewport-y\"&&!l.layout.get(\"symbol-sort-key\").isConstant(),this._currentTileIndex=0,this._currentPartIndex=0,this._seenCrossTileIDs={},this._bucketParts=[]}continuePlacement(l,d,v,b,M){let O=this._bucketParts;for(;this._currentTileIndexB.sortKey-U.sortKey));this._currentPartIndex!this._forceFullPlacement&&n.h.now()-b>2;for(;this._currentPlacementIndex>=0;){let O=d[l[this._currentPlacementIndex]],B=this.placement.collisionIndex.transform.zoom;if(O.type===\"symbol\"&&(!O.minzoom||O.minzoom<=B)&&(!O.maxzoom||O.maxzoom>B)){if(this._inProgressLayer||(this._inProgressLayer=new Um(O)),this._inProgressLayer.continuePlacement(v[O.source],this.placement,this._showCollisionBoxes,O,M))return;delete this._inProgressLayer}this._currentPlacementIndex--}this._done=!0}commit(l){return this.placement.commit(l),this.placement}}let Ss=512/n.N/2;class nh{constructor(l,d,v){this.tileID=l,this.bucketInstanceId=v,this._symbolsByKey={};let b=new Map;for(let M=0;M({x:Math.floor(U.anchorX*Ss),y:Math.floor(U.anchorY*Ss)})),crossTileIDs:O.map(U=>U.crossTileID)};if(B.positions.length>128){let U=new n.av(B.positions.length,16,Uint16Array);for(let{x:W,y:Z}of B.positions)U.add(W,Z);U.finish(),delete B.positions,B.index=U}this._symbolsByKey[M]=B}}getScaledCoordinates(l,d){let{x:v,y:b,z:M}=this.tileID.canonical,{x:O,y:B,z:U}=d.canonical,W=Ss/Math.pow(2,U-M),Z=(B*n.N+l.anchorY)*W,$=b*n.N*Ss;return{x:Math.floor((O*n.N+l.anchorX)*W-v*n.N*Ss),y:Math.floor(Z-$)}}findMatches(l,d,v){let b=this.tileID.canonical.zl)}}class ai{constructor(){this.maxCrossTileID=0}generate(){return++this.maxCrossTileID}}class ka{constructor(){this.indexes={},this.usedCrossTileIDs={},this.lng=0}handleWrapJump(l){let d=Math.round((l-this.lng)/360);if(d!==0)for(let v in this.indexes){let b=this.indexes[v],M={};for(let O in b){let B=b[O];B.tileID=B.tileID.unwrapTo(B.tileID.wrap+d),M[B.tileID.key]=B}this.indexes[v]=M}this.lng=l}addBucket(l,d,v){if(this.indexes[l.overscaledZ]&&this.indexes[l.overscaledZ][l.key]){if(this.indexes[l.overscaledZ][l.key].bucketInstanceId===d.bucketInstanceId)return!1;this.removeBucketCrossTileIDs(l.overscaledZ,this.indexes[l.overscaledZ][l.key])}for(let M=0;Ml.overscaledZ)for(let B in O){let U=O[B];U.tileID.isChildOf(l)&&U.findMatches(d.symbolInstances,l,b)}else{let B=O[l.scaledTo(Number(M)).key];B&&B.findMatches(d.symbolInstances,l,b)}}for(let M=0;M{d[v]=!0});for(let v in this.layerIndexes)d[v]||delete this.layerIndexes[v]}}let ln=(T,l)=>n.x(T,l&&l.filter(d=>d.identifier!==\"source.canvas\")),Dn=n.F(n.ax,[\"addLayer\",\"removeLayer\",\"setPaintProperty\",\"setLayoutProperty\",\"setFilter\",\"addSource\",\"removeSource\",\"setLayerZoomRange\",\"setLight\",\"setTransition\",\"setGeoJSONSourceData\",\"setGlyphs\",\"setSprite\"]),Vm=n.F(n.ax,[\"setCenter\",\"setZoom\",\"setBearing\",\"setPitch\"]),Go=n.aw();class Gn extends n.E{constructor(l,d={}){super(),this.map=l,this.dispatcher=new ih(bo(),this,l._getMapId()),this.imageManager=new ue,this.imageManager.setEventedParent(this),this.glyphManager=new Sr(l._requestManager,d.localIdeographFontFamily),this.lineAtlas=new No(256,512),this.crossTileSymbolIndex=new Dc,this._spritesImagesIds={},this._layers={},this._order=[],this.sourceCaches={},this.zoomHistory=new n.ay,this._loaded=!1,this._availableImages=[],this._resetUpdates(),this.dispatcher.broadcast(\"setReferrer\",n.az());let v=this;this._rtlTextPluginCallback=Gn.registerForPluginStateChange(b=>{v.dispatcher.broadcast(\"syncRTLPluginState\",{pluginStatus:b.pluginStatus,pluginURL:b.pluginURL},(M,O)=>{if(n.aA(M),O&&O.every(B=>B))for(let B in v.sourceCaches){let U=v.sourceCaches[B].getSource().type;U!==\"vector\"&&U!==\"geojson\"||v.sourceCaches[B].reload()}})}),this.on(\"data\",b=>{if(b.dataType!==\"source\"||b.sourceDataType!==\"metadata\")return;let M=this.sourceCaches[b.sourceId];if(!M)return;let O=M.getSource();if(O&&O.vectorLayerIds)for(let B in this._layers){let U=this._layers[B];U.source===O.id&&this._validateLayer(U)}})}loadURL(l,d={},v){this.fire(new n.k(\"dataloading\",{dataType:\"style\"})),d.validate=typeof d.validate!=\"boolean\"||d.validate;let b=this.map._requestManager.transformRequest(l,Q.Style);this._request=n.f(b,(M,O)=>{this._request=null,M?this.fire(new n.j(M)):O&&this._load(O,d,v)})}loadJSON(l,d={},v){this.fire(new n.k(\"dataloading\",{dataType:\"style\"})),this._request=n.h.frame(()=>{this._request=null,d.validate=d.validate!==!1,this._load(l,d,v)})}loadEmpty(){this.fire(new n.k(\"dataloading\",{dataType:\"style\"})),this._load(Go,{validate:!1})}_load(l,d,v){var b;let M=d.transformStyle?d.transformStyle(v,l):l;if(!d.validate||!ln(this,n.y(M))){this._loaded=!0,this.stylesheet=M;for(let O in M.sources)this.addSource(O,M.sources[O],{validate:!1});M.sprite?this._loadSprite(M.sprite):this.imageManager.setLoaded(!0),this.glyphManager.setURL(M.glyphs),this._createLayers(),this.light=new zl(this.stylesheet.light),this.map.setTerrain((b=this.stylesheet.terrain)!==null&&b!==void 0?b:null),this.fire(new n.k(\"data\",{dataType:\"style\"})),this.fire(new n.k(\"style.load\"))}}_createLayers(){let l=n.aB(this.stylesheet.layers);this.dispatcher.broadcast(\"setLayers\",l),this._order=l.map(d=>d.id),this._layers={},this._serializedLayers=null;for(let d of l){let v=n.aC(d);v.setEventedParent(this,{layer:{id:d.id}}),this._layers[d.id]=v}}_loadSprite(l,d=!1,v=void 0){this.imageManager.setLoaded(!1),this._spriteRequest=function(b,M,O,B){let U=kt(b),W=U.length,Z=O>1?\"@2x\":\"\",$={},st={},At={};for(let{id:pt,url:yt}of U){let dt=M.transformRequest(M.normalizeSpriteURL(yt,Z,\".json\"),Q.SpriteJSON),Ft=`${pt}_${dt.url}`;$[Ft]=n.f(dt,(Bt,Qt)=>{delete $[Ft],st[pt]=Qt,Xt(B,st,At,Bt,W)});let Ht=M.transformRequest(M.normalizeSpriteURL(yt,Z,\".png\"),Q.SpriteImage),St=`${pt}_${Ht.url}`;$[St]=j.getImage(Ht,(Bt,Qt)=>{delete $[St],At[pt]=Qt,Xt(B,st,At,Bt,W)})}return{cancel(){for(let pt of Object.values($))pt.cancel()}}}(l,this.map._requestManager,this.map.getPixelRatio(),(b,M)=>{if(this._spriteRequest=null,b)this.fire(new n.j(b));else if(M)for(let O in M){this._spritesImagesIds[O]=[];let B=this._spritesImagesIds[O]?this._spritesImagesIds[O].filter(U=>!(U in M)):[];for(let U of B)this.imageManager.removeImage(U),this._changedImages[U]=!0;for(let U in M[O]){let W=O===\"default\"?U:`${O}:${U}`;this._spritesImagesIds[O].push(W),W in this.imageManager.images?this.imageManager.updateImage(W,M[O][U],!1):this.imageManager.addImage(W,M[O][U]),d&&(this._changedImages[W]=!0)}}this.imageManager.setLoaded(!0),this._availableImages=this.imageManager.listImages(),d&&(this._changed=!0),this.dispatcher.broadcast(\"setImages\",this._availableImages),this.fire(new n.k(\"data\",{dataType:\"style\"})),v&&v(b)})}_unloadSprite(){for(let l of Object.values(this._spritesImagesIds).flat())this.imageManager.removeImage(l),this._changedImages[l]=!0;this._spritesImagesIds={},this._availableImages=this.imageManager.listImages(),this._changed=!0,this.dispatcher.broadcast(\"setImages\",this._availableImages),this.fire(new n.k(\"data\",{dataType:\"style\"}))}_validateLayer(l){let d=this.sourceCaches[l.source];if(!d)return;let v=l.sourceLayer;if(!v)return;let b=d.getSource();(b.type===\"geojson\"||b.vectorLayerIds&&b.vectorLayerIds.indexOf(v)===-1)&&this.fire(new n.j(new Error(`Source layer \"${v}\" does not exist on source \"${b.id}\" as specified by style layer \"${l.id}\".`)))}loaded(){if(!this._loaded||Object.keys(this._updatedSources).length)return!1;for(let l in this.sourceCaches)if(!this.sourceCaches[l].loaded())return!1;return!!this.imageManager.isLoaded()}_serializeByIds(l){let d=this._serializedAllLayers();if(!l||l.length===0)return Object.values(d);let v=[];for(let b of l)d[b]&&v.push(d[b]);return v}_serializedAllLayers(){let l=this._serializedLayers;if(l)return l;l=this._serializedLayers={};let d=Object.keys(this._layers);for(let v of d){let b=this._layers[v];b.type!==\"custom\"&&(l[v]=b.serialize())}return l}hasTransitions(){if(this.light&&this.light.hasTransition())return!0;for(let l in this.sourceCaches)if(this.sourceCaches[l].hasTransition())return!0;for(let l in this._layers)if(this._layers[l].hasTransition())return!0;return!1}_checkLoaded(){if(!this._loaded)throw new Error(\"Style is not done loading.\")}update(l){if(!this._loaded)return;let d=this._changed;if(this._changed){let b=Object.keys(this._updatedLayers),M=Object.keys(this._removedLayers);(b.length||M.length)&&this._updateWorkerLayers(b,M);for(let O in this._updatedSources){let B=this._updatedSources[O];if(B===\"reload\")this._reloadSource(O);else{if(B!==\"clear\")throw new Error(`Invalid action ${B}`);this._clearSource(O)}}this._updateTilesForChangedImages(),this._updateTilesForChangedGlyphs();for(let O in this._updatedPaintProps)this._layers[O].updateTransitions(l);this.light.updateTransitions(l),this._resetUpdates()}let v={};for(let b in this.sourceCaches){let M=this.sourceCaches[b];v[b]=M.used,M.used=!1}for(let b of this._order){let M=this._layers[b];M.recalculate(l,this._availableImages),!M.isHidden(l.zoom)&&M.source&&(this.sourceCaches[M.source].used=!0)}for(let b in v){let M=this.sourceCaches[b];v[b]!==M.used&&M.fire(new n.k(\"data\",{sourceDataType:\"visibility\",dataType:\"source\",sourceId:b}))}this.light.recalculate(l),this.z=l.zoom,d&&this.fire(new n.k(\"data\",{dataType:\"style\"}))}_updateTilesForChangedImages(){let l=Object.keys(this._changedImages);if(l.length){for(let d in this.sourceCaches)this.sourceCaches[d].reloadTilesForDependencies([\"icons\",\"patterns\"],l);this._changedImages={}}}_updateTilesForChangedGlyphs(){if(this._glyphsDidChange){for(let l in this.sourceCaches)this.sourceCaches[l].reloadTilesForDependencies([\"glyphs\"],[\"\"]);this._glyphsDidChange=!1}}_updateWorkerLayers(l,d){this.dispatcher.broadcast(\"updateLayers\",{layers:this._serializeByIds(l),removedIds:d})}_resetUpdates(){this._changed=!1,this._updatedLayers={},this._removedLayers={},this._updatedSources={},this._updatedPaintProps={},this._changedImages={},this._glyphsDidChange=!1}setState(l,d={}){this._checkLoaded();let v=this.serialize();if(l=d.transformStyle?d.transformStyle(v,l):l,ln(this,n.y(l)))return!1;(l=n.aD(l)).layers=n.aB(l.layers);let b=n.aE(v,l).filter(O=>!(O.command in Vm));if(b.length===0)return!1;let M=b.filter(O=>!(O.command in Dn));if(M.length>0)throw new Error(`Unimplemented: ${M.map(O=>O.command).join(\", \")}.`);for(let O of b)O.command!==\"setTransition\"&&this[O.command].apply(this,O.args);return this.stylesheet=l,this._serializedLayers=null,!0}addImage(l,d){if(this.getImage(l))return this.fire(new n.j(new Error(`An image named \"${l}\" already exists.`)));this.imageManager.addImage(l,d),this._afterImageUpdated(l)}updateImage(l,d){this.imageManager.updateImage(l,d)}getImage(l){return this.imageManager.getImage(l)}removeImage(l){if(!this.getImage(l))return this.fire(new n.j(new Error(`An image named \"${l}\" does not exist.`)));this.imageManager.removeImage(l),this._afterImageUpdated(l)}_afterImageUpdated(l){this._availableImages=this.imageManager.listImages(),this._changedImages[l]=!0,this._changed=!0,this.dispatcher.broadcast(\"setImages\",this._availableImages),this.fire(new n.k(\"data\",{dataType:\"style\"}))}listImages(){return this._checkLoaded(),this.imageManager.listImages()}addSource(l,d,v={}){if(this._checkLoaded(),this.sourceCaches[l]!==void 0)throw new Error(`Source \"${l}\" already exists.`);if(!d.type)throw new Error(`The type property must be defined, but only the following properties were given: ${Object.keys(d).join(\", \")}.`);if([\"vector\",\"raster\",\"geojson\",\"video\",\"image\"].indexOf(d.type)>=0&&this._validate(n.y.source,`sources.${l}`,d,null,v))return;this.map&&this.map._collectResourceTiming&&(d.collectResourceTiming=!0);let b=this.sourceCaches[l]=new ls(l,d,this.dispatcher);b.style=this,b.setEventedParent(this,()=>({isSourceLoaded:b.loaded(),source:b.serialize(),sourceId:l})),b.onAdd(this.map),this._changed=!0}removeSource(l){if(this._checkLoaded(),this.sourceCaches[l]===void 0)throw new Error(\"There is no source with this ID\");for(let v in this._layers)if(this._layers[v].source===l)return this.fire(new n.j(new Error(`Source \"${l}\" cannot be removed while layer \"${v}\" is using it.`)));let d=this.sourceCaches[l];delete this.sourceCaches[l],delete this._updatedSources[l],d.fire(new n.k(\"data\",{sourceDataType:\"metadata\",dataType:\"source\",sourceId:l})),d.setEventedParent(null),d.onRemove(this.map),this._changed=!0}setGeoJSONSourceData(l,d){if(this._checkLoaded(),this.sourceCaches[l]===void 0)throw new Error(`There is no source with this ID=${l}`);let v=this.sourceCaches[l].getSource();if(v.type!==\"geojson\")throw new Error(`geojsonSource.type is ${v.type}, which is !== 'geojson`);v.setData(d),this._changed=!0}getSource(l){return this.sourceCaches[l]&&this.sourceCaches[l].getSource()}addLayer(l,d,v={}){this._checkLoaded();let b=l.id;if(this.getLayer(b))return void this.fire(new n.j(new Error(`Layer \"${b}\" already exists on this map.`)));let M;if(l.type===\"custom\"){if(ln(this,n.aF(l)))return;M=n.aC(l)}else{if(\"source\"in l&&typeof l.source==\"object\"&&(this.addSource(b,l.source),l=n.aD(l),l=n.e(l,{source:b})),this._validate(n.y.layer,`layers.${b}`,l,{arrayIndex:-1},v))return;M=n.aC(l),this._validateLayer(M),M.setEventedParent(this,{layer:{id:b}})}let O=d?this._order.indexOf(d):this._order.length;if(d&&O===-1)this.fire(new n.j(new Error(`Cannot add layer \"${b}\" before non-existing layer \"${d}\".`)));else{if(this._order.splice(O,0,b),this._layerOrderChanged=!0,this._layers[b]=M,this._removedLayers[b]&&M.source&&M.type!==\"custom\"){let B=this._removedLayers[b];delete this._removedLayers[b],B.type!==M.type?this._updatedSources[M.source]=\"clear\":(this._updatedSources[M.source]=\"reload\",this.sourceCaches[M.source].pause())}this._updateLayer(M),M.onAdd&&M.onAdd(this.map)}}moveLayer(l,d){if(this._checkLoaded(),this._changed=!0,!this._layers[l])return void this.fire(new n.j(new Error(`The layer '${l}' does not exist in the map's style and cannot be moved.`)));if(l===d)return;let v=this._order.indexOf(l);this._order.splice(v,1);let b=d?this._order.indexOf(d):this._order.length;d&&b===-1?this.fire(new n.j(new Error(`Cannot move layer \"${l}\" before non-existing layer \"${d}\".`))):(this._order.splice(b,0,l),this._layerOrderChanged=!0)}removeLayer(l){this._checkLoaded();let d=this._layers[l];if(!d)return void this.fire(new n.j(new Error(`Cannot remove non-existing layer \"${l}\".`)));d.setEventedParent(null);let v=this._order.indexOf(l);this._order.splice(v,1),this._layerOrderChanged=!0,this._changed=!0,this._removedLayers[l]=d,delete this._layers[l],this._serializedLayers&&delete this._serializedLayers[l],delete this._updatedLayers[l],delete this._updatedPaintProps[l],d.onRemove&&d.onRemove(this.map)}getLayer(l){return this._layers[l]}getLayersOrder(){return[...this._order]}hasLayer(l){return l in this._layers}setLayerZoomRange(l,d,v){this._checkLoaded();let b=this.getLayer(l);b?b.minzoom===d&&b.maxzoom===v||(d!=null&&(b.minzoom=d),v!=null&&(b.maxzoom=v),this._updateLayer(b)):this.fire(new n.j(new Error(`Cannot set the zoom range of non-existing layer \"${l}\".`)))}setFilter(l,d,v={}){this._checkLoaded();let b=this.getLayer(l);if(b){if(!n.aG(b.filter,d))return d==null?(b.filter=void 0,void this._updateLayer(b)):void(this._validate(n.y.filter,`layers.${b.id}.filter`,d,null,v)||(b.filter=n.aD(d),this._updateLayer(b)))}else this.fire(new n.j(new Error(`Cannot filter non-existing layer \"${l}\".`)))}getFilter(l){return n.aD(this.getLayer(l).filter)}setLayoutProperty(l,d,v,b={}){this._checkLoaded();let M=this.getLayer(l);M?n.aG(M.getLayoutProperty(d),v)||(M.setLayoutProperty(d,v,b),this._updateLayer(M)):this.fire(new n.j(new Error(`Cannot style non-existing layer \"${l}\".`)))}getLayoutProperty(l,d){let v=this.getLayer(l);if(v)return v.getLayoutProperty(d);this.fire(new n.j(new Error(`Cannot get style of non-existing layer \"${l}\".`)))}setPaintProperty(l,d,v,b={}){this._checkLoaded();let M=this.getLayer(l);M?n.aG(M.getPaintProperty(d),v)||(M.setPaintProperty(d,v,b)&&this._updateLayer(M),this._changed=!0,this._updatedPaintProps[l]=!0):this.fire(new n.j(new Error(`Cannot style non-existing layer \"${l}\".`)))}getPaintProperty(l,d){return this.getLayer(l).getPaintProperty(d)}setFeatureState(l,d){this._checkLoaded();let v=l.source,b=l.sourceLayer,M=this.sourceCaches[v];if(M===void 0)return void this.fire(new n.j(new Error(`The source '${v}' does not exist in the map's style.`)));let O=M.getSource().type;O===\"geojson\"&&b?this.fire(new n.j(new Error(\"GeoJSON sources cannot have a sourceLayer parameter.\"))):O!==\"vector\"||b?(l.id===void 0&&this.fire(new n.j(new Error(\"The feature id parameter must be provided.\"))),M.setFeatureState(b,l.id,d)):this.fire(new n.j(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}removeFeatureState(l,d){this._checkLoaded();let v=l.source,b=this.sourceCaches[v];if(b===void 0)return void this.fire(new n.j(new Error(`The source '${v}' does not exist in the map's style.`)));let M=b.getSource().type,O=M===\"vector\"?l.sourceLayer:void 0;M!==\"vector\"||O?d&&typeof l.id!=\"string\"&&typeof l.id!=\"number\"?this.fire(new n.j(new Error(\"A feature id is required to remove its specific state property.\"))):b.removeFeatureState(O,l.id,d):this.fire(new n.j(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}getFeatureState(l){this._checkLoaded();let d=l.source,v=l.sourceLayer,b=this.sourceCaches[d];if(b!==void 0)return b.getSource().type!==\"vector\"||v?(l.id===void 0&&this.fire(new n.j(new Error(\"The feature id parameter must be provided.\"))),b.getFeatureState(v,l.id)):void this.fire(new n.j(new Error(\"The sourceLayer parameter must be provided for vector source types.\")));this.fire(new n.j(new Error(`The source '${d}' does not exist in the map's style.`)))}getTransition(){return n.e({duration:300,delay:0},this.stylesheet&&this.stylesheet.transition)}serialize(){if(!this._loaded)return;let l=n.aH(this.sourceCaches,M=>M.serialize()),d=this._serializeByIds(this._order),v=this.map.getTerrain()||void 0,b=this.stylesheet;return n.aI({version:b.version,name:b.name,metadata:b.metadata,light:b.light,center:b.center,zoom:b.zoom,bearing:b.bearing,pitch:b.pitch,sprite:b.sprite,glyphs:b.glyphs,transition:b.transition,sources:l,layers:d,terrain:v},M=>M!==void 0)}_updateLayer(l){this._updatedLayers[l.id]=!0,l.source&&!this._updatedSources[l.source]&&this.sourceCaches[l.source].getSource().type!==\"raster\"&&(this._updatedSources[l.source]=\"reload\",this.sourceCaches[l.source].pause()),this._serializedLayers=null,this._changed=!0}_flattenAndSortRenderedFeatures(l){let d=O=>this._layers[O].type===\"fill-extrusion\",v={},b=[];for(let O=this._order.length-1;O>=0;O--){let B=this._order[O];if(d(B)){v[B]=O;for(let U of l){let W=U[B];if(W)for(let Z of W)b.push(Z)}}}b.sort((O,B)=>B.intersectionZ-O.intersectionZ);let M=[];for(let O=this._order.length-1;O>=0;O--){let B=this._order[O];if(d(B))for(let U=b.length-1;U>=0;U--){let W=b[U].feature;if(v[W.layer.id]{let pe=Ft.featureSortOrder;if(pe){let he=pe.indexOf($t.featureIndex);return pe.indexOf(oe.featureIndex)-he}return oe.featureIndex-$t.featureIndex});for(let $t of Qt)Bt.push($t)}}for(let Ft in pt)pt[Ft].forEach(Ht=>{let St=Ht.feature,Bt=W[B[Ft].source].getFeatureState(St.layer[\"source-layer\"],St.id);St.source=St.layer.source,St.layer[\"source-layer\"]&&(St.sourceLayer=St.layer[\"source-layer\"]),St.state=Bt});return pt}(this._layers,O,this.sourceCaches,l,d,this.placement.collisionIndex,this.placement.retainedQueryData)),this._flattenAndSortRenderedFeatures(M)}querySourceFeatures(l,d){d&&d.filter&&this._validate(n.y.filter,\"querySourceFeatures.filter\",d.filter,null,d);let v=this.sourceCaches[l];return v?function(b,M){let O=b.getRenderableIds().map(W=>b.getTileByID(W)),B=[],U={};for(let W=0;W{cl[b]=M})(l,d),d.workerSourceURL?void this.dispatcher.broadcast(\"loadWorkerSource\",{name:l,url:d.workerSourceURL},v):v(null,null))}getLight(){return this.light.getLight()}setLight(l,d={}){this._checkLoaded();let v=this.light.getLight(),b=!1;for(let O in l)if(!n.aG(l[O],v[O])){b=!0;break}if(!b)return;let M={now:n.h.now(),transition:n.e({duration:300,delay:0},this.stylesheet.transition)};this.light.setLight(l,d),this.light.updateTransitions(M)}_validate(l,d,v,b,M={}){return(!M||M.validate!==!1)&&ln(this,l.call(n.y,n.e({key:d,style:this.serialize(),value:v,styleSpec:n.v},b)))}_remove(l=!0){this._request&&(this._request.cancel(),this._request=null),this._spriteRequest&&(this._spriteRequest.cancel(),this._spriteRequest=null),n.aJ.off(\"pluginStateChange\",this._rtlTextPluginCallback);for(let d in this._layers)this._layers[d].setEventedParent(null);for(let d in this.sourceCaches){let v=this.sourceCaches[d];v.setEventedParent(null),v.onRemove(this.map)}this.imageManager.setEventedParent(null),this.setEventedParent(null),this.dispatcher.remove(l)}_clearSource(l){this.sourceCaches[l].clearTiles()}_reloadSource(l){this.sourceCaches[l].resume(),this.sourceCaches[l].reload()}_updateSources(l){for(let d in this.sourceCaches)this.sourceCaches[d].update(l,this.map.terrain)}_generateCollisionBoxes(){for(let l in this.sourceCaches)this._reloadSource(l)}_updatePlacement(l,d,v,b,M=!1){let O=!1,B=!1,U={};for(let W of this._order){let Z=this._layers[W];if(Z.type!==\"symbol\")continue;if(!U[Z.source]){let st=this.sourceCaches[Z.source];U[Z.source]=st.getRenderableIds(!0).map(At=>st.getTileByID(At)).sort((At,pt)=>pt.tileID.overscaledZ-At.tileID.overscaledZ||(At.tileID.isLessThan(pt.tileID)?-1:1))}let $=this.crossTileSymbolIndex.addLayer(Z,U[Z.source],l.center.lng);O=O||$}if(this.crossTileSymbolIndex.pruneUnusedLayers(this._order),((M=M||this._layerOrderChanged||v===0)||!this.pauseablePlacement||this.pauseablePlacement.isDone()&&!this.placement.stillRecent(n.h.now(),l.zoom))&&(this.pauseablePlacement=new Vl(l,this.map.terrain,this._order,M,d,v,b,this.placement),this._layerOrderChanged=!1),this.pauseablePlacement.isDone()?this.placement.setStale():(this.pauseablePlacement.continuePlacement(this._order,this._layers,U),this.pauseablePlacement.isDone()&&(this.placement=this.pauseablePlacement.commit(n.h.now()),B=!0),O&&this.pauseablePlacement.placement.setStale()),B||O)for(let W of this._order){let Z=this._layers[W];Z.type===\"symbol\"&&this.placement.updateLayerOpacities(Z,U[Z.source])}return!this.pauseablePlacement.isDone()||this.placement.hasTransitions(n.h.now())}_releaseSymbolFadeTiles(){for(let l in this.sourceCaches)this.sourceCaches[l].releaseSymbolFadeTiles()}getImages(l,d,v){this.imageManager.getImages(d.icons,v),this._updateTilesForChangedImages();let b=this.sourceCaches[d.source];b&&b.setDependencies(d.tileID.key,d.type,d.icons)}getGlyphs(l,d,v){this.glyphManager.getGlyphs(d.stacks,v);let b=this.sourceCaches[d.source];b&&b.setDependencies(d.tileID.key,d.type,[\"\"])}getResource(l,d,v){return n.m(d,v)}getGlyphsUrl(){return this.stylesheet.glyphs||null}setGlyphs(l,d={}){this._checkLoaded(),l&&this._validate(n.y.glyphs,\"glyphs\",l,null,d)||(this._glyphsDidChange=!0,this.stylesheet.glyphs=l,this.glyphManager.entries={},this.glyphManager.setURL(l))}addSprite(l,d,v={},b){this._checkLoaded();let M=[{id:l,url:d}],O=[...kt(this.stylesheet.sprite),...M];this._validate(n.y.sprite,\"sprite\",O,null,v)||(this.stylesheet.sprite=O,this._loadSprite(M,!0,b))}removeSprite(l){this._checkLoaded();let d=kt(this.stylesheet.sprite);if(d.find(v=>v.id===l)){if(this._spritesImagesIds[l])for(let v of this._spritesImagesIds[l])this.imageManager.removeImage(v),this._changedImages[v]=!0;d.splice(d.findIndex(v=>v.id===l),1),this.stylesheet.sprite=d.length>0?d:void 0,delete this._spritesImagesIds[l],this._availableImages=this.imageManager.listImages(),this._changed=!0,this.dispatcher.broadcast(\"setImages\",this._availableImages),this.fire(new n.k(\"data\",{dataType:\"style\"}))}else this.fire(new n.j(new Error(`Sprite \"${l}\" doesn't exists on this map.`)))}getSprite(){return kt(this.stylesheet.sprite)}setSprite(l,d={},v){this._checkLoaded(),l&&this._validate(n.y.sprite,\"sprite\",l,null,d)||(this.stylesheet.sprite=l,l?this._loadSprite(l,!0,v):(this._unloadSprite(),v&&v(null)))}}Gn.registerForPluginStateChange=n.aK;var So=n.Q([{name:\"a_pos\",type:\"Int16\",components:2}]),jl=\"attribute vec3 a_pos3d;uniform mat4 u_matrix;uniform float u_ele_delta;varying vec2 v_texture_pos;varying float v_depth;void main() {float extent=8192.0;float ele_delta=a_pos3d.z==1.0 ? u_ele_delta : 0.0;v_texture_pos=a_pos3d.xy/extent;gl_Position=u_matrix*vec4(a_pos3d.xy,get_elevation(a_pos3d.xy)-ele_delta,1.0);v_depth=gl_Position.z/gl_Position.w;}\";let Ki={prelude:_i(`#ifdef GL_ES\nprecision mediump float;\n#else\n#if !defined(lowp)\n#define lowp\n#endif\n#if !defined(mediump)\n#define mediump\n#endif\n#if !defined(highp)\n#define highp\n#endif\n#endif\n`,`#ifdef GL_ES\nprecision highp float;\n#else\n#if !defined(lowp)\n#define lowp\n#endif\n#if !defined(mediump)\n#define mediump\n#endif\n#if !defined(highp)\n#define highp\n#endif\n#endif\nvec2 unpack_float(const float packedValue) {int packedIntValue=int(packedValue);int v0=packedIntValue/256;return vec2(v0,packedIntValue-v0*256);}vec2 unpack_opacity(const float packedOpacity) {int intOpacity=int(packedOpacity)/2;return vec2(float(intOpacity)/127.0,mod(packedOpacity,2.0));}vec4 decode_color(const vec2 encodedColor) {return vec4(unpack_float(encodedColor[0])/255.0,unpack_float(encodedColor[1])/255.0\n);}float unpack_mix_vec2(const vec2 packedValue,const float t) {return mix(packedValue[0],packedValue[1],t);}vec4 unpack_mix_color(const vec4 packedColors,const float t) {vec4 minColor=decode_color(vec2(packedColors[0],packedColors[1]));vec4 maxColor=decode_color(vec2(packedColors[2],packedColors[3]));return mix(minColor,maxColor,t);}vec2 get_pattern_pos(const vec2 pixel_coord_upper,const vec2 pixel_coord_lower,const vec2 pattern_size,const float tile_units_to_pixels,const vec2 pos) {vec2 offset=mod(mod(mod(pixel_coord_upper,pattern_size)*256.0,pattern_size)*256.0+pixel_coord_lower,pattern_size);return (tile_units_to_pixels*pos+offset)/pattern_size;}\n#ifdef TERRAIN3D\nuniform sampler2D u_terrain;uniform float u_terrain_dim;uniform mat4 u_terrain_matrix;uniform vec4 u_terrain_unpack;uniform float u_terrain_exaggeration;uniform highp sampler2D u_depth;\n#endif\nconst highp vec4 bitSh=vec4(256.*256.*256.,256.*256.,256.,1.);const highp vec4 bitShifts=vec4(1.)/bitSh;highp float unpack(highp vec4 color) {return dot(color,bitShifts);}highp float depthOpacity(vec3 frag) {\n#ifdef TERRAIN3D\nhighp float d=unpack(texture2D(u_depth,frag.xy*0.5+0.5))+0.0001-frag.z;return 1.0-max(0.0,min(1.0,-d*500.0));\n#else\nreturn 1.0;\n#endif\n}float calculate_visibility(vec4 pos) {\n#ifdef TERRAIN3D\nvec3 frag=pos.xyz/pos.w;highp float d=depthOpacity(frag);if (d > 0.95) return 1.0;return (d+depthOpacity(frag+vec3(0.0,0.01,0.0)))/2.0;\n#else\nreturn 1.0;\n#endif\n}float ele(vec2 pos) {\n#ifdef TERRAIN3D\nvec4 rgb=(texture2D(u_terrain,pos)*255.0)*u_terrain_unpack;return rgb.r+rgb.g+rgb.b-u_terrain_unpack.a;\n#else\nreturn 0.0;\n#endif\n}float get_elevation(vec2 pos) {\n#ifdef TERRAIN3D\nvec2 coord=(u_terrain_matrix*vec4(pos,0.0,1.0)).xy*u_terrain_dim+1.0;vec2 f=fract(coord);vec2 c=(floor(coord)+0.5)/(u_terrain_dim+2.0);float d=1.0/(u_terrain_dim+2.0);float tl=ele(c);float tr=ele(c+vec2(d,0.0));float bl=ele(c+vec2(0.0,d));float br=ele(c+vec2(d,d));float elevation=mix(mix(tl,tr,f.x),mix(bl,br,f.x),f.y);return elevation*u_terrain_exaggeration;\n#else\nreturn 0.0;\n#endif\n}`),background:_i(`uniform vec4 u_color;uniform float u_opacity;void main() {gl_FragColor=u_color*u_opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}`,\"attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}\"),backgroundPattern:_i(`uniform vec2 u_pattern_tl_a;uniform vec2 u_pattern_br_a;uniform vec2 u_pattern_tl_b;uniform vec2 u_pattern_br_b;uniform vec2 u_texsize;uniform float u_mix;uniform float u_opacity;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(u_pattern_tl_a/u_texsize,u_pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(u_pattern_tl_b/u_texsize,u_pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_mix)*u_opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}`,\"uniform mat4 u_matrix;uniform vec2 u_pattern_size_a;uniform vec2 u_pattern_size_b;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_scale_a;uniform float u_scale_b;uniform float u_tile_units_to_pixels;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_a*u_pattern_size_a,u_tile_units_to_pixels,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_b*u_pattern_size_b,u_tile_units_to_pixels,a_pos);}\"),circle:_i(`varying vec3 v_data;varying float v_visibility;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define mediump float radius\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define highp vec4 stroke_color\n#pragma mapbox: define mediump float stroke_width\n#pragma mapbox: define lowp float stroke_opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize mediump float radius\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize highp vec4 stroke_color\n#pragma mapbox: initialize mediump float stroke_width\n#pragma mapbox: initialize lowp float stroke_opacity\nvec2 extrude=v_data.xy;float extrude_length=length(extrude);lowp float antialiasblur=v_data.z;float antialiased_blur=-max(blur,antialiasblur);float opacity_t=smoothstep(0.0,antialiased_blur,extrude_length-1.0);float color_t=stroke_width < 0.01 ? 0.0 : smoothstep(antialiased_blur,0.0,extrude_length-radius/(radius+stroke_width));gl_FragColor=v_visibility*opacity_t*mix(color*opacity,stroke_color*stroke_opacity,color_t);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}`,`uniform mat4 u_matrix;uniform bool u_scale_with_map;uniform bool u_pitch_with_map;uniform vec2 u_extrude_scale;uniform lowp float u_device_pixel_ratio;uniform highp float u_camera_to_center_distance;attribute vec2 a_pos;varying vec3 v_data;varying float v_visibility;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define mediump float radius\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define highp vec4 stroke_color\n#pragma mapbox: define mediump float stroke_width\n#pragma mapbox: define lowp float stroke_opacity\nvoid main(void) {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize mediump float radius\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize highp vec4 stroke_color\n#pragma mapbox: initialize mediump float stroke_width\n#pragma mapbox: initialize lowp float stroke_opacity\nvec2 extrude=vec2(mod(a_pos,2.0)*2.0-1.0);vec2 circle_center=floor(a_pos*0.5);float ele=get_elevation(circle_center);v_visibility=calculate_visibility(u_matrix*vec4(circle_center,ele,1.0));if (u_pitch_with_map) {vec2 corner_position=circle_center;if (u_scale_with_map) {corner_position+=extrude*(radius+stroke_width)*u_extrude_scale;} else {vec4 projected_center=u_matrix*vec4(circle_center,0,1);corner_position+=extrude*(radius+stroke_width)*u_extrude_scale*(projected_center.w/u_camera_to_center_distance);}gl_Position=u_matrix*vec4(corner_position,ele,1);} else {gl_Position=u_matrix*vec4(circle_center,ele,1);if (u_scale_with_map) {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*u_camera_to_center_distance;} else {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*gl_Position.w;}}lowp float antialiasblur=1.0/u_device_pixel_ratio/(radius+stroke_width);v_data=vec3(extrude.x,extrude.y,antialiasblur);}`),clippingMask:_i(\"void main() {gl_FragColor=vec4(1.0);}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}\"),heatmap:_i(`uniform highp float u_intensity;varying vec2 v_extrude;\n#pragma mapbox: define highp float weight\n#define GAUSS_COEF 0.3989422804014327\nvoid main() {\n#pragma mapbox: initialize highp float weight\nfloat d=-0.5*3.0*3.0*dot(v_extrude,v_extrude);float val=weight*u_intensity*GAUSS_COEF*exp(d);gl_FragColor=vec4(val,1.0,1.0,1.0);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}`,`uniform mat4 u_matrix;uniform float u_extrude_scale;uniform float u_opacity;uniform float u_intensity;attribute vec2 a_pos;varying vec2 v_extrude;\n#pragma mapbox: define highp float weight\n#pragma mapbox: define mediump float radius\nconst highp float ZERO=1.0/255.0/16.0;\n#define GAUSS_COEF 0.3989422804014327\nvoid main(void) {\n#pragma mapbox: initialize highp float weight\n#pragma mapbox: initialize mediump float radius\nvec2 unscaled_extrude=vec2(mod(a_pos,2.0)*2.0-1.0);float S=sqrt(-2.0*log(ZERO/weight/u_intensity/GAUSS_COEF))/3.0;v_extrude=S*unscaled_extrude;vec2 extrude=v_extrude*radius*u_extrude_scale;vec4 pos=vec4(floor(a_pos*0.5)+extrude,0,1);gl_Position=u_matrix*pos;}`),heatmapTexture:_i(`uniform sampler2D u_image;uniform sampler2D u_color_ramp;uniform float u_opacity;varying vec2 v_pos;void main() {float t=texture2D(u_image,v_pos).r;vec4 color=texture2D(u_color_ramp,vec2(t,0.5));gl_FragColor=color*u_opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(0.0);\n#endif\n}`,\"uniform mat4 u_matrix;uniform vec2 u_world;attribute vec2 a_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos*u_world,0,1);v_pos.x=a_pos.x;v_pos.y=1.0-a_pos.y;}\"),collisionBox:_i(\"varying float v_placed;varying float v_notUsed;void main() {float alpha=0.5;gl_FragColor=vec4(1.0,0.0,0.0,1.0)*alpha;if (v_placed > 0.5) {gl_FragColor=vec4(0.0,0.0,1.0,0.5)*alpha;}if (v_notUsed > 0.5) {gl_FragColor*=.1;}}\",\"attribute vec2 a_pos;attribute vec2 a_anchor_pos;attribute vec2 a_extrude;attribute vec2 a_placed;attribute vec2 a_shift;uniform mat4 u_matrix;uniform vec2 u_extrude_scale;uniform float u_camera_to_center_distance;varying float v_placed;varying float v_notUsed;void main() {vec4 projectedPoint=u_matrix*vec4(a_anchor_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);gl_Position=u_matrix*vec4(a_pos,get_elevation(a_pos),1.0);gl_Position.xy+=(a_extrude+a_shift)*u_extrude_scale*gl_Position.w*collision_perspective_ratio;v_placed=a_placed.x;v_notUsed=a_placed.y;}\"),collisionCircle:_i(\"varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;void main() {float alpha=0.5*min(v_perspective_ratio,1.0);float stroke_radius=0.9*max(v_perspective_ratio,1.0);float distance_to_center=length(v_extrude);float distance_to_edge=abs(distance_to_center-v_radius);float opacity_t=smoothstep(-stroke_radius,0.0,-distance_to_edge);vec4 color=mix(vec4(0.0,0.0,1.0,0.5),vec4(1.0,0.0,0.0,1.0),v_collision);gl_FragColor=color*alpha*opacity_t;}\",\"attribute vec2 a_pos;attribute float a_radius;attribute vec2 a_flags;uniform mat4 u_matrix;uniform mat4 u_inv_matrix;uniform vec2 u_viewport_size;uniform float u_camera_to_center_distance;varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;vec3 toTilePosition(vec2 screenPos) {vec4 rayStart=u_inv_matrix*vec4(screenPos,-1.0,1.0);vec4 rayEnd =u_inv_matrix*vec4(screenPos, 1.0,1.0);rayStart.xyz/=rayStart.w;rayEnd.xyz /=rayEnd.w;highp float t=(0.0-rayStart.z)/(rayEnd.z-rayStart.z);return mix(rayStart.xyz,rayEnd.xyz,t);}void main() {vec2 quadCenterPos=a_pos;float radius=a_radius;float collision=a_flags.x;float vertexIdx=a_flags.y;vec2 quadVertexOffset=vec2(mix(-1.0,1.0,float(vertexIdx >=2.0)),mix(-1.0,1.0,float(vertexIdx >=1.0 && vertexIdx <=2.0)));vec2 quadVertexExtent=quadVertexOffset*radius;vec3 tilePos=toTilePosition(quadCenterPos);vec4 clipPos=u_matrix*vec4(tilePos,1.0);highp float camera_to_anchor_distance=clipPos.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);float padding_factor=1.2;v_radius=radius;v_extrude=quadVertexExtent*padding_factor;v_perspective_ratio=collision_perspective_ratio;v_collision=collision;gl_Position=vec4(clipPos.xyz/clipPos.w,1.0)+vec4(quadVertexExtent*padding_factor/u_viewport_size*2.0,0.0,0.0);}\"),debug:_i(\"uniform highp vec4 u_color;uniform sampler2D u_overlay;varying vec2 v_uv;void main() {vec4 overlay_color=texture2D(u_overlay,v_uv);gl_FragColor=mix(u_color,overlay_color,overlay_color.a);}\",\"attribute vec2 a_pos;varying vec2 v_uv;uniform mat4 u_matrix;uniform float u_overlay_scale;void main() {v_uv=a_pos/8192.0;gl_Position=u_matrix*vec4(a_pos*u_overlay_scale,get_elevation(a_pos),1);}\"),fill:_i(`#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float opacity\ngl_FragColor=color*opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}`,`attribute vec2 a_pos;uniform mat4 u_matrix;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float opacity\ngl_Position=u_matrix*vec4(a_pos,0,1);}`),fillOutline:_i(`varying vec2 v_pos;\n#pragma mapbox: define highp vec4 outline_color\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 outline_color\n#pragma mapbox: initialize lowp float opacity\nfloat dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=outline_color*(alpha*opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}`,`attribute vec2 a_pos;uniform mat4 u_matrix;uniform vec2 u_world;varying vec2 v_pos;\n#pragma mapbox: define highp vec4 outline_color\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 outline_color\n#pragma mapbox: initialize lowp float opacity\ngl_Position=u_matrix*vec4(a_pos,0,1);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}`),fillOutlinePattern:_i(`uniform vec2 u_texsize;uniform sampler2D u_image;uniform float u_fade;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);float dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=mix(color1,color2,u_fade)*alpha*opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}`,`uniform mat4 u_matrix;uniform vec2 u_world;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;gl_Position=u_matrix*vec4(a_pos,0,1);vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,a_pos);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}`),fillPattern:_i(`#ifdef GL_ES\nprecision highp float;\n#endif\nuniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_fade)*opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}`,`uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileZoomRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileZoomRatio,a_pos);}`),fillExtrusion:_i(`varying vec4 v_color;void main() {gl_FragColor=v_color;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}`,`uniform mat4 u_matrix;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;uniform float u_vertical_gradient;uniform lowp float u_opacity;attribute vec2 a_pos;attribute vec4 a_normal_ed;\n#ifdef TERRAIN3D\nattribute vec2 a_centroid;\n#endif\nvarying vec4 v_color;\n#pragma mapbox: define highp float base\n#pragma mapbox: define highp float height\n#pragma mapbox: define highp vec4 color\nvoid main() {\n#pragma mapbox: initialize highp float base\n#pragma mapbox: initialize highp float height\n#pragma mapbox: initialize highp vec4 color\nvec3 normal=a_normal_ed.xyz;\n#ifdef TERRAIN3D\nfloat height_terrain3d_offset=get_elevation(a_centroid);float base_terrain3d_offset=height_terrain3d_offset-(base > 0.0 ? 0.0 : 10.0);\n#else\nfloat height_terrain3d_offset=0.0;float base_terrain3d_offset=0.0;\n#endif\nbase=max(0.0,base)+base_terrain3d_offset;height=max(0.0,height)+height_terrain3d_offset;float t=mod(normal.x,2.0);gl_Position=u_matrix*vec4(a_pos,t > 0.0 ? height : base,1);float colorvalue=color.r*0.2126+color.g*0.7152+color.b*0.0722;v_color=vec4(0.0,0.0,0.0,1.0);vec4 ambientlight=vec4(0.03,0.03,0.03,1.0);color+=ambientlight;float directional=clamp(dot(normal/16384.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((1.0-colorvalue+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_color.r+=clamp(color.r*directional*u_lightcolor.r,mix(0.0,0.3,1.0-u_lightcolor.r),1.0);v_color.g+=clamp(color.g*directional*u_lightcolor.g,mix(0.0,0.3,1.0-u_lightcolor.g),1.0);v_color.b+=clamp(color.b*directional*u_lightcolor.b,mix(0.0,0.3,1.0-u_lightcolor.b),1.0);v_color*=u_opacity;}`),fillExtrusionPattern:_i(`uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\n#pragma mapbox: define lowp float base\n#pragma mapbox: define lowp float height\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float base\n#pragma mapbox: initialize lowp float height\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);vec4 mixedColor=mix(color1,color2,u_fade);gl_FragColor=mixedColor*v_lighting;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}`,`uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_height_factor;uniform vec3 u_scale;uniform float u_vertical_gradient;uniform lowp float u_opacity;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;attribute vec2 a_pos;attribute vec4 a_normal_ed;\n#ifdef TERRAIN3D\nattribute vec2 a_centroid;\n#endif\nvarying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\n#pragma mapbox: define lowp float base\n#pragma mapbox: define lowp float height\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float base\n#pragma mapbox: initialize lowp float height\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec3 normal=a_normal_ed.xyz;float edgedistance=a_normal_ed.w;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;\n#ifdef TERRAIN3D\nfloat height_terrain3d_offset=get_elevation(a_centroid);float base_terrain3d_offset=height_terrain3d_offset-(base > 0.0 ? 0.0 : 10.0);\n#else\nfloat height_terrain3d_offset=0.0;float base_terrain3d_offset=0.0;\n#endif\nbase=max(0.0,base)+base_terrain3d_offset;height=max(0.0,height)+height_terrain3d_offset;float t=mod(normal.x,2.0);float z=t > 0.0 ? height : base;gl_Position=u_matrix*vec4(a_pos,z,1);vec2 pos=normal.x==1.0 && normal.y==0.0 && normal.z==16384.0\n? a_pos\n: vec2(edgedistance,z*u_height_factor);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,pos);v_lighting=vec4(0.0,0.0,0.0,1.0);float directional=clamp(dot(normal/16383.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((0.5+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_lighting.rgb+=clamp(directional*u_lightcolor,mix(vec3(0.0),vec3(0.3),1.0-u_lightcolor),vec3(1.0));v_lighting*=u_opacity;}`),hillshadePrepare:_i(`#ifdef GL_ES\nprecision highp float;\n#endif\nuniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_dimension;uniform float u_zoom;uniform vec4 u_unpack;float getElevation(vec2 coord,float bias) {vec4 data=texture2D(u_image,coord)*255.0;data.a=-1.0;return dot(data,u_unpack)/4.0;}void main() {vec2 epsilon=1.0/u_dimension;float a=getElevation(v_pos+vec2(-epsilon.x,-epsilon.y),0.0);float b=getElevation(v_pos+vec2(0,-epsilon.y),0.0);float c=getElevation(v_pos+vec2(epsilon.x,-epsilon.y),0.0);float d=getElevation(v_pos+vec2(-epsilon.x,0),0.0);float e=getElevation(v_pos,0.0);float f=getElevation(v_pos+vec2(epsilon.x,0),0.0);float g=getElevation(v_pos+vec2(-epsilon.x,epsilon.y),0.0);float h=getElevation(v_pos+vec2(0,epsilon.y),0.0);float i=getElevation(v_pos+vec2(epsilon.x,epsilon.y),0.0);float exaggerationFactor=u_zoom < 2.0 ? 0.4 : u_zoom < 4.5 ? 0.35 : 0.3;float exaggeration=u_zoom < 15.0 ? (u_zoom-15.0)*exaggerationFactor : 0.0;vec2 deriv=vec2((c+f+f+i)-(a+d+d+g),(g+h+h+i)-(a+b+b+c))/pow(2.0,exaggeration+(19.2562-u_zoom));gl_FragColor=clamp(vec4(deriv.x/2.0+0.5,deriv.y/2.0+0.5,1.0,1.0),0.0,1.0);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}`,\"uniform mat4 u_matrix;uniform vec2 u_dimension;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);highp vec2 epsilon=1.0/u_dimension;float scale=(u_dimension.x-2.0)/u_dimension.x;v_pos=(a_texture_pos/8192.0)*scale+epsilon;}\"),hillshade:_i(`uniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_latrange;uniform vec2 u_light;uniform vec4 u_shadow;uniform vec4 u_highlight;uniform vec4 u_accent;\n#define PI 3.141592653589793\nvoid main() {vec4 pixel=texture2D(u_image,v_pos);vec2 deriv=((pixel.rg*2.0)-1.0);float scaleFactor=cos(radians((u_latrange[0]-u_latrange[1])*(1.0-v_pos.y)+u_latrange[1]));float slope=atan(1.25*length(deriv)/scaleFactor);float aspect=deriv.x !=0.0 ? atan(deriv.y,-deriv.x) : PI/2.0*(deriv.y > 0.0 ? 1.0 :-1.0);float intensity=u_light.x;float azimuth=u_light.y+PI;float base=1.875-intensity*1.75;float maxValue=0.5*PI;float scaledSlope=intensity !=0.5 ? ((pow(base,slope)-1.0)/(pow(base,maxValue)-1.0))*maxValue : slope;float accent=cos(scaledSlope);vec4 accent_color=(1.0-accent)*u_accent*clamp(intensity*2.0,0.0,1.0);float shade=abs(mod((aspect+azimuth)/PI+0.5,2.0)-1.0);vec4 shade_color=mix(u_shadow,u_highlight,shade)*sin(scaledSlope)*clamp(intensity*2.0,0.0,1.0);gl_FragColor=accent_color*(1.0-shade_color.a)+shade_color;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}`,\"uniform mat4 u_matrix;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos=a_texture_pos/8192.0;}\"),line:_i(`uniform lowp float u_device_pixel_ratio;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);gl_FragColor=color*(alpha*opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}`,`\n#define scale 0.015873016\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform vec2 u_units_to_pixels;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_linesofar;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define mediump float gapwidth\n#pragma mapbox: define lowp float offset\n#pragma mapbox: define mediump float width\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump float gapwidth\n#pragma mapbox: initialize lowp float offset\n#pragma mapbox: initialize mediump float width\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;\n#ifdef TERRAIN3D\nv_gamma_scale=1.0;\n#else\nfloat extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;\n#endif\nv_width2=vec2(outset,inset);}`),lineGradient:_i(`uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;varying highp vec2 v_uv;\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);vec4 color=texture2D(u_image,v_uv);gl_FragColor=color*(alpha*opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}`,`\n#define scale 0.015873016\nattribute vec2 a_pos_normal;attribute vec4 a_data;attribute float a_uv_x;attribute float a_split_index;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_units_to_pixels;uniform float u_image_height;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp vec2 v_uv;\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define mediump float gapwidth\n#pragma mapbox: define lowp float offset\n#pragma mapbox: define mediump float width\nvoid main() {\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump float gapwidth\n#pragma mapbox: initialize lowp float offset\n#pragma mapbox: initialize mediump float width\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;highp float texel_height=1.0/u_image_height;highp float half_texel_height=0.5*texel_height;v_uv=vec2(a_uv_x,a_split_index*texel_height-half_texel_height);vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;\n#ifdef TERRAIN3D\nv_gamma_scale=1.0;\n#else\nfloat extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;\n#endif\nv_width2=vec2(outset,inset);}`),linePattern:_i(`#ifdef GL_ES\nprecision highp float;\n#endif\nuniform lowp float u_device_pixel_ratio;uniform vec2 u_texsize;uniform float u_fade;uniform mediump vec3 u_scale;uniform sampler2D u_image;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;vec2 pattern_size_a=vec2(display_size_a.x*fromScale/tileZoomRatio,display_size_a.y);vec2 pattern_size_b=vec2(display_size_b.x*toScale/tileZoomRatio,display_size_b.y);float aspect_a=display_size_a.y/v_width;float aspect_b=display_size_b.y/v_width;float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float x_a=mod(v_linesofar/pattern_size_a.x*aspect_a,1.0);float x_b=mod(v_linesofar/pattern_size_b.x*aspect_b,1.0);float y=0.5*v_normal.y+0.5;vec2 texel_size=1.0/u_texsize;vec2 pos_a=mix(pattern_tl_a*texel_size-texel_size,pattern_br_a*texel_size+texel_size,vec2(x_a,y));vec2 pos_b=mix(pattern_tl_b*texel_size-texel_size,pattern_br_b*texel_size+texel_size,vec2(x_b,y));vec4 color=mix(texture2D(u_image,pos_a),texture2D(u_image,pos_b),u_fade);gl_FragColor=color*alpha*opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}`,`\n#define scale 0.015873016\n#define LINE_DISTANCE_SCALE 2.0\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform vec2 u_units_to_pixels;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float offset\n#pragma mapbox: define mediump float gapwidth\n#pragma mapbox: define mediump float width\n#pragma mapbox: define lowp float floorwidth\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float offset\n#pragma mapbox: initialize mediump float gapwidth\n#pragma mapbox: initialize mediump float width\n#pragma mapbox: initialize lowp float floorwidth\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;\n#ifdef TERRAIN3D\nv_gamma_scale=1.0;\n#else\nfloat extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;\n#endif\nv_linesofar=a_linesofar;v_width2=vec2(outset,inset);v_width=floorwidth;}`),lineSDF:_i(`uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;uniform float u_sdfgamma;uniform float u_mix;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define mediump float width\n#pragma mapbox: define lowp float floorwidth\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump float width\n#pragma mapbox: initialize lowp float floorwidth\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float sdfdist_a=texture2D(u_image,v_tex_a).a;float sdfdist_b=texture2D(u_image,v_tex_b).a;float sdfdist=mix(sdfdist_a,sdfdist_b,u_mix);alpha*=smoothstep(0.5-u_sdfgamma/floorwidth,0.5+u_sdfgamma/floorwidth,sdfdist);gl_FragColor=color*(alpha*opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}`,`\n#define scale 0.015873016\n#define LINE_DISTANCE_SCALE 2.0\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_patternscale_a;uniform float u_tex_y_a;uniform vec2 u_patternscale_b;uniform float u_tex_y_b;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define mediump float gapwidth\n#pragma mapbox: define lowp float offset\n#pragma mapbox: define mediump float width\n#pragma mapbox: define lowp float floorwidth\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump float gapwidth\n#pragma mapbox: initialize lowp float offset\n#pragma mapbox: initialize mediump float width\n#pragma mapbox: initialize lowp float floorwidth\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;\n#ifdef TERRAIN3D\nv_gamma_scale=1.0;\n#else\nfloat extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;\n#endif\nv_tex_a=vec2(a_linesofar*u_patternscale_a.x/floorwidth,normal.y*u_patternscale_a.y+u_tex_y_a);v_tex_b=vec2(a_linesofar*u_patternscale_b.x/floorwidth,normal.y*u_patternscale_b.y+u_tex_y_b);v_width2=vec2(outset,inset);}`),raster:_i(`uniform float u_fade_t;uniform float u_opacity;uniform sampler2D u_image0;uniform sampler2D u_image1;varying vec2 v_pos0;varying vec2 v_pos1;uniform float u_brightness_low;uniform float u_brightness_high;uniform float u_saturation_factor;uniform float u_contrast_factor;uniform vec3 u_spin_weights;void main() {vec4 color0=texture2D(u_image0,v_pos0);vec4 color1=texture2D(u_image1,v_pos1);if (color0.a > 0.0) {color0.rgb=color0.rgb/color0.a;}if (color1.a > 0.0) {color1.rgb=color1.rgb/color1.a;}vec4 color=mix(color0,color1,u_fade_t);color.a*=u_opacity;vec3 rgb=color.rgb;rgb=vec3(dot(rgb,u_spin_weights.xyz),dot(rgb,u_spin_weights.zxy),dot(rgb,u_spin_weights.yzx));float average=(color.r+color.g+color.b)/3.0;rgb+=(average-rgb)*u_saturation_factor;rgb=(rgb-0.5)*u_contrast_factor+0.5;vec3 u_high_vec=vec3(u_brightness_low,u_brightness_low,u_brightness_low);vec3 u_low_vec=vec3(u_brightness_high,u_brightness_high,u_brightness_high);gl_FragColor=vec4(mix(u_high_vec,u_low_vec,rgb)*color.a,color.a);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}`,\"uniform mat4 u_matrix;uniform vec2 u_tl_parent;uniform float u_scale_parent;uniform float u_buffer_scale;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos0;varying vec2 v_pos1;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos0=(((a_texture_pos/8192.0)-0.5)/u_buffer_scale )+0.5;v_pos1=(v_pos0*u_scale_parent)+u_tl_parent;}\"),symbolIcon:_i(`uniform sampler2D u_texture;varying vec2 v_tex;varying float v_fade_opacity;\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\nlowp float alpha=opacity*v_fade_opacity;gl_FragColor=texture2D(u_texture,v_tex)*alpha;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}`,`const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform highp float u_camera_to_center_distance;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform float u_fade_change;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform vec2 u_texsize;varying vec2 v_tex;varying float v_fade_opacity;\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;vec2 a_minFontScale=a_pixeloffset.zw/256.0;float ele=get_elevation(a_pos);highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,ele,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\ncamera_to_anchor_distance/u_camera_to_center_distance :\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),ele,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,ele,1.0);float z=float(u_pitch_with_map)*projected_pos.z/projected_pos.w;gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*max(a_minFontScale,fontScale)+a_pxoffset/16.0),z,1.0);v_tex=a_tex/u_texsize;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float visibility=calculate_visibility(projectedPoint);v_fade_opacity=max(0.0,min(visibility,fade_opacity[0]+fade_change));}`),symbolSDF:_i(`#define SDF_PX 8.0\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;uniform bool u_is_text;varying vec2 v_data0;varying vec3 v_data1;\n#pragma mapbox: define highp vec4 fill_color\n#pragma mapbox: define highp vec4 halo_color\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float halo_width\n#pragma mapbox: define lowp float halo_blur\nvoid main() {\n#pragma mapbox: initialize highp vec4 fill_color\n#pragma mapbox: initialize highp vec4 halo_color\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float halo_width\n#pragma mapbox: initialize lowp float halo_blur\nfloat EDGE_GAMMA=0.105/u_device_pixel_ratio;vec2 tex=v_data0.xy;float gamma_scale=v_data1.x;float size=v_data1.y;float fade_opacity=v_data1[2];float fontScale=u_is_text ? size/24.0 : size;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float inner_edge=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);inner_edge=inner_edge+gamma*gamma_scale;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(inner_edge-gamma_scaled,inner_edge+gamma_scaled,dist);if (u_is_halo) {lowp float halo_edge=(6.0-halo_width/fontScale)/SDF_PX;alpha=min(smoothstep(halo_edge-gamma_scaled,halo_edge+gamma_scaled,dist),1.0-alpha);}gl_FragColor=color*(alpha*opacity*fade_opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}`,`const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;varying vec2 v_data0;varying vec3 v_data1;\n#pragma mapbox: define highp vec4 fill_color\n#pragma mapbox: define highp vec4 halo_color\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float halo_width\n#pragma mapbox: define lowp float halo_blur\nvoid main() {\n#pragma mapbox: initialize highp vec4 fill_color\n#pragma mapbox: initialize highp vec4 halo_color\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float halo_width\n#pragma mapbox: initialize lowp float halo_blur\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;float ele=get_elevation(a_pos);highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,ele,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\ncamera_to_anchor_distance/u_camera_to_center_distance :\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),ele,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,ele,1.0);float z=float(u_pitch_with_map)*projected_pos.z/projected_pos.w;gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale+a_pxoffset),z,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float visibility=calculate_visibility(projectedPoint);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(visibility,fade_opacity[0]+fade_change));v_data0=a_tex/u_texsize;v_data1=vec3(gamma_scale,size,interpolated_fade_opacity);}`),symbolTextAndIcon:_i(`#define SDF_PX 8.0\n#define SDF 1.0\n#define ICON 0.0\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform sampler2D u_texture_icon;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;varying vec4 v_data0;varying vec4 v_data1;\n#pragma mapbox: define highp vec4 fill_color\n#pragma mapbox: define highp vec4 halo_color\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float halo_width\n#pragma mapbox: define lowp float halo_blur\nvoid main() {\n#pragma mapbox: initialize highp vec4 fill_color\n#pragma mapbox: initialize highp vec4 halo_color\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float halo_width\n#pragma mapbox: initialize lowp float halo_blur\nfloat fade_opacity=v_data1[2];if (v_data1.w==ICON) {vec2 tex_icon=v_data0.zw;lowp float alpha=opacity*fade_opacity;gl_FragColor=texture2D(u_texture_icon,tex_icon)*alpha;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\nreturn;}vec2 tex=v_data0.xy;float EDGE_GAMMA=0.105/u_device_pixel_ratio;float gamma_scale=v_data1.x;float size=v_data1.y;float fontScale=size/24.0;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}`,`const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;uniform vec2 u_texsize_icon;varying vec4 v_data0;varying vec4 v_data1;\n#pragma mapbox: define highp vec4 fill_color\n#pragma mapbox: define highp vec4 halo_color\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float halo_width\n#pragma mapbox: define lowp float halo_blur\nvoid main() {\n#pragma mapbox: initialize highp vec4 fill_color\n#pragma mapbox: initialize highp vec4 halo_color\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float halo_width\n#pragma mapbox: initialize lowp float halo_blur\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);float is_sdf=a_size[0]-2.0*a_size_min;float ele=get_elevation(a_pos);highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,ele,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\ncamera_to_anchor_distance/u_camera_to_center_distance :\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=size/24.0;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),ele,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,ele,1.0);float z=float(u_pitch_with_map)*projected_pos.z/projected_pos.w;gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale),z,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float visibility=calculate_visibility(projectedPoint);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(visibility,fade_opacity[0]+fade_change));v_data0.xy=a_tex/u_texsize;v_data0.zw=a_tex/u_texsize_icon;v_data1=vec4(gamma_scale,size,interpolated_fade_opacity,is_sdf);}`),terrain:_i(\"uniform sampler2D u_texture;varying vec2 v_texture_pos;void main() {gl_FragColor=texture2D(u_texture,v_texture_pos);}\",jl),terrainDepth:_i(\"varying float v_depth;const highp vec4 bitSh=vec4(256.*256.*256.,256.*256.,256.,1.);const highp vec4 bitMsk=vec4(0.,vec3(1./256.0));highp vec4 pack(highp float value) {highp vec4 comp=fract(value*bitSh);comp-=comp.xxyz*bitMsk;return comp;}void main() {gl_FragColor=pack(v_depth);}\",jl),terrainCoords:_i(\"precision mediump float;uniform sampler2D u_texture;uniform float u_terrain_coords_id;varying vec2 v_texture_pos;void main() {vec4 rgba=texture2D(u_texture,v_texture_pos);gl_FragColor=vec4(rgba.r,rgba.g,rgba.b,u_terrain_coords_id);}\",jl)};function _i(T,l){let d=/#pragma mapbox: ([\\w]+) ([\\w]+) ([\\w]+) ([\\w]+)/g,v=l.match(/attribute ([\\w]+) ([\\w]+)/g),b=T.match(/uniform ([\\w]+) ([\\w]+)([\\s]*)([\\w]*)/g),M=l.match(/uniform ([\\w]+) ([\\w]+)([\\s]*)([\\w]*)/g),O=M?M.concat(b):b,B={};return{fragmentSource:T=T.replace(d,(U,W,Z,$,st)=>(B[st]=!0,W===\"define\"?`\n#ifndef HAS_UNIFORM_u_${st}\nvarying ${Z} ${$} ${st};\n#else\nuniform ${Z} ${$} u_${st};\n#endif\n`:`\n#ifdef HAS_UNIFORM_u_${st}\n ${Z} ${$} ${st} = u_${st};\n#endif\n`)),vertexSource:l=l.replace(d,(U,W,Z,$,st)=>{let At=$===\"float\"?\"vec2\":\"vec4\",pt=st.match(/color/)?\"color\":At;return B[st]?W===\"define\"?`\n#ifndef HAS_UNIFORM_u_${st}\nuniform lowp float u_${st}_t;\nattribute ${Z} ${At} a_${st};\nvarying ${Z} ${$} ${st};\n#else\nuniform ${Z} ${$} u_${st};\n#endif\n`:pt===\"vec4\"?`\n#ifndef HAS_UNIFORM_u_${st}\n ${st} = a_${st};\n#else\n ${Z} ${$} ${st} = u_${st};\n#endif\n`:`\n#ifndef HAS_UNIFORM_u_${st}\n ${st} = unpack_mix_${pt}(a_${st}, u_${st}_t);\n#else\n ${Z} ${$} ${st} = u_${st};\n#endif\n`:W===\"define\"?`\n#ifndef HAS_UNIFORM_u_${st}\nuniform lowp float u_${st}_t;\nattribute ${Z} ${At} a_${st};\n#else\nuniform ${Z} ${$} u_${st};\n#endif\n`:pt===\"vec4\"?`\n#ifndef HAS_UNIFORM_u_${st}\n ${Z} ${$} ${st} = a_${st};\n#else\n ${Z} ${$} ${st} = u_${st};\n#endif\n`:`\n#ifndef HAS_UNIFORM_u_${st}\n ${Z} ${$} ${st} = unpack_mix_${pt}(a_${st}, u_${st}_t);\n#else\n ${Z} ${$} ${st} = u_${st};\n#endif\n`}),staticAttributes:v,staticUniforms:O}}class Gl{constructor(){this.boundProgram=null,this.boundLayoutVertexBuffer=null,this.boundPaintVertexBuffers=[],this.boundIndexBuffer=null,this.boundVertexOffset=null,this.boundDynamicVertexBuffer=null,this.vao=null}bind(l,d,v,b,M,O,B,U,W){this.context=l;let Z=this.boundPaintVertexBuffers.length!==b.length;for(let $=0;!Z&&$({u_depth:new n.aL($t,oe.u_depth),u_terrain:new n.aL($t,oe.u_terrain),u_terrain_dim:new n.aM($t,oe.u_terrain_dim),u_terrain_matrix:new n.aN($t,oe.u_terrain_matrix),u_terrain_unpack:new n.aO($t,oe.u_terrain_unpack),u_terrain_exaggeration:new n.aM($t,oe.u_terrain_exaggeration)}))(l,Qt),this.binderUniforms=v?v.getUniforms(l,Qt):[]}draw(l,d,v,b,M,O,B,U,W,Z,$,st,At,pt,yt,dt,Ft,Ht){let St=l.gl;if(this.failedToCreate)return;if(l.program.set(this.program),l.setDepthMode(v),l.setStencilMode(b),l.setColorMode(M),l.setCullFace(O),U){l.activeTexture.set(St.TEXTURE2),St.bindTexture(St.TEXTURE_2D,U.depthTexture),l.activeTexture.set(St.TEXTURE3),St.bindTexture(St.TEXTURE_2D,U.texture);for(let Qt in this.terrainUniforms)this.terrainUniforms[Qt].set(U[Qt])}for(let Qt in this.fixedUniforms)this.fixedUniforms[Qt].set(B[Qt]);yt&&yt.setUniforms(l,this.binderUniforms,At,{zoom:pt});let Bt=0;switch(d){case St.LINES:Bt=2;break;case St.TRIANGLES:Bt=3;break;case St.LINE_STRIP:Bt=1}for(let Qt of st.get()){let $t=Qt.vaos||(Qt.vaos={});($t[W]||($t[W]=new Gl)).bind(l,this,Z,yt?yt.getPaintVertexBuffers():[],$,Qt.vertexOffset,dt,Ft,Ht),St.drawElements(d,Qt.primitiveLength*Bt,St.UNSIGNED_SHORT,Qt.primitiveOffset*Bt*2)}}}function rs(T,l,d){let v=1/Dt(d,1,l.transform.tileZoom),b=Math.pow(2,d.tileID.overscaledZ),M=d.tileSize*Math.pow(2,l.transform.tileZoom)/b,O=M*(d.tileID.canonical.x+d.tileID.wrap*b),B=M*d.tileID.canonical.y;return{u_image:0,u_texsize:d.imageAtlasTexture.size,u_scale:[v,T.fromScale,T.toScale],u_fade:T.t,u_pixel_coord_upper:[O>>16,B>>16],u_pixel_coord_lower:[65535&O,65535&B]}}let Gp=(T,l,d,v)=>{let b=l.style.light,M=b.properties.get(\"position\"),O=[M.x,M.y,M.z],B=function(){var W=new n.A(9);return n.A!=Float32Array&&(W[1]=0,W[2]=0,W[3]=0,W[5]=0,W[6]=0,W[7]=0),W[0]=1,W[4]=1,W[8]=1,W}();b.properties.get(\"anchor\")===\"viewport\"&&function(W,Z){var $=Math.sin(Z),st=Math.cos(Z);W[0]=st,W[1]=$,W[2]=0,W[3]=-$,W[4]=st,W[5]=0,W[6]=0,W[7]=0,W[8]=1}(B,-l.transform.angle),function(W,Z,$){var st=Z[0],At=Z[1],pt=Z[2];W[0]=st*$[0]+At*$[3]+pt*$[6],W[1]=st*$[1]+At*$[4]+pt*$[7],W[2]=st*$[2]+At*$[5]+pt*$[8]}(O,O,B);let U=b.properties.get(\"color\");return{u_matrix:T,u_lightpos:O,u_lightintensity:b.properties.get(\"intensity\"),u_lightcolor:[U.r,U.g,U.b],u_vertical_gradient:+d,u_opacity:v}},Wl=(T,l,d,v,b,M,O)=>n.e(Gp(T,l,d,v),rs(M,l,O),{u_height_factor:-Math.pow(2,b.overscaledZ)/O.tileSize/8}),_d=T=>({u_matrix:T}),yd=(T,l,d,v)=>n.e(_d(T),rs(d,l,v)),vd=(T,l)=>({u_matrix:T,u_world:l}),xd=(T,l,d,v,b)=>n.e(yd(T,l,d,v),{u_world:b}),lt=(T,l,d,v)=>{let b=T.transform,M,O;if(v.paint.get(\"circle-pitch-alignment\")===\"map\"){let B=Dt(d,1,b.zoom);M=!0,O=[B,B]}else M=!1,O=b.pixelsToGLUnits;return{u_camera_to_center_distance:b.cameraToCenterDistance,u_scale_with_map:+(v.paint.get(\"circle-pitch-scale\")===\"map\"),u_matrix:T.translatePosMatrix(l.posMatrix,d,v.paint.get(\"circle-translate\"),v.paint.get(\"circle-translate-anchor\")),u_pitch_with_map:+M,u_device_pixel_ratio:T.pixelRatio,u_extrude_scale:O}},ft=(T,l,d)=>{let v=Dt(d,1,l.zoom),b=Math.pow(2,l.zoom-d.tileID.overscaledZ),M=d.tileID.overscaleFactor();return{u_matrix:T,u_camera_to_center_distance:l.cameraToCenterDistance,u_pixels_to_tile_units:v,u_extrude_scale:[l.pixelsToGLUnits[0]/(v*b),l.pixelsToGLUnits[1]/(v*b)],u_overscale_factor:M}},Lt=(T,l,d=1)=>({u_matrix:T,u_color:l,u_overlay:0,u_overlay_scale:d}),Kt=T=>({u_matrix:T}),ge=(T,l,d,v)=>({u_matrix:T,u_extrude_scale:Dt(l,1,d),u_intensity:v});function Qe(T,l){let d=Math.pow(2,l.canonical.z),v=l.canonical.y;return[new n.U(0,v/d).toLngLat().lat,new n.U(0,(v+1)/d).toLngLat().lat]}let ti=(T,l,d,v)=>{let b=T.transform;return{u_matrix:jm(T,l,d,v),u_ratio:1/Dt(l,1,b.zoom),u_device_pixel_ratio:T.pixelRatio,u_units_to_pixels:[1/b.pixelsToGLUnits[0],1/b.pixelsToGLUnits[1]]}},is=(T,l,d,v,b)=>n.e(ti(T,l,d,b),{u_image:0,u_image_height:v}),Ts=(T,l,d,v,b)=>{let M=T.transform,O=Ra(l,M);return{u_matrix:jm(T,l,d,b),u_texsize:l.imageAtlasTexture.size,u_ratio:1/Dt(l,1,M.zoom),u_device_pixel_ratio:T.pixelRatio,u_image:0,u_scale:[O,v.fromScale,v.toScale],u_fade:v.t,u_units_to_pixels:[1/M.pixelsToGLUnits[0],1/M.pixelsToGLUnits[1]]}},Vs=(T,l,d,v,b,M)=>{let O=T.lineAtlas,B=Ra(l,T.transform),U=d.layout.get(\"line-cap\")===\"round\",W=O.getDash(v.from,U),Z=O.getDash(v.to,U),$=W.width*b.fromScale,st=Z.width*b.toScale;return n.e(ti(T,l,d,M),{u_patternscale_a:[B/$,-W.height/2],u_patternscale_b:[B/st,-Z.height/2],u_sdfgamma:O.width/(256*Math.min($,st)*T.pixelRatio)/2,u_image:0,u_tex_y_a:W.y,u_tex_y_b:Z.y,u_mix:b.t})};function Ra(T,l){return 1/Dt(T,1,l.tileZoom)}function jm(T,l,d,v){return T.translatePosMatrix(v?v.posMatrix:l.tileID.posMatrix,l,d.paint.get(\"line-translate\"),d.paint.get(\"line-translate-anchor\"))}let Ox=(T,l,d,v,b)=>{return{u_matrix:T,u_tl_parent:l,u_scale_parent:d,u_buffer_scale:1,u_fade_t:v.mix,u_opacity:v.opacity*b.paint.get(\"raster-opacity\"),u_image0:0,u_image1:1,u_brightness_low:b.paint.get(\"raster-brightness-min\"),u_brightness_high:b.paint.get(\"raster-brightness-max\"),u_saturation_factor:(O=b.paint.get(\"raster-saturation\"),O>0?1-1/(1.001-O):-O),u_contrast_factor:(M=b.paint.get(\"raster-contrast\"),M>0?1/(1-M):1+M),u_spin_weights:Bx(b.paint.get(\"raster-hue-rotate\"))};var M,O};function Bx(T){T*=Math.PI/180;let l=Math.sin(T),d=Math.cos(T);return[(2*d+1)/3,(-Math.sqrt(3)*l-d+1)/3,(Math.sqrt(3)*l-d+1)/3]}let l_=(T,l,d,v,b,M,O,B,U,W)=>{let Z=b.transform;return{u_is_size_zoom_constant:+(T===\"constant\"||T===\"source\"),u_is_size_feature_constant:+(T===\"constant\"||T===\"camera\"),u_size_t:l?l.uSizeT:0,u_size:l?l.uSize:0,u_camera_to_center_distance:Z.cameraToCenterDistance,u_pitch:Z.pitch/360*2*Math.PI,u_rotate_symbol:+d,u_aspect_ratio:Z.width/Z.height,u_fade_change:b.options.fadeDuration?b.symbolFadeChange:1,u_matrix:M,u_label_plane_matrix:O,u_coord_matrix:B,u_is_text:+U,u_pitch_with_map:+v,u_texsize:W,u_texture:0}},c_=(T,l,d,v,b,M,O,B,U,W,Z)=>{let $=b.transform;return n.e(l_(T,l,d,v,b,M,O,B,U,W),{u_gamma_scale:v?Math.cos($._pitch)*$.cameraToCenterDistance:1,u_device_pixel_ratio:b.pixelRatio,u_is_halo:+Z})},gf=(T,l,d,v,b,M,O,B,U,W)=>n.e(c_(T,l,d,v,b,M,O,B,!0,U,!0),{u_texsize_icon:W,u_texture_icon:1}),Gm=(T,l,d)=>({u_matrix:T,u_opacity:l,u_color:d}),fl=(T,l,d,v,b,M)=>n.e(function(O,B,U,W){let Z=U.imageManager.getPattern(O.from.toString()),$=U.imageManager.getPattern(O.to.toString()),{width:st,height:At}=U.imageManager.getPixelSize(),pt=Math.pow(2,W.tileID.overscaledZ),yt=W.tileSize*Math.pow(2,U.transform.tileZoom)/pt,dt=yt*(W.tileID.canonical.x+W.tileID.wrap*pt),Ft=yt*W.tileID.canonical.y;return{u_image:0,u_pattern_tl_a:Z.tl,u_pattern_br_a:Z.br,u_pattern_tl_b:$.tl,u_pattern_br_b:$.br,u_texsize:[st,At],u_mix:B.t,u_pattern_size_a:Z.displaySize,u_pattern_size_b:$.displaySize,u_scale_a:B.fromScale,u_scale_b:B.toScale,u_tile_units_to_pixels:1/Dt(W,1,U.transform.tileZoom),u_pixel_coord_upper:[dt>>16,Ft>>16],u_pixel_coord_lower:[65535&dt,65535&Ft]}}(v,M,d,b),{u_matrix:T,u_opacity:l}),Wm={fillExtrusion:(T,l)=>({u_matrix:new n.aN(T,l.u_matrix),u_lightpos:new n.aP(T,l.u_lightpos),u_lightintensity:new n.aM(T,l.u_lightintensity),u_lightcolor:new n.aP(T,l.u_lightcolor),u_vertical_gradient:new n.aM(T,l.u_vertical_gradient),u_opacity:new n.aM(T,l.u_opacity)}),fillExtrusionPattern:(T,l)=>({u_matrix:new n.aN(T,l.u_matrix),u_lightpos:new n.aP(T,l.u_lightpos),u_lightintensity:new n.aM(T,l.u_lightintensity),u_lightcolor:new n.aP(T,l.u_lightcolor),u_vertical_gradient:new n.aM(T,l.u_vertical_gradient),u_height_factor:new n.aM(T,l.u_height_factor),u_image:new n.aL(T,l.u_image),u_texsize:new n.aQ(T,l.u_texsize),u_pixel_coord_upper:new n.aQ(T,l.u_pixel_coord_upper),u_pixel_coord_lower:new n.aQ(T,l.u_pixel_coord_lower),u_scale:new n.aP(T,l.u_scale),u_fade:new n.aM(T,l.u_fade),u_opacity:new n.aM(T,l.u_opacity)}),fill:(T,l)=>({u_matrix:new n.aN(T,l.u_matrix)}),fillPattern:(T,l)=>({u_matrix:new n.aN(T,l.u_matrix),u_image:new n.aL(T,l.u_image),u_texsize:new n.aQ(T,l.u_texsize),u_pixel_coord_upper:new n.aQ(T,l.u_pixel_coord_upper),u_pixel_coord_lower:new n.aQ(T,l.u_pixel_coord_lower),u_scale:new n.aP(T,l.u_scale),u_fade:new n.aM(T,l.u_fade)}),fillOutline:(T,l)=>({u_matrix:new n.aN(T,l.u_matrix),u_world:new 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n.aR(T,l.u_accent)}),hillshadePrepare:(T,l)=>({u_matrix:new n.aN(T,l.u_matrix),u_image:new n.aL(T,l.u_image),u_dimension:new n.aQ(T,l.u_dimension),u_zoom:new n.aM(T,l.u_zoom),u_unpack:new n.aO(T,l.u_unpack)}),line:(T,l)=>({u_matrix:new n.aN(T,l.u_matrix),u_ratio:new n.aM(T,l.u_ratio),u_device_pixel_ratio:new n.aM(T,l.u_device_pixel_ratio),u_units_to_pixels:new n.aQ(T,l.u_units_to_pixels)}),lineGradient:(T,l)=>({u_matrix:new n.aN(T,l.u_matrix),u_ratio:new n.aM(T,l.u_ratio),u_device_pixel_ratio:new n.aM(T,l.u_device_pixel_ratio),u_units_to_pixels:new n.aQ(T,l.u_units_to_pixels),u_image:new n.aL(T,l.u_image),u_image_height:new n.aM(T,l.u_image_height)}),linePattern:(T,l)=>({u_matrix:new n.aN(T,l.u_matrix),u_texsize:new n.aQ(T,l.u_texsize),u_ratio:new n.aM(T,l.u_ratio),u_device_pixel_ratio:new n.aM(T,l.u_device_pixel_ratio),u_image:new n.aL(T,l.u_image),u_units_to_pixels:new n.aQ(T,l.u_units_to_pixels),u_scale:new n.aP(T,l.u_scale),u_fade:new n.aM(T,l.u_fade)}),lineSDF:(T,l)=>({u_matrix:new n.aN(T,l.u_matrix),u_ratio:new n.aM(T,l.u_ratio),u_device_pixel_ratio:new n.aM(T,l.u_device_pixel_ratio),u_units_to_pixels:new n.aQ(T,l.u_units_to_pixels),u_patternscale_a:new n.aQ(T,l.u_patternscale_a),u_patternscale_b:new n.aQ(T,l.u_patternscale_b),u_sdfgamma:new n.aM(T,l.u_sdfgamma),u_image:new n.aL(T,l.u_image),u_tex_y_a:new n.aM(T,l.u_tex_y_a),u_tex_y_b:new n.aM(T,l.u_tex_y_b),u_mix:new n.aM(T,l.u_mix)}),raster:(T,l)=>({u_matrix:new n.aN(T,l.u_matrix),u_tl_parent:new n.aQ(T,l.u_tl_parent),u_scale_parent:new n.aM(T,l.u_scale_parent),u_buffer_scale:new n.aM(T,l.u_buffer_scale),u_fade_t:new n.aM(T,l.u_fade_t),u_opacity:new n.aM(T,l.u_opacity),u_image0:new n.aL(T,l.u_image0),u_image1:new n.aL(T,l.u_image1),u_brightness_low:new n.aM(T,l.u_brightness_low),u_brightness_high:new n.aM(T,l.u_brightness_high),u_saturation_factor:new n.aM(T,l.u_saturation_factor),u_contrast_factor:new n.aM(T,l.u_contrast_factor),u_spin_weights:new n.aP(T,l.u_spin_weights)}),symbolIcon:(T,l)=>({u_is_size_zoom_constant:new n.aL(T,l.u_is_size_zoom_constant),u_is_size_feature_constant:new n.aL(T,l.u_is_size_feature_constant),u_size_t:new n.aM(T,l.u_size_t),u_size:new n.aM(T,l.u_size),u_camera_to_center_distance:new n.aM(T,l.u_camera_to_center_distance),u_pitch:new n.aM(T,l.u_pitch),u_rotate_symbol:new n.aL(T,l.u_rotate_symbol),u_aspect_ratio:new n.aM(T,l.u_aspect_ratio),u_fade_change:new n.aM(T,l.u_fade_change),u_matrix:new n.aN(T,l.u_matrix),u_label_plane_matrix:new n.aN(T,l.u_label_plane_matrix),u_coord_matrix:new n.aN(T,l.u_coord_matrix),u_is_text:new n.aL(T,l.u_is_text),u_pitch_with_map:new n.aL(T,l.u_pitch_with_map),u_texsize:new n.aQ(T,l.u_texsize),u_texture:new n.aL(T,l.u_texture)}),symbolSDF:(T,l)=>({u_is_size_zoom_constant:new n.aL(T,l.u_is_size_zoom_constant),u_is_size_feature_constant:new n.aL(T,l.u_is_size_feature_constant),u_size_t:new n.aM(T,l.u_size_t),u_size:new n.aM(T,l.u_size),u_camera_to_center_distance:new 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n.aM(T,l.u_aspect_ratio),u_fade_change:new n.aM(T,l.u_fade_change),u_matrix:new n.aN(T,l.u_matrix),u_label_plane_matrix:new n.aN(T,l.u_label_plane_matrix),u_coord_matrix:new n.aN(T,l.u_coord_matrix),u_is_text:new n.aL(T,l.u_is_text),u_pitch_with_map:new n.aL(T,l.u_pitch_with_map),u_texsize:new n.aQ(T,l.u_texsize),u_texsize_icon:new n.aQ(T,l.u_texsize_icon),u_texture:new n.aL(T,l.u_texture),u_texture_icon:new n.aL(T,l.u_texture_icon),u_gamma_scale:new n.aM(T,l.u_gamma_scale),u_device_pixel_ratio:new n.aM(T,l.u_device_pixel_ratio),u_is_halo:new n.aL(T,l.u_is_halo)}),background:(T,l)=>({u_matrix:new n.aN(T,l.u_matrix),u_opacity:new n.aM(T,l.u_opacity),u_color:new n.aR(T,l.u_color)}),backgroundPattern:(T,l)=>({u_matrix:new n.aN(T,l.u_matrix),u_opacity:new n.aM(T,l.u_opacity),u_image:new n.aL(T,l.u_image),u_pattern_tl_a:new n.aQ(T,l.u_pattern_tl_a),u_pattern_br_a:new n.aQ(T,l.u_pattern_br_a),u_pattern_tl_b:new n.aQ(T,l.u_pattern_tl_b),u_pattern_br_b:new 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M=l.gl;this.buffer=M.createBuffer(),l.bindVertexBuffer.set(this.buffer),M.bufferData(M.ARRAY_BUFFER,d.arrayBuffer,this.dynamicDraw?M.DYNAMIC_DRAW:M.STATIC_DRAW),this.dynamicDraw||delete d.arrayBuffer}bind(){this.context.bindVertexBuffer.set(this.buffer)}updateData(l){if(l.length!==this.length)throw new Error(`Length of new data is ${l.length}, which doesn't match current length of ${this.length}`);let d=this.context.gl;this.bind(),d.bufferSubData(d.ARRAY_BUFFER,0,l.arrayBuffer)}enableAttributes(l,d){for(let v=0;v0){let 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vu=T.translatePosMatrix(pe.posMatrix,he,M,O),_h=Ft||b&&$t||yu?Jm:jr,Ws=T.translatePosMatrix(ql,he,M,O,!0),Ps=Ee&&d.paint.get(b?\"text-halo-width\":\"icon-halo-width\").constantOr(1)!==0,Eo;Eo=Ee?be.iconsInText?gf(pr.kind,Jr,Ht,dt,T,vu,_h,Ws,ei,hs):c_(pr.kind,Jr,Ht,dt,T,vu,_h,Ws,b,ei,!0):l_(pr.kind,Jr,Ht,dt,T,vu,_h,Ws,b,ei);let yh={program:Gi,buffers:Ze,uniformValues:Eo,atlasTexture:On,atlasTextureIcon:Bn,atlasInterpolation:tn,atlasInterpolationIcon:Gs,isSDF:Ee,hasHalo:Ps};if(St&&be.canOverlap){Bt=!0;let Fn=Ze.segments.get();for(let fs of Fn)oe.push({segments:new n.S([fs]),sortKey:fs.sortKey,state:yh,terrainData:Vr})}else oe.push({segments:Ze.segments,sortKey:0,state:yh,terrainData:Vr})}Bt&&oe.sort((pe,he)=>pe.sortKey-he.sortKey);for(let pe of oe){let he=pe.state;if(st.activeTexture.set(At.TEXTURE0),he.atlasTexture.bind(he.atlasInterpolation,At.CLAMP_TO_EDGE),he.atlasTextureIcon&&(st.activeTexture.set(At.TEXTURE1),he.atlasTextureIcon&&he.atlasTextureIcon.bind(he.atlasInterpolationIcon,At.CLAMP_TO_EDGE)),he.isSDF){let be=he.uniformValues;he.hasHalo&&(be.u_is_halo=1,e0(he.buffers,pe.segments,d,T,he.program,Qt,Z,$,be,pe.terrainData)),be.u_is_halo=0}e0(he.buffers,pe.segments,d,T,he.program,Qt,Z,$,he.uniformValues,pe.terrainData)}}function e0(T,l,d,v,b,M,O,B,U,W){let Z=v.context;b.draw(Z,Z.gl.TRIANGLES,M,O,B,It.disabled,U,W,d.id,T.layoutVertexBuffer,T.indexBuffer,l,d.paint,v.transform.zoom,T.programConfigurations.get(d.id),T.dynamicLayoutVertexBuffer,T.opacityVertexBuffer)}function Ed(T,l,d,v,b){if(!d||!v||!v.imageAtlas)return;let M=v.imageAtlas.patternPositions,O=M[d.to.toString()],B=M[d.from.toString()];if(!O&&B&&(O=B),!B&&O&&(B=O),!O||!B){let U=b.getPaintProperty(l);O=M[U],B=M[U]}O&&B&&T.setConstantPatternPositions(O,B)}function Pd(T,l,d,v,b,M,O){let B=T.context.gl,U=\"fill-pattern\",W=d.paint.get(U),Z=W&&W.constantOr(1),$=d.getCrossfadeParameters(),st,At,pt,yt,dt;O?(At=Z&&!d.getPaintProperty(\"fill-outline-color\")?\"fillOutlinePattern\":\"fillOutline\",st=B.LINES):(At=Z?\"fillPattern\":\"fill\",st=B.TRIANGLES);let Ft=W.constantOr(null);for(let Ht of v){let St=l.getTile(Ht);if(Z&&!St.patternsLoaded())continue;let Bt=St.getBucket(d);if(!Bt)continue;let Qt=Bt.programConfigurations.get(d.id),$t=T.useProgram(At,Qt),oe=T.style.map.terrain&&T.style.map.terrain.getTerrainData(Ht);Z&&(T.context.activeTexture.set(B.TEXTURE0),St.imageAtlasTexture.bind(B.LINEAR,B.CLAMP_TO_EDGE),Qt.updatePaintBuffers($)),Ed(Qt,U,Ft,St,d);let pe=oe?Ht:null,he=T.translatePosMatrix(pe?pe.posMatrix:Ht.posMatrix,St,d.paint.get(\"fill-translate\"),d.paint.get(\"fill-translate-anchor\"));if(O){yt=Bt.indexBuffer2,dt=Bt.segments2;let be=[B.drawingBufferWidth,B.drawingBufferHeight];pt=At===\"fillOutlinePattern\"&&Z?xd(he,T,$,St,be):vd(he,be)}else yt=Bt.indexBuffer,dt=Bt.segments,pt=Z?yd(he,T,$,St):_d(he);$t.draw(T.context,st,b,T.stencilModeForClipping(Ht),M,It.disabled,pt,oe,d.id,Bt.layoutVertexBuffer,yt,dt,d.paint,T.transform.zoom,Qt)}}function Id(T,l,d,v,b,M,O){let B=T.context,U=B.gl,W=\"fill-extrusion-pattern\",Z=d.paint.get(W),$=Z.constantOr(1),st=d.getCrossfadeParameters(),At=d.paint.get(\"fill-extrusion-opacity\"),pt=Z.constantOr(null);for(let yt of v){let dt=l.getTile(yt),Ft=dt.getBucket(d);if(!Ft)continue;let Ht=T.style.map.terrain&&T.style.map.terrain.getTerrainData(yt),St=Ft.programConfigurations.get(d.id),Bt=T.useProgram($?\"fillExtrusionPattern\":\"fillExtrusion\",St);$&&(T.context.activeTexture.set(U.TEXTURE0),dt.imageAtlasTexture.bind(U.LINEAR,U.CLAMP_TO_EDGE),St.updatePaintBuffers(st)),Ed(St,W,pt,dt,d);let Qt=T.translatePosMatrix(yt.posMatrix,dt,d.paint.get(\"fill-extrusion-translate\"),d.paint.get(\"fill-extrusion-translate-anchor\")),$t=d.paint.get(\"fill-extrusion-vertical-gradient\"),oe=$?Wl(Qt,T,$t,At,yt,st,dt):Gp(Qt,T,$t,At);Bt.draw(B,B.gl.TRIANGLES,b,M,O,It.backCCW,oe,Ht,d.id,Ft.layoutVertexBuffer,Ft.indexBuffer,Ft.segments,d.paint,T.transform.zoom,St,T.style.map.terrain&&Ft.centroidVertexBuffer)}}function Fx(T,l,d,v,b,M,O){let B=T.context,U=B.gl,W=d.fbo;if(!W)return;let Z=T.useProgram(\"hillshade\"),$=T.style.map.terrain&&T.style.map.terrain.getTerrainData(l);B.activeTexture.set(U.TEXTURE0),U.bindTexture(U.TEXTURE_2D,W.colorAttachment.get()),Z.draw(B,U.TRIANGLES,b,M,O,It.disabled,((st,At,pt,yt)=>{let dt=pt.paint.get(\"hillshade-shadow-color\"),Ft=pt.paint.get(\"hillshade-highlight-color\"),Ht=pt.paint.get(\"hillshade-accent-color\"),St=pt.paint.get(\"hillshade-illumination-direction\")*(Math.PI/180);pt.paint.get(\"hillshade-illumination-anchor\")===\"viewport\"&&(St-=st.transform.angle);let Bt=!st.options.moving;return{u_matrix:yt?yt.posMatrix:st.transform.calculatePosMatrix(At.tileID.toUnwrapped(),Bt),u_image:0,u_latrange:Qe(0,At.tileID),u_light:[pt.paint.get(\"hillshade-exaggeration\"),St],u_shadow:dt,u_highlight:Ft,u_accent:Ht}})(T,d,v,$?l:null),$,v.id,T.rasterBoundsBuffer,T.quadTriangleIndexBuffer,T.rasterBoundsSegments)}function r0(T,l,d,v,b,M){let O=T.context,B=O.gl,U=l.dem;if(U&&U.data){let W=U.dim,Z=U.stride,$=U.getPixels();if(O.activeTexture.set(B.TEXTURE1),O.pixelStoreUnpackPremultiplyAlpha.set(!1),l.demTexture=l.demTexture||T.getTileTexture(Z),l.demTexture){let At=l.demTexture;At.update($,{premultiply:!1}),At.bind(B.NEAREST,B.CLAMP_TO_EDGE)}else l.demTexture=new qt(O,$,B.RGBA,{premultiply:!1}),l.demTexture.bind(B.NEAREST,B.CLAMP_TO_EDGE);O.activeTexture.set(B.TEXTURE0);let st=l.fbo;if(!st){let At=new qt(O,{width:W,height:W,data:null},B.RGBA);At.bind(B.LINEAR,B.CLAMP_TO_EDGE),st=l.fbo=O.createFramebuffer(W,W,!0,!1),st.colorAttachment.set(At.texture)}O.bindFramebuffer.set(st.framebuffer),O.viewport.set([0,0,W,W]),T.useProgram(\"hillshadePrepare\").draw(O,B.TRIANGLES,v,b,M,It.disabled,((At,pt)=>{let yt=pt.stride,dt=n.Z();return n.aS(dt,0,n.N,-n.N,0,0,1),n.$(dt,dt,[0,-n.N,0]),{u_matrix:dt,u_image:1,u_dimension:[yt,yt],u_zoom:At.overscaledZ,u_unpack:pt.getUnpackVector()}})(l.tileID,U),null,d.id,T.rasterBoundsBuffer,T.quadTriangleIndexBuffer,T.rasterBoundsSegments),l.needsHillshadePrepare=!1}}function f_(T,l,d,v,b,M){let O=v.paint.get(\"raster-fade-duration\");if(!M&&O>0){let B=n.h.now(),U=(B-T.timeAdded)/O,W=l?(B-l.timeAdded)/O:-1,Z=d.getSource(),$=b.coveringZoomLevel({tileSize:Z.tileSize,roundZoom:Z.roundZoom}),st=!l||Math.abs(l.tileID.overscaledZ-$)>Math.abs(T.tileID.overscaledZ-$),At=st&&T.refreshedUponExpiration?1:n.ad(st?U:1-W,0,1);return T.refreshedUponExpiration&&U>=1&&(T.refreshedUponExpiration=!1),l?{opacity:1,mix:1-At}:{opacity:At,mix:0}}return{opacity:1,mix:0}}let d_=new n.aT(1,0,0,1),yf=new n.aT(0,1,0,1),Ba=new n.aT(0,0,1,1),Wn=new n.aT(1,0,1,1),p_=new n.aT(0,1,1,1);function Cd(T,l,d,v){Xp(T,0,l+d/2,T.transform.width,d,v)}function $p(T,l,d,v){Xp(T,l-d/2,0,d,T.transform.height,v)}function Xp(T,l,d,v,b,M){let O=T.context,B=O.gl;B.enable(B.SCISSOR_TEST),B.scissor(l*T.pixelRatio,d*T.pixelRatio,v*T.pixelRatio,b*T.pixelRatio),O.clear({color:M}),B.disable(B.SCISSOR_TEST)}function i0(T,l,d){let v=T.context,b=v.gl,M=d.posMatrix,O=T.useProgram(\"debug\"),B=ci.disabled,U=Je.disabled,W=T.colorModeForRenderPass(),Z=\"$debug\",$=T.style.map.terrain&&T.style.map.terrain.getTerrainData(d);v.activeTexture.set(b.TEXTURE0);let st=l.getTileByID(d.key).latestRawTileData,At=Math.floor((st&&st.byteLength||0)/1024),pt=l.getTile(d).tileSize,yt=512/Math.min(pt,512)*(d.overscaledZ/T.transform.zoom)*.5,dt=d.canonical.toString();d.overscaledZ!==d.canonical.z&&(dt+=` => ${d.overscaledZ}`),function(Ft,Ht){Ft.initDebugOverlayCanvas();let St=Ft.debugOverlayCanvas,Bt=Ft.context.gl,Qt=Ft.debugOverlayCanvas.getContext(\"2d\");Qt.clearRect(0,0,St.width,St.height),Qt.shadowColor=\"white\",Qt.shadowBlur=2,Qt.lineWidth=1.5,Qt.strokeStyle=\"white\",Qt.textBaseline=\"top\",Qt.font=\"bold 36px Open Sans, sans-serif\",Qt.fillText(Ht,5,5),Qt.strokeText(Ht,5,5),Ft.debugOverlayTexture.update(St),Ft.debugOverlayTexture.bind(Bt.LINEAR,Bt.CLAMP_TO_EDGE)}(T,`${dt} ${At}kB`),O.draw(v,b.TRIANGLES,B,U,Ji.alphaBlended,It.disabled,Lt(M,n.aT.transparent,yt),null,Z,T.debugBuffer,T.quadTriangleIndexBuffer,T.debugSegments),O.draw(v,b.LINE_STRIP,B,U,W,It.disabled,Lt(M,n.aT.red),$,Z,T.debugBuffer,T.tileBorderIndexBuffer,T.debugSegments)}function Cn(T,l,d){let v=T.context,b=v.gl,M=T.colorModeForRenderPass(),O=new ci(b.LEQUAL,ci.ReadWrite,T.depthRangeFor3D),B=T.useProgram(\"terrain\"),U=l.getTerrainMesh();v.bindFramebuffer.set(null),v.viewport.set([0,0,T.width,T.height]);for(let W of d){let Z=T.renderToTexture.getTexture(W),$=l.getTerrainData(W.tileID);v.activeTexture.set(b.TEXTURE0),b.bindTexture(b.TEXTURE_2D,Z.texture);let st={u_matrix:T.transform.calculatePosMatrix(W.tileID.toUnwrapped()),u_texture:0,u_ele_delta:l.getMeshFrameDelta(T.transform.zoom)};B.draw(v,b.TRIANGLES,O,Je.disabled,M,It.backCCW,st,$,\"terrain\",U.vertexBuffer,U.indexBuffer,U.segments)}}class ah{constructor(l,d){this.context=new Oc(l),this.transform=d,this._tileTextures={},this.terrainFacilitator={dirty:!0,matrix:n.Z(),renderTime:0},this.setup(),this.numSublayers=ls.maxUnderzooming+ls.maxOverzooming+1,this.depthEpsilon=1/Math.pow(2,16),this.crossTileSymbolIndex=new Dc}resize(l,d,v){if(this.width=Math.floor(l*v),this.height=Math.floor(d*v),this.pixelRatio=v,this.context.viewport.set([0,0,this.width,this.height]),this.style)for(let b of this.style._order)this.style._layers[b].resize()}setup(){let l=this.context,d=new n.a_;d.emplaceBack(0,0),d.emplaceBack(n.N,0),d.emplaceBack(0,n.N),d.emplaceBack(n.N,n.N),this.tileExtentBuffer=l.createVertexBuffer(d,So.members),this.tileExtentSegments=n.S.simpleSegment(0,0,4,2);let v=new n.a_;v.emplaceBack(0,0),v.emplaceBack(n.N,0),v.emplaceBack(0,n.N),v.emplaceBack(n.N,n.N),this.debugBuffer=l.createVertexBuffer(v,So.members),this.debugSegments=n.S.simpleSegment(0,0,4,5);let b=new n.V;b.emplaceBack(0,0,0,0),b.emplaceBack(n.N,0,n.N,0),b.emplaceBack(0,n.N,0,n.N),b.emplaceBack(n.N,n.N,n.N,n.N),this.rasterBoundsBuffer=l.createVertexBuffer(b,Jn.members),this.rasterBoundsSegments=n.S.simpleSegment(0,0,4,2);let M=new n.a_;M.emplaceBack(0,0),M.emplaceBack(1,0),M.emplaceBack(0,1),M.emplaceBack(1,1),this.viewportBuffer=l.createVertexBuffer(M,So.members),this.viewportSegments=n.S.simpleSegment(0,0,4,2);let O=new n.a$;O.emplaceBack(0),O.emplaceBack(1),O.emplaceBack(3),O.emplaceBack(2),O.emplaceBack(0),this.tileBorderIndexBuffer=l.createIndexBuffer(O);let B=new n.b0;B.emplaceBack(0,1,2),B.emplaceBack(2,1,3),this.quadTriangleIndexBuffer=l.createIndexBuffer(B);let U=this.context.gl;this.stencilClearMode=new Je({func:U.ALWAYS,mask:0},0,255,U.ZERO,U.ZERO,U.ZERO)}clearStencil(){let l=this.context,d=l.gl;this.nextStencilID=1,this.currentStencilSource=void 0;let v=n.Z();n.aS(v,0,this.width,this.height,0,0,1),n.a0(v,v,[d.drawingBufferWidth,d.drawingBufferHeight,0]),this.useProgram(\"clippingMask\").draw(l,d.TRIANGLES,ci.disabled,this.stencilClearMode,Ji.disabled,It.disabled,Kt(v),null,\"$clipping\",this.viewportBuffer,this.quadTriangleIndexBuffer,this.viewportSegments)}_renderTileClippingMasks(l,d){if(this.currentStencilSource===l.source||!l.isTileClipped()||!d||!d.length)return;this.currentStencilSource=l.source;let v=this.context,b=v.gl;this.nextStencilID+d.length>256&&this.clearStencil(),v.setColorMode(Ji.disabled),v.setDepthMode(ci.disabled);let M=this.useProgram(\"clippingMask\");this._tileClippingMaskIDs={};for(let O of d){let B=this._tileClippingMaskIDs[O.key]=this.nextStencilID++,U=this.style.map.terrain&&this.style.map.terrain.getTerrainData(O);M.draw(v,b.TRIANGLES,ci.disabled,new Je({func:b.ALWAYS,mask:0},B,255,b.KEEP,b.KEEP,b.REPLACE),Ji.disabled,It.disabled,Kt(O.posMatrix),U,\"$clipping\",this.tileExtentBuffer,this.quadTriangleIndexBuffer,this.tileExtentSegments)}}stencilModeFor3D(){this.currentStencilSource=void 0,this.nextStencilID+1>256&&this.clearStencil();let l=this.nextStencilID++,d=this.context.gl;return new Je({func:d.NOTEQUAL,mask:255},l,255,d.KEEP,d.KEEP,d.REPLACE)}stencilModeForClipping(l){let d=this.context.gl;return new Je({func:d.EQUAL,mask:255},this._tileClippingMaskIDs[l.key],0,d.KEEP,d.KEEP,d.REPLACE)}stencilConfigForOverlap(l){let d=this.context.gl,v=l.sort((O,B)=>B.overscaledZ-O.overscaledZ),b=v[v.length-1].overscaledZ,M=v[0].overscaledZ-b+1;if(M>1){this.currentStencilSource=void 0,this.nextStencilID+M>256&&this.clearStencil();let O={};for(let B=0;B=0;this.currentLayer--){let U=this.style._layers[v[this.currentLayer]],W=b[U.source],Z=M[U.source];this._renderTileClippingMasks(U,Z),this.renderLayer(this,W,U,Z)}for(this.renderPass=\"translucent\",this.currentLayer=0;this.currentLayerdt.source&&!dt.isHidden(Z)?[W.sourceCaches[dt.source]]:[]),At=st.filter(dt=>dt.getSource().type===\"vector\"),pt=st.filter(dt=>dt.getSource().type!==\"vector\"),yt=dt=>{(!$||$.getSource().maxzoomyt(dt)),$||pt.forEach(dt=>yt(dt)),$}(this.style,this.transform.zoom);U&&function(W,Z,$){for(let st=0;st<$.length;st++)i0(W,Z,$[st])}(this,U,U.getVisibleCoordinates())}this.options.showPadding&&function(U){let W=U.transform.padding;Cd(U,U.transform.height-(W.top||0),3,d_),Cd(U,W.bottom||0,3,yf),$p(U,W.left||0,3,Ba),$p(U,U.transform.width-(W.right||0),3,Wn);let Z=U.transform.centerPoint;(function($,st,At,pt){Xp($,st-1,At-10,2,20,pt),Xp($,st-10,At-1,20,2,pt)})(U,Z.x,U.transform.height-Z.y,p_)}(this),this.context.setDefault()}renderLayer(l,d,v,b){if(!v.isHidden(this.transform.zoom)&&(v.type===\"background\"||v.type===\"custom\"||(b||[]).length))switch(this.id=v.id,v.type){case\"symbol\":(function(M,O,B,U,W){if(M.renderPass!==\"translucent\")return;let Z=Je.disabled,$=M.colorModeForRenderPass();(B._unevaluatedLayout.hasValue(\"text-variable-anchor\")||B._unevaluatedLayout.hasValue(\"text-variable-anchor-offset\"))&&function(st,At,pt,yt,dt,Ft,Ht){let St=At.transform,Bt=dt===\"map\",Qt=Ft===\"map\";for(let $t of st){let oe=yt.getTile($t),pe=oe.getBucket(pt);if(!pe||!pe.text||!pe.text.segments.get().length)continue;let he=n.ah(pe.textSizeData,St.zoom),be=Dt(oe,1,At.transform.zoom),Ze=ve($t.posMatrix,Qt,Bt,At.transform,be),Kr=pt.layout.get(\"icon-text-fit\")!==\"none\"&&pe.hasIconData();if(he){let Ee=Math.pow(2,St.zoom-oe.tileID.overscaledZ);Qp(pe,Bt,Qt,Ht,St,Ze,$t.posMatrix,Ee,he,Kr,At.style.map.terrain?(pr,tr)=>At.style.map.terrain.getElevation($t,pr,tr):null)}}}(U,M,B,O,B.layout.get(\"text-rotation-alignment\"),B.layout.get(\"text-pitch-alignment\"),W),B.paint.get(\"icon-opacity\").constantOr(1)!==0&&wt(M,O,B,U,!1,B.paint.get(\"icon-translate\"),B.paint.get(\"icon-translate-anchor\"),B.layout.get(\"icon-rotation-alignment\"),B.layout.get(\"icon-pitch-alignment\"),B.layout.get(\"icon-keep-upright\"),Z,$),B.paint.get(\"text-opacity\").constantOr(1)!==0&&wt(M,O,B,U,!0,B.paint.get(\"text-translate\"),B.paint.get(\"text-translate-anchor\"),B.layout.get(\"text-rotation-alignment\"),B.layout.get(\"text-pitch-alignment\"),B.layout.get(\"text-keep-upright\"),Z,$),O.map.showCollisionBoxes&&(Km(M,O,B,U,B.paint.get(\"text-translate\"),B.paint.get(\"text-translate-anchor\"),!0),Km(M,O,B,U,B.paint.get(\"icon-translate\"),B.paint.get(\"icon-translate-anchor\"),!1))})(l,d,v,b,this.style.placement.variableOffsets);break;case\"circle\":(function(M,O,B,U){if(M.renderPass!==\"translucent\")return;let W=B.paint.get(\"circle-opacity\"),Z=B.paint.get(\"circle-stroke-width\"),$=B.paint.get(\"circle-stroke-opacity\"),st=!B.layout.get(\"circle-sort-key\").isConstant();if(W.constantOr(1)===0&&(Z.constantOr(1)===0||$.constantOr(1)===0))return;let At=M.context,pt=At.gl,yt=M.depthModeForSublayer(0,ci.ReadOnly),dt=Je.disabled,Ft=M.colorModeForRenderPass(),Ht=[];for(let St=0;StSt.sortKey-Bt.sortKey);for(let St of Ht){let{programConfiguration:Bt,program:Qt,layoutVertexBuffer:$t,indexBuffer:oe,uniformValues:pe,terrainData:he}=St.state;Qt.draw(At,pt.TRIANGLES,yt,dt,Ft,It.disabled,pe,he,B.id,$t,oe,St.segments,B.paint,M.transform.zoom,Bt)}})(l,d,v,b);break;case\"heatmap\":(function(M,O,B,U){if(B.paint.get(\"heatmap-opacity\")!==0)if(M.renderPass===\"offscreen\"){let W=M.context,Z=W.gl,$=Je.disabled,st=new Ji([Z.ONE,Z.ONE],n.aT.transparent,[!0,!0,!0,!0]);(function(At,pt,yt){let dt=At.gl;At.activeTexture.set(dt.TEXTURE1),At.viewport.set([0,0,pt.width/4,pt.height/4]);let Ft=yt.heatmapFbo;if(Ft)dt.bindTexture(dt.TEXTURE_2D,Ft.colorAttachment.get()),At.bindFramebuffer.set(Ft.framebuffer);else{let Ht=dt.createTexture();dt.bindTexture(dt.TEXTURE_2D,Ht),dt.texParameteri(dt.TEXTURE_2D,dt.TEXTURE_WRAP_S,dt.CLAMP_TO_EDGE),dt.texParameteri(dt.TEXTURE_2D,dt.TEXTURE_WRAP_T,dt.CLAMP_TO_EDGE),dt.texParameteri(dt.TEXTURE_2D,dt.TEXTURE_MIN_FILTER,dt.LINEAR),dt.texParameteri(dt.TEXTURE_2D,dt.TEXTURE_MAG_FILTER,dt.LINEAR),Ft=yt.heatmapFbo=At.createFramebuffer(pt.width/4,pt.height/4,!1,!1),function(St,Bt,Qt,$t){var oe,pe;let he=St.gl,be=(oe=St.HALF_FLOAT)!==null&&oe!==void 0?oe:he.UNSIGNED_BYTE,Ze=(pe=St.RGBA16F)!==null&&pe!==void 0?pe:he.RGBA;he.texImage2D(he.TEXTURE_2D,0,Ze,Bt.width/4,Bt.height/4,0,he.RGBA,be,null),$t.colorAttachment.set(Qt)}(At,pt,Ht,Ft)}})(W,M,B),W.clear({color:n.aT.transparent});for(let At=0;At{let St=n.Z();n.aS(St,0,yt.width,yt.height,0,0,1);let Bt=yt.context.gl;return{u_matrix:St,u_world:[Bt.drawingBufferWidth,Bt.drawingBufferHeight],u_image:0,u_color_ramp:1,u_opacity:dt.paint.get(\"heatmap-opacity\")}})(W,Z),null,Z.id,W.viewportBuffer,W.quadTriangleIndexBuffer,W.viewportSegments,Z.paint,W.transform.zoom)}(M,B))})(l,d,v,b);break;case\"line\":(function(M,O,B,U){if(M.renderPass!==\"translucent\")return;let W=B.paint.get(\"line-opacity\"),Z=B.paint.get(\"line-width\");if(W.constantOr(1)===0||Z.constantOr(1)===0)return;let $=M.depthModeForSublayer(0,ci.ReadOnly),st=M.colorModeForRenderPass(),At=B.paint.get(\"line-dasharray\"),pt=B.paint.get(\"line-pattern\"),yt=pt.constantOr(1),dt=B.paint.get(\"line-gradient\"),Ft=B.getCrossfadeParameters(),Ht=yt?\"linePattern\":At?\"lineSDF\":dt?\"lineGradient\":\"line\",St=M.context,Bt=St.gl,Qt=!0;for(let $t of U){let oe=O.getTile($t);if(yt&&!oe.patternsLoaded())continue;let pe=oe.getBucket(B);if(!pe)continue;let he=pe.programConfigurations.get(B.id),be=M.context.program.get(),Ze=M.useProgram(Ht,he),Kr=Qt||Ze.program!==be,Ee=M.style.map.terrain&&M.style.map.terrain.getTerrainData($t),pr=pt.constantOr(null);if(pr&&oe.imageAtlas){let Jr=oe.imageAtlas,Vr=Jr.patternPositions[pr.to.toString()],ei=Jr.patternPositions[pr.from.toString()];Vr&&ei&&he.setConstantPatternPositions(Vr,ei)}let tr=Ee?