pax_global_header00006660000000000000000000000064151470617210014516gustar00rootroot0000000000000052 comment=d0c4b5631b05df8495135957f46970a226d94c7d pointpats-2.5.5/000077500000000000000000000000001514706172100135505ustar00rootroot00000000000000pointpats-2.5.5/.github/000077500000000000000000000000001514706172100151105ustar00rootroot00000000000000pointpats-2.5.5/.github/release.yml000066400000000000000000000005171514706172100172560ustar00rootroot00000000000000changelog: exclude: labels: - ignore-for-release authors: - dependabot categories: - title: Bug Fixes labels: - bug - title: Enhancements labels: - enhancement - title: Maintenance labels: - maintenance - title: Other Changes labels: - "*"pointpats-2.5.5/.github/workflows/000077500000000000000000000000001514706172100171455ustar00rootroot00000000000000pointpats-2.5.5/.github/workflows/build_docs.yml000066400000000000000000000037411514706172100220040ustar00rootroot00000000000000# Building and hosting documentation for PACKAGE_NAME # # Notes: # - After the first run of this workflow: # - Within the project repo, navigate to Setting/Github Pages # - set `the source branch to `gh-pages/(root))`. # - Uncomment everything below the following line to enable the workflow. #--------------------------------------------------------------------------- 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 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/envs/314-latest.yaml] experimental: [false] defaults: run: shell: bash -l {0} steps: - name: checkout repo uses: actions/checkout@v4 - name: setup micromamba uses: mamba-org/setup-micromamba@v2 with: environment-file: ${{ matrix.environment-file }} micromamba-version: 'latest' - name: Install Dependencies run: | # Install ipykernel and jupyter into the 'test' environment micromamba install -n test ipykernel jupyter -c conda-forge -y - name: Register Jupyter kernel "python3" (for nbsphinx) run: | python -m ipykernel install --sys-prefix --name python3 --display-name "Python 3" jupyter kernelspec list - name: make docs run: | pip install -e . --no-deps --force-reinstall cd docs; make html - name: Publish to Github Pages uses: peaceiris/actions-gh-pages@v4 with: github_token: ${{ secrets.GITHUB_TOKEN }} publish_dir: docs/build/html/ keep_files: false pointpats-2.5.5/.github/workflows/release_and_publish.yml000066400000000000000000000030711514706172100236610ustar00rootroot00000000000000# Release package on GitHub and publish to PyPI # IMPORTANT -- 1 MANUAL STEP # * FOLLOWING TAGGED RELEASE # - update CHANGELOG.md #-------------------------------------------------- name: Release & Publish 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: name: Create release & publish to PyPI runs-on: ubuntu-latest permissions: id-token: write # MANDATORY for trusted publishing to PyPI contents: write # MANDATORY for the Github release action steps: - name: Checkout repo uses: actions/checkout@v6 - name: Set up python uses: actions/setup-python@v6 with: python-version: "3.x" - name: Install Dependencies run: | python -m pip install --upgrade pip python -m pip install --upgrade build twine python -m build twine check --strict dist/* - name: Create Release Notes uses: actions/github-script@v8 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 pointpats-2.5.5/.github/workflows/tests.yaml000066400000000000000000000031041514706172100211710ustar00rootroot00000000000000name: Tests on: push: branches: [main] pull_request: branches: - "*" schedule: - cron: "0 0 * * 1,4" workflow_dispatch: inputs: version: description: Manual CI Run default: test required: false jobs: Test: name: ${{ matrix.os }}, ${{ matrix.environment-file }} runs-on: ${{ matrix.os }} strategy: fail-fast: false matrix: os: [ubuntu-latest] environment-file: - ci/envs/311-oldest.yaml - ci/envs/311-latest.yaml - ci/envs/312-latest.yaml - ci/envs/313-latest.yaml - ci/envs/314-latest.yaml - ci/envs/314-dev.yaml include: - environment-file: ci/envs/314-latest.yaml os: macos-15-intel # Intel - environment-file: ci/envs/314-latest.yaml os: macos-latest # Apple Silicon - environment-file: ci/envs/314-latest.yaml os: windows-latest defaults: run: shell: bash -l {0} steps: - uses: actions/checkout@v4 - name: setup micromamba uses: mamba-org/setup-micromamba@v2 with: environment-file: ${{ matrix.environment-file }} micromamba-version: "latest" - name: Install pointpats run: pip install . - name: Test pointpats run: | pytest \ -v \ -n logical \ --color yes \ --cov pointpats \ --cov-append \ --cov-report term-missing \ --cov-report xml . - uses: codecov/codecov-action@v5 pointpats-2.5.5/.gitignore000066400000000000000000000004271514706172100155430ustar00rootroot00000000000000*.swp *.pyc .rope* .idea/ notebooks/.ipynb_checkpoints/ .DS_Store .ipynb_checkpoints/ *.bak .eggs/ *.egg-info/ # Packages *.egg *.egg-info dist build eggs parts bin var sdist develop-eggs .installed.cfg lib lib64 __pycache__ doc/_build doc/generated docs/_build docs/generatedpointpats-2.5.5/LICENSE.txt000066400000000000000000000027011514706172100153730ustar00rootroot00000000000000Copyright 2017-, pysal-pointpats 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. pointpats-2.5.5/README.md000066400000000000000000000045331514706172100150340ustar00rootroot00000000000000# pointpats: Point Pattern Analysis in PySAL [![Continuous Integration](https://github.com/pysal/pointpats/actions/workflows/tests.yaml/badge.svg)](https://github.com/pysal/pointpats/actions/workflows/tests.yaml) [![codecov](https://codecov.io/gh/pysal/pointpats/branch/main/graph/badge.svg)](https://codecov.io/gh/pysal/pointpats) [![Documentation](https://img.shields.io/static/v1.svg?label=docs&message=current&color=9cf)](http://pysal.org/pointpats/) [![PyPI version](https://badge.fury.io/py/pointpats.svg)](https://badge.fury.io/py/pointpats) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.7706219.svg)](https://doi.org/10.5281/zenodo.7706219) Statistical analysis of planar point patterns. This package is part of [PySAL](https://pysal.org): The Python Spatial Analysis Library. ## Introduction This [pointpats](https://github.com/pysal/pointpats) package is intended to support the statistical analysis of planar point patterns. It currently works on cartesian coordinates. Users with data in geographic coordinates need to project their data prior to using this module. ## Documentation Online documentation is available [here](http://pysal.org/pointpats/). ## Installation Install pointpats by running: $ pip install pointpats ## Development pointpats development is hosted on [github](https://github.com/pysal/pointpats). As part of the PySAL project, pointpats development follows [PySAL's documentation for developers](https://pysal.org/docs/devs/) and the PySAL [development guidelines](https://github.com/pysal/pysal/wiki). ## Bug reports To search for or report bugs, please see pointpats' [issues](https://github.com/pysal/pointpats/issues). ## BibTeX Citation ``` @software{wei_kang_2023_7706219, author = {Wei Kang and Levi John Wolf and Sergio Rey and Hu Shao and Mridul Seth and Martin Fleischmann and Sugam Srivastava and James Gaboardi and Giovanni Palla and Dani Arribas-Bel and Qiusheng Wu}, title = {pysal/pointpats: pointpats 2.3.0}, month = mar, year = 2023, publisher = {Zenodo}, version = {v2.3.0}, doi = {10.5281/zenodo.7706219}, url = {https://doi.org/10.5281/zenodo.7706219} } ``` pointpats-2.5.5/ci/000077500000000000000000000000001514706172100141435ustar00rootroot00000000000000pointpats-2.5.5/ci/envs/000077500000000000000000000000001514706172100151165ustar00rootroot00000000000000pointpats-2.5.5/ci/envs/311-latest.yaml000066400000000000000000000004311514706172100175760ustar00rootroot00000000000000name: test channels: - conda-forge dependencies: - python=3.11 - geopandas - libpysal - matplotlib - numpy - pandas - scipy - shapely # tests - scikit-learn - statsmodels - pytest - pytest-cov - pytest-xdist - codecov - pip - pip: - KDEpy pointpats-2.5.5/ci/envs/311-oldest.yaml000066400000000000000000000004351514706172100176000ustar00rootroot00000000000000name: test channels: - conda-forge dependencies: - python=3.11 - geopandas=1.0 - libpysal=4.10 - matplotlib=3.9 - numpy=1.26 - pandas=2.2 - scipy=1.12 - shapely=2.1 # tests - scikit-learn=1.4 - statsmodels - pytest - pytest-cov - pytest-xdist - codecov pointpats-2.5.5/ci/envs/312-latest.yaml000066400000000000000000000004311514706172100175770ustar00rootroot00000000000000name: test channels: - conda-forge dependencies: - python=3.12 - geopandas - libpysal - matplotlib - numpy - pandas - scipy - shapely # tests - scikit-learn - statsmodels - pytest - pytest-cov - pytest-xdist - codecov - pip - pip: - KDEpy pointpats-2.5.5/ci/envs/313-latest.yaml000066400000000000000000000004311514706172100176000ustar00rootroot00000000000000name: test channels: - conda-forge dependencies: - python=3.13 - geopandas - libpysal - matplotlib - numpy - pandas - scipy - shapely # tests - scikit-learn - statsmodels - pytest - pytest-cov - pytest-xdist - codecov - pip - pip: - KDEpy pointpats-2.5.5/ci/envs/314-dev.yaml000066400000000000000000000011001514706172100170550ustar00rootroot00000000000000name: test channels: - conda-forge dependencies: - python=3.14 - folium - libpysal - mapclassify - matplotlib - numpy - pandas - shapely # tests - codecov - pytest - pytest-cov - pytest-xdist - pyproj - pip - pip: - --pre \ --index-url https://pypi.anaconda.org/scientific-python-nightly-wheels/simple \ --extra-index-url https://pypi.org/simple - KDEpy - scipy - scikit-learn - statsmodels - git+https://github.com/pysal/libpysal.git - git+https://github.com/geopandas/geopandas.git pointpats-2.5.5/ci/envs/314-latest.yaml000066400000000000000000000010401514706172100175760ustar00rootroot00000000000000name: test channels: - conda-forge dependencies: - python=3.14 - geopandas - libpysal - matplotlib - numpy - pandas - scipy - shapely # tests - scikit-learn - statsmodels - pytest - pytest-cov - pytest-xdist - codecov - pip - pip: - KDEpy # for docs build action (this env only) - sphinx - nbsphinx - numpydoc - sphinxcontrib-bibtex - sphinx_bootstrap_theme # notebook execution support (required by nbsphinx) - ipykernel - jupyter_client - nbconvert - nbformat - nbclient pointpats-2.5.5/codecov.yml000066400000000000000000000005421514706172100157160ustar00rootroot00000000000000codecov: notify: after_n_builds: 7 coverage: range: 50..95 round: nearest precision: 1 status: project: default: threshold: 5% patch: default: threshold: 20% target: 60% ignore: - "tests/*" comment: layout: "reach, diff, files" behavior: once after_n_builds: 7 require_changes: truepointpats-2.5.5/docs/000077500000000000000000000000001514706172100145005ustar00rootroot00000000000000pointpats-2.5.5/docs/Makefile000066400000000000000000000014161514706172100161420ustar00rootroot00000000000000# Minimal makefile for Sphinx documentation # # You can set these variables from the command line. SPHINXOPTS = SPHINXBUILD = sphinx-build SPHINXPROJ = PACKAGE_NAME SOURCEDIR = . 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PrBwf̘67BgC@4dRD" B=0 ESuuP(3g~ƌӧO'?C,_|1c 'O߿xWUU`0 EGSS\.߻wolllQQQ}$ɒ%Kvw@LLZkӧ{}W\9tDѪ-[vS>|Z/>k׮=[o0eʔk׮ M@!$0 E޽*&Md2t钷 K%pjL&;{,uJΜ9իSN ,k׮]cǎ}03fٳgΝ;(׏B%PdeeAAA Ș={P($t̙[[[hTjY`ٓ&M|2ܿ$>|X p'RB[CB+oڵksY`ݻihhx7m۶ggg~)88뫭$..n /!`PP_p!''a„9sLj⚛mll|||񁁁7o$ ]v%$$\tj(R ߏ>}:YvZSS=!!鮊Bh)Jm!B=s [class*='col-'] { display: flex; flex-direction: 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: 1 0 auto; flex-direction: column; } .row.equal-height > [class*='col-'] > .thumbnail > .caption > .flex-text { flex-grow: 1; } .row.equal-height > [class*='col-'] > .thumbnail > img { width: 100%; height: 200px; /* 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: 600px; width: 100%; margin: 0; overflow-y: auto; } .table-scrollable { margin: 0; padding: 0; } .label { color: #ff0000; /*font-size: 100%;*/ } div.body { max-width: 1080px; } pointpats-2.5.5/docs/_static/references.bib000066400000000000000000000105741514706172100207340ustar00rootroot00000000000000%% This BibTeX bibliography file was created using BibDesk. %% https://bibdesk.sourceforge.io/ %% Created for weikang at 2020-04-22 14:18:22 -0700 %% Saved with string encoding Unicode (UTF-8) @inbook{Sullivan2010, Author = {O'Sullivan, D. and Unwin, D. J.}, Booktitle = {Geographic Information Analysis}, Chapter = {5}, Date-Added = {2020-04-22 14:18:04 -0700}, Date-Modified = {2020-04-22 14:18:21 -0700}, Doi = {10.1002/9780470549094.ch5}, Pages = {121-156}, Publisher = {John Wiley \& Sons, Ltd}, Title = {Point Pattern Analysis}, Year = {2010}, Bdsk-Url-1 = {https://doi.org/10.1002/9780470549094.ch5}} @article{Baker:2004, Author = {Baker, Rose D}, Date-Added = {2019-11-20 20:48:20 -0800}, Date-Modified = {2019-11-20 20:48:48 -0800}, Journal = {Acta tropica}, Number = {3}, Pages = {291--299}, Publisher = {Elsevier}, Title = {Identifying space--time disease clusters}, Volume = {91}, Year = {2004}} @article{Jacquez:1996, Author = {Jacquez, Geoffrey M}, Date-Added = {2019-11-20 20:47:22 -0800}, Date-Modified = {2019-11-20 20:47:31 -0800}, Journal = {Statistics in medicine}, Number = {18}, Pages = {1935--1949}, Publisher = {Wiley Online Library}, Title = {A k nearest neighbour test for space--time interaction}, Volume = {15}, Year = {1996}} @article{Mantel:1967, Author = {Mantel, Nathan}, Date-Added = {2019-11-20 20:45:32 -0800}, Date-Modified = {2019-11-20 20:52:18 -0800}, Issn = {0008-5472}, Journal = {Cancer research}, Month = {February}, Number = {2}, Pages = {209---220}, Title = {The detection of disease clustering and a generalized regression approach}, Volume = {27}, Year = {1967}, Bdsk-Url-1 = {http://europepmc.org/abstract/MED/6018555}} @article{Rogerson:2001, Author = {P. Rogerson}, Year = {2001}, Journal = {Journal of the Royal Statistical Society, Series A}, Volume = {164}, Pages = {87--96}, Title = {Monitoring point patterns for the development of space-time clusters} } @article{Knox:1964, Author = {E. G. Knox and M. S. Bartlett}, Date-Added = {2019-11-20 20:42:48 -0800}, Date-Modified = {2019-11-20 20:43:06 -0800}, Issn = {00359254, 14679876}, Journal = {Journal of the Royal Statistical Society. Series C (Applied Statistics)}, Number = {1}, Pages = {25--30}, Publisher = {[Wiley, Royal Statistical Society]}, Title = {The Detection of Space-Time Interactions}, Url = {http://www.jstor.org/stable/2985220}, Volume = {13}, Year = {1964}, Bdsk-Url-1 = {http://www.jstor.org/stable/2985220}} @article{VanLieshout1996, Abstract = {The strength and range of interpoint interactions in a spatial point process can be quantified by the function J = (1 - G)/(1 - F), where G is the nearest-neighbour distance distribution function and F the empty space function of the process. J(r) is identically equal to 1 for a Poisson process; values of J(r) smaller or larger than 1 indicate clustering or regularity, respectively. We show that, for a large class of point processes, J(r) is constant for distances r greater than the range of spatial interaction. Hence both the range and type of interaction can be inferred from J without parametric model assumptions. It is also possible to evaluate J(r) explicitly for many point process models, so that J is also useful for parameter estimation. Various properties are derived, including the fact that the J function of the superposition of independent point processes is a weighted mean of the J functions of the individual processes. Estimators of J can be constructed from standard estimators of F and G. We compute estimates of J for several standard point pattern datasets and implement a Monte Carlo test for complete spatial randomness.}, Author = {Lieshout, M. N. M. and Baddeley, A. J.}, Date-Added = {2018-11-12 17:23:48 -0800}, Date-Modified = {2018-11-12 17:24:22 -0800}, Doi = {10.1111/j.1467-9574.1996.tb01501.x}, Journal = {Statistica Neerlandica}, Keywords = {clustering, empty space function, J-statistic, Monte Carlo inference, nearest-neighbour distance distribution, Nguyen-Zessin formula, point process, spatial interaction, spatial statistics, regularity}, Number = {3}, Pages = {344-361}, Title = {A nonparametric measure of spatial interaction in point patterns}, Volume = {50}, Year = {1996}, Bdsk-Url-1 = {https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1467-9574.1996.tb01501.x}, Bdsk-Url-2 = {https://doi.org/10.1111/j.1467-9574.1996.tb01501.x}} pointpats-2.5.5/docs/api.rst000066400000000000000000000032541514706172100160070ustar00rootroot00000000000000.. _api_ref: .. currentmodule:: pointpats API reference ============= .. _pointpattern_api: Point Pattern -------------- .. autosummary:: :toctree: generated/ PointPattern .. _pointprocess_api: Point Processes --------------- .. autosummary:: :toctree: generated/ PointProcess PoissonPointProcess PoissonClusterPointProcess .. _centrgraphy_api: Centrography ------------ .. autosummary:: :toctree: generated/ minimum_bounding_rectangle minimum_bounding_circle minimum_rotated_rectangle hull mean_center weighted_mean_center manhattan_median std_distance euclidean_median ellipse dtot .. _density_api: Density ------- .. autosummary:: :toctree: generated/ plot_density .. _quadrat_api: Quadrat Based Statistics ------------------------ .. autosummary:: :toctree: generated/ RectangleM HexagonM QStatistic .. _distance_api: Distance Based Statistics -------------------------- .. autosummary:: :toctree: generated/ f g k j l f_test g_test k_test j_test l_test .. _window_api: Window functions ---------------- .. autosummary:: :toctree: generated/ Window as_window poly_from_bbox to_ccf Random distributions -------------------- .. autosummary:: :toctree: generated/ random.poisson random.normal random.cluster_poisson random.cluster_normal Space-Time Interaction Tests ----------------------------- .. autosummary:: :toctree: generated/ SpaceTimeEvents Knox KnoxLocal mantel jacquez modified_knox Visualization --------------- .. autosummary:: :toctree: generated/ plot_densitypointpats-2.5.5/docs/conf.py000066400000000000000000000245551514706172100160120ustar00rootroot00000000000000# -*- coding: utf-8 -*- # # pointpats documentation build configuration file, created by # sphinx-quickstart on Nov 12 16:37:22 2018. # # This file is execfile() 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. # 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, os import sphinx_bootstrap_theme sys.path.insert(0, os.path.abspath("../")) import pointpats # -- 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", ] # sphinx_gallery_conf = { # # path to your examples scripts # 'examples_dirs': '../examples', # # path where to save gallery generated examples # 'gallery_dirs': 'auto_examples', # 'backreferences_dir': False, # } # 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 = "pointpats" 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 = pointpats.__version__ release = pointpats.__version__ # 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 bibtex_bibfiles = ["_static/references.bib"] # -- 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 = "%s v%s Manual" % (project, version) # (Optional) Logo. Should be small enough to fit the navbar (ideally 24x24). # Path should be relative to the ``_static`` files directory. # html_logo = "_static/images/CGS_logo.jpg" # html_logo = "_static/images/CGS_logo_green.png" # html_logo = "_static/images/pysal_logo_small.jpg" 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": "pointpats", # 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", "navbar_links": [ ("Installation", "installation"), ("User Guide", "user-guide/intro"), ("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 = "pointpatsdoc" # -- 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, "pointpats.tex", "pointpats 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, "pointpats", "pointpats 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, "pointpats", "pointpats Documentation", author, "pointpats", "One line description of project.", "Miscellaneous", ), ] # ----------------------------------------------------------------------------- # Napoleon configuration # ----------------------------------------------------------------------------- # numpydoc_show_class_members = True # numpydoc_class_members_toctree = False # # napoleon_use_ivar = True # ----------------------------------------------------------------------------- # Autosummary # ----------------------------------------------------------------------------- # Generate the API documentation when building autosummary_generate = True # avoid showing members twice numpydoc_show_class_members = False numpydoc_use_plots = True # automatically document class members autodoc_default_options = { "members": True, "undoc-members": True, "inherited-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 = {"python": ('https://docs.python.org/3', None), 'numpy': ('https://numpy.org/doc/stable', None), 'scipy': ('https://docs.scipy.org/doc/scipy', None), 'libpysal': ('https://pysal.org/libpysal/', None), 'pandas': ('https://pandas.pydata.org/pandas-docs/stable/', None), 'matplotlib':("https://matplotlib.org/stable/", None), 'KDEpy':("https://kdepy.readthedocs.io/en/latest/", None), 'statsmodels':("https://www.statsmodels.org/stable/", None), } # 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}}, } # 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}", ] # --- nbsphinx execution policy --------------------------------------------- nbsphinx_execute = "always" nbsphinx_timeout = 600 # nbsphinx_allow_errors = True # optional mathjax3_config = { "TeX": {"equationNumbers": {"autoNumber": "AMS", "useLabelIds": True}}, } pointpats-2.5.5/docs/index.rst000066400000000000000000000105221514706172100163410ustar00rootroot00000000000000.. pointpats documentation master file. Point Pattern Analysis (pointpats) ================================== Statistical analysis of planar point patterns in Python. `pointpats` is an open-source library for the analysis of planar point patterns and a subpackage of the Python Spatial Analysis Library, `PySAL`_. Key workflows (notebooks) ------------------------- Explore the main workflows through executable notebooks: .. raw:: html
Centrography notebook
Centrography

Centroids, dispersion measures, and visual summaries.
Quadrat statistics notebook
Quadrat statistics

Grid-based summaries and tests for spatial randomness.
Distance statistics notebook
Distance-based statistics