$t:null,Gi=yt?Ts(M,oe,B,Ft,tr):At?Vs(M,oe,B,At,Ft,tr):dt?is(M,oe,B,pe.lineClipsArray.length,tr):ti(M,oe,B,tr);if(yt)St.activeTexture.set(Bt.TEXTURE0),oe.imageAtlasTexture.bind(Bt.LINEAR,Bt.CLAMP_TO_EDGE),he.updatePaintBuffers(Ft);else if(At&&(Kr||M.lineAtlas.dirty))St.activeTexture.set(Bt.TEXTURE0),M.lineAtlas.bind(St);else if(dt){let Jr=pe.gradients[B.id],Vr=Jr.texture;if(B.gradientVersion!==Jr.version){let ei=256;if(B.stepInterpolant){let On=O.getSource().maxzoom,tn=$t.canonical.z===On?Math.ceil(1<0?d.pop():null}isPatternMissing(l){if(!l)return!1;if(!l.from||!l.to)return!0;let d=this.imageManager.getPattern(l.from.toString()),v=this.imageManager.getPattern(l.to.toString());return!d||!v}useProgram(l,d){this.cache=this.cache||{};let v=l+(d?d.cacheKey:\"\")+(this._showOverdrawInspector?\"/overdraw\":\"\")+(this.style.map.terrain?\"/terrain\":\"\");return this.cache[v]||(this.cache[v]=new pu(this.context,Ki[l],d,Wm[l],this._showOverdrawInspector,this.style.map.terrain)),this.cache[v]}setCustomLayerDefaults(){this.context.unbindVAO(),this.context.cullFace.setDefault(),this.context.activeTexture.setDefault(),this.context.pixelStoreUnpack.setDefault(),this.context.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.context.pixelStoreUnpackFlipY.setDefault()}setBaseState(){let l=this.context.gl;this.context.cullFace.set(!1),this.context.viewport.set([0,0,this.width,this.height]),this.context.blendEquation.set(l.FUNC_ADD)}initDebugOverlayCanvas(){this.debugOverlayCanvas==null&&(this.debugOverlayCanvas=document.createElement(\"canvas\"),this.debugOverlayCanvas.width=512,this.debugOverlayCanvas.height=512,this.debugOverlayTexture=new qt(this.context,this.debugOverlayCanvas,this.context.gl.RGBA))}destroy(){this.debugOverlayTexture&&this.debugOverlayTexture.destroy()}overLimit(){let{drawingBufferWidth:l,drawingBufferHeight:d}=this.context.gl;return this.width!==l||this.height!==d}}class fi{constructor(l,d){this.points=l,this.planes=d}static fromInvProjectionMatrix(l,d,v){let b=Math.pow(2,v),M=[[-1,1,-1,1],[1,1,-1,1],[1,-1,-1,1],[-1,-1,-1,1],[-1,1,1,1],[1,1,1,1],[1,-1,1,1],[-1,-1,1,1]].map(B=>{let U=1/(B=n.ag([],B,l))[3]/d*b;return n.b3(B,B,[U,U,1/B[3],U])}),O=[[0,1,2],[6,5,4],[0,3,7],[2,1,5],[3,2,6],[0,4,5]].map(B=>{let U=function(st,At){var pt=At[0],yt=At[1],dt=At[2],Ft=pt*pt+yt*yt+dt*dt;return Ft>0&&(Ft=1/Math.sqrt(Ft)),st[0]=At[0]*Ft,st[1]=At[1]*Ft,st[2]=At[2]*Ft,st}([],function(st,At,pt){var yt=At[0],dt=At[1],Ft=At[2],Ht=pt[0],St=pt[1],Bt=pt[2];return st[0]=dt*Bt-Ft*St,st[1]=Ft*Ht-yt*Bt,st[2]=yt*St-dt*Ht,st}([],ut([],M[B[0]],M[B[1]]),ut([],M[B[2]],M[B[1]]))),W=-((Z=U)[0]*($=M[B[1]])[0]+Z[1]*$[1]+Z[2]*$[2]);var Z,$;return U.concat(W)});return new fi(M,O)}}class mu{constructor(l,d){this.min=l,this.max=d,this.center=function(v,b,M){return v[0]=.5*b[0],v[1]=.5*b[1],v[2]=.5*b[2],v}([],function(v,b,M){return v[0]=b[0]+M[0],v[1]=b[1]+M[1],v[2]=b[2]+M[2],v}([],this.min,this.max))}quadrant(l){let d=[l%2==0,l<2],v=K(this.min),b=K(this.max);for(let M=0;M=0&&O++;if(O===0)return 0;O!==d.length&&(v=!1)}if(v)return 2;for(let b=0;b<3;b++){let M=Number.MAX_VALUE,O=-Number.MAX_VALUE;for(let B=0;Bthis.max[b]-this.min[b])return 0}return 1}}class vf{constructor(l=0,d=0,v=0,b=0){if(isNaN(l)||l<0||isNaN(d)||d<0||isNaN(v)||v<0||isNaN(b)||b<0)throw new Error(\"Invalid value for edge-insets, top, bottom, left and right must all be numbers\");this.top=l,this.bottom=d,this.left=v,this.right=b}interpolate(l,d,v){return d.top!=null&&l.top!=null&&(this.top=n.B.number(l.top,d.top,v)),d.bottom!=null&&l.bottom!=null&&(this.bottom=n.B.number(l.bottom,d.bottom,v)),d.left!=null&&l.left!=null&&(this.left=n.B.number(l.left,d.left,v)),d.right!=null&&l.right!=null&&(this.right=n.B.number(l.right,d.right,v)),this}getCenter(l,d){let v=n.ad((this.left+l-this.right)/2,0,l),b=n.ad((this.top+d-this.bottom)/2,0,d);return new n.P(v,b)}equals(l){return this.top===l.top&&this.bottom===l.bottom&&this.left===l.left&&this.right===l.right}clone(){return new vf(this.top,this.bottom,this.left,this.right)}toJSON(){return{top:this.top,bottom:this.bottom,left:this.left,right:this.right}}}class Kp{constructor(l,d,v,b,M){this.tileSize=512,this.maxValidLatitude=85.051129,this._renderWorldCopies=M===void 0||!!M,this._minZoom=l||0,this._maxZoom=d||22,this._minPitch=v??0,this._maxPitch=b??60,this.setMaxBounds(),this.width=0,this.height=0,this._center=new n.L(0,0),this._elevation=0,this.zoom=0,this.angle=0,this._fov=.6435011087932844,this._pitch=0,this._unmodified=!0,this._edgeInsets=new vf,this._posMatrixCache={},this._alignedPosMatrixCache={},this._minEleveationForCurrentTile=0}clone(){let l=new Kp(this._minZoom,this._maxZoom,this._minPitch,this.maxPitch,this._renderWorldCopies);return l.apply(this),l}apply(l){this.tileSize=l.tileSize,this.latRange=l.latRange,this.width=l.width,this.height=l.height,this._center=l._center,this._elevation=l._elevation,this._minEleveationForCurrentTile=l._minEleveationForCurrentTile,this.zoom=l.zoom,this.angle=l.angle,this._fov=l._fov,this._pitch=l._pitch,this._unmodified=l._unmodified,this._edgeInsets=l._edgeInsets.clone(),this._calcMatrices()}get minZoom(){return this._minZoom}set minZoom(l){this._minZoom!==l&&(this._minZoom=l,this.zoom=Math.max(this.zoom,l))}get maxZoom(){return this._maxZoom}set maxZoom(l){this._maxZoom!==l&&(this._maxZoom=l,this.zoom=Math.min(this.zoom,l))}get minPitch(){return this._minPitch}set minPitch(l){this._minPitch!==l&&(this._minPitch=l,this.pitch=Math.max(this.pitch,l))}get maxPitch(){return this._maxPitch}set maxPitch(l){this._maxPitch!==l&&(this._maxPitch=l,this.pitch=Math.min(this.pitch,l))}get renderWorldCopies(){return this._renderWorldCopies}set renderWorldCopies(l){l===void 0?l=!0:l===null&&(l=!1),this._renderWorldCopies=l}get worldSize(){return this.tileSize*this.scale}get centerOffset(){return this.centerPoint._sub(this.size._div(2))}get size(){return new n.P(this.width,this.height)}get bearing(){return-this.angle/Math.PI*180}set bearing(l){let d=-n.b5(l,-180,180)*Math.PI/180;this.angle!==d&&(this._unmodified=!1,this.angle=d,this._calcMatrices(),this.rotationMatrix=function(){var v=new n.A(4);return n.A!=Float32Array&&(v[1]=0,v[2]=0),v[0]=1,v[3]=1,v}(),function(v,b,M){var O=b[0],B=b[1],U=b[2],W=b[3],Z=Math.sin(M),$=Math.cos(M);v[0]=O*$+U*Z,v[1]=B*$+W*Z,v[2]=O*-Z+U*$,v[3]=B*-Z+W*$}(this.rotationMatrix,this.rotationMatrix,this.angle))}get pitch(){return this._pitch/Math.PI*180}set pitch(l){let d=n.ad(l,this.minPitch,this.maxPitch)/180*Math.PI;this._pitch!==d&&(this._unmodified=!1,this._pitch=d,this._calcMatrices())}get fov(){return this._fov/Math.PI*180}set fov(l){l=Math.max(.01,Math.min(60,l)),this._fov!==l&&(this._unmodified=!1,this._fov=l/180*Math.PI,this._calcMatrices())}get zoom(){return this._zoom}set zoom(l){let d=Math.min(Math.max(l,this.minZoom),this.maxZoom);this._zoom!==d&&(this._unmodified=!1,this._zoom=d,this.tileZoom=Math.max(0,Math.floor(d)),this.scale=this.zoomScale(d),this._constrain(),this._calcMatrices())}get center(){return this._center}set center(l){l.lat===this._center.lat&&l.lng===this._center.lng||(this._unmodified=!1,this._center=l,this._constrain(),this._calcMatrices())}get elevation(){return this._elevation}set elevation(l){l!==this._elevation&&(this._elevation=l,this._constrain(),this._calcMatrices())}get padding(){return this._edgeInsets.toJSON()}set padding(l){this._edgeInsets.equals(l)||(this._unmodified=!1,this._edgeInsets.interpolate(this._edgeInsets,l,1),this._calcMatrices())}get centerPoint(){return this._edgeInsets.getCenter(this.width,this.height)}isPaddingEqual(l){return this._edgeInsets.equals(l)}interpolatePadding(l,d,v){this._unmodified=!1,this._edgeInsets.interpolate(l,d,v),this._constrain(),this._calcMatrices()}coveringZoomLevel(l){let d=(l.roundZoom?Math.round:Math.floor)(this.zoom+this.scaleZoom(this.tileSize/l.tileSize));return Math.max(0,d)}getVisibleUnwrappedCoordinates(l){let d=[new n.b6(0,l)];if(this._renderWorldCopies){let v=this.pointCoordinate(new n.P(0,0)),b=this.pointCoordinate(new n.P(this.width,0)),M=this.pointCoordinate(new n.P(this.width,this.height)),O=this.pointCoordinate(new n.P(0,this.height)),B=Math.floor(Math.min(v.x,b.x,M.x,O.x)),U=Math.floor(Math.max(v.x,b.x,M.x,O.x)),W=1;for(let Z=B-W;Z<=U+W;Z++)Z!==0&&d.push(new n.b6(Z,l))}return d}coveringTiles(l){var d,v;let b=this.coveringZoomLevel(l),M=b;if(l.minzoom!==void 0&&bl.maxzoom&&(b=l.maxzoom);let O=this.pointCoordinate(this.getCameraPoint()),B=n.U.fromLngLat(this.center),U=Math.pow(2,b),W=[U*O.x,U*O.y,0],Z=[U*B.x,U*B.y,0],$=fi.fromInvProjectionMatrix(this.invProjMatrix,this.worldSize,b),st=l.minzoom||0;!l.terrain&&this.pitch<=60&&this._edgeInsets.top<.1&&(st=b);let At=l.terrain?2/Math.min(this.tileSize,l.tileSize)*this.tileSize:3,pt=St=>({aabb:new mu([St*U,0,0],[(St+1)*U,U,0]),zoom:0,x:0,y:0,wrap:St,fullyVisible:!1}),yt=[],dt=[],Ft=b,Ht=l.reparseOverscaled?M:b;if(this._renderWorldCopies)for(let St=1;St<=3;St++)yt.push(pt(-St)),yt.push(pt(St));for(yt.push(pt(0));yt.length>0;){let St=yt.pop(),Bt=St.x,Qt=St.y,$t=St.fullyVisible;if(!$t){let Ze=St.aabb.intersects($);if(Ze===0)continue;$t=Ze===2}let oe=l.terrain?W:Z,pe=St.aabb.distanceX(oe),he=St.aabb.distanceY(oe),be=Math.max(Math.abs(pe),Math.abs(he));if(St.zoom===Ft||be>At+(1<=st){let Ze=Ft-St.zoom,Kr=W[0]-.5-(Bt<>1),pr=St.zoom+1,tr=St.aabb.quadrant(Ze);if(l.terrain){let Gi=new n.O(pr,St.wrap,pr,Kr,Ee),Jr=l.terrain.getMinMaxElevation(Gi),Vr=(d=Jr.minElevation)!==null&&d!==void 0?d:this.elevation,ei=(v=Jr.maxElevation)!==null&&v!==void 0?v:this.elevation;tr=new mu([tr.min[0],tr.min[1],Vr],[tr.max[0],tr.max[1],ei])}yt.push({aabb:tr,zoom:pr,x:Kr,y:Ee,wrap:St.wrap,fullyVisible:$t})}}return dt.sort((St,Bt)=>St.distanceSq-Bt.distanceSq).map(St=>St.tileID)}resize(l,d){this.width=l,this.height=d,this.pixelsToGLUnits=[2/l,-2/d],this._constrain(),this._calcMatrices()}get unmodified(){return this._unmodified}zoomScale(l){return Math.pow(2,l)}scaleZoom(l){return Math.log(l)/Math.LN2}project(l){let d=n.ad(l.lat,-this.maxValidLatitude,this.maxValidLatitude);return new n.P(n.G(l.lng)*this.worldSize,n.H(d)*this.worldSize)}unproject(l){return new n.U(l.x/this.worldSize,l.y/this.worldSize).toLngLat()}get point(){return this.project(this.center)}getCameraPosition(){return{lngLat:this.pointLocation(this.getCameraPoint()),altitude:Math.cos(this._pitch)*this.cameraToCenterDistance/this._pixelPerMeter+this.elevation}}recalculateZoom(l){let d=this.pointLocation(this.centerPoint,l),v=l.getElevationForLngLatZoom(d,this.tileZoom);if(!(this.elevation-v))return;let b=this.getCameraPosition(),M=n.U.fromLngLat(b.lngLat,b.altitude),O=n.U.fromLngLat(d,v),B=M.x-O.x,U=M.y-O.y,W=M.z-O.z,Z=Math.sqrt(B*B+U*U+W*W),$=this.scaleZoom(this.cameraToCenterDistance/Z/this.tileSize);this._elevation=v,this._center=d,this.zoom=$}setLocationAtPoint(l,d){let v=this.pointCoordinate(d),b=this.pointCoordinate(this.centerPoint),M=this.locationCoordinate(l),O=new n.U(M.x-(v.x-b.x),M.y-(v.y-b.y));this.center=this.coordinateLocation(O),this._renderWorldCopies&&(this.center=this.center.wrap())}locationPoint(l,d){return d?this.coordinatePoint(this.locationCoordinate(l),d.getElevationForLngLatZoom(l,this.tileZoom),this.pixelMatrix3D):this.coordinatePoint(this.locationCoordinate(l))}pointLocation(l,d){return this.coordinateLocation(this.pointCoordinate(l,d))}locationCoordinate(l){return n.U.fromLngLat(l)}coordinateLocation(l){return l&&l.toLngLat()}pointCoordinate(l,d){if(d){let st=d.pointCoordinate(l);if(st!=null)return st}let v=[l.x,l.y,0,1],b=[l.x,l.y,1,1];n.ag(v,v,this.pixelMatrixInverse),n.ag(b,b,this.pixelMatrixInverse);let M=v[3],O=b[3],B=v[1]/M,U=b[1]/O,W=v[2]/M,Z=b[2]/O,$=W===Z?0:(0-W)/(Z-W);return new n.U(n.B.number(v[0]/M,b[0]/O,$)/this.worldSize,n.B.number(B,U,$)/this.worldSize)}coordinatePoint(l,d=0,v=this.pixelMatrix){let b=[l.x*this.worldSize,l.y*this.worldSize,d,1];return n.ag(b,b,v),new n.P(b[0]/b[3],b[1]/b[3])}getBounds(){let l=Math.max(0,this.height/2-this.getHorizon());return new Si().extend(this.pointLocation(new n.P(0,l))).extend(this.pointLocation(new n.P(this.width,l))).extend(this.pointLocation(new n.P(this.width,this.height))).extend(this.pointLocation(new n.P(0,this.height)))}getMaxBounds(){return this.latRange&&this.latRange.length===2&&this.lngRange&&this.lngRange.length===2?new Si([this.lngRange[0],this.latRange[0]],[this.lngRange[1],this.latRange[1]]):null}getHorizon(){return Math.tan(Math.PI/2-this._pitch)*this.cameraToCenterDistance*.85}setMaxBounds(l){l?(this.lngRange=[l.getWest(),l.getEast()],this.latRange=[l.getSouth(),l.getNorth()],this._constrain()):(this.lngRange=null,this.latRange=[-this.maxValidLatitude,this.maxValidLatitude])}calculatePosMatrix(l,d=!1){let v=l.key,b=d?this._alignedPosMatrixCache:this._posMatrixCache;if(b[v])return b[v];let M=l.canonical,O=this.worldSize/this.zoomScale(M.z),B=M.x+Math.pow(2,M.z)*l.wrap,U=n.ao(new Float64Array(16));return n.$(U,U,[B*O,M.y*O,0]),n.a0(U,U,[O/n.N,O/n.N,1]),n.a1(U,d?this.alignedProjMatrix:this.projMatrix,U),b[v]=new Float32Array(U),b[v]}customLayerMatrix(){return this.mercatorMatrix.slice()}_constrain(){if(!this.center||!this.width||!this.height||this._constraining)return;this._constraining=!0;let l,d,v,b,M=-90,O=90,B=-180,U=180,W=this.size,Z=this._unmodified;if(this.latRange){let At=this.latRange;M=n.H(At[1])*this.worldSize,O=n.H(At[0])*this.worldSize,l=O-MO&&(b=O-pt)}if(this.lngRange){let At=(B+U)/2,pt=n.b5($.x,At-this.worldSize/2,At+this.worldSize/2),yt=W.x/2;pt-ytU&&(v=U-yt)}v===void 0&&b===void 0||(this.center=this.unproject(new n.P(v!==void 0?v:$.x,b!==void 0?b:$.y)).wrap()),this._unmodified=Z,this._constraining=!1}_calcMatrices(){if(!this.height)return;let l=this.centerOffset,d=this.point.x,v=this.point.y;this.cameraToCenterDistance=.5/Math.tan(this._fov/2)*this.height,this._pixelPerMeter=n.b7(1,this.center.lat)*this.worldSize;let b=n.ao(new Float64Array(16));n.a0(b,b,[this.width/2,-this.height/2,1]),n.$(b,b,[1,-1,0]),this.labelPlaneMatrix=b,b=n.ao(new Float64Array(16)),n.a0(b,b,[1,-1,1]),n.$(b,b,[-1,-1,0]),n.a0(b,b,[2/this.width,2/this.height,1]),this.glCoordMatrix=b;let M=this.cameraToCenterDistance+this._elevation*this._pixelPerMeter/Math.cos(this._pitch),O=Math.min(this.elevation,this._minEleveationForCurrentTile),B=M-O*this._pixelPerMeter/Math.cos(this._pitch),U=O<0?B:M,W=Math.PI/2+this._pitch,Z=this._fov*(.5+l.y/this.height),$=Math.sin(Z)*U/Math.sin(n.ad(Math.PI-W-Z,.01,Math.PI-.01)),st=this.getHorizon(),At=2*Math.atan(st/this.cameraToCenterDistance)*(.5+l.y/(2*st)),pt=Math.sin(At)*U/Math.sin(n.ad(Math.PI-W-At,.01,Math.PI-.01)),yt=Math.min($,pt),dt=1.01*(Math.cos(Math.PI/2-this._pitch)*yt+U),Ft=this.height/50;b=new Float64Array(16),n.b8(b,this._fov,this.width/this.height,Ft,dt),b[8]=2*-l.x/this.width,b[9]=2*l.y/this.height,n.a0(b,b,[1,-1,1]),n.$(b,b,[0,0,-this.cameraToCenterDistance]),n.b9(b,b,this._pitch),n.ae(b,b,this.angle),n.$(b,b,[-d,-v,0]),this.mercatorMatrix=n.a0([],b,[this.worldSize,this.worldSize,this.worldSize]),n.a0(b,b,[1,1,this._pixelPerMeter]),this.pixelMatrix=n.a1(new Float64Array(16),this.labelPlaneMatrix,b),n.$(b,b,[0,0,-this.elevation]),this.projMatrix=b,this.invProjMatrix=n.as([],b),this.pixelMatrix3D=n.a1(new Float64Array(16),this.labelPlaneMatrix,b);let Ht=this.width%2/2,St=this.height%2/2,Bt=Math.cos(this.angle),Qt=Math.sin(this.angle),$t=d-Math.round(d)+Bt*Ht+Qt*St,oe=v-Math.round(v)+Bt*St+Qt*Ht,pe=new Float64Array(b);if(n.$(pe,pe,[$t>.5?$t-1:$t,oe>.5?oe-1:oe,0]),this.alignedProjMatrix=pe,b=n.as(new Float64Array(16),this.pixelMatrix),!b)throw new Error(\"failed to invert matrix\");this.pixelMatrixInverse=b,this._posMatrixCache={},this._alignedPosMatrixCache={}}maxPitchScaleFactor(){if(!this.pixelMatrixInverse)return 1;let l=this.pointCoordinate(new n.P(0,0)),d=[l.x*this.worldSize,l.y*this.worldSize,0,1];return n.ag(d,d,this.pixelMatrix)[3]/this.cameraToCenterDistance}getCameraPoint(){let l=Math.tan(this._pitch)*(this.cameraToCenterDistance||1);return this.centerPoint.add(new n.P(0,l))}getCameraQueryGeometry(l){let d=this.getCameraPoint();if(l.length===1)return[l[0],d];{let v=d.x,b=d.y,M=d.x,O=d.y;for(let B of l)v=Math.min(v,B.x),b=Math.min(b,B.y),M=Math.max(M,B.x),O=Math.max(O,B.y);return[new n.P(v,b),new n.P(M,b),new n.P(M,O),new n.P(v,O),new n.P(v,b)]}}}function lh(T,l){let d,v=!1,b=null,M=null,O=()=>{b=null,v&&(T.apply(M,d),b=setTimeout(O,l),v=!1)};return(...B)=>(v=!0,M=this,d=B,b||O(),b)}class Ld{constructor(l){this._getCurrentHash=()=>{let d=window.location.hash.replace(\"#\",\"\");if(this._hashName){let v;return d.split(\"&\").map(b=>b.split(\"=\")).forEach(b=>{b[0]===this._hashName&&(v=b)}),(v&&v[1]||\"\").split(\"/\")}return d.split(\"/\")},this._onHashChange=()=>{let d=this._getCurrentHash();if(d.length>=3&&!d.some(v=>isNaN(v))){let v=this._map.dragRotate.isEnabled()&&this._map.touchZoomRotate.isEnabled()?+(d[3]||0):this._map.getBearing();return this._map.jumpTo({center:[+d[2],+d[1]],zoom:+d[0],bearing:v,pitch:+(d[4]||0)}),!0}return!1},this._updateHashUnthrottled=()=>{let d=window.location.href.replace(/(#.+)?$/,this.getHashString());try{window.history.replaceState(window.history.state,null,d)}catch{}},this._updateHash=lh(this._updateHashUnthrottled,300),this._hashName=l&&encodeURIComponent(l)}addTo(l){return this._map=l,addEventListener(\"hashchange\",this._onHashChange,!1),this._map.on(\"moveend\",this._updateHash),this}remove(){return removeEventListener(\"hashchange\",this._onHashChange,!1),this._map.off(\"moveend\",this._updateHash),clearTimeout(this._updateHash()),delete this._map,this}getHashString(l){let d=this._map.getCenter(),v=Math.round(100*this._map.getZoom())/100,b=Math.ceil((v*Math.LN2+Math.log(512/360/.5))/Math.LN10),M=Math.pow(10,b),O=Math.round(d.lng*M)/M,B=Math.round(d.lat*M)/M,U=this._map.getBearing(),W=this._map.getPitch(),Z=\"\";if(Z+=l?`/${O}/${B}/${v}`:`${v}/${B}/${O}`,(U||W)&&(Z+=\"/\"+Math.round(10*U)/10),W&&(Z+=`/${Math.round(W)}`),this._hashName){let $=this._hashName,st=!1,At=window.location.hash.slice(1).split(\"&\").map(pt=>{let yt=pt.split(\"=\")[0];return yt===$?(st=!0,`${yt}=${Z}`):pt}).filter(pt=>pt);return st||At.push(`${$}=${Z}`),`#${At.join(\"&\")}`}return`#${Z}`}}let ch={linearity:.3,easing:n.ba(0,0,.3,1)},Jp=n.e({deceleration:2500,maxSpeed:1400},ch),tA=n.e({deceleration:20,maxSpeed:1400},ch),A_=n.e({deceleration:1e3,maxSpeed:360},ch),m_=n.e({deceleration:1e3,maxSpeed:90},ch);class n0{constructor(l){this._map=l,this.clear()}clear(){this._inertiaBuffer=[]}record(l){this._drainInertiaBuffer(),this._inertiaBuffer.push({time:n.h.now(),settings:l})}_drainInertiaBuffer(){let l=this._inertiaBuffer,d=n.h.now();for(;l.length>0&&d-l[0].time>160;)l.shift()}_onMoveEnd(l){if(this._drainInertiaBuffer(),this._inertiaBuffer.length<2)return;let d={zoom:0,bearing:0,pitch:0,pan:new n.P(0,0),pinchAround:void 0,around:void 0};for(let{settings:M}of this._inertiaBuffer)d.zoom+=M.zoomDelta||0,d.bearing+=M.bearingDelta||0,d.pitch+=M.pitchDelta||0,M.panDelta&&d.pan._add(M.panDelta),M.around&&(d.around=M.around),M.pinchAround&&(d.pinchAround=M.pinchAround);let v=this._inertiaBuffer[this._inertiaBuffer.length-1].time-this._inertiaBuffer[0].time,b={};if(d.pan.mag()){let M=uh(d.pan.mag(),v,n.e({},Jp,l||{}));b.offset=d.pan.mult(M.amount/d.pan.mag()),b.center=this._map.transform.center,pl(b,M)}if(d.zoom){let M=uh(d.zoom,v,tA);b.zoom=this._map.transform.zoom+M.amount,pl(b,M)}if(d.bearing){let M=uh(d.bearing,v,A_);b.bearing=this._map.transform.bearing+n.ad(M.amount,-179,179),pl(b,M)}if(d.pitch){let M=uh(d.pitch,v,m_);b.pitch=this._map.transform.pitch+M.amount,pl(b,M)}if(b.zoom||b.bearing){let M=d.pinchAround===void 0?d.around:d.pinchAround;b.around=M?this._map.unproject(M):this._map.getCenter()}return this.clear(),n.e(b,{noMoveStart:!0})}}function pl(T,l){(!T.duration||T.durationd.unproject(U)),B=M.reduce((U,W,Z,$)=>U.add(W.div($.length)),new n.P(0,0));super(l,{points:M,point:B,lngLats:O,lngLat:d.unproject(B),originalEvent:v}),this._defaultPrevented=!1}}class g_ extends n.k{preventDefault(){this._defaultPrevented=!0}get defaultPrevented(){return this._defaultPrevented}constructor(l,d,v){super(l,{originalEvent:v}),this._defaultPrevented=!1}}class js{constructor(l,d){this._map=l,this._clickTolerance=d.clickTolerance}reset(){delete this._mousedownPos}wheel(l){return this._firePreventable(new g_(l.type,this._map,l))}mousedown(l,d){return this._mousedownPos=d,this._firePreventable(new la(l.type,this._map,l))}mouseup(l){this._map.fire(new la(l.type,this._map,l))}click(l,d){this._mousedownPos&&this._mousedownPos.dist(d)>=this._clickTolerance||this._map.fire(new la(l.type,this._map,l))}dblclick(l){return this._firePreventable(new la(l.type,this._map,l))}mouseover(l){this._map.fire(new la(l.type,this._map,l))}mouseout(l){this._map.fire(new la(l.type,this._map,l))}touchstart(l){return this._firePreventable(new kd(l.type,this._map,l))}touchmove(l){this._map.fire(new kd(l.type,this._map,l))}touchend(l){this._map.fire(new kd(l.type,this._map,l))}touchcancel(l){this._map.fire(new kd(l.type,this._map,l))}_firePreventable(l){if(this._map.fire(l),l.defaultPrevented)return{}}isEnabled(){return!0}isActive(){return!1}enable(){}disable(){}}class gu{constructor(l){this._map=l}reset(){this._delayContextMenu=!1,this._ignoreContextMenu=!0,delete this._contextMenuEvent}mousemove(l){this._map.fire(new la(l.type,this._map,l))}mousedown(){this._delayContextMenu=!0,this._ignoreContextMenu=!1}mouseup(){this._delayContextMenu=!1,this._contextMenuEvent&&(this._map.fire(new la(\"contextmenu\",this._map,this._contextMenuEvent)),delete this._contextMenuEvent)}contextmenu(l){this._delayContextMenu?this._contextMenuEvent=l:this._ignoreContextMenu||this._map.fire(new la(l.type,this._map,l)),this._map.listens(\"contextmenu\")&&l.preventDefault()}isEnabled(){return!0}isActive(){return!1}enable(){}disable(){}}class Ln{constructor(l){this._map=l}get transform(){return this._map._requestedCameraState||this._map.transform}get center(){return{lng:this.transform.center.lng,lat:this.transform.center.lat}}get zoom(){return this.transform.zoom}get pitch(){return this.transform.pitch}get bearing(){return this.transform.bearing}unproject(l){return this.transform.pointLocation(n.P.convert(l),this._map.terrain)}}class eA{constructor(l,d){this._map=l,this._tr=new Ln(l),this._el=l.getCanvasContainer(),this._container=l.getContainer(),this._clickTolerance=d.clickTolerance||1}isEnabled(){return!!this._enabled}isActive(){return!!this._active}enable(){this.isEnabled()||(this._enabled=!0)}disable(){this.isEnabled()&&(this._enabled=!1)}mousedown(l,d){this.isEnabled()&&l.shiftKey&&l.button===0&&(c.disableDrag(),this._startPos=this._lastPos=d,this._active=!0)}mousemoveWindow(l,d){if(!this._active)return;let v=d;if(this._lastPos.equals(v)||!this._box&&v.dist(this._startPos)M.fitScreenCoordinates(v,b,this._tr.bearing,{linear:!0})};this._fireEvent(\"boxzoomcancel\",l)}keydown(l){this._active&&l.keyCode===27&&(this.reset(),this._fireEvent(\"boxzoomcancel\",l))}reset(){this._active=!1,this._container.classList.remove(\"maplibregl-crosshair\"),this._box&&(c.remove(this._box),this._box=null),c.enableDrag(),delete this._startPos,delete this._lastPos}_fireEvent(l,d){return this._map.fire(new n.k(l,{originalEvent:d}))}}function ca(T,l){if(T.length!==l.length)throw new Error(`The number of touches and points are not equal - touches ${T.length}, points ${l.length}`);let d={};for(let v=0;vthis.numTouches)&&(this.aborted=!0),this.aborted||(this.startTime===void 0&&(this.startTime=l.timeStamp),v.length===this.numTouches&&(this.centroid=function(b){let M=new n.P(0,0);for(let O of b)M._add(O);return M.div(b.length)}(d),this.touches=ca(v,d)))}touchmove(l,d,v){if(this.aborted||!this.centroid)return;let b=ca(v,d);for(let M in this.touches){let O=b[M];(!O||O.dist(this.touches[M])>30)&&(this.aborted=!0)}}touchend(l,d,v){if((!this.centroid||l.timeStamp-this.startTime>500)&&(this.aborted=!0),v.length===0){let b=!this.aborted&&this.centroid;if(this.reset(),b)return b}}}class Rd{constructor(l){this.singleTap=new Fa(l),this.numTaps=l.numTaps,this.reset()}reset(){this.lastTime=1/0,delete this.lastTap,this.count=0,this.singleTap.reset()}touchstart(l,d,v){this.singleTap.touchstart(l,d,v)}touchmove(l,d,v){this.singleTap.touchmove(l,d,v)}touchend(l,d,v){let b=this.singleTap.touchend(l,d,v);if(b){let M=l.timeStamp-this.lastTime<500,O=!this.lastTap||this.lastTap.dist(b)<30;if(M&&O||this.reset(),this.count++,this.lastTime=l.timeStamp,this.lastTap=b,this.count===this.numTaps)return this.reset(),b}}}class Al{constructor(l){this._tr=new Ln(l),this._zoomIn=new Rd({numTouches:1,numTaps:2}),this._zoomOut=new Rd({numTouches:2,numTaps:1}),this.reset()}reset(){this._active=!1,this._zoomIn.reset(),this._zoomOut.reset()}touchstart(l,d,v){this._zoomIn.touchstart(l,d,v),this._zoomOut.touchstart(l,d,v)}touchmove(l,d,v){this._zoomIn.touchmove(l,d,v),this._zoomOut.touchmove(l,d,v)}touchend(l,d,v){let b=this._zoomIn.touchend(l,d,v),M=this._zoomOut.touchend(l,d,v),O=this._tr;return b?(this._active=!0,l.preventDefault(),setTimeout(()=>this.reset(),0),{cameraAnimation:B=>B.easeTo({duration:300,zoom:O.zoom+1,around:O.unproject(b)},{originalEvent:l})}):M?(this._active=!0,l.preventDefault(),setTimeout(()=>this.reset(),0),{cameraAnimation:B=>B.easeTo({duration:300,zoom:O.zoom-1,around:O.unproject(M)},{originalEvent:l})}):void 0}touchcancel(){this.reset()}enable(){this._enabled=!0}disable(){this._enabled=!1,this.reset()}isEnabled(){return this._enabled}isActive(){return this._active}}class za{constructor(l){this._enabled=!!l.enable,this._moveStateManager=l.moveStateManager,this._clickTolerance=l.clickTolerance||1,this._moveFunction=l.move,this._activateOnStart=!!l.activateOnStart,l.assignEvents(this),this.reset()}reset(l){this._active=!1,this._moved=!1,delete this._lastPoint,this._moveStateManager.endMove(l)}_move(...l){let d=this._moveFunction(...l);if(d.bearingDelta||d.pitchDelta||d.around||d.panDelta)return this._active=!0,d}dragStart(l,d){this.isEnabled()&&!this._lastPoint&&this._moveStateManager.isValidStartEvent(l)&&(this._moveStateManager.startMove(l),this._lastPoint=d.length?d[0]:d,this._activateOnStart&&this._lastPoint&&(this._active=!0))}dragMove(l,d){if(!this.isEnabled())return;let v=this._lastPoint;if(!v)return;if(l.preventDefault(),!this._moveStateManager.isValidMoveEvent(l))return void this.reset(l);let b=d.length?d[0]:d;return!this._moved&&b.dist(v){T.mousedown=T.dragStart,T.mousemoveWindow=T.dragMove,T.mouseup=T.dragEnd,T.contextmenu=function(l){l.preventDefault()}},Na=({enable:T,clickTolerance:l,bearingDegreesPerPixelMoved:d=.8})=>{let v=new rA({checkCorrectEvent:b=>c.mouseButton(b)===0&&b.ctrlKey||c.mouseButton(b)===2});return new za({clickTolerance:l,move:(b,M)=>({bearingDelta:(M.x-b.x)*d}),moveStateManager:v,enable:T,assignEvents:fh})},co=({enable:T,clickTolerance:l,pitchDegreesPerPixelMoved:d=-.5})=>{let v=new rA({checkCorrectEvent:b=>c.mouseButton(b)===0&&b.ctrlKey||c.mouseButton(b)===2});return new za({clickTolerance:l,move:(b,M)=>({pitchDelta:(M.y-b.y)*d}),moveStateManager:v,enable:T,assignEvents:fh})};class Ge{constructor(l,d){this._minTouches=l.cooperativeGestures?2:1,this._clickTolerance=l.clickTolerance||1,this._map=d,this.reset()}reset(){this._active=!1,this._touches={},this._sum=new n.P(0,0),setTimeout(()=>{this._cancelCooperativeMessage=!1},200)}touchstart(l,d,v){return this._calculateTransform(l,d,v)}touchmove(l,d,v){if(this._map._cooperativeGestures&&(this._minTouches===2&&v.length<2&&!this._cancelCooperativeMessage?this._map._onCooperativeGesture(l,!1,v.length):this._cancelCooperativeMessage||(this._cancelCooperativeMessage=!0)),this._active&&!(v.length0&&(this._active=!0);let b=ca(v,d),M=new n.P(0,0),O=new n.P(0,0),B=0;for(let W in b){let Z=b[W],$=this._touches[W];$&&(M._add(Z),O._add(Z.sub($)),B++,b[W]=Z)}if(this._touches=b,BMath.abs(T.x)}class zx extends Dd{constructor(l){super(),this._map=l}reset(){super.reset(),this._valid=void 0,delete this._firstMove,delete this._lastPoints}touchstart(l,d,v){super.touchstart(l,d,v),this._currentTouchCount=v.length}_start(l){this._lastPoints=l,a0(l[0].sub(l[1]))&&(this._valid=!1)}_move(l,d,v){if(this._map._cooperativeGestures&&this._currentTouchCount<3)return;let b=l[0].sub(this._lastPoints[0]),M=l[1].sub(this._lastPoints[1]);return this._valid=this.gestureBeginsVertically(b,M,v.timeStamp),this._valid?(this._lastPoints=l,this._active=!0,{pitchDelta:(b.y+M.y)/2*-.5}):void 0}gestureBeginsVertically(l,d,v){if(this._valid!==void 0)return this._valid;let b=l.mag()>=2,M=d.mag()>=2;if(!b&&!M)return;if(!b||!M)return this._firstMove===void 0&&(this._firstMove=v),v-this._firstMove<100&&void 0;let O=l.y>0==d.y>0;return a0(l)&&a0(d)&&O}}let dh={panStep:100,bearingStep:15,pitchStep:10};class y_{constructor(l){this._tr=new Ln(l);let d=dh;this._panStep=d.panStep,this._bearingStep=d.bearingStep,this._pitchStep=d.pitchStep,this._rotationDisabled=!1}reset(){this._active=!1}keydown(l){if(l.altKey||l.ctrlKey||l.metaKey)return;let d=0,v=0,b=0,M=0,O=0;switch(l.keyCode){case 61:case 107:case 171:case 187:d=1;break;case 189:case 109:case 173:d=-1;break;case 37:l.shiftKey?v=-1:(l.preventDefault(),M=-1);break;case 39:l.shiftKey?v=1:(l.preventDefault(),M=1);break;case 38:l.shiftKey?b=1:(l.preventDefault(),O=-1);break;case 40:l.shiftKey?b=-1:(l.preventDefault(),O=1);break;default:return}return this._rotationDisabled&&(v=0,b=0),{cameraAnimation:B=>{let U=this._tr;B.easeTo({duration:300,easeId:\"keyboardHandler\",easing:l0,zoom:d?Math.round(U.zoom)+d*(l.shiftKey?2:1):U.zoom,bearing:U.bearing+v*this._bearingStep,pitch:U.pitch+b*this._pitchStep,offset:[-M*this._panStep,-O*this._panStep],center:U.center},{originalEvent:l})}}}enable(){this._enabled=!0}disable(){this._enabled=!1,this.reset()}isEnabled(){return this._enabled}isActive(){return this._active}disableRotation(){this._rotationDisabled=!0}enableRotation(){this._rotationDisabled=!1}}function l0(T){return T*(2-T)}let c0=4.000244140625;class bf{constructor(l,d){this._onTimeout=v=>{this._type=\"wheel\",this._delta-=this._lastValue,this._active||this._start(v)},this._map=l,this._tr=new Ln(l),this._el=l.getCanvasContainer(),this._triggerRenderFrame=d,this._delta=0,this._defaultZoomRate=.01,this._wheelZoomRate=.0022222222222222222}setZoomRate(l){this._defaultZoomRate=l}setWheelZoomRate(l){this._wheelZoomRate=l}isEnabled(){return!!this._enabled}isActive(){return!!this._active||this._finishTimeout!==void 0}isZooming(){return!!this._zooming}enable(l){this.isEnabled()||(this._enabled=!0,this._aroundCenter=!!l&&l.around===\"center\")}disable(){this.isEnabled()&&(this._enabled=!1)}wheel(l){if(!this.isEnabled())return;if(this._map._cooperativeGestures){if(!l[this._map._metaKey])return;l.preventDefault()}let d=l.deltaMode===WheelEvent.DOM_DELTA_LINE?40*l.deltaY:l.deltaY,v=n.h.now(),b=v-(this._lastWheelEventTime||0);this._lastWheelEventTime=v,d!==0&&d%c0==0?this._type=\"wheel\":d!==0&&Math.abs(d)<4?this._type=\"trackpad\":b>400?(this._type=null,this._lastValue=d,this._timeout=setTimeout(this._onTimeout,40,l)):this._type||(this._type=Math.abs(b*d)<200?\"trackpad\":\"wheel\",this._timeout&&(clearTimeout(this._timeout),this._timeout=null,d+=this._lastValue)),l.shiftKey&&d&&(d/=4),this._type&&(this._lastWheelEvent=l,this._delta-=d,this._active||this._start(l)),l.preventDefault()}_start(l){if(!this._delta)return;this._frameId&&(this._frameId=null),this._active=!0,this.isZooming()||(this._zooming=!0),this._finishTimeout&&(clearTimeout(this._finishTimeout),delete this._finishTimeout);let d=c.mousePos(this._el,l),v=this._tr;this._around=n.L.convert(this._aroundCenter?v.center:v.unproject(d)),this._aroundPoint=v.transform.locationPoint(this._around),this._frameId||(this._frameId=!0,this._triggerRenderFrame())}renderFrame(){if(!this._frameId||(this._frameId=null,!this.isActive()))return;let l=this._tr.transform;if(this._delta!==0){let B=this._type===\"wheel\"&&Math.abs(this._delta)>c0?this._wheelZoomRate:this._defaultZoomRate,U=2/(1+Math.exp(-Math.abs(this._delta*B)));this._delta<0&&U!==0&&(U=1/U);let W=typeof this._targetZoom==\"number\"?l.zoomScale(this._targetZoom):l.scale;this._targetZoom=Math.min(l.maxZoom,Math.max(l.minZoom,l.scaleZoom(W*U))),this._type===\"wheel\"&&(this._startZoom=l.zoom,this._easing=this._smoothOutEasing(200)),this._delta=0}let d=typeof this._targetZoom==\"number\"?this._targetZoom:l.zoom,v=this._startZoom,b=this._easing,M,O=!1;if(this._type===\"wheel\"&&v&&b){let B=Math.min((n.h.now()-this._lastWheelEventTime)/200,1),U=b(B);M=n.B.number(v,d,U),B<1?this._frameId||(this._frameId=!0):O=!0}else M=d,O=!0;return this._active=!0,O&&(this._active=!1,this._finishTimeout=setTimeout(()=>{this._zooming=!1,this._triggerRenderFrame(),delete this._targetZoom,delete this._finishTimeout},200)),{noInertia:!0,needsRenderFrame:!O,zoomDelta:M-l.zoom,around:this._aroundPoint,originalEvent:this._lastWheelEvent}}_smoothOutEasing(l){let d=n.bb;if(this._prevEase){let v=this._prevEase,b=(n.h.now()-v.start)/v.duration,M=v.easing(b+.01)-v.easing(b),O=.27/Math.sqrt(M*M+1e-4)*.01,B=Math.sqrt(.0729-O*O);d=n.ba(O,B,.25,1)}return this._prevEase={start:n.h.now(),duration:l,easing:d},d}reset(){this._active=!1,this._zooming=!1,delete this._targetZoom,this._finishTimeout&&(clearTimeout(this._finishTimeout),delete this._finishTimeout)}}class u0{constructor(l,d){this._clickZoom=l,this._tapZoom=d}enable(){this._clickZoom.enable(),this._tapZoom.enable()}disable(){this._clickZoom.disable(),this._tapZoom.disable()}isEnabled(){return this._clickZoom.isEnabled()&&this._tapZoom.isEnabled()}isActive(){return this._clickZoom.isActive()||this._tapZoom.isActive()}}class iA{constructor(l){this._tr=new Ln(l),this.reset()}reset(){this._active=!1}dblclick(l,d){return l.preventDefault(),{cameraAnimation:v=>{v.easeTo({duration:300,zoom:this._tr.zoom+(l.shiftKey?-1:1),around:this._tr.unproject(d)},{originalEvent:l})}}}enable(){this._enabled=!0}disable(){this._enabled=!1,this.reset()}isEnabled(){return this._enabled}isActive(){return this._active}}class nA{constructor(){this._tap=new Rd({numTouches:1,numTaps:1}),this.reset()}reset(){this._active=!1,delete this._swipePoint,delete this._swipeTouch,delete this._tapTime,delete this._tapPoint,this._tap.reset()}touchstart(l,d,v){if(!this._swipePoint)if(this._tapTime){let b=d[0],M=l.timeStamp-this._tapTime<500,O=this._tapPoint.dist(b)<30;M&&O?v.length>0&&(this._swipePoint=b,this._swipeTouch=v[0].identifier):this.reset()}else this._tap.touchstart(l,d,v)}touchmove(l,d,v){if(this._tapTime){if(this._swipePoint){if(v[0].identifier!==this._swipeTouch)return;let b=d[0],M=b.y-this._swipePoint.y;return this._swipePoint=b,l.preventDefault(),this._active=!0,{zoomDelta:M/128}}}else this._tap.touchmove(l,d,v)}touchend(l,d,v){if(this._tapTime)this._swipePoint&&v.length===0&&this.reset();else{let b=this._tap.touchend(l,d,v);b&&(this._tapTime=l.timeStamp,this._tapPoint=b)}}touchcancel(){this.reset()}enable(){this._enabled=!0}disable(){this._enabled=!1,this.reset()}isEnabled(){return this._enabled}isActive(){return this._active}}class ph{constructor(l,d,v){this._el=l,this._mousePan=d,this._touchPan=v}enable(l){this._inertiaOptions=l||{},this._mousePan.enable(),this._touchPan.enable(),this._el.classList.add(\"maplibregl-touch-drag-pan\")}disable(){this._mousePan.disable(),this._touchPan.disable(),this._el.classList.remove(\"maplibregl-touch-drag-pan\")}isEnabled(){return this._mousePan.isEnabled()&&this._touchPan.isEnabled()}isActive(){return this._mousePan.isActive()||this._touchPan.isActive()}}class us{constructor(l,d,v){this._pitchWithRotate=l.pitchWithRotate,this._mouseRotate=d,this._mousePitch=v}enable(){this._mouseRotate.enable(),this._pitchWithRotate&&this._mousePitch.enable()}disable(){this._mouseRotate.disable(),this._mousePitch.disable()}isEnabled(){return this._mouseRotate.isEnabled()&&(!this._pitchWithRotate||this._mousePitch.isEnabled())}isActive(){return this._mouseRotate.isActive()||this._mousePitch.isActive()}}class _u{constructor(l,d,v,b){this._el=l,this._touchZoom=d,this._touchRotate=v,this._tapDragZoom=b,this._rotationDisabled=!1,this._enabled=!0}enable(l){this._touchZoom.enable(l),this._rotationDisabled||this._touchRotate.enable(l),this._tapDragZoom.enable(),this._el.classList.add(\"maplibregl-touch-zoom-rotate\")}disable(){this._touchZoom.disable(),this._touchRotate.disable(),this._tapDragZoom.disable(),this._el.classList.remove(\"maplibregl-touch-zoom-rotate\")}isEnabled(){return this._touchZoom.isEnabled()&&(this._rotationDisabled||this._touchRotate.isEnabled())&&this._tapDragZoom.isEnabled()}isActive(){return this._touchZoom.isActive()||this._touchRotate.isActive()||this._tapDragZoom.isActive()}disableRotation(){this._rotationDisabled=!0,this._touchRotate.disable()}enableRotation(){this._rotationDisabled=!1,this._touchZoom.isEnabled()&&this._touchRotate.enable()}}let Bc=T=>T.zoom||T.drag||T.pitch||T.rotate;class h0 extends n.k{}function Od(T){return T.panDelta&&T.panDelta.mag()||T.zoomDelta||T.bearingDelta||T.pitchDelta}class f0{constructor(l,d){this.handleWindowEvent=b=>{this.handleEvent(b,`${b.type}Window`)},this.handleEvent=(b,M)=>{if(b.type===\"blur\")return void this.stop(!0);this._updatingCamera=!0;let O=b.type===\"renderFrame\"?void 0:b,B={needsRenderFrame:!1},U={},W={},Z=b.touches,$=Z?this._getMapTouches(Z):void 0,st=$?c.touchPos(this._el,$):c.mousePos(this._el,b);for(let{handlerName:yt,handler:dt,allowed:Ft}of this._handlers){if(!dt.isEnabled())continue;let Ht;this._blockedByActive(W,Ft,yt)?dt.reset():dt[M||b.type]&&(Ht=dt[M||b.type](b,st,$),this.mergeHandlerResult(B,U,Ht,yt,O),Ht&&Ht.needsRenderFrame&&this._triggerRenderFrame()),(Ht||dt.isActive())&&(W[yt]=dt)}let At={};for(let yt in this._previousActiveHandlers)W[yt]||(At[yt]=O);this._previousActiveHandlers=W,(Object.keys(At).length||Od(B))&&(this._changes.push([B,U,At]),this._triggerRenderFrame()),(Object.keys(W).length||Od(B))&&this._map._stop(!0),this._updatingCamera=!1;let{cameraAnimation:pt}=B;pt&&(this._inertia.clear(),this._fireEvents({},{},!0),this._changes=[],pt(this._map))},this._map=l,this._el=this._map.getCanvasContainer(),this._handlers=[],this._handlersById={},this._changes=[],this._inertia=new n0(l),this._bearingSnap=d.bearingSnap,this._previousActiveHandlers={},this._eventsInProgress={},this._addDefaultHandlers(d);let v=this._el;this._listeners=[[v,\"touchstart\",{passive:!0}],[v,\"touchmove\",{passive:!1}],[v,\"touchend\",void 0],[v,\"touchcancel\",void 0],[v,\"mousedown\",void 0],[v,\"mousemove\",void 0],[v,\"mouseup\",void 0],[document,\"mousemove\",{capture:!0}],[document,\"mouseup\",void 0],[v,\"mouseover\",void 0],[v,\"mouseout\",void 0],[v,\"dblclick\",void 0],[v,\"click\",void 0],[v,\"keydown\",{capture:!1}],[v,\"keyup\",void 0],[v,\"wheel\",{passive:!1}],[v,\"contextmenu\",void 0],[window,\"blur\",void 0]];for(let[b,M,O]of this._listeners)c.addEventListener(b,M,b===document?this.handleWindowEvent:this.handleEvent,O)}destroy(){for(let[l,d,v]of this._listeners)c.removeEventListener(l,d,l===document?this.handleWindowEvent:this.handleEvent,v)}_addDefaultHandlers(l){let d=this._map,v=d.getCanvasContainer();this._add(\"mapEvent\",new js(d,l));let b=d.boxZoom=new eA(d,l);this._add(\"boxZoom\",b),l.interactive&&l.boxZoom&&b.enable();let M=new Al(d),O=new iA(d);d.doubleClickZoom=new u0(O,M),this._add(\"tapZoom\",M),this._add(\"clickZoom\",O),l.interactive&&l.doubleClickZoom&&d.doubleClickZoom.enable();let B=new nA;this._add(\"tapDragZoom\",B);let U=d.touchPitch=new zx(d);this._add(\"touchPitch\",U),l.interactive&&l.touchPitch&&d.touchPitch.enable(l.touchPitch);let W=Na(l),Z=co(l);d.dragRotate=new us(l,W,Z),this._add(\"mouseRotate\",W,[\"mousePitch\"]),this._add(\"mousePitch\",Z,[\"mouseRotate\"]),l.interactive&&l.dragRotate&&d.dragRotate.enable();let $=(({enable:Ft,clickTolerance:Ht})=>{let St=new rA({checkCorrectEvent:Bt=>c.mouseButton(Bt)===0&&!Bt.ctrlKey});return new za({clickTolerance:Ht,move:(Bt,Qt)=>({around:Qt,panDelta:Qt.sub(Bt)}),activateOnStart:!0,moveStateManager:St,enable:Ft,assignEvents:fh})})(l),st=new Ge(l,d);d.dragPan=new ph(v,$,st),this._add(\"mousePan\",$),this._add(\"touchPan\",st,[\"touchZoom\",\"touchRotate\"]),l.interactive&&l.dragPan&&d.dragPan.enable(l.dragPan);let At=new o0,pt=new __;d.touchZoomRotate=new _u(v,pt,At,B),this._add(\"touchRotate\",At,[\"touchPan\",\"touchZoom\"]),this._add(\"touchZoom\",pt,[\"touchPan\",\"touchRotate\"]),l.interactive&&l.touchZoomRotate&&d.touchZoomRotate.enable(l.touchZoomRotate);let yt=d.scrollZoom=new bf(d,()=>this._triggerRenderFrame());this._add(\"scrollZoom\",yt,[\"mousePan\"]),l.interactive&&l.scrollZoom&&d.scrollZoom.enable(l.scrollZoom);let dt=d.keyboard=new y_(d);this._add(\"keyboard\",dt),l.interactive&&l.keyboard&&d.keyboard.enable(),this._add(\"blockableMapEvent\",new gu(d))}_add(l,d,v){this._handlers.push({handlerName:l,handler:d,allowed:v}),this._handlersById[l]=d}stop(l){if(!this._updatingCamera){for(let{handler:d}of this._handlers)d.reset();this._inertia.clear(),this._fireEvents({},{},l),this._changes=[]}}isActive(){for(let{handler:l}of this._handlers)if(l.isActive())return!0;return!1}isZooming(){return!!this._eventsInProgress.zoom||this._map.scrollZoom.isZooming()}isRotating(){return!!this._eventsInProgress.rotate}isMoving(){return!!Bc(this._eventsInProgress)||this.isZooming()}_blockedByActive(l,d,v){for(let b in l)if(b!==v&&(!d||d.indexOf(b)<0))return!0;return!1}_getMapTouches(l){let d=[];for(let v of l)this._el.contains(v.target)&&d.push(v);return d}mergeHandlerResult(l,d,v,b,M){if(!v)return;n.e(l,v);let O={handlerName:b,originalEvent:v.originalEvent||M};v.zoomDelta!==void 0&&(d.zoom=O),v.panDelta!==void 0&&(d.drag=O),v.pitchDelta!==void 0&&(d.pitch=O),v.bearingDelta!==void 0&&(d.rotate=O)}_applyChanges(){let l={},d={},v={};for(let[b,M,O]of this._changes)b.panDelta&&(l.panDelta=(l.panDelta||new n.P(0,0))._add(b.panDelta)),b.zoomDelta&&(l.zoomDelta=(l.zoomDelta||0)+b.zoomDelta),b.bearingDelta&&(l.bearingDelta=(l.bearingDelta||0)+b.bearingDelta),b.pitchDelta&&(l.pitchDelta=(l.pitchDelta||0)+b.pitchDelta),b.around!==void 0&&(l.around=b.around),b.pinchAround!==void 0&&(l.pinchAround=b.pinchAround),b.noInertia&&(l.noInertia=b.noInertia),n.e(d,M),n.e(v,O);this._updateMapTransform(l,d,v),this._changes=[]}_updateMapTransform(l,d,v){let b=this._map,M=b._getTransformForUpdate(),O=b.terrain;if(!(Od(l)||O&&this._terrainMovement))return this._fireEvents(d,v,!0);let{panDelta:B,zoomDelta:U,bearingDelta:W,pitchDelta:Z,around:$,pinchAround:st}=l;st!==void 0&&($=st),b._stop(!0),$=$||b.transform.centerPoint;let At=M.pointLocation(B?$.sub(B):$);W&&(M.bearing+=W),Z&&(M.pitch+=Z),U&&(M.zoom+=U),O?this._terrainMovement||!d.drag&&!d.zoom?d.drag&&this._terrainMovement?M.center=M.pointLocation(M.centerPoint.sub(B)):M.setLocationAtPoint(At,$):(this._terrainMovement=!0,this._map._elevationFreeze=!0,M.setLocationAtPoint(At,$),this._map.once(\"moveend\",()=>{this._map._elevationFreeze=!1,this._terrainMovement=!1,M.recalculateZoom(b.terrain)})):M.setLocationAtPoint(At,$),b._applyUpdatedTransform(M),this._map._update(),l.noInertia||this._inertia.record(l),this._fireEvents(d,v,!0)}_fireEvents(l,d,v){let b=Bc(this._eventsInProgress),M=Bc(l),O={};for(let Z in l){let{originalEvent:$}=l[Z];this._eventsInProgress[Z]||(O[`${Z}start`]=$),this._eventsInProgress[Z]=l[Z]}!b&&M&&this._fireEvent(\"movestart\",M.originalEvent);for(let Z in O)this._fireEvent(Z,O[Z]);M&&this._fireEvent(\"move\",M.originalEvent);for(let Z in l){let{originalEvent:$}=l[Z];this._fireEvent(Z,$)}let B={},U;for(let Z in this._eventsInProgress){let{handlerName:$,originalEvent:st}=this._eventsInProgress[Z];this._handlersById[$].isActive()||(delete this._eventsInProgress[Z],U=d[$]||st,B[`${Z}end`]=U)}for(let Z in B)this._fireEvent(Z,B[Z]);let W=Bc(this._eventsInProgress);if(v&&(b||M)&&!W){this._updatingCamera=!0;let Z=this._inertia._onMoveEnd(this._map.dragPan._inertiaOptions),$=st=>st!==0&&-this._bearingSnap{delete this._frameId,this.handleEvent(new h0(\"renderFrame\",{timeStamp:l})),this._applyChanges()})}_triggerRenderFrame(){this._frameId===void 0&&(this._frameId=this._requestFrame())}}class v_ extends n.E{constructor(l,d){super(),this._renderFrameCallback=()=>{let v=Math.min((n.h.now()-this._easeStart)/this._easeOptions.duration,1);this._onEaseFrame(this._easeOptions.easing(v)),v<1&&this._easeFrameId?this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback):this.stop()},this._moving=!1,this._zooming=!1,this.transform=l,this._bearingSnap=d.bearingSnap,this.on(\"moveend\",()=>{delete this._requestedCameraState})}getCenter(){return new n.L(this.transform.center.lng,this.transform.center.lat)}setCenter(l,d){return this.jumpTo({center:l},d)}panBy(l,d,v){return l=n.P.convert(l).mult(-1),this.panTo(this.transform.center,n.e({offset:l},d),v)}panTo(l,d,v){return this.easeTo(n.e({center:l},d),v)}getZoom(){return this.transform.zoom}setZoom(l,d){return this.jumpTo({zoom:l},d),this}zoomTo(l,d,v){return this.easeTo(n.e({zoom:l},d),v)}zoomIn(l,d){return this.zoomTo(this.getZoom()+1,l,d),this}zoomOut(l,d){return this.zoomTo(this.getZoom()-1,l,d),this}getBearing(){return this.transform.bearing}setBearing(l,d){return this.jumpTo({bearing:l},d),this}getPadding(){return this.transform.padding}setPadding(l,d){return this.jumpTo({padding:l},d),this}rotateTo(l,d,v){return this.easeTo(n.e({bearing:l},d),v)}resetNorth(l,d){return this.rotateTo(0,n.e({duration:1e3},l),d),this}resetNorthPitch(l,d){return this.easeTo(n.e({bearing:0,pitch:0,duration:1e3},l),d),this}snapToNorth(l,d){return Math.abs(this.getBearing()){if(this._zooming&&(v.zoom=n.B.number(b,U,$t)),this._rotating&&(v.bearing=n.B.number(M,W,$t)),this._pitching&&(v.pitch=n.B.number(O,Z,$t)),this._padding&&(v.interpolatePadding(B,$,$t),At=v.centerPoint.add(st)),this.terrain&&!l.freezeElevation&&this._updateElevation($t),St)v.setLocationAtPoint(St,Bt);else{let oe=v.zoomScale(v.zoom-b),pe=U>b?Math.min(2,Ht):Math.max(.5,Ht),he=Math.pow(pe,1-$t),be=v.unproject(dt.add(Ft.mult($t*he)).mult(oe));v.setLocationAtPoint(v.renderWorldCopies?be.wrap():be,At)}this._applyUpdatedTransform(v),this._fireMoveEvents(d)},$t=>{this.terrain&&this._finalizeElevation(),this._afterEase(d,$t)},l),this}_prepareEase(l,d,v={}){this._moving=!0,d||v.moving||this.fire(new n.k(\"movestart\",l)),this._zooming&&!v.zooming&&this.fire(new n.k(\"zoomstart\",l)),this._rotating&&!v.rotating&&this.fire(new n.k(\"rotatestart\",l)),this._pitching&&!v.pitching&&this.fire(new n.k(\"pitchstart\",l))}_prepareElevation(l){this._elevationCenter=l,this._elevationStart=this.transform.elevation,this._elevationTarget=this.terrain.getElevationForLngLatZoom(l,this.transform.tileZoom),this._elevationFreeze=!0}_updateElevation(l){this.transform._minEleveationForCurrentTile=this.terrain.getMinTileElevationForLngLatZoom(this._elevationCenter,this.transform.tileZoom);let d=this.terrain.getElevationForLngLatZoom(this._elevationCenter,this.transform.tileZoom);if(l<1&&d!==this._elevationTarget){let v=this._elevationTarget-this._elevationStart;this._elevationStart+=l*(v-(d-(v*l+this._elevationStart))/(1-l)),this._elevationTarget=d}this.transform.elevation=n.B.number(this._elevationStart,this._elevationTarget,l)}_finalizeElevation(){this._elevationFreeze=!1,this.transform.recalculateZoom(this.terrain)}_getTransformForUpdate(){return this.transformCameraUpdate?(this._requestedCameraState||(this._requestedCameraState=this.transform.clone()),this._requestedCameraState):this.transform}_applyUpdatedTransform(l){if(!this.transformCameraUpdate)return;let d=l.clone(),{center:v,zoom:b,pitch:M,bearing:O,elevation:B}=this.transformCameraUpdate(d);v&&(d.center=v),b!==void 0&&(d.zoom=b),M!==void 0&&(d.pitch=M),O!==void 0&&(d.bearing=O),B!==void 0&&(d.elevation=B),this.transform.apply(d)}_fireMoveEvents(l){this.fire(new n.k(\"move\",l)),this._zooming&&this.fire(new n.k(\"zoom\",l)),this._rotating&&this.fire(new n.k(\"rotate\",l)),this._pitching&&this.fire(new n.k(\"pitch\",l))}_afterEase(l,d){if(this._easeId&&d&&this._easeId===d)return;delete this._easeId;let v=this._zooming,b=this._rotating,M=this._pitching;this._moving=!1,this._zooming=!1,this._rotating=!1,this._pitching=!1,this._padding=!1,v&&this.fire(new n.k(\"zoomend\",l)),b&&this.fire(new n.k(\"rotateend\",l)),M&&this.fire(new n.k(\"pitchend\",l)),this.fire(new n.k(\"moveend\",l))}flyTo(l,d){if(!l.essential&&n.h.prefersReducedMotion){let tr=n.F(l,[\"center\",\"zoom\",\"bearing\",\"pitch\",\"around\"]);return this.jumpTo(tr,d)}this.stop(),l=n.e({offset:[0,0],speed:1.2,curve:1.42,easing:n.bb},l);let v=this._getTransformForUpdate(),b=this.getZoom(),M=this.getBearing(),O=this.getPitch(),B=this.getPadding(),U=\"zoom\"in l?n.ad(+l.zoom,v.minZoom,v.maxZoom):b,W=\"bearing\"in l?this._normalizeBearing(l.bearing,M):M,Z=\"pitch\"in l?+l.pitch:O,$=\"padding\"in l?l.padding:v.padding,st=v.zoomScale(U-b),At=n.P.convert(l.offset),pt=v.centerPoint.add(At),yt=v.pointLocation(pt),dt=n.L.convert(l.center||yt);this._normalizeCenter(dt);let Ft=v.project(yt),Ht=v.project(dt).sub(Ft),St=l.curve,Bt=Math.max(v.width,v.height),Qt=Bt/st,$t=Ht.mag();if(\"minZoom\"in l){let tr=n.ad(Math.min(l.minZoom,b,U),v.minZoom,v.maxZoom),Gi=Bt/v.zoomScale(tr-b);St=Math.sqrt(Gi/$t*2)}let oe=St*St;function pe(tr){let Gi=(Qt*Qt-Bt*Bt+(tr?-1:1)*oe*oe*$t*$t)/(2*(tr?Qt:Bt)*oe*$t);return Math.log(Math.sqrt(Gi*Gi+1)-Gi)}function he(tr){return(Math.exp(tr)-Math.exp(-tr))/2}function be(tr){return(Math.exp(tr)+Math.exp(-tr))/2}let Ze=pe(!1),Kr=function(tr){return be(Ze)/be(Ze+St*tr)},Ee=function(tr){return Bt*((be(Ze)*(he(Gi=Ze+St*tr)/be(Gi))-he(Ze))/oe)/$t;var Gi},pr=(pe(!0)-Ze)/St;if(Math.abs($t)<1e-6||!isFinite(pr)){if(Math.abs(Bt-Qt)<1e-6)return this.easeTo(l,d);let tr=Qtl.maxDuration&&(l.duration=0),this._zooming=!0,this._rotating=M!==W,this._pitching=Z!==O,this._padding=!v.isPaddingEqual($),this._prepareEase(d,!1),this.terrain&&this._prepareElevation(dt),this._ease(tr=>{let Gi=tr*pr,Jr=1/Kr(Gi);v.zoom=tr===1?U:b+v.scaleZoom(Jr),this._rotating&&(v.bearing=n.B.number(M,W,tr)),this._pitching&&(v.pitch=n.B.number(O,Z,tr)),this._padding&&(v.interpolatePadding(B,$,tr),pt=v.centerPoint.add(At)),this.terrain&&!l.freezeElevation&&this._updateElevation(tr);let Vr=tr===1?dt:v.unproject(Ft.add(Ht.mult(Ee(Gi))).mult(Jr));v.setLocationAtPoint(v.renderWorldCopies?Vr.wrap():Vr,pt),this._applyUpdatedTransform(v),this._fireMoveEvents(d)},()=>{this.terrain&&this._finalizeElevation(),this._afterEase(d)},l),this}isEasing(){return!!this._easeFrameId}stop(){return this._stop()}_stop(l,d){if(this._easeFrameId&&(this._cancelRenderFrame(this._easeFrameId),delete this._easeFrameId,delete this._onEaseFrame),this._onEaseEnd){let v=this._onEaseEnd;delete this._onEaseEnd,v.call(this,d)}if(!l){let v=this.handlers;v&&v.stop(!1)}return this}_ease(l,d,v){v.animate===!1||v.duration===0?(l(1),d()):(this._easeStart=n.h.now(),this._easeOptions=v,this._onEaseFrame=l,this._onEaseEnd=d,this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback))}_normalizeBearing(l,d){l=n.b5(l,-180,180);let v=Math.abs(l-d);return Math.abs(l-360-d)180?-360:v<-180?360:0}queryTerrainElevation(l){return this.terrain?this.terrain.getElevationForLngLatZoom(n.L.convert(l),this.transform.tileZoom)-this.transform.elevation:null}}class ua{constructor(l={}){this._toggleAttribution=()=>{this._container.classList.contains(\"maplibregl-compact\")&&(this._container.classList.contains(\"maplibregl-compact-show\")?(this._container.setAttribute(\"open\",\"\"),this._container.classList.remove(\"maplibregl-compact-show\")):(this._container.classList.add(\"maplibregl-compact-show\"),this._container.removeAttribute(\"open\")))},this._updateData=d=>{!d||d.sourceDataType!==\"metadata\"&&d.sourceDataType!==\"visibility\"&&d.dataType!==\"style\"&&d.type!==\"terrain\"||this._updateAttributions()},this._updateCompact=()=>{this._map.getCanvasContainer().offsetWidth<=640||this._compact?this._compact===!1?this._container.setAttribute(\"open\",\"\"):this._container.classList.contains(\"maplibregl-compact\")||this._container.classList.contains(\"maplibregl-attrib-empty\")||(this._container.setAttribute(\"open\",\"\"),this._container.classList.add(\"maplibregl-compact\",\"maplibregl-compact-show\")):(this._container.setAttribute(\"open\",\"\"),this._container.classList.contains(\"maplibregl-compact\")&&this._container.classList.remove(\"maplibregl-compact\",\"maplibregl-compact-show\"))},this._updateCompactMinimize=()=>{this._container.classList.contains(\"maplibregl-compact\")&&this._container.classList.contains(\"maplibregl-compact-show\")&&this._container.classList.remove(\"maplibregl-compact-show\")},this.options=l}getDefaultPosition(){return\"bottom-right\"}onAdd(l){return this._map=l,this._compact=this.options&&this.options.compact,this._container=c.create(\"details\",\"maplibregl-ctrl maplibregl-ctrl-attrib\"),this._compactButton=c.create(\"summary\",\"maplibregl-ctrl-attrib-button\",this._container),this._compactButton.addEventListener(\"click\",this._toggleAttribution),this._setElementTitle(this._compactButton,\"ToggleAttribution\"),this._innerContainer=c.create(\"div\",\"maplibregl-ctrl-attrib-inner\",this._container),this._updateAttributions(),this._updateCompact(),this._map.on(\"styledata\",this._updateData),this._map.on(\"sourcedata\",this._updateData),this._map.on(\"terrain\",this._updateData),this._map.on(\"resize\",this._updateCompact),this._map.on(\"drag\",this._updateCompactMinimize),this._container}onRemove(){c.remove(this._container),this._map.off(\"styledata\",this._updateData),this._map.off(\"sourcedata\",this._updateData),this._map.off(\"terrain\",this._updateData),this._map.off(\"resize\",this._updateCompact),this._map.off(\"drag\",this._updateCompactMinimize),this._map=void 0,this._compact=void 0,this._attribHTML=void 0}_setElementTitle(l,d){let v=this._map._getUIString(`AttributionControl.${d}`);l.title=v,l.setAttribute(\"aria-label\",v)}_updateAttributions(){if(!this._map.style)return;let l=[];if(this.options.customAttribution&&(Array.isArray(this.options.customAttribution)?l=l.concat(this.options.customAttribution.map(b=>typeof b!=\"string\"?\"\":b)):typeof this.options.customAttribution==\"string\"&&l.push(this.options.customAttribution)),this._map.style.stylesheet){let b=this._map.style.stylesheet;this.styleOwner=b.owner,this.styleId=b.id}let d=this._map.style.sourceCaches;for(let b in d){let M=d[b];if(M.used||M.usedForTerrain){let O=M.getSource();O.attribution&&l.indexOf(O.attribution)<0&&l.push(O.attribution)}}l=l.filter(b=>String(b).trim()),l.sort((b,M)=>b.length-M.length),l=l.filter((b,M)=>{for(let O=M+1;O=0)return!1;return!0});let v=l.join(\" | \");v!==this._attribHTML&&(this._attribHTML=v,l.length?(this._innerContainer.innerHTML=v,this._container.classList.remove(\"maplibregl-attrib-empty\")):this._container.classList.add(\"maplibregl-attrib-empty\"),this._updateCompact(),this._editLink=null)}}class un{constructor(l={}){this._updateCompact=()=>{let d=this._container.children;if(d.length){let v=d[0];this._map.getCanvasContainer().offsetWidth<=640||this._compact?this._compact!==!1&&v.classList.add(\"maplibregl-compact\"):v.classList.remove(\"maplibregl-compact\")}},this.options=l}getDefaultPosition(){return\"bottom-left\"}onAdd(l){this._map=l,this._compact=this.options&&this.options.compact,this._container=c.create(\"div\",\"maplibregl-ctrl\");let d=c.create(\"a\",\"maplibregl-ctrl-logo\");return d.target=\"_blank\",d.rel=\"noopener nofollow\",d.href=\"https://maplibre.org/\",d.setAttribute(\"aria-label\",this._map._getUIString(\"LogoControl.Title\")),d.setAttribute(\"rel\",\"noopener nofollow\"),this._container.appendChild(d),this._container.style.display=\"block\",this._map.on(\"resize\",this._updateCompact),this._updateCompact(),this._container}onRemove(){c.remove(this._container),this._map.off(\"resize\",this._updateCompact),this._map=void 0,this._compact=void 0}}class sA{constructor(){this._queue=[],this._id=0,this._cleared=!1,this._currentlyRunning=!1}add(l){let d=++this._id;return this._queue.push({callback:l,id:d,cancelled:!1}),d}remove(l){let d=this._currentlyRunning,v=d?this._queue.concat(d):this._queue;for(let b of v)if(b.id===l)return void(b.cancelled=!0)}run(l=0){if(this._currentlyRunning)throw new Error(\"Attempting to run(), but is already running.\");let d=this._currentlyRunning=this._queue;this._queue=[];for(let v of d)if(!v.cancelled&&(v.callback(l),this._cleared))break;this._cleared=!1,this._currentlyRunning=!1}clear(){this._currentlyRunning&&(this._cleared=!0),this._queue=[]}}let d0={\"AttributionControl.ToggleAttribution\":\"Toggle attribution\",\"AttributionControl.MapFeedback\":\"Map feedback\",\"FullscreenControl.Enter\":\"Enter fullscreen\",\"FullscreenControl.Exit\":\"Exit fullscreen\",\"GeolocateControl.FindMyLocation\":\"Find my location\",\"GeolocateControl.LocationNotAvailable\":\"Location not available\",\"LogoControl.Title\":\"Mapbox logo\",\"NavigationControl.ResetBearing\":\"Reset bearing to north\",\"NavigationControl.ZoomIn\":\"Zoom in\",\"NavigationControl.ZoomOut\":\"Zoom out\",\"ScaleControl.Feet\":\"ft\",\"ScaleControl.Meters\":\"m\",\"ScaleControl.Kilometers\":\"km\",\"ScaleControl.Miles\":\"mi\",\"ScaleControl.NauticalMiles\":\"nm\",\"TerrainControl.enableTerrain\":\"Enable terrain\",\"TerrainControl.disableTerrain\":\"Disable terrain\"};var Ah=n.Q([{name:\"a_pos3d\",type:\"Int16\",components:3}]);class x_ extends n.E{constructor(l){super(),this.sourceCache=l,this._tiles={},this._renderableTilesKeys=[],this._sourceTileCache={},this.minzoom=0,this.maxzoom=22,this.tileSize=512,this.deltaZoom=1,l.usedForTerrain=!0,l.tileSize=this.tileSize*2**this.deltaZoom}destruct(){this.sourceCache.usedForTerrain=!1,this.sourceCache.tileSize=null}update(l,d){this.sourceCache.update(l,d),this._renderableTilesKeys=[];let v={};for(let b of l.coveringTiles({tileSize:this.tileSize,minzoom:this.minzoom,maxzoom:this.maxzoom,reparseOverscaled:!1,terrain:d}))v[b.key]=!0,this._renderableTilesKeys.push(b.key),this._tiles[b.key]||(b.posMatrix=new Float64Array(16),n.aS(b.posMatrix,0,n.N,0,n.N,0,1),this._tiles[b.key]=new ao(b,this.tileSize));for(let b in this._tiles)v[b]||delete this._tiles[b]}freeRtt(l){for(let d in this._tiles){let v=this._tiles[d];(!l||v.tileID.equals(l)||v.tileID.isChildOf(l)||l.isChildOf(v.tileID))&&(v.rtt=[])}}getRenderableTiles(){return this._renderableTilesKeys.map(l=>this.getTileByID(l))}getTileByID(l){return this._tiles[l]}getTerrainCoords(l){let d={};for(let v of this._renderableTilesKeys){let b=this._tiles[v].tileID;if(b.canonical.equals(l.canonical)){let M=l.clone();M.posMatrix=new Float64Array(16),n.aS(M.posMatrix,0,n.N,0,n.N,0,1),d[v]=M}else if(b.canonical.isChildOf(l.canonical)){let M=l.clone();M.posMatrix=new Float64Array(16);let O=b.canonical.z-l.canonical.z,B=b.canonical.x-(b.canonical.x>>O<>O<>O;n.aS(M.posMatrix,0,W,0,W,0,1),n.$(M.posMatrix,M.posMatrix,[-B*W,-U*W,0]),d[v]=M}else if(l.canonical.isChildOf(b.canonical)){let M=l.clone();M.posMatrix=new Float64Array(16);let O=l.canonical.z-b.canonical.z,B=l.canonical.x-(l.canonical.x>>O<>O<>O;n.aS(M.posMatrix,0,n.N,0,n.N,0,1),n.$(M.posMatrix,M.posMatrix,[B*W,U*W,0]),n.a0(M.posMatrix,M.posMatrix,[1/2**O,1/2**O,0]),d[v]=M}}return d}getSourceTile(l,d){let v=this.sourceCache._source,b=l.overscaledZ-this.deltaZoom;if(b>v.maxzoom&&(b=v.maxzoom),b=v.minzoom&&(!M||!M.dem);)M=this.sourceCache.getTileByID(l.scaledTo(b--).key);return M}tilesAfterTime(l=Date.now()){return Object.values(this._tiles).filter(d=>d.timeAdded>=l)}}class b_{constructor(l,d,v){this.painter=l,this.sourceCache=new x_(d),this.options=v,this.exaggeration=typeof v.exaggeration==\"number\"?v.exaggeration:1,this.qualityFactor=2,this.meshSize=128,this._demMatrixCache={},this.coordsIndex=[],this._coordsTextureSize=1024}getDEMElevation(l,d,v,b=n.N){var M;if(!(d>=0&&d=0&&vl.canonical.z&&(l.canonical.z>=b?M=l.canonical.z-b:n.w(\"cannot calculate elevation if elevation maxzoom > source.maxzoom\"));let O=l.canonical.x-(l.canonical.x>>M<>M<>8<<4|M>>8,d[O+3]=0;let v=new n.R({width:this._coordsTextureSize,height:this._coordsTextureSize},new Uint8Array(d.buffer)),b=new qt(l,v,l.gl.RGBA,{premultiply:!1});return b.bind(l.gl.NEAREST,l.gl.CLAMP_TO_EDGE),this._coordsTexture=b,b}pointCoordinate(l){let d=new Uint8Array(4),v=this.painter.context,b=v.gl;v.bindFramebuffer.set(this.getFramebuffer(\"coords\").framebuffer),b.readPixels(l.x,this.painter.height/devicePixelRatio-l.y-1,1,1,b.RGBA,b.UNSIGNED_BYTE,d),v.bindFramebuffer.set(null);let M=d[0]+(d[2]>>4<<8),O=d[1]+((15&d[2])<<8),B=this.coordsIndex[255-d[3]],U=B&&this.sourceCache.getTileByID(B);if(!U)return null;let W=this._coordsTextureSize,Z=(1<0&&Math.sign(M)<0||!v&&Math.sign(b)<0&&Math.sign(M)>0?(b=360*Math.sign(M)+b,n.G(b)):d}}class Nx{constructor(l,d,v){this._context=l,this._size=d,this._tileSize=v,this._objects=[],this._recentlyUsed=[],this._stamp=0}destruct(){for(let l of this._objects)l.texture.destroy(),l.fbo.destroy()}_createObject(l){let d=this._context.createFramebuffer(this._tileSize,this._tileSize,!0,!0),v=new qt(this._context,{width:this._tileSize,height:this._tileSize,data:null},this._context.gl.RGBA);return v.bind(this._context.gl.LINEAR,this._context.gl.CLAMP_TO_EDGE),d.depthAttachment.set(this._context.createRenderbuffer(this._context.gl.DEPTH_STENCIL,this._tileSize,this._tileSize)),d.colorAttachment.set(v.texture),{id:l,fbo:d,texture:v,stamp:-1,inUse:!1}}getObjectForId(l){return this._objects[l]}useObject(l){l.inUse=!0,this._recentlyUsed=this._recentlyUsed.filter(d=>l.id!==d),this._recentlyUsed.push(l.id)}stampObject(l){l.stamp=++this._stamp}getOrCreateFreeObject(){for(let d of this._recentlyUsed)if(!this._objects[d].inUse)return this._objects[d];if(this._objects.length>=this._size)throw new Error(\"No free RenderPool available, call freeAllObjects() required!\");let l=this._createObject(this._objects.length);return this._objects.push(l),l}freeObject(l){l.inUse=!1}freeAllObjects(){for(let l of this._objects)this.freeObject(l)}isFull(){return!(this._objects.length!l.inUse)===!1}}let Mo={background:!0,fill:!0,line:!0,raster:!0,hillshade:!0};class oA{constructor(l,d){this.painter=l,this.terrain=d,this.pool=new Nx(l.context,30,d.sourceCache.tileSize*d.qualityFactor)}destruct(){this.pool.destruct()}getTexture(l){return this.pool.getObjectForId(l.rtt[this._stacks.length-1].id).texture}prepareForRender(l,d){this._stacks=[],this._prevType=null,this._rttTiles=[],this._renderableTiles=this.terrain.sourceCache.getRenderableTiles(),this._renderableLayerIds=l._order.filter(v=>!l._layers[v].isHidden(d)),this._coordsDescendingInv={};for(let v in l.sourceCaches){this._coordsDescendingInv[v]={};let b=l.sourceCaches[v].getVisibleCoordinates();for(let M of b){let O=this.terrain.sourceCache.getTerrainCoords(M);for(let B in O)this._coordsDescendingInv[v][B]||(this._coordsDescendingInv[v][B]=[]),this._coordsDescendingInv[v][B].push(O[B])}}this._coordsDescendingInvStr={};for(let v of l._order){let b=l._layers[v],M=b.source;if(Mo[b.type]&&!this._coordsDescendingInvStr[M]){this._coordsDescendingInvStr[M]={};for(let O in this._coordsDescendingInv[M])this._coordsDescendingInvStr[M][O]=this._coordsDescendingInv[M][O].map(B=>B.key).sort().join()}}for(let v of this._renderableTiles)for(let b in this._coordsDescendingInvStr){let M=this._coordsDescendingInvStr[b][v.tileID.key];M&&M!==v.rttCoords[b]&&(v.rtt=[])}}renderLayer(l){if(l.isHidden(this.painter.transform.zoom))return!1;let d=l.type,v=this.painter,b=this._renderableLayerIds[this._renderableLayerIds.length-1]===l.id;if(Mo[d]&&(this._prevType&&Mo[this._prevType]||this._stacks.push([]),this._prevType=d,this._stacks[this._stacks.length-1].push(l.id),!b))return!0;if(Mo[this._prevType]||Mo[d]&&b){this._prevType=d;let M=this._stacks.length-1,O=this._stacks[M]||[];for(let B of this._renderableTiles){if(this.pool.isFull()&&(Cn(this.painter,this.terrain,this._rttTiles),this._rttTiles=[],this.pool.freeAllObjects()),this._rttTiles.push(B),B.rtt[M]){let W=this.pool.getObjectForId(B.rtt[M].id);if(W.stamp===B.rtt[M].stamp){this.pool.useObject(W);continue}}let U=this.pool.getOrCreateFreeObject();this.pool.useObject(U),this.pool.stampObject(U),B.rtt[M]={id:U.id,stamp:U.stamp},v.context.bindFramebuffer.set(U.fbo.framebuffer),v.context.clear({color:n.aT.transparent,stencil:0}),v.currentStencilSource=void 0;for(let W=0;W{T.touchstart=T.dragStart,T.touchmoveWindow=T.dragMove,T.touchend=T.dragEnd},aA={showCompass:!0,showZoom:!0,visualizePitch:!1};class Bd{constructor(l,d,v=!1){this.mousedown=O=>{this.startMouse(n.e({},O,{ctrlKey:!0,preventDefault:()=>O.preventDefault()}),c.mousePos(this.element,O)),c.addEventListener(window,\"mousemove\",this.mousemove),c.addEventListener(window,\"mouseup\",this.mouseup)},this.mousemove=O=>{this.moveMouse(O,c.mousePos(this.element,O))},this.mouseup=O=>{this.mouseRotate.dragEnd(O),this.mousePitch&&this.mousePitch.dragEnd(O),this.offTemp()},this.touchstart=O=>{O.targetTouches.length!==1?this.reset():(this._startPos=this._lastPos=c.touchPos(this.element,O.targetTouches)[0],this.startTouch(O,this._startPos),c.addEventListener(window,\"touchmove\",this.touchmove,{passive:!1}),c.addEventListener(window,\"touchend\",this.touchend))},this.touchmove=O=>{O.targetTouches.length!==1?this.reset():(this._lastPos=c.touchPos(this.element,O.targetTouches)[0],this.moveTouch(O,this._lastPos))},this.touchend=O=>{O.targetTouches.length===0&&this._startPos&&this._lastPos&&this._startPos.dist(this._lastPos){this.mouseRotate.reset(),this.mousePitch&&this.mousePitch.reset(),this.touchRotate.reset(),this.touchPitch&&this.touchPitch.reset(),delete this._startPos,delete this._lastPos,this.offTemp()},this._clickTolerance=10;let b=l.dragRotate._mouseRotate.getClickTolerance(),M=l.dragRotate._mousePitch.getClickTolerance();this.element=d,this.mouseRotate=Na({clickTolerance:b,enable:!0}),this.touchRotate=(({enable:O,clickTolerance:B,bearingDegreesPerPixelMoved:U=.8})=>{let W=new s0;return new za({clickTolerance:B,move:(Z,$)=>({bearingDelta:($.x-Z.x)*U}),moveStateManager:W,enable:O,assignEvents:wf})})({clickTolerance:b,enable:!0}),this.map=l,v&&(this.mousePitch=co({clickTolerance:M,enable:!0}),this.touchPitch=(({enable:O,clickTolerance:B,pitchDegreesPerPixelMoved:U=-.5})=>{let W=new s0;return new za({clickTolerance:B,move:(Z,$)=>({pitchDelta:($.y-Z.y)*U}),moveStateManager:W,enable:O,assignEvents:wf})})({clickTolerance:M,enable:!0})),c.addEventListener(d,\"mousedown\",this.mousedown),c.addEventListener(d,\"touchstart\",this.touchstart,{passive:!1}),c.addEventListener(d,\"touchcancel\",this.reset)}startMouse(l,d){this.mouseRotate.dragStart(l,d),this.mousePitch&&this.mousePitch.dragStart(l,d),c.disableDrag()}startTouch(l,d){this.touchRotate.dragStart(l,d),this.touchPitch&&this.touchPitch.dragStart(l,d),c.disableDrag()}moveMouse(l,d){let v=this.map,{bearingDelta:b}=this.mouseRotate.dragMove(l,d)||{};if(b&&v.setBearing(v.getBearing()+b),this.mousePitch){let{pitchDelta:M}=this.mousePitch.dragMove(l,d)||{};M&&v.setPitch(v.getPitch()+M)}}moveTouch(l,d){let v=this.map,{bearingDelta:b}=this.touchRotate.dragMove(l,d)||{};if(b&&v.setBearing(v.getBearing()+b),this.touchPitch){let{pitchDelta:M}=this.touchPitch.dragMove(l,d)||{};M&&v.setPitch(v.getPitch()+M)}}off(){let l=this.element;c.removeEventListener(l,\"mousedown\",this.mousedown),c.removeEventListener(l,\"touchstart\",this.touchstart,{passive:!1}),c.removeEventListener(window,\"touchmove\",this.touchmove,{passive:!1}),c.removeEventListener(window,\"touchend\",this.touchend),c.removeEventListener(l,\"touchcancel\",this.reset),this.offTemp()}offTemp(){c.enableDrag(),c.removeEventListener(window,\"mousemove\",this.mousemove),c.removeEventListener(window,\"mouseup\",this.mouseup),c.removeEventListener(window,\"touchmove\",this.touchmove,{passive:!1}),c.removeEventListener(window,\"touchend\",this.touchend)}}let Hn;function uo(T,l,d){if(T=new n.L(T.lng,T.lat),l){let v=new n.L(T.lng-360,T.lat),b=new n.L(T.lng+360,T.lat),M=d.locationPoint(T).distSqr(l);d.locationPoint(v).distSqr(l)180;){let v=d.locationPoint(T);if(v.x>=0&&v.y>=0&&v.x<=d.width&&v.y<=d.height)break;T.lng>d.center.lng?T.lng-=360:T.lng+=360}return T}let ji={center:\"translate(-50%,-50%)\",top:\"translate(-50%,0)\",\"top-left\":\"translate(0,0)\",\"top-right\":\"translate(-100%,0)\",bottom:\"translate(-50%,-100%)\",\"bottom-left\":\"translate(0,-100%)\",\"bottom-right\":\"translate(-100%,-100%)\",left:\"translate(0,-50%)\",right:\"translate(-100%,-50%)\"};function w_(T,l,d){let v=T.classList;for(let b in ji)v.remove(`maplibregl-${d}-anchor-${b}`);v.add(`maplibregl-${d}-anchor-${l}`)}class mh extends n.E{constructor(l){if(super(),this._onKeyPress=d=>{let v=d.code,b=d.charCode||d.keyCode;v!==\"Space\"&&v!==\"Enter\"&&b!==32&&b!==13||this.togglePopup()},this._onMapClick=d=>{let v=d.originalEvent.target,b=this._element;this._popup&&(v===b||b.contains(v))&&this.togglePopup()},this._update=d=>{if(!this._map)return;let v=this._map.loaded()&&!this._map.isMoving();(d?.type===\"terrain\"||d?.type===\"render\"&&!v)&&this._map.once(\"render\",this._update),this._map.transform.renderWorldCopies&&(this._lngLat=uo(this._lngLat,this._pos,this._map.transform)),this._pos=this._map.project(this._lngLat)._add(this._offset);let b=\"\";this._rotationAlignment===\"viewport\"||this._rotationAlignment===\"auto\"?b=`rotateZ(${this._rotation}deg)`:this._rotationAlignment===\"map\"&&(b=`rotateZ(${this._rotation-this._map.getBearing()}deg)`);let M=\"\";this._pitchAlignment===\"viewport\"||this._pitchAlignment===\"auto\"?M=\"rotateX(0deg)\":this._pitchAlignment===\"map\"&&(M=`rotateX(${this._map.getPitch()}deg)`),d&&d.type!==\"moveend\"||(this._pos=this._pos.round()),c.setTransform(this._element,`${ji[this._anchor]} translate(${this._pos.x}px, ${this._pos.y}px) ${M} ${b}`),this._map.terrain&&!this._opacityTimeout&&(this._opacityTimeout=setTimeout(()=>{let O=this._map.unproject(this._pos),B=40075016686e-3*Math.abs(Math.cos(this._lngLat.lat*Math.PI/180))/Math.pow(2,this._map.transform.tileZoom+8);this._element.style.opacity=O.distanceTo(this._lngLat)>20*B?\"0.2\":\"1.0\",this._opacityTimeout=null},100))},this._onMove=d=>{if(!this._isDragging){let v=this._clickTolerance||this._map._clickTolerance;this._isDragging=d.point.dist(this._pointerdownPos)>=v}this._isDragging&&(this._pos=d.point.sub(this._positionDelta),this._lngLat=this._map.unproject(this._pos),this.setLngLat(this._lngLat),this._element.style.pointerEvents=\"none\",this._state===\"pending\"&&(this._state=\"active\",this.fire(new n.k(\"dragstart\"))),this.fire(new n.k(\"drag\")))},this._onUp=()=>{this._element.style.pointerEvents=\"auto\",this._positionDelta=null,this._pointerdownPos=null,this._isDragging=!1,this._map.off(\"mousemove\",this._onMove),this._map.off(\"touchmove\",this._onMove),this._state===\"active\"&&this.fire(new n.k(\"dragend\")),this._state=\"inactive\"},this._addDragHandler=d=>{this._element.contains(d.originalEvent.target)&&(d.preventDefault(),this._positionDelta=d.point.sub(this._pos).add(this._offset),this._pointerdownPos=d.point,this._state=\"pending\",this._map.on(\"mousemove\",this._onMove),this._map.on(\"touchmove\",this._onMove),this._map.once(\"mouseup\",this._onUp),this._map.once(\"touchend\",this._onUp))},this._anchor=l&&l.anchor||\"center\",this._color=l&&l.color||\"#3FB1CE\",this._scale=l&&l.scale||1,this._draggable=l&&l.draggable||!1,this._clickTolerance=l&&l.clickTolerance||0,this._isDragging=!1,this._state=\"inactive\",this._rotation=l&&l.rotation||0,this._rotationAlignment=l&&l.rotationAlignment||\"auto\",this._pitchAlignment=l&&l.pitchAlignment&&l.pitchAlignment!==\"auto\"?l.pitchAlignment:this._rotationAlignment,l&&l.element)this._element=l.element,this._offset=n.P.convert(l&&l.offset||[0,0]);else{this._defaultMarker=!0,this._element=c.create(\"div\"),this._element.setAttribute(\"aria-label\",\"Map marker\");let d=c.createNS(\"http://www.w3.org/2000/svg\",\"svg\"),v=41,b=27;d.setAttributeNS(null,\"display\",\"block\"),d.setAttributeNS(null,\"height\",`${v}px`),d.setAttributeNS(null,\"width\",`${b}px`),d.setAttributeNS(null,\"viewBox\",`0 0 ${b} ${v}`);let M=c.createNS(\"http://www.w3.org/2000/svg\",\"g\");M.setAttributeNS(null,\"stroke\",\"none\"),M.setAttributeNS(null,\"stroke-width\",\"1\"),M.setAttributeNS(null,\"fill\",\"none\"),M.setAttributeNS(null,\"fill-rule\",\"evenodd\");let O=c.createNS(\"http://www.w3.org/2000/svg\",\"g\");O.setAttributeNS(null,\"fill-rule\",\"nonzero\");let B=c.createNS(\"http://www.w3.org/2000/svg\",\"g\");B.setAttributeNS(null,\"transform\",\"translate(3.0, 29.0)\"),B.setAttributeNS(null,\"fill\",\"#000000\");let U=[{rx:\"10.5\",ry:\"5.25002273\"},{rx:\"10.5\",ry:\"5.25002273\"},{rx:\"9.5\",ry:\"4.77275007\"},{rx:\"8.5\",ry:\"4.29549936\"},{rx:\"7.5\",ry:\"3.81822308\"},{rx:\"6.5\",ry:\"3.34094679\"},{rx:\"5.5\",ry:\"2.86367051\"},{rx:\"4.5\",ry:\"2.38636864\"}];for(let Ft of U){let Ht=c.createNS(\"http://www.w3.org/2000/svg\",\"ellipse\");Ht.setAttributeNS(null,\"opacity\",\"0.04\"),Ht.setAttributeNS(null,\"cx\",\"10.5\"),Ht.setAttributeNS(null,\"cy\",\"5.80029008\"),Ht.setAttributeNS(null,\"rx\",Ft.rx),Ht.setAttributeNS(null,\"ry\",Ft.ry),B.appendChild(Ht)}let W=c.createNS(\"http://www.w3.org/2000/svg\",\"g\");W.setAttributeNS(null,\"fill\",this._color);let Z=c.createNS(\"http://www.w3.org/2000/svg\",\"path\");Z.setAttributeNS(null,\"d\",\"M27,13.5 C27,19.074644 20.250001,27.000002 14.75,34.500002 C14.016665,35.500004 12.983335,35.500004 12.25,34.500002 C6.7499993,27.000002 0,19.222562 0,13.5 C0,6.0441559 6.0441559,0 13.5,0 C20.955844,0 27,6.0441559 27,13.5 Z\"),W.appendChild(Z);let $=c.createNS(\"http://www.w3.org/2000/svg\",\"g\");$.setAttributeNS(null,\"opacity\",\"0.25\"),$.setAttributeNS(null,\"fill\",\"#000000\");let st=c.createNS(\"http://www.w3.org/2000/svg\",\"path\");st.setAttributeNS(null,\"d\",\"M13.5,0 C6.0441559,0 0,6.0441559 0,13.5 C0,19.222562 6.7499993,27 12.25,34.5 C13,35.522727 14.016664,35.500004 14.75,34.5 C20.250001,27 27,19.074644 27,13.5 C27,6.0441559 20.955844,0 13.5,0 Z M13.5,1 C20.415404,1 26,6.584596 26,13.5 C26,15.898657 24.495584,19.181431 22.220703,22.738281 C19.945823,26.295132 16.705119,30.142167 13.943359,33.908203 C13.743445,34.180814 13.612715,34.322738 13.5,34.441406 C13.387285,34.322738 13.256555,34.180814 13.056641,33.908203 C10.284481,30.127985 7.4148684,26.314159 5.015625,22.773438 C2.6163816,19.232715 1,15.953538 1,13.5 C1,6.584596 6.584596,1 13.5,1 Z\"),$.appendChild(st);let At=c.createNS(\"http://www.w3.org/2000/svg\",\"g\");At.setAttributeNS(null,\"transform\",\"translate(6.0, 7.0)\"),At.setAttributeNS(null,\"fill\",\"#FFFFFF\");let pt=c.createNS(\"http://www.w3.org/2000/svg\",\"g\");pt.setAttributeNS(null,\"transform\",\"translate(8.0, 8.0)\");let yt=c.createNS(\"http://www.w3.org/2000/svg\",\"circle\");yt.setAttributeNS(null,\"fill\",\"#000000\"),yt.setAttributeNS(null,\"opacity\",\"0.25\"),yt.setAttributeNS(null,\"cx\",\"5.5\"),yt.setAttributeNS(null,\"cy\",\"5.5\"),yt.setAttributeNS(null,\"r\",\"5.4999962\");let dt=c.createNS(\"http://www.w3.org/2000/svg\",\"circle\");dt.setAttributeNS(null,\"fill\",\"#FFFFFF\"),dt.setAttributeNS(null,\"cx\",\"5.5\"),dt.setAttributeNS(null,\"cy\",\"5.5\"),dt.setAttributeNS(null,\"r\",\"5.4999962\"),pt.appendChild(yt),pt.appendChild(dt),O.appendChild(B),O.appendChild(W),O.appendChild($),O.appendChild(At),O.appendChild(pt),d.appendChild(O),d.setAttributeNS(null,\"height\",v*this._scale+\"px\"),d.setAttributeNS(null,\"width\",b*this._scale+\"px\"),this._element.appendChild(d),this._offset=n.P.convert(l&&l.offset||[0,-14])}if(this._element.classList.add(\"maplibregl-marker\"),this._element.addEventListener(\"dragstart\",d=>{d.preventDefault()}),this._element.addEventListener(\"mousedown\",d=>{d.preventDefault()}),w_(this._element,this._anchor,\"marker\"),l&&l.className)for(let d of l.className.split(\" \"))this._element.classList.add(d);this._popup=null}addTo(l){return this.remove(),this._map=l,l.getCanvasContainer().appendChild(this._element),l.on(\"move\",this._update),l.on(\"moveend\",this._update),l.on(\"terrain\",this._update),this.setDraggable(this._draggable),this._update(),this._map.on(\"click\",this._onMapClick),this}remove(){return this._opacityTimeout&&(clearTimeout(this._opacityTimeout),delete this._opacityTimeout),this._map&&(this._map.off(\"click\",this._onMapClick),this._map.off(\"move\",this._update),this._map.off(\"moveend\",this._update),this._map.off(\"mousedown\",this._addDragHandler),this._map.off(\"touchstart\",this._addDragHandler),this._map.off(\"mouseup\",this._onUp),this._map.off(\"touchend\",this._onUp),this._map.off(\"mousemove\",this._onMove),this._map.off(\"touchmove\",this._onMove),delete this._map),c.remove(this._element),this._popup&&this._popup.remove(),this}getLngLat(){return this._lngLat}setLngLat(l){return this._lngLat=n.L.convert(l),this._pos=null,this._popup&&this._popup.setLngLat(this._lngLat),this._update(),this}getElement(){return this._element}setPopup(l){if(this._popup&&(this._popup.remove(),this._popup=null,this._element.removeEventListener(\"keypress\",this._onKeyPress),this._originalTabIndex||this._element.removeAttribute(\"tabindex\")),l){if(!(\"offset\"in l.options)){let b=Math.abs(13.5)/Math.SQRT2;l.options.offset=this._defaultMarker?{top:[0,0],\"top-left\":[0,0],\"top-right\":[0,0],bottom:[0,-38.1],\"bottom-left\":[b,-1*(38.1-13.5+b)],\"bottom-right\":[-b,-1*(38.1-13.5+b)],left:[13.5,-1*(38.1-13.5)],right:[-13.5,-1*(38.1-13.5)]}:this._offset}this._popup=l,this._lngLat&&this._popup.setLngLat(this._lngLat),this._originalTabIndex=this._element.getAttribute(\"tabindex\"),this._originalTabIndex||this._element.setAttribute(\"tabindex\",\"0\"),this._element.addEventListener(\"keypress\",this._onKeyPress)}return this}getPopup(){return this._popup}togglePopup(){let l=this._popup;return l?(l.isOpen()?l.remove():l.addTo(this._map),this):this}getOffset(){return this._offset}setOffset(l){return this._offset=n.P.convert(l),this._update(),this}addClassName(l){this._element.classList.add(l)}removeClassName(l){this._element.classList.remove(l)}toggleClassName(l){return this._element.classList.toggle(l)}setDraggable(l){return this._draggable=!!l,this._map&&(l?(this._map.on(\"mousedown\",this._addDragHandler),this._map.on(\"touchstart\",this._addDragHandler)):(this._map.off(\"mousedown\",this._addDragHandler),this._map.off(\"touchstart\",this._addDragHandler))),this}isDraggable(){return this._draggable}setRotation(l){return this._rotation=l||0,this._update(),this}getRotation(){return this._rotation}setRotationAlignment(l){return this._rotationAlignment=l||\"auto\",this._update(),this}getRotationAlignment(){return this._rotationAlignment}setPitchAlignment(l){return this._pitchAlignment=l&&l!==\"auto\"?l:this._rotationAlignment,this._update(),this}getPitchAlignment(){return this._pitchAlignment}}let kn={positionOptions:{enableHighAccuracy:!1,maximumAge:0,timeout:6e3},fitBoundsOptions:{maxZoom:15},trackUserLocation:!1,showAccuracyCircle:!0,showUserLocation:!0},wn=0,Sf=!1,Es={maxWidth:100,unit:\"metric\"};function gh(T,l,d){let v=d&&d.maxWidth||100,b=T._container.clientHeight/2,M=T.unproject([0,b]),O=T.unproject([v,b]),B=M.distanceTo(O);if(d&&d.unit===\"imperial\"){let U=3.2808*B;U>5280?Wo(l,v,U/5280,T._getUIString(\"ScaleControl.Miles\")):Wo(l,v,U,T._getUIString(\"ScaleControl.Feet\"))}else d&&d.unit===\"nautical\"?Wo(l,v,B/1852,T._getUIString(\"ScaleControl.NauticalMiles\")):B>=1e3?Wo(l,v,B/1e3,T._getUIString(\"ScaleControl.Kilometers\")):Wo(l,v,B,T._getUIString(\"ScaleControl.Meters\"))}function Wo(T,l,d,v){let b=function(M){let O=Math.pow(10,`${Math.floor(M)}`.length-1),B=M/O;return B=B>=10?10:B>=5?5:B>=3?3:B>=2?2:B>=1?1:function(U){let W=Math.pow(10,Math.ceil(-Math.log(U)/Math.LN10));return Math.round(U*W)/W}(B),O*B}(d);T.style.width=l*(b/d)+\"px\",T.innerHTML=`${b} ${v}`}let p0={closeButton:!0,closeOnClick:!0,focusAfterOpen:!0,className:\"\",maxWidth:\"240px\"},Fd=[\"a[href]\",\"[tabindex]:not([tabindex='-1'])\",\"[contenteditable]:not([contenteditable='false'])\",\"button:not([disabled])\",\"input:not([disabled])\",\"select:not([disabled])\",\"textarea:not([disabled])\"].join(\", \");function Tf(T){if(T){if(typeof T==\"number\"){let l=Math.round(Math.abs(T)/Math.SQRT2);return{center:new n.P(0,0),top:new n.P(0,T),\"top-left\":new n.P(l,l),\"top-right\":new n.P(-l,l),bottom:new n.P(0,-T),\"bottom-left\":new n.P(l,-l),\"bottom-right\":new n.P(-l,-l),left:new n.P(T,0),right:new n.P(-T,0)}}if(T instanceof n.P||Array.isArray(T)){let l=n.P.convert(T);return{center:l,top:l,\"top-left\":l,\"top-right\":l,bottom:l,\"bottom-left\":l,\"bottom-right\":l,left:l,right:l}}return{center:n.P.convert(T.center||[0,0]),top:n.P.convert(T.top||[0,0]),\"top-left\":n.P.convert(T[\"top-left\"]||[0,0]),\"top-right\":n.P.convert(T[\"top-right\"]||[0,0]),bottom:n.P.convert(T.bottom||[0,0]),\"bottom-left\":n.P.convert(T[\"bottom-left\"]||[0,0]),\"bottom-right\":n.P.convert(T[\"bottom-right\"]||[0,0]),left:n.P.convert(T.left||[0,0]),right:n.P.convert(T.right||[0,0])}}return Tf(new n.P(0,0))}let Ho={extend:(T,...l)=>n.e(T,...l),run(T){T()},logToElement(T,l=!1,d=\"log\"){let v=window.document.getElementById(d);v&&(l&&(v.innerHTML=\"\"),v.innerHTML+=`
${T}`)}},lA=o;class bi{static get version(){return lA}static get workerCount(){return lo.workerCount}static set workerCount(l){lo.workerCount=l}static get maxParallelImageRequests(){return n.c.MAX_PARALLEL_IMAGE_REQUESTS}static set maxParallelImageRequests(l){n.c.MAX_PARALLEL_IMAGE_REQUESTS=l}static get workerUrl(){return n.c.WORKER_URL}static set workerUrl(l){n.c.WORKER_URL=l}static addProtocol(l,d){n.c.REGISTERED_PROTOCOLS[l]=d}static removeProtocol(l){delete n.c.REGISTERED_PROTOCOLS[l]}}return bi.Map=class extends v_{constructor(T){if(n.bg.mark(n.bh.create),(T=n.e({},dr,T)).minZoom!=null&&T.maxZoom!=null&&T.minZoom>T.maxZoom)throw new Error(\"maxZoom must be greater than or equal to minZoom\");if(T.minPitch!=null&&T.maxPitch!=null&&T.minPitch>T.maxPitch)throw new Error(\"maxPitch must be greater than or equal to minPitch\");if(T.minPitch!=null&&T.minPitch<0)throw new Error(\"minPitch must be greater than or equal to 0\");if(T.maxPitch!=null&&T.maxPitch>85)throw new Error(\"maxPitch must be less than or equal to 85\");if(super(new Kp(T.minZoom,T.maxZoom,T.minPitch,T.maxPitch,T.renderWorldCopies),{bearingSnap:T.bearingSnap}),this._cooperativeGesturesOnWheel=l=>{this._onCooperativeGesture(l,l[this._metaKey],1)},this._contextLost=l=>{l.preventDefault(),this._frame&&(this._frame.cancel(),this._frame=null),this.fire(new n.k(\"webglcontextlost\",{originalEvent:l}))},this._contextRestored=l=>{this._setupPainter(),this.resize(),this._update(),this.fire(new n.k(\"webglcontextrestored\",{originalEvent:l}))},this._onMapScroll=l=>{if(l.target===this._container)return this._container.scrollTop=0,this._container.scrollLeft=0,!1},this._onWindowOnline=()=>{this._update()},this._interactive=T.interactive,this._cooperativeGestures=T.cooperativeGestures,this._metaKey=navigator.platform.indexOf(\"Mac\")===0?\"metaKey\":\"ctrlKey\",this._maxTileCacheSize=T.maxTileCacheSize,this._maxTileCacheZoomLevels=T.maxTileCacheZoomLevels,this._failIfMajorPerformanceCaveat=T.failIfMajorPerformanceCaveat,this._preserveDrawingBuffer=T.preserveDrawingBuffer,this._antialias=T.antialias,this._trackResize=T.trackResize,this._bearingSnap=T.bearingSnap,this._refreshExpiredTiles=T.refreshExpiredTiles,this._fadeDuration=T.fadeDuration,this._crossSourceCollisions=T.crossSourceCollisions,this._crossFadingFactor=1,this._collectResourceTiming=T.collectResourceTiming,this._renderTaskQueue=new sA,this._controls=[],this._mapId=n.a2(),this._locale=n.e({},d0,T.locale),this._clickTolerance=T.clickTolerance,this._overridePixelRatio=T.pixelRatio,this._maxCanvasSize=T.maxCanvasSize,this.transformCameraUpdate=T.transformCameraUpdate,this._imageQueueHandle=j.addThrottleControl(()=>this.isMoving()),this._requestManager=new et(T.transformRequest),typeof T.container==\"string\"){if(this._container=document.getElementById(T.container),!this._container)throw new Error(`Container '${T.container}' not found.`)}else{if(!(T.container instanceof HTMLElement))throw new Error(\"Invalid type: 'container' must be a String or HTMLElement.\");this._container=T.container}if(T.maxBounds&&this.setMaxBounds(T.maxBounds),this._setupContainer(),this._setupPainter(),this.on(\"move\",()=>this._update(!1)),this.on(\"moveend\",()=>this._update(!1)),this.on(\"zoom\",()=>this._update(!0)),this.on(\"terrain\",()=>{this.painter.terrainFacilitator.dirty=!0,this._update(!0)}),this.once(\"idle\",()=>{this._idleTriggered=!0}),typeof window<\"u\"){addEventListener(\"online\",this._onWindowOnline,!1);let l=!1,d=lh(v=>{this._trackResize&&!this._removed&&this.resize(v)._update()},50);this._resizeObserver=new ResizeObserver(v=>{l?d(v):l=!0}),this._resizeObserver.observe(this._container)}this.handlers=new f0(this,T),this._cooperativeGestures&&this._setupCooperativeGestures(),this._hash=T.hash&&new Ld(typeof T.hash==\"string\"&&T.hash||void 0).addTo(this),this._hash&&this._hash._onHashChange()||(this.jumpTo({center:T.center,zoom:T.zoom,bearing:T.bearing,pitch:T.pitch}),T.bounds&&(this.resize(),this.fitBounds(T.bounds,n.e({},T.fitBoundsOptions,{duration:0})))),this.resize(),this._localIdeographFontFamily=T.localIdeographFontFamily,this._validateStyle=T.validateStyle,T.style&&this.setStyle(T.style,{localIdeographFontFamily:T.localIdeographFontFamily}),T.attributionControl&&this.addControl(new ua({customAttribution:T.customAttribution})),T.maplibreLogo&&this.addControl(new un,T.logoPosition),this.on(\"style.load\",()=>{this.transform.unmodified&&this.jumpTo(this.style.stylesheet)}),this.on(\"data\",l=>{this._update(l.dataType===\"style\"),this.fire(new n.k(`${l.dataType}data`,l))}),this.on(\"dataloading\",l=>{this.fire(new n.k(`${l.dataType}dataloading`,l))}),this.on(\"dataabort\",l=>{this.fire(new n.k(\"sourcedataabort\",l))})}_getMapId(){return this._mapId}addControl(T,l){if(l===void 0&&(l=T.getDefaultPosition?T.getDefaultPosition():\"top-right\"),!T||!T.onAdd)return this.fire(new n.j(new Error(\"Invalid argument to map.addControl(). Argument must be a control with onAdd and onRemove methods.\")));let d=T.onAdd(this);this._controls.push(T);let v=this._controlPositions[l];return l.indexOf(\"bottom\")!==-1?v.insertBefore(d,v.firstChild):v.appendChild(d),this}removeControl(T){if(!T||!T.onRemove)return this.fire(new n.j(new Error(\"Invalid argument to map.removeControl(). Argument must be a control with onAdd and onRemove methods.\")));let l=this._controls.indexOf(T);return l>-1&&this._controls.splice(l,1),T.onRemove(this),this}hasControl(T){return this._controls.indexOf(T)>-1}calculateCameraOptionsFromTo(T,l,d,v){return v==null&&this.terrain&&(v=this.terrain.getElevationForLngLatZoom(d,this.transform.tileZoom)),super.calculateCameraOptionsFromTo(T,l,d,v)}resize(T){var l;let d=this._containerDimensions(),v=d[0],b=d[1],M=this._getClampedPixelRatio(v,b);if(this._resizeCanvas(v,b,M),this.painter.resize(v,b,M),this.painter.overLimit()){let B=this.painter.context.gl;this._maxCanvasSize=[B.drawingBufferWidth,B.drawingBufferHeight];let U=this._getClampedPixelRatio(v,b);this._resizeCanvas(v,b,U),this.painter.resize(v,b,U)}this.transform.resize(v,b),(l=this._requestedCameraState)===null||l===void 0||l.resize(v,b);let O=!this._moving;return O&&(this.stop(),this.fire(new n.k(\"movestart\",T)).fire(new n.k(\"move\",T))),this.fire(new n.k(\"resize\",T)),O&&this.fire(new n.k(\"moveend\",T)),this}_getClampedPixelRatio(T,l){let{0:d,1:v}=this._maxCanvasSize,b=this.getPixelRatio(),M=T*b,O=l*b;return Math.min(M>d?d/M:1,O>v?v/O:1)*b}getPixelRatio(){var T;return(T=this._overridePixelRatio)!==null&&T!==void 0?T:devicePixelRatio}setPixelRatio(T){this._overridePixelRatio=T,this.resize()}getBounds(){return this.transform.getBounds()}getMaxBounds(){return this.transform.getMaxBounds()}setMaxBounds(T){return this.transform.setMaxBounds(Si.convert(T)),this._update()}setMinZoom(T){if((T=T??-2)>=-2&&T<=this.transform.maxZoom)return this.transform.minZoom=T,this._update(),this.getZoom()=this.transform.minZoom)return this.transform.maxZoom=T,this._update(),this.getZoom()>T&&this.setZoom(T),this;throw new Error(\"maxZoom must be greater than the current minZoom\")}getMaxZoom(){return this.transform.maxZoom}setMinPitch(T){if((T=T??0)<0)throw new Error(\"minPitch must be greater than or equal to 0\");if(T>=0&&T<=this.transform.maxPitch)return this.transform.minPitch=T,this._update(),this.getPitch()85)throw new Error(\"maxPitch must be less than or equal to 85\");if(T>=this.transform.minPitch)return this.transform.maxPitch=T,this._update(),this.getPitch()>T&&this.setPitch(T),this;throw new Error(\"maxPitch must be greater than the current minPitch\")}getMaxPitch(){return this.transform.maxPitch}getRenderWorldCopies(){return this.transform.renderWorldCopies}setRenderWorldCopies(T){return this.transform.renderWorldCopies=T,this._update()}getCooperativeGestures(){return this._cooperativeGestures}setCooperativeGestures(T){return this._cooperativeGestures=T,this._cooperativeGestures?this._setupCooperativeGestures():this._destroyCooperativeGestures(),this}project(T){return this.transform.locationPoint(n.L.convert(T),this.style&&this.terrain)}unproject(T){return this.transform.pointLocation(n.P.convert(T),this.terrain)}isMoving(){var T;return this._moving||((T=this.handlers)===null||T===void 0?void 0:T.isMoving())}isZooming(){var T;return this._zooming||((T=this.handlers)===null||T===void 0?void 0:T.isZooming())}isRotating(){var T;return this._rotating||((T=this.handlers)===null||T===void 0?void 0:T.isRotating())}_createDelegatedListener(T,l,d){if(T===\"mouseenter\"||T===\"mouseover\"){let v=!1;return{layer:l,listener:d,delegates:{mousemove:M=>{let O=this.getLayer(l)?this.queryRenderedFeatures(M.point,{layers:[l]}):[];O.length?v||(v=!0,d.call(this,new la(T,this,M.originalEvent,{features:O}))):v=!1},mouseout:()=>{v=!1}}}}if(T===\"mouseleave\"||T===\"mouseout\"){let v=!1;return{layer:l,listener:d,delegates:{mousemove:O=>{(this.getLayer(l)?this.queryRenderedFeatures(O.point,{layers:[l]}):[]).length?v=!0:v&&(v=!1,d.call(this,new la(T,this,O.originalEvent)))},mouseout:O=>{v&&(v=!1,d.call(this,new la(T,this,O.originalEvent)))}}}}{let v=b=>{let M=this.getLayer(l)?this.queryRenderedFeatures(b.point,{layers:[l]}):[];M.length&&(b.features=M,d.call(this,b),delete b.features)};return{layer:l,listener:d,delegates:{[T]:v}}}}on(T,l,d){if(d===void 0)return super.on(T,l);let v=this._createDelegatedListener(T,l,d);this._delegatedListeners=this._delegatedListeners||{},this._delegatedListeners[T]=this._delegatedListeners[T]||[],this._delegatedListeners[T].push(v);for(let b in v.delegates)this.on(b,v.delegates[b]);return this}once(T,l,d){if(d===void 0)return super.once(T,l);let v=this._createDelegatedListener(T,l,d);for(let b in v.delegates)this.once(b,v.delegates[b]);return this}off(T,l,d){return d===void 0?super.off(T,l):(this._delegatedListeners&&this._delegatedListeners[T]&&(v=>{let b=this._delegatedListeners[T];for(let M=0;Mthis._updateStyle(T,l));let d=this.style&&l.transformStyle?this.style.serialize():void 0;return this.style&&(this.style.setEventedParent(null),this.style._remove(!T)),T?(this.style=new Gn(this,l||{}),this.style.setEventedParent(this,{style:this.style}),typeof T==\"string\"?this.style.loadURL(T,l,d):this.style.loadJSON(T,l,d),this):(delete this.style,this)}_lazyInitEmptyStyle(){this.style||(this.style=new Gn(this,{}),this.style.setEventedParent(this,{style:this.style}),this.style.loadEmpty())}_diffStyle(T,l){if(typeof T==\"string\"){let d=this._requestManager.transformRequest(T,Q.Style);n.f(d,(v,b)=>{v?this.fire(new n.j(v)):b&&this._updateDiff(b,l)})}else typeof T==\"object\"&&this._updateDiff(T,l)}_updateDiff(T,l){try{this.style.setState(T,l)&&this._update(!0)}catch(d){n.w(`Unable to perform style diff: ${d.message||d.error||d}. Rebuilding the style from scratch.`),this._updateStyle(T,l)}}getStyle(){if(this.style)return this.style.serialize()}isStyleLoaded(){return this.style?this.style.loaded():n.w(\"There is no style added to the map.\")}addSource(T,l){return this._lazyInitEmptyStyle(),this.style.addSource(T,l),this._update(!0)}isSourceLoaded(T){let l=this.style&&this.style.sourceCaches[T];if(l!==void 0)return l.loaded();this.fire(new n.j(new Error(`There is no source with ID '${T}'`)))}setTerrain(T){if(this.style._checkLoaded(),this._terrainDataCallback&&this.style.off(\"data\",this._terrainDataCallback),T){let l=this.style.sourceCaches[T.source];if(!l)throw new Error(`cannot load terrain, because there exists no source with ID: ${T.source}`);for(let d in this.style._layers){let v=this.style._layers[d];v.type===\"hillshade\"&&v.source===T.source&&n.w(\"You are using the same source for a hillshade layer and for 3D terrain. Please consider using two separate sources to improve rendering quality.\")}this.terrain=new b_(this.painter,l,T),this.painter.renderToTexture=new oA(this.painter,this.terrain),this.transform._minEleveationForCurrentTile=this.terrain.getMinTileElevationForLngLatZoom(this.transform.center,this.transform.tileZoom),this.transform.elevation=this.terrain.getElevationForLngLatZoom(this.transform.center,this.transform.tileZoom),this._terrainDataCallback=d=>{d.dataType===\"style\"?this.terrain.sourceCache.freeRtt():d.dataType===\"source\"&&d.tile&&(d.sourceId!==T.source||this._elevationFreeze||(this.transform._minEleveationForCurrentTile=this.terrain.getMinTileElevationForLngLatZoom(this.transform.center,this.transform.tileZoom),this.transform.elevation=this.terrain.getElevationForLngLatZoom(this.transform.center,this.transform.tileZoom)),this.terrain.sourceCache.freeRtt(d.tile.tileID))},this.style.on(\"data\",this._terrainDataCallback)}else this.terrain&&this.terrain.sourceCache.destruct(),this.terrain=null,this.painter.renderToTexture&&this.painter.renderToTexture.destruct(),this.painter.renderToTexture=null,this.transform._minEleveationForCurrentTile=0,this.transform.elevation=0;return this.fire(new n.k(\"terrain\",{terrain:T})),this}getTerrain(){var T,l;return(l=(T=this.terrain)===null||T===void 0?void 0:T.options)!==null&&l!==void 0?l:null}areTilesLoaded(){let T=this.style&&this.style.sourceCaches;for(let l in T){let d=T[l]._tiles;for(let v in d){let b=d[v];if(b.state!==\"loaded\"&&b.state!==\"errored\")return!1}}return!0}addSourceType(T,l,d){return this._lazyInitEmptyStyle(),this.style.addSourceType(T,l,d)}removeSource(T){return this.style.removeSource(T),this._update(!0)}getSource(T){return this.style.getSource(T)}addImage(T,l,d={}){let{pixelRatio:v=1,sdf:b=!1,stretchX:M,stretchY:O,content:B}=d;if(this._lazyInitEmptyStyle(),!(l instanceof HTMLImageElement||n.a(l))){if(l.width===void 0||l.height===void 0)return this.fire(new n.j(new Error(\"Invalid arguments to map.addImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`\")));{let{width:U,height:W,data:Z}=l,$=l;return this.style.addImage(T,{data:new n.R({width:U,height:W},new Uint8Array(Z)),pixelRatio:v,stretchX:M,stretchY:O,content:B,sdf:b,version:0,userImage:$}),$.onAdd&&$.onAdd(this,T),this}}{let{width:U,height:W,data:Z}=n.h.getImageData(l);this.style.addImage(T,{data:new n.R({width:U,height:W},Z),pixelRatio:v,stretchX:M,stretchY:O,content:B,sdf:b,version:0})}}updateImage(T,l){let d=this.style.getImage(T);if(!d)return this.fire(new n.j(new Error(\"The map has no image with that id. If you are adding a new image use `map.addImage(...)` instead.\")));let v=l instanceof HTMLImageElement||n.a(l)?n.h.getImageData(l):l,{width:b,height:M,data:O}=v;if(b===void 0||M===void 0)return this.fire(new n.j(new Error(\"Invalid arguments to map.updateImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`\")));if(b!==d.data.width||M!==d.data.height)return this.fire(new n.j(new Error(\"The width and height of the updated image must be that same as the previous version of the image\")));let B=!(l instanceof HTMLImageElement||n.a(l));return d.data.replace(O,B),this.style.updateImage(T,d),this}getImage(T){return this.style.getImage(T)}hasImage(T){return T?!!this.style.getImage(T):(this.fire(new n.j(new Error(\"Missing required image id\"))),!1)}removeImage(T){this.style.removeImage(T)}loadImage(T,l){j.getImage(this._requestManager.transformRequest(T,Q.Image),l)}listImages(){return this.style.listImages()}addLayer(T,l){return this._lazyInitEmptyStyle(),this.style.addLayer(T,l),this._update(!0)}moveLayer(T,l){return this.style.moveLayer(T,l),this._update(!0)}removeLayer(T){return this.style.removeLayer(T),this._update(!0)}getLayer(T){return this.style.getLayer(T)}getLayersOrder(){return this.style.getLayersOrder()}setLayerZoomRange(T,l,d){return this.style.setLayerZoomRange(T,l,d),this._update(!0)}setFilter(T,l,d={}){return this.style.setFilter(T,l,d),this._update(!0)}getFilter(T){return this.style.getFilter(T)}setPaintProperty(T,l,d,v={}){return this.style.setPaintProperty(T,l,d,v),this._update(!0)}getPaintProperty(T,l){return this.style.getPaintProperty(T,l)}setLayoutProperty(T,l,d,v={}){return this.style.setLayoutProperty(T,l,d,v),this._update(!0)}getLayoutProperty(T,l){return this.style.getLayoutProperty(T,l)}setGlyphs(T,l={}){return this._lazyInitEmptyStyle(),this.style.setGlyphs(T,l),this._update(!0)}getGlyphs(){return this.style.getGlyphsUrl()}addSprite(T,l,d={}){return this._lazyInitEmptyStyle(),this.style.addSprite(T,l,d,v=>{v||this._update(!0)}),this}removeSprite(T){return this._lazyInitEmptyStyle(),this.style.removeSprite(T),this._update(!0)}getSprite(){return this.style.getSprite()}setSprite(T,l={}){return this._lazyInitEmptyStyle(),this.style.setSprite(T,l,d=>{d||this._update(!0)}),this}setLight(T,l={}){return this._lazyInitEmptyStyle(),this.style.setLight(T,l),this._update(!0)}getLight(){return this.style.getLight()}setFeatureState(T,l){return this.style.setFeatureState(T,l),this._update()}removeFeatureState(T,l){return this.style.removeFeatureState(T,l),this._update()}getFeatureState(T){return this.style.getFeatureState(T)}getContainer(){return this._container}getCanvasContainer(){return this._canvasContainer}getCanvas(){return this._canvas}_containerDimensions(){let T=0,l=0;return this._container&&(T=this._container.clientWidth||400,l=this._container.clientHeight||300),[T,l]}_setupContainer(){let T=this._container;T.classList.add(\"maplibregl-map\");let l=this._canvasContainer=c.create(\"div\",\"maplibregl-canvas-container\",T);this._interactive&&l.classList.add(\"maplibregl-interactive\"),this._canvas=c.create(\"canvas\",\"maplibregl-canvas\",l),this._canvas.addEventListener(\"webglcontextlost\",this._contextLost,!1),this._canvas.addEventListener(\"webglcontextrestored\",this._contextRestored,!1),this._canvas.setAttribute(\"tabindex\",\"0\"),this._canvas.setAttribute(\"aria-label\",\"Map\"),this._canvas.setAttribute(\"role\",\"region\");let d=this._containerDimensions(),v=this._getClampedPixelRatio(d[0],d[1]);this._resizeCanvas(d[0],d[1],v);let b=this._controlContainer=c.create(\"div\",\"maplibregl-control-container\",T),M=this._controlPositions={};[\"top-left\",\"top-right\",\"bottom-left\",\"bottom-right\"].forEach(O=>{M[O]=c.create(\"div\",`maplibregl-ctrl-${O} `,b)}),this._container.addEventListener(\"scroll\",this._onMapScroll,!1)}_setupCooperativeGestures(){this._cooperativeGesturesScreen=c.create(\"div\",\"maplibregl-cooperative-gesture-screen\",this._container);let T=typeof this._cooperativeGestures!=\"boolean\"&&this._cooperativeGestures.windowsHelpText?this._cooperativeGestures.windowsHelpText:\"Use Ctrl + scroll to zoom the map\";navigator.platform.indexOf(\"Mac\")===0&&(T=typeof this._cooperativeGestures!=\"boolean\"&&this._cooperativeGestures.macHelpText?this._cooperativeGestures.macHelpText:\"Use \\u2318 + scroll to zoom the map\"),this._cooperativeGesturesScreen.innerHTML=`\n