G, K, and L functions, envelopes, and interaction structure.
Quickstart ---------- Install the latest release: .. code-block:: bash pip install pointpats or using conda-forge: .. code-block:: bash conda install -c conda-forge pointpats A minimal example: .. code-block:: python import numpy as np from pointpats import PointPattern # toy coordinates coords = np.random.random((100, 2)) pp = PointPattern(coords) print("n:", pp.n) print("mean center:", pp.mean_center) print("average nearest-neighbor distance:", pp.nnd.mean()) What you can do with ``pointpats`` ---------------------------------- - Build and summarize **point pattern objects** from coordinate data. - Compute **centrographic measures** (mean center, standard distance, ellipses). - Perform **quadrat statistics** for tests of complete spatial randomness. - Use **distance-based functions** (G, K, L) and simulation envelopes. - Work with **marked patterns** and **simulated point processes**. User Guide ---------- The full user guide is organized around executable notebooks: .. toctree:: :maxdepth: 1 :caption: User Guide user-guide/intro .. toctree:: :maxdepth: 2 :caption: Reference :hidden: Installation API References Part of the PySAL ecosystem --------------------------- ``pointpats`` is part of the `PySAL`_ family of spatial analysis libraries, alongside components for spatial weights, regression, clustering, and more. - Source code: https://github.com/pysal/pointpats - Bug reports and feature requests: https://github.com/pysal/pointpats/issues Citation -------- If you use ``pointpats`` in your work, please cite the Zenodo record: .. code-block:: bibtex @software{wei_kang_2023_7706219, author = {Wei Kang and Levi John Wolf and Sergio Rey and Hu Shao and Mridul Seth and Martin Fleischmann and Sugam Srivastava and James Gaboardi and Giovanni Palla and Dani Arribas-Bel and Qiusheng Wu}, title = {pysal/pointpats: pointpats 2.3.0}, year = {2023}, publisher = {Zenodo}, version = {v2.3.0}, doi = {10.5281/zenodo.7706219}, url = {https://doi.org/10.5281/zenodo.7706219} } .. _PySAL: https://github.com/pysal/pysal pointpats-2.5.5/docs/installation.rst000066400000000000000000000031331514706172100177330ustar00rootroot00000000000000.. Installation Installation ============ Currently, pointpats supports Python >= `3.11`_. Please make sure that you are operating in a Python 3 environment. Installing released version --------------------------- pointpats is available on the `Python Package Index`_. Therefore, you can either install directly with `pip` from the command line:: pip install -U pointpats 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 . You may also install the latest stable pointpats via `conda-forge`_ channel by running:: $ conda install --channel conda-forge pointpats Installing development version ------------------------------ Potentially, you might want to use the newest features in the development version of pointpats on github - `pysal/pointpats`_ while have not been incorporated in the Pypi released version. You can achieve that by installing `pysal/pointpats`_ by running the following from a command shell:: pip install git+https://github.com/pysal/pointpats.git You can also `fork`_ the `pysal/pointpats`_ repo and create a local clone of your fork. By making changes to your local clone and submitting a pull request to `pysal/pointpats`_, you can contribute to the pointpats development. .. _3.11: https://docs.python.org/3.11/ .. _Python Package Index: https://pypi.org/project/pointpats/ .. _pysal/pointpats: https://github.com/pysal/pointpats .. _fork: https://help.github.com/articles/fork-a-repo/ .. _conda-forge: https://github.com/conda-forge/pointpats-feedstockpointpats-2.5.5/docs/make.bat000066400000000000000000000014471514706172100161130ustar00rootroot00000000000000@ECHO OFF pushd %~dp0 REM Command file for Sphinx documentation if "%SPHINXBUILD%" == "" ( set SPHINXBUILD=python -msphinx ) set SOURCEDIR=. set BUILDDIR=_build set SPHINXPROJ=pointpats if "%1" == "" goto help %SPHINXBUILD% >NUL 2>NUL if errorlevel 9009 ( echo. echo.The Sphinx module was not found. Make sure you have Sphinx installed, echo.then set the SPHINXBUILD environment variable to point to the full echo.path of the 'sphinx-build' executable. Alternatively you may add the echo.Sphinx directory to PATH. echo. echo.If you don't have Sphinx installed, grab it from echo.http://sphinx-doc.org/ exit /b 1 ) %SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% goto end :help %SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% :end popd pointpats-2.5.5/docs/references.rst000066400000000000000000000001461514706172100173540ustar00rootroot00000000000000.. reference for the docs References ========== .. bibliography:: _static/references.bib :cited: pointpats-2.5.5/docs/user-guide/000077500000000000000000000000001514706172100165515ustar00rootroot00000000000000pointpats-2.5.5/docs/user-guide/Quadrat_statistics.ipynb000066400000000000000000000262441514706172100234770ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Quadrat-based statistics in `pointpats`\n", "\n", "This notebook demonstrates quadrat-based methods for analyzing planar point\n", "patterns using the current `pointpats.quadrat_statistics` implementation.\n", "\n", "The workflow is:\n", "\n", "1. Represent the point pattern as a NumPy array of coordinates with shape `(n, 2)`.\n", "2. Construct a rectangular or hexagonal quadrat tessellation over the **minimum\n", " bounding box (MBB)** of the points.\n", "3. Compute a Pearson chi-square statistic from per-quadrat counts.\n", "4. Optionally, obtain a **Monte Carlo p-value** by simulating CSR realizations\n", " within the same study window, with reproducibility controlled by `rng`.\n", "\n", "Key implementation facts (relevant for interpretation):\n", "\n", "- The default chi-square statistic uses **equal expected counts** across included cells.\n", "- A `window` may be supplied; if omitted, the MBB rectangle is used.\n", "- Monte Carlo inference makes the reference distribution window-aware, but it does not\n", " change how expected counts are defined.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Quadrat statistics and CSR\n", "\n", "Quadrat analysis partitions space into a set of non-overlapping cells and compares\n", "the observed counts per cell to what is expected under **complete spatial randomness (CSR)**.\n", "\n", "Let $O_i$ be the count in cell \\(i\\), and let \\(k\\) be the number of cells\n", "included in the analysis. The implemented test statistic is the Pearson chi-square\n", "statistic with **equal expected counts** across included cells:\n", "\n", "$$\n", "X^2 = \\sum_{i=1}^k \\frac{(O_i - \\bar{O})^2}{\\bar{O}},\n", "\\qquad\n", "\\bar{O} = \\frac{1}{k} \\sum_{i=1}^k O_i.\n", "$$\n", "\n", "Analytical inference compares $X^2$ to a chi-square distribution with $(k-1)$\n", "degrees of freedom. Simulation-based inference (Monte Carlo) instead compares the\n", "observed $X^2$ to the sampling distribution obtained from CSR realizations\n", "generated in the same window.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Example: the juvenile delinquency point pattern\n", "\n", "We will illustrate the quadrat workflow using the `juvenile.shp` example dataset.\n", "The dataset is read as a GeoDataFrame and then converted to a NumPy coordinate array\n", "`xy` with shape `(n, 2)`.\n", "\n", "These coordinates are passed directly to `QStatistic`.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import libpysal as ps\n", "import numpy as np\n", "import geopandas as gpd\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import warnings\n", "warnings.filterwarnings(\"ignore\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Import `pointpats.quadrat_statistics` and compute quadrat statistics.\n", "\n", "The main entry point is:\n", "\n", "```python\n", "QStatistic(points, shape=\"rectangle\"|\"hexagon\", realizations=0, window=None,\n", " drop_nonintersecting=False, rng=None)\n", "```\n", "\n", "- `points` is always a NumPy array of shape `(n, 2)`.\n", "- `shape` selects the tessellation type.\n", "- `realizations > 0` enables Monte Carlo inference.\n", "- `rng` controls reproducibility of simulations.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from pointpats.quadrat_statistics import QStatistic" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Open the point shapefile and extract coordinates.\n", "\n", "Here we use `GeoDataFrame.get_coordinates()` to obtain an `(n, 2)` NumPy array,\n", "which is the required input type for `QStatistic`.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "juv = gpd.read_file((ps.examples.get_path(\"juvenile.shp\")))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "juv.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "xy = juv.get_coordinates()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "xy" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "resRect = QStatistic(xy)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "resRect.chi2_pvalue" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "resRect.plot()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "resHex = QStatistic(xy, shape='hexagon')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "resHex.plot()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "resHex.chi2_pvalue" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Simulation-based inference (Monte Carlo)\n", "\n", "When `realizations > 0`, CSR realizations are generated **within the same window**\n", "and the chi-square statistic is recomputed for each realization. The Monte Carlo\n", "p-value uses the standard +1 correction:\n", "\n", "$$\n", "p = \\frac{\\#\\{X^2_{sim} \\ge X^2_{obs}\\} + 1}{R + 1},\n", "$$\n", "\n", "where $R$ is the number of realizations.\n", "\n", "### Reproducibility\n", "\n", "All simulation-based results can be made reproducible by supplying `rng`:\n", "\n", "- `None` (default): create a new `numpy.random.default_rng()`\n", "- integer seed\n", "- `numpy.random.Generator`\n", "- legacy `numpy.random.RandomState` (wrapped)\n", "\n", "The same generator is passed through the CSR simulation code, ensuring repeatable\n", "Monte Carlo p-values.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from numpy.random import default_rng" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "rng = default_rng(42)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "resHex_sim = QStatistic(xy, shape='hexagon', realizations=99, rng=rng)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "resHex_sim.chi2_r_pvalue\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "resHex_sim.chi2_realizations" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "resHex_sim.chi2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4. Irregular windows\n", "\n", "Quadrat grids are constructed over the MBB of the points. If the study window is\n", "irregular (e.g., a polygon), some grid cells may lie entirely outside the window.\n", "\n", "- If such outside cells are included, they contribute guaranteed zeros and can\n", " distort the test.\n", "\n", "\n", "### How Monte Carlo interacts with this\n", "\n", "Comparing the observed chi-square statistic to simulated chi-square values is a\n", "valid Monte Carlo test **for the statistic being used**, because the same grid,\n", "window, and inclusion rule are applied to both observed and simulated patterns.\n", "\n", "Monte Carlo inference makes the *reference distribution* window-aware, but it does\n", "not convert the test into an area-proportional quadrat test when the window is irregular.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from shapely.geometry import Polygon" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "window = Polygon([ [0, 0], [1, 0], [0, 1] ])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from pointpats.random import poisson" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pp = poisson(window, 100/1, rng=42)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pp" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "qs = QStatistic(pp, nx=5, ny=5)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "qs.plot()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "qs.chi2_pvalue" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "qsw = QStatistic(pp, nx=5, ny=5, window=window, realizations=99, rng=42)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "qsw.chi2_r_pvalue" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "qsw.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 5. Recap\n", "\n", "In this notebook, we:\n", "\n", "- represented a point pattern as an `(n, 2)` NumPy coordinate array,\n", "- computed quadrat counts using rectangular and hexagonal tessellations over the MBB,\n", "- used a Pearson chi-square statistic with equal expected counts across included cells,\n", "- obtained either an analytical p-value or a Monte Carlo p-value from CSR realizations,\n", "- clarified the issues associated with irregular windows, and\n", "- ensured reproducibility of simulations via an explicit `rng`." ] } ], "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": "3.14.2" }, "widgets": { "state": {}, "version": "1.1.2" } }, "nbformat": 4, "nbformat_minor": 4 } pointpats-2.5.5/docs/user-guide/centrography.ipynb000066400000000000000000000570141514706172100223300ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Centrography in `pointpats`\n", "\n", "This notebook introduces **centrographic statistics** for planar point\n", "patterns, focusing on:\n", "\n", "- Measures of **central tendency** (mean center, weighted mean center,\n", " Manhattan and Euclidean medians)\n", "- Measures of **dispersion and orientation** (standard distance and\n", " the standard deviational ellipse)\n", "- Basic **shape descriptors** (convex hull and minimum bounding rectangle)\n", "\n", "We will:\n", "\n", "1. Construct a simple example point pattern and compute its centrographic\n", " summaries using functions from `pointpats.centrography`.\n", "2. Visualize central tendency, dispersion, and shape diagnostics to build\n", " intuition about how these measures behave.\n", "3. Explore an additional example based on simulated point patterns within the\n", " Virginia state border under complete spatial randomness (CSR).\n", "\n", "This notebook is designed to live in the `docs/user-guide/` folder and be\n", "executed automatically as part of the `pointpats` documentation build.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The python file **centrography.py** contains several functions with which we can conduct centrography analysis.\n", "* Central Tendency\n", " 1. mean_center: calculate the mean center of the unmarked point pattern.\n", " 2. weighted_mean_center: calculate the weighted mean center of the marked point pattern.\n", " 3. manhattan_median: calculate the manhattan median\n", " 4. euclidean_median: calculate the Euclidean median\n", "* Dispersion and Orientation\n", " 1. std_distance: calculate the standard distance\n", "* Shape Analysis\n", " 1. hull: calculate the convex hull of the point pattern\n", " 2. mbr: calculate the minimum bounding box (rectangle)\n", " \n", "All of the above functions operate on a series of coordinate pairs. That is, the data type of the first argument should be $(n,2)$ array_like. In case that you have a point pattern (PointPattern instance), you need to pass its attribute \"points\" instead of itself to these functions." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "from pointpats import PointPattern\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "points = [[66.22, 32.54], [22.52, 22.39], [31.01, 81.21],\n", " [9.47, 31.02], [30.78, 60.10], [75.21, 58.93],\n", " [79.26, 7.68], [8.23, 39.93], [98.73, 77.17],\n", " [89.78, 42.53], [65.19, 92.08], [54.46, 8.48]]\n", "pp = PointPattern(points) #create a point pattern \"pp\" from list\n", "pp.points " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "type(pp.points)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can use PointPattern class method **plot** to visualize **pp**." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pp.plot()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#import centragraphy analysis functions \n", "from pointpats.centrography import hull, mean_center, minimum_bounding_rectangle, weighted_mean_center, manhattan_median, std_distance,euclidean_median,ellipse" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Central tendency\n", "\n", "Central Tendency concerns about the center point of the two-dimensional distribution. It is similar to the first moment of a one-dimensional distribution. There are several ways to measure central tendency, each having pros and cons. We need to carefully select the appropriate measure according to our objective and data status.\n", "\n", "### Mean Center $(x_{mc},y_{mc})$\n", "\n", "$$x_{mc}=\\frac{1}{n} \\sum^n_{i=1}x_i$$\n", "$$y_{mc}=\\frac{1}{n} \\sum^n_{i=1}y_i$$" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "mc = mean_center(pp.points)\n", "mc" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pp.plot()\n", "plt.plot(mc[0], mc[1], 'b^', label='Mean Center')\n", "plt.legend(numpoints=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1.1 Weighted mean center $(x_{wmc}, y_{wmc})$\n", "\n", "$$x_{wmc}=\\sum^n_{i=1} \\frac{w_i x_i}{\\sum^n_{i=1}w_i}$$\n", "$$y_{wmc}=\\sum^n_{i=1} \\frac{w_i y_i}{\\sum^n_{i=1}w_i}$$\n", "\n", "Weighted mean center is meant for marked point patterns. Aside from the first argument which is a seris of $(x,y)$ coordinates in **weighted_mean_center** function, we need to specify its second argument which is the weight for each event point." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "weights = np.arange(12)\n", "weights" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "wmc = weighted_mean_center(pp.points, weights)\n", "wmc" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pp.plot() #use class method \"plot\" to visualize point pattern\n", "plt.plot(mc[0], mc[1], 'b^', label='Mean Center') \n", "plt.plot(wmc[0], wmc[1], 'gd', label='Weighted Mean Center')\n", "plt.legend(numpoints=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1.2 Manhattan median $(x_{mm}, y_{mm})$\n", "\n", "$$min f(x_{mm},y_{mm})= \\sum^n_{i=1}(|x_i-x_{mm}|+|y_i-y_{mm}|)$$\n", "\n", "The Manhattan median is the location which minimizes the absolute distance to all the event points. It is an extension of the median measure in one-dimensional space to two-dimensional space. Since in one-dimensional space, a median is the number separating the higher half of a dataset from the lower half, we define the Manhattan median as a tuple whose first element is the median of $x$ coordinates and second element is the median of $y$ coordinates.\n", "\n", "Though Manhattan median can be found very quickly, it is not unique if you have even number of points. In this case, pysal handles the Manhattan median the same way as numpy.median: return the average of the two middle values." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#get the number of points in point pattern \"pp\"\n", "pp.n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#Manhattan Median is not unique for \"pp\"\n", "mm = manhattan_median(pp.points)\n", "mm" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pp.plot()\n", "plt.plot(mc[0], mc[1], 'b^', label='Mean Center')\n", "plt.plot(wmc[0], wmc[1], 'gd', label='Weighted Mean Center')\n", "plt.plot(mm[0], mm[1], 'rv', label='Manhattan Median')\n", "plt.legend(numpoints=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1.3 Euclidean median $(x_{em}, y_{em})$\n", "\n", "$$min f(x_{em},y_{em})= \\sum^n_{i=1} \\sqrt{(x_i-x_{em})^2+(y_i-y_{em})^2}$$\n", "\n", "The Euclidean Median is the location from which the sum of the Euclidean distances to all points in a distribution is a minimum. It is an optimization problem and very important for more general location allocation problems. There is no closed form solution. We can use first iterative algorithm (Kuhn and Kuenne, 1962) to approximate Euclidean Median. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below, we define a function named median_center with the first argument **points** a series of $(x,y)$ coordinates and the second argument **crit** the convergence criterion." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def median_center(points, crit=0.0001):\n", " points = np.asarray(points)\n", " x0, y0 = points.mean(axis=0)\n", " dx = np.inf\n", " dy = np.inf\n", " iteration = 0\n", " while np.abs(dx) > crit or np.abs(dy) > crit:\n", " xd = points[:, 0] - x0\n", " yd = points[:, 1] - y0\n", " d = np.sqrt(xd*xd + yd*yd)\n", " w = 1./d\n", " w = w / w.sum()\n", " x1 = w * points[:, 0]\n", " x1 = x1.sum()\n", " y1 = w * points[:, 1]\n", " y1 = y1.sum()\n", " dx = x1 - x0\n", " dy = y1 - y0\n", " iteration +=1 \n", " print(x0, x1, dx, dy, d.sum(), iteration)\n", " x0 = x1\n", " y0 = y1\n", " \n", " return x1, y1" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "median_center(pp.points, crit=.0001)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After 18 iterations, the convergence criterion is reached. The Euclidean Median is $(54.167594287646125,44.424308658832047)$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also call the function **euclidean_median** in pysal to calculate the Euclidean Median." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "em = euclidean_median(pp.points)\n", "em" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The two results we get from **euclidean_median** function in pysal and the **median_center** function we define here are very much the same." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pp.plot()\n", "plt.plot(mc[0], mc[1], 'b^', label='Mean Center')\n", "plt.plot(wmc[0], wmc[1], 'gd', label='Weighted Mean Center')\n", "plt.plot(mm[0], mm[1], 'rv', label='Manhattan Median')\n", "plt.plot(em[0], em[1], 'm+', label='Euclidean Median')\n", "plt.legend(numpoints=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Dispersion and orientation\n", "\n", "### Standard Distance & Standard Distance Circle\n", "\n", "$$SD = \\displaystyle \\sqrt{\\frac{\\sum^n_{i=1}(x_i-x_{m})^2}{n} + \\frac{\\sum^n_{i=1}(y_i-y_{m})^2}{n}}$$\n", "\n", "The Standard distance is closely related to the usual definition of the standard deviation of a data set, and it provides a measure of how dispersed the events are around their mean center $(x_m,y_m)$. Taken together, these measurements can be used to plot a summary circle (standard distance circle) for the point pattern, centered at $(x_m,y_m)$ with radius $SD$, as shown below." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "stdd = std_distance(pp.points)\n", "stdd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot mean center as well as the standard distance circle." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "circle1=plt.Circle((mc[0], mc[1]),stdd,color='r')\n", "ax = pp.plot(get_ax=True, title='Standard Distance Circle')\n", "ax.add_artist(circle1)\n", "plt.plot(mc[0], mc[1], 'b^', label='Mean Center')\n", "ax.set_aspect('equal')\n", "plt.legend(numpoints=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From the above figure, we can observe that there are five points outside the standard distance circle which are potential outliers." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Standard deviational ellipse\n", "\n", "Compared with standard distance circle which measures dispersion using a single parameter $SD$, standard deviational ellipse measures dispersion and trend in two dimensions through angle of rotation $\\theta$, dispersion along major axis $s_x$ and dispersion along minor axis $s_y$:\n", "\n", "* Major axis defines the direction of maximum spread in the distribution. $s_x$ is the semi-major axis (half the length of the major axis):\n", "\n", "$$ s_x = \\displaystyle \\sqrt{\\frac{2(\\sum_{i=1}^n (x_i-\\bar{x})\\cos(\\theta) - \\sum_{i=1}^n (y_i-\\bar{y})\\sin(\\theta))^2}{n-2}}$$\n", "\n", "* Minor axis defines the direction of minimum spread and is orthogonal to major axis. $s_y$ is the semi-minor axis (half the length of the minor axis):\n", "\n", "$$ s_y = \\displaystyle \\sqrt{\\frac{2(\\sum_{i=1}^n (x_i-\\bar{x})\\sin(\\theta) - \\sum_{i=1}^n (y_i-\\bar{y})\\cos(\\theta))^2}{n-2}}$$\n", "\n", "* The ellipse is rotated clockwise through an angle $\\theta$:\n", "\n", "$$\\theta = \\displaystyle \\arctan{\\{ (\\sum_i(x_i-\\bar{x})^2-\\sum_i(y_i-\\bar{y})^2) + \\frac{[(\\sum_i(x_i-\\bar{x})^2-\\sum_i(y_i-\\bar{y})^2)^2 + 4(\\sum_i(x-\\bar{x})(y_i-\\bar{y}))^2]^\\frac{1}{2}}{2\\sum_i(x-\\bar{x})(y_i-\\bar{y})}\\}}$$\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sx, sy, theta = ellipse(pp.points)\n", "sx, sy, theta" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "theta_degree = np.degrees(theta) #need degree of rotation to plot the ellipse\n", "theta_degree" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Standard Deviational Ellipse for the point pattern is rotated clockwise by $63.25^{\\circ}$." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from matplotlib.patches import Ellipse\n", "from pylab import figure, show,rand\n", "fig = figure()\n", "#ax = fig.add_subplot(111, aspect='equal')\n", "e = Ellipse(xy=mean_center(pp.points), width=sx*2, height=sy*2, angle=-theta_degree) #angle is rotation in degrees (anti-clockwise)\n", "ax = pp.plot(get_ax=True, title='Standard Deviational Ellipse')\n", "ax.add_artist(e)\n", "e.set_clip_box(ax.bbox)\n", "e.set_facecolor([0.8,0,0])\n", "e.set_edgecolor([1,0,0])\n", "ax.set_xlim(0,100)\n", "ax.set_ylim(0,100)\n", "ax.set_aspect('equal')\n", "plt.plot(mc[0], mc[1], 'b^', label='Mean Center')\n", "plt.legend(numpoints=1)\n", "show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4. Shape analysis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.1 Convex hull\n", "\n", "[Convex hull](https://en.wikipedia.org/wiki/Convex_hull)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "hull(pp.points)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By specifying \"hull\" argument **True** in PointPattern class method **plot**, we can easily plot convex hull of the point pattern." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pp.plot(title='Centers', hull=True ) #plot point pattern \"pp\" as well as its convex hull\n", "plt.plot(mc[0], mc[1], 'b^', label='Mean Center')\n", "plt.plot(wmc[0], wmc[1], 'gd', label='Weighted Mean Center')\n", "plt.plot(mm[0], mm[1], 'rv', label='Manhattan Median')\n", "plt.plot(em[0], em[1], 'm+', label='Euclidean Median')\n", "plt.legend(numpoints=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.2 Minimum bounding rectangle\n", "\n", "[Minimum bounding rectangle](https://en.wikipedia.org/wiki/Minimum_bounding_rectangle)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "minimum_bounding_rectangle(pp.points)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Thus, four vertices of the minimum bounding rectangle is $(8.23,7.68),(98.73,7.68),(98.73,92.08),(8.23,92.08)$." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pp.plot(title='Centers', window=True ) #plot point pattern \"pp\" as well as its Minimum Bounding Rectangle\n", "plt.plot(mc[0], mc[1], 'b^', label='Mean Center')\n", "plt.plot(wmc[0], wmc[1], 'gd', label='Weighted Mean Center')\n", "plt.plot(mm[0], mm[1], 'rv', label='Manhattan Median')\n", "plt.plot(em[0], em[1], 'm+', label='Euclidean Median')\n", "plt.legend(numpoints=1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pp.plot(title='Centers', hull=True , window=True )#plot point pattern \"pp\", convex hull, and Minimum Bounding Rectangle\n", "plt.plot(mc[0], mc[1], 'b^', label='Mean Center')\n", "plt.plot(wmc[0], wmc[1], 'gd', label='Weighted Mean Center')\n", "plt.plot(mm[0], mm[1], 'rv', label='Manhattan Median')\n", "plt.plot(em[0], em[1], 'm+', label='Euclidean Median')\n", "plt.legend(numpoints=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot Standard Distance Circle and Convex Hull." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "circle1=plt.Circle((mc[0], mc[1]),stdd,color='r',alpha=0.2)\n", "ax = pp.plot(get_ax=True, title='Standard Distance Circle', hull=True)\n", "ax.add_artist(circle1)\n", "plt.plot(mc[0], mc[1], 'b^', label='Mean Center')\n", "ax.set_aspect('equal')\n", "plt.legend(numpoints=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 5. Additional example: simulated patterns in Virginia\n", "\n", "We apply the centrography statistics and visualization to 2 simulated random datasets." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#from pysal.contrib import shapely_ext\n", "from libpysal.cg import shapely_ext\n", "from pointpats import PoissonPointProcess as csr\n", "import libpysal as ps\n", "from pointpats import as_window\n", "#import pysal_examples\n", "\n", "# open \"vautm17n\" polygon shapefile\n", "va = ps.io.open(ps.examples.get_path(\"vautm17n.shp\"))\n", "\n", "# Create the exterior polygons for VA from the union of the county shapes\n", "polys = [shp for shp in va]\n", "state = shapely_ext.cascaded_union(polys)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 5.1 Simulate a 100-point dataset within the Virginia state border under CSR (complete spatial randomness)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pp = csr(as_window(state), 100, 1, asPP=True).realizations[0]\n", "pp.plot(window=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pp.plot(window=True, hull=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "mc = mean_center(pp.points)\n", "mm = manhattan_median(pp.points)\n", "em = euclidean_median(pp.points)\n", "pp.plot(title='Centers', hull=True , window=True )#plot point pattern \"pp\", convex hull, and Minimum Bounding Rectangle\n", "plt.plot(mc[0], mc[1], 'c^', label='Mean Center')\n", "plt.plot(mm[0], mm[1], 'rv', label='Manhattan Median')\n", "plt.plot(em[0], em[1], 'm+', label='Euclidean Median')\n", "plt.legend(numpoints=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 5.2 Simulate a 500-point dataset within the Virginia state border under CSR (complete spatial randomness)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pp = csr(as_window(state), 500, 1, asPP=True).realizations[0]\n", "pp.plot(window=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pp.plot(window=True, hull=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "mc = mean_center(pp.points)\n", "mm = manhattan_median(pp.points)\n", "em = euclidean_median(pp.points)\n", "pp.plot(title='Centers', hull=True , window=True )#plot point pattern \"pp\", convex hull, and Minimum Bounding Rectangle\n", "plt.plot(mc[0], mc[1], 'c^', label='Mean Center')\n", "plt.plot(mm[0], mm[1], 'rv', label='Manhattan Median')\n", "plt.plot(em[0], em[1], 'm+', label='Euclidean Median')\n", "plt.legend(numpoints=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we calculate the Euclidean distances between every event point and Mean Center (Euclidean Median), and sum them up, we can see that Euclidean Median is the optimal point in iterms of minimizing the Euclidean distances to all the event points." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from pointpats import dtot\n", "\n", "print(dtot(mc.tolist(), pp.points) > dtot(em.tolist(), pp.points))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 6. Recap\n", "\n", "In this notebook, we:\n", "\n", "- Reviewed a range of **centrographic statistics** for planar point patterns,\n", " including measures of central tendency, dispersion, and shape.\n", "- Used functions in `pointpats.centrography` to compute and visualize\n", " the mean center, weighted mean center, Manhattan and Euclidean medians.\n", "- Explored measures of spread and orientation via the **standard distance**\n", " and **standard deviational ellipse**.\n", "- Illustrated basic **shape descriptors**, such as the convex hull and\n", " minimum bounding rectangle, and examined how these relate to dispersion.\n", "- Worked through an additional example based on simulated point patterns\n", " within the Virginia state border under CSR.\n", "\n", "Together with the quadrat- and distance-based statistics notebooks, these\n", "tools provide a complementary set of summaries for exploring spatial point\n", "pattern structure in `pointpats`.\n" ] } ], "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": "3.12.2" }, "widgets": { "state": {}, "version": "1.1.2" } }, "nbformat": 4, "nbformat_minor": 4 } pointpats-2.5.5/docs/user-guide/intro.rst000066400000000000000000000011421514706172100204340ustar00rootroot00000000000000========== User Guide ========== This user guide covers essential features of pointpats, mostly in the form of interactive Jupyter notebooks. Reading this guide, you will learn: - descriptive statistics for point patterns - quadrat analysis of point patterns - distance-based tests for clustering in point patterns Notebooks cover just a small selection of functions as an illustration of principles. For a full overview of pointpats's capabilities, head to the `API <../api.rst>`_. .. toctree:: :maxdepth: 1 centrography sd_ellipse Quadrat_statistics ripley random marks pointpats-2.5.5/docs/user-guide/marks.ipynb000066400000000000000000003467161514706172100207520ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "id": "b046916b", "metadata": {}, "source": [ "# Working with marks in `pointpats`\n", "\n", "In addition to the *locations* of events, a point pattern can have extra attributes\n", "attached to each point: these are called **marks**.\n", "\n", "Examples of marks:\n", "\n", "- species of a tree (categorical)\n", "- crime type (categorical)\n", "- tree diameter (continuous)\n", "- number of injuries at an accident (count)\n", "\n", "In `pointpats`, a **marked point pattern** is represented by a\n", "`PointPattern` whose underlying data frame (`pp.df`) has extra columns\n", "beyond the coordinates, or by adding marks using the `add_marks` method.\n", "You can also split a marked pattern into separate unmarked patterns using\n", "`explode`. \n", "\n", "In this notebook we will:\n", "\n", "1. Create an unmarked point pattern.\n", "2. Attach categorical and numeric marks with `add_marks`.\n", "3. Explore marks in the underlying pandas DataFrame.\n", "4. Visualize the pattern by mark category.\n", "5. Use `explode` to split the pattern into one pattern per mark level.\n", "6. Compute a **weighted mean center** using a numeric mark as weights. \n" ] }, { "cell_type": "markdown", "id": "f133c33f", "metadata": {}, "source": [ "## Setup and imports\n", "\n", "We will simulate points in the unit square `[0, 1] × [0, 1]` using the\n", "random distributions in `pointpats.random` and then work with their marks.\n" ] }, { "cell_type": "code", "execution_count": 1, "id": "4dcf9af3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0., 0., 1., 1.])" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "\n", "from pointpats import PointPattern\n", "from pointpats import random as ppr\n", "from pointpats import weighted_mean_center, mean_center # centrography functions\n", "\n", "# Make results reproducible\n", "np.random.seed(42)\n", "\n", "# Define a simple square hull as a bounding box: [xmin, ymin, xmax, ymax]\n", "hull = np.array([0.0, 0.0, 1.0, 1.0])\n", "hull" ] }, { "cell_type": "markdown", "id": "67e5f4cb", "metadata": {}, "source": [ "## 1. Create an unmarked point pattern\n", "\n", "First, we simulate 200 locations from a homogeneous Poisson point process\n", "in the unit square and construct a `PointPattern` from the coordinates. \n" ] }, { "cell_type": "code", "execution_count": 2, "id": "ee524a72", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(200, 2)" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Simulate 200 points in the unit square (unmarked pattern)\n", "coords = ppr.poisson(hull, size=200)\n", "coords.shape" ] }, { "cell_type": "code", "execution_count": 3, "id": "ab0d2505", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Point Pattern\n", "200 points\n", "Bounding rectangle [(0.005061583846218687,0.009197051616629648), (0.9905051420006733,0.9900538501042633)]\n", "Area of window: 0.9665790135416406\n", "Intensity estimate for window: 206.91531390401323\n", " x y\n", "0 0.374540 0.950714\n", "1 0.731994 0.598658\n", "2 0.156019 0.155995\n", "3 0.058084 0.866176\n", "4 0.601115 0.708073\n" ] }, { "data": { "text/html": [ "
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" ], "text/plain": [ " x y\n", "0 0.374540 0.950714\n", "1 0.731994 0.598658\n", "2 0.156019 0.155995\n", "3 0.058084 0.866176\n", "4 0.601115 0.708073" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Build an unmarked PointPattern\n", "pp = PointPattern(coords)\n", "\n", "pp.summary() # quick textual summary\n", "pp.df.head() # underlying DataFrame with coordinates only (x, y)" ] }, { "cell_type": "markdown", "id": "688e56e9", "metadata": {}, "source": [ "At this stage, `pp` has only coordinates (`x`, `y`) and **no marks**.\n", "\n", "Internally, `PointPattern` stores the data in a pandas DataFrame (`pp.df`).\n", "Any extra columns we add will be treated as marks (attributes) of the\n", "points. \n" ] }, { "cell_type": "markdown", "id": "11f0062c", "metadata": {}, "source": [ "## 2. Add categorical and numeric marks with `add_marks`\n", "\n", "We will assign two marks to each point:\n", "\n", "- `type`: a **categorical** mark taking values `'A'` or `'B'`.\n", "- `value`: a **continuous** mark drawn from a normal distribution.\n", "\n", "We use the `add_marks(marks, mark_names=None)` method of `PointPattern`\n", "to attach these attributes. \n" ] }, { "cell_type": "code", "execution_count": 4, "id": "58797dbf", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " x y type value\n", "0 0.374540 0.950714 A 1.071554\n", "1 0.731994 0.598658 B 0.838322\n", "2 0.156019 0.155995 A 1.532274\n", "3 0.058084 0.866176 B 0.655587\n", "4 0.601115 0.708073 A 5.707410" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "n = pp.n\n", "\n", "# Categorical mark: two types, A and B\n", "types = np.random.choice([\"A\", \"B\"], size=n, p=[0.6, 0.4])\n", "\n", "# Numeric mark: e.g. \"intensity\" or \"size\" for each event\n", "values = np.random.gamma(shape=2.0, scale=1.0, size=n)\n", "\n", "# Attach both marks to the point pattern\n", "pp.add_marks([types, values], mark_names=[\"type\", \"value\"])\n", "\n", "pp.df.head()" ] }, { "cell_type": "markdown", "id": "72f763b1", "metadata": {}, "source": [ "Now `pp` is a **marked point pattern**. The DataFrame has four columns:\n", "`x`, `y` (coordinates) and `type`, `value` (marks).\n", "\n", "We can work with marks using standard pandas tools, for example to\n", "compute simple summaries by type:" ] }, { "cell_type": "code", "execution_count": 5, "id": "1b8e99e8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "type\n", "A 114\n", "B 86\n", "Name: count, dtype: int64" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Counts by categorical mark\n", "pp.df[\"type\"].value_counts()" ] }, { "cell_type": "code", "execution_count": 6, "id": "4a80a2a4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "type\n", "A 2.000269\n", "B 1.736881\n", "Name: value, dtype: float64" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Mean of the numeric mark by type\n", "pp.df.groupby(\"type\")[\"value\"].mean()" ] }, { "cell_type": "markdown", "id": "51984ed0", "metadata": {}, "source": [ "## 3. Visualizing a marked point pattern\n", "\n", "Let’s write a helper to plot points colored by a categorical mark, here\n", "`type`.\n" ] }, { "cell_type": "code", "execution_count": 7, "id": "24547460", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def plot_marked_pattern(pp, mark_column=\"type\", ax=None, title=None):\n", " \"\"\"Plot a marked point pattern, coloring by a categorical mark.\n", "\n", " Parameters\n", " ----------\n", " pp : pointpats.PointPattern\n", " Marked point pattern.\n", " mark_column : str\n", " Column in `pp.df` containing categorical marks.\n", " ax : matplotlib.axes.Axes, optional\n", " Axis to plot on.\n", " title : str, optional\n", " Plot title.\n", " \"\"\"\n", " if ax is None:\n", " fig, ax = plt.subplots(figsize=(5, 5))\n", "\n", " df = pp.df\n", " categories = df[mark_column].astype(\"category\")\n", " cats = categories.cat.categories\n", "\n", " # Basic color cycle\n", " colors = plt.rcParams[\"axes.prop_cycle\"].by_key()[\"color\"]\n", "\n", " for i, cat in enumerate(cats):\n", " sub = df[categories == cat]\n", " ax.scatter(sub[pp.coord_names[0]], sub[pp.coord_names[1]],\n", " s=15, label=f\"{mark_column} = {cat}\",\n", " color=colors[i % len(colors)], alpha=0.8)\n", "\n", " # Use the window of the pattern to set plot limits\n", " xmin, ymin, xmax, ymax = pp.window.bbox\n", " ax.set_xlim(xmin, xmax)\n", " ax.set_ylim(ymin, ymax)\n", " ax.set_aspect(\"equal\", adjustable=\"box\")\n", " ax.set_xlabel(pp.coord_names[0])\n", " ax.set_ylabel(pp.coord_names[1])\n", " if title:\n", " ax.set_title(title)\n", " ax.legend(loc=\"best\")\n", "\n", " return ax\n", "\n", "\n", "plot_marked_pattern(pp, mark_column=\"type\",\n", " title=\"Marked point pattern colored by type\")\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "efef5a3e", "metadata": {}, "source": [ "The colors distinguish the mark categories (`type = 'A'` vs `type = 'B'`).\n", "You can adapt the plotting function to map numeric marks to size or\n", "color gradients if desired." ] }, { "cell_type": "markdown", "id": "ae87e7b0", "metadata": {}, "source": [ "## 4. Splitting a marked pattern with `explode`\n", "\n", "To analyze each mark category as its own point pattern, we can use the\n", "`explode(mark)` method of `PointPattern`. It returns a list of\n", "`PointPattern` objects, one per unique value of the mark. \n" ] }, { "cell_type": "code", "execution_count": 8, "id": "b6421f39", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['A', 'B'], dtype='object')" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Unique categories in the \"type\" mark\n", "categories = pp.df[\"type\"].astype(\"category\").cat.categories\n", "categories" ] }, { "cell_type": "code", "execution_count": 9, "id": "b367b558", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Explode into a list of unmarked patterns, one per category\n", "pps_by_type = pp.explode(\"type\")\n", "\n", "len(pps_by_type) # should match the number of unique categories" ] }, { "cell_type": "code", "execution_count": 10, "id": "e7870bdb", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Category 'A' -> 114 points\n", "Category 'B' -> 86 points\n" ] } ], "source": [ "for cat, sub_pp in zip(categories, pps_by_type):\n", " print(f\"Category {cat!r} -> {sub_pp.n} points\")" ] }, { "cell_type": "markdown", "id": "f4d3b414", "metadata": {}, "source": [ "We can visualize each category in its own panel to compare their\n", "spatial distributions." ] }, { "cell_type": "code", "execution_count": 15, "id": "e2a5098e-f418-41b6-8857-7313589a58e1", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(1, len(pps_by_type), figsize=(5 * len(pps_by_type), 4))\n", "if len(pps_by_type) == 1:\n", " axes = [axes]\n", "\n", "for ax, cat, sub_pp in zip(axes, categories, pps_by_type):\n", " df = sub_pp.df\n", " xname, yname = sub_pp.coord_names\n", "\n", " ax.scatter(df[xname], df[yname], s=15, alpha=0.8)\n", " xmin, ymin, xmax, ymax = sub_pp.window.bbox\n", " ax.set_xlim(xmin, xmax)\n", " ax.set_ylim(ymin, ymax)\n", " ax.set_aspect(\"equal\", adjustable=\"box\")\n", " ax.set_title(f\"type = {cat}\")\n", " ax.set_xlabel(xname)\n", " ax.set_ylabel(yname)\n", "\n", "plt.tight_layout()\n", "plt.show()\n" ] }, { "cell_type": "markdown", "id": "5711efc6", "metadata": {}, "source": [ "Each panel now shows the points belonging to a single mark category.\n", "\n", "This is useful when you want to treat each type as its **own** point\n", "pattern (e.g., trees of different species, crimes of different types)\n", "and compare their intensities, clustering, or nearest-neighbor behavior." ] }, { "cell_type": "markdown", "id": "80cda364", "metadata": {}, "source": [ "## 5. Using numeric marks as weights: weighted mean center\n", "\n", "Numeric marks are often used as **weights** when summarizing the pattern.\n", "For example, you might weight accidents by the number of injuries or\n", "trees by their biomass.\n", "\n", "The function `weighted_mean_center(points, weights)` computes a\n", "weighted mean center, where each point contributes proportionally to its\n", "weight. \n", "\n", "Here we compare the (unweighted) mean center of the pattern to the\n", "weighted mean center using the `value` mark as weights." ] }, { "cell_type": "code", "execution_count": 12, "id": "b11cb2d4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([0.49831839, 0.49006298]), array([0.50793006, 0.47675179]))" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Unweighted mean center\n", "mc = mean_center(pp.points)\n", "\n", "# Weighted mean center using the numeric mark \"value\" as weights\n", "wmc = weighted_mean_center(pp.points, pp.df[\"value\"].values)\n", "\n", "mc, wmc" ] }, { "cell_type": "code", "execution_count": 13, "id": "e09e6e5c", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(5, 5))\n", "\n", "# Plot marked pattern with small gray points\n", "ax.scatter(pp.df[pp.coord_names[0]], pp.df[pp.coord_names[1]],\n", " s=10, alpha=0.5, label=\"events\")\n", "\n", "xmin, ymin, xmax, ymax = pp.window.bbox\n", "ax.set_xlim(xmin, xmax)\n", "ax.set_ylim(ymin, ymax)\n", "ax.set_aspect(\"equal\", adjustable=\"box\")\n", "\n", "# Add unweighted and weighted mean centers\n", "ax.plot(mc[0], mc[1], \"b^\", markersize=10, label=\"mean center\")\n", "ax.plot(wmc[0], wmc[1], \"ro\", markersize=10, label=\"weighted mean center\")\n", "\n", "ax.set_xlabel(pp.coord_names[0])\n", "ax.set_ylabel(pp.coord_names[1])\n", "ax.set_title(\"Unweighted vs weighted mean center\")\n", "ax.legend()\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "5a67d5d9", "metadata": {}, "source": [ "The weighted mean center is pulled toward points with larger `value`.\n", "This is a simple example of how numeric marks can affect spatial\n", "summaries.\n" ] }, { "cell_type": "markdown", "id": "ff56dbf8", "metadata": {}, "source": [ "## 6. Creating a marked pattern directly from an array\n", "\n", "Instead of starting with an unmarked pattern and calling `add_marks`,\n", "you can also pass a full `(n, p)` array (coordinates + attributes) into\n", "`PointPattern` and specify `names` for the columns. \n", "\n", "In this example we build a second point pattern whose coordinates and\n", "marks are all stored in a single NumPy array:" ] }, { "cell_type": "code", "execution_count": 14, "id": "16c80159", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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00.3745400.9507140.01.071554
10.7319940.5986581.00.838322
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" ], "text/plain": [ " x y type_code value\n", "0 0.374540 0.950714 0.0 1.071554\n", "1 0.731994 0.598658 1.0 0.838322\n", "2 0.156019 0.155995 0.0 1.532274\n", "3 0.058084 0.866176 1.0 0.655587\n", "4 0.601115 0.708073 0.0 5.707410" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Build a new (n, 4) array: x, y, type_code, value\n", "type_code = (types == \"B\").astype(float) # 0 for A, 1 for B\n", "points_with_marks = np.column_stack([pp.points, type_code, values])\n", "\n", "names = [\"x\", \"y\", \"type_code\", \"value\"]\n", "pp2 = PointPattern(points_with_marks, names=names)\n", "\n", "pp2.df.head()" ] }, { "cell_type": "markdown", "id": "e5d08eb8", "metadata": {}, "source": [ "Here, `pp2` is also a marked point pattern: `x`, `y` are coordinates,\n", "and `type_code`, `value` are marks.\n", "\n", "This pattern (constructing a full `(n, p)` table and passing it to\n", "`PointPattern`) is convenient when your data already live in a\n", "DataFrame or when loading from a CSV or GIS table." ] }, { "cell_type": "markdown", "id": "09d0bb9f", "metadata": {}, "source": [ "## Recap\n", "\n", "In this notebook, we have:\n", "\n", "- Built an **unmarked** `PointPattern` from simulated coordinates.\n", "- Attached **categorical** and **numeric** marks with `add_marks`.\n", "- Used `pp.df` (a pandas DataFrame) to summarize marks by category.\n", "- Visualized a marked point pattern with color by categorical mark.\n", "- Applied `explode` to split a marked pattern into separate patterns,\n", " one per mark category.\n", "- Computed a **weighted mean center** using a numeric mark as weights.\n", "- Created a marked pattern directly from a full `(n, p)` array with\n", " coordinate and mark columns.\n", "\n", "These tools form the core of working with **marked point patterns** in\n", "`pointpats`. You can combine them with quadrat statistics, distance-based\n", "functions, and space–time tests for more advanced analyses.\n" ] } ], "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": "3.12.2" } }, "nbformat": 4, "nbformat_minor": 5 } pointpats-2.5.5/docs/user-guide/random.ipynb000066400000000000000000010213671514706172100211060ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "id": "5212af16", "metadata": {}, "source": [ "# Simulating random point patterns with `pointpats.random`\n", "\n", "This notebook illustrates how to use the `pointpats.random` module to simulate\n", "random point patterns inside a study region (the *hull*).\n", "\n", "We will look at several families of random patterns:\n", "\n", "1. **Homogeneous Poisson** (`random.poisson`)\n", "2. **Normal cluster** (`random.normal`)\n", "3. **Poisson cluster (Neyman–Scott)** (`random.cluster_poisson`)\n", "4. **Normal cluster with random seeds** (`random.cluster_normal`)\n", "\n", "All examples use a simple square window as the hull:\n", "\n", "$$[0, 1] \\times [0, 1].$$\n" ] }, { "cell_type": "code", "execution_count": 1, "id": "6003346e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0., 0., 1., 1.])" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "from pointpats import random as ppr # pointpats.random\n", "\n", "# Make results reproducible\n", "np.random.seed(12345)\n", "\n", "# Define a simple square hull as a bounding box:\n", "# [xmin, ymin, xmax, ymax]\n", "hull = np.array([0.0, 0.0, 1.0, 1.0])\n", "\n", "hull" ] }, { "cell_type": "markdown", "id": "11dc1585", "metadata": {}, "source": [ "## Helper function for plotting point patterns\n", "\n", "We'll use a small helper to visualize realizations of the random point processes." ] }, { "cell_type": "code", "execution_count": 2, "id": "fd2889cf", "metadata": {}, "outputs": [], "source": [ "def plot_pattern(points, hull, ax=None, title=None):\n", " \"\"\"Plot a 2D point pattern inside a rectangular hull.\n", "\n", " Parameters\n", " ----------\n", " points : array-like, shape (n_points, 2)\n", " Coordinates of the points.\n", " hull : array-like, shape (4,)\n", " Bounding box [xmin, ymin, xmax, ymax].\n", " ax : matplotlib.axes.Axes, optional\n", " Axis to plot on.\n", " title : str, optional\n", " Title for the plot.\n", " \"\"\"\n", " points = np.asarray(points)\n", "\n", " if ax is None:\n", " fig, ax = plt.subplots(figsize=(4, 4))\n", "\n", " xmin, ymin, xmax, ymax = hull\n", " ax.scatter(points[:, 0], points[:, 1], s=10, alpha=0.7)\n", " ax.set_xlim(xmin, xmax)\n", " ax.set_ylim(ymin, ymax)\n", " ax.set_aspect(\"equal\", adjustable=\"box\")\n", " ax.set_xlabel(\"x\")\n", " ax.set_ylabel(\"y\")\n", " if title:\n", " ax.set_title(title)\n", "\n", " return ax" ] }, { "cell_type": "markdown", "id": "f05bca84", "metadata": {}, "source": [ "## 1. Homogeneous Poisson process (`random.poisson`)\n", "\n", "The **homogeneous Poisson point process** is the canonical model of\n", "*complete spatial randomness* (CSR):\n", "\n", "- Each point is independently and uniformly distributed in the hull.\n", "- The expected number of points is controlled by the **intensity**\n", " (points per unit area).\n", "\n", "A typical interface (check the installed `pointpats` version for details) allows usage patterns like:\n", "\n", "- `poisson(hull, size=n)` — simulate `n` points once.\n", "- `poisson(hull, intensity=lambda_, size=r)` — draw `r` *replications* from a Poisson\n", " process with intensity `lambda_` points per unit area.\n" ] }, { "cell_type": "code", "execution_count": 3, "id": "870559d8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(100, 2)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# A single realization with exactly 100 points\n", "points_poisson_fixed = ppr.poisson(hull, size=100)\n", "\n", "points_poisson_fixed.shape" ] }, { "cell_type": "code", "execution_count": 4, "id": "bb7f6f94", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(4, 4))\n", "plot_pattern(points_poisson_fixed, hull, ax=ax,\n", " title=\"Homogeneous Poisson process (n = 100)\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 5, "id": "0ac04ea4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(4, 200, 2)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Simulate 4 replications with intensity λ = 200 points per unit area\n", "lambda_ = 200 # expected number of points in unit square\n", "n_replications = 4\n", "\n", "points_poisson_intensity = ppr.poisson(hull, intensity=lambda_, size=n_replications)\n", "\n", "points_poisson_intensity.shape" ] }, { "cell_type": "code", "execution_count": 6, "id": "b0e48b36", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(2, 2, figsize=(6, 6))\n", "\n", "for i, ax in enumerate(axes.ravel()):\n", " plot_pattern(points_poisson_intensity[i], hull, ax=ax,\n", " title=f\"Poisson, λ={lambda_}, rep {i+1}\")\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "ff89ebf4", "metadata": {}, "source": [ "## 2. Normal cluster (`random.normal`)\n", "\n", "`random.normal` simulates points from a **bivariate normal distribution**\n", "truncated to the hull.\n", "\n", "A typical interface looks like:\n", "\n", "```python\n", "ppr.normal(hull, center=None, cov=None, size=None)\n", "```\n", "\n", "Key arguments:\n", "\n", "- `center`: 2D location of the cluster center. If `None`, often the centroid of the hull.\n", "- `cov`: covariance structure (scalar = iid variance, or 2×2 matrix).\n", "- `size`: number of points (and optionally replications).\n", "\n", "We'll start with a single tight cluster in the center of the window." ] }, { "cell_type": "code", "execution_count": 7, "id": "c34bfe45", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(200, 2)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 200 points in a single bivariate normal cluster\n", "points_normal = ppr.normal(hull, size=200)\n", "\n", "points_normal.shape" ] }, { "cell_type": "code", "execution_count": 8, "id": "7fbf7901", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(4, 4))\n", "plot_pattern(points_normal, hull, ax=ax,\n", " title=\"Normal cluster (default center & spread)\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 9, "id": "a58f6a59", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Three different covariance structures\n", "covariances = [\n", " 0.05, # scalar: isotropic variance\n", " np.array([[0.05, 0.0], [0.0, 0.01]]), # elongated along x\n", " np.array([[0.01, 0.0], [0.0, 0.05]]), # elongated along y\n", "]\n", "\n", "fig, axes = plt.subplots(1, 3, figsize=(12, 4))\n", "\n", "for cov, ax in zip(covariances, axes):\n", " pts = ppr.normal(hull, size=300, cov=cov)\n", " plot_pattern(pts, hull, ax=ax, title=f\"cov =\\n{cov}\")\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "f74ce910", "metadata": {}, "source": [ "## 3. Poisson cluster process (`random.cluster_poisson`)\n", "\n", "`cluster_poisson` implements a simple **Neyman–Scott**–type cluster process:\n", "\n", "1. \"Seed\" points are drawn from a Poisson process.\n", "2. Around each seed, a small cloud of points is placed within a radius.\n", "\n", "A typical use looks like:\n", "\n", "```python\n", "ppr.cluster_poisson(\n", " hull,\n", " intensity=None,\n", " size=None,\n", " n_seeds=2,\n", " cluster_radius=None,\n", ")\n", "```\n", "\n", "Key arguments:\n", "\n", "- `n_seeds`: number of cluster centers.\n", "- `cluster_radius`: radius of each cluster. If `None`, it may default to a value\n", " based on distances between seeds.\n", "- `size`: controls how many points you simulate in total and how many replications.\n", "\n", "Here we simulate 300 points organized into a few clusters." ] }, { "cell_type": "code", "execution_count": 10, "id": "f1137841", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(300, 2)" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "n_points = 300\n", "n_replications = 1\n", "n_seeds = 4\n", "\n", "points_cluster_poisson = ppr.cluster_poisson(\n", " hull,\n", " size=(n_points, n_replications),\n", " n_seeds=n_seeds,\n", " # cluster_radius can be None (default) or a scalar / array\n", ")\n", "\n", "points_cluster_poisson.shape" ] }, { "cell_type": "code", "execution_count": 11, "id": "3601664c", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(4, 4))\n", "plot_pattern(points_cluster_poisson, hull, ax=ax,\n", " title=f\"Poisson cluster process (n={n_points}, seeds={n_seeds})\")\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "b43e4bc9", "metadata": {}, "source": [ "## 4. Normal clusters with random seeds (`random.cluster_normal`)\n", "\n", "`cluster_normal` builds clusters whose centers are themselves drawn from a\n", "Poisson process, but the points within clusters are drawn from a **normal**\n", "distribution around each seed:\n", "\n", "```python\n", "ppr.cluster_normal(hull, cov=None, size=None, n_seeds=2)\n", "```\n", "\n", "- `cov` controls the within-cluster spread.\n", "- `n_seeds` controls how many clusters you get.\n", "- `size` controls total number of points and replications.\n" ] }, { "cell_type": "code", "execution_count": 12, "id": "7baf7061", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(300, 2)" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "n_points = 300\n", "n_replications = 1\n", "n_seeds = 5\n", "\n", "points_cluster_normal = ppr.cluster_normal(\n", " hull,\n", " size=(n_points, n_replications),\n", " n_seeds=n_seeds,\n", " # cov=None uses a default based on distances between seeds\n", ")\n", "\n", "points_cluster_normal.shape" ] }, { "cell_type": "code", "execution_count": 13, "id": "2d02eb84", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(4, 4))\n", "plot_pattern(points_cluster_normal, hull, ax=ax,\n", " title=f\"Normal cluster process (n={n_points}, seeds={n_seeds})\")\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "f21340aa", "metadata": {}, "source": [ "## Recap\n", "\n", "In this notebook we have:\n", "\n", "- Used `pointpats.random.poisson` to simulate homogeneous Poisson\n", " point patterns under complete spatial randomness.\n", "- Used `pointpats.random.normal` to generate truncated bivariate normal clusters.\n", "- Used `pointpats.random.cluster_poisson` for Poisson cluster processes\n", " (Neyman–Scott–like).\n", "- Used `pointpats.random.cluster_normal` for clustered patterns with\n", " normally distributed clusters.\n", "\n", "These simulated patterns are useful as:\n", "\n", "- **Null models** for hypothesis testing (e.g., against observed patterns).\n", "- **Toy data** for teaching, examples, and algorithm development.\n", "\n", "You can adapt the hull, intensities, and covariance structures to match\n", "your own study regions and applications." ] }, { "cell_type": "code", "execution_count": null, "id": "92eec114-d372-4458-b4f3-514bd34a2b7f", "metadata": {}, "outputs": [], "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": "3.12.2" } }, "nbformat": 4, "nbformat_minor": 5 } pointpats-2.5.5/docs/user-guide/ripley.ipynb000066400000000000000000011201401514706172100211170ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "id": "8aaf7749", "metadata": {}, "source": [ "# Distance-based statistics in `pointpats`\n", "\n", "This notebook provides an introduction to the\n", "**distance-based statistics** available in `pointpats`, focusing on:\n", "\n", "- Ripley’s **G** (nearest-neighbor) function\n", "- Ripley’s **F** (empty-space) function\n", "- Ripley’s **K** and **L** functions (inter-event distances)\n", "- Simulation-based **envelopes** using `g_test` and `k_test`\n", "\n", "We will:\n", "\n", "1. Construct two toy point patterns in the unit square: one **CSR** (completely\n", " spatially random) and one **clustered**.\n", "2. Compare their distance-based statistics using the functional API:\n", " `f`, `g`, `k`, and `l`.\n", "3. Use `g_test` and `k_test` to generate Monte Carlo simulation envelopes\n", " under CSR and visualize departures from complete spatial randomness.\n", "\n", "This notebook is designed to live in the `docs/user-guide/` folder and be\n", "executed automatically as part of the `pointpats` documentation build." ] }, { "cell_type": "markdown", "id": "c485e8fa", "metadata": {}, "source": [ "## 1. Setup and imports\n", "\n", "We work in the unit square `[0, 1] × [0, 1]` and simulate patterns using\n", "the random distributions in `pointpats.random`." ] }, { "cell_type": "code", "execution_count": 5, "id": "5747bcbe", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0., 0., 1., 1.])" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "from pointpats import PointPattern, random\n", "from pointpats import f, g, k, l, g_test, k_test\n", "\n", "# Make results reproducible\n", "np.random.seed(12345)\n", "\n", "# Unit square hull encoded as [xmin, ymin, xmax, ymax]\n", "hull = np.array([0.0, 0.0, 1.0, 1.0])\n", "hull" ] }, { "cell_type": "markdown", "id": "f74d9e8f", "metadata": {}, "source": [ "## 2. Constructing example point patterns\n", "\n", "We construct two patterns within the same hull:\n", "\n", "- a **CSR** pattern from a homogeneous Poisson process; and\n", "- a **clustered** pattern from a Poisson cluster process.\n" ] }, { "cell_type": "code", "execution_count": 6, "id": "3d12eeab", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(200, 200)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "n_points = 200\n", "\n", "# CSR pattern: homogeneous Poisson process in the hull\n", "coords_csr = random.poisson(hull, size=n_points)\n", "pp_csr = PointPattern(coords_csr)\n", "\n", "# Clustered pattern: Poisson cluster process\n", "coords_cluster = random.cluster_poisson(\n", " hull,\n", " size=n_points,\n", " n_seeds=5,\n", ")\n", "pp_cluster = PointPattern(coords_cluster)\n", "\n", "pp_csr.n, pp_cluster.n" ] }, { "cell_type": "markdown", "id": "c4241430", "metadata": {}, "source": [ "### Helper: plotting point patterns\n", "\n", "We will reuse a simple helper to visualize point patterns in their hull." ] }, { "cell_type": "code", "execution_count": 7, "id": "a77ea131", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def plot_pattern(points, hull, ax=None, title=None):\n", " \"\"\"Plot a 2D point pattern inside a rectangular hull.\n", "\n", " Parameters\n", " ----------\n", " points : array-like of shape (n, 2)\n", " Point coordinates.\n", " hull : array-like of length 4\n", " Bounding box [xmin, ymin, xmax, ymax].\n", " ax : matplotlib.axes.Axes, optional\n", " Axis to plot on.\n", " title : str, optional\n", " Plot title.\n", " \"\"\"\n", " points = np.asarray(points)\n", "\n", " if ax is None:\n", " fig, ax = plt.subplots(figsize=(4, 4))\n", "\n", " xmin, ymin, xmax, ymax = hull\n", " ax.scatter(points[:, 0], points[:, 1], s=10, alpha=0.7)\n", " ax.set_xlim(xmin, xmax)\n", " ax.set_ylim(ymin, ymax)\n", " ax.set_aspect(\"equal\", adjustable=\"box\")\n", " ax.set_xlabel(\"x\")\n", " ax.set_ylabel(\"y\")\n", " if title:\n", " ax.set_title(title)\n", "\n", " return ax\n", "\n", "\n", "fig, axes = plt.subplots(1, 2, figsize=(8, 4))\n", "plot_pattern(coords_csr, hull, ax=axes[0], title=\"CSR (Poisson)\")\n", "plot_pattern(coords_cluster, hull, ax=axes[1], title=\"Clustered\")\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "b4dc3fc8", "metadata": {}, "source": [ "## 3. Distance-based summary functions: F, G, K, L\n", "\n", "`pointpats` provides a functional API for distance-based statistics.\n", "Each function takes `coordinates` and returns a pair `(support, values)`:\n", "\n", "```python\n", "support, G_vals = g(coordinates)\n", "support, F_vals = f(coordinates, hull=hull)\n", "support, K_vals = k(coordinates)\n", "support, L_vals = l(coordinates, linearized=True)\n", "```\n", "\n", "- **G(d)**: nearest-neighbor distribution from events to events.\n", "- **F(d)**: empty-space function from random locations to events\n", " (requires a hull when distances are not precomputed).\n", "- **K(d)**: cumulative inter-event distance function.\n", "- **L(d)**: scaled and shifted version of K; for CSR, linearized L\n", " should be close to zero across distances." ] }, { "cell_type": "code", "execution_count": 8, "id": "f494e7bc", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((20,), (20,))" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# G: nearest-neighbor distribution\n", "support_g_csr, G_csr = g(coords_csr)\n", "support_g_cluster, G_cluster = g(coords_cluster)\n", "\n", "# F: empty-space function (needs hull when distances=None)\n", "support_f_csr, F_csr = f(coords_csr, hull=hull)\n", "support_f_cluster, F_cluster = f(coords_cluster, hull=hull)\n", "\n", "# K: inter-event distance function\n", "support_k_csr, K_csr = k(coords_csr)\n", "support_k_cluster, K_cluster = k(coords_cluster)\n", "\n", "# L: linearized version of K\n", "support_l_csr, L_csr = l(coords_csr, linearized=True)\n", "support_l_cluster, L_cluster = l(coords_cluster, linearized=True)\n", "\n", "(support_g_csr.shape, support_k_csr.shape)" ] }, { "cell_type": "markdown", "id": "c7ba6b75", "metadata": {}, "source": [ "### 3.1 Comparing G and F\n", "\n", "We first compare the G and F functions for the CSR and clustered patterns.\n", "Clustering tends to increase the probability of short distances, which is\n", "reflected differently in G and F." ] }, { "cell_type": "code", "execution_count": 9, "id": "bfdad630", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(1, 2, figsize=(10, 4))\n", "\n", "# G function\n", "ax = axes[0]\n", "ax.plot(support_g_csr, G_csr, label=\"CSR\", lw=2)\n", "ax.plot(support_g_cluster, G_cluster, label=\"Clustered\", lw=2)\n", "ax.set_xlabel(\"distance\")\n", "ax.set_ylabel(\"G(d)\")\n", "ax.set_title(\"Ripley's G function\")\n", "ax.legend()\n", "\n", "# F function\n", "ax = axes[1]\n", "ax.plot(support_f_csr, F_csr, label=\"CSR\", lw=2)\n", "ax.plot(support_f_cluster, F_cluster, label=\"Clustered\", lw=2)\n", "ax.set_xlabel(\"distance\")\n", "ax.set_ylabel(\"F(d)\")\n", "ax.set_title(\"Ripley's F function\")\n", "ax.legend()\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "a4e52fb0", "metadata": {}, "source": [ "### 3.2 Comparing K and L\n", "\n", "Under complete spatial randomness (CSR) with intensity `lambda`, we expect:\n", "\n", "- `K(d)` to grow like `pi * d^2`.\n", "- `L(d)` (linearized) to be close to zero.\n", "\n", "Departures above zero in L suggest clustering, while departures below zero\n", "suggest inhibition (regularity)." ] }, { "cell_type": "code", "execution_count": 10, "id": "fdea0ce1", "metadata": {}, "outputs": [ { "data": { "image/png": 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J/5a1XrZfg5zfb7zMld5nOTo6vvZJ5xd9P8lXsadPn55qGtBkyb/bQ4cOZenSpYwYMQJvb2/s7e3RaDR06dIlzelCnzdOwbM2bNjAtGnT+OKLL2jZsqXJey/zf+BlJX+/RYsWfeVtiJcjSbcQWcDMzAw/Pz+aNm3KvHnz+Oyzz164zrO3tymKwpUrV6hZs2a665QvX55du3bxxhtvZOiPfM+ePfH19eXOnTusWrWKVq1aZegWriJFitCnTx/69OlDbGwsb731FpMnT87wQUm5cuXYvn07jRs3xsfHh4MHD6Y6I5tRyfHev3/fZNCWZ8+yv4zy5ctz/vz557Z5mbPBZcqUYdeuXTx48MDkavfly5eN7wshRG4k/Vvme7pfe9rr9mt6vZ6LFy+mm1RCxvu25H4rKCiI//3vfybvBQUFZWq/VrlyZa5du5Zp20tL8hVlOzs7mjVr9ty2v//+O7169WLGjBnGZY8fP07183pZ//zzD7169aJdu3aMHz8+zRgz8n8g+bsPDg6mXLlyxuWRkZHpnry4du2a8Yq9yB5ye7kQWaRJkyZ4enoye/ZsHj9+/ML2v/zyi8nzaL///jt37tzhnXfeSXedTp06odPp+OKLL1K9l5SUlKpD6Nq1KxqNhuHDh/Pvv//SvXv3F8Z19+5dk3rBggWpUKEC8fHxL1z3aTVq1GDLli3ExsbSvHnzVFOMZFRyR3ngwAHjsri4OJYvX/5K2wPDnJVnzpwx3pr4tOSz4snPWGWkk3333XfR6XTMmzfPZPmsWbPQaDTP/ZkKIUROJ/1b5rKzs6No0aIm/RrADz/88MrbbNeuHVqtlqlTp6a6Gvv01XBbW9sM9Wv16tXD2dmZhQsXmnw/f//9N5cuXaJVq1avHOuzvL29OX/+fJb+HDw8PChfvjzfffcdsbGxqd6PjIw0ls3MzFLdQTB37twMTX+WntjYWN5//31KlizJ8uXL0zz5kdH/A82aNcPc3Jy5c+eaxDl79ux0Pz8gIABvb+9Xjl+8PLnSLUQWGjNmDB07dmTZsmWpBhR5VpEiRWjUqBF9+vQhPDyc2bNnU6FChefOad24cWMGDBiAn58fp0+fpkWLFpibmxMcHMy6deuYM2cOHTp0MLZ3cnKiZcuWrFu3DgcHhwx1klWrVqVJkyZ4eHhQpEgRTp48ye+//86QIUMy/kU84e3tzfr162nTpg3Nmzfn4MGDLz0lVosWLShdujR9+/ZlzJgxmJmZsWTJEpycnAgNDX3pmMDwc/r999/p2LEjH330ER4eHty7d49NmzaxcOFCatWqRfny5XFwcGDhwoUUKlQIW1tbvLy80nx2q02bNjRt2pT/+7//IyQkhFq1arFjxw42btzIiBEjTJ7ZEkKI3Ej6N8MgnMl3MD2tV69euLq6ZmgbyT7++GO++eYbPv74Y+rVq8eBAwf4559/XmobT6tQoQL/93//xxdffMGbb77JBx98gKWlJSdOnKBEiRL4+fkBhuRzwYIFfPnll1SoUAFnZ+dUV7IBzM3NmTZtGn369KFx48Z07drVOGWYm5sbI0eOfOVYn/Xee+/xxRdfsH//flq0aJFp232aVqtl8eLFvPPOO1SrVo0+ffpQsmRJbt26xd69e7Gzs+Ovv/4CoHXr1qxYsQJ7e3uqVq2Kv78/u3bteq0pPadMmcLFixeZMGFCqvmyy5cvj7e3d4b/DyTPge7n50fr1q159913OXXqFH///Xeat49HRERw9uxZBg8e/Mrxi1egypjpQuQhydNAnDhxItV7Op1OKV++vFK+fHnj9BHpTW/x22+/KePGjVOcnZ0Va2trpVWrVsr169dNtvfslCrJFi1apHh4eCjW1tZKoUKFlBo1aiiffvqpcvv27VRtk6dq6d+/f4b278svv1Q8PT0VBwcHxdraWqlcubLy1VdfKQkJCc9d73lTqqxZs0bRarVK/fr1TaaReRZpTBmmKIoSEBCgeHl5KRYWFkrp0qWVmTNnpjtlWFrTwTz7M1AURbl7964yZMgQpWTJkoqFhYVSqlQppVevXkpUVJSxzcaNG5WqVasqBQoUMJneJa2fy4MHD5SRI0cqJUqUUMzNzRV3d3dl+vTpJtN5PG8fy5Qpk+Y0H0IIkV2kf0tb8n6l9zp48GC666Y1ZZiiGKal6tu3r2Jvb68UKlRI6dSpkxIREZHulGGRkZEm66c3JdWSJUuUOnXqKJaWlkrhwoWVxo0bKzt37jS+HxYWprRq1UopVKiQAhh/fs9OGZZszZo1xu0VKVJE6datm3Lz5s0M7WN6U36mpWbNmkrfvn1fuI/p/c49e+yR3rRsp06dUj744APF0dFRsbS0VMqUKaN06tRJ2b17t7HNf//9p/Tp00cpWrSoUrBgQcXHx0e5fPlyqn76ef9fno09eVq1tF7P9v0Z+T+g0+mUKVOmKMWLF1esra2VJk2aKOfPn0/zWGLBggWKjY3Nc4+/RObTKEoOGXFBiHxq3759NG3alHXr1pmctc8qGzdupF27dhw4cMBkKi0hhBAiM0n/Jl7VihUrGDx4MKGhoSbjt4jXV6dOHZo0acKsWbPUDiVfkWe6hchnfvrpJ8qVK0ejRo3UDkUIIYTINNK/5R3dunWjdOnSzJ8/X+1Q8pRt27YRHBzMuHHj1A4l35FnuoXIJ1avXs3Zs2fZsmULc+bMkbkZhRBC5AnSv+U9Wq32hbOKiJfXsmXLNAeOE1lPkm4h8omuXbtSsGBB+vbty6BBg9QORwghhMgU0r8JIXI6eaZbCCGEEEIIIYTIIvJMtxBCCCGEEEIIkUUk6RZCCCGEEEIIIbKIPNP9ivR6Pbdv36ZQoUIyYIcQQogspSgKDx48oESJEmi1cr78eaR/FkIIkV0y3D+rOUl4snnz5illypRRLC0tFU9PT+XYsWPPbb927VqlUqVKiqWlpVK9enVly5YtxvcSEhKUTz/9VKlevbpiY2OjFC9eXOnRo4dy69Ytk22UKVMm1WT0fn5+GY75xo0b6U5qLy95yUte8pJXVrxu3Ljxch1sPiT9s7zkJS95ySu7Xy/qn1W/0r1mzRp8fX1ZuHAhXl5ezJ49Gx8fH4KCgnB2dk7V/siRI3Tt2hU/Pz9at27NqlWraNeuHYGBgVSvXp2HDx8SGBjI559/Tq1atfjvv/8YPnw4bdu25eTJkybbmjp1Kv369TPWCxUqlOG4k9veuHEDOzu7V9x7IYQQ4sViYmJwdXV9qX4qO8yfP5/p06cTFhZGrVq1mDt3Lp6enum2X7duHZ9//jkhISG4u7szbdo03n333TTbDhw4kB9//JFZs2YxYsSIDMck/bMQQojsktH+WfXRy728vKhfvz7z5s0DDLeFubq6MnToUD777LNU7Tt37kxcXBybN282LmvQoAG1a9dm4cKFaX7GiRMn8PT05Pr165QuXRoANzc3RowY8VId+dNiYmKwt7cnOjpaOnUhhBBZKif2OWvWrKFnz54mJ83XrVv33JPmb731lslJ82nTphlPmj/tzz//ZMqUKURGRjJmzJiX6qtz4nclhBAib8pon6Pqg2EJCQkEBATQrFkz4zKtVkuzZs3w9/dPcx1/f3+T9gA+Pj7ptgeIjo5Go9Hg4OBgsvybb77B0dGROnXqMH36dJKSkl59Z4QQQoh8ZObMmfTr148+ffpQtWpVFi5ciI2NDUuWLEmz/Zw5c2jZsiVjxoyhSpUqfPHFF9StW9d40j3ZrVu3GDp0KL/++ivm5ubZsStCCCFEllL19vKoqCh0Oh3FihUzWV6sWDEuX76c5jphYWFptg8LC0uz/ePHjxk7dixdu3Y1OfswbNgw6tatS5EiRThy5Ajjxo3jzp07zJw5M83txMfHEx8fb6zHxMRkaB+FEEKIvCb5pPm4ceOMyzJy0tzX19dkmY+PDxs2bDDW9Xo9PXr0YMyYMVSrVi1LYhdCCCGym+rPdGelxMREOnXqhKIoLFiwwOS9pzv+mjVrYmFhwYABA/Dz88PS0jLVtvz8/JgyZUqWxyyEEELkdFl10nzatGkUKFCAYcOGZTgWOSkuhBAip1M16S5atChmZmaEh4ebLA8PD8fFxSXNdVxcXDLUPjnhvn79Onv27Hnhc11eXl4kJSUREhJCpUqVUr0/btw4k0Q9+aH5F9HpdCQmJr6wnciZzM3NMTMzUzsMIYTI8wICApgzZw6BgYEvNdWXnBQXIu+S42ihtszKBVRNui0sLPDw8GD37t20a9cOMNxatnv3boYMGZLmOt7e3uzevdtkUJWdO3fi7e1trCcn3MHBwezduxdHR8cXxnL69Gm0Wm2ag78AWFpapnkFPD2KohAWFsb9+/czvI7ImRwcHHBxcZH5XoUQ4omsOGl+8OBBIiIijAOeguGAe9SoUcyePZuQkJA0t/uqJ8WFEDmXHEeLnCQzcgHVby/39fWlV69e1KtXD09PT2bPnk1cXBx9+vQBoGfPnpQsWRI/Pz8Ahg8fTuPGjZkxYwatWrVi9erVnDx5kkWLFgGGhLtDhw4EBgayefNmdDqd8da1IkWKYGFhgb+/P8eOHaNp06YUKlQIf39/Ro4cSffu3SlcuHCm7FfyHwpnZ2dsbGwkYcuFFEXh4cOHREREAFC8eHGVIxJCiJwhK06a9+jRI82BUnv06GE8JkjLy54UF0LkfHIcLXKCzMwFVE+6O3fuTGRkJBMnTiQsLIzatWuzbds243NfoaGhaLUpg6w3bNiQVatWMWHCBMaPH4+7uzsbNmwwTjdy69YtNm3aBEDt2rVNPmvv3r00adIES0tLVq9ezeTJk4mPj6ds2bKMHDky1QAvr0qn0xn/UGTkKrvIuaytrQGIiIjA2dlZbjUXQognMvukuaOjY6o+09zcHBcXlzQf+xJC5E1yHC1ykszKBVRPugGGDBmS7pnxffv2pVrWsWNHOnbsmGZ7Nzc3XjT1eN26dTl69OhLx5lRyc+e2NjYZNlniOyT/HNMTEyUpFsIIZ7I7JPmQggBchwtcp7MyAVyRNKdV8mtMHmD/ByFECJtmXnSPC3pPccthMj75PhL5BSZ8buofXETIYQQQrysLWfvsPNi+IsbCiHE69AlwcEZcPeq2pEIIdIhSbcQQgiRyc7fimbUutP0X3GSubuDX/jYkxBCvJKoYFjiA7unwoZBoNepHZEQ2SokJASNRsPp06fVDuW5JOkWqYSFhTF06FDKlSuHpaUlrq6utGnTht27dwNw5swZ2rZti7OzM1ZWVri5udG5c2fjyH7Jv/zJryJFitC4cWMOHjyo5m4JIUS2iIqNZ8CKAB4n6lEUCL33UO2QhBB5jV4Px36EhW/CrZOGZTePQ2jWjVkkMuZFx9Egx9JNmjQxmckio3r37m2cMSOZq6srd+7cyfHjg0jSLUyEhITg4eHBnj17mD59OufOnWPbtm00bdqUwYMHExkZydtvv02RIkXYvn07ly5dYunSpZQoUYK4uDiTbe3atYs7d+5w4MABSpQoQevWrVPN0SqEEHlJok7PoF8DuXX/EQB1Sjvw5fvV5dlEIUTmuX8DVrwHf38KSYa/NRQpDx/tALc31I0tn3vRcTQgx9KZzMzMDBcXFwoUyOFDlSnilURHRyuAEh0dneq9R48eKRcvXlQePXqkQmSv55133lFKliypxMbGpnrvv//+U/7880+lQIECSmJiYrrbuHbtmgIop06dMi47e/asAigbN27MirCzVG7+eQohsteEP88pZcZuVsqM3azU/3KnEhadOX83ntfnCFPyXYk8S69XlFO/KsrXpRRlkl3Ka8toRYlPfdyWW+Xm464XHUcripLlx9Lnzp1TWrZsqdja2irOzs5K9+7dlcjISEVRFGXv3r2Kubm5cuDAAWP7adOmKU5OTkpYWJiiKIrSuHFjZfDgwcrgwYMVOzs7xdHRUZkwYYKi1+uN6zx+/FgZNWqUUqJECcXGxkbx9PRU9u7daxLHoUOHlMaNGyvW1taKg4OD0qJFC+XevXtKr169FMDkde3aNSUpKUn56KOPFDc3N8XKykqpWLGiMnv2bOP2Jk2alGq9vXv3pvld7du3T6lfv75iYWGhuLi4KGPHjjX5vhs3bqwMHTpUGTNmjFK4cGGlWLFiyqRJk9L9Tp/3O5nRPkeudAuje/fusW3bNgYPHoytrW2q9x0cHHBxcSEpKYk///wzw88oPnr0iF9++QUACwuLTI1ZCCFyit+Oh7Li6HUALMy0/NjDg2J2VipHJYTIE2IjYU132PAJxMcYltmVhB4b4N3pYJH6uE1kr4wcRwNZeix9//59/ve//1GnTh1OnjzJtm3bCA8Pp1OnTkDKbd09evQgOjqaU6dO8fnnn7N48WLjdI8Ay5cvp0CBAhw/fpw5c+Ywc+ZMFi9ebHx/yJAh+Pv7s3r1as6ePUvHjh1p2bIlwcHBAJw+fZq3336bqlWr4u/vz6FDh2jTpg06nY45c+bg7e1Nv379uHPnDnfu3MHV1RW9Xk+pUqVYt24dFy9eZOLEiYwfP561a9cCMHr0aDp16kTLli2N6zVs2DDVd3Dr1i3effdd6tevz5kzZ1iwYAE///wzX375pUm75cuXY2try7Fjx/j222+ZOnUqO3fuzNDP41Xk8OvweUebuYeIfBCf7Z/rVMiSv4Y2ylDbK1euoCgKlStXTrdNgwYNGD9+PB9++CEDBw7E09OT//3vf/Ts2dPkPysY5mTVarU8fPgQRVHw8PDg7bfffq39EUKInOhkyD0mbjxvrH/1fnXqlC6sYkRCiDzj0l/w1wh4GJWyrFZXaPkNWDuoFZUqkpOtpxUuXJiyZcvy+PFjLl68mGqdunXrAhAUFJTq9m03NzeKFClCZGQkN27cMHmvUKFCuLu7Zzi2jBxHQ9YeS8+bN486derw9ddfG5ctWbIEV1dX/vnnHypWrMiXX37Jzp076d+/P+fPn6dXr160bdvWZDuurq7MmjULjUZDpUqVOHfuHLNmzaJfv36EhoaydOlSQkNDKVGiBGBIiLdt28bSpUv5+uuv+fbbb6lXrx4//PCDcZvVqlUzli0sLLCxscHFxcW4zMzMjClTphjrZcuWxd/fn7Vr19KpUycKFiyItbU18fHxJus964cffsDV1ZV58+ah0WioXLkyt2/fZuzYsUycOBGt1nDNuWbNmkyaNAkAd3d35s2bx+7du2nevHm6234dknRnk8gH8YTFPFY7jOfK6Nm2r776Cl9fX/bs2cOxY8dYuHAhX3/9NQcOHKBGjRrGdmvWrKFy5cqcP3+eTz/9lGXLlmFubp5V4QshhCruRD9i4MpAEnWGv6F93nCjYz1XlaMSQuQJu6capgNLZuMIbeZAlTbqxaSiH3/80SQxA+jWrRsrV67k5s2beHh4pFon+fi2d+/eHD1qOtDcihUr6N69O2vXrmXIkCEm77Vo0YLt27dnOLaMHkdD1h1Lnzlzhr1791KwYMFU7129epWKFStiYWHBr7/+Ss2aNSlTpgyzZs1K1bZBgwYmY5F4e3szY8YMdDod586dQ6fTUbFiRZN14uPjcXR0BAxXujt27Jjh7yPZ/PnzWbJkCaGhoTx69IiEhARq1679Utu4dOkS3t7eJvG/8cYbxMbGcvPmTUqXLg0Yku6nFS9e3DiQXVaQpDubOBWyzPGf6+7ujkaj4fLlyy9s6+joSMeOHenYsSNff/01derU4bvvvmP58uXGNq6urri7u+Pu7k5SUhLvv/8+58+fx9JSne9CCCEy2+NEHQNWBBAVa7iTqWF5R/7v3SoqRyWEyBPOrDZNuCu1gjazoaCzaiGpbcCAAamuyhYubLirqFSpUgQEBKS77rJly9K80g3QqVMnvL29Td4rVKjQS8X2MsfRkDXH0rGxsbRp04Zp06aleq948eLG8pEjRwDDLfH37t1L83b49MTGxmJmZkZAQABmZmYm7yUn+9bW1hneXrLVq1czevRoZsyYgbe3N4UKFWL69OkcO3bspbeVEc+evNBoNOj1+iz5LJCkO9tk9BZvNRUpUgQfHx/mz5/PsGHDUv0HvH//vvF5lKdZWFhQvnz5VH/IntahQwcmTpzIDz/8wMiRIzM7dCGEyHaKojBu/TnO3owGwLWINfM/rEsBMxkuRQjxmm4FwqZhKfUWX4L3EMjnMyEUL17cJHl8mpWVlfFW8rRUqlQp3fecnJxwcnJ6rdhe9TgaMu9Yum7duvzxxx+4ubmlO5r31atXGTlyJD/99BNr1qyhV69e7Nq1y3jbNZAq0T169Cju7u6YmZlRp04ddDodERERvPnmm2l+Rs2aNdm9e3equxKe3l+dznRO+cOHD9OwYUMGDRpkEuuL1ntWlSpV+OOPP1AUxXi1+/DhwxQqVIhSpUo9d92sJEcGwsT8+fPR6XR4enryxx9/EBwczKVLl/j+++/x9vZm8+bNdO/enc2bN/PPP/8QFBTEd999x9atW3nvvffS3a5Go2HYsGF88803PHwoc9YKIXK/nw9d489TtwCwNjdjUY96FLaVwSKFEK8pNsIwaJruyVhAHr2h4dB8n3DnBi86jgay9Fh68ODB3Lt3j65du3LixAmuXr3K9u3b6dOnDzqdDp1OR/fu3fHx8aFPnz4sXbqUs2fPMmPGDJPthIaG4uvrS1BQEL/99htz585l+PDhAFSsWJFu3brRs2dP1q9fz7Vr1zh+/Dh+fn5s2bIFgHHjxnHixAkGDRrE2bNnuXz5MgsWLCAqyjAugZubG8eOHSMkJISoqCj0ej3u7u6cPHmS7du3888///D5559z4sQJk7jc3Nw4e/YsQUFBREVFkZiYmOo7GDRoEDdu3GDo0KFcvnyZjRs3MmnSJHx9fU1OLGQ3SbqFiXLlyhEYGEjTpk0ZNWoU1atXp3nz5uzevZsFCxZQtWpVbGxsGDVqFLVr16ZBgwasXbuWxYsX06NHj+duu1evXiQmJjJv3rxs2hshhMgaB/6J5Outl4z1mZ1qUaW4nYoRCSHyhKQEWNsTYgwn9HD1gnemqxuTyLAXHUcDWXosXaJECQ4fPoxOp6NFixbUqFGDESNG4ODggFar5auvvuL69ev8+OOPgOHOgUWLFjFhwgTOnDlj3E7Pnj159OgRnp6eDB48mOHDh9O/f3/j+0uXLqVnz56MGjWKSpUq0a5dO06cOGF8XrpixYrs2LGDM2fO4Onpibe3Nxs3bjRefR89ejRmZmZUrVoVJycnQkNDGTBgAB988AGdO3fGy8uLu3fvmlz1BujXrx+VKlWiXr16ODk5cfjw4VTfQcmSJdm6dSvHjx+nVq1aDBw4kL59+zJhwoQX/fiylEZ5maf+hVFMTAz29vZER0djZ2d6oPX48WOuXbtG2bJlsbKS6WJyO/l5CiGeFhIVx3vzDxP9yHCGfej/KjCqRfq3LWaG5/U5wpR8VyJX2+wLJ382lAsVh/77oVCx56+Tx8hxl7qaNGlC7dq1mT17ttqh5BjP+53MaJ8jV7qFEEKIDIqNT6LfLyeNCXezKs6MbFbxBWsJIUQGBCxLSbjNLKHzr/ku4RYir5KkWwghhMgAvV7Bd81pgiNiAajgXJBZnWuj1cpzlkKI1xR6DLaMTqm3ngWlUk9/JYTInWT0ciGEECID5uwOZsfFcADsrArwU896FLJKf75UIYTIkJjbsLYH6J8MCuU1EOp0UzcmkW/t27dP7RDyJLnSLYQQQrzAtvNhzNkdDIBWA3M/rEvZohmf11QIIdKU+NgwUnms4YQebm8apgcTQuQpknQLIYQQz3E5LAbftaeN9bEtK9O44uvN5yqEECgKbPGFWwGGun1p6LgMzOQOGiHyGkm6hRBCiHTcf5hA/18CeJigA+C92iXo/1Y5laMSQuQJR3+A078aygWsocuvYFtU3ZiEEFlCkm4hhBAiDUk6PUNWnSL03kMAqpe0Y1r7mmg0MnCaEOI1Hf8Jto9Pqb83D4rXVC8eIUSWkqRbCCGESIPf35c5dCUKgKIFLfixRz2szM1UjkoIkeudWAxbnxqpvPFYqNFBvXiEEFlOkm4hhBDiGX8E3OTnQ9cAKKDV8EM3D0o6WKsclRAi1zvxM2wZlVJv5AtNxqkXjxAiW0jSLV6aRqNhw4YNaoeRafbt24dGo+H+/ftqhyKEyAFO37jPuD/PGetT3quGZ9kiKkYkhMgTTvxsGDgtWaOR8PZEkEdWhMjzJOkWqYSFhTF06FDKlSuHpaUlrq6utGnTht27d2f6Z0nCK4TISSJiHjNgxUkSkvQAfOhVmm5eZVSOSgiR651cYppwvzEC3p4kCXce9KLj6DNnztC2bVucnZ2xsrLCzc2Nzp07ExERAUBISAgajcb4KlKkCI0bN+bgwYNq7pZ4TZJ0CxMhISF4eHiwZ88epk+fzrlz59i2bRtNmzZl8ODBaoeXLkVRSEpKUjsMIUQuFp+kY+DKAMJj4gGo71aYyW2qqRyVECLXO7kUNo9Mqb8xHJpNloQ7D3rRcXRkZCRvv/02RYoUYfv27Vy6dImlS5dSokQJ4uLiTLa1a9cu7ty5w4EDByhRogStW7cmPDxcpT0Tr0uSbmFi0KBBaDQajh8/Tvv27alYsSLVqlXD19eXo0ePpmqf1pXq06dPo9FoCAkJAeD69eu0adOGwoULY2trS7Vq1di6dSshISE0bdoUgMKFC6PRaOjduzcAer0ePz8/ypYti7W1NbVq1eL3339P9bl///03Hh4eWFpacujQoReuB7B161YqVqyItbU1TZs2NcYphMjfJm+6SGDofQCK21vxQzcPLApINymEeA0nl8LmESn1N4ZDsymScOdRLzqOPnz4MNHR0SxevJg6depQtmxZmjZtyqxZsyhbtqzJthwdHXFxcaF69eqMHz+emJgYjh07ptKeiddVQO0ARM5x7949tm3bxldffYWtrW2q9x0cHF5pu4MHDyYhIYEDBw5ga2vLxYsXKViwIK6urvzxxx+0b9+eoKAg7OzssLY2DFTk5+fHypUrWbhwIe7u7hw4cIDu3bvj5ORE48aNjdv+7LPP+O677yhXrhyFCxd+4Xo3btzggw8+YPDgwfTv35+TJ08yatSo9EIXQuQTB4Mj+e14KACWBbQs6lEPp0KWKkclhMjVApaZJtwNh0nCnYdl5DjaxcWFpKQk/vzzTzp06JChKSgfPXrEL7/8AoCFhUWmxy2yhyTd2eXHxhAbkf2fW9AZBuzPUNMrV66gKAqVK1fO1BBCQ0Np3749NWrUAKBcuXLG94oUMQxO5OzsbEzq4+Pj+frrr9m1axfe3t7GdQ4dOsSPP/5oknRPnTqV5s2bZ3i9BQsWUL58eWbMmAFApUqVOHfuHNOmTcvUfRZC5B6PE3VM3HjBWJ/ctho1StmrGJEQItc7swb+Gp5SbzgUmk+VhPs1tJl7iMgH8dn+uU6FLPlraKMXtsvIcXSDBg0YP348H374IQMHDsTT05P//e9/9OzZk2LFipm0bdiwIVqtlocPH6IoCh4eHrz99tuvvT9CHZJ0Z5fYCHhwW+0onktRlCzZ7rBhw/jkk0/YsWMHzZo1o3379tSsWTPd9leuXOHhw4fGZDpZQkICderUMVlWr169l1rv0qVLeHl5mbyfnKALIfKnRQf+5VqU4Vm6emUK07meq8oRCSFytdgI03m4vYdA8y8k4X5NkQ/iCYt5rHYY6crocfRXX32Fr68ve/bs4dixYyxcuJCvv/6aAwcOGC9QAaxZs4bKlStz/vx5Pv30U5YtW4a5uXlWhS+ymCTd2aWgc47/XHd3dzQaDZcvX87wOlqt4XnHp//QJCYmmrT5+OOP8fHxYcuWLezYsQM/Pz9mzJjB0KFD09xmbGwsAFu2bKFkyZIm71lamt7u+fTtOy+znhBCAFy/G8e8vVcAMNNq+PL96mi1cmAshHgNu6dAfIyhXLMLtPhSEu5MoNYjPxn93Jc5jnZ0dKRjx4507NiRr7/+mjp16vDdd9+xfPlyYxtXV1fc3d1xd3cnKSmJ999/n/Pnz8sxbS4lSXd2yeAt3moqUqQIPj4+zJ8/n2HDhqV6HuX+/fupnut2cnIC4M6dOxQuXBgwDKT2LFdXVwYOHMjAgQMZN24cP/30E0OHDjU+m6LT6Yxtq1atiqWlJaGhoSa3kr9IRtarUqUKmzZtMlmW1gBxQoi8T1EUJm68YJwe7KM33KjsYqdyVEKIXO1mAJxaaShb2kvCnYkycou3ml7lOBoMz2mXL18+1ejlT+vQoQMTJ07khx9+YOTIkem2EzmXDMsqTMyfPx+dToenpyd//PEHwcHBXLp0ie+//z7N27ArVKiAq6srkydPJjg4mC1bthifl042YsQItm/fzrVr1wgMDGTv3r1UqVIFgDJlyqDRaNi8eTORkZHExsZSqFAhRo8ezciRI1m+fDlXr14lMDCQuXPnmpwBfFZG1hs4cCDBwcGMGTOGoKAgVq1axbJlyzLvCxRC5Brbzoex/59IwDBa+YhmFVWOSAiRq+n18PenKfWm46Cgk3rxiGz3ouPozZs30717dzZv3sw///xDUFAQ3333HVu3buW9995Ld7sajYZhw4bxzTff8PDhw2zcI5FZJOkWJsqVK0dgYCBNmzZl1KhRVK9enebNm7N7924WLFiQqr25uTm//fYbly9fpmbNmkybNo0vv/zSpI1Op2Pw4MFUqVKFli1bUrFiRX744QcASpYsyZQpU/jss88oVqwYQ4YMAeCLL77g888/x8/Pz7jeli1bUk2n8KwXrVe6dGn++OMPNmzYQK1atYzP0Qgh8pfY+CSm/HXRWJ/Upiq2lnLzlxDiNZxdDbdOGspOlaH+x+rGI7Ldi46jq1atio2NDaNGjaJ27do0aNCAtWvXsnjxYnr06PHcbffq1YvExETmzZuXTXsjMpNGyarRs/K4mJgY7O3tiY6Oxs7O9HbEx48fc+3aNcqWLYuVlZVKEYrMIj9PIfKer7Zc5KeD1wBoUsmJpb3rZ2jqFrU8r88RpuS7Eqp4HANzPSDuyUw1PTZA+aaqhpRbyXGXyGme9zuZ0T5HrnQLIYTIVy6HxbDkcAhgmJN7atvqOTrhFkLkAgempyTcVdpIwi2EMCFJtxBCiHxDr1eY8Od5dHrDTV6Dm1agtKONylEJIXK1qGA4+uQRPDNLw+BpQgjxFEm6hRBC5Bu/B9zk5PX/AChb1JYBjcupHJEQIldTFNj2GeifTJf6xnAo7KZqSEKInEeSbiGEEPnCf3EJ+P19yVj/4r3qWBYwUzEiIUSu9892uLLLULYrBY1kOichRGqSdAshhMgXpm27zH8PDVej2tQqQSP3oipHJITI1ZLiDVe5k7X4AizkcRUhRGqSdGchvV6vdggiE8jP8YmTS2HLaLh/Q+1IhHhpAdf/Y/UJw+9uQcsCTGhVReWIhBC5nv98+M8wCwJlGkG199WNRwiRY8mkpFnAwsICrVbL7du3cXJywsLCQkbGzYUURSEhIYHIyEi0Wi0WFhZqh6SepATDyKwxtyBwOQw/C3bF1Y5KiAxJ0umZsOG8sT6qRUWK2ck0NEKI1xBzBw58ZyhrtPDONJBjPSFEOiTpzgJarZayZcty584dbt++rXY44jXZ2NhQunRptNp8fGPI2dWGhBug/NuScItcZbn/dS7diQGganE7ejQoo3JEQohcb9ckSIwzlOv1BZfq6sYjhMjRJOnOIhYWFpQuXZqkpCR0Op3a4YhXZGZmRoECBfL3nQq6JDg4M6X+1mj1YhHiJYVFP2bmjiDAcBHqq/erU8AsH59AE0K8vtCjcHaNoWxdGJqOVzceIUSOJ0l3FtJoNJibm2Nubq52KEK8ugt/pjyzVrYxlKqnbjxCvIQvtlwkLsFw4rNL/dLUKV1Y5YiEELla4iPY/NQI5f+bADZF1ItH5DoajYY///yTdu3aqR1Kpti3bx9Nmzblv//+w8HBQe1wciw53S+ESJ9eDwdnpNTlKrfIRQ78E8mWs3cAcLS1YGzLSipHJITI9XZNhoiLhrJLDfDoo2o4IucJCwtj6NChlCtXDktLS1xdXWnTpg27d+/O9M/at28fGo2G+/fvZ/q2RebKEUn3/PnzcXNzw8rKCi8vL44fP/7c9uvWraNy5cpYWVlRo0YNtm7danwvMTGRsWPHUqNGDWxtbSlRogQ9e/ZM9Wz1vXv36NatG3Z2djg4ONC3b19iY2OzZP+EyLWCtkLkk3mNXb3A7U114xEigx4n6pi4MWXwtHHvVsHBJh8PhiiEeH3BO+HYQkO5gBV88BNozdSNSeQoISEheHh4sGfPHqZPn865c+fYtm0bTZs2ZfDgwWqHly5FUUhKSlI7jDxN9aR7zZo1+Pr6MmnSJAIDA6lVqxY+Pj5ERESk2f7IkSN07dqVvn37curUKdq1a0e7du04f95wcPXw4UMCAwP5/PPPCQwMZP369QQFBdG2bVuT7XTr1o0LFy6wc+dONm/ezIEDB+jfv3+W768QuYaiGEYsT/bmaBmZVeQaC/dfJeTuQwA83YrQvm5JlSMSQuRqsZGw4ZOUeosvwVmmHhSmBg0ahEaj4fjx47Rv356KFStSrVo1fH19OXr0aKr2aV2pPn36NBqNhpCQEACuX79OmzZtKFy4MLa2tlSrVo2tW7cSEhJC06ZNAShcuDAajYbevXsDhulu/fz8KFu2LNbW1tSqVYvff/891ef+/fffeHh4YGlpyaFDh164HsDWrVupWLEi1tbWNG3a1BineAFFZZ6ensrgwYONdZ1Op5QoUULx8/NLs32nTp2UVq1amSzz8vJSBgwYkO5nHD9+XAGU69evK4qiKBcvXlQA5cSJE8Y2f//9t6LRaJRbt25lKO7o6GgFUKKjozPUXohcJ3inokyyM7wWNFIUvV7tiITIkGuRsYr7/21VyozdrJQft0W5fCdG7ZBem/Q5GSfflch0er2irOyY0ieu7Ch9YhZ69OiRcvHiReXRo0dqh/JS7t69q2g0GuXrr79+bjtA+fPPPxVFUZS9e/cqgPLff/8Z3z916pQCKNeuXVMURVFatWqlNG/eXDl79qxy9epV5a+//lL279+vJCUlKX/88YcCKEFBQcqdO3eU+/fvK4qiKF9++aVSuXJlZdu2bcrVq1eVpUuXKpaWlsq+fftMPrdmzZrKjh07lCtXrih379594XqhoaGKpaWl4uvrq1y+fFlZuXKlUqxYsVT7kNc873cyo32OqgOpJSQkEBAQwLhx44zLtFotzZo1w9/fP811/P398fX1NVnm4+PDhg0b0v2c6OhoNBqN8eF+f39/HBwcqFcvZUCoZs2aodVqOXbsGO+///6r75QQecWBp57lfnOUXOUWuYKiKEzcdIGEJD0AfRuVpZJLIZWjEkLkaicWQ/B2Q9nWCd6bL32iGn5sDLFp3wmbpQo6w4D9L2x25coVFEWhcuXKmfrxoaGhtG/fnho1agBQrlw543tFihgG8XN2djbmOfHx8Xz99dfs2rULb29v4zqHDh3ixx9/pHHjxsb1p06dSvPmzTO83oIFCyhfvjwzZhiOEStVqsS5c+eYNm1apu5zXqRq0h0VFYVOp6NYsWImy4sVK8bly5fTXCcsLCzN9mFhYWm2f/z4MWPHjqVr167Y2dkZt+Hs7GzSrkCBAhQpUiTd7cTHxxMfH2+sx8TEPH/nhMjNQg5D6BFDuWglqNL2+e2FyCG2ngvjwD+RAJSwt2LY2+4qRySEyNUiLsGOCSn1dgugoJN68eRnsRHw4PaL26lEUZQs2e6wYcP45JNP2LFjB82aNaN9+/bUrFkz3fZXrlzh4cOHxmQ6WUJCAnXq1DFZ9vQFyIysd+nSJby8vEzeT07QxfOp/kx3VkpMTKRTp04oisKCBQtea1t+fn7Y29sbX66urpkUpRA50MHvUspv+oI2T/+pEHlEbHwSUzdfMNYntqmGraXMjJmVMnMgVIDJkydTuXJlbG1tKVy4MM2aNePYsWNZuQtCpC8pHv74GJIeG+qeA8C9+fPXEVmnoDMUKpH9r4LOL44NcHd3R6PRpHvhMC3aJ8dXTyfsiYmJJm0+/vhj/v33X3r06MG5c+eoV68ec+fOTXebyQNDb9myhdOnTxtfFy9eTPV8tq2t7SutJ16eqkcjRYsWxczMjPDwcJPl4eHhuLi4pLmOi4tLhtonJ9zXr19nz549xqvcydt4dqC2pKQk7t27l+7njhs3zuS29piYGEm8Rd50KwCu7jGUHcpA9Q7qxiNEBs3a+Q/hMYY7kv5X2RmfasVesIZ4HckDoS5cuBAvLy9mz56Nj48PQUFBqe4mg5SBUP38/GjdujWrVq2iXbt2BAYGUr16dQAqVqzIvHnzKFeuHI8ePWLWrFm0aNGCK1eu4OQkVxdFNts1BcKfzILgXBWaT1U3nvwuA7d4q6lIkSL4+Pgwf/58hg0bZpLQAty/fz/VPNbJf9fu3LlD4cKFAcNAas9ydXVl4MCBDBw4kHHjxvHTTz8xdOhQLCwMs3LodDpj26pVq2JpaUloaKjJreQvkpH1qlSpwqZNm0yWpTVAnEhN1ctXFhYWeHh4mMxbp9fr2b17d7q3Knh7e6ea527nzp0m7ZMT7uDgYHbt2oWjo2Oqbdy/f5+AgADjsj179qDX61PdMpHM0tISOzs7k5cQedLBmSnlRiPATK4Uipzv4u0Ylh0JAcCygJbJbaqhkWcus9TMmTPp168fffr0oWrVqixcuBAbGxuWLFmSZvs5c+bQsmVLxowZQ5UqVfjiiy+oW7cu8+bNM7b58MMPadasGeXKlaNatWrMnDmTmJgYzp49m127JYTBld1wdL6hbGYJ7ReDuZW6MYkcb/78+eh0Ojw9Pfnjjz8IDg7m0qVLfP/992nmNhUqVMDV1ZXJkycTHBzMli1bjM9LJxsxYgTbt2/n2rVrBAYGsnfvXqpUMYycX6ZMGTQaDZs3byYyMpLY2FgKFSrE6NGjGTlyJMuXL+fq1asEBgYyd+5cli9fnm7sGVlv4MCBBAcHM2bMGIKCgli1ahXLli3LvC8wL8uCAd5eyurVqxVLS0tl2bJlysWLF5X+/fsrDg4OSlhYmKIoitKjRw/ls88+M7Y/fPiwUqBAAeW7775TLl26pEyaNEkxNzdXzp07pyiKoiQkJCht27ZVSpUqpZw+fVq5c+eO8RUfH2/cTsuWLZU6deoox44dUw4dOqS4u7srXbt2zXDcMjqqyJPCLqSMzvpdJUVJfKx2REK8kE6nV96ff0gpM3azUmbsZmXu7n/UDinT5bQ+Jz4+XjEzMzOOwJusZ8+eStu2bdNcx9XVVZk1a5bJsokTJyo1a9ZM9zOmT5+u2NvbK5GRkRmOLad9VyIXio1SlOnuKf2h/wK1I8pXcuvo5clu376tDB48WClTpoxiYWGhlCxZUmnbtq2yd+9eRVFMRy9XFEU5dOiQUqNGDcXKykp58803lXXr1pmMXj5kyBClfPnyiqWlpeLk5KT06NFDiYqKMq4/depUxcXFRdFoNEqvXr0URVEUvV6vzJ49W6lUqZJibm6uODk5KT4+Psr+/fsVRUl71PSMrKcoivLXX38pFSpUUCwtLZU333xTWbJkiYxenoE+R/WkW1EUZe7cuUrp0qUVCwsLxdPTUzl69KjxvcaNGxt/gZKtXbtWqVixomJhYaFUq1ZN2bJli/G9a9euKUCar+RfdkUxDOvftWtXpWDBgoqdnZ3Sp08f5cGDBxmOWTp1kSet+yjlIOPIfLWjESJDVh+/bky4m363V3mcmKR2SJkup/U5t27dUgDlyJEjJsvHjBmjeHp6prmOubm5smrVKpNl8+fPV5ydnU2W/fXXX4qtra2i0WiUEiVKKMePH39uLI8fP1aio6ONrxs3buSo70rkMnq9oqzqktIXrvhApgfLZrk96RZ5T66fMizZkCFDGDJkSJrv7du3L9Wyjh070rFjxzTbu7m5ZWj0wCJFirBq1aqXilOIPO3uVbiw3lC2cQSPXurGI0QG3ItLwO/vlEFrvnivOpYFzFSMSLyupk2bcvr0aaKiovjpp5/o1KkTx44dS/M5cTAMdDplypRsjlLkWUe+h6AnA/zZFIX3fpDpwYQQr02GJBZCGByaBYphbmMaDAIL2+e3FyIH+ObvS9x/aBjptW2tErxRoajKEeUPWTkQqq2tLRUqVKBBgwb8/PPPFChQgJ9//jndWMaNG0d0dLTxdePGjVfcK5GvJSXA5pGwc2LKsvfmQyEZkFEI8fok6RZCwP0bcGa1oWxpD5791I1HiAw4GXKPtSdvAlDIsgATWldROaL8I6sGQk2LXq8nPj4+3fdloFPx2mIj4Zf34ORTgwC+9SlUaqleTEKIPCVH3F4uhFDZke9B/2ReSK/+YGWvbjxCvMDjRB3j1p8z1kf7VMK5kIwsnJ18fX3p1asX9erVw9PTk9mzZxMXF0efPn0A6NmzJyVLlsTPzw+A4cOH07hxY2bMmEGrVq1YvXo1J0+eZNGiRQDExcXx1Vdf0bZtW4oXL05UVBTz58/n1q1b6T5SJsRru3MGVneD6Cd3SJhZQps5ULurunEJIfIUSbqFyO8e3YfAFYayuQ14faJqOEJkxIwdQQRHxAJQo6Q93RuUUTmi/Kdz585ERkYyceJEwsLCqF27Ntu2baNYMcPtuKGhoWi1KTfUNWzYkFWrVjFhwgTGjx+Pu7s7GzZsMM7RbWZmxuXLl1m+fDlRUVE4OjpSv359Dh48SLVq1VTZR5HHnf8DNgyGpEeGekEX6PIrlKqnblxCiDxHkm4h8ruza1MOOGp3A1vH57cXQmXH/r3L4kPXALAw0/Jdx1qYaWWgIzVk5kCoVlZWrF+/PjPDEyJtej3s/RIOPjUfcsl60Hkl2BVXLy5hQq/Xqx2CEEDm/C5K0i1EfqYoELA0pV7vI/ViESIDYuOTGP37GZInqRjVoiKVXAqpG5QQIvd4HAPr+8E/21KW1e4GrWaCuTyikhNYWFig1Wq5ffs2Tk5OWFhYoJER5IUKFEUhISGByMhItFotFhYWr7wtSbqFyM9uHIOIi4ayqxcUq6puPEK8wFdbLnHjnuHOjPpuhfn4zXIqRySEyDXuXoXfukJUkKGuMQOfr8BroEwLloNotVrKli3LnTt3uH37ttrhCIGNjQ2lS5c2eWTqZUnSLUR+dlKucovcY29QBL8dDwXAxsJMbisXQmRcyCFY0x0e/WeoWzlAx2VQvqmaUYl0WFhYULp0aZKSktDpdGqHI/IxMzMzChQo8Np3W0jSLUR+9fAeXPjTULZygKrvqRqOEM9z/2ECY38/a6yPf7cKZRxlLnkhRAac+hX+Gp4yS4dTFei6CorInTI5mUajwdzcHHNzc7VDEeK1SdItRH515jfQPZn7tvaHYG6tbjxCPMfEjReIeGD4fX2rohPdvEqrHJEQIsfT62HPVDg0K2VZhWbQYSlYyXzuQojsI0m3EPmRopjeWu7RR71YhHiBLWfvsOmM4bk+O6sCfNu+pgyqI4R4voSH8OcAuLQpZZlnf/DxAzM5/BVCZC/5qyNEfnT9MNwNNpTLNAKniurGI0Q6Ih48ZsKGc8b61Peq42IvIwwLIZ7jQRj81gVunzLUNVpoOQ28+qsblxAi35KkW4j8yGQANbnKLXImRVEY98c5/ntoeA7zneouvFe7hMpRCSFytLBzsKoLxNw01C0KQYclULGFunEJIfI1SbqFyG/iouDiRkPZxhGqtFE3HiHSsS7gJrsvRwBQtKAFX7arLreVCyHSF7QN/ugLCbGGur0rfLgGilVTNy4hRL4nSbcQ+c3pX1NGcK3dDQpYqhuPEGm4+d9Dpv510Vj3+6AmjgXld1UIkYbbp+HkEji1AhS9YVlJD+jyGxQqpmpoQggBknQLkb/o9RCwLKXu0VutSIRIl16vMGbdWWLjkwDo4FGK5lXlwFkI8ZSEODi/3pBs3w40fa/a+9BugczKIYTIMSTpFiI/ubYf7v1rKJdtDI7l1Y1HiDQs9w/B/9+7AJR0sGZim6oqRySEyDEiLhnGJTmzGuKjTd+zKAhvjIA3R4FWq0p4QgiRFkm6hchPAp4eQO0j9eIQIh1XI2P55u/Lxvr0DjWxszJXMSIhhOr0ejj/h+GqduiR1O+71DD0aTU6gmWh7I9PCCFeQJJuIfKLB+FweYuhbOsMlVupG48Qz0jS6fFde4b4JMMzmb0butGwQlGVoxJCqG7bWDi+yHRZAWuo3t6QbJesCzLIohAiB5OkW4j84vRK0BuekaVOdzCTq4ciZ1m4/ypnbtwHoFxRW8a2rKxuQEII9UUFw4nFKXWnyoZEu2ZnsHZQLSwhhHgZknQLkR+YDKCmAY9eakYjRCoXbkczZ3cwAFoNfNepFtYWZipHJYRQ3YHvUkYkb/wZNPlMrmoLIXIdGWVCiPzg6h64H2ooV3gbCrupGo4QT4tP0uG75gyJOgWAT5qUp27pwipHJYRQ3d2rcG6toWxdBBoOlYRbCJErSdItRH7w9ABqHn3Ui0OINMzaGUxQ+AMAqhS3Y/jbFVWOSAiRIxyckXKV23swWBZUNx4hhHhFknQLkdfF3Iagvw3lQsWhYkt14xHiKQHX77HowFUAzM00zOxUC4sC0jUJke/du2aYFgzAygE8+6sajhBCvA45shEirwtcAYrOUK7bE8xkKAeRMzxMSMJ37Rn0hrvKGdm8IlWK26kblBAiZzg4I6Xv8h4MVvK3QQiRe0nSLURelpQAgcsNZY3WkHQLkUP4bb3M9bsPAahb2oEBb5VXOSIhRI7w33U485uhbGkvV7mFELmeJN1C5GXnf4eYW4ZyxZZgX0rdeIR44mBwJCuOXgfA2tyMGZ1qY6aVAZKEEMChmSlTXDb4RKYGE0LkepJ0C5FX6fVweE5K/Y3h6sUixFOiHyUyZt1ZY33cu5UpW9RWxYiEEDnG/Rtw6ldD2dIOGgxUNx4hhMgEknQLkVcFb4fIy4ayawMo3UDdeIR4YsqmC4TFPAagUYWidPcqo3JEQogc49As0Ccayl4DwFqmDxRC5H6SdAuRVx2anVJuNEKtKIQwse18GOtPGR55KGRVgG871EQrt5ULIQCib8GpFYayRUFoMEjdeIQQIpNI0i1EXhR6FG4cNZSdKoO7j7rxCAFExcbzf3+eM9Ynt6lGCQdrFSMSQuQoh+eALsFQ9uwHNkXUjUcIITKJJN1C5EVPX+V+Yzho5b+6UJdOrzByzWnuxhkOqFtULcYHdUuqHJUQIseIuQMBywxlc1vwHqpqOEIIkZnkSFyIvCbiEvzzt6FsVxKqd1A3HiGAObuDORgcBUDRghZ8/UENNBq5rVwI8cSR70EXbyjX7wu2jurGI4QQmUiSbiHymsPfp5S9B0MBC/ViEQLYezmC73cHA6DVwNyudSla0FLlqIQQOcaDcDi5xFAuYA0Nh6kbjxBCZDJJuoXIS6Jvwrm1hrKVA9TtpWo4Qty495ARa04b65+2rIx3ebmCJYR4ypHvIckwowH1+0JBJ3XjEUKITCZJtxB5if8PoE8ylD37g2VBdeMR+drjRB2f/BpA9CPD9D8tqhZjwFvlVI5KCJGjxEbCiZ8N5QJWcpVbCJEnSdItRF7x8F7KIDQFrA3zmwqhoil/XeD8rRgA3Bxt+K5TLXmOWwhhyn8uJD0ylD36QKFi6sYjhBBZQJJuIfKKE4shMc5QrtMdbIuqG4/I19aevMFvx28AYGWuZUF3D+yszFWOSgiRozyOhhNPnuU2szTMtiGEEHmQJN1C5AUJD+HYQkNZYwYNh6gbj8jXzt+K5vMN5431r9+vQZXidipGJITIkU4uhYQHhnLtD8GuuLrxCCFEFpGkW4i84PSv8PCuoVztfSjspmo4Iv+KfpjIoF8DiU/SA9C9QWk+qFtK5aiEEDlOUjwcXfCkooGGMi+3ECLvkqRbiNxOl2QY+TWZ3J4nVKLXK/iuPU3ovYcA1Cplz+etq6oclRAiRzq3DmLDDOUqrcGxvLrxCCFEFpKkW4jc7uIGuB9qKJd/G4rXVDUckX8t2H+V3ZcjAChsY84P3T2wLGCmclRCiBxHr4fDT50sbigni4UQeZsk3ULkZooCh2an1BuNUCsSkc8dCo5ixo4gADQamNOlDiUdrFWOSgiRIwVvhyjD3wtKNwTX+urGI4QQWUySbiFys6u7IfycoVyiLri9qW48Il+6ff8Rw1afQq8Y6iObVeStik7qBiWEyLkOyyNRQoj8RZJuIXKzw3NSyo1GGC4xCpGNEpL0DPo1kHtxCQA0qeTEkKYVVI5KCJFj3TgOoUcM5aKVwL2FuvEIIUQ2kKRbiNwq6gpcO2AoFykPlVurG4/Il77acpHTN+4DUKqwNbM710arlZM/Qoh0PH2y+I1hoJVDUSFE3id/6YTIrU6tSCnX6wNaGbBKZK8Np26x3P86ABYFtCzo5oGDjYXKUQkhcqyoK3B5i6Fc0AVqdFQ3HiGEyCaqJ93z58/Hzc0NKysrvLy8OH78+HPbr1u3jsqVK2NlZUWNGjXYunWryfvr16+nRYsWODo6otFoOH36dKptNGnSBI1GY/IaOHBgZu6WEFlLlwRnfjOUtQWgZhd14xH5TlDYA8atP2esT21bjRql7FWMSAiR4/nPBZ4M/tDgEyhgqWo4QgiRXVRNutesWYOvry+TJk0iMDCQWrVq4ePjQ0RERJrtjxw5QteuXenbty+nTp2iXbt2tGvXjvPnzxvbxMXF0ahRI6ZNm/bcz+7Xrx937twxvr799ttM3TchslTwDogNN5QrvQMFZdAqkX0ePE7kk5UBPErUAdDRoxSd67uqHJUQIkeLjYDTT04WWxQy3KElhBD5hKpJ98yZM+nXrx99+vShatWqLFy4EBsbG5YsWZJm+zlz5tCyZUvGjBlDlSpV+OKLL6hbty7z5s0ztunRowcTJ06kWbNmz/1sGxsbXFxcjC87O7tM3TchstTTt5bX6aleHCLfURSFMevO8m9UHABVi9vxRbvqaGQQPyHE8xz7EXTxhnK93mAld8YIIfIP1ZLuhIQEAgICTJJjrVZLs2bN8Pf3T3Mdf3//VMm0j49Puu2f59dff6Vo0aJUr16dcePG8fDhw+e2j4+PJyYmxuQlhCoehME/2w3lQiWgwtvqxiPylcUHr7HtQhgAdlYFWNjdAytzGU9ACPEc8bFwYrGhrDUHr0/UjUcIIbJZAbU+OCoqCp1OR7FixUyWFytWjMuXL6e5TlhYWJrtw8LCXuqzP/zwQ8qUKUOJEiU4e/YsY8eOJSgoiPXr16e7jp+fH1OmTHmpzxEiS5z5DRTDbb3U7ioDqIlsc+zfu3yzLeXv88xOtSntaKNiREKIXOHUCnh831Cu0RHsS6oajhBCZDfVkm419e/f31iuUaMGxYsX5+233+bq1auUL18+zXXGjRuHr6+vsR4TE4OrqzzDKLKZosCplSn1Ot3Vi0XkKxExjxny2yl0esMgSIOblqdZ1WIvWEsIke/pEsF/fkq94VD1YhFCCJWolnQXLVoUMzMzwsPDTZaHh4fj4uKS5jouLi4v1T6jvLy8ALhy5Uq6SbelpSWWljLKplBZ6FG4e8VQdnsTipRTNx6RLyTq9AxeFUjkA8PzmG9UcMS3eSWVoxJC5AoX/oToG4ayuw8Uq6puPEIIoQLVnum2sLDAw8OD3bt3G5fp9Xp2796Nt7d3mut4e3ubtAfYuXNnuu0zKnlaseLFi7/WdoTIciYDqPVQLw6Rr3y77TInQv4DoLi9Fd93qYOZVgZOE0K8gKLA4e9T6m8MUy8WIYRQkaqjl/v6+vLTTz+xfPlyLl26xCeffEJcXBx9+himkejZsyfjxo0zth8+fDjbtm1jxowZXL58mcmTJ3Py5EmGDBlibHPv3j1Onz7NxYsXAQgKCuL06dPG576vXr3KF198QUBAACEhIWzatImePXvy1ltvUbNmzWzceyFe0uMYwxUDAEt7qNpW3XhEvrD13B1+OngNAHMzDfO71cWxoNz1Iwzmz5+Pm5sbVlZWeHl5cfz48ee2X7duHZUrV8bKyooaNWqwdetW43uJiYmMHTuWGjVqYGtrS4kSJejZsye3b9/O6t0QWeXqHgg/ZyiX9IAyb6gbjxBCqETVpLtz58589913TJw4kdq1a3P69Gm2bdtmHCwtNDSUO3fuGNs3bNiQVatWsWjRImrVqsXvv//Ohg0bqF69urHNpk2bqFOnDq1atQKgS5cu1KlTh4ULFwKGK+y7du2iRYsWVK5cmVGjRtG+fXv++uuvbNxzIV7BhfWQ+GSU/RodwNxa3XhEnnclIpYx684Y65+3rkrd0oVVjEjkJGvWrMHX15dJkyYRGBhIrVq18PHxISIiIs32R44coWvXrvTt25dTp07Rrl072rVrx/nz5wF4+PAhgYGBfP755wQGBrJ+/XqCgoJo21ZOMOZKCQ9h56SUesNhIFMLCiHyKY2iKIraQeRGMTEx2NvbEx0dLXN8i+zx09tw66Sh3H8flKijajgib3vwOJF28w9zNdIwH/d7tUswu3NtmY9bJTmxz/Hy8qJ+/frMmzcPMDwi5urqytChQ/nss89Ste/cuTNxcXFs3rzZuKxBgwbUrl3beGL8WSdOnMDT05Pr169TunTpDMWVE7+rfEdRYMMnhtk2AJwqwydHZLYNIUSek9E+R9Ur3UKIDIq4lJJwF6sBxWurGo7I2/R6hdHrzhgT7krFCuH3QQ1JuIVRQkICAQEBNGvWzLhMq9XSrFkz/P3901zH39/fpD2Aj49Puu0BoqOj0Wg0ODg4ZErcIpucWJyScJvbQsdlknALIfK1fDllmBC5TuBTA6jV7SG36IkstWD/VbZfMMwUYWdVgB97eGBjId2FSBEVFYVOpzM+DpasWLFiXL58Oc11wsLC0myfPObKsx4/fszYsWPp2rXrc68exMfHEx8fb6zHxMRkdDdEVrhxHLaljMfDe/PAuYp68QghRA4gV7qFyOmSEuDsakPZzBJqdFQ3HpGn7QuK4LsdQYDh3M6cLnVwK2qrclQiv0lMTKRTp04oisKCBQue29bPzw97e3vjy9XVNZuiFKk8CIe1PUGfaKh7D4HqH6gbkxBC5ACSdAuR0wVthYd3DeUqrcGmiLrxiDwr9O5Dhq8+TfJIHyObVaRpZWd1gxI5UtGiRTEzMyM8PNxkeXh4OC4uLmmu4+LikqH2yQn39evX2blz5wufyx43bhzR0dHG140bN15hj8Rr0yXC733gwZMBcMs0gmZT1I1JCCFyCEm6hcjpZG5ukQ0eJegYsDKA6EeGK1TNqhRjSNMKKkclcioLCws8PDzYvXu3cZler2f37t14e3unuY63t7dJe4CdO3eatE9OuIODg9m1axeOjo4vjMXS0hI7OzuTl1DBrslw/bChXKgEdFwKZvJYihBCgDzTLUTOFn0Trjw5SHUoDWUbqxuPyJMUReGz9We5dMfwLGy5orbM7FwLrVbGDhDp8/X1pVevXtSrVw9PT09mz55NXFwcffr0AaBnz56ULFkSPz8/AIYPH07jxo2ZMWMGrVq1YvXq1Zw8eZJFixYBhoS7Q4cOBAYGsnnzZnQ6nfF57yJFimBhYaHOjooXO/8H+BtGsUdrDp1+gYJyl4wQQiSTpFuInOz0b8CTe31rdwet3JwiMt+SwyFsPH0bAFsLM37s4YGdlbnKUYmcrnPnzkRGRjJx4kTCwsKoXbs227ZtMw6WFhoaivapv1kNGzZk1apVTJgwgfHjx+Pu7s6GDRuoXr06ALdu3WLTpk0A1K5d2+Sz9u7dS5MmTbJlv8RLirgEG4em1N/5BlzrqxePEELkQDJP9yuSeUBFltPr4fvacP86oIER58BBBggSmevov3fptvgYOr2hK1jQrS7v1CiuclTiWdLnZJx8V9nocTQsagr3rhrqtbpCuwUyw4YQIt+QebqFyO1CDj5JuIHy/5OEW2S6O9GPGLIq0Jhwf9KkvCTcQoiM0evhz09SEm6XGtB6liTcQgiRBkm6hcipTj0zN7cQmSg+ScfAlYFExSYA8KZ7UUa3qKRyVEKIXGP/NAjaYihbOUDnlWBurWpIQgiRU0nSLURO9CAMLm40lK2LQKV31Y1H5DmTNl7gzI37AJQqbM33XepgJgOnCSEy4ugC2P/Nk4oG2v8Mhd3UjEgIIXI0SbqFyImOLQSd4QokHr2ggKW68Yg8ZdWxUFafMMxlbFlAy8LuHhS2lZGhhRAZcGolbPsspd7iS3Bvpl48QgiRC0jSLUROE/8ATiwxlM0swGuguvGIPCUw9D8mbTpvrH/TvgbVS9qrGJEQIte48Cdsemqk8rc+hYZD1ItHCCFyCUm6hchpAn+B+GhDuWYnKOSibjwiz4h8EM+glYEk6gwDp/Vu6Mb7dUqpHJUQIlf4Zwf80Q8UvaHu9Qk0Ha9uTEIIkUtI0i1ETqJLBP8fUuoNh6kXi8hTEnV6Bv8aSFjMYwA83Yrwf62qqByVECJXCDkEa3uAPtFQr9MdfL6WkcqFECKDJOkWIie58CfE3DSUK7YEJxlNWmSOr7Zc4njIPQCK2Vkyr1sdzM2kCxBCvMCtAFjVBZIMJ+yo2g7afA9a+fshhBAZVeBVV7x27RoHDx7k+vXrPHz4ECcnJ+rUqYO3tzdWVlaZGaMQ+YOiwOHvU+pylVtkkj9P3WTZkRAAzM00LOjugXMh+Tud34WGhpr04dWqVcPSUgZtFE8Jvwgr20PCA0O9QnP44CfQmqkblxBC5DIvnXT/+uuvzJkzh5MnT1KsWDFKlCiBtbU19+7d4+rVq1hZWdGtWzfGjh1LmTJlsiJmIfKmf/dC+DlDuaQHlGmobjwiTzh/K5rP/jhnrE9pW526pQurGJFQU0hICAsWLGD16tXcvHkTRVGM71lYWPDmm2/Sv39/2rdvj1auZOZvd6/Cinbw6D9DvUwj6LwCCshMB0II8bJeqketU6cO33//Pb179+b69evcuXOHgIAADh06xMWLF4mJiWHjxo3o9Xrq1avHunXrsipuIfKeZ69yy7Ny4jX9F5fAwJUBxCcZBj7qUt+VD71KqxyVUMuwYcOoVasW165d48svv+TixYtER0eTkJBAWFgYW7dupVGjRkycOJGaNWty4sQJtUMWaom+Cb+0g9hwQ71EXej6G5hbqxqWEELkVhrl6dPcL7B9+3Z8fHwy1Pbu3buEhITg4eHxysHlZDExMdjb2xMdHY2dnZ3a4Yjc7s5Z+PFNQ7lwWRgaILfvidei0yv0Xnqcg8FRANRydWDtgAZYFpDfq9woM/qccePGMXr0aBwdHV/Ydtu2bTx8+JAPPvjglT5LTdI/v6a4u7C0JUT9Y6g7V4XeW8CmiLpxCSFEDpTRPuelbi/PaMIN4OjomKGOXQgBHJmbUvYeLAm3eG3f7QgyJtyOthYs6FZXEu58zs/PL8NtW7ZsmYWRiBwr/gH82j4l4S5cFnr8KQm3EEK8ppdKumNiYjLcVs4uC5FB90Ph/B+Gso0j1O6mbjwi1/v73B0W7LsKgJlWw/xudSnhILeFCiGeI/Ex/NYVbp8y1AsVh54boJCLqmEJIURe8FJJt4ODA5oMPmeq0+leKSAh8p2jC0B58v+lfj+wsFE3HpGrBYc/YPS6M8b6+Her0KCc3HUkDOOyZLQPDwwMzOJoRI6iS4LfP4KQg4a6dWHDFe7CbqqGJYQQecVLJd179+41lkNCQvjss8/o3bs33t7eAPj7+7N8+fKXuoVNiHzt0X8QsNxQLmAFnv3UjUfkalGx8fRfEUBcguEkznu1S/DRG27qBiVyjHbt2hnLjx8/5ocffqBq1arGPvzo0aNcuHCBQYMGqRShUIVeD38Ng6Athrq5LXT7HZyrqBuXEELkIS+VdDdu3NhYnjp1KjNnzqRr167GZW3btqVGjRosWrSIXr16ZV6UQuRVJ5dAYpyhXLsb2BZVNx6Ra0U/SqTnz8e5FmX4fapS3I5vPqiZ4SubIu+bNGmSsfzxxx8zbNgwvvjii1Rtbty4kd2hCbUoCuz8HE7/aqibWUCXX6FUPXXjEkKIPOaVJ+H09/enXr3Uf5Tr1avH8ePHXysoIfKFpHg49uOTisYwgJoQr+BhQhIfLTvBxTuGcTeK21vxU08PrC1k4DSRtnXr1tGzZ89Uy7t3784ff/yhQkRCFQdngP88Q1mjhfaLoXxTdWMSQog86JWTbldXV3766adUyxcvXoyrq+trBSVEvnB2TcocqFXagGN5deMRuVJ8ko4BKwIIuP4fYBipfEVfL0oVlrEBRPqsra05fPhwquWHDx/GyspKhYhEtjvxM+x56k6H1rOh6nuqhSOEEHnZS91e/rRZs2bRvn17/v77b7y8vAA4fvw4wcHBcpZciBfR602nCXtjuHqxiFwrSadn2G+njFODFbIqwPKPPKngXFDlyERON2LECD755BMCAwPx9PQE4NixYyxZsoTPP/9c5ehEljv/B2wZlVJvNgU85LFAIYTIKq+cdL/77rsEBwezYMECLl26BECbNm0YOHCgXOkW4kWCt6fMg1q6oTw/J16aXq/w6e9n2X7BcLeEtbkZy/rUp3pJe5UjE7nBZ599Rrly5ZgzZw4rV64EoEqVKixdupROnTqpHJ3IUld2wfr+gGKovzECGo1QMSAhhMj7NIqiKGoHkRvFxMRgb29PdHS0zEkuXo6iwM8t4OaTsQ+6roZK76gbk8hVFEVh0qYL/OJ/HQALMy0/967Hm+5OKkcmsor0ORkn39Vz3AyA5a0h8aGhXrcXtJkDMuCiEEK8koz2OS/1THdoaOhLBXHr1q2Xai9EvnB2TUrCXbQSuPuoG4/Idb7bEWRMuM20Gr7vWkcSbvFCco49n4u6Aqs6piTcVdpC61mScAshRDZ4qaS7fv36DBgwgBMnTqTbJjo6mp9++onq1avLs91CPOvRf7BjQkrd5yvQvvJ4hiIfWrj/KvP3XjXWv21fk5bVXVSMSOQW1apVY/Xq1SQkJDy3XXBwMJ988gnffPNNNkUmstyDMFj5Pjy8a6i7vQkf/ARameFACCGyw0s9033x4kW++uormjdvjpWVFR4eHpQoUQIrKyv+++8/Ll68yIULF6hbty7ffvst7777blbFLUTutOdLiIs0lKu0Bffm6sYjcpWVR6/zzd+XjfWp71WjvUcpFSMSucncuXMZO3YsgwYNonnz5tSrVy9VH37o0CHOnz/P0KFD+eSTT9QOWWSGx9GwsgPcf3K3YrEahrm4zWWUeiGEyC6v9Ez3o0eP2LJlC4cOHeL69es8evSIokWLUqdOHXx8fChTpgyFChXKinhzDHlmTLy0W4Hw0/8ABcxtYchxsJeESWTMn6du4rv2DMl/scf4VGJw0wrqBiWyTWb2OYcOHWLNmjUcPHgwzT68W7duFC5cOJMiz37SPz8l8TH82gFCDhrqDqWh704oJHfHCCFEZshon/PSo5fPmjWLkSNH0qFDBzp06JDq/QcPHtCyZcs05/8UIt/S62DzSIyjxTb5TBJukWE7LoQxet1ZY8I9sHF5SbjFK2vUqBGNGjVK872bN28yduxYFi1alM1RiUyn18H6fikJt40jdP9TEm4hhFDBSz9MOn78eH755Zc034uLi6Nly5bcvXv3tQMTIk85uQTunDaUnapAA7ltU2TM4StRDFl1Cp3ekHF3b1CasS0rqRyVyKvu3r3Lzz//rHYY4nUpCvz9KVzaZKib28KH66ConKwTQgg1vHTSvWLFCgYMGMCmTZtMlsfGxuLj40NkZCR79+7NtACFyPViI2D3Fyn11jPBzFy9eESuEXD9P/r9cpIEnR6AdrVLMLVtdTQy2rAQ4nkOfAcnFhvK2gLQ+Rco5aFuTEIIkY+99O3lHTp04P79+3Tt2pUtW7bQpEkT4uLieOeddwgPD2f//v0UL148K2IVInfa8TnERxvKtT6EMg3VjUfkChdvx9Bn6XEeJugAaF61GNM71kKrlYRbCPEcActh75cp9fd+gArN1ItHCCHEyyfdAB9//DH37t3jvffeY+PGjUycOJHbt2+zf/9+SpQokdkxCpF7hRyCs6sNZSsHaPHFc5sLAfBvZCw9lxwj5nESAG9UcGRu1zqYm8n0ckKI5/hnB2wekVJv/gXU6qxaOEIIIQxeKekG+PTTT7l37x5vv/02bm5u7Nu3j1KlZGAoIYySEmDLqJR6s0lgW1S9eESucOv+I7ovPkZUrGEu5bqlHVjUox5W5jKfrnh9H3zwwXPfv3//fvYEIjKfLhG2jgbF8DgK3kPgjWHqxiSEEAJ4haT72Q7b3NycokWLMnz4cJPl69evf73IhMjtjv4AkU/mVC7pAXV7qRuPyPEiH8TTffExbkc/BqBKcTuW9vbE1vKVz48KYcLe3v6F7/fs2TObohGZ6tzvcP+6oVz2LcNVbiGEEDnCSx/JPdthd+3aNdOCESLPuH8D9k8zlDVaaDUDtHKlUqQv+mEiPX4+xrWoOADKFbXll488sbeRQfdE5lm6dKnaIYisoNfBwRkp9SbjQSuPowghRE7x0km3dNhCZMC2zyDxoaFc/2MoUUfdeESOFhufRK+lx7kc9gCAkg7WrPzYC6dClipHJoTIFS5uhLvBhnKZRlDGW914hBBCmJDToEJktn+2w+XNhrKtMzT9P3XjETna40Qd/X85yekb9wEoWtCSlR97UcLBWt3AhBC5g15vmCIsWeMx6sUihBAiTZJ0C5GZEuIMA9kk8/kKrB1UC0fkbPcfJtB3+QmOXL0LgJ1VAVb09aRsUVuVIxNC5Br//A0RFwzlUvWhbGN14xFCCJGKjM4jRGba+zXcDzWU3d6EGh3VjUfkWJfDYuj/SwCh9wyPIdhYmLHsI0+qFLdTOTIhRK6hKHBgekr9rTGg0agXjxBCiDSpfqV7/vz5uLm5YWVlhZeXF8ePH39u+3Xr1lG5cmWsrKyoUaMGW7duNXl//fr1tGjRAkdHRzQaDadPn061jcePHzN48GAcHR0pWLAg7du3Jzw8PDN3S+RHtwINI5YDmFlCmzly8CPStOXsHd6ff8SYcDvaWrD8I0/qli6scmRCiFzlym64fcpQdqkJ7i3UjUcIIUSaVE2616xZg6+vL5MmTSIwMJBatWrh4+NDREREmu2PHDlC165d6du3L6dOnaJdu3a0a9eO8+fPG9vExcXRqFEjpk2blu7njhw5kr/++ot169axf/9+bt++/cK5S4V4Ll0i/DUsZX7UJmPBsby6MYkcR6dX+HbbZQavCuRRog6AGiXt+WtoI+q7FVE5OiFErqIocODblLpc5RZCiBxLoyiKotaHe3l5Ub9+febNmweAXq/H1dWVoUOH8tlnn6Vq37lzZ+Li4ti8ebNxWYMGDahduzYLFy40aRsSEkLZsmU5deoUtWvXNi6Pjo7GycmJVatW0aFDBwAuX75MlSpV8Pf3p0GDBhmKPSYmBnt7e6Kjo7Gzk9tB871Ds2DXZEO5WHXovw/MZKonkSL6YSLD15xiX1CkcdkHdUry9Qc1sDKX6eTE80mfk3H55ru6dhCWtzaUnSrDJ/4yTZgQQmSzjPY5qv11TkhIICAggGbNmqUEo9XSrFkz/P3901zH39/fpD2Aj49Puu3TEhAQQGJiosl2KleuTOnSpZ+7nfj4eGJiYkxeQgBw9yrs+8ZQ1mihzfeScAsT/4Q/4L35h4wJt5lWw8TWVZnRqZYk3EKIV/P0Ve43R0vCLYQQOZhqf6GjoqLQ6XQUK1bMZHmxYsUICwtLc52wsLCXap/eNiwsLHBwcHip7fj5+WFvb298ubq6ZvgzRR6mKLB5BCQ9NtS9BkIpD1VDEjnLtvN3eH/+YULuGp7fLmxjzoq+nnzUqCwauRVUCPEqQo/BtQOGcpHyUF0ekRNCiJxMTotm0Lhx44iOjja+bty4oXZIIic4/WvKgY99aZmTWxjp9QozdwQxcGUgcQmG57erFrdj05BGNCxfVOXohBC52sGn5uV+0xe0cseMEELkZKpNGVa0aFHMzMxSjRoeHh6Oi4tLmuu4uLi8VPv0tpGQkMD9+/dNrna/aDuWlpZYWlpm+HNEPhAbAdufSrJbzwLLgurFI3KMmMeJjFx9mt2XUwaFbFurBNPa18TaQg6OhRCv4fYpCN5hKNu7Qs3O6sYjhBDihVS70m1hYYGHhwe7d+82LtPr9ezevRtvb+801/H29jZpD7Bz585026fFw8MDc3Nzk+0EBQURGhr6UtsRgm2fweP7hnKNjuDe7LnNRf5wJSKWdvMPGxNurQb+790qzOlSWxJuIcTrO/DUVe5GI2QMESGEyAVUu9IN4OvrS69evahXrx6enp7Mnj2buLg4+vTpA0DPnj0pWbIkfn5+AAwfPpzGjRszY8YMWrVqxerVqzl58iSLFi0ybvPevXuEhoZy+/ZtwJBQg+EKt4uLC/b29vTt2xdfX1+KFCmCnZ0dQ4cOxdvbO8MjlwvBP9vh/B+GsnURaPmNuvGIHGHXxXBGrDlNbHwSAA425sztWoc33Z1UjkwIkSeEX4TLT2ZwKVQcandXNx4hhBAZomrS3blzZyIjI5k4cSJhYWHUrl2bbdu2GQdLCw0NRfvUaJwNGzZk1apVTJgwgfHjx+Pu7s6GDRuoXr26sc2mTZuMSTtAly5dAJg0aRKTJ08GYNasWWi1Wtq3b098fDw+Pj788MMP2bDHIk+IfwCbfVPqPl+DrTyjm5/p9Qpz91xh1q5/jMsquxRiUY96lHa0UTEyIUSecnBGSrnhMDC3Ui8WIYQQGabqPN25Wb6ZB1Sk9vdYOPZkXvhyTaHHnyCjUOdbDx4nMmrtGXZcTBlvolWN4kzvWBMbC1XPa4o8RPqcjMuz31XUFZhfHxQ92BSFEefAQk7qCSGEmjLa58gRoRAv48YJOPajoVzA2jB4miTc+da/kbH0XxHAlYhYwPCr8KlPZQY2LifTgQkhMtfhWYaEG6DhEEm4hRAiF5Epw4TIqEf3YcNA4MnNIU3HQ5GyakYkVLT3cgTvzT9sTLjtrAqwtHd9PmlSXhJukW/Mnz8fNzc3rKys8PLy4vjx489tv27dOipXroyVlRU1atRg69atJu+vX7+eFi1a4OjoiEaj4fTp01kYfS4SGwln1xrKVvZQr6+68QghhHgpknQLkRG6JPi9D9y9YqgXrwUNBqkbk1CFoijM33uFj5af4MFjw4BpFYsVZNOQRjSp5KxydEJknzVr1uDr68ukSZMIDAykVq1a+Pj4EBERkWb7I0eO0LVrV/r27cupU6do164d7dq14/z588Y2cXFxNGrUiGnTpmXXbuQOJ5eALsFQrtsLrPLQbfNCCJEPyDPdryjPPjMm0vb0c9zWRaDfHrnKnQ8l6vSM/eMs6wNvGZe1rObCd51qUdBSntYRWScn9jleXl7Ur1+fefPmAYZpP11dXRk6dCifffZZqvadO3cmLi6OzZs3G5c1aNCA2rVrs3DhQpO2ISEhlC1bllOnTlG7du2XiisnflevJSkBZleH2HDQaGH4GXAorXZUQgghyHifI1e6hXiRk0tTEm5tAei8QhLufOhRgo6BKwKMCbdGA6NbVOSHbnUl4Rb5TkJCAgEBATRr1sy4TKvV0qxZM/z9/dNcx9/f36Q9gI+PT7rtxRMX/jQk3ACVW0vCLYQQuZAcKQrxPNcOwtbRKfVWM8GtkXrxCFVEP0rk4+UnOBHyHwAWZlq+71qbltWLqxyZEOqIiopCp9MZp/hMVqxYMS5fvpzmOmFhYWm2DwsLe61Y4uPjiY+PN9ZjYmJea3s5iqLAsQUp9QafqBeLEEKIVyZXuoVIz71/YW0P0Bue26XBIPDopW5MIttFxDym84/+xoS7oGUBln1UXxJuIXIIPz8/7O3tjS9XV1e1Q8o8N47B7VOGcvFaUNpb3XiEEEK8Ekm6hUjL42hY1QUeGRItKjSD5l+oG5PIdiFRcbRfeITLYQ8AcLS1YHX/BjQsX1TlyIRQV9GiRTEzMyM8PNxkeXh4OC4uLmmu4+Li8lLtM2rcuHFER0cbXzdu3Hit7eUoR5+6yu31iUxRKYQQuZQk3UI8S6+D3/tCVJChXrQSdFgCZvI0Rn5y4XY0HRb6c+PeIwBKOlizbqA31UvaqxyZEOqzsLDAw8OD3bt3G5fp9Xp2796Nt3faV2O9vb1N2gPs3Lkz3fYZZWlpiZ2dnckrT4i+CZf+MpRtnaH6B+rGI4QQ4pVJFiHEs3Z8Dld2GsrWheHD1YZ5UUW+cezfu3y8/CQP4lOmBPvlIy9c7K1UjkyInMPX15devXpRr149PD09mT17NnFxcfTp0weAnj17UrJkSfz8/AAYPnw4jRs3ZsaMGbRq1YrVq1dz8uRJFi1aZNzmvXv3CA0N5fbt2wAEBRlOfrq4uLz2FfFc5/hPoOgM5XofQQFLdeMRQgjxyiTpFuJpgb/A0fmGsrYAdFoBRcqpG5PIVjsvhjNkVSDxSXoAPMoU5ude9XCwsVA5MiFyls6dOxMZGcnEiRMJCwujdu3abNu2zThYWmhoKFptyg11DRs2ZNWqVUyYMIHx48fj7u7Ohg0bqF69urHNpk2bjEk7QJcuXQCYNGkSkydPzp4dywkS4iBgmaFsZmFIuoUQQuRaMk/3K8pz84AKuH4ElrcFfaKh3no21Ovz3FVE3rLu5A0+W38Ond7wZ7FJJScWdPPA2sJM5chEfid9Tsblie/q5BLYPNJQrtUV3l/4/PZCCCFUkdE+R650CwGQlADr+6ck3J4DJOHOZ37cfxW/v1OmOmpXuwTTO9bC3EyGvhBCZCNFgWM/ptS9BqoXixBCiEwhSbcQAGdWQfSTEW/LNAKfr9WNR2QbRVH45u/L/HjgX+Oy3g3dmNi6KlqtjBQshMhm/+6FyCcnAEt7Q4naqoYjhBDi9UnSLYQuEQ7OTKk3nyIjlecTSTo949afY13ATeOyUc0rMuR/FdDI1DxCCDU8PU1Yg0/Ui0MIIUSmkcxCiHPr4P51Q7n8/6BUPXXjEdnicaKOob+dYudFw7zBGg188V51ujcoo3JkQoh8K+oKBO8wlO1doVIrdeMRQgiRKSTpFvmbXgcHvkupv/WperGIbBPzOJF+y09y7No9AMzNNMzuXIdWNYurHJkQIl87/tSz3J795K4rIYTII+Svucjfzq+He1cNZbc3oYy3uvGILBf5IJ5eS45z8U4MADYWZizqUY9G7kVVjkwIka89ug+nfjWUzW2gbk9VwxFCCJF5JOkW+ZdeDwemp9Qby1XuvO7GvYf0+PkYIXcfAlDE1oKlvetTy9VB3cCEEOLUSkiMM5RrdQHrwurGI4QQItNI0i3yr0sbISrIUC7tbbjSLfKsy2Ex9Pz5OBEP4gEoYW/FL329qOBcUOXIhBD5nl5nemu5TBMmhBB5iiTdIn/S62H/U1e53xpjGElL5EknQu7Rd9kJYh4nAVDBuSC/fORJCQdrlSMTQggg6G+4H2ool38bnCqpG48QQohMJUm3yJ+CtkLEBUO5ZD3DqOUiT9pzOZxBvwbyOFEPQC1XB5b1rk9hWwuVIxNCiCeOLUwpyzRhQgiR50jSLfIfRYED36bUG38qV7nzqPWBNxnz+1l0egWAN92LsrC7B7aW8qdPCJFD3DkLIQcNZUd3w5VuIYQQeYoceYr8J3gH3DljKBevBe4t1I1HZInFB//lyy2XjPXWNYszs1NtLApoVYxKCCGeYXKVeyBo5W+UEELkNZJ0i/xFUWD/U1e535Kr3HmNoihM3x7ED/uuGpf1aFCGyW2rYaaVn7UQIgeJjYRz6wxlK3uo1VXdeIQQQmQJSbpF/nJ1D9w6aSg7V4NK76obj8hUOr3ChA3n+O34DeOyEc3cGf62Oxo5uSKEyGlOLgFdgqFctxdY2KobjxBCiCwhSbfIP569yt14jNzGl4fEJ+kYsfo0f58PAww3MExpW42e3m7qBiaEEGlJiocTiw1ljRl49lc3HiGEEFlGkm6Rf4QchBtHDeWilaDKe+rGIzJNbHwS/X85yZGrdwEooNUwo1Mt3qtdUuXIhBAiHefXQ1yEoVylNTi4qhuPEEKILCNJt8g/TJ7llqvceUVUbDx9lp7g3K1oAKzNzVjYw4PGFZ1UjkwIIdKhKHD0h5R6g0HqxSKEECLLSdIt8ofr/ilTshQpD9U/UDcekSlu/veQnj8f59+oOAAcbMxZ0rs+dUsXVjkyIYR4jlB/CDtrKJeoA65e6sYjhBAiS0nSLfKH/dNSym+NBq2ZerGITPFP+AN6/HyM8Jh4AFzsrFjR1xP3YoVUjkwIIV7g2avcMtCjEELkaZJ0i7zv2kH4d6+h7FAGanRUNx7x2gKu/8dHy04Q/SgRgHJFbfmlryelCtuoHJkQQrzAf9fh8hZDuaALVG2najhCCCGyniTdIm9TFNg9JaXeZByYmasXj3ht+4Ii+GRlII8SdQDULGXP0t71cSxoqXJkQgiRAccXgaI3lOt/DAUs1I1HCCFElpOkW+RtQX/DzROGslMVqNlJ3XjEa9l4+haj1p4hSa8A8EYFR37sUY+ClvKnTAiRC8Q/gMAVhrKZJdTro248QgghsoUcqYq8S6+DPV+k1P83QZ7lzsWWHwlh8l8XUAz5Nu9Ud2F2l9pYFpCfqRAilzj9G8QbZlqgZiewLapuPEIIIbKFJN0i7zr3O0RcNJRL1oPKrdSNR7wSRVGYtSuY73cHG5d19SzNl+2qY6aVwYeEELmEXg/HFqbUG3yiXixCCCGylSTdIm9KSoC9X6XU354oo8PmQjq9wqRN51l5NNS4bEjTCoxqURGN/DyFELnJlZ1w76qhXPYtKFZN3XiEEEJkG0m6Rd4UuBzuXzeUyzWBco1VDUe8vPgkHb5rz7Dl7B3jsomtq/JRo7IqRiWEEK/o2WnChBBC5BuSdIu8JyEODkxPqb89Ub1YxCuJi09iwIoADl2JAqCAVsP0jjV5v04plSMTQohXEH4R/t1nKBcuC+4+qoYjhBAie0nSLfKeYz9CbLihXKUtlPRQNx7xUs7djGbsH2e5eCcGACtzLQu6edC0srPKkQkhxCt6+llur4Gg1aoXixBCiGwnSbfIWx79B4dnG8oarWHEcpEr3H+YwPTtQaw6HmocodzOqgBLetennlsRdYMTQohXFXcXzq4xlC3toE43deMRQgiR7STpFnnL4Tnw+Ml0LLU+BKdK6sYjXkivV/g94CbfbLvMvbgE4/IKzgWZ/2FdKrkUUjE6IYR4TQFLIemxoVynB1jK3zQhhMhvJOkWeceDMDj65BY+Mwto8pm68YgXOn8rmokbzxMYet+4zMbCjBHN3OndsCwWBeQWTCFELpYUD8d/elLRgGc/VcMRQgihDkm6Rd5xYDokPTKU6/UFB1d14xHpin6UyMwdQaw4eh29krK8Vc3iTGhVheL21uoFJ4QQmeXc7xAbZihXaQ1FZPYFIYTIjyTpFnnDvWsQsMxQNreFN0epGo5Im6Io/BF4i2/+vkRUbMqt5OWcbJnatjqN3IuqGJ0QQmQiRQH/+Sl176HqxSKEEEJVknSLvGGfH+iTDGXvwVDQSd14RCqX7sQwceN5ToT8Z1xmbW7GsLfd6dtIbiUXQuQxV/dAxAVDuZQnlPZSNx4hhBCqyRFHufPnz8fNzQ0rKyu8vLw4fvz4c9uvW7eOypUrY2VlRY0aNdi6davJ+4qiMHHiRIoXL461tTXNmjUjODjYpI2bmxsajcbk9c0332T6volsEH4Bzq41lK0LQ8Mh6sYjTMQ8TmTKXxdoPfeQScL9TnUXdo1qzCdNykvCLYTIe/znpZSlXxJCiHxN9SPdNWvW4Ovry6RJkwgMDKRWrVr4+PgQERGRZvsjR47QtWtX+vbty6lTp2jXrh3t2rXj/Pnzxjbffvst33//PQsXLuTYsWPY2tri4+PD48ePTbY1depU7ty5Y3wNHSq3fuU6ej3smAA8eTC4kS9Y2asakjBQFIU/T93kf9/tZ+nhEHRPHt4uW9SW5R95sqC7ByUd5NltIUQeFHbecKUboLAbVG6tajhCCCHUpXrSPXPmTPr160efPn2oWrUqCxcuxMbGhiVLlqTZfs6cObRs2ZIxY8ZQpUoVvvjiC+rWrcu8eYYzyoqiMHv2bCZMmMB7771HzZo1+eWXX7h9+zYbNmww2VahQoVwcXExvmxtbbN6d0VmOzQj5cCmUAkZGTaHCAp7QOdFRxm55gxRsfEAWJlrGeNTiW0j3qRxRbn9XwiRhz39LHeDQaA1Uy8WIYQQqlM16U5ISCAgIIBmzZoZl2m1Wpo1a4a/v3+a6/j7+5u0B/Dx8TG2v3btGmFhYSZt7O3t8fLySrXNb775BkdHR+rUqcP06dNJSkrKrF0T2eHKLtjz1ZOKBt6bC+Zy5VRNsfFJfLn5Iu9+f5Dj1+4Zl7eoWoydIxszuGkFLAvIwacQIg+LuQPn1hnKVvZQu5u68QghhFCdqgOpRUVFodPpKFasmMnyYsWKcfny5TTXCQsLS7N9WFiY8f3kZem1ARg2bBh169alSJEiHDlyhHHjxnHnzh1mzpyZ5ufGx8cTHx9vrMfExGRwL0WW+O86/PExxtvKm/4fVGj23FVE1lEUhb/O3uGrLRcJj0n5f1LG0YbJbarRtLKzitEJIUQ2Or4I9ImGcr2+YFlQ3XiEEEKoLt+OXu7r62ss16xZEwsLCwYMGICfnx+Wlpap2vv5+TFlypTsDFGkJ/ERrO0Bj54MylXxHZkiTEUxjxPxXXOaXZdSxmGwLKBlUJMKDGhcDitzubIthMgnEuLg5JPH47Tm4Nlf3XiEEELkCKreXl60aFHMzMwIDw83WR4eHo6Li0ua67i4uDy3ffK/L7NNAC8vL5KSkggJCUnz/XHjxhEdHW183bhx47n7JrKIosCW0XDnjKFepBy8vxC0qg9PkC9diXhAu3mHTRLutys7s3NkY4Y3c5eEWwiRv5z6FR7fN5RrdAS74qqGI4QQImdQNVOxsLDAw8OD3bt3G5fp9Xp2796Nt7d3mut4e3ubtAfYuXOnsX3ZsmVxcXExaRMTE8OxY8fS3SbA6dOn0Wq1ODunfRuspaUldnZ2Ji+hgoClcHqloWxuA51XgrWDqiHlVzsvhtNu/hH+jYoDwMHGnEU9PPi5d31KO9qoHJ0QQmQzvQ6OPjWAmvdg9WIRQgiRo6h+e7mvry+9evWiXr16eHp6Mnv2bOLi4ujTpw8APXv2pGTJkvj5+QEwfPhwGjduzIwZM2jVqhWrV6/m5MmTLFq0CACNRsOIESP48ssvcXd3p2zZsnz++eeUKFGCdu3aAYbB2I4dO0bTpk0pVKgQ/v7+jBw5ku7du1O4cGFVvgeRATdPwtZPU+pt50KxaurFk0/p9QpzdgczZ3ewcVlll0L81LMerkUk2RZC5FOXN8N/IYZyuabgUl3VcIQQQuQcqifdnTt3JjIykokTJxIWFkbt2rXZtm2bcSC00NBQtE/dOtywYUNWrVrFhAkTGD9+PO7u7mzYsIHq1VM6t08//ZS4uDj69+/P/fv3adSoEdu2bcPKygowXLVevXo1kydPJj4+nrJlyzJy5EiT57xFDhMbCWt7pgxO4/UJ1Oigbkz50IPHiYxcc4Zdl1Ie32hdszjfdqiJjYXqf06EEEI9R+allBsOUS8OIYQQOY5GURRF7SByo5iYGOzt7YmOjpZbzbOaLglWtIOQg4Z6aW/o9ReYmasaVn7zb2Qs/X45ydVIw+3kWg182rIyA94qh0ajUTk6IfI26XMyTpXv6sZx+Lm5oexcFT45AvJ3UQgh8ryM9jlyaUrkfLunpCTcBYtBx2WScGezPZfDGf7baR7EG+ayt7MqwNwP69K4opPKkQkhRA5wZG5K2XuIJNxCCCFMSNItcrYLG+DI94aytgB0+gUKpT8KvchciqIwf+8VZuz8h+R7YioWK8iiHvVwK2qrbnBCCJET3PsXLv1lKBcsJo8+CSGESEWSbpFzXTsA65+a49TnayjdQL148pm4+CRGrzvD3+fDjMveqe7Cdx1rYWspfzqEEAKAowuAJ2clPftDAUtVwxFCCJHzyJGzyJlunIBVXUAXb6jX7GI4mBHZIiQqjv4rTvJPeCxguFNydItKDGpSXp7fFkKIZA/vwamnprGs95G68QghhMiRJOkWOU/Yefi1PSQaBuyi4jvw3jx5Ri6b7P8nkqGrAol5bHh+u5BVAeZ0qc3/KhdTOTIhhMhB7ofCtnGQ+NBQr9MdbIqk2fRubDxnb0Zz6/4jqhQvRM1SDpibadNsK4QQIu+RpFvkLFFXDCOVP4421Mu+JQOnZRNFUVi4/1+mb7+M/smdkhWcC7KohwflnAqqG5wQQuQUD8Lh4Aw4uSRlGkuNFhp8AkBsfBLnbkZz9uZ9zt6M5szN+9z875HJJmwszPAsW4SG5R1pWL4oVYrbYaaVE8tCCJFXSdItco77ofDLexAXaaiXqg9dfgNzK3XjygceJiQx5vezbDl7x7isRdVizOhUi0JWcsJDCJG2+fPnM336dMLCwqhVqxZz587F09Mz3fbr1q3j888/JyQkBHd3d6ZNm8a7775rfF9RFCZNmsRPP/3E/fv3eeONN1iwYAHu7u7ZsTvP9/CeYWDPYz+mXN0GkizsOFrpU/7c9YAzN/dzNTKWF03G+jBBx76gSPYFGfo7e2tzGpQrQsPyRfEu74i7c0F5lEcIIfIQSbpFzvAg3JBwx9w01IvVgG7rwFKusGa1G/ce0u+Xk1wOe2BcNrJZRYb+rwJaufIihEjHmjVr8PX1ZeHChXh5eTF79mx8fHwICgrC2dk5VfsjR47QtWtX/Pz8aN26NatWraJdu3YEBgZSvXp1AL799lu+//57li9fTtmyZfn888/x8fHh4sWLWFmpdAI2PhaOLUA5/D2a+Bjj4kdYsfT/27vzuKir/X/grxmGGQaGVWBg2EFQQBRFxR0rU6vrcttsU+vrL723rEyz7VZWt5t1y5teq2t1u9lmiy3QqiGZ+5KaioKAgqAsA8iOwMDM+f0xODCCis4MA/h6Ph48cD7nzIfzOcK85z3nfM7RT8WamptQ87sLgNOdPl3p6IC4AHcMDnRHoKcSB09VYeeJMyitbTLVqW5oxsajWmw8qgUAeKsUGB3Rr3UkvB+CvZyZhBMR9WISIS71eSx1pqsboVMXnK0A1t4ElGYYH/eLBO77GVBxD2hb255TjoWfHUDVWeMUSZVChjdmxeP6GN6/TdST9MSYk5iYiBEjRuDNN98EABgMBgQFBeGhhx7Ck08+2aH+rFmzUF9fjx9++MF0bNSoUYiPj8eaNWsghIBGo8GSJUvw2GOPAQCqq6uhVquxdu1a3HHHHV1ql7X6qkBbgert7yI8cw1cWipNx5uEDJ/qJ+Htlhkoh7vZcxwdJBjo54bBge4YEuiBwUHu6O+jguy8+7eFEDhRVo9dJ8qx88QZ7Mo9Y3od7kyAh9KUhI+O6Ad/d+UVXxcREVlPV2MOR7rJvhprgE9uaUu43YOBOclMuG1MCIH/bsvD8p8zTfdvh3u74N05w9Hfl7MLiOjidDod9u/fj6eeesp0TCqVYtKkSdi1a1enz9m1axcWL15sdmzKlClITk4GAOTl5aGkpASTJk0ylbu7uyMxMRG7du26YNLd1NSEpqa2UeOaGuNo9MGDB6FStb2eeXp6IiwsDI2NjcjIyOhwnmHDhgEAsrKy0FBdDvVPcxCHclN5i5DiK/0E/LvlZhTBGxIAgW4yRPs6Y3xsEAb5u0JXdhJyBwmAZgBlOFtUBtEvDnCQ4sSJE6iurjb7mZPDAzB7dALOVFTgtz9ykF7ahPRSHY6W6dDY0jYmUljVgK/2n8ZX+42j6RpXB4yP8sPkQf6IUDagoqLC7LxqtRoBAQGora1FTk6OWZmjoyPi4uIAAOnp6WhuNk/2IyMj4erqisLCQmi1WrOyfv36ISQkBA0NDcjMzDQrk0gkGDp0KAAgMzMTDQ3m97GHhYXB09MTWq0WhYWFZmXu7u6IiIhAc3Mz0tPTcb4hQ4bAwcEBOTk5qK2tNSsLCgqCj48PKioqcPLkSbMyFxcXDBgwAABw4MCBDueNiYmBk5MT8vLyUFlZaVbm7+8Pf39/1NTU4Pjx42ZlCoUCsbGxAIDDhw+jpaXFrDwqKgoqlQqnT59GaWmpWZm3tzeCg4Nx9uxZHDt2zKxMKpUiPj4eAJCRkYHGxkaz8vDwcHh4eKCkpARFRUVmZR4eHggPD4dOp8ORI0c6XGt8fDykUimys7NRV1dnVhYcHAxvb2+Ul5ejoKDArEylUiEqKgoGgwEHDx7scN5BgwZBLpcjNzcXVVVVZmUajQZ+fn6oqqpCbm6uWZmTkxNiYmIAGP9WDQaDWfnAgQPh7OyMgoIClJeXm5X5+voiMDAQdXV1yM7ONiuTyWQYPHgwAODo0aNmrw0A0L9/f7i5uaG4uBjFxcVmZZfzGlFfX29WFhoaCi8vL5SVleHUqVNmZa6uroiMjIRer8ehQ4c6nDcuLg6Ojo6dvkYEBARArVajsrISeXl5ZmVKpRLR0dEAgD/++APnj6NGR0dDqVQiPz8fZ86cMSvja4SRtV4jukTQFamurhYARHV1tb2b0ns11QvxvxuEWOZm/HotUojy4/ZuVZ+XWVwt7npvlwh54gfT130f7BXVDTp7N42ILqCnxZzCwkIBQOzcudPs+NKlS8XIkSM7fY6jo6NYt26d2bG33npL+Pr6CiGE2LFjhwAgioqKzOrcdttt4vbbb79gW5YtWyZg3Cj7ol933323EEKInJycTsvPGTVqlAAgvnpoqCk+pTwzRcz6x0di8vNfCLeRNwtFUJyQyJUCgJg8ebIQou3/6Pyv0tJSIYQQ06ZN61C2YsUKIYQQX375pXmZ1EHETZwm3vw1R9z93m4RvORrs9fs9l/jH36jw3mffPJJIYQQmzdv7lAWEBBgutaAgIAO5Zs3bxZCCPHkk092KJs3b54QQogjR450KJPL5abzDh06tEP5l19+KYQQYsWKFR3Kpk2bJoQQorS0tNM+PPd7P3ny5A5lb775phBCiI8//rhD2ahRo0xt6uy8OTk5Qggh7r777g5ly5YtE0IIsWHDhg5lERERpvN6e3t3KD/3d/Hoo492KHvggQeEEELs37+/Q5mrq6vpvDExMR3KU1JShBBCvPzyyx3Kbr31ViGEEKdOner0WhsbG4UQQiQlJXUoe++994QQQrz33nsdypKSkoQQQjQ2NnZ63lOnTgkhhLj11ls7lL388stCCCFSUlI6lMXExJiu1dXVtUP5/v37hRBCPPDAAx3KHn30USGEEDt37uxQ5u3tbTpvREREh/INGzYIITp/3bjc14j2Xx9//LEQQog333yzQ5lNXiMAMXToUFOb5HJ5h/IjR44IIYSYN29ehzK+RljvNeLrr782a8OFcHr5FeqJU/16Fd1Z4PO7gNzNxsdKT+OUct9o+7arDztT14R/pWbjs70FptFtAHjo2v54dFIU798m6sF6WswpKipCQEAAdu7cidGjR5uOP/7449iyZQv27NnT4TlyuRwffvgh7rzzTtOxt99+Gy+88AK0Wi127tyJsWPHoqioCP7+/qY6t99+OyQSCb744otO29LZSHdQUBC2bNlyxSPd9fX1yDpxAoPz3sWxsDmIHz0ZEYFqu41i7d13AFnlxlHw9NImZJ9phr7d6/iS0R4YG9Q25ZyjWEYc6W7DkW4jjnQb8TXCyBqvEadPn0ZQUNAl4zOT7ivU094A9SqN1cC6WUBB6xREuSsw9zsgYJh929VH6VoM+GjXSaxKy0FtY9sbg0BPJV6YHovronn/NlFP19Nijk6ng7OzM7766ivMnDnTdHzu3LmoqqpCSkpKh+cEBwdj8eLFWLRokenYsmXLkJycjEOHDiE3NxcRERH4448/TIkHACQlJSE+Ph6rVq3qUtt6Wl/ZQn1TCz7enY9XfjYmbc5yB6Q8OBaRalc7t4yI6OrS1ZgjvWAJkS3UnwE+nN6WcCvcgHu+ZsJtA0II/HpMi6krt+KlHzNNCbeL3AFLpwzApsVJTLiJ6IrI5XIkJCQgLS3NdMxgMCAtLc1s5Lu90aNHm9UHgNTUVFP9sLAw+Pn5mdWpqanBnj17LnjOq5WLQoYFE8Jx87AAAMYtyP7yyX7UNbVc4plERGQPXEiNuk9tiXFbsLLW6VTO/YB7vgE08XZtVl+Uo63F33/MxNbsMtMxiQS4dVgglk4ZAF837n1ORJZZvHgx5s6di+HDh2PkyJFYuXIl6uvrcd999wEA5syZg4CAACxfvhwA8MgjjyApKQkrVqzATTfdhM8//xz79u3Du+++C8A49XDRokV46aWXEBkZadoyTKPRmI2mk5FEIsE/ZsYho6gGx0pqcaKsHo9/dQhv3TWM24sREfUwTLqpe1TmGxPuytb7UVz9gdnJgO9Auzarr6k6q8Mbqdn4ZE8B9O1u3B4e4oll02IRF+hux9YRUV8ya9YslJWV4bnnnkNJSQni4+OxYcMGqNXGGTQFBQWQStsm1I0ZMwbr1q3DM888g6effhqRkZFITk427dENGO8Jr6+vx/z581FVVYVx48Zhw4YN9tuju4dTyh2w5p4ETHtzO2obW/BTegne356H/zc+3N5NIyKidnhP9xW6Gu4Zs5qybGPCXdu66IdHMDDnO8ArzL7t6kOa9QZ8ujsfb2zKQXVD24IXAR5KPHnDQPxpsD9HPoh6Mcacrrsa+yo1Q4v7P9oHAHCQSvDZ/aMwMszLzq0iIur7eE839Qwl6cAHN7Ql3N5RwP9tZMJtRVuyy3DDqm14/vsMU8KtdHTA4uujkLYkCdOGaJhwExH1YdfHqPHAxAgAgN4g8OC6AyitabzEs4iIqLtwejnZzqm9wKe3GlcrBwC/OOCebwGVj33b1UecKKvDP37MxK/HzLcjuXloAB6fOhB+7pyOSUR0tVgyeQAOna7CjuNnUFbbhIXr/sCn9yfC0YHjK0RE9sZXYrKN3N+Aj2a2JdyBI4G5PzDhtoLqs8148fsMTHljq1nCPTTYA98+MAb/mhXPhJuI6CrjIJXg33cMhX/r6//ekxV49edjl3gWERF1B450k/Xt+wD46THA0Lp1SVgScMc6QKGyb7t6uRa9AZ/9fgr/+iULlWfb7tv2c3PCkzcMxPQhGkilnEZORHS16qdS4K27h2HWO7vQrBf47/Y8DAvxxI1x/vZuGhHRVY1JN1mPQQ/88gyw++22YwNuBG79AHDkyOuVatEb8GN6Md7efAJZ2lrTcYVMigVJEfhLUjic5fxTJiIiYFiwJ579UwyeSzkKAFi6/hCi1K7o78sPvomI7IXv1Mk6GmuAr+cBOb+0HRv1ADD5JUDqYL929WL1TS344vdTeH97HgqrGszKpg/R4IkbBiLAQ2mn1hERUU81e1QIDuRXIvlgEep1evzlk/1IeXAsXBR820dEZA989SXLVZ4E1t0BlGUaH0tlwI2vA8Pvs2uzequy2iZ8uPMkPt6db7b9FwDEB3ng2T9FIyGEW8EQEVHnJBIJXr45DpnFtcjS1uJ4aR2e+PowVt85lLtZEBHZAZNuskzBbuDzu4CzZ4yPnTyAWR8DYRPs2qzeKK+8Hu9ty8VX+09D12IwK5s4wAcLJkRgVLgX3zAREdElOctlWDM7AdNXb0dtUwt+OFyMYcGe+L9x3LKTiKi7MemmK3fwM+D7hwG9zvi4X3/gri+BfhH2bVcvc6CgEu9uycXGjBII0XZcJpVgerwG8yeEY6Cfm/0aSEREvVKYtwtev30IFny8HwDw8k+ZGBzojuGhnC1FRNSdmHTT5TMYgF9fBLa/0XYsfCJw21pA6WmvVvUqBoPAr8dK8e7WXOw9WWFWplLIcOfIINw3Ngwa3rNNREQWmBLrhwVJ4XhnSy5aDAIPrjuAHx4aDx9Xhc1+ZmW9DqmZWkT4qJAQwvcFRERMuunytOiAb+cDR79tOzZ8HnDDq4CDo/3a1Us0teiR8kcR3t2Wi+OldWZlvq4K3Dc2DHclBsNdyb4kIiLrWDp5AA6fqsau3DPQ1jThoc8O4JN5iZA5SK36c46V1GDtjpP49o9CNLXeJjUi1BMPXNMfE6N8eHsUEV21JEK0n9BKXVVTUwN3d3dUV1fDze0qmfrb3AB8MRs4nmp8LJECU18FEufbt129QHVDM9btKcAHO/JQWttkVtbfV4X548MxY6gGChlXeieijq7KmHOF2FedK6ttwp9Wb4O2xhiDFkwIx1M3Rlt8Xr1BIC1Tiw92nMSu3DMXrBerccOD1/THlFg/OEiZfBNR39DVmMORbuqaplrjCuX5242PZU7A7R8DUZPt264erri6AR/sOIl1ewpQ19RiVjYy1AvzJ4Tj2oG+kPINCBER2ZCPqwJv3z0Ms97ZjRaDwDtbczE02ANTB/lf0fmqG5qxft8pfLjrJE5VmG9rqVLIMG2IP34/WWma1XW0qAYPfHoA4T4u+GtSBGYODYCjlUfaiYh6KibddGlnK4BPbwUKjQuxQO4K3PUFEDrWvu3qgXQtBhwtqsb+/ErszavAr8dK0WJom0wikQBTYvwwPykcw4J5nxsREXWfhBAvPHNTNJ7/PgMA8Nj6w4hUuyLCR9XlcxwvrcOHO0/i6wOncVanNysL83bB3NEhuHV4EFQKGQwGgV8ySvDW5hNIL6wGAOSW1WPpV4exclMOFiSF4/bhQXBy5CwvIurbOL38Cl0109fqSoGPZgKlR42PlZ7APV8DAQl2bVZPUXVWh/35ldiXX4n9+ZU4dKrKdB9be3KZFLcMC8T948MQfhlvboiIgKso5lgB++rihBB45POD+O5QEQAgSq1C8oNj4Sy/8DiMwSCwJacMH+w4ia3ZZR3KJ0T54L4xoUiK8ul05pYQAttyyvHm5uPYm2e+eKi3So5548Jxz6hguDpxPRMi6l26GnOYdF+hqyKoV50CPpoBVJwwPnbxBeakAOoY+7bLToQQyCuvNybYJyuxv6Cyw2Jo5/N0dsTdiSGYOybUpivFElHfdlXEHCthX11afVMLZr61AzmtMWxGvAYrZ8V3WOisrqkFX+8/jQ93nkRueb1ZmbPcAbcMC8TcMSHo7+va5Z/9+8kKvL35ODZnmSfvbk4yzB0TivvGhsHLRX6FV0ZE1L2YdNtYnw/qZ04YE+7qU8bH7kHGhPsq2oO7sVmPI4XV2JdfiX0nK3GgoBIV9bqLPifIS4nhIV4YFuKJ4SGeiFK7csEYIrJYn485VsS+6poTZXWY8eYO03ojL0yPxdwxoQCA/DP1+HBnPtbvO4Xa89YjCfRU4t4xobhteJBFO20cLarG27+dwE/pxWj/TlTp6IC7EoNx//hw+Lk7XfH5iYi6A5NuG+vTQV171DilvL7U+NgrwphwewTZtVm2Vl7XhP2t08T3nazAkcIa6PQdp4qfI5NKEBvgjuEhnkhoTbJ93fgGgYisr0/HHCtjX3Xdz+nF+OunBwAYY9qy6bHYklWKtGOlOP/d4ejwfrhvbCiui1Zb9cPkE2V1eGfLCXxzoNBsDRRHBwluTQjEggkRCPV2sdrPIyKyJibdNtZng/rpfcZF0xoqjY99Y4HZ3wKuavu2y8oMBoETZXWmUez9+RU4eebsRZ/jrnREQmuCnRDiiSGBHlDKufgLEdlen405NsC+ujwv/5SJd7fmdlqmkEnx56EBuHdsKAb62bYvC6sa8N7WXHy2t8BsbRSpBJg2RIO/ToyweRuIiC4Xk24b63NBXQhg3/vAhqcAfesU6oAE4O6vAGcv+7bNChp0ehw6XWUaxT5QUIXqhuaLPifM28U0gp0Q4okIHxW39iIiu+hzMceG2FeXp0VvwF3/3WO2wJm/uxNmjw7BnSOC4dnN91eX1zXhf9vz8PGu/A5T22fGa/DMn2LgreIaKUTUMzDptrE+FdSb6oAfHgXSv2w7FjoeuPMzQNH1xVF6ktKaRrNR7KNFNWbT1s4nd5AiLrBtqviwEE8GdSLqMfpUzLEx9tXlK6ttwtPfpqOpxYBZw4MwJVYNmZ330K5uaMYnu/Px/vY8s/VU3JWO+NuN0bhteGCHhd+IiLobk24b6zNBvSwL+HIOUHas7dioB4BJLwCy3rF6qN4gkK2tbV1VvAL78itxurLhos/p5yI3LXY2PNQTgwLcoZBxqjgR9Ux9JuZ0A/ZV33JW14LP9p7Cv9NyzGaoJYZ54eWb4y5rj3EiImvrasy58KaM1PelfwV89zDQ3LoNiNwVmPEmEDvTrs3qisKqBqQeLcGvWWX4I7+ywxS080X6qkz3Yg8P9UJoP2d+Qk5ERNTDOctlmDcuDDPiNXjphwwkHzTuL74nrwI3rNyGB6/pj79MDOcH50TUozHpvhq1NAEbnwZ+/2/bMd9Y4PaPAO/+9mvXRQghcLy0DhuPlmDjUS3SC6svWFchk2JIkIdpFHtYsCc8nHvHqD0RERF15K1SYOUdQ/HnYYF4JjkdpyoaoNMb8MambHx3qBDLbx6MkWG9fw0aa6prakFqRgkO5Fch1NsF1wzwQThnBlAvU93QjMziGmhrGjE40ANhvXQ3A04vv0K9dvpaZT6w/l6g6EDbsfi7gRtfB+TOdmtWZwwGgYOnq7DxaAlSj2qRW17faT1fV4UpuR4e6oUYfzfIZfa9F42IyJp6bcyxA/ZV39eg02NVWg7e25YLfbv1Wu4YEYSnboiGu/OV7x/e2+laDNiaXYaUQ0VIzShBY7P51qeh/ZwxcYAvrhnoi8QwLzg5coYA9QxCCJyubEBGcQ0yimqQUVyDzOKaDreM9vdVYVK0GtfH+CI+yNOqWxheCd7TbWO9Lqgb9ED6euDnJ4DGKuMxmZMx2R42265Na0/XYsDu3DPGRDtDi9Lapk7rDQpww5QYP0yO9UOUWsWp4kTUp/W6mGNH7KurR0ZRDZ76Nh2HTlWZjnmrFHhuWgymDfa/at4bGAwC+/IrkXywED+lF6Pq7MV3ZznHyVGKsRHemDjQF9cM8EGgZ/cOvgghUFLTiIp6HaQSCaQSCSQS4zZxknOPgbbjUomxDJJ2dTp+VylkV83/fW/V1KJHjrbOlGBnFhuT7NrGi98uej5vlRzXDvTF9TF+GNff2y5b+TLptrFeE9QNBiAjGfjtFaA8q+24Z5hxOrn/YLs17ZyzuhZsySrDxqMlSDtW2ukfnFQCjAj1wpRYP0yOVXd7YCAisqdeE3N6APbV1UVvEPhkdz5e25iFunbruyRF+eClmYMQ5NU33y8IIXCspBbJBwvx/cEiFFU3dqjj4eyIm+L8MSlajWxtLTZnlWLfycoL7uYS6avCtQN9MXGAL4aHesLRSivYCyFQVN2IbG0tjmvrkFNai5zSOhzX1l1yTZ4r4eViTMQmRasxPtIbLgreTWtPlfU6U1J9bgT7eGndRXcVOsdF7oBofzdE+7vBW6XA9uNl2J9fic6eqpBJMT7SG9fHqHHtQDV8XLtnFyIm3TbW44O6EMCxH4DNy4HSo+Zl0dOAGW8BTu72aRuAinodNmVq8cvREmzLKUdTi6FDHblMigmR3pgc64frBvqiH7fwIqKrVI+POT0I++rqVFzdgOe/O4qNR7WmY06OUiy+Pgr/NzbM7lugWcupirP47lARUg4WIltb16Fc6eiA62PUmBGvwfhInw6329U0NmNHTjk2Z5Vic1YZyi4wo9BVIcO4SG9cM8AXEwf4wNfN6ZJtMxgECqsajEm1tg7Z2jocL63F8dI61Ov0V3bBFpLLpBgT0Q/XRasxKdoX/u5Ku7SjL2vRG1Ba24Ti6gYUVjWiqKoBRVUNOFVxFlkltZ1+INQZf3cnRPu7IcbfDTEa4/dgL2dIz5s+fqauCb8eK8WmTC22Zpejobnj75ZEAsQHeeD6GDWuj1ajv6/tZsUy6baxHhvUhQCyNwKb/wGUHDYvCxoFXPs3IGzCFZ/eYBDQ6Q1oajGgqUUPXYsBuhbjY12LwVjWbIBOrzcdb2pX52xTC3acKMfevIpOP6VydZLh2oG+mBLrh6QoH346SUSEHhxzeiD21dVt49ESLEs5ipKatjf6Mf5uWH5zHIYEedivYRY4U9eEn9KLkXKwCPvyKzuUO0glmBDpjRnxAbg+Rt3l904Gg0BGcQ1+a03A/yjofAQRAGI1brhmgC+uGeiDwYEeKKpqQHbrqLVx9LoOx0vrOk2ALiTQU4kotSvUrQm9EAJCAAYhYBCtj9H22CAEYCo/V8dY79zjZr0BB09V4ewFkvxBAW64bqAa18eoEatx4zT0LqhpbDYl0u2TauNXI0pqGs3WVrgUB6kE/X1UpsQ6RmMcyfZyufxFjxub9dh5ohypGcYk/EIfIoX0c8b10WpMilFjeIinVT+E61VJ91tvvYXXXnsNJSUlGDJkCFavXo2RI0desP769evx7LPP4uTJk4iMjMSrr76KG2+80VQuhMCyZcvw3nvvoaqqCmPHjsV//vMfREZGmupUVFTgoYcewvfffw+pVIpbbrkFq1atgkrVtVUde1xQFwI4kQZsfhko3G9eFjAcuOZpIOJa40c/56lvajF9OlVc1YCiauMfVHF1A4qrGlF5VmdKqJv11v918XFVYHKMGlNi/TAqvB8XQSMiOk+Pizk9GPuKahub8frGLHy0Ox/n3uVKJcCc0aF4bMoAqHrBB/r1TS1IzdAi5WAhtuWUdzoVNyHEEzPjNbgxzt8qswEr63XYmlOGzcdKsSW7DJUXuDdcIgG6mj1IJECwlzMifVXo7+uKKLUKkb6uiPB1gbPcNv8Pjc167M49g02ZWqRllqL4AiOtfm5OuC7aF5Ni1Bgd3q9PLSonRNsgmWkQrFlvGhxrO643/buxWY8z9ToUtkuqi6saLboFQKWQmY1cx2jc0N9XZZO+NhgEDhdWY1OGFqkZWmRpazut5+HsiGsHGP/fJ0T5WPx60GuS7i+++AJz5szBmjVrkJiYiJUrV2L9+vXIysqCr69vh/o7d+7EhAkTsHz5cvzpT3/CunXr8Oqrr+LAgQMYNGgQAODVV1/F8uXL8eGHHyIsLAzPPvss0tPTkZGRAScn46dpN9xwA4qLi/HOO++gubkZ9913H0aMGIF169Z1qd12D+pCABW5QN5W4OQ2IG8bUF9qXsd/CJonPIUS3wnGRLra+IlUcev3c39QNZe5aIE1hPZzbr0/2w9Dgzw6TB0hIqI2do85vQj7is75o6AST32TjmMlbW++/d2d8OKMQbg+Rt3t7Wls1qOmsRk1DS2t35tR09jS+r0Zta3/Lq1twvaczqfNRvqqMHNoAKYP0dj0fnW9QeDQ6Sr8dsw4Cn6xrVoB44caIf1cEOmrQmRrYt3fV2WzBKurhBA4WlRjSsAvdB3OcgeMj/TGddFqXDvQF9496JbGs7oWs/ft5wbHiqoaUFGvM5tZ2j6J7g6ezo7QeCih8VAiwEMJjYeT6bHGXQlfV4Xd3uMXnDmLTZnGBHzvyYpOR+MH+rliw6IrnwEM9KKkOzExESNGjMCbb74JADAYDAgKCsJDDz2EJ598skP9WbNmob6+Hj/88IPp2KhRoxAfH481a9ZACAGNRoMlS5bgscceAwBUV1dDrVZj7dq1uOOOO5CZmYmYmBj8/vvvGD58OABgw4YNuPHGG3H69GloNJpLtru7g7quxYCzpXloyd0Ch5Pb4Fy0C4qzxZ3WPS0Px0dOdyH5bDzK6nVd/jSyMy5yB/RTKaCQSSGXSU3f5TKHtmMOUigcpZA7nKvjcF7dtmNyBykifFxsem8FEVFfw0Sy69hX1F6z3oD3t+dh5aZss+2zrhnggzBvldlq2cZ/m6+YbVoZ27RidruVtVufK5VI0NRi6DShrm1NqGsaWqDTX1kipHF3wrR4DWbGB2Cgn6td3j+V1jZiS1YZNmeV4mT5WePotVqFSLUrIn1VCPN26RUjxcXVDUjLLEVaphY7TpzpNDmVSIBhwZ7GUfBoNYI8nSGXSW2yNZXeIFBa22ieVLebxl1c3XDBGQe2JpdJoXFvl0R7KBHQLqn2d3ey2WwFa6s+24zNWaVIzdRiS1aZadHFBUnheOqGaIvO3SuSbp1OB2dnZ3z11VeYOXOm6fjcuXNRVVWFlJSUDs8JDg7G4sWLsWjRItOxZcuWITk5GYcOHUJubi4iIiLwxx9/ID4+3lQnKSkJ8fHxWLVqFf73v/9hyZIlqKxsuy+mpaUFTk5OWL9+Pf785z9fsu3WCup7vvwnDDVaGJobgOazEM0NkLY0QqpvhIO+EQ76JshFEzxQg0BJ+QXPUyecsNcwEF/pJ+Bnw0gIXHqKtqODBH7uTtC4t/3xaFo/pfJvPebmxG0XiIjsjYlk17GvqDMFZ87ib8np2JZz4fdSPYmHsyNujPPHzPgADA/x5IxAG6hvasG2nHKkZWrx67FSnKnXXbS+g1TSNqDk0HFgSeFw/gCVsZ5xYMpYR0BAW21MsgurGqCtaezSKt4XopBJ4eTYNtilaDcwpmjXPoWj+SCZwrF18Kz12Llr8FA6IsBTCX93Jfq5yPvk79257Yk3ZWpxa0IgBgd6WHS+rsYcu348UV5eDr1eD7XafIqPWq3GsWPHOn1OSUlJp/VLSkpM5eeOXazO+VPXZTIZvLy8THXO19TUhKamtpvza2pqLnV5XaLJ/ABBoujilTr5fW8QcvxuGIDdhhjsMsQgXYShpd1/p0QC+KgU8G/9VMrf3ZhUB3go4e+hhMbdCd4q+035ICIiIuouwf2c8dH/jcR3h4rw4vcZl0ywrMXVSQY3J0e4KR3h5iSDm9Kx02PGx63HnRzh7+FktS27qHMuChmmDvLD1EF+0BsEDp6qxKbMUmzK0CKntOPK8HqDwFmd/oKLtFmbTNo6ONY6ddu/3b81Hkr4ezjBzcmxW9rSl8hlUkyI8sGEKJ9u/bm9Y05AD7B8+XK88MILVj9vs1QOXOJvVw8pmiROKJD3R65qGE57jECVVxxcnF3g5yTDbIUMrk7GF3GVQgZ3pSPUbk5ckIyIiIiolUQiwYz4AEyJ9UNWSW3rCGPbKtiG1tWzxbmVsdFuFe1zjw2t9dB+tW1A5iCBe/vkWekIlVzGwY1ewkEqQUKIFxJCvPDE1IHIP1OPTZml2HXiDOqbWow79ugNpl172u/c06S/snuoL3Q/tL+78bGPq8ImU9rJPuyadHt7e8PBwQFardbsuFarhZ+fX6fP8fPzu2j9c9+1Wi38/f3N6pybbu7n54fSUvNFx1paWlBRUXHBn/vUU09h8eLFpsc1NTUICgrqwlVe3NnrXsZhXSMUShconVVwdlZBqXKFs7MLJI5KwNEZDg6OcAYwsPWLiIiIiK6Mk6NDr90+jLpHSD8XzBsXhnnjwrpUXwiBZn3riuHN5gl6++1zhRBQt97aqZT3/HvgyXrsmnTL5XIkJCQgLS3NdE+3wWBAWloaFi5c2OlzRo8ejbS0NLN7ulNTUzF69GgAQFhYGPz8/JCWlmZKsmtqarBnzx789a9/NZ2jqqoK+/fvR0JCAgDg119/hcFgQGJiYqc/V6FQQKGw/kqGg8bcZPVzEhERERFR95BIJJDLJJDLpL1iSzrqfnb/rVi8eDHmzp2L4cOHY+TIkVi5ciXq6+tx3333AQDmzJmDgIAALF++HADwyCOPICkpCStWrMBNN92Ezz//HPv27cO7774LwPhLv2jRIrz00kuIjIw0bRmm0WhMiX10dDSmTp2K+++/H2vWrEFzczMWLlyIO+64o0srlxMRERERERF1hd2T7lmzZqGsrAzPPfccSkpKEB8fjw0bNpgWQisoKIBU2nZv8pgxY7Bu3To888wzePrppxEZGYnk5GTTHt0A8Pjjj6O+vh7z589HVVUVxo0bhw0bNpj26AaATz/9FAsXLsR1110HqVSKW265Bf/+97+778KJiIiIiIioz7P7Pt29FbckISKi7sKY03XsKyIi6i5djTlc3pqIiIiIiIjIRph0ExEREREREdkIk24iIiIiIiIiG2HSTURERERERGQjTLqJiIiIiIiIbIRJNxEREREREZGNMOkmIiIiIiIishGZvRvQW53b3rympsbOLSEior7uXKw5F3vowhifiYiou3Q1PjPpvkK1tbUAgKCgIDu3hIiIrha1tbVwd3e3dzN6NMZnIiLqbpeKzxLBj82viMFgQFFREVxdXSGRSCw6V01NDYKCgnDq1Cm4ublZqYVXD/afZdh/lmH/XTn2XdcJIVBbWwuNRgOplHeGXQzjc8/B/rMM+88y7D/LsP+6pqvxmSPdV0gqlSIwMNCq53Rzc+MvtQXYf5Zh/1mG/Xfl2HddwxHurmF87nnYf5Zh/1mG/WcZ9t+ldSU+8+NyIiIiIiIiIhth0k1ERERERERkI0y6ewCFQoFly5ZBoVDYuym9EvvPMuw/y7D/rhz7jno6/o5ahv1nGfafZdh/lmH/WRcXUiMiIiIiIiKyEY50ExEREREREdkIk24iIiIiIiIiG2HSTURERERERGQjTLpt4K233kJoaCicnJyQmJiIvXv3XrT++vXrMXDgQDg5OSEuLg4//fSTWbkQAs899xz8/f2hVCoxadIk5OTk2PIS7Mqa/dfc3IwnnngCcXFxcHFxgUajwZw5c1BUVGTry7Aba//+tfeXv/wFEokEK1eutHKrew5b9F9mZiamT58Od3d3uLi4YMSIESgoKLDVJdiVtfuvrq4OCxcuRGBgIJRKJWJiYrBmzRpbXgL1YYzPlmF8tgzjs2UYny3D+Gxngqzq888/F3K5XPzvf/8TR48eFffff7/w8PAQWq220/o7duwQDg4O4p///KfIyMgQzzzzjHB0dBTp6emmOq+88opwd3cXycnJ4tChQ2L69OkiLCxMNDQ0dNdldRtr919VVZWYNGmS+OKLL8SxY8fErl27xMiRI0VCQkJ3Xla3scXv3znffPONGDJkiNBoNOKNN96w8ZXYhy367/jx48LLy0ssXbpUHDhwQBw/flykpKRc8Jy9mS367/777xcRERFi8+bNIi8vT7zzzjvCwcFBpKSkdNdlUR/B+GwZxmfLMD5bhvHZMozP9sek28pGjhwpHnzwQdNjvV4vNBqNWL58eaf1b7/9dnHTTTeZHUtMTBQLFiwQQghhMBiEn5+feO2110zlVVVVQqFQiM8++8wGV2Bf1u6/zuzdu1cAEPn5+dZpdA9iq/47ffq0CAgIEEeOHBEhISF9Nqjbov9mzZol7rnnHts0uIexRf/FxsaKF1980azOsGHDxN/+9jcrtpyuBozPlmF8tgzjs2UYny3D+Gx/nF5uRTqdDvv378ekSZNMx6RSKSZNmoRdu3Z1+pxdu3aZ1QeAKVOmmOrn5eWhpKTErI67uzsSExMveM7eyhb915nq6mpIJBJ4eHhYpd09ha36z2AwYPbs2Vi6dCliY2Nt0/gewBb9ZzAY8OOPPyIqKgpTpkyBr68vEhMTkZycbLPrsBdb/f6NGTMG3333HQoLCyGEwObNm5GdnY3Jkyfb5kKoT2J8tgzjs2UYny3D+GwZxueegUm3FZWXl0Ov10OtVpsdV6vVKCkp6fQ5JSUlF61/7vvlnLO3skX/na+xsRFPPPEE7rzzTri5uVmn4T2Erfrv1VdfhUwmw8MPP2z9Rvcgtui/0tJS1NXV4ZVXXsHUqVPxyy+/4M9//jNuvvlmbNmyxTYXYie2+v1bvXo1YmJiEBgYCLlcjqlTp+Ktt97ChAkTrH8R1GcxPluG8dkyjM+WYXy2DONzzyCzdwOIuktzczNuv/12CCHwn//8x97N6RX279+PVatW4cCBA5BIJPZuTq9jMBgAADNmzMCjjz4KAIiPj8fOnTuxZs0aJCUl2bN5vcLq1auxe/dufPfddwgJCcHWrVvx4IMPQqPRdPgUnoh6J8bny8f4bBnGZ8sxPl8ejnRbkbe3NxwcHKDVas2Oa7Va+Pn5dfocPz+/i9Y/9/1yztlb2aL/zjkX0PPz85GamtrnPkUHbNN/27ZtQ2lpKYKDgyGTySCTyZCfn48lS5YgNDTUJtdhL7boP29vb8hkMsTExJjViY6O7nOro9qi/xoaGvD000/jX//6F6ZNm4bBgwdj4cKFmDVrFl5//XXbXAj1SYzPlmF8tgzjs2UYny3D+NwzMOm2IrlcjoSEBKSlpZmOGQwGpKWlYfTo0Z0+Z/To0Wb1ASA1NdVUPywsDH5+fmZ1ampqsGfPngues7eyRf8BbQE9JycHmzZtQr9+/WxzAXZmi/6bPXs2Dh8+jIMHD5q+NBoNli5dio0bN9ruYuzAFv0nl8sxYsQIZGVlmdXJzs5GSEiIla/AvmzRf83NzWhuboZUah6qHBwcTKMURF3B+GwZxmfLMD5bhvHZMozPPYR913Hrez7//HOhUCjE2rVrRUZGhpg/f77w8PAQJSUlQgghZs+eLZ588klT/R07dgiZTCZef/11kZmZKZYtW9bpliQeHh4iJSVFHD58WMyYMaNPb0lizf7T6XRi+vTpIjAwUBw8eFAUFxebvpqamuxyjbZki9+/8/Xl1VFt0X/ffPONcHR0FO+++67IyckRq1evFg4ODmLbtm3dfn22Zov+S0pKErGxsWLz5s0iNzdXfPDBB8LJyUm8/fbb3X591LsxPluG8dkyjM+WYXy2DOOz/THptoHVq1eL4OBgIZfLxciRI8Xu3btNZUlJSWLu3Llm9b/88ksRFRUl5HK5iI2NFT/++KNZucFgEM8++6xQq9VCoVCI6667TmRlZXXHpdiFNfsvLy9PAOj0a/Pmzd10Rd3L2r9/5+vLQV0I2/Tf+++/L/r37y+cnJzEkCFDRHJysq0vw26s3X/FxcXi3nvvFRqNRjg5OYkBAwaIFStWCIPB0B2XQ30M47NlGJ8tw/hsGcZnyzA+25dECCHsM8ZORERERERE1Lfxnm4iIiIiIiIiG2HSTURERERERGQjTLqJiIiIiIiIbIRJNxEREREREZGNMOkmIiIiIiIishEm3UREREREREQ2wqSbiIiIiIiIyEaYdBMRERERERHZCJNuIurUxIkTsWjRIgBAaGgoVq5cadf2EBEREeMzUW/EpJuILun333/H/Pnzu1SXbwCIiIi6B+MzUe8gs3cDiKjn8/HxsXcTiIiI6DyMz0S9A0e6iQj19fWYM2cOVCoV/P39sWLFCrPy9p+OCyHw/PPPIzg4GAqFAhqNBg8//DAA45S3/Px8PProo5BIJJBIJACAM2fO4M4770RAQACcnZ0RFxeHzz77zOxnTJw4EQ8//DAef/xxeHl5wc/PD88//7xZnaqqKixYsABqtRpOTk4YNGgQfvjhB1P59u3bMX78eCiVSgQFBeHhhx9GfX29lXuLiIioezA+E/UNTLqJCEuXLsWWLVuQkpKCX375Bb/99hsOHDjQad2vv/4ab7zxBt555x3k5OQgOTkZcXFxAIBvvvkGgYGBePHFF1FcXIzi4mIAQGNjIxISEvDjjz/iyJEjmD9/PmbPno29e/eanfvDDz+Ei4sL9uzZg3/+85948cUXkZqaCgAwGAy44YYbsGPHDnzyySfIyMjAK6+8AgcHBwDAiRMnMHXqVNxyyy04fPgwvvjiC2zfvh0LFy60VbcRERHZFOMzUR8hiOiqVltbK+Ryufjyyy9Nx86cOSOUSqV45JFHhBBChISEiDfeeEMIIcSKFStEVFSU0Ol0nZ6vfd2Luemmm8SSJUtMj5OSksS4cePM6owYMUI88cQTQgghNm7cKKRSqcjKyur0fPPmzRPz5883O7Zt2zYhlUpFQ0PDJdtDRETUkzA+E/UdHOkmusqdOHECOp0OiYmJpmNeXl4YMGBAp/Vvu+02NDQ0IDw8HPfffz++/fZbtLS0XPRn6PV6/P3vf0dcXBy8vLygUqmwceNGFBQUmNUbPHiw2WN/f3+UlpYCAA4ePIjAwEBERUV1+jMOHTqEtWvXQqVSmb6mTJkCg8GAvLy8S/YDERFRT8L4TNR3cCE1IrosQUFByMrKwqZNm5CamooHHngAr732GrZs2QJHR8dOn/Paa69h1apVWLlyJeLi4uDi4oJFixZBp9OZ1Tv/+RKJBAaDAQCgVCov2q66ujosWLDAdP9ae8HBwZdziURERL0O4zNRz8Wkm+gqFxERAUdHR+zZs8cU/CorK5GdnY2kpKROn6NUKjFt2jRMmzYNDz74IAYOHIj09HQMGzYMcrkcer3erP6OHTswY8YM3HPPPQCM939lZ2cjJiamy+0cPHgwTp8+jezs7E4/TR82bBgyMjLQv3//Lp+TiIiop2J8Juo7OL2c6CqnUqkwb948LF26FL/++iuOHDmCe++9F1Jp5y8Pa9euxfvvv48jR44gNzcXn3zyCZRKJUJCQgAYV1LdunUrCgsLUV5eDgCIjIxEamoqdu7ciczMTCxYsABarfay2pmUlIQJEybglltuQWpqKvLy8vDzzz9jw4YNAIAnnngCO3fuxMKFC3Hw4EHk5OQgJSWFC7UQEVGvxPhM1Hcw6SYivPbaaxg/fjymTZuGSZMmYdy4cUhISOi0roeHB9577z2MHTsWgwcPxqZNm/D999+jX79+AIAXX3wRJ0+eREREhGn/0GeeeQbDhg3DlClTMHHiRPj5+WHmzJmX3c6vv/4aI0aMwJ133omYmBg8/vjjpk/tBw8ejC1btiA7Oxvjx4/H0KFD8dxzz0Gj0VxZpxAREdkZ4zNR3yARQgh7N4KIiIiIiIioL+JINxEREREREZGNMOkmIiIiIiIishEm3UREREREREQ2wqSbiIiIiIiIyEaYdBMRERERERHZCJNuIiIiIiIiIhth0k1ERERERERkI0y6iYiIiIiIiGyESTcRERERERGRjTDpJiIiIiIiIrIRJt1ERERERERENsKkm4iIiIiIiMhG/j8Pj9eRCUZnOgAAAABJRU5ErkJggg==", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(1, 2, figsize=(10, 4))\n", "\n", "# K function\n", "ax = axes[0]\n", "ax.plot(support_k_csr, K_csr, label=\"CSR\", lw=2)\n", "ax.plot(support_k_cluster, K_cluster, label=\"Clustered\", lw=2)\n", "ax.set_xlabel(\"distance\")\n", "ax.set_ylabel(\"K(d)\")\n", "ax.set_title(\"Ripley's K function\")\n", "ax.legend()\n", "\n", "# L function\n", "ax = axes[1]\n", "ax.axhline(0.0, color=\"k\", ls=\"--\", lw=1, label=\"CSR expectation\")\n", "ax.plot(support_l_csr, L_csr, label=\"CSR\", lw=2)\n", "ax.plot(support_l_cluster, L_cluster, label=\"Clustered\", lw=2)\n", "ax.set_xlabel(\"distance\")\n", "ax.set_ylabel(\"L(d)\")\n", "ax.set_title(\"Ripley's L function (linearized)\")\n", "ax.legend()\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "5c171ed7", "metadata": {}, "source": [ "## 4. Simulation envelopes with `g_test` and `k_test`\n", "\n", "The functional API (`f`, `g`, `k`, `l`) gives a single curve for a given\n", "pattern. To assess whether this curve is consistent with CSR, `pointpats`\n", "provides test functions like `g_test` and `k_test` that:\n", "\n", "1. Simulate many CSR patterns in the same hull.\n", "2. Compute the statistic for each simulation.\n", "3. Return the observed statistic, the simulated statistics, and Monte Carlo\n", " p-values at each distance.\n", "\n", "We will apply `g_test` and `k_test` to the **clustered** pattern and visualize\n", "the simulation envelopes." ] }, { "cell_type": "code", "execution_count": 11, "id": "d74a1211", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((20,), (199, 20), (199, 20))" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "n_sims = 199\n", "\n", "g_cluster = g_test(\n", " coords_cluster,\n", " hull=hull,\n", " keep_simulations=True,\n", " n_simulations=n_sims,\n", ")\n", "\n", "k_cluster = k_test(\n", " coords_cluster,\n", " hull=hull,\n", " keep_simulations=True,\n", " n_simulations=n_sims,\n", ")\n", "\n", "g_cluster.support.shape, g_cluster.simulations.shape, k_cluster.simulations.shape" ] }, { "cell_type": "markdown", "id": "6c4128d8", "metadata": {}, "source": [ "### Helper: plotting simulation envelopes\n", "\n", "We define a small helper that takes a test result and produces a two-panel\n", "plot showing:\n", "\n", "- the observed function and a simulation envelope; and\n", "- the point pattern used in the test." ] }, { "cell_type": "code", "execution_count": 12, "id": "fcc769bd", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def plot_envelope(test_result, coordinates, name=\"G\"):\n", " \"\"\"Plot simulation envelopes and the observed distance function.\n", "\n", " Parameters\n", " ----------\n", " test_result : namedtuple\n", " Result from g_test, k_test, etc. Must have attributes\n", " `support`, `statistic`, and `simulations`.\n", " coordinates : array-like of shape (n, 2)\n", " The point pattern being tested.\n", " name : str, default \"G\"\n", " Name of the statistic, used for labels and titles.\n", " \"\"\"\n", " support = test_result.support\n", " observed = test_result.statistic\n", " sims = test_result.simulations\n", "\n", " fig, axes = plt.subplots(\n", " 1,\n", " 2,\n", " figsize=(10, 3),\n", " gridspec_kw=dict(width_ratios=(2, 1)),\n", " )\n", "\n", " # Left: simulation envelope + observed\n", " ax = axes[0]\n", "\n", " # All simulations as faint lines\n", " ax.plot(support, sims.T, color=\"0.8\", alpha=0.1)\n", "\n", " # 95% envelope\n", " lower = np.percentile(sims, 2.5, axis=0)\n", " upper = np.percentile(sims, 97.5, axis=0)\n", " ax.fill_between(\n", " support,\n", " lower,\n", " upper,\n", " color=\"0.9\",\n", " alpha=0.7,\n", " label=\"95% envelope\",\n", " )\n", "\n", " # Observed\n", " ax.plot(support, observed, lw=2, label=\"observed\")\n", "\n", " ax.set_xlabel(\"distance\")\n", " ax.set_ylabel(f\"{name}(d)\")\n", " ax.set_title(f\"Ripley's {name} with CSR envelope\")\n", " ax.legend()\n", "\n", " # Right: the pattern itself\n", " ax = axes[1]\n", " coords = np.asarray(coordinates)\n", " ax.scatter(coords[:, 0], coords[:, 1], s=10, alpha=0.7)\n", " xmin, ymin, xmax, ymax = hull\n", " ax.set_xlim(xmin, xmax)\n", " ax.set_ylim(ymin, ymax)\n", " ax.set_xticks([])\n", " ax.set_yticks([])\n", " ax.set_aspect(\"equal\", adjustable=\"box\")\n", " ax.set_title(\"Pattern\")\n", "\n", " fig.tight_layout()\n", " plt.show()\n", "\n", "\n", "plot_envelope(g_cluster, coords_cluster, name=\"G\")" ] }, { "cell_type": "code", "execution_count": 13, "id": "0caf89eb", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_envelope(k_cluster, coords_cluster, name=\"K\")" ] }, { "cell_type": "markdown", "id": "8039d778", "metadata": {}, "source": [ "In these plots, departures of the observed curve outside the simulation\n", "envelope indicate scales (distances) where the clustered pattern is unlikely\n", "to have arisen under CSR.\n", "\n", "For example, if the observed G curve lies above the envelope at short\n", "distances, that suggests an excess of close neighbors, consistent with\n", "clustering. If it lies below, that suggests inhibition or regular spacing.\n" ] }, { "cell_type": "markdown", "id": "36cefc61", "metadata": {}, "source": [ "## 5. Recap\n", "\n", "In this notebook, we have:\n", "\n", "- Constructed **CSR** and **clustered** point patterns in a simple\n", " rectangular window.\n", "- Used the functional API (`f`, `g`, `k`, `l`) to compute distance-based\n", " statistics directly from coordinate arrays.\n", "- Compared how **G**, **F**, **K**, and **L** respond to CSR vs clustering.\n", "- Used `g_test` and `k_test` to generate **simulation envelopes** under CSR\n", " and visualize departures from complete spatial randomness.\n" ] } ], "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": "3.14.2" } }, "nbformat": 4, "nbformat_minor": 5 } pointpats-2.5.5/docs/user-guide/sd_ellipse.ipynb000066400000000000000000010244721514706172100217510ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "id": "9c9ec342-b3cc-4628-9b8b-2100134c77b6", "metadata": {}, "source": [ "# Standard deviational ellipse in `pointpats`\n", "\n", "This notebook demonstrates how to compute and visualize the\n", "**standard deviational ellipse** (SDE) for planar point patterns using\n", "`pointpats.centrography`.\n", "\n", "The SDE provides a compact summary of:\n", "\n", "- **Central location** of the point pattern\n", "- **Dispersion** along two principal axes\n", "- **Orientation** of the pattern in the plane\n", "- Optional **weighting** of points (e.g., counts, magnitudes, intensities)\n", "\n", "We will:\n", "\n", "1. Construct a simple example point pattern and compute its standard\n", " deviational ellipse.\n", "2. Show how the SDE computation **dispatches on input type**, including\n", " NumPy arrays and GeoPandas GeoDataFrames.\n", "3. Compare **unweighted** and **weighted** ellipses to see how weights\n", " affect the ellipse size and orientation.\n", "4. Explore additional options in the ellipse construction, including\n", " corrections that align or differ from CrimeStat-style ellipses.\n", "\n" ] }, { "cell_type": "code", "execution_count": 2, "id": "b6fbd94a-9b3a-47a5-a04a-f16b9146f9ed", "metadata": {}, "outputs": [], "source": [ "import geopandas as gpd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import geopandas as gpd\n", "from pointpats import ellipse" ] }, { "cell_type": "code", "execution_count": 3, "id": "8c22f909-f5d5-4c26-a8a0-2f1118eef336", "metadata": {}, "outputs": [], "source": [ "import pointpats\n" ] }, { "cell_type": "markdown", "id": "5fa8c9eb-b023-4d3e-8914-2fd690cbcf0b", "metadata": {}, "source": [ "## 1. Example point pattern\n", "\n", "- 100 points\n", "- randomly distributed in [(0,0), (10, 10)]" ] }, { "cell_type": "code", "execution_count": 4, "id": "a567c58f-dec2-4ca6-b7d5-c8fedbce2a46", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "\n", "seed = 65647437836358831880808032086803839626\n", "rng = np.random.default_rng(seed)\n", "points = rng.integers(0, 100, (50, 2))" ] }, { "cell_type": "code", "execution_count": 5, "id": "90686986-ba5c-4a56-9694-5ba521946be0", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "points_gdf = gpd.GeoDataFrame(geometry=gpd.points_from_xy(points[:,0], points[:,1]))\n", "\n", "points_gdf.plot()" ] }, { "cell_type": "markdown", "id": "ade6fe62-313e-4e4f-bced-a16fd9f84f55", "metadata": {}, "source": [ "## 2. Dispatching on input type\n", "\n", "`ellipse` can take the following inputs\n", "\n", "- points as a 2-D numpy array\n", "- a geodataframe with a point GeoSeries\n", "\n", "It is important to note that the return types differ between these two cases." ] }, { "cell_type": "markdown", "id": "c08e18cb-59ae-4952-80c6-9ea7203364f0", "metadata": {}, "source": [ "### 2.1 Passing an array" ] }, { "cell_type": "code", "execution_count": 6, "id": "f381e968-3868-4a44-bc4f-f8fb620bba81", "metadata": {}, "outputs": [], "source": [ "ellipse_ = ellipse(points)" ] }, { "cell_type": "markdown", "id": "20caa5b8-fd65-4c94-b18f-f9069ca4f2ba", "metadata": {}, "source": [ "This will return a tuple with\n", "- length of the major axis\n", "- length of the minor axis\n", "- angle of rotation (in radians)" ] }, { "cell_type": "code", "execution_count": 7, "id": "706c3026-a75c-4a46-a848-f22d58694118", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(43.85494662229593, 36.28453973005919, -0.9362557045365753)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ellipse_" ] }, { "cell_type": "markdown", "id": "68de8ebb-0354-4c7a-beff-bd07db2a221d", "metadata": {}, "source": [ "### 2.2 Passing a GeoDataFrame" ] }, { "cell_type": "code", "execution_count": 8, "id": "6b1272a6-5180-4211-936f-9fb70044b82d", "metadata": {}, "outputs": [], "source": [ "ellipse_poly = ellipse(points_gdf)" ] }, { "cell_type": "markdown", "id": "459548fb-2bc5-4e11-95fe-9f989006aeb0", "metadata": {}, "source": [ "This will return a `shapely` `Polygon`" ] }, { "cell_type": "code", "execution_count": 9, "id": "3420d0d1-5c2a-491c-abd9-9ea2b7d64a0b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "shapely.geometry.polygon.Polygon" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(ellipse_poly)" ] }, { "cell_type": "markdown", "id": "c4dcaefb-cd34-4272-a8c1-c7dd45079bbb", "metadata": {}, "source": [ "## 3. Unweighted and weighted ellipses\n", "\n", "The default is to treat the points as an unmarked point pattern and construct the ellipse accordingly." ] }, { "cell_type": "code", "execution_count": 10, "id": "23e497c8-3a62-4d37-8a74-6c59fd94f03c", "metadata": {}, "outputs": [], "source": [ "ellipse_gdf = gpd.GeoDataFrame(geometry=[ellipse_poly])" ] }, { "cell_type": "code", "execution_count": 11, "id": "c3dd02c1-e139-4413-8044-5e15bef12149", "metadata": {}, "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": [ "base = ellipse_gdf.plot()\n", "points_gdf.plot(ax=base, color='k')" ] }, { "cell_type": "markdown", "id": "73b29b5a-96be-428d-ba68-3b7bc41ca7a1", "metadata": {}, "source": [ "### 3.1 Weighted points\n", "\n", "If marks are available to attach to each point, the weighted standard deviational ellipse can be constructed. Here we use use the $y$ coordinate as the weight for demonstration:" ] }, { "cell_type": "code", "execution_count": 12, "id": "143ddf77-89b6-4ef4-8405-0ce17b2a54ba", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'Marked Point Pattern')" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Normalize or scale marker size (you can tune this)\n", "size_scale = 20 # try 10, 20, 50 etc. to get a good size visually\n", "marker_sizes = points[:,1] * size_scale\n", "\n", "fig, ax = plt.subplots(figsize=(8, 6))\n", "base = points_gdf.plot(ax=ax, markersize=marker_sizes, alpha=0.6, color='cornflowerblue', edgecolor='k')\n", "points_gdf.plot(ax=base, color='k')\n", "ax.set_title(\"Marked Point Pattern\", fontsize=14)" ] }, { "cell_type": "code", "execution_count": 13, "id": "4536e782-5e00-445f-86bc-14691eabf366", "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "" ], "text/plain": [ "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ellipse_w_poly = ellipse(points_gdf, weights=points[:,1])\n", "ellipse_w_poly" ] }, { "cell_type": "code", "execution_count": 14, "id": "b54a2dc5-8927-41ae-a732-623c3b039bf2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([51.1 , 36.88])" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mc = pointpats.mean_center(points)\n", "mc" ] }, { "cell_type": "code", "execution_count": 15, "id": "66738152-5476-4c4d-8c98-3f9d7002d3c2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([47.3302603 , 59.13665944])" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wmc = pointpats.weighted_mean_center(points, points[:,1])\n", "wmc" ] }, { "cell_type": "code", "execution_count": 16, "id": "bb956941-df8b-4410-922f-f34695889519", "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "size_scale = 20 \n", "marker_sizes = points[:,1] * size_scale\n", "\n", "fig, ax = plt.subplots(figsize=(8, 6))\n", "\n", "ellipse_gdf.plot(ax=ax)\n", "gpd.GeoSeries([ellipse_w_poly]).plot(color='yellow', ax=ax)\n", "points_gdf.plot(ax=ax, color='k')\n", "base = points_gdf.plot(ax=ax, markersize=marker_sizes, alpha=0.6, color='cornflowerblue', edgecolor='k')\n", "points_gdf.plot(ax=ax, color='k')\n", "ax.plot(*mc, marker='o', color='red', markersize=10, label='mean center')\n", "ax.plot(*wmc, marker='o', color='green', markersize=10, label='weighted mean center')\n", "ax.legend()\n", "ax.set_title(\"Marked Point Pattern\", fontsize=14);" ] }, { "cell_type": "markdown", "id": "e068b84a-83b8-4f29-939a-6b4b420abd6f", "metadata": {}, "source": [ "## 4. Other options\n", "\n", "The default option constructs the standard deviational ellipse following the method used in CrimeStat." ] }, { "cell_type": "code", "execution_count": 17, "id": "f5efab64-b13e-42a6-8d05-d68391ad8869", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(43.85494662229593, 36.28453973005919, -0.9362557045365753)" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ellipse(points)" ] }, { "cell_type": "markdown", "id": "af44e1e8-cbe7-4bcd-b496-864a6d3822f5", "metadata": {}, "source": [ "The default construction can be overridden in one of two (or both) ways.\n", "\n", "The first employs the `yuill` method which employs a different estimator for the major and minor axes lengths:" ] }, { "cell_type": "code", "execution_count": 18, "id": "04800f10-f44b-442e-a0ff-fc833e0f6680", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(31.01013014519952, 25.65704409535755, -0.9362557045365753)" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ellipse(points, method='yuill', crimestatCorr=False) " ] }, { "cell_type": "markdown", "id": "b98d38be-0b7e-420b-b63c-dc2f821d6a55", "metadata": {}, "source": [ "This results in shorter axes lengths relative to to crimestat.\n", "\n", "The second approach drops the degrees of freedom correction used in the default:" ] }, { "cell_type": "code", "execution_count": 19, "id": "93d51f17-71a9-435a-8d5e-9255c465e579", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(42.96889676864705, 35.551443156155464, -0.9362557045365753)" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ellipse(points, method='yuill', degfreedCorr=False) " ] }, { "cell_type": "markdown", "id": "987d22d8-639f-4ff4-82b4-1a2bfd1c21c2", "metadata": {}, "source": [ "Again, this shortens the estimated axes.\n", "\n", "Finally, both corrections can be toggled off:" ] }, { "cell_type": "code", "execution_count": 20, "id": "55ee9923-9dcb-4580-a9a1-3388cce22fc5", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(30.383598285215058, 25.138666536685605, -0.9362557045365753)" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ellipse(points, method='yuill', crimestatCorr=False, degfreedCorr=False) " ] }, { "cell_type": "markdown", "id": "660bc907", "metadata": {}, "source": [ "## 5. Recap\n", "\n", "In this notebook, we:\n", "\n", "- Introduced the **standard deviational ellipse** (SDE) as a summary of\n", " the central location, dispersion, and orientation of a point pattern.\n", "- Showed how `pointpats.centrography` can construct an SDE from both\n", " NumPy arrays and GeoPandas GeoDataFrames.\n", "- Compared **unweighted** and **weighted** ellipses to highlight how\n", " point weights influence the ellipse axes and rotation.\n", "- Explored additional construction options, including corrections that\n", " control consistency with CrimeStat-style ellipses.\n", "\n", "The SDE complements other centrographic measures (mean center, standard\n", "distance) and the quadrat- and distance-based statistics notebooks to\n", "provide a richer description of spatial point pattern structure in\n", "`pointpats`.\n" ] } ], "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": "3.12.2" } }, "nbformat": 4, "nbformat_minor": 5 } pointpats-2.5.5/docs/user-guide/sd_fires.parquet000066400000000000000000027560611514706172100217730ustar00rootroot00000000000000PAR1LH@L T P  D t  X p  l  d   `  |  \ < @   8 x      h  (@@,@@(@@?@ Pǁ p$ (" 4J8cD,q0sD 1@ A,b, 3M5V8.191\(328385920,80796526 RX03@ n 56206285P0300-2012-VMP-010Z08: RX02^!46 F02541500qRX0>3-0B4-004% 5052 3922 190708228!p230:0049040" 0840!00!504400)w11% 7001!x45800871010602604574! 5643#6 #RY0306  5642956H80061 2829kUnit ) 62431>S6264996 3 %E%4M 3961.91489326 783502B j6{)T1%}4V5V=Q.EE797605100-21949))219A060 4313 "2 .^9V S31014 S u5A207 I624.~MVU.RX304MVU}u4-999J087 S 25491%1F923A552f581983M@E#F@0NY  7q 0099.D1e0095Q 3053q3a!i$ 11291 4248%(000465) 2608710 /!@% )19822)R 2886FDC7.0922b4223^.a226 08309 ! 8983 5uy2332416 000517 aY 30731x2y.08=.07V=԰27&{0A64442B-1AEC-4D9B-AAA2-41A7DBEA23A2}&*5750C9E6-5226-4F0B-8499-C4C138690993}T437EF696-947D-4B38-A7FC-8A8BD7A0B5ED}*3E40AE3B-AB71-4ED5-A86E-D133204C46E0}*C0CE759D-D46B-40CA-8673-401CA16951E3}y( 6001=Y2.j33 f32C8A61-FDA4-4D12-A58E-5EFE17CDFAF8}.6.l=Ѹ 8273I12 <.<2.15Q2309IW>j641M901 7WFPOOMACV%|.<2 6002R?2 1979:516* (0000000DC69,6 (&{C32C8A61-FDA4-4D12-A58E-5EFE17CDFAF8}0H?  ! 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(10,10))\n", "ax = fig.add_subplot(111)\n", "ax.set_xlim(8, 10)\n", "ax.set_ylim(13,16)\n", "ax.plot([p[0] for p in points], [p[-1] for p in points], 'r')\n", "ax.add_patch(des.PolygonPatch(mbc_poly, fc='white', ec='black'))\n", "chull = pointpats.hull(points)\n", "ax.plot([p[0] for p in chull], [p[-1] for p in chull], 'm')\n", "ax.plot([p[0] for p in constraints], [p[-1] for p in constraints], '^b')\n", "ax.plot([p[0] for p in inset], [p[-1] for p in inset], 'ob')\n", "ax.plot([p[0] for p in removed], [p[-1] for p in removed], 'xb')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Cool. How fast is this?" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "import time" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "def demo_mbc(chains):\n", " for cidx, chain in enumerate(chains):\n", " points = chain\n", " start = time.time()\n", " (radius, center), inset, removed, constraints = pointpats.skyum(chain)\n", " elapsed = time.time() - start\n", " mbc_poly = sgeom.Point(*center).buffer(radius)\n", " fig = plt.figure(figsize=(8,8))\n", " ax = fig.add_subplot(111)\n", " parray = ps.common.np.array(points)\n", " ax.set_xlim(parray[:,0].min()*.98, parray[:,0].max()*1.02)\n", " ax.set_ylim(parray[:,1].min()*.98, parray[:,1].max()*1.02)\n", " ax.plot([p[0] for p in points], [p[-1] for p in points], 'r')\n", " ax.add_patch(des.PolygonPatch(mbc_poly, fc='white', ec='black'))\n", " chull = pointpats.hull(points)\n", " #ax.plot([p[0] for p in chull], [p[-1] for p in chull], '--m')\n", " ax.plot([p[0] for p in constraints], [p[-1] for p in constraints], '^b')\n", " #ax.plot([p[0] for p in inset], [p[-1] for p in inset], 'ob')\n", " ax.plot([p[0][0] for p in removed[:-1]], [p[0][1] for p in removed[:-1]], 'xc')\n", " ax.plot(removed[-1][0][0], removed[-1][0][1], '*k')\n", " plt.title('Shape #{}, Elapsed Time: {}'.format(cidx, elapsed))\n", " #print(removed)\n", " nonboundary = [p for p in chull.tolist() if p not in constraints]\n", " succeeded = [mbc_poly.contains(sgeom.Point(p)) for p in nonboundary]\n", " for i,v in enumerate(succeeded):\n", " print(\"Point {i}: {tf}\".format(i=i, tf=v))\n", " if not v:\n", " ax.plot(chull.tolist()[i][0], chull.tolist()[i][1], 'gH')\n", " plt.show()\n", " plt.clf()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Point 0: True\n", "Point 1: True\n", "Point 2: True\n", "Point 3: True\n", "Point 4: True\n", "Point 5: True\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Point 0: True\n", "Point 1: True\n", "Point 2: True\n", "Point 3: True\n", "Point 4: True\n", "Point 5: True\n", "Point 6: True\n", "Point 7: True\n", "Point 8: True\n" ] }, { "data": { "text/plain": [ "
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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Point 0: False\n", "Point 1: True\n", "Point 2: True\n", "Point 3: True\n", "Point 4: True\n" ] }, { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", 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mIdAdWHOMdUcA1xaoMhEREcki19C21k4DtoeZNRIYDNhjrPsNkF7g6kREROSwAh3TNsb0BNZba38sjCKMMbcaY+YaY+Zu2bKlMJ5SRESkxMl3aBtjqgBDgQcKqwhr7cvW2gRrbUJcXFxhPa2IiEiJUpCW9klAU+BHY8yvQANgnjGmbmEWJiIiIlmVy+8K1tqfgNqZj73gTrDWbi3EukRERCSbvJzyNR6YCbQ0xqwzxtx0jGUTjDGjQx5/C7wLdPPWvaAwihYRESmNcm1pW2uTcpnfJOT+XODmkMcdj6c4EREROUIjoomIiASEQltERCQgFNoiIiIBodAWEREJCIW2iIhIQCi0RUREAkKhLSIiEhAKbRERkYBQaIuIiASEQltERCQgFNoiIiIBodAWEREJCIW2iIhIQCi0RUREAkKhLSIiEhAKbRERkYBQaIuIiASEQltERCQgFNoiIiIBodAWEREJCIW2iIhIQCi0RUREAkKhLSIiEhAKbRERkYBQaIuIiASEQltERCQgFNoiIiIBodAWEREJCIW2iIhIQCi0RUREAkKhLSIiEhAKbRERkYBQaIuIiASEQltERCQgFNoiIiIBodAWEREJCIW2iIhIQCi0RUREAkKhLSIiEhAKbRERkYBQaIuIiASEQltERCQgFNoiIiIBodAWEREJiFxD2xjzmjFmszFmUZh5g4wx1hgTm8O61xljVni36wqjYBERkdIqLy3tN4ALs080xjQEugNrwq1kjKkJPAicDZwFPGiMiSlwpSIiIqVcrqFtrZ0GbA8zayQwGLA5rHoBMMlau91amwpMIkz4i4iISN4U6Ji2MaYnsN5a++MxFjsBWBvyeJ03Ldzz3WqMmWuMmbtly5aClCQiIlLi5Tu0jTFVgKHAA7ktGmZa2Fa5tfZla22CtTYhLi4uvyWJiIiUCuUKsM5JQFPgR2MMQANgnjHmLGvtxpDl1gFdQh43AKYUrEwRybRt2zZWr17Nzp07SU9PZ9euXYdvoY/Dzdu9ezfly5cnKiqK6OhooqKijrqf0+PatWvTtGlTypcv7/dHIFJq5Tu0rbU/AbUzHxtjfgUSrLVbsy06EXgspPNZD2BIAesUKVV27NjBihUrwt4yMjI48cQTqVGjxlHhGhUVRd26dXMM4KpVq3LgwIEcgz7z8YYNG7I8Tk9PZ+PGjaxfv54GDRrQvHnzo26NGzemXLmCtANEJK9y/Q8zxozHtZhjjTHrgAetta/msGwCcLu19mZr7XZjzMPAHG/2cGttuA5tIqXSgQMHWLRoEcuXLz8qmPft25clELt3785f//pXmjdvTmxsLN5ermK3f/9+Vq9efbjmxYsX8/HHH7NixQo2bdpE48aNjwrzU045hXr16vlSr0hJY6zNqfO3PxISEuzcuXP9LkOk0O3bt4/vv/+eadOmMXXqVL7//nsaNWrEySefnCXkWrRoQZ06dXwL5oLat28fq1atOmoD5Mcff6RWrVp06tSJzp0706lTJ5o0aeJ3uSIRxRjzg7U2IdflFNoiRWPXrl3MnDmTqVOnMm3aNH744Qdat259OLjOO+88YmJK/tAFGRkZLFq06PDGyrRp06hUqVKWEG/evHngNlJECpNCW6SY7dixg+nTpx8Opp9++okzzjjjcDC1b9+e6Ohov8v0nbWWZcuWMW3atMNBfvDgwSwhHh8fT5kyGmVZSg+FtkgxWL16NRMmTOD9999n6dKlnHXWWYeD55xzzqFy5cp+lxjxrLX8+uuvhzd2pk6dSlpaGhdccAFJSUn06NGDChUq+F2mSJFSaIsUkU2bNpGcnMz48eNZsWIFV111Fb1796Z9+/YKl0Kybt06PvnkE8aNG8fSpUv585//TJ8+fejYsSNly5b1uzyRQqfQFilEO3bs4MMPP2T8+PHMnj2byy67jKSkJLp3767zlovYb7/9xjvvvMO4cePYsmULvXv3pk+fPrRt21bHwaXEUGiLHKe9e/fy2WefMW7cOCZPnkzXrl1JSkri0ksvpUqVKn6XVyotWbKE8ePHM27cOIwxJCUlkZSURKtWrfwuTeS4KLRFCuDAgQN8/fXXjBs3js8++4yEhASSkpL485//TI0aNfwuTzzWWubOncv48eN55513qF27NklJSVxzzTU0atTI7/JE8k2hLZIP6enpjB49mpEjR1K/fn369u3L1VdfTd26df0uTXJx6NAhpk2bxvjx43n//fdJTExk8ODBnHXWWX6XJpJneQ1tnVMhpdrGjRsZOnQoTZs2ZebMmbz//vvMmjWLO++8U4EdEGXLliUxMZGXX36Z3377jY4dO3L11VfTpUsXPv/8cyKtYSJyPBTaUiotX76c2267jVatWpGamsqsWbNITk6mXbt2fpcmxyEqKoq77rqLlStXcssttzBkyBDatGnDmDFj2L9/v9/liRw3hbaUKt9//z1XXnklHTp0oG7duixbtowXX3yRZs2a+V2aFKLy5cvTt29fFixYwDPPPMOYMWM46aSTePbZZ0lPT/e7PJECU2hLiZeRkcH//vc/OnfuTO/evenSpQu//vorDz30ELVr1879CSSwjDH06NGDr7/+mo8++ojZs2fTtGlT7rvvPjZu3Jj7E4hEGIW2lFgHDhzgzTffpE2bNtx///3cfvvtrFy5kjvvvJOqVav6XZ4Us7Zt2zJhwgRmz57Nzp07adWqFbfeeivLly/3uzSRPFNoS4n05Zdfcuqpp/Lmm28ycuRI5s2bR1JSkq73LJx44on85z//Yfny5dSvX5/zzjuPgQMHkpqa6ndpIrlSaEuJsnLlSnr27Mmdd97J008/zTfffEP37t01cpYcJS4ujn/+858sXryYgwcPcvLJJ/PSSy9x6NAhv0sTyZFCW0qEXbt2MWTIEM455xw6dOjAokWLuPTSSxXWkqvY2FhefPFFJk6cyNtvv01CQgLffvut32WJhKXQlkCz1jJ27FhOPvlk1q9fz8KFC7nnnnuoWLGi36VJwJx++ulMnTqVe++9l759+5KUlMTatWv9LkskC4W2BNYPP/zAeeedx3PPPce7777LmDFjqF+/vt9lSYAZY+jduzdLliyhefPmnH766Tz66KPs27fP79JEAIW2BNDmzZu55ZZbuPTSS7npppuYPXs25557rt9lSQlStWpVhg8fzty5c5k3bx7x8fF89NFHGl1NfKfQlsA4cOAAzz33HK1btyY6OpolS5Zw4403UqaM/oylaDRt2pT333+fl19+maFDh9KjRw8WL17sd1lSiunbTgJhyZIltGvXjs8//5xp06bx7LPP6qpbUmzOP/98FixYwGWXXUbnzp155JFH1MtcfKHQlohmreXll1+mY8eODBgwgIkTJ+rayeKL8uXLM3DgQObPn8/XX39Nt27d1FFNip1CWyLW9u3bueqqq3jxxRf59ttvueWWW3QKl/iuQYMGfPPNN/To0YOEhAQ+/PBDv0uSUkShLRFp6tSpnH766TRs2JBZs2apdS0RpWzZstx33318/PHH/OMf/+D2229nz549fpclpYBCWyLKwYMHeeCBB7jmmmv473//y3PPPUelSpX8LkskrHPOOYf58+ezc+dO2rVrx8KFC/0uSUo4hbZEjF9//ZXOnTsza9Ys5s2bx8UXX+x3SSK5ql69Om+//TaDBw+mW7du/Pvf/9apYVJkFNoSEd555x3OOussrrjiCr788kvq1avnd0kieWaM4brrrmPGjBmMGTOGnj17smXLFr/LkhJIoS2+2rVrFzfeeCPDhg3jiy++YNCgQTrvWgKrefPmTJ8+nfj4eM444wy++eYbv0uSEkbfjuKbZcuW0bZtW6y1zJs3j7Zt2/pdkshxq1ChAk8++SSvv/46/fr148EHH9Tucik0Cm3xxfTp0+ncuTODBw/m9ddfJyoqyu+SRApV9+7dmT9/PhMnTuS6665j//79fpckJYBCW4rd+++/z+WXX86bb77JTTfd5Hc5IkWmdu3aTJ48mbS0NC6++GLS0tL8LkkCTqEtxer5559n4MCBTJw4kQsuuMDvckSKXJUqVfjggw9o0aIFnTp1Yv369X6XJAGm0JZikZGRwaBBgxg1ahTTp0/nzDPP9LskkWJTtmxZXnjhBfr06UP79u1ZtGiR3yVJQJXzuwAp+fbt28d1113H77//zvTp06lZs6bfJYkUO2MM99xzDw0aNKBr164kJyfTpUsXv8uSgFFLW4pUamoqF1xwARkZGUyaNEmBLaVe3759mTBhAr169WL8+PF+lyMBo9CWIvPbb7/RoUMH2rZtyzvvvKPhSEU8Xbt25ZtvvuGee+5hxIgROiVM8kyhLUViwYIFdOjQgVtuuYVnn31WA6aIZHPqqaceHkFt4MCBuj635Im+SaXQff311/To0YORI0fy97//3e9yRCJWgwYN+O677/j555+56qqr2Ldvn98lSYRTaEuhmjp1Kn369OG9997j6quv9rsckYhXvXp1vvzyS8qXL0/v3r05ePCg3yVJBFNoS6GZN28eV199NePHj6dTp05+lyMSGBUqVGDs2LEcOHCAm266iYyMDL9Lkgil0JZCsXTpUi655BJeeuklunXr5nc5IoFToUIF3nvvPVatWsXf//53dU6TsBTactzWrFnDBRdcwGOPPcYVV1zhdzkigVWlShU+++wzpk6dyvDhw/0uRyKQBleR47J582a6d+/O3/72N2644Qa/yxEJvBo1ajBx4kQ6duxITEwMAwcO9LskiSC5trSNMa8ZYzYbYxaFTHvYGLPQGLPAGPOVMaZ+Dus+aYxZ5N16F2bh4r+0tDQuvPBCevXqpV7iIoWoTp06fPXVV4wYMYK33nrL73IkguRl9/gbwIXZpo2w1rax1p4OfAY8kH0lY8wlwJnA6cDZwP8ZY6odX7kSKfbu3UvPnj1p3769duOJFIEmTZrw1VdfMXjwYD755BO/y5EIkWtoW2unAduzTdsZ8rAqEK7HRDww1Vp70Fq7G/iRo8NfAujAgQNcffXVNGjQgOeffx5jjN8liZRIrVq14tNPP+Xmm29mypQpfpcjEaDAHdGMMY8aY9YCfQnT0saF9EXGmCrGmFggEWhY0NeTyJCRkcH1118PwBtvvKGRzkSKWEJCAu+88w69evVi7ty5fpcjPivwN661dqi1tiHwNnBHmPlfAZ8DM4DxwEwg7KgBxphbjTFzjTFzt2zZUtCSpIhZaxk4cCDr1q3j3XffpXz58n6XJFIqJCYm8sorr3DZZZexZMkSv8sRHxVGM2kccGW4GdbaR621p1truwMGWJHDci9baxOstQlxcXGFUJIUhWeffZbp06fzySefULlyZb/LESlV/vSnP/Hkk09y0UUXsW3bNr/LEZ8UKLSNMc1DHvYEloZZpqwxppZ3vw3QBviqIK8n/ps+fTpPPfUUH330EdWrV/e7HJFSqV+/flx11VX069dPo6aVUnk55Stz13ZLY8w6Y8xNwBPeaVwLgR7AXd6yCcaY0d6q5YFvjTGLgZeBv1hrNahuAG3ZsoVrrrmG1157jcaNG/tdjkip9vjjj7Njxw6efPJJv0sRH5hIGyovISHBqrNF5Dh06BAXXXQRbdu25fHHH/e7HBEB1q1bR0JCAhMmTKBLly5+lyOFwBjzg7U2Ibfl1PVXjumRRx5h//79PPzww36XIiKeBg0aMGbMGPr27cvGjRv9LkeKkUJbcjRp0iReeuklxo8fT7lyGvFWJJL06NGDm2++maSkJF3OsxRRaEtY69evp1+/frz99tvUq1fP73JEJIwHHniAsmXL8s9//tPvUqSYKLTlKAcOHOCaa65hwIABJCYm+l2OiOSgbNmyvP3227zxxht88cUXfpcjxUChLUcZOnQoUVFR3HfffX6XIiK5qFOnDuPHj+eGG25gzZo1fpcjRUyhLVl88sknvPPOO7z11lsaolQkIDp27Mjdd99Nr1692L9/v9/lSBHSt7Ictnr1am6++WYmTJhAbGys3+WISD4MGjSI2rVrM3jwYL9LkSKk0BbAnY99zTXXMGTIEM4991y/yxGRfCpTpgxvvvkmH3/8MZ9++qnf5UgRUWgLAKNGjaJSpUr87W9/87sUESmgmJgYRo8ezR133MHu3bszxGs3AAAgAElEQVT9LkeKgEJb2LBhAw899BAvvviiro0tEnDdunXjvPPOY/jw4X6XIkVAoS3cfffd3HzzzbRu3drvUkSkEDzzzDO89tprLFq0yO9SpJAptEu5SZMmMXPmTIYNG+Z3KSJSSOrWrctDDz1E//79dTWwEkahXYrt27ePAQMG8J///IcqVar4XY6IFKLbbruNP/74gzfffJMNG6BzZ9Aw5cGn0C7FnnzySVq3bs2ll17qdykiUsjKli3Lf//7X4YMGcLQofv47jvQdX+CT6FdSq1YsYJ///vf/Otf//K7FBEpImeecQY3nftnxr4BGRnw+utqbQedQrsUstYyYMAA7r33Xho1auR3OSJS2NasgSeegFNPJfWjUzDWTT50SK3toFNol0LJycls3LiRu+66y+9SRKSwbN8OL78MnTpB48YwZAgbKp/I6+VvZT+VANi/X63toFNolzJpaWncfffdjBo1ivLly/tdjogcj3374L334IoroG5duO022LLFNadXreLhdp+QYcplWUWt7WArl/siUpIMGzaMiy66iA4dOvhdiogUxKFDMHUqvP22C+ydO11g33EH9O0LZ54J3iBJM2e61nWo/fthxgwf6pZCodAuRRYtWsQ777zD4sWL/S5FRPLDWvjxRxfU48fD+vUQFQVXXumCumtXKFv2qNXmz/fudOoEZcvyUJcuLF68mHfeead465dCo9AuRR599FH+8Y9/UKtWLb9LEZG8+O03GDfOhfXPP0O5cnDhhfDMM3DZZZDX8RXi4mDZMgYNGsSJJ57I4sWLiY+PL9rapUjomHYpsWzZMr755hv69+/vdykicizbt8NLL7nWcZMmcN99UL06vPgibNgAn34KvXvnPbABYmNhyxaqVq3KXXfdxWOPPVZk5UvRUku7lHj88ce58847iY6O9rsUEclu3z747DMYOxY+/xwOHICTT3Y9xvr0gRNPPL7nj4uDbdsgI4MBAwZw0kknsXLlSpo1a1Y49UuxUWiXAqtXr+bTTz9l1apVfpcikidPrVlDu+hoEmNiDk9LSU1lTno6g0vK2AKZHcrGjoX338/aoewvf4Ezzjjcoey4xcW519uxg+o1azJgwACeeOIJRo8eXTjPL8VGoV0KPPHEE9x+++3UqFHD71JE8qRddDS9Fi8mOT6exJgYUrZupdfSpSQ3bAi//+5apn/84X6G3s/8uX+/GwLM2vA/s08rUwZq1oRatdwtNtb9jIoqvOCErB3Kxo1z7yUPHcqOW1yc+7llC9SsycCBA2nevDnDhg2jcePGhf96UmQU2iXcunXrePfdd1m+fLnfpYjk7tAh+PlnEr//nuTffqNXair9P/qIUZdeSvJDD5G4YEHx1lO+fNYQD73lNK1GjaODN1yHsosugmefzV+HsoKKjXU/t2yBli2pVasWt9xyC0899RQvvPBC0b62FCqFdgk3YsQIbrzxRmIz/2lFIoW1sG4dzJ4N33/vbj/8ALt3A5BYsyb9K1fm4b/8hWFr1pDYpw/ccANUqgQVK2b9mX1ahQqu9VymjGsph/4MN+3gQdcBbNu2I7etW4++v3TpkWkHD4Z/X8ZATMyRIM/IcO8RoH1716Hs6quPBGlxCG1pe+6++25atWrF0KFDqV+/fvHVIsdFoV2Cbdq0ibfeeouff/7Z71JE3DHbOXOOhPTs2a43NLiQPf10uPFGOPtsOOssUmrVYtSSJQyrX59R5cuTeNllWY5xF7ratfO+rLXu/YSGfPagz7zt3Vt4HcoKKjO0t249PKlOnTr069ePZ555hmeeecafuiTfFNol2DPPPEPfvn2pV6+e36VICXTMzmL16sFPP2UN6CVLXNgBNG8O3brBWWe5kD7tNNdCDnme0GPaiTVqZHnsO2PcaVjVq/sXxPkRuns8xKBBg2jTpg333nsvcZnBLhFNoV1Cbdu2jdGjR7OguI8BSqlxuLNYq1YkpqWRMn8+vapWJXnsWDe85r59bsHYWBfMvXu7n+3auU5fxzAnPT1LQCfGxJAcH8+c9PTICO2gqVTJdXjLFtoNGjSgV69ejBw5UuduB4SxmVu+ESIhIcHOnTvX7zIC74EHHmDDhg288sorfpciJVjKr7/S6+ef6f/++4zq2ZPkxx8nsVy5Iy3os892A4QUZg9sKZgTT3TH1MeOzTJ59erVJCQksHLlSmK0QeQbY8wP1tqE3JZTS7sESktL48UXX2R2ZucXkaKweDGJl15K/27deLhfP4ZVqEDid9+5HtcSeeLishzTztS0aVN69uzJ888/z4MPPuhDYZIfGsa0BBo7diznn38+JwbhWJsE0xdfwLnnktK0KaP69mVY48aMspaUXbv8rkxyEhd31O7xTIMHD2bUqFEczKlHvEQMhXYJNGbMGG644Qa/y5CSyFp47jm49FJSevSg14MPknzaaQxv2pTk+Hh6LV5MSmqq31VKON744+G0atWKJk2aMGnSpGIuSvJLoV3CLF26lLVr19KtWze/S5GSZv9+uO02+Pvf4fLLmfP44ySfemrYzmISgTJb2jn0Y+rXrx9jxowp5qIkv3RMu4R566236Nu3L+XK6VcrhWjbNrjqKpgyBYYOheHDGVzm6G3+xJgY9e6OVHFxrkf/nj1QtepRs3v37s2QIUNIS0ujevXqPhQoeaGWdgmSkZHBW2+9Rb9+/fwuRUqS//3P7VqdMsX1PH7kETeKmARLmFHRQtWqVYtu3brx3nvvFWNRkl/6zytBpk6dSq1atTj11FP9LkVKip9+gksvdfdfeMFd1EKCKYcBVkJpF3nkU2iXIGPGjFErWwrPrFnQuTOccAIsWgR//avfFcnxyKWlDXDxxRezePFiVq9eXUxFSX4ptEuI3bt389FHH5GUlOR3KVISfPMNnH++G7nsu++gdWu/K5LjFWb88ewqVKhA7969GZttABaJHArtEuKjjz6iffv21K1b1+9SJOg+/hguvhiaNoVvv3Ujmknw5aGlDUd2kUfaaJniKLRLCO0al0IxdixceaW74tbUqaCLzZQc0dFutLpcQrtdu3aULVuWWbNmFVNhkh8K7RJg/fr1zJkzh549e/pdigTZiy/Ctde649hff53rRT0kYIw55qhoRxYz6pAWwRTaJcC4ceO48sorqVy5st+lSFA9/jgMGACXXeZO8YqO9rsiKQp5CG2Av/zlLyQnJ/PHH38UQ1GSH7mGtjHmNWPMZmPMopBpDxtjFhpjFhhjvjLG1M9h3aeMMT8bY5YYY543Rpf6KWzWWt58803tGpeCsRbuvRfuu8+dzvX+++4yjlIy5XDRkOwaNWrEaaedxmeffVYMRUl+5KWl/QZwYbZpI6y1bay1pwOfAQ9kX8kY0x7oALQBTgHaAZ2Pq1o5yvLly0lLS6NDhw5+lyJBc+gQ9O8PTz7pfo4Zoyt0lXR5bGkDJCUl8e677xZxQZJfuYa2tXYasD3btJ0hD6sC4boZWqASUAGoCJQHNhW4Uglr8uTJnH/++ZTRCFWSHwcOQL9+8NJLMGSIGzhFf0Ml3zEuGpJd9+7dSUlJUS/yCFPg/1JjzKPGmLVAX8K0tK21M4EUYIN3m2itXVLQ15PwJk+eTNeuXf0uQ4Jk7174859h3Dh44gl47DHXSUlKvrg4SEtzF3/JRZMmTahatSqLFy8uhsIkrwoc2tbaodbahsDbwB3Z5xtjmgGtgAbACUBXY0yncM9ljLnVGDPXGDN3Sx63AsWNNZ6SkkJiYqLfpUhQpKfDJZe4zmajRsE99/hdkRSnzHO1t23L0+Jdu3Zl8uTJRViQ5Fdh7A8bB1wZZvoVwCxr7S5r7S7gC+CccE9grX3ZWptgrU2Iy/yjklz99NNP1KxZkwYNGvhdigTBtm3QrRtMm+bOx779dr8rkuKWxwFWMim0I0+BQtsY0zzkYU9gaZjF1gCdjTHljDHlcZ3QtHu8EGnXuOQqIwNWrYIPPnDnXy9cCB9+CH36+F2Z+CEPFw0JlZiYyNSpUzl06FARFiX5ketFl40x44EuQKwxZh3wIHCxMaYlkAH8BtzuLZsA3G6tvRl4D+gK/ITrlPaltfbTongTpdXkyZO59tpr/S5DIsWuXe6qXAsXwo8/utvChW46QPXq8MUXoMMppVc+W9r16tWjTp06LFiwgLZt2xZhYZJXuYa2tTbcFShezWHZucDN3v1DwG3HVZ3k6ODBg3z77be8+mrYX4UEgbWwb58L1dDb7t3HfhxuWloarF3rnhNcQLdpA9dfD6ed5m6nnAIagKd0y8NFQ7Lr2rUrKSkpCu0IkWtoS2SaN28eDRs2pHbt2n6XUuSeWrOGdtHRJMbEHJ6WkprKnPR0BjdqVPQFHDhwJCR37z76lp/p2QM3IyPvdVSuDFFRWW9Vq0KdOu5+ixZHArpRI/UIl6PVrOn+LvLR4bdr1668+uqrDBo0qAgLk7xSaAdUaTqe3S46ml6LF5McH09iTAwpqamHHx928GDuoXmsED3WtAMH8ldwZrhWrZr1dsIJ4UM39H50dNZpmbcqVaBs2cL9YKX0KVvWBXc+QrtLly7ccMMNHDhwgPIafMd3Cu2Amjx5MgMGDPC7jGKRGBNDcnw8vebMof/MmYxq25bkV14hcf78I8Ga3zGSK1XKGqiZoVmv3tHTwi2X07QqVTRIiUS2fIyKBlCrVi1OOukk5syZQ/v27YuwMMkLhXYA/fHHH8ycOZPk5GS/Syk2iTEx9P/8cx6+/HKGzZhBYsWK7vSl/ARq6E2tVimt8jj+eKjMU78U2v5TaAfQ7NmzadmyJTVq1PC7lGKTkprKqB49GPbVV4y65BIS+/fPcoxbRPIoLg6WLcvXKomJiYwcOZL777+/iIqSvNJ+vAAqbaOgHT6GvWABw0eOJPnkk+m1eDEpqal+lyYSPPkYfzxTp06d+P7773Wpzgig0A6ghQsXlqrTL+akp7tOaHXrwr59JO7YQXJ8PHPS0/0uTSR44uLc6Hj5OHOhWrVqNGjQgBUrVhRhYZIXCu0AWrZsGS1btvS7jGIzuFEjtys8s7f4zz+TGBNTPKd7iZQ0cXHusqw7duRrtZYtW7Isn7vVpfAptAPm0KFDrFy5khYtWvhdSvELCW0RKaB8joqWSaEdGRTaAbNmzRpiY2OpWrWq36UUv2rVoGFD0KUCRQoun+OPZ1JoRwaFdsCUtl3jR4mPV0tb5HiopR1oCu2AKfWh3bo1LFnijsmJSP4VYPxxOBLaNnN8e/GFQjtgFNqt3UU2fv3V70pEgqmALe3Y2FiMMWzJ53pSuBTaAVPqQ1ud0USOT8WKboz7fIavMUa7yCOAQjtgFNpeaKszmkjBFWCAFdBx7Uig0A6QXbt2sX37dhqV5vOTM3uQq6UtUnD5vGhIJoW2/xTaAbJ8+XKaNWtGmdJ+FSn1IBc5PgW4aAgotCNBKf/2D5ZSv2s8k3qQixwftbQDS6EdIAptj3qQixyfzGPa+Tx9q1mzZvz2228cOHCgiAqT3Ci0A2TVqlU0b97c7zL8px7kIscnLs5t+O7ena/VKlasSL169fjtt9+KqDDJjUI7QFJTU6lZs6bfZfhPPchFjk8BB1gBqFmzJjvyebERKTwK7QBJT08nOjra7zL8px7kIsengAOsAERHR5Ouy+L6RqEdIArtEOpBLlJwBbxoCCi0/abQDhCFdgj1IBcpOLW0A0uhHSAK7RDqQS5ScMdxTFuh7a9yfhcgeVeqQ/vQIdeynjkTZs2CqVPd9F9+gZNO8rc2kaCJjoYKFdTSDiCFdkAcOnSIffv2UbVqVb9LKR5bt8L337uAnjkTZs+GzC+KWrXgnHPgppugQwd/6xQJImMKPP64QttfCu2A2LVrF1WrVsUY43cphe/gQfjppyOt6FmzYMUKN69sWWjTBq691gX1OedAs2buS0dECq6Ao6JFR0ezdu3aIihI8kKhHRAlatf4xo1HWtCzZsHcubBnj5tXpw6ce65rRZ97LrRtC6Vl74JIcSrg+ONRUVFqaftIoR0Q6enpREVF+V1G/u3fD/PnH2lBz5wJmaMplS8PZ54Jt9xypBXduLFa0SLFIS6uQB05tXvcXwrtgIiElvZTa9bQLjqaxJiYw9NSUlOZk57O4EaN3DjG69ZlbUXPmwd//OEWbtjQBfNdd7mfZ5wBlSr59G5ESjkd0w4khXZA7Nq1y/fQbhcdTa/Fi0mOjycxJoaUjRvptXQpycuWwaRJLqh//90tXKkSJCTAnXceaUWfcIKv9YtIiLg4SEtze8MqVMjzatHR0ezatasIC5NjUWgHRCS0tBNjYkiOj6fX/Pn0/+YbRrVrR/JDD5G4YIE77Sox8UhAn3aa2/0tIpEp81ztbdugXr08r6aWtr8U2gGxZ88eqlSp4ncZJMbE0H/+fB7u3p1hq1aROHw4nH021K7td2kikh+ho6LlI7QrV67M3r17i6goyY1GRAuIKlWqsCezh7WPUlJTGdWmDcPGjGFUy5aknHeeAlskiAo4/vjevXupXLlyERQkeaHQDohI2CWVkprqjmnv28fw118nuVw5ei1eTEpqqq91iUgBFHD88Ug4VFeaKbQDIhJCe056uuuE1rIlAIlLlpAcH88cHd8SCZ46ddzPTZvytZpC2186ph0QkRDagxs1cneqVYOKFWHRIhJvuCHLKWAiEhA1a7rOohs25Gs1hba/1NIOiIgahahsWV3PWiTojIG6dd0IhfkQ2IGeSgiFdkBEQks7i1NOgUWL/K5CRI5HvXpqaQeMQjsgoqKi2LNnDxkZGX6X4rRuDevXw44dflciIgVVwJa2Qts/Cu2AKFu2LJUqVYqI074A19IG7SIXCbJ69fId2pEwOmNpptAOkIjaRZ4Z2tpFLhJcdeu6U74OHszzKmpp+0uhHSARFdqNGkFUlEJbJMjq1XMX+tm8Oc+rKLT9lWtoG2NeM8ZsNsYsCpn2sDFmoTFmgTHmK2NM/TDrJXrzM2/7jDGXF/YbKE0iKrSNcce1tXtcJLjq1nU/89EZTaHtr7y0tN8ALsw2bYS1to219nTgM+CB7CtZa1Ostad7y3QF9gBfHWe9pVpEhTa41vby5X5XISIFlTnmeD6Oayu0/ZVraFtrpwHbs03bGfKwKmBzeZqrgC+stRHSiyqYIiq016+HTz+Frl39rkRECkot7cAp8IhoxphHgX5AGpCYy+LXAM8W9LXEiYmJYdu2bX6X4Tz8MBw6BA895HclIlJQmUOZ5qOlvX37dmrUqFFEBUluCtwRzVo71FrbEHgbuCOn5Ywx9YBTgYnHWOZWY8xcY8zcLfkcvL40adasGStWrPC7DFixAkaPhltvhaZN/a5GRAqqYkU3nGkeW9r79u1jw4YNNG7cuIgLk5wURu/xccCVx5jfC/jQWnsgpwWstS9baxOstQlxmVeekaO0bNmSZcuW+V0GPPCA+2e//36/KxGR45WPAVZWrlxJkyZNKF++fBEXJTkpUGgbY5qHPOwJLD3G4knA+IK8jmQVEaG9YAFMmAB/+9uR42EiElz5GMp02bJltPSu8if+yMspX+OBmUBLY8w6Y8xNwBPGmEXGmIVAD+Aub9kEY8zokHWbAA2BqUVQe6nTokULVq5cyaFDh/wr4r77ICYG/u///KtBRApPPlraCm3/5doRzVqbFGbyqzksOxe4OeTxr8AJBS1OsqpatSqxsbGsWbOGpn4cS/72W/jiC3jySVBHFJGSIXMoU2vd+AvHsGzZMjp16lRMhUk4GhEtYHzbRW4tDBni/sHvyLHfoYgETd26sHcv7NyZ66JqaftPoR0wvoX2//4H06e7TmhVqhT/64tI0cjjACvWWoV2BFBoB4wvoZ2RAUOHwkknwU03Fe9ri0jRyuMAK1u2bMEYQ2xsbDEUJTlRaAeML6E9YQIsXOgGVNGpHiIlSx5b2pmtbJPLcW8pWgrtgCn20N6/H4YNg9NOg969i+91RaR45LGlrV3jkaHAw5iKPxo1asT27dvZtWsXUVFRRf+Cr74Kv/zijmmX0TaeSIlTo4YbLCmPLW3xl76FA6ZMmTI0a9aM5cVxda09e2D4cDjvPLjooqJ/PREpfsa41rZa2oGg0A6gYttF/vzzbuv78cdzPX9TRAIsDwOsKLQjg0I7gNq0acO8efOK9kVSU90gKpdc4lraIlJy5TKUaVpaGr///jvNmjUrxqIkHIV2ACUmJpKSklK0LzJiBOzYAY8+WrSvIyL+y6Wl/e2333L22WdTsWLFYixKwlFoB9BZZ53FsmXLSE1NLZoX2LABnnsO+vRxvcZFpGSrVw+2boUD4S/GOHnyZBITE4u5KAlHoR1AFSpUoH379kydWkTXYXnkEffPO3x40Ty/iESWzNO+Nm0KO3vy5Ml07dq1GAuSnCi0A6pr165Mnjy58J/4l1/g5Zfh5pvdCGgiUvIdY4CVrVu3snr1ahISEoq5KAlHoR1QXbt2LZrj2g884EY9Gzas8J9bRCLTMQZYmTJlCueddx7lNRpiRFBoB9QZZ5zBunXr2JTD7qwC+eknGDcOBg6E+vUL73lFJLIdo6WtXeORRaEdUOXKlaNjx45MmTKl8J506FCoVg3uuafwnlNEIl/t2u5nmJZ2SkqKOqFFEIV2gBXqce3p0+HTT11gx8QUznOKSDBUqAC1ah3V0v7999/ZtGkTp+kskoih0A6wQgtta+G++6BOHbdrXERKnzADrKSkpNClSxfKli3rU1GSnUI7wE455RR27NjB2rVrj++JJk6EadNc57OqVQunOBEJljADrOh4duRRaAdYmTJljn90tIwMGDIEmjaFW24pvOJEJFjCtLQV2pFHoR1wx72L/N13YcECN5BKhQqFV5iIBEtmS9taAFavXs3evXtp1aqVz4VJKIV2wHXt2pWvv/6ajIyM/K984IDbJX7KKZCUVPjFiUhw1KsHf/zhrjkATJo0icTERIyu8BdRFNoB17x5c2rWrMm3336b/5Vffx1WrIDHHgN1NBEp3TIHWPGOa48bN46rr77ax4IknHJ+FyDHxxhDv379GDNmDJ07d877inv3wkMPwbnnwqWXFl2BEnyLFsGf/gT79vldSd5ERbnxBqpXP3KrVcsNGHTCCe5Wv7676apVR9Srxwbgmmuu4enRo1m0aBGXXHKJ31VJNgrtEqBPnz60bt2af//731SpUiVvK73wAvz+uxsBTbu/5Fg++cSNSX/jjVAmwnfOWQu7dkFaGuzc6VqNaWmwbZvbUM0uNvZIkGe/Va7sNlSOdbPWbSBk30gIfVy1ajD+x+rW5WHgu59+YsCAAfTq1UuX4oxAxnqdDiJFQkKCnTt3rt9lBM6FF17IddddR1Jejk2npcGJJ8JZZ8EXXxR9cRJsl10GK1fCkiV+V1Jw1rpjtevXH/u2eXPhv3aZMllD/FgBn/k4tFNo9u/oYz3Oz7IhjytfdRX7wlyWs1KlSuwNt7Ejhc4Y84O1NtersqilXUJk7iLPKbSfWrOGdtHRJMbEwNNPw/btpAwfzpw1axjcqFExVyuBYS3MmAGXX+53JcfHGDfSX0yM63iZk/373WlP69e7+5UqHblVrHj0Y2OOtOwzbzt3HvtxWpp7jaVLjzzO4TrWxeUXYBDwEbAHd9gtKSmJZ555xte65GgK7RLi8ssvZ8CAAWzYsIF6mYP/h2gXHU2vxYtJrlePxJEjSfnb3+i1fz/J0dE+VCuBsXw5bN8O7dv7XUnxqFABGjd2t7yKjna3Bg0K9prWul7boaF+8GDWZbLvXi/Mx8ZQD6j22GPs++ADypYpQ0ZGBtWrV6duZuc0iRgK7RKiSpUqXHHFFYwbN45//OMfR81PjIkhOT6eXnPn0r9XL0ZdfjnJ8fGu5S3+sBZ+/hlSUtxu2YceirxjxjNmuJ+lJbT9YMyR1nudOr6VsSkjg1tuvZXx48fTs2dPNoa54pf4T6FdgvTr14+77rorbGiDC+7+u3bxcL9+DMvcVS7Fx1pYtsyFdEoKTJkCW7YcmT9okDumGUlmzIAaNaBlS78rkSL2wQcf8MEHH7B06VLeeustv8uRHETYZr0cj06dOrFjxw5+/PHHsPNTUlMZFRPDsDFjGJWWRkpqajFXWMpY6zpwvfIK9OnjeiS3agV//SvMnAkXXgivvQYnnwwJCZEX2ODqPPfcyNsDIEVizJgx9OvXz+8y5Bj0n1iClClThmuvvZYxY8YcNS8lNdUd027UiOGvv07y6tX0WrxYwV3Yfv3VDVrTrx80agTNm8Ott7qWdZcu8PLLbkCbNWtgzBh3/vPy5XDRRX5XfrQdO9zue+0aLxW2bt1KSkoKV111ld+lyDFo93gJc+2119KlSxeefPJJypU78uudk57ujmHXqAHR0STOn0/yFVcwJz1du8mPx7p1R3Z3p6S40AaIi3MhnZjobi1bhj9Xd9Ikd9GWSAztWbPcT4V2qTBhwgQuueQSqlWr5ncpcgwK7RKmZcuWNG7cmEmTJnFRSBBkOa2rRQtYsYLEmBgFdn5t3Jg1pFeudNNjYlxI3323C+nWrfM2oMbnn0PNmu6c+UgzY4bbLR6JtUmhGzNmDMOHD/e7DMmFQrsE6tevH6+//nqW0M6ieXP4/vviLSqotmxxHcYyQ3rpUje9WjXo3Nkdn05MhDZt8n/cNyMDvvwSevSIzLHfZ8xw7ysqyu9KpIj9/PPPrF27lvPPP9/vUiQXCu0S6C9/+QsPPvggK1eupFmzZkcv0KIFJCe7c0M1TGFW27fD1KlHQnrRIje9alXo1MkN5ZmYCGeccfxBO3++O9UrEneNHzrkNuzUKalUeOqpp/jrX/+a5ZCaRCb9hkqgatWqMWDAAB5//HFeffXVoxdo3ty18n75xfVmLs3S0mDatCMh/eOPrtd35crQoYO7ZGliouvdXb584b525hCyF1xQuM9bGBYtciN96Xh2iffLL7/w2WefsWrVKr9LkTxQaGj31PgAAB7GSURBVJdQAwcOpHnz5jzwwAM0zj66U4sW7ueKFaUvtPfuzbq7e948twFTsaI7temf/3QhfdZZRb8X4osvoG1bXwfUyJEGVSk1nnjiCfr370+NGjX8LkXyQKFdQtWsWZNbbrmFp556ihdeeCHrzObN3c/ly4u/ML/s3+9Ot3rkEdi0ybWazz4bhg51IX3uuW5EquJiLcyZ4zYYWrd2x45PO839bNPGndPt55WhZsxw11du0sS/GqTIrV27lvfee4/lpem7IOAU2iXY3Xffzcknn8zQoUOpX7/+kRkxMa7H8i+/+FdccTl0CN5+Gx580J2O1amTO4+6Uyd3nNovxsDEiTB5Mixc6AYxmTDhyPyaNY8EeGaYt27tdtsXhxkzXCs7CJeUlAIbMWIEN910E7GxsX6XInmk0C7BateuzXXXXcfTTz/Ns88+m3Vm5cqu9VlSWQsffwz33+8GCDnjDBg1yh0/jpQgyjyHO9OOHfDTTy7EFy50x9dHj4Y9e9z8MmXcoY3srfKGDQv3PW3a5Dbo+vcvvOeUiLNx40bGjh3L4sWL/S5F8kGhXcINGjSIU089lSFDhhAXF+d3OcVj8mQYMgRmzz7SU/7KKyN/KM4aNaBjR3fLlJEBq1ZlDfI5c9x7Cl0ve6v8lFOgSpWC1TFzpvup49kl2rPPPkvfvn11Ja+AUWiXcCeccAK9e/dm5MiRPPbYY36XU7TmzIH77oOvv3aXSRw9Gq67DoJ8GkuZMq4PQvPmbsMj086dR1rlP/7ofr7+Ouze7eYb49bJ3ipv3DjHVvnha67PmOEuUXnmmaSkpjInPV3XXC9htm7dyujRo3O8ToFErgB/m0le3XPPPbRt25ZBgwZRs2ZNv8spfIsXu93gH34IsbHw7LNu125xdiwrbtWquVPSOnQ4Mi0jA1avzhrk8+fDe+9lXS8zwNu0cRs3tWtD7dq0q1jRjU+/YQOJbduSsnevexwfX/zvT4rUv/71L6666ioaNmzodymST8Zae+wFjHkNuBTYbK09xZv2MPAnIAPYDFxvrf09zLqNgNFAQ8ACF1trfz3W6yUkJNi5c+fm/53IMd144400btyYBx980E1o0MBdZWr0aH8LOx6//upO0XrrLdepbNAg+PvfITra78oiS3q6O+86M8gzb+npRy2acs459LrnHvpv3syo1q11zfUSaMeOHTRr1ozZs2dz4okn+l2OeIwxP1hrE3JdLg+h3QnYBYwJCe1q1tqd3v2BQLy19vYw604BHrXWTjLGRAEZ1to9x3o9hXbRWL58OR06dGDVqlXuggAnnOBG4gpiaG/aBI8+Cv/9r9t9fMcdcO+9rpUteZOR4S52snGjG5Vt82Y3ZOvmzTzQuDEPt2nDsMaNGd60qd+VSiF79NFHWbZsWdirAYp/8hraue4et9ZOM8Y0yTZtZ8jDqrhWdPYC4oFy1tpJ3jq7cnstKTotWrSge/fuvPjii9x7771uYqT0os6rHTtgxAh47jk3BOtNN8GwYW6vgeRPmTLu0qHZjlWnpKYyavFihtWvz6jffyexRg21tEuQXbt28a9//Ytp06b5XYoUUIG70xpjHjXGrAX6Ag+EWaQFsMMY84ExZr4xZoQxJuxgzcaYW40xc40xc7ds2VLQkiQX999/PyNHjmTr1q1+l5I/e/bAk0/CiSfCY49Bz56wZAm89JICuxAdvuZ6fDzDmzYlOT5e11wvYZ5++mm6devGySef7HcpUkAFDm1r7VBrbUPgbeCOMIuUAzoCg4B2wInA9Tk818vW2gRrbUKpOS3JB/Hx8SQlJXHPPff4XUrurHXnVz/2GJx0ktv93b6961g1fvyRUd2k0By+5rrXsk6MiSE5Pp45YY59S/CsWLGCF154gREjRvhdihyHXI9pA3i7xz/LPKadbV5j4H/Z5xljzgGesNZ28R5fC5xjrR1wrNfSMe2itXPnTuLj4/lgdzN67P6caXOr0KaN31V5DhyA776DTz5xt8wR27p0gYcfhvPO87U8kaCy1tKjRw8uuugi7r77br/LkTAK7Zh2Dk/e3Fq7wnvYE1gaZrE5QMz/t3fn0VHW9x7H318SBVkEKkjZVMSwFEWEVGUnIosgFIoIAaRKgeJl6XHpoSVFqpZTvQroUQ8HXImSEESOoIIsFgItAg2LLHIFBEUBCyibFAgJv/vHzOVGCGQSkvnlST6vc+aQeeYZ8vllJvPJs5tZdefcQeAuQG3s2dVXX82UKVMYfH9jjnIVAwb8/9UnvTh6NHRN6fnzYcGC0HbrsmXh7rth7Fi4917IeQpWEcm3tLQ0Dh48yJgxY3xHkcuUZ2mbWSrQAahmZt8CE4BuZtaQ0CFfXwMjwvPGAyOcc0Odc9lm9jjwiZkZsA54tWiGIflx00338QUAxtatjk3tRtO0SXZoj/Lzb5Ur53uHtXMn6cixA9NPTtLx1VfwwQehol6+HLKyoHp16N07tL26Uye/5wUXKUGOHj3KY489xpw5c3S97BIgotXj0aTV40Xv5pth61YHGOBoUnYnWyq1gtx2UCtfPrSzV26FXrt26LEaNX5y1rGcOzQlVK3Ksh9+4P5Nm5i9fj0JM2aEjhGG0GVBe/YM3e64A2Jy3U9RRC7D6NGjyczMZNq0ab6jyCUU6epxCa6NG0P7d4UKO/Tv1tNxbFp7kKYNT8O+faHjd/fuvfC2cmXo8TNnfvqflikTuoxjuMgTatdmduPG3H/qFA9v28bU665j9oQJJGzeHDqv9qRJ0KOHdiYTKWIZGRm8++67uihICaIl7VImtJR9/lRHkyYW2bbts2dDS+Q5yzy3kj9yhCceeoinBw9mfEYGT9WsGTqZS0k8japIMZSdnc0dd9zB6NGj+c1vfuM7juRBS9qSqy+/zG2q8eWX/7e6PA9lypw7VzW33XbR2Zbt38/UHTtCJ+m44goSdDpMkaiaOnUqFStWZPDgwb6jSCFSaZcyJ09eOG3QoEHUrl0beLZQvseyw4e5f9cuZt9yCwlVq5JwzTU/2cYtIkVr//79PPnkk6Snp2NBO/OhXFIxv8CwRMOkSZN444032FJIx37pJB0ifj366KMMGzaMX+gKbSWOtmkLEFqVlpKSQnp6OmXK6G85kaBasmQJw4cPZ+vWrZQvX953HIlQpNu09eksAAwfPpysrCwmT57sO4qIFND333/PsGHDeOWVV1TYJZS2aQsAMTExzJo1i9tvv52WLVvSunVr35FEJB/Onj3L4MGD6dOnD926dfMdR4qIlrTlnOuvv5433niD/v37o6utiQTLs88+y5EjR3jmmWd8R5EipNKWn+jevTuDBg1i4MCBZGdn+44jIhFYvnw5L774ImlpaVxxxRW+40gRUmnLBZ5++mkyMzP561//6juKiOThu+++Y+DAgSQnJ1NH15cv8VTacoHY2FhSU1OZNm0aS5cu9R1HRC4iOzubxMREhg4dSufOnX3HkShQaUuuatasycyZM3nggQfYu3ev7zgikosJEyYQExPDE0884TuKRIlKWy4qISGBkSNH0r9/f86cf5EQEfFq4cKFvPXWW8ycOZMYXSGv1FBpyyWNGzeOihUrkpSU5DuKiITt2bOHhx56iNTUVGrUqOE7jkSRSlsuqUyZMrz99tukpaUxf/5833FESr3MzEzuv/9+Hn30Udq2bes7jkSZSlvyVK1aNWbNmsXQoUPZvXu37zgipdrYsWO59tprefzxx31HEQ9U2hKRli1bkpSURK9evThy5IjvOCKl0ptvvsm8efOYMWOGrhFQSulVl4iNGTOGhIQEunfvzokTJ3zHESlV5s6dS1JSEh9//DFVdYnbUkulLREzMyZPnsxNN93EfffdR2Zmpu9IIqXC0qVLGTFiBB999BENGjTwHUc8UmlLvpQpU4bXX3+dK6+8ksGDB+tUpyJFbM2aNQwYMID33nuP2267zXcc8UylLfkWGxtLWloaBw4cYOTIkRS3a7KLlBRbtmzhV7/6FW+++ab2FBdApS0FVK5cOebNm8e6det0DLdIEdi9ezddu3ZlypQpdO/e3XccKSZ0PW0psEqVKrFw4ULatWtH1apV+cMf/uA7kkiJsH//fjp16kRSUhKJiYm+40gxotKWy1KtWjUWL15M27ZtqVq1KkOHDvUdSSTQfvjhB7p06cJDDz3Eww8/7DuOFDMqbblsderUYfHixbRv357KlSvTt29f35FEAunEiRN0796dTp06MW7cON9xpBhSaUuhiIuLY8GCBXTu3JnKlSvrMoEi+XT69Gl69+5N48aNef755zEz35GkGNKOaFJomjVrxty5cxk4cCDLli3zHUckME6dOkViYiIVK1Zk+vTpKmy5KJW2FKo2bdowe/Zs+vXrR2pqqu84IsXe4cOH6dKlCzExMaSmphIbqxWgcnEqbSl0CQkJfPLJJ4wdO5bnnntOx3GLXMSePXto3bo1LVq0IC0tjbJly/qOJMWcSluKxC233MKqVatITk7m97//vc6cJnKejRs30qpVK4YNG8bkyZN1ARCJiN4lUmTq1KnDypUr2bJlC3379uXkyZO+I4kUC0uWLKFz585MmTKFRx55xHccCRCVthSpKlWqsHDhQq666io6duzIoUOHfEcS8So5OZlBgwbx3nvv6fBIyTeVthS5smXL8vbbb9O+fXtat27Nrl27fEcSiTrnHBMnTmTChAksX75c5xKXAtFuihIVZcqU4W9/+xt16tShTZs2zJ8/n/j4eN+xRKIiKyuLkSNH8q9//YtVq1ZRs2ZN35EkoFTaElUjR46kdu3a3HPPPcyYMYNu3br5jiRSpE6cOEH//v05c+YM6enpVKpUyXckCTCtHpeo69WrF/Pnz2fIkCFMnz7ddxyRIvPdd9+RkJBA9erV+eCDD1TYctlU2uJFy5YtWbFiBS+88AIPPPAAx44d8x1JpFAtWrSI5s2b06NHD15//XWuuOIK35GkBFBpizcNGjQgIyOD8uXL07x5c9auXes7kshlO336NI899hjDhg0jJSWF8ePH67SkUmhU2uJV+fLlmTZtGs8++yw9evTgmWee4ezZs75jiRTIF198QcuWLdm1axcbNmygQ4cOviNJCaPSlmKhT58+ZGRksHDhQjp16sS+fft8RxKJmHOO119/nTZt2jB8+HDmzp3LNddc4zuWlEAqbSk26taty9///nc6dOhA8+bN+eCDD3xHEsnTkSNH6N+/Py+++CLLly9nxIgRWh0uRUalLcVKTEwM48ePZ+7cuYwZM4ZRo0bp9KdSbP3zn/+kWbNmXHvttaxdu5YmTZr4jiQlXJ6lbWZvmNkBM9uSY9rTZrbJzDaa2WIzq3WR52aH59loZvMLM7iUbK1atWLDhg0cOnSI22+/na1bt/qOJHJOVlYWTz31FH369OGll17ipZdeoly5cr5jSSkQyZL2W0DX86Y955xr6pxrBnwIPHGR5550zjUL33peRk4phapUqUJqaiqPPPIIHTp0YOrUqbrMp3i3Z88eEhISWLFiBevXr6dHjx6+I0kpkmdpO+dWAD+cNy3nQbUVAH2SSpEwM4YMGcI//vEPXnvtNe6++262bNmS9xNFCllmZiaTJ0+mRYsW3HvvvSxevJhatXJdyShSZAq8TdvMJprZN8BALr6kXc7MMsxstZn1Kuj3EmnYsCFr1qyhd+/e3HXXXYwZM4bDhw/7jiWlxMcff0zTpk1ZsmQJK1euZOzYsbr+tXhR4Hedcy7JOVcXmAmMushs1znn4oEBwAtmVj+3mcxseLjcMw4ePFjQSFLCxcbGMmrUKD7//HPOnDlDo0aNmDZtGtnZ2b6jSQm1c+dOevbsyahRo3j++edZsGABjRo18h1LSrHC+FMxBeiT2wPOuX3hf3cBy4HbLjLfdOdcvHMuvnr16oUQSUqyatWqMXXqVBYtWsTMmTOJj49n5cqVvmNJCfLjjz/ypz/9iTvvvJPWrVuzdetW7r33Xh3KJd4VqLTNLC7H3Z7A/+QyT1UzKxv+uhrQGvi8IN9PJDfNmjUjPT2dP/7xjwwcOJDExES++eYb37EkwJxzvPPOOzRq1Ii9e/eyadMmxo4dS9myZX1HEwEiO+QrFfgUaGhm35rZb4FnzGyLmW0COgO/D88bb2avhZ/aGMgws8+AZcAzzjmVthQqM6Nfv35s27aNuLg4mjVrxsSJEzl16pTvaBIw69ato02bNrzwwgu8++67JCcna0czKXasuB1CEx8f7zIyMnzHkIDavXs3jz/+OBs2bGDSpEn06tVLqzTlkg4cOEBSUhIffvghEydO5MEHH9ROZhJ1ZrYuvA/YJemdKSVKvXr1eO+993j11VcZP3487du3Z8GCBTq+Wy7w73//m6SkJJo0aUKlSpXYtm0bQ4YMUWFLsaZ3p5RIHTt2ZOPGjYwYMYJx48bRtGlTkpOTyczM9B1NPNu+fTu/+93vaNSoEUeOHGH16tVMnjyZKlWq+I4mkieVtpRYsbGxDBgw4Nyq8uTkZOrXr8/kyZM5fvy473gSZWvWrKFPnz60bt2an//852zfvp1XXnmF+vVzPRJVpFhSaUuJZ2Z07tyZpUuX8v7777N27Vrq1avHuHHj+O6773zHkyJ09uxZPvroI9q3b0+/fv3o0KEDX331FU8++SQ6vFSCSKUtpUqLFi2YNWsWa9eu5dixYzRu3Jjhw4fzxRdf+I4mhSgzM5MZM2bQtGlT/vznPzNixAh27tzJ6NGjqVChgu94IgWm0pZS6cYbb+Tll19m+/bt1KpVi7Zt2/LrX/+a1atX+44ml+HYsWNMmjSJ+vXr88477zBlyhTWr19PYmIisbGxvuOJXDaVtpRq1atX5y9/+Qu7d+/mrrvuIjExkV/+8pdMmTKFffv2+Y4nEcjKymLJkiUMGTKEevXqkZGRwbx581iyZAmdOnXSIX9Soug4bZEcsrKyWLZsGSkpKcybN49bb72VAQMG0KdPH372s5/5jidhzjlWr15Namoqs2fPpm7duiQmJtKvXz9q167tO55IvkV6nLZKW+QiTp06xcKFC0lNTWXRokW0a9eOAQMG0LNnT20X9WTz5s2kpqYya9YsypYtS2JiIomJicTFxeX9ZJFiTKUtUoiOHz/O+++/T2pqKqtWreKee+4hMTGRrl27cuWVV/qOV6Lt2rWLWbNmkZKSwrFjx84V9a233qpV31JiqLRFisihQ4eYM2cOKSkpfP755/Tu3Zt+/frRpk0bypUr5zteifD1118zb948UlJS2LVrF3379iUxMZFWrVrpjGVSIqm0RaLgm2++IS0tjTlz5rBlyxaaN29O+/btadeuHa1atdJq9Ag459i5cyfp6emsWLGC9PR0Tp8+TZcuXRgwYAAdO3bUnt9S4qm0RaLs+PHjfPrpp+fKZ8OGDdx8883nSrxNmzZUrlzZd0zvzp49y7Zt2879nFasWEFMTAzt27c/97Nq0KCBVn1LqaLSFvHs5MmTrFmz5lw5rV27lri4uHPF1LZtW6pVq+Y7ZpHLzs7ms88+O1fQK1asoEqVKrRr1+7cz+KGG25QSUupptIWKWYyMzPJyMg4V+KrVq2iZs2aNGzYkLi4uJ/cateuHbhttydOnGDnzp3s2LHjJ7fNmzdTq1atcyXdtm1b6tSp4zuuSLGi0hYp5rKysti2bdsFJbd9+3aOHj1K/fr1LyjzuLg4atas6W2p9OTJk3z55ZcXZN6xYweHDx/mxhtvvCBvkyZNdJ5vkTyotEUC7Pjx47kute7YsYP//Oc/1KtXjypVqlCpUiUqVqz4k9v503Ler1ChAmfOnOHHH388dzt+/HhE9/fv38+BAweoV69ern9M1KlTJ3BrB0SKC5W2SAl19OhRdu/ezbFjxy4o2IsVb85pV155Za7lnte0GjVqcN111xETE+P7RyBS4kRa2jqOQiRgKleuTLNmzXzHEBEPtC5LREQkIFTaIiIiAaHSFhERCQiVtoiISECotEVERAJCpS0iIhIQKm0REZGAUGmLiIgEhEpbREQkIFTaIiIiAaHSFhERCQiVtoiISECotEVERAJCpS0iIhIQKm0REZGAUGmLiIgEhEpbREQkIFTaIiIiAaHSFhERCQiVtoiISECotEVERAJCpS0iIhIQKm0REZGAUGmLiIgEhEpbREQkIPIsbTN7w8wOmNmWHNOeNrNNZrbRzBabWa1LPP9qM9trZi8XVmgREZHSKJIl7beArudNe84519Q51wz4EHjiEs9/GkgvWDwRERH5P3mWtnNuBfDDedOO5bhbAXC5PdfMWgA1gMWXkVFERESA2II+0cwmAoOBo0BCLo+XASYBDwAdC/p9REREJKTAO6I555Kcc3WBmcCoXGb5L2CBc+6bvP4vMxtuZhlmlnHw4MGCRhIRESnRCmPv8RSgTy7TWwKjzOwr4HlgsJk9k9t/4Jyb7pyLd87FV69evRAiiYiIlDwFWj1uZnHOuR3huz2B/zl/HufcwBzzPwjEO+f+WJDvJyIiIhGUtpmlAh2Aamb2LTAB6GZmDYGzwNfAiPC88cAI59zQIkssIiJSSplzue747U18fLzLyMjwHUNERCRqzGydcy4+r/l0RjQREZGAUGmLiIgEhEpbREQkIFTaIiIiAaHSFhERCQiVtoiISECotEVERAJCpS0iIhIQKm0REZGAUGmLiIgEhEpbREQkIFTaIiIiAaHSFhERCQiVtoiISECotEVERAJCpS0iIhIQKm0REZGAUGmLiIgEhEpbREQkIFTaIiIiAaHSFhERCQiVtoiISECotEVERAJCpS0iIhIQKm0REZGAUGmLiIgEhEpbREQkIFTaIiIiAaHSFhERCQiVtoiISECotEVERAJCpS0iIhIQKm0REZGAUGmLiIgEhEpbREQkIFTaIiIiAaHSFhERCQiVtoiISECotEVERAJCpS0iIhIQ5pzzneEnzOwg8LXvHDlUAw75DlEENK5g0biCp6SOTeMqGtc756rnNVOxK+3ixswynHPxvnMUNo0rWDSu4CmpY9O4/NLqcRERkYBQaYuIiASESjtv030HKCIaV7BoXMFTUsemcXmkbdoiIiIBoSVtERGRgFBph5nZI2a21cy2mFmqmZU77/F2ZrbezLLM7D5fOfMrgnE9amafm9kmM/vEzK73lTU/IhjXCDPbbGYbzewfZvYLX1nzI69x5ZjvPjNzZlbs93aFiF6vB83sYPj12mhmQ31lzY9IXi8zuz/8O7bVzFJ85MyvCF6vKTleq+1mdsRX1vyKYGzXmdkyM9sQ/lzs5itrrpxzpf4G1AZ2A1eF788GHjxvnhuApkAycJ/vzIU4rgSgfPjrh4E037kLaVxX5/i6J/Cx79yFMa7w9ErACmA1EO87dyG9Xg8CL/vOWgTjigM2AFXD96/1nbswxnXe/KOBN3znLsTXbDrwcPjrXwBf+c6d86Yl7f8XC1xlZrFAeWBfzgedc1855zYBZ32Euwx5jWuZc+4/4burgTpRzldQeY3rWI67FYCg7LxxyXGFPQ38N3AqmsEuUyTjCqK8xjUMeMU5dxjAOXcgyvkKKj+vVyKQGpVUhSOvsTng6vDXlXN53CuVNuCc2ws8D+wB9gNHnXOL/aa6fAUY12+BhdHIdjkiHZeZjTSzLwkV3Jjopsy/SMZlZrcBdZ1zH3qIWCD5eB/2Ca+OnGNmdaMasgAiHFcDoIGZ/dPMVptZ12jnzK/8fG6EN6fVA/4evYQFF+HY/gIMMrNvgQWE1iQUGyptwMyqAr8i9OarBVQws0F+U12+/IwrPD0eeC56CQsm0nE5515xztUHxgJ/jm7K/MtrXGZWBpgCPOYnYcFE+Hp9ANzgnGsKLAVmRDdl/kU4rlhCq8g7EFoifc3MqkQzZ37l8/OwPzDHOZcdrXyXI8KxJQJvOefqAN2At8O/e8VCsQni2d3AbufcQefcGWAu0MpzpsIQ0bjM7G4gCejpnDsd5YwFkd/XaxbQKyrJLk9e46oE3AwsN7OvgDuB+QHYGS3P18s5932O996rQIsoZyyISN6H3wLznHNnnHO7gS8IlXhxlp/fr/4Ea9V4JGP7LaFt3TjnPgXKETovebGg0g7ZA9xpZuXNzICOwDbPmQpDnuMKr26dRqiwg7K9LZJx5fxg7A7siGK+grrkuJxzR51z1ZxzNzjnbiC0D0JP51yGn7gRi+T1qpnjbs/zHy+mIvnceJ/Qzp6YWTVCq8t3RTVl/kX0eWhmDYGqwKdRznc5IhnbnvB0zKwxodI+GNWUl6DSBpxza4A5wHpgM6Gfy3Qze8rMegKY2S/D2zj6AtPMbKu3wBGKZFyEVodXBN4NH74x30/ayEU4rlHhwzo2Ao8Cv/GTNnIRjitwIhzXmPDr9Rmh/Q8e9BI2HyIc1yLgezP7HFgG/ME5972XwBHKx/swEZjlwrtZB0GEY3sMGBZ+L6YS2ru82IxRZ0QTEREJCC1pi4iIBIRKW0REJCBU2iIiIgGh0hYREQkIlbaIiEhAqLRFREQCQqUtIiISECptERGRgPhf2zmUm8QcjjYAAAAASUVORK5CYII=\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Point 0: False\n", "Point 1: True\n", "Point 2: True\n", "Point 3: True\n", "Point 4: True\n" ] }, { "data": { "text/plain": [ "
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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Point 0: True\n", "Point 1: True\n", "Point 2: True\n", "Point 3: True\n", "Point 4: True\n", "Point 5: True\n", "Point 6: True\n", "Point 7: True\n" ] }, { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Point 0: True\n", "Point 1: True\n", "Point 2: True\n", "Point 3: True\n", "Point 4: True\n", "Point 5: True\n", "Point 6: True\n", "Point 7: True\n" ] }, { "data": { "text/plain": [ "