${T}
\n
${typeof this._cooperativeGestures!=\"boolean\"&&this._cooperativeGestures.mobileHelpText?this._cooperativeGestures.mobileHelpText:\"Use two fingers to move the map\"}
\n `,this._cooperativeGesturesScreen.setAttribute(\"aria-hidden\",\"true\"),this._canvasContainer.addEventListener(\"wheel\",this._cooperativeGesturesOnWheel,!1),this._canvasContainer.classList.add(\"maplibregl-cooperative-gestures\")}_destroyCooperativeGestures(){c.remove(this._cooperativeGesturesScreen),this._canvasContainer.removeEventListener(\"wheel\",this._cooperativeGesturesOnWheel,!1),this._canvasContainer.classList.remove(\"maplibregl-cooperative-gestures\")}_resizeCanvas(T,l,d){this._canvas.width=Math.floor(d*T),this._canvas.height=Math.floor(d*l),this._canvas.style.width=`${T}px`,this._canvas.style.height=`${l}px`}_setupPainter(){let T={alpha:!0,stencil:!0,depth:!0,failIfMajorPerformanceCaveat:this._failIfMajorPerformanceCaveat,preserveDrawingBuffer:this._preserveDrawingBuffer,antialias:this._antialias||!1},l=null;this._canvas.addEventListener(\"webglcontextcreationerror\",v=>{l={requestedAttributes:T},v&&(l.statusMessage=v.statusMessage,l.type=v.type)},{once:!0});let d=this._canvas.getContext(\"webgl2\",T)||this._canvas.getContext(\"webgl\",T);if(!d){let v=\"Failed to initialize WebGL\";throw l?(l.message=v,new Error(JSON.stringify(l))):new Error(v)}this.painter=new ah(d,this.transform),f.testSupport(d)}_onCooperativeGesture(T,l,d){return!l&&d<2&&(this._cooperativeGesturesScreen.classList.add(\"maplibregl-show\"),setTimeout(()=>{this._cooperativeGesturesScreen.classList.remove(\"maplibregl-show\")},100)),!1}loaded(){return!this._styleDirty&&!this._sourcesDirty&&!!this.style&&this.style.loaded()}_update(T){return this.style&&this.style._loaded?(this._styleDirty=this._styleDirty||T,this._sourcesDirty=!0,this.triggerRepaint(),this):this}_requestRenderFrame(T){return this._update(),this._renderTaskQueue.add(T)}_cancelRenderFrame(T){this._renderTaskQueue.remove(T)}_render(T){let l=this._idleTriggered?this._fadeDuration:0;if(this.painter.context.setDirty(),this.painter.setBaseState(),this._renderTaskQueue.run(T),this._removed)return;let d=!1;if(this.style&&this._styleDirty){this._styleDirty=!1;let b=this.transform.zoom,M=n.h.now();this.style.zoomHistory.update(b,M);let O=new n.a8(b,{now:M,fadeDuration:l,zoomHistory:this.style.zoomHistory,transition:this.style.getTransition()}),B=O.crossFadingFactor();B===1&&B===this._crossFadingFactor||(d=!0,this._crossFadingFactor=B),this.style.update(O)}this.style&&this._sourcesDirty&&(this._sourcesDirty=!1,this.style._updateSources(this.transform)),this.terrain?(this.terrain.sourceCache.update(this.transform,this.terrain),this.transform._minEleveationForCurrentTile=this.terrain.getMinTileElevationForLngLatZoom(this.transform.center,this.transform.tileZoom),this._elevationFreeze||(this.transform.elevation=this.terrain.getElevationForLngLatZoom(this.transform.center,this.transform.tileZoom))):(this.transform._minEleveationForCurrentTile=0,this.transform.elevation=0),this._placementDirty=this.style&&this.style._updatePlacement(this.painter.transform,this.showCollisionBoxes,l,this._crossSourceCollisions),this.painter.render(this.style,{showTileBoundaries:this.showTileBoundaries,showOverdrawInspector:this._showOverdrawInspector,rotating:this.isRotating(),zooming:this.isZooming(),moving:this.isMoving(),fadeDuration:l,showPadding:this.showPadding}),this.fire(new n.k(\"render\")),this.loaded()&&!this._loaded&&(this._loaded=!0,n.bg.mark(n.bh.load),this.fire(new n.k(\"load\"))),this.style&&(this.style.hasTransitions()||d)&&(this._styleDirty=!0),this.style&&!this._placementDirty&&this.style._releaseSymbolFadeTiles();let v=this._sourcesDirty||this._styleDirty||this._placementDirty;return v||this._repaint?this.triggerRepaint():!this.isMoving()&&this.loaded()&&this.fire(new n.k(\"idle\")),!this._loaded||this._fullyLoaded||v||(this._fullyLoaded=!0,n.bg.mark(n.bh.fullLoad)),this}redraw(){return this.style&&(this._frame&&(this._frame.cancel(),this._frame=null),this._render(0)),this}remove(){var T;this._hash&&this._hash.remove();for(let d of this._controls)d.onRemove(this);this._controls=[],this._frame&&(this._frame.cancel(),this._frame=null),this._renderTaskQueue.clear(),this.painter.destroy(),this.handlers.destroy(),delete this.handlers,this.setStyle(null),typeof window<\"u\"&&removeEventListener(\"online\",this._onWindowOnline,!1),j.removeThrottleControl(this._imageQueueHandle),(T=this._resizeObserver)===null||T===void 0||T.disconnect();let l=this.painter.context.gl.getExtension(\"WEBGL_lose_context\");l&&l.loseContext(),this._canvas.removeEventListener(\"webglcontextrestored\",this._contextRestored,!1),this._canvas.removeEventListener(\"webglcontextlost\",this._contextLost,!1),c.remove(this._canvasContainer),c.remove(this._controlContainer),this._cooperativeGestures&&this._destroyCooperativeGestures(),this._container.classList.remove(\"maplibregl-map\"),n.bg.clearMetrics(),this._removed=!0,this.fire(new n.k(\"remove\"))}triggerRepaint(){this.style&&!this._frame&&(this._frame=n.h.frame(T=>{n.bg.frame(T),this._frame=null,this._render(T)}))}get showTileBoundaries(){return!!this._showTileBoundaries}set showTileBoundaries(T){this._showTileBoundaries!==T&&(this._showTileBoundaries=T,this._update())}get showPadding(){return!!this._showPadding}set showPadding(T){this._showPadding!==T&&(this._showPadding=T,this._update())}get showCollisionBoxes(){return!!this._showCollisionBoxes}set showCollisionBoxes(T){this._showCollisionBoxes!==T&&(this._showCollisionBoxes=T,T?this.style._generateCollisionBoxes():this._update())}get showOverdrawInspector(){return!!this._showOverdrawInspector}set showOverdrawInspector(T){this._showOverdrawInspector!==T&&(this._showOverdrawInspector=T,this._update())}get repaint(){return!!this._repaint}set repaint(T){this._repaint!==T&&(this._repaint=T,this.triggerRepaint())}get vertices(){return!!this._vertices}set vertices(T){this._vertices=T,this._update()}get version(){return nr}getCameraTargetElevation(){return this.transform.elevation}},bi.NavigationControl=class{constructor(T){this._updateZoomButtons=()=>{let l=this._map.getZoom(),d=l===this._map.getMaxZoom(),v=l===this._map.getMinZoom();this._zoomInButton.disabled=d,this._zoomOutButton.disabled=v,this._zoomInButton.setAttribute(\"aria-disabled\",d.toString()),this._zoomOutButton.setAttribute(\"aria-disabled\",v.toString())},this._rotateCompassArrow=()=>{let l=this.options.visualizePitch?`scale(${1/Math.pow(Math.cos(this._map.transform.pitch*(Math.PI/180)),.5)}) rotateX(${this._map.transform.pitch}deg) rotateZ(${this._map.transform.angle*(180/Math.PI)}deg)`:`rotate(${this._map.transform.angle*(180/Math.PI)}deg)`;this._compassIcon.style.transform=l},this._setButtonTitle=(l,d)=>{let v=this._map._getUIString(`NavigationControl.${d}`);l.title=v,l.setAttribute(\"aria-label\",v)},this.options=n.e({},aA,T),this._container=c.create(\"div\",\"maplibregl-ctrl maplibregl-ctrl-group\"),this._container.addEventListener(\"contextmenu\",l=>l.preventDefault()),this.options.showZoom&&(this._zoomInButton=this._createButton(\"maplibregl-ctrl-zoom-in\",l=>this._map.zoomIn({},{originalEvent:l})),c.create(\"span\",\"maplibregl-ctrl-icon\",this._zoomInButton).setAttribute(\"aria-hidden\",\"true\"),this._zoomOutButton=this._createButton(\"maplibregl-ctrl-zoom-out\",l=>this._map.zoomOut({},{originalEvent:l})),c.create(\"span\",\"maplibregl-ctrl-icon\",this._zoomOutButton).setAttribute(\"aria-hidden\",\"true\")),this.options.showCompass&&(this._compass=this._createButton(\"maplibregl-ctrl-compass\",l=>{this.options.visualizePitch?this._map.resetNorthPitch({},{originalEvent:l}):this._map.resetNorth({},{originalEvent:l})}),this._compassIcon=c.create(\"span\",\"maplibregl-ctrl-icon\",this._compass),this._compassIcon.setAttribute(\"aria-hidden\",\"true\"))}onAdd(T){return this._map=T,this.options.showZoom&&(this._setButtonTitle(this._zoomInButton,\"ZoomIn\"),this._setButtonTitle(this._zoomOutButton,\"ZoomOut\"),this._map.on(\"zoom\",this._updateZoomButtons),this._updateZoomButtons()),this.options.showCompass&&(this._setButtonTitle(this._compass,\"ResetBearing\"),this.options.visualizePitch&&this._map.on(\"pitch\",this._rotateCompassArrow),this._map.on(\"rotate\",this._rotateCompassArrow),this._rotateCompassArrow(),this._handler=new Bd(this._map,this._compass,this.options.visualizePitch)),this._container}onRemove(){c.remove(this._container),this.options.showZoom&&this._map.off(\"zoom\",this._updateZoomButtons),this.options.showCompass&&(this.options.visualizePitch&&this._map.off(\"pitch\",this._rotateCompassArrow),this._map.off(\"rotate\",this._rotateCompassArrow),this._handler.off(),delete this._handler),delete this._map}_createButton(T,l){let d=c.create(\"button\",T,this._container);return d.type=\"button\",d.addEventListener(\"click\",l),d}},bi.GeolocateControl=class extends n.E{constructor(T){super(),this._onSuccess=l=>{if(this._map){if(this._isOutOfMapMaxBounds(l))return this._setErrorState(),this.fire(new n.k(\"outofmaxbounds\",l)),this._updateMarker(),void this._finish();if(this.options.trackUserLocation)switch(this._lastKnownPosition=l,this._watchState){case\"WAITING_ACTIVE\":case\"ACTIVE_LOCK\":case\"ACTIVE_ERROR\":this._watchState=\"ACTIVE_LOCK\",this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-active\");break;case\"BACKGROUND\":case\"BACKGROUND_ERROR\":this._watchState=\"BACKGROUND\",this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-background-error\"),this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-background\");break;default:throw new Error(`Unexpected watchState ${this._watchState}`)}this.options.showUserLocation&&this._watchState!==\"OFF\"&&this._updateMarker(l),this.options.trackUserLocation&&this._watchState!==\"ACTIVE_LOCK\"||this._updateCamera(l),this.options.showUserLocation&&this._dotElement.classList.remove(\"maplibregl-user-location-dot-stale\"),this.fire(new n.k(\"geolocate\",l)),this._finish()}},this._updateCamera=l=>{let d=new n.L(l.coords.longitude,l.coords.latitude),v=l.coords.accuracy,b=this._map.getBearing(),M=n.e({bearing:b},this.options.fitBoundsOptions),O=Si.fromLngLat(d,v);this._map.fitBounds(O,M,{geolocateSource:!0})},this._updateMarker=l=>{if(l){let d=new n.L(l.coords.longitude,l.coords.latitude);this._accuracyCircleMarker.setLngLat(d).addTo(this._map),this._userLocationDotMarker.setLngLat(d).addTo(this._map),this._accuracy=l.coords.accuracy,this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()}else this._userLocationDotMarker.remove(),this._accuracyCircleMarker.remove()},this._onZoom=()=>{this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()},this._onError=l=>{if(this._map){if(this.options.trackUserLocation)if(l.code===1){this._watchState=\"OFF\",this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-active\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-background-error\"),this._geolocateButton.disabled=!0;let d=this._map._getUIString(\"GeolocateControl.LocationNotAvailable\");this._geolocateButton.title=d,this._geolocateButton.setAttribute(\"aria-label\",d),this._geolocationWatchID!==void 0&&this._clearWatch()}else{if(l.code===3&&Sf)return;this._setErrorState()}this._watchState!==\"OFF\"&&this.options.showUserLocation&&this._dotElement.classList.add(\"maplibregl-user-location-dot-stale\"),this.fire(new n.k(\"error\",l)),this._finish()}},this._finish=()=>{this._timeoutId&&clearTimeout(this._timeoutId),this._timeoutId=void 0},this._setupUI=l=>{if(this._map){if(this._container.addEventListener(\"contextmenu\",d=>d.preventDefault()),this._geolocateButton=c.create(\"button\",\"maplibregl-ctrl-geolocate\",this._container),c.create(\"span\",\"maplibregl-ctrl-icon\",this._geolocateButton).setAttribute(\"aria-hidden\",\"true\"),this._geolocateButton.type=\"button\",l===!1){n.w(\"Geolocation support is not available so the GeolocateControl will be disabled.\");let d=this._map._getUIString(\"GeolocateControl.LocationNotAvailable\");this._geolocateButton.disabled=!0,this._geolocateButton.title=d,this._geolocateButton.setAttribute(\"aria-label\",d)}else{let d=this._map._getUIString(\"GeolocateControl.FindMyLocation\");this._geolocateButton.title=d,this._geolocateButton.setAttribute(\"aria-label\",d)}this.options.trackUserLocation&&(this._geolocateButton.setAttribute(\"aria-pressed\",\"false\"),this._watchState=\"OFF\"),this.options.showUserLocation&&(this._dotElement=c.create(\"div\",\"maplibregl-user-location-dot\"),this._userLocationDotMarker=new mh({element:this._dotElement}),this._circleElement=c.create(\"div\",\"maplibregl-user-location-accuracy-circle\"),this._accuracyCircleMarker=new mh({element:this._circleElement,pitchAlignment:\"map\"}),this.options.trackUserLocation&&(this._watchState=\"OFF\"),this._map.on(\"zoom\",this._onZoom)),this._geolocateButton.addEventListener(\"click\",this.trigger.bind(this)),this._setup=!0,this.options.trackUserLocation&&this._map.on(\"movestart\",d=>{d.geolocateSource||this._watchState!==\"ACTIVE_LOCK\"||d.originalEvent&&d.originalEvent.type===\"resize\"||(this._watchState=\"BACKGROUND\",this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-active\"),this.fire(new n.k(\"trackuserlocationend\")))})}},this.options=n.e({},kn,T)}onAdd(T){return this._map=T,this._container=c.create(\"div\",\"maplibregl-ctrl maplibregl-ctrl-group\"),function(l,d=!1){Hn===void 0||d?window.navigator.permissions!==void 0?window.navigator.permissions.query({name:\"geolocation\"}).then(v=>{Hn=v.state!==\"denied\",l(Hn)}).catch(()=>{Hn=!!window.navigator.geolocation,l(Hn)}):(Hn=!!window.navigator.geolocation,l(Hn)):l(Hn)}(this._setupUI),this._container}onRemove(){this._geolocationWatchID!==void 0&&(window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0),this.options.showUserLocation&&this._userLocationDotMarker&&this._userLocationDotMarker.remove(),this.options.showAccuracyCircle&&this._accuracyCircleMarker&&this._accuracyCircleMarker.remove(),c.remove(this._container),this._map.off(\"zoom\",this._onZoom),this._map=void 0,wn=0,Sf=!1}_isOutOfMapMaxBounds(T){let l=this._map.getMaxBounds(),d=T.coords;return l&&(d.longitudel.getEast()||d.latitudel.getNorth())}_setErrorState(){switch(this._watchState){case\"WAITING_ACTIVE\":this._watchState=\"ACTIVE_ERROR\",this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-active\"),this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-active-error\");break;case\"ACTIVE_LOCK\":this._watchState=\"ACTIVE_ERROR\",this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-active\"),this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-waiting\");break;case\"BACKGROUND\":this._watchState=\"BACKGROUND_ERROR\",this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-background\"),this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-background-error\"),this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-waiting\");break;case\"ACTIVE_ERROR\":break;default:throw new Error(`Unexpected watchState ${this._watchState}`)}}_updateCircleRadius(){let T=this._map.getBounds(),l=T.getSouthEast(),d=T.getNorthEast(),v=l.distanceTo(d),b=Math.ceil(this._accuracy/(v/this._map._container.clientHeight)*2);this._circleElement.style.width=`${b}px`,this._circleElement.style.height=`${b}px`}trigger(){if(!this._setup)return n.w(\"Geolocate control triggered before added to a map\"),!1;if(this.options.trackUserLocation){switch(this._watchState){case\"OFF\":this._watchState=\"WAITING_ACTIVE\",this.fire(new n.k(\"trackuserlocationstart\"));break;case\"WAITING_ACTIVE\":case\"ACTIVE_LOCK\":case\"ACTIVE_ERROR\":case\"BACKGROUND_ERROR\":wn--,Sf=!1,this._watchState=\"OFF\",this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-active\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-background-error\"),this.fire(new n.k(\"trackuserlocationend\"));break;case\"BACKGROUND\":this._watchState=\"ACTIVE_LOCK\",this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-background\"),this._lastKnownPosition&&this._updateCamera(this._lastKnownPosition),this.fire(new n.k(\"trackuserlocationstart\"));break;default:throw new Error(`Unexpected watchState ${this._watchState}`)}switch(this._watchState){case\"WAITING_ACTIVE\":this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-active\");break;case\"ACTIVE_LOCK\":this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-active\");break;case\"OFF\":break;default:throw new Error(`Unexpected watchState ${this._watchState}`)}if(this._watchState===\"OFF\"&&this._geolocationWatchID!==void 0)this._clearWatch();else if(this._geolocationWatchID===void 0){let T;this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-waiting\"),this._geolocateButton.setAttribute(\"aria-pressed\",\"true\"),wn++,wn>1?(T={maximumAge:6e5,timeout:0},Sf=!0):(T=this.options.positionOptions,Sf=!1),this._geolocationWatchID=window.navigator.geolocation.watchPosition(this._onSuccess,this._onError,T)}}else window.navigator.geolocation.getCurrentPosition(this._onSuccess,this._onError,this.options.positionOptions),this._timeoutId=setTimeout(this._finish,1e4);return!0}_clearWatch(){window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0,this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-waiting\"),this._geolocateButton.setAttribute(\"aria-pressed\",\"false\"),this.options.showUserLocation&&this._updateMarker(null)}},bi.AttributionControl=ua,bi.LogoControl=un,bi.ScaleControl=class{constructor(T){this._onMove=()=>{gh(this._map,this._container,this.options)},this.setUnit=l=>{this.options.unit=l,gh(this._map,this._container,this.options)},this.options=n.e({},Es,T)}getDefaultPosition(){return\"bottom-left\"}onAdd(T){return this._map=T,this._container=c.create(\"div\",\"maplibregl-ctrl maplibregl-ctrl-scale\",T.getContainer()),this._map.on(\"move\",this._onMove),this._onMove(),this._container}onRemove(){c.remove(this._container),this._map.off(\"move\",this._onMove),this._map=void 0}},bi.FullscreenControl=class extends n.E{constructor(T={}){super(),this._onFullscreenChange=()=>{(window.document.fullscreenElement||window.document.mozFullScreenElement||window.document.webkitFullscreenElement||window.document.msFullscreenElement)===this._container!==this._fullscreen&&this._handleFullscreenChange()},this._onClickFullscreen=()=>{this._isFullscreen()?this._exitFullscreen():this._requestFullscreen()},this._fullscreen=!1,T&&T.container&&(T.container instanceof HTMLElement?this._container=T.container:n.w(\"Full screen control 'container' must be a DOM element.\")),\"onfullscreenchange\"in document?this._fullscreenchange=\"fullscreenchange\":\"onmozfullscreenchange\"in document?this._fullscreenchange=\"mozfullscreenchange\":\"onwebkitfullscreenchange\"in document?this._fullscreenchange=\"webkitfullscreenchange\":\"onmsfullscreenchange\"in document&&(this._fullscreenchange=\"MSFullscreenChange\")}onAdd(T){return this._map=T,this._container||(this._container=this._map.getContainer()),this._controlContainer=c.create(\"div\",\"maplibregl-ctrl maplibregl-ctrl-group\"),this._setupUI(),this._controlContainer}onRemove(){c.remove(this._controlContainer),this._map=null,window.document.removeEventListener(this._fullscreenchange,this._onFullscreenChange)}_setupUI(){let T=this._fullscreenButton=c.create(\"button\",\"maplibregl-ctrl-fullscreen\",this._controlContainer);c.create(\"span\",\"maplibregl-ctrl-icon\",T).setAttribute(\"aria-hidden\",\"true\"),T.type=\"button\",this._updateTitle(),this._fullscreenButton.addEventListener(\"click\",this._onClickFullscreen),window.document.addEventListener(this._fullscreenchange,this._onFullscreenChange)}_updateTitle(){let T=this._getTitle();this._fullscreenButton.setAttribute(\"aria-label\",T),this._fullscreenButton.title=T}_getTitle(){return this._map._getUIString(this._isFullscreen()?\"FullscreenControl.Exit\":\"FullscreenControl.Enter\")}_isFullscreen(){return this._fullscreen}_handleFullscreenChange(){this._fullscreen=!this._fullscreen,this._fullscreenButton.classList.toggle(\"maplibregl-ctrl-shrink\"),this._fullscreenButton.classList.toggle(\"maplibregl-ctrl-fullscreen\"),this._updateTitle(),this._fullscreen?(this.fire(new n.k(\"fullscreenstart\")),this._map._cooperativeGestures&&(this._prevCooperativeGestures=this._map._cooperativeGestures,this._map.setCooperativeGestures())):(this.fire(new n.k(\"fullscreenend\")),this._prevCooperativeGestures&&(this._map.setCooperativeGestures(this._prevCooperativeGestures),delete this._prevCooperativeGestures))}_exitFullscreen(){window.document.exitFullscreen?window.document.exitFullscreen():window.document.mozCancelFullScreen?window.document.mozCancelFullScreen():window.document.msExitFullscreen?window.document.msExitFullscreen():window.document.webkitCancelFullScreen?window.document.webkitCancelFullScreen():this._togglePseudoFullScreen()}_requestFullscreen(){this._container.requestFullscreen?this._container.requestFullscreen():this._container.mozRequestFullScreen?this._container.mozRequestFullScreen():this._container.msRequestFullscreen?this._container.msRequestFullscreen():this._container.webkitRequestFullscreen?this._container.webkitRequestFullscreen():this._togglePseudoFullScreen()}_togglePseudoFullScreen(){this._container.classList.toggle(\"maplibregl-pseudo-fullscreen\"),this._handleFullscreenChange(),this._map.resize()}},bi.TerrainControl=class{constructor(T){this._toggleTerrain=()=>{this._map.getTerrain()?this._map.setTerrain(null):this._map.setTerrain(this.options),this._updateTerrainIcon()},this._updateTerrainIcon=()=>{this._terrainButton.classList.remove(\"maplibregl-ctrl-terrain\"),this._terrainButton.classList.remove(\"maplibregl-ctrl-terrain-enabled\"),this._map.terrain?(this._terrainButton.classList.add(\"maplibregl-ctrl-terrain-enabled\"),this._terrainButton.title=this._map._getUIString(\"TerrainControl.disableTerrain\")):(this._terrainButton.classList.add(\"maplibregl-ctrl-terrain\"),this._terrainButton.title=this._map._getUIString(\"TerrainControl.enableTerrain\"))},this.options=T}onAdd(T){return this._map=T,this._container=c.create(\"div\",\"maplibregl-ctrl maplibregl-ctrl-group\"),this._terrainButton=c.create(\"button\",\"maplibregl-ctrl-terrain\",this._container),c.create(\"span\",\"maplibregl-ctrl-icon\",this._terrainButton).setAttribute(\"aria-hidden\",\"true\"),this._terrainButton.type=\"button\",this._terrainButton.addEventListener(\"click\",this._toggleTerrain),this._updateTerrainIcon(),this._map.on(\"terrain\",this._updateTerrainIcon),this._container}onRemove(){c.remove(this._container),this._map.off(\"terrain\",this._updateTerrainIcon),this._map=void 0}},bi.Popup=class extends n.E{constructor(T){super(),this.remove=()=>(this._content&&c.remove(this._content),this._container&&(c.remove(this._container),delete this._container),this._map&&(this._map.off(\"move\",this._update),this._map.off(\"move\",this._onClose),this._map.off(\"click\",this._onClose),this._map.off(\"remove\",this.remove),this._map.off(\"mousemove\",this._onMouseMove),this._map.off(\"mouseup\",this._onMouseUp),this._map.off(\"drag\",this._onDrag),delete this._map),this.fire(new n.k(\"close\")),this),this._onMouseUp=l=>{this._update(l.point)},this._onMouseMove=l=>{this._update(l.point)},this._onDrag=l=>{this._update(l.point)},this._update=l=>{if(!this._map||!this._lngLat&&!this._trackPointer||!this._content)return;if(!this._container){if(this._container=c.create(\"div\",\"maplibregl-popup\",this._map.getContainer()),this._tip=c.create(\"div\",\"maplibregl-popup-tip\",this._container),this._container.appendChild(this._content),this.options.className)for(let O of this.options.className.split(\" \"))this._container.classList.add(O);this._trackPointer&&this._container.classList.add(\"maplibregl-popup-track-pointer\")}if(this.options.maxWidth&&this._container.style.maxWidth!==this.options.maxWidth&&(this._container.style.maxWidth=this.options.maxWidth),this._map.transform.renderWorldCopies&&!this._trackPointer&&(this._lngLat=uo(this._lngLat,this._pos,this._map.transform)),this._trackPointer&&!l)return;let d=this._pos=this._trackPointer&&l?l:this._map.project(this._lngLat),v=this.options.anchor,b=Tf(this.options.offset);if(!v){let O=this._container.offsetWidth,B=this._container.offsetHeight,U;U=d.y+b.bottom.ythis._map.transform.height-B?[\"bottom\"]:[],d.xthis._map.transform.width-O/2&&U.push(\"right\"),v=U.length===0?\"bottom\":U.join(\"-\")}let M=d.add(b[v]).round();c.setTransform(this._container,`${ji[v]} translate(${M.x}px,${M.y}px)`),w_(this._container,v,\"popup\")},this._onClose=()=>{this.remove()},this.options=n.e(Object.create(p0),T)}addTo(T){return this._map&&this.remove(),this._map=T,this.options.closeOnClick&&this._map.on(\"click\",this._onClose),this.options.closeOnMove&&this._map.on(\"move\",this._onClose),this._map.on(\"remove\",this.remove),this._update(),this._focusFirstElement(),this._trackPointer?(this._map.on(\"mousemove\",this._onMouseMove),this._map.on(\"mouseup\",this._onMouseUp),this._container&&this._container.classList.add(\"maplibregl-popup-track-pointer\"),this._map._canvasContainer.classList.add(\"maplibregl-track-pointer\")):this._map.on(\"move\",this._update),this.fire(new n.k(\"open\")),this}isOpen(){return!!this._map}getLngLat(){return this._lngLat}setLngLat(T){return this._lngLat=n.L.convert(T),this._pos=null,this._trackPointer=!1,this._update(),this._map&&(this._map.on(\"move\",this._update),this._map.off(\"mousemove\",this._onMouseMove),this._container&&this._container.classList.remove(\"maplibregl-popup-track-pointer\"),this._map._canvasContainer.classList.remove(\"maplibregl-track-pointer\")),this}trackPointer(){return this._trackPointer=!0,this._pos=null,this._update(),this._map&&(this._map.off(\"move\",this._update),this._map.on(\"mousemove\",this._onMouseMove),this._map.on(\"drag\",this._onDrag),this._container&&this._container.classList.add(\"maplibregl-popup-track-pointer\"),this._map._canvasContainer.classList.add(\"maplibregl-track-pointer\")),this}getElement(){return this._container}setText(T){return this.setDOMContent(document.createTextNode(T))}setHTML(T){let l=document.createDocumentFragment(),d=document.createElement(\"body\"),v;for(d.innerHTML=T;v=d.firstChild,v;)l.appendChild(v);return this.setDOMContent(l)}getMaxWidth(){var T;return(T=this._container)===null||T===void 0?void 0:T.style.maxWidth}setMaxWidth(T){return this.options.maxWidth=T,this._update(),this}setDOMContent(T){if(this._content)for(;this._content.hasChildNodes();)this._content.firstChild&&this._content.removeChild(this._content.firstChild);else this._content=c.create(\"div\",\"maplibregl-popup-content\",this._container);return this._content.appendChild(T),this._createCloseButton(),this._update(),this._focusFirstElement(),this}addClassName(T){this._container&&this._container.classList.add(T)}removeClassName(T){this._container&&this._container.classList.remove(T)}setOffset(T){return this.options.offset=T,this._update(),this}toggleClassName(T){if(this._container)return this._container.classList.toggle(T)}_createCloseButton(){this.options.closeButton&&(this._closeButton=c.create(\"button\",\"maplibregl-popup-close-button\",this._content),this._closeButton.type=\"button\",this._closeButton.setAttribute(\"aria-label\",\"Close popup\"),this._closeButton.innerHTML=\"×\",this._closeButton.addEventListener(\"click\",this._onClose))}_focusFirstElement(){if(!this.options.focusAfterOpen||!this._container)return;let T=this._container.querySelector(Fd);T&&T.focus()}},bi.Marker=mh,bi.Style=Gn,bi.LngLat=n.L,bi.LngLatBounds=Si,bi.Point=n.P,bi.MercatorCoordinate=n.U,bi.Evented=n.E,bi.AJAXError=n.bi,bi.config=n.c,bi.CanvasSource=Vo,bi.GeoJSONSource=Xi,bi.ImageSource=ki,bi.RasterDEMTileSource=Rc,bi.RasterTileSource=kc,bi.VectorTileSource=ll,bi.VideoSource=ts,bi.setRTLTextPlugin=n.bj,bi.getRTLTextPluginStatus=n.bk,bi.prewarm=function(){bo().acquire(oi)},bi.clearPrewarmedResources=function(){let T=ul;T&&(T.isPreloaded()&&T.numActive()===1?(T.release(oi),ul=null):console.warn(\"Could not clear WebWorkers since there are active Map instances that still reference it. 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this.transform(t,r)}},jE,GE;function nat(){return jE||(jE=new ss([0,0,0,0,0,0,0,0,0]),Object.freeze(jE)),jE}function sat(){return GE||(GE=new ss,Object.freeze(GE)),GE}function oat(e){return e[0]=1,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=1,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=1,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function s7(e,t){if(e===t){var r=t[1],i=t[2],s=t[3],n=t[6],o=t[7],c=t[11];e[1]=t[4],e[2]=t[8],e[3]=t[12],e[4]=r,e[6]=t[9],e[7]=t[13],e[8]=i,e[9]=n,e[11]=t[14],e[12]=s,e[13]=o,e[14]=c}else e[0]=t[0],e[1]=t[4],e[2]=t[8],e[3]=t[12],e[4]=t[1],e[5]=t[5],e[6]=t[9],e[7]=t[13],e[8]=t[2],e[9]=t[6],e[10]=t[10],e[11]=t[14],e[12]=t[3],e[13]=t[7],e[14]=t[11],e[15]=t[15];return e}function Sb(e,t){var r=t[0],i=t[1],s=t[2],n=t[3],o=t[4],c=t[5],f=t[6],_=t[7],w=t[8],I=t[9],R=t[10],N=t[11],j=t[12],Q=t[13],et=t[14],Y=t[15],K=r*c-i*o,J=r*f-s*o,ut=r*_-n*o,Et=i*f-s*c,kt=i*_-n*c,Xt=s*_-n*f,qt=w*Q-I*j,le=w*et-R*j,ue=w*Y-N*j,De=I*et-R*Q,Ke=I*Y-N*Q,rr=R*Y-N*et,Sr=K*rr-J*Ke+ut*De+Et*ue-kt*le+Xt*qt;return 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e[0]=J*i+ut*c+Et*I+kt*Q,e[1]=J*s+ut*f+Et*R+kt*et,e[2]=J*n+ut*_+Et*N+kt*Y,e[3]=J*o+ut*w+Et*j+kt*K,J=r[4],ut=r[5],Et=r[6],kt=r[7],e[4]=J*i+ut*c+Et*I+kt*Q,e[5]=J*s+ut*f+Et*R+kt*et,e[6]=J*n+ut*_+Et*N+kt*Y,e[7]=J*o+ut*w+Et*j+kt*K,J=r[8],ut=r[9],Et=r[10],kt=r[11],e[8]=J*i+ut*c+Et*I+kt*Q,e[9]=J*s+ut*f+Et*R+kt*et,e[10]=J*n+ut*_+Et*N+kt*Y,e[11]=J*o+ut*w+Et*j+kt*K,J=r[12],ut=r[13],Et=r[14],kt=r[15],e[12]=J*i+ut*c+Et*I+kt*Q,e[13]=J*s+ut*f+Et*R+kt*et,e[14]=J*n+ut*_+Et*N+kt*Y,e[15]=J*o+ut*w+Et*j+kt*K,e}function ag(e,t,r){var i=r[0],s=r[1],n=r[2],o,c,f,_,w,I,R,N,j,Q,et,Y;return t===e?(e[12]=t[0]*i+t[4]*s+t[8]*n+t[12],e[13]=t[1]*i+t[5]*s+t[9]*n+t[13],e[14]=t[2]*i+t[6]*s+t[10]*n+t[14],e[15]=t[3]*i+t[7]*s+t[11]*n+t[15]):(o=t[0],c=t[1],f=t[2],_=t[3],w=t[4],I=t[5],R=t[6],N=t[7],j=t[8],Q=t[9],et=t[10],Y=t[11],e[0]=o,e[1]=c,e[2]=f,e[3]=_,e[4]=w,e[5]=I,e[6]=R,e[7]=N,e[8]=j,e[9]=Q,e[10]=et,e[11]=Y,e[12]=o*i+w*s+j*n+t[12],e[13]=c*i+I*s+Q*n+t[13],e[14]=f*i+R*s+et*n+t[14],e[15]=_*i+N*s+Y*n+t[15]),e}function By(e,t,r){var i=r[0],s=r[1],n=r[2];return e[0]=t[0]*i,e[1]=t[1]*i,e[2]=t[2]*i,e[3]=t[3]*i,e[4]=t[4]*s,e[5]=t[5]*s,e[6]=t[6]*s,e[7]=t[7]*s,e[8]=t[8]*n,e[9]=t[9]*n,e[10]=t[10]*n,e[11]=t[11]*n,e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e}function a7(e,t,r,i){var s=i[0],n=i[1],o=i[2],c=Math.hypot(s,n,o),f,_,w,I,R,N,j,Q,et,Y,K,J,ut,Et,kt,Xt,qt,le,ue,De,Ke,rr,Sr,Li;return c0&&(o=1/Math.sqrt(o)),e[0]=r*o,e[1]=i*o,e[2]=s*o,e[3]=n*o,e}function _7(e,t){return e[0]*t[0]+e[1]*t[1]+e[2]*t[2]+e[3]*t[3]}function y7(e,t,r,i){var s=t[0],n=t[1],o=t[2],c=t[3];return e[0]=s+i*(r[0]-s),e[1]=n+i*(r[1]-n),e[2]=o+i*(r[2]-o),e[3]=c+i*(r[3]-c),e}function Nh(e,t,r){var i=t[0],s=t[1],n=t[2],o=t[3];return e[0]=r[0]*i+r[4]*s+r[8]*n+r[12]*o,e[1]=r[1]*i+r[5]*s+r[9]*n+r[13]*o,e[2]=r[2]*i+r[6]*s+r[10]*n+r[14]*o,e[3]=r[3]*i+r[7]*s+r[11]*n+r[15]*o,e}function v7(e,t,r){var i=t[0],s=t[1],n=t[2],o=r[0],c=r[1],f=r[2],_=r[3],w=_*i+c*n-f*s,I=_*s+f*i-o*n,R=_*n+o*s-c*i,N=-o*i-c*s-f*n;return e[0]=w*_+N*-o+I*-f-R*-c,e[1]=I*_+N*-c+R*-o-w*-f,e[2]=R*_+N*-f+w*-c-I*-o,e[3]=t[3],e}var b4t=function(){var e=cat();return function(t,r,i,s,n,o){var c,f;for(r||(r=4),i||(i=0),s?f=Math.min(s*r+i,t.length):f=t.length,c=i;cMath.PI*2)throw Error(\"expected radians\")}function Aat(e,t,r,i,s,n){let o=2*n/(r-t),c=2*n/(s-i),f=(r+t)/(r-t),_=(s+i)/(s-i),w=-1,I=-1,R=-2*n;return e[0]=o,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=c,e[6]=0,e[7]=0,e[8]=f,e[9]=_,e[10]=w,e[11]=I,e[12]=0,e[13]=0,e[14]=R,e[15]=0,e}function b7(){var e=new ya(4);return ya!=Float32Array&&(e[0]=0,e[1]=0,e[2]=0),e[3]=1,e}function w7(e){return e[0]=0,e[1]=0,e[2]=0,e[3]=1,e}function iD(e,t,r){r=r*.5;var i=Math.sin(r);return e[0]=i*t[0],e[1]=i*t[1],e[2]=i*t[2],e[3]=Math.cos(r),e}function nD(e,t,r){var i=t[0],s=t[1],n=t[2],o=t[3],c=r[0],f=r[1],_=r[2],w=r[3];return e[0]=i*w+o*c+s*_-n*f,e[1]=s*w+o*f+n*c-i*_,e[2]=n*w+o*_+i*f-s*c,e[3]=o*w-i*c-s*f-n*_,e}function S7(e,t,r){r*=.5;var i=t[0],s=t[1],n=t[2],o=t[3],c=Math.sin(r),f=Math.cos(r);return e[0]=i*f+o*c,e[1]=s*f+n*c,e[2]=n*f-s*c,e[3]=o*f-i*c,e}function T7(e,t,r){r*=.5;var i=t[0],s=t[1],n=t[2],o=t[3],c=Math.sin(r),f=Math.cos(r);return e[0]=i*f-n*c,e[1]=s*f+o*c,e[2]=n*f+i*c,e[3]=o*f-s*c,e}function M7(e,t,r){r*=.5;var i=t[0],s=t[1],n=t[2],o=t[3],c=Math.sin(r),f=Math.cos(r);return e[0]=i*f+s*c,e[1]=s*f-i*c,e[2]=n*f+o*c,e[3]=o*f-n*c,e}function E7(e,t){var r=t[0],i=t[1],s=t[2];return e[0]=r,e[1]=i,e[2]=s,e[3]=Math.sqrt(Math.abs(1-r*r-i*i-s*s)),e}function Mb(e,t,r,i){var s=t[0],n=t[1],o=t[2],c=t[3],f=r[0],_=r[1],w=r[2],I=r[3],R,N,j,Q,et;return N=s*f+n*_+o*w+c*I,N<0&&(N=-N,f=-f,_=-_,w=-w,I=-I),1-N>zh?(R=Math.acos(N),j=Math.sin(R),Q=Math.sin((1-i)*R)/j,et=Math.sin(i*R)/j):(Q=1-i,et=i),e[0]=Q*s+et*f,e[1]=Q*n+et*_,e[2]=Q*o+et*w,e[3]=Q*c+et*I,e}function P7(e,t){var r=t[0],i=t[1],s=t[2],n=t[3],o=r*r+i*i+s*s+n*n,c=o?1/o:0;return e[0]=-r*c,e[1]=-i*c,e[2]=-s*c,e[3]=n*c,e}function I7(e,t){return e[0]=-t[0],e[1]=-t[1],e[2]=-t[2],e[3]=t[3],e}function sD(e,t){var r=t[0]+t[4]+t[8],i;if(r>0)i=Math.sqrt(r+1),e[3]=.5*i,i=.5/i,e[0]=(t[5]-t[7])*i,e[1]=(t[6]-t[2])*i,e[2]=(t[1]-t[3])*i;else{var s=0;t[4]>t[0]&&(s=1),t[8]>t[s*3+s]&&(s=2);var n=(s+1)%3,o=(s+2)%3;i=Math.sqrt(t[s*3+s]-t[n*3+n]-t[o*3+o]+1),e[s]=.5*i,i=.5/i,e[3]=(t[n*3+o]-t[o*3+n])*i,e[n]=(t[n*3+s]+t[s*3+n])*i,e[o]=(t[o*3+s]+t[s*3+o])*i}return e}var C7=p7;var L7=Fy,k7=_7,R7=y7,D7=A7;var O7=m7;var B7=g7;var F7=function(){var e=qR(),t=ZR(1,0,0),r=ZR(0,1,0);return function(i,s,n){var o=YR(s,n);return o<-.999999?(Dy(e,t,s),zE(e)<1e-6&&Dy(e,r,s),Wj(e,e),iD(i,e,Math.PI),i):o>.999999?(i[0]=0,i[1]=0,i[2]=0,i[3]=1,i):(Dy(e,s,n),i[0]=e[0],i[1]=e[1],i[2]=e[2],i[3]=1+o,B7(i,i))}}(),R4t=function(){var e=b7(),t=b7();return function(r,i,s,n,o,c){return Mb(e,i,o,c),Mb(t,s,n,c),Mb(r,e,t,2*c*(1-c)),r}}(),D4t=function(){var e=Xj();return function(t,r,i,s){return e[0]=i[0],e[3]=i[1],e[6]=i[2],e[1]=s[0],e[4]=s[1],e[7]=s[2],e[2]=-r[0],e[5]=-r[1],e[8]=-r[2],B7(t,sD(t,e))}}();var gat=[0,0,0,1],lg=class extends np{constructor(t=0,r=0,i=0,s=1){super(-0,-0,-0,-0),Array.isArray(t)&&arguments.length===1?this.copy(t):this.set(t,r,i,s)}copy(t){return this[0]=t[0],this[1]=t[1],this[2]=t[2],this[3]=t[3],this.check()}set(t,r,i,s){return this[0]=t,this[1]=r,this[2]=i,this[3]=s,this.check()}fromObject(t){return this[0]=t.x,this[1]=t.y,this[2]=t.z,this[3]=t.w,this.check()}fromMatrix3(t){return sD(this,t),this.check()}fromAxisRotation(t,r){return iD(this,t,r),this.check()}identity(){return w7(this),this.check()}setAxisAngle(t,r){return this.fromAxisRotation(t,r)}get ELEMENTS(){return 4}get x(){return this[0]}set x(t){this[0]=Qi(t)}get y(){return this[1]}set y(t){this[1]=Qi(t)}get z(){return this[2]}set z(t){this[2]=Qi(t)}get w(){return this[3]}set w(t){this[3]=Qi(t)}len(){return D7(this)}lengthSquared(){return O7(this)}dot(t){return k7(this,t)}rotationTo(t,r){return F7(this,t,r),this.check()}add(t){return C7(this,this,t),this.check()}calculateW(){return E7(this,this),this.check()}conjugate(){return I7(this,this),this.check()}invert(){return P7(this,this),this.check()}lerp(t,r,i){return i===void 0?this.lerp(this,t,r):(R7(this,t,r,i),this.check())}multiplyRight(t){return nD(this,this,t),this.check()}multiplyLeft(t){return nD(this,t,this),this.check()}normalize(){let t=this.len(),r=t>0?1/t:0;return this[0]=this[0]*r,this[1]=this[1]*r,this[2]=this[2]*r,this[3]=this[3]*r,t===0&&(this[3]=1),this.check()}rotateX(t){return S7(this,this,t),this.check()}rotateY(t){return T7(this,this,t),this.check()}rotateZ(t){return M7(this,this,t),this.check()}scale(t){return L7(this,this,t),this.check()}slerp(t,r,i){let s,n,o;switch(arguments.length){case 1:({start:s=gat,target:n,ratio:o}=t);break;case 2:s=this,n=t,o=r;break;default:s=t,n=r,o=i}return Mb(this,s,n,o),this.check()}transformVector4(t,r=new wb){return v7(r,t,this),QA(r,4)}lengthSq(){return this.lengthSquared()}setFromAxisAngle(t,r){return this.setAxisAngle(t,r)}premultiply(t){return this.multiplyLeft(t)}multiply(t){return this.multiplyRight(t)}};var YE={EPSILON1:.1,EPSILON2:.01,EPSILON3:.001,EPSILON4:1e-4,EPSILON5:1e-5,EPSILON6:1e-6,EPSILON7:1e-7,EPSILON8:1e-8,EPSILON9:1e-9,EPSILON10:1e-10,EPSILON11:1e-11,EPSILON12:1e-12,EPSILON13:1e-13,EPSILON14:1e-14,EPSILON15:1e-15,EPSILON16:1e-16,EPSILON17:1e-17,EPSILON18:1e-18,EPSILON19:1e-19,EPSILON20:1e-20,PI_OVER_TWO:Math.PI/2,PI_OVER_FOUR:Math.PI/4,PI_OVER_SIX:Math.PI/6,TWO_PI:Math.PI*2};var oD=`#if (defined(SHADER_TYPE_FRAGMENT) && defined(LIGHTING_FRAGMENT)) || (defined(SHADER_TYPE_VERTEX) && defined(LIGHTING_VERTEX))\n\nstruct AmbientLight {\n vec3 color;\n};\n\nstruct PointLight {\n vec3 color;\n vec3 position;\n vec3 attenuation;\n};\n\nstruct DirectionalLight {\n vec3 color;\n vec3 direction;\n};\n\nuniform AmbientLight lighting_uAmbientLight;\nuniform PointLight lighting_uPointLight[MAX_LIGHTS];\nuniform DirectionalLight lighting_uDirectionalLight[MAX_LIGHTS];\nuniform int lighting_uPointLightCount;\nuniform int lighting_uDirectionalLightCount;\n\nuniform bool lighting_uEnabled;\n\nfloat getPointLightAttenuation(PointLight pointLight, float distance) {\n return pointLight.attenuation.x\n + pointLight.attenuation.y * distance\n + pointLight.attenuation.z * distance * distance;\n}\n\n#endif\n`;var _at={lightSources:{}};function aD(){let{color:e=[0,0,0],intensity:t=1}=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{};return e.map(r=>r*t/255)}function yat(e){let{ambientLight:t,pointLights:r=[],directionalLights:i=[]}=e,s={};return t?s[\"lighting_uAmbientLight.color\"]=aD(t):s[\"lighting_uAmbientLight.color\"]=[0,0,0],r.forEach((n,o)=>{s[\"lighting_uPointLight[\".concat(o,\"].color\")]=aD(n),s[\"lighting_uPointLight[\".concat(o,\"].position\")]=n.position,s[\"lighting_uPointLight[\".concat(o,\"].attenuation\")]=n.attenuation||[1,0,0]}),s.lighting_uPointLightCount=r.length,i.forEach((n,o)=>{s[\"lighting_uDirectionalLight[\".concat(o,\"].color\")]=aD(n),s[\"lighting_uDirectionalLight[\".concat(o,\"].direction\")]=n.direction}),s.lighting_uDirectionalLightCount=i.length,s}function z7(){let e=arguments.length>0&&arguments[0]!==void 0?arguments[0]:_at;if(\"lightSources\"in e){let{ambientLight:t,pointLights:r,directionalLights:i}=e.lightSources||{};return t||r&&r.length>0||i&&i.length>0?Object.assign({},yat({ambientLight:t,pointLights:r,directionalLights:i}),{lighting_uEnabled:!0}):{lighting_uEnabled:!1}}if(\"lights\"in e){let t={pointLights:[],directionalLights:[]};for(let r of e.lights||[])switch(r.type){case\"ambient\":t.ambientLight=r;break;case\"directional\":t.directionalLights.push(r);break;case\"point\":t.pointLights.push(r);break;default:}return z7({lightSources:t})}return{}}var lD={name:\"lights\",vs:oD,fs:oD,getUniforms:z7,defines:{MAX_LIGHTS:3}};var vat=new Uint8Array([0,255,255,255]),xat={pickingSelectedColor:null,pickingHighlightColor:vat,pickingActive:!1,pickingAttribute:!1};function bat(){let e=arguments.length>0&&arguments[0]!==void 0?arguments[0]:xat,t={};if(e.pickingSelectedColor!==void 0)if(!e.pickingSelectedColor)t.picking_uSelectedColorValid=0;else{let r=e.pickingSelectedColor.slice(0,3);t.picking_uSelectedColorValid=1,t.picking_uSelectedColor=r}if(e.pickingHighlightColor){let r=Array.from(e.pickingHighlightColor,i=>i/255);Number.isFinite(r[3])||(r[3]=1),t.picking_uHighlightColor=r}return e.pickingActive!==void 0&&(t.picking_uActive=!!e.pickingActive,t.picking_uAttribute=!!e.pickingAttribute),t}var wat=`uniform bool picking_uActive;\nuniform bool picking_uAttribute;\nuniform vec3 picking_uSelectedColor;\nuniform bool picking_uSelectedColorValid;\n\nout vec4 picking_vRGBcolor_Avalid;\n\nconst float COLOR_SCALE = 1. / 255.;\n\nbool picking_isColorValid(vec3 color) {\n return dot(color, vec3(1.0)) > 0.001;\n}\n\nbool isVertexPicked(vec3 vertexColor) {\n return\n picking_uSelectedColorValid &&\n !picking_isColorValid(abs(vertexColor - picking_uSelectedColor));\n}\n\nvoid picking_setPickingColor(vec3 pickingColor) {\n if (picking_uActive) {\n picking_vRGBcolor_Avalid.a = float(picking_isColorValid(pickingColor));\n\n if (!picking_uAttribute) {\n picking_vRGBcolor_Avalid.rgb = pickingColor * COLOR_SCALE;\n }\n } else {\n picking_vRGBcolor_Avalid.a = float(isVertexPicked(pickingColor));\n }\n}\n\nvoid picking_setPickingAttribute(float value) {\n if (picking_uAttribute) {\n picking_vRGBcolor_Avalid.r = value;\n }\n}\nvoid picking_setPickingAttribute(vec2 value) {\n if (picking_uAttribute) {\n picking_vRGBcolor_Avalid.rg = value;\n }\n}\nvoid picking_setPickingAttribute(vec3 value) {\n if (picking_uAttribute) {\n picking_vRGBcolor_Avalid.rgb = value;\n }\n}\n`,Sat=`uniform bool picking_uActive;\nuniform vec3 picking_uSelectedColor;\nuniform vec4 picking_uHighlightColor;\n\nin vec4 picking_vRGBcolor_Avalid;\nvec4 picking_filterHighlightColor(vec4 color) {\n if (picking_uActive) {\n return color;\n }\n bool selected = bool(picking_vRGBcolor_Avalid.a);\n\n if (selected) {\n float highLightAlpha = picking_uHighlightColor.a;\n float blendedAlpha = highLightAlpha + color.a * (1.0 - highLightAlpha);\n float highLightRatio = highLightAlpha / blendedAlpha;\n\n vec3 blendedRGB = mix(color.rgb, picking_uHighlightColor.rgb, highLightRatio);\n return vec4(blendedRGB, blendedAlpha);\n } else {\n return color;\n }\n}\nvec4 picking_filterPickingColor(vec4 color) {\n if (picking_uActive) {\n if (picking_vRGBcolor_Avalid.a == 0.0) {\n discard;\n }\n return picking_vRGBcolor_Avalid;\n }\n return color;\n}\nvec4 picking_filterColor(vec4 color) {\n vec4 highightColor = picking_filterHighlightColor(color);\n return picking_filterPickingColor(highightColor);\n}\n\n`,QE={name:\"picking\",vs:wat,fs:Sat,getUniforms:bat};var cD=`\nuniform float lighting_uAmbient;\nuniform float lighting_uDiffuse;\nuniform float lighting_uShininess;\nuniform vec3 lighting_uSpecularColor;\n\nvec3 lighting_getLightColor(vec3 surfaceColor, vec3 light_direction, vec3 view_direction, vec3 normal_worldspace, vec3 color) {\n vec3 halfway_direction = normalize(light_direction + view_direction);\n float lambertian = dot(light_direction, normal_worldspace);\n float specular = 0.0;\n if (lambertian > 0.0) {\n float specular_angle = max(dot(normal_worldspace, halfway_direction), 0.0);\n specular = pow(specular_angle, lighting_uShininess);\n }\n lambertian = max(lambertian, 0.0);\n return (lambertian * lighting_uDiffuse * surfaceColor + specular * lighting_uSpecularColor) * color;\n}\n\nvec3 lighting_getLightColor(vec3 surfaceColor, vec3 cameraPosition, vec3 position_worldspace, vec3 normal_worldspace) {\n vec3 lightColor = surfaceColor;\n\n if (lighting_uEnabled) {\n vec3 view_direction = normalize(cameraPosition - position_worldspace);\n lightColor = lighting_uAmbient * surfaceColor * lighting_uAmbientLight.color;\n\n for (int i = 0; i < MAX_LIGHTS; i++) {\n if (i >= lighting_uPointLightCount) {\n break;\n }\n PointLight pointLight = lighting_uPointLight[i];\n vec3 light_position_worldspace = pointLight.position;\n vec3 light_direction = normalize(light_position_worldspace - position_worldspace);\n lightColor += lighting_getLightColor(surfaceColor, light_direction, view_direction, normal_worldspace, pointLight.color);\n }\n\n for (int i = 0; i < MAX_LIGHTS; i++) {\n if (i >= lighting_uDirectionalLightCount) {\n break;\n }\n DirectionalLight directionalLight = lighting_uDirectionalLight[i];\n lightColor += lighting_getLightColor(surfaceColor, -directionalLight.direction, view_direction, normal_worldspace, directionalLight.color);\n }\n }\n return lightColor;\n}\n\nvec3 lighting_getSpecularLightColor(vec3 cameraPosition, vec3 position_worldspace, vec3 normal_worldspace) {\n vec3 lightColor = vec3(0, 0, 0);\n vec3 surfaceColor = vec3(0, 0, 0);\n\n if (lighting_uEnabled) {\n vec3 view_direction = normalize(cameraPosition - position_worldspace);\n\n for (int i = 0; i < MAX_LIGHTS; i++) {\n if (i >= lighting_uPointLightCount) {\n break;\n }\n PointLight pointLight = lighting_uPointLight[i];\n vec3 light_position_worldspace = pointLight.position;\n vec3 light_direction = normalize(light_position_worldspace - position_worldspace);\n lightColor += lighting_getLightColor(surfaceColor, light_direction, view_direction, normal_worldspace, pointLight.color);\n }\n\n for (int i = 0; i < MAX_LIGHTS; i++) {\n if (i >= lighting_uDirectionalLightCount) {\n break;\n }\n DirectionalLight directionalLight = lighting_uDirectionalLight[i];\n lightColor += lighting_getLightColor(surfaceColor, -directionalLight.direction, view_direction, normal_worldspace, directionalLight.color);\n }\n }\n return lightColor;\n}\n`;var Tat={};function Mat(e){let{ambient:t=.35,diffuse:r=.6,shininess:i=32,specularColor:s=[30,30,30]}=e;return{lighting_uAmbient:t,lighting_uDiffuse:r,lighting_uShininess:i,lighting_uSpecularColor:s.map(n=>n/255)}}function N7(){let e=arguments.length>0&&arguments[0]!==void 0?arguments[0]:Tat;if(!(\"material\"in e))return{};let{material:t}=e;return t?Mat(t):{lighting_uEnabled:!1}}var Zf={name:\"gouraud-lighting\",dependencies:[lD],vs:cD,defines:{LIGHTING_VERTEX:1},getUniforms:N7},Ny={name:\"phong-lighting\",dependencies:[lD],fs:cD,defines:{LIGHTING_FRAGMENT:1},getUniforms:N7};var Eat=`attribute float transform_elementID;\nvec2 transform_getPixelSizeHalf(vec2 size) {\n return vec2(1.) / (2. * size);\n}\n\nvec2 transform_getPixelIndices(vec2 texSize, vec2 pixelSizeHalf) {\n float yIndex = floor((transform_elementID / texSize[0]) + 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j)et.push(this._getHash(J)),et.push(this._getHash(s[J]));for(let J of Q)Y.push(this._getHash(J)),Y.push(this._getHash(n[J]));let K=\"\".concat(w,\"/\").concat(I,\"D\").concat(et.join(\"/\"),\"M\").concat(R.join(\"/\"),\"I\").concat(Y.join(\"/\"),\"V\").concat(N.join(\"/\"),\"H\").concat(this.stateHash,\"B\").concat(c).concat(f?\"T\":\"\");if(!this._programCache[K]){let J=VR(this.gl,{vs:r,fs:i,modules:_,inject:n,defines:s,hookFunctions:this._hookFunctions,transpileToGLSL100:f});this._programCache[K]=new rp(this.gl,{hash:K,vs:J.vs,fs:J.fs,varyings:o,bufferMode:c}),this._getUniforms[K]=J.getUniforms||(ut=>{}),this._useCounts[K]=0}return this._useCounts[K]++,this._programCache[K]}getUniforms(t){return this._getUniforms[t.hash]||null}release(t){let r=t.hash;this._useCounts[r]--,this._useCounts[r]===0&&(this._programCache[r].delete(),delete this._programCache[r],delete this._getUniforms[r],delete this._useCounts[r])}_getHash(t){return this._hashes[t]===void 0&&(this._hashes[t]=this._hashCounter++),this._hashes[t]}_getModuleList(){let t=arguments.length>0&&arguments[0]!==void 0?arguments[0]:[],r=new Array(this._defaultModules.length+t.length),i={},s=0;for(let n=0,o=this._defaultModules.length;n{},Rat={},fn=class{constructor(t){let r=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{},{id:i=ta(\"model\")}=r;ye(Jd(t)),this.id=i,this.gl=t,this.id=r.id||ta(\"Model\"),this.lastLogTime=0,this.animated=!1,this.initialize(r)}initialize(t){this.props={},this.programManager=t.programManager||Uh.getDefaultProgramManager(this.gl),this._programManagerState=-1,this._managedProgram=!1;let{program:r=null,vs:i,fs:s,modules:n,defines:o,inject:c,varyings:f,bufferMode:_,transpileToGLSL100:w}=t;this.programProps={program:r,vs:i,fs:s,modules:n,defines:o,inject:c,varyings:f,bufferMode:_,transpileToGLSL100:w},this.program=null,this.vertexArray=null,this._programDirty=!0,this.userData={},this.needsRedraw=!0,this._attributes={},this.attributes={},this.uniforms={},this.pickable=!0,this._checkProgram(),this.setUniforms(Object.assign({},this.getModuleUniforms(t.moduleSettings))),this.drawMode=t.drawMode!==void 0?t.drawMode:4,this.vertexCount=t.vertexCount||0,this.geometryBuffers={},this.isInstanced=t.isInstanced||t.instanced||t.instanceCount>0,this._setModelProps(t),this.geometry={},ye(this.drawMode!==void 0&&Number.isFinite(this.vertexCount),kat)}setProps(t){this._setModelProps(t)}delete(){for(let t in this._attributes)this._attributes[t]!==this.attributes[t]&&this._attributes[t].delete();this._managedProgram&&(this.programManager.release(this.program),this._managedProgram=!1),this.vertexArray.delete(),this._deleteGeometryBuffers()}getDrawMode(){return this.drawMode}getVertexCount(){return this.vertexCount}getInstanceCount(){return this.instanceCount}getAttributes(){return this.attributes}getProgram(){return this.program}setProgram(t){let{program:r,vs:i,fs:s,modules:n,defines:o,inject:c,varyings:f,bufferMode:_,transpileToGLSL100:w}=t;this.programProps={program:r,vs:i,fs:s,modules:n,defines:o,inject:c,varyings:f,bufferMode:_,transpileToGLSL100:w},this._programDirty=!0}getUniforms(){return this.uniforms}setDrawMode(t){return this.drawMode=t,this}setVertexCount(t){return ye(Number.isFinite(t)),this.vertexCount=t,this}setInstanceCount(t){return ye(Number.isFinite(t)),this.instanceCount=t,this}setGeometry(t){return this.drawMode=t.drawMode,this.vertexCount=t.getVertexCount(),this._deleteGeometryBuffers(),this.geometryBuffers=U7(this.gl,t),this.vertexArray.setAttributes(this.geometryBuffers),this}setAttributes(){let t=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{};if(Wf(t))return this;let r={};for(let i in t){let s=t[i];r[i]=s.getValue?s.getValue():s}return this.vertexArray.setAttributes(r),this}setUniforms(){let t=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{};return Object.assign(this.uniforms,t),this}getModuleUniforms(t){this._checkProgram();let r=this.programManager.getUniforms(this.program);return r?r(t):{}}updateModuleSettings(t){let r=this.getModuleUniforms(t||{});return this.setUniforms(r)}clear(t){return Hf(this.program.gl,t),this}draw(){let t=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{};this._checkProgram();let{moduleSettings:r=null,framebuffer:i,uniforms:s={},attributes:n={},transformFeedback:o=this.transformFeedback,parameters:c={},vertexArray:f=this.vertexArray}=t;this.setAttributes(n),this.updateModuleSettings(r),this.setUniforms(s);let _;He.priority>=Uy&&(_=this._logDrawCallStart(Uy));let w=this.vertexArray.getDrawParams(),{isIndexed:I=w.isIndexed,indexType:R=w.indexType,indexOffset:N=w.indexOffset,vertexArrayInstanced:j=w.isInstanced}=this.props;j&&!this.isInstanced&&He.warn(\"Found instanced attributes on non-instanced model\",this.id)();let{isInstanced:Q,instanceCount:et}=this,{onBeforeRender:Y=V7,onAfterRender:K=V7}=this.props;Y(),this.program.setUniforms(this.uniforms);let J=this.program.draw(Object.assign(Rat,t,{logPriority:_,uniforms:null,framebuffer:i,parameters:c,drawMode:this.getDrawMode(),vertexCount:this.getVertexCount(),vertexArray:f,transformFeedback:o,isIndexed:I,indexType:R,isInstanced:Q,instanceCount:et,offset:I?N:0}));return K(),He.priority>=Uy&&this._logDrawCallEnd(_,f,i),J}transform(){let t=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{},{discard:r=!0,feedbackBuffers:i,unbindModels:s=[]}=t,{parameters:n}=t;i&&this._setFeedbackBuffers(i),r&&(n=Object.assign({},n,{35977:r})),s.forEach(o=>o.vertexArray.unbindBuffers());try{this.draw(Object.assign({},t,{parameters:n}))}finally{s.forEach(o=>o.vertexArray.bindBuffers())}return this}render(){let t=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{};return He.warn(\"Model.render() is deprecated. Use Model.setUniforms() and Model.draw()\")(),this.setUniforms(t).draw()}_setModelProps(t){Object.assign(this.props,t),\"uniforms\"in t&&this.setUniforms(t.uniforms),\"pickable\"in t&&(this.pickable=t.pickable),\"instanceCount\"in t&&(this.instanceCount=t.instanceCount),\"geometry\"in t&&this.setGeometry(t.geometry),\"attributes\"in t&&this.setAttributes(t.attributes),\"_feedbackBuffers\"in t&&this._setFeedbackBuffers(t._feedbackBuffers)}_checkProgram(){if(!(this._programDirty||this.programManager.stateHash!==this._programManagerState))return;let{program:r}=this.programProps;if(r)this._managedProgram=!1;else{let{vs:i,fs:s,modules:n,inject:o,defines:c,varyings:f,bufferMode:_,transpileToGLSL100:w}=this.programProps;r=this.programManager.get({vs:i,fs:s,modules:n,inject:o,defines:c,varyings:f,bufferMode:_,transpileToGLSL100:w}),this.program&&this._managedProgram&&this.programManager.release(this.program),this._programManagerState=this.programManager.stateHash,this._managedProgram=!0}ye(r instanceof rp,\"Model needs a program\"),this._programDirty=!1,r!==this.program&&(this.program=r,this.vertexArray?this.vertexArray.setProps({program:this.program,attributes:this.vertexArray.attributes}):this.vertexArray=new Iy(this.gl,{program:this.program}),this.setUniforms(Object.assign({},this.getModuleUniforms())))}_deleteGeometryBuffers(){for(let t in this.geometryBuffers){let r=this.geometryBuffers[t][0]||this.geometryBuffers[t];r instanceof Fr&&r.delete()}}_setAnimationProps(t){this.animated&&ye(t,\"Model.draw(): animated uniforms but no animationProps\")}_setFeedbackBuffers(){let t=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{};if(Wf(t))return this;let{gl:r}=this.program;return this.transformFeedback=this.transformFeedback||new ip(r,{program:this.program}),this.transformFeedback.setBuffers(t),this}_logDrawCallStart(t){let r=t>3?0:Lat;if(!(Date.now()-this.lastLogTime>> DRAWING MODEL \".concat(this.id),{collapsed:He.level<=2})(),t}_logDrawCallEnd(t,r,i,s){if(t===void 0)return;let n=CR({vertexArray:r,header:\"\".concat(this.id,\" attributes\"),attributes:this._attributes}),{table:o,unusedTable:c,unusedCount:f}=ME({header:\"\".concat(this.id,\" uniforms\"),program:this.program,uniforms:Object.assign({},this.program.uniforms,i)}),{table:_,count:w}=ME({header:\"\".concat(this.id,\" uniforms\"),program:this.program,uniforms:Object.assign({},this.program.uniforms,i),undefinedOnly:!0});w>0&&He.log(\"MISSING UNIFORMS\",Object.keys(_))(),f>0&&He.log(\"UNUSED UNIFORMS\",Object.keys(c))();let I=LR(this.vertexArray.configuration);He.table(t,n)(),He.table(t,o)(),He.table(t+1,I)(),s&&s.log({logLevel:Uy,message:\"Rendered to \".concat(s.id)}),He.groupEnd(Uy)()}};var Eb=class{constructor(t){let r=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{};this.gl=t,this.currentIndex=0,this.feedbackMap={},this.varyings=null,this.bindings=[],this.resources={},this._initialize(r),Object.seal(this)}setupResources(t){for(let r of this.bindings)this._setupTransformFeedback(r,t)}updateModelProps(){let t=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{},{varyings:r}=this;return r.length>0&&(t=Object.assign({},t,{varyings:r})),t}getDrawOptions(){let t=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{},r=this.bindings[this.currentIndex],{sourceBuffers:i,transformFeedback:s}=r;return{attributes:Object.assign({},i,t.attributes),transformFeedback:s}}swap(){return this.feedbackMap?(this.currentIndex=this._getNextIndex(),!0):!1}update(){let t=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{};this._setupBuffers(t)}getBuffer(t){let{feedbackBuffers:r}=this.bindings[this.currentIndex],i=t?r[t]:null;return i?i instanceof Fr?i:i.buffer:null}getData(){let t=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{},{varyingName:r}=t,i=this.getBuffer(r);return i?i.getData():null}delete(){for(let t in this.resources)this.resources[t].delete()}_initialize(){let t=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{};this._setupBuffers(t),this.varyings=t.varyings||Object.keys(this.bindings[this.currentIndex].feedbackBuffers),this.varyings.length>0&&ye(fr(this.gl))}_getFeedbackBuffers(t){let{sourceBuffers:r={}}=t,i={};if(this.bindings[this.currentIndex]&&Object.assign(i,this.bindings[this.currentIndex].feedbackBuffers),this.feedbackMap)for(let s in this.feedbackMap){let n=this.feedbackMap[s];s in r&&(i[n]=s)}Object.assign(i,t.feedbackBuffers);for(let s in i){let n=i[s];if(typeof n==\"string\"){let o=r[n],{byteLength:c,usage:f,accessor:_}=o;i[s]=this._createNewBuffer(s,{byteLength:c,usage:f,accessor:_})}}return i}_setupBuffers(){let t=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{},{sourceBuffers:r=null}=t;Object.assign(this.feedbackMap,t.feedbackMap);let i=this._getFeedbackBuffers(t);this._updateBindings({sourceBuffers:r,feedbackBuffers:i})}_setupTransformFeedback(t,r){let{model:i}=r,{program:s}=i;t.transformFeedback=new ip(this.gl,{program:s,buffers:t.feedbackBuffers})}_updateBindings(t){if(this.bindings[this.currentIndex]=this._updateBinding(this.bindings[this.currentIndex],t),this.feedbackMap){let{sourceBuffers:r,feedbackBuffers:i}=this._swapBuffers(this.bindings[this.currentIndex]),s=this._getNextIndex();this.bindings[s]=this._updateBinding(this.bindings[s],{sourceBuffers:r,feedbackBuffers:i})}}_updateBinding(t,r){return t?(Object.assign(t.sourceBuffers,r.sourceBuffers),Object.assign(t.feedbackBuffers,r.feedbackBuffers),t.transformFeedback&&t.transformFeedback.setBuffers(t.feedbackBuffers),t):{sourceBuffers:Object.assign({},r.sourceBuffers),feedbackBuffers:Object.assign({},r.feedbackBuffers)}}_swapBuffers(t){if(!this.feedbackMap)return null;let r=Object.assign({},t.sourceBuffers),i=Object.assign({},t.feedbackBuffers);for(let s in this.feedbackMap){let n=this.feedbackMap[s];r[s]=t.feedbackBuffers[n],i[n]=t.sourceBuffers[s],ye(i[n]instanceof 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Fr(this.gl,{data:r,accessor:{size:1}}),this.elementCount=t}_updateBindings(t){if(this.bindings[this.currentIndex]=this._updateBinding(this.bindings[this.currentIndex],t),this._swapTexture){let{sourceTextures:r,targetTexture:i}=this._swapTextures(this.bindings[this.currentIndex]),s=this._getNextIndex();this.bindings[s]=this._updateBinding(this.bindings[s],{sourceTextures:r,targetTexture:i})}}_updateBinding(t,r){let{sourceBuffers:i,sourceTextures:s,targetTexture:n}=r;if(t||(t={sourceBuffers:{},sourceTextures:{},targetTexture:null}),Object.assign(t.sourceTextures,s),Object.assign(t.sourceBuffers,i),n){t.targetTexture=n;let{width:o,height:c}=n,{framebuffer:f}=t;f?(f.update({attachments:{36064:n},resizeAttachments:!1}),f.resize({width:o,height:c})):t.framebuffer=new yi(this.gl,{id:\"transform-framebuffer\",width:o,height:c,attachments:{36064:n}})}return t}_setSourceTextureParameters(){let t=this.currentIndex,{sourceTextures:r}=this.bindings[t];for(let i in r)r[i].setParameters(Nat)}_swapTextures(t){if(!this._swapTexture)return null;let r=Object.assign({},t.sourceTextures);r[this._swapTexture]=t.targetTexture;let i=t.sourceTextures[this._swapTexture];return{sourceTextures:r,targetTexture:i}}_createNewTexture(t){let r=yE(t,{parameters:{10241:9728,10240:9728,10242:33071,10243:33071},pixelStore:{37440:!1}});return this.ownTexture&&this.ownTexture.delete(),this.ownTexture=r,r}_getNextIndex(){return(this.currentIndex+1)%2}_processVertexShader(){let t=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{},{sourceTextures:r,targetTexture:i}=this.bindings[this.currentIndex],{vs:s,uniforms:n,targetTextureType:o,inject:c,samplerTextureMap:f}=G7({vs:t.vs,sourceTextureMap:r,targetTextureVarying:this.targetTextureVarying,targetTexture:i}),_=Ly([t.inject||{},c]);this.targetTextureType=o,this.samplerTextureMap=f;let w=t._fs||bb({version:Py(s),input:this.targetTextureVarying,inputType:o,output:Uat}),I=this.hasSourceTextures||this.targetTextureVarying?[uD].concat(t.modules||[]):t.modules;return{vs:s,fs:w,modules:I,uniforms:n,inject:_}}};var nc=class{static isSupported(t){return fr(t)}constructor(t){let r=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{};this.gl=t,this.model=null,this.elementCount=0,this.bufferTransform=null,this.textureTransform=null,this.elementIDBuffer=null,this._initialize(r),Object.seal(this)}delete(){let{model:t,bufferTransform:r,textureTransform:i}=this;t&&t.delete(),r&&r.delete(),i&&i.delete()}run(){let t=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{},{clearRenderTarget:r=!0}=t,i=this._updateDrawOptions(t);r&&i.framebuffer&&i.framebuffer.clear({color:!0}),this.model.transform(i)}swap(){let t=!1,r=[this.bufferTransform,this.textureTransform].filter(Boolean);for(let i of r)t=t||i.swap();ye(t,\"Nothing to swap\")}getBuffer(){let 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vec3(0.0)\n);\n`),elt=`\n`.concat(Z7,`\n\nstruct FragmentGeometry {\n vec2 uv;\n} geometry;\n\nfloat smoothedge(float edge, float x) {\n return smoothstep(edge - SMOOTH_EDGE_RADIUS, edge + SMOOTH_EDGE_RADIUS, x);\n}\n`),Y7={name:\"geometry\",vs:tlt,fs:elt};var rlt=Object.keys(Yr).map(e=>\"const int COORDINATE_SYSTEM_\".concat(e,\" = \").concat(Yr[e],\";\")).join(\"\"),ilt=Object.keys(Ja).map(e=>\"const int PROJECTION_MODE_\".concat(e,\" = \").concat(Ja[e],\";\")).join(\"\"),nlt=Object.keys(po).map(e=>\"const int UNIT_\".concat(e.toUpperCase(),\" = \").concat(po[e],\";\")).join(\"\"),Q7=\"\".concat(rlt,`\n`).concat(ilt,`\n`).concat(nlt,`\n\nuniform int project_uCoordinateSystem;\nuniform int project_uProjectionMode;\nuniform float project_uScale;\nuniform bool project_uWrapLongitude;\nuniform vec3 project_uCommonUnitsPerMeter;\nuniform vec3 project_uCommonUnitsPerWorldUnit;\nuniform vec3 project_uCommonUnitsPerWorldUnit2;\nuniform vec4 project_uCenter;\nuniform mat4 project_uModelMatrix;\nuniform mat4 project_uViewProjectionMatrix;\nuniform vec2 project_uViewportSize;\nuniform float project_uDevicePixelRatio;\nuniform float project_uFocalDistance;\nuniform vec3 project_uCameraPosition;\nuniform vec3 project_uCoordinateOrigin;\nuniform vec3 project_uCommonOrigin;\nuniform bool project_uPseudoMeters;\n\nconst float TILE_SIZE = 512.0;\nconst float PI = 3.1415926536;\nconst float WORLD_SCALE = TILE_SIZE / (PI * 2.0);\nconst vec3 ZERO_64_LOW = vec3(0.0);\nconst float EARTH_RADIUS = 6370972.0;\nconst float GLOBE_RADIUS = 256.0;\nfloat project_size_at_latitude(float lat) {\n float y = clamp(lat, -89.9, 89.9);\n return 1.0 / cos(radians(y));\n}\n\nfloat project_size() {\n if (project_uProjectionMode == PROJECTION_MODE_WEB_MERCATOR &&\n project_uCoordinateSystem == COORDINATE_SYSTEM_LNGLAT &&\n project_uPseudoMeters == false) {\n \n if (geometry.position.w == 0.0) {\n return project_size_at_latitude(geometry.worldPosition.y);\n }\n \n float y = geometry.position.y / TILE_SIZE * 2.0 - 1.0;\n float y2 = y * y;\n float y4 = y2 * y2;\n float y6 = y4 * y2;\n return 1.0 + 4.9348 * y2 + 4.0587 * y4 + 1.5642 * y6;\n }\n return 1.0;\n}\n\nfloat project_size_at_latitude(float meters, float lat) {\n return meters * project_uCommonUnitsPerMeter.z * project_size_at_latitude(lat);\n}\nfloat project_size(float meters) {\n return meters * project_uCommonUnitsPerMeter.z * project_size();\n}\n\nvec2 project_size(vec2 meters) {\n return meters * project_uCommonUnitsPerMeter.xy * project_size();\n}\n\nvec3 project_size(vec3 meters) {\n return meters * project_uCommonUnitsPerMeter * project_size();\n}\n\nvec4 project_size(vec4 meters) {\n return vec4(meters.xyz * project_uCommonUnitsPerMeter, meters.w);\n}\nmat3 project_get_orientation_matrix(vec3 up) {\n vec3 uz = normalize(up);\n vec3 ux = abs(uz.z) == 1.0 ? vec3(1.0, 0.0, 0.0) : normalize(vec3(uz.y, -uz.x, 0));\n vec3 uy = cross(uz, ux);\n return mat3(ux, uy, uz);\n}\n\nbool project_needs_rotation(vec3 commonPosition, out mat3 transform) {\n if (project_uProjectionMode == PROJECTION_MODE_GLOBE) {\n transform = project_get_orientation_matrix(commonPosition);\n return true;\n }\n return false;\n}\nvec3 project_normal(vec3 vector) {\n vec4 normal_modelspace = project_uModelMatrix * vec4(vector, 0.0);\n vec3 n = normalize(normal_modelspace.xyz * project_uCommonUnitsPerMeter);\n mat3 rotation;\n if (project_needs_rotation(geometry.position.xyz, rotation)) {\n n = rotation * n;\n }\n return n;\n}\n\nvec4 project_offset_(vec4 offset) {\n float dy = offset.y;\n vec3 commonUnitsPerWorldUnit = project_uCommonUnitsPerWorldUnit + project_uCommonUnitsPerWorldUnit2 * dy;\n return vec4(offset.xyz * commonUnitsPerWorldUnit, offset.w);\n}\nvec2 project_mercator_(vec2 lnglat) {\n float x = lnglat.x;\n if (project_uWrapLongitude) {\n x = mod(x + 180., 360.0) - 180.;\n }\n float y = clamp(lnglat.y, -89.9, 89.9);\n return vec2(\n radians(x) + PI,\n PI + log(tan_fp32(PI * 0.25 + radians(y) * 0.5))\n ) * WORLD_SCALE;\n}\n\nvec3 project_globe_(vec3 lnglatz) {\n float lambda = radians(lnglatz.x);\n float phi = radians(lnglatz.y);\n float cosPhi = cos(phi);\n float D = (lnglatz.z / EARTH_RADIUS + 1.0) * GLOBE_RADIUS;\n\n return vec3(\n sin(lambda) * cosPhi,\n -cos(lambda) * cosPhi,\n sin(phi)\n ) * D;\n}\nvec4 project_position(vec4 position, vec3 position64Low) {\n vec4 position_world = project_uModelMatrix * position;\n if (project_uProjectionMode == PROJECTION_MODE_WEB_MERCATOR) {\n if (project_uCoordinateSystem == COORDINATE_SYSTEM_LNGLAT) {\n return vec4(\n project_mercator_(position_world.xy),\n project_size_at_latitude(position_world.z, position_world.y),\n position_world.w\n );\n }\n if (project_uCoordinateSystem == COORDINATE_SYSTEM_CARTESIAN) {\n position_world.xyz += project_uCoordinateOrigin;\n }\n }\n if (project_uProjectionMode == PROJECTION_MODE_GLOBE) {\n if (project_uCoordinateSystem == COORDINATE_SYSTEM_LNGLAT) {\n return vec4(\n project_globe_(position_world.xyz),\n position_world.w\n );\n }\n }\n if (project_uProjectionMode == PROJECTION_MODE_WEB_MERCATOR_AUTO_OFFSET) {\n if (project_uCoordinateSystem == COORDINATE_SYSTEM_LNGLAT) {\n if (abs(position_world.y - project_uCoordinateOrigin.y) > 0.25) {\n return vec4(\n project_mercator_(position_world.xy) - project_uCommonOrigin.xy,\n project_size(position_world.z),\n position_world.w\n );\n }\n }\n }\n if (project_uProjectionMode == PROJECTION_MODE_IDENTITY ||\n (project_uProjectionMode == PROJECTION_MODE_WEB_MERCATOR_AUTO_OFFSET &&\n (project_uCoordinateSystem == COORDINATE_SYSTEM_LNGLAT ||\n project_uCoordinateSystem == COORDINATE_SYSTEM_CARTESIAN))) {\n position_world.xyz -= project_uCoordinateOrigin;\n }\n return project_offset_(position_world) + project_offset_(project_uModelMatrix * vec4(position64Low, 0.0));\n}\n\nvec4 project_position(vec4 position) {\n return project_position(position, ZERO_64_LOW);\n}\n\nvec3 project_position(vec3 position, vec3 position64Low) {\n vec4 projected_position = project_position(vec4(position, 1.0), position64Low);\n return projected_position.xyz;\n}\n\nvec3 project_position(vec3 position) {\n vec4 projected_position = project_position(vec4(position, 1.0), ZERO_64_LOW);\n return projected_position.xyz;\n}\n\nvec2 project_position(vec2 position) {\n vec4 projected_position = project_position(vec4(position, 0.0, 1.0), ZERO_64_LOW);\n return projected_position.xy;\n}\n\nvec4 project_common_position_to_clipspace(vec4 position, mat4 viewProjectionMatrix, vec4 center) {\n return viewProjectionMatrix * position + center;\n}\nvec4 project_common_position_to_clipspace(vec4 position) {\n return project_common_position_to_clipspace(position, project_uViewProjectionMatrix, project_uCenter);\n}\nvec2 project_pixel_size_to_clipspace(vec2 pixels) {\n vec2 offset = pixels / project_uViewportSize * project_uDevicePixelRatio * 2.0;\n return offset * project_uFocalDistance;\n}\n\nfloat project_size_to_pixel(float meters) {\n return project_size(meters) * project_uScale;\n}\nfloat project_size_to_pixel(float size, int unit) {\n if (unit == UNIT_METERS) return project_size_to_pixel(size);\n if (unit == UNIT_COMMON) return size * project_uScale;\n return size;\n}\nfloat project_pixel_size(float pixels) {\n return pixels / project_uScale;\n}\nvec2 project_pixel_size(vec2 pixels) {\n return pixels / project_uScale;\n}\n`);function slt(e,t){if(e===t)return!0;if(Array.isArray(e)){let r=e.length;if(!t||t.length!==r)return!1;for(let i=0;i{for(let s in i)if(!slt(i[s],t[s])){r=e(i),t=i;break}return r}}var $7=[0,0,0,0],olt=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0],X7=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],alt=[0,0,0],K7=[0,0,0],llt=Yf(ult);function hD(e,t,r=K7){r.length<3&&(r=[r[0],r[1],0]);let i=r,s,n=!0;switch(t===Yr.LNGLAT_OFFSETS||t===Yr.METER_OFFSETS?s=r:s=e.isGeospatial?[Math.fround(e.longitude),Math.fround(e.latitude),0]:null,e.projectionMode){case Ja.WEB_MERCATOR:(t===Yr.LNGLAT||t===Yr.CARTESIAN)&&(s=[0,0,0],n=!1);break;case Ja.WEB_MERCATOR_AUTO_OFFSET:t===Yr.LNGLAT?i=s:t===Yr.CARTESIAN&&(i=[Math.fround(e.center[0]),Math.fround(e.center[1]),0],s=e.unprojectPosition(i),i[0]-=r[0],i[1]-=r[1],i[2]-=r[2]);break;case Ja.IDENTITY:i=e.position.map(Math.fround),i[2]=i[2]||0;break;case Ja.GLOBE:n=!1,s=null;break;default:n=!1}return{geospatialOrigin:s,shaderCoordinateOrigin:i,offsetMode:n}}function clt(e,t,r){let{viewMatrixUncentered:i,projectionMatrix:s}=e,{viewMatrix:n,viewProjectionMatrix:o}=e,c=$7,f=$7,_=e.cameraPosition,{geospatialOrigin:w,shaderCoordinateOrigin:I,offsetMode:R}=hD(e,t,r);return R&&(f=e.projectPosition(w||I),_=[_[0]-f[0],_[1]-f[1],_[2]-f[2]],f[3]=1,c=Nh([],f,o),n=i||n,o=qf([],s,n),o=qf([],o,olt)),{viewMatrix:n,viewProjectionMatrix:o,projectionCenter:c,originCommon:f,cameraPosCommon:_,shaderCoordinateOrigin:I,geospatialOrigin:w}}function J7({viewport:e,devicePixelRatio:t=1,modelMatrix:r=null,coordinateSystem:i=Yr.DEFAULT,coordinateOrigin:s=K7,autoWrapLongitude:n=!1}){i===Yr.DEFAULT&&(i=e.isGeospatial?Yr.LNGLAT:Yr.CARTESIAN);let o=llt({viewport:e,devicePixelRatio:t,coordinateSystem:i,coordinateOrigin:s});return o.project_uWrapLongitude=n,o.project_uModelMatrix=r||X7,o}function ult({viewport:e,devicePixelRatio:t,coordinateSystem:r,coordinateOrigin:i}){let{projectionCenter:s,viewProjectionMatrix:n,originCommon:o,cameraPosCommon:c,shaderCoordinateOrigin:f,geospatialOrigin:_}=clt(e,r,i),w=e.getDistanceScales(),I=[e.width*t,e.height*t],R=Nh([],[0,0,-e.focalDistance,1],e.projectionMatrix)[3]||1,N={project_uCoordinateSystem:r,project_uProjectionMode:e.projectionMode,project_uCoordinateOrigin:f,project_uCommonOrigin:o.slice(0,3),project_uCenter:s,project_uPseudoMeters:!!e._pseudoMeters,project_uViewportSize:I,project_uDevicePixelRatio:t,project_uFocalDistance:R,project_uCommonUnitsPerMeter:w.unitsPerMeter,project_uCommonUnitsPerWorldUnit:w.unitsPerMeter,project_uCommonUnitsPerWorldUnit2:alt,project_uScale:e.scale,project_uWrapLongitude:!1,project_uViewProjectionMatrix:n,project_uModelMatrix:X7,project_uCameraPosition:c};if(_){let j=e.getDistanceScales(_);switch(r){case Yr.METER_OFFSETS:N.project_uCommonUnitsPerWorldUnit=j.unitsPerMeter,N.project_uCommonUnitsPerWorldUnit2=j.unitsPerMeter2;break;case Yr.LNGLAT:case Yr.LNGLAT_OFFSETS:e._pseudoMeters||(N.project_uCommonUnitsPerMeter=j.unitsPerMeter),N.project_uCommonUnitsPerWorldUnit=j.unitsPerDegree,N.project_uCommonUnitsPerWorldUnit2=j.unitsPerDegree2;break;case Yr.CARTESIAN:N.project_uCommonUnitsPerWorldUnit=[1,1,j.unitsPerMeter[2]],N.project_uCommonUnitsPerWorldUnit2=[0,0,j.unitsPerMeter2[2]];break;default:break}}return N}var hlt={};function flt(e=hlt){return\"viewport\"in e?J7(e):{}}var Vh={name:\"project\",dependencies:[CE,Y7],vs:Q7,getUniforms:flt};function fD(){return[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]}function JA(e,t){let r=Nh([],t,e);return Fy(r,r,1/r[3]),r}function dD(e,t){let r=e%t;return r<0?t+r:r}function tG(e,t,r){return r*t+(1-r)*e}function kb(e,t,r){return er?r:e}function dlt(e){return Math.log(e)*Math.LOG2E}var Vy=Math.log2||dlt;function Bu(e,t){if(!e)throw new Error(t||\"@math.gl/web-mercator: assertion failed.\")}var jh=Math.PI,eG=jh/4,Fu=jh/180,pD=180/jh,jy=512,KE=4003e4,Gy=85.051129,rG=1.5;function Rb(e){return Math.pow(2,e)}function JE(e){return Vy(e)}function va(e){let[t,r]=e;Bu(Number.isFinite(t)),Bu(Number.isFinite(r)&&r>=-90&&r<=90,\"invalid latitude\");let i=t*Fu,s=r*Fu,n=jy*(i+jh)/(2*jh),o=jy*(jh+Math.log(Math.tan(eG+s*.5)))/(2*jh);return[n,o]}function oc(e){let[t,r]=e,i=t/jy*(2*jh)-jh,s=2*(Math.atan(Math.exp(r/jy*(2*jh)-jh))-eG);return[i*pD,s*pD]}function AD(e){let{latitude:t}=e;Bu(Number.isFinite(t));let r=Math.cos(t*Fu);return JE(KE*r)-9}function Db(e){let t=Math.cos(e*Fu);return jy/KE/t}function Wy(e){let{latitude:t,longitude:r,highPrecision:i=!1}=e;Bu(Number.isFinite(t)&&Number.isFinite(r));let s=jy,n=Math.cos(t*Fu),o=s/360,c=o/n,f=s/KE/n,_={unitsPerMeter:[f,f,f],metersPerUnit:[1/f,1/f,1/f],unitsPerDegree:[o,c,f],degreesPerUnit:[1/o,1/c,1/f]};if(i){let w=Fu*Math.tan(t*Fu)/n,I=o*w/2,R=s/KE*w,N=R/c*f;_.unitsPerDegree2=[0,I,R],_.unitsPerMeter2=[N,0,N]}return _}function Ob(e,t){let[r,i,s]=e,[n,o,c]=t,{unitsPerMeter:f,unitsPerMeter2:_}=Wy({longitude:r,latitude:i,highPrecision:!0}),w=va(e);w[0]+=n*(f[0]+_[0]*o),w[1]+=o*(f[1]+_[1]*o);let I=oc(w),R=(s||0)+(c||0);return Number.isFinite(s)||Number.isFinite(c)?[I[0],I[1],R]:I}function tP(e){let{height:t,pitch:r,bearing:i,altitude:s,scale:n,center:o}=e,c=fD();ag(c,c,[0,0,-s]),WE(c,c,-r*Fu),HE(c,c,i*Fu);let f=n/t;return By(c,c,[f,f,f]),o&&ag(c,c,Gj([],o)),c}function mD(e){let{width:t,height:r,altitude:i,pitch:s=0,offset:n,center:o,scale:c,nearZMultiplier:f=1,farZMultiplier:_=1}=e,{fovy:w=cg(rG)}=e;i!==void 0&&(w=cg(i));let I=w*Fu,R=s*Fu,N=Bb(w),j=N;o&&(j+=o[2]*c/Math.cos(R)/r);let Q=I*(.5+(n?n[1]:0)/r),et=Math.sin(Q)*j/Math.sin(kb(Math.PI/2-R-Q,.01,Math.PI-.01)),Y=Math.sin(R)*et+j,K=j*10,J=Math.min(Y*_,K);return{fov:I,aspect:t/r,focalDistance:N,near:f,far:J}}function cg(e){return 2*Math.atan(.5/e)*pD}function Bb(e){return .5/Math.tan(.5*e*Fu)}function Hy(e,t){let[r,i,s=0]=e;return Bu(Number.isFinite(r)&&Number.isFinite(i)&&Number.isFinite(s)),JA(t,[r,i,s,1])}function Qf(e,t,r=0){let[i,s,n]=e;if(Bu(Number.isFinite(i)&&Number.isFinite(s),\"invalid pixel coordinate\"),Number.isFinite(n))return JA(t,[i,s,n,1]);let o=JA(t,[i,s,0,1]),c=JA(t,[i,s,1,1]),f=o[2],_=c[2],w=f===_?0:((r||0)-f)/(_-f);return kE([],o,c,w)}function Fb(e){let{width:t,height:r,bounds:i,minExtent:s=0,maxZoom:n=24,offset:o=[0,0]}=e,[[c,f],[_,w]]=i,I=plt(e.padding),R=va([c,kb(w,-Gy,Gy)]),N=va([_,kb(f,-Gy,Gy)]),j=[Math.max(Math.abs(N[0]-R[0]),s),Math.max(Math.abs(N[1]-R[1]),s)],Q=[t-I.left-I.right-Math.abs(o[0])*2,r-I.top-I.bottom-Math.abs(o[1])*2];Bu(Q[0]>0&&Q[1]>0);let et=Q[0]/j[0],Y=Q[1]/j[1],K=(I.right-I.left)/2/et,J=(I.top-I.bottom)/2/Y,ut=[(N[0]+R[0])/2+K,(N[1]+R[1])/2+J],Et=oc(ut),kt=Math.min(n,Vy(Math.abs(Math.min(et,Y))));return Bu(Number.isFinite(kt)),{longitude:Et[0],latitude:Et[1],zoom:kt}}function plt(e=0){return typeof e==\"number\"?{top:e,bottom:e,left:e,right:e}:(Bu(Number.isFinite(e.top)&&Number.isFinite(e.bottom)&&Number.isFinite(e.left)&&Number.isFinite(e.right)),e)}var iG=Math.PI/180;function zb(e,t=0){let{width:r,height:i,unproject:s}=e,n={targetZ:t},o=s([0,i],n),c=s([r,i],n),f,_,w=e.fovy?.5*e.fovy*iG:Math.atan(.5/e.altitude),I=(90-e.pitch)*iG;return w>I-.01?(f=nG(e,0,t),_=nG(e,r,t)):(f=s([0,0],n),_=s([r,0],n)),[o,c,_,f]}function nG(e,t,r){let{pixelUnprojectionMatrix:i}=e,s=JA(i,[t,0,1,1]),n=JA(i,[t,e.height,1,1]),c=(r*e.distanceScales.unitsPerMeter[2]-s[2])/(n[2]-s[2]),f=kE([],s,n,c),_=oc(f);return _.push(r),_}var sG=512;function eP(e){let{width:t,height:r,pitch:i=0}=e,{longitude:s,latitude:n,zoom:o,bearing:c=0}=e;(s<-180||s>180)&&(s=dD(s+180,360)-180),(c<-180||c>180)&&(c=dD(c+180,360)-180);let f=Vy(r/sG);if(o<=f)o=f,n=0;else{let _=r/2/Math.pow(2,o),w=oc([0,_])[1];if(nI&&(n=I)}}return{width:t,height:r,longitude:s,latitude:n,zoom:o,pitch:i,bearing:c}}var oG=.01,mlt=[\"longitude\",\"latitude\",\"zoom\"],aG={curve:1.414,speed:1.2};function rP(e,t,r,i){let{startZoom:s,startCenterXY:n,uDelta:o,w0:c,u1:f,S:_,rho:w,rho2:I,r0:R}=lG(e,t,i);if(fo?0:w}function lG(e,t,r){r=Object.assign({},aG,r);let i=r.curve,s=e.zoom,n=[e.longitude,e.latitude],o=Rb(s),c=t.zoom,f=[t.longitude,t.latitude],_=Rb(c-s),w=va(n),I=va(f),R=Nj([],I,w),N=Math.max(e.width,e.height),j=N/_,Q=Bj(R)*o,et=Math.max(Q,oG),Y=i*i,K=(j*j-N*N+Y*Y*et*et)/(2*N*Y*et),J=(j*j-N*N-Y*Y*et*et)/(2*j*Y*et),ut=Math.log(Math.sqrt(K*K+1)-K),Et=Math.log(Math.sqrt(J*J+1)-J),kt=(Et-ut)/i;return{startZoom:s,startCenterXY:w,uDelta:R,w0:N,u1:Q,S:kt,rho:i,rho2:Y,r0:ut,r1:Et}}var _lt=`\nconst int max_lights = 2;\nuniform mat4 shadow_uViewProjectionMatrices[max_lights];\nuniform vec4 shadow_uProjectCenters[max_lights];\nuniform bool shadow_uDrawShadowMap;\nuniform bool shadow_uUseShadowMap;\nuniform int shadow_uLightId;\nuniform float shadow_uLightCount;\n\nvarying vec3 shadow_vPosition[max_lights];\n\nvec4 shadow_setVertexPosition(vec4 position_commonspace) {\n if (shadow_uDrawShadowMap) {\n return project_common_position_to_clipspace(position_commonspace, shadow_uViewProjectionMatrices[shadow_uLightId], shadow_uProjectCenters[shadow_uLightId]);\n }\n if (shadow_uUseShadowMap) {\n for (int i = 0; i < max_lights; i++) {\n if(i < int(shadow_uLightCount)) {\n vec4 shadowMap_position = project_common_position_to_clipspace(position_commonspace, shadow_uViewProjectionMatrices[i], shadow_uProjectCenters[i]);\n shadow_vPosition[i] = (shadowMap_position.xyz / shadowMap_position.w + 1.0) / 2.0;\n }\n }\n }\n return gl_Position;\n}\n`,ylt=`\nconst int max_lights = 2;\nuniform bool shadow_uDrawShadowMap;\nuniform bool shadow_uUseShadowMap;\nuniform sampler2D shadow_uShadowMap0;\nuniform sampler2D shadow_uShadowMap1;\nuniform vec4 shadow_uColor;\nuniform float shadow_uLightCount;\n\nvarying vec3 shadow_vPosition[max_lights];\n\nconst vec4 bitPackShift = vec4(1.0, 255.0, 65025.0, 16581375.0);\nconst vec4 bitUnpackShift = 1.0 / bitPackShift;\nconst vec4 bitMask = vec4(1.0 / 255.0, 1.0 / 255.0, 1.0 / 255.0, 0.0);\n\nfloat shadow_getShadowWeight(vec3 position, sampler2D shadowMap) {\n vec4 rgbaDepth = texture2D(shadowMap, position.xy);\n\n float z = dot(rgbaDepth, bitUnpackShift);\n return smoothstep(0.001, 0.01, position.z - z);\n}\n\nvec4 shadow_filterShadowColor(vec4 color) {\n if (shadow_uDrawShadowMap) {\n vec4 rgbaDepth = fract(gl_FragCoord.z * bitPackShift);\n rgbaDepth -= rgbaDepth.gbaa * bitMask;\n return rgbaDepth;\n }\n if (shadow_uUseShadowMap) {\n float shadowAlpha = 0.0;\n shadowAlpha += shadow_getShadowWeight(shadow_vPosition[0], shadow_uShadowMap0);\n if(shadow_uLightCount > 1.0) {\n shadowAlpha += shadow_getShadowWeight(shadow_vPosition[1], shadow_uShadowMap1);\n }\n shadowAlpha *= shadow_uColor.a / shadow_uLightCount;\n float blendedAlpha = shadowAlpha + color.a * (1.0 - shadowAlpha);\n\n return vec4(\n mix(color.rgb, shadow_uColor.rgb, shadowAlpha / blendedAlpha),\n blendedAlpha\n );\n }\n return color;\n}\n`,vlt=Yf(Tlt),xlt=Yf(Mlt),blt=[0,0,0,1],wlt=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0];function Slt(e,t){let[r,i,s]=e,n=Qf([r,i,s],t);return Number.isFinite(s)?n:[n[0],n[1],0]}function Tlt({viewport:e,center:t}){return new En(e.viewProjectionMatrix).invert().transform(t)}function Mlt({viewport:e,shadowMatrices:t}){let r=[],i=e.pixelUnprojectionMatrix,s=e.isGeospatial?void 0:1,n=[[0,0,s],[e.width,0,s],[0,e.height,s],[e.width,e.height,s],[0,0,-1],[e.width,0,-1],[0,e.height,-1],[e.width,e.height,-1]].map(o=>Slt(o,i));for(let o of t){let c=o.clone().translate(new Ve(e.center).negate()),f=n.map(w=>c.transform(w)),_=new En().ortho({left:Math.min(...f.map(w=>w[0])),right:Math.max(...f.map(w=>w[0])),bottom:Math.min(...f.map(w=>w[1])),top:Math.max(...f.map(w=>w[1])),near:Math.min(...f.map(w=>-w[2])),far:Math.max(...f.map(w=>-w[2]))});r.push(_.multiplyRight(o))}return r}function Elt(e,t){let{shadowEnabled:r=!0}=e;if(!r||!e.shadowMatrices||!e.shadowMatrices.length)return{shadow_uDrawShadowMap:!1,shadow_uUseShadowMap:!1};let i={shadow_uDrawShadowMap:!!e.drawToShadowMap,shadow_uUseShadowMap:e.shadowMaps?e.shadowMaps.length>0:!1,shadow_uColor:e.shadowColor||blt,shadow_uLightId:e.shadowLightId||0,shadow_uLightCount:e.shadowMatrices.length},s=vlt({viewport:e.viewport,center:t.project_uCenter}),n=[],o=xlt({shadowMatrices:e.shadowMatrices,viewport:e.viewport}).slice();for(let c=0;c0?i[\"shadow_uShadowMap\".concat(c)]=e.shadowMaps[c]:i[\"shadow_uShadowMap\".concat(c)]=e.dummyShadowMap;return i}var Nb={name:\"shadow\",dependencies:[Vh],vs:_lt,fs:ylt,inject:{\"vs:DECKGL_FILTER_GL_POSITION\":`\n position = shadow_setVertexPosition(geometry.position);\n `,\"fs:DECKGL_FILTER_COLOR\":`\n color = shadow_filterShadowColor(color);\n `},getUniforms:(e={},t={})=>\"viewport\"in e&&(e.drawToShadowMap||e.shadowMaps&&e.shadowMaps.length>0)?Elt(e,t):{}};var Plt={color:[255,255,255],intensity:1},cG=[{color:[255,255,255],intensity:1,direction:[-1,3,-1]},{color:[255,255,255],intensity:.9,direction:[1,-8,-2.5]}],Ilt=[0,0,0,200/255],qy=class{constructor(t={}){G(this,\"id\",\"lighting-effect\"),G(this,\"props\",void 0),G(this,\"shadowColor\",Ilt),G(this,\"shadow\",void 0),G(this,\"ambientLight\",void 0),G(this,\"directionalLights\",void 0),G(this,\"pointLights\",void 0),G(this,\"shadowPasses\",[]),G(this,\"shadowMaps\",[]),G(this,\"dummyShadowMap\",null),G(this,\"programManager\",void 0),G(this,\"shadowMatrices\",void 0),this.setProps(t)}setProps(t){this.ambientLight=null,this.directionalLights=[],this.pointLights=[];for(let r in t){let i=t[r];switch(i.type){case\"ambient\":this.ambientLight=i;break;case\"directional\":this.directionalLights.push(i);break;case\"point\":this.pointLights.push(i);break;default:}}this._applyDefaultLights(),this.shadow=this.directionalLights.some(r=>r.shadow),this.props=t}preRender(t,{layers:r,layerFilter:i,viewports:s,onViewportActive:n,views:o}){if(this.shadow){this.shadowMatrices=this._calculateMatrices(),this.shadowPasses.length===0&&this._createShadowPasses(t),this.programManager||(this.programManager=Uh.getDefaultProgramManager(t),Nb&&this.programManager.addDefaultModule(Nb)),this.dummyShadowMap||(this.dummyShadowMap=new pi(t,{width:1,height:1}));for(let c=0;ci.getProjectedLight({layer:t})),pointLights:this.pointLights.map(i=>i.getProjectedLight({layer:t}))},r}cleanup(){for(let t of this.shadowPasses)t.delete();this.shadowPasses.length=0,this.shadowMaps.length=0,this.dummyShadowMap&&(this.dummyShadowMap.delete(),this.dummyShadowMap=null),this.shadow&&this.programManager&&(this.programManager.removeDefaultModule(Nb),this.programManager=null)}_calculateMatrices(){let t=[];for(let r of this.directionalLights){let i=new En().lookAt({eye:new Ve(r.direction).negate()});t.push(i)}return t}_createShadowPasses(t){for(let r=0;rs&&(n=s);let o=this._pool,c=t.BYTES_PER_ELEMENT*n,f=o.findIndex(_=>_.byteLength>=c);if(f>=0){let _=new t(o.splice(f,1)[0],0,n);return i&&_.fill(0),_}return new t(n)}_release(t){if(!ArrayBuffer.isView(t))return;let r=this._pool,{buffer:i}=t,{byteLength:s}=i,n=r.findIndex(o=>o.byteLength>=s);n<0?r.push(i):(n>0||r.lengththis.opts.poolSize&&r.shift()}},Gh=new _D;function Yy(){return[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]}function hG(e){return[e[12],e[13],e[14]]}function fG(e){return{left:Zy(e[3]+e[0],e[7]+e[4],e[11]+e[8],e[15]+e[12]),right:Zy(e[3]-e[0],e[7]-e[4],e[11]-e[8],e[15]-e[12]),bottom:Zy(e[3]+e[1],e[7]+e[5],e[11]+e[9],e[15]+e[13]),top:Zy(e[3]-e[1],e[7]-e[5],e[11]-e[9],e[15]-e[13]),near:Zy(e[3]+e[2],e[7]+e[6],e[11]+e[10],e[15]+e[14]),far:Zy(e[3]-e[2],e[7]-e[6],e[11]-e[10],e[15]-e[14])}}var uG=new Ve;function Zy(e,t,r,i){uG.set(e,t,r);let s=uG.len();return{distance:i/s,normal:new Ve(-e/s,-t/s,-r/s)}}function Clt(e){return e-Math.fround(e)}var Ub;function iP(e,t){let{size:r=1,startIndex:i=0}=t,s=t.endIndex!==void 0?t.endIndex:e.length,n=(s-i)/r;Ub=Gh.allocate(Ub,n,{type:Float32Array,size:r*2});let o=i,c=0;for(;osuper.render({target:o,layers:t,layerFilter:r,views:i,viewports:s,onViewportActive:n,cullRect:I,effects:R?.filter(ut=>ut.useInPicking),pass:N,isPicking:!0,moduleParameters:Q}));return this._colorEncoderState=null,{decodePickingColor:Y&&Flt.bind(null,Y),stats:K}}shouldDrawLayer(t){let{pickable:r,operation:i}=t.props;return r&&i.includes(\"draw\")||i.includes(\"terrain\")||i.includes(\"mask\")}getModuleParameters(){return{pickingActive:1,pickingAttribute:this.pickZ,lightSources:{}}}getLayerParameters(t,r,i){let s={...t.props.parameters},{pickable:n,operation:o}=t.props;return this._colorEncoderState?n&&o.includes(\"draw\")&&(Object.assign(s,gG),s.blend=!0,s.blendColor=Blt(this._colorEncoderState,t,i)):s.blend=!1,o.includes(\"terrain\")&&(s.blend=!1),s}_resetColorEncoder(t){return this._colorEncoderState=t?null:{byLayer:new Map,byAlpha:[]},this._colorEncoderState}};function Blt(e,t,r){let{byLayer:i,byAlpha:s}=e,n,o=i.get(t);return o?(o.viewports.push(r),n=o.a):(n=i.size+1,n<=255?