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wYcMG2zGEEEKILKOUStc0x9I9LoQQQngJKdpCCCGEl5CiLYQQQngJKdpCCCGEl5CiLYQQQngJKdpCCCGEl5CiLYQQQngJKdpCCCGEl5CiLYQQQngJKdpCCCGEl5CiLYQQQngJKdpCCCGEl5CiLYQQQngJKdpCCCGEl5CiLYQQQngJKdpCCCGEl5CiLYQQQngJKdpCCCGEl5CiLYQQQngJKdpCCCGEl5CiLYQQQngJKdpCCCGEl5CiLYQQQngJKdpCCCGEl5CiLYQQQngJKdpCCCGEl5CiLYQQQngJKdpCCCGEl5CiLYQQQngJKdpCCCGEl5CiLYQQQngJKdpCCCGEl5CiLYQQQngJKdpCCCGEl5CiLYQQQngJKdpCCCGEl0izaCulPldKHVNKbXO5bpBSaqtSaotSaqlS6o5U7jtUKbVdKbVTKfVfpZRyZ3ghhBDCn6SnpT0JeCTZdcO01pW01lWA+cBbye+klLofqANUAioANYB6t5RWCCGE8GNpFm2t9Y/AqWTXnXW5mBvQKd0VyAEEA9mBbMA/GU4qhBBC+LmgjN5RKTUYiALOAA2SL9da/6yUWgkcARQwRmu9M5XHeg54DqBYsWIZjSSEEEL4tAwPRNNav6G1Dge+BHolX66UKg2UBYoCdwIPKaXqpvJY47XW1bXW1QsVKpTRSEIIIYRPc8fo8WlA2xSufwxYp7U+p7U+BywC7nPD8wkhhBB+KUNFWyl1t8vFlsCuFG52EKinlApSSmXDDEJLsXtcCCGEEGlLc5u2UuoroD5QUCl1GHgbaKaUKgMkAv8Dujm3rQ5001o/C3wNPAT8jhmUtlhr/V1mvAghhBDCHyitUxr4bU/16tX1hg0bbMcQQgghsoxSaqPWunpat5MZ0YQQQggvIUVbCCGE8BJStIUQQggvIUVbCCGE8BIZnhFNCJH1tNZcuHCB2NjYK6e4uLg0z6e0LCAggFy5cpErVy5y5sx55Xzyy2ktCw4ORo4FJETWkKIthIdITEzkn3/+4dChQxw8ePCav0nnjx07RnBwcLqLatLlvHnzUqRIkSvLcubMSWJiYorF/cSJE+ku/LGxsQAULVqU8PBwwsPDKVas2DV/w8PDyZcvnxR2IdxAirYQWUBrzenTp68pwMnP//XXX+TLl++6gle7du0r52+//XaCgjzra3vhwgUOHz58zWvZsmUL33777ZXrtNbXvS7X8+Hh4eTMmdP2SxHC48l+2kK42fnz5/ntt9/YtGkTmzdvZtOmTezZs4eAgIAbFq6iRYuSI0cO2/EzxZkzZ264wnL48GFCQ0MpW7YsERERVK1alYiICO69916PW0kRIjOkdz9tKdpC3ILo6OgrhTnp78GDBylXrtw1xadMmTLkzZvXdlyPlZiYyLFjx9i2bds17+Xhw4epUKECERERV97PChUq+OzKjfBfUrSFcLOjR49eU1A2bdrEyZMnqVy58jUFumzZsmTLls12XJ8QExNzXa/F7t27ufvuu68p5JUrVyYkJMR2XCEyTIq2ELfg3Llz/PDDD6xbt+5Kgb506dI1hSIiIoLSpUsTECB7TmalCxcuXNci37ZtG0WLFr3y/3nwwQepUaMGgYGBtuMKkS5StIW4CYmJifz2228sXbqUJUuWsH79emrUqMEDDzxwpRCEh4fLCGgPFR8fz65du9i0aRMbN25kxYoV/P333zRs2JAmTZrQpEkTihYtajumEKmSoi1EGo4dO3alSC9btozQ0NArP/D169cnT548tiOKW/D3339f8/8tXLgwjRs3pkmTJtSrV09GqwuPIkVbiGQuXbrETz/9dOWHfN++fTz00EM0adKExo0bU7JkSdsRRSZJSEhg06ZNLFmyhKVLl7J582Zq1659ZSWtfPny0osirJKiLfye1po9e/awZMkSlixZwo8//kiZMmWu/FDXqlVLBoz5qbNnz7JixYorn42LFy9eaYU3atSIAgUK2I4o/IwUbeGXEhMTWb16NTNmzGDx4sVcvHjxSpF++OGH5cdYXCdp5S6pB+aHH36gTJkyNGvWjI4dO3L33Xfbjij8gBRt4Vf++OMPpkyZwtSpUwkJCeHJJ5+kefPm0u0pbtqlS5dYu3Ytc+fO5auvvuKuu+4iKiqK9u3bkz9/ftvxhI+Soi183smTJ5k+fTpTpkzhf//7H0888QRPPfUUlStXlkIt3OLy5cssW7aMyZMns2jRIho2bEhUVBTNmjUjODjYdjzhQ6RoC5908eJFFixYwJQpU1i5ciXNmjUjKiqKhx9+WKa7FJnqzJkzfP3110yePJkdO3bQrl07oqKiqFmzpqwkilsmRVv4DK0169atY/LkycyaNYuKFSsSFRVF27ZtCQ0NtR1P+KEDBw4wdepUJk+ejFKKp556iieffJISJUrYjia8lBRt4fX27dvH1KlTmTJlCoGBgVd+GIsXL247mhCAWaH89ddfmTx5MjNmzKB8+fJERUURGRkpc82LmyJFW3ilCxcuMG3aNCZNmsTOnTtp3749UVFR1KhRQ7oghUe7dOkSCxcuZPLkySxfvpymTZvy7LPP0rBhQ/nsijRJ0RZe5fTp04wbN47//ve/VK1ala5du9K0aVMZ7CO80smTJ5kxYwZjxowhR44c9O3bl8jISBl3IVKV3qItRzoQVv3111+88sor3HXXXezcuZOlS5eycOFCWrVqJQVbeK0CBQrQo0cPtm3bxjvvvMOYMWMoU6YMH330EXFxcbbjCS8mRVtYsXPnTp5++mkqVqxIfHw8W7ZsYfLkyVSsWNF2NCHcJiAggBYtWrBmzRomT57MkiVLKFGiBIMGDeLUqVO24wkvJEVbZKm1a9fSqlUr6tevz1133cXu3bsZOXIkxYoVsx1NiExVp04d5s2bx8qVK9m/fz+lS5emT58+HDx40HY04UWkaItMl5iYyHfffccDDzzAk08+SZMmTdi/fz8DBgyQaUWF3ylXrhyff/45W7duJTAwkCpVqhAVFcW2bdtsRxNeQIq2yDSXLl3iiy++oGLFirz99tv06tWLP//8kx49epArVy7b8YSwqmjRonzwwQfs3buXsmXL0qhRIx599FF+/PFHPG2AsPAcUrSF28XExDB8+HBKlSrFlClTGDlyJBs3bqRDhw4yelaIZMLCwnj99dfZv38/LVq04JlnnuH+++9n7ty5JCYm2o4nPIwUbeE28fHxjBs3jtKlS/PLL78wd+5cvv/+exo1aiT7qQqRhhw5ctC1a1d27drFyy+/zODBg4mIiGDVqlW2owkPIs0e4Rbff/89ffr0oWDBgixdupTKlSvbjiSEVwoMDCQyMpK2bdsya9YsOnfuTLVq1Rg2bBh33XWX7XjCMmlpi1uye/duWrVqRdeuXRk4cCArVqyQgi2EGyilaNeuHTt37iQiIoKaNWvy+uuvExMTYzuasEiKtsiQM2fO8Oqrr1K7dm3uv/9+tm/fzmOPPSbd4EK4Wc6cOXnjjTfYunUrf//9N2XKlOHzzz+X7d1+Soq2uCkJCQmMHz+eMmXKcOrUKbZt20a/fv3IkSOH7WhC+LQ77riDL774gnnz5jFhwgRq1KjB6tWrbccSWUy2aYt0W7VqFS+++CKhoaEsXLiQiIgI25GE8Ds1atRgzZo1TJ8+nY4dO1K7dm2GDBkihwX1E9LSFmnat28fbdq0oXPnzrzxxhv88MMPUrCFsEgpxeOPP86uXbsoX7481apV48033+TcuXO2o4lMJkVbpOrs2bO89tpr1KxZk+rVq7Nz507+9a9/yXZrITxErly5eOutt/jtt984cOAA9957L5MnT5bt3T5Mira4jtaaKVOmcO+993L06FG2bt1K//79yZkzp+1oQogUFC1alKlTp/L1118zduxY7rvvPjZu3Gg7lsgEsk1bXOPEiRM899xz7Nmzh3nz5lGjRg3bkYQQ6XTffffx888/M3XqVJo2bcoLL7xAv379ZCZCHyItbXHF4sWLqVy5MqVKlWL9+vVSsIXwQgEBAURFRbFp0yZWrFhBvXr12Ldvn+1Ywk2kaAtiY2Pp1asXXbt2ZerUqQwbNozs2bPbjiWEuAVFixZl2bJlREZGUqtWLSZOnCgHIvEBUrT93KZNm6hWrRrR0dH89ttvNGjQwHYkIYSbBAQE0KdPH1asWMGIESOIjIzkxIkTtmOJWyBF208lJCTw3nvv8cgjj/DWW2/x5Zdfki9fPtuxhBCZoGLFiqxfv5677rqLypUrs3jxYtuRRAbJ6AQ/tH//fqKioggKCmLDhg0UK1bMdiQhRCbLnj07w4YNo1mzZnTq1IlWrVoxZMgQOba9l5GWth/RWvPFF19Qs2ZNWrduzfLly6VgC+FnGjRowG+//cbJkyepVq0amzZtsh1J3ARpafuJkydP0rVrV/744w+WL19OpUqVbEcSQlgSFhbGtGnTmDZtGo888gh9+vShb9++BAYG2o4m0iAtbT+QdHzr4sWLs379+iwr2EMPHmRldPQ1162MjmbowYNZ8vxCiBt74okn2LBhA0uXLqV+/focOHDAdiSRBinaPiwxMZHXX3+dZ555hi+++IIPP/wwS4/GVSMkhHY7dlwp3Cujo2m3Ywc1QkKyLIMQ4saKFSvG8uXLadWqFTVq1ODbb7+1HUncgHSP+6jY2FiioqI4duwYmzdvpmDBglmeoUFYGDNLlqTdL7/QfccOxpUrx8yNG2mwaBHkygW5c5tT0vnkf13PZ8uWaTmHHjxIjZAQGoSFXbluZXQ062Ni6Cvb/IUfCAgI4JVXXqFu3bq0bt2avXv38uKLL8pxBjyQFG0fdPToUVq2bEmZMmVYtmyZ1YlSGuzeTfeZMxkUFcWA2bNpMGkSnD8PNzvJQ1BQ6gU9PUX/BstqZM9Oux07mFmuHA3Cwq70CMwsVy5T3hMhPFXNmjX5+eefad68Obt37+a///2vTIHqYZSnzZBTvXp1vWHDBtsxvNbvv/9OixYteOaZZ3jzzTetrymvXLqUdrGxdC9QgHEBAaYw5ssHFy9CbKwp4El/Xc+n9je9t4mPv7mcERG0e+stuv/6K+OaNmVm+fLXtLyF8Cdnzpyhffv2AMycOZPQ0FDLiXyfUmqj1rp6WreTVSgfsnjxYqKiohg1ahSPP/647TimxRoUxMx33qHBpEk0KFbsmhYtOXJA/vyZ8+SXL99U0W9w/jzdt29nUKNGDMidWwq28Gt58+Zl/vz5PP/889SpU4f58+dTvHhx27EEUrR9xkcffcSgQYOYM2cOderUsR0HgPUxMcw8eZIGW7ZAzpxmG3e5cqyPicn8opgtG+TLZ07psDI6mnFbtjBg8mTGRUXRIDpaCrfwa0FBQYwdO5ZRo0Zx//33M2fOHGrWrGk7lt+T0eNeLiEhgT59+jB69GjWrFnjMQUboG+xYjQ4c8ZccI7F3SAszOMGd13Zhp03LwMnTmSmczn57mpC+BulFC+++CLjxo2jefPmfP3117Yj+T1paXuxc+fO8cQTT3D+/HnWrl1LmCe2DGNjzV8PnipxfUyM6bJ3xnc0+N//mFm/ftb0CAjhBVq2bMmSJUto1aoVe/fupW/fvtbHy/graWl7qcOHD/Pggw9SqFAhFi1a5JkFGyAuzvx1WtqeqG+xYqY4h4VB9uzw998e2SMghE0RERH8/PPPTJ8+nf/7v//j0qVLtiP5JSnaXuTIEahXD5Yt+53atWvToUMHPvvsM4KDg21HS11S0c7CSV0yTCm44w74+2/bSYTwSEWLFmX16tUcO3aMpk2bEi2bkLKcFG0vMmgQrF6tadHiV0aMGEG/fv08v4sqNtYU7AAv+ajdeSf89ZftFEJ4rDx58jBnzhwqVapE7dq12bt3r+1IfkW2aXuJI4cT+Hx8AloHoxKieCDme1ixAkJDzSkkxPzNlcu0GD1FXJxHd41f5447YPNmM/mLJ72PQniQwMBARowYQenSpalfvz6rVq2iVKlStmP5BSnaXqJbx53ohNIAJMYnMKjLPsbS6/obBgRcLeSup6Sintop+fKQEDML2a2Ki/PoQWjXKVYMZs4027dLlYLSpa/+TTpfpIj39BwIkYl69uxJUFAQDz/8MD/88IMc6jcLSNH2AjNm/Mh3P9ZAY7YLXyJDwN0fAAAgAElEQVQHE7N3Z8BXEdye7SScPZvyKSbG/D11Cg4cuHr9uXPpe+JcuTJe9JNOZ896V0v71VehaFHYs8ecNm+Gb765doa1nDnhrrtSLujFirlnZUcIL9G1a1diY2OvFO4iRYrYjuTT0vx1UUp9DjwKHNNaV3CuGwS0AhKBY0BnrfV1o3eUUsWAz4BwQAPNtNYH3JbeD6xZs4ZOnfYQlO0BLl++en2CDmDQ97UZOzYDD5qQYAq3a2FPq/Annfbvv/ZyeqYLrVIlAyEtue02eOGFa6+Lj4eDB2HvXlPIk/7u2QNLl14dbAemYJcoYZYBDBgAt99+7alwYTP3uRA+ok+fPlcK96pVqyhUqJDtSD4rzbnHlVJ1gXPAZJeiHaq1Puucfx4op7XulsJ9VwGDtdbLlFJ5gEStdeyNnk/mHr9q/fr1NG/enNDQPezde/3cv1WqmIagNVqbOcTTKvi1akH9+haDZqLERDOsP3lBnznTLFcq5YOj5MlzbSGvWhX69YPAwKzNL4Qb9e/fn8WLF7NixQrypXM2QmG4be5xrfWPSqkSya4763IxN6YVnTxAOSBIa73MuU86+2QFwNatW3n00UeZMGECOyufpkZIAg169YK1a2H//iuHjgSL25CUMiPDc+QwLVR/FBBgRpzfeSfUrXv1+hkzzN/4eDhxAo4evfb0zz9Xz//2G3z9tRkE17mzlZchhDsMHjyY2NhYmjZtytKlSwkJCbEdyedkeOObUmowEAWcARqkcJN7gNNKqW+AksD3wGta64SMPqe/2LVrF4888gijR4+mRYsW5EmaZrNaNRpMn87K48dp9+efcuhIbxAUdLU1nRqtTW/EW29Bhw7esU+7EClQSjFixAi6du1KixYtWLhwIbm8aSCqF8jwEFit9Rta63DgS0hpGDNBwIPAK0AN4C6gc0qPpZR6Tim1QSm14fjx4xmN5BP27t1Lo0aNeO+992jXrh3AlQNttKtcmbc6dbr2SFnC+ykFQ4bAoUNkbJCCEJ5DKcW4ceMIDw+nTZs2XLx40XYkn+KO/VamAW1TuP4wsFlrvU9rHQ/MBSJSegCt9XitdXWtdXV/HsBw6NAhHn74Yd544w06dep0zbIGYWF0L1CAQVFRdF+/Xgq2r2nQAJo0gcGD4fRp22mEuCWBgYFMnDiRPHny0L59ey67jqIVtyRDRVspdbfLxZbArhRuth4IU0olVeGHgB0ZeT5/cPToURo2bEjv3r3p1u26MX3m0JHnzzPg4EHGlS7NyqVLLaQUmer99yE62rS6hfByQUFBTJs2jcuXL/PUU0+RkCBbRt0hzaKtlPoK+Bkoo5Q6rJR6BnhfKbVNKbUVaAy84Ny2ulLqMwBn2/UrwHKl1O+AAj7NpNfh1U6cOMHDDz9MVFQUL7300nXLrxw6slw5BrZvz8wJE2h38SIrjx61kFZkmipVoGNHGDlSplIVPiE4OJjZs2dz/Phxnn32WRITE21H8npp7vKV1fxtl6/z589Tt25dGjduzLvvvpviXOJDDx6kRkjI1S7xFStY+fLLrO/Zk77PPpvFiUWm2r8fypSBTp3gU1nHFb7h3LlzNGnShBo1ajBy5EjbcTxSenf5krkYLdJa89xzz1GhQoVUCza4HDoyyUMP0aB8efr26AF//JFFaUWWKFkSevSAzz+H1avh8GGzW9jJk3DmjDkAy+XLKe/77WWOHDlCvXr1OCo9Rj4vT548LFiwgIULFzJ16lTbcbyatLQtGjt2LJ9++ilr1669+d0i/vnHtMiqVYPvv5eDW/iS48fNlKgxMTe+XWCg2aUsWzZzSjqf0nU5cpjHvPde87m5914z9arF3ct69OjBJ598QteuXfnoo4+s5RBZ5/fff+ehhx5ixYoVVKxY0XYcj5LelrYUbUvWrVtHy5Yt+fnnnzN+dJyPP4bu3WHqVLMtVPiOXbtg3TozOcvly+aUdD4j150/b2ZqO3To6nMEBJiWffHiprAHBJgVgYCAq6ebuZzO2+YcOJALKUx/myNHDuJcp4QVPmnq1KkMHDiQ9evXkzdvXttxPIYUbQ92/PhxqlWrxpgxY2jZsmXGHygxEe6/32wH3bXLHJlKiBs5dw7+/NN8Xv74w/w9dMh8lhISzN/k59O6nN5ljiOYEapzgVggV65cPPbYY3zwwQfcfqNJaITP6NmzJ0eOHGH27Nmpbhb0N26bxlS4V0JCAk888QRPPvnkrRVsMK2Xjz82XeT9+8O4ce4JKXxXnjwQEWFOWU1rSEigSGIiofffz4WNG8mRPTsXLlwgNDRUCrYfGT58OHXr1uWDDz7g1VdftR3Hq8hAtCz29ttvk5iYyMCBA93zgFWqwPPPwyefmO5UITyVUmYbe3Aw/+zZQ7fChVn3yy9069ZNBqP5mezZszNr1iw+/PBDVq1aZTuOV5Hu8Sw0f/58evTowYYNG7jNnQfYiImBsmWhYEHYsEGO5yw82x9/mIFwI0bAiy/aTiMsWrZsGZ06dWLDhg3ccccdtuNYJbt8eZh9+/bxzDPPMGPGDPcWbICQEBg1yhwtavRo9z62EO42c6Zpdf/rX7aTCMsaNWpEjx49aNeunUx1mk5StLNAXFwcbdu2ZcCAAdSuXTtznqRNG2jWzBwp6vDhzHkOIdxhxgx44AFzOFPh9/r370++fPno27ev7SheQYp2JtNa07NnT8qWLUvPnj0z74mUgjFjzG4+0uUoPNX27ebkHMFOiICAAKZMmcK8efOYOXOm7TgeT4p2JpswYQK//PIL48ePz/xdG0qWNC3t2bNhwYLMfS4hMmLmTLPXQ2Sk7STCg4SFhfH111/Ts2dPdu7caTuOR5OBaJlo48aNNG3alNWrV1OmTJmsedJLl6ByZbN7zY4d5gdSCE+gtRkweccdsGKF7TTCA33++ecMGzaMX3/9lZCQENtxspQMRLPs8uXLdOrUiVGjRmVdwQYIDoZ//9uM0J03L+ueV4i0bN1qPpft29tOIjxUly5dqFGjBgMGDLAdxWNJ0c4kI0eOpGjRonTo0CHrn7xtWzPP9Pvv+8SBJYSPGD/eTGXapo3tJMKDDR8+nK+++ootW7bYjuKRpGhngoMHDzJkyBDGjBljZ4q+oCB49VX49VeQiQuEJ/jqK/joI+jaFQoVsp1GeLCCBQsyePBgunfvLsffToEU7Uzwwgsv0Lt3b0qXLm0vRKdOcPvt8N579jIIAbB+PXTpAnXrmglVhEhDly5dUErx2Wef2Y7icaRou9n8+fPZtm0b/fr1sxskRw7o0weWLYONG+1mEf7r77+hdWuzAvn112bMhRBpCAgIYNy4cbz55pscP37cdhyPIkXbjWJjY+nduzdjx44lh8XjFF/RrRvkzQtDhthOIvxRXBw89hicOWMGRUq3uLgJlStX5sknn5RJV5KRou1G7777LjVr1qRx48a2oxihodCjh2nh7N5tO43wJ1rDc8+ZcRVTpkClSrYTCS/0zjvvsGzZMlavXm07iseQou0mu3bt4uOPP2b48OG2o1zrhRcge3YYNsx2EuEvtDabZqZOhUGDTGtbiAwICQlhxIgR9OjRQ+Ymd0jRdoOkqUrffPNN7vS0+ZQLFzaDgL74wmxfFCIzaQ29e5sD2Lz4Irzxhu1EwstFRkZyxx13MGrUKNtRPIIUbTf46quvOHnyJL169bIdJWWvvAIJCTJyV2SuxESzOWbsWPOZGz7czIkvxC1QSjF27Fjef/99Dh06ZDuOdVK0b9Hp06d55ZVXGDduHEGeehzrkiXNLFQffwzR0bbTCF+UmGj2wf74Y3jtNRg6VAq2cJvSpUvTu3dvXnjhBdtRrJOifYsGDBhA8+bNM++Qm+7y2mtw7pyZ4EIId0pIgGeegc8+gzffhHfflYIt3K5fv378/vvvLPDzgyFJ0b4FGzduZNasWbz//vu2o6StYkVo3txsa4yNtZ1G+IqEBHj6aZg0ycx5P2iQFGyRKXLkyMHYsWPp3bs3cXFxtuNYI0X7Frz11lu8/fbbFChQwHaU9HntNTh+HCZOtJ1E+IL4eIiKMrt0DRoEb79tO5HwcY0bN6ZSpUp+PVOaHJozg37//XeaNGnCvn37PGMilfR64AH46y/Yt09aRCLjLl+GJ580x8d+7z2zQihEFvjll19o3749u3fvJlu2bLbjuI0cmjOTDRs2jOeff967CjaYIywdOGBmqRIiIy5fhscfNwV72DAp2CJL1apVixIlSjBr1izbUayQop0BBw8eZMGCBXTr1s12lJuX1JUvo8hFRly6BO3awezZZhfCV16xnUj4ob59+zJ06FA8rac4K0jRzoARI0bQpUsX8uXLZzvKzQsLM39PnbKbQ3ifixchMhLmzoXRo83kKUJY0LRpUxISEli6dKntKFlOivZNOnXqFF988QUveusPVlLRlpa2uBkXLphNK999Z3Yb9NSJhIRfUEpdaW37GynaN+mjjz6idevWnjddaXpJ0RY3Ky7OHF5z4UIYPx66d7edSAg6dOjA7t278YaBy+4kRfsmxMXFMWbMGF599VXbUTIuf37zV7rHRXrExkLLlrB0KUyYAP/3f7YTCQFAtmzZeOmll/yutS1F+yZMmjSJWrVqUbZsWdtRMk5a2iK9zp+HRx+F5cvN5ClduthOJMQ1nn32WVauXMmePXtsR8kyUrTTKSEhgQ8++MD7D8ieM6c5VKcUbXEj585Bs2bwww9m8pSoKNuJhLhOnjx56NatGx9++KHtKFlGinY6zZ49myJFilCnTh3bUW5d/vxStEXqYmLgkUfgp59g2jTo2NF2IiFS1bt3b6ZPn84///xjO0qWkKKdDlprhg4d6v2t7CRhYbJNW6Ts0CFo3Bh++QWmTzdHhxPCg91222106NCB0aNH246SJaRop8OKFSuIjY3l0UcftR3FPcLCpKUtrpWYaHblKl8etm41s51FRtpOJUS6vPzyy3z88cfExMTYjpLppGinw/Dhw3nllVcICPCRt0uKtnD1xx9Qrx707An33QfbtsFjj9lOJUS6lS5dmoceeohJkybZjpLpfKQKZZ5Tp06xZs0a2vtSN2H+/NI9Lswc4u++C5Urw/btZoT4kiVQsqTtZELctM6dOzNjxgzbMTKdFO00fPvttzRs2JDcuXPbjuI+0tIWly+b7dVvvGH2w965Ezp1kiO/Ca/VsGFDtm/fzpEjR2xHyVRStNMwe/Zs2rZtazuGe4WFwdmzkJBgO4mwIT4ennoK5syBkSPN9uvChW2nEuKWZM+enebNmzNnzhzbUTKVFO0bOHv2LD/88IPvDEBLkjQr2unTdnOIrJeQYCZJmTEDhg6FF16wnUgIt2nbti2zZ8+2HSNTSdG+gYULF/Lggw+SN29e21HcS4705b8GDzaTpQwaBN48Ha8QKWjSpAkbNmzgxIkTtqNkGinaN+CTXeMgU5n6qyNHYMgQ+Ne/4M03bacRwu1y5cpF48aNmTdvnu0omUaKdipiY2NZunQpLVu2tB3F/aRo+6eBA+HSJXjvPdtJhMg0vt5FLkU7FUuWLKF69eoULFjQdhT3kyN9+Z8//oBPP4Vu3aBUKdtphMg0zZs3Z82aNZz20TE7UrRT4bNd4yAtbX/0xhvmYDEDBthOIkSmCgkJoX79+syfP992lEwhRTsFFy9eZMGCBTzmq7NCSdH2L+vWwezZZuDZbbfZTiNEpvPlLnIp2ilYvnw55cuXp0iRIrajZI7gYMidW4q2P9Aa+vY1+2G/9JLtNEJkiZYtW7JixQrOnTtnO4rbSdFOgU93jSeRI335h4ULYfVqePttyJPHdhohskRYWBj33XcfixYtsh3F7aRoJxMfH8+3335LmzZtbEfJXDKVqe9LSIDXXoPSpeHZZ22nESJL+WoXuRTtZNauXUuxYsUoXry47SiZS4q275syxRyx6913IVs222mEyFKtW7dm0aJFxMfH247iVlK0k/n111+pU6eO7RiZr0AB+Ocf2ylEZrlwAd56C2rWlONiC7902223UbhwYXbt2mU7iltJ0U5m8+bNVK1a1XaMzFe9OuzaJYXbV40ZA4cOwfvvy5G7hN+qWrUqmzdvth3DraRoJ+M3RfuRR8zfJUvs5hDuFx1tusSbNoUGDWynEcIaKdo+LjY2lgMHDlCuXDnbUTJflSpmN6DFi20nEe72/vvmCG7vv287iRBW+WLRDrIdwJNs3bqVsmXLEhwcbDtK5gsIgCZNYMECM8o4MNB2IuEOcXHw3/+a/2fHjhAScu0pNPT661K7TZ485nMihJeqWrUqW7ZsQWuN8pHNRFK0XfhN13iSRx6ByZNhwwaoVct2GuEO2bJB//6wZw/ExMDZs6bVfeiQuZx0SkxM3+Plzn1rhd/1lD27bF8XWeq2224jV65cHDhwgJIlS9qO4xZStF34XdFu3Nj8iC5eLEXbVwQFpT2/uNamRZ5U1F2LefJTSsuTVgCSll24kP5s6Sn8aa0UhIZKL4BIt6QucinaPmjTpk107tzZdoysU6CA2SVo8WIzY5bwD0pBrlzmVLjwrT9efHz6C37y5e7uBUhv0ZdeAL+RVLR9ZcIsKdqOy5cvs2PHDipVqmQ7StZq2hTeeQdOnjRFXIibFRRkJutJOhDNrbjVXoDDh69d7u5egJw5r55y5Ej9ckrLMqFnYOjBg9QICaGBy3u/Mjqa9TEx9C1WzO3P542qVq3K559/bjuG26RZtJVSnwOPAse01hWc6wYBrYBE4BjQWWv9dyr3DwV2AnO01r3cFdzddu7cSbFixcjjb/MzP/II/PvfsGwZdOhgO43wd1nVC5CelYJb6QVISXBw+gp+elYAnPM1cuaknVLMzJ2bBvfey8r4eNrt2MFMf9gDJp18bQR5elrak4AxwGSX64ZprQcAKKWeB94CuqVy/0HAD7eQMUv43fbsJNWrmxb24sVStIXvcXcvQHy86QlIOl24kPL5m1mWdDp1KuVlly+nGqkBMLNKFdq9/Tbd581jXNOmzCxX7pqWt78rUaIEsbGxHDt2jNt84NC0aRZtrfWPSqkSya4763IxN6BTuq9SqhpQGFgMVM9wyizgt0U7MNAMSFu0CC5eNNv2hBDXU8qMzs+WzXShZ5WEhNRXAC5coEFcHN337WPQww8zIEcOKdjJKKWutLabNGliO84ty/BGFqXUYKXUIaAjpqWdfHkA8CHwajoe6zml1Aal1Ibjx49nNNIt2bx5MxEREVae27qnn4Zjx2DgQNtJhBDJBQaaQXcFC0J4ONx9N1SqZPb4qFePlbVqMa5CBQZMnsy4U6dYKQcCuo4vdZFnuGhrrd/QWocDXwIpbavuASzUWh9Kx2ON11pX11pXL1SoUEYjZZjWmi1btlClSpUsf26P0KgRdO4MQ4aYfbaFEF5hZXS02YZdsSIDt29n5oQJtNuxQwp3MlK0rzUNaJvC9bWBXkqpA8AHQJRSyiPnVYyNjeXy5csULFjQdhR7RoyA2283xfviRdtphBDpsD4m5uo27MhIGsyYwcz8+VkfE2M7mkcJDw/nyJEjtmO4RYaKtlLqbpeLLYHrjn2mte6otS6mtS4BvAJM1lq/lqGUmez06dPky5fPdgy78uWDTz+F7dvNLmBCCI/Xt1ixq9uw25q2U4OFC2V3r2TCwsI4ffq07RhukWbRVkp9BfwMlFFKHVZKPQO8r5TappTaCjQGXnBuW10p9VmmJs4Ep0+fJkwGb5h9tp9+2nSTr19vO40Q4mbcdRdERMDXX9tO4nHy5ctHtI9sMkizaGutH9daF9FaZ9NaF9VaT9Bat9VaV9BaV9Jat9Ba/+XcdoPW+tkUHmOSJ++jHR0dLS3tJMOHQ5Eipps8vRNTCCE8Q2QkrFtn9i8XV+TLl89/Wtr+QLrHXSR1k+/YId3kQngbp4ucb76xm8PD5MmTh7i4OOLj421HuWVStJGifZ2mTaFLFxg6FH791XYaIUR63XOP2R1MusivERAQQN68eX2itS1FG9mmnaLhw+GOO6SbXAhvExkJP/0Ef6c4s7Tf8pUucinayDbtFOXNC599Bjt3yqQrQniTyEgz5eqcObaTeBQp2j5EusdT0aQJNG8OM2faTiKESK+yZaFcOekiTyYsLMwnRpBL0UaK9g2VKgUnTthOIYS4GZGR8OOP8M8/tpN4DGlp+xDZpn0DBQvCmTM3PNKQEMLDREaaw4jOnWs7iceQou1DZJv2DSRN7XrypN0cQoj0q1DBjCSXLvIrpGj7EOkev4Gkoi1d5EJ4D6VMa3vlSvnuOmSbtg+Ron0DUrSF8E6RkeZY3PPm2U7iEaSl7UMuXbpEtmzZbMfwTFK0hfBOVaqY+cilixyAwMBALvvA2Bwp2pg1sDNnztiO4ZmkaAvhnZQy05ouXw4+0C18q86ePUvevHltx7hlUrTxrcO2uV2BAuavFG0hvE9kpNnz47vvbCexzlf2EpKijW8dts3tgoMhNFSKthDeqEYNCA+XLnJ8Z+ySFG18Z4BCpilYUIq2EN4oaRT5kiVw9qztNFZJ0fYhUrTTUKCAFG0hvFVkJFy6BPPn205iVXR0tHSP+wrZpp2GvHn9fi1dCK91333miH1+3kUuLW0fItu00xASAufO2U4hhMiIgAAzinzRIr/+HkvR9iHSPZ6GPHn8+ssuhNeLjIQLF2DhQttJrJHucR8iRTsNefJATIztFEKIjKpTBwoX9usucmlp+xAp2mmQlrYQ3i0wENq0gQULIDbWdposd+HCBbTW5MiRw3aUWyZFG9+ZSD7ThISYrrX4eNtJhBAZFRlpCvbixbaTZLmkVrZSynaUWyZFG2lppylPHvP3/Hm7OYQQGVe3rplzwQ+7yH1lezZI0QakaKcpqWjLdm0hvFdQEDz2mJnS9MIF22mylK9szwYp2gCEhoYSGxvLBT/7IKdbUtGW7dpCeLfISPM9XrrUdpIsdfz4cfLnz287hltI0cYcsq1MmTJs377ddhTPFBJi/krRFsK7NWgAYWF+10W+detWKlSoYDuGW0jRdlStWpXNmzfbjuGZpKUthG/Ilg1at4Zvv4WLF22nyTKbN2+matWqtmO4hRRtR0REhBTt1Mg2bSF8R2QknDljjrPtJ6Ro+yBpad+AtLSF8B0NG5rjCfhJF/np06c5duwYd999t+0obiFF21GlShW2bt1KQkKC7SieR7ZpC+E7smeHli1h7ly4fNl2mky3ZcsWKlWqRGBgoO0obiFF25E3b14KFy7Mn3/+aTuK55GWthC+JTISoqNh5UrbSTKdL3WNgxTta0gXeSpy5zZ/ZZu2EL6hcWOzMu4HXeSbN28mIiLCdgy3kaLtQop2KoKCIEcOaWkL4Sty5IAWLWDOHJ+fnlha2j5MivYNyDG1hfAtkZFw4gT8+KPtJJkmLi6OvXv3Ur58edtR3EaKtoukoq21th3F88iRvoTwLY88Arly+XQX+bZt27jnnnvInj277ShuI0XbRZEiRQgODubQoUO2o3geOaa2EL4lVy5o3hy++QZ8dK8ZX+saByna15Eu8lRI97gQvicyEv75B376yXaSTLFp0yYp2r5OinYqpHtcCN/TrJkZlOajXeTS0vYDERERrF+/3nYMzyNFWwjfkycPNG0Ks2dDYqLtNG516dIltm/fTuXKlW1HcSsp2sk0aNCANWvWcP78edtRPIts0xbCN0VGwt9/w7p1tpO41fLly6lcuTKhoaG2o7iVFO1k8ufPT61atVi0aJHtKJ5FtmkL4ZsefRSCg32ui3z27Nm0bdvWdgy3k6KdgrZt2zJ79mzbMTzC0IMHWRkdfU33+MroaIYePGg5mRDCLUJDoUkTU7R9ZHfX+Ph45s2bR5s2bWxHcTsp2ilo3bo1ixYt4sKFC7ajWFcjJIR2O3aw8s474eJFVh4/TrsdO6iRdBARIYT3i4yEQ4fAR8bz/PjjjxQvXpwSJUrYjuJ2QbYDeKLChQtTqVIlvv/+ex599FHbcaxqEBbGzHLlaBcXR/enn2bcr78yc9EiGpw5Y1rfefKYrvP8+eHJJyFfPtuRhRA3q0ULyJbNtLZr1rSd5pb5atc4gPK02b+qV6+uN2zYYDsGo0aNYsuWLUycONF2FI/w1vr1DDp/ngErVjBw3jzTVR4TY/7GxZkbVa0Ky5ZBgQJ2wwohbl6zZrBrF+zdC0rZTpNhiYmJFC1alFWrVnHPPffYjpNuSqmNWuvqad1OusdT0aZNG7777jsu+8HxZtOyMjqacZcuMaB4ccY1acLKVavMF/vYMYiNNcfkXbAAduyAhx6C48dtRxZC3KzISNi/H7x8noqff/6ZAgUKeFXBvhlStFMRHh5OqVKlWLVqle0oVq2Mjqbdjh3MLFeOgSVLmq7yHTvM4LQkQUFmLf277+DPP6FBAzPLkhDCe7RqBYGBXj+K3Je7xkGK9g3JKHJYHxPDzHLlaBAWBlzdxr0+pX22GzUyLe59+0zhPno0i9MKITKsQAHTUzZrlteOItda880330jR9ldt27Zl7ty5JPjoZPrp0bdYsSsFO0mDsDD6FiuW8h0eeggWLYKDB6F+fWlxC+FNIiNhzx74/XfbSTJk06ZNBAcHU6FCBdtRMo0U7RsoVaoUt99+Oz/56GT6maZePVO4//c/6NXLdhohRHq1bg0BAWZaUy+U1DWuvHggXVqkaKehbdu2fPPNN7ZjeJ8HH4Q33zTbxxYutJ1GCJEet91mVrq9cLu21trnt2eDFO00JRVtT9s1ziu8+iqULQs9e5pR5kIIzxcZafYE2bHDdpKbsn37di5cuEC1atVsR8lUUrTTUK5cOXLnzs3atWttR/E+wcHw8cdw4AAMHGg7jRAiPR57zOyn7WVd5NOnT6dNmzY+3TUOUrTTpUePHgwfPtx2DO9Uty48/TR8+CFs22Y7jRAiLUWKQJ06XtVFfv78ecaPH0+3bt1sR8l0UrTToUuXLqxevZo//gAD6TUAACAASURBVPjDdhTvNHQo5M0LXbv63DF7hfBJkZGwdauZd8ELTJw4kTp16lCmTBnbUTKdFO10yJ07Nz169ODDDz+0HcU7FSwIH3wAa9fChAm20wgh0pJ0dCwv6CKPj4/nww8/pF+/frajZAkp2unUq1cvvv76a47KhCEZ06mTGZXar5+Z/lQI4bnCw+G++7yii3zWrFmEh4dz33332Y6SJaRop1PBggXp2LEjo0aNsh3FOyllBqWdOwcvv2w7jRAiLZGRsGmTmeHQQ2mtGTp0qN+0skGK9k156aWX+PTTTzl79qztKN7p3ntNS3vqVFi+3HYaIcSNJO3v7MFd5MuWLePy5cs0bdrUdpQsI0X7JpQsWZLGjRszfvx421G8V//+UKoUdO8OFy7YTiOESE2JElC9ukd3kQ8dOpS+ffsSEOA/pcx/XqmbvPrqq4wcOZJLly7ZjuKdcuaEceNg9254/33baYQQNxIZCb/+aqYk9jAbN27kzz//pEOHDrajZKk0i7ZS6nOl1DGl1DaX6wYppbYqpbYopZYqpe5I4X5VlFI/K6W2O7dt7+7wNlStWpXy5cvz5Zdf2o7ivRo1gscfh/feA9mNTgjPldRF7oFTOQ8dOpQ+ffoQHBxsO0qWUmlNz6mUqgucAyZrrSs414Vqrc86558HymmtuyW73z2A1lrvdor6RqCs1vr0jZ6vevXqesOGDRl+QVlh+fLl9O7dm23btvlVt4xbHT1qtnFXqwbff28GqgkhPE/VqpArF3jQgZP27t1LrVq12L9/PyEhIbbjuIVSaqPWunpat0uz4mitfwROJbvOdSRWbuC6yq+1/lNrvds5/zdwDCiU1vN5g4ceeoicOXMyf/5821G81+23m+7xFSvMwDQhhGeKjDRzLPz1l+0kV3z44Yd07drVZwr2zchwM1EpNVgpdQjoCLyVxm1rAsHA3ow+nydRStGvXz+GDh1qO4p3e+45sy/oyy/DqVNp314IkfUiI81fD+kiP3bsGF999RXPP/+87ShWZLhoa63f0FqHA18CqR40WSlVBJgCPK21TnEOS6XUc0qpDUqpDcePH89opCzVpk0bjhw5IsfavhUBAfDJJ6Zg+9F+lkJ4lTJloEIFjxlFPnr0aNq3b0/hwoVtR7HCHRtkpwEpHsBUKRUKLADe1FqvS+0BtNbjtdbVtdbVCxXyjh70oKAg+vfvT9++fUmU+bQzrlIl6NMHPvsM1qyxnUYIkZLISFi92oxFsejw4cOMGzeOV1991WoOmzJUtJVSd7tcbAnsSuE2wcAczAC2WRmL59mefvpp4uPjmTRpku0o3u3f/4ZixeCppzxqu5kQwhEZCVrDnDlWY7z00kv06NGDUqVKWc1hU3p2+foK+Bkoo5Q6rJR6BnhfKbVNKbUVaAy84Ny2ulLqM+eu7YC6QGdn17AtSqkqmfMy7AgICGDcuHG8/vrrnDx50nYc75U7t9ledvIkNG5s/gohPEe5cmZvD4td5EuWLGHjxo28/vrr1jJ4gjR3+cpq3rDLV3LPP/88cXFxfPrpp7ajeLdVq+CRR0yX+fLl4IcjQ4XwWAMGwLvvmi7yLN6MGRcXR8WKFRk9erTPTlnqtl2+RNoGDRrEwoULWbt2re0o3q1+fZg1yxykoFUrmeZUCE8SGQmJiTB3bpY/9ZAhQ6hSpYrPFuybIUXbDfLmzcsHH3xA9+7diY+Ptx3Hu7VoAV98YVrd7dvD5cu2EwkhwPSAlS6d5V3ku3fvZsyYMYwYMSJLn9dTSdF2kw4dOlCoUCFGjx5tO4r369gRxoyBb7+FZ54xa/dCCLuUMq3t5cuzbNyJ1ppevXrx+uuvEx4eniXP6emkaLuJUoqPPvqIwYMH85eMgL51PXrAf/4DU6bAiy+akatCCLsiIyEhwaxQZ4FZs2Zx5MgRv51IJSVStN3onnvuoXv37vTp08d2FN/Qv7+ZLW30aLNbmBDCrogIc8jOLOgiP3v2LC+99BLjxo0jW7Zsmf583kKKtpv179+fDRs2sGTJEttRvJ9SMGyY6SIfOBBGjrSdSAj/ltRFvmwZnL7hsZ9u2dtvv02TJk2oU6dOpj6Pt5Gi7WY5c+ZkzJgx9OrViwsy+vnWKWWmOm3b1sycNnGi7URC+LfISDNA9LvvMu0ptmzZwrRp0xgyZEimPYe3kqKdCZo1a0alSpXkA+cugYHw5Zdm4pVnn/WYAxcI4Zdq1oTw8EzrIk9MTKR79+4MHjyYggULZspzeDMp2plk5MiRjB49mt27d9uO4huyZzfFulYtePxxcwxuIUTWU8r0fC1ZAmfPpn37mzRhwgSUUnTp0sXtj+0LpGhnkvDwcN5++22eeOIJ6SZ3l9y5YcECM51i69awLtVj0AghMlNkJFy8aL6PbrRz50769+/Pxx9/zP+3d+fxUVX3/8dfhySQsAihIvtaNhMgEIkCIsqWUJFQZRF+QBSVFrGgBbQCLix1qRUr1lZBZFXEiNjypSABDCA7ERBlEZAdIoIgAgmBJOf3xw0RMEgIIXfu5P18POYxmZk7M585hLznnHvvOUWKKJ5yola5jv70pz9Ro0YNnnjiCbdL8R+hoc43/IoV4Xe/g6++crsikcKneXPn/2A+DpGfOnWKLl268PLLL9OoUaN8e11/o9C+jowxTJo0iSVLljB16lS3y/EfFSo4R6+WKOHs59650+2KRAqXIkWcIfJ58+DUqWt+OWstjzzyCM2bN+fhhx/OhwL9l0L7OitVqhSzZ89m6NChfPnll26X4z9q1ICEBOco1vbttaSnSEHr2tVZH2D+/Gt+qX/+859s376dN998Mx8K828K7QIQFhbGG2+8QZcuXfjxOp/bWKiEhTl/MI4e1ZKeIgWtZUu46aZrHiJfuXIlL7zwArNmzSIkJCSfivNfCu0C0rNnT+6++24eeOABMjWXdv6JinLOF/32W2cf98mTblckUjgEBMB99zkHo6Wk5OklDh8+zP3338+kSZOoVatWPhfonxTaBejVV1/lyJEjvPLKK26X4l+0pKeIO7p2hdOnnYNDr1J6ejo9e/bkwQcfpGPHjtehOP+k0C5ARYsWJT4+nnHjxrF48WK3y/EvnTrBlCmQmOj0vletcrsiEf93553wm9/kaYj8mWeeITAwkJFaV+CqKLQLWJUqVXj//ffp3bs3Bw4ccLsc/9K7tzNU/uOPcPvt8NhjcOKE21WJ+K/AQLj3Xuf/XVparp/2n//8hw8++IAZM2YQEBBwHQv0PwptF7Rp04bHH3+cbt26cfbsWbfL8S/33ANbtsCgQfDWW87BarNn+8bSnl98AYcPu12FSP7q2tU5lmThwlxtvmPHDv7whz8QHx+vaUrzQKHtkqeeeoqbbrqJoUOHul2K/ylVylkRbM0aKFfOOZ/097+H/fvdqefbb533b9oURoxwpwaR66VNG2fSo1wMkaekpNClSxdGjRrFbbfdVgDF+R+FtkuKFCnC1KlTmTdvHh988IHb5finqChISnKW91y40Ol1v/EGZGQUzPufPAnDhjnvu2gRlCkDyckF894iBSUoyDkA9L//hV8ZObTW0r9/fyIiIujfv38BFuhfFNouKlOmDB9//DGDBg1ixYoVbpfjnwIDYehQ2LzZOa/08cedKRg3brx+75mZCdOmQb168PLL0KMHbN8Ot9xy3dcgFnFF167O7/Znn112k7/97W9s3LiR8ePHY4wpwOL8i0LbZREREbz//vvce++9JCUluV2O/6pZ05lyccYM2LvXGap+6inndJX8kpYGy5dDixbwwAPO8oWrV8PUqVCpktPTPn48/95PxFe0awc33HDZIfI33niDd955h/nz51O8ePECLs6/BLpdgEB0dDQTJ07knnvuYeHChTRs2NDtkvyTMc6ynjExTmD//e/O+d0DBzr7wYODf76EhOR8+8QJJ/T37HGuL7wkJzsHvFWo4AR1797OHM3nhYYqtMU/FSsGsbHwySfOAaBBQdkPTZw4kbFjx7J06VIqV67sYpH+QaHtI2JjY0lNTSUmJobExETq1avndkn+q2xZmDgR4uLgj3+EIUPy9jpBQU5vukYN54tA9epOj/7ee50vAZdSaIs/69oV3nsPli51et7A+++/z/PPP8+SJUuoUaOGu/X5CYW2D7n//vtJTU2lffv2LF26lJo1a7pdkn9r1crZ1338uDOL2vlLamrOt1NToWRJJ6SrV3d61FdzjmloqDOEnprq9NxF/El0NJQoQfLUBHqMaUdc3P945pmhLFq0iDp16rhdnd9QaPuYBx98kJSUFNq2bcuyZcuoUqWK2yX5tyJFnBmdCkJoqHN9/LhCW/xPSAjccw9jZt3M52mWtWsPs3LlPMLDw92uzK8otH3QgAEDLgru8uXLu12S5Ifzof3jj86BaSJ+JrlNLyZ/2A6LwdoHqFhRs53lNx097qOGDh1Kr169aNeuHT9oyUn/cGFPW8QfpKfDunXOQZ0dOzJqQDKZOKdzWRvAmDEu1+eH1NP2Yc8++yynT58mJiaGxYsXU7p0abdLkmtRpoxzrdAWr0pPhw0bYMkS5/L559nL4e6u3JRJGX04RzDgzLMyeTI8+6xz+IfkD/W0fZgxhpdffpnmzZtz9913c+rUKbdLkmuhnrZ4zSU9acqWhVtvdU6Z3LULevWCmTPZungx4ccGYAOLXvT0jAzU285n6mn7OGMM48aNo1+/fsTGxvK///2PEB3E5E0KbfGKQ4dgwABnhrOsnjT16zshfdddzpKcWd3nb775hnZt2nDTTV+zd+/F+7DPnoWVKwu4dj+nnrYHFClShAkTJlChQgW6dOlCamqq2yVJXmh4XLzAWnjoIUhIyO5Jk5wMW7c6E6fcf392YO/cuZP27dvz17/+lT17QrGWX1w2bHD58/gZhbZHBAQEMHXqVMqWLUvr1q05rCUevScw0Jl0RaEtvuzdd2HBAmdI/JKQvtDy5ctp2bIlzz33HH379nWh0MJJoe0hQUFBTJ8+nQ4dOtCsWTM2b97sdklytUJDtWiI+K69e2HwYGjdGh599LKbzZgxg/vuu4+pU6fyyCOPFGCBon3aHmOMYeTIkdSpU4fWrVvz3nvvER0d7XZZkluaylR8VWamMyxuLUyadPG8+VmstYwePZrJkyfz2Wef0aBBAxcKLdzU0/aoXr168fHHHxMXF8f48ePdLkdySyt9ia8aP9458GzsWGeq3kukpaXRp08f5s2bx+rVqxXYLlFoe9gdd9zB8uXLee211xgyZAgZGRlulyRXop62+KJdu+DJJ6F9e+jX7xcPHz16lHbt2pGWlkZiYiIVdOK1axTaHle7dm1WrVrF+vXr6dKlC6fzc31oyX8KbfERr+zbR+Lx4z8PiwcEkPjmm7yyf/9F223bto1mzZrRsmVLPvzwQ62H7TKFth8oW7YsCxYsIDQ0lFatWnHo0CG3S5LLUWiLj4gqVYruW7aQOHkyLF1K4ttv0/3IEaIuWFb2s88+484772T48OG89NJLFMlhP7cULB2I5ieKFi3KpEmTePnll2nWrBlz5syhcePGbpcllwoNhZQUZ9aJokWvvL0UPpmZzhKuFy4Pm5+XrNdufeYM8dWq0f2hh3j0hRd4q1o14sPCaJ01CdDkyZN5+umnmTlzJq1bt3a5UeQ8hbYfMcYwbNgwateuTXR0NJMnT6Zjx45ulyUXunClr5tucrcWubKMDNiz51fDL79CNPty9uy11x0c/OuX0FAIDqZ1cDCPHjrEmBYteLZSJVqHhpKZmcmIESOIj49n6dKl1K9f/9rrkXyj0PZD3bp1o2rVqtx33308/fTTDBo0yO2S5LwLZ0VTaPu+AQNgwoSre44xVw7NG2+8/GPFijlrU1/pNYoVy/n+okWdGnIh8fhx3tqyhWcrVeKtQ4doERLCuwMHkpyczJo1a7jxxhvz0GhyPSm0/VSzZs1YuXIlHTt2ZNOmTbz++uuULFnS7bJE8497h7Xw3//+PNHIlUL0/CUwMNeh6abE48fpvmVL9pB4ndOn6ZSUxJ3Vq7No+nSCg4PdLlFyoKMK/FiNGjVYtWoV6enpNGnShNWrV7tdkii0veOrr+DwYYiLg27doFMn55SoO+6AqCho2BDq1IGqVaFcOWeK2qAgTwQ2wLqTJ4kPC+OuMmWYMGECf77rLvp//z3tBw5UYPsw9bT93A033MCUKVOYNWsWnTt35tFHH+WZZ54hMFD/9K5QaHtHQoJz3b69u3VcJ09Vq8b3339PbGwsBw4cYNmyZYSFhbldllyBetqFRNeuXdmwYQOrVq3i9ttvZ8eOHW6XVDgptL0jIQHCw6FyZbcruS7mzp1LREQEDRo0YM2aNQpsj1BoFyKVKlVi/vz59O7dm+bNmzNhwgSstW6XVbicP80rPd3dOuTXpabCsmXgh/P6nz59mv79+zNw4EDi4+N56aWXKKrTDz1DoV3IFClShIEDB7Js2TLeeustOnfuzPfff+92WSK+Zfly51QsPxsaX7t2LU2aNCE1NZWNGzdyxx13uF2SXCWFdiEVFhbGmjVrCA8PJyIigrlz57pdkojvSEhwRkVatXK7knyRnp7O6NGj6dSpEy+88AJTp06ldOnSbpcleaCjkQqxokWL8tJLL/G73/2OuLg45s6dy9ixYylRooTbpYm4KyEBWrYEP/i/sHPnTvr06UOpUqVYv349lf10H31hoZ620KpVK7788ktSUlJo0qQJa9eudbskEfckJ8OmTZ7fn22tZeLEiTRv3pwePXrw6aefKrD9gHraAkDp0qWZNm0aH330EZ06deKxxx5j+PDhOjVMCp9Fi5xrD4f2kSNH6NevH3v27GHJkiWEh4e7XZLkE/W05SLdunVj/fr1rFy5koiICBLOn6sqUlgsXOhMlhIR4XYlV+3cuXOMGzeO8PBw6tWrl33civgPdaPkFypXrsz8+fOZM2cOAwYM4Oabb2bs2LHUrVvX7dK8T6fY+TZrnf3Z7dqBx5ahnDdvHoMHD6ZGjRosWbJE5137KYW25MgYQ+fOnenQoQNvvPEGLVq0IC4ujueee44y5xe9kLzzyFSXfsla+OEHZ991cjJ8993PP+/d60xd6qGh8a1btzJ48GB27drFa6+9xt13343R75ffUmjLrypWrBhPPvkkcXFxPPvss9SvX5+RI0fSr18/AgIC3C5P5GfnzjkBfGEIXxrKyclOKJ8798vnlywJFSs6gR0bW/D1X6Vjx44xatQoZsyYwfDhw3nsscc0SUohoNCWXClfvjwTJkxgwIABPPHEE/z73//m9ddfp02bNm6XJoXR2bMwZAh8883PoXz0aM7b3nijE8YVK0JYGFSo8PPtihV/vu2RVfDS09N5++23GT16NF26dGHLli2UK1fO7bKkgCi05ao0btyYxMREZs+ezSOPPEJERASvvvoqv/3tb90uTQqTTz6BN9+EJk2gVi1o0SLnIC5f3ll5y08kJCTw5z//mQoVKrBo0SIaNWrkdklSwBTactWMMXTp0oWOHTvyj3/8g9tuu42HHnqIZ555hhtuuMHt8qQwmDzZWRJz3TooBLtptm/fzpAhQ9i6dStjx44lNjZW+60LKW8dHik+JTg4mGHDhvHVV19x9OhR6tWrx8SJE8nIyHC7NPFnBw44R3g/8IDfB/aPP/7I4MGDadGiBa1atWLz5s107txZgV2IXTG0jTGTjDHfG2O+vuC+McaYTcaYjcaYBGNMpcs89wFjzI6sywP5Wbj4jooVKzJp0iTmzp3LlClTiIqKYs6cOWRmZrpdmvijadOcI8AffNDtSq6bU6dO8frrr1O/fn1OnjzJ5s2befLJJylWrJjbpYnLctPTngJ0uOS+v1trG1lrGwNzgecufZIxpizwPHAbcCvwvDEm9NrKFV92yy238PnnnzNixAhGjRpFw4YNmTJlCmfPnnW7NN+h87SvjbXO0HirVuCHx1EcOXKE5557jpo1a7JixQo+/fRT3nnnHcqXL+92aeIjrhja1tplwLFL7vvpgpslgJz+EsUAC621x6y1x4GF/DL8xc+c39+dlJTEuHHjmDFjBrVq1WLs2LH89NNPV36BwkLDm3mzYgXs3Al9+7pdSb7atWsXjz32GPXq1ePw4cOsXLmSjz76iMaNG7tdmviYPO/TNsa8YIzZD/Qih542UBnYf8HtA1n3SSFgjKFdu3YkJCQwZ84c1q1bR61atRg+fDiHDx92uzzxqsmTnZW3unZ1u5J8sWHDBnr27Mmtt95K6dKl2bJlC+PHj6dOnTpulyY+Ks+hba0dYa2tCrwP/CmHTXLqSuQ4NmiM+YMxJskYk3TkyJG8liQ+KjIykpkzZ7J27VpOnDhB/fr1+eMf/8iOHTvcLk285PRpiI+H7t09c051Tqy1LFq0iOjoaDp16kTTpk3ZtWsXL774IhUqVHC7PPFx+XH0+AygSw73HwCqXnC7CnAopxew1k6w1ja11jbVJAH+q1atWvzrX/9i+/btlC9fnhYtWtCtWzfWrVvndmniBbNmwalTnh0az8jIID4+nqioKAYNGkTPnj3ZtWsXQ4YM0amSkmt5Cm1jzIVjN7HAthw2WwBEG2NCsw5Ai866Twq5cuXKMXr0aHbv3k3Lli3p2rUrbdq0YcGCBVgdqCWXM3ky1K4NLVu6XclVSU1N5a233qJu3bqMGzeO5557jq+//pq+fftq2lG5ark55esDYBVQzxhzwBjzMPCyMeZrY8wmnDB+PGvbpsaYiQDW2mPAGGBd1mV01n0iAJQsWZLHH3+cnTt30rdvX4YOHUqTJk2YMWMGaWlpbpcnvmTXLli61DnNyyMH8R09epQXXniBmjVrMn/+fKZNm8aKFSuIjY2liMdWEBPfkZujx3taaytaa4OstVWste9aa7tYaxtknfbVyVp7MGvbJGvtIxc8d5K1tnbWZfL1/CDiXUFBQfTp04dNmzbx4osv8u6771K5cmUGDBjAqlWr1PsWmDLFCeu4OLcr+VVpaWl8/PHHdO7cmdq1a7Nz504WL17MnDlzuP32290uT/yAvu6JzzDGcPfdd7N48WK++OILqlSpQt++falbty5jxoxh9+7dbpd47fQF5OplZsLUqdC+vTN1qY+x1rJy5Ur69+9PpUqV+Pe//829997L/v37mTx5MuHh4W6XKH5Ec4+LT6pevTrDhw9n2LBhJCUlMW3aNG699VZuvvlm+vTpQ7du3by9rrdHhnivmbVO6GZk5P2yYQPs2wd/+5vbn+Yi3377Le+99x7Tp08nKCiIuLg4NmzYQLVq1dwuTfyYQlt8mjGGqKgooqKiGDt2LPPnz2f69OkMHTqUDh060KdPH2JiYgjyo5WcfNY//wnjxl1d4ObXVLZly8Lvf58/r3UNjh8/zkcffcS0adPYvn07PXr0YObMmdxyyy2aD1wKhPG1/YVNmza1SUlJbpchPu7YsWPEx8czffp0du7cSY8ePYiLiyMyMtK3/3gePQrlyjkB+KecpjfwUZmZUL06hIRA8+ZQpIizWEd+Xa70evXqQf36rnz0c+fO8emnnzJt2jQSEhKIjo4mLi6ODh066Mui5BtjzBfW2qZX2k49bfGksmXL0r9/f/r378+OHTt477336NatG8HBwcTFxdGrVy+q+uD+T89au9ZZXWv6dOjd2+1qrjtrLUlJSUyfPp2ZM2dSt25d4uLimDBhAqGhWkJB3KPQFs+rU6cOo0aNYuTIkaxYsYJp06YRERFBlSpViImJISYmhpYtWxIcHOx2qd41axYEBUGnTm5Xct388MMPLFy4kAULFpCQkEDx4sXp3bs3q1at4rd+uDiJeJOGx8Uvpaens27dOhYsWMCCBQvYvHkzLVu2zA7xevXquTOM7sXhcWuhZk1o0ADmznW7mnxz7tw5Vq9eTUJCAgsWLGDbtm3ceeed2b8jtWvX9u1dLeJXNDwuhVpgYCDNmzenefPmjBw5kmPHjrF48WISEhIYO3Ysxhiio6OJiYmhbdu2BT/k6WNfln/VF1/A3r0wcqTblVyz3bt3Z3+RS0xMpFatWsTExPDKK6/QokULzVAmPk89bSl0rLVs27Yt+4/38uXLadiwYXYPKyoqioCAgOvz5mfPQqVKUKsWrFwJgR743vyXv8Brr8Hhw85R3B5y6tQplixZkv1vfeLEiewva+3bt9c61eIzctvTVmhLoXfmzBk+//zz7GHSgwcP0rZtW6Kjo2nVqhW1a9fO32kn4+Ph/vvhr3+FESPy73WvB2ud+b7r1oX5892u5orOnDnDpk2b+Oyzz0hISGDdunVERUVlfyFr1KiRphAVn6TQFsmjQ4cOZQf4mjVrOHr0KBERETRp0oTIyEiaNGlCWFjYtZ3u06MHzJ7tHJXduHH+FZ/fNmyAyEiYOBEeftjtai5y8uRJNm7cyIYNG1i/fj0bNmxgx44d1KlTh1atWhETE8Ndd91FSQ8v4ymFh0JbJJ8cP36cjRs3sn79+uxw2LNnD2FhYURGRmYHeaNGjQgJCcndi/7wA4SHQ/nyTnAXK3Z9P0RejRjhzET23Xdw442ulXH06NGLwnn9+vUcPHiQhg0bZn+ZioyMJDw8XGcJiCcptEWuo9OnT7Np06aLQmTbtm3UqlUrO8QjIyNp3LgxpUuXzvlF/u//IDaW5EEv0WPj03z4IVSoULCf41dZ60xqUr06LFxYQG9pOXjw4C8C+sSJExeNdERGRlKvXj0CvXBMgEguKLRFCtjZs2fZvHnzRWGzadMmKlSoQP369alatSpVq1alWrVq2dfVRo1i4NTbGG/60/9Rw7/+5fangFf27SOqVClaHzgAjRrB22+T2L07606e5Kl8mFf75MmT7N+/n/3797Nv377sn/fu3cvXX38NcNEIRmRkJDVr1tS+aPFrCm0RH5CRkcH27dvZsWPHRSF1/vrkwUxSMraTRgjBpLCudCQV4Opt+AAACZJJREFUQk4QEBxMUPHiFC1enKASJTBBQc7kJgVwSTSG7mlpxC9bRuuRI0ncsYPuycnEh4XR+gqnxp09e5aDBw9eFMaXfua0tLSLvrxc+HN4eDiVKlXS+dFS6Ci0RTygf/9MJr9rOZseQJA5S9eyn/CXMiNIO32acykpZKSmQno6JYoWpURQEMGBgQQBQdYSCARmZhJgLQGZmQRkZFAkM5MiGRmYa1yoI7FxY7o//zxxq1bxTnQ0/ZKTqfbDD6SmppKSkkJKSkr2zz/99BMHDhxg//79HD16lIoVK142lKtVq0bZsmUVyiKXUGiL+LjkZOd07TNnfr4vJAR27bp43/aZM2c4cOAA+/bt47vvvrsoOC8MzwsvZ1JSOHv6NGezwj89NTX7OjMtjeJBQQRaS5GMDEoFB1OyWDFKFC2afV2iaFFWxsaS2KEDDdavp9nWrRQvXpyQkBCKFy+efQkJCaFkyZJUqVKFqlWrUrFixet3jruIH9OMaCI+bsyYX65cmZHh3H/hvu3g4GBq165N7dq18+V9rbWkpqYSEBBA0aJFc+z1Jh4/zvgtW3i2UiXeCgri//Xte8WhcRG5/hTaIi5ZtcqZIO1CZ886E6VdT8YYihcvftnHE48fp/uWLdn7sFuXKXPRbRFxj0JbxCUbNrhdQc7WnTx5UUC3Dg0lPiyMdSdPKrRFXKbQFpGL5HRaV+vQUAW2iA/QiY8iIiIeodAWERHxCIW2iIiIRyi0RUREPEKhLSIi4hEKbREREY9QaIuIiHiEQltERMQjFNoiIiIeodAWERHxCIW2iIiIRyi0RUREPEKhLSIi4hEKbREREY9QaIuIiHiEQltERMQjFNoiIiIeodAWERHxCIW2iIiIRyi0RUREPEKhLSIi4hEKbREREY9QaIuIiHiEQltERMQjFNoiIiIeodAWERHxCIW2iIiIRyi0RUREPEKhLSIi4hEKbREREY9QaIuIiHiEQltERMQjFNoiIiIeodAWERHxCIW2iIiIRyi0RUREPEKhLSIi4hEKbREREY9QaIuIiHiEQltERMQjFNoiIiIeccXQNsZMMsZ8b4z5+oL7/m6M2WaM2WSM+cQYU+Yyz/2zMWazMeZrY8wHxpjg/CxeRESkMMlNT3sK0OGS+xYCDay1jYDtwLBLn2SMqQwMAppaaxsAAUCPa6pWRESkELtiaFtrlwHHLrkvwVqbnnVzNVDlMk8PBEKMMYFAceDQNdQqIiJSqOXHPu2HgPmX3mmtPQi8CuwDkoET1tqEfHg/ERGRQumaQtsYMwJIB97P4bFQoDNQE6gElDDG9L7M6/zBGJNkjEk6cuTItZQkIiLit/Ic2saYB4B7gF7WWpvDJu2A3dbaI9bac8BsoEVOr2WtnWCtbWqtbVquXLm8liQiIuLX8hTaxpgOwF+AWGttymU22wc0M8YUN8YYoC2wNW9lioiISG5O+foAWAXUM8YcMMY8DLwJlAIWGmM2GmPeztq2kjFmHoC1dg0wC1gPfJX1XhOuz8cQERHxfybnkW33NG3a1CYlJbldhoiISIExxnxhrW16pe00I5qIiIhHKLRFREQ8QqEtIiLiEQptERERj1Boi4iIeIRCW0RExCMU2iIiIh6h0BYREfEIhbaIiIhHKLRFREQ8QqEtIiLiEQptERERj1Boi4iIeIRCW0RExCMU2iIiIh6h0BYREfEIhbaIiIhHKLRFREQ8QqEtIiLiEQptERERj1Boi4iIeIRCW0RExCMU2iIiIh6h0BYREfEIhbaIiIhHKLRFREQ8QqEtIiLiEQptERERj1Boi4iIeIRCW0RExCMU2iIiIh6h0BYREfEIhbaIiIhHKLRFREQ8QqEtIiLiEQptERERjzDWWrdruIgx5giw1+06fNSNwFG3iygk1NYFR21dcNTWBedq27q6tbbclTbyudCWyzPGJFlrm7pdR2Ggti44auuCo7YuONerrTU8LiIi4hEKbREREY9QaHvLBLcLKETU1gVHbV1w1NYF57q0tfZpi4iIeIR62iIiIh6h0PYxxph6xpiNF1x+MsY8cck2pY0x/2eM+dIYs9kY09eter3OGPPnrDb82hjzgTEm+JLHixljPjTG7DTGrDHG1HCnUu/LRVsPNsZsMcZsMsYsNsZUd6tWr7tSW1+wXVdjjDXG6IjyPMpNWxtjumf9bm82xsy4lvdTaPsYa+031trG1trGwC1ACvDJJZs9Bmyx1kYAdwFjjTFFC7ZS7zPGVAYGAU2ttQ2AAKDHJZs9DBy31tYG/gH8rWCr9A+5bOsNWY83AmYBrxRslf4hl22NMaZU1nZrCrZC/5GbtjbG1AGGAbdba8OBJ37xQldBoe3b2gLfWmsvnWzGAqWMMQYoCRwD0gu6OD8RCIQYYwKB4sChSx7vDEzN+nkW0Dar3eXq/WpbW2sTrbUpWTdXA1UKuD5/cqXfa4AxOF+MzhRkYX7oSm3dD/iXtfY4gLX2+2t5M4W2b+sBfJDD/W8CN+P8cnwFPG6tzSzIwvyBtfYg8CqwD0gGTlhrEy7ZrDKwP2v7dOAE8JuCrNMf5LKtL/QwML8gavM3uWlrY0wToKq1dq4LJfqNXP5e1wXqGmNWGGNWG2M6XMt7KrR9VNZwdyzwUQ4PxwAbgUpAY+BNY8wNBVieXzDGhOL0pGvitGUJY0zvSzfL4ak65eIq5bKtz2/bG2gK/L3gKvQfV2prY0wRnF09Q9yp0H/k8vc6EKiDsyuzJzDRGFMmr++p0PZdvwPWW2sP5/BYX2C2dewEdgP1C7Q6/9AO2G2tPWKtPQfMBlpcss0BoCpA1vBXaZzdEXJ1ctPWGGPaASOAWGttWgHX6C+u1NalgAbAEmPMHqAZMEcHo+VJbv+G/Ndae85auxv4BifE80Sh7bt6kvPQODhDMW0BjDHlgXrArgKqy5/sA5oZY4pn7aduC2y9ZJs5wANZP3cFPrOa3CAvrtjWWUO243EC+5r2+xVyv9rW1toT1tobrbU1rLU1cI4fiLXWJrlTrqfl5m/If4DWAMaYG3GGy/P891qh7YOMMcWB9jjf2s7f198Y0z/r5highTHmK2Ax8BdrrVbuuUrW2jU4B5etxzk2oAgwwRgz2hgTm7XZu8BvjDE7gcHA064U63G5bOu/4xxY+VHW6Y5z3KnW23LZ1pIPctnWC4AfjDFbgETgSWvtD3l9T82IJiIi4hHqaYuIiHiEQltERMQjFNoiIiIeodAWERHxCIW2iIiIRyi0RUREPEKhLSIi4hEKbREREY/4/2nZRMkERf8cAAAAAElFTkSuQmCC\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Point 0: True\n", "Point 1: True\n", "Point 2: True\n", "Point 3: True\n", "Point 4: True\n", "Point 5: True\n", "Point 6: True\n", "Point 7: True\n" ] }, { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Point 0: True\n", "Point 1: True\n", "Point 2: True\n", "Point 3: True\n", "Point 4: True\n", "Point 5: True\n", "Point 6: True\n", "Point 7: True\n", "Point 8: True\n", "Point 9: True\n" ] }, { "data": { "text/plain": [ "
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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Point 0: True\n", "Point 1: True\n", "Point 2: True\n", "Point 3: True\n", "Point 4: True\n", "Point 5: True\n", "Point 6: True\n", "Point 7: True\n", "Point 8: True\n", "Point 9: True\n" ] }, { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Point 0: True\n", "Point 1: True\n", "Point 2: True\n", "Point 3: True\n" ] }, { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Point 0: True\n", "Point 1: True\n", "Point 2: True\n", "Point 3: True\n", "Point 4: True\n" ] }, { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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WEREJCIW2iIhIQCi0RUREAkKhLSIiEhAKbRERkYBQaIuIiASEQltERCQgFNoiIiIBodAWEREJCIW2iIhIQCi0RUREAkKhLSIiEhAKbRERkYBQaIuIiASEQltERCQgFNoiIiIBodAWEREJCIW2iIhIQCi0RUREAkKhLSIiEhAKbRERkYBQaIuIiASEQltERCQgshTaZjbKzLaZ2bKwx542syVmtsjMpplZlQyWq2FmC0LzLDez/jlZvIiISDTJakt7NNA13WPPO+caOeeaAJ8BgzNYbjPQOjRPS+ChjMJdRERETi9Loe2cmwXsTPfY3rC7JQGXwXJHnHOHQ3eLZvX5RERE5LcKZWdhM3sG6A3sATpkMs8FwGSgNvAX59ym7DyniIhItMpWy9c594hz7gLgPeCuTOZZ75xrhBfafcysckbzmdmdZjbfzOZv3749O2WJiIhEpJzqrh4HdD/VDKEW9nKgbSbT33DOxTjnYipVqpRDZYmIiESOsw5tM7s47O7VwMoM5qlmZsVDv5cH2gCrzvY5RUREolmW9mmb2XjgcqCimW0AHgfizawukAr8BPQPzRsD9HfO3QFcAvzDzBxgwAvOuaU5/ipERESigDn3m4O+fRcTE+Pmz5/vdxkiIiJ5wswWOOdiTjefhmCJiIgEhEJbREQkIBTaIiIiAaHQFhERCQiFtoiISEAotEVERAJCoS0iIhIQCm0REZGAUGiLiIgEhEJbREQkIBTaIiIiAaHQFhERCQiFtoiISEAotEVERAJCoS0iIhIQCm0REZGAUGiLiIgEhEJbREQkIBTaIiIiAaHQFhERCQiFtoiISEAotEVERAJCoS0iIhIQCm0REZGAUGiLiIgEhEJbREQkIBTaIiIiAaHQFhERCQiFtoiISEAotEVERAJCoS0iIhIQCm0REZGAUGiLiIgEhEJbREQkIBTaIiIiAaHQFhERCQiFtoiISEAotEVERAJCoS0iIhIQCm0REZGAUGiLiIgEhEJbREQkIBTaIiIiAaHQFhERCYjThraZjTKzbWa2LOyxp81siZktMrNpZlYlg+WamNkcM1semvfGnC5eREQkmmSlpT0a6Jruseedc42cc02Az4DBGSx3AOjtnGsQWv5lMyuXnWJFRESiWaHTzeCcm2VmNdM9tjfsbknAZbDcD2G/bzKzbUAlYPfZFisiIhLNThvamTGzZ4DewB6gw2nmbQEUAdac7fOJiIhEu7M+EM0594hz7gLgPeCuzOYzs/OBd4E/OOdSTzHfnWY238zmb9++/WzLEhERiVg5cfT4OKB7RhPMrAwwGXjUOZd0qpU4595wzsU452IqVaqUA2WJiIhElrMKbTO7OOzu1cDKDOYpAnwEjHHOTTq78kRERCTNafdpm9l44HKgopltAB4H4s2sLpAK/AT0D80bA/R3zt0B9ADaARXMrG9odX2dc4ty+kWIiIhEA3PuNwd++y4mJsbNnz/f7zJERETyhJktcM7FnG4+nRFNREQkIBTaIiIiAaHQFhERCQiFtoiISEAotEVERAJCoS0iIhIQCm0REZGAUGiLiIgEhEJbREQkIBTaIiIiAaHQFhERCQiFtoiISEAotEVERAJCoS0iIhIQCm0REZGAUGiLiIgEhEJbREQkIBTaIiIiAaHQFhERCQiFtoiISEAotEVERAJCoS0iIhIQCm0REZGAUGiLiIgEhEJbREQkIBTaIiIiAaHQFhERCQiFtoiISEAotEVERAJCoS0iIhIQCm0REZGAUGiLiIgERCG/CxCRnOWc4+jRoxw6dIhDhw5x+PDhE79n9tixY8coVqzYiVvRokVPup/+saJFi1KwYEG/X6pI1FFoi+Rzx48fZ/v27WzdupUtW7Zk+nPbtm3s37+fQ4cOUbBgwd+E7qnCuGDBghw5cuS04R7+eKFChShWrBjnnHMO5513HpUrVz7lz1KlSvm9KUUCT6Et4rNDhw6xevVqVq5cyapVq1i9ejWbN28+EcY7d+6kXLlyGQZh48aNT9yvXLkyZcqUyZNWcFpr/uDBg+zYseM3XyCWLFnymy8WZnZS7dWrV6du3brUq1ePevXqcd5552FmuVq3SNCZc87vGn4jJibGzZ8/3+8yRHKMc46tW7eyatWqE+G8cuVKVq5cyaZNm6hVqxb16tWjbt26XHzxxVSpUuVEwFWqVInChQv7/RKyxTnHvn372Lp164kgX7du3Unb49ChQye2QVqQ161bl9q1a1OsWDG/X4JIrjKzBc65mNPOp9AWyVl79+5l3rx5zJs3j++///5EMBUsWPCkMEr7vVatWoEP5Zzw888/s2rVqpO+0KxatYp169ZRrVq1E9vsd7/7HbGxsdSpU4cCBXQsrUQGhbZIHkhNTeWHH35gzpw5JCUlMWfOHJKTk2natCnNmzenQYMGJ8KmYsWKfpcbSEePHiU5OflEkC9atIikpCT27NlDbGwssbGxtGrVihYtWlC2bFm/yxU5KwptkVywZ88e5s6deyKg586dS9myZWnVqhWtWrUiNjaWxo0bU6RIEb9LjXibN28mKSnpxN/if//7HzVr1jzpb1GvXj21xiUQFNoiOWDLli1MnTqVxMREkpKSWLduHZdeeumJ1l1sbCznnXee32UKXot8yZIlJ/V67Ny5k5YtWxIbG0unTp2IjY2lUCEdfyv5j0Jb5CykpqayYMECJk+ezOTJk/nxxx/p1KkTl19+ObGxsTRq1Ej7nwNk69atzJ07l2+//ZapU6eSkpLClVdeSUJCAl27dqVChQp+lygCKLRFsmzPnj1MmzaNyZMnM2XKFCpUqEBCQgIJCQm0adNGIR1BNm7cyOeff87kyZOZOXMmDRo0OPG3bty4sYaciW8U2iKZcM6xcuXKE63p+fPnc9lll5348K5Vq5bfJQIwLCWF5qVL06F8+ROPzdy1i3n79vFg9eo+Vpa7Nm/eTM+ePZkwYUKu7no4fPgwX3/99YkQP3jwIPHx8SQkJNCxY0edDEbyVFZDW0doSMRZtAjKlYMlS359zDnH/Pnzuffee7nooou48sorWbNmDQ888ABbtmxhypQp3HXXXfkmsAGaly5NjxUrmLlrF+AFdo8VK2heurTPlWVDaiocPQqHDsH+/bBnD/z8M2zbBps2wfr1PP3gg8yePZunnnoqV0spWrQoXbp04eWXX2b16tXMmDGDSy65hH/961+cf/75dOnShZEjR7Jnz55crUPkTKilLRGnYUNYvhwaNICpUzcyduxYxowZw6FDh+jduzfXXXcdDRs2zNmu0NRUOH4cjh07s5+nmWcm0KN4cQYcPMjrxYoxcedOOhw4kGPrz/I8ObW+U3zeFAcOZfB4sWLFOHjwYM79rbJg3759TJs2jfHjxzN9+nTi4uLo06cPnTt31jnXJVfkWPe4mY0CrgK2Oecahh57GrgGSAW2AX2dc5syWPYLIBaY7Zy7KqvFK7TlbC1aBE2bOsAAxz8KXkq76rupVa0a55Qrh+VkOIX/zEWD//AHnu7dm8fGjOGpt98+s4ULFYKCBX/7M6PHsjNvDqxv8/79DJo4kY8XLOAAUKJECX7/+9/zwgsv+HqE/s8//8yECRN45513WL9+Pb169aJ37940bNjQt5ok8uRkaLcD9gNjwkK7jHNub+j3u4H6zrn+GSzbESgB9FNoS25yzpGYmMg111zI7t1VSQvtBkV+YFnt63I+ePJomZlAj4MHGVCsGK8fOsTEChXoULJk1tYfwPHJA3r04I1JkyhSuDBHjh+nX79+vPbaa36XdcL333/PmDFjePfdd6lcuTJ9+vThpptuolKlSn6XJgGX1dA+7YBF59wsM6uZ7rG9YXdLAhkmv3PuKzO7/HTPIXK2kpOTGTNmDGPGjAEas3v3h3iBDWAsP1KXJeOX06iRj0WepbR92BMbNaJD+fJ0SLtftepJB6dFkq3r19MfuHPUKN6YM4fNmzf7XdJJLrnkEp577jmGDBnCjBkzeOeddxg8eDCXX345ffr0ISEhQSfWkVx11l/FzewZM1sP3AIMzm4hZnanmc03s/nbt2/P7uokgjnnmDlzJvHx8bRs2ZKdO3cyceJEihcPD+xf3Xxz3teYE+bt28fE+vVPBHSH8uWZWL8+8/bt87my3PNhjx68CjTu2pVXX32VDz/80O+SMlSwYEE6d+7M2LFjSUlJ4eqrr+bll1+mevXqDBkyhJ07d/pdokSoLB2IFmppf5bWPZ5u2sNAMefc45ksezkwSN3jkl3Hjh3jww8/5Pnnn2fv3r385S9/oVevXieuAFW8uHdQcnrFikEeH8ckZ+vPf4YxY2D3bgjgmOkVK1bwwgsv8PHHH3Prrbdy3333UbNmTb/LkgDIyyFf44DuObAekQwdOHCA1157jbp16/LKK6/w6KOP8v3333PHHXecdMnGgwe9g5OdAzf3OxyGe2+cAjtI1qyBCy8MZGAD1K9fn1GjRrF06VKKFSvGpZdeys0338zChQv9Lk0ixFmFtpldHHb3amBlzpQj8qsdO3bw5JNPUqtWLb788kveffddvvnmG6655prTXwQiJgYqVoQpU/KmWMkZyclw0UV+V5FtVatWZejQoaxdu5ZmzZrRrVs3OnfuzLRp08iPw2wlOE4b2mY2HpgD1DWzDWZ2O/B3M1tmZkuALsA9oXljzOzNsGUTgUlAx9CyV+bKq5CIsmbNGgYOHEidOnXYuHEjs2bN4qOPPqJ169ZZX0mBAtC1K3zxhTeGWvK/48dh7VqvpR0hypQpw6BBg0hOTqZXr1488MADNG3alPfee4+jR4/6XZ4E0GlD2zl3k3PufOdcYedcNefcW8657s65hs65Rs65bs65jaF55zvn7ghbtq1zrpJzrnho2am5+WIk2FavXk3Pnj2JjY2lXLlyrFixgjfeeIO6deue3Qrj4mDHDtDxEcGwaRMcORIRLe30ihQpQp8+fViyZAnPPfccb775JrVr12b48OEcy+Vx/hJZgjeQUyLOzp07uffee2nVqhWNGzcmOTmZZ555Jvsn1LjySm/f6Oef50yhkrvWrPF+RlBLOz0zIy4ujpkzZzJp0iQmTJhA48aNmTJlirrNJUsU2uKbI0eO8PLLL1OvXj0OHz7MihUrePjhhymdU+fWrlABWrbUfu2gSE72fkZgSzsjLVq0YMaMGTz33HPce++9dO3alaVLl/pdluRzCm3Jc845PvroIxo0aMC0adOYOXMmr7/+Oueee27OP1l8PMybBxr7n/+tWeOdze2CC/yuJM+YGVdffTXLli3jqquuomPHjtx5551s2bLF79Ikn1JoS56aP38+7du3Z/Dgwbz66qt8/vnnNGjQIPeeMC7OGwM2VYdT5HvJyVC9OkTh9csLFy7Mn//8Z1atWkWZMmVo0KABzzzzTJ5fKEXyP4W25In169dz6623cvXVV3PrrbeyaNEiunTpkvtP3KwZnHuu9msHQYQM98qO8uXL88ILL/Ddd9+xaNEi6taty9ixY0nVCAgJUWhLrjp69ChPPfUUTZo0oUaNGqxatYo//vGPeXd5wwIFvNb21KnekCLJv9JOrCJcdNFFTJo0iXHjxvHPf/6Tli1b6gQtAii0JRctW7aMli1bkpSUxMKFCxkyZEjOHWR2JuLiYOdO+O67vH9uyZo9e+Dnn6O+pZ3eZZddRlJSEgMHDuTKK69kyJAhGiIW5RTakuOOHz/O888/T4cOHfjTn/7E5MmTqV69un8FdenitbjVRZ5/pR05rpb2bxQoUIC+ffuyYMECZs2aRevWrVm5UiehjFYKbclRP/74I+3bt2fy5Ml899133HHHHZjf55EuXx5atdLQr/wsyoZ7nY0LLriAqVOn8oc//IHLLruMl19+Wfu6o5BCW3KEc47XX3+d2NhYunfvzowZM6hVq5bfZf0qPh4WLAANpcmfouDEKjnBzBgwYABJSUm8//77XHHFFaxdu9bvsiQPKbQl2zZs2MCVV17J22+/TWJiIvfdd9/pL+iR1+LivJ8a+pU/JSd7J8MpW9bvSgKhdu3afP311yQkJNCiRQtGjhypM6pFiXz2ySpB4pzj3XffpVmzZrRr145vv/2WSy65xO+yMtakCZx/vvZr51fJyWpln6GCBQuNEKqXAAAgAElEQVTyl7/8hf/+978MHz6cq666ik2bNvldluQyhbaclUOHDtG3b1+GDh3K1KlTefTRRylUqJDfZWXOzLvq17RpoKNv8x8N9zprDRo0ICkpiebNm9OsWTO+/vprv0uSXKTQljO2detWrrjiCg4cOMB3331H06ZN/S4pa+LjYfduSEryuxIJd+wY/PSTDkLLhsKFC/PEE08wduxYevTowZtvvnn6hSSQFNpyRhYvXkzLli3p3LkzEyZMoESJEn6XlHWdOnnnttZR5PlLSop34hu1tLOtU6dOJCYmMmzYMO677z6O64RCEUehLVn2ySef0KlTJ/7+97/z5JNP5r+DzU6nXDlo00b7tfMbDffKUXXq1GHu3LksXbqUbt26sWfPHr9LkhwUsE9d8YNzjqFDhzJw4EAmT55Mz549/S7p7MXFwaJFoAN28g8N98px5cuXZ8qUKdSqVYtWrVqxJm0bS+AptOWUDh8+TN++fZk4cSJJSUm0aNHC75KyJz7e+/nFF/7WIb9KToYiRaBqVb8riSiFCxfm1Vdf5a677qJNmzY6QC1CKLQlU9u2bTtxwFliYiLVqlXzu6Ts+93vvHDQfu38Y80aqFnTO95Actyf/vSnEweovfXWW36XI9mk0JYMJScn07JlSzp16hS8A85OxczrIp82DY4e9bsaAV2SMw+kHaA2dOhQHn30UZ2IJcAU2vIbKSkpdOzYkQcffDCYB5ydTlwc7N0Lc+b4XYk4pzHaeaROnTp8++23fPLJJzz99NN+lyNnKcI+jSW7Nm3aRMeOHbn77rsZMGCA3+Xkjk6doFAhHUWeH+zc6X2BUks7T1SsWJHp06czbtw4hg4d6nc5chYU2nLC1q1b6dixI7fffjv33Xef3+XknjJl4LLLtF87P9AlOfNc5cqV+eqrrxg5ciQvv/yy3+XIGVJoCwA7duygU6dO3HjjjTz00EN+l5P74uNhyRLYsMHvSqKbhnv5omrVqsyYMYNXXnmF119/3e9y5AwotIXdu3fTpUsXEhISePzxx/0uJ2+kXfVLrW1/qaXtm+rVqzN9+nSeffZZRo0a5Xc5kkUK7Si3d+9eunbtSrt27XjuuecwM79LyhsNGsAFFyi0/bZmDVSuDCVL+l1JVLrooouYPn06jz32GOPGjfO7HMmCfHxZJsltv/zyCwkJCTRt2pSXXnopegIbvKFf8fHw3ntw5Ih3cg/Jexru5bu6desybdo0OnXqRJEiRbj++uv9LklOQS3tKOWco1evXlx44YW8+uqr0RXYaeLiYP9++OYbvyuJXrqOdr7QoEEDpkyZwp/+9CeSdBW8fE2hHaVefPFFNm7cyBtvvBF547CzqmNHKFxYQ7/8cvgwrF+vlnY+0aRJE0aOHMmNN97Izz//7Hc5koko/bSObrNnz2bYsGFMmjSJokWL+l2Of0qVgnbttF/bLz/95J1cRS3tfOOaa66hR48e9OrVi9TUVL/LkQwotKPMtm3buOmmmxg1ahQ1atTwuxz/xcfD8uXeNZ0lb6UN91JLO1959tln2b9/P88++6zfpUgGFNpR5Pjx49x888307t2bhIQEv8vJHzT0yz8a7pUvFS5cmAkTJvDaa68xffp0v8uRdBTaUeTJJ58kNTWVJ5980u9S8o969bwrTGm/dt5bswaKF4fzzvO7EkmnSpUqvPvuu9x6661s3LjR73IkjEI7SnzxxRe89dZbjBs3jkKFNNLvhLSrfn31lXdglOSdtCPHo3HkQgB07NiRgQMHcuONN3JUV8TLNxTaUSAlJYW+ffsyfvx4zlOr5rfi4+GXXyAx0e9KoouGe+V7f/vb3yhTpgx/+9vf/C5FQhTaEc45R9++fbn33ntp166d3+XkTx06eCdX0X7tvOOcTqwSAAUKFODdd99lwoQJzJgxw+9yBIV2xPvwww/ZsWMHgwYN8ruU/KtkSbj8cu3Xzkvbtnm9G2pp53sVKlTgpZde4t577+XYsWN+lxP1FNoR7ODBgwwaNIiXX35Z+7FPJy4OVq6EtWv9riQ6aLhXoFx33XVUqFCBkSNH+l1K1FNoR7AXX3yRZs2accUVV/hdSv4XH+/9VBd53tBwr0AxM1555RWeeOIJdu7c6Xc5UU2hHaE2btzIiy++yPPPP+93KcFw8cVegCi088aaNd5R4zVr+l2JZFGjRo3o3r07TzzxhN+lRDWFdoR66KGH6N+/PxeqJZM1aVf9+uorOHTI72oiX3IyVK0KxYr5XYmcgaeeeorx48ezfPlyv0uJWgrtCDRnzhxmzJjBww8/7HcpwRIXBwcPwqxZflcS+das0f7sAKpYsSKPPfYY9913H845v8uJSgrtCJOamso999zD3//+d0qVKuV3OcFy+eVey09Hkec+jdEOrAEDBrBhwwY+/fRTv0uJSgrtCDN27FgKFCjALbfc4ncpwVOihIZ+5YUDB2DzZoV2QBUuXJiXXnqJ+++/X2dK80GWQtvMRpnZNjNbFvbY02a2xMwWmdk0M6uSybJ9zGx16NYnpwqX33LO8cILL/D0009H7zWysys+Hlavhh9/9LuSyJU2rE7d44F15ZVXUqVKFf7v//7P71KiTlY/2UcDXdM99rxzrpFzrgnwGTA4/UJmdg7wONASaAE8bmblz75cOZWkpCQOHjxIx44d/S4luHTVr9yn4V4RYcCAAQwfPtzvMqJOlkLbOTcL2Jnusb1hd0sCGR2VcCXwpXNup3NuF/Alvw1/ySEjRoygX79+amVnR+3a3vAvhXbu0YlVIsJ1113H4sWL+VG9UnkqW5/uZvaMma0HbiGDljZQFVgfdn9D6DHJYTt37uTjjz+mb9++fpcSfPHxMHOmdyS55LzkZChdGipU8LsSyYaiRYvSt29f3njjDb9LiSrZCm3n3CPOuQuA94C7Mpglo2vuZThOwMzuNLP5ZjZ/+/bt2SkrKo0ZM4aEhAQqVqzodynBFxfnjdX+73/9riQypQ330iU5A+/OO+9k9OjRHNZlbfNMTvWjjgO6Z/D4BuCCsPvVgE0ZrcA594ZzLsY5F1OpUqUcKis6OOcYPnw4/fv397uUyNC+PRQvrqPIc4uGe0WM2rVr07hxYz744AO/S4kaZx3aZnZx2N2rgZUZzDYV6GJm5UMHoHUJPSY5aNasWRQsWJDLLrvM71IiQ7FicMUVXmjrBBI5KzXVO3pc+7MjRv/+/RkxYoTfZUSNrA75Gg/MAeqa2QYzux34u5ktM7MleGF8T2jeGDN7E8A5txN4GpgXuj0Vekxy0PDhw+nXrx+m7sacEx/vtQhXr/a7ksiyaRMcPqyWdgS5+uqrWb16NStWrPC7lKhg+fFUdDExMW7+/Pl+lxEIu3fvpkaNGqxbt47y5TWaLsesXesFy8svwz33+F1N5Jg1y9v9MHUqdOnidzWSQx555BEOHz7MCy+84HcpgWVmC5xzMaebT2ODAu6bb74hJiZGgZ3TatWCevW0XzunabhXRIqPj2fmzJl+lxEVFNoBN2vWLNq1a+d3GZEpLg6+/hp++cXvSiJHcjIULAjVq/tdieSgmJgYVq1axd69e08/s2SLQjvgEhMTadu2rd9lRKb4eG//q1oQOWfNGi+wCxf2uxLJQUWLFiUmJoZvv/3W71IinkI7wA4cOMDixYuJjY31u5TI1LYtlCyps6PlJA33iljt2rVjli5rm+sU2gE2d+5cGjVqRIkSJfwuJTIVLQodO2roV05KTtb+7AjVtm1bEhMT/S4j4im0AywxMVH7s3NbXBysWwerVvldSfDt2wfbt6ulHaFatWrFwoULOXTokN+lRDSFdoDNmjVL+7NzW9pVv3QUefalXd1LLe2IVKpUKerXr893333ndykRTaEdUEePHmXu3Lm0adPG71IiW40aUL++9mvnhLThXmppRyx1kec+hXZArVixggsuuEDjs/NCfLw39Gv/fr8rCTZdRzvitW7dmrlz5/pdRkRTaAfUli1bqFatmt9lRIe4ODh6FL76yu9Kgm3NGjjnHChXzu9KJJdUrVqVrVu3+l1GRFNoB9SOHTt0Gc68ctllUKqUusizS8O9Il6lSpXYsWOH32VENIV2QG3fvh1dwjSPFCkCnTpp6Fd2pV1HWyJWxYoV2b59u99lRDSFdkCppZ3H4uNh/XrQlYzOzrFj8NNPamlHuDJlynDo0CEOHz7sdykRS6EdUArtPKahX9mzYYMX3GppRzQzo2LFivz8889+lxKxFNoBpe7xPFatGvzud9qvfbY03CtqqIs8dym0A0otbR/Ex0NiIuhKRmdOJ1aJGhUrVtTBaLlIoR1QCm0fxMV5Xbwa+nXm1qzxruxVtarflUgu0xHkuUuhHVB79+6ldOnSfpcRXVq3hjJltF/7bCQnQ61a3rW0JaKVLFmSffv2+V1GxFJoB1S5cuXYs2eP32VEl8KFoXNnb7+2hn6dmTVrtD87Suzdu5eyZcv6XUbEUmgHlPYb+SQ+HjZuhKVL/a4kWHRilaihXXe5q5DfBcjZqVSpko7Q9EPXrt7PKVOgUSN/awmKXbtg925YvRpeecXbxZDRrXRp78xzBdSWCDKNbMldCu2AUkvbJ1WqQJMm3n7tv/7V72qCoUABb7t9+aV3O53SpTMP9vCAP930IkVypPxhKSk0L12aDmEX55m5axfz9u3jwerVc+Q5Iola2rlLoR1QCm0fxcXBsGGwZw9o393plS3r7VI4ehT27fOGzKX9zMpt3z7YtOnkx7JyTEGxYln7AnCaLwLNS5Wix4oVTKxfnw7lyzNz164T9+Vkqamp7Ny5kwoVKvhdSsRSaAdUpUqV+OGHH/wuIzrFx8Nzz3mtxuuv97ua4Chc2LvK1znnZG89zsEvv2Qe8Kf6ArB+/cnzZuF0mx0KFGBi69b0ePBBBpQrx+sFCpwIcDnZnj17KFWqFIULF/a7lIil0A6oihUr8s033/hdRnSKjfUuLzllikLbD2bevu9Spbxu9+w4fDhLrf4OO3Yw4KOPeLp3bx6rVk2BnYnt27erazyXKbQDSt3jPipUCLp0+XXol5nfFcnZKlrUu50maGZu2cLrSUk8tmEDrxcuTIdy5RTcGdD+7NynwzQDqkqVKqSkpPhdRvSKi4PNm2HxYr8rkVw2c9cueqxZw8Qnn+Sp1auZWL8+PVasYOauXX6Xlu+kpKRQJbu9H3JKCu2AqlevHtu3b2fbtm1+lxKd0oZ+6exoEW/evn3ePuyVK+HgQTqUL8/E+vWZp7N+/cY333xDq1at/C4joim0A6pgwYK0bt2axMREv0uJTuedB82aKbSjwIPVq3td4cWLw8GDAHQoX17DvTIwa9Ys2rZt63cZEU2hHWDt2rVTaPtkWEoKM2+5BebM8U4egteNOky7LCJXWGjLb+3atYvk5GSaNWvmdykRTaEdYG3btmXWrFl+lxGVmpcuTY9mzZjZqBFMm3Zi7G5zXcQlcim0T+mbb76hZcuWGu6VyxTaARYTE8MPP/ygC4f4oEP58kz83e/o8cQTDN68+aSTb0iEUmifUmJiorrG84BCO8CKFClC8+bN+fbbb/0uJSp1qFCBAT/+yNNNmjDg/PMV2JFOoX1Ks2bNol27dn6XEfEU2gHXtm1b7df2ycxdu3i9SRMeGzOG11NSNAQo0im0M3XgwAGWLFlCy5Yt/S4l4im0A06h7Y8T55++6CKeGj2aiYsXa+xupFNoZ2ru3Lk0atSIEiVK+F1KxFNoB1zr1q1ZtmwZW7Zs8buUqHJi7G7NmtCyJR3GjdPY3Uin0M7UJ598QufOnf0uIyootAOuZMmSdO/enbffftvvUqLKibG74F1AZN48Ohw9qrG7kUyhnaGDBw8yduxYbrvtNr9LiQoK7QjQv39/3njjDVJTU/0uJTrFx3vnIP/iC78rkdyk0M7QpEmTaNGiBTVr1vS7lKig0I4AMTExVKhQgWnTpvldSnRq2tQ7Q5rOjhbZFNoZGj58OP379/e7jKih0I4Q/fv3Z/jw4X6XEZ0KFPAuIDJ1Khw75nc1klsU2r+xZMkS1q9fT3x8vN+lRA2FdoTo2bMns2bNYsOGDX6XEp3i42H3bu+0phKZ0kLbOb8ryTdGjBjBH//4RwoV0lWe84pCO0KUKlWKm266iTfffNPvUqJT587edbbVRR65ihf3fh4+7G8d+cT+/fsZP348t99+u9+lRBWFdgTp168fb775JsfURZv3ypaFyy5TaEeytNBWFzkA48ePp3379lStWtXvUqKKQjuCNGrUiBo1avDpp5/6XUp0io+HJUtAuygik0L7BOecDkDzyWlD28xGmdk2M1sW9tjzZrbSzJaY2UdmVi6TZe8xs2VmttzM7s3JwiVjf/3rX3n00Uc5evSo36VEn7SDcdTajkwK7RMmTZpEamqqTqjig6y0tEcDXdM99iXQ0DnXCPgBeDj9QmbWEPgj0AJoDFxlZhdnq1o5rW7dulGlShUdSe6H+vWhenWFdqRKO0VnlIf2gQMH+Mtf/sIrr7xCgQLqrM1rp93izrlZwM50j01zzqXtOE0CqmWw6CVAknPuQGjer4HfZ7NeOQ0z46WXXuLpp59mx44dfpcTXcwgIQGmT9fBSpFILW0Ann/+eWJjY3VFL5/kxNek24ApGTy+DGhnZhXMrAQQD1yQA88np9GwYUNuvPFGHn/8cb9LiT7x8fDLL6CLuEQehTYpKSn885//ZNiwYX6XErWyFdpm9ghwDHgv/TTn3PfAULyu9C+AxaF5M1vXnWY238zmb9++PTtlCfDkk08yadIkli5d6ncp0aVDByhaFCZP9rsSyWkKbf76179y1113UaNGDb9LiVpnHdpm1ge4CrjFuYzPNuCce8s518w51w6vi311Zutzzr3hnItxzsVUqlTpbMuSkHPOOYfHH3+ce+65h0z+PJIbSpb0glv7tSNPWmgfOOBvHT5JTExk9uzZPPjgg36XEtXOKrTNrCvwV+Bq51ym72AzOzf0szpwHTD+bJ5Pzk6/fv3Ytm0bH330kd+lRJf4ePjhB/jxR78rkZwUxS3t48ePc8899zBs2DBKlizpdzlRLStDvsYDc4C6ZrbBzG4H/g2UBr40s0VmNjw0bxUzC29ifGBmK4BPgYHOuV05/xIkM4UKFeKVV17hgQce4NChQ36XEz3i4ryfUzI61EMCK4pD++2336ZEiRL07NnT71KinuXHrtOYmBg3f/58v8uIGNdffz01atTgH//4h9+lRI+6daFWLV2uM5Js3w7nngv/+hfcdZff1eSZlJQUmjdvzueff86ll17qdzkRy8wWOOdiTjefBtlFgREjRvDBBx/w4Ycf+l1K9EhIgP/+1zuSXCJDFLa0jxw5wg033MCgQYMU2PmEQjsKVKhQgYkTJ9K/f39+1H7WvBEf743VnjnT70okp0RhaA8aNIjzzz+fQYMG+V2KhCi0o0SLFi0YPHgwN9xwAwej6EPHN23bekeSa+hX5ChYEAoXjprQnjhxIpMnT2b06NGYmd/lSIhCO4oMHDiQunXrcvfdd/tdSuQrWhQ6dfKGfuXD40bkLKVdUzvCrVq1ioEDBzJp0iTKlcvw0hLiE4V2FDEzRo4cSWJiIu+8847f5US+hARISYEVK/yuRHJKFIT2gQMHuP7663nmmWdo1qyZ3+VIOgrtKFO6dGk++OADBg0apLOl5ba0oV860UrkiPDQds4xYMAAmjZtyh//+Ee/y5EMKLSjUIMGDXjxxRe5/vrr2bt3r9/lRK5q1aBRI+3XjiQRHtpvvfUWCxYs4PXXX9d+7HxKoR2lbr31Vjp06EDPnj05rCtS5Z74eJg9G/bs8bsSyQkRHNpfffUVDz/8MO+//77OepaPKbSj2L/+9S+KFStGz549OXr0qN/lRKaEBDh+HL780u9KJCdEaGgnJibSs2dP3n//ferVq+d3OXIKCu0oVrhwYf7zn/9w9OhRevXqxbFjmV6ETc5WbCyUK6f92pEiAkM7KSmJ7t27M378eNq3b+93OXIaCu0oV6RIEd5//3127drFbbfdRmpqqt8lRZZCheDKK73Q1rYNvggL7f/9739cc801jB49mk6dOvldjmSBQlsoVqwYH3/8MSkpKfTr10/BndPi42HrVli40O9KJLsiKLSXLFlCfHw8I0aMID4+3u9yJIsU2gJAiRIl+Oyzz1ixYgV33323rsGdk7p2BTN1kUeCCAnt77//nq5du/LKK69w7bXX+l2OnAGFtpxQqlQpPv/8c+bOncugQYMU3Dnl3HOheXOFdiSIgNBevXo1nTt3ZujQodx4441+lyNnSKEtJylbtixTp05lxowZPPLIIwrunBIfD3Pnepd3lOAKeGivXbuWTp068cQTT3Drrbf6XY6cBYW2/MY555zDtGnTmDJlCrfddpvGceeE+HjvHORTp/pdiWRHgEP722+/pU2bNjz88MPccccdfpcjZ0mhLRmqVKkSs2fPZs+ePXTq1IntaiFmz6WXet3k6iIPtuLFvXH3ATuvwZgxY7j22mt566236N+/v9/lSDYotCVTJUuW5P3336d9+/a0aNGCZcuW+V1ScBUo4J2L/IsvQOPhgytg19ROTU3loYce4sknn2TmzJnEpZ0PXwJLoS2nVKBAAYYMGcKQIUPo0KEDn332md8lBVd8POza5e3blmAKUGjv37+f6667jjlz5jB37lwaNGjgd0mSAxTakiW33HILn376Kf369eOFF17QAWpno0sXKFhQXeRBFpDQ/umnn2jTpg0VK1bkyy+/pGLFin6XJDlEoS1ZFhsby5w5cxg7diy33367DlA7U+XKQZs2Cu0gC0Boz5kzh1atWtG3b19GjhxJkSJF/C5JcpBCW85I9erVmT17Nrt379YBamcjPh4WLYKNG/2uRM5GPg/td999l2uuuYY333yT++67T5fXjEAKbTljpUqVOnGAWtOmTZkyZYrfJQVH2ukitc2CKZ+G9t69e7nttttOHHCm05JGLoW2nJW0A9TGjBnDgAED6NevH/v27fO7rPyvYUO44AJ1kQdVPgztGTNm0KhRIwoXLszChQt1wFmEU2hLtlxxxRUsXryYo0eP0rhxY2bNmuV3Sfmbmdfa/vJLOHLE72rkTOWj0D5w4AB33303vXv35vXXX2fEiBGULl3a77Iklym0JdvKli3LqFGjeOWVV+jZsyf3338/B/PBh1q+FR8P+/dDYqLflciZyiehnZSURJMmTdi5cydLlizR+OsootCWHNOtWzeWLl3Kxo0bufTSS5k/f77fJeVPV1wBRYqoizyIfA7tw4cP87e//Y1rr72WZ599lrFjx3LOOef4Uov4Q6EtOapChQpMmDCBwYMHk5CQwOOPP84RdQOfrFQpuPxyhXYQ+RjaixcvpkWLFixfvpzFixdz/fXX53kN4j+FtuSKnj17snDhQubNm0dsbCwLFizwu6T8JT4eVq6E5GS/K5Ez4UNoHzp0iCFDhtCpUyfuv/9+Pv74YypXrpxnzy/5i0Jbck2VKlWYPHkyd911F1dddRV9+vRhw4YNfpeVP6QNyVFrO1jyMLSdc/znP//hkksuYf78+SxYsIA+ffpo7HWUU2hLrjIzbrvtNlatWkW1atVo3LgxgwcPZv/+/X6X5q+LL4batRXaQVOkiDcCIJdDe86cObRu3Zrnn3+e0aNH8/HHH1O9evVcfU4JBoW25IkyZcrwzDPPsHDhQpKTk6lbty6jRo3i+PHjfpfmn4QEmDkTDhzwuxLJKjOvtZ1Lf7O1a9dy44030qNHDwYMGMC8efNo3759rjyXBJNCW/JU9erVGTt2LB999BFvv/02zZo1Y/r06X6X5Y/4eDh0yAtuCY7ixXO8pb17924efPBBmjdvTsOGDVm1ahW9e/emQAF9RMvJ9I4QX7Ro0YJZs2YxePBg+vfvz1VXXcX333/vd1l5q107KFFCXeRBk4OhffToUV599VXq1avHzp07Wbp0KY899hglSpTIkfVL5FFoi2/MjO7du7N8+XKuuOIK2rdvz8CBA1m/fr3fpeWNYsWgY0cvtHWp0+DIgdA+fvw4H374IY0aNeLjjz9m2rRpvPnmm5x//vk5VKREKoW2+K5o0aLcf//9fP/995QoUYImTZrQu3dvlixZ4ndpuS8hAdat84Z/STBkI7QPHjzIiBEjuOSSSxg6dCj/+Mc/mDZtGo0aNcrhIiVSKbQl36hQoQLPP/88a9asoX79+nTt2pW4uDhmzJiBi9SWaNrpJydP9rcOybqzCO2dO3cyZMgQatWqxaeffsqbb75JUlIS8fHxGsIlZ0ShLflOuXLleOihh1i7di033HADAwcOpHnz5kyYMIFjx475XV7Oql7du/KX9msHxxmE9rp167jnnnuoXbs2a9as4auvvuKzzz6jXbt2Cms5KwptybeKFi3KbbfdxvLly3n88cf597//TZ06dfj3v//NL7/84nd5OSchwbt4yN69flciWZGF0F64cCE333wzl156KcWKFWPp0qW8/fbbumymZJtCW/K9AgUK0K1bNxITE3nvvfeYMWMGtWrVYvDgwWzbts3v8rIvPh6OHYNoHfoWNJmEtnOOadOm0blzZ7p160azZs1ITk5m6NChVK1a1YdCJRIptCVQWrVqxYcffsjs2bPZunUrderU4fe//z0fffRRYC9MMuyCC5jZps1J+7Vn7trFsJQUH6uSzGzmfNqvGcWWLd79tWvX8uSTT1K7dm0eeOABevXqRXJyMoMGDaJs2bL+FisRR6EtgVSnTh1GjBhBSkoKV111FS+99BJVq1blz3/+M/Pnzw/UgWvNy5WjxyOPMHPdOnCOmbt20WPFCpqXLu13aZKBp1d0Z/bhGG66aQXt2rWjRYsW7NixgwkTJrBkyRL69OlDkSJF/C5TIpTlxw+3mJgYp2sxy5lKTk7m3XffZcyYMRQrVow+ffpwyy23BKJrcsl2ZlQAAA9tSURBVOZ//kOPYsUYsG0br9eqxcQVK+iwa5c3fts5SE399ffMHsutefx87rx4LIvLudRUNh0/l9qpqzlEcQoUOMzIkV/Rq1cnhbRkm5ktcM7FnHY+hbZEGuccs2fP5p133uGDDz6gRYsW9OnTh2uvvTb/nmlq+3YGP/EET99wA4+NGcNTb7/96zQz71agwK+/Z/ZYVubJyeWiYF0/79zJ0uXLWbZ8OR8c/xffHLqZo64IRYrAHXfAq6/697aRyJFjoW1mo4CrgG3OuYahx54HugFHgDXAH5xzuzNY9j7gDsABS0PzHTpdUQptySkHDhzg448/5p133uG7776je/fu/P73v6dDhw75KsDTusQHnHcer2/ezMT69elwzjl+lxW1Vq9ezeTJkxk3bhwbNmygV69exMffTlxcXQ6FfYIVL+5dEv288/yrVSJDVkM7K/u0RwNd0z32JdDQOdcI+AF4OIMCqgJ3AzGhsC8I9MzC84nkmBIlSnDzzTczdepUli1bRr169Rg2bBjnnXceCQkJvPbaa/z000++1pgW2BPr1+epiy5iYoMG9Pj+e2bu2uVrXdHkyJEjTJ8+nfvuu486derQrl07li5dylNPPUVKSgrDhg1j4sS6pKaevNzx4/D00/7ULNEpS93jZlYT+CytpZ1u2u+B651zt6R7vCqQBDQG9gIfA/90zk073fOppS25bdeuXUybNo3JkyczZcoUKleuTEJCAvHx8bRu3ZrChQvnWS3DUlJoXro0HcqXP/HYzF27mLdvHw/qGsq5ZvPmzXz++edMnjyZr776iksuuYSEhAQSEhJo0qTJb66w1bQpLFr02/U0aQILF+ZR0RKxcnSf9mlC+1NggnNubAbT7gGeAQ4C09IHe2YU2pKXjh8/zrx585g8eTKTJ09m3bp1dO7cmYSEBOLi4qhUqZLfJUoOSE1NPenvnJycTJcuXUhISKBr166ce+65fpcoUSxPQtvMHgFigOtcuhWZWXngA+BGYDcwCXg/o3APzX8ncCdA9erVL/W7y1Ki16ZNm5gyZcpJLbDLL7+c2NhYWrVqReXKlf0uUbLg6NGjLFmyhKSkJL799lu+/PJLzj333BOt6VatWuVpj4rIqeR6aJtZH6A/0NE5dyCDZW4Aujrnbg/d7w3EOuf+dLrnU0tb8ovDhw8ze/ZsZs+eTVJSEklJSZQvX/5EgLdq1YrGjRvrwz8f2LJlC0lJScyZM4ekpCT+97//UaNGDVq1akVsbCwdO3akZs2afpcpkqGshnahs1x5V+CvQPuMAjskBYg1sxJ43eMdASWxBErRokXp2LEjHTt2BLwu1lWrVp0Ih5EjR5KcnEzTpk1PhEOrVq10XeRcduTIERYvXnwioOfMmcOePXto2bIlrVq14tFHH6VFixY6I5lEnKwM+RoPXA5UBLYCj+MdLV4U+Dk0W5Jzrr+ZVQHedM7Fh5Z9Eq97/BiwELjDOXf4dEWppS1BsnfvXr777rsTAZKUlESpUqVo2bIl9evXp27dutSrV486depQsmRJv8sNFOccGzZsYNWqVaxcuZKVK1eyaNEiFi1axIUXXnjSF6U6der85uAxkaDQyVVEfOKcY/Xq1cybN4+VK1eeCJzVq1dTqVIl6tWrdyLI036vWrVqVF+q8cCBA6xevfqk7bVy5Up++OEHypQpc9L2atiwIc2bN6dMmTJ+l/3/7dxrjFx1Gcfx369surs0raVlLUUqtcaOJQRB9gX6wkuqSa2kmmgMjSQ0IESjEJUYJPjCaOILMSEmEEkFxBgs0QbwEkBqxBBNS9JALcilBqG1Yne3tIXQ0i1bHl/MYTPsbU5ndy7P7veTnMycM/8z++yT/85vz5kzA8wYQhvoMCdPntS+fftGA6k2oI4ePapKpaJKpaLVq1dr+fLlOuuss7Rs2bLR256ennb/Cg2JCB06dEgDAwM6cODA6O3evXtH+zAwMKBVq1aNBvPb/8xUKhVOcWNOILSBRI4cOTIa4Hv27BkNt7cDbnBwUL29ve8I8bG3y5Yt06JFi9Td3a2enp7Rpbu7e0aO4kdGRnT8+PHRZXh4WG+88YYOHjw4LpBrbwcHB7VgwYJx9a5YsWI0oFeuXKmuroYusQFmBUIbmEUiQocPH35HGE4UkK+//vq4YB0eHtb8+fPfEeK1od7T06PTTjtNw8PD4/atXZek3t7ecfsvXbp0wn8gau93d3e3uYNAZ2vq1eMAWsu2lixZoiVLlmjNmjWntO9bb72lEydOTBnKIyMjk4b62+scCQPtx18hMMvNmzdvNIB5fxjIjc9HAACQBKENAEAShDYAAEkQ2gAAJEFoAwCQBKENAEAShDYAAEkQ2gAAJEFoAwCQBKENAEAShDYAAEkQ2gAAJEFoAwCQBKENAEAShDYAAEkQ2gAAJEFoAwCQBKENAEAShDYAAEkQ2gAAJEFoAwCQBKENAEAShDYAAEkQ2gAAJEFoAwCQBKENAEAShDYAAEkQ2gAAJEFoAwCQBKENAEAShDYAAEkQ2gAAJEFoAwCQBKENAEAShDYAAEkQ2gAAJEFoAwCQBKENAEAShDYAAEkQ2gAAJFE3tG3fZXvQ9tM12262/Zzt3bbvt714gv0qtnfVLK/Z/uZM/wIAAMwVZY6075a0bsy2bZLOj4gLJO2RdOPYnSLi+Yi4MCIulHSxpGOS7p9euQAAzF11QzsiHpN0aMy2RyJipFjdIemcOk+zVtILEbG3oSoBAMCMvKd9paSH6oy5TNKWqQbYvsb2Tts7h4aGZqAsAABml2mFtu2bJI1IumeKMfMlbZD026meKyI2R0R/RPT39fVNpywAAGalrkZ3tH2FpEslrY2ImGLoZyQ9EREDjf4sAADQYGjbXifpBkkfj4hjdYZvVJ1T4wAAoL4yH/naImm7pIrt/bavknSrpIWSthUf57q9GHu27Qdr9j1d0qcl3deU6gEAmEPqHmlHxMYJNt85ydiXJa2vWT8maWnD1QEAgFF8IxoAAEkQ2gAAJEFoAwCQBKENAEAShDYAAEkQ2gAAJEFoAwCQBKENAEAShDYAAEkQ2gAAJEFoAwCQBKENAEAShDYAAEkQ2gAAJEFoAwCQBKENAEAShDYAAEkQ2gAAJEFoAwCQBKENAEAShDYAAEkQ2gAAJEFoAwCQBKENAEAShDYAAEkQ2gAAJEFoAwCQBKENAEAShDYAAEkQ2gAAJEFoAwCQBKENAEAShDYAAEkQ2gAAJEFoAwCQBKENAEAShDYAAEkQ2gAAJEFoAwCQBKENAEAShDYAAEkQ2gAAJEFoAwCQBKENAEASdUPb9l22B20/XbPtZtvP2d5t+37biyfZd7HtrcXYZ21/ZCaLBwBgLilzpH23pHVjtm2TdH5EXCBpj6QbJ9n3p5IejogPSvqQpGcbrBMAgDmvbmhHxGOSDo3Z9khEjBSrOySdM3Y/24skfUzSncU+JyLiyLQrBgBgjpqJ97SvlPTQBNtXSRqS9AvbT9q+w/aCGfh5AADMSdMKbds3SRqRdM8ED3dJ+rCkn0XERZKOSvruFM91je2dtncODQ1NpywAAGalhkPb9hWSLpX05YiICYbsl7Q/Ih4v1reqGuITiojNEdEfEf19fX2NlgUAwKzVUGjbXifpBkkbIuLYRGMi4oCk/9iuFJvWSnqmoSoBAECpj3xtkbRdUsX2fttXSbpV0kJJ22zvsn17MfZs2w/W7H6tpHts75Z0oaQfzfhvAADAHNFVb0BEbJxg852TjH1Z0vqa9V2S+huuDgAAjOIb0QAASILQBgAgCUIbAIAkCG0AAJIgtAEASILQBgAgCUIbAIAkCG0AAJIgtAEASILQBgAgCUIbAIAkCG0AAJIgtAEASILQBgAgCUIbAIAkCG0AAJIgtAEASILQBgAgCUIbAIAkCG0AAJJwRLS7hnFsD0naW2LomZIONrmcuYreNgd9bQ762hz0tTkm6uu5EdFXb8eODO2ybO+MiP521zEb0dvmoK/NQV+bg742x3T6yulxAACSILQBAEgie2hvbncBsxi9bQ762hz0tTnoa3M03NfU72kDADCXZD/SBgBgzkgR2ra/Zfuftp+2vcV2z5jHv2r7Kdu7bP/N9nntqjWTen2tGfdF22Gbq0hLKDFfN9keKubrLttfaVet2ZSZs7a/ZPuZYtyv21FnNiXm7C0183WP7SPtqjWTEn19r+1HbT9pe7ft9XWfNCI6epH0HkkvSuot1n8jadOYMYtq7m+Q9HC76+70pUxfi+0LJT0maYek/nbX3elLyfm6SdKt7a4121Kytx+Q9KSkM4r1d7e77k5fyr4W1Iy/VtJd7a6705eS83WzpK8V98+T9FK9501xpC2pS1Kv7S5Jp0t6ufbBiHitZnWBJN6oL2fKvhZ+KOnHko63srDkyvQVjanX26sl3RYRhyUpIgZbXF9WpzJnN0ra0pKq8qvX15C0qLj/rgkeH6fjQzsi/ivpJ5L2SfqfpFcj4pGx42x/3fYLqgbMda2tMp8yfbV9kaQVEfHHNpSYUtn5KukLxemwrbZXtLTIpEr2drWk1bb/bnuH7XWtrjObU5izsn2upPdJ+kvrKsypZF+/L+ly2/slPajqWYwpdXxo2z5D0udUnShnS1pg+/Kx4yLitoh4v6QbJH2vtVXmU6+vtudJukXS9e2pMKeS8/UPklZGxAWS/izpl62tMqeSve1S9RT5J1Q9IrzD9uJW1plN2dfYwmWStkbEyVbVl1XJvm6UdHdEnCNpvaRfFa+9k+r40Jb0KUkvRsRQRLwp6T5JH51i/L2SPt+SynKr19eFks6X9FfbL0m6RNLvuRitrrrzNSJeiYjhYvXnki5ucY1ZlXkt2C/pdxHxZkS8KOl5VUMckzuV19jLxKnxssr09SpV3+tWRGyX1KPq95JPKkNo75N0ie3TbVvSWknP1g6wXftH+VlJ/2phfVlN2deIeDUizoyIlRGxUtUL0TZExM72lJtGmfm6vGZ1w9jHMam6vZX0gKRPSpLtM1U9Xf7vllaZT5m+ynZF0hmStre4vqzK9HVfsV2216ga2kNTPWnHh3ZEPC5pq6QnJD2las2bbf/A9oZi2DeKy+p3Sfq2pCvaU20eJfuKU1Syr9cV8/Ufql5/saktxSZTsrd/kvSK7WckPSrpOxHxSlsKTuIUXgs2Sro3ikudMbWSfb1e0tXFa8EWVa8un7K/fCMaAABJdPyRNgAAqCK0AQBIgtAGACAJQhsAgCQIbQAAkiC0AQBIgtAGACAJQhsAgCT+D83PKTOLNRYIAAAAAElFTkSuQmCC\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Point 0: True\n", "Point 1: True\n" ] }, { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Point 0: True\n", "Point 1: True\n", "Point 2: True\n", "Point 3: True\n", "Point 4: True\n" ] }, { "data": { "text/plain": [ "
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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Point 0: True\n", "Point 1: True\n", "Point 2: True\n", "Point 3: True\n", "Point 4: True\n", "Point 5: True\n" ] }, { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Point 0: True\n", "Point 1: True\n", "Point 2: True\n", "Point 3: True\n", "Point 4: True\n", "Point 5: True\n", "Point 6: True\n", "Point 7: True\n" ] }, { "data": { "text/plain": [ "
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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Point 0: True\n", "Point 1: True\n", "Point 2: True\n", "Point 3: True\n", "Point 4: True\n", "Point 5: True\n" ] }, { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Point 0: False\n", "Point 1: False\n", "Point 2: False\n", "Point 3: False\n", "Point 4: False\n", "Point 5: False\n", "Point 6: True\n", "Point 7: False\n", "Point 8: False\n" ] }, { "data": { "text/plain": [ "
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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Point 0: True\n", "Point 1: True\n", "Point 2: True\n" ] }, { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Point 0: True\n", "Point 1: True\n", "Point 2: True\n", "Point 3: True\n", "Point 4: True\n", "Point 5: True\n" ] }, { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Point 0: True\n", "Point 1: True\n", "Point 2: True\n", "Point 3: True\n", "Point 4: True\n", "Point 5: True\n", "Point 6: True\n", "Point 7: True\n", "Point 8: False\n", "Point 9: False\n", "Point 10: False\n", "Point 11: True\n", "Point 12: True\n", "Point 13: True\n", "Point 14: True\n", "Point 15: True\n", "Point 16: True\n" ] }, { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Point 0: True\n", "Point 1: True\n", "Point 2: True\n", "Point 3: True\n", "Point 4: True\n", "Point 5: True\n", "Point 6: True\n", "Point 7: True\n" ] }, { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Point 0: True\n", "Point 1: True\n", "Point 2: True\n", "Point 3: True\n", "Point 4: True\n", "Point 5: True\n" ] }, { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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iIuJjCnIREREfU5CLiIj4mIJcRETExxTkIiIiPqYgFxER8TEFuYiIiI8pyEVERHxMQS4iIuJjCnIREREfU5CLiIj4mIJcRETExxTkIiIiPqYgFxER8TEFuYiIiI8pyEVERHxMQS4iIuJjCnIREREfU5CLiIj4mIJcRETExxTkIiIiPpZtkBtjXjbG7DHGbAg6b6IxZrMxZr0xZp4xpnwWlx1hjNlojNlgjHnDGBMVyuJFRETCXU5G5K8A3TKctxA431rbHNgCPJDxQsaYGsA/gFbW2vOBCGBAvqoVERGRdLINcmvtUmB/hvM+ttaeCvy5EqiZxcWLA9HGmOJAKeCXfNQqIiIiGYRiG/ktwIcZz7TW/gxMAnYCvwIHrLUfh+D2REREJCBfQW6MeQg4BczKZFks8FegHlAdKG2MueEs1zXEGLPaGLN67969+SlLREQkbOQ5yI0xNwG9gHhrrc1klc7Aj9bavdbak8A7QLusrs9aO8Na28pa26py5cp5LUtERCSs5CnIjTHdgFHA1dba5CxW2wm0NcaUMsYY4Arg27yVKSIiIpnJye5nbwArgMbGmF3GmMHAFCAGWGiMWWeMmRZYt7oxZgGAtXYVMBdYC3wTuK0ZBXM3REREwpPJvCvurlatWtnVq1e7XYaIiEihMMassda2ystldWQ3ERERH1OQi4iI+JiCXERExMcU5CIiIj6mIBcREfExBbmIiIiPKchFRER8TEEuIiLiYwpyERERH1OQi4iI+JiCXERExMcU5CIiIj6mIBcREfExBbmIiIiPKchFRER8TEEuIiLiYwpyERERH1OQi4iI+JiCXERExMcU5CIiIj6mIBcREfExBbmIiIiPKchFRER8TEEuIiLiYwpykTBmreXEiROkpKS4XYqI5FFxtwsQkZw5efIkv/76Kz/99BN79uwhOTmZ5ORkjh49muXPsy1L+wmQmppKZGQk0dHRlCpVKl8/y5QpQ/Xq1alVqxaVKlXCGOPyIydStCnIRTzg1KlTp0N6165d6X6m/b53716qVKlCrVq1qFKlCqVKlTodnmlBWrly5SwDNqvzIiMjsdZy/PjxHId/8O+7d+9O9/ehQ4f4+eef2bVrF0eOHKFGjRrUqlWLWrVqUbNmzXQ/a9WqRYUKFRT2IvmgIBcpBL/99hs//vhjpgGdNsKuXLnyGYHXrl27079XrVqV4sUL5l/WGENUVBRRUVHExsaG7HqTk5PZtWtXuvv89ddfM3/+/NP3/9ixY9SsWTNduAcHfsOGDSldunTIahIpaoy11u0aztCqVSu7evVqt8sQybXU1FS2bt3KV199xbp161i3bh1fffUVJ06coGHDhmeMRtN+r1atGpGRkW6X74rDhw+fEfbBv2/bto06deoQFxdHXFwcLVu2JC4ujnPOOcft0kVCxhizxlrbKk+XVZCL5M3Ro0fZsGFDusD+5ptvqFSpUrrAiYuLo1atWmof59GJEyfYvHnz6cc47fGOjo5O9xi3bNmS+vXrU6yY5vCK/yjIRQrY77//ni6w161bxw8//EDjxo1PB0lcXBwtWrQIaWtaMmetZceOHaefk7TnJSkpiebNm6cL+GbNmhEVFeV2ySJnpSAXCaGTJ0/yxRdf8Mknn7B69WrWrVvHgQMHaNGiRbqRdtOmTSlZsqTb5UqQ/fv38/XXX6cbuX///fc0atSIuLg4LrnkErp06UKDBg3UIRFPUZCL5IO1li1btrBw4UIWLlzIkiVLqFu3Ll26dKFt27bExcVRr149tWx96tixY2zcuJGvvvqKpUuXsmjRIkqWLEmXLl3o0qULV1xxBRUqVHC7TAlzCnKRXNq3bx+LFi06Hd6pqamn39g7d+6siVRFmLWWjRs3nn7uly1bxnnnnXf6+b/kkkvUaZFCpyAXycaxY8f4/PPPT795b926lcsuu+z0m/d5552nVmuYOn78OCtWrDj92ti8eTN/+ctfTr82mjVrpteGFDgFuUgG1lrWr19/+s15+fLlnH/++affnNu2bRu2u3vJ2e3fv59PP/309Gvn2LFjdO7c+XS3plq1am6XKEWQglwEZ9T9wQcfMG/ePBYuXEjZsmVPB3enTp0oX7682yWKD/3www+nQz0xMZEaNWpw5ZVXMmDAAC666CKN1iUkFOQStlJTU1myZAkJCQnMmzePli1b0r9/f6688krq1q3rdnlSxKSkpLB69WoWLFjA66+/TrFixYiPjyc+Pp4GDRq4XZ74mIJcwoq1lq+//ppZs2bxxhtvULlyZW644QYGDBhAjRo13C5PwoS1li+++IJZs2bx1ltvUb9+feLj47nuuuuoXLmy2+WJzyjIJSzs2LGD119/nVmzZnH48OHTI6GmTZu6XZqEuZMnT7Jo0SJmzZrF/PnzadeuHfHx8fTu3VvHiZccUZBLkfX7778zZ84cZs2axbfffku/fv2Ij4+nXbt22q9bPOnIkSO89957JCQksHz5cnr16kV8fDxdunQpsC+9Ef9TkEuRcvToUf7zn/8wa9YsFi9eTLdu3YiPj6dbt26UKFHC7fJEcmzPnj289dZbzJo1ix9//JH+/ftzww03cPHFF2uSnKSjIBffS01NJTExkYSEBN59911atWpFfHw8ffv2pWzZsm6XJ5JvW7du5fXXXychIYHU1FTi4+MZOHCgJskJkL8gV29SXHXs2DFeeuklLrjgAkaMGMEFF1xw+qhbgwYNUohLkdGwYUMeeeQRvvvuO9544w0OHDhA27Zt6dOnD8uWLcOLgyrxB43IxRW///4706ZNY8qUKcTFxXHvvfdyxRVXqN0oYeXIkSO88sorPP3001SqVImRI0fSp08fIiIi3C5NCplG5OIbP/zwA8OGDaNRo0Zs3bqVjz/+mA8//JDOnTsrxCXslC5dmjvvvJPvvvuO+++/n6effppGjRoxefJkDh8+7HZ54hMKcikUK1as4Nprr6VNmzaULVuWDRs2MHPmTC644AK3SxNxXUREBH379uXzzz8nISGBxYsXU69ePR588EF+/fVXt8sTj1OQS4FJSUnhnXfeoX379sTHx9OhQwe2b9/O2LFjqV69utvlSZiZsHMniUlJ6c5LTEpiws6dLlWUuXbt2vH222+zYsUKDh48SLNmzbjlllvYsGGD26WJRynIJeSSk5N5/vnnady4MRMmTGDEiBF8//33DB8+nDJlyrhdnoSp1jEx9N+06XSYJyYl0X/TJlrHxLhcWeYaNmzIlClT+P7772nQoAFdunShe/fuLFq0SBPjJB1NdpOQ+e2335gyZQrTp0+nffv2jBw5knbt2mnbt89M2LmT1jExdIqNPX1eYlISXx46xP21a7tYGZCaCsePZ306duysyxMjI+lfvz5Dd+9marVqzN63j04lS0LZsmeeSpUCD712jx07xuuvv86TTz5JZGQkI0eO5LrrrtO3+BUR2o9cXPXzzz/z+OOPM2fOHAYMGMCIESNo1KiR22VJHqWNVGc3aUKnmBgS9+6l/w8/MLtaNTpFRJw9SHNyyiZsz3o6dSrf9++Rm2/miYEDefjVV3l85sysVyxW7M9Qj4nJPOyzOgWvHxMDITyiW2pqKh999BGTJk1iy5Yt3Hfffdxxxx2nD5b0668wYAC89RZUrRqym5UCpiAXVxw+fJiJEycyZcoUbrvtNkaOHEmlSpXcLktCIHH/fvovXcrQ999n6tVXM/uxx+i0bl3er9AYKFnyz1NUVPq/83PKxXUlnjhB/x9/ZGiVKkzdvZvZFSvS6cQJOHQIDh7M/SmnM8tLlcp58J/tVLJkui7B2rVr+ec//8n333/PhAkT6N27N3feaZg+He64A557Lu9PmRQuBbkUqpSUFF555RUeeeQROnbsyNixY6lTp47bZUkoWcsjgwc7I9eff+bxffvyF77Fi7vepj7daWjalE6xsWf8nSepqU6Y5+VDQPCHhwMHICUl+9uLjMw0+H9NTmbZ+vXsLVmXe/as4HhKCaKjYds2jcr9Ij9BriP4S64sXLiQkSNHUrZsWebNm8fFF1/sdklSABL37WPq1Vfz8E8/MbV+fTr16JH3sPOILw8dShfanWJjmd20KV8eOpT3+xbcfs8Pa51NDrn9AHDwIPz2G9UOHuTayEiG7r4Na1MBSEmxPPGE0ag8DGhELjmyceNG7rvvPrZs2cKECRPo06ePJrEVUYlJSfTfuJHZw4fT6frrSbzttvyPXKXA/for1K9nOXb8z//L4sVPsmnTMRo18ubMfPmTjuwmBea3337jjjvuoFOnTnTt2pVNmzbRt29fhXgR9uWhQ8yuU8fZJh4ZmW7kKt71xBOQmmFclpoKLVrMZdq0aZwKwURB8SYFuWTq6NGjjB07lmbNmlGqVCk2b97M3Xffra8RDQP3165Np6go54/Ark2dYmPd3/VMzmrFCjhxIv0H7NTUSGrVuo633nqLFi1asGDBAu2DXgQpyCWd1NRUEhISaNy4MWvWrGHlypU89dRTVKhQwe3SpDCdPOn81D7KvvHVV2D3/Y7FYJ+djLXOpvfvvivFp59+yrhx4xgxYgRdu3Zl/fr1bpcrIaQgl9M+++wz2rRpw7PPPsusWbN4++23adiwodtliRsU5P5ULPCWnpqa7mxjDFdffTUbNmzgr3/9K126dOHWW2/VcdyLCAW5sHfvXq677jpuuOEG7r77blauXMmll17qdlniJgW5P6UFeRa7skVGRjJs2DC+++47YmNjOf/883nqqadIzRD84i8K8jD33nvv0bx5c2rXrs3mzZuJj4+nWDG9LMLeiRPOT82J8JcsRuQZlS9fnokTJ7Jq1SreeecdOnXqxI8//lgIBUpB0Dt2mDpw4ACDBg3innvuYfbs2UycOJHo6Gi3yxKv0Ijcn3IY5GkaNmzIkiVL6NWrFxdffDEvvPCCJsP5kII8DH3yySc0b96c6Ohovv76a7XR5UwKcn/KZZCD813o9913H4sXL2batGn06tWLX375pYAKlIKgIA8jycnJDB8+nJtuuonp06czdepUfa2oZE5B7k95CPI0zZo1Y+XKlbRq1YqWLVvy5ptvhrg4KSgK8jCxcuVK4uLiSEpK4ptvvqFbt25ulyRepiD3p3wEOTiT4R577DHmz5/PY489xnXXXcfvv/8ewgKlICjIi7jjx4/z4IMP0rt3b8aOHUtCQgKxOsymZEdB7k8REc7PfM5Cb926NWvXrqVGjRo0b96cDz74IATFSUHJNsiNMS8bY/YYYzYEnTfRGLPZGLPeGDPPGFM+i8uWN8bMDaz7rTHmklAWL2f39ddfc/HFF7Nx40a+/vprrr32WrdLEr9QkPtT2qGTQ7A7WXR0NE899RSvv/46w4YNY/DgwRw8eDDf1yuhl5MR+StAxj7sQuB8a21zYAvwQBaXfQb4r7X2PKAF8G0e65RcOHXqFOPGjaNz587cc889vPvuu1SpUsXtssRPFOT+ZIxzCuF+4R06dGD9+vVERETQvHlzFi9eHLLrltDI9mtMrbVLjTF1M5z3cdCfK4EzhnrGmLLAZcCgwGVOACfyXqrkxJYtW7jpppsoXbo0a9asobaOjy15kRbk2o/cf4oVC2mQA8TExDBjxgwWLFhAfHw8/fr1Y9y4cdpl1SNCsY38FuDDTM6vD+wFZhpjvjLGvGiMKR2C25MszJo1i/bt2xMfH8/HH3+sEJe8SzsgjEbk/lMAQZ6mR48erF+/nt9++42LLrqILVu2FMjtSO7kK8iNMQ8Bp4BZmSwuDlwITLXWtgSOAKPPcl1DjDGrjTGr9+7dm5+ywk5qaioPPPAADz/8MImJiQwbNkxHZ5P8UWvdvwowyAEqVqzIG2+8wYgRI7j00ktZtGhRgd2W5Eye3+2NMTcBvYB4m/mhgHYBu6y1qwJ/z8UJ9kxZa2dYa1tZa1tVrlw5r2WFncOHD9O3b18+//xzVq1axfnnn+92SVIUpH13dfFst76J1xRwkKe57bbbeOutt7jhhht4/vnnC/z2JGt5CnJjTDdgFHC1tTY5s3WstbuBn4wxjQNnXQFsylOVkqkdO3bQvn17KlWqxKJFi9AHIAmZtJF4WqCLfxQrluWXpoRax44d+fzzz5kyZQp///vfOZnWyZFClZOQ1okYAAAgAElEQVTdz94AVgCNjTG7jDGDgSlADLDQGLPOGDMtsG51Y8yCoIsPB2YZY9YDccDYkN+DMLV8+XIuueQSBg0axAsvvEAJTUqSUIqKcn4ePepuHZJ7hTQiT9OgQQNWrFjB9u3b6d69O/v37y+02xZHTmatX5/J2S9lse4vQI+gv9cBrfJcnWTq1VdfZeTIkfz73/+me/fubpcjRVFakB875m4dknuFHOQA5cqV4z//+Q/33Xcfbdu25T//+Q+NGzfO/oISEtoA5iMpKSk8+OCDzJ07l8WLF9O0aVO3S5KiKm23IgW5/7gQ5OB8+cpTTz1Fs2bNuPTSS0lISKBr166FXkc40tRmnzh06BB9+vRh1apVrFq1SiEuBUutdf9yKcjTDB48mLlz5zJw4EAmT56sr0UtBApyH9i+fTvt2rWjatWqfPzxx1SqVMntkqSoU5D7V0SEq0EOcNlll7FixQqmT5/O0KFDNQmugCnIPW7ZsmVccskl3HbbbUyfPl2T2qRwpLXWjx93tw7JPZdH5Gnq1avH8uXL2bVrF127dtW3qBUgBbmHzZw5k759+zJz5kz+8Y9/YNK+EEGkoGlE7l8eCXKAsmXL8t5779GqVSvatGnDt9/q6zYKgia7edRjjz3Ga6+9xpIlS2jSpInb5Ui40ax1//JQkIMzCW7ixIk0bdqUDh068N5773HJJfoizFBSkHvQE088wVtvvcXnn3+uby0TdyjI/ctjQZ7m5ptvpmrVqvz1r39l/vz5XHzxxW6XVGSote4x48aNY9asWXz66acKcXGPdj/zL48GOUD37t156aWX6NWrF2vWrHG7nCJDQe4hEydOZObMmXz66adUrVrV7XIknJUs6fzUNnL/8XCQA1x11VXMmDGDHj168NVXX7ldTpGg1rpHPP3000yfPp3FixdTvXp1t8uRcFesmPNd5BqR+4/Hgxygd+/epKSk0L17dz766CNatGjhdkm+piD3gMmTJzN58mQWL15MzZo13S5HxBEVpSD3o0L80pT8uOaaa0hJSaFbt24sXLhQ39yYDwpylz3//PM8+eSTLF68mNq1a7tdjsifoqPVWvcjH4zI0/Tv359Tp07RtWtXFi1apCNW5pGC3EUzZsxg/PjxLF68mLp167pdjkh6GpH7k4+CHOBvf/sbKSkpdOnShU8++YTzzjvP7ZJ8R0HukpdffpknnniCxMRE6tev73Y5ImdSkPuTBw7Rmls33ngjKSkpdO7cmcTERBo1auR2Sb6iIHfBv//9bx555BE+/fRTGjZs6HY5IpmLjlaQ+5HPRuRpBg0axKlTp7j88stZvHgxDRo0cLsk31CQF7KEhAQeeOABPv30U84991y3yxHJWlSUtpH7kU+DHODWW28lJSXldJjXq1fP7ZJ8QUFeiN58803uu+8+Fi1apO1A4n1qrfuTj4Mc4Pbbb083Mq9Tp47bJXmegryQLFq0iLvvvpuFCxfSrFkzt8sRyV5UFOgbq/zH50EOcOedd3Lq1Ck6d+7MF198QWxsrNsleZqO7FYItm/fzg033MCbb77JBRdc4HY5IjmjbeT+VASCHOCuu+6iR48e3HDDDaQWgftTkBTkBSw5OZk+ffowevRoOnbs6HY5IjmnbeT+VESCHGDSpEkcPnyYMWPGuF2KpynIC5C1lttvv51mzZpx1113uV2OSO5oG7k/FaEgj4yMZPbs2bzyyiu8++67bpfjWdpGXoAmT57Mhg0b+PzzzzHGuF2OSO4oyP2pCAU5QJUqVZg7dy69evXivPPO00ThTGhEXkCWLFnC2LFjmTdvHqVKlXK7HJHc0zZyf/LJsdZz4+KLL2bcuHH07t2bgwcPul2O5yjIC8BPP/3E9ddfz2uvvaZDr4p/aRu5PxWxEXmawYMH06lTJ2666SZNfstAQR5ix44d45prrmHEiBF06dLF7XJE8i4qyhnZnTrldiWSGz48RGtOPfPMM+zZs4exY8e6XYqnKMhDyFrL3//+d+rVq8fIkSPdLkckf6KinJ9qr/tLER2RA5QoUYI5c+Ywbdo0FixY4HY5nqEgD6Fp06bx5Zdf8tJLL2lym/hfdLTzU+11fynCQQ5QvXp13nrrLW6++Wa2bt3qdjmeoCAPkc8//5wxY8Ywb948ypQp43Y5IvmnEbk/FfEgB2jfvj1jxoyhd+/eHD582O1yXKcgD4FffvmF/v37M3PmTH2bmRQdCnJ/CoMgB7jjjjto06YNt9xyC9Zat8txlYI8n06cOMG1117L3//+d3r06OF2OSKhoyD3pzAJcmMMzz33HNu3b2fixIlul+MqBXk+jR07lgoVKvDAAw+4XYpIaGkbuT+FSZADREVF8fbbbzNp0iS+/vprt8txjYI8HzZv3syUKVOYNm0axYrpoZQiRiNyfwqjIAeoVasWY8eO5fbbbyeliB0IJ6eUPnlkreWOO+7gkUceoWbNmm6XIxJ6CnJfmbBzJ4lJSemCPDEpiQk7d7pcWcG75ZZbiIyMZNq0aW6X4goFeR7NnDmT5ORk7rzzTrdLESkYCnJfaR0TQ/9Nm0isWRNSU0lMSqL/pk20jolxu7QCV6xYMWbMmMGYMWP4+eef3S6n0CnI82DPnj2MHj2aGTNmEBER4XY5IgVD28h9pVNsLLObNqV/16480rMn/TdtYnbTpnSKjXW7tELRpEkThg4dGpbfNKkgz4N7772XgQMHEhcX53YpIgVHI3Lf6RQby9BNm3iiVy+GVq8eNiGe5sEHH2T9+vX85z//cbuUQqUgz6VFixbx2Wef8dhjj7ldikjBUpD7TmJSElObNOHh//6Xqb/84mwzDyNRUVFMmzaNYcOGhdWBYhTkuXD06FHuuOMOnnvuOUqXLu12OSIFKy3I1Vr3hbRt4rPffZfHFy922uybNoVdmF9++eV07NiRRx55xO1SCk1xtwvwk3/9619ceOGF9OzZ0+1SRELHWvj9d/jxR+e0bZvzM+041sePu1uf5MiXhw4528Q3b4bo6NPbzL88dCjsWuxPPvkkzZo1Iz4+nosuusjtcgqcgjyHNm7cyIwZM1i/fr3bpYjkXnIybN+ePqiDfz90KP36lSpB/fowYAD89a+ulCy5c3/t2s4vR4/COecAzjbzcAtxgEqVKjFhwgSGDBnCqlWrKF68aEdd0b53IZKamsqQIUN4/PHHqVatmtvliJwpJQV27TozoNN+7t6dfv3oaCeo69WDjh2dn/XqOefVrQthsMtSkZWc/OceB2Fs4MCB/Pvf/2bKlCncfffdbpdToBTkOfDiiy+SmprK7bff7nYpEq6shf37zwzotN937oSTJ/9cv1gxqF3bCecePf4M7bSf55wD+qrdoik5GUqVcrsK1xljmDZtGu3ateOaa66hVq1abpdUYBTk2di9ezf//Oc/+eSTT3QYVilYeW1/t2oF/funH1XXqgWRka7cDXHZ0aMK8oBzzz2Xf/zjHwwbNox3330XU0Q/vCrIszF69GhuueUWLrjgArdLEb/LT/u7Q4f0o2q1vyUraq2nM2rUKOLi4vjwww+L7DdUKsjP4scff2T+/Pn88MMPbpcifpCX9netWk4wB7e/08Ja7W/JC7XW0ylZsiSPP/44TzzxBN27dy+So3IF+VlMnDiRIUOGUK5cObdLEa8Ibn9nNqo+W/u7X7/0o2q1vyXUTp50Oj8K8nT69u3LP//5T5YsWULHjh3dLifkFORZ2L17N2+++SabN292uxQpTCkp8PPPWY+qM2t/pwVzcPs77aT2txSm5GTnp1rr6URERDBq1CjGjRunIA8nTz/9NPHx8ZwT2B9TioiM7e+Mo+rs2t/BM7/V/havSQtyjcjPcMMNN/Doo4+yZs2aIneQGAV5JpKSknjxxRdZu3at26VIXhw9+mdI57T9Xa/eme3vevWcXbjU/ha/SDucroL8DCVKlGDkyJGMGzeOuXPnul1OSCnIM/Hcc89x1VVXUadOHbdLkcxkbH9nDOvs2t/Bo2q1v6UoUWv9rG699VbGjh3Lt99+S5MmTdwuJ2QU5BkcOXKEyZMns3jxYrdLCV95bX+nHfwkY1BXqaL2t4QHjcjPqnTp0gwfPpz//d//5ZVXXnG7nJBRkGfw4osv8pe//KVIfVrzpKNHndnfWU0qy679HRzWan+LOLSNPFt33nknDRs2ZMeOHUWm66ogD3LixAkmTZrEvHnz3C7Fcybs3EnrmJh0X8CQmJTEl4cO/fllDcHy2v6uVw8uu0ztb5G8UGs9W7Gxsdx6661MmjSJyZMnu11OSCjIgyQkJNCkSRNatWrldime0zomxvmu46ZN6RQbS+L+/fTfuJHZkZGwcuWZo+odO7JvfweHtdrfIvmn1nqOjBgxgqZNm/Lwww8XiT2TFOQBKSkpjB8/nhkzZrhdiielfbdx/y++YOjixUxt25bZY8bQad26P1dKa39feCFce63a3yKFTSPyHKlatSoDBgzg//7v/xg7dqzb5eSbgjzg7bffpmLFinTo0MHtUjyrU2wsQ+fO5Yn4eB7+9ls6DRwIjz76Z1ir/S3iLm0jz7H77ruPVq1aMWrUKN8fvVNf5wVYaxk3bhwPPvhgkTwOb6gkJiUxtVcvHt66lanNm5M4aBD07g3NmyvERbxArfUcq1evHj169OD55593u5R8U5ADiYmJnDx5kp49e7pdimclJiU528jHj+fx775z2uybNpGYlOR2aSKSRq31XBk9ejTPPPMMp06dcruUfFGQA2+++SYDBw7U942fxZeHDjkT3TZsgNTU09vMv8y4m5iIuCc52Zk0WrKk25X4QrNmzahZs6bvjxsS9tvIT506xbx58/jiiy/cLsXTTu9iVqwYpKYCzjbz4N3RRMRlR486bXVtIsyxfv36MWfOHDp37ux2KXkW9kPQxYsXU6dOHerVq+d2Kf4QFOQi4jHJyWqr51K/fv2YN2+er9vrYR/kc+bMoV+/fm6X4R8KchHvSk7WRLdcql+/PrVq1WLJkiVul5JnYR3kaW11BXkuGOMcC11EvCettS65ktZe96uwDvIlS5ZQu3Zt6tev73Yp/qERuYh3qbWeJ/369eOdd97xbXs9rINcbfU8UJCLeJdG5HnSoEEDatWqxdKlS90uJU/CNsjVVs8jBbmId2kbeZ75ub2ebZAbY142xuwxxmwIOm+iMWazMWa9MWaeMab8WS4fYYz5yhgzP1RFh8LSpUupWbOm2uq5pSAX8S611vMsrb2ekpLidim5lpMR+StAtwznLQTOt9Y2B7YAD5zl8ncB3+apugKktnoeKchFvEut9Txr0KABNWrU8GV7Pdsgt9YuBfZnOO9ja23arICVQM3MLmuMqQn0BF7MZ50hlZKSwjvvvKMgzwsFuYh3aUSeL35tr4diG/ktwIdZLPs/4H7AU+/8S5cupUaNGjRo0MDtUvxHQS7iXdpGni9+ba/nK8iNMQ8Bp4BZmSzrBeyx1q7J4XUNMcasNsas3rt3b37Kypba6vlQrJj2IxfxKrXW86Vhw4ZUq1aNzz77zO1SciXPQW6MuQnoBcRbm+k7e3vgamPMduBN4HJjTEJW12etnWGtbWWtbVW5cuW8lpWt1NRUtdXzwxiNyEW8KDXVCXK11vPFj+31PAW5MaYbMAq42lqbnNk61toHrLU1rbV1gQHAp9baG/JcaYhs2bKF6OhoGjZs6HYp/qTWuog3HTvm/NSIPF+6du1a9Ebkxpg3gBVAY2PMLmPMYGAKEAMsNMasM8ZMC6xb3RizoEArzqe1a9dy0UUXuV2GfynIRbzp6FHnp4I8X84//3y2bt3K0bTH0wey/RpTa+31mZz9Uhbr/gL0yOT8xcDiXNZWIBTk+aQgF/Gm5EBzVK31fImKiqJx48Z88803XHzxxW6XkyNhd2S3NWvWcOGFF7pdhn8pyEW8KS3INSLPtwsvvJA1a3I0T9sTwirIU1NT+eqrrxTk+aEgF/EmtdZD5qKLLmLt2rVul5FjYRXk27Zto2zZshTkrPgiT0Eu4k1qrYeMRuQetnbtWo3G80tBLuJNGpGHTPPmzdm8eTPHjx93u5QcCbsg10S3fDJGB4QR8SKNyEOmVKlSNGjQgI0bN7pdSo6EVZBrolsIaEQu4k2a7BZSfmqvh02QW2s1Ig8FBbmIN6m1HlJ+mvAWNkG+Y8cOSpYsSdWqVd0uxd8U5CLepNZ6SGlE7kGa6BYiCnIRb1JrPaTi4uLYsGEDJ0+edLuUbIVVkKutHgIKchFvSmuta0QeEmXKlKFOnTp8++23bpeSrbAJck10CxEFuYg3JSdDZCQUz/bI25JDfmmvh02Qr1+/nri4OLfL8D8FuYg3JSerrR5icXFxrF+/3u0yshUWQW6tZe/evVSpUsXtUvxPQS7iTUePKshDrGrVquzdu9ftMrIVFkGenJxMREQEUVFRbpfifzogjIg3JSdr+3iIlS9fnqSkJLfLyFZYBPkff/xBbGys22UUDRqRi3jTsWNQsqTbVRQpsbGx/PHHH26Xka2wCPKkpCQFeagoyEW8q1hYvKUXmtjYWI3IvSIpKYny5cu7XUbRoCAXkTCh1rqHqLUeQgpyEQkTaq17iFrrIaQgF5EwER0dTWpqKseOHXO7lLNSkEvuKMhFJEwYY3zRXg+bINc28hBRkItIGPHDhLewCHJtIw8hBbmIhBE/bCcPiyBXaz2EdEAYEW/S/2WBUGvdI9RaDyGNyEW8yxi3Kyhy1Fr3CLXWQ0hBLiJhRK11j1BrPYQU5CISRjQi94jk5GSi9WUCoaEgF5EwUrJkSe1H7gXlypXj4MGDbpdRNCjIRSSMHD58mJiYGLfLOKuwCHI/zDr0DQW5iIQRP0yWDosg98NkBd9QkItIGPHDHKuwCXKNyEPEGAW5iIQNP+z1FBZBrtZ6CBUrpgNPiHiR/i8LhFrrHqHWegiptS7iXTogTMipte4Raq2HkIJcRMKIWuseoSAPIQW5iISJ1NRUDhw4oNa6F2gbeQgpyEUkTBw6dIjo6GiKFy/udilnFRZBrm3kIaQgF5Ew4Ye2OoRRkGtEHiIKchEJE36Y6AZhEuRqrYeQglxEwoQfdj2DMAnytNa61X6W+acDwoh4k97fQk6tdQ8pUaIEJUqU4MiRI26X4n86IIyId2k/8pBSa91jatSowY4dO9wuw//UWheRMLFjxw6qV6/udhnZCpsgb9myJWvXrnW7DP9TkItImFi7di0XXnih22VkK2yC/KKLLlKQh4KCXETCxNq1a7nooovcLiNbYRPkF154IWvWrHG7DP9L20au7eQiUoTt3r2bY8eOUadOHbdLyVZYBfm6detI1Wgyf4oFXjIKchEpwtLa6sYHEwjDJsgrVKhAxYoV+f77790uxd/SglwfiESkCFuzZo0vto9DGAU5OKNybSfPp7RPpwpyEW9Rlyyk/DLRDcIsyDXhLQQ0IhfxLh+0gf3CLxPdIMyCXBPeQkDbyEWkiNu3bx8HDhygfv36bpeSI2EX5GvXrtWhWvNDI3IRKeLWrl1Ly5YtKVbMHxHpjypD5JxzziEmJoZt27a5XYp/KchFpIjz00Q3CLMgB014yzcFuYgUcX6a6AZhGOSa8JZPCnIRKeL8NNENwjDINeEtnxTkIlKEJSUlsWfPHho1auR2KTkWtkGuI7zlkYJcRIqwNWvW0KJFCyIiItwuJcfCLsirV69O9erVWb58udul+JMOCCPiTdobJyTef/99rrzySrfLyJWwC3KAfv36MWfOHLfL8CeNyEW8SweEyZfU1FTefvtt+vXr53YpuRK2QT537ly11/NCB4QRkSJq+fLlVKhQgfPOO8/tUnIlLIO8SZMmxMbGsmLFCrdL8R+NyEWkiJozZ47vRuMQpkEOaq/nmYJcRIqg1NRU5s6dqyD3E7XX80hBLiJF0IoVK4iNjaVJkyZul5JrYRvkTZs2pVy5cqxcudLtUvxFQS4iRZBf2+oQxkEOaq/niYJcRIoYP7fVQUGu9npuaT9yEW/SniR5tnLlSsqVK0fTpk3dLiVPwjrImzVrRkxMDKtWrXK7FP/QiFzEu7QfeZ74ua0OYR7k4IzKZ8+e7XYZ/qEgF5EiJDU1tegHuTHmZWPMHmPMhqDzJhpjNhtj1htj5hljymdyuVrGmERjzLfGmI3GmLtCXXwoqL2eSzogjIgUIStXrqRs2bI0a9bM7VLyLCcj8leAbhnOWwicb61tDmwBHsjkcqeAe621TYC2wJ3GGM9tgFB7PZc0IheRImTOnDn079/f7TLyJdsgt9YuBfZnOO9ja+2pwJ8rgZqZXO5Xa+3awO+HgG+BGvmuOMSMMZq9nhsKchEpIvw+Wz1NKLaR3wJ8eLYVjDF1gZaAJ4e98fHxJCQkcOTIEbdL8T4FuYinTNi5k8SkpHTnJSYlMWHnTpcq8o8FCxZQqVIlX7fVIZ9Bbox5CKeFPuss65QB3gbuttYePMt6Q4wxq40xq/fu3ZufsnLt3HPP5dJLL+XFF18s1Nv1JQW5iKe0jomh/6ZNJNaqBTgh3n/TJlrHxLhcmbdZaxk7diyjR492u5R8y3OQG2NuAnoB8dZmPvPJGBOJE+KzrLXvnO36rLUzrLWtrLWtKleunNey8uyBBx5g0qRJnDhxotBv21cU5CKe0ik2ltlNm9K/Z08e6daN/ps2MbtpUzrFxrpdmqctXbqUvXv3cu2117pdSr7lKciNMd2AUcDV1trkLNYxwEvAt9bap/JeYuFo1aoVTZo0ISEhwe1SvE0HhBHxnE6xsQxds4YnunZlaPXqCvEcGDduHKNGjSIiIsLtUvItJ7ufvQGsABobY3YZYwYDU4AYYKExZp0xZlpg3erGmAWBi7YHbgQuD6yzzhjTo2DuRmg8+OCDjB8/npSUFLdL8S6NyEU8JzEpiaktW/Lw8uVM/eWXM7aZS3pr1qxhw4YN3HjjjW6XEhLFs1vBWnt9Jme/lMW6vwA9Ar8vA3x1mKEOHTpQsWJF3nnnHd/PYiwwCnIRT0nbJj77+efpVLEinYYOVXs9G+PHj+fee++lZMmSbpcSEmF/ZLdgxhgefPBBxo4dSxab/UUHhBHxlC8PHXJCe/lyiI09vc38y0OH3C7NkzZv3sySJUu47bbb3C4lZBTkGfTs2ZOUlBQ++ugjt0vxJo3IRTzl/tq16VS2LBw4ABUqAM428/tr13a5Mm+aMGECw4cPp0yZMm6XEjIK8gyKFSvG6NGjGTt2rNuleJOCXMR7/vjD+RkIcsnczp07ee+99xg2bJjbpYSUgjwT/fv35+eff2bZsmVul+I9CnIR79kfOPimtomf1aRJkxg8eDCxRexxUpBnonjx4tx///2MGzfO7VK8R0Eu4j1ps9Q1Is/Snj17SEhIYMSIEW6XEnIK8izcdNNNrFu3jnXr1rldirdoP3IR79GIPFvPPPMM1113HdWqVXO7lJBTkGchKiqKESNGMH78eLdL8RaNyEW8RyPyszpw4ADTp0/nvvvuc7uUAqEgP4vbb7+dTz75hC1btrhdincoyEW8RyPys5o6dSrdunWjfv36bpdSIBTkZxETE8O9997LPffco/3K02g/chHvSRuRK8jP8Msvv/Dkk0/y0EMPuV1KgVGQZ+Oee+5h+/btzJ071+1SvEEjchHv2b8fypSBEiXcrsRz7rrrLm6//XaaNGnidikFRkGejRIlSjB9+nTuvvtuDhw44HY57lOQi3jP/v0ajWdi/vz5rFu3rkiPxkFBniPt27enV69ePPDAA26X4j4FuYj3JCVpolsGhw8fZtiwYUybNo3o6Gi3yylQCvIcGj9+PO+++y4rVqxwuxR3KchFvEcj8jM8+uijXHbZZVxxxRVul1Lgsv32M3HExsby9NNPM2TIENauXUtkZKTbJblDQS7iPUlJ0Lix21V4xldffUVCQgIbNmxwu5RCoRF5LvTv35+aNWvy5JNPul2Ke3RAGBHv0Yj8tJSUFIYMGcL48eOpXLmy2+UUCgV5LhhjeP7555k0aRLbtm1zuxx3aEQu4j3aRn7ac889R+nSpRk0aJDbpRQaBXku1atXj/vvv5+hQ4eG577lCnIRbzl6FI4dU5ADP/30E48//jjTp0/HpHUPw4CCPA9GjBjB7t27eeONN9wupfDpgDAi3qKjup02fPhwhg8fTuMwmy+gIM+DyMhIZsyYwb333sv+tH+icKERuYi36DjrALz77rts3ryZ0aNHu11KoVOQ51GbNm245pprGDVqlNulFC4FuYi3aETOwYMHGT58ONOnT6dkyZJul1PoFOT58D//8z98+OGHfPbZZ26XUngU5CLeohE5//znP+natSsdOnRwuxRXaD/yfChXrhzPPvsst9xyC19++SXly5d3u6SCpyAX8ZYwH5F/8sknzJkzJ2z2Gc+MRuT51LdvX7p3786NN95IajiEm/YjF/GWMB6Rb9++nfj4eF5//XUqVqzodjmuUZCHwJNPPsmBAwd4/PHH3S6l4GlELuIt+/c7/5cxMW5XUqiOHj1K3759GTVqFJ06dXK7HFcpyEMgMjKSOXPm8NJLL/H++++7XU7BUpCLeEvaUd2Khc/bubX29FeT3n333W6X4zptIw+RKlWqMHfuXK666io+++yzorsfo4JcxFvC8KhuU6ZMYf369SxfvjysDvySlfD5CFcI2rRpw9ixY+nduzcHDx50u5yCoQPCiHhLmB1nfcmSJfzP//wP8+bNo1SpUm6X4wkK8hC79dZb6dChA4MGDSqak980IhfxljAake/atYvrr7+eV199lXr16rldjmcoyAvAM888w+7duxk/frzbpYSeglzEW8JkRH7s2DGuueYa7rrrLrp27ep2OZ6ibeQFoGTJksydO5fWrVvTsmVLunfv7nZJoaMgF/GWMBiRW2u58847qV27Nvfff7/b5XiORuQFpHr16syePZtBgwaxdetWt8sJHQW5iHekpjr+ufYAABxtSURBVDpBXsRH5NOnT2fVqlXMnDlTk9syoSAvQO3bt+fRRx+lT58+HD582O1yQkMHhBHxjoMHnYmnRXhEvnz5ch555BHmzZtHmTJl3C7HkxTkBWzo0KG0bt2awYMHF43vL9eIXMQ70g7PWkSD/JdffqF///7MnDmTRo0auV2OZynIC5gxhueff55t27YxceJEt8vJPwW5iHcU4eOsHz9+nH79+nH77bfTs2dPt8vxNE12KwRRUVG88847tGvXjkqVKnHLLbe4XVLeKchFvKOIHmf9xIkTXHfddVSvXp2HHnrI7XI8T0FeSGrVqsUnn3zC5ZdfTvHixRk4cKDbJeWNDggj4h1FcER+8uRJrr/+eqy1zJo1i2JhdOjZvFKQF6Jzzz2XRYsWccUVVxAREUF8fLzbJeWeJruJeEcRG5GfOnWK+Ph4jh8/zttvv02JEiXcLskXFOSF7LzzzuPjjz+mc+fOFC9enOuuu87tknLHGOekIBdxXxEakZ86dYobb7yRQ4cOMW/ePEqWLOl2Sb6hIHdBs2bN+Pjjj+nSpQsRERFce+21bpeUO8WKKchFvCApCaKjISrK7UryJSUlhZtvvpl9+/bx/vvvE+Xz+1PYFOQuueCCC/jvf//LlVdeSUREBH369HG7pJxTkIt4w/79vm+rp6amMnjwYH7++Wfmz59PdHS02yX5joLcRXFxcXz44Yd0796diIgIrr76ardLyhm11kW8wefHWU9NTWXIkCH8+OOPLFiwQN9mlkeaDuiyCy+8kPnz53PrrbfywQcfuF1OzmhELuINPj7OempqKkOHDmXz5s188MEHlC5d2u2SfEtB7gGtW7fm/fff5+abb+ajjz5yu5zsKchFvMGnI3JrLcOHD+ebb77hww8/1KFX80lB7hFt27bl3Xff5cYbb2TRokVul3N2CnIRb/DhiNxay4gRI1i9ejUffvghMTExbpfkewpyD2nXrh1vv/02f/vb30hMTHS7nKwVK6YDwoh4gc9G5NZaRo4cybJly/joo48oV66c2yUVCQpyj7n00kuZPXs2/fv3926Ya0Qu4r7jxyE52Tcjcmsto0aNIjExkY8//pjy5cu7XVKRoSD3oI4dOzJ79myuu+46Zs6c6XY5Z1KQi7gv7ahuPhiRHz16lOuvv54lS5awcOFCKvjkw4dfKMg9qlOnTixdupSxY8dy7733kpKS4nZJf1KQi7jPJ19h+ssvv3DZZZcRERHB4sWLqVixotslFTkKcg8777zzWLVqFevWrePqq6/m4MGDbpfk0H7kIu7zwXHWV69eTZs2bejTpw8JCQk62EsBUZB7XIUKFfjvf/9L7dq1ueSSS9i2bZvbJWlELuIFHj/O+ltvvUX37t159tlnefDBBzFpX7gkIacg94HIyEimTp3K3//+d9q1a8eSJUvcLUhBLuI+j47IU1NTGTNmDPfffz8LFy701+GnfUpB7iN33nknr732Gv369ePFF190rxAFuYj7PDgiT05OZsCAAXz00UesWrWKuLg4t0sKCwpyn+nSpQufffYZEyZMYMSIEZw6darwi1CQi7gvKcmZr+KRfbF37drFpZdeSsmSJUlMTKRq1apulxQ2FOQ+1LhxY1atWsWGDRu46qqrOHDgQOEWoAPCiLju159+okOxYuzeu9ftUvjiiy9o27Yt/fr149VXX9XXkBYyBblPxcbGsmDBAho0aEDbtm3ZunVr4d24RuQirnvis89YlpLC448/7modb7zxBj179uS5555j9OjRmtTmAn2NqY9FRkYyZcoUpk6dSvv27XnzzTfp1KlTwd+wglwk9FJSnHb5vn1nPUUvWMCxoI7Y1KlTmTp1KlFRURw9erTQyk1NTeXRRx/ltdde45NPPqF58+aFdtuSnoK8CBg6dCiNGzdmwIABPProowwdOrRgPxUryEXOzlo4cCDbUE532r8/601WpUpBpUpQqRLbLruMkdu38+7PP5N86hSlSpWiT58+TJo0qdDu3oEDBxg8eDC7d+/miy++4Jxzzim025YzKciLiMsvv5xly5YxYMAAFixYwAsvvEC1atUK5sZ0QBgJJ9bCkSO5C+Xff4esJqJGRkLlyqeDmRYt/vw9s1PFik6QB1QDyg4dyrEZM4iKiuLYsWOULVu20CaXffrpp9x888306NGDWbNmUbJkyUK5XcmagrwIadSoEStXruRf//oXcXFxPPPMMwwYMCD0N6QRufjZsWO5C+V9+5wvKMlMsWLpQ7dxY2jf/uzBXKaM82E4H3777TfuuOMOhgwZwowZM/j111/zdX05kZyczOjRo3nnnXd48cUX6datW4HfpuSMsR6cfdyqVSu7evVqt8vwtS+//JKBAwfSvHlznn/++dAe37hZM2jSBObODd11iuTFyZPO6Dc3oXzkSNbXFxv7Z+AGj5qzOpUr54R5Ebdy5UoGDhzIxRdfzOTJk4n10L7rRYUxZo21tlVeLqsReRHVunVr1q5dy0MPPUTz5s2ZMWMGPXv2DM2Va0QuBSElBf744+whvHdv+r/PtutlTMyfgXvOOdC06dlDuUIFKK63xGAnTpxgzJgxvPzyy0yZMoVrr73W7ZIkE3rVFmHR0dE89dRTXH311dx8883MmzePp556irJly+bvihXkkh1r4eDB3E/2yup1FRWVfoRcv37225W17TZf1q9fz4033kidOnVYt26dDvDiYQryMNCxY0fWr1/PPffcQ4sWLZg5cyYdO3bM+xXqgDDhxVpITs79duWzTfYKDt0LLsi+hR002UsK1qlTp5g4cSJPPfUUEyf+f3t3Hx1Vfedx/P0lCoIEhKAeQmCDhseCBsnyEKuImNbCIqssizCeRRdLOavVWtYqUksrLVTFPXYLR5e2HJAG8VTB5Yg9Go2iHIMVJGBCJAiyEEXCKqCGx0x++8dMsgnkYTLJzJ1LPq9z5mRyuTP5frkz9zNz7+/e+yQzZszQseEJTkHeRiQnJ/OHP/yBDRs2EAgEmDJlCosWLYrusoL6Ru5vJ082f7/yyZP1P1e7dqFvv9WB268fjB7deCgnJ7d4sJfERmlpKTNmzKBTp05s3bqVPn36eF2SREBB3sZMmDCBHTt2cM899zBs2DCee+45RowY0bwnUZAnjjNnQpukmxPK337b8PPVHuzVuzcMG9Z4KF9ySZsY7HW+q6qqYunSpfzqV79i/vz53HPPPbTTcvUNBXkblJKSwpo1a3jhhReYOHEis2bN4tFHH6V9+/aRPYGOI4+NqqqIzuxV53b0aMPPV3uwV48eoSMNNNhLzrJ//37uuusuKioqeO+99+jfv7/XJUkzNfmuNbPlwD8A5c65IeFpTwITgdPAHuAu59w5axQzuxn4HZAE/NE599tWrF1aaOrUqVx//fXcfffdjBo1imXLlpGVFcHRD/pG3jTn4JtvGh5t3dLBXn37arCXtEgwGGT58uU88sgjPPDAA/zsZz/jAn2Q86VIltoKYAnwXK1pecBc51ylmT0OzAUeqv0gM0sClgI5QBnwgZmtd87tbI3CpXX07NmTV155hZUrV3LLLbcwbtw4Fi5cSO/evRt+UFsM8mgGe505U/9zXXBB3dAdMiSywV7aryyt5I033mDOnDkkJyfzxhtvcPXVV3tdkrRAk0HunHvHzNLPmvZ6rV83A/UdXDgC+MQ5txfAzNYAkwAFeYIxM+68804mT57ME088QWZmJrNnz+bhhx8mOTn53Af4PchPnWr+YK+GLkZhVnew15VXwsiRjYdyly4KZfHEzp07efDBB9m1axePP/44t912m0aknwdaYzvKvwIv1DO9F3Cg1u9lwMhW+HsSI8nJySxYsIAf/ehHzJs3j/79+/PLX/6SmTNn1t3klkhBXlnZ/MFe33zT8PNdcsn/B26vXk2fB/uSSyApKX79ikShvLyc+fPn8+KLL/LII4+wdu1anSP9PNKiIDezeUAlkFvfP9czrcGDj81sFjAL0CEPHktLS2PlypVs3bqVOXPm8Pvf/57FixfXnFu5sKIfN2z/He/sgFa9cmFVVdNn9jr7duRIw8/XufO558FuarDXhRe2YkMi3jpx4gRPP/00Tz31FHfccQcff/xx656uWRJC1EFuZjMIDYIb5+o/YXsZUHtHaxrweUPP55xbBiyD0LnWo61LWs/w4cN56623WL9+Pffffz/p6eksXryYOz6ex7HgxUyfDkVFDTzYudBhTpGEcfVAsC+/bPibfocOdUP3mmsaPyd2SkpogJhIG1RVVcWaNWuYO3cuw4cPp6CggH79+nldlsRIVEEeHo3+EDDGOXe8gdk+APqZWV/gM+B2YHpUVYpnzIxJkyYxfvx4nn32WcaMuY8jx/MBo7jYsWP2M1yVVFx/QJ8+Xf+TJiXVDd2mzoHdowdcfLH2K4tEYNOmTfz0pz/FOceqVau4/vrrvS5JYqzJq5+Z2fPADUAP4BAwn9Ao9Q7Al+HZNjvnZptZKqHDzMaHHzseeJrQ4WfLnXO/iaQoXf0scQ0aVMnHHycR2nPi+A5FFKWMbTqIa9+6dlUoi7SyTz75hIceeogtW7awcOFCpk2bppO6+EhLrn6my5hKxAoLQyf6qsvx2GPrmTdvolYaIh746quvWLBgAatWrWLOnDn85Cc/ie7Uy+KplgS51rwSsTvuqH/6okXfISsri5deeolgMBjfokTaqK+++oqFCxcycOBATp48SXFxMXPnzlWIt0EKconYnj31TTWcu5JHH32UxYsX079/f5YsWUJFRUW8yxNpE/bu3cuPf/xjMjIyKC0t5e233+aZZ57h8ssv97o08YiCXCJ24kRoMPrZtxMnjFtvvZWCggJWrVpFfn4+6enpzJs3j4MHD3pdtsh5YfPmzUyZMoURI0bQuXNnioqKWLFiBYMHD/a6NPGYglxaVXZ2NmvXrqWgoICjR48yePBgZs6cSXFxsdelifhOMBhk3bp1fPe732XatGlcd9117Nu3j0WLFpGamup1eZIgFOQSExkZGSxdupTdu3fTt29fxo0bx/jx48nPzycRB1iKJJLjx4/zzDPPMHDgQBYtWsT999/P7t27ue++++jcubPX5UmCUZBLTPXo0YOf//zn7Nu3j8mTJ3PvvfdyzTXXkJuby5mGLioi0kYdOnSIX/ziF6Snp/Paa6+xfPly3n//faZMmaIrk0mDFOQSFxdddBEzZ86kqKiIX//61/zpT3/iyiuvZPHixRw7dszr8kQ8VVJSwg9/+EMGDhxIeXk57777Li+//DLXXXedLmoiTVKQS1y1a9eOCRMmkJ+fz7p16/jwww+54oormDNnDvv37/e6PJG4cc7x9ttvM3HiRG644QbS0tIoLS3l2WefZcCAAV6XJz6iIBfPDB8+nNWrV7Nt2zYAMjMzycnJYeXKlXz99dceVycSG3v27OGxxx5j4MCBzJ49m4kTJ7Jv3z7mz5/PpZde6nV54kMKcvFcnz59eOqppygrK+Puu+/mpZdeonfv3kydOpX169dzuqFztov4RHl5OUuWLGHUqFGMHj2aw4cPs3LlSkpKSpg1a5ZO4iItolO0SkL68ssv+ctf/kJubi4lJSVMmTKFQCBAdna2TgUrvlBRUcHLL79Mbm4u7733HhMmTCAQCJCTk8OFulyunEXnWpfz2r59+1i9ejW5ublUVFQQCAQIBAI6EYYknMrKSvLy8sjNzeWVV14hOzubQCDApEmTdNiYNEpBLm2Cc47t27eTm5vL6tWrueyyywgEAkybNo1evXp5XZ60Uc45/va3v5Gbm8sLL7xA3759CQQCTJ06lcsuu8zr8sQnFOTS5gSDQTZu3Ehubi7r1q1j2LBhBAIBJk+eTNeuXb0uT9qA0tLSmg+V7dq1IxAIMH36dDIyMrwuTXxIQS5t2smTJ9mwYQN//vOfyc/PZ+zYsXzve98jJyeHjIwMHYcrreLMmTO8//775OXl8eqrr3LgwAFuv/12AoEAWVlZep1JiyjIRcKOHDnChg0byMvLIy8vj/bt29eE+o033khKSorXJYpPOOfYtWtXzWtp48aNXHHFFeTk5PD973+fMWPG6Gxr0moU5CL1cM6xc+fOmhXxu+++y4ABA8jJySEnJ4fs7Gw6dOjgdZmSQA4fPsybb75Z85pxztW8XsaNG6d93hIzCnKRCJw+fZqCgoKalXRJSQnXXnttzYp6yJAh2jzaxpw8eZJNmzbVvCb27NnDmDFjal4TAwYM0GtC4kJBLhKFI0eOkJ+fX7MSP378ODfddFPNSrxnz55elyitrKqqih07dtQs84KCAoYOHVqzzEeOHKljvMUTCnKRVrB3796aFXx+fj69evVi5MiRZGZmkpmZydVXX01ycrLXZUqEnHN8/vnnbNu2jcLCQrZt28amTZvo2rVrTXCPHTtWRzlIQlCQi7SyYDDIhx9+yJYtW2pCoLi4mNTUVDIzMxk2bFhNwPfs2VObXz0WDAbZtWsXhYWFNcursLAQM6uzrEaPHk16errX5YqcQ0EuEgeVlZWUlpbWhER1YCQlJdUERXVo9OvXj6SkJK9LPi9VVFTw0Ucf1QnsoqKimg9ZtZeDPmSJXyjIRTzinOOzzz6rEyqFhYUcOnSIoUOH1gRLZmYmGRkZdO/eXcESocrKSg4ePMjOnTvrfHDav38/gwYNqhPYV111FV26dPG6ZJGoKchFEsyxY8fYvn17TQAVFhby6aefcurUKdLS0ujdu3fNz9r309LS6Nat23kf9sFgkC+++IIDBw5QVlbGgQMHzrlfXl5Ojx49GDBgQJ3N44MGDdKANDnvKMhFfOLbb7+tN7hqT6usrDwn3M8O/K5duyZs2AeDQQ4dOtRon1988QUpKSmN9pmamqrAljZDQS5yHvn6668pKytrNAhPnTpFp06d6NSpEx07dmzxz2AwyPHjxzlx4kSLf544cYKUlJRGP4ikpqbSvn17r/+rRRJGS4Jc5xcUSTBdunRh8ODBjV6mtTowIwnV2tOOHj1a77xJSUkNhnxKSkqzPxzom7RI/CjIRXyoY8eOdOzYke7du3tdioh4rJ3XBYiIiEj0FOQiIiI+piAXERHxMQW5iIiIjynIRUREfExBLiIi4mMKchERER9TkIuIiPiYglxERMTHFOQiIiI+piAXERHxMQW5iIiIjynIRUREfExBLiIi4mMKchERER9TkIuIiPiYglxERMTHFOQiIiI+piAXERHxMQW5iIiIjynIRUREfExBLiIi4mMKchERER9TkIuIiPiYglxERMTHFOQiIiI+piAXERHxMQW5iIiIjynIRUREfExBLiIi4mMKchERER9TkIuIiPiYglxERMTHFOQiIiI+piAXERHxMQW5iIiIjynIRUREfExBLiIi4mMKchERER9rMsjNbLmZlZtZUa1pU8ys2MyqzCyrkcc+EJ6vyMyeN7OLWqtwERERiewb+Qrg5rOmFQG3Ae809CAz6wXcB2Q554YAScDt0ZUpIiIi9bmgqRmcc++YWfpZ00oAzCyS5+9oZmeATsDnUVUpIiIi9YrZPnLn3GfAYmA/cBA45px7vaH5zWyWmW0xsy2HDx+OVVkiIiLnlZgFuZl1AyYBfYFU4GIzu6Oh+Z1zy5xzWc65rEsvvTRWZYmIiJxXYjlq/SbgU+fcYefcGWAtkB3DvyciItLmxDLI9wOjzKyThXamjwNKYvj3RERE2pxIDj97HigABphZmZnNNLNbzawMGA1sMLPXwvOmmtmrAM6594EXgQ+Bj8J/a1mM+hAREWmTzDnndQ3nyMrKclu2bPG6DBERkbgws63OuQbPy9IYndlNRETExxTkIiIiPqYgFxER8TEFuYiIiI8pyEVERHxMQS4iIuJjCnIREREfU5CLiIj4mIJcRETExxTkIiIiPqYgFxER8TEFuYiIiI8pyEVERHwsIa9+ZmaHgf/xuo4I9QD+1+siWoH6SCzqI7Goj8RyPvbxd865S6N5koQMcj8xsy3RXnoukaiPxKI+Eov6SCzqoy5tWhcREfExBbmIiIiPKchbbpnXBbQS9ZFY1EdiUR+JRX3Uon3kIiIiPqZv5CIiIj6mIG+AmS03s3IzK6o1rbuZ5ZnZ7vDPbvU87u/MbKuZFZpZsZnNjm/l59QTVR+15u1iZp+Z2ZL4VNxgHVH3YWbB8PIoNLP18au63lpa0kcfM3vdzErMbKeZpcer7npqifb9MbbWsig0s5Nm9o/xrb5OPS1ZHk+E3+MlZvafZmbxq/ycWlrSx+NmVhS+TY1f1fXWUl8fU8L/z1Vm1uAIbzO72cx2mdknZvZwfCpusJaW9HHOY5uiIG/YCuDms6Y9DLzpnOsHvBn+/WwHgWznXCYwEnjYzFJjWWgTVhBdH9UWABtjU1qzrCD6Pk445zLDt1tiWGMkVhB9H88BTzrnBgEjgPJYFRmBFUTRh3PureplAdwIHAdej3GtjVlBFH2YWTZwLXAVMAT4e2BMTCtt3Aqi62MCcA1Qvb560My6xLbURq3g3D6KgNuAdxp6kJklAUuBHwCDgWlmNjhGNUZiBVH00chjG6Ugb4Bz7h3gq7MmTwJWhu+vBM75JuGcO+2cOxX+tQMe/x9H2weAmQ0HLsfbFS3Qsj4SSbR9hFdKFzjn8sLP861z7ngsa21MKy2PfwL+6tM+HHAR0J7Q+/xC4FCMymxSC/oYDGx0zlU65yqA7TQzRFpTfX0450qcc7uaeOgI4BPn3F7n3GlgDaH+PdGCPhpalo1SkDfP5c65gwDhn5fVN5OZ9TazHcAB4HHn3OdxrDESTfZhZu2Ap4AH41xbc0S0PICLzGyLmW32cjNuIyLpoz9w1MzWmtk2M3sy/C0kkUS6PKrdDjwf86qar8k+nHMFwFuEtsAdBF5zzpXEtcqmRbI8tgM/MLNOZtYDGAv0jmONraUXofVttbLwtDZBQR4DzrkDzrmrgAxghpld7nVNUfg34FXn3IEm50x8fcJnT5oOPG1mV3pdUBQuAK4D/p3QZtwrgDu9LKglzKwnMBR4zetaomFmGcAgII1QYNxoZtd7W1XzOedeB14F3iP0oaoAqPS0qOjUNz6hzRySpSBvnkPhFVD1iqjRfZThb+LFhFbAiSSSPkYD95rZPmAx8C9m9tv4lRiRiJZH9RYR59xe4G1gWLwKjFAkfZQB28KbDiuBlwnt20wkzXl//DOwzjl3Ji6VNU8kfdwKbA7v4vgW+CswKo41RiLS98dvwuMWcggF4u441thayqi7JSENSLQtoTGjIG+e9cCM8P0ZwH+fPYOZpZlZx/D9boQGxDS5XyTOmuzDORdwzvVxzqUT+hb4nHPO05Gg9YhkeXQzsw7h+z0ILY+dcaswMk32AXwAdDOz6osq3Ig/+6g2jcTcrA6R9bEfGGNmF5jZhYQGuiXapvVI3h9JZpYSvn8VocF7no+JicIHQD8z62tm7QnttvH0CJW4cs7pVs+N0ErmIHCG0Ke9mUAKodGfu8M/u4fnzQL+GL6fA+wgtO9pBzDLj32c9Rx3Akv82AeQDXwUXh4fATP92MdZr62PCI1sbe/TPtKBz4B2Xi6LFr6ukoD/IhTeO4H/8GkfF4Xr3wlsBjITsI9bw/dPERpQ+Fp43lRCu/+qHzseKAX2APN83Mc5j23q7+nMbiIiIj6mTesiIiI+piAXERHxMQW5iIiIjynIRUREfExBLiIi4mMKchERER9TkIuIiPiYglxERMTH/g8GyXPBvserwgAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Point 0: True\n", "Point 1: True\n", "Point 2: True\n", "Point 3: True\n", "Point 4: True\n", "Point 5: True\n", "Point 6: True\n", "Point 7: True\n", "Point 8: True\n", "Point 9: True\n", "Point 10: True\n", "Point 11: True\n" ] }, { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Point 0: True\n", "Point 1: True\n", "Point 2: True\n", "Point 3: True\n", "Point 4: True\n", "Point 5: True\n", "Point 6: True\n", "Point 7: True\n", "Point 8: True\n", "Point 9: True\n" ] }, { "data": { "text/plain": [ "
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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Point 0: True\n", "Point 1: True\n", "Point 2: True\n", "Point 3: True\n" ] }, { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Point 0: True\n", "Point 1: True\n", "Point 2: True\n", "Point 3: True\n" ] }, { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Point 0: True\n", "Point 1: True\n", "Point 2: True\n", "Point 3: True\n", "Point 4: True\n", "Point 5: True\n", "Point 6: True\n", "Point 7: True\n", "Point 8: True\n", "Point 9: True\n" ] }, { "data": { "text/plain": [ "
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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Point 0: True\n", "Point 1: True\n", "Point 2: True\n" ] }, { "data": { "text/plain": [ "
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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Point 0: True\n", "Point 1: True\n", "Point 2: True\n" ] }, { "data": { "text/plain": [ "
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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Point 0: True\n", "Point 1: True\n", "Point 2: True\n", "Point 3: True\n", "Point 4: True\n", "Point 5: True\n", "Point 6: True\n" ] }, { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Point 0: True\n", "Point 1: True\n", "Point 2: True\n", "Point 3: True\n", "Point 4: True\n" ] }, { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Point 0: True\n", "Point 1: True\n", "Point 2: True\n", "Point 3: True\n", "Point 4: True\n", "Point 5: True\n", "Point 6: True\n", "Point 7: True\n" ] }, { "data": { "text/plain": [ "
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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Point 0: True\n", "Point 1: True\n", "Point 2: True\n", "Point 3: True\n", "Point 4: True\n", "Point 5: True\n", "Point 6: True\n", "Point 7: True\n", "Point 8: True\n" ] }, { "data": { "text/plain": [ "
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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Point 0: True\n", "Point 1: True\n", "Point 2: True\n", "Point 3: True\n", "Point 4: True\n", "Point 5: True\n", "Point 6: True\n", "Point 7: True\n", "Point 8: True\n" ] }, { "data": { "text/plain": [ "
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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Point 0: True\n", "Point 1: True\n", "Point 2: True\n", "Point 3: True\n", "Point 4: True\n" ] }, { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Point 0: True\n", "Point 1: True\n", "Point 2: True\n", "Point 3: True\n", "Point 4: True\n" ] }, { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Point 0: True\n", "Point 1: True\n", "Point 2: True\n", "Point 3: True\n" ] }, { "data": { "text/plain": [ "
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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Point 0: True\n", "Point 1: True\n" ] }, { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Point 0: True\n", "Point 1: True\n", "Point 2: True\n", "Point 3: True\n", "Point 4: True\n", "Point 5: True\n", "Point 6: True\n", "Point 7: True\n", "Point 8: True\n", "Point 9: True\n", "Point 10: True\n" ] }, { "data": { "text/plain": [ "
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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Point 0: True\n", "Point 1: True\n", "Point 2: True\n", "Point 3: True\n", "Point 4: True\n", "Point 5: True\n", "Point 6: True\n" ] }, { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Point 0: True\n", "Point 1: True\n", "Point 2: True\n" ] }, { "data": { "text/plain": [ "
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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Point 0: True\n", "Point 1: True\n", "Point 2: True\n", "Point 3: True\n" ] }, { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Point 0: True\n", "Point 1: True\n", "Point 2: True\n", "Point 3: True\n", "Point 4: True\n", "Point 5: True\n", "Point 6: True\n", "Point 7: True\n" ] }, { "data": { "text/plain": [ "