(o={a:n,layer:t,viewports:[r]},i.set(t,o),s[n]=o):(or.warn(\"Too many pickable layers, only picking the first 255\")(),n=0)),[0,0,0,n/255]}function Flt(e,t){let r=e.byAlpha[t[3]];return r&&{pickedLayer:r.layer,pickedViewports:r.viewports,pickedObjectIndex:r.layer.decodePickingColor(t)}}var tm={NO_STATE:\"Awaiting 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State transferred from previous layer\",INITIALIZED:\"Initialized\",AWAITING_GC:\"Discarded. Awaiting garbage collection\",AWAITING_FINALIZATION:\"No longer matched. Awaiting garbage collection\",FINALIZED:\"Finalized! Awaiting garbage collection\"},Qy=Symbol.for(\"component\"),zu=Symbol.for(\"propTypes\"),nP=Symbol.for(\"deprecatedProps\"),sp=Symbol.for(\"asyncPropDefaults\"),$f=Symbol.for(\"asyncPropOriginal\"),Wh=Symbol.for(\"asyncPropResolved\");function op(e,t=()=>!0){return Array.isArray(e)?_G(e,t,[]):t(e)?[e]:[]}function _G(e,t,r){let i=-1;for(;++i0}delete(){}getData(){return this.isLoaded?this._error?Promise.reject(this._error):this._content:this._loader.then(()=>this.getData())}setData(t,r){if(t===this._data&&!r)return;this._data=t;let i=++this._loadCount,s=t;typeof t==\"string\"&&(s=jA(t)),s instanceof Promise?(this.isLoaded=!1,this._loader=s.then(n=>{this._loadCount===i&&(this.isLoaded=!0,this._error=void 0,this._content=n)}).catch(n=>{this._loadCount===i&&(this.isLoaded=!0,this._error=n||!0)})):(this.isLoaded=!0,this._error=void 0,this._content=t);for(let n of this._subscribers)n.onChange(this.getData())}};var jb=class{constructor({gl:t,protocol:r}){G(this,\"protocol\",void 0),G(this,\"_context\",void 0),G(this,\"_resources\",void 0),G(this,\"_consumers\",void 0),G(this,\"_pruneRequest\",void 0),this.protocol=r||\"resource://\",this._context={gl:t,resourceManager:this},this._resources={},this._consumers={},this._pruneRequest=null}contains(t){return t.startsWith(this.protocol)?!0:t in this._resources}add({resourceId:t,data:r,forceUpdate:i=!1,persistent:s=!0}){let n=this._resources[t];n?n.setData(r,i):(n=new Vb(t,r,this._context),this._resources[t]=n),n.persistent=s}remove(t){let r=this._resources[t];r&&(r.delete(),delete this._resources[t])}unsubscribe({consumerId:t}){let r=this._consumers[t];if(r){for(let i in r){let s=r[i],n=this._resources[s.resourceId];n&&n.unsubscribe(s)}delete this._consumers[t],this.prune()}}subscribe({resourceId:t,onChange:r,consumerId:i,requestId:s=\"default\"}){let{_resources:n,protocol:o}=this;t.startsWith(o)&&(t=t.replace(o,\"\"),n[t]||this.add({resourceId:t,data:null,persistent:!1}));let c=n[t];if(this._track(i,s,c,r),c)return c.getData()}prune(){this._pruneRequest||(this._pruneRequest=setTimeout(()=>this._prune(),0))}finalize(){for(let t in this._resources)this._resources[t].delete()}_track(t,r,i,s){let n=this._consumers,o=n[t]=n[t]||{},c=o[r]||{},f=c.resourceId&&this._resources[c.resourceId];f&&(f.unsubscribe(c),this.prune()),i&&(o[r]=c,c.onChange=s,c.resourceId=i.id,i.subscribe(c))}_prune(){this._pruneRequest=null;for(let t of Object.keys(this._resources)){let r=this._resources[t];!r.persistent&&!r.inUse()&&(r.delete(),delete this._resources[t])}}};var zlt=`\nvec4 project_position_to_clipspace(\n vec3 position, vec3 position64Low, vec3 offset, out vec4 commonPosition\n) {\n vec3 projectedPosition = project_position(position, position64Low);\n mat3 rotation;\n if (project_needs_rotation(projectedPosition, rotation)) {\n // offset is specified as ENU\n // when in globe projection, rotate offset so that the ground alighs with the surface of the globe\n offset = rotation * offset;\n }\n commonPosition = vec4(projectedPosition + offset, 1.0);\n return project_common_position_to_clipspace(commonPosition);\n}\n\nvec4 project_position_to_clipspace(\n vec3 position, vec3 position64Low, vec3 offset\n) {\n vec4 commonPosition;\n return project_position_to_clipspace(position, position64Low, offset, commonPosition);\n}\n`,Rs={name:\"project32\",dependencies:[Vh],vs:zlt};var Ao={inject:{\"vs:DECKGL_FILTER_GL_POSITION\":`\n // for picking depth values\n picking_setPickingAttribute(position.z / position.w);\n `,\"vs:DECKGL_FILTER_COLOR\":`\n picking_setPickingColor(geometry.pickingColor);\n `,\"fs:#decl\":`\nuniform bool picking_uAttribute;\n `,\"fs:DECKGL_FILTER_COLOR\":{order:99,injection:`\n // use highlight color if this fragment belongs to the selected object.\n color = picking_filterHighlightColor(color);\n\n // use picking color if rendering to picking FBO.\n color = picking_filterPickingColor(color);\n `}},...QE};var Nlt=[Vh],Ult=[\"vs:DECKGL_FILTER_SIZE(inout vec3 size, VertexGeometry 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Gf({id:\"deck.gl\"}),viewport:s||new ac({id:\"DEFAULT-INITIAL-VIEWPORT\"}),timeline:n||new KA,resourceManager:this.resourceManager,onError:void 0},Object.seal(this)}finalize(){this.resourceManager.finalize();for(let t of this.layers)this._finalizeLayer(t)}needsRedraw(t={clearRedrawFlags:!1}){let r=this._needsRedraw;t.clearRedrawFlags&&(this._needsRedraw=!1);for(let i of this.layers){let s=i.getNeedsRedraw(t);r=r||s}return r}needsUpdate(){return this._nextLayers&&this._nextLayers!==this._lastRenderedLayers?\"layers changed\":this._needsUpdate}setNeedsRedraw(t){this._needsRedraw=this._needsRedraw||t}setNeedsUpdate(t){this._needsUpdate=this._needsUpdate||t}getLayers({layerIds:t}={}){return t?this.layers.filter(r=>t.find(i=>r.id.indexOf(i)===0)):this.layers}setProps(t){\"debug\"in t&&(this._debug=t.debug),\"userData\"in t&&(this.context.userData=t.userData),\"layers\"in t&&(this._nextLayers=t.layers),\"onError\"in t&&(this.context.onError=t.onError)}setLayers(t,r){Ls(Vlt,this,r,t),this._lastRenderedLayers=t;let i=op(t,Boolean);for(let s of i)s.context=this.context;this._updateLayers(this.layers,i)}updateLayers(){let t=this.needsUpdate();t&&(this.setNeedsRedraw(\"updating layers: \".concat(t)),this.setLayers(this._nextLayers||this._lastRenderedLayers,t)),this._nextLayers=null}_handleError(t,r,i){i.raiseError(r,\"\".concat(t,\" of \").concat(i))}_updateLayers(t,r){let i={};for(let o of t)i[o.id]?or.warn(\"Multiple old layers with same id \".concat(o.id))():i[o.id]=o;let s=[];this._updateSublayersRecursively(r,i,s),this._finalizeOldLayers(i);let n=!1;for(let o of s)if(o.hasUniformTransition()){n=\"Uniform transition in \".concat(o);break}this._needsUpdate=n,this.layers=s}_updateSublayersRecursively(t,r,i){for(let s of t){s.context=this.context;let n=r[s.id];n===null&&or.warn(\"Multiple new layers with same id \".concat(s.id))(),r[s.id]=null;let o=null;try{this._debug&&n!==s&&s.validateProps(),n?(this._transferLayerState(n,s),this._updateLayer(s)):this._initializeLayer(s),i.push(s),o=s.isComposite?s.getSubLayers():null}catch(c){this._handleError(\"matching\",c,s)}o&&this._updateSublayersRecursively(o,r,i)}}_finalizeOldLayers(t){for(let r in t){let i=t[r];i&&this._finalizeLayer(i)}}_initializeLayer(t){try{t._initialize(),t.lifecycle=tm.INITIALIZED}catch(r){this._handleError(\"initialization\",r,t)}}_transferLayerState(t,r){r._transferState(t),r.lifecycle=tm.MATCHED,r!==t&&(t.lifecycle=tm.AWAITING_GC)}_updateLayer(t){try{t._update()}catch(r){this._handleError(\"update\",r,t)}}_finalizeLayer(t){this._needsRedraw=this._needsRedraw||\"finalized \".concat(t),t.lifecycle=tm.AWAITING_FINALIZATION;try{t._finalize(),t.lifecycle=tm.FINALIZED}catch(r){this._handleError(\"finalization\",r,t)}}};function mo(e,t,r){if(e===t)return!0;if(!r||!e||!t)return!1;if(Array.isArray(e)){if(!Array.isArray(t)||e.length!==t.length)return!1;for(let i=0;ir.containsPixel(t)):this._viewports}getViews(){let t={};return this.views.forEach(r=>{t[r.id]=r}),t}getView(t){return this.views.find(r=>r.id===t)}getViewState(t){let r=typeof t==\"string\"?this.getView(t):t,i=r&&this.viewState[r.getViewStateId()]||this.viewState;return r?r.filterViewState(i):i}getViewport(t){return this._viewportMap[t]}unproject(t,r){let i=this.getViewports(),s={x:t[0],y:t[1]};for(let n=i.length-1;n>=0;--n){let o=i[n];if(o.containsPixel(s)){let c=t.slice();return c[0]-=o.x,c[1]-=o.y,o.unproject(c,r)}}return null}setProps(t){t.views&&this._setViews(t.views),t.viewState&&this._setViewState(t.viewState),(\"width\"in t||\"height\"in t)&&this._setSize(t.width,t.height),this._isUpdating||this._update()}_update(){this._isUpdating=!0,this._needsUpdate&&(this._needsUpdate=!1,this._rebuildViewports()),this._needsUpdate&&(this._needsUpdate=!1,this._rebuildViewports()),this._isUpdating=!1}_setSize(t,r){(t!==this.width||r!==this.height)&&(this.width=t,this.height=r,this.setNeedsUpdate(\"Size changed\"))}_setViews(t){t=op(t,Boolean),this._diffViews(t,this.views)&&this.setNeedsUpdate(\"views changed\"),this.views=t}_setViewState(t){t?(!mo(t,this.viewState,3)&&this.setNeedsUpdate(\"viewState changed\"),this.viewState=t):or.warn(\"missing `viewState` or `initialViewState`\")()}_onViewStateChange(t,r){this._eventCallbacks.onViewStateChange&&this._eventCallbacks.onViewStateChange({...r,viewId:t})}_createController(t,r){let i=r.type;return new i({timeline:this.timeline,eventManager:this._eventManager,onViewStateChange:this._onViewStateChange.bind(this,r.id),onStateChange:this._eventCallbacks.onInteractionStateChange,makeViewport:n=>{var o;return(o=this.getView(t.id))===null||o===void 0?void 0:o.makeViewport({viewState:n,width:this.width,height:this.height})}})}_updateController(t,r,i,s){let n=t.controller;if(n&&i){let o={...r,...n,id:t.id,x:i.x,y:i.y,width:i.width,height:i.height};return(!s||s.constructor!==n.type)&&(s=this._createController(t,o)),s&&s.setProps(o),s}return null}_rebuildViewports(){let{views:t}=this,r=this.controllers;this._viewports=[],this.controllers={};let i=!1;for(let s=t.length;s--;){let n=t[s],o=this.getViewState(n),c=n.makeViewport({viewState:o,width:this.width,height:this.height}),f=r[n.id],_=!!n.controller;_&&!f&&(i=!0),(i||!_)&&f&&(f.finalize(),f=null),this.controllers[n.id]=this._updateController(n,o,c,f),c&&this._viewports.unshift(c)}for(let s in r){let n=r[s];n&&!this.controllers[s]&&n.finalize()}this._buildViewportMap()}_buildViewportMap(){this._viewportMap={},this._viewports.forEach(t=>{t.id&&(this._viewportMap[t.id]=this._viewportMap[t.id]||t)})}_diffViews(t,r){return t.length!==r.length?!0:t.some((i,s)=>!t[s].equals(r[s]))}};var Glt=/([0-9]+\\.?[0-9]*)(%|px)/;function ap(e){switch(typeof e){case\"number\":return{position:e,relative:!1};case\"string\":let t=Glt.exec(e);if(t&&t.length>=3){let r=t[2]===\"%\",i=parseFloat(t[1]);return{position:r?i/100:i,relative:r}}default:throw new Error(\"Could not parse position string \".concat(e))}}function lp(e,t){return e.relative?Math.round(e.position*t):e.position}function _r(e,t){if(!e)throw new Error(t||\"deck.gl: assertion failed.\")}var Xc=class{constructor(t){G(this,\"id\",void 0),G(this,\"viewportInstance\",void 0),G(this,\"_x\",void 0),G(this,\"_y\",void 0),G(this,\"_width\",void 0),G(this,\"_height\",void 0),G(this,\"_padding\",void 0),G(this,\"props\",void 0);let{id:r,x:i=0,y:s=0,width:n=\"100%\",height:o=\"100%\",padding:c=null,viewportInstance:f}=t||{};_r(!f||f instanceof ac),this.viewportInstance=f,this.id=r||this.constructor.displayName||\"view\",this.props={...t,id:this.id},this._x=ap(i),this._y=ap(s),this._width=ap(n),this._height=ap(o),this._padding=c&&{left:ap(c.left||0),right:ap(c.right||0),top:ap(c.top||0),bottom:ap(c.bottom||0)},this.equals=this.equals.bind(this),Object.seal(this)}equals(t){return this===t?!0:this.viewportInstance?t.viewportInstance?this.viewportInstance.equals(t.viewportInstance):!1:this.ViewportType===t.ViewportType&&mo(this.props,t.props,2)}makeViewport({width:t,height:r,viewState:i}){if(this.viewportInstance)return this.viewportInstance;i=this.filterViewState(i);let s=this.getDimensions({width:t,height:r});return!s.height||!s.width?null:new this.ViewportType({...i,...this.props,...s})}getViewStateId(){let{viewState:t}=this.props;return typeof t==\"string\"?t:t?.id||this.id}filterViewState(t){if(this.props.viewState&&typeof this.props.viewState==\"object\"){if(!this.props.viewState.id)return this.props.viewState;let r={...t};for(let i in this.props.viewState)i!==\"id\"&&(r[i]=this.props.viewState[i]);return r}return t}getDimensions({width:t,height:r}){let i={x:lp(this._x,t),y:lp(this._y,r),width:lp(this._width,t),height:lp(this._height,r)};return this._padding&&(i.padding={left:lp(this._padding.left,t),top:lp(this._padding.top,r),right:lp(this._padding.right,t),bottom:lp(this._padding.bottom,r)}),i}get controller(){let t=this.props.controller;return t?t===!0?{type:this.ControllerType}:typeof t==\"function\"?{type:t}:{type:this.ControllerType,...t}:null}};var Kc=class{constructor(t){G(this,\"_inProgress\",void 0),G(this,\"_handle\",void 0),G(this,\"_timeline\",void 0),G(this,\"time\",void 0),G(this,\"settings\",void 0),this._inProgress=!1,this._handle=null,this._timeline=t,this.time=0,this.settings={duration:0}}get inProgress(){return this._inProgress}start(t){var r,i;this.cancel(),this.settings=t,this._inProgress=!0,(r=(i=this.settings).onStart)===null||r===void 0||r.call(i,this)}end(){if(this._inProgress){var t,r;this._timeline.removeChannel(this._handle),this._handle=null,this._inProgress=!1,(t=(r=this.settings).onEnd)===null||t===void 0||t.call(r,this)}}cancel(){if(this._inProgress){var t,r;(t=(r=this.settings).onInterrupt)===null||t===void 0||t.call(r,this),this._timeline.removeChannel(this._handle),this._handle=null,this._inProgress=!1}}update(){var t,r;if(!this._inProgress)return!1;if(this._handle===null){let{_timeline:i,settings:s}=this;this._handle=i.addChannel({delay:i.getTime(),duration:s.duration})}return this.time=this._timeline.getTime(this._handle),this._onUpdate(),(t=(r=this.settings).onUpdate)===null||t===void 0||t.call(r,this),this._timeline.isFinished(this._handle)&&this.end(),!0}_onUpdate(){}};var vG=()=>{},bD={BREAK:1,SNAP_TO_END:2,IGNORE:3},Wlt=e=>e,Hlt=bD.BREAK,Hb=class{constructor(t){G(this,\"getControllerState\",void 0),G(this,\"props\",void 0),G(this,\"propsInTransition\",void 0),G(this,\"transition\",void 0),G(this,\"onViewStateChange\",void 0),G(this,\"onStateChange\",void 0),G(this,\"_onTransitionUpdate\",r=>{let{time:i,settings:{interpolator:s,startProps:n,endProps:o,duration:c,easing:f}}=r,_=f(i/c),w=s.interpolateProps(n,o,_);this.propsInTransition=this.getControllerState({...this.props,...w}).getViewportProps(),this.onViewStateChange({viewState:this.propsInTransition,oldViewState:this.props})}),this.getControllerState=t.getControllerState,this.propsInTransition=null,this.transition=new Kc(t.timeline),this.onViewStateChange=t.onViewStateChange||vG,this.onStateChange=t.onStateChange||vG}finalize(){this.transition.cancel()}getViewportInTransition(){return this.propsInTransition}processViewStateChange(t){let r=!1,i=this.props;if(this.props=t,!i||this._shouldIgnoreViewportChange(i,t))return!1;if(this._isTransitionEnabled(t)){let s=i;if(this.transition.inProgress){let{interruption:n,endProps:o}=this.transition.settings;s={...i,...n===bD.SNAP_TO_END?o:this.propsInTransition||i}}this._triggerTransition(s,t),r=!0}else this.transition.cancel();return r}updateTransition(){this.transition.update()}_isTransitionEnabled(t){let{transitionDuration:r,transitionInterpolator:i}=t;return(r>0||r===\"auto\")&&!!i}_isUpdateDueToCurrentTransition(t){return this.transition.inProgress&&this.propsInTransition?this.transition.settings.interpolator.arePropsEqual(t,this.propsInTransition):!1}_shouldIgnoreViewportChange(t,r){return this.transition.inProgress?this.transition.settings.interruption===bD.IGNORE||this._isUpdateDueToCurrentTransition(r):this._isTransitionEnabled(r)?r.transitionInterpolator.arePropsEqual(t,r):!0}_triggerTransition(t,r){let i=this.getControllerState(t),s=this.getControllerState(r).shortestPathFrom(i),n=r.transitionInterpolator,o=n.getDuration?n.getDuration(t,r):r.transitionDuration;if(o===0)return;let c=n.initializeProps(t,s);this.propsInTransition={};let f={duration:o,easing:r.transitionEasing||Wlt,interpolator:n,interruption:r.transitionInterruption||Hlt,startProps:c.start,endProps:c.end,onStart:r.onTransitionStart,onUpdate:this._onTransitionUpdate,onInterrupt:this._onTransitionEnd(r.onTransitionInterrupt),onEnd:this._onTransitionEnd(r.onTransitionEnd)};this.transition.start(f),this.onStateChange({inTransition:!0}),this.updateTransition()}_onTransitionEnd(t){return r=>{this.propsInTransition=null,this.onStateChange({inTransition:!1,isZooming:!1,isPanning:!1,isRotating:!1}),t?.(r)}}};var hg=class{constructor(t){G(this,\"_propsToCompare\",void 0),G(this,\"_propsToExtract\",void 0),G(this,\"_requiredProps\",void 0);let{compare:r,extract:i,required:s}=t;this._propsToCompare=r,this._propsToExtract=i||r,this._requiredProps=s}arePropsEqual(t,r){for(let i of this._propsToCompare)if(!(i in t)||!(i in r)||!Ro(t[i],r[i]))return!1;return!0}initializeProps(t,r){let i={},s={};for(let n of this._propsToExtract)(n in t||n in r)&&(i[n]=t[n],s[n]=r[n]);return this._checkRequiredProps(i),this._checkRequiredProps(s),{start:i,end:s}}getDuration(t,r){return r.transitionDuration}_checkRequiredProps(t){this._requiredProps&&this._requiredProps.forEach(r=>{let i=t[r];_r(Number.isFinite(i)||Array.isArray(i),\"\".concat(r,\" is required for transition\"))})}};var qlt=[\"longitude\",\"latitude\",\"zoom\",\"bearing\",\"pitch\"],Zlt=[\"longitude\",\"latitude\",\"zoom\"],fg=class extends hg{constructor(t={}){let r=Array.isArray(t)?t:t.transitionProps,i=Array.isArray(t)?{}:t;i.transitionProps=Array.isArray(r)?{compare:r,required:r}:r||{compare:qlt,required:Zlt},super(i.transitionProps),G(this,\"opts\",void 0),this.opts=i}initializeProps(t,r){let i=super.initializeProps(t,r),{makeViewport:s,around:n}=this.opts;if(s&&n){let o=s(t),c=s(r),f=o.unproject(n);i.start.around=n,Object.assign(i.end,{around:c.project(f),aroundPosition:f,width:r.width,height:r.height})}return i}interpolateProps(t,r,i){let s={};for(let n of this._propsToExtract)s[n]=il(t[n]||0,r[n]||0,i);if(r.aroundPosition&&this.opts.makeViewport){let n=this.opts.makeViewport({...r,...s});Object.assign(s,n.panByPosition(r.aroundPosition,il(t.around,r.around,i)))}return s}};var em={transitionDuration:0},Ylt=300,sP=e=>1-(1-e)*(1-e),$y={WHEEL:[\"wheel\"],PAN:[\"panstart\",\"panmove\",\"panend\"],PINCH:[\"pinchstart\",\"pinchmove\",\"pinchend\"],TRIPLE_PAN:[\"tripanstart\",\"tripanmove\",\"tripanend\"],DOUBLE_TAP:[\"doubletap\"],KEYBOARD:[\"keydown\"]},dg={},qb=class{constructor(t){G(this,\"props\",void 0),G(this,\"state\",{}),G(this,\"transitionManager\",void 0),G(this,\"eventManager\",void 0),G(this,\"onViewStateChange\",void 0),G(this,\"onStateChange\",void 0),G(this,\"makeViewport\",void 0),G(this,\"_controllerState\",void 0),G(this,\"_events\",{}),G(this,\"_interactionState\",{isDragging:!1}),G(this,\"_customEvents\",[]),G(this,\"_eventStartBlocked\",null),G(this,\"_panMove\",!1),G(this,\"invertPan\",!1),G(this,\"dragMode\",\"rotate\"),G(this,\"inertia\",0),G(this,\"scrollZoom\",!0),G(this,\"dragPan\",!0),G(this,\"dragRotate\",!0),G(this,\"doubleClickZoom\",!0),G(this,\"touchZoom\",!0),G(this,\"touchRotate\",!1),G(this,\"keyboard\",!0),this.transitionManager=new Hb({...t,getControllerState:r=>new this.ControllerState(r),onViewStateChange:this._onTransition.bind(this),onStateChange:this._setInteractionState.bind(this)}),this.handleEvent=this.handleEvent.bind(this),this.eventManager=t.eventManager,this.onViewStateChange=t.onViewStateChange||(()=>{}),this.onStateChange=t.onStateChange||(()=>{}),this.makeViewport=t.makeViewport}set events(t){this.toggleEvents(this._customEvents,!1),this.toggleEvents(t,!0),this._customEvents=t,this.props&&this.setProps(this.props)}finalize(){for(let r in this._events)if(this._events[r]){var t;(t=this.eventManager)===null||t===void 0||t.off(r,this.handleEvent)}this.transitionManager.finalize()}handleEvent(t){this._controllerState=void 0;let r=this._eventStartBlocked;switch(t.type){case\"panstart\":return r?!1:this._onPanStart(t);case\"panmove\":return this._onPan(t);case\"panend\":return this._onPanEnd(t);case\"pinchstart\":return r?!1:this._onPinchStart(t);case\"pinchmove\":return this._onPinch(t);case\"pinchend\":return this._onPinchEnd(t);case\"tripanstart\":return r?!1:this._onTriplePanStart(t);case\"tripanmove\":return this._onTriplePan(t);case\"tripanend\":return this._onTriplePanEnd(t);case\"doubletap\":return this._onDoubleTap(t);case\"wheel\":return this._onWheel(t);case\"keydown\":return this._onKeyDown(t);default:return!1}}get controllerState(){return this._controllerState=this._controllerState||new this.ControllerState({makeViewport:this.makeViewport,...this.props,...this.state}),this._controllerState}getCenter(t){let{x:r,y:i}=this.props,{offsetCenter:s}=t;return[s.x-r,s.y-i]}isPointInBounds(t,r){let{width:i,height:s}=this.props;if(r&&r.handled)return!1;let n=t[0]>=0&&t[0]<=i&&t[1]>=0&&t[1]<=s;return n&&r&&r.stopPropagation(),n}isFunctionKeyPressed(t){let{srcEvent:r}=t;return!!(r.metaKey||r.altKey||r.ctrlKey||r.shiftKey)}isDragging(){return this._interactionState.isDragging||!1}blockEvents(t){let r=setTimeout(()=>{this._eventStartBlocked===r&&(this._eventStartBlocked=null)},t);this._eventStartBlocked=r}setProps(t){t.dragMode&&(this.dragMode=t.dragMode),this.props=t,\"transitionInterpolator\"in t||(t.transitionInterpolator=this._getTransitionProps().transitionInterpolator),this.transitionManager.processViewStateChange(t);let{inertia:r}=t;this.inertia=Number.isFinite(r)?r:r===!0?Ylt:0;let{scrollZoom:i=!0,dragPan:s=!0,dragRotate:n=!0,doubleClickZoom:o=!0,touchZoom:c=!0,touchRotate:f=!1,keyboard:_=!0}=t,w=!!this.onViewStateChange;this.toggleEvents($y.WHEEL,w&&i),this.toggleEvents($y.PAN,w),this.toggleEvents($y.PINCH,w&&(c||f)),this.toggleEvents($y.TRIPLE_PAN,w&&f),this.toggleEvents($y.DOUBLE_TAP,w&&o),this.toggleEvents($y.KEYBOARD,w&&_),this.scrollZoom=i,this.dragPan=s,this.dragRotate=n,this.doubleClickZoom=o,this.touchZoom=c,this.touchRotate=f,this.keyboard=_}updateTransition(){this.transitionManager.updateTransition()}toggleEvents(t,r){this.eventManager&&t.forEach(i=>{this._events[i]!==r&&(this._events[i]=r,r?this.eventManager.on(i,this.handleEvent):this.eventManager.off(i,this.handleEvent))})}updateViewport(t,r=null,i={}){let s={...t.getViewportProps(),...r},n=this.controllerState!==t;if(this.state=t.getState(),this._setInteractionState(i),n){let o=this.controllerState&&this.controllerState.getViewportProps();this.onViewStateChange&&this.onViewStateChange({viewState:s,interactionState:this._interactionState,oldViewState:o})}}_onTransition(t){this.onViewStateChange({...t,interactionState:this._interactionState})}_setInteractionState(t){Object.assign(this._interactionState,t),this.onStateChange(this._interactionState)}_onPanStart(t){let r=this.getCenter(t);if(!this.isPointInBounds(r,t))return!1;let i=this.isFunctionKeyPressed(t)||t.rightButton||!1;(this.invertPan||this.dragMode===\"pan\")&&(i=!i);let s=this.controllerState[i?\"panStart\":\"rotateStart\"]({pos:r});return this._panMove=i,this.updateViewport(s,em,{isDragging:!0}),!0}_onPan(t){return this.isDragging()?this._panMove?this._onPanMove(t):this._onPanRotate(t):!1}_onPanEnd(t){return this.isDragging()?this._panMove?this._onPanMoveEnd(t):this._onPanRotateEnd(t):!1}_onPanMove(t){if(!this.dragPan)return!1;let r=this.getCenter(t),i=this.controllerState.pan({pos:r});return this.updateViewport(i,em,{isDragging:!0,isPanning:!0}),!0}_onPanMoveEnd(t){let{inertia:r}=this;if(this.dragPan&&r&&t.velocity){let i=this.getCenter(t),s=[i[0]+t.velocityX*r/2,i[1]+t.velocityY*r/2],n=this.controllerState.pan({pos:s}).panEnd();this.updateViewport(n,{...this._getTransitionProps(),transitionDuration:r,transitionEasing:sP},{isDragging:!1,isPanning:!0})}else{let i=this.controllerState.panEnd();this.updateViewport(i,null,{isDragging:!1,isPanning:!1})}return!0}_onPanRotate(t){if(!this.dragRotate)return!1;let r=this.getCenter(t),i=this.controllerState.rotate({pos:r});return this.updateViewport(i,em,{isDragging:!0,isRotating:!0}),!0}_onPanRotateEnd(t){let{inertia:r}=this;if(this.dragRotate&&r&&t.velocity){let i=this.getCenter(t),s=[i[0]+t.velocityX*r/2,i[1]+t.velocityY*r/2],n=this.controllerState.rotate({pos:s}).rotateEnd();this.updateViewport(n,{...this._getTransitionProps(),transitionDuration:r,transitionEasing:sP},{isDragging:!1,isRotating:!0})}else{let i=this.controllerState.rotateEnd();this.updateViewport(i,null,{isDragging:!1,isRotating:!1})}return!0}_onWheel(t){if(!this.scrollZoom)return!1;let r=this.getCenter(t);if(!this.isPointInBounds(r,t))return!1;t.srcEvent.preventDefault();let{speed:i=.01,smooth:s=!1}=this.scrollZoom===!0?{}:this.scrollZoom,{delta:n}=t,o=2/(1+Math.exp(-Math.abs(n*i)));n<0&&o!==0&&(o=1/o);let c=this.controllerState.zoom({pos:r,scale:o});return this.updateViewport(c,{...this._getTransitionProps({around:r}),transitionDuration:s?250:1},{isZooming:!0,isPanning:!0}),!0}_onTriplePanStart(t){let r=this.getCenter(t);if(!this.isPointInBounds(r,t))return!1;let i=this.controllerState.rotateStart({pos:r});return this.updateViewport(i,em,{isDragging:!0}),!0}_onTriplePan(t){if(!this.touchRotate||!this.isDragging())return!1;let r=this.getCenter(t);r[0]-=t.deltaX;let i=this.controllerState.rotate({pos:r});return this.updateViewport(i,em,{isDragging:!0,isRotating:!0}),!0}_onTriplePanEnd(t){if(!this.isDragging())return!1;let{inertia:r}=this;if(this.touchRotate&&r&&t.velocityY){let i=this.getCenter(t),s=[i[0],i[1]+=t.velocityY*r/2],n=this.controllerState.rotate({pos:s});this.updateViewport(n,{...this._getTransitionProps(),transitionDuration:r,transitionEasing:sP},{isDragging:!1,isRotating:!0}),this.blockEvents(r)}else{let i=this.controllerState.rotateEnd();this.updateViewport(i,null,{isDragging:!1,isRotating:!1})}return!0}_onPinchStart(t){let r=this.getCenter(t);if(!this.isPointInBounds(r,t))return!1;let i=this.controllerState.zoomStart({pos:r}).rotateStart({pos:r});return dg._startPinchRotation=t.rotation,dg._lastPinchEvent=t,this.updateViewport(i,em,{isDragging:!0}),!0}_onPinch(t){if(!this.touchZoom&&!this.touchRotate||!this.isDragging())return!1;let r=this.controllerState;if(this.touchZoom){let{scale:i}=t,s=this.getCenter(t);r=r.zoom({pos:s,scale:i})}if(this.touchRotate){let{rotation:i}=t;r=r.rotate({deltaAngleX:dg._startPinchRotation-i})}return this.updateViewport(r,em,{isDragging:!0,isPanning:this.touchZoom,isZooming:this.touchZoom,isRotating:this.touchRotate}),dg._lastPinchEvent=t,!0}_onPinchEnd(t){if(!this.isDragging())return!1;let{inertia:r}=this,{_lastPinchEvent:i}=dg;if(this.touchZoom&&r&&i&&t.scale!==i.scale){let s=this.getCenter(t),n=this.controllerState.rotateEnd(),o=Math.log2(t.scale),c=(o-Math.log2(i.scale))/(t.deltaTime-i.deltaTime),f=Math.pow(2,o+c*r/2);n=n.zoom({pos:s,scale:f}).zoomEnd(),this.updateViewport(n,{...this._getTransitionProps({around:s}),transitionDuration:r,transitionEasing:sP},{isDragging:!1,isPanning:this.touchZoom,isZooming:this.touchZoom,isRotating:!1}),this.blockEvents(r)}else{let s=this.controllerState.zoomEnd().rotateEnd();this.updateViewport(s,null,{isDragging:!1,isPanning:!1,isZooming:!1,isRotating:!1})}return dg._startPinchRotation=null,dg._lastPinchEvent=null,!0}_onDoubleTap(t){if(!this.doubleClickZoom)return!1;let r=this.getCenter(t);if(!this.isPointInBounds(r,t))return!1;let i=this.isFunctionKeyPressed(t),s=this.controllerState.zoom({pos:r,scale:i?.5:2});return this.updateViewport(s,this._getTransitionProps({around:r}),{isZooming:!0,isPanning:!0}),this.blockEvents(100),!0}_onKeyDown(t){if(!this.keyboard)return!1;let r=this.isFunctionKeyPressed(t),{zoomSpeed:i,moveSpeed:s,rotateSpeedX:n,rotateSpeedY:o}=this.keyboard===!0?{}:this.keyboard,{controllerState:c}=this,f,_={};switch(t.srcEvent.code){case\"Minus\":f=r?c.zoomOut(i).zoomOut(i):c.zoomOut(i),_.isZooming=!0;break;case\"Equal\":f=r?c.zoomIn(i).zoomIn(i):c.zoomIn(i),_.isZooming=!0;break;case\"ArrowLeft\":r?(f=c.rotateLeft(n),_.isRotating=!0):(f=c.moveLeft(s),_.isPanning=!0);break;case\"ArrowRight\":r?(f=c.rotateRight(n),_.isRotating=!0):(f=c.moveRight(s),_.isPanning=!0);break;case\"ArrowUp\":r?(f=c.rotateUp(o),_.isRotating=!0):(f=c.moveUp(s),_.isPanning=!0);break;case\"ArrowDown\":r?(f=c.rotateDown(o),_.isRotating=!0):(f=c.moveDown(s),_.isPanning=!0);break;default:return!1}return this.updateViewport(f,this._getTransitionProps(),_),!0}_getTransitionProps(t){let{transition:r}=this;return!r||!r.transitionInterpolator?em:t?{...r,transitionInterpolator:new fg({...t,...r.transitionInterpolator.opts,makeViewport:this.controllerState.makeViewport})}:r}};var Zb=class{constructor(t,r){G(this,\"_viewportProps\",void 0),G(this,\"_state\",void 0),this._viewportProps=this.applyConstraints(t),this._state=r}getViewportProps(){return this._viewportProps}getState(){return this._state}};var xG=5,Qlt=1.2,wD=class extends Zb{constructor(t){let{width:r,height:i,latitude:s,longitude:n,zoom:o,bearing:c=0,pitch:f=0,altitude:_=1.5,position:w=[0,0,0],maxZoom:I=20,minZoom:R=0,maxPitch:N=60,minPitch:j=0,startPanLngLat:Q,startZoomLngLat:et,startRotatePos:Y,startBearing:K,startPitch:J,startZoom:ut,normalize:Et=!0}=t;_r(Number.isFinite(n)),_r(Number.isFinite(s)),_r(Number.isFinite(o)),super({width:r,height:i,latitude:s,longitude:n,zoom:o,bearing:c,pitch:f,altitude:_,maxZoom:I,minZoom:R,maxPitch:N,minPitch:j,normalize:Et,position:w},{startPanLngLat:Q,startZoomLngLat:et,startRotatePos:Y,startBearing:K,startPitch:J,startZoom:ut}),G(this,\"makeViewport\",void 0),this.makeViewport=t.makeViewport}panStart({pos:t}){return this._getUpdatedState({startPanLngLat:this._unproject(t)})}pan({pos:t,startPos:r}){let i=this.getState().startPanLngLat||this._unproject(r);if(!i)return this;let n=this.makeViewport(this.getViewportProps()).panByPosition(i,t);return this._getUpdatedState(n)}panEnd(){return this._getUpdatedState({startPanLngLat:null})}rotateStart({pos:t}){return this._getUpdatedState({startRotatePos:t,startBearing:this.getViewportProps().bearing,startPitch:this.getViewportProps().pitch})}rotate({pos:t,deltaAngleX:r=0,deltaAngleY:i=0}){let{startRotatePos:s,startBearing:n,startPitch:o}=this.getState();if(!s||n===void 0||o===void 0)return this;let c;return t?c=this._getNewRotation(t,s,o,n):c={bearing:n+r,pitch:o+i},this._getUpdatedState(c)}rotateEnd(){return this._getUpdatedState({startBearing:null,startPitch:null})}zoomStart({pos:t}){return this._getUpdatedState({startZoomLngLat:this._unproject(t),startZoom:this.getViewportProps().zoom})}zoom({pos:t,startPos:r,scale:i}){let{startZoom:s,startZoomLngLat:n}=this.getState();if(n||(s=this.getViewportProps().zoom,n=this._unproject(r)||this._unproject(t)),!n)return this;let{maxZoom:o,minZoom:c}=this.getViewportProps(),f=s+Math.log2(i);f=Il(f,c,o);let _=this.makeViewport({...this.getViewportProps(),zoom:f});return this._getUpdatedState({zoom:f,..._.panByPosition(n,t)})}zoomEnd(){return this._getUpdatedState({startZoomLngLat:null,startZoom:null})}zoomIn(t=2){return this._zoomFromCenter(t)}zoomOut(t=2){return this._zoomFromCenter(1/t)}moveLeft(t=100){return this._panFromCenter([t,0])}moveRight(t=100){return this._panFromCenter([-t,0])}moveUp(t=100){return this._panFromCenter([0,t])}moveDown(t=100){return this._panFromCenter([0,-t])}rotateLeft(t=15){return this._getUpdatedState({bearing:this.getViewportProps().bearing-t})}rotateRight(t=15){return this._getUpdatedState({bearing:this.getViewportProps().bearing+t})}rotateUp(t=10){return this._getUpdatedState({pitch:this.getViewportProps().pitch+t})}rotateDown(t=10){return this._getUpdatedState({pitch:this.getViewportProps().pitch-t})}shortestPathFrom(t){let r=t.getViewportProps(),i={...this.getViewportProps()},{bearing:s,longitude:n}=i;return Math.abs(s-r.bearing)>180&&(i.bearing=s<0?s+360:s-360),Math.abs(n-r.longitude)>180&&(i.longitude=n<0?n+360:n-360),i}applyConstraints(t){let{maxZoom:r,minZoom:i,zoom:s}=t;t.zoom=Il(s,i,r);let{maxPitch:n,minPitch:o,pitch:c}=t;t.pitch=Il(c,o,n);let{normalize:f=!0}=t;return f&&Object.assign(t,eP(t)),t}_zoomFromCenter(t){let{width:r,height:i}=this.getViewportProps();return this.zoom({pos:[r/2,i/2],scale:t})}_panFromCenter(t){let{width:r,height:i}=this.getViewportProps();return this.pan({startPos:[r/2,i/2],pos:[r/2+t[0],i/2+t[1]]})}_getUpdatedState(t){return new this.constructor({makeViewport:this.makeViewport,...this.getViewportProps(),...this.getState(),...t})}_unproject(t){let r=this.makeViewport(this.getViewportProps());return t&&r.unproject(t)}_getNewRotation(t,r,i,s){let n=t[0]-r[0],o=t[1]-r[1],c=t[1],f=r[1],{width:_,height:w}=this.getViewportProps(),I=n/_,R=0;o>0?Math.abs(w-f)>xG&&(R=o/(f-w)*Qlt):o<0&&f>xG&&(R=1-c/f),R=Il(R,-1,1);let{minPitch:N,maxPitch:j}=this.getViewportProps(),Q=s+180*I,et=i;return R>0?et=i+R*(j-i):R<0&&(et=i-R*(N-i)),{pitch:et,bearing:Q}}},Yb=class extends qb{constructor(...t){super(...t),G(this,\"ControllerState\",wD),G(this,\"transition\",{transitionDuration:300,transitionInterpolator:new fg({transitionProps:{compare:[\"longitude\",\"latitude\",\"zoom\",\"bearing\",\"pitch\",\"position\"],required:[\"longitude\",\"latitude\",\"zoom\"]}})}),G(this,\"dragMode\",\"pan\")}setProps(t){t.position=t.position||[0,0,0];let r=this.props;super.setProps(t),(!r||r.height!==t.height)&&this.updateViewport(new this.ControllerState({makeViewport:this.makeViewport,...t,...this.state}))}};var Xy=class extends Xc{get ViewportType(){return lc}get ControllerType(){return Yb}};G(Xy,\"displayName\",\"MapView\");var $lt=new qy;function Xlt(e,t){var r,i;let s=(r=e.order)!==null&&r!==void 0?r:1/0,n=(i=t.order)!==null&&i!==void 0?i:1/0;return s-n}var Qb=class{constructor(){G(this,\"effects\",void 0),G(this,\"_resolvedEffects\",[]),G(this,\"_defaultEffects\",[]),G(this,\"_needsRedraw\",void 0),this.effects=[],this._needsRedraw=\"Initial render\",this._setEffects([])}addDefaultEffect(t){let r=this._defaultEffects;if(!r.find(i=>i.id===t.id)){let i=r.findIndex(s=>Xlt(s,t)>0);i<0?r.push(t):r.splice(i,0,t),this._setEffects(this.effects)}}setProps(t){\"effects\"in t&&(mo(t.effects,this.effects,1)||this._setEffects(t.effects))}needsRedraw(t={clearRedrawFlags:!1}){let r=this._needsRedraw;return t.clearRedrawFlags&&(this._needsRedraw=!1),r}getEffects(){return this._resolvedEffects}_setEffects(t){let r={};for(let s of this.effects)r[s.id]=s;let i=[];for(let s of t){let n=r[s.id];n&&n!==s?n.setProps?(n.setProps(s.props),i.push(n)):(n.cleanup(),i.push(s)):i.push(s),delete r[s.id]}for(let s in r)r[s].cleanup();this.effects=i,this._resolvedEffects=i.concat(this._defaultEffects),t.some(s=>s instanceof qy)||this._resolvedEffects.push($lt),this._needsRedraw=\"effects changed\"}finalize(){for(let t of this._resolvedEffects)t.cleanup();this.effects.length=0,this._resolvedEffects.length=0,this._defaultEffects.length=0}};var $b=class extends sc{shouldDrawLayer(t){let{operation:r}=t.props;return r.includes(\"draw\")||r.includes(\"terrain\")}};var Klt=\"deckRenderer.renderLayers\",Xb=class{constructor(t){G(this,\"gl\",void 0),G(this,\"layerFilter\",void 0),G(this,\"drawPickingColors\",void 0),G(this,\"drawLayersPass\",void 0),G(this,\"pickLayersPass\",void 0),G(this,\"renderCount\",void 0),G(this,\"_needsRedraw\",void 0),G(this,\"renderBuffers\",void 0),G(this,\"lastPostProcessEffect\",void 0),this.gl=t,this.layerFilter=null,this.drawPickingColors=!1,this.drawLayersPass=new $b(t),this.pickLayersPass=new ug(t),this.renderCount=0,this._needsRedraw=\"Initial render\",this.renderBuffers=[],this.lastPostProcessEffect=null}setProps(t){this.layerFilter!==t.layerFilter&&(this.layerFilter=t.layerFilter,this._needsRedraw=\"layerFilter changed\"),this.drawPickingColors!==t.drawPickingColors&&(this.drawPickingColors=t.drawPickingColors,this._needsRedraw=\"drawPickingColors changed\")}renderLayers(t){if(!t.viewports.length)return;let r=this.drawPickingColors?this.pickLayersPass:this.drawLayersPass,i={layerFilter:this.layerFilter,isPicking:this.drawPickingColors,...t,target:t.target||yi.getDefaultFramebuffer(this.gl)};i.effects&&this._preRender(i.effects,i);let s=this.lastPostProcessEffect?this.renderBuffers[0]:i.target,n=r.render({...i,target:s});i.effects&&this._postRender(i.effects,i),this.renderCount++,Ls(Klt,this,n,t)}needsRedraw(t={clearRedrawFlags:!1}){let r=this._needsRedraw;return t.clearRedrawFlags&&(this._needsRedraw=!1),r}finalize(){let{renderBuffers:t}=this;for(let r of t)r.delete();t.length=0}_preRender(t,r){this.lastPostProcessEffect=null,r.preRenderStats=r.preRenderStats||{};for(let i of t)r.preRenderStats[i.id]=i.preRender(this.gl,r),i.postRender&&(this.lastPostProcessEffect=i.id);this.lastPostProcessEffect&&this._resizeRenderBuffers()}_resizeRenderBuffers(){let{renderBuffers:t}=this;t.length===0&&t.push(new yi(this.gl),new yi(this.gl));for(let r of t)r.resize()}_postRender(t,r){let{renderBuffers:i}=this,s={...r,inputBuffer:i[0],swapBuffer:i[1],target:null};for(let n of t)if(n.postRender){if(n.id===this.lastPostProcessEffect){s.target=r.target,n.postRender(this.gl,s);break}let o=n.postRender(this.gl,s);s.inputBuffer=o,s.swapBuffer=o===i[0]?i[1]:i[0]}}};var Jlt={pickedColor:null,pickedObjectIndex:-1};function bG({pickedColors:e,decodePickingColor:t,deviceX:r,deviceY:i,deviceRadius:s,deviceRect:n}){let{x:o,y:c,width:f,height:_}=n,w=s*s,I=-1,R=0;for(let N=0;N<_;N++){let j=N+c-i,Q=j*j;if(Q>w)R+=4*f;else for(let et=0;et=0){let K=et+o-r,J=K*K+Q;J<=w&&(w=J,I=R)}R+=4}}if(I>=0){let N=e.slice(I,I+4),j=t(N);if(j){let Q=Math.floor(I/4/f),et=I/4-Q*f;return{...j,pickedColor:N,pickedX:o+et,pickedY:c+Q}}or.error(\"Picked non-existent layer. Is picking buffer corrupt?\")()}return Jlt}function wG({pickedColors:e,decodePickingColor:t}){let r=new Map;if(e){for(let i=0;i=0){let n=e.slice(i,i+4),o=n.join(\",\");if(!r.has(o)){let c=t(n);c?r.set(o,{...c,color:n}):or.error(\"Picked non-existent layer. Is picking buffer corrupt?\")()}}}return Array.from(r.values())}function SD({pickInfo:e,viewports:t,pixelRatio:r,x:i,y:s,z:n}){let o=t[0];t.length>1&&(o=tct(e?.pickedViewports||t,{x:i,y:s}));let c;if(o){let f=[i-o.x,s-o.y];n!==void 0&&(f[2]=n),c=o.unproject(f)}return{color:null,layer:null,viewport:o,index:-1,picked:!1,x:i,y:s,pixel:[i,s],coordinate:c,devicePixel:e&&\"pickedX\"in e?[e.pickedX,e.pickedY]:void 0,pixelRatio:r}}function SG(e){let{pickInfo:t,lastPickedInfo:r,mode:i,layers:s}=e,{pickedColor:n,pickedLayer:o,pickedObjectIndex:c}=t,f=o?[o]:[];if(i===\"hover\"){let I=r.index,R=r.layerId,N=o?o.props.id:null;if(N!==R||c!==I){if(N!==R){let j=s.find(Q=>Q.props.id===R);j&&f.unshift(j)}r.layerId=N,r.index=c,r.info=null}}let _=SD(e),w=new Map;return w.set(null,_),f.forEach(I=>{let R={..._};I===o&&(R.color=n,R.index=c,R.picked=!0),R=TD({layer:I,info:R,mode:i});let N=R.layer;I===o&&i===\"hover\"&&(r.info=R),w.set(N.id,R),i===\"hover\"&&N.updateAutoHighlight(R)}),w}function TD({layer:e,info:t,mode:r}){for(;e&&t;){let i=t.layer||null;t.sourceLayer=i,t.layer=e,t=e.getPickingInfo({info:t,mode:r,sourceLayer:i}),e=e.parent}return t}function tct(e,t){for(let r=e.length-1;r>=0;r--){let i=e[r];if(i.containsPixel(t))return i}return e[0]}var Kb=class{constructor(t){G(this,\"gl\",void 0),G(this,\"pickingFBO\",void 0),G(this,\"depthFBO\",void 0),G(this,\"pickLayersPass\",void 0),G(this,\"layerFilter\",void 0),G(this,\"lastPickedInfo\",void 0),G(this,\"_pickable\",!0),this.gl=t,this.pickLayersPass=new ug(t),this.lastPickedInfo={index:-1,layerId:null,info:null}}setProps(t){\"layerFilter\"in t&&(this.layerFilter=t.layerFilter),\"_pickable\"in t&&(this._pickable=t._pickable)}finalize(){this.pickingFBO&&this.pickingFBO.delete(),this.depthFBO&&(this.depthFBO.color.delete(),this.depthFBO.delete())}pickObject(t){return this._pickClosestObject(t)}pickObjects(t){return this._pickVisibleObjects(t)}getLastPickedObject({x:t,y:r,layers:i,viewports:s},n=this.lastPickedInfo.info){let o=n&&n.layer&&n.layer.id,c=n&&n.viewport&&n.viewport.id,f=o?i.find(R=>R.id===o):null,_=c&&s.find(R=>R.id===c)||s[0],w=_&&_.unproject([t-_.x,r-_.y]);return{...n,...{x:t,y:r,viewport:_,coordinate:w,layer:f}}}_resizeBuffer(){var t,r;let{gl:i}=this;if(!this.pickingFBO&&(this.pickingFBO=new yi(i),yi.isSupported(i,{colorBufferFloat:!0}))){let s=new yi(i);s.attach({36064:new pi(i,{format:fr(i)?34836:6408,type:5126})}),this.depthFBO=s}(t=this.pickingFBO)===null||t===void 0||t.resize({width:i.canvas.width,height:i.canvas.height}),(r=this.depthFBO)===null||r===void 0||r.resize({width:i.canvas.width,height:i.canvas.height})}_getPickable(t){if(this._pickable===!1)return null;let r=t.filter(i=>this.pickLayersPass.shouldDrawLayer(i)&&!i.isComposite);return r.length?r:null}_pickClosestObject({layers:t,views:r,viewports:i,x:s,y:n,radius:o=0,depth:c=1,mode:f=\"query\",unproject3D:_,onViewportActive:w,effects:I}){let R=this._getPickable(t),N=El(this.gl);if(!R)return{result:[],emptyInfo:SD({viewports:i,x:s,y:n,pixelRatio:N})};this._resizeBuffer();let j=Sy(this.gl,[s,n],!0),Q=[j.x+Math.floor(j.width/2),j.y+Math.floor(j.height/2)],et=Math.round(o*N),{width:Y,height:K}=this.pickingFBO,J=this._getPickingRect({deviceX:Q[0],deviceY:Q[1],deviceRadius:et,deviceWidth:Y,deviceHeight:K}),ut={x:s-o,y:n-o,width:o*2+1,height:o*2+1},Et,kt=[],Xt=new Set;for(let qt=0;qt=_)break;let De=kt[ue],Ke={color:De.pickedColor,layer:null,index:De.pickedObjectIndex,picked:!0,x:s,y:n,pixelRatio:N};Ke=TD({layer:De.pickedLayer,info:Ke,mode:f});let rr=(le=Ke.object)!==null&&le!==void 0?le:\"\".concat(Ke.layer.id,\"[\").concat(Ke.index,\"]\");Xt.has(rr)||Xt.set(rr,Ke)}return Array.from(Xt.values())}_drawAndSample({layers:t,views:r,viewports:i,onViewportActive:s,deviceRect:n,cullRect:o,effects:c,pass:f},_=!1){let w=_?this.depthFBO:this.pickingFBO,I={layers:t,layerFilter:this.layerFilter,views:r,viewports:i,onViewportActive:s,pickingFBO:w,deviceRect:n,cullRect:o,effects:c,pass:f,pickZ:_,preRenderStats:{}};for(let K of c)K.useInPicking&&(I.preRenderStats[K.id]=K.preRender(this.gl,I));let{decodePickingColor:R}=this.pickLayersPass.render(I),{x:N,y:j,width:Q,height:et}=n,Y=new(_?Float32Array:Uint8Array)(Q*et*4);return Dh(w,{sourceX:N,sourceY:j,sourceWidth:Q,sourceHeight:et,target:Y}),{pickedColors:Y,decodePickingColor:R}}_getPickingRect({deviceX:t,deviceY:r,deviceRadius:i,deviceWidth:s,deviceHeight:n}){let o=Math.max(0,t-i),c=Math.max(0,r-i),f=Math.min(s,t+i+1)-o,_=Math.min(n,r+i+1)-c;return f<=0||_<=0?null:{x:o,y:c,width:f,height:_}}};var ect={zIndex:\"1\",position:\"absolute\",pointerEvents:\"none\",color:\"#a0a7b4\",backgroundColor:\"#29323c\",padding:\"10px\",top:\"0\",left:\"0\",display:\"none\"},Jb=class{constructor(t){G(this,\"el\",null),G(this,\"isVisible\",!1);let r=t.parentElement;r&&(this.el=document.createElement(\"div\"),this.el.className=\"deck-tooltip\",Object.assign(this.el.style,ect),r.appendChild(this.el))}setTooltip(t,r,i){let s=this.el;if(s){if(typeof t==\"string\")s.innerText=t;else if(t)t.text&&(s.innerText=t.text),t.html&&(s.innerHTML=t.html),t.className&&(s.className=t.className);else{this.isVisible=!1,s.style.display=\"none\";return}this.isVisible=!0,s.style.display=\"block\",s.style.transform=\"translate(\".concat(r,\"px, \").concat(i,\"px)\"),t&&typeof t==\"object\"&&\"style\"in t&&Object.assign(s.style,t.style)}}remove(){this.el&&(this.el.remove(),this.el=null)}};var pg=Ri(TG());var rct={mousedown:1,mousemove:2,mouseup:4};function ict(e,t){for(let r=0;r0&&i.type===\"pointerdown\"&&(ict(s,n=>n.pointerId===i.pointerId)||s.push(i)),t.call(this,i)}}function EG(e){e.prototype.handler=function(r){let i=rct[r.type];i&1&&r.button>=0&&(this.pressed=!0),i&2&&r.which===0&&(i=4),this.pressed&&(i&4&&(this.pressed=!1),this.callback(this.manager,i,{pointers:[r],changedPointers:[r],pointerType:\"mouse\",srcEvent:r}))}}MG(pg.PointerEventInput);EG(pg.MouseInput);var PG=pg.Manager,Hh=pg;var qh=class{constructor(t,r,i){this.element=t,this.callback=r,this.options={enable:!0,...i}}};var IG=Hh?[[Hh.Pan,{event:\"tripan\",pointers:3,threshold:0,enable:!1}],[Hh.Rotate,{enable:!1}],[Hh.Pinch,{enable:!1}],[Hh.Swipe,{enable:!1}],[Hh.Pan,{threshold:0,enable:!1}],[Hh.Press,{enable:!1}],[Hh.Tap,{event:\"doubletap\",taps:2,enable:!1}],[Hh.Tap,{event:\"anytap\",enable:!1}],[Hh.Tap,{enable:!1}]]:null,MD={tripan:[\"rotate\",\"pinch\",\"pan\"],rotate:[\"pinch\"],pinch:[\"pan\"],pan:[\"press\",\"doubletap\",\"anytap\",\"tap\"],doubletap:[\"anytap\"],anytap:[\"tap\"]},CG={doubletap:[\"tap\"]},LG={pointerdown:\"pointerdown\",pointermove:\"pointermove\",pointerup:\"pointerup\",touchstart:\"pointerdown\",touchmove:\"pointermove\",touchend:\"pointerup\",mousedown:\"pointerdown\",mousemove:\"pointermove\",mouseup:\"pointerup\"},Ky={KEY_EVENTS:[\"keydown\",\"keyup\"],MOUSE_EVENTS:[\"mousedown\",\"mousemove\",\"mouseup\",\"mouseover\",\"mouseout\",\"mouseleave\"],WHEEL_EVENTS:[\"wheel\",\"mousewheel\"]},kG={tap:\"tap\",anytap:\"anytap\",doubletap:\"doubletap\",press:\"press\",pinch:\"pinch\",pinchin:\"pinch\",pinchout:\"pinch\",pinchstart:\"pinch\",pinchmove:\"pinch\",pinchend:\"pinch\",pinchcancel:\"pinch\",rotate:\"rotate\",rotatestart:\"rotate\",rotatemove:\"rotate\",rotateend:\"rotate\",rotatecancel:\"rotate\",tripan:\"tripan\",tripanstart:\"tripan\",tripanmove:\"tripan\",tripanup:\"tripan\",tripandown:\"tripan\",tripanleft:\"tripan\",tripanright:\"tripan\",tripanend:\"tripan\",tripancancel:\"tripan\",pan:\"pan\",panstart:\"pan\",panmove:\"pan\",panup:\"pan\",pandown:\"pan\",panleft:\"pan\",panright:\"pan\",panend:\"pan\",pancancel:\"pan\",swipe:\"swipe\",swipeleft:\"swipe\",swiperight:\"swipe\",swipeup:\"swipe\",swipedown:\"swipe\"},ED={click:\"tap\",anyclick:\"anytap\",dblclick:\"doubletap\",mousedown:\"pointerdown\",mousemove:\"pointermove\",mouseup:\"pointerup\",mouseover:\"pointerover\",mouseout:\"pointerout\",mouseleave:\"pointerleave\"};var RG=typeof navigator<\"u\"&&navigator.userAgent?navigator.userAgent.toLowerCase():\"\",Ag=typeof window<\"u\"?window:global;var aP=!1;try{let e={get passive(){return aP=!0,!0}};Ag.addEventListener(\"test\",null,e),Ag.removeEventListener(\"test\",null)}catch{aP=!1}var nct=RG.indexOf(\"firefox\")!==-1,{WHEEL_EVENTS:sct}=Ky,DG=\"wheel\",OG=4.000244140625,oct=40,act=.25,tw=class extends qh{constructor(t,r,i){super(t,r,i),this.handleEvent=s=>{if(!this.options.enable)return;let n=s.deltaY;Ag.WheelEvent&&(nct&&s.deltaMode===Ag.WheelEvent.DOM_DELTA_PIXEL&&(n/=Ag.devicePixelRatio),s.deltaMode===Ag.WheelEvent.DOM_DELTA_LINE&&(n*=oct)),n!==0&&n%OG===0&&(n=Math.floor(n/OG)),s.shiftKey&&n&&(n=n*act),this.callback({type:DG,center:{x:s.clientX,y:s.clientY},delta:-n,srcEvent:s,pointerType:\"mouse\",target:s.target})},this.events=(this.options.events||[]).concat(sct),this.events.forEach(s=>t.addEventListener(s,this.handleEvent,aP?{passive:!1}:!1))}destroy(){this.events.forEach(t=>this.element.removeEventListener(t,this.handleEvent))}enableEventType(t,r){t===DG&&(this.options.enable=r)}};var{MOUSE_EVENTS:lct}=Ky,BG=\"pointermove\",FG=\"pointerover\",zG=\"pointerout\",NG=\"pointerenter\",UG=\"pointerleave\",ew=class extends qh{constructor(t,r,i){super(t,r,i),this.handleEvent=n=>{this.handleOverEvent(n),this.handleOutEvent(n),this.handleEnterEvent(n),this.handleLeaveEvent(n),this.handleMoveEvent(n)},this.pressed=!1;let{enable:s}=this.options;this.enableMoveEvent=s,this.enableLeaveEvent=s,this.enableEnterEvent=s,this.enableOutEvent=s,this.enableOverEvent=s,this.events=(this.options.events||[]).concat(lct),this.events.forEach(n=>t.addEventListener(n,this.handleEvent))}destroy(){this.events.forEach(t=>this.element.removeEventListener(t,this.handleEvent))}enableEventType(t,r){t===BG&&(this.enableMoveEvent=r),t===FG&&(this.enableOverEvent=r),t===zG&&(this.enableOutEvent=r),t===NG&&(this.enableEnterEvent=r),t===UG&&(this.enableLeaveEvent=r)}handleOverEvent(t){this.enableOverEvent&&t.type===\"mouseover\"&&this._emit(FG,t)}handleOutEvent(t){this.enableOutEvent&&t.type===\"mouseout\"&&this._emit(zG,t)}handleEnterEvent(t){this.enableEnterEvent&&t.type===\"mouseenter\"&&this._emit(NG,t)}handleLeaveEvent(t){this.enableLeaveEvent&&t.type===\"mouseleave\"&&this._emit(UG,t)}handleMoveEvent(t){if(this.enableMoveEvent)switch(t.type){case\"mousedown\":t.button>=0&&(this.pressed=!0);break;case\"mousemove\":t.which===0&&(this.pressed=!1),this.pressed||this._emit(BG,t);break;case\"mouseup\":this.pressed=!1;break;default:}}_emit(t,r){this.callback({type:t,center:{x:r.clientX,y:r.clientY},srcEvent:r,pointerType:\"mouse\",target:r.target})}};var{KEY_EVENTS:cct}=Ky,VG=\"keydown\",jG=\"keyup\",rw=class extends qh{constructor(t,r,i){super(t,r,i),this.handleEvent=s=>{let n=s.target||s.srcElement;n.tagName===\"INPUT\"&&n.type===\"text\"||n.tagName===\"TEXTAREA\"||(this.enableDownEvent&&s.type===\"keydown\"&&this.callback({type:VG,srcEvent:s,key:s.key,target:s.target}),this.enableUpEvent&&s.type===\"keyup\"&&this.callback({type:jG,srcEvent:s,key:s.key,target:s.target}))},this.enableDownEvent=this.options.enable,this.enableUpEvent=this.options.enable,this.events=(this.options.events||[]).concat(cct),t.tabIndex=this.options.tabIndex||0,t.style.outline=\"none\",this.events.forEach(s=>t.addEventListener(s,this.handleEvent))}destroy(){this.events.forEach(t=>this.element.removeEventListener(t,this.handleEvent))}enableEventType(t,r){t===VG&&(this.enableDownEvent=r),t===jG&&(this.enableUpEvent=r)}};var GG=\"contextmenu\",iw=class extends qh{constructor(t,r,i){super(t,r,i),this.handleEvent=s=>{this.options.enable&&this.callback({type:GG,center:{x:s.clientX,y:s.clientY},srcEvent:s,pointerType:\"mouse\",target:s.target})},t.addEventListener(\"contextmenu\",this.handleEvent)}destroy(){this.element.removeEventListener(\"contextmenu\",this.handleEvent)}enableEventType(t,r){t===GG&&(this.options.enable=r)}};var uct={pointerdown:1,pointermove:2,pointerup:4,mousedown:1,mousemove:2,mouseup:4},hct=1,fct=2,dct=3,pct=0,Act=1,mct=2,gct=1,_ct=2,yct=4;function WG(e){let t=uct[e.srcEvent.type];if(!t)return null;let{buttons:r,button:i,which:s}=e.srcEvent,n=!1,o=!1,c=!1;return t===4||t===2&&!Number.isFinite(r)?(n=s===hct,o=s===fct,c=s===dct):t===2?(n=!!(r&gct),o=!!(r&yct),c=!!(r&_ct)):t===1&&(n=i===pct,o=i===Act,c=i===mct),{leftButton:n,middleButton:o,rightButton:c}}function HG(e,t){let r=e.center;if(!r)return null;let i=t.getBoundingClientRect(),s=i.width/t.offsetWidth||1,n=i.height/t.offsetHeight||1,o={x:(r.x-i.left-t.clientLeft)/s,y:(r.y-i.top-t.clientTop)/n};return{center:r,offsetCenter:o}}var PD={srcElement:\"root\",priority:0},nw=class{constructor(t){this.handleEvent=r=>{if(this.isEmpty())return;let i=this._normalizeEvent(r),s=r.srcEvent.target;for(;s&&s!==i.rootElement;){if(this._emit(i,s),i.handled)return;s=s.parentNode}this._emit(i,\"root\")},this.eventManager=t,this.handlers=[],this.handlersByElement=new Map,this._active=!1}isEmpty(){return!this._active}add(t,r,i,s=!1,n=!1){let{handlers:o,handlersByElement:c}=this,f=PD;typeof i==\"string\"||i&&i.addEventListener?f={...PD,srcElement:i}:i&&(f={...PD,...i});let _=c.get(f.srcElement);_||(_=[],c.set(f.srcElement,_));let w={type:t,handler:r,srcElement:f.srcElement,priority:f.priority};s&&(w.once=!0),n&&(w.passive=!0),o.push(w),this._active=this._active||!w.passive;let I=_.length-1;for(;I>=0&&!(_[I].priority>=w.priority);)I--;_.splice(I+1,0,w)}remove(t,r){let{handlers:i,handlersByElement:s}=this;for(let n=i.length-1;n>=0;n--){let o=i[n];if(o.type===t&&o.handler===r){i.splice(n,1);let c=s.get(o.srcElement);c.splice(c.indexOf(o),1),c.length===0&&s.delete(o.srcElement)}}this._active=i.some(n=>!n.passive)}_emit(t,r){let i=this.handlersByElement.get(r);if(i){let s=!1,n=()=>{t.handled=!0},o=()=>{t.handled=!0,s=!0},c=[];for(let f=0;f{t.srcEvent.preventDefault()},stopImmediatePropagation:null,stopPropagation:null,handled:!1,rootElement:r}}};var vct={events:null,recognizers:null,recognizerOptions:{},Manager:PG,touchAction:\"none\",tabIndex:0},Jy=class{constructor(t=null,r){this._onBasicInput=s=>{let{srcEvent:n}=s,o=LG[n.type];o&&this.manager.emit(o,s)},this._onOtherEvent=s=>{this.manager.emit(s.type,s)},this.options={...vct,...r},this.events=new Map,this.setElement(t);let{events:i}=this.options;i&&this.on(i)}getElement(){return this.element}setElement(t){if(this.element&&this.destroy(),this.element=t,!t)return;let{options:r}=this,i=r.Manager;this.manager=new i(t,{touchAction:r.touchAction,recognizers:r.recognizers||IG}).on(\"hammer.input\",this._onBasicInput),r.recognizers||Object.keys(MD).forEach(s=>{let n=this.manager.get(s);n&&MD[s].forEach(o=>{n.recognizeWith(o)})});for(let s in r.recognizerOptions){let n=this.manager.get(s);if(n){let o=r.recognizerOptions[s];delete o.enable,n.set(o)}}this.wheelInput=new tw(t,this._onOtherEvent,{enable:!1}),this.moveInput=new ew(t,this._onOtherEvent,{enable:!1}),this.keyInput=new rw(t,this._onOtherEvent,{enable:!1,tabIndex:r.tabIndex}),this.contextmenuInput=new iw(t,this._onOtherEvent,{enable:!1});for(let[s,n]of this.events)n.isEmpty()||(this._toggleRecognizer(n.recognizerName,!0),this.manager.on(s,n.handleEvent))}destroy(){this.element&&(this.wheelInput.destroy(),this.moveInput.destroy(),this.keyInput.destroy(),this.contextmenuInput.destroy(),this.manager.destroy(),this.wheelInput=null,this.moveInput=null,this.keyInput=null,this.contextmenuInput=null,this.manager=null,this.element=null)}on(t,r,i){this._addEventHandler(t,r,i,!1)}once(t,r,i){this._addEventHandler(t,r,i,!0)}watch(t,r,i){this._addEventHandler(t,r,i,!1,!0)}off(t,r){this._removeEventHandler(t,r)}_toggleRecognizer(t,r){let{manager:i}=this;if(!i)return;let s=i.get(t);if(s&&s.options.enable!==r){s.set({enable:r});let n=CG[t];n&&!this.options.recognizers&&n.forEach(o=>{let c=i.get(o);r?(c.requireFailure(t),s.dropRequireFailure(o)):c.dropRequireFailure(t)})}this.wheelInput.enableEventType(t,r),this.moveInput.enableEventType(t,r),this.keyInput.enableEventType(t,r),this.contextmenuInput.enableEventType(t,r)}_addEventHandler(t,r,i,s,n){if(typeof t!=\"string\"){i=r;for(let w in t)this._addEventHandler(w,t[w],i,s,n);return}let{manager:o,events:c}=this,f=ED[t]||t,_=c.get(f);_||(_=new nw(this),c.set(f,_),_.recognizerName=kG[f]||f,o&&o.on(f,_.handleEvent)),_.add(t,r,i,s,n),_.isEmpty()||this._toggleRecognizer(_.recognizerName,!0)}_removeEventHandler(t,r){if(typeof t!=\"string\"){for(let o in t)this._removeEventHandler(o,t[o]);return}let{events:i}=this,s=ED[t]||t,n=i.get(s);if(n&&(n.remove(t,r),n.isEmpty())){let{recognizerName:o}=n,c=!1;for(let f of i.values())if(f.recognizerName===o&&!f.isEmpty()){c=!0;break}c||this._toggleRecognizer(o,!1)}}};function mg(){}var xct=({isDragging:e})=>e?\"grabbing\":\"grab\",qG={id:\"\",width:\"100%\",height:\"100%\",style:null,viewState:null,initialViewState:null,pickingRadius:0,layerFilter:null,glOptions:{},parameters:{},parent:null,gl:null,canvas:null,layers:[],effects:[],views:null,controller:null,useDevicePixels:!0,touchAction:\"none\",eventRecognizerOptions:{},_framebuffer:null,_animate:!1,_pickable:!0,_typedArrayManagerProps:{},_customRender:null,onWebGLInitialized:mg,onResize:mg,onViewStateChange:mg,onInteractionStateChange:mg,onBeforeRender:mg,onAfterRender:mg,onLoad:mg,onError:e=>or.error(e.message,e.cause)(),onHover:null,onClick:null,onDragStart:null,onDrag:null,onDragEnd:null,_onMetrics:null,getCursor:xct,getTooltip:null,debug:!1,drawPickingColors:!1},cp=class{constructor(t){G(this,\"props\",void 0),G(this,\"width\",0),G(this,\"height\",0),G(this,\"userData\",{}),G(this,\"canvas\",null),G(this,\"viewManager\",null),G(this,\"layerManager\",null),G(this,\"effectManager\",null),G(this,\"deckRenderer\",null),G(this,\"deckPicker\",null),G(this,\"eventManager\",null),G(this,\"tooltip\",null),G(this,\"metrics\",void 0),G(this,\"animationLoop\",void 0),G(this,\"stats\",void 0),G(this,\"viewState\",void 0),G(this,\"cursorState\",void 0),G(this,\"_needsRedraw\",void 0),G(this,\"_pickRequest\",void 0),G(this,\"_lastPointerDownInfo\",null),G(this,\"_metricsCounter\",void 0),G(this,\"_onPointerMove\",r=>{let{_pickRequest:i}=this;if(r.type===\"pointerleave\")i.x=-1,i.y=-1,i.radius=0;else{if(r.leftButton||r.rightButton)return;{let s=r.offsetCenter;if(!s)return;i.x=s.x,i.y=s.y,i.radius=this.props.pickingRadius}}this.layerManager&&(this.layerManager.context.mousePosition={x:i.x,y:i.y}),i.event=r}),G(this,\"_onEvent\",r=>{let i=nR[r.type],s=r.offsetCenter;if(!i||!s||!this.layerManager)return;let n=this.layerManager.getLayers(),o=this.deckPicker.getLastPickedObject({x:s.x,y:s.y,layers:n,viewports:this.getViewports(s)},this._lastPointerDownInfo),{layer:c}=o,f=c&&(c[i.handler]||c.props[i.handler]),_=this.props[i.handler],w=!1;f&&(w=f.call(c,o,r)),!w&&_&&_(o,r)}),G(this,\"_onPointerDown\",r=>{let i=r.offsetCenter,s=this._pick(\"pickObject\",\"pickObject Time\",{x:i.x,y:i.y,radius:this.props.pickingRadius});this._lastPointerDownInfo=s.result[0]||s.emptyInfo}),this.props={...qG,...t},t=this.props,this._needsRedraw=\"Initial render\",this._pickRequest={mode:\"hover\",x:-1,y:-1,radius:0,event:null},this.cursorState={isHovering:!1,isDragging:!1},t.viewState&&t.initialViewState&&or.warn(\"View state tracking is disabled. Use either `initialViewState` for auto update or `viewState` for manual update.\")(),vy()===\"IE\"&&or.warn(\"IE 11 is not supported\")(),this.viewState=t.initialViewState,t.gl||typeof document<\"u\"&&(this.canvas=this._createCanvas(t)),this.animationLoop=this._createAnimationLoop(t),this.stats=new Gf({id:\"deck.gl\"}),this.metrics={fps:0,setPropsTime:0,updateAttributesTime:0,framesRedrawn:0,pickTime:0,pickCount:0,gpuTime:0,gpuTimePerFrame:0,cpuTime:0,cpuTimePerFrame:0,bufferMemory:0,textureMemory:0,renderbufferMemory:0,gpuMemory:0},this._metricsCounter=0,this.setProps(t),t._typedArrayManagerProps&&Gh.setOptions(t._typedArrayManagerProps),this.animationLoop.start()}finalize(){var t,r,i,s,n,o,c,f;if((t=this.animationLoop)===null||t===void 0||t.stop(),this.animationLoop=null,this._lastPointerDownInfo=null,(r=this.layerManager)===null||r===void 0||r.finalize(),this.layerManager=null,(i=this.viewManager)===null||i===void 0||i.finalize(),this.viewManager=null,(s=this.effectManager)===null||s===void 0||s.finalize(),this.effectManager=null,(n=this.deckRenderer)===null||n===void 0||n.finalize(),this.deckRenderer=null,(o=this.deckPicker)===null||o===void 0||o.finalize(),this.deckPicker=null,(c=this.eventManager)===null||c===void 0||c.destroy(),this.eventManager=null,(f=this.tooltip)===null||f===void 0||f.remove(),this.tooltip=null,!this.props.canvas&&!this.props.gl&&this.canvas){var _;(_=this.canvas.parentElement)===null||_===void 0||_.removeChild(this.canvas),this.canvas=null}}setProps(t){this.stats.get(\"setProps Time\").timeStart(),\"onLayerHover\"in t&&or.removed(\"onLayerHover\",\"onHover\")(),\"onLayerClick\"in t&&or.removed(\"onLayerClick\",\"onClick\")(),t.initialViewState&&!mo(this.props.initialViewState,t.initialViewState,3)&&(this.viewState=t.initialViewState),Object.assign(this.props,t),this._setCanvasSize(this.props);let r=Object.create(this.props);Object.assign(r,{views:this._getViews(),width:this.width,height:this.height,viewState:this._getViewState()}),this.animationLoop.setProps(r),this.layerManager&&(this.viewManager.setProps(r),this.layerManager.activateViewport(this.getViewports()[0]),this.layerManager.setProps(r),this.effectManager.setProps(r),this.deckRenderer.setProps(r),this.deckPicker.setProps(r)),this.stats.get(\"setProps Time\").timeEnd()}needsRedraw(t={clearRedrawFlags:!1}){if(!this.layerManager)return!1;if(this.props._animate)return\"Deck._animate\";let r=this._needsRedraw;t.clearRedrawFlags&&(this._needsRedraw=!1);let i=this.viewManager.needsRedraw(t),s=this.layerManager.needsRedraw(t),n=this.effectManager.needsRedraw(t),o=this.deckRenderer.needsRedraw(t);return r=r||i||s||n||o,r}redraw(t){if(!this.layerManager)return;let r=this.needsRedraw({clearRedrawFlags:!0});r=t||r,r&&(this.stats.get(\"Redraw Count\").incrementCount(),this.props._customRender?this.props._customRender(r):this._drawLayers(r))}get isInitialized(){return this.viewManager!==null}getViews(){return _r(this.viewManager),this.viewManager.views}getViewports(t){return _r(this.viewManager),this.viewManager.getViewports(t)}getCanvas(){return this.canvas}pickObject(t){let r=this._pick(\"pickObject\",\"pickObject Time\",t).result;return r.length?r[0]:null}pickMultipleObjects(t){return t.depth=t.depth||10,this._pick(\"pickObject\",\"pickMultipleObjects Time\",t).result}pickObjects(t){return this._pick(\"pickObjects\",\"pickObjects Time\",t)}_addResources(t,r=!1){for(let i in t)this.layerManager.resourceManager.add({resourceId:i,data:t[i],forceUpdate:r})}_removeResources(t){for(let r of t)this.layerManager.resourceManager.remove(r)}_addDefaultEffect(t){this.effectManager.addDefaultEffect(t)}_pick(t,r,i){_r(this.deckPicker);let{stats:s}=this;s.get(\"Pick Count\").incrementCount(),s.get(r).timeStart();let n=this.deckPicker[t]({layers:this.layerManager.getLayers(i),views:this.viewManager.getViews(),viewports:this.getViewports(i),onViewportActive:this.layerManager.activateViewport,effects:this.effectManager.getEffects(),...i});return s.get(r).timeEnd(),n}_createCanvas(t){let r=t.canvas;return typeof r==\"string\"&&(r=document.getElementById(r),_r(r)),r||(r=document.createElement(\"canvas\"),r.id=t.id||\"deckgl-overlay\",(t.parent||document.body).appendChild(r)),Object.assign(r.style,t.style),r}_setCanvasSize(t){if(!this.canvas)return;let{width:r,height:i}=t;if(r||r===0){let n=Number.isFinite(r)?\"\".concat(r,\"px\"):r;this.canvas.style.width=n}if(i||i===0){var s;let n=Number.isFinite(i)?\"\".concat(i,\"px\"):i;this.canvas.style.position=((s=t.style)===null||s===void 0?void 0:s.position)||\"absolute\",this.canvas.style.height=n}}_updateCanvasSize(){var t,r;let{canvas:i}=this;if(!i)return;let s=(t=i.clientWidth)!==null&&t!==void 0?t:i.width,n=(r=i.clientHeight)!==null&&r!==void 0?r:i.height;if(s!==this.width||n!==this.height){var o,c;this.width=s,this.height=n,(o=this.viewManager)===null||o===void 0||o.setProps({width:s,height:n}),(c=this.layerManager)===null||c===void 0||c.activateViewport(this.getViewports()[0]),this.props.onResize({width:s,height:n})}}_createAnimationLoop(t){let{width:r,height:i,gl:s,glOptions:n,debug:o,onError:c,onBeforeRender:f,onAfterRender:_,useDevicePixels:w}=t;return new rg({width:r,height:i,useDevicePixels:w,autoResizeDrawingBuffer:!s,autoResizeViewport:!1,gl:s,onCreateContext:I=>Ty({...n,...I,canvas:this.canvas,debug:o,onContextLost:()=>this._onContextLost()}),onInitialize:I=>this._setGLContext(I.gl),onRender:this._onRenderFrame.bind(this),onBeforeRender:f,onAfterRender:_,onError:c})}_getViewState(){return this.props.viewState||this.viewState}_getViews(){let t=this.props.views||[new Xy({id:\"default-view\"})];return t=Array.isArray(t)?t:[t],t.length&&this.props.controller&&(t[0].props.controller=this.props.controller),t}_onContextLost(){let{onError:t}=this.props;this.animationLoop&&t&&t(new Error(\"WebGL context is lost\"))}_pickAndCallback(){let{_pickRequest:t}=this;if(t.event){let{result:i,emptyInfo:s}=this._pick(\"pickObject\",\"pickObject Time\",t);this.cursorState.isHovering=i.length>0;let n=s,o=!1;for(let c of i){var r;n=c,o=((r=c.layer)===null||r===void 0?void 0:r.onHover(c,t.event))||o}if(!o&&this.props.onHover&&this.props.onHover(n,t.event),this.props.getTooltip&&this.tooltip){let c=this.props.getTooltip(n);this.tooltip.setTooltip(c,n.x,n.y)}t.event=null}}_updateCursor(){let t=this.props.parent||this.canvas;t&&(t.style.cursor=this.props.getCursor(this.cursorState))}_setGLContext(t){if(this.layerManager)return;this.canvas||(this.canvas=t.canvas,q0(t,{enable:!0,copyState:!0})),this.tooltip=new Jb(this.canvas),Ml(t,{blend:!0,blendFunc:[770,771,1,771],polygonOffsetFill:!0,depthTest:!0,depthFunc:515}),this.props.onWebGLInitialized(t);let r=new KA;r.play(),this.animationLoop.attachTimeline(r),this.eventManager=new Jy(this.props.parent||t.canvas,{touchAction:this.props.touchAction,recognizerOptions:this.props.eventRecognizerOptions,events:{pointerdown:this._onPointerDown,pointermove:this._onPointerMove,pointerleave:this._onPointerMove}});for(let s in nR)this.eventManager.on(s,this._onEvent);this.viewManager=new Wb({timeline:r,eventManager:this.eventManager,onViewStateChange:this._onViewStateChange.bind(this),onInteractionStateChange:this._onInteractionStateChange.bind(this),views:this._getViews(),viewState:this._getViewState(),width:this.width,height:this.height});let i=this.viewManager.getViewports()[0];this.layerManager=new Gb(t,{deck:this,stats:this.stats,viewport:i,timeline:r}),this.effectManager=new Qb,this.deckRenderer=new Xb(t),this.deckPicker=new Kb(t),this.setProps(this.props),this._updateCanvasSize(),this.props.onLoad()}_drawLayers(t,r){let{gl:i}=this.layerManager.context;Ml(i,this.props.parameters),this.props.onBeforeRender({gl:i}),this.deckRenderer.renderLayers({target:this.props._framebuffer,layers:this.layerManager.getLayers(),viewports:this.viewManager.getViewports(),onViewportActive:this.layerManager.activateViewport,views:this.viewManager.getViews(),pass:\"screen\",effects:this.effectManager.getEffects(),...r}),this.props.onAfterRender({gl:i})}_onRenderFrame(t){this._getFrameStats(),this._metricsCounter++%60===0&&(this._getMetrics(),this.stats.reset(),or.table(4,this.metrics)(),this.props._onMetrics&&this.props._onMetrics(this.metrics)),this._updateCanvasSize(),this._updateCursor(),this.tooltip.isVisible&&this.viewManager.needsRedraw()&&this.tooltip.setTooltip(null),this.layerManager.updateLayers(),this._pickAndCallback(),this.redraw(),this.viewManager&&this.viewManager.updateViewStates()}_onViewStateChange(t){let r=this.props.onViewStateChange(t)||t.viewState;this.viewState&&(this.viewState={...this.viewState,[t.viewId]:r},this.props.viewState||this.viewManager&&this.viewManager.setProps({viewState:this.viewState}))}_onInteractionStateChange(t){this.cursorState.isDragging=t.isDragging||!1,this.props.onInteractionStateChange(t)}_getFrameStats(){let{stats:t}=this;t.get(\"frameRate\").timeEnd(),t.get(\"frameRate\").timeStart();let r=this.animationLoop.stats;t.get(\"GPU Time\").addTime(r.get(\"GPU Time\").lastTiming),t.get(\"CPU Time\").addTime(r.get(\"CPU Time\").lastTiming)}_getMetrics(){let{metrics:t,stats:r}=this;t.fps=r.get(\"frameRate\").getHz(),t.setPropsTime=r.get(\"setProps Time\").time,t.updateAttributesTime=r.get(\"Update Attributes\").time,t.framesRedrawn=r.get(\"Redraw Count\").count,t.pickTime=r.get(\"pickObject Time\").time+r.get(\"pickMultipleObjects Time\").time+r.get(\"pickObjects Time\").time,t.pickCount=r.get(\"Pick Count\").count,t.gpuTime=r.get(\"GPU Time\").time,t.cpuTime=r.get(\"CPU Time\").time,t.gpuTimePerFrame=r.get(\"GPU Time\").getAverageTime(),t.cpuTimePerFrame=r.get(\"CPU Time\").getAverageTime();let i=Du.get(\"Memory Usage\");t.bufferMemory=i.get(\"Buffer Memory\").count,t.textureMemory=i.get(\"Texture Memory\").count,t.renderbufferMemory=i.get(\"Renderbuffer Memory\").count,t.gpuMemory=i.get(\"GPU Memory\").count}};G(cp,\"defaultProps\",qG);G(cp,\"VERSION\",xV);var gg=class{constructor(t,r){G(this,\"opts\",void 0),G(this,\"source\",void 0),this.opts=r,this.source=t}get value(){return this.source.value}getValue(){let t=this.source.getBuffer(),r=this.getAccessor();if(t)return[t,r];let{value:i}=this.source,{size:s}=r,n=i;if(i&&i.length!==s){n=new Float32Array(s);let o=r.elementOffset||0;for(let c=0;c=n){let o=new Array(s).fill(1/0),c=new Array(s).fill(-1/0);for(let f=0;fc[_]&&(c[_]=w)}t=[o,c]}}return this.state.bounds=t,t}setData(t){let{state:r}=this,i;ArrayBuffer.isView(t)?i={value:t}:t instanceof Fr?i={buffer:t}:i=t;let s={...this.settings,...i};if(r.bufferAccessor=s,r.bounds=null,i.constant){let n=i.value;if(n=this._normalizeValue(n,[],0),this.settings.normalized&&(n=this.normalizeConstant(n)),!(!r.constant||!this._areValuesEqual(n,this.value)))return!1;r.externalBuffer=null,r.constant=!0,this.value=n}else if(i.buffer){let n=i.buffer;r.externalBuffer=n,r.constant=!1,this.value=i.value||null;let o=i.value instanceof Float64Array;s.type=i.type||n.accessor.type,s.bytesPerElement=n.accessor.BYTES_PER_ELEMENT*(o?2:1),s.stride=lP(s)}else if(i.value){this._checkExternalBuffer(i);let n=i.value;r.externalBuffer=null,r.constant=!1,this.value=n,s.bytesPerElement=n.BYTES_PER_ELEMENT,s.stride=lP(s);let{buffer:o,byteOffset:c}=this;this.doublePrecision&&n instanceof Float64Array&&(n=iP(n,s));let f=n.byteLength+c+s.stride*2;o.byteLength(r+128)/255*2-1);case 5122:return new Float32Array(t).map(r=>(r+32768)/65535*2-1);case 5121:return new Float32Array(t).map(r=>r/255);case 5123:return new Float32Array(t).map(r=>r/65535);default:return t}}_normalizeValue(t,r,i){let{defaultValue:s,size:n}=this.settings;if(Number.isFinite(t))return r[i]=t,r;if(!t){let o=n;for(;--o>=0;)r[i+o]=s[o];return r}switch(n){case 4:r[i+3]=Number.isFinite(t[3])?t[3]:s[3];case 3:r[i+2]=Number.isFinite(t[2])?t[2]:s[2];case 2:r[i+1]=Number.isFinite(t[1])?t[1]:s[1];case 1:r[i+0]=Number.isFinite(t[0])?t[0]:s[0];break;default:let o=n;for(;--o>=0;)r[i+o]=Number.isFinite(t[o])?t[o]:s[o]}return r}_areValuesEqual(t,r){if(!t||!r)return!1;let{size:i}=this;for(let s=0;s0&&($G.length=e.length,i=$G):i=QG,(t>0||Number.isFinite(r))&&(i=(Array.isArray(i)?i:Array.from(i)).slice(t,r),s.index=t-1),{iterable:i,objectInfo:s}}function cP(e){return e&&e[Symbol.asyncIterator]}function uP(e,t){let{size:r,stride:i,offset:s,startIndices:n,nested:o}=t,c=e.BYTES_PER_ELEMENT,f=i?i/c:r,_=s?s/c:0,w=Math.floor((e.length-_)/f);return(I,{index:R,target:N})=>{if(!n){let Y=R*f+_;for(let K=0;K=t[1]))return e;let r=[],i=e.length,s=0;for(let n=0;nt[1]?r.push(o):t=[Math.min(o[0],t[0]),Math.max(o[1],t[1])]}return r.splice(s,0,t),r}function ID(e){let{source:t,target:r,start:i=0,size:s,getData:n}=e,o=e.end||r.length,c=t.length,f=o-i;if(c>f){r.set(t.subarray(0,f),i);return}if(r.set(t,i),!n)return;let _=c;for(;_i(w+c,I)),_=Math.min(s.length,n.length);for(let w=1;w<_;w++){let I=s[w]*r,R=n[w]*r;ID({source:e.subarray(o,I),target:t,start:c,end:R,size:r,getData:f}),o=I,c=R}return ce},spring:{stiffness:.05,damping:.5}};function hP(e,t){if(!e)return null;Number.isFinite(e)&&(e={type:\"interpolation\",duration:e});let r=e.type||\"interpolation\";return{...Sct[r],...t,...e,type:r}}function fP(e,t){let r=t.getBuffer();return r?[r,{divisor:0,size:t.size,normalized:t.settings.normalized}]:t.value}function dP(e){switch(e){case 1:return\"float\";case 2:return\"vec2\";case 3:return\"vec3\";case 4:return\"vec4\";default:throw new Error('No defined attribute type for size \"'.concat(e,'\"'))}}function pP(e){e.push(e.shift())}function aw(e,t){let{doublePrecision:r,settings:i,value:s,size:n}=e,o=r&&s instanceof Float64Array?2:1;return(i.noAlloc?s.length:t*n)*o}function AP({buffer:e,numInstances:t,attribute:r,fromLength:i,fromStartIndices:s,getData:n=o=>o}){let o=r.doublePrecision&&r.value instanceof Float64Array?2:1,c=r.size*o,f=r.byteOffset,_=r.startIndices,w=s&&_,I=aw(r,t),R=r.isConstant;if(!w&&i>=I)return;let N=R?r.value:r.getBuffer().getData({srcByteOffset:f});if(r.settings.normalized&&!R){let Y=n;n=(K,J)=>r.normalizeConstant(Y(K,J))}let j=R?(Y,K)=>n(N,K):(Y,K)=>n(N.subarray(Y,Y+c),K),Q=e.getData({length:i}),et=new Float32Array(I);JG({source:Q,target:et,sourceStartIndices:s,targetStartIndices:_,size:c,getData:j}),e.byteLengtht[n])]:t[r];return hP(s,i)}setNeedsUpdate(t=this.id,r){if(this.state.needsUpdate=this.state.needsUpdate||t,this.setNeedsRedraw(t),r){let{startRow:i=0,endRow:s=1/0}=r;this.state.updateRanges=KG(this.state.updateRanges,[i,s])}else this.state.updateRanges=ow}clearNeedsUpdate(){this.state.needsUpdate=!1,this.state.updateRanges=XG}setNeedsRedraw(t=this.id){this.state.needsRedraw=this.state.needsRedraw||t}allocate(t){let{state:r,settings:i}=this;return i.noAlloc?!1:i.update?(super.allocate(t,r.updateRanges!==ow),!0):!1}updateBuffer({numInstances:t,data:r,props:i,context:s}){if(!this.needsUpdate())return!1;let{state:{updateRanges:n},settings:{update:o,noAlloc:c}}=this,f=!0;if(o){for(let[_,w]of n)o.call(s,this,{data:r,startRow:_,endRow:w,props:i,numInstances:t});if(this.value)if(this.constant||this.buffer.byteLengthw?_.set(J,Q):(t._normalizeValue(J,Y.target,0),xD({target:_,source:Y.target,start:Q,count:ut}));Q+=ut*w}else t._normalizeValue(J,_,Q),Q+=w}}_validateAttributeUpdaters(){let{settings:t}=this;if(!(t.noAlloc||typeof t.update==\"function\"))throw new Error(\"Attribute \".concat(this.id,\" missing update or accessor\"))}_checkAttributeArray(){let{value:t}=this,r=Math.min(4,this.size);if(t&&t.length>=r){let i=!0;switch(r){case 4:i=i&&Number.isFinite(t[3]);case 3:i=i&&Number.isFinite(t[2]);case 2:i=i&&Number.isFinite(t[1]);case 1:i=i&&Number.isFinite(t[0]);break;default:i=!1}if(!i)throw new Error(\"Illegal attribute generated for \".concat(this.id))}}};var lw=class{constructor({gl:t,attribute:r,timeline:i}){G(this,\"gl\",void 0),G(this,\"type\",\"interpolation\"),G(this,\"attributeInTransition\",void 0),G(this,\"settings\",void 0),G(this,\"attribute\",void 0),G(this,\"transition\",void 0),G(this,\"currentStartIndices\",void 0),G(this,\"currentLength\",void 0),G(this,\"transform\",void 0),G(this,\"buffers\",void 0),this.gl=t,this.transition=new Kc(i),this.attribute=r,this.attributeInTransition=new up(t,r.settings),this.currentStartIndices=r.startIndices,this.currentLength=0,this.transform=Mct(t,r);let s={byteLength:0,usage:35050};this.buffers=[new Fr(t,s),new Fr(t,s)]}get inProgress(){return this.transition.inProgress}start(t,r){if(t.duration<=0){this.transition.cancel();return}this.settings=t;let{gl:i,buffers:s,attribute:n}=this;pP(s);let o={numInstances:r,attribute:n,fromLength:this.currentLength,fromStartIndices:this.currentStartIndices,getData:t.enter};for(let c of s)AP({buffer:c,...o});this.currentStartIndices=n.startIndices,this.currentLength=aw(n,r),this.attributeInTransition.setData({buffer:s[1],value:n.value}),this.transition.start(t),this.transform.update({elementCount:Math.floor(this.currentLength/n.size),sourceBuffers:{aFrom:s[0],aTo:fP(i,n)},feedbackBuffers:{vCurrent:s[1]}})}update(){let t=this.transition.update();if(t){let{duration:r,easing:i}=this.settings,{time:s}=this.transition,n=s/r;i&&(n=i(n)),this.transform.run({uniforms:{time:n}})}return t}cancel(){this.transition.cancel(),this.transform.delete();for(let t of this.buffers)t.delete();this.buffers.length=0}},Tct=`\n#define SHADER_NAME interpolation-transition-vertex-shader\n\nuniform float time;\nattribute ATTRIBUTE_TYPE aFrom;\nattribute ATTRIBUTE_TYPE aTo;\nvarying ATTRIBUTE_TYPE vCurrent;\n\nvoid main(void) {\n vCurrent = mix(aFrom, aTo, time);\n gl_Position = vec4(0.0);\n}\n`;function Mct(e,t){let r=dP(t.size);return new nc(e,{vs:Tct,defines:{ATTRIBUTE_TYPE:r},varyings:[\"vCurrent\"]})}var cw=class{constructor({gl:t,attribute:r,timeline:i}){G(this,\"gl\",void 0),G(this,\"type\",\"spring\"),G(this,\"attributeInTransition\",void 0),G(this,\"settings\",void 0),G(this,\"attribute\",void 0),G(this,\"transition\",void 0),G(this,\"currentStartIndices\",void 0),G(this,\"currentLength\",void 0),G(this,\"texture\",void 0),G(this,\"framebuffer\",void 0),G(this,\"transform\",void 0),G(this,\"buffers\",void 0),this.gl=t,this.type=\"spring\",this.transition=new Kc(i),this.attribute=r,this.attributeInTransition=new up(t,{...r.settings,normalized:!1}),this.currentStartIndices=r.startIndices,this.currentLength=0,this.texture=Pct(t),this.framebuffer=Ict(t,this.texture),this.transform=Ect(t,r,this.framebuffer);let s={byteLength:0,usage:35050};this.buffers=[new Fr(t,s),new Fr(t,s),new Fr(t,s)]}get inProgress(){return this.transition.inProgress}start(t,r){let{gl:i,buffers:s,attribute:n}=this,o={numInstances:r,attribute:n,fromLength:this.currentLength,fromStartIndices:this.currentStartIndices,getData:t.enter};for(let c of s)AP({buffer:c,...o});this.settings=t,this.currentStartIndices=n.startIndices,this.currentLength=aw(n,r),this.attributeInTransition.setData({buffer:s[1],value:n.value}),this.transition.start({...t,duration:1/0}),this.transform.update({elementCount:Math.floor(this.currentLength/n.size),sourceBuffers:{aTo:fP(i,n)}})}update(){let{buffers:t,transform:r,framebuffer:i,transition:s}=this;if(!s.update())return!1;let o=this.settings;return r.update({sourceBuffers:{aPrev:t[0],aCur:t[1]},feedbackBuffers:{vNext:t[2]}}),r.run({framebuffer:i,discard:!1,clearRenderTarget:!0,uniforms:{stiffness:o.stiffness,damping:o.damping},parameters:{depthTest:!1,blend:!0,viewport:[0,0,1,1],blendFunc:[1,1],blendEquation:[32776,32776]}}),pP(t),this.attributeInTransition.setData({buffer:t[1],value:this.attribute.value}),Dh(i)[0]>0||s.end(),!0}cancel(){this.transition.cancel(),this.transform.delete();for(let t of this.buffers)t.delete();this.buffers.length=0,this.texture.delete(),this.framebuffer.delete()}};function Ect(e,t,r){let i=dP(t.size);return new nc(e,{framebuffer:r,vs:`\n#define SHADER_NAME spring-transition-vertex-shader\n\n#define EPSILON 0.00001\n\nuniform float stiffness;\nuniform float damping;\nattribute ATTRIBUTE_TYPE aPrev;\nattribute ATTRIBUTE_TYPE aCur;\nattribute ATTRIBUTE_TYPE aTo;\nvarying ATTRIBUTE_TYPE vNext;\nvarying float vIsTransitioningFlag;\n\nATTRIBUTE_TYPE getNextValue(ATTRIBUTE_TYPE cur, ATTRIBUTE_TYPE prev, ATTRIBUTE_TYPE dest) {\n ATTRIBUTE_TYPE velocity = cur - prev;\n ATTRIBUTE_TYPE delta = dest - cur;\n ATTRIBUTE_TYPE spring = delta * stiffness;\n ATTRIBUTE_TYPE damper = velocity * -1.0 * damping;\n return spring + damper + velocity + cur;\n}\n\nvoid main(void) {\n bool isTransitioning = length(aCur - aPrev) > EPSILON || length(aTo - aCur) > EPSILON;\n vIsTransitioningFlag = isTransitioning ? 1.0 : 0.0;\n\n vNext = getNextValue(aCur, aPrev, aTo);\n gl_Position = vec4(0, 0, 0, 1);\n gl_PointSize = 100.0;\n}\n`,fs:`\n#define SHADER_NAME spring-transition-is-transitioning-fragment-shader\n\nvarying float vIsTransitioningFlag;\n\nvoid main(void) {\n if (vIsTransitioningFlag == 0.0) {\n discard;\n }\n gl_FragColor = vec4(1.0);\n}`,defines:{ATTRIBUTE_TYPE:i},varyings:[\"vNext\"]})}function Pct(e){return new pi(e,{data:new Uint8Array(4),format:6408,type:5121,border:0,mipmaps:!1,dataFormat:6408,width:1,height:1})}function Ict(e,t){return new yi(e,{id:\"spring-transition-is-transitioning-framebuffer\",width:1,height:1,attachments:{36064:t}})}var Cct={interpolation:lw,spring:cw},uw=class{constructor(t,{id:r,timeline:i}){G(this,\"id\",void 0),G(this,\"isSupported\",void 0),G(this,\"gl\",void 0),G(this,\"timeline\",void 0),G(this,\"transitions\",void 0),G(this,\"needsRedraw\",void 0),G(this,\"numInstances\",void 0),this.id=r,this.gl=t,this.timeline=i,this.transitions={},this.needsRedraw=!1,this.numInstances=1,this.isSupported=nc.isSupported(t)}finalize(){for(let t in this.transitions)this._removeTransition(t)}update({attributes:t,transitions:r,numInstances:i}){this.numInstances=i||1;for(let s in t){let n=t[s],o=n.getTransitionSetting(r);o&&this._updateAttribute(s,n,o)}for(let s in this.transitions){let n=t[s];(!n||!n.getTransitionSetting(r))&&this._removeTransition(s)}}hasAttribute(t){let r=this.transitions[t];return r&&r.inProgress}getAttributes(){let t={};for(let r in this.transitions){let i=this.transitions[r];i.inProgress&&(t[r]=i.attributeInTransition)}return t}run(){if(!this.isSupported||this.numInstances===0)return!1;for(let r in this.transitions)this.transitions[r].update()&&(this.needsRedraw=!0);let t=this.needsRedraw;return this.needsRedraw=!1,t}_removeTransition(t){this.transitions[t].cancel(),delete this.transitions[t]}_updateAttribute(t,r,i){let s=this.transitions[t],n=!s||s.type!==i.type;if(n){if(!this.isSupported){or.warn(\"WebGL2 not supported by this browser. Transition for \".concat(t,\" is disabled.\"))();return}s&&this._removeTransition(t);let o=Cct[i.type];o?this.transitions[t]=new o({attribute:r,timeline:this.timeline,gl:this.gl}):(or.error(\"unsupported transition type '\".concat(i.type,\"'\"))(),n=!1)}(n||r.needsRedraw())&&(this.needsRedraw=!0,this.transitions[t].start(i,this.numInstances))}};var t9=\"attributeManager.invalidate\",Lct=\"attributeManager.updateStart\",kct=\"attributeManager.updateEnd\",Rct=\"attribute.updateStart\",Dct=\"attribute.allocate\",Oct=\"attribute.updateEnd\",Xf=class{constructor(t,{id:r=\"attribute-manager\",stats:i,timeline:s}={}){G(this,\"id\",void 0),G(this,\"gl\",void 0),G(this,\"attributes\",void 0),G(this,\"updateTriggers\",void 0),G(this,\"needsRedraw\",void 0),G(this,\"userData\",void 0),G(this,\"stats\",void 0),G(this,\"attributeTransitionManager\",void 0),G(this,\"mergeBoundsMemoized\",Yf(dG)),this.id=r,this.gl=t,this.attributes={},this.updateTriggers={},this.needsRedraw=!0,this.userData={},this.stats=i,this.attributeTransitionManager=new uw(t,{id:\"\".concat(r,\"-transitions\"),timeline:s}),Object.seal(this)}finalize(){for(let t in this.attributes)this.attributes[t].delete();this.attributeTransitionManager.finalize()}getNeedsRedraw(t={clearRedrawFlags:!1}){let r=this.needsRedraw;return this.needsRedraw=this.needsRedraw&&!t.clearRedrawFlags,r&&this.id}setNeedsRedraw(){this.needsRedraw=!0}add(t){this._add(t)}addInstanced(t){this._add(t,{instanced:1})}remove(t){for(let r of t)this.attributes[r]!==void 0&&(this.attributes[r].delete(),delete this.attributes[r])}invalidate(t,r){let i=this._invalidateTrigger(t,r);Ls(t9,this,t,i)}invalidateAll(t){for(let r in this.attributes)this.attributes[r].setNeedsUpdate(r,t);Ls(t9,this,\"all\")}update({data:t,numInstances:r,startIndices:i=null,transitions:s,props:n={},buffers:o={},context:c={}}){let f=!1;Ls(Lct,this),this.stats&&this.stats.get(\"Update Attributes\").timeStart();for(let _ in this.attributes){let w=this.attributes[_],I=w.settings.accessor;w.startIndices=i,w.numInstances=r,n[_]&&or.removed(\"props.\".concat(_),\"data.attributes.\".concat(_))(),w.setExternalBuffer(o[_])||w.setBinaryValue(typeof I==\"string\"?o[I]:void 0,t.startIndices)||typeof I==\"string\"&&!o[I]&&w.setConstantValue(n[I])||w.needsUpdate()&&(f=!0,this._updateAttribute({attribute:w,numInstances:r,data:t,props:n,context:c})),this.needsRedraw=this.needsRedraw||w.needsRedraw()}f&&Ls(kct,this,r),this.stats&&this.stats.get(\"Update Attributes\").timeEnd(),this.attributeTransitionManager.update({attributes:this.attributes,numInstances:r,transitions:s})}updateTransition(){let{attributeTransitionManager:t}=this,r=t.run();return this.needsRedraw=this.needsRedraw||r,r}getAttributes(){return this.attributes}getBounds(t){let r=t.map(i=>{var s;return(s=this.attributes[i])===null||s===void 0?void 0:s.getBounds()});return this.mergeBoundsMemoized(r)}getChangedAttributes(t={clearChangedFlags:!1}){let{attributes:r,attributeTransitionManager:i}=this,s={...i.getAttributes()};for(let n in r){let o=r[n];o.needsRedraw(t)&&!i.hasAttribute(n)&&(s[n]=o)}return s}getShaderAttributes(t,r={}){t||(t=this.getAttributes());let i={};for(let s in t)r[s]||Object.assign(i,t[s].getShaderAttributes());return i}_add(t,r={}){for(let i in t){let s=t[i];this.attributes[i]=this._createAttribute(i,s,r)}this._mapUpdateTriggersToAttributes()}_createAttribute(t,r,i){let s={...r,id:t,size:r.isIndexed&&1||r.size||1,divisor:i.instanced?1:r.divisor||0};return new up(this.gl,s)}_mapUpdateTriggersToAttributes(){let t={};for(let r in this.attributes)this.attributes[r].getUpdateTriggers().forEach(s=>{t[s]||(t[s]=[]),t[s].push(r)});this.updateTriggers=t}_invalidateTrigger(t,r){let{attributes:i,updateTriggers:s}=this,n=s[t];return n&&n.forEach(o=>{let c=i[o];c&&c.setNeedsUpdate(c.id,r)}),n}_updateAttribute(t){let{attribute:r,numInstances:i}=t;if(Ls(Rct,r),r.constant){r.setConstantValue(r.value);return}r.allocate(i)&&Ls(Dct,r,i),r.updateBuffer(t)&&(this.needsRedraw=!0,Ls(Oct,r,i))}};var hw=class extends Kc{get value(){return this._value}_onUpdate(){let{time:t,settings:{fromValue:r,toValue:i,duration:s,easing:n}}=this,o=n(t/s);this._value=il(r,i,o)}};var e9=1e-5;function r9(e,t,r,i,s){let n=t-e,c=(r-t)*s,f=-n*i;return c+f+n+t}function Bct(e,t,r,i,s){if(Array.isArray(r)){let n=[];for(let o=0;o0}add(t,r,i,s){let{transitions:n}=this;if(n.has(t)){let f=n.get(t),{value:_=f.settings.fromValue}=f;r=_,this.remove(t)}if(s=hP(s),!s)return;let o=Fct[s.type];if(!o){or.error(\"unsupported transition type '\".concat(s.type,\"'\"))();return}let c=new o(this.timeline);c.start({...s,fromValue:r,toValue:i}),n.set(t,c)}remove(t){let{transitions:r}=this;r.has(t)&&(r.get(t).cancel(),r.delete(t))}update(){let t={};for(let[r,i]of this.transitions)i.update(),t[r]=i.value,i.inProgress||this.remove(r);return t}clear(){for(let t of this.transitions.keys())this.remove(t)}};function s9(e){let t=e[zu];for(let r in t){let i=t[r],{validate:s}=i;if(s&&!s(e[r],i))throw new Error(\"Invalid prop \".concat(r,\": \").concat(e[r]))}}function o9(e,t){let r=pw({newProps:e,oldProps:t,propTypes:e[zu],ignoreProps:{data:null,updateTriggers:null,extensions:null,transitions:null}}),i=Nct(e,t),s=!1;return i||(s=Uct(e,t)),{dataChanged:i,propsChanged:r,updateTriggersChanged:s,extensionsChanged:Vct(e,t),transitionsChanged:zct(e,t)}}function zct(e,t){if(!e.transitions)return!1;let r={},i=e[zu],s=!1;for(let n in e.transitions){let o=i[n],c=o&&o.type;(c===\"number\"||c===\"color\"||c===\"array\")&&CD(e[n],t[n],o)&&(r[n]=!0,s=!0)}return s?r:!1}function pw({newProps:e,oldProps:t,ignoreProps:r={},propTypes:i={},triggerName:s=\"props\"}){if(t===e)return!1;if(typeof e!=\"object\"||e===null||typeof t!=\"object\"||t===null)return\"\".concat(s,\" changed shallowly\");for(let n of Object.keys(e))if(!(n in r)){if(!(n in t))return\"\".concat(s,\".\").concat(n,\" added\");let o=CD(e[n],t[n],i[n]);if(o)return\"\".concat(s,\".\").concat(n,\" \").concat(o)}for(let n of Object.keys(t))if(!(n in r)){if(!(n in e))return\"\".concat(s,\".\").concat(n,\" dropped\");if(!Object.hasOwnProperty.call(e,n)){let o=CD(e[n],t[n],i[n]);if(o)return\"\".concat(s,\".\").concat(n,\" \").concat(o)}}return!1}function CD(e,t,r){let i=r&&r.equal;return i&&!i(e,t,r)||!i&&(i=e&&t&&e.equals,i&&!i.call(e,t))?\"changed deeply\":!i&&t!==e?\"changed shallowly\":null}function Nct(e,t){if(t===null)return\"oldProps is null, initial diff\";let r=!1,{dataComparator:i,_dataDiff:s}=e;return i?i(e.data,t.data)||(r=\"Data comparator detected a change\"):e.data!==t.data&&(r=\"A new data container was supplied\"),r&&s&&(r=s(e.data,t.data)||r),r}function Uct(e,t){if(t===null)return{all:!0};if(\"all\"in e.updateTriggers&&n9(e,t,\"all\"))return{all:!0};let r={},i=!1;for(let s in 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n=0;n{},this.oldProps=null,this.oldAsyncProps=null}finalize(){for(let t in this.asyncProps){let r=this.asyncProps[t];r&&r.type&&r.type.release&&r.type.release(r.resolvedValue,r.type,this.component)}this.asyncProps={},this.component=null,this.resetOldProps()}getOldProps(){return this.oldAsyncProps||this.oldProps||iut}resetOldProps(){this.oldAsyncProps=null,this.oldProps=this.component?this.component.props:null}hasAsyncProp(t){return t in this.asyncProps}getAsyncProp(t){let r=this.asyncProps[t];return r&&r.resolvedValue}isAsyncPropLoading(t){if(t){let r=this.asyncProps[t];return!!(r&&r.pendingLoadCount>0&&r.pendingLoadCount!==r.resolvedLoadCount)}for(let r in this.asyncProps)if(this.isAsyncPropLoading(r))return!0;return!1}reloadAsyncProp(t,r){this._watchPromise(t,Promise.resolve(r))}setAsyncProps(t){this.component=t[Qy]||this.component;let r=t[Wh]||{},i=t[$f]||t,s=t[sp]||{};for(let n in r){let o=r[n];this._createAsyncPropData(n,s[n]),this._updateAsyncProp(n,o),r[n]=this.getAsyncProp(n)}for(let n in i){let o=i[n];this._createAsyncPropData(n,s[n]),this._updateAsyncProp(n,o)}}_fetch(t,r){return null}_onResolve(t,r){}_onError(t,r){}_updateAsyncProp(t,r){if(this._didAsyncInputValueChange(t,r)){if(typeof r==\"string\"&&(r=this._fetch(t,r)),r instanceof Promise){this._watchPromise(t,r);return}if(cP(r)){this._resolveAsyncIterable(t,r);return}this._setPropValue(t,r)}}_freezeAsyncOldProps(){if(!this.oldAsyncProps&&this.oldProps){this.oldAsyncProps=Object.create(this.oldProps);for(let t in this.asyncProps)Object.defineProperty(this.oldAsyncProps,t,{enumerable:!0,value:this.oldProps[t]})}}_didAsyncInputValueChange(t,r){let i=this.asyncProps[t];return r===i.resolvedValue||r===i.lastValue?!1:(i.lastValue=r,!0)}_setPropValue(t,r){this._freezeAsyncOldProps();let i=this.asyncProps[t];i&&(r=this._postProcessValue(i,r),i.resolvedValue=r,i.pendingLoadCount++,i.resolvedLoadCount=i.pendingLoadCount)}_setAsyncPropValue(t,r,i){let s=this.asyncProps[t];s&&i>=s.resolvedLoadCount&&r!==void 0&&(this._freezeAsyncOldProps(),s.resolvedValue=r,s.resolvedLoadCount=i,this.onAsyncPropUpdated(t,r))}_watchPromise(t,r){let i=this.asyncProps[t];if(i){i.pendingLoadCount++;let s=i.pendingLoadCount;r.then(n=>{this.component&&(n=this._postProcessValue(i,n),this._setAsyncPropValue(t,n,s),this._onResolve(t,n))}).catch(n=>{this._onError(t,n)})}}async _resolveAsyncIterable(t,r){if(t!==\"data\"){this._setPropValue(t,r);return}let i=this.asyncProps[t];if(!i)return;i.pendingLoadCount++;let s=i.pendingLoadCount,n=[],o=0;for await(let c of r){if(!this.component)return;let{dataTransform:f}=this.component.props;f?n=f(c,n):n=n.concat(c),Object.defineProperty(n,\"__diff\",{enumerable:!1,value:[{startRow:o,endRow:n.length}]}),o=n.length,this._setAsyncPropValue(t,n,s)}this._onResolve(t,n)}_postProcessValue(t,r){let i=t.type;return i&&this.component&&(i.release&&i.release(t.resolvedValue,i,this.component),i.transform)?i.transform(r,i,this.component):r}_createAsyncPropData(t,r){if(!this.asyncProps[t]){let s=this.component&&this.component.props[zu];this.asyncProps[t]={type:s&&s[t],lastValue:null,resolvedValue:r,pendingLoadCount:0,resolvedLoadCount:0}}}};var gw=class extends mw{constructor({attributeManager:t,layer:r}){super(r),G(this,\"attributeManager\",void 0),G(this,\"needsRedraw\",void 0),G(this,\"needsUpdate\",void 0),G(this,\"subLayers\",void 0),G(this,\"usesPickingColorCache\",void 0),G(this,\"hasPickingBuffer\",void 0),G(this,\"changeFlags\",void 0),G(this,\"viewport\",void 0),G(this,\"uniformTransitions\",void 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extends _g{constructor(...t){super(...t),G(this,\"internalState\",null),G(this,\"lifecycle\",tm.NO_STATE),G(this,\"context\",void 0),G(this,\"state\",void 0),G(this,\"parent\",null)}static get componentName(){return Object.prototype.hasOwnProperty.call(this,\"layerName\")?this.layerName:\"\"}get root(){let t=this;for(;t.parent;)t=t.parent;return t}toString(){let t=this.constructor.layerName||this.constructor.name;return\"\".concat(t,\"({id: '\").concat(this.props.id,\"'})\")}project(t){_r(this.internalState);let r=this.internalState.viewport||this.context.viewport,i=vD(t,{viewport:r,modelMatrix:this.props.modelMatrix,coordinateOrigin:this.props.coordinateOrigin,coordinateSystem:this.props.coordinateSystem}),[s,n,o]=Hy(i,r.pixelProjectionMatrix);return t.length===2?[s,n]:[s,n,o]}unproject(t){return _r(this.internalState),(this.internalState.viewport||this.context.viewport).unproject(t)}projectPosition(t,r){_r(this.internalState);let i=this.internalState.viewport||this.context.viewport;return mG(t,{viewport:i,modelMatrix:this.props.modelMatrix,coordinateOrigin:this.props.coordinateOrigin,coordinateSystem:this.props.coordinateSystem,...r})}get isComposite(){return!1}setState(t){this.setChangeFlags({stateChanged:!0}),Object.assign(this.state,t),this.setNeedsRedraw()}setNeedsRedraw(){this.internalState&&(this.internalState.needsRedraw=!0)}setNeedsUpdate(){this.internalState&&(this.context.layerManager.setNeedsUpdate(String(this)),this.internalState.needsUpdate=!0)}get isLoaded(){return this.internalState?!this.internalState.isAsyncPropLoading():!1}get wrapLongitude(){return this.props.wrapLongitude}isPickable(){return this.props.pickable&&this.props.visible}getModels(){return this.state&&(this.state.models||this.state.model&&[this.state.model])||[]}setModuleParameters(t){for(let r of this.getModels())r.updateModuleSettings(t)}getAttributeManager(){return this.internalState&&this.internalState.attributeManager}getCurrentLayer(){return this.internalState&&this.internalState.layer}getLoadOptions(){return this.props.loadOptions}use64bitPositions(){let{coordinateSystem:t}=this.props;return t===Yr.DEFAULT||t===Yr.LNGLAT||t===Yr.CARTESIAN}onHover(t,r){return this.props.onHover&&this.props.onHover(t,r)||!1}onClick(t,r){return this.props.onClick&&this.props.onClick(t,r)||!1}nullPickingColor(){return[0,0,0]}encodePickingColor(t,r=[]){return r[0]=t+1&255,r[1]=t+1>>8&255,r[2]=t+1>>8>>8&255,r}decodePickingColor(t){_r(t instanceof Uint8Array);let[r,i,s]=t;return r+i*256+s*65536-1}getNumInstances(){return Number.isFinite(this.props.numInstances)?this.props.numInstances:this.state&&this.state.numInstances!