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LSGgLIYQQLiGhLYQQQriEhLYQQgjhEhLaQgghhEtIaAshhBAuIaEthBBCuISEthBCCOESEtpCCCGES0hoCyGEEC4hoS2EEEK4hIS2EEII4RIS2kIIIYRLSGgLIYQQLpFjaCulJiulDiqlNnjdd69SaqNSKk0pFZ3NY8OUUp8rpX5RSm1WSt2cX4ULIYQQBU1uWtpTgfYZ7tsAdAO+y+GxY4GvtdZ1gQbA5istUAghhBBGcE4raK2/U0rVyHDfZgClVJaPU0qVAm4H+ngecw44l+dKhRBCiALOl/u0awFJwBSl1Fql1CSlVHEfbk8IIYQIaL4M7WCgMTBea90IOAk8m9XKSqlHlVIJSqmEpKQkH5YlhBBCuJMvQ3sPsEdrvcrz8+eYEM+U1nqi1jpaax1drlw5H5YlhBBCuJPPQltrvR/YrZS63nPXncAmX21PCCGECHS5OeTrE2AlcL1Sao9Sqp9S6h6l1B7gZiBOKbXIs26EUmqh18P/DsxQSiUCDYHX8v8lCCGEEAVDbkaP35/Foi8zWXcv0NHr53VAlsdxCyGEECL3ZEY0IYQQwiUktIUQQgiXkNAWQgghXEJCWwghhHAJCW0hhBDCJSS0hRBCCJeQ0BZCCCFcQkJbCCGEcAkJbSGEEMIlJLSFEEIIl5DQFkIIIVxCQlsIIYRwCQltIYQQwiUktIUQQgiXkNAWQgghXEJCWwghhHAJCW0hhBDCJSS0hRBCCJeQ0BZCCCFcQkJbCCGEcAkJbSGEEMIlJLSFEEIIl5DQFkIIIVxCQlsIIYRwCQltIYQQwiUktIUQQgiXkNAWQgghXEJCWwghhHAJCW0hhBDCJSS0hRBCCJeQ0BZCCCFcQkJbCCGEcAkJbSGEEMIlJLSFEEIIl5DQFkIIIVwi2HYBQgjfOX/+PKdOneLUqVOcPHky17fTr8+ePUtISAjFihWjWLFiFC9e/JLr3NwODpaPGSHyi/w3CeFSZ86cYdeuXezcuZOdO3eyY8eOC7d37tzJvn37SEtLyzFoM/5cvnz5C7eLFi3K2bNnLwn2pKQkdu7ceVngZ/YF4OTJkwQHBxMREUH16tUvudSoUYPq1atTtWpVihYtavvtFMIVJLSFcKjjx49nGsbpl6NHj1KlSpVLgrB169YXbkdERFgPQ601Z8+e5Y8//rjky8X333/Pxx9/zM6dO/njjz8oU6ZMlqFevXp1SpQoYfV1COEUSmttu4bLREdH64SEBNtlCOEXWmu2bdtGQkIC8fHxxMfH8/PPP3Pu3LlMAyz9UqlSJYKC3D8sJTU1lb179172pST9y8quXbsoVqwY9evXJzo6mqZNmxIdHU2NGjVQStkuX4h8oZRarbWOznE9CW0h/EdrzZ49e4iPj78Q0qtXr6ZEiRKXBFKDBg0oV66chBLmPTtw4ADr1q275IvN+fPniY6OvuR9i4iIsF2uEHkioS2EAxw8ePCSgE5ISEBrfSFk0q8rVKhgu1TX2bt37yXvbXx8PCEhIZe8r9HR0ZQtW9Z2qULkSEJbCD/TWrN+/XoWLVrETz/9RHx8PMnJyZe1BqtWrSotaB/QWrNjx45LgnzNmjWUKVOG6OhomjVrRvv27YmMjJT3XziOhLYQfnDy5EmWLFlCXFwccXFxhISE0KFDB1q0aEHTpk2pXbu2BIRFaWlp/PrrryQkJLB8+XIWLlxIUFAQnTp1olOnTrRs2ZLQ0FDbZQohoS2Er2zfvv1CSC9fvpymTZvSuXNnOnXqxHXXXSch7WBaazZu3Hjh97du3TruuOOOCyFetWpV2yWKAkpCW4h8cv78eX744YcLH/SHDx+mY8eOdOrUiTZt2lC6dGnbJYo8Onz4MIsWLSIuLo6vv/6aiIiICwHevHlzmRhG+I2EthBX4cCBA3z11VfExcWxePFirr322gsf5k2aNAmIQ63EpVJTU1m1atWFL2e7d++mXbt2dOrUifbt21OmTBnbJYoAJqEtxBU6dOgQM2bMYMaMGfzyyy+0bt2aTp060aFDBypVqmS7POFne/bsYeHChSxYsIBly5ZRv359HnjgAe6//37pXRH5TkJbiFxIS0vj22+/ZdKkSXz99dd07NiRPn360LJlS4oUKWK7POEQZ86cYfHixUydOpXFixfTtWtX+vXrx2233SZjGES+kNAWIhu7d+9mypQpTJkyhbCwMPr160fPnj255pprbJfmGqN27aJpyZLEhIdfuG/pkSPEJyfzTLVqFivzraSkJKZPn86kSZNISUnhoYceonfv3tIbI65KbkNbdsyJAuPcuXN8/vnndOjQgYYNG7J//36++OIL1q5dyxNPPCGBfYWalixJ902bWHrkCGACu/umTTQtWdJyZb5Vrlw5nnzySTZu3MhHH33Eb7/9RmRkJF27dmX+/PmkpKRcsv6+fXDHHbB/v6WCRUCRlrYIeJs2beLDDz/k448/JjIykn79+tGtWzeKFStmuzTXW3rkCN0TExnw9deMb9GC2ZMnE7NrF4SEQNGi5pJ++2ruy255cDBY7qI+ceIEs2fPZtKkSezYsYPevXvz0EMPUadOHQYOhPffh/794d13rZYpHEy6x0WBduLECT799FM+/PBDdu7ceeFD9Nprr7VdWmBJS+OFl15iZKtWDF+zhhHLl8PZs+Zy5syl15ndl5+fPyVKmAAvXNhcW7r9x4EDLFuxgv/98APFKzTkve3fcC61CKGhsH07VKyYfy9ZBI7chrYchCgCyrZt2xg1ahSzZ8+mZcuWDBs2jPbt22d6vG1B3Sebn5Z+8gnjGzdm+NGjjG/WjJi+fS95P7OlNaSk5C7cs7tv7lxISIBHH4Xz581zpqTk7vbJk1e2/vnz5pKNysDfPJeBx+8D0gA4d/Y8zzxzhmnTAnv3gfAtCW0REH799Vdee+01FixYwMCBA9m8eTMVc2jSpO+TnR0ZSUx4+IV9srMjI/1Utbst3bGD7iVKMHvOHGKmTiXm6NFL3s8cKWVap4ULw9XsB3/++bw/Nq/S0rIOds/P+/bBlLa1OXfWDB1KTSvM9OkpwDOMGDGQGjVq+L9u4XoS2sLVNm/ezKuvvsqiRYsYNGgQv/32G2FhYbl6bExYGLNTUui+ahUDvvmG8TExzF64kJizZ6FCBShf3ly8b5ctC4UK+fhVuUP8l18ye8YMYqZOBaWICQ9ndmQk8cnJuW9tu1VQ0MX96lkY+RakZej9L1IkhI0bu9GkSRPuuecehg4dSu3atX1crAgkEtrClX7++WdeeeUVli1bRmxsLO+99x6lSpXK3YN374aPPoKpU4nZto0Bjz3GyB49GL58OTErV8LBg+aSYRQwYFqHZcteDPGMoe59u3x5s581EK1cyTNPPglPPw1RURfujgkPD/zAzqWVK+HcuUvvO3dOkZbWnK1btzJ27FiaNWtGp06deO6557juuuvsFCpcRQaiCVdZt24dI0eOZPny5QwZMoQBAwZQIjfBeOYM/Pe/MGUKfPON2Z/asiVLBwyge0QEAypXZvzevRe7drWGo0fhwIGLIe59O+PPx45lvt1ixbIOeLe24lNSIDoaDh+GTZsC94tJfmrb1rxfGT7Xjh49yttvv824ceNo27Ytzz33HJGye6ZAkoFoIqAkJCQwcuRI4uPjeeaZZ5g+fXrOh2xpDatXm6CeOdOEcLVqMHw49O7N0vDwS/bBxoSFXbpPNv1St27OBZ49m3Wgp/+8Zw+sWZP7Vnx2AW+zFf/OO7B+PcyZI4GdW1FRMGECpKZe8sUsLCyM4cOHM3jwYN577z1iYmJo2bIlzz//PPXq1bNYsHAqaWkLR/vxxx8ZOXIkiYmJ/POf/6Rfv345n/84KQk+/tiE9c8/m2N5u3WDvn2hVSuzPxKLo8e1hiNHcteCz2srPmPA51cr/o8/zJeY22+HBQusHx/tGpMnQ79+sHUrZHPY4YkTJ5gwYQJvvvkmN998M8OHD6dRo0Z+LFTYIsdpC1f76aefeP7559myZQtDhw6lb9++FM1m0A8pKfDVVyao5883P990kwnqHj0gl4PTHOnMGfNFJKeAP3DArHelrfjMWvTFi2deS48e5hCrjRuhVi3fvu5AEh9v/h6//BLuvjvH1U+dOsXEiRN54403aNKkCSNHjqRBgwZ+KFTYIt3jwpUOHTrE0KFDWbBgAS+//DK9e/fO/sQdmzeboJ42zYRW+fIweLAJ6xtv9F/hvhQSAlWrmktO0tLMboCcAn71avPz8eOZP493Kz491IsUgU8/hREjJLCvVPp+6g0bchXaxYoVIzY2lv79+/PBBx/Qtm1b7r//fkaMGJH7AZciIEloC0dIS0tjypQpDBs2jPvuu49NmzZlfejWsWMmPCZPhlWrzGxUnTqZoO7Y0Rz3W1AFBcE115hLbvbFp7fiswv4XbvMAKqDB82+2aef9v3rCDTFi5svOhs2XNHDQkJC+Pvf/87999/Ps88+yw033MDo0aPp0aOHnF2sgJLucWHd+vXrGTBgAKmpqYwfP57GjRtfvlJaGixbZoJ6zhw4fdq0pPv2hQceMK1B4VtpZmav9DEB4gp17Qrbtl1xcHtbsWIFAwcOpEyZMrz77rvUzc0XM+EKcpYv4XjHjx8nNjaWNm3a0LdvX1auXHl5YO/YAS+/DLVrw513msFPvXvDTz+ZQWZDhkhg+0tQkAT21YiKgi1bLj94+wq0aNGChIQEunTpwq233sqwYcM4depUPhYpnE7+A4Xfaa2ZNWsWN9xwA8nJyWzcuJFHHnmEoPRAOHUKZswwIV2zpgnta6819+3bB+PHQ9OmMnJZuEtUlBkkuGXLVT1NcHAwgwcPJjExkR07dhAZGcm8efPyqUjhdLJPW/jVL7/8wuOPP86ff/7JZ599RosWLcwCrc3+6SlTYNYsM0AqPbB794bq1e0WLsTVSp85bsMGyIdjsCMiIpg5cyZLlizh8ccf54MPPmDcuHHUrFnzqp9bOJe0tIVfnDp1iueee45bb72Vu+66i9WrV5vA3r8f3njD7J+++WaYPt2Mrl26FH77DV54QQJbBIbrrzeDJq9in3Zm7rzzTtavX0+LFi1o2rQpr776KmfPns3XbQjnkNAWPjdv3jwiIyPZvn07iYmJxA4cSPD8+dClC1SpAs88Y46j/uADE+IffQQtW8r+UxFYihQxwZ3PoQ1QtGhRhg4dSkJCAj/99BP169fnm2++yfftCPuke1z4THJyMv379ychIYEPP/yQO8uXh9GjzWxlSUlQsSI89RT06ZO7w5OEcLuoKDPRio/UqFGDuXPnMn/+fB599FHatGnD2LFjc55FULiGNGWET6xdu5YmTZpQLjiYnwcM4M5nn4X69c281bfdZkaB794N//63BLYoOKKiYPt2OHnSp5u56667SExM5MSJE9x0001s3rzZp9sT/iOhLfLFvn1wxx2wb5/mvbff5rWWLfmmbFnGfPopRf7xD3OYy5gxsHcvfPGFmQwlWDp6RAGTPhht0yafb6pkyZLMmDGDwYMHc/vtt/PRRx/5fJvC93IMbaXUZKXUQaXUBq/77lVKbVRKpSmlsj0YXClVSCm1Vim1ID8KFs40ciT88L2mb+QndBsyhM+OH6f6L7/AI4+YKTPXrTPTi5Yta7tUIexJD+2ff/bL5pRSPPzwwyxdupTXX3+d3r17c+LECb9sW/hGblraU4H2Ge7bAHQDvsvF4wcD0jcTwPbtgynvnyNNK747ejepLbqZaUb37oW334bGjeWYaiHAHMYYGuqTwWjZiYqKIj4+nqCgIJo2bcrPfvrSIPJfjqGttf4OOJzhvs1a6xxnCFBKVQE6AZPyXKFwNK01f/3rOlI9U1ymFgnltRtnQffu5kQXQoiLChUyJw/xc2gDFC9enClTpjB06FBatWrFBx98gBOnsRbZ8/U+7THAM0Caj7cjLDh06BBt2/Zm5cq6nMcE9LlziilTzJFbQohMREVZCe10vXr14vvvv+ftt9+mZ8+eHM/qTG/CkXwW2kqpzsBBrfXqXK7/qFIqQSmVkJSU5KuyRD5Zvnw5jRo1IinpMQoXvvQ816mpZh+3ECIT9eqZfUqHDlkroW7duqxatYrSpUvTpEkT1qxZY60WcWV82dK+BeiilNoBzAJtZj+ZAAAgAElEQVRaKaU+zmplrfVErXW01jq6XLlyPixLXI20tDT+9a9/8Ze//IX33nsPpW7h3LlL91efOwcrVlgqUAinSx+MtnGj1TJCQ0OZMGECr7zyCu3bt+edd96R7nIX8Floa62Haq2raK1rAD2Ab7XWD/hqe8L3jhw5QocOHYiLiyM+Pp7OnTuzdq2ZNlynpKKLl0APGozWsHat7WqFcCjvOcgd4L777mPFihVMmTKFv/71rzK63OFyc8jXJ8BK4Hql1B6lVD+l1D1KqT3AzUCcUmqRZ90IpdRC35YsbNizZw+33XYbN9xwA8uWLaNq1aqXrlCoEDRoANLNJkT2IiLMtL0OGsF97bXXsmLFCsLCwmjVqhWyi9K5cjN6/H6tdSWtdWFPy/lDrfWXnttFtdYVtNbtPOvu1Vp3zOQ5lmmtO/viBQjf27x5M7fccgu9e/fmrbfeIjirSVEaNzbHY6fJuEMhsqSU9cFomSlatCiTJk2ibdu23HLLLfz++++2SxKZkBnRRLZWrlxJTEwMI0eO5Omnn0Zld7x148Zw4oQ5O5cQImvpoe2wfchKKV555RUGDRrEbbfdxvr1622XJDKQ0BZZiouLo0uXLkyePJlevXrl/IBGjcy1dJELkb2oKDh61ExA5EBPPPEE//nPf2jTpg3Lli2zXY7wIqEtMjV16lT69evH/Pnz6djxsj0emYuMNKcflNAWInv16plrh3WRe+vevTuzZs2ie/fufPHFF7bLER5yxgZxCa01o0aNYvz48Sxbtoy6V3IGriJFzIeRDB0XIns33miuN2yAdu3s1pKNVq1asWjRIjp37szBgwcZMGCA7ZIKPAltcUFaWhpPPvkkS5YsYfny5VSuXPnKn6RRI5gzx+yrk/nGhchcmTJQqZKjW9rpGjVqxPfff0+7du3Yv38/L730UvZjW4RPSfe4AODcuXM88MADrFmzhu+++y5vgQ1mMNrhw7BrV/4WKESgceAI8qzUqlWLH374gQULFtC/f39SUlJsl1RgSWgLkpOT6dSpE6dPn2bRokWEh4fn/ckaNzbX0kUuRPaiosysaKmptivJlQoVKrBs2TK2b9/Ovffey+nTp22XVCBJaBdwBw8eJCYmhlq1avHZZ58RGhp6dU9Yrx4EBclgNCFyEhUFp0+Di46HLlmyJHFxcYSEhNCuXTuOHj1qu6QCR0K7ADt06BAtW7akQ4cOTJgwIetJU65EsWJwww0S2kLkxGHTmeZWkSJFmDFjBo0aNaJ169YkJyfbLqlAkdAuoE6dOkXnzp3p1KkTI0eOzN+BJY0aSfe4EDmJjDTXLgttgKCgIMaMGUPjxo3p1q0b586ds11SgSGhXQCdP3+e7t27U6dOHV5//fX830DjxmbSiAMH8v+5hQgUJUpArVquDG0ws6e99957FC9enD59+pAm0xf7hYR2AaO15rHHHiM1NZUPP/yQoCAf/AnIYDQhcsdFI8gzExwczCeffMLu3bsZMmSInNrTDyS0C5jnnnuOjRs38tlnn1G4cGHfbKRhQ3Mt+7WFyF5UFGzZYk5C71KhoaHMmzePb775hjfeeMN2OQFPJlcpQMaNG8ecOXP44YcfKFGihO82VLo01K4toS1ETqKiICXFBHf61KYuFB4ezqJFi7jllluoUKECvXv3tl1SwJLQLiBmzZrFqFGjWL58OWXLlvX9Bhs3htWrfb8dIdzMewS5i0MboHLlynz99de0bNmSsmXL0qlTJ9slBSTpHi8AFi9ezKBBg1i4cCHVq1f3z0YbNYLt2+HIEf9sTwg3uv56CA529X5tb3Xr1uW///0vffr04ccff7RdTkCS0A5wa9asoWfPnnz++efUr1/ffxtOH4y2bp3/timE2xQpAtddFzChDdC8eXOmTp3K3XffzebNm22XE3AktAPYtm3b6Ny5MxMmTOD222/378bTz60tI8iFyF69egEV2gCdOnXi9ddfp0OHDvzxxx+2ywkoEtoB6sCBA7Rr144XX3yRbt26+b+A8uWhcmUZjCZETqKizK6kkydtV5KvevfuzcCBA2nfvj1HZDdZvpHQDkCnT5+mY8eOPPjggzz22GP2CmncWEJbiJykD0bbtMluHT7w9NNP07p1a+6++27Onz9vu5yAIKEdgIYMGcK1117LCy+8YLeQxo3NoSwB1oIQIl+5dA7y3FBK8eabb1KsWDFeeukl2+UEBAntADNnzhy+/vprJk6caP9E9Y0aQVoaJCbarUMIJ6tZE0JD4eefbVfiE0FBQUydOpWpU6fy7bff2i7H9SS0A8iuXbvo378/M2fOpHTp0rbLuTiCXLrIhchaoULm5CEB2NJOV6FCBaZOnUqvXr1ISkqyXY6rSWgHiJSUFHr27MmQIUNo3ry57XKMKlWgTBkZQS5ETlw+B3lutGnThgcffFBOLnKVJLQDxIgRIyhWrBhPP/207VIuUkoGowmRG1FRsG8fHDpkuxKfGjFiBIcPH2bs2LG2S3EtCe0AsGzZMiZNmsS0adN8c9auq9G4sWlBuPiECEL4XPoUphs32q3DxwoXLszMmTP517/+xWqZ5jhPHPYJL67Un3/+yYMPPsiUKVOoWLGi7XIu16gRnD8f8B9GQlyVAB5BnlHNmjV555136NGjB8nJybbLcR0JbRfTWvPQQw/Ro0cP2rVrZ7uczMlgNCFyFhEBYWEFIrQBunfvTsuWLRk4cKDtUlxHQtvF3n77bfbv38+rr75qu5Ss1a4NJUvKYDQhsqOUaW0H6GFfmRk7diyrV69m2rRptktxFQltl1q7di0jR47kk08+oUiRIrbLyVpQEDRsKC1tIXKSPoJca9uV+EWxYsX49NNPGTJkCL/++qvtclxDQtuFTpw4QY8ePRg7diy1a9e2XU7OGjeG9eshNdV2JUI4V1QUHD0Ke/farsRv6tWrx8svv0yPHj04e/as7XJcQULbhQYPHswtt9xCz549bZeSO40bw6lTIN+mhchaARqM5m3AgAHUqFGDYcOG2S7FFSS0XWbFihUsWrSIcePG2S4l99JP0yld5EJkrXJlc33ggN06/EwpxQcffMD06dP5uQDt088rCW0XSUtLY/Dgwfz73/+mRIkStsvJvRtugJAQCW0hshMebq6PHrVbhwVlypThhRdeIDY2Fl1A9unnlYS2i0ybNo3g4GD3dIunCw42k0fICHIhspZ+voACeu7p/v37c+DAAebOnWu7FEeT0HaJ5ORkhg0bxtixY50361luNG5sQlu+RQuRueBgc3hkAWxpAwQHB/PWW28xZMgQGZSWDRd++hdMr732Gm3atOGmm26yXUreNG5sPox27LBdiRDOFRZWYFvaYE4qEhUVxZgxY2yX4ljBtgsQOdu2bRsTJ0509yAN78FoNWvarUUIpwoLK7At7XSjR4/m5ptvplevXlSqVMl2OY4jLW0XeOqppxgyZAgRERG2S8m7evXMeYNlMJoQWQsPL/ChXadOHR566CGee+4526U4koS2wy1ZsoR169bx5JNP2i7l6oSEwI03ymA0IbJTwLvH0z3//PN89dVXJCQk2C7FcSS0HSwlJYXY2FhGjx5NSEiI7XKuXqNGsHq1DEYTIivS0gagVKlSvPLKKwwePFgOActAQtvBJk6cSNmyZenWrZvtUvJH48Zw8CDs22e7EiGcSVraF/Tp04czZ84wa9Ys26U4ioS2Qx0+fJiXX36ZMWPGoJSyXU7+SD9Np3SRC5G58HBIToaUFNuVWFeoUCHGjh3LP//5T06ePGm7HMeQ0Haol19+mXvuuYcGDRrYLiX/NGhgTkEog9GEyFxYmLk+ftxuHQ5x66230qJFC9544w3bpTiGHPLlQL/99hszZsxg8+bNtkvJXyVLQp06EtpCZCU9tI8cgWuusVuLQ4waNYpGjRrx2GOPySFgSEvbkcaNG8ejjz5KuXLlbJeS/9JnRhNCXK4Azz+elWrVqnHfffcxfvx426U4goS2wxw7doyPP/6YgQMH2i7FNxo1gp074dAh25UI4TzeLW1xwaBBg3j//fc5c+aM7VKsk9B2mMmTJ9O+fXuqVKliuxTfuPFGcy2tbSEuJy3tTNWtW5cmTZrwySef2C7FOgltB0lNTWXcuHEMHjzYdin5LzUVxo+HXr3MzGhFitiuSAjnkZZ2lgYPHsyYMWMK/HHbEtoOMm/ePCpWrEizZs1sl5K/vvsOmjSBgQPNCPK1a+H2221XJYTzpIe2tLQv07ZtW86fP8+yZctsl2KVhLaDjB07ltjYWNtl5J/du+H+++GOO0zL4bPPYMkSMw+5EOJyJUqYnigJ7csopRg8eDBjx461XYpVEtoOsXbtWrZt2xYYs58lJ8PIkVC3Lvz3v/Dii7B5M/z1r+Y4bSFE5pSSWdGy8eCDD7J8+XK2bdtmuxRrJLQdYuzYsTz++OMULlzYdil5l5wM//oX1KgBL7wAHTrAL7/ASy9BsWK2qxPCHWT+8SwVK1aMfv368fbbb9suxRoJbQc4cOAAc+fO5ZFHHrFdSt54h/WwYdCiBcTHw+efQ/XqtqsTwl2kpZ2txx9/nOnTp3O8gM4aJ6HtABMmTOC+++6jTJkytku5MhnD+uab4aefYP58iI62XZ0Q7hQWJi3tbFStWpU2bdowZcoU26VYIaFt2dmzZ5kwYQKDBg2yXUruZRXWCxZA06a2qxPC3aR7PEexsbGMGzeO1NRU26X4nYS2ZZ9++in169cnMjLSdim5c/SoCWkJayF8Q7rHc9S8eXPKli1LXFyc7VL8TkLbsjFjxrjnMK/z5+Hee2HLFli4UMJaCF+QlnauxMbGMmbMGNtl+J2EtkVbtmzhwIEDtGvXznYpOdPaTI6yeDF88IEZGS6EyH9hYXD2LJw+bbsSR/vLX/7C2rVr2b9/v+1S/EpC26J58+bRpUsXgoJc8Gt44w2YNAmeew769LFdjRCBS+Yfz5UiRYrQvn17FixYYLsUv3JBWgSu9NB2vC++gH/+E+67D0aMsF2NEIFNpjLNtS5dujB37lzbZfiVhLYlSUlJJCYmEhMTY7uU7K1aBQ88YI69njoV3NArIISbyUlDcq19+/b873//4+TJk7ZL8Rv5BLYkLi6ONm3aEBISYruUrO3YAV26QESEmY7UybUKESikezzXwsPDadq0KYsXL7Zdit9IaFvi+K7xY8egUyc4dw7i4qBcOdsVCVEwSEv7inTp0oV58+bZLsNvJLQtOHPmDEuWLKFjx462S8lc+qFdv/4Kc+aYE38IIfxDWtpXpEuXLixYsKDATLQioW3Bt99+S4MGDShbtqztUi6nNTz+OHzzjTm0y+n73IUINKVLm2sJ7VypWbMmFSpUYNWqVbZL8QsJbQsc3TU+erQJ62HD5NAuIWwoWhRCQ6V7/AoUpC5yCW0/S0tLY968eXTt2tV2KZf74gt45hlzaNfIkbarEaLgklnRroiEtvCZ1atXU7p0aerUqWO7lEv99JM5tOvmm+XQLiFsk/nHr0h0dDRHjhxh69attkvxOflk9jNHdo3v2AF33QWVKsHcuXJolxC2SUv7igQFBXHXXXcViNa2hLafOS605dAuIZxHzql9xQpKF7mEth/t2bOHvXv30rx5c9ulGN6Hdn3xBdxwg+2KhBBgWtrSPX5F7rzzTtauXcvRAP+yk2NoK6UmK6UOKqU2eN13r1Jqo1IqTSkVncXjqiqlliqlNnvWHZyfhbvR6tWruemmmyhUqJCV7Y/atYul6R8EnkO7liYlMerTT6FVKys1CSEyIS3tKxYaGkpUVBTr16+3XYpP5aalPRVon+G+DUA34LtsHpcCDNFa3wA0Bx5XSkXmpchAkZiYSP369a1tv2nJknTftMkE9+jRLI2Pp/vrr9NUjsUWwlnSQzstzXYlrlK/fn0SExNtl+FTOYa21vo74HCG+zZrrbfk8Lh9Wus1ntvJwGag8lXU6nq2QzsmPJzZkZF0X7uWFzZvpvtrrzE7OpqY9BmYhBDOEB5uesOSk21X4ioS2vlEKVUDaARkOWWNUupRpVSCUiohKSnJH2X5XWJiIg0aNLBaQ0x4OAMWLmRkr14MuPZaYq65xmo9QohMyPzjedKgQQMJ7aullCoBfAHEaq2PZ7We1nqi1jpaax1dLgBHMJ88eZLdu3dz3XXXWa1j6ZEjjL/jDoavXcv4gwcv7uMWQjiHzD+eJ1FRUWzYsCGg5yH3aWgrpQpjAnuG1nqOL7fldBs3bqRu3boEBwdbq2HpkSN037SJ2R9+yIjly01Xefo+biGEc6S3tCW0r0jp0qUpX74827Zts12Kz/gstJVSCvgQ2Ky1/o+vtuMWtvdnA8QnJzM7MpKYPXvgzJkL+7jjZb+ZEM4i3eN5Fuhd5Lk55OsTYCVwvVJqj1Kqn1LqHqXUHuBmIE4ptcizboRSaqHnobcADwKtlFLrPBeHnovS99avX299f/Yz1aqZQWchIXDmDGD2cT9TrZrVuoQQGUj3eJ7Vr18/oA/7yrGvVmt9fxaLvsxk3b1AR8/tHwB1VdUFkMTERO6++27bZRihoXDqlO0qhBBZkZZ2njVo0IBp06bZLsNnZEY0P9BaO6J7/AKvlrYQwoFKlQKlpKWdB4F+2JeEth/s3r2b0NBQHDMqPiQETp+2XYUQIitBQVC6tIR2HtSqVYukpCSOHTtmuxSfkND2A0e1ssF0j0tLWwhnk9Nz5kmhQoWIiori559/tl2KT0ho+4HjQlta2kI4n5yeM88CuYtcQtsPNmzYQFRUlO0yLpKWthDOJy3tPKtXrx4bNmzIeUUXktD2g6SkJCpWrGi7jItkIJoQzict7TyrUKECgTodtoS2Hxw7dozSpUvbLuOi9NDW2nYlQoisyOk586x06dIyEE3kneNCOzTUXJ89a7cOIUTWwsOlezyPJLTFVTl69KizQjskxFzLYDQhnCsszEyCdO6c7Upcp3Tp0hwN0F4KCW0/OHbsGGHpMxw5QXpLW/ZrC+FcctKQPAsLC5OWtsibM2fOkJaWRkh669YJpKUthPPJ/ON5Jt3jIs/S92ebk545hLS0hXC+lBRzHcDnhvaV0NBQUlJSOBuA43YktH3McV3jcLGlLaEthHP99BOULAnXXWe7EtdRSgVsF7mEto85buQ4SPe4EG6wciXcdBMUKmS7ElcK1C5yCW0fc2RoS/e4EM528iQkJsLNN9uuxLUktEWeHD161Lnd49LSFsKZ4uPNvmwJ7TwL1MO+JLR9TFraQogrtnKluW7e3G4dLib7tEWeODK0paUthLOtXAnXXw/XXGO7EteS7nGRJ8eOHaNUqVK2y7iUtLSFcC6tTWhLK/uqlChRguTkZNtl5DsJbR8rWrSo844VlEO+hHCubdvgzz9lf/ZVSklJoXDhwrbLyHcS2j7myP0q0j0uhHO99x4EBUHr1rYrcTVHzpGRDyS0fcyR+1WkpS2EM+3cCe++C336QO3atqtxNcedqCmfSGj7mCNDu1AhKFxYWtpCOM2LL4JS8NJLtitxPUcOAs4HEto+FhYW5sxjBYsWlZa2EE6yYQNMmwZPPAFVq9quxvWke1zkiSNb2ufOwYkTF0/9J4Sw77nnzFzjQ4fariQgSPe4yBNHhvb+/eY6IsJuHUIIY/lymDcP/vlPKFPGdjUBQbrHRZ44snt83z5zXamS3TqEEOa47GefhYoVYfBg29UEhJSUFE6fPk2JEiVsl5Lvgm0XEOhKlCjBqVOnSE1NpZBTztaTHtrS0hbCvrg4+OEHc6hX8eK2qwkIx48fp2TJkgQFBV67NPBekcMEBQVRsmRJjh8/bruUi/buNdfS0hbCrtRUsw+7dm14+GHb1QSMQO0aB2lp+0V6F3l4eLjtUox9+8zkDeXL265EiIJt5kwzanzWLHMYpsgXjjy7Yj6RlrYfOG4w2t69UKGCOV5bCGHH2bMwfDg0agT33mu7moAiLW1xVRwX2vv2yf5sIWx7/30zA9rEiabnS+QbCW1xVcLCwjhy5IjtMi7atw+qVLFdhRAFV2oqvPKKmeRo4ULYvBlq1DCXmjXBaWcGdJlAPUYbJLT9olatWvz222+2y7ho71646SbbVQhRcCkFnTtDfDxMmgQnT166PDz8YoB7h3n67QA8lCk/bd26ldoBOne7hLYf1K9fn2XLltkuwzh/HpKSZOS4EDYFBcHkyea21nDoEOzYAb//bq7Tb2/eDF99dfl5AsqWzTzMa9aE6tWhWDE/vhjnWb9+PX379rVdhk9IaPtBgwYNGDt2rO0yjAMHzIeE7NMWwhmUMiFctixER1++XGs4ePDSME+/nZgI8+ebQW3eype/NMy9w7169Ytn+vMyatcumpYsSYzXUS5LjxwhPjmZZ6pVy69X6xeJiYk0aNDAdhk+IaHtB5GRkfz666+cO3eOIkWK2C1GZkMTwl2UMkd7VKgAzZpdvjwtzXwZz9hK37EDVq+GOXNMD5u3SpUuC/OmNWvSvUgRZtetS0z58iw9coTumzYxOzLS5y8xPx09epTDhw9Ts2ZN26X4hIS2H4SGhlKjRg22bNlCvXr17BYjs6EJEViCgkwIV6oELVpcvjw11fzfZ9ZS//FHmD0bUlOJAWY3bEj3F19kgFKML1uW2ZGRl7S83SAxMZGoqKiAnA0NJLT9pkGDBqxfv95+aG/ZYq4rV7ZbhxDCPwoVMkeLVKkCt956+fKUFDM49fffidmxgwGLFjHyvvsYHhHhusCGwO4aBwltv6lfvz6JiYm2yzAzLzVpYk5OIIQQwcFQrRpUq8bS+vUZX6YMw3/8kfGFCxMTFua64E5MTKRhw4a2y/CZwOw/cCBHhPaGDbBmDfTqZbcOIYTjXNiHPW8eI+bMYXZkJN03bWKpk+aYyIX169dTv35922X4jIS2nzgitKdPN9+qe/SwW4cQwnHik5PNPmxPd3lMeDizIyOJT062XVqupaamsnHjRvu7IX1Iusf9pGrVqpw+fZqkpCTKlSvn/wJSU+Hjj6FDBzlRiBDiMhcO64qIgP37zeC08HBXdY9v27aNcuXKBexsaCAtbb9RStltbX/7rRlsIl3jQojsRESYL/lJSbYruWKJiYkB3TUOEtp+ZTW0p02DsDAzdaIQQmQl/XDQvXvt1pEHEtoiX6Uf9uV3J06YCRa6d890JiQhhLjAxaG9fv36gD7cCyS0/ap+/fqsW7fO/xueMwdOnZKucSFEztJnS3RhaK9bty6gB6GBhLZfNWzYkJ07d7J//37/bnjaNKhVK/PZkoQQwlv6HA4uC+0tW7aQkpJCnTp1bJfiUxLaflSkSBHatWvHggUL/LbNfQkJ3LFkCfvvucfMYSyEENkpXNgcYeKy0J43bx533XVXwE5fmk4O+fKzrl27MnPmTB5++GG/bG/kE0/wAzDi5595b80ac3IBrS+9DuT7OneGtm398l4LETAiIi6ep8Al5s2bx7Bhw2yX4XNKa227hstER0frhIQE22X4xJEjR6hevTr79u2jePHiPttOaGgoZ86cuez+EOD05au7i1LmEhR08dr7dvr1yZPQsCHEx9uuWAh36dwZdu8GGwNn8yApKYlrr72WAwcOEOLSwbZKqdVa60zOzXopaWn7WXh4OE2bNmXx4sV07drVZ9vZvn07Tz31FP+dM4dTZ85QrEgR7mnenNH9+kGZMrkLPX/fl5vHpF9y44knzIQyWsuuASGuRJ06sHSpa/534uLiaN26tWsD+0pIaFvQpUsX5s2b59PQrlSpEqVKleLMuXOEhIRw5tw5St14IxUL0gjyOnXg2DEzSYTMAidErozatYumDRoQc+qU2a9duTJLjxwhPjn54qxpDjNv3jzuvvtu22X4RWDvsXeoLl26sGDBAlJTU326nQMHDtC/f39+/PFH+vfv7/9R67Zdd5253rrVbh1CuEjTkiXpXr06Sxs2hK1bL5xIpGnJkrZLy9SZM2dYsmQJHTt2tF2KX0hL24KaNWtSoUIFVq1aRQsfHoY1Z86cC7ffffddn23HsdIP/di6FW65xW4tQrhETHg4sytWpPuLLzLg0CHGb9pkTiTi0DnIv/32Wxo0aEDZsmVtl+IX0tK2JL2LXPhQjRrmrGa//mq7EiFcJaZOHQbMm8fIMmUYEBHh2MAG0zXepUsX22X4jYS2JRLafhAcDDVrSve4EFdoaXIy47t2ZfjmzYzfu9ex59ROS0tj/vz5Ph0f5DQS2pZER0dz5MgRtkqg+NZ110loC3EF0vdhz544kRHff8/syEi6b9rkyOBes2YNpUqVCvhZ0LxJaFsSFBTEXXfdxfz5822XEtjq1DGh7cD5CIRwovjkZLMP+88/4dAhs487MpL45GTbpV2moHWNg4S2VV27dmXu3Lm2ywhsdeqYk6W4bEpGIWx5plo1sw+7TBk4dAgwg9OceLjX3LlzJbSF/7Rq1YrExET++OMP26UErpQUc33+vN06hHAbr9B2oi1btnDgwAGaN29uuxS/ktC2KDQ0lAceeIDx48fbLiVwLVxoWts1atiuRAh3cXhov/322zzyyCMUKlTIdil+JaFt2aBBg5g4cSKnT7t+RnDnOXkSli2DTp1sVyKE+1xzDSQnw7lztiu5zJEjR5g5cyYDBgywXYrfSWhbVqdOHZo1a8aMGTNslxJ4liyBs2cltIXIizJlzPXhw3bryMSHH35Ip06diIiIsF2K30loO8DgwYMZO3YsTjzjmqvFxUGJEnD77bYrEcJ90kPbYV3kKSkpvPPOOwwePNh2KVZIaDvAnXfeidaab7/91nYpgUNrsz+7TRsoUsR2NUK4j0Nb2nPnzqVKlSpER+d4FsuAJKHtAEopYmNjGTNmjO1SAkdiIuzZI13jQuSVQ1vaY8aMITY21nYZ1khoO8Tf/vY3Vq1aJTOk5Ze4OHNdQM78I0S+c2BoJyQksGvXrgJzGs7MSGg7RGhoKI888gjjxo2zXUpgiHhgbEUAACAASURBVIuDxo2hUiXblQjhTg4M7bFjx/LEE08QHFxwT1Apoe0gAwcOZMaMGRw9etR2Ke526BD8+KN0jQtxNYoXN+NBHBLa+/btIy4ujocffth2KVZJaDtI5cqVad++PZMnT7Zdirvt2QNpaXDDDbYrEcK9lHLUBCvjx4+nR48ehDv4NKH+kGNoK6UmK6UOKqU2eN13r1Jqo1IqTSmV5RA+pVR7pdQWpdRvSqln86voQBYbG8u4ceNISZ9+U1y56683p+XcsCHndYUQWXNIaJ85c4b333+fQYMG2S7Futy0tKcC7TPctwHoBnyX1YOUUoWAd4EOQCRwv1IqMm9lFhw33XQTERERcq7tqxESYlrZa9farkQIVxq1a5c5FadXaC89coRRu3ZZqWfmzJk0adKEunXrWtm+k+QY2lrr74DDGe7brLXeksNDbwJ+01pv11qfA2YBBedM5VchNjaWN998UyZbuRqNGkloC5FHTUuWNOfQrl8fDh26cI7tpiVL+r2WtLQ03nrrrQI7mUpGvtynXRnY7fXzHs99IgfdunXj+PHjfPnll7ZLca9GjWD/fnMRQlyRmPBwZp8/T/c2bXihdWu6b9pkzrFtYX/y1KlTKVWqFG3btvX7tp3Il6GtMrkvy6ajUupRpVSCUiohKSnJh2U5X3BwMGPGjGHIkCGcOXPGdjnu1LChuV63zm4dQrjNnj1w//3ExMQwYMkSRt5zDwMiIqwE9vHjx3nuuecYO3YsSmUWKQWPL0N7D1DV6+cqwN6sVtZaT9RaR2uto8uVK+fDstzhzjvvpGHDhvznP/+xXYo7pYe2dJELkTtnz8Jrr5mBnP/9L0vfeovxf/0rw6tXZ/zevWYft5+98sordOjQocBOWZoZXx6hHg/UUUrVBP4AegA9fbi9gDN69GiaNWtGnz59CuTZbK5KWBjUrCktbSFyY8ECiI2FbdvgnntY+uqrdD98+EKXeExYmN+7yLdu3crkyZPZIEeBXCI3h3x9AqwErldK7VFK9VNK3aOU2gPcDMQppRZ51o1QSi0E0FqnAE8Ai4DNwGyt9UZfvZBAVLt2bR5++GGGDh1quxR3athQWtpCZOfXX80kRHfdBYULw//9H8yZQ3zx4pcEdEx4OLMjI4lPTvZbaU899RRPP/00FStW9Ns23UA5cYRydHS0TkhIsF2GIyQnJ3P99dfz5Zdf0qxZM9vluMvIkfDCC3D8OFgY9SqEY504Aa+8Av/5jzlE8qWX4O9/N8HtAN988w0DBgxg48aNFC1a1HY5fqGUWq21znE/gMyI5nAlS5bktddeY/DgwaSlpdkux10aNTLXiYl26xDCKbSGmTPNfuvXX4eePU1r+8knHRPYKSkpxMbGMnr06AIT2FdCQtsFevXqRWpqKjNnzrRdirvIYDQhLlq3Dm6/Hf72N3MinRUrYOpUcFj384QJE6hUqRJdu8q0HpmR0HaBoKAgxo4dy7PPPsuJEydsl+MelStD2bKwZo3tSoSw59AhGDgQmjSBX36BDz6An36Cm2+2XdllDh06xIgRI3jrrbfkEK8sSGi7RIsWLbjjjjt4/fXXbZfiHkpBy5Ywf745nEWIguaPP6BuXZg4ER5/3HSFP/wwBDnzo/+ll17i3nvvpV69erZLcSxn/uZEpv7973/z3nvvsWPHDtuluEe/fvDnnzB3ru1KhPC/hQvN3/+yZTBuHDj4DFkbNmxg1qxZvPzyy7ZLcTQJbRepWrUqgwcP5umnn7Zdinu0aQPVqpkuQSEKmuXLoVw5uOUW25VkS2vNP/7xD4YPH07ZsmVtl+NoEtou89RTTxEfH89XX31luxR3KFTIdAcuXgzbt9uuRgj/WrECWrQwu4oc7NNPP+WPP/5gwIABtktxPAltlylWrBgfffQRDz30EPv27bNdjjv07Wv24U2aZLsSIfwnKQm2bjWh7WC///47gwYNYvr06RR2yGFnTiah7UJ33HEHjz76KL169ZJjt3OjShXo2BGmTIHz521XI4R/rFhhrh3cNX7+/Hnuv/9+hg4dSpMmTWyX4woS2i41fPhwzp49y6hRo2yX4g6PPGJO0xkXZ7sSIfxjxQozYYqDw3D48OGUKVOG2NhY26W4hoS2SwUHBzNjxgzeeustfvzxR9vlOF/HjhARYQ59EaIgWLHCBHZIiO1KMvXNN9/w8ccfM3XqVDkm+wpIaLtY1apVef/99+nZsydHjx61XY6zBQfDQw/B11/Drl22qxHCt86ehfh4x3aNHzhwgD59+jBt2jTkVMxXRkLb5e6++246dOjAo48+ihNP/uIo/fqZ68mT7dYhhK+tXWuC24GD0NLS0ujduzd9+/alVatWtstxHQntADB69Gh++eUXJsno6OzVqAFt28KHH0Jqqu1qhPCd9EFoDgztN998k+TkZF566SXbpbiShHYACA0N5dNPP2Xo0KFs2rTJdjnO9sgjsGeP6SYXIlAtXw61ajnuZCA//fQTb7zxBjNnziQ4OPj/27v7+JzrxY/jr8/kZu4V3ZDcy03RmblZDqYS7aKU3NXPyTKh9Oue0+l3nMqpE6WcjrtTjYwf2kmhTaQIzU0bG0aRQkmMaGYI2+f3x5f9kLLYru/1va738/HYY7Nd2/XevrP39f18vtfn43YcT1JpB4nGjRvz0ksv0bt3b44cOeJ2nMDVrRtcfrlWSJPgZe3/L6oSQA4ePEjfvn2ZOHEitWrVcjuOZ6m0g8iAAQNo0qQJTzzxhNtRAlepUtC/PyQlwa5dbqcRKXrbtztPbwyg0rbWMnjwYDp16kSPHj3cjuNpKu0gYozhjTfeYMGCBbz33ntuxwlccXHOnPaUKW4nESl6KSnO6wC6cvztt99m/fr1vPbaa25H8TyVdpCpVKkSM2fOZPDgwWzevNntOIGpQQPo2NFZ1vTYMbfTiBStFSugQgVo2tTtJACkp6czbNgw3nnnHcLDw92O43kq7SDUunVrRo8eTZcuXdilIeBze+IJZxjx6afdTiJStFasgDZtnM1yXPbNN9/QtWtXJk2aRNMAeRDhdSrtINW/f38GDRpEly5dtPDKufh88NBD8Oqr2mtbgsfBg7BhQ0AMje/Zs4fOnTvz17/+VfPYRUilHcSGDx9Ox44duf3223VF+bm88gpERDgXpu3Y4XYakYu3ejXk57t+EVpOTg4xMTHcc889DB482NUswUalHcSMMbz22mtUr16de++9lzwtKHKmMmUgMdH5I9e7t+a3xftSUpxtaFu3di3CsWPHuOuuu4iMjNQCKsVApR3kwsLCmDp1KgcPHuTBBx/UUqdnq1fPuSBt9Wr4y1/cTiNycVasgOuvh4oVXbn7U0uUli9fngkTJmgjkGKg0g4BpUuX5r333iMtLY3nnnvO7TiBp2dPZ357zBiYN8/tNCIXJi8PVq1ybWjcWsvjjz/O999/z4wZMygRABfCBSOVdoioWLEi8+fPZ/r06UyaNMntOIFH89vidZmZkJPjWmmPHj2aTz75hHnz5umpXcVIpR1CrrjiChYuXMjzzz/P7Nmz3Y4TWE7Nb+flaX5bvOnUJiEuXDk+ZcoUJk6cyIIFC6hcubLf7z+UqLRDTL169UhKSmLw4MEsXbrU7TiBRfPb4kUbN8KgQc7aA7VqObvZ+VFSUhJPP/00CxYsoEaNGn6971Ck0g5BERERzJo1i549e7Ju3Tq34wQWzW+LF+Tnw/z5zlaz110HCQlw772waBH48eKvlStXEhsby9y5c2nUqJHf7jeUqbRD1M0338y//vUvfD4fX3/9tdtxAovmtyVQHToE48dD48bOAkEbN8ILL8B33zk71zVo4LcomZmZdO/enYSEBFq7+BSzUKPSDmG9e/dmxIgRtGvXjvT0dLfjBA7Nb0ug2b4dnnwSrr4ahg6FypVhxgzn/X/5C1St6tc4KSkp3HzzzYwdO5bbbrvNr/cd6lTaIe6BBx7g9ddfp3PnzixevNjtOIFD89viNmth2TLo0cP5fRw7Fm67DVaudH4v+/aFkiX9HuuDDz6ge/fuTJ06lb59+/r9/kOdSlu4++67SUxMpE+fPiQmJrodJ3Boflvc8PPPzhx1ixbQoQN8+ikMH+6cVc+c6WwG4pLJkyfzwAMPkJycTJcuXVzLEcoucTuABIbo6GgWLVpETEwMWVlZDB061O1IgeGVV5wzm/79IT3duTpXpDjs2QOTJsHEic7bTZrAG284F5iVLetqNGst//jHP3jzzTdZunQpDRs2dDVPKFNpS4HmzZvz2Wef0blzZ3bv3s3IkSO1DOGp+e2ICOjeHRYvhipV3E4lwea11+DPf3aun/D54JFH4JZb/Hol+K/Jz8/n0UcfZenSpaSkpFC9enW3I4U0DY/LGerUqUNKSgoLFy5k4MCBnDhxwu1I7qtXD955BzZtgk6dQFudSlGbONFZM3zzZkhKcn7PAqCwf/75Z/r27cu6detYunSpCjsAqLTlF6pVq8aSJUv47rvv6NGjB4cPH3Y7kvu6dIHZs2H9eue5sdnZbieSYJKV5Sw/GkDDzgcPHiQmJoYTJ06wcOFCrXQWIFTack7ly5fngw8+oEKFCtx6663s37/f7Uju69rVKe6MDOjcGQ4edDuRBINjx5wHgdWquZ2kwO7du4mOjqZhw4YkJiZSpkwZtyPJSSpt+VWlSpUqWDihXbt27Ny50+1I7uvWDf7zH1izxjn7VnHLxdq713l9+eXu5jhp69attG3blu7duzNhwgTt1hVgVNrym8LCwhgzZgyxsbG0bduWL774wu1I7rvjDufitNRU53mzOTluJxIvC6DSXrt2Le3bt2fYsGGMGDFCF6IGIJW2FMqTTz7J3//+dzp06MD777/vdhz33XknzJrlLHIRE+MsLylyIbKynNcuD49Pnz6dzp07M378eAYNGuRqFvl1esqXFFq/fv1o0KABffr0YcmSJbz88suULl3a7Vju6dHDWeyib1+nuOfPh/Ll3U4lXnOqtF06087NzeXhhx9mxYoVfPLJJzRr1syVHFI4OtOW36VNmzakp6ezc+dObrzxRrZu3ep2JHf17An/+7+QkuJcqJab63Yi8RoXh8c3btxIq1atOHHiBGlpaSpsD1Bpy+9WpUoVZs+eTWxsLFFRUbzzzjtuR3JX794wfTosX+5cqKanyMnvkZXlrCFeqZLf7tJay+TJk4mOjuapp54iISGB8hol8gQNj8sFMcYwdOhQoqKi6N27N4sXL2bs2LGEh4e7Hc0dffs6exz/6U9w++3wwQcQqj8L+X2yspz5bD9d9JWTk8OQIUPIyMhg6dKlNGnSxC/3K0VDZ9pyUVq0aMHatWvJzs6mdevWfPnll25Hcs+998LbbztLnd5xBxw54nYi8YK9e/12EVpGRgYtWrQgPDyczz//XIXtQSptuWgVK1Zk5syZPPzww7Rr146EhAS3I7mnXz+YMgU+/thZq/zoUbcTSaDLyir2+WxrLRMmTKBTp048++yzvPnmm5R1eRMSuTAaHpciYYxh4MCBtGnThl69erFkyRLGjRtHuXLl3I7mf/fd5wyVDxgAvXo5q6i5sO+xeMTevVC/frF9+ezsbOLi4ti6dSspKSnaocvjdKYtRer6668nNTUVay0tW7Zkw4YNbkdyR2wsjB/vzG0PGOCUuMi5nJrTLgapqalERERwxRVXsHLlShV2EFBpS5ErX748b7/9NsOHD+emm25i/Pjx5OXluR3L/4YMgZEjYdo0eOwxsNbtRBJojhxxFuYp4uHxEydO8PLLL+Pz+Rg9ejTjxo3T+uFBQqUtxea+++5j+fLlzJw5k6ioKNasWeN2JP975hmnsF9/3SlwkdMVw3O0U1JSaNGiBR999BGrVq2iR48eRfa1xX0qbSlWjRo1YtmyZTz44IP4fD6GDh3KT6G0H7Ux8Morzjz33/4G48a5nUgCSREuYbp3717uv/9+evfuzTPPPMNHH31E3bp1L/rrSmBRaUuxCwsLo3///mzatIm8vDwaN27MtGnTsKEyXBwWBm+95TwN7OGHnRXURKBIljDNz8/n3//+N02bNqVKlSp88cUX9OrVS5t9BCmVtvjNpZdeysSJE5k7dy5jx44lOjqajRs3uh3LPy65xNlgpGNH56w7OdntRBIITg2PX+CZ9po1a4iKiiIhIYGPP/6YMWPGUKFChSIMKIFGpS1+16pVKz7//HN69epFdHQ0w4YN41Ao7JJVpgzMmQM33AB33+0seyqh7QLPtH/66SeGDh2Kz+dj8ODBLF++XOuGhwiVtriiRIkSPPTQQ2RmZrJ7926aNGnC7Nmzg3/IvGJF+PBDqFXL2WAkPd3tROKmvXudB3OFXPfbWsv06dNp3LgxJ06cYNOmTcTGxhIWpj/loUJHWlx1xRVXkJCQwLRp0xgxYgQxMTHBv3NYtWqwaJGzQUSXLvDVV24nErf8jnXHN27cSMeOHXn11VeZM2cOkyZN4tJLL/VDSAkkKm0JCB06dCAjI4ObbrqJNm3a8Oyzz3IkmNfurlnTKW5roVMn2LnT7UTihkIsYXro0CGGDx9OdHQ0PXv2JDU1ldatW/spoAQalbYEjJIlS/LUU0+Rnp5OZmYmDRo04J///Gfwlve118KCBbB/P9x6K+zb53Yi8be9e3+1tHNychg1ahT169dn165dbNiwgYceeogSJUr4OaQEEpW2BJyaNWvy7rvvMm/ePD799FPq1q3LmDFjyM3NdTta0YuIcJY6/eYbiImBnBy3E4k/nWMJ0+zsbF544QXq1atHRkYGH3/8MdOmTePKK690KaQEEpW2BKyIiAjef/99Fi5cyOrVq6lbty6jRo0iJ9iKrUMH+M9/YO1auPNO+PlntxOJP1jLD3vC6LB4BLt3w4EDB3juueeoX78+mzdvZtmyZcycOZPrrrvO7aQSQFTaEvCaNWtGYmIiixcvZt26ddSrV48XXniB7Oxst6MVnW7dnC09P/kE7rkHTpxwO5EUt9xcRv78FJ99X4euXVfToEEDduzYwcqVK0lISKBRo0ZuJ5QApK05xTOaNm3KjBkz+PLLL3nxxRepV68eQ4cO5ZFHHqFKlSpux7t4/fo589uPPgqDBjmrqGlVK3fl58OxYxf+cvz4r37smy05TGEU+TaMjIw/kJKyhtata7n9HUuAU2mL5zRq1IiEhAS2bt3Kiy++SP369RkyZAiPPfYYl112mdvxLs4jj8D+/YzesoWWo0bRcfjwguJecuAAqTk5DLvmGpdDXqT8/N8ssyJ5KaqvX0y70+WFhfGP/H+Rj3NsS5QoRUJCLXRRuJyPCcTFLCIjI21aWprbMcQjtm3bxksvvcS7777LwIEDefzxx7m8iLc69CtrWTJyJL2aNSNx1y46PvggSw4coNemTSQ2aULHc40qWFv8RVhURVlcQ/8lSkCpUv57KVnyd91+1759vPL660ydMYM7ewxl+vTn+fnn/x9JCQ93rkfU9WahyRizxlobed7bqbQlWHz77beMGjWKmTNnctdddxEXF0fr1q29uXFCfj5Lhg2j1x//yJDPP2fijTeS+OabdMzMPHdRHj9ePDlOL8LfWVJ+LcuSJZ2sAcZay7Jly4iPjycpKYnY2FiefPJJRo68ivh459CdUqoUxMXB+PHu5RX3qLQlZO3evZupU6cSHx9PyZIlGTBgAP369aNaEWx/6FfHjzNi8mRGXnstf121iufXrvV/OQZgEXrBrl27mDp1KpMnT6Z06dIFv4NVq1YF4A9/gIyMX37eDTdoZdtQpdKWkGetZfny5cTHxzN37lxuueUW4uLi6NSpkycWqDg1JD6kenUm7tr160PjEhCOHz/O/PnziY+PZ/ny5dx9993ExcXRqlUrb472iF+ptEVOk52dzaxZs3jrrbfYs2cP/fv3JzY2ljp16rgd7ZzOnsM+75y2uGbLli1MnjyZqVOnUrduXeLi4ujZsyflC7kJiAgUvrT1PG0JCZUqVWLQoEGkpqaSlJREdnY2LVu2pFOnTsyaNYujR4+6HfEMqTk5ZxR0xypVSGzShNRgW1jGo3Jzc5k6dSrt27enXbt25OXlsXjxYlJSUoiNjVVhS7HRmbaErKNHjzJnzhzi4+NJT0/nnnvuYcCAATRv3tztaBKArLWkpaURHx9PYmIiUVFRDBgwgK5du1KqVCm344nHFfZMu1DP0zbGTAa6AlnW2utOvu9S4B2gNrAd6GWtPXCOzx0N+HDO6hcBj9hAfKQgIadMmTL06dOHPn36sG3bNqZMmULXrl2pWrUqt99+Oz6fj8jISO1VHMLy8vJYvXo1SUlJzJs3j8OHD3P//fezfv16rr76arfjSQgq1Jm2MaY9cAhIOK20RwP7rbUvGWP+DFSx1g4/6/NuBF4G2p9812fA09baT3/r/nSmLW7Jy8tj+fLlJCcnk5yczI8//khMTAw+n49OnTpRqVIltyNKMdu/fz8LFy4kOTmZBQsWUKNGDXw+Hz6fj6ioKD2Ik2JR5BeiGWNqA0mnlfZmINpa+4Mx5irgU2vttWd9ThQwDvgjYIBlQD9r7Re/dV8qbQkU27ZtKyjwlJQUIiMjC/6AX3vttboqOAhYa8nMzCw4zuvWrSM6Ohqfz0dMTAw1a9Z0O6KEAH+U9k/W2sqnffyAtfYXl7UaY14B4nBKe5y19pnz3ZdKWwJRbm4uixcvJikpieTkZEqXLo3P56Nr16506NCB0qVLux1RCunw4cMsWbKEpKQk5s+fT1hYWMGxjI6OpkyZMm5HlBBTpHPaFxGiPtAYODX5s8gY095au+wct30AeADgGq+vrSxBqVy5cnTr1o1u3bphrWX9+vUkJyfz3HPPkZmZSceOHQvOzmrUqOF2XDnL9u3bmT9/PsnJySxfvpyIiAh8Ph8ffvghjRs31qiJeEJxD48/BZSx1o48+e8RwFFr7ejfui+daYvX7Nu3r2AedOHChZQrV46WLVsSGRlJy5YtadGiRXDsROYR+/btIy0tjbS0NFJTU0lLS+P48eN06dIFn89H586dqVy58vm/kIif+GN4/GXgx9MuRLvUWjvsrM/pDQwEuuAMjy8AxlprP/it+1Jpi5dZa/n6668LyiI1NZX09HSuvPLKghKPjIwkIiJCz+ctAgcPHmTNmjVnFPSPP/5IixYtzvh5165dW2fTErCKtLSNMTOBaKAqsAf4GzAHSASuAb4Felpr9xtjIoHB1to4Y0wJYALO1eMWWGCtffx896fSlmCTl5fHl19+eUaRZ2ZmUqdOnTOKpXnz5ppP/Q2HDx8mIyPjjJ/jzp07ad68+Rk/x4YNG+oqb/EULWMqEuCOHTtGZmZmQfmkpqayZcsWGjVqxA033EDt2rWpVasWtWrVonbt2tSoUYOSJUu6HbvYHTt2jJ07d7Jjxw62b99e8Do9PZ2vvvqKJk2anFHQTZs25ZJLivXyHJFip9IW8aAjR46QkZHBhg0b2LFjxxnFtWfPHq688sozivzU27Vq1eKaa64hPDzc7W/hvA4fPlzwvZ39Pe7YsYOsrCyuuuqqX3yPzZs35/rrr9dIhAQllbZIkDl+/HjBGei5Xr777jsqV65cUHLVq1enfPnylC1blrJly1KuXLlzvn32v8uUKXPOuV9rLUePHiU3N5fDhw8XvJz+73O9fejQIb7//vuCnDk5OdSsWfMXDzpOvdSoUUNnzhJyAuIpXyJSdEqWLEmdOnV+dWey/Px8du/eXVCOP/zwA7m5uRw6dIisrKzzluupt48dO3ZGgZ8q6iNHjlC6dOnffABwrrerVatG27ZtC0r58ssv13yzyAVSaYsEibCwMKpXr0716tWJioq64K+Tl5dXUOJHjx4lPDycsmXLEh4e7ol9yEWCmUpbRM5QokQJKlSoQIUKFdyOIiJn0RiViIiIR6i0RUREPEKlLSIi4hEqbREREY9QaYuIiHiESltERMQjVNoiIiIeodIWERHxCJW2iIiIR6i0RUREPEKlLSIi4hEqbREREY9QaYuIiHiESltERMQjVNoiIiIeodIWERHxCJW2iIiIR6i0RUREPEKlLSIi4hEqbREREY9QaYuIiHiESltERMQjVNoiIiIeodIWERHxCJW2iIiIR6i0RUREPEKlLSIi4hEqbREREY9QaYuIiHiESltERMQjVNoiIiIeodIWERHxCJW2iIiIR6i0RUREPEKlLSIi4hEqbREREY9QaYuIiHiESltERMQjVNoiIiIeodIWERHxCJW2iIiIR6i0RUREPEKlLSIi4hEqbREREY9QaYuIiHiESltERMQjVNoiIiIeodIWERHxCJW2iIiIR6i0RUREPEKlLSIi4hEqbREREY9QaYuIiHiESltERMQjVNoiIiIeodIWERHxCJW2iIiIR6i0RUREPEKlLSIi4hEqbREREY9QaYuIiHiESltERMQjVNoiIiIeodIWERHxCJW2iIiIR6i0RUREPEKlLSIi4hEqbREREY8w1lq3M/yCMWYvsMPtHAGuKrDP7RCi4xAgdBwCg47Dhatlra12vhsFZGnL+Rlj0qy1kW7nCHU6DoFBxyEw6DgUPw2Pi4iIeIRKW0RExCNU2t71htsBBNBxCBQ6DoFBx6GYaU5bRETEI3SmLSIi4hEq7QBnjHnMGLPRGJNpjJlpjClzjtv0MsZsOnm7GW7kDHbnOw7GmGuMMUuMMenGmPXGmBi3sgYzY8wjJ4/BRmPMo+f4uDHGvG6M2XryOES4kTOYFeIY3HvyZ7/eGLPCGNPcjZzBSqUdwIwxNYD/BiKttdcBJYA+Z92mAfA00NZa2xT4xX8iuTiFOQ7A/wCJ1to/nPzYBP+mDH7GmOuAgUAroDnQ9eTv/+luAxqcfHkAmOjXkEGukMdgG9DBWtsMGInmuYuUSjvwXQKEG2MuAcoCu876+EBgvLX2AIC1NsvP+ULF+Y6DBSqefLvSOT4uF68xsMpaoJ5tbgAAAlNJREFUe9haewJYCtx51m3uABKsYxVQ2Rhzlb+DBrHzHgNr7YpTf4+AVcDVfs4Y1FTaAcxa+z3wCvAt8AOQba396KybNQQaGmNSjDGrjDFd/J0z2BXyODwL/JcxZicwH3jYryFDQybQ3hhzmTGmLBAD1DzrNjWA7077986T75OiUZhjcLoBwId+SRYiVNoBzBhTBefMoQ5QHShnjPmvs252Cc5QYDTQF3jLGFPZnzmDXSGPQ1/gbWvt1Th/yKYZY/T/qwhZa78ARgGLgAXAOuDEWTcz5/rUYo4WMgp5DAAwxnTEKe3hfgsYAvRHJbDdAmyz1u611h4H3gNuPOs2O4G51trj1tptwGacEpeiU5jjMABIBLDWrgTK4KzDLEXIWhtvrY2w1rYH9gNfnXWTnZx55nc1mqooUoU4BhhjmgFvAXdYa3/0d8ZgptIObN8CbYwxZY0xBrgZ+OKs28wBOgIYY6riDJd/49eUwa8wx+Hbk+/HGNMYp7T3+jVlCDDGXH7y9TXAXcDMs24yD/jTyavI2+BMZfzg55hB7XzH4OT73wP6WWu3+D9hcLvE7QDy66y1q40x7wJrcYag0oE3jDHPA2nW2nnAQuBWY8wmIA94So9si1Yhj8MTwJvGmMdwhmP7W61cVBxmG2MuA44DD1lrDxhjBgNYayfhXE8QA2wFDgOxriUNXuc7BiOAy4AJzmNcTmgTkaKjFdFEREQ8QsPjIiIiHqHSFhER8QiVtoiIiEeotEVERDxCpS0iIuIRKm0RERGPUGmLiIh4hEpbRETEI/4PEDh2SbvDGmUAAAAASUVORK5CYII=\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Point 0: False\n", "Point 1: True\n", "Point 2: True\n", "Point 3: True\n", "Point 4: False\n" ] }, { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Point 0: True\n", "Point 1: True\n", "Point 2: True\n", "Point 3: True\n", "Point 4: True\n", "Point 5: True\n", "Point 6: True\n", "Point 7: True\n" ] }, { "data": { "text/plain": [ "
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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Point 0: True\n", "Point 1: True\n", "Point 2: True\n", "Point 3: True\n", "Point 4: True\n", "Point 5: True\n", "Point 6: True\n", "Point 7: True\n", "Point 8: True\n", "Point 9: True\n" ] }, { "data": { "text/plain": [ "
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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Point 0: True\n", "Point 1: True\n", "Point 2: True\n", "Point 3: True\n", "Point 4: True\n", "Point 5: True\n" ] }, { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "demo_mbc(chains)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 9.33329678, 13.27241993],\n", " [ 9.12427711, 12.63424015],\n", " [ 9.3860054 , 12.59624004],\n", " [ 9.47149754, 12.5957098 ],\n", " [ 9.55582809, 12.59519005],\n", " [ 9.72367477, 12.59519958],\n", " [10.01543999, 12.72404957],\n", " [10.09543037, 12.87689972],\n", " [10.09350967, 12.90021992],\n", " [10.08250999, 13.03376961],\n", " [10.02766991, 13.29854012],\n", " [ 9.67700958, 13.29658985]])" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pointpats.hull(chains[8])" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(*pointpats.hull(chains[8]).T.tolist())\n", "plt.plot(*pointpats.hull(chains[8])[5].T.tolist(), markerfacecolor='k', marker='o')\n", "plt.plot(*pointpats.hull(chains[8])[6].T.tolist(), markerfacecolor='k', marker='o')\n", "plt.plot(*pointpats.hull(chains[8])[7].T.tolist(), markerfacecolor='k', marker='o')" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0.914822771306765, (8.333136366132898, 13.026633376705332))" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pointpats._circle(chains[8][-5], chains[8][-4], chains[8][-3])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" }, "widgets": { "state": {}, "version": "1.1.2" } }, "nbformat": 4, "nbformat_minor": 1 } pointpats-2.5.5/notebooks/Quadrat_statistics.ipynb000066400000000000000000004001251514706172100224730ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Quadrat Based Statistical Method for Planar Point Patterns\n", "\n", "**Authors: Serge Rey , Wei Kang and Hu Shao **\n", "\n", "## Introduction\n", "\n", "In this notebook, we are going to introduce how to apply quadrat statistics to a point pattern to infer whether it comes from a CSR process.\n", "\n", "1. In [Quadrat Statistic](#Quadrat-Statistic) we introduce the concept of quadrat based method.\n", "2. We illustrate how to use the module **quadrat_statistics.py** through an example dataset **juvenile** in [Juvenile Example](#Juvenile-Example)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Quadrat Statistic\n", "\n", "In the previous notebooks, we introduced the concept of Complete Spatial Randomness (CSR) process which serves as the benchmark process. Utilizing CSR properties, we can discriminate those that are not from a CSR process. Quadrat statistic is one such method. Since a CSR process has two major characteristics:\n", "1. Uniform: each location has equal probability of getting a point (where an event happens).\n", "2. Independent: location of event points are independent.\n", "\n", "We can imagine that for any point pattern, if the underlying process is a CSR process, the expected point counts inside any cell of area $|A|$ should be $\\lambda |A|$ ($\\lambda$ is the intensity which is uniform across the study area for a CSR). Thus, if we impose a $m \\times k$ rectangular tessellation over the study area (window), we can easily calculate the expected number of points inside each cell under the null of CSR. By comparing the observed point counts against the expected counts and calculate a $\\chi^2$ test statistic, we can decide whether to reject the null based on the position of the $\\chi^2$ test statistic in the sampling distribution. \n", "\n", "$$\\chi^2 = \\sum^m_{i=1} \\sum^k_{j=1} \\frac{[x_{i,j}-E(x_{i,j})]^2}{\\lambda |A_{i,j}|}$$\n", "\n", "There are two ways to construct the sampling distribution and acquire a p-value:\n", "1. Analytical sampling distribution: a $\\chi^2$ distribution of $m \\times k -1$ degree of freedom. We can refer to the $\\chi^2$ distribution table to acquire the p-value. If it is smaller than $0.05$, we will reject the null at the $95\\%$ confidence level.\n", "2. Empirical sampling distribution: a distribution constructed from a large number of $\\chi^2$ test statistics for simulations under the null of CSR. If the $\\chi^2$ test statistic for the observed point pattern is among the largest $5%$ test statistics, we would say that it is very unlikely that it is the outcome of a CSR process at the $95\\%$ confidence level. Then, the null is rejected. A pseudo p-value can be calculated based on which we can use the same rule as p-value to make the decision:\n", "$$p(\\chi^2) = \\frac{1+\\sum^{nsim}_{i=1}\\phi_i}{nsim+1}$$\n", "where \n", "$$ \n", "\\phi_i =\n", " \\begin{cases}\n", " 1 & \\quad \\text{if } \\psi_i^2 \\geq \\chi^2 \\\\\n", " 0 & \\quad \\text{otherwise } \\\\\n", " \\end{cases}\n", "$$\n", "\n", "$nsim$ is the number of simulations, $\\psi_i^2$ is the $\\chi^2$ test statistic calculated for each simulated point pattern, $\\chi^2$ is the $\\chi^2$ test statistic calculated for the observed point pattern, $\\phi_i$ is an indicator variable.\n", "\n", "We are going to introduce how to use the **quadrat_statistics.py** module to perform quadrat based method using either of the above two approaches to constructing the sampling distribution and acquire a p-value.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Juvenile Example" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/wk0110/My Drive (weikang9009@gmail.com)/python_repos/pysal-refactor/libpysal/libpysal/cg/alpha_shapes.py:42: NumbaDeprecationWarning: \u001b[1mThe 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\u001b[0m\n", " def nb_dist(x, y):\n", "/Users/wk0110/My Drive (weikang9009@gmail.com)/python_repos/pysal-refactor/libpysal/libpysal/cg/alpha_shapes.py:168: NumbaDeprecationWarning: \u001b[1mThe 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\u001b[0m\n", " def get_faces(triangle):\n", "/Users/wk0110/My Drive (weikang9009@gmail.com)/python_repos/pysal-refactor/libpysal/libpysal/cg/alpha_shapes.py:202: NumbaDeprecationWarning: \u001b[1mThe 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\u001b[0m\n", " def build_faces(faces, triangles_is, num_triangles, num_faces_single):\n", "/Users/wk0110/My Drive (weikang9009@gmail.com)/python_repos/pysal-refactor/libpysal/libpysal/cg/alpha_shapes.py:264: NumbaDeprecationWarning: \u001b[1mThe 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\u001b[0m\n", " def nb_mask_faces(mask, faces):\n", "/Users/wk0110/opt/anaconda3/lib/python3.9/site-packages/geopandas/_compat.py:112: UserWarning: The Shapely GEOS version (3.10.2-CAPI-1.16.0) is incompatible with the GEOS version PyGEOS was compiled with (3.10.4-CAPI-1.16.2). Conversions between both will be slow.\n", " warnings.warn(\n" ] } ], "source": [ "import libpysal as ps\n", "import numpy as np\n", "from pointpats import PointPattern, as_window\n", "from pointpats import PoissonPointProcess as csr\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import warnings\n", "warnings.filterwarnings(\"ignore\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Import the quadrat_statistics module to conduct quadrat-based method. \n", "\n", "Among the three major classes in the module, **RectangleM, HexagonM, QStatistic**, the first two are aimed at imposing a tessellation (rectangular or hexagonal shape) over the minimum bounding rectangle of the point pattern and calculate the number of points falling in each cell; **QStatistic** is the main class with which we can calculate a p-value, as well as a pseudo p-value to help us make the decision of rejecting the null or not." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import pointpats.quadrat_statistics as qs" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['HexagonM',\n", " 'PointPattern',\n", " 'QStatistic',\n", " 'RectangleM',\n", " '__all__',\n", " '__author__',\n", " '__builtins__',\n", " '__cached__',\n", " '__doc__',\n", " '__file__',\n", " '__loader__',\n", " '__name__',\n", " '__package__',\n", " '__spec__',\n", " 'math',\n", " 'np',\n", " 'plt',\n", " 'scipy']" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dir(qs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Open the point shapefile \"juvenile.shp\"." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "juv = ps.io.open(ps.examples.get_path(\"juvenile.shp\"))" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "168" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(juv) # 168 point events in total" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[94., 93.],\n", " [80., 95.],\n", " [79., 90.],\n", " [78., 92.],\n", " [76., 92.],\n", " [66., 93.],\n", " [64., 90.],\n", " [27., 70.],\n", " [58., 88.],\n", " [57., 92.],\n", " [53., 92.],\n", " [50., 90.],\n", " [49., 90.],\n", " [32., 90.],\n", " [31., 87.],\n", " [22., 87.],\n", " [21., 87.],\n", " [21., 86.],\n", " [22., 81.],\n", " [23., 83.],\n", " [27., 85.],\n", " [27., 84.],\n", " [27., 83.],\n", " [27., 82.],\n", " [30., 84.],\n", " [31., 84.],\n", " [31., 84.],\n", " [32., 83.],\n", " [33., 81.],\n", " [32., 79.],\n", " [32., 76.],\n", " [33., 77.],\n", " [34., 86.],\n", " [34., 84.],\n", " [38., 82.],\n", " [39., 81.],\n", " [40., 80.],\n", " [41., 83.],\n", " [43., 75.],\n", " [44., 81.],\n", " [46., 81.],\n", " [47., 82.],\n", " [47., 81.],\n", " [48., 80.],\n", " [48., 81.],\n", " [50., 85.],\n", " [51., 84.],\n", " [52., 83.],\n", " [55., 85.],\n", " [57., 88.],\n", " [57., 81.],\n", " [60., 87.],\n", " [69., 80.],\n", " [71., 82.],\n", " [72., 81.],\n", " [74., 82.],\n", " [75., 81.],\n", " [77., 88.],\n", " [80., 88.],\n", " [82., 77.],\n", " [66., 62.],\n", " [64., 71.],\n", " [59., 63.],\n", " [55., 64.],\n", " [53., 68.],\n", " [52., 59.],\n", " [51., 61.],\n", " [50., 75.],\n", " [50., 74.],\n", " [45., 61.],\n", " [44., 60.],\n", " [43., 59.],\n", " [42., 61.],\n", " [39., 71.],\n", " [37., 67.],\n", " [35., 70.],\n", " [31., 68.],\n", " [30., 71.],\n", " [29., 61.],\n", " [26., 69.],\n", " [24., 68.],\n", " [ 7., 52.],\n", " [11., 53.],\n", " [34., 50.],\n", " [36., 47.],\n", " [37., 45.],\n", " [37., 56.],\n", " [38., 55.],\n", " [38., 50.],\n", " [39., 52.],\n", " [41., 52.],\n", " [47., 49.],\n", " [50., 57.],\n", " [52., 56.],\n", " [53., 55.],\n", " [56., 57.],\n", " [69., 52.],\n", " [69., 50.],\n", " [71., 51.],\n", " [71., 51.],\n", " [73., 48.],\n", " [74., 48.],\n", " [75., 46.],\n", " [75., 46.],\n", " [86., 51.],\n", " [87., 51.],\n", " [87., 52.],\n", " [90., 52.],\n", " [91., 51.],\n", " [87., 42.],\n", " [81., 39.],\n", " [80., 43.],\n", " [79., 37.],\n", " [78., 38.],\n", " [75., 44.],\n", " [73., 41.],\n", " [71., 44.],\n", " [68., 29.],\n", " [62., 33.],\n", " [61., 35.],\n", " [60., 34.],\n", " [58., 36.],\n", " [54., 30.],\n", " [52., 38.],\n", " [52., 36.],\n", " [47., 37.],\n", " [46., 36.],\n", " [45., 33.],\n", " [36., 32.],\n", " [22., 39.],\n", " [21., 38.],\n", " [22., 35.],\n", " [21., 36.],\n", " [22., 30.],\n", " [19., 29.],\n", " [17., 40.],\n", " [14., 41.],\n", " [13., 36.],\n", " [10., 34.],\n", " [ 7., 37.],\n", " [ 2., 39.],\n", " [21., 16.],\n", " [22., 14.],\n", " [29., 17.],\n", " [30., 25.],\n", " [32., 26.],\n", " [39., 28.],\n", " [40., 26.],\n", " [40., 26.],\n", " [42., 25.],\n", " [43., 24.],\n", " [43., 16.],\n", " [48., 16.],\n", " [51., 25.],\n", " [52., 26.],\n", " [57., 27.],\n", " [60., 22.],\n", " [63., 24.],\n", " [64., 23.],\n", " [64., 27.],\n", " [71., 25.],\n", " [50., 10.],\n", " [48., 12.],\n", " [45., 14.],\n", " [33., 8.],\n", " [31., 7.],\n", " [32., 6.],\n", " [31., 8.]])" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "juv_points = np.array([event for event in juv]) # get x,y coordinates for all the points\n", "juv_points" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Construct a point pattern from numpy array **juv_points**." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pp_juv = PointPattern(juv_points)\n", "pp_juv" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Point Pattern\n", "168 points\n", "Bounding rectangle [(2.0,6.0), (94.0,95.0)]\n", "Area of window: 8188.0\n", "Intensity estimate for window: 0.02051783097215437\n", " x y\n", "0 94.0 93.0\n", "1 80.0 95.0\n", "2 79.0 90.0\n", "3 78.0 92.0\n", "4 76.0 92.0\n" ] } ], "source": [ "pp_juv.summary()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pp_juv.plot(window= True, title= \"Point pattern\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Rectangle quadrats & analytical sampling distribution\n", "\n", "We can impose rectangle tessellation over mbb of the point pattern by specifying **shape** as \"rectangle\". We can also specify the number of rectangles in each row and column. For the current analysis, we use the $3 \\times 3$ rectangle grids." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "q_r = qs.QStatistic(pp_juv,shape= \"rectangle\",nx = 3, ny = 3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use the plot method to plot the quadrats as well as the number of points falling in each quadrat." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "q_r.plot()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "909.7777777777778" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "q_r.mr.rectangle_width * q_r.mr.rectangle_height #calculate the area of each grid cell " ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "33.107142857142854" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "q_r.chi2 #chi-squared test statistic for the observed point pattern" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "8" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "q_r.df #degree of freedom" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "5.890978545159614e-05" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "q_r.chi2_pvalue # analytical pvalue" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since the p-value based on the analytical $\\chi^2$ distribution (degree of freedom = 8) is 0.0000589, much smaller than 0.05. We might determine that the underlying process is not CSR. We can also turn to empirical sampling distribution to ascertain our decision." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Rectangle quadrats & empirical sampling distribution\n", "\n", "To construct a empirical sampling distribution, we need to simulate CSR within the window of the observed point pattern a lot of times. Here, we generate 999 point patterns under the null of CSR." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "csr_process = csr(pp_juv.window, pp_juv.n, 999, asPP=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We specify parameter **realizations** as the point process instance which contains 999 CSR realizations." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "q_r_e = qs.QStatistic(pp_juv,shape= \"rectangle\",nx = 3, ny = 3, realizations = csr_process)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.001" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "q_r_e.chi2_r_pvalue" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The pseudo p-value is 0.002, which is smaller than 0.05. Thus, we reject the null at the $95\\%$ confidence level." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Hexagon quadrats & analytical sampling distribution\n", "\n", "We can also impose hexagon tessellation over mbb of the point pattern by specifying **shape** as \"hexagon\". We can also specify the length of the hexagon edge. For the current analysis, we specify it as 15." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "q_h = qs.QStatistic(pp_juv,shape= \"hexagon\",lh = 15)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "q_h.plot()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "129.38095238095238" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "q_h.chi2 #chi-squared test statistic for the observed point pattern" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "19" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "q_h.df #degree of freedom" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.909272893094198e-18" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "q_h.chi2_pvalue # analytical pvalue" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Similar to the inference of rectangle tessellation, since the analytical p-value is much smaller than 0.05, we reject the null of CSR. The point pattern is not random." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Hexagon quadrats & empirical sampling distribution" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "q_h_e = qs.QStatistic(pp_juv,shape= \"hexagon\",lh = 15, realizations = csr_process)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.001" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "q_h_e.chi2_r_pvalue" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Because 0.001 is smaller than 0.05, we reject the null." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "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": "3.9.16" }, "widgets": { "state": {}, "version": "1.1.2" } }, "nbformat": 4, "nbformat_minor": 1 } pointpats-2.5.5/notebooks/centrography.ipynb000066400000000000000000000607061514706172100213340ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Centrography of Point Patterns\n", "\n", "**Authors: Serge Rey and Wei Kang **\n", "\n", "## Introduction\n", "\n", "Centrography refers to a set of descriptive statistics that provide summary descriptions of point patterns.\n", "\n", "This notebook introduces three types of centrography analysis for point patterns in pysal.\n", "* [Central Tendency](#Central-Tendency)\n", "* [Dispersion and Orientation](#Dispersion-and-Orientation)\n", "* [Shape Analysis](#Shape-Analysis)\n", "\n", "We also illustrate centrography analysis using two simulated datasets. See [Another Example](#Another-Example)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The python file **centrography.py** contains several functions with which we can conduct centrography analysis.\n", "* Central Tendency\n", " 1. mean_center: calculate the mean center of the unmarked point pattern.\n", " 2. weighted_mean_center: calculate the weighted mean center of the marked point pattern.\n", " 3. manhattan_median: calculate the manhattan median\n", " 4. euclidean_median: calculate the Euclidean median\n", "* Dispersion and Orientation\n", " 1. std_distance: calculate the standard distance\n", "* Shape Analysis\n", " 1. hull: calculate the convex hull of the point pattern\n", " 2. mbr: calculate the minimum bounding box (rectangle)\n", " \n", "All of the above functions operate on a series of coordinate pairs. That is, the data type of the first argument should be $(n,2)$ array_like. In case that you have a point pattern (PointPattern instance), you need to pass its attribute \"points\" instead of itself to these functions." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "from pointpats import PointPattern\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "points = [[66.22, 32.54], [22.52, 22.39], [31.01, 81.21],\n", " [9.47, 31.02], [30.78, 60.10], [75.21, 58.93],\n", " [79.26, 7.68], [8.23, 39.93], [98.73, 77.17],\n", " [89.78, 42.53], [65.19, 92.08], [54.46, 8.48]]\n", "pp = PointPattern(points) #create a point pattern \"pp\" from list\n", "pp.points " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "type(pp.points)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can use PointPattern class method **plot** to visualize **pp**." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pp.plot()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#import centragraphy analysis functions \n", "from pointpats.centrography import hull, mean_center, minimum_bounding_rectangle, weighted_mean_center, manhattan_median, std_distance,euclidean_median,ellipse" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Central Tendency\n", "\n", "Central Tendency concerns about the center point of the two-dimensional distribution. It is similar to the first moment of a one-dimensional distribution. There are several ways to measure central tendency, each having pros and cons. We need to carefully select the appropriate measure according to our objective and data status.\n", "\n", "### Mean Center $(x_{mc},y_{mc})$\n", "\n", "$$x_{mc}=\\frac{1}{n} \\sum^n_{i=1}x_i$$\n", "$$y_{mc}=\\frac{1}{n} \\sum^n_{i=1}y_i$$" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "mc = mean_center(pp.points)\n", "mc" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pp.plot()\n", "plt.plot(mc[0], mc[1], 'b^', label='Mean Center')\n", "plt.legend(numpoints=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Weighted Mean Center $(x_{wmc},y_{wmc})$\n", "\n", "$$x_{wmc}=\\sum^n_{i=1} \\frac{w_i x_i}{\\sum^n_{i=1}w_i}$$\n", "$$y_{wmc}=\\sum^n_{i=1} \\frac{w_i y_i}{\\sum^n_{i=1}w_i}$$\n", "\n", "Weighted mean center is meant for marked point patterns. Aside from the first argument which is a seris of $(x,y)$ coordinates in **weighted_mean_center** function, we need to specify its second argument which is the weight for each event point." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "weights = np.arange(12)\n", "weights" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "wmc = weighted_mean_center(pp.points, weights)\n", "wmc" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pp.plot() #use class method \"plot\" to visualize point pattern\n", "plt.plot(mc[0], mc[1], 'b^', label='Mean Center') \n", "plt.plot(wmc[0], wmc[1], 'gd', label='Weighted Mean Center')\n", "plt.legend(numpoints=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Manhattan Median $(x_{mm},y_{mm})$\n", "\n", "$$min f(x_{mm},y_{mm})= \\sum^n_{i=1}(|x_i-x_{mm}|+|y_i-y_{mm}|)$$\n", "\n", "The Manhattan median is the location which minimizes the absolute distance to all the event points. It is an extension of the median measure in one-dimensional space to two-dimensional space. Since in one-dimensional space, a median is the number separating the higher half of a dataset from the lower half, we define the Manhattan median as a tuple whose first element is the median of $x$ coordinates and second element is the median of $y$ coordinates.\n", "\n", "Though Manhattan median can be found very quickly, it is not unique if you have even number of points. In this case, pysal handles the Manhattan median the same way as numpy.median: return the average of the two middle values." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#get the number of points in point pattern \"pp\"\n", "pp.n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#Manhattan Median is not unique for \"pp\"\n", "mm = manhattan_median(pp.points)\n", "mm" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pp.plot()\n", "plt.plot(mc[0], mc[1], 'b^', label='Mean Center')\n", "plt.plot(wmc[0], wmc[1], 'gd', label='Weighted Mean Center')\n", "plt.plot(mm[0], mm[1], 'rv', label='Manhattan Median')\n", "plt.legend(numpoints=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Euclidean Median $(x_{em},y_{em})$\n", "\n", "$$min f(x_{em},y_{em})= \\sum^n_{i=1} \\sqrt{(x_i-x_{em})^2+(y_i-y_{em})^2}$$\n", "\n", "The Euclidean Median is the location from which the sum of the Euclidean distances to all points in a distribution is a minimum. It is an optimization problem and very important for more general location allocation problems. There is no closed form solution. We can use first iterative algorithm (Kuhn and Kuenne, 1962) to approximate Euclidean Median. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below, we define a function named median_center with the first argument **points** a series of $(x,y)$ coordinates and the second argument **crit** the convergence criterion." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def median_center(points, crit=0.0001):\n", " points = np.asarray(points)\n", " x0, y0 = points.mean(axis=0)\n", " dx = np.inf\n", " dy = np.inf\n", " iteration = 0\n", " while np.abs(dx) > crit or np.abs(dy) > crit:\n", " xd = points[:, 0] - x0\n", " yd = points[:, 1] - y0\n", " d = np.sqrt(xd*xd + yd*yd)\n", " w = 1./d\n", " w = w / w.sum()\n", " x1 = w * points[:, 0]\n", " x1 = x1.sum()\n", " y1 = w * points[:, 1]\n", " y1 = y1.sum()\n", " dx = x1 - x0\n", " dy = y1 - y0\n", " iteration +=1 \n", " print(x0, x1, dx, dy, d.sum(), iteration)\n", " x0 = x1\n", " y0 = y1\n", " \n", " return x1, y1" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "median_center(pp.points, crit=.0001)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After 18 iterations, the convergence criterion is reached. The Euclidean Median is $(54.167594287646125,44.424308658832047)$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also call the function **euclidean_median** in pysal to calculate the Euclidean Median." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "em = euclidean_median(pp.points)\n", "em" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The two results we get from **euclidean_median** function in pysal and the **median_center** function we define here are very much the same." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pp.plot()\n", "plt.plot(mc[0], mc[1], 'b^', label='Mean Center')\n", "plt.plot(wmc[0], wmc[1], 'gd', label='Weighted Mean Center')\n", "plt.plot(mm[0], mm[1], 'rv', label='Manhattan Median')\n", "plt.plot(em[0], em[1], 'm+', label='Euclidean Median')\n", "plt.legend(numpoints=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Dispersion and Orientation\n", "\n", "### Standard Distance & Standard Distance Circle\n", "\n", "$$SD = \\displaystyle \\sqrt{\\frac{\\sum^n_{i=1}(x_i-x_{m})^2}{n} + \\frac{\\sum^n_{i=1}(y_i-y_{m})^2}{n}}$$\n", "\n", "The Standard distance is closely related to the usual definition of the standard deviation of a data set, and it provides a measure of how dispersed the events are around their mean center $(x_m,y_m)$. Taken together, these measurements can be used to plot a summary circle (standard distance circle) for the point pattern, centered at $(x_m,y_m)$ with radius $SD$, as shown below." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "stdd = std_distance(pp.points)\n", "stdd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot mean center as well as the standard distance circle." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "circle1=plt.Circle((mc[0], mc[1]),stdd,color='r')\n", "ax = pp.plot(get_ax=True, title='Standard Distance Circle')\n", "ax.add_artist(circle1)\n", "plt.plot(mc[0], mc[1], 'b^', label='Mean Center')\n", "ax.set_aspect('equal')\n", "plt.legend(numpoints=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From the above figure, we can observe that there are five points outside the standard distance circle which are potential outliers." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Standard Deviational Ellipse\n", "\n", "Compared with standard distance circle which measures dispersion using a single parameter $SD$, standard deviational ellipse measures dispersion and trend in two dimensions through angle of rotation $\\theta$, dispersion along major axis $s_x$ and dispersion along minor axis $s_y$:\n", "\n", "* Major axis defines the direction of maximum spread in the distribution. $s_x$ is the semi-major axis (half the length of the major axis):\n", "\n", "$$ s_x = \\displaystyle \\sqrt{\\frac{2(\\sum_{i=1}^n (x_i-\\bar{x})\\cos(\\theta) - \\sum_{i=1}^n (y_i-\\bar{y})\\sin(\\theta))^2}{n-2}}$$\n", "\n", "* Minor axis defines the direction of minimum spread and is orthogonal to major axis. $s_y$ is the semi-minor axis (half the length of the minor axis):\n", "\n", "$$ s_y = \\displaystyle \\sqrt{\\frac{2(\\sum_{i=1}^n (x_i-\\bar{x})\\sin(\\theta) - \\sum_{i=1}^n (y_i-\\bar{y})\\cos(\\theta))^2}{n-2}}$$\n", "\n", "* The ellipse is rotated clockwise through an angle $\\theta$:\n", "\n", "$$\\theta = \\displaystyle \\arctan{\\{ (\\sum_i(x_i-\\bar{x})^2-\\sum_i(y_i-\\bar{y})^2) + \\frac{[(\\sum_i(x_i-\\bar{x})^2-\\sum_i(y_i-\\bar{y})^2)^2 + 4(\\sum_i(x-\\bar{x})(y_i-\\bar{y}))^2]^\\frac{1}{2}}{2\\sum_i(x-\\bar{x})(y_i-\\bar{y})}\\}}$$\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sx, sy, theta = ellipse(pp.points)\n", "sx, sy, theta" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "theta_degree = np.degrees(theta) #need degree of rotation to plot the ellipse\n", "theta_degree" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Standard Deviational Ellipse for the point pattern is rotated clockwise by $63.25^{\\circ}$." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from matplotlib.patches import Ellipse\n", "from pylab import figure, show,rand\n", "fig = figure()\n", "#ax = fig.add_subplot(111, aspect='equal')\n", "e = Ellipse(xy=mean_center(pp.points), width=sx*2, height=sy*2, angle=-theta_degree) #angle is rotation in degrees (anti-clockwise)\n", "ax = pp.plot(get_ax=True, title='Standard Deviational Ellipse')\n", "ax.add_artist(e)\n", "e.set_clip_box(ax.bbox)\n", "e.set_facecolor([0.8,0,0])\n", "e.set_edgecolor([1,0,0])\n", "ax.set_xlim(0,100)\n", "ax.set_ylim(0,100)\n", "ax.set_aspect('equal')\n", "plt.plot(mc[0], mc[1], 'b^', label='Mean Center')\n", "plt.legend(numpoints=1)\n", "show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Shape Analysis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### [Convex Hull](https://en.wikipedia.org/wiki/Convex_hull)\n", "\n", "The convex hull of a point pattern *pp* is the smallest convex set that contains *pp*. We can call function **hull** to caculate the convex hull." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "hull(pp.points)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By specifying \"hull\" argument **True** in PointPattern class method **plot**, we can easily plot convex hull of the point pattern." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pp.plot(title='Centers', hull=True ) #plot point pattern \"pp\" as well as its convex hull\n", "plt.plot(mc[0], mc[1], 'b^', label='Mean Center')\n", "plt.plot(wmc[0], wmc[1], 'gd', label='Weighted Mean Center')\n", "plt.plot(mm[0], mm[1], 'rv', label='Manhattan Median')\n", "plt.plot(em[0], em[1], 'm+', label='Euclidean Median')\n", "plt.legend(numpoints=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### [Minimum Bounding Rectangle](https://en.wikipedia.org/wiki/Minimum_bounding_rectangle)\n", "\n", "Minimum Bounding Rectangle (Box) is the same as the minimum bounding Rectangle of its convex hull. Thus, it is almost always bigger than convex hull.\n", "\n", "We can call **mbr** function to calculate the leftmost, downmost, rightmost, and upmost value of the vertices of minimum bounding rectangle." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "minimum_bounding_rectangle(pp.points)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Thus, four vertices of the minimum bounding rectangle is $(8.23,7.68),(98.73,7.68),(98.73,92.08),(8.23,92.08)$." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pp.plot(title='Centers', window=True ) #plot point pattern \"pp\" as well as its Minimum Bounding Rectangle\n", "plt.plot(mc[0], mc[1], 'b^', label='Mean Center')\n", "plt.plot(wmc[0], wmc[1], 'gd', label='Weighted Mean Center')\n", "plt.plot(mm[0], mm[1], 'rv', label='Manhattan Median')\n", "plt.plot(em[0], em[1], 'm+', label='Euclidean Median')\n", "plt.legend(numpoints=1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pp.plot(title='Centers', hull=True , window=True )#plot point pattern \"pp\", convex hull, and Minimum Bounding Rectangle\n", "plt.plot(mc[0], mc[1], 'b^', label='Mean Center')\n", "plt.plot(wmc[0], wmc[1], 'gd', label='Weighted Mean Center')\n", "plt.plot(mm[0], mm[1], 'rv', label='Manhattan Median')\n", "plt.plot(em[0], em[1], 'm+', label='Euclidean Median')\n", "plt.legend(numpoints=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot Standard Distance Circle and Convex Hull." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "circle1=plt.Circle((mc[0], mc[1]),stdd,color='r',alpha=0.2)\n", "ax = pp.plot(get_ax=True, title='Standard Distance Circle', hull=True)\n", "ax.add_artist(circle1)\n", "plt.plot(mc[0], mc[1], 'b^', label='Mean Center')\n", "ax.set_aspect('equal')\n", "plt.legend(numpoints=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Another Example\n", "\n", "We apply the centrography statistics and visualization to 2 simulated random datasets." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#from pysal.contrib import shapely_ext\n", "from libpysal.cg import shapely_ext\n", "from pointpats import PoissonPointProcess as csr\n", "import libpysal as ps\n", "from pointpats import as_window\n", "#import pysal_examples\n", "\n", "# open \"vautm17n\" polygon shapefile\n", "va = ps.io.open(ps.examples.get_path(\"vautm17n.shp\"))\n", "\n", "# Create the exterior polygons for VA from the union of the county shapes\n", "polys = [shp for shp in va]\n", "state = shapely_ext.cascaded_union(polys)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Simulate a 100-point dataset within VA state border from a CSR (complete spatial randomness) process." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pp = csr(as_window(state), 100, 1, asPP=True).realizations[0]\n", "pp.plot(window=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pp.plot(window=True, hull=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "mc = mean_center(pp.points)\n", "mm = manhattan_median(pp.points)\n", "em = euclidean_median(pp.points)\n", "pp.plot(title='Centers', hull=True , window=True )#plot point pattern \"pp\", convex hull, and Minimum Bounding Rectangle\n", "plt.plot(mc[0], mc[1], 'c^', label='Mean Center')\n", "plt.plot(mm[0], mm[1], 'rv', label='Manhattan Median')\n", "plt.plot(em[0], em[1], 'm+', label='Euclidean Median')\n", "plt.legend(numpoints=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot Standard Distance Circle of the simulated point pattern." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sx, sy, theta = ellipse(pp.points)\n", "sx, sy, theta\n", "theta_degree = np.degrees(theta) #need degree of rotation to plot the ellipse\n", "from matplotlib.patches import Ellipse\n", "from pylab import figure, show,rand\n", "fig = figure()\n", "#ax = fig.add_subplot(111, aspect='equal')\n", "e = Ellipse(xy=mean_center(pp.points), width=sx*2, height=sy*2, angle=-theta_degree)\n", "ax = pp.plot(get_ax=True, title='Standard Deviational Ellipse')\n", "ax.add_artist(e)\n", "e.set_clip_box(ax.bbox)\n", "e.set_facecolor([0.8,0,0])\n", "e.set_edgecolor([1,0,0])\n", "ax.set_xlim(300000,1000000)\n", "ax.set_ylim(4050000,4350000)\n", "#ax.set_aspect('equal')\n", "plt.plot(mc[0], mc[1], 'c^', label='Mean Center')\n", "plt.legend(numpoints=1)\n", "show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Simulate a 500-point dataset within VA state border from a CSR (complete spatial randomness) process." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pp = csr(as_window(state), 500, 1, asPP=True).realizations[0]\n", "pp.plot(window=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pp.plot(window=True, hull=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "mc = mean_center(pp.points)\n", "mm = manhattan_median(pp.points)\n", "em = euclidean_median(pp.points)\n", "pp.plot(title='Centers', hull=True , window=True )#plot point pattern \"pp\", convex hull, and Minimum Bounding Rectangle\n", "plt.plot(mc[0], mc[1], 'c^', label='Mean Center')\n", "plt.plot(mm[0], mm[1], 'rv', label='Manhattan Median')\n", "plt.plot(em[0], em[1], 'm+', label='Euclidean Median')\n", "plt.legend(numpoints=1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sx, sy, theta = ellipse(pp.points)\n", "sx, sy, theta\n", "theta_degree = np.degrees(theta) #need degree of rotation to plot the ellipse\n", "from matplotlib.patches import Ellipse\n", "from pylab import figure, show,rand\n", "fig = figure()\n", "#ax = fig.add_subplot(111, aspect='equal')\n", "e = Ellipse(xy=mean_center(pp.points), width=sx*2, height=sy*2, angle=-theta_degree)\n", "ax = pp.plot(get_ax=True, title='Standard Deviational Ellipse')\n", "ax.add_artist(e)\n", "e.set_clip_box(ax.bbox)\n", "e.set_facecolor([0.8,0,0])\n", "e.set_edgecolor([1,0,0])\n", "ax.set_xlim(300000,1000000)\n", "ax.set_ylim(4050000,4350000)\n", "#ax.set_aspect('equal')\n", "plt.plot(mc[0], mc[1], 'c^', label='Mean Center')\n", "plt.legend(numpoints=1)\n", "show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we calculate the Euclidean distances between every event point and Mean Center (Euclidean Median), and sum them up, we can see that Euclidean Median is the optimal point in iterms of minimizing the Euclidean distances to all the event points." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from pointpats import dtot\n", "print(dtot(mc, pp.points))\n", "print(dtot(em, pp.points))\n", "print(dtot(mc, pp.points) > dtot(em, pp.points))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "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": "3.12.2" }, "widgets": { "state": {}, "version": "1.1.2" } }, "nbformat": 4, "nbformat_minor": 4 } pointpats-2.5.5/notebooks/distance_statistics-numpy-oriented.ipynb000066400000000000000000012414741514706172100256540ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Distance Based Statistical Method for Planar Point Patterns\n", "\n", "**Authors: Serge Rey and Wei Kang **\n", "\n", "## Introduction\n", "\n", "Distance based methods for point patterns are of three types:\n", "\n", "* [Mean Nearest Neighbor Distance Statistics](#Mean-Nearest-Neighbor-Distance-Statistics)\n", "* [Nearest Neighbor Distance Functions](#Nearest-Neighbor-Distance-Functions)\n", "* [Interevent Distance Functions](#Interevent-Distance-Functions)\n", "\n", "In addition, we are going to introduce a computational technique [Simulation Envelopes](#Simulation-Envelopes) to aid in making inferences about the data generating process. An [example](#CSR-Example) is used to demonstrate how to use and interprete simulation envelopes." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from scipy import spatial\n", "import libpysal as ps\n", "import numpy as np\n", "from pointpats import ripley\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Mean Nearest Neighbor Distance Statistics\n", "\n", "The nearest neighbor(s) for a point $u$ is the point(s) $N(u)$ which meet the condition\n", "$$d_{u,N(u)} \\leq d_{u,j} \\forall j \\in S - u$$\n", "\n", "The distance between the nearest neighbor(s) $N(u)$ and the point $u$ is nearest neighbor distance for $u$. After searching for nearest neighbor(s) for all the points and calculating the corresponding distances, we are able to calculate mean nearest neighbor distance by averaging these distances.\n", "\n", "It was demonstrated by Clark and Evans(1954) that mean nearest neighbor distance statistics distribution is a normal distribution under null hypothesis (underlying spatial process is CSR). We can utilize the test statistics to determine whether the point pattern is the outcome of CSR. If not, is it the outcome of cluster or regular\n", "spatial process?" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "points = np.array([[66.22, 32.54], [22.52, 22.39], [31.01, 81.21],\n", " [9.47, 31.02], [30.78, 60.10], [75.21, 58.93],\n", " [79.26, 7.68], [8.23, 39.93], [98.73, 77.17],\n", " [89.78, 42.53], [65.19, 92.08], [54.46, 8.48]])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Nearest Neighbor Distance Functions\n", "\n", "Nearest neighbour distance distribution functions (including the nearest “event-to-event” and “point-event” distance distribution functions) of a point process are cumulative distribution functions of several kinds -- $G, F, J$. By comparing the distance function of the observed point pattern with that of the point pattern from a CSR process, we are able to infer whether the underlying spatial process of the observed point pattern is CSR or not for a given confidence level." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### $G$ function - event-to-event\n", "\n", "The $G$ function is a kind of \"cumulative\" density describing the distribution of distances within a point pattern. For a given distance $d$, $G(d)$ is the proportion of nearest neighbor distances that are less than $d$. To express this, we first need to define the nearest neighbor distance, which is the smallest distance from each observation $i$ to some other observation $j$, where $j \\neq i$:\n", "$$ min_{j\\neq i}\\{d_{ij}\\} = d^*_i $$\n", "\n", "With this, we can define the $G$ function as a cumulative density function:\n", "$$G(d) = \\frac{1}{N}\\sum_{i=1}^N \\mathcal{I}(d^*_i < d)$$\n", "where $\\mathcal{I}(.)$ is an *indicator function* that is $1$ when the argument is true and is zero otherwise. In simple terms, $G(d)$ gives the percentage of of nearest neighbor distances ($d^*_i$) that are smaller than $d$; when $d$ is very small, $G(d)$ is close to zero. When $d$ is large, $G(d)$ approaches one. \n", "\n", "Analytical results about $G$ are available assuming that the \"null\" process of locating points in the study area is completely spatially random. In a completely spatially random process, the $G(d)$ value should be:\n", "$$\n", "G(d) = 1-e^{-\\lambda \\pi d^2}\n", "$$\n", "Practically, we assess statistical significance for the $G(d)$ function using simulations, where a known spatially-random process is generated and then analyzed. This partially accounts for issues with irregularly-shaped study areas, where locations of points are constrained. \n", "\n", "In practice, we use the `ripley.g_test` function to conduct a test on the $G(d)$. It estimates a value of $G(d)$ for a set of values (called the `support`). To compute the $G$ function for ten values of $d$ ranging from the smallest possible to the largest values in the data:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "g_test = ripley.g_test(points, support=10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "All statistical tests in the `pointpats.distance_statistics` return a `collections.namedtuple` object with the following properties:\n", "- `support`, which contains the distance values ($d$) used to compute the distance statistic. \n", "- `statistic`, which expresses the value of the requested function at each value of $d$ in the `support`. \n", "- `pvalue`, which expresses the fraction of observed simulations (under a completely spatially random process) that are more extreme than the observed statistics. \n", "- `simulations`, which stores the simulated values of the statistic under a spatially random process. Generally, this is *not* saved (for efficiency reasons), but can be requested using `keep_simulations`. " ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 0. , 3.84791574, 7.69583148, 11.54374723, 15.39166297,\n", " 19.23957871, 23.08749445, 26.93541019, 30.78332593, 34.63124168])" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "g_test.support" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0. , 0. , 0. , 0.16666667, 0.16666667,\n", " 0.25 , 0.58333333, 0.83333333, 0.91666667, 1. ])" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "g_test.statistic" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0.00e+00, 0.00e+00, 0.00e+00, 2.89e-02, 1.10e-03, 1.00e-04,\n", " 4.30e-03, 6.10e-02, 7.33e-02, 0.00e+00])" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "g_test.pvalue" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "g_test.simulations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To make a plot of the statistic, the `statistic` is generally plotted on the vertical axis and the `support` on the horizontal axis. Here, we will show the median simulated value of $G(d)$ as well." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "g_test = ripley.g_test(points, support=10, keep_simulations=True)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(g_test.support, np.median(g_test.simulations, axis=0), \n", " color='k', label='simulated')\n", "plt.plot(g_test.support, g_test.statistic, \n", " marker='x', color='orangered', label='observed')\n", "plt.legend()\n", "plt.xlabel('Distance')\n", "plt.ylabel('G Function')\n", "plt.title('G Function Plot')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, the $G$ function increases very slowly at small distances and the line is below the typical simulated value (shown in black). We can verify the visual intuition here by looking at the p-value for each point and plotting the simulated $G(d)$ curves, too:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# grab the middle 95% of simulations using numpy:\n", "middle_95pct = np.percentile(g_test.simulations, q=(2.5, 97.5), axis=0)\n", "# use the fill_between function to color between the 2.5% and 97.5% envelope\n", "plt.fill_between(g_test.support, *middle_95pct, \n", " color='lightgrey', label='simulated')\n", "\n", "# plot the line for the observed value of G(d)\n", "plt.plot(g_test.support, g_test.statistic, \n", " color='orangered', label='observed')\n", "# and plot the support points depending on whether their p-value is smaller than .05\n", "plt.scatter(g_test.support, g_test.statistic, \n", " cmap='viridis', c=g_test.pvalue < .01)\n", "plt.legend()\n", "plt.xlabel('Distance')\n", "plt.ylabel('G Function')\n", "plt.title('G Function Plot')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From this, we can see that there is statistically significant \"dispersion\" at small values of $d$, since there are *too few* nearest neighbor distances observed between $0 < d < 25$. Once we get to very large distances, the simulation envelope covers the observed statistic. As such, we can say that the point pattern recorded in `points` is unusally dispersed. \n", "\n", "To evaluate the $G(d)$ function without considering any statistical significance or simulations, you can use the `g_function` in the `ripley` module, which simply returns the distances & values of $G(d)$. " ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([ 0. , 1.82269693, 3.64539386, 5.46809079, 7.29078772,\n", " 9.11348465, 10.93618158, 12.75887851, 14.58157544, 16.40427237,\n", " 18.2269693 , 20.04966623, 21.87236316, 23.69506009, 25.51775702,\n", " 27.34045395, 29.16315088, 30.98584782, 32.80854475, 34.63124168]),\n", " array([0. , 0. , 0. , 0. , 0. ,\n", " 0.16666667, 0.16666667, 0.16666667, 0.16666667, 0.25 ,\n", " 0.25 , 0.25 , 0.41666667, 0.58333333, 0.75 ,\n", " 0.83333333, 0.83333333, 0.91666667, 0.91666667, 1. ]))" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ripley.g_function(points)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### $F$ function - \"point-event\" \n", "\n", "When the number of events in a point pattern is small, $G$ function is rough. For the pattern contained in `points`, there are only 12 observations! This means that there are only 12 nearest neighbor distances, and thus only 12 possible values for the $G(d)$ statistic, at any $d$. \n", "\n", "One way to get around this is to turn to an alternative, the $F(d)$ function. This is analogous to the $G(d)$ function, but measures the nearest neighbor distance *from* a set of known randomly-distributed points *to* a point in the observed pattern. Another way of thinking about $F(d)$ is that it reflects a *between-pattern* measure of dispersion, where one pattern is completely spatially random and the other pattern is our observed pattern. In contrast, $G(d)$ is a *within-pattern* measure of dispersion. \n", "\n", "For a randomly simulated point pattern of size $N_s$, this makes the $F(d)$ function:\n", "\n", "$$F(d) = \\frac{1}{N_s} \\sum_k^{N_s} \\mathcal{I}(d^*_k < d)$$\n", "\n", "This can have $N_s$ possible values for any $d$, and thus can give a much more fine-grained view of the point pattern. In this sense, the $F(d)$ function is often called the *empty space function*, as it measures the distance from random points in \"empty space\" to the \"filled\" points in our point pattern. The number of those random points governs how \"fine-grained\" our measure of the observed point pattern can be. \n", "\n", "Just like the `ripley.g_test`, this function is evaluated for every $d$ in a support. Further, we can provide *custom* values for `support`, just in case we have known distance values of interest. \n", "\n", "Below, we'll use the same ten `support` values from $G(d)$ function. And, let's constrain the \"simulated\" point patterns to fall within the convex hull of our original point pattern: " ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "f_test = ripley.f_test(points, support = g_test.support, keep_simulations=True, hull='convex')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since the $F(d)$ function is very smooth, we can see the $F(d)$ statistic and its simulations clearly by plotting their values directly as lines. For the simulated values, we will make them very transparent. As before we will visualize statistical significance using the `pvalue` attribute:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(f_test.support, f_test.simulations.T, alpha=.01, color='k')\n", "plt.plot(f_test.support, f_test.statistic, color='red')\n", "\n", "plt.scatter(f_test.support, f_test.statistic, \n", " cmap='viridis', c=f_test.pvalue < .05,\n", " zorder=4 # make sure they plot on top\n", " )\n", "\n", "plt.xlabel('Distance')\n", "plt.ylabel('F Function')\n", "plt.title('F Function Plot')\n", "plt.show()\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From this we see that the values of the $F$ function are *too high* for distances from about 15 to 25, and (in contrast) for values between $5 < d < 10$, the $F(d)$ function has too few short distances. When the observed $F(d)$ values are too large, then the pattern is too dispersed, or regular. If the empirical $F(d)$ tends to fall below the simulated values, then it reflects clustering. This is the *opposite* of the interpretation of the $G(d)$ function above, so be careful!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### $J$ function - a combination of \"event-event\" and \"point-event\"\n", "\n", "The $J$ function combines the $G$ and $F$ function, in an attempt to provide an immediate graphical indication of the clustering both internally and with respect to the empty space distribution. Practically, the $J(d)$ function is computed as a kind of \"relative clustering ratio\":\n", "\n", "$$J(d) = \\frac{1-G(d)}{1-F(d)}$$\n", "\n", "where the numerator captures the clustering due to within-pattern distances and the denominator captures that for the pattern-to-empty distances. This means that when $J(d)<1$, the underlying point process is a cluster point process, and when $J(d)=1$, the underlying point process is a random point process; otherwise, it is a dispersed point process.\n", "\n", "This function can suffer from numerical stability issues; as $G(d)$ and $F(d)$ both approach $1$, the $J$ ratio can become chaotic. Further, when $G$ or $F$ reaches one, the $J$ function changes abruptly. As such, the $J$ function is often *truncated* to the first $1$ (either in $F(d)$ or $G(d)$), and any $d$ where both $F$ and $G$ are $1$ is assigned a $J$ value of $1$. " ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/lw17329/Dropbox/dev/pointpats/pointpats/ripley.py:894: UserWarning: requested 20 bins to evaluate the J function, but it reaches infinity at d=25.5178, meaning only 14 bins will be used to characterize the J function.\n", " tree, distances=distances, **core_kwargs\n" ] } ], "source": [ "jp1 = ripley.j_test(points, support=20)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see from the warning above, the $J$ function did encounter numerical stability issues at about $d=25$. To address this, `pointpats` truncated the $J$ function to only have 14 values in its support, rather than the $20$ requested. " ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'J Function')" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(jp1.support, jp1.statistic, color='orangered')\n", "plt.axhline(1, linestyle=':', color='k')\n", "plt.xlabel('Distance')\n", "plt.ylabel('J Function')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From the above figure, we see that the $J$ function is above the $J(d)=1$ horizontal line, especially as $d$ gets large. This suggests that the process is over-dispersed. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Interevent Distance Functions\n", "\n", "While both the $F(d)$ and $G(d)$ functions are useful, they only consider the distance between each point $i$ and its nearest point. Earlier we spelled this distance $d_i^*$, and the distance between $i$ and $j$ was $d_{ij}$. So, note that $d_{i}^*$ is the *only* term that matters for $F$ and $G$, if $d_{ij}$ changes (but $j$ isn't closest to $i$), then the $F$ and $G$ functions generally remain the same. \n", "\n", "So, further statistical summary functions have been developed to consider the *whole* distance distribution, not only the nearest neighbor distances. These functions (still considered part of the \"Ripley\" alphabet, are the $K$, and $L$ functions. \n", "\n", "#### $K$ function\n", "\n", "The $K(d)$ function is a scaled version of the cumulative density function for *all* distances within a point pattern. As such, it's a \"relative\" of the $G$ function that considers all distances, not just the nearest neighbor distances. Practically, the $K(d)$ function can be thought of as the percentage of all distances that are less than $d$. Therefore, for a threshold distance $d$, the $K$ function is defined as:\n", "\n", "$$K(d) = \\frac{1}{N\\hat\\lambda} \\underset{i=1}{\\overset{N}{\\sum}}\\underset{j=1}{\\overset{N}{\\sum}} \\mathcal{I}\\left(d_ij < d\\right)$$\n", "\n", "In this equation, $\\hat\\lambda$ is the *intensity* of the point process. This represents how many points (on average) you would expect in a unit area. You can think of this as an analogue to the *density* of the points in the pattern: large values of $\\hat\\lambda$ mean many points per area, and small values of $\\hat\\lambda$ mean there are fewer points per area. Generally, this parameter is unknown, and is modelled using the average number of points in the study area. This assumes that the intensity of the point pattern is *constant* or *homogeneous* over the study area.\n", "\n", "In the same manner as before, we can construct a set of $K(d)$ function evaluations for random point patterns, and compare them to the observed $K(d)$ function we saw in our original data." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "k_test = ripley.k_test(points, keep_simulations=True)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(k_test.support, k_test.simulations.T, color='k', alpha=.01)\n", "plt.plot(k_test.support, k_test.statistic, color='orangered')\n", "\n", "plt.scatter(k_test.support, k_test.statistic, \n", " cmap='viridis', c=k_test.pvalue < .05,\n", " zorder=4 # make sure they plot on top\n", " )\n", "\n", "plt.xlabel('Distance')\n", "plt.ylabel('K Function')\n", "plt.title('K Function Plot')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Again, we can see that the envelopes are generally above the observed function, meaining that our point pattern is dispersed. We can draw this conclusion because the distances are *too small*, suggesting the pattern is less clustered than otherwise woudl be expected. When points are too regular, their distances tend to be smaller than if they were distributed randomly. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### $L$ function - \"interevent\"\n", "\n", "The $L$ function is a scaled version of $K$ function, defined in order to assist with interpretation. The expected value of the $K(d)$ function *increases* with $d$; this makes sense, since the number of pairs of points closer than $d$ will increase as $d$ increases. So, we can define a normalization of $K$ that *removes* this increase as $d$ increases. \n", "\n", "$$L(d) = \\sqrt{\\frac{K(d)}{\\pi}}-d$$\n", "\n", "For a pattern that is spatially random, $L(d)$ is $0$ at all $d$ values. So, we can use this standardization to make it easier to visualize the results of the $K$ function:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "l_test = ripley.l_test(points, keep_simulations=True)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(l_test.support, l_test.simulations.T, color='k', alpha=.01)\n", "plt.plot(l_test.support, l_test.statistic, color='orangered')\n", "\n", "plt.scatter(l_test.support, l_test.statistic, \n", " cmap='viridis', c=l_test.pvalue < .05,\n", " zorder=4 # make sure they plot on top\n", " )\n", "\n", "plt.xlabel('Distance')\n", "plt.ylabel('K Function')\n", "plt.title('K Function Plot')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## CSR Example\n", "\n", "In this example, we are going to generate a point pattern as the \"observed\" point pattern. This ensures that the data generating process is completely spatially random. Then, we will simulate CSR in the same domain for 100 times and construct evaluate the ripley functions for these simulations. " ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "import geopandas\n", "df = geopandas.read_file(ps.examples.get_path(\"vautm17n.shp\"))\n", "state = df.geometry.cascaded_union" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Generate the point pattern **pp** (size 100) from CSR as the \"observed\" point pattern." ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "pattern = ripley.simulate(state, size=100)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "before we go any further, let's visualize these simulated values:" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df.plot()\n", "plt.scatter(*pattern.T, color='orangered', marker='.')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And, let's check if there are 100 points:" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(100, 2)" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pattern.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Yep! So, next to simulate a set of realizations in the same manner, we can use the `size` argument again, just like the `numpy.random` simulators. This means that, to simulate $K$ realizations of a pattern of size $N$, then we use `simulate(hull, size=(N,K)`. For just one realization, we can use `size=N`. " ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "random_realizations = ripley.simulate(state, size=(100,100))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To show the random pattern is truly random, we can visualize all of the points:" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df.plot(facecolor='none', edgecolor='k')\n", "plt.scatter(*random_realizations.T, marker='.', s=2)\n", "plt.scatter(*pattern.T, color='orangered', marker='.')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's now compute the `G` function for the observed pattern as well as all the realizations we just made. " ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [], "source": [ "observed_g = ripley.g_function(pattern)\n", "comparison_g = [ripley.g_function(realization, support=observed_g[0]) \n", " for realization in random_realizations]" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(*observed_g, color='orangered')\n", "[plt.plot(*comparison, color='k', alpha=.01) \n", " for comparison in comparison_g]\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "All other functions work identically!" ] } ], "metadata": { "kernelspec": { "display_name": "Analysis", "language": "python", "name": "ana" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.6" }, "widgets": { "state": {}, "version": "1.1.2" } }, "nbformat": 4, "nbformat_minor": 4 } pointpats-2.5.5/notebooks/distance_statistics.ipynb000066400000000000000000033430431514706172100226740ustar00rootroot00000000000000 distance_statistics-numpy-oriented