==void 0?this.state.numInstances:a9(this.props.data)}getStartIndices(){return this.props.startIndices?this.props.startIndices:this.state&&this.state.startIndices?this.state.startIndices:null}getBounds(){var t;return(t=this.getAttributeManager())===null||t===void 0?void 0:t.getBounds([\"positions\",\"instancePositions\"])}getShaders(t){for(let r of this.props.extensions)t=tv(t,r.getShaders.call(this,r));return t}shouldUpdateState(t){return t.changeFlags.propsOrDataChanged}updateState(t){let r=this.getAttributeManager(),{dataChanged:i}=t.changeFlags;if(i&&r)if(Array.isArray(i))for(let s of i)r.invalidateAll(s);else r.invalidateAll();if(r){let{props:s}=t,n=this.internalState.hasPickingBuffer,o=Number.isInteger(s.highlightedObjectIndex)||s.pickable||s.extensions.some(c=>c.getNeedsPickingBuffer.call(this,c));if(n!==o){this.internalState.hasPickingBuffer=o;let{pickingColors:c,instancePickingColors:f}=r.attributes,_=c||f;_&&(o&&_.constant&&(_.constant=!1,r.invalidate(_.id)),!_.value&&!o&&(_.constant=!0,_.value=[0,0,0]))}}}finalizeState(t){for(let i of this.getModels())i.delete();let r=this.getAttributeManager();r&&r.finalize(),this.context&&this.context.resourceManager.unsubscribe({consumerId:this.id}),this.internalState&&(this.internalState.uniformTransitions.clear(),this.internalState.finalize())}draw(t){for(let r of this.getModels())r.draw(t)}getPickingInfo({info:t,mode:r,sourceLayer:i}){let{index:s}=t;return s>=0&&Array.isArray(this.props.data)&&(t.object=this.props.data[s]),t}raiseError(t,r){var i,s;if(r&&(t=new Error(\"\".concat(r,\": \").concat(t.message),{cause:t})),!((i=(s=this.props).onError)!==null&&i!==void 0&&i.call(s,t))){var n,o;(n=this.context)===null||n===void 0||(o=n.onError)===null||o===void 0||o.call(n,t,this)}}getNeedsRedraw(t={clearRedrawFlags:!1}){return this._getNeedsRedraw(t)}needsUpdate(){return this.internalState?this.internalState.needsUpdate||this.hasUniformTransition()||this.shouldUpdateState(this._getUpdateParams()):!1}hasUniformTransition(){var t;return((t=this.internalState)===null||t===void 0?void 0:t.uniformTransitions.active)||!1}activateViewport(t){if(!this.internalState)return;let r=this.internalState.viewport;this.internalState.viewport=t,(!r||!uut({oldViewport:r,viewport:t}))&&(this.setChangeFlags({viewportChanged:!0}),this.isComposite?this.needsUpdate()&&this.setNeedsUpdate():this._update())}invalidateAttribute(t=\"all\"){let r=this.getAttributeManager();r&&(t===\"all\"?r.invalidateAll():r.invalidate(t))}updateAttributes(t){for(let r of this.getModels())this._setModelAttributes(r,t)}_updateAttributes(){let t=this.getAttributeManager();if(!t)return;let r=this.props,i=this.getNumInstances(),s=this.getStartIndices();t.update({data:r.data,numInstances:i,startIndices:s,props:r,transitions:r.transitions,buffers:r.data.attributes,context:this});let n=t.getChangedAttributes({clearChangedFlags:!0});this.updateAttributes(n)}_updateAttributeTransition(){let t=this.getAttributeManager();t&&t.updateTransition()}_updateUniformTransition(){let{uniformTransitions:t}=this.internalState;if(t.active){let r=t.update(),i=Object.create(this.props);for(let s in r)Object.defineProperty(i,s,{value:r[s]});return i}return this.props}calculateInstancePickingColors(t,{numInstances:r}){if(t.constant)return;let i=Math.floor(Kf.length/3);if(this.internalState.usesPickingColorCache=!0,id9&&or.warn(\"Layer has too many data objects. Picking might not be able to distinguish all objects.\")(),Kf=Gh.allocate(Kf,r,{size:3,copy:!0,maxCount:Math.max(r,d9)});let s=Math.floor(Kf.length/3),n=[];for(let o=i;o(or.deprecated(\"layer.state.attributeManager\",\"layer.getAttributeManager()\")(),t)}),this.internalState.uniformTransitions=new dw(this.context.timeline),this.internalState.onAsyncPropUpdated=this._onAsyncPropUpdated.bind(this),this.internalState.setAsyncProps(this.props),this.initializeState(this.context);for(let r of this.props.extensions)r.initializeState.call(this,this.context,r);this.setChangeFlags({dataChanged:\"init\",propsChanged:\"init\",viewportChanged:!0,extensionsChanged:!0}),this._update()}_transferState(t){Ls(lut,this,this===t);let{state:r,internalState:i}=t;this!==t&&(this.internalState=i,this.state=r,this.internalState.setAsyncProps(this.props),this._diffProps(this.props,this.internalState.getOldProps()))}_update(){let t=this.needsUpdate();if(Ls(out,this,t),!t)return;let r=this.props,i=this.context,s=this.internalState,n=i.viewport,o=this._updateUniformTransition();s.propsInTransition=o,i.viewport=s.viewport||n,this.props=o;try{let c=this._getUpdateParams(),f=this.getModels();if(i.gl)this.updateState(c);else try{this.updateState(c)}catch{}for(let w of this.props.extensions)w.updateState.call(this,c,w);let _=this.getModels()[0]!==f[0];this._postUpdate(c,_)}finally{i.viewport=n,this.props=r,this._clearChangeFlags(),s.needsUpdate=!1,s.resetOldProps()}}_finalize(){Ls(aut,this),this.finalizeState(this.context);for(let t of this.props.extensions)t.finalizeState.call(this,this.context,t)}_drawLayer({moduleParameters:t=null,uniforms:r={},parameters:i={}}){this._updateAttributeTransition();let s=this.props,n=this.context;this.props=this.internalState.propsInTransition||s;let o=this.props.opacity;r.opacity=Math.pow(o,1/2.2);try{t&&this.setModuleParameters(t);let{getPolygonOffset:c}=this.props,f=c&&c(r)||[0,0];Ml(n.gl,{polygonOffset:f}),Mn(n.gl,i,()=>{let _={moduleParameters:t,uniforms:r,parameters:i,context:n};for(let w of this.props.extensions)w.draw.call(this,_,w);this.draw(_)})}finally{this.props=s}}getChangeFlags(){var t;return(t=this.internalState)===null||t===void 0?void 0:t.changeFlags}setChangeFlags(t){if(!this.internalState)return;let{changeFlags:r}=this.internalState;for(let s in t)if(t[s]){let n=!1;switch(s){case\"dataChanged\":let o=t[s],c=r[s];o&&Array.isArray(c)&&(r.dataChanged=Array.isArray(o)?c.concat(o):o,n=!0);default:r[s]||(r[s]=t[s],n=!0)}n&&Ls(nut,this,s,t)}let i=!!(r.dataChanged||r.updateTriggersChanged||r.propsChanged||r.extensionsChanged);r.propsOrDataChanged=i,r.somethingChanged=i||r.viewportChanged||r.stateChanged}_clearChangeFlags(){this.internalState.changeFlags={dataChanged:!1,propsChanged:!1,updateTriggersChanged:!1,viewportChanged:!1,stateChanged:!1,extensionsChanged:!1,propsOrDataChanged:!1,somethingChanged:!1}}_diffProps(t,r){let i=o9(t,r);if(i.updateTriggersChanged)for(let n in i.updateTriggersChanged)i.updateTriggersChanged[n]&&this.invalidateAttribute(n);if(i.transitionsChanged)for(let n in i.transitionsChanged){var s;this.internalState.uniformTransitions.add(n,r[n],t[n],(s=t.transitions)===null||s===void 0?void 0:s[n])}return this.setChangeFlags(i)}validateProps(){s9(this.props)}updateAutoHighlight(t){this.props.autoHighlight&&!Number.isInteger(this.props.highlightedObjectIndex)&&this._updateAutoHighlight(t)}_updateAutoHighlight(t){let r={pickingSelectedColor:t.picked?t.color:null},{highlightColor:i}=this.props;t.picked&&typeof i==\"function\"&&(r.pickingHighlightColor=i(t)),this.setModuleParameters(r),this.setNeedsRedraw()}_getAttributeManager(){let t=this.context;return new Xf(t.gl,{id:this.props.id,stats:t.stats,timeline:t.timeline})}_postUpdate(t,r){let{props:i,oldProps:s}=t;this.setNeedsRedraw(),this._updateAttributes();let{model:n}=this.state;n?.setInstanceCount(this.getNumInstances());let{autoHighlight:o,highlightedObjectIndex:c,highlightColor:f}=i;if(r||s.autoHighlight!==o||s.highlightedObjectIndex!==c||s.highlightColor!==f){let _={};o||(_.pickingSelectedColor=null),Array.isArray(f)&&(_.pickingHighlightColor=f),(r||c!==s.highlightedObjectIndex)&&(_.pickingSelectedColor=Number.isFinite(c)&&c>=0?this.encodePickingColor(c):null),this.setModuleParameters(_)}}_getUpdateParams(){return{props:this.props,oldProps:this.internalState.getOldProps(),context:this.context,changeFlags:this.internalState.changeFlags}}_getNeedsRedraw(t){if(!this.internalState)return!1;let r=!1;r=r||this.internalState.needsRedraw&&this.id;let i=this.getAttributeManager(),s=i?i.getNeedsRedraw(t):!1;if(r=r||s,r)for(let n of this.props.extensions)n.onNeedsRedraw.call(this,n);return this.internalState.needsRedraw=this.internalState.needsRedraw&&!t.clearRedrawFlags,r}_onAsyncPropUpdated(){this._diffProps(this.props,this.internalState.getOldProps()),this.setNeedsUpdate()}};G(dn,\"defaultProps\",hut);G(dn,\"layerName\",\"Layer\");var fut=\"compositeLayer.renderLayers\",Ni=class extends dn{get isComposite(){return!0}get isLoaded(){return super.isLoaded&&this.getSubLayers().every(t=>t.isLoaded)}getSubLayers(){return this.internalState&&this.internalState.subLayers||[]}initializeState(t){}setState(t){super.setState(t),this.setNeedsUpdate()}getPickingInfo({info:t}){let{object:r}=t;return r&&r.__source&&r.__source.parent&&r.__source.parent.id===this.id&&(t.object=r.__source.object,t.index=r.__source.index),t}filterSubLayer(t){return!0}shouldRenderSubLayer(t,r){return r&&r.length}getSubLayerClass(t,r){let{_subLayerProps:i}=this.props;return i&&i[t]&&i[t].type||r}getSubLayerRow(t,r,i){return t.__source={parent:this,object:r,index:i},t}getSubLayerAccessor(t){if(typeof t==\"function\"){let r={index:-1,data:this.props.data,target:[]};return(i,s)=>i&&i.__source?(r.index=i.__source.index,t(i.__source.object,r)):t(i,s)}return t}getSubLayerProps(t={}){var r;let{opacity:i,pickable:s,visible:n,parameters:o,getPolygonOffset:c,highlightedObjectIndex:f,autoHighlight:_,highlightColor:w,coordinateSystem:I,coordinateOrigin:R,wrapLongitude:N,positionFormat:j,modelMatrix:Q,extensions:et,fetch:Y,operation:K,_subLayerProps:J}=this.props,ut={id:\"\",updateTriggers:{},opacity:i,pickable:s,visible:n,parameters:o,getPolygonOffset:c,highlightedObjectIndex:f,autoHighlight:_,highlightColor:w,coordinateSystem:I,coordinateOrigin:R,wrapLongitude:N,positionFormat:j,modelMatrix:Q,extensions:et,fetch:Y,operation:K},Et=J&&t.id&&J[t.id],kt=Et&&Et.updateTriggers,Xt=t.id||\"sublayer\";if(Et){let qt=this.props[zu],le=t.type?t.type._propTypes:{};for(let ue in Et){let De=le[ue]||qt[ue];De&&De.type===\"accessor\"&&(Et[ue]=this.getSubLayerAccessor(Et[ue]))}}Object.assign(ut,t,Et),ut.id=\"\".concat(this.props.id,\"-\").concat(Xt),ut.updateTriggers={all:(r=this.props.updateTriggers)===null||r===void 0?void 0:r.all,...t.updateTriggers,...kt};for(let qt of et){let le=qt.getSubLayerProps.call(this,qt);le&&Object.assign(ut,le,{updateTriggers:Object.assign(ut.updateTriggers,le.updateTriggers)})}return ut}_updateAutoHighlight(t){for(let r of this.getSubLayers())r.updateAutoHighlight(t)}_getAttributeManager(){return null}_postUpdate(t,r){let i=this.internalState.subLayers,s=!i||this.needsUpdate();if(s){let n=this.renderLayers();i=op(n,Boolean),this.internalState.subLayers=i}Ls(fut,this,s,i);for(let n of i)n.parent=this}};G(Ni,\"layerName\",\"CompositeLayer\");var gP=Math.PI/180,p9=180/Math.PI,_P=6370972,ev=256;function dut(){let e=ev/_P,t=Math.PI/180*ev;return{unitsPerMeter:[e,e,e],unitsPerMeter2:[0,0,0],metersPerUnit:[1/e,1/e,1/e],unitsPerDegree:[t,t,e],unitsPerDegree2:[0,0,0],degreesPerUnit:[1/t,1/t,1/e]}}var rv=class extends ac{constructor(t={}){let{latitude:r=0,longitude:i=0,zoom:s=0,nearZMultiplier:n=.1,farZMultiplier:o=2,resolution:c=10}=t,{height:f,altitude:_=1.5}=t;f=f||1,_=Math.max(.75,_);let w=new En().lookAt({eye:[0,-_,0],up:[0,0,1]}),I=Math.pow(2,s);w.rotateX(r*gP),w.rotateZ(-i*gP),w.scale(I/f);let R=Math.atan(.5/_),N=ev*2*I/f;super({...t,height:f,viewMatrix:w,longitude:i,latitude:r,zoom:s,distanceScales:dut(),fovyRadians:R*2,focalDistance:_,near:n,far:Math.min(2,1/N+1)*_*o}),G(this,\"longitude\",void 0),G(this,\"latitude\",void 0),G(this,\"resolution\",void 0),this.latitude=r,this.longitude=i,this.resolution=c}get projectionMode(){return Ja.GLOBE}getDistanceScales(){return this.distanceScales}getBounds(t={}){let r={targetZ:t.z||0},i=this.unproject([0,this.height/2],r),s=this.unproject([this.width/2,0],r),n=this.unproject([this.width,this.height/2],r),o=this.unproject([this.width/2,this.height],r);return n[0]this.longitude&&(i[0]-=360),[Math.min(i[0],n[0],s[0],o[0]),Math.min(i[1],n[1],s[1],o[1]),Math.max(i[0],n[0],s[0],o[0]),Math.max(i[1],n[1],s[1],o[1])]}unproject(t,{topLeft:r=!0,targetZ:i}={}){let[s,n,o]=t,c=r?n:this.height-n,{pixelUnprojectionMatrix:f}=this,_;if(Number.isFinite(o))_=OD(f,[s,c,o,1]);else{let N=OD(f,[s,c,-1,1]),j=OD(f,[s,c,1,1]),Q=((i||0)/_P+1)*ev,et=NE(FE([],N,j)),Y=NE(N),K=NE(j),ut=4*((4*Y*K-(et-Y-K)**2)/16)/et,Et=Math.sqrt(Y-ut),kt=Math.sqrt(Math.max(0,Q*Q-ut)),Xt=(Et-kt)/Math.sqrt(et);_=Hj([],N,j,Xt)}let[w,I,R]=this.unprojectPosition(_);return Number.isFinite(o)?[w,I,R]:Number.isFinite(i)?[w,I,i]:[w,I]}projectPosition(t){let[r,i,s=0]=t,n=r*gP,o=i*gP,c=Math.cos(o),f=(s/_P+1)*ev;return[Math.sin(n)*c*f,-Math.cos(n)*c*f,Math.sin(o)*f]}unprojectPosition(t){let[r,i,s]=t,n=zE(t),o=Math.asin(s/n),f=Math.atan2(r,-i)*p9,_=o*p9,w=(n/ev-1)*_P;return[f,_,w]}projectFlat(t){return t}unprojectFlat(t){return t}panByPosition(t,r){let i=this.unproject(r);return{longitude:t[0]-i[0]+this.longitude,latitude:t[1]-i[1]+this.latitude}}};function OD(e,t){let r=Nh([],t,e);return Fy(r,r,1/r[3]),r}var put=new En().lookAt({eye:[0,0,1]});function Aut({width:e,height:t,near:r,far:i,padding:s}){let n=-e/2,o=e/2,c=-t/2,f=t/2;if(s){let{left:_=0,right:w=0,top:I=0,bottom:R=0}=s,N=Il((_+e-w)/2,0,e)-e/2,j=Il((I+t-R)/2,0,t)-t/2;n-=N,o-=N,c+=j,f+=j}return new En().ortho({left:n,right:o,bottom:c,top:f,near:r,far:i})}var iv=class extends ac{constructor(t){let{width:r,height:i,near:s=.1,far:n=1e3,zoom:o=0,target:c=[0,0,0],padding:f=null,flipY:_=!0}=t,w=Array.isArray(o)?o[0]:o,I=Array.isArray(o)?o[1]:o,R=Math.min(w,I),N=Math.pow(2,R),j;if(w!==I){let Q=Math.pow(2,w),et=Math.pow(2,I);j={unitsPerMeter:[Q/N,et/N,1],metersPerUnit:[N/Q,N/et,1]}}super({...t,longitude:void 0,position:c,viewMatrix:put.clone().scale([N,N*(_?-1:1),N]),projectionMatrix:Aut({width:r||1,height:i||1,padding:f,near:s,far:n}),zoom:R,distanceScales:j})}projectFlat([t,r]){let{unitsPerMeter:i}=this.distanceScales;return[t*i[0],r*i[1]]}unprojectFlat([t,r]){let{metersPerUnit:i}=this.distanceScales;return[t*i[0],r*i[1]]}panByPosition(t,r){let i=Qf(r,this.pixelUnprojectionMatrix),s=this.projectFlat(t),n=$A([],s,LE([],i)),o=$A([],this.center,n);return{target:this.unprojectFlat(o)}}};var cc=class{static get componentName(){return Object.prototype.hasOwnProperty.call(this,\"extensionName\")?this.extensionName:\"\"}constructor(t){G(this,\"opts\",void 0),t&&(this.opts=t)}equals(t){return this===t?!0:this.constructor===t.constructor&&mo(this.opts,t.opts,1)}getShaders(t){return null}getSubLayerProps(t){let{defaultProps:r}=t.constructor,i={updateTriggers:{}};for(let s in r)if(s in this.props){let n=r[s],o=this.props[s];i[s]=o,n&&n.type===\"accessor\"&&(i.updateTriggers[s]=this.props.updateTriggers[s],typeof o==\"function\"&&(i[s]=this.getSubLayerAccessor(o)))}return i}initializeState(t,r){}updateState(t,r){}onNeedsRedraw(t){}getNeedsPickingBuffer(t){return!1}draw(t,r){}finalizeState(t,r){}};G(cc,\"defaultProps\",{});G(cc,\"extensionName\",\"LayerExtension\");var BD={bearing:0,pitch:0,position:[0,0,0]},mut={speed:1.2,curve:1.414},nv=class extends 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0);let{attributes:r={}}=t;this.typedArrayManager=Gh,this.attributes={},this._attributeDefs=r,this.opts=t,this.updateGeometry(t)}updateGeometry(t){Object.assign(this.opts,t);let{data:r,buffers:i={},getGeometry:s,geometryBuffer:n,positionFormat:o,dataChanged:c,normalize:f=!0}=this.opts;if(this.data=r,this.getGeometry=s,this.positionSize=n&&n.size||(o===\"XY\"?2:3),this.buffers=i,this.normalize=f,n&&(_r(r.startIndices),this.getGeometry=this.getGeometryFromBuffer(n),f||(i.positions=n)),this.geometryBuffer=i.positions,Array.isArray(c))for(let _ of c)this._rebuildGeometry(_);else this._rebuildGeometry()}updatePartialGeometry({startRow:t,endRow:r}){this._rebuildGeometry({startRow:t,endRow:r})}getGeometryFromBuffer(t){let r=t.value||t;return ArrayBuffer.isView(r)?uP(r,{size:this.positionSize,offset:t.offset,stride:t.stride,startIndices:this.data.startIndices}):null}_allocate(t,r){let{attributes:i,buffers:s,_attributeDefs:n,typedArrayManager:o}=this;for(let c in n)if(c in 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Uint32Array([t.buffer[1]>>>16,t.buffer[1]&65535,t.buffer[0]>>>16,t.buffer[0]&65535]),s=r[3]*i[3];this.buffer[0]=s&65535;let n=s>>>16;return s=r[2]*i[3],n+=s,s=r[3]*i[2]>>>0,n+=s,this.buffer[0]+=n<<16,this.buffer[1]=n>>>0>>16,this.buffer[1]+=r[1]*i[3]+r[2]*i[2]+r[3]*i[1],this.buffer[1]+=r[0]*i[3]+r[1]*i[2]+r[2]*i[1]+r[3]*i[0]<<16,this}_plus(t){let r=this.buffer[0]+t.buffer[0]>>>0;this.buffer[1]+=t.buffer[1],r>>0&&++this.buffer[1],this.buffer[0]=r}lessThan(t){return this.buffer[1]>>0,r[2]=this.buffer[2]+t.buffer[2]>>>0,r[1]=this.buffer[1]+t.buffer[1]>>>0,r[0]=this.buffer[0]+t.buffer[0]>>>0,r[0]>>0&&++r[1],r[1]>>0&&++r[2],r[2]>>0&&++r[3],this.buffer[3]=r[3],this.buffer[2]=r[2],this.buffer[1]=r[1],this.buffer[0]=r[0],this}hex(){return`${Tv(this.buffer[3])} ${Tv(this.buffer[2])} ${Tv(this.buffer[1])} ${Tv(this.buffer[0])}`}static multiply(t,r){return new e(new Uint32Array(t.buffer)).times(r)}static add(t,r){return new e(new Uint32Array(t.buffer)).plus(r)}static from(t,r=new Uint32Array(4)){return e.fromString(typeof t==\"string\"?t:t.toString(),r)}static fromNumber(t,r=new Uint32Array(4)){return e.fromString(t.toString(),r)}static fromString(t,r=new Uint32Array(4)){let i=t.startsWith(\"-\"),s=t.length,n=new e(r);for(let o=i?1:0;o0&&this.readData(t,i)||new Uint8Array(0)}readOffsets(t,r){return this.readData(t,r)}readTypeIds(t,r){return this.readData(t,r)}readData(t,{length:r,offset:i}=this.nextBufferRange()){return this.bytes.subarray(i,i+r)}readDictionary(t){return this.dictionaries.get(t.id)}},g3=class extends o2{constructor(t,r,i,s,n){super(new Uint8Array(0),r,i,s,n),this.sources=t}readNullBitmap(t,r,{offset:i}=this.nextBufferRange()){return r<=0?new Uint8Array(0):Sg(this.sources[i])}readOffsets(t,{offset:r}=this.nextBufferRange()){return Ai(Uint8Array,Ai(t.OffsetArrayType,this.sources[r]))}readTypeIds(t,{offset:r}=this.nextBufferRange()){return 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i=()=>{this[r]=tB(this.model.get(t))};this.model.on(`change:${t}`,i),this.callbacks.set(`change:${t}`,i)}finalize(){for(let[t,r]of Object.entries(this.callbacks))this.model.off(t,r)}};async function L3(e,t){let r=[];for(let i of t)r.push(e.get_model(i.slice(10)));return await Promise.all(r)}function Jt(e){return e!=null}function DH(e,t=20){let r;return(...s)=>{clearTimeout(r),r=setTimeout(()=>e(...s),t)}}var bdt=`\n uniform bool brushing_enabled;\n uniform int brushing_target;\n uniform vec2 brushing_mousePos;\n uniform float brushing_radius;\n\n #ifdef NON_INSTANCED_MODEL\n attribute vec2 brushingTargets;\n #else\n attribute vec2 instanceBrushingTargets;\n #endif\n\n varying float brushing_isVisible;\n\n bool brushing_isPointInRange(vec2 position) {\n if (!brushing_enabled) {\n return true;\n }\n vec2 source_commonspace = project_position(position);\n vec2 target_commonspace = project_position(brushing_mousePos);\n float distance = length((target_commonspace - source_commonspace) / 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i=this.getAttributeManager();i&&i.add({brushingTargets:{size:2,accessor:\"getBrushingTarget\",shaderAttributes:{brushingTargets:{divisor:0},instanceBrushingTargets:{divisor:1}}}}),this.state.onMouseMove=()=>{var s;(s=this.getCurrentLayer())===null||s===void 0||s.setNeedsRedraw()},t.deck&&t.deck.eventManager.on({pointermove:this.state.onMouseMove,pointerleave:this.state.onMouseMove})}finalizeState(t,r){t.deck&&t.deck.eventManager.off({pointermove:this.state.onMouseMove,pointerleave:this.state.onMouseMove})}};G(vm,\"defaultProps\",Mdt);G(vm,\"extensionName\",\"BrushingExtension\");var BH=`\nuniform DATAFILTER_TYPE filter_min;\nuniform DATAFILTER_TYPE filter_softMin;\nuniform DATAFILTER_TYPE filter_softMax;\nuniform DATAFILTER_TYPE filter_max;\nuniform bool filter_useSoftMargin;\nuniform bool filter_enabled;\nuniform bool filter_transformSize;\n\n#ifdef NON_INSTANCED_MODEL\n #define DATAFILTER_ATTRIB filterValues\n #define DATAFILTER_ATTRIB_64LOW filterValues64Low\n#else\n #define DATAFILTER_ATTRIB instanceFilterValues\n #define DATAFILTER_ATTRIB_64LOW instanceFilterValues64Low\n#endif\n\nattribute DATAFILTER_TYPE DATAFILTER_ATTRIB;\n#ifdef DATAFILTER_DOUBLE\n attribute DATAFILTER_TYPE DATAFILTER_ATTRIB_64LOW;\n\n uniform DATAFILTER_TYPE filter_min64High;\n uniform DATAFILTER_TYPE filter_max64High;\n#endif\n\nvarying float dataFilter_value;\n\nfloat dataFilter_reduceValue(float value) {\n return value;\n}\nfloat dataFilter_reduceValue(vec2 value) {\n return min(value.x, value.y);\n}\nfloat dataFilter_reduceValue(vec3 value) {\n return min(min(value.x, value.y), value.z);\n}\nfloat dataFilter_reduceValue(vec4 value) {\n return min(min(value.x, value.y), min(value.z, value.w));\n}\nvoid dataFilter_setValue(DATAFILTER_TYPE valueFromMin, DATAFILTER_TYPE valueFromMax) {\n if (filter_enabled) {\n if (filter_useSoftMargin) {\n dataFilter_value = dataFilter_reduceValue(\n smoothstep(filter_min, filter_softMin, valueFromMin) *\n (1.0 - smoothstep(filter_softMax, filter_max, valueFromMax))\n );\n } else {\n dataFilter_value = dataFilter_reduceValue(\n step(filter_min, valueFromMin) * step(valueFromMax, filter_max)\n );\n }\n } else {\n dataFilter_value = 1.0;\n }\n}\n`,FH=`\nuniform bool filter_transformColor;\nvarying float dataFilter_value;\n`;function zH(e){if(!e||!(\"extensions\"in e))return{};let{filterRange:t=[-1,1],filterEnabled:r=!0,filterTransformSize:i=!0,filterTransformColor:s=!0}=e,n=e.filterSoftRange||t;return{...Number.isFinite(t[0])?{filter_min:t[0],filter_softMin:n[0],filter_softMax:n[1],filter_max:t[1]}:{filter_min:t.map(o=>o[0]),filter_softMin:n.map(o=>o[0]),filter_softMax:n.map(o=>o[1]),filter_max:t.map(o=>o[1])},filter_enabled:r,filter_useSoftMargin:!!e.filterSoftRange,filter_transformSize:r&&i,filter_transformColor:r&&s}}function Edt(e){if(!e||!(\"extensions\"in e))return{};let t=zH(e);if(Number.isFinite(t.filter_min)){let r=Math.fround(t.filter_min);t.filter_min-=r,t.filter_softMin-=r,t.filter_min64High=r;let i=Math.fround(t.filter_max);t.filter_max-=i,t.filter_softMax-=i,t.filter_max64High=i}else{let r=t.filter_min.map(Math.fround);t.filter_min=t.filter_min.map((s,n)=>s-r[n]),t.filter_softMin=t.filter_softMin.map((s,n)=>s-r[n]),t.filter_min64High=r;let i=t.filter_max.map(Math.fround);t.filter_max=t.filter_max.map((s,n)=>s-i[n]),t.filter_softMax=t.filter_softMax.map((s,n)=>s-i[n]),t.filter_max64High=i}return t}var NH={\"vs:#main-start\":`\n #ifdef DATAFILTER_DOUBLE\n dataFilter_setValue(\n DATAFILTER_ATTRIB - filter_min64High + DATAFILTER_ATTRIB_64LOW,\n DATAFILTER_ATTRIB - filter_max64High + DATAFILTER_ATTRIB_64LOW\n );\n #else\n dataFilter_setValue(DATAFILTER_ATTRIB, DATAFILTER_ATTRIB);\n #endif\n `,\"vs:#main-end\":`\n if (dataFilter_value == 0.0) {\n gl_Position = vec4(0.);\n }\n `,\"vs:DECKGL_FILTER_SIZE\":`\n if (filter_transformSize) {\n size = size * dataFilter_value;\n }\n `,\"fs:DECKGL_FILTER_COLOR\":`\n if (dataFilter_value == 0.0) discard;\n if (filter_transformColor) {\n color.a *= dataFilter_value;\n }\n `},UH={name:\"data-filter\",vs:BH,fs:FH,inject:NH,getUniforms:zH},VH={name:\"data-filter-fp64\",vs:BH,fs:FH,inject:NH,getUniforms:Edt};var Pdt=`#define SHADER_NAME data-filter-vertex-shader\n\n#ifdef FLOAT_TARGET\n attribute float filterIndices;\n attribute float filterPrevIndices;\n#else\n attribute vec2 filterIndices;\n attribute vec2 filterPrevIndices;\n#endif\n\nvarying vec4 vColor;\nconst float component = 1.0 / 255.0;\n\nvoid main() {\n #ifdef FLOAT_TARGET\n dataFilter_value *= float(filterIndices != filterPrevIndices);\n gl_Position = vec4(0.0, 0.0, 0.0, 1.0);\n vColor = vec4(0.0, 0.0, 0.0, 1.0);\n #else\n // Float texture is not supported: pack result into 4 channels x 256 px x 64px\n dataFilter_value *= float(filterIndices.x != filterPrevIndices.x);\n float col = filterIndices.x;\n float row = filterIndices.y * 4.0;\n float channel = floor(row);\n row = fract(row);\n vColor = component * vec4(bvec4(channel == 0.0, channel == 1.0, channel == 2.0, channel == 3.0));\n gl_Position = vec4(col * 2.0 - 1.0, row * 2.0 - 1.0, 0.0, 1.0);\n #endif\n gl_PointSize = 1.0;\n}\n`,Idt=`#define SHADER_NAME data-filter-fragment-shader\nprecision highp float;\n\nvarying vec4 vColor;\n\nvoid main() {\n if (dataFilter_value < 0.5) {\n discard;\n }\n gl_FragColor = vColor;\n}\n`;function jH(e){return!!(e.getExtension(\"EXT_float_blend\")&&(e.getExtension(\"EXT_color_buffer_float\")||e.getExtension(\"WEBGL_color_buffer_float\")))}function GH(e,t){return t?new yi(e,{width:1,height:1,attachments:{36064:new pi(e,{format:fr(e)?34836:6408,type:5126,mipmaps:!1})}}):new yi(e,{width:256,height:64,depth:!1})}function WH(e,t,r){return t.defines.NON_INSTANCED_MODEL=1,r&&(t.defines.FLOAT_TARGET=1),new fn(e,{id:\"data-filter-aggregation-model\",vertexCount:1,isInstanced:!1,drawMode:0,vs:Pdt,fs:Idt,...t})}var HH={blend:!0,blendFunc:[1,1,1,1],blendEquation:[32774,32774],depthTest:!1};var Ldt={getFilterValue:{type:\"accessor\",value:0},onFilteredItemsChange:{type:\"function\",value:null,optional:!0},filterEnabled:!0,filterRange:[-1,1],filterSoftRange:null,filterTransformSize:!0,filterTransformColor:!0},qH={1:\"float\",2:\"vec2\",3:\"vec3\",4:\"vec4\"},xm=class extends cc{constructor({filterSize:t=1,fp64:r=!1,countItems:i=!1}={}){if(!qH[t])throw new Error(\"filterSize out of range\");super({filterSize:t,fp64:r,countItems:i})}getShaders(t){let{filterSize:r,fp64:i}=t.opts;return{modules:[i?VH:UH],defines:{DATAFILTER_TYPE:qH[r],DATAFILTER_DOUBLE:!!i}}}initializeState(t,r){let i=this.getAttributeManager();i&&i.add({filterValues:{size:r.opts.filterSize,type:r.opts.fp64?5130:5126,accessor:\"getFilterValue\",shaderAttributes:{filterValues:{divisor:0},instanceFilterValues:{divisor:1}}}});let{gl:s}=this.context;if(i&&r.opts.countItems){let n=jH(s);i.add({filterIndices:{size:n?1:2,vertexOffset:1,type:5121,normalized:!0,accessor:(f,{index:_})=>{let w=f&&f.__source?f.__source.index:_;return n?(w+1)%255:[(w+1)%255,Math.floor(w/255)%255]},shaderAttributes:{filterPrevIndices:{vertexOffset:0},filterIndices:{vertexOffset:1}}}});let o=GH(s,n),c=WH(s,r.getShaders.call(this,r),n);this.setState({filterFBO:o,filterModel:c})}}updateState({props:t,oldProps:r}){if(this.state.filterModel){let s=this.getAttributeManager().attributes.filterValues.needsUpdate()||t.filterEnabled!==r.filterEnabled||t.filterRange!==r.filterRange||t.filterSoftRange!==r.filterSoftRange;s&&this.setState({filterNeedsUpdate:s})}}draw(t,r){let{filterFBO:i,filterModel:s,filterNeedsUpdate:n}=this.state,{onFilteredItemsChange:o}=this.props;if(n&&o&&s){let{attributes:{filterValues:c,filterIndices:f}}=this.getAttributeManager();s.setVertexCount(this.getNumInstances());let{gl:_}=this.context;Hf(_,{framebuffer:i,color:[0,0,0,0]}),s.updateModuleSettings(t.moduleParameters).setAttributes({...c.getShaderAttributes(),...f&&f.getShaderAttributes()}).draw({framebuffer:i,parameters:{...HH,viewport:[0,0,i.width,i.height]}});let w=Dh(i),I=0;for(let R=0;R 0.0) {\n if (dashAlignMode == 0.0) {\n offset = vDashOffset;\n } else {\n unitLength = vPathLength / round(vPathLength / unitLength);\n offset = solidLength / 2.0;\n }\n\n float unitOffset = mod(vPathPosition.y + offset, unitLength);\n\n if (gapLength > 0.0 && unitOffset > solidLength) {\n if (capType <= 0.5) {\n if (!(dashGapPickable && picking_uActive)) {\n discard;\n }\n } else {\n float distToEnd = length(vec2(\n min(unitOffset - solidLength, unitLength - unitOffset),\n vPathPosition.x\n ));\n if (distToEnd > 1.0) {\n if (!(dashGapPickable && picking_uActive)) {\n discard;\n }\n }\n }\n }\n }\n`}},YH={inject:{\"vs:#decl\":`\nattribute float instanceOffsets;\n`,\"vs:DECKGL_FILTER_SIZE\":`\n float offsetWidth = abs(instanceOffsets * 2.0) + 1.0;\n size *= offsetWidth;\n`,\"vs:#main-end\":`\n float offsetWidth = abs(instanceOffsets * 2.0) + 1.0;\n float offsetDir = sign(instanceOffsets);\n vPathPosition.x = (vPathPosition.x + offsetDir) * offsetWidth - offsetDir;\n vPathPosition.y *= offsetWidth;\n vPathLength *= offsetWidth;\n`,\"fs:#main-start\":`\n float isInside;\n isInside = step(-1.0, vPathPosition.x) * step(vPathPosition.x, 1.0);\n if (isInside == 0.0) {\n discard;\n }\n`}};var kdt={getDashArray:{type:\"accessor\",value:[0,0]},getOffset:{type:\"accessor\",value:0},dashJustified:!1,dashGapPickable:!1},bm=class extends cc{constructor({dash:t=!1,offset:r=!1,highPrecisionDash:i=!1}={}){super({dash:t||i,offset:r,highPrecisionDash:i})}isEnabled(t){return\"pathTesselator\"in t.state}getShaders(t){if(!t.isEnabled(this))return null;let r={};return t.opts.dash&&(r=tv(r,ZH)),t.opts.offset&&(r=tv(r,YH)),r}initializeState(t,r){let i=this.getAttributeManager();!i||!r.isEnabled(this)||(r.opts.dash&&i.addInstanced({instanceDashArrays:{size:2,accessor:\"getDashArray\"}}),r.opts.highPrecisionDash&&i.addInstanced({instanceDashOffsets:{size:1,accessor:\"getPath\",transform:r.getDashOffsets.bind(this)}}),r.opts.offset&&i.addInstanced({instanceOffsets:{size:1,accessor:\"getOffset\"}}))}updateState(t,r){if(!r.isEnabled(this))return;let i={};r.opts.dash&&(i.dashAlignMode=this.props.dashJustified?1:0,i.dashGapPickable=!!this.props.dashGapPickable),this.state.model.setUniforms(i)}getDashOffsets(t){let r=[0],i=this.props.positionFormat===\"XY\"?2:3,s=Array.isArray(t[0]),n=s?t.length:t.length/i,o,c;for(let f=0;f0&&(r[f]=r[f-1]+$j(c,o)),c=o;return r}};G(bm,\"defaultProps\",kdt);G(bm,\"extensionName\",\"PathStyleExtension\");var Rdt=`\n#ifdef NON_INSTANCED_MODEL\nattribute float collisionPriorities;\n#else\nattribute float instanceCollisionPriorities;\n#endif\n\nuniform sampler2D collision_texture;\nuniform bool collision_sort;\nuniform bool collision_enabled;\n\nvec2 collision_getCoords(vec4 position) {\n vec4 collision_clipspace = project_common_position_to_clipspace(position);\n return (1.0 + collision_clipspace.xy / collision_clipspace.w) / 2.0;\n}\n\nfloat collision_match(vec2 tex, vec3 pickingColor) {\n vec4 collision_pickingColor = texture2D(collision_texture, tex);\n float delta = dot(abs(collision_pickingColor.rgb - pickingColor), vec3(1.0));\n float e = 0.001;\n return step(delta, e);\n}\n\nfloat collision_isVisible(vec2 texCoords, vec3 pickingColor) {\n if (!collision_enabled) {\n return 1.0;\n }\n\n // Visibility test, sample area of 5x5 pixels in order to fade in/out.\n // Due to the locality, the lookups will be cached\n // This reduces the flicker present when objects are shown/hidden\n const int N = 2;\n float accumulator = 0.0;\n vec2 step = vec2(1.0 / project_uViewportSize);\n\n const float floatN = float(N);\n vec2 delta = -floatN * step;\n for(int i = -N; i <= N; i++) {\n delta.x = -step.x * floatN;\n for(int j = -N; j <= N; j++) {\n accumulator += collision_match(texCoords + delta, pickingColor);\n delta.x += step.x;\n }\n delta.y += step.y;\n }\n\n float W = 2.0 * floatN + 1.0;\n return pow(accumulator / (W * W), 2.2);\n}\n`,Ddt={\"vs:#decl\":`\n float collision_fade = 1.0;\n`,\"vs:DECKGL_FILTER_GL_POSITION\":`\n if (collision_sort) {\n #ifdef NON_INSTANCED_MODEL\n float collisionPriority = collisionPriorities;\n #else\n float collisionPriority = instanceCollisionPriorities;\n #endif\n position.z = -0.001 * collisionPriority * position.w; // Support range -1000 -> 1000\n }\n\n if (collision_enabled) {\n vec4 collision_common_position = project_position(vec4(geometry.worldPosition, 1.0));\n vec2 collision_texCoords = collision_getCoords(collision_common_position);\n collision_fade = collision_isVisible(collision_texCoords, geometry.pickingColor / 255.0);\n if (collision_fade < 0.0001) {\n // Position outside clip space bounds to discard\n position = vec4(0.0, 0.0, 2.0, 1.0);\n }\n }\n `,\"vs:DECKGL_FILTER_COLOR\":`\n color.a *= collision_fade;\n `},Odt=(e,t)=>{if(!e||!(\"dummyCollisionMap\"in e))return{};let{collisionFBO:r,drawToCollisionMap:i,dummyCollisionMap:s}=e;return{collision_sort:!!i,collision_texture:!i&&r?r:s}},QH={name:\"collision\",dependencies:[Vh],vs:Rdt,inject:Ddt,getUniforms:Odt};var B2=class extends sc{renderCollisionMap(t,r){let i=this.gl,s=1;return Mn(i,{scissorTest:!0,scissor:[s,s,t.width-2*s,t.height-2*s],clearColor:[0,0,0,0],blend:!1,depthTest:!0,depthRange:[0,1]},()=>this.render({...r,target:t,pass:\"collision\"}))}getModuleParameters(){return{drawToCollisionMap:!0,pickingActive:1,pickingAttribute:!1,lightSources:{}}}};var F2=class extends sc{constructor(t,r){super(t,r),G(this,\"maskMap\",void 0),G(this,\"fbo\",void 0);let{mapSize:i=2048}=r;this.maskMap=new pi(t,{width:i,height:i,parameters:{10241:9729,10240:9729,10242:33071,10243:33071}}),this.fbo=new yi(t,{id:\"maskmap\",width:i,height:i,attachments:{36064:this.maskMap}})}render(t){let r=this.gl,i=[!1,!1,!1,!1];return i[t.channel]=!0,Mn(r,{clearColor:[255,255,255,255],blend:!0,blendFunc:[0,1],blendEquation:32778,colorMask:i,depthTest:!1},()=>super.render({...t,target:this.fbo,pass:\"mask\"}))}shouldDrawLayer(t){return t.props.operation.includes(\"mask\")}delete(){this.fbo.delete(),this.maskMap.delete()}};function $H(e,t){let r=[1/0,1/0,-1/0,-1/0];for(let i of e){let s=i.getBounds();if(s){let n=i.projectPosition(s[0],{viewport:t,autoOffset:!1}),o=i.projectPosition(s[1],{viewport:t,autoOffset:!1});r[0]=Math.min(r[0],n[0]),r[1]=Math.min(r[1],n[1]),r[2]=Math.max(r[2],o[0]),r[3]=Math.max(r[3],o[1])}}return Number.isFinite(r[0])?r:null}var Bdt=2048;function XH(e){let{bounds:t,viewport:r,border:i=0}=e,{isGeospatial:s}=r;if(t[2]<=t[0]||t[3]<=t[1])return null;let n=r.unprojectPosition([(t[0]+t[2])/2,(t[1]+t[3])/2,0]),{width:o,height:c,zoom:f}=e;if(f===void 0){o=o-i*2,c=c-i*2;let _=Math.min(o/(t[2]-t[0]),c/(t[3]-t[1]));f=Math.min(Math.log2(_),20)}else if(!o||!c){let _=2**f;o=Math.round(Math.abs(t[2]-t[0])*_),c=Math.round(Math.abs(t[3]-t[1])*_);let w=Bdt-i*2;if(o>w||c>w){let I=w/Math.max(o,c);o=Math.round(o*I),c=Math.round(c*I),f+=Math.log2(I)}}return s?new lc({id:r.id,x:i,y:i,width:o,height:c,longitude:n[0],latitude:n[1],zoom:f,orthographic:!0}):new iv({id:r.id,x:i,y:i,width:o,height:c,target:n,zoom:f,flipY:!1})}function Fdt(e,t){let r;if(t&&t.length===2){let[n,o]=t,c=e.getBounds({z:n}),f=e.getBounds({z:o});r=[Math.min(c[0],f[0]),Math.min(c[1],f[1]),Math.max(c[2],f[2]),Math.max(c[3],f[3])]}else r=e.getBounds();let i=e.projectPosition(r.slice(0,2)),s=e.projectPosition(r.slice(2,4));return[i[0],i[1],s[0],s[1]]}function KH(e,t,r){if(!e)return[0,0,1,1];let i=Fdt(t,r),s=zdt(i);return e[2]-e[0]<=s[2]-s[0]&&e[3]-e[1]<=s[3]-s[1]?e:[Math.max(e[0],s[0]),Math.max(e[1],s[1]),Math.min(e[2],s[2]),Math.min(e[3],s[3])]}function zdt(e){let t=e[2]-e[0],r=e[3]-e[1],i=(e[0]+e[2])/2,s=(e[1]+e[3])/2;return[i-t,s-r,i+t,s+r]}var z2=class{constructor(){G(this,\"id\",\"mask-effect\"),G(this,\"props\",null),G(this,\"useInPicking\",!0),G(this,\"order\",0),G(this,\"dummyMaskMap\",void 0),G(this,\"channels\",[]),G(this,\"masks\",null),G(this,\"maskPass\",void 0),G(this,\"maskMap\",void 0),G(this,\"lastViewport\",void 0)}preRender(t,{layers:r,layerFilter:i,viewports:s,onViewportActive:n,views:o,isPicking:c}){let f=!1;if(this.dummyMaskMap||(this.dummyMaskMap=new pi(t,{width:1,height:1})),c)return{didRender:f};let _=r.filter(N=>N.props.visible&&N.props.operation.includes(\"mask\"));if(_.length===0)return this.masks=null,this.channels.length=0,{didRender:f};this.masks={},this.maskPass||(this.maskPass=new F2(t,{id:\"default-mask\"}),this.maskMap=this.maskPass.maskMap);let w=this._sortMaskChannels(_),I=s[0],R=!this.lastViewport||!this.lastViewport.equals(I);if(I.resolution!==void 0)return or.warn(\"MaskExtension is not supported in GlobeView\")(),{didRender:f};for(let N in w){let j=this._renderChannel(w[N],{layerFilter:i,onViewportActive:n,views:o,viewport:I,viewportChanged:R});f||(f=j)}return{didRender:f}}_renderChannel(t,{layerFilter:r,onViewportActive:i,views:s,viewport:n,viewportChanged:o}){let c=!1,f=this.channels[t.index];if(!f)return c;let _=t===f||t.layers.length!==f.layers.length||t.layers.some((w,I)=>w!==f.layers[I]||w.props.transitions)||t.layerBounds.some((w,I)=>w!==f.layerBounds[I]);if(t.bounds=f.bounds,t.maskBounds=f.maskBounds,this.channels[t.index]=t,_||o){this.lastViewport=n;let w=$H(t.layers,n);if(t.bounds=w&&KH(w,n),_||!Ro(t.bounds,f.bounds)){let{maskPass:I,maskMap:R}=this,N=w&&XH({bounds:t.bounds,viewport:n,width:R.width,height:R.height,border:1});t.maskBounds=N?N.getBounds():[0,0,1,1],I.render({pass:\"mask\",channel:t.index,layers:t.layers,layerFilter:r,viewports:N?[N]:[],onViewportActive:i,views:s,moduleParameters:{devicePixelRatio:1}}),c=!0}}return this.masks[t.id]={index:t.index,bounds:t.maskBounds,coordinateOrigin:t.coordinateOrigin,coordinateSystem:t.coordinateSystem},c}_sortMaskChannels(t){let r={},i=0;for(let s of t){let{id:n}=s.root,o=r[n];if(!o){if(++i>4){or.warn(\"Too many mask layers. The max supported is 4\")();continue}o={id:n,index:this.channels.findIndex(c=>c?.id===n),layers:[],layerBounds:[],coordinateOrigin:s.root.props.coordinateOrigin,coordinateSystem:s.root.props.coordinateSystem},r[n]=o}o.layers.push(s),o.layerBounds.push(s.getBounds())}for(let s=0;s<4;s++){let n=this.channels[s];(!n||!(n.id in r))&&(this.channels[s]=null)}for(let s in r){let n=r[s];n.index<0&&(n.index=this.channels.findIndex(o=>!o),this.channels[n.index]=n)}return r}getModuleParameters(){return{maskMap:this.masks?this.maskMap:this.dummyMaskMap,maskChannels:this.masks}}cleanup(){this.dummyMaskMap&&(this.dummyMaskMap.delete(),this.dummyMaskMap=void 0),this.maskPass&&(this.maskPass.delete(),this.maskPass=void 0,this.maskMap=void 0),this.lastViewport=void 0,this.masks=null,this.channels.length=0}};var eB=2,N2=class{constructor(){G(this,\"id\",\"collision-filter-effect\"),G(this,\"props\",null),G(this,\"useInPicking\",!0),G(this,\"order\",1),G(this,\"channels\",{}),G(this,\"collisionFilterPass\",void 0),G(this,\"collisionFBOs\",{}),G(this,\"dummyCollisionMap\",void 0),G(this,\"lastViewport\",void 0)}preRender(t,{effects:r,layers:i,layerFilter:s,viewports:n,onViewportActive:o,views:c,isPicking:f,preRenderStats:_={}}){var w;if(this.dummyCollisionMap||(this.dummyCollisionMap=new pi(t,{width:1,height:1})),f)return;let I=i.filter(({props:{visible:Y,collisionEnabled:K}})=>Y&&K);if(I.length===0){this.channels={};return}this.collisionFilterPass||(this.collisionFilterPass=new B2(t,{id:\"default-collision-filter\"}));let R=r?.filter(Y=>Y.constructor===z2),N=(w=_[\"mask-effect\"])===null||w===void 0?void 0:w.didRender,j=this._groupByCollisionGroup(t,I),Q=n[0],et=!this.lastViewport||!this.lastViewport.equals(Q)||N;for(let Y in j){let K=this.collisionFBOs[Y],J=j[Y];K.resize({width:t.canvas.width/eB,height:t.canvas.height/eB}),this._render(J,{effects:R,layerFilter:s,onViewportActive:o,views:c,viewport:Q,viewportChanged:et})}}_render(t,{effects:r,layerFilter:i,onViewportActive:s,views:n,viewport:o,viewportChanged:c}){let{collisionGroup:f}=t,_=this.channels[f];if(!_)return;let w=c||t===_||!mo(_.layers,t.layers,1)||t.layerBounds.some((I,R)=>!Ro(I,_.layerBounds[R]))||t.allLayersLoaded!==_.allLayersLoaded||t.layers.some(I=>I.props.transitions);if(this.channels[f]=t,w){this.lastViewport=o;let I=this.collisionFBOs[f];this.collisionFilterPass.renderCollisionMap(I,{pass:\"collision-filter\",isPicking:!0,layers:t.layers,effects:r,layerFilter:i,viewports:o?[o]:[],onViewportActive:s,views:n,moduleParameters:{dummyCollisionMap:this.dummyCollisionMap,devicePixelRatio:El(I.gl)/eB}})}}_groupByCollisionGroup(t,r){let i={};for(let s of r){let{collisionGroup:n}=s.props,o=i[n];o||(o={collisionGroup:n,layers:[],layerBounds:[],allLayersLoaded:!0},i[n]=o),o.layers.push(s),o.layerBounds.push(s.getBounds()),s.isLoaded||(o.allLayersLoaded=!1)}for(let s of Object.keys(i))this.collisionFBOs[s]||this.createFBO(t,s),this.channels[s]||(this.channels[s]=i[s]);for(let s of Object.keys(this.collisionFBOs))i[s]||this.destroyFBO(s);return i}getModuleParameters(t){let{collisionGroup:r}=t.props,{collisionFBOs:i,dummyCollisionMap:s}=this;return{collisionFBO:i[r],dummyCollisionMap:s}}cleanup(){this.dummyCollisionMap&&(this.dummyCollisionMap.delete(),this.dummyCollisionMap=void 0),this.channels={};for(let t of Object.keys(this.collisionFBOs))this.destroyFBO(t);this.collisionFBOs={},this.lastViewport=void 0}createFBO(t,r){let{width:i,height:s}=t.canvas,n=new pi(t,{width:i,height:s,parameters:{10241:9728,10240:9728,10242:33071,10243:33071}}),o=new el(t,{format:33189,width:i,height:s});this.collisionFBOs[r]=new yi(t,{id:\"Collision-\".concat(r),width:i,height:s,attachments:{36064:n,36096:o}})}destroyFBO(t){let r=this.collisionFBOs[t];for(let i of Object.values(r.attachments))i.delete();r.delete(),delete this.collisionFBOs[t]}};var Ndt={getCollisionPriority:{type:\"accessor\",value:0},collisionEnabled:!0,collisionGroup:{type:\"string\",value:\"default\"},collisionTestProps:{}},wm=class extends cc{getShaders(){return{modules:[QH]}}draw({uniforms:t,context:r,moduleParameters:i}){let{collisionEnabled:s}=this.props,{collisionFBO:n,drawToCollisionMap:o}=i,c=s&&!!n;t.collision_enabled=c,o&&(this.props=this.clone(this.props.collisionTestProps).props)}initializeState(t,r){var i;if(this.getAttributeManager()===null)return;(i=this.context.deck)===null||i===void 0||i._addDefaultEffect(new N2),this.getAttributeManager().add({collisionPriorities:{size:1,accessor:\"getCollisionPriority\",shaderAttributes:{collisionPriorities:{divisor:0},instanceCollisionPriorities:{divisor:1}}}})}getNeedsPickingBuffer(){return this.props.collisionEnabled}};G(wm,\"defaultProps\",Ndt);G(wm,\"extensionName\",\"CollisionFilterExtension\");var Ng=class extends zg{static extensionType;constructor(t,r){super(t,r)}},U2=class extends Ng{static extensionType=\"brushing\";extensionInstance;constructor(t,r,i){super(t,i),this.extensionInstance=new vm,r.initRegularAttribute(\"brushing_enabled\",\"brushingEnabled\"),r.initRegularAttribute(\"brushing_target\",\"brushingTarget\"),r.initRegularAttribute(\"brushing_radius\",\"brushingRadius\"),r.initVectorizedAccessor(\"get_brushing_target\",\"getBrushingTarget\"),r.extensionLayerPropertyNames=[...r.extensionLayerPropertyNames,\"brushingEnabled\",\"brushingTarget\",\"brushingRadius\",\"getBrushingTarget\"]}},V2=class extends Ng{static extensionType=\"collision-filter\";extensionInstance;constructor(t,r,i){super(t,i),this.extensionInstance=new wm,r.initRegularAttribute(\"collision_enabled\",\"collisionEnabled\"),r.initRegularAttribute(\"collision_group\",\"collisionGroup\"),r.initRegularAttribute(\"collision_test_props\",\"collisionTestProps\"),r.initVectorizedAccessor(\"get_collision_priority\",\"getCollisionPriority\"),r.extensionLayerPropertyNames=[...r.extensionLayerPropertyNames,\"collisionEnabled\",\"collisionGroup\",\"collisionTestProps\",\"getCollisionPriority\"]}},k3=class extends Ng{static extensionType=\"data-filter\";extensionInstance;constructor(t,r,i){super(t,i);let s=this.model.get(\"filter_size\");this.extensionInstance=new xm({filterSize:s}),r.initRegularAttribute(\"filter_enabled\",\"filterEnabled\"),r.initRegularAttribute(\"filter_range\",\"filterRange\"),r.initRegularAttribute(\"filter_soft_range\",\"filterSoftRange\"),r.initRegularAttribute(\"filter_transform_size\",\"filterTransformSize\"),r.initRegularAttribute(\"filter_transform_color\",\"filterTransformColor\"),r.initVectorizedAccessor(\"get_filter_value\",\"getFilterValue\"),r.extensionLayerPropertyNames=[...r.extensionLayerPropertyNames,\"filterEnabled\",\"filterRange\",\"filterSoftRange\",\"filterTransformSize\",\"filterTransformColor\",\"getFilterValue\"]}},j2=class extends Ng{static extensionType=\"path-style\";extensionInstance;constructor(t,r,i){super(t,i);let s=this.model.get(\"dash\"),n=this.model.get(\"high_precision_dash\"),o=this.model.get(\"offset\");this.extensionInstance=new bm({...Jt(s)?{dash:s}:{},...Jt(n)?{highPrecisionDash:n}:{},...Jt(o)?{offset:o}:{}}),r.initRegularAttribute(\"dash_gap_pickable\",\"dashGapPickable\"),r.initRegularAttribute(\"dash_justified\",\"dashJustified\"),r.initVectorizedAccessor(\"get_dash_array\",\"getDashArray\"),r.initVectorizedAccessor(\"get_offset\",\"getOffset\"),r.extensionLayerPropertyNames=[...r.extensionLayerPropertyNames,\"dashGapPickable\",\"dashJustified\",\"getDashArray\",\"getOffset\"]}};async function rB(e,t,r){let i=e.get(\"_extension_type\"),s;switch(i){case U2.extensionType:s=new U2(e,t,r);break;case V2.extensionType:s=new V2(e,t,r);break;case k3.extensionType:s=new k3(e,t,r);break;case j2.extensionType:s=new j2(e,t,r);break;default:throw new Error(`no known model for extension type ${i}`)}return await s.loadSubModels(),s}var Ug=class extends zg{pickable;visible;opacity;autoHighlight;extensions;extensionLayerPropertyNames=[];constructor(t,r){super(t,r),this.initRegularAttribute(\"pickable\",\"pickable\"),this.initRegularAttribute(\"visible\",\"visible\"),this.initRegularAttribute(\"opacity\",\"opacity\"),this.initRegularAttribute(\"auto_highlight\",\"autoHighlight\"),this.extensions=[]}async loadSubModels(){await this.initLayerExtensions()}extensionInstances(){return this.extensions.map(t=>t.extensionInstance)}extensionProps(){let t={};for(let r of this.extensionLayerPropertyNames)Jt(this[r])&&(t[r]=this[r]);return t}onClick(t){t.index&&(this.model.set(\"selected_index\",t.index),this.model.save_changes())}baseLayerProps(){return{extensions:this.extensionInstances(),...this.extensionProps(),id:this.model.model_id,pickable:this.pickable,visible:this.visible,opacity:this.opacity,autoHighlight:this.autoHighlight,onClick:this.onClick.bind(this)}}async initLayerExtensions(){let t=async()=>{let r=this.model.get(\"extensions\");if(!r){this.extensions=[];return}let i=await L3(this.model.widget_manager,r),s=[];for(let n of i){let o=await rB(n,this,this.updateStateCallback);s.push(o)}this.extensions=s};await t(),this.model.off(\"change:extensions\"),this.model.on(\"change:extensions\",t),this.callbacks.set(\"change:extensions\",t)}};var JH=`#define SHADER_NAME arc-layer-vertex-shader\n\nattribute vec3 positions;\nattribute vec4 instanceSourceColors;\nattribute vec4 instanceTargetColors;\nattribute vec3 instanceSourcePositions;\nattribute vec3 instanceSourcePositions64Low;\nattribute vec3 instanceTargetPositions;\nattribute vec3 instanceTargetPositions64Low;\nattribute vec3 instancePickingColors;\nattribute float instanceWidths;\nattribute float instanceHeights;\nattribute float instanceTilts;\n\nuniform bool greatCircle;\nuniform bool useShortestPath;\nuniform float numSegments;\nuniform float opacity;\nuniform float widthScale;\nuniform float widthMinPixels;\nuniform float widthMaxPixels;\nuniform int widthUnits;\n\nvarying vec4 vColor;\nvarying vec2 uv;\nvarying float isValid;\n\nfloat paraboloid(float distance, float sourceZ, float targetZ, float ratio) {\n\n float deltaZ = targetZ - sourceZ;\n float dh = distance * instanceHeights;\n if (dh == 0.0) {\n return sourceZ + deltaZ * ratio;\n }\n float unitZ = deltaZ / dh;\n float p2 = unitZ * unitZ + 1.0;\n float dir = step(deltaZ, 0.0);\n float z0 = mix(sourceZ, targetZ, dir);\n float r = mix(ratio, 1.0 - ratio, dir);\n return sqrt(r * (p2 - r)) * dh + z0;\n}\nvec2 getExtrusionOffset(vec2 line_clipspace, float offset_direction, float width) {\n vec2 dir_screenspace = normalize(line_clipspace * project_uViewportSize);\n dir_screenspace = vec2(-dir_screenspace.y, dir_screenspace.x);\n\n return dir_screenspace * offset_direction * width / 2.0;\n}\n\nfloat getSegmentRatio(float index) {\n return smoothstep(0.0, 1.0, index / (numSegments - 1.0));\n}\n\nvec3 interpolateFlat(vec3 source, vec3 target, float segmentRatio) {\n float distance = length(source.xy - target.xy);\n float z = paraboloid(distance, source.z, target.z, segmentRatio);\n\n float tiltAngle = radians(instanceTilts);\n vec2 tiltDirection = normalize(target.xy - source.xy);\n vec2 tilt = vec2(-tiltDirection.y, tiltDirection.x) * z * sin(tiltAngle);\n\n return vec3(\n mix(source.xy, target.xy, segmentRatio) + tilt,\n z * cos(tiltAngle)\n );\n}\nfloat getAngularDist (vec2 source, vec2 target) {\n vec2 sourceRadians = radians(source);\n vec2 targetRadians = radians(target);\n vec2 sin_half_delta = sin((sourceRadians - targetRadians) / 2.0);\n vec2 shd_sq = sin_half_delta * sin_half_delta;\n\n float a = shd_sq.y + cos(sourceRadians.y) * cos(targetRadians.y) * shd_sq.x;\n return 2.0 * asin(sqrt(a));\n}\n\nvec3 interpolateGreatCircle(vec3 source, vec3 target, vec3 source3D, vec3 target3D, float angularDist, float t) {\n vec2 lngLat;\n if(abs(angularDist - PI) < 0.001) {\n lngLat = (1.0 - t) * source.xy + t * target.xy;\n } else {\n float a = sin((1.0 - t) * angularDist);\n float b = sin(t * angularDist);\n vec3 p = source3D.yxz * a + target3D.yxz * b;\n lngLat = degrees(vec2(atan(p.y, -p.x), atan(p.z, length(p.xy))));\n }\n\n float z = paraboloid(angularDist * EARTH_RADIUS, source.z, target.z, t);\n\n return vec3(lngLat, z);\n}\n\nvoid main(void) {\n geometry.worldPosition = instanceSourcePositions;\n geometry.worldPositionAlt = instanceTargetPositions;\n\n float segmentIndex = positions.x;\n float segmentRatio = getSegmentRatio(segmentIndex);\n float prevSegmentRatio = getSegmentRatio(max(0.0, segmentIndex - 1.0));\n float nextSegmentRatio = getSegmentRatio(min(numSegments - 1.0, segmentIndex + 1.0));\n float indexDir = mix(-1.0, 1.0, step(segmentIndex, 0.0));\n isValid = 1.0;\n\n uv = vec2(segmentRatio, positions.y);\n geometry.uv = uv;\n geometry.pickingColor = instancePickingColors;\n\n vec4 curr;\n vec4 next;\n vec3 source;\n vec3 target;\n\n if ((greatCircle || project_uProjectionMode == PROJECTION_MODE_GLOBE) && project_uCoordinateSystem == COORDINATE_SYSTEM_LNGLAT) {\n source = project_globe_(vec3(instanceSourcePositions.xy, 0.0));\n target = project_globe_(vec3(instanceTargetPositions.xy, 0.0));\n float angularDist = getAngularDist(instanceSourcePositions.xy, instanceTargetPositions.xy);\n\n vec3 prevPos = interpolateGreatCircle(instanceSourcePositions, instanceTargetPositions, source, target, angularDist, prevSegmentRatio);\n vec3 currPos = interpolateGreatCircle(instanceSourcePositions, instanceTargetPositions, source, target, angularDist, segmentRatio);\n vec3 nextPos = interpolateGreatCircle(instanceSourcePositions, instanceTargetPositions, source, target, angularDist, nextSegmentRatio);\n\n if (abs(currPos.x - prevPos.x) > 180.0) {\n indexDir = -1.0;\n isValid = 0.0;\n } else if (abs(currPos.x - nextPos.x) > 180.0) {\n indexDir = 1.0;\n isValid = 0.0;\n }\n nextPos = indexDir < 0.0 ? prevPos : nextPos;\n nextSegmentRatio = indexDir < 0.0 ? prevSegmentRatio : nextSegmentRatio;\n\n if (isValid == 0.0) {\n nextPos.x += nextPos.x > 0.0 ? -360.0 : 360.0;\n float t = ((currPos.x > 0.0 ? 180.0 : -180.0) - currPos.x) / (nextPos.x - currPos.x);\n currPos = mix(currPos, nextPos, t);\n segmentRatio = mix(segmentRatio, nextSegmentRatio, t);\n }\n\n vec3 currPos64Low = mix(instanceSourcePositions64Low, instanceTargetPositions64Low, segmentRatio);\n vec3 nextPos64Low = mix(instanceSourcePositions64Low, instanceTargetPositions64Low, nextSegmentRatio);\n \n curr = project_position_to_clipspace(currPos, currPos64Low, vec3(0.0), geometry.position);\n next = project_position_to_clipspace(nextPos, nextPos64Low, vec3(0.0));\n \n } else {\n vec3 source_world = instanceSourcePositions;\n vec3 target_world = instanceTargetPositions;\n if (useShortestPath) {\n source_world.x = mod(source_world.x + 180., 360.0) - 180.;\n target_world.x = mod(target_world.x + 180., 360.0) - 180.;\n\n float deltaLng = target_world.x - source_world.x;\n if (deltaLng > 180.) target_world.x -= 360.;\n if (deltaLng < -180.) source_world.x -= 360.;\n }\n source = project_position(source_world, instanceSourcePositions64Low);\n target = project_position(target_world, instanceTargetPositions64Low);\n float antiMeridianX = 0.0;\n\n if (useShortestPath) {\n if (project_uProjectionMode == PROJECTION_MODE_WEB_MERCATOR_AUTO_OFFSET) {\n antiMeridianX = -(project_uCoordinateOrigin.x + 180.) / 360. * TILE_SIZE;\n }\n float thresholdRatio = (antiMeridianX - source.x) / (target.x - source.x);\n\n if (prevSegmentRatio <= thresholdRatio && nextSegmentRatio > thresholdRatio) {\n isValid = 0.0;\n indexDir = sign(segmentRatio - thresholdRatio);\n segmentRatio = thresholdRatio;\n }\n }\n\n nextSegmentRatio = indexDir < 0.0 ? prevSegmentRatio : nextSegmentRatio;\n vec3 currPos = interpolateFlat(source, target, segmentRatio);\n vec3 nextPos = interpolateFlat(source, target, nextSegmentRatio);\n\n if (useShortestPath) {\n if (nextPos.x < antiMeridianX) {\n currPos.x += TILE_SIZE;\n nextPos.x += TILE_SIZE;\n }\n }\n\n curr = project_common_position_to_clipspace(vec4(currPos, 1.0));\n next = project_common_position_to_clipspace(vec4(nextPos, 1.0));\n geometry.position = vec4(currPos, 1.0);\n }\n float widthPixels = clamp(\n project_size_to_pixel(instanceWidths * widthScale, widthUnits),\n widthMinPixels, widthMaxPixels\n );\n vec3 offset = vec3(\n getExtrusionOffset((next.xy - curr.xy) * indexDir, positions.y, widthPixels),\n 0.0);\n DECKGL_FILTER_SIZE(offset, geometry);\n DECKGL_FILTER_GL_POSITION(curr, geometry);\n gl_Position = curr + vec4(project_pixel_size_to_clipspace(offset.xy), 0.0, 0.0);\n\n vec4 color = mix(instanceSourceColors, instanceTargetColors, segmentRatio);\n vColor = vec4(color.rgb, color.a * opacity);\n DECKGL_FILTER_COLOR(vColor, geometry);\n}\n`;var tq=`#define SHADER_NAME arc-layer-fragment-shader\n\nprecision highp float;\n\nvarying vec4 vColor;\nvarying vec2 uv;\nvarying float isValid;\n\nvoid main(void) {\n if (isValid == 0.0) {\n discard;\n }\n\n gl_FragColor = vColor;\n geometry.uv = uv;\n\n DECKGL_FILTER_COLOR(gl_FragColor, geometry);\n}\n`;var R3=[0,0,0,255],Udt={getSourcePosition:{type:\"accessor\",value:e=>e.sourcePosition},getTargetPosition:{type:\"accessor\",value:e=>e.targetPosition},getSourceColor:{type:\"accessor\",value:R3},getTargetColor:{type:\"accessor\",value:R3},getWidth:{type:\"accessor\",value:1},getHeight:{type:\"accessor\",value:1},getTilt:{type:\"accessor\",value:0},greatCircle:!1,numSegments:{type:\"number\",value:50,min:1},widthUnits:\"pixels\",widthScale:{type:\"number\",value:1,min:0},widthMinPixels:{type:\"number\",value:0,min:0},widthMaxPixels:{type:\"number\",value:Number.MAX_SAFE_INTEGER,min:0}},Tp=class extends dn{constructor(...t){super(...t),G(this,\"state\",void 0)}getBounds(){var t;return(t=this.getAttributeManager())===null||t===void 0?void 0:t.getBounds([\"instanceSourcePositions\",\"instanceTargetPositions\"])}getShaders(){return super.getShaders({vs:JH,fs:tq,modules:[Rs,Ao]})}get wrapLongitude(){return!1}initializeState(){this.getAttributeManager().addInstanced({instanceSourcePositions:{size:3,type:5130,fp64:this.use64bitPositions(),transition:!0,accessor:\"getSourcePosition\"},instanceTargetPositions:{size:3,type:5130,fp64:this.use64bitPositions(),transition:!0,accessor:\"getTargetPosition\"},instanceSourceColors:{size:this.props.colorFormat.length,type:5121,normalized:!0,transition:!0,accessor:\"getSourceColor\",defaultValue:R3},instanceTargetColors:{size:this.props.colorFormat.length,type:5121,normalized:!0,transition:!0,accessor:\"getTargetColor\",defaultValue:R3},instanceWidths:{size:1,transition:!0,accessor:\"getWidth\",defaultValue:1},instanceHeights:{size:1,transition:!0,accessor:\"getHeight\",defaultValue:1},instanceTilts:{size:1,transition:!0,accessor:\"getTilt\",defaultValue:0}})}updateState(t){super.updateState(t);let{props:r,oldProps:i,changeFlags:s}=t;if(s.extensionsChanged||s.propsChanged&&r.numSegments!==i.numSegments){var n;let{gl:o}=this.context;(n=this.state.model)===null||n===void 0||n.delete(),this.state.model=this._getModel(o),this.getAttributeManager().invalidateAll()}}draw({uniforms:t}){let{widthUnits:r,widthScale:i,widthMinPixels:s,widthMaxPixels:n,greatCircle:o,wrapLongitude:c}=this.props;this.state.model.setUniforms(t).setUniforms({greatCircle:o,widthUnits:po[r],widthScale:i,widthMinPixels:s,widthMaxPixels:n,useShortestPath:c}).draw()}_getModel(t){let{id:r,numSegments:i}=this.props,s=[];for(let o=0;o0&&j>0&&(c[I++]=w-n,c[I++]=w-n-1,c[I++]=w-1,c[I++]=w-n,c[I++]=w-1,c[I++]=w),w++}}return{vertexCount:o,positions:_,indices:c,texCoords:f}}function Gdt(e){let t=new Float64Array(12);for(let r=0;r 0.5) {\n vTexPos = geometry.worldPosition.xy;\n }\n\n vec4 color = vec4(0.0);\n DECKGL_FILTER_COLOR(color, geometry);\n}\n`;var Hdt=`\nvec3 packUVsIntoRGB(vec2 uv) {\n // Extract the top 8 bits. We want values to be truncated down so we can add a fraction\n vec2 uv8bit = floor(uv * 256.);\n\n // Calculate the normalized remainders of u and v parts that do not fit into 8 bits\n // Scale and clamp to 0-1 range\n vec2 uvFraction = fract(uv * 256.);\n vec2 uvFraction4bit = floor(uvFraction * 16.);\n\n // Remainder can be encoded in blue channel, encode as 4 bits for pixel coordinates\n float fractions = uvFraction4bit.x + uvFraction4bit.y * 16.;\n\n return vec3(uv8bit, fractions) / 255.;\n}\n`,rq=`\n#define SHADER_NAME bitmap-layer-fragment-shader\n\n#ifdef GL_ES\nprecision highp float;\n#endif\n\nuniform sampler2D bitmapTexture;\n\nvarying vec2 vTexCoord;\nvarying vec2 vTexPos;\n\nuniform float desaturate;\nuniform vec4 transparentColor;\nuniform vec3 tintColor;\nuniform float opacity;\n\nuniform float coordinateConversion;\nuniform vec4 bounds;\n\n/* projection utils */\nconst float TILE_SIZE = 512.0;\nconst float PI = 3.1415926536;\nconst float WORLD_SCALE = TILE_SIZE / PI / 2.0;\n\n// from degrees to Web Mercator\nvec2 lnglat_to_mercator(vec2 lnglat) {\n float x = lnglat.x;\n float y = clamp(lnglat.y, -89.9, 89.9);\n return vec2(\n radians(x) + PI,\n PI + log(tan(PI * 0.25 + radians(y) * 0.5))\n ) * WORLD_SCALE;\n}\n\n// from Web Mercator to degrees\nvec2 mercator_to_lnglat(vec2 xy) {\n xy /= WORLD_SCALE;\n return degrees(vec2(\n xy.x - PI,\n atan(exp(xy.y - PI)) * 2.0 - PI * 0.5\n ));\n}\n/* End projection utils */\n\n// apply desaturation\nvec3 color_desaturate(vec3 color) {\n float luminance = (color.r + color.g + color.b) * 0.333333333;\n return mix(color, vec3(luminance), desaturate);\n}\n\n// apply tint\nvec3 color_tint(vec3 color) {\n return color * tintColor;\n}\n\n// blend with background color\nvec4 apply_opacity(vec3 color, float alpha) {\n if (transparentColor.a == 0.0) {\n return vec4(color, alpha);\n }\n float blendedAlpha = alpha + transparentColor.a * (1.0 - alpha);\n float highLightRatio = alpha / blendedAlpha;\n vec3 blendedRGB = mix(transparentColor.rgb, color, highLightRatio);\n return vec4(blendedRGB, blendedAlpha);\n}\n\nvec2 getUV(vec2 pos) {\n return vec2(\n (pos.x - bounds[0]) / (bounds[2] - bounds[0]),\n (pos.y - bounds[3]) / (bounds[1] - bounds[3])\n );\n}\n\n`.concat(Hdt,`\n\nvoid main(void) {\n vec2 uv = vTexCoord;\n if (coordinateConversion < -0.5) {\n vec2 lnglat = mercator_to_lnglat(vTexPos);\n uv = getUV(lnglat);\n } else if (coordinateConversion > 0.5) {\n vec2 commonPos = lnglat_to_mercator(vTexPos);\n uv = getUV(commonPos);\n }\n vec4 bitmapColor = texture2D(bitmapTexture, uv);\n\n gl_FragColor = apply_opacity(color_tint(color_desaturate(bitmapColor.rgb)), bitmapColor.a * opacity);\n\n geometry.uv = uv;\n DECKGL_FILTER_COLOR(gl_FragColor, geometry);\n\n if (picking_uActive && !picking_uAttribute) {\n // Since instance information is not used, we can use picking color for pixel index\n gl_FragColor.rgb = packUVsIntoRGB(uv);\n }\n}\n`);var qdt={image:{type:\"image\",value:null,async:!0},bounds:{type:\"array\",value:[1,0,0,1],compare:!0},_imageCoordinateSystem:Yr.DEFAULT,desaturate:{type:\"number\",min:0,max:1,value:0},transparentColor:{type:\"color\",value:[0,0,0,0]},tintColor:{type:\"color\",value:[255,255,255]},textureParameters:{type:\"object\",ignore:!0}},Mp=class extends dn{constructor(...t){super(...t),G(this,\"state\",void 0)}getShaders(){return super.getShaders({vs:eq,fs:rq,modules:[Rs,Ao]})}initializeState(){let t=this.getAttributeManager();t.remove([\"instancePickingColors\"]);let r=!0;t.add({indices:{size:1,isIndexed:!0,update:i=>i.value=this.state.mesh.indices,noAlloc:r},positions:{size:3,type:5130,fp64:this.use64bitPositions(),update:i=>i.value=this.state.mesh.positions,noAlloc:r},texCoords:{size:2,update:i=>i.value=this.state.mesh.texCoords,noAlloc:r}})}updateState({props:t,oldProps:r,changeFlags:i}){let s=this.getAttributeManager();if(i.extensionsChanged){var n;let{gl:o}=this.context;(n=this.state.model)===null||n===void 0||n.delete(),this.state.model=this._getModel(o),s.invalidateAll()}if(t.bounds!==r.bounds){let o=this.state.mesh,c=this._createMesh();this.state.model.setVertexCount(c.vertexCount);for(let f in c)o&&o[f]!==c[f]&&s.invalidate(f);this.setState({mesh:c,...this._getCoordinateUniforms()})}else t._imageCoordinateSystem!==r._imageCoordinateSystem&&this.setState(this._getCoordinateUniforms())}getPickingInfo(t){let{image:r}=this.props,i=t.info;if(!i.color||!r)return i.bitmap=null,i;let{width:s,height:n}=r;i.index=0;let o=Zdt(i.color),c=[Math.floor(o[0]*s),Math.floor(o[1]*n)];return i.bitmap={size:{width:s,height:n},uv:o,pixel:c},i}disablePickingIndex(){this.setState({disablePicking:!0})}restorePickingColors(){this.setState({disablePicking:!1})}_updateAutoHighlight(t){super._updateAutoHighlight({...t,color:this.encodePickingColor(0)})}_createMesh(){let{bounds:t}=this.props,r=t;return iq(t)&&(r=[[t[0],t[1]],[t[0],t[3]],[t[2],t[3]],[t[2],t[1]]]),iB(r,this.context.viewport.resolution)}_getModel(t){return t?new fn(t,{...this.getShaders(),id:this.props.id,geometry:new $n({drawMode:4,vertexCount:6}),isInstanced:!1}):null}draw(t){let{uniforms:r,moduleParameters:i}=t,{model:s,coordinateConversion:n,bounds:o,disablePicking:c}=this.state,{image:f,desaturate:_,transparentColor:w,tintColor:I}=this.props;i.pickingActive&&c||f&&s&&s.setUniforms(r).setUniforms({bitmapTexture:f,desaturate:_,transparentColor:w.map(R=>R/255),tintColor:I.slice(0,3).map(R=>R/255),coordinateConversion:n,bounds:o}).draw()}_getCoordinateUniforms(){let{LNGLAT:t,CARTESIAN:r,DEFAULT:i}=Yr,{_imageCoordinateSystem:s}=this.props;if(s!==i){let{bounds:n}=this.props;if(!iq(n))throw new Error(\"_imageCoordinateSystem only supports rectangular bounds\");let o=this.context.viewport.resolution?t:r;if(s=s===t?t:r,s===t&&o===r)return{coordinateConversion:-1,bounds:n};if(s===r&&o===t){let c=va([n[0],n[1]]),f=va([n[2],n[3]]);return{coordinateConversion:1,bounds:[c[0],c[1],f[0],f[1]]}}}return{coordinateConversion:0,bounds:[0,0,0,0]}}};G(Mp,\"layerName\",\"BitmapLayer\");G(Mp,\"defaultProps\",qdt);function Zdt(e){let[t,r,i]=e,s=(i&240)/256,n=(i&15)/16;return[(t+n)/256,(r+s)/256]}function iq(e){return Number.isFinite(e[0])}var nq=`#define SHADER_NAME icon-layer-vertex-shader\n\nattribute vec2 positions;\n\nattribute vec3 instancePositions;\nattribute vec3 instancePositions64Low;\nattribute float instanceSizes;\nattribute float instanceAngles;\nattribute vec4 instanceColors;\nattribute vec3 instancePickingColors;\nattribute vec4 instanceIconFrames;\nattribute float instanceColorModes;\nattribute vec2 instanceOffsets;\nattribute vec2 instancePixelOffset;\n\nuniform float sizeScale;\nuniform vec2 iconsTextureDim;\nuniform float sizeMinPixels;\nuniform float sizeMaxPixels;\nuniform bool billboard;\nuniform int sizeUnits;\n\nvarying float vColorMode;\nvarying vec4 vColor;\nvarying vec2 vTextureCoords;\nvarying vec2 uv;\n\nvec2 rotate_by_angle(vec2 vertex, float angle) {\n float angle_radian = angle * PI / 180.0;\n float cos_angle = cos(angle_radian);\n float sin_angle = sin(angle_radian);\n mat2 rotationMatrix = mat2(cos_angle, -sin_angle, sin_angle, cos_angle);\n return rotationMatrix * vertex;\n}\n\nvoid main(void) {\n geometry.worldPosition = instancePositions;\n geometry.uv = positions;\n geometry.pickingColor = instancePickingColors;\n uv = positions;\n\n vec2 iconSize = instanceIconFrames.zw;\n float sizePixels = clamp(\n project_size_to_pixel(instanceSizes * sizeScale, sizeUnits), \n sizeMinPixels, sizeMaxPixels\n );\n float instanceScale = iconSize.y == 0.0 ? 0.0 : sizePixels / iconSize.y;\n vec2 pixelOffset = positions / 2.0 * iconSize + instanceOffsets;\n pixelOffset = rotate_by_angle(pixelOffset, instanceAngles) * instanceScale;\n pixelOffset += instancePixelOffset;\n pixelOffset.y *= -1.0;\n\n if (billboard) {\n gl_Position = project_position_to_clipspace(instancePositions, instancePositions64Low, vec3(0.0), geometry.position);\n DECKGL_FILTER_GL_POSITION(gl_Position, geometry);\n vec3 offset = vec3(pixelOffset, 0.0);\n DECKGL_FILTER_SIZE(offset, geometry);\n gl_Position.xy += project_pixel_size_to_clipspace(offset.xy);\n\n } else {\n vec3 offset_common = vec3(project_pixel_size(pixelOffset), 0.0);\n DECKGL_FILTER_SIZE(offset_common, geometry);\n gl_Position = project_position_to_clipspace(instancePositions, instancePositions64Low, offset_common, geometry.position); \n DECKGL_FILTER_GL_POSITION(gl_Position, geometry);\n }\n\n vTextureCoords = mix(\n instanceIconFrames.xy,\n instanceIconFrames.xy + iconSize,\n (positions.xy + 1.0) / 2.0\n ) / iconsTextureDim;\n\n vColor = instanceColors;\n DECKGL_FILTER_COLOR(vColor, geometry);\n\n vColorMode = instanceColorModes;\n}\n`;var sq=`#define SHADER_NAME icon-layer-fragment-shader\n\nprecision highp float;\n\nuniform float opacity;\nuniform sampler2D iconsTexture;\nuniform float alphaCutoff;\n\nvarying float vColorMode;\nvarying vec4 vColor;\nvarying vec2 vTextureCoords;\nvarying vec2 uv;\n\nvoid main(void) {\n geometry.uv = uv;\n\n vec4 texColor = texture2D(iconsTexture, vTextureCoords);\n vec3 color = mix(texColor.rgb, vColor.rgb, vColorMode);\n float a = texColor.a * opacity * vColor.a;\n\n if (a < alphaCutoff) {\n discard;\n }\n\n gl_FragColor = vec4(color, a);\n DECKGL_FILTER_COLOR(gl_FragColor, geometry);\n}\n`;var Ydt=1024,Qdt=4,oq=()=>{},aq={10241:9987,10240:9729,10242:33071,10243:33071};function $dt(e){return Math.pow(2,Math.ceil(Math.log2(e)))}function Xdt(e,t,r,i){let s=Math.min(r/t.width,i/t.height),n=Math.floor(t.width*s),o=Math.floor(t.height*s);return s===1?{data:t,width:n,height:o}:(e.canvas.height=o,e.canvas.width=n,e.clearRect(0,0,n,o),e.drawImage(t,0,0,t.width,t.height,0,0,n,o),{data:e.canvas,width:n,height:o})}function G2(e){return e&&(e.id||e.url)}function Kdt(e,t,r,i){let s=e.width,n=e.height,o=new pi(e.gl,{width:t,height:r,parameters:i});return gE(e,o,{targetY:0,width:s,height:n}),e.delete(),o}function lq(e,t,r){for(let i=0;io&&(lq(r,c,s),i=0,s=n+s+t,n=0,c=[]),c.push({icon:_,xOffset:i}),i=i+R+t,n=Math.max(n,I)}}return c.length>0&&lq(r,c,s),{mapping:r,rowHeight:n,xOffset:i,yOffset:s,canvasWidth:o,canvasHeight:$dt(n+s+t)}}function tpt(e,t,r){if(!e||!t)return null;r=r||{};let i={},{iterable:s,objectInfo:n}=Jc(e);for(let o of s){n.index++;let c=t(o,n),f=G2(c);if(!c)throw new Error(\"Icon is missing.\");if(!c.url)throw new Error(\"Icon url is missing.\");!i[f]&&(!r[f]||c.url!==r[f].url)&&(i[f]={...c,source:o,sourceIndex:n.index})}return i}var W2=class{constructor(t,{onUpdate:r=oq,onError:i=oq}){G(this,\"gl\",void 0),G(this,\"onUpdate\",void 0),G(this,\"onError\",void 0),G(this,\"_loadOptions\",null),G(this,\"_texture\",null),G(this,\"_externalTexture\",null),G(this,\"_mapping\",{}),G(this,\"_textureParameters\",null),G(this,\"_pendingCount\",0),G(this,\"_autoPacking\",!1),G(this,\"_xOffset\",0),G(this,\"_yOffset\",0),G(this,\"_rowHeight\",0),G(this,\"_buffer\",Qdt),G(this,\"_canvasWidth\",Ydt),G(this,\"_canvasHeight\",0),G(this,\"_canvas\",null),this.gl=t,this.onUpdate=r,this.onError=i}finalize(){var t;(t=this._texture)===null||t===void 0||t.delete()}getTexture(){return this._texture||this._externalTexture}getIconMapping(t){let r=this._autoPacking?G2(t):t;return this._mapping[r]||{}}setProps({loadOptions:t,autoPacking:r,iconAtlas:i,iconMapping:s,textureParameters:n}){if(t&&(this._loadOptions=t),r!==void 0&&(this._autoPacking=r),s&&(this._mapping=s),i){var o;(o=this._texture)===null||o===void 0||o.delete(),this._texture=null,this._externalTexture=i}n&&(this._textureParameters=n)}get isLoaded(){return this._pendingCount===0}packIcons(t,r){if(!this._autoPacking||typeof document>\"u\")return;let i=Object.values(tpt(t,r,this._mapping)||{});if(i.length>0){let{mapping:s,xOffset:n,yOffset:o,rowHeight:c,canvasHeight:f}=Jdt({icons:i,buffer:this._buffer,canvasWidth:this._canvasWidth,mapping:this._mapping,rowHeight:this._rowHeight,xOffset:this._xOffset,yOffset:this._yOffset});this._rowHeight=c,this._mapping=s,this._xOffset=n,this._yOffset=o,this._canvasHeight=f,this._texture||(this._texture=new pi(this.gl,{width:this._canvasWidth,height:this._canvasHeight,parameters:this._textureParameters||aq})),this._texture.height!==this._canvasHeight&&(this._texture=Kdt(this._texture,this._canvasWidth,this._canvasHeight,this._textureParameters||aq)),this.onUpdate(),this._canvas=this._canvas||document.createElement(\"canvas\"),this._loadIcons(i)}}_loadIcons(t){let r=this._canvas.getContext(\"2d\",{willReadFrequently:!0});for(let i of t)this._pendingCount++,jA(i.url,this._loadOptions).then(s=>{let n=G2(i),o=this._mapping[n],{x:c,y:f,width:_,height:w}=o,{data:I,width:R,height:N}=Xdt(r,s,_,w);this._texture.setSubImageData({data:I,x:c+(_-R)/2,y:f+(w-N)/2,width:R,height:N}),o.width=R,o.height=N,this._texture.generateMipmap(),this.onUpdate()}).catch(s=>{this.onError({url:i.url,source:i.source,sourceIndex:i.sourceIndex,loadOptions:this._loadOptions,error:s})}).finally(()=>{this._pendingCount--})}};var cq=[0,0,0,255],ept={iconAtlas:{type:\"image\",value:null,async:!0},iconMapping:{type:\"object\",value:{},async:!0},sizeScale:{type:\"number\",value:1,min:0},billboard:!0,sizeUnits:\"pixels\",sizeMinPixels:{type:\"number\",min:0,value:0},sizeMaxPixels:{type:\"number\",min:0,value:Number.MAX_SAFE_INTEGER},alphaCutoff:{type:\"number\",value:.05,min:0,max:1},getPosition:{type:\"accessor\",value:e=>e.position},getIcon:{type:\"accessor\",value:e=>e.icon},getColor:{type:\"accessor\",value:cq},getSize:{type:\"accessor\",value:1},getAngle:{type:\"accessor\",value:0},getPixelOffset:{type:\"accessor\",value:[0,0]},onIconError:{type:\"function\",value:null,optional:!0},textureParameters:{type:\"object\",ignore:!0}},Ep=class extends dn{constructor(...t){super(...t),G(this,\"state\",void 0)}getShaders(){return super.getShaders({vs:nq,fs:sq,modules:[Rs,Ao]})}initializeState(){this.state={iconManager:new W2(this.context.gl,{onUpdate:this._onUpdate.bind(this),onError:this._onError.bind(this)})},this.getAttributeManager().addInstanced({instancePositions:{size:3,type:5130,fp64:this.use64bitPositions(),transition:!0,accessor:\"getPosition\"},instanceSizes:{size:1,transition:!0,accessor:\"getSize\",defaultValue:1},instanceOffsets:{size:2,accessor:\"getIcon\",transform:this.getInstanceOffset},instanceIconFrames:{size:4,accessor:\"getIcon\",transform:this.getInstanceIconFrame},instanceColorModes:{size:1,type:5121,accessor:\"getIcon\",transform:this.getInstanceColorMode},instanceColors:{size:this.props.colorFormat.length,type:5121,normalized:!0,transition:!0,accessor:\"getColor\",defaultValue:cq},instanceAngles:{size:1,transition:!0,accessor:\"getAngle\"},instancePixelOffset:{size:2,transition:!0,accessor:\"getPixelOffset\"}})}updateState(t){super.updateState(t);let{props:r,oldProps:i,changeFlags:s}=t,n=this.getAttributeManager(),{iconAtlas:o,iconMapping:c,data:f,getIcon:_,textureParameters:w}=r,{iconManager:I}=this.state,R=o||this.internalState.isAsyncPropLoading(\"iconAtlas\");if(I.setProps({loadOptions:r.loadOptions,autoPacking:!R,iconAtlas:o,iconMapping:R?c:null,textureParameters:w}),R?i.iconMapping!==r.iconMapping&&n.invalidate(\"getIcon\"):(s.dataChanged||s.updateTriggersChanged&&(s.updateTriggersChanged.all||s.updateTriggersChanged.getIcon))&&I.packIcons(f,_),s.extensionsChanged){var N;let{gl:j}=this.context;(N=this.state.model)===null||N===void 0||N.delete(),this.state.model=this._getModel(j),n.invalidateAll()}}get isLoaded(){return super.isLoaded&&this.state.iconManager.isLoaded}finalizeState(t){super.finalizeState(t),this.state.iconManager.finalize()}draw({uniforms:t}){let{sizeScale:r,sizeMinPixels:i,sizeMaxPixels:s,sizeUnits:n,billboard:o,alphaCutoff:c}=this.props,{iconManager:f}=this.state,_=f.getTexture();_&&this.state.model.setUniforms(t).setUniforms({iconsTexture:_,iconsTextureDim:[_.width,_.height],sizeUnits:po[n],sizeScale:r,sizeMinPixels:i,sizeMaxPixels:s,billboard:o,alphaCutoff:c}).draw()}_getModel(t){let r=[-1,-1,-1,1,1,1,1,-1];return new fn(t,{...this.getShaders(),id:this.props.id,geometry:new $n({drawMode:6,attributes:{positions:{size:2,value:new Float32Array(r)}}}),isInstanced:!0})}_onUpdate(){this.setNeedsRedraw()}_onError(t){var r;let i=(r=this.getCurrentLayer())===null||r===void 0?void 0:r.props.onIconError;i?i(t):or.error(t.error.message)()}getInstanceOffset(t){let{width:r,height:i,anchorX:s=r/2,anchorY:n=i/2}=this.state.iconManager.getIconMapping(t);return[r/2-s,i/2-n]}getInstanceColorMode(t){return this.state.iconManager.getIconMapping(t).mask?1:0}getInstanceIconFrame(t){let{x:r,y:i,width:s,height:n}=this.state.iconManager.getIconMapping(t);return[r,i,s,n]}};G(Ep,\"defaultProps\",ept);G(Ep,\"layerName\",\"IconLayer\");var uq=`#define SHADER_NAME point-cloud-layer-vertex-shader\n\nattribute vec3 positions;\nattribute vec3 instanceNormals;\nattribute vec4 instanceColors;\nattribute vec3 instancePositions;\nattribute vec3 instancePositions64Low;\nattribute vec3 instancePickingColors;\n\nuniform float opacity;\nuniform float radiusPixels;\nuniform int sizeUnits;\n\nvarying vec4 vColor;\nvarying vec2 unitPosition;\n\nvoid main(void) {\n geometry.worldPosition = instancePositions;\n geometry.normal = project_normal(instanceNormals);\n unitPosition = positions.xy;\n geometry.uv = unitPosition;\n geometry.pickingColor = instancePickingColors;\n vec3 offset = vec3(positions.xy * project_size_to_pixel(radiusPixels, sizeUnits), 0.0);\n DECKGL_FILTER_SIZE(offset, geometry);\n\n gl_Position = project_position_to_clipspace(instancePositions, instancePositions64Low, vec3(0.), geometry.position);\n DECKGL_FILTER_GL_POSITION(gl_Position, geometry);\n gl_Position.xy += project_pixel_size_to_clipspace(offset.xy);\n vec3 lightColor = lighting_getLightColor(instanceColors.rgb, project_uCameraPosition, geometry.position.xyz, geometry.normal);\n vColor = vec4(lightColor, instanceColors.a * opacity);\n DECKGL_FILTER_COLOR(vColor, geometry);\n}\n`;var hq=`#define SHADER_NAME point-cloud-layer-fragment-shader\n\nprecision highp float;\n\nvarying vec4 vColor;\nvarying vec2 unitPosition;\n\nvoid main(void) {\n geometry.uv = unitPosition;\n\n float distToCenter = length(unitPosition);\n\n if (distToCenter > 1.0) {\n discard;\n }\n\n gl_FragColor = vColor;\n DECKGL_FILTER_COLOR(gl_FragColor, geometry);\n}\n`;var fq=[0,0,0,255],dq=[0,0,1],rpt={sizeUnits:\"pixels\",pointSize:{type:\"number\",min:0,value:10},getPosition:{type:\"accessor\",value:e=>e.position},getNormal:{type:\"accessor\",value:dq},getColor:{type:\"accessor\",value:fq},material:!0,radiusPixels:{deprecatedFor:\"pointSize\"}};function ipt(e){let{header:t,attributes:r}=e;!t||!r||(e.length=t.vertexCount,r.POSITION&&(r.instancePositions=r.POSITION),r.NORMAL&&(r.instanceNormals=r.NORMAL),r.COLOR_0&&(r.instanceColors=r.COLOR_0))}var Pp=class extends dn{getShaders(){return super.getShaders({vs:uq,fs:hq,modules:[Rs,Zf,Ao]})}initializeState(){this.getAttributeManager().addInstanced({instancePositions:{size:3,type:5130,fp64:this.use64bitPositions(),transition:!0,accessor:\"getPosition\"},instanceNormals:{size:3,transition:!0,accessor:\"getNormal\",defaultValue:dq},instanceColors:{size:this.props.colorFormat.length,type:5121,normalized:!0,transition:!0,accessor:\"getColor\",defaultValue:fq}})}updateState(t){let{changeFlags:r,props:i}=t;if(super.updateState(t),r.extensionsChanged){var s;let{gl:n}=this.context;(s=this.state.model)===null||s===void 0||s.delete(),this.state.model=this._getModel(n),this.getAttributeManager().invalidateAll()}r.dataChanged&&ipt(i.data)}draw({uniforms:t}){let{pointSize:r,sizeUnits:i}=this.props;this.state.model.setUniforms(t).setUniforms({sizeUnits:po[i],radiusPixels:r}).draw()}_getModel(t){let r=[];for(let i=0;i<3;i++){let s=i/3*Math.PI*2;r.push(Math.cos(s)*2,Math.sin(s)*2,0)}return new fn(t,{...this.getShaders(),id:this.props.id,geometry:new $n({drawMode:4,attributes:{positions:new Float32Array(r)}}),isInstanced:!0})}};G(Pp,\"layerName\",\"PointCloudLayer\");G(Pp,\"defaultProps\",rpt);var pq=`#define SHADER_NAME scatterplot-layer-vertex-shader\n\nattribute vec3 positions;\n\nattribute vec3 instancePositions;\nattribute vec3 instancePositions64Low;\nattribute float instanceRadius;\nattribute float instanceLineWidths;\nattribute vec4 instanceFillColors;\nattribute vec4 instanceLineColors;\nattribute vec3 instancePickingColors;\n\nuniform float opacity;\nuniform float radiusScale;\nuniform float radiusMinPixels;\nuniform float radiusMaxPixels;\nuniform float lineWidthScale;\nuniform float lineWidthMinPixels;\nuniform float lineWidthMaxPixels;\nuniform float stroked;\nuniform bool filled;\nuniform bool antialiasing;\nuniform bool billboard;\nuniform int radiusUnits;\nuniform int lineWidthUnits;\n\nvarying vec4 vFillColor;\nvarying vec4 vLineColor;\nvarying vec2 unitPosition;\nvarying float innerUnitRadius;\nvarying float outerRadiusPixels;\n\n\nvoid main(void) {\n geometry.worldPosition = instancePositions;\n outerRadiusPixels = clamp(\n project_size_to_pixel(radiusScale * instanceRadius, radiusUnits),\n radiusMinPixels, radiusMaxPixels\n );\n float lineWidthPixels = clamp(\n project_size_to_pixel(lineWidthScale * instanceLineWidths, lineWidthUnits),\n lineWidthMinPixels, lineWidthMaxPixels\n );\n outerRadiusPixels += stroked * lineWidthPixels / 2.0;\n float edgePadding = antialiasing ? (outerRadiusPixels + SMOOTH_EDGE_RADIUS) / outerRadiusPixels : 1.0;\n unitPosition = edgePadding * positions.xy;\n geometry.uv = unitPosition;\n geometry.pickingColor = instancePickingColors;\n\n innerUnitRadius = 1.0 - stroked * lineWidthPixels / outerRadiusPixels;\n \n if (billboard) {\n gl_Position = project_position_to_clipspace(instancePositions, instancePositions64Low, vec3(0.0), geometry.position);\n DECKGL_FILTER_GL_POSITION(gl_Position, geometry);\n vec3 offset = edgePadding * positions * outerRadiusPixels;\n DECKGL_FILTER_SIZE(offset, geometry);\n gl_Position.xy += project_pixel_size_to_clipspace(offset.xy);\n } else {\n vec3 offset = edgePadding * positions * project_pixel_size(outerRadiusPixels);\n DECKGL_FILTER_SIZE(offset, geometry);\n gl_Position = project_position_to_clipspace(instancePositions, instancePositions64Low, offset, geometry.position);\n DECKGL_FILTER_GL_POSITION(gl_Position, geometry);\n }\n vFillColor = vec4(instanceFillColors.rgb, instanceFillColors.a * opacity);\n DECKGL_FILTER_COLOR(vFillColor, geometry);\n vLineColor = vec4(instanceLineColors.rgb, instanceLineColors.a * opacity);\n DECKGL_FILTER_COLOR(vLineColor, geometry);\n}\n`;var Aq=`#define SHADER_NAME scatterplot-layer-fragment-shader\n\nprecision highp float;\n\nuniform bool filled;\nuniform float stroked;\nuniform bool antialiasing;\n\nvarying vec4 vFillColor;\nvarying vec4 vLineColor;\nvarying vec2 unitPosition;\nvarying float innerUnitRadius;\nvarying float outerRadiusPixels;\n\nvoid main(void) {\n geometry.uv = unitPosition;\n\n float distToCenter = length(unitPosition) * outerRadiusPixels;\n float inCircle = antialiasing ? \n smoothedge(distToCenter, outerRadiusPixels) : \n step(distToCenter, outerRadiusPixels);\n\n if (inCircle == 0.0) {\n discard;\n }\n\n if (stroked > 0.5) {\n float isLine = antialiasing ? \n smoothedge(innerUnitRadius * outerRadiusPixels, distToCenter) :\n step(innerUnitRadius * outerRadiusPixels, distToCenter);\n\n if (filled) {\n gl_FragColor = mix(vFillColor, vLineColor, isLine);\n } else {\n if (isLine == 0.0) {\n discard;\n }\n gl_FragColor = vec4(vLineColor.rgb, vLineColor.a * isLine);\n }\n } else if (!filled) {\n discard;\n } else {\n gl_FragColor = vFillColor;\n }\n\n gl_FragColor.a *= inCircle;\n DECKGL_FILTER_COLOR(gl_FragColor, geometry);\n}\n`;var mq=[0,0,0,255],npt={radiusUnits:\"meters\",radiusScale:{type:\"number\",min:0,value:1},radiusMinPixels:{type:\"number\",min:0,value:0},radiusMaxPixels:{type:\"number\",min:0,value:Number.MAX_SAFE_INTEGER},lineWidthUnits:\"meters\",lineWidthScale:{type:\"number\",min:0,value:1},lineWidthMinPixels:{type:\"number\",min:0,value:0},lineWidthMaxPixels:{type:\"number\",min:0,value:Number.MAX_SAFE_INTEGER},stroked:!1,filled:!0,billboard:!1,antialiasing:!0,getPosition:{type:\"accessor\",value:e=>e.position},getRadius:{type:\"accessor\",value:1},getFillColor:{type:\"accessor\",value:mq},getLineColor:{type:\"accessor\",value:mq},getLineWidth:{type:\"accessor\",value:1},strokeWidth:{deprecatedFor:\"getLineWidth\"},outline:{deprecatedFor:\"stroked\"},getColor:{deprecatedFor:[\"getFillColor\",\"getLineColor\"]}},Ku=class extends dn{getShaders(){return super.getShaders({vs:pq,fs:Aq,modules:[Rs,Ao]})}initializeState(){this.getAttributeManager().addInstanced({instancePositions:{size:3,type:5130,fp64:this.use64bitPositions(),transition:!0,accessor:\"getPosition\"},instanceRadius:{size:1,transition:!0,accessor:\"getRadius\",defaultValue:1},instanceFillColors:{size:this.props.colorFormat.length,transition:!0,normalized:!0,type:5121,accessor:\"getFillColor\",defaultValue:[0,0,0,255]},instanceLineColors:{size:this.props.colorFormat.length,transition:!0,normalized:!0,type:5121,accessor:\"getLineColor\",defaultValue:[0,0,0,255]},instanceLineWidths:{size:1,transition:!0,accessor:\"getLineWidth\",defaultValue:1}})}updateState(t){if(super.updateState(t),t.changeFlags.extensionsChanged){var r;let{gl:i}=this.context;(r=this.state.model)===null||r===void 0||r.delete(),this.state.model=this._getModel(i),this.getAttributeManager().invalidateAll()}}draw({uniforms:t}){let{radiusUnits:r,radiusScale:i,radiusMinPixels:s,radiusMaxPixels:n,stroked:o,filled:c,billboard:f,antialiasing:_,lineWidthUnits:w,lineWidthScale:I,lineWidthMinPixels:R,lineWidthMaxPixels:N}=this.props;this.state.model.setUniforms(t).setUniforms({stroked:o?1:0,filled:c,billboard:f,antialiasing:_,radiusUnits:po[r],radiusScale:i,radiusMinPixels:s,radiusMaxPixels:n,lineWidthUnits:po[w],lineWidthScale:I,lineWidthMinPixels:R,lineWidthMaxPixels:N}).draw()}_getModel(t){let r=[-1,-1,0,1,-1,0,1,1,0,-1,1,0];return new fn(t,{...this.getShaders(),id:this.props.id,geometry:new $n({drawMode:6,vertexCount:4,attributes:{positions:{size:3,value:new Float32Array(r)}}}),isInstanced:!0})}};G(Ku,\"defaultProps\",npt);G(Ku,\"layerName\",\"ScatterplotLayer\");var Kv={CLOCKWISE:1,COUNTER_CLOCKWISE:-1};function Vg(e,t,r={}){return gq(e,r)!==t?(spt(e,r),!0):!1}function gq(e,t={}){return Math.sign(D3(e,t))}function D3(e,t={}){let{start:r=0,end:i=e.length}=t,s=t.size||2,n=0;for(let o=r,c=i-s;o0){let s=!0;for(let n=0;nt[2]&&(r|=2),e[1]t[3]&&(r|=8),r}function Z2(e,t){let{size:r=2,broken:i=!1,gridResolution:s=10,gridOffset:n=[0,0],startIndex:o=0,endIndex:c=e.length}=t||{},f=(c-o)/r,_=[],w=[_],I=Sm(e,0,r,o),R,N,j=vq(I,s,n,[]),Q=[];xc(_,I);for(let et=1;etr&&(_=[],w.push(_),xc(_,I)),N=q2(R,j)}xc(_,R),H2(I,R)}return i?w:w[0]}var _q=0,apt=1;function B3(e,t){for(let r=0;r=0?(xc(_,N)&&I.push(Q),ut+=j):I.length&&(I[I.length-1]=_q),H2(et,N),Y=j,K=Q;return[J?{pos:f,types:t&&w}:null,ut?{pos:_,types:t&&I}:null]}function vq(e,t,r,i){let s=Math.floor((e[0]-r[0])/t)*t+r[0],n=Math.floor((e[1]-r[1])/t)*t+r[1];return i[0]=s,i[1]=n,i[2]=s+t,i[3]=n+t,i}function lpt(e,t,r){r&8?(e[1]+=t,e[3]+=t):r&4?(e[1]-=t,e[3]-=t):r&2?(e[0]+=t,e[2]+=t):r&1&&(e[0]-=t,e[2]-=t)}function cpt(e,t,r,i){let s=1/0,n=-1/0,o=1/0,c=-1/0;for(let f=0;fn?_:n,o=wc?w:c}return i[0][0]=s,i[0][1]=o,i[1][0]=n,i[1][1]=c,i}var upt=85.051129;function nB(e,t){let{size:r=2,startIndex:i=0,endIndex:s=e.length,normalize:n=!0}=t||{},o=e.slice(i,s);xq(o,r,0,s-i);let c=Z2(o,{size:r,broken:!0,gridResolution:360,gridOffset:[-180,-180]});if(n)for(let f of c)bq(f,r);return c}function sB(e,t=null,r){let{size:i=2,normalize:s=!0,edgeTypes:n=!1}=r||{};t=t||[];let o=[],c=[],f=0,_=0;for(let I=0;I<=t.length;I++){let R=t[I]||e.length,N=_,j=hpt(e,i,f,R);for(let Q=j;Qs&&(s=c,n=o-1)}return n}function fpt(e,t,r,i,s=upt){let n=e[r],o=e[i-t];if(Math.abs(n-o)>180){let c=Sm(e,0,t,r);c[0]+=Math.round((o-n)/360)*360,xc(e,c),c[1]=Math.sign(c[1])*s,xc(e,c),c[0]=n,xc(e,c)}}function xq(e,t,r,i){let s=e[0],n;for(let o=r;o180||c<-180)&&(n-=Math.round(c/360)*360),e[o]=s=n}}function bq(e,t){let r,i=e.length/t;for(let n=0;n=i),s=s.flatMap(N=>[N[0],N[1]]),Vg(s,Kv.COUNTER_CLOCKWISE));let n=r>0,o=i+1,c=n?o*3+1:i,f=Math.PI*2/i,_=new Uint16Array(n?i*3*2:0),w=new Float32Array(c*3),I=new Float32Array(c*3),R=0;if(n){for(let N=0;N 0.0 && instanceElevations >= 0.0);\n float dotRadius = radius * coverage * shouldRender;\n\n geometry.pickingColor = instancePickingColors;\n vec3 centroidPosition = vec3(instancePositions.xy, instancePositions.z + elevation);\n vec3 centroidPosition64Low = instancePositions64Low;\n vec2 offset = (rotationMatrix * positions.xy * strokeOffsetRatio + offset) * dotRadius;\n if (radiusUnits == UNIT_METERS) {\n offset = project_size(offset);\n }\n vec3 pos = vec3(offset, 0.);\n DECKGL_FILTER_SIZE(pos, geometry);\n\n gl_Position = project_position_to_clipspace(centroidPosition, centroidPosition64Low, pos, geometry.position);\n geometry.normal = project_normal(vec3(rotationMatrix * normals.xy, normals.z));\n DECKGL_FILTER_GL_POSITION(gl_Position, geometry);\n if (extruded && !isStroke) {\n#ifdef FLAT_SHADING\n position_commonspace = geometry.position;\n vColor = vec4(color.rgb, color.a * opacity);\n#else\n vec3 lightColor = lighting_getLightColor(color.rgb, project_uCameraPosition, geometry.position.xyz, geometry.normal);\n vColor = vec4(lightColor, color.a * opacity);\n#endif\n } else {\n vColor = vec4(color.rgb, color.a * opacity);\n }\n DECKGL_FILTER_COLOR(vColor, geometry);\n}\n`;var Sq=`#version 300 es\n#define SHADER_NAME column-layer-fragment-shader\n\nprecision highp float;\n\nuniform vec3 project_uCameraPosition;\nuniform bool extruded;\nuniform bool isStroke;\n\nout vec4 fragColor;\n\nin vec4 vColor;\n#ifdef FLAT_SHADING\nin vec4 position_commonspace;\n#endif\n\nvoid main(void) {\n fragColor = vColor;\n#ifdef FLAT_SHADING\n if (extruded && !isStroke && !picking_uActive) {\n vec3 normal = normalize(cross(dFdx(position_commonspace.xyz), dFdy(position_commonspace.xyz)));\n fragColor.rgb = lighting_getLightColor(vColor.rgb, project_uCameraPosition, position_commonspace.xyz, normal);\n }\n#endif\n DECKGL_FILTER_COLOR(fragColor, geometry);\n}\n`;var F3=[0,0,0,255],Apt={diskResolution:{type:\"number\",min:4,value:20},vertices:null,radius:{type:\"number\",min:0,value:1e3},angle:{type:\"number\",value:0},offset:{type:\"array\",value:[0,0]},coverage:{type:\"number\",min:0,max:1,value:1},elevationScale:{type:\"number\",min:0,value:1},radiusUnits:\"meters\",lineWidthUnits:\"meters\",lineWidthScale:1,lineWidthMinPixels:0,lineWidthMaxPixels:Number.MAX_SAFE_INTEGER,extruded:!0,wireframe:!1,filled:!0,stroked:!1,getPosition:{type:\"accessor\",value:e=>e.position},getFillColor:{type:\"accessor\",value:F3},getLineColor:{type:\"accessor\",value:F3},getLineWidth:{type:\"accessor\",value:1},getElevation:{type:\"accessor\",value:1e3},material:!0,getColor:{deprecatedFor:[\"getFillColor\",\"getLineColor\"]}},af=class extends dn{getShaders(){let{gl:t}=this.context,r=!fr(t),i={},s=this.props.flatShading&&$0(t,Ii.GLSL_DERIVATIVES);return s&&(i.FLAT_SHADING=1),super.getShaders({vs:wq,fs:Sq,defines:i,transpileToGLSL100:r,modules:[Rs,s?Ny:Zf,Ao]})}initializeState(){this.getAttributeManager().addInstanced({instancePositions:{size:3,type:5130,fp64:this.use64bitPositions(),transition:!0,accessor:\"getPosition\"},instanceElevations:{size:1,transition:!0,accessor:\"getElevation\"},instanceFillColors:{size:this.props.colorFormat.length,type:5121,normalized:!0,transition:!0,accessor:\"getFillColor\",defaultValue:F3},instanceLineColors:{size:this.props.colorFormat.length,type:5121,normalized:!0,transition:!0,accessor:\"getLineColor\",defaultValue:F3},instanceStrokeWidths:{size:1,accessor:\"getLineWidth\",transition:!0}})}updateState(t){super.updateState(t);let{props:r,oldProps:i,changeFlags:s}=t,n=s.extensionsChanged||r.flatShading!==i.flatShading;if(n){var o;let{gl:c}=this.context;(o=this.state.model)===null||o===void 0||o.delete(),this.state.model=this._getModel(c),this.getAttributeManager().invalidateAll()}(n||r.diskResolution!==i.diskResolution||r.vertices!==i.vertices||(r.extruded||r.stroked)!