Distance Based Statistical Method for Planar Point Patterns

Authors: Serge Rey sjsrey@gmail.com and Wei Kang weikang9009@gmail.com

Introduction

Distance based methods for point patterns are of three types:

In addition, we are going to introduce a computational technique Simulation Envelopes to aid in making inferences about the data generating process. An example is used to demonstrate how to use and interprete simulation envelopes.

In [1]:
from scipy import spatial
import libpysal as ps
import numpy as np
from pointpats import (PointPattern, PoissonPointProcess, as_window, 
                       G, F, J, K, L, Genv, Fenv, Jenv, Kenv, Lenv)
from pointpats import ripley
%matplotlib inline
import matplotlib.pyplot as plt

Mean Nearest Neighbor Distance Statistics

The nearest neighbor(s) for a point $u$ is the point(s) $N(u)$ which meet the condition $$d_{u,N(u)} \leq d_{u,j} \forall j \in S - u$$

The distance between the nearest neighbor(s) $N(u)$ and the point $u$ is nearest neighbor distance for $u$. After searching for nearest neighbor(s) for all the points and calculating the corresponding distances, we are able to calculate mean nearest neighbor distance by averaging these distances.

It was demonstrated by Clark and Evans(1954) that mean nearest neighbor distance statistics distribution is a normal distribution under null hypothesis (underlying spatial process is CSR). We can utilize the test statistics to determine whether the point pattern is the outcome of CSR. If not, is it the outcome of cluster or regular spatial process?

Mean nearest neighbor distance statistic

$$\bar{d}_{min}=\frac{1}{n} \sum_{i=1}^n d_{min}(s_i)$$
In [2]:
points = np.array([[66.22, 32.54], [22.52, 22.39], [31.01, 81.21],
                   [9.47, 31.02],  [30.78, 60.10], [75.21, 58.93],
                   [79.26,  7.68], [8.23, 39.93],  [98.73, 77.17],
                   [89.78, 42.53], [65.19, 92.08], [54.46, 8.48]])
pp = PointPattern(points)
kdt = spatial.KDTree(points)

Nearest Neighbor Distance Functions

Nearest neighbour distance distribution functions (including the nearest “event-to-event” and “point-event” distance distribution functions) of a point process are cumulative distribution functions of several kinds -- $G, F, J$. By comparing the distance function of the observed point pattern with that of the point pattern from a CSR process, we are able to infer whether the underlying spatial process of the observed point pattern is CSR or not for a given confidence level.

$G$ function - event-to-event

The $G$ function is defined as follows: for a given distance $d$, $G(d)$ is the proportion of nearest neighbor distances that are less than $d$. $$G(d) = \sum_{i=1}^n \frac{ \phi_i^d}{n}$$

$$ \phi_i^d = \begin{cases} 1 & \quad \text{if } d_{min}(s_i)<d \\ 0 & \quad \text{otherwise } \\ \end{cases} $$

If the underlying point process is a CSR process, $G$ function has an expectation of: $$ G(d) = 1-e(-\lambda \pi d^2) $$ However, if the $G$ function plot is above the expectation this reflects clustering, while departures below expectation reflect dispersion.

In [3]:
gp1 = G(pp, intervals=20)
gp1.plot()
In [4]:
support, gfunction = ripley.g_function(points, support=20)
In [5]:
plt.plot(*ripley.g_function(points, support=20), marker='x')
plt.plot(gp1.d, gp1.ev)
plt.plot(gp1.d, gp1.G, marker='+')
Out[5]:
[<matplotlib.lines.Line2D at 0x128fea460>]

in the q-q plot the csr function is now a diagonal line which serves to make accessment of departures from csr visually easier.

It is obvious that the above $G$ increases very slowly at small distances and the line is below the expected value for a CSR process (green line). We might think that the underlying spatial process is regular point process. However, this visual inspection is not enough for a final conclusion. In Simulation Envelopes, we are going to demonstrate how to simulate data under CSR many times and construct the $95\%$ simulation envelope for $G$.

$F$ function - "point-event"

When the number of events in a point pattern is small, $G$ function is rough (see the $G$ function plot for the 12 size point pattern above). One way to get around this is to turn to $F$ funtion where a given number of randomly distributed points are generated in the domain and the nearest event neighbor distance is calculated for each point. The cumulative distribution of all nearest event neighbor distances is called $F$ function.

In [6]:
fp1 = F(pp, intervals=20) # The default is to randomly generate 100 points.
In [7]:
plt.plot(*ripley.f_function(points, support=20), marker='x')
plt.plot(fp1.d, fp1.ev)
plt.plot(fp1.d, fp1.F, marker='+')
Out[7]:
[<matplotlib.lines.Line2D at 0x1290be8b0>]

$J$ function - a combination of "event-event" and "point-event"

$J$ function is defined as follows:

$$J(d) = \frac{1-G(d)}{1-F(d)}$$

If $J(d)<1$, the underlying point process is a cluster point process; if $J(d)=1$, the underlying point process is a random point process; otherwise, it is a regular point process.

In [8]:
jp1 = J(pp, intervals=20)
In [9]:
plt.plot(*ripley.j_function(points, support=20), marker='x')
plt.plot(jp1.d, jp1.ev)
plt.plot(jp1.d, jp1.j, marker='+')

/Users/lw17329/Dropbox/dev/pointpats/pointpats/ripley.py:656: UserWarning: requested 20 bins to evaluate the J function, but it reaches infinity at d=25.5178, meaning only 14 bins will be used to characterize the J function.
  warnings.warn(
Out[9]:
[<matplotlib.lines.Line2D at 0x1291aa340>]

From the above figure, we can observe that $J$ function is obviously above the $J(d)=1$ horizontal line. It is approaching infinity with nearest neighbor distance increasing. We might tend to conclude that the underlying point process is a regular one.

Interevent Distance Functions

Nearest neighbor distance functions consider only the nearest neighbor distances, "event-event", "point-event" or the combination. Thus, distances to higer order neighbors are ignored, which might reveal important information regarding the point process. Interevent distance functions, including $K$ and $L$ functions, are proposed to consider distances between all pairs of event points. Similar to $G$, $F$ and $J$ functions, $K$ and $L$ functions are also cumulative distribution function.

$K$ function - "interevent"

Given distance $d$, $K(d)$ is defined as: $$K(d) = \frac{\sum_{i=1}^n \sum_{j=1}^n \psi_{ij}(d)}{n \hat{\lambda}}$$

where $$ \psi_{ij}(d) = \begin{cases} 1 & \quad \text{if } d_{ij}<d \\ 0 & \quad \text{otherwise } \\ \end{cases} $$

$\sum_{j=1}^n \psi_{ij}(d)$ is the number of events within a circle of radius $d$ centered on event $s_i$ .

Still, we use CSR as the benchmark (null hypothesis) and see how the $K$ funtion estimated from the observed point pattern deviate from that under CSR, which is $K(d)=\pi d^2$. $K(d)<\pi d^2$ indicates that the underlying point process is a regular point process. $K(d)>\pi d^2$ indicates that the underlying point process is a cluster point process.

In [10]:
kp1 = K(pp, intervals=20)
kp1.plot()
In [11]:
plt.plot(*ripley.k_function(points, support=20))
plt.plot(kp1.d, kp1.ev)
plt.plot(kp1.d, kp1.k)
Out[11]:
[<matplotlib.lines.Line2D at 0x129342730>]

$L$ function - "interevent"

$L$ function is a scaled version of $K$ function, defined as: $$L(d) = \sqrt{\frac{K(d)}{\pi}}-d$$

In [12]:
lp1 = L(pp)
lp1.plot()
In [13]:
plt.plot(*ripley.l_function(points, support=20, linearized=True))
plt.plot(lp1.d, lp1.l)
Out[13]:
[<matplotlib.lines.Line2D at 0x129467760>]

Simulation Envelopes

A Simulation envelope is a computer intensive technique for inferring whether an observed pattern significantly deviates from what would be expected under a specific process. Here, we always use CSR as the benchmark. In order to construct a simulation envelope for a given function, we need to simulate CSR a lot of times, say $1000$ times. Then, we can calculate the function for each simulated point pattern. For every distance $d$, we sort the function values of the $1000$ simulated point patterns. Given a confidence level, say $95\%$, we can acquire the $25$th and $975$th value for every distance $d$. Thus, a simulation envelope is constructed.

Simulation Envelope for G function

Genv class in pysal.

In [14]:
realizations = PoissonPointProcess(pp.window, pp.n, 100, asPP=True) # simulate CSR 100 times
genv = Genv(pp, intervals=20, realizations=realizations) # call Genv to generate simulation envelope
genv
Out[14]:
<pointpats.distance_statistics.Genv at 0x12942bb50>
In [15]:
genv.observed
Out[15]:
array([[ 0.        ,  0.        ],
       [ 1.73156208,  0.        ],
       [ 3.46312417,  0.        ],
       [ 5.19468625,  0.        ],
       [ 6.92624834,  0.        ],
       [ 8.65781042,  0.        ],
       [10.3893725 ,  0.16666667],
       [12.12093459,  0.16666667],
       [13.85249667,  0.16666667],
       [15.58405875,  0.16666667],
       [17.31562084,  0.25      ],
       [19.04718292,  0.25      ],
       [20.77874501,  0.25      ],
       [22.51030709,  0.58333333],
       [24.24186917,  0.58333333],
       [25.97343126,  0.83333333],
       [27.70499334,  0.83333333],
       [29.43655542,  0.83333333],
       [31.16811751,  0.91666667],
       [32.89967959,  0.91666667],
       [34.63124168,  1.        ],
       [36.36280376,  1.        ]])
In [16]:
genv.plot()

In the above figure, LB and UB comprise the simulation envelope. CSR is the mean function calculated from the simulated data. G is the function estimated from the observed point pattern. It is well below the simulation envelope. We can infer that the underlying point process is a regular one.

In [17]:
g_test = ripley.g_test(points, keep_replications=True)
g_test
Out[17]:
GtestResult(support=array([ 0.        ,  1.82269693,  3.64539386,  5.46809079,  7.29078772,
        9.11348465, 10.93618158, 12.75887851, 14.58157544, 16.40427237,
       18.2269693 , 20.04966623, 21.87236316, 23.69506009, 25.51775702,
       27.34045395, 29.16315088, 30.98584782, 32.80854475, 34.63124168]), statistic=array([0.        , 0.        , 0.        , 0.        , 0.        ,
       0.0952381 , 0.0952381 , 0.0952381 , 0.0952381 , 0.19047619,
       0.19047619, 0.19047619, 0.28571429, 0.47619048, 0.57142857,
       0.76190476, 0.85714286, 0.95238095, 0.95238095, 1.        ]), pvalue=array([0.    , 0.    , 0.    , 0.    , 0.    , 0.3179, 0.1553, 0.0608,
       0.0189, 0.0252, 0.0058, 0.002 , 0.0014, 0.0084, 0.0102, 0.075 ,
       0.1346, 0.3083, 0.1204, 0.    ]), replications=array([[0.        , 0.        , 0.        , ..., 0.        , 0.        ,
        0.        ],
       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,
        0.        ],
       [0.        , 0.2       , 0.        , ..., 0.        , 0.10526316,
        0.        ],
       ...,
       [0.95833333, 0.95      , 1.        , ..., 0.85714286, 1.        ,
        0.95238095],
       [1.        , 1.        , 1.        , ..., 0.95238095, 1.        ,
        0.95238095],
       [1.        , 1.        , 1.        , ..., 1.        , 1.        ,
        1.        ]]))
In [18]:
plt.fill_between(g_test.support, 
                 *np.percentile(g_test.replications, 
                                q=(2.5, 97.5), axis=1),
                 color='grey', alpha=.2, label='95% CI')
plt.plot(g_test.support, g_test.statistic, color='orangered', label='Observed')
plt.legend()
plt.show()

Simulation Envelope for F function

Fenv class in pysal.

In [19]:
fenv = Fenv(pp, intervals=20, realizations=realizations)
fenv.plot()
In [20]:
f_test = ripley.f_test(points, keep_replications=True)
f_test
Out[20]:
FtestResult(support=array([ 0.        ,  1.82269693,  3.64539386,  5.46809079,  7.29078772,
        9.11348465, 10.93618158, 12.75887851, 14.58157544, 16.40427237,
       18.2269693 , 20.04966623, 21.87236316, 23.69506009, 25.51775702,
       27.34045395, 29.16315088, 30.98584782, 32.80854475, 34.63124168]), statistic=array([0.   , 0.01 , 0.045, 0.103, 0.193, 0.297, 0.41 , 0.521, 0.646,
       0.775, 0.869, 0.931, 0.971, 0.997, 1.   , 1.   , 1.   , 1.   ,
       1.   , 1.   ]), pvalue=array([0.    , 0.0409, 0.013 , 0.0082, 0.0718, 0.1941, 0.3569, 0.4831,
       0.2651, 0.0693, 0.0267, 0.0136, 0.008 , 0.002 , 0.0042, 0.0211,
       0.0604, 0.1262, 0.2283, 0.    ]), replications=array([[0.        , 0.        , 0.        , ..., 0.        , 0.        ,
        0.        ],
       [0.01003009, 0.01484624, 0.015     , ..., 0.01778243, 0.0167364 ,
        0.0079096 ],
       [0.05616851, 0.05408271, 0.068     , ..., 0.07217573, 0.06276151,
        0.0519774 ],
       ...,
       [0.98996991, 0.95015907, 0.998     , ..., 0.95711297, 0.96025105,
        0.96497175],
       [0.99498495, 0.97348887, 1.        , ..., 0.9832636 , 0.98221757,
        0.98418079],
       [1.        , 1.        , 1.        , ..., 1.        , 1.        ,
        1.        ]]))
In [21]:
plt.fill_between(f_test.support, 
                 *np.percentile(f_test.replications, 
                                q=(2.5, 97.5), axis=1),
                 color='grey', alpha=.2, label='95% CI')
plt.plot(f_test.support, f_test.statistic, color='orangered',
         label='Observed')
plt.legend()
plt.show()

Simulation Envelope for J function

Jenv class in pysal.

In [22]:
jenv = Jenv(pp, intervals=20, realizations=realizations)
jenv.plot()
In [23]:
j_test = ripley.j_test(points, keep_replications=True)
j_test
Out[23]:
JtestResult(support=array([ 0.        ,  1.82269693,  3.64539386,  5.46809079,  7.29078772,
        9.11348465, 10.93618158, 12.75887851, 14.58157544, 16.40427237,
       18.2269693 , 20.04966623, 21.87236316, 23.69506009]), statistic=array([  1.        ,   1.00908174,   1.05374078,   1.14810563,
         1.27226463,   1.27420999,   1.53186275,   1.97472354,
         2.85388128,   3.86597938,   6.30252101,  12.29508197,
        26.51515152, 208.33333333]), pvalue=array([0.000e+00, 9.720e-02, 3.000e-01, 4.309e-01, 2.423e-01, 2.479e-01,
       1.459e-01, 6.450e-02, 1.790e-02, 1.160e-02, 4.300e-03, 1.500e-03,
       1.000e-03, 1.000e-04]), replications=array([[1.        , 1.        , 1.        , ..., 1.        , 1.        ,
        1.        ],
       [1.01425439, 1.01754386, 1.01853871, ..., 1.02073171, 1.01914414,
        1.01380898],
       [0.74425287, 1.06879607, 1.06864989, ..., 1.08279431, 1.09167672,
        1.05635492],
       ...,
       [1.15625   , 1.02112676, 2.55890411, ..., 0.        , 1.86213992,
        0.        ],
       [2.5       , 2.68518519, 5.49411765, ..., 0.        , 4.18981481,
        0.        ],
       [1.        , 1.        , 1.        , ..., 1.        , 1.        ,
        1.        ]]))
In [24]:
plt.fill_between(j_test.support, 
                 *np.percentile(j_test.replications, 
                                q=(2.5, 97.5), axis=1),
                 color='grey', alpha=.2, label='95% CI')
plt.plot(j_test.support, j_test.statistic, color='orangered',
         label='Observed')
plt.legend()
plt.show()

Simulation Envelope for K function

Kenv class in pysal.

In [25]:
kenv = Kenv(pp, intervals=20, realizations=realizations)
kenv.plot()
In [26]:
k_test = ripley.k_test(points, keep_replications=True)
k_test
Out[26]:
KtestResult(support=array([ 0.        ,  1.82269693,  3.64539386,  5.46809079,  7.29078772,
        9.11348465, 10.93618158, 12.75887851, 14.58157544, 16.40427237,
       18.2269693 , 20.04966623, 21.87236316, 23.69506009, 25.51775702,
       27.34045395, 29.16315088, 30.98584782, 32.80854475, 34.63124168]), statistic=array([   0.        ,    0.        ,    0.        ,    0.        ,
          0.        ,  106.08611111,  106.08611111,  106.08611111,
        106.08611111,  212.17222222,  212.17222222,  212.17222222,
        318.25833333,  530.43055556,  636.51666667,  848.68888889,
       1060.86111111, 1273.03333333, 1273.03333333, 1273.03333333]), pvalue=array([0.    , 0.    , 0.    , 0.    , 0.    , 0.3985, 0.2023, 0.0788,
       0.0232, 0.0451, 0.0097, 0.001 , 0.0029, 0.031 , 0.0352, 0.1081,
       0.2332, 0.3624, 0.1898, 0.0815]), replications=array([[   0.        ,    0.        ,    0.        , ...,    0.        ,
           0.        ,    0.        ],
       [   0.        ,    0.        ,    0.        , ...,    0.        ,
           0.        ,    0.        ],
       [   0.        ,    0.        ,    0.        , ...,   77.81536443,
           0.        ,    0.        ],
       ...,
       [1197.29622961, 1501.02714205, 1245.41812316, ..., 1634.12265299,
        1482.57903984, 1393.33205578],
       [1473.59535951, 1626.11273722, 1512.29343527, ..., 1711.93801742,
        1575.24022983, 1486.2208595 ],
       [1841.99419939, 1751.19833239, 1690.21031   , ..., 1867.56874628,
        1667.90141982, 1486.2208595 ]]))
In [27]:
plt.plot(k_test.support, 
         k_test.replications, 
         color='k', alpha=.01)
plt.plot(k_test.support, 
         k_test.replications[:,0], 
         color='k', label='Simulations')
plt.plot(k_test.support, k_test.statistic, color='orangered',
         label='Observed')
plt.title('Alternative Plot Style')
plt.legend()
plt.show()

Simulation Envelope for L function

Lenv class in pysal.

In [28]:
lenv = Lenv(pp, intervals=20, realizations=realizations)
lenv.plot()
In [29]:
l_test = ripley.l_test(points, linearized=True, keep_replications=True)
l_test
Out[29]:
LtestResult(support=array([ 0.        ,  1.82269693,  3.64539386,  5.46809079,  7.29078772,
        9.11348465, 10.93618158, 12.75887851, 14.58157544, 16.40427237,
       18.2269693 , 20.04966623, 21.87236316, 23.69506009, 25.51775702,
       27.34045395, 29.16315088, 30.98584782, 32.80854475, 34.63124168]), statistic=array([  0.        ,  -1.82269693,  -3.64539386,  -5.46809079,
        -7.29078772,  -3.30243845,  -5.12513538,  -6.94783231,
        -8.77052924,  -8.18621202, -10.00890895, -11.83160588,
       -11.8073359 , -10.70116577, -11.28365896, -10.90433326,
       -10.7870093 , -10.85579328, -12.67849021, -14.50118714]), pvalue=array([0.    , 0.    , 0.    , 0.    , 0.    , 0.4021, 0.2052, 0.0869,
       0.0273, 0.0449, 0.0112, 0.0021, 0.0027, 0.0314, 0.0342, 0.1121,
       0.2296, 0.364 , 0.1906, 0.0819]), replications=array([[  0.        ,   0.        ,   0.        , ...,   0.        ,
          0.        ,   0.        ],
       [ -1.82269693,  -1.82269693,  -1.82269693, ...,  -1.82269693,
         -1.82269693,   2.58715172],
       [ -3.64539386,  -3.64539386,   1.38442619, ...,  -3.64539386,
          1.34537542,   0.76445479],
       ...,
       [-12.6454847 , -10.97749319,  -9.06137052, ...,  -9.76660799,
        -11.65668151, -12.27644449],
       [-12.99868847, -12.18440487, -10.31450566, ...,  -9.88914623,
        -12.23107585, -13.08710202],
       [-14.8213854 , -13.40917683, -12.13720259, ..., -11.1724894 ,
        -13.45720087, -14.42277641]]))
In [30]:
plt.plot(l_test.support, 
         l_test.replications, 
         color='k', alpha=.01)
plt.plot(l_test.support, 
         l_test.replications[:,0], 
         color='k', label='Simulations')
plt.plot(l_test.support, l_test.statistic, color='orangered',
         label='Observed')
plt.title('Alternative Plot Style')
plt.legend()
plt.show()

CSR Example

In this example, we are going to generate a point pattern as the "observed" point pattern. The data generating process is CSR. Then, we will simulate CSR in the same domain for 100 times and construct a simulation envelope for each function.

In [31]:
from libpysal.cg import shapely_ext, asShape
from pointpats import Window
import geopandas
df = geopandas.read_file(ps.examples.get_path("vautm17n.shp"))
state = df.geometry.cascaded_union

Generate the point pattern pp (size 100) from CSR as the "observed" point pattern.

In [32]:
a = [[1],[1,2]]
np.asarray(a)
Out[32]:
array([list([1]), list([1, 2])], dtype=object)
In [33]:
n = 100
samples = 1
pp = PoissonPointProcess(Window(asShape(state).parts), 
                         n, samples, asPP=True)
pp.realizations[0]
Out[33]:
<pointpats.pointpattern.PointPattern at 0x1303d2070>
In [34]:
pp.n
Out[34]:
100

Simulate CSR in the same domian for 100 times which would be used for constructing simulation envelope under the null hypothesis of CSR.

In [35]:
csrs = PoissonPointProcess(pp.window, 100, 100, asPP=True)
csrs
Out[35]:
<pointpats.process.PoissonPointProcess at 0x1303d2730>
In [36]:
import importlib
ripley = importlib.reload(ripley)
In [46]:
simulations = ripley.simulate(state, size=(100,100))
In [59]:
plt.scatter(*simulations[23].T)
geopandas.GeoDataFrame(geometry=[state]).boundary.plot(ax=plt.gca())
Out[59]:
<matplotlib.axes._subplots.AxesSubplot at 0x13a8cdc40>

Construct the simulation envelope for $G$ function.

In [60]:
genv = Genv(pp.realizations[0], realizations=csrs)
genv.plot()
In [63]:
ripley.g_test(simulations[10], hull=state).pvalues

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-63-b71a997a58e0> in <module>
----> 1 ripley.g_test(simulations[10], hull=state).pvalues

AttributeError: 'GtestResult' object has no attribute 'pvalues'

Since the "observed" $G$ is well contained by the simulation envelope, we infer that the underlying point process is a random process.

In [39]:
genv.low # lower bound of the simulation envelope for G
Out[39]:
array([0.  , 0.04, 0.25, 0.55, 0.76, 0.88, 0.93, 0.96, 0.97, 0.98, 0.98,
       0.98])
In [40]:
genv.high # higher bound of the simulation envelope for G
Out[40]:
array([0.  , 0.2 , 0.51, 0.73, 0.9 , 0.96, 0.99, 1.  , 1.  , 1.  , 1.  ,
       1.  ])

Construct the simulation envelope for $F$ function.

In [41]:
fenv = Fenv(pp.realizations[0], realizations=csrs)
fenv.plot()

Construct the simulation envelope for $J$ function.

In [42]:
jenv = Jenv(pp.realizations[0], realizations=csrs)
jenv.plot()

Construct the simulation envelope for $K$ function.

In [43]:
kenv = Kenv(pp.realizations[0], realizations=csrs)
kenv.plot()

Construct the simulation envelope for $L$ function.

In [44]:
lenv = Lenv(pp.realizations[0], realizations=csrs)
lenv.plot()
In [ ]:
pointpats-2.5.5/notebooks/marks.ipynb000066400000000000000000003131051514706172100177360ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Marked Point Pattern\n", "\n", "In addition to the [unmarked point pattern](pointpattern.ipynb), non-binary attributes might be associated with each point, leading to the so-called marked point pattern. The charactertistics of a marked point pattern are:\n", "\n", "* Location pattern of the events are of interest\n", "* Stochastic attribute attached to the events is of interest\n", "\n", "Unmarked point pattern can be modified to be a marked point pattern using the method **add_marks** while the method **explode** could decompose a marked point pattern into a sequence of unmarked point patterns. Both methods belong to the class **PointPattern**." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from pointpats import PoissonPointProcess, PoissonClusterPointProcess, Window, poly_from_bbox, PointPattern\n", "import libpysal as ps\n", "from libpysal.cg import shapely_ext\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# open the virginia polygon shapefile\n", "va = ps.io.open(ps.examples.get_path(\"virginia.shp\"))\n", "polys = [shp for shp in va]" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# Create the exterior polygons for VA from the union of the county shapes\n", "state = shapely_ext.cascaded_union(polys)\n", "# create window from virginia state boundary\n", "window = Window(state.parts)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[-83.67526245117188, 36.541481018066406, -75.24258422851562, 39.45690155029297]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "window.bbox" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(-78.85183583334933, 37.51851209850039)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "window.centroid" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "samples = PoissonPointProcess(window, 200, 1, conditioning=False, asPP=False)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "csr = PointPattern(samples.realizations[0])" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "cx, cy = window.centroid" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-78.85183583334933" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cx" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "37.51851209850039" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cy" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "west = csr.points.x < cx\n", "south = csr.points.y < cy\n", "east = 1 - west\n", "north = 1 - south" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Create an attribute named quad which has a value for each event." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "quad = 1 * east * north + 2 * west * north + 3 * west * south + 4 * east * south" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "pandas.core.series.Series" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(quad)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 2\n", "1 3\n", "2 3\n", "3 4\n", "4 1\n", "5 3\n", "6 1\n", "7 4\n", "8 2\n", "9 3\n", "10 4\n", "11 4\n", "12 3\n", "13 1\n", "14 1\n", "15 1\n", "16 4\n", "17 1\n", "18 1\n", "19 3\n", "20 4\n", "21 3\n", "22 1\n", "23 3\n", "24 1\n", "25 1\n", "26 4\n", "27 1\n", "28 3\n", "29 3\n", " ..\n", "170 2\n", "171 2\n", "172 3\n", "173 3\n", "174 1\n", "175 4\n", "176 3\n", "177 3\n", "178 4\n", "179 1\n", "180 1\n", "181 3\n", "182 3\n", "183 1\n", "184 2\n", "185 3\n", "186 1\n", "187 2\n", "188 4\n", "189 1\n", "190 4\n", "191 1\n", "192 3\n", "193 3\n", "194 4\n", "195 3\n", "196 1\n", "197 1\n", "198 4\n", "199 4\n", "Length: 200, dtype: int64" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "quad" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Attach the attribute quad to the point pattern " ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "csr.add_marks([quad], mark_names=['quad'])" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " x y quad\n", "0 -79.603948 37.791190 2\n", "1 -80.079205 37.396681 3\n", "2 -79.464397 36.625981 3\n", "3 -76.437205 36.884895 4\n", "4 -78.545956 37.992603 1\n", "5 -80.241928 36.991135 3\n", "6 -77.898430 37.716846 1\n", "7 -76.498353 37.321863 4\n", "8 -79.900184 37.854658 2\n", "9 -81.102672 36.633735 3\n", "10 -77.281811 36.976553 4\n", "11 -77.083152 37.303132 4\n", "12 -83.035154 36.618248 3\n", "13 -77.997824 38.483939 1\n", "14 -76.576682 37.932985 1\n", "15 -77.048965 37.697935 1\n", "16 -78.330254 37.421786 4\n", "17 -78.420495 38.125428 1\n", "18 -77.379883 38.801099 1\n", "19 -82.102351 36.838275 3\n", "20 -77.139652 36.681456 4\n", "21 -81.411197 37.009286 3\n", "22 -78.377109 38.110156 1\n", "23 -82.348030 37.260970 3\n", "24 -78.540305 37.759264 1\n", "25 -78.708559 38.778273 1\n", "26 -77.119341 37.441159 4\n", "27 -77.532402 37.827257 1\n", "28 -81.025392 36.963752 3\n", "29 -81.234484 37.187202 3\n", ".. ... ... ...\n", "170 -79.175687 38.412747 2\n", "171 -79.466336 37.677601 2\n", "172 -80.503082 36.784620 3\n", "173 -79.654007 36.620456 3\n", "174 -78.283744 38.436626 1\n", "175 -76.917112 36.713404 4\n", "176 -82.234255 36.978863 3\n", "177 -81.012936 37.186895 3\n", "178 -77.979839 36.758554 4\n", "179 -77.979268 38.366633 1\n", "180 -75.550882 37.940486 1\n", "181 -81.031949 37.194569 3\n", "182 -80.957128 37.150309 3\n", "183 -77.766785 37.647643 1\n", "184 -79.411096 37.705193 2\n", "185 -80.691844 36.673324 3\n", "186 -78.752009 38.137110 1\n", "187 -79.160647 38.047687 2\n", "188 -78.244262 37.137316 4\n", "189 -77.641999 38.612349 1\n", "190 -77.961376 37.257348 4\n", "191 -77.664101 37.612087 1\n", "192 -82.073349 37.151563 3\n", "193 -80.029721 36.650720 3\n", "194 -77.015099 36.892940 4\n", "195 -81.938727 36.759000 3\n", "196 -78.653617 38.637913 1\n", "197 -78.353159 38.009389 1\n", "198 -76.652473 36.918815 4\n", "199 -76.806542 37.477773 4\n", "\n", "[200 rows x 3 columns]" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "csr.df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Explode a marked point pattern into a sequence of individual point patterns. Since the mark quad has 4 unique values, the sequence will be of length 4." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "csr_q = csr.explode('quad')" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "4" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(csr_q)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "csr" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Point Pattern\n", "200 points\n", "Bounding rectangle [(-83.5775552379073,36.58477411642467), (-75.55088173741038,39.23835955804836)]\n", "Area of window: 21.299463945585302\n", "Intensity estimate for window: 9.389907676125041\n", " x y quad\n", "0 -79.603948 37.791190 2\n", "1 -80.079205 37.396681 3\n", "2 -79.464397 36.625981 3\n", "3 -76.437205 36.884895 4\n", "4 -78.545956 37.992603 1\n" ] } ], "source": [ "csr.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Plot the 4 individual sequences" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "plt.xlim?" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
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\n", 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\n", 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.xlim()\n", "for ppn in csr_q:\n", " ppn.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Plot the 4 unmarked point patterns using the same axes for a convenient comparison of locations" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "x0, y0, x1, y1 = csr.mbb\n", "ylim = (y0, y1)\n", "xlim = (x0, x1)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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\n", 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\n", 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for ppn in csr_q:\n", " ppn.plot()\n", " plt.xlim(xlim)\n", " plt.ylim(ylim)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 1 } pointpats-2.5.5/notebooks/pointpattern.ipynb000066400000000000000000001042541514706172100213530ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Planar Point Patterns in PySAL\n", "\n", "**Author: Serge Rey and Wei Kang **\n", "\n", "## Introduction\n", "This notebook introduces the basic PointPattern class in PySAL and covers the following:\n", "\n", "* [What is a point pattern?](#What-is-a-point-pattern?)\n", "* [Creating Point Patterns](#Creating-Point-Patterns)\n", "* [Atributes of Point Patterns](#Attributes-of-PySAL-Point-Patterns)\n", "* [Intensity Estimates](#Intensity-Estimates)\n", "* [Next steps](#Next-steps)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## What is a point pattern?\n", "\n", "We introduce basic terminology here and point the interested reader to more [detailed references](#References) on the underlying theory of the statistical analysis of point patterns.\n", "\n", "### Points and Event Points\n", "\n", "To start we consider a series of *point locations*, $(s_1, s_2, \\ldots, s_n)$ in a study region $\\Re$. We limit our focus here to a two-dimensional space so that $s_j = (x_j, y_j)$ is the spatial coordinate pair for point location $j$.\n", "\n", "We will be interested in two different types of points.\n", "\n", "#### Event Points\n", "\n", "*Event Points* are locations where something of interest has occurred. The term *event* is very general here and could be used to represent a wide variety of phenomena. Some examples include:\n", "\n", "* [locations of individual plants of a certain species](http://link.springer.com/chapter/10.1007/978-3-642-01976-0_7#page-1)\n", "* [archeological sites](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwjA46Si2oTKAhUU1GMKHZUBCBEQFgghMAA&url=http%3A%2F%2Fdiscovery.ucl.ac.uk%2F11345%2F&usg=AFQjCNG5dKBcsVJQZ9M20U5AOMTt3P6AWQ&sig2=Nt8ViSs8Q2G_-q1BSnNvKg&bvm=bv.110151844,d.cGc)\n", "* [addresses of disease cases](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwiy7NSE2oTKAhUOyWMKHb7cDA4QFgghMAA&url=http%3A%2F%2Fwww.jstor.org%2Fstable%2F622936&usg=AFQjCNExfettAsU3i-Hs7twmB6_iVkghUA&sig2=tPROSM6wMtbZT0qlg_N6Hw&bvm=bv.110151844,d.cGc)\n", "* [locations of crimes](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&cad=rja&uact=8&ved=0ahUKEwiogfbl2YTKAhVT42MKHfTFCdUQFggqMAE&url=https%3A%2F%2Fgeodacenter.asu.edu%2Fsystem%2Ffiles%2Fpoints.pdf&usg=AFQjCNFase8ykAPuopayUDHQRvgj8S4Vsw&sig2=Ezzx45MLZIFaepvcOjV-aw)\n", "* the [distribution of neurons](http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2889688/)\n", "\n", "among [many others](https://en.wikipedia.org/wiki/Point_process).\n", "\n", "It is important to recognize that in the statistical analysis of point patterns the interest extends beyond the observed point pattern at hand.\n", "The observed patterns are viewed as realizations from some underlying spatial stochastic process.\n", "\n", "\n", "#### Arbitrary Points\n", "\n", "The second type of point we consider are those locations where the phenomena of interest has not been observed. These go by various names such as \"empty space\" or \"regular\" points, and at first glance might seem less interesting to a spatial analayst. However, these types of points play a central role in a class of point pattern methods that we explore below.\n", "\n", "\n", "### Point Pattern Analysis\n", "\n", "The analysis of event points focuses on a number of different characteristics of the collective spatial pattern that is observed. Often the pattern is jugded against the hypothesis of complete spatial randomness (CSR). That is, one assumes that the point events arise independently of one another and with constant probability across $\\Re$, loosely speaking.\n", "\n", "Of course, many of the empirical point patterns we encounter do not appear to be generated from such a simple stochastic process. The depatures from CSR can be due to two types of effects.\n", "\n", "#### First order effects\n", "\n", "For a point process, the first-order properties pertain to the intensity of the process across space. Whether and how the intensity of the point pattern varies within our study region are questions that assume center stage. Such variation in the itensity of the pattern of, say, addresses of individuals with a certain type of non-infectious disease may reflect the underlying population density. In other words, although the point pattern of disease cases may display variation in intensity in our study region, and thus violate the constant probability of an event condition, that spatial drift in the pattern intensity could be driven by an underlying covariate. \n", "\n", "\n", "\n", "#### Second order effects\n", "\n", "The second channel by which departures from CSR can arise is through interaction and dependence between events in space. The canonical example being contagious diseases whereby the presence of an infected individual increases the probability of subsequent additional cases nearby.\n", "\n", "\n", "When a pattern departs from expectation under CSR, this is suggestive that the underlying process may have some spatial structure that merits further investigation. Thus methods for detection of deviations from CSR and testing for alternative processes have given rise to a large literature in point pattern statistics." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "### Methods of Point Pattern Analysis in PySAL\n", "\n", "The points module in PySAL implements basic methods of point pattern analysis organized into the following groups:\n", "\n", "* Point Processing\n", "* Centrography and Visualization\n", "* Quadrat Based Methods\n", "* Distance Based Methods\n", "\n", "In the remainder of this notebook we shall focus on point processing." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import libpysal as ps\n", "import numpy as np\n", "from pointpats import PointPattern" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Creating Point Patterns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### From lists\n", "\n", "We can build a point pattern by using Python lists of coordinate pairs $(s_0, s_1,\\ldots, s_m)$ as follows:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "points = [[66.22, 32.54], [22.52, 22.39], [31.01, 81.21],\n", " [9.47, 31.02], [30.78, 60.10], [75.21, 58.93],\n", " [79.26, 7.68], [8.23, 39.93], [98.73, 77.17],\n", " [89.78, 42.53], [65.19, 92.08], [54.46, 8.48]]\n", "p1 = PointPattern(points)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 8.23, 7.68, 98.73, 92.08])" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "p1.mbb" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Thus $s_0 = (66.22, 32.54), \\ s_{11}=(54.46, 8.48)$." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Point Pattern\n", "12 points\n", "Bounding rectangle [(8.23,7.68), (98.73,92.08)]\n", "Area of window: 7638.200000000002\n", "Intensity estimate for window: 0.0015710507711240865\n", " x y\n", "0 66.22 32.54\n", "1 22.52 22.39\n", "2 31.01 81.21\n", "3 9.47 31.02\n", "4 30.78 60.10\n" ] } ], "source": [ "p1.summary()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "pandas.core.frame.DataFrame" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(p1.points)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[66.22, 32.54],\n", " [22.52, 22.39],\n", " [31.01, 81.21],\n", " [ 9.47, 31.02],\n", " [30.78, 60.1 ],\n", " [75.21, 58.93],\n", " [79.26, 7.68],\n", " [ 8.23, 39.93],\n", " [98.73, 77.17],\n", " [89.78, 42.53],\n", " [65.19, 92.08],\n", " [54.46, 8.48]])" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.asarray(p1.points)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 8.23, 7.68, 98.73, 92.08])" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "p1.mbb" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### From numpy arrays" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[66.22, 32.54],\n", " [22.52, 22.39],\n", " [31.01, 81.21],\n", " [ 9.47, 31.02],\n", " [30.78, 60.1 ],\n", " [75.21, 58.93],\n", " [79.26, 7.68],\n", " [ 8.23, 39.93],\n", " [98.73, 77.17],\n", " [89.78, 42.53],\n", " [65.19, 92.08],\n", " [54.46, 8.48]])" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "points = np.asarray(points)\n", "points" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Point Pattern\n", "12 points\n", "Bounding rectangle [(8.23,7.68), (98.73,92.08)]\n", "Area of window: 7638.200000000002\n", "Intensity estimate for window: 0.0015710507711240865\n", " x y\n", "0 66.22 32.54\n", "1 22.52 22.39\n", "2 31.01 81.21\n", "3 9.47 31.02\n", "4 30.78 60.10\n" ] } ], "source": [ "p1_np = PointPattern(points)\n", "p1_np.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### From shapefiles\n", "\n", "This example uses 200 randomly distributed points within the counties of Virginia. Coordinates are for UTM zone 17 N." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Point Pattern\n", "200 points\n", "Bounding rectangle [(273959.664381352,4049220.903414295), (972595.9895779632,4359604.85977962)]\n", "Area of window: 216845506675.0557\n", "Intensity estimate for window: 9.223156295311261e-10\n", " x y\n", "0 865322.486181 4.150317e+06\n", "1 774479.213103 4.258993e+06\n", "2 308048.692232 4.054700e+06\n", "3 670711.529980 4.258864e+06\n", "4 666254.475614 4.256514e+06\n" ] } ], "source": [ "f = ps.examples.get_path('vautm17n_points.shp')\n", "fo = ps.io.open(f)\n", "pp_va = PointPattern(np.asarray([pnt for pnt in fo]))\n", "fo.close()\n", "pp_va.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Attributes of PySAL Point Patterns" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Point Pattern\n", "200 points\n", "Bounding rectangle [(273959.664381352,4049220.903414295), (972595.9895779632,4359604.85977962)]\n", "Area of window: 216845506675.0557\n", "Intensity estimate for window: 9.223156295311261e-10\n", " x y\n", "0 865322.486181 4.150317e+06\n", "1 774479.213103 4.258993e+06\n", "2 308048.692232 4.054700e+06\n", "3 670711.529980 4.258864e+06\n", "4 666254.475614 4.256514e+06\n" ] } ], "source": [ "pp_va.summary()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " x y\n", "195 876485.065262 4.148120e+06\n", "196 621600.111400 4.177462e+06\n", "197 450246.610116 4.106031e+06\n", "198 740919.375814 4.359605e+06\n", "199 797522.610898 4.208606e+06" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pp_va.tail()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Intensity Estimates" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The intensity of a point process at point $s_i$ can be defined as:\n", "\n", "$$\\lambda(s_j) = \\lim \\limits_{|\\mathbf{A}s_j| \\to 0} \\left \\{ \\frac{E(Y(\\mathbf{A}s_j)}{|\\mathbf{A}s_j|} \\right \\} $$\n", "\n", "where $\\mathbf{A}s_j$ is a small region surrounding location $s_j$ with area $|\\mathbf{A}s_j|$, and $E(Y(\\mathbf{A}s_j)$ is the expected number of event points in $\\mathbf{A}s_j$. \n", "\n", "The intensity is the mean number of event points per unit of area at point $s_j$. \n", "\n", "\n", "\n", "Recall that one of the implications of CSR is that the intensity of the point process is constant in our study area $\\Re$. In other words $\\lambda(s_j) = \\lambda(s_{j+1}) = \\ldots = \\lambda(s_n) = \\lambda \\ \\forall s_j \\in \\Re$. Thus, if the area of $\\Re$ = $|\\Re|$ the expected number of event points in the study region is: $E(Y(\\Re)) = \\lambda |\\Re|.$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In PySAL, the intensity is estimated by using a geometric object to encode the study region. We refer to this as the window, $W$. The reason for distinguishing between $\\Re$ and $W$ is that the latter permits alternative definitions of the bounding object." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Intensity estimates are based on the following:**\n", "$$\\hat{\\lambda} = \\frac{n}{|W|}$$\n", "\n", "where $n$ is the number of points in the *window* $W$, and $|W|$ is the area of $W$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Intensity based on minimum bounding box:**\n", "$$\\hat{\\lambda}_{mbb} = \\frac{n}{|W_{mbb}|}$$\n", "\n", "where $W_{mbb}$ is the minimum bounding box for the point pattern." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "9.223156295311263e-10" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pp_va.lambda_mbb" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Intensity based on convex hull:**\n", "$$\\hat{\\lambda}_{hull} = \\frac{n}{|W_{hull}|}$$\n", "\n", "where $W_{hull}$ is the convex hull for the point pattern." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.5973789098179388e-09" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pp_va.lambda_hull" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Next steps" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "There is more to learn about point patterns in PySAL. \n", "\n", "The [centrographic notebook](centrography.ipynb) illustrates a number of spatial descriptive statistics and visualization of point patterns.\n", "\n", "Clearly the window chosen will impact the intensity estimate. For more on **windows** see the [window notebook](window.ipynb).\n", "\n", "To test if your point pattern departs from complete spatial randomness see the [distance statistics notebook](distance_statistics.ipynb) and [quadrat statistics notebook](Quadrat_statistics.ipynb).\n", "\n", "\n", "To simulate different types of point processes in various windows see [process notebook](process.ipynb).\n", "\n", "If you have point pattern data with additional attributes associated with each point see how to handle this in the [marks notebook](marks.ipynb).\n", "\n" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 1 } pointpats-2.5.5/notebooks/process.ipynb000066400000000000000000007675711514706172100203230ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Point Processes\n", "\n", "**Author: Serge Rey and Wei Kang **\n", "\n", "## Introduction\n", "\n", "One philosophy of applying inferential statistics to spatial data is to think in terms of spatial processes and their possible realizations. In this view, an observed map pattern is one of the possible patterns that might have been generated by a hypothesized process. In this notebook, we are going to regard point patterns as the outcome of point processes. There are three major types of point process, which will result in three types of point patterns:\n", "\n", "* [Random Patterns](#Random-Patterns)\n", "* [Clustered Patterns](#Clustered-Patterns)\n", "* [Regular Patterns](#Regular-Patterns)\n", "\n", "We will investigate how to generate these point patterns via simulation (Data Generating Processes (DGP) is the correponding point process), and inspect how these resulting point patterns differ from each other visually. In [Quadrat statistics notebook](Quadrat_statistics.ipynb) and [distance statistics notebook](distance_statistics.ipynb), we will adpot some statistics to infer whether it is a [Complete Spaital Randomness](https://en.wikipedia.org/wiki/Complete_spatial_randomness) (CSR) process.\n", "\n", "A python file named \"process.py\" contains several point process classes with which we can generate point patterns of different types." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from pointpats import PoissonPointProcess, PoissonClusterPointProcess, Window, poly_from_bbox, PointPattern\n", "import libpysal as ps\n", "from libpysal.cg import shapely_ext\n", "%matplotlib inline\n", "import numpy as np\n", "#import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Random Patterns\n", "\n", "Random point patterns are the outcome of CSR. CSR has two major characteristics:\n", "1. Uniform: each location has equal probability of getting a point (where an event happens)\n", "2. Independent: location of event points are independent\n", "\n", "It usually serves as the null hypothesis in testing whether a point pattern is the outcome of a random process.\n", "\n", "There are two types of CSR:\n", "* $N$-conditioned CSR: $N$ is fixed\n", " * Given the total number of events $N$ occurring within an area $A$, the locations of the $N$ events represent an independent random sample of $N$ locations where each location is equally likely to be chosen as an event.\n", "* $\\lambda$-conditioned CSR: $N$ is randomly generated from a Poisson process.\n", " * The number of events occurring within a finite region $A$ is a random variable $\\dot{N}$ following a Poisson distribution with mean $\\lambda|A|$, with $|A|$ denoting area of $A$ and $\\lambda$ denoting the intensity of the point pattern.\n", " * Given the total number of events $\\dot{N}$ occurring within an area $A$, the locations of the $\\dot{N}$ events represent an independent random sample of $\\dot{N}$ locations where each location is equally likely to be chosen as an event." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Simulating CSR\n", "We are going to generate several point patterns (200 events) from CSR within Virginia state boundary." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# open the virginia polygon shapefile\n", "va = ps.io.open(ps.examples.get_path(\"virginia.shp\"))\n", "polys = [shp for shp in va]" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# Create the exterior polygons for VA from the union of the county shapes\n", "state = shapely_ext.cascaded_union(polys)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# create window from virginia state boundary\n", "window = Window(state.parts)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 1. Generate a point series from N-conditioned CSR " ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# simulate a csr process in the same window (200 points, 1 realization)\n", "# by specifying \"asPP\" false, we can generate a point series\n", "# by specifying \"conditioning\" false, we can simulate a N-conditioned CSR\n", "np.random.seed(5)\n", "samples = PoissonPointProcess(window, 200, 1, conditioning=False, asPP=False)\n", "samples" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[-76.3326571 , 36.57893856],\n", " [-81.93206633, 37.0243966 ],\n", " [-79.55664806, 37.35242254],\n", " [-78.5166233 , 37.55701527],\n", " [-77.21660795, 38.26514268],\n", " [-82.09226973, 37.00701809],\n", " [-77.44823305, 38.6714618 ],\n", " [-79.95384378, 37.99268412],\n", " 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36.77163754],\n", " [-78.55997549, 36.9372054 ],\n", " [-80.74982401, 36.69837703],\n", " [-79.89144123, 37.27287164],\n", " [-77.53568375, 38.42234669],\n", " [-79.36034573, 37.9199658 ],\n", " [-78.39624506, 39.22046697],\n", " [-77.32624847, 37.32763411],\n", " [-77.14780326, 38.1270279 ],\n", " [-80.24638938, 37.5142178 ],\n", " [-77.41027396, 36.97299833],\n", " [-78.73229552, 37.60233533],\n", " [-79.50446982, 38.04796476],\n", " [-77.34484259, 37.05615369],\n", " [-80.66964982, 37.07084403],\n", " [-77.15297781, 37.29870784],\n", " [-78.28959166, 39.29418715],\n", " [-77.10310375, 37.3812618 ],\n", " [-78.11943302, 37.92836454],\n", " [-80.31267194, 36.665347 ],\n", " [-76.6777552 , 36.75870423],\n", " [-79.31751436, 38.06910198],\n", " [-77.02234401, 38.29308642],\n", " [-77.44257801, 38.34724139],\n", " [-77.54221373, 37.50425399],\n", " [-75.67749041, 37.81841772],\n", " [-80.21661887, 37.67742691],\n", " [-78.75115924, 38.71767437],\n", " [-78.95485683, 36.59501015],\n", " 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[-77.102715 , 36.9571268 ],\n", " [-79.16601272, 37.50364778],\n", " [-78.17995667, 37.56372944],\n", " [-78.55397235, 38.94719771],\n", " [-82.21842212, 37.31977937],\n", " [-75.70804637, 37.73079071],\n", " [-76.86774363, 37.59858498],\n", " [-79.2410832 , 36.73533614],\n", " [-75.63397197, 37.85672189],\n", " [-78.43974651, 36.73714428],\n", " [-79.63776485, 38.06933981],\n", " [-78.32258504, 38.01500577],\n", " [-77.85944265, 36.88932439],\n", " [-77.86902482, 39.14909625],\n", " [-81.97464747, 36.8508439 ],\n", " [-78.99980174, 37.44186754],\n", " [-77.36680988, 38.99916544],\n", " [-79.9150312 , 37.36377025],\n", " [-80.36600514, 36.67015317],\n", " [-77.42381708, 37.2241776 ],\n", " [-77.93652737, 38.17731926]])" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "samples.realizations[0] # simulated event points" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# build a point pattern from the simulated point series\n", "pp_csr = PointPattern(samples.realizations[0])\n", "pp_csr" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pp_csr.plot(window=True, hull=True, title='Random Point Pattern')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "200" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pp_csr.n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 2. Generate a point series from $\\lambda$-conditioned CSR" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# simulate a csr process in the same window (200 points, 1 realization)\n", "# by specifying \"asPP\" false, we can generate a point series\n", "# by specifying \"conditioning\" True, we can simulate a lamda-conditioned CSR\n", "np.random.seed(5)\n", "samples = PoissonPointProcess(window, 200, 1, conditioning=True, asPP=False)\n", "samples" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[-79.55664806, 38.25720619],\n", " [-78.5166233 , 38.30532302],\n", " [-77.21660795, 37.57582983],\n", " [-77.44823305, 36.74925133],\n", " [-79.95384378, 37.55525777],\n", " [-81.36397951, 36.71747455],\n", " [-80.18215183, 37.35242254],\n", " [-78.37289635, 38.26514268],\n", " 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[-77.54221373, 37.3812618 ],\n", " [-75.71630753, 37.92836454],\n", " [-80.21661887, 36.75870423],\n", " [-78.75115924, 38.06910198],\n", " [-76.86872936, 37.50425399],\n", " [-79.21340288, 37.25364651],\n", " [-77.77941742, 39.02429976],\n", " [-77.3124049 , 38.7084587 ],\n", " [-77.9591418 , 38.05055288],\n", " [-77.9213301 , 38.71767437],\n", " [-77.66093307, 36.59501015],\n", " [-77.21170491, 37.13898883],\n", " [-81.91956901, 36.81108808],\n", " [-76.68169985, 36.66281858],\n", " [-77.30664489, 36.92944526],\n", " [-81.85918569, 36.80543902],\n", " [-76.86645645, 37.57131542],\n", " [-80.68521276, 37.18782022],\n", " [-81.41932788, 36.70007358],\n", " [-78.8063798 , 38.30071805],\n", " [-77.14952496, 37.89347582],\n", " [-79.07890941, 37.5601488 ],\n", " [-83.00313472, 36.75272866],\n", " [-78.68101007, 38.87234553],\n", " [-77.63204774, 37.61550741],\n", " [-78.88306754, 38.05667036],\n", " [-77.45255789, 37.3391586 ],\n", " [-78.47299022, 37.4129918 ],\n", " [-79.02176971, 37.37256883],\n", " [-78.28147935, 37.91147075],\n", " [-79.58518375, 37.78361184],\n", " [-77.77610921, 37.97641435],\n", " [-80.99224637, 36.61994736],\n", " [-80.58914692, 36.8579181 ],\n", " [-77.80923356, 37.58006348],\n", " [-78.86249696, 36.73878708],\n", " [-82.21675753, 36.74153288],\n", " [-83.24670184, 36.69004968],\n", " [-81.28732939, 36.87113389],\n", " [-75.84594392, 37.54487864],\n", " [-79.22235094, 36.87297199],\n", " [-78.48547431, 38.52479965],\n", " [-78.20476584, 38.54799414],\n", " [-77.89852106, 38.1201838 ],\n", " [-81.37405706, 37.04456699],\n", " [-78.02587914, 37.64685616],\n", " [-78.42418842, 36.88536942],\n", " [-76.68842544, 36.74893391],\n", " [-80.81565379, 36.84864385],\n", " [-77.83641513, 37.17473795],\n", " [-77.50393746, 38.0087299 ],\n", " [-82.66226354, 37.12507361],\n", " [-81.50584616, 36.91097209],\n", " [-78.50980015, 38.35200834],\n", " [-77.99991705, 39.2235099 ],\n", " [-77.75281574, 38.65985656],\n", " [-79.18848841, 37.81637356],\n", " [-75.99527387, 36.67900083],\n", " [-78.10679754, 38.48234391],\n", " [-77.72774175, 37.44640927],\n", " [-79.16298721, 38.41845743],\n", " [-80.79353455, 36.6361113 ],\n", " [-79.09248227, 38.09955844],\n", " [-79.43627162, 37.75365148],\n", " [-82.44550608, 36.66466322],\n", " [-78.88007206, 38.4383976 ]])" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "samples.realizations[0] # simulated points" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# build a point pattern from the simulated point series\n", "pp_csr = PointPattern(samples.realizations[0])\n", "pp_csr" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pp_csr.plot(window=True, hull=True, title='Random Point Pattern')" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "188" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pp_csr.n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The simulated point pattern has $194$ events rather than the Possion mean $200$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 3. Generate a point pattern from N-conditioned CSR " ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# simulate a csr process in the same window (200 points, 1 realization)\n", "# by specifying \"asPP\" True, we can generate a point pattern\n", "# by specifying \"conditioning\" false, we can simulate a N-conditioned CSR\n", "np.random.seed(5)\n", "samples = PoissonPointProcess(window, 200, 1, conditioning=False, asPP=True)\n", "samples" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pp_csr = samples.realizations[0] # simulated point pattern\n", "pp_csr" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pp_csr.plot(window=True, hull=True, title='Random Point Pattern')" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "200" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pp_csr.n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 4. Generate a point pattern of size 200 from a $\\lambda$-conditioned CSR" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# simulate a csr process in the same window (200 points, 1 realization)\n", "# by specifying \"asPP\" True, we can generate a point pattern\n", "# by specifying \"conditioning\" True, we can simulate a lamda-conditioned CSR\n", "np.random.seed(5)\n", "samples = PoissonPointProcess(window, 200, 1, conditioning=True, asPP=True)\n", "samples" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pp_csr = samples.realizations[0] # simulated point pattern\n", "pp_csr" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pp_csr.plot(window=True, hull=True, title='Random Point Pattern')" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "188" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pp_csr.n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Clustered Patterns\n", "\n", "Clustered Patterns are more grouped than random patterns. Visually, we can observe more points at short distances. There are two sources of clustering:\n", "\n", "* Contagion: presence of events at one location affects probability of events at another location (correlated point process)\n", "* Heterogeneity: intensity $\\lambda$ varies with location (heterogeneous Poisson point process)\n", "\n", "We are going to focus on simulating correlated point process in this notebook. One example of correlated point process is Poisson cluster process. Two stages are involved in simulating a Poisson cluster process. First, parent events are simulted from a $\\lambda$-conditioned or $N$-conditioned CSR. Second, $n$ offspring events for each parent event are simulated within a circle of radius $r$ centered on the parent. Offspring events are independently and identically distributed." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 1. Simulate a Poisson cluster process of size 200 with 10 parents and 20 children within 0.5 units of each parent (parent events: $N$-conditioned CSR)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.random.seed(5)\n", "csamples = PoissonClusterPointProcess(window, 200, 10, 0.5, 1, asPP=True, conditioning=False)\n", "csamples" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{0: {'n': 200}}" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "csamples.parameters #number of total events for each realization " ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{0: 10}" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "csamples.num_parents #number of parent events for each realization " ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "20" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "csamples.children # number of children events centered on each parent event" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pp_pcp = csamples.realizations[0]\n", "pp_pcp" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pp_pcp.plot(window=True, hull=True, title='Clustered Point Pattern') #plot the first realization" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is obvious that there are several clusters in the above point pattern." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 2. Simulate a Poisson cluster process of size 200 with 10 parents and 20 children within 0.5 units of each parent (parent events: $\\lambda$-conditioned CSR)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "np.random.seed(10)\n", "csamples = PoissonClusterPointProcess(window, 200, 10, 0.5, 1, asPP=True, conditioning=True)\n", "csamples" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{0: {'n': 260}}" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "csamples.parameters #number of events for the realization might not be equal to 200" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{0: 13}" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "csamples.num_parents #number of parent events for the realization, not equal to 10" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "20" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "csamples.children # number of children events centered on each parent event" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pp_pcp = csamples.realizations[0]\n", "pp_pcp.plot(window=True, hull=True, title='Clustered Point Pattern')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 3. Simulate a Poisson cluster process of size 200 with 5 parents and 40 children within 0.5 units of each parent (parent events: $N$-conditioned CSR)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "np.random.seed(10)\n", "csamples = PoissonClusterPointProcess(window, 200, 5, 0.5, 1, asPP=True)\n", "pp_pcp = csamples.realizations[0]\n", "pp_pcp.plot(window=True, hull=True, title='Clustered Point Pattern')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 1 } pointpats-2.5.5/notebooks/spacetime.ipynb000066400000000000000000211446061514706172100206060ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "id": "627b4984-0793-4262-8d4a-e9e8a57d6eae", "metadata": {}, "source": [ "# Space-Time Event Clustering" ] }, { "cell_type": "code", "execution_count": 1, "id": "cb851e55-2395-439a-a306-ed8709743fa2", "metadata": { "execution": { "iopub.execute_input": "2023-09-28T18:28:33.832250Z", "iopub.status.busy": "2023-09-28T18:28:33.832122Z", "iopub.status.idle": "2023-09-28T18:28:35.997248Z", "shell.execute_reply": "2023-09-28T18:28:35.996983Z", "shell.execute_reply.started": "2023-09-28T18:28:33.832232Z" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Author: eli knaap & serge rey\n", "\n", "Last updated: 2023-09-28\n", "\n", "Python implementation: CPython\n", "Python version : 3.11.5\n", "IPython version : 8.15.0\n", "\n", "pointpats: 2.3.0\n", "geopandas: 0.13.2\n", "libpysal : 4.7.0\n", "\n" ] } ], "source": [ "%load_ext autoreload\n", "%autoreload 2\n", "\n", "%load_ext watermark\n", "%watermark -a 'eli knaap & serge rey' -v -d -u -p pointpats,geopandas,libpysal" ] }, { "cell_type": "markdown", "id": "0eba3a13-fdac-4ca7-8b3c-1d7107868434", "metadata": {}, "source": [ "A classic tradition in spatial analysis examines the clustering of events in time and space. A set of \"events\" in this case is a dataset with both a geographic location (i.e. a point with X and Y coordinates) and a temporal location (i.e. a timestamp or some record of *when* something occurred). Events that cluster togther in time and space can often be substantively important across a wide variety of social, behavioral, and natural sciences. For example in epidemiology, a cluster of events may help identify a disease outbreak, but understanding clustering in space-time event data is widely applicable in many fields. Further, the data are often already present in the most useful format. Example research questions and geocoded/timestamped data might include:" ] }, { "cell_type": "markdown", "id": "28654afa-af0d-4a95-b9cd-08b1379abf0e", "metadata": {}, "source": [ "**public health & epidemiology**, e.g. to describe outbreaks or help identify inflection points in an epidemic\n", "- infections\n", "- vaccines\n", "- overdoses\n", "\n", "**transportation**, e.g. to identify dangerous intersections or high-demand transit locations\n", "- traffic accidents\n", "- taxi hails\n", "- DUI violations\n", "\n", "**housing** e.g. to identify transitioning land markets or residential displacement\n", "- development permits\n", "- foreclosures\n", "- evictions\n", "\n", "**marketing** e.g. to understand which campaigns do better in which locations\n", "- sales \n", "- ad clicks\n", "- conversions\n", "\n", "**criminology** e.g. to understand inequality in police enforcement or victimization\n", "- stop and frisk incidents\n", "- burglaries\n", "- firearm sales\n", "\n", "**earth & climate science** e.g. to examine impacts of climate change on catastrophic events \n", "- volcanic eruptions\n", "- earthquakes\n", "- hurricane landfalls" ] }, { "cell_type": "markdown", "id": "ad34cb76-dad0-4411-a957-7056d03b40f3", "metadata": {}, "source": [ "One of the most commonly-used approaches is the [Knox statistic](https://doi.org/10.2307/2985220), first formulated in epidemiology to study disease outbreaks. The Knox statistic looks for clustering of events in space and time using two distance thresholds that define a set of \"spatial neighbors\" who are nearby in the geographical sense, and a set of \"temporal neighbors\" who are nearby in time. These thresholds are commonly called *delta* ($\\delta$, for distance) and *tau* ($\\tau$, for time) respectively.\n", "\n", "To use a Knox statistic, we adopt the null hypothesis that there is no space-time interaction between events, and we look for non-random groups in the data. That is, if we find there are more events than expected within the two thresholds (i.e. more space-time neighbors than expected), then there is evidence to reject the null in favor of space-time clustering." ] }, { "cell_type": "code", "execution_count": 2, "id": "54a26566-16fe-46dc-8b4b-e3552dabb5cb", "metadata": { "execution": { "iopub.execute_input": "2023-09-28T18:28:35.998277Z", "iopub.status.busy": "2023-09-28T18:28:35.998147Z", "iopub.status.idle": "2023-09-28T18:28:36.016390Z", "shell.execute_reply": "2023-09-28T18:28:36.015826Z", "shell.execute_reply.started": "2023-09-28T18:28:35.998269Z" }, "tags": [] }, "outputs": [], "source": [ "import geopandas as gpd\n", "import pandas as pd\n", "from pointpats import Knox, KnoxLocal, plot_density" ] }, { "cell_type": "markdown", "id": "7567944c-a141-4971-8850-fb3bd613411a", "metadata": {}, "source": [ "## Traffic Collisions in San Diego County" ] }, { "cell_type": "markdown", "id": "b3bac236-42c3-47a7-8fef-578a74a1fd09", "metadata": {}, "source": [ "To demonstrate the Knox and KnoxLocal functionality in `pointpats`, we will use the example of traffic collisions in San Diego using an open dataset collected from the CA Highway Patrol\n", "\n", "These data contain an extract from from dates 1/1/2001 - 8/1/2023. More information on the dataset including field codes is\n", "available from \n" ] }, { "cell_type": "code", "execution_count": 3, "id": "8c63c59a-1168-477d-991d-8c4e01e301c2", "metadata": { "execution": { "iopub.execute_input": "2023-09-28T18:28:36.017038Z", "iopub.status.busy": "2023-09-28T18:28:36.016915Z", "iopub.status.idle": "2023-09-28T18:28:36.521712Z", "shell.execute_reply": "2023-09-28T18:28:36.521390Z", "shell.execute_reply.started": "2023-09-28T18:28:36.017027Z" }, "tags": [] }, "outputs": [], "source": [ "# currently, you can download this file from\n", "# https://www.dropbox.com/scl/fi/ddduihxo5mnhbtkhsp3vb/sd_collisions.parquet?rlkey=cy19kaxu0zalcpewd3u8gzdwr&dl=0\n", "\n", "sd_collisions = gpd.read_parquet(\"sd_collisions.parquet\")" ] }, { "cell_type": "code", "execution_count": 4, "id": "df7cdbd4-206e-41d3-b98e-40a8cf371f89", "metadata": { "execution": { "iopub.execute_input": "2023-09-28T18:28:36.522216Z", "iopub.status.busy": "2023-09-28T18:28:36.522129Z", "iopub.status.idle": "2023-09-28T18:28:36.679004Z", "shell.execute_reply": "2023-09-28T18:28:36.678546Z", "shell.execute_reply.started": "2023-09-28T18:28:36.522206Z" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 183525 entries, 0 to 183524\n", "Data columns (total 80 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 index 183525 non-null int64 \n", " 1 CASE_ID 183525 non-null int64 \n", " 2 ACCIDENT_YEAR 183525 non-null int64 \n", " 3 PROC_DATE 183525 non-null int64 \n", " 4 JURIS 183525 non-null object \n", " 5 COLLISION_DATE 183525 non-null datetime64[us]\n", " 6 COLLISION_TIME 183525 non-null int64 \n", " 7 OFFICER_ID 183424 non-null object \n", " 8 REPORTING_DISTRICT 11628 non-null object \n", " 9 DAY_OF_WEEK 183525 non-null int64 \n", " 10 CHP_SHIFT 183525 non-null int64 \n", " 11 POPULATION 183525 non-null int64 \n", " 12 CNTY_CITY_LOC 183525 non-null int64 \n", " 13 SPECIAL_COND 183525 non-null int64 \n", " 14 BEAT_TYPE 183525 non-null int64 \n", " 15 CHP_BEAT_TYPE 183525 non-null object \n", " 16 CITY_DIVISION_LAPD 0 non-null object \n", " 17 CHP_BEAT_CLASS 183525 non-null int64 \n", " 18 BEAT_NUMBER 182139 non-null object \n", " 19 PRIMARY_RD 183525 non-null object \n", " 20 SECONDARY_RD 183522 non-null object \n", " 21 DISTANCE 183525 non-null float64 \n", " 22 DIRECTION 169765 non-null object \n", " 23 INTERSECTION 183525 non-null object \n", " 24 WEATHER_1 183525 non-null object \n", " 25 WEATHER_2 183525 non-null object \n", " 26 STATE_HWY_IND 183520 non-null object \n", " 27 CALTRANS_COUNTY 41699 non-null object \n", " 28 CALTRANS_DISTRICT 41699 non-null float64 \n", " 29 STATE_ROUTE 41699 non-null float64 \n", " 30 ROUTE_SUFFIX 41699 non-null object \n", " 31 POSTMILE_PREFIX 41699 non-null object \n", " 32 POSTMILE 41699 non-null float64 \n", " 33 LOCATION_TYPE 41699 non-null object \n", " 34 RAMP_INTERSECTION 41699 non-null object \n", " 35 SIDE_OF_HWY 41698 non-null object \n", " 36 TOW_AWAY 183105 non-null object \n", " 37 COLLISION_SEVERITY 183525 non-null int64 \n", " 38 NUMBER_KILLED 183525 non-null int64 \n", " 39 NUMBER_INJURED 183525 non-null int64 \n", " 40 PARTY_COUNT 183525 non-null object \n", " 41 PRIMARY_COLL_FACTOR 183525 non-null object \n", " 42 PCF_CODE_OF_VIOL 183525 non-null object \n", " 43 PCF_VIOL_CATEGORY 183525 non-null object \n", " 44 PCF_VIOLATION 175576 non-null float64 \n", " 45 PCF_VIOL_SUBSECTION 52938 non-null object \n", " 46 HIT_AND_RUN 183525 non-null object \n", " 47 TYPE_OF_COLLISION 183525 non-null object \n", " 48 MVIW 183525 non-null object \n", " 49 PED_ACTION 183525 non-null object \n", " 50 ROAD_SURFACE 183525 non-null object \n", " 51 ROAD_COND_1 183525 non-null object \n", " 52 ROAD_COND_2 183525 non-null object \n", " 53 LIGHTING 183525 non-null object \n", " 54 CONTROL_DEVICE 183525 non-null object \n", " 55 CHP_ROAD_TYPE 183525 non-null float64 \n", " 56 PEDESTRIAN_ACCIDENT 2484 non-null object \n", " 57 BICYCLE_ACCIDENT 1787 non-null object \n", " 58 MOTORCYCLE_ACCIDENT 12177 non-null object \n", " 59 TRUCK_ACCIDENT 8740 non-null object \n", " 60 NOT_PRIVATE_PROPERTY 183525 non-null object \n", " 61 ALCOHOL_INVOLVED 20612 non-null object \n", " 62 STWD_VEHTYPE_AT_FAULT 183525 non-null object \n", " 63 CHP_VEHTYPE_AT_FAULT 183011 non-null object \n", " 64 COUNT_SEVERE_INJ 183525 non-null int64 \n", " 65 COUNT_VISIBLE_INJ 183525 non-null int64 \n", " 66 COUNT_COMPLAINT_PAIN 183525 non-null int64 \n", " 67 COUNT_PED_KILLED 183525 non-null int64 \n", " 68 COUNT_PED_INJURED 183525 non-null int64 \n", " 69 COUNT_BICYCLIST_KILLED 183525 non-null int64 \n", " 70 COUNT_BICYCLIST_INJURED 183525 non-null int64 \n", " 71 COUNT_MC_KILLED 183525 non-null int64 \n", " 72 COUNT_MC_INJURED 183525 non-null object \n", " 73 PRIMARY_RAMP 183525 non-null object \n", " 74 SECONDARY_RAMP 183525 non-null object \n", " 75 LATITUDE 183525 non-null float64 \n", " 76 LONGITUDE 183525 non-null float64 \n", " 77 year 183525 non-null object \n", " 78 geometry 183525 non-null geometry \n", " 79 time_in_days 183525 non-null int64 \n", "dtypes: datetime64[us](1), float64(8), geometry(1), int64(24), object(46)\n", "memory usage: 112.0+ MB\n" ] } ], "source": [ "sd_collisions.info()" ] }, { "cell_type": "code", "execution_count": 5, "id": "9936c02e-45ad-450b-869e-9486140de683", "metadata": { "execution": { "iopub.execute_input": "2023-09-28T18:28:36.679722Z", "iopub.status.busy": "2023-09-28T18:28:36.679636Z", "iopub.status.idle": "2023-09-28T18:28:39.836898Z", "shell.execute_reply": "2023-09-28T18:28:39.836578Z", "shell.execute_reply.started": "2023-09-28T18:28:36.679714Z" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "image/png": { "height": 428, "width": 547 } }, "output_type": "display_data" } ], "source": [ "sd_collisions.plot()" ] }, { "cell_type": "markdown", "id": "acec3e57-df13-42f6-9acc-abb4201fdd09", "metadata": {}, "source": [ "Because these points are so dense, a common way to search for \"hotspots\" is to plot a map using a kernel density estimator (KDE), which we can do for all events, or for subsets of events like pedestrian or bicycle collisions" ] }, { "cell_type": "code", "execution_count": 6, "id": "406951bc-3397-48cb-b745-996114d09262", "metadata": { "execution": { "iopub.execute_input": "2023-09-28T18:28:39.837522Z", "iopub.status.busy": "2023-09-28T18:28:39.837386Z", "iopub.status.idle": "2023-09-28T18:29:08.791800Z", "shell.execute_reply": "2023-09-28T18:29:08.791168Z", "shell.execute_reply.started": "2023-09-28T18:28:39.837505Z" }, "tags": [] }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "image/png": { "height": 428, "width": 556 } }, "output_type": "display_data" } ], "source": [ "# note you need statsmodels installed to run this line\n", "ax = plot_density(sd_collisions, bandwidth=2000)" ] }, { "cell_type": "code", "execution_count": 7, "id": "6b011a4a-4e6b-42ed-881d-00766b279cff", "metadata": { "execution": { "iopub.execute_input": "2023-09-28T18:29:08.793861Z", "iopub.status.busy": "2023-09-28T18:29:08.793695Z", "iopub.status.idle": "2023-09-28T18:29:08.901582Z", "shell.execute_reply": "2023-09-28T18:29:08.901193Z", "shell.execute_reply.started": "2023-09-28T18:29:08.793852Z" }, "tags": [] }, "outputs": [], "source": [ "ped_collisions = sd_collisions[sd_collisions[\"PEDESTRIAN_ACCIDENT\"] == \"Y\"]\n", "bike_collisions = sd_collisions[sd_collisions[\"BICYCLE_ACCIDENT\"] == \"Y\"]" ] }, { "cell_type": "code", "execution_count": 8, "id": "630b7e28-5830-4c5e-9725-eb89acb5ac2f", "metadata": { "execution": { "iopub.execute_input": "2023-09-28T18:29:08.902087Z", "iopub.status.busy": "2023-09-28T18:29:08.901990Z", "iopub.status.idle": "2023-09-28T18:29:09.522702Z", "shell.execute_reply": "2023-09-28T18:29:09.522439Z", "shell.execute_reply.started": "2023-09-28T18:29:08.902075Z" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "image/png": { "height": 428, "width": 556 } }, "output_type": "display_data" } ], "source": [ "plot_density(ped_collisions, bandwidth=2000)" ] }, { "cell_type": "code", "execution_count": 9, "id": "5879b0e5-f85f-4b6c-8b71-691a0b9173d8", "metadata": { "execution": { "iopub.execute_input": "2023-09-28T18:29:09.523258Z", "iopub.status.busy": "2023-09-28T18:29:09.523175Z", "iopub.status.idle": "2023-09-28T18:29:10.026371Z", "shell.execute_reply": "2023-09-28T18:29:10.026082Z", "shell.execute_reply.started": "2023-09-28T18:29:09.523250Z" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "image/png": { "height": 428, "width": 575 } }, "output_type": "display_data" } ], "source": [ "plot_density(bike_collisions, bandwidth=2000)" ] }, { "cell_type": "markdown", "id": "83fb87d6-3f90-4360-acb0-430f65c2ecf8", "metadata": {}, "source": [ "Plotting a heatmap of these points using a kernel-density estimator can give us a sense for how the events cluster in space, but it ignores the time dimension entirely. We can see that the heatmaps for pedestriand and bicycle collisions are a little different, but it is not clear why. It could be because there are more cycle commuters in some regions of the county versus others (so more opportunities for a collision to occur) or because collision risk is higher in some places, or both. Comparisons across kernel density maps for these two classes of events is complicated because we don't know whether they share the same population-at-risk surface underneath or risks surfaces." ] }, { "cell_type": "code", "execution_count": null, "id": "8517a1d1-bc60-47b5-9bd9-01a41c1e7027", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "511290fb-e99e-400e-bb32-3e041338d15a", "metadata": { "execution": { "iopub.execute_input": "2023-09-25T23:07:16.128413Z", "iopub.status.busy": "2023-09-25T23:07:16.127888Z", "iopub.status.idle": "2023-09-25T23:07:16.136324Z", "shell.execute_reply": "2023-09-25T23:07:16.133940Z", "shell.execute_reply.started": "2023-09-25T23:07:16.128387Z" }, "tags": [] }, "source": [ "## Global Clustering" ] }, { "cell_type": "markdown", "id": "d0e9ec39-0a27-41b5-aa5a-ff43350dde6c", "metadata": {}, "source": [ "The classic Knox statistic is a test for global clustering. It examines whether the pattern of spatio-temporal interaction in the events is random or not." ] }, { "cell_type": "markdown", "id": "e11d8b9a-53a5-4e56-b8d9-321c5afc8a55", "metadata": {}, "source": [ ".The two critical parameters for the Knox statistic are `delta` and `tau` which define our neighborhood thresholds in space and time. The `delta` argument is measured in the units of the geodataframe CRS, and `tau` can be measured using any time measurement represented by an integer. \n", "\n", "Here, we'll measure time by the number of days elapsed since the start of data collection. Since the collision date is stored as a string, we can convert to a pandas datetime dtype, then take the difference from the minium date, stored in days (so our time is measured in days)." ] }, { "cell_type": "code", "execution_count": 10, "id": "6ed7a122-8962-496b-ac64-76df0e4a0ec6", "metadata": { "execution": { "iopub.execute_input": "2023-09-28T18:29:10.026955Z", "iopub.status.busy": "2023-09-28T18:29:10.026875Z", "iopub.status.idle": "2023-09-28T18:29:10.812379Z", "shell.execute_reply": "2023-09-28T18:29:10.811996Z", "shell.execute_reply.started": "2023-09-28T18:29:10.026946Z" }, "tags": [] }, "outputs": [], "source": [ "# convert to datetime\n", "sd_collisions.COLLISION_DATE = pd.to_datetime(\n", " sd_collisions.COLLISION_DATE, format=\"%Y%m%d\"\n", ")\n", "\n", "min_date = sd_collisions.COLLISION_DATE.min()\n", "\n", "sd_collisions[\"time_in_days\"] = sd_collisions.COLLISION_DATE.apply(\n", " lambda x: x - min_date\n", ").dt.days" ] }, { "cell_type": "markdown", "id": "f5ce3702-2d6b-43b6-b670-99fc211c478a", "metadata": { "tags": [] }, "source": [ "### Pedestrian Collisions" ] }, { "cell_type": "markdown", "id": "941724fc-93a8-4ef8-b11e-8eabea8ce76e", "metadata": {}, "source": [ "Here we set delta to `2000` and tau to `30`, meaning our space-time neighborhood is looking for clusters of events that occur within one month and two kilometers of one another." ] }, { "cell_type": "code", "execution_count": 11, "id": "5b6c5a44-6e52-4036-97f2-60fb137e72f6", "metadata": { "execution": { "iopub.execute_input": "2023-09-28T18:29:10.812902Z", "iopub.status.busy": "2023-09-28T18:29:10.812812Z", "iopub.status.idle": "2023-09-28T18:29:12.065712Z", "shell.execute_reply": "2023-09-28T18:29:12.065358Z", "shell.execute_reply.started": "2023-09-28T18:29:10.812892Z" }, "tags": [] }, "outputs": [], "source": [ "ped_knox = Knox.from_dataframe(\n", " ped_collisions, time_col=\"time_in_days\", delta=2000, tau=30\n", ")" ] }, { "cell_type": "code", "execution_count": 12, "id": "04d82ca1-d416-499d-8c57-95c46f58c6a4", "metadata": { "execution": { "iopub.execute_input": "2023-09-28T18:29:12.066162Z", "iopub.status.busy": "2023-09-28T18:29:12.066085Z", "iopub.status.idle": "2023-09-28T18:29:12.086886Z", "shell.execute_reply": "2023-09-28T18:29:12.086274Z", "shell.execute_reply.started": "2023-09-28T18:29:12.066153Z" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "5.88418203051333e-15" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ped_knox.p_poisson" ] }, { "cell_type": "code", "execution_count": 13, "id": "ba792f93-9918-4e57-8e87-239d77c0211b", "metadata": { "execution": { "iopub.execute_input": "2023-09-28T18:29:12.087523Z", "iopub.status.busy": "2023-09-28T18:29:12.087438Z", "iopub.status.idle": "2023-09-28T18:29:12.111885Z", "shell.execute_reply": "2023-09-28T18:29:12.111468Z", "shell.execute_reply.started": "2023-09-28T18:29:12.087515Z" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "0.01" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ped_knox.p_sim" ] }, { "cell_type": "markdown", "id": "503194de-72e1-423d-9b0d-0e80b2512d01", "metadata": { "execution": { "iopub.execute_input": "2023-09-25T23:02:45.683997Z", "iopub.status.busy": "2023-09-25T23:02:45.683640Z", "iopub.status.idle": "2023-09-25T23:02:45.690196Z", "shell.execute_reply": "2023-09-25T23:02:45.689461Z", "shell.execute_reply.started": "2023-09-25T23:02:45.683950Z" }, "tags": [] }, "source": [ "### Bike Collisions" ] }, { "cell_type": "code", "execution_count": 14, "id": "3951f670-f343-4057-a5f3-0c20f334a82d", "metadata": { "execution": { "iopub.execute_input": "2023-09-28T18:29:12.112370Z", "iopub.status.busy": "2023-09-28T18:29:12.112284Z", "iopub.status.idle": "2023-09-28T18:29:12.830391Z", "shell.execute_reply": "2023-09-28T18:29:12.830032Z", "shell.execute_reply.started": "2023-09-28T18:29:12.112362Z" }, "tags": [] }, "outputs": [], "source": [ "bike_knox = Knox.from_dataframe(\n", " bike_collisions, time_col=\"time_in_days\", delta=2000, tau=30\n", ")" ] }, { "cell_type": "code", "execution_count": 15, "id": "51e685cd-755b-4ae8-a5e2-5951a532944e", "metadata": { "execution": { "iopub.execute_input": "2023-09-28T18:29:12.830890Z", "iopub.status.busy": "2023-09-28T18:29:12.830809Z", "iopub.status.idle": "2023-09-28T18:29:12.856553Z", "shell.execute_reply": "2023-09-28T18:29:12.856145Z", "shell.execute_reply.started": "2023-09-28T18:29:12.830879Z" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "0.0" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bike_knox.p_poisson" ] }, { "cell_type": "code", "execution_count": 16, "id": "e76f0478-d1fc-4d44-9797-939d8d31c62c", "metadata": { "execution": { "iopub.execute_input": "2023-09-28T18:29:12.857460Z", "iopub.status.busy": "2023-09-28T18:29:12.857244Z", "iopub.status.idle": "2023-09-28T18:29:12.879023Z", "shell.execute_reply": "2023-09-28T18:29:12.878681Z", "shell.execute_reply.started": "2023-09-28T18:29:12.857450Z" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "0.01" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bike_knox.p_sim" ] }, { "cell_type": "markdown", "id": "eec6a518-cdf0-4318-aadc-a52e4888b501", "metadata": {}, "source": [ "In both the pedestrian and bicycle collision data the p-values (for both analytical and permutation-based inference) are significant, demonstrating evidence of space-time clustering in the collisions. That is, some times and places appear to be more dangerous than others. Using a local Knox statistic, we can start investigating where and when these places might be." ] }, { "cell_type": "markdown", "id": "0a3c5f0a-8578-435c-9b32-ea3c3d0b8ad6", "metadata": {}, "source": [ "## Local Clustering" ] }, { "cell_type": "markdown", "id": "b4f91bd9-90b7-486e-8efe-e5431a1d3015", "metadata": {}, "source": [ "As with a statistic like Moran's $I$, the Knox statistic can be decomposed into a local version that describes, at an observation level, whether the event at a given location and time is a member of a significant space-time cluster. This can be considered a kind of \"hotspot\" analysis where the locally significant values identify particular pockets of the study region (and time period) where events are clustered." ] }, { "cell_type": "markdown", "id": "3f8a11d0-6d9b-40ef-866b-d69cef31de8e", "metadata": { "tags": [] }, "source": [ "### Pedestrian Collisions" ] }, { "cell_type": "code", "execution_count": 17, "id": "8a09a92f-cc41-4b89-910e-1fd9393c51e2", "metadata": { "execution": { "iopub.execute_input": "2023-09-28T18:29:12.879624Z", "iopub.status.busy": "2023-09-28T18:29:12.879539Z", "iopub.status.idle": "2023-09-28T18:29:30.907616Z", "shell.execute_reply": "2023-09-28T18:29:30.907325Z", "shell.execute_reply.started": "2023-09-28T18:29:12.879616Z" }, "tags": [] }, "outputs": [], "source": [ "ped_knox_local = KnoxLocal.from_dataframe(\n", " ped_collisions.set_index(\"CASE_ID\"), time_col=\"time_in_days\", delta=2000, tau=30\n", ")" ] }, { "cell_type": "markdown", "id": "b64e7f2f-f7df-41fb-93b0-fb92bb066ed5", "metadata": {}, "source": [ "As a local statistic, the resulting `KnoxLocal` class no longer has a single $p$-value, but a value for each observation. Since `permutations` is included as a default argument in the KnoxLocal class, p-values from both the analytical and simulation-based inference are available as attributes on the fitted class" ] }, { "cell_type": "code", "execution_count": 18, "id": "429c3cd5-54ae-445e-ae3b-f6d00bdbcb34", "metadata": { "execution": { "iopub.execute_input": "2023-09-28T18:29:30.908199Z", "iopub.status.busy": "2023-09-28T18:29:30.908114Z", "iopub.status.idle": "2023-09-28T18:29:30.929299Z", "shell.execute_reply": "2023-09-28T18:29:30.928722Z", "shell.execute_reply.started": "2023-09-28T18:29:30.908190Z" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "array([1. , 0.17142085, 1. , ..., 1. , 1. ,\n", " 1. ])" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ped_knox_local.p_hypergeom" ] }, { "cell_type": "code", "execution_count": 19, "id": "536363be-4560-4bfa-aa0e-4fef7582ab58", "metadata": { "execution": { "iopub.execute_input": "2023-09-28T18:29:30.929821Z", "iopub.status.busy": "2023-09-28T18:29:30.929726Z", "iopub.status.idle": "2023-09-28T18:29:30.952740Z", "shell.execute_reply": "2023-09-28T18:29:30.952324Z", "shell.execute_reply.started": "2023-09-28T18:29:30.929812Z" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "array([1. , 0.19, 1. , ..., 1. , 1. , 1. ])" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\n", "ped_knox_local.p_sims" ] }, { "cell_type": "markdown", "id": "00377341-8cb3-4999-bf23-8b38e5d12007", "metadata": {}, "source": [ "Together with the space-time neighbor relationships, we can use these p-values to identify local \"hotspots\"" ] }, { "cell_type": "code", "execution_count": 20, "id": "1517a950-70af-4b1d-b627-118d258576ac", "metadata": { "execution": { "iopub.execute_input": "2023-09-28T18:29:30.953383Z", "iopub.status.busy": "2023-09-28T18:29:30.953295Z", "iopub.status.idle": "2023-09-28T18:29:30.984332Z", "shell.execute_reply": "2023-09-28T18:29:30.983953Z", "shell.execute_reply.started": "2023-09-28T18:29:30.953374Z" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " pvalue focal_time focal neighbor orientation\n", "0 0.05 3760 9263257 9250758 lag\n", "1 0.03 4431 9547792 9546979 lag\n", "2 0.03 4431 9547792 9560633 lead\n", "3 0.01 2399 90533779 8424929 lead\n", "4 0.01 2399 90533779 90604293 lag\n", ".. ... ... ... ... ...\n", "478 0.01 702 5950645 5925674 lag\n", "479 0.01 691 5925674 5901518 lag\n", "480 0.01 691 5925674 5970599 lead\n", "481 0.01 691 5925674 5849678 lag\n", "482 0.01 691 5925674 5950645 lead\n", "\n", "[483 rows x 5 columns]" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ped_knox_local.hotspots()" ] }, { "cell_type": "markdown", "id": "5a1bf9f1-585e-4fb2-ace3-c7186e028e7a", "metadata": {}, "source": [ "Observations are identified as hotspots if the observed number of space-time neighbors at that location exceeds the expected number. This means that *every observation* in the dataset is assigned a p-value that determines whether it is a significant locus of space-time interaction. From a graph theoretic perspective, the number of space-time neighbors for a unit is the number of incident edges for that node, with the graph being formed as the intersection of two other graphs: one for temporal neighbors and one for spatial neighbors. p-values for each node are determined as the probability of exceeding the number of incident edges in the space-time graph given the number of incident edges in each of the temporal and spatial graphs for that unit under the assumption of no space-time interaction." ] }, { "cell_type": "markdown", "id": "778111b9-a2c3-4654-b8c1-3f65dc0a484e", "metadata": {}, "source": [ "Since we can use either computational/permutation-based inference or an analyical approximation, we can also use the analytical p-values to defined hotspots" ] }, { "cell_type": "code", "execution_count": 21, "id": "b26db9d4-896c-4579-bfd6-cfb97a269283", "metadata": { "execution": { "iopub.execute_input": "2023-09-28T18:29:30.984763Z", "iopub.status.busy": "2023-09-28T18:29:30.984684Z", "iopub.status.idle": "2023-09-28T18:29:31.009976Z", "shell.execute_reply": "2023-09-28T18:29:31.009690Z", "shell.execute_reply.started": "2023-09-28T18:29:30.984756Z" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " pvalue focal_time focal neighbor orientation\n", "0 0.017825 4431 9547792 9546979 lag\n", "1 0.017825 4431 9547792 9560633 lead\n", "2 0.001876 2399 90533779 8424929 lead\n", "3 0.001876 2399 90533779 90604293 lag\n", "4 0.001876 2399 90533779 8411306 lag\n", ".. ... ... ... ... ...\n", "517 0.000168 702 5950645 5925674 lag\n", "518 0.000417 691 5925674 5901518 lag\n", "519 0.000417 691 5925674 5970599 lead\n", "520 0.000417 691 5925674 5849678 lag\n", "521 0.000417 691 5925674 5950645 lead\n", "\n", "[522 rows x 5 columns]" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ped_knox_local.hotspots(inference=\"analytic\")" ] }, { "cell_type": "markdown", "id": "d60136bf-73bb-473f-9ea7-4c474366dd55", "metadata": {}, "source": [ "And finally we can plot the hotspots to see where they are. By default:\n", "\n", "- any observation that is itself significant shows up as red\n", "- any observation that is not itself significant, but is a space-time neighbor of a significant observation shows up as yellow\n", "- any observation that is insignificant (at the defined `crit` value) shows up as gray" ] }, { "cell_type": "code", "execution_count": 22, "id": "58e44270-15de-4bca-baa0-22ceaa8ef4cd", "metadata": { "execution": { "iopub.execute_input": "2023-09-28T18:29:31.014260Z", "iopub.status.busy": "2023-09-28T18:29:31.014118Z", "iopub.status.idle": "2023-09-28T18:29:31.220630Z", "shell.execute_reply": "2023-09-28T18:29:31.220241Z", "shell.execute_reply.started": "2023-09-28T18:29:31.014249Z" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "image/png": { "height": 428, "width": 544 } }, "output_type": "display_data" } ], "source": [ "ped_knox_local.plot()" ] }, { "cell_type": "markdown", "id": "a02c60cd-8aff-4e11-b700-224d5216e24f", "metadata": {}, "source": [ "You can also customize the plot if you like" ] }, { "cell_type": "code", "execution_count": 23, "id": "e330e520-6fff-41c3-90aa-3fb37924bccd", "metadata": { "execution": { "iopub.execute_input": "2023-09-28T18:29:31.221081Z", "iopub.status.busy": "2023-09-28T18:29:31.221000Z", "iopub.status.idle": "2023-09-28T18:29:37.316682Z", "shell.execute_reply": "2023-09-28T18:29:37.316382Z", "shell.execute_reply.started": "2023-09-28T18:29:31.221072Z" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "(439112.26800894685, 590276.0408081682, 3594614.155634063, 3710165.9779328993)" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "image/png": { "height": 553, "width": 717 } }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "import contextily as ctx\n", "\n", "f, ax = plt.subplots(figsize=(9, 9))\n", "ax = ped_knox_local.plot(point_kwargs={\"alpha\": 0.2}, plot_edges=False, ax=ax)\n", "ctx.add_basemap(ax, source=ctx.providers.CartoDB.Positron, crs=sd_collisions.crs)\n", "ax.axis(\"off\")" ] }, { "cell_type": "markdown", "id": "8b90e7a0-4a9e-4a59-83ad-c6298f800c4f", "metadata": {}, "source": [ "For convenience, there is also an interactive version of the hot spot map that uses the same defaults. In the interactive version, an edge (line) is drawn between members of space-time neighborhoods. This makes it easy to identify the \"neighborhood\" of significant events" ] }, { "cell_type": "code", "execution_count": 24, "id": "69c84801-f894-4663-aec5-830d291091be", "metadata": { "execution": { "iopub.execute_input": "2023-09-28T18:29:37.317253Z", "iopub.status.busy": "2023-09-28T18:29:37.317104Z", "iopub.status.idle": "2023-09-28T18:29:37.703601Z", "shell.execute_reply": "2023-09-28T18:29:37.703296Z", "shell.execute_reply.started": "2023-09-28T18:29:37.317245Z" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "
Make this Notebook Trusted to load map: File -> Trust Notebook
" ], "text/plain": [ "" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ped_knox_local.explore(plot_edges=True)" ] }, { "cell_type": "markdown", "id": "65e2cc8c-fe2b-48c8-b180-551a13596b5b", "metadata": { "execution": { "iopub.execute_input": "2023-09-25T23:02:45.683997Z", "iopub.status.busy": "2023-09-25T23:02:45.683640Z", "iopub.status.idle": "2023-09-25T23:02:45.690196Z", "shell.execute_reply": "2023-09-25T23:02:45.689461Z", "shell.execute_reply.started": "2023-09-25T23:02:45.683950Z" }, "tags": [] }, "source": [ "### Bike Collisions" ] }, { "cell_type": "code", "execution_count": 25, "id": "14f651f1-ff08-43fd-ae71-9937fcec01d3", "metadata": { "execution": { "iopub.execute_input": "2023-09-28T18:29:37.704203Z", "iopub.status.busy": "2023-09-28T18:29:37.704050Z", "iopub.status.idle": "2023-09-28T18:29:45.849978Z", "shell.execute_reply": "2023-09-28T18:29:45.849635Z", "shell.execute_reply.started": "2023-09-28T18:29:37.704195Z" }, "tags": [] }, "outputs": [], "source": [ "bike_knox_local = KnoxLocal.from_dataframe(\n", " bike_collisions, time_col=\"time_in_days\", delta=2000, tau=30\n", ")" ] }, { "cell_type": "code", "execution_count": 26, "id": "d28c677b-2225-407a-baed-674e2b6d99bb", "metadata": { "execution": { "iopub.execute_input": "2023-09-28T18:29:45.850440Z", "iopub.status.busy": "2023-09-28T18:29:45.850364Z", "iopub.status.idle": "2023-09-28T18:29:46.088569Z", "shell.execute_reply": "2023-09-28T18:29:46.088203Z", "shell.execute_reply.started": "2023-09-28T18:29:45.850432Z" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "
Make this Notebook Trusted to load map: File -> Trust Notebook
" ], "text/plain": [ "" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bike_knox_local.explore()" ] }, { "cell_type": "code", "execution_count": null, "id": "2b2f5012-f0a2-4fd3-8cd6-f1673d44d41f", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "4c561263-9500-4297-a33d-d4989e43727c", "metadata": {}, "source": [ "Comparing these maps, it looks like with these `delta` and `tau` parameters, the maps show similarities but also differences. Places like downtown, La Presa, or Escondido can be collision hotspots for both cyclists and pedestrians. But for cyclists, Coronado and Carlsbad show up as particularly dangerous locations. " ] }, { "cell_type": "markdown", "id": "1d331c17-6321-4e6c-8749-f0e002210898", "metadata": {}, "source": [ "Continuing with the analysis, the difference between these two hotspot locations could be because of differences in the local environment, such as poorer cycling infrastructure in Coronado and Carlsbad. But these hotspots could also just reflect that these places have greater numbers of cyclists, and thus, tend to have more frequent collisions." ] }, { "cell_type": "markdown", "id": "cc398eb9-adac-464d-a6da-3c294910445c", "metadata": {}, "source": [ "**Note**: these data span a long time horizon, and in this simplified demonstration, we haven't considered the [population shift bias](https://www.sciencedirect.com/science/article/abs/pii/S0198971512000397?via%3Dihub). In the case of vehicle collisions shown here, there is reason to believe that the underlying population at risk may have shifted over time, which could introduce a known bias into our inference. One important assumption of the Knox statistic is that population growth is constant over the geographic subareas over time, but San Diego county is a large, sprawling region and it's possible there is more traffic activity in some parts of the region in later time periods due to uneven urban development. If so, this increase in activity will lead bias out count of observed space-time neighbors upwards. It is important to consider the performance of the Knox statistic (and its local variant) any time the underlying population surface may vary over time." ] }, { "cell_type": "code", "execution_count": null, "id": "03fc0bbb-d466-4e52-852c-69426d8fe844", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:pointpats]", "language": "python", "name": "conda-env-pointpats-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.5" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": {}, "version_major": 2, "version_minor": 0 } } }, "nbformat": 4, "nbformat_minor": 5 } pointpats-2.5.5/notebooks/standard_deviational_ellipse.ipynb000066400000000000000000010240461514706172100245210ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "id": "9c9ec342-b3cc-4628-9b8b-2100134c77b6", "metadata": {}, "source": [ "# Standard Deviational Ellipse \n", "\n", "\n", "\n", "## Overview\n", "This notebook demonstrates how to compute and visualize **standard deviational ellipses** for a set of spatial points using the `pointpats` package from `pysal`. Standard deviational ellipses are useful for summarizing the **direction, dispersion, and central tendency** of spatial point patterns, and they can optionally account for weights such as counts, magnitudes, or intensities.\n", "\n", "## Goals\n", "- Compute unweighted and weighted standard deviational ellipses for a synthetic point pattern.\n", "- Visualize the ellipses over the original point data using GeoPandas and Matplotlib.\n", "- Explore how marker size (based on an attribute) and weights influence the shape and orientation of the resulting ellipse.\n", "- Demonstrate other options for ellipse construction.\n" ] }, { "cell_type": "code", "execution_count": 1, "id": "aaddb9ea-7869-4b22-8e2e-20c2c05c33e3", "metadata": {}, "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2\n" ] }, { "cell_type": "code", "execution_count": 2, "id": "b6fbd94a-9b3a-47a5-a04a-f16b9146f9ed", "metadata": {}, "outputs": [], "source": [ "# imports\n", "import geopandas as gpd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import geopandas as gpd\n", "\n", "from pointpats import ellipse" ] }, { "cell_type": "code", "execution_count": 3, "id": "8c22f909-f5d5-4c26-a8a0-2f1118eef336", "metadata": {}, "outputs": [], "source": [ "import pointpats\n" ] }, { "cell_type": "markdown", "id": "5fa8c9eb-b023-4d3e-8914-2fd690cbcf0b", "metadata": {}, "source": [ "## Example Point Pattern\n", "\n", "- 100 points\n", "- randomly distributed in [(0,0), (10, 10)]" ] }, { "cell_type": "code", "execution_count": 4, "id": "a567c58f-dec2-4ca6-b7d5-c8fedbce2a46", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "\n", "seed = 65647437836358831880808032086803839626\n", "rng = np.random.default_rng(seed)\n", "points = rng.integers(0, 100, (50, 2))" ] }, { "cell_type": "code", "execution_count": 5, "id": "90686986-ba5c-4a56-9694-5ba521946be0", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "points_gdf = gpd.GeoDataFrame(geometry=gpd.points_from_xy(points[:,0], points[:,1]))\n", "\n", "points_gdf.plot()" ] }, { "cell_type": "markdown", "id": "ade6fe62-313e-4e4f-bced-a16fd9f84f55", "metadata": {}, "source": [ "## Dispatching on input type\n", "\n", "`ellipse` can take the following inputs\n", "\n", "- points as a 2-D numpy array\n", "- a geodataframe with a point GeoSeries\n", "\n", "It is important to note that the return types differ between these two cases." ] }, { "cell_type": "markdown", "id": "c08e18cb-59ae-4952-80c6-9ea7203364f0", "metadata": {}, "source": [ "### Passing an array" ] }, { "cell_type": "code", "execution_count": 6, "id": "f381e968-3868-4a44-bc4f-f8fb620bba81", "metadata": {}, "outputs": [], "source": [ "ellipse_ = ellipse(points)" ] }, { "cell_type": "markdown", "id": "20caa5b8-fd65-4c94-b18f-f9069ca4f2ba", "metadata": {}, "source": [ "This will return a tuple with\n", "- length of the major axis\n", "- length of the minor axis\n", "- angle of rotation (in radians)" ] }, { "cell_type": "code", "execution_count": 7, "id": "706c3026-a75c-4a46-a848-f22d58694118", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(np.float64(43.85494662229593),\n", " np.float64(36.28453973005919),\n", " np.float64(-0.9362557045365753))" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ellipse_" ] }, { "cell_type": "markdown", "id": "68de8ebb-0354-4c7a-beff-bd07db2a221d", "metadata": {}, "source": [ "### Passing a GeoDataFrame" ] }, { "cell_type": "code", "execution_count": 8, "id": "6b1272a6-5180-4211-936f-9fb70044b82d", "metadata": {}, "outputs": [], "source": [ "ellipse_poly = ellipse(points_gdf)" ] }, { "cell_type": "markdown", "id": "459548fb-2bc5-4e11-95fe-9f989006aeb0", "metadata": {}, "source": [ "This will return a `shapely` `Polygon`" ] }, { "cell_type": "code", "execution_count": 9, "id": "3420d0d1-5c2a-491c-abd9-9ea2b7d64a0b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "shapely.geometry.polygon.Polygon" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(ellipse_poly)" ] }, { "cell_type": "markdown", "id": "c4dcaefb-cd34-4272-a8c1-c7dd45079bbb", "metadata": {}, "source": [ "## Unweighted and Weighted Ellipses\n", "\n", "The default is to treat the points as an unmarked point pattern and construct the ellipse accordingly." ] }, { "cell_type": "code", "execution_count": 10, "id": "23e497c8-3a62-4d37-8a74-6c59fd94f03c", "metadata": {}, "outputs": [], "source": [ "ellipse_gdf = gpd.GeoDataFrame(geometry=[ellipse_poly])" ] }, { "cell_type": "code", "execution_count": 11, "id": "c3dd02c1-e139-4413-8044-5e15bef12149", "metadata": {}, "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": [ "base = ellipse_gdf.plot()\n", "points_gdf.plot(ax=base, color='k')" ] }, { "cell_type": "markdown", "id": "73b29b5a-96be-428d-ba68-3b7bc41ca7a1", "metadata": {}, "source": [ "### Weighted Points\n", "\n", "If marks are available to attach to each point, the weighted standard deviational ellipse can be constructed. Here we use use the $y$ coordinate as the weight for demonstration:" ] }, { "cell_type": "code", "execution_count": 12, "id": "143ddf77-89b6-4ef4-8405-0ce17b2a54ba", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'Marked Point Pattern')" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Normalize or scale marker size (you can tune this)\n", "size_scale = 20 # try 10, 20, 50 etc. to get a good size visually\n", "marker_sizes = points[:,1] * size_scale\n", "\n", "fig, ax = plt.subplots(figsize=(8, 6))\n", "base = points_gdf.plot(ax=ax, markersize=marker_sizes, alpha=0.6, color='cornflowerblue', edgecolor='k')\n", "points_gdf.plot(ax=base, color='k')\n", "ax.set_title(\"Marked Point Pattern\", fontsize=14)" ] }, { "cell_type": "code", "execution_count": 13, "id": "4536e782-5e00-445f-86bc-14691eabf366", "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "" ], "text/plain": [ "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ellipse_w_poly = ellipse(points_gdf, weights=points[:,1])\n", "ellipse_w_poly" ] }, { "cell_type": "code", "execution_count": 14, "id": "b54a2dc5-8927-41ae-a732-623c3b039bf2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([51.1 , 36.88])" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mc = pointpats.mean_center(points)\n", "mc" ] }, { "cell_type": "code", "execution_count": 15, "id": "66738152-5476-4c4d-8c98-3f9d7002d3c2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([47.3302603 , 59.13665944])" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wmc = pointpats.weighted_mean_center(points, points[:,1])\n", "wmc" ] }, { "cell_type": "code", "execution_count": 16, "id": "bb956941-df8b-4410-922f-f34695889519", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "size_scale = 20 \n", "marker_sizes = points[:,1] * size_scale\n", "\n", "fig, ax = plt.subplots(figsize=(8, 6))\n", "\n", "ellipse_gdf.plot(ax=ax)\n", "gpd.GeoSeries([ellipse_w_poly]).plot(color='yellow', ax=ax)\n", "points_gdf.plot(ax=ax, color='k')\n", "base = points_gdf.plot(ax=ax, markersize=marker_sizes, alpha=0.6, color='cornflowerblue', edgecolor='k')\n", "points_gdf.plot(ax=ax, color='k')\n", "ax.plot(*mc, marker='o', color='red', markersize=10, label='mean center')\n", "ax.plot(*wmc, marker='o', color='green', markersize=10, label='weighted mean center')\n", "ax.legend()\n", "ax.set_title(\"Marked Point Pattern\", fontsize=14);" ] }, { "cell_type": "markdown", "id": "e068b84a-83b8-4f29-939a-6b4b420abd6f", "metadata": {}, "source": [ "## Other options\n", "\n", "The default option constructs the standard deviational ellipse following the method used in CrimeStat." ] }, { "cell_type": "code", "execution_count": 17, "id": "f5efab64-b13e-42a6-8d05-d68391ad8869", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(np.float64(43.85494662229593),\n", " np.float64(36.28453973005919),\n", " np.float64(-0.9362557045365753))" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ellipse(points)" ] }, { "cell_type": "markdown", "id": "af44e1e8-cbe7-4bcd-b496-864a6d3822f5", "metadata": {}, "source": [ "The default construction can be overridden in one of two (or both) ways.\n", "\n", "The first employs the `yuill` method which employs a different estimator for the major and minor axes lengths:" ] }, { "cell_type": "code", "execution_count": 18, "id": "04800f10-f44b-442e-a0ff-fc833e0f6680", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(np.float64(31.01013014519952),\n", " np.float64(25.65704409535755),\n", " np.float64(-0.9362557045365753))" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ellipse(points, method='yuill', crimestatCorr=False) " ] }, { "cell_type": "markdown", "id": "b98d38be-0b7e-420b-b63c-dc2f821d6a55", "metadata": {}, "source": [ "This results in shorter axes lengths relative to to crimestat.\n", "\n", "The second approach drops the degrees of freedom correction used in the default:" ] }, { "cell_type": "code", "execution_count": 19, "id": "93d51f17-71a9-435a-8d5e-9255c465e579", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(np.float64(42.96889676864705),\n", " np.float64(35.551443156155464),\n", " np.float64(-0.9362557045365753))" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ellipse(points, method='yuill', degfreedCorr=False) " ] }, { "cell_type": "markdown", "id": "987d22d8-639f-4ff4-82b4-1a2bfd1c21c2", "metadata": {}, "source": [ "Again, this shortens the estimated axes.\n", "\n", "Finally, both corrections can be toggled off:" ] }, { "cell_type": "code", "execution_count": 20, "id": "55ee9923-9dcb-4580-a9a1-3388cce22fc5", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(np.float64(30.383598285215058),\n", " np.float64(25.138666536685605),\n", " np.float64(-0.9362557045365753))" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ellipse(points, method='yuill', crimestatCorr=False, degfreedCorr=False) " ] } ], "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": "3.12.9" } }, "nbformat": 4, "nbformat_minor": 5 } pointpats-2.5.5/notebooks/window.ipynb000066400000000000000000001600211514706172100201250ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Point Pattern Windows\n", "\n", "**Author: Serge Rey **\n", "\n", "## Introduction\n", "Windows play several important roles in the analysis of planar point patterns. As we saw in the [introductory notebook](pointpattern.ipynb), the area of the window can be used to develop estimates of the intensity of the point pattern. A window also defines the domain for the point pattern and can support corrections for so-called edge effects in the statistical analysis of point patterns. However, there are different ways to define a window for a point pattern.\n", "\n", "This notebook provides an overview of how to work with windows and covers the following:\n", "\n", "* [Creating a window](#Creating-a-Window)\n", "* [Window attributes](#Window-Attributes)\n", "* [Window methods](#Window-Methods)\n", "* [Multi-part windows](#Multi-part-Windows)\n", "* [Windows and point pattern intensity revisited](#Windows-and-point-pattern-intensity-revisited)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Creating a Window\n", "\n", "We will first continue on with an example from the [introductory notebook](pointpattern.ipynb). Recall this uses 200 randomly distributed points within the counties of Virginia. Coordinates are for UTM zone 17 N." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import libpysal as ps\n", "import numpy as np\n", "from pointpats import PointPattern" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Point Pattern\n", "200 points\n", "Bounding rectangle [(273959.664381352,4049220.903414295), (972595.9895779632,4359604.85977962)]\n", "Area of window: 216845506675.0557\n", "Intensity estimate for window: 9.223156295311261e-10\n", " x y\n", "0 865322.486181 4.150317e+06\n", "1 774479.213103 4.258993e+06\n", "2 308048.692232 4.054700e+06\n", "3 670711.529980 4.258864e+06\n", "4 666254.475614 4.256514e+06\n" ] } ], "source": [ "f = ps.examples.get_path('vautm17n_points.shp')\n", "fo = ps.io.open(f)\n", "pp_va = PointPattern(np.asarray([pnt for pnt in fo]))\n", "fo.close()\n", "pp_va.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From the summary method we see that the **Bounding Rectangle** is reported along with the **Area of the window** for the point pattern. Two things to note here. \n", "\n", "First, the only argument we passed in to the `PointPattern`s constructor was the array of coordinates for the 200 points. In this case PySAL finds the [minimum bounding box](https://en.wikipedia.org/wiki/Minimum_bounding_rectangle) for the point pattern and uses this as the window.\n", "\n", "The second thing to note is that the area of the window in this case is simply the area of the bounding rectangle. Because we are using projected coordinates (UTM) the unit of measure for the area is in square meters." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Window Attributes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The window is an attribute of the `PointPattern`. It is also an object with its own attributes:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "216845506675.0557" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pp_va.window.area" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[273959.664381352, 4049220.903414295, 972595.9895779632, 4359604.85977962]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pp_va.window.bbox" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The bounding box is given in left, bottom, right, top ordering." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(623277.8269796579, 4204412.881596957)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pp_va.window.centroid" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[[(273959.664381352, 4049220.903414295),\n", " (273959.664381352, 4359604.85977962),\n", " (972595.9895779632, 4359604.85977962),\n", " (972595.9895779632, 4049220.903414295),\n", " (273959.664381352, 4049220.903414295)]]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pp_va.window.parts" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `parts` attribute for the `window` is a list of polygons. In this case the window has only a single part and it is a rectangular polygon with vertices listed clockwise in closed cartographic form." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Window Methods\n", "\n", "A window has several basic geometric operations that are heavily used in some of the other modules in the the `Point` package. Most of this is done under the hood and the user typically doesn't see this. However, there can be times when direct access to these method can be handy. Let's explore.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The window supports basic point containment checks:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pp_va.window.contains_point((623277.82697965798, 4204412.8815969583))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This also applies to sequences of points:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "pnts = ((-623277.82697965798, 4204412.8815969583),\n", " (623277.82697965798, 4204412.8815969583),\n", " (1000.01, 200.9))" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[array([ 623277.82697966, 4204412.88159696])]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pnts_in = pp_va.window.filter_contained(pnts)\n", "pnts_in" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Multi-part Windows\n", "\n", "Thus far our window was a simple bounding box. There many instances when the relevant containing geometry for a point pattern is more complex. Examples include multi-part polygons and polygons with holes.\n", "\n", "Here we construct such a window, one with two parts and one hole." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "parts = [[(0.0, 0.0), (0.0, 10.0), (10.0, 10.0), (10.0, 0.0)],\n", " [(11.,11.), (11.,20.), (20.,20.), (20.,11.)]]\n", "holes = [[(3.0,3.0), (6.0, 3.0), (6.0, 6.0), (3.0, 6.0)]]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will plot this using matplotlib to get a better understanding of the challenges that this type of window presents for statistical analysis of the associated point pattern." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "p0 = np.asarray(parts[0])\n", "plt.plot(p0[:,0], p0[:,1])\n", "plt.xlim(-10,20)\n", "t = plt.ylim(-10,20) # silence the output of ylim" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Not, quite what we wanted, as the first part of our multi-part polygon is a ring, but it was not encoded in closed cartographic form:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 0., 0.],\n", " [ 0., 10.],\n", " [10., 10.],\n", " [10., 0.]])" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "p0" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can fix this with a helper function from the `window` module:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[(0.0, 0.0), (0.0, 10.0), (10.0, 10.0), (10.0, 0.0)]\n", "[(0.0, 0.0), (0.0, 10.0), (10.0, 10.0), (10.0, 0.0), (0.0, 0.0)]\n" ] } ], "source": [ "from pointpats.window import to_ccf\n", "print(parts[0])\n", "print(to_ccf(parts[0])) #get closed ring" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from pointpats.window import to_ccf\n", "p0 = np.asarray(to_ccf(parts[0]))\n", "plt.plot(p0[:,0], p0[:,1])\n", "plt.xlim(-10,20)\n", "t=plt.ylim(-10,20)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can print all the rings composing our window: two exterior rings, and one hole:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for part in parts:\n", " part = np.asarray(to_ccf(part))\n", " plt.plot(part[:,0], part[:,1], 'b')\n", "for hole in holes:\n", " hole = np.asarray(to_ccf(hole))\n", " plt.plot(hole[:,0], hole[:,1], 'r')\n", "plt.xlim(-10,30)\n", "t = plt.ylim(-10,30)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The red hole is associated with the first exterior ring.\n", "\n", "With this visual representation, consider the problem of testing whether or not this multi-part window contains one or more points in a sequence:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pnts = [(12,12), (4,4), (2,2), (25,1), (5,20)]\n", "for pnt in pnts:\n", " plt.plot(pnt[0], pnt[1], 'g.')\n", "\n", "for part in parts:\n", " part = np.asarray(to_ccf(part))\n", " plt.plot(part[:,0], part[:,1], 'b')\n", "for hole in holes:\n", " hole = np.asarray(to_ccf(hole))\n", " plt.plot(hole[:,0], hole[:,1], 'r')\n", "plt.xlim(-10,30)\n", "t = plt.ylim(-10,30)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Of the five points two are clearly outside of both of the exterior rings. The three remaining points are each contained in one of the bounding boxes for an exterior ring. However, one of these points is also contained in the hole ring, and thus is not contained in the exterior ring associated with that hole.\n", "\n", "We can create a Window object from the parts and holes to demonstrate how to evaluate these containment checks." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "from pointpats import Window\n", "window = Window(parts, holes)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[[(0.0, 0.0), (0.0, 10.0), (10.0, 10.0), (10.0, 0.0), (0.0, 0.0)],\n", " [(11.0, 11.0), (11.0, 20.0), (20.0, 20.0), (20.0, 11.0), (11.0, 11.0)]]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "window.parts" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[[(3.0, 3.0), (3.0, 6.0), (6.0, 6.0), (6.0, 3.0), (3.0, 3.0)]]" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "window.holes" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[0.0, 0.0, 20.0, 20.0]" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "window.bbox" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "172.0" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "window.area" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pnts = [(12,12), (4,4), (2,2), (25,1), (5,20)]\n", "for pnt in pnts:\n", " plt.plot(pnt[0], pnt[1], 'g.') #plot the five points in green\n", "\n", "for part in parts:\n", " part = np.asarray(to_ccf(part))\n", " plt.plot(part[:,0], part[:,1], 'b') #plot \"parts\" in blue\n", "for hole in holes:\n", " hole = np.asarray(to_ccf(hole))\n", " plt.plot(hole[:,0], hole[:,1], 'r') #plot \"hole\" in red \n", " \n", "from pointpats.window import poly_from_bbox\n", "poly = np.asarray(poly_from_bbox(window.bbox).vertices)\n", "plt.plot(poly[:,0], poly[:,1], 'm-.') #plot the minimum bounding box in magenta\n", "\n", "plt.xlim(-10,30)\n", "t = plt.ylim(-10,30)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we have extended the figure to include the bounding box for the multi-part window (in cyan). Now we can call the `filter_contained` method of the window on the point sequence:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[array([12, 12]), array([2, 2])]" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pin = window.filter_contained(pnts)\n", "pin" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This was a lot of code just to illustrate that the methods of a window can be used to identify topological relationships between points and the window's constituent parts. Let's turn to a less contrived example to see this in action.\n", "\n", "Here we will make use of PySAL's [shapely extension](https://pysal.readthedocs.org/en/latest/users/tutorials/shapely.html) to create a multi-part window from the county shapefile for Virgina." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "from libpysal.cg import shapely_ext\n", "import numpy as np\n", "from pointpats.window import poly_from_bbox, as_window, Window\n", "import libpysal as ps\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "va = ps.io.open(ps.examples.get_path(\"vautm17n.shp\")) #open \"vautm17n\" polygon shapefile\n", "polys = [shp for shp in va]\n", "vapnts = ps.io.open(ps.examples.get_path(\"vautm17n_points.shp\")) #open \"vautm17n_points\" point shapefile\n", "points = [shp for shp in vapnts]" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "136\n" ] } ], "source": [ "print(len(polys))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The county shapefile `vautm17n.shp` has 136 shapes of the `polygon` type. Some of these are composed of multiple-rings and holes to reflect the [interesting history](https://en.wikipedia.org/wiki/List_of_counties_in_Virginia) of political boundaries in that State.\n", "Fortunately, with our window class we can handle these. We will come back to this shortly.\n", "\n", "First we are going to build up a realistic window for our point pattern based on a *cascaded union* made possible via [Shapely](https://pypi.python.org/pypi/Shapely) through the [PySAL shapely extension](https://pysal.readthedocs.org/en/latest/users/tutorials/shapely.html)." ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "cu = shapely_ext.cascaded_union(polys)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This creates a PySAL Polygon:" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "libpysal.cg.shapes.Polygon" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(cu)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can construct a Window from this polygon instance using the helper function `as_window`:" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "w = as_window(cu)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[[]]" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "w.holes" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(w.parts)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The window has three parts consisting of the union of mainland counties and two \"island\" parts associated with Accomack and Northampton counties and has no holes.\n", "\n", "Since this a window, we can access its properties:" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[260694.99205079858, 4044845.4484747574, 1005496.0048517315, 4370839.043748417]" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "w.bbox" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(689097.7340935213, 4155195.0497352206)" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "w.centroid" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "w.contains_point(w.centroid)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So the centroid for our new window is contained by the window. Such a result is not guaranteed as the geometry of the window could be complex such that the centroid falls outside of the window.\n", "\n", "Let's continue on with a more interesting query. Since we know the window centroid is contained in the Window, we can find which individual county contains the centroid. \n", "\n", "Our strategy is a simple one to illustrate the useful nature of the Window. We will create a sequence of Windows, one for each county and use them to carry out a containment test." ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "#create a window for each of the individual counties in the state\n", "windows = [as_window(county) for county in polys]" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[(67, )]" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#check each county for containment of the window's centroid\n", "cent_poly = [ (i, county) for i,county in enumerate(windows) if county.contains_point(w.centroid)]\n", "cent_poly" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [], "source": [ "i, cent_poly = cent_poly[0]" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[674997.5183093206, 4119217.2472937624, 713300.2226730094, 4159075.43995212]" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cent_poly.bbox" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What we did here was create a window for each of the individual counties in the state. With these in hand we checked each one for containment of the window's centroid. The result is we see the window (count) with index 67 is the only one that contains the centroid point.\n", "\n", "The point of this exercise is not to use an inefficient brute force exhaustive search to find this county. There are more efficient spatial indices in PySAL that we could use for such a query. Rather, we wanted to explicitly check each window to ensure that only one contained the centroid.\n", "\n", "As we will see in elsewhere in this series of notebooks, this type of decomposition can support highly flexible types of spatial analysis." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Windows and point pattern intensity revisited\n", "\n", "Returning to the central use of Windows, we saw in the [introductory notebook](pointpattern.ipynb) that the area of the Window is used to form the estimate of intensity for the point pattern:" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Point Pattern\n", "200 points\n", "Bounding rectangle [(273959.664381352,4049220.903414295), (972595.9895779632,4359604.85977962)]\n", "Area of window: 216845506675.0557\n", "Intensity estimate for window: 9.223156295311261e-10\n", " x y\n", "0 865322.486181 4.150317e+06\n", "1 774479.213103 4.258993e+06\n", "2 308048.692232 4.054700e+06\n", "3 670711.529980 4.258864e+06\n", "4 666254.475614 4.256514e+06\n" ] } ], "source": [ "f = ps.examples.get_path('vautm17n_points.shp') #open \"vautm17n_points\" point shapefile\n", "fo = ps.io.open(f)\n", "pnts = np.asarray([pnt for pnt in fo])\n", "fo.close()\n", "pp_va = PointPattern(pnts)\n", "pp_va.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here the default is to form the minimum bounding rectangle and use that as the window for the point pattern and, in turn, to implment the intesity estimation." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can override the default by passing a window object in to the constructor for the point pattner. Here we use our window that was formed from the county cascading union above:" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Point Pattern\n", "200 points\n", "Bounding rectangle [(273959.664381352,4049220.903414295), (972595.9895779632,4359604.85977962)]\n", "Area of window: 103195696155.68987\n", "Intensity estimate for window: 1.9380653210407425e-09\n", " x y\n", "0 865322.486181 4.150317e+06\n", "1 774479.213103 4.258993e+06\n", "2 308048.692232 4.054700e+06\n", "3 670711.529980 4.258864e+06\n", "4 666254.475614 4.256514e+06\n" ] } ], "source": [ "pp_va_union = PointPattern(pnts, window=w)\n", "pp_va_union.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here, the window is redefined. Thus, window related attributes **Area of window** and **Intensity estimate for window** are changed. However, the **Bounding rectangle** remains unchanged since it is not relavant to the definition of window.\n", "\n", "Close examination of the summary report from reveals that while the bounding rectangles for the two point pattern instances are identical (as they should be), the area of the windows are substantially different:" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2.1013037825521717" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pp_va.window.area / pp_va_union.window.area" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "as are the intensity estimates:" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.47589501732368955" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pp_va.lambda_window / pp_va_union.lambda_window" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 1 } pointpats-2.5.5/pointpats/000077500000000000000000000000001514706172100155715ustar00rootroot00000000000000pointpats-2.5.5/pointpats/__init__.py000066400000000000000000000006601514706172100177040ustar00rootroot00000000000000import contextlib from importlib.metadata import PackageNotFoundError, version from .pointpattern import PointPattern from .window import as_window, poly_from_bbox, to_ccf, Window from .centrography import * from .process import * from .quadrat_statistics import * from .distance_statistics import * from .spacetime import * from .kde import * with contextlib.suppress(PackageNotFoundError): __version__ = version("pointpats")pointpats-2.5.5/pointpats/centrography.py000066400000000000000000000736271514706172100206670ustar00rootroot00000000000000""" Centrographic measures for point patterns - documentation """ __author__ = "Serge Rey sjsrey@gmail.com" __all__ = [ "hull", "mean_center", "weighted_mean_center", "manhattan_median", "std_distance", "euclidean_median", "ellipse", "minimum_rotated_rectangle", "minimum_bounding_rectangle", "minimum_bounding_circle", "dtot", "_circle", ] import copy import math import sys import warnings from collections.abc import Sequence from functools import singledispatch import numpy as np import shapely from geopandas.base import GeoPandasBase from libpysal.cg import is_clockwise from numpy.typing import NDArray from scipy.optimize import minimize from scipy.spatial import ConvexHull not_clockwise = lambda x: not is_clockwise(x) MAXD = sys.float_info.max MIND = sys.float_info.min @singledispatch def minimum_bounding_rectangle(points): """ Find minimum bounding rectangle of a point array. Parameters ---------- points : arraylike array representing a point pattern Returns ------- rectangle minimum bounding rectangle of a given point pattern Examples -------- >>> import numpy as np >>> import geopandas as gpd Create an array of point coordinates. >>> coords = np.array( ... [ ... [66.22, 32.54], ... [22.52, 22.39], ... [31.01, 81.21], ... [9.47, 31.02], ... [30.78, 60.10], ... [75.21, 58.93], ... [79.26, 7.68], ... [8.23, 39.93], ... [98.73, 77.17], ... [89.78, 42.53], ... [65.19, 92.08], ... [54.46, 8.48], ... ] ... ) Passing an array of coordinates returns a tuple capturing the bounds. >>> minimum_bounding_rectangle(coords) (np.float64(8.23), np.float64(7.68), np.float64(98.73), np.float64(92.08)) Passing a GeoPandas object returns a shapely geometry. >>> geoms = gpd.GeoSeries.from_xy(*coords.T) >>> minimum_bounding_rectangle(geoms) """ try: points = np.asarray(points) return minimum_bounding_rectangle(points) except AttributeError as e: raise NotImplementedError from e @minimum_bounding_rectangle.register def _(points: np.ndarray) -> tuple[float, float, float, float]: points = np.asarray(points) min_x = min_y = MAXD max_x = max_y = MIND x, y = zip(*points, strict=True) min_x = min(x) min_y = min(y) max_x = max(x) max_y = max(y) return min_x, min_y, max_x, max_y @minimum_bounding_rectangle.register def _(points: GeoPandasBase) -> shapely.Polygon: return shapely.envelope(shapely.geometrycollections(points.geometry.values)) @singledispatch def minimum_rotated_rectangle(points, return_angle=False): """ Compute the minimum rotated rectangle for a point array. This is the smallest enclosing rectangle (possibly rotated) for the input point set. It is computed using Shapely. Parameters ---------- points : arraylike array representing a point pattern return_angle : bool whether to return the angle (in degrees) of the angle between the horizontal axis of the rectanle and the first side (i.e. length). Returns ------- rectangle | tuple(rectangle, angle) Examples -------- >>> import numpy as np >>> import geopandas as gpd Create an array of point coordinates. >>> coords = np.array( ... [ ... [66.22, 32.54], ... [22.52, 22.39], ... [31.01, 81.21], ... [9.47, 31.02], ... [30.78, 60.10], ... [75.21, 58.93], ... [79.26, 7.68], ... [8.23, 39.93], ... [98.73, 77.17], ... [89.78, 42.53], ... [65.19, 92.08], ... [54.46, 8.48], ... ] ... ) Passing an array of coordinates returns an array capturing the corners. >>> minimum_rotated_rectangle(coords) array([[ 75.5990843 , -0.72615725], [ 4.08727852, 30.41752523], [ 36.40164577, 104.61744544], [107.91345156, 73.47376296]]) >>> minimum_rotated_rectangle(coords, return_angle=True) (array([[ 75.5990843 , -0.72615725], [ 4.08727852, 30.41752523], [ 36.40164577, 104.61744544], [107.91345156, 73.47376296]]), 66.466678613503) Passing a GeoPandas object returns a shapely geometry. >>> geoms = gpd.GeoSeries.from_xy(*coords.T) >>> minimum_rotated_rectangle(geoms) >>> minimum_rotated_rectangle(geoms, return_angle=True) (, 66.466678613503) """ # noqa: E501 try: points = np.asarray(points) return minimum_rotated_rectangle(points, return_angle=return_angle) except AttributeError as e: raise NotImplementedError from e def _get_mrr_angle(out_points): angle = ( math.degrees( math.atan2( out_points[1][1] - out_points[0][1], out_points[1][0] - out_points[0][0], ) ) % 90 ) return angle @minimum_rotated_rectangle.register def _( points: np.ndarray, return_angle: bool = False ) -> NDArray[np.float64] | tuple[NDArray[np.float64], float]: out_points = shapely.get_coordinates( shapely.minimum_rotated_rectangle(shapely.multipoints(points)) )[:-1] if return_angle: return (out_points[::-1], _get_mrr_angle(out_points)) return out_points[::-1] @minimum_rotated_rectangle.register def _( points: GeoPandasBase, return_angle: bool = False ) -> shapely.Polygon | tuple[shapely.Polygon, float]: rectangle = shapely.minimum_rotated_rectangle(shapely.multipoints(points.geometry)) if return_angle: out_points = shapely.get_coordinates(rectangle)[:-1] return (rectangle, _get_mrr_angle(out_points)) return rectangle @singledispatch def hull(points): """ Find convex hull of a point array. Parameters ---------- points : arraylike array representing a point pattern Returns ------- rectangle convex hull of a given point pattern Examples -------- >>> import numpy as np >>> import geopandas as gpd Create an array of point coordinates. >>> coords = np.array( ... [ ... [66.22, 32.54], ... [22.52, 22.39], ... [31.01, 81.21], ... [9.47, 31.02], ... [30.78, 60.10], ... [75.21, 58.93], ... [79.26, 7.68], ... [8.23, 39.93], ... [98.73, 77.17], ... [89.78, 42.53], ... [65.19, 92.08], ... [54.46, 8.48], ... ] ... ) Passing an array of coordinates returns an array capturing the vertices. >>> hull(coords) array([[31.01, 81.21], [ 8.23, 39.93], [ 9.47, 31.02], [22.52, 22.39], [54.46, 8.48], [79.26, 7.68], [89.78, 42.53], [98.73, 77.17], [65.19, 92.08]]) Passing a GeoPandas object returns a shapely geometry. >>> geoms = gpd.GeoSeries.from_xy(*coords.T) >>> hull(geoms) """ try: points = np.asarray(points) return hull(points) except AttributeError as e: raise NotImplementedError from e @hull.register def _(points: np.ndarray) -> NDArray[np.float64]: h = ConvexHull(points) return points[h.vertices] @hull.register def _(points: GeoPandasBase) -> shapely.Polygon: return shapely.convex_hull(shapely.multipoints(points.geometry)) @singledispatch def mean_center(points): """ Find mean center of a point array. Parameters ---------- points : arraylike array representing a point pattern Returns ------- center center of a given point pattern Examples -------- >>> import numpy as np >>> import geopandas as gpd Create an array of point coordinates. >>> coords = np.array( ... [ ... [66.22, 32.54], ... [22.52, 22.39], ... [31.01, 81.21], ... [9.47, 31.02], ... [30.78, 60.10], ... [75.21, 58.93], ... [79.26, 7.68], ... [8.23, 39.93], ... [98.73, 77.17], ... [89.78, 42.53], ... [65.19, 92.08], ... [54.46, 8.48], ... ] ... ) Passing an array of coordinates returns an array capturing the center. >>> mean_center(coords) array([52.57166667, 46.17166667]) Passing a GeoPandas object returns a shapely geometry. >>> geoms = gpd.GeoSeries.from_xy(*coords.T) >>> mean_center(geoms) """ try: points = np.asarray(points) return mean_center(points) except AttributeError as e: raise NotImplementedError from e @mean_center.register def _(points: np.ndarray) -> NDArray[np.float64]: return points.mean(axis=0) @mean_center.register def _(points: GeoPandasBase) -> shapely.Point: coords = shapely.get_coordinates(points.geometry) return shapely.Point(mean_center(coords)) @singledispatch def weighted_mean_center(points, weights): """ Find weighted mean center of a marked point pattern. Parameters ---------- points : arraylike array representing a point pattern weights : arraylike a series of attribute values of length n. Returns ------- center center of a given point pattern Examples -------- >>> import numpy as np >>> import geopandas as gpd Create an array of point coordinates. >>> coords = np.array( ... [ ... [66.22, 32.54], ... [22.52, 22.39], ... [31.01, 81.21], ... [9.47, 31.02], ... [30.78, 60.10], ... [75.21, 58.93], ... [79.26, 7.68], ... [8.23, 39.93], ... [98.73, 77.17], ... [89.78, 42.53], ... [65.19, 92.08], ... [54.46, 8.48], ... ] ... ) >>> weight = np.arange(1, 13, 1) Passing an array of coordinates returns an array capturing the center. >>> weighted_mean_center(coords, weight) array([59.29448718, 47.52282051]) Passing a GeoPandas object returns a shapely geometry. >>> geoms = gpd.GeoSeries.from_xy(*coords.T) >>> weighted_mean_center(geoms, weight) """ try: points = np.asarray(points) return weighted_mean_center(points, weights) except AttributeError as e: raise NotImplementedError from e @weighted_mean_center.register def _(points: np.ndarray, weights: Sequence) -> NDArray[np.float64]: points, weights = np.asarray(points), np.asarray(weights) w = weights * 1.0 / weights.sum() w.shape = (1, len(points)) return np.dot(w, points)[0] @weighted_mean_center.register def _(points: GeoPandasBase, weights) -> shapely.Point: coords = shapely.get_coordinates(points.geometry) return shapely.Point(weighted_mean_center(coords, weights)) @singledispatch def manhattan_median(points): """ Find manhattan median of a point array. Parameters ---------- points : arraylike array representing a point pattern Returns ------- median manhattan median of a given point pattern Examples -------- >>> import numpy as np >>> import geopandas as gpd Create an array of point coordinates. >>> coords = np.array( ... [ ... [66.22, 32.54], ... [22.52, 22.39], ... [31.01, 81.21], ... [9.47, 31.02], ... [30.78, 60.10], ... [75.21, 58.93], ... [79.26, 7.68], ... [8.23, 39.93], ... [98.73, 77.17], ... [89.78, 42.53], ... [65.19, 92.08], ... ] ... ) Passing an array of coordinates returns an array capturing the median. >>> manhattan_median(coords) array([65.19, 42.53]) Passing a GeoPandas object returns a shapely geometry. >>> geoms = gpd.GeoSeries.from_xy(*coords.T) >>> manhattan_median(geoms) """ try: points = np.asarray(points) return manhattan_median(points) except AttributeError as e: raise NotImplementedError from e @manhattan_median.register def _(points: np.ndarray) -> NDArray[np.float64]: if not len(points) % 2: warnings.warn( "Manhattan Median is not unique for even point patterns.", stacklevel=3 ) return np.median(points, axis=0) @manhattan_median.register def _(points: GeoPandasBase) -> shapely.Point: coords = shapely.get_coordinates(points.geometry) return shapely.Point(manhattan_median(coords)) @singledispatch def std_distance(points) -> np.float64: """ Calculate standard distance of a point array. Parameters ---------- points : arraylike array representing a point pattern Returns ------- distance standard distance of a given point pattern Examples -------- >>> import numpy as np >>> import geopandas as gpd Create an array of point coordinates. >>> coords = np.array( ... [ ... [66.22, 32.54], ... [22.52, 22.39], ... [31.01, 81.21], ... [9.47, 31.02], ... [30.78, 60.10], ... [75.21, 58.93], ... [79.26, 7.68], ... [8.23, 39.93], ... [98.73, 77.17], ... [89.78, 42.53], ... [65.19, 92.08], ... ] ... ) Passing an array of coordinates returns a tuple capturing the bounds. >>> std_distance(coords) np.float64(40.21575987282547) The same applies to a GeoPandas object. >>> geoms = gpd.GeoSeries.from_xy(*coords.T) >>> std_distance(geoms) np.float64(40.21575987282547) """ try: points = np.asarray(points) return std_distance(points) except AttributeError as e: raise NotImplementedError from e @std_distance.register def _(points: np.ndarray) -> np.float64: n, _ = points.shape m = points.mean(axis=0) return np.sqrt(((points * points).sum(axis=0) / n - m * m).sum()) @std_distance.register def _(points: GeoPandasBase) -> np.float64: coords = shapely.get_coordinates(points.geometry) return std_distance(coords) @singledispatch def ellipse(points, weights=None, method="crimestat", crimestatCorr=True, degfreedCorr=True): """ Computes a weighted standard deviational ellipse for a set of point geometries. Parameters ---------- points : array-like Array representing a point pattern. weights : array-like, optional Array of weights for each point. method : str Correction method to apply. Must be either 'crimestat' or 'yuill'. crimestatCorr : bool Whether to apply the CrimeStat correction (used only if `method == 'yuill'`). degfreedCorr : bool Whether to apply degrees-of-freedom correction (used only if `method == 'yuill'`). Apply degrees-of-freedom correction if method == 'yuill'. Returns ------- tuple(major_axis_length, minor_axis_length, rotation_angle) | ellipse Notes ----- Implements approach from: https://www.icpsr.umich.edu/CrimeStat/files/CrimeStatChapter.4.pdf Examples -------- >>> import numpy as np >>> import geopandas as gpd Create an array of point coordinates. >>> coords = np.array( ... [ ... [66.22, 32.54], ... [22.52, 22.39], ... [31.01, 81.21], ... [9.47, 31.02], ... [30.78, 60.10], ... [75.21, 58.93], ... [79.26, 7.68], ... [8.23, 39.93], ... [98.73, 77.17], ... [89.78, 42.53], ... [65.19, 92.08], ... ] ... ) Passing an array of coordinates returns a tuple capturing the semi-major axis, semi-minor axis and clockwise rotation angle of the ellipse. >>> ellipse(coords) (np.float64(50.13029459102783), np.float64(37.95222670267865), np.float64(0.3465582095042642)) Passing a GeoPandas object returns a shapely geometry. >>> geoms = gpd.GeoSeries.from_xy(*coords.T) >>> ellipse(geoms) """ try: points = np.asarray(points) return ellipse(points, weights, method, crimestatCorr, degfreedCorr) except AttributeError as e: raise NotImplementedError @ellipse.register def _( points: np.ndarray, weights=None, method='crimestat', crimestatCorr=True, degfreedCorr=True ) -> tuple[float, float, float]: method = method.lower() if method not in ("crimestat", "yuill"): raise ValueError("`method` must be either 'crimestat' or 'yuill'") x = points[:, 0] y = points[:, 1] if weights is None: weights = np.ones(len(points)) else: weights = np.asarray(weights) if len(weights) != len(points): raise ValueError("weights must have same length as points") w = weights sumw = w.sum() meanx = np.average(x, weights=w) meany = np.average(y, weights=w) xm = x - meanx ym = y - meany xyw = np.sum(xm * ym * w) x2w = np.sum(xm**2 * w) y2w = np.sum(ym**2 * w) den = 2 * xyw left = x2w - y2w right = np.sqrt(left**2 + 4 * xyw**2) if den == 0: theta1 = 0 theta2 = np.pi / 2 else: theta1 = np.arctan(-(left + right) / den) theta2 = np.arctan(-(left - right) / den) term1 = np.sum(w * (ym * np.cos(theta1) - xm * np.sin(theta1))**2) term2 = np.sum(w * (ym * np.cos(theta2) - xm * np.sin(theta2))**2) sx = np.sqrt(term1 / sumw) sy = np.sqrt(term2 / sumw) n = len(points) if method == "crimestat": correction = (np.sqrt(2) * np.sqrt(n)) / np.sqrt(n - 2) sx *= correction sy *= correction elif method == "yuill": if crimestatCorr: sx *= np.sqrt(2) sy *= np.sqrt(2) if degfreedCorr: sx *= np.sqrt(n) / np.sqrt(n - 2) sy *= np.sqrt(n) / np.sqrt(n - 2) if sy > sx: major_axis, minor_axis = sy, sx major_angle = theta1 else: major_axis, minor_axis = sx, sy major_angle = theta2 return major_axis, minor_axis, major_angle @ellipse.register def _(points: GeoPandasBase, weights=None, method='crimestat', crimestatCorr=True, degfreedCorr=True) -> shapely.Polygon: coords = shapely.get_coordinates(points.geometry) major, minor, rotation = ellipse(coords, weights=weights, method=method, crimestatCorr=crimestatCorr, degfreedCorr=degfreedCorr) if weights is None: centre = mean_center(points).buffer(1) else: centre = weighted_mean_center(points, weights=weights).buffer(1) scaled = shapely.affinity.scale(centre, major, minor) rotated = shapely.affinity.rotate(scaled, rotation, use_radians=True) return rotated @singledispatch def dtot(coord, points) -> float: """ Sum of Euclidean distances between event points and a selected point. Parameters ---------- coord starting point points : arraylike array representing a point pattern Returns ------- distance sum of Euclidean distances. Examples -------- >>> import numpy as np >>> import geopandas as gpd >>> import shapely Create an array of point coordinates. >>> coords = np.array( ... [ ... [66.22, 32.54], ... [22.52, 22.39], ... [31.01, 81.21], ... [9.47, 31.02], ... [30.78, 60.10], ... [75.21, 58.93], ... [79.26, 7.68], ... [8.23, 39.93], ... [98.73, 77.17], ... [89.78, 42.53], ... [65.19, 92.08], ... ] ... ) Passing an array of coordinates returns a tuple capturing the bounds. >>> point = [30.78, 60.10] >>> dtot(point, coords) np.float64(465.3617957739617) The same applies to a GeoPandas object. >>> point = shapely.Point(point) >>> geoms = gpd.GeoSeries.from_xy(*coords.T) >>> dtot(point, geoms) np.float64(465.3617957739617) """ try: coord = np.asarray(coord) points = np.asarray(points) return dtot(coord, points) except AttributeError as e: raise NotImplementedError from e @dtot.register def _(coord: np.ndarray, points: np.ndarray) -> float: xd = points[:, 0] - coord[0] yd = points[:, 1] - coord[1] d = np.sqrt(xd * xd + yd * yd).sum() return d @dtot.register def _(coord: shapely.Point, points: GeoPandasBase) -> float: coord = shapely.get_coordinates(coord).flatten() points = shapely.get_coordinates(points.geometry) return dtot(coord, points) @singledispatch def euclidean_median(points): """ Calculate the Euclidean median for a point pattern. Parameters ---------- points : arraylike array representing a point pattern Returns ------- median Euclidean median of a given point pattern Examples -------- >>> import numpy as np >>> import geopandas as gpd Create an array of point coordinates. >>> coords = np.array( ... [ ... [66.22, 32.54], ... [22.52, 22.39], ... [31.01, 81.21], ... [9.47, 31.02], ... [30.78, 60.10], ... [75.21, 58.93], ... [79.26, 7.68], ... [8.23, 39.93], ... [98.73, 77.17], ... [89.78, 42.53], ... [65.19, 92.08], ... ] ... ) Passing an array of coordinates returns an array capturing the median. >>> euclidean_median(coords) array([53.51770575, 49.6572671 ]) Passing a GeoPandas object returns a shapely geometry. >>> geoms = gpd.GeoSeries.from_xy(*coords.T) >>> euclidean_median(geoms) """ try: points = np.asarray(points) return euclidean_median(points) except AttributeError as e: raise NotImplementedError from e @euclidean_median.register def _(points: np.ndarray) -> NDArray[np.float64]: start = mean_center(points) res = minimize(dtot, start, args=(points,)) return res["x"] @euclidean_median.register def _(points: GeoPandasBase) -> shapely.Point: coords = shapely.get_coordinates(points.geometry) return shapely.Point(euclidean_median(coords)) @singledispatch def minimum_bounding_circle(points): """ Implements Skyum (1990)'s algorithm for the minimum bounding circle in R^2. Store points clockwise. Find p in S that maximizes angle(prec(p), p, succ(p) THEN radius(prec( p), p, succ(p)). This is also called the lexicographic maximum, and is the last entry of a list of (radius, angle) in lexicographical order. * If angle(prec(p), p, succ(p)) <= 90 degrees, then finish. * If not, remove p from set. Parameters ---------- points : arraylike array representing a point pattern Returns ------- circle minimum bounding circle Examples -------- >>> import numpy as np >>> import geopandas as gpd Create an array of point coordinates. >>> coords = np.array( ... [ ... [66.22, 32.54], ... [22.52, 22.39], ... [31.01, 81.21], ... [9.47, 31.02], ... [30.78, 60.10], ... [75.21, 58.93], ... [79.26, 7.68], ... [8.23, 39.93], ... [98.73, 77.17], ... [89.78, 42.53], ... [65.19, 92.08], ... ] ... ) Passing an array of coordinates returns an tuple of (x, y), radius. >>> minimum_bounding_circle(coords) ((55.244520477497474, 51.88135107645883), np.float64(50.304102155590726)) Passing a GeoPandas object returns a shapely geometry. >>> geoms = gpd.GeoSeries.from_xy(*coords.T) >>> minimum_bounding_circle(geoms) """ try: points = np.asarray(points) return minimum_bounding_circle(points) except AttributeError as e: raise NotImplementedError from e @minimum_bounding_circle.register def _(points: np.ndarray) -> tuple[tuple[float, float], float]: try: from numba import njit HAS_NUMBA = True except ImportError: HAS_NUMBA = False points = hull(points) if not_clockwise(points): points = points[::-1] if not_clockwise(points): raise Exception("Points are neither clockwise nor counterclockwise") POINTS = copy.deepcopy(points) if HAS_NUMBA: # noqa: SIM108 circ = _skyum_numba(POINTS)[0] else: circ = _skyum_lists(POINTS)[0] return (circ[1], circ[2]), circ[0] @minimum_bounding_circle.register def _(points: GeoPandasBase) -> shapely.Polygon: coords = shapely.get_coordinates(points.geometry) (x, y), r = minimum_bounding_circle(coords) return shapely.Point(x, y).buffer(r) def _skyum_lists(points): points = points.tolist() removed = [] i = 0 while True: angles = [ _angle( _prec(p, points), p, _succ(p, points), ) for p in points ] circles = [ _circle( _prec(p, points), p, _succ(p, points), ) for p in points ] radii = [c[0] for c in circles] lexord = np.lexsort((radii, angles)) # confusing as hell defaults... lexmax = lexord[-1] candidate = ( _prec(points[lexmax], points), points[lexmax], _succ(points[lexmax], points), ) if angles[lexmax] <= (np.pi / 2.0): # print("Constrained by points: {}".format(candidate)) return _circle(*candidate), points, removed, candidate else: try: removed.append((points.pop(lexmax), i)) except IndexError: raise Exception("Construction of Minimum Bounding Circle failed!") i += 1 try: from numba import njit, boolean HAS_NUMBA = True @njit(fastmath=True) def _skyum_numba(points): i = 0 complete = False while not complete: complete, points, candidate, circle = _skyum_iteration(points) if complete: return circle, points, None, candidate @njit(fastmath=True) def _skyum_iteration(points): points = points.reshape(-1, 2) n = points.shape[0] angles = np.empty((n,)) circles = np.empty((n, 3)) for i in range(n): p = points[(i - 1) % n] q = points[i % n] r = points[(i + 1) % n] angles[i] = _angle(p, q, r) circles[i] = _circle(p, q, r) radii = circles[:, 0] # workaround for no lexsort in numba angle_argmax = angles.argmax() angle_max = angles[angle_argmax] # the maximum radius for the largest angle lexmax = (radii * (angles == angle_max)).argmax() if angles[lexmax] <= (np.pi / 2.0): return True, points, lexmax, circles[lexmax] else: mask = np.ones((n,), dtype=boolean) mask[lexmax] = False new_points = points[mask, :] return False, new_points, lexmax, circles[lexmax] except ModuleNotFoundError: def njit(func, **kwargs): return func @njit def _angle(p, q, r): p = np.asarray(p) q = np.asarray(q) r = np.asarray(r) pq = p - q rq = r - q magnitudes = np.linalg.norm(pq) * np.linalg.norm(rq) return np.abs(np.arccos(np.dot(pq, rq) / magnitudes)) def _prec(p, l): """ retrieve the predecessor of p in list l """ pos = l.index(p) if pos - 1 < 0: return l[-1] else: return l[pos - 1] def _succ(p, l): """ retrieve the successor of p in list l """ pos = l.index(p) if pos + 1 >= len(l): return l[0] else: return l[pos + 1] @njit def _euclidean_distance(px, py, qx, qy): return np.sqrt((px - qx) ** 2 + (py - qy) ** 2) @njit def _circle(p, q, r, dmetric=_euclidean_distance): """ Returns (radius, (center_x, center_y)) of the circumscribed circle by the triangle pqr. note, this does not assume that p!=q!=r """ px, py = p qx, qy = q rx, ry = r angle = np.abs(_angle(p, q, r)) if np.abs(angle - np.pi) < 1e-5: # angle is pi radius = _euclidean_distance(px, py, rx, ry) / 2.0 center_x = (px + rx) / 2.0 center_y = (py + ry) / 2.0 elif np.abs(angle) < 1e-5: # angle is zero radius = _euclidean_distance(px, py, qx, qy) / 2.0 center_x = (px + qx) / 2.0 center_y = (py + qy) / 2.0 else: D = 2 * (px * (qy - ry) + qx * (ry - py) + rx * (py - qy)) center_x = ( (px**2 + py**2) * (qy - ry) + (qx**2 + qy**2) * (ry - py) + (rx**2 + ry**2) * (py - qy) ) / float(D) center_y = ( (px**2 + py**2) * (rx - qx) + (qx**2 + qy**2) * (px - rx) + (rx**2 + ry**2) * (qx - px) ) / float(D) radius = _euclidean_distance(center_x, center_y, px, py) return radius, center_x, center_y pointpats-2.5.5/pointpats/distance_statistics.py000066400000000000000000001201651514706172100222140ustar00rootroot00000000000000import warnings from collections import namedtuple import geopandas import numpy import shapely from scipy import interpolate, spatial from joblib import Parallel, delayed from .geometry import ( TREE_TYPES, ) from .geometry import ( area as _area, ) from .geometry import ( build_best_tree as _build_best_tree, ) from .geometry import ( k_neighbors as _k_neighbors, ) from .geometry import ( prepare_hull as _prepare_hull, ) from .random import poisson __all__ = [ "f", "g", "k", "j", "l", "f_test", "g_test", "k_test", "j_test", "l_test", ] def _prepare(coordinates, support, distances, metric, hull, edge_correction): """ prepare the arguments to convert into a standard format 1. cast the coordinates to a numpy array 2. precomputed metrics must have distances provided 3. metrics must be callable or string 4. warn if distances are specified and metric is not default 5. make distances a numpy.ndarray 6. construct the support, accepting: - num_steps -> a linspace with len(support) == num_steps from zero to a quarter of the bounding box's smallest side - (stop, ) -> a linspace with len(support) == 20 from zero to stop - (start, stop) -> a linspace with len(support) == 20 from start to stop - (start, stop, num_steps) -> a linspace with len(support) == num_steps from start to stop - numpy.ndarray -> passed through """ # Throw early if edge correction is requested if edge_correction is not None: raise NotImplementedError("Edge correction is not currently implemented.") if isinstance(coordinates, geopandas.GeoDataFrame | geopandas.GeoSeries): coordinates = shapely.get_coordinates(coordinates.geometry) # cast to coordinate array if isinstance(coordinates, TREE_TYPES): tree = coordinates coordinates = tree.data else: coordinates = numpy.asarray(coordinates) hull = _prepare_hull(coordinates, hull) # evaluate distances if (distances is None) and metric == "precomputed": raise ValueError( "If metric =`precomputed` then distances must" " be provided as a (n,n) numpy array." ) if not (isinstance(metric, str) or callable(metric)): raise TypeError( f"`metric` argument must be callable or a string. Recieved: {metric}" ) if distances is not None and metric != "euclidean": warnings.warn( "Distances were provided. The specified metric will be ignored." " To use precomputed distances with a custom distance metric," " do not specify a `metric` argument.", stacklevel=2, ) metric = "euclidean" if support is None: support = 20 if isinstance(support, int): # if just n_steps, use the max nnd # this is O(n log n) for kdtrees & balltrees tmp_tree = _build_best_tree(coordinates, metric=metric) max_dist = _k_neighbors(tmp_tree, coordinates, 1)[0].max() support = numpy.linspace(0, max_dist, num=support) # otherwise, we need to build it using (start, stop, step) semantics elif isinstance(support, tuple): if len(support) == 1: # assuming this is with zero implicit start support = numpy.linspace(0, support[0], num=20) # default support n bins elif len(support) == 2: support = numpy.linspace(*support, num=20) # default support n bins elif len(support) == 3: support = numpy.linspace(support[0], support[1], num=support[2]) else: # try to use it as is try: support = numpy.asarray(support) except: raise TypeError( "`support` must be a tuple (either (start, stop, step), (start, stop) or (stop,))," " an int describing the number of breaks to use to evalute the function," " or an iterable containing the breaks to use to evaluate the function." " Recieved object of type {}: {}".format(type(support), support) ) return coordinates, support, distances, metric, hull, edge_correction # ------------------------------------------------------------# # Statistical Functions # # ------------------------------------------------------------# def f( coordinates, support=None, distances=None, metric="euclidean", hull=None, edge_correction=None, ): """ Ripley's F function The so-called "empty space" function, this is the cumulative density function of the distances from a random set of points to the known points in the pattern. Parameters ---------- coordinates : geopandas object | numpy.ndarray of shape (n,2) input coordinates to function support : tuple of length 1, 2, or 3, int, or numpy.ndarray tuple, encoding (stop,), (start, stop), or (start, stop, num) int, encoding number of equally-spaced intervals numpy.ndarray, used directly within numpy.histogram distances: numpy.ndarray, (n, p) or (p,) distances from every point in a random point set of size p to some point in `coordinates` metric: str or callable distance metric to use when building search tree hull: bounding box, scipy.spatial.ConvexHull, shapely.geometry.Polygon the hull used to construct a random sample pattern, if distances is None edge_correction: bool or str whether or not to conduct edge correction. Not yet implemented. Returns ------- a tuple containing the support values used to evalute the function and the values of the function at each distance value in the support. """ coordinates, support, distances, metric, hull, _ = _prepare( coordinates, support, distances, metric, hull, edge_correction ) if distances is not None: n = coordinates.shape[0] if distances.ndim == 2: k, p = distances.shape if k == p == n: warnings.warn( f"A full distance matrix is not required for this function, and" f" the intput matrix is a square {n},{n} matrix. Only the" f" distances from p random points to their nearest neighbor within" f" the pattern is required, as an {n},p matrix. Assuming the" f" provided distance matrix has rows pertaining to input" f" pattern and columns pertaining to the output points.", stacklevel=2, ) distances = distances.min(axis=0) elif k == n: distances = distances.min(axis=0) else: raise ValueError( f"Distance matrix should have the same rows as the input" f" coordinates with p columns, where n may be equal to p." f" Recieved an {k},{p} distance matrix for {n} coordinates" ) elif distances.ndim == 1: p = len(distances) else: # Do 1000 empties. Users can control this by computing their own # empty space distribution. n_empty_points = 1000 randoms = poisson(hull=hull, size=(n_empty_points, 1)) try: tree except NameError: tree = _build_best_tree(coordinates, metric) finally: distances, _ = tree.query(randoms, k=1) distances = distances.squeeze() counts, bins = numpy.histogram(distances, bins=support) fracs = numpy.cumsum(counts) / counts.sum() return bins, numpy.asarray([0, *fracs]) def g( coordinates, support=None, distances=None, metric="euclidean", edge_correction=None, ): """ Ripley's G function The G function is computed from the cumulative density function of the nearest neighbor distances between points in the pattern. Parameters ---------- coordinates : geopandas object | numpy.ndarray of shape (n,2) input coordinates to function support : tuple of length 1, 2, or 3, int, or numpy.ndarray tuple, encoding (stop,), (start, stop), or (start, stop, num) int, encoding number of equally-spaced intervals numpy.ndarray, used directly within numpy.histogram distances: numpy.ndarray, (n, n) or (n,) distances from every point in the point to another point in `coordinates` metric: str or callable distance metric to use when building search tree edge_correction: bool or str whether or not to conduct edge correction. Not yet implemented. Returns ------- a tuple containing the support values used to evalute the function and the values of the function at each distance value in the support. """ coordinates, support, distances, metric, *_ = _prepare( coordinates, support, distances, metric, None, edge_correction ) if distances is not None: if distances.ndim == 2: if distances.shape[0] == distances.shape[1] == coordinates.shape[0]: warnings.warn( "The full distance matrix is not required for this function," " only the distance to the nearest neighbor within the pattern." " Computing this and discarding the rest.", stacklevel=2, ) distances = distances.min(axis=1) else: k, p = distances.shape n = coordinates.shape[0] raise ValueError( " Input distance matrix has an invalid shape: {k},{p}." " Distances supplied can either be 2 dimensional" " square matrices with the same number of rows" " as `coordinates` ({n}) or 1 dimensional and contain" " the shortest distance from each point in " " `coordinates` to some other point in coordinates." ) elif distances.ndim == 1: if distances.shape[0] != coordinates.shape[0]: raise ValueError( f"Distances are not aligned with coordinates! Distance" f" matrix must be (n_coordinates, n_coordinates), but recieved" f" {distances.shape} instead of ({coordinates.shape[0]},)" ) else: raise ValueError( "Distances supplied can either be 2 dimensional" " square matrices with the same number of rows" " as `coordinates` or 1 dimensional and contain" " the shortest distance from each point in " " `coordinates` to some other point in coordinates." " Input matrix was {distances.ndim} dimensioanl" ) else: try: tree except NameError: tree = _build_best_tree(coordinates, metric) finally: distances, indices = _k_neighbors(tree, coordinates, k=1) counts, bins = numpy.histogram(distances.squeeze(), bins=support) fracs = numpy.cumsum(counts) / counts.sum() return bins, numpy.asarray([0, *fracs]) def j( coordinates, support=None, distances=None, metric="euclidean", hull=None, edge_correction=None, truncate=True, ): """ Ripely's J function The so-called "spatial hazard" function, this is a function relating the F and G functions. Parameters ---------- coordinates : geopandas object | numpy.ndarray, (n,2) input coordinates to function support : tuple of length 1, 2, or 3, int, or numpy.ndarray tuple, encoding (stop,), (start, stop), or (start, stop, num) int, encoding number of equally-spaced intervals numpy.ndarray, used directly within numpy.histogram distances: tuple of numpy.ndarray precomputed distances to use to evaluate the j function. The first must be of shape (n,n) or (n,) and is used in the g function. the second must be of shape (n,p) or (p,) (with p possibly equal to n) used in the f function. metric: str or callable distance metric to use when building search tree hull: bounding box, scipy.spatial.ConvexHull, shapely.geometry.Polygon the hull used to construct a random sample pattern for the f function. edge_correction: bool or str whether or not to conduct edge correction. Not yet implemented. truncate: bool (default: True) whether or not to truncate the results when the F function reaches one. If the F function is one but the G function is less than one, this function will return numpy.nan values. Returns ------- a tuple containing the support values used to evalute the function and the values of the function at each distance value in the support. """ if distances is not None: g_distances, f_distances = distances else: g_distances = f_distances = None fsupport, fstats = f( coordinates, support=support, distances=f_distances, metric=metric, hull=hull, edge_correction=edge_correction, ) gsupport, gstats = g( coordinates, support=support, distances=g_distances, metric=metric, edge_correction=edge_correction, ) if isinstance(support, numpy.ndarray): if not numpy.allclose(gsupport, support): gfunction = interpolate.interp1d(gsupport, gstats, fill_value=1) gstats = gfunction(support) gsupport = support if not (numpy.allclose(gsupport, fsupport)): ffunction = interpolate.interp1d(fsupport, fstats, fill_value=1) fstats = ffunction(gsupport) fsupport = gsupport with numpy.errstate(invalid="ignore", divide="ignore"): hazard_ratio = (1 - gstats) / (1 - fstats) both_zero = (gstats == 1) & (fstats == 1) hazard_ratio[both_zero] = numpy.nan if truncate: result = _truncate(gsupport, hazard_ratio) if len(result[1]) != len(hazard_ratio): warnings.warn( f"requested {support} bins to evaluate the J function, but" f" it reaches infinity at d={result[0][-1]:.4f}, meaning only" f" {len(result[0])} bins will be used to characterize the J function.", stacklevel=2, ) return result else: return gsupport, hazard_ratio def k( coordinates, support=None, distances=None, metric="euclidean", edge_correction=None, ): """ Ripley's K function This function counts the number of pairs of points that are closer than a given distance. As d increases, K approaches the number of point pairs. coordinates : geopandas object | numpy.ndarray, (n,2) input coordinates to function support : tuple of length 1, 2, or 3, int, or numpy.ndarray tuple, encoding (stop,), (start, stop), or (start, stop, num) int, encoding number of equally-spaced intervals numpy.ndarray, used directly within numpy.histogram distances: numpy.ndarray, (n, p) or (p,) distances from every point in a random point set of size p to some point in `coordinates` metric: str or callable distance metric to use when building search tree hull: bounding box, scipy.spatial.ConvexHull, shapely.geometry.Polygon the hull used to construct a random sample pattern, if distances is None edge_correction: bool or str whether or not to conduct edge correction. Not yet implemented. Returns ------- a tuple containing the support values used to evalute the function and the values of the function at each distance value in the support. """ coordinates, support, distances, metric, hull, edge_correction = _prepare( coordinates, support, distances, metric, None, edge_correction ) n = coordinates.shape[0] upper_tri_n = n * (n - 1) * 0.5 if distances is not None: if distances.ndim == 1: if distances.shape[0] != upper_tri_n: raise ValueError( f"Shape of inputted distances is not square, nor is the upper triangular" f" matrix matching the number of input points. The shape of the input matrix" f" is {distances.shape}, but required shape is ({upper_tri_n},) or ({n},{n})" ) upper_tri_distances = distances elif distances.shape[0] == distances.shape[1] == n: upper_tri_distances = distances[numpy.triu_indices_from(distances, k=1)] else: raise ValueError( f"Shape of inputted distances is not square, nor is the upper triangular" f" matrix matching the number of input points. The shape of the input matrix" f" is {distances.shape}, but required shape is ({upper_tri_n},) or ({n},{n})" ) else: upper_tri_distances = spatial.distance.pdist(coordinates, metric=metric) n_pairs_less_than_d = (upper_tri_distances < support.reshape(-1, 1)).sum(axis=1) intensity = n / _area(hull) k_estimate = ((n_pairs_less_than_d * 2) / n) / intensity return support, k_estimate def l( coordinates, support=None, permutations=9999, distances=None, metric="euclidean", edge_correction=None, linearized=False, ): """ Ripley's L function This is a scaled and shifted version of the K function that accounts for the K function's increasing expected value as distances increase. This means that the L function, for a completely random pattern, should be close to zero at all distance values in the support. Parameters ---------- coordinates : geopandas object | numpy.ndarray, (n,2) input coordinates to function support : tuple of length 1, 2, or 3, int, or numpy.ndarray tuple, encoding (stop,), (start, stop), or (start, stop, num) int, encoding number of equally-spaced intervals numpy.ndarray, used directly within numpy.histogram distances: numpy.ndarray, (n, p) or (p,) distances from every point in a random point set of size p to some point in `coordinates` metric: str or callable distance metric to use when building search tree hull: bounding box, scipy.spatial.ConvexHull, shapely.geometry.Polygon the hull used to construct a random sample pattern, if distances is None edge_correction: bool or str whether or not to conduct edge correction. Not yet implemented. linearized : bool whether or not to subtract l from its expected value (support) at each distance bin. This centers the l function on zero for all distances. Proposed by Besag (1977) Returns ------- a tuple containing the support values used to evalute the function and the values of the function at each distance value in the support. """ support, k_estimate = k( coordinates, support=support, distances=distances, metric=metric, edge_correction=edge_correction, ) l = numpy.sqrt(k_estimate / numpy.pi) if linearized: return support, l - support return support, l # ------------------------------------------------------------# # Statistical Tests based on Ripley Functions # # ------------------------------------------------------------# FtestResult = namedtuple( "FtestResult", ("support", "statistic", "pvalue", "simulations") ) GtestResult = namedtuple( "GtestResult", ("support", "statistic", "pvalue", "simulations") ) JtestResult = namedtuple( "JtestResult", ("support", "statistic", "pvalue", "simulations") ) KtestResult = namedtuple( "KtestResult", ("support", "statistic", "pvalue", "simulations") ) LtestResult = namedtuple( "LtestResult", ("support", "statistic", "pvalue", "simulations") ) _ripley_dispatch = { "F": (f, FtestResult), "G": (g, GtestResult), "J": (j, JtestResult), "K": (k, KtestResult), "L": (l, LtestResult), } def _ripley_test( calltype, coordinates, support=None, distances=None, metric="euclidean", hull=None, edge_correction=None, keep_simulations=False, n_simulations=9999, n_jobs=-1, **kwargs, ): if isinstance(coordinates, geopandas.GeoDataFrame | geopandas.GeoSeries): coordinates = shapely.get_coordinates(coordinates.geometry) stat_function, result_container = _ripley_dispatch.get(calltype) core_kwargs = dict( support=support, metric=metric, edge_correction=edge_correction, ) tree = _build_best_tree(coordinates, metric=metric) hull = _prepare_hull(coordinates, hull) empty_space_points = None if calltype in ("F", "J"): # these require simulations core_kwargs["hull"] = hull # amortize to avoid doing this every time empty_space_points = poisson(coordinates, size=(1000, 1)) if distances is None: # Note: We now use the original coordinates' tree to calculate the observed distance # for the first time, as per the original logic flow. empty_space_distances, _ = _k_neighbors(tree, empty_space_points, k=1) if calltype == "F": distances = empty_space_distances.squeeze() else: # calltype == 'J': n_distances, _ = _k_neighbors(tree, coordinates, k=1) distances = (n_distances.squeeze(), empty_space_distances.squeeze()) else: pass core_kwargs.update(**kwargs) observed_support, observed_statistic = stat_function( coordinates, distances=distances, **core_kwargs ) # The original function passed the tree, but the wrapper functions expect coordinates # Corrected to pass coordinates, relying on stat_function to manage the tree internally. core_kwargs["support"] = observed_support n_observations = coordinates.shape[0] # --- PARALLEL SIMULATION BLOCK --- if n_simulations <= 0: warnings.warn("n_simulations must be positive. No simulations performed.") simulations_array = numpy.empty((0, len(observed_support))) else: simulations_list = Parallel(n_jobs=n_jobs)( delayed(_run_one_ripley_simulation)( calltype, n_observations, hull, stat_function, metric, empty_space_points, core_kwargs, observed_support, ) for _ in range(n_simulations) ) simulations_array = numpy.array(simulations_list) # --- END PARALLEL SIMULATION BLOCK --- # --- VECTORIZED P-VALUE CALCULATION --- if simulations_array.shape[0] == 0: pvalues = numpy.nan * numpy.ones_like(observed_support) else: # Calculate how many simulations are as extreme as the observed statistic pvalues_count = (simulations_array >= observed_statistic).sum(axis=0) # Conservative p-value calculation pvalues = (pvalues_count + 1) / (n_simulations + 1) pvalues = numpy.minimum(pvalues, 1 - pvalues) # --- END P-VALUE CALCULATION --- return result_container( observed_support, observed_statistic, pvalues, simulations_array if keep_simulations else None, ) def f_test( coordinates, support=None, distances=None, metric="euclidean", hull=None, edge_correction=None, keep_simulations=False, n_simulations=9999, n_jobs=-1, ): """ Ripley's F function The so-called "empty space" function, this is the cumulative density function of the distances from a random set of points to the known points in the pattern. When the estimated statistic is larger than simulated values at a given distance, then the pattern is considered "dispersed" or "regular" Parameters ---------- coordinates : geopandas object | numpy.ndarray, (n,2) input coordinates to function support : tuple of length 1, 2, or 3, int, or numpy.ndarray tuple, encoding (stop,), (start, stop), or (start, stop, num) int, encoding number of equally-spaced intervals numpy.ndarray, used directly within numpy.histogram distances: numpy.ndarray, (n, p) or (p,) distances from every point in a random point set of size p to some point in `coordinates` metric: str or callable distance metric to use when building search tree hull: bounding box, scipy.spatial.ConvexHull, shapely.geometry.Polygon the hull used to construct a random sample pattern, if distances is None edge_correction: bool or str whether or not to conduct edge correction. Not yet implemented. keep_simulations: bool whether or not to keep the simulation envelopes. If so, will be returned as the result's simulations attribute n_simulations: int how many simulations to conduct, assuming that the reference pattern has complete spatial randomness. n_jobs : int (default: -1) The number of CPU cores to use for running the Monte Carlo simulations. Simulations are independent and can be run in parallel to significantly reduce execution time. * If ``n_jobs = -1``, all available CPU cores will be used. * If ``n_jobs = 1``, the execution will be forced to run sequentially (serially), disabling parallel processing. This is often useful for debugging or testing purposes. * If ``n_jobs > 1``, that specific number of cores will be used. Returns ------- a named tuple with properties - support, the exact distance values used to evalute the statistic - statistic, the values of the statistic at each distance - pvalue, the percent of simulations that were as extreme as the observed value - simulations, the distribution of simulated statistics (shaped (n_simulations, n_support_points)) or None if keep_simulations=False (which is the default) """ return _ripley_test( "F", coordinates, support=support, distances=distances, metric=metric, hull=hull, edge_correction=edge_correction, keep_simulations=keep_simulations, n_simulations=n_simulations, n_jobs=n_jobs, ) def g_test( coordinates, support=None, distances=None, metric="euclidean", hull=None, edge_correction=None, keep_simulations=False, n_simulations=9999, n_jobs=-1, ): """ Ripley's G function The G function is computed from the cumulative density function of the nearest neighbor distances between points in the pattern. When the G function is below the simulated values, it suggests dispersion. Parameters ---------- coordinates : geopandas object | numpy.ndarray, (n,2) input coordinates to function support : tuple of length 1, 2, or 3, int, or numpy.ndarray tuple, encoding (stop,), (start, stop), or (start, stop, num) int, encoding number of equally-spaced intervals numpy.ndarray, used directly within numpy.histogram distances: numpy.ndarray, (n, p) or (p,) distances from every point in a random point set of size p to some point in `coordinates` metric: str or callable distance metric to use when building search tree hull: bounding box, scipy.spatial.ConvexHull, shapely.geometry.Polygon the hull used to construct a random sample pattern, if distances is None edge_correction: bool or str whether or not to conduct edge correction. Not yet implemented. keep_simulations: bool whether or not to keep the simulation envelopes. If so, will be returned as the result's simulations attribute n_simulations: int how many simulations to conduct, assuming that the reference pattern has complete spatial randomness. n_jobs : int (default: -1) The number of CPU cores to use for running the Monte Carlo simulations. Simulations are independent and can be run in parallel to significantly reduce execution time. * If ``n_jobs = -1``, all available CPU cores will be used. * If ``n_jobs = 1``, the execution will be forced to run sequentially (serially), disabling parallel processing. This is often useful for debugging or testing purposes. * If ``n_jobs > 1``, that specific number of cores will be used. Returns ------- a named tuple with properties - support, the exact distance values used to evalute the statistic - statistic, the values of the statistic at each distance - pvalue, the percent of simulations that were as extreme as the observed value - simulations, the distribution of simulated statistics (shaped (n_simulations, n_support_points)) or None if keep_simulations=False (which is the default) """ return _ripley_test( "G", coordinates, support=support, distances=distances, metric=metric, hull=hull, edge_correction=edge_correction, keep_simulations=keep_simulations, n_simulations=n_simulations, n_jobs=n_jobs, ) def j_test( coordinates, support=None, distances=None, metric="euclidean", hull=None, edge_correction=None, truncate=True, keep_simulations=False, n_simulations=9999, n_jobs=-1, ): """ Ripley's J function The so-called "spatial hazard" function, this is a function relating the F and G functions. When the J function is consistently below 1, then it indicates clustering. When consistently above 1, it suggests dispersion. coordinates : geopandas object | numpy.ndarray, (n,2) input coordinates to function support : tuple of length 1, 2, or 3, int, or numpy.ndarray tuple, encoding (stop,), (start, stop), or (start, stop, num) int, encoding number of equally-spaced intervals numpy.ndarray, used directly within numpy.histogram distances: numpy.ndarray, (n, p) or (p,) distances from every point in a random point set of size p to some point in `coordinates` metric: str or callable distance metric to use when building search tree hull: bounding box, scipy.spatial.ConvexHull, shapely.geometry.Polygon the hull used to construct a random sample pattern, if distances is None edge_correction: bool or str whether or not to conduct edge correction. Not yet implemented. keep_simulations: bool whether or not to keep the simulation envelopes. If so, will be returned as the result's simulations attribute n_simulations: int how many simulations to conduct, assuming that the reference pattern has complete spatial randomness. n_jobs : int (default: -1) The number of CPU cores to use for running the Monte Carlo simulations. Simulations are independent and can be run in parallel to significantly reduce execution time. * If ``n_jobs = -1``, all available CPU cores will be used. * If ``n_jobs = 1``, the execution will be forced to run sequentially (serially), disabling parallel processing. This is often useful for debugging or testing purposes. * If ``n_jobs > 1``, that specific number of cores will be used. Returns ------- a named tuple with properties - support, the exact distance values used to evalute the statistic - statistic, the values of the statistic at each distance - pvalue, the percent of simulations that were as extreme as the observed value - simulations, the distribution of simulated statistics (shaped (n_simulations, n_support_points)) or None if keep_simulations=False (which is the default) """ result = _ripley_test( "J", coordinates, support=support, distances=distances, metric=metric, hull=hull, edge_correction=edge_correction, keep_simulations=keep_simulations, n_simulations=n_simulations, n_jobs=n_jobs, truncate=False, ) if truncate: result_trunc = _truncate(*result) result_trunc = JtestResult(*result_trunc) if len(result_trunc.statistic) != len(result.statistic): warnings.warn( f"requested {support} bins to evaluate the J function, but" f" it reaches infinity at d={result[0][-1]:.4f}, meaning only" f" {len(result[0])} bins will be used to characterize the J function.", stacklevel=2, ) return result_trunc else: return result def k_test( coordinates, support=None, distances=None, metric="euclidean", hull=None, edge_correction=None, keep_simulations=False, n_simulations=9999, n_jobs=-1, ): """ Ripley's K function This function counts the number of pairs of points that are closer than a given distance. As d increases, K approaches the number of point pairs. When the K function is below simulated values, it suggests that the pattern is dispersed. Parameters ---------- coordinates : geopandas object | numpy.ndarray, (n,2) input coordinates to function support : tuple of length 1, 2, or 3, int, or numpy.ndarray tuple, encoding (stop,), (start, stop), or (start, stop, num) int, encoding number of equally-spaced intervals numpy.ndarray, used directly within numpy.histogram distances: numpy.ndarray, (n, p) or (p,) distances from every point in a random point set of size p to some point in `coordinates` metric: str or callable distance metric to use when building search tree hull: bounding box, scipy.spatial.ConvexHull, shapely.geometry.Polygon the hull used to construct a random sample pattern, if distances is None edge_correction: bool or str whether or not to conduct edge correction. Not yet implemented. keep_simulations: bool whether or not to keep the simulation envelopes. If so, will be returned as the result's simulations attribute n_simulations: int how many simulations to conduct, assuming that the reference pattern has complete spatial randomness. n_jobs : int (default: -1) The number of CPU cores to use for running the Monte Carlo simulations. Simulations are independent and can be run in parallel to significantly reduce execution time. * If ``n_jobs = -1``, all available CPU cores will be used. * If ``n_jobs = 1``, the execution will be forced to run sequentially (serially), disabling parallel processing. This is often useful for debugging or testing purposes. * If ``n_jobs > 1``, that specific number of cores will be used. Returns ------- a named tuple with properties - support, the exact distance values used to evalute the statistic - statistic, the values of the statistic at each distance - pvalue, the percent of simulations that were as extreme as the observed value - simulations, the distribution of simulated statistics (shaped (n_simulations, n_support_points)) or None if keep_simulations=False (which is the default) """ return _ripley_test( "K", coordinates, support=support, distances=distances, metric=metric, hull=hull, edge_correction=edge_correction, keep_simulations=keep_simulations, n_simulations=n_simulations, n_jobs=n_jobs, ) def l_test( coordinates, support=None, distances=None, metric="euclidean", hull=None, edge_correction=None, linearized=False, keep_simulations=False, n_simulations=9999, n_jobs=-1, ): """ Ripley's L function This is a scaled and shifted version of the K function that accounts for the K function's increasing expected value as distances increase. This means that the L function, for a completely random pattern, should be close to zero at all distance values in the support. When the L function is negative, this suggests dispersion. Parameters ---------- coordinates : geopandas object | numpy.ndarray, (n,2) input coordinates to function support : tuple of length 1, 2, or 3, int, or numpy.ndarray tuple, encoding (stop,), (start, stop), or (start, stop, num) int, encoding number of equally-spaced intervals numpy.ndarray, used directly within numpy.histogram distances: numpy.ndarray, (n, p) or (p,) distances from every point in a random point set of size p to some point in `coordinates` metric: str or callable distance metric to use when building search tree hull: bounding box, scipy.spatial.ConvexHull, shapely.geometry.Polygon the hull used to construct a random sample pattern, if distances is None edge_correction: bool or str whether or not to conduct edge correction. Not yet implemented. keep_simulations: bool whether or not to keep the simulation envelopes. If so, will be returned as the result's simulations attribute n_simulations: int how many simulations to conduct, assuming that the reference pattern has complete spatial randomness. n_jobs : int (default: -1) The number of CPU cores to use for running the Monte Carlo simulations. Simulations are independent and can be run in parallel to significantly reduce execution time. * If ``n_jobs = -1``, all available CPU cores will be used. * If ``n_jobs = 1``, the execution will be forced to run sequentially (serially), disabling parallel processing. This is often useful for debugging or testing purposes. * If ``n_jobs > 1``, that specific number of cores will be used. Returns ------- a named tuple with properties - support, the exact distance values used to evalute the statistic - statistic, the values of the statistic at each distance - pvalue, the percent of simulations that were as extreme as the observed value - simulations, the distribution of simulated statistics (shaped (n_simulations, n_support_points)) or None if keep_simulations=False (which is the default) """ return _ripley_test( "L", coordinates, support=support, distances=distances, metric=metric, hull=hull, edge_correction=edge_correction, keep_simulations=keep_simulations, n_simulations=n_simulations, n_jobs=n_jobs, linearized=linearized, ) def _run_one_ripley_simulation( calltype, n_observations, hull, stat_function, metric, empty_space_points, core_kwargs, observed_support, ): """Run a single Monte Carlo simulation for a Ripley function test.""" # 1. Generate a random point pattern (CSR) random_i = poisson(hull, size=n_observations) current_kwargs = core_kwargs.copy() current_kwargs["support"] = observed_support # 2. Prepare distances for F/J tests if calltype in ("F", "J"): random_tree = _build_best_tree(random_i, metric) empty_distances, _ = random_tree.query(empty_space_points, k=1) if calltype == "F": current_kwargs["distances"] = empty_distances.squeeze() else: # calltype == 'J': n_distances, _ = _k_neighbors(random_tree, random_i, k=1) current_kwargs["distances"] = ( n_distances.squeeze(), empty_distances.squeeze(), ) # 3. Calculate the Ripley statistic for the simulated pattern # rep_support is ignored as we rely on observed_support _, simulations_i = stat_function(random_i, **current_kwargs) return simulations_i def _truncate(support, realizations, *rest): is_invalid = numpy.isinf(realizations) | numpy.isnan(realizations) first_inv = is_invalid.argmax() if not is_invalid.any(): return support, realizations, *rest elif first_inv < len(realizations): return ( support[:first_inv], realizations[:first_inv], *[r[:first_inv] if r is not None else None for r in rest], ) pointpats-2.5.5/pointpats/geometry.py000066400000000000000000000307071514706172100200050ustar00rootroot00000000000000import numpy from scipy import spatial from functools import singledispatch from libpysal.cg import alpha_shape_auto from libpysal.cg.kdtree import Arc_KDTree import warnings __all__ = ["area", "bbox", "contains", "k_neighbors", "build_best_tree", "prepare_hull"] # ------------------------------------------------------------# # Utilities and dispatching # # ------------------------------------------------------------# TREE_TYPES = (spatial.KDTree, Arc_KDTree) try: from sklearn.neighbors import KDTree, BallTree TREE_TYPES = (*TREE_TYPES, KDTree, BallTree) except ModuleNotFoundError: pass HULL_TYPES = ( numpy.ndarray, spatial.ConvexHull, ) ## Define default dispatches and special dispatches without GEOS ### AREA @singledispatch def area(shape): """ If a shape has an area attribute, return it. Works for: shapely.geometry.Polygon """ try: return shape.area except AttributeError: return area(numpy.asarray(shape)) @area.register def _(shape: spatial.ConvexHull): """ If a shape is a convex hull from scipy, assure it's 2-dimensional and then use its volume. """ assert shape.points.shape[1] == 2 return shape.volume @area.register def _(shape: numpy.ndarray): """ If a shape describes a bounding box, compute length times width """ assert len(shape) == 4, "shape is not a bounding box!" width, height = shape[2] - shape[0], shape[3] - shape[1] return numpy.abs(width * height) ### bounding box @singledispatch def bbox(shape): """ If a shape can be cast to an array, use that. Works for: lists of tuples scikit memory arrays """ return bbox(numpy.asarray(shape)) @bbox.register def _(shape: numpy.ndarray): """ If a shape is an array of points, compute the minima/maxima or let it pass through if it's 1 dimensional & length 4 """ if (shape.ndim == 1) & (len(shape) == 4): return shape return numpy.array([*shape.min(axis=0), *shape.max(axis=0)]) @bbox.register def _(shape: spatial.ConvexHull): """ For scipy.spatial.ConvexHulls, compute the bounding box from their boundary points. """ return bbox(shape.points[shape.vertices]) ### contains @singledispatch def contains(shape, x, y): """ Try to use the shape's contains method directly on XY. Does not currently work on anything. """ raise NotImplementedError() return shape.contains((x, y)) @contains.register def _(shape: numpy.ndarray, x: float, y: float): """ If provided an ndarray, assume it's a bbox and return whether the point falls inside """ xmin, xmax = shape[0], shape[2] ymin, ymax = shape[1], shape[3] in_x = (xmin <= x) and (x <= xmax) in_y = (ymin <= y) and (y <= ymax) return in_x & in_y @contains.register def _(shape: spatial.Delaunay, x: float, y: float): """ For points and a delaunay triangulation, use the find_simplex method to identify whether a point is inside the triangulation. If the returned simplex index is -1, then the point is not within a simplex of the triangulation. """ return shape.find_simplex((x, y)) >= 0 @contains.register def _(shape: spatial.ConvexHull, x: float, y: float): """ For convex hulls, convert their exterior first into a Delaunay triangulation and then use the delaunay dispatcher. """ exterior = shape.points[shape.vertices] delaunay = spatial.Delaunay(exterior) return contains(delaunay, x, y) ### centroid @singledispatch def centroid(shape): """ Assume the input is a shape with a centroid method: """ return shape.centroid @centroid.register def _(shape: numpy.ndarray): """ Handle point arrays or bounding boxes """ from .centrography import mean_center if shape.ndim == 2: return mean_center(shape).squeeze() elif shape.ndim == 1: assert shape.shape == (4,) xmin, ymin, xmax, ymax = shape return numpy.column_stack( (numpy.mean((xmin, xmax)), numpy.mean((ymin, ymax))) ).squeeze() else: raise TypeError( f"Centroids are only implemented in 2 dimensions," f" but input has {shape.ndim} dimensinos" ) @centroid.register def _(shape: spatial.ConvexHull): """ Treat convex hulls as arrays of points """ return centroid(shape.points[shape.vertices]) from shapely.geometry.base import BaseGeometry as _BaseGeometry from shapely.geometry import ( Polygon as _ShapelyPolygon, MultiPolygon as _ShapelyMultiPolygon, ) from shapely.geometry import Point as _ShapelyPoint HULL_TYPES = (*HULL_TYPES, _ShapelyPolygon, _ShapelyMultiPolygon) @contains.register def _(shape: _BaseGeometry, x: float, y: float): """ If we know we're working with a shapely polygon, then use the contains method & cast input coords to a shapely point """ return shape.contains(_ShapelyPoint((x, y))) @bbox.register def _(shape: _BaseGeometry): """ If a shape is an array of points, compute the minima/maxima or let it pass through if it's 1 dimensional & length 4 """ return numpy.asarray(list(shape.bounds)) @centroid.register def _(shape: _BaseGeometry): """ Handle shapely, which requires explicit centroid extraction """ return numpy.asarray(list(shape.centroid.coords)).squeeze() import shapely HULL_TYPES = (*HULL_TYPES, shapely.Geometry) @area.register def _(shape: shapely.Geometry): """ If we know we're working with a shapely polygon, then use shapely.area """ return shapely.area(shape) @contains.register def _(shape: shapely.Geometry, x: float, y: float): """ If we know we're working with a shapely polygon, then use shapely.within casting the points to a shapely object too """ return shapely.within(shapely.points((x, y)), shape) @bbox.register def _(shape: shapely.Geometry): """ If we know we're working with a shapely polygon, then use shapely.bounds """ return shapely.bounds(shape) @centroid.register def _(shape: shapely.Geometry): """ if we know we're working with a shapely polygon, then use shapely.centroid """ return shapely.coordinates.get_coordinates(shapely.centroid(shape)).squeeze() # ------------------------------------------------------------# # Constructors for trees, prepared inputs, & neighbors # # ------------------------------------------------------------# def build_best_tree(coordinates, metric): """ Build the best query tree that can support the application. Chooses from: 1. sklearn.KDTree if available and metric is simple 2. sklearn.BallTree if available and metric is complicated Parameters ---------- coordinates : numpy.ndarray array of coordinates over which to build the tree. metric : string or callable either a metric supported by sklearn KDTrees or BallTrees, or a callabe function. If sklearn is not installed, then this must be euclidean. Returns ------- : a distance tree a KDTree from either scikit-learn or scipy, or a BallTree if available. Notes ----- This will return a scikit-learn KDTree if the metric is supported and sklearn can be imported. If the metric is not supported by KDTree, a BallTree will be used if sklearn can be imported. If the metric is a user-defined callable function, a Ball Tree will be used if sklearn can be imported. If sklearn can't be imported, then a scipy.spatial.KDTree will be used if the metric is euclidean. Otherwise, an error will be raised. """ coordinates = numpy.asarray(coordinates) tree = spatial.KDTree try: import sklearn from sklearn.neighbors import KDTree, BallTree from packaging.version import Version if Version(sklearn.__version__) == Version("1.3.0"): kdtree_valid_metrics = KDTree.valid_metrics() balltree_valid_metrics = BallTree.valid_metrics() else: kdtree_valid_metrics = KDTree.valid_metrics balltree_valid_metrics = BallTree.valid_metrics if metric in kdtree_valid_metrics: tree = lambda coordinates: KDTree(coordinates, metric=metric) elif metric in balltree_valid_metrics: tree = lambda coordinates: BallTree(coordinates, metric=metric) elif callable(metric): warnings.warn( "Distance metrics defined in pure Python may " " have unacceptable performance!", stacklevel=2, ) tree = lambda coordinates: BallTree(coordinates, metric=metric) else: raise KeyError( f"Metric {metric} not found in set of available types." f"BallTree metrics: {balltree_valid_metrics}, and" f"scikit KDTree metrics: {kdtree_valid_metrics}." ) except ModuleNotFoundError as e: if metric not in ("l2", "euclidean"): raise KeyError( f"Metric {metric} requested, but this requires" f" scikit-learn to use. Without scikit-learn, only" f" euclidean distance metric is supported." ) return tree(coordinates) def k_neighbors(tree, coordinates, k, **kwargs): """ Query a kdtree for k neighbors, handling the self-neighbor case in the case of coincident points. Arguments ---------- tree : distance tree a distance tree, such as a scipy KDTree or sklearn KDTree or BallTree that supports a query argument. coordinates : numpy.ndarray of shape n,2 coordinates to query for their neighbors within the tree. k : int number of neighbors to query in the tree **kwargs : mappable arguments that may need to be passed down to the tree.query() function Returns -------- a tuple of (distances, indices) that is assured to not include the point itself in its query result. """ distances, indices = tree.query(coordinates, k=k + 1, **kwargs) n, ks = distances.shape assert ks == k + 1 full_indices = numpy.arange(n) other_index_mask = indices != full_indices.reshape(n, 1) has_k_indices = other_index_mask.sum(axis=1) == (k + 1) other_index_mask[has_k_indices, -1] = False distances = distances[other_index_mask].reshape(n, k) indices = indices[other_index_mask].reshape(n, k) return distances, indices def prepare_hull(coordinates, hull=None): """ Construct a hull from the coordinates given a hull type Will either return: - a bounding box array of [xmin, ymin, xmax, ymax] - a scipy.spatial.ConvexHull object from the Qhull library - a shapely shape using alpha_shape_auto Parameters --------- coordinates : numpy.ndarray of shape (n,2) Points to use to construct a hull hull : string or a pre-computed hull A string denoting what kind of hull to compute (if required) or a hull that has already been computed Returns -------- hull : A geometry-like object This encodes the "space" in which to simulate the normal pattern. All points will lie within this hull. Supported values are: - a bounding box encoded in a numpy array as numpy.array([xmin, ymin, xmax, ymax]) - an (N,2) array of points for which the bounding box will be computed & used - a shapely polygon/multipolygon - a shapely geometry - a scipy.spatial.ConvexHull """ if isinstance(hull, numpy.ndarray): assert len(hull) == 4, f"bounding box provided is not shaped correctly! {hull}" assert hull.ndim == 1, f"bounding box provided is not shaped correctly! {hull}" return hull if (hull is None) or (hull == "bbox"): return bbox(coordinates) if isinstance(hull, (_ShapelyPolygon, _ShapelyMultiPolygon)): return hull if isinstance(hull, shapely.Geometry): return hull if isinstance(hull, str): if hull.startswith("convex"): return spatial.ConvexHull(coordinates) elif hull.startswith("alpha") or hull.startswith("α"): return alpha_shape_auto(coordinates) elif isinstance(hull, spatial.ConvexHull): return hull raise ValueError( f"Hull type {hull} not in the set of valid options:" f" (None, 'bbox', 'convex', 'alpha', 'α', " f" shapely.geometry.Polygon, shapely.Geometry)" ) pointpats-2.5.5/pointpats/kde.py000066400000000000000000000136261514706172100167160ustar00rootroot00000000000000import numpy as np def plot_density( data, bandwidth, kernel=None, resolution=100, levels=10, fill=False, margin=0.1, ax=None, figsize=None, **kwargs, ): """Plot kernel density of a given point pattern The KDE can be done either using :class:`statsmodels.nonparametric.KDEMultivariate`, which is used when ``kernel=None``, or using :class:`KDEpy.FFTKDE` when kernel is set. :class:`~KDEpy.FFTKDE` tends to be generally faster in most cases but may need different than ``"gaussian"`` kernel to resolve in some cases. For small data of up to 10 000 points, the difference is not noticeable. For larger data, specify ``bandwidth`` to enforce the use of :class:`~KDEpy.FFTKDE`. Note that while being faster, :class:`~KDEpy.FFTKDE` may in some case result in erroneous KDE. KDE is plotted using matplotlib's :meth:`~matplotlib.pyplot.contour` or :meth:`~matplotlib.pyplot.contourf` function to plot the density. If MultiPoints are given, each point is treated as separate observation. Parameters ---------- data : array or geopandas object Array with a shape (2, n) containing coordinates of points or a geopandas object with (Multi)Point geometry. Assumes projected coordinates, geographical coordinates (latitude, longitude) are not supported. bandwidth : float bandwidth in the units of CRS in which data is kernel : str | None, optional The kernel function. If None, defaults to the Gaussian kernel and statsmodels implementation. If set, uses KDEpy implementation. See :meth:`KDEpy.FFTKDE._available_kernels.keys()` for choices. resolution : int | tuple(int, int), optional resolution of the grid used to evaluate the probability density function. If tuple, each dimension of the grid is specified separately. By default 100 levels : int or array-like, optional Determines the number and positions of the contour lines / regions. See the documentation of :meth:`~matplotlib.pyplot.contour` for details. By default 10 fill : bool, optional Fill the area between contour lines, by default False margin : float, optional The factor of the margin by which the extent of the data will be expanded when creating the grid. 0.1 means 10% on each side, by default 0.1. Only used with the ``statsmodels`` implementation. ax : matplotlib.axes.Axes (default None) axes on which to draw the plot figsize : tuple of integers (default None) Size of the resulting ``matplotlib.figure.Figure``. If the argument ``ax`` is given explicitly, ``figsize`` is ignored. **kwargs Keyword arguments passed to :meth:`~matplotlib.pyplot.contour` or :meth:`~matplotlib.pyplot.contourf` used for further styling of the plot, for example ``cmap``, ``linewidths``, ``linestyles``, or `alpha`. See the documentation of :meth:`~matplotlib.pyplot.contour` for details. Returns ------- matplotlib.axes.Axes matplotlib axes instance with the contour plot """ if kernel is None: try: import statsmodels.api as sm except ImportError as err: raise ImportError( "statsmodels is required for `plot_density` when kernel" "is not specified." ) from err engine = "sm" else: try: from KDEpy import FFTKDE except ImportError as err: raise ImportError( "KDEpy is required for `plot_density` when kernel is not None." ) from err engine = "kdepy" try: import matplotlib.pyplot as plt except ImportError as err: raise ImportError("matplotlib is required for `plot_density`") from err if ax is None: _, ax = plt.subplots(figsize=figsize) ax.set_aspect("equal") # bandwidth is fixed, hence aspect shall be equal if isinstance(data, np.ndarray): pass else: # geopandas if not data.geom_type.str.contains("Point").all(): raise ValueError( "data contain non-point geometries. " "Only (Multi)Points are supported." ) data = data.get_coordinates().values if engine == "sm": dens_u = sm.nonparametric.KDEMultivariate( data=[data[:, 0], data[:, 1]], var_type="cc", bw=[bandwidth, bandwidth], ) xmax = data[:, 0].max() xmin = data[:, 0].min() ymax = data[:, 1].max() ymin = data[:, 1].min() # get margin to go beyond the extent to avoid cutting of countour lines x_margin = (xmax - xmin) * margin y_margin = (ymax - ymin) * margin if isinstance(resolution, tuple): x_res, y_res = resolution elif isinstance(resolution, (float, int)): x_res = resolution y_res = resolution elif resolution is None: x_res = 100 y_res = 100 else: raise ValueError("Unsupported option for `resolution`.") # create mesh for predicting KDE on with more space around the points x_mesh, y_mesh = np.meshgrid( np.linspace(xmin - x_margin, xmax + x_margin, x_res), np.linspace(ymin - y_margin, ymax + y_margin, y_res), ) # get the prediction pred = dens_u.pdf(np.vstack([x_mesh.flatten(), y_mesh.flatten()]).T) z = pred.reshape(x_mesh.shape) else: kde = FFTKDE(bw=bandwidth, kernel=kernel) grid, points = kde.fit(data).evaluate(resolution) x_mesh, y_mesh = np.unique(grid[:, 0]), np.unique(grid[:, 1]) z = points.reshape(resolution, resolution).T if fill: ax.contourf(x_mesh, y_mesh, z, levels=levels, **kwargs) else: ax.contour(x_mesh, y_mesh, z, levels=levels, **kwargs) return ax pointpats-2.5.5/pointpats/pointpattern.py000066400000000000000000000357231514706172100207040ustar00rootroot00000000000000""" Planar Point Pattern Class """ import numpy as np import sys from libpysal.cg import KDTree from .centrography import hull from .window import as_window, poly_from_bbox from .util import cached_property import pandas as pd from matplotlib import pyplot as plt from matplotlib.collections import PatchCollection from matplotlib.patches import Polygon __author__ = "Serge Rey sjsrey@gmail.com" __all__ = ["PointPattern"] if sys.version_info[0] > 2: xrange = range class PointPattern(object): """ Planar Point Pattern Class 2-D. Parameters ---------- points: array (n,p), n points with p >= 2 attributes on each point. Two attributes must comprise the spatial coordinate pair. Default is that the first two attributes are the x and y spatial coordinates. window: :class:`.Window` Bounding geometric object for the point pattern. If not specified, window will be set to the minimum bounding rectangle of the point pattern. names: list The names of the attributes. coord_names: list The names of the attributes defining the two spatial coordinates. Examples -------- >>> from pointpats import PointPattern >>> points = [[66.22, 32.54], [22.52, 22.39], [31.01, 81.21], ... [9.47, 31.02], [30.78, 60.10], [75.21, 58.93], ... [79.26, 7.68], [8.23, 39.93], [98.73, 77.17], ... [89.78, 42.53], [65.19, 92.08], [54.46, 8.48]] >>> pp = PointPattern(points) >>> pp.n 12 >>> pp.mean_nnd 21.612139802089246 >>> pp.lambda_mbb 0.0015710507711240867 >>> pp.lambda_hull 0.0022667153468973137 >>> pp.hull_area 5294.00395 >>> pp.mbb_area 7638.200000000001 """ def __init__(self, points, window=None, names=None, coord_names=None): # first two series in df are x, y unless coor_names and names are # specified self.df = pd.DataFrame(points) n, p = self.df.shape self._n_marks = p - 2 if coord_names is None: if names is not None: coord_names = names[:2] else: coord_names = ["x", "y"] if names is None: col_names = coord_names if p > 2: for m in range(2, p): col_names.append("mark_{}".format(m - 2)) coord_names = coord_names[:2] else: col_names = names self.coord_names = coord_names self._x, self._y = coord_names self.df.columns = col_names self.points = self.df.loc[:, [self._x, self._y]] self._n, self._p = self.points.shape if window is None: self.set_window(as_window(poly_from_bbox(self.mbb))) else: self.set_window(window) self._facade() def __len__(self): """Return the number of points. Use the expression 'len(pp)'. Returns ------- length : int The number of points in the point pattern. Examples -------- >>> from pointpats import PointPattern >>> points = [[1, 3], [4, 5], [0,0]] >>> pp = PointPattern(points) >>> len(pp) 3 """ return len(self.df) def __contains__(self, n): """Return True if n is a point (a tuple of coordinates), False otherwise. Use the expression 'n in pp'. Examples -------- >>> from pointpats import PointPattern >>> points = [[1, 3], [4, 5], [0,0]] >>> pp = PointPattern(points) >>> [1, 3] in pp True """ name = self.df.columns.values.tolist() return ((self.df[name[0]] == n[0]) & (self.df[name[1]] == n[1])).any() def set_window(self, window): try: self._window = window except: print("not a valid Window object") def get_window(self): """ Bounding geometry for the point pattern :class:`.window.Window` """ if not hasattr(self, "_window") or self._window is None: # use bbox as window self.set_window(as_window(poly_from_bbox(self.mbb))) return self._window window = property(get_window, set_window) def summary(self): """ Description of the point pattern. """ print("Point Pattern") print("{} points".format(self.n)) print("Bounding rectangle [({},{}), ({},{})]".format(*self.mbb)) print("Area of window: {}".format(self.window.area)) print("Intensity estimate for window: {}".format(self.lambda_window)) print(self.head()) def add_marks(self, marks, mark_names=None): if mark_names is None: nm = range(len(marks)) mark_names = ["mark_{}".format(self._n_marks + 1 + j) for j in nm] for name, mark in zip(mark_names, marks): self.df[name] = mark self._n_marks += 1 def plot(self, window=False, title="Point Pattern", hull=False, get_ax=False): """ Plot function for a point pattern. Parameters ---------- window : boolean If window is True, plot window of the point pattern. If not, don't plot window. title : string Name of the figure. hull : boolean If hull is True, plot convex hull of the point pattern. If not, don't plot convex hull. get_ax : boolean If get_ax is True, return the current plot ax. Returns ------- ax : matplotlib.axes._subplots.AxesSubplot Current plot ax. Only return it when get_ax is True. """ fig, ax = plt.subplots() plt.plot(self.df[self._x], self.df[self._y], ".") # plt.scatter(self.df[self._x], self.df[self._y]) plt.title(title) if window: patches = [] for part in self.window.parts: p = Polygon(np.asarray(part)) patches.append(p) ax.add_collection( PatchCollection(patches, facecolor="none", edgecolor="k", alpha=0.3) ) if hull: patches = [] p = Polygon(self.hull) patches.append(p) ax.add_collection( PatchCollection(patches, facecolor="none", edgecolor="k", alpha=0.3) ) ax.set_aspect("equal") # plt.plot(x, y, '.') if get_ax: return ax def _mbb(self): """ Minimum bounding box """ mins = self.points.min(axis=0) maxs = self.points.max(axis=0) return np.hstack((mins, maxs)) mbb = cached_property(_mbb) def _mbb_area(self): """ Area of minimum bounding box """ return np.prod(self.mbb[[2, 3]] - self.mbb[[0, 1]]) mbb_area = cached_property(_mbb_area) def _n(self): """ Number of points """ return self.points.shape[0] n = cached_property(_n) def _rot(self): """ Ripley's rule of thumb for distance range in plotting k and related functions One-quarter the smallest side of the mbb. """ w, s, e, n = self.mbb return 0.25 * min(e - w, n - s) rot = cached_property(_rot) def _lambda_mbb(self): """ Intensity based on minimum bounding box """ return self.n * 1.0 / self.mbb_area lambda_mbb = cached_property(_lambda_mbb) def _hull(self): """ Points defining convex hull in counterclockwise order """ return hull(self.points) hull = cached_property(_hull) def _lambda_window(self): """ Intensity estimate based on area of window The intensity of a point process at point :math:`s_j` can be defined as: .. math:: \\lambda(s_j) = \\lim \\limits_{|\\mathbf{A}s_j| \\to 0} \\left \\{ \\frac{E(Y(\\mathbf{A}s_j)}{|\\mathbf{A}s_j|} \\right \\} where :math:`\\mathbf{A}s_j` is a small region surrounding location :math:`s_j` with area :math:`|\\mathbf{A}s_j|`, and :math:`E(Y(\\mathbf{A}s_j))` is the expected number of event points in :math:`\\mathbf{A}s_j`. The intensity is the mean number of event points per unit of area at point :math:`s_j`. """ return self.n / self.window.area lambda_window = cached_property(_lambda_window) def _hull_area(self): """ Area of convex hull """ h = self.hull if not np.all(h[0] == h[-1]): # not in closed cartographic form h = np.vstack((h, h[0])) s = h[:-1, 0] * h[1:, 1] - h[1:, 0] * h[:-1, 1] return s.sum() / 2.0 hull_area = cached_property(_hull_area) def _lambda_hull(self): """ Intensity based on convex hull """ return self.n * 1.0 / self.hull_area lambda_hull = cached_property(_lambda_hull) def _build_tree(self): return KDTree(self.points) tree = cached_property(_build_tree) def knn(self, k=1): """ Find k nearest neighbors for each point in the pattern Parameters ---------- k: int number of nearest neighbors to find Returns ------- nn: array (n x k) row i column j contains the id for i's jth nearest neighbor nnd: array(n x k) row i column j contains the distance between i and its jth nearest neighbor """ if k < 1: raise ValueError("k must be at least 1") nn = self.tree.query(self.tree.data, k=k + 1) return nn[1][:, 1:], nn[0][:, 1:] def _nn_sum(self): """ Nearest neighbor distances """ ids, nnd = self.knn(1) return nnd nnd = cached_property(_nn_sum) # nearest neighbor distances def _min_nnd(self): """ Min nearest neighbor distance """ return self.nnd.min() min_nnd = cached_property(_min_nnd) def _max_nnd(self): """ Max nearest neighbor distance """ return self.nnd.max() max_nnd = cached_property(_max_nnd) def _mean_nnd(self): """ Mean nearest neighbor distance """ return self.nnd.mean() mean_nnd = cached_property(_mean_nnd) def find_pairs(self, r): """ Find all pairs of points in the pattern that are within r units of each other Parameters ---------- r: float diameter of pair circle Returns ------- s: set pairs of points within r units of each other """ return self.tree.query_pairs(r) def knn_other(self, other, k=1): """ Find k nearest neighbors in the pattern for each point in other Parameters ---------- other: PointPattern :py:class:`pointpats.PointPattern` k: int number of nearest neighbors to find Returns ------- nn: array (n x k) row i column j contains the id for i's jth nearest neighbor nnd: array(n x k) row i column j contains the distance between i and its jth nearest neighbor """ if k < 1: raise ValueError("k must be at least 1") try: nn = self.tree.query(np.asarray(other.points), k=k) except: nn = self.tree.query(np.asarray(other), k=k) return nn[1], nn[0] def explode(self, mark): """ Explode a marked point pattern into a sequence of individual point patterns. If the mark has k unique values, then the sequence will be of length k. Parameters ---------- mark: string The label of the mark to use for the subsetting Returns ------- pps: list sequence of :class:`PointPattern` instances """ uv = np.unique(self.df[mark]) pps = [self.df[self.df[mark] == v] for v in uv] names = self.df.columns.values.tolist() cnames = self.coord_names return [PointPattern(pp, names=names, coord_names=cnames) for pp in pps] def unique(self): """Remove duplicate points in the point pattern. Two points in a point pattern are deemed to be identical if their coordinates are the same, and their marks are the same (if any) Returns ------- pp: list A deduplicated :class:`PointPattern` instance Examples -------- >>> from pointpats import PointPattern >>> points = [[1.2, 2.1], [1.2, 2.1], [0, 1], [1, 2]] >>> pp = PointPattern(points) >>> pp.unique().df x y 0 1.2 2.1 2 0.0 1.0 3 1.0 2.0 """ names = self.df.columns.values.tolist() coord_names = self.coord_names window = self.set_window unique_df = self.df.drop_duplicates() return PointPattern( unique_df, names=names, coord_names=coord_names, window=window ) def superimpose(self, point_pattern): """Returns a superimposed point pattern. Parameters ---------- point_pattern: :class:`PointPattern` instance Returns ------- superimposed : :class:`PointPattern` instance Examples -------- >>> from pointpats import PointPattern >>> points1 = [[1, 3], [4, 5], [0, 0]] >>> points2 = [[5, 6], [1, 4], [0, 0]] >>> pp1 = PointPattern(points1) >>> pp2 = PointPattern(points2) >>> pp1.superimpose(pp2).points x y 0 1 3 1 4 5 2 0 0 0 5 6 1 1 4 """ names_pp1 = self.df.columns.values.tolist() cnames_pp1 = self.coord_names names_pp2 = point_pattern.df.columns.values.tolist() cnames_pp2 = point_pattern.coord_names if names_pp1 != names_pp2 or cnames_pp1 != cnames_pp2: raise TypeError( "Both point patterns should have similar\ attributes and spatial coordinates " ) pp = pd.concat((self.df, point_pattern.df)) pp = pp.drop_duplicates() return PointPattern(pp, names=names_pp1, coord_names=cnames_pp1) def flip_coordinates(self): """Flips the coordinates of a point pattern. Doesn't change the structure of data frame. This function swaps `_x` and `_y` variables, which are used to represent coordinates. """ self._x, self._y = self._y, self._x # Pandas facade def _facade(self): self.head = self.df.head self.tail = self.df.tail pointpats-2.5.5/pointpats/process.py000066400000000000000000000375541514706172100176370ustar00rootroot00000000000000""" Simulation of planar point processes TODO - inhibition process(es) - optimize draws for complex windows - documentation """ __author__ = "Serge Rey sjsrey@gmail.com" __all__ = ["PointProcess", "PoissonPointProcess", "PoissonClusterPointProcess"] import numpy as np import libpysal as ps from numpy.random import poisson from .pointpattern import PointPattern as PP import warnings warnings.filterwarnings( "ignore", "Objects based on the `Geometry` class will", FutureWarning ) def runif_in_circle(n, radius=1.0, center=(0.0, 0.0), burn=2, verbose=False): """ Generate n points within a circle of given radius. Parameters ---------- n : int Number of points. radius : float Radius of the circle. center : tuple Coordinates of the center. Returns ------- : array (n+1, 2), coordinates of generated points as well as the center. """ good = np.zeros((n, 2), float) c = 0 r = radius r2 = r * r it = 0 while c < n: x = np.random.uniform(-r, r, (burn * n, 1)) y = np.random.uniform(-r, r, (burn * n, 1)) ids = np.where(x * x + y * y <= r2) candidates = np.hstack((x, y))[ids[0]] nc = candidates.shape[0] need = n - c if nc > need: # more than we need good[c:] = candidates[:need] else: # use them all and keep going good[c : c + nc] = candidates c += nc it += 1 if verbose: print("Iterations: {}".format(it)) return good + np.asarray(center) class PointProcess(object): """ Point Process base class. Parameters ---------- window : :py:class:`~.window.Window` Bounding geometric object to contain point process realizations. n : int Size of each realization. samples : list Number of realizations. asPP : bool Control the data type of value in the "realizations" dictionary. If True, the data type is point pattern as defined in pointpattern.py; if False, the data type is an two-dimensional array. Attributes ---------- realizations : dictionary The key is the index of each realization, and the value is simulated event points for each realization. The data type of the value is controlled by the parameter "asPP". parameters : dictionary Dictionary of a dictionary. The key is the index of each realization, and the value is a dictionary with the key 'n' and the value size of each realization. """ def __init__(self, window, n, samples, asPP=False, **args): super(PointProcess, self).__init__() self.window = window self.n = n self.samples = samples self.args = args self.realizations = {} self.setup() for sample in range(samples): self.realizations[sample] = self.draw(self.parameters[sample]) if asPP: for sample in self.realizations: points = self.realizations[sample] self.realizations[sample] = PP(points, window=self.window) warnings.warn( "These point pattern simulators are deprecated! Please replace them" " with equivalent functions in pointpats.random", DeprecationWarning, stacklevel=2, ) def draw(self, parameter): """ Generate a series of point coordinates within the given window. Parameters ---------- parameter : dictionary Key: 'n'. Value: size of the realization. Returns ------- : array A series of point coordinates. """ c = 0 sample = [] n = parameter["n"] while c < n: pnts = self.realize(n) pnts = [ps.cg.shapes.Point((x[0], y[0])) for x, y in pnts] pins = self.window.filter_contained(pnts) sample.extend(pins) c = len(sample) return np.array([np.asarray(p) for p in sample[:n]]) def realize(self): pass def setup(self): pass class PoissonPointProcess(PointProcess): """ Poisson point process including :math:`N`-conditioned CSR process and :math:`\\lambda`-conditioned CSR process. Parameters ---------- window : :py:class:`~.window.Window` Bounding geometric object to contain point process realizations. n : int Size of each realization. samples : list Number of realizations. conditioning : bool If True, use the :math:`\\lambda`-conditioned CSR process, number of events would vary across realizations; if False, use the :math:`N`-conditioned CSR process. asPP : bool Control the data type of value in the "realizations" dictionary. If True, the data type is point pattern as defined in pointpattern.py; if False, the data type is an two-dimensional array. Attributes ---------- realizations : dictionary The key is the index of each realization, and the value is simulated event points for each realization. The data type of the value is controlled by the parameter "asPP". parameters : dictionary Dictionary of a dictionary. The key is the index of each realization, and the value is a dictionary with the key 'n' and the value: 1. always equal to the parameter n in the case of N-conditioned process. For example, {0:{'n':100},1:{'n':100},2:{'n':100}} 2. randomly generated from a Possion process in the case of lambda-conditioned process. For example, {0:{'n':97},1:{'n':100},2:{'n':98}} Examples -------- >>> import libpysal as ps >>> import numpy as np >>> from pointpats import Window >>> from libpysal.cg import shapely_ext Open the virginia polygon shapefile >>> va = ps.io.open(ps.examples.get_path("virginia.shp")) Create the exterior polygons for VA from the union of the county shapes >>> polys = [shp for shp in va] >>> state = shapely_ext.cascaded_union(polys) Create window from virginia state boundary >>> window = Window(state.parts) 1. Simulate a :math:`N`-conditioned csr process in the same window (10 points, 2 realizations) >>> np.random.seed(5) >>> samples1 = PoissonPointProcess(window, 10, 2, conditioning=False, asPP=False) >>> samples1.realizations[0] # the first realized event points array([[-81.80326547, 36.77687577], [-78.5166233 , 37.34055832], [-77.21660795, 37.7491503 ], [-79.30361037, 37.40467853], [-78.61625258, 36.61234487], [-81.43369537, 37.13784646], [-80.91302108, 36.60834063], [-76.90806444, 37.95525903], [-76.33475868, 36.62635347], [-79.71621808, 37.27396618]]) 2. Simulate a :math:`\\lambda`-conditioned csr process in the same window (10 points, 2 realizations) >>> np.random.seed(5) >>> samples2 = PoissonPointProcess(window, 10, 2, conditioning=True, asPP=True) >>> samples2.realizations[0].n # the size of first realized point pattern 10 >>> samples2.realizations[1].n # the size of second realized point pattern 13 """ def __init__(self, window, n, samples, conditioning=False, asPP=False): self.conditioning = conditioning super(PoissonPointProcess, self).__init__(window, n, samples, asPP) def setup(self): """ Generate the number of events for each realization. If "conditioning" is False, all the event numbers are the same; if it is True, the event number is a random variable following a Poisson distribution. """ self.parameters = {} if self.conditioning: lambdas = poisson(self.n, self.samples) for i, l in enumerate(lambdas): self.parameters[i] = {"n": l} else: for i in range(self.samples): self.parameters[i] = {"n": self.n} def realize(self, n): """ Generate n points which are randomly and independently distributed in the minimum bounding box of "window". Parameters ---------- n : int Number of point events. Returns ------- : array (n,2), n point coordinates. """ l, b, r, t = self.window.bbox xs = np.random.uniform(l, r, (n, 1)) ys = np.random.uniform(b, t, (n, 1)) return zip(xs, ys) class PoissonClusterPointProcess(PointProcess): """ Poisson cluster point process (Neyman Scott). Two stages: 1. parent CSR process: :math:`N`-conditioned or :math:`\\lambda`-conditioned. If parent events follow a :math:`\\lambda`-conditioned CSR process, the number of parent events varies across realizations. 2. child process: fixed number of points in circle centered on each parent. Parameters ---------- window : :py:class:`~.window.Window` Bounding geometric object to contain point process realizations. n : int Size of each realization. parents : int Number of parents. radius : float Radius of the circle centered on each parent. samples : list Number of realizations. asPP : bool Control the data type of value in the "realizations" dictionary. If True, the data type is point pattern as defined in pointpattern.py; if False, the data type is an two-dimensional array. conditioning : bool If True, use the :math:`lambda`-conditioned CSR process for parent events, leading to varied number of parent events across realizations; if False, use the :math:`N`-conditioned CSR process. Attributes ---------- children : int Number of childrens centered on each parent. Can be considered as local intensity. num_parents : dictionary The key is the index of each realization. The value is the number of parent events for each realization. realizations : dictionary The key is the index of each realization, and the value is simulated event points for each realization. The data type of the value is controlled by the parameter "asPP". parameters : dictionary Dictionary of a dictionary. The key is the index of each realization, and the value is a dictionary with the key 'n' and the value always equal to the parameter n in the case of N-conditioned process. For example, {0:{'n':100},1:{'n':100},2:{'n':100}} 2. randomly generated from a Possion process in the case of lambda-conditioned process. For example, {0:{'n':97},1:{'n':100},2:{'n':98}} Examples -------- >>> import libpysal as ps >>> import numpy as np >>> from pointpats import Window >>> from libpysal.cg import shapely_ext Open the virginia polygon shapefile >>> va = ps.io.open(ps.examples.get_path("virginia.shp")) Create the exterior polygons for VA from the union of the county shapes >>> polys = [shp for shp in va] >>> state = shapely_ext.cascaded_union(polys) Create window from virginia state boundary >>> window = Window(state.parts) 1. Simulate a Poisson cluster process of size 200 with 10 parents and 20 children within 0.5 units of each parent (parent events: :math:`N`-conditioned CSR) >>> np.random.seed(10) >>> samples1 = PoissonClusterPointProcess(window, 200, 10, 0.5, 1, asPP=True, conditioning=False) >>> samples1.parameters # number of events for the realization {0: {'n': 200}} >>> samples1.num_parents #number of parent events for each realization {0: 10} >>> samples1.children # number of children events centered on each parent event 20 2. Simulate a Poisson cluster process of size 200 with 10 parents and 20 children within 0.5 units of each parent (parent events: :math:`\\lambda`-conditioned CSR) >>> np.random.seed(10) >>> samples2 = PoissonClusterPointProcess(window, 200, 10, 0.5, 1, asPP=True, conditioning=True) >>> samples2.parameters # number of events for the realization might not be equal to 200 {0: {'n': 260}} >>> samples2.num_parents #number of parent events for each realization {0: 13} >>> samples2.children # number of children events centered on each parent event 20 """ def __init__( self, window, n, parents, radius, samples, keep=False, asPP=False, conditioning=False, ): self.conditioning = conditioning self.parents = parents self.children = int(np.ceil(n * 1.0 / parents)) self.radius = radius self.keep = keep super(PoissonClusterPointProcess, self).__init__(window, n, samples, asPP) def setup(self): """ Generate the number of events for each realization. If "conditioning" is False, all the event numbers are the same; if it is True, the number of parents is a random variable following a Poisson distribution, resulting in varied number of events. """ self.parameters = {} self.num_parents = {} if self.conditioning: lambdas = poisson(self.parents, self.samples) for i, l in enumerate(lambdas): num = l * self.children self.parameters[i] = {"n": num} self.num_parents[i] = l else: for i in range(self.samples): self.parameters[i] = {"n": self.n} self.num_parents[i] = self.parents def realize(self, n): """ Generate n points which are distributed in a clustered fashion in the minimum bounding box of "window". Parameters ---------- n : int Number of point events. Returns ------- res : array (n,2), n point coordinates. """ l, b, r, t = self.window.bbox d = self.radius # get parent points pxs = np.random.uniform(l, r, (int(n / self.children), 1)) pys = np.random.uniform(b, t, (int(n / self.children), 1)) cents = np.hstack((pxs, pys)) # generate children points pnts = [runif_in_circle(self.children, d, center) for center in cents] res = np.vstack(np.asarray(pnts)) if self.keep: res = np.vstack((np.asarray(cents), res)) np.random.shuffle(res) # so we don't truncate in a biased fashion return res pointpats-2.5.5/pointpats/quadrat_statistics.py000066400000000000000000000615111514706172100220620ustar00rootroot00000000000000""" Quadrat statistics for planar point patterns - First argument everywhere is a NumPy ndarray of point coordinates (n, 2). - MBB is computed internally from the array. - Optional `window` (shapely geometry). If omitted, uses the MBB rectangle. - Optional `rng` for reproducible simulation-based inference. - Rectangle and hexagon grids supported. - Plotting is self-contained (matplotlib only): scatters points, draws window, draws grid, annotates counts, and optionally annotates per-cell chi-square contributions. """ __author__ = "Serge Rey, Wei Kang, Hu Shao" __all__ = ["RectangleM", "HexagonM", "QStatistic"] import math import geopandas as gpd import numpy as np import scipy.stats from matplotlib import pyplot as plt from matplotlib.collections import PatchCollection from matplotlib.patches import Polygon as MplPolygon from shapely.geometry import MultiPolygon, Polygon, box from .random import poisson # ----------------------------------------------------------------------------- # Helpers # ----------------------------------------------------------------------------- def _as_points_array(points) -> np.ndarray: """ Normalize input into an (n, 2) float ndarray. Accepts: - array-like shaped (n, 2) - GeoPandas GeoSeries / GeometryArray of Points (and optionally MultiPoints) - PointPattern Returns: - np.ndarray of shape (n, 2) Raises: - ValueError for empty inputs or non-2D coordinate inputs - TypeError for unsupported geometry types """ if isinstance(points, gpd.GeoDataFrame): points = points.geometry if isinstance(points, gpd.GeoSeries): points = points.dropna() if not points.geom_type.isin(["Point", "MultiPoint"]).all(): raise TypeError( "GeoSeries must contain Point geometries (optionally MultiPoint). " f"Got geometry type: {getattr(points, 'geom_type', type(points).__name__)}" ) coords = points.get_coordinates() if len(coords) == 0: raise ValueError("no non-empty Point geometries.") return coords.values.astype(float) # ---- PointPattern --- if hasattr(points, "points"): return points.points.values # ---- Generic array-like path ---- pts = np.asarray(points) if pts.ndim != 2 or pts.shape[1] != 2: raise ValueError(f"points must be a (n, 2) array-like. Got shape {pts.shape}.") if pts.size == 0: raise ValueError("points is empty; cannot compute mbb.") return pts def _compute_mbb(points: np.ndarray) -> np.ndarray: x_min = float(np.min(points[:, 0])) y_min = float(np.min(points[:, 1])) x_max = float(np.max(points[:, 0])) y_max = float(np.max(points[:, 1])) return np.array([x_min, y_min, x_max, y_max], dtype=float) def _ensure_window(window, mbb): if window is None: return box(mbb[0], mbb[1], mbb[2], mbb[3]) return window def _coerce_rng(rng): """Coerce rng input to a NumPy Generator. Parameters ---------- rng : None | int | numpy.random.Generator | numpy.random.RandomState | numpy.random.BitGenerator Random number generator specification. Returns ------- numpy.random.Generator """ if rng is None: return np.random.default_rng() if isinstance(rng, np.random.Generator): return rng # Accept legacy RandomState by deriving a seed from it. if isinstance(rng, np.random.RandomState): # Draw a seed deterministically from the RandomState stream. seed = int(rng.randint(0, 2**32 - 1, dtype=np.uint32)) return np.random.default_rng(seed) # If a BitGenerator is passed, wrap it. if isinstance(rng, np.random.BitGenerator): return np.random.Generator(rng) # Int-like seeds if isinstance(rng, (int, np.integer)): return np.random.default_rng(int(rng)) # Fall back to NumPy’s coercion (e.g., SeedSequence) return np.random.default_rng(rng) def _window_to_paths(window_geom): """Return list of (x, y) arrays for plotting boundary(ies).""" paths = [] if window_geom is None: return paths if isinstance(window_geom, Polygon): x, y = window_geom.exterior.xy paths.append((np.asarray(x), np.asarray(y))) for ring in window_geom.interiors: x, y = ring.xy paths.append((np.asarray(x), np.asarray(y))) return paths if isinstance(window_geom, MultiPolygon): for poly in window_geom.geoms: paths.extend(_window_to_paths(poly)) return paths if hasattr(window_geom, "geoms"): for g in window_geom.geoms: paths.extend(_window_to_paths(g)) return paths def _scatter_points(ax, points): ax.scatter(points[:, 0], points[:, 1], s=10) def _draw_window(ax, window_geom): for x, y in _window_to_paths(window_geom): ax.plot(x, y, lw=1.5) def _cell_center_from_bounds(x0, y0, x1, y1): return (0.5 * (x0 + x1), 0.5 * (y0 + y1)) # ----------------------------------------------------------------------------- # Rectangle grid # ----------------------------------------------------------------------------- class RectangleM: """ Rectangle grid structure for quadrat-based method. """ def __init__( self, pp, count_column=3, count_row=3, rectangle_width=0, rectangle_height=0, window=None, ): self.points = _as_points_array(pp) self.mbb = _compute_mbb(self.points) self.window = _ensure_window(window, self.mbb) x_range = self.mbb[2] - self.mbb[0] y_range = self.mbb[3] - self.mbb[1] if rectangle_width and rectangle_height: self.rectangle_width = float(rectangle_width) self.rectangle_height = float(rectangle_height) self.count_column = int(math.ceil(x_range / self.rectangle_width)) self.count_row = int(math.ceil(y_range / self.rectangle_height)) else: self.count_column = int(count_column) self.count_row = int(count_row) self.rectangle_width = x_range / float(self.count_column) self.rectangle_height = y_range / float(self.count_row) self.num = self.count_column * self.count_row def point_location_sta(self): dict_id_count = { j + i * self.count_column: 0 for i in range(self.count_row) for j in range(self.count_column) } x_min, y_min = self.mbb[0], self.mbb[1] for point in self.points: index_x = (point[0] - x_min) // self.rectangle_width index_y = (point[1] - y_min) // self.rectangle_height if index_x == self.count_column: index_x -= 1 if index_y == self.count_row: index_y -= 1 cell_id = int(index_y) * self.count_column + int(index_x) dict_id_count[cell_id] += 1 return dict_id_count def _rect_cell_bounds(self, ix, iy): x0 = self.mbb[0] + ix * self.rectangle_width y0 = self.mbb[1] + iy * self.rectangle_height x1 = x0 + self.rectangle_width y1 = y0 + self.rectangle_height return x0, y0, x1, y1 def _rect_cell_polygon(self, ix, iy): x0, y0, x1, y1 = self._rect_cell_bounds(ix, iy) return box(x0, y0, x1, y1) def _iter_cells(self): for iy in range(self.count_row): for ix in range(self.count_column): cell_id = ix + iy * self.count_column poly = self._rect_cell_polygon(ix, iy) x0, y0, x1, y1 = self._rect_cell_bounds(ix, iy) cx, cy = _cell_center_from_bounds(x0, y0, x1, y1) yield cell_id, poly, (cx, cy) def plot( self, title="Quadrat Count", show="counts", chi2_contrib=None, ): fig, ax = plt.subplots() ax.set_title(title) _scatter_points(ax, self.points) _draw_window(ax, self.window) dict_id_count = self.point_location_sta() patches = [] ann_xy = [] cell_ids = [] for cell_id, poly, (cx, cy) in self._iter_cells(): patches.append(MplPolygon(np.asarray(poly.exterior.coords), closed=True)) ann_xy.append((cx, cy)) cell_ids.append(cell_id) pc = PatchCollection(patches, linewidths=1.0, edgecolor="red", facecolor="none") ax.add_collection(pc) if show == "counts": for (cx, cy), cell_id in zip(ann_xy, cell_ids): ax.text( cx, cy, str(dict_id_count.get(cell_id, 0)), ha="center", va="center" ) elif show == "chi2": if chi2_contrib is None: raise ValueError('chi2_contrib must be provided when show="chi2".') chi2_contrib = np.asarray(chi2_contrib, dtype=float) if chi2_contrib.shape[0] != len(cell_ids): raise ValueError( "chi2_contrib length must match number of plotted cells " f"({len(cell_ids)}). Got {chi2_contrib.shape[0]}." ) pc.set_array(chi2_contrib) pc.set_alpha(0.6) pc.set_facecolor(None) plt.colorbar(pc, ax=ax, label="Chi-square contribution") for (cx, cy), v in zip(ann_xy, chi2_contrib): ax.text(cx, cy, f"{v:.2f}", ha="center", va="center") else: raise ValueError('show must be "counts" or "chi2".') ax.set_aspect("equal", adjustable="box") return ax, cell_ids # ----------------------------------------------------------------------------- # Hexagon grid # ----------------------------------------------------------------------------- class HexagonM: """ Hexagon grid structure for quadrat-based method. """ def __init__(self, pp, lh, window=None): self.points = _as_points_array(pp) self.h_length = float(lh) self.mbb = _compute_mbb(self.points) self.window = _ensure_window(window, self.mbb) range_x = self.mbb[2] - self.mbb[0] range_y = self.mbb[3] - self.mbb[1] self.count_column = 1 if self.h_length / 2.0 < range_x: temp = math.ceil((range_x - self.h_length / 2.0) / (1.5 * self.h_length)) self.count_column += int(temp) self.semi_height = self.h_length * math.cos(math.pi / 6.0) self.count_row_even = 1 if self.semi_height < range_y: temp = math.ceil((range_y - self.semi_height) / (2.0 * self.semi_height)) self.count_row_even += int(temp) self.count_row_odd = int(math.ceil(range_y / (2.0 * self.semi_height))) self.num = self.count_row_odd * ( (self.count_column // 2) + (self.count_column % 2) ) + self.count_row_even * (self.count_column // 2) def point_location_sta(self): semi_cell_length = self.h_length / 2.0 dict_id_count = {} for i in range(self.count_row_even): for j in range(self.count_column): if ( self.count_row_even != self.count_row_odd and i == self.count_row_even - 1 and j % 2 == 1 ): continue dict_id_count[j + i * self.count_column] = 0 x_min = self.mbb[0] y_min = self.mbb[1] for point in self.points: intercept_degree_x = (point[0] - x_min) // semi_cell_length possible_y_index_even = int( (point[1] + self.semi_height - y_min) / (2.0 * self.semi_height) ) possible_y_index_odd = int((point[1] - y_min) / (2.0 * self.semi_height)) if intercept_degree_x % 3 != 1: center_index_x = int((intercept_degree_x + 1) // 3) center_index_y = possible_y_index_odd if center_index_x % 2 == 0: center_index_y = possible_y_index_even dict_id_count[center_index_x + center_index_y * self.count_column] += 1 else: center_index_x = int(intercept_degree_x // 3) center_x = center_index_x * semi_cell_length * 3.0 + x_min center_index_y = possible_y_index_odd center_y = (center_index_y * 2.0 + 1.0) * self.semi_height + y_min if center_index_x % 2 == 0: center_index_y = possible_y_index_even center_y = center_index_y * 2.0 * self.semi_height + y_min if point[1] > center_y: x0, y0 = center_x + self.h_length, center_y x1, y1 = center_x + semi_cell_length, center_y + self.semi_height indicator = -( point[1] - ( (y0 - y1) / (x0 - x1) * point[0] + (x0 * y1 - x1 * y0) / (x0 - x1) ) ) else: x0, y0 = center_x + semi_cell_length, center_y - self.semi_height x1, y1 = center_x + self.h_length, center_y indicator = point[1] - ( (y0 - y1) / (x0 - x1) * point[0] + (x0 * y1 - x1 * y0) / (x0 - x1) ) if indicator <= 0: center_index_x += 1 center_index_y = possible_y_index_odd if center_index_x % 2 == 0: center_index_y = possible_y_index_even dict_id_count[center_index_x + center_index_y * self.count_column] += 1 return dict_id_count def _hex_vertices(self, cx, cy): sh = self.semi_height lh = self.h_length return np.array( [ [cx + lh, cy], [cx + lh / 2.0, cy + sh], [cx - lh / 2.0, cy + sh], [cx - lh, cy], [cx - lh / 2.0, cy - sh], [cx + lh / 2.0, cy - sh], [cx + lh, cy], ], dtype=float, ) def _iter_cells(self): dict_id_count = self.point_location_sta() x_min = self.mbb[0] y_min = self.mbb[1] for cell_id in dict_id_count.keys(): ix = cell_id % self.count_column iy = cell_id // self.count_column cx = ix * self.h_length / 2.0 * 3.0 + x_min cy = iy * self.semi_height * 2.0 + y_min if ix % 2 == 1: cy = (iy * 2.0 + 1.0) * self.semi_height + y_min verts = self._hex_vertices(cx, cy) poly = Polygon(verts[:-1]) yield cell_id, poly, (cx, cy) def plot( self, title="Quadrat Count", show="counts", chi2_contrib=None, ): fig, ax = plt.subplots() ax.set_title(title) _scatter_points(ax, self.points) _draw_window(ax, self.window) dict_id_count = self.point_location_sta() patches = [] ann_xy = [] cell_ids = [] for cell_id, poly, (cx, cy) in self._iter_cells(): patches.append(MplPolygon(np.asarray(poly.exterior.coords), closed=True)) ann_xy.append((cx, cy)) cell_ids.append(cell_id) pc = PatchCollection(patches, linewidths=1.0, edgecolor="red", facecolor="none") ax.add_collection(pc) if show == "counts": for (cx, cy), cell_id in zip(ann_xy, cell_ids): ax.text( cx, cy, str(dict_id_count.get(cell_id, 0)), ha="center", va="center" ) elif show == "chi2": if chi2_contrib is None: raise ValueError('chi2_contrib must be provided when show="chi2".') chi2_contrib = np.asarray(chi2_contrib, dtype=float) if chi2_contrib.shape[0] != len(cell_ids): raise ValueError( "chi2_contrib length must match number of plotted cells " f"({len(cell_ids)}). Got {chi2_contrib.shape[0]}." ) pc.set_array(chi2_contrib) pc.set_alpha(0.6) pc.set_facecolor(None) plt.colorbar(pc, ax=ax, label="Chi-square contribution") for (cx, cy), v in zip(ann_xy, chi2_contrib): ax.text(cx, cy, f"{v:.2f}", ha="center", va="center") else: raise ValueError('show must be "counts" or "chi2".') ax.set_aspect("equal", adjustable="box") return ax, cell_ids # ----------------------------------------------------------------------------- # Quadrat statistic # ----------------------------------------------------------------------------- class QStatistic: """Pearson chi-square quadrat test for complete spatial randomness (CSR). This class partitions the study region into quadrats (rectangles or hexagons), counts the number of events falling in each quadrat, and evaluates departure from CSR using a Pearson chi-square statistic under an equal-intensity CSR null model. The primary outputs are the observed chi-square statistic and its analytical p-value (from the chi-square distribution). Optionally, a simulation-based (Monte Carlo) p-value can be computed by generating CSR realizations within the study window and recomputing the chi-square statistic for each realization. Parameters ---------- pp : :class: `.PointPattern` or array_like Event coordinates as an (n, 2) array-like of floats (x, y). shape : {"rectangle", "hexagon"}, default="rectangle" Quadrat tessellation type. nx : int, default=3 Number of columns when `shape="rectangle"` and `rectangle_width` / `rectangle_height` are not provided. ny : int, default=3 Number of rows when `shape="rectangle"` and `rectangle_width` / `rectangle_height` are not provided. rectangle_width : float, default=0 Target rectangle width. Must be provided together with `rectangle_height`. When both are provided and non-zero, `nx` and `ny` are ignored and the grid dimensions are computed from the point-set MBB. rectangle_height : float, default=0 Target rectangle height. Must be provided together with `rectangle_width`. When both are provided and non-zero, `nx` and `ny` are ignored and the grid dimensions are computed from the point-set MBB. lh : float, default=10 Hexagon side length when `shape="hexagon"`. realizations : int, default=0 Number of CSR simulations used to compute a Monte Carlo p-value. If 0, no simulation-based inference is performed. window : shapely geometry, optional Study window (e.g., Polygon or MultiPolygon). If None, the window is taken to be the axis-aligned MBB rectangle of `points`. rng : None | int | numpy.random.Generator | numpy.random.RandomState, optional Random number generator control for reproducible CSR simulations when `realizations > 0`. - None: create a new ``numpy.random.default_rng()`` - int: use as a seed for ``numpy.random.default_rng(seed)`` - Generator: used as-is - RandomState: wrapped via ``numpy.random.default_rng(RandomState)`` Attributes ---------- points : numpy.ndarray (n, 2) array of event coordinates. mbb : numpy.ndarray Bounding box of `points` as ``[xmin, ymin, xmax, ymax]``. window : shapely geometry Study window used for simulation and plotting. shape : str Quadrat tessellation type: "rectangle" or "hexagon". rng : numpy.random.Generator RNG used for CSR simulations (always stored as a Generator). mr : RectangleM or HexagonM Tessellation manager instance. cell_ids : list of int Cell identifiers included in the test statistic (all grid cells). chi2 : float Observed Pearson chi-square statistic. df : int Degrees of freedom for the analytical chi-square reference distribution, equal to ``k - 1`` where ``k`` is the number of included cells. chi2_pvalue : float Analytical p-value from the chi-square distribution. chi2_contrib : numpy.ndarray Per-cell chi-square contributions aligned with `cell_ids`, computed as ``(O - E)^2 / E`` with ``E = mean(O)`` over included cells. chi2_realizations : numpy.ndarray Simulated chi-square statistics for CSR realizations. Present only when `realizations > 0`. chi2_r_pvalue : float Monte Carlo p-value based on `chi2_realizations`, computed using the standard +1 correction: ``( #{T_sim >= T_obs} + 1 ) / (realizations + 1)``. Present only when `realizations > 0`. Notes ----- - The analytical test uses ``scipy.stats.chisquare`` with expected counts equal across cells (i.e., ``E = mean(O)``). This corresponds to a homogeneous CSR null with equal-area cells. For irregular windows, a fully-correct analytical reference would area-weight expectations. - The simulation-based null generates CSR realizations within `window` with intensity ``n / area(window)``. Reproducibility depends on passing `rng` through to the underlying CSR generator (``poisson(..., rng=...)``). See Also -------- RectangleM : Rectangular tessellation over the point-set MBB. HexagonM : Hexagonal tessellation over the point-set MBB. """ def __init__( self, pp, shape="rectangle", nx=3, ny=3, rectangle_width=0, rectangle_height=0, lh=10, realizations=0, window=None, rng=None, ): self.points = _as_points_array(pp) self.mbb = _compute_mbb(self.points) self.window = _ensure_window(window, self.mbb) self.shape = shape self.rng = _coerce_rng(rng) if shape == "rectangle": self.mr = RectangleM( self.points, count_column=nx, count_row=ny, rectangle_width=rectangle_width, rectangle_height=rectangle_height, window=self.window, ) elif shape == "hexagon": self.mr = HexagonM(self.points, lh, window=self.window) else: raise ValueError('shape must be either "rectangle" or "hexagon".') dict_id_count = self.mr.point_location_sta() obs_counts = np.asarray(list(dict_id_count.values()), dtype=float) self.cell_ids = list(dict_id_count.keys()) self.chi2, self.chi2_pvalue = scipy.stats.chisquare(obs_counts) self.df = obs_counts.size - 1 expected = obs_counts.mean() if obs_counts.size else np.nan if obs_counts.size and expected > 0: self.chi2_contrib = (obs_counts - expected) ** 2 / expected else: self.chi2_contrib = np.full_like(obs_counts, np.nan, dtype=float) # simulation-based inference under CSR (reproducible via rng) if realizations and realizations > 0: n = self.points.shape[0] intensity = n / float(self.window.area) reals = poisson(self.window, intensity, realizations, rng=self.rng) chi2_realizations = [] for i in range(realizations): ri = reals[i] pts_i = getattr(ri, "points", ri) pts_i = _as_points_array(pts_i) if shape == "rectangle": mr_temp = RectangleM( pts_i, count_column=self.mr.count_column, count_row=self.mr.count_row, window=self.window, ) else: mr_temp = HexagonM(pts_i, self.mr.h_length, window=self.window) dtemp = mr_temp.point_location_sta() sim_counts = np.asarray(list(dtemp.values()), dtype=float) chi2_sim, _p = scipy.stats.chisquare(sim_counts) chi2_realizations.append(chi2_sim) self.chi2_realizations = np.asarray(chi2_realizations, dtype=float) self.chi2_r_pvalue = (np.sum(self.chi2_realizations >= self.chi2) + 1.0) / ( realizations + 1.0 ) def plot(self, title="Quadrat Count", show="counts"): if show == "counts": ax, _cell_ids = self.mr.plot(title=title, show="counts") return ax if show == "chi2": # get plotted ids _, plotted_ids = self.mr.plot(title=title, show="counts") plt.close() contrib_map = {cid: v for cid, v in zip(self.cell_ids, self.chi2_contrib)} plotted_contrib = np.asarray( [contrib_map.get(cid, np.nan) for cid in plotted_ids], dtype=float ) ax, _ = self.mr.plot(title=title, show="chi2", chi2_contrib=plotted_contrib) return ax raise ValueError('show must be either "counts" or "chi2".') pointpats-2.5.5/pointpats/random.py000066400000000000000000000720361514706172100174330ustar00rootroot00000000000000"""Spatial Point Pattern Simulation Module ======================================= This module provides tools to simulate spatial point patterns for use in spatial statistics, geographic analysis, and spatial modeling. Patterns can be generated within arbitrary hulls, defined as polygons, bounding boxes, or other spatial objects. Currently supported point process models include: - `poisson`: Simulates a homogeneous Poisson point process. - `normal`: Simulates points from a multivariate normal distribution, constrained within a hull. - `cluster_poisson`: Simulates cluster patterns using a two-stage Poisson process. - `cluster_normal`: Simulates clusters around randomly placed seed points using a normal distribution. - `strauss`: Simulates a Strauss process with pairwise interaction constraints. Each distribution supports control over intensity, point count, replication, and randomness. Geometry utilities support arbitrary spatial hulls and containment logic. """ import numpy from scipy.spatial import cKDTree from .geometry import ( spatial, area as _area, centroid as _centroid, contains as _contains, bbox as _bbox, prepare_hull as _prepare_hull, HULL_TYPES, ) # ------------------------------------------------------------ # # Utilities # # ------------------------------------------------------------ # def parse_size_and_intensity(hull, intensity=None, size=None): """ Given a hull, an intensity, and a size int/tuple, correctly compute the resulting missing quantities. Defaults to 100 points in one replication, meaning the intensity will be computed on the fly if nothing is provided. Parameters ---------- hull : A geometry-like object This encodes the "space" in which to simulate the normal pattern. All points will lie within this hull. Supported values are: - a bounding box encoded in a numpy array as numpy.array([xmin, ymin, xmax, ymax]) - an (N,2) array of points for which the bounding box will be computed & used - a shapely polygon/multipolygon - a scipy convexh hull intensity : float the number of observations per unit area in the hull to use. If provided, then the number of observations is determined using the intensity * area(hull) and the size is assumed to represent n_replications (if provided). size : tuple or int a tuple of (n_observations, n_replications), where the first number is the number of points to simulate in each replication and the second number is the number of total replications. So, (10, 4) indicates 10 points, 4 times. If an integer is provided and intensity is None, n_replications is assumed to be 1. If size is an integer and intensity is also provided, then size indicates n_replications, and the number of observations is computed on the fly using intensity and area. """ if size is None: if intensity is not None: # if intensity is provided, assume # n_observations n_observations = int(_area(hull) * intensity) else: # default to 100 points n_observations = 100 intensity = n_observations / _area(hull) n_simulations = 1 size = (n_observations, n_simulations) elif isinstance(size, tuple): if len(size) == 2 and intensity is None: n_observations, n_simulations = size intensity = n_observations / _area(hull) elif len(size) == 2 and intensity is not None: raise ValueError( "Either intensity or size as (n observations, n simulations)" " can be provided. Providing both creates statistical conflicts." " between the requested intensity and implied intensity by" " the number of observations and the area of the hull. If" " you want to specify the intensity, use the intensity argument" " and set size equal to the number of simulations." ) else: raise ValueError( f"Intensity and size not understood. Provide size as a tuple" f" containing (number of observations, number of simulations)" f" with no specified intensity, or an intensity and size equal" f" to the number of simulations." f" Recieved: `intensity={intensity}, size={size}`" ) elif isinstance(size, int): # assume int size with specified intensity means n_simulations at x intensity if intensity is not None: n_observations = int(intensity * _area(hull)) n_simulations = size else: # assume we have one replication at the specified number of points n_simulations = 1 n_observations = size intensity = n_observations / _area(hull) else: raise ValueError( f"Intensity and size not understood. Provide size as a tuple" f" containing (number of observations, number of simulations)" f" with no specified intensity, or an intensity and size equal" f" to the number of simulations." f" Recieved: `intensity={intensity}, size={size}`" ) return (n_observations, n_simulations, intensity) # ------------------------------------------------------------ # # Distributions # # ------------------------------------------------------------ # def poisson(hull, intensity=None, size=None, rng=None): """ Simulate a poisson random point process with a specified intensity. Parameters ---------- hull : A geometry-like object This encodes the "space" in which to simulate the normal pattern. All points will lie within this hull. Supported values are: - a bounding box encoded in a numpy array as numpy.array([xmin, ymin, xmax, ymax]) - an (N,2) array of points for which the bounding box will be computed & used - a shapely polygon/multipolygon - a scipy convexh hull intensity : float the number of observations per unit area in the hull to use. If provided, then size must be an integer describing the number of replications to use. size : tuple or int a tuple of (n_observations, n_replications), where the first number is the number of points to simulate in each replication and the second number is the number of total replications. So, (10, 4) indicates 10 points, 4 times. If an integer is provided and intensity is None, n_replications is assumed to be 1. If size is an integer and intensity is also provided, then size indicates n_replications, and the number of observations is computed from the intensity. rng : int, numpy.random.Generator, or None, optional A source of randomness. This can be: - A `numpy.random.Generator` instance (recommended) - An `int` seed, used to initialize a new Generator - `None` (default), which uses a new `numpy.random.default_rng()` instance This interface follows Scientific Python SPEC 7, ensuring consistent and reproducible random number generation across libraries. Returns ------- : numpy.ndarray either an (n_replications, n_observations, 2) or (n_observations,2) array containing the simulated realizations. """ if isinstance(hull, numpy.ndarray): if hull.shape == (4,): hull = hull else: hull = _prepare_hull(hull) n_observations, n_simulations, intensity = parse_size_and_intensity( hull, intensity=intensity, size=size ) result = numpy.empty((n_simulations, n_observations, 2)) bbox = _bbox(hull) rng = numpy.random.default_rng(rng) for i_replication in range(n_simulations): generating = True i_observation = 0 while i_observation < n_observations: x, y = ( rng.uniform(bbox[0], bbox[2]), rng.uniform(bbox[1], bbox[3]), ) if _contains(hull, x, y): result[i_replication, i_observation] = (x, y) i_observation += 1 return result.squeeze() def normal(hull, center=None, cov=None, size=None, rng=None): """ Simulate a multivariate random normal point cluster Parameters ---------- hull : A geometry-like object This encodes the "space" in which to simulate the normal pattern. All points will lie within this hull. Supported values are: - a bounding box encoded in a numpy array as numpy.array([xmin, ymin, xmax, ymax]) - an (N,2) array of points for which the bounding box will be computed & used - a shapely polygon/multipolygon - a scipy convexh hull center : iterable of shape (2, ) A point where the simulations will be centered. cov : float or a numpy array of shape (2,2) either the standard deviation of an independent and identically distributed normal distribution, or a 2 by 2 covariance matrix expressing the covariance of the x and y for the distribution. Default is half of the width or height of the hull's bounding box, whichever is larger. size : tuple or int a tuple of (n_observations, n_replications), where the first number is the number of points to simulate in each replication and the second number is the number of total replications. So, (10, 4) indicates 10 points, 4 times. If an integer is provided, n_replications is assumed to be 1. rng : int, numpy.random.Generator, or None, optional A source of randomness. This can be: - A `numpy.random.Generator` instance (recommended) - An `int` seed, used to initialize a new Generator - `None` (default), which uses a new `numpy.random.default_rng()` instance This interface follows Scientific Python SPEC 7, ensuring consistent and reproducible random number generation across libraries. Returns ------- : numpy.ndarray either an (n_replications, n_observations, 2) or (n_observations,2) array containing the simulated realizations. """ if isinstance(hull, numpy.ndarray): if hull.shape == (4,): hull = hull else: hull = _prepare_hull(hull) if center is None: center = _centroid(hull) n_observations, n_simulations, intensity = parse_size_and_intensity( hull, intensity=None, size=size ) if cov is None: bbox = _bbox(hull) width = bbox[2] - bbox[0] height = bbox[3] - bbox[1] cov = numpy.maximum(width / 2, height / 2) ** 2 if isinstance(cov, (int, float)): sd = cov cov = numpy.eye(2) * sd elif isinstance(cov, numpy.ndarray): if cov.ndim == 2: assert cov.shape == (2, 2), "Bivariate covariance matrices must be 2 by 2" elif cov.ndim == 3: assert cov.shape[1:] == ( 2, 2, ), "3-dimensional covariance matrices must have shape (n_simulations, 2,2)" assert ( cov.shape[0] == n_simulations ), "3-dimensional covariance matrices must have shape (n_simulations, 2,2)" else: raise ValueError( "`cov` argument must be a float (signifying a standard deviation)" " or a 2 by 2 array expressing the covariance matrix of the " " bivariate normal distribution." ) result = numpy.empty((n_simulations, n_observations, 2)) bbox = _bbox(hull) rng = numpy.random.default_rng(rng) for i_replication in range(n_simulations): generating = True i_observation = 0 replication_cov = cov[i] if cov.ndim == 3 else cov replication_sd = numpy.diagonal(replication_cov) ** 0.5 replication_cor = (1 / replication_sd) * replication_cov * (1 / replication_sd) while i_observation < n_observations: # candidate = numpy.random.multivariate_normal((0, 0), replication_cor) candidate = rng.multivariate_normal((0, 0), replication_cor) x, y = center + candidate * replication_sd if _contains(hull, x, y): result[i_replication, i_observation] = (x, y) i_observation += 1 return result.squeeze() def cluster_poisson( hull, intensity=None, size=None, n_seeds=2, cluster_radius=None, rng=None ): """ Simulate a cluster poisson random point process with a specified intensity & number of seeds. A cluster poisson process is a poisson process where the center of each "cluster" is itself distributed according to a spatial poisson process. Parameters ---------- hull : A geometry-like object This encodes the "space" in which to simulate the normal pattern. All points will lie within this hull. Supported values are: - a bounding box encoded in a numpy array as numpy.array([xmin, ymin, xmax, ymax]) - an (N,2) array of points for which the bounding box will be computed & used - a shapely polygon/multipolygon - a scipy convexh hull intensity : float the number of observations per unit area in the hull to use. If provided, then size must be an integer describing the number of replications to use. size : tuple or int a tuple of (n_observations, n_replications), where the first number is the number of points to simulate in each replication and the second number is the number of total replications. So, (10, 4) indicates 10 points, 4 times. If an integer is provided and intensity is None, n_replications is assumed to be 1. If size is an integer and intensity is also provided, then size indicates n_replications, and the number of observations is computed from the intensity. n_seeds : int the number of sub-clusters to use. cluster_radius : float or iterable the radius of each cluster. If a float, the same radius is used for all clusters. If an array, then there must be the same number of radii as clusters. If None, 50% of the minimum inter-point distance is used, which may fluctuate across replications. rng : int, numpy.random.Generator, or None, optional A source of randomness. This can be: - A `numpy.random.Generator` instance (recommended) - An `int` seed, used to initialize a new Generator - `None` (default), which uses a new `numpy.random.default_rng()` instance This interface follows Scientific Python SPEC 7, ensuring consistent and reproducible random number generation across libraries. Returns ------- : numpy.ndarray either an (n_replications, n_observations, 2) or (n_observations,2) array containing the simulated realizations. """ if isinstance(hull, numpy.ndarray): if hull.shape == (4,): hull = hull else: hull = _prepare_hull(hull) if isinstance(cluster_radius, numpy.ndarray): cluster_radii = cluster_radius.flatten() assert len(cluster_radii) == n_seeds, ( f"number of radii provided ({len(cluster_radii)})" f"does not match number of clusters requested" f" ({n_seeds})." ) elif isinstance(cluster_radius, (int, float)): cluster_radii = [cluster_radius] * n_seeds n_observations, n_simulations, intensity = parse_size_and_intensity( hull, intensity=intensity, size=size ) result = numpy.empty((n_simulations, n_observations, 2)) hull_area = _area(hull) rng = numpy.random.default_rng(rng) center_seeds = rng.integers(100_000, size=n_simulations) for i_replication in range(n_simulations): seeds = poisson(hull, size=n_seeds, rng=[center_seeds[i_replication]]) if cluster_radius is None: # default cluster radius is one half the minimum distance between seeds cluster_radii = [spatial.distance.pdist(seeds).min() * 0.5] * n_seeds clusters = numpy.array_split(result[i_replication], n_seeds) for i_cluster, radius in enumerate(cluster_radii): seed = seeds[i_cluster] cluster_points = clusters[i_cluster] n_in_cluster = len(cluster_points) if n_in_cluster == 1: clusters[i_cluster] = seed continue if n_in_cluster < 1: raise Exception( "There are too many clusters requested for the " " inputted number of samples. Reduce `n_seeds` or" " increase the number of sampled points." ) candidates = _uniform_circle( n_in_cluster - 1, radius=radius, center=seed, hull=hull, rng=rng, ) clusters[i_cluster] = numpy.row_stack((seed, candidates)) result[i_replication] = numpy.row_stack(clusters) return result.squeeze() def cluster_normal(hull, cov=None, size=None, n_seeds=2, rng=None): """ Simulate a cluster poisson random point process with a specified intensity & number of seeds. A cluster poisson process is a poisson process where the center of each "cluster" is itself distributed according to a spatial poisson process. Parameters ---------- hull : A geometry-like object This encodes the "space" in which to simulate the normal pattern. All points will lie within this hull. Supported values are: - a bounding box encoded in a numpy array as numpy.array([xmin, ymin, xmax, ymax]) - an (N,2) array of points for which the bounding box will be computed & used - a shapely polygon/multipolygon - a scipy convexh hull cov : float, int, or numpy.ndarray of shape (2,2) The covariance structure for clusters. By default, this is the squared average distance between cluster seeds. size : tuple or int a tuple of (n_observations, n_replications), where the first number is the number of points to simulate in each replication and the second number is the number of total replications. So, (10, 4) indicates 10 points, 4 times. If an integer is provided and intensity is None, n_replications is assumed to be 1. If size is an integer and intensity is also provided, then size indicates n_replications, and the number of observations is computed from the intensity. n_seeds : int the number of sub-clusters to use. rng : int, numpy.random.Generator, or None, optional A source of randomness. This can be: - A `numpy.random.Generator` instance (recommended) - An `int` seed, used to initialize a new Generator - `None` (default), which uses a new `numpy.random.default_rng()` instance This interface follows Scientific Python SPEC 7, ensuring consistent and reproducible random number generation across libraries. Returns ------- : numpy.ndarray either an (n_replications, n_observations, 2) or (n_observations,2) array containing the simulated realizations. """ if isinstance(hull, numpy.ndarray): if hull.shape == (4,): hull = hull else: hull = _prepare_hull(hull) n_observations, n_simulations, intensity = parse_size_and_intensity( hull, intensity=None, size=size ) result = numpy.empty((n_simulations, n_observations, 2)) rng = numpy.random.default_rng(rng) seeds = rng.integers(100_000, size=n_simulations) for i_replication in range(n_simulations): centers = poisson(hull, size=n_seeds, rng=seeds[i_replication]) if cov is None: cov = spatial.distance.pdist(centers).mean() ** 2 clusters = numpy.array_split(result[i_replication], n_seeds) candidate_seeds = rng.integers(100_000, size=n_seeds) for i_cluster, center in enumerate(centers): cluster_points = clusters[i_cluster] n_in_cluster = len(cluster_points) if n_in_cluster == 1: clusters[i_cluster] = center continue if n_in_cluster < 1: raise Exception( "There are too many clusters requested for the " " inputted number of samples. Reduce `n_seeds` or" " increase the number of sampled points." ) candidates = normal( hull, center=center, cov=cov, size=n_in_cluster - 1, rng=candidate_seeds[i_cluster], ) clusters[i_cluster] = numpy.row_stack((center, candidates)) result[i_replication] = numpy.row_stack(clusters) return result.squeeze() def _uniform_circle( n, radius=1.0, center=(0.0, 0.0), burn=2, verbose=False, hull=None, seed=None, rng=None, ): """ Generate n points within a circle of given radius. Parameters ---------- n : int Number of points. radius : float Radius of the circle. center : tuple Coordinates of the center. burn : int number of coordinates to simulate at a time. This is the "chunk" size sent to numpy.random.uniform each iteration of the rejection sampler seed : int or None, optional A seed to initialize the NumPy default random number generator (`numpy.random.default_rng`). If `None` (the default), the generator is initialized with entropy from the operating system, producing different sequences each time. Setting a specific integer seed ensures that the sequence of random numbers is reproducible. rng : int, numpy.random.Generator, or None, optional A source of randomness. This can be: - A `numpy.random.Generator` instance (recommended) - An `int` seed, used to initialize a new Generator - `None` (default), which uses a new `numpy.random.default_rng()` instance This interface follows Scientific Python SPEC 7, ensuring consistent and reproducible random number generation across libraries. Returns ------- : array (n, 2), coordinates of generated points """ good = numpy.zeros((n, 2), float) c = 0 center_x, center_y = center r = radius r2 = r * r it = 0 if rng is None: rng = numpy.random.default_rng(seed) while c < n: x = rng.uniform(-r, r, (burn * n, 1)) y = rng.uniform(-r, r, (burn * n, 1)) if hull is None: in_hull = True else: in_hull = numpy.asarray( [ _contains(hull, xi + center_x, yi + center_y) for xi, yi in numpy.column_stack((x, y)) ] ).reshape(-1, 1) ids, *_ = numpy.where(((x * x + y * y) <= r2) & in_hull) candidates = numpy.hstack((x, y))[ids] nc = candidates.shape[0] need = n - c if nc > need: # more than we need good[c:] = candidates[:need] else: # use them all and keep going good[c : c + nc] = candidates c += nc it += 1 if verbose: print("Iterations: {}".format(it)) return good + numpy.asarray(center) def _pairwise_count_kdtree(points, r): """Count number of pairs within distance r using KDTree""" tree = cKDTree(points) pairs = tree.query_pairs(r) return len(pairs) def strauss( hull, intensity=None, size=None, gamma=0.1, r=0.05, n_iter=5000, rng=None, max_iter=10, ): """ Simulate a realization of the Strauss spatial point process using Metropolis-Hastings within a Gibbs sampler. Parameters ---------- hull : array-like or geometry-like The spatial domain in which to simulate the point pattern. Supported formats include: - A bounding box as a NumPy array: np.array([xmin, ymin, xmax, ymax]) - An (N, 2) array of 2D points (bounding box is inferred) - A Shapely Polygon or MultiPolygon - A SciPy ConvexHull intensity : float, optional Target number of points per unit area. Used to derive the number of points if `size` is not explicitly given. Default is 100. size : int or tuple of (int, int), optional Either the number of points to generate (if `intensity` is None), or a tuple (n_observations, n_replications). If an integer and `intensity` is provided, it is interpreted as the number of replications. gamma : float, optional Strauss interaction parameter: - `gamma < 1`: inhibition (repulsion) - `gamma = 1`: homogeneous Poisson process (no interaction) - `gamma > 1`: clustering Default is 0.1. r : float, optional Interaction radius. Pairs of points closer than this distance contribute to the Strauss interaction term. Default is 0.05. n_iter : int, optional Number of iterations for the Metropolis-Hastings sampler per replication. Default is 5000. rng : int, numpy.random.Generator, or None, optional A source of randomness. This can be: - A `numpy.random.Generator` instance (recommended) - An `int` seed, used to initialize a new Generator - `None` (default), which uses a new `numpy.random.default_rng()` instance This interface follows Scientific Python SPEC 7, ensuring consistent and reproducible random number generation across libraries. max_iter : int, optional Maximum number of regeneration attempts per replication if a valid point pattern of the desired size is not achieved. Default is 10. Returns ------- numpy.ndarray A simulated realization of the Strauss point process: - If `n_replications` > 1: shape (n_replications, n_observations, 2) - If `n_replications` = 1: shape (n_observations, 2) Raises ------ RuntimeError If a valid pattern with the desired number of points cannot be generated within `max_iter` attempts. This may indicate that the parameter combination (especially low `gamma` and large `r`) makes the configuration too restrictive. Notes ----- The Strauss process models spatial inhibition or clustering via pairwise interactions. This implementation uses a Metropolis-Hastings sampler with biased birth-death moves to encourage convergence toward the desired number of points. """ if isinstance(hull, numpy.ndarray): if hull.shape != (4,): hull = _prepare_hull(hull) n_observations, n_simulations, intensity = parse_size_and_intensity( hull, intensity=intensity, size=size ) result = numpy.empty((n_simulations, n_observations, 2)) rng = numpy.random.default_rng(rng) for i_replication in range(n_simulations): found = False gen_iter = 0 while not found and gen_iter < max_iter: # Over-sample initially to improve convergence initial_n = int(n_observations * 1.5) points = poisson(hull, intensity=initial_n, rng=rng) for _ in range(n_iter): if len(points) < n_observations or rng.random() < 0.5: new_point = poisson(hull, 1, rng=rng) trial_points = numpy.vstack([points, new_point]) k_old = _pairwise_count_kdtree(points, r) k_new = _pairwise_count_kdtree(trial_points, r) ratio = n_observations * (gamma ** (k_new - k_old)) ratio /= len(points) + 1 if rng.random() < min(1, ratio): points = trial_points else: # death move if len(points) > n_observations: idx = rng.integers(0, len(points)) trial_points = numpy.delete(points, idx, axis=0) k_old = _pairwise_count_kdtree(points, r) k_new = _pairwise_count_kdtree(trial_points, r) den = n_observations * (gamma ** (k_old - k_new)) if den == 0: raise RuntimeError( "The number of points requested is too large for the specified " "values of gamma and radius. Try reducing `intensity` or increasing " "`gamma`, `radius`, or `max_iter`." ) ratio = len(points) / den if rng.random() < min(1, ratio): points = trial_points if points.shape[0] >= n_observations: result[i_replication] = points[:n_observations] found = True gen_iter += 1 if not found: raise RuntimeError( "The number of points requested is too large for the specified " "values of gamma and radius. Try reducing `intensity` or increasing " "`gamma`, `radius`, or `max_iter`." ) return result.squeeze() pointpats-2.5.5/pointpats/spacetime.py000066400000000000000000001614521514706172100201260ustar00rootroot00000000000000""" Methods for identifying space-time interaction in spatio-temporal event data. """ __author__ = ( "Eli Knaap ", "Nicholas Malizia ", "Sergio J. Rey ", "Philip Stephens ", ) __all__ = [ "SpaceTimeEvents", "knox", "mantel", "jacquez", "modified_knox", "Knox", "KnoxLocal", ] import os from datetime import date from warnings import warn import geopandas as gpd import libpysal as lps import numpy as np import pandas import pandas as pd import scipy.stats as stats from libpysal import cg from libpysal.graph import Graph from pandas.api.types import is_numeric_dtype from scipy.spatial import KDTree from scipy.stats import hypergeom, poisson from shapely.geometry import LineString class SpaceTimeEvents: """ Method for reformatting event data stored in a shapefile for use in calculating metrics of spatio-temporal interaction. Parameters ---------- path : string the path to the appropriate shapefile, including the file name and extension time : string column header in the DBF file indicating the column containing the time stamp. infer_timestamp : bool, optional if the column containing the timestamp is formatted as calendar dates, try to coerce them into Python datetime objects (the default is False). Attributes ---------- n : int number of events. x : array (n, 1), array of the x coordinates for the events. y : array (n, 1), array of the y coordinates for the events. t : array (n, 1), array of the temporal coordinates for the events. space : array (n, 2), array of the spatial coordinates (x,y) for the events. time : array (n, 2), array of the temporal coordinates (t,1) for the events, the second column is a vector of ones. Examples -------- Read in the example shapefile data, ensuring to omit the file extension. In order to successfully create the event data the .dbf file associated with the shapefile should have a column of values that are a timestamp for the events. This timestamp may be a numerical value or a date. Date inference was added in version 1.6. >>> import libpysal as lps >>> path = lps.examples.get_path("burkitt.shp") >>> from pointpats import SpaceTimeEvents Create an instance of SpaceTimeEvents from a shapefile, where the temporal information is stored in a column named "T". >>> events = SpaceTimeEvents(path,'T') See how many events are in the instance. >>> events.n 188 Check the spatial coordinates of the first event. >>> events.space[0] array([300., 302.]) Check the time of the first event. >>> events.t[0] array([413.]) Calculate the time difference between the first two events. >>> events.t[1] - events.t[0] array([59.]) New, in 1.6, date support: Now, create an instance of SpaceTimeEvents from a shapefile, where the temporal information is stored in a column named "DATE". >>> events = SpaceTimeEvents(path,'DATE') See how many events are in the instance. >>> events.n 188 Check the spatial coordinates of the first event. >>> events.space[0] array([300., 302.]) Check the time of the first event. Note that this value is equivalent to 413 days after January 1, 1900. >>> events.t[0][0] datetime.date(1901, 2, 16) Calculate the time difference between the first two events. >>> (events.t[1][0] - events.t[0][0]).days 59 """ def __init__(self, path, time_col, infer_timestamp=False): shp = lps.io.open(path) head, tail = os.path.split(path) dbf_tail = tail.split(".")[0] + ".dbf" dbf = lps.io.open(lps.examples.get_path(dbf_tail)) # extract the spatial coordinates from the shapefile x = [coords[0] for coords in shp] y = [coords[1] for coords in shp] self.n = n = len(shp) x = np.array(x) y = np.array(y) self.x = np.reshape(x, (n, 1)) self.y = np.reshape(y, (n, 1)) self.space = np.hstack((self.x, self.y)) # extract the temporal information from the database if infer_timestamp: col = dbf.by_col(time_col) if isinstance(col[0], date): day1 = min(col) col = [(d - day1).days for d in col] t = np.array(col) else: print( "Unable to parse your time column as Python datetime \ objects, proceeding as integers." ) t = np.array(col) else: t = np.array(dbf.by_col(time_col)) line = np.ones((n, 1)) self.t = np.reshape(t, (n, 1)) self.time = np.hstack((self.t, line)) # close open objects dbf.close() shp.close() def knox(s_coords, t_coords, delta, tau, permutations=99, debug=False): """ Knox test for spatio-temporal interaction. :cite:`Knox:1964` Parameters ---------- s_coords : array (n, 2), spatial coordinates. t_coords : array (n, 1), temporal coordinates. delta : float threshold for proximity in space. tau : float threshold for proximity in time. permutations : int, optional the number of permutations used to establish pseudo- significance (the default is 99). debug : bool, optional if true, debugging information is printed (the default is False). Returns ------- knox_result : dictionary contains the statistic (stat) for the test and the associated p-value (pvalue). stat : float value of the knox test for the dataset. pvalue : float pseudo p-value associated with the statistic. counts : int count of space time neighbors. Examples -------- >>> import numpy as np >>> import libpysal as lps >>> from pointpats import SpaceTimeEvents, knox Read in the example data and create an instance of SpaceTimeEvents. >>> path = lps.examples.get_path("burkitt.shp") >>> events = SpaceTimeEvents(path,'T') Set the random seed generator. This is used by the permutation based inference to replicate the pseudo-significance of our example results - the end-user will normally omit this step. >>> np.random.seed(100) Run the Knox test with distance and time thresholds of 20 and 5, respectively. This counts the events that are closer than 20 units in space, and 5 units in time. >>> result = knox(events.space, events.t, delta=20, tau=5, permutations=99) Next, we examine the results. First, we call the statistic from the results dictionary. This reports that there are 13 events close in both space and time, according to our threshold definitions. >>> result['stat'] == 13 True Next, we look at the pseudo-significance of this value, calculated by permuting the timestamps and rerunning the statistics. In this case, the results indicate there is likely no space-time interaction between the events. >>> print("%2.2f"%result['pvalue']) 0.17 """ warn("This function is deprecated. Use Knox", DeprecationWarning, stacklevel=2) # Do a kdtree on space first as the number of ties (identical points) is # likely to be lower for space than time. kd_s = cg.KDTree(s_coords) neigh_s = kd_s.query_pairs(delta) tau2 = tau * tau ids = np.array(list(neigh_s)) # For the neighboring pairs in space, determine which are also time # neighbors d_t = (t_coords[ids[:, 0]] - t_coords[ids[:, 1]]) ** 2 n_st = sum(d_t <= tau2) knox_result = {"stat": n_st[0]} if permutations: joint = np.zeros((permutations, 1), int) for p in range(permutations): np.random.shuffle(t_coords) d_t = (t_coords[ids[:, 0]] - t_coords[ids[:, 1]]) ** 2 joint[p] = np.sum(d_t <= tau2) larger = sum(joint >= n_st[0]) if (permutations - larger) < larger: larger = permutations - larger p_sim = (larger + 1.0) / (permutations + 1.0) knox_result["pvalue"] = p_sim return knox_result def mantel( s_coords, t_coords, permutations=99, scon=1.0, spow=-1.0, tcon=1.0, tpow=-1.0 ): """ Standardized Mantel test for spatio-temporal interaction. :cite:`Mantel:1967` Parameters ---------- s_coords : array (n, 2), spatial coordinates. t_coords : array (n, 1), temporal coordinates. permutations : int, optional the number of permutations used to establish pseudo- significance (the default is 99). scon : float, optional constant added to spatial distances (the default is 1.0). spow : float, optional value for power transformation for spatial distances (the default is -1.0). tcon : float, optional constant added to temporal distances (the default is 1.0). tpow : float, optional value for power transformation for temporal distances (the default is -1.0). Returns ------- mantel_result : dictionary contains the statistic (stat) for the test and the associated p-value (pvalue). stat : float value of the knox test for the dataset. pvalue : float pseudo p-value associated with the statistic. Examples -------- >>> import numpy as np >>> import libpysal as lps >>> from pointpats import SpaceTimeEvents, mantel Read in the example data and create an instance of SpaceTimeEvents. >>> path = lps.examples.get_path("burkitt.shp") >>> events = SpaceTimeEvents(path,'T') Set the random seed generator. This is used by the permutation based inference to replicate the pseudo-significance of our example results - the end-user will normally omit this step. >>> np.random.seed(100) The standardized Mantel test is a measure of matrix correlation between the spatial and temporal distance matrices of the event dataset. The following example runs the standardized Mantel test without a constant or transformation; however, as recommended by :cite:`Mantel:1967`, these should be added by the user. This can be done by adjusting the constant and power parameters. >>> result = mantel(events.space, events.t, 99, scon=1.0, spow=-1.0, tcon=1.0, tpow=-1.0) Next, we examine the result of the test. >>> print("%6.6f"%result['stat']) 0.048368 Finally, we look at the pseudo-significance of this value, calculated by permuting the timestamps and rerunning the statistic for each of the 99 permutations. According to these parameters, the results indicate space-time interaction between the events. >>> print("%2.2f"%result['pvalue']) 0.01 """ t = t_coords s = s_coords n = len(t) # calculate the spatial and temporal distance matrices for the events distmat = cg.distance_matrix(s) timemat = cg.distance_matrix(t) # calculate the transformed standardized statistic timevec = (timemat[np.tril_indices(timemat.shape[0], k=-1)] + tcon) ** tpow distvec = (distmat[np.tril_indices(distmat.shape[0], k=-1)] + scon) ** spow stat = stats.pearsonr(timevec, distvec)[0].sum() # return the results (if no inference) if not permutations: return stat # loop for generating a random distribution to assess significance dist = [] for _i in range(permutations): trand = _shuffle_matrix(timemat, np.arange(n)) timevec = (trand[np.tril_indices(trand.shape[0], k=-1)] + tcon) ** tpow m = stats.pearsonr(timevec, distvec)[0].sum() dist.append(m) # establish the pseudo significance of the observed statistic distribution = np.array(dist) greater = np.ma.masked_greater_equal(distribution, stat) count = np.ma.count_masked(greater) pvalue = (count + 1.0) / (permutations + 1.0) # report the results mantel_result = {"stat": stat, "pvalue": pvalue} return mantel_result def jacquez(s_coords, t_coords, k, permutations=99): """ Jacquez k nearest neighbors test for spatio-temporal interaction. :cite:`Jacquez:1996` Parameters ---------- s_coords : array (n, 2), spatial coordinates. t_coords : array (n, 1), temporal coordinates. k : int the number of nearest neighbors to be searched. permutations : int, optional the number of permutations used to establish pseudo- significance (the default is 99). Returns ------- jacquez_result : dictionary contains the statistic (stat) for the test and the associated p-value (pvalue). stat : float value of the Jacquez k nearest neighbors test for the dataset. pvalue : float p-value associated with the statistic (normally distributed with k-1 df). Examples -------- >>> import numpy as np >>> import libpysal as lps >>> from pointpats import SpaceTimeEvents, jacquez Read in the example data and create an instance of SpaceTimeEvents. >>> path = lps.examples.get_path("burkitt.shp") >>> events = SpaceTimeEvents(path,'T') The Jacquez test counts the number of events that are k nearest neighbors in both time and space. The following runs the Jacquez test on the example data and reports the resulting statistic. In this case, there are 12 instances where events are nearest neighbors in both space and time. # turning off as kdtree changes from scipy < 0.12 return 13 >>> np.random.seed(100) >>> result = jacquez(events.space, events.t ,k=3,permutations=99) >>> print(result['stat']) 12 The significance of this can be assessed by calling the p- value from the results dictionary, as shown below. Again, no space-time interaction is observed. >>> result['pvalue'] < 0.01 False """ time = t_coords space = s_coords n = len(time) # calculate the nearest neighbors in space and time separately knnt = lps.weights.KNN.from_array(time, k) knns = lps.weights.KNN.from_array(space, k) nnt = knnt.neighbors nns = knns.neighbors knn_sum = 0 # determine which events are nearest neighbors in both space and time for i in range(n): t_neighbors = nnt[i] s_neighbors = nns[i] check = set(t_neighbors) inter = check.intersection(s_neighbors) count = len(inter) knn_sum += count stat = knn_sum # return the results (if no inference) if not permutations: return stat # loop for generating a random distribution to assess significance dist = [] for _p in range(permutations): j = 0 trand = np.random.permutation(time) knnt = lps.weights.KNN.from_array(trand, k) nnt = knnt.neighbors for i in range(n): t_neighbors = nnt[i] s_neighbors = nns[i] check = set(t_neighbors) inter = check.intersection(s_neighbors) count = len(inter) j += count dist.append(j) # establish the pseudo significance of the observed statistic distribution = np.array(dist) greater = np.ma.masked_greater_equal(distribution, stat) count = np.ma.count_masked(greater) pvalue = (count + 1.0) / (permutations + 1.0) # report the results jacquez_result = {"stat": stat, "pvalue": pvalue} return jacquez_result def modified_knox(s_coords, t_coords, delta, tau, permutations=99): """ Baker's modified Knox test for spatio-temporal interaction. :cite:`Baker:2004` Parameters ---------- s_coords : array (n, 2), spatial coordinates. t_coords : array (n, 1), temporal coordinates. delta : float threshold for proximity in space. tau : float threshold for proximity in time. permutations : int, optional the number of permutations used to establish pseudo- significance (the default is 99). Returns ------- modknox_result : dictionary contains the statistic (stat) for the test and the associated p-value (pvalue). stat : float value of the modified knox test for the dataset. pvalue : float pseudo p-value associated with the statistic. Examples -------- >>> import numpy as np >>> import libpysal as lps >>> from pointpats import SpaceTimeEvents, modified_knox Read in the example data and create an instance of SpaceTimeEvents. >>> path = lps.examples.get_path("burkitt.shp") >>> events = SpaceTimeEvents(path, 'T') Set the random seed generator. This is used by the permutation based inference to replicate the pseudo-significance of our example results - the end-user will normally omit this step. >>> np.random.seed(100) Run the modified Knox test with distance and time thresholds of 20 and 5, respectively. This counts the events that are closer than 20 units in space, and 5 units in time. >>> result = modified_knox(events.space, events.t, delta=20, tau=5, permutations=99) Next, we examine the results. First, we call the statistic from the results dictionary. This reports the difference between the observed and expected Knox statistic. >>> print("%2.8f" % result['stat']) 2.81016043 Next, we look at the pseudo-significance of this value, calculated by permuting the timestamps and rerunning the statistics. In this case, the results indicate there is likely no space-time interaction. >>> print("%2.2f" % result['pvalue']) 0.11 """ s = s_coords t = t_coords n = len(t) # calculate the spatial and temporal distance matrices for the events sdistmat = cg.distance_matrix(s) tdistmat = cg.distance_matrix(t) # identify events within thresholds spacmat = np.ones((n, n)) spacbin = sdistmat <= delta spacmat = spacmat * spacbin timemat = np.ones((n, n)) timebin = tdistmat <= tau timemat = timemat * timebin # calculate the observed (original) statistic knoxmat = timemat * spacmat obsstat = knoxmat.sum() - n # calculate the expectated value ssumvec = np.reshape((spacbin.sum(axis=0) - 1), (n, 1)) tsumvec = np.reshape((timebin.sum(axis=0) - 1), (n, 1)) expstat = (ssumvec * tsumvec).sum() # calculate the modified stat stat = (obsstat - (expstat / (n - 1.0))) / 2.0 # return results (if no inference) if not permutations: return stat distribution = [] # loop for generating a random distribution to assess significance for _p in range(permutations): rtdistmat = _shuffle_matrix(tdistmat, list(range(n))) timemat = np.ones((n, n)) timebin = rtdistmat <= tau timemat = timemat * timebin # calculate the observed knox again knoxmat = timemat * spacmat obsstat = knoxmat.sum() - n # calculate the expectated value again ssumvec = np.reshape((spacbin.sum(axis=0) - 1), (n, 1)) tsumvec = np.reshape((timebin.sum(axis=0) - 1), (n, 1)) expstat = (ssumvec * tsumvec).sum() # calculate the modified stat tempstat = (obsstat - (expstat / (n - 1.0))) / 2.0 distribution.append(tempstat) # establish the pseudo significance of the observed statistic distribution = np.array(distribution) greater = np.ma.masked_greater_equal(distribution, stat) count = np.ma.count_masked(greater) pvalue = (count + 1.0) / (permutations + 1.0) # return results modknox_result = {"stat": stat, "pvalue": pvalue} return modknox_result def _shuffle_matrix(X, ids): """ Random permutation of rows and columns of a matrix Parameters ---------- X : array (k, k), array to be permuted. ids : array range (k, ). Returns ------- : array (k, k) with rows and columns randomly shuffled. """ np.random.shuffle(ids) return X[ids, :][:, ids] def _knox(s_coords, t_coords, delta, tau, permutations=99, keep=False): """ Parameters ========== s_coords: array-like spatial coordinates t_coords: array-like temporal coordinates delta: float distance threshold tau: float temporal threshold permutations: int number of permutations keep: bool return values from permutations (default False) Returns ------- results : dict Dictionary containing global counts, contingency tables, p-values, and intermediate neighbor/pair structures. Keys include: Counts (global) - ``"ns"`` : float Total number of spatial neighbor pairs, denoted ``NS``. - ``"nt"`` : float Total number of temporal neighbor pairs, denoted ``NT``. - ``"nst"`` : float Total number of space-time neighbor pairs, denoted ``NST``. - ``"pairs"`` : float Total number of unordered pairs, ``n * (n - 1) / 2``. Tables - ``"observed"`` : ndarray of shape (2, 2) Observed contingency table:: [[NST, NS - NST], [NT - NST, pairs - (NS + NT - NST)]] - ``"expected"`` : ndarray of shape (2, 2) Expected contingency table under independence. The expected space-time count is ``ENST = NS * NT / pairs``. Inference - ``"p_value_poisson"`` : float One-sided analytic p-value using a Poisson model for ``NST`` with mean equal to the expected space-time count. - ``"p_value_sim"`` : float, optional One-sided permutation p-value (present when ``permutations > 0``). - ``"exceedence"`` : int, optional Number of permutations with statistic >= observed (present when ``permutations > 0``). - ``"st_perm"`` : ndarray of shape (permutations,), optional Permutation distribution of the statistic (present when ``permutations > 0`` and ``keep=True``). Neighbor and pair structures - ``"sneighbors"`` : list of sets Spatial neighbors for each event index. - ``"tneighbors"`` : list of sets Temporal neighbors for each event index. - ``"stneighbors"`` : list of sets Intersection of spatial and temporal neighbors for each index. - ``"st_pairs"`` : set of tuple(int, int) Set of unordered index pairs that are neighbors in both space and time. Notes ----- - All pair counts are **unordered** (i.e., each pair is counted once), hence the division by 2 after summing directed neighbor counts. - The permutation procedure randomly reassigns temporal neighbor labels to spatial neighborhoods via index permutations, generating a null distribution for the global space-time neighbor count. """ n = s_coords.shape[0] stree = KDTree(s_coords) ttree = KDTree(t_coords) sneighbors = stree.query_ball_tree(stree, r=delta) sneighbors = [ set(neighbors).difference([i]) for i, neighbors in enumerate(sneighbors) ] tneighbors = ttree.query_ball_tree(ttree, r=tau) tneighbors = [ set(neighbors).difference([i]) for i, neighbors in enumerate(tneighbors) ] # number of spatial neighbor pairs ns = np.array([len(neighbors) for neighbors in sneighbors]) # by i NS = ns.sum() / 2 # total # number of temporal neigbor pairs nt = np.array([len(neighbors) for neighbors in tneighbors]) NT = nt.sum() / 2 # s-t neighbors (list of lists) stneighbors = [ sneighbors_i.intersection(tneighbors_i) for sneighbors_i, tneighbors_i in zip(sneighbors, tneighbors) ] # number of spatio-temporal neigbor pairs nst = np.array([len(neighbors) for neighbors in stneighbors]) NST = nst.sum() / 2 all_pairs = [] pairs = {} for i, neigh in enumerate(stneighbors): if len(neigh) > 0: all_pairs.extend([sorted((i, j)) for j in neigh]) st_pairs = {tuple(l) for l in all_pairs} # ENST: expected number of spatio-temporal neighbors under HO pairs = n * (n - 1) / 2 ENST = NS * NT / pairs # observed table observed = np.zeros((2, 2)) NS_ = NS - NST # spatial only NT_ = NT - NST # temporal only observed[0, 0] = NST observed[0, 1] = NS_ observed[1, 0] = NT_ observed[1, 1] = pairs - NST - NS_ - NT_ # expected table expected = np.zeros((2, 2)) expected[0, 0] = ENST expected[0, 1] = NS - expected[0, 0] expected[1, 0] = NT - expected[0, 0] expected[1, 1] = pairs - expected.sum() p_value_poisson = 1 - poisson.cdf(NST, expected[0, 0]) results = {} results["ns"] = ns.sum() / 2 results["nt"] = nt.sum() / 2 results["nst"] = nst.sum() / 2 results["pairs"] = pairs results["expected"] = expected results["observed"] = observed results["p_value_poisson"] = p_value_poisson results["st_pairs"] = st_pairs results["sneighbors"] = sneighbors results["tneighbors"] = tneighbors results["stneighbors"] = stneighbors if permutations > 0: exceedence = 0 n = len(sneighbors) ids = np.arange(n) if keep: ST = np.zeros(permutations) for perm in range(permutations): st = 0 rids = np.random.permutation(ids) for i in range(n): ri = rids[i] tni = tneighbors[ri] rjs = [rids[j] for j in sneighbors[i]] sti = [j for j in rjs if j in tni] st += len(sti) st /= 2 if st >= results["nst"]: exceedence += 1 if keep: ST[perm] = st results["p_value_sim"] = (exceedence + 1) / (permutations + 1) results["exceedence"] = exceedence if keep: results["st_perm"] = ST return results class Knox: """Global Knox statistic for space-time interactions. Parameters ---------- s_coords: array-like spatial coordinates of point events t_coords: array-like temporal coordinates of point events (floats or ints, not dateTime) delta: float spatial threshold defining distance below which pairs are spatial neighbors tau: float temporal threshold defining distance below which pairs are temporal neighbors permutations: int number of random permutations for inference keep: bool whether to store realized values of the statistic under permutations Attributes ---------- s_coords: array-like spatial coordinates of point events t_coords: array-like temporal coordinates of point events (floats or ints, not dateTime) delta: float spatial threshold defining distance below which pairs are spatial neighbors tau: float temporal threshold defining distance below which pairs are temporal neighbors permutations: int number of random permutations for inference keep: bool whether to store realized values of the statistic under permutations nst: int number of space-time pairs p_poisson: float Analytical p-value under Poisson assumption p_sim: float Pseudo p-value based on random permutations expected: array Two-by-two array with expected counts under the null of no space-time interactions. ``[[NST, NS_], [NT_, N__]]`` where ``NST`` is the expected number of space-time pairs, ``NS_`` is the expected number of spatial (but not also temporal) pairs, ``NT_`` is the number of expected temporal (but not also spatial pairs), ``N__`` is the number of pairs that are neighor spatial or temporal neighbors. observed: array Same structure as expected with the observed pair classifications sim: array Global statistics from permutations (if keep=True) Notes ----- Technical details can be found in :cite:`Rogerson:2001` Examples -------- >>> import libpysal >>> path = libpysal.examples.get_path('burkitt.shp') >>> import geopandas >>> df = geopandas.read_file(path) >>> from pointpats.spacetime import Knox >>> global_knox = Knox(df[['X', 'Y']], df[["T"]], delta=20, tau=5) >>> global_knox.statistic_ 13 >>> global_knox.p_poisson 0.14624558197140414 >>> global_knox.observed array([[1.300e+01, 3.416e+03], [3.900e+01, 1.411e+04]]) >>> global_knox.expected array([[1.01438161e+01, 3.41885618e+03], [4.18561839e+01, 1.41071438e+04]]) >>> hasattr(global_knox, 'sim') False >>> import numpy >>> numpy.random.seed(12345) >>> global_knox = Knox(df[['X', 'Y']], df[["T"]], delta=20, tau=5, keep=True) >>> hasattr(global_knox, 'sim') True >>> global_knox.p_sim 0.21 """ def __init__(self, s_coords, t_coords, delta, tau, permutations=99, keep=False): self.s_coords = s_coords self.t_coords = t_coords self.delta = delta self.tau = tau self.permutations = permutations self.keep = keep results = _knox(s_coords, t_coords, delta, tau, permutations, keep) self.nst = int(results["nst"]) if permutations > 0: self.p_sim = results["p_value_sim"] if keep: self.sim = results["st_perm"] self.p_poisson = results["p_value_poisson"] self.observed = results["observed"] self.expected = results["expected"] self.statistic_ = self.nst @classmethod def from_dataframe( cls, dataframe, time_col: int, delta: int, tau: int, permutations: int = 99, keep: bool = False, ): """Compute a Knox statistic from a dataframe of Point observations Parameters ---------- dataframe : geopandas.GeoDataFrame geodataframe holding observations. Should be in a projected coordinate system with geometries stored as Point time_col : str column in the dataframe storing the time values (integer coordinate) for each observation. For example if the observations are stored with a timestamp, the time_col should be converted to a series of integers representing, e.g. hours, days, seconds, etc. delta : int delta parameter defining the spatial neighbor threshold measured in the same units as the dataframe CRS tau : int tau parameter defining the temporal neihgbor threshold (in the units measured by `time_col`) permutations : int, optional permutations to use for computation inference, by default 99 keep : bool whether to store realized values of the statistic under permutations Returns ------- pointpats.spacetime.Knox a fitted Knox class """ s_coords, t_coords = _spacetime_points_to_arrays(dataframe, time_col) return cls(s_coords, dataframe[[time_col]], delta, tau, permutations, keep) def _knox_local(s_coords, t_coords, delta, tau, permutations=99, keep=False): """ Parameters ---------- s_coords: array (n,2) spatial coordinates t_coords: array (n,1) temporal coordinates delta: numeric spatial threshold distance for neighbor relation tau: numeric temporal threshold distance for neighbor relation permutations: int number of permutations for conditional randomization inference keep: bool whether to store local statistics from the permtuations """ # think about passing in the global object as an option to avoid recomputing the trees res = _knox(s_coords, t_coords, delta, tau, permutations=permutations) sneighbors = {i: tuple(ns) for i, ns in enumerate(res["sneighbors"])} tneighbors = {i: tuple(nt) for i, nt in enumerate(res["tneighbors"])} n = len(s_coords) ids = np.arange(n) res["nsti"] = np.zeros(n) # number of observed st_pairs for observation i res["nsi"] = [len(r) for r in res["sneighbors"]] res["nti"] = [len(r) for r in res["tneighbors"]] for pair in res["st_pairs"]: i, j = pair res["nsti"][i] += 1 res["nsti"][j] += 1 nsti = res["nsti"] nsi = res["nsi"] res["nti"] # rather than do n*permutations, we reuse the permutations # ensuring that each permutation is conditional on a focal unit i # for each of the permutations we loop over i and swap labels between the # label at index i in the current permutation and the label at the index # assigned i in the permutation. if permutations > 0: exceedence = np.zeros(n) if keep: STI = np.zeros((n, permutations)) for perm in range(permutations): rids = np.random.permutation(ids) for i in range(n): rids_i = rids.copy() # set observed value of focal unit i # swap with value assigned to rids[i] # example # 0 1 2 (ids) # 2 0 1 (rids) # i=0 # 0 2 1 (rids_i) # i=1 # 2 1 0 (rids_i) # i=2 # 1 0 2 (rids_i) rids_i[rids == i] = rids[i] rids_i[i] = i # calculate local stat rjs = [rids_i[j] for j in sneighbors[i]] tni = tneighbors[i] sti = [j for j in rjs if j in tni] count = len(sti) if count >= res["nsti"][i]: exceedence[i] += 1 if keep: STI[i, perm] = count if keep: res["sti_perm"] = STI res["exceedence_pvalue"] = (exceedence + 1) / (permutations + 1) res["exceedences"] = exceedence # analytical inference ntjis = [len(r) for r in res["tneighbors"]] n1 = n - 1 hg_pvalues = [ 1 - hypergeom.cdf(nsti[i] - 1, n1, ntjis, nsi[i]).mean() for i in range(n) ] res["hg_pvalues"] = np.array(hg_pvalues) # identification of hot spots adjlist = [] for i, j in res["st_pairs"]: adjlist.append([i, j]) adjlist.append([j, i]) adjlist = pandas.DataFrame(data=adjlist, columns=["focal", "neighbor"]) adjlist = adjlist.sort_values(by=["focal", "neighbor"]) adjlist.reset_index(drop=True, inplace=True) adjlist["orientation"] = "" for index, row in adjlist.iterrows(): focal = row["focal"] neighbor = row["neighbor"] ft = t_coords[focal] nt = t_coords[neighbor] if ft < nt: adjlist.iloc[index, 2] = "lead" elif ft > nt: adjlist.iloc[index, 2] = "lag" else: adjlist.iloc[index, 2] = "coincident" res["stadjlist"] = adjlist return res class KnoxLocal: """Local Knox statistics for space-time interactions. Parameters ---------- s_coords: array (nx2) spatial coordinates of point events t_coords: array (nx1) temporal coordinates of point events (floats or ints, not dateTime) delta: float spatial threshold defining distance below which pairs are spatial neighbors tau: float temporal threshold defining distance below which pairs are temporal neighbors permutations: int number of random permutations for inference keep: bool whether to store realized values of the statistic under permutations conditional: bool whether to include conditional permutation inference crit: float signifcance level for local statistics crs: str (optional) coordinate reference system string for s_coords Attributes ---------- s_coords: array (nx2) spatial coordinates of point events t_coords: array (nx1) temporal coordinates of point events (floats or ints, not dateTime) delta: float spatial threshold defining distance below which pairs are spatial neighbors tau: float temporal threshold defining distance below which pairs are temporal neighbors permutations: int number of random permutations for inference keep: bool whether to store realized values of the statistic under permutations nst: int number of space-time pairs (global) p_poisson: float Analytical p-value under Poisson assumption (global) p_sim: float Pseudo p-value based on random permutations (global) expected: array Two-by-two array with expected counts under the null of no space-time interactions. ``[[NST, NS_], [NT_, N__]]`` where ``NST`` is the expected number of space-time pairs, ``NS_`` is the expected number of spatial (but not also temporal) pairs, ``NT_`` is the number of expected temporal (but not also spatial pairs), ``N__`` is the number of pairs that are neighor spatial or temporal neighbors. (global) observed: array Same structure as expected with the observed pair classifications (global) sim: array Global statistics from permutations (if keep=True and keep=True) (global) p_sims: array Local psuedo p-values from conditional permutations (if permutations>0) sims: array Local statistics from conditional permutations (if keep=True and permutations>0) nsti: array Local statistics p_hypergeom: array Analytical p-values based on hypergeometric distribution Notes ----- Technical details can be found in :cite:`Rogerson:2001`. The conditional permutation inference is unique to pysal.pointpats. Examples -------- >>> import libpysal >>> path = libpysal.examples.get_path('burkitt.shp') >>> import geopandas >>> df = geopandas.read_file(path) >>> from pointpats.spacetime import Knox >>> import numpy >>> numpy.random.seed(12345) >>> local_knox = KnoxLocal(df[['X', 'Y']], df[["T"]], delta=20, tau=5, keep=True) >>> local_knox.statistic_.shape (188,) >>> lres = local_knox >>> gt0ids = numpy.where(lres.nsti>0) >>> gt0ids # doctest: +NORMALIZE_WHITESPACE (array([ 25, 26, 30, 31, 35, 36, 41, 42, 46, 47, 51, 52, 102, 103, 116, 118, 122, 123, 137, 138, 139, 140, 158, 159, 162, 163]),) >>> lres.nsti[gt0ids] array([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.]) >>> lres.p_hypergeom[gt0ids] array([0.1348993 , 0.14220663, 0.07335085, 0.08400282, 0.1494317 , 0.21524073, 0.0175806 , 0.04599869, 0.17523687, 0.18209188, 0.19111321, 0.16830444, 0.13734428, 0.14703242, 0.06796364, 0.03192559, 0.13734428, 0.17523687, 0.12998154, 0.1933476 , 0.13244507, 0.13244507, 0.12502644, 0.14703242, 0.12502644, 0.12998154]) >>> lres.p_sims[gt0ids] array([0.3 , 0.33, 0.11, 0.17, 0.3 , 0.42, 0.06, 0.06, 0.33, 0.34, 0.36, 0.38, 0.3 , 0.29, 0.41, 0.19, 0.31, 0.39, 0.18, 0.39, 0.48, 0.41, 0.22, 0.41, 0.39, 0.32]) """ def __init__( self, s_coords, t_coords, delta, tau, permutations=99, keep=False, crit=0.05, crs=None, ids=None, ): if not isinstance(t_coords, np.ndarray): raise ValueError("t_coords should be numpy.ndarray type") if not isinstance(s_coords, np.ndarray): raise ValueError("s_coords should be numpy.ndarray type") n_s, k = s_coords.shape rangeids = list(range(n_s)) if k < 2: raise ValueError("s_coords shape required to be nx2") n_t, k = t_coords.shape if n_s != n_t: raise ValueError("t_coords and s_coords need to be same length") if ids is not None: if len(ids) != n_s: raise ValueError("`ids` must have the same length as the inputs") else: ids = rangeids self._ids = ids self.s_coords = s_coords self.t_coords = t_coords self.delta = delta self.tau = tau self.permutations = permutations self.keep = keep self.crit = crit results = _knox_local(s_coords, t_coords, delta, tau, permutations, keep) self.adjlist = results["stadjlist"] self.nst = int(results["nst"]) if permutations > 0: self.p_sim = results["p_value_sim"] if keep: self.sim = results["sti_perm"] self.p_poisson = results["p_value_poisson"] self.observed = results["observed"] self.expected = results["expected"] self.p_hypergeom = results["hg_pvalues"] if permutations > 0: self.p_sims = results["exceedence_pvalue"] if keep: self.sims = results["sti_perm"] self.nsti = results["nsti"] # self.hotspots = results["hotspots"] self._crs = crs self.statistic_ = self.nsti self._id_map = dict(zip(rangeids, self._ids)) self.adjlist["focal"] = self.adjlist["focal"].replace(self._id_map) self.adjlist["neighbor"] = self.adjlist["neighbor"].replace(self._id_map) # reconstruct df geom = gpd.points_from_xy(self.s_coords[:, 0], self.s_coords[:, 1]) _gdf = gpd.GeoDataFrame(geometry=geom, crs=self._crs, index=self._ids) _gdf["time"] = self.t_coords if permutations > 0: _gdf["p_sim"] = self.p_sims _gdf["p_hypergeom"] = self.p_hypergeom self._gdf_static = _gdf @property def _gdf(self): return self._gdf_static.copy() @classmethod def from_dataframe( cls, dataframe, time_col: str, delta: int, tau: int, permutations: int = 99, keep: bool = False, ): """Compute a set of local Knox statistics from a dataframe of Point observations Parameters ---------- dataframe : geopandas.GeoDataFrame dataframe holding observations. Should be in a projected coordinate system with geometries stored as Points time_col : str column in the dataframe storing the time values (integer coordinate) for each observation. For example if the observations are stored with a timestamp, the time_col should be converted to a series of integers representing, e.g. hours, days, seconds, etc. delta : int delta parameter defining the spatial neighbor threshold measured in the same units as the dataframe CRS tau : int tau parameter defining the temporal neihgbor threshold (in the units measured by `time_col`) permutations : int, optional permutations to use for computational inference, by default 99 keep : bool whether to store realized values of the statistic under permutations Returns ------- pointpats.spacetime.LocalKnox a fitted KnoxLocal class """ s_coords, t_coords = _spacetime_points_to_arrays(dataframe, time_col) return cls( s_coords, t_coords, delta, tau, permutations, keep, crs=dataframe.crs, ids=dataframe.index.values, ) def hotspots(self, crit=0.05, inference="permutation", keep_neighbors=True): """Table of significant space-time clusters that define local hotspots. Parameters ---------- crit : float, optional critical value for statistical inference, by default 0.05 inference : str, optional whether p-values should use permutation or analutical inference, by default "permutation" keep_neighbors: bool whether to included nonsignificant members of hotspots. While these observations are not themselves significant, these still define the spatial extent of the cluster, and the the focal observation cannot become significant without their presence. If True, return all members of a significant hotspot, else return only the significant locations Returns ------- pandas.DataFrame dataframe of significant hotspots Raises ------ ValueError if `inference` is not in {'permutation', 'analytic'} """ if inference == "permutation": if not hasattr(self, "p_sim"): warn( "Pseudo-p values not availalable. Permutation-based p-values require " "fitting the KnoxLocal class using `permutations` set to a large " "number. Using analytic p-values instead", stacklevel=1, ) col = "p_hypergeom" else: col = "p_sim" elif inference == "analytic": col = "p_hypergeom" else: raise ValueError("inference must be either `permutation` or `analytic`") # determine hot spots pdf_sig = self._gdf[self._gdf[col] <= crit][[col, "time"]].rename( columns={col: "pvalue", "time": "focal_time"} ) # if keep_neighbors, we want to include a 'cluster' column denoting which # cluster nonsig observations belong to. Need to use a graph for that temp_neighbors = self.adjlist[ (self.adjlist.focal.isin(pdf_sig.index.values)) | self.adjlist.neighbor.isin(pdf_sig.index.values) ] pdf_sig = pd.concat([pdf_sig, self._gdf[self._gdf.index.isin(temp_neighbors.neighbor.values) ][[col, "time"]].rename( columns={col: "pvalue", "time": "focal_time"} ) ]) pdf_sig = pdf_sig.merge( temp_neighbors, how='outer', left_index=True, right_on="focal" ).reset_index(drop=True) # significant focals can be neighbors of others (dupes) pdf_sig = pdf_sig.groupby("focal").first().reset_index() graph = Graph.from_adjacency(pdf_sig.assign(weight=1)) pdf_sig["cluster"] = graph.component_labels.values if not keep_neighbors : pdf_sig = pdf_sig[pdf_sig.pvalue<=crit] return self._gdf[["geometry"]].merge( pdf_sig.copy(), left_index=True, right_on="focal" ) def plot( self, colors: dict = {"focal": "red", "neighbor": "yellow", "nonsig": "grey"}, crit: float = 0.05, inference: str = "permutation", point_kwargs: dict = None, plot_edges: bool = True, edge_color: str = "black", edge_kwargs: dict = None, ax=None, ): """plot hotspots Parameters ---------- colors : dict, optional mapping of colors to hotspot values, by default {"focal": "red", "neighbor": "yellow", "nonsig": "grey"} crit : float, optional critical value for assessing statistical sgifnicance, by default 0.05 inference : str, optional whether to use permutation or analytic inference, by default "permutation" point_kwargs : dict, optional additional keyword arguments passsed to point plot, by default None plot_edges : bool, optional whether to plot edges connecting members of the same hotspot subgraph, by default True edge_color : str, optional color of edges when plot_edges is True, by default 'black' edge_kwargs : dict, optional additional keyword arguments passsed to edge plot, by default None ax : matplotlib.axes.Axes, optional axes object on which to create the plot, by default None Returns ------- matplotlib.axes.Axes plot of local space-time hotspots """ if point_kwargs is None: point_kwargs = dict() if edge_kwargs is None: edge_kwargs = dict() g = self._gdf.copy() g["color"] = colors["nonsig"] g["pvalue"] = self.p_hypergeom if inference == "permutation": if not hasattr(self, "p_sims"): warn( "Pseudo-p values not availalable. Permutation-based p-values require " "fitting the KnoxLocal class using `permutations` set to a large " "number. Using analytic p-values instead" ) g["pvalue"] = self.p_hypergeom else: g["pvalue"] = self.p_sims elif inference == "analytic": g["pvalue"] = self.p_hypergeom else: raise ValueError("inference must be either `permutation` or `analytic`") mask = g[g.pvalue <= crit].index.values neighbors = self.adjlist[self.adjlist.focal.isin(mask)].neighbor.unique() g.loc[neighbors, "color"] = colors["neighbor"] g.loc[g.pvalue <= crit, "color"] = colors["focal"] m = g[g.color == colors["nonsig"]].plot( color=colors["nonsig"], ax=ax, **point_kwargs ) g[g.color == colors["neighbor"]].plot( ax=m, color=colors["neighbor"], **point_kwargs ) g[g.color == colors["focal"]].plot(ax=m, color=colors["focal"], **point_kwargs) if plot_edges: # edges between hotspot and st-neighbors ghs = self.hotspots(crit=crit, inference=inference) ghs = ghs.dropna() origins = g.loc[ghs.focal].geometry destinations = g.loc[ghs.neighbor].geometry ods = zip(origins, destinations) lines = gpd.GeoSeries([LineString(od) for od in ods], crs=g.crs) lines.plot(ax=m, color=edge_color, **edge_kwargs) return m def explore( self, crit: float = 0.05, inference: str = "permutation", radius: int = 5, style_kwds: dict = None, tiles: str = "CartoDB Positron", plot_edges: bool = True, edge_weight: int = 2, edge_color: str = "black", colors: dict = {"focal": "red", "neighbor": "yellow", "nonsig": "grey"}, ): """Interactive plotting for space-time hotspots. Parameters ---------- crit : float, optional critical value for statistical inference, by default 0.05 inference : str, optional which p-value to use for determining hotspots. Either "permutation" or "analytic", by default "permutation" radius : int, optional radius of the circlemarker plotted by folium, passed to geopandas.GeoDataFrame.explore style_kwds as a convenience. Ignored if `style_kwds` is passed directly, by default 5 style_kwds : dict, optional additional style kewords passed to GeoDataFrame.explore, by default None tiles : str, optional tileset passed to GeoDataFrame.explore `tiles` argument, by default "CartoDB Positron" plot_edges : bool, optional Whether to include lines drawn between members of a singnificant hotspot, by default True edge_weight : int, optional line thickness when `plot_edges=True`, by default 2 edge_color : str, optional color of line when `plot_edges=True`, by default "black" colors : dict, optional mapping of observation type to color, by default {"focal": "red", "neighbor": "yellow", "nonsig": "grey"} Returns ------- folium.Map an interactive map showing locally-significant spacetime hotspots """ if style_kwds is None: style_kwds = {"radius": radius} g = self._gdf.copy() g["color"] = colors["nonsig"] if inference == "permutation": if not hasattr(self, "p_sims"): warn( "Pseudo-p values not availalable. Permutation-based p-values require " "fitting the KnoxLocal class using `permutations` set to a large " "number. Using analytic p-values instead" ) g["pvalue"] = self.p_hypergeom else: g["pvalue"] = self.p_sims elif inference == "analytic": g["pvalue"] = self.p_hypergeom else: raise ValueError("inference must be either `permutation` or `analytic`") mask = g[g.pvalue <= crit].index.values neighbors = self.adjlist[self.adjlist.focal.isin(mask)].neighbor.unique() # this is clunky, but enforces plotting order so significance is prioritized g.loc[neighbors, "color"] = colors["neighbor"] g.loc[g.pvalue <= crit, "color"] = colors["focal"] nbs = self.adjlist.groupby("focal").agg(list)["neighbor"] g = g.reset_index().merge(nbs, left_on="index", right_index=True, how="left") m = g[g.color == colors["nonsig"]].explore( color="grey", style_kwds=style_kwds, tiles=tiles ) blues = g[g.color == colors["neighbor"]] if blues.shape[0] == 0: warn("empty neighbor set.") else: m = blues.explore(m=m, color=colors["neighbor"], style_kwds=style_kwds) m = g[g.color == colors["focal"]].explore( m=m, color=colors["focal"], style_kwds=style_kwds ) if plot_edges: # edges between hotspot and st-neighbors g = g.set_index("index") ghs = self.hotspots(crit=crit, inference=inference) ghs = ghs.dropna() origins = g.loc[ghs.focal].geometry destinations = g.loc[ghs.neighbor].geometry ods = zip(origins, destinations) lines = gpd.GeoSeries([LineString(od) for od in ods], crs=g.crs) lines.explore(m=m, color=edge_color, style_kwds={"weight": edge_weight}) return m def _gdfhs(self, crit=0.05, inference="permutation"): # merge df with self.hotspots return gpd.GeoDataFrame( self._gdf.merge( self.hotspots(crit=crit, inference=inference), left_index=True, right_on="focal", ) ) def _spacetime_points_to_arrays(dataframe, time_col): """convert long-form geodataframe into arrays for kdtree Parameters ---------- dataframe : geopandas.GeoDataFrame geodataframe with point geometries time_col : str name of the column on dataframe that stores time values Returns ------- tuple two numpy arrays holding spatial coodinates s_coords (n,2) and temporal coordinates t_coords (n,1) """ if dataframe.crs is None: warn( "There is no CRS set on the dataframe. The KDTree will assume coordinates " "are stored in Euclidean distances" ) else: if dataframe.crs.is_geographic: raise ValueError( "The input dataframe must be in a projected coordinate system." ) assert dataframe.geom_type.unique().tolist() == [ "Point" ], "The Knox statistic is only defined for Point geometries" # kdtree wont operate on datetime if is_numeric_dtype(dataframe[time_col].dtype) is False: raise ValueError( "The time values must be stored as " f"a numeric dtype but the column {time_col} is stored as " f"{dataframe[time_col].dtype}" ) s_coords = np.vstack((dataframe.geometry.x.values, dataframe.geometry.y.values)).T t_coords = np.vstack(dataframe[time_col].values) return s_coords, t_coords pointpats-2.5.5/pointpats/tests/000077500000000000000000000000001514706172100167335ustar00rootroot00000000000000pointpats-2.5.5/pointpats/tests/__init__.py000066400000000000000000000000001514706172100210320ustar00rootroot00000000000000pointpats-2.5.5/pointpats/tests/test_centrography.py000066400000000000000000000115261514706172100230560ustar00rootroot00000000000000import numpy as np import shapely import pytest import geopandas as gpd from pointpats import centrography from libpysal.common import RTOL # points from Ebdon, D. (1985) Statistics for Geographers. Second Edition points = np.array([(1, 2), (2, 3), (2, 2), (2, 1), (3, 4), (3, 3), (3, 1), (4, 4)]) geoms = gpd.GeoSeries.from_xy(*points.T) sequence = points.tolist() dispatch_types = pytest.mark.parametrize( "points", [points, geoms, sequence], ids=["ndarray", "geoseries", "list"] ) @pytest.mark.skipif( shapely.geos_version < (3, 12, 0), reason="Requires GEOS 3.12.0 to use correct algorithm", ) @dispatch_types def test_minimum_rotated_rectangle(points): mrr = centrography.minimum_rotated_rectangle(points) known = np.array([[2.5, 0.5], [5.0, 3.0], [3.5, 4.5], [1.0, 2.0]]) if isinstance(points, gpd.GeoSeries): assert shapely.Polygon(known).normalize().equals_exact(mrr.normalize(), 1e-5) else: for i in range(5): success = np.allclose(mrr, np.roll(known, i, axis=0)) if success: break if not success: raise AssertionError( f"Minimum Rotated Rectangle cannot be" f"aligned with correct answer:" f"\ncomputed {mrr}\nknown: {known}" ) @pytest.mark.skipif( shapely.geos_version < (3, 12, 0), reason="Requires GEOS 3.12.0 to use correct algorithm", ) @dispatch_types def test_minimum_rotated_rectangle_angle(points): _, angle = centrography.minimum_rotated_rectangle(points, return_angle=True) np.testing.assert_allclose(angle, 45.0, RTOL) @dispatch_types def test_minimum_bounding_rectangle(points): res = centrography.minimum_bounding_rectangle(points) if isinstance(points, gpd.GeoSeries): assert shapely.box(1, 1, 4, 4).normalize().equals_exact(res.normalize(), 1e-5) else: min_x, min_y, max_x, max_y = res np.testing.assert_allclose(min_x, 1, RTOL) np.testing.assert_allclose(min_y, 1, RTOL) np.testing.assert_allclose(max_x, 4, RTOL) np.testing.assert_allclose(max_y, 4, RTOL) @dispatch_types def test_hull(points): hull = centrography.hull(points) exp = np.array( [ [1, 2], [2, 1], [3, 1], [4, 4], [3, 4], ] ) if isinstance(points, gpd.GeoSeries): assert shapely.Polygon(exp).normalize().equals_exact(hull.normalize(), 1e-5) else: np.testing.assert_array_equal(hull, exp) @dispatch_types def test_mean_center(points): exp = np.array([2.5, 2.5]) res = centrography.mean_center(points) if isinstance(points, gpd.GeoSeries): exp = shapely.Point(exp) assert exp.equals_exact(res, 1e-5) else: np.testing.assert_array_almost_equal(res, exp) @dispatch_types def test_std_distance(points): std = centrography.std_distance(points) np.testing.assert_allclose(std, 1.4142135623730951, RTOL) @dispatch_types def test_euclidean_median(points): euclidean = centrography.euclidean_median(points) res = np.array([2.34998979, 2.48670953]) if isinstance(points, gpd.GeoSeries): assert isinstance(euclidean, shapely.Point) euclidean = shapely.get_coordinates(euclidean).flatten() np.testing.assert_array_almost_equal(euclidean, res, decimal=3) @dispatch_types def test_minimum_bounding_circle(points): res = centrography.minimum_bounding_circle(points) x = 2.642857142857143 y = 2.7857142857142856 r = 1.821078397711709 if isinstance(points, gpd.GeoSeries): assert shapely.Point(x, y).equals_exact(res.centroid, 1e-5) np.testing.assert_allclose(np.sqrt(res.area / np.pi), r, 0.1) else: np.testing.assert_allclose(res[0][0], x, RTOL) np.testing.assert_allclose(res[0][1], y, RTOL) np.testing.assert_allclose(res[1], r, RTOL) @dispatch_types def test_weighted_mean_center(points): res = centrography.weighted_mean_center(points, np.tile(np.array([1, 2]), 4)) exp = np.array([2.5833333, 2.5833333]) if isinstance(points, gpd.GeoSeries): exp = shapely.Point(exp) assert exp.equals_exact(res, 1e-5) else: np.testing.assert_array_almost_equal(res, exp) @dispatch_types def test_manhattan_median(points): with pytest.warns(UserWarning, match="Manhattan Median is not unique for even"): res = centrography.manhattan_median(points) exp = np.array([2.5, 2.5]) if isinstance(points, gpd.GeoSeries): exp = shapely.Point(exp) assert exp.equals_exact(res, 1e-5) else: np.testing.assert_array_almost_equal(res, exp) @dispatch_types def test_dtot(points): coord = [20, 30] if isinstance(points, gpd.GeoSeries): coord = shapely.Point(*coord) res = centrography.dtot(coord, points) np.testing.assert_allclose(res, 260.8212494704881, RTOL) pointpats-2.5.5/pointpats/tests/test_distance_statistics.py000066400000000000000000000041611514706172100244120ustar00rootroot00000000000000import numpy as np import pytest from shapely.geometry import box from pointpats.random import ( poisson, normal, cluster_poisson, cluster_normal, _pairwise_count_kdtree, ) @pytest.fixture def hull(): return box(0, 0, 1, 1) @pytest.fixture def rng(): return np.random.default_rng(42) # Use a square as a simple hull square_hull = np.array([[0, 0], [0, 10], [10, 10], [10, 0]]) def test_poisson_output_shape(): result = poisson(square_hull, intensity=1, size=2, rng=42) assert result.shape == (2, 100, 2) # default is 100 points def test_normal_output_with_custom_cov(): cov = np.array([[1, 0.5], [0.5, 2]]) result = normal(square_hull, cov=cov, size=(20, 3), rng=42) assert result.shape == (3, 20, 2) def test_all_points_within_hull_poisson(): result = poisson(square_hull, size=(10, 2), rng=42) for sim in result: for x, y in sim: assert 0 <= x <= 10 and 0 <= y <= 10 def test_poisson_seed_consistency(): a = poisson(square_hull, intensity=1, size=2, rng=42) b = poisson(square_hull, intensity=1, size=2, rng=42) np.testing.assert_allclose(a, b) def test_cluster_poisson_shapes(): result = cluster_poisson(square_hull, size=(20, 2), n_seeds=4, rng=123) assert result.shape == (2, 20, 2) def test_cluster_poisson_seed_consistency(): a = cluster_poisson(square_hull, size=(20, 2), n_seeds=4, rng=123) b = cluster_poisson(square_hull, size=(20, 2), n_seeds=4, rng=123) np.testing.assert_allclose(a, b) def test_value_error_on_conflicting_inputs(): with pytest.raises(ValueError): poisson(square_hull, intensity=1.0, size=(10, 2)) def test_cluster_normal_output(): result = cluster_normal(square_hull, size=(50, 3), n_seeds=5, rng=100) assert result.shape == (3, 50, 2) def test_pairwise_count_kdtree_basic(): # Create a square of 4 points within 1 unit distance of each other points = np.array([[0.0, 0.0], [0.0, 0.5], [0.5, 0.0], [0.5, 0.5]]) r = 0.75 # should connect all pairs within a 0.75 radius count = _pairwise_count_kdtree(points, r) assert count == 6 # 4 points, 6 unique pairs pointpats-2.5.5/pointpats/tests/test_ellipse.py000066400000000000000000000125131514706172100220030ustar00rootroot00000000000000import numpy as np import geopandas as gpd import pytest from shapely.geometry import Point from pointpats import ellipse @pytest.fixture def sample_coords(): return np.array([ [66.22, 32.54], [22.52, 22.39], [31.01, 81.21], [9.47, 31.02], [30.78, 60.10], [75.21, 58.93], [79.26, 7.68], [8.23, 39.93], [98.73, 77.17], [89.78, 42.53], [65.19, 92.08], ]) @pytest.fixture def sample_points(): seed = 65647437836358831880808032086803839626 rng = np.random.default_rng(seed) points = rng.integers(0, 100, (50, 2)) return points @pytest.fixture def sample_weights(): return np.arange(1, 12) @pytest.fixture def sample_geoseries(sample_coords): return gpd.GeoSeries([Point(x, y) for x, y in sample_coords]) def test_ellipse_numpy_no_weights(sample_coords): major, minor, rotation = ellipse(sample_coords) assert isinstance(major, float) assert isinstance(minor, float) assert isinstance(rotation, float) assert major > 0 assert minor > 0 assert -np.pi <= rotation <= np.pi def test_ellipse_numpy_with_weights(sample_coords, sample_weights): major, minor, rotation = ellipse(sample_coords, weights=sample_weights) assert isinstance(major, float) assert isinstance(minor, float) assert isinstance(rotation, float) def test_ellipse_list_input(sample_coords): coords_list = sample_coords.tolist() major, minor, rotation = ellipse(coords_list) assert isinstance(major, float) assert isinstance(minor, float) assert isinstance(rotation, float) def test_ellipse_list_with_weights(sample_coords, sample_weights): coords_list = sample_coords.tolist() weights_list = sample_weights.tolist() major, minor, rotation = ellipse(coords_list, weights=weights_list) assert isinstance(major, float) assert isinstance(minor, float) assert isinstance(rotation, float) def test_ellipse_geoseries(sample_geoseries): ellipse_ = ellipse(sample_geoseries) assert ellipse_.geom_type == "Polygon" assert ellipse_.is_valid def test_ellipse_geoseries_with_weights(sample_geoseries, sample_weights): ellipse_ = ellipse(sample_geoseries, weights=sample_weights) assert ellipse_.geom_type == "Polygon" assert ellipse_.is_valid def test_invalid_method(sample_coords): with pytest.raises(ValueError, match="`method` must be either 'crimestat' or 'yuill'"): ellipse(sample_coords, method="invalidmethod") def test_weights_length_mismatch(sample_coords): wrong_weights = np.arange(5) with pytest.raises(ValueError): ellipse(sample_coords, weights=wrong_weights) def test_unweighted(sample_points): result = ellipse(sample_points) expected = (np.float64(43.85494662229593), np.float64(36.28453973005919), np.float64(-0.9362557045365753)) assert isinstance(result, tuple) assert len(result) == 3 for r, e in zip(result, expected): assert isinstance(r, np.float64) np.testing.assert_almost_equal(r, e, decimal=8) def test_weighted(sample_points): points = sample_points points_gdf = gpd.GeoDataFrame(geometry=gpd.points_from_xy(points[:,0], points[:,1])) result = ellipse(points_gdf, weights=sample_points[:,1]).area expected = 4085.3662956683897 np.testing.assert_almost_equal(result, expected, decimal=8) def test_params(sample_points): result = ellipse(sample_points) expected = (np.float64(43.85494662229593), np.float64(36.28453973005919), np.float64(-0.9362557045365753)) assert isinstance(result, tuple) assert len(result) == 3 for r, e in zip(result, expected): assert isinstance(r, np.float64) np.testing.assert_almost_equal(r, e, decimal=8) result = ellipse(sample_points, method='yuill') expected = (np.float64(43.85494662229593), np.float64(36.28453973005919), np.float64(-0.9362557045365753)) assert isinstance(result, tuple) assert len(result) == 3 for r, e in zip(result, expected): assert isinstance(r, np.float64) np.testing.assert_almost_equal(r, e, decimal=8) result = ellipse(sample_points, method='yuill', crimestatCorr=False) expected = (np.float64(31.01013014519952), np.float64(25.65704409535755), np.float64(-0.9362557045365753)) assert len(result) == 3 for r, e in zip(result, expected): assert isinstance(r, np.float64) np.testing.assert_almost_equal(r, e, decimal=8) result = ellipse(sample_points, method='yuill', degfreedCorr=False) expected = (np.float64(42.96889676864705), np.float64(35.551443156155464), np.float64(-0.9362557045365753)) assert len(result) == 3 for r, e in zip(result, expected): assert isinstance(r, np.float64) np.testing.assert_almost_equal(r, e, decimal=8) result = ellipse(sample_points, method='yuill', crimestatCorr=False, degfreedCorr=False) expected = (np.float64(30.383598285215058), np.float64(25.138666536685605), np.float64(-0.9362557045365753)) assert len(result) == 3 for r, e in zip(result, expected): assert isinstance(r, np.float64) np.testing.assert_almost_equal(r, e, decimal=8) pointpats-2.5.5/pointpats/tests/test_kde.py000066400000000000000000000065471514706172100211230ustar00rootroot00000000000000import numpy as np import pytest from packaging.version import Version from pointpats import plot_density matplotlib = pytest.importorskip("matplotlib") statsmodels = pytest.importorskip("statsmodels") KDEpy = pytest.importorskip("KDEpy") MPL_310 = Version(matplotlib.__version__) >= Version("3.10.0") @pytest.mark.skipif(not MPL_310, reason="ContourSet is composed differently in < 3.10") class TestDensity: def setup_method(self): self.points = np.array( [ [66.22, 32.54], [22.52, 22.39], [31.01, 81.21], [9.47, 31.02], [30.78, 60.10], [75.21, 58.93], [79.26, 7.68], [8.23, 39.93], [98.73, 77.17], [89.78, 42.53], [65.19, 92.08], [54.46, 8.48], ] ) def test_default(self): ax = plot_density(self.points, 10) contourset = ax.collections[0] assert len(ax.collections[0].get_paths()) == 12 assert not contourset.filled np.testing.assert_array_equal(contourset.get_linewidths(), np.array([1.5] * 12)) np.testing.assert_array_equal( contourset.get_edgecolor()[5], np.array([0.143343, 0.522773, 0.556295, 1.0]), ) def test_bandwidth(self): ax = plot_density(self.points, 1) assert len(ax.collections[0].get_paths()) == 10 def test_resolution(self): ax = plot_density(self.points, 10, resolution=200) contourset = ax.collections[0] assert len(contourset.get_paths()) == 12 def test_margin(self): ax = plot_density(self.points, 10, margin=0.3) contourset = ax.collections[0] assert len(contourset.get_paths()) == 12 def test_kdepy(self): ax = plot_density(self.points, 10, kernel="gaussian") contourset = ax.collections[0] assert len(contourset.get_paths()) == 12 np.testing.assert_array_equal(contourset.get_linewidths(), np.array([1.5] * 12)) def test_levels(self): ax = plot_density(self.points, 10, levels=5) contourset = ax.collections[0] assert len(contourset.get_paths()) == 7 def test_fill(self): ax = plot_density(self.points, 10, fill=True) contourset = ax.collections[0] assert contourset.get_edgecolor().shape == (0, 4) assert contourset.filled np.testing.assert_array_equal( contourset.get_facecolor()[0], np.array([0.279566, 0.067836, 0.391917, 1.0]), ) def test_geopandas(self): geopandas = pytest.importorskip("geopandas") gs = geopandas.GeoSeries.from_xy(*self.points.T) ax = plot_density(gs, 10) contourset = ax.collections[0] assert len(contourset.get_paths()) == 12 np.testing.assert_array_equal(contourset.get_linewidths(), np.array([1.5] * 12)) def test_kwargs(self): ax = plot_density( self.points, 10, cmap="magma", linewidths=0.5, linestyles="-." ) contourset = ax.collections[0] assert len(contourset.get_paths()) == 12 np.testing.assert_array_equal(contourset.get_linewidths(), np.array([0.5] * 12)) np.testing.assert_array_equal( contourset.get_edgecolor()[5], np.array([0.639216, 0.189921, 0.49415, 1.0]), ) pointpats-2.5.5/pointpats/tests/test_pointpattern.py000066400000000000000000000107241514706172100230770ustar00rootroot00000000000000import unittest import numpy as np from ..pointpattern import PointPattern from libpysal.common import RTOL class TestPointPattern(unittest.TestCase): def setUp(self): self.points = [[66.22, 32.54], [22.52, 22.39], [31.01, 81.21], [9.47, 31.02], [30.78, 60.10], [75.21, 58.93], [79.26, 7.68], [8.23, 39.93], [98.73, 77.17], [89.78, 42.53], [65.19, 92.08], [54.46, 8.48]] self.pp = PointPattern(self.points) self.assertEqual(len(self.pp), 12) self.assertTrue([66.22, 32.54] in self.pp) def test_point_pattern_n(self): self.assertEqual(self.pp.n, 12) def test_point_pattern_mean_nnd(self): np.testing.assert_allclose(self.pp.mean_nnd, 21.612139802089246, RTOL) def test_point_pattern_lambda_mbb(self): np.testing.assert_allclose(self.pp.lambda_mbb, 0.0015710507711240867, RTOL) def test_point_pattern_lambda_hull(self): np.testing.assert_allclose(self.pp.lambda_hull, 0.0022667153468973137, RTOL) def test_point_pattern_hull_area(self): np.testing.assert_allclose(self.pp.hull_area, 5294.0039500000003, RTOL) def test_point_pattern_mbb_area(self): np.testing.assert_allclose(self.pp.mbb_area, 7638.2000000000007, RTOL) def test_point_pattern_min_nnd(self): np.testing.assert_allclose(self.pp.min_nnd, 8.9958712752017522, RTOL) def test_point_pattern_max_nnd(self): np.testing.assert_allclose(self.pp.max_nnd, 34.63124167568931, RTOL) def test_point_pattern_find_pairs(self): self.assertEqual(self.pp.find_pairs(10), {(3, 7)}) self.assertEqual(self.pp.find_pairs(20), {(3, 7), (1, 3)}) def test_point_pattern_knn(self): knn = self.pp.knn(1) nn = np.array([[9], [3], [4], [7], [2], [9], [11], [3], [5], [5], [5], [6]]) nnd = np.array([[25.59050019], [15.64542745], [21.11125292], [8.99587128], [21.11125292], [21.93729473], [24.81289987], [8.99587128], [29.76387072], [21.93729473], [34.63124168], [24.81289987]]) np.testing.assert_array_equal(knn[0], nn) np.testing.assert_array_almost_equal(knn[1], nnd) def test_point_pattern_knn_error(self): self.assertRaises(ValueError, self.pp.knn, k=0) def test_point_pattern_knn_other(self): knn = self.pp.knn_other(self.pp) nn = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]) nnd = np.zeros(12) np.testing.assert_array_equal(knn[0], nn) np.testing.assert_array_equal(knn[1], nnd) knn = self.pp.knn_other([0, 0], k=12) nn = np.array([1, 3, 7, 11, 4, 0, 6, 2, 5, 9, 10, 8]) nnd = np.array([31.75629859, 32.43333625, 40.76932425, 55.11625894, 67.52346555, 73.78306039, 79.63121247, 86.92919072, 95.54731289, 99.34409545, 112.82048794, 125.31090056]) np.testing.assert_array_equal(knn[0], nn) np.testing.assert_array_almost_equal(knn[1], nnd) def test_point_pattern_knn_other_error(self): knn_other = self.pp.knn_other self.assertRaises(ValueError, knn_other, self.pp, k=0) def test_point_pattern_explode(self): explosion = self.pp.explode('x') for ppattern in explosion: np.testing.assert_array_equal(ppattern.df.iloc[0], self.pp.df.loc[ppattern.df.index[0]]) def test_point_pattern_flip_coordinates(self): pp_flip = PointPattern(self.points, coord_names=['The x coordinate', 'The y coordinate']) coord = pp_flip.coord_names x_coord, y_coord = pp_flip._x, pp_flip._y # Flip the coordinates pp_flip.flip_coordinates() coord_flipped = pp_flip.coord_names x_coord_flipped, y_coord_flipped = pp_flip._x, pp_flip._y self.assertEqual(x_coord, y_coord_flipped) self.assertEqual(y_coord, x_coord_flipped) self.assertEqual(coord, coord_flipped) # Flip the coordinates again, they should return to the intial values. pp_flip.flip_coordinates() coord_again = pp_flip.coord_names x_coord_again, y_coord_again = pp_flip._x, pp_flip._y self.assertEqual(x_coord, x_coord_again) self.assertEqual(y_coord, y_coord_again) self.assertEqual(coord, coord_again) pointpats-2.5.5/pointpats/tests/test_quadrat_statistics.py000066400000000000000000000365311514706172100242670ustar00rootroot00000000000000import importlib import math import numpy as np import pytest import scipy.stats from shapely.geometry import Point, MultiPoint, Polygon, MultiPolygon, box import geopandas as gpd import pointpats.quadrat_statistics as m import pointpats # ----------------------------------------------------------------------------- # Fixtures # ----------------------------------------------------------------------------- @pytest.fixture def pointpattern(): """ Return a simple pointpats.PointPattern with 3 points. """ pts = np.array( [ [0.0, 0.0], [1.0, 1.0], [2.0, 2.0], ] ) return pointpats.PointPattern(pts) @pytest.fixture def pts_simple(): # Four points spanning a 2x2 square (mbb = [0,0,2,2]) return np.array( [ [0.0, 0.0], [0.5, 0.5], [1.5, 1.5], [2.0, 2.0], # on max boundary ], dtype=float, ) @pytest.fixture def pts_random_small(): rng = np.random.default_rng(123) return rng.random((50, 2), dtype=float) * 100.0 @pytest.fixture def window_poly(): # Simple rectangular polygon window return box(0.0, 0.0, 10.0, 5.0) @pytest.fixture def window_poly_with_hole(): outer = box(0.0, 0.0, 10.0, 10.0) hole = box(4.0, 4.0, 6.0, 6.0) return Polygon(outer.exterior.coords, holes=[hole.exterior.coords]) @pytest.fixture def window_multipoly(): a = box(0.0, 0.0, 1.0, 1.0) b = box(2.0, 2.0, 3.0, 3.0) return MultiPolygon([a, b]) # ----------------------------------------------------------------------------- # Helper tests # ----------------------------------------------------------------------------- def test_as_points_array_accepts_arraylike(): pts = m._as_points_array([[0, 1], [2, 3.5]]) assert isinstance(pts, np.ndarray) assert pts.shape == (2, 2) assert pts.dtype.kind in ("f", "i") # will often be float, but allow int input def test_as_points_array_rejects_bad_shape(): with pytest.raises(ValueError, match=r"points must be a \(n, 2\)"): m._as_points_array([1, 2, 3]) with pytest.raises(ValueError, match=r"points must be a \(n, 2\)"): m._as_points_array(np.zeros((3, 3))) def test_as_points_array_rejects_empty(): with pytest.raises(ValueError, match="points is empty"): m._as_points_array(np.empty((0, 2))) def test_compute_mbb(): pts = np.array([[1, 2], [3, 4], [-1, 10]], dtype=float) mbb = m._compute_mbb(pts) assert mbb.shape == (4,) assert np.allclose(mbb, [-1.0, 2.0, 3.0, 10.0]) def test_ensure_window_none_uses_box(): mbb = np.array([0.0, 1.0, 2.0, 3.0], dtype=float) w = m._ensure_window(None, mbb) assert w.equals(box(0.0, 1.0, 2.0, 3.0)) def test_coerce_rng_none(): rng = m._coerce_rng(None) assert isinstance(rng, np.random.Generator) def test_coerce_rng_int(): rng = m._coerce_rng(123) assert isinstance(rng, np.random.Generator) # Determinism check rng2 = m._coerce_rng(123) assert rng.random() == rng2.random() def test_coerce_rng_numpy_integer(): rng = m._coerce_rng(np.int64(456)) assert isinstance(rng, np.random.Generator) def test_coerce_rng_generator_passthrough(): gen = np.random.default_rng(789) out = m._coerce_rng(gen) assert out is gen def test_coerce_rng_bitgenerator(): bitgen = np.random.PCG64(123) rng = m._coerce_rng(bitgen) assert isinstance(rng, np.random.Generator) assert rng.bit_generator is bitgen def test_coerce_rng_randomstate(): rs = np.random.RandomState(321) rng = m._coerce_rng(rs) assert isinstance(rng, np.random.Generator) # Reproduce the seed used by _coerce_rng: it is the FIRST randint draw from RS(321). rs2 = np.random.RandomState(321) seed = int(rs2.randint(0, 2**32 - 1, dtype=np.uint32)) expected = np.random.default_rng(seed) assert rng.random() == expected.random() def test_coerce_rng_seedsequence(): ss = np.random.SeedSequence(999) rng = m._coerce_rng(ss) assert isinstance(rng, np.random.Generator) def test_window_to_paths_polygon_and_hole(window_poly_with_hole): paths = m._window_to_paths(window_poly_with_hole) # exterior + 1 hole -> 2 paths assert len(paths) == 2 for x, y in paths: assert isinstance(x, np.ndarray) assert isinstance(y, np.ndarray) assert x.shape == y.shape assert x.ndim == 1 def test_window_to_paths_multipolygon(window_multipoly): paths = m._window_to_paths(window_multipoly) assert len(paths) == 2 # two polygons -> two exterior rings @pytest.mark.skipif( pytest.importorskip("geopandas", reason="geopandas not installed") is None, reason="geopandas not installed", ) def test_as_points_array_geoseries_points_and_multipoints(): import geopandas as gpd s = gpd.GeoSeries([Point(0, 0), MultiPoint([Point(1, 1), Point(2, 2)]), None]) pts = m._as_points_array(s) assert pts.shape == (3, 2) assert np.allclose(pts, np.array([[0, 0], [1, 1], [2, 2]], dtype=float)) @pytest.mark.skipif( pytest.importorskip("geopandas", reason="geopandas not installed") is None, reason="geopandas not installed", ) def test_as_points_array_geoseries_rejects_non_point_geoms(): import geopandas as gpd poly = box(0, 0, 1, 1) s = gpd.GeoSeries([poly]) with pytest.raises(TypeError, match="GeoSeries must contain Point geometries"): m._as_points_array(s) @pytest.mark.skipif( pytest.importorskip("geopandas", reason="geopandas not installed") is None, reason="geopandas not installed", ) def test_as_points_array_geoseries_all_empty_errors(): import geopandas as gpd s = gpd.GeoSeries([None, Point()]) # Point() is empty in shapely with pytest.raises(ValueError, match="no non-empty Point geometries"): m._as_points_array(s) def test_as_points_array_pointpattern(pointpattern): assert isinstance(m._as_points_array(pointpattern), np.ndarray) # ----------------------------------------------------------------------------- # RectangleM tests # ----------------------------------------------------------------------------- def test_rectangle_grid_default_dimensions(pts_simple): r = m.RectangleM(pts_simple, count_column=2, count_row=2) assert r.count_column == 2 assert r.count_row == 2 assert r.num == 4 assert np.allclose(r.mbb, [0.0, 0.0, 2.0, 2.0]) # cell size should be 1x1 assert math.isclose(r.rectangle_width, 1.0) assert math.isclose(r.rectangle_height, 1.0) def test_rectangle_grid_width_height_override(pts_simple): # range_x=2, range_y=2, width=0.75 -> ceil(2/0.75)=3 columns r = m.RectangleM(pts_simple, rectangle_width=0.75, rectangle_height=1.0) assert r.count_column == 3 assert r.count_row == 2 assert r.num == 6 def test_rectangle_point_location_counts_sum_to_n(pts_random_small): r = m.RectangleM(pts_random_small, count_column=5, count_row=4) d = r.point_location_sta() assert len(d) == r.num assert sum(d.values()) == pts_random_small.shape[0] def test_rectangle_boundary_point_in_last_cell(pts_simple): # The point [2,2] lies on max boundary; code clamps index==count to last cell r = m.RectangleM(pts_simple, count_column=2, count_row=2) d = r.point_location_sta() # bottom-left cell id 0 contains [0,0] and [0.5,0.5] assert d[0] == 2 # top-right cell id 3 contains [1.5,1.5] and [2,2] assert d[3] == 2 def test_rectangle_plot_counts_smoke(pts_simple): r = m.RectangleM(pts_simple, count_column=2, count_row=2) ax, cell_ids = r.plot(show="counts") assert ax is not None assert len(cell_ids) == r.num def test_rectangle_plot_chi2_requires_contrib(pts_simple): r = m.RectangleM(pts_simple, count_column=2, count_row=2) with pytest.raises(ValueError, match="chi2_contrib must be provided"): r.plot(show="chi2", chi2_contrib=None) def test_rectangle_plot_chi2_length_mismatch(pts_simple): r = m.RectangleM(pts_simple, count_column=2, count_row=2) with pytest.raises(ValueError, match="length must match number of plotted cells"): r.plot(show="chi2", chi2_contrib=np.zeros(r.num - 1)) def test_rectangle_plot_invalid_show(pts_simple): r = m.RectangleM(pts_simple, count_column=2, count_row=2) with pytest.raises(ValueError, match='show must be "counts" or "chi2"'): r.plot(show="nope") # ----------------------------------------------------------------------------- # HexagonM tests # ----------------------------------------------------------------------------- def test_hexagon_grid_basic_invariants(pts_random_small): h = m.HexagonM(pts_random_small, lh=10.0) assert h.h_length == 10.0 assert h.semi_height > 0 assert h.count_column >= 1 assert h.count_row_even >= 1 assert h.count_row_odd >= 1 assert h.num >= 1 def test_hexagon_point_location_counts_sum_to_n(pts_random_small): h = m.HexagonM(pts_random_small, lh=10.0) d = h.point_location_sta() assert sum(d.values()) == pts_random_small.shape[0] # dict keys correspond to the number of cells included assert len(d) >= 1 def test_hexagon_plot_counts_smoke(pts_simple): h = m.HexagonM(pts_simple, lh=1.0) ax, cell_ids = h.plot(show="counts") assert ax is not None assert len(cell_ids) == len(h.point_location_sta()) def test_hexagon_plot_chi2_requires_contrib(pts_simple): h = m.HexagonM(pts_simple, lh=1.0) with pytest.raises(ValueError, match="chi2_contrib must be provided"): h.plot(show="chi2", chi2_contrib=None) def test_hexagon_plot_chi2_length_mismatch(pts_simple): h = m.HexagonM(pts_simple, lh=1.0) k = len(h.point_location_sta()) with pytest.raises(ValueError, match="length must match number of plotted cells"): h.plot(show="chi2", chi2_contrib=np.zeros(k - 1)) def test_hexagon_plot_invalid_show(pts_simple): h = m.HexagonM(pts_simple, lh=1.0) with pytest.raises(ValueError, match='show must be "counts" or "chi2"'): h.plot(show="nope") # ----------------------------------------------------------------------------- # QStatistic tests (analytical) # ----------------------------------------------------------------------------- def test_qstatistic_rectangle_matches_scipy(pts_simple): qs = m.QStatistic(pts_simple, shape="rectangle", nx=2, ny=2, realizations=0) d = qs.mr.point_location_sta() obs = np.asarray(list(d.values()), dtype=float) chi2_ref, p_ref = scipy.stats.chisquare(obs) assert math.isclose(qs.chi2, chi2_ref, rel_tol=1e-12, abs_tol=0.0) assert math.isclose(qs.chi2_pvalue, p_ref, rel_tol=1e-12, abs_tol=0.0) assert qs.df == obs.size - 1 assert len(qs.cell_ids) == obs.size def test_qstatistic_hexagon_basic(pts_random_small): qs = m.QStatistic(pts_random_small, shape="hexagon", lh=10.0, realizations=0) assert qs.shape == "hexagon" assert qs.df == len(qs.cell_ids) - 1 assert np.isfinite(qs.chi2) assert 0.0 <= qs.chi2_pvalue <= 1.0 def test_qstatistic_contrib_formula(pts_simple): qs = m.QStatistic(pts_simple, shape="rectangle", nx=2, ny=2, realizations=0) d = qs.mr.point_location_sta() obs = np.asarray(list(d.values()), dtype=float) expected = obs.mean() contrib_ref = ( (obs - expected) ** 2 / expected if expected > 0 else np.full_like(obs, np.nan) ) assert np.allclose(qs.chi2_contrib, contrib_ref, equal_nan=True) def test_qstatistics_geopandas_input(pts_simple): gs = gpd.GeoSeries.from_xy(pts_simple[:, 0], pts_simple[:, 1]) gdf = gs.to_frame("geometry") qs_gs = m.QStatistic(gs, shape="rectangle", nx=2, ny=2, realizations=0) qs_gdf = m.QStatistic(gdf, shape="rectangle", nx=2, ny=2, realizations=0) for qs in [qs_gs, qs_gdf]: d = qs.mr.point_location_sta() obs = np.asarray(list(d.values()), dtype=float) chi2_ref, p_ref = scipy.stats.chisquare(obs) assert math.isclose(qs.chi2, chi2_ref, rel_tol=1e-12, abs_tol=0.0) assert math.isclose(qs.chi2_pvalue, p_ref, rel_tol=1e-12, abs_tol=0.0) assert qs.df == obs.size - 1 assert len(qs.cell_ids) == obs.size def test_qstatistic_invalid_shape_raises(pts_simple): with pytest.raises( ValueError, match='shape must be either "rectangle" or "hexagon"' ): m.QStatistic(pts_simple, shape="triangle") def test_qstatistic_plot_counts_smoke(pts_simple): qs = m.QStatistic(pts_simple, shape="rectangle", nx=2, ny=2, realizations=0) ax = qs.plot(show="counts") assert ax is not None def test_qstatistic_plot_chi2_smoke(pts_simple): qs = m.QStatistic(pts_simple, shape="rectangle", nx=2, ny=2, realizations=0) ax = qs.plot(show="chi2") assert ax is not None def test_qstatistic_plot_invalid_show(pts_simple): qs = m.QStatistic(pts_simple, shape="rectangle", nx=2, ny=2, realizations=0) with pytest.raises(ValueError, match='show must be either "counts" or "chi2"'): qs.plot(show="nope") # ----------------------------------------------------------------------------- # QStatistic tests (simulation branch via poisson stub) # ----------------------------------------------------------------------------- class _PoissonReturn: """Mimic an object with a `.points` attribute.""" def __init__(self, points): self.points = points def test_qstatistic_simulation_branch_calls_poisson_and_sets_outputs( monkeypatch, pts_simple, window_poly ): # Make points fit inside the window for clarity pts = np.array([[1.0, 1.0], [2.0, 1.0], [3.0, 2.0], [4.0, 4.0]], dtype=float) calls = {} def poisson_stub(window, intensity, realizations, rng=None): # record call args for assertions calls["window"] = window calls["intensity"] = intensity calls["realizations"] = realizations calls["rng_is_generator"] = isinstance(rng, np.random.Generator) # deterministic realizations: identical copies (chi2_sim == chi2_obs) return [_PoissonReturn(pts.copy()) for _ in range(realizations)] # Patch the imported poisson symbol in the module under test monkeypatch.setattr(m, "poisson", poisson_stub) qs = m.QStatistic( pts, shape="rectangle", nx=2, ny=2, realizations=5, window=window_poly, rng=123, ) assert calls["realizations"] == 5 assert calls["rng_is_generator"] is True assert math.isclose( calls["intensity"], pts.shape[0] / window_poly.area, rel_tol=1e-12 ) assert hasattr(qs, "chi2_realizations") assert qs.chi2_realizations.shape == (5,) # Because stub returns same points each time, simulated chi2 equals observed chi2. assert np.allclose(qs.chi2_realizations, qs.chi2) # With chi2_sim == chi2_obs, #{>=} == R, so p = (R+1)/(R+1) = 1 assert math.isclose(qs.chi2_r_pvalue, 1.0, rel_tol=0.0, abs_tol=0.0) def test_qstatistic_simulation_accepts_realizations_object_without_points_attr( monkeypatch, pts_simple, window_poly ): pts = np.array([[1.0, 1.0], [2.0, 1.0], [3.0, 2.0], [4.0, 4.0]], dtype=float) def poisson_stub(window, intensity, realizations, rng=None): # Return raw arrays (code path uses getattr(ri, "points", ri)) return [pts.copy() for _ in range(realizations)] monkeypatch.setattr(m, "poisson", poisson_stub) qs = m.QStatistic( pts, shape="rectangle", nx=2, ny=2, realizations=3, window=window_poly, rng=np.random.default_rng(0), ) assert qs.chi2_realizations.shape == (3,) assert 0.0 <= qs.chi2_r_pvalue <= 1.0 pointpats-2.5.5/pointpats/tests/test_spacetime.py000066400000000000000000000260261514706172100223240ustar00rootroot00000000000000from warnings import warn import geopandas as gpd import libpysal as lps import numpy from pytest import approx import pytest import matplotlib.pyplot as plt from pointpats import ( Knox, KnoxLocal, SpaceTimeEvents, jacquez, knox, mantel, modified_knox, ) class TestKnox: def setup_method(self): path = lps.examples.get_path("burkitt.shp") self.gdf = gpd.read_file(path) def test_knox(self): global_knox = Knox(self.gdf[["X", "Y"]], self.gdf[["T"]], delta=20, tau=5) assert global_knox.statistic_ == 13 assert global_knox.p_poisson == 0.14624558197140414 assert hasattr(global_knox, "sim") == False numpy.testing.assert_array_equal( global_knox.observed, [[1.300e01, 3.416e03], [3.900e01, 1.411e04]] ) numpy.testing.assert_allclose( global_knox.expected, [[1.01438161e01, 3.41885618e03], [4.1856139e01, 1.41071438e04]], rtol=1e-5, atol=0, ) numpy.random.seed(12345) global_knox = Knox( self.gdf[["X", "Y"]], self.gdf[["T"]], delta=20, tau=5, keep=True ) assert global_knox.statistic_ == 13 assert hasattr(global_knox, "sim") == True assert global_knox.p_sim == 0.21 def test_knox_from_gdf(self): gdf = self.gdf.copy() # not technically the correct CRS... gdf.crs = 21096 global_knox = Knox.from_dataframe(gdf, time_col="T", delta=20, tau=5) assert global_knox.statistic_ == 13 assert global_knox.p_poisson == 0.14624558197140414 assert hasattr(global_knox, "sim") == False numpy.testing.assert_array_equal( global_knox.observed, [[1.300e01, 3.416e03], [3.900e01, 1.411e04]] ) numpy.testing.assert_allclose( global_knox.expected, [[1.01438161e01, 3.41885618e03], [4.1856139e01, 1.41071438e04]], rtol=1e-5, atol=0, ) # no CRS should raise a warning global_knox = Knox.from_dataframe(self.gdf, time_col="T", delta=20, tau=5) numpy.testing.assert_allclose( global_knox.expected, [[1.01438161e01, 3.41885618e03], [4.1856139e01, 1.41071438e04]], rtol=1e-5, atol=0, ) # unprojected coords try: gdf.crs = 4326 global_knox = Knox.from_dataframe(gdf, time_col="T", delta=20, tau=5) except ValueError: warn("successfully caught crs error") pass # non-numeric type for time try: gdf["T"] = gdf["T"].astype("O") global_knox = Knox.from_dataframe(gdf, time_col="T", delta=20, tau=5) except ValueError: warn("successfully caught dtype error") pass numpy.random.seed(12345) global_knox = Knox( self.gdf[["X", "Y"]], self.gdf[["T"]], delta=20, tau=5, keep=True ) assert global_knox.statistic_ == 13 assert hasattr(global_knox, "sim") == True assert global_knox.p_sim == 0.21 class TestKnoxLocal: def setup_method(self): path = lps.examples.get_path("burkitt.shp") self.gdf = gpd.read_file(path) def test_knox_local(self): numpy.random.seed(12345) local_knox = KnoxLocal( self.gdf[["X", "Y"]].values, self.gdf[["T"]].values, delta=20, tau=5, keep=True, ) assert local_knox.statistic_.shape == (188,) lres = local_knox gt0ids = numpy.where(lres.nsti > 0) numpy.testing.assert_array_equal( gt0ids, [ [ 25, 26, 30, 31, 35, 36, 41, 42, 46, 47, 51, 52, 102, 103, 116, 118, 122, 123, 137, 138, 139, 140, 158, 159, 162, 163, ] ], ) numpy.testing.assert_allclose( lres.p_hypergeom[gt0ids], [ 0.1348993, 0.14220663, 0.07335085, 0.08400282, 0.1494317, 0.21524073, 0.0175806, 0.04599869, 0.17523687, 0.18209188, 0.19111321, 0.16830444, 0.13734428, 0.14703242, 0.06796364, 0.03192559, 0.13734428, 0.17523687, 0.12998154, 0.1933476, 0.13244507, 0.13244507, 0.12502644, 0.14703242, 0.12502644, 0.12998154, ], rtol=1e-5, atol=0, ) numpy.testing.assert_array_equal( lres.p_sims[gt0ids], [ 0.3, 0.33, 0.11, 0.17, 0.3, 0.42, 0.06, 0.06, 0.33, 0.34, 0.36, 0.38, 0.3, 0.29, 0.41, 0.19, 0.31, 0.39, 0.18, 0.39, 0.48, 0.41, 0.22, 0.41, 0.39, 0.32, ], ) def test_knox_local_from_gdf(self): gdf = self.gdf gdf.crs = 21096 numpy.random.seed(12345) local_knox = KnoxLocal.from_dataframe( gdf, time_col="T", delta=20, tau=5, keep=True ) assert local_knox.statistic_.shape == (188,) lres = local_knox gt0ids = numpy.where(lres.nsti > 0) numpy.testing.assert_array_equal( gt0ids, [ [ 25, 26, 30, 31, 35, 36, 41, 42, 46, 47, 51, 52, 102, 103, 116, 118, 122, 123, 137, 138, 139, 140, 158, 159, 162, 163, ] ], ) numpy.testing.assert_allclose( lres.p_hypergeom[gt0ids], [ 0.1348993, 0.14220663, 0.07335085, 0.08400282, 0.1494317, 0.21524073, 0.0175806, 0.04599869, 0.17523687, 0.18209188, 0.19111321, 0.16830444, 0.13734428, 0.14703242, 0.06796364, 0.03192559, 0.13734428, 0.17523687, 0.12998154, 0.1933476, 0.13244507, 0.13244507, 0.12502644, 0.14703242, 0.12502644, 0.12998154, ], rtol=1e-5, atol=0, ) numpy.testing.assert_array_equal( lres.p_sims[gt0ids], [ 0.3, 0.33, 0.11, 0.17, 0.3, 0.42, 0.06, 0.06, 0.33, 0.34, 0.36, 0.38, 0.3, 0.29, 0.41, 0.19, 0.31, 0.39, 0.18, 0.39, 0.48, 0.41, 0.22, 0.41, 0.39, 0.32, ], ) def test_explore(self): gdf = self.gdf.copy() gdf.crs = 21096 numpy.random.seed(12345) m = KnoxLocal.from_dataframe( gdf, time_col="T", delta=20, tau=5, keep=True ).explore() numpy.testing.assert_array_almost_equal( m.get_bounds(), [ [-0.0005034046601185694, 28.514258651567], [0.0008675512091255166, 28.514975377735905], ], ) # old folium returns 5, new folium returns 3 assert len(m.to_dict()["children"]) >= 3 def test_hotspots_without_neighbors(self): gdf = self.gdf.copy() gdf = gdf.set_crs(21096) numpy.random.seed(1) knox = KnoxLocal.from_dataframe( gdf, time_col="T", delta=20, tau=5, ).hotspots(keep_neighbors=False, inference='analytic') assert knox.shape == (3,7) def test_hotspots_with_neighbors(self): gdf = self.gdf.copy() gdf = gdf.set_crs(21096) knox = KnoxLocal.from_dataframe( gdf, time_col="T", delta=20, tau=5, ).hotspots(keep_neighbors=True, inference='analytic') assert knox.shape == (4,7) @pytest.mark.mpl_image_compare def test_plot(self): gdf = self.gdf.copy() gdf.crs = 21096 fig, ax2 = plt.subplots(figsize=(30,18)) lk = KnoxLocal.from_dataframe( gdf, time_col="T", delta=20, tau=5, keep=True) lk.plot(inference='analytic', ax=ax2) assert fig # old tests refactored to pytest class TestSpaceTimeEvents: def setup_method(self): path = lps.examples.get_path("burkitt.shp") self.events = SpaceTimeEvents(path, "T") def test_space_time_events(self): assert self.events.n == 188 def test_knox(self): result = knox(self.events.space, self.events.t, delta=20, tau=5, permutations=1) assert result["stat"] == 13.0 def test_mantel(self): result = mantel( self.events.space, self.events.time, 1, scon=0.0, spow=1.0, tcon=0.0, tpow=1.0, ) assert result["stat"] == approx(0.014154, rel=1e-4) def test_jacquez(self): result = jacquez(self.events.space, self.events.t, k=3, permutations=1) assert result["stat"] == 12 def test_modified_knox(self): result = modified_knox( self.events.space, self.events.t, delta=20, tau=5, permutations=1 ) assert result["stat"] == approx(2.810160, rel=1e-4) pointpats-2.5.5/pointpats/util.py000066400000000000000000000007561514706172100171300ustar00rootroot00000000000000__all__ = ['cached_property'] import functools def cached_property(fun): """A memoize decorator for class properties. Adapted from: http://code.activestate.com/recipes/576563-cached-property/ """ @functools.wraps(fun) def get(self): try: return self._cache[fun] except AttributeError: self._cache = {} except KeyError: pass ret = self._cache[fun] = fun(self) return ret return property(get) pointpats-2.5.5/pointpats/window.py000066400000000000000000000034421514706172100174550ustar00rootroot00000000000000""" Window class for point patterns """ __author__ = "Serge Rey sjsrey@gmail.com" import warnings import libpysal as ps import numpy as np __all__ = ["as_window", "poly_from_bbox", "to_ccf", "Window"] warnings.filterwarnings( "ignore", "Objects based on the `Geometry` class will", FutureWarning ) def poly_from_bbox(bbox): l, b, r, t = bbox c = [(l, b), (l, t), (r, t), (r, b), (l, b)] return ps.cg.shapes.Polygon(c) def to_ccf(poly): if poly[-1] != poly[0]: poly.append(poly[0]) return poly def as_window(pysal_polygon): """ Convert a libpysal polygon to a Window. Parameters ---------- pysal_polygon: libpysal.cg.shapes.Polygon libpysal Polygon instance. Returns ------- Window A Window instance. """ if pysal_polygon.holes == [[]]: return Window(pysal_polygon.parts) else: return Window(pysal_polygon.parts, pysal_polygon.holes) class Window(ps.cg.Polygon): """ Geometric container for point patterns. A window is used to define the area over which the pattern is observed. This area is used in estimating the intensity of the point pattern. See :attr:`PointPattern.lambda_window`. Parameters ---------- parts: sequence A sequence of rings which bound the positive space point pattern. holes: sequence A sequence of rings which bound holes in the polygons that bound the point pattern. """ def __init__(self, parts, holes=[]): if holes: super(Window, self).__init__(parts, holes) else: super(Window, self).__init__(parts) def filter_contained(self, points): return [np.asarray(pnt) for pnt in points if self.contains_point(pnt)] pointpats-2.5.5/pyproject.toml000066400000000000000000000042671514706172100164750ustar00rootroot00000000000000[build-system] requires = ["setuptools>=61.0", "setuptools_scm[toml]>=6.2"] build-backend = "setuptools.build_meta" [tool.setuptools_scm] [project] name = "pointpats" dynamic = ["version"] authors = [ { name = "Serge Rey", email = "sjsrey@gmail.com" }, { name = "Hu Shao", email = "shaohutiger@gmail.com" }, ] maintainers = [{ name = "pysal contributors" }] license = { text = "BSD 3-Clause" } description = "Methods and Functions for planar point pattern analysis" keywords = [ "spatial statistics, point patterns" ] readme = "README.md" 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 = [ "libpysal >=4.10", "geopandas >= 1.0", "matplotlib >=3.9", "numpy >=1.26", "pandas >=2.2", "scipy >=1.12", "shapely >=2.1" ] [project.urls] Home = "https://github.com/pysal/pointpats/" Repository = "https://github.com/pysal/pointpats" [project.optional-dependencies] dev = ["pre-commit"] docs = [ "nbsphinx", "numpydoc", "pandoc", "sphinx", "sphinxcontrib-bibtex", "sphinx_bootstrap_theme", "mkdocs-jupyter", "myst-parser" ] tests = [ "codecov", "coverage", "folium", "mapclassify", "pytest", "pytest-mpl", "pytest-cov", "pytest-xdist", "scikit-learn >=1.4", "statsmodels", "watermark", ] [tool.setuptools.packages.find] include = ["pointpats", "pointpats.*"] [tool.black] line-length = 88 [tool.ruff] line-length = 88 select = ["E", "F", "W", "I", "UP", "N", "B", "A", "C4", "SIM", "ARG"] ignore = [ "B006", "B008", "B009", "B010", "C408", "E731", "F401", "F403", "N803", "N806", "N999", "UP007" ] exclude = ["pointpats/tests/*", "docs/*"] [tool.coverage.run] source = ["./pointpats"] [tool.coverage.report] exclude_lines = [ "if self.debug:", "pragma: no cover", "raise NotImplementedError", "except ModuleNotFoundError:", "except ImportError", ] ignore_errors = true omit = ["pointpats/tests/*", "docs/conf.py"]