==(i.extruded||i.stroked))&&this._updateGeometry(r)}getGeometry(t,r,i){let s=new Q2({radius:1,height:i?2:0,vertices:r,nradial:t}),n=0;if(r)for(let o=0;o=t.length&&(r+=1-t.length/s);let n=r*s;return i[0]=t[n],i[1]=t[n+1],i[2]=s===3&&t[n+2]||0,i}isClosed(t){if(!this.normalize)return!!this.opts.loop;let{positionSize:r}=this,i=t.length-r;return t[0]===t[i]&&t[1]===t[i+1]&&(r===2||t[2]===t[i+2])}};function Mq(e){return Array.isArray(e[0])}var Eq=`#define SHADER_NAME path-layer-vertex-shader\n\nattribute vec2 positions;\n\nattribute float instanceTypes;\nattribute vec3 instanceStartPositions;\nattribute vec3 instanceEndPositions;\nattribute vec3 instanceLeftPositions;\nattribute vec3 instanceRightPositions;\nattribute vec3 instanceLeftPositions64Low;\nattribute vec3 instanceStartPositions64Low;\nattribute vec3 instanceEndPositions64Low;\nattribute vec3 instanceRightPositions64Low;\nattribute float instanceStrokeWidths;\nattribute vec4 instanceColors;\nattribute vec3 instancePickingColors;\n\nuniform float widthScale;\nuniform float widthMinPixels;\nuniform float widthMaxPixels;\nuniform float jointType;\nuniform float capType;\nuniform float miterLimit;\nuniform bool billboard;\nuniform int widthUnits;\n\nuniform float opacity;\n\nvarying vec4 vColor;\nvarying vec2 vCornerOffset;\nvarying float vMiterLength;\nvarying vec2 vPathPosition;\nvarying float vPathLength;\nvarying float vJointType;\n\nconst float EPSILON = 0.001;\nconst vec3 ZERO_OFFSET = vec3(0.0);\n\nfloat flipIfTrue(bool flag) {\n return -(float(flag) * 2. - 1.);\n}\nvec3 getLineJoinOffset(\n vec3 prevPoint, vec3 currPoint, vec3 nextPoint,\n vec2 width\n) {\n bool isEnd = positions.x > 0.0;\n float sideOfPath = positions.y;\n float isJoint = float(sideOfPath == 0.0);\n\n vec3 deltaA3 = (currPoint - prevPoint);\n vec3 deltaB3 = (nextPoint - currPoint);\n\n mat3 rotationMatrix;\n bool needsRotation = !billboard && project_needs_rotation(currPoint, rotationMatrix);\n if (needsRotation) {\n deltaA3 = deltaA3 * rotationMatrix;\n deltaB3 = deltaB3 * rotationMatrix;\n }\n vec2 deltaA = deltaA3.xy / width;\n vec2 deltaB = deltaB3.xy / width;\n\n float lenA = length(deltaA);\n float lenB = length(deltaB);\n\n vec2 dirA = lenA > 0. ? normalize(deltaA) : vec2(0.0, 0.0);\n vec2 dirB = lenB > 0. ? normalize(deltaB) : vec2(0.0, 0.0);\n\n vec2 perpA = vec2(-dirA.y, dirA.x);\n vec2 perpB = vec2(-dirB.y, dirB.x);\n vec2 tangent = dirA + dirB;\n tangent = length(tangent) > 0. ? normalize(tangent) : perpA;\n vec2 miterVec = vec2(-tangent.y, tangent.x);\n vec2 dir = isEnd ? dirA : dirB;\n vec2 perp = isEnd ? perpA : perpB;\n float L = isEnd ? lenA : lenB;\n float sinHalfA = abs(dot(miterVec, perp));\n float cosHalfA = abs(dot(dirA, miterVec));\n float turnDirection = flipIfTrue(dirA.x * dirB.y >= dirA.y * dirB.x);\n float cornerPosition = sideOfPath * turnDirection;\n\n float miterSize = 1.0 / max(sinHalfA, EPSILON);\n miterSize = mix(\n min(miterSize, max(lenA, lenB) / max(cosHalfA, EPSILON)),\n miterSize,\n step(0.0, cornerPosition)\n );\n\n vec2 offsetVec = mix(miterVec * miterSize, perp, step(0.5, cornerPosition))\n * (sideOfPath + isJoint * turnDirection);\n bool isStartCap = lenA == 0.0 || (!isEnd && (instanceTypes == 1.0 || instanceTypes == 3.0));\n bool isEndCap = lenB == 0.0 || (isEnd && (instanceTypes == 2.0 || instanceTypes == 3.0));\n bool isCap = isStartCap || isEndCap;\n if (isCap) {\n offsetVec = mix(perp * sideOfPath, dir * capType * 4.0 * flipIfTrue(isStartCap), isJoint);\n vJointType = capType;\n } else {\n vJointType = jointType;\n }\n vPathLength = L;\n vCornerOffset = offsetVec;\n vMiterLength = dot(vCornerOffset, miterVec * turnDirection);\n vMiterLength = isCap ? isJoint : vMiterLength;\n\n vec2 offsetFromStartOfPath = vCornerOffset + deltaA * float(isEnd);\n vPathPosition = vec2(\n dot(offsetFromStartOfPath, perp),\n dot(offsetFromStartOfPath, dir)\n );\n geometry.uv = vPathPosition;\n\n float isValid = step(instanceTypes, 3.5);\n vec3 offset = vec3(offsetVec * width * isValid, 0.0);\n\n if (needsRotation) {\n offset = rotationMatrix * offset;\n }\n return offset;\n}\nvoid clipLine(inout vec4 position, vec4 refPosition) {\n if (position.w < EPSILON) {\n float r = (EPSILON - refPosition.w) / (position.w - refPosition.w);\n position = refPosition + (position - refPosition) * r;\n }\n}\n\nvoid main() {\n geometry.pickingColor = instancePickingColors;\n\n vColor = vec4(instanceColors.rgb, instanceColors.a * opacity);\n\n float isEnd = positions.x;\n\n vec3 prevPosition = mix(instanceLeftPositions, instanceStartPositions, isEnd);\n vec3 prevPosition64Low = mix(instanceLeftPositions64Low, instanceStartPositions64Low, isEnd);\n\n vec3 currPosition = mix(instanceStartPositions, instanceEndPositions, isEnd);\n vec3 currPosition64Low = mix(instanceStartPositions64Low, instanceEndPositions64Low, isEnd);\n\n vec3 nextPosition = mix(instanceEndPositions, instanceRightPositions, isEnd);\n vec3 nextPosition64Low = mix(instanceEndPositions64Low, instanceRightPositions64Low, isEnd);\n\n geometry.worldPosition = currPosition;\n vec2 widthPixels = vec2(clamp(\n project_size_to_pixel(instanceStrokeWidths * widthScale, widthUnits),\n widthMinPixels, widthMaxPixels) / 2.0);\n vec3 width;\n\n if (billboard) {\n vec4 prevPositionScreen = project_position_to_clipspace(prevPosition, prevPosition64Low, ZERO_OFFSET);\n vec4 currPositionScreen = project_position_to_clipspace(currPosition, currPosition64Low, ZERO_OFFSET, geometry.position);\n vec4 nextPositionScreen = project_position_to_clipspace(nextPosition, nextPosition64Low, ZERO_OFFSET);\n\n clipLine(prevPositionScreen, currPositionScreen);\n clipLine(nextPositionScreen, currPositionScreen);\n clipLine(currPositionScreen, mix(nextPositionScreen, prevPositionScreen, isEnd));\n\n width = vec3(widthPixels, 0.0);\n DECKGL_FILTER_SIZE(width, geometry);\n\n vec3 offset = getLineJoinOffset(\n prevPositionScreen.xyz / prevPositionScreen.w,\n currPositionScreen.xyz / currPositionScreen.w,\n nextPositionScreen.xyz / nextPositionScreen.w,\n project_pixel_size_to_clipspace(width.xy)\n );\n\n DECKGL_FILTER_GL_POSITION(currPositionScreen, geometry);\n gl_Position = vec4(currPositionScreen.xyz + offset * currPositionScreen.w, currPositionScreen.w);\n } else {\n prevPosition = project_position(prevPosition, prevPosition64Low);\n currPosition = project_position(currPosition, currPosition64Low);\n nextPosition = project_position(nextPosition, nextPosition64Low);\n\n width = vec3(project_pixel_size(widthPixels), 0.0);\n DECKGL_FILTER_SIZE(width, geometry);\n\n vec3 offset = getLineJoinOffset(prevPosition, currPosition, nextPosition, width.xy);\n geometry.position = vec4(currPosition + offset, 1.0);\n gl_Position = project_common_position_to_clipspace(geometry.position);\n DECKGL_FILTER_GL_POSITION(gl_Position, geometry);\n }\n DECKGL_FILTER_COLOR(vColor, geometry);\n}\n`;var Pq=`#define SHADER_NAME path-layer-fragment-shader\n\nprecision highp float;\n\nuniform float miterLimit;\n\nvarying vec4 vColor;\nvarying vec2 vCornerOffset;\nvarying float vMiterLength;\nvarying vec2 vPathPosition;\nvarying float vPathLength;\nvarying float vJointType;\n\nvoid main(void) {\n geometry.uv = vPathPosition;\n\n if (vPathPosition.y < 0.0 || vPathPosition.y > vPathLength) {\n if (vJointType > 0.5 && length(vCornerOffset) > 1.0) {\n discard;\n }\n if (vJointType < 0.5 && vMiterLength > miterLimit + 1.0) {\n discard;\n }\n }\n gl_FragColor = vColor;\n\n DECKGL_FILTER_COLOR(gl_FragColor, geometry);\n}\n`;var Iq=[0,0,0,255],_pt={widthUnits:\"meters\",widthScale:{type:\"number\",min:0,value:1},widthMinPixels:{type:\"number\",min:0,value:0},widthMaxPixels:{type:\"number\",min:0,value:Number.MAX_SAFE_INTEGER},jointRounded:!1,capRounded:!1,miterLimit:{type:\"number\",min:0,value:4},billboard:!1,_pathType:null,getPath:{type:\"accessor\",value:e=>e.path},getColor:{type:\"accessor\",value:Iq},getWidth:{type:\"accessor\",value:1},rounded:{deprecatedFor:[\"jointRounded\",\"capRounded\"]}},aB={enter:(e,t)=>t.length?t.subarray(t.length-e.length):e},bc=class extends dn{constructor(...t){super(...t),G(this,\"state\",void 0)}getShaders(){return super.getShaders({vs:Eq,fs:Pq,modules:[Rs,Ao]})}get wrapLongitude(){return!1}initializeState(){this.getAttributeManager().addInstanced({positions:{size:3,vertexOffset:1,type:5130,fp64:this.use64bitPositions(),transition:aB,accessor:\"getPath\",update:this.calculatePositions,noAlloc:!0,shaderAttributes:{instanceLeftPositions:{vertexOffset:0},instanceStartPositions:{vertexOffset:1},instanceEndPositions:{vertexOffset:2},instanceRightPositions:{vertexOffset:3}}},instanceTypes:{size:1,type:5121,update:this.calculateSegmentTypes,noAlloc:!0},instanceStrokeWidths:{size:1,accessor:\"getWidth\",transition:aB,defaultValue:1},instanceColors:{size:this.props.colorFormat.length,type:5121,normalized:!0,accessor:\"getColor\",transition:aB,defaultValue:Iq},instancePickingColors:{size:3,type:5121,accessor:(i,{index:s,target:n})=>this.encodePickingColor(i&&i.__source?i.__source.index:s,n)}}),this.setState({pathTesselator:new $2({fp64:this.use64bitPositions()})})}updateState(t){super.updateState(t);let{props:r,changeFlags:i}=t,s=this.getAttributeManager();if(i.dataChanged||i.updateTriggersChanged&&(i.updateTriggersChanged.all||i.updateTriggersChanged.getPath)){let{pathTesselator:c}=this.state,f=r.data.attributes||{};c.updateGeometry({data:r.data,geometryBuffer:f.getPath,buffers:f,normalize:!r._pathType,loop:r._pathType===\"loop\",getGeometry:r.getPath,positionFormat:r.positionFormat,wrapLongitude:r.wrapLongitude,resolution:this.context.viewport.resolution,dataChanged:i.dataChanged}),this.setState({numInstances:c.instanceCount,startIndices:c.vertexStarts}),i.dataChanged||s.invalidateAll()}if(i.extensionsChanged){var o;let{gl:c}=this.context;(o=this.state.model)===null||o===void 0||o.delete(),this.state.model=this._getModel(c),s.invalidateAll()}}getPickingInfo(t){let r=super.getPickingInfo(t),{index:i}=r,{data:s}=this.props;return s[0]&&s[0].__source&&(r.object=s.find(n=>n.__source.index===i)),r}disablePickingIndex(t){let{data:r}=this.props;if(r[0]&&r[0].__source)for(let i=0;i=1&&e[0].length>=2&&Number.isFinite(e[0][0])}function Fpt(e){let t=e[0],r=e[e.length-1];return t[0]===r[0]&&t[1]===r[1]&&t[2]===r[2]}function zpt(e,t,r,i){for(let s=0;sc/t));let n=tx(e),o=i&&t===3;if(r){let c=n.length;n=n.slice();let f=[];for(let _=0;_f&&c>_||(f>_?(r||(n=n.slice()),zq(n,0,2,1)):(r||(n=n.slice()),zq(n,2,0,1)))}return(0,Nq.default)(n,s,t)}var eS=class extends rm{constructor(t){let{fp64:r,IndexType:i=Uint32Array}=t;super({...t,attributes:{positions:{size:3,type:r?Float64Array:Float32Array},vertexValid:{type:Uint8ClampedArray,size:1},indices:{type:i,size:1}}})}get(t){let{attributes:r}=this;return t===\"indices\"?r.indices&&r.indices.subarray(0,this.vertexCount):r[t]}updateGeometry(t){super.updateGeometry(t);let r=this.buffers.indices;if(r)this.vertexCount=(r.value||r).length;else if(this.data&&!this.getGeometry)throw new Error(\"missing indices buffer\")}normalizeGeometry(t){if(this.normalize){let r=G3(t,this.positionSize);return this.opts.resolution?Y2(tx(r),tS(r),{size:this.positionSize,gridResolution:this.opts.resolution,edgeTypes:!0}):this.opts.wrapLongitude?sB(tx(r),tS(r),{size:this.positionSize,maxLatitude:86,edgeTypes:!0}):r}return t}getGeometrySize(t){if(jq(t)){let r=0;for(let i of t)r+=this.getGeometrySize(i);return r}return tx(t).length/this.positionSize}getGeometryFromBuffer(t){return this.normalize||!this.buffers.indices?super.getGeometryFromBuffer(t):null}updateGeometryAttributes(t,r){if(t&&jq(t))for(let i of t){let s=this.getGeometrySize(i);r.geometrySize=s,this.updateGeometryAttributes(i,r),r.vertexStart+=s,r.indexStart=this.indexStarts[r.geometryIndex+1]}else this._updateIndices(t,r),this._updatePositions(t,r),this._updateVertexValid(t,r)}_updateIndices(t,{geometryIndex:r,vertexStart:i,indexStart:s}){let{attributes:n,indexStarts:o,typedArrayManager:c}=this,f=n.indices;if(!f||!t)return;let _=s,w=Uq(t,this.positionSize,this.opts.preproject,this.opts.full3d);f=c.allocate(f,s+w.length,{copy:!0});for(let I=0;I2?o[f*n+2]:0;s[c*3]=_,s[c*3+1]=w,s[c*3+2]=I}}_updateVertexValid(t,{vertexStart:r,geometrySize:i}){let{positionSize:s}=this,n=this.attributes.vertexValid,o=t&&tS(t);if(t&&t.edgeTypes?n.set(t.edgeTypes,r):n.fill(1,r,r+i),o)for(let c=0;c0&&!Number.isFinite(e[0])}var W3=`\nattribute vec2 vertexPositions;\nattribute float vertexValid;\n\nuniform bool extruded;\nuniform bool isWireframe;\nuniform float elevationScale;\nuniform float opacity;\n\nvarying vec4 vColor;\n\nstruct PolygonProps {\n vec4 fillColors;\n vec4 lineColors;\n vec3 positions;\n vec3 nextPositions;\n vec3 pickingColors;\n vec3 positions64Low;\n vec3 nextPositions64Low;\n float elevations;\n};\n\nvec3 project_offset_normal(vec3 vector) {\n if (project_uCoordinateSystem == COORDINATE_SYSTEM_LNGLAT ||\n project_uCoordinateSystem == COORDINATE_SYSTEM_LNGLAT_OFFSETS) {\n return normalize(vector * project_uCommonUnitsPerWorldUnit);\n }\n return project_normal(vector);\n}\n\nvoid calculatePosition(PolygonProps props) {\n#ifdef IS_SIDE_VERTEX\n if(vertexValid < 0.5){\n gl_Position = vec4(0.);\n return;\n }\n#endif\n\n vec3 pos;\n vec3 pos64Low;\n vec3 normal;\n vec4 colors = isWireframe ? props.lineColors : props.fillColors;\n\n geometry.worldPosition = props.positions;\n geometry.worldPositionAlt = props.nextPositions;\n geometry.pickingColor = props.pickingColors;\n\n#ifdef IS_SIDE_VERTEX\n pos = mix(props.positions, props.nextPositions, vertexPositions.x);\n pos64Low = mix(props.positions64Low, props.nextPositions64Low, vertexPositions.x);\n#else\n pos = props.positions;\n pos64Low = props.positions64Low;\n#endif\n\n if (extruded) {\n pos.z += props.elevations * vertexPositions.y * elevationScale;\n }\n gl_Position = project_position_to_clipspace(pos, pos64Low, vec3(0.), geometry.position);\n\n DECKGL_FILTER_GL_POSITION(gl_Position, geometry);\n\n if (extruded) {\n #ifdef IS_SIDE_VERTEX\n normal = vec3(\n props.positions.y - props.nextPositions.y + (props.positions64Low.y - props.nextPositions64Low.y),\n props.nextPositions.x - props.positions.x + (props.nextPositions64Low.x - props.positions64Low.x),\n 0.0);\n normal = project_offset_normal(normal);\n #else\n normal = project_normal(vec3(0.0, 0.0, 1.0));\n #endif\n geometry.normal = normal;\n vec3 lightColor = lighting_getLightColor(colors.rgb, project_uCameraPosition, geometry.position.xyz, normal);\n vColor = vec4(lightColor, colors.a * opacity);\n } else {\n vColor = vec4(colors.rgb, colors.a * opacity);\n }\n DECKGL_FILTER_COLOR(vColor, geometry);\n}\n`;var Gq=`#define SHADER_NAME solid-polygon-layer-vertex-shader\n\nattribute vec3 positions;\nattribute vec3 positions64Low;\nattribute float elevations;\nattribute vec4 fillColors;\nattribute vec4 lineColors;\nattribute vec3 pickingColors;\n\n`.concat(W3,`\n\nvoid main(void) {\n PolygonProps props;\n\n props.positions = positions;\n props.positions64Low = positions64Low;\n props.elevations = elevations;\n props.fillColors = fillColors;\n props.lineColors = lineColors;\n props.pickingColors = pickingColors;\n\n calculatePosition(props);\n}\n`);var Wq=`#define SHADER_NAME solid-polygon-layer-vertex-shader-side\n#define IS_SIDE_VERTEX\n\n\nattribute vec3 instancePositions;\nattribute vec3 nextPositions;\nattribute vec3 instancePositions64Low;\nattribute vec3 nextPositions64Low;\nattribute float instanceElevations;\nattribute vec4 instanceFillColors;\nattribute vec4 instanceLineColors;\nattribute vec3 instancePickingColors;\n\n`.concat(W3,`\n\nvoid main(void) {\n PolygonProps props;\n\n #if RING_WINDING_ORDER_CW == 1\n props.positions = instancePositions;\n props.positions64Low = instancePositions64Low;\n props.nextPositions = nextPositions;\n props.nextPositions64Low = nextPositions64Low;\n #else\n props.positions = nextPositions;\n props.positions64Low = nextPositions64Low;\n props.nextPositions = instancePositions;\n props.nextPositions64Low = instancePositions64Low;\n #endif\n props.elevations = instanceElevations;\n props.fillColors = instanceFillColors;\n props.lineColors = instanceLineColors;\n props.pickingColors = instancePickingColors;\n\n calculatePosition(props);\n}\n`);var Hq=`#define SHADER_NAME solid-polygon-layer-fragment-shader\n\nprecision highp float;\n\nvarying vec4 vColor;\n\nvoid main(void) {\n gl_FragColor = vColor;\n\n DECKGL_FILTER_COLOR(gl_FragColor, geometry);\n}\n`;var q3=[0,0,0,255],Npt={filled:!0,extruded:!1,wireframe:!1,_normalize:!0,_windingOrder:\"CW\",_full3d:!1,elevationScale:{type:\"number\",min:0,value:1},getPolygon:{type:\"accessor\",value:e=>e.polygon},getElevation:{type:\"accessor\",value:1e3},getFillColor:{type:\"accessor\",value:q3},getLineColor:{type:\"accessor\",value:q3},material:!0},H3={enter:(e,t)=>t.length?t.subarray(t.length-e.length):e},wc=class extends dn{constructor(...t){super(...t),G(this,\"state\",void 0)}getShaders(t){return super.getShaders({vs:t===\"top\"?Gq:Wq,fs:Hq,defines:{RING_WINDING_ORDER_CW:!this.props._normalize&&this.props._windingOrder===\"CCW\"?0:1},modules:[Rs,Zf,Ao]})}get wrapLongitude(){return!1}initializeState(){let{gl:t,viewport:r}=this.context,{coordinateSystem:i}=this.props,{_full3d:s}=this.props;r.isGeospatial&&i===Yr.DEFAULT&&(i=Yr.LNGLAT);let n;i===Yr.LNGLAT&&(s?n=r.projectPosition.bind(r):n=r.projectFlat.bind(r)),this.setState({numInstances:0,polygonTesselator:new eS({preproject:n,fp64:this.use64bitPositions(),IndexType:!t||Oh(t,Ii.ELEMENT_INDEX_UINT32)?Uint32Array:Uint16Array})});let o=this.getAttributeManager(),c=!0;o.remove([\"instancePickingColors\"]),o.add({indices:{size:1,isIndexed:!0,update:this.calculateIndices,noAlloc:c},positions:{size:3,type:5130,fp64:this.use64bitPositions(),transition:H3,accessor:\"getPolygon\",update:this.calculatePositions,noAlloc:c,shaderAttributes:{positions:{vertexOffset:0,divisor:0},instancePositions:{vertexOffset:0,divisor:1},nextPositions:{vertexOffset:1,divisor:1}}},vertexValid:{size:1,divisor:1,type:5121,update:this.calculateVertexValid,noAlloc:c},elevations:{size:1,transition:H3,accessor:\"getElevation\",shaderAttributes:{elevations:{divisor:0},instanceElevations:{divisor:1}}},fillColors:{size:this.props.colorFormat.length,type:5121,normalized:!0,transition:H3,accessor:\"getFillColor\",defaultValue:q3,shaderAttributes:{fillColors:{divisor:0},instanceFillColors:{divisor:1}}},lineColors:{size:this.props.colorFormat.length,type:5121,normalized:!0,transition:H3,accessor:\"getLineColor\",defaultValue:q3,shaderAttributes:{lineColors:{divisor:0},instanceLineColors:{divisor:1}}},pickingColors:{size:3,type:5121,accessor:(f,{index:_,target:w})=>this.encodePickingColor(f&&f.__source?f.__source.index:_,w),shaderAttributes:{pickingColors:{divisor:0},instancePickingColors:{divisor:1}}}})}getPickingInfo(t){let r=super.getPickingInfo(t),{index:i}=r,{data:s}=this.props;return s[0]&&s[0].__source&&(r.object=s.find(n=>n.__source.index===i)),r}disablePickingIndex(t){let{data:r}=this.props;if(r[0]&&r[0].__source)for(let i=0;if.delete()),this.setState(this._getModels(this.context.gl)),n.invalidateAll()}}updateGeometry({props:t,oldProps:r,changeFlags:i}){if(i.dataChanged||i.updateTriggersChanged&&(i.updateTriggersChanged.all||i.updateTriggersChanged.getPolygon)){let{polygonTesselator:n}=this.state,o=t.data.attributes||{};n.updateGeometry({data:t.data,normalize:t._normalize,geometryBuffer:o.getPolygon,buffers:o,getGeometry:t.getPolygon,positionFormat:t.positionFormat,wrapLongitude:t.wrapLongitude,resolution:this.context.viewport.resolution,fp64:this.use64bitPositions(),dataChanged:i.dataChanged,full3d:t._full3d}),this.setState({numInstances:n.instanceCount,startIndices:n.vertexStarts}),i.dataChanged||this.getAttributeManager().invalidateAll()}}_getModels(t){let{id:r,filled:i,extruded:s}=this.props,n,o;if(i){let c=this.getShaders(\"top\");c.defines.NON_INSTANCED_MODEL=1,n=new fn(t,{...c,id:\"\".concat(r,\"-top\"),drawMode:4,attributes:{vertexPositions:new Float32Array([0,1])},uniforms:{isWireframe:!1,isSideVertex:!1},vertexCount:0,isIndexed:!0})}return s&&(o=new fn(t,{...this.getShaders(\"side\"),id:\"\".concat(r,\"-side\"),geometry:new $n({drawMode:1,vertexCount:4,attributes:{vertexPositions:{size:2,value:new Float32Array([1,0,0,0,0,1,1,1])}}}),instanceCount:0,isInstanced:1}),o.userData.excludeAttributes={indices:!0}),{models:[o,n].filter(Boolean),topModel:n,sideModel:o}}calculateIndices(t){let{polygonTesselator:r}=this.state;t.startIndices=r.indexStarts,t.value=r.get(\"indices\")}calculatePositions(t){let{polygonTesselator:r}=this.state;t.startIndices=r.vertexStarts,t.value=r.get(\"positions\")}calculateVertexValid(t){t.value=this.state.polygonTesselator.get(\"vertexValid\")}};G(wc,\"defaultProps\",Npt);G(wc,\"layerName\",\"SolidPolygonLayer\");function Z3({data:e,getIndex:t,dataRange:r,replace:i}){let{startRow:s=0,endRow:n=1/0}=r,o=e.length,c=o,f=o;for(let R=0;RR&&N>=s&&(c=R),N>=n){f=R;break}}let _=c,I=f-c!==i.length?e.slice(f):void 0;for(let R=0;Re.polygon},getFillColor:{type:\"accessor\",value:Upt},getLineColor:{type:\"accessor\",value:qq},getLineWidth:{type:\"accessor\",value:1},getElevation:{type:\"accessor\",value:1e3},material:!0},lf=class extends Ni{initializeState(){this.state={paths:[]},this.props.getLineDashArray&&or.removed(\"getLineDashArray\",\"PathStyleExtension\")()}updateState({changeFlags:t}){let r=t.dataChanged||t.updateTriggersChanged&&(t.updateTriggersChanged.all||t.updateTriggersChanged.getPolygon);if(r&&Array.isArray(t.dataChanged)){let i=this.state.paths.slice(),s=t.dataChanged.map(n=>Z3({data:i,getIndex:o=>o.__source.index,dataRange:n,replace:this._getPaths(n)}));this.setState({paths:i,pathsDiff:s})}else r&&this.setState({paths:this._getPaths(),pathsDiff:null})}_getPaths(t={}){let{data:r,getPolygon:i,positionFormat:s,_normalize:n}=this.props,o=[],c=s===\"XY\"?2:3,{startRow:f,endRow:_}=t,{iterable:w,objectInfo:I}=Jc(r,f,_);for(let R of w){I.index++;let N=i(R,I);n&&(N=G3(N,c));let{holeIndices:j}=N,Q=N.positions||N;if(j)for(let et=0;et<=j.length;et++){let Y=Q.slice(j[et-1]||0,j[et]||Q.length);o.push(this.getSubLayerRow({path:Y},R,I.index))}else o.push(this.getSubLayerRow({path:Q},R,I.index))}return o}renderLayers(){let{data:t,_dataDiff:r,stroked:i,filled:s,extruded:n,wireframe:o,_normalize:c,_windingOrder:f,elevationScale:_,transitions:w,positionFormat:I}=this.props,{lineWidthUnits:R,lineWidthScale:N,lineWidthMinPixels:j,lineWidthMaxPixels:Q,lineJointRounded:et,lineMiterLimit:Y,lineDashJustified:K}=this.props,{getFillColor:J,getLineColor:ut,getLineWidth:Et,getLineDashArray:kt,getElevation:Xt,getPolygon:qt,updateTriggers:le,material:ue}=this.props,{paths:De,pathsDiff:Ke}=this.state,rr=this.getSubLayerClass(\"fill\",wc),Sr=this.getSubLayerClass(\"stroke\",bc),Li=this.shouldRenderSubLayer(\"fill\",De)&&new rr({_dataDiff:r,extruded:n,elevationScale:_,filled:s,wireframe:o,_normalize:c,_windingOrder:f,getElevation:Xt,getFillColor:J,getLineColor:n&&o?ut:qq,material:ue,transitions:w},this.getSubLayerProps({id:\"fill\",updateTriggers:le&&{getPolygon:le.getPolygon,getElevation:le.getElevation,getFillColor:le.getFillColor,lineColors:n&&o,getLineColor:le.getLineColor}}),{data:t,positionFormat:I,getPolygon:qt}),oo=!n&&i&&this.shouldRenderSubLayer(\"stroke\",De)&&new Sr({_dataDiff:Ke&&(()=>Ke),widthUnits:R,widthScale:N,widthMinPixels:j,widthMaxPixels:Q,jointRounded:et,miterLimit:Y,dashJustified:K,_pathType:\"loop\",transitions:w&&{getWidth:w.getLineWidth,getColor:w.getLineColor,getPath:w.getPolygon},getColor:this.getSubLayerAccessor(ut),getWidth:this.getSubLayerAccessor(Et),getDashArray:this.getSubLayerAccessor(kt)},this.getSubLayerProps({id:\"stroke\",updateTriggers:le&&{getWidth:le.getLineWidth,getColor:le.getLineColor,getDashArray:le.getLineDashArray}}),{data:De,positionFormat:I,getPath:zl=>zl.path});return[!n&&Li,oo,n&&Li]}};G(lf,\"layerName\",\"PolygonLayer\");G(lf,\"defaultProps\",Vpt);function Zq(e,t){if(!e)return null;let r=\"startIndices\"in e?e.startIndices[t]:t,i=e.featureIds.value[r];return r!==-1?jpt(e,i,r):null}function jpt(e,t,r){let i={properties:{...e.properties[t]}};for(let s in e.numericProps)i.properties[s]=e.numericProps[s].value[r];return i}function Yq(e,t){let r={points:null,lines:null,polygons:null};for(let i in r){let s=e[i].globalFeatureIds.value;r[i]=new Uint8ClampedArray(s.length*3);let n=[];for(let o=0;o 0.0) {\n float inFill = alpha;\n float inBorder = smoothstep(outlineBuffer - gamma, outlineBuffer + gamma, distance);\n color = mix(outlineColor, vColor, inFill);\n alpha = inBorder;\n }\n }\n float a = alpha * color.a;\n \n if (a < alphaCutoff) {\n discard;\n }\n\n gl_FragColor = vec4(color.rgb, a * opacity);\n }\n\n DECKGL_FILTER_COLOR(gl_FragColor, geometry);\n}\n`;var dB=192/256,$q=[],Gpt={getIconOffsets:{type:\"accessor\",value:e=>e.offsets},alphaCutoff:.001,smoothing:.1,outlineWidth:0,outlineColor:{type:\"color\",value:[0,0,0,255]}},Gg=class extends Ep{constructor(...t){super(...t),G(this,\"state\",void 0)}getShaders(){return{...super.getShaders(),fs:Qq}}initializeState(){super.initializeState(),this.getAttributeManager().addInstanced({instanceOffsets:{size:2,accessor:\"getIconOffsets\"},instancePickingColors:{type:5121,size:3,accessor:(r,{index:i,target:s})=>this.encodePickingColor(i,s)}})}updateState(t){super.updateState(t);let{props:r,oldProps:i}=t,{outlineColor:s}=r;s!==i.outlineColor&&(s=s.map(n=>n/255),s[3]=Number.isFinite(s[3])?s[3]:1,this.setState({outlineColor:s})),!r.sdf&&r.outlineWidth&&or.warn(\"\".concat(this.id,\": fontSettings.sdf is required to render outline\"))()}draw(t){let{sdf:r,smoothing:i,outlineWidth:s}=this.props,{outlineColor:n}=this.state,o=s?Math.max(i,dB*(1-s)):-1;if(t.uniforms={...t.uniforms,sdfBuffer:dB,outlineBuffer:o,gamma:i,sdf:!!r,outlineColor:n},super.draw(t),r&&s){let{iconManager:c}=this.state;c.getTexture()&&this.state.model.draw({uniforms:{outlineBuffer:dB}})}}getInstanceOffset(t){return t?Array.from(t).flatMap(r=>super.getInstanceOffset(r)):$q}getInstanceColorMode(t){return 1}getInstanceIconFrame(t){return t?Array.from(t).flatMap(r=>super.getInstanceIconFrame(r)):$q}};G(Gg,\"defaultProps\",Gpt);G(Gg,\"layerName\",\"MultiIconLayer\");var rS=class{constructor({fontSize:t=24,buffer:r=3,radius:i=8,cutoff:s=.25,fontFamily:n=\"sans-serif\",fontWeight:o=\"normal\",fontStyle:c=\"normal\"}={}){this.buffer=r,this.cutoff=s,this.radius=i;let f=this.size=t+r*4,_=this._createCanvas(f),w=this.ctx=_.getContext(\"2d\",{willReadFrequently:!0});w.font=`${c} ${o} ${t}px ${n}`,w.textBaseline=\"alphabetic\",w.textAlign=\"left\",w.fillStyle=\"black\",this.gridOuter=new Float64Array(f*f),this.gridInner=new Float64Array(f*f),this.f=new Float64Array(f),this.z=new Float64Array(f+1),this.v=new Uint16Array(f)}_createCanvas(t){let r=document.createElement(\"canvas\");return r.width=r.height=t,r}draw(t){let{width:r,actualBoundingBoxAscent:i,actualBoundingBoxDescent:s,actualBoundingBoxLeft:n,actualBoundingBoxRight:o}=this.ctx.measureText(t),c=Math.ceil(i),f=0,_=Math.max(0,Math.min(this.size-this.buffer,Math.ceil(o-n))),w=Math.min(this.size-this.buffer,c+Math.ceil(s)),I=_+2*this.buffer,R=w+2*this.buffer,N=Math.max(I*R,0),j=new Uint8ClampedArray(N),Q={data:j,width:I,height:R,glyphWidth:_,glyphHeight:w,glyphTop:c,glyphLeft:f,glyphAdvance:r};if(_===0||w===0)return Q;let{ctx:et,buffer:Y,gridInner:K,gridOuter:J}=this;et.clearRect(Y,Y,_,w),et.fillText(t,Y,Y+c);let ut=et.getImageData(Y,Y,_,w);J.fill(1e20,0,N),K.fill(0,0,N);for(let Et=0;Et0?le*le:0,K[qt]=le<0?le*le:0}}Xq(J,0,0,I,R,I,this.f,this.v,this.z),Xq(K,Y,Y,_,w,I,this.f,this.v,this.z);for(let Et=0;Et-1);f++,n[f]=c,o[f]=_,o[f+1]=1e20}for(let c=0,f=0;cs&&(_=0,f++),n[I]={x:_+i,y:c+f*w+i,width:R,height:w,layoutWidth:R,layoutHeight:r},_+=R+i*2}return{mapping:n,xOffset:_,yOffset:c+f*w,canvasHeight:qpt(c+(f+1)*w)}}function tZ(e,t,r,i){let s=0;for(let o=t;oi&&(oc){let I=tZ(e,c,f,s);_+I>i&&(oi&&(I=eZ(e,c,f,i,s,n),o=n[n.length-1])),c=f,_+=I}return _}function Ypt(e,t,r,i,s=0,n){n===void 0&&(n=e.length);let o=[];return t===\"break-all\"?eZ(e,s,n,r,i,o):Zpt(e,s,n,r,i,o),o}function Qpt(e,t,r,i,s,n){let o=0,c=0;for(let f=t;f0,I=[0,0],R=[0,0],N=0,j=0,Q=0;for(let Y=0;Y<=o;Y++){let K=n[Y];if((K===`\n`||Y===o)&&(Q=Y),Q>j){let J=w?Ypt(n,r,i,s,j,Q):Hpt;for(let ut=0;ut<=J.length;ut++){let Et=ut===0?j:J[ut-1],kt=ut1||f>0){let N=e.constructor;R=new N(_);for(let j=0;j<_;j++)R[j]=e[j*c+f]}for(let N=0;N=0&&this._order.splice(r,1)}_appendOrder(t){this._order.push(t)}};function $pt(){let e=[];for(let t=32;t<128;t++)e.push(String.fromCharCode(t));return e}var Wg={fontFamily:\"Monaco, monospace\",fontWeight:\"normal\",characterSet:$pt(),fontSize:64,buffer:4,sdf:!1,cutoff:.25,radius:12,smoothing:.1},nZ=1024,sZ=.9,oZ=1.2,lZ=3,Y3=new ex(lZ);function Xpt(e,t){let r;typeof t==\"string\"?r=new Set(Array.from(t)):r=new Set(t);let i=Y3.get(e);if(!i)return r;for(let s in i.mapping)r.has(s)&&r.delete(s);return r}function Kpt(e,t){for(let r=0;r=lZ,\"Invalid cache limit\"),Y3=new ex(e)}var iS=class{constructor(){G(this,\"props\",{...Wg}),G(this,\"_key\",void 0),G(this,\"_atlas\",void 0)}get texture(){return this._atlas}get mapping(){return this._atlas&&this._atlas.mapping}get scale(){let{fontSize:t,buffer:r}=this.props;return(t*oZ+r*2)/t}setProps(t={}){Object.assign(this.props,t),this._key=this._getKey();let r=Xpt(this._key,this.props.characterSet),i=Y3.get(this._key);if(i&&r.size===0){this._atlas!==i&&(this._atlas=i);return}let s=this._generateFontAtlas(r,i);this._atlas=s,Y3.set(this._key,s)}_generateFontAtlas(t,r){let{fontFamily:i,fontWeight:s,fontSize:n,buffer:o,sdf:c,radius:f,cutoff:_}=this.props,w=r&&r.data;w||(w=document.createElement(\"canvas\"),w.width=nZ);let I=w.getContext(\"2d\",{willReadFrequently:!0});aZ(I,i,n,s);let{mapping:R,canvasHeight:N,xOffset:j,yOffset:Q}=Jq({getFontWidth:et=>I.measureText(et).width,fontHeight:n*oZ,buffer:o,characterSet:t,maxCanvasWidth:nZ,...r&&{mapping:r.mapping,xOffset:r.xOffset,yOffset:r.yOffset}});if(w.height!==N){let et=I.getImageData(0,0,w.width,w.height);w.height=N,I.putImageData(et,0,0)}if(aZ(I,i,n,s),c){let et=new rS({fontSize:n,buffer:o,radius:f,cutoff:_,fontFamily:i,fontWeight:\"\".concat(s)});for(let Y of t){let{data:K,width:J,height:ut,glyphTop:Et}=et.draw(Y);R[Y].width=J,R[Y].layoutOffsetY=n*sZ-Et;let kt=I.createImageData(J,ut);Kpt(K,kt),I.putImageData(kt,R[Y].x,R[Y].y)}}else for(let et of t)I.fillText(et,R[et].x,R[et].y+o+n*sZ);return{xOffset:j,yOffset:Q,mapping:R,data:w,width:w.width,height:w.height}}_getKey(){let{fontFamily:t,fontWeight:r,fontSize:i,buffer:s,sdf:n,radius:o,cutoff:c}=this.props;return n?\"\".concat(t,\" \").concat(r,\" \").concat(i,\" \").concat(s,\" \").concat(o,\" \").concat(c):\"\".concat(t,\" \").concat(r,\" \").concat(i,\" \").concat(s)}};var uZ=`#define SHADER_NAME text-background-layer-vertex-shader\n\nattribute vec2 positions;\n\nattribute vec3 instancePositions;\nattribute vec3 instancePositions64Low;\nattribute vec4 instanceRects;\nattribute float instanceSizes;\nattribute float instanceAngles;\nattribute vec2 instancePixelOffsets;\nattribute float instanceLineWidths;\nattribute vec4 instanceFillColors;\nattribute vec4 instanceLineColors;\nattribute vec3 instancePickingColors;\n\nuniform bool billboard;\nuniform float opacity;\nuniform float sizeScale;\nuniform float sizeMinPixels;\nuniform float sizeMaxPixels;\nuniform vec4 padding;\nuniform int sizeUnits;\n\nvarying vec4 vFillColor;\nvarying vec4 vLineColor;\nvarying float vLineWidth;\nvarying vec2 uv;\nvarying vec2 dimensions;\n\nvec2 rotate_by_angle(vec2 vertex, float angle) {\n float angle_radian = radians(angle);\n float cos_angle = cos(angle_radian);\n float sin_angle = sin(angle_radian);\n mat2 rotationMatrix = mat2(cos_angle, -sin_angle, sin_angle, cos_angle);\n return rotationMatrix * vertex;\n}\n\nvoid main(void) {\n geometry.worldPosition = instancePositions;\n geometry.uv = positions;\n geometry.pickingColor = instancePickingColors;\n uv = positions;\n vLineWidth = instanceLineWidths;\n float sizePixels = clamp(\n project_size_to_pixel(instanceSizes * sizeScale, sizeUnits),\n sizeMinPixels, sizeMaxPixels\n );\n\n dimensions = instanceRects.zw * sizePixels + padding.xy + padding.zw;\n\n vec2 pixelOffset = (positions * instanceRects.zw + instanceRects.xy) * sizePixels + mix(-padding.xy, padding.zw, positions);\n pixelOffset = rotate_by_angle(pixelOffset, instanceAngles);\n pixelOffset += instancePixelOffsets;\n pixelOffset.y *= -1.0;\n\n if (billboard) {\n gl_Position = project_position_to_clipspace(instancePositions, instancePositions64Low, vec3(0.0), geometry.position);\n DECKGL_FILTER_GL_POSITION(gl_Position, geometry);\n vec3 offset = vec3(pixelOffset, 0.0);\n DECKGL_FILTER_SIZE(offset, geometry);\n gl_Position.xy += project_pixel_size_to_clipspace(offset.xy);\n } else {\n vec3 offset_common = vec3(project_pixel_size(pixelOffset), 0.0);\n DECKGL_FILTER_SIZE(offset_common, geometry);\n gl_Position = project_position_to_clipspace(instancePositions, instancePositions64Low, offset_common, geometry.position);\n DECKGL_FILTER_GL_POSITION(gl_Position, geometry);\n }\n vFillColor = vec4(instanceFillColors.rgb, instanceFillColors.a * opacity);\n DECKGL_FILTER_COLOR(vFillColor, geometry);\n vLineColor = vec4(instanceLineColors.rgb, instanceLineColors.a * opacity);\n DECKGL_FILTER_COLOR(vLineColor, geometry);\n}\n`;var hZ=`#define SHADER_NAME text-background-layer-fragment-shader\n\nprecision highp float;\n\nuniform bool stroked;\n\nvarying vec4 vFillColor;\nvarying vec4 vLineColor;\nvarying float vLineWidth;\nvarying vec2 uv;\nvarying vec2 dimensions;\n\nvoid main(void) {\n geometry.uv = uv;\n\n vec2 pixelPosition = uv * dimensions;\n if (stroked) {\n float distToEdge = min(\n min(pixelPosition.x, dimensions.x - pixelPosition.x),\n min(pixelPosition.y, dimensions.y - pixelPosition.y)\n );\n float isBorder = smoothedge(distToEdge, vLineWidth);\n gl_FragColor = mix(vFillColor, vLineColor, isBorder);\n } else {\n gl_FragColor = vFillColor;\n }\n\n DECKGL_FILTER_COLOR(gl_FragColor, geometry);\n}\n`;var Jpt={billboard:!0,sizeScale:1,sizeUnits:\"pixels\",sizeMinPixels:0,sizeMaxPixels:Number.MAX_SAFE_INTEGER,padding:{type:\"array\",value:[0,0,0,0]},getPosition:{type:\"accessor\",value:e=>e.position},getSize:{type:\"accessor\",value:1},getAngle:{type:\"accessor\",value:0},getPixelOffset:{type:\"accessor\",value:[0,0]},getBoundingRect:{type:\"accessor\",value:[0,0,0,0]},getFillColor:{type:\"accessor\",value:[0,0,0,255]},getLineColor:{type:\"accessor\",value:[0,0,0,255]},getLineWidth:{type:\"accessor\",value:1}},Hg=class extends dn{constructor(...t){super(...t),G(this,\"state\",void 0)}getShaders(){return super.getShaders({vs:uZ,fs:hZ,modules:[Rs,Ao]})}initializeState(){this.getAttributeManager().addInstanced({instancePositions:{size:3,type:5130,fp64:this.use64bitPositions(),transition:!0,accessor:\"getPosition\"},instanceSizes:{size:1,transition:!0,accessor:\"getSize\",defaultValue:1},instanceAngles:{size:1,transition:!0,accessor:\"getAngle\"},instanceRects:{size:4,accessor:\"getBoundingRect\"},instancePixelOffsets:{size:2,transition:!0,accessor:\"getPixelOffset\"},instanceFillColors:{size:4,transition:!0,normalized:!0,type:5121,accessor:\"getFillColor\",defaultValue:[0,0,0,255]},instanceLineColors:{size:4,transition:!0,normalized:!0,type:5121,accessor:\"getLineColor\",defaultValue:[0,0,0,255]},instanceLineWidths:{size:1,transition:!0,accessor:\"getLineWidth\",defaultValue:1}})}updateState(t){super.updateState(t);let{changeFlags:r}=t;if(r.extensionsChanged){var i;let{gl:s}=this.context;(i=this.state.model)===null||i===void 0||i.delete(),this.state.model=this._getModel(s),this.getAttributeManager().invalidateAll()}}draw({uniforms:t}){let{billboard:r,sizeScale:i,sizeUnits:s,sizeMinPixels:n,sizeMaxPixels:o,getLineWidth:c}=this.props,{padding:f}=this.props;f.length<4&&(f=[f[0],f[1],f[0],f[1]]),this.state.model.setUniforms(t).setUniforms({billboard:r,stroked:!!c,padding:f,sizeUnits:po[s],sizeScale:i,sizeMinPixels:n,sizeMaxPixels:o}).draw()}_getModel(t){let r=[0,0,1,0,1,1,0,1];return new fn(t,{...this.getShaders(),id:this.props.id,geometry:new $n({drawMode:6,vertexCount:4,attributes:{positions:{size:2,value:new Float32Array(r)}}}),isInstanced:!0})}};G(Hg,\"defaultProps\",Jpt);G(Hg,\"layerName\",\"TextBackgroundLayer\");var fZ={start:1,middle:0,end:-1},dZ={top:1,center:0,bottom:-1},pB=[0,0,0,255],tAt=1,eAt={billboard:!0,sizeScale:1,sizeUnits:\"pixels\",sizeMinPixels:0,sizeMaxPixels:Number.MAX_SAFE_INTEGER,background:!1,getBackgroundColor:{type:\"accessor\",value:[255,255,255,255]},getBorderColor:{type:\"accessor\",value:pB},getBorderWidth:{type:\"accessor\",value:0},backgroundPadding:{type:\"array\",value:[0,0,0,0]},characterSet:{type:\"object\",value:Wg.characterSet},fontFamily:Wg.fontFamily,fontWeight:Wg.fontWeight,lineHeight:tAt,outlineWidth:{type:\"number\",value:0,min:0},outlineColor:{type:\"color\",value:pB},fontSettings:{type:\"object\",value:{},compare:1},wordBreak:\"break-word\",maxWidth:{type:\"number\",value:-1},getText:{type:\"accessor\",value:e=>e.text},getPosition:{type:\"accessor\",value:e=>e.position},getColor:{type:\"accessor\",value:pB},getSize:{type:\"accessor\",value:32},getAngle:{type:\"accessor\",value:0},getTextAnchor:{type:\"accessor\",value:\"middle\"},getAlignmentBaseline:{type:\"accessor\",value:\"center\"},getPixelOffset:{type:\"accessor\",value:[0,0]},backgroundColor:{deprecatedFor:[\"background\",\"getBackgroundColor\"]}},cf=class extends Ni{constructor(...t){super(...t),G(this,\"state\",void 0),G(this,\"getBoundingRect\",(r,i)=>{let{size:[s,n]}=this.transformParagraph(r,i),{fontSize:o}=this.state.fontAtlasManager.props;s/=o,n/=o;let{getTextAnchor:c,getAlignmentBaseline:f}=this.props,_=fZ[typeof c==\"function\"?c(r,i):c],w=dZ[typeof f==\"function\"?f(r,i):f];return[(_-1)*s/2,(w-1)*n/2,s,n]}),G(this,\"getIconOffsets\",(r,i)=>{let{getTextAnchor:s,getAlignmentBaseline:n}=this.props,{x:o,y:c,rowWidth:f,size:[_,w]}=this.transformParagraph(r,i),I=fZ[typeof s==\"function\"?s(r,i):s],R=dZ[typeof n==\"function\"?n(r,i):n],N=o.length,j=new Array(N*2),Q=0;for(let et=0;et0&&or.warn(\"v8.9 breaking change: TextLayer maxWidth is now relative to text size\")()}updateState(t){let{props:r,oldProps:i,changeFlags:s}=t;(s.dataChanged||s.updateTriggersChanged&&(s.updateTriggersChanged.all||s.updateTriggersChanged.getText))&&this._updateText(),(this._updateFontAtlas()||r.lineHeight!==i.lineHeight||r.wordBreak!==i.wordBreak||r.maxWidth!==i.maxWidth)&&this.setState({styleVersion:this.state.styleVersion+1})}getPickingInfo({info:t}){return t.object=t.index>=0?this.props.data[t.index]:null,t}_updateFontAtlas(){let{fontSettings:t,fontFamily:r,fontWeight:i}=this.props,{fontAtlasManager:s,characterSet:n}=this.state,o={...t,characterSet:n,fontFamily:r,fontWeight:i};if(!s.mapping)return s.setProps(o),!0;for(let c in o)if(o[c]!==s.props[c])return s.setProps(o),!0;return!1}_updateText(){var t;let{data:r,characterSet:i}=this.props,s=(t=r.attributes)===null||t===void 0?void 0:t.getText,{getText:n}=this.props,o=r.startIndices,c,f=i===\"auto\"&&new Set;if(s&&o){let{texts:_,characterCount:w}=iZ({...ArrayBuffer.isView(s)?{value:s}:s,length:r.length,startIndices:o,characterSet:f});c=w,n=(I,{index:R})=>_[R]}else{let{iterable:_,objectInfo:w}=Jc(r);o=[0],c=0;for(let I of _){w.index++;let R=Array.from(n(I,w)||\"\");f&&R.forEach(f.add,f),c+=R.length,o.push(c)}}this.setState({getText:n,startIndices:o,numInstances:c,characterSet:f||i})}transformParagraph(t,r){let{fontAtlasManager:i}=this.state,s=i.mapping,n=this.state.getText,{wordBreak:o,lineHeight:c,maxWidth:f}=this.props,_=n(t,r)||\"\";return rZ(_,c,o,f*i.props.fontSize,s)}renderLayers(){let{startIndices:t,numInstances:r,getText:i,fontAtlasManager:{scale:s,texture:n,mapping:o},styleVersion:c}=this.state,{data:f,_dataDiff:_,getPosition:w,getColor:I,getSize:R,getAngle:N,getPixelOffset:j,getBackgroundColor:Q,getBorderColor:et,getBorderWidth:Y,backgroundPadding:K,background:J,billboard:ut,fontSettings:Et,outlineWidth:kt,outlineColor:Xt,sizeScale:qt,sizeUnits:le,sizeMinPixels:ue,sizeMaxPixels:De,transitions:Ke,updateTriggers:rr}=this.props,Sr=this.getSubLayerClass(\"characters\",Gg),Li=this.getSubLayerClass(\"background\",Hg);return[J&&new Li({getFillColor:Q,getLineColor:et,getLineWidth:Y,padding:K,getPosition:w,getSize:R,getAngle:N,getPixelOffset:j,billboard:ut,sizeScale:qt,sizeUnits:le,sizeMinPixels:ue,sizeMaxPixels:De,transitions:Ke&&{getPosition:Ke.getPosition,getAngle:Ke.getAngle,getSize:Ke.getSize,getFillColor:Ke.getBackgroundColor,getLineColor:Ke.getBorderColor,getLineWidth:Ke.getBorderWidth,getPixelOffset:Ke.getPixelOffset}},this.getSubLayerProps({id:\"background\",updateTriggers:{getPosition:rr.getPosition,getAngle:rr.getAngle,getSize:rr.getSize,getFillColor:rr.getBackgroundColor,getLineColor:rr.getBorderColor,getLineWidth:rr.getBorderWidth,getPixelOffset:rr.getPixelOffset,getBoundingRect:{getText:rr.getText,getTextAnchor:rr.getTextAnchor,getAlignmentBaseline:rr.getAlignmentBaseline,styleVersion:c}}}),{data:f.attributes&&f.attributes.background?{length:f.length,attributes:f.attributes.background}:f,_dataDiff:_,autoHighlight:!1,getBoundingRect:this.getBoundingRect}),new Sr({sdf:Et.sdf,smoothing:Number.isFinite(Et.smoothing)?Et.smoothing:Wg.smoothing,outlineWidth:kt/(Et.radius||Wg.radius),outlineColor:Xt,iconAtlas:n,iconMapping:o,getPosition:w,getColor:I,getSize:R,getAngle:N,getPixelOffset:j,billboard:ut,sizeScale:qt*s,sizeUnits:le,sizeMinPixels:ue*s,sizeMaxPixels:De*s,transitions:Ke&&{getPosition:Ke.getPosition,getAngle:Ke.getAngle,getColor:Ke.getColor,getSize:Ke.getSize,getPixelOffset:Ke.getPixelOffset}},this.getSubLayerProps({id:\"characters\",updateTriggers:{all:rr.getText,getPosition:rr.getPosition,getAngle:rr.getAngle,getColor:rr.getColor,getSize:rr.getSize,getPixelOffset:rr.getPixelOffset,getIconOffsets:{getTextAnchor:rr.getTextAnchor,getAlignmentBaseline:rr.getAlignmentBaseline,styleVersion:c}}}),{data:f,_dataDiff:_,startIndices:t,numInstances:r,getIconOffsets:this.getIconOffsets,getIcon:i})]}static set fontAtlasCacheLimit(t){cZ(t)}};G(cf,\"defaultProps\",eAt);G(cf,\"layerName\",\"TextLayer\");var nS={circle:{type:Ku,props:{filled:\"filled\",stroked:\"stroked\",lineWidthMaxPixels:\"lineWidthMaxPixels\",lineWidthMinPixels:\"lineWidthMinPixels\",lineWidthScale:\"lineWidthScale\",lineWidthUnits:\"lineWidthUnits\",pointRadiusMaxPixels:\"radiusMaxPixels\",pointRadiusMinPixels:\"radiusMinPixels\",pointRadiusScale:\"radiusScale\",pointRadiusUnits:\"radiusUnits\",pointAntialiasing:\"antialiasing\",pointBillboard:\"billboard\",getFillColor:\"getFillColor\",getLineColor:\"getLineColor\",getLineWidth:\"getLineWidth\",getPointRadius:\"getRadius\"}},icon:{type:Ep,props:{iconAtlas:\"iconAtlas\",iconMapping:\"iconMapping\",iconSizeMaxPixels:\"sizeMaxPixels\",iconSizeMinPixels:\"sizeMinPixels\",iconSizeScale:\"sizeScale\",iconSizeUnits:\"sizeUnits\",iconAlphaCutoff:\"alphaCutoff\",iconBillboard:\"billboard\",getIcon:\"getIcon\",getIconAngle:\"getAngle\",getIconColor:\"getColor\",getIconPixelOffset:\"getPixelOffset\",getIconSize:\"getSize\"}},text:{type:cf,props:{textSizeMaxPixels:\"sizeMaxPixels\",textSizeMinPixels:\"sizeMinPixels\",textSizeScale:\"sizeScale\",textSizeUnits:\"sizeUnits\",textBackground:\"background\",textBackgroundPadding:\"backgroundPadding\",textFontFamily:\"fontFamily\",textFontWeight:\"fontWeight\",textLineHeight:\"lineHeight\",textMaxWidth:\"maxWidth\",textOutlineColor:\"outlineColor\",textOutlineWidth:\"outlineWidth\",textWordBreak:\"wordBreak\",textCharacterSet:\"characterSet\",textBillboard:\"billboard\",textFontSettings:\"fontSettings\",getText:\"getText\",getTextAngle:\"getAngle\",getTextColor:\"getColor\",getTextPixelOffset:\"getPixelOffset\",getTextSize:\"getSize\",getTextAnchor:\"getTextAnchor\",getTextAlignmentBaseline:\"getAlignmentBaseline\",getTextBackgroundColor:\"getBackgroundColor\",getTextBorderColor:\"getBorderColor\",getTextBorderWidth:\"getBorderWidth\"}}},sS={type:bc,props:{lineWidthUnits:\"widthUnits\",lineWidthScale:\"widthScale\",lineWidthMinPixels:\"widthMinPixels\",lineWidthMaxPixels:\"widthMaxPixels\",lineJointRounded:\"jointRounded\",lineCapRounded:\"capRounded\",lineMiterLimit:\"miterLimit\",lineBillboard:\"billboard\",getLineColor:\"getColor\",getLineWidth:\"getWidth\"}},Q3={type:wc,props:{extruded:\"extruded\",filled:\"filled\",wireframe:\"wireframe\",elevationScale:\"elevationScale\",material:\"material\",_full3d:\"_full3d\",getElevation:\"getElevation\",getFillColor:\"getFillColor\",getLineColor:\"getLineColor\"}};function rx({type:e,props:t}){let r={};for(let i in t)r[i]=e.defaultProps[t[i]];return r}function $3(e,t){let{transitions:r,updateTriggers:i}=e.props,s={updateTriggers:{},transitions:r&&{getPosition:r.geometry}};for(let n in t){let o=t[n],c=e.props[n];n.startsWith(\"get\")&&(c=e.getSubLayerAccessor(c),s.updateTriggers[o]=i[n],r&&(s.transitions[o]=r[n])),s[o]=c}return s}function AZ(e){if(Array.isArray(e))return e;switch(or.assert(e.type,\"GeoJSON does not have type\"),e.type){case\"Feature\":return[e];case\"FeatureCollection\":return or.assert(Array.isArray(e.features),\"GeoJSON does not have features array\"),e.features;default:return[{geometry:e}]}}function AB(e,t,r={}){let i={pointFeatures:[],lineFeatures:[],polygonFeatures:[],polygonOutlineFeatures:[]},{startRow:s=0,endRow:n=e.length}=r;for(let o=s;o{c.push(r({geometry:{type:\"Point\",coordinates:I}},i,s))});break;case\"LineString\":f.push(r({geometry:e},i,s));break;case\"MultiLineString\":o.forEach(I=>{f.push(r({geometry:{type:\"LineString\",coordinates:I}},i,s))});break;case\"Polygon\":_.push(r({geometry:e},i,s)),o.forEach(I=>{w.push(r({geometry:{type:\"LineString\",coordinates:I}},i,s))});break;case\"MultiPolygon\":o.forEach(I=>{_.push(r({geometry:{type:\"Polygon\",coordinates:I}},i,s)),I.forEach(R=>{w.push(r({geometry:{type:\"LineString\",coordinates:R}},i,s))})});break;default:}}var rAt={Point:1,MultiPoint:2,LineString:2,MultiLineString:3,Polygon:3,MultiPolygon:4};function iAt(e,t){let r=rAt[e];for(or.assert(r,\"Unknown GeoJSON type \".concat(e));t&&--r>0;)t=t[0];return t&&Number.isFinite(t[0])}function mZ(){return{points:{},lines:{},polygons:{},polygonsOutline:{}}}function X3(e){return e.geometry.coordinates}function gZ(e,t){let r=mZ(),{pointFeatures:i,lineFeatures:s,polygonFeatures:n,polygonOutlineFeatures:o}=e;return r.points.data=i,r.points._dataDiff=t.pointFeatures&&(()=>t.pointFeatures),r.points.getPosition=X3,r.lines.data=s,r.lines._dataDiff=t.lineFeatures&&(()=>t.lineFeatures),r.lines.getPath=X3,r.polygons.data=n,r.polygons._dataDiff=t.polygonFeatures&&(()=>t.polygonFeatures),r.polygons.getPolygon=X3,r.polygonsOutline.data=o,r.polygonsOutline._dataDiff=t.polygonOutlineFeatures&&(()=>t.polygonOutlineFeatures),r.polygonsOutline.getPath=X3,r}function _Z(e,t){let r=mZ(),{points:i,lines:s,polygons:n}=e,o=Yq(e,t);return r.points.data={length:i.positions.value.length/i.positions.size,attributes:{...i.attributes,getPosition:i.positions,instancePickingColors:{size:3,value:o.points}},properties:i.properties,numericProps:i.numericProps,featureIds:i.featureIds},r.lines.data={length:s.pathIndices.value.length-1,startIndices:s.pathIndices.value,attributes:{...s.attributes,getPath:s.positions,instancePickingColors:{size:3,value:o.lines}},properties:s.properties,numericProps:s.numericProps,featureIds:s.featureIds},r.lines._pathType=\"open\",r.polygons.data={length:n.polygonIndices.value.length-1,startIndices:n.polygonIndices.value,attributes:{...n.attributes,getPolygon:n.positions,pickingColors:{size:3,value:o.polygons}},properties:n.properties,numericProps:n.numericProps,featureIds:n.featureIds},r.polygons._normalize=!1,n.triangles&&(r.polygons.data.attributes.indices=n.triangles.value),r.polygonsOutline.data={length:n.primitivePolygonIndices.value.length-1,startIndices:n.primitivePolygonIndices.value,attributes:{...n.attributes,getPath:n.positions,instancePickingColors:{size:3,value:o.polygons}},properties:n.properties,numericProps:n.numericProps,featureIds:n.featureIds},r.polygonsOutline._pathType=\"open\",r}var nAt=[\"points\",\"linestrings\",\"polygons\"],sAt={...rx(nS.circle),...rx(nS.icon),...rx(nS.text),...rx(sS),...rx(Q3),stroked:!0,filled:!0,extruded:!1,wireframe:!1,_full3d:!1,iconAtlas:{type:\"object\",value:null},iconMapping:{type:\"object\",value:{}},getIcon:{type:\"accessor\",value:e=>e.properties.icon},getText:{type:\"accessor\",value:e=>e.properties.text},pointType:\"circle\",getRadius:{deprecatedFor:\"getPointRadius\"}},Mm=class extends Ni{initializeState(){this.state={layerProps:{},features:{}}}updateState({props:t,changeFlags:r}){if(!r.dataChanged)return;let{data:i}=this.props,s=i&&\"points\"in i&&\"polygons\"in i&&\"lines\"in i;this.setState({binary:s}),s?this._updateStateBinary({props:t,changeFlags:r}):this._updateStateJSON({props:t,changeFlags:r})}_updateStateBinary({props:t,changeFlags:r}){let i=_Z(t.data,this.encodePickingColor);this.setState({layerProps:i})}_updateStateJSON({props:t,changeFlags:r}){let 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n=Math.sin(r),o=Dl(this.e,r,n);i=this.x0+this.a*this.k0*Ce(t-this.long0),s=this.y0-this.a*this.k0*Math.log(o)}return e.x=i,e.y=s,e}function HAt(e){var t=e.x-this.x0,r=e.y-this.y0,i,s;if(this.sphere)s=de-2*Math.atan(Math.exp(-r/(this.a*this.k0)));else{var n=Math.exp(-r/(this.a*this.k0));if(s=Lp(this.e,n),s===-9999)return null}return i=Ce(this.long0+t/(this.a*this.k0)),e.x=i,e.y=s,e}var qAt=[\"Mercator\",\"Popular Visualisation Pseudo Mercator\",\"Mercator_1SP\",\"Mercator_Auxiliary_Sphere\",\"merc\"],GZ={init:GAt,forward:WAt,inverse:HAt,names:qAt};function ZAt(){}function WZ(e){return e}var YAt=[\"longlat\",\"identity\"],HZ={init:ZAt,forward:WZ,inverse:WZ,names:YAt};var QAt=[GZ,HZ],sI={},oI=[];function qZ(e,t){var r=oI.length;return e.names?(oI[r]=e,e.names.forEach(function(i){sI[i.toLowerCase()]=r}),this):(console.log(t),!0)}function $At(e){if(!e)return!1;var t=e.toLowerCase();if(typeof sI[t]<\"u\"&&oI[sI[t]])return oI[sI[t]]}function XAt(){QAt.forEach(qZ)}var 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s=1e-12,n=s*s,o=30,c,f,_,w,I,R,N,j,Q,et,Y,K,J,ut=e.x,Et=e.y,kt=e.z?e.z:0,Xt,qt,le;if(c=Math.sqrt(ut*ut+Et*Et),f=Math.sqrt(ut*ut+Et*Et+kt*kt),c/rn&&Ji.y||j>i.x||Yc&&Math.abs(f.y)>c);if(o<0)return console.log(\"Inverse grid shift iterator failed to converge.\"),i;i.x=Ce(n.x+r.ll[0]),i.y=n.y+r.ll[1]}else isNaN(n.x)||(i.x=e.x+n.x,i.y=e.y+n.y);return i}function nY(e,t){var r={x:e.x/t.del[0],y:e.y/t.del[1]},i={x:Math.floor(r.x),y:Math.floor(r.y)},s={x:r.x-1*i.x,y:r.y-1*i.y},n={x:Number.NaN,y:Number.NaN},o;if(i.x<0||i.x>=t.lim[0]||i.y<0||i.y>=t.lim[1])return n;o=i.y*t.lim[0]+i.x;var c={x:t.cvs[o][0],y:t.cvs[o][1]};o++;var f={x:t.cvs[o][0],y:t.cvs[o][1]};o+=t.lim[0];var _={x:t.cvs[o][0],y:t.cvs[o][1]};o--;var w={x:t.cvs[o][0],y:t.cvs[o][1]},I=s.x*s.y,R=s.x*(1-s.y),N=(1-s.x)*(1-s.y),j=(1-s.x)*s.y;return n.x=N*c.x+R*f.x+j*w.x+I*_.x,n.y=N*c.y+R*f.y+j*w.y+I*_.y,n}function EB(e,t,r){var i=r.x,s=r.y,n=r.z||0,o,c,f,_={};for(f=0;f<3;f++)if(!(t&&f===2&&r.z===void 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t.lat&&t.lon?[t.lon,t.lat,t.lon,t.lat]:[t.left,t.bottom,t.right,t.top]}function LB(e){var t=kB(mY(e.toUpperCase()));return t.lat&&t.lon?[t.lon,t.lat]:[(t.left+t.right)/2,(t.top+t.bottom)/2]}function IB(e){return e*(Math.PI/180)}function hY(e){return 180*(e/Math.PI)}function umt(e){var t=e.lat,r=e.lon,i=6378137,s=.00669438,n=.9996,o,c,f,_,w,I,R,N=IB(t),j=IB(r),Q,et;et=Math.floor((r+180)/6)+1,r===180&&(et=60),t>=56&&t<64&&r>=3&&r<12&&(et=32),t>=72&&t<84&&(r>=0&&r<9?et=31:r>=9&&r<21?et=33:r>=21&&r<33?et=35:r>=33&&r<42&&(et=37)),o=(et-1)*6-180+3,Q=IB(o),c=s/(1-s),f=i/Math.sqrt(1-s*Math.sin(N)*Math.sin(N)),_=Math.tan(N)*Math.tan(N),w=c*Math.cos(N)*Math.cos(N),I=Math.cos(N)*(j-Q),R=i*((1-s/4-3*s*s/64-5*s*s*s/256)*N-(3*s/8+3*s*s/32+45*s*s*s/1024)*Math.sin(2*N)+(15*s*s/256+45*s*s*s/1024)*Math.sin(4*N)-35*s*s*s/3072*Math.sin(6*N));var Y=n*f*(I+(1-_+w)*I*I*I/6+(5-18*_+_*_+72*w-58*c)*I*I*I*I*I/120)+5e5,K=n*(R+f*Math.tan(N)*(I*I/2+(5-_+9*w+4*w*w)*I*I*I*I/24+(61-58*_+_*_+600*w-330*c)*I*I*I*I*I*I/720));return t<0&&(K+=1e7),{northing:Math.round(K),easting:Math.round(Y),zoneNumber:et,zoneLetter:hmt(t)}}function kB(e){var t=e.northing,r=e.easting,i=e.zoneLetter,s=e.zoneNumber;if(s<0||s>60)return null;var n=.9996,o=6378137,c=.00669438,f,_=(1-Math.sqrt(1-c))/(1+Math.sqrt(1-c)),w,I,R,N,j,Q,et,Y,K,J=r-5e5,ut=t;i<\"N\"&&(ut-=1e7),et=(s-1)*6-180+3,f=c/(1-c),Q=ut/n,Y=Q/(o*(1-c/4-3*c*c/64-5*c*c*c/256)),K=Y+(3*_/2-27*_*_*_/32)*Math.sin(2*Y)+(21*_*_/16-55*_*_*_*_/32)*Math.sin(4*Y)+151*_*_*_/96*Math.sin(6*Y),w=o/Math.sqrt(1-c*Math.sin(K)*Math.sin(K)),I=Math.tan(K)*Math.tan(K),R=f*Math.cos(K)*Math.cos(K),N=o*(1-c)/Math.pow(1-c*Math.sin(K)*Math.sin(K),1.5),j=J/(w*n);var Et=K-w*Math.tan(K)/N*(j*j/2-(5+3*I+10*R-4*R*R-9*f)*j*j*j*j/24+(61+90*I+298*R+45*I*I-252*f-3*R*R)*j*j*j*j*j*j/720);Et=hY(Et);var kt=(j-(1+2*I+R)*j*j*j/6+(5-2*R+28*I-3*R*R+8*f+24*I*I)*j*j*j*j*j/120)/Math.cos(K);kt=et+hY(kt);var Xt;if(e.accuracy){var qt=kB({northing:e.northing+e.accuracy,easting:e.easting+e.accuracy,zoneLetter:e.zoneLetter,zoneNumber:e.zoneNumber});Xt={top:qt.lat,right:qt.lon,bottom:Et,left:kt}}else Xt={lat:Et,lon:kt};return Xt}function hmt(e){var t=\"Z\";return 84>=e&&e>=72?t=\"X\":72>e&&e>=64?t=\"W\":64>e&&e>=56?t=\"V\":56>e&&e>=48?t=\"U\":48>e&&e>=40?t=\"T\":40>e&&e>=32?t=\"S\":32>e&&e>=24?t=\"R\":24>e&&e>=16?t=\"Q\":16>e&&e>=8?t=\"P\":8>e&&e>=0?t=\"N\":0>e&&e>=-8?t=\"M\":-8>e&&e>=-16?t=\"L\":-16>e&&e>=-24?t=\"K\":-24>e&&e>=-32?t=\"J\":-32>e&&e>=-40?t=\"H\":-40>e&&e>=-48?t=\"G\":-48>e&&e>=-56?t=\"F\":-56>e&&e>=-64?t=\"E\":-64>e&&e>=-72?t=\"D\":-72>e&&e>=-80&&(t=\"C\"),t}function fmt(e,t){var r=\"00000\"+e.easting,i=\"00000\"+e.northing;return e.zoneNumber+e.zoneLetter+dmt(e.easting,e.northing,e.zoneNumber)+r.substr(r.length-5,t)+i.substr(i.length-5,t)}function dmt(e,t,r){var i=AY(r),s=Math.floor(e/1e5),n=Math.floor(t/1e5)%20;return pmt(s,n,i)}function AY(e){var t=e%uY;return t===0&&(t=uY),t}function pmt(e,t,r){var i=r-1,s=fY.charCodeAt(i),n=dY.charCodeAt(i),o=s+e-1,c=n+t,f=!1;o>mS&&(o=o-mS+ux-1,f=!0),(o===Mc||sMc||(o>Mc||sth||(o>th||smS&&(o=o-mS+ux-1),c>AS?(c=c-AS+ux-1,f=!0):f=!1,(c===Mc||nMc||(c>Mc||nth||(c>th||nAS&&(c=c-AS+ux-1);var _=String.fromCharCode(o)+String.fromCharCode(c);return _}function mY(e){if(e&&e.length===0)throw\"MGRSPoint coverting from nothing\";for(var t=e.length,r=null,i=\"\",s,n=0;!/[A-Z]/.test(s=e.charAt(n));){if(n>=2)throw\"MGRSPoint bad conversion from: \"+e;i+=s,n++}var o=parseInt(i,10);if(n===0||n+3>t)throw\"MGRSPoint bad conversion from: \"+e;var c=e.charAt(n++);if(c<=\"A\"||c===\"B\"||c===\"Y\"||c>=\"Z\"||c===\"I\"||c===\"O\")throw\"MGRSPoint zone letter \"+c+\" not handled: \"+e;r=e.substring(n,n+=2);for(var f=AY(o),_=Amt(r.charAt(0),f),w=mmt(r.charAt(1),f);w0&&(Q=1e5/Math.pow(10,R),et=e.substring(n,n+R),N=parseFloat(et)*Q,Y=e.substring(n+R),j=parseFloat(Y)*Q),K=N+_,J=j+w,{easting:K,northing:J,zoneLetter:c,zoneNumber:o,accuracy:Q}}function Amt(e,t){for(var r=fY.charCodeAt(t-1),i=1e5,s=!1;r!==e.charCodeAt(0);){if(r++,r===Mc&&r++,r===th&&r++,r>mS){if(s)throw\"Bad character: \"+e;r=ux,s=!0}i+=1e5}return i}function mmt(e,t){if(e>\"V\")throw\"MGRSPoint given invalid Northing \"+e;for(var r=dY.charCodeAt(t-1),i=0,s=!1;r!==e.charCodeAt(0);){if(r++,r===Mc&&r++,r===th&&r++,r>AS){if(s)throw\"Bad character: \"+e;r=ux,s=!0}i+=1e5}return i}function gmt(e){var t;switch(e){case\"C\":t=11e5;break;case\"D\":t=2e6;break;case\"E\":t=28e5;break;case\"F\":t=37e5;break;case\"G\":t=46e5;break;case\"H\":t=55e5;break;case\"J\":t=64e5;break;case\"K\":t=73e5;break;case\"L\":t=82e5;break;case\"M\":t=91e5;break;case\"N\":t=0;break;case\"P\":t=8e5;break;case\"Q\":t=17e5;break;case\"R\":t=26e5;break;case\"S\":t=35e5;break;case\"T\":t=44e5;break;case\"U\":t=53e5;break;case\"V\":t=62e5;break;case\"W\":t=7e6;break;case\"X\":t=79e5;break;default:t=-1}if(t>=0)return t;throw\"Invalid zone letter: \"+e}function hx(e,t,r){if(!(this instanceof hx))return new hx(e,t,r);if(Array.isArray(e))this.x=e[0],this.y=e[1],this.z=e[2]||0;else if(typeof e==\"object\")this.x=e.x,this.y=e.y,this.z=e.z||0;else if(typeof e==\"string\"&&typeof t>\"u\"){var i=e.split(\",\");this.x=parseFloat(i[0],10),this.y=parseFloat(i[1],10),this.z=parseFloat(i[2],10)||0}else this.x=e,this.y=t,this.z=r||0;console.warn(\"proj4.Point will be removed in version 3, use proj4.toPoint\")}hx.fromMGRS=function(e){return new hx(LB(e))};hx.prototype.toMGRS=function(e){return CB([this.x,this.y],e)};var gY=hx;var _mt=1,ymt=.25,_Y=.046875,yY=.01953125,vY=.01068115234375,vmt=.75,xmt=.46875,bmt=.013020833333333334,wmt=.007120768229166667,Smt=.3645833333333333,Tmt=.005696614583333333,Mmt=.3076171875;function hI(e){var t=[];t[0]=_mt-e*(ymt+e*(_Y+e*(yY+e*vY))),t[1]=e*(vmt-e*(_Y+e*(yY+e*vY)));var r=e*e;return t[2]=r*(xmt-e*(bmt+e*wmt)),r*=e,t[3]=r*(Smt-e*Tmt),t[4]=r*e*Mmt,t}function Xg(e,t,r,i){return r*=t,t*=t,i[0]*e-r*(i[1]+t*(i[2]+t*(i[3]+t*i[4])))}var Emt=20;function fI(e,t,r){for(var i=1/(1-t),s=e,n=Emt;n;--n){var o=Math.sin(s),c=1-t*o*o;if(c=(Xg(s,o,Math.cos(s),r)-e)*(c*Math.sqrt(c))*i,s-=c,Math.abs(c)Se?Math.tan(r):0,Q=Math.pow(j,2),et=Math.pow(Q,2);s=1-this.es*Math.pow(c,2),w=w/Math.sqrt(s);var Y=Xg(r,c,f,this.en);n=this.a*(this.k0*w*(1+I/6*(1-Q+R+I/20*(5-18*Q+et+14*R-58*Q*R+I/42*(61+179*et-et*Q-479*Q)))))+this.x0,o=this.a*(this.k0*(Y-this.ml0+c*i*w/2*(1+I/12*(5-Q+9*R+4*N+I/30*(61+et-58*Q+270*R-330*Q*R+I/56*(1385+543*et-et*Q-3111*Q))))))+this.y0}else{var _=f*Math.sin(i);if(Math.abs(Math.abs(_)-1)=1){if(_-1>Se)return 93;o=0}else o=Math.acos(o);r<0&&(o=-o),o=this.a*this.k0*(o-this.lat0)+this.y0}return e.x=n,e.y=o,e}function Cmt(e){var t,r,i,s,n=(e.x-this.x0)*(1/this.a),o=(e.y-this.y0)*(1/this.a);if(this.es)if(t=this.ml0+o/this.k0,r=fI(t,this.es,this.en),Math.abs(r)Se?Math.tan(r):0,j=this.ep2*Math.pow(R,2),Q=Math.pow(j,2),et=Math.pow(N,2),Y=Math.pow(et,2);t=1-this.es*Math.pow(I,2);var K=n*Math.sqrt(t)/this.k0,J=Math.pow(K,2);t=t*N,i=r-t*J/(1-this.es)*.5*(1-J/12*(5+3*et-9*j*et+j-4*Q-J/30*(61+90*et-252*j*et+45*Y+46*j-J/56*(1385+3633*et+4095*Y+1574*Y*et)))),s=Ce(this.long0+K*(1-J/6*(1+2*et+j-J/20*(5+28*et+24*Y+8*j*et+6*j-J/42*(61+662*et+1320*Y+720*Y*et))))/R)}else i=de*pd(o),s=0;else{var c=Math.exp(n/this.k0),f=.5*(c-1/c),_=this.lat0+o/this.k0,w=Math.cos(_);t=Math.sqrt((1-Math.pow(w,2))/(1+Math.pow(f,2))),i=Math.asin(t),o<0&&(i=-i),f===0&&w===0?s=0:s=Ce(Math.atan2(f,w)+this.long0)}return e.x=s,e.y=i,e}var Lmt=[\"Fast_Transverse_Mercator\",\"Fast Transverse Mercator\"],fx={init:Pmt,forward:Imt,inverse:Cmt,names:Lmt};function dI(e){var t=Math.exp(e);return t=(t-1/t)/2,t}function Ta(e,t){e=Math.abs(e),t=Math.abs(t);var r=Math.max(e,t),i=Math.min(e,t)/(r||1);return r*Math.sqrt(1+Math.pow(i,2))}function xY(e){var t=1+e,r=t-1;return r===0?e:e*Math.log(t)/r}function bY(e){var t=Math.abs(e);return t=xY(t*(1+t/(Ta(1,t)+1))),e<0?-t:t}function pI(e,t){for(var r=2*Math.cos(2*t),i=e.length-1,s=e[i],n=0,o;--i>=0;)o=-n+r*s+e[i],n=s,s=o;return t+o*Math.sin(2*t)}function wY(e,t){for(var r=2*Math.cos(t),i=e.length-1,s=e[i],n=0,o;--i>=0;)o=-n+r*s+e[i],n=s,s=o;return Math.sin(t)*o}function SY(e){var t=Math.exp(e);return t=(t+1/t)/2,t}function RB(e,t,r){for(var i=Math.sin(t),s=Math.cos(t),n=dI(r),o=SY(r),c=2*s*o,f=-2*i*n,_=e.length-1,w=e[_],I=0,R=0,N=0,j,Q;--_>=0;)j=R,Q=I,R=w,I=N,w=-j+c*R-f*I+e[_],N=-Q+f*R+c*I;return c=i*o,f=s*n,[c*w-f*N,c*N+f*w]}function kmt(){if(!this.approx&&(isNaN(this.es)||this.es<=0))throw new Error('Incorrect elliptical usage. Try using the +approx option in the proj string, or PROJECTION[\"Fast_Transverse_Mercator\"] in the WKT.');this.approx&&(fx.init.apply(this),this.forward=fx.forward,this.inverse=fx.inverse),this.x0=this.x0!==void 0?this.x0:0,this.y0=this.y0!==void 0?this.y0:0,this.long0=this.long0!==void 0?this.long0:0,this.lat0=this.lat0!==void 0?this.lat0:0,this.cgb=[],this.cbg=[],this.utg=[],this.gtu=[];var e=this.es/(1+Math.sqrt(1-this.es)),t=e/(2-e),r=t;this.cgb[0]=t*(2+t*(-2/3+t*(-2+t*(116/45+t*(26/45+t*(-2854/675)))))),this.cbg[0]=t*(-2+t*(2/3+t*(4/3+t*(-82/45+t*(32/45+t*(4642/4725)))))),r=r*t,this.cgb[1]=r*(7/3+t*(-8/5+t*(-227/45+t*(2704/315+t*(2323/945))))),this.cbg[1]=r*(5/3+t*(-16/15+t*(-13/9+t*(904/315+t*(-1522/945))))),r=r*t,this.cgb[2]=r*(56/15+t*(-136/35+t*(-1262/105+t*(73814/2835)))),this.cbg[2]=r*(-26/15+t*(34/21+t*(8/5+t*(-12686/2835)))),r=r*t,this.cgb[3]=r*(4279/630+t*(-332/35+t*(-399572/14175))),this.cbg[3]=r*(1237/630+t*(-12/5+t*(-24832/14175))),r=r*t,this.cgb[4]=r*(4174/315+t*(-144838/6237)),this.cbg[4]=r*(-734/315+t*(109598/31185)),r=r*t,this.cgb[5]=r*(601676/22275),this.cbg[5]=r*(444337/155925),r=Math.pow(t,2),this.Qn=this.k0/(1+t)*(1+r*(1/4+r*(1/64+r/256))),this.utg[0]=t*(-.5+t*(2/3+t*(-37/96+t*(1/360+t*(81/512+t*(-96199/604800)))))),this.gtu[0]=t*(.5+t*(-2/3+t*(5/16+t*(41/180+t*(-127/288+t*(7891/37800)))))),this.utg[1]=r*(-1/48+t*(-1/15+t*(437/1440+t*(-46/105+t*(1118711/3870720))))),this.gtu[1]=r*(13/48+t*(-3/5+t*(557/1440+t*(281/630+t*(-1983433/1935360))))),r=r*t,this.utg[2]=r*(-17/480+t*(37/840+t*(209/4480+t*(-5569/90720)))),this.gtu[2]=r*(61/240+t*(-103/140+t*(15061/26880+t*(167603/181440)))),r=r*t,this.utg[3]=r*(-4397/161280+t*(11/504+t*(830251/7257600))),this.gtu[3]=r*(49561/161280+t*(-179/168+t*(6601661/7257600))),r=r*t,this.utg[4]=r*(-4583/161280+t*(108847/3991680)),this.gtu[4]=r*(34729/80640+t*(-3418889/1995840)),r=r*t,this.utg[5]=r*(-20648693/638668800),this.gtu[5]=r*(212378941/319334400);var i=pI(this.cbg,this.lat0);this.Zb=-this.Qn*(i+wY(this.gtu,2*i))}function Rmt(e){var t=Ce(e.x-this.long0),r=e.y;r=pI(this.cbg,r);var i=Math.sin(r),s=Math.cos(r),n=Math.sin(t),o=Math.cos(t);r=Math.atan2(i,o*s),t=Math.atan2(n*s,Ta(i,s*o)),t=bY(Math.tan(t));var c=RB(this.gtu,2*r,2*t);r=r+c[0],t=t+c[1];var f,_;return Math.abs(t)<=2.623395162778?(f=this.a*(this.Qn*t)+this.x0,_=this.a*(this.Qn*r+this.Zb)+this.y0):(f=1/0,_=1/0),e.x=f,e.y=_,e}function Dmt(e){var t=(e.x-this.x0)*(1/this.a),r=(e.y-this.y0)*(1/this.a);r=(r-this.Zb)/this.Qn,t=t/this.Qn;var i,s;if(Math.abs(t)<=2.623395162778){var n=RB(this.utg,2*r,2*t);r=r+n[0],t=t+n[1],t=Math.atan(dI(t));var o=Math.sin(r),c=Math.cos(r),f=Math.sin(t),_=Math.cos(t);r=Math.atan2(o*_,Ta(f,_*c)),t=Math.atan2(f,_*c),i=Ce(t+this.long0),s=pI(this.cgb,r)}else i=1/0,s=1/0;return e.x=i,e.y=s,e}var Omt=[\"Extended_Transverse_Mercator\",\"Extended Transverse Mercator\",\"etmerc\",\"Transverse_Mercator\",\"Transverse Mercator\",\"Gauss Kruger\",\"Gauss_Kruger\",\"tmerc\"],dx={init:kmt,forward:Rmt,inverse:Dmt,names:Omt};function TY(e,t){if(e===void 0){if(e=Math.floor((Ce(t)+Math.PI)*30/Math.PI)+1,e<0)return 0;if(e>60)return 60}return e}var Bmt=\"etmerc\";function Fmt(){var e=TY(this.zone,this.long0);if(e===void 0)throw new Error(\"unknown utm zone\");this.lat0=0,this.long0=(6*Math.abs(e)-183)*vs,this.x0=5e5,this.y0=this.utmSouth?1e7:0,this.k0=.9996,dx.init.apply(this),this.forward=dx.forward,this.inverse=dx.inverse}var zmt=[\"Universal Transverse Mercator System\",\"utm\"],MY={init:Fmt,names:zmt,dependsOn:Bmt};function AI(e,t){return Math.pow((1-e)/(1+e),t)}var Nmt=20;function Umt(){var e=Math.sin(this.lat0),t=Math.cos(this.lat0);t*=t,this.rc=Math.sqrt(1-this.es)/(1-this.es*e*e),this.C=Math.sqrt(1+this.es*t*t/(1-this.es)),this.phic0=Math.asin(e/this.C),this.ratexp=.5*this.C*this.e,this.K=Math.tan(.5*this.phic0+Ui)/(Math.pow(Math.tan(.5*this.lat0+Ui),this.C)*AI(this.e*e,this.ratexp))}function Vmt(e){var t=e.x,r=e.y;return e.y=2*Math.atan(this.K*Math.pow(Math.tan(.5*r+Ui),this.C)*AI(this.e*Math.sin(r),this.ratexp))-de,e.x=this.C*t,e}function jmt(e){for(var t=1e-14,r=e.x/this.C,i=e.y,s=Math.pow(Math.tan(.5*i+Ui)/this.K,1/this.C),n=Nmt;n>0&&(i=2*Math.atan(s*AI(this.e*Math.sin(e.y),-.5*this.e))-de,!(Math.abs(i-e.y)0?this.con=1:this.con=-1),this.cons=Math.sqrt(Math.pow(1+this.e,1+this.e)*Math.pow(1-this.e,1-this.e)),this.k0===1&&!isNaN(this.lat_ts)&&Math.abs(this.coslat0)<=Se&&Math.abs(Math.cos(this.lat_ts))>Se&&(this.k0=.5*this.cons*ol(this.e,Math.sin(this.lat_ts),Math.cos(this.lat_ts))/Dl(this.e,this.con*this.lat_ts,this.con*Math.sin(this.lat_ts))),this.ms1=ol(this.e,this.sinlat0,this.coslat0),this.X0=2*Math.atan(this.ssfn_(this.lat0,this.sinlat0,this.e))-de,this.cosX0=Math.cos(this.X0),this.sinX0=Math.sin(this.X0))}function $mt(e){var t=e.x,r=e.y,i=Math.sin(r),s=Math.cos(r),n,o,c,f,_,w,I=Ce(t-this.long0);return Math.abs(Math.abs(t-this.long0)-Math.PI)<=Se&&Math.abs(r+this.lat0)<=Se?(e.x=NaN,e.y=NaN,e):this.sphere?(n=2*this.k0/(1+this.sinlat0*i+this.coslat0*s*Math.cos(I)),e.x=this.a*n*s*Math.sin(I)+this.x0,e.y=this.a*n*(this.coslat0*i-this.sinlat0*s*Math.cos(I))+this.y0,e):(o=2*Math.atan(this.ssfn_(r,i,this.e))-de,f=Math.cos(o),c=Math.sin(o),Math.abs(this.coslat0)<=Se?(_=Dl(this.e,r*this.con,this.con*i),w=2*this.a*this.k0*_/this.cons,e.x=this.x0+w*Math.sin(t-this.long0),e.y=this.y0-this.con*w*Math.cos(t-this.long0),e):(Math.abs(this.sinlat0)0?t=Ce(this.long0+Math.atan2(e.x,-1*e.y)):t=Ce(this.long0+Math.atan2(e.x,e.y)):t=Ce(this.long0+Math.atan2(e.x*Math.sin(c),o*this.coslat0*Math.cos(c)-e.y*this.sinlat0*Math.sin(c))),e.x=t,e.y=r,e)}else if(Math.abs(this.coslat0)<=Se){if(o<=Se)return r=this.lat0,t=this.long0,e.x=t,e.y=r,e;e.x*=this.con,e.y*=this.con,i=o*this.cons/(2*this.a*this.k0),r=this.con*Lp(this.e,i),t=this.con*Ce(this.con*this.long0+Math.atan2(e.x,-1*e.y))}else s=2*Math.atan(o*this.cosX0/(2*this.a*this.k0*this.ms1)),t=this.long0,o<=Se?n=this.X0:(n=Math.asin(Math.cos(s)*this.sinX0+e.y*Math.sin(s)*this.cosX0/o),t=Ce(this.long0+Math.atan2(e.x*Math.sin(s),o*this.cosX0*Math.cos(s)-e.y*this.sinX0*Math.sin(s)))),r=-1*Lp(this.e,Math.tan(.5*(de+n)));return e.x=t,e.y=r,e}var Kmt=[\"stere\",\"Stereographic_South_Pole\",\"Polar Stereographic (variant B)\",\"Polar_Stereographic\"],PY={init:Qmt,forward:$mt,inverse:Xmt,names:Kmt,ssfn_:Ymt};function Jmt(){var e=this.lat0;this.lambda0=this.long0;var t=Math.sin(e),r=this.a,i=this.rf,s=1/i,n=2*s-Math.pow(s,2),o=this.e=Math.sqrt(n);this.R=this.k0*r*Math.sqrt(1-n)/(1-n*Math.pow(t,2)),this.alpha=Math.sqrt(1+n/(1-n)*Math.pow(Math.cos(e),4)),this.b0=Math.asin(t/this.alpha);var c=Math.log(Math.tan(Math.PI/4+this.b0/2)),f=Math.log(Math.tan(Math.PI/4+e/2)),_=Math.log((1+o*t)/(1-o*t));this.K=c-this.alpha*f+this.alpha*o/2*_}function t0t(e){var t=Math.log(Math.tan(Math.PI/4-e.y/2)),r=this.e/2*Math.log((1+this.e*Math.sin(e.y))/(1-this.e*Math.sin(e.y))),i=-this.alpha*(t+r)+this.K,s=2*(Math.atan(Math.exp(i))-Math.PI/4),n=this.alpha*(e.x-this.lambda0),o=Math.atan(Math.sin(n)/(Math.sin(this.b0)*Math.tan(s)+Math.cos(this.b0)*Math.cos(n))),c=Math.asin(Math.cos(this.b0)*Math.sin(s)-Math.sin(this.b0)*Math.cos(s)*Math.cos(n));return e.y=this.R/2*Math.log((1+Math.sin(c))/(1-Math.sin(c)))+this.y0,e.x=this.R*o+this.x0,e}function e0t(e){for(var t=e.x-this.x0,r=e.y-this.y0,i=t/this.R,s=2*(Math.atan(Math.exp(r/this.R))-Math.PI/4),n=Math.asin(Math.cos(this.b0)*Math.sin(s)+Math.sin(this.b0)*Math.cos(s)*Math.cos(i)),o=Math.atan(Math.sin(i)/(Math.cos(this.b0)*Math.cos(i)-Math.sin(this.b0)*Math.tan(s))),c=this.lambda0+o/this.alpha,f=0,_=n,w=-1e3,I=0;Math.abs(_-w)>1e-7;){if(++I>20)return;f=1/this.alpha*(Math.log(Math.tan(Math.PI/4+n/2))-this.K)+this.e*Math.log(Math.tan(Math.PI/4+Math.asin(this.e*Math.sin(_))/2)),w=_,_=2*Math.atan(Math.exp(f))-Math.PI/2}return e.x=c,e.y=_,e}var r0t=[\"somerc\"],IY={init:Jmt,forward:t0t,inverse:e0t,names:r0t};var px=1e-7;function i0t(e){var t=[\"Hotine_Oblique_Mercator\",\"Hotine_Oblique_Mercator_Azimuth_Natural_Origin\"],r=typeof e.PROJECTION==\"object\"?Object.keys(e.PROJECTION)[0]:e.PROJECTION;return\"no_uoff\"in e||\"no_off\"in e||t.indexOf(r)!==-1}function n0t(){var e,t,r,i,s,n,o,c,f,_,w=0,I,R=0,N=0,j=0,Q=0,et=0,Y=0,K;this.no_off=i0t(this),this.no_rot=\"no_rot\"in this;var J=!1;\"alpha\"in this&&(J=!0);var ut=!1;if(\"rectified_grid_angle\"in this&&(ut=!0),J&&(Y=this.alpha),ut&&(w=this.rectified_grid_angle*vs),J||ut)R=this.longc;else if(N=this.long1,Q=this.lat1,j=this.long2,et=this.lat2,Math.abs(Q-et)<=px||(e=Math.abs(Q))<=px||Math.abs(e-de)<=px||Math.abs(Math.abs(this.lat0)-de)<=px||Math.abs(Math.abs(et)-de)<=px)throw new Error;var Et=1-this.es;t=Math.sqrt(Et),Math.abs(this.lat0)>Se?(c=Math.sin(this.lat0),r=Math.cos(this.lat0),e=1-this.es*c*c,this.B=r*r,this.B=Math.sqrt(1+this.es*this.B*this.B/Et),this.A=this.B*this.k0*t/e,i=this.B*t/(r*Math.sqrt(e)),s=i*i-1,s<=0?s=0:(s=Math.sqrt(s),this.lat0<0&&(s=-s)),this.E=s+=i,this.E*=Math.pow(Dl(this.e,this.lat0,c),this.B)):(this.B=1/t,this.A=this.k0,this.E=i=s=1),J||ut?(J?(I=Math.asin(Math.sin(Y)/i),ut||(w=Y)):(I=w,Y=Math.asin(i*Math.sin(I))),this.lam0=R-Math.asin(.5*(s-1/s)*Math.tan(I))/this.B):(n=Math.pow(Dl(this.e,Q,Math.sin(Q)),this.B),o=Math.pow(Dl(this.e,et,Math.sin(et)),this.B),s=this.E/n,f=(o-n)/(o+n),_=this.E*this.E,_=(_-o*n)/(_+o*n),e=N-j,e<-Math.pi?j-=Em:e>Math.pi&&(j+=Em),this.lam0=Ce(.5*(N+j)-Math.atan(_*Math.tan(.5*this.B*(N-j))/f)/this.B),I=Math.atan(2*Math.sin(this.B*Ce(N-this.lam0))/(s-1/s)),w=Y=Math.asin(i*Math.sin(I))),this.singam=Math.sin(I),this.cosgam=Math.cos(I),this.sinrot=Math.sin(w),this.cosrot=Math.cos(w),this.rB=1/this.B,this.ArB=this.A*this.rB,this.BrA=1/this.ArB,K=this.A*this.B,this.no_off?this.u_0=0:(this.u_0=Math.abs(this.ArB*Math.atan(Math.sqrt(i*i-1)/Math.cos(Y))),this.lat0<0&&(this.u_0=-this.u_0)),s=.5*I,this.v_pole_n=this.ArB*Math.log(Math.tan(Ui-s)),this.v_pole_s=this.ArB*Math.log(Math.tan(Ui+s))}function s0t(e){var t={},r,i,s,n,o,c,f,_;if(e.x=e.x-this.lam0,Math.abs(Math.abs(e.y)-de)>Se){if(o=this.E/Math.pow(Dl(this.e,e.y,Math.sin(e.y)),this.B),c=1/o,r=.5*(o-c),i=.5*(o+c),n=Math.sin(this.B*e.x),s=(r*this.singam-n*this.cosgam)/i,Math.abs(Math.abs(s)-1)0?this.v_pole_n:this.v_pole_s,f=this.ArB*e.y;return this.no_rot?(t.x=f,t.y=_):(f-=this.u_0,t.x=_*this.cosrot+f*this.sinrot,t.y=f*this.cosrot-_*this.sinrot),t.x=this.a*t.x+this.x0,t.y=this.a*t.y+this.y0,t}function o0t(e){var t,r,i,s,n,o,c,f={};if(e.x=(e.x-this.x0)*(1/this.a),e.y=(e.y-this.y0)*(1/this.a),this.no_rot?(r=e.y,t=e.x):(r=e.x*this.cosrot-e.y*this.sinrot,t=e.y*this.cosrot+e.x*this.sinrot+this.u_0),i=Math.exp(-this.BrA*r),s=.5*(i-1/i),n=.5*(i+1/i),o=Math.sin(this.BrA*t),c=(o*this.cosgam+s*this.singam)/n,Math.abs(Math.abs(c)-1)Se?this.ns=Math.log(i/c)/Math.log(s/f):this.ns=t,isNaN(this.ns)&&(this.ns=t),this.f0=i/(this.ns*Math.pow(s,this.ns)),this.rh=this.a*this.f0*Math.pow(_,this.ns),this.title||(this.title=\"Lambert Conformal Conic\")}}function c0t(e){var t=e.x,r=e.y;Math.abs(2*Math.abs(r)-Math.PI)<=Se&&(r=pd(r)*(de-2*Se));var i=Math.abs(Math.abs(r)-de),s,n;if(i>Se)s=Dl(this.e,r,Math.sin(r)),n=this.a*this.f0*Math.pow(s,this.ns);else{if(i=r*this.ns,i<=0)return null;n=0}var o=this.ns*Ce(t-this.long0);return e.x=this.k0*(n*Math.sin(o))+this.x0,e.y=this.k0*(this.rh-n*Math.cos(o))+this.y0,e}function u0t(e){var t,r,i,s,n,o=(e.x-this.x0)/this.k0,c=this.rh-(e.y-this.y0)/this.k0;this.ns>0?(t=Math.sqrt(o*o+c*c),r=1):(t=-Math.sqrt(o*o+c*c),r=-1);var f=0;if(t!==0&&(f=Math.atan2(r*o,r*c)),t!==0||this.ns>0){if(r=1/this.ns,i=Math.pow(t/(this.a*this.f0),r),s=Lp(this.e,i),s===-9999)return null}else s=-de;return n=Ce(f/this.ns+this.long0),e.x=n,e.y=s,e}var h0t=[\"Lambert Tangential Conformal Conic Projection\",\"Lambert_Conformal_Conic\",\"Lambert_Conformal_Conic_1SP\",\"Lambert_Conformal_Conic_2SP\",\"lcc\",\"Lambert Conic Conformal (1SP)\",\"Lambert Conic Conformal (2SP)\"],LY={init:l0t,forward:c0t,inverse:u0t,names:h0t};function f0t(){this.a=6377397155e-3,this.es=.006674372230614,this.e=Math.sqrt(this.es),this.lat0||(this.lat0=.863937979737193),this.long0||(this.long0=.7417649320975901-.308341501185665),this.k0||(this.k0=.9999),this.s45=.785398163397448,this.s90=2*this.s45,this.fi0=this.lat0,this.e2=this.es,this.e=Math.sqrt(this.e2),this.alfa=Math.sqrt(1+this.e2*Math.pow(Math.cos(this.fi0),4)/(1-this.e2)),this.uq=1.04216856380474,this.u0=Math.asin(Math.sin(this.fi0)/this.alfa),this.g=Math.pow((1+this.e*Math.sin(this.fi0))/(1-this.e*Math.sin(this.fi0)),this.alfa*this.e/2),this.k=Math.tan(this.u0/2+this.s45)/Math.pow(Math.tan(this.fi0/2+this.s45),this.alfa)*this.g,this.k1=this.k0,this.n0=this.a*Math.sqrt(1-this.e2)/(1-this.e2*Math.pow(Math.sin(this.fi0),2)),this.s0=1.37008346281555,this.n=Math.sin(this.s0),this.ro0=this.k1*this.n0/Math.tan(this.s0),this.ad=this.s90-this.uq}function d0t(e){var t,r,i,s,n,o,c,f=e.x,_=e.y,w=Ce(f-this.long0);return t=Math.pow((1+this.e*Math.sin(_))/(1-this.e*Math.sin(_)),this.alfa*this.e/2),r=2*(Math.atan(this.k*Math.pow(Math.tan(_/2+this.s45),this.alfa)/t)-this.s45),i=-w*this.alfa,s=Math.asin(Math.cos(this.ad)*Math.sin(r)+Math.sin(this.ad)*Math.cos(r)*Math.cos(i)),n=Math.asin(Math.cos(r)*Math.sin(i)/Math.cos(s)),o=this.n*n,c=this.ro0*Math.pow(Math.tan(this.s0/2+this.s45),this.n)/Math.pow(Math.tan(s/2+this.s45),this.n),e.y=c*Math.cos(o)/1,e.x=c*Math.sin(o)/1,this.czech||(e.y*=-1,e.x*=-1),e}function p0t(e){var t,r,i,s,n,o,c,f,_=e.x;e.x=e.y,e.y=_,this.czech||(e.y*=-1,e.x*=-1),o=Math.sqrt(e.x*e.x+e.y*e.y),n=Math.atan2(e.y,e.x),s=n/Math.sin(this.s0),i=2*(Math.atan(Math.pow(this.ro0/o,1/this.n)*Math.tan(this.s0/2+this.s45))-this.s45),t=Math.asin(Math.cos(this.ad)*Math.sin(i)-Math.sin(this.ad)*Math.cos(i)*Math.cos(s)),r=Math.asin(Math.cos(i)*Math.sin(s)/Math.cos(t)),e.x=this.long0-r/this.alfa,c=t,f=0;var w=0;do e.y=2*(Math.atan(Math.pow(this.k,-1/this.alfa)*Math.pow(Math.tan(t/2+this.s45),1/this.alfa)*Math.pow((1+this.e*Math.sin(c))/(1-this.e*Math.sin(c)),this.e/2))-this.s45),Math.abs(c-e.y)<1e-10&&(f=1),c=e.y,w+=1;while(f===0&&w<15);return w>=15?null:e}var A0t=[\"Krovak\",\"krovak\"],kY={init:f0t,forward:d0t,inverse:p0t,names:A0t};function zo(e,t,r,i,s){return e*s-t*Math.sin(2*s)+r*Math.sin(4*s)-i*Math.sin(6*s)}function kp(e){return 1-.25*e*(1+e/16*(3+1.25*e))}function Rp(e){return .375*e*(1+.25*e*(1+.46875*e))}function Dp(e){return .05859375*e*e*(1+.75*e)}function Op(e){return e*e*e*(35/3072)}function Bp(e,t,r){var i=t*r;return e/Math.sqrt(1-i*i)}function ff(e){return Math.abs(e)1e-7?(r=e*t,(1-e*e)*(t/(1-r*r)-.5/e*Math.log((1-r)/(1+r)))):2*t}var v0t=1,x0t=2,b0t=3,w0t=4;function S0t(){var e=Math.abs(this.lat0);if(Math.abs(e-de)0){var t;switch(this.qp=df(this.e,1),this.mmf=.5/(1-this.es),this.apa=R0t(this.es),this.mode){case this.N_POLE:this.dd=1;break;case this.S_POLE:this.dd=1;break;case this.EQUIT:this.rq=Math.sqrt(.5*this.qp),this.dd=1/this.rq,this.xmf=1,this.ymf=.5*this.qp;break;case this.OBLIQ:this.rq=Math.sqrt(.5*this.qp),t=Math.sin(this.lat0),this.sinb1=df(this.e,t)/this.qp,this.cosb1=Math.sqrt(1-this.sinb1*this.sinb1),this.dd=Math.cos(this.lat0)/(Math.sqrt(1-this.es*t*t)*this.rq*this.cosb1),this.ymf=(this.xmf=this.rq)/this.dd,this.xmf*=this.dd;break}}else this.mode===this.OBLIQ&&(this.sinph0=Math.sin(this.lat0),this.cosph0=Math.cos(this.lat0))}function T0t(e){var t,r,i,s,n,o,c,f,_,w,I=e.x,R=e.y;if(I=Ce(I-this.long0),this.sphere){if(n=Math.sin(R),w=Math.cos(R),i=Math.cos(I),this.mode===this.OBLIQ||this.mode===this.EQUIT){if(r=this.mode===this.EQUIT?1+w*i:1+this.sinph0*n+this.cosph0*w*i,r<=Se)return null;r=Math.sqrt(2/r),t=r*w*Math.sin(I),r*=this.mode===this.EQUIT?n:this.cosph0*n-this.sinph0*w*i}else if(this.mode===this.N_POLE||this.mode===this.S_POLE){if(this.mode===this.N_POLE&&(i=-i),Math.abs(R+this.lat0)=0?(t=(_=Math.sqrt(o))*s,r=i*(this.mode===this.S_POLE?_:-_)):t=r=0;break}}return e.x=this.a*t+this.x0,e.y=this.a*r+this.y0,e}function M0t(e){e.x-=this.x0,e.y-=this.y0;var t=e.x/this.a,r=e.y/this.a,i,s,n,o,c,f,_;if(this.sphere){var w=0,I,R=0;if(I=Math.sqrt(t*t+r*r),s=I*.5,s>1)return null;switch(s=2*Math.asin(s),(this.mode===this.OBLIQ||this.mode===this.EQUIT)&&(R=Math.sin(s),w=Math.cos(s)),this.mode){case this.EQUIT:s=Math.abs(I)<=Se?0:Math.asin(r*R/I),t*=R,r=w*I;break;case this.OBLIQ:s=Math.abs(I)<=Se?this.lat0:Math.asin(w*this.sinph0+r*R*this.cosph0/I),t*=R*this.cosph0,r=(w-Math.sin(s)*this.sinph0)*I;break;case this.N_POLE:r=-r,s=de-s;break;case this.S_POLE:s-=de;break}i=r===0&&(this.mode===this.EQUIT||this.mode===this.OBLIQ)?0:Math.atan2(t,r)}else{if(_=0,this.mode===this.OBLIQ||this.mode===this.EQUIT){if(t/=this.dd,r*=this.dd,f=Math.sqrt(t*t+r*r),f1&&(e=e>1?1:-1),Math.asin(e)}function B0t(){Math.abs(this.lat1+this.lat2)Se?this.ns0=(this.ms1*this.ms1-this.ms2*this.ms2)/(this.qs2-this.qs1):this.ns0=this.con,this.c=this.ms1*this.ms1+this.ns0*this.qs1,this.rh=this.a*Math.sqrt(this.c-this.ns0*this.qs0)/this.ns0)}function F0t(e){var t=e.x,r=e.y;this.sin_phi=Math.sin(r),this.cos_phi=Math.cos(r);var i=df(this.e3,this.sin_phi),s=this.a*Math.sqrt(this.c-this.ns0*i)/this.ns0,n=this.ns0*Ce(t-this.long0),o=s*Math.sin(n)+this.x0,c=this.rh-s*Math.cos(n)+this.y0;return e.x=o,e.y=c,e}function z0t(e){var t,r,i,s,n,o;return e.x-=this.x0,e.y=this.rh-e.y+this.y0,this.ns0>=0?(t=Math.sqrt(e.x*e.x+e.y*e.y),i=1):(t=-Math.sqrt(e.x*e.x+e.y*e.y),i=-1),s=0,t!==0&&(s=Math.atan2(i*e.x,i*e.y)),i=t*this.ns0/this.a,this.sphere?o=Math.asin((this.c-i*i)/(2*this.ns0)):(r=(this.c-i*i)/this.ns0,o=this.phi1z(this.e3,r)),n=Ce(s/this.ns0+this.long0),e.x=n,e.y=o,e}function N0t(e,t){var r,i,s,n,o,c=Ec(.5*t);if(e0||Math.abs(o)<=Se?(c=this.x0+this.a*n*r*Math.sin(i)/o,f=this.y0+this.a*n*(this.cos_p14*t-this.sin_p14*r*s)/o):(c=this.x0+this.infinity_dist*r*Math.sin(i),f=this.y0+this.infinity_dist*(this.cos_p14*t-this.sin_p14*r*s)),e.x=c,e.y=f,e}function G0t(e){var t,r,i,s,n,o;return e.x=(e.x-this.x0)/this.a,e.y=(e.y-this.y0)/this.a,e.x/=this.k0,e.y/=this.k0,(t=Math.sqrt(e.x*e.x+e.y*e.y))?(s=Math.atan2(t,this.rc),r=Math.sin(s),i=Math.cos(s),o=Ec(i*this.sin_p14+e.y*r*this.cos_p14/t),n=Math.atan2(e.x*r,t*this.cos_p14*i-e.y*this.sin_p14*r),n=Ce(this.long0+n)):(o=this.phic0,n=0),e.x=n,e.y=o,e}var W0t=[\"gnom\"],BY={init:V0t,forward:j0t,inverse:G0t,names:W0t};function FY(e,t){var r=1-(1-e*e)/(2*e)*Math.log((1-e)/(1+e));if(Math.abs(Math.abs(t)-r)<1e-6)return t<0?-1*de:de;for(var i=Math.asin(.5*t),s,n,o,c,f=0;f<30;f++)if(n=Math.sin(i),o=Math.cos(i),c=e*n,s=Math.pow(1-c*c,2)/(2*o)*(t/(1-e*e)-n/(1-c*c)+.5/e*Math.log((1-c)/(1+c))),i+=s,Math.abs(s)<=1e-10)return i;return NaN}function H0t(){this.sphere||(this.k0=ol(this.e,Math.sin(this.lat_ts),Math.cos(this.lat_ts)))}function q0t(e){var t=e.x,r=e.y,i,s,n=Ce(t-this.long0);if(this.sphere)i=this.x0+this.a*n*Math.cos(this.lat_ts),s=this.y0+this.a*Math.sin(r)/Math.cos(this.lat_ts);else{var o=df(this.e,Math.sin(r));i=this.x0+this.a*this.k0*n,s=this.y0+this.a*o*.5/this.k0}return e.x=i,e.y=s,e}function Z0t(e){e.x-=this.x0,e.y-=this.y0;var t,r;return this.sphere?(t=Ce(this.long0+e.x/this.a/Math.cos(this.lat_ts)),r=Math.asin(e.y/this.a*Math.cos(this.lat_ts))):(r=FY(this.e,2*e.y*this.k0/this.a),t=Ce(this.long0+e.x/(this.a*this.k0))),e.x=t,e.y=r,e}var Y0t=[\"cea\"],zY={init:H0t,forward:q0t,inverse:Z0t,names:Y0t};function Q0t(){this.x0=this.x0||0,this.y0=this.y0||0,this.lat0=this.lat0||0,this.long0=this.long0||0,this.lat_ts=this.lat_ts||0,this.title=this.title||\"Equidistant Cylindrical (Plate Carre)\",this.rc=Math.cos(this.lat_ts)}function $0t(e){var t=e.x,r=e.y,i=Ce(t-this.long0),s=ff(r-this.lat0);return e.x=this.x0+this.a*i*this.rc,e.y=this.y0+this.a*s,e}function X0t(e){var t=e.x,r=e.y;return e.x=Ce(this.long0+(t-this.x0)/(this.a*this.rc)),e.y=ff(this.lat0+(r-this.y0)/this.a),e}var K0t=[\"Equirectangular\",\"Equidistant_Cylindrical\",\"eqc\"],NY={init:Q0t,forward:$0t,inverse:X0t,names:K0t};var UY=20;function J0t(){this.temp=this.b/this.a,this.es=1-Math.pow(this.temp,2),this.e=Math.sqrt(this.es),this.e0=kp(this.es),this.e1=Rp(this.es),this.e2=Dp(this.es),this.e3=Op(this.es),this.ml0=this.a*zo(this.e0,this.e1,this.e2,this.e3,this.lat0)}function tgt(e){var t=e.x,r=e.y,i,s,n,o=Ce(t-this.long0);if(n=o*Math.sin(r),this.sphere)Math.abs(r)<=Se?(i=this.a*o,s=-1*this.a*this.lat0):(i=this.a*Math.sin(n)/Math.tan(r),s=this.a*(ff(r-this.lat0)+(1-Math.cos(n))/Math.tan(r)));else if(Math.abs(r)<=Se)i=this.a*o,s=-1*this.ml0;else{var c=Bp(this.a,this.e,Math.sin(r))/Math.tan(r);i=c*Math.sin(n),s=this.a*zo(this.e0,this.e1,this.e2,this.e3,r)-this.ml0+c*(1-Math.cos(n))}return e.x=i+this.x0,e.y=s+this.y0,e}function egt(e){var t,r,i,s,n,o,c,f,_;if(i=e.x-this.x0,s=e.y-this.y0,this.sphere)if(Math.abs(s+this.a*this.lat0)<=Se)t=Ce(i/this.a+this.long0),r=0;else{o=this.lat0+s/this.a,c=i*i/this.a/this.a+o*o,f=o;var w;for(n=UY;n;--n)if(w=Math.tan(f),_=-1*(o*(f*w+1)-f-.5*(f*f+c)*w)/((f-o)/w-1),f+=_,Math.abs(_)<=Se){r=f;break}t=Ce(this.long0+Math.asin(i*Math.tan(f)/this.a)/Math.sin(r))}else if(Math.abs(s+this.ml0)<=Se)r=0,t=Ce(this.long0+i/this.a);else{o=(this.ml0+s)/this.a,c=i*i/this.a/this.a+o*o,f=o;var I,R,N,j,Q;for(n=UY;n;--n)if(Q=this.e*Math.sin(f),I=Math.sqrt(1-Q*Q)*Math.tan(f),R=this.a*zo(this.e0,this.e1,this.e2,this.e3,f),N=this.e0-2*this.e1*Math.cos(2*f)+4*this.e2*Math.cos(4*f)-6*this.e3*Math.cos(6*f),j=R/this.a,_=(o*(I*j+1)-j-.5*I*(j*j+c))/(this.es*Math.sin(2*f)*(j*j+c-2*o*j)/(4*I)+(o-j)*(I*N-2/Math.sin(2*f))-N),f-=_,Math.abs(_)<=Se){r=f;break}I=Math.sqrt(1-this.es*Math.pow(Math.sin(r),2))*Math.tan(r),t=Ce(this.long0+Math.asin(i*I/this.a)/Math.sin(r))}return e.x=t,e.y=r,e}var rgt=[\"Polyconic\",\"poly\"],VY={init:J0t,forward:tgt,inverse:egt,names:rgt};function igt(){this.A=[],this.A[1]=.6399175073,this.A[2]=-.1358797613,this.A[3]=.063294409,this.A[4]=-.02526853,this.A[5]=.0117879,this.A[6]=-.0055161,this.A[7]=.0026906,this.A[8]=-.001333,this.A[9]=67e-5,this.A[10]=-34e-5,this.B_re=[],this.B_im=[],this.B_re[1]=.7557853228,this.B_im[1]=0,this.B_re[2]=.249204646,this.B_im[2]=.003371507,this.B_re[3]=-.001541739,this.B_im[3]=.04105856,this.B_re[4]=-.10162907,this.B_im[4]=.01727609,this.B_re[5]=-.26623489,this.B_im[5]=-.36249218,this.B_re[6]=-.6870983,this.B_im[6]=-1.1651967,this.C_re=[],this.C_im=[],this.C_re[1]=1.3231270439,this.C_im[1]=0,this.C_re[2]=-.577245789,this.C_im[2]=-.007809598,this.C_re[3]=.508307513,this.C_im[3]=-.112208952,this.C_re[4]=-.15094762,this.C_im[4]=.18200602,this.C_re[5]=1.01418179,this.C_im[5]=1.64497696,this.C_re[6]=1.9660549,this.C_im[6]=2.5127645,this.D=[],this.D[1]=1.5627014243,this.D[2]=.5185406398,this.D[3]=-.03333098,this.D[4]=-.1052906,this.D[5]=-.0368594,this.D[6]=.007317,this.D[7]=.0122,this.D[8]=.00394,this.D[9]=-.0013}function ngt(e){var t,r=e.x,i=e.y,s=i-this.lat0,n=r-this.long0,o=s/Yg*1e-5,c=n,f=1,_=0;for(t=1;t<=10;t++)f=f*o,_=_+this.A[t]*f;var w=_,I=c,R=1,N=0,j,Q,et=0,Y=0;for(t=1;t<=6;t++)j=R*w-N*I,Q=N*w+R*I,R=j,N=Q,et=et+this.B_re[t]*R-this.B_im[t]*N,Y=Y+this.B_im[t]*R+this.B_re[t]*N;return e.x=Y*this.a+this.x0,e.y=et*this.a+this.y0,e}function sgt(e){var t,r=e.x,i=e.y,s=r-this.x0,n=i-this.y0,o=n/this.a,c=s/this.a,f=1,_=0,w,I,R=0,N=0;for(t=1;t<=6;t++)w=f*o-_*c,I=_*o+f*c,f=w,_=I,R=R+this.C_re[t]*f-this.C_im[t]*_,N=N+this.C_im[t]*f+this.C_re[t]*_;for(var j=0;j.999999999999&&(r=.999999999999),t=Math.asin(r);var i=Ce(this.long0+e.x/(.900316316158*this.a*Math.cos(t)));i<-Math.PI&&(i=-Math.PI),i>Math.PI&&(i=Math.PI),r=(2*t+Math.sin(2*t))/Math.PI,Math.abs(r)>1&&(r=1);var s=Math.asin(r);return e.x=i,e.y=s,e}var ygt=[\"Mollweide\",\"moll\"],HY={init:mgt,forward:ggt,inverse:_gt,names:ygt};function vgt(){Math.abs(this.lat1+this.lat2)=0?(r=Math.sqrt(e.x*e.x+e.y*e.y),t=1):(r=-Math.sqrt(e.x*e.x+e.y*e.y),t=-1);var n=0;if(r!==0&&(n=Math.atan2(t*e.x,t*e.y)),this.sphere)return s=Ce(this.long0+n/this.ns),i=ff(this.g-r/this.a),e.x=s,e.y=i,e;var o=this.g-r/this.a;return i=Kg(o,this.e0,this.e1,this.e2,this.e3),s=Ce(this.long0+n/this.ns),e.x=s,e.y=i,e}var wgt=[\"Equidistant_Conic\",\"eqdc\"],qY={init:vgt,forward:xgt,inverse:bgt,names:wgt};function Sgt(){this.R=this.a}function Tgt(e){var t=e.x,r=e.y,i=Ce(t-this.long0),s,n;Math.abs(r)<=Se&&(s=this.x0+this.R*i,n=this.y0);var o=Ec(2*Math.abs(r/Math.PI));(Math.abs(i)<=Se||Math.abs(Math.abs(r)-de)<=Se)&&(s=this.x0,r>=0?n=this.y0+Math.PI*this.R*Math.tan(.5*o):n=this.y0+Math.PI*this.R*-Math.tan(.5*o));var c=.5*Math.abs(Math.PI/i-i/Math.PI),f=c*c,_=Math.sin(o),w=Math.cos(o),I=w/(_+w-1),R=I*I,N=I*(2/_-1),j=N*N,Q=Math.PI*this.R*(c*(I-j)+Math.sqrt(f*(I-j)*(I-j)-(j+f)*(R-j)))/(j+f);i<0&&(Q=-Q),s=this.x0+Q;var et=f+I;return Q=Math.PI*this.R*(N*et-c*Math.sqrt((j+f)*(f+1)-et*et))/(j+f),r>=0?n=this.y0+Q:n=this.y0-Q,e.x=s,e.y=n,e}function Mgt(e){var t,r,i,s,n,o,c,f,_,w,I,R,N;return e.x-=this.x0,e.y-=this.y0,I=Math.PI*this.R,i=e.x/I,s=e.y/I,n=i*i+s*s,o=-Math.abs(s)*(1+n),c=o-2*s*s+i*i,f=-2*o+1+2*s*s+n*n,N=s*s/f+(2*c*c*c/f/f/f-9*o*c/f/f)/27,_=(o-c*c/3/f)/f,w=2*Math.sqrt(-_/3),I=3*N/_/w,Math.abs(I)>1&&(I>=0?I=1:I=-1),R=Math.acos(I)/3,e.y>=0?r=(-w*Math.cos(R+Math.PI/3)-c/3/f)*Math.PI:r=-(-w*Math.cos(R+Math.PI/3)-c/3/f)*Math.PI,Math.abs(i)2*de*this.a?void 0:(r=t/this.a,i=Math.sin(r),s=Math.cos(r),n=this.long0,Math.abs(t)<=Se?o=this.lat0:(o=Ec(s*this.sin_p12+e.y*i*this.cos_p12/t),c=Math.abs(this.lat0)-de,Math.abs(c)<=Se?this.lat0>=0?n=Ce(this.long0+Math.atan2(e.x,-e.y)):n=Ce(this.long0-Math.atan2(-e.x,e.y)):n=Ce(this.long0+Math.atan2(e.x*i,t*this.cos_p12*s-e.y*this.sin_p12*i))),e.x=n,e.y=o,e)):(f=kp(this.es),_=Rp(this.es),w=Dp(this.es),I=Op(this.es),Math.abs(this.sin_p12-1)<=Se?(R=this.a*zo(f,_,w,I,de),t=Math.sqrt(e.x*e.x+e.y*e.y),N=R-t,o=Kg(N/this.a,f,_,w,I),n=Ce(this.long0+Math.atan2(e.x,-1*e.y)),e.x=n,e.y=o,e):Math.abs(this.sin_p12+1)<=Se?(R=this.a*zo(f,_,w,I,de),t=Math.sqrt(e.x*e.x+e.y*e.y),N=t-R,o=Kg(N/this.a,f,_,w,I),n=Ce(this.long0+Math.atan2(e.x,e.y)),e.x=n,e.y=o,e):(t=Math.sqrt(e.x*e.x+e.y*e.y),et=Math.atan2(e.x,e.y),j=Bp(this.a,this.e,this.sin_p12),Y=Math.cos(et),K=this.e*this.cos_p12*Y,J=-K*K/(1-this.es),ut=3*this.es*(1-J)*this.sin_p12*this.cos_p12*Y/(1-this.es),Et=t/j,kt=Et-J*(1+J)*Math.pow(Et,3)/6-ut*(1+3*J)*Math.pow(Et,4)/24,Xt=1-J*kt*kt/2-Et*kt*kt*kt/6,Q=Math.asin(this.sin_p12*Math.cos(kt)+this.cos_p12*Math.sin(kt)*Y),n=Ce(this.long0+Math.asin(Math.sin(et)*Math.sin(kt)/Math.cos(Q))),qt=Math.sin(Q),o=Math.atan2((qt-this.es*Xt*this.sin_p12)*Math.tan(Q),qt*(1-this.es)),e.x=n,e.y=o,e))}var Lgt=[\"Azimuthal_Equidistant\",\"aeqd\"],YY={init:Pgt,forward:Igt,inverse:Cgt,names:Lgt};function kgt(){this.sin_p14=Math.sin(this.lat0),this.cos_p14=Math.cos(this.lat0)}function Rgt(e){var t,r,i,s,n,o,c,f,_=e.x,w=e.y;return i=Ce(_-this.long0),t=Math.sin(w),r=Math.cos(w),s=Math.cos(i),o=this.sin_p14*t+this.cos_p14*r*s,n=1,(o>0||Math.abs(o)<=Se)&&(c=this.a*n*r*Math.sin(i),f=this.y0+this.a*n*(this.cos_p14*t-this.sin_p14*r*s)),e.x=c,e.y=f,e}function Dgt(e){var t,r,i,s,n,o,c;return e.x-=this.x0,e.y-=this.y0,t=Math.sqrt(e.x*e.x+e.y*e.y),r=Ec(t/this.a),i=Math.sin(r),s=Math.cos(r),o=this.long0,Math.abs(t)<=Se?(c=this.lat0,e.x=o,e.y=c,e):(c=Ec(s*this.sin_p14+e.y*i*this.cos_p14/t),n=Math.abs(this.lat0)-de,Math.abs(n)<=Se?(this.lat0>=0?o=Ce(this.long0+Math.atan2(e.x,-e.y)):o=Ce(this.long0-Math.atan2(-e.x,e.y)),e.x=o,e.y=c,e):(o=Ce(this.long0+Math.atan2(e.x*i,t*this.cos_p14*s-e.y*this.sin_p14*i)),e.x=o,e.y=c,e))}var Ogt=[\"ortho\"],QY={init:kgt,forward:Rgt,inverse:Dgt,names:Ogt};var bs={FRONT:1,RIGHT:2,BACK:3,LEFT:4,TOP:5,BOTTOM:6},An={AREA_0:1,AREA_1:2,AREA_2:3,AREA_3:4};function Bgt(){this.x0=this.x0||0,this.y0=this.y0||0,this.lat0=this.lat0||0,this.long0=this.long0||0,this.lat_ts=this.lat_ts||0,this.title=this.title||\"Quadrilateralized Spherical Cube\",this.lat0>=de-Ui/2?this.face=bs.TOP:this.lat0<=-(de-Ui/2)?this.face=bs.BOTTOM:Math.abs(this.long0)<=Ui?this.face=bs.FRONT:Math.abs(this.long0)<=de+Ui?this.face=this.long0>0?bs.RIGHT:bs.LEFT:this.face=bs.BACK,this.es!==0&&(this.one_minus_f=1-(this.a-this.b)/this.a,this.one_minus_f_squared=this.one_minus_f*this.one_minus_f)}function Fgt(e){var t={x:0,y:0},r,i,s,n,o,c,f={value:0};if(e.x-=this.long0,this.es!==0?r=Math.atan(this.one_minus_f_squared*Math.tan(e.y)):r=e.y,i=e.x,this.face===bs.TOP)n=de-r,i>=Ui&&i<=de+Ui?(f.value=An.AREA_0,s=i-de):i>de+Ui||i<=-(de+Ui)?(f.value=An.AREA_1,s=i>0?i-xs:i+xs):i>-(de+Ui)&&i<=-Ui?(f.value=An.AREA_2,s=i+de):(f.value=An.AREA_3,s=i);else if(this.face===bs.BOTTOM)n=de+r,i>=Ui&&i<=de+Ui?(f.value=An.AREA_0,s=-i+de):i=-Ui?(f.value=An.AREA_1,s=-i):i<-Ui&&i>=-(de+Ui)?(f.value=An.AREA_2,s=-i-de):(f.value=An.AREA_3,s=i>0?-i+xs:-i-xs);else{var _,w,I,R,N,j,Q;this.face===bs.RIGHT?i=Ax(i,+de):this.face===bs.BACK?i=Ax(i,+xs):this.face===bs.LEFT&&(i=Ax(i,-de)),R=Math.sin(r),N=Math.cos(r),j=Math.sin(i),Q=Math.cos(i),_=N*Q,w=N*j,I=R,this.face===bs.FRONT?(n=Math.acos(_),s=gI(n,I,w,f)):this.face===bs.RIGHT?(n=Math.acos(w),s=gI(n,I,-_,f)):this.face===bs.BACK?(n=Math.acos(-_),s=gI(n,I,-w,f)):this.face===bs.LEFT?(n=Math.acos(-w),s=gI(n,I,_,f)):(n=s=0,f.value=An.AREA_0)}return c=Math.atan(12/xs*(s+Math.acos(Math.sin(s)*Math.cos(Ui))-de)),o=Math.sqrt((1-Math.cos(n))/(Math.cos(c)*Math.cos(c))/(1-Math.cos(Math.atan(1/Math.cos(s))))),f.value===An.AREA_1?c+=de:f.value===An.AREA_2?c+=xs:f.value===An.AREA_3&&(c+=1.5*xs),t.x=o*Math.cos(c),t.y=o*Math.sin(c),t.x=t.x*this.a+this.x0,t.y=t.y*this.a+this.y0,e.x=t.x,e.y=t.y,e}function zgt(e){var t={lam:0,phi:0},r,i,s,n,o,c,f,_,w,I={value:0};if(e.x=(e.x-this.x0)/this.a,e.y=(e.y-this.y0)/this.a,i=Math.atan(Math.sqrt(e.x*e.x+e.y*e.y)),r=Math.atan2(e.y,e.x),e.x>=0&&e.x>=Math.abs(e.y)?I.value=An.AREA_0:e.y>=0&&e.y>=Math.abs(e.x)?(I.value=An.AREA_1,r-=de):e.x<0&&-e.x>=Math.abs(e.y)?(I.value=An.AREA_2,r=r<0?r+xs:r-xs):(I.value=An.AREA_3,r+=de),w=xs/12*Math.tan(r),o=Math.sin(w)/(Math.cos(w)-1/Math.sqrt(2)),c=Math.atan(o),s=Math.cos(r),n=Math.tan(i),f=1-s*s*n*n*(1-Math.cos(Math.atan(1/Math.cos(c)))),f<-1?f=-1:f>1&&(f=1),this.face===bs.TOP)_=Math.acos(f),t.phi=de-_,I.value===An.AREA_0?t.lam=c+de:I.value===An.AREA_1?t.lam=c<0?c+xs:c-xs:I.value===An.AREA_2?t.lam=c-de:t.lam=c;else if(this.face===bs.BOTTOM)_=Math.acos(f),t.phi=_-de,I.value===An.AREA_0?t.lam=-c+de:I.value===An.AREA_1?t.lam=-c:I.value===An.AREA_2?t.lam=-c-de:t.lam=c<0?-c-xs:-c+xs;else{var R,N,j;R=f,w=R*R,w>=1?j=0:j=Math.sqrt(1-w)*Math.sin(c),w+=j*j,w>=1?N=0:N=Math.sqrt(1-w),I.value===An.AREA_1?(w=N,N=-j,j=w):I.value===An.AREA_2?(N=-N,j=-j):I.value===An.AREA_3&&(w=N,N=j,j=-w),this.face===bs.RIGHT?(w=R,R=-N,N=w):this.face===bs.BACK?(R=-R,N=-N):this.face===bs.LEFT&&(w=R,R=N,N=-w),t.phi=Math.acos(-j)-de,t.lam=Math.atan2(N,R),this.face===bs.RIGHT?t.lam=Ax(t.lam,-de):this.face===bs.BACK?t.lam=Ax(t.lam,-xs):this.face===bs.LEFT&&(t.lam=Ax(t.lam,+de))}if(this.es!==0){var Q,et,Y;Q=t.phi<0?1:0,et=Math.tan(t.phi),Y=this.b/Math.sqrt(et*et+this.one_minus_f_squared),t.phi=Math.atan(Math.sqrt(this.a*this.a-Y*Y)/(this.one_minus_f*Y)),Q&&(t.phi=-t.phi)}return t.lam+=this.long0,e.x=t.lam,e.y=t.phi,e}function gI(e,t,r,i){var s;return eUi&&s<=de+Ui?(i.value=An.AREA_1,s-=de):s>de+Ui||s<=-(de+Ui)?(i.value=An.AREA_2,s=s>=0?s-xs:s+xs):(i.value=An.AREA_3,s+=de)),s}function Ax(e,t){var r=e+t;return r<-xs?r+=Em:r>+xs&&(r-=Em),r}var Ngt=[\"Quadrilateralized Spherical Cube\",\"Quadrilateralized_Spherical_Cube\",\"qsc\"],$Y={init:Bgt,forward:Fgt,inverse:zgt,names:Ngt};var DB=[[1,22199e-21,-715515e-10,31103e-10],[.9986,-482243e-9,-24897e-9,-13309e-10],[.9954,-83103e-8,-448605e-10,-986701e-12],[.99,-.00135364,-59661e-9,36777e-10],[.9822,-.00167442,-449547e-11,-572411e-11],[.973,-.00214868,-903571e-10,18736e-12],[.96,-.00305085,-900761e-10,164917e-11],[.9427,-.00382792,-653386e-10,-26154e-10],[.9216,-.00467746,-10457e-8,481243e-11],[.8962,-.00536223,-323831e-10,-543432e-11],[.8679,-.00609363,-113898e-9,332484e-11],[.835,-.00698325,-640253e-10,934959e-12],[.7986,-.00755338,-500009e-10,935324e-12],[.7597,-.00798324,-35971e-9,-227626e-11],[.7186,-.00851367,-701149e-10,-86303e-10],[.6732,-.00986209,-199569e-9,191974e-10],[.6213,-.010418,883923e-10,624051e-11],[.5722,-.00906601,182e-6,624051e-11],[.5322,-.00677797,275608e-9,624051e-11]],gS=[[-520417e-23,.0124,121431e-23,-845284e-16],[.062,.0124,-126793e-14,422642e-15],[.124,.0124,507171e-14,-160604e-14],[.186,.0123999,-190189e-13,600152e-14],[.248,.0124002,710039e-13,-224e-10],[.31,.0123992,-264997e-12,835986e-13],[.372,.0124029,988983e-12,-311994e-12],[.434,.0123893,-369093e-11,-435621e-12],[.4958,.0123198,-102252e-10,-345523e-12],[.5571,.0121916,-154081e-10,-582288e-12],[.6176,.0119938,-241424e-10,-525327e-12],[.6769,.011713,-320223e-10,-516405e-12],[.7346,.0113541,-397684e-10,-609052e-12],[.7903,.0109107,-489042e-10,-104739e-11],[.8435,.0103431,-64615e-9,-140374e-14],[.8936,.00969686,-64636e-9,-8547e-9],[.9394,.00840947,-192841e-9,-42106e-10],[.9761,.00616527,-256e-6,-42106e-10],[1,.00328947,-319159e-9,-42106e-10]],XY=.8487,KY=1.3523,JY=Sc/5,Ugt=1/JY,mx=18,_I=function(e,t){return e[0]+t*(e[1]+t*(e[2]+t*e[3]))},Vgt=function(e,t){return e[1]+t*(2*e[2]+t*3*e[3])};function jgt(e,t,r,i){for(var s=t;i;--i){var n=e(s);if(s-=n,Math.abs(n)=mx&&(i=mx-1),r=Sc*(r-Ugt*i);var s={x:_I(DB[i],r)*t,y:_I(gS[i],r)};return e.y<0&&(s.y=-s.y),s.x=s.x*this.a*XY+this.x0,s.y=s.y*this.a*KY+this.y0,s}function Hgt(e){var t={x:(e.x-this.x0)/(this.a*XY),y:Math.abs(e.y-this.y0)/(this.a*KY)};if(t.y>=1)t.x/=DB[mx][0],t.y=e.y<0?-de:de;else{var r=Math.floor(t.y*mx);for(r<0?r=0:r>=mx&&(r=mx-1);;)if(gS[r][0]>t.y)--r;else if(gS[r+1][0]<=t.y)++r;else break;var i=gS[r],s=5*(t.y-i[0])/(gS[r+1][0]-i[0]);s=jgt(function(n){return(_I(i,n)-t.y)/Vgt(i,n)},s,Se,100),t.x/=_I(DB[r],s),t.y=(5*r+s)*vs,e.y<0&&(t.y=-t.y)}return t.x=Ce(t.x+this.long0),t}var qgt=[\"Robinson\",\"robin\"],tQ={init:Ggt,forward:Wgt,inverse:Hgt,names:qgt};function Zgt(){this.name=\"geocent\"}function Ygt(e){var t=aI(e,this.es,this.a);return t}function Qgt(e){var t=lI(e,this.es,this.a,this.b);return t}var $gt=[\"Geocentric\",\"geocentric\",\"geocent\",\"Geocent\"],eQ={init:Zgt,forward:Ygt,inverse:Qgt,names:$gt};var al={N_POLE:0,S_POLE:1,EQUIT:2,OBLIQ:3},_S={h:{def:1e5,num:!0},azi:{def:0,num:!0,degrees:!0},tilt:{def:0,num:!0,degrees:!0},long0:{def:0,num:!0},lat0:{def:0,num:!0}};function Xgt(){if(Object.keys(_S).forEach(function(r){if(typeof this[r]>\"u\")this[r]=_S[r].def;else{if(_S[r].num&&isNaN(this[r]))throw new Error(\"Invalid parameter value, must be numeric \"+r+\" = \"+this[r]);_S[r].num&&(this[r]=parseFloat(this[r]))}_S[r].degrees&&(this[r]=this[r]*vs)}.bind(this)),Math.abs(Math.abs(this.lat0)-de)1e10)throw new Error(\"Invalid height\");this.p=1+this.pn1,this.rp=1/this.p,this.h1=1/this.pn1,this.pfact=(this.p+1)*this.h1,this.es=0;var e=this.tilt,t=this.azi;this.cg=Math.cos(t),this.sg=Math.sin(t),this.cw=Math.cos(e),this.sw=Math.sin(e)}function Kgt(e){e.x-=this.long0;var t=Math.sin(e.y),r=Math.cos(e.y),i=Math.cos(e.x),s,n;switch(this.mode){case al.OBLIQ:n=this.sinph0*t+this.cosph0*r*i;break;case al.EQUIT:n=r*i;break;case al.S_POLE:n=-t;break;case al.N_POLE:n=t;break}switch(n=this.pn1/(this.p-n),s=n*r*Math.sin(e.x),this.mode){case al.OBLIQ:n*=this.cosph0*t-this.sinph0*r*i;break;case al.EQUIT:n*=t;break;case al.N_POLE:n*=-(r*i);break;case al.S_POLE:n*=r*i;break}var o,c;return o=n*this.cg+s*this.sg,c=1/(o*this.sw*this.h1+this.cw),s=(s*this.cg-n*this.sg)*this.cw*c,n=o*c,e.x=s*this.a,e.y=n*this.a,e}function Jgt(e){e.x/=this.a,e.y/=this.a;var t={x:e.x,y:e.y},r,i,s;s=1/(this.pn1-e.y*this.sw),r=this.pn1*e.x*s,i=this.pn1*e.y*this.cw*s,e.x=r*this.cg+i*this.sg,e.y=i*this.cg-r*this.sg;var n=Ta(e.x,e.y);if(Math.abs(n)1e10)throw new Error;if(this.radius_g=1+this.radius_g_1,this.C=this.radius_g*this.radius_g-1,this.es!==0){var e=1-this.es,t=1/e;this.radius_p=Math.sqrt(e),this.radius_p2=e,this.radius_p_inv2=t,this.shape=\"ellipse\"}else this.radius_p=1,this.radius_p2=1,this.radius_p_inv2=1,this.shape=\"sphere\";this.title||(this.title=\"Geostationary Satellite View\")}function r_t(e){var t=e.x,r=e.y,i,s,n,o;if(t=t-this.long0,this.shape===\"ellipse\"){r=Math.atan(this.radius_p2*Math.tan(r));var c=this.radius_p/Ta(this.radius_p*Math.cos(r),Math.sin(r));if(s=c*Math.cos(t)*Math.cos(r),n=c*Math.sin(t)*Math.cos(r),o=c*Math.sin(r),(this.radius_g-s)*s-n*n-o*o*this.radius_p_inv2<0)return e.x=Number.NaN,e.y=Number.NaN,e;i=this.radius_g-s,this.flip_axis?(e.x=this.radius_g_1*Math.atan(n/Ta(o,i)),e.y=this.radius_g_1*Math.atan(o/i)):(e.x=this.radius_g_1*Math.atan(n/i),e.y=this.radius_g_1*Math.atan(o/Ta(n,i)))}else this.shape===\"sphere\"&&(i=Math.cos(r),s=Math.cos(t)*i,n=Math.sin(t)*i,o=Math.sin(r),i=this.radius_g-s,this.flip_axis?(e.x=this.radius_g_1*Math.atan(n/Ta(o,i)),e.y=this.radius_g_1*Math.atan(o/i)):(e.x=this.radius_g_1*Math.atan(n/i),e.y=this.radius_g_1*Math.atan(o/Ta(n,i))));return e.x=e.x*this.a,e.y=e.y*this.a,e}function i_t(e){var t=-1,r=0,i=0,s,n,o,c;if(e.x=e.x/this.a,e.y=e.y/this.a,this.shape===\"ellipse\"){this.flip_axis?(i=Math.tan(e.y/this.radius_g_1),r=Math.tan(e.x/this.radius_g_1)*Ta(1,i)):(r=Math.tan(e.x/this.radius_g_1),i=Math.tan(e.y/this.radius_g_1)*Ta(1,r));var f=i/this.radius_p;if(s=r*r+f*f+t*t,n=2*this.radius_g*t,o=n*n-4*s*this.C,o<0)return e.x=Number.NaN,e.y=Number.NaN,e;c=(-n-Math.sqrt(o))/(2*s),t=this.radius_g+c*t,r*=c,i*=c,e.x=Math.atan2(r,t),e.y=Math.atan(i*Math.cos(e.x)/t),e.y=Math.atan(this.radius_p_inv2*Math.tan(e.y))}else if(this.shape===\"sphere\"){if(this.flip_axis?(i=Math.tan(e.y/this.radius_g_1),r=Math.tan(e.x/this.radius_g_1)*Math.sqrt(1+i*i)):(r=Math.tan(e.x/this.radius_g_1),i=Math.tan(e.y/this.radius_g_1)*Math.sqrt(1+r*r)),s=r*r+i*i+t*t,n=2*this.radius_g*t,o=n*n-4*s*this.C,o<0)return e.x=Number.NaN,e.y=Number.NaN,e;c=(-n-Math.sqrt(o))/(2*s),t=this.radius_g+c*t,r*=c,i*=c,e.x=Math.atan2(r,t),e.y=Math.atan(i*Math.cos(e.x)/t)}return e.x=e.x+this.long0,e}var n_t=[\"Geostationary Satellite View\",\"Geostationary_Satellite\",\"geos\"],iQ={init:e_t,forward:r_t,inverse:i_t,names:n_t};function nQ(e){e.Proj.projections.add(fx),e.Proj.projections.add(dx),e.Proj.projections.add(MY),e.Proj.projections.add(EY),e.Proj.projections.add(PY),e.Proj.projections.add(IY),e.Proj.projections.add(CY),e.Proj.projections.add(LY),e.Proj.projections.add(kY),e.Proj.projections.add(RY),e.Proj.projections.add(DY),e.Proj.projections.add(OY),e.Proj.projections.add(BY),e.Proj.projections.add(zY),e.Proj.projections.add(NY),e.Proj.projections.add(VY),e.Proj.projections.add(jY),e.Proj.projections.add(GY),e.Proj.projections.add(WY),e.Proj.projections.add(HY),e.Proj.projections.add(qY),e.Proj.projections.add(ZY),e.Proj.projections.add(YY),e.Proj.projections.add(QY),e.Proj.projections.add($Y),e.Proj.projections.add(tQ),e.Proj.projections.add(eQ),e.Proj.projections.add(rQ),e.Proj.projections.add(iQ)}Tc.defaultDatum=\"WGS84\";Tc.Proj=Pm;Tc.WGS84=new 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bI=class{minX;minY;maxX;maxY;constructor(){this.minX=1/0,this.minY=1/0,this.maxX=-1/0,this.maxY=-1/0}updateBbox(t){t.minXthis.maxX&&(this.maxX=t.maxX),t.maxY>this.maxY&&(this.maxY=t.maxY)}updateCoord(t,r){tthis.maxX&&(this.maxX=t),r>this.maxY&&(this.maxY=r)}};function __t(e,t){switch(t.metadata.get(\"ARROW:extension:name\")){case Im.POINT:return gQ(e);case Im.LINESTRING:case Im.MULTIPOINT:return _Q(e);case Im.POLYGON:case Im.MULTILINESTRING:return yQ(e);case Im.MULTIPOLYGON:return v_t(e);default:throw new Error(\"Unknown ext type name\")}}function y_t(e){let r=xS(e).values,i=new bI;for(let s=0;svQ(r)));let t=new mm({type:new dc,nullValues:[null]});t.set(e.length-1,null);for(let r=0;rxQ(r,t));return}for(let r=0;rvS(n,t)));let r=[];for(let n of e.children)r.push(vS(n,t));let i;e.dictionary!==void 0&&(i=vS(e.dictionary,t));let s={[Oi.OFFSET]:yI(e.buffers[Oi.OFFSET],t),[Oi.DATA]:yI(e.buffers[Oi.DATA],t),[Oi.VALIDITY]:yI(e.buffers[Oi.VALIDITY],t),[Oi.TYPE]:yI(e.buffers[Oi.TYPE],t)};return new Fi(e.type,e.offset,e.length,e._nullCount,s,r,i)}function vI(e){if(\"data\"in e)return e.data.some(r=>vI(r));for(let r of e.children)if(vI(r))return!0;if(e.dictionary!==void 0&&vI(e.dictionary))return!0;let t=[Oi.OFFSET,Oi.DATA,Oi.VALIDITY,Oi.TYPE];for(let r of t)if(e.buffers[r]!==void 0&&bQ(e.buffers[r]))return!0;return!1}function bQ(e){return!(e.byteOffset===0&&e.byteLength===e.buffer.byteLength)}function yI(e,t){return e===void 0||!t&&!bQ(e)?e:e.slice()}function xI(e,t=!1){if(\"data\"in e){let i=[],s=[];for(let o of e.data){let[c,f]=xI(o);i.push(c),s.push(...f)}return[new xr(i),s]}e=vS(e,t);let r=[];for(let i=0;i1)throw new Error(\"expected 1 field\");return new sl(t[0])}case Ot.Struct:{let t=e.children.map(yS);return new pn(t)}case Ot.Union:{let t=e.children.map(yS);return new pc(e.mode,e.typeIds,t)}case Ot.FixedSizeBinary:return new Qu(e.byteWidth);case Ot.FixedSizeList:{let t=e.children.map(yS);if(t.length>1)throw new Error(\"expected 1 field\");return new Ll(e.listSize,t[0])}case Ot.Map:{let t=e.children.map(yS);if(t.length>1)throw new Error(\"expected 1 field\");let r=t[0];return new Ac(r,e.keysSorted)}case Ot.Duration:return new Yu(e.unit);default:throw new Error(`unknown type ${e}`)}}function yS(e){let t=wQ(e.type);return new si(e.name,t,e.nullable,e.metadata)}function UB(e){let t=e.children.map(s=>UB(s)),r=e.dictionary?SQ(e.dictionary):void 0,i={[Oi.OFFSET]:e.valueOffsets,[Oi.DATA]:e.values,[Oi.VALIDITY]:e.nullBitmap,[Oi.TYPE]:e.typeIds};return new Fi(wQ(e.type),e.offset,e.length,e._nullCount,i,t,r)}function SQ(e){return new xr(e.data.map(t=>UB(t)))}var VB=Object.freeze({__proto__:null,hardClone:vS,isShared:vI,preparePostMessage:xI,rehydrateData:UB,rehydrateVector:SQ});function E_t(e,t,r){let i=e.fields.findIndex(s=>s.name===r||s.metadata.get(\"ARROW:extension:name\")===t);return i!==-1?i:null}function P_t(e,t){let{index:r,data:i}=e,s=r;i.invertedGeomOffsets!==void 0&&(s=i.invertedGeomOffsets[r]);let n={data:i.data,length:i.length,attributes:i.attributes},o={index:s,data:n,target:e.target};return t(o)}function ro(e){let{props:t,propName:r,propInput:i,chunkIdx:s,geomCoordOffsets:n}=e;if(i!==void 0)if(i instanceof xr){let o=i.data[s];if(ze.isFixedSizeList(o)){_r(o.children.length===1);let c=o.children[0].values;n&&(c=EI(c,o.type.listSize,n)),t.data.attributes[r]={value:c,size:o.type.listSize,normalized:!0}}else if(ze.isFloat(o)){let c=o.values;n&&(c=EI(c,1,n)),t.data.attributes[r]={value:c,size:1}}}else typeof i==\"function\"?t[r]=(o,c)=>r===\"getPolygonOffset\"?i(o,c):P_t(c,i):t[r]=i}function EI(e,t,r){let i=r[r.length-1],s=new e.constructor(i*t);for(let n=0;n(t[i+1]=t[i]+r.length,t),new Uint32Array(e.length+1))}function no(e,t){let r=[],i=[];for(let[s,n]of Object.entries(e))s.startsWith(\"get\")&&n instanceof xr&&(r.push(n),s.endsWith(\"Color\")&&i.push(n));I_t(t,r);for(let s of i)C_t(s)}function I_t(e,t){for(let r of t)_r(e.batches.length===r.data.length);for(let r of t)for(let i=0;ithis.data):this.content}get isLoaded(){return this._isLoaded&&!this._needsReload}get isLoading(){return!!this._loader&&!this._isCancelled}get needsReload(){return this._needsReload||this._isCancelled}get byteLength(){let t=this.content?this.content.byteLength:0;return Number.isFinite(t)||console.error(\"byteLength not defined in tile data\"),t}async _loadData({getData:t,requestScheduler:r,onLoad:i,onError:s}){let{index:n,id:o,bbox:c,userData:f,zoom:_}=this,w=this._loaderId;this._abortController=new AbortController;let{signal:I}=this._abortController,R=await r.scheduleRequest(this,Q=>Q.isSelected?1:-1);if(!R){this._isCancelled=!0;return}if(this._isCancelled){R.done();return}let N=null,j;try{N=await t({index:n,id:o,bbox:c,userData:f,zoom:_,signal:I})}catch(Q){j=Q||!0}finally{R.done()}if(w===this._loaderId){if(this._loader=void 0,this.content=N,this._isCancelled&&!N){this._isLoaded=!1;return}this._isLoaded=!0,this._isCancelled=!1,j?s(j,this):i(this)}}loadData(t){return this._isLoaded=!1,this._isCancelled=!1,this._needsReload=!1,this._loaderId++,this._loader=this._loadData(t),this._loader}setNeedsReload(){this.isLoading&&(this.abort(),this._loader=void 0),this._needsReload=!0}abort(){var t;this.isLoaded||(this._isCancelled=!0,(t=this._abortController)===null||t===void 0||t.abort())}};var so={OUTSIDE:-1,INTERSECTING:0,INSIDE:1};var IQ=new Ve,O_t=new Ve,Jg=class e{constructor(t=[0,0,0],r=[0,0,0],i){G(this,\"center\",void 0),G(this,\"halfDiagonal\",void 0),G(this,\"minimum\",void 0),G(this,\"maximum\",void 0),i=i||IQ.copy(t).add(r).scale(.5),this.center=new Ve(i),this.halfDiagonal=new Ve(r).subtract(this.center),this.minimum=new Ve(t),this.maximum=new Ve(r)}clone(){return new e(this.minimum,this.maximum,this.center)}equals(t){return this===t||!!t&&this.minimum.equals(t.minimum)&&this.maximum.equals(t.maximum)}transform(t){return this.center.transformAsPoint(t),this.halfDiagonal.transform(t),this.minimum.transform(t),this.maximum.transform(t),this}intersectPlane(t){let{halfDiagonal:r}=this,i=O_t.from(t.normal),s=r.x*Math.abs(i.x)+r.y*Math.abs(i.y)+r.z*Math.abs(i.z),n=this.center.dot(i)+t.distance;return n-s>0?so.INSIDE:n+s<0?so.OUTSIDE:so.INTERSECTING}distanceTo(t){return Math.sqrt(this.distanceSquaredTo(t))}distanceSquaredTo(t){let r=IQ.from(t).subtract(this.center),{halfDiagonal:i}=this,s=0,n;return n=Math.abs(r.x)-i.x,n>0&&(s+=n*n),n=Math.abs(r.y)-i.y,n>0&&(s+=n*n),n=Math.abs(r.z)-i.z,n>0&&(s+=n*n),s}};var TS=new Ve,CQ=new Ve,t_=class e{constructor(t=[0,0,0],r=0){G(this,\"center\",void 0),G(this,\"radius\",void 0),this.radius=-0,this.center=new Ve,this.fromCenterRadius(t,r)}fromCenterRadius(t,r){return this.center.from(t),this.radius=r,this}fromCornerPoints(t,r){return r=TS.from(r),this.center=new Ve().from(t).add(r).scale(.5),this.radius=this.center.distance(r),this}equals(t){return this===t||!!t&&this.center.equals(t.center)&&this.radius===t.radius}clone(){return new e(this.center,this.radius)}union(t){let r=this.center,i=this.radius,s=t.center,n=t.radius,o=TS.copy(s).subtract(r),c=o.magnitude();if(i>=c+n)return this.clone();if(n>=c+i)return t.clone();let f=(i+c+n)*.5;return CQ.copy(o).scale((-i+f)/c).add(r),this.center.copy(CQ),this.radius=f,this}expand(t){let i=TS.from(t).subtract(this.center).magnitude();return i>this.radius&&(this.radius=i),this}transform(t){this.center.transform(t);let r=c7(TS,t);return this.radius=Math.max(r[0],Math.max(r[1],r[2]))*this.radius,this}distanceSquaredTo(t){let r=this.distanceTo(t);return r*r}distanceTo(t){let i=TS.from(t).subtract(this.center);return Math.max(0,i.len()-this.radius)}intersectPlane(t){let r=this.center,i=this.radius,n=t.normal.dot(r)+t.distance;return n<-i?so.OUTSIDE:n=f?so.INSIDE:so.INTERSECTING}distanceTo(t){return Math.sqrt(this.distanceSquaredTo(t))}distanceSquaredTo(t){let r=F_t.from(t).subtract(this.center),i=this.halfAxes,s=i.getColumn(0,II),n=i.getColumn(1,CI),o=i.getColumn(2,LI),c=s.magnitude(),f=n.magnitude(),_=o.magnitude();s.normalize(),n.normalize(),o.normalize();let w=0,I;return I=Math.abs(r.dot(s))-c,I>0&&(w+=I*I),I=Math.abs(r.dot(n))-f,I>0&&(w+=I*I),I=Math.abs(r.dot(o))-_,I>0&&(w+=I*I),w}computePlaneDistances(t,r,i=[-0,-0]){let s=Number.POSITIVE_INFINITY,n=Number.NEGATIVE_INFINITY,o=this.center,c=this.halfAxes,f=c.getColumn(0,II),_=c.getColumn(1,CI),w=c.getColumn(2,LI),I=z_t.copy(f).add(_).add(w).add(o),R=N_t.copy(I).subtract(t),N=r.dot(R);return s=Math.min(N,s),n=Math.max(N,n),I.copy(o).add(f).add(_).subtract(w),R.copy(I).subtract(t),N=r.dot(R),s=Math.min(N,s),n=Math.max(N,n),I.copy(o).add(f).subtract(_).add(w),R.copy(I).subtract(t),N=r.dot(R),s=Math.min(N,s),n=Math.max(N,n),I.copy(o).add(f).subtract(_).subtract(w),R.copy(I).subtract(t),N=r.dot(R),s=Math.min(N,s),n=Math.max(N,n),o.copy(I).subtract(f).add(_).add(w),R.copy(I).subtract(t),N=r.dot(R),s=Math.min(N,s),n=Math.max(N,n),o.copy(I).subtract(f).add(_).subtract(w),R.copy(I).subtract(t),N=r.dot(R),s=Math.min(N,s),n=Math.max(N,n),o.copy(I).subtract(f).subtract(_).add(w),R.copy(I).subtract(t),N=r.dot(R),s=Math.min(N,s),n=Math.max(N,n),o.copy(I).subtract(f).subtract(_).subtract(w),R.copy(I).subtract(t),N=r.dot(R),s=Math.min(N,s),n=Math.max(N,n),i[0]=s,i[1]=n,i}transform(t){this.center.transformAsPoint(t);let r=this.halfAxes.getColumn(0,II);r.transformAsPoint(t);let i=this.halfAxes.getColumn(1,CI);i.transformAsPoint(t);let s=this.halfAxes.getColumn(2,LI);return s.transformAsPoint(t),this.halfAxes=new ss([...r,...i,...s]),this}getTransform(){throw new Error(\"not implemented\")}};var LQ=new Ve,kQ=new Ve,Af=class e{constructor(t=[0,0,1],r=0){G(this,\"normal\",void 0),G(this,\"distance\",void 0),this.normal=new Ve,this.distance=-0,this.fromNormalDistance(t,r)}fromNormalDistance(t,r){return Bh(Number.isFinite(r)),this.normal.from(t).normalize(),this.distance=r,this}fromPointNormal(t,r){t=LQ.from(t),this.normal.from(r).normalize();let i=-this.normal.dot(t);return this.distance=i,this}fromCoefficients(t,r,i,s){return this.normal.set(t,r,i),Bh(Ro(this.normal.len(),1)),this.distance=s,this}clone(){return new e(this.normal,this.distance)}equals(t){return Ro(this.distance,t.distance)&&Ro(this.normal,t.normal)}getPointDistance(t){return this.normal.dot(t)+this.distance}transform(t){let r=kQ.copy(this.normal).transformAsVector(t).normalize(),i=this.normal.scale(-this.distance).transform(t);return this.fromPointNormal(i,r)}projectPointOntoPlane(t,r=[0,0,0]){t=LQ.from(t);let i=this.getPointDistance(t),s=kQ.copy(this.normal).scale(i);return t.subtract(s).to(r)}};var RQ=[new Ve([1,0,0]),new Ve([0,1,0]),new Ve([0,0,1])],DQ=new Ve,U_t=new Ve,Rse=new Af(new Ve(1,0,0),0),Ad=class e{constructor(t=[]){G(this,\"planes\",void 0),this.planes=t}fromBoundingSphere(t){this.planes.length=2*RQ.length;let r=t.center,i=t.radius,s=0;for(let n of RQ){let o=this.planes[s],c=this.planes[s+1];o||(o=this.planes[s]=new Af),c||(c=this.planes[s+1]=new Af);let f=DQ.copy(n).scale(-i).add(r),_=-n.dot(f);o.fromPointNormal(f,n);let w=DQ.copy(n).scale(i).add(r),I=U_t.copy(n).negate(),R=-I.dot(w);c.fromPointNormal(w,I),s+=2}return this}computeVisibility(t){let r=so.INSIDE;for(let i of this.planes)switch(t.intersectPlane(i)){case so.OUTSIDE:return so.OUTSIDE;case so.INTERSECTING:r=so.INTERSECTING;break;default:}return r}computeVisibilityWithPlaneMask(t,r){if(Bh(Number.isFinite(r),\"parentPlaneMask is required.\"),r===e.MASK_OUTSIDE||r===e.MASK_INSIDE)return r;let i=e.MASK_INSIDE,s=this.planes;for(let n=0;nf;)q_t(c,kI),OQ.copy(kI).transpose(),c.multiplyRight(kI),c.multiplyLeft(OQ),o.multiplyRight(kI),++s>2&&(++n,s=0);return t.unitary=o.toTarget(t.unitary),t.diagonal=c.toTarget(t.diagonal),t}function W_t(e){let t=0;for(let r=0;r<9;++r){let i=e[r];t+=i*i}return Math.sqrt(t)}var GB=[1,0,0],WB=[2,2,1];function H_t(e){let t=0;for(let r=0;r<3;++r){let i=e[md.getElementIndex(WB[r],GB[r])];t+=2*i*i}return Math.sqrt(t)}function q_t(e,t){let r=YE.EPSILON15,i=0,s=1;for(let _=0;_<3;++_){let w=Math.abs(e[md.getElementIndex(WB[_],GB[_])]);w>i&&(s=_,i=w)}let n=GB[s],o=WB[s],c=1,f=0;if(Math.abs(e[md.getElementIndex(o,n)])>r){let _=e[md.getElementIndex(o,o)],w=e[md.getElementIndex(n,n)],I=e[md.getElementIndex(o,n)],R=(_-w)/2/I,N;R<0?N=-1/(-R+Math.sqrt(1+R*R)):N=1/(R+Math.sqrt(1+R*R)),c=1/Math.sqrt(1+N*N),f=N*c}return ss.IDENTITY.to(t),t[md.getElementIndex(n,n)]=t[md.getElementIndex(o,o)]=c,t[md.getElementIndex(o,n)]=f,t[md.getElementIndex(n,o)]=-f,t}var Cm=new Ve,Z_t=new Ve,Y_t=new Ve,Q_t=new Ve,$_t=new Ve,X_t=new ss,K_t={diagonal:new ss,unitary:new ss};function HB(e,t=new yx){if(!e||e.length===0)return t.halfAxes=new ss([0,0,0,0,0,0,0,0,0]),t.center=new Ve,t;let r=e.length,i=new Ve(0,0,0);for(let le of e)i.add(le);let s=1/r;i.multiplyByScalar(s);let n=0,o=0,c=0,f=0,_=0,w=0;for(let le of e){let ue=Cm.copy(le).subtract(i);n+=ue.x*ue.x,o+=ue.x*ue.y,c+=ue.x*ue.z,f+=ue.y*ue.y,_+=ue.y*ue.z,w+=ue.z*ue.z}n*=s,o*=s,c*=s,f*=s,_*=s,w*=s;let I=X_t;I[0]=n,I[1]=o,I[2]=c,I[3]=o,I[4]=f,I[5]=_,I[6]=c,I[7]=_,I[8]=w;let{unitary:R}=RI(I,K_t),N=t.halfAxes.copy(R),j=N.getColumn(0,Y_t),Q=N.getColumn(1,Q_t),et=N.getColumn(2,$_t),Y=-Number.MAX_VALUE,K=-Number.MAX_VALUE,J=-Number.MAX_VALUE,ut=Number.MAX_VALUE,Et=Number.MAX_VALUE,kt=Number.MAX_VALUE;for(let le of e)Cm.copy(le),Y=Math.max(Cm.dot(j),Y),K=Math.max(Cm.dot(Q),K),J=Math.max(Cm.dot(et),J),ut=Math.min(Cm.dot(j),ut),Et=Math.min(Cm.dot(Q),Et),kt=Math.min(Cm.dot(et),kt);j=j.multiplyByScalar(.5*(ut+Y)),Q=Q.multiplyByScalar(.5*(Et+K)),et=et.multiplyByScalar(.5*(kt+J)),t.center.copy(j).add(Q).add(et);let Xt=Z_t.set(Y-ut,K-Et,J-kt).multiplyByScalar(.5),qt=new ss([Xt[0],0,0,0,Xt[1],0,0,0,Xt[2]]);return t.halfAxes.multiplyRight(qt),t}var vx=512,BQ=3,FQ=[[.5,.5],[0,0],[0,1],[1,0],[1,1]],zQ=FQ.concat([[0,.5],[.5,0],[1,.5],[.5,1]]),J_t=zQ.concat([[.25,.5],[.75,.5]]),qB=class e{constructor(t,r,i){G(this,\"x\",void 0),G(this,\"y\",void 0),G(this,\"z\",void 0),G(this,\"childVisible\",void 0),G(this,\"selected\",void 0),G(this,\"_children\",void 0),this.x=t,this.y=r,this.z=i}get children(){if(!this._children){let t=this.x*2,r=this.y*2,i=this.z+1;this._children=[new e(t,r,i),new e(t,r+1,i),new e(t+1,r,i),new e(t+1,r+1,i)]}return this._children}update(t){let{viewport:r,cullingVolume:i,elevationBounds:s,minZ:n,maxZ:o,bounds:c,offset:f,project:_}=t,w=this.getBoundingVolume(s,f,_);if(c&&!this.insideBounds(c)||i.computeVisibility(w)<0)return!1;if(!this.childVisible){let{z:R}=this;if(R=n){let N=w.distanceTo(r.cameraPosition)*r.scale/r.height;R+=Math.floor(Math.log2(N))}if(R>=o)return this.selected=!0,!0}this.selected=!1,this.childVisible=!0;for(let R of this.children)R.update(t);return!0}getSelected(t=[]){if(this.selected&&t.push(this),this._children)for(let r of this._children)r.getSelected(t);return t}insideBounds([t,r,i,s]){let n=Math.pow(2,this.z),o=vx/n;return this.x*ot&&(this.y+1)*o>r}getBoundingVolume(t,r,i){if(i){let f=this.z<1?J_t:this.z<2?zQ:FQ,_=[];for(let w of f){let I=DI(this.x+w[0],this.y+w[1],this.z);I[2]=t[0],_.push(i(I)),t[0]!==t[1]&&(I[2]=t[1],_.push(i(I)))}return HB(_)}let s=Math.pow(2,this.z),n=vx/s,o=this.x*n+r*vx,c=vx-(this.y+1)*n;return new Jg([o,c,t[0]],[o+n,c+n,t[1]])}};function NQ(e,t,r,i){let s=e instanceof rv&&e.resolution?e.projectPosition:null,n=Object.values(e.getFrustumPlanes()).map(({normal:N,distance:j})=>new Af(N.clone().negate(),j)),o=new Ad(n),c=e.distanceScales.unitsPerMeter[2],f=r&&r[0]*c||0,_=r&&r[1]*c||0,w=e instanceof lc&&e.pitch<=60?t:0;if(i){let[N,j,Q,et]=i,Y=va([N,et]),K=va([Q,j]);i=[Y[0],vx-Y[1],K[0],vx-K[1]]}let I=new qB(0,0,0),R={viewport:e,project:s,cullingVolume:o,elevationBounds:[f,_],minZ:w,maxZ:t,bounds:i,offset:0};if(I.update(R),e instanceof lc&&e.subViewports&&e.subViewports.length>1){for(R.offset=-1;I.update(R)&&!(--R.offset<-BQ););for(R.offset=1;I.update(R)&&!(++R.offset>BQ););}return I.getSelected()}var zp=512,tyt=[-1/0,-1/0,1/0,1/0],YB={type:\"object\",value:null,validate:(e,t)=>t.optional&&e===null||typeof e==\"string\"||Array.isArray(e)&&e.every(r=>typeof r==\"string\"),equal:(e,t)=>{if(e===t)return!0;if(!Array.isArray(e)||!Array.isArray(t))return!1;let r=e.length;if(r!==t.length)return!1;for(let 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jQ({viewport:e,z:t=0,cullRect:r}){return(e.subViewports||[e]).map(s=>ZB(s,t,r))}function ZB(e,t,r){if(!Array.isArray(t)){let n=r.x-e.x,o=r.y-e.y,{width:c,height:f}=r,_={targetZ:t},w=e.unproject([n,o],_),I=e.unproject([n+c,o],_),R=e.unproject([n,o+f],_),N=e.unproject([n+c,o+f],_);return[Math.min(w[0],I[0],R[0],N[0]),Math.min(w[1],I[1],R[1],N[1]),Math.max(w[0],I[0],R[0],N[0]),Math.max(w[1],I[1],R[1],N[1])]}let i=ZB(e,t[0],r),s=ZB(e,t[1],r);return[Math.min(i[0],s[0]),Math.min(i[1],s[1]),Math.max(i[2],s[2]),Math.max(i[3],s[3])]}function iyt(e,t,r){return r?VQ(e,r).map(s=>s*t/zp):e.map(i=>i*t/zp)}function $B(e,t){return Math.pow(2,e)*zp/t}function DI(e,t,r){let i=$B(r,zp),s=e/i*360-180,n=Math.PI-2*Math.PI*t/i,o=180/Math.PI*Math.atan(.5*(Math.exp(n)-Math.exp(-n)));return[s,o]}function UQ(e,t,r,i){let s=$B(r,i);return[e/s*zp,t/s*zp]}function XB(e,t,r,i,s=zp){if(e.isGeospatial){let[_,w]=DI(t,r,i),[I,R]=DI(t+1,r+1,i);return{west:_,north:w,east:I,south:R}}let[n,o]=UQ(t,r,i,s),[c,f]=UQ(t+1,r+1,i,s);return{left:n,top:o,right:c,bottom:f}}function nyt(e,t,r,i,s){let n=ryt(e,null,i),o=$B(t,r),[c,f,_,w]=iyt(n,o,s),I=[];for(let R=Math.floor(c);R<_;R++)for(let N=Math.floor(f);Nt&&(_=t);let w=s;return o&&c&&s&&!e.isGeospatial&&(w=VQ(s,o)),e.isGeospatial?NQ(e,_,i,s):nyt(e,_,n,w||tyt,c)}function GQ(e){let t={},r;return i=>{for(let s in i)if(!syt(i[s],t[s])){r=e(i),t=i;break}return r}}function syt(e,t){if(e===t)return!0;if(Array.isArray(e)){let r=e.length;if(!t||t.length!==r)return!1;for(let i=0;i{}},uyt={extent:null,tileSize:512,maxZoom:null,minZoom:null,maxCacheSize:null,maxCacheByteSize:null,refinementStrategy:\"best-available\",zRange:null,maxRequests:6,zoomOffset:0,onTileLoad:()=>{},onTileUnload:()=>{},onTileError:()=>{}},MS=class{constructor(t){G(this,\"opts\",void 0),G(this,\"_requestScheduler\",void 0),G(this,\"_cache\",void 0),G(this,\"_dirty\",void 0),G(this,\"_tiles\",void 0),G(this,\"_cacheByteSize\",void 0),G(this,\"_viewport\",void 0),G(this,\"_zRange\",void 0),G(this,\"_selectedTiles\",void 0),G(this,\"_frameNumber\",void 0),G(this,\"_modelMatrix\",void 0),G(this,\"_modelMatrixInverse\",void 0),G(this,\"_maxZoom\",void 0),G(this,\"_minZoom\",void 0),G(this,\"onTileLoad\",void 0),G(this,\"_getCullBounds\",GQ(jQ)),this.opts={...uyt,...t},this.onTileLoad=r=>{var i,s;(i=(s=this.opts).onTileLoad)===null||i===void 0||i.call(s,r),this.opts.maxCacheByteSize&&(this._cacheByteSize+=r.byteLength,this._resizeCache())},this._requestScheduler=new py({maxRequests:t.maxRequests,throttleRequests:!!(t.maxRequests&&t.maxRequests>0)}),this._cache=new Map,this._tiles=[],this._dirty=!1,this._cacheByteSize=0,this._viewport=null,this._selectedTiles=null,this._frameNumber=0,this._modelMatrix=new En,this._modelMatrixInverse=new En,this.setOptions(t)}get tiles(){return this._tiles}get selectedTiles(){return this._selectedTiles}get isLoaded(){return this._selectedTiles!==null&&this._selectedTiles.every(t=>t.isLoaded)}get needsReload(){return this._selectedTiles!==null&&this._selectedTiles.some(t=>t.needsReload)}setOptions(t){Object.assign(this.opts,t),Number.isFinite(t.maxZoom)&&(this._maxZoom=Math.floor(t.maxZoom)),Number.isFinite(t.minZoom)&&(this._minZoom=Math.ceil(t.minZoom))}finalize(){for(let t of this._cache.values())t.isLoading&&t.abort();this._cache.clear(),this._tiles=[],this._selectedTiles=null}reloadAll(){for(let t of this._cache.keys()){let r=this._cache.get(t);!this._selectedTiles||!this._selectedTiles.includes(r)?this._cache.delete(t):r.setNeedsReload()}}update(t,{zRange:r,modelMatrix:i}={}){let s=new En(i),n=!s.equals(this._modelMatrix);if(!this._viewport||!t.equals(this._viewport)||!Ro(this._zRange,r)||n){n&&(this._modelMatrixInverse=s.clone().invert(),this._modelMatrix=s),this._viewport=t,this._zRange=r;let c=this.getTileIndices({viewport:t,maxZoom:this._maxZoom,minZoom:this._minZoom,zRange:r,modelMatrix:this._modelMatrix,modelMatrixInverse:this._modelMatrixInverse});this._selectedTiles=c.map(f=>this._getTile(f,!0)),this._dirty&&this._rebuildTree()}else this.needsReload&&(this._selectedTiles=this._selectedTiles.map(c=>this._getTile(c.index,!0)));let o=this.updateTileStates();return this._pruneRequests(),this._dirty&&this._resizeCache(),o&&this._frameNumber++,this._frameNumber}isTileVisible(t,r){if(!t.isVisible)return!1;if(r&&this._viewport){let i=this._getCullBounds({viewport:this._viewport,z:this._zRange,cullRect:r}),{bbox:s}=t;for(let[n,o,c,f]of i){let _;if(\"west\"in s)_=s.westn&&s.southo;else{let w=Math.min(s.top,s.bottom),I=Math.max(s.top,s.bottom);_=s.leftn&&wo}if(_)return!0}return!1}return!0}getTileIndices({viewport:t,maxZoom:r,minZoom:i,zRange:s,modelMatrix:n,modelMatrixInverse:o}){let{tileSize:c,extent:f,zoomOffset:_}=this.opts;return KB({viewport:t,maxZoom:r,minZoom:i,zRange:s,tileSize:c,extent:f,modelMatrix:n,modelMatrixInverse:o,zoomOffset:_})}getTileId(t){return\"\".concat(t.x,\"-\").concat(t.y,\"-\").concat(t.z)}getTileZoom(t){return t.z}getTileMetadata(t){let{tileSize:r}=this.opts;return{bbox:XB(this._viewport,t.x,t.y,t.z,r)}}getParentIndex(t){let r=Math.floor(t.x/2),i=Math.floor(t.y/2),s=t.z-1;return{x:r,y:i,z:s}}updateTileStates(){let t=this.opts.refinementStrategy||ES,r=new Array(this._cache.size),i=0;for(let s of this._cache.values())r[i++]=s.isVisible,s.isSelected=!1,s.isVisible=!1;for(let s of this._selectedTiles)s.isSelected=!0,s.isVisible=!0;(typeof t==\"function\"?t:cyt[t])(Array.from(this._cache.values())),i=0;for(let s of this._cache.values())if(r[i++]!==s.isVisible)return!0;return!1}_pruneRequests(){let{maxRequests:t=0}=this.opts,r=[],i=0;for(let s of 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g=16777216,Vi=Math.max(fe,g);Vi>4,g=(Tt&15)<<4|Ms>>2,Vi=(Ms&3)<<6|cs,Be=Be+String.fromCharCode(br),Ms!==64&&(Be=Be+String.fromCharCode(g)),cs!==64&&(Be=Be+String.fromCharCode(Vi));while(li>2]=p,g[k+4>>2]=m,k=(C|0)!=0,k&&(g[C>>2]=0),ji(p,m)|0)return Nt=1,wt=Wt,Nt|0;g[Nt>>2]=0;t:do if((y|0)>=1)if(k)for(it=0,ot=1,Ct=1,L=0,k=p;;){if(!(L|it)){if(k=Wn(k,m,4,Nt)|0,m=It()|0,(k|0)==0&(m|0)==0){k=2;break t}if(ji(k,m)|0){k=1;break t}}if(k=Wn(k,m,g[16+(it<<2)>>2]|0,Nt)|0,m=It()|0,(k|0)==0&(m|0)==0){k=2;break t}if(p=S+(Ct<<3)|0,g[p>>2]=k,g[p+4>>2]=m,g[C+(Ct<<2)>>2]=ot,L=L+1|0,p=(L|0)==(ot|0),z=it+1|0,H=(z|0)==6,ji(k,m)|0){k=1;break t}if(ot=ot+(H&p&1)|0,(ot|0)>(y|0)){k=0;break}else it=p?H?0:z:it,Ct=Ct+1|0,L=p?0:L}else for(it=0,ot=1,Ct=1,L=0,k=p;;){if(!(L|it)){if(k=Wn(k,m,4,Nt)|0,m=It()|0,(k|0)==0&(m|0)==0){k=2;break t}if(ji(k,m)|0){k=1;break t}}if(k=Wn(k,m,g[16+(it<<2)>>2]|0,Nt)|0,m=It()|0,(k|0)==0&(m|0)==0){k=2;break t}if(p=S+(Ct<<3)|0,g[p>>2]=k,g[p+4>>2]=m,L=L+1|0,p=(L|0)==(ot|0),z=it+1|0,H=(z|0)==6,ji(k,m)|0){k=1;break t}if(ot=ot+(H&p&1)|0,(ot|0)>(y|0)){k=0;break}else it=p?H?0:z:it,Ct=Ct+1|0,L=p?0:L}else k=0;while(!1);return Nt=k,wt=Wt,Nt|0}function Ba(p,m,y,S,C,k,L){p=p|0,m=m|0,y=y|0,S=S|0,C=C|0,k=k|0,L=L|0;var z=0,H=0,it=0,ot=0,Ct=0,Nt=0,Wt=0;if(Ct=wt,wt=wt+16|0,ot=Ct,(p|0)==0&(m|0)==0){wt=Ct;return}if(z=Yo(p|0,m|0,k|0,((k|0)<0)<<31>>31|0)|0,It()|0,H=S+(z<<3)|0,Nt=H,Wt=g[Nt>>2]|0,Nt=g[Nt+4>>2]|0,it=(Wt|0)==(p|0)&(Nt|0)==(m|0),!((Wt|0)==0&(Nt|0)==0|it))do z=(z+1|0)%(k|0)|0,H=S+(z<<3)|0,Wt=H,Nt=g[Wt>>2]|0,Wt=g[Wt+4>>2]|0,it=(Nt|0)==(p|0)&(Wt|0)==(m|0);while(!((Nt|0)==0&(Wt|0)==0|it));if(z=C+(z<<2)|0,it&&(g[z>>2]|0)<=(L|0)){wt=Ct;return}if(Wt=H,g[Wt>>2]=p,g[Wt+4>>2]=m,g[z>>2]=L,(L|0)>=(y|0)){wt=Ct;return}Wt=L+1|0,g[ot>>2]=0,Nt=Wn(p,m,2,ot)|0,Ba(Nt,It()|0,y,S,C,k,Wt),g[ot>>2]=0,Nt=Wn(p,m,3,ot)|0,Ba(Nt,It()|0,y,S,C,k,Wt),g[ot>>2]=0,Nt=Wn(p,m,1,ot)|0,Ba(Nt,It()|0,y,S,C,k,Wt),g[ot>>2]=0,Nt=Wn(p,m,5,ot)|0,Ba(Nt,It()|0,y,S,C,k,Wt),g[ot>>2]=0,Nt=Wn(p,m,4,ot)|0,Ba(Nt,It()|0,y,S,C,k,Wt),g[ot>>2]=0,Nt=Wn(p,m,6,ot)|0,Ba(Nt,It()|0,y,S,C,k,Wt),wt=Ct}function Wn(p,m,y,S){p=p|0,m=m|0,y=y|0,S=S|0;var C=0,k=0,L=0,z=0,H=0,it=0,ot=0,Ct=0,Nt=0,Wt=0;if((g[S>>2]|0)>0){C=0;do y=Na(y)|0,C=C+1|0;while((C|0)<(g[S>>2]|0))}z=me(p|0,m|0,45)|0,It()|0,H=z&127,k=Es(p,m)|0,C=me(p|0,m|0,52)|0,It()|0,C=C&15;t:do if(!C)L=6;else for(;;){if(Ct=(15-C|0)*3|0,Nt=me(p|0,m|0,Ct|0)|0,It()|0,Nt=Nt&7,Wt=(Ho(C)|0)==0,C=C+-1|0,ot=ke(7,0,Ct|0)|0,m=m&~(It()|0),Ct=ke(g[(Wt?464:48)+(Nt*28|0)+(y<<2)>>2]|0,0,Ct|0)|0,it=It()|0,y=g[(Wt?672:256)+(Nt*28|0)+(y<<2)>>2]|0,p=Ct|p&~ot,m=it|m,!y){y=0;break t}if(!C){L=6;break}}while(!1);(L|0)==6&&(Wt=g[880+(H*28|0)+(y<<2)>>2]|0,Nt=ke(Wt|0,0,45)|0,p=Nt|p,m=It()|0|m&-1040385,y=g[4304+(H*28|0)+(y<<2)>>2]|0,(Wt&127|0)==127&&(Wt=ke(g[880+(H*28|0)+20>>2]|0,0,45)|0,m=It()|0|m&-1040385,y=g[4304+(H*28|0)+20>>2]|0,p=Wo(Wt|p,m)|0,m=It()|0,g[S>>2]=(g[S>>2]|0)+1)),L=me(p|0,m|0,45)|0,It()|0,L=L&127;t:do if(fi(L)|0){e:do if((Es(p,m)|0)==1){if((H|0)!=(L|0))if(ch(L,g[7728+(H*28|0)>>2]|0)|0){p=Fd(p,m)|0,k=1,m=It()|0;break}else{p=Wo(p,m)|0,k=1,m=It()|0;break}switch(k|0){case 5:{p=Fd(p,m)|0,m=It()|0,g[S>>2]=(g[S>>2]|0)+5,k=0;break e}case 3:{p=Wo(p,m)|0,m=It()|0,g[S>>2]=(g[S>>2]|0)+1,k=0;break e}default:return Nt=0,Wt=0,Je(Nt|0),Wt|0}}else k=0;while(!1);if((y|0)>0){C=0;do p=gh(p,m)|0,m=It()|0,C=C+1|0;while((C|0)!=(y|0))}if((H|0)!=(L|0)){if(!(mu(L)|0)){if((k|0)!=0|(Es(p,m)|0)!=5)break;g[S>>2]=(g[S>>2]|0)+1;break}switch(z&127){case 8:case 118:break t;default:}(Es(p,m)|0)!=3&&(g[S>>2]=(g[S>>2]|0)+1)}}else if((y|0)>0){C=0;do p=Wo(p,m)|0,m=It()|0,C=C+1|0;while((C|0)!=(y|0))}while(!1);return g[S>>2]=((g[S>>2]|0)+y|0)%6|0,Nt=m,Wt=p,Je(Nt|0),Wt|0}function p_(p,m,y,S){p=p|0,m=m|0,y=y|0,S=S|0;var C=0,k=0,L=0,z=0,H=0,it=0,ot=0,Ct=0;if(Ct=wt,wt=wt+16|0,ot=Ct,!y)return ot=S,g[ot>>2]=p,g[ot+4>>2]=m,ot=0,wt=Ct,ot|0;g[ot>>2]=0;t:do if(ji(p,m)|0)p=1;else{if(k=(y|0)>0,k){C=0,it=p;do{if(it=Wn(it,m,4,ot)|0,m=It()|0,(it|0)==0&(m|0)==0){p=2;break t}if(C=C+1|0,ji(it,m)|0){p=1;break t}}while((C|0)<(y|0));if(H=S,g[H>>2]=it,g[H+4>>2]=m,H=y+-1|0,k){k=0,L=1,C=it,p=m;do{if(C=Wn(C,p,2,ot)|0,p=It()|0,(C|0)==0&(p|0)==0){p=2;break t}if(z=S+(L<<3)|0,g[z>>2]=C,g[z+4>>2]=p,L=L+1|0,ji(C,p)|0){p=1;break t}k=k+1|0}while((k|0)<(y|0));z=0,k=L;do{if(C=Wn(C,p,3,ot)|0,p=It()|0,(C|0)==0&(p|0)==0){p=2;break t}if(L=S+(k<<3)|0,g[L>>2]=C,g[L+4>>2]=p,k=k+1|0,ji(C,p)|0){p=1;break t}z=z+1|0}while((z|0)<(y|0));L=0;do{if(C=Wn(C,p,1,ot)|0,p=It()|0,(C|0)==0&(p|0)==0){p=2;break t}if(z=S+(k<<3)|0,g[z>>2]=C,g[z+4>>2]=p,k=k+1|0,ji(C,p)|0){p=1;break t}L=L+1|0}while((L|0)<(y|0));L=0;do{if(C=Wn(C,p,5,ot)|0,p=It()|0,(C|0)==0&(p|0)==0){p=2;break t}if(z=S+(k<<3)|0,g[z>>2]=C,g[z+4>>2]=p,k=k+1|0,ji(C,p)|0){p=1;break t}L=L+1|0}while((L|0)<(y|0));L=0;do{if(C=Wn(C,p,4,ot)|0,p=It()|0,(C|0)==0&(p|0)==0){p=2;break t}if(z=S+(k<<3)|0,g[z>>2]=C,g[z+4>>2]=p,k=k+1|0,ji(C,p)|0){p=1;break t}L=L+1|0}while((L|0)<(y|0));for(L=0;;){if(C=Wn(C,p,6,ot)|0,p=It()|0,(C|0)==0&(p|0)==0){p=2;break t}if((L|0)!=(H|0))if(z=S+(k<<3)|0,g[z>>2]=C,g[z+4>>2]=p,!(ji(C,p)|0))k=k+1|0;else{p=1;break t}if(L=L+1|0,(L|0)>=(y|0)){L=it,k=m;break}}}else L=it,C=it,k=m,p=m}else L=S,g[L>>2]=p,g[L+4>>2]=m,L=p,C=p,k=m,p=m;p=((L|0)!=(C|0)|(k|0)!=(p|0))&1}while(!1);return ot=p,wt=Ct,ot|0}function Cd(p,m){p=p|0,m=m|0;var y=0,S=0,C=0,k=0,L=0,z=0;if(k=wt,wt=wt+48|0,C=k+8|0,S=k,z=p,L=g[z+4>>2]|0,y=S,g[y>>2]=g[z>>2],g[y+4>>2]=L,Ee(S,C),C=uh(C,m)|0,m=g[S>>2]|0,S=g[p+8>>2]|0,(S|0)<=0)return z=m,L=(C|0)<(z|0),z=L?z:C,z=z+12|0,wt=k,z|0;y=g[p+12>>2]|0,p=0;do m=(g[y+(p<<3)>>2]|0)+m|0,p=p+1|0;while((p|0)<(S|0));return z=(C|0)<(m|0),z=z?m:C,z=z+12|0,wt=k,z|0}function $p(p,m,y){p=p|0,m=m|0,y=y|0;var S=0,C=0,k=0,L=0,z=0,H=0;if(z=wt,wt=wt+48|0,S=z+8|0,C=z,!(Xp(p,m,y)|0)){wt=z;return}if(H=p,k=g[H+4>>2]|0,L=C,g[L>>2]=g[H>>2],g[L+4>>2]=k,Ee(C,S),L=uh(S,m)|0,m=g[C>>2]|0,k=g[p+8>>2]|0,(k|0)>0){C=g[p+12>>2]|0,S=0;do m=(g[C+(S<<3)>>2]|0)+m|0,S=S+1|0;while((S|0)!=(k|0))}if(m=(L|0)<(m|0)?m:L,(m|0)<=-12){wt=z;return}H=m+11|0,Fc(y|0,0,(((H|0)>0?H:0)<<3)+8|0)|0,wt=z}function Xp(p,m,y){p=p|0,m=m|0,y=y|0;var S=0,C=0,k=0,L=0,z=0,H=0,it=0,ot=0,Ct=0,Nt=0,Wt=0,re=0,ne=0,Le=0,We=0,te=0,_e=0,Ut=0,$e=0,er=0,we=0,je=0,Zr=0,qi=0,Ei=0,hn=0,Pi=0,Sn=0,yn=0,Or=0;if(Or=wt,wt=wt+112|0,hn=Or+80|0,H=Or+72|0,Pi=Or,Sn=Or+56|0,it=p+8|0,yn=ho((g[it>>2]<<5)+32|0)|0,yn||Mi(22848,22448,800,22456),pr(p,yn),k=p,S=g[k+4>>2]|0,z=H,g[z>>2]=g[k>>2],g[z+4>>2]=S,Ee(H,hn),z=uh(hn,m)|0,S=g[H>>2]|0,k=g[it>>2]|0,(k|0)>0){L=g[p+12>>2]|0,C=0;do S=(g[L+(C<<3)>>2]|0)+S|0,C=C+1|0;while((C|0)!=(k|0))}if(z=(z|0)<(S|0)?S:z,Ei=z+12|0,C=Ua(Ei,8)|0,ot=Ua(Ei,8)|0,g[hn>>2]=0,Zr=p,qi=g[Zr+4>>2]|0,S=H,g[S>>2]=g[Zr>>2],g[S+4>>2]=qi,S=i0(H,Ei,m,hn,C,ot)|0,S|0)return Gr(C),Gr(ot),Gr(yn),yn=S,wt=Or,yn|0;t:do if((g[it>>2]|0)>0){for(k=p+12|0,S=0;L=i0((g[k>>2]|0)+(S<<3)|0,Ei,m,hn,C,ot)|0,S=S+1|0,!(L|0);)if((S|0)>=(g[it>>2]|0))break t;return Gr(C),Gr(ot),Gr(yn),yn=L,wt=Or,yn|0}while(!1);(z|0)>-12&&Fc(ot|0,0,((Ei|0)>1?Ei:1)<<3|0)|0;t:do if((g[hn>>2]|0)>0){qi=((Ei|0)<0)<<31>>31,Ut=C,$e=ot,er=C,we=C,je=ot,Zr=C,S=C,Le=C,We=ot,te=ot,_e=ot,C=ot;e:for(;;){for(ne=g[hn>>2]|0,Wt=0,re=0,k=0;;){L=Pi,z=L+56|0;do g[L>>2]=0,L=L+4|0;while((L|0)<(z|0));if(m=Ut+(Wt<<3)|0,H=g[m>>2]|0,m=g[m+4>>2]|0,yf(H,m,1,Pi,0)|0){L=Pi,z=L+56|0;do g[L>>2]=0,L=L+4|0;while((L|0)<(z|0));L=Ua(7,4)|0,L|0&&(Ba(H,m,1,Pi,L,7,0),Gr(L))}Nt=0;do{Ct=Pi+(Nt<<3)|0,ot=g[Ct>>2]|0,Ct=g[Ct+4>>2]|0;r:do if(!((ot|0)==0&(Ct|0)==0)){if(H=Yo(ot|0,Ct|0,Ei|0,qi|0)|0,It()|0,L=y+(H<<3)|0,z=L,m=g[z>>2]|0,z=g[z+4>>2]|0,!((m|0)==0&(z|0)==0))for(it=0;;){if((it|0)>(Ei|0))break e;if((m|0)==(ot|0)&(z|0)==(Ct|0))break r;if(H=(H+1|0)%(Ei|0)|0,L=y+(H<<3)|0,z=L,m=g[z>>2]|0,z=g[z+4>>2]|0,(m|0)==0&(z|0)==0)break;it=it+1|0}(ot|0)==0&(Ct|0)==0||(l(ot,Ct,Sn),tr(p,yn,Sn)|0&&(it=L,g[it>>2]=ot,g[it+4>>2]=Ct,it=$e+(k<<3)|0,g[it>>2]=ot,g[it+4>>2]=Ct,k=k+1|0))}while(!1);Nt=Nt+1|0}while(Nt>>>0<7);if(re=re+1|0,(re|0)>=(ne|0))break;Wt=Wt+1|0}if((ne|0)>0&&Fc(er|0,0,ne<<3|0)|0,g[hn>>2]=k,(k|0)>0)ot=C,Ct=_e,Nt=Zr,Wt=te,re=We,ne=$e,C=Le,_e=S,te=we,We=er,Le=ot,S=Ct,Zr=je,je=Nt,we=Wt,er=re,$e=Ut,Ut=ne;else break t}return Gr(we),Gr(je),Gr(yn),yn=-1,wt=Or,yn|0}else S=ot;while(!1);return Gr(yn),Gr(C),Gr(S),yn=0,wt=Or,yn|0}function i0(p,m,y,S,C,k){p=p|0,m=m|0,y=y|0,S=S|0,C=C|0,k=k|0;var L=0,z=0,H=0,it=0,ot=0,Ct=0,Nt=0,Wt=0,re=0,ne=0,Le=0,We=0,te=0,_e=0,Ut=0,$e=0,er=0,we=0,je=0,Zr=0,qi=0,Ei=0,hn=0;if(qi=wt,wt=wt+48|0,er=qi+32|0,we=qi+16|0,je=qi,L=g[p>>2]|0,(L|0)<=0)return Zr=0,wt=qi,Zr|0;We=p+4|0,te=er+8|0,_e=we+8|0,Ut=je+8|0,$e=((m|0)<0)<<31>>31,Le=0;t:for(;;){z=g[We>>2]|0,re=z+(Le<<4)|0,g[er>>2]=g[re>>2],g[er+4>>2]=g[re+4>>2],g[er+8>>2]=g[re+8>>2],g[er+12>>2]=g[re+12>>2],(Le|0)==(L+-1|0)?(g[we>>2]=g[z>>2],g[we+4>>2]=g[z+4>>2],g[we+8>>2]=g[z+8>>2],g[we+12>>2]=g[z+12>>2]):(re=z+(Le+1<<4)|0,g[we>>2]=g[re>>2],g[we+4>>2]=g[re+4>>2],g[we+8>>2]=g[re+8>>2],g[we+12>>2]=g[re+12>>2]),re=la(er,we,y)|0;e:do if((re|0)>0){ne=+(re|0),Wt=0;r:for(;;){hn=+(re-Wt|0),Ei=+(Wt|0),Tt[je>>3]=+Tt[er>>3]*hn/ne+ +Tt[we>>3]*Ei/ne,Tt[Ut>>3]=+Tt[te>>3]*hn/ne+ +Tt[_e>>3]*Ei/ne,Ct=lA(je,y)|0,Nt=It()|0,z=Yo(Ct|0,Nt|0,m|0,$e|0)|0,It()|0,L=k+(z<<3)|0,H=L,it=g[H>>2]|0,H=g[H+4>>2]|0;i:do if((it|0)==0&(H|0)==0)Zr=14;else for(ot=0;;){if((ot|0)>(m|0)){L=1;break i}if((it|0)==(Ct|0)&(H|0)==(Nt|0)){L=7;break i}if(z=(z+1|0)%(m|0)|0,L=k+(z<<3)|0,H=L,it=g[H>>2]|0,H=g[H+4>>2]|0,(it|0)==0&(H|0)==0){Zr=14;break}else ot=ot+1|0}while(!1);switch((Zr|0)==14&&(Zr=0,(Ct|0)==0&(Nt|0)==0?L=7:(g[L>>2]=Ct,g[L+4>>2]=Nt,L=g[S>>2]|0,ot=C+(L<<3)|0,g[ot>>2]=Ct,g[ot+4>>2]=Nt,g[S>>2]=L+1,L=0)),L&7){case 7:case 0:break;default:break r}if(Wt=Wt+1|0,(re|0)<=(Wt|0)){Zr=8;break e}}if(L|0){L=-1,Zr=20;break t}}else Zr=8;while(!1);if((Zr|0)==8&&(Zr=0),Le=Le+1|0,L=g[p>>2]|0,(Le|0)>=(L|0)){L=0,Zr=20;break}}return(Zr|0)==20?(wt=qi,L|0):0}function Cn(p,m,y){p=p|0,m=m|0,y=y|0;var S=0,C=0,k=0,L=0,z=0,H=0,it=0;if(it=wt,wt=wt+176|0,H=it,(m|0)<1){vu(y,0,0),wt=it;return}L=p,L=me(g[L>>2]|0,g[L+4>>2]|0,52)|0,It()|0,vu(y,(m|0)>6?m:6,L&15),L=0;do{if(S=p+(L<<3)|0,d(g[S>>2]|0,g[S+4>>2]|0,H),S=g[H>>2]|0,(S|0)>0){z=0;do k=H+8+(z<<4)|0,z=z+1|0,S=H+8+(((z|0)%(S|0)|0)<<4)|0,C=yh(y,S,k)|0,C?Ps(y,C)|0:Eo(y,k,S)|0,S=g[H>>2]|0;while((z|0)<(S|0))}L=L+1|0}while((L|0)!=(m|0));wt=it}function ah(p,m,y){p=p|0,m=m|0,y=y|0;var S=0,C=0,k=0,L=0;if(k=wt,wt=wt+32|0,S=k,C=k+16|0,Cn(p,m,C),g[y>>2]=0,g[y+4>>2]=0,g[y+8>>2]=0,p=Ws(C)|0,!p){Ht(y)|0,_h(C),wt=k;return}do{m=yt(y)|0;do dt(m,p)|0,L=p+16|0,g[S>>2]=g[L>>2],g[S+4>>2]=g[L+4>>2],g[S+8>>2]=g[L+8>>2],g[S+12>>2]=g[L+12>>2],Ps(C,p)|0,p=Fn(C,S)|0;while(p|0);p=Ws(C)|0}while(p|0);Ht(y)|0,_h(C),wt=k}function fi(p){return p=p|0,g[7728+(p*28|0)+16>>2]|0}function mu(p){return p=p|0,(p|0)==4|(p|0)==117|0}function vf(p){return p=p|0,g[11152+((g[p>>2]|0)*216|0)+((g[p+4>>2]|0)*72|0)+((g[p+8>>2]|0)*24|0)+(g[p+12>>2]<<3)>>2]|0}function Kp(p){return p=p|0,g[11152+((g[p>>2]|0)*216|0)+((g[p+4>>2]|0)*72|0)+((g[p+8>>2]|0)*24|0)+(g[p+12>>2]<<3)+4>>2]|0}function lh(p,m){p=p|0,m=m|0,p=7728+(p*28|0)|0,g[m>>2]=g[p>>2],g[m+4>>2]=g[p+4>>2],g[m+8>>2]=g[p+8>>2],g[m+12>>2]=g[p+12>>2]}function Ld(p,m){p=p|0,m=m|0;var y=0,S=0;if(m>>>0>20)return m=-1,m|0;do if((g[11152+(m*216|0)>>2]|0)!=(p|0))if((g[11152+(m*216|0)+8>>2]|0)!=(p|0))if((g[11152+(m*216|0)+16>>2]|0)!=(p|0))if((g[11152+(m*216|0)+24>>2]|0)!=(p|0))if((g[11152+(m*216|0)+32>>2]|0)!=(p|0))if((g[11152+(m*216|0)+40>>2]|0)!=(p|0))if((g[11152+(m*216|0)+48>>2]|0)!=(p|0))if((g[11152+(m*216|0)+56>>2]|0)!=(p|0))if((g[11152+(m*216|0)+64>>2]|0)!=(p|0))if((g[11152+(m*216|0)+72>>2]|0)!=(p|0))if((g[11152+(m*216|0)+80>>2]|0)!=(p|0))if((g[11152+(m*216|0)+88>>2]|0)!=(p|0))if((g[11152+(m*216|0)+96>>2]|0)!=(p|0))if((g[11152+(m*216|0)+104>>2]|0)!=(p|0))if((g[11152+(m*216|0)+112>>2]|0)!=(p|0))if((g[11152+(m*216|0)+120>>2]|0)!=(p|0))if((g[11152+(m*216|0)+128>>2]|0)!=(p|0))if((g[11152+(m*216|0)+136>>2]|0)==(p|0))p=2,y=1,S=2;else{if((g[11152+(m*216|0)+144>>2]|0)==(p|0)){p=0,y=2,S=0;break}if((g[11152+(m*216|0)+152>>2]|0)==(p|0)){p=0,y=2,S=1;break}if((g[11152+(m*216|0)+160>>2]|0)==(p|0)){p=0,y=2,S=2;break}if((g[11152+(m*216|0)+168>>2]|0)==(p|0)){p=1,y=2,S=0;break}if((g[11152+(m*216|0)+176>>2]|0)==(p|0)){p=1,y=2,S=1;break}if((g[11152+(m*216|0)+184>>2]|0)==(p|0)){p=1,y=2,S=2;break}if((g[11152+(m*216|0)+192>>2]|0)==(p|0)){p=2,y=2,S=0;break}if((g[11152+(m*216|0)+200>>2]|0)==(p|0)){p=2,y=2,S=1;break}if((g[11152+(m*216|0)+208>>2]|0)==(p|0)){p=2,y=2,S=2;break}else p=-1;return p|0}else p=2,y=1,S=1;else p=2,y=1,S=0;else p=1,y=1,S=2;else p=1,y=1,S=1;else p=1,y=1,S=0;else p=0,y=1,S=2;else p=0,y=1,S=1;else p=0,y=1,S=0;else p=2,y=0,S=2;else p=2,y=0,S=1;else p=2,y=0,S=0;else p=1,y=0,S=2;else p=1,y=0,S=1;else p=1,y=0,S=0;else p=0,y=0,S=2;else p=0,y=0,S=1;else p=0,y=0,S=0;while(!1);return m=g[11152+(m*216|0)+(y*72|0)+(p*24|0)+(S<<3)+4>>2]|0,m|0}function ch(p,m){return p=p|0,m=m|0,(g[7728+(p*28|0)+20>>2]|0)==(m|0)?(m=1,m|0):(m=(g[7728+(p*28|0)+24>>2]|0)==(m|0),m|0)}function Jp(p,m){return p=p|0,m=m|0,g[880+(p*28|0)+(m<<2)>>2]|0}function tA(p,m){return p=p|0,m=m|0,(g[880+(p*28|0)>>2]|0)==(m|0)?(m=0,m|0):(g[880+(p*28|0)+4>>2]|0)==(m|0)?(m=1,m|0):(g[880+(p*28|0)+8>>2]|0)==(m|0)?(m=2,m|0):(g[880+(p*28|0)+12>>2]|0)==(m|0)?(m=3,m|0):(g[880+(p*28|0)+16>>2]|0)==(m|0)?(m=4,m|0):(g[880+(p*28|0)+20>>2]|0)==(m|0)?(m=5,m|0):((g[880+(p*28|0)+24>>2]|0)==(m|0)?6:7)|0}function A_(){return 122}function m_(p){p=p|0;var m=0,y=0,S=0;m=0;do ke(m|0,0,45)|0,S=It()|0|134225919,y=p+(m<<3)|0,g[y>>2]=-1,g[y+4>>2]=S,m=m+1|0;while((m|0)!=122)}function n0(p){return p=p|0,+Tt[p+16>>3]<+Tt[p+24>>3]|0}function pl(p,m){p=p|0,m=m|0;var y=0,S=0,C=0;return y=+Tt[m>>3],!(y>=+Tt[p+8>>3])||!(y<=+Tt[p>>3])?(m=0,m|0):(S=+Tt[p+16>>3],y=+Tt[p+24>>3],C=+Tt[m+8>>3],m=C>=y,p=C<=S&1,S>2]=0,k=k+4|0;while((k|0)<(z|0));return O(m,C),k=C,z=g[k>>2]|0,k=g[k+4>>2]|0,l(z,k,y),d(z,k,S),H=+Bc(y,S+8|0),Tt[y>>3]=+Tt[p>>3],k=y+8|0,Tt[k>>3]=+Tt[p+16>>3],Tt[S>>3]=+Tt[p+8>>3],z=S+8|0,Tt[z>>3]=+Tt[p+24>>3],it=+Bc(y,S),z=~~+Ji(+(it*it/+ml(+ +li(+((+Tt[k>>3]-+Tt[z>>3])/(+Tt[y>>3]-+Tt[S>>3]))),3)/(H*(H*2.59807621135)*.8))),wt=L,(z|0?z:1)|0}function la(p,m,y){p=p|0,m=m|0,y=y|0;var S=0,C=0,k=0,L=0,z=0,H=0,it=0;z=wt,wt=wt+288|0,S=z+264|0,C=z+96|0,k=z,L=k,H=L+96|0;do g[L>>2]=0,L=L+4|0;while((L|0)<(H|0));return O(y,k),H=k,L=g[H>>2]|0,H=g[H+4>>2]|0,l(L,H,S),d(L,H,C),it=+Bc(S,C+8|0),H=~~+Ji(+(+Bc(p,m)/(it*2))),wt=z,(H|0?H:1)|0}function kd(p,m,y,S){p=p|0,m=m|0,y=y|0,S=S|0,g[p>>2]=m,g[p+4>>2]=y,g[p+8>>2]=S}function g_(p,m){p=p|0,m=m|0;var y=0,S=0,C=0,k=0,L=0,z=0,H=0,it=0,ot=0,Ct=0,Nt=0;Ct=m+8|0,g[Ct>>2]=0,H=+Tt[p>>3],L=+li(+H),it=+Tt[p+8>>3],z=+li(+it)/.8660254037844386,L=L+z*.5,y=~~L,p=~~z,L=L-+(y|0),z=z-+(p|0);do if(L<.5)if(L<.3333333333333333)if(g[m>>2]=y,z<(L+1)*.5){g[m+4>>2]=p;break}else{p=p+1|0,g[m+4>>2]=p;break}else if(Nt=1-L,p=(!(z>2]=p,Nt<=z&z>2]=y;break}else{g[m>>2]=y;break}else{if(!(L<.6666666666666666))if(y=y+1|0,g[m>>2]=y,z>2]=p;break}else{p=p+1|0,g[m+4>>2]=p;break}if(z<1-L){if(g[m+4>>2]=p,L*2+-1>2]=y;break}}else p=p+1|0,g[m+4>>2]=p;y=y+1|0,g[m>>2]=y}while(!1);do if(H<0)if(p&1){ot=(p+1|0)/2|0,ot=zd(y|0,((y|0)<0)<<31>>31|0,ot|0,((ot|0)<0)<<31>>31|0)|0,y=~~(+(y|0)-((+(ot>>>0)+4294967296*+(It()|0))*2+1)),g[m>>2]=y;break}else{ot=(p|0)/2|0,ot=zd(y|0,((y|0)<0)<<31>>31|0,ot|0,((ot|0)<0)<<31>>31|0)|0,y=~~(+(y|0)-(+(ot>>>0)+4294967296*+(It()|0))*2),g[m>>2]=y;break}while(!1);ot=m+4|0,it<0&&(y=y-((p<<1|1|0)/2|0)|0,g[m>>2]=y,p=0-p|0,g[ot>>2]=p),S=p-y|0,(y|0)<0?(C=0-y|0,g[ot>>2]=S,g[Ct>>2]=C,g[m>>2]=0,p=S,y=0):C=0,(p|0)<0&&(y=y-p|0,g[m>>2]=y,C=C-p|0,g[Ct>>2]=C,g[ot>>2]=0,p=0),k=y-C|0,S=p-C|0,(C|0)<0&&(g[m>>2]=k,g[ot>>2]=S,g[Ct>>2]=0,p=S,y=k,C=0),S=(p|0)<(y|0)?p:y,S=(C|0)<(S|0)?C:S,!((S|0)<=0)&&(g[m>>2]=y-S,g[ot>>2]=p-S,g[Ct>>2]=C-S)}function js(p){p=p|0;var m=0,y=0,S=0,C=0,k=0,L=0;m=g[p>>2]|0,L=p+4|0,y=g[L>>2]|0,(m|0)<0&&(y=y-m|0,g[L>>2]=y,k=p+8|0,g[k>>2]=(g[k>>2]|0)-m,g[p>>2]=0,m=0),(y|0)<0?(m=m-y|0,g[p>>2]=m,k=p+8|0,C=(g[k>>2]|0)-y|0,g[k>>2]=C,g[L>>2]=0,y=0):(C=p+8|0,k=C,C=g[C>>2]|0),(C|0)<0&&(m=m-C|0,g[p>>2]=m,y=y-C|0,g[L>>2]=y,g[k>>2]=0,C=0),S=(y|0)<(m|0)?y:m,S=(C|0)<(S|0)?C:S,!((S|0)<=0)&&(g[p>>2]=m-S,g[L>>2]=y-S,g[k>>2]=C-S)}function gu(p,m){p=p|0,m=m|0;var y=0,S=0;S=g[p+8>>2]|0,y=+((g[p+4>>2]|0)-S|0),Tt[m>>3]=+((g[p>>2]|0)-S|0)-y*.5,Tt[m+8>>3]=y*.8660254037844386}function Ln(p,m,y){p=p|0,m=m|0,y=y|0,g[y>>2]=(g[m>>2]|0)+(g[p>>2]|0),g[y+4>>2]=(g[m+4>>2]|0)+(g[p+4>>2]|0),g[y+8>>2]=(g[m+8>>2]|0)+(g[p+8>>2]|0)}function eA(p,m,y){p=p|0,m=m|0,y=y|0,g[y>>2]=(g[p>>2]|0)-(g[m>>2]|0),g[y+4>>2]=(g[p+4>>2]|0)-(g[m+4>>2]|0),g[y+8>>2]=(g[p+8>>2]|0)-(g[m+8>>2]|0)}function ca(p,m){p=p|0,m=m|0;var y=0,S=0;y=Oc(g[p>>2]|0,m)|0,g[p>>2]=y,y=p+4|0,S=Oc(g[y>>2]|0,m)|0,g[y>>2]=S,p=p+8|0,m=Oc(g[p>>2]|0,m)|0,g[p>>2]=m}function Fa(p){p=p|0;var m=0,y=0,S=0,C=0,k=0,L=0,z=0;L=g[p>>2]|0,z=(L|0)<0,S=(g[p+4>>2]|0)-(z?L:0)|0,k=(S|0)<0,C=(k?0-S|0:0)+((g[p+8>>2]|0)-(z?L:0))|0,y=(C|0)<0,p=y?0:C,m=(k?0:S)-(y?C:0)|0,C=(z?0:L)-(k?S:0)-(y?C:0)|0,y=(m|0)<(C|0)?m:C,y=(p|0)<(y|0)?p:y,S=(y|0)>0,p=p-(S?y:0)|0,m=m-(S?y:0)|0;t:do switch(C-(S?y:0)|0){case 0:switch(m|0){case 0:return z=p|0?(p|0)==1?1:7:0,z|0;case 1:return z=p|0?(p|0)==1?3:7:2,z|0;default:break t}case 1:switch(m|0){case 0:return z=p|0?(p|0)==1?5:7:4,z|0;case 1:{if(!p)p=6;else break t;return p|0}default:break t}default:}while(!1);return z=7,z|0}function Rd(p){p=p|0;var m=0,y=0,S=0,C=0,k=0,L=0,z=0;L=p+8|0,y=g[L>>2]|0,m=(g[p>>2]|0)-y|0,z=p+4|0,y=(g[z>>2]|0)-y|0,S=_n(+((m*3|0)-y|0)/7)|0,g[p>>2]=S,m=_n(+((y<<1)+m|0)/7)|0,g[z>>2]=m,g[L>>2]=0,y=m-S|0,(S|0)<0?(k=0-S|0,g[z>>2]=y,g[L>>2]=k,g[p>>2]=0,m=y,S=0,y=k):y=0,(m|0)<0&&(S=S-m|0,g[p>>2]=S,y=y-m|0,g[L>>2]=y,g[z>>2]=0,m=0),k=S-y|0,C=m-y|0,(y|0)<0?(g[p>>2]=k,g[z>>2]=C,g[L>>2]=0,m=C,C=k,y=0):C=S,S=(m|0)<(C|0)?m:C,S=(y|0)<(S|0)?y:S,!((S|0)<=0)&&(g[p>>2]=C-S,g[z>>2]=m-S,g[L>>2]=y-S)}function Al(p){p=p|0;var m=0,y=0,S=0,C=0,k=0,L=0,z=0;L=p+8|0,y=g[L>>2]|0,m=(g[p>>2]|0)-y|0,z=p+4|0,y=(g[z>>2]|0)-y|0,S=_n(+((m<<1)+y|0)/7)|0,g[p>>2]=S,m=_n(+((y*3|0)-m|0)/7)|0,g[z>>2]=m,g[L>>2]=0,y=m-S|0,(S|0)<0?(k=0-S|0,g[z>>2]=y,g[L>>2]=k,g[p>>2]=0,m=y,S=0,y=k):y=0,(m|0)<0&&(S=S-m|0,g[p>>2]=S,y=y-m|0,g[L>>2]=y,g[z>>2]=0,m=0),k=S-y|0,C=m-y|0,(y|0)<0?(g[p>>2]=k,g[z>>2]=C,g[L>>2]=0,m=C,C=k,y=0):C=S,S=(m|0)<(C|0)?m:C,S=(y|0)<(S|0)?y:S,!((S|0)<=0)&&(g[p>>2]=C-S,g[z>>2]=m-S,g[L>>2]=y-S)}function za(p){p=p|0;var m=0,y=0,S=0,C=0,k=0,L=0,z=0;m=g[p>>2]|0,L=p+4|0,y=g[L>>2]|0,z=p+8|0,S=g[z>>2]|0,C=y+(m*3|0)|0,g[p>>2]=C,y=S+(y*3|0)|0,g[L>>2]=y,m=(S*3|0)+m|0,g[z>>2]=m,S=y-C|0,(C|0)<0?(m=m-C|0,g[L>>2]=S,g[z>>2]=m,g[p>>2]=0,y=S,S=0):S=C,(y|0)<0&&(S=S-y|0,g[p>>2]=S,m=m-y|0,g[z>>2]=m,g[L>>2]=0,y=0),k=S-m|0,C=y-m|0,(m|0)<0?(g[p>>2]=k,g[L>>2]=C,g[z>>2]=0,S=k,m=0):C=y,y=(C|0)<(S|0)?C:S,y=(m|0)<(y|0)?m:y,!((y|0)<=0)&&(g[p>>2]=S-y,g[L>>2]=C-y,g[z>>2]=m-y)}function hh(p){p=p|0;var m=0,y=0,S=0,C=0,k=0,L=0,z=0;C=g[p>>2]|0,L=p+4|0,m=g[L>>2]|0,z=p+8|0,y=g[z>>2]|0,S=(m*3|0)+C|0,C=y+(C*3|0)|0,g[p>>2]=C,g[L>>2]=S,m=(y*3|0)+m|0,g[z>>2]=m,y=S-C|0,(C|0)<0?(m=m-C|0,g[L>>2]=y,g[z>>2]=m,g[p>>2]=0,C=0):y=S,(y|0)<0&&(C=C-y|0,g[p>>2]=C,m=m-y|0,g[z>>2]=m,g[L>>2]=0,y=0),k=C-m|0,S=y-m|0,(m|0)<0?(g[p>>2]=k,g[L>>2]=S,g[z>>2]=0,C=k,m=0):S=y,y=(S|0)<(C|0)?S:C,y=(m|0)<(y|0)?m:y,!((y|0)<=0)&&(g[p>>2]=C-y,g[L>>2]=S-y,g[z>>2]=m-y)}function rA(p,m){p=p|0,m=m|0;var y=0,S=0,C=0,k=0,L=0,z=0;(m+-1|0)>>>0>=6||(C=(g[15472+(m*12|0)>>2]|0)+(g[p>>2]|0)|0,g[p>>2]=C,z=p+4|0,S=(g[15472+(m*12|0)+4>>2]|0)+(g[z>>2]|0)|0,g[z>>2]=S,L=p+8|0,m=(g[15472+(m*12|0)+8>>2]|0)+(g[L>>2]|0)|0,g[L>>2]=m,y=S-C|0,(C|0)<0?(m=m-C|0,g[z>>2]=y,g[L>>2]=m,g[p>>2]=0,S=0):(y=S,S=C),(y|0)<0&&(S=S-y|0,g[p>>2]=S,m=m-y|0,g[L>>2]=m,g[z>>2]=0,y=0),k=S-m|0,C=y-m|0,(m|0)<0?(g[p>>2]=k,g[z>>2]=C,g[L>>2]=0,S=k,m=0):C=y,y=(C|0)<(S|0)?C:S,y=(m|0)<(y|0)?m:y,!((y|0)<=0)&&(g[p>>2]=S-y,g[z>>2]=C-y,g[L>>2]=m-y))}function s0(p){p=p|0;var m=0,y=0,S=0,C=0,k=0,L=0,z=0;C=g[p>>2]|0,L=p+4|0,m=g[L>>2]|0,z=p+8|0,y=g[z>>2]|0,S=m+C|0,C=y+C|0,g[p>>2]=C,g[L>>2]=S,m=y+m|0,g[z>>2]=m,y=S-C|0,(C|0)<0?(m=m-C|0,g[L>>2]=y,g[z>>2]=m,g[p>>2]=0,S=0):(y=S,S=C),(y|0)<0&&(S=S-y|0,g[p>>2]=S,m=m-y|0,g[z>>2]=m,g[L>>2]=0,y=0),k=S-m|0,C=y-m|0,(m|0)<0?(g[p>>2]=k,g[L>>2]=C,g[z>>2]=0,S=k,m=0):C=y,y=(C|0)<(S|0)?C:S,y=(m|0)<(y|0)?m:y,!((y|0)<=0)&&(g[p>>2]=S-y,g[L>>2]=C-y,g[z>>2]=m-y)}function fh(p){p=p|0;var m=0,y=0,S=0,C=0,k=0,L=0,z=0;m=g[p>>2]|0,L=p+4|0,S=g[L>>2]|0,z=p+8|0,y=g[z>>2]|0,C=S+m|0,g[p>>2]=C,S=y+S|0,g[L>>2]=S,m=y+m|0,g[z>>2]=m,y=S-C|0,(C|0)<0?(m=m-C|0,g[L>>2]=y,g[z>>2]=m,g[p>>2]=0,S=0):(y=S,S=C),(y|0)<0&&(S=S-y|0,g[p>>2]=S,m=m-y|0,g[z>>2]=m,g[L>>2]=0,y=0),k=S-m|0,C=y-m|0,(m|0)<0?(g[p>>2]=k,g[L>>2]=C,g[z>>2]=0,S=k,m=0):C=y,y=(C|0)<(S|0)?C:S,y=(m|0)<(y|0)?m:y,!((y|0)<=0)&&(g[p>>2]=S-y,g[L>>2]=C-y,g[z>>2]=m-y)}function Na(p){switch(p=p|0,p|0){case 1:{p=5;break}case 5:{p=4;break}case 4:{p=6;break}case 6:{p=2;break}case 2:{p=3;break}case 3:{p=1;break}default:}return p|0}function co(p){switch(p=p|0,p|0){case 1:{p=3;break}case 3:{p=2;break}case 2:{p=6;break}case 6:{p=4;break}case 4:{p=5;break}case 5:{p=1;break}default:}return p|0}function Ge(p){p=p|0;var m=0,y=0,S=0,C=0,k=0,L=0,z=0;m=g[p>>2]|0,L=p+4|0,y=g[L>>2]|0,z=p+8|0,S=g[z>>2]|0,C=y+(m<<1)|0,g[p>>2]=C,y=S+(y<<1)|0,g[L>>2]=y,m=(S<<1)+m|0,g[z>>2]=m,S=y-C|0,(C|0)<0?(m=m-C|0,g[L>>2]=S,g[z>>2]=m,g[p>>2]=0,y=S,S=0):S=C,(y|0)<0&&(S=S-y|0,g[p>>2]=S,m=m-y|0,g[z>>2]=m,g[L>>2]=0,y=0),k=S-m|0,C=y-m|0,(m|0)<0?(g[p>>2]=k,g[L>>2]=C,g[z>>2]=0,S=k,m=0):C=y,y=(C|0)<(S|0)?C:S,y=(m|0)<(y|0)?m:y,!((y|0)<=0)&&(g[p>>2]=S-y,g[L>>2]=C-y,g[z>>2]=m-y)}function Dd(p){p=p|0;var m=0,y=0,S=0,C=0,k=0,L=0,z=0;C=g[p>>2]|0,L=p+4|0,m=g[L>>2]|0,z=p+8|0,y=g[z>>2]|0,S=(m<<1)+C|0,C=y+(C<<1)|0,g[p>>2]=C,g[L>>2]=S,m=(y<<1)+m|0,g[z>>2]=m,y=S-C|0,(C|0)<0?(m=m-C|0,g[L>>2]=y,g[z>>2]=m,g[p>>2]=0,C=0):y=S,(y|0)<0&&(C=C-y|0,g[p>>2]=C,m=m-y|0,g[z>>2]=m,g[L>>2]=0,y=0),k=C-m|0,S=y-m|0,(m|0)<0?(g[p>>2]=k,g[L>>2]=S,g[z>>2]=0,C=k,m=0):S=y,y=(S|0)<(C|0)?S:C,y=(m|0)<(y|0)?m:y,!((y|0)<=0)&&(g[p>>2]=C-y,g[L>>2]=S-y,g[z>>2]=m-y)}function Hl(p,m){p=p|0,m=m|0;var y=0,S=0,C=0,k=0,L=0,z=0;return L=(g[p>>2]|0)-(g[m>>2]|0)|0,z=(L|0)<0,S=(g[p+4>>2]|0)-(g[m+4>>2]|0)-(z?L:0)|0,k=(S|0)<0,C=(z?0-L|0:0)+(g[p+8>>2]|0)-(g[m+8>>2]|0)+(k?0-S|0:0)|0,p=(C|0)<0,m=p?0:C,y=(k?0:S)-(p?C:0)|0,C=(z?0:L)-(k?S:0)-(p?C:0)|0,p=(y|0)<(C|0)?y:C,p=(m|0)<(p|0)?m:p,S=(p|0)>0,m=m-(S?p:0)|0,y=y-(S?p:0)|0,p=C-(S?p:0)|0,p=(p|0)>-1?p:0-p|0,y=(y|0)>-1?y:0-y|0,m=(m|0)>-1?m:0-m|0,m=(y|0)>(m|0)?y:m,((p|0)>(m|0)?p:m)|0}function xf(p,m){p=p|0,m=m|0;var y=0;y=g[p+8>>2]|0,g[m>>2]=(g[p>>2]|0)-y,g[m+4>>2]=(g[p+4>>2]|0)-y}function __(p,m){p=p|0,m=m|0;var y=0,S=0,C=0,k=0,L=0,z=0;S=g[p>>2]|0,g[m>>2]=S,p=g[p+4>>2]|0,L=m+4|0,g[L>>2]=p,z=m+8|0,g[z>>2]=0,y=p-S|0,(S|0)<0?(p=0-S|0,g[L>>2]=y,g[z>>2]=p,g[m>>2]=0,S=0):(y=p,p=0),(y|0)<0&&(S=S-y|0,g[m>>2]=S,p=p-y|0,g[z>>2]=p,g[L>>2]=0,y=0),k=S-p|0,C=y-p|0,(p|0)<0?(g[m>>2]=k,g[L>>2]=C,g[z>>2]=0,y=C,C=k,p=0):C=S,S=(y|0)<(C|0)?y:C,S=(p|0)<(S|0)?p:S,!((S|0)<=0)&&(g[m>>2]=C-S,g[L>>2]=y-S,g[z>>2]=p-S)}function Oe(p){p=p|0;var m=0,y=0,S=0,C=0;m=p+8|0,C=g[m>>2]|0,y=C-(g[p>>2]|0)|0,g[p>>2]=y,S=p+4|0,p=(g[S>>2]|0)-C|0,g[S>>2]=p,g[m>>2]=0-(p+y)}function o0(p){p=p|0;var m=0,y=0,S=0,C=0,k=0,L=0,z=0;y=g[p>>2]|0,m=0-y|0,g[p>>2]=m,L=p+8|0,g[L>>2]=0,z=p+4|0,S=g[z>>2]|0,C=S+y|0,(y|0)>0?(g[z>>2]=C,g[L>>2]=y,g[p>>2]=0,m=0,S=C):y=0,(S|0)<0?(k=m-S|0,g[p>>2]=k,y=y-S|0,g[L>>2]=y,g[z>>2]=0,C=k-y|0,m=0-y|0,(y|0)<0?(g[p>>2]=C,g[z>>2]=m,g[L>>2]=0,S=m,y=0):(S=0,C=k)):C=m,m=(S|0)<(C|0)?S:C,m=(y|0)<(m|0)?y:m,!((m|0)<=0)&&(g[p>>2]=C-m,g[z>>2]=S-m,g[L>>2]=y-m)}function a0(p,m,y){p=p|0,m=m|0,y=y|0;var S=0,C=0;S=wt,wt=wt+16|0,C=S,zx(p,m,y,C),g_(C,y+4|0),wt=S}function zx(p,m,y,S){p=p|0,m=m|0,y=y|0,S=S|0;var C=0,k=0,L=0,z=0,H=0;if(H=wt,wt=wt+32|0,k=H,ql(p,k),g[y>>2]=0,C=+jr(15888,k),L=+jr(15912,k),L>2]=1,C=L),L=+jr(15936,k),L>2]=2,C=L),L=+jr(15960,k),L>2]=3,C=L),L=+jr(15984,k),L>2]=4,C=L),L=+jr(16008,k),L>2]=5,C=L),L=+jr(16032,k),L>2]=6,C=L),L=+jr(16056,k),L>2]=7,C=L),L=+jr(16080,k),L>2]=8,C=L),L=+jr(16104,k),L>2]=9,C=L),L=+jr(16128,k),L>2]=10,C=L),L=+jr(16152,k),L>2]=11,C=L),L=+jr(16176,k),L>2]=12,C=L),L=+jr(16200,k),L>2]=13,C=L),L=+jr(16224,k),L>2]=14,C=L),L=+jr(16248,k),L>2]=15,C=L),L=+jr(16272,k),L>2]=16,C=L),L=+jr(16296,k),L>2]=17,C=L),L=+jr(16320,k),L>2]=18,C=L),L=+jr(16344,k),L>2]=19,C=L),L=+_f(+(1-C*.5)),L<1e-16){g[S>>2]=0,g[S+4>>2]=0,g[S+8>>2]=0,g[S+12>>2]=0,wt=H;return}if(y=g[y>>2]|0,C=+Tt[16368+(y*24|0)>>3],C=+ph(C-+ph(+Od(15568+(y<<4)|0,p))),Ho(m)|0?z=+ph(C+-.3334731722518321):z=C,C=+To(+L)/.381966011250105,(m|0)>0){k=0;do C=C*2.6457513110645907,k=k+1|0;while((k|0)!=(m|0))}L=+Ur(+z)*C,Tt[S>>3]=L,z=+hi(+z)*C,Tt[S+8>>3]=z,wt=H}function dh(p,m,y,S,C){p=p|0,m=m|0,y=y|0,S=S|0,C=C|0;var k=0,L=0;if(k=+hs(p),k<1e-16){m=15568+(m<<4)|0,g[C>>2]=g[m>>2],g[C+4>>2]=g[m+4>>2],g[C+8>>2]=g[m+8>>2],g[C+12>>2]=g[m+12>>2];return}if(L=+qr(+ +Tt[p+8>>3],+ +Tt[p>>3]),(y|0)>0){p=0;do k=k/2.6457513110645907,p=p+1|0;while((p|0)!=(y|0))}S?(k=k/3,y=(Ho(y)|0)==0,k=+Md(+((y?k:k/2.6457513110645907)*.381966011250105))):(k=+Md(+(k*.381966011250105)),Ho(y)|0&&(L=+ph(L+.3334731722518321))),f0(15568+(m<<4)|0,+ph(+Tt[16368+(m*24|0)>>3]-L),k,C)}function y_(p,m,y){p=p|0,m=m|0,y=y|0;var S=0,C=0;S=wt,wt=wt+16|0,C=S,gu(p+4|0,C),dh(C,g[p>>2]|0,m,0,y),wt=S}function l0(p,m,y,S,C){p=p|0,m=m|0,y=y|0,S=S|0,C=C|0;var k=0,L=0,z=0,H=0,it=0,ot=0,Ct=0,Nt=0,Wt=0,re=0,ne=0,Le=0,We=0,te=0,_e=0,Ut=0,$e=0,er=0,we=0,je=0,Zr=0,qi=0,Ei=0,hn=0,Pi=0,Sn=0,yn=0,Or=0;if(Sn=wt,wt=wt+272|0,k=Sn+256|0,te=Sn+240|0,Ei=Sn,hn=Sn+224|0,Pi=Sn+208|0,_e=Sn+176|0,Ut=Sn+160|0,$e=Sn+192|0,er=Sn+144|0,we=Sn+128|0,je=Sn+112|0,Zr=Sn+96|0,qi=Sn+80|0,g[k>>2]=m,g[te>>2]=g[p>>2],g[te+4>>2]=g[p+4>>2],g[te+8>>2]=g[p+8>>2],g[te+12>>2]=g[p+12>>2],c0(te,k,Ei),g[C>>2]=0,te=S+y+((S|0)==5&1)|0,(te|0)<=(y|0)){wt=Sn;return}H=g[k>>2]|0,it=hn+4|0,ot=_e+4|0,Ct=y+5|0,Nt=16848+(H<<2)|0,Wt=16928+(H<<2)|0,re=we+8|0,ne=je+8|0,Le=Zr+8|0,We=Pi+4|0,z=y;t:for(;;){L=Ei+(((z|0)%5|0)<<4)|0,g[Pi>>2]=g[L>>2],g[Pi+4>>2]=g[L+4>>2],g[Pi+8>>2]=g[L+8>>2],g[Pi+12>>2]=g[L+12>>2];do;while((bf(Pi,H,0,1)|0)==2);if((z|0)>(y|0)&(Ho(m)|0)!=0){if(g[_e>>2]=g[Pi>>2],g[_e+4>>2]=g[Pi+4>>2],g[_e+8>>2]=g[Pi+8>>2],g[_e+12>>2]=g[Pi+12>>2],gu(it,Ut),S=g[_e>>2]|0,k=g[17008+(S*80|0)+(g[hn>>2]<<2)>>2]|0,g[_e>>2]=g[18608+(S*80|0)+(k*20|0)>>2],L=g[18608+(S*80|0)+(k*20|0)+16>>2]|0,(L|0)>0){p=0;do s0(ot),p=p+1|0;while((p|0)<(L|0))}switch(L=18608+(S*80|0)+(k*20|0)+4|0,g[$e>>2]=g[L>>2],g[$e+4>>2]=g[L+4>>2],g[$e+8>>2]=g[L+8>>2],ca($e,(g[Nt>>2]|0)*3|0),Ln(ot,$e,ot),js(ot),gu(ot,er),yn=+(g[Wt>>2]|0),Tt[we>>3]=yn*3,Tt[re>>3]=0,Or=yn*-1.5,Tt[je>>3]=Or,Tt[ne>>3]=yn*2.598076211353316,Tt[Zr>>3]=Or,Tt[Le>>3]=yn*-2.598076211353316,g[17008+((g[_e>>2]|0)*80|0)+(g[Pi>>2]<<2)>>2]|0){case 1:{p=je,S=we;break}case 3:{p=Zr,S=je;break}case 2:{p=we,S=Zr;break}default:{p=12;break t}}Bn(Ut,er,S,p,qi),dh(qi,g[_e>>2]|0,H,1,C+8+(g[C>>2]<<4)|0),g[C>>2]=(g[C>>2]|0)+1}if((z|0)<(Ct|0)&&(gu(We,_e),dh(_e,g[Pi>>2]|0,H,1,C+8+(g[C>>2]<<4)|0),g[C>>2]=(g[C>>2]|0)+1),g[hn>>2]=g[Pi>>2],g[hn+4>>2]=g[Pi+4>>2],g[hn+8>>2]=g[Pi+8>>2],g[hn+12>>2]=g[Pi+12>>2],z=z+1|0,(z|0)>=(te|0)){p=3;break}}if((p|0)==3){wt=Sn;return}else(p|0)==12&&Mi(22474,22521,581,22531)}function c0(p,m,y){p=p|0,m=m|0,y=y|0;var S=0,C=0,k=0,L=0,z=0,H=0;H=wt,wt=wt+128|0,S=H+64|0,C=H,k=S,L=20208,z=k+60|0;do g[k>>2]=g[L>>2],k=k+4|0,L=L+4|0;while((k|0)<(z|0));k=C,L=20272,z=k+60|0;do g[k>>2]=g[L>>2],k=k+4|0,L=L+4|0;while((k|0)<(z|0));z=(Ho(g[m>>2]|0)|0)==0,S=z?S:C,C=p+4|0,Ge(C),Dd(C),Ho(g[m>>2]|0)|0&&(hh(C),g[m>>2]=(g[m>>2]|0)+1),g[y>>2]=g[p>>2],m=y+4|0,Ln(C,S,m),js(m),g[y+16>>2]=g[p>>2],m=y+20|0,Ln(C,S+12|0,m),js(m),g[y+32>>2]=g[p>>2],m=y+36|0,Ln(C,S+24|0,m),js(m),g[y+48>>2]=g[p>>2],m=y+52|0,Ln(C,S+36|0,m),js(m),g[y+64>>2]=g[p>>2],y=y+68|0,Ln(C,S+48|0,y),js(y),wt=H}function bf(p,m,y,S){p=p|0,m=m|0,y=y|0,S=S|0;var C=0,k=0,L=0,z=0,H=0,it=0,ot=0,Ct=0,Nt=0,Wt=0,re=0;if(re=wt,wt=wt+32|0,Nt=re+12|0,z=re,Wt=p+4|0,Ct=g[16928+(m<<2)>>2]|0,ot=(S|0)!=0,Ct=ot?Ct*3|0:Ct,C=g[Wt>>2]|0,it=p+8|0,L=g[it>>2]|0,ot){if(k=p+12|0,S=g[k>>2]|0,C=L+C+S|0,(C|0)==(Ct|0))return Wt=1,wt=re,Wt|0;H=k}else H=p+12|0,S=g[H>>2]|0,C=L+C+S|0;if((C|0)<=(Ct|0))return Wt=0,wt=re,Wt|0;do if((S|0)>0){if(S=g[p>>2]|0,(L|0)>0){k=18608+(S*80|0)+60|0,S=p;break}S=18608+(S*80|0)+40|0,y?(kd(Nt,Ct,0,0),eA(Wt,Nt,z),fh(z),Ln(z,Nt,Wt),k=S,S=p):(k=S,S=p)}else k=18608+((g[p>>2]|0)*80|0)+20|0,S=p;while(!1);if(g[S>>2]=g[k>>2],C=k+16|0,(g[C>>2]|0)>0){S=0;do s0(Wt),S=S+1|0;while((S|0)<(g[C>>2]|0))}return p=k+4|0,g[Nt>>2]=g[p>>2],g[Nt+4>>2]=g[p+4>>2],g[Nt+8>>2]=g[p+8>>2],m=g[16848+(m<<2)>>2]|0,ca(Nt,ot?m*3|0:m),Ln(Wt,Nt,Wt),js(Wt),ot?S=((g[it>>2]|0)+(g[Wt>>2]|0)+(g[H>>2]|0)|0)==(Ct|0)?1:2:S=2,Wt=S,wt=re,Wt|0}function u0(p,m){p=p|0,m=m|0;var y=0;do y=bf(p,m,0,1)|0;while((y|0)==2);return y|0}function iA(p,m,y,S,C){p=p|0,m=m|0,y=y|0,S=S|0,C=C|0;var k=0,L=0,z=0,H=0,it=0,ot=0,Ct=0,Nt=0,Wt=0,re=0,ne=0,Le=0,We=0,te=0,_e=0,Ut=0,$e=0,er=0,we=0,je=0,Zr=0,qi=0,Ei=0;if(Zr=wt,wt=wt+240|0,k=Zr+224|0,$e=Zr+208|0,er=Zr,we=Zr+192|0,je=Zr+176|0,Le=Zr+160|0,We=Zr+144|0,te=Zr+128|0,_e=Zr+112|0,Ut=Zr+96|0,g[k>>2]=m,g[$e>>2]=g[p>>2],g[$e+4>>2]=g[p+4>>2],g[$e+8>>2]=g[p+8>>2],g[$e+12>>2]=g[p+12>>2],nA($e,k,er),g[C>>2]=0,ne=S+y+((S|0)==6&1)|0,(ne|0)<=(y|0)){wt=Zr;return}H=g[k>>2]|0,it=y+6|0,ot=16928+(H<<2)|0,Ct=We+8|0,Nt=te+8|0,Wt=_e+8|0,re=we+4|0,L=0,z=y,S=-1;t:for(;;){if(k=(z|0)%6|0,p=er+(k<<4)|0,g[we>>2]=g[p>>2],g[we+4>>2]=g[p+4>>2],g[we+8>>2]=g[p+8>>2],g[we+12>>2]=g[p+12>>2],p=L,L=bf(we,H,0,1)|0,(z|0)>(y|0)&(Ho(m)|0)!=0&&(p|0)!=1&&(g[we>>2]|0)!=(S|0)){switch(gu(er+(((k+5|0)%6|0)<<4)+4|0,je),gu(er+(k<<4)+4|0,Le),qi=+(g[ot>>2]|0),Tt[We>>3]=qi*3,Tt[Ct>>3]=0,Ei=qi*-1.5,Tt[te>>3]=Ei,Tt[Nt>>3]=qi*2.598076211353316,Tt[_e>>3]=Ei,Tt[Wt>>3]=qi*-2.598076211353316,k=g[$e>>2]|0,g[17008+(k*80|0)+(((S|0)==(k|0)?g[we>>2]|0:S)<<2)>>2]|0){case 1:{p=te,S=We;break}case 3:{p=_e,S=te;break}case 2:{p=We,S=_e;break}default:{p=8;break t}}Bn(je,Le,S,p,Ut),!(qo(je,Ut)|0)&&!(qo(Le,Ut)|0)&&(dh(Ut,g[$e>>2]|0,H,1,C+8+(g[C>>2]<<4)|0),g[C>>2]=(g[C>>2]|0)+1)}if((z|0)<(it|0)&&(gu(re,je),dh(je,g[we>>2]|0,H,1,C+8+(g[C>>2]<<4)|0),g[C>>2]=(g[C>>2]|0)+1),z=z+1|0,(z|0)>=(ne|0)){p=3;break}else S=g[we>>2]|0}if((p|0)==3){wt=Zr;return}else(p|0)==8&&Mi(22557,22521,746,22602)}function nA(p,m,y){p=p|0,m=m|0,y=y|0;var S=0,C=0,k=0,L=0,z=0,H=0;H=wt,wt=wt+160|0,S=H+80|0,C=H,k=S,L=20336,z=k+72|0;do g[k>>2]=g[L>>2],k=k+4|0,L=L+4|0;while((k|0)<(z|0));k=C,L=20416,z=k+72|0;do g[k>>2]=g[L>>2],k=k+4|0,L=L+4|0;while((k|0)<(z|0));z=(Ho(g[m>>2]|0)|0)==0,S=z?S:C,C=p+4|0,Ge(C),Dd(C),Ho(g[m>>2]|0)|0&&(hh(C),g[m>>2]=(g[m>>2]|0)+1),g[y>>2]=g[p>>2],m=y+4|0,Ln(C,S,m),js(m),g[y+16>>2]=g[p>>2],m=y+20|0,Ln(C,S+12|0,m),js(m),g[y+32>>2]=g[p>>2],m=y+36|0,Ln(C,S+24|0,m),js(m),g[y+48>>2]=g[p>>2],m=y+52|0,Ln(C,S+36|0,m),js(m),g[y+64>>2]=g[p>>2],m=y+68|0,Ln(C,S+48|0,m),js(m),g[y+80>>2]=g[p>>2],y=y+84|0,Ln(C,S+60|0,y),js(y),wt=H}function ph(p){p=+p;var m=0;return m=p<0?p+6.283185307179586:p,+(p>=6.283185307179586?m+-6.283185307179586:m)}function us(p,m){return p=p|0,m=m|0,+li(+(+Tt[p>>3]-+Tt[m>>3]))<17453292519943298e-27?(m=+li(+(+Tt[p+8>>3]-+Tt[m+8>>3]))<17453292519943298e-27,m|0):(m=0,m|0)}function _u(p,m){p=p|0,m=m|0;var y=0,S=0,C=0,k=0;return C=+Tt[m>>3],S=+Tt[p>>3],k=+hi(+((C-S)*.5)),y=+hi(+((+Tt[m+8>>3]-+Tt[p+8>>3])*.5)),y=k*k+y*(+Ur(+C)*+Ur(+S)*y),+(+qr(+ +bn(+y),+ +bn(+(1-y)))*2)}function Bc(p,m){p=p|0,m=m|0;var y=0,S=0,C=0,k=0;return C=+Tt[m>>3],S=+Tt[p>>3],k=+hi(+((C-S)*.5)),y=+hi(+((+Tt[m+8>>3]-+Tt[p+8>>3])*.5)),y=k*k+y*(+Ur(+C)*+Ur(+S)*y),+(+qr(+ +bn(+y),+ +bn(+(1-y)))*2*6371.007180918475)}function h0(p,m){p=p|0,m=m|0;var y=0,S=0,C=0,k=0;return C=+Tt[m>>3],S=+Tt[p>>3],k=+hi(+((C-S)*.5)),y=+hi(+((+Tt[m+8>>3]-+Tt[p+8>>3])*.5)),y=k*k+y*(+Ur(+C)*+Ur(+S)*y),+(+qr(+ +bn(+y),+ +bn(+(1-y)))*2*6371.007180918475*1e3)}function Od(p,m){p=p|0,m=m|0;var y=0,S=0,C=0,k=0,L=0;return k=+Tt[m>>3],S=+Ur(+k),C=+Tt[m+8>>3]-+Tt[p+8>>3],L=S*+hi(+C),y=+Tt[p>>3],+ +qr(+L,+(+hi(+k)*+Ur(+y)-+Ur(+C)*(S*+hi(+y))))}function f0(p,m,y,S){p=p|0,m=+m,y=+y,S=S|0;var C=0,k=0,L=0,z=0;if(y<1e-16){g[S>>2]=g[p>>2],g[S+4>>2]=g[p+4>>2],g[S+8>>2]=g[p+8>>2],g[S+12>>2]=g[p+12>>2];return}k=m<0?m+6.283185307179586:m,k=m>=6.283185307179586?k+-6.283185307179586:k;do if(k<1e-16)m=+Tt[p>>3]+y,Tt[S>>3]=m,C=S;else{if(C=+li(+(k+-3.141592653589793))<1e-16,m=+Tt[p>>3],C){m=m-y,Tt[S>>3]=m,C=S;break}if(L=+Ur(+y),y=+hi(+y),m=L*+hi(+m)+ +Ur(+k)*(y*+Ur(+m)),m=m>1?1:m,m=+h_(+(m<-1?-1:m)),Tt[S>>3]=m,+li(+(m+-1.5707963267948966))<1e-16){Tt[S>>3]=1.5707963267948966,Tt[S+8>>3]=0;return}if(+li(+(m+1.5707963267948966))<1e-16){Tt[S>>3]=-1.5707963267948966,Tt[S+8>>3]=0;return}if(z=+Ur(+m),k=y*+hi(+k)/z,y=+Tt[p>>3],m=(L-+hi(+m)*+hi(+y))/+Ur(+y)/z,L=k>1?1:k,m=m>1?1:m,m=+Tt[p+8>>3]+ +qr(+(L<-1?-1:L),+(m<-1?-1:m)),m>3.141592653589793)do m=m+-6.283185307179586;while(m>3.141592653589793);if(m<-3.141592653589793)do m=m+6.283185307179586;while(m<-3.141592653589793);Tt[S+8>>3]=m;return}while(!1);if(+li(+(m+-1.5707963267948966))<1e-16){Tt[C>>3]=1.5707963267948966,Tt[S+8>>3]=0;return}if(+li(+(m+1.5707963267948966))<1e-16){Tt[C>>3]=-1.5707963267948966,Tt[S+8>>3]=0;return}if(m=+Tt[p+8>>3],m>3.141592653589793)do m=m+-6.283185307179586;while(m>3.141592653589793);if(m<-3.141592653589793)do m=m+6.283185307179586;while(m<-3.141592653589793);Tt[S+8>>3]=m}function v_(p){return p=p|0,+ +Tt[20496+(p<<3)>>3]}function ua(p){return p=p|0,+ +Tt[20624+(p<<3)>>3]}function un(p){return p=p|0,+ +Tt[20752+(p<<3)>>3]}function sA(p){return p=p|0,+ +Tt[20880+(p<<3)>>3]}function d0(p){p=p|0;var m=0;return m=21008+(p<<3)|0,p=g[m>>2]|0,Je(g[m+4>>2]|0),p|0}function Ah(p,m,y){p=p|0,m=m|0,y=y|0;var S=0,C=0,k=0,L=0,z=0,H=0,it=0,ot=0,Ct=0,Nt=0;return Nt=+Tt[m>>3],ot=+Tt[p>>3],H=+hi(+((Nt-ot)*.5)),k=+Tt[m+8>>3],it=+Tt[p+8>>3],L=+hi(+((k-it)*.5)),z=+Ur(+ot),Ct=+Ur(+Nt),L=H*H+L*(Ct*z*L),L=+qr(+ +bn(+L),+ +bn(+(1-L)))*2,H=+Tt[y>>3],Nt=+hi(+((H-Nt)*.5)),S=+Tt[y+8>>3],k=+hi(+((S-k)*.5)),C=+Ur(+H),k=Nt*Nt+k*(Ct*C*k),k=+qr(+ +bn(+k),+ +bn(+(1-k)))*2,H=+hi(+((ot-H)*.5)),S=+hi(+((it-S)*.5)),S=H*H+S*(z*C*S),S=+qr(+ +bn(+S),+ +bn(+(1-S)))*2,C=(L+k+S)*.5,+(+Md(+ +bn(+(+To(+(C*.5))*+To(+((C-L)*.5))*+To(+((C-k)*.5))*+To(+((C-S)*.5)))))*4)}function x_(p,m){p=p|0,m=m|0;var y=0,S=0,C=0,k=0,L=0;if(k=wt,wt=wt+192|0,S=k+168|0,C=k,l(p,m,S),d(p,m,C),m=g[C>>2]|0,(m|0)<=0)return y=0,wt=k,+y;if(y=+Ah(C+8|0,C+8+(((m|0)!=1&1)<<4)|0,S)+0,(m|0)==1)return wt=k,+y;p=1;do L=p,p=p+1|0,y=y+ +Ah(C+8+(L<<4)|0,C+8+(((p|0)%(m|0)|0)<<4)|0,S);while((p|0)<(m|0));return wt=k,+y}function b_(p,m){p=p|0,m=m|0;var y=0,S=0,C=0,k=0,L=0;if(k=wt,wt=wt+192|0,S=k+168|0,C=k,l(p,m,S),d(p,m,C),m=g[C>>2]|0,(m|0)>0){if(y=+Ah(C+8|0,C+8+(((m|0)!=1&1)<<4)|0,S)+0,(m|0)!=1){p=1;do L=p,p=p+1|0,y=y+ +Ah(C+8+(L<<4)|0,C+8+(((p|0)%(m|0)|0)<<4)|0,S);while((p|0)<(m|0))}}else y=0;return wt=k,+(y*6371.007180918475*6371.007180918475)}function Nx(p,m){p=p|0,m=m|0;var y=0,S=0,C=0,k=0,L=0;if(k=wt,wt=wt+192|0,S=k+168|0,C=k,l(p,m,S),d(p,m,C),m=g[C>>2]|0,(m|0)>0){if(y=+Ah(C+8|0,C+8+(((m|0)!=1&1)<<4)|0,S)+0,(m|0)!=1){p=1;do L=p,p=p+1|0,y=y+ +Ah(C+8+(L<<4)|0,C+8+(((p|0)%(m|0)|0)<<4)|0,S);while((p|0)<(m|0))}}else y=0;return wt=k,+(y*6371.007180918475*6371.007180918475*1e3*1e3)}function Mo(p,m){p=p|0,m=m|0;var y=0,S=0,C=0,k=0,L=0,z=0,H=0,it=0;if(L=wt,wt=wt+176|0,k=L,pt(p,m,k),p=g[k>>2]|0,(p|0)<=1)return C=0,wt=L,+C;m=p+-1|0,p=0,y=0,S=+Tt[k+8>>3],C=+Tt[k+16>>3];do p=p+1|0,H=S,S=+Tt[k+8+(p<<4)>>3],it=+hi(+((S-H)*.5)),z=C,C=+Tt[k+8+(p<<4)+8>>3],z=+hi(+((C-z)*.5)),z=it*it+z*(+Ur(+S)*+Ur(+H)*z),y=y+ +qr(+ +bn(+z),+ +bn(+(1-z)))*2;while((p|0)<(m|0));return wt=L,+y}function oA(p,m){p=p|0,m=m|0;var y=0,S=0,C=0,k=0,L=0,z=0,H=0,it=0;if(L=wt,wt=wt+176|0,k=L,pt(p,m,k),p=g[k>>2]|0,(p|0)<=1)return C=0,wt=L,+C;m=p+-1|0,p=0,y=0,S=+Tt[k+8>>3],C=+Tt[k+16>>3];do p=p+1|0,H=S,S=+Tt[k+8+(p<<4)>>3],it=+hi(+((S-H)*.5)),z=C,C=+Tt[k+8+(p<<4)+8>>3],z=+hi(+((C-z)*.5)),z=it*it+z*(+Ur(+H)*+Ur(+S)*z),y=y+ +qr(+ +bn(+z),+ +bn(+(1-z)))*2;while((p|0)!=(m|0));return it=y*6371.007180918475,wt=L,+it}function nr(p,m){p=p|0,m=m|0;var y=0,S=0,C=0,k=0,L=0,z=0,H=0,it=0;if(L=wt,wt=wt+176|0,k=L,pt(p,m,k),p=g[k>>2]|0,(p|0)<=1)return C=0,wt=L,+C;m=p+-1|0,p=0,y=0,S=+Tt[k+8>>3],C=+Tt[k+16>>3];do p=p+1|0,H=S,S=+Tt[k+8+(p<<4)>>3],it=+hi(+((S-H)*.5)),z=C,C=+Tt[k+8+(p<<4)+8>>3],z=+hi(+((C-z)*.5)),z=it*it+z*(+Ur(+H)*+Ur(+S)*z),y=y+ +qr(+ +bn(+z),+ +bn(+(1-z)))*2;while((p|0)!=(m|0));return it=y*6371.007180918475*1e3,wt=L,+it}function dr(p,m){return p=p|0,m=m|0,m=me(p|0,m|0,52)|0,It()|0,m&15|0}function wf(p,m){return p=p|0,m=m|0,m=me(p|0,m|0,45)|0,It()|0,m&127|0}function aA(p,m){p=p|0,m=m|0;var y=0,S=0,C=0,k=0,L=0,z=0;if(!(!0&(m&-16777216|0)==134217728)||(L=me(p|0,m|0,45)|0,It()|0,L=L&127,L>>>0>121))return m=0,m|0;y=me(p|0,m|0,52)|0,It()|0,y=y&15;do if(y|0){for(C=1,S=0;;){if(k=me(p|0,m|0,(15-C|0)*3|0)|0,It()|0,k=k&7,(k|0)!=0&(S^1))if((k|0)==1&(fi(L)|0)!=0){z=0,S=13;break}else S=1;if((k|0)==7){z=0,S=13;break}if(C>>>0>>0)C=C+1|0;else{S=9;break}}if((S|0)==9){if((y|0)==15)z=1;else break;return z|0}else if((S|0)==13)return z|0}while(!1);for(;;){if(z=me(p|0,m|0,(14-y|0)*3|0)|0,It()|0,!((z&7|0)==7&!0)){z=0,S=13;break}if(y>>>0<14)y=y+1|0;else{z=1,S=13;break}}return(S|0)==13?z|0:0}function Bd(p,m,y){p=p|0,m=m|0,y=y|0;var S=0,C=0;if(S=me(p|0,m|0,52)|0,It()|0,S=S&15,(S|0)>=(y|0)){if((S|0)!=(y|0))if(y>>>0<=15){if(C=ke(y|0,0,52)|0,p=C|p,m=It()|0|m&-15728641,(S|0)>(y|0))do C=ke(7,0,(14-y|0)*3|0)|0,y=y+1|0,p=C|p,m=It()|0|m;while((y|0)<(S|0))}else m=0,p=0}else m=0,p=0;return Je(m|0),p|0}function Hn(p,m,y){return p=p|0,m=m|0,y=y|0,p=me(p|0,m|0,52)|0,It()|0,p=p&15,(y|0)<16&(p|0)<=(y|0)?(y=Ze(7,y-p|0)|0,y|0):(y=0,y|0)}function uo(p,m,y,S){p=p|0,m=m|0,y=y|0,S=S|0;var C=0,k=0,L=0,z=0,H=0,it=0,ot=0;if(L=me(p|0,m|0,52)|0,It()|0,L=L&15,!!((y|0)<16&(L|0)<=(y|0))){if((L|0)==(y|0)){y=S,g[y>>2]=p,g[y+4>>2]=m;return}if(H=Ze(7,y-L|0)|0,it=(H|0)/7|0,z=me(p|0,m|0,45)|0,It()|0,!(fi(z&127)|0))k=0;else{t:do if(!L)C=0;else for(k=1;;){if(C=me(p|0,m|0,(15-k|0)*3|0)|0,It()|0,C=C&7,C|0)break t;if(k>>>0>>0)k=k+1|0;else{C=0;break}}while(!1);k=(C|0)==0}if(ot=ke(L+1|0,0,52)|0,C=It()|0|m&-15728641,z=(14-L|0)*3|0,m=ke(7,0,z|0)|0,m=(ot|p)&~m,L=C&~(It()|0),uo(m,L,y,S),C=S+(it<<3)|0,!k){ot=ke(1,0,z|0)|0,uo(ot|m,It()|0|L,y,C),ot=C+(it<<3)|0,H=ke(2,0,z|0)|0,uo(H|m,It()|0|L,y,ot),ot=ot+(it<<3)|0,H=ke(3,0,z|0)|0,uo(H|m,It()|0|L,y,ot),ot=ot+(it<<3)|0,H=ke(4,0,z|0)|0,uo(H|m,It()|0|L,y,ot),ot=ot+(it<<3)|0,H=ke(5,0,z|0)|0,uo(H|m,It()|0|L,y,ot),H=ke(6,0,z|0)|0,uo(H|m,It()|0|L,y,ot+(it<<3)|0);return}k=C+(it<<3)|0,(H|0)>6&&(H=C+8|0,ot=(k>>>0>H>>>0?k:H)+-1+(0-C)|0,Fc(C|0,0,ot+8&-8|0)|0,C=H+(ot>>>3<<3)|0),ot=ke(2,0,z|0)|0,uo(ot|m,It()|0|L,y,C),ot=C+(it<<3)|0,H=ke(3,0,z|0)|0,uo(H|m,It()|0|L,y,ot),ot=ot+(it<<3)|0,H=ke(4,0,z|0)|0,uo(H|m,It()|0|L,y,ot),ot=ot+(it<<3)|0,H=ke(5,0,z|0)|0,uo(H|m,It()|0|L,y,ot),H=ke(6,0,z|0)|0,uo(H|m,It()|0|L,y,ot+(it<<3)|0)}}function ji(p,m){p=p|0,m=m|0;var y=0,S=0,C=0;if(C=me(p|0,m|0,45)|0,It()|0,!(fi(C&127)|0))return C=0,C|0;C=me(p|0,m|0,52)|0,It()|0,C=C&15;t:do if(!C)y=0;else for(S=1;;){if(y=me(p|0,m|0,(15-S|0)*3|0)|0,It()|0,y=y&7,y|0)break t;if(S>>>0>>0)S=S+1|0;else{y=0;break}}while(!1);return C=(y|0)==0&1,C|0}function w_(p,m,y){p=p|0,m=m|0,y=y|0;var S=0,C=0;if(S=me(p|0,m|0,52)|0,It()|0,S=S&15,(y|0)<16&(S|0)<=(y|0)){if((S|0)!=(y|0)&&(C=ke(y|0,0,52)|0,p=C|p,m=It()|0|m&-15728641,(S|0)<(y|0)))do C=ke(7,0,(14-S|0)*3|0)|0,S=S+1|0,p=p&~C,m=m&~(It()|0);while((S|0)<(y|0))}else m=0,p=0;return Je(m|0),p|0}function mh(p,m,y){p=p|0,m=m|0,y=y|0;var S=0,C=0,k=0,L=0,z=0,H=0,it=0,ot=0,Ct=0,Nt=0,Wt=0,re=0,ne=0,Le=0,We=0,te=0,_e=0,Ut=0,$e=0,er=0,we=0,je=0;if(!y)return we=0,we|0;if(C=p,S=g[C>>2]|0,C=g[C+4>>2]|0,!0&(C&15728640|0)==0){if((y|0)<=0||(we=m,g[we>>2]=S,g[we+4>>2]=C,(y|0)==1))return we=0,we|0;S=1;do $e=p+(S<<3)|0,er=g[$e+4>>2]|0,we=m+(S<<3)|0,g[we>>2]=g[$e>>2],g[we+4>>2]=er,S=S+1|0;while((S|0)!=(y|0));return S=0,S|0}if($e=y<<3,er=ho($e)|0,!er)return we=-3,we|0;if(Va(er|0,p|0,$e|0)|0,Ut=Ua(y,8)|0,!Ut)return Gr(er),we=-3,we|0;S=y;t:for(;;){L=er,ot=g[L>>2]|0,L=g[L+4>>2]|0,te=me(ot|0,L|0,52)|0,It()|0,te=te&15,_e=te+-1|0,We=(S|0)>0;e:do if(We){if(Le=((S|0)<0)<<31>>31,re=ke(_e|0,0,52)|0,ne=It()|0,_e>>>0>15)for(C=0,p=ot,y=L;;){if(!((p|0)==0&(y|0)==0)){if(k=me(p|0,y|0,52)|0,It()|0,k=k&15,z=(k|0)<(_e|0),k=(k|0)==(_e|0),it=z?0:k?p:0,p=z?0:k?y:0,y=Yo(it|0,p|0,S|0,Le|0)|0,It()|0,k=Ut+(y<<3)|0,z=k,H=g[z>>2]|0,z=g[z+4>>2]|0,(H|0)==0&(z|0)==0)y=it;else for(re=0,Wt=y,Nt=z,y=it;;){if((re|0)>(S|0)){we=41;break t}if((H|0)==(y|0)&(Nt&-117440513|0)==(p|0)){it=me(H|0,Nt|0,56)|0,It()|0,it=it&7,Ct=it+1|0,ne=me(H|0,Nt|0,45)|0,It()|0;r:do if(!(fi(ne&127)|0))z=7;else{if(H=me(H|0,Nt|0,52)|0,It()|0,H=H&15,!H){z=6;break}for(z=1;;){if(ne=ke(7,0,(15-z|0)*3|0)|0,!((ne&y|0)==0&((It()|0)&p|0)==0)){z=7;break r}if(z>>>0>>0)z=z+1|0;else{z=6;break}}}while(!1);if((it+2|0)>>>0>z>>>0){we=51;break t}ne=ke(Ct|0,0,56)|0,p=It()|0|p&-117440513,z=k,g[z>>2]=0,g[z+4>>2]=0,z=Wt,y=ne|y}else z=(Wt+1|0)%(S|0)|0;if(k=Ut+(z<<3)|0,Nt=k,H=g[Nt>>2]|0,Nt=g[Nt+4>>2]|0,(H|0)==0&(Nt|0)==0)break;re=re+1|0,Wt=z}ne=k,g[ne>>2]=y,g[ne+4>>2]=p}if(C=C+1|0,(C|0)>=(S|0))break e;y=er+(C<<3)|0,p=g[y>>2]|0,y=g[y+4>>2]|0}for(C=0,p=ot,y=L;;){if(!((p|0)==0&(y|0)==0)){if(z=me(p|0,y|0,52)|0,It()|0,z=z&15,(z|0)>=(_e|0)){if((z|0)!=(_e|0)&&(p=p|re,y=y&-15728641|ne,z>>>0>=te>>>0)){k=_e;do Wt=ke(7,0,(14-k|0)*3|0)|0,k=k+1|0,p=Wt|p,y=It()|0|y;while(k>>>0>>0)}}else p=0,y=0;if(z=Yo(p|0,y|0,S|0,Le|0)|0,It()|0,k=Ut+(z<<3)|0,H=k,it=g[H>>2]|0,H=g[H+4>>2]|0,!((it|0)==0&(H|0)==0))for(Wt=0;;){if((Wt|0)>(S|0)){we=41;break t}if((it|0)==(p|0)&(H&-117440513|0)==(y|0)){Ct=me(it|0,H|0,56)|0,It()|0,Ct=Ct&7,Nt=Ct+1|0,je=me(it|0,H|0,45)|0,It()|0;r:do if(!(fi(je&127)|0))H=7;else{if(it=me(it|0,H|0,52)|0,It()|0,it=it&15,!it){H=6;break}for(H=1;;){if(je=ke(7,0,(15-H|0)*3|0)|0,!((je&p|0)==0&((It()|0)&y|0)==0)){H=7;break r}if(H>>>0>>0)H=H+1|0;else{H=6;break}}}while(!1);if((Ct+2|0)>>>0>H>>>0){we=51;break t}je=ke(Nt|0,0,56)|0,y=It()|0|y&-117440513,Nt=k,g[Nt>>2]=0,g[Nt+4>>2]=0,p=je|p}else z=(z+1|0)%(S|0)|0;if(k=Ut+(z<<3)|0,H=k,it=g[H>>2]|0,H=g[H+4>>2]|0,(it|0)==0&(H|0)==0)break;Wt=Wt+1|0}je=k,g[je>>2]=p,g[je+4>>2]=y}if(C=C+1|0,(C|0)>=(S|0))break e;y=er+(C<<3)|0,p=g[y>>2]|0,y=g[y+4>>2]|0}}while(!1);if((S+5|0)>>>0<11){we=99;break}if(ne=Ua((S|0)/6|0,8)|0,!ne){we=58;break}e:do if(We){Wt=0,Nt=0;do{if(z=Ut+(Wt<<3)|0,p=z,C=g[p>>2]|0,p=g[p+4>>2]|0,!((C|0)==0&(p|0)==0)){H=me(C|0,p|0,56)|0,It()|0,H=H&7,y=H+1|0,it=p&-117440513,je=me(C|0,p|0,45)|0,It()|0;r:do if(fi(je&127)|0){if(Ct=me(C|0,p|0,52)|0,It()|0,Ct=Ct&15,Ct|0)for(k=1;;){if(je=ke(7,0,(15-k|0)*3|0)|0,!((C&je|0)==0&(it&(It()|0)|0)==0))break r;if(k>>>0>>0)k=k+1|0;else break}p=ke(y|0,0,56)|0,C=p|C,p=It()|0|it,y=z,g[y>>2]=C,g[y+4>>2]=p,y=H+2|0}while(!1);(y|0)==7&&(je=ne+(Nt<<3)|0,g[je>>2]=C,g[je+4>>2]=p&-117440513,Nt=Nt+1|0)}Wt=Wt+1|0}while((Wt|0)!=(S|0));if(We){if(re=((S|0)<0)<<31>>31,Ct=ke(_e|0,0,52)|0,Wt=It()|0,_e>>>0>15)for(p=0,C=0;;){do if(!((ot|0)==0&(L|0)==0)){for(H=me(ot|0,L|0,52)|0,It()|0,H=H&15,k=(H|0)<(_e|0),H=(H|0)==(_e|0),z=k?0:H?ot:0,H=k?0:H?L:0,k=Yo(z|0,H|0,S|0,re|0)|0,It()|0,y=0;;){if((y|0)>(S|0)){we=98;break t}if(je=Ut+(k<<3)|0,it=g[je+4>>2]|0,(it&-117440513|0)==(H|0)&&(g[je>>2]|0)==(z|0)){we=70;break}if(k=(k+1|0)%(S|0)|0,je=Ut+(k<<3)|0,(g[je>>2]|0)==(z|0)&&(g[je+4>>2]|0)==(H|0))break;y=y+1|0}if((we|0)==70&&(we=0,!0&(it&117440512|0)==100663296))break;je=m+(C<<3)|0,g[je>>2]=ot,g[je+4>>2]=L,C=C+1|0}while(!1);if(p=p+1|0,(p|0)>=(S|0)){S=Nt;break e}L=er+(p<<3)|0,ot=g[L>>2]|0,L=g[L+4>>2]|0}for(p=0,C=0;;){do if(!((ot|0)==0&(L|0)==0)){if(H=me(ot|0,L|0,52)|0,It()|0,H=H&15,(H|0)>=(_e|0))if((H|0)!=(_e|0))if(y=ot|Ct,k=L&-15728641|Wt,H>>>0>>0)H=k;else{z=_e;do je=ke(7,0,(14-z|0)*3|0)|0,z=z+1|0,y=je|y,k=It()|0|k;while(z>>>0>>0);H=k}else y=ot,H=L;else y=0,H=0;for(z=Yo(y|0,H|0,S|0,re|0)|0,It()|0,k=0;;){if((k|0)>(S|0)){we=98;break t}if(je=Ut+(z<<3)|0,it=g[je+4>>2]|0,(it&-117440513|0)==(H|0)&&(g[je>>2]|0)==(y|0)){we=93;break}if(z=(z+1|0)%(S|0)|0,je=Ut+(z<<3)|0,(g[je>>2]|0)==(y|0)&&(g[je+4>>2]|0)==(H|0))break;k=k+1|0}if((we|0)==93&&(we=0,!0&(it&117440512|0)==100663296))break;je=m+(C<<3)|0,g[je>>2]=ot,g[je+4>>2]=L,C=C+1|0}while(!1);if(p=p+1|0,(p|0)>=(S|0)){S=Nt;break e}L=er+(p<<3)|0,ot=g[L>>2]|0,L=g[L+4>>2]|0}}else C=0,S=Nt}else C=0,S=0;while(!1);if(Fc(Ut|0,0,$e|0)|0,Va(er|0,ne|0,S<<3|0)|0,Gr(ne),S)m=m+(C<<3)|0;else break}return(we|0)==41?(Gr(er),Gr(Ut),je=-1,je|0):(we|0)==51?(Gr(er),Gr(Ut),je=-2,je|0):(we|0)==58?(Gr(er),Gr(Ut),je=-3,je|0):(we|0)==98?(Gr(ne),Gr(er),Gr(Ut),je=-1,je|0):((we|0)==99&&Va(m|0,er|0,S<<3|0)|0,Gr(er),Gr(Ut),je=0,je|0)}function kn(p,m,y,S,C){p=p|0,m=m|0,y=y|0,S=S|0,C=C|0;var k=0,L=0,z=0,H=0,it=0,ot=0;if((m|0)<=0)return C=0,C|0;if((C|0)>=16){for(k=0;;){if(ot=p+(k<<3)|0,!((g[ot>>2]|0)==0&(g[ot+4>>2]|0)==0)){k=14;break}if(k=k+1|0,(k|0)>=(m|0)){L=0,k=16;break}}if((k|0)==14)return((S|0)>0?-2:-1)|0;if((k|0)==16)return L|0}k=0,ot=0;t:for(;;){it=p+(ot<<3)|0,z=it,L=g[z>>2]|0,z=g[z+4>>2]|0;do if(!((L|0)==0&(z|0)==0)){if((k|0)>=(S|0)){L=-1,k=16;break t}if(H=me(L|0,z|0,52)|0,It()|0,H=H&15,(H|0)>(C|0)){L=-2,k=16;break t}if((H|0)==(C|0)){it=y+(k<<3)|0,g[it>>2]=L,g[it+4>>2]=z,k=k+1|0;break}if(L=(Ze(7,C-H|0)|0)+k|0,(L|0)>(S|0)){L=-1,k=16;break t}uo(g[it>>2]|0,g[it+4>>2]|0,C,y+(k<<3)|0),k=L}while(!1);if(ot=ot+1|0,(ot|0)>=(m|0)){L=0,k=16;break}}return(k|0)==16?L|0:0}function wn(p,m,y){p=p|0,m=m|0,y=y|0;var S=0,C=0,k=0,L=0;if((m|0)<=0)return y=0,y|0;if((y|0)>=16){for(S=0;;){if(L=p+(S<<3)|0,!((g[L>>2]|0)==0&(g[L+4>>2]|0)==0)){S=-1,C=13;break}if(S=S+1|0,(S|0)>=(m|0)){S=0,C=13;break}}if((C|0)==13)return S|0}S=0,L=0;t:for(;;){C=p+(L<<3)|0,k=g[C>>2]|0,C=g[C+4>>2]|0;do if(!((k|0)==0&(C|0)==0)){if(C=me(k|0,C|0,52)|0,It()|0,C=C&15,(C|0)>(y|0)){S=-1,C=13;break t}if((C|0)==(y|0)){S=S+1|0;break}else{S=(Ze(7,y-C|0)|0)+S|0;break}}while(!1);if(L=L+1|0,(L|0)>=(m|0)){C=13;break}}return(C|0)==13?S|0:0}function Sf(p,m){return p=p|0,m=m|0,m=me(p|0,m|0,52)|0,It()|0,m&1|0}function Es(p,m){p=p|0,m=m|0;var y=0,S=0,C=0;if(C=me(p|0,m|0,52)|0,It()|0,C=C&15,!C)return C=0,C|0;for(S=1;;){if(y=me(p|0,m|0,(15-S|0)*3|0)|0,It()|0,y=y&7,y|0){S=5;break}if(S>>>0>>0)S=S+1|0;else{y=0,S=5;break}}return(S|0)==5?y|0:0}function gh(p,m){p=p|0,m=m|0;var y=0,S=0,C=0,k=0,L=0,z=0,H=0;if(H=me(p|0,m|0,52)|0,It()|0,H=H&15,!H)return z=m,H=p,Je(z|0),H|0;for(z=1,y=0;;){k=(15-z|0)*3|0,S=ke(7,0,k|0)|0,C=It()|0,L=me(p|0,m|0,k|0)|0,It()|0,k=ke(Na(L&7)|0,0,k|0)|0,L=It()|0,p=k|p&~S,m=L|m&~C;t:do if(!y)if((k&S|0)==0&(L&C|0)==0)y=0;else if(S=me(p|0,m|0,52)|0,It()|0,S=S&15,!S)y=1;else{y=1;e:for(;;){switch(L=me(p|0,m|0,(15-y|0)*3|0)|0,It()|0,L&7){case 1:break e;case 0:break;default:{y=1;break 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0:break;default:{y=1;break t}}if(y>>>0>>0)y=y+1|0;else{y=1;break t}}for(y=1;;)if(C=(15-y|0)*3|0,k=ke(7,0,C|0)|0,L=m&~(It()|0),m=me(p|0,m|0,C|0)|0,It()|0,m=ke(co(m&7)|0,0,C|0)|0,p=p&~k|m,m=L|(It()|0),y>>>0>>0)y=y+1|0;else{y=1;break}}while(!1);if(z>>>0>>0)z=z+1|0;else break}return Je(m|0),p|0}function Fd(p,m){p=p|0,m=m|0;var y=0,S=0,C=0,k=0,L=0;if(S=me(p|0,m|0,52)|0,It()|0,S=S&15,!S)return y=m,S=p,Je(y|0),S|0;for(y=1;L=(15-y|0)*3|0,k=ke(7,0,L|0)|0,C=m&~(It()|0),m=me(p|0,m|0,L|0)|0,It()|0,m=ke(co(m&7)|0,0,L|0)|0,p=m|p&~k,m=It()|0|C,y>>>0>>0;)y=y+1|0;return Je(m|0),p|0}function Tf(p,m){p=p|0,m=m|0;var y=0,S=0,C=0,k=0,L=0,z=0,H=0,it=0,ot=0;if(H=wt,wt=wt+64|0,z=H+40|0,S=H+24|0,C=H+12|0,k=H,ke(m|0,0,52)|0,y=It()|0|134225919,!m)return(g[p+4>>2]|0)>2||(g[p+8>>2]|0)>2||(g[p+12>>2]|0)>2?(L=0,z=0,Je(L|0),wt=H,z|0):(ke(vf(p)|0,0,45)|0,L=It()|0|y,z=-1,Je(L|0),wt=H,z|0);if(g[z>>2]=g[p>>2],g[z+4>>2]=g[p+4>>2],g[z+8>>2]=g[p+8>>2],g[z+12>>2]=g[p+12>>2],L=z+4|0,(m|0)>0)for(p=-1;g[S>>2]=g[L>>2],g[S+4>>2]=g[L+4>>2],g[S+8>>2]=g[L+8>>2],m&1?(Rd(L),g[C>>2]=g[L>>2],g[C+4>>2]=g[L+4>>2],g[C+8>>2]=g[L+8>>2],za(C)):(Al(L),g[C>>2]=g[L>>2],g[C+4>>2]=g[L+4>>2],g[C+8>>2]=g[L+8>>2],hh(C)),eA(S,C,k),js(k),ot=(15-m|0)*3|0,it=ke(7,0,ot|0)|0,y=y&~(It()|0),ot=ke(Fa(k)|0,0,ot|0)|0,p=ot|p&~it,y=It()|0|y,(m|0)>1;)m=m+-1|0;else p=-1;t:do if((g[L>>2]|0)<=2&&(g[z+8>>2]|0)<=2&&(g[z+12>>2]|0)<=2){if(S=vf(z)|0,m=ke(S|0,0,45)|0,m=m|p,p=It()|0|y&-1040385,k=Kp(z)|0,!(fi(S)|0)){if((k|0)<=0)break;for(C=0;;){if(S=me(m|0,p|0,52)|0,It()|0,S=S&15,S)for(y=1;ot=(15-y|0)*3|0,z=me(m|0,p|0,ot|0)|0,It()|0,it=ke(7,0,ot|0)|0,p=p&~(It()|0),ot=ke(Na(z&7)|0,0,ot|0)|0,m=m&~it|ot,p=p|(It()|0),y>>>0>>0;)y=y+1|0;if(C=C+1|0,(C|0)==(k|0))break t}}C=me(m|0,p|0,52)|0,It()|0,C=C&15;e:do if(C){y=1;r:for(;;){switch(ot=me(m|0,p|0,(15-y|0)*3|0)|0,It()|0,ot&7){case 1:break r;case 0:break;default:break e}if(y>>>0>>0)y=y+1|0;else break e}if(ch(S,g[z>>2]|0)|0)for(y=1;z=(15-y|0)*3|0,it=ke(7,0,z|0)|0,ot=p&~(It()|0),p=me(m|0,p|0,z|0)|0,It()|0,p=ke(co(p&7)|0,0,z|0)|0,m=m&~it|p,p=ot|(It()|0),y>>>0>>0;)y=y+1|0;else for(y=1;ot=(15-y|0)*3|0,z=me(m|0,p|0,ot|0)|0,It()|0,it=ke(7,0,ot|0)|0,p=p&~(It()|0),ot=ke(Na(z&7)|0,0,ot|0)|0,m=m&~it|ot,p=p|(It()|0),y>>>0>>0;)y=y+1|0}while(!1);if((k|0)>0){y=0;do m=gh(m,p)|0,p=It()|0,y=y+1|0;while((y|0)!=(k|0))}}else 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t}}if(S>>>0>>0)S=S+1|0;else{S=m;break t}}for(C=1,S=m;m=(15-C|0)*3|0,L=ke(7,0,m|0)|0,z=S&~(It()|0),S=me(p|0,S|0,m|0)|0,It()|0,S=ke(co(S&7)|0,0,m|0)|0,p=p&~L|S,S=z|(It()|0),C>>>0>>0;)C=C+1|0}else S=m;while(!1);if(z=7728+(it*28|0)|0,g[y>>2]=g[z>>2],g[y+4>>2]=g[z+4>>2],g[y+8>>2]=g[z+8>>2],g[y+12>>2]=g[z+12>>2],!(bi(p,S,y)|0)){wt=ot;return}if(L=y+4|0,g[H>>2]=g[L>>2],g[H+4>>2]=g[L+4>>2],g[H+8>>2]=g[L+8>>2],k=me(p|0,S|0,52)|0,It()|0,z=k&15,k&1?(hh(L),k=z+1|0):k=z,!(fi(it)|0))S=0;else{t:do if(!z)S=0;else for(m=1;;){if(C=me(p|0,S|0,(15-m|0)*3|0)|0,It()|0,C=C&7,C|0){S=C;break t}if(m>>>0>>0)m=m+1|0;else{S=0;break}}while(!1);S=(S|0)==4&1}if(!(bf(y,k,S,0)|0))(k|0)!=(z|0)&&(g[L>>2]=g[H>>2],g[L+4>>2]=g[H+4>>2],g[L+8>>2]=g[H+8>>2]);else{if(fi(it)|0)do;while(bf(y,k,0,0)|0);(k|0)!=(z|0)&&Al(L)}wt=ot}function l(p,m,y){p=p|0,m=m|0,y=y|0;var S=0,C=0;S=wt,wt=wt+16|0,C=S,T(p,m,C),m=me(p|0,m|0,52)|0,It()|0,y_(C,m&15,y),wt=S}function d(p,m,y){p=p|0,m=m|0,y=y|0;var S=0,C=0,k=0,L=0,z=0;L=wt,wt=wt+16|0,k=L,T(p,m,k),S=me(p|0,m|0,45)|0,It()|0,S=(fi(S&127)|0)==0,C=me(p|0,m|0,52)|0,It()|0,C=C&15;t:do if(!S){if(C|0)for(S=1;;){if(z=ke(7,0,(15-S|0)*3|0)|0,!((z&p|0)==0&((It()|0)&m|0)==0))break t;if(S>>>0>>0)S=S+1|0;else break}l0(k,C,0,5,y),wt=L;return}while(!1);iA(k,C,0,6,y),wt=L}function v(p,m){p=p|0,m=m|0;var y=0,S=0,C=0;if(S=me(p|0,m|0,45)|0,It()|0,!(fi(S&127)|0))return S=2,S|0;if(S=me(p|0,m|0,52)|0,It()|0,S=S&15,!S)return S=5,S|0;for(y=1;;){if(C=ke(7,0,(15-y|0)*3|0)|0,!((C&p|0)==0&((It()|0)&m|0)==0)){y=2,p=6;break}if(y>>>0>>0)y=y+1|0;else{y=5,p=6;break}}return(p|0)==6?y|0:0}function b(p,m,y){p=p|0,m=m|0,y=y|0;var S=0,C=0,k=0,L=0,z=0,H=0,it=0,ot=0,Ct=0;Ct=wt,wt=wt+128|0,it=Ct+112|0,k=Ct+96|0,ot=Ct,C=me(p|0,m|0,52)|0,It()|0,z=C&15,g[it>>2]=z,L=me(p|0,m|0,45)|0,It()|0,L=L&127;t:do if(fi(L)|0){if(z|0)for(S=1;;){if(H=ke(7,0,(15-S|0)*3|0)|0,!((H&p|0)==0&((It()|0)&m|0)==0)){C=0;break t}if(S>>>0>>0)S=S+1|0;else break}if(C&1)C=1;else{H=ke(z+1|0,0,52)|0,ot=It()|0|m&-15728641,it=ke(7,0,(14-z|0)*3|0)|0,b((H|p)&~it,ot&~(It()|0),y),wt=Ct;return}}else C=0;while(!1);T(p,m,k),C?(c0(k,it,ot),H=5):(nA(k,it,ot),H=6);t:do if(fi(L)|0)if(!z)S=20;else for(S=1;;){if(L=ke(7,0,(15-S|0)*3|0)|0,!((L&p|0)==0&((It()|0)&m|0)==0)){S=8;break t}if(S>>>0>>0)S=S+1|0;else{S=20;break}}else S=8;while(!1);if(Fc(y|0,-1,S|0)|0,C){C=0;do{for(k=ot+(C<<4)|0,u0(k,g[it>>2]|0)|0,k=g[k>>2]|0,S=0;L=y+(S<<2)|0,z=g[L>>2]|0,!((z|0)==-1|(z|0)==(k|0));)S=S+1|0;g[L>>2]=k,C=C+1|0}while((C|0)!=(H|0))}else{C=0;do{for(k=ot+(C<<4)|0,bf(k,g[it>>2]|0,0,1)|0,k=g[k>>2]|0,S=0;L=y+(S<<2)|0,z=g[L>>2]|0,!((z|0)==-1|(z|0)==(k|0));)S=S+1|0;g[L>>2]=k,C=C+1|0}while((C|0)!=(H|0))}wt=Ct}function M(){return 12}function O(p,m){p=p|0,m=m|0;var y=0,S=0,C=0,k=0,L=0,z=0,H=0;if(ke(p|0,0,52)|0,z=It()|0|134225919,(p|0)<1){S=0,y=0;do fi(S)|0&&(ke(S|0,0,45)|0,L=z|(It()|0),p=m+(y<<3)|0,g[p>>2]=-1,g[p+4>>2]=L,y=y+1|0),S=S+1|0;while((S|0)!=122);return}L=0,y=0;do{if(fi(L)|0){for(ke(L|0,0,45)|0,S=1,C=-1,k=z|(It()|0);H=ke(7,0,(15-S|0)*3|0)|0,C=C&~H,k=k&~(It()|0),(S|0)!=(p|0);)S=S+1|0;H=m+(y<<3)|0,g[H>>2]=C,g[H+4>>2]=k,y=y+1|0}L=L+1|0}while((L|0)!=122)}function B(p,m,y,S){p=p|0,m=m|0,y=y|0,S=S|0;var C=0,k=0,L=0,z=0,H=0,it=0;if(z=wt,wt=wt+64|0,L=z,(p|0)==(y|0)&(m|0)==(S|0)|(!1|(m&2013265920|0)!=134217728|(!1|(S&2013265920|0)!=134217728))||(C=me(p|0,m|0,52)|0,It()|0,C=C&15,k=me(y|0,S|0,52)|0,It()|0,(C|0)!=(k&15|0)))return L=0,wt=z,L|0;if(k=C+-1|0,C>>>0>1&&(it=Bd(p,m,k)|0,H=It()|0,k=Bd(y,S,k)|0,(it|0)==(k|0)&(H|0)==(It()|0))&&(k=(C^15)*3|0,C=me(p|0,m|0,k|0)|0,It()|0,C=C&7,k=me(y|0,S|0,k|0)|0,It()|0,k=k&7,(C|0)==0|(k|0)==0||(g[21136+(C<<2)>>2]|0)==(k|0)||(g[21168+(C<<2)>>2]|0)==(k|0)))return it=1,wt=z,it|0;C=L,k=C+56|0;do g[C>>2]=0,C=C+4|0;while((C|0)<(k|0));return f_(p,m,1,L),it=L,!((g[it>>2]|0)==(y|0)&&(g[it+4>>2]|0)==(S|0))&&(it=L+8|0,!((g[it>>2]|0)==(y|0)&&(g[it+4>>2]|0)==(S|0)))&&(it=L+16|0,!((g[it>>2]|0)==(y|0)&&(g[it+4>>2]|0)==(S|0)))&&(it=L+24|0,!((g[it>>2]|0)==(y|0)&&(g[it+4>>2]|0)==(S|0)))&&(it=L+32|0,!((g[it>>2]|0)==(y|0)&&(g[it+4>>2]|0)==(S|0)))&&(it=L+40|0,!((g[it>>2]|0)==(y|0)&&(g[it+4>>2]|0)==(S|0)))?(C=L+48|0,C=((g[C>>2]|0)==(y|0)?(g[C+4>>2]|0)==(S|0):0)&1):C=1,it=C,wt=z,it|0}function U(p,m,y,S){p=p|0,m=m|0,y=y|0,S=S|0;var C=0,k=0,L=0,z=0,H=0,it=0,ot=0;if(it=wt,wt=wt+16|0,L=it,!(B(p,m,y,S)|0))return z=0,H=0,Je(z|0),wt=it,H|0;for(z=m&-2130706433,C=(ji(p,m)|0)==0,C=C?1:2;g[L>>2]=0,ot=Wn(p,m,C,L)|0,k=C+1|0,!((ot|0)==(y|0)&(It()|0)==(S|0));)if(k>>>0<7)C=k;else{C=0,p=0,H=6;break}return(H|0)==6?(Je(C|0),wt=it,p|0):(ot=ke(C|0,0,56)|0,H=z|(It()|0)|268435456,ot=p|ot,Je(H|0),wt=it,ot|0)}function W(p,m){p=p|0,m=m|0;var y=0;return y=!0&(m&2013265920|0)==268435456,Je((y?m&-2130706433|134217728:0)|0),(y?p:0)|0}function Z(p,m){p=p|0,m=m|0;var y=0,S=0,C=0;return S=wt,wt=wt+16|0,y=S,!0&(m&2013265920|0)==268435456?(C=me(p|0,m|0,56)|0,It()|0,g[y>>2]=0,y=Wn(p,m&-2130706433|134217728,C&7,y)|0,m=It()|0,Je(m|0),wt=S,y|0):(m=0,y=0,Je(m|0),wt=S,y|0)}function $(p,m){p=p|0,m=m|0;var y=0;if(!(!0&(m&2013265920|0)==268435456))return y=0,y|0;switch(y=me(p|0,m|0,56)|0,It()|0,y&7){case 0:case 7:return y=0,y|0;default:}return y=m&-2130706433|134217728,!0&(m&117440512|0)==16777216&(ji(p,y)|0)!=0?(y=0,y|0):(y=aA(p,y)|0,y|0)}function st(p,m,y){p=p|0,m=m|0,y=y|0;var S=0,C=0,k=0,L=0,z=0;k=wt,wt=wt+16|0,S=k,L=!0&(m&2013265920|0)==268435456,C=m&-2130706433|134217728,z=y,g[z>>2]=L?p:0,g[z+4>>2]=L?C:0,L?(m=me(p|0,m|0,56)|0,It()|0,g[S>>2]=0,p=Wn(p,C,m&7,S)|0,m=It()|0):(p=0,m=0),z=y+8|0,g[z>>2]=p,g[z+4>>2]=m,wt=k}function At(p,m,y){p=p|0,m=m|0,y=y|0;var S=0,C=0;C=(ji(p,m)|0)==0,m=m&-2130706433,S=y,g[S>>2]=C?p:0,g[S+4>>2]=C?m|285212672:0,S=y+8|0,g[S>>2]=p,g[S+4>>2]=m|301989888,S=y+16|0,g[S>>2]=p,g[S+4>>2]=m|318767104,S=y+24|0,g[S>>2]=p,g[S+4>>2]=m|335544320,S=y+32|0,g[S>>2]=p,g[S+4>>2]=m|352321536,y=y+40|0,g[y>>2]=p,g[y+4>>2]=m|369098752}function pt(p,m,y){p=p|0,m=m|0,y=y|0;var S=0,C=0,k=0,L=0,z=0;if(L=wt,wt=wt+16|0,k=L,S=me(p|0,m|0,56)|0,It()|0,z=!0&(m&2013265920|0)==268435456,C=z?p:0,p=z?m&-2130706433|134217728:0,m=yu(C,p,S&7)|0,(m|0)==-1){g[y>>2]=0,wt=L;return}T(C,p,k),S=me(C|0,p|0,52)|0,It()|0,S=S&15,ji(C,p)|0?l0(k,S,m,2,y):iA(k,S,m,2,y),wt=L}function yt(p){p=p|0;var m=0,y=0,S=0;return m=Ua(1,12)|0,m||Mi(22691,22646,49,22704),y=p+4|0,S=g[y>>2]|0,S|0?(S=S+8|0,g[S>>2]=m,g[y>>2]=m,m|0):(g[p>>2]|0&&Mi(22721,22646,61,22744),S=p,g[S>>2]=m,g[y>>2]=m,m|0)}function dt(p,m){p=p|0,m=m|0;var y=0,S=0;return S=ho(24)|0,S||Mi(22758,22646,78,22772),g[S>>2]=g[m>>2],g[S+4>>2]=g[m+4>>2],g[S+8>>2]=g[m+8>>2],g[S+12>>2]=g[m+12>>2],g[S+16>>2]=0,m=p+4|0,y=g[m>>2]|0,y|0?(g[y+16>>2]=S,g[m>>2]=S,S|0):(g[p>>2]|0&&Mi(22787,22646,82,22772),g[p>>2]=S,g[m>>2]=S,S|0)}function Ft(p){p=p|0;var m=0,y=0,S=0,C=0;if(p)for(S=1;;){if(m=g[p>>2]|0,m|0)do{if(y=g[m>>2]|0,y|0)do C=y,y=g[y+16>>2]|0,Gr(C);while(y|0);C=m,m=g[m+8>>2]|0,Gr(C)}while(m|0);if(m=p,p=g[p+8>>2]|0,S||Gr(m),p)S=0;else break}}function Ht(p){p=p|0;var m=0,y=0,S=0,C=0,k=0,L=0,z=0,H=0,it=0,ot=0,Ct=0,Nt=0,Wt=0,re=0,ne=0,Le=0,We=0,te=0,_e=0,Ut=0,$e=0,er=0,we=0,je=0,Zr=0,qi=0,Ei=0,hn=0,Pi=0,Sn=0,yn=0,Or=0;if(C=p+8|0,g[C>>2]|0)return Or=1,Or|0;if(S=g[p>>2]|0,!S)return Or=0,Or|0;m=S,y=0;do y=y+1|0,m=g[m+8>>2]|0;while(m|0);if(y>>>0<2)return Or=0,Or|0;Sn=ho(y<<2)|0,Sn||Mi(22807,22646,317,22826),Pi=ho(y<<5)|0,Pi||Mi(22848,22646,321,22826),g[p>>2]=0,er=p+4|0,g[er>>2]=0,g[C>>2]=0,y=0,hn=0,$e=0,Ct=0;t:for(;;){if(ot=g[S>>2]|0,ot){k=0,L=ot;do{if(H=+Tt[L+8>>3],m=L,L=g[L+16>>2]|0,it=(L|0)==0,C=it?ot:L,z=+Tt[C+8>>3],+li(+(H-z))>3.141592653589793){Or=14;break}k=k+(z-H)*(+Tt[m>>3]+ +Tt[C>>3])}while(!it);if((Or|0)==14){Or=0,k=0,m=ot;do Ut=+Tt[m+8>>3],Ei=m+16|0,qi=g[Ei>>2]|0,qi=qi|0?qi:ot,_e=+Tt[qi+8>>3],k=k+(+Tt[m>>3]+ +Tt[qi>>3])*((_e<0?_e+6.283185307179586:_e)-(Ut<0?Ut+6.283185307179586:Ut)),m=g[(m|0?Ei:S)>>2]|0;while(m|0)}k>0?(g[Sn+(hn<<2)>>2]=S,hn=hn+1|0,C=$e,m=Ct):Or=19}else Or=19;if((Or|0)==19){Or=0;do if(y){if(m=y+8|0,g[m>>2]|0){Or=21;break t}if(y=Ua(1,12)|0,!y){Or=23;break t}g[m>>2]=y,C=y+4|0,L=y,m=Ct}else if(Ct){C=er,L=Ct+8|0,m=S,y=p;break}else if(g[p>>2]|0){Or=27;break t}else{C=er,L=p,m=S,y=p;break}while(!1);if(g[L>>2]=S,g[C>>2]=S,L=Pi+($e<<5)|0,it=g[S>>2]|0,it){for(ot=Pi+($e<<5)+8|0,Tt[ot>>3]=17976931348623157e292,Ct=Pi+($e<<5)+24|0,Tt[Ct>>3]=17976931348623157e292,Tt[L>>3]=-17976931348623157e292,Nt=Pi+($e<<5)+16|0,Tt[Nt>>3]=-17976931348623157e292,We=17976931348623157e292,te=-17976931348623157e292,C=0,Wt=it,H=17976931348623157e292,ne=17976931348623157e292,Le=-17976931348623157e292,z=-17976931348623157e292;k=+Tt[Wt>>3],Ut=+Tt[Wt+8>>3],Wt=g[Wt+16>>2]|0,re=(Wt|0)==0,_e=+Tt[(re?it:Wt)+8>>3],k>3]=k,H=k),Ut>3]=Ut,ne=Ut),k>Le?Tt[L>>3]=k:k=Le,Ut>z&&(Tt[Nt>>3]=Ut,z=Ut),We=Ut>0&Utte?Ut:te,C=C|+li(+(Ut-_e))>3.141592653589793,!re;)Le=k;C&&(Tt[Nt>>3]=te,Tt[Ct>>3]=We)}else g[L>>2]=0,g[L+4>>2]=0,g[L+8>>2]=0,g[L+12>>2]=0,g[L+16>>2]=0,g[L+20>>2]=0,g[L+24>>2]=0,g[L+28>>2]=0;C=$e+1|0}if(Ei=S+8|0,S=g[Ei>>2]|0,g[Ei>>2]=0,S)$e=C,Ct=m;else{Or=45;break}}if((Or|0)==21)Mi(22624,22646,35,22658);else if((Or|0)==23)Mi(22678,22646,37,22658);else if((Or|0)==27)Mi(22721,22646,61,22744);else if((Or|0)==45){t:do if((hn|0)>0){for(Ei=(C|0)==0,Zr=C<<2,qi=(p|0)==0,je=0,m=0;;){if(we=g[Sn+(je<<2)>>2]|0,Ei)Or=73;else{if($e=ho(Zr)|0,!$e){Or=50;break}if(er=ho(Zr)|0,!er){Or=52;break}e:do if(qi)y=0;else{for(C=0,y=0,L=p;S=Pi+(C<<5)|0,St(g[L>>2]|0,S,g[we>>2]|0)|0?(g[$e+(y<<2)>>2]=L,g[er+(y<<2)>>2]=S,re=y+1|0):re=y,L=g[L+8>>2]|0,L;)C=C+1|0,y=re;if((re|0)>0)if(S=g[$e>>2]|0,(re|0)==1)y=S;else for(Nt=0,Wt=-1,y=S,Ct=S;;){for(it=g[Ct>>2]|0,S=0,L=0;C=g[g[$e+(L<<2)>>2]>>2]|0,(C|0)==(it|0)?ot=S:ot=S+((St(C,g[er+(L<<2)>>2]|0,g[it>>2]|0)|0)&1)|0,L=L+1|0,(L|0)!=(re|0);)S=ot;if(C=(ot|0)>(Wt|0),y=C?Ct:y,S=Nt+1|0,(S|0)==(re|0))break e;Nt=S,Wt=C?ot:Wt,Ct=g[$e+(S<<2)>>2]|0}else y=0}while(!1);if(Gr($e),Gr(er),y){if(C=y+4|0,S=g[C>>2]|0,S)y=S+8|0;else if(g[y>>2]|0){Or=70;break}g[y>>2]=we,g[C>>2]=we}else Or=73}if((Or|0)==73){if(Or=0,m=g[we>>2]|0,m|0)do er=m,m=g[m+16>>2]|0,Gr(er);while(m|0);Gr(we),m=2}if(je=je+1|0,(je|0)>=(hn|0)){yn=m;break t}}(Or|0)==50?Mi(22863,22646,249,22882):(Or|0)==52?Mi(22901,22646,252,22882):(Or|0)==70&&Mi(22721,22646,61,22744)}else yn=0;while(!1);return Gr(Sn),Gr(Pi),Or=yn,Or|0}return 0}function St(p,m,y){p=p|0,m=m|0,y=y|0;var S=0,C=0,k=0,L=0,z=0,H=0,it=0,ot=0;if(!(pl(m,y)|0)||(m=n0(m)|0,it=+Tt[y>>3],S=+Tt[y+8>>3],S=m&S<0?S+6.283185307179586:S,p=g[p>>2]|0,!p))return p=0,p|0;if(m){m=0,y=p;t:for(;;){for(;L=+Tt[y>>3],H=+Tt[y+8>>3],y=y+16|0,ot=g[y>>2]|0,ot=ot|0?ot:p,k=+Tt[ot>>3],C=+Tt[ot+8>>3],L>k?(z=L,L=H):(z=k,k=L,L=C,C=H),!!(itz);)if(y=g[y>>2]|0,!y){y=22;break t}if(H=C<0?C+6.283185307179586:C,L=L<0?L+6.283185307179586:L,S=L==S|H==S?S+-2220446049250313e-31:S,H=H+(it-k)/(z-k)*(L-H),(H<0?H+6.283185307179586:H)>S&&(m=m^1),y=g[y>>2]|0,!y){y=22;break}}if((y|0)==22)return m|0}else{m=0,y=p;t:for(;;){for(;L=+Tt[y>>3],H=+Tt[y+8>>3],y=y+16|0,ot=g[y>>2]|0,ot=ot|0?ot:p,k=+Tt[ot>>3],C=+Tt[ot+8>>3],L>k?(z=L,L=H):(z=k,k=L,L=C,C=H),!!(itz);)if(y=g[y>>2]|0,!y){y=22;break t}if(S=L==S|C==S?S+-2220446049250313e-31:S,C+(it-k)/(z-k)*(L-C)>S&&(m=m^1),y=g[y>>2]|0,!y){y=22;break}}if((y|0)==22)return m|0}return 0}function Bt(p,m,y,S,C){p=p|0,m=m|0,y=y|0,S=S|0,C=C|0;var k=0,L=0,z=0,H=0,it=0,ot=0,Ct=0,Nt=0,Wt=0,re=0,ne=0,Le=0,We=0,te=0;if(te=wt,wt=wt+32|0,We=te+16|0,Le=te,k=me(p|0,m|0,52)|0,It()|0,k=k&15,Wt=me(y|0,S|0,52)|0,It()|0,(k|0)!=(Wt&15|0))return We=1,wt=te,We|0;if(it=me(p|0,m|0,45)|0,It()|0,it=it&127,ot=me(y|0,S|0,45)|0,It()|0,ot=ot&127,Wt=(it|0)!=(ot|0),Wt){if(z=tA(it,ot)|0,(z|0)==7)return We=2,wt=te,We|0;H=tA(ot,it)|0,(H|0)==7?Mi(22925,22949,151,22959):(re=z,L=H)}else re=0,L=0;Ct=fi(it)|0,Nt=fi(ot)|0,g[We>>2]=0,g[We+4>>2]=0,g[We+8>>2]=0,g[We+12>>2]=0;do if(re){if(ot=g[4304+(it*28|0)+(re<<2)>>2]|0,z=(ot|0)>0,Nt)if(z){it=0,H=y,z=S;do H=p0(H,z)|0,z=It()|0,L=co(L)|0,(L|0)==1&&(L=co(1)|0),it=it+1|0;while((it|0)!=(ot|0));ot=L,it=H,H=z}else ot=L,it=y,H=S;else if(z){it=0,H=y,z=S;do H=Fd(H,z)|0,z=It()|0,L=co(L)|0,it=it+1|0;while((it|0)!=(ot|0));ot=L,it=H,H=z}else ot=L,it=y,H=S;if(bi(it,H,We)|0,Wt||Mi(22972,22949,181,22959),z=(Ct|0)!=0,L=(Nt|0)!=0,z&L&&Mi(22999,22949,182,22959),z){if(L=Es(p,m)|0,br[22032+(L*7|0)+re>>0]|0){k=3;break}H=g[21200+(L*28|0)+(re<<2)>>2]|0,it=H,ne=26}else if(L){if(L=Es(it,H)|0,br[22032+(L*7|0)+ot>>0]|0){k=4;break}it=0,H=g[21200+(ot*28|0)+(L<<2)>>2]|0,ne=26}else L=0;if((ne|0)==26)if((H|0)<=-1&&Mi(23030,22949,212,22959),(it|0)<=-1&&Mi(23053,22949,213,22959),(H|0)>0){z=We+4|0,L=0;do fh(z),L=L+1|0;while((L|0)!=(H|0));L=it}else L=it;if(g[Le>>2]=0,g[Le+4>>2]=0,g[Le+8>>2]=0,rA(Le,re),k|0)for(;Ho(k)|0?za(Le):hh(Le),(k|0)>1;)k=k+-1|0;if((L|0)>0){k=0;do fh(Le),k=k+1|0;while((k|0)!=(L|0))}ne=We+4|0,Ln(ne,Le,ne),js(ne),ne=50}else if(bi(y,S,We)|0,(Ct|0)!=0&(Nt|0)!=0)if((ot|0)!=(it|0)&&Mi(23077,22949,243,22959),L=Es(p,m)|0,k=Es(y,S)|0,br[22032+(L*7|0)+k>>0]|0)k=5;else if(L=g[21200+(L*28|0)+(k<<2)>>2]|0,(L|0)>0){z=We+4|0,k=0;do fh(z),k=k+1|0;while((k|0)!=(L|0));ne=50}else ne=50;else ne=50;while(!1);return(ne|0)==50&&(k=We+4|0,g[C>>2]=g[k>>2],g[C+4>>2]=g[k+4>>2],g[C+8>>2]=g[k+8>>2],k=0),We=k,wt=te,We|0}function Qt(p,m,y,S){p=p|0,m=m|0,y=y|0,S=S|0;var C=0,k=0,L=0,z=0,H=0,it=0,ot=0,Ct=0,Nt=0,Wt=0,re=0,ne=0,Le=0,We=0,te=0;if(re=wt,wt=wt+48|0,L=re+36|0,z=re+24|0,H=re+12|0,it=re,k=me(p|0,m|0,52)|0,It()|0,k=k&15,Nt=me(p|0,m|0,45)|0,It()|0,Nt=Nt&127,ot=fi(Nt)|0,ke(k|0,0,52)|0,Le=It()|0|134225919,ne=S,g[ne>>2]=-1,g[ne+4>>2]=Le,!k)return(g[y>>2]|0)>1||(g[y+4>>2]|0)>1||(g[y+8>>2]|0)>1||(C=Jp(Nt,Fa(y)|0)|0,(C|0)==127)?(Le=1,wt=re,Le|0):(Wt=ke(C|0,0,45)|0,ne=It()|0,Nt=S,ne=g[Nt+4>>2]&-1040385|ne,Le=S,g[Le>>2]=g[Nt>>2]|Wt,g[Le+4>>2]=ne,Le=0,wt=re,Le|0);for(g[L>>2]=g[y>>2],g[L+4>>2]=g[y+4>>2],g[L+8>>2]=g[y+8>>2];g[z>>2]=g[L>>2],g[z+4>>2]=g[L+4>>2],g[z+8>>2]=g[L+8>>2],Ho(k)|0?(Rd(L),g[H>>2]=g[L>>2],g[H+4>>2]=g[L+4>>2],g[H+8>>2]=g[L+8>>2],za(H)):(Al(L),g[H>>2]=g[L>>2],g[H+4>>2]=g[L+4>>2],g[H+8>>2]=g[L+8>>2],hh(H)),eA(z,H,it),js(it),ne=S,We=g[ne>>2]|0,ne=g[ne+4>>2]|0,te=(15-k|0)*3|0,y=ke(7,0,te|0)|0,ne=ne&~(It()|0),te=ke(Fa(it)|0,0,te|0)|0,ne=It()|0|ne,Le=S,g[Le>>2]=te|We&~y,g[Le+4>>2]=ne,(k|0)>1;)k=k+-1|0;t:do if((g[L>>2]|0)<=1&&(g[L+4>>2]|0)<=1&&(g[L+8>>2]|0)<=1){k=Fa(L)|0,z=Jp(Nt,k)|0,(z|0)==127?it=0:it=fi(z)|0;e:do if(k){if(ot){if(L=21408+((Es(p,m)|0)*28|0)+(k<<2)|0,L=g[L>>2]|0,(L|0)>0){y=0;do k=Na(k)|0,y=y+1|0;while((y|0)!=(L|0))}if((k|0)==1){C=3;break t}y=Jp(Nt,k)|0,(y|0)==127&&Mi(23104,22949,376,23134),fi(y)|0?Mi(23147,22949,377,23134):(Wt=L,Ct=k,C=y)}else Wt=0,Ct=k,C=z;if(H=g[4304+(Nt*28|0)+(Ct<<2)>>2]|0,(H|0)<=-1&&Mi(23178,22949,384,23134),!it){if((Wt|0)<=-1&&Mi(23030,22949,417,23134),Wt|0){L=S,k=0,y=g[L>>2]|0,L=g[L+4>>2]|0;do y=Wo(y,L)|0,L=It()|0,te=S,g[te>>2]=y,g[te+4>>2]=L,k=k+1|0;while((k|0)<(Wt|0))}if((H|0)<=0){k=54;break}for(L=S,k=0,y=g[L>>2]|0,L=g[L+4>>2]|0;;)if(y=Wo(y,L)|0,L=It()|0,te=S,g[te>>2]=y,g[te+4>>2]=L,k=k+1|0,(k|0)==(H|0)){k=54;break e}}if(z=tA(C,Nt)|0,(z|0)==7&&Mi(22925,22949,393,23134),k=S,y=g[k>>2]|0,k=g[k+4>>2]|0,(H|0)>0){L=0;do y=Wo(y,k)|0,k=It()|0,te=S,g[te>>2]=y,g[te+4>>2]=k,L=L+1|0;while((L|0)!=(H|0))}if(y=Es(y,k)|0,te=mu(C)|0,y=g[(te?21824:21616)+(z*28|0)+(y<<2)>>2]|0,(y|0)<=-1&&Mi(23030,22949,412,23134),!y)k=54;else{z=S,k=0,L=g[z>>2]|0,z=g[z+4>>2]|0;do L=gh(L,z)|0,z=It()|0,te=S,g[te>>2]=L,g[te+4>>2]=z,k=k+1|0;while((k|0)<(y|0));k=54}}else if((ot|0)!=0&(it|0)!=0)if(te=Es(p,m)|0,k=S,k=21408+(te*28|0)+((Es(g[k>>2]|0,g[k+4>>2]|0)|0)<<2)|0,k=g[k>>2]|0,(k|0)<=-1&&Mi(23201,22949,433,23134),!k)C=z,k=55;else{L=S,C=0,y=g[L>>2]|0,L=g[L+4>>2]|0;do y=Wo(y,L)|0,L=It()|0,te=S,g[te>>2]=y,g[te+4>>2]=L,C=C+1|0;while((C|0)<(k|0));C=z,k=54}else C=z,k=54;while(!1);if((k|0)==54&&it&&(k=55),(k|0)==55&&(te=S,(Es(g[te>>2]|0,g[te+4>>2]|0)|0)==1)){C=4;break}te=S,Le=g[te>>2]|0,te=g[te+4>>2]&-1040385,We=ke(C|0,0,45)|0,te=te|(It()|0),C=S,g[C>>2]=Le|We,g[C+4>>2]=te,C=0}else C=2;while(!1);return te=C,wt=re,te|0}function $t(p,m,y,S,C){p=p|0,m=m|0,y=y|0,S=S|0,C=C|0;var k=0,L=0;return L=wt,wt=wt+16|0,k=L,p=Bt(p,m,y,S,k)|0,p||(xf(k,C),p=0),wt=L,p|0}function oe(p,m,y,S){p=p|0,m=m|0,y=y|0,S=S|0;var C=0,k=0;return C=wt,wt=wt+16|0,k=C,__(y,k),S=Qt(p,m,k,S)|0,wt=C,S|0}function pe(p,m,y,S){p=p|0,m=m|0,y=y|0,S=S|0;var C=0,k=0,L=0;return L=wt,wt=wt+32|0,C=L+12|0,k=L,!(Bt(p,m,p,m,C)|0)&&!(Bt(p,m,y,S,k)|0)?p=Hl(C,k)|0:p=-1,wt=L,p|0}function he(p,m,y,S){p=p|0,m=m|0,y=y|0,S=S|0;var C=0,k=0,L=0;return L=wt,wt=wt+32|0,C=L+12|0,k=L,!(Bt(p,m,p,m,C)|0)&&!(Bt(p,m,y,S,k)|0)?p=Hl(C,k)|0:p=-1,wt=L,(p>>>31^1)+p|0}function be(p,m,y,S,C){p=p|0,m=m|0,y=y|0,S=S|0,C=C|0;var k=0,L=0,z=0,H=0,it=0,ot=0,Ct=0,Nt=0,Wt=0,re=0,ne=0,Le=0,We=0,te=0,_e=0,Ut=0,$e=0,er=0;if($e=wt,wt=wt+48|0,k=$e+24|0,L=$e+12|0,Ut=$e,!(Bt(p,m,p,m,k)|0)&&!(Bt(p,m,y,S,L)|0)){if(_e=Hl(k,L)|0,(_e|0)<0)return Ut=_e,wt=$e,Ut|0;for(g[k>>2]=0,g[k+4>>2]=0,g[k+8>>2]=0,g[L>>2]=0,g[L+4>>2]=0,g[L+8>>2]=0,Bt(p,m,p,m,k)|0,Bt(p,m,y,S,L)|0,Oe(k),Oe(L),_e?(ot=g[k>>2]|0,Wt=+(_e|0),Le=k+4|0,Ct=g[Le>>2]|0,We=k+8|0,Nt=g[We>>2]|0,te=k,y=ot,S=Ct,k=Nt,re=+((g[L>>2]|0)-ot|0)/Wt,ne=+((g[L+4>>2]|0)-Ct|0)/Wt,Wt=+((g[L+8>>2]|0)-Nt|0)/Wt):(S=k+4|0,Nt=k+8|0,Le=S,We=Nt,te=k,y=g[k>>2]|0,S=g[S>>2]|0,k=g[Nt>>2]|0,re=0,ne=0,Wt=0),g[Ut>>2]=y,Nt=Ut+4|0,g[Nt>>2]=S,Ct=Ut+8|0,g[Ct>>2]=k,ot=0;;){H=+(ot|0),er=re*H+ +(y|0),z=ne*H+ +(g[Le>>2]|0),H=Wt*H+ +(g[We>>2]|0),S=~~+Mf(+er),L=~~+Mf(+z),y=~~+Mf(+H),er=+li(+(+(S|0)-er)),z=+li(+(+(L|0)-z)),H=+li(+(+(y|0)-H));do if(er>z&er>H)S=0-(L+y)|0,k=L;else if(it=0-S|0,z>H){k=it-y|0;break}else{k=L,y=it-L|0;break}while(!1);if(g[Ut>>2]=S,g[Nt>>2]=k,g[Ct>>2]=y,o0(Ut),Qt(p,m,Ut,C+(ot<<3)|0)|0,(ot|0)==(_e|0))break;ot=ot+1|0,y=g[te>>2]|0}return Ut=0,wt=$e,Ut|0}return Ut=-1,wt=$e,Ut|0}function Ze(p,m){p=p|0,m=m|0;var y=0;if(!m)return y=1,y|0;y=p,p=1;do p=Oc(m&1|0?y:1,p)|0,m=m>>1,y=Oc(y,y)|0;while(m|0);return p|0}function Kr(p,m,y){p=p|0,m=m|0,y=y|0;var S=0,C=0,k=0,L=0,z=0,H=0,it=0,ot=0,Ct=0,Nt=0;if(!(pl(m,y)|0)||(m=n0(m)|0,Nt=+Tt[y>>3],S=+Tt[y+8>>3],S=m&S<0?S+6.283185307179586:S,Ct=g[p>>2]|0,(Ct|0)<=0))return Ct=0,Ct|0;if(ot=g[p+4>>2]|0,m){m=0,y=-1,p=0;t:for(;;){for(it=p;L=+Tt[ot+(it<<4)>>3],H=+Tt[ot+(it<<4)+8>>3],p=(y+2|0)%(Ct|0)|0,k=+Tt[ot+(p<<4)>>3],C=+Tt[ot+(p<<4)+8>>3],L>k?(z=L,L=H):(z=k,k=L,L=C,C=H),!!(Ntz);)if(y=it+1|0,(y|0)<(Ct|0))p=it,it=y,y=p;else{y=22;break t}if(H=C<0?C+6.283185307179586:C,L=L<0?L+6.283185307179586:L,S=L==S|H==S?S+-2220446049250313e-31:S,H=H+(Nt-k)/(z-k)*(L-H),(H<0?H+6.283185307179586:H)>S&&(m=m^1),p=it+1|0,(p|0)>=(Ct|0)){y=22;break}else y=it}if((y|0)==22)return m|0}else{m=0,y=-1,p=0;t:for(;;){for(it=p;L=+Tt[ot+(it<<4)>>3],H=+Tt[ot+(it<<4)+8>>3],p=(y+2|0)%(Ct|0)|0,k=+Tt[ot+(p<<4)>>3],C=+Tt[ot+(p<<4)+8>>3],L>k?(z=L,L=H):(z=k,k=L,L=C,C=H),!!(Ntz);)if(y=it+1|0,(y|0)<(Ct|0))p=it,it=y,y=p;else{y=22;break t}if(S=L==S|C==S?S+-2220446049250313e-31:S,C+(Nt-k)/(z-k)*(L-C)>S&&(m=m^1),p=it+1|0,(p|0)>=(Ct|0)){y=22;break}else y=it}if((y|0)==22)return m|0}return 0}function Ee(p,m){p=p|0,m=m|0;var y=0,S=0,C=0,k=0,L=0,z=0,H=0,it=0,ot=0,Ct=0,Nt=0,Wt=0,re=0,ne=0,Le=0,We=0,te=0;if(re=g[p>>2]|0,!re){g[m>>2]=0,g[m+4>>2]=0,g[m+8>>2]=0,g[m+12>>2]=0,g[m+16>>2]=0,g[m+20>>2]=0,g[m+24>>2]=0,g[m+28>>2]=0;return}if(ne=m+8|0,Tt[ne>>3]=17976931348623157e292,Le=m+24|0,Tt[Le>>3]=17976931348623157e292,Tt[m>>3]=-17976931348623157e292,We=m+16|0,Tt[We>>3]=-17976931348623157e292,!