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ICJjb29yZGluYXRlX3N5c3RlbSI6IHsic3VidHlwZSI6ICJDYXJ0ZXNpYW4iLCAiYXhpcyI6IFt7Im5hbWUiOiAiRWFzdGluZyIsICJhYmJyZXZpYXRpb24iOiAiWCIsICJkaXJlY3Rpb24iOiAiZWFzdCIsICJ1bml0IjogIm1ldHJlIn0sIHsibmFtZSI6ICJOb3J0aGluZyIsICJhYmJyZXZpYXRpb24iOiAiWSIsICJkaXJlY3Rpb24iOiAibm9ydGgiLCAidW5pdCI6ICJtZXRyZSJ9XX0sICJzY29wZSI6ICJTdGF0ZS13aWRlIHNwYXRpYWwgZGF0YSBtYW5hZ2VtZW50LiIsICJhcmVhIjogIlVuaXRlZCBTdGF0ZXMgKFVTQSkgLSBDYWxpZm9ybmlhLiIsICJiYm94IjogeyJzb3V0aF9sYXRpdHVkZSI6IDMyLjUzLCAid2VzdF9sb25naXR1ZGUiOiAtMTI0LjQ1LCAibm9ydGhfbGF0aXR1ZGUiOiA0Mi4wMSwgImVhc3RfbG9uZ2l0dWRlIjogLTExNC4xMn0sICJpZCI6IHsiYXV0aG9yaXR5IjogIkVQU0ciLCAiY29kZSI6IDMzMTB9fSwgImdlb21ldHJ5X3R5cGVzIjogWyJQb2ludCJdLCAiYmJveCI6IFsyNDUwMzYuNTEzNTkwOTAyNSwgLTU5NzMzNC4yNzE2MDc4NzIzLCAzNTEyMzEuMjc4MTk2MjYzNzcsIC00OTc3NjMuMDM1NDY5MDA3OTddfX0sICJ2ZXJzaW9uIjogIjEuMC4wIiwgImNyZWF0b3IiOiB7ImxpYnJhcnkiOiAiZ2VvcGFuZGFzIiwgInZlcnNpb24iOiAiMS4wLjEifX0AAAADAAAAZ2VvADDj//+cCgAABAAAAI8KAAB7ImluZGV4X2NvbHVtbnMiOiBbIl9faW5kZXhfbGV2ZWxfMF9fIl0sICJjb2x1bW5faW5kZXhlcyI6IFt7Im5hbWUiOiBudWxsLCAiZmllbGRfbmFtZSI6IG51bGwsICJwYW5kYXNfdHlwZSI6ICJ1bmljb2RlIiwgIm51bXB5X3R5cGUiOiAib2JqZWN0IiwgIm1ldGFkYXRhIjogeyJlbmNvZGluZyI6ICJVVEYtOCJ9fV0sICJjb2x1bW5zIjogW3sibmFtZSI6ICJZRUFSXyIsICJmaWVsZF9uYW1lIjogIllFQVJfIiwgInBhbmRhc190eXBlIjogImZsb2F0NjQiLCAibnVtcHlfdHlwZSI6ICJmbG9hdDY0IiwgIm1ldGFkYXRhIjogbnVsbH0sIHsibmFtZSI6ICJTVEFURSIsICJmaWVsZF9uYW1lIjogIlNUQVRFIiwgInBhbmRhc190eXBlIjogInVuaWNvZGUiLCAibnVtcHlfdHlwZSI6ICJvYmplY3QiLCAibWV0YWRhdGEiOiBudWxsfSwgeyJuYW1lIjogIkFHRU5DWSIsICJmaWVsZF9uYW1lIjogIkFHRU5DWSIsICJwYW5kYXNfdHlwZSI6ICJ1bmljb2RlIiwgIm51bXB5X3R5cGUiOiAib2JqZWN0IiwgIm1ldGFkYXRhIjogbnVsbH0sIHsibmFtZSI6ICJVTklUX0lEIiwgImZpZWxkX25hbWUiOiAiVU5JVF9JRCIsICJwYW5kYXNfdHlwZSI6ICJ1bmljb2RlIiwgIm51bXB5X3R5cGUiOiAib2JqZWN0IiwgIm1ldGFkYXRhIjogbnVsbH0sIHsibmFtZSI6ICJUUkVBVE1FTlRfSUQiLCAiZmllbGRfbmFtZSI6ICJUUkVBVE1FTlRfSUQiLCAicGFuZGFzX3R5cGUiOiAidW5pY29kZSIsICJudW1weV90eXBlIjogIm9iamVjdCIsICJtZXRhZGF0YSI6IG51bGx9LCB7Im5hbWUiOiAiVFJFQVRNRU5UX05BTUUiLCAiZmllbGRfbmFtZSI6ICJUUkVBVE1FTlRfTkFNRSIsICJwYW5kYXNfdHlwZSI6ICJ1bmljb2RlIiwgIm51bXB5X3R5cGUiOiAib2JqZWN0IiwgIm1ldGFkYXRhIjogbnVsbH0sIHsibmFtZSI6ICJUUkVBVE1FTlRfVFlQRSIsICJmaWVsZF9uYW1lIjogIlRSRUFUTUVOVF9UWVBFIiwgInBhbmRhc190eXBlIjogImZsb2F0NjQiLCAibnVtcHlfdHlwZSI6ICJmbG9hdDY0IiwgIm1ldGFkYXRhIjogbnVsbH0sIHsibmFtZSI6ICJTVEFSVF9EQVRFIiwgImZpZWxkX25hbWUiOiAiU1RBUlRfREFURSIsICJwYW5kYXNfdHlwZSI6ICJkYXRldGltZXR6IiwgIm51bXB5X3R5cGUiOiAiZGF0ZXRpbWU2NFtuc10iLCAibWV0YWRhdGEiOiB7InRpbWV6b25lIjogIlVUQyJ9fSwgeyJuYW1lIjogIkVORF9EQVRFIiwgImZpZWxkX25hbWUiOiAiRU5EX0RBVEUiLCAicGFuZGFzX3R5cGUiOiAiZGF0ZXRpbWV0eiIsICJudW1weV90eXBlIjogImRhdGV0aW1lNjRbbnNdIiwgIm1ldGFkYXRhIjogeyJ0aW1lem9uZSI6ICJVVEMifX0sIHsibmFtZSI6ICJUUkVBVEVEX0FDIiwgImZpZWxkX25hbWUiOiAiVFJFQVRFRF9BQyIsICJwYW5kYXNfdHlwZSI6ICJmbG9hdDY0IiwgIm51bXB5X3R5cGUiOiAiZmxvYXQ2NCIsICJtZXRhZGF0YSI6IG51bGx9LCB7Im5hbWUiOiAiR0lTX0FDUkVTIiwgImZpZWxkX25hbWUiOiAiR0lTX0FDUkVTIiwgInBhbmRhc190eXBlIjogImZsb2F0MzIiLCAibnVtcHlfdHlwZSI6ICJmbG9hdDMyIiwgIm1ldGFkYXRhIjogbnVsbH0sIHsibmFtZSI6ICJSWF9DT05TVU0iLCAiZmllbGRfbmFtZSI6ICJSWF9DT05TVU0iLCAicGFuZGFzX3R5cGUiOiAiZmxvYXQ2NCIsICJudW1weV90eXBlIjogImZsb2F0NjQiLCAibWV0YWRhdGEiOiBudWxsfSwgeyJuYW1lIjogIlBSRV9DT05fQ0xBU1MiLCAiZmllbGRfbmFtZSI6ICJQUkVfQ09OX0NMQVNTIiwgInBhbmRhc190eXBlIjogImZsb2F0NjQiLCAibnVtcHlfdHlwZSI6ICJmbG9hdDY0IiwgIm1ldGFkYXRhIjogbnVsbH0sIHsibmFtZSI6ICJQT1NUX0NPTl9DTEFTUyIsICJmaWVsZF9uYW1lIjogIlBPU1RfQ09OX0NMQVNTIiwgInBhbmRhc190eXBlIjogImZsb2F0NjQiLCAibnVtcHlfdHlwZSI6ICJmbG9hdDY0IiwgIm1ldGFkYXRhIjogbnVsbH0sIHsibmFtZSI6ICJTaGFwZV9MZW5ndGgiLCAiZmllbGRfbmFtZSI6ICJTaGFwZV9MZW5ndGgiLCAicGFuZGFzX3R5cGUiOiAiZmxvYXQ2NCIsICJudW1weV90eXBlIjogImZsb2F0NjQiLCAibWV0YWRhdGEiOiBudWxsfSwgeyJuYW1lIjogIlNoYXBlX0FyZWEiLCAiZmllbGRfbmFtZSI6ICJTaGFwZV9BcmVhIiwgInBhbmRhc190eXBlIjogImZsb2F0NjQiLCAibnVtcHlfdHlwZSI6ICJmbG9hdDY0IiwgIm1ldGFkYXRhIjogbnVsbH0sIHsibmFtZSI6ICJnZW9tZXRyeSIsICJmaWVsZF9uYW1lIjogImdlb21ldHJ5IiwgInBhbmRhc190eXBlIjogImVtcHR5IiwgIm51bXB5X3R5cGUiOiAib2JqZWN0IiwgIm1ldGFkYXRhIjogbnVsbH0sIHsibmFtZSI6ICJ5ZWFyIiwgImZpZWxkX25hbWUiOiAieWVhciIsICJwYW5kYXNfdHlwZSI6ICJpbnQ2NCIsICJudW1weV90eXBlIjogImludDY0IiwgIm1ldGFkYXRhIjogbnVsbH0sIHsibmFtZSI6ICJjZW50cm9pZCIsICJmaWVsZF9uYW1lIjogImNlbnRyb2lkIiwgInBhbmRhc190eXBlIjogImVtcHR5IiwgIm51bXB5X3R5cGUiOiAib2JqZWN0IiwgIm1ldGFkYXRhIjogbnVsbH0sIHsibmFtZSI6IG51bGwsICJmaWVsZF9uYW1lIjogIl9faW5kZXhfbGV2ZWxfMF9fIiwgInBhbmRhc190eXBlIjogImludDY0IiwgIm51bXB5X3R5cGUiOiAiaW50NjQiLCAibWV0YWRhdGEiOiBudWxsfV0sICJjcmVhdG9yIjogeyJsaWJyYXJ5IjogInB5YXJyb3ciLCAidmVyc2lvbiI6ICIxNy4wLjAifSwgInBhbmRhc192ZXJzaW9uIjogIjIuMi4zIn0ABgAAAHBhbmRhcwAAFAAAAGQVAAAkFQAA+BQAAMwUAACYFAAAZBQAACwUAADkEwAApBMAAHATAAA8EwAACBMAANASAACYEgAAYBIAACwSAABgCQAAEAkAAEwAAAAEAAAA/Or//wAAAQIQAAAALAAAAAQAAAAAAAAAEQAAAF9faW5kZXhfbGV2ZWxfMF9fAAAACAAMAAgABwAIAAAAAAAAAUAAAAAG9///AAABBBQAAACwCAAACAAAABgAAAAAAAAACAAAAGNlbnRyb2lkAAAAAAIAAABQCAAABAAAAKzu//8kCAAABAAAABcIAAB7ImNycyI6ICJ7XCIkc2NoZW1hXCI6XCJodHRwczovL3Byb2oub3JnL3NjaGVtYXMvdjAuNy9wcm9qanNvbi5zY2hlbWEuanNvblwiLFwidHlwZVwiOlwiUHJvamVjdGVkQ1JTXCIsXCJuYW1lXCI6XCJO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parquet-cpp-arrow version 17.0.0EgPAR1pointpats-2.5.5/environment.yml000066400000000000000000000002451514706172100166400ustar00rootroot00000000000000name: pointpats channels: - conda-forge - defaults dependencies: - python - geopandas - libpysal - matplotlib - numpy - pandas - scipy - shapley pointpats-2.5.5/notebooks/000077500000000000000000000000001514706172100155535ustar00rootroot00000000000000pointpats-2.5.5/notebooks/Minimum bounding circle.ipynb000066400000000000000000062443731514706172100232640ustar00rootroot00000000000000{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import matplotlib.collections as mplc\n", "import libpysal as ps\n", "from shapely import geometry as sgeom\n", "import descartes as des\n", "import pointpats \n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "data = ps.io.open(ps.examples.get_path('columbus.shp')).read()\n", "chains = [chain.parts[0] for chain in data]" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[(8.624129295349121, 14.236980438232422),\n", " (8.559700012207031, 14.742449760437012),\n", " (8.809452056884766, 14.734430313110352),\n", " (8.808412551879883, 14.636520385742188),\n", " (8.919304847717285, 14.638500213623047),\n", " (9.087138175964355, 14.63049030303955),\n", " (9.09996509552002, 14.244830131530762),\n", " (9.015047073364258, 14.241840362548828),\n", " (9.008951187133789, 13.995059967041016),\n", " (8.818140029907227, 14.002050399780273),\n", " (8.653305053710938, 14.008090019226074),\n", " (8.642902374267578, 14.089710235595703),\n", " (8.63259220123291, 14.1705904006958),\n", " (8.625825881958008, 14.22367000579834),\n", " (8.624129295349121, 14.236980438232422)]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "points = chains[0]\n", "points" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's plot that polygon by interpreting it in Shapely and using its draw behavior." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "" ], "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "poly = sgeom.Polygon(points)\n", "poly" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nifty. Now, I've implemented Skyum's method for finding the Minimum Bounding Circle for a set of points in `centrography`. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Right now, there's some extra printing. Essentially, if you have sufficiently straight lines on the boundary, the equations for the circumcenter of the tuple $(p,q,r)$ explodes. Thus, I test if $\\angle (p,q,r)$ identifies a circle whose diameter is $(p,r)$ or $(p,q)$. There are two triplets of straight enough lines, so their circle equations are modified, and I retain printing for bug diagnostics." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "(radius, center), inset, removed, constraints = pointpats.skyum(points)\n", "#p,q,r = cent.skyum(points)\n", "#mbc = cent._circle(points[p], points[q], points[r])\n", "#mbc = cent._circle()\n", "mbc_poly = sgeom.Point(*center).buffer(radius)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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