((re|0)<=0)){for(Nt=g[p+4>>2]|0,it=17976931348623157e292,ot=-17976931348623157e292,Ct=0,p=-1,k=17976931348623157e292,L=17976931348623157e292,H=-17976931348623157e292,S=-17976931348623157e292,Wt=0;y=+Tt[Nt+(Wt<<4)>>3],z=+Tt[Nt+(Wt<<4)+8>>3],p=p+2|0,C=+Tt[Nt+(((p|0)==(re|0)?0:p)<<4)+8>>3],y>3]=y,k=y),z>3]=z,L=z),y>H?Tt[m>>3]=y:y=H,z>S&&(Tt[We>>3]=z,S=z),it=z>0&zot?z:ot,Ct=Ct|+li(+(z-C))>3.141592653589793,p=Wt+1|0,(p|0)!=(re|0);)te=Wt,H=y,Wt=p,p=te;Ct&&(Tt[We>>3]=ot,Tt[Le>>3]=it)}}function pr(p,m){p=p|0,m=m|0;var y=0,S=0,C=0,k=0,L=0,z=0,H=0,it=0,ot=0,Ct=0,Nt=0,Wt=0,re=0,ne=0,Le=0,We=0,te=0,_e=0,Ut=0,$e=0,er=0,we=0;if(re=g[p>>2]|0,re){if(ne=m+8|0,Tt[ne>>3]=17976931348623157e292,Le=m+24|0,Tt[Le>>3]=17976931348623157e292,Tt[m>>3]=-17976931348623157e292,We=m+16|0,Tt[We>>3]=-17976931348623157e292,(re|0)>0){for(C=g[p+4>>2]|0,Nt=17976931348623157e292,Wt=-17976931348623157e292,S=0,y=-1,H=17976931348623157e292,it=17976931348623157e292,Ct=-17976931348623157e292,L=-17976931348623157e292,te=0;k=+Tt[C+(te<<4)>>3],ot=+Tt[C+(te<<4)+8>>3],er=y+2|0,z=+Tt[C+(((er|0)==(re|0)?0:er)<<4)+8>>3],k>3]=k,H=k),ot>3]=ot,it=ot),k>Ct?Tt[m>>3]=k:k=Ct,ot>L&&(Tt[We>>3]=ot,L=ot),Nt=ot>0&otWt?ot:Wt,S=S|+li(+(ot-z))>3.141592653589793,y=te+1|0,(y|0)!=(re|0);)er=te,Ct=k,te=y,y=er;S&&(Tt[We>>3]=Wt,Tt[Le>>3]=Nt)}}else g[m>>2]=0,g[m+4>>2]=0,g[m+8>>2]=0,g[m+12>>2]=0,g[m+16>>2]=0,g[m+20>>2]=0,g[m+24>>2]=0,g[m+28>>2]=0;if(er=p+8|0,y=g[er>>2]|0,!((y|0)<=0)){$e=p+12|0,Ut=0;do if(C=g[$e>>2]|0,S=Ut,Ut=Ut+1|0,Le=m+(Ut<<5)|0,We=g[C+(S<<3)>>2]|0,We){if(te=m+(Ut<<5)+8|0,Tt[te>>3]=17976931348623157e292,p=m+(Ut<<5)+24|0,Tt[p>>3]=17976931348623157e292,Tt[Le>>3]=-17976931348623157e292,_e=m+(Ut<<5)+16|0,Tt[_e>>3]=-17976931348623157e292,(We|0)>0){for(re=g[C+(S<<3)+4>>2]|0,Nt=17976931348623157e292,Wt=-17976931348623157e292,C=0,S=-1,ne=0,H=17976931348623157e292,it=17976931348623157e292,ot=-17976931348623157e292,L=-17976931348623157e292;k=+Tt[re+(ne<<4)>>3],Ct=+Tt[re+(ne<<4)+8>>3],S=S+2|0,z=+Tt[re+(((S|0)==(We|0)?0:S)<<4)+8>>3],k>3]=k,H=k),Ct>3]=Ct,it=Ct),k>ot?Tt[Le>>3]=k:k=ot,Ct>L&&(Tt[_e>>3]=Ct,L=Ct),Nt=Ct>0&CtWt?Ct:Wt,C=C|+li(+(Ct-z))>3.141592653589793,S=ne+1|0,(S|0)!=(We|0);)we=ne,ne=S,ot=k,S=we;C&&(Tt[_e>>3]=Wt,Tt[p>>3]=Nt)}}else g[Le>>2]=0,g[Le+4>>2]=0,g[Le+8>>2]=0,g[Le+12>>2]=0,g[Le+16>>2]=0,g[Le+20>>2]=0,g[Le+24>>2]=0,g[Le+28>>2]=0,y=g[er>>2]|0;while((Ut|0)<(y|0))}}function tr(p,m,y){p=p|0,m=m|0,y=y|0;var S=0,C=0,k=0;if(!(Kr(p,m,y)|0))return C=0,C|0;if(C=p+8|0,(g[C>>2]|0)<=0)return C=1,C|0;for(S=p+12|0,p=0;;){if(k=p,p=p+1|0,Kr((g[S>>2]|0)+(k<<3)|0,m+(p<<5)|0,y)|0){p=0,S=6;break}if((p|0)>=(g[C>>2]|0)){p=1,S=6;break}}return(S|0)==6?p|0:0}function Gi(){return 8}function Jr(){return 16}function Vr(){return 168}function ei(){return 8}function On(){return 16}function tn(){return 12}function Gs(){return 8}function hs(p){p=p|0;var m=0,y=0;return y=+Tt[p>>3],m=+Tt[p+8>>3],+ +bn(+(y*y+m*m))}function Bn(p,m,y,S,C){p=p|0,m=m|0,y=y|0,S=S|0,C=C|0;var k=0,L=0,z=0,H=0,it=0,ot=0,Ct=0,Nt=0;it=+Tt[p>>3],H=+Tt[m>>3]-it,z=+Tt[p+8>>3],L=+Tt[m+8>>3]-z,Ct=+Tt[y>>3],k=+Tt[S>>3]-Ct,Nt=+Tt[y+8>>3],ot=+Tt[S+8>>3]-Nt,k=(k*(z-Nt)-(it-Ct)*ot)/(H*ot-L*k),Tt[C>>3]=it+H*k,Tt[C+8>>3]=z+L*k}function qo(p,m){return p=p|0,m=m|0,+Tt[p>>3]!=+Tt[m>>3]?(m=0,m|0):(m=+Tt[p+8>>3]==+Tt[m+8>>3],m|0)}function jr(p,m){p=p|0,m=m|0;var y=0,S=0,C=0;return C=+Tt[p>>3]-+Tt[m>>3],S=+Tt[p+8>>3]-+Tt[m+8>>3],y=+Tt[p+16>>3]-+Tt[m+16>>3],+(C*C+S*S+y*y)}function ql(p,m){p=p|0,m=m|0;var y=0,S=0,C=0;y=+Tt[p>>3],S=+Ur(+y),y=+hi(+y),Tt[m+16>>3]=y,y=+Tt[p+8>>3],C=S*+Ur(+y),Tt[m>>3]=C,y=S*+hi(+y),Tt[m+8>>3]=y}function Zl(p,m){p=p|0,m=m|0;var y=0,S=0,C=0,k=0,L=0,z=0,H=0,it=0;if(it=wt,wt=wt+32|0,C=it+16|0,k=it,T(p,m,C),L=wf(p,m)|0,H=Es(p,m)|0,lh(L,k),m=Ld(L,g[C>>2]|0)|0,!(fi(L)|0))return H=m,wt=it,H|0;do switch(L|0){case 4:{p=0,y=14;break}case 14:{p=1,y=14;break}case 24:{p=2,y=14;break}case 38:{p=3,y=14;break}case 49:{p=4,y=14;break}case 58:{p=5,y=14;break}case 63:{p=6,y=14;break}case 72:{p=7,y=14;break}case 83:{p=8,y=14;break}case 97:{p=9,y=14;break}case 107:{p=10,y=14;break}case 117:{p=11,y=14;break}default:z=0,S=0}while(!1);return(y|0)==14&&(z=g[22096+(p*24|0)+8>>2]|0,S=g[22096+(p*24|0)+16>>2]|0),p=g[C>>2]|0,(p|0)!=(g[k>>2]|0)&&(L=mu(L)|0,p=g[C>>2]|0,L|(p|0)==(S|0)&&(m=(m+1|0)%6|0)),(H|0)==3&(p|0)==(S|0)?(H=(m+5|0)%6|0,wt=it,H|0):(H|0)==5&(p|0)==(z|0)?(H=(m+1|0)%6|0,wt=it,H|0):(H=m,wt=it,H|0)}function yu(p,m,y){p=p|0,m=m|0,y=y|0;var S=0,C=0;return S=ji(p,m)|0,(y+-1|0)>>>0>5||(C=(S|0)!=0,(y|0)==1&C)?(y=-1,y|0):(S=Zl(p,m)|0,C?(y=(5-S+(g[22384+(y<<2)>>2]|0)|0)%5|0,y|0):(y=(6-S+(g[22416+(y<<2)>>2]|0)|0)%6|0,y|0))}function vu(p,m,y){p=p|0,m=m|0,y=y|0;var S=0;(m|0)>0?(S=Ua(m,4)|0,g[p>>2]=S,S||Mi(23230,23253,40,23267)):g[p>>2]=0,g[p+4>>2]=m,g[p+8>>2]=0,g[p+12>>2]=y}function _h(p){p=p|0;var m=0,y=0,S=0,C=0,k=0,L=0,z=0;C=p+4|0,k=p+12|0,L=p+8|0;t:for(;;){for(y=g[C>>2]|0,m=0;;){if((m|0)>=(y|0))break t;if(S=g[p>>2]|0,z=g[S+(m<<2)>>2]|0,!z)m=m+1|0;else break}m=S+(~~(+li(+(+dl(10,+ +(15-(g[k>>2]|0)|0))*(+Tt[z>>3]+ +Tt[z+8>>3])))%+(y|0))>>>0<<2)|0,y=g[m>>2]|0;e:do if(y|0){if(S=z+32|0,(y|0)==(z|0))g[m>>2]=g[S>>2];else{if(y=y+32|0,m=g[y>>2]|0,!m)break;for(;(m|0)!=(z|0);)if(y=m+32|0,m=g[y>>2]|0,!m)break e;g[y>>2]=g[S>>2]}Gr(z),g[L>>2]=(g[L>>2]|0)+-1}while(!1)}Gr(g[p>>2]|0)}function Ws(p){p=p|0;var m=0,y=0,S=0;for(S=g[p+4>>2]|0,y=0;;){if((y|0)>=(S|0)){m=0,y=4;break}if(m=g[(g[p>>2]|0)+(y<<2)>>2]|0,!m)y=y+1|0;else{y=4;break}}return(y|0)==4?m|0:0}function Ps(p,m){p=p|0,m=m|0;var y=0,S=0,C=0,k=0;if(y=~~(+li(+(+dl(10,+ +(15-(g[p+12>>2]|0)|0))*(+Tt[m>>3]+ +Tt[m+8>>3])))%+(g[p+4>>2]|0))>>>0,y=(g[p>>2]|0)+(y<<2)|0,S=g[y>>2]|0,!S)return k=1,k|0;k=m+32|0;do if((S|0)!=(m|0)){if(y=g[S+32>>2]|0,!y)return k=1,k|0;for(C=y;;){if((C|0)==(m|0)){C=8;break}if(y=g[C+32>>2]|0,y)S=C,C=y;else{y=1,C=10;break}}if((C|0)==8){g[S+32>>2]=g[k>>2];break}else if((C|0)==10)return y|0}else g[y>>2]=g[k>>2];while(!1);return Gr(m),k=p+8|0,g[k>>2]=(g[k>>2]|0)+-1,k=0,k|0}function Eo(p,m,y){p=p|0,m=m|0,y=y|0;var S=0,C=0,k=0,L=0;k=ho(40)|0,k||Mi(23283,23253,98,23296),g[k>>2]=g[m>>2],g[k+4>>2]=g[m+4>>2],g[k+8>>2]=g[m+8>>2],g[k+12>>2]=g[m+12>>2],C=k+16|0,g[C>>2]=g[y>>2],g[C+4>>2]=g[y+4>>2],g[C+8>>2]=g[y+8>>2],g[C+12>>2]=g[y+12>>2],g[k+32>>2]=0,C=~~(+li(+(+dl(10,+ +(15-(g[p+12>>2]|0)|0))*(+Tt[m>>3]+ +Tt[m+8>>3])))%+(g[p+4>>2]|0))>>>0,C=(g[p>>2]|0)+(C<<2)|0,S=g[C>>2]|0;do if(!S)g[C>>2]=k;else{for(;!(us(S,m)|0&&us(S+16|0,y)|0);)if(C=g[S+32>>2]|0,S=C|0?C:S,!(g[S+32>>2]|0)){L=10;break}if((L|0)==10){g[S+32>>2]=k;break}return Gr(k),L=S,L|0}while(!1);return L=p+8|0,g[L>>2]=(g[L>>2]|0)+1,L=k,L|0}function yh(p,m,y){p=p|0,m=m|0,y=y|0;var S=0,C=0;if(C=~~(+li(+(+dl(10,+ +(15-(g[p+12>>2]|0)|0))*(+Tt[m>>3]+ +Tt[m+8>>3])))%+(g[p+4>>2]|0))>>>0,C=g[(g[p>>2]|0)+(C<<2)>>2]|0,!C)return y=0,y|0;if(!y){for(p=C;;){if(us(p,m)|0){S=10;break}if(p=g[p+32>>2]|0,!p){p=0,S=10;break}}if((S|0)==10)return p|0}for(p=C;;){if(us(p,m)|0&&us(p+16|0,y)|0){S=10;break}if(p=g[p+32>>2]|0,!p){p=0,S=10;break}}return(S|0)==10?p|0:0}function Fn(p,m){p=p|0,m=m|0;var y=0;if(y=~~(+li(+(+dl(10,+ +(15-(g[p+12>>2]|0)|0))*(+Tt[m>>3]+ +Tt[m+8>>3])))%+(g[p+4>>2]|0))>>>0,p=g[(g[p>>2]|0)+(y<<2)>>2]|0,!p)return y=0,y|0;for(;;){if(us(p,m)|0){m=5;break}if(p=g[p+32>>2]|0,!p){p=0,m=5;break}}return(m|0)==5?p|0:0}function fs(){return 23312}function Zo(p){return p=+p,+ +Ux(+p)}function _n(p){return p=+p,~~+Zo(p)|0}function ho(p){p=p|0;var m=0,y=0,S=0,C=0,k=0,L=0,z=0,H=0,it=0,ot=0,Ct=0,Nt=0,Wt=0,re=0,ne=0,Le=0,We=0,te=0,_e=0,Ut=0,$e=0;$e=wt,wt=wt+16|0,Nt=$e;do if(p>>>0<245){if(it=p>>>0<11?16:p+11&-8,p=it>>>3,Ct=g[5829]|0,y=Ct>>>p,y&3|0)return m=(y&1^1)+p|0,p=23356+(m<<1<<2)|0,y=p+8|0,S=g[y>>2]|0,C=S+8|0,k=g[C>>2]|0,(k|0)==(p|0)?g[5829]=Ct&~(1<>2]=p,g[y>>2]=k),Ut=m<<3,g[S+4>>2]=Ut|3,Ut=S+Ut+4|0,g[Ut>>2]=g[Ut>>2]|1,Ut=C,wt=$e,Ut|0;if(ot=g[5831]|0,it>>>0>ot>>>0){if(y|0)return m=2<>>12&16,m=m>>>z,y=m>>>5&8,m=m>>>y,k=m>>>2&4,m=m>>>k,p=m>>>1&2,m=m>>>p,S=m>>>1&1,S=(y|z|k|p|S)+(m>>>S)|0,m=23356+(S<<1<<2)|0,p=m+8|0,k=g[p>>2]|0,z=k+8|0,y=g[z>>2]|0,(y|0)==(m|0)?(p=Ct&~(1<>2]=m,g[p>>2]=y,p=Ct),Ut=S<<3,L=Ut-it|0,g[k+4>>2]=it|3,C=k+it|0,g[C+4>>2]=L|1,g[k+Ut>>2]=L,ot|0&&(S=g[5834]|0,m=ot>>>3,y=23356+(m<<1<<2)|0,m=1<>2]|0):(g[5829]=p|m,m=y,p=y+8|0),g[p>>2]=S,g[m+12>>2]=S,g[S+8>>2]=m,g[S+12>>2]=y),g[5831]=L,g[5834]=C,Ut=z,wt=$e,Ut|0;if(k=g[5830]|0,k){for(y=(k&0-k)+-1|0,C=y>>>12&16,y=y>>>C,S=y>>>5&8,y=y>>>S,L=y>>>2&4,y=y>>>L,z=y>>>1&2,y=y>>>z,H=y>>>1&1,H=g[23620+((S|C|L|z|H)+(y>>>H)<<2)>>2]|0,y=H,z=H,H=(g[H+4>>2]&-8)-it|0;p=g[y+16>>2]|0,!(!p&&(p=g[y+20>>2]|0,!p));)L=(g[p+4>>2]&-8)-it|0,C=L>>>0>>0,y=p,z=C?p:z,H=C?L:H;if(L=z+it|0,L>>>0>z>>>0){C=g[z+24>>2]|0,m=g[z+12>>2]|0;do if((m|0)==(z|0)){if(p=z+20|0,m=g[p>>2]|0,!m&&(p=z+16|0,m=g[p>>2]|0,!m)){y=0;break}for(;;)if(S=m+20|0,y=g[S>>2]|0,y)m=y,p=S;else if(S=m+16|0,y=g[S>>2]|0,y)m=y,p=S;else break;g[p>>2]=0,y=m}else y=g[z+8>>2]|0,g[y+12>>2]=m,g[m+8>>2]=y,y=m;while(!1);do if(C|0){if(m=g[z+28>>2]|0,p=23620+(m<<2)|0,(z|0)==(g[p>>2]|0)){if(g[p>>2]=y,!y){g[5830]=k&~(1<>2]|0)==(z|0)?Ut:C+20|0)>>2]=y,!y)break;g[y+24>>2]=C,m=g[z+16>>2]|0,m|0&&(g[y+16>>2]=m,g[m+24>>2]=y),m=g[z+20>>2]|0,m|0&&(g[y+20>>2]=m,g[m+24>>2]=y)}while(!1);return H>>>0<16?(Ut=H+it|0,g[z+4>>2]=Ut|3,Ut=z+Ut+4|0,g[Ut>>2]=g[Ut>>2]|1):(g[z+4>>2]=it|3,g[L+4>>2]=H|1,g[L+H>>2]=H,ot|0&&(S=g[5834]|0,m=ot>>>3,y=23356+(m<<1<<2)|0,m=1<>2]|0):(g[5829]=m|Ct,m=y,p=y+8|0),g[p>>2]=S,g[m+12>>2]=S,g[S+8>>2]=m,g[S+12>>2]=y),g[5831]=H,g[5834]=L),Ut=z+8|0,wt=$e,Ut|0}else Ct=it}else Ct=it}else Ct=it}else if(p>>>0<=4294967231)if(p=p+11|0,it=p&-8,S=g[5830]|0,S){C=0-it|0,p=p>>>8,p?it>>>0>16777215?H=31:(Ct=(p+1048320|0)>>>16&8,ne=p<>>16&4,ne=ne<>>16&2,H=14-(z|Ct|H)+(ne<>>15)|0,H=it>>>(H+7|0)&1|H<<1):H=0,y=g[23620+(H<<2)>>2]|0;t:do if(!y)y=0,p=0,ne=61;else for(p=0,z=it<<((H|0)==31?0:25-(H>>>1)|0),k=0;;){if(L=(g[y+4>>2]&-8)-it|0,L>>>0>>0)if(L)p=y,C=L;else{p=y,C=0,ne=65;break t}if(ne=g[y+20>>2]|0,y=g[y+16+(z>>>31<<2)>>2]|0,k=(ne|0)==0|(ne|0)==(y|0)?k:ne,y)z=z<<1;else{y=k,ne=61;break}}while(!1);if((ne|0)==61){if((y|0)==0&(p|0)==0){if(p=2<>>12&16,Ct=Ct>>>L,k=Ct>>>5&8,Ct=Ct>>>k,z=Ct>>>2&4,Ct=Ct>>>z,H=Ct>>>1&2,Ct=Ct>>>H,y=Ct>>>1&1,p=0,y=g[23620+((k|L|z|H|y)+(Ct>>>y)<<2)>>2]|0}y?ne=65:(z=p,L=C)}if((ne|0)==65)for(k=y;;)if(Ct=(g[k+4>>2]&-8)-it|0,y=Ct>>>0>>0,C=y?Ct:C,p=y?k:p,y=g[k+16>>2]|0,y||(y=g[k+20>>2]|0),y)k=y;else{z=p,L=C;break}if(z|0&&L>>>0<((g[5831]|0)-it|0)>>>0&&(ot=z+it|0,ot>>>0>z>>>0)){k=g[z+24>>2]|0,m=g[z+12>>2]|0;do if((m|0)==(z|0)){if(p=z+20|0,m=g[p>>2]|0,!m&&(p=z+16|0,m=g[p>>2]|0,!m)){m=0;break}for(;;)if(C=m+20|0,y=g[C>>2]|0,y)m=y,p=C;else if(C=m+16|0,y=g[C>>2]|0,y)m=y,p=C;else break;g[p>>2]=0}else Ut=g[z+8>>2]|0,g[Ut+12>>2]=m,g[m+8>>2]=Ut;while(!1);do if(k){if(p=g[z+28>>2]|0,y=23620+(p<<2)|0,(z|0)==(g[y>>2]|0)){if(g[y>>2]=m,!m){S=S&~(1<>2]|0)==(z|0)?Ut:k+20|0)>>2]=m,!m)break;g[m+24>>2]=k,p=g[z+16>>2]|0,p|0&&(g[m+16>>2]=p,g[p+24>>2]=m),p=g[z+20>>2]|0,p&&(g[m+20>>2]=p,g[p+24>>2]=m)}while(!1);t:do if(L>>>0<16)Ut=L+it|0,g[z+4>>2]=Ut|3,Ut=z+Ut+4|0,g[Ut>>2]=g[Ut>>2]|1;else{if(g[z+4>>2]=it|3,g[ot+4>>2]=L|1,g[ot+L>>2]=L,m=L>>>3,L>>>0<256){y=23356+(m<<1<<2)|0,p=g[5829]|0,m=1<>2]|0):(g[5829]=p|m,m=y,p=y+8|0),g[p>>2]=ot,g[m+12>>2]=ot,g[ot+8>>2]=m,g[ot+12>>2]=y;break}if(m=L>>>8,m?L>>>0>16777215?y=31:(_e=(m+1048320|0)>>>16&8,Ut=m<<_e,te=(Ut+520192|0)>>>16&4,Ut=Ut<>>16&2,y=14-(te|_e|y)+(Ut<>>15)|0,y=L>>>(y+7|0)&1|y<<1):y=0,m=23620+(y<<2)|0,g[ot+28>>2]=y,p=ot+16|0,g[p+4>>2]=0,g[p>>2]=0,p=1<>2]=ot,g[ot+24>>2]=m,g[ot+12>>2]=ot,g[ot+8>>2]=ot;break}m=g[m>>2]|0;e:do if((g[m+4>>2]&-8|0)!=(L|0)){for(S=L<<((y|0)==31?0:25-(y>>>1)|0);y=m+16+(S>>>31<<2)|0,p=g[y>>2]|0,!!p;)if((g[p+4>>2]&-8|0)==(L|0)){m=p;break e}else S=S<<1,m=p;g[y>>2]=ot,g[ot+24>>2]=m,g[ot+12>>2]=ot,g[ot+8>>2]=ot;break t}while(!1);_e=m+8|0,Ut=g[_e>>2]|0,g[Ut+12>>2]=ot,g[_e>>2]=ot,g[ot+8>>2]=Ut,g[ot+12>>2]=m,g[ot+24>>2]=0}while(!1);return Ut=z+8|0,wt=$e,Ut|0}else Ct=it}else Ct=it;else Ct=-1;while(!1);if(y=g[5831]|0,y>>>0>=Ct>>>0)return m=y-Ct|0,p=g[5834]|0,m>>>0>15?(Ut=p+Ct|0,g[5834]=Ut,g[5831]=m,g[Ut+4>>2]=m|1,g[p+y>>2]=m,g[p+4>>2]=Ct|3):(g[5831]=0,g[5834]=0,g[p+4>>2]=y|3,Ut=p+y+4|0,g[Ut>>2]=g[Ut>>2]|1),Ut=p+8|0,wt=$e,Ut|0;if(L=g[5832]|0,L>>>0>Ct>>>0)return te=L-Ct|0,g[5832]=te,Ut=g[5835]|0,_e=Ut+Ct|0,g[5835]=_e,g[_e+4>>2]=te|1,g[Ut+4>>2]=Ct|3,Ut=Ut+8|0,wt=$e,Ut|0;if(g[5947]|0?p=g[5949]|0:(g[5949]=4096,g[5948]=4096,g[5950]=-1,g[5951]=-1,g[5952]=0,g[5940]=0,g[5947]=Nt&-16^1431655768,p=4096),z=Ct+48|0,H=Ct+47|0,k=p+H|0,C=0-p|0,it=k&C,it>>>0<=Ct>>>0||(p=g[5939]|0,p|0&&(ot=g[5937]|0,Nt=ot+it|0,Nt>>>0<=ot>>>0|Nt>>>0>p>>>0)))return Ut=0,wt=$e,Ut|0;t:do if(g[5940]&4)m=0,ne=143;else{y=g[5835]|0;e:do if(y){for(S=23764;Nt=g[S>>2]|0,!(Nt>>>0<=y>>>0&&(Nt+(g[S+4>>2]|0)|0)>>>0>y>>>0);)if(p=g[S+8>>2]|0,p)S=p;else{ne=128;break e}if(m=k-L&C,m>>>0<2147483647)if(p=en(m|0)|0,(p|0)==((g[S>>2]|0)+(g[S+4>>2]|0)|0)){if((p|0)!=-1){L=m,k=p,ne=145;break t}}else S=p,ne=136;else m=0}else ne=128;while(!1);do if((ne|0)==128)if(y=en(0)|0,(y|0)!=-1&&(m=y,Wt=g[5948]|0,re=Wt+-1|0,m=(re&m|0?(re+m&0-Wt)-m|0:0)+it|0,Wt=g[5937]|0,re=m+Wt|0,m>>>0>Ct>>>0&m>>>0<2147483647)){if(Nt=g[5939]|0,Nt|0&&re>>>0<=Wt>>>0|re>>>0>Nt>>>0){m=0;break}if(p=en(m|0)|0,(p|0)==(y|0)){L=m,k=y,ne=145;break t}else S=p,ne=136}else m=0;while(!1);do if((ne|0)==136){if(y=0-m|0,!(z>>>0>m>>>0&(m>>>0<2147483647&(S|0)!=-1)))if((S|0)==-1){m=0;break}else{L=m,k=S,ne=145;break t}if(p=g[5949]|0,p=H-m+p&0-p,p>>>0>=2147483647){L=m,k=S,ne=145;break t}if((en(p|0)|0)==-1){en(y|0)|0,m=0;break}else{L=p+m|0,k=S,ne=145;break t}}while(!1);g[5940]=g[5940]|4,ne=143}while(!1);if((ne|0)==143&&it>>>0<2147483647&&(te=en(it|0)|0,re=en(0)|0,Le=re-te|0,We=Le>>>0>(Ct+40|0)>>>0,!((te|0)==-1|We^1|te>>>0>>0&((te|0)!=-1&(re|0)!=-1)^1))&&(L=We?Le:m,k=te,ne=145),(ne|0)==145){m=(g[5937]|0)+L|0,g[5937]=m,m>>>0>(g[5938]|0)>>>0&&(g[5938]=m),H=g[5835]|0;t:do if(H){for(m=23764;;){if(p=g[m>>2]|0,y=g[m+4>>2]|0,(k|0)==(p+y|0)){ne=154;break}if(S=g[m+8>>2]|0,S)m=S;else break}if((ne|0)==154&&(_e=m+4|0,(g[m+12>>2]&8|0)==0)&&k>>>0>H>>>0&p>>>0<=H>>>0){g[_e>>2]=y+L,Ut=(g[5832]|0)+L|0,te=H+8|0,te=te&7|0?0-te&7:0,_e=H+te|0,te=Ut-te|0,g[5835]=_e,g[5832]=te,g[_e+4>>2]=te|1,g[H+Ut+4>>2]=40,g[5836]=g[5951];break}for(k>>>0<(g[5833]|0)>>>0&&(g[5833]=k),y=k+L|0,m=23764;;){if((g[m>>2]|0)==(y|0)){ne=162;break}if(p=g[m+8>>2]|0,p)m=p;else break}if((ne|0)==162&&!(g[m+12>>2]&8|0)){g[m>>2]=k,ot=m+4|0,g[ot>>2]=(g[ot>>2]|0)+L,ot=k+8|0,ot=k+(ot&7|0?0-ot&7:0)|0,m=y+8|0,m=y+(m&7|0?0-m&7:0)|0,it=ot+Ct|0,z=m-ot-Ct|0,g[ot+4>>2]=Ct|3;e:do if((H|0)==(m|0))Ut=(g[5832]|0)+z|0,g[5832]=Ut,g[5835]=it,g[it+4>>2]=Ut|1;else{if((g[5834]|0)==(m|0)){Ut=(g[5831]|0)+z|0,g[5831]=Ut,g[5834]=it,g[it+4>>2]=Ut|1,g[it+Ut>>2]=Ut;break}if(p=g[m+4>>2]|0,(p&3|0)==1){L=p&-8,S=p>>>3;r:do if(p>>>0<256)if(p=g[m+8>>2]|0,y=g[m+12>>2]|0,(y|0)==(p|0)){g[5829]=g[5829]&~(1<>2]=y,g[y+8>>2]=p;break}else{k=g[m+24>>2]|0,p=g[m+12>>2]|0;do if((p|0)==(m|0)){if(y=m+16|0,S=y+4|0,p=g[S>>2]|0,p)y=S;else if(p=g[y>>2]|0,!p){p=0;break}for(;;)if(C=p+20|0,S=g[C>>2]|0,S)p=S,y=C;else if(C=p+16|0,S=g[C>>2]|0,S)p=S,y=C;else break;g[y>>2]=0}else Ut=g[m+8>>2]|0,g[Ut+12>>2]=p,g[p+8>>2]=Ut;while(!1);if(!k)break;y=g[m+28>>2]|0,S=23620+(y<<2)|0;do if((g[S>>2]|0)!=(m|0)){if(Ut=k+16|0,g[((g[Ut>>2]|0)==(m|0)?Ut:k+20|0)>>2]=p,!p)break r}else{if(g[S>>2]=p,p|0)break;g[5830]=g[5830]&~(1<>2]=k,y=m+16|0,S=g[y>>2]|0,S|0&&(g[p+16>>2]=S,g[S+24>>2]=p),y=g[y+4>>2]|0,!y)break;g[p+20>>2]=y,g[y+24>>2]=p}while(!1);m=m+L|0,C=L+z|0}else C=z;if(m=m+4|0,g[m>>2]=g[m>>2]&-2,g[it+4>>2]=C|1,g[it+C>>2]=C,m=C>>>3,C>>>0<256){y=23356+(m<<1<<2)|0,p=g[5829]|0,m=1<>2]|0):(g[5829]=p|m,m=y,p=y+8|0),g[p>>2]=it,g[m+12>>2]=it,g[it+8>>2]=m,g[it+12>>2]=y;break}m=C>>>8;do if(!m)S=0;else{if(C>>>0>16777215){S=31;break}_e=(m+1048320|0)>>>16&8,Ut=m<<_e,te=(Ut+520192|0)>>>16&4,Ut=Ut<>>16&2,S=14-(te|_e|S)+(Ut<>>15)|0,S=C>>>(S+7|0)&1|S<<1}while(!1);if(m=23620+(S<<2)|0,g[it+28>>2]=S,p=it+16|0,g[p+4>>2]=0,g[p>>2]=0,p=g[5830]|0,y=1<>2]=it,g[it+24>>2]=m,g[it+12>>2]=it,g[it+8>>2]=it;break}m=g[m>>2]|0;r:do if((g[m+4>>2]&-8|0)!=(C|0)){for(S=C<<((S|0)==31?0:25-(S>>>1)|0);y=m+16+(S>>>31<<2)|0,p=g[y>>2]|0,!!p;)if((g[p+4>>2]&-8|0)==(C|0)){m=p;break r}else S=S<<1,m=p;g[y>>2]=it,g[it+24>>2]=m,g[it+12>>2]=it,g[it+8>>2]=it;break e}while(!1);_e=m+8|0,Ut=g[_e>>2]|0,g[Ut+12>>2]=it,g[_e>>2]=it,g[it+8>>2]=Ut,g[it+12>>2]=m,g[it+24>>2]=0}while(!1);return Ut=ot+8|0,wt=$e,Ut|0}for(m=23764;p=g[m>>2]|0,!(p>>>0<=H>>>0&&(Ut=p+(g[m+4>>2]|0)|0,Ut>>>0>H>>>0));)m=g[m+8>>2]|0;C=Ut+-47|0,p=C+8|0,p=C+(p&7|0?0-p&7:0)|0,C=H+16|0,p=p>>>0>>0?H:p,m=p+8|0,y=L+-40|0,te=k+8|0,te=te&7|0?0-te&7:0,_e=k+te|0,te=y-te|0,g[5835]=_e,g[5832]=te,g[_e+4>>2]=te|1,g[k+y+4>>2]=40,g[5836]=g[5951],y=p+4|0,g[y>>2]=27,g[m>>2]=g[5941],g[m+4>>2]=g[5942],g[m+8>>2]=g[5943],g[m+12>>2]=g[5944],g[5941]=k,g[5942]=L,g[5944]=0,g[5943]=m,m=p+24|0;do _e=m,m=m+4|0,g[m>>2]=7;while((_e+8|0)>>>0>>0);if((p|0)!=(H|0)){if(k=p-H|0,g[y>>2]=g[y>>2]&-2,g[H+4>>2]=k|1,g[p>>2]=k,m=k>>>3,k>>>0<256){y=23356+(m<<1<<2)|0,p=g[5829]|0,m=1<>2]|0):(g[5829]=p|m,m=y,p=y+8|0),g[p>>2]=H,g[m+12>>2]=H,g[H+8>>2]=m,g[H+12>>2]=y;break}if(m=k>>>8,m?k>>>0>16777215?S=31:(_e=(m+1048320|0)>>>16&8,Ut=m<<_e,te=(Ut+520192|0)>>>16&4,Ut=Ut<>>16&2,S=14-(te|_e|S)+(Ut<>>15)|0,S=k>>>(S+7|0)&1|S<<1):S=0,y=23620+(S<<2)|0,g[H+28>>2]=S,g[H+20>>2]=0,g[C>>2]=0,m=g[5830]|0,p=1<>2]=H,g[H+24>>2]=y,g[H+12>>2]=H,g[H+8>>2]=H;break}m=g[y>>2]|0;e:do if((g[m+4>>2]&-8|0)!=(k|0)){for(S=k<<((S|0)==31?0:25-(S>>>1)|0);y=m+16+(S>>>31<<2)|0,p=g[y>>2]|0,!!p;)if((g[p+4>>2]&-8|0)==(k|0)){m=p;break e}else S=S<<1,m=p;g[y>>2]=H,g[H+24>>2]=m,g[H+12>>2]=H,g[H+8>>2]=H;break t}while(!1);_e=m+8|0,Ut=g[_e>>2]|0,g[Ut+12>>2]=H,g[_e>>2]=H,g[H+8>>2]=Ut,g[H+12>>2]=m,g[H+24>>2]=0}}else Ut=g[5833]|0,(Ut|0)==0|k>>>0>>0&&(g[5833]=k),g[5941]=k,g[5942]=L,g[5944]=0,g[5838]=g[5947],g[5837]=-1,g[5842]=23356,g[5841]=23356,g[5844]=23364,g[5843]=23364,g[5846]=23372,g[5845]=23372,g[5848]=23380,g[5847]=23380,g[5850]=23388,g[5849]=23388,g[5852]=23396,g[5851]=23396,g[5854]=23404,g[5853]=23404,g[5856]=23412,g[5855]=23412,g[5858]=23420,g[5857]=23420,g[5860]=23428,g[5859]=23428,g[5862]=23436,g[5861]=23436,g[5864]=23444,g[5863]=23444,g[5866]=23452,g[5865]=23452,g[5868]=23460,g[5867]=23460,g[5870]=23468,g[5869]=23468,g[5872]=23476,g[5871]=23476,g[5874]=23484,g[5873]=23484,g[5876]=23492,g[5875]=23492,g[5878]=23500,g[5877]=23500,g[5880]=23508,g[5879]=23508,g[5882]=23516,g[5881]=23516,g[5884]=23524,g[5883]=23524,g[5886]=23532,g[5885]=23532,g[5888]=23540,g[5887]=23540,g[5890]=23548,g[5889]=23548,g[5892]=23556,g[5891]=23556,g[5894]=23564,g[5893]=23564,g[5896]=23572,g[5895]=23572,g[5898]=23580,g[5897]=23580,g[5900]=23588,g[5899]=23588,g[5902]=23596,g[5901]=23596,g[5904]=23604,g[5903]=23604,Ut=L+-40|0,te=k+8|0,te=te&7|0?0-te&7:0,_e=k+te|0,te=Ut-te|0,g[5835]=_e,g[5832]=te,g[_e+4>>2]=te|1,g[k+Ut+4>>2]=40,g[5836]=g[5951];while(!1);if(m=g[5832]|0,m>>>0>Ct>>>0)return te=m-Ct|0,g[5832]=te,Ut=g[5835]|0,_e=Ut+Ct|0,g[5835]=_e,g[_e+4>>2]=te|1,g[Ut+4>>2]=Ct|3,Ut=Ut+8|0,wt=$e,Ut|0}return Ut=fs()|0,g[Ut>>2]=12,Ut=0,wt=$e,Ut|0}function Gr(p){p=p|0;var m=0,y=0,S=0,C=0,k=0,L=0,z=0,H=0;if(p){y=p+-8|0,C=g[5833]|0,p=g[p+-4>>2]|0,m=p&-8,H=y+m|0;do if(p&1)z=y,L=y;else{if(S=g[y>>2]|0,!(p&3)||(L=y+(0-S)|0,k=S+m|0,L>>>0>>0))return;if((g[5834]|0)==(L|0)){if(p=H+4|0,m=g[p>>2]|0,(m&3|0)!=3){z=L,m=k;break}g[5831]=k,g[p>>2]=m&-2,g[L+4>>2]=k|1,g[L+k>>2]=k;return}if(y=S>>>3,S>>>0<256)if(p=g[L+8>>2]|0,m=g[L+12>>2]|0,(m|0)==(p|0)){g[5829]=g[5829]&~(1<>2]=m,g[m+8>>2]=p,z=L,m=k;break}C=g[L+24>>2]|0,p=g[L+12>>2]|0;do if((p|0)==(L|0)){if(m=L+16|0,y=m+4|0,p=g[y>>2]|0,p)m=y;else if(p=g[m>>2]|0,!p){p=0;break}for(;;)if(S=p+20|0,y=g[S>>2]|0,y)p=y,m=S;else if(S=p+16|0,y=g[S>>2]|0,y)p=y,m=S;else break;g[m>>2]=0}else z=g[L+8>>2]|0,g[z+12>>2]=p,g[p+8>>2]=z;while(!1);if(C){if(m=g[L+28>>2]|0,y=23620+(m<<2)|0,(g[y>>2]|0)==(L|0)){if(g[y>>2]=p,!p){g[5830]=g[5830]&~(1<>2]|0)==(L|0)?z:C+20|0)>>2]=p,!p){z=L,m=k;break}g[p+24>>2]=C,m=L+16|0,y=g[m>>2]|0,y|0&&(g[p+16>>2]=y,g[y+24>>2]=p),m=g[m+4>>2]|0,m?(g[p+20>>2]=m,g[m+24>>2]=p,z=L,m=k):(z=L,m=k)}else z=L,m=k}while(!1);if(!(L>>>0>=H>>>0)&&(p=H+4|0,S=g[p>>2]|0,!!(S&1))){if(S&2)g[p>>2]=S&-2,g[z+4>>2]=m|1,g[L+m>>2]=m,C=m;else{if((g[5835]|0)==(H|0)){if(H=(g[5832]|0)+m|0,g[5832]=H,g[5835]=z,g[z+4>>2]=H|1,(z|0)!=(g[5834]|0))return;g[5834]=0,g[5831]=0;return}if((g[5834]|0)==(H|0)){H=(g[5831]|0)+m|0,g[5831]=H,g[5834]=L,g[z+4>>2]=H|1,g[L+H>>2]=H;return}C=(S&-8)+m|0,y=S>>>3;do if(S>>>0<256)if(m=g[H+8>>2]|0,p=g[H+12>>2]|0,(p|0)==(m|0)){g[5829]=g[5829]&~(1<>2]=p,g[p+8>>2]=m;break}else{k=g[H+24>>2]|0,p=g[H+12>>2]|0;do if((p|0)==(H|0)){if(m=H+16|0,y=m+4|0,p=g[y>>2]|0,p)m=y;else if(p=g[m>>2]|0,!p){y=0;break}for(;;)if(S=p+20|0,y=g[S>>2]|0,y)p=y,m=S;else if(S=p+16|0,y=g[S>>2]|0,y)p=y,m=S;else break;g[m>>2]=0,y=p}else y=g[H+8>>2]|0,g[y+12>>2]=p,g[p+8>>2]=y,y=p;while(!1);if(k|0){if(p=g[H+28>>2]|0,m=23620+(p<<2)|0,(g[m>>2]|0)==(H|0)){if(g[m>>2]=y,!y){g[5830]=g[5830]&~(1<>2]|0)==(H|0)?S:k+20|0)>>2]=y,!y)break;g[y+24>>2]=k,p=H+16|0,m=g[p>>2]|0,m|0&&(g[y+16>>2]=m,g[m+24>>2]=y),p=g[p+4>>2]|0,p|0&&(g[y+20>>2]=p,g[p+24>>2]=y)}}while(!1);if(g[z+4>>2]=C|1,g[L+C>>2]=C,(z|0)==(g[5834]|0)){g[5831]=C;return}}if(p=C>>>3,C>>>0<256){y=23356+(p<<1<<2)|0,m=g[5829]|0,p=1<>2]|0):(g[5829]=m|p,p=y,m=y+8|0),g[m>>2]=z,g[p+12>>2]=z,g[z+8>>2]=p,g[z+12>>2]=y;return}p=C>>>8,p?C>>>0>16777215?S=31:(L=(p+1048320|0)>>>16&8,H=p<>>16&4,H=H<>>16&2,S=14-(k|L|S)+(H<>>15)|0,S=C>>>(S+7|0)&1|S<<1):S=0,p=23620+(S<<2)|0,g[z+28>>2]=S,g[z+20>>2]=0,g[z+16>>2]=0,m=g[5830]|0,y=1<>2]=z,g[z+24>>2]=p,g[z+12>>2]=z,g[z+8>>2]=z;else{p=g[p>>2]|0;e:do if((g[p+4>>2]&-8|0)!=(C|0)){for(S=C<<((S|0)==31?0:25-(S>>>1)|0);y=p+16+(S>>>31<<2)|0,m=g[y>>2]|0,!!m;)if((g[m+4>>2]&-8|0)==(C|0)){p=m;break e}else S=S<<1,p=m;g[y>>2]=z,g[z+24>>2]=p,g[z+12>>2]=z,g[z+8>>2]=z;break t}while(!1);L=p+8|0,H=g[L>>2]|0,g[H+12>>2]=z,g[L>>2]=z,g[z+8>>2]=H,g[z+12>>2]=p,g[z+24>>2]=0}while(!1);if(H=(g[5837]|0)+-1|0,g[5837]=H,!(H|0)){for(p=23772;p=g[p>>2]|0,p;)p=p+8|0;g[5837]=-1}}}}function Ua(p,m){p=p|0,m=m|0;var y=0;return p?(y=Oc(m,p)|0,(m|p)>>>0>65535&&(y=((y>>>0)/(p>>>0)|0|0)==(m|0)?y:-1)):y=0,p=ho(y)|0,!p||!(g[p+-4>>2]&3)||Fc(p|0,0,y|0)|0,p|0}function S_(p,m,y,S){return p=p|0,m=m|0,y=y|0,S=S|0,y=p+y>>>0,Je(m+S+(y>>>0

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xx=16;var IS=8,sae=Ma.sizeOfH3Index(),YQ=Ma.sizeOfGeoCoord(),Ayt=Ma.sizeOfGeoBoundary(),oae=Ma.sizeOfGeoPolygon(),aae=Ma.sizeOfGeofence(),lae=Ma.sizeOfLinkedGeoPolygon(),cae=Ma.sizeOfCoordIJ(),qQ={m:\"m\",m2:\"m2\",km:\"km\",km2:\"km2\",rads:\"rads\",rads2:\"rads2\"};function myt(e){if(typeof e!=\"number\"||e<0||e>15||Math.floor(e)!==e)throw new Error(\"Invalid resolution: \"+e)}var gyt=/[^0-9a-fA-F]/;function bx(e){if(Array.isArray(e)&&e.length===2&&Number.isInteger(e[0])&&Number.isInteger(e[1]))return e;if(typeof e!=\"string\"||gyt.test(e))return[0,0];var t=parseInt(e.substring(0,e.length-8),xx),r=parseInt(e.substring(e.length-8),xx);return[r,t]}function ZQ(e){if(e>=0)return e.toString(xx);e=e&2147483647;var t=QQ(8,e.toString(xx)),r=(parseInt(t[0],xx)+8).toString(xx);return t=r+t.substring(1),t}function _yt(e,t){return ZQ(t)+QQ(8,ZQ(e))}function QQ(e,t){for(var r=e-t.length,i=\"\",s=0;s180?r[0]-=360:i<-180&&(r[0]+=360)}}function Myt(e,t,r){let[i,s]=FI(e),n=t.length;n$(t,s);let o=t[0]===t[n-1]?n-1:n;for(let c=0;ce.hexagon},extruded:!0},Np=class e extends Ni{constructor(...t){super(...t),G(this,\"state\",void 0)}initializeState(){e._checkH3Lib(),this.state={edgeLengthKM:0,resolution:-1}}shouldUpdateState({changeFlags:t}){return this._shouldUseHighPrecision()?t.propsOrDataChanged:t.somethingChanged}updateState({props:t,changeFlags:r}){if(t.highPrecision!==!0&&(r.dataChanged||r.updateTriggersChanged&&r.updateTriggersChanged.getHexagon)){let i=this._calculateH3DataProps();this.setState(i)}this._updateVertices(this.context.viewport)}_calculateH3DataProps(){let t=-1,r=!1,i=!1,{iterable:s,objectInfo:n}=Jc(this.props.data);for(let o of s){n.index++;let c=this.props.getHexagon(o,n),f=KQ(c);if(t<0){if(t=f,!this.props.highPrecision)break}else if(t!==f){i=!0;break}if(XQ(c)){r=!0;break}}return{resolution:t,edgeLengthKM:t>=0?r$(t,\"km\"):0,hasMultipleRes:i,hasPentagon:r}}_shouldUseHighPrecision(){if(this.props.highPrecision===\"auto\"){let{resolution:t,hasPentagon:r,hasMultipleRes:i}=this.state,{viewport:s}=this.context;return!!s?.resolution||i||r||t>=0&&t<=5}return this.props.highPrecision}_updateVertices(t){if(this._shouldUseHighPrecision())return;let{resolution:r,edgeLengthKM:i,centerHex:s}=this.state;if(r<0)return;let n=this.props.centerHexagon||JQ(t.latitude,t.longitude,r);if(s===n)return;if(s){let R=e$(s,n);if(R>=0&&R*i{let N=t.projectFlat(R);return[(N[0]-w)/o[0],(N[1]-I)/o[1]]}),this.setState({centerHex:n,vertices:c})}renderLayers(){return this._shouldUseHighPrecision()?this._renderPolygonLayer():this._renderColumnLayer()}_getForwardProps(){let{elevationScale:t,material:r,coverage:i,extruded:s,wireframe:n,stroked:o,filled:c,lineWidthUnits:f,lineWidthScale:_,lineWidthMinPixels:w,lineWidthMaxPixels:I,getFillColor:R,getElevation:N,getLineColor:j,getLineWidth:Q,transitions:et,updateTriggers:Y}=this.props;return{elevationScale:t,extruded:s,coverage:i,wireframe:n,stroked:o,filled:c,lineWidthUnits:f,lineWidthScale:_,lineWidthMinPixels:w,lineWidthMaxPixels:I,material:r,getElevation:N,getFillColor:R,getLineColor:j,getLineWidth:Q,transitions:et,updateTriggers:{getFillColor:Y.getFillColor,getElevation:Y.getElevation,getLineColor:Y.getLineColor,getLineWidth:Y.getLineWidth}}}_renderPolygonLayer(){let{data:t,getHexagon:r,updateTriggers:i,coverage:s}=this.props,n=this.getSubLayerClass(\"hexagon-cell-hifi\",lf),o=this._getForwardProps();return o.updateTriggers.getPolygon=Iyt(i.getHexagon,s),new n(o,this.getSubLayerProps({id:\"hexagon-cell-hifi\",updateTriggers:o.updateTriggers}),{data:t,_normalize:!1,_windingOrder:\"CCW\",positionFormat:\"XY\",getPolygon:(c,f)=>{let _=r(c,f);return Pyt(i$(_,s))}})}_renderColumnLayer(){let{data:t,getHexagon:r,updateTriggers:i}=this.props,s=this.getSubLayerClass(\"hexagon-cell\",af),n=this._getForwardProps();return n.updateTriggers.getPosition=i.getHexagon,new s(n,this.getSubLayerProps({id:\"hexagon-cell\",flatShading:!0,updateTriggers:n.updateTriggers}),{data:t,diskResolution:6,radius:1,vertices:this.state.vertices,getPosition:Eyt.bind(null,r)})}};G(Np,\"defaultProps\",Cyt);G(Np,\"layerName\",\"H3HexagonLayer\");G(Np,\"_checkH3Lib\",()=>{});var{data:Sae,getHexagon:Tae,...Lyt}=Np.defaultProps,kyt={_validate:!0},Mae={...Lyt,...kyt};var s$=[[255,255,178],[254,217,118],[254,178,76],[253,141,60],[240,59,32],[189,0,38]];function o$(e,t=!1,r=Float32Array){let i;if(Number.isFinite(e[0]))i=new r(e);else{i=new r(e.length*4);let s=0;for(let n=0;nc[0]),r=e.map(c=>c[1]),i=Math.min.apply(null,t),s=Math.max.apply(null,t),n=Math.min.apply(null,r),o=Math.max.apply(null,r);return[i,n,s,o]}function u$(e,t){return t[0]>=e[0]&&t[2]<=e[2]&&t[1]>=e[1]&&t[3]<=e[3]}var l$=new Float32Array(12);function tF(e,t=2){let r=0;for(let i of e)for(let s=0;s 0.) {\n maxValue = colorDomain[1];\n minValue = colorDomain[0];\n }\n vIntensityMax = intensity / maxValue;\n vIntensityMin = intensity / minValue;\n}\n`;var A$=`#define SHADER_NAME triangle-layer-fragment-shader\n\nprecision highp float;\n\nuniform float opacity;\nuniform sampler2D texture;\nuniform sampler2D colorTexture;\nuniform float aggregationMode;\n\nvarying vec2 vTexCoords;\nvarying float vIntensityMin;\nvarying float vIntensityMax;\n\nvec4 getLinearColor(float value) {\n float factor = clamp(value * vIntensityMax, 0., 1.);\n vec4 color = texture2D(colorTexture, vec2(factor, 0.5));\n color.a *= min(value * vIntensityMin, 1.0);\n return color;\n}\n\nvoid main(void) {\n vec4 weights = texture2D(texture, vTexCoords);\n float weight = weights.r;\n\n if (aggregationMode > 0.5) {\n weight /= max(1.0, weights.a);\n }\n if (weight <= 0.) {\n discard;\n }\n\n vec4 linearColor = getLinearColor(weight);\n linearColor.a *= opacity;\n gl_FragColor =linearColor;\n}\n`;var Sx=class extends dn{getShaders(){return{vs:p$,fs:A$,modules:[Rs]}}initializeState({gl:t}){this.getAttributeManager().add({positions:{size:3,noAlloc:!0},texCoords:{size:2,noAlloc:!0}}),this.setState({model:this._getModel(t)})}_getModel(t){let{vertexCount:r}=this.props;return new fn(t,{...this.getShaders(),id:this.props.id,geometry:new $n({drawMode:6,vertexCount:r})})}draw({uniforms:t}){let{model:r}=this.state,{texture:i,maxTexture:s,colorTexture:n,intensity:o,threshold:c,aggregationMode:f,colorDomain:_}=this.props;r.setUniforms({...t,texture:i,maxTexture:s,colorTexture:n,intensity:o,threshold:c,aggregationMode:f,colorDomain:_}).draw()}};G(Sx,\"layerName\",\"TriangleLayer\");var m$=`attribute vec3 positions;\nattribute vec3 positions64Low;\nattribute float weights;\nvarying vec4 weightsTexture;\nuniform float radiusPixels;\nuniform float textureWidth;\nuniform vec4 commonBounds;\nuniform float weightsScale;\nvoid main()\n{\n weightsTexture = vec4(weights * weightsScale, 0., 0., 1.);\n\n float radiusTexels = project_pixel_size(radiusPixels) * textureWidth / (commonBounds.z - commonBounds.x);\n gl_PointSize = radiusTexels * 2.;\n\n vec3 commonPosition = project_position(positions, positions64Low);\n gl_Position.xy = (commonPosition.xy - commonBounds.xy) / (commonBounds.zw - commonBounds.xy) ;\n gl_Position.xy = (gl_Position.xy * 2.) - (1.);\n}\n`;var g$=`varying vec4 weightsTexture;\nfloat gaussianKDE(float u){\n return pow(2.71828, -u*u/0.05555)/(1.77245385*0.166666);\n}\nvoid main()\n{\n float dist = length(gl_PointCoord - vec2(0.5, 0.5));\n if (dist > 0.5) {\n discard;\n }\n gl_FragColor = weightsTexture * gaussianKDE(2. * dist);\n DECKGL_FILTER_COLOR(gl_FragColor, geometry);\n}\n`;var _$=`attribute vec4 inTexture;\nvarying vec4 outTexture;\n\nvoid main()\n{\noutTexture = inTexture;\ngl_Position = vec4(0, 0, 0, 1.);\ngl_PointSize = 1.0;\n}\n`;var y$=`varying vec4 outTexture;\nvoid main() {\n gl_FragColor = outTexture;\n gl_FragColor.g = outTexture.r / max(1.0, outTexture.a);\n}\n`;var Dyt=2,eF={mipmaps:!1,parameters:{10240:9729,10241:9729,10242:33071,10243:33071},dataFormat:6408},v$=[0,0],Oyt={SUM:0,MEAN:1},Byt={getPosition:{type:\"accessor\",value:e=>e.position},getWeight:{type:\"accessor\",value:1},intensity:{type:\"number\",min:0,value:1},radiusPixels:{type:\"number\",min:1,max:100,value:50},colorRange:s$,threshold:{type:\"number\",min:0,max:1,value:.05},colorDomain:{type:\"array\",value:null,optional:!0},aggregation:\"SUM\",weightsTextureSize:{type:\"number\",min:128,max:2048,value:2048},debounceTimeout:{type:\"number\",min:0,max:1e3,value:500}},Fyt=[Ii.BLEND_EQUATION_MINMAX,Ii.TEXTURE_FLOAT],zyt=[Ii.COLOR_ATTACHMENT_RGBA32F,Ii.FLOAT_BLEND],Nyt={data:{props:[\"radiusPixels\"]}},Up=class extends wx{constructor(...t){super(...t),G(this,\"state\",void 0)}initializeState(){let{gl:t}=this.context;if(!Oh(t,Fyt)){this.setState({supported:!1}),or.error(\"HeatmapLayer: \".concat(this.id,\" is not supported on this browser\"))();return}super.initializeAggregationLayer(Nyt),this.setState({supported:!0,colorDomain:v$}),this._setupTextureParams(),this._setupAttributes(),this._setupResources()}shouldUpdateState({changeFlags:t}){return t.somethingChanged}updateState(t){this.state.supported&&(super.updateState(t),this._updateHeatmapState(t))}_updateHeatmapState(t){let{props:r,oldProps:i}=t,s=this._getChangeFlags(t);(s.dataChanged||s.viewportChanged)&&(s.boundsChanged=this._updateBounds(s.dataChanged),this._updateTextureRenderingBounds()),s.dataChanged||s.boundsChanged?(clearTimeout(this.state.updateTimer),this.setState({isWeightMapDirty:!0})):s.viewportZoomChanged&&this._debouncedUpdateWeightmap(),r.colorRange!==i.colorRange&&this._updateColorTexture(t),this.state.isWeightMapDirty&&this._updateWeightmap(),this.setState({zoom:t.context.viewport.zoom})}renderLayers(){if(!this.state.supported)return[];let{weightsTexture:t,triPositionBuffer:r,triTexCoordBuffer:i,maxWeightsTexture:s,colorTexture:n,colorDomain:o}=this.state,{updateTriggers:c,intensity:f,threshold:_,aggregation:w}=this.props,I=this.getSubLayerClass(\"triangle\",Sx);return new I(this.getSubLayerProps({id:\"triangle-layer\",updateTriggers:c}),{coordinateSystem:Yr.DEFAULT,data:{attributes:{positions:r,texCoords:i}},vertexCount:4,maxTexture:s,colorTexture:n,aggregationMode:Oyt[w]||0,texture:t,intensity:f,threshold:_,colorDomain:o})}finalizeState(t){super.finalizeState(t);let{weightsTransform:r,weightsTexture:i,maxWeightTransform:s,maxWeightsTexture:n,triPositionBuffer:o,triTexCoordBuffer:c,colorTexture:f,updateTimer:_}=this.state;r?.delete(),i?.delete(),s?.delete(),n?.delete(),o?.delete(),c?.delete(),f?.delete(),_&&clearTimeout(_)}_getAttributeManager(){return new Xf(this.context.gl,{id:this.props.id,stats:this.context.stats})}_getChangeFlags(t){let r={},{dimensions:i}=this.state;r.dataChanged=this.isAttributeChanged()||this.isAggregationDirty(t,{compareAll:!0,dimension:i.data}),r.viewportChanged=t.changeFlags.viewportChanged;let{zoom:s}=this.state;return(!t.context.viewport||t.context.viewport.zoom!==s)&&(r.viewportZoomChanged=!0),r}_createTextures(){let{gl:t}=this.context,{textureSize:r,format:i,type:s}=this.state;this.setState({weightsTexture:new pi(t,{width:r,height:r,format:i,type:s,...eF}),maxWeightsTexture:new pi(t,{format:i,type:s,...eF})})}_setupAttributes(){this.getAttributeManager().add({positions:{size:3,type:5130,accessor:\"getPosition\"},weights:{size:1,accessor:\"getWeight\"}}),this.setState({positionAttributeName:\"positions\"})}_setupTextureParams(){let{gl:t}=this.context,{weightsTextureSize:r}=this.props,i=Math.min(r,wy(t,3379)),s=Oh(t,zyt),{format:n,type:o}=d$({gl:t,floatTargetSupport:s}),c=s?1:1/255;this.setState({textureSize:i,format:n,type:o,weightsScale:c}),s||or.warn(\"HeatmapLayer: \".concat(this.id,\" rendering to float texture not supported, fallingback to low precession format\"))()}getShaders(t){return super.getShaders(t===\"max-weights-transform\"?{vs:_$,_fs:y$}:{vs:m$,_fs:g$})}_createWeightsTransform(t={}){var r;let{gl:i}=this.context,{weightsTransform:s}=this.state,{weightsTexture:n}=this.state;(r=s)===null||r===void 0||r.delete(),s=new nc(i,{id:\"\".concat(this.id,\"-weights-transform\"),elementCount:1,_targetTexture:n,_targetTextureVarying:\"weightsTexture\",...t}),this.setState({weightsTransform:s})}_setupResources(){let{gl:t}=this.context;this._createTextures();let{textureSize:r,weightsTexture:i,maxWeightsTexture:s}=this.state,n=this.getShaders(\"weights-transform\");this._createWeightsTransform(n);let o=this.getShaders(\"max-weights-transform\"),c=new nc(t,{id:\"\".concat(this.id,\"-max-weights-transform\"),_sourceTextures:{inTexture:i},_targetTexture:s,_targetTextureVarying:\"outTexture\",...o,elementCount:r*r});this.setState({weightsTexture:i,maxWeightsTexture:s,maxWeightTransform:c,zoom:null,triPositionBuffer:new Fr(t,{byteLength:48,accessor:{size:3}}),triTexCoordBuffer:new Fr(t,{byteLength:48,accessor:{size:2}})})}updateShaders(t){this._createWeightsTransform(t)}_updateMaxWeightValue(){let{maxWeightTransform:t}=this.state;t.run({parameters:{blend:!0,depthTest:!1,blendFunc:[1,1],blendEquation:32776}})}_updateBounds(t=!1){let{viewport:r}=this.context,i=[r.unproject([0,0]),r.unproject([r.width,0]),r.unproject([r.width,r.height]),r.unproject([0,r.height])].map(c=>c.map(Math.fround)),s=c$(i),n={visibleWorldBounds:s,viewportCorners:i},o=!1;if(t||!this.state.worldBounds||!u$(this.state.worldBounds,s)){let c=this._worldToCommonBounds(s),f=this._commonToWorldBounds(c);this.props.coordinateSystem===Yr.LNGLAT&&(f[1]=Math.max(f[1],-85.051129),f[3]=Math.min(f[3],85.051129),f[0]=Math.max(f[0],-360),f[2]=Math.min(f[2],360));let _=this._worldToCommonBounds(f);n.worldBounds=f,n.normalizedCommonBounds=_,o=!0}return this.setState(n),o}_updateTextureRenderingBounds(){let{triPositionBuffer:t,triTexCoordBuffer:r,normalizedCommonBounds:i,viewportCorners:s}=this.state,{viewport:n}=this.context;t.subData(tF(s,3));let o=s.map(c=>f$(n.projectPosition(c),i));r.subData(tF(o,2))}_updateColorTexture(t){let{colorRange:r}=t.props,{colorTexture:i}=this.state,s=o$(r,!1,Uint8Array);i?i.setImageData({data:s,width:r.length}):i=new pi(this.context.gl,{data:s,width:r.length,height:1,...eF}),this.setState({colorTexture:i})}_updateWeightmap(){let{radiusPixels:t,colorDomain:r,aggregation:i}=this.props,{weightsTransform:s,worldBounds:n,textureSize:o,weightsTexture:c,weightsScale:f}=this.state;this.state.isWeightMapDirty=!1;let _=this._worldToCommonBounds(n,{useLayerCoordinateSystem:!0});if(r&&i===\"SUM\"){let{viewport:I}=this.context,R=I.distanceScales.metersPerUnit[2]*(_[2]-_[0])/o;this.state.colorDomain=r.map(N=>N*R*f)}else this.state.colorDomain=r||v$;let w={radiusPixels:t,commonBounds:_,textureWidth:o,weightsScale:f};s.update({elementCount:this.getNumInstances()}),Mn(this.context.gl,{clearColor:[0,0,0,0]},()=>{s.run({uniforms:w,parameters:{blend:!0,depthTest:!1,blendFunc:[1,1],blendEquation:32774},clearRenderTarget:!0,attributes:this.getAttributes(),moduleSettings:this.getModuleSettings()})}),this._updateMaxWeightValue(),c.setParameters({10240:9729,10241:9729})}_debouncedUpdateWeightmap(t=!1){let{updateTimer:r}=this.state,{debounceTimeout:i}=this.props;t?(r=null,this._updateBounds(!0),this._updateTextureRenderingBounds(),this.setState({isWeightMapDirty:!0})):(this.setState({isWeightMapDirty:!1}),clearTimeout(r),r=setTimeout(this._debouncedUpdateWeightmap.bind(this,!0),i)),this.setState({updateTimer:r})}_worldToCommonBounds(t,r={}){let{useLayerCoordinateSystem:i=!1}=r,[s,n,o,c]=t,{viewport:f}=this.context,{textureSize:_}=this.state,{coordinateSystem:w}=this.props,I=i&&(w===Yr.LNGLAT_OFFSETS||w===Yr.METER_OFFSETS),R=I?f.projectPosition(this.props.coordinateOrigin):[0,0],N=_*Dyt/f.scale,j,Q;return i&&!I?(j=this.projectPosition([s,n,0]),Q=this.projectPosition([o,c,0])):(j=f.projectPosition([s,n,0]),Q=f.projectPosition([o,c,0])),h$([j[0]-R[0],j[1]-R[1],Q[0]-R[0],Q[1]-R[1]],N,N)}_commonToWorldBounds(t){let[r,i,s,n]=t,{viewport:o}=this.context,c=o.unprojectPosition([r,i]),f=o.unprojectPosition([s,n]);return c.slice(0,2).concat(f.slice(0,2))}};G(Up,\"layerName\",\"HeatmapLayer\");G(Up,\"defaultProps\",Byt);var{data:Ale,getPosition:mle,...Uyt}=Up.defaultProps,x$={_validate:!0},Vyt={...Uyt,...x$},CS=class extends Ni{static defaultProps=Vyt;static layerName=\"GeoArrowHeatmapLayer\";renderLayers(){let{data:t}=this.props,r=ws(t,Kn.POINT);if(r!==null)return this._renderLayersPoint(r);let i=this.props.getPosition;if(i!==void 0&&Ci.isPointVector(i))return this._renderLayersPoint(i);throw new Error(\"getPosition not GeoArrow point\")}_renderLayersPoint(t){let{data:r}=this.props;this.props._validate&&(_r(Ci.isPointVector(t)),no(this.props,r));let[i,s]=io(this.props,[\"getPosition\"]),n=vo(r.data),o=[];for(let c=0;cr.text()),earcutWorkerPool:null}}async initEarcutPool(){if(this.state.earcutWorkerPool)return this.state.earcutWorkerPool;let t=await this.state.earcutWorkerRequest;if(!t||window?.location?.href.startsWith(\"file://\"))return null;try{let r=RX(()=>LX(kX.fromText(t)),8);return this.state.earcutWorkerPool=r,this.state.earcutWorkerPool}catch{return null}}async finalizeState(t){await this.state?.earcutWorkerPool?.terminate(),console.log(\"terminated\")}async updateData(){let{data:t}=this.props,r=await this._updateEarcut(t),i=vo(t.data);this.setState({table:this.props.data,triangles:r,tableOffsets:i})}async _updateEarcut(t){let r=ws(t,Kn.POLYGON);if(r!==null)return this._earcutPolygonVector(r);let i=ws(t,Kn.MULTIPOLYGON);if(i!==null)return this._earcutMultiPolygonVector(i);let s=this.props.getPolygon;if(s!==void 0&&Ci.isPolygonVector(s))return this._earcutPolygonVector(s);if(s!==void 0&&Ci.isMultiPolygonVector(s))return this._earcutMultiPolygonVector(s);throw new Error(\"geometryColumn not Polygon or MultiPolygon\")}async _earcutPolygonVector(t){let r=await this.initEarcutPool();if(!r)return this._earcutPolygonVectorMainThread(t);let i=new Array(t.data.length);console.time(\"earcut\");for(let s=0;s{let _=await f(LF(o,c));i[s]=_})}return await r.completed(),console.timeEnd(\"earcut\"),i}_earcutPolygonVectorMainThread(t){let r=new Array(t.data.length);for(let i=0;i{let w=await _(LF(c,f));i[s]=w})}return await r.completed(),console.timeEnd(\"earcut\"),i}_earcutMultiPolygonVectorMainThread(t){let r=new Array(t.data.length);for(let i=0;iDX(t))):e}function OX(e){if(\"data\"in e)return new xr(e.data.map(o=>OX(o)));let t=e.valueOffsets,r=vi.getMultiPolygonChild(e),i=r.valueOffsets,s=vi.getPolygonChild(r),n=new Int32Array(t.length);for(let o=0;o{this.table=O2(this.model.get(t))};this.model.on(`change:${t}`,r),this.callbacks.set(`change:${t}`,r)}},tC=class extends mf{static layerType=\"arc\";greatCircle;numSegments;widthUnits;widthScale;widthMinPixels;widthMaxPixels;getSourcePosition;getTargetPosition;getSourceColor;getTargetColor;getWidth;getHeight;getTilt;constructor(t,r){super(t,r),this.initRegularAttribute(\"great_circle\",\"greatCircle\"),this.initRegularAttribute(\"num_segments\",\"numSegments\"),this.initRegularAttribute(\"width_units\",\"widthUnits\"),this.initRegularAttribute(\"width_scale\",\"widthScale\"),this.initRegularAttribute(\"width_min_pixels\",\"widthMinPixels\"),this.initRegularAttribute(\"width_max_pixels\",\"widthMaxPixels\"),this.initVectorizedAccessor(\"get_source_position\",\"getSourcePosition\"),this.initVectorizedAccessor(\"get_target_position\",\"getTargetPosition\"),this.initVectorizedAccessor(\"get_source_color\",\"getSourceColor\"),this.initVectorizedAccessor(\"get_target_color\",\"getTargetColor\"),this.initVectorizedAccessor(\"get_width\",\"getWidth\"),this.initVectorizedAccessor(\"get_height\",\"getHeight\"),this.initVectorizedAccessor(\"get_tilt\",\"getTilt\")}layerProps(){return{data:this.table,getSourcePosition:this.getSourcePosition,getTargetPosition:this.getTargetPosition,...Jt(this.greatCircle)&&{greatCircle:this.greatCircle},...Jt(this.numSegments)&&{numSegments:this.numSegments},...Jt(this.widthUnits)&&{widthUnits:this.widthUnits},...Jt(this.widthScale)&&{widthScale:this.widthScale},...Jt(this.widthMinPixels)&&{widthMinPixels:this.widthMinPixels},...Jt(this.widthMaxPixels)&&{widthMaxPixels:this.widthMaxPixels},...Jt(this.getSourceColor)&&{getSourceColor:this.getSourceColor},...Jt(this.getTargetColor)&&{getTargetColor:this.getTargetColor},...Jt(this.getWidth)&&{getWidth:this.getWidth},...Jt(this.getHeight)&&{getHeight:this.getHeight},...Jt(this.getTilt)&&{getTilt:this.getTilt}}}render(){return new wS({...this.baseLayerProps(),...this.layerProps()})}},eC=class extends Ug{static layerType=\"bitmap\";image;bounds;desaturate;transparentColor;tintColor;constructor(t,r){super(t,r),this.initRegularAttribute(\"image\",\"image\"),this.initRegularAttribute(\"bounds\",\"bounds\"),this.initRegularAttribute(\"desaturate\",\"desaturate\"),this.initRegularAttribute(\"transparent_color\",\"transparentColor\"),this.initRegularAttribute(\"tint_color\",\"tintColor\")}layerProps(){return{...Jt(this.image)&&{image:this.image},...Jt(this.bounds)&&{bounds:this.bounds},...Jt(this.desaturate)&&{desaturate:this.desaturate},...Jt(this.transparentColor)&&{transparentColor:this.transparentColor},...Jt(this.tintColor)&&{tintColor:this.tintColor}}}render(){return new Mp({...this.baseLayerProps(),...this.layerProps(),data:void 0,pickable:!1})}},rC=class extends Ug{static layerType=\"bitmap-tile\";data;tileSize;zoomOffset;maxZoom;minZoom;extent;maxCacheSize;maxCacheByteSize;refinementStrategy;maxRequests;desaturate;transparentColor;tintColor;constructor(t,r){super(t,r),this.initRegularAttribute(\"data\",\"data\"),this.initRegularAttribute(\"tile_size\",\"tileSize\"),this.initRegularAttribute(\"zoom_offset\",\"zoomOffset\"),this.initRegularAttribute(\"max_zoom\",\"maxZoom\"),this.initRegularAttribute(\"min_zoom\",\"minZoom\"),this.initRegularAttribute(\"extent\",\"extent\"),this.initRegularAttribute(\"max_cache_size\",\"maxCacheSize\"),this.initRegularAttribute(\"max_cache_byte_size\",\"maxCacheByteSize\"),this.initRegularAttribute(\"refinement_strategy\",\"refinementStrategy\"),this.initRegularAttribute(\"max_requests\",\"maxRequests\"),this.initRegularAttribute(\"desaturate\",\"desaturate\"),this.initRegularAttribute(\"transparent_color\",\"transparentColor\"),this.initRegularAttribute(\"tint_color\",\"tintColor\")}bitmapLayerProps(){return{...Jt(this.desaturate)&&{desaturate:this.desaturate},...Jt(this.transparentColor)&&{transparentColor:this.transparentColor},...Jt(this.tintColor)&&{tintColor:this.tintColor}}}layerProps(){return{data:this.data,...Jt(this.tileSize)&&{tileSize:this.tileSize},...Jt(this.zoomOffset)&&{zoomOffset:this.zoomOffset},...Jt(this.maxZoom)&&{maxZoom:this.maxZoom},...Jt(this.minZoom)&&{minZoom:this.minZoom},...Jt(this.extent)&&{extent:this.extent},...Jt(this.maxCacheSize)&&{maxCacheSize:this.maxCacheSize},...Jt(this.maxCacheByteSize)&&{maxCacheByteSize:this.maxCacheByteSize},...Jt(this.refinementStrategy)&&{refinementStrategy:this.refinementStrategy},...Jt(this.maxRequests)&&{maxRequests:this.maxRequests}}}render(){return new Lm({...this.baseLayerProps(),...this.layerProps(),renderSubLayers:t=>{let[r,i]=t.tile.boundingBox;return new Mp(t,{...this.bitmapLayerProps(),data:void 0,image:t.data,bounds:[r[0],r[1],i[0],i[1]]})}})}},iC=class extends mf{static layerType=\"column\";diskResolution;radius;angle;vertices;offset;coverage;elevationScale;filled;stroked;extruded;wireframe;flatShading;radiusUnits;lineWidthUnits;lineWidthScale;lineWidthMinPixels;lineWidthMaxPixels;material;getPosition;getFillColor;getLineColor;getElevation;getLineWidth;constructor(t,r){super(t,r),this.initRegularAttribute(\"disk_resolution\",\"diskResolution\"),this.initRegularAttribute(\"radius\",\"radius\"),this.initRegularAttribute(\"angle\",\"angle\"),this.initRegularAttribute(\"vertices\",\"vertices\"),this.initRegularAttribute(\"offset\",\"offset\"),this.initRegularAttribute(\"coverage\",\"coverage\"),this.initRegularAttribute(\"elevation_scale\",\"elevationScale\"),this.initRegularAttribute(\"filled\",\"filled\"),this.initRegularAttribute(\"stroked\",\"stroked\"),this.initRegularAttribute(\"extruded\",\"extruded\"),this.initRegularAttribute(\"wireframe\",\"wireframe\"),this.initRegularAttribute(\"flat_shading\",\"flatShading\"),this.initRegularAttribute(\"radius_units\",\"radiusUnits\"),this.initRegularAttribute(\"line_width_units\",\"lineWidthUnits\"),this.initRegularAttribute(\"line_width_scale\",\"lineWidthScale\"),this.initRegularAttribute(\"line_width_min_pixels\",\"lineWidthMinPixels\"),this.initRegularAttribute(\"line_width_max_pixels\",\"lineWidthMaxPixels\"),this.initRegularAttribute(\"material\",\"material\"),this.initVectorizedAccessor(\"get_position\",\"getPosition\"),this.initVectorizedAccessor(\"get_fill_color\",\"getFillColor\"),this.initVectorizedAccessor(\"get_line_color\",\"getLineColor\"),this.initVectorizedAccessor(\"get_elevation\",\"getElevation\"),this.initVectorizedAccessor(\"get_line_width\",\"getLineWidth\")}layerProps(){return{data:this.table,...Jt(this.diskResolution)&&{diskResolution:this.diskResolution},...Jt(this.radius)&&{radius:this.radius},...Jt(this.angle)&&{angle:this.angle},...Jt(this.vertices)&&this.vertices!==void 0&&{vertices:this.vertices},...Jt(this.offset)&&{offset:this.offset},...Jt(this.coverage)&&{coverage:this.coverage},...Jt(this.elevationScale)&&{elevationScale:this.elevationScale},...Jt(this.filled)&&{filled:this.filled},...Jt(this.stroked)&&{stroked:this.stroked},...Jt(this.extruded)&&{extruded:this.extruded},...Jt(this.wireframe)&&{wireframe:this.wireframe},...Jt(this.flatShading)&&{flatShading:this.flatShading},...Jt(this.radiusUnits)&&{radiusUnits:this.radiusUnits},...Jt(this.lineWidthUnits)&&{lineWidthUnits:this.lineWidthUnits},...Jt(this.lineWidthScale)&&{lineWidthScale:this.lineWidthScale},...Jt(this.lineWidthMinPixels)&&{lineWidthMinPixels:this.lineWidthMinPixels},...Jt(this.lineWidthMaxPixels)&&{lineWidthMaxPixels:this.lineWidthMaxPixels},...Jt(this.material)&&{material:this.material},...Jt(this.getPosition)&&{getPosition:this.getPosition},...Jt(this.getFillColor)&&{getFillColor:this.getFillColor},...Jt(this.getLineColor)&&{getLineColor:this.getLineColor},...Jt(this.getElevation)&&{getElevation:this.getElevation},...Jt(this.getLineWidth)&&{getLineWidth:this.getLineWidth}}}render(){return new SS({...this.baseLayerProps(),...this.layerProps()})}},nC=class extends mf{static layerType=\"heatmap\";radiusPixels;colorRange;intensity;threshold;colorDomain;aggregation;weightsTextureSize;debounceTimeout;getPosition;getWeight;constructor(t,r){super(t,r),this.initRegularAttribute(\"radius_pixels\",\"radiusPixels\"),this.initRegularAttribute(\"color_range\",\"colorRange\"),this.initRegularAttribute(\"intensity\",\"intensity\"),this.initRegularAttribute(\"threshold\",\"threshold\"),this.initRegularAttribute(\"color_domain\",\"colorDomain\"),this.initRegularAttribute(\"aggregation\",\"aggregation\"),this.initRegularAttribute(\"weights_texture_size\",\"weightsTextureSize\"),this.initRegularAttribute(\"debounce_timeout\",\"debounceTimeout\"),this.initVectorizedAccessor(\"get_position\",\"getPosition\"),this.initVectorizedAccessor(\"get_weight\",\"getWeight\")}layerProps(){return{data:this.table,...Jt(this.radiusPixels)&&{radiusPixels:this.radiusPixels},...Jt(this.colorRange)&&{colorRange:this.colorRange},...Jt(this.intensity)&&{intensity:this.intensity},...Jt(this.threshold)&&{threshold:this.threshold},...Jt(this.colorDomain)&&{colorDomain:this.colorDomain},...Jt(this.aggregation)&&{aggregation:this.aggregation},...Jt(this.weightsTextureSize)&&{weightsTextureSize:this.weightsTextureSize},...Jt(this.debounceTimeout)&&{debounceTimeout:this.debounceTimeout},...Jt(this.getPosition)&&{getPosition:this.getPosition},...Jt(this.getWeight)&&{getWeight:this.getWeight}}}render(){return new CS({...this.baseLayerProps(),...this.layerProps()})}},QS=class extends mf{static layerType=\"path\";widthUnits;widthScale;widthMinPixels;widthMaxPixels;jointRounded;capRounded;miterLimit;billboard;getColor;getWidth;constructor(t,r){super(t,r),this.initRegularAttribute(\"width_units\",\"widthUnits\"),this.initRegularAttribute(\"width_scale\",\"widthScale\"),this.initRegularAttribute(\"width_min_pixels\",\"widthMinPixels\"),this.initRegularAttribute(\"width_max_pixels\",\"widthMaxPixels\"),this.initRegularAttribute(\"joint_rounded\",\"jointRounded\"),this.initRegularAttribute(\"cap_rounded\",\"capRounded\"),this.initRegularAttribute(\"miter_limit\",\"miterLimit\"),this.initRegularAttribute(\"billboard\",\"billboard\"),this.initVectorizedAccessor(\"get_color\",\"getColor\"),this.initVectorizedAccessor(\"get_width\",\"getWidth\")}layerProps(){return{data:this.table,...Jt(this.widthUnits)&&{widthUnits:this.widthUnits},...Jt(this.widthScale)&&{widthScale:this.widthScale},...Jt(this.widthMinPixels)&&{widthMinPixels:this.widthMinPixels},...Jt(this.widthMaxPixels)&&{widthMaxPixels:this.widthMaxPixels},...Jt(this.jointRounded)&&{jointRounded:this.jointRounded},...Jt(this.capRounded)&&{capRounded:this.capRounded},...Jt(this.miterLimit)&&{miterLimit:this.miterLimit},...Jt(this.billboard)&&{billboard:this.billboard},...Jt(this.getColor)&&{getColor:this.getColor},...Jt(this.getWidth)&&{getWidth:this.getWidth}}}render(){return new e_({...this.baseLayerProps(),...this.layerProps()})}},sC=class extends mf{static layerType=\"point-cloud\";sizeUnits;pointSize;getColor;getNormal;constructor(t,r){super(t,r),this.initRegularAttribute(\"size_units\",\"sizeUnits\"),this.initRegularAttribute(\"point_size\",\"pointSize\"),this.initVectorizedAccessor(\"get_color\",\"getColor\"),this.initVectorizedAccessor(\"get_normal\",\"getNormal\")}layerProps(){return{data:this.table,...Jt(this.sizeUnits)&&{sizeUnits:this.sizeUnits},...Jt(this.pointSize)&&{pointSize:this.pointSize},...Jt(this.getColor)&&{getColor:this.getColor},...Jt(this.getNormal)&&{getNormal:this.getNormal}}}render(){return new LS({...this.baseLayerProps(),...this.layerProps()})}},oC=class extends mf{static layerType=\"polygon\";stroked;filled;extruded;wireframe;elevationScale;lineWidthUnits;lineWidthScale;lineWidthMinPixels;lineWidthMaxPixels;lineJointRounded;lineMiterLimit;getFillColor;getLineColor;getLineWidth;getElevation;constructor(t,r){super(t,r),this.initRegularAttribute(\"stroked\",\"stroked\"),this.initRegularAttribute(\"filled\",\"filled\"),this.initRegularAttribute(\"extruded\",\"extruded\"),this.initRegularAttribute(\"wireframe\",\"wireframe\"),this.initRegularAttribute(\"elevation_scale\",\"elevationScale\"),this.initRegularAttribute(\"line_width_units\",\"lineWidthUnits\"),this.initRegularAttribute(\"line_width_scale\",\"lineWidthScale\"),this.initRegularAttribute(\"line_width_min_pixels\",\"lineWidthMinPixels\"),this.initRegularAttribute(\"line_width_max_pixels\",\"lineWidthMaxPixels\"),this.initRegularAttribute(\"line_joint_rounded\",\"lineJointRounded\"),this.initRegularAttribute(\"line_miter_limit\",\"lineMiterLimit\"),this.initVectorizedAccessor(\"get_fill_color\",\"getFillColor\"),this.initVectorizedAccessor(\"get_line_color\",\"getLineColor\"),this.initVectorizedAccessor(\"get_line_width\",\"getLineWidth\"),this.initVectorizedAccessor(\"get_elevation\",\"getElevation\")}layerProps(){return{data:this.table,...Jt(this.stroked)&&{stroked:this.stroked},...Jt(this.filled)&&{filled:this.filled},...Jt(this.extruded)&&{extruded:this.extruded},...Jt(this.wireframe)&&{wireframe:this.wireframe},...Jt(this.elevationScale)&&{elevationScale:this.elevationScale},...Jt(this.lineWidthUnits)&&{lineWidthUnits:this.lineWidthUnits},...Jt(this.lineWidthScale)&&{lineWidthScale:this.lineWidthScale},...Jt(this.lineWidthMinPixels)&&{lineWidthMinPixels:this.lineWidthMinPixels},...Jt(this.lineWidthMaxPixels)&&{lineWidthMaxPixels:this.lineWidthMaxPixels},...Jt(this.lineJointRounded)&&{lineJointRounded:this.lineJointRounded},...Jt(this.lineMiterLimit)&&{lineMiterLimit:this.lineMiterLimit},...Jt(this.getFillColor)&&{getFillColor:this.getFillColor},...Jt(this.getLineColor)&&{getLineColor:this.getLineColor},...Jt(this.getLineWidth)&&{getLineWidth:this.getLineWidth},...Jt(this.getElevation)&&{getElevation:this.getElevation}}}render(){return new qS({...this.baseLayerProps(),...this.layerProps()})}},$S=class extends mf{static layerType=\"scatterplot\";radiusUnits;radiusScale;radiusMinPixels;radiusMaxPixels;lineWidthUnits;lineWidthScale;lineWidthMinPixels;lineWidthMaxPixels;stroked;filled;billboard;antialiasing;getRadius;getFillColor;getLineColor;getLineWidth;constructor(t,r){super(t,r),this.initRegularAttribute(\"radius_units\",\"radiusUnits\"),this.initRegularAttribute(\"radius_scale\",\"radiusScale\"),this.initRegularAttribute(\"radius_min_pixels\",\"radiusMinPixels\"),this.initRegularAttribute(\"radius_max_pixels\",\"radiusMaxPixels\"),this.initRegularAttribute(\"line_width_units\",\"lineWidthUnits\"),this.initRegularAttribute(\"line_width_scale\",\"lineWidthScale\"),this.initRegularAttribute(\"line_width_min_pixels\",\"lineWidthMinPixels\"),this.initRegularAttribute(\"line_width_max_pixels\",\"lineWidthMaxPixels\"),this.initRegularAttribute(\"stroked\",\"stroked\"),this.initRegularAttribute(\"filled\",\"filled\"),this.initRegularAttribute(\"billboard\",\"billboard\"),this.initRegularAttribute(\"antialiasing\",\"antialiasing\"),this.initVectorizedAccessor(\"get_radius\",\"getRadius\"),this.initVectorizedAccessor(\"get_fill_color\",\"getFillColor\"),this.initVectorizedAccessor(\"get_line_color\",\"getLineColor\"),this.initVectorizedAccessor(\"get_line_width\",\"getLineWidth\")}layerProps(){return{data:this.table,...Jt(this.radiusUnits)&&{radiusUnits:this.radiusUnits},...Jt(this.radiusScale)&&{radiusScale:this.radiusScale},...Jt(this.radiusMinPixels)&&{radiusMinPixels:this.radiusMinPixels},...Jt(this.radiusMaxPixels)&&{radiusMaxPixels:this.radiusMaxPixels},...Jt(this.lineWidthUnits)&&{lineWidthUnits:this.lineWidthUnits},...Jt(this.lineWidthScale)&&{lineWidthScale:this.lineWidthScale},...Jt(this.lineWidthMinPixels)&&{lineWidthMinPixels:this.lineWidthMinPixels},...Jt(this.lineWidthMaxPixels)&&{lineWidthMaxPixels:this.lineWidthMaxPixels},...Jt(this.stroked)&&{stroked:this.stroked},...Jt(this.filled)&&{filled:this.filled},...Jt(this.billboard)&&{billboard:this.billboard},...Jt(this.antialiasing)&&{antialiasing:this.antialiasing},...Jt(this.getRadius)&&{getRadius:this.getRadius},...Jt(this.getFillColor)&&{getFillColor:this.getFillColor},...Jt(this.getLineColor)&&{getLineColor:this.getLineColor},...Jt(this.getLineWidth)&&{getLineWidth:this.getLineWidth}}}render(){return new ZS({...this.baseLayerProps(),...this.layerProps()})}},XS=class extends mf{static layerType=\"solid-polygon\";filled;extruded;wireframe;elevationScale;getElevation;getFillColor;getLineColor;constructor(t,r){super(t,r),this.initRegularAttribute(\"filled\",\"filled\"),this.initRegularAttribute(\"extruded\",\"extruded\"),this.initRegularAttribute(\"wireframe\",\"wireframe\"),this.initRegularAttribute(\"elevation_scale\",\"elevationScale\"),this.initVectorizedAccessor(\"get_elevation\",\"getElevation\"),this.initVectorizedAccessor(\"get_fill_color\",\"getFillColor\"),this.initVectorizedAccessor(\"get_line_color\",\"getLineColor\")}layerProps(){return{data:this.table,...Jt(this.filled)&&{filled:this.filled},...Jt(this.extruded)&&{extruded:this.extruded},...Jt(this.wireframe)&&{wireframe:this.wireframe},...Jt(this.elevationScale)&&{elevationScale:this.elevationScale},...Jt(this.getElevation)&&{getElevation:this.getElevation},...Jt(this.getFillColor)&&{getFillColor:this.getFillColor},...Jt(this.getLineColor)&&{getLineColor:this.getLineColor}}}render(){return new o_({...this.baseLayerProps(),...this.layerProps()})}},aC=class extends mf{static layerType=\"text\";billboard;sizeScale;sizeUnits;sizeMinPixels;sizeMaxPixels;getBackgroundColor;getBorderColor;getBorderWidth;backgroundPadding;characterSet;fontFamily;fontWeight;lineHeight;outlineWidth;outlineColor;fontSettings;wordBreak;maxWidth;getText;getPosition;getColor;getSize;getAngle;getTextAnchor;getAlignmentBaseline;getPixelOffset;constructor(t,r){super(t,r),this.initRegularAttribute(\"billboard\",\"billboard\"),this.initRegularAttribute(\"size_scale\",\"sizeScale\"),this.initRegularAttribute(\"size_units\",\"sizeUnits\"),this.initRegularAttribute(\"size_min_pixels\",\"sizeMinPixels\"),this.initRegularAttribute(\"size_max_pixels\",\"sizeMaxPixels\"),this.initRegularAttribute(\"background_padding\",\"backgroundPadding\"),this.initRegularAttribute(\"character_set\",\"characterSet\"),this.initRegularAttribute(\"font_family\",\"fontFamily\"),this.initRegularAttribute(\"font_weight\",\"fontWeight\"),this.initRegularAttribute(\"line_height\",\"lineHeight\"),this.initRegularAttribute(\"outline_width\",\"outlineWidth\"),this.initRegularAttribute(\"outline_color\",\"outlineColor\"),this.initRegularAttribute(\"font_settings\",\"fontSettings\"),this.initRegularAttribute(\"word_break\",\"wordBreak\"),this.initRegularAttribute(\"max_width\",\"maxWidth\"),this.initVectorizedAccessor(\"get_background_color\",\"getBackgroundColor\"),this.initVectorizedAccessor(\"get_border_color\",\"getBorderColor\"),this.initVectorizedAccessor(\"get_border_width\",\"getBorderWidth\"),this.initVectorizedAccessor(\"get_text\",\"getText\"),this.initVectorizedAccessor(\"get_position\",\"getPosition\"),this.initVectorizedAccessor(\"get_color\",\"getColor\"),this.initVectorizedAccessor(\"get_size\",\"getSize\"),this.initVectorizedAccessor(\"get_angle\",\"getAngle\"),this.initVectorizedAccessor(\"get_text_anchor\",\"getTextAnchor\"),this.initVectorizedAccessor(\"get_alignment_baseline\",\"getAlignmentBaseline\"),this.initVectorizedAccessor(\"get_pixel_offset\",\"getPixelOffset\")}layerProps(){return{data:this.table,getText:this.getText,...Jt(this.billboard)&&{billboard:this.billboard},...Jt(this.sizeScale)&&{sizeScale:this.sizeScale},...Jt(this.sizeUnits)&&{sizeUnits:this.sizeUnits},...Jt(this.sizeMinPixels)&&{sizeMinPixels:this.sizeMinPixels},...Jt(this.sizeMaxPixels)&&{sizeMaxPixels:this.sizeMaxPixels},...Jt(this.backgroundPadding)&&{backgroundPadding:this.backgroundPadding},...Jt(this.characterSet)&&{characterSet:this.characterSet},...Jt(this.fontFamily)&&{fontFamily:this.fontFamily},...Jt(this.fontWeight)&&{fontWeight:this.fontWeight},...Jt(this.lineHeight)&&{lineHeight:this.lineHeight},...Jt(this.outlineWidth)&&{outlineWidth:this.outlineWidth},...Jt(this.outlineColor)&&{outlineColor:this.outlineColor},...Jt(this.fontSettings)&&{fontSettings:this.fontSettings},...Jt(this.wordBreak)&&{wordBreak:this.wordBreak},...Jt(this.maxWidth)&&{maxWidth:this.maxWidth},...Jt(this.getBackgroundColor)&&{getBackgroundColor:this.getBackgroundColor},...Jt(this.getBorderColor)&&{getBorderColor:this.getBorderColor},...Jt(this.getBorderWidth)&&{getBorderWidth:this.getBorderWidth},...Jt(this.getPosition)&&{getPosition:this.getPosition},...Jt(this.getColor)&&{getColor:this.getColor},...Jt(this.getSize)&&{getSize:this.getSize},...Jt(this.getAngle)&&{getAngle:this.getAngle},...Jt(this.getTextAnchor)&&{getTextAnchor:this.getTextAnchor},...Jt(this.getAlignmentBaseline)&&{getAlignmentBaseline:this.getAlignmentBaseline},...Jt(this.getPixelOffset)&&{getPixelOffset:this.getPixelOffset}}}render(){return 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BLK90 GI59 GI69 GI79 \\\n", "0 27077 0 0.000000 ... 0.024534 0.285235 0.372336 0.342104 \n", "1 53019 0 0.000000 ... 0.317712 0.256158 0.360665 0.361928 \n", "2 53065 0 1.863863 ... 0.210030 0.283999 0.394083 0.357566 \n", "3 53047 0 2.612330 ... 0.155922 0.258540 0.371218 0.381240 \n", "4 53051 0 0.000000 ... 0.134605 0.243263 0.365614 0.358706 \n", "\n", " GI89 FH60 FH70 FH80 FH90 \\\n", "0 0.336455 11.279621 5.4 5.663881 9.515860 \n", "1 0.360640 10.053476 2.6 10.079576 11.397059 \n", "2 0.369942 9.258437 5.6 6.812127 10.352015 \n", "3 0.394519 9.039900 8.1 10.084926 12.840340 \n", "4 0.387848 8.243930 4.1 7.557643 10.313002 \n", "\n", " geometry \n", "0 POLYGON ((49050.745 2838555.999, 49054.346 285... \n", "1 POLYGON ((-1704187.741 2978490.561, -1690089.6... \n", "2 POLYGON ((-1598348.829 2964085.947, -1605943.0... \n", "3 POLYGON ((-1713271.344 2979541.451, -1712880.7... \n", "4 POLYGON ((-1574802.183 3066600.159, -1545432.6... \n", "\n", "[5 rows x 70 columns]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gdf = gpd.read_file(examples.get_path(\"NAT.shp\")).to_crs(epsg=5070)\n", "gdf.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Using legendgrams with plots can be simple:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2025-07-11T20:24:27.184875Z", "iopub.status.busy": "2025-07-11T20:24:27.184695Z", "iopub.status.idle": "2025-07-11T20:24:27.417068Z", "shell.execute_reply": "2025-07-11T20:24:27.416858Z", "shell.execute_reply.started": "2025-07-11T20:24:27.184863Z" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = gdf.plot(\"GI89\")\n", "ax.axis(\"off\")\n", "classifier = mapclassify.EqualInterval(gdf[\"GI89\"], k=100)\n", "classifier.plot_legendgram(ax=ax)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### but you can also tweak quite a bit, like the size & location:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll make the legend a little longer & shorter, as well as moving it to the lower right:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2025-07-11T20:24:27.417496Z", "iopub.status.busy": "2025-07-11T20:24:27.417413Z", "iopub.status.idle": "2025-07-11T20:24:27.603011Z", "shell.execute_reply": "2025-07-11T20:24:27.602807Z", "shell.execute_reply.started": "2025-07-11T20:24:27.417488Z" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = gdf.plot(\"GI89\")\n", "ax.axis(\"off\")\n", "classifier = mapclassify.EqualInterval(gdf[\"GI89\"], k=100)\n", "classifier.plot_legendgram(ax=ax, legend_size=(\"60%\", \"12%\"), loc=\"lower right\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can clip the display to a smaller range to cut off any dangling long tails and use different scheme:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2025-07-11T20:24:27.603403Z", "iopub.status.busy": "2025-07-11T20:24:27.603331Z", "iopub.status.idle": "2025-07-11T20:24:27.848606Z", "shell.execute_reply": "2025-07-11T20:24:27.848401Z", "shell.execute_reply.started": "2025-07-11T20:24:27.603394Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = gdf.plot(\"GI89\", scheme=\"Quantiles\")\n", "ax.axis(\"off\")\n", "classifier = mapclassify.Quantiles(gdf[\"GI89\"])\n", "hax = classifier.plot_legendgram(\n", " ax=ax,\n", " legend_size=(\"50%\", \"12%\"),\n", " loc=\"lower left\",\n", " clip=(0.3, 0.5),\n", " vlines=True,\n", ")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2025-07-11T20:24:27.849833Z", "iopub.status.busy": "2025-07-11T20:24:27.849752Z", "iopub.status.idle": "2025-07-11T20:24:27.859698Z", "shell.execute_reply": "2025-07-11T20:24:27.859442Z", "shell.execute_reply.started": "2025-07-11T20:24:27.849824Z" } }, "outputs": [], "source": [ "ret = hax.hist(classifier.y, bins=50, color=\"0.1\")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "execution": { "iopub.execute_input": "2025-07-11T20:24:27.859968Z", "iopub.status.busy": "2025-07-11T20:24:27.859904Z", "iopub.status.idle": "2025-07-11T20:24:27.861739Z", "shell.execute_reply": "2025-07-11T20:24:27.861546Z", "shell.execute_reply.started": "2025-07-11T20:24:27.859962Z" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ret[2]" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "execution": { "iopub.execute_input": "2025-07-11T20:24:27.862107Z", "iopub.status.busy": "2025-07-11T20:24:27.862039Z", "iopub.status.idle": "2025-07-11T20:24:27.864008Z", "shell.execute_reply": "2025-07-11T20:24:27.863852Z", "shell.execute_reply.started": "2025-07-11T20:24:27.862100Z" } }, "outputs": [ { "data": { "text/plain": [ "(np.float64(0.0), np.float64(237.0375))" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hax.get_ylim()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When calling `plot_legendgram()` the most recently plotted `cmap` is used unless otherwise stipulated:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "execution": { "iopub.execute_input": "2025-07-11T20:24:27.864391Z", "iopub.status.busy": "2025-07-11T20:24:27.864316Z", "iopub.status.idle": "2025-07-11T20:24:28.050947Z", "shell.execute_reply": "2025-07-11T20:24:28.050727Z", "shell.execute_reply.started": "2025-07-11T20:24:27.864383Z" } }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = gdf.plot(\"GI89\", cmap=\"plasma\", scheme=\"Quantiles\")\n", "ax.axis(\"off\")\n", "classifier = mapclassify.Quantiles(gdf[\"GI89\"])\n", "hax = classifier.plot_legendgram(\n", " ax=ax,\n", " legend_size=(\"15%\", \"30%\"),\n", " loc=\"lower right\",\n", " clip=(0.3, 0.4),\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Further, you can work directly on the Axes, if you prefer very fine-grained control over the plot parameters. `legendgram` returns the Axes on which the legendgram was plotted, so you can modify it after the fact:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "execution": { "iopub.execute_input": "2025-07-11T20:24:28.051350Z", "iopub.status.busy": "2025-07-11T20:24:28.051280Z", "iopub.status.idle": "2025-07-11T20:24:28.056671Z", "shell.execute_reply": "2025-07-11T20:24:28.056486Z", "shell.execute_reply.started": "2025-07-11T20:24:28.051341Z" } }, "outputs": [ { "data": { "text/html": [ "
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NAMESTATE_NAMESTATE_FIPSCNTY_FIPSFIPSSTFIPSCOFIPSFIPSNOSOUTHHR60...BLK90GI59GI69GI79GI89FH60FH70FH80FH90geometry
2255RiversideCalifornia0606506065665606504.898903...5.433210.2840.3767370.3832260.3681779.7699599.211.14561212.67834POLYGON ((-1841135.909 1346087.732, -1926639.5...
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1 rows × 70 columns

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" ], "text/plain": [ " NAME STATE_NAME STATE_FIPS CNTY_FIPS FIPS STFIPS COFIPS FIPSNO \\\n", "2255 Riverside California 06 065 06065 6 65 6065 \n", "\n", " SOUTH HR60 ... BLK90 GI59 GI69 GI79 GI89 \\\n", "2255 0 4.898903 ... 5.43321 0.284 0.376737 0.383226 0.368177 \n", "\n", " FH60 FH70 FH80 FH90 \\\n", "2255 9.769959 9.2 11.145612 12.67834 \n", "\n", " geometry \n", "2255 POLYGON ((-1841135.909 1346087.732, -1926639.5... \n", "\n", "[1 rows x 70 columns]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "riverside_loc = gdf.iloc[2255]\n", "riverside = gpd.GeoDataFrame(riverside_loc.to_frame().T)\n", "riverside" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "execution": { "iopub.execute_input": "2025-07-11T20:24:28.057008Z", "iopub.status.busy": "2025-07-11T20:24:28.056928Z", "iopub.status.idle": "2025-07-11T20:24:28.283360Z", "shell.execute_reply": "2025-07-11T20:24:28.283149Z", "shell.execute_reply.started": "2025-07-11T20:24:28.056999Z" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = gdf.plot(\"GI89\", cmap=\"Blues\", figsize=(10, 10))\n", "ax.axis(\"off\")\n", "classifier = mapclassify.EqualInterval(gdf[\"GI89\"], k=100)\n", "hax = classifier.plot_legendgram(ax=ax, legend_size=(\"65%\", \"25%\"))\n", "\n", "# riverside in red\n", "riverside.plot(\"GI89\", linewidth=2, edgecolor=\"r\", ax=ax)\n", "# mark Riverside's Gini in the legend\n", "hax.vlines(riverside_loc[\"GI89\"], 0, 1, color=\"r\", transform=hax.transAxes)" ] } ], "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": "3.13.5" } }, "nbformat": 4, "nbformat_minor": 4 } mapclassify-2.10.0/notebooks/value_by_alpha.ipynb000066400000000000000000030202311503430131600221230ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Value-by-Alpha Choropleth\n", "\n", "Value-by-Alpha Choropleth plotting with `mapclassify`\n", "\n", "* *This notebook is adapted from one originally written by [@slumnitz](https://github.com/slumnitz)* in the `splot` project (original [here](https://github.com/pysal/splot/blob/b8361cb5f4685d0945e08cbf9172ba701ce57c44/notebooks/mapping_vba.ipynb)).\n", "\n", "## Set up\n", "\n", "### Imports" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "execution": { "iopub.execute_input": "2025-07-11T19:53:33.802055Z", "iopub.status.busy": "2025-07-11T19:53:33.801867Z", "iopub.status.idle": "2025-07-11T19:53:36.020933Z", "shell.execute_reply": "2025-07-11T19:53:36.020715Z", "shell.execute_reply.started": "2025-07-11T19:53:33.802034Z" } }, "outputs": [], "source": [ "import geopandas\n", "import libpysal\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Data Preparation\n", "\n", "Load example data into a `geopandas.GeoDataFrame` and inspect column names. In this example we will use the `columbus.shp` file containing neighborhood crime data of 1980." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2025-07-11T19:53:36.021345Z", "iopub.status.busy": "2025-07-11T19:53:36.021213Z", "iopub.status.idle": "2025-07-11T19:53:36.652926Z", "shell.execute_reply": "2025-07-11T19:53:36.652705Z", "shell.execute_reply.started": "2025-07-11T19:53:36.021336Z" } }, "outputs": [ { "data": { "text/plain": [ "Index(['AREA', 'PERIMETER', 'COLUMBUS_', 'COLUMBUS_I', 'POLYID', 'NEIG',\n", " 'HOVAL', 'INC', 'CRIME', 'OPEN', 'PLUMB', 'DISCBD', 'X', 'Y', 'NSA',\n", " 'NSB', 'EW', 'CP', 'THOUS', 'NEIGNO', 'geometry'],\n", " dtype='object')" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "link_to_data = libpysal.examples.get_path(\"columbus.shp\")\n", "gdf = geopandas.read_file(link_to_data)\n", "gdf.columns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Our two variables of interest in `gdf`:\n", "* `x`: `HOVAL` – housing value (in $1,000); the rgb variable\n", "* `y`: `CRIME` – residential burglaries and vehicle thefts per 1000 households; the alpha variable" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2025-07-11T19:53:36.653398Z", "iopub.status.busy": "2025-07-11T19:53:36.653267Z", "iopub.status.idle": "2025-07-11T19:53:36.654812Z", "shell.execute_reply": "2025-07-11T19:53:36.654617Z", "shell.execute_reply.started": "2025-07-11T19:53:36.653390Z" } }, "outputs": [], "source": [ "x = \"HOVAL\"\n", "y = \"CRIME\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create Value-by-Alpha Choropleths\n", "\n", "### What is a Value by Alpha choropleth?\n", "\n", "In a nutshell, a Value-by-Alpha Choropleth is a bivariate choropleth that uses the values of the second input variable `y` as a transparency mask, determining how much of the choropleth displaying the values of a first variable `x` is shown. In comparison to a cartogram, Value-By-Alpha choropleths will not distort shapes and sizes but modify the alpha channel (transparency) of polygons according to the second input variable `y`.\n", "\n", "Let's look at a couple of examples." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2025-07-11T19:53:36.655257Z", "iopub.status.busy": "2025-07-11T19:53:36.655159Z", "iopub.status.idle": "2025-07-11T19:53:37.313978Z", "shell.execute_reply": "2025-07-11T19:53:37.313740Z", "shell.execute_reply.started": "2025-07-11T19:53:36.655250Z" } }, "outputs": [], "source": [ "from mapclassify.value_by_alpha import vba_choropleth" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can create a value by alpha map using `vba_choropleth`.\n", "\n", "We will plot a Value-by-Alpha Choropleth with `x` (`HOVAL`) defining the rgb values and `y` (`CRIME`) defining the alpha value. For comparison we plot a choropleth of `x` with `gdf.plot()`:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2025-07-11T19:53:37.314460Z", "iopub.status.busy": "2025-07-11T19:53:37.314323Z", "iopub.status.idle": "2025-07-11T19:53:37.618909Z", "shell.execute_reply": "2025-07-11T19:53:37.618679Z", "shell.execute_reply.started": "2025-07-11T19:53:37.314452Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Create new figure\n", "fig, axs = plt.subplots(2, 2, figsize=(15, 10))\n", "\n", "# use gdf.plot() to create regular choropleth\n", "axs[0, 0].set_title(\"normal Choropleth\")\n", "axs[0, 0].set_axis_off()\n", "gdf.plot(column=x, scheme=\"quantiles\", cmap=\"RdBu\", ax=axs[0, 0])\n", "\n", "# use vba_choropleth to create Value-by-Alpha Choropleth\n", "axs[0, 1].set_title(\"Value-by-Alpha Choropleth\")\n", "axs[0, 1].set_axis_off()\n", "vba_choropleth(\n", " x,\n", " y,\n", " gdf,\n", " x_classification_kwds={\"classifier\": \"quantiles\"},\n", " y_classification_kwds={\"classifier\": \"quantiles\"},\n", " cmap=\"RdBu\",\n", " ax=axs[0, 1],\n", ")\n", "\n", "# create a VBA Choropleth - divergent=False, revert_alpha=False\n", "axs[1, 0].set_title(\"divergent=False, revert_alpha=False\")\n", "axs[1, 0].set_axis_off()\n", "vba_choropleth(\n", " x,\n", " y,\n", " gdf,\n", " x_classification_kwds={\"classifier\": \"quantiles\"},\n", " y_classification_kwds={\"classifier\": \"quantiles\"},\n", " cmap=\"RdBu\",\n", " ax=axs[1, 0],\n", " divergent=True,\n", " revert_alpha=False,\n", ")\n", "\n", "# create a VBA Choropleth - divergent=False, revert_alpha=True\n", "axs[1, 1].set_title(\"divergent=False, revert_alpha=True\")\n", "axs[1, 1].set_axis_off()\n", "vba_choropleth(\n", " x,\n", " y,\n", " gdf,\n", " x_classification_kwds={\"classifier\": \"quantiles\"},\n", " y_classification_kwds={\"classifier\": \"quantiles\"},\n", " cmap=\"RdBu\",\n", " ax=axs[1, 1],\n", " divergent=False,\n", " revert_alpha=True,\n", ")\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can see the original choropleth is fading into transparency wherever there is a high `y` value.\n", "\n", "#### Classification & binning options\n", "\n", "You can use the option to bin or classify your `x` and `y` values, and display the alternative color and alpha ranges:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2025-07-11T19:53:37.620223Z", "iopub.status.busy": "2025-07-11T19:53:37.620139Z", "iopub.status.idle": "2025-07-11T19:53:37.795919Z", "shell.execute_reply": "2025-07-11T19:53:37.795669Z", "shell.execute_reply.started": "2025-07-11T19:53:37.620215Z" } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Create new figure\n", "fig, axs = plt.subplots(2, 2, figsize=(15, 10))\n", "\n", "# classify with Quantiles\n", "axs[0, 0].set_axis_off()\n", "vba_choropleth(\n", " x,\n", " y,\n", " gdf,\n", " cmap=\"viridis\",\n", " ax=axs[0, 0],\n", " x_classification_kwds={\"classifier\": \"quantiles\", \"k\": 3},\n", " y_classification_kwds={\"classifier\": \"quantiles\", \"k\": 3},\n", ")\n", "\n", "# classifier with Natural Breaks\n", "axs[0, 1].set_axis_off()\n", "vba_choropleth(\n", " x,\n", " y,\n", " gdf,\n", " cmap=\"viridis\",\n", " ax=axs[0, 1],\n", " x_classification_kwds={\"classifier\": \"natural_breaks\"},\n", " y_classification_kwds={\"classifier\": \"natural_breaks\"},\n", ")\n", "\n", "# classify with Standard Deviation Mean\n", "axs[1, 0].set_axis_off()\n", "vba_choropleth(\n", " x,\n", " y,\n", " gdf,\n", " cmap=\"viridis\",\n", " ax=axs[1, 0],\n", " x_classification_kwds={\"classifier\": \"std_mean\"},\n", " y_classification_kwds={\"classifier\": \"std_mean\"},\n", ")\n", "\n", "# classify with Fisher Jenks\n", "axs[1, 1].set_axis_off()\n", "vba_choropleth(\n", " x,\n", " y,\n", " gdf,\n", " cmap=\"viridis\",\n", " ax=axs[1, 1],\n", " x_classification_kwds={\"classifier\": \"fisher_jenks\", \"k\": 3},\n", " y_classification_kwds={\"classifier\": \"fisher_jenks\", \"k\": 3},\n", ")\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### User-defined colors\n", "\n", "Instead of using a colormap you can also pass a list of colors:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "execution": { "iopub.execute_input": "2025-07-11T19:53:37.796430Z", "iopub.status.busy": "2025-07-11T19:53:37.796339Z", "iopub.status.idle": "2025-07-11T19:53:37.797988Z", "shell.execute_reply": "2025-07-11T19:53:37.797810Z", "shell.execute_reply.started": "2025-07-11T19:53:37.796419Z" } }, "outputs": [], "source": [ "color_list = [\"#a1dab4\", \"#41b6c4\", \"#225ea8\"]" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "execution": { "iopub.execute_input": "2025-07-11T19:53:37.798367Z", "iopub.status.busy": "2025-07-11T19:53:37.798300Z", "iopub.status.idle": "2025-07-11T19:53:37.825519Z", "shell.execute_reply": "2025-07-11T19:53:37.825294Z", "shell.execute_reply.started": "2025-07-11T19:53:37.798359Z" } }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "_, ax = vba_choropleth(\n", " x,\n", " y,\n", " gdf,\n", " cmap=color_list,\n", " x_classification_kwds={\"classifier\": \"quantiles\", \"k\": 3},\n", " y_classification_kwds={\"classifier\": \"quantiles\"},\n", ")\n", "ax.set_axis_off()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Transposing transparency\n", "\n", "Sometimes it is important in geospatial analysis to actually see the high values and let the small values fade out. With the `revert_alpha = True` argument, you can revert the transparency of the `y` values so that high `y` values will more transparent and low values will more opaque." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "execution": { "iopub.execute_input": "2025-07-11T19:53:37.825999Z", "iopub.status.busy": "2025-07-11T19:53:37.825926Z", "iopub.status.idle": "2025-07-11T19:53:37.896177Z", "shell.execute_reply": "2025-07-11T19:53:37.895953Z", "shell.execute_reply.started": "2025-07-11T19:53:37.825991Z" } }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Create new figure\n", "fig, axs = plt.subplots(1, 2, figsize=(20, 10))\n", "\n", "# create a vba_choropleth\n", "axs[0].set_title(\"revert_alpha = False\")\n", "axs[0].set_axis_off()\n", "vba_choropleth(\n", " x,\n", " y,\n", " gdf,\n", " x_classification_kwds={\"classifier\": \"quantiles\"},\n", " y_classification_kwds={\"classifier\": \"quantiles\"},\n", " cmap=\"RdBu\",\n", " ax=axs[0],\n", " revert_alpha=False,\n", ")\n", "\n", "# set revert_alpha argument to True\n", "axs[1].set_title(\"revert_alpha = True\")\n", "axs[1].set_axis_off()\n", "vba_choropleth(\n", " x,\n", " y,\n", " gdf,\n", " x_classification_kwds={\"classifier\": \"quantiles\"},\n", " y_classification_kwds={\"classifier\": \"quantiles\"},\n", " cmap=\"RdBu\",\n", " ax=axs[1],\n", " revert_alpha=True,\n", ")\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Displaying divergency\n", "\n", "You can use the `divergent` argument to display divergent alpha values. This means values at the extremes of your data range will be displayed with an alpha value of 1. Values towards the middle of your data range will be mapped increasingly transparent approaching an alpha value of 0." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "execution": { "iopub.execute_input": "2025-07-11T19:53:37.896589Z", "iopub.status.busy": "2025-07-11T19:53:37.896518Z", "iopub.status.idle": "2025-07-11T19:53:37.964458Z", "shell.execute_reply": "2025-07-11T19:53:37.964248Z", "shell.execute_reply.started": "2025-07-11T19:53:37.896580Z" } }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# create new figure\n", "fig, axs = plt.subplots(1, 2, figsize=(20, 10))\n", "\n", "# create a vba_choropleth\n", "axs[0].set_title(\"divergent = False\")\n", "axs[0].set_axis_off()\n", "vba_choropleth(x, y, gdf, cmap=\"RdBu\", divergent=False, ax=axs[0])\n", "\n", "# set divergent to True\n", "axs[1].set_title(\"divergent = True\")\n", "axs[1].set_axis_off()\n", "vba_choropleth(x, y, gdf, cmap=\"RdBu\", divergent=True, ax=axs[1])\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create your own cmap for plotting\n", "\n", "### Colormap shifting\n", "\n", "Sometimes you need to display divergent values with a natural midpoint not overlapping with the median of your data. For example, if you measure the temperature over a country ranging from -2 to 10 degrees Celsius. Or if you need to assess whether a certain threshold is reached.\n", "\n", "For cases like this, we can use `shift_colormap`." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "execution": { "iopub.execute_input": "2025-07-11T19:53:37.964872Z", "iopub.status.busy": "2025-07-11T19:53:37.964787Z", "iopub.status.idle": "2025-07-11T19:53:37.966330Z", "shell.execute_reply": "2025-07-11T19:53:37.966140Z", "shell.execute_reply.started": "2025-07-11T19:53:37.964863Z" } }, "outputs": [], "source": [ "from mapclassify.value_by_alpha import shift_colormap" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "execution": { "iopub.execute_input": "2025-07-11T19:53:37.966770Z", "iopub.status.busy": "2025-07-11T19:53:37.966664Z", "iopub.status.idle": "2025-07-11T19:53:37.973107Z", "shell.execute_reply": "2025-07-11T19:53:37.972929Z", "shell.execute_reply.started": "2025-07-11T19:53:37.966763Z" } }, "outputs": [ { "data": { "image/png": "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", "text/html": [ "
RdBu_08
\"RdBu_08
under
bad
over
" ], "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# shift the midpoint to the 80th percentile of your datarange\n", "mid08 = shift_colormap(\"RdBu\", midpoint=0.8, name=\"RdBu_08\")\n", "mid08" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "execution": { "iopub.execute_input": "2025-07-11T19:53:37.973534Z", "iopub.status.busy": "2025-07-11T19:53:37.973436Z", "iopub.status.idle": "2025-07-11T19:53:37.980803Z", "shell.execute_reply": "2025-07-11T19:53:37.980438Z", "shell.execute_reply.started": "2025-07-11T19:53:37.973526Z" } }, "outputs": [ { "data": { "image/png": "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", "text/html": [ "
RdBu_02
\"RdBu_02
under
bad
over
" ], "text/plain": [ "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# shift the midpoint to the 20th percentile of your datarange\n", "mid02 = shift_colormap(\"RdBu\", midpoint=0.2, name=\"RdBu_02\")\n", "mid02" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "execution": { "iopub.execute_input": "2025-07-11T19:53:37.981374Z", "iopub.status.busy": "2025-07-11T19:53:37.981249Z", "iopub.status.idle": "2025-07-11T19:53:38.047920Z", "shell.execute_reply": "2025-07-11T19:53:38.047718Z", "shell.execute_reply.started": "2025-07-11T19:53:37.981332Z" } }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# create new figure\n", "fig, axs = plt.subplots(1, 2, figsize=(20, 10))\n", "\n", "# vba_choropleth with cmap mid08\n", "vba_choropleth(x, y, gdf, cmap=mid08, ax=axs[0], divergent=True)\n", "axs[0].set_axis_off()\n", "\n", "# vba_choropleth with cmap mid02\n", "vba_choropleth(x, y, gdf, cmap=mid02, ax=axs[1], divergent=True)\n", "axs[1].set_axis_off()\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Colormap truncation\n", "\n", "You may also want to truncate minimum and maximum values for demonstrative or merely aesthetic purposes.\n", "\n", "For cases like this, we can use `truncate_colormap`." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "execution": { "iopub.execute_input": "2025-07-11T19:53:38.048385Z", "iopub.status.busy": "2025-07-11T19:53:38.048310Z", "iopub.status.idle": "2025-07-11T19:53:38.049870Z", "shell.execute_reply": "2025-07-11T19:53:38.049672Z", "shell.execute_reply.started": "2025-07-11T19:53:38.048376Z" } }, "outputs": [], "source": [ "from mapclassify.value_by_alpha import truncate_colormap" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "execution": { "iopub.execute_input": "2025-07-11T19:53:38.050190Z", "iopub.status.busy": "2025-07-11T19:53:38.050126Z", "iopub.status.idle": "2025-07-11T19:53:38.054243Z", "shell.execute_reply": "2025-07-11T19:53:38.054057Z", "shell.execute_reply.started": "2025-07-11T19:53:38.050182Z" } }, "outputs": [ { "data": { "image/png": "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", "text/html": [ "
trunc(RdBu,0.20,0.60)
\"trunc(RdBu,0.20,0.60)
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" ], "text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# truncate to the 20th-60th percentile of your datarange\n", "trunc0206 = truncate_colormap(\"RdBu\", minval=0.2, maxval=0.6, n=2)\n", "trunc0206" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "execution": { "iopub.execute_input": "2025-07-11T19:53:38.054690Z", "iopub.status.busy": "2025-07-11T19:53:38.054591Z", "iopub.status.idle": "2025-07-11T19:53:38.059064Z", "shell.execute_reply": "2025-07-11T19:53:38.058822Z", "shell.execute_reply.started": "2025-07-11T19:53:38.054682Z" } }, "outputs": [ { "data": { "image/png": "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", "text/html": [ "
trunc(RdBu,0.40,0.90)
\"trunc(RdBu,0.40,0.90)
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" ], "text/plain": [ "" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# truncate to the 40th-90th percentile of your datarange\n", "trunc0409 = truncate_colormap(\"RdBu\", minval=0.4, maxval=0.9, n=2)\n", "trunc0409" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "execution": { "iopub.execute_input": "2025-07-11T19:53:38.059518Z", "iopub.status.busy": "2025-07-11T19:53:38.059433Z", "iopub.status.idle": "2025-07-11T19:53:38.123678Z", "shell.execute_reply": "2025-07-11T19:53:38.123449Z", "shell.execute_reply.started": "2025-07-11T19:53:38.059509Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# create new figure\n", "fig, axs = plt.subplots(1, 2, figsize=(20, 10))\n", "\n", "# vba_choropleth with cmap trunc0206\n", "vba_choropleth(x, y, gdf, cmap=trunc0206, ax=axs[0], divergent=True)\n", "axs[0].set_axis_off()\n", "\n", "# vba_choropleth with cmap trunc0409\n", "vba_choropleth(x, y, gdf, cmap=trunc0409, ax=axs[1], divergent=True)\n", "axs[1].set_axis_off()\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Add a legend\n", "\n", "If your values are classified, you have the option to add a legend to your map." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "execution": { "iopub.execute_input": "2025-07-11T19:53:38.124138Z", "iopub.status.busy": "2025-07-11T19:53:38.124065Z", "iopub.status.idle": "2025-07-11T19:53:38.252136Z", "shell.execute_reply": "2025-07-11T19:53:38.251914Z", "shell.execute_reply.started": "2025-07-11T19:53:38.124130Z" } }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(15, 10))\n", "ax = fig.add_subplot(111)\n", "\n", "gdf.plot(ax=ax, fc=\"none\", ec=\"k\", lw=0.1)\n", "vba_choropleth(\n", " x,\n", " y,\n", " gdf,\n", " ax=ax,\n", " x_classification_kwds={\"classifier\": \"quantiles\", \"k\": 5},\n", " y_classification_kwds={\"classifier\": \"quantiles\", \"k\": 5},\n", " legend=True,\n", " legend_kwargs={\"x_label\": x, \"y_label\": y},\n", ")\n", "ax.set_axis_off()\n", "\n", "plt.show()" ] } ], "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": "3.13.5" } }, "nbformat": 4, "nbformat_minor": 4 } mapclassify-2.10.0/pyproject.toml000066400000000000000000000070431503430131600170210ustar00rootroot00000000000000[build-system] requires = ["setuptools>=61.0", "setuptools_scm[toml]>=6.2"] build-backend = "setuptools.build_meta" [tool.setuptools_scm] [project] name = "mapclassify" dynamic = ["version"] maintainers = [ { name = "Serge Rey", email = "sjsrey@gmail.com" }, { name = "Wei Kang", email = "weikang9009@gmail.com" }, ] license = { text = "BSD 3-Clause" } description = "Classification Schemes for Choropleth Maps." keywords = ["spatial statistics", "geovisualization"] readme = { text = """\ `mapclassify` implements a family of classification schemes for choropleth maps. Its focus is on the determination of the number of classes, and the assignment of observations to those classes. It is intended for use with upstream mapping and geovisualization packages (see `geopandas`_ and `geoplot`_) that handle the rendering of the maps. For further theoretical background see "`Choropleth Mapping`_" in Rey, S.J., D. Arribas-Bel, and L.J. Wolf (2020) "Geographic Data Science with PySAL and the PyData Stack”. .. _geopandas: https://geopandas.org/mapping.html .. _geoplot: https://residentmario.github.io/geoplot/user_guide/Customizing_Plots.html .. _Choropleth Mapping: https://geographicdata.science/book/notebooks/05_choropleth.html """, content-type = "text/x-rst" } classifiers = [ "Programming Language :: Python :: 3", "License :: OSI Approved :: BSD License", "Operating System :: OS Independent", "Intended Audience :: Science/Research", "Topic :: Scientific/Engineering :: GIS", ] requires-python = ">=3.11" dependencies = [ "networkx>=3.2", "numpy>=1.26", "pandas>=2.1", "scikit-learn>=1.4", "scipy>=1.12", ] [project.urls] Home = "https://pysal.org/mapclassify/" Repository = "https://github.com/pysal/mapclassify" [project.optional-dependencies] speedups = [ "numba>=0.58" ] dev = [ "ruff", "pre-commit", "watermark", ] docs = [ "nbsphinx", "numpydoc", "sphinx>=1.4.3", "sphinx-gallery", "sphinxcontrib-bibtex", "sphinx_bootstrap_theme", ] spatial = [ "geopandas", "libpysal", "matplotlib", "shapely", ] notebooks = [ "mapclassify[spatial]", "geodatasets", "ipywidgets", "jupyterlab", "lonboard", "pyarrow", "pydeck", "seaborn", ] tests = [ "mapclassify[spatial]", "pytest", "pytest-cov", "pytest-xdist", "pytest-doctestplus", ] all = ["mapclassify[speedups,dev,docs,notebooks,tests]"] [tool.setuptools.packages.find] include = ["mapclassify", "mapclassify.*"] [tool.ruff] line-length = 88 extend-include = [ "*.ipynb", "docs/conf.py" ] [tool.ruff.lint] select = ["E", "F", "W", "I", "UP", "N", "B", "A", "C4", "SIM", "ARG"] ignore = [ "B006", # Do not use mutable data structures for argument defaults ] [tool.ruff.lint.per-file-ignores] "*__init__.py" = [ "F401", # imported but unused "F403", # star import; unable to detect undefined names ] "_classify_API.py" = [ "N999", # Invalid module name ] "*.ipynb" = [ "C416", # Unnecessary dict comprehension (rewrite using `dict()`) "F401", # imported but unused ] "*tests/test_*.py" = [ "A004", # Import is shadowing a Python builtin "N802", # Function name should be lowercase ] "docs/conf.py" = [ "A001", # Variable is shadowing a Python builtin ] [tool.coverage.run] source = ["./mapclassify"] [tool.coverage.report] exclude_lines = [ "if self.debug:", "pragma: no cover", "raise NotImplementedError", "except ModuleNotFoundError:", "except ImportError", ] ignore_errors = true omit = ["mapclassify/tests/*", "docs/conf.